code/OGWD_himalayas_composite_lagA.ipynb

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    "import xarray as xr\n",
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import MultipleLocator\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from dask.distributed import Client, LocalCluster, get_task_stream\n",
    "\n",
    "#from itertools import product, combinations\n",
    "#from cftime import DatetimeNoLeap\n",
    "#from scipy.stats import ks_2samp, epps_singleton_2samp, ttest_ind, wilcoxon, mannwhitneyu\n",
    "#from collections import ChainMap"
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    "sns.set_style('whitegrid')\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}/'"
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    "# Functions"
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    "def open_date_file(file_path):\n",
    "    df = pd.read_csv(file_path, index_col=0, parse_dates=True)\n",
    "    df['BeginDate'] = df.BeginDate.apply(lambda t: pd.to_datetime(t, format='%Y-%m-%d'))\n",
    "    return df"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:   (lag: 21, lat: 48, lon: 96, plev: 63)\n",
       "Coordinates:\n",
       "  * plev      (plev) float64 1e+05 9.25e+04 8.5e+04 8e+04 ... 0.2 0.15 0.1 0.07\n",
       "  * lon       (lon) float64 0.0 3.75 7.5 11.25 15.0 ... 345.0 348.8 352.5 356.2\n",
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       "  * lag       (lag) int64 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10\n",
       "Data variables:\n",
       "    accelogw  (lag, plev, lat, lon) float32 ...</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-a142f882-e631-4551-bd9b-7d5a0f7978a9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a142f882-e631-4551-bd9b-7d5a0f7978a9' 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'>lag</span>: 21</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></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-3bde9f5c-de57-4cf0-a176-af9d9d898ab1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3bde9f5c-de57-4cf0-a176-af9d9d898ab1' 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'>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-99f67e49-1853-48b4-a822-40c02e326db7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99f67e49-1853-48b4-a822-40c02e326db7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbb62f82-1884-41e8-91b9-232bc82eab7c' class='xr-var-data-in' type='checkbox'><label for='data-dbb62f82-1884-41e8-91b9-232bc82eab7c' 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'>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-e0e099ce-ec58-4a9c-933e-e1d090782a13' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e0e099ce-ec58-4a9c-933e-e1d090782a13' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3248af63-a131-4bad-a62b-e31d46addd73' class='xr-var-data-in' type='checkbox'><label for='data-3248af63-a131-4bad-a62b-e31d46addd73' 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'>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-c86bef54-126c-4dd7-bfe5-b6755b133964' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c86bef54-126c-4dd7-bfe5-b6755b133964' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d0b909e-50ec-4892-8fab-dddab7e57526' class='xr-var-data-in' type='checkbox'><label for='data-0d0b909e-50ec-4892-8fab-dddab7e57526' 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'>lag</span></div><div class='xr-var-dims'>(lag)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-10 -9 -8 -7 -6 -5 ... 5 6 7 8 9 10</div><input id='attrs-6c04df64-ab4c-45b8-a943-85a517690271' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6c04df64-ab4c-45b8-a943-85a517690271' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c9e705e-a6b3-4f0e-be66-5dbed36bad47' class='xr-var-data-in' type='checkbox'><label for='data-1c9e705e-a6b3-4f0e-be66-5dbed36bad47' 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([-10,  -9,  -8,  -7,  -6,  -5,  -4,  -3,  -2,  -1,   0,   1,   2,   3,\n",
       "         4,   5,   6,   7,   8,   9,  10])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ff047028-c1f9-4f00-a39d-cec0f5caf297' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ff047028-c1f9-4f00-a39d-cec0f5caf297' 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>accelogw</span></div><div class='xr-var-dims'>(lag, plev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f4695669-1536-4214-8dd4-fadd4116747d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f4695669-1536-4214-8dd4-fadd4116747d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fbd23ea-d93b-4652-9ee7-9accc6274a04' class='xr-var-data-in' type='checkbox'><label for='data-0fbd23ea-d93b-4652-9ee7-9accc6274a04' 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>[6096384 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0894a7e6-c333-45ee-a217-f78068f9139c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0894a7e6-c333-45ee-a217-f78068f9139c' 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:   (lag: 21, lat: 48, lon: 96, plev: 63)\n",
       "Coordinates:\n",
       "  * plev      (plev) float64 1e+05 9.25e+04 8.5e+04 8e+04 ... 0.2 0.15 0.1 0.07\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",
       "  * lag       (lag) int64 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10\n",
       "Data variables:\n",
       "    accelogw  (lag, plev, lat, lon) float32 ..."
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sel_var = 'accelogw'\n",
    "DJF_bool = '_DJFonly'\n",
    "cesta = f'{root_path}composites_woSSW{DJF_bool}/'\n",
    "ds_comp = xr.open_dataset(f'{cesta}{sel_var}_absolute_comp_himalayas_20days.nc')\n",
    "ds_comp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "74c3c1ff-e6e4-4da7-9ee0-b5f2958d41ab",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-17T15:34:39.603830Z",
     "iopub.status.busy": "2022-01-17T15:34:39.603318Z",
     "iopub.status.idle": "2022-01-17T15:34:39.845405Z",
     "shell.execute_reply": "2022-01-17T15:34:39.844644Z",
     "shell.execute_reply.started": "2022-01-17T15:34:39.603772Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fac5158ca90>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_comp['accelogw'].sel(\n",
    "    lag = 0,\n",
    "    lat = slice(0,None),\n",
    "    plev = 7000\n",
    ").mean('lon').plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1be42f1a-2a80-46ba-8192-a2651d495647",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-15T15:53:25.720144Z",
     "iopub.status.busy": "2021-10-15T15:53:25.719516Z",
     "iopub.status.idle": "2021-10-15T15:53:25.779801Z",
     "shell.execute_reply": "2021-10-15T15:53:25.779014Z",
     "shell.execute_reply.started": "2021-10-15T15:53:25.720074Z"
    },
    "tags": []
   },
   "outputs": [
    {
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       "  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,\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:    (bnds: 2, lat: 48, lon: 96, month: 12, plev: 63)\n",
       "Coordinates:\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",
       "  * month      (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    time_bnds  (month, bnds) float64 3.382e+04 3.382e+04 ... 3.416e+04 3.416e+04\n",
       "    lat_bnds   (month, lat, bnds) float64 -90.0 -85.32 -85.32 ... 85.32 90.0\n",
       "    lon_bnds   (month, lon, bnds) float64 0.0 1.875 1.875 ... 354.4 354.4 358.1\n",
       "    plev_bnds  (month, plev, bnds) float64 1e+05 9.25e+04 9.25e+04 ... 0.07 0.0\n",
       "    accelogw   (month, plev, lat, lon) float64 ...</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-0a39c668-dfdf-4478-9274-4f8eaaa30e16' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0a39c668-dfdf-4478-9274-4f8eaaa30e16' 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'>month</span>: 12</li><li><span class='xr-has-index'>plev</span>: 63</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-0eb8ddab-ff8b-4309-bf45-01ad6cb35fc3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0eb8ddab-ff8b-4309-bf45-01ad6cb35fc3' 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'>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-c250df33-4dcc-4c1c-8e98-7a4b3c726682' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c250df33-4dcc-4c1c-8e98-7a4b3c726682' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aaa9c7d2-c81d-464d-a76c-cfccd6eb03ff' class='xr-var-data-in' type='checkbox'><label for='data-aaa9c7d2-c81d-464d-a76c-cfccd6eb03ff' 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-c929ae26-fa5e-4112-86d3-5e3d58684e77' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c929ae26-fa5e-4112-86d3-5e3d58684e77' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57b8075e-e7a0-4430-a6cc-c2dc921500b9' class='xr-var-data-in' type='checkbox'><label for='data-57b8075e-e7a0-4430-a6cc-c2dc921500b9' 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-fc3a9098-2f73-41ec-8647-33dd79964867' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc3a9098-2f73-41ec-8647-33dd79964867' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ffe49d02-e2c1-40c2-83f3-99eb83b38cd3' class='xr-var-data-in' type='checkbox'><label for='data-ffe49d02-e2c1-40c2-83f3-99eb83b38cd3' 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'>month</span></div><div class='xr-var-dims'>(month)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-13e6e969-f69d-418f-9f5d-875b65254054' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-13e6e969-f69d-418f-9f5d-875b65254054' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b955692c-c5b9-4bae-9203-de1a8ef52460' class='xr-var-data-in' type='checkbox'><label for='data-b955692c-c5b9-4bae-9203-de1a8ef52460' 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([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-af7c6f10-06f5-46ef-b6f2-cc53fe34bd11' class='xr-section-summary-in' type='checkbox'  checked><label for='section-af7c6f10-06f5-46ef-b6f2-cc53fe34bd11' 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'>(month, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c97701ae-d6c3-45e4-8ac8-36c3f11cc7da' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c97701ae-d6c3-45e4-8ac8-36c3f11cc7da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa0b0562-bdd3-49c3-a9e4-9e1c3939f0c0' class='xr-var-data-in' type='checkbox'><label for='data-aa0b0562-bdd3-49c3-a9e4-9e1c3939f0c0' 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([[33823.375, 33823.625],\n",
       "       [33852.875, 33853.125],\n",
       "       [33882.375, 33882.625],\n",
       "       [33912.875, 33913.125],\n",
       "       [33943.375, 33943.625],\n",
       "       [33973.875, 33974.125],\n",
       "       [34004.375, 34004.625],\n",
       "       [34035.375, 34035.625],\n",
       "       [34065.875, 34066.125],\n",
       "       [34096.375, 34096.625],\n",
       "       [34126.875, 34127.125],\n",
       "       [34157.375, 34157.625]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(month, lat, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cdba7c20-ff19-40c0-a25c-220a714794be' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cdba7c20-ff19-40c0-a25c-220a714794be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3408e127-b89c-49d5-9997-bebe2d3f4a94' class='xr-var-data-in' type='checkbox'><label for='data-3408e127-b89c-49d5-9997-bebe2d3f4a94' 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([[[-90.      , -85.319016],\n",
       "        [-85.319016, -81.627991],\n",
       "        ...,\n",
       "        [ 81.627991,  85.319016],\n",
       "        [ 85.319016,  90.      ]],\n",
       "\n",
       "       [[-90.      , -85.319016],\n",
       "        [-85.319016, -81.627991],\n",
       "        ...,\n",
       "        [ 81.627991,  85.319016],\n",
       "        [ 85.319016,  90.      ]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[-90.      , -85.319016],\n",
       "        [-85.319016, -81.627991],\n",
       "        ...,\n",
       "        [ 81.627991,  85.319016],\n",
       "        [ 85.319016,  90.      ]],\n",
       "\n",
       "       [[-90.      , -85.319016],\n",
       "        [-85.319016, -81.627991],\n",
       "        ...,\n",
       "        [ 81.627991,  85.319016],\n",
       "        [ 85.319016,  90.      ]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(month, lon, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-81568ce4-a069-43c9-be58-d3130e87a457' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-81568ce4-a069-43c9-be58-d3130e87a457' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e9323e2-b590-47fa-a340-2c778af639bc' class='xr-var-data-in' type='checkbox'><label for='data-8e9323e2-b590-47fa-a340-2c778af639bc' 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([[[  0.   ,   1.875],\n",
       "        [  1.875,   5.625],\n",
       "        ...,\n",
       "        [350.625, 354.375],\n",
       "        [354.375, 358.125]],\n",
       "\n",
       "       [[  0.   ,   1.875],\n",
       "        [  1.875,   5.625],\n",
       "        ...,\n",
       "        [350.625, 354.375],\n",
       "        [354.375, 358.125]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[  0.   ,   1.875],\n",
       "        [  1.875,   5.625],\n",
       "        ...,\n",
       "        [350.625, 354.375],\n",
       "        [354.375, 358.125]],\n",
       "\n",
       "       [[  0.   ,   1.875],\n",
       "        [  1.875,   5.625],\n",
       "        ...,\n",
       "        [350.625, 354.375],\n",
       "        [354.375, 358.125]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>plev_bnds</span></div><div class='xr-var-dims'>(month, plev, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5fa0f8dd-78e6-410c-8ea1-6710eebb7a97' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5fa0f8dd-78e6-410c-8ea1-6710eebb7a97' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-adb64763-bbd5-468f-b9ac-3768bd3c2d7b' class='xr-var-data-in' type='checkbox'><label for='data-adb64763-bbd5-468f-b9ac-3768bd3c2d7b' 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([[[1.00e+05, 9.25e+04],\n",
       "        [9.25e+04, 8.50e+04],\n",
       "        ...,\n",
       "        [1.00e-01, 7.00e-02],\n",
       "        [7.00e-02, 0.00e+00]],\n",
       "\n",
       "       [[1.00e+05, 9.25e+04],\n",
       "        [9.25e+04, 8.50e+04],\n",
       "        ...,\n",
       "        [1.00e-01, 7.00e-02],\n",
       "        [7.00e-02, 0.00e+00]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[1.00e+05, 9.25e+04],\n",
       "        [9.25e+04, 8.50e+04],\n",
       "        ...,\n",
       "        [1.00e-01, 7.00e-02],\n",
       "        [7.00e-02, 0.00e+00]],\n",
       "\n",
       "       [[1.00e+05, 9.25e+04],\n",
       "        [9.25e+04, 8.50e+04],\n",
       "        ...,\n",
       "        [1.00e-01, 7.00e-02],\n",
       "        [7.00e-02, 0.00e+00]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>accelogw</span></div><div class='xr-var-dims'>(month, plev, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-49548c93-1a7b-4d66-8d8f-66bcc7ec516c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-49548c93-1a7b-4d66-8d8f-66bcc7ec516c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1ebed9c-a868-4136-ba35-de99ed69c0e2' class='xr-var-data-in' type='checkbox'><label for='data-d1ebed9c-a868-4136-ba35-de99ed69c0e2' 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>[3483648 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-231babf6-d582-43e6-8366-72b67eb45cff' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-231babf6-d582-43e6-8366-72b67eb45cff' 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:    (bnds: 2, lat: 48, lon: 96, month: 12, plev: 63)\n",
       "Coordinates:\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",
       "  * month      (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    time_bnds  (month, bnds) float64 ...\n",
       "    lat_bnds   (month, lat, bnds) float64 ...\n",
       "    lon_bnds   (month, lon, bnds) float64 ...\n",
       "    plev_bnds  (month, plev, bnds) float64 ...\n",
       "    accelogw   (month, plev, lat, lon) float64 ..."
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "infile = f'{root_path}{sel_var}/{sel_var}_climatology_woSSW.nc'\n",
    "ds_clim = xr.open_dataset(infile)\n",
    "ds_clim"
   ]
  },
  {
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   "id": "9218dd6d-00f5-4d04-926d-ed2a700cc58d",
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     "iopub.execute_input": "2021-10-15T15:54:32.773484Z",
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     "iopub.status.idle": "2021-10-15T15:54:32.792669Z",
     "shell.execute_reply": "2021-10-15T15:54:32.791723Z",
     "shell.execute_reply.started": "2021-10-15T15:54:32.773421Z"
    },
    "tags": []
   },
   "outputs": [
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       "array(-0.76062438)\n",
       "Coordinates:\n",
       "    plev     float64 7e+03</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'accelogw'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-38cf0535-41d0-4895-aa59-b99e37922cf2' class='xr-array-in' type='checkbox' checked><label for='section-38cf0535-41d0-4895-aa59-b99e37922cf2' 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>-0.7606</span></div><div class='xr-array-data'><pre>array(-0.76062438)</pre></div></div></li><li class='xr-section-item'><input id='section-8545b113-83f2-45f7-8a1f-d65c3ab0eb86' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8545b113-83f2-45f7-8a1f-d65c3ab0eb86' class='xr-section-summary' >Coordinates: <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>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-049123ec-9ec8-4835-a33e-e7a262208ea8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-049123ec-9ec8-4835-a33e-e7a262208ea8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a558f175-837c-4ef7-881c-315fa1c00ea3' class='xr-var-data-in' type='checkbox'><label for='data-a558f175-837c-4ef7-881c-315fa1c00ea3' 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(7000.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8ef2adf3-8e86-4a31-b4af-ad42a8ad109c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8ef2adf3-8e86-4a31-b4af-ad42a8ad109c' 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 'accelogw' ()>\n",
       "array(-0.76062438)\n",
       "Coordinates:\n",
       "    plev     float64 7e+03"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_clim[sel_var].sel(plev = 7000, \n",
    "                     month = [12,1,2],\n",
    "                     lat = slice(20,40)).mean(['lon','lat','month'])*24*3600#.plot(label = 'ZM')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9adc02f9-32c4-43ab-9f39-654bc532c6f9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-15T15:49:15.400819Z",
     "iopub.status.busy": "2021-10-15T15:49:15.400297Z",
     "iopub.status.idle": "2021-10-15T15:49:15.564761Z",
     "shell.execute_reply": "2021-10-15T15:49:15.564038Z",
     "shell.execute_reply.started": "2021-10-15T15:49:15.400763Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fce9370e790>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_comp[sel_var].sel(plev = 7000, lat = slice(20,40)).mean(['lon','lat']).plot(label = 'ZM')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fa5141bb-2689-49be-a836-755fa6e672ac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-15T15:49:53.315695Z",
     "iopub.status.busy": "2021-10-15T15:49:53.314944Z",
     "iopub.status.idle": "2021-10-15T15:49:53.521848Z",
     "shell.execute_reply": "2021-10-15T15:49:53.520608Z",
     "shell.execute_reply.started": "2021-10-15T15:49:53.315631Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fce9407cd90>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_comp[sel_var].sel(plev = 7000, lat = slice(20,40), lon = slice(70,102.5)).mean(['lon','lat']).plot(label = 'HI')\n",
    "ds_comp[sel_var].sel(plev = 7000, lat = slice(20,40)).mean(['lon','lat']).plot(label = 'ZM')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4c6131c-fc9f-49cd-a6c1-22b93dc8dfc7",
   "metadata": {},
   "source": [
    "# acceldiv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc9c53f5-79b9-4ad1-bf05-7b257b5ab2ab",
   "metadata": {},
   "source": [
    "## Open data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f4573f29-8147-4ae1-a348-787ede201ed6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-24T15:46:19.558730Z",
     "iopub.status.busy": "2021-11-24T15:46:19.558163Z",
     "iopub.status.idle": "2021-11-24T15:46:35.315932Z",
     "shell.execute_reply": "2021-11-24T15:46:35.314592Z",
     "shell.execute_reply.started": "2021-11-24T15:46:19.558675Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_4016793/423478415.py:7: RuntimeWarning: Converting a CFTimeIndex with dates from a non-standard calendar, 'noleap', to a pandas.DatetimeIndex, which uses dates from the standard calendar.  This may lead to subtle errors in operations that depend on the length of time between dates.\n",
      "  ds['time'] = ds.indexes['time'].to_datetimeindex()\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "\n",
       ".xr-attrs dt {\n",
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       "\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "\n",
       ".xr-icon-database,\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:   (lat: 48, plev: 63, time: 11680)\n",
       "Coordinates:\n",
       "  * time      (time) datetime64[ns] 1979-01-01 1979-01-02 ... 2010-12-31\n",
       "  * plev      (plev) float64 1e+03 925.0 850.0 800.0 ... 0.0015 0.001 0.0007\n",
       "  * lat       (lat) float64 -87.16 -83.48 -79.78 -76.07 ... 79.78 83.48 87.16\n",
       "Data variables:\n",
       "    accelogw  (time, plev, lat) float64 1.238e-11 1.238e-11 ... 0.0007593</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-11b51127-f981-445b-b00d-70c57008b31b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-11b51127-f981-445b-b00d-70c57008b31b' 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'>plev</span>: 63</li><li><span class='xr-has-index'>time</span>: 11680</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5f4ec488-9f1f-4445-a550-2fce8ac0ccce' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5f4ec488-9f1f-4445-a550-2fce8ac0ccce' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1979-01-01 ... 2010-12-31</div><input id='attrs-c0f60059-4d0a-428e-a092-c359e2aaf673' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c0f60059-4d0a-428e-a092-c359e2aaf673' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9084eec8-cf73-4fd3-bbc3-2db20effd999' class='xr-var-data-in' type='checkbox'><label for='data-9084eec8-cf73-4fd3-bbc3-2db20effd999' 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([&#x27;1979-01-01T00:00:00.000000000&#x27;, &#x27;1979-01-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;1979-01-03T00:00:00.000000000&#x27;, ..., &#x27;2010-12-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-12-30T00:00:00.000000000&#x27;, &#x27;2010-12-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</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+03 925.0 850.0 ... 0.001 0.0007</div><input id='attrs-7848a971-d239-4763-8b49-10a6cbe55a47' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7848a971-d239-4763-8b49-10a6cbe55a47' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c2e5b17-7599-46d4-a618-aee94f1ec7cb' class='xr-var-data-in' type='checkbox'><label for='data-7c2e5b17-7599-46d4-a618-aee94f1ec7cb' 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>hPa</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+03, 9.25e+02, 8.50e+02, 8.00e+02, 7.80e+02, 7.50e+02, 7.00e+02,\n",
       "       6.50e+02, 6.00e+02, 5.00e+02, 4.50e+02, 4.00e+02, 3.50e+02, 3.00e+02,\n",
       "       2.85e+02, 2.50e+02, 2.00e+02, 1.70e+02, 1.50e+02, 1.30e+02, 1.15e+02,\n",
       "       1.00e+02, 9.00e+01, 8.00e+01, 7.00e+01, 6.00e+01, 5.00e+01, 4.00e+01,\n",
       "       3.50e+01, 3.00e+01, 2.50e+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,\n",
       "       5.00e-02, 4.00e-02, 3.00e-02, 2.00e-02, 1.50e-02, 1.00e-02, 7.00e-03,\n",
       "       5.00e-03, 4.00e-03, 3.00e-03, 2.00e-03, 1.50e-03, 1.00e-03, 7.00e-04])</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-ee6dc266-863c-4c7f-84c5-b535e54bedd5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ee6dc266-863c-4c7f-84c5-b535e54bedd5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7fdff5ff-c877-4dc0-9c11-2c0b294090cc' class='xr-var-data-in' type='checkbox'><label for='data-7fdff5ff-c877-4dc0-9c11-2c0b294090cc' 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></ul></div></li><li class='xr-section-item'><input id='section-f181a351-722f-4c15-b2be-8c4f37c3d883' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f181a351-722f-4c15-b2be-8c4f37c3d883' 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>accelogw</span></div><div class='xr-var-dims'>(time, plev, lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.238e-11 1.238e-11 ... 0.0007593</div><input id='attrs-800a460b-4d1a-447a-bdde-c061b44339b5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-800a460b-4d1a-447a-bdde-c061b44339b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df43cd05-167b-4d98-b6b3-eb2fa5fc5ee9' class='xr-var-data-in' type='checkbox'><label for='data-df43cd05-167b-4d98-b6b3-eb2fa5fc5ee9' 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([[[ 1.23758724e-11,  1.23758724e-11,  5.02456482e-06, ...,\n",
       "         -3.14397159e-03, -1.15644052e-02, -2.25869724e-05],\n",
       "        [-3.30984560e-09, -2.86175871e-09, -1.92774617e-01, ...,\n",
       "         -4.90887659e-02, -7.62095097e-02,  3.32035438e-06],\n",
       "        [ 2.04648324e-09,  5.15082567e-01, -8.23322345e-02, ...,\n",
       "         -6.29512592e-02,  2.07477268e-01, -3.36019103e-05],\n",
       "        ...,\n",
       "        [ 2.37006692e-01,  1.51837059e+00,  1.70511067e+00, ...,\n",
       "          1.07700980e-01,  1.50639802e-02, -1.27101405e-03],\n",
       "        [-8.44231576e-02,  1.42543211e+00,  3.96133981e+00, ...,\n",
       "          7.42336708e-02,  1.33889100e-02, -7.13216490e-04],\n",
       "        [-6.85596822e-02,  7.97821864e-01,  1.37970823e+00, ...,\n",
       "          9.38647866e-02,  1.71764736e-02, -5.04631905e-04]],\n",
       "\n",
       "       [[-1.85801438e-10, -1.85801438e-10,  5.02436664e-06, ...,\n",
       "         -2.75596626e-02, -1.47409826e-02,  2.57910170e-06],\n",
       "        [ 3.33968596e-09,  2.69362190e-09, -2.07396744e-01, ...,\n",
       "         -5.60692438e-01, -6.79187398e-01, -1.55227917e-04],\n",
       "        [ 3.15354456e-09,  6.18868792e-02, -1.09760584e-01, ...,\n",
       "         -3.07936731e-01, -5.30242332e-01, -9.11916728e-05],\n",
       "...\n",
       "        [-2.09258162e-01, -3.98675972e-01, -3.84496274e-01, ...,\n",
       "         -2.78350364e+00,  2.45931582e-02, -3.00616421e-04],\n",
       "        [-1.63061542e-01, -2.89144324e-01, -3.53494153e-01, ...,\n",
       "         -3.67944632e-01,  3.38426226e-02,  3.77720592e-04],\n",
       "        [-1.66441133e-01, -2.53459297e-01, -3.22974274e-01, ...,\n",
       "         -2.74582991e-02,  4.28806707e-02,  7.59217410e-04]],\n",
       "\n",
       "       [[ 1.08726222e-09,  1.08726222e-09, -3.54525216e-06, ...,\n",
       "          5.53835291e-02, -1.58710998e-03,  2.09145083e-05],\n",
       "        [ 1.87879169e-09,  1.38721500e-09,  3.57978674e-03, ...,\n",
       "          4.74849214e-02,  1.17938907e-01,  2.39746662e-05],\n",
       "        [ 1.45419924e-09, -2.08025002e-01, -1.59385553e-02, ...,\n",
       "          4.31396923e-01,  2.39586726e-01,  5.45440496e-06],\n",
       "        ...,\n",
       "        [-2.08143043e-01, -3.80985322e-01,  1.13926809e-01, ...,\n",
       "         -6.11076599e-02,  3.71371467e-02, -3.00662557e-04],\n",
       "        [-1.59646038e-01, -2.88282447e-01, -4.37309544e-01, ...,\n",
       "          8.64482774e-02,  3.38426430e-02,  3.77740970e-04],\n",
       "        [-1.65853021e-01, -2.51660301e-01, -3.96105702e-01, ...,\n",
       "          1.16561668e-01,  4.28807416e-02,  7.59288240e-04]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b96f2fd0-6255-4a3c-8bf2-f5ae9b240e6d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b96f2fd0-6255-4a3c-8bf2-f5ae9b240e6d' 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:   (lat: 48, plev: 63, time: 11680)\n",
       "Coordinates:\n",
       "  * time      (time) datetime64[ns] 1979-01-01 1979-01-02 ... 2010-12-31\n",
       "  * plev      (plev) float64 1e+03 925.0 850.0 800.0 ... 0.0015 0.001 0.0007\n",
       "  * lat       (lat) float64 -87.16 -83.48 -79.78 -76.07 ... 79.78 83.48 87.16\n",
       "Data variables:\n",
       "    accelogw  (time, plev, lat) float64 1.238e-11 1.238e-11 ... 0.0007593"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sel_var = 'accelogw'\n",
    "infile = f'{root_path}{sel_var }/{sel_var}_6hrPlev_CMAM_CMAM30-SD_r1i1p1_19790101-20101231_anomalies_zm.nc'\n",
    "ds = xr.open_dataset(infile)\n",
    "ds = ds.sortby('time')\n",
    "ds[sel_var] = ds[sel_var]*24*3600.\n",
    "ds = ds.resample(time = '1D').mean('time')\n",
    "ds['time'] = ds.indexes['time'].to_datetimeindex()\n",
    "ds['plev'] = ds['plev']/100.\n",
    "ds['plev'].attrs['units'] = 'hPa'\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0adf86e-b389-4eb9-b639-7a9e922af34c",
   "metadata": {},
   "source": [
    "# 20 days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3eee3fa4-b947-4451-9af4-0b0ab17f6d80",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-24T15:46:35.318062Z",
     "iopub.status.busy": "2021-11-24T15:46:35.317728Z",
     "iopub.status.idle": "2021-11-24T15:46:35.618899Z",
     "shell.execute_reply": "2021-11-24T15:46:35.618182Z",
     "shell.execute_reply.started": "2021-11-24T15:46:35.318033Z"
    },
    "tags": []
   },
   "outputs": [
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       "  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:   (lag: 21, plev: 1, time: 1565, what: 3)\n",
       "Coordinates:\n",
       "  * time      (time) datetime64[ns] 1979-01-06 1979-01-07 ... 2010-12-30\n",
       "  * plev      (plev) float64 70.0\n",
       "  * lag       (lag) int64 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10\n",
       "  * what      (what) &lt;U9 &#x27;himalayas&#x27; &#x27;eastasia&#x27; &#x27;westamer&#x27;\n",
       "Data variables:\n",
       "    accelogw  (what, lag, time, plev) float64 -0.1214 nan nan ... nan -0.6962</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-13c60a93-9404-4efc-b64b-246ec063e398' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-13c60a93-9404-4efc-b64b-246ec063e398' 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'>lag</span>: 21</li><li><span class='xr-has-index'>plev</span>: 1</li><li><span class='xr-has-index'>time</span>: 1565</li><li><span class='xr-has-index'>what</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-45d05a8f-868d-4cca-83e0-c6dfbbe70437' class='xr-section-summary-in' type='checkbox'  checked><label for='section-45d05a8f-868d-4cca-83e0-c6dfbbe70437' 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'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1979-01-06 ... 2010-12-30</div><input id='attrs-97930f8e-0a85-4937-a6de-564686d1011a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-97930f8e-0a85-4937-a6de-564686d1011a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7448e08-0002-4a3a-b34c-6f40c5bb7b40' class='xr-var-data-in' type='checkbox'><label for='data-b7448e08-0002-4a3a-b34c-6f40c5bb7b40' 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([&#x27;1979-01-06T00:00:00.000000000&#x27;, &#x27;1979-01-07T00:00:00.000000000&#x27;,\n",
       "       &#x27;1979-01-08T00:00:00.000000000&#x27;, ..., &#x27;2010-12-28T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-12-29T00:00:00.000000000&#x27;, &#x27;2010-12-30T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</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'>70.0</div><input id='attrs-c951b0df-3701-4751-8dba-93a44fde7827' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c951b0df-3701-4751-8dba-93a44fde7827' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd59ca31-855c-4fb1-aca4-11d65009e928' class='xr-var-data-in' type='checkbox'><label for='data-fd59ca31-855c-4fb1-aca4-11d65009e928' 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>hPa</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([70.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lag</span></div><div class='xr-var-dims'>(lag)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-10 -9 -8 -7 -6 -5 ... 5 6 7 8 9 10</div><input id='attrs-382719de-ea65-47bb-8445-b1fc8024d5f4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-382719de-ea65-47bb-8445-b1fc8024d5f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-532e188d-f54d-4812-a0a1-1b2f95a4d1af' class='xr-var-data-in' type='checkbox'><label for='data-532e188d-f54d-4812-a0a1-1b2f95a4d1af' 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([-10,  -9,  -8,  -7,  -6,  -5,  -4,  -3,  -2,  -1,   0,   1,   2,   3,\n",
       "         4,   5,   6,   7,   8,   9,  10])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>what</span></div><div class='xr-var-dims'>(what)</div><div class='xr-var-dtype'>&lt;U9</div><div class='xr-var-preview xr-preview'>&#x27;himalayas&#x27; &#x27;eastasia&#x27; &#x27;westamer&#x27;</div><input id='attrs-c9ba5754-e5ff-46db-89c3-459e14b3aea1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c9ba5754-e5ff-46db-89c3-459e14b3aea1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4522c80a-126a-4d28-abe9-d34248dda78b' class='xr-var-data-in' type='checkbox'><label for='data-4522c80a-126a-4d28-abe9-d34248dda78b' 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([&#x27;himalayas&#x27;, &#x27;eastasia&#x27;, &#x27;westamer&#x27;], dtype=&#x27;&lt;U9&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-58415c96-16d4-458a-bdd9-532374c57b00' class='xr-section-summary-in' type='checkbox'  checked><label for='section-58415c96-16d4-458a-bdd9-532374c57b00' 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>accelogw</span></div><div class='xr-var-dims'>(what, lag, time, plev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.1214 nan nan ... nan nan -0.6962</div><input id='attrs-9e3451f4-647e-4a43-8b8b-aa794d5162dc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9e3451f4-647e-4a43-8b8b-aa794d5162dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa04d113-b28e-4b69-a1f8-4f130593c2e9' class='xr-var-data-in' type='checkbox'><label for='data-aa04d113-b28e-4b69-a1f8-4f130593c2e9' 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([[[[-1.21385152e-01],\n",
       "         [            nan],\n",
       "         [            nan],\n",
       "         ...,\n",
       "         [            nan],\n",
       "         [            nan],\n",
       "         [            nan]],\n",
       "\n",
       "        [[            nan],\n",
       "         [-3.17276678e-01],\n",
       "         [            nan],\n",
       "         ...,\n",
       "         [            nan],\n",
       "         [            nan],\n",
       "         [            nan]],\n",
       "\n",
       "        [[            nan],\n",
       "         [            nan],\n",
       "         [-9.57147223e-05],\n",
       "         ...,\n",
       "...\n",
       "         ...,\n",
       "         [-3.39274403e-01],\n",
       "         [            nan],\n",
       "         [            nan]],\n",
       "\n",
       "        [[            nan],\n",
       "         [            nan],\n",
       "         [            nan],\n",
       "         ...,\n",
       "         [            nan],\n",
       "         [-4.54129757e-01],\n",
       "         [            nan]],\n",
       "\n",
       "        [[            nan],\n",
       "         [            nan],\n",
       "         [            nan],\n",
       "         ...,\n",
       "         [            nan],\n",
       "         [            nan],\n",
       "         [-6.96210132e-01]]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-da3686a6-26f9-4a03-8c17-11946cfa7fff' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-da3686a6-26f9-4a03-8c17-11946cfa7fff' 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:   (lag: 21, plev: 1, time: 1565, what: 3)\n",
       "Coordinates:\n",
       "  * time      (time) datetime64[ns] 1979-01-06 1979-01-07 ... 2010-12-30\n",
       "  * plev      (plev) float64 70.0\n",
       "  * lag       (lag) int64 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10\n",
       "  * what      (what) <U9 'himalayas' 'eastasia' 'westamer'\n",
       "Data variables:\n",
       "    accelogw  (what, lag, time, plev) float64 -0.1214 nan nan ... nan -0.6962"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name_ls = ['himalayas','eastasia','westamer']\n",
    "ds_ls2 = []\n",
    "max_lag = 10\n",
    "g_method = 'dayofyear' # 'month'\n",
    "timescale = 20\n",
    "\n",
    "for name in name_ls:\n",
    "    df_dates = open_date_file(f\"accelogw_{name}_hotspot@70hPa_{timescale}dayts_indexes.csv\")\n",
    "    \n",
    "    ds_ls = []\n",
    "    for il, lag in enumerate(range(-max_lag,max_lag+1)):\n",
    "        #print(lag)\n",
    "        dates = df_dates.set_index('BeginDate')\n",
    "\n",
    "        dates = dates.index + pd.Timedelta(str(lag)+' days')\n",
    "        #filter lags withi 29th February\n",
    "        #dates = dates[dates.apply(lambda x: not (x.day in [29] and x.month in [2]))]\n",
    "        dates = dates[~((dates.month == 2) & (dates.day == 29))]\n",
    "        #filter dates shited to year out of range\n",
    "        #dates = dates[dates.apply(lambda x: not (x.year in [1978,2011]))]\n",
    "        dates =  dates[(dates.year != 2011) & (dates.year != 1978)]\n",
    "        dates = dates[(dates.month == 12) | (dates.month == 1) | (dates.month == 2)]\n",
    "        \n",
    "        # plev = [300,200,30,10]\n",
    "        ds_sel_dates = ds.sel(time = dates, plev = [70], lat = slice(20,40)).mean('lat') \n",
    "        ds_ls.append(ds_sel_dates)\n",
    "        \n",
    "    temp = xr.concat(ds_ls, dim = 'lag')\n",
    "    temp['lag'] = range(-max_lag, max_lag+1)\n",
    "    ds_ls2.append(temp)\n",
    "    \n",
    "ds_all = xr.concat(ds_ls2, dim = 'what')\n",
    "ds_all['what'] = name_ls\n",
    "ds_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f2a75555-b931-4deb-8b43-bdb5c7281a78",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-24T15:46:35.619962Z",
     "iopub.status.busy": "2021-11-24T15:46:35.619796Z",
     "iopub.status.idle": "2021-11-24T15:46:35.664703Z",
     "shell.execute_reply": "2021-11-24T15:46:35.663948Z",
     "shell.execute_reply.started": "2021-11-24T15:46:35.619942Z"
    },
    "tags": []
   },
   "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>variable</th>\n",
       "      <th>what</th>\n",
       "      <th>lag</th>\n",
       "      <th>plev</th>\n",
       "      <th>accelogw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>accelogw</td>\n",
       "      <td>himalayas</td>\n",
       "      <td>-10</td>\n",
       "      <td>70.0</td>\n",
       "      <td>-0.121385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>accelogw</td>\n",
       "      <td>himalayas</td>\n",
       "      <td>-10</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>accelogw</td>\n",
       "      <td>himalayas</td>\n",
       "      <td>-10</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>accelogw</td>\n",
       "      <td>himalayas</td>\n",
       "      <td>-10</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>accelogw</td>\n",
       "      <td>himalayas</td>\n",
       "      <td>-10</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   variable       what  lag  plev  accelogw\n",
       "0  accelogw  himalayas  -10  70.0 -0.121385\n",
       "1  accelogw  himalayas  -10  70.0       NaN\n",
       "2  accelogw  himalayas  -10  70.0       NaN\n",
       "3  accelogw  himalayas  -10  70.0       NaN\n",
       "4  accelogw  himalayas  -10  70.0       NaN"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all = ds_all.to_array().to_dataframe(name = sel_var).reset_index().drop(columns=['time'])#)\n",
    "df_all.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16baad01-982b-42ab-9d73-b512d06d3303",
   "metadata": {},
   "source": [
    "## visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8140dcce-f39f-4d43-82bd-567131dbf224",
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     "iopub.status.idle": "2021-11-24T15:49:55.833456Z",
     "shell.execute_reply": "2021-11-24T15:49:55.832698Z",
     "shell.execute_reply.started": "2021-11-24T15:49:51.086245Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sel_what = 'himalayas'\n",
    "threshold_dict = dict(aspect_ratio = 2.3, \n",
    "                      centroid_latitude = 72,\n",
    "                      uref_ratio = 0.4,\n",
    "                      kurtosis = 1.7,\n",
    "                      objective_area = None)\n",
    "                      \n",
    "mask = (df_all['what'] == sel_what) \n",
    "data = df_all[mask]#.drop(columns = '')\n",
    "\n",
    "g = sns.FacetGrid(data, row = 'plev', sharey=True,  height = 4, aspect = 3)#, margin_titles = True)\n",
    "g.map(sns.lineplot, \"lag\", sel_var, n_boot=10000)\n",
    "g.map(plt.axvline, x=0, c='gray', lw = 2)\n",
    "#g.map(plt.axhline, y=0, c='k', ls = 'dashed')\n",
    "#g.map(plt.axhline, y=0, c='k')\n",
    "g.axes[0][0].xaxis.set_major_locator(MultipleLocator(2))\n",
    "g.axes[0][0].set_ylabel('OGWD [m/s/day]')\n",
    "g.axes[-1][0].set_xlabel('lag [day]')\n",
    "#g.axes[0][0].set_title('Zonally averaged at 70 hPa and 20-40°N')\n",
    "g.axes[0][0].set_title('')\n",
    "plt.savefig(f'OGWD_ZM_composite.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e5036e5-dc49-415c-93a5-246423c30ea9",
   "metadata": {},
   "source": [
    "# 10 days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "87b3f6b5-1afd-49e4-ab00-0711a0b55410",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-15T15:04:42.248556Z",
     "iopub.status.busy": "2021-09-15T15:04:42.247991Z",
     "iopub.status.idle": "2021-09-15T15:04:42.638878Z",
     "shell.execute_reply": "2021-09-15T15:04:42.637719Z",
     "shell.execute_reply.started": "2021-09-15T15:04:42.248496Z"
    },
    "tags": []
   },
   "outputs": [
    {
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       "\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,\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:   (lag: 21, plev: 1, time: 1707, what: 3)\n",
       "Coordinates:\n",
       "  * time      (time) datetime64[ns] 1979-01-06 1979-01-07 ... 2010-12-30\n",
       "  * plev      (plev) float64 70.0\n",
       "  * lag       (lag) int64 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10\n",
       "  * what      (what) &lt;U9 &#x27;himalayas&#x27; &#x27;eastasia&#x27; &#x27;westamer&#x27;\n",
       "Data variables:\n",
       "    accelogw  (what, lag, time, plev) float64 -0.1214 nan nan ... nan -0.6962</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-a2d2b0ea-8c22-40fd-9cf8-d126558421cf' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a2d2b0ea-8c22-40fd-9cf8-d126558421cf' 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'>lag</span>: 21</li><li><span class='xr-has-index'>plev</span>: 1</li><li><span class='xr-has-index'>time</span>: 1707</li><li><span class='xr-has-index'>what</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-afc72b01-b046-46f2-a52c-95c39c551668' class='xr-section-summary-in' type='checkbox'  checked><label for='section-afc72b01-b046-46f2-a52c-95c39c551668' 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'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1979-01-06 ... 2010-12-30</div><input id='attrs-968b7510-d6a2-449c-8372-ff11931e7b07' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-968b7510-d6a2-449c-8372-ff11931e7b07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5a4acea-d480-48ad-8d08-95d289af6596' class='xr-var-data-in' type='checkbox'><label for='data-a5a4acea-d480-48ad-8d08-95d289af6596' 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([&#x27;1979-01-06T00:00:00.000000000&#x27;, &#x27;1979-01-07T00:00:00.000000000&#x27;,\n",
       "       &#x27;1979-01-08T00:00:00.000000000&#x27;, ..., &#x27;2010-12-28T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-12-29T00:00:00.000000000&#x27;, &#x27;2010-12-30T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</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'>70.0</div><input id='attrs-d381be1d-dc35-434c-8ab8-ca9ef5c8bf15' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d381be1d-dc35-434c-8ab8-ca9ef5c8bf15' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13a0bc91-812a-43a0-a06c-dffd77c7fb18' class='xr-var-data-in' type='checkbox'><label for='data-13a0bc91-812a-43a0-a06c-dffd77c7fb18' 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>hPa</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([70.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lag</span></div><div class='xr-var-dims'>(lag)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-10 -9 -8 -7 -6 -5 ... 5 6 7 8 9 10</div><input id='attrs-39d7b39d-6185-4bb3-9b8f-53c450a8ad3e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-39d7b39d-6185-4bb3-9b8f-53c450a8ad3e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a433808a-cec5-4e40-bc94-e587596fa59d' class='xr-var-data-in' type='checkbox'><label for='data-a433808a-cec5-4e40-bc94-e587596fa59d' 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([-10,  -9,  -8,  -7,  -6,  -5,  -4,  -3,  -2,  -1,   0,   1,   2,   3,\n",
       "         4,   5,   6,   7,   8,   9,  10])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>what</span></div><div class='xr-var-dims'>(what)</div><div class='xr-var-dtype'>&lt;U9</div><div class='xr-var-preview xr-preview'>&#x27;himalayas&#x27; &#x27;eastasia&#x27; &#x27;westamer&#x27;</div><input id='attrs-1ca82058-b1f4-4579-a433-ceab639d0d29' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1ca82058-b1f4-4579-a433-ceab639d0d29' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6282f2cc-9569-43b5-8976-a188fd7c16d0' class='xr-var-data-in' type='checkbox'><label for='data-6282f2cc-9569-43b5-8976-a188fd7c16d0' 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([&#x27;himalayas&#x27;, &#x27;eastasia&#x27;, &#x27;westamer&#x27;], dtype=&#x27;&lt;U9&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2a7aac8e-10bb-4a46-9451-c2fdd34dbe28' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2a7aac8e-10bb-4a46-9451-c2fdd34dbe28' 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>accelogw</span></div><div class='xr-var-dims'>(what, lag, time, plev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.1214 nan nan ... nan nan -0.6962</div><input id='attrs-f9b3dd9f-0bb8-46ec-81a4-6bce3be56859' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f9b3dd9f-0bb8-46ec-81a4-6bce3be56859' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2c23398-a618-4979-96d7-19be46f98778' class='xr-var-data-in' type='checkbox'><label for='data-a2c23398-a618-4979-96d7-19be46f98778' 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([[[[-1.21385152e-01],\n",
       "         [            nan],\n",
       "         [            nan],\n",
       "         ...,\n",
       "         [            nan],\n",
       "         [            nan],\n",
       "         [            nan]],\n",
       "\n",
       "        [[            nan],\n",
       "         [-3.17276678e-01],\n",
       "         [            nan],\n",
       "         ...,\n",
       "         [            nan],\n",
       "         [            nan],\n",
       "         [            nan]],\n",
       "\n",
       "        [[            nan],\n",
       "         [            nan],\n",
       "         [-9.57147223e-05],\n",
       "         ...,\n",
       "...\n",
       "         ...,\n",
       "         [-3.39274403e-01],\n",
       "         [            nan],\n",
       "         [            nan]],\n",
       "\n",
       "        [[            nan],\n",
       "         [            nan],\n",
       "         [            nan],\n",
       "         ...,\n",
       "         [            nan],\n",
       "         [-4.54129757e-01],\n",
       "         [            nan]],\n",
       "\n",
       "        [[            nan],\n",
       "         [            nan],\n",
       "         [            nan],\n",
       "         ...,\n",
       "         [            nan],\n",
       "         [            nan],\n",
       "         [-6.96210132e-01]]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-590dad14-aa40-4aad-bc4a-8131b7233dfe' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-590dad14-aa40-4aad-bc4a-8131b7233dfe' 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:   (lag: 21, plev: 1, time: 1707, what: 3)\n",
       "Coordinates:\n",
       "  * time      (time) datetime64[ns] 1979-01-06 1979-01-07 ... 2010-12-30\n",
       "  * plev      (plev) float64 70.0\n",
       "  * lag       (lag) int64 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10\n",
       "  * what      (what) <U9 'himalayas' 'eastasia' 'westamer'\n",
       "Data variables:\n",
       "    accelogw  (what, lag, time, plev) float64 -0.1214 nan nan ... nan -0.6962"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name_ls = ['himalayas','eastasia','westamer']\n",
    "ds_ls2 = []\n",
    "max_lag = 10\n",
    "g_method = 'dayofyear' # 'month'\n",
    "timescale = 10\n",
    "\n",
    "for name in name_ls:\n",
    "    df_dates = open_date_file(f\"accelogw_{name}_hotspot@70hPa_{timescale}dayts_indexes.csv\")\n",
    "    \n",
    "    ds_ls = []\n",
    "    for il, lag in enumerate(range(-max_lag,max_lag+1)):\n",
    "        #print(lag)\n",
    "        dates = df_dates.set_index('BeginDate')\n",
    "\n",
    "        dates = dates.index + pd.Timedelta(str(lag)+' days')\n",
    "        #filter lags withi 29th February\n",
    "        #dates = dates[dates.apply(lambda x: not (x.day in [29] and x.month in [2]))]\n",
    "        dates = dates[~((dates.month == 2) & (dates.day == 29))]\n",
    "        #filter dates shited to year out of range\n",
    "        #dates = dates[dates.apply(lambda x: not (x.year in [1978,2011]))]\n",
    "        dates =  dates[(dates.year != 2011) & (dates.year != 1978)]\n",
    "        dates = dates[(dates.month == 12) | (dates.month == 1) | (dates.month == 2)]\n",
    "        \n",
    "        # plev = [300,200,30,10]\n",
    "        ds_sel_dates = ds.sel(time = dates, plev = [70], lat = slice(20,40)).mean('lat') \n",
    "        ds_ls.append(ds_sel_dates)\n",
    "        \n",
    "    temp = xr.concat(ds_ls, dim = 'lag')\n",
    "    temp['lag'] = range(-max_lag, max_lag+1)\n",
    "    ds_ls2.append(temp)\n",
    "    \n",
    "ds_all = xr.concat(ds_ls2, dim = 'what')\n",
    "ds_all['what'] = name_ls\n",
    "ds_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "097cba57-8661-4f56-9d5f-25434b20eb36",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-15T15:04:46.317690Z",
     "iopub.status.busy": "2021-09-15T15:04:46.317155Z",
     "iopub.status.idle": "2021-09-15T15:04:46.368773Z",
     "shell.execute_reply": "2021-09-15T15:04:46.367777Z",
     "shell.execute_reply.started": "2021-09-15T15:04:46.317630Z"
    },
    "tags": []
   },
   "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>variable</th>\n",
       "      <th>what</th>\n",
       "      <th>lag</th>\n",
       "      <th>plev</th>\n",
       "      <th>accelogw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>accelogw</td>\n",
       "      <td>himalayas</td>\n",
       "      <td>-10</td>\n",
       "      <td>70.0</td>\n",
       "      <td>-0.121385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>accelogw</td>\n",
       "      <td>himalayas</td>\n",
       "      <td>-10</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>accelogw</td>\n",
       "      <td>himalayas</td>\n",
       "      <td>-10</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>accelogw</td>\n",
       "      <td>himalayas</td>\n",
       "      <td>-10</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>accelogw</td>\n",
       "      <td>himalayas</td>\n",
       "      <td>-10</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   variable       what  lag  plev  accelogw\n",
       "0  accelogw  himalayas  -10  70.0 -0.121385\n",
       "1  accelogw  himalayas  -10  70.0       NaN\n",
       "2  accelogw  himalayas  -10  70.0       NaN\n",
       "3  accelogw  himalayas  -10  70.0       NaN\n",
       "4  accelogw  himalayas  -10  70.0       NaN"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all = ds_all.to_array().to_dataframe(name = sel_var).reset_index().drop(columns=['time'])#)\n",
    "df_all.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "976b0fe5-55a5-4e66-811b-252dda1ddc1d",
   "metadata": {},
   "source": [
    "## visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fd4c1eb3-7c70-49f8-9257-fbb4eea9f621",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-15T15:04:47.666705Z",
     "iopub.status.busy": "2021-09-15T15:04:47.666106Z",
     "iopub.status.idle": "2021-09-15T15:04:52.268355Z",
     "shell.execute_reply": "2021-09-15T15:04:52.267560Z",
     "shell.execute_reply.started": "2021-09-15T15:04:47.666639Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Zonally averaged at 70 hPa and 20-40°N')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sel_what = 'himalayas'\n",
    "threshold_dict = dict(aspect_ratio = 2.3, \n",
    "                      centroid_latitude = 72,\n",
    "                      uref_ratio = 0.4,\n",
    "                      kurtosis = 1.7,\n",
    "                      objective_area = None)\n",
    "                      \n",
    "mask = (df_all['what'] == sel_what) \n",
    "data = df_all[mask]#.drop(columns = '')\n",
    "\n",
    "g = sns.FacetGrid(data, row = 'plev', sharey=True,  height = 4, aspect = 3)#, margin_titles = True)\n",
    "g.map(sns.lineplot, \"lag\", sel_var, n_boot=10000)\n",
    "g.map(plt.axvline, x=0, c='k')\n",
    "#g.map(plt.axhline, y=0, c='k', ls = 'dashed')\n",
    "#g.map(plt.axhline, y=0, c='k')\n",
    "g.axes[0][0].xaxis.set_major_locator(MultipleLocator(2))\n",
    "g.axes[0][0].set_ylabel('OGWD [m/s/day]')\n",
    "g.axes[-1][0].set_xlabel('lag [day]')\n",
    "g.axes[0][0].set_title('Zonally averaged at 70 hPa and 20-40°N')"
   ]
  }
 ],
 "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
}