code/OGWD+refr_index_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 itertools import product\n",
    "from cftime import DatetimeNoLeap\n",
    "from string import ascii_lowercase, ascii_uppercase\n",
    "\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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       "<xarray.core.options.set_options at 0x7f481c453f10>"
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    "sns.set_style('whitegrid')\n",
    "plt.rcParams.update({'font.size': 22})\n",
    "xr.set_options(keep_attrs=True,display_style='html')\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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   "metadata": {},
   "source": [
    "# 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\n",
    "\n",
    "def tm_mean_xr(x, dims, q = [0.1,0.9]):\n",
    "    qs = x.quantile(q, dim = dims)\n",
    "    return x.where((x > qs.sel(quantile = q[0])) & (x < qs.sel(quantile = q[1]))).mean(dims)"
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    "# acceldiv"
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   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "## Open data"
   ]
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      "/tmp/ipykernel_2671735/423478415.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"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:   (lat: 48, plev: 63, time: 11680)\n",
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       "  * time      (time) datetime64[ns] 1979-01-01 1979-01-02 ... 2010-12-31\n",
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       "  * 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-a7e9b7a0-41ff-4bf5-b329-efc09d8c24a0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a7e9b7a0-41ff-4bf5-b329-efc09d8c24a0' 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-a36c98f7-2f5a-4964-a30b-cdc4edd0b4e7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a36c98f7-2f5a-4964-a30b-cdc4edd0b4e7' 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-ad79a06f-be4d-466c-b645-c38e867912c4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ad79a06f-be4d-466c-b645-c38e867912c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73321a01-782a-431d-808c-afb209ca7466' class='xr-var-data-in' type='checkbox'><label for='data-73321a01-782a-431d-808c-afb209ca7466' 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-2b085e44-82da-48eb-ac9c-03a0b7f85b27' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2b085e44-82da-48eb-ac9c-03a0b7f85b27' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-423766ee-a610-4d39-b4b5-409d611cf36c' class='xr-var-data-in' type='checkbox'><label for='data-423766ee-a610-4d39-b4b5-409d611cf36c' 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-996d1b6d-b41b-46d7-a46f-08b7d0ad80d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-996d1b6d-b41b-46d7-a46f-08b7d0ad80d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8921561b-4f29-42eb-ab9d-aa96ba27d412' class='xr-var-data-in' type='checkbox'><label for='data-8921561b-4f29-42eb-ab9d-aa96ba27d412' 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-6f55e2fa-46f0-4eaf-a347-04a578ed136d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6f55e2fa-46f0-4eaf-a347-04a578ed136d' 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-32a9d0ff-94f4-467e-a60a-2a87d768fef9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-32a9d0ff-94f4-467e-a60a-2a87d768fef9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-768f4a2d-7d26-46be-84b6-9dbdb600ab23' class='xr-var-data-in' type='checkbox'><label for='data-768f4a2d-7d26-46be-84b6-9dbdb600ab23' 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-060b4155-70ef-4eb2-b9c9-c7b36dab7146' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-060b4155-70ef-4eb2-b9c9-c7b36dab7146' 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": 7,
     "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": 25,
   "id": "640d7e80-b9ff-4b3b-8183-e43bed750cca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-09T16:36:20.569290Z",
     "iopub.status.busy": "2022-02-09T16:36:20.568721Z",
     "iopub.status.idle": "2022-02-09T16:36:20.578968Z",
     "shell.execute_reply": "2022-02-09T16:36:20.577571Z",
     "shell.execute_reply.started": "2022-02-09T16:36:20.569227Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(hourly_index_temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "22e2915b-5ac0-477f-87e3-4bd2b6b47fc9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-09T16:34:42.240799Z",
     "iopub.status.busy": "2022-02-09T16:34:42.240099Z",
     "iopub.status.idle": "2022-02-09T16:34:42.250156Z",
     "shell.execute_reply": "2022-02-09T16:34:42.249234Z",
     "shell.execute_reply.started": "2022-02-09T16:34:42.240733Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "hourly_index_temp = [DatetimeNoLeap(*x, hour) for x, hour in product(zip(dates.year, dates.month, dates.day), range(0,24,6))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f4302216-bea6-485b-a342-30fa05674cda",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-09T16:37:03.434450Z",
     "iopub.status.busy": "2022-02-09T16:37:03.433928Z",
     "iopub.status.idle": "2022-02-09T16:37:03.531717Z",
     "shell.execute_reply": "2022-02-09T16:37:03.531005Z",
     "shell.execute_reply.started": "2022-02-09T16:37:03.434394Z"
    },
    "tags": []
   },
   "outputs": [
    {
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       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:   (lat: 6, plev: 1, time: 100)\n",
       "Coordinates:\n",
       "  * plev      (plev) float64 70.0\n",
       "  * lat       (lat) float64 20.41 24.12 27.83 31.54 35.26 38.97\n",
       "  * time      (time) object 1980-01-23 00:00:00 ... 2010-12-30 18:00:00\n",
       "    month     (time) int64 1 1 1 1 2 2 2 2 2 2 2 ... 12 12 2 2 2 2 12 12 12 12\n",
       "Data variables:\n",
       "    accelogw  (time, plev, lat) float64 -0.08245 -0.2654 ... -1.057 0.3821</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-cd63b2ba-faee-4e6a-8ecd-98da20a67b99' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cd63b2ba-faee-4e6a-8ecd-98da20a67b99' 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>: 6</li><li><span class='xr-has-index'>plev</span>: 1</li><li><span class='xr-has-index'>time</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-3d44d772-d4a3-4419-ad0f-7097ea3a2040' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3d44d772-d4a3-4419-ad0f-7097ea3a2040' 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'>70.0</div><input id='attrs-e4cf1be8-1f5f-4e6a-9a50-7ebb341ac98b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e4cf1be8-1f5f-4e6a-9a50-7ebb341ac98b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb817dcb-35fd-497c-b6ea-d611f037f649' class='xr-var-data-in' type='checkbox'><label for='data-fb817dcb-35fd-497c-b6ea-d611f037f649' 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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>20.41 24.12 27.83 31.54 35.26 38.97</div><input id='attrs-76817d42-63b7-4c70-a9da-df7dfee64363' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-76817d42-63b7-4c70-a9da-df7dfee64363' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6997ce7a-7c2f-4e06-ab10-4bea20aad4d8' class='xr-var-data-in' type='checkbox'><label for='data-6997ce7a-7c2f-4e06-ab10-4bea20aad4d8' 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([20.411238, 24.122348, 27.833444, 31.544523, 35.25558 , 38.96661 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1980-01-23 00:00:00 ... 2010-12-...</div><input id='attrs-e8e639ef-02f1-45d8-8900-4f46dedb753c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e8e639ef-02f1-45d8-8900-4f46dedb753c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe587e88-c873-402d-9113-352d0ed86c94' class='xr-var-data-in' type='checkbox'><label for='data-fe587e88-c873-402d-9113-352d0ed86c94' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1980, 1, 23, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1980, 1, 23, 6, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1980, 1, 23, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1980, 1, 23, 18, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1980, 2, 25, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1980, 2, 25, 6, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1980, 2, 25, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1980, 2, 25, 18, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1981, 2, 20, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1981, 2, 20, 6, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1981, 2, 20, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1981, 2, 20, 18, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1982, 1, 12, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1982, 1, 12, 6, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1982, 1, 12, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1982, 1, 12, 18, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1982, 2, 6, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1982, 2, 6, 6, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1982, 2, 6, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1982, 2, 6, 18, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1983, 12, 14, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1983, 12, 14, 6, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1983, 12, 14, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1983, 12, 14, 18, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1985, 1, 7, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1985, 1, 7, 6, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1985, 1, 7, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1985, 1, 7, 18, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1988, 1, 28, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1988, 1, 28, 6, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1988, 1, 28, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1988, 1, 28, 18, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1990, 1, 20, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1990, 1, 20, 6, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1990, 1, 20, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1990, 1, 20, 18, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1990, 12, 4, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1990, 12, 4, 6, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1990, 12, 4, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1990, 12, 4, 18, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1994, 1, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1994, 1, 15, 6, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1994, 1, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1994, 1, 15, 18, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1994, 12, 6, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1994, 12, 6, 6, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1994, 12, 6, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1994, 12, 6, 18, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1995, 12, 14, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1995, 12, 14, 6, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1995, 12, 14, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1995, 12, 14, 18, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1996, 1, 26, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1996, 1, 26, 6, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1996, 1, 26, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1996, 1, 26, 18, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1997, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1997, 1, 1, 6, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1997, 1, 1, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1997, 1, 1, 18, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1997, 2, 3, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1997, 2, 3, 6, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1997, 2, 3, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1997, 2, 3, 18, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1998, 12, 2, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1998, 12, 2, 6, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1998, 12, 2, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1998, 12, 2, 18, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1999, 1, 7, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1999, 1, 7, 6, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1999, 1, 7, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1999, 1, 7, 18, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2000, 1, 22, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2000, 1, 22, 6, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2000, 1, 22, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2000, 1, 22, 18, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2007, 12, 12, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2007, 12, 12, 6, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2007, 12, 12, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2007, 12, 12, 18, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2008, 2, 7, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2008, 2, 7, 6, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2008, 2, 7, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2008, 2, 7, 18, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2009, 1, 4, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2009, 1, 4, 6, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2009, 1, 4, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2009, 1, 4, 18, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2009, 12, 18, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2009, 12, 18, 6, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2009, 12, 18, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2009, 12, 18, 18, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2010, 2, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2010, 2, 1, 6, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2010, 2, 1, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2010, 2, 1, 18, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2010, 12, 30, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2010, 12, 30, 6, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2010, 12, 30, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2010, 12, 30, 18, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>month</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 1 1 1 2 2 2 ... 2 2 2 12 12 12 12</div><input id='attrs-38ffd52c-1610-4434-9c03-f3b3211cd4ce' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-38ffd52c-1610-4434-9c03-f3b3211cd4ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1490d7c5-c376-44a4-9bbc-966b15cf6d75' class='xr-var-data-in' type='checkbox'><label for='data-1490d7c5-c376-44a4-9bbc-966b15cf6d75' 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,  1,  1,  1,  2,  2,  2,  2,  2,  2,  2,  2,  1,  1,  1,  1,  2,  2,\n",
       "        2,  2, 12, 12, 12, 12,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "       12, 12, 12, 12,  1,  1,  1,  1, 12, 12, 12, 12, 12, 12, 12, 12,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  2,  2,  2,  2, 12, 12, 12, 12,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1, 12, 12, 12, 12,  2,  2,  2,  2,  1,  1,  1,  1, 12, 12,\n",
       "       12, 12,  2,  2,  2,  2, 12, 12, 12, 12])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d87fc924-b7dd-46ca-a165-8b64cd6b53b6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d87fc924-b7dd-46ca-a165-8b64cd6b53b6' 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'>-0.08245 -0.2654 ... -1.057 0.3821</div><input id='attrs-34ac58c5-5379-42ec-b30c-f79a25e6e17d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-34ac58c5-5379-42ec-b30c-f79a25e6e17d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-46074a57-f819-4963-92f5-f8cd4c4f2258' class='xr-var-data-in' type='checkbox'><label for='data-46074a57-f819-4963-92f5-f8cd4c4f2258' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-8.24477773e-02, -2.65375417e-01,  7.27874794e-02,\n",
       "         -8.38991229e-01,  9.91717023e-01,  3.98370191e-01]],\n",
       "\n",
       "       [[-5.33819578e-02, -2.32413718e-01, -4.92667555e-01,\n",
       "         -5.06500941e-01,  9.87751445e-01,  7.38743688e-02]],\n",
       "\n",
       "       [[-1.23942433e-01, -3.17770740e-01, -8.65629694e-01,\n",
       "         -7.18024978e-01, -2.21747076e-01, -1.46258187e-01]],\n",
       "\n",
       "       [[-1.14021964e-01, -3.38537496e-01, -4.81043587e-01,\n",
       "         -3.63267784e-01, -4.18806410e-01, -1.50085271e-01]],\n",
       "\n",
       "       [[-1.57380325e-01, -4.08611064e-01, -1.33102559e+00,\n",
       "         -3.24057557e-01,  7.87395392e-01,  2.18475297e-01]],\n",
       "\n",
       "       [[-1.48446889e-01, -2.99119917e-01, -1.18590184e+00,\n",
       "         -1.16936118e+00,  5.59525149e-01,  2.35304899e-01]],\n",
       "\n",
       "       [[-2.12347739e-01, -4.13418665e-01, -6.76733386e-01,\n",
       "         -1.24748852e+00,  2.24218815e-01,  3.44826800e-01]],\n",
       "...\n",
       "       [[ 9.10725371e-02,  1.09394217e-01,  1.12069081e-01,\n",
       "          4.03832564e-01,  8.28955107e-01,  2.62684344e-01]],\n",
       "\n",
       "       [[ 8.17955456e-02, -1.17785168e-01, -1.59251983e+00,\n",
       "         -4.28834855e-01,  8.38892708e-01,  5.52112089e-01]],\n",
       "\n",
       "       [[ 8.99721611e-02, -1.52295842e-02, -4.31597074e-01,\n",
       "         -3.33974451e-01,  7.26901402e-01,  7.93258358e-01]],\n",
       "\n",
       "       [[-2.68649196e-01, -1.90423971e-01, -1.95874915e-01,\n",
       "         -6.24456812e-01, -1.23896920e+00, -2.15079349e-01]],\n",
       "\n",
       "       [[-3.30707414e-01, -1.44757415e-01, -5.86411144e-01,\n",
       "         -1.43607967e+00, -1.45033359e+00,  7.72029612e-02]],\n",
       "\n",
       "       [[-3.17358475e-01, -5.45623871e-01, -1.34717793e+00,\n",
       "         -1.91460943e+00, -9.30125231e-01, -1.31837729e-02]],\n",
       "\n",
       "       [[-3.38364112e-01, -7.33225793e-01, -1.76874057e+00,\n",
       "         -1.52092821e+00, -1.05728376e+00,  3.82117716e-01]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f292ff72-24c0-43dc-8aeb-9a99f18f3b32' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f292ff72-24c0-43dc-8aeb-9a99f18f3b32' 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: 6, plev: 1, time: 100)\n",
       "Coordinates:\n",
       "  * plev      (plev) float64 70.0\n",
       "  * lat       (lat) float64 20.41 24.12 27.83 31.54 35.26 38.97\n",
       "  * time      (time) object 1980-01-23 00:00:00 ... 2010-12-30 18:00:00\n",
       "    month     (time) int64 1 1 1 1 2 2 2 2 2 2 2 ... 12 12 2 2 2 2 12 12 12 12\n",
       "Data variables:\n",
       "    accelogw  (time, plev, lat) float64 -0.08245 -0.2654 ... -1.057 0.3821"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.sel(time = hourly_index_temp, plev = [70], lat = slice(20,40))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a199d7db-0301-4107-bc2c-e9d259a283d0",
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     "iopub.execute_input": "2022-02-09T16:34:54.634186Z",
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     "iopub.status.idle": "2022-02-09T16:34:54.666086Z",
     "shell.execute_reply": "2022-02-09T16:34:54.665419Z",
     "shell.execute_reply.started": "2022-02-09T16:34:54.634123Z"
    },
    "tags": []
   },
   "outputs": [
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       ".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:   (lat: 6, plev: 1, time: 25)\n",
       "Coordinates:\n",
       "  * plev      (plev) float64 70.0\n",
       "  * lat       (lat) float64 20.41 24.12 27.83 31.54 35.26 38.97\n",
       "  * time      (time) datetime64[ns] 1980-01-23 1980-02-25 ... 2010-12-30\n",
       "    month     (time) int64 1 2 2 1 2 12 1 1 1 12 1 ... 1 2 12 1 1 12 2 1 12 2 12\n",
       "Data variables:\n",
       "    accelogw  (time, plev, lat) float64 -0.08245 -0.2654 ... -1.239 -0.2151</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-f1eb010f-e3e0-4340-994b-d2a5df8e5607' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f1eb010f-e3e0-4340-994b-d2a5df8e5607' 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>: 6</li><li><span class='xr-has-index'>plev</span>: 1</li><li><span class='xr-has-index'>time</span>: 25</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e8a741ea-2a7e-43f8-948a-4ff7433b724b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e8a741ea-2a7e-43f8-948a-4ff7433b724b' 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'>70.0</div><input id='attrs-dd3926d3-6ed7-49c2-a42d-17fc49866ca0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dd3926d3-6ed7-49c2-a42d-17fc49866ca0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16bc5a39-3b8d-4ec9-93c9-90e6a8a17699' class='xr-var-data-in' type='checkbox'><label for='data-16bc5a39-3b8d-4ec9-93c9-90e6a8a17699' 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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>20.41 24.12 27.83 31.54 35.26 38.97</div><input id='attrs-c4c11011-35ab-4b8d-bd77-b94e2829f140' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c4c11011-35ab-4b8d-bd77-b94e2829f140' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e0a9ac2e-f25b-43c7-abaa-7a2c69195764' class='xr-var-data-in' type='checkbox'><label for='data-e0a9ac2e-f25b-43c7-abaa-7a2c69195764' 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([20.411238, 24.122348, 27.833444, 31.544523, 35.25558 , 38.96661 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1980-01-23 ... 2010-12-30</div><input id='attrs-8ab77373-d458-46d3-b031-085c9fd2e45d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8ab77373-d458-46d3-b031-085c9fd2e45d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6bf1e28a-1f11-43fe-8e29-5ebb09601c9c' class='xr-var-data-in' type='checkbox'><label for='data-6bf1e28a-1f11-43fe-8e29-5ebb09601c9c' 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;1980-01-23T00:00:00.000000000&#x27;, &#x27;1980-02-25T00:00:00.000000000&#x27;,\n",
       "       &#x27;1981-02-20T00:00:00.000000000&#x27;, &#x27;1982-01-12T00:00:00.000000000&#x27;,\n",
       "       &#x27;1982-02-06T00:00:00.000000000&#x27;, &#x27;1983-12-14T00:00:00.000000000&#x27;,\n",
       "       &#x27;1985-01-07T00:00:00.000000000&#x27;, &#x27;1988-01-28T00:00:00.000000000&#x27;,\n",
       "       &#x27;1990-01-20T00:00:00.000000000&#x27;, &#x27;1990-12-04T00:00:00.000000000&#x27;,\n",
       "       &#x27;1994-01-15T00:00:00.000000000&#x27;, &#x27;1994-12-06T00:00:00.000000000&#x27;,\n",
       "       &#x27;1995-12-14T00:00:00.000000000&#x27;, &#x27;1996-01-26T00:00:00.000000000&#x27;,\n",
       "       &#x27;1997-01-01T00:00:00.000000000&#x27;, &#x27;1997-02-03T00:00:00.000000000&#x27;,\n",
       "       &#x27;1998-12-02T00:00:00.000000000&#x27;, &#x27;1999-01-07T00:00:00.000000000&#x27;,\n",
       "       &#x27;2000-01-22T00:00:00.000000000&#x27;, &#x27;2007-12-12T00:00:00.000000000&#x27;,\n",
       "       &#x27;2008-02-07T00:00:00.000000000&#x27;, &#x27;2009-01-04T00:00:00.000000000&#x27;,\n",
       "       &#x27;2009-12-18T00:00:00.000000000&#x27;, &#x27;2010-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-12-30T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>month</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 2 1 2 12 1 ... 1 12 2 1 12 2 12</div><input id='attrs-7a21f243-abd1-41af-8f6e-043214d04670' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7a21f243-abd1-41af-8f6e-043214d04670' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b018bbb3-7ed5-48a1-9118-2131ebfd3d04' class='xr-var-data-in' type='checkbox'><label for='data-b018bbb3-7ed5-48a1-9118-2131ebfd3d04' 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,  2,  1,  2, 12,  1,  1,  1, 12,  1, 12, 12,  1,  1,  2, 12,  1,\n",
       "        1, 12,  2,  1, 12,  2, 12])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-37019060-6327-4b89-bf5a-9141d9e7fc77' class='xr-section-summary-in' type='checkbox'  checked><label for='section-37019060-6327-4b89-bf5a-9141d9e7fc77' 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'>-0.08245 -0.2654 ... -1.239 -0.2151</div><input id='attrs-3d439f5f-5ea5-4348-9799-d93b500e1d53' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3d439f5f-5ea5-4348-9799-d93b500e1d53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-344af9a2-2341-4a2a-ba43-786a6cdb3665' class='xr-var-data-in' type='checkbox'><label for='data-344af9a2-2341-4a2a-ba43-786a6cdb3665' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-8.24477773e-02, -2.65375417e-01,  7.27874794e-02,\n",
       "         -8.38991229e-01,  9.91717023e-01,  3.98370191e-01]],\n",
       "\n",
       "       [[-1.57380325e-01, -4.08611064e-01, -1.33102559e+00,\n",
       "         -3.24057557e-01,  7.87395392e-01,  2.18475297e-01]],\n",
       "\n",
       "       [[ 9.61175106e-02,  3.95363628e-01,  6.07234012e-01,\n",
       "         -8.65045059e-01, -1.52379906e+00, -7.90735838e-01]],\n",
       "\n",
       "       [[ 4.91279459e-02, -2.32901157e-02,  4.97532637e-02,\n",
       "          5.78502964e-01,  9.59676467e-02, -4.20178121e-01]],\n",
       "\n",
       "       [[-2.28634608e-02,  7.93838599e-02,  1.98301390e-01,\n",
       "          8.03820463e-01,  9.08095496e-01,  1.11241975e-01]],\n",
       "\n",
       "       [[-8.01503031e-02, -8.94199103e-02,  3.00975979e-01,\n",
       "          4.51235761e-01, -4.19169730e-01,  4.81723285e-01]],\n",
       "\n",
       "       [[ 3.21813818e-02, -1.37756693e-01, -7.39602787e-01,\n",
       "         -7.66142703e-01,  3.67739276e-01,  7.64908787e-01]],\n",
       "...\n",
       "       [[ 2.93337787e-02, -3.11669284e-02, -2.16452098e-01,\n",
       "         -4.26243137e-02, -6.70851635e-01, -6.35355138e-01]],\n",
       "\n",
       "       [[ 9.95404657e-03, -1.47560687e-01,  3.54753562e-01,\n",
       "         -6.74315754e-02,  3.20584344e-01,  6.42248607e-01]],\n",
       "\n",
       "       [[ 9.45437657e-02,  2.62924801e-01,  5.69591221e-01,\n",
       "          2.04563980e-01, -8.75909345e-02,  5.56366444e-01]],\n",
       "\n",
       "       [[ 4.43652504e-02, -5.99747789e-04, -8.75382959e-02,\n",
       "          2.07848761e-02, -4.72730372e-02,  3.53901065e-01]],\n",
       "\n",
       "       [[-3.09861259e-02,  7.37329676e-02, -5.25161658e-01,\n",
       "         -8.02890386e-01, -1.44015913e+00, -1.52512336e+00]],\n",
       "\n",
       "       [[ 6.78292143e-02,  2.27241290e-01,  5.40631270e-01,\n",
       "         -9.57651397e-02,  7.59727738e-01,  2.29012721e-01]],\n",
       "\n",
       "       [[-2.68649196e-01, -1.90423971e-01, -1.95874915e-01,\n",
       "         -6.24456812e-01, -1.23896920e+00, -2.15079349e-01]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-001033a9-fb59-4c1d-b296-1211a4f4da8f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-001033a9-fb59-4c1d-b296-1211a4f4da8f' 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: 6, plev: 1, time: 25)\n",
       "Coordinates:\n",
       "  * plev      (plev) float64 70.0\n",
       "  * lat       (lat) float64 20.41 24.12 27.83 31.54 35.26 38.97\n",
       "  * time      (time) datetime64[ns] 1980-01-23 1980-02-25 ... 2010-12-30\n",
       "    month     (time) int64 1 2 2 1 2 12 1 1 1 12 1 ... 1 2 12 1 1 12 2 1 12 2 12\n",
       "Data variables:\n",
       "    accelogw  (time, plev, lat) float64 -0.08245 -0.2654 ... -1.239 -0.2151"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.sel(time = dates, plev = [70], lat = slice(20,40))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3eee3fa4-b947-4451-9af4-0b0ab17f6d80",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-02T13:41:32.065077Z",
     "iopub.status.busy": "2022-03-02T13:41:32.064483Z",
     "iopub.status.idle": "2022-03-02T13:41:32.848907Z",
     "shell.execute_reply": "2022-03-02T13:41:32.847700Z",
     "shell.execute_reply.started": "2022-03-02T13:41:32.065026Z"
    },
    "tags": []
   },
   "outputs": [
    {
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       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\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: 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-506a916f-cdec-44af-848c-0be2f21e6aed' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-506a916f-cdec-44af-848c-0be2f21e6aed' 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-fa90fd3b-72e9-49b9-b3c9-954adfe9c29e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fa90fd3b-72e9-49b9-b3c9-954adfe9c29e' 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-657a5958-9985-48e5-b4b4-c81a495a78f4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-657a5958-9985-48e5-b4b4-c81a495a78f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f164316-667a-4228-8a0f-7c25ea9c1ec5' class='xr-var-data-in' type='checkbox'><label for='data-6f164316-667a-4228-8a0f-7c25ea9c1ec5' 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-5acadcab-bb79-4e84-bcb6-59479943c516' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5acadcab-bb79-4e84-bcb6-59479943c516' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-98802e74-ba0b-408c-a3e7-cab57a867e79' class='xr-var-data-in' type='checkbox'><label for='data-98802e74-ba0b-408c-a3e7-cab57a867e79' 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-9a093f04-9caf-4554-95d8-ce72ebd7bf0f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9a093f04-9caf-4554-95d8-ce72ebd7bf0f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32900e4f-2fd3-4d56-868e-52a050e08827' class='xr-var-data-in' type='checkbox'><label for='data-32900e4f-2fd3-4d56-868e-52a050e08827' 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-7171124b-7f2d-4a07-ae60-f92898662ecc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7171124b-7f2d-4a07-ae60-f92898662ecc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d4f854f-e1a2-4528-a87c-160e3c296a8a' class='xr-var-data-in' type='checkbox'><label for='data-9d4f854f-e1a2-4528-a87c-160e3c296a8a' 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-e4320846-3ddc-4b82-9ad4-bb5fa1b35670' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e4320846-3ddc-4b82-9ad4-bb5fa1b35670' 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-f324e0a6-eb4d-4ee2-9485-04fa7c365fbc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f324e0a6-eb4d-4ee2-9485-04fa7c365fbc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17260283-581b-4f63-a2db-15f886526614' class='xr-var-data-in' type='checkbox'><label for='data-17260283-581b-4f63-a2db-15f886526614' 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-623def60-8435-4398-b4cc-006cdb0839c0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-623def60-8435-4398-b4cc-006cdb0839c0' 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": 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 = 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",
    "        hourly_index_temp = [DatetimeNoLeap(*x, hour) for x, hour in product(zip(dates.year, dates.month, dates.day), range(0,24,6))]\n",
    "        # plev = [300,200,30,10]\n",
    "        ds_sel_dates = ds.sel(time = dates, plev = [70], lat = slice(20,40)).mean('lat') # daily\n",
    "        #ds_sel_dates = ds.sel(time = hourly_index_temp, plev = [70], lat = slice(20,40)) # 6-hourly\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": "f2a75555-b931-4deb-8b43-bdb5c7281a78",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-02T13:41:38.699093Z",
     "iopub.status.busy": "2022-03-02T13:41:38.697921Z",
     "iopub.status.idle": "2022-03-02T13:41:38.753580Z",
     "shell.execute_reply": "2022-03-02T13:41:38.752590Z",
     "shell.execute_reply.started": "2022-03-02T13:41:38.699003Z"
    },
    "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": "16baad01-982b-42ab-9d73-b512d06d3303",
   "metadata": {},
   "source": [
    "## visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8140dcce-f39f-4d43-82bd-567131dbf224",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-09T16:37:48.958104Z",
     "iopub.status.busy": "2022-02-09T16:37:48.957425Z",
     "iopub.status.idle": "2022-02-09T16:37:55.203794Z",
     "shell.execute_reply": "2022-02-09T16:37:55.203036Z",
     "shell.execute_reply.started": "2022-02-09T16:37:48.958045Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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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_ylbabel('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": "d5481d3b-057b-4c56-adf0-53921d16172d",
   "metadata": {},
   "source": [
    "# Refractive index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "16146c2d-31f1-470a-9380-7917abece437",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-02T13:41:47.336095Z",
     "iopub.status.busy": "2022-03-02T13:41:47.335500Z",
     "iopub.status.idle": "2022-03-02T13:42:23.973842Z",
     "shell.execute_reply": "2022-03-02T13:42:23.972862Z",
     "shell.execute_reply.started": "2022-03-02T13:41:47.336046Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2671735/3836862584.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"
     ]
    },
    {
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       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:     (lat: 48, plev: 63, time: 11680, wavenumber: 3)\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",
       "  * wavenumber  (wavenumber) int64 1 2 3\n",
       "Data variables:\n",
       "    refr_index  (time, wavenumber, plev, lat) float64 1.6e+08 ... -3.095e+08</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-871c29f7-eef9-4abe-b4a5-a9c28f090ef1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-871c29f7-eef9-4abe-b4a5-a9c28f090ef1' 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><li><span class='xr-has-index'>wavenumber</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1e653827-1835-434b-9959-84aea89e3adb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1e653827-1835-434b-9959-84aea89e3adb' 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'>1979-01-01 ... 2010-12-31</div><input id='attrs-af1d08db-6143-4a2a-bb8c-b1bf73d4479d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-af1d08db-6143-4a2a-bb8c-b1bf73d4479d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a86d790-71eb-44e9-90b9-ae98802f723a' class='xr-var-data-in' type='checkbox'><label for='data-2a86d790-71eb-44e9-90b9-ae98802f723a' 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-f7e2db65-6d92-459e-96b2-cfd8ab44fa12' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f7e2db65-6d92-459e-96b2-cfd8ab44fa12' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8761c4b-349e-483b-8a29-ba23f5b13111' class='xr-var-data-in' type='checkbox'><label for='data-d8761c4b-349e-483b-8a29-ba23f5b13111' 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-2772276b-937c-433c-b757-16e5fdd4eebe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2772276b-937c-433c-b757-16e5fdd4eebe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71a6a474-4bf8-486a-9428-9a90b00f0e95' class='xr-var-data-in' type='checkbox'><label for='data-71a6a474-4bf8-486a-9428-9a90b00f0e95' 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'>wavenumber</span></div><div class='xr-var-dims'>(wavenumber)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3</div><input id='attrs-23882dd7-acfb-4583-8ce6-b82c94e6eed1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-23882dd7-acfb-4583-8ce6-b82c94e6eed1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-39b3f942-ce54-4a51-8c81-f4c3643f238b' class='xr-var-data-in' type='checkbox'><label for='data-39b3f942-ce54-4a51-8c81-f4c3643f238b' 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])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2b7df272-4ffc-4350-afb5-c05311d18760' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2b7df272-4ffc-4350-afb5-c05311d18760' 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>refr_index</span></div><div class='xr-var-dims'>(time, wavenumber, plev, lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.6e+08 -8.903e+06 ... -3.095e+08</div><input id='attrs-af2804b6-41ed-4fd3-91f6-e1611e5ef51c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-af2804b6-41ed-4fd3-91f6-e1611e5ef51c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26fc07d7-ae83-4087-a02a-02e91274c6ff' class='xr-var-data-in' type='checkbox'><label for='data-26fc07d7-ae83-4087-a02a-02e91274c6ff' 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([[[[ 1.60032991e+08, -8.90290256e+06,  6.99961750e+08, ...,\n",
       "           1.42866096e+08,  1.34261305e+08,  1.97663901e+09],\n",
       "         [ 1.56838483e+08,  1.53626496e+08,  3.07692350e+08, ...,\n",
       "           9.80989189e+07,  1.49999638e+08, -6.67069912e+08],\n",
       "         [ 2.20297768e+08,  4.36586548e+07,  2.08391410e+09, ...,\n",
       "           9.40547429e+07, -7.95280797e+08,  8.25260699e+06],\n",
       "         ...,\n",
       "         [ 8.97103105e+08,  4.11030951e+05,  1.35545890e+08, ...,\n",
       "           1.19652743e+07,  1.51815719e+07, -6.80742569e+05],\n",
       "         [ 2.97321442e+08, -1.30624409e+07, -1.39321254e+07, ...,\n",
       "          -5.93015437e+06, -2.85704337e+07, -3.82705394e+08],\n",
       "         [-5.93753954e+07,  1.00999109e+08,  9.24129959e+07, ...,\n",
       "          -4.20029151e+07,  2.50058032e+07, -1.42401408e+09]],\n",
       "\n",
       "        [[ 1.60032991e+08, -8.90290256e+06,  6.99961750e+08, ...,\n",
       "           1.42866096e+08,  1.34261305e+08,  1.97663901e+09],\n",
       "         [ 1.56838483e+08,  1.53626496e+08,  3.07692350e+08, ...,\n",
       "           9.80989189e+07,  1.49999638e+08, -6.67069912e+08],\n",
       "         [ 2.20297768e+08,  4.36586548e+07,  2.08391410e+09, ...,\n",
       "           9.40547429e+07, -7.95280797e+08,  8.25260699e+06],\n",
       "...\n",
       "         [ 4.40299324e+08, -7.17984993e+07, -1.91461869e+07, ...,\n",
       "          -7.49500000e+07,  2.38312974e+07,  4.22410317e+08],\n",
       "         [ 1.48997553e+09,  1.33641793e+08,  1.32772073e+08, ...,\n",
       "           2.65402905e+07,  2.78755181e+08,  6.63290146e+07],\n",
       "         [ 6.03650424e+08, -1.35361764e+08,  1.05324410e+07, ...,\n",
       "           2.12184327e+07,  5.75865420e+07, -3.09474328e+08]],\n",
       "\n",
       "        [[ 2.48740801e+07,  2.02397225e+10,  9.49675077e+06, ...,\n",
       "          -1.14453699e+09, -2.77659475e+08, -3.67032944e+08],\n",
       "         [ 3.16002646e+07, -3.22125140e+08, -2.50804323e+08, ...,\n",
       "          -5.09602541e+08,  2.22231641e+08, -3.43337881e+07],\n",
       "         [ 1.81714838e+07,  8.06288259e+09, -2.86712383e+08, ...,\n",
       "          -2.88996873e+08,  1.39162704e+09,  3.06256983e+08],\n",
       "         ...,\n",
       "         [ 4.40299324e+08, -7.17984993e+07, -1.91461869e+07, ...,\n",
       "          -7.49500000e+07,  2.38312974e+07,  4.22410317e+08],\n",
       "         [ 1.48997553e+09,  1.33641793e+08,  1.32772073e+08, ...,\n",
       "           2.65402905e+07,  2.78755181e+08,  6.63290146e+07],\n",
       "         [ 6.03650424e+08, -1.35361764e+08,  1.05324410e+07, ...,\n",
       "           2.12184327e+07,  5.75865420e+07, -3.09474328e+08]]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-190db6c5-9d00-4870-8866-8fc365d775b0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-190db6c5-9d00-4870-8866-8fc365d775b0' 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, wavenumber: 3)\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",
       "  * wavenumber  (wavenumber) int64 1 2 3\n",
       "Data variables:\n",
       "    refr_index  (time, wavenumber, plev, lat) float64 1.6e+08 ... -3.095e+08"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sel_var = 'refr_index'\n",
    "what = 'anomalies' # 'absolute'#\n",
    "infile = f'{root_path}{sel_var }/{sel_var}_6hrPlev_CMAM_CMAM30-SD_r1i1p1_19790101-20101231_{what}.nc'\n",
    "ds = xr.open_dataset(infile)\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": "code",
   "execution_count": 11,
   "id": "bc0aecda-72a9-4aff-aa79-e3cf2021c633",
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     "iopub.execute_input": "2022-03-02T13:42:23.976416Z",
     "iopub.status.busy": "2022-03-02T13:42:23.975897Z",
     "iopub.status.idle": "2022-03-02T13:42:25.722894Z",
     "shell.execute_reply": "2022-03-02T13:42:25.721933Z",
     "shell.execute_reply.started": "2022-03-02T13:42:23.976369Z"
    },
    "tags": []
   },
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:     (lag: 21, time: 1565, wavenumber: 3, what: 3)\n",
       "Coordinates:\n",
       "  * time        (time) datetime64[ns] 1979-01-06 1979-01-07 ... 2010-12-30\n",
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       "  * what        (what) &lt;U9 &#x27;himalayas&#x27; &#x27;eastasia&#x27; &#x27;westamer&#x27;\n",
       "Data variables:\n",
       "    refr_index  (what, lag, time, wavenumber) float64 -2.03e+05 ... -5.521e+05</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-5abbeb8a-7fe1-4a43-9a96-9254160cc7f8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5abbeb8a-7fe1-4a43-9a96-9254160cc7f8' 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'>time</span>: 1565</li><li><span class='xr-has-index'>wavenumber</span>: 3</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-64cf5492-cca1-48bd-a904-fcc8b74a156a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-64cf5492-cca1-48bd-a904-fcc8b74a156a' 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-5a599c0c-33f7-4ee3-b242-ed10c9505001' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5a599c0c-33f7-4ee3-b242-ed10c9505001' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a43ab13c-d5c0-4f16-b8e3-45fa438a48e4' class='xr-var-data-in' type='checkbox'><label for='data-a43ab13c-d5c0-4f16-b8e3-45fa438a48e4' 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'>wavenumber</span></div><div class='xr-var-dims'>(wavenumber)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3</div><input id='attrs-b46459b6-357a-4dfe-b104-46188657e752' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b46459b6-357a-4dfe-b104-46188657e752' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03129bd9-4eca-437e-ad06-a6ed4a171fc0' class='xr-var-data-in' type='checkbox'><label for='data-03129bd9-4eca-437e-ad06-a6ed4a171fc0' 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])</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-cecce4d0-1c41-4e34-b0ac-f6462664bf14' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cecce4d0-1c41-4e34-b0ac-f6462664bf14' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd7d3e2b-85bd-4073-b7a2-2135701c29e2' class='xr-var-data-in' type='checkbox'><label for='data-dd7d3e2b-85bd-4073-b7a2-2135701c29e2' 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-a2984e08-e94b-4ebc-bbc1-171c185b1d5a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a2984e08-e94b-4ebc-bbc1-171c185b1d5a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19720533-ef4a-4562-8126-574657ee4913' class='xr-var-data-in' type='checkbox'><label for='data-19720533-ef4a-4562-8126-574657ee4913' 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-57f64f35-e9d2-4112-b198-ca137e6e2c28' class='xr-section-summary-in' type='checkbox'  checked><label for='section-57f64f35-e9d2-4112-b198-ca137e6e2c28' 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>refr_index</span></div><div class='xr-var-dims'>(what, lag, time, wavenumber)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-2.03e+05 -2.03e+05 ... -5.521e+05</div><input id='attrs-549ee420-a4bf-46b9-a20e-85a3d27cf684' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-549ee420-a4bf-46b9-a20e-85a3d27cf684' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b4bae5b-cdd0-4d6e-b567-992c9f1e7031' class='xr-var-data-in' type='checkbox'><label for='data-9b4bae5b-cdd0-4d6e-b567-992c9f1e7031' 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([[[[-202985.08165793, -202985.08165793, -202985.08165793],\n",
       "         [             nan,              nan,              nan],\n",
       "         [             nan,              nan,              nan],\n",
       "         ...,\n",
       "         [             nan,              nan,              nan],\n",
       "         [             nan,              nan,              nan],\n",
       "         [             nan,              nan,              nan]],\n",
       "\n",
       "        [[             nan,              nan,              nan],\n",
       "         [-507155.7704302 , -507155.7704302 , -507155.7704302 ],\n",
       "         [             nan,              nan,              nan],\n",
       "         ...,\n",
       "         [             nan,              nan,              nan],\n",
       "         [             nan,              nan,              nan],\n",
       "         [             nan,              nan,              nan]],\n",
       "\n",
       "        [[             nan,              nan,              nan],\n",
       "         [             nan,              nan,              nan],\n",
       "         [-695786.61762889, -695786.61762889, -695786.61762889],\n",
       "         ...,\n",
       "...\n",
       "         ...,\n",
       "         [-621418.38586543, -621418.38586543, -621418.38586543],\n",
       "         [             nan,              nan,              nan],\n",
       "         [             nan,              nan,              nan]],\n",
       "\n",
       "        [[             nan,              nan,              nan],\n",
       "         [             nan,              nan,              nan],\n",
       "         [             nan,              nan,              nan],\n",
       "         ...,\n",
       "         [             nan,              nan,              nan],\n",
       "         [-328230.29589334, -328230.29589334, -328230.29589334],\n",
       "         [             nan,              nan,              nan]],\n",
       "\n",
       "        [[             nan,              nan,              nan],\n",
       "         [             nan,              nan,              nan],\n",
       "         [             nan,              nan,              nan],\n",
       "         ...,\n",
       "         [             nan,              nan,              nan],\n",
       "         [             nan,              nan,              nan],\n",
       "         [-552098.25894827, -552098.25894827, -552098.25894827]]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a1754d6e-f94a-457b-bf53-867e1b22dd73' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a1754d6e-f94a-457b-bf53-867e1b22dd73' 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, time: 1565, wavenumber: 3, what: 3)\n",
       "Coordinates:\n",
       "  * time        (time) datetime64[ns] 1979-01-06 1979-01-07 ... 2010-12-30\n",
       "  * wavenumber  (wavenumber) int64 1 2 3\n",
       "  * lag         (lag) int64 -10 -9 -8 -7 -6 -5 -4 -3 -2 ... 2 3 4 5 6 7 8 9 10\n",
       "  * what        (what) <U9 'himalayas' 'eastasia' 'westamer'\n",
       "Data variables:\n",
       "    refr_index  (what, lag, time, wavenumber) float64 -2.03e+05 ... -5.521e+05"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name_ls = ['himalayas','eastasia','westamer']\n",
    "ds_ls2 = []\n",
    "max_lag = 10\n",
    "g_method = 'dayofyear' # 'month'\n",
    "\n",
    "for name in name_ls:\n",
    "    df_dates = open_date_file(f\"accelogw_{name}_hotspot@70hPa_20dayts_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",
    "        hourly_index_temp = [DatetimeNoLeap(*x, hour) for x, hour in product(zip(dates.year, dates.month, dates.day), range(0,24,6))]\n",
    "        \n",
    "        ds_sel_dates = ds.sel(time = dates, \\\n",
    "                              plev = slice(200,70), \\\n",
    "                              lat = slice(35,45))#.mean(['lat','plev'])\n",
    "        \"\"\"\n",
    "        ds_sel_dates = ds.sel(time = hourly_index_temp, \\\n",
    "                              plev = slice(200,70), \\\n",
    "                              lat = slice(35,45))#.mean(['lat','plev']) \n",
    "        \"\"\"\n",
    "        temp = tm_mean_xr(ds_sel_dates, ['lat','plev'])#(trim_mean(ds_sel_dates['refr_index'].stack(points = ('lat','plev')).values, 0.1, axis=2)\n",
    "        ds_ls.append(temp)#xr.DataArray(temp, dims = ['wavenumber']))\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": 12,
   "id": "6124e632-d277-46fc-9f7e-ca24ed97aff7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-02T13:42:25.724957Z",
     "iopub.status.busy": "2022-03-02T13:42:25.724375Z",
     "iopub.status.idle": "2022-03-02T13:42:25.813980Z",
     "shell.execute_reply": "2022-03-02T13:42:25.813089Z",
     "shell.execute_reply.started": "2022-03-02T13:42:25.724908Z"
    },
    "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>wavenumber</th>\n",
       "      <th>accelogw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>refr_index</td>\n",
       "      <td>himalayas</td>\n",
       "      <td>-10</td>\n",
       "      <td>1</td>\n",
       "      <td>-202985.081658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>refr_index</td>\n",
       "      <td>himalayas</td>\n",
       "      <td>-10</td>\n",
       "      <td>2</td>\n",
       "      <td>-202985.081658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>refr_index</td>\n",
       "      <td>himalayas</td>\n",
       "      <td>-10</td>\n",
       "      <td>3</td>\n",
       "      <td>-202985.081658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>refr_index</td>\n",
       "      <td>himalayas</td>\n",
       "      <td>-10</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>refr_index</td>\n",
       "      <td>himalayas</td>\n",
       "      <td>-10</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     variable       what  lag  wavenumber       accelogw\n",
       "0  refr_index  himalayas  -10           1 -202985.081658\n",
       "1  refr_index  himalayas  -10           2 -202985.081658\n",
       "2  refr_index  himalayas  -10           3 -202985.081658\n",
       "3  refr_index  himalayas  -10           1            NaN\n",
       "4  refr_index  himalayas  -10           2            NaN"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all2 = ds_all.to_array().to_dataframe(name = sel_var).reset_index().drop(columns=['time'])#)\n",
    "df_all2 = df_all2.rename(columns={'refr_index':'accelogw'})\n",
    "df_all2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2ac30dbe-f01c-4985-a2d1-a9787038a58f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-09T16:44:47.322048Z",
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     "iopub.status.idle": "2022-02-09T16:44:52.068169Z",
     "shell.execute_reply": "2022-02-09T16:44:52.067450Z",
     "shell.execute_reply.started": "2022-02-09T16:44:47.321985Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 43.76000000000002, 'lag [day]')"
      ]
     },
     "execution_count": 43,
     "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_all2['what'] == sel_what) & (df_all2['wavenumber'] == 1) \n",
    "data = df_all2[mask]#.drop(columns = '')\n",
    "\n",
    "g = sns.FacetGrid(data,  sharey=True,  height = 4, aspect = 3)#, margin_titles = True) row = 'wavenumber',\n",
    "g.map(sns.lineplot, \"lag\", 'accelogw', n_boot=10000)\n",
    "g.map(plt.axvline, x=0, c='gray', lw = 2)\n",
    "#g.set(yscale='log')\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('refr. index')\n",
    "g.axes[0][0].set_xlabel('lag [day]')\n",
    "#plt.savefig(f'refr_lagA_composite_CI.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6ab2463c-239a-4eb3-9aa2-2e4484b4e035",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-09T16:42:55.680693Z",
     "iopub.status.busy": "2022-02-09T16:42:55.680115Z",
     "iopub.status.idle": "2022-02-09T16:42:55.688748Z",
     "shell.execute_reply": "2022-02-09T16:42:55.687967Z",
     "shell.execute_reply.started": "2022-02-09T16:42:55.680630Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3549420"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.shape[0]+df_all2.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c6f8edb2-82f9-4d1f-b4b0-fba3aab39737",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-02T13:48:05.599085Z",
     "iopub.status.busy": "2022-03-02T13:48:05.598555Z",
     "iopub.status.idle": "2022-03-02T13:48:05.672120Z",
     "shell.execute_reply": "2022-03-02T13:48:05.671256Z",
     "shell.execute_reply.started": "2022-03-02T13:48:05.599036Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(65730, 6)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sel_what = 'himalayas'\n",
    "mask = (df_all['what'] == sel_what) \n",
    "data = df_all[mask]\n",
    "mask = (df_all2['what'] == sel_what) & (df_all2['wavenumber'] == 1) \n",
    "data2 = df_all2[mask]\n",
    "df_conc = pd.concat([data2, data])\n",
    "df_conc.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e1ac527b-58ea-4e71-8359-e8383cf47ab9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-09T16:51:29.428486Z",
     "iopub.status.busy": "2022-02-09T16:51:29.427969Z",
     "iopub.status.idle": "2022-02-09T16:51:29.436158Z",
     "shell.execute_reply": "2022-02-09T16:51:29.435516Z",
     "shell.execute_reply.started": "2022-02-09T16:51:29.428432Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 1)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.axes.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ed07124d-690a-4e3b-b797-572f1b3cbeb7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-02T13:51:05.041140Z",
     "iopub.status.busy": "2022-03-02T13:51:05.039526Z",
     "iopub.status.idle": "2022-03-02T13:51:19.647770Z",
     "shell.execute_reply": "2022-03-02T13:51:19.646304Z",
     "shell.execute_reply.started": "2022-03-02T13:51:05.041032Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 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",
    "#.drop(columns = '')\n",
    "\n",
    "g = sns.FacetGrid(df_conc,  sharey=False,  row = 'variable', height = 4, aspect = 3)#, margin_titles = True) row = 'wavenumber',\n",
    "g.map(sns.lineplot, \"lag\", 'accelogw', n_boot=10000)\n",
    "g.map(plt.axvline, x=0, c='gray', lw = 2)\n",
    "#g.set(yscale='log')\n",
    "#g.map(plt.axhline, y=0, c='k')\n",
    "g.axes[1][0].xaxis.set_major_locator(MultipleLocator(2))\n",
    "g.axes[1][0].set_ylabel('OGWD [m/s/day]')\n",
    "g.axes[1][0].set_title('')\n",
    "g.axes[0][0].set_ylabel('refr. index')\n",
    "g.axes[0][0].set_xlabel('lag [day]')\n",
    "g.axes[0][0].set_title('')\n",
    "\n",
    "for i, ax in enumerate(g.axes.flat): #range(3): \n",
    "    ax.text(0.06, 0.15, ascii_uppercase[i], transform=ax.transAxes,\n",
    "                      fontsize=24, fontweight='bold', va='top', \n",
    "            bbox={'alpha': 0.7, 'facecolor': 'white'})\n",
    "plt.savefig(f'OGWDzm+refr_lagA_composite_CI_daily.pdf', bbox_inches='tight')"
   ]
  }
 ],
 "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
}