{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "through-treasurer",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "from datetime import datetime, timedelta\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "promising-preparation",
   "metadata": {},
   "source": [
    "I want to plot the simulated winds for WLEF ( at 30 and 100m) with the measured winds. Both 1 minute and 30 minute averaged."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "prospective-joining",
   "metadata": {},
   "source": [
    "For WLEF I don't have to go through Brian's processed data. Can pull them in from the server. "
   ]
  },
  {
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   "execution_count": 2,
   "id": "photographic-tonight",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/mnt/c/Users/Sreenath/Documents/palm/Cheyenne/realistic_IOP03'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "empirical-aircraft",
   "metadata": {},
   "outputs": [],
   "source": [
    "tower_data = xr.open_dataset('tower_data/tower_and_meta_data.nc')\n",
    "tower_data.close()\n",
    "\n",
    "#subset for 2019 Sep 24 and Sep25\n",
    "f1 = tower_data.time>=267\n",
    "f2 = tower_data.time<=269"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "intensive-marshall",
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculate time variable and offset for UTC\n",
    "tower_data['time'] = (tower_data.DOY+tower_data.hour/24+tower_data.minute/60/24 - 5/24)\n",
    "tower_data = tower_data.set_coords(['time'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "devoted-invention",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:   (timestep: 5616, tower: 17)\n",
       "Coordinates:\n",
       "  * timestep  (timestep) float32 0.0 1.0 2.0 ... 5.613e+03 5.614e+03 5.615e+03\n",
       "  * tower     (tower) |S3 b&#x27;nw1&#x27; b&#x27;nw2&#x27; b&#x27;nw3&#x27; b&#x27;nw4&#x27; ... b&#x27;se4&#x27; b&#x27;se5&#x27; b&#x27;se6&#x27;\n",
       "    time      (timestep) float64 170.8 170.8 170.8 170.9 ... 287.7 287.7 287.8\n",
       "Data variables:\n",
       "    cov_wT    (timestep, tower) float32 nan nan -0.0267 nan ... nan nan nan nan\n",
       "    cov_wH2O  (timestep, tower) float32 nan nan 0.03993 nan ... nan nan nan nan\n",
       "    cov_wCO2  (timestep, tower) float32 nan nan 1.119 nan ... nan nan nan nan\n",
       "    f_Hs      (timestep, tower) float32 nan nan -30.16 nan ... nan nan nan nan\n",
       "    f_Hl      (timestep, tower) float32 nan nan 68.47 nan ... nan nan nan nan\n",
       "    f_CO2     (timestep, tower) float32 nan nan 1.119 nan ... nan nan nan nan\n",
       "    ustar     (timestep, tower) float32 nan nan 0.1886 0.3965 ... nan nan nan\n",
       "    wstar     (timestep, tower) float32 9.969e+36 9.969e+36 ... 9.969e+36\n",
       "    R_net     (timestep, tower) float32 nan nan nan 59.42 ... nan nan nan nan\n",
       "    ghf       (timestep, tower) float32 8.721 nan 11.6 12.22 ... -0.0 -0.0 -0.0\n",
       "    S_store   (timestep, tower) float32 nan nan nan nan 0.0 ... 0.0 0.0 0.0 0.0\n",
       "    T_store   (timestep, tower) float32 nan nan -0.4062 nan ... nan nan nan nan\n",
       "    Q_store   (timestep, tower) float32 nan nan 0.04815 nan ... nan nan nan nan\n",
       "    u         (timestep, tower) float32 nan nan -0.4987 -2.516 ... nan nan nan\n",
       "    v         (timestep, tower) float32 nan nan -1.604 -1.946 ... nan nan nan\n",
       "    w         (timestep, tower) float32 nan nan -0.1089 0.6126 ... nan nan nan\n",
       "    T         (timestep, tower) float32 nan nan 293.8 294.1 ... nan nan nan nan\n",
       "    q         (timestep, tower) float32 nan nan 10.11 nan ... nan nan nan nan\n",
       "    year      (timestep) float32 2.019e+03 2.019e+03 ... 2.019e+03 2.019e+03\n",
       "    month     (timestep) float32 6.0 6.0 6.0 6.0 6.0 ... 10.0 10.0 10.0 10.0\n",
       "    day       (timestep) float32 20.0 20.0 20.0 20.0 ... 14.0 14.0 14.0 14.0\n",
       "    DOY       (timestep) int32 171 171 171 171 171 171 ... 287 287 287 287 287\n",
       "    hour      (timestep) float32 0.0 0.0 1.0 1.0 2.0 ... 22.0 22.0 23.0 23.0\n",
       "    minute    (timestep) float32 0.0 30.0 0.0 30.0 0.0 ... 0.0 30.0 0.0 30.0\n",
       "    z_meas    (tower) float32 32.0 12.0 3.0 32.0 32.0 ... 32.0 3.0 12.0 32.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-fb0021df-bc00-4546-9332-7987b562dc9a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fb0021df-bc00-4546-9332-7987b562dc9a' 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'>timestep</span>: 5616</li><li><span class='xr-has-index'>tower</span>: 17</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-979a4be8-eb97-4072-8a4a-2ba58ee115f4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-979a4be8-eb97-4072-8a4a-2ba58ee115f4' 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'>timestep</span></div><div class='xr-var-dims'>(timestep)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0 ... 5.614e+03 5.615e+03</div><input id='attrs-7462a79f-c57f-44f3-8c07-a8b9f02226c0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7462a79f-c57f-44f3-8c07-a8b9f02226c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4118e5dd-7163-4e09-a24b-dbccb54fe18f' class='xr-var-data-in' type='checkbox'><label for='data-4118e5dd-7163-4e09-a24b-dbccb54fe18f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.000e+00, 1.000e+00, 2.000e+00, ..., 5.613e+03, 5.614e+03, 5.615e+03],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>tower</span></div><div class='xr-var-dims'>(tower)</div><div class='xr-var-dtype'>|S3</div><div class='xr-var-preview xr-preview'>b&#x27;nw1&#x27; b&#x27;nw2&#x27; ... b&#x27;se5&#x27; b&#x27;se6&#x27;</div><input id='attrs-1d4eb23e-fdba-4307-a6c2-898b19815c00' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1d4eb23e-fdba-4307-a6c2-898b19815c00' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a13d9a24-b00e-43ff-b429-e4ffc5270626' class='xr-var-data-in' type='checkbox'><label for='data-a13d9a24-b00e-43ff-b429-e4ffc5270626' 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([b&#x27;nw1&#x27;, b&#x27;nw2&#x27;, b&#x27;nw3&#x27;, b&#x27;nw4&#x27;, b&#x27;ne1&#x27;, b&#x27;ne2&#x27;, b&#x27;ne3&#x27;, b&#x27;ne4&#x27;, b&#x27;sw1&#x27;,\n",
       "       b&#x27;sw2&#x27;, b&#x27;sw3&#x27;, b&#x27;sw4&#x27;, b&#x27;se2&#x27;, b&#x27;se3&#x27;, b&#x27;se4&#x27;, b&#x27;se5&#x27;, b&#x27;se6&#x27;],\n",
       "      dtype=&#x27;|S3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(timestep)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>170.8 170.8 170.8 ... 287.7 287.8</div><input id='attrs-224ca96a-7c95-4490-a824-91a48e214d76' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-224ca96a-7c95-4490-a824-91a48e214d76' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02eaa85b-5041-412c-a3ce-ef71bf272ba2' class='xr-var-data-in' type='checkbox'><label for='data-02eaa85b-5041-412c-a3ce-ef71bf272ba2' 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([170.79166667, 170.8125    , 170.83333333, ..., 287.72916669,\n",
       "       287.74999998, 287.77083331])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-22e9857e-4b49-49f3-ab36-eeedc03f8e57' class='xr-section-summary-in' type='checkbox'  ><label for='section-22e9857e-4b49-49f3-ab36-eeedc03f8e57' class='xr-section-summary' >Data variables: <span>(25)</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>cov_wT</span></div><div class='xr-var-dims'>(timestep, tower)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bd46c56a-e455-408b-b04b-f581f13bdbad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd46c56a-e455-408b-b04b-f581f13bdbad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99f67231-c559-4f5f-aeb0-c97df647b3bc' class='xr-var-data-in' type='checkbox'><label for='data-99f67231-c559-4f5f-aeb0-c97df647b3bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>K m s-1</dd></dl></div><div class='xr-var-data'><pre>array([[      nan,       nan, -0.026703, ...,       nan,       nan,       nan],\n",
       "       [      nan,       nan, -0.025013, ...,       nan,       nan,       nan],\n",
       "       [      nan,       nan, -0.031405, ...,       nan,       nan,       nan],\n",
       "       ...,\n",
       "       [      nan,       nan, -0.016991, ...,       nan,       nan,       nan],\n",
       "       [      nan,       nan, -0.053063, ...,       nan,       nan,       nan],\n",
       "       [      nan,       nan,       nan, ...,       nan,       nan,       nan]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cov_wH2O</span></div><div class='xr-var-dims'>(timestep, tower)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fbe04d85-4eb5-4a5a-b86e-d566d9f2d277' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fbe04d85-4eb5-4a5a-b86e-d566d9f2d277' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b65013a-140c-4337-be66-0ae9c5cd30f5' class='xr-var-data-in' type='checkbox'><label for='data-6b65013a-140c-4337-be66-0ae9c5cd30f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg kg-1 m s-1</dd></dl></div><div class='xr-var-data'><pre>array([[     nan,      nan, 0.039932, ...,      nan,      nan,      nan],\n",
       "       [     nan,      nan, 0.034378, ...,      nan,      nan,      nan],\n",
       "       [     nan,      nan, 0.038521, ...,      nan,      nan,      nan],\n",
       "       ...,\n",
       "       [     nan,      nan, 0.00862 , ...,      nan,      nan,      nan],\n",
       "       [     nan,      nan, 0.018269, ...,      nan,      nan,      nan],\n",
       "       [     nan,      nan, 0.003931, ...,      nan,      nan,      nan]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cov_wCO2</span></div><div class='xr-var-dims'>(timestep, tower)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d4a4d312-21d9-4b61-bced-7576d4e6f847' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d4a4d312-21d9-4b61-bced-7576d4e6f847' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3799ec0-5516-41f6-a4ff-66c9baa4f388' class='xr-var-data-in' type='checkbox'><label for='data-f3799ec0-5516-41f6-a4ff-66c9baa4f388' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>mymol mol-1 m s-1</dd></dl></div><div class='xr-var-data'><pre>array([[      nan,       nan,  1.119354, ...,       nan,       nan,       nan],\n",
       "       [      nan,       nan,  3.143258, ...,       nan,       nan,       nan],\n",
       "       [      nan,       nan,  5.404742, ...,       nan,       nan,       nan],\n",
       "       ...,\n",
       "       [      nan,       nan,  1.128974, ...,       nan,       nan,       nan],\n",
       "       [      nan,       nan, -1.063081, ...,       nan,       nan,       nan],\n",
       "       [      nan,       nan,  2.326698, ...,       nan,       nan,       nan]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>f_Hs</span></div><div class='xr-var-dims'>(timestep, tower)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3649c8c2-8975-4226-92e4-02cf4f4111b9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3649c8c2-8975-4226-92e4-02cf4f4111b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c2f0602-81b3-4388-9b04-fc5c55d54515' class='xr-var-data-in' type='checkbox'><label for='data-9c2f0602-81b3-4388-9b04-fc5c55d54515' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><pre>array([[       nan,        nan, -30.159956, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan, -28.3329  , ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan, -35.68713 , ...,        nan,        nan,\n",
       "               nan],\n",
       "       ...,\n",
       "       [       nan,        nan, -20.262838, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan, -63.793026, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>f_Hl</span></div><div class='xr-var-dims'>(timestep, tower)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c26cf6ba-c359-468e-9446-6fd68257bf2a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c26cf6ba-c359-468e-9446-6fd68257bf2a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-731980eb-1d70-4223-a023-5eb2cd497413' class='xr-var-data-in' type='checkbox'><label for='data-731980eb-1d70-4223-a023-5eb2cd497413' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><pre>array([[      nan,       nan, 68.46817 , ...,       nan,       nan,       nan],\n",
       "       [      nan,       nan, 59.172993, ...,       nan,       nan,       nan],\n",
       "       [      nan,       nan, 66.57911 , ...,       nan,       nan,       nan],\n",
       "       ...,\n",
       "       [      nan,       nan, 15.806147, ...,       nan,       nan,       nan],\n",
       "       [      nan,       nan, 33.847023, ...,       nan,       nan,       nan],\n",
       "       [      nan,       nan,  7.353924, ...,       nan,       nan,       nan]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>f_CO2</span></div><div class='xr-var-dims'>(timestep, tower)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-aac048d5-7d7d-43c2-aed1-b67990974799' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aac048d5-7d7d-43c2-aed1-b67990974799' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-904e075f-a257-4f60-a0d7-b16792f2026c' class='xr-var-data-in' type='checkbox'><label for='data-904e075f-a257-4f60-a0d7-b16792f2026c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><pre>array([[      nan,       nan,  1.119354, ...,       nan,       nan,       nan],\n",
       "       [      nan,       nan,  3.143258, ...,       nan,       nan,       nan],\n",
       "       [      nan,       nan,  5.404742, ...,       nan,       nan,       nan],\n",
       "       ...,\n",
       "       [      nan,       nan,  1.128974, ...,       nan,       nan,       nan],\n",
       "       [      nan,       nan, -1.063081, ...,       nan,       nan,       nan],\n",
       "       [      nan,       nan,  2.326698, ...,       nan,       nan,       nan]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ustar</span></div><div class='xr-var-dims'>(timestep, tower)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7045144b-9aad-416b-b392-5614b27c8f9b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7045144b-9aad-416b-b392-5614b27c8f9b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ac45e82-02dd-46c1-beae-10ba329e3c85' class='xr-var-data-in' type='checkbox'><label for='data-8ac45e82-02dd-46c1-beae-10ba329e3c85' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><pre>array([[     nan,      nan, 0.188587, ..., 0.237281,      nan,      nan],\n",
       "       [     nan,      nan, 0.138664, ..., 0.197773,      nan,      nan],\n",
       "       [     nan,      nan, 0.168554, ..., 0.173564,      nan,      nan],\n",
       "       ...,\n",
       "       [     nan,      nan, 0.16697 , ...,      nan,      nan,      nan],\n",
       "       [     nan,      nan, 0.247247, ...,      nan,      nan,      nan],\n",
       "       [     nan,      nan, 0.026412, ...,      nan,      nan,      nan]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wstar</span></div><div class='xr-var-dims'>(timestep, tower)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3fead853-2868-4e61-bc2a-e68aec9983d5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3fead853-2868-4e61-bc2a-e68aec9983d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-595b8ca7-811d-473f-bf76-21ea61b6c0ea' class='xr-var-data-in' type='checkbox'><label for='data-595b8ca7-811d-473f-bf76-21ea61b6c0ea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><pre>array([[9.96921e+36, 9.96921e+36, 9.96921e+36, ..., 9.96921e+36, 9.96921e+36,\n",
       "        9.96921e+36],\n",
       "       [9.96921e+36, 9.96921e+36, 9.96921e+36, ..., 9.96921e+36, 9.96921e+36,\n",
       "        9.96921e+36],\n",
       "       [9.96921e+36, 9.96921e+36, 9.96921e+36, ..., 9.96921e+36, 9.96921e+36,\n",
       "        9.96921e+36],\n",
       "       ...,\n",
       "       [9.96921e+36, 9.96921e+36, 9.96921e+36, ..., 9.96921e+36, 9.96921e+36,\n",
       "        9.96921e+36],\n",
       "       [9.96921e+36, 9.96921e+36, 9.96921e+36, ..., 9.96921e+36, 9.96921e+36,\n",
       "        9.96921e+36],\n",
       "       [9.96921e+36, 9.96921e+36, 9.96921e+36, ..., 9.96921e+36, 9.96921e+36,\n",
       "        9.96921e+36]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>R_net</span></div><div class='xr-var-dims'>(timestep, tower)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-edc10bd4-5ccf-461b-86bd-86c5cad1f843' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-edc10bd4-5ccf-461b-86bd-86c5cad1f843' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22ba4743-2d46-4ad3-ad4e-733da721d00a' class='xr-var-data-in' type='checkbox'><label for='data-22ba4743-2d46-4ad3-ad4e-733da721d00a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><pre>array([[       nan,        nan,        nan, ...,  98.8553  ,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,  11.278423,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ..., -25.517384,        nan,\n",
       "               nan],\n",
       "       ...,\n",
       "       [       nan,        nan, -34.673714, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan, -66.01016 , ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan, -67.23102 , ...,        nan,        nan,\n",
       "               nan]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ghf</span></div><div class='xr-var-dims'>(timestep, tower)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7d9a4012-5726-4899-a660-ee72330acae1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7d9a4012-5726-4899-a660-ee72330acae1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a7a194b-8b34-4709-ab46-6170ff16527e' class='xr-var-data-in' type='checkbox'><label for='data-1a7a194b-8b34-4709-ab46-6170ff16527e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><pre>array([[ 8.721212,       nan, 11.599653, ..., -0.      ,       nan,       nan],\n",
       "       [ 8.282206,       nan, 11.66592 , ..., -0.      ,       nan,       nan],\n",
       "       [ 7.710792,       nan, 11.447599, ..., -0.      ,       nan,       nan],\n",
       "       ...,\n",
       "       [-0.      ,       nan, -8.79652 , ..., -0.      , -0.      , -0.      ],\n",
       "       [-0.      ,       nan, -8.580209, ..., -0.      , -0.      , -0.      ],\n",
       "       [-0.      ,       nan, -8.399653, ..., -0.      , -0.      , -0.      ]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>S_store</span></div><div class='xr-var-dims'>(timestep, tower)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9399c169-08c9-4f09-80c9-ff22b474742d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9399c169-08c9-4f09-80c9-ff22b474742d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f282fe1b-7fbb-443c-9cee-24d223bded04' class='xr-var-data-in' type='checkbox'><label for='data-f282fe1b-7fbb-443c-9cee-24d223bded04' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><pre>array([[     nan,      nan,      nan, ..., 0.      ,      nan,      nan],\n",
       "       [     nan,      nan,      nan, ..., 0.      ,      nan,      nan],\n",
       "       [     nan,      nan,      nan, ..., 0.      ,      nan,      nan],\n",
       "       ...,\n",
       "       [0.      ,      nan, 6.315117, ..., 0.      , 0.      , 0.      ],\n",
       "       [0.      ,      nan, 6.956841, ..., 0.      , 0.      , 0.      ],\n",
       "       [0.      ,      nan, 5.308681, ..., 0.      , 0.      , 0.      ]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>T_store</span></div><div class='xr-var-dims'>(timestep, tower)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-06b0affa-ae66-4208-9d30-cd14934a81ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-06b0affa-ae66-4208-9d30-cd14934a81ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73a0ac2e-bb99-4a18-94c9-de018d44c5d2' class='xr-var-data-in' type='checkbox'><label for='data-73a0ac2e-bb99-4a18-94c9-de018d44c5d2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><pre>array([[      nan,       nan, -0.406208, ...,       nan,       nan,       nan],\n",
       "       [      nan,       nan, -0.938196, ...,       nan,       nan,       nan],\n",
       "       [      nan,       nan, -0.464067, ...,       nan,       nan,       nan],\n",
       "       ...,\n",
       "       [      nan,       nan, -1.533359, ...,       nan,       nan,       nan],\n",
       "       [      nan,       nan, -2.515758, ...,       nan,       nan,       nan],\n",
       "       [      nan,       nan, -0.859207, ...,       nan,       nan,       nan]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Q_store</span></div><div class='xr-var-dims'>(timestep, tower)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e2ba5da0-a9ca-4461-a319-1a0b353dfd21' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e2ba5da0-a9ca-4461-a319-1a0b353dfd21' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e71d8025-906a-411b-8578-427355b5bdf1' class='xr-var-data-in' type='checkbox'><label for='data-e71d8025-906a-411b-8578-427355b5bdf1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><pre>array([[      nan,       nan,  0.048146, ...,       nan,       nan,       nan],\n",
       "       [      nan,       nan,  0.682183, ...,       nan,       nan,       nan],\n",
       "       [      nan,       nan, -0.220659, ...,       nan,       nan,       nan],\n",
       "       ...,\n",
       "       [      nan,       nan,  0.408843, ...,       nan,       nan,       nan],\n",
       "       [      nan,       nan,  0.308482, ...,       nan,       nan,       nan],\n",
       "       [      nan,       nan, -0.25063 , ...,       nan,       nan,       nan]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u</span></div><div class='xr-var-dims'>(timestep, tower)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d6fab2d2-c1c7-47f0-baaa-eb867368cd53' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6fab2d2-c1c7-47f0-baaa-eb867368cd53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca18806e-a884-4601-bab4-6c4eab2c35b4' class='xr-var-data-in' type='checkbox'><label for='data-ca18806e-a884-4601-bab4-6c4eab2c35b4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><pre>array([[      nan,       nan, -0.498715, ..., -0.914024,       nan,       nan],\n",
       "       [      nan,       nan, -0.464199, ..., -0.92605 ,       nan,       nan],\n",
       "       [      nan,       nan, -0.562639, ..., -0.682546,       nan,       nan],\n",
       "       ...,\n",
       "       [      nan,       nan,  0.59202 , ...,       nan,       nan,       nan],\n",
       "       [      nan,       nan,  0.006304, ...,       nan,       nan,       nan],\n",
       "       [      nan,       nan, -0.017322, ...,       nan,       nan,       nan]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(timestep, tower)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-301b5e5d-2fde-416c-ac1a-97a40be3db38' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-301b5e5d-2fde-416c-ac1a-97a40be3db38' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c92d8dc-e970-436b-ab23-03599bdb3e1f' class='xr-var-data-in' type='checkbox'><label for='data-0c92d8dc-e970-436b-ab23-03599bdb3e1f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><pre>array([[      nan,       nan, -1.604266, ..., -1.032957,       nan,       nan],\n",
       "       [      nan,       nan, -1.113737, ..., -0.956631,       nan,       nan],\n",
       "       [      nan,       nan, -1.154891, ..., -0.697563,       nan,       nan],\n",
       "       ...,\n",
       "       [      nan,       nan,  0.956378, ...,       nan,       nan,       nan],\n",
       "       [      nan,       nan,  0.014832, ...,       nan,       nan,       nan],\n",
       "       [      nan,       nan, -0.091024, ...,       nan,       nan,       nan]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>w</span></div><div class='xr-var-dims'>(timestep, tower)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8b4e4134-afa6-4cda-829f-a912c1fa022a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b4e4134-afa6-4cda-829f-a912c1fa022a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c79d538-e923-49ef-bbe1-13a41963d416' class='xr-var-data-in' type='checkbox'><label for='data-6c79d538-e923-49ef-bbe1-13a41963d416' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><pre>array([[      nan,       nan, -0.10888 , ..., -0.034653,       nan,       nan],\n",
       "       [      nan,       nan, -0.066821, ..., -0.033728,       nan,       nan],\n",
       "       [      nan,       nan, -0.086447, ...,  0.011597,       nan,       nan],\n",
       "       ...,\n",
       "       [      nan,       nan,  0.048318, ...,       nan,       nan,       nan],\n",
       "       [      nan,       nan,  0.013532, ...,       nan,       nan,       nan],\n",
       "       [      nan,       nan,  0.006933, ...,       nan,       nan,       nan]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>T</span></div><div class='xr-var-dims'>(timestep, tower)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-84e7052d-af9c-4e95-b938-c0f429f6790b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84e7052d-af9c-4e95-b938-c0f429f6790b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31bd9fdd-f3ef-4bdd-924d-a90846b68918' class='xr-var-data-in' type='checkbox'><label for='data-31bd9fdd-f3ef-4bdd-924d-a90846b68918' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><pre>array([[      nan,       nan, 293.81552, ..., 293.75693,       nan,       nan],\n",
       "       [      nan,       nan, 292.8149 , ..., 293.0093 ,       nan,       nan],\n",
       "       [      nan,       nan, 291.88367, ..., 291.42538,       nan,       nan],\n",
       "       ...,\n",
       "       [      nan,       nan, 280.16534, ...,       nan,       nan,       nan],\n",
       "       [      nan,       nan, 277.7816 , ...,       nan,       nan,       nan],\n",
       "       [      nan,       nan, 275.7071 , ...,       nan,       nan,       nan]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q</span></div><div class='xr-var-dims'>(timestep, tower)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ed336fc2-25c9-4a4a-af4b-cff7645b430d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed336fc2-25c9-4a4a-af4b-cff7645b430d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15e37f44-efa5-41ce-8bcc-413b06a7fb07' class='xr-var-data-in' type='checkbox'><label for='data-15e37f44-efa5-41ce-8bcc-413b06a7fb07' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>g/Kg</dd></dl></div><div class='xr-var-data'><pre>array([[      nan,       nan, 10.109797, ...,       nan,       nan,       nan],\n",
       "       [      nan,       nan, 10.622018, ...,       nan,       nan,       nan],\n",
       "       [      nan,       nan, 10.771578, ...,       nan,       nan,       nan],\n",
       "       ...,\n",
       "       [      nan,       nan,  6.533478, ...,       nan,       nan,       nan],\n",
       "       [      nan,       nan,  7.097571, ...,       nan,       nan,       nan],\n",
       "       [      nan,       nan,  6.768535, ...,       nan,       nan,       nan]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>year</span></div><div class='xr-var-dims'>(timestep)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fca1e529-d7d2-4fcf-b3e5-86b1faf89dfa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fca1e529-d7d2-4fcf-b3e5-86b1faf89dfa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60d32cf9-ef93-4282-9a98-9fb7931b0dad' class='xr-var-data-in' type='checkbox'><label for='data-60d32cf9-ef93-4282-9a98-9fb7931b0dad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>YYYY</dd></dl></div><div class='xr-var-data'><pre>array([2019., 2019., 2019., ..., 2019., 2019., 2019.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>month</span></div><div class='xr-var-dims'>(timestep)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0baba9b0-22ac-4d14-b201-b30305c39145' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0baba9b0-22ac-4d14-b201-b30305c39145' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fe7dc65-100c-4bf1-8341-41d40238368b' class='xr-var-data-in' type='checkbox'><label for='data-0fe7dc65-100c-4bf1-8341-41d40238368b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>MM</dd></dl></div><div class='xr-var-data'><pre>array([ 6.,  6.,  6., ..., 10., 10., 10.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>day</span></div><div class='xr-var-dims'>(timestep)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fc252434-b32a-44c7-8ee8-9907dd9a4cb7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc252434-b32a-44c7-8ee8-9907dd9a4cb7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6fb168e9-268d-4790-aa36-0f6a231caabe' class='xr-var-data-in' type='checkbox'><label for='data-6fb168e9-268d-4790-aa36-0f6a231caabe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>DD</dd></dl></div><div class='xr-var-data'><pre>array([20., 20., 20., ..., 14., 14., 14.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOY</span></div><div class='xr-var-dims'>(timestep)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6b27ea08-1eac-4296-bfde-fbedbf2cf883' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6b27ea08-1eac-4296-bfde-fbedbf2cf883' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-012cdeea-e05a-4b34-832a-9010f90bf212' class='xr-var-data-in' type='checkbox'><label for='data-012cdeea-e05a-4b34-832a-9010f90bf212' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>array([171, 171, 171, ..., 287, 287, 287], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hour</span></div><div class='xr-var-dims'>(timestep)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 1.0 1.0 ... 22.0 23.0 23.0</div><input id='attrs-cb57c902-9dc7-45a9-a0f5-ffbfae722f48' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cb57c902-9dc7-45a9-a0f5-ffbfae722f48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f38d849-3e52-4d4d-aed5-9976143f3609' class='xr-var-data-in' type='checkbox'><label for='data-9f38d849-3e52-4d4d-aed5-9976143f3609' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>hh</dd></dl></div><div class='xr-var-data'><pre>array([ 0.,  0.,  1., ..., 22., 23., 23.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>minute</span></div><div class='xr-var-dims'>(timestep)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 30.0 0.0 30.0 ... 30.0 0.0 30.0</div><input id='attrs-66c8cc20-ccb2-459e-b3fa-7670daafcdc2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-66c8cc20-ccb2-459e-b3fa-7670daafcdc2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6b086a8-85bd-41c2-bf98-7e91a7b4f7e9' class='xr-var-data-in' type='checkbox'><label for='data-a6b086a8-85bd-41c2-bf98-7e91a7b4f7e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>mm</dd></dl></div><div class='xr-var-data'><pre>array([ 0., 30.,  0., ..., 30.,  0., 30.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z_meas</span></div><div class='xr-var-dims'>(tower)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ff04a28f-3cb1-4af3-85de-a258edbf0744' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ff04a28f-3cb1-4af3-85de-a258edbf0744' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20832a33-6220-4c6d-8e01-9a7140e72442' class='xr-var-data-in' type='checkbox'><label for='data-20832a33-6220-4c6d-8e01-9a7140e72442' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([32., 12.,  3., 32., 32., 32., 32., 32., 32., 25., 32., 32., 32., 32.,\n",
       "        3., 12., 32.], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-67021186-631c-459f-bc63-b7ef59698389' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-67021186-631c-459f-bc63-b7ef59698389' 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:   (timestep: 5616, tower: 17)\n",
       "Coordinates:\n",
       "  * timestep  (timestep) float32 0.0 1.0 2.0 ... 5.613e+03 5.614e+03 5.615e+03\n",
       "  * tower     (tower) |S3 b'nw1' b'nw2' b'nw3' b'nw4' ... b'se4' b'se5' b'se6'\n",
       "    time      (timestep) float64 170.8 170.8 170.8 170.9 ... 287.7 287.7 287.8\n",
       "Data variables:\n",
       "    cov_wT    (timestep, tower) float32 ...\n",
       "    cov_wH2O  (timestep, tower) float32 ...\n",
       "    cov_wCO2  (timestep, tower) float32 ...\n",
       "    f_Hs      (timestep, tower) float32 ...\n",
       "    f_Hl      (timestep, tower) float32 ...\n",
       "    f_CO2     (timestep, tower) float32 ...\n",
       "    ustar     (timestep, tower) float32 ...\n",
       "    wstar     (timestep, tower) float32 ...\n",
       "    R_net     (timestep, tower) float32 ...\n",
       "    ghf       (timestep, tower) float32 ...\n",
       "    S_store   (timestep, tower) float32 ...\n",
       "    T_store   (timestep, tower) float32 ...\n",
       "    Q_store   (timestep, tower) float32 ...\n",
       "    u         (timestep, tower) float32 ...\n",
       "    v         (timestep, tower) float32 ...\n",
       "    w         (timestep, tower) float32 ...\n",
       "    T         (timestep, tower) float32 ...\n",
       "    q         (timestep, tower) float32 ...\n",
       "    year      (timestep) float32 ...\n",
       "    month     (timestep) float32 ...\n",
       "    day       (timestep) float32 ...\n",
       "    DOY       (timestep) int32 ...\n",
       "    hour      (timestep) float32 0.0 0.0 1.0 1.0 2.0 ... 22.0 22.0 23.0 23.0\n",
       "    minute    (timestep) float32 0.0 30.0 0.0 30.0 0.0 ... 0.0 30.0 0.0 30.0\n",
       "    z_meas    (tower) float32 ..."
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tower_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "native-magnet",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f71ac588a50>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tower_data['T'].mean(dim='tower').plot(color='black',x='time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "continued-mechanism",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f71ac33bcd0>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#subset for 2019 Sep 24 and Sep25\n",
    "f1 = tower_data.time>=267\n",
    "f2 = tower_data.time<268\n",
    "tower_data['T'].where(f1 & f2).mean(dim='tower').plot(color='black',x='time',marker = '*', figsize=(14,12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "excited-official",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48,)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(tower_data['T'].where(f1 & f2,drop=True).mean(dim='tower'))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}