{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bad674ea",
   "metadata": {},
   "source": [
    "## Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a5151687",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:39:23.359432Z",
     "start_time": "2022-08-30T18:39:23.355554Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import metview as mv\n",
    "import xarray as xr\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import binsreg\n",
    "from ipynb.fs.full.asm import seg2trans\n",
    "from ipynb.fs.full.asm import load_reg_nc\n",
    "from ipynb.fs.full.asm import binscatter\n",
    "sns.set_theme(style='darkgrid')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d14b0079",
   "metadata": {},
   "source": [
    "## Load data: segments over the Malay Peninsula and Sumatra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "04e2b3dd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:42:23.242680Z",
     "start_time": "2022-08-30T18:42:23.222696Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('/bog/amuttaqin/Datasets/Derived/segments_mc.csv')\n",
    "# Executed in 5ms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ea9fd852",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:42:26.243926Z",
     "start_time": "2022-08-30T18:42:26.239855Z"
    }
   },
   "outputs": [],
   "source": [
    "df_MS = df[(df['mainland'] == 'MalayPenin') | (df['mainland'] == 'Sumatra')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8f9f8110",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:42:28.343367Z",
     "start_time": "2022-08-30T18:42:28.328585Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        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>mainland</th>\n",
       "      <th>lat1</th>\n",
       "      <th>lon1</th>\n",
       "      <th>lat2</th>\n",
       "      <th>lon2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MalayPenin</td>\n",
       "      <td>14.9000</td>\n",
       "      <td>97.8000</td>\n",
       "      <td>13.6000</td>\n",
       "      <td>98.3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MalayPenin</td>\n",
       "      <td>11.6110</td>\n",
       "      <td>98.7840</td>\n",
       "      <td>8.9230</td>\n",
       "      <td>98.3240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MalayPenin</td>\n",
       "      <td>7.7000</td>\n",
       "      <td>99.2000</td>\n",
       "      <td>6.7000</td>\n",
       "      <td>99.9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MalayPenin</td>\n",
       "      <td>5.9050</td>\n",
       "      <td>100.3740</td>\n",
       "      <td>4.2000</td>\n",
       "      <td>100.6520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MalayPenin</td>\n",
       "      <td>3.7670</td>\n",
       "      <td>100.9110</td>\n",
       "      <td>3.1260</td>\n",
       "      <td>101.3420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MalayPenin</td>\n",
       "      <td>2.8460</td>\n",
       "      <td>101.3300</td>\n",
       "      <td>1.4800</td>\n",
       "      <td>103.4100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MalayPenin</td>\n",
       "      <td>2.6190</td>\n",
       "      <td>103.7610</td>\n",
       "      <td>1.8000</td>\n",
       "      <td>104.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MalayPenin</td>\n",
       "      <td>4.7330</td>\n",
       "      <td>103.4260</td>\n",
       "      <td>2.8760</td>\n",
       "      <td>103.4260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>MalayPenin</td>\n",
       "      <td>6.8860</td>\n",
       "      <td>101.4170</td>\n",
       "      <td>5.2500</td>\n",
       "      <td>103.2100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>MalayPenin</td>\n",
       "      <td>9.1580</td>\n",
       "      <td>99.8250</td>\n",
       "      <td>7.9680</td>\n",
       "      <td>100.2980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>MalayPenin</td>\n",
       "      <td>12.2010</td>\n",
       "      <td>99.9590</td>\n",
       "      <td>10.2710</td>\n",
       "      <td>99.2050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>MalayPenin</td>\n",
       "      <td>13.4418</td>\n",
       "      <td>100.0866</td>\n",
       "      <td>13.4982</td>\n",
       "      <td>100.8962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>MalayPenin</td>\n",
       "      <td>12.6900</td>\n",
       "      <td>101.8400</td>\n",
       "      <td>11.4220</td>\n",
       "      <td>103.1180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>MalayPenin</td>\n",
       "      <td>10.5970</td>\n",
       "      <td>104.0140</td>\n",
       "      <td>10.1000</td>\n",
       "      <td>105.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>MalayPenin</td>\n",
       "      <td>9.8011</td>\n",
       "      <td>104.8860</td>\n",
       "      <td>8.9580</td>\n",
       "      <td>104.8031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>MalayPenin</td>\n",
       "      <td>9.0500</td>\n",
       "      <td>105.4300</td>\n",
       "      <td>9.3415</td>\n",
       "      <td>106.1600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>MalayPenin</td>\n",
       "      <td>10.4270</td>\n",
       "      <td>107.3000</td>\n",
       "      <td>11.3500</td>\n",
       "      <td>108.9910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>MalayPenin</td>\n",
       "      <td>12.0000</td>\n",
       "      <td>109.2500</td>\n",
       "      <td>14.0000</td>\n",
       "      <td>109.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Sumatra</td>\n",
       "      <td>4.8500</td>\n",
       "      <td>95.4000</td>\n",
       "      <td>3.7500</td>\n",
       "      <td>96.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Sumatra</td>\n",
       "      <td>3.7000</td>\n",
       "      <td>96.8000</td>\n",
       "      <td>2.8000</td>\n",
       "      <td>97.6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Sumatra</td>\n",
       "      <td>2.4000</td>\n",
       "      <td>97.7000</td>\n",
       "      <td>1.9000</td>\n",
       "      <td>98.6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Sumatra</td>\n",
       "      <td>1.5000</td>\n",
       "      <td>98.8000</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>99.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Sumatra</td>\n",
       "      <td>-0.1353</td>\n",
       "      <td>99.7852</td>\n",
       "      <td>-1.9247</td>\n",
       "      <td>100.8829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Sumatra</td>\n",
       "      <td>-2.2500</td>\n",
       "      <td>100.8600</td>\n",
       "      <td>-3.6400</td>\n",
       "      <td>102.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Sumatra</td>\n",
       "      <td>-3.9500</td>\n",
       "      <td>102.3000</td>\n",
       "      <td>-5.9000</td>\n",
       "      <td>104.7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Sumatra</td>\n",
       "      <td>-2.9500</td>\n",
       "      <td>106.0000</td>\n",
       "      <td>-5.7000</td>\n",
       "      <td>105.8000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Sumatra</td>\n",
       "      <td>-1.0200</td>\n",
       "      <td>104.3800</td>\n",
       "      <td>-1.9000</td>\n",
       "      <td>104.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Sumatra</td>\n",
       "      <td>-0.7500</td>\n",
       "      <td>103.3000</td>\n",
       "      <td>-1.1500</td>\n",
       "      <td>104.3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Sumatra</td>\n",
       "      <td>0.0210</td>\n",
       "      <td>103.8162</td>\n",
       "      <td>0.5620</td>\n",
       "      <td>103.3926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Sumatra</td>\n",
       "      <td>2.1906</td>\n",
       "      <td>100.5620</td>\n",
       "      <td>2.6828</td>\n",
       "      <td>100.2093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Sumatra</td>\n",
       "      <td>4.0750</td>\n",
       "      <td>98.3160</td>\n",
       "      <td>2.9300</td>\n",
       "      <td>99.9600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Sumatra</td>\n",
       "      <td>5.1500</td>\n",
       "      <td>97.6000</td>\n",
       "      <td>4.4000</td>\n",
       "      <td>98.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Sumatra</td>\n",
       "      <td>5.2500</td>\n",
       "      <td>96.3000</td>\n",
       "      <td>5.2500</td>\n",
       "      <td>97.4000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      mainland     lat1      lon1     lat2      lon2\n",
       "0   MalayPenin  14.9000   97.8000  13.6000   98.3000\n",
       "1   MalayPenin  11.6110   98.7840   8.9230   98.3240\n",
       "2   MalayPenin   7.7000   99.2000   6.7000   99.9000\n",
       "3   MalayPenin   5.9050  100.3740   4.2000  100.6520\n",
       "4   MalayPenin   3.7670  100.9110   3.1260  101.3420\n",
       "5   MalayPenin   2.8460  101.3300   1.4800  103.4100\n",
       "6   MalayPenin   2.6190  103.7610   1.8000  104.2000\n",
       "7   MalayPenin   4.7330  103.4260   2.8760  103.4260\n",
       "8   MalayPenin   6.8860  101.4170   5.2500  103.2100\n",
       "9   MalayPenin   9.1580   99.8250   7.9680  100.2980\n",
       "10  MalayPenin  12.2010   99.9590  10.2710   99.2050\n",
       "11  MalayPenin  13.4418  100.0866  13.4982  100.8962\n",
       "12  MalayPenin  12.6900  101.8400  11.4220  103.1180\n",
       "13  MalayPenin  10.5970  104.0140  10.1000  105.1000\n",
       "14  MalayPenin   9.8011  104.8860   8.9580  104.8031\n",
       "15  MalayPenin   9.0500  105.4300   9.3415  106.1600\n",
       "16  MalayPenin  10.4270  107.3000  11.3500  108.9910\n",
       "17  MalayPenin  12.0000  109.2500  14.0000  109.2500\n",
       "18     Sumatra   4.8500   95.4000   3.7500   96.5000\n",
       "19     Sumatra   3.7000   96.8000   2.8000   97.6000\n",
       "20     Sumatra   2.4000   97.7000   1.9000   98.6000\n",
       "21     Sumatra   1.5000   98.8000   0.4000   99.1000\n",
       "22     Sumatra  -0.1353   99.7852  -1.9247  100.8829\n",
       "23     Sumatra  -2.2500  100.8600  -3.6400  102.2000\n",
       "24     Sumatra  -3.9500  102.3000  -5.9000  104.7000\n",
       "25     Sumatra  -2.9500  106.0000  -5.7000  105.8000\n",
       "26     Sumatra  -1.0200  104.3800  -1.9000  104.5000\n",
       "27     Sumatra  -0.7500  103.3000  -1.1500  104.3000\n",
       "28     Sumatra   0.0210  103.8162   0.5620  103.3926\n",
       "29     Sumatra   2.1906  100.5620   2.6828  100.2093\n",
       "30     Sumatra   4.0750   98.3160   2.9300   99.9600\n",
       "31     Sumatra   5.1500   97.6000   4.4000   98.2500\n",
       "32     Sumatra   5.2500   96.3000   5.2500   97.4000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_MS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0ef81da7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:42:33.361129Z",
     "start_time": "2022-08-30T18:42:33.358804Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(33, 5)\n"
     ]
    }
   ],
   "source": [
    "print(df_MS.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a32fbd7a",
   "metadata": {},
   "source": [
    "## Convert segments into transects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d3aaff3b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:42:55.644527Z",
     "start_time": "2022-08-30T18:42:54.955445Z"
    }
   },
   "outputs": [],
   "source": [
    "transects03, transects_qc03 = seg2trans(seg=df_MS, dist2coast=0.3, dist2trnsc=0.3)\n",
    "# Executed in 503ms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7ae2c98e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:43:01.048878Z",
     "start_time": "2022-08-30T18:43:00.677006Z"
    }
   },
   "outputs": [],
   "source": [
    "transects015, transects_qc015 = seg2trans(seg=df_MS, dist2coast=0.15, dist2trnsc=0.3)\n",
    "# Executed in 368ms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "04a1d6ed",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:43:10.198502Z",
     "start_time": "2022-08-30T18:43:10.187391Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat1</th>\n",
       "      <th>lon1</th>\n",
       "      <th>lat2</th>\n",
       "      <th>lon2</th>\n",
       "      <th>segment_index</th>\n",
       "      <th>lsm1</th>\n",
       "      <th>lsm2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13.7223</td>\n",
       "      <td>97.9315</td>\n",
       "      <td>13.9377</td>\n",
       "      <td>98.4915</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000095</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14.0023</td>\n",
       "      <td>97.8238</td>\n",
       "      <td>14.2177</td>\n",
       "      <td>98.3839</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.2823</td>\n",
       "      <td>97.7161</td>\n",
       "      <td>14.4977</td>\n",
       "      <td>98.2762</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000335</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.5623</td>\n",
       "      <td>97.6085</td>\n",
       "      <td>14.7777</td>\n",
       "      <td>98.1685</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.1348</td>\n",
       "      <td>98.0559</td>\n",
       "      <td>9.0336</td>\n",
       "      <td>98.6473</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.952226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>4.9715</td>\n",
       "      <td>98.1517</td>\n",
       "      <td>4.5785</td>\n",
       "      <td>97.6983</td>\n",
       "      <td>32</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.1982</td>\n",
       "      <td>97.9552</td>\n",
       "      <td>4.8052</td>\n",
       "      <td>97.5018</td>\n",
       "      <td>32</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>5.5500</td>\n",
       "      <td>96.5500</td>\n",
       "      <td>4.9500</td>\n",
       "      <td>96.5500</td>\n",
       "      <td>33</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>5.5500</td>\n",
       "      <td>96.8500</td>\n",
       "      <td>4.9500</td>\n",
       "      <td>96.8500</td>\n",
       "      <td>33</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>5.5500</td>\n",
       "      <td>97.1500</td>\n",
       "      <td>4.9500</td>\n",
       "      <td>97.1500</td>\n",
       "      <td>33</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>152 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        lat1     lon1     lat2     lon2  segment_index      lsm1      lsm2\n",
       "0    13.7223  97.9315  13.9377  98.4915              1  0.000095  1.000000\n",
       "1    14.0023  97.8238  14.2177  98.3839              1  0.000000  1.000000\n",
       "2    14.2823  97.7161  14.4977  98.2762              1  0.000335  1.000000\n",
       "3    14.5623  97.6085  14.7777  98.1685              1  0.000000  1.000000\n",
       "4     9.1348  98.0559   9.0336  98.6473              2  0.000000  0.952226\n",
       "..       ...      ...      ...      ...            ...       ...       ...\n",
       "148   4.9715  98.1517   4.5785  97.6983             32  0.000000  1.000000\n",
       "149   5.1982  97.9552   4.8052  97.5018             32  0.000000  1.000000\n",
       "150   5.5500  96.5500   4.9500  96.5500             33  0.000000  1.000000\n",
       "151   5.5500  96.8500   4.9500  96.8500             33  0.000000  1.000000\n",
       "152   5.5500  97.1500   4.9500  97.1500             33  0.000000  1.000000\n",
       "\n",
       "[152 rows x 7 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transects_qc03"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2a5d9a4f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:43:14.758180Z",
     "start_time": "2022-08-30T18:43:14.746118Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat1</th>\n",
       "      <th>lon1</th>\n",
       "      <th>lat2</th>\n",
       "      <th>lon2</th>\n",
       "      <th>segment_index</th>\n",
       "      <th>lsm1</th>\n",
       "      <th>lsm2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14.0562</td>\n",
       "      <td>97.9638</td>\n",
       "      <td>14.1638</td>\n",
       "      <td>98.2438</td>\n",
       "      <td>1</td>\n",
       "      <td>5.047917e-02</td>\n",
       "      <td>0.999575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.3362</td>\n",
       "      <td>97.8562</td>\n",
       "      <td>14.4438</td>\n",
       "      <td>98.1362</td>\n",
       "      <td>1</td>\n",
       "      <td>2.120972e-03</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.6162</td>\n",
       "      <td>97.7485</td>\n",
       "      <td>14.7239</td>\n",
       "      <td>98.0285</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.857749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.1095</td>\n",
       "      <td>98.2037</td>\n",
       "      <td>9.0589</td>\n",
       "      <td>98.4994</td>\n",
       "      <td>2</td>\n",
       "      <td>1.331404e-01</td>\n",
       "      <td>0.987720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9.4052</td>\n",
       "      <td>98.2543</td>\n",
       "      <td>9.3546</td>\n",
       "      <td>98.5500</td>\n",
       "      <td>2</td>\n",
       "      <td>7.014036e-03</td>\n",
       "      <td>0.916576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>4.8732</td>\n",
       "      <td>98.0384</td>\n",
       "      <td>4.6768</td>\n",
       "      <td>97.8116</td>\n",
       "      <td>32</td>\n",
       "      <td>1.192093e-07</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.0999</td>\n",
       "      <td>97.8419</td>\n",
       "      <td>4.9035</td>\n",
       "      <td>97.6152</td>\n",
       "      <td>32</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>5.4000</td>\n",
       "      <td>96.5500</td>\n",
       "      <td>5.1000</td>\n",
       "      <td>96.5500</td>\n",
       "      <td>33</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.999997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>5.4000</td>\n",
       "      <td>96.8500</td>\n",
       "      <td>5.1000</td>\n",
       "      <td>96.8500</td>\n",
       "      <td>33</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>5.4000</td>\n",
       "      <td>97.1500</td>\n",
       "      <td>5.1000</td>\n",
       "      <td>97.1500</td>\n",
       "      <td>33</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.953211</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>149 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        lat1     lon1     lat2     lon2  segment_index          lsm1      lsm2\n",
       "1    14.0562  97.9638  14.1638  98.2438              1  5.047917e-02  0.999575\n",
       "2    14.3362  97.8562  14.4438  98.1362              1  2.120972e-03  1.000000\n",
       "3    14.6162  97.7485  14.7239  98.0285              1  0.000000e+00  0.857749\n",
       "4     9.1095  98.2037   9.0589  98.4994              2  1.331404e-01  0.987720\n",
       "5     9.4052  98.2543   9.3546  98.5500              2  7.014036e-03  0.916576\n",
       "..       ...      ...      ...      ...            ...           ...       ...\n",
       "148   4.8732  98.0384   4.6768  97.8116             32  1.192093e-07  1.000000\n",
       "149   5.0999  97.8419   4.9035  97.6152             32  0.000000e+00  1.000000\n",
       "150   5.4000  96.5500   5.1000  96.5500             33  0.000000e+00  0.999997\n",
       "151   5.4000  96.8500   5.1000  96.8500             33  0.000000e+00  1.000000\n",
       "152   5.4000  97.1500   5.1000  97.1500             33  0.000000e+00  0.953211\n",
       "\n",
       "[149 rows x 7 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transects_qc015"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00944564",
   "metadata": {},
   "source": [
    "## Display the segments and transects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "52570e3b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:43:37.788492Z",
     "start_time": "2022-08-30T18:43:37.784053Z"
    }
   },
   "outputs": [],
   "source": [
    "region_segments = df_MS[[\"lat1\",\"lon1\",\"lat2\",\"lon2\"]].to_numpy().tolist()\n",
    "region_transects = transects_qc03[[\"lat1\",\"lon1\",\"lat2\",\"lon2\"]].to_numpy().tolist()\n",
    "# # Executed in 4ms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6dea961c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:43:48.582704Z",
     "start_time": "2022-08-30T18:43:40.835898Z"
    }
   },
   "outputs": [],
   "source": [
    "geolines_segments = []\n",
    "for i in range(len(region_segments)):\n",
    "    lnss = mv.mvl_geoline(*region_segments[i],1)\n",
    "    geolines_segments.append(lnss)\n",
    "\n",
    "geolines_transects = []\n",
    "for j in range(len(region_transects)):\n",
    "    lnst = mv.mvl_geoline(*region_transects[j],1)\n",
    "    geolines_transects.append(lnst)\n",
    "# Executed in 7.84s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5677f15d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:43:55.416521Z",
     "start_time": "2022-08-30T18:43:52.641490Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mv.setoutput(\"jupyter\", plot_widget=False, output_width=900)\n",
    "#mv.setoutput(mv.pdf_output(output_name=\"/bog/amuttaqin/Figures/\"+reg+\"-trans-segs\"))\n",
    "my_view = mv.geoview(\n",
    "    map_area_definition=\"corners\", \n",
    "    area=[-8,92,16,112],\n",
    "    subpage_y_length=80,\n",
    "    subpage_y_position=10, \n",
    "    subpage_x_position=21,\n",
    "    subpage_frame_colour=\"black\",\n",
    "    subpage_frame_thickness=10)\n",
    "my_coast = mv.mcoast(\n",
    "    map_coastline_resolution = \"high\",\n",
    "    map_coastline_thickness = 2,\n",
    "    map_coastline_sea_shade = \"on\",\n",
    "    map_coastline_sea_shade_colour = \"RGB(0.4845,0.6572,0.9351)\",\n",
    "    map_coastline_land_shade = \"on\",\n",
    "    map_coastline_land_shade_colour = \"beige\",\n",
    "    map_cities = \"on\",\n",
    "    map_cities_font_size = 3,\n",
    "    map_cities_marker = \"circle\",\n",
    "    map_cities_marker_colour = \"black\",\n",
    "    map_cities_marker_height = 2,\n",
    "    map_grid_line_style = \"dash\",\n",
    "    map_grid_latitude_increment = 4, #5 \n",
    "    map_grid_longitude_increment = 4, #5\n",
    "    map_label =\"on\",\n",
    "    map_label_height = 0.7,\n",
    "    map_label_top = \"off\",\n",
    "    map_label_latitude_frequency=1,\n",
    "    map_label_longitude_frequency=1)\n",
    "pltLst = []\n",
    "graph_segs = mv.mgraph(\n",
    "    graph_line_colour  = \"red\",\n",
    "    graph_line_thickness = 8.0,\n",
    "    graph_line_style = \"solid\")\n",
    "pltLst.extend([geolines_segments, graph_segs])\n",
    "graph_trans = mv.mgraph(\n",
    "   graph_line_colour  = \"blue\",\n",
    "   graph_line_thickness = 5.0,\n",
    "   graph_line_style = \"solid\")\n",
    "pltLst.extend([geolines_transects, graph_trans])\n",
    "mv.plot(my_view, my_coast, pltLst)\n",
    "# Executed in 1.78s"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55d9359d",
   "metadata": {},
   "source": [
    "## Load reanalysis datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af1d6ac9",
   "metadata": {},
   "source": [
    "### Load skin temperature over ocean and land"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "17f8dcc8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:44:42.166060Z",
     "start_time": "2022-08-30T18:44:41.887024Z"
    }
   },
   "outputs": [],
   "source": [
    "filein1 = \"/bog/amuttaqin/Datasets/ERA5/skto/1991-2020_skto_\"\n",
    "filein2 = \"/bog/amuttaqin/Datasets/ERA5-Land/skt/1991-2020_skt_\"\n",
    "\n",
    "skto_MS = load_reg_nc(filein1, 'MalayPenin_Sumatra')\n",
    "sktl_MS = load_reg_nc(filein2, 'MalayPenin_Sumatra')\n",
    "# Executed in 512ms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fb07cfb",
   "metadata": {},
   "source": [
    "### Load winds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "54c24357",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:44:45.260146Z",
     "start_time": "2022-08-30T18:44:44.966280Z"
    }
   },
   "outputs": [],
   "source": [
    "filein3 = \"/bog/amuttaqin/Datasets/ERA5/uv/uv_low_mean/1991-2020_uv_low_mean_\"\n",
    "filein4 = \"/bog/amuttaqin/Datasets/ERA5/uv/uv_multilevel/1991-2020_uv_multilevel_\"\n",
    "\n",
    "windlm_MS = load_reg_nc(filein3, 'MalayPenin_Sumatra')\n",
    "windml_MS = load_reg_nc(filein4, 'MalayPenin_Sumatra')\n",
    "# Executed in 1.11s"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54c99708",
   "metadata": {},
   "source": [
    "### Convert regular dataset into chunked/dask dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "94bacd7b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:44:48.162217Z",
     "start_time": "2022-08-30T18:44:48.155105Z"
    }
   },
   "outputs": [],
   "source": [
    "skto_MS = skto_MS.chunk(chunks={\"time\":24*365*2})\n",
    "sktl_MS = sktl_MS.chunk(chunks={\"time\":24*365*2})\n",
    "windlm_MS = windlm_MS.chunk(chunks={\"time\":24*365*2})\n",
    "windml_MS = windml_MS.chunk(chunks={\"time\":24*365*2})\n",
    "# Executed in 9ms"
   ]
  },
  {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;var235&#x27; (time: 262992, lat: 89, lon: 73)&gt;\n",
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       "Attributes:\n",
       "    table:    128</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'var235'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 262992</li><li><span class='xr-has-index'>lat</span>: 89</li><li><span class='xr-has-index'>lon</span>: 73</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-9585c621-679f-4f81-a665-8e870f97da9e' class='xr-array-in' type='checkbox' checked><label for='section-9585c621-679f-4f81-a665-8e870f97da9e' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(17520, 89, 73), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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       "  <text x=\"35.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,80.706734)\">262992</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-2d46a816-5b44-4397-af0e-7e7b36fa6cd7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2d46a816-5b44-4397-af0e-7e7b36fa6cd7' 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'>1991-01-01 ... 2020-12-31T23:00:00</div><input id='attrs-8bc6c4f7-7cd7-4da8-b1c5-066879cae0ae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8bc6c4f7-7cd7-4da8-b1c5-066879cae0ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-800cfee9-e44e-46b9-ad38-7ddb7a448fc5' class='xr-var-data-in' type='checkbox'><label for='data-800cfee9-e44e-46b9-ad38-7ddb7a448fc5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1991-01-01T00:00:00.000000000&#x27;, &#x27;1991-01-01T01:00:00.000000000&#x27;,\n",
       "       &#x27;1991-01-01T02:00:00.000000000&#x27;, ..., &#x27;2020-12-31T21:00:00.000000000&#x27;,\n",
       "       &#x27;2020-12-31T22:00:00.000000000&#x27;, &#x27;2020-12-31T23:00:00.000000000&#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'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>94.0 94.25 94.5 ... 111.8 112.0</div><input id='attrs-f4d00f29-aaf0-470d-85f3-345b66cdd415' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4d00f29-aaf0-470d-85f3-345b66cdd415' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d7d0a39-d280-4fd7-b336-0ce2bbcc1c79' class='xr-var-data-in' type='checkbox'><label for='data-1d7d0a39-d280-4fd7-b336-0ce2bbcc1c79' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([ 94.  ,  94.25,  94.5 ,  94.75,  95.  ,  95.25,  95.5 ,  95.75,  96.  ,\n",
       "        96.25,  96.5 ,  96.75,  97.  ,  97.25,  97.5 ,  97.75,  98.  ,  98.25,\n",
       "        98.5 ,  98.75,  99.  ,  99.25,  99.5 ,  99.75, 100.  , 100.25, 100.5 ,\n",
       "       100.75, 101.  , 101.25, 101.5 , 101.75, 102.  , 102.25, 102.5 , 102.75,\n",
       "       103.  , 103.25, 103.5 , 103.75, 104.  , 104.25, 104.5 , 104.75, 105.  ,\n",
       "       105.25, 105.5 , 105.75, 106.  , 106.25, 106.5 , 106.75, 107.  , 107.25,\n",
       "       107.5 , 107.75, 108.  , 108.25, 108.5 , 108.75, 109.  , 109.25, 109.5 ,\n",
       "       109.75, 110.  , 110.25, 110.5 , 110.75, 111.  , 111.25, 111.5 , 111.75,\n",
       "       112.  ])</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'>15.0 14.75 14.5 ... -6.5 -6.75 -7.0</div><input id='attrs-5dcf2e60-f0d1-409d-b93f-0d8db1b9deaf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5dcf2e60-f0d1-409d-b93f-0d8db1b9deaf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d759c250-86f6-4a43-837c-d29a16905f45' class='xr-var-data-in' type='checkbox'><label for='data-d759c250-86f6-4a43-837c-d29a16905f45' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([15.  , 14.75, 14.5 , 14.25, 14.  , 13.75, 13.5 , 13.25, 13.  , 12.75,\n",
       "       12.5 , 12.25, 12.  , 11.75, 11.5 , 11.25, 11.  , 10.75, 10.5 , 10.25,\n",
       "       10.  ,  9.75,  9.5 ,  9.25,  9.  ,  8.75,  8.5 ,  8.25,  8.  ,  7.75,\n",
       "        7.5 ,  7.25,  7.  ,  6.75,  6.5 ,  6.25,  6.  ,  5.75,  5.5 ,  5.25,\n",
       "        5.  ,  4.75,  4.5 ,  4.25,  4.  ,  3.75,  3.5 ,  3.25,  3.  ,  2.75,\n",
       "        2.5 ,  2.25,  2.  ,  1.75,  1.5 ,  1.25,  1.  ,  0.75,  0.5 ,  0.25,\n",
       "        0.  , -0.25, -0.5 , -0.75, -1.  , -1.25, -1.5 , -1.75, -2.  , -2.25,\n",
       "       -2.5 , -2.75, -3.  , -3.25, -3.5 , -3.75, -4.  , -4.25, -4.5 , -4.75,\n",
       "       -5.  , -5.25, -5.5 , -5.75, -6.  , -6.25, -6.5 , -6.75, -7.  ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-35045b18-f4ef-40d8-a09d-d946fdfafe5f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-35045b18-f4ef-40d8-a09d-d946fdfafe5f' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>table :</span></dt><dd>128</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'var235' (time: 262992, lat: 89, lon: 73)>\n",
       "dask.array<xarray-var235, shape=(262992, 89, 73), dtype=float32, chunksize=(17520, 89, 73), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1991-01-01 ... 2020-12-31T23:00:00\n",
       "  * lon      (lon) float64 94.0 94.25 94.5 94.75 ... 111.2 111.5 111.8 112.0\n",
       "  * lat      (lat) float64 15.0 14.75 14.5 14.25 14.0 ... -6.25 -6.5 -6.75 -7.0\n",
       "Attributes:\n",
       "    table:    128"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skto_MS.var235"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80ecf828",
   "metadata": {},
   "source": [
    "## Extract reanalysis aligned with transects"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5be3168",
   "metadata": {},
   "source": [
    "### U850"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f9660ffd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:48:54.488176Z",
     "start_time": "2022-08-30T18:44:57.025578Z"
    }
   },
   "outputs": [],
   "source": [
    "U850_2d = windml_MS.sel(plev=85000)\n",
    "\n",
    "lat2 = transects_qc015[\"lat2\"].to_numpy().tolist()\n",
    "lon2 = transects_qc015[\"lon2\"].to_numpy().tolist()\n",
    "\n",
    "lat2 = xr.DataArray(lat2, dims='segment')\n",
    "lon2 = xr.DataArray(lon2, dims='segment')\n",
    "\n",
    "U850 = U850_2d.sel(lat=lat2, lon=lon2, method=\"nearest\")\n",
    "\n",
    "U850 = U850.compute()\n",
    "# Executed in 5m 29s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f33fdc4a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:48:57.338073Z",
     "start_time": "2022-08-30T18:48:57.319524Z"
    }
   },
   "outputs": [
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       ".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:  (time: 262992, segment: 149)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1991-01-01 ... 2020-12-31T23:00:00\n",
       "    lon      (segment) float64 98.25 98.25 98.0 98.5 ... 97.5 96.5 96.75 97.25\n",
       "    lat      (segment) float64 14.25 14.5 14.75 9.0 9.25 ... 5.0 5.0 5.0 5.0\n",
       "    plev     float64 8.5e+04\n",
       "Dimensions without coordinates: segment\n",
       "Data variables:\n",
       "    var131   (time, segment) float32 -0.909 -0.5633 -0.1785 ... -3.934 -5.405\n",
       "    var132   (time, segment) float32 2.181 2.203 3.203 ... 0.9597 1.098 0.7605\n",
       "Attributes:\n",
       "    CDI:          Climate Data Interface version 2.0.3 (https://mpimet.mpg.de...\n",
       "    Conventions:  CF-1.6\n",
       "    institution:  European Centre for Medium-Range Weather Forecasts\n",
       "    history:      Wed Jul 06 22:21:16 2022: cdo -O -s -f nc4c -z zip -copy /b...\n",
       "    CDO:          Climate Data Operators version 2.0.3 (https://mpimet.mpg.de...</pre><div class='xr-wrap' style='display:none'><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-e21ca59f-bb9a-4dbb-9025-2953c25ba725' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e21ca59f-bb9a-4dbb-9025-2953c25ba725' 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'>time</span>: 262992</li><li><span>segment</span>: 149</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-878216ad-6796-4bb0-bd00-a437a4503e6d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-878216ad-6796-4bb0-bd00-a437a4503e6d' 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'>1991-01-01 ... 2020-12-31T23:00:00</div><input id='attrs-c526c81a-f21a-4b09-8b05-49f655d7eea7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c526c81a-f21a-4b09-8b05-49f655d7eea7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58f191fc-e119-4517-b71d-1df8b809fdfd' class='xr-var-data-in' type='checkbox'><label for='data-58f191fc-e119-4517-b71d-1df8b809fdfd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1991-01-01T00:00:00.000000000&#x27;, &#x27;1991-01-01T01:00:00.000000000&#x27;,\n",
       "       &#x27;1991-01-01T02:00:00.000000000&#x27;, ..., &#x27;2020-12-31T21:00:00.000000000&#x27;,\n",
       "       &#x27;2020-12-31T22:00:00.000000000&#x27;, &#x27;2020-12-31T23:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(segment)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>98.25 98.25 98.0 ... 96.75 97.25</div><input id='attrs-75048073-c22f-4e68-ba6a-ba9cc01823fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-75048073-c22f-4e68-ba6a-ba9cc01823fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-257d29b2-ddbe-4d72-887f-8a63f2f6a006' class='xr-var-data-in' type='checkbox'><label for='data-257d29b2-ddbe-4d72-887f-8a63f2f6a006' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([ 98.25,  98.25,  98.  ,  98.5 ,  98.5 ,  98.5 ,  98.75,  98.75,  98.75,\n",
       "        98.75,  99.  , 100.  ,  99.75,  99.5 ,  99.5 , 100.75, 100.75, 100.75,\n",
       "       100.5 , 100.5 , 101.25, 101.25, 103.25, 103.  , 102.75, 102.5 , 102.25,\n",
       "       102.  , 101.75, 101.5 , 104.  , 103.75, 103.75, 103.25, 103.25, 103.25,\n",
       "       103.25, 103.25, 103.25, 103.  , 102.75, 102.5 , 102.25, 102.  , 102.  ,\n",
       "       101.75, 101.5 , 100.  , 100.  ,  99.75,  99.75,  99.25,  99.25,  99.5 ,\n",
       "        99.5 ,  99.5 ,  99.75, 100.25, 100.75, 103.  , 103.  , 102.75, 102.25,\n",
       "       102.  , 105.  , 104.5 , 104.25, 105.  , 105.  , 105.5 , 106.  , 107.5 ,\n",
       "       107.75, 108.  , 108.25, 108.5 , 108.75, 109.  , 109.  , 109.  , 109.  ,\n",
       "       109.  ,  96.5 ,  96.25,  96.  ,  95.75,  95.75,  97.5 ,  97.5 ,  97.25,\n",
       "        97.  ,  98.5 ,  98.25,  98.  ,  99.25,  99.  ,  99.  , 100.75, 100.75,\n",
       "       100.5 , 100.5 , 100.25, 100.  , 102.25, 102.  , 101.75, 101.5 , 101.25,\n",
       "       101.  , 104.75, 104.5 , 104.25, 104.  , 103.75, 103.5 , 103.25, 103.  ,\n",
       "       102.75, 102.5 , 105.75, 105.75, 105.75, 105.75, 105.75, 105.75, 105.75,\n",
       "       105.75, 105.75, 104.25, 104.25, 104.  , 103.75, 103.5 , 103.5 , 103.5 ,\n",
       "       100.25, 100.25,  99.75,  99.5 ,  99.25,  99.  ,  98.75,  98.5 ,  98.  ,\n",
       "        97.75,  97.5 ,  96.5 ,  96.75,  97.25])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(segment)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>14.25 14.5 14.75 ... 5.0 5.0 5.0</div><input id='attrs-0b6b0100-c7eb-47a7-b228-0846114938a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0b6b0100-c7eb-47a7-b228-0846114938a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9cbe829a-c045-4ba6-88d4-f7375596b434' class='xr-var-data-in' type='checkbox'><label for='data-9cbe829a-c045-4ba6-88d4-f7375596b434' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([14.25, 14.5 , 14.75,  9.  ,  9.25,  9.75, 10.  , 10.25, 10.75, 11.25,\n",
       "       11.5 ,  7.  ,  7.25,  7.5 ,  7.75,  4.5 ,  4.75,  5.  ,  5.25,  5.75,\n",
       "        3.5 ,  3.75,  1.75,  2.  ,  2.  ,  2.25,  2.25,  2.5 ,  2.75,  2.75,\n",
       "        1.75,  2.25,  2.5 ,  3.  ,  3.25,  3.75,  4.  ,  4.25,  4.5 ,  5.25,\n",
       "        5.5 ,  5.75,  5.75,  6.  ,  6.25,  6.5 ,  6.75,  8.  ,  8.25,  8.75,\n",
       "        9.  , 10.5 , 10.75, 11.25, 11.5 , 11.75, 12.  , 13.5 , 13.75, 11.75,\n",
       "       11.75, 12.  , 12.5 , 12.75, 10.25, 10.5 , 10.5 ,  9.25,  9.5 ,  9.25,\n",
       "        9.5 , 10.75, 10.75, 11.  , 11.  , 11.25, 11.5 , 12.25, 12.5 , 13.25,\n",
       "       13.5 , 13.75,  4.  ,  4.25,  4.5 ,  4.5 ,  4.75,  3.  ,  3.25,  3.5 ,\n",
       "        3.75,  2.25,  2.25,  2.5 ,  0.75,  1.  ,  1.25, -1.5 , -1.25, -1.  ,\n",
       "       -0.75, -0.5 , -0.25, -3.5 , -3.25, -3.  , -2.75, -2.5 , -2.25, -5.75,\n",
       "       -5.5 , -5.25, -5.  , -5.  , -4.75, -4.5 , -4.25, -4.25, -4.  , -5.5 ,\n",
       "       -5.25, -5.  , -4.5 , -4.25, -4.  , -3.75, -3.5 , -3.  , -1.75, -1.25,\n",
       "       -1.25, -1.  , -1.  ,  0.  ,  0.25,  2.25,  2.5 ,  3.  ,  3.  ,  3.25,\n",
       "        3.5 ,  3.75,  3.75,  4.5 ,  4.75,  5.  ,  5.  ,  5.  ,  5.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>plev</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>8.5e+04</div><input id='attrs-ef4ef87a-8a5e-423d-8e57-9dedd3c5bcb2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ef4ef87a-8a5e-423d-8e57-9dedd3c5bcb2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-324261fa-7490-4335-b829-e1f4b63e3141' class='xr-var-data-in' type='checkbox'><label for='data-324261fa-7490-4335-b829-e1f4b63e3141' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_pressure</dd><dt><span>long_name :</span></dt><dd>pressure</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>axis :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>array(85000.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-287a687a-184c-4406-bae9-babf326d1d3f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-287a687a-184c-4406-bae9-babf326d1d3f' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>var131</span></div><div class='xr-var-dims'>(time, segment)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.909 -0.5633 ... -3.934 -5.405</div><input id='attrs-43a523aa-9807-4d41-9ff9-084efccb83eb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-43a523aa-9807-4d41-9ff9-084efccb83eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d537747b-ef72-4dd2-a6e4-f020488be3fa' class='xr-var-data-in' type='checkbox'><label for='data-d537747b-ef72-4dd2-a6e4-f020488be3fa' 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>table :</span></dt><dd>128</dd></dl></div><div class='xr-var-data'><pre>array([[-0.90896606, -0.56326294, -0.17849731, ..., -0.9831848 ,\n",
       "        -1.2859192 , -1.9382629 ],\n",
       "       [-1.1718597 , -0.67381287, -0.08787537, ..., -1.2206879 ,\n",
       "        -1.5702972 , -2.2812347 ],\n",
       "       [-1.4719849 , -0.9290161 , -0.20440674, ..., -1.7005005 ,\n",
       "        -1.9348755 , -2.6126099 ],\n",
       "       ...,\n",
       "       [-1.572525  , -4.0783844 , -1.3166656 , ..., -4.2326813 ,\n",
       "        -4.996353  , -6.043228  ],\n",
       "       [-0.29776   , -2.577057  , -0.45401   , ..., -2.8504944 ,\n",
       "        -3.6512756 , -4.6336975 ],\n",
       "       [-0.73872375, -2.9633331 , -0.65864563, ..., -3.0629425 ,\n",
       "        -3.9340363 , -5.4047394 ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>var132</span></div><div class='xr-var-dims'>(time, segment)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>2.181 2.203 3.203 ... 1.098 0.7605</div><input id='attrs-4d219dd6-7f21-4a84-8a2a-e959d9da61e5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4d219dd6-7f21-4a84-8a2a-e959d9da61e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c1f3d74-bb5f-45a7-a285-4665fb835dff' class='xr-var-data-in' type='checkbox'><label for='data-4c1f3d74-bb5f-45a7-a285-4665fb835dff' 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>table :</span></dt><dd>128</dd></dl></div><div class='xr-var-data'><pre>array([[ 2.1814423 ,  2.2029266 ,  3.2029266 , ...,  0.32987976,\n",
       "         0.6833954 ,  0.568161  ],\n",
       "       [ 2.1390686 ,  2.0765686 ,  2.9535217 , ...,  0.21133423,\n",
       "         0.5746155 ,  0.3343811 ],\n",
       "       [ 2.280655  ,  2.1341705 ,  2.8685455 , ...,  0.22401428,\n",
       "         0.6771393 ,  0.35682678],\n",
       "       ...,\n",
       "       [ 1.236557  ,  0.92796326, -1.6110992 , ...,  0.6564789 ,\n",
       "         1.2482758 ,  0.32835388],\n",
       "       [ 2.039627  ,  2.2056427 , -0.5209198 , ...,  1.5161896 ,\n",
       "         1.537674  ,  0.7583771 ],\n",
       "       [ 1.5944977 ,  1.9831696 , -0.65940857, ...,  0.95973206,\n",
       "         1.0984039 ,  0.7605133 ]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-370e58e1-8a7f-4c54-b150-c62a4cebafd3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-370e58e1-8a7f-4c54-b150-c62a4cebafd3' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 2.0.3 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>institution :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>history :</span></dt><dd>Wed Jul 06 22:21:16 2022: cdo -O -s -f nc4c -z zip -copy /bog/amuttaqin/Datasets/ERA5/uv/uv_multilevel/1991-2020_uv_multilevel_MalayPenin_Sumatra.grib /bog/amuttaqin/Datasets/ERA5/uv/uv_multilevel/1991-2020_uv_multilevel_MalayPenin_Sumatra.nc</dd><dt><span>CDO :</span></dt><dd>Climate Data Operators version 2.0.3 (https://mpimet.mpg.de/cdo)</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (time: 262992, segment: 149)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1991-01-01 ... 2020-12-31T23:00:00\n",
       "    lon      (segment) float64 98.25 98.25 98.0 98.5 ... 97.5 96.5 96.75 97.25\n",
       "    lat      (segment) float64 14.25 14.5 14.75 9.0 9.25 ... 5.0 5.0 5.0 5.0\n",
       "    plev     float64 8.5e+04\n",
       "Dimensions without coordinates: segment\n",
       "Data variables:\n",
       "    var131   (time, segment) float32 -0.909 -0.5633 -0.1785 ... -3.934 -5.405\n",
       "    var132   (time, segment) float32 2.181 2.203 3.203 ... 0.9597 1.098 0.7605\n",
       "Attributes:\n",
       "    CDI:          Climate Data Interface version 2.0.3 (https://mpimet.mpg.de...\n",
       "    Conventions:  CF-1.6\n",
       "    institution:  European Centre for Medium-Range Weather Forecasts\n",
       "    history:      Wed Jul 06 22:21:16 2022: cdo -O -s -f nc4c -z zip -copy /b...\n",
       "    CDO:          Climate Data Operators version 2.0.3 (https://mpimet.mpg.de..."
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "U850"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "76d5ba2f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:49:00.617416Z",
     "start_time": "2022-08-30T18:49:00.614146Z"
    }
   },
   "outputs": [],
   "source": [
    "U850 = U850.assign_coords(segment=transects_qc015['segment_index'].to_numpy().tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "41fb23c4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:49:02.459483Z",
     "start_time": "2022-08-30T18:49:02.456921Z"
    }
   },
   "outputs": [],
   "source": [
    "U850 = U850.rename({'var131':'u', 'var132':'v'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "95be6fdc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:49:04.091083Z",
     "start_time": "2022-08-30T18:49:04.088833Z"
    }
   },
   "outputs": [],
   "source": [
    "U850 = U850.drop_vars({'lon', 'lat', 'plev'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8d1fd329",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:49:06.076876Z",
     "start_time": "2022-08-30T18:49:06.063193Z"
    }
   },
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (time: 262992, segment: 149)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1991-01-01 ... 2020-12-31T23:00:00\n",
       "  * segment  (segment) int64 1 1 1 2 2 2 2 2 2 2 ... 31 31 31 32 32 32 33 33 33\n",
       "Data variables:\n",
       "    u        (time, segment) float32 -0.909 -0.5633 -0.1785 ... -3.934 -5.405\n",
       "    v        (time, segment) float32 2.181 2.203 3.203 ... 0.9597 1.098 0.7605\n",
       "Attributes:\n",
       "    CDI:          Climate Data Interface version 2.0.3 (https://mpimet.mpg.de...\n",
       "    Conventions:  CF-1.6\n",
       "    institution:  European Centre for Medium-Range Weather Forecasts\n",
       "    history:      Wed Jul 06 22:21:16 2022: cdo -O -s -f nc4c -z zip -copy /b...\n",
       "    CDO:          Climate Data Operators version 2.0.3 (https://mpimet.mpg.de...</pre><div class='xr-wrap' style='display:none'><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-06083957-32db-431a-be8b-7200df83c773' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-06083957-32db-431a-be8b-7200df83c773' 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'>time</span>: 262992</li><li><span class='xr-has-index'>segment</span>: 149</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-36146516-22e1-42f7-864d-5a29269e2b87' class='xr-section-summary-in' type='checkbox'  checked><label for='section-36146516-22e1-42f7-864d-5a29269e2b87' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>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'>1991-01-01 ... 2020-12-31T23:00:00</div><input id='attrs-bfd77b62-3c71-4b49-b8a9-665e022c31e5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bfd77b62-3c71-4b49-b8a9-665e022c31e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9d14260-49ff-4dcb-b49d-f040e8c14f09' class='xr-var-data-in' type='checkbox'><label for='data-b9d14260-49ff-4dcb-b49d-f040e8c14f09' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1991-01-01T00:00:00.000000000&#x27;, &#x27;1991-01-01T01:00:00.000000000&#x27;,\n",
       "       &#x27;1991-01-01T02:00:00.000000000&#x27;, ..., &#x27;2020-12-31T21:00:00.000000000&#x27;,\n",
       "       &#x27;2020-12-31T22:00:00.000000000&#x27;, &#x27;2020-12-31T23:00:00.000000000&#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'>segment</span></div><div class='xr-var-dims'>(segment)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 1 1 2 2 2 2 ... 32 32 32 33 33 33</div><input id='attrs-f237164b-31e6-4b8c-9b8f-5f217f483cdb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f237164b-31e6-4b8c-9b8f-5f217f483cdb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71cede18-4a99-4ab6-a3ec-f23a92c30926' class='xr-var-data-in' type='checkbox'><label for='data-71cede18-4a99-4ab6-a3ec-f23a92c30926' 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,  2,  2,  2,  2,  2,  2,  2,  2,  3,  3,  3,  3,  4,  4,  4,\n",
       "        4,  4,  5,  5,  6,  6,  6,  6,  6,  6,  6,  6,  7,  7,  7,  8,  8,  8,\n",
       "        8,  8,  8,  9,  9,  9,  9,  9,  9,  9,  9, 10, 10, 10, 10, 11, 11, 11,\n",
       "       11, 11, 11, 12, 12, 13, 13, 13, 13, 13, 14, 14, 14, 15, 15, 16, 16, 17,\n",
       "       17, 17, 17, 17, 17, 18, 18, 18, 18, 18, 19, 19, 19, 19, 19, 20, 20, 20,\n",
       "       20, 21, 21, 21, 22, 22, 22, 23, 23, 23, 23, 23, 23, 24, 24, 24, 24, 24,\n",
       "       24, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 26, 26, 26, 26, 26, 26, 26,\n",
       "       26, 26, 27, 27, 28, 28, 28, 29, 29, 30, 30, 31, 31, 31, 31, 31, 31, 32,\n",
       "       32, 32, 33, 33, 33])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7e7af04f-923b-4900-931b-cad0d8108428' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7e7af04f-923b-4900-931b-cad0d8108428' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>u</span></div><div class='xr-var-dims'>(time, segment)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.909 -0.5633 ... -3.934 -5.405</div><input id='attrs-5f311b73-b373-4c73-9d5b-afe0587745e9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f311b73-b373-4c73-9d5b-afe0587745e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-166c43e4-b487-4f64-abf3-4129d7ceaaeb' class='xr-var-data-in' type='checkbox'><label for='data-166c43e4-b487-4f64-abf3-4129d7ceaaeb' 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>table :</span></dt><dd>128</dd></dl></div><div class='xr-var-data'><pre>array([[-0.90896606, -0.56326294, -0.17849731, ..., -0.9831848 ,\n",
       "        -1.2859192 , -1.9382629 ],\n",
       "       [-1.1718597 , -0.67381287, -0.08787537, ..., -1.2206879 ,\n",
       "        -1.5702972 , -2.2812347 ],\n",
       "       [-1.4719849 , -0.9290161 , -0.20440674, ..., -1.7005005 ,\n",
       "        -1.9348755 , -2.6126099 ],\n",
       "       ...,\n",
       "       [-1.572525  , -4.0783844 , -1.3166656 , ..., -4.2326813 ,\n",
       "        -4.996353  , -6.043228  ],\n",
       "       [-0.29776   , -2.577057  , -0.45401   , ..., -2.8504944 ,\n",
       "        -3.6512756 , -4.6336975 ],\n",
       "       [-0.73872375, -2.9633331 , -0.65864563, ..., -3.0629425 ,\n",
       "        -3.9340363 , -5.4047394 ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(time, segment)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>2.181 2.203 3.203 ... 1.098 0.7605</div><input id='attrs-6d94e5ce-3f89-478a-914e-033ea1673eae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6d94e5ce-3f89-478a-914e-033ea1673eae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1dfa11e6-17d5-430d-951d-0726e1e58dba' class='xr-var-data-in' type='checkbox'><label for='data-1dfa11e6-17d5-430d-951d-0726e1e58dba' 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>table :</span></dt><dd>128</dd></dl></div><div class='xr-var-data'><pre>array([[ 2.1814423 ,  2.2029266 ,  3.2029266 , ...,  0.32987976,\n",
       "         0.6833954 ,  0.568161  ],\n",
       "       [ 2.1390686 ,  2.0765686 ,  2.9535217 , ...,  0.21133423,\n",
       "         0.5746155 ,  0.3343811 ],\n",
       "       [ 2.280655  ,  2.1341705 ,  2.8685455 , ...,  0.22401428,\n",
       "         0.6771393 ,  0.35682678],\n",
       "       ...,\n",
       "       [ 1.236557  ,  0.92796326, -1.6110992 , ...,  0.6564789 ,\n",
       "         1.2482758 ,  0.32835388],\n",
       "       [ 2.039627  ,  2.2056427 , -0.5209198 , ...,  1.5161896 ,\n",
       "         1.537674  ,  0.7583771 ],\n",
       "       [ 1.5944977 ,  1.9831696 , -0.65940857, ...,  0.95973206,\n",
       "         1.0984039 ,  0.7605133 ]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-07235c37-800a-4086-abe6-8d1e7f1d5ad1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-07235c37-800a-4086-abe6-8d1e7f1d5ad1' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 2.0.3 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>institution :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>history :</span></dt><dd>Wed Jul 06 22:21:16 2022: cdo -O -s -f nc4c -z zip -copy /bog/amuttaqin/Datasets/ERA5/uv/uv_multilevel/1991-2020_uv_multilevel_MalayPenin_Sumatra.grib /bog/amuttaqin/Datasets/ERA5/uv/uv_multilevel/1991-2020_uv_multilevel_MalayPenin_Sumatra.nc</dd><dt><span>CDO :</span></dt><dd>Climate Data Operators version 2.0.3 (https://mpimet.mpg.de/cdo)</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (time: 262992, segment: 149)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1991-01-01 ... 2020-12-31T23:00:00\n",
       "  * segment  (segment) int64 1 1 1 2 2 2 2 2 2 2 ... 31 31 31 32 32 32 33 33 33\n",
       "Data variables:\n",
       "    u        (time, segment) float32 -0.909 -0.5633 -0.1785 ... -3.934 -5.405\n",
       "    v        (time, segment) float32 2.181 2.203 3.203 ... 0.9597 1.098 0.7605\n",
       "Attributes:\n",
       "    CDI:          Climate Data Interface version 2.0.3 (https://mpimet.mpg.de...\n",
       "    Conventions:  CF-1.6\n",
       "    institution:  European Centre for Medium-Range Weather Forecasts\n",
       "    history:      Wed Jul 06 22:21:16 2022: cdo -O -s -f nc4c -z zip -copy /b...\n",
       "    CDO:          Climate Data Operators version 2.0.3 (https://mpimet.mpg.de..."
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "U850"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d448522d",
   "metadata": {},
   "source": [
    "### U900-1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "851cf64a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:50:35.195504Z",
     "start_time": "2022-08-30T18:49:09.262768Z"
    }
   },
   "outputs": [],
   "source": [
    "lat2 = transects_qc015[\"lat2\"].to_numpy().tolist()\n",
    "lon2 = transects_qc015[\"lon2\"].to_numpy().tolist()\n",
    "\n",
    "lat2 = xr.DataArray(lat2, dims='segment')\n",
    "lon2 = xr.DataArray(lon2, dims='segment')\n",
    "\n",
    "U910 = windlm_MS.sel(lat=lat2, lon=lon2, method=\"nearest\")\n",
    "\n",
    "U910 = U910.compute()\n",
    "# Executed in 5m 29s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5f47502d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:50:43.356001Z",
     "start_time": "2022-08-30T18:50:43.338766Z"
    }
   },
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (time: 262992, segment: 149)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1991-01-01 ... 2020-12-31T23:00:00\n",
       "    lon      (segment) float64 98.25 98.25 98.0 98.5 ... 97.5 96.5 96.75 97.25\n",
       "    lat      (segment) float64 14.25 14.5 14.75 9.0 9.25 ... 5.0 5.0 5.0 5.0\n",
       "Dimensions without coordinates: segment\n",
       "Data variables:\n",
       "    var131   (time, segment) float32 -1.283 -1.097 -0.9882 ... -1.197 -1.753\n",
       "    var132   (time, segment) float32 -1.331 -1.02 -1.839 ... 0.8333 1.284 1.469\n",
       "Attributes:\n",
       "    CDI:          Climate Data Interface version 2.0.3 (https://mpimet.mpg.de...\n",
       "    Conventions:  CF-1.6\n",
       "    institution:  European Centre for Medium-Range Weather Forecasts\n",
       "    history:      Wed Jul 06 16:25:02 2022: cdo -O -s -f nc4c -z zip -copy /b...\n",
       "    CDO:          Climate Data Operators version 2.0.3 (https://mpimet.mpg.de...</pre><div class='xr-wrap' style='display:none'><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-28118061-f349-4361-ad25-9725d231fdd7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-28118061-f349-4361-ad25-9725d231fdd7' 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'>time</span>: 262992</li><li><span>segment</span>: 149</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-cb6670f3-653d-4a65-aabe-5ad358289bba' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cb6670f3-653d-4a65-aabe-5ad358289bba' 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'>1991-01-01 ... 2020-12-31T23:00:00</div><input id='attrs-7d59ff51-1216-43a6-8e89-b769f977666d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7d59ff51-1216-43a6-8e89-b769f977666d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36ffb482-1546-49ce-a61e-e648a5c3646b' class='xr-var-data-in' type='checkbox'><label for='data-36ffb482-1546-49ce-a61e-e648a5c3646b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1991-01-01T00:00:00.000000000&#x27;, &#x27;1991-01-01T01:00:00.000000000&#x27;,\n",
       "       &#x27;1991-01-01T02:00:00.000000000&#x27;, ..., &#x27;2020-12-31T21:00:00.000000000&#x27;,\n",
       "       &#x27;2020-12-31T22:00:00.000000000&#x27;, &#x27;2020-12-31T23:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(segment)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>98.25 98.25 98.0 ... 96.75 97.25</div><input id='attrs-5cbbbe18-357e-4dd3-a78b-d802ac36ab45' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5cbbbe18-357e-4dd3-a78b-d802ac36ab45' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3191f6d-794e-4adb-aa29-268ec4a14ab3' class='xr-var-data-in' type='checkbox'><label for='data-e3191f6d-794e-4adb-aa29-268ec4a14ab3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([ 98.25,  98.25,  98.  ,  98.5 ,  98.5 ,  98.5 ,  98.75,  98.75,  98.75,\n",
       "        98.75,  99.  , 100.  ,  99.75,  99.5 ,  99.5 , 100.75, 100.75, 100.75,\n",
       "       100.5 , 100.5 , 101.25, 101.25, 103.25, 103.  , 102.75, 102.5 , 102.25,\n",
       "       102.  , 101.75, 101.5 , 104.  , 103.75, 103.75, 103.25, 103.25, 103.25,\n",
       "       103.25, 103.25, 103.25, 103.  , 102.75, 102.5 , 102.25, 102.  , 102.  ,\n",
       "       101.75, 101.5 , 100.  , 100.  ,  99.75,  99.75,  99.25,  99.25,  99.5 ,\n",
       "        99.5 ,  99.5 ,  99.75, 100.25, 100.75, 103.  , 103.  , 102.75, 102.25,\n",
       "       102.  , 105.  , 104.5 , 104.25, 105.  , 105.  , 105.5 , 106.  , 107.5 ,\n",
       "       107.75, 108.  , 108.25, 108.5 , 108.75, 109.  , 109.  , 109.  , 109.  ,\n",
       "       109.  ,  96.5 ,  96.25,  96.  ,  95.75,  95.75,  97.5 ,  97.5 ,  97.25,\n",
       "        97.  ,  98.5 ,  98.25,  98.  ,  99.25,  99.  ,  99.  , 100.75, 100.75,\n",
       "       100.5 , 100.5 , 100.25, 100.  , 102.25, 102.  , 101.75, 101.5 , 101.25,\n",
       "       101.  , 104.75, 104.5 , 104.25, 104.  , 103.75, 103.5 , 103.25, 103.  ,\n",
       "       102.75, 102.5 , 105.75, 105.75, 105.75, 105.75, 105.75, 105.75, 105.75,\n",
       "       105.75, 105.75, 104.25, 104.25, 104.  , 103.75, 103.5 , 103.5 , 103.5 ,\n",
       "       100.25, 100.25,  99.75,  99.5 ,  99.25,  99.  ,  98.75,  98.5 ,  98.  ,\n",
       "        97.75,  97.5 ,  96.5 ,  96.75,  97.25])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(segment)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>14.25 14.5 14.75 ... 5.0 5.0 5.0</div><input id='attrs-4912bf72-3cba-4394-a3ae-6c0c400fbcd3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4912bf72-3cba-4394-a3ae-6c0c400fbcd3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a992ef5-ed3c-4797-8712-59811929c126' class='xr-var-data-in' type='checkbox'><label for='data-9a992ef5-ed3c-4797-8712-59811929c126' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([14.25, 14.5 , 14.75,  9.  ,  9.25,  9.75, 10.  , 10.25, 10.75, 11.25,\n",
       "       11.5 ,  7.  ,  7.25,  7.5 ,  7.75,  4.5 ,  4.75,  5.  ,  5.25,  5.75,\n",
       "        3.5 ,  3.75,  1.75,  2.  ,  2.  ,  2.25,  2.25,  2.5 ,  2.75,  2.75,\n",
       "        1.75,  2.25,  2.5 ,  3.  ,  3.25,  3.75,  4.  ,  4.25,  4.5 ,  5.25,\n",
       "        5.5 ,  5.75,  5.75,  6.  ,  6.25,  6.5 ,  6.75,  8.  ,  8.25,  8.75,\n",
       "        9.  , 10.5 , 10.75, 11.25, 11.5 , 11.75, 12.  , 13.5 , 13.75, 11.75,\n",
       "       11.75, 12.  , 12.5 , 12.75, 10.25, 10.5 , 10.5 ,  9.25,  9.5 ,  9.25,\n",
       "        9.5 , 10.75, 10.75, 11.  , 11.  , 11.25, 11.5 , 12.25, 12.5 , 13.25,\n",
       "       13.5 , 13.75,  4.  ,  4.25,  4.5 ,  4.5 ,  4.75,  3.  ,  3.25,  3.5 ,\n",
       "        3.75,  2.25,  2.25,  2.5 ,  0.75,  1.  ,  1.25, -1.5 , -1.25, -1.  ,\n",
       "       -0.75, -0.5 , -0.25, -3.5 , -3.25, -3.  , -2.75, -2.5 , -2.25, -5.75,\n",
       "       -5.5 , -5.25, -5.  , -5.  , -4.75, -4.5 , -4.25, -4.25, -4.  , -5.5 ,\n",
       "       -5.25, -5.  , -4.5 , -4.25, -4.  , -3.75, -3.5 , -3.  , -1.75, -1.25,\n",
       "       -1.25, -1.  , -1.  ,  0.  ,  0.25,  2.25,  2.5 ,  3.  ,  3.  ,  3.25,\n",
       "        3.5 ,  3.75,  3.75,  4.5 ,  4.75,  5.  ,  5.  ,  5.  ,  5.  ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bcc66a50-efb1-4c42-b31b-59e29c9cb5f9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bcc66a50-efb1-4c42-b31b-59e29c9cb5f9' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>var131</span></div><div class='xr-var-dims'>(time, segment)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.283 -1.097 ... -1.197 -1.753</div><input id='attrs-47309978-0268-475d-a871-d576a107a7a5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-47309978-0268-475d-a871-d576a107a7a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2afbb0b2-8555-46c0-81b8-c4785120b0b8' class='xr-var-data-in' type='checkbox'><label for='data-2afbb0b2-8555-46c0-81b8-c4785120b0b8' 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>table :</span></dt><dd>128</dd></dl></div><div class='xr-var-data'><pre>array([[-1.283104  , -1.0970688 , -0.98818207, ...,  0.25351715,\n",
       "         0.07334137, -0.61318207],\n",
       "       [-1.2089767 , -0.97753143, -1.0200119 , ..., -0.01805878,\n",
       "        -0.20653534, -0.8163986 ],\n",
       "       [-1.0812225 , -0.9357147 , -1.0392303 , ..., -0.6036835 ,\n",
       "        -0.5533905 , -1.0631561 ],\n",
       "       ...,\n",
       "       [-9.279419  , -6.3218994 , -8.124634  , ..., -1.4234619 ,\n",
       "        -1.6773682 , -1.7633057 ],\n",
       "       [-8.676918  , -5.772133  , -7.6393204 , ..., -0.77555084,\n",
       "        -0.98600006, -1.3199844 ],\n",
       "       [-8.831406  , -5.880234  , -7.598984  , ..., -0.85484314,\n",
       "        -1.19664   , -1.7527924 ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>var132</span></div><div class='xr-var-dims'>(time, segment)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.331 -1.02 -1.839 ... 1.284 1.469</div><input id='attrs-9f41c4de-7896-4a86-8962-658ac2dfe51d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f41c4de-7896-4a86-8962-658ac2dfe51d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d19d56ea-3c99-4d44-9fde-41ffd271a668' class='xr-var-data-in' type='checkbox'><label for='data-d19d56ea-3c99-4d44-9fde-41ffd271a668' 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>table :</span></dt><dd>128</dd></dl></div><div class='xr-var-data'><pre>array([[-1.3314972 , -1.020462  , -1.8388214 , ...,  0.77153015,\n",
       "         1.1055145 ,  0.6186981 ],\n",
       "       [-1.0243607 , -0.877388  , -1.833931  , ...,  0.30962372,\n",
       "         0.61870575,  0.5103073 ],\n",
       "       [-0.7408066 , -0.70809174, -1.5660019 , ..., -0.29549408,\n",
       "         0.08341217,  0.5277481 ],\n",
       "       ...,\n",
       "       [-1.6232605 , -0.58517456, -0.9093933 , ...,  0.8508606 ,\n",
       "         1.2825012 ,  1.7073059 ],\n",
       "       [-1.2233582 , -0.3937683 , -0.5666199 , ...,  0.89871216,\n",
       "         1.309845  ,  1.4099426 ],\n",
       "       [-1.3478851 , -0.32884216, -0.51390076, ...,  0.8332672 ,\n",
       "         1.2844391 ,  1.4685211 ]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-73feec53-256e-45d3-9aa8-643984fd5e0a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-73feec53-256e-45d3-9aa8-643984fd5e0a' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 2.0.3 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>institution :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>history :</span></dt><dd>Wed Jul 06 16:25:02 2022: cdo -O -s -f nc4c -z zip -copy /bog/amuttaqin/Datasets/ERA5/uv/uv_low_mean/1991-2020_uv_low_mean_MalayPenin_Sumatra.grib /bog/amuttaqin/Datasets/ERA5/uv/uv_low_mean/1991-2020_uv_low_mean_MalayPenin_Sumatra.nc</dd><dt><span>CDO :</span></dt><dd>Climate Data Operators version 2.0.3 (https://mpimet.mpg.de/cdo)</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (time: 262992, segment: 149)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1991-01-01 ... 2020-12-31T23:00:00\n",
       "    lon      (segment) float64 98.25 98.25 98.0 98.5 ... 97.5 96.5 96.75 97.25\n",
       "    lat      (segment) float64 14.25 14.5 14.75 9.0 9.25 ... 5.0 5.0 5.0 5.0\n",
       "Dimensions without coordinates: segment\n",
       "Data variables:\n",
       "    var131   (time, segment) float32 -1.283 -1.097 -0.9882 ... -1.197 -1.753\n",
       "    var132   (time, segment) float32 -1.331 -1.02 -1.839 ... 0.8333 1.284 1.469\n",
       "Attributes:\n",
       "    CDI:          Climate Data Interface version 2.0.3 (https://mpimet.mpg.de...\n",
       "    Conventions:  CF-1.6\n",
       "    institution:  European Centre for Medium-Range Weather Forecasts\n",
       "    history:      Wed Jul 06 16:25:02 2022: cdo -O -s -f nc4c -z zip -copy /b...\n",
       "    CDO:          Climate Data Operators version 2.0.3 (https://mpimet.mpg.de..."
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "U910"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d79f3f25",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:50:47.179094Z",
     "start_time": "2022-08-30T18:50:47.176124Z"
    }
   },
   "outputs": [],
   "source": [
    "U910 = U910.assign_coords(segment=transects_qc015['segment_index'].to_numpy().tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "70219aa8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:50:47.609584Z",
     "start_time": "2022-08-30T18:50:47.607196Z"
    }
   },
   "outputs": [],
   "source": [
    "U910 = U910.rename({'var131':'u', 'var132':'v'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8b2513cd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:50:47.874917Z",
     "start_time": "2022-08-30T18:50:47.872933Z"
    }
   },
   "outputs": [],
   "source": [
    "U910 = U910.drop_vars({'lon', 'lat'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a4e1e0c8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:50:48.675242Z",
     "start_time": "2022-08-30T18:50:48.661989Z"
    }
   },
   "outputs": [
    {
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       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --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 !important;\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:  (time: 262992, segment: 149)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1991-01-01 ... 2020-12-31T23:00:00\n",
       "  * segment  (segment) int64 1 1 1 2 2 2 2 2 2 2 ... 31 31 31 32 32 32 33 33 33\n",
       "Data variables:\n",
       "    u        (time, segment) float32 -1.283 -1.097 -0.9882 ... -1.197 -1.753\n",
       "    v        (time, segment) float32 -1.331 -1.02 -1.839 ... 0.8333 1.284 1.469\n",
       "Attributes:\n",
       "    CDI:          Climate Data Interface version 2.0.3 (https://mpimet.mpg.de...\n",
       "    Conventions:  CF-1.6\n",
       "    institution:  European Centre for Medium-Range Weather Forecasts\n",
       "    history:      Wed Jul 06 16:25:02 2022: cdo -O -s -f nc4c -z zip -copy /b...\n",
       "    CDO:          Climate Data Operators version 2.0.3 (https://mpimet.mpg.de...</pre><div class='xr-wrap' style='display:none'><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-78bd4e8a-4c22-4afd-9909-440241778248' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-78bd4e8a-4c22-4afd-9909-440241778248' 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'>time</span>: 262992</li><li><span class='xr-has-index'>segment</span>: 149</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-595917d5-ca6c-4dfd-873b-377aada57a71' class='xr-section-summary-in' type='checkbox'  checked><label for='section-595917d5-ca6c-4dfd-873b-377aada57a71' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>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'>1991-01-01 ... 2020-12-31T23:00:00</div><input id='attrs-5ac958ed-765a-4970-82be-3ccb2bbc9c6a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ac958ed-765a-4970-82be-3ccb2bbc9c6a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-444a3664-d308-4dca-b153-86b7acca9c03' class='xr-var-data-in' type='checkbox'><label for='data-444a3664-d308-4dca-b153-86b7acca9c03' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1991-01-01T00:00:00.000000000&#x27;, &#x27;1991-01-01T01:00:00.000000000&#x27;,\n",
       "       &#x27;1991-01-01T02:00:00.000000000&#x27;, ..., &#x27;2020-12-31T21:00:00.000000000&#x27;,\n",
       "       &#x27;2020-12-31T22:00:00.000000000&#x27;, &#x27;2020-12-31T23:00:00.000000000&#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'>segment</span></div><div class='xr-var-dims'>(segment)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 1 1 2 2 2 2 ... 32 32 32 33 33 33</div><input id='attrs-6edfe6a8-c61a-4784-bf24-af884faeb192' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6edfe6a8-c61a-4784-bf24-af884faeb192' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10f725b6-07f6-40f6-921b-a39b9503e9ad' class='xr-var-data-in' type='checkbox'><label for='data-10f725b6-07f6-40f6-921b-a39b9503e9ad' 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,  2,  2,  2,  2,  2,  2,  2,  2,  3,  3,  3,  3,  4,  4,  4,\n",
       "        4,  4,  5,  5,  6,  6,  6,  6,  6,  6,  6,  6,  7,  7,  7,  8,  8,  8,\n",
       "        8,  8,  8,  9,  9,  9,  9,  9,  9,  9,  9, 10, 10, 10, 10, 11, 11, 11,\n",
       "       11, 11, 11, 12, 12, 13, 13, 13, 13, 13, 14, 14, 14, 15, 15, 16, 16, 17,\n",
       "       17, 17, 17, 17, 17, 18, 18, 18, 18, 18, 19, 19, 19, 19, 19, 20, 20, 20,\n",
       "       20, 21, 21, 21, 22, 22, 22, 23, 23, 23, 23, 23, 23, 24, 24, 24, 24, 24,\n",
       "       24, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 26, 26, 26, 26, 26, 26, 26,\n",
       "       26, 26, 27, 27, 28, 28, 28, 29, 29, 30, 30, 31, 31, 31, 31, 31, 31, 32,\n",
       "       32, 32, 33, 33, 33])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-84e8fc13-d79f-4e6c-ab2c-fec09e25180d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-84e8fc13-d79f-4e6c-ab2c-fec09e25180d' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>u</span></div><div class='xr-var-dims'>(time, segment)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.283 -1.097 ... -1.197 -1.753</div><input id='attrs-a9c63c84-0613-43f7-b425-d7f4d13cb170' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a9c63c84-0613-43f7-b425-d7f4d13cb170' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a53f0fee-d38d-4ee9-b988-2756f901760d' class='xr-var-data-in' type='checkbox'><label for='data-a53f0fee-d38d-4ee9-b988-2756f901760d' 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>table :</span></dt><dd>128</dd></dl></div><div class='xr-var-data'><pre>array([[-1.283104  , -1.0970688 , -0.98818207, ...,  0.25351715,\n",
       "         0.07334137, -0.61318207],\n",
       "       [-1.2089767 , -0.97753143, -1.0200119 , ..., -0.01805878,\n",
       "        -0.20653534, -0.8163986 ],\n",
       "       [-1.0812225 , -0.9357147 , -1.0392303 , ..., -0.6036835 ,\n",
       "        -0.5533905 , -1.0631561 ],\n",
       "       ...,\n",
       "       [-9.279419  , -6.3218994 , -8.124634  , ..., -1.4234619 ,\n",
       "        -1.6773682 , -1.7633057 ],\n",
       "       [-8.676918  , -5.772133  , -7.6393204 , ..., -0.77555084,\n",
       "        -0.98600006, -1.3199844 ],\n",
       "       [-8.831406  , -5.880234  , -7.598984  , ..., -0.85484314,\n",
       "        -1.19664   , -1.7527924 ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(time, segment)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.331 -1.02 -1.839 ... 1.284 1.469</div><input id='attrs-356acb20-e050-42dd-9921-c74e8f4baf37' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-356acb20-e050-42dd-9921-c74e8f4baf37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f3fdc52-a547-4f93-ad3a-13ce74333708' class='xr-var-data-in' type='checkbox'><label for='data-8f3fdc52-a547-4f93-ad3a-13ce74333708' 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>table :</span></dt><dd>128</dd></dl></div><div class='xr-var-data'><pre>array([[-1.3314972 , -1.020462  , -1.8388214 , ...,  0.77153015,\n",
       "         1.1055145 ,  0.6186981 ],\n",
       "       [-1.0243607 , -0.877388  , -1.833931  , ...,  0.30962372,\n",
       "         0.61870575,  0.5103073 ],\n",
       "       [-0.7408066 , -0.70809174, -1.5660019 , ..., -0.29549408,\n",
       "         0.08341217,  0.5277481 ],\n",
       "       ...,\n",
       "       [-1.6232605 , -0.58517456, -0.9093933 , ...,  0.8508606 ,\n",
       "         1.2825012 ,  1.7073059 ],\n",
       "       [-1.2233582 , -0.3937683 , -0.5666199 , ...,  0.89871216,\n",
       "         1.309845  ,  1.4099426 ],\n",
       "       [-1.3478851 , -0.32884216, -0.51390076, ...,  0.8332672 ,\n",
       "         1.2844391 ,  1.4685211 ]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-87337ace-8114-4870-973a-75fffcff70d0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-87337ace-8114-4870-973a-75fffcff70d0' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 2.0.3 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>institution :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>history :</span></dt><dd>Wed Jul 06 16:25:02 2022: cdo -O -s -f nc4c -z zip -copy /bog/amuttaqin/Datasets/ERA5/uv/uv_low_mean/1991-2020_uv_low_mean_MalayPenin_Sumatra.grib /bog/amuttaqin/Datasets/ERA5/uv/uv_low_mean/1991-2020_uv_low_mean_MalayPenin_Sumatra.nc</dd><dt><span>CDO :</span></dt><dd>Climate Data Operators version 2.0.3 (https://mpimet.mpg.de/cdo)</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (time: 262992, segment: 149)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1991-01-01 ... 2020-12-31T23:00:00\n",
       "  * segment  (segment) int64 1 1 1 2 2 2 2 2 2 2 ... 31 31 31 32 32 32 33 33 33\n",
       "Data variables:\n",
       "    u        (time, segment) float32 -1.283 -1.097 -0.9882 ... -1.197 -1.753\n",
       "    v        (time, segment) float32 -1.331 -1.02 -1.839 ... 0.8333 1.284 1.469\n",
       "Attributes:\n",
       "    CDI:          Climate Data Interface version 2.0.3 (https://mpimet.mpg.de...\n",
       "    Conventions:  CF-1.6\n",
       "    institution:  European Centre for Medium-Range Weather Forecasts\n",
       "    history:      Wed Jul 06 16:25:02 2022: cdo -O -s -f nc4c -z zip -copy /b...\n",
       "    CDO:          Climate Data Operators version 2.0.3 (https://mpimet.mpg.de..."
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "U910"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f10e755",
   "metadata": {},
   "source": [
    "### Delta-T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "89b69aba",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:55:00.242692Z",
     "start_time": "2022-08-30T18:51:49.137952Z"
    }
   },
   "outputs": [],
   "source": [
    "lat1 = transects_qc03[\"lat1\"].to_numpy().tolist()\n",
    "lon1 = transects_qc03[\"lon1\"].to_numpy().tolist()\n",
    "lat2 = transects_qc03[\"lat2\"].to_numpy().tolist()\n",
    "lon2 = transects_qc03[\"lon2\"].to_numpy().tolist()\n",
    "\n",
    "lat1 = xr.DataArray(lat1, dims='segment')\n",
    "lon1 = xr.DataArray(lon1, dims='segment')\n",
    "lat2 = xr.DataArray(lat2, dims='segment')\n",
    "lon2 = xr.DataArray(lon2, dims='segment')\n",
    "\n",
    "ts1 = skto_MS.sel(lat=lat1, lon=lon1, method=\"nearest\")\n",
    "ts2 = sktl_MS.sel(lat=lat2, lon=lon2, method=\"nearest\")\n",
    "\n",
    "ts1 = ts1.compute()\n",
    "ts2 = ts2.compute()\n",
    "\n",
    "tdiff = ts2 - ts1\n",
    "deltaT = tdiff.compute()\n",
    "# Executed in 3m 23s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "914d9fd2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:55:03.733608Z",
     "start_time": "2022-08-30T18:55:03.721722Z"
    }
   },
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (time: 262992, segment: 152)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1991-01-01 ... 2020-12-31T23:00:00\n",
       "Dimensions without coordinates: segment\n",
       "Data variables:\n",
       "    var235   (time, segment) float32 -8.87 -10.9 -11.56 ... -9.326 -9.422 -6.655</pre><div class='xr-wrap' style='display:none'><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-a515f818-00b8-42fe-99ed-d353ec06f87a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a515f818-00b8-42fe-99ed-d353ec06f87a' 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'>time</span>: 262992</li><li><span>segment</span>: 152</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2a2fedb9-3fc1-4aaf-bdb9-188dffc526e3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2a2fedb9-3fc1-4aaf-bdb9-188dffc526e3' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span 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'>1991-01-01 ... 2020-12-31T23:00:00</div><input id='attrs-71181413-89a6-40f1-af1c-87e551fa6290' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-71181413-89a6-40f1-af1c-87e551fa6290' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2996e014-d1d5-48d5-954e-6860cd3fb79f' class='xr-var-data-in' type='checkbox'><label for='data-2996e014-d1d5-48d5-954e-6860cd3fb79f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1991-01-01T00:00:00.000000000&#x27;, &#x27;1991-01-01T01:00:00.000000000&#x27;,\n",
       "       &#x27;1991-01-01T02:00:00.000000000&#x27;, ..., &#x27;2020-12-31T21:00:00.000000000&#x27;,\n",
       "       &#x27;2020-12-31T22:00:00.000000000&#x27;, &#x27;2020-12-31T23:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-31089258-31d6-465b-820d-c57a380d1818' class='xr-section-summary-in' type='checkbox'  checked><label for='section-31089258-31d6-465b-820d-c57a380d1818' 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>var235</span></div><div class='xr-var-dims'>(time, segment)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-8.87 -10.9 ... -9.422 -6.655</div><input id='attrs-e0c2d2ba-a38f-4ab2-8d87-794f27711511' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e0c2d2ba-a38f-4ab2-8d87-794f27711511' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-059a4ee1-0b01-4d7a-b021-3c4245681fe2' class='xr-var-data-in' type='checkbox'><label for='data-059a4ee1-0b01-4d7a-b021-3c4245681fe2' 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([[ -8.870117 , -10.895508 , -11.563477 , ...,  -9.547852 ,\n",
       "         -9.74707  ,  -7.397461 ],\n",
       "       [ -7.744873 ,  -8.508545 ,  -9.141357 , ...,  -6.369873 ,\n",
       "         -7.7058105,  -4.6081543],\n",
       "       [ -1.3996582,  -0.6906738,  -2.710205 , ...,  -5.2180176,\n",
       "         -6.2355957,  -2.6418457],\n",
       "       ...,\n",
       "       [ -8.974609 , -10.1171875, -10.246094 , ...,  -9.03125  ,\n",
       "         -9.136719 ,  -6.451172 ],\n",
       "       [ -9.20459  , -10.546387 , -10.800293 , ...,  -9.118652 ,\n",
       "         -9.228027 ,  -6.5249023],\n",
       "       [ -9.5998535, -10.929932 , -11.32251  , ...,  -9.326416 ,\n",
       "         -9.422119 ,  -6.654541 ]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8b7713e9-6b2d-4ef7-bdb6-4a84f6c3670a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8b7713e9-6b2d-4ef7-bdb6-4a84f6c3670a' 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:  (time: 262992, segment: 152)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1991-01-01 ... 2020-12-31T23:00:00\n",
       "Dimensions without coordinates: segment\n",
       "Data variables:\n",
       "    var235   (time, segment) float32 -8.87 -10.9 -11.56 ... -9.326 -9.422 -6.655"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deltaT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5dc84c34",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:55:04.054380Z",
     "start_time": "2022-08-30T18:55:04.051292Z"
    }
   },
   "outputs": [],
   "source": [
    "deltaT = deltaT.assign_coords(segment=transects_qc03['segment_index'].to_numpy().tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8f845b5a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:55:04.470333Z",
     "start_time": "2022-08-30T18:55:04.467788Z"
    }
   },
   "outputs": [],
   "source": [
    "deltaT = deltaT.rename({'var235':'skt'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2dd373fa",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:55:04.820817Z",
     "start_time": "2022-08-30T18:55:04.809582Z"
    }
   },
   "outputs": [
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       "Dimensions:  (time: 262992, segment: 152)\n",
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       "  * segment  (segment) int64 1 1 1 1 2 2 2 2 2 2 ... 31 31 31 32 32 32 33 33 33\n",
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       "    skt      (time, segment) float32 -8.87 -10.9 -11.56 ... -9.326 -9.422 -6.655</pre><div class='xr-wrap' style='display:none'><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-d967dff2-529d-4169-a397-d5e7324dea31' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d967dff2-529d-4169-a397-d5e7324dea31' 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'>time</span>: 262992</li><li><span class='xr-has-index'>segment</span>: 152</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2d5a4503-89a9-4456-96e0-9460cd9ddc7c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2d5a4503-89a9-4456-96e0-9460cd9ddc7c' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>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'>1991-01-01 ... 2020-12-31T23:00:00</div><input id='attrs-046ff1aa-6f85-47a3-87c8-17c196fb6c16' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-046ff1aa-6f85-47a3-87c8-17c196fb6c16' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c809a9c-ebc8-4f6b-bf63-f2abf4540114' class='xr-var-data-in' type='checkbox'><label for='data-0c809a9c-ebc8-4f6b-bf63-f2abf4540114' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1991-01-01T00:00:00.000000000&#x27;, &#x27;1991-01-01T01:00:00.000000000&#x27;,\n",
       "       &#x27;1991-01-01T02:00:00.000000000&#x27;, ..., &#x27;2020-12-31T21:00:00.000000000&#x27;,\n",
       "       &#x27;2020-12-31T22:00:00.000000000&#x27;, &#x27;2020-12-31T23:00:00.000000000&#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'>segment</span></div><div class='xr-var-dims'>(segment)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 1 1 1 2 2 2 ... 32 32 32 33 33 33</div><input id='attrs-f070178f-774d-48d3-b2a7-3ea2ac9e9ace' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f070178f-774d-48d3-b2a7-3ea2ac9e9ace' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-428427ab-2868-4ca3-a22f-b5144ae42de4' class='xr-var-data-in' type='checkbox'><label for='data-428427ab-2868-4ca3-a22f-b5144ae42de4' 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,  3,  3,  3,  3,  4,  4,\n",
       "        4,  4,  4,  5,  5,  6,  6,  6,  6,  6,  6,  6,  6,  7,  7,  7,  8,  8,\n",
       "        8,  8,  8,  8,  9,  9,  9,  9,  9,  9,  9,  9, 10, 10, 10, 10, 11, 11,\n",
       "       11, 11, 11, 11, 12, 12, 13, 13, 13, 13, 13, 13, 14, 14, 14, 15, 15, 16,\n",
       "       16, 17, 17, 17, 17, 17, 17, 18, 18, 18, 18, 18, 18, 19, 19, 19, 19, 19,\n",
       "       20, 20, 20, 20, 21, 21, 21, 22, 22, 22, 23, 23, 23, 23, 23, 23, 24, 24,\n",
       "       24, 24, 24, 24, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 26, 26, 26, 26,\n",
       "       26, 26, 26, 26, 26, 27, 27, 28, 28, 28, 29, 29, 30, 30, 31, 31, 31, 31,\n",
       "       31, 31, 32, 32, 32, 33, 33, 33])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7dc16750-d4c4-4344-8dc4-1c466ce7880e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7dc16750-d4c4-4344-8dc4-1c466ce7880e' 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>skt</span></div><div class='xr-var-dims'>(time, segment)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-8.87 -10.9 ... -9.422 -6.655</div><input id='attrs-1071abd6-e032-4e46-b024-bc1f2fc11c16' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1071abd6-e032-4e46-b024-bc1f2fc11c16' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d06c1763-a73b-45db-a5e5-b59ff7487074' class='xr-var-data-in' type='checkbox'><label for='data-d06c1763-a73b-45db-a5e5-b59ff7487074' 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([[ -8.870117 , -10.895508 , -11.563477 , ...,  -9.547852 ,\n",
       "         -9.74707  ,  -7.397461 ],\n",
       "       [ -7.744873 ,  -8.508545 ,  -9.141357 , ...,  -6.369873 ,\n",
       "         -7.7058105,  -4.6081543],\n",
       "       [ -1.3996582,  -0.6906738,  -2.710205 , ...,  -5.2180176,\n",
       "         -6.2355957,  -2.6418457],\n",
       "       ...,\n",
       "       [ -8.974609 , -10.1171875, -10.246094 , ...,  -9.03125  ,\n",
       "         -9.136719 ,  -6.451172 ],\n",
       "       [ -9.20459  , -10.546387 , -10.800293 , ...,  -9.118652 ,\n",
       "         -9.228027 ,  -6.5249023],\n",
       "       [ -9.5998535, -10.929932 , -11.32251  , ...,  -9.326416 ,\n",
       "         -9.422119 ,  -6.654541 ]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c515e26a-d2ca-46a2-bf91-166772c77b24' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c515e26a-d2ca-46a2-bf91-166772c77b24' 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>"
      ],
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       "<xarray.Dataset>\n",
       "Dimensions:  (time: 262992, segment: 152)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1991-01-01 ... 2020-12-31T23:00:00\n",
       "  * segment  (segment) int64 1 1 1 1 2 2 2 2 2 2 ... 31 31 31 32 32 32 33 33 33\n",
       "Data variables:\n",
       "    skt      (time, segment) float32 -8.87 -10.9 -11.56 ... -9.326 -9.422 -6.655"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deltaT"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "764a08f6",
   "metadata": {},
   "source": [
    "## Combine variables into a table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69406ffc",
   "metadata": {},
   "source": [
    "### Focus on certain segment on limited period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4549a4ae",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:55:12.642191Z",
     "start_time": "2022-08-30T18:55:12.639782Z"
    }
   },
   "outputs": [],
   "source": [
    "segn=2\n",
    "ystr='1991'\n",
    "yend='2020'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a0577fc0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:55:13.985150Z",
     "start_time": "2022-08-30T18:55:13.832143Z"
    }
   },
   "outputs": [],
   "source": [
    "u850 = U850.sel(segment=segn).sel(time=slice(ystr,yend)).mean(dim='segment')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "01051c7f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:55:14.302009Z",
     "start_time": "2022-08-30T18:55:14.290757Z"
    }
   },
   "outputs": [
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       "Data variables:\n",
       "    u        (time) float32 -4.506 -4.909 -5.294 -5.677 ... -9.2 -7.274 -6.819\n",
       "    v        (time) float32 0.05547 0.1442 0.3991 0.8005 ... 1.386 1.681 1.63</pre><div class='xr-wrap' style='display:none'><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-f81ee23d-1c9a-4e92-b4b9-65318156743f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f81ee23d-1c9a-4e92-b4b9-65318156743f' 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'>time</span>: 262992</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-253f8204-5fb5-4b24-aa73-b091965eacf5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-253f8204-5fb5-4b24-aa73-b091965eacf5' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span 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'>1991-01-01 ... 2020-12-31T23:00:00</div><input id='attrs-25526bc0-65c3-4013-9a82-0a28d387bccd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-25526bc0-65c3-4013-9a82-0a28d387bccd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7d8f14b-f95f-443d-892b-4c09932388d2' class='xr-var-data-in' type='checkbox'><label for='data-e7d8f14b-f95f-443d-892b-4c09932388d2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1991-01-01T00:00:00.000000000&#x27;, &#x27;1991-01-01T01:00:00.000000000&#x27;,\n",
       "       &#x27;1991-01-01T02:00:00.000000000&#x27;, ..., &#x27;2020-12-31T21:00:00.000000000&#x27;,\n",
       "       &#x27;2020-12-31T22:00:00.000000000&#x27;, &#x27;2020-12-31T23:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-14a290cf-9582-408f-a4b0-09774607395d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-14a290cf-9582-408f-a4b0-09774607395d' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>u</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-4.506 -4.909 ... -7.274 -6.819</div><input id='attrs-61696d10-7113-43b6-aba2-f93944ae34de' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-61696d10-7113-43b6-aba2-f93944ae34de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4bd6323-7f6c-4333-ad6b-ac96e224b455' class='xr-var-data-in' type='checkbox'><label for='data-c4bd6323-7f6c-4333-ad6b-ac96e224b455' 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([-4.506134 , -4.9094086, -5.293762 , ..., -9.199722 , -7.2740784,\n",
       "       -6.819046 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.05547 0.1442 ... 1.681 1.63</div><input id='attrs-5579ec09-cb38-45bc-9fc7-f6fcc42cf322' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5579ec09-cb38-45bc-9fc7-f6fcc42cf322' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9af3c977-a352-4a70-b52b-43abd8cb0698' class='xr-var-data-in' type='checkbox'><label for='data-9af3c977-a352-4a70-b52b-43abd8cb0698' 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([0.0554657 , 0.14419556, 0.3990631 , ..., 1.3857269 , 1.6809845 ,\n",
       "       1.6298981 ], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4192e2fb-d550-4dd0-b38f-977846b76a6c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4192e2fb-d550-4dd0-b38f-977846b76a6c' 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:  (time: 262992)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1991-01-01 ... 2020-12-31T23:00:00\n",
       "Data variables:\n",
       "    u        (time) float32 -4.506 -4.909 -5.294 -5.677 ... -9.2 -7.274 -6.819\n",
       "    v        (time) float32 0.05547 0.1442 0.3991 0.8005 ... 1.386 1.681 1.63"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u850"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f37ab4b7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:55:18.419693Z",
     "start_time": "2022-08-30T18:55:18.414986Z"
    }
   },
   "outputs": [],
   "source": [
    "u850_spd = np.sqrt(u850.u**2+u850.v**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "547a617b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:55:19.562407Z",
     "start_time": "2022-08-30T18:55:19.550976Z"
    }
   },
   "outputs": [],
   "source": [
    "u850_dir = np.mod((180+(180/np.pi)*np.arctan2(u850.u,u850.v)), 360)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7f6f5cf7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:55:19.963024Z",
     "start_time": "2022-08-30T18:55:19.852098Z"
    }
   },
   "outputs": [],
   "source": [
    "u910 = U910.sel(segment=segn).sel(time=slice(ystr,yend)).mean(dim='segment')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d696dc75",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:55:20.479299Z",
     "start_time": "2022-08-30T18:55:20.475490Z"
    }
   },
   "outputs": [],
   "source": [
    "u910_spd = np.sqrt(u910.u**2+u910.v**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a7cc232a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:55:20.751876Z",
     "start_time": "2022-08-30T18:55:20.740657Z"
    }
   },
   "outputs": [],
   "source": [
    "u910_dir = np.mod((180+(180/np.pi)*np.arctan2(u910.u,u910.v)), 360)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "65ca1a54",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:55:21.022916Z",
     "start_time": "2022-08-30T18:55:20.976335Z"
    }
   },
   "outputs": [],
   "source": [
    "delt = deltaT.sel(segment=segn).sel(time=slice(ystr,yend)).mean(dim='segment')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "25b004ef",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:55:21.370292Z",
     "start_time": "2022-08-30T18:55:21.361227Z"
    }
   },
   "outputs": [
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       "  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:  (time: 262992)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1991-01-01 ... 2020-12-31T23:00:00\n",
       "Data variables:\n",
       "    skt      (time) float32 -7.833 -4.797 -2.688 0.905 ... -5.764 -5.886 -6.045</pre><div class='xr-wrap' style='display:none'><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-b15bb4a2-e2cd-4fa8-8ab9-d1c2c3120ad2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b15bb4a2-e2cd-4fa8-8ab9-d1c2c3120ad2' 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'>time</span>: 262992</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8aa6852c-158a-4532-9e7c-a28bcc274f5f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8aa6852c-158a-4532-9e7c-a28bcc274f5f' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span 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'>1991-01-01 ... 2020-12-31T23:00:00</div><input id='attrs-e0ad96a2-8dfb-4c14-bb72-392f167013c9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e0ad96a2-8dfb-4c14-bb72-392f167013c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62c92c6b-31b7-44ad-bb44-20df41d953ed' class='xr-var-data-in' type='checkbox'><label for='data-62c92c6b-31b7-44ad-bb44-20df41d953ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1991-01-01T00:00:00.000000000&#x27;, &#x27;1991-01-01T01:00:00.000000000&#x27;,\n",
       "       &#x27;1991-01-01T02:00:00.000000000&#x27;, ..., &#x27;2020-12-31T21:00:00.000000000&#x27;,\n",
       "       &#x27;2020-12-31T22:00:00.000000000&#x27;, &#x27;2020-12-31T23:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f1631e1e-e8db-4942-859d-fd3f9a11f18e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f1631e1e-e8db-4942-859d-fd3f9a11f18e' 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>skt</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-7.833 -4.797 ... -5.886 -6.045</div><input id='attrs-a652cb40-af7f-4037-bf46-b9cf92386ade' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a652cb40-af7f-4037-bf46-b9cf92386ade' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20ee82e1-df2c-4511-bdfb-10c19e91db65' class='xr-var-data-in' type='checkbox'><label for='data-20ee82e1-df2c-4511-bdfb-10c19e91db65' 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([-7.833008 , -4.797119 , -2.6875   , ..., -5.763672 , -5.8862305,\n",
       "       -6.045166 ], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8e290b5a-b709-462d-8c3a-de8fea4501a8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8e290b5a-b709-462d-8c3a-de8fea4501a8' 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:  (time: 262992)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1991-01-01 ... 2020-12-31T23:00:00\n",
       "Data variables:\n",
       "    skt      (time) float32 -7.833 -4.797 -2.688 0.905 ... -5.764 -5.886 -6.045"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6964521",
   "metadata": {},
   "source": [
    "### Combine into a table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ee54e81a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:55:25.017331Z",
     "start_time": "2022-08-30T18:55:24.933479Z"
    }
   },
   "outputs": [],
   "source": [
    "years = u850.time.dt.year\n",
    "months = u850.time.dt.month\n",
    "days = u850.time.dt.day\n",
    "hours = u850.time.dt.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "24abc7d0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:56:39.591454Z",
     "start_time": "2022-08-30T18:55:25.294137Z"
    }
   },
   "outputs": [],
   "source": [
    "df_sbd = pd.DataFrame(years, columns=['Year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f420f2ad",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:57:36.715385Z",
     "start_time": "2022-08-30T18:57:36.709642Z"
    }
   },
   "outputs": [],
   "source": [
    "df_sbd['Month'] = months\n",
    "df_sbd['Day'] = days\n",
    "df_sbd['Hour'] = hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "bb6680d6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:57:37.231212Z",
     "start_time": "2022-08-30T18:57:37.225826Z"
    }
   },
   "outputs": [],
   "source": [
    "df_sbd['u850_u'] = u850.u\n",
    "df_sbd['u850_v'] = u850.v\n",
    "df_sbd['u850_spd'] = u850_spd\n",
    "df_sbd['u850_dir'] = u850_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "76249145",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:57:37.614570Z",
     "start_time": "2022-08-30T18:57:37.609742Z"
    }
   },
   "outputs": [],
   "source": [
    "df_sbd['u910_u'] = u910.u\n",
    "df_sbd['u910_v'] = u910.v\n",
    "df_sbd['u910_spd'] = u910_spd\n",
    "df_sbd['u910_dir'] = u910_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a6bc1924",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:57:38.073878Z",
     "start_time": "2022-08-30T18:57:38.071167Z"
    }
   },
   "outputs": [],
   "source": [
    "df_sbd['delt'] = delt.skt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "7c0af33f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:57:38.908901Z",
     "start_time": "2022-08-30T18:57:38.893864Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Hour</th>\n",
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       "      <td>75.355194</td>\n",
       "      <td>-2.687500</td>\n",
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       "      <th>3</th>\n",
       "      <td>1991</td>\n",
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       "      <td>-5.676514</td>\n",
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       "      <td>76.237701</td>\n",
       "      <td>0.905029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1991</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>-6.077362</td>\n",
       "      <td>1.232758</td>\n",
       "      <td>6.201131</td>\n",
       "      <td>101.466537</td>\n",
       "      <td>-1.248970</td>\n",
       "      <td>-0.136230</td>\n",
       "      <td>1.256378</td>\n",
       "      <td>83.775116</td>\n",
       "      <td>1.577637</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>262987</th>\n",
       "      <td>2020</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>19</td>\n",
       "      <td>-9.462204</td>\n",
       "      <td>0.358109</td>\n",
       "      <td>9.468978</td>\n",
       "      <td>92.167397</td>\n",
       "      <td>-7.781830</td>\n",
       "      <td>-2.198517</td>\n",
       "      <td>8.086430</td>\n",
       "      <td>74.223991</td>\n",
       "      <td>-5.661133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262988</th>\n",
       "      <td>2020</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>20</td>\n",
       "      <td>-9.441513</td>\n",
       "      <td>0.860519</td>\n",
       "      <td>9.480646</td>\n",
       "      <td>95.207664</td>\n",
       "      <td>-7.798843</td>\n",
       "      <td>-2.313690</td>\n",
       "      <td>8.134809</td>\n",
       "      <td>73.475914</td>\n",
       "      <td>-5.719971</td>\n",
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       "    <tr>\n",
       "      <th>262989</th>\n",
       "      <td>2020</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>21</td>\n",
       "      <td>-9.199722</td>\n",
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       "      <td>8.164022</td>\n",
       "      <td>72.388451</td>\n",
       "      <td>-5.763672</td>\n",
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       "    <tr>\n",
       "      <th>262990</th>\n",
       "      <td>2020</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>22</td>\n",
       "      <td>-7.274078</td>\n",
       "      <td>1.680984</td>\n",
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       "      <td>103.012199</td>\n",
       "      <td>-6.863991</td>\n",
       "      <td>-2.822540</td>\n",
       "      <td>7.421664</td>\n",
       "      <td>67.647057</td>\n",
       "      <td>-5.886230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262991</th>\n",
       "      <td>2020</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>23</td>\n",
       "      <td>-6.819046</td>\n",
       "      <td>1.629898</td>\n",
       "      <td>7.011131</td>\n",
       "      <td>103.442703</td>\n",
       "      <td>-7.045761</td>\n",
       "      <td>-3.098129</td>\n",
       "      <td>7.696827</td>\n",
       "      <td>66.264175</td>\n",
       "      <td>-6.045166</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>262992 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Year  Month  Day  Hour    u850_u    u850_v  u850_spd    u850_dir  \\\n",
       "0       1991      1    1     0 -4.506134  0.055466  4.506475   90.705215   \n",
       "1       1991      1    1     1 -4.909409  0.144196  4.911526   91.682365   \n",
       "2       1991      1    1     2 -5.293762  0.399063  5.308783   94.311005   \n",
       "3       1991      1    1     3 -5.676514  0.800537  5.732685   98.027267   \n",
       "4       1991      1    1     4 -6.077362  1.232758  6.201131  101.466537   \n",
       "...      ...    ...  ...   ...       ...       ...       ...         ...   \n",
       "262987  2020     12   31    19 -9.462204  0.358109  9.468978   92.167397   \n",
       "262988  2020     12   31    20 -9.441513  0.860519  9.480646   95.207664   \n",
       "262989  2020     12   31    21 -9.199722  1.385727  9.303501   98.565903   \n",
       "262990  2020     12   31    22 -7.274078  1.680984  7.465784  103.012199   \n",
       "262991  2020     12   31    23 -6.819046  1.629898  7.011131  103.442703   \n",
       "\n",
       "          u910_u    u910_v  u910_spd   u910_dir      delt  \n",
       "0      -1.273766 -0.173355  1.285508  82.249855 -7.833008  \n",
       "1      -1.188408 -0.261360  1.216808  77.596703 -4.797119  \n",
       "2      -1.249680 -0.326561  1.291643  75.355194 -2.687500  \n",
       "3      -1.104790 -0.270592  1.137445  76.237701  0.905029  \n",
       "4      -1.248970 -0.136230  1.256378  83.775116  1.577637  \n",
       "...          ...       ...       ...        ...       ...  \n",
       "262987 -7.781830 -2.198517  8.086430  74.223991 -5.661133  \n",
       "262988 -7.798843 -2.313690  8.134809  73.475914 -5.719971  \n",
       "262989 -7.781372 -2.470123  8.164022  72.388451 -5.763672  \n",
       "262990 -6.863991 -2.822540  7.421664  67.647057 -5.886230  \n",
       "262991 -7.045761 -3.098129  7.696827  66.264175 -6.045166  \n",
       "\n",
       "[262992 rows x 13 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sbd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "fbd2932d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:57:39.318940Z",
     "start_time": "2022-08-30T18:57:39.196760Z"
    }
   },
   "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>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>u850_u</th>\n",
       "      <th>u850_v</th>\n",
       "      <th>u850_spd</th>\n",
       "      <th>u850_dir</th>\n",
       "      <th>u910_u</th>\n",
       "      <th>u910_v</th>\n",
       "      <th>u910_spd</th>\n",
       "      <th>u910_dir</th>\n",
       "      <th>delt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>262992.000000</td>\n",
       "      <td>262992.000000</td>\n",
       "      <td>262992.000000</td>\n",
       "      <td>262992.0000</td>\n",
       "      <td>262992.000000</td>\n",
       "      <td>262992.000000</td>\n",
       "      <td>262992.000000</td>\n",
       "      <td>262992.000000</td>\n",
       "      <td>262992.000000</td>\n",
       "      <td>262992.000000</td>\n",
       "      <td>262992.000000</td>\n",
       "      <td>262992.000000</td>\n",
       "      <td>262992.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2005.500365</td>\n",
       "      <td>6.522723</td>\n",
       "      <td>15.730243</td>\n",
       "      <td>11.5000</td>\n",
       "      <td>1.185023</td>\n",
       "      <td>0.586932</td>\n",
       "      <td>7.343678</td>\n",
       "      <td>175.247208</td>\n",
       "      <td>0.429039</td>\n",
       "      <td>0.318832</td>\n",
       "      <td>3.904252</td>\n",
       "      <td>183.024734</td>\n",
       "      <td>-3.542529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.655852</td>\n",
       "      <td>3.448766</td>\n",
       "      <td>8.800365</td>\n",
       "      <td>6.9222</td>\n",
       "      <td>7.973019</td>\n",
       "      <td>2.517784</td>\n",
       "      <td>4.210372</td>\n",
       "      <td>90.585472</td>\n",
       "      <td>4.031723</td>\n",
       "      <td>1.630821</td>\n",
       "      <td>1.989186</td>\n",
       "      <td>93.303352</td>\n",
       "      <td>2.809393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1991.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-21.837509</td>\n",
       "      <td>-10.659149</td>\n",
       "      <td>0.020824</td>\n",
       "      <td>0.005997</td>\n",
       "      <td>-10.699860</td>\n",
       "      <td>-6.373917</td>\n",
       "      <td>0.007671</td>\n",
       "      <td>0.003967</td>\n",
       "      <td>-13.196777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1998.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5.7500</td>\n",
       "      <td>-5.063541</td>\n",
       "      <td>-1.035202</td>\n",
       "      <td>3.952651</td>\n",
       "      <td>91.397085</td>\n",
       "      <td>-3.015207</td>\n",
       "      <td>-0.820305</td>\n",
       "      <td>2.306136</td>\n",
       "      <td>90.014122</td>\n",
       "      <td>-5.570801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2005.500000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>11.5000</td>\n",
       "      <td>-0.523323</td>\n",
       "      <td>0.450310</td>\n",
       "      <td>6.743049</td>\n",
       "      <td>149.131424</td>\n",
       "      <td>0.658276</td>\n",
       "      <td>0.269722</td>\n",
       "      <td>3.831619</td>\n",
       "      <td>224.789307</td>\n",
       "      <td>-4.256836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2013.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>17.2500</td>\n",
       "      <td>8.060249</td>\n",
       "      <td>2.074112</td>\n",
       "      <td>10.236098</td>\n",
       "      <td>263.122650</td>\n",
       "      <td>3.855843</td>\n",
       "      <td>1.360210</td>\n",
       "      <td>5.373474</td>\n",
       "      <td>260.099037</td>\n",
       "      <td>-1.752136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2020.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>23.0000</td>\n",
       "      <td>23.439957</td>\n",
       "      <td>13.934631</td>\n",
       "      <td>24.574062</td>\n",
       "      <td>359.975708</td>\n",
       "      <td>11.165215</td>\n",
       "      <td>7.246506</td>\n",
       "      <td>11.188865</td>\n",
       "      <td>359.992126</td>\n",
       "      <td>10.198730</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Year          Month            Day         Hour  \\\n",
       "count  262992.000000  262992.000000  262992.000000  262992.0000   \n",
       "mean     2005.500365       6.522723      15.730243      11.5000   \n",
       "std         8.655852       3.448766       8.800365       6.9222   \n",
       "min      1991.000000       1.000000       1.000000       0.0000   \n",
       "25%      1998.000000       4.000000       8.000000       5.7500   \n",
       "50%      2005.500000       7.000000      16.000000      11.5000   \n",
       "75%      2013.000000      10.000000      23.000000      17.2500   \n",
       "max      2020.000000      12.000000      31.000000      23.0000   \n",
       "\n",
       "              u850_u         u850_v       u850_spd       u850_dir  \\\n",
       "count  262992.000000  262992.000000  262992.000000  262992.000000   \n",
       "mean        1.185023       0.586932       7.343678     175.247208   \n",
       "std         7.973019       2.517784       4.210372      90.585472   \n",
       "min       -21.837509     -10.659149       0.020824       0.005997   \n",
       "25%        -5.063541      -1.035202       3.952651      91.397085   \n",
       "50%        -0.523323       0.450310       6.743049     149.131424   \n",
       "75%         8.060249       2.074112      10.236098     263.122650   \n",
       "max        23.439957      13.934631      24.574062     359.975708   \n",
       "\n",
       "              u910_u         u910_v       u910_spd       u910_dir  \\\n",
       "count  262992.000000  262992.000000  262992.000000  262992.000000   \n",
       "mean        0.429039       0.318832       3.904252     183.024734   \n",
       "std         4.031723       1.630821       1.989186      93.303352   \n",
       "min       -10.699860      -6.373917       0.007671       0.003967   \n",
       "25%        -3.015207      -0.820305       2.306136      90.014122   \n",
       "50%         0.658276       0.269722       3.831619     224.789307   \n",
       "75%         3.855843       1.360210       5.373474     260.099037   \n",
       "max        11.165215       7.246506      11.188865     359.992126   \n",
       "\n",
       "                delt  \n",
       "count  262992.000000  \n",
       "mean       -3.542529  \n",
       "std         2.809393  \n",
       "min       -13.196777  \n",
       "25%        -5.570801  \n",
       "50%        -4.256836  \n",
       "75%        -1.752136  \n",
       "max        10.198730  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sbd.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d4c29a0",
   "metadata": {},
   "source": [
    "### Regular scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "a9ad609b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:57:42.641752Z",
     "start_time": "2022-08-30T18:57:42.639388Z"
    }
   },
   "outputs": [],
   "source": [
    "# fig, ax = plt.subplots(nrows=1, ncols=1, dpi=200, figsize=(10,6))\n",
    "\n",
    "# graph = sns.scatterplot(x='delt', y='u910_spd', data=df_sbd, s=100);\n",
    "\n",
    "# ax.set_xlabel('$\\Delta$T [Land - Ocean] ($^\\circ$C)', fontsize=16)\n",
    "# ax.set_ylabel('$U_{1000-950}$ (m/s)', fontsize=16)\n",
    "# ax.tick_params(axis='both', labelsize=14)\n",
    "# ax.set_title(r\"$\\bf{Scatterplot}$: $\\Delta$T vs $U_{1000-950}$\", fontsize=16)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daa68a9f",
   "metadata": {},
   "source": [
    "## Apply filter to get Sea Breeze Days (SBD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "66e0a9ad",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:57:43.641559Z",
     "start_time": "2022-08-30T18:57:43.637100Z"
    }
   },
   "outputs": [],
   "source": [
    "# Filter No 1 (delt >= 3)\n",
    "#df_sbd['delt_flag'] = \n",
    "filtered_sbd = df_sbd[(df_sbd.delt >= 3) & (df_sbd.u850_spd <= 6)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "a175755d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:57:44.438436Z",
     "start_time": "2022-08-30T18:57:44.436337Z"
    }
   },
   "outputs": [],
   "source": [
    "# fig2, ax2 = plt.subplots(nrows=1, ncols=1, dpi=200, figsize=(10,6))\n",
    "\n",
    "# graph2 = sns.scatterplot(x='delt', y='u910_spd', data=filtered_sbd, s=100);\n",
    "\n",
    "# ax2.set_xlabel('$\\Delta$T [Land - Ocean] ($^\\circ$C)', fontsize=16)\n",
    "# ax2.set_ylabel('$U_{1000-950}$ (m/s)', fontsize=16)\n",
    "# ax2.tick_params(axis='both', labelsize=14)\n",
    "# ax2.set_title(r\"$\\bf{Scatterplot}$: $\\Delta$T vs $U_{1000-950}$\", fontsize=16)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf0f0a24",
   "metadata": {},
   "source": [
    "## Binned scatter plot for filtered SBD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "146ab98e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:57:46.414207Z",
     "start_time": "2022-08-30T18:57:45.799618Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>group</th>\n",
       "      <th>delt</th>\n",
       "      <th>bin</th>\n",
       "      <th>isknot</th>\n",
       "      <th>mid</th>\n",
       "      <th>u910_spd</th>\n",
       "      <th>ci_l</th>\n",
       "      <th>ci_r</th>\n",
       "      <th>ci</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Full Sample</td>\n",
       "      <td>3.054403</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.101916</td>\n",
       "      <td>1.931213</td>\n",
       "      <td>2.288351</td>\n",
       "      <td>0.357138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Full Sample</td>\n",
       "      <td>3.172303</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.094251</td>\n",
       "      <td>1.937212</td>\n",
       "      <td>2.217170</td>\n",
       "      <td>0.279958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Full Sample</td>\n",
       "      <td>3.293199</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.057169</td>\n",
       "      <td>1.924133</td>\n",
       "      <td>2.173294</td>\n",
       "      <td>0.249161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Full Sample</td>\n",
       "      <td>3.405656</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.074748</td>\n",
       "      <td>1.970821</td>\n",
       "      <td>2.206253</td>\n",
       "      <td>0.235432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Full Sample</td>\n",
       "      <td>3.529979</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.142379</td>\n",
       "      <td>1.989200</td>\n",
       "      <td>2.223324</td>\n",
       "      <td>0.234125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         group      delt  bin  isknot  mid  u910_spd      ci_l      ci_r  \\\n",
       "0  Full Sample  3.054403    0       0    0  2.101916  1.931213  2.288351   \n",
       "1  Full Sample  3.172303    1       0    0  2.094251  1.937212  2.217170   \n",
       "2  Full Sample  3.293199    2       0    0  2.057169  1.924133  2.173294   \n",
       "3  Full Sample  3.405656    3       0    0  2.074748  1.970821  2.206253   \n",
       "4  Full Sample  3.529979    4       0    0  2.142379  1.989200  2.223324   \n",
       "\n",
       "         ci  \n",
       "0  0.357138  \n",
       "1  0.279958  \n",
       "2  0.249161  \n",
       "3  0.235432  \n",
       "4  0.234125  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Estimate binsreg 1\n",
    "df_est = binscatter(x='delt', y='u910_spd', data=filtered_sbd, ci=(3,3))\n",
    "df_est.head(n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "35dc1477",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:57:49.238028Z",
     "start_time": "2022-08-30T18:57:48.676390Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig3, ax3 = plt.subplots(nrows=1, ncols=1, dpi=200, figsize=(10,6))\n",
    "\n",
    "graph3 = sns.scatterplot(x='delt', y='u910_spd', data=df_est, s=100);\n",
    "ax3.errorbar('delt', 'u910_spd', yerr='ci', data=df_est, ls='', lw=4, alpha=0.3);\n",
    "\n",
    "ax3.set_xlabel('$\\Delta$T [Land - Ocean] ($^\\circ$C)', fontsize=16)\n",
    "ax3.set_ylabel('$U_{1000-950}$ (m/s)', fontsize=16)\n",
    "ax3.tick_params(axis='both', labelsize=14)\n",
    "ax3.set_title(r\"$\\bf{Binned scatterplot}$: $\\Delta$T vs $U_{1000-950}$\", fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "58996988",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-30T18:57:52.569822Z",
     "start_time": "2022-08-30T18:57:52.230114Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig4, ax4 = plt.subplots(nrows=1, ncols=1, dpi=150)#, figsize=(10,6))\n",
    "\n",
    "graph4 = sns.regplot(x=\"delt\", y=\"u910_spd\", data=df_est, fit_reg=True, order=2, ci=0, scatter_kws={\"s\": 50});\n",
    "ax4.errorbar('delt', 'u910_spd', yerr='ci', data=df_est, ls='', lw=2, alpha=0.3);\n",
    "\n",
    "ax4.set_xlabel('$\\Delta$T [Land - Ocean] ($^\\circ$C)', fontsize=14)\n",
    "ax4.set_ylabel('$U_{1000-950}$ (m/s)', fontsize=14)\n",
    "ax4.tick_params(axis='both', labelsize=12)\n",
    "ax4.set_title(r\"$\\bf{Binned scatterplot}$: $\\Delta$T vs $U_{1000-950}$\", fontsize=14)\n",
    "plt.annotate('Segment = '+str(segn), xy=(0.05, 0.9), xycoords='axes fraction', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9fdba800",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "80f534d9",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "680f351f",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "014dcb4f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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