{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot CTD profiles against Bottle data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.collections as mcol\n",
    "from matplotlib.legend_handler import HandlerLineCollection, HandlerTuple\n",
    "from matplotlib.lines import Line2D\n",
    "from matplotlib.ticker import ScalarFormatter,AutoMinorLocator\n",
    "import matplotlib as mpl\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "sns.set_style(\"white\")\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load CTD profiles from .cnv "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seabird.cnv.fCNV at 0x25cfb7debe0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from seabird.cnv import fCNV\n",
    "myCTD= 'C:/Users/malek/Downloads/notebooks_2021/DissolvedOxygen/data/cast043.cnv'\n",
    "profile = fCNV(myCTD)\n",
    "profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sbe_model': '9',\n",
       " 'LATITUDE': 40.827666666666666,\n",
       " 'LONGITUDE': 2.786,\n",
       " 'gps_datetime': 'Mar 04 2022 20:00:27',\n",
       " 'seasave': 'V 7.26.2.13',\n",
       " 'instrument_type': 'CTD',\n",
       " 'nquan': '46',\n",
       " 'nvalues': '303',\n",
       " 'start_time': 'Mar 04 2022 20:00:27 [NMEA time, header]',\n",
       " 'bad_flag': '-9.990e-29',\n",
       " 'file_type': 'ascii',\n",
       " 'md5': '086159f4155aabddc09de3ff935b72d3',\n",
       " 'datetime': datetime.datetime(2022, 3, 4, 20, 0, 27),\n",
       " 'filename': 'cast043.cnv'}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# header of the cnv file\n",
    "profile.attrs "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['scan',\n",
       " 'PRES',\n",
       " 'TEMP',\n",
       " 'TEMP2',\n",
       " 'T2-T190C',\n",
       " 'CNDC',\n",
       " 'CNDC2',\n",
       " 'C2-C1S/m',\n",
       " 'ph',\n",
       " 'flECO-AFL',\n",
       " 'turbWETntu0',\n",
       " 'CStarAt0',\n",
       " 'CStarTr0',\n",
       " 'par',\n",
       " 'oxygenvoltage',\n",
       " 'v0',\n",
       " 'v1',\n",
       " 'v2',\n",
       " 'v3',\n",
       " 'v4',\n",
       " 'v5',\n",
       " 'v7',\n",
       " 'nbf',\n",
       " 'bpos',\n",
       " 'pumps',\n",
       " 'LATITUDE',\n",
       " 'LONGITUDE',\n",
       " 'timeS',\n",
       " 'timeJ',\n",
       " 'PSAL',\n",
       " 'PSAL2',\n",
       " 'secS-priS',\n",
       " 'soundspeed',\n",
       " 'sigma-�00',\n",
       " 'sigma-�11',\n",
       " 'D2-D1',\n",
       " 'potemperature',\n",
       " 'potemperature2',\n",
       " 'tsa',\n",
       " 'sbeox0PS',\n",
       " 'sbeox0Mg/L',\n",
       " 'oxygen_ml_L',\n",
       " 'DEPTH',\n",
       " 'descentrate',\n",
       " 'nbin',\n",
       " 'flag']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list of varible inside the .cnv file\n",
    "profile.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#extract my CTD variables\n",
    "temp= profile['TEMP'] #Temperature\n",
    "pres= profile['PRES'] #Pressure\n",
    "sal= profile['PSAL'] #Salinity\n",
    "oxy= profile['oxygen_ml_L'] #Oxygen\n",
    "lon= profile.attrs['LONGITUDE']\n",
    "lat= profile.attrs['LATITUDE']\n",
    "#del(profile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "    .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>scan</th>\n",
       "      <th>PRES</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>TEMP2</th>\n",
       "      <th>T2-T190C</th>\n",
       "      <th>CNDC</th>\n",
       "      <th>CNDC2</th>\n",
       "      <th>C2-C1S/m</th>\n",
       "      <th>ph</th>\n",
       "      <th>flECO-AFL</th>\n",
       "      <th>...</th>\n",
       "      <th>potemperature</th>\n",
       "      <th>potemperature2</th>\n",
       "      <th>tsa</th>\n",
       "      <th>sbeox0PS</th>\n",
       "      <th>sbeox0Mg/L</th>\n",
       "      <th>oxygen_ml_L</th>\n",
       "      <th>DEPTH</th>\n",
       "      <th>descentrate</th>\n",
       "      <th>nbin</th>\n",
       "      <th>flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>501.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.8180</td>\n",
       "      <td>13.8251</td>\n",
       "      <td>0.0071</td>\n",
       "      <td>4.521783</td>\n",
       "      <td>4.522591</td>\n",
       "      <td>0.000792</td>\n",
       "      <td>8.108</td>\n",
       "      <td>0.9301</td>\n",
       "      <td>...</td>\n",
       "      <td>13.8172</td>\n",
       "      <td>13.8243</td>\n",
       "      <td>-60.554</td>\n",
       "      <td>98.597</td>\n",
       "      <td>8.0487</td>\n",
       "      <td>5.6320</td>\n",
       "      <td>4.961</td>\n",
       "      <td>0.228</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>544.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.8118</td>\n",
       "      <td>13.8057</td>\n",
       "      <td>-0.0061</td>\n",
       "      <td>4.521471</td>\n",
       "      <td>4.520493</td>\n",
       "      <td>-0.000959</td>\n",
       "      <td>8.108</td>\n",
       "      <td>0.9228</td>\n",
       "      <td>...</td>\n",
       "      <td>13.8109</td>\n",
       "      <td>13.8048</td>\n",
       "      <td>-60.885</td>\n",
       "      <td>98.374</td>\n",
       "      <td>8.0314</td>\n",
       "      <td>5.6199</td>\n",
       "      <td>5.953</td>\n",
       "      <td>0.646</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>581.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.7577</td>\n",
       "      <td>13.7583</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>4.515952</td>\n",
       "      <td>4.515686</td>\n",
       "      <td>-0.000267</td>\n",
       "      <td>8.109</td>\n",
       "      <td>0.9241</td>\n",
       "      <td>...</td>\n",
       "      <td>13.7567</td>\n",
       "      <td>13.7572</td>\n",
       "      <td>-62.068</td>\n",
       "      <td>97.830</td>\n",
       "      <td>7.9957</td>\n",
       "      <td>5.5949</td>\n",
       "      <td>6.946</td>\n",
       "      <td>0.556</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>636.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.7194</td>\n",
       "      <td>13.7220</td>\n",
       "      <td>0.0026</td>\n",
       "      <td>4.511754</td>\n",
       "      <td>4.511758</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>8.109</td>\n",
       "      <td>0.9459</td>\n",
       "      <td>...</td>\n",
       "      <td>13.7182</td>\n",
       "      <td>13.7208</td>\n",
       "      <td>-62.701</td>\n",
       "      <td>97.900</td>\n",
       "      <td>8.0076</td>\n",
       "      <td>5.6033</td>\n",
       "      <td>7.938</td>\n",
       "      <td>0.417</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>683.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.6931</td>\n",
       "      <td>13.6929</td>\n",
       "      <td>-0.0001</td>\n",
       "      <td>4.508945</td>\n",
       "      <td>4.508666</td>\n",
       "      <td>-0.000276</td>\n",
       "      <td>8.109</td>\n",
       "      <td>0.9598</td>\n",
       "      <td>...</td>\n",
       "      <td>13.6917</td>\n",
       "      <td>13.6916</td>\n",
       "      <td>-63.172</td>\n",
       "      <td>97.783</td>\n",
       "      <td>8.0024</td>\n",
       "      <td>5.5996</td>\n",
       "      <td>8.930</td>\n",
       "      <td>0.607</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    scan  PRES     TEMP    TEMP2  T2-T190C      CNDC     CNDC2  C2-C1S/m  \\\n",
       "0  501.0   5.0  13.8180  13.8251    0.0071  4.521783  4.522591  0.000792   \n",
       "1  544.0   6.0  13.8118  13.8057   -0.0061  4.521471  4.520493 -0.000959   \n",
       "2  581.0   7.0  13.7577  13.7583    0.0005  4.515952  4.515686 -0.000267   \n",
       "3  636.0   8.0  13.7194  13.7220    0.0026  4.511754  4.511758 -0.000001   \n",
       "4  683.0   9.0  13.6931  13.6929   -0.0001  4.508945  4.508666 -0.000276   \n",
       "\n",
       "      ph  flECO-AFL  ...  potemperature  potemperature2     tsa  sbeox0PS  \\\n",
       "0  8.108     0.9301  ...        13.8172         13.8243 -60.554    98.597   \n",
       "1  8.108     0.9228  ...        13.8109         13.8048 -60.885    98.374   \n",
       "2  8.109     0.9241  ...        13.7567         13.7572 -62.068    97.830   \n",
       "3  8.109     0.9459  ...        13.7182         13.7208 -62.701    97.900   \n",
       "4  8.109     0.9598  ...        13.6917         13.6916 -63.172    97.783   \n",
       "\n",
       "   sbeox0Mg/L  oxygen_ml_L  DEPTH  descentrate  nbin  flag  \n",
       "0      8.0487       5.6320  4.961        0.228  16.0   0.0  \n",
       "1      8.0314       5.6199  5.953        0.646  36.0   0.0  \n",
       "2      7.9957       5.5949  6.946        0.556  44.0   0.0  \n",
       "3      8.0076       5.6033  7.938        0.417  57.0   0.0  \n",
       "4      8.0024       5.5996  8.930        0.607  40.0   0.0  \n",
       "\n",
       "[5 rows x 46 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfP = profile.as_DataFrame()\n",
    "dfP.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### laod bottle data from .ros file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sal00</th>\n",
       "      <th>t090C</th>\n",
       "      <th>sbeox0Mg/L</th>\n",
       "      <th>sbeox0ML/L</th>\n",
       "      <th>oxsolML/L</th>\n",
       "      <th>sbeox0V</th>\n",
       "      <th>sigma-�00</th>\n",
       "      <th>depSM</th>\n",
       "      <th>scan</th>\n",
       "      <th>flag</th>\n",
       "      <th>pressure</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nbf</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.653419</td>\n",
       "      <td>13.798240</td>\n",
       "      <td>5.235092</td>\n",
       "      <td>3.663210</td>\n",
       "      <td>5.700533</td>\n",
       "      <td>1.775888</td>\n",
       "      <td>29.067177</td>\n",
       "      <td>300.950187</td>\n",
       "      <td>11637.5</td>\n",
       "      <td>False</td>\n",
       "      <td>303.519604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38.653357</td>\n",
       "      <td>13.798571</td>\n",
       "      <td>5.236445</td>\n",
       "      <td>3.664145</td>\n",
       "      <td>5.700499</td>\n",
       "      <td>1.776861</td>\n",
       "      <td>29.067037</td>\n",
       "      <td>300.070735</td>\n",
       "      <td>11686.0</td>\n",
       "      <td>False</td>\n",
       "      <td>302.631878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38.619135</td>\n",
       "      <td>13.815920</td>\n",
       "      <td>5.424422</td>\n",
       "      <td>3.795682</td>\n",
       "      <td>5.699724</td>\n",
       "      <td>1.839204</td>\n",
       "      <td>29.032857</td>\n",
       "      <td>178.578735</td>\n",
       "      <td>14662.0</td>\n",
       "      <td>False</td>\n",
       "      <td>180.049551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38.608559</td>\n",
       "      <td>13.789186</td>\n",
       "      <td>5.561151</td>\n",
       "      <td>3.891369</td>\n",
       "      <td>5.703162</td>\n",
       "      <td>1.865794</td>\n",
       "      <td>29.030222</td>\n",
       "      <td>173.023633</td>\n",
       "      <td>14787.0</td>\n",
       "      <td>False</td>\n",
       "      <td>174.446265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>38.532004</td>\n",
       "      <td>13.617664</td>\n",
       "      <td>6.133955</td>\n",
       "      <td>4.292172</td>\n",
       "      <td>5.725614</td>\n",
       "      <td>2.011655</td>\n",
       "      <td>29.006202</td>\n",
       "      <td>129.596319</td>\n",
       "      <td>15750.0</td>\n",
       "      <td>False</td>\n",
       "      <td>130.648149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>38.529253</td>\n",
       "      <td>13.609388</td>\n",
       "      <td>6.134794</td>\n",
       "      <td>4.292763</td>\n",
       "      <td>5.726667</td>\n",
       "      <td>2.013994</td>\n",
       "      <td>29.005767</td>\n",
       "      <td>127.133204</td>\n",
       "      <td>15798.0</td>\n",
       "      <td>False</td>\n",
       "      <td>128.164327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>38.498516</td>\n",
       "      <td>13.681202</td>\n",
       "      <td>6.115670</td>\n",
       "      <td>4.279386</td>\n",
       "      <td>5.719479</td>\n",
       "      <td>2.044355</td>\n",
       "      <td>28.965082</td>\n",
       "      <td>78.823341</td>\n",
       "      <td>16900.5</td>\n",
       "      <td>False</td>\n",
       "      <td>79.453295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>38.498586</td>\n",
       "      <td>13.686627</td>\n",
       "      <td>6.263518</td>\n",
       "      <td>4.382841</td>\n",
       "      <td>5.718851</td>\n",
       "      <td>2.052129</td>\n",
       "      <td>28.963908</td>\n",
       "      <td>76.704755</td>\n",
       "      <td>16947.0</td>\n",
       "      <td>False</td>\n",
       "      <td>77.317265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>38.335351</td>\n",
       "      <td>13.536167</td>\n",
       "      <td>7.406816</td>\n",
       "      <td>5.182845</td>\n",
       "      <td>5.742068</td>\n",
       "      <td>2.316708</td>\n",
       "      <td>28.868159</td>\n",
       "      <td>35.237531</td>\n",
       "      <td>18878.0</td>\n",
       "      <td>False</td>\n",
       "      <td>35.515286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>38.336973</td>\n",
       "      <td>13.536584</td>\n",
       "      <td>7.344208</td>\n",
       "      <td>5.139043</td>\n",
       "      <td>5.741961</td>\n",
       "      <td>2.321516</td>\n",
       "      <td>28.869288</td>\n",
       "      <td>34.026857</td>\n",
       "      <td>18935.0</td>\n",
       "      <td>False</td>\n",
       "      <td>34.295020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>38.258915</td>\n",
       "      <td>13.787668</td>\n",
       "      <td>7.974941</td>\n",
       "      <td>5.580385</td>\n",
       "      <td>5.715746</td>\n",
       "      <td>2.496674</td>\n",
       "      <td>28.754262</td>\n",
       "      <td>4.389206</td>\n",
       "      <td>20320.5</td>\n",
       "      <td>False</td>\n",
       "      <td>4.423412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>38.259224</td>\n",
       "      <td>13.791620</td>\n",
       "      <td>7.980802</td>\n",
       "      <td>5.584498</td>\n",
       "      <td>5.715281</td>\n",
       "      <td>2.495584</td>\n",
       "      <td>28.753635</td>\n",
       "      <td>4.184980</td>\n",
       "      <td>20362.0</td>\n",
       "      <td>False</td>\n",
       "      <td>4.217755</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         sal00      t090C  sbeox0Mg/L  sbeox0ML/L  oxsolML/L   sbeox0V  \\\n",
       "nbf                                                                      \n",
       "1    38.653419  13.798240    5.235092    3.663210   5.700533  1.775888   \n",
       "2    38.653357  13.798571    5.236445    3.664145   5.700499  1.776861   \n",
       "3    38.619135  13.815920    5.424422    3.795682   5.699724  1.839204   \n",
       "4    38.608559  13.789186    5.561151    3.891369   5.703162  1.865794   \n",
       "5    38.532004  13.617664    6.133955    4.292172   5.725614  2.011655   \n",
       "6    38.529253  13.609388    6.134794    4.292763   5.726667  2.013994   \n",
       "7    38.498516  13.681202    6.115670    4.279386   5.719479  2.044355   \n",
       "8    38.498586  13.686627    6.263518    4.382841   5.718851  2.052129   \n",
       "9    38.335351  13.536167    7.406816    5.182845   5.742068  2.316708   \n",
       "10   38.336973  13.536584    7.344208    5.139043   5.741961  2.321516   \n",
       "11   38.258915  13.787668    7.974941    5.580385   5.715746  2.496674   \n",
       "12   38.259224  13.791620    7.980802    5.584498   5.715281  2.495584   \n",
       "\n",
       "     sigma-�00       depSM     scan   flag    pressure  \n",
       "nbf                                                     \n",
       "1    29.067177  300.950187  11637.5  False  303.519604  \n",
       "2    29.067037  300.070735  11686.0  False  302.631878  \n",
       "3    29.032857  178.578735  14662.0  False  180.049551  \n",
       "4    29.030222  173.023633  14787.0  False  174.446265  \n",
       "5    29.006202  129.596319  15750.0  False  130.648149  \n",
       "6    29.005767  127.133204  15798.0  False  128.164327  \n",
       "7    28.965082   78.823341  16900.5  False   79.453295  \n",
       "8    28.963908   76.704755  16947.0  False   77.317265  \n",
       "9    28.868159   35.237531  18878.0  False   35.515286  \n",
       "10   28.869288   34.026857  18935.0  False   34.295020  \n",
       "11   28.754262    4.389206  20320.5  False    4.423412  \n",
       "12   28.753635    4.184980  20362.0  False    4.217755  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Load btl.ros files\n",
    "from ctd import rosette_summary\n",
    "ros = rosette_summary('C:/Users/malek/Downloads/notebooks_2021/DissolvedOxygen/data/cast 043_040322.ros')\n",
    "ros = ros.groupby(ros.index).mean()\n",
    "ros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(ros)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#extract my btl variables\n",
    "tempbtl= ros.t090C #Temperature\n",
    "presbtl= ros.pressure #Pressure\n",
    "salbtl= ros.sal00 #Salinity\n",
    "oxybtl= ros.loc[:,\"sbeox0ML/L\"] #Oxygen\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load winkler "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import pandas as pd\n",
    "\n",
    "winkler= pd.read_csv(\"C:/Users/malek/Downloads/notebooks_2021/DissolvedOxygen/data/winkler43.csv\", delimiter=';')\n",
    "winkler\n",
    "oxywink= winkler['O2ml/l_1']\n",
    "preswink= winkler['PRESSURE']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot temperature, salinity and dissolved oxygen profile "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2, ax3) = plt.subplots(1,3, figsize=(10,6), dpi=100,  sharey=True)\n",
    "plt.suptitle(profile.attrs['filename'], fontsize=15)\n",
    "\n",
    "# Temperature\n",
    "ax1.plot(temp,pres,'-r')\n",
    "ax1.plot(tempbtl,presbtl,'*k')\n",
    "ax1.set_ylabel('Pressure (db)')\n",
    "ax1.set_ylim(ax1.get_ylim()[::-1])\n",
    "ax1.set_xlabel('Temperature (°C)')\n",
    "ax1.xaxis.set_label_position('top')\n",
    "ax1.xaxis.set_ticks_position('top') \n",
    "ax1.yaxis.set_ticks_position('left') \n",
    "ax1.set_ylim([ 400,0])\n",
    "ax1.grid(True)\n",
    "ax1.legend( ('CTD temperature', 'bottle'), loc='lower right', shadow=True)\n",
    "\n",
    "# Salinity\n",
    "ax2.plot(sal,pres,'-')\n",
    "ax2.plot(salbtl,presbtl,'*k')\n",
    "ax2.set_xlabel('Salinity')\n",
    "ax2.xaxis.set_label_position('top') \n",
    "ax2.xaxis.set_ticks_position('top') \n",
    "ax2.set_ylim([ 400,0])\n",
    "ax2.grid(True)\n",
    "ax2.legend( ('CTD salinity', 'bottle'), loc='lower right', shadow=True)\n",
    "\n",
    "# dissoledOxygen\n",
    "ax3.plot(oxy,pres,'-g')\n",
    "ax3.plot(oxybtl,presbtl,'*k')\n",
    "ax3.plot(oxywink,preswink,'om')\n",
    "\n",
    "ax3.set_xlabel('Dissolved oxygen (ml/L)')\n",
    "ax3.xaxis.set_label_position('top')\n",
    "ax3.xaxis.set_ticks_position('top') \n",
    "ax3.set_ylim([ 400,0])\n",
    "ax3.grid(True)\n",
    "ax3.legend( ('CTD oxygen', 'Niskin bottle', 'Winkler oxygen'), loc='lower right', shadow=True)\n",
    "\n",
    "FIG= profile.attrs['filename'][0:7]\n",
    "plt.savefig(os.path.join(str(FIG))+'.png')"
   ]
  }
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