vertical_section.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "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>pres</th>\n",
       "      <th>temp</th>\n",
       "      <th>sal</th>\n",
       "      <th>oxy</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>19.718</td>\n",
       "      <td>35.934</td>\n",
       "      <td>4.3</td>\n",
       "      <td>-31.4972</td>\n",
       "      <td>110.2333</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>19.722</td>\n",
       "      <td>35.936</td>\n",
       "      <td>4.3</td>\n",
       "      <td>-31.4972</td>\n",
       "      <td>110.2333</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>19.720</td>\n",
       "      <td>35.940</td>\n",
       "      <td>4.3</td>\n",
       "      <td>-31.4972</td>\n",
       "      <td>110.2333</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>19.728</td>\n",
       "      <td>35.942</td>\n",
       "      <td>4.3</td>\n",
       "      <td>-31.4972</td>\n",
       "      <td>110.2333</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>19.718</td>\n",
       "      <td>35.932</td>\n",
       "      <td>4.3</td>\n",
       "      <td>-31.4972</td>\n",
       "      <td>110.2333</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pres    temp     sal  oxy      lat       lon  year\n",
       "0     1  19.718  35.934  4.3 -31.4972  110.2333  1994\n",
       "1     3  19.722  35.936  4.3 -31.4972  110.2333  1994\n",
       "2     5  19.720  35.940  4.3 -31.4972  110.2333  1994\n",
       "3     7  19.728  35.942  4.3 -31.4972  110.2333  1994\n",
       "4     9  19.718  35.932  4.3 -31.4972  110.2333  1994"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('ctd_data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "groups = df.groupby(['lon', 'lat'])\n",
    "\n",
    "(lon, lat), group = list(groups)[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88b0ed9390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes, zoomed_inset_axes, mark_inset\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(4, 6))\n",
    "ax.invert_yaxis()\n",
    "\n",
    "ax.plot(group['temp'], group['pres'])\n",
    "\n",
    "axins = inset_axes(ax,\n",
    "                   width='30%',\n",
    "                   height=1,\n",
    "                   loc=7)\n",
    "\n",
    "axins.plot(group['temp'], group['pres'])\n",
    "axins.set_ylim(0, 100)\n",
    "axins.set_xlim(1.5, 1.9)\n",
    "axins.xaxis.tick_top()\n",
    "mark_inset(ax, axins, loc1=2, loc2=4, fc='none', ec='0.5')\n",
    "axins.invert_yaxis()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:IOOS]",
   "language": "python",
   "name": "conda-env-IOOS-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}