Fig12.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.stats as st\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>time</th>\n",
       "      <th>fluviometry_data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>26.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>26.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>26.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>26.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>27.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>361</td>\n",
       "      <td>27</td>\n",
       "      <td>12</td>\n",
       "      <td>2016</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>25.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>362</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>2016</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>25.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>363</td>\n",
       "      <td>29</td>\n",
       "      <td>12</td>\n",
       "      <td>2016</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>26.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>364</td>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "      <td>2016</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>26.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>365</td>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "      <td>2016</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>26.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>366 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     day  month  year      time  fluviometry_data\n",
       "0      1      1  2016  00:00:00             26.83\n",
       "1      2      1  2016  00:00:00             26.88\n",
       "2      3      1  2016  00:00:00             26.92\n",
       "3      4      1  2016  00:00:00             26.96\n",
       "4      5      1  2016  00:00:00             27.01\n",
       "..   ...    ...   ...       ...               ...\n",
       "361   27     12  2016  00:00:00             25.75\n",
       "362   28     12  2016  00:00:00             25.97\n",
       "363   29     12  2016  00:00:00             26.12\n",
       "364   30     12  2016  00:00:00             26.27\n",
       "365   31     12  2016  00:00:00             26.59\n",
       "\n",
       "[366 rows x 5 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('fluviometry_data.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
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    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 0\n",
    "months_i = []\n",
    "for index, row in df.iterrows():\n",
    "    if row['month'] != i:\n",
    "        months_i.append(index)\n",
    "        i += 1 \n",
    "\n",
    "plt.figure(figsize=(17,4))\n",
    "plt.rc('font', size=16)\n",
    "plt.rc('axes', titlesize=16)\n",
    "\n",
    "month = df['month']\n",
    "masl = df['fluviometry_data']\n",
    "plt.plot(masl, label='fluviometry')\n",
    "plt.ylabel('MASL')\n",
    "plt.xlabel('Months')\n",
    "rot = 90\n",
    "plt.xticks(months_i, ['jan', 'feb', 'mar', 'apr', 'may', 'jun', 'jul', 'aug', 'sept', 'oct', 'nov', 'dec'], rotation=rot)\n",
    "plt.xlim([0, len(masl)])\n",
    "# plt.ylim([0.1, np.max(cotas['Complexity'])])\n",
    "plt.legend(loc = 'upper right', ncol=5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  },
  "toc": {
   "nav_menu": {
    "height": "49.1333px",
    "width": "252px"
   },
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": "block",
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}