TahameDataZeSluncarne.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ahoj\n",
    "\n",
    "Použijme Pandas - knihovnu na zpracovávání tabulkových dat, [dokumentace tady](http://pandas.pydata.org/pandas-docs/version/0.18.1/) - k otevření dat z počítače ve sluncárně."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import datetime\n",
    "log = pd.read_csv(\"http://radio-slunce.local/all.csv\",\n",
    "                  parse_dates=True, date_parser=lambda ts: datetime.datetime.fromtimestamp(float(ts)),\n",
    "                  index_col=0, header=None,\n",
    "                  delim_whitespace=True, names=['SigLevel'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A vykresleme je!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb427698c>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Np0DCX1V3qztWjXOWAQvC9lsi8jHQF3grXfyRMdeDyspKKutyYm2GZGPzj/s7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\nnmH7WuA8zHEArIcxFFPcWoe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      "text/plain": [
       "<matplotlib.figure.Figure at 0xb4455d6c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "log.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SigLevel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-08-19 17:35:53.390</th>\n",
       "      <td>-12.007863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-19 17:38:07.760</th>\n",
       "      <td>-12.006884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-19 17:40:22.140</th>\n",
       "      <td>-12.002869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-19 17:42:36.510</th>\n",
       "      <td>-12.000285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-19 17:44:50.890</th>\n",
       "      <td>-12.001890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-19 17:47:05.390</th>\n",
       "      <td>-12.007481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-19 17:49:19.760</th>\n",
       "      <td>-12.006593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-19 17:51:34.140</th>\n",
       "      <td>-12.006522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-19 17:53:48.510</th>\n",
       "      <td>-12.008027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-19 17:56:02.890</th>\n",
       "      <td>-12.004855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-19 17:58:17.390</th>\n",
       "      <td>-12.011170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-19 18:00:31.760</th>\n",
       "      <td>-12.009473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-19 18:02:46.140</th>\n",
       "      <td>-12.009619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-19 18:05:00.510</th>\n",
       "      <td>-12.007837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-19 18:07:14.890</th>\n",
       "      <td>-12.009206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-19 18:09:29.390</th>\n",
       "      <td>-12.011169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-19 18:11:43.760</th>\n",
       "      <td>-12.008692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-19 18:13:58.140</th>\n",
       "      <td>-12.007495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-19 18:16:12.510</th>\n",
       "      <td>-12.010168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-19 18:18:26.890</th>\n",
       "      <td>-12.004189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-19 18:20:41.390</th>\n",
       "      <td>-12.006321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-19 18:22:55.760</th>\n",
       "      <td>-12.006645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-19 18:25:10.140</th>\n",
       "      <td>-11.996785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-19 18:27:24.510</th>\n",
       "      <td>-11.996511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-19 18:29:38.890</th>\n",
       "      <td>-11.995121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-19 18:31:53.390</th>\n",
       "      <td>-11.998647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-19 18:34:07.760</th>\n",
       "      <td>-11.993179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-19 18:36:22.140</th>\n",
       "      <td>-11.997225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-19 18:38:36.510</th>\n",
       "      <td>-11.999101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-19 18:40:50.890</th>\n",
       "      <td>-11.998509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-28 10:18:00.780</th>\n",
       "      <td>-13.326913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-28 10:20:15.160</th>\n",
       "      <td>-13.333344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-28 10:22:29.540</th>\n",
       "      <td>-13.344358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-28 10:24:44.030</th>\n",
       "      <td>-13.344702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-28 10:26:58.410</th>\n",
       "      <td>-13.375147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-28 10:29:12.780</th>\n",
       "      <td>-13.371780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-28 10:31:27.160</th>\n",
       "      <td>-13.365622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-28 10:33:41.540</th>\n",
       "      <td>-13.385050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-28 10:35:56.030</th>\n",
       "      <td>-13.375622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-28 10:38:10.410</th>\n",
       "      <td>-13.356455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-28 10:40:24.780</th>\n",
       "      <td>-13.343168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-28 10:42:39.160</th>\n",
       "      <td>-13.349736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-28 10:44:53.540</th>\n",
       "      <td>-13.353804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-28 10:47:08.030</th>\n",
       "      <td>-13.366563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-28 10:49:22.410</th>\n",
       "      <td>-13.393693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-28 10:51:36.780</th>\n",
       "      <td>-13.432814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-28 10:53:51.160</th>\n",
       "      <td>-13.469905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-28 10:56:05.540</th>\n",
       "      <td>-13.472269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-28 10:58:20.030</th>\n",
       "      <td>-13.413502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-28 11:00:34.410</th>\n",
       "      <td>-13.340753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-28 11:02:48.780</th>\n",
       "      <td>-13.336333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-28 11:05:03.160</th>\n",
       "      <td>-13.344470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-28 11:07:17.540</th>\n",
       "      <td>-13.344990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-28 11:09:32.030</th>\n",
       "      <td>-13.410786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-28 11:11:46.410</th>\n",
       "      <td>-13.427565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-28 11:14:00.790</th>\n",
       "      <td>-13.431616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-28 11:16:15.160</th>\n",
       "      <td>-13.407078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-28 11:18:29.540</th>\n",
       "      <td>-13.375332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-28 11:20:44.030</th>\n",
       "      <td>-13.476637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-28 11:22:58.410</th>\n",
       "      <td>-13.422397</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>189161 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          SigLevel\n",
       "2015-08-19 17:35:53.390 -12.007863\n",
       "2015-08-19 17:38:07.760 -12.006884\n",
       "2015-08-19 17:40:22.140 -12.002869\n",
       "2015-08-19 17:42:36.510 -12.000285\n",
       "2015-08-19 17:44:50.890 -12.001890\n",
       "2015-08-19 17:47:05.390 -12.007481\n",
       "2015-08-19 17:49:19.760 -12.006593\n",
       "2015-08-19 17:51:34.140 -12.006522\n",
       "2015-08-19 17:53:48.510 -12.008027\n",
       "2015-08-19 17:56:02.890 -12.004855\n",
       "2015-08-19 17:58:17.390 -12.011170\n",
       "2015-08-19 18:00:31.760 -12.009473\n",
       "2015-08-19 18:02:46.140 -12.009619\n",
       "2015-08-19 18:05:00.510 -12.007837\n",
       "2015-08-19 18:07:14.890 -12.009206\n",
       "2015-08-19 18:09:29.390 -12.011169\n",
       "2015-08-19 18:11:43.760 -12.008692\n",
       "2015-08-19 18:13:58.140 -12.007495\n",
       "2015-08-19 18:16:12.510 -12.010168\n",
       "2015-08-19 18:18:26.890 -12.004189\n",
       "2015-08-19 18:20:41.390 -12.006321\n",
       "2015-08-19 18:22:55.760 -12.006645\n",
       "2015-08-19 18:25:10.140 -11.996785\n",
       "2015-08-19 18:27:24.510 -11.996511\n",
       "2015-08-19 18:29:38.890 -11.995121\n",
       "2015-08-19 18:31:53.390 -11.998647\n",
       "2015-08-19 18:34:07.760 -11.993179\n",
       "2015-08-19 18:36:22.140 -11.997225\n",
       "2015-08-19 18:38:36.510 -11.999101\n",
       "2015-08-19 18:40:50.890 -11.998509\n",
       "...                            ...\n",
       "2016-07-28 10:18:00.780 -13.326913\n",
       "2016-07-28 10:20:15.160 -13.333344\n",
       "2016-07-28 10:22:29.540 -13.344358\n",
       "2016-07-28 10:24:44.030 -13.344702\n",
       "2016-07-28 10:26:58.410 -13.375147\n",
       "2016-07-28 10:29:12.780 -13.371780\n",
       "2016-07-28 10:31:27.160 -13.365622\n",
       "2016-07-28 10:33:41.540 -13.385050\n",
       "2016-07-28 10:35:56.030 -13.375622\n",
       "2016-07-28 10:38:10.410 -13.356455\n",
       "2016-07-28 10:40:24.780 -13.343168\n",
       "2016-07-28 10:42:39.160 -13.349736\n",
       "2016-07-28 10:44:53.540 -13.353804\n",
       "2016-07-28 10:47:08.030 -13.366563\n",
       "2016-07-28 10:49:22.410 -13.393693\n",
       "2016-07-28 10:51:36.780 -13.432814\n",
       "2016-07-28 10:53:51.160 -13.469905\n",
       "2016-07-28 10:56:05.540 -13.472269\n",
       "2016-07-28 10:58:20.030 -13.413502\n",
       "2016-07-28 11:00:34.410 -13.340753\n",
       "2016-07-28 11:02:48.780 -13.336333\n",
       "2016-07-28 11:05:03.160 -13.344470\n",
       "2016-07-28 11:07:17.540 -13.344990\n",
       "2016-07-28 11:09:32.030 -13.410786\n",
       "2016-07-28 11:11:46.410 -13.427565\n",
       "2016-07-28 11:14:00.790 -13.431616\n",
       "2016-07-28 11:16:15.160 -13.407078\n",
       "2016-07-28 11:18:29.540 -13.375332\n",
       "2016-07-28 11:20:44.030 -13.476637\n",
       "2016-07-28 11:22:58.410 -13.422397\n",
       "\n",
       "[189161 rows x 1 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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