{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TEST P 20190411"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import serial\n",
    "import time\n",
    "import datetime\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-deep')\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test Notes\n",
    "\n",
    "### Configuration ###\n",
    "\n",
    "* Test: `O`\n",
    "* NAL Power: `Internal 12VDC (modified`\n",
    "* NAL Serial GND: `Connected to 12VDC GND`\n",
    "* CPF Power: `On, 12/5 Enabled`\n",
    "* CPF Electronics: `In Housing`\n",
    "* Motherboard: `Modified`\n",
    "### Log\n",
    "\n",
    "* `2019-04-11T10:58` Running on cpf modified electronics and cpf modem, but attaching to an external antenna far away. \n",
    "*\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = '20190411T105800_cpflog.csv'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date,time,csq\n",
      "01-Jan-2007,01:18:22.731,5\n",
      "01-Jan-2007,01:18:31.363,5\n",
      "01-Jan-2007,01:18:39.994,5\n",
      "01-Jan-2007,01:18:52.941,5\n",
      "01-Jan-2007,01:18:57.257,4\n",
      "01-Jan-2007,01:19:10.203,5\n",
      "01-Jan-2007,01:19:22.611,5\n",
      "01-Jan-2007,01:19:31.242,5\n",
      "01-Jan-2007,01:19:39.874,4\n",
      "01-Jan-2007,01:19:52.821,4\n",
      "01-Jan-2007,01:19:57.136,4\n",
      "01-Jan-2007,01:20:05.768,3\n",
      "01-Jan-2007,01:20:18.715,4\n",
      "01-Jan-2007,01:20:27.347,4\n",
      "01-Jan-2007,01:20:43.530,4\n",
      "01-Jan-2007,01:20:47.846,4\n",
      "01-Jan-2007,01:21:00.793,2\n",
      "01-Jan-2007,01:21:13.741,0\n",
      "01-Jan-2007,01:21:22.372,0\n",
      "01-Jan-2007,01:21:31.004,0\n",
      "01-Jan-2007,01:21:43.411,0\n",
      "01-Jan-2007,01:21:47.727,0\n",
      "01-Jan-2007,01:22:00.674,2\n",
      "01-Jan-2007,01:22:06.153,0\n",
      "01-Jan-2007,01:22:22.432,2\n",
      "01-Jan-2007,01:22:31.064,4\n",
      "01-Jan-2007,01:22:44.010,4\n",
      "01-Jan-2007,01:22:48.325,4\n",
      "01-Jan-2007,01:23:01.272,4\n",
      "01-Jan-2007,01:23:14.218,4\n",
      "01-Jan-2007,01:23:22.849,5\n",
      "01-Jan-2007,01:23:31.750,5\n",
      "01-Jan-2007,01:23:44.697,5\n",
      "01-Jan-2007,01:23:49.012,5\n",
      "01-Jan-2007,01:24:01.959,4\n",
      "01-Jan-2007,01:24:14.906,3\n",
      "01-Jan-2007,01:24:23.537,5\n",
      "01-Jan-2007,01:24:32.168,4\n",
      "01-Jan-2007,01:24:44.575,4\n",
      "01-Jan-2007,01:24:48.890,5\n",
      "01-Jan-2007,01:25:03.366,5\n",
      "01-Jan-2007,01:25:07.682,5\n",
      "01-Jan-2007,01:25:24.945,5\n",
      "01-Jan-2007,01:25:34.564,5\n",
      "01-Jan-2007,01:25:43.195,5\n",
      "01-Jan-2007,01:25:51.827,5\n",
      "01-Jan-2007,01:26:04.774,5\n",
      "01-Jan-2007,01:26:09.089,5\n",
      "01-Jan-2007,01:26:22.036,5\n",
      "01-Jan-2007,01:26:34.983,5\n",
      "01-Jan-2007,01:26:45.232,5\n",
      "01-Jan-2007,01:26:49.548,5\n",
      "01-Jan-2007,01:27:02.495,5\n",
      "01-Jan-2007,01:27:15.442,5\n",
      "01-Jan-2007,01:27:18.139,5\n",
      "01-Jan-2007,01:27:32.704,5\n",
      "01-Jan-2007,01:27:43.763,5\n",
      "01-Jan-2007,01:27:52.395,5\n",
      "01-Jan-2007,01:28:05.342,5\n",
      "01-Jan-2007,01:28:09.657,4\n",
      "01-Jan-2007,01:28:22.604,5\n",
      "01-Jan-2007,01:28:35.012,5\n",
      "01-Jan-2007,01:28:43.643,5\n",
      "01-Jan-2007,01:28:52.275,4\n",
      "01-Jan-2007,01:29:05.222,4\n",
      "01-Jan-2007,01:29:09.538,5\n",
      "01-Jan-2007,01:29:25.452,4\n",
      "01-Jan-2007,01:29:28.062,4\n",
      "01-Jan-2007,01:29:42.715,2\n",
      "01-Jan-2007,01:29:55.662,2\n",
      "01-Jan-2007,01:29:59.978,2\n",
      "01-Jan-2007,01:30:13.194,3\n",
      "01-Jan-2007,01:30:26.142,1\n",
      "01-Jan-2007,01:30:34.773,0\n",
      "01-Jan-2007,01:30:43.405,1\n",
      "01-Jan-2007,01:30:56.352,0\n",
      "01-Jan-2007,01:31:00.667,0\n",
      "01-Jan-2007,01:31:13.614,3\n",
      "01-Jan-2007,01:31:26.562,2\n",
      "01-Jan-2007,01:31:35.102,2\n",
      "01-Jan-2007,01:31:43.643,3\n",
      "01-Jan-2007,01:31:56.591,4\n",
      "01-Jan-2007,01:32:00.726,4\n",
      "01-Jan-2007,01:32:13.942,4\n",
      "01-Jan-2007,01:32:26.889,5\n",
      "01-Jan-2007,01:32:28.867,5\n",
      "01-Jan-2007,01:32:44.151,5\n",
      "01-Jan-2007,01:32:57.098,5\n",
      "01-Jan-2007,01:33:01.413,5\n",
      "01-Jan-2007,01:33:14.360,5\n",
      "01-Jan-2007,01:33:26.768,5\n",
      "01-Jan-2007,01:33:35.398,5\n",
      "01-Jan-2007,01:33:44.030,5\n",
      "01-Jan-2007,01:33:56.976,5\n",
      "01-Jan-2007,01:34:01.292,5\n",
      "01-Jan-2007,01:34:17.115,5\n",
      "01-Jan-2007,01:34:21.431,5\n",
      "01-Jan-2007,01:34:34.378,5\n",
      "01-Jan-2007,01:34:47.325,4\n",
      "01-Jan-2007,01:34:55.597,5\n",
      "01-Jan-2007,01:35:04.228,4\n",
      "01-Jan-2007,01:35:14.477,4\n",
      "01-Jan-2007,01:35:27.423,4\n",
      "01-Jan-2007,01:35:30.480,4\n",
      "01-Jan-2007,01:35:44.686,5\n",
      "01-Jan-2007,01:35:57.633,4\n",
      "01-Jan-2007,01:36:01.948,3\n"
     ]
    }
   ],
   "source": [
    "!echo date,time,csq> 20190411T105800_cpflog.csv\n",
    "!grep '+CSQ' 20190411T105800_cpflog.txt | awk '{print $2\",\"$3\",\"$7}' | sed 's/+CSQ://g'>> 20190411T105800_cpflog.csv\n",
    "!cat 20190411T105800_cpflog.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.read_csv(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 107 entries, 0 to 106\n",
      "Data columns (total 3 columns):\n",
      "date    107 non-null object\n",
      "time    107 non-null object\n",
      "csq     107 non-null int64\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 1.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>date</th>\n",
       "      <th>time</th>\n",
       "      <th>csq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01-Jan-2007</td>\n",
       "      <td>01:18:22.731</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01-Jan-2007</td>\n",
       "      <td>01:18:31.363</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01-Jan-2007</td>\n",
       "      <td>01:18:39.994</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01-Jan-2007</td>\n",
       "      <td>01:18:52.941</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01-Jan-2007</td>\n",
       "      <td>01:18:57.257</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date          time  csq\n",
       "0  01-Jan-2007  01:18:22.731    5\n",
       "1  01-Jan-2007  01:18:31.363    5\n",
       "2  01-Jan-2007  01:18:39.994    5\n",
       "3  01-Jan-2007  01:18:52.941    5\n",
       "4  01-Jan-2007  01:18:57.257    4"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['date', 'time', 'csq'], dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## \n",
    "df2.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Test F: Housing Closed, CPF On, 5V On, 12V On, CPF connected to NAL')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.arange(7)-0.5\n",
    "\n",
    "fig = df2.csq.hist(figsize=(10,5), align='mid', bins=bins, edgecolor=\"black\")\n",
    "bins = np.arange(7)-0.5\n",
    "fig.set_xlabel(\"CSQ REPLY (RSSI)\")\n",
    "fig.set_ylabel(\"COUNT\")\n",
    "fig.set_title(\"Test F: Housing Closed, CPF On, 5V On, 12V On, CPF connected to NAL\")\n",
    "#Determine Test Length\n",
    "#date_str = f\"Test Start: {df2.date[0]}\\nTest End: {df2.date.iloc[-1]} \"\n",
    "#plt.text(0,150,date_str);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Time')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot CSQ over time\n",
    "import matplotlib.dates as mdates\n",
    "ax = df2.plot(kind='line',x='time',y='csq', figsize=(10,5))\n",
    "ax.xaxis.set_major_locator(plt.MaxNLocator(4))\n",
    "ax.set_title(\"Test F Time Series\")\n",
    "ax.set_ylabel(\"CSQ (RSSI)\")\n",
    "ax.set_xlabel(\"Time\")"
   ]
  },
  {
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}