LoRa data extraction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import math\n",
    "df = pd.read_csv(\"all.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CRC result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fedba006588>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fedba000da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.status.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRC_BAD    112537\n",
       "CRC_OK       8834\n",
       "Name: status, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.status.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modulation used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fedbb4f85c0>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kDyV5MMl7W/21SXYlebT9PLnVk+S6JAtJ7k9y9shYG1r7R5NsGKmfk+SB1ue6JJnkl5Uk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\n1M3QkCR1MzQkSd0MDUlSN0NDktTt/wEm9ayyLRts2gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fedbb54b6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.modulation.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LORA    121343\n",
       "FSK         28\n",
       "Name: modulation, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.modulation.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Frequency used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fedbb4c1320>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fedbb4d82e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.frequency.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "868300000    28937\n",
       "868500000    16461\n",
       "868100000    16424\n",
       "867300000    12415\n",
       "867100000    12172\n",
       "867700000    12075\n",
       "867900000    12013\n",
       "867500000    10846\n",
       "868800000       28\n",
       "Name: frequency, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.frequency.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RSSI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fedc302e9b0>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lwCzwfeAdAFV1IMmHgPtauw8evjkNvAv4NHAKwxvR3oyWpAka52ml/wUc63MHF8/RvoDr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\n/H+/7K4sByDrDAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fedc303ab70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.RSSI.hist(bins=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spreading factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fedba01cc18>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Onp6eiohX0A6MqzPzG6X5iXJpifLzydK+Czi2Y/MFpW2y9gXjtEuS+qSXp6cCuAK4LzP/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\nqvZ/braFAtrDvj0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fedc1def828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.datarate.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 50000     0.000231\n",
       "SF11       0.000239\n",
       "SF10       0.000255\n",
       "SF09       0.003131\n",
       "SF08       0.048183\n",
       "SF12       0.105437\n",
       "SF07       0.842524\n",
       "Name: datarate, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.datarate.value_counts().sort_values() / df.datarate.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Size of the payload (bytes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Count')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7AwcAXwRmSpqe9kbmAFvT9FuBw4EtkqYDBwLPlNqHlefZTUSsBFYC9PX1RX9//6Q7XalU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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fedc2f92eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df[\"size\"].hist(bins=100)\n",
    "ax.set_xlabel(\"Payload size (bytes)\")\n",
    "ax.set_ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Global payload transmission (in bytes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13496697"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"size\"].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Frame over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f24cae642b0>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Fzsyk+ekszE/jRHMP5y8tGNbrwyksv33tXFL9Xv7lqlX86MVjfOG6Myd83ovz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\nKKWUsmlAUEopBcD/B4jECmMh+WKuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f24cae68780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.set_index(pd.DatetimeIndex(df[\"UTC timestamp\"])).resample(\"D\").count()[\"payload\"].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f24cb0a60b8>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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H7riub8dW2TxDl6RCLCjQI+LaiHgqIvZHxOZuFSVJal/Hl1wi4gzgb4BrgIPA1yNie2Y+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\nLkmFMNAlqRAGuiQVwkCXpEIY6JJUiP8HDQLDk7kRKToAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f24cb0b2908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.set_index(pd.DatetimeIndex(df[\"UTC timestamp\"])).resample(\"D\").count()[\"payload\"].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0026295581134033637"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"size\"].sum() / ((21531 / 8)* (1 / (2*math.e)) * (4 * 30 * 24 * 60 * 60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}