LoRa data extraction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "df = pd.read_csv(\"all.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CRC result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f24baa13da0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f24baa17f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.status.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRC_BAD    112537\n",
       "CRC_OK       8834\n",
       "Name: status, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.status.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modulation used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f24bf75f208>"
      ]
     },
     "execution_count": 15,
     "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 0x7f24bf789128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.modulation.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LORA    121343\n",
       "FSK         28\n",
       "Name: modulation, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.modulation.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Frequency used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f24bf16e5f8>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f24bf6f12b0>"
      ]
     },
     "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": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f24bf6fa9b0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VX05PXDVw4jrTY924JqYCx6Ex1cZhzLenymyo24CHM/Pv2zZtAlaU5RXAnW315dFYDDxX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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f24bf13ebe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.RSSI.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spreading factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f24cb5e67b8>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fH4+eNtTV406GfvmMTVUdHT09FRFvoxkYV2XmDaX56XJpifLzmdK+AziiZfe5pW1P7XPH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\nzdCQJFX7P1ymp5KRSHOvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f24c6ddf4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.datarate.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SF7       102258\n",
       "SF12       12797\n",
       "SF8         5848\n",
       "SF9          380\n",
       "SF10          31\n",
       "SF11          29\n",
       " 50000        28\n",
       "Name: datarate, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.datarate.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Size of the payload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f24cab23390>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IeCoijpXalRFxJCJOlufVTY+zWxFxICLORcTTHbVZ+4u2e8v+Ph4RW5obeXfm6PcLEXGm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\nOEiSKv8HP87cRor8occAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f24cab265f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"size\"].hist()"
   ]
  },
  {
   "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": 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
}