FactoryIO_LocalDependanciesRemoved.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "international-houston",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "informative-sitting",
   "metadata": {},
   "outputs": [],
   "source": [
    "import zipfile\n",
    "from zipfile import ZipFile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "frequent-squad",
   "metadata": {},
   "outputs": [],
   "source": [
    "file_name = \"data/moving2.zip\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "returning-anaheim",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File Name                                             Modified             Size\n",
      "moving2.csv                                    2021-03-11 16:08:44     35879985\n"
     ]
    }
   ],
   "source": [
    "with ZipFile(file_name, 'r') as zip:\n",
    "    # printing all the contents of the zip file\n",
    "    zip.printdir()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "emotional-transsexual",
   "metadata": {},
   "source": [
    "<h3 style=\"color:green;\">SCREENSHOT OF RUNNING SCENE IN FACTORY IO</h3>\n",
    "<p>Factory I/O is a 3D factory simulation for learning automation technologies. Designed to be easy to use, it allows to quickly build a virtual factory using a selection of common industrial parts.\n",
    "\n",
    "Factory I/O also includes many scenes inspired by typical industrial applications, ranging from beginner to advanced difficulty levels.\n",
    "\n",
    "The most common scenario is to use Factory I/O as a PLC training platform since PLC are the most common controllers found in industrial applications. However, it can also be used with microcontrollers, SoftPLC, Modbus, among many other technologies.</p>\n",
    "\n",
    "<a href=\"url\">https://factoryio.com/</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "advance-substitute",
   "metadata": {},
   "source": [
    "![title](images/Conveyor.PNG)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "determined-ecuador",
   "metadata": {},
   "source": [
    "<h3 style=\"color:green;\">SCREENSHOT OF CONTROL IO CONFIGURATOR</h3>\n",
    "<p>CONTROL I/O is an easy to use, brand independent SoftPLC, designed from the ground up for FACTORY I/O.<br> The goal of CONTROL I/O is to provide a simple and intuitive experience for anyone starting in the world of automation.</p>\n",
    "<a href=\"url\">https://docs.factoryio.com</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "level-deficit",
   "metadata": {},
   "source": [
    "![title](images/Control_IO.PNG)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "southern-scout",
   "metadata": {},
   "source": [
    "<h3 style=\"color:green;\">APPLYING THE GENERATED MODEL<h3>\n",
    "<p style=\"font-weight: normal;\">Using the model created in the previous process against the current running conditions.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "requested-safety",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing the csv file created in factory io\n",
    "#File currently contains 6 Columns and 682,211 Rows\n",
    "data = pd.read_csv('data/moving2.zip')\n",
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "relative-japanese",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Sensor/Actuator</th>\n",
       "      <th>Data Type</th>\n",
       "      <th>Name</th>\n",
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       "      <th>1</th>\n",
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       "      <td>Sensor</td>\n",
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       "      <th>4</th>\n",
       "      <td>Sensor</td>\n",
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       "      <td>6.783351</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Actuator</td>\n",
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       "      <td>498</td>\n",
       "      <td>False</td>\n",
       "      <td>6.783351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Actuator</td>\n",
       "      <td>Bool</td>\n",
       "      <td>Roller Conveyor (6m) 1</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>6.783351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Actuator</td>\n",
       "      <td>Bool</td>\n",
       "      <td>Roller Conveyor (6m) 2</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>6.783351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Actuator</td>\n",
       "      <td>Bool</td>\n",
       "      <td>Curved Roller Conveyor 1 CCW</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>6.783351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Actuator</td>\n",
       "      <td>Bool</td>\n",
       "      <td>Emitter 1 (Emit)</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>6.783351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Actuator</td>\n",
       "      <td>Bool</td>\n",
       "      <td>Remover 1 (Remove)</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "      <td>6.783351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Actuator</td>\n",
       "      <td>Int</td>\n",
       "      <td>FACTORY I/O (Camera Position)</td>\n",
       "      <td>240</td>\n",
       "      <td>0</td>\n",
       "      <td>6.783351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Actuator</td>\n",
       "      <td>Int</td>\n",
       "      <td>Emitter 1 (Base)</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>6.783351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Actuator</td>\n",
       "      <td>Int</td>\n",
       "      <td>Emitter 1 (Part)</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6.783351</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Sensor</td>\n",
       "      <td>Bool</td>\n",
       "      <td>FACTORY I/O (Running)</td>\n",
       "      <td>496</td>\n",
       "      <td>True</td>\n",
       "      <td>6.883351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Sensor</td>\n",
       "      <td>Bool</td>\n",
       "      <td>FACTORY I/O (Paused)</td>\n",
       "      <td>497</td>\n",
       "      <td>False</td>\n",
       "      <td>6.883351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Sensor</td>\n",
       "      <td>Bool</td>\n",
       "      <td>FACTORY I/O (Reset)</td>\n",
       "      <td>498</td>\n",
       "      <td>False</td>\n",
       "      <td>6.883351</td>\n",
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      "text/plain": [
       "   Sensor/Actuator  Data Type                           Name   Address  \\\n",
       "0           Sensor       Bool          FACTORY I/O (Running)       496   \n",
       "1           Sensor       Bool           FACTORY I/O (Paused)       497   \n",
       "2           Sensor       Bool            FACTORY I/O (Reset)       498   \n",
       "3           Sensor       Bool       Retroreflective Sensor 1         0   \n",
       "4           Sensor      Float       FACTORY I/O (Time Scale)       240   \n",
       "5           Sensor        Int  Light Array Emitter 1 (Value)         0   \n",
       "6         Actuator       Bool              FACTORY I/O (Run)       496   \n",
       "7         Actuator       Bool            FACTORY I/O (Pause)       497   \n",
       "8         Actuator       Bool            FACTORY I/O (Reset)       498   \n",
       "9         Actuator       Bool         Roller Conveyor (6m) 1         1   \n",
       "10        Actuator       Bool         Roller Conveyor (6m) 2         0   \n",
       "11        Actuator       Bool   Curved Roller Conveyor 1 CCW         2   \n",
       "12        Actuator       Bool               Emitter 1 (Emit)         3   \n",
       "13        Actuator       Bool             Remover 1 (Remove)         4   \n",
       "14        Actuator        Int  FACTORY I/O (Camera Position)       240   \n",
       "15        Actuator        Int               Emitter 1 (Base)         0   \n",
       "16        Actuator        Int               Emitter 1 (Part)         1   \n",
       "17          Sensor       Bool          FACTORY I/O (Running)       496   \n",
       "18          Sensor       Bool           FACTORY I/O (Paused)       497   \n",
       "19          Sensor       Bool            FACTORY I/O (Reset)       498   \n",
       "\n",
       "    Current Value   Time Elapsed  \n",
       "0            True       6.783351  \n",
       "1           False       6.783351  \n",
       "2           False       6.783351  \n",
       "3           False       6.783351  \n",
       "4               1       6.783351  \n",
       "5               0       6.783351  \n",
       "6            True       6.783351  \n",
       "7           False       6.783351  \n",
       "8           False       6.783351  \n",
       "9            True       6.783351  \n",
       "10           True       6.783351  \n",
       "11           True       6.783351  \n",
       "12           True       6.783351  \n",
       "13           True       6.783351  \n",
       "14              0       6.783351  \n",
       "15            100       6.783351  \n",
       "16              0       6.783351  \n",
       "17           True       6.883351  \n",
       "18          False       6.883351  \n",
       "19          False       6.883351  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check data is imported\n",
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "conditional-drawing",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Subseting all Light Array Emitter data\n",
    "filteredLAE = df[df[' Name']=='Light Array Emitter 1 (Value)']\n",
    "#converting the current value column to numeric\n",
    "#using a copy to prevent index warning\n",
    "filteredLAE = filteredLAE.copy()\n",
    "filteredLAE[' Current Value'] = pd.to_numeric(filteredLAE[' Current Value'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fatty-copying",
   "metadata": {},
   "outputs": [
    {
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       "      <th>682181</th>\n",
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      "text/plain": [
       "       Sensor/Actuator  Data Type                           Name   Address  \\\n",
       "5               Sensor        Int  Light Array Emitter 1 (Value)         0   \n",
       "22              Sensor        Int  Light Array Emitter 1 (Value)         0   \n",
       "39              Sensor        Int  Light Array Emitter 1 (Value)         0   \n",
       "56              Sensor        Int  Light Array Emitter 1 (Value)         0   \n",
       "73              Sensor        Int  Light Array Emitter 1 (Value)         0   \n",
       "...                ...        ...                            ...       ...   \n",
       "682130          Sensor        Int  Light Array Emitter 1 (Value)         0   \n",
       "682147          Sensor        Int  Light Array Emitter 1 (Value)         0   \n",
       "682164          Sensor        Int  Light Array Emitter 1 (Value)         0   \n",
       "682181          Sensor        Int  Light Array Emitter 1 (Value)         0   \n",
       "682198          Sensor        Int  Light Array Emitter 1 (Value)         0   \n",
       "\n",
       "         Current Value   Time Elapsed   Sensor Value Difference  \n",
       "5                    0       6.783351                       NaN  \n",
       "22                   0       6.883351                       0.0  \n",
       "39                   0       6.975019                       0.0  \n",
       "56                   0       7.066686                       0.0  \n",
       "73                   0       7.166686                       0.0  \n",
       "...                ...            ...                       ...  \n",
       "682130             192    4014.250000                       0.0  \n",
       "682147             192    4014.341000                       0.0  \n",
       "682164             192    4014.457000                       0.0  \n",
       "682181             192    4014.548000                       0.0  \n",
       "682198             192    4014.656000                       0.0  \n",
       "\n",
       "[40130 rows x 7 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filteredLAE[' Sensor Value Difference'] = filteredLAE[' Current Value'].diff()\n",
    "filteredLAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "elegant-obligation",
   "metadata": {},
   "outputs": [],
   "source": [
    "#filteredLAE.to_csv (r'C:\\Users\\a.sethu\\Desktop\\Python\\export.csv', index = False, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "informational-cowboy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Sensor/Actuator</th>\n",
       "      <th>Data Type</th>\n",
       "      <th>Name</th>\n",
       "      <th>Address</th>\n",
       "      <th>Current Value</th>\n",
       "      <th>Time Elapsed</th>\n",
       "      <th>Sensor Value Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1909</th>\n",
       "      <td>Sensor</td>\n",
       "      <td>Int</td>\n",
       "      <td>Light Array Emitter 1 (Value)</td>\n",
       "      <td>0</td>\n",
       "      <td>192</td>\n",
       "      <td>17.96656</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2266</th>\n",
       "      <td>Sensor</td>\n",
       "      <td>Int</td>\n",
       "      <td>Light Array Emitter 1 (Value)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20.07486</td>\n",
       "      <td>-192.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2538</th>\n",
       "      <td>Sensor</td>\n",
       "      <td>Int</td>\n",
       "      <td>Light Array Emitter 1 (Value)</td>\n",
       "      <td>0</td>\n",
       "      <td>192</td>\n",
       "      <td>21.68317</td>\n",
       "      <td>192.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2912</th>\n",
       "      <td>Sensor</td>\n",
       "      <td>Int</td>\n",
       "      <td>Light Array Emitter 1 (Value)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.88313</td>\n",
       "      <td>-192.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3694</th>\n",
       "      <td>Sensor</td>\n",
       "      <td>Int</td>\n",
       "      <td>Light Array Emitter 1 (Value)</td>\n",
       "      <td>0</td>\n",
       "      <td>192</td>\n",
       "      <td>28.46640</td>\n",
       "      <td>128.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679869</th>\n",
       "      <td>Sensor</td>\n",
       "      <td>Int</td>\n",
       "      <td>Light Array Emitter 1 (Value)</td>\n",
       "      <td>0</td>\n",
       "      <td>192</td>\n",
       "      <td>4000.99300</td>\n",
       "      <td>192.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680226</th>\n",
       "      <td>Sensor</td>\n",
       "      <td>Int</td>\n",
       "      <td>Light Array Emitter 1 (Value)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4003.09300</td>\n",
       "      <td>-192.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681076</th>\n",
       "      <td>Sensor</td>\n",
       "      <td>Int</td>\n",
       "      <td>Light Array Emitter 1 (Value)</td>\n",
       "      <td>0</td>\n",
       "      <td>192</td>\n",
       "      <td>4008.08200</td>\n",
       "      <td>192.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681450</th>\n",
       "      <td>Sensor</td>\n",
       "      <td>Int</td>\n",
       "      <td>Light Array Emitter 1 (Value)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4010.25700</td>\n",
       "      <td>-192.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681994</th>\n",
       "      <td>Sensor</td>\n",
       "      <td>Int</td>\n",
       "      <td>Light Array Emitter 1 (Value)</td>\n",
       "      <td>0</td>\n",
       "      <td>192</td>\n",
       "      <td>4013.46100</td>\n",
       "      <td>192.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1297 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sensor/Actuator  Data Type                           Name   Address  \\\n",
       "1909            Sensor        Int  Light Array Emitter 1 (Value)         0   \n",
       "2266            Sensor        Int  Light Array Emitter 1 (Value)         0   \n",
       "2538            Sensor        Int  Light Array Emitter 1 (Value)         0   \n",
       "2912            Sensor        Int  Light Array Emitter 1 (Value)         0   \n",
       "3694            Sensor        Int  Light Array Emitter 1 (Value)         0   \n",
       "...                ...        ...                            ...       ...   \n",
       "679869          Sensor        Int  Light Array Emitter 1 (Value)         0   \n",
       "680226          Sensor        Int  Light Array Emitter 1 (Value)         0   \n",
       "681076          Sensor        Int  Light Array Emitter 1 (Value)         0   \n",
       "681450          Sensor        Int  Light Array Emitter 1 (Value)         0   \n",
       "681994          Sensor        Int  Light Array Emitter 1 (Value)         0   \n",
       "\n",
       "         Current Value   Time Elapsed   Sensor Value Difference  \n",
       "1909               192       17.96656                      64.0  \n",
       "2266                 0       20.07486                    -192.0  \n",
       "2538               192       21.68317                     192.0  \n",
       "2912                 0       23.88313                    -192.0  \n",
       "3694               192       28.46640                     128.0  \n",
       "...                ...            ...                       ...  \n",
       "679869             192     4000.99300                     192.0  \n",
       "680226               0     4003.09300                    -192.0  \n",
       "681076             192     4008.08200                     192.0  \n",
       "681450               0     4010.25700                    -192.0  \n",
       "681994             192     4013.46100                     192.0  \n",
       "\n",
       "[1297 rows x 7 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filtering sensor trigger data points from the data by removing \n",
    "#all all data with current value = 0\n",
    "filteredLAE = filteredLAE[filteredLAE[' Sensor Value Difference'] != 0]\n",
    "filteredLAE = filteredLAE[filteredLAE[' Current Value']!=128 ]\n",
    "filteredLAE = filteredLAE[filteredLAE[' Current Value']!=64 ]\n",
    "#Dropping al NaN values\n",
    "filteredLAE.dropna(subset = [' Sensor Value Difference'], inplace=True)\n",
    "filteredLAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "sonic-cambodia",
   "metadata": {},
   "outputs": [],
   "source": [
    "filteredLAE=filteredLAE.copy()\n",
    "filteredLAE[filteredLAE[' Sensor Value Difference'] > 0]\n",
    "filteredLAE['Trigger Status']=\"Off\"\n",
    "filteredLAE['Trigger Status'] = filteredLAE.apply(lambda x: 'On' if x[' Sensor Value Difference']>0 else 'Off',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "steady-headset",
   "metadata": {},
   "outputs": [],
   "source": [
    "filteredLAE['Trigger Interval'] = filteredLAE[\" Time Elapsed\"].diff(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "naval-liberal",
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocessedDF = filteredLAE[filteredLAE['Trigger Status']=='On']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "mineral-newsletter",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sensor/Actuator</th>\n",
       "      <th>Data Type</th>\n",
       "      <th>Name</th>\n",
       "      <th>Address</th>\n",
       "      <th>Current Value</th>\n",
       "      <th>Time Elapsed</th>\n",
       "      <th>Sensor Value Difference</th>\n",
       "      <th>Trigger Status</th>\n",
       "      <th>Trigger Interval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2538</th>\n",
       "      <td>Sensor</td>\n",
       "      <td>Int</td>\n",
       "      <td>Light Array Emitter 1 (Value)</td>\n",
       "      <td>0</td>\n",
       "      <td>192</td>\n",
       "      <td>21.68317</td>\n",
       "      <td>192.0</td>\n",
       "      <td>On</td>\n",
       "      <td>3.71661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3694</th>\n",
       "      <td>Sensor</td>\n",
       "      <td>Int</td>\n",
       "      <td>Light Array Emitter 1 (Value)</td>\n",
       "      <td>0</td>\n",
       "      <td>192</td>\n",
       "      <td>28.46640</td>\n",
       "      <td>128.0</td>\n",
       "      <td>On</td>\n",
       "      <td>6.78323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4731</th>\n",
       "      <td>Sensor</td>\n",
       "      <td>Int</td>\n",
       "      <td>Light Array Emitter 1 (Value)</td>\n",
       "      <td>0</td>\n",
       "      <td>192</td>\n",
       "      <td>34.56642</td>\n",
       "      <td>192.0</td>\n",
       "      <td>On</td>\n",
       "      <td>6.10002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6312</th>\n",
       "      <td>Sensor</td>\n",
       "      <td>Int</td>\n",
       "      <td>Light Array Emitter 1 (Value)</td>\n",
       "      <td>0</td>\n",
       "      <td>192</td>\n",
       "      <td>43.86673</td>\n",
       "      <td>192.0</td>\n",
       "      <td>On</td>\n",
       "      <td>9.30031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7094</th>\n",
       "      <td>Sensor</td>\n",
       "      <td>Int</td>\n",
       "      <td>Light Array Emitter 1 (Value)</td>\n",
       "      <td>0</td>\n",
       "      <td>192</td>\n",
       "      <td>48.47523</td>\n",
       "      <td>192.0</td>\n",
       "      <td>On</td>\n",
       "      <td>4.60850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>678611</th>\n",
       "      <td>Sensor</td>\n",
       "      <td>Int</td>\n",
       "      <td>Light Array Emitter 1 (Value)</td>\n",
       "      <td>0</td>\n",
       "      <td>192</td>\n",
       "      <td>3993.63800</td>\n",
       "      <td>192.0</td>\n",
       "      <td>On</td>\n",
       "      <td>6.09300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679393</th>\n",
       "      <td>Sensor</td>\n",
       "      <td>Int</td>\n",
       "      <td>Light Array Emitter 1 (Value)</td>\n",
       "      <td>0</td>\n",
       "      <td>192</td>\n",
       "      <td>3998.21200</td>\n",
       "      <td>192.0</td>\n",
       "      <td>On</td>\n",
       "      <td>4.57400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679869</th>\n",
       "      <td>Sensor</td>\n",
       "      <td>Int</td>\n",
       "      <td>Light Array Emitter 1 (Value)</td>\n",
       "      <td>0</td>\n",
       "      <td>192</td>\n",
       "      <td>4000.99300</td>\n",
       "      <td>192.0</td>\n",
       "      <td>On</td>\n",
       "      <td>2.78100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681076</th>\n",
       "      <td>Sensor</td>\n",
       "      <td>Int</td>\n",
       "      <td>Light Array Emitter 1 (Value)</td>\n",
       "      <td>0</td>\n",
       "      <td>192</td>\n",
       "      <td>4008.08200</td>\n",
       "      <td>192.0</td>\n",
       "      <td>On</td>\n",
       "      <td>7.08900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681994</th>\n",
       "      <td>Sensor</td>\n",
       "      <td>Int</td>\n",
       "      <td>Light Array Emitter 1 (Value)</td>\n",
       "      <td>0</td>\n",
       "      <td>192</td>\n",
       "      <td>4013.46100</td>\n",
       "      <td>192.0</td>\n",
       "      <td>On</td>\n",
       "      <td>5.37900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>648 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sensor/Actuator  Data Type                           Name   Address  \\\n",
       "2538            Sensor        Int  Light Array Emitter 1 (Value)         0   \n",
       "3694            Sensor        Int  Light Array Emitter 1 (Value)         0   \n",
       "4731            Sensor        Int  Light Array Emitter 1 (Value)         0   \n",
       "6312            Sensor        Int  Light Array Emitter 1 (Value)         0   \n",
       "7094            Sensor        Int  Light Array Emitter 1 (Value)         0   \n",
       "...                ...        ...                            ...       ...   \n",
       "678611          Sensor        Int  Light Array Emitter 1 (Value)         0   \n",
       "679393          Sensor        Int  Light Array Emitter 1 (Value)         0   \n",
       "679869          Sensor        Int  Light Array Emitter 1 (Value)         0   \n",
       "681076          Sensor        Int  Light Array Emitter 1 (Value)         0   \n",
       "681994          Sensor        Int  Light Array Emitter 1 (Value)         0   \n",
       "\n",
       "         Current Value   Time Elapsed   Sensor Value Difference  \\\n",
       "2538               192       21.68317                     192.0   \n",
       "3694               192       28.46640                     128.0   \n",
       "4731               192       34.56642                     192.0   \n",
       "6312               192       43.86673                     192.0   \n",
       "7094               192       48.47523                     192.0   \n",
       "...                ...            ...                       ...   \n",
       "678611             192     3993.63800                     192.0   \n",
       "679393             192     3998.21200                     192.0   \n",
       "679869             192     4000.99300                     192.0   \n",
       "681076             192     4008.08200                     192.0   \n",
       "681994             192     4013.46100                     192.0   \n",
       "\n",
       "       Trigger Status  Trigger Interval  \n",
       "2538               On           3.71661  \n",
       "3694               On           6.78323  \n",
       "4731               On           6.10002  \n",
       "6312               On           9.30031  \n",
       "7094               On           4.60850  \n",
       "...               ...               ...  \n",
       "678611             On           6.09300  \n",
       "679393             On           4.57400  \n",
       "679869             On           2.78100  \n",
       "681076             On           7.08900  \n",
       "681994             On           5.37900  \n",
       "\n",
       "[648 rows x 9 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessedDF = preprocessedDF.copy()\n",
    "preprocessedDF['Trigger Interval'] = preprocessedDF[\" Time Elapsed\"].diff(1)\n",
    "preprocessedDF.dropna(subset = ['Trigger Interval'], inplace=True)\n",
    "preprocessedDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "paperback-jonathan",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([], [])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "preprocessedDF=preprocessedDF.reset_index(drop=True)\n",
    "plt.figure(figsize=(12,8)) #Plotting the data points on the plot\n",
    "sns.stripplot(x='Trigger Interval',data = preprocessedDF,jitter= True,edgecolor = 'None')\n",
    "sns.despine\n",
    "locs,labels = plt.xticks()\n",
    "plt.yticks([])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "following-possibility",
   "metadata": {},
   "outputs": [],
   "source": [
    "import jenkspy\n",
    "#Finding the breaks using the Jenks natural breaks optimzation method\n",
    "breaks = jenkspy.jenks_breaks(preprocessedDF['Trigger Interval'],nb_class = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "impressive-discovery",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2.57300000000032, 4.816000000000031, 7.506499999999988, 10.33400000000006]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#show ranges\n",
    "breaks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "overhead-hydrogen",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Trigger Interval'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Box plot\n",
    "import numpy as np\n",
    "sns.boxplot(x='Trigger Interval', data=preprocessedDF, whis=np.inf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "accepting-scene",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "array_list = []\n",
    "B = plt.boxplot(preprocessedDF['Trigger Interval'])\n",
    "[array_list.append(item.get_ydata()) for item in B['whiskers']]\n",
    "download_list = []\n",
    "for item in array_list:\n",
    "    download_list.append(item[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "behind-intro",
   "metadata": {},
   "source": [
    "<h3 style=\"color:green;\">COMPARE AGAINST IDEAL RANGE</h3>\n",
    "<p>Images below show normal expected range obtained from the training dataset, comparing this against the above data shows potential issue at both the upper <strong>[6.2]</strong> and lower <strong>[4.2]</strong> expected thresholds.</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "decimal-flower",
   "metadata": {},
   "source": [
    "<table align=\"center\">\n",
    "    <tr>\n",
    "        <th><img src=images/idealRange.PNG></th>\n",
    "        <th><img src=images/idealBoxplot.PNG></th>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "identified-jungle",
   "metadata": {},
   "outputs": [],
   "source": [
    "#All code below this is tp be ignored was used only for research purposes\n",
    "#Removing data outside the range of3.8 to 8.7 for testing purposes\n",
    "dummydata = preprocessedDF[preprocessedDF['Trigger Interval']>3.8]\n",
    "dummydata = dummydata[dummydata['Trigger Interval']<8.7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "empirical-palestine",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dummydata=dummydata.reset_index(drop=True)\n",
    "import jenkspy\n",
    "#Finding the breaks using the Jenks natural breaks optimzation method\n",
    "breaks = jenkspy.jenks_breaks(dummydata['Trigger Interval'],nb_class = 3)\n",
    "#seaborn library for plotting\n",
    "import seaborn as sns\n",
    "plt.figure(figsize=(12,8)) #Plotting the data points on the plot\n",
    "sns.stripplot(x='Trigger Interval',data = dummydata,jitter= True,edgecolor = 'None')\n",
    "sns.despine\n",
    "locs,labels = plt.xticks()\n",
    "plt.yticks([])\n",
    "#Drawing vertical lines usng the data points as x values\n",
    "for b in breaks:\n",
    "    plt.vlines(b,ymin= -0.2,ymax=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "shaped-shipping",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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