alpha_lc.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import binascii\n",
    "import pygraphviz as pgv\n",
    "import graphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "################################################################\n",
    "#create the checksum from an dataframe with unsorted motion IDs#\n",
    "################################################################\n",
    "\n",
    "def CRC32(IDs):\n",
    "    crc = binascii.crc32(str(np.asarray(IDs)))\n",
    "    return np.uint32(crc)\n",
    "                         \n",
    "#########################################################\n",
    "#calculate duration and checksum with unsorted motion ID#\n",
    "#########################################################\n",
    "\n",
    "def aggregate(x):\n",
    "    names = {\n",
    "        'caseID': x['caseID'].max(),\n",
    "        'checksum': CRC32(x['motionID'])\n",
    "        }\n",
    "\n",
    "    return pd.Series(names, index=['caseID', 'checksum'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read event log from CSV\n",
    "eventLog = pd.read_csv('C:/Users/Wolfgang/Documents/PhD/syntheticLog/inducedVariantsLogMin.csv', index_col = False)\n",
    "#change start and end time column values to tyoe timestamp\n",
    "eventLog['startTime'] = pd.to_datetime(eventLog['startTime'], format = '%d/%m/%Y %H:%M:%S')\n",
    "eventLog['completeTime'] = pd.to_datetime(eventLog['completeTime'], format = '%d/%m/%Y  %H:%M:%S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#copy and filter by style 1\n",
    "style1Log = eventLog.loc[(eventLog['style'] == 1)].copy()\n",
    "#delete not needed columns\n",
    "del style1Log['style']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create a blank result matrix based on the event logs motion ID\n",
    "DSM = style1Log.groupby('motionID').size().reset_index(name='counts')[['motionID']].copy()\n",
    "\n",
    "#create columns matching the rows and fill with false\n",
    "for index, row in DSM.iterrows():\n",
    "    DSM[row['motionID']] = False\n",
    "\n",
    "#make motionID the index\n",
    "DSM.set_index('motionID', inplace=True)\n",
    "oDSM = DSM.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#split log into start and complete events\n",
    "#create a copy\n",
    "style1aLog = style1Log.copy()\n",
    "#delete complete column\n",
    "del style1aLog['completeTime']\n",
    "#rename start column\n",
    "style1aLog.rename(columns = {'startTime':'timestamp'}, inplace = True)\n",
    "#add type column = start\n",
    "style1aLog['type'] = 'start'\n",
    "\n",
    "#create a copy\n",
    "style1bLog = style1Log.copy()\n",
    "#delete start column\n",
    "del style1bLog['startTime']\n",
    "#rename complete column\n",
    "style1bLog.rename(columns = {'completeTime':'timestamp'}, inplace = True)\n",
    "#add type column = start\n",
    "style1bLog['type'] = 'complete'\n",
    "\n",
    "#concat the two pandas\n",
    "styleLog = pd.concat([style1aLog,style1bLog])\n",
    "#sort by timestamp, type and motion ID\n",
    "styleLog = styleLog.sort_values(['timestamp','type','motionID'] ,ascending = [True, True, True])\n",
    "#index newly\n",
    "styleLog.reset_index(inplace=True, drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create a summary over the log with start and complete events which includes the checksum over the motionID\n",
    "logSummary = styleLog.groupby(['caseID']).apply(aggregate)\n",
    "\n",
    "#find for each trace class (defined through the checksum) the ID of a matching case\n",
    "traces = logSummary.groupby(['checksum']).min()['caseID'].reset_index(name='caseID')\n",
    "\n",
    "#get the case for each trace class found\n",
    "traceLog = styleLog.loc[styleLog['caseID'].isin(traces['caseID'])]\n",
    "traceLog.reset_index(inplace=True, drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "          1   2   4   5   6   7   8   9   10  11  13\n",
       "motionID                                            \n",
       "1          0   0   0   0   0   0   0   0   0   0   0\n",
       "2          1   0   0   0   0   0   0   0   0   0   0\n",
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       "13         0   0   0   0   0   0   0   0   0   1   0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#iterate through the condensed log\n",
    "for index, row in traceLog.iterrows():\n",
    "    #if a complete event is found store the current motion ID as column number and the current case number\n",
    "    if traceLog.loc[index]['type'] == 'complete':\n",
    "        columnValue = traceLog.loc[index]['motionID']\n",
    "        currentCase = traceLog.loc[index]['caseID']\n",
    "        iterationPointer = index\n",
    "        startFound = 0\n",
    "        #iterrate through the remainder of that case and make a mark in the DSM for every start found prior to another complete\n",
    "        while ((traceLog.loc[iterationPointer]['type'] == 'start') or (startFound == 0)) and traceLog.loc[iterationPointer]['caseID'] == currentCase:\n",
    "            if traceLog.loc[iterationPointer]['type'] == 'start':\n",
    "                startFound = 1\n",
    "                rowValue = traceLog.loc[iterationPointer]['motionID']\n",
    "                DSM.loc[rowValue, columnValue] = True\n",
    "            iterationPointer += 1\n",
    "            if iterationPointer == len(traceLog):\n",
    "                break\n",
    "\n",
    "DSM.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "DSM.astype(int).to_csv('C:\\\\Users\\\\Wolfgang\\\\Documents\\\\PhD\\\\syntheticLog\\\\matrix1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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      "text/plain": [
       "          1   2   4   5   6   7   8   9   10  11  13\n",
       "motionID                                            \n",
       "1          1   0   0   0   0   0   0   0   0   0   0\n",
       "2          1   1   1   1   1   0   0   0   0   0   0\n",
       "4          1   1   1   1   1   1   0   0   0   0   0\n",
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       "11         1   1   1   1   1   1   1   1   1   1   0\n",
       "13         1   1   1   1   1   1   1   1   1   1   1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#iterate through the condensed log\n",
    "for index, row in traceLog.iterrows():\n",
    "    #if a start event is found store the current motion ID as column number and the current case number\n",
    "    if traceLog.loc[index]['type'] == 'start':\n",
    "        columnValue = traceLog.loc[index]['motionID']\n",
    "        currentCase = traceLog.loc[index]['caseID']\n",
    "        iterationPointer = index\n",
    "        #iterrate through the remainder of that case and make a mark in the oDSM for every complete found\n",
    "        while traceLog.loc[iterationPointer]['caseID'] == currentCase:\n",
    "            if traceLog.loc[iterationPointer]['type'] == 'complete':\n",
    "                rowValue = traceLog.loc[iterationPointer]['motionID']\n",
    "                oDSM.loc[rowValue, columnValue] = True\n",
    "            iterationPointer += 1\n",
    "            if iterationPointer == len(traceLog):\n",
    "                break\n",
    "\n",
    "oDSM.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "oDSM.astype(int).to_csv('C:\\\\Users\\\\Wolfgang\\\\Documents\\\\PhD\\\\syntheticLog\\\\matrix2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
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   "outputs": [
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       "motionID  1   2   4   5   6   7   8   9   10  11  13\n",
       "1          0   0   0   0   0   0   0   0   0   0   0\n",
       "2          1   0   0   0   0   0   0   0   0   0   0\n",
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       "8          1   1   1   0   1   0   0   0   0   0   0\n",
       "9          1   1   1   0   1   0   1   0   0   0   0\n",
       "10         1   1   1   0   1   0   1   1   0   0   0\n",
       "11         1   1   1   1   1   1   1   1   1   0   0\n",
       "13         1   1   1   1   1   1   1   1   1   1   0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
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    }
   ],
   "source": [
    "#transpose matrix\n",
    "oDSM = oDSM.T\n",
    "#invert matrix\n",
    "oDSM = ~oDSM\n",
    "oDSM.astype(int)"
   ]
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  {
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   "execution_count": 13,
   "metadata": {
    "scrolled": false
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   "outputs": [
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       "          1   2   4   5   6   7   8   9   10  11  13\n",
       "motionID                                            \n",
       "1          0   0   0   0   0   0   0   0   0   0   0\n",
       "2          1   0   0   0   0   0   0   0   0   0   0\n",
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       "13         0   0   0   0   0   0   0   0   0   1   0"
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    "#DSM AND oDSM\n",
    "resultMatrix = (DSM & oDSM).astype(int)\n",
    "resultMatrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "resultMatrix.to_csv('C:\\\\Users\\\\Wolfgang\\\\Documents\\\\PhD\\\\syntheticLog\\\\matrix3.csv')"
   ]
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      ],
      "text/plain": [
       "<graphviz.files.Source at 0x7d6fad0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create list with labels\n",
    "label = ['load part', 'close clamps 1', 'close clamps 2', 'close clamps 3', 'initiate R01', 'initiate R02', 'R01 weld / clear', 'R02 weld / reposition', 'open clamps 3', 'R02 weld / clear', 'open clamps 1', 'open clamps 2', 'unload part']\n",
    "\n",
    "#draw graph of result matrix\n",
    "#define graph\n",
    "G = pgv.AGraph(strict=True, directed=True)\n",
    "G.graph_attr['rankdir'] = 'TB'\n",
    "\n",
    "#iterate through matrix and create nodes\n",
    "rowCount = 1\n",
    "for index, row in resultMatrix.iterrows():\n",
    "    if rowCount == 1 or rowCount == len(resultMatrix):\n",
    "        G.add_node(index, label=label[index-1], shape='box', style='filled, rounded', fillcolor='white', fontcolor='black', xlp=\"\")\n",
    "    else:\n",
    "        G.add_node(index, label=label[index-1], shape='box', style='filled', fillcolor='white', fontcolor='black', xlp=\"\")\n",
    "    rowCount += 1\n",
    "\n",
    "#iterate through matrix and create edges\n",
    "for index, row in resultMatrix.iterrows():\n",
    "    for column in resultMatrix.columns:\n",
    "        if row[column]:\n",
    "            G.add_edge(column, index, label='', penwidth=2)\n",
    "\n",
    "            \n",
    "graphviz.Source(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "G.draw('C:\\\\Users\\\\Wolfgang\\\\Documents\\\\PhD\\\\syntheticLog\\\\oneVariantsStyle1Model.eps', prog='dot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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