gobmk_with_stateFunction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "States and functions instantiated\n"
     ]
    }
   ],
   "source": [
    "from random import *\n",
    "import numpy as np\n",
    "from scipy.stats import truncnorm\n",
    "from operator import add\n",
    "\n",
    "seed (100)\n",
    "\n",
    "## Global variables\n",
    "global N, NUM_OF_ITERATION\n",
    "global STATE0, STATE1, STATE2, STATE3, STATE4, STATE5, STATE6\n",
    "global BASE, OPT\n",
    "global TDM, GREEDY, GENERIC\n",
    "global iCache, dCache\n",
    "global PROB\n",
    "\n",
    "global TDM_OPT, GREEDY_OPT, GENERIC_OPT\n",
    "global OPT_LIST\n",
    "global METHOD\n",
    "global STATE_COUNT\n",
    "\n",
    "## Instantiating global variables\n",
    "## N is the total number of cycles the process runs for\n",
    "N = 100000000\n",
    "\n",
    "## Number of iterations for the complete run\n",
    "## 10000 takes around 2 min to run\n",
    "NUMBER_OF_ITERATION = 10\n",
    "\n",
    "## BASE list has all the base cycle values that were generated\n",
    "## OPT list has the optimized values for that cycle post optimization\n",
    "BASE = []\n",
    "OPT = []\n",
    "\n",
    "## States\n",
    "STATE0 = {'meanCycle':  40000, 'stdDev':  1000, 'min': 35000, 'max': 45000, 'base': BASE, 'opt': OPT}\n",
    "STATE1 = {'meanCycle':  18000, 'stdDev':  1000, 'min': 14000, 'max': 22000, 'base': BASE, 'opt': OPT}\n",
    "STATE2 = {'meanCycle':  130000, 'stdDev':  10000, 'min': 100000, 'max': 160000, 'base': BASE, 'opt': OPT}\n",
    "STATE3 = {'meanCycle':  600000, 'stdDev':  10000, 'min': 550000, 'max': 650000, 'base': BASE, 'opt': OPT}\n",
    "STATE4 = {'meanCycle':  260000, 'stdDev':  10000, 'min': 220000, 'max': 300000, 'base': BASE, 'opt': OPT}\n",
    "STATE5 = {'meanCycle':  50000, 'stdDev':  1000, 'min': 45000, 'max': 55000, 'base': BASE, 'opt': OPT}\n",
    "STATE6 = {'meanCycle':  25000, 'stdDev':  1000, 'min': 20000, 'max': 30000, 'base': BASE, 'opt': OPT}\n",
    "\n",
    "## name key added to ##M print the name\n",
    "TDM = {'name': 'TDM', 'freqSampling': 2000, 'lat': 500, 'stdDev': 20, 'quality': 0.7}\n",
    "GREEDY = {'name': 'GREEDY', 'freqSampling': 25000, 'lat': 1000, 'stdDev': 100, 'quality': 0.9}\n",
    "GENERIC = {'name': 'GENERIC', 'freqSampling': 50000, 'lat': 2000, 'stdDev': 500, 'quality': 1}\n",
    "\n",
    "## CycleRatio ==> is the percentage of cycles that the technique uses from the total cycles\n",
    "iCache = {'name': 'iCahche', 'lat': 250, 'stdDev': 10, 'quality': 0.2, 'cycleRatio': 0.56}\n",
    "dCache = {'name': 'dCache', 'lat': 400, 'stdDev': 50, 'quality': 0.3, 'cycleRatio': 0.09}\n",
    "ROB = {'name': 'ROB', 'lat': 300, 'stdDev': 50, 'quality': 0.1, 'cycleRatio': 0.10}\n",
    "IQ = {'name': 'IQ', 'lat': 400, 'stdDev': 50, 'quality': 0.1, 'cycleRatio': 0.25}\n",
    "\n",
    "## techList is the list containing all the techniques \n",
    "## it is fed into the getOptCycles to get the configuration time for the max of the techniques\n",
    "techList = [iCache, dCache, ROB, IQ]\n",
    "\n",
    "## Clearing the global opt lists\n",
    "TDM_OPT = []\n",
    "GREEDY_OPT = []\n",
    "GENERIC_OPT = []\n",
    "\n",
    "## Create OPT_LIST contains the speedup for all the variations of techniques\n",
    "OPT_LIST = [TDM_OPT, GREEDY_OPT, GENERIC_OPT]\n",
    "\n",
    "## Create a list of methods, do not create strings 'TDM' XX\n",
    "METHOD = [TDM, GREEDY, GENERIC]\n",
    "\n",
    "## State Count \n",
    "STATE_COUNT = {'state0': 0, 'state1': 0, 'state2': 0, 'state3': 0, 'state4': 0, 'state5': 0, 'state6': 0}\n",
    "## This list is not need as we have encoded the techniques in \n",
    "## the getOptCycles using the getActiveTech function\n",
    "#TECH = [iCache, dCache]\n",
    "\n",
    "\n",
    "## --------------------------------------------------------------- ##\n",
    "\n",
    "def state_0():\n",
    "    ##Dprint \"State_0\"\n",
    "    STATE_COUNT['state0'] = STATE_COUNT['state0'] + 1\n",
    "    \n",
    "    ## Lets take that the mean values of cycles in state0 is 50000 and std Dev is 5000\n",
    "    global STATE0, BASE, OPT\n",
    "    \n",
    "    baseCycles = round(getTruncatedNormal(STATE0['meanCycle'], STATE0['stdDev'],\\\n",
    "                                         STATE0['min'], STATE0['max']))\n",
    "    BASE.append(baseCycles)\n",
    "    \n",
    "    ##MwriteStr = \"State0 \" + str(baseCycles) + \"\\n\"\n",
    "    ##Mf.write(writeStr)\n",
    "    \n",
    "    ##Dprint \"Opt Cycle Satrt: 0\\n\"\n",
    "    ## For the first iteration we need to start with time=0\n",
    "#     if (len(BASE) == 1):\n",
    "#         optStateCycles = getOptCycles(baseCycles, 0, TDM, techList, getActiveTech(10))\n",
    "#     else:\n",
    "#         ## startCycles for next iteration is the baseCycle from previous state\n",
    "#         optStateCycles = getOptCycles(baseCycles, getTotalCycles(BASE, len(BASE)-1), TDM, techList, getActiveTech(10))\n",
    "    \n",
    "#     OPT.append(optStateCycles)\n",
    "    \n",
    "    ## \n",
    "    #for i in range(len(BASE)):\n",
    "        ###M print \"BASE[%d] = %f\" %(i, BASE[i])\n",
    "    \n",
    "    ## ------------- Wrong way to code ---------------##\n",
    "#     print \"Before Entering if loop: State0\\n\"\n",
    "#     if random() <= 0.5:\n",
    "#         print\"State0_1\\n\"\n",
    "#         return state_1\n",
    "#     elif random() > 0.5:\n",
    "#         print\"State0_0\\n\"\n",
    "#         return state_0\n",
    "#     else: ## else is very important, otherwise the code is breaking even if you consider all the scenarios\n",
    "#         print\"Default: 0\\n\"\n",
    "#         return state_0\n",
    "#     elif random() > 0.6:\n",
    "#         print\"State0_3\\n\"\n",
    "#         return state_3\n",
    "\n",
    "    ## ------------------- Correct Way ------------------##\n",
    "    rand = random()\n",
    "#     if rand <= 0.4:\n",
    "#         return state_0\n",
    "#     elif rand > 0.4 and rand <= 0.8:\n",
    "#         return state_1\n",
    "#     elif rand > 0.8 and rand <= 0.9:\n",
    "#         return state_2\n",
    "#     elif rand > 0.9:\n",
    "#         return state_3\n",
    "    \n",
    "    return returnState(rand)\n",
    "    \n",
    "def state_1():\n",
    "    ##Dprint \"State_1\"\n",
    "    STATE_COUNT['state1'] = STATE_COUNT['state1'] + 1\n",
    "    ##\n",
    "    global STATE1, BASE, OPT\n",
    "    \n",
    "    ## baseCycles for the state at the current execution time\n",
    "    ## random number from the normal distribution with mean=STATE1['meanCycle']\n",
    "    ## and stdDev = STATE1['stdDev']\n",
    "    #baseCycles = round(np.random.normal(STATE1['meanCycle'], STATE1['stdDev']))\n",
    "    baseCycles = round(getTruncatedNormal(STATE1['meanCycle'], STATE1['stdDev'],\\\n",
    "                                         STATE1['min'], STATE1['max']))\n",
    "    BASE.append(baseCycles)\n",
    "    \n",
    "    ##MwriteStr = \"State1 \" + str(baseCycles) + \"\\n\"\n",
    "    ##Mf.write(writeStr)\n",
    "    ##Dprint \"Opt Cycle Satrt: 1\\n\"\n",
    "#     if (len(BASE) == 1):\n",
    "#         optStateCycles = getOptCycles(baseCycles, 0, TDM, techList, getActiveTech(10))\n",
    "#     else:\n",
    "#         optStateCycles = getOptCycles(baseCycles, getTotalCycles(BASE, len(BASE)-1), TDM, techList, getActiveTech(10))\n",
    "    \n",
    "#     OPT.append(optStateCycles)\n",
    "    \n",
    "    #for i in range(len(BASE)):\n",
    "        ###M print \"BASE[%d]= %f\" %(i, BASE[i])\n",
    "    \n",
    "    ##Dprint \"Before Entering if loop: State1\\n\"    \n",
    "    ## Do not create random number in the comparison \n",
    "    ## create a variable with random() and then check for the condition\n",
    "    ## if you do if random() elif random() => these are both generating different random numbers\n",
    "    ## and can break your code even if you consider all the possibilities, since each number get generated \n",
    "    ## independently of each other.\n",
    "    ## Wrong way to code\n",
    "#     if random() <= 0.5:\n",
    "#         print\"State1_0\\n\"\n",
    "#         return state_0\n",
    "#     elif random() > 0.5:\n",
    "#         print\"State1_1\\n\"\n",
    "#         return state_1\n",
    "#     else:\n",
    "#         print\"Default: 1\\n\"\n",
    "#         return state_1\n",
    "    \n",
    "    rand = random()\n",
    "#     if rand <= 0.4:\n",
    "#         return state_0\n",
    "#     elif rand > 0.4 and rand <= 0.8:\n",
    "#         return state_1\n",
    "#     elif rand > 0.8 and rand <= 0.9:\n",
    "#         return state_2\n",
    "#     elif rand > 0.9:\n",
    "#         return state_3\n",
    "\n",
    "    return returnState(rand)\n",
    "    \n",
    "def state_2():\n",
    "    ##M print \"State_2\"\n",
    "    ##\n",
    "    STATE_COUNT['state2'] = STATE_COUNT['state2'] + 1\n",
    "    global STATE2, BASE, OPT\n",
    "    \n",
    "    baseCycles = round(getTruncatedNormal(STATE2['meanCycle'], STATE2['stdDev'],\\\n",
    "                                         STATE2['min'], STATE2['max']))\n",
    "    BASE.append(baseCycles)\n",
    "    \n",
    "#     if (len(BASE) == 1):\n",
    "#         optStateCycles = getOptCycles(baseCycles, 0, TDM, techList, getActiveTech(10))\n",
    "#     else:\n",
    "#         optStateCycles = getOptCycles(baseCycles, getTotalCycles(BASE, len(BASE)-1), TDM, techList, getActiveTech(10))\n",
    "    \n",
    "#     OPT.append(optStateCycles)\n",
    "    \n",
    "    ##MwriteStr = \"State2 \" + str(baseCycles) + \"\\n\"\n",
    "    ##Mf.write(writeStr)\n",
    "    \n",
    "    rand = random()\n",
    "    return returnState(rand)\n",
    "\n",
    "def state_3():\n",
    "    ##M print \"State_3\"\n",
    "    STATE_COUNT['state3'] = STATE_COUNT['state3'] + 1\n",
    "    ##\n",
    "    global STATE3, BASE, OPT\n",
    "    \n",
    "    baseCycles = round(getTruncatedNormal(STATE3['meanCycle'], STATE3['stdDev'],\\\n",
    "                                         STATE3['min'], STATE3['max']))\n",
    "    BASE.append(baseCycles)\n",
    "    \n",
    "    ##MwriteStr = \"State3 \" + str(baseCycles) + \"\\n\"\n",
    "    ##Mf.write(writeStr)\n",
    "    \n",
    "#     if (len(BASE) == 1):\n",
    "#         optStateCycles = getOptCycles(baseCycles, 0, TDM, techList, getActiveTech(10))\n",
    "#     else:\n",
    "#         optStateCycles = getOptCycles(baseCycles, getTotalCycles(BASE, len(BASE)-1), TDM, techList, getActiveTech(10))\n",
    "    \n",
    "#     OPT.append(optStateCycles)\n",
    "    \n",
    "    rand = random()\n",
    "    return returnState(rand)\n",
    "\n",
    "def state_4():\n",
    "    ##M print \"State_4\"\n",
    "    STATE_COUNT['state4'] = STATE_COUNT['state4'] + 1\n",
    "    ##\n",
    "    global STATE4, BASE, OPT\n",
    "    \n",
    "    baseCycles = round(getTruncatedNormal(STATE4['meanCycle'], STATE4['stdDev'],\\\n",
    "                                         STATE4['min'], STATE4['max']))\n",
    "    BASE.append(baseCycles)\n",
    "    \n",
    "    ##MwriteStr = \"State3 \" + str(baseCycles) + \"\\n\"\n",
    "    ##Mf.write(writeStr)\n",
    "    \n",
    "#     if (len(BASE) == 1):\n",
    "#         optStateCycles = getOptCycles(baseCycles, 0, TDM, techList, getActiveTech(10))\n",
    "#     else:\n",
    "#         optStateCycles = getOptCycles(baseCycles, getTotalCycles(BASE, len(BASE)-1), TDM, techList, getActiveTech(10))\n",
    "    \n",
    "#     OPT.append(optStateCycles)\n",
    "    \n",
    "    rand = random()\n",
    "    return returnState(rand)\n",
    "\n",
    "def state_5():\n",
    "    ##M print \"State_5\"\n",
    "    STATE_COUNT['state5'] = STATE_COUNT['state5'] + 1\n",
    "    ##\n",
    "    global STATE5, BASE, OPT\n",
    "    \n",
    "    baseCycles = round(getTruncatedNormal(STATE5['meanCycle'], STATE5['stdDev'],\\\n",
    "                                         STATE5['min'], STATE5['max']))\n",
    "    BASE.append(baseCycles)\n",
    "    \n",
    "    ##MwriteStr = \"State3 \" + str(baseCycles) + \"\\n\"\n",
    "    ##Mf.write(writeStr)\n",
    "    \n",
    "#     if (len(BASE) == 1):\n",
    "#         optStateCycles = getOptCycles(baseCycles, 0, TDM, techList, getActiveTech(10))\n",
    "#     else:\n",
    "#         optStateCycles = getOptCycles(baseCycles, getTotalCycles(BASE, len(BASE)-1), TDM, techList, getActiveTech(10))\n",
    "    \n",
    "#     OPT.append(optStateCycles)\n",
    "    \n",
    "    rand = random()\n",
    "    return returnState(rand)\n",
    "\n",
    "def state_6():\n",
    "    ##M print \"State_6\"\n",
    "    STATE_COUNT['state6'] = STATE_COUNT['state6'] + 1\n",
    "    ##\n",
    "    global STATE6, BASE, OPT\n",
    "    \n",
    "    baseCycles = round(getTruncatedNormal(STATE6['meanCycle'], STATE6['stdDev'],\\\n",
    "                                         STATE6['min'], STATE6['max']))\n",
    "    BASE.append(baseCycles)\n",
    "    \n",
    "    ##MwriteStr = \"State3 \" + str(baseCycles) + \"\\n\"\n",
    "    ##Mf.write(writeStr)\n",
    "    \n",
    "#     if (len(BASE) == 1):\n",
    "#         optStateCycles = getOptCycles(baseCycles, 0, TDM, techList, getActiveTech(10))\n",
    "#     else:\n",
    "#         optStateCycles = getOptCycles(baseCycles, getTotalCycles(BASE, len(BASE)-1), TDM, techList, getActiveTech(10))\n",
    "    \n",
    "#     OPT.append(optStateCycles)\n",
    "    \n",
    "    rand = random()\n",
    "    return returnState(rand)\n",
    "\n",
    "\n",
    "## Define the state function return for each state beased on the pobabilities\n",
    "def returnState(randVar):\n",
    "    if randVar <= 0.03:\n",
    "        return state_0\n",
    "    elif randVar > 0.03 and randVar <= 0.35:\n",
    "        return state_1\n",
    "    elif randVar > 0.35 and randVar <= 0.378:\n",
    "        return state_2\n",
    "    elif randVar > 0.378 and randVar <= 0.385:\n",
    "        return state_3\n",
    "    elif randVar > 0.385 and randVar <= 0.40:\n",
    "        return state_4\n",
    "    elif randVar > 0.40 and randVar <= 0.50:\n",
    "        return state_5\n",
    "    elif randVar > 0.50 and randVar <= 1:\n",
    "        return state_6\n",
    "    \n",
    "## Other functions\n",
    "## get the techniques that are active for a given simulation\n",
    "def getActiveTech(x):\n",
    "    if (x == 0):\n",
    "        return (0, 0, 0, 0)\n",
    "    elif (x==1):\n",
    "        return (0, 0, 0, 1)\n",
    "    elif (x==2):\n",
    "        return (0, 0, 1, 0)\n",
    "    elif (x==3):\n",
    "        return (0, 0, 1, 1)\n",
    "    elif (x==4):\n",
    "        return (0, 1, 0, 0)\n",
    "    elif (x==5):\n",
    "        return (0, 1, 0, 1)\n",
    "    elif (x==6):\n",
    "        return (0, 1, 1, 0)\n",
    "    elif (x==7):\n",
    "        return (0, 1, 1, 1)\n",
    "    elif (x==8):\n",
    "        return (1, 0, 0, 0)\n",
    "    elif (x==9):\n",
    "        return (1, 0, 0, 1)\n",
    "    elif (x==10):\n",
    "        return (1, 0, 1, 0)\n",
    "    elif (x==11):\n",
    "        return (1, 0, 1, 1)\n",
    "    elif (x==12):\n",
    "        return (1, 1, 0, 0)\n",
    "    elif (x==13):\n",
    "        return (1, 1, 0, 1)\n",
    "    elif (x==14):\n",
    "        return (1, 1, 1, 0)\n",
    "    elif (x==15):\n",
    "        return (1, 1, 1, 1)\n",
    "\n",
    "###############################################\n",
    "\n",
    "## sum() => internal function can also be used\n",
    "## Usage sum(list)\n",
    "def getTotalCycles(list1, index):\n",
    "    tot = 0\n",
    "    for i in range(index):\n",
    "        tot = tot + list1[i]\n",
    "    return tot\n",
    "\n",
    "###############################################\n",
    "\n",
    "## getOptCycles takes in input basecycle, time at which the state started,\n",
    "## method and technique applied and returns the optimized time for that state, that method \n",
    "## and technique\n",
    "\n",
    "## baseCycles => is generated randomly based on mean and variance for a state\n",
    "## startCycles => is the time at which the previous state finished. Start of this state in globale\n",
    "## time\n",
    "## method => method, TDM, GREEDY, GENERIC\n",
    "## method['lat'] => gives the latency time for the method\n",
    "## techList => technique applied, iCache, dCache, ROB etc. Used to get config time for \n",
    "##          all the techniques and max will be used for process\n",
    "## activeTech ==> is the tuple with the active techniques \n",
    "\n",
    "def getOptCycles(baseCycles, startCycles, method, techList, activeTech):\n",
    "    ###M print \"method= %s\\n%s['freqSampling'] = %d\" %(method, method, method['freqSampling'])\n",
    "    ###M print \"Type of Method = %s\" %(type(method))\n",
    "    samplingRate = method['freqSampling']\n",
    "    \n",
    "    ## samplingTime is the time in the state where the technique will sample for optimisation\n",
    "    ## If the startCyles is 0 => Starting the simulation\n",
    "    ## startCycles is divisible by sampling rate => We discussed to go on to the next \n",
    "    ## sampling cycle\n",
    "    if (startCycles % samplingRate == 0 or startCycles == 0):\n",
    "        samplingTime = samplingRate\n",
    "    elif (startCycles % samplingRate != 0):\n",
    "        ## Since we know the starting time, we divide it by samplingRate to see how much \n",
    "        ## it is above the samplingRate. Now we subtract it from the samplingRate to get \n",
    "        ## how further down will the next sampling be done.\n",
    "        ## We multiply 2*samplingRate since the program will take at least freqSampling\n",
    "        ## to optimize.\n",
    "        #samplingTime = startCycles + (samplingRate - np.remainder(startCycles, samplingRate))\n",
    "        ## For this run we assume the sampling point can be the first sampling point and need \n",
    "        ## not wait for the next sampling point. One more assumption is that the latency \n",
    "        ## should account for the time it needs to configure the state variables for optimization\n",
    "        ## Because of thois we use 1*samplingRate - ....\n",
    "        samplingTime = (1*samplingRate) - np.remainder(startCycles, samplingRate)\n",
    "    \n",
    "    if (samplingTime > baseCycles):\n",
    "        ##M print \"Sampling Time greater than baseCycles ==> No optimisation possible\\n\"\n",
    "        ##M print \"baseCycles= %d\\nsamplingTime= %d\\n\" %(baseCycles, samplingTime)\n",
    "        return baseCycles\n",
    "    else:\n",
    "        ## globalSamplingTime => Gives the sampling time with respect to global time\n",
    "        globalSamplingTime = startCycles + samplingTime\n",
    "\n",
    "        ## optTime => Represents the time taken by method to optimize\n",
    "        optTime = round(np.random.normal(method['lat'], method['stdDev']))\n",
    "\n",
    "        ## configTimeList => Represents the list of time taken by technique to configure\n",
    "        #configTime = round(np.random.normal(tech['lat'], tech['stdDev']))\n",
    "        configTimeList = []\n",
    "        for ii in range(len(techList)):\n",
    "            configTimeList.append(round(np.random.normal(techList[ii]['lat'], techList[ii]['stdDev'])))\n",
    "        \n",
    "        ## configTime is the max of all the cpnfiguration times required, since they can happen in parallel\n",
    "        configTime = max(configTimeList)\n",
    "\n",
    "        ## timeleft => Represents the time left after sampling and optimisation and configuration time\n",
    "        timeLeft = baseCycles - (samplingTime + optTime + configTime)\n",
    "        if (timeLeft < 0):\n",
    "            ##M print \"Cannot optimize because there is no time left for optimisation\\n\"\n",
    "            ##M print \"timeLeft < 0\\n\"\n",
    "            ##M print \"baseCycles= %d\\nstartCycles= %d\\nsamplingTime= %d\\nglobalSamplingTime=%d\\noptTime= %d\\\n",
    "        ##M\\nconfigTime= %d\\ntimeLeft= %d\\n\"\\\n",
    "        ##M% (baseCycles, startCycles, samplingTime, globalSamplingTime, optTime, \\\n",
    "        ##M   configTime, timeLeft)\n",
    "            return baseCycles\n",
    "        \n",
    "        else:\n",
    "            ## this time is now optimized according to the method\n",
    "            ##opt_timeLeft = round(method['quality']*timeLeft)\n",
    "            a1 = activeTech[0]\n",
    "            a2 = activeTech[1]\n",
    "            a3 = activeTech[2]\n",
    "            a4 = activeTech[3]\n",
    "#             opt_timeLeft = round(timeLeft*method['quality']*(a1*iCache['cycleRatio']*(1-iCache['quality']) + \\\n",
    "#                                           a2*dCache['cycleRatio']*(1-dCache['quality']) + \\\n",
    "#                                           a3*ROB['cycleRatio']*(1-ROB['quality']) + \\\n",
    "#                                           a4*IQ['cycleRatio']*(1-IQ['quality']) \\\n",
    "#                                           ) + \\\n",
    "#                                 timeLeft*(1-(a1*iCache['cycleRatio'] +\\\n",
    "#                                            a2*dCache['cycleRatio'] +\\\n",
    "#                                            a3*ROB['cycleRatio'] +\\\n",
    "#                                            a4*IQ['cycleRatio'])\\\n",
    "#                                         )\\\n",
    "#                                 )\n",
    "            \n",
    "            opt_timeLeft = round(timeLeft*(a1*iCache['cycleRatio']*((1-iCache['quality']) + iCache['quality']*(1-method['quality'])) +\\\n",
    "                                          a2*dCache['cycleRatio']*((1-dCache['quality']) + dCache['quality']*(1-method['quality']) ) +\\\n",
    "                                          a3*ROB['cycleRatio']*((1-ROB['quality']) + ROB['quality']*(1-method['quality']) ) +\\\n",
    "                                          a4*IQ['cycleRatio']*((1-IQ['quality']) + IQ['quality']*(1-method['quality']) )  \n",
    "                                          ) + \\\n",
    "                                timeLeft*(1-(a1*iCache['cycleRatio'] +\\\n",
    "                                           a2*dCache['cycleRatio'] +\\\n",
    "                                           a3*ROB['cycleRatio'] +\\\n",
    "                                           a4*IQ['cycleRatio'])\\\n",
    "                                        )\\\n",
    "                                ) \n",
    "\n",
    "\n",
    "            newStateCycles = samplingTime + optTime + configTime + opt_timeLeft\n",
    "\n",
    "            ##M print \"\\nbaseCycles= %d\\nstartCycles= %d\\nsamplingTime= %d\\nglobalSamplingTime=%d\\noptTime= %d\\\n",
    "            ##M\\nconfigTime= %d\\ntimeLeft= %d\\nopt_timeLeft=%d\\nnewStateCycles= %d\\n\"\\\n",
    "            ##M% (baseCycles, startCycles, samplingTime, globalSamplingTime, optTime, \\\n",
    "            ##M   configTime, timeLeft, opt_timeLeft, newStateCycles)\n",
    "            \n",
    "            ##M print \"\\tiCache= %d, %d\\n\\tdCAche= %d, %d\\n\\tROB= %d, %d\\n\\tIQ= %d, %d\\n\" \\\n",
    "            ##M%(round(method['quality']*timeLeft*a1*iCache['cycleRatio']*(1-iCache['quality'])), round(timeLeft*iCache['cycleRatio']),\\\n",
    "            ##M  round(method['quality']*timeLeft*a2*dCache['cycleRatio']*(1-dCache['quality'])), round(timeLeft*dCache['cycleRatio']),\\\n",
    "            ##M  round(method['quality']*timeLeft*a3*ROB['cycleRatio']*(1-ROB['quality'])), round(timeLeft*ROB['cycleRatio']),\\\n",
    "            ##M  round(method['quality']*timeLeft*a4*IQ['cycleRatio']*(1-IQ['quality'])), round(timeLeft*IQ['cycleRatio'])\\\n",
    "            ##M )\n",
    "\n",
    "            ## returning the optimized time the state takes because of the technique and method applied\n",
    "            return newStateCycles\n",
    "\n",
    "#######################################################    \n",
    "\n",
    "## getTruncatedNormal gets random number by normal distribution within 'low' and 'up' given\n",
    "## the mean and standard deviation of the numbers.\n",
    "## rvs ==> is random variable continuous \n",
    "## Refer ==> https://stackoverflow.com/questions/36894191/how-to-get-a-normal-distribution-within-a-range-in-numpy\n",
    "\n",
    "def getTruncatedNormal (mean, stdDev, low, up):\n",
    "    return truncnorm ((low-mean)/stdDev, (up-mean)/stdDev, loc=mean, scale=stdDev).rvs()\n",
    "\n",
    "###M print \"truncated Normal number between %d and %d with mean=%d and stdDev = %d is %d\"\\\n",
    "#% (numMin, numMax, numMean, numSd, getTruncatedNormal(numMean, numSd, numMin, numMax).rvs() )    \n",
    "################\n",
    "\n",
    "print \"States and functions instantiated\"\n",
    "## ------------------------------------------------------------- ##\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "TOTAL_CYCLES= 100000000\n",
      "NUMBER_OF_ITERATION= 10\n",
      "TDM_OPT= [1.0, 1.0168332543630616, 1.0066658989805495, 1.0182043625263044, 1.0801064977394692, 1.023726947063333, 1.0356624002194388, 1.0251167967214552, 1.0997715168586475, 1.0878872719134409, 1.1013769488212433, 1.0428138433195406, 1.1078402030860961, 1.1218321899563886, 1.1094678180999848, 1.1302279836832692]\n",
      "GREEDY_OPT= [1.0, 1.0141400678839119, 1.0056085563585495, 1.0152884565541722, 1.0666289665877127, 1.0199080468145858, 1.0298685483453425, 1.021070443626964, 1.0827314810488793, 1.0730128296822634, 1.0840366050062957, 1.0358181745533401, 1.0893115338651422, 1.1006811059317567, 1.09063536766007, 1.1074848336819678]\n",
      "GENERIC_OPT= [1.0, 1.0112026606099245, 1.0044497147732763, 1.0121089710446241, 1.0522248030526147, 1.0157527040050232, 1.0235810052014549, 1.0166637494880795, 1.0646268654103248, 1.057151754955117, 1.0656471420157316, 1.0282509417526164, 1.0696793655827617, 1.0783834677417192, 1.0706962949434293, 1.0835438036034362]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# # global variables\n",
    "# # create OPT list for each Method\n",
    "# from operator import add\n",
    "\n",
    "# global TDM_OPT, GREEDY_OPT, GENERIC_OPT\n",
    "# global OPT_LIST\n",
    "# global METHOD\n",
    "# global NUM_OF_ITERATION\n",
    "\n",
    "# TDM_OPT = []\n",
    "# GREEDY_OPT = []\n",
    "# GENERIC_OPT = []\n",
    "\n",
    "# ## Create OPT_LIST contains the speedup for all the variations of techniques\n",
    "# OPT_LIST = [TDM_OPT, GREEDY_OPT, GENERIC_OPT]\n",
    "\n",
    "# ## name key added to print the name\n",
    "# TDM = {'name': 'TDM', 'freqSampling': 2000, 'lat': 500, 'stdDev': 20, 'quality': 0.8}\n",
    "# GREEDY = {'name': 'GREEDY', 'freqSampling': 4000, 'lat': 1500, 'stdDev': 100, 'quality': 0.9}\n",
    "# GENERIC = {'name': 'GENERIC', 'freqSampling': 20000, 'lat': 5000, 'stdDev': 500, 'quality': 1}\n",
    "\n",
    "# ## CycleRatio ==> is the percentage of cycles that the technique uses from the total cycles\n",
    "# iCache = {'name': 'iCahche', 'lat': 250, 'stdDev': 10, 'quality': 0.2, 'cycleRatio': 0.6}\n",
    "# dCache = {'name': 'dCache', 'lat': 400, 'stdDev': 50, 'quality': 0.3, 'cycleRatio': 0.1}\n",
    "# ROB = {'name': 'ROB', 'lat': 300, 'stdDev': 50, 'quality': 0.1, 'cycleRatio': 0.1}\n",
    "# IQ = {'name': 'IQ', 'lat': 400, 'stdDev': 50, 'quality': 0.1, 'cycleRatio': 0.2}\n",
    "\n",
    "# ## techList is the list containing all the techniques \n",
    "# ## it is fed into the getOptCycles to get the configuration time for the max of the techniques\n",
    "# techList = [iCache, dCache, ROB, IQ]\n",
    "\n",
    "# ## Create a list of methods, do not create strings 'TDM' XX\n",
    "# METHOD = [TDM, GREEDY, GENERIC]\n",
    "\n",
    "# ## This list is not need as we have encoded the techniques in \n",
    "# ## the getOptCycles using the getActiveTech function\n",
    "# #TECH = [iCache, dCache]\n",
    "\n",
    "# ## Number of iterations for the complete run\n",
    "# ## 10000 takes around 2 min to run\n",
    "# NUMBER_OF_ITERATION = 10  \n",
    "\n",
    "###############################################################################\n",
    "## BASE is empty and and is will contain the base numbers of the states\n",
    "## optList has the empty list to which the optCycles will be appended\n",
    "## optList = [pdmList, greedyList, genericList]\n",
    "def runMCF(BASE, optList):\n",
    "    state = state_0\n",
    "\n",
    "    count = 0\n",
    "\n",
    "    while state:\n",
    "        state = state()\n",
    "        ## when the sum of base goes above N we stop\n",
    "        ## Stops close to the maximum number of cycles that we want to execute\n",
    "        if sum(BASE) > N:\n",
    "            ## state => None. while loop stops\n",
    "            ##Dprint\"sum(BASE)= %d, N= %d\\nBASE= %s\\n\" %(sum(BASE), N, BASE)\n",
    "            state = None\n",
    "    \n",
    "    ##Dprint\"\\nBASE2 = %s, Sum= %d\\n\" %(BASE, sum(BASE))\n",
    "    wrStr = \"Base Length: \" + str(len(BASE)) + \" , Base Sum: \" + str(sum(BASE)) + \"\\n\"\n",
    "    f.write(wrStr)\n",
    "    # ##M => Commented last to refrain from seeing all the debuggin outputs\n",
    "    ##Mprint \"\\ntype of BASE = %s\\n\" %(type(BASE))\n",
    "    #totalBaseCycles = getTotalCycles(BASE, len(BASE))\n",
    "\n",
    "    ##Mfor i in range(len(BASE)):\n",
    "    ##M    print \"BASE[%d]= %d, OPT[%d]=%d\" %(i, BASE[i], i, OPT[i])\n",
    "\n",
    "    #print \"\\nTotal Base Cycles = %d\" %(totalBaseCycles)\n",
    "\n",
    "    ### \n",
    "    #getOptCycles(BASE[1], BASE[0], TDM, iCache)\n",
    "\n",
    "    ##Mprint \"\\nTotal Base = %d, Total Opt = %d\\nTotal Speed Up = %f\" \\\n",
    "    ##M% (sum(BASE), sum(OPT), sum(BASE)/sum(OPT))\n",
    "\n",
    "    ############################################################\n",
    "\n",
    "    ## Once BASE is created, we can use it to get speedup for \n",
    "    ## various METHODS and TECHNIQUES\n",
    "\n",
    "    ## interList is the list to store intermediate optimisation cycles \n",
    "    ## for calculating the speedup \n",
    "    interList = []\n",
    "\n",
    "    ## groupList goes over the Methods in the order of activation of techniques\n",
    "    groupList = [0,1,2,4,8,3,5,6,9,10,12,7,11,13,14,15]\n",
    "\n",
    "    for i in range(len(METHOD)):\n",
    "        ## When we have a ist of list , the only way to makeit zero is by calling it by its name\n",
    "        ## pdmList = [] does not work even though pdmList is a list within optList\n",
    "        optList[i]=[]\n",
    "        ## This loop iterates over all combination of techniques\n",
    "        for j in groupList:\n",
    "            iterList = []\n",
    "            for b in range(len(BASE)):\n",
    "                #print \"Ooptimisation Method = %s , Tech = %s\" %(METHOD[j], TECH[i])\n",
    "                #print \"Type of method = %s type of TDM variable=%s\\nb=%d type of b=%s\" \\\n",
    "                #%(type(METHOD[j]), type(TDM), b, type(b))\n",
    "                if (b==0):\n",
    "                    optCycle = getOptCycles(BASE[b], getTotalCycles(BASE, 0), METHOD[i], techList, getActiveTech(j))\n",
    "                else:\n",
    "                    optCycle = getOptCycles(BASE[b], getTotalCycles(BASE, b), METHOD[i], techList, getActiveTech(j))\n",
    "\n",
    "                #optList = iterList\n",
    "                #print \"\\nname= %s, optList len = %d\\n\" %(METHOD_LIST[j], len(optList))\n",
    "                iterList.append(optCycle)\n",
    "\n",
    "\n",
    "            ##Mprint \"\\n############################\\n\"\n",
    "            ##Mprint \"Ooptimisation Method = %s , Tech = %d\" %(METHOD[i]['name'], j)\n",
    "            ##Mfor k in range(len(BASE)):\n",
    "            ##M    print \"BASE[%d]= %d, iterList[%d]=%d\" %(k, BASE[k], k, iterList[k])\n",
    "            ##Mprint \"\\nTotal Base = %d, Total Opt = %d\\nTotal Speed Up = %f\" \\\n",
    "            ##M% (sum(BASE), sum(iterList), sum(BASE)/sum(iterList))\n",
    "            ##Mprint \"\\n############################\\n\"\n",
    "\n",
    "            speedUp = sum(BASE)/sum(iterList)\n",
    "            #optList = OPT_LIST[i]\n",
    "            optList[i].append(speedUp)\n",
    "            ##Mprint \"optList[%d] = %s\" %(i, optList[i])\n",
    "\n",
    "    ## TDM_OPT and GREEDY_OPT will have the speedup's according to the activated techniques 0000, 0001, 0010..1111\n",
    "    ## Format the list to print only upto 4 decimal places\n",
    "    formatted_TDM_OPT = ['%.4f' % elem for elem in optList[0]]\n",
    "    formatted_GREEDY_OPT = ['%.4f' % elem for elem in optList[1]]\n",
    "    formatted_GENERIC_OPT = ['%.4f' % elm for elm in optList[2]]\n",
    "    ##Pprint \"TDM= %s\\nGREEDY= %s\\nGENERIC= %s\\n\" %(formatted_TDM_OPT, formatted_GREEDY_OPT, formatted_GENERIC_OPT)\n",
    "    \n",
    "    ## return optList with all the optimized lists for an iteration\n",
    "    return optList\n",
    "\n",
    "######## End of function runMCF\n",
    "## --------------------------------------------------------------- ##\n",
    "\n",
    "## These list contain the optCycle values for each method for each run\n",
    "pdmList = []\n",
    "greedyList = []\n",
    "genericList = []\n",
    "BASE = []\n",
    "TDM_OPT=[]\n",
    "GREEDY_OPT=[]\n",
    "GENERIC_OPT=[]\n",
    "\n",
    "##Mprint \"TDM_OPT= %s\\nGREEDY_OPT= %s\\nGENERIC_OPT= %s\\n\" %(TDM_OPT, GREEDY_OPT, GENERIC_OPT)\n",
    "\n",
    "optList = [pdmList, greedyList, genericList]\n",
    "##Mprint \"optList = %s\" %(optList)\n",
    "\n",
    "## ---------------------------------------------------------------- ##\n",
    "## opening file to write\n",
    "## Always close the file handle\n",
    "f = open(\"gobmk_withStateFunc.txt\", 'w')\n",
    "f.write(\"This is the first line\\n\")\n",
    "\n",
    "## ---------------------------------------------------------------- ##\n",
    "\n",
    "## ---------- Iterating over the complete process NUMBER_OF_ITERATION times ------------ ##\n",
    "for p in range(NUMBER_OF_ITERATION):\n",
    "    ##Mprint \"\\nBefore runFFT\\n\"\n",
    "    optListReturn = runMCF(BASE, optList)\n",
    "    ##Mprint \"\\nFinished runFFT\\n\"\n",
    "    ##Mprint\"\\nLoop Num= %d\" %(p)\n",
    "    \n",
    "    ## Create 3 list of pdm, greedy and generic from the returned list\n",
    "    ## Do not use the optReturn[0] to do anyoperation as it currupts the list\n",
    "    ## Always create separate lists fromt the list of list and then operate on it\n",
    "    list1 = optList[0][:]\n",
    "    list2 = optList[1][:]\n",
    "    list3 = optList[2][:]\n",
    "    \n",
    "    ##Mprint \"optList= %s\\n\\nlist1= %s\\n\\nlist2= %s\\n\\nlist3= %s\\n\" \\\n",
    "    ##M%(optList, list1, list2, list3)\n",
    "    ##Mprint \"optList= %s\\nlist1= %s\\nlist2= %s\\nlist3= %s\\n\" \\\n",
    "    ##M%(type(optList), type(list1), type(list2), type(list3))\n",
    "    \n",
    "#     list1 = [x*2 for x in list1]\n",
    "#     optList[0] = []\n",
    "#     print \"\\nlist1= %s\\n \\nTDM_OPT= %s\\n\\noptList= %s\" %(list1, TDM_OPT, optList)\n",
    "    # Creates a list of all state_count*meancycle values for each state\n",
    "    # np.sort sorts the the keys in ascending order, normally dict.keys() iterates randomly over keys\n",
    "    stateDictList = [STATE0, STATE1, STATE2, STATE3, STATE4, STATE5, STATE6]\n",
    "    avgProductList = [stateDict['meanCycle']*STATE_COUNT[keyStateCount] \\\n",
    "                          for keyStateCount, stateDict in zip(np.sort(STATE_COUNT.keys()), stateDictList)]\n",
    "    # reduce uses the lambda function x+y, which iterates one by one over the entire \n",
    "    # stateCountList to give the sum.\n",
    "    stateProductTotal = reduce(lambda x,y: x+y, avgProductList)\n",
    "        \n",
    "    # The type becomes str when we use format\n",
    "    state0_percentage = \"{0:4.4f}\".format(avgProductList[0]*1.0/stateProductTotal)\n",
    "    state1_percentage = \"{0:4.4f}\".format(avgProductList[1]*1.0/stateProductTotal)\n",
    "    state2_percentage = \"{0:4.4f}\".format(avgProductList[2]*1.0/stateProductTotal)\n",
    "    state3_percentage = \"{0:4.4f}\".format(avgProductList[3]*1.0/stateProductTotal)\n",
    "    state4_percentage = \"{0:4.4f}\".format(avgProductList[4]*1.0/stateProductTotal)\n",
    "    state5_percentage = \"{0:4.4f}\".format(avgProductList[5]*1.0/stateProductTotal)\n",
    "    state6_percentage = \"{0:4.4f}\".format(avgProductList[6]*1.0/stateProductTotal)\n",
    "\n",
    "    \n",
    "    if (p==0):\n",
    "        ##Mprint \"loop0\"\n",
    "        TDM_OPT = list1[:]\n",
    "        GREEDY_OPT = list2[:]\n",
    "        GENERIC_OPT = list3[:]\n",
    "        \n",
    "        \n",
    "        f.write(\"State0: \" + str(STATE_COUNT['state0']) + \"(\" + state0_percentage + \")\" + \\\n",
    "                \" , State1: \" + str(STATE_COUNT['state1']) + \"(\" + state1_percentage + \")\" + \\\n",
    "               \" , State2: \" + str(STATE_COUNT['state2']) + \"(\" + state2_percentage + \")\" + \\\n",
    "                \" , State3: \" + str(STATE_COUNT['state3']) + \"(\" + state3_percentage + \")\" + \\\n",
    "                \", State4: \" + str(STATE_COUNT['state4']) + \"(\" + state4_percentage + \")\" + \\\n",
    "                \" , State5: \" + str(STATE_COUNT['state5']) + \"(\" + state5_percentage + \")\" + \\\n",
    "                \", State6: \" + str(STATE_COUNT['state6']) + \"(\" + state6_percentage + \")\" + \"\\n\")\n",
    "        f.write(\"TDM: \" + str(list1[len(list1)-1]) + \" , GREEDY: \" + str(list2[len(list2)-1]) + \\\n",
    "                \" , GENERIC: \" + str(list3[len(list3)-1]) + \"\\n\")\n",
    "        f.write(\"Iteration 1 complete\\n\\n\")\n",
    "    \n",
    "        ##Mprint \"\\nTDM_OPT= %s\\n\\nGREEDY_OPT= %s\\n\\nGENERIC_OPT= %s\" %(TDM_OPT, GREEDY_OPT, GENERIC_OPT)\n",
    "\n",
    "    else:\n",
    "       \n",
    "        ## Adding contents of the each iteration to its coressponding old value\n",
    "        ## This will be divided by the total number of iterations to get an average value\n",
    "        TDM_OPT = map(add, list1, TDM_OPT)\n",
    "        GREEDY_OPT = map(add, list2, GREEDY_OPT)\n",
    "        GENERIC_OPT = map(add, list3, GENERIC_OPT)\n",
    "        \n",
    "        f.write(\"State0: \" + str(STATE_COUNT['state0']) + \"(\" + state0_percentage + \")\" + \\\n",
    "                \" , State1: \" + str(STATE_COUNT['state1']) + \"(\" + state1_percentage + \")\" + \\\n",
    "               \" , State2: \" + str(STATE_COUNT['state2']) + \"(\" + state2_percentage + \")\" + \\\n",
    "                \" , State3: \" + str(STATE_COUNT['state3']) + \"(\" + state3_percentage + \")\" + \\\n",
    "                \", State4: \" + str(STATE_COUNT['state4']) + \"(\" + state4_percentage + \")\" + \\\n",
    "                \" , State5: \" + str(STATE_COUNT['state5']) + \"(\" + state5_percentage + \")\" + \\\n",
    "                \", State6: \" + str(STATE_COUNT['state6']) + \"(\" + state6_percentage + \")\" + \"\\n\")\n",
    "        f.write(\"TDM: \" + str(list1[len(list1)-1]) + \" , GREEDY: \" + str(list2[len(list2)-1]) + \\\n",
    "                \" , GENERIC: \" + str(list3[len(list3)-1]) + \"\\n\")\n",
    "        iterationStr = \"Iteration \" + str(p+1) + \" completed\\n\\n\"\n",
    "        f.write(iterationStr)\n",
    "        \n",
    "        ##Mprint \"\\nTDM_OPT= %s\\n\\nGREEDY_OPT= %s\\n\\nGENERIC_OPT= %s\" %(TDM_OPT, GREEDY_OPT, GENERIC_OPT)\n",
    "\n",
    "    ## Clearing the contents of the lists for next iteration\n",
    "    BASE = []\n",
    "    ## Clearing the STATE_COUNT Variable\n",
    "    STATE_COUNT['state0'] = 0\n",
    "    STATE_COUNT['state1'] = 0\n",
    "    STATE_COUNT['state2'] = 0\n",
    "    STATE_COUNT['state3'] = 0\n",
    "    STATE_COUNT['state4'] = 0\n",
    "    STATE_COUNT['state5'] = 0\n",
    "    STATE_COUNT['state6'] = 0\n",
    "    ## this doe not work when we have to clear contents of a list within a list\n",
    "    #pdmList = []\n",
    "    #greedyList = []\n",
    "    #genericList = []\n",
    "    \n",
    "\n",
    "##Mprint\"optListReturn= %d, %s\" %(len(optListReturn), optListReturn)\n",
    "\n",
    "## Averaging the values of all the iterations\n",
    "TDM_OPT = [x/NUMBER_OF_ITERATION for x in TDM_OPT]\n",
    "GREEDY_OPT = [x/NUMBER_OF_ITERATION for x in GREEDY_OPT]\n",
    "GENERIC_OPT = [x/NUMBER_OF_ITERATION for x in GENERIC_OPT]\n",
    "\n",
    "print \"\\nTOTAL_CYCLES= %d\\nNUMBER_OF_ITERATION= %d\\nTDM_OPT= %s\\nGREEDY_OPT= %s\\nGENERIC_OPT= %s\\n\" \\\n",
    "%(N,NUMBER_OF_ITERATION, TDM_OPT, GREEDY_OPT, GENERIC_OPT)\n",
    "\n",
    "## closing the file handle\n",
    "f.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABnIAAANnCAYAAAAFm8aLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7834b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## taken from http://matplotlib.org/examples/api/barchart_demo.html\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "numBars = 16\n",
    "## making the multiplier bigger reduces the size of bar's\n",
    "index = 1.5*np.arange(numBars) # location of groups\n",
    "width = 0.35 #width of each bar\n",
    "\n",
    "#fig, ax = plt.subplots()\n",
    "#rect1 = ax.bar(index, TDM_OPT, width, color='b')\n",
    "#rect2 = ax.bar(index+width, GREEDY_OPT, width, color='g')\n",
    "#rect3 = ax.bar(index+2*width, GENERIC_OPT, width, color='r')\n",
    "## Adding labels and titles\n",
    "#ax.set_ylabel('Speed Up')\n",
    "#ax.set_title('Speed up for various Methods')\n",
    "#ax.set_xticks(index+1.5*width)\n",
    "#ax.set_xticklabels(('0000', '0001', '0010', '0011',\\\n",
    "#                    '0100', '0101', '0110', '0111',\\\n",
    "#                    '1000', '1001', '1010', '1011',\\\n",
    "#                    '1100', '1101', '1110', '1111'))\n",
    "#ax.legend((rect1[0], rect2[0], rect3[0]), ('TDM', 'GREEDY', 'GENERIC'))\n",
    "\n",
    "fig = plt.figure(figsize=(20,10))\n",
    "rect1 = plt.bar(index, TDM_OPT, width, color='b')\n",
    "rect2 = plt.bar(index+width, GREEDY_OPT, width, color='g')\n",
    "rect3 = plt.bar(index+2*width, GENERIC_OPT, width, color='orange')\n",
    "\n",
    "tickLabel = (('0000', '0001', '0010', '0100', '1000',\\\n",
    "            '0011', '0101', '0110', '1001', '1010', '1100',\\\n",
    "            '0111', '1011', '1101', '1110',\\\n",
    "            '1111'))\n",
    "\n",
    "## Adding labels and titles\n",
    "plt.ylabel('Speed-up', fontsize=20)\n",
    "plt.xlabel('Optimized Components  [iCache, dCache, RUU, IQ]', fontsize=20)\n",
    "\n",
    "##title = 'Speed up of MCF,   Total iterations=' + str(N) + '   simulated for=' + str(NUMBER_OF_ITERATION) + ' times'\n",
    "##plt.title(title)\n",
    "plt.xticks(index+1.5*width, tickLabel, fontsize=18)\n",
    "plt.yticks(fontsize=18)\n",
    "\n",
    "plt.legend((rect1[0], rect2[0], rect3[0]), ('TDM', 'GREEDY', 'GENETIC'), fontsize=16, loc='lower right')\n",
    "plt.grid()\n",
    "\n",
    "plt.show()\n",
    "fig.savefig('gobmk_withStateFunc.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}