evaluation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "import os\n",
    "import pandas as pd\n",
    "import math\n",
    "import numpy as np\n",
    "from pygini import gini\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['figure.figsize']\n",
    "\n",
    "workflowNames = {\n",
    "    \"rnaseq\" : \"RNA-Seq\",\n",
    "    \"sarek\" : \"Sarek\",\n",
    "    \"chipseq\" : \"Chip-Seq\",\n",
    "    \"rangeland\" : \"Rangeland\",\n",
    "    \"Synthetic_Blast_200\" : \"Syn. BLAST\",\n",
    "    \"Synthetic_Bwa_200\" : \"Syn. BWA\",\n",
    "    \"Synthetic_Cycles_200\" : \"Syn. Cycles\",\n",
    "    \"Synthetic_Genome_200\" : \"Syn. Genome\",\n",
    "    \"Synthetic_Montage_200\" : \"Syn. Montage\",\n",
    "    \"Synthetic_Seismology_200\" : \"Syn. Seismology\",\n",
    "    \"Synthetic_Soykb_200\" : \"Syn. Soykb\",\n",
    "    \"allIntoOne\" : \"All in One\",\n",
    "    \"chain\" : \"Chain\",\n",
    "    \"fork\" : \"Fork\",\n",
    "    \"group\" : \"Group\",\n",
    "    \"groupMultiple\" : \"Group Multiple\"\n",
    "}\n",
    "\n",
    "filesystemNames = {\n",
    "    \"nfs\" : \"NFS\",\n",
    "    \"ceph\" : \"Ceph\"\n",
    "}\n",
    "\n",
    "inputDataInKBByWorkflow = {\n",
    "    \"rnaseq\" :                    145902340,\n",
    "    \"sarek\" :                     215862528,\n",
    "    \"chipseq\" :                   148084620,\n",
    "    \"rangeland\" :                 317947164,\n",
    "    \"Synthetic_Blast_200\":        23471389869 / 1024,\n",
    "    \"Synthetic_Bwa_200\":          20884270268 / 1024,\n",
    "    \"Synthetic_Cycles_200\":       21949826112 / 1024,\n",
    "    \"Synthetic_Genome_200\":       23511485272 / 1024,\n",
    "    \"Synthetic_Montage_200\":      21243943207 / 1024,\n",
    "    \"Synthetic_Seismology_200\":   22177568000 / 1024,\n",
    "    \"Synthetic_Soykb_200\":        23913638451 / 1024,\n",
    "    \"allIntoOne\" :                0,\n",
    "    \"chain\" :                     0,\n",
    "    \"fork\" :                      0,\n",
    "    \"group\" :                     0,\n",
    "    \"groupMultiple\" :             0\n",
    "}\n",
    "\n",
    "\n",
    "multirow = {\n",
    "    \"rnaseq\" : \"\\\\multirow{4}{*}{\\\\cellcolor{white}\\\\rotatebox{90}{Real-World}}\",\n",
    "    \"Synthetic_Blast_200\" : \"\\\\multirow{7}{*}{\\\\cellcolor{white}\\\\rotatebox{90}{Synthetic}}\",\n",
    "    \"allIntoOne\" : \"\\\\multirow{5}{*}{\\\\cellcolor{white}\\\\rotatebox{90}{Pattern}}\",\n",
    "}\n",
    "\n",
    "decimalPlaces = \"1\"\n",
    "floatFormatString = \"{:,.\" + decimalPlaces + \"f}\"\n",
    "plotsFolder = \"plots\" + os.sep\n",
    "\n",
    "def formatNumber(value):\n",
    "    if ( value is None ):\n",
    "        return \"N/A\"\n",
    "    return floatFormatString.format(value)\n",
    "\n",
    "original_fontsize = plt.rcParams['font.size']\n",
    "\n",
    "def formatForTable( value ):\n",
    "    if ( value is None ):\n",
    "        return \"N/A\"\n",
    "    if ( type(value) is str ):\n",
    "        return value\n",
    "    if ( type(value) is int ):\n",
    "        return str(value)\n",
    "    if ( type(value) is float or type(value) is np.float64 ):\n",
    "        return \"{:.3f}\".format(value)\n",
    "    print( \"Unknown type: \" + str(type(value)) + \" for value: \" + str(value) )\n",
    "    return str(value)\n",
    "\n",
    "def tableToCSV( table, header, filename ):\n",
    "    csv = []\n",
    "    csv.append( \";\".join(header) )\n",
    "    for row in table:\n",
    "        if ( row in [\"\\\\hline\",\"\\\\hdashline\"]  ):\n",
    "            continue\n",
    "        csv.append( \";\".join([formatForTable( x ) for x in row]) )\n",
    "    with open( filename, \"w\" ) as f:\n",
    "        f.write( \"\\n\".join(csv) )\n",
    "\n",
    "os.makedirs(plotsFolder, exist_ok=True)\n",
    "os.makedirs(\"evaluation\", exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "resultsFolder = \"results\"\n",
    "\n",
    "def readLocalData( folder ):\n",
    "    # read dataOnNode.csv in folder\n",
    "    dataOnNode = pd.read_csv( folder + os.sep + \"dataOnNode.csv\", sep=\";\" )\n",
    "    # convert to dict\n",
    "    d = {}\n",
    "    for index, row in dataOnNode.iterrows():\n",
    "        sizeInKB = int(row[\"dataOnNode\"])\n",
    "        sizeInGB = 0\n",
    "        # 4 byte if empty\n",
    "        if ( sizeInKB > 4 ):\n",
    "            sizeInGB = sizeInKB / 1024 / 1024\n",
    "        d[row[\"node\"]] = sizeInGB\n",
    "\n",
    "    return d\n",
    "\n",
    "def readSharedData( folder ):\n",
    "    # read dataOnNode.csv in folder\n",
    "    dataOnNode = pd.read_csv( folder + os.sep + \"dataOnSharedFS.csv\", sep=\";\", header=None, names=[\"folder\", \"dataInFolder\"] )\n",
    "    # convert to dict\n",
    "    d = {}\n",
    "    for index, row in dataOnNode.iterrows():\n",
    "        sizeInKB = 0 if math.isnan(row[\"dataInFolder\"])  else int(row[\"dataInFolder\"])\n",
    "        sizeInGB = 0\n",
    "        # 4 byte if empty\n",
    "        if ( sizeInKB > 4 ):\n",
    "            sizeInGB = sizeInKB / 1024 / 1024\n",
    "        d[row[\"folder\"]] = sizeInGB\n",
    "    return d\n",
    "\n",
    "def readTrace( folder ):\n",
    "    trace = pd.read_csv( folder + os.sep + \"trace.csv\", sep=\",\" )\n",
    "    d = {}\n",
    "    d[\"numberPhysicalTasks\"] = trace[\"native_id\"].unique().size\n",
    "    d[\"numberAbstractTasks\"] = trace[\"process\"].unique().size\n",
    "    d[\"numberTasksByNode\"] = trace[\"hostname\"].groupby(trace[\"hostname\"]).count().to_dict()\n",
    "    d[\"tasksStatus\"] = trace[\"status\"].groupby(trace[\"status\"]).count().to_dict()\n",
    "\n",
    "    start = trace[\"submit\"].min()\n",
    "    end = trace[\"complete\"].max()\n",
    "    try:\n",
    "        d[\"makespan\"] = int(end) - int(start)\n",
    "    except:\n",
    "        print( \"Error calculating makespan for \" + folder )\n",
    "\n",
    "    d[\"allocCpuHours\"] = ((trace[\"realtime\"] / 3600000) * trace[\"cpus\"]).sum()\n",
    "    d[\"usedCpuHours\"] = ((trace[\"realtime\"] / 3600000) * (trace[\"%cpu\"] / 100)).sum()\n",
    "    d[\"runtimeSumInHours\"] = (trace[\"realtime\"] / 3600000).sum()\n",
    "    d[\"task-node\"] = trace[[\"name\", \"hostname\"]]\n",
    "\n",
    "    return d\n",
    "\n",
    "def readScheduler( folder, strategy ):\n",
    "    if ( strategy != \"la\" ):\n",
    "        return None\n",
    "\n",
    "    maxModelCreated = 0\n",
    "    modelCreated = []\n",
    "    with open( folder + os.sep + \"scheduler.log\" ) as f:\n",
    "        while True:\n",
    "            line = f.readline()\n",
    "            if ( \"Model created in\" in line ):\n",
    "                # example: Model created in 2ms ( 6 vars )\n",
    "                time = int(line.split( \"Model created in \" )[1].split( \"ms\" )[0])\n",
    "                modelCreated.append( time )\n",
    "                if ( time > maxModelCreated ):\n",
    "                    maxModelCreated = time\n",
    "            if ( not line ):\n",
    "                break\n",
    "\n",
    "    return {\n",
    "        \"modelCreatedInMs\" : {\n",
    "            \"values\" : modelCreated,\n",
    "            \"max\" : maxModelCreated,\n",
    "            \"avg\" : np.mean(modelCreated),\n",
    "            \"median\" : np.median(modelCreated),\n",
    "            \"99thPercentile\" : np.percentile(modelCreated, 99),\n",
    "            \"95thPercentile\" : np.percentile(modelCreated, 95),\n",
    "            \"90thPercentile\" : np.percentile(modelCreated, 90),\n",
    "        },\n",
    "    }\n",
    "\n",
    "def flatDataframe( df ):\n",
    "    if len(df) == 0:\n",
    "        return df\n",
    "    result = []\n",
    "    for (name, node), group_df in df.groupby(['task', 'node']):\n",
    "        start_events = group_df[group_df['state'] == 'start'].sort_values(by=['time']).reset_index()\n",
    "        finish_events = group_df[group_df['state'] == 'finished'].sort_values(by=['time']).reset_index()\n",
    "        if ( len(start_events) > len(finish_events) + 1 ):\n",
    "            print( \"Error: start and finish events are not equal for \" + name + \" on \" + node )\n",
    "            continue\n",
    "        lastTime = 0\n",
    "        for index, row in start_events.iterrows():\n",
    "            if ( index >= len(finish_events) ):\n",
    "                result.append({\n",
    "                    'task': name,\n",
    "                    'node': node,\n",
    "                    'filesToCopy': row['filesToCopy'],\n",
    "                    'exitcode': np.nan,\n",
    "                    'starttime': row['time'],\n",
    "                    'endtime': np.nan\n",
    "                })\n",
    "                continue\n",
    "            finish_row = finish_events.iloc[index]\n",
    "            if ( row['time'] < lastTime ):\n",
    "                print( \"Error: start time is before last finish time\" )\n",
    "            lastTime = finish_row['time']\n",
    "            result.append({\n",
    "                'task': name,\n",
    "                'node': node,\n",
    "                'filesToCopy': row['filesToCopy'],\n",
    "                'exitcode': finish_row['exitCode'],\n",
    "                'starttime': row['time'],\n",
    "                'endtime': finish_row['time']\n",
    "            })\n",
    "    result = pd.DataFrame(result)\n",
    "    if ( len(result[result[\"starttime\"] > result[\"endtime\"]])):\n",
    "        print( \"Error: start time is after end time\" )\n",
    "    return result\n",
    "\n",
    "def readCopyTasks( folder, strategy ):\n",
    "    if ( strategy != \"la\" ):\n",
    "        return None\n",
    "    maxSolvedIn = 0\n",
    "    solvedIn = []\n",
    "    maxVars = 0\n",
    "    vars = []\n",
    "    solutionQuality = {}\n",
    "    copytasks = []\n",
    "    with open( folder + os.sep + \"copytasks.csv\" ) as f:\n",
    "        while True:\n",
    "            line = f.readline()\n",
    "            if ( line.startswith(\"Solved in\") ):\n",
    "                # example: Solved in 9ms ( 6 vars ) + solution is: OPTIMAL\n",
    "                time = int(line.split( \"Solved in \" )[1].split( \"ms\" )[0])\n",
    "                solvedIn.append( time )\n",
    "                if ( time > maxSolvedIn ):\n",
    "                    maxSolvedIn = time\n",
    "                quality = line.split( \" vars ) + solution is: \" )[1]\n",
    "                quality = quality.split( \"\\n\" )[0]\n",
    "                if ( quality not in solutionQuality ):\n",
    "                    solutionQuality[quality] = 0\n",
    "                solutionQuality[quality] += 1\n",
    "                var = int(line.split( \"vars\" )[0].split( \"(\" )[1].strip())\n",
    "                vars.append( var )\n",
    "                if ( var > maxVars ):\n",
    "                    maxVars = var\n",
    "            if ( \"\\\"start\\\"\" in line or \"\\\"finished(\" in line ):\n",
    "                task = line.split( \"\\\"\")[1]\n",
    "                node = line.split( \"\\\"\")[3]\n",
    "                filesToCopy = int(line.split( \"\\\"\")[5])\n",
    "                state = line.split( \"\\\"\")[7]\n",
    "                exitCode = -1\n",
    "                if ( state.startswith( \"finished\" ) ):\n",
    "                    exitCode = int(state.split( \"(\" )[1].split( \")\" )[0])\n",
    "                    state = \"finished\"\n",
    "                time = int(line.split( \"\\\"\")[8][1:])\n",
    "                copytasks.append( { \n",
    "                    \"task\" : task, \n",
    "                    \"node\" : node, \n",
    "                    \"filesToCopy\" : filesToCopy, \n",
    "                    \"state\" : state, \n",
    "                    \"exitCode\" : exitCode, \n",
    "                    \"time\" : time } \n",
    "                    )\n",
    "            if ( not line ):\n",
    "                break\n",
    "\n",
    "    allCopyTasks = flatDataframe( pd.DataFrame( copytasks ))\n",
    "    #check duplicates\n",
    "\n",
    "\n",
    "    return {\n",
    "        \"solvedInInMs\" : {\n",
    "            \"values\" : solvedIn,\n",
    "            \"max\" : maxSolvedIn,\n",
    "            \"avg\" : np.mean(solvedIn),\n",
    "            \"median\" : np.median(solvedIn),\n",
    "            \"99thPercentile\" : np.percentile(solvedIn, 99),\n",
    "            \"95thPercentile\" : np.percentile(solvedIn, 95),\n",
    "            \"90thPercentile\" : np.percentile(solvedIn, 90),\n",
    "        },\n",
    "        \"vars\" : {\n",
    "            \"values\" : vars,\n",
    "            \"max\" : maxVars,\n",
    "            \"avg\" : np.mean(vars),\n",
    "            \"median\" : np.median(vars),\n",
    "            \"99thPercentile\" : np.percentile(vars, 99),\n",
    "            \"95thPercentile\" : np.percentile(vars, 95),\n",
    "            \"90thPercentile\" : np.percentile(vars, 90),\n",
    "        },\n",
    "        \"solutionQuality\" : solutionQuality,\n",
    "        \"copytasks\" : allCopyTasks\n",
    "    }\n",
    "\n",
    "def readExecution( folder ):\n",
    "    run = folder.split( os.sep )[-1]\n",
    "    if ( len( run ) > 1 ):\n",
    "        return None\n",
    "    run = int( folder.split( os.sep )[-1] )\n",
    "    strategy = folder.split( os.sep )[-2]\n",
    "    filesystem = strategy.split( \"-\" )[1]\n",
    "    strategy = strategy.split( \"-\" )[0]\n",
    "    workflow = folder.split( os.sep )[-3]\n",
    "    networkspeed = folder.split( os.sep )[-4]\n",
    "    nodes = 8\n",
    "    if ( \"-\" in networkspeed ):\n",
    "        nodes = int( networkspeed.split( \"-\" )[1].split( \"nodes\" )[0] )\n",
    "        networkspeed = networkspeed.split( \"-\" )[0]\n",
    "    networkspeed = int( networkspeed.split( \"gbit\" )[0] )\n",
    "    return { \n",
    "            \"folder\" : folder,\n",
    "            \"run\" : run,\n",
    "            \"strategy\" : strategy,\n",
    "            \"filesystem\" : filesystem,\n",
    "            \"workflow\" : workflow,\n",
    "            \"networkspeed\" : networkspeed,\n",
    "            \"nodes\" : nodes,\n",
    "            \"localData\" : readLocalData( folder ),\n",
    "            \"sharedData\" : readSharedData( folder ),\n",
    "            \"inputDatainGB\" : inputDataInKBByWorkflow[workflow] / 1024 / 1024,\n",
    "            \"trace\" : readTrace( folder ),\n",
    "            \"scheduler\" : readScheduler( folder, strategy ),\n",
    "            \"copytasks\" : readCopyTasks( folder, strategy )\n",
    "         }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "resultsRAW = []\n",
    "for networkspeed in os.listdir(resultsFolder):\n",
    "    # check if workflow is a folder\n",
    "    if ( not os.path.isdir( resultsFolder + os.sep + networkspeed ) ):\n",
    "        continue\n",
    "    for workflow in os.listdir(resultsFolder + os.sep + networkspeed):\n",
    "        if ( workflow == \"ignore\" ):\n",
    "            continue\n",
    "        for strategy in os .listdir(resultsFolder + os.sep + networkspeed + os.sep + workflow):\n",
    "            for run in os.listdir(resultsFolder + os.sep + networkspeed + os.sep + workflow + os.sep + strategy):\n",
    "                folder = resultsFolder + os.sep + networkspeed + os.sep + workflow + os.sep + strategy + os.sep + run\n",
    "                result = readExecution( folder )\n",
    "                if ( result != None ):\n",
    "                    resultsRAW.append( result )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "for r in resultsRAW:\n",
    "    if ( r[\"strategy\"] == \"la\" and r[\"nodes\"] != 1 ):\n",
    "        copytasks = r[\"copytasks\"][\"copytasks\"]\n",
    "        copytasksGrouped = copytasks.groupby([\"task\",\"node\"]).size().reset_index(name='counts').sort_values(by=['counts'], ascending=False)\n",
    "        tasksOnNode = r[\"trace\"]['task-node'].rename(columns={\"name\": \"task\", \"hostname\" : \"node\"}).groupby([\"task\",\"node\"]).size().reset_index(name='counts').sort_values(by=['counts'], ascending=False)\n",
    "        tasksOnNode = tasksOnNode.rename(columns={\"counts\": \"# executed\"})\n",
    "        copytasksGrouped = copytasksGrouped.rename(columns={\"counts\": \"# copies\"})\n",
    "        result_outer = pd.merge(copytasksGrouped, tasksOnNode, how='outer', left_on=['task', 'node'], right_on=['task', 'node']).fillna(0)\n",
    "\n",
    "        # how many tasks where not copied at all\n",
    "        notCopied = 0\n",
    "        # which tasks where copied to the node it was executed (useful COPs)\n",
    "        usefulCopied = 0\n",
    "        # how many COPs that were not used (useless COPs)\n",
    "        uselessCopied = 0\n",
    "\n",
    "        for index, row in result_outer.iterrows():\n",
    "            if row[\"# copies\"] == 0:\n",
    "                notCopied += row[\"# executed\"]\n",
    "            elif row[\"# copies\"] == row[\"# executed\"]:\n",
    "                usefulCopied += row[\"# copies\"]\n",
    "            elif row[\"# copies\"] > row[\"# executed\"]:\n",
    "                usefulCopied += row[\"# executed\"]\n",
    "                uselessCopied += row[\"# copies\"] - row[\"# executed\"]\n",
    "            elif row[\"# copies\"] < row[\"# executed\"]:\n",
    "                usefulCopied += row[\"# copies\"]\n",
    "                notCopied += row[\"# executed\"] - row[\"# copies\"]\n",
    "            else:\n",
    "                print(\"case not handled\", row)\n",
    "\n",
    "        if ( notCopied + usefulCopied != r[\"trace\"]['numberPhysicalTasks'] ):\n",
    "            print(r[\"workflow\"], notCopied, usefulCopied, notCopied +  usefulCopied, r[\"trace\"]['numberPhysicalTasks'])\n",
    "        r[\"copytasks\"][\"notCopied\"] = notCopied\n",
    "        r[\"copytasks\"][\"usefulCopied\"] = usefulCopied\n",
    "        r[\"copytasks\"][\"uselessCopied\"] = uselessCopied"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# check if runs are missing\n",
    "def checkIfThreeRunsExist( networkspeed, workflow, strategy, filesystem, nodes ):\n",
    "    first = False\n",
    "    second = False\n",
    "    third = False\n",
    "    for i in range( len( resultsRAW ) ):\n",
    "        if ( resultsRAW[i][\"networkspeed\"] == networkspeed \n",
    "            and resultsRAW[i][\"workflow\"] == workflow \n",
    "            and resultsRAW[i][\"strategy\"] == strategy \n",
    "            and resultsRAW[i][\"filesystem\"] == filesystem \n",
    "            and resultsRAW[i][\"nodes\"] == nodes\n",
    "            ):\n",
    "            if ( resultsRAW[i][\"run\"] == 1 ):\n",
    "                first = True\n",
    "            elif ( resultsRAW[i][\"run\"] == 2 ):\n",
    "                second = True\n",
    "            elif ( resultsRAW[i][\"run\"] == 3 ):\n",
    "                third = True\n",
    "            else:\n",
    "                print( \"Run \" + str(resultsRAW[i][\"run\"]) + \" found for \" + str(networkspeed) + \"gbit \" + workflow + \" \" + strategy + \" \" + filesystem )\n",
    "    missing = \"\"\n",
    "    if ( not first ):\n",
    "        missing += \"1 \"\n",
    "    if ( not second ):\n",
    "        missing += \"2 \"\n",
    "    if ( not third ):\n",
    "        missing += \"3 \"\n",
    "    if ( missing != \"\" ):\n",
    "        print( filesystem + \" \" + strategy + \" \" + str(networkspeed) + \"gbit \" + workflow + \" missing: \" + missing )\n",
    "    return first and second and third"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking if all runs exist for 1 gbit and 8 nodes\n"
     ]
    }
   ],
   "source": [
    "print ( \"Checking if all runs exist for 1 gbit and 8 nodes\" )\n",
    "for filesystem in [\"ceph\", \"nfs\"]:\n",
    "    for strategy in [\"cws\", \"la\", \"orig\"]:\n",
    "        for workflow in workflowNames:\n",
    "            if ( not checkIfThreeRunsExist( 1, workflow, strategy, filesystem, 8 ) ):\n",
    "                continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking if all runs exist for scalability experiment\n",
      "Nodes: 1 networkspeed: 1gbit\n",
      "Nodes: 2 networkspeed: 1gbit\n",
      "Nodes: 4 networkspeed: 1gbit\n",
      "Nodes: 6 networkspeed: 1gbit\n",
      "Nodes: 8 networkspeed: 1gbit\n",
      "Nodes: 8 networkspeed: 2gbit\n"
     ]
    }
   ],
   "source": [
    "print ( \"Checking if all runs exist for scalability experiment\" )\n",
    "for networkspeed in [1, 2]:\n",
    "    nodes = [8] if networkspeed == 2 else [1,2,4,6,8]\n",
    "    for node in nodes:\n",
    "        print( \"Nodes: \" + str(node) + \" networkspeed: \" + str(networkspeed) + \"gbit\" )\n",
    "        for filesystem in [\"ceph\", \"nfs\"]:\n",
    "            strategies = [\"cws\", \"la\", \"orig\"] if networkspeed == 2 else [\"cws\", \"la\"]\n",
    "            for strategy in strategies:\n",
    "                for workflow in [\"allIntoOne\", \"chain\", \"chipseq\", \"fork\", \"group\", \"groupMultiple\"]:\n",
    "                    if ( not checkIfThreeRunsExist( networkspeed, workflow, strategy, filesystem, node ) ):\n",
    "                        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum time to solve our model\n",
      "Our model was solved in maximum: 2150ms, median: 11.0ms, average: 26.69184790042402ms, 99th percentile: 112.0ms, 95th percentile: 100.0ms, 90th percentile: 83.0ms\n",
      "Solution quality: {'OPTIMAL': 219330}\n"
     ]
    }
   ],
   "source": [
    "print(\"Maximum time to solve our model\")\n",
    "values = []\n",
    "solutionQuality = {}\n",
    "for e in resultsRAW:\n",
    "    if ( e[\"copytasks\"] != None ):\n",
    "        values += e[\"copytasks\"][\"solvedInInMs\"][\"values\"]\n",
    "        for quality in e[\"copytasks\"][\"solutionQuality\"]:\n",
    "            if ( quality not in solutionQuality ):\n",
    "                solutionQuality[quality] = 0\n",
    "            solutionQuality[quality] += e[\"copytasks\"][\"solutionQuality\"][quality]\n",
    "print( \"Our model was solved in maximum: \" + str(max(values)) + \n",
    "      \"ms, median: \" + str(np.median(values)) + \n",
    "      \"ms, average: \" + str(np.mean(values)) + \n",
    "      \"ms, 99th percentile: \" + str(np.percentile(values, 99)) + \n",
    "      \"ms, 95th percentile: \" + str(np.percentile(values, 95)) + \n",
    "      \"ms, 90th percentile: \" + str(np.percentile(values, 90)) + \"ms\" )\n",
    "print( \"Solution quality: \" + str(solutionQuality) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def createTableBody( rows, columns ):\n",
    "    result = []\n",
    "    rowIndex = 1\n",
    "    for r in rows:\n",
    "        if ( r in [\"\\\\hline\",\"\\\\hdashline\"]  ):\n",
    "            result.append( r )\n",
    "            continue\n",
    "        r = [ formatNumber(x) if (type(x) is not str) else x for x in r ]\n",
    "        #finalRow = \"\" if rowIndex % 2 == 0 else \"\\\\rowcolor{lightgray}\"\n",
    "        finalRow = \"\"\n",
    "        stringToJoin = \" & \"\n",
    "        if ( rowIndex % 2 == 0 ):\n",
    "            stringToJoin = \" & \\\\cellcolor{lightgray}\"\n",
    "            if ( r[0] != \"\" ):\n",
    "                finalRow = \"\\\\cellcolor{lightgray}\"\n",
    "        finalRow += stringToJoin.join(np.array(r)[columns]) + \" \\\\\\\\\"#\n",
    "        result.append( finalRow )\n",
    "        rowIndex += 1\n",
    "    return \"\\n\".join(result)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\\begin{table}\n",
      "\\centering\n",
      "\\caption{Evaluation workflows, including real-world workflows, synthetic workflows, and workflow patterns.}\n",
      "\\footnotesize\n",
      "\\begin{tabular}{c|l|r|r|r|r|r}\n",
      "& \\multicolumn{1}{p{1.2cm}|}{\\parbox[c][2.5em]{\\hsize}{\\centering Workflow}} & \\multicolumn{1}{p{0.67cm}|}{\\parbox[c][2.5em]{\\hsize}{\\centering Inputs in GB}} & \\multicolumn{1}{p{1.05cm}|}{\\parbox[c][2.5em]{\\hsize}{\\centering Generated GB}} & \\multicolumn{1}{p{0.4cm}|}{\\parbox[c][2.5em]{\\hsize}{\\centering Fac-tor}} & \\multicolumn{1}{p{0.85cm}|}{\\parbox[c][2.5em]{\\hsize}{\\centering Abstract tasks}} & \\multicolumn{1}{p{0.8cm}}{\\parbox[c][2.5em]{\\hsize}{\\centering Physical tasks}} \\\\\n",
      "\\hline\n",
      "\\multirow{4}{*}{\\cellcolor{white}\\rotatebox{90}{Real-World}} & RNA-Seq & 139.1 & 598.3 & 4.3 & 53 & 1,269 \\\\\n",
      " & \\cellcolor{lightgray}Sarek & \\cellcolor{lightgray}205.9 & \\cellcolor{lightgray}918.8 & \\cellcolor{lightgray}4.5 & \\cellcolor{lightgray}49 & \\cellcolor{lightgray}8,656 \\\\\n",
      " & Chip-Seq & 141.2 & 787.2 & 5.6 & 48 & 3,537 \\\\\n",
      " & \\cellcolor{lightgray}Rangeland & \\cellcolor{lightgray}303.2 & \\cellcolor{lightgray}274.0 & \\cellcolor{lightgray}0.9 & \\cellcolor{lightgray}8 & \\cellcolor{lightgray}3,184 \\\\\n",
      "\\hdashline\n",
      "\\multirow{7}{*}{\\cellcolor{white}\\rotatebox{90}{Synthetic}} & Syn. BLAST & 21.9 & 151.0 & 6.9 & 4 & 198 \\\\\n",
      " & \\cellcolor{lightgray}Syn. BWA & \\cellcolor{lightgray}19.4 & \\cellcolor{lightgray}152.8 & \\cellcolor{lightgray}7.9 & \\cellcolor{lightgray}5 & \\cellcolor{lightgray}198 \\\\\n",
      " & Syn. Cycles & 20.4 & 157.9 & 7.7 & 7 & 198 \\\\\n",
      " & \\cellcolor{lightgray}Syn. Genome & \\cellcolor{lightgray}21.9 & \\cellcolor{lightgray}154.7 & \\cellcolor{lightgray}7.1 & \\cellcolor{lightgray}5 & \\cellcolor{lightgray}198 \\\\\n",
      " & Syn. Montage & 19.8 & 168.8 & 8.5 & 8 & 198 \\\\\n",
      " & \\cellcolor{lightgray}Syn. Seismology & \\cellcolor{lightgray}20.7 & \\cellcolor{lightgray}150.7 & \\cellcolor{lightgray}7.3 & \\cellcolor{lightgray}2 & \\cellcolor{lightgray}198 \\\\\n",
      " & Syn. Soykb & 22.3 & 160.0 & 7.2 & 14 & 196 \\\\\n",
      "\\hdashline\n",
      "\\cellcolor{lightgray}\\multirow{5}{*}{\\cellcolor{white}\\rotatebox{90}{Pattern}} & \\cellcolor{lightgray}All in One & \\cellcolor{lightgray}0.0 & \\cellcolor{lightgray}180.3 & \\cellcolor{lightgray}- & \\cellcolor{lightgray}2 & \\cellcolor{lightgray}101 \\\\\n",
      " & Chain & 0.0 & 180.3 & - & 2 & 200 \\\\\n",
      " & \\cellcolor{lightgray}Fork & \\cellcolor{lightgray}0.0 & \\cellcolor{lightgray}99.4 & \\cellcolor{lightgray}- & \\cellcolor{lightgray}2 & \\cellcolor{lightgray}101 \\\\\n",
      " & Group & 0.0 & 180.3 & - & 2 & 134 \\\\\n",
      " & \\cellcolor{lightgray}Group Multiple & \\cellcolor{lightgray}0.0 & \\cellcolor{lightgray}270.5 & \\cellcolor{lightgray}- & \\cellcolor{lightgray}3 & \\cellcolor{lightgray}160 \\\\\n",
      "\\end{tabular}\n",
      "\\label{tab:workflow_input}\n",
      "\\end{table}\n"
     ]
    }
   ],
   "source": [
    "table = []\n",
    "\n",
    "columns = [\n",
    "    (1.2, \"Workflow\"),\n",
    "    (0.67, \"Inputs in GB\"),\n",
    "    (1.05, \"Generated GB\"),\n",
    "    (0.4, \"Fac-tor\"),\n",
    "    (0.85, \"Abstract tasks\"),\n",
    "    (0.8, \"Physical tasks\")\n",
    "]\n",
    "\n",
    "table.append(\"\\\\begin{table}\")\n",
    "table.append(\"\\\\centering\")\n",
    "table.append(\"\\\\caption{Evaluation workflows, including real-world workflows, synthetic workflows, and workflow patterns.}\")\n",
    "table.append(\"\\\\footnotesize\")\n",
    "table.append(\"\\\\begin{tabular}{c|l|r|r|r|r|r}\")\n",
    "\n",
    "headerString = \"\"\n",
    "for column in columns:\n",
    "    s = \"\\\\multicolumn{1}{p{\" + str(column[0]) + \"cm}|}{\\\\parbox[c][2.5em]{\\\\hsize}{\\\\centering \" + column[1] + \"}} & \"\n",
    "    if ( column[1] == columns[-1][1] ):\n",
    "        s = s.replace( \"cm}|}{\\\\parbox\", \"cm}}{\\\\parbox\" )\n",
    "    headerString += s\n",
    "\n",
    "table.append(\"& \" + headerString[:-2] + \"\\\\\\\\\")\n",
    "table.append(\"\\\\hline\")\n",
    "\n",
    "row = []\n",
    "\n",
    "for workflow in workflowNames:\n",
    "    generated = 1\n",
    "    abstractTasks = []\n",
    "    physicalTasks = []\n",
    "    sharedData = []\n",
    "    for e in resultsRAW:\n",
    "        if ( e[\"workflow\"] == workflow ):\n",
    "            abstractTasks.append( e[\"trace\"][\"numberAbstractTasks\"] )\n",
    "            physicalTasks.append( e[\"trace\"][\"numberPhysicalTasks\"] )\n",
    "            if ( e[\"strategy\"] != \"la\" ):\n",
    "                sharedData.append( e[\"sharedData\"][\"workData\"] )\n",
    "    if ( min(abstractTasks) != max(abstractTasks) ):\n",
    "        print( \"Abstract tasks differ for \" + workflow )\n",
    "    if ( min(physicalTasks) != max(physicalTasks) ):\n",
    "        print( \"Physical tasks differ for \" + workflow )\n",
    "    abstractTasks = max(abstractTasks)\n",
    "    physicalTasks = max(physicalTasks)\n",
    "    minData = min(sharedData)\n",
    "    maxData = max(sharedData)\n",
    "    if ( minData < maxData - 0.4 ):\n",
    "        print( \"\\x1b[31mShared data differs by \" + str(maxData - minData) + \" for \" + workflow + \"(\" + str(sharedData) + \")\\x1b[0m\" )\n",
    "    if ( len(sharedData) > 0 ):\n",
    "        generated = np.median(sharedData)\n",
    "    inputs = inputDataInKBByWorkflow[workflow] / 1024 / 1024\n",
    "\n",
    "    currentRow = [ \n",
    "        multirow[workflow] if workflow in multirow else \"\",\n",
    "        workflowNames[workflow], \n",
    "        inputs, \n",
    "        generated, \n",
    "        generated / inputs if inputDataInKBByWorkflow[workflow] > 0 else \"-\", \n",
    "        \"{:,.0f}\".format(abstractTasks), \n",
    "        \"{:,.0f}\".format(physicalTasks) \n",
    "    ]\n",
    "    if workflow == \"Synthetic_Blast_200\" or workflow == \"allIntoOne\":\n",
    "        row.append(\"\\\\hdashline\")\n",
    "    row.append( currentRow )\n",
    "\n",
    "table.append( createTableBody( row, range(len(row[0])) ) )\n",
    "tableToCSV( row, [x[1].replace(\"-\",\"\") for x in columns], \"evaluation\" + os.sep + \"workflowInputs.csv\" )\n",
    "\n",
    "table.append(\"\\\\end{tabular}\")\n",
    "table.append(\"\\\\label{tab:workflow_input}\")\n",
    "table.append(\"\\\\end{table}\")\n",
    "\n",
    "\n",
    "print( \"\\n\" * 3)\n",
    "print( \"\\n\".join(table) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getResultsForWorkflow( workflow, filesystem, networkspeed, nodes, strategies = [\"orig\", \"cws\", \"la\"] ):\n",
    "    makespan = {}\n",
    "    usedCpuHoursAbsolute = {}\n",
    "    usedCpuHoursRelative = {}\n",
    "    allocCpuHours = {}\n",
    "    notCopied =  []\n",
    "    usefulCopied = []\n",
    "    uselessCopied = []\n",
    "    physicalTasks = 0\n",
    "\n",
    "\n",
    "\n",
    "    for strategy in strategies:\n",
    "        makespan[strategy] = []\n",
    "        usedCpuHoursAbsolute[strategy] = []\n",
    "        usedCpuHoursRelative[strategy] = []\n",
    "        allocCpuHours[strategy] = []\n",
    "\n",
    "    for e in resultsRAW:\n",
    "        if ( e[\"workflow\"] == workflow and e[\"filesystem\"] == filesystem and e[\"nodes\"] == nodes and e[\"networkspeed\"] == networkspeed ):\n",
    "            for strategy in strategies:\n",
    "                if ( e[\"strategy\"] == strategy ):\n",
    "                    makespan[strategy].append( e[\"trace\"][\"makespan\"] )\n",
    "                    usedCpuHoursAbsolute[strategy].append( e[\"trace\"][\"usedCpuHours\"] )\n",
    "                    allocCpuHours[strategy].append( e[\"trace\"][\"allocCpuHours\"] )\n",
    "                    usedCpuHoursRelative[strategy].append( e[\"trace\"][\"usedCpuHours\"] / e[\"trace\"][\"allocCpuHours\"] )\n",
    "                    if ( e[\"strategy\"] == \"la\" and e[\"nodes\"] != 1 ):\n",
    "                        notCopied.append( e[\"copytasks\"][\"notCopied\"] )\n",
    "                        usefulCopied.append( e[\"copytasks\"][\"usefulCopied\"] )\n",
    "                        uselessCopied.append( e[\"copytasks\"][\"uselessCopied\"] )\n",
    "                    physicalTasks = e[\"trace\"][\"numberPhysicalTasks\"]\n",
    "                    \n",
    "    for strategy in strategies:\n",
    "        if ( len(makespan[strategy]) < 3 ):\n",
    "            print( \"\\x1b[31m\" + workflow + \": \" + str(3-len(makespan[strategy])) + \" missing run(s) for \" + strategy + \"-\" + filesystem + \" (\" + str(nodes) + \" nodes)\\x1b[0m\" )\n",
    "\n",
    "    results = {}\n",
    "    for strategy in strategies:\n",
    "        results[ \"makespan-\" + strategy ] = makespan[strategy]\n",
    "        results[ \"usedCpuHoursAbsolute-\" + strategy ] = usedCpuHoursAbsolute[strategy]\n",
    "        results[ \"usedCpuHoursRelative-\" + strategy ] = usedCpuHoursRelative[strategy]\n",
    "        results[ \"allocCpuHours-\" + strategy ] = allocCpuHours[strategy]\n",
    "        if ( strategy == \"la\" ):\n",
    "            results[ \"notCopied\" ] = notCopied\n",
    "            results[ \"usefulCopied\" ] = usefulCopied\n",
    "            results[ \"uselessCopied\" ] = uselessCopied\n",
    "    results[ \"physicalTasks\" ] = physicalTasks\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Runtime for different system\n",
      "Data: 16 rows, 24 columns, format: 24\n",
      "\\begingroup\n",
      "\\setlength{\\tabcolsep}{0.82mm}\n",
      "\\begin{table*}\n",
      "\\centering\n",
      "\\caption{Results for workflow execution using Ceph and NFS with eight nodes and 1 Gbit network speed. The original median makespan is given in minutes, and the median change in makespan for the CWS and our WOW approach is compared to the original in percent. The median allocated CPU hours for the original execution are determined by summing the product of each task's runtime and the number of allocated CPUs over all tasks. As with makespan, we report the relative change for CWS and WOW over the original. Finally, we show the median of how many of the tasks ran without needing any COPs because all required data was local (\"none\") and the median of how many of the COPs transferred data that was used by tasks (\"used\"). A greener background indicates better values and a redder background indicates worse values.}\n",
      "\\footnotesize\n",
      "\\begin{tabular}{c|l|r|r|r||r|r|r||r|r||r|r|r||r|r|r||r|r|r|}\n",
      " & \\multicolumn{1}{c|}{} & \\multicolumn{8}{c||}{Ceph} & \\multicolumn{8}{c|}{NFS}\\\\\n",
      " & \\multicolumn{1}{c|}{Workflow} & \\multicolumn{3}{c||}{Makespan [min]} & \\multicolumn{3}{c||}{CPU allocated [h]} & \\multicolumn{2}{c||}{WOW COPs} & \\multicolumn{3}{c||}{Makespan [min]} & \\multicolumn{3}{c||}{CPU allocated [h]} & \\multicolumn{2}{c|}{WOW COPs}\\\\\n",
      " &  & Orig & CWS & WOW & Orig & CWS & WOW & none & used & Orig & CWS & WOW & Orig & CWS & WOW & none & used\\\\\n",
      "\\hline\n",
      "\\multirow{4}{*}{\\cellcolor{white}\\rotatebox{90}{Real-World}} & RNA-Seq & 181.1 & \\cellcolor[RGB]{236,255,236} -6.1\\% & \\cellcolor[RGB]{230,255,230} -18.3\\% & 474.4 & \\cellcolor[RGB]{255,236,236} 7.9\\% & \\cellcolor[RGB]{233,255,233} -13.6\\% & \\cellcolor[RGB]{194,255,194} 90.1\\% & \\cellcolor[RGB]{225,255,225} 28.8\\% & 413.0 & \\cellcolor[RGB]{238,255,238} -2.6\\% & \\cellcolor[RGB]{213,255,213} -53.2\\% & 1,354.6 & \\cellcolor[RGB]{255,233,233} 12.4\\% & \\cellcolor[RGB]{209,255,209} -60.8\\% & \\cellcolor[RGB]{195,255,195} 89.1\\% & \\cellcolor[RGB]{228,255,228} 22.9\\% \\\\\n",
      " & \\cellcolor{lightgray}Sarek & \\cellcolor{lightgray}305.0 & \\cellcolor{lightgray}\\cellcolor[RGB]{236,255,236} -7.0\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{237,255,237} -4.2\\% & \\cellcolor{lightgray}997.9 & \\cellcolor{lightgray}\\cellcolor[RGB]{255,239,239} 1.2\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{237,255,237} -4.1\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{197,255,197} 86.0\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{219,255,219} 40.4\\% & \\cellcolor{lightgray}557.5 & \\cellcolor{lightgray}\\cellcolor[RGB]{239,255,239} -1.3\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{218,255,218} -42.6\\% & \\cellcolor{lightgray}1,806.1 & \\cellcolor{lightgray}\\cellcolor[RGB]{239,255,239} -0.4\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{219,255,219} -40.4\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{197,255,197} 85.8\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{220,255,220} 39.1\\% \\\\\n",
      " & Chip-Seq & 221.1 & \\cellcolor[RGB]{255,237,237} 4.9\\% & \\cellcolor[RGB]{232,255,232} -15.4\\% & 710.3 & \\cellcolor[RGB]{255,236,236} 7.1\\% & \\cellcolor[RGB]{237,255,237} -6.0\\% & \\cellcolor[RGB]{200,255,200} 79.6\\% & \\cellcolor[RGB]{230,255,230} 18.6\\% & 375.0 & \\cellcolor[RGB]{255,235,235} 9.6\\% & \\cellcolor[RGB]{217,255,217} -44.8\\% & 1,293.8 & \\cellcolor[RGB]{255,232,232} 15.7\\% & \\cellcolor[RGB]{218,255,218} -42.3\\% & \\cellcolor[RGB]{200,255,200} 78.9\\% & \\cellcolor[RGB]{231,255,231} 17.7\\% \\\\\n",
      " & \\cellcolor{lightgray}Rangeland & \\cellcolor{lightgray}166.0 & \\cellcolor{lightgray}\\cellcolor[RGB]{239,255,239} -1.9\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{237,255,237} -4.3\\% & \\cellcolor{lightgray}468.3 & \\cellcolor{lightgray}\\cellcolor[RGB]{255,239,239} 0.1\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{238,255,238} -2.1\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{195,255,195} 88.7\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{209,255,209} 60.7\\% & \\cellcolor{lightgray}181.2 & \\cellcolor{lightgray}\\cellcolor[RGB]{239,255,239} -0.7\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{233,255,233} -13.4\\% & \\cellcolor{lightgray}476.2 & \\cellcolor{lightgray}\\cellcolor[RGB]{255,239,239} 0.7\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{238,255,238} -2.8\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{195,255,195} 88.7\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{207,255,207} 64.3\\% \\\\\n",
      "\\hdashline\n",
      "\\multirow{7}{*}{\\cellcolor{white}\\rotatebox{90}{Synthetic}} & Syn. BLAST & 35.0 & \\cellcolor[RGB]{255,239,239} 0.5\\% & \\cellcolor[RGB]{219,255,219} -41.6\\% & 1.5 & \\cellcolor[RGB]{233,255,233} -12.9\\% & \\cellcolor[RGB]{214,255,214} -51.3\\% & \\cellcolor[RGB]{190,255,190} 99.0\\% & \\cellcolor[RGB]{230,255,230} 18.2\\% & 55.6 & \\cellcolor[RGB]{255,239,239} 0.7\\% & \\cellcolor[RGB]{209,255,209} -60.8\\% & 3.9 & \\cellcolor[RGB]{236,255,236} -6.2\\% & \\cellcolor[RGB]{208,255,208} -62.6\\% & \\cellcolor[RGB]{190,255,190} 99.0\\% & \\cellcolor[RGB]{230,255,230} 18.2\\% \\\\\n",
      " & \\cellcolor{lightgray}Syn. BWA & \\cellcolor{lightgray}37.7 & \\cellcolor{lightgray}\\cellcolor[RGB]{239,255,239} -1.0\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{209,255,209} -61.1\\% & \\cellcolor{lightgray}12.0 & \\cellcolor{lightgray}\\cellcolor[RGB]{239,255,239} -0.6\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{200,255,200} -78.8\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{190,255,190} 100.0\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{255,255,255} 0.0\\% & \\cellcolor{lightgray}81.7 & \\cellcolor{lightgray}\\cellcolor[RGB]{255,239,239} 1.2\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{198,255,198} -82.7\\% & \\cellcolor{lightgray}65.7 & \\cellcolor{lightgray}\\cellcolor[RGB]{239,255,239} -0.9\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{191,255,191} -96.0\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{190,255,190} 100.0\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{255,255,255} 0.0\\% \\\\\n",
      " & Syn. Cycles & 20.0 & \\cellcolor[RGB]{255,238,238} 3.6\\% & \\cellcolor[RGB]{216,255,216} -47.9\\% & 9.2 & \\cellcolor[RGB]{237,255,237} -4.2\\% & \\cellcolor[RGB]{200,255,200} -78.4\\% & \\cellcolor[RGB]{195,255,195} 89.9\\% & \\cellcolor[RGB]{214,255,214} 51.1\\% & 55.6 & \\cellcolor[RGB]{238,255,238} -2.8\\% & \\cellcolor[RGB]{199,255,199} -81.3\\% & 30.9 & \\cellcolor[RGB]{236,255,236} -6.9\\% & \\cellcolor[RGB]{194,255,194} -91.3\\% & \\cellcolor[RGB]{195,255,195} 89.4\\% & \\cellcolor[RGB]{215,255,215} 48.8\\% \\\\\n",
      " & \\cellcolor{lightgray}Syn. Genome & \\cellcolor{lightgray}28.6 & \\cellcolor{lightgray}\\cellcolor[RGB]{237,255,237} -4.7\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{209,255,209} -62.0\\% & \\cellcolor{lightgray}22.8 & \\cellcolor{lightgray}\\cellcolor[RGB]{255,239,239} 0.5\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{195,255,195} -88.7\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{193,255,193} 92.9\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{213,255,213} 53.8\\% & \\cellcolor{lightgray}92.9 & \\cellcolor{lightgray}\\cellcolor[RGB]{255,239,239} 0.7\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{196,255,196} -86.3\\% & \\cellcolor{lightgray}100.2 & \\cellcolor{lightgray}\\cellcolor[RGB]{255,238,238} 3.3\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{192,255,192} -95.0\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{193,255,193} 92.9\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{216,255,216} 47.8\\% \\\\\n",
      " & Syn. Montage & 31.4 & \\cellcolor[RGB]{238,255,238} -2.8\\% & \\cellcolor[RGB]{217,255,217} -44.6\\% & 7.4 & \\cellcolor[RGB]{255,233,233} 13.2\\% & \\cellcolor[RGB]{195,255,195} -88.8\\% & \\cellcolor[RGB]{209,255,209} 61.6\\% & \\cellcolor[RGB]{202,255,202} 74.0\\% & 85.8 & \\cellcolor[RGB]{238,255,238} -2.0\\% & \\cellcolor[RGB]{200,255,200} -78.7\\% & 27.2 & \\cellcolor[RGB]{255,236,236} 6.8\\% & \\cellcolor[RGB]{193,255,193} -93.0\\% & \\cellcolor[RGB]{209,255,209} 61.1\\% & \\cellcolor[RGB]{203,255,203} 72.6\\% \\\\\n",
      " & \\cellcolor{lightgray}Syn. Seismology & \\cellcolor{lightgray}31.4 & \\cellcolor{lightgray}\\cellcolor[RGB]{255,239,239} 0.8\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{229,255,229} -20.9\\% & \\cellcolor{lightgray}1.5 & \\cellcolor{lightgray}\\cellcolor[RGB]{232,255,232} -15.9\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{218,255,218} -42.6\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{190,255,190} 99.5\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{215,255,215} 50.0\\% & \\cellcolor{lightgray}45.5 & \\cellcolor{lightgray}\\cellcolor[RGB]{255,239,239} 0.5\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{216,255,216} -47.4\\% & \\cellcolor{lightgray}2.4 & \\cellcolor{lightgray}\\cellcolor[RGB]{239,255,239} -1.6\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{222,255,222} -35.5\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{190,255,190} 99.5\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{215,255,215} 50.0\\% \\\\\n",
      " & Syn. Soykb & 31.6 & \\cellcolor[RGB]{237,255,237} -4.0\\% & \\cellcolor[RGB]{211,255,211} -56.9\\% & 7.9 & \\cellcolor[RGB]{255,233,233} 13.2\\% & \\cellcolor[RGB]{202,255,202} -74.8\\% & \\cellcolor[RGB]{194,255,194} 91.8\\% & \\cellcolor[RGB]{210,255,210} 58.5\\% & 65.7 & \\cellcolor[RGB]{239,255,239} -1.4\\% & \\cellcolor[RGB]{203,255,203} -72.9\\% & 29.3 & \\cellcolor[RGB]{238,255,238} -3.3\\% & \\cellcolor[RGB]{193,255,193} -92.6\\% & \\cellcolor[RGB]{196,255,196} 87.8\\% & \\cellcolor[RGB]{213,255,213} 53.3\\% \\\\\n",
      "\\hdashline\n",
      "\\cellcolor{lightgray}\\multirow{5}{*}{\\cellcolor{white}\\rotatebox{90}{Pattern}} & \\cellcolor{lightgray}All in One & \\cellcolor{lightgray}32.5 & \\cellcolor{lightgray}\\cellcolor[RGB]{238,255,238} -2.8\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{215,255,215} -49.3\\% & \\cellcolor{lightgray}2.5 & \\cellcolor{lightgray}\\cellcolor[RGB]{237,255,237} -5.2\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{205,255,205} -69.1\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{190,255,190} 99.0\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{215,255,215} 50.0\\% & \\cellcolor{lightgray}40.6 & \\cellcolor{lightgray}\\cellcolor[RGB]{255,239,239} 0.1\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{209,255,209} -60.1\\% & \\cellcolor{lightgray}2.7 & \\cellcolor{lightgray}\\cellcolor[RGB]{255,238,238} 2.5\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{204,255,204} -71.4\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{190,255,190} 99.0\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{215,255,215} 50.0\\% \\\\\n",
      " & Chain & 16.2 & \\cellcolor[RGB]{255,238,238} 2.8\\% & \\cellcolor[RGB]{196,255,196} -86.4\\% & 40.3 & \\cellcolor[RGB]{233,255,233} -13.4\\% & \\cellcolor[RGB]{192,255,192} -96.0\\% & \\cellcolor[RGB]{190,255,190} 98.5\\% & \\cellcolor[RGB]{237,255,237} 5.1\\% & 38.5 & \\cellcolor[RGB]{255,237,237} 5.0\\% & \\cellcolor[RGB]{192,255,192} -94.5\\% & 112.5 & \\cellcolor[RGB]{222,255,222} -35.4\\% & \\cellcolor[RGB]{190,255,190} -98.6\\% & \\cellcolor[RGB]{190,255,190} 99.0\\% & \\cellcolor[RGB]{237,255,237} 5.3\\% \\\\\n",
      " & \\cellcolor{lightgray}Fork & \\cellcolor{lightgray}9.6 & \\cellcolor{lightgray}\\cellcolor[RGB]{230,255,230} -18.5\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{201,255,201} -76.6\\% & \\cellcolor{lightgray}14.7 & \\cellcolor{lightgray}\\cellcolor[RGB]{204,255,204} -70.2\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{195,255,195} -89.6\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{190,255,190} 99.0\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{232,255,232} 14.3\\% & \\cellcolor{lightgray}18.2 & \\cellcolor{lightgray}\\cellcolor[RGB]{239,255,239} -1.6\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{195,255,195} -88.4\\% & \\cellcolor{lightgray}30.2 & \\cellcolor{lightgray}\\cellcolor[RGB]{235,255,235} -8.3\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{192,255,192} -95.0\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{190,255,190} 99.0\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{232,255,232} 14.3\\% \\\\\n",
      " & Group & 14.2 & \\cellcolor[RGB]{238,255,238} -3.9\\% & \\cellcolor[RGB]{200,255,200} -78.3\\% & 14.2 & \\cellcolor[RGB]{255,237,237} 4.6\\% & \\cellcolor[RGB]{191,255,191} -96.8\\% & \\cellcolor[RGB]{201,255,201} 76.1\\% & \\cellcolor[RGB]{196,255,196} 86.8\\% & 34.5 & \\cellcolor[RGB]{238,255,238} -3.3\\% & \\cellcolor[RGB]{194,255,194} -90.4\\% & 51.0 & \\cellcolor[RGB]{238,255,238} -2.8\\% & \\cellcolor[RGB]{190,255,190} -99.1\\% & \\cellcolor[RGB]{202,255,202} 75.4\\% & \\cellcolor[RGB]{199,255,199} 80.5\\% \\\\\n",
      " & \\cellcolor{lightgray}Group Multiple & \\cellcolor{lightgray}21.3 & \\cellcolor{lightgray}\\cellcolor[RGB]{239,255,239} -0.9\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{199,255,199} -80.1\\% & \\cellcolor{lightgray}35.6 & \\cellcolor{lightgray}\\cellcolor[RGB]{237,255,237} -5.6\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{190,255,190} -98.2\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{204,255,204} 71.2\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{193,255,193} 92.6\\% & \\cellcolor{lightgray}49.7 & \\cellcolor{lightgray}\\cellcolor[RGB]{255,239,239} 0.3\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{194,255,194} -90.7\\% & \\cellcolor{lightgray}98.6 & \\cellcolor{lightgray}\\cellcolor[RGB]{255,238,238} 2.8\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{190,255,190} -99.4\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{205,255,205} 68.1\\% & \\cellcolor{lightgray}\\cellcolor[RGB]{196,255,196} 86.2\\% \\\\\n",
      "\\end{tabular}\n",
      "\\label{tab:workflow_results_1gbit}\n",
      "\\end{table*}\n",
      "\\endgroup\n"
     ]
    }
   ],
   "source": [
    "print( \"Runtime for different system\" )\n",
    "\n",
    "\n",
    "def findMaxValues( data ):\n",
    "    maxValues = []\n",
    "    minValues = []\n",
    "    for i in range( len(data[0]) ):\n",
    "        maxValues.append( -10000 )\n",
    "        minValues.append( 10000 )\n",
    "    \n",
    "    for row in data:\n",
    "        index = 0\n",
    "        for r in row:\n",
    "            if ( index != 0 ):\n",
    "                if ( r > maxValues[index] ):\n",
    "                    maxValues[index] = r\n",
    "                if ( r < minValues[index] ):\n",
    "                    minValues[index] = r\n",
    "            index += 1\n",
    "    return maxValues, minValues\n",
    "\n",
    "def colorForValue( value, lowest = \"green\", highest = \"red\"):\n",
    "    if ( lowest == \"red\" ):\n",
    "        value = -value\n",
    "    \n",
    "    value = value * 0.5 + np.sign(value) * 15 # stronger colors for values close to 0\n",
    "    if ( value < 0 ):\n",
    "        r = 255 + value\n",
    "        g = 255\n",
    "        b = 255 + value\n",
    "    else:\n",
    "        r = 255\n",
    "        g = 255 - value\n",
    "        b = 255 - value\n",
    "    return \"\\\\cellcolor[RGB]{\" + str(int(r)) + \",\" + str(int(g)) + \",\" + str(int(b)) + \"}\"\n",
    "\n",
    "def calculateChangeTwoValues( old, new ):\n",
    "    return (np.median(new) - np.median(old)) / np.median(old) * 100\n",
    "\n",
    "def calculateChange( orig, cws, la, divideOrigBy = 1 ):\n",
    "    cwsChange = calculateChangeTwoValues( orig, cws )\n",
    "    laChange = calculateChangeTwoValues( orig, la )\n",
    "    return [ np.median(orig) / divideOrigBy, cwsChange, laChange ]\n",
    "\n",
    "def formatPercent( value ):\n",
    "    return colorForValue(value) + \" \" + formatNumber(value) + \"\\\\%\"\n",
    "\n",
    "def formatPercentOnlyPositive( value ):\n",
    "    return colorForValue(value,\"red\",\"green\") + \" \" + formatNumber(value) + \"\\\\%\"\n",
    "\n",
    "def noFormat( value ):\n",
    "    return value\n",
    "    \n",
    "\n",
    "def genTable( networkspeed, nodes ):\n",
    "\n",
    "    table = []\n",
    "    table.append(\"\\\\begingroup\") \n",
    "    table.append(\"\\\\setlength{\\\\tabcolsep}{0.82mm}\")\n",
    "    table.append(\"\\\\begin{table*}\")\n",
    "    table.append(\"\\\\centering\")\n",
    "    table.append(\"\\\\caption{Results for workflow execution using Ceph and NFS with eight nodes and 1 Gbit network speed. \"\n",
    "    + \"The original median makespan is given in minutes, and the median change in makespan for the CWS and our WOW approach is compared to the original in percent. \"\n",
    "    + \"The median allocated CPU hours for the original execution are determined by summing the product of each task's runtime and the number of allocated CPUs over all tasks. \"\n",
    "    + \"As with makespan, we report the relative change for CWS and WOW over the original. \"\n",
    "    + \"Finally, we show the median of how many of the tasks ran without needing any COPs because all required data was local (\\\"none\\\") and the median of how many of \"\n",
    "    + \"the COPs transferred data that was used by tasks (\\\"used\\\"). A greener background indicates better values and a redder background indicates worse values.}\")\n",
    "    table.append(\"\\\\footnotesize\")\n",
    "\n",
    "    data = []\n",
    "\n",
    "    header = None\n",
    "    columnDef = None\n",
    "    header1 = None\n",
    "    header2 = None\n",
    "    header3 = None\n",
    "    formatMethod = None\n",
    "\n",
    "    for workflow in workflowNames:    \n",
    "        resultsCeph = getResultsForWorkflow( workflow, \"ceph\", networkspeed, nodes )\n",
    "        resultsNFS = getResultsForWorkflow( workflow, \"nfs\", networkspeed, nodes )\n",
    "\n",
    "        notCopiedCeph = np.array(resultsCeph[\"notCopied\"])\n",
    "        usefulCopiedCeph = np.array(resultsCeph[\"usefulCopied\"])\n",
    "        uselessCopiedCeph = np.array(resultsCeph[\"uselessCopied\"])\n",
    "\n",
    "        notCopiedNFS = np.array(resultsNFS[\"notCopied\"])\n",
    "        usefulCopiedNFS = np.array(resultsNFS[\"usefulCopied\"])\n",
    "        uselessCopiedNFS = np.array(resultsNFS[\"uselessCopied\"])\n",
    "\n",
    "        physicalTasks = resultsCeph[\"physicalTasks\"]\n",
    "\n",
    "        header = [ \"workflow\" ]\n",
    "        columnDef = [ \"c\", \"l\" ]\n",
    "        header1 = [ \"\", \"\\\\multicolumn{1}{c|}{}\" ]\n",
    "        header2 = [ \"\", \"\\\\multicolumn{1}{c|}{Workflow}\" ]\n",
    "        header3 = [ \"\", \"\" ]\n",
    "        formatMethod = [ noFormat, noFormat ]\n",
    "        \n",
    "        row = [multirow[workflow] if workflow in multirow else \"\", workflowNames[workflow]]\n",
    "        \n",
    "        header1 = header1 + [ \"\\\\multicolumn{8}{c||}{Ceph}\" ]\n",
    "\n",
    "        header2 = header2 + [ \"\\\\multicolumn{3}{c||}{Makespan [min]}\" ]\n",
    "        header3 = header3 + [ \"Orig\", \"CWS\", \"WOW\" ]\n",
    "        header = header + [ \"Makespan in m (Orig Ceph)\", \"Improvement over Orig (CWS Ceph)\", \"Improvement over Orig (WOW Ceph)\" ]\n",
    "        columnDef = columnDef + [ \"r\", \"r\", \"r|\" ]\n",
    "        formatMethod = formatMethod + [ formatNumber, formatPercent, formatPercent ]\n",
    "        row = row + calculateChange( resultsCeph[\"makespan-orig\"], resultsCeph[\"makespan-cws\"], resultsCeph[\"makespan-la\"], 60000 ) # convert to minutes\n",
    "        \n",
    "        #header2 = header2 + [ \"\\\\multicolumn{3}{c||}{CPU hours used}\" ]\n",
    "        #header3 = header3 + [ \"Orig\", \"CWS\", \"WOW\" ]\n",
    "        header = header + [ \"CPU hours used (Orig Ceph)\", \"Improvement over Orig (CWS Ceph)\", \"Improvement over Orig (WOW Ceph)\" ]\n",
    "        #columnDef = columnDef + [ \"r\", \"r\", \"r|\" ]\n",
    "        formatMethod = formatMethod + [ formatNumber, formatPercent, formatPercent ]\n",
    "        row = row + calculateChange( resultsCeph[\"usedCpuHoursAbsolute-orig\"], resultsCeph[\"usedCpuHoursAbsolute-cws\"], resultsCeph[\"usedCpuHoursAbsolute-la\"] )\n",
    "        \n",
    "        header2 = header2 + [ \"\\\\multicolumn{3}{c||}{CPU allocated [h]}\" ]\n",
    "        header3 = header3 + [ \"Orig\", \"CWS\", \"WOW\" ]\n",
    "        header = header + [ \"CPU hours allocated (Orig Ceph)\", \"Improvement over Orig (CWS Ceph)\", \"Improvement over Orig (WOW Ceph)\" ]\n",
    "        columnDef = columnDef + [ \"r\", \"r\", \"r|\" ]\n",
    "        formatMethod = formatMethod + [ formatNumber, formatPercent, formatPercent ]\n",
    "        row = row + calculateChange( resultsCeph[\"allocCpuHours-orig\"], resultsCeph[\"allocCpuHours-cws\"], resultsCeph[\"allocCpuHours-la\"] )\n",
    "\n",
    "        header2 = header2 + [ \"\\\\multicolumn{2}{c||}{WOW COPs}\" ]\n",
    "        header3 = header3 + [ \"none\", \"used\" ]\n",
    "        header = header + [ \"Not copied (Ceph)\", \"Useless copies (Ceph)\" ]\n",
    "        columnDef = columnDef + [ \"r\", \"r|\" ]\n",
    "        formatMethod = formatMethod + [ formatPercentOnlyPositive, formatPercentOnlyPositive ]\n",
    "        row = row +  [ \n",
    "            np.median(notCopiedCeph) / physicalTasks * 100, \n",
    "            np.median(usefulCopiedNFS / (usefulCopiedCeph + uselessCopiedCeph)) * 100, \n",
    "            ]\n",
    "\n",
    "\n",
    "        \n",
    "        header1 = header1 + [ \"\\\\multicolumn{8}{c|}{NFS}\" ]\n",
    "\n",
    "        header2 = header2 + [ \"\\\\multicolumn{3}{c||}{Makespan [min]}\" ]\n",
    "        header3 = header3 + [ \"Orig\", \"CWS\", \"WOW\" ]\n",
    "        header = header + [ \"Makespan in m (Orig NFS)\", \"Improvement over Orig (CWS NFS)\", \"Improvement over Orig (WOW NFS)\" ]\n",
    "        columnDef = columnDef + [ \"r\", \"r\", \"r|\" ]\n",
    "        formatMethod = formatMethod + [ formatNumber, formatPercent, formatPercent ]\n",
    "        row = row + calculateChange( resultsNFS[\"makespan-orig\"], resultsNFS[\"makespan-cws\"], resultsNFS[\"makespan-la\"], 60000 ) # convert to minutes\n",
    "\n",
    "        #header2 = header2 + [ \"\\\\multicolumn{3}{c||}{CPU hours used}\" ]\n",
    "        #header3 = header3 + [ \"Orig\", \"CWS\", \"WOW\" ]\n",
    "        header = header + [ \"CPU hours used (Orig NFS)\", \"Improvement over Orig (CWS NFS)\", \"Improvement over Orig (WOW NFS)\" ]\n",
    "        #columnDef = columnDef + [ \"r\", \"r\", \"r\" ]\n",
    "        formatMethod = formatMethod + [ formatNumber, formatPercent, formatPercent ]\n",
    "        row = row + calculateChange( resultsNFS[\"usedCpuHoursAbsolute-orig\"], resultsNFS[\"usedCpuHoursAbsolute-cws\"], resultsNFS[\"usedCpuHoursAbsolute-la\"] )\n",
    "\n",
    "        header2 = header2 + [ \"\\\\multicolumn{3}{c||}{CPU allocated [h]}\" ]\n",
    "        header3 = header3 + [ \"Orig\", \"CWS\", \"WOW\" ]\n",
    "        header = header + [ \"CPU hours allocated (Orig NFS)\", \"Improvement over Orig (CWS NFS)\", \"Improvement over Orig (WOW NFS)\" ]\n",
    "        columnDef = columnDef + [ \"r\", \"r\", \"r|\" ]\n",
    "        formatMethod = formatMethod + [ formatNumber, formatPercent, formatPercent ]\n",
    "        row = row + calculateChange( resultsNFS[\"allocCpuHours-orig\"], resultsNFS[\"allocCpuHours-cws\"], resultsNFS[\"allocCpuHours-la\"] )\n",
    "\n",
    "        header2 = header2 + [ \"\\\\multicolumn{2}{c|}{WOW COPs}\" ]\n",
    "        header3 = header3 + [ \"none\", \"used\" ]\n",
    "        header = header + [ \"Not copied (NFS)\", \"Useless copies (NFS)\" ]\n",
    "        columnDef = columnDef + [ \"r\", \"r\", \"r\" ]\n",
    "        formatMethod = formatMethod + [ formatPercentOnlyPositive, formatPercentOnlyPositive ]\n",
    "        row = row +  [ \n",
    "            np.median(notCopiedNFS) / physicalTasks * 100, \n",
    "            np.median(usefulCopiedNFS / (usefulCopiedNFS + uselessCopiedNFS)) * 100 \n",
    "            ]\n",
    "        \n",
    "        data.append( row )\n",
    "\n",
    "        \n",
    "    tableToCSV( data, header, \"evaluation\" + os.sep + \"results1Gbit.csv\" )\n",
    "\n",
    "    table.append( \"\\\\begin{tabular}{\" + \"|\".join(columnDef) + \"|}\" )\n",
    "    table.append( \" & \".join(header1) + \"\\\\\\\\\" )\n",
    "    table.append( \" & \".join(header2) + \"\\\\\\\\\" )\n",
    "    table.append( \" & \".join(header3) + \"\\\\\\\\\" )\n",
    "    table.append(\"\\\\hline\")\n",
    "        \n",
    "    print ( \"Data: \" + str(len(data)) + \" rows, \" + str(len(data[0])) + \" columns, format: \" + str(len(formatMethod) ))\n",
    "\n",
    "    elementsToUse = [0,\n",
    "                        1,2,3, #makespan\n",
    "                        #4,5,6, #cpu hours used\n",
    "                        7,8,9, #cpu hours allocated\n",
    "                        10,11, #copy tasks\n",
    "                        12,13,14, #makespan\n",
    "                        #15,16,17, #cpu hours used\n",
    "                        18,19,20, #cpu hours allocated\n",
    "                        21,22 # copy tasks\n",
    "    ]\n",
    "\n",
    "    elementsToUse = [0] + [ x + 1 for x in elementsToUse ]\n",
    "\n",
    "    formatedData = []\n",
    "    for r in data:\n",
    "        rFormated = [ formatMethod[i](r[i]) for i in range(len(r)) ]\n",
    "        formatedData.append( rFormated )\n",
    "\n",
    "    formatedData.insert( 4, \"\\\\hdashline\" )\n",
    "    formatedData.insert( 12, \"\\\\hdashline\" )\n",
    "\n",
    "    table.append( createTableBody( formatedData, elementsToUse ) )\n",
    "    \n",
    "    table.append(\"\\\\end{tabular}\")\n",
    "    table.append(\"\\\\label{tab:workflow_results_1gbit}\")\n",
    "    table.append(\"\\\\end{table*}\")\n",
    "    table.append(\"\\\\endgroup\") \n",
    "\n",
    "\n",
    "    print( \"\\n\".join(table) )\n",
    "\n",
    "genTable( 1, 8 )\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Runtime change for 2Gbit\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\\begingroup\n",
      "\\setlength{\\tabcolsep}{1.4mm}\n",
      "\\begin{table}\n",
      "\\centering\n",
      "\\caption{Change of the makespan of workflow patterns when the network bandwidth changes from 1Gbit to 2Gbit.}\n",
      "\\footnotesize\n",
      "\\begin{tabular}{l|r|r|r||r|r|r}\n",
      "\\multicolumn{1}{c|}{} & \\multicolumn{3}{c||}{Ceph} & \\multicolumn{3}{c}{NFS} \\\\\n",
      "Workflow & Orig & CWS & WOW & Orig & CWS & WOW \\\\\n",
      "\\hline\n",
      "All in One & \\textcolor{blue}{-46.0\\%} & \\textcolor{blue}{-46.2\\%} & \\textcolor{blue}{-34.1\\%} & -49.5\\% & \\textcolor{blue}{-49.6\\%} & \\textcolor{blue}{-33.1\\%} \\\\\n",
      "\\cellcolor{lightgray}Chain & \\cellcolor{lightgray}-27.5\\% & \\cellcolor{lightgray}-27.4\\% & \\cellcolor{lightgray}-2.0\\% & \\cellcolor{lightgray}\\textcolor{blue}{-50.9\\%} & \\cellcolor{lightgray}-49.4\\% & \\cellcolor{lightgray}\\textbf{\\textcolor{red}{1.1\\%}} \\\\\n",
      "Chip-Seq & \\textbf{\\textcolor{red}{-7.9\\%}} & \\textbf{\\textcolor{red}{-10.5\\%}} & \\textbf{\\textcolor{red}{0.0\\%}} & \\textbf{\\textcolor{red}{-31.5\\%}} & \\textbf{\\textcolor{red}{-34.0\\%}} & -9.6\\% \\\\\n",
      "\\cellcolor{lightgray}Fork & \\cellcolor{lightgray}-27.7\\% & \\cellcolor{lightgray}-28.7\\% & \\cellcolor{lightgray}-22.4\\% & \\cellcolor{lightgray}-47.5\\% & \\cellcolor{lightgray}-46.9\\% & \\cellcolor{lightgray}-16.8\\% \\\\\n",
      "Group & -34.9\\% & -33.5\\% & -23.0\\% & -50.1\\% & -47.1\\% & -28.2\\% \\\\\n",
      "\\cellcolor{lightgray}Group Multiple & \\cellcolor{lightgray}-33.7\\% & \\cellcolor{lightgray}-37.0\\% & \\cellcolor{lightgray}-27.1\\% & \\cellcolor{lightgray}-48.8\\% & \\cellcolor{lightgray}-48.6\\% & \\cellcolor{lightgray}-32.7\\% \\\\\n",
      "\\end{tabular}\n",
      "\\label{tab:workflow_results_2gbit}\n",
      "\\end{table}\n",
      "\\endgroup\n"
     ]
    }
   ],
   "source": [
    "print( \"Runtime change for 2Gbit\" )\n",
    "\n",
    "table = []    \n",
    "table.append(\"\\\\begingroup\") \n",
    "table.append(\"\\\\setlength{\\\\tabcolsep}{1.4mm}\")\n",
    "table.append(\"\\\\begin{table}\")\n",
    "table.append(\"\\\\centering\")\n",
    "table.append(\"\\\\caption{Change of the makespan of workflow patterns when the network bandwidth changes from 1Gbit to 2Gbit.}\")\n",
    "table.append(\"\\\\footnotesize\")\n",
    "table.append(\"\\\\begin{tabular}{l|r|r|r||r|r|r}\")\n",
    "table.append(\"\\\\multicolumn{1}{c|}{} & \\\\multicolumn{3}{c||}{Ceph} & \\\\multicolumn{3}{c}{NFS} \\\\\\\\\")\n",
    "table.append(\"Workflow & Orig & CWS & WOW & Orig & CWS & WOW \\\\\\\\\")\n",
    "table.append(\"\\\\hline\")\n",
    "\n",
    "rows = []\n",
    "\n",
    "for workflow in [\"allIntoOne\", \"chain\", \"chipseq\", \"fork\", \"group\", \"groupMultiple\"]:\n",
    "    resultsCeph1 = getResultsForWorkflow( workflow, \"ceph\", 1, 8 )\n",
    "    resultsNFS1 = getResultsForWorkflow( workflow, \"nfs\", 1, 8 )\n",
    "    resultsCeph2 = getResultsForWorkflow( workflow, \"ceph\", 2, 8 )\n",
    "    resultsNFS2 = getResultsForWorkflow( workflow, \"nfs\", 2, 8 )\n",
    "\n",
    "    changeOrigCeph = calculateChangeTwoValues(resultsCeph1[\"makespan-orig\"], resultsCeph2[\"makespan-orig\"])\n",
    "    changeOrigNFS = calculateChangeTwoValues(resultsNFS1[\"makespan-orig\"], resultsNFS2[\"makespan-orig\"])\n",
    "    changeCwsCeph = calculateChangeTwoValues(resultsCeph1[\"makespan-cws\"], resultsCeph2[\"makespan-cws\"])\n",
    "    changeCwsNFS = calculateChangeTwoValues(resultsNFS1[\"makespan-cws\"], resultsNFS2[\"makespan-cws\"])\n",
    "    changeLaCeph = calculateChangeTwoValues(resultsCeph1[\"makespan-la\"], resultsCeph2[\"makespan-la\"])\n",
    "    changeLaNFS = calculateChangeTwoValues(resultsNFS1[\"makespan-la\"], resultsCeph2[\"makespan-la\"])\n",
    "\n",
    "    changeCPUAllocOrigCeph = calculateChangeTwoValues(resultsCeph1[\"allocCpuHours-orig\"], resultsCeph2[\"allocCpuHours-orig\"])\n",
    "    changeCPUAllocCWSCeph = calculateChangeTwoValues(resultsCeph1[\"allocCpuHours-cws\"], resultsCeph2[\"allocCpuHours-cws\"])\n",
    "    changeCPUAllocLACeph = calculateChangeTwoValues(resultsCeph1[\"allocCpuHours-la\"], resultsCeph2[\"allocCpuHours-la\"])\n",
    "    changeCPUUsedOrigCeph = calculateChangeTwoValues(resultsCeph1[\"usedCpuHoursAbsolute-orig\"], resultsCeph2[\"usedCpuHoursAbsolute-orig\"])\n",
    "    changeCPUUsedCWSCeph = calculateChangeTwoValues(resultsCeph1[\"usedCpuHoursAbsolute-cws\"], resultsCeph2[\"usedCpuHoursAbsolute-cws\"])\n",
    "    changeCPUUsedLACeph = calculateChangeTwoValues(resultsCeph1[\"usedCpuHoursAbsolute-la\"], resultsCeph2[\"usedCpuHoursAbsolute-la\"])\n",
    "\n",
    "    changeCPUAllocOrigNFS = calculateChangeTwoValues(resultsNFS1[\"allocCpuHours-orig\"], resultsNFS2[\"allocCpuHours-orig\"])\n",
    "    changeCPUAllocCWSNFS = calculateChangeTwoValues(resultsNFS1[\"allocCpuHours-cws\"], resultsNFS2[\"allocCpuHours-cws\"])\n",
    "    changeCPUAllocLANFS = calculateChangeTwoValues(resultsNFS1[\"allocCpuHours-la\"], resultsNFS2[\"allocCpuHours-la\"])\n",
    "    changeCPUUsedOrigNFS = calculateChangeTwoValues(resultsNFS1[\"usedCpuHoursAbsolute-orig\"], resultsNFS2[\"usedCpuHoursAbsolute-orig\"])\n",
    "    changeCPUUsedCWSNFS = calculateChangeTwoValues(resultsNFS1[\"usedCpuHoursAbsolute-cws\"], resultsNFS2[\"usedCpuHoursAbsolute-cws\"])\n",
    "    changeCPUUsedLANFS = calculateChangeTwoValues(resultsNFS1[\"usedCpuHoursAbsolute-la\"], resultsNFS2[\"usedCpuHoursAbsolute-la\"])\n",
    "\n",
    "    rows.append( [ \n",
    "                    workflowNames[workflow],\n",
    "                    changeOrigCeph, \n",
    "                    changeCwsCeph, \n",
    "                    changeLaCeph, \n",
    "                    changeOrigNFS, \n",
    "                    changeCwsNFS, \n",
    "                    changeLaNFS,\n",
    "                    changeCPUAllocOrigCeph,\n",
    "                    changeCPUAllocCWSCeph,\n",
    "                    changeCPUAllocLACeph,\n",
    "                    changeCPUUsedOrigCeph,\n",
    "                    changeCPUUsedCWSCeph,\n",
    "                    changeCPUUsedLACeph,\n",
    "                    changeCPUAllocOrigNFS,\n",
    "                    changeCPUAllocCWSNFS,\n",
    "                    changeCPUAllocLANFS,\n",
    "                    changeCPUUsedOrigNFS,\n",
    "                    changeCPUUsedCWSNFS,\n",
    "                    changeCPUUsedLANFS\n",
    "                  ] )\n",
    "header = [ \"Workflow\",   \n",
    "        \"Change Orig (Ceph)\",\n",
    "        \"Change CWS (Ceph)\",\n",
    "        \"Change WOW (Ceph)\",\n",
    "        \"Change Orig (NFS)\",\n",
    "        \"Change CWS (NFS)\",\n",
    "        \"Change WOW (NFS)\",\n",
    "        \"Change CPU Alloc Orig (Ceph)\",\n",
    "        \"Change CPU Alloc CWS (Ceph)\",\n",
    "        \"Change CPU Alloc WOW (Ceph)\",\n",
    "        \"Change CPU Used Orig (Ceph)\",\n",
    "        \"Change CPU Used CWS (Ceph)\",\n",
    "        \"Change CPU Used WOW (Ceph)\",\n",
    "        \"Change CPU Alloc Orig (NFS)\",\n",
    "        \"Change CPU Alloc CWS (NFS)\",\n",
    "        \"Change CPU Alloc WOW (NFS)\",\n",
    "        \"Change CPU Used Orig (NFS)\",\n",
    "        \"Change CPU Used CWS (NFS)\",\n",
    "        \"Change CPU Used WOW (NFS)\",\n",
    "        ]  \n",
    "#tableToCSV( rows, header, \"evaluation\" + os.sep + \"change2Gbit.csv\" )\n",
    "change2Gbit = pd.DataFrame(rows, columns=header)    \n",
    "change2Gbit = change2Gbit.set_index(\"Workflow\")\n",
    "change2Gbit.to_csv(\"evaluation\" + os.sep + \"change2Gbit.csv\")\n",
    "\n",
    "maxValues, minValues = findMaxValues( rows )\n",
    "\n",
    "for row in rows:\n",
    "    rowString = []\n",
    "    for r in row:\n",
    "        index = row.index(r)\n",
    "        if ( index == 0 ):\n",
    "            rnew = r\n",
    "        else:\n",
    "            rnew = formatNumber(r)\n",
    "            rnew = rnew + \"\\%\"\n",
    "            if r == maxValues[index]:\n",
    "                rnew = \"\\\\textbf{\\\\textcolor{red}{\" + rnew + \"}}\"\n",
    "            if r == minValues[index]:\n",
    "                rnew = \"\\\\textcolor{blue}{\" + rnew + \"}\"\n",
    "        rowString.append( rnew )\n",
    "    rows[rows.index(row)] = rowString\n",
    "\n",
    "table.append( createTableBody( rows, range(7) ) )\n",
    "    \n",
    "table.append(\"\\\\end{tabular}\")\n",
    "table.append(\"\\\\label{tab:workflow_results_2gbit}\")\n",
    "table.append(\"\\\\end{table}\")\n",
    "table.append(\"\\\\endgroup\") \n",
    "\n",
    "print( \"\\n\" * 3 )\n",
    "print( \"\\n\".join(table) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>Change Orig (Ceph)</th>\n",
       "      <th>Change CWS (Ceph)</th>\n",
       "      <th>Change WOW (Ceph)</th>\n",
       "      <th>Change Orig (NFS)</th>\n",
       "      <th>Change CWS (NFS)</th>\n",
       "      <th>Change WOW (NFS)</th>\n",
       "      <th>Change CPU Alloc Orig (Ceph)</th>\n",
       "      <th>Change CPU Alloc CWS (Ceph)</th>\n",
       "      <th>Change CPU Alloc WOW (Ceph)</th>\n",
       "      <th>Change CPU Used Orig (Ceph)</th>\n",
       "      <th>Change CPU Used CWS (Ceph)</th>\n",
       "      <th>Change CPU Used WOW (Ceph)</th>\n",
       "      <th>Change CPU Alloc Orig (NFS)</th>\n",
       "      <th>Change CPU Alloc CWS (NFS)</th>\n",
       "      <th>Change CPU Alloc WOW (NFS)</th>\n",
       "      <th>Change CPU Used Orig (NFS)</th>\n",
       "      <th>Change CPU Used CWS (NFS)</th>\n",
       "      <th>Change CPU Used WOW (NFS)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Workflow</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>All in One</th>\n",
       "      <td>-45.994015</td>\n",
       "      <td>-46.150231</td>\n",
       "      <td>-34.108140</td>\n",
       "      <td>-49.532869</td>\n",
       "      <td>-49.589797</td>\n",
       "      <td>-33.083295</td>\n",
       "      <td>-45.151478</td>\n",
       "      <td>-42.141006</td>\n",
       "      <td>9.509717</td>\n",
       "      <td>-6.639365</td>\n",
       "      <td>-0.370105</td>\n",
       "      <td>-21.306496</td>\n",
       "      <td>-44.422976</td>\n",
       "      <td>-45.988177</td>\n",
       "      <td>7.964893</td>\n",
       "      <td>-6.620868</td>\n",
       "      <td>-10.761952</td>\n",
       "      <td>-21.828916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chain</th>\n",
       "      <td>-27.495809</td>\n",
       "      <td>-27.417907</td>\n",
       "      <td>-2.036254</td>\n",
       "      <td>-50.881758</td>\n",
       "      <td>-49.351145</td>\n",
       "      <td>1.097225</td>\n",
       "      <td>-26.838515</td>\n",
       "      <td>-31.451102</td>\n",
       "      <td>0.870476</td>\n",
       "      <td>-3.445002</td>\n",
       "      <td>-0.309473</td>\n",
       "      <td>1.995695</td>\n",
       "      <td>-53.872885</td>\n",
       "      <td>-58.686980</td>\n",
       "      <td>0.211641</td>\n",
       "      <td>-7.284066</td>\n",
       "      <td>-7.592748</td>\n",
       "      <td>0.324722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chip-Seq</th>\n",
       "      <td>-7.877546</td>\n",
       "      <td>-10.519077</td>\n",
       "      <td>0.030660</td>\n",
       "      <td>-31.517182</td>\n",
       "      <td>-34.007687</td>\n",
       "      <td>-9.644314</td>\n",
       "      <td>-5.497423</td>\n",
       "      <td>-6.948105</td>\n",
       "      <td>-0.184691</td>\n",
       "      <td>0.517691</td>\n",
       "      <td>0.058241</td>\n",
       "      <td>0.016036</td>\n",
       "      <td>-32.511010</td>\n",
       "      <td>-33.881769</td>\n",
       "      <td>-8.266094</td>\n",
       "      <td>2.816353</td>\n",
       "      <td>0.045433</td>\n",
       "      <td>-0.380219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fork</th>\n",
       "      <td>-27.659810</td>\n",
       "      <td>-28.744560</td>\n",
       "      <td>-22.366385</td>\n",
       "      <td>-47.459424</td>\n",
       "      <td>-46.926742</td>\n",
       "      <td>-16.807396</td>\n",
       "      <td>-15.274960</td>\n",
       "      <td>3.634135</td>\n",
       "      <td>-1.232546</td>\n",
       "      <td>-5.394009</td>\n",
       "      <td>1.147733</td>\n",
       "      <td>0.949965</td>\n",
       "      <td>-56.571981</td>\n",
       "      <td>-58.442963</td>\n",
       "      <td>0.566870</td>\n",
       "      <td>-1.716158</td>\n",
       "      <td>-0.443730</td>\n",
       "      <td>0.657227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Group</th>\n",
       "      <td>-34.871406</td>\n",
       "      <td>-33.504095</td>\n",
       "      <td>-23.030089</td>\n",
       "      <td>-50.052310</td>\n",
       "      <td>-47.144757</td>\n",
       "      <td>-28.234045</td>\n",
       "      <td>-28.320407</td>\n",
       "      <td>-27.871157</td>\n",
       "      <td>2.960227</td>\n",
       "      <td>1.169369</td>\n",
       "      <td>1.965725</td>\n",
       "      <td>4.566066</td>\n",
       "      <td>-51.716714</td>\n",
       "      <td>-51.214443</td>\n",
       "      <td>1.140091</td>\n",
       "      <td>-0.159523</td>\n",
       "      <td>-0.350972</td>\n",
       "      <td>1.489585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Group Multiple</th>\n",
       "      <td>-33.684473</td>\n",
       "      <td>-36.975008</td>\n",
       "      <td>-27.148326</td>\n",
       "      <td>-48.795273</td>\n",
       "      <td>-48.603707</td>\n",
       "      <td>-32.692315</td>\n",
       "      <td>-30.506489</td>\n",
       "      <td>-29.287778</td>\n",
       "      <td>4.022206</td>\n",
       "      <td>0.568960</td>\n",
       "      <td>2.975602</td>\n",
       "      <td>2.966921</td>\n",
       "      <td>-49.644016</td>\n",
       "      <td>-51.384371</td>\n",
       "      <td>1.904770</td>\n",
       "      <td>-0.448704</td>\n",
       "      <td>-0.740117</td>\n",
       "      <td>2.111090</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Change Orig (Ceph)  Change CWS (Ceph)  Change WOW (Ceph)  \\\n",
       "Workflow                                                                   \n",
       "All in One              -45.994015         -46.150231         -34.108140   \n",
       "Chain                   -27.495809         -27.417907          -2.036254   \n",
       "Chip-Seq                 -7.877546         -10.519077           0.030660   \n",
       "Fork                    -27.659810         -28.744560         -22.366385   \n",
       "Group                   -34.871406         -33.504095         -23.030089   \n",
       "Group Multiple          -33.684473         -36.975008         -27.148326   \n",
       "\n",
       "                Change Orig (NFS)  Change CWS (NFS)  Change WOW (NFS)  \\\n",
       "Workflow                                                                \n",
       "All in One             -49.532869        -49.589797        -33.083295   \n",
       "Chain                  -50.881758        -49.351145          1.097225   \n",
       "Chip-Seq               -31.517182        -34.007687         -9.644314   \n",
       "Fork                   -47.459424        -46.926742        -16.807396   \n",
       "Group                  -50.052310        -47.144757        -28.234045   \n",
       "Group Multiple         -48.795273        -48.603707        -32.692315   \n",
       "\n",
       "                Change CPU Alloc Orig (Ceph)  Change CPU Alloc CWS (Ceph)  \\\n",
       "Workflow                                                                    \n",
       "All in One                        -45.151478                   -42.141006   \n",
       "Chain                             -26.838515                   -31.451102   \n",
       "Chip-Seq                           -5.497423                    -6.948105   \n",
       "Fork                              -15.274960                     3.634135   \n",
       "Group                             -28.320407                   -27.871157   \n",
       "Group Multiple                    -30.506489                   -29.287778   \n",
       "\n",
       "                Change CPU Alloc WOW (Ceph)  Change CPU Used Orig (Ceph)  \\\n",
       "Workflow                                                                   \n",
       "All in One                         9.509717                    -6.639365   \n",
       "Chain                              0.870476                    -3.445002   \n",
       "Chip-Seq                          -0.184691                     0.517691   \n",
       "Fork                              -1.232546                    -5.394009   \n",
       "Group                              2.960227                     1.169369   \n",
       "Group Multiple                     4.022206                     0.568960   \n",
       "\n",
       "                Change CPU Used CWS (Ceph)  Change CPU Used WOW (Ceph)  \\\n",
       "Workflow                                                                 \n",
       "All in One                       -0.370105                  -21.306496   \n",
       "Chain                            -0.309473                    1.995695   \n",
       "Chip-Seq                          0.058241                    0.016036   \n",
       "Fork                              1.147733                    0.949965   \n",
       "Group                             1.965725                    4.566066   \n",
       "Group Multiple                    2.975602                    2.966921   \n",
       "\n",
       "                Change CPU Alloc Orig (NFS)  Change CPU Alloc CWS (NFS)  \\\n",
       "Workflow                                                                  \n",
       "All in One                       -44.422976                  -45.988177   \n",
       "Chain                            -53.872885                  -58.686980   \n",
       "Chip-Seq                         -32.511010                  -33.881769   \n",
       "Fork                             -56.571981                  -58.442963   \n",
       "Group                            -51.716714                  -51.214443   \n",
       "Group Multiple                   -49.644016                  -51.384371   \n",
       "\n",
       "                Change CPU Alloc WOW (NFS)  Change CPU Used Orig (NFS)  \\\n",
       "Workflow                                                                 \n",
       "All in One                        7.964893                   -6.620868   \n",
       "Chain                             0.211641                   -7.284066   \n",
       "Chip-Seq                         -8.266094                    2.816353   \n",
       "Fork                              0.566870                   -1.716158   \n",
       "Group                             1.140091                   -0.159523   \n",
       "Group Multiple                    1.904770                   -0.448704   \n",
       "\n",
       "                Change CPU Used CWS (NFS)  Change CPU Used WOW (NFS)  \n",
       "Workflow                                                              \n",
       "All in One                     -10.761952                 -21.828916  \n",
       "Chain                           -7.592748                   0.324722  \n",
       "Chip-Seq                         0.045433                  -0.380219  \n",
       "Fork                            -0.443730                   0.657227  \n",
       "Group                           -0.350972                   1.489585  \n",
       "Group Multiple                  -0.740117                   2.111090  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "change2Gbit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>Orig Ceph</th>\n",
       "      <th>CWS Ceph</th>\n",
       "      <th>WOW Ceph</th>\n",
       "      <th>Orig NFS</th>\n",
       "      <th>CWS NFS</th>\n",
       "      <th>WOW NFS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Workflow</th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>All in One</th>\n",
       "      <td>-0.842537</td>\n",
       "      <td>-4.009225</td>\n",
       "      <td>-43.617857</td>\n",
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       "      <td>-3.601619</td>\n",
       "      <td>-41.048188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chain</th>\n",
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       "      <td>-2.906730</td>\n",
       "      <td>2.991127</td>\n",
       "      <td>9.335835</td>\n",
       "      <td>0.885584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chip-Seq</th>\n",
       "      <td>-2.380122</td>\n",
       "      <td>-3.570972</td>\n",
       "      <td>0.215351</td>\n",
       "      <td>0.993828</td>\n",
       "      <td>-0.125918</td>\n",
       "      <td>-1.378220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fork</th>\n",
       "      <td>-12.384850</td>\n",
       "      <td>-32.378695</td>\n",
       "      <td>-21.133838</td>\n",
       "      <td>9.112557</td>\n",
       "      <td>11.516221</td>\n",
       "      <td>-17.374266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Group</th>\n",
       "      <td>-6.550999</td>\n",
       "      <td>-5.632938</td>\n",
       "      <td>-25.990316</td>\n",
       "      <td>1.664405</td>\n",
       "      <td>4.069685</td>\n",
       "      <td>-29.374136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Group Multiple</th>\n",
       "      <td>-3.177984</td>\n",
       "      <td>-7.687231</td>\n",
       "      <td>-31.170532</td>\n",
       "      <td>0.848743</td>\n",
       "      <td>2.780664</td>\n",
       "      <td>-34.597085</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Orig Ceph   CWS Ceph   WOW Ceph  Orig NFS    CWS NFS  \\\n",
       "Workflow                                                               \n",
       "All in One      -0.842537  -4.009225 -43.617857 -5.109893  -3.601619   \n",
       "Chain           -0.657294   4.033195  -2.906730  2.991127   9.335835   \n",
       "Chip-Seq        -2.380122  -3.570972   0.215351  0.993828  -0.125918   \n",
       "Fork           -12.384850 -32.378695 -21.133838  9.112557  11.516221   \n",
       "Group           -6.550999  -5.632938 -25.990316  1.664405   4.069685   \n",
       "Group Multiple  -3.177984  -7.687231 -31.170532  0.848743   2.780664   \n",
       "\n",
       "                  WOW NFS  \n",
       "Workflow                   \n",
       "All in One     -41.048188  \n",
       "Chain            0.885584  \n",
       "Chip-Seq        -1.378220  \n",
       "Fork           -17.374266  \n",
       "Group          -29.374136  \n",
       "Group Multiple -34.597085  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diffAllocatedChange2Gbit = pd.DataFrame({\n",
    "    'Orig Ceph': change2Gbit['Change Orig (Ceph)'] - change2Gbit['Change CPU Alloc Orig (Ceph)'],\n",
    "    'CWS Ceph': change2Gbit['Change CWS (Ceph)'] - change2Gbit['Change CPU Alloc CWS (Ceph)'],\n",
    "    'WOW Ceph': change2Gbit['Change WOW (Ceph)'] - change2Gbit['Change CPU Alloc WOW (Ceph)'],\n",
    "    'Orig NFS': change2Gbit['Change Orig (NFS)'] - change2Gbit['Change CPU Alloc Orig (NFS)'],\n",
    "    'CWS NFS': change2Gbit['Change CWS (NFS)'] - change2Gbit['Change CPU Alloc CWS (NFS)'],\n",
    "    'WOW NFS': change2Gbit['Change WOW (NFS)'] - change2Gbit['Change CPU Alloc WOW (NFS)'],\n",
    "    }, index=change2Gbit.index)\n",
    "diffAllocatedChange2Gbit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>Orig Ceph</th>\n",
       "      <th>CWS Ceph</th>\n",
       "      <th>WOW Ceph</th>\n",
       "      <th>Orig NFS</th>\n",
       "      <th>CWS NFS</th>\n",
       "      <th>WOW NFS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Workflow</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>All in One</th>\n",
       "      <td>-39.354650</td>\n",
       "      <td>-45.780125</td>\n",
       "      <td>-12.801644</td>\n",
       "      <td>-42.912001</td>\n",
       "      <td>-38.827845</td>\n",
       "      <td>-11.254379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chain</th>\n",
       "      <td>-24.050807</td>\n",
       "      <td>-27.108434</td>\n",
       "      <td>-4.031949</td>\n",
       "      <td>-43.597692</td>\n",
       "      <td>-41.758396</td>\n",
       "      <td>0.772503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chip-Seq</th>\n",
       "      <td>-8.395237</td>\n",
       "      <td>-10.577319</td>\n",
       "      <td>0.014624</td>\n",
       "      <td>-34.333535</td>\n",
       "      <td>-34.053120</td>\n",
       "      <td>-9.264095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fork</th>\n",
       "      <td>-22.265801</td>\n",
       "      <td>-29.892294</td>\n",
       "      <td>-23.316350</td>\n",
       "      <td>-45.743266</td>\n",
       "      <td>-46.483012</td>\n",
       "      <td>-17.464623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Group</th>\n",
       "      <td>-36.040775</td>\n",
       "      <td>-35.469820</td>\n",
       "      <td>-27.596155</td>\n",
       "      <td>-49.892787</td>\n",
       "      <td>-46.793785</td>\n",
       "      <td>-29.723630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Group Multiple</th>\n",
       "      <td>-34.253433</td>\n",
       "      <td>-39.950611</td>\n",
       "      <td>-30.115246</td>\n",
       "      <td>-48.346569</td>\n",
       "      <td>-47.863590</td>\n",
       "      <td>-34.803405</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Orig Ceph   CWS Ceph   WOW Ceph   Orig NFS    CWS NFS  \\\n",
       "Workflow                                                                \n",
       "All in One     -39.354650 -45.780125 -12.801644 -42.912001 -38.827845   \n",
       "Chain          -24.050807 -27.108434  -4.031949 -43.597692 -41.758396   \n",
       "Chip-Seq        -8.395237 -10.577319   0.014624 -34.333535 -34.053120   \n",
       "Fork           -22.265801 -29.892294 -23.316350 -45.743266 -46.483012   \n",
       "Group          -36.040775 -35.469820 -27.596155 -49.892787 -46.793785   \n",
       "Group Multiple -34.253433 -39.950611 -30.115246 -48.346569 -47.863590   \n",
       "\n",
       "                  WOW NFS  \n",
       "Workflow                   \n",
       "All in One     -11.254379  \n",
       "Chain            0.772503  \n",
       "Chip-Seq        -9.264095  \n",
       "Fork           -17.464623  \n",
       "Group          -29.723630  \n",
       "Group Multiple -34.803405  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diffUsedChange2Gbit = pd.DataFrame({\n",
    "    'Orig Ceph': change2Gbit['Change Orig (Ceph)'] - change2Gbit['Change CPU Used Orig (Ceph)'],\n",
    "    'CWS Ceph': change2Gbit['Change CWS (Ceph)'] - change2Gbit['Change CPU Used CWS (Ceph)'],\n",
    "    'WOW Ceph': change2Gbit['Change WOW (Ceph)'] - change2Gbit['Change CPU Used WOW (Ceph)'],\n",
    "    'Orig NFS': change2Gbit['Change Orig (NFS)'] - change2Gbit['Change CPU Used Orig (NFS)'],\n",
    "    'CWS NFS': change2Gbit['Change CWS (NFS)'] - change2Gbit['Change CPU Used CWS (NFS)'],\n",
    "    'WOW NFS': change2Gbit['Change WOW (NFS)'] - change2Gbit['Change CPU Used WOW (NFS)'],\n",
    "    }, index=change2Gbit.index)\n",
    "diffUsedChange2Gbit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Runtime depending on the \n",
      "[<matplotlib.lines.Line2D object at 0x000001F55689C2E0>, <matplotlib.lines.Line2D object at 0x000001F55687F760>, <matplotlib.lines.Line2D object at 0x000001F55687F9A0>, <matplotlib.collections.PathCollection object at 0x000001F55A11AF40>, <matplotlib.lines.Line2D object at 0x000001F559D2E9A0>, <matplotlib.collections.PathCollection object at 0x000001F55BAAB9D0>, <matplotlib.lines.Line2D object at 0x000001F55BAABD30>]\n",
      "[Text(0, 0, 'WOW efficiency'), Text(0, 0, 'CWS efficiency'), Text(0, 0, 'Ideal efficiency'), Text(0, 0, 'WOW makespan'), Text(0, 0, 'Median WOW'), Text(0, 0, 'CWS makespan'), Text(0, 0, 'Median CWS')]\n"
     ]
    },
    {
     "data": {
      "image/png": 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Gq1atsilv2bIl5cuXt95/8MEH8fb2TtcsrtjWp3379qXYiglmV9Mff/xhc5s0aZL1+OOPP862bdvo2LEj+/bt4+OPP6Z169aUKlWK5cuXp3huBwcHHn30UWuX66FDh7h27Rpvv/02hmGwbds2wGyNql69+n2N4cuI1y0lsb8HSY2JKleuHEWKFLHeEr4uTk5O9O/f33rfxcWF/v37c/nyZXbt2nVP8axbt46IiAieeOIJa1n+/Pn5+++/OX/+/D2dU/IWddvdp88//5xKlSrh5OREsWLFqFy5svXNNzAwEMMwqFixYpKPTdj9UqpUqTQPCj99+jQODg6JZosVL16c/Pnzc/r0aZvysmXLJnuuhN1JsV0Ofn5+icpjYmIIDg62NuVv2bKFkSNHsm3bNm7dumVTPzg42HqupJ4HoECBAjZjZI4fP84zzzyTbKxgvq6HDh2y6XaI714G4sa+XvG7M8H8Q12uXLlEr2dav1fe3t6AOZ4iPRK+VrGJVMLxRElJ6uetUqVK3Lp1iytXrlCwYEGuX79uc7xIkSLWsSatW7emdevW3Lp1i127dvHDDz8wc+ZM2rdvz+HDhylatCiBgYEEBwcnO24n9nsQ+7oljKlIkSI2yWFyTp8+TcmSJRMlnw888IDN+WOl5WcsLV544QXGjh3LmDFj6NSpU7L1HB0dadmyZYrnqlevHr/88guRkZHs27ePJUuWMGXKFJ599ln27t2b4lT5xo0bM2rUKG7fvs2mTZsoUaIEDz30EDVr1mTTpk08/vjjbN68mc6dO6fr+hLKqNctObHfv7CwsETHli1bxt27d9m3bx9vvPFGouMlS5bEw8PDpqxSpUqAOdbpkUceSXc8v/32G3Xr1rXpbv/444/p1asXfn5+1KlTh7Zt29KzZ0/KlSuX7vNL7qfk6T7Vr1/fOtsuoZiYGCwWC6tWrUpyEGTCT2H3MvstrYskpnTu5KYFJ1du/P9A2uPHj9OiRQuqVKnC5MmT8fPzw8XFhZUrVzJlyhRiYmLSdb60iomJoUaNGkyePDnJ4wmTvsyQ1u9VhQoVcHJyYv/+/ek6f0a9VkmJnT4f38mTJylTpoxNWb58+WjcuDGNGzemcOHCjB49mlWrVtGrVy9iYmIoWrQo3377bZLPkVxim9ky6nWLbX3q3bs3y5Yty4jQcHFxoV69etSrV49KlSrRp08ffvzxR+sEhqQ0atSIu3fvsm3bNjZt2mRtgWzcuDGbNm3i8OHDXLlyxaZF915k5s8bxP0eJDWrtGnTpgAZPnM0JStXrkzUcty5c2caN27MkiVLWLNmDZ988gkTJkzgl19+sWmhEgElT5mqfPnyGIZB2bJlrZ+UMoq/vz8xMTEEBgZaP4UDXLp0iZs3b+Lv75+hz5eUFStWEBERwfLly20+ud5Lt1ms8uXLpzptv3z58uzbt48WLVpk2Arbsa/XkSNHbD5pRkZGcvLkyVRbF5KTL18+HnvsMdatW0dQUFCWJHaBgYGJyo4ePUq+fPkoUqQIrq6u/PHHHzbHixcvnuI5Yz8gxK7jU758edauXUvDhg1TTCRjX9fAwECb1/XKlStpatXw9/dn7dq1hIaG2rQ+HT582Ob8maF79+6MGzeO0aNH07Fjxww9d8LXMzn169fHxcWFTZs2sWnTJuusuSZNmvDVV1/x559/Wu+nxN4r0Xt4eNCsWTM2bNjAuXPnKFWqVJofe/78ecLDw21an44ePQqQKOGPL7lrPnDgAGfOnKFdu3aJjpUoUYJBgwYxaNAgLl++zEMPPcSHH36o5EkS0ZinTPT000/j6OjI6NGjE32CMwyDa9eu3fO527ZtC5izx+KLbY1J6g9DRov9tBr/2oKDg5k7d+49n/OZZ56xdm0kFPs8nTt35ty5c3z11VeJ6ty+fZvw8PB0P2/Lli1xcXHh008/tbmer7/+muDg4Pt6PUeOHIlhGPTo0SPJbotdu3Yxf/78ez5/Qtu2bbOZXh0UFMSyZcto1aoVjo6OFChQgJYtW9rcYtfIiX0zTih2jF1st2bnzp2Jjo5m7NixiepGRUVZp9e3bNkSZ2dnPvvsM5vXNeHPbXLatm1LdHQ006dPtymfMmUKFoslU9/UYluf9u7dm+r4pOT89ddfSbbeJHw9k+Pm5ka9evX47rvvOHPmjE3L0+3bt/n0008pX748JUqUSPE8sYlHZqwwnlYffPAB0dHRdO/ePcnfg+RauaKiovjyyy+t9yMjI/nyyy8pUqSIzdi+hJK75pUrV1pnMseKjo5ONEazaNGilCxZMtXlTyRvUstTJipfvjzjxo1jxIgRnDp1ik6dOuHl5cXJkydZsmQJL7/8cpJ9/GlRs2ZNevXqxaxZs7h58yZNmzZlx44dzJ8/n06dOiXqlskMrVq1wsXFhQ4dOtC/f3/CwsL46quvKFq0aKqfqJPz5ptv8tNPP/Hcc8/x4osvUqdOHa5fv87y5cuZOXMmNWvWpEePHixevJgBAwbw119/0bBhQ6Kjozl8+DCLFy+2rmeVHkWKFGHEiBGMHj2aNm3a0LFjR44cOcIXX3xBvXr1UlwIMTWPPvoon3/+OYMGDaJKlSo2K4yvX7+e5cuXM27cuHs+f0LVq1endevWNksVgLnOTmqefPJJypYtS4cOHShfvjzh4eGsXbuWFStWUK9ePetqzE2bNqV///4EBASwd+9eWrVqhbOzM4GBgfz4449MmzaNZ599liJFivDGG28QEBBA+/btadu2LXv27GHVqlXWKfMp6dChA82bN+fdd9/l1KlT1KxZkzVr1rBs2TIGDx5sM8g5M8SOfdq7d+89Pf61117j1q1bPPXUU1SpUoXIyEi2bt3KDz/8QJkyZdI06aBx48Z89NFH+Pj4UKNGDcB8Y69cuTJHjhxJ01pZsUnGu+++S5cuXXB2dqZDhw6JxhLdixUrVrBv3z4A7t69y7///mv9ee7YsaN1KZDGjRszffp0XnvtNSpWrGhdYTwyMpKjR4/y7bff4uLikqgVtGTJkkyYMIFTp05RqVIlfvjhB/bu3cusWbNSXLYj9ppff/11WrdujaOjI126dOG3337jiSeesGmZCg0NxdfXl2effZaaNWvi6enJ2rVr2blzp80EABGrLJ7dl2vELlWwc+fOVOv+/PPPRqNGjQwPDw/Dw8PDqFKlivHKK68YR44csdZp2rSpUa1atSQfn9RSBYZhGHfv3jVGjx5tlC1b1nB2djb8/PyMESNGGHfu3LGp5+/vb7Rr1y7R42OXKvjxxx/TdG1JTddevny58eCDDxpubm5GmTJljAkTJhhz5sxJNC06uRiaNm1qM7XbMMypy6+++qpRqlQpw8XFxfD19TV69eplXL161VonMjLSmDBhglGtWjXD1dXVKFCggFGnTh1j9OjRRnBwcOIXMZXriDV9+nSjSpUqhrOzs1GsWDFj4MCBNlPAY2NO7nuVkl27dhndunUzSpYsaTg7OxsFChQwWrRoYcyfP9+6FED8KfMJkWD6eXJLFbzyyivGN998Y1SsWNFwdXU1ateubfz1119pivG7774zunTpYpQvX95wd3c33NzcjKpVqxrvvvuuERISkqj+rFmzjDp16hju7u6Gl5eXUaNGDWP48OHG+fPnrXWio6ON0aNHGyVKlDDc3d2NZs2aGQcOHDD8/f1TXarAMAwjNDTUGDJkiPV1q1ixovHJJ5/YLH0Q/9oTSsvzpPS6x/4+JPyZSe73Mr5Vq1YZL774olGlShXD09PTcHFxMSpUqGC89tprxqVLl1J8bKzffvvNABItHdK3b18DML7++utEj0n4s2IYhjF27FijVKlShoODg83v5/28boaR/JINJFg6INaePXuMnj17GqVLlzZcXFwMDw8P48EHHzSGDRtms1yCYcT9rv3zzz9GgwYNDDc3N8Pf39+YPn26Tb2kliqIiooyXnvtNaNIkSKGxWIxAOPmzZuGk5OTsXjxYpvHR0REGG+++aZRs2ZNw8vLy/Dw8DBq1qxpfPHFF6lev+RNFsPIoBGBImJ3FouFV155JVE3V3ZUpkwZmjVrZl0VWiSzLV68mBdeeIGrV6/azAQWSS+NeRIRkTwhf/78fPrpp0qc5L5pzJOIiOQJrVq1sncIkkuo5UlEREQkHTTmSURERCQd1PIkIiIikg5KnkRERETSIUcNGI+KimLPnj0UK1bMuvmuiIhIbhcTE8OlS5eoXbt2lu4DKEnLUd+BPXv2UL9+fXuHISIiYhc7duygXr169g4jz8tRyVOxYsUA84cntb2cREREcosLFy5Qv3596/ug2FeOSp5iu+pKlCiBr6+vnaMRERHJWhqykj3ouyAiIiKSDkqeRERERNJByZOIiIhIOih5EhEREUkHJU8iIiIi6aDkSURERCQdlDyJiIiIpIOSJxEREZF0UPIkIiIikg5KnkRERHKhb/79hv4r+lN3Vl1cx7liGW1h3t55ydYPiQhh6Oqh+E/1x3WcK2WmluHNNW8SFhmWZP0YI4bP/v6MGjNq4P6hO0U+KULXn7ty4saJRHWjY6IZv2k85aaVw+cjHx5f+Dj/XfkvyfPO2jWLfB/m4/j14/d03VlByZOIiEgu9N6695i1exang09TwjPl/WDDI8NpOq8pU7ZPoUrhKgx5ZAiVC1dm4raJPDb/Me5E3Un0mP4r+vP6769jGAav13+dNhXa8MuhX6j3VT0CrwXa1J2yfQrvrnuXB4o8wIu1XmTvxb20WtgqUWJ2IfQCw/8YzqhmoyhfsPz9vwiZJE8nT9euwbx55v8iIiK5yeyOszn1v1NcefMKA+oOSLHux1s+Zu/FvbzV8C1Wd1/NRy0/YnX31bzV8C12nt/JlG1TbOr/dfIvZu+ZTRP/Juzuv5sJj09g4VMLWfr8Uq7fvs6rq161qf/V7q9oWa4lv3X7jSltpvBL5184F3qOX4/+alPvlZWvUK5AOYY2GJoxL0ImydbJU0REBCEhIdZbaGhoxpw4JgYMg2vXYP78/0+eDMMsFxERyQValmuJf37/VOsZhsHsPbPxdPHk/Sbv2xx7v8n7eLp4MnvPbJvyr3Z/BcDY5mNxcXSxlj9R8QmalWnGmuNrOBN8xloeFBxEnRJ1rPfrlqwLYFPnl0O/sPzIcmZ3nI2Tg1M6rjTrZevkKSAgAB8fH+utatWqGXPi+fNh1CgICTHvh4SY9xcsyJjzi4iI5BCB1wM5H3qehn4N8XDxsDnm4eJBQ7+GnLhxgqDgIGv5+lPr8XA2jyXUunxrADac2mAt8/PxY8/FPdb7uy/sBqC0T2kAgu8E8+rKVxnyyBAeKvFQxl1cJsnWqd2IESMYOjSu6e7cuXMZk0CVLQvLlnFrz7uEevZh5JhAajvHUKWcIyXP7cYhYUoZFgZr10KjhlC4SPLnvXvX/N/Z+f5jFBGRHK9K4Srkc86XYecLDQ0lJPaDP+Dq6oqrq+t9nTN2fFLFghWTPF6xYEVWH19N4PVA/Hz8CI8M50LYBaoXrY6jg2OS9cFMymL1rd2X4WuH035ReyoVqsQ3/35DSa+StK/UHoA3/3gTd2d3xjQfc1/XklWydfKU8Ici/g/MfWnWjBv+tQh4cwO7atRhF/A9wClgdgqP+yVjnl5ERPKGXS/vytCWlIQNCCNHjmTUqFH3dc7giGAAfNx8kjzu7ept1rsTbFvfNW31AYY2GEpEdARf7/maTWc2Ua9kPT594lM8XTzZcGoDs3fPZk2PNTg7OjNs9TDm7p3Lrbu3aFOhDbM6zKKoR9H7usaMlq2Tp8yyfj1MmZKfaJrz8j8zOZKvNobFiebNoW1bcLrXV+XCBTgbBPXqp1xvwQJwc4POnZOvEx0NAwdCz57QqFHy9YKC4K918OxzkC/Bp5vly+H8eejWzbwfGgorVkDJktCxY9quSURE7lmVwlUy9Hz//fcfpUqVst6/31anrOLo4Mh7Td7jvSbv2ZTfibrDy7++TM+aPWlZriUBmwL4fOfnTGk9BV9vX15d9Sq9l/Zm5Qsr7RR50vJk8nTyJNSqBYM7XKHAm99zY2RLpq4oTxlXqO+X8mM//xyKFoXnnkviYAkgLR8w3kpDpehoeOczKFcOihVLvt5lRzgVCcVqgaen7bHKIbBqKuydAh4ecOkSeHlB826Q3Ceh5cvN56xePQ0XIiIiWcnLywtvb+8MPWdsC1L8lqL4QiLMXp/Ylilr/Yi01U/JmA1juHnnJpNbTwZg2t/T6FmzJwPrDbSeq/uS7hy5eoTKhSun9ZIyXbYeMJ5ZevWC0aOhgHc0YP4/erTZyJMaFxfzlukcHaFBg5QTJ4CaNWHGjMSJE0CzZjB3Lvj5ma1iZcqY95s1S/58P/0E+/en/Jxnz5qtYqdPp1wvNBSuX0+5joiI2FXFQonHKMUXWx47lsnDxYMSniU4eeMk0THRqdZPzr+X/uWTrZ8wrc00CroXJPhOMJfCL1GreC1rndolagNw+Orh9F1UJsuTyZN1QHihQmYmVaiQbXkK+vWDJ59Muc7167BrF0RE3F+cGaJAAXj9dfPrt94y76dkwQLo2jXlOo6OZutUwm7ChH75BV5+OfUY//jDfMFERCTLVSxYkZJeJdkStIXwyHCbY+GR4WwJ2kLZ/GXx84nrmmlapinhd81jCa0+vhqAJv5Nkn3O6Jho+i7vS+vyrelSvYvNsYioiERfWyyW9F9YJsqTyZNVoULQu7c1ecoo27bBG29Ahw4wZAgsWZKhp7e/EiXgzTehSAozDwFatoR33039fL/+Clu3plwnLAwGDUq9VezWLTN7NYzUn1dERLBYLPSt3ZewyDDGbhxrc2zsxrGERYbR76F+NuUvP2R+MH7/r/eJjI60lq8KXMX6U+tpVb5VimtMTft7GoeuHmJGuxnWMh83H0p4lmDlsbjxTSsDza8fKPzAvV9gJsiTY54yW9u2ULUq7N4Ne/bAmTOpPyZXKlXKvKVm2rTU60RHm92OHh4p11u3DiZPNpeWSOmTypYtZlNjgwapP7eISA40e/dsNp/ZDMD+y/utZetPrQegUelG9H2oLwDDGw5n2ZFlTNgygT0X9/BQ8YfYfXE3a46voV7Jegx+ZLDNuZuXbU7f2n2ZvWc2D335EO0qtuNC2AV+OPgDBd0L8tkTnyUb16mbp/jgrw8IaBFg05oF8L+H/8fbf75Nu0Xt8PXyZc7eObSv1N7atZhdWAwj53xEP3v2LH5+fgQFBeHr62vvcDJMRIQ5JMnfP+X3+3t27Zo5y65DhwxvZct2Ll+GEyfgkUdSrjdypPlipzTF1zBg8GB4/nl49NHk60VEmC1ePj5p6/sVEUmne3n/6720N/P3zU/2eK+avZjXaZ71fvCdYEatH8XPh37mYthFSniV4LmqzzGy6Ui8XL0SPT7GiGH6junM2jWLY9eP4eniSctyLfnwsQ9T3Jeu9TetCYkIYcuLW3Cw2P7NjI6JZsSfI5i7dy7hkeE8UfEJvmz/JYXzFU7TNWcVJU/ZwM6dMHy4uc+ef+or6UtGMYyUs9WoKJg0Cdq0MQfmJ2fbNnjnHXOwfUrJ6e7d5iD6pk3vPeaYGDPm+HEbhnlT4iaSa+XW97+cSn9ts4EaNWDiRChd2t6R5DGpNfM5OZmD7FNKnAAqV4axY82Wp5Rs2GAmWKn54ANYvTrpY7FbC129CnfuwI0b2lpIRCSLacxTNuDmBnXqpF5vwgSzbu3a5jpVGbzUh9yrggVTXsg01pAhaRvIXrBg8mO7/n9rIXr3hvBws56TEzRvnq6QRUTk3qnlKQdxdze7+EaOhE6dYO/etD3u2jWzS/DatUwMTtImLYPaBg9OPhmLv3YXmM2VKa3d9fvvMGfOPQQqIiLJUctTDhK7XNOlS+YsvkqV0va4a9fM3p5HH83948XzhAIFzFas/v3NRCultbtCQtK2UOnevWYiVrBgRkUpIpJrKXnKgYoVM8cwp2bxYnPh8QoVMj8myaZS2j8xVnS0OWNh4EB46qnMj0lEJIdTt10udvasuQRC7DCbnDOvUrKUgwMsXAiPPZZyve3bzT2MQkOzJi4RkWxKyVMuNnQoPP00fPb/a5V99pk5OUtygQRbC90Xi8VszkxttmDhwub6WUntoxjfunWwefP9xyUikk0pecrF1q83J2WdPWuOJz571ry/fr1dw5KMkElbC6WoQgVzi5zUBr1v2mSu4J4Sw4ArV9QcKiI5ksY85WInT5pLGsSOKb5xA6ZOhVOn4upcvWo2KIhkmJEjU0+Kbtwwx2ONHZu2ZR5ERLIRJU+5WK9etotOFygAo0ebi1QD7NgB771nDmPp0sVcLkgkQ6TWOpUvH3z4ITyQymafGzaYXYDvvJNJexeJiKSfuu1yseR264gtr1kTnn3WXCZowAA4ejTrYpM8zs3NXDsjpWUWIOntaJLyzz95eAduEclqSp7yMFdXePllmDHDfG86ccLeEYkk0Ly52eqUmokTk9/SJpbGV4lIBlFHjVCpkplAOTraOxKRezRnjrleVUqOHjX3Kpw2TTtwi8h9UcuTAOZ4Jw0pkRwrXz7w8kq5Tv785todqc2Q2LjR3D9QRCQZSp5EJG8oVsycHZHcpsuxjhyBXbtSP9+ZMxARkTGxiUiOouRJ0uTIEbh5095RiGSBfv1gzJiU60RHw0svwcqV9/dcMTGJx2IZRtyUWBHJlpQ8SaoMwxyPG7tSuYgAkyenvkbVrl0wbBjcupX08fnzYdSouKX/b9ww7y9YkJGRikgG04BxSZXFAhMmpD4eVyTPcHSEGjVSr+fsbI61cndP+njZsub4qu7doXJlOH7c/IVr3jxDwxWRjKXkSdKkYEF7RyCSAz34oHlLTrNm5oJr3bvDnj1QrZq56npq61+JiF2p205ExJ4KFIBJk8yvX389+cQpJAQWLjT3VBIRu8qzydPly3ELF1++bO9ocr47d9StJ3LPktsOIL4LF+DHH81fNhGxqzybPIWHJ/213JsZM2DgQDh2zN6RiORSlSub46NKlUq53m+/wdKlWRKSSF6V55Kny5fNW/yW76tX48rl3rRpA1FR0L8/zJ4NkZH2jkgkBylUyNzJu1ChlOulZZ+/M2e015JIJrMYRs7Z8Ons2bP4+fkRFBSEr6/vPZ0jtb87OefVyH7u3oVFi+Dnn2HWLChe3N4RiUiSwsNh3DhzraoKFewdjaRBRrz/ScbRbDvJMM7O5ofn554zd8sQkWwqNNRciNPZ2d6RiORISp4kwylxEsnmihc3F29LzaJFEBRkbqgsIlZ5bsxTWJh5O3AgrszHB65dM8tFROT/FS4MJUvaOwqRbCfPJU8eHuYtfutIu3bmWKfU9guVjHH0qMaWieQIrVpBjx4p14mIgGeegW3bsiYmkWwgzyVPsYoWjft61qzUJ7lIxjh50pyRp7+zIrnE3bvQsWPqSyhERupTk+QaeTZ58vAwf4/V4pS1ypaFTz6BRx6xdyQikiE8Pc2ZIqVLp1xv0SKznkguoAHjkuXq1rV3BCKS5Ro0SL11CuDKFXOsVWrryojYUZ5teUpo82Z47TV7RyEikktVrgyPP55ynagoeOEFrZAu2Z6Sp/93/Trs2mXuvSn2FRVl7whExG7GjTNbqVJy7Zq5hILGUImdKHn6fx06wNat4O1t70jytogI6NsX5s3TFi8ieY6TE9Svn/r2BL//DgMGKHkSu1Hy9P/UvZ49WCzQtCl88405K++//+wdkYhkOx07wqRJ4JDKW9iOHXDpUtbEJHmKkifJVlxcoE8f+PJLcHWFmTP14VJEEvDygipVUq4TEwOjR8O6dVkTk+Qpmm2XQGAg/P03dO9u70jytvLl4fPPzTFoahUUkXRzcIDvv0+93tmzcPAgNG9ufnoTSQO1PCWwYoW50fj16/aORBwdoUABe0chIjmWl5d5S8m+fTB1qvkHRySNlDwl8MILEB2dtg8sIiKSw7VrB0uWpJ48LVliTskWIZsnTxEREYSEhFhvoaGhmf6cxYpBmzYwf36mP5Xcp4gItRCKSAZwc0u9zp9/pm0GS0xM4oGahmGWS66RrZOngIAAfHx8rLeqVatmyfMOGgSNG2u9oezuu++gXz8ziRIRyVTTp6c+GPbkSRg4EN59F27cMMtu3IBRo2DBgkwPUbKOxTCy71ymiIgIIuK9M547d46qVasSFBSEr6+vHSOT7CA42Bzn+eij9o5ERASzW+/jj81PdIYBtWvDnj3mrJfBg6FZs3s+9dmzZ/Hz89P7XzaRrWfbubq64urqar0fouW/JR4fHyVOIpKN1KkDP/xgtja9/z5s2ADVqsHYsZr9kstk6247ERGRHKdAAXj9dfPr119X4pQLKXlKwbJlEBBg7yjkfmTfTmkREcmplDyl4MgR2L5db8A51ZEj5hYvhw/bOxIREclNlDyl4M03zdYnrXCdM8UuFvzKKzBjBty5Y994RCQPKVQIevUy/5dcJ+8lT8HB5nL8STl71jz+/5Q05Wxly5pJU9++sHQp/PqrvSMSkTyjUCHo3VvJUy6VrWfbZbjgYHMFzMuXYf168POLOxYUZE4jLVoUfv/dnMolOZ6jI3TtCk2amN9aERGR+5W3Wp5CQ83E6cQJM1EKCjLLYxOnEyfM4wlWMt+7F1atyupgJSOVKgXOzvaOQkREcoO8lTz5+potTuXKxSVQW7fGJU7lypnHEyxANn26uWisVtcXERGRvJU8gdlVFz+BatjQNnGK35X3/3r1gtOnYePGLI9WskhwsGZViohI2uS95Ck42BwJvnChbfnChWZ5vAHjsRo1MnMrbRacO0VHwxtvwBdf2DsSERHJCfLmgPHz5xMf69rV/L9kyUQDxi0WGDYMtDtM7uToCC++CIUL2zsSERHJCfJW8hQaai5HELtUQenS8N13ZuJ05oxZFhNj1ksw227QoCyOVbJUgwb2jkBERHKKvNVtFxJizqZLyeXLamISERGRZOWt5AniVr50cjJbmxo2NP93crI9LiIikoMZhsEvh36h+fzmlJhUgnwf5qPy9Mr0X9GfEzdOJKofEhHC0NVD8Z/qj+s4V8pMLcOba94kLDIsUd0bt2/QZ1kfik0sRvGJxXlx2YvcuH0jyTi6/dyN6l9U52703Qy/RnvJW8lT1aqwa5fZXRcVZXssKsos37XLrJeMr7/WwPG8aNEimDULIiLsHYmISNq8seYNnln8DEeuHqFT5U68Vv81yuYvy1e7v6LWzFocuHzAWjc8Mpym85oyZfsUqhSuwpBHhlC5cGUmbpvIY/Mf406U7f5WPZb04Nt/v6VDpQ60q9iOhf8upNfSXoliWBm4kh8O/sDsjrNxdsw9i+3lrTFPANWqweLF5hS6+AmUk5NZXq1aig/fuhU8PMzlCyTvcHCAn36CTZvMmXk1a9o7IhGR5F0Mu8jUv6fi7+PPvgH78HGLG8c7ZdsUhq4ZyuRtk5nz5BwAPt7yMXsv7uWthm/xUcuPrHXfXvs2E7ZMYMq2KYxoPAKAC6EX+C3wN8Y1H8e7Td4FoEz+Mnyw/gMuhl2kuGdxAMIiwxj420BeqfcKj/g+klWXniXyVssTmKuJd+uWdMtTt25xq44nY/Zs+PTTTIxPsqUuXeCrr8x5BMOGpT50TkTEnk7dPEWMEUPD0g1tEieA9pXaA3Dl1hXA7N6bvWc2ni6evN/kfZu67zd5H08XT2bvmW0tCwox3yfrlKxjLatbsi4AZ4LPWMve+fMdDMNgfIvxGXhl2UPeSp7Ono1bTdwpQaObk1PcquPJbRyMhkTlZf7+ZuI8ZYr2yROR7K1iwYq4OLqw5cwWQiJsJ0H9etTcJb1F2RYABF4P5HzoeRr6NcTDxcOmroeLBw39GnLixgmCgs2kyc/bXEx6z4U91nq7L+wGoLRPaQC2n93O5zs/Z0a7GXi6eGbCFdpX3uq28/KC/PnNRCkqylz5cuFC6NEjLqHKn9+sJ5IEBweoUcPeUYhIXhUaGkpIvBnhrq6uuLq6JqpXKF8hPmrxEcPWDKPK9Co8WflJvF292XdpH+tOrmNQ3UG8Wv9VAAKvBQJmwpWUigUrsvr4agKvB+Ln40cJrxK0rdiWketHcuLGCQwMFuxbQIdKHSjuWZy70Xfpt6Ifnat1pl2ldpnwKthf3mp5Cg2F69fjEqf16+HRR2HsWPN4VJR5PMHGwEnZvBn+/jtzwxUREYmvatWq+Pj4WG8BAQHJ1h3SYAjfP/s9YZFhzNw1k4+3fszq46t52PdhutXohpOD2X4SHGHurJGwey+Wt6u3We9O3A4cC59aSNcaXVl2ZBkrjq7ghQdfYH4nczZVwOYAzoeeZ1qbaVwKu0Sn7zvhNs6NAhMKMGz1MKJjojPktbCnvNfyVLy42XwQfx+7Z58113b6+GMoVixNLU/Dh5vjX1atytyQJeeJiIAkPgiKiNy3//77j1KlSlnvJ9XqFGvMhjGM2ziOMc3H0P3B7uR3y8/ei3sZsnoIzeY34+fOP9Oxcsd7iqOge0FrshTfoSuHGL9pPDPbz6SoR1Faf9Oaw1cPs/i5xZwPPc/g3wdTzLMYwxsOv6fnzS7yVsuTj4+59cqGDbYbALu4wIAB5s6/CbZmSU7v3rBmTdI7vUjedeaMObj8v//sHYmI5EZeXl54e3tbb8klT2tPrGXk+pG8Wv9V3m70Nr7evni6eNKodCNWdF2Bs4Mzw9YMA8DH1XzPi9+yFF/smKnkWqZiGYZBvxX9aOzfmN61enP46mHWHF9DQIsAOlbuyIC6A+jxYA+mbJ9yr5efbeSt5AnMxMjXN+ljvr5pSpwAOncGZ2f49tsMjE1yvIIFoW1bKF/e3pGISF62KtDsFmlepnmiY8U9i1OlcBWOXT9GWGQYFQuZY50Crwcmea7Y8uTGRMWa8c8M9lzcw5ftvwTgyNUjANQqXstap3aJ2lwMu5hsopZT5L3kKTV37qReB3Nc+ZAhmnUltjw9oV8/dduJiH1FRkcCccsRJHTl1hUcLA44OzhTsWBFSnqVZEvQFsIjw23qhUeGsyVoC2Xzl8XPxy/JcwGcCznHiD9HMLrZaMoVKGdzLCIqItHXlhw+dV3JU3x79phjotLY5xIQoMUyRUQk+2lYuiEAk7dNTtTKM/OfmZwNOUsD3wa4OrlisVjoW7svYZFhjN041qbu2I1jCYsMo99D/VJ8vkErB1GhYAWGPDLEWlalcBXAXGU81spjKynpVdI6CD2nylsDxlNTrRoMHWr2vYiIiORQz1V9jhn/zGDj6Y1Uml6JjpU6kt8tP7sv7mbdyXW4O7kzufVka/3hDYez7MgyJmyZwJ6Le3io+EPsvribNcfXUK9kPQY/MjjZ51p8cDErA1eyo+8OHB0creWVC1emTYU2jNowitPBp7kQdoG1J9Yy8fGJmXnpWcJiGIZh7yDS6uzZs/j5+REUFIRvcuOWRLKx6Gj45BNo3x6qV7d3NCKSU9zL+19EVARTtk9h8cHFHLl2hMjoSIp5FKN52ea80+gdHijygE394DvBjFo/ip8P/czFsIuU8CrBc1WfY2TTkXi5Jj0L/cbtGzzw+QP0qtmLCY9PSHT8cvhlBv42kN+P/Y67kzsv1X6JgJYBOFhydseXkqf7FBMDEydC3brw2GP2jkayu5s34Z134PBh6NTJHB/l7m7vqEQku8uO7395Wc5O/bIBBwdYsQL27bN3JJIT5M8Pn30GgwaZa4S99569IxIRkfTSmKeknDhhrkWwcCE88ECq1TdsMJMokbRwdDTXZW3Y0FybVUREchYlT0kpVQoqVTK3a0kDJU5yL0qUMG8iIpKzKHlKiqsrLFpk7yhEREQkG1KbSQaJjobVq80eP5GMEBMDOWc6h4hI3qHkKYPExECPHvDFF/aORHKLZcvgjTfS3HssIiJZRMlTSq5ehZdeMueVp8LZGbp1g2++0ZudZIzSpeHBB8Epic71a9dg3jzzfxERyVpKnlLi5WWuQRAUlKbqvXrBpUtm953I/apTJ/ntf65dg/nzlTyJiNiDBoynxNUV/vknzdVr1YIXXjCnoouIiEjupOQpA1ksZredSGaLHUiuAeUiIllP3XYiOcyNG+Yq5QCDB5sDy6Oj7RqSiEieouQpLW7fhk8/hcBAe0ciedz69dC7N5w9C/Xrm5MTpk41Vyz/7Tc7Bycikkeo2y4tLBYICAAfH6hYMdXqISEwaZI5/qlSpSyIT/KMkyfNsXWDB0OBAmYr1LhxcP16muc1iIjIfVLylBZubua7lptbmqq7uJgzoR58UMmTZKxevWy3AypQwEzUY2LMHF9ERDKfkqe0SmPiFFv1xAnteScZL7mfKf2siYhkHf3JzSR6M5PsZvFiOHTI3lGIiOR8eotPD8OADRvgzBl7RyKSLnfvmou3pmGxfBERSYWSp/SIiICnn07zYk6RkfDLL+YAchF7cnaGr76CDh3sHYmISM6n5Ck93Nxg504YMSJN1S9fNqeQ//hjJsclkgYODknvkyciIumj5Cm9ypVL87QmX19o2dKceSeS3RkGLFkCN2/aOxIRkewtWydPERERhISEWG+hoaH2DindevWCTZvg+HF7RyKSsvPnYdYseP55mDYNLlywd0QiItlTtk6eAgIC8PHxsd6qVq1q75DiXL5s3lLx1FPQurU+zUv2V6oU/PCDubjrX3/BW29p7zwRkaRYDCP7/nmMiIggIiLCev/cuXNUrVqVoKAgfH197RdYVJTZJ9erF0yYYL84RDLJnTtmy1PZsvaOREQAzp49i5+fn/3f/wTI5otkurq64urqar0fkl2mrTk5mR/Ra9SwdyQimcLNTYmTiEhysnW3XbbWtCkULGjvKETs5sIFczkOEZG8RslTFjl/3hxDEh5u70hEMsaYMTB+vL2jEBHJekqe7ldMTJoyoshImDsX/vsvC2ISyQIjRkCPHvaOQkQk62XrMU/ZnmFA3brQqhV89FGKVcuUMVuftEih5BalS9s7AhER+9Bb+f2wWODNN6FixTRVV+IkeU1wMPj42DsKEZGMpbfz+9W1q70jEMmWIiOhTx+z1bVrV7ORNo2L84uIZGsa85TFwsPNve6y7+paIhnD0RH+9z+4dQuGD4f+/c2vRURyOrU8ZSTDSPWj9fbt0LkzbN0KDRpkUVwiduDoaK7o0aQJ7NkDu3ZBvnz2jkpE5P6p5Smj9OoF77+farXmzcHPD+bNy/yQRLIDiwUeegj69bN3JCIiGUPJU0apVQvSsPeegwP07GkuUH77duaHJSIiIhlL3XYZZciQNFft1cvswrh61WyFEhHzA8X58zB4sAaWi0j2puTJDipWhFWr7B2FSPbi6Wkua6DESUSyOyVPIpIttGtn7whERNJGY54y2scfw+TJ9o5CREREMomSp4x286Z5S4PDh2H0aK35JJJWc+bATz9psoWI2JeSp4w2fry53XwanDwJX30Fly5lckwiuYBhwLVrMGMGdOlibrQdFmbvqEQkL1LyZEetW8Pp01C8uL0jEcn+YreS/PZbaNkSli41t4AREclqSp7syMHBXIVZRNKueHF47TVzm6OCBe0djYjkRUqeMsuKFbBggb2jEMm1XFzsHYGI5FVKnjLL77/DsmVpqnrzptZ9EsloZ8/Cpk0QE2PvSEQkt1HylFmmToWff05T1W++gY4d4cqVzA1JJC/ZuBE+/RSiouwdiYjkNkqeMouzc5qrdu1qDoZdtCgT4xHJY7p1M2ezqntPRDKakqdsoFAhaN8e5s+3dyQiuUv+/PaOQERyIyVPme3oUVi9OtVqfftCuXJw61YWxCQiABw7BufO2TsKEclplDxltqlTzcVpUllGvG1bc+XkfPmyJiwRMVt7e/SAUaPgyBF7RyMiGemJb59gyaElRMdEZ/i5lTxltrFjYccObRUvkg299x4MHgyBgTBgAGzbZu+IRCSjrD62mmd/fBbfKb6MWDuCY9ePZdi5lTxltkKFwM3N3lGISBJcXc2ZrgsWmK1PdevaOyIRySjHXj/G8EeH42BxYMKWCVSeXpkWC1rw/YHviYy+v+0JLIaRc7alPXv2LH5+fgQFBeHr62vvcDLFjh2wcye88oq9IxERkewiL7z/ZZbomGh+Pfors/fM5vdjvxNjxFDArQA9a/ak70N9qVqkarrPqZanrHL7Nvz7b6rVNm+GmTO1No1IdhMTo42IRXIiRwdHnqzyJCu6ruDM4DOMaTaG/G75mfb3NGrMqEGjOY2Yv3c+d6LupPmcSp6yyttvQ7t2qS53/OqrZo7l5JRFcYlImvz9N3TuDBcv2jsSEblXJbxK8FajtwhoEUAJzxIYhsHWoK28uPxFfCf78smWT4gxUt+WQG/RWWXoUDMzckg5X9WCfiLZU+XK0K8fFCtm70hE5F4cvXaU2btns2DfAq7cuoKroys9avagV81e7L6wm+k7pvP2n29zOfwyn7T6JMVzacyTiIhINqf3v3tzJ+oOPx78kdl7ZrP5zGYMw6BK4Sq8XOdletXsRQH3Ata6EVERPL7wcY5cO8KlNy6leF61PGVTly6Z06cbNbJ3JCKSVlFR4OiolUkke1lyaAlf/PMFuy/sJjwynBJeJXjE9xE+bvkxfj5+1nohESGMWj+Knw/9zMWwi5TwLMFzVZ9jZLOReLp42pzzxu0bDF0zlJWBK7FgoW3FtkxqNckmGYnV7edu/HvpX/b034OzY9q3Lrtfr658lUX7FxEcEYyzgzPPV3ue/nX607RM0yTruzq50rp8a7YEbUn13Eqe7OH6dShYMMUqo0fD8uVw+rT5x1hEsr+lS2HVKujSBZo319hFsS/DMBjw6wBm7Z5F+QLl6VKtC16uXpwPPc+G0xs4HXzamjyFR4bTdF5T9l7cS6vyrehavSt7Lu5h4raJbDi9gY19NuLmFLfsTo8lPVhzfA09a/bEMAwW/LuAq7eusrzrcpsYVgau5IeDP7DlxS1ZmjgBfLHzC8oXLM+IRiPoU7sPhfMVTvUxzco044MmH6R+ciMHCQoKMgAjKCjI3qHcu/HjDcPX1zDu3k2x2vbthgGGsWZNFsUlIvftwAHDeOstw2jWzDCef94wtm61d0SSW9zL+9/UbVMNRmEM+nWQERUdlej43ei496EP1n1gMArjrT/esqnz1h9vGYzCGL9xvLXsfMh5g1EY4zaMs5aNWT/GYBTGhdAL1rLQiFCj9JTSxmsrX0tzzBlp7fG1mXZuzbbLap06wbRpqbbr168PVapos2CRnKRaNfjoI/jqK6hRAzw9U3+MSGa4ffc2ozeMplyBckx7YhqODom7MJwczKZRwzCYvWc2ni6evN/kfZs67zd5H08XT2bvmW0tCwoJAqBOyTrWsrolzRVmzwSfsZa98+c7GIbB+BbjM+7C0qFFuRaZdm4lT1ntgQfg6adT7YuzWGDgQP3xFcmJKlSAd981E6i0uHYN5s0z/xfJCGuOr+HGnRt0qtyJ6Jhofjn0Cx9t/oiZ/8xMtE1J4PVAzoeep6FfQzxcPGyOebh40NCvISdunCAo2Eya/LzNrr49F/ZY6+2+sBuA0j6lAdh+djuf7/ycGe1mJBovlVXm751PnVl1OB96Psnj50PPU2dWHRbtX5Tuc6tHPht7/XV7RyAiWeHaNbOV+dFHzR2dRJITGhpKSEiI9b6rqyuurq6J6u26sAswF4h8cOaDHL121HrMweLAkEeGMLHVRAACrwUCULFgxSSfs2LBiqw+vprA64H4+fhRwqsEbSu2ZeT6kZy4cQIDgwX7FtChUgeKexbnbvRd+q3oR+dqnWlXqV2GXXt6zds3DxdHF0p6lUzyeEmvkrg7ufP1nq/pVqNbus6tlid7SmXBTBHJvW7fNieGnDmTel2RWFWrVsXHx8d6CwgISLLe5fDLAEzeNhkfVx929N1B6IhQNvbeSKVClZi0bRIzds4AIDgiGAAfN58kz+Xt6m3WuxNsLVv41EK61ujKsiPLWHF0BS88+ALzO5njTAI2B3A+9DzT2kzjUtglOn3fCbdxbhSYUIBhq4cRHROdMS9GKv678h+1i9dOsU6t4rX478p/6T63Wp7s5dtvYfx4czlxTacTyXMuX4Zz58zNiW/fNstyzqp7Yi///fcfpUqVst5PqtUJsK6S7eLowtIuS62tL439G/Pjcz9Sc2ZNJm2bxMB6A+8pjoLuBa3JUnyHrhxi/KbxzGw/k6IeRWn9TWsOXz3M4ucWcz70PIN/H0wxz2IMbzj8np43PYLvBFPALfHSCfF5u3pz4/aNdJ9bLU/2Ur06PPss3El9L501a+DHH7MgJhHJMv7+MGuWuavAZ5+ZZZ99BjfS/3dc8hAvLy+8vb2tt+SSJx9XsxWpbsm6ibqtqhetTrkC5Th+4zg379y01o3fshRfSITZTZhcy1QswzDot6Ifjf0b07tWbw5fPcya42sIaBFAx8odGVB3AD0e7MGU7VPSdc33qqRXSfZe2ptinX2X9lHMM/3bBih5speaNc02ew+PVKsuXgwLF2ZBTCKSpdavh9694exZaNbM/L93b7M8VmCgevgl/SoXrgxAfrf8SR6PLb999zYVC5ljnQKvByZZN7Y8uTFRsWb8M4M9F/fwZfsvAThy9Qhgdo3Fql2iNhfDLiabqGWkluVasvrYav44/keSx9ccX8Pvx36ndfnW6T63uu1ygOnTwc0t9XoikrOcPAm1asHgwVCggNnqNHUqnDplHr94EV5+GYoWNRfdbNHCnMmnFcwlNc3LNAfg0NVDiY7djb7LsevH8HD2oIhHEYp7FqekV0m2BG0hPDLcZsZdeGQ4W4K2UDZ/WZvVyBM6F3KOEX+OYHQzc3mE+CKiIhJ9bcmCH+IRjUbww8EfaLuoLT0e7MHj5R6nlHcpzoWcY82JNXzz7zd4u3ozotGIdJ9bLU85gBInkdypVy+zAbrA/w/LKFDAvN+zp3m/aFFzWbgGDWD1anjjDYjOmrG2ksOVL1ieVuVbcez6MWbvnm1z7KPNH3Hzzk2eeuApnBycsFgs9K3dl7DIMMZuHGtTd+zGsYRFhtHvoX4pPt+glYOoULACQx4ZYi2rUrgKYK4yHmvlsZWU9CppHYSemcoWKMtv3X6jmEcx5u2dR/cl3Wk+vzndl3Rn/t75lPAswaoXVlG2QNl0n1sbA9vb+vXwySfmXiwaOC4iyYiOhqAgKFPG3pGIPdzL+9/x68d5dM6jXA6/TLuK7ahSuAp7Lu5h3cl1+Pv4s73vdop7FgfMFqaGcxqy79I+WpVvxUPFH2L3xd2sOb6GeiXrsaH3Btyd3ZN8nsUHF/PCLy+wo+8Oapewnd32xLdPsPbEWvrU6sOFsAv8evRXJj4+kWGPDru/FyQdIqMjWXZ4GTvO7SA4Ipj8bvmpX6o+HSt3xMXR5Z7OqeTJ3v75B8aNg9mzoXDK++6cPg03b5rDpUREEoqOhtBQyJ/f3pFIRrvX97+g4CA+WP8Bvx/7nWu3rlHcszgdK3fkg6YfUNSjqE3d4DvBthsDe/3/xsBNR+Ll6pXk+W/cvsEDnz9Ar5q9mPD4hETHL4dfZuBvA/n92O+4O7nzUu2XCGgZgIMlZ3d8KXnKQR57zBzr8Oef9o5ERLKjHTtgxAhYsADizWaXXCCvv/9lNxownoP07Akvvmguqle6tL2jEZHspkoVeOstKJn0gsoieU5kdCRLDy9l57md3Lxzk2gj8aBBCxa+fvLrdJ03WydPERERRETEjdIPDQ21YzT298wz8Mor5rIF775r72hEJLvx9oZWrVKvFxMDDjm710QkVadvnubxhY9z/MZxUupks1hyWfIUEBDA6NGj7R1G1jh8GGbMgMmTkx047uUFL72kVYhF5N6FhUGfPuY+eo89Zm5erERKcqMhq4dw7PoxetTswYu1XsTX2xcnh4xJe7L1mKeELU/nzp2jatWqubPPd8cO6NrVHNCk6TQikkmCg+H77+Gvv+DSJbOLb/58cMrWH6VFY57SL/9H+alXqh5/9Eh6kcz7ka1/XRLuFh1/J+lcp149cylhfQQUkUzk4wP9+0O/fnDwoLlQpxInyY1ijJhUNwa+V/qVyS4sFi0bLCJZxsHB7LKrUcPekYhkjod9H05yhfWMoGaOHMgwzP3uNm60dyQiktsdOwavvgqXL9s7EpH0+ajFR6w7uY6f/vspw8+tlqfs5vp1c0DCgAHJduFZLOaWDY88Ak2aZHF8IpKnxMRAkSJQsKC9IxFJn98Cf6N5meY8/9PzNPVvykMlHkpyWxgLFt5v+n66zp2tB4wnlCcGzG3fbmZEO3emuJT4rVuQL18WxiUiInaTJ97/MpjD6LR1rlksFqI/SN+mkWp5ym4efhguXIBChVKspsRJRLKLqCiYOhUaNoS6dcHZ2d4RicBfvf7KtHMrecpuLJZUEycRkezk8mU4cAB++81cqLNJExg8WHudi301LdM0086t5CmHO3wY3N3B39/ekYhIXlWyJMydCydOwLp1cO6cEifJ3TTbLruKiYH1683/U6jSogVMnJh1YYmIJMVigfLlzfWjRo2ydzQipqiYKKZsm0L9r+rjHeCN05i4NqO9F/cy6LdBHL12NN3nVfKUXf3zDzRvnuJ6BA4O8MIL8N13EBmZhbGJiNyHq1dh0SJzqxiRzHL77m2az2/OG3+8weng03i7emMQN0eubP6yzN07lwX7FqT73Eqesqt69eDvv6Fpyn22vXrBtWvmWAMRkZwgMNBckUUkM43fNJ4tZ7YQ0CKAi8Mu0vehvjbHfdx8aOrflNXHV6f73EqesiuLBerXT3XV8WrVoH17c78qEZGcoEED+Pln8PS0dySSm/1w8Aeal23O8IbDsVgsWEj8flquQDnOBJ9J97k1YDwXWLHC3hGIiKRPassZGAZs2QJ16piTYkTS60zwGZ6q8lSKdbxcvAi+k/7WByVPOcHFi1C0qDYNFpE8IygI3n8f3Nzg0UfNyTGPPKI/g5J2Xq5eXL6V8r5Cx28cp4hHkXSfWz+G2d3hw+DrC3/+ae9IRESyTOnS8O235qSYkyfh00+1d7qkzyO+j7DiyApu3rmZ5PGg4CBWBq6kiX/69zlT8pTdVa4Mc+aYA8hTEBlprrNy5EgWxSUikslKloTu3c0/gV9+qeRJ0ufNR9/kxp0btFjQgi1nthAVEwXArbu3+PPEn7T+pjVRMVEMfWRous+tbrvszmKBnj3TVG3ECBg50sy3RERyEx+flI/fuWPubFW2bNbEI9lfE/8mTH9iOv/7/X80mRfXuuQV4AWAo8WRL9p9QZ2SddJ9biVPuYSzMxw7ptkrIpI3bd0KY8eaSyAUK2bvaCS7GFhvIM3KNGPmPzP5+9zfXL99HW9Xbx4u9TCD6g2iWtFq93ReJU85SXS02cSUzIhJJU4iklc1bgxTpihxksQeKPIA056YlqHn1JinnOL8ebM9+q/M2yVaRCSncnaGWrVSr3f7dqaHInmAWp5yihIlzLFPvr4pVjMM2LMHypWD/PmzJjQRkZzgyhVz9l7duvDYY9CwodaQyk1iF7ss5VUKRwfHdC1+WdqndLqeS8lTTmGxwLhxqVa7etVcmPyzz2DgwCyIS0Qkh3B3h/79Yd06+PBDKFQIfvgBHB3tHZlkhDJTy2CxWDj0yiEqFapkvZ8aCxaiPohK13MpecplihSBNm1g/nwlTyIi8Xl6wjPPmLcLF+DUKSVOuUnPmj2xWCz4uPrY3M8MSp5yoV69oHNnc33NKlXsHY2ISPZTooR5S0lMjNnor/WlcoZ5nealeD8jacB4TnPzJrRta7Y7J6NDB3PgZFBQlkUlIpLr7NsH3bqZY6VE4lPylNP4+KS6Wpybmzlo/PHHsygmEZFcqEABaNYMChe2dyRyL86GnGX5keXJbs9y4/YNlh9ZzrmQc+k+t5KnnMZige++M6eKiIhIpilTxhxgfi/ddteuwbx55v9iH+M2jqPPsj64OyU9pTKfcz5eXPYiAZsD0n1uJU8iIiL36PZtePNNWL4cgoPjyq9dMyfuKHmyn3Un19GqfCtcnVyTPO7q5Eqr8q1Ye2Jtus+t5CkXCwmB6dPN/0VEJOPdvGn+P22aOYvvvffMgeZif+dCz1HGp0yKdfx9/DkXqm67vOPOHXMn4C1bkq0SEgLDhsG2bVkYl4hIHlKiBHzyCfz4I7zyirmOsYODuWAxxP0vWc/F0YWQiJRbD0IiQrCQ/n5ZJU85lasrrF9v7gacDF9fOHjQXPfJYoHLl7MuPBGRvKRgQXjqKRgwAG7cMBcqBvP/GzfsG1teVaNoDVYcXUFEVESSx+9E3WH50eXUKFYj3edW8pRTWSzmNuK9eqVYzds7i+IRERHWr4feveHsWXOm3tmz5v316+0aVp7Up1YfzoacpeP3HTlx44TNsePXj/Pk909yPvQ8fWv3Tfe5tUhmTpbCFJDYVqbTp+PK4n9dtGgmxSQikoedPGmuszd4sLnUwY0bMHWquZq5ZK0+tfuw8thKfv7vZ6pMr0LZAmUp5VWKc6HnOHnjJFExUTxf/Xn61O6T7nNbDCPn9MiePXsWPz8/goKC8E1lg9y8LrWptTnnuy4iknPExJhjntJanlZ6/7s3hmHw+c7P+WLnFxy+ethaXrVIVV6p9woD693bPmZqecrpoqPNkYqVKsFDD9k7GhGRPC25BOl+Eie5dxaLhVfrv8qr9V8lPDKc4IhgfFx98HDxuK/z6tuZ0zk4wKhR8Ouv6XrYnTuZE46IiEh25OHiQUmvkvedOIFannI+iwV27gQvr3Q9zM0tk+IRERHJ5ZQ85QZJJE5hYeb/p05B9erm1wcOmNsNJLR6NRw9aq5RoqZlERHJicpNK4fFYmFtj7WULVCWctPKpelxFouF468fT9dz6a0yl/LwMG/xk6UyZeLK49uxA1asUOIkIiI5V4wRQ4wRY3PfSMO/+I9JK7U85Sb//QeOjlC5croe9v77EBVlWxYeDvny3duGmCIiIlnt1OBTKd7PSGpryC0MAzp1gkmTbIo9PMxDhpG4xSk+pwRp9P/+B/XqwZo1WtZARESyv6Grh7Lm+Brr/TPBZ1LdnuVeKXnKLSwWc8Zd7J4A96l7d3B2htat4bHHzDFRIiIi2dXU7VPZfna79X7ZaWWZun1qpjyXkqfcpFIlc8+7DNCsmbn7y/Ll5rIG2uZFRESyM08XT27dvWW9n5lrgGvMkyTLYoEOHcxbfIYBQUFQurR94hIREUmoYqGK/HLoF56q8hQlvEoAcPPOTc4En0n1saV90veGpu1ZcqNbtyA4GEqUyJTTr1wJTz4J//4LDzyQKU8hIiLx6P0vdd/t/47uS7pb7xuGgSUNs54sWIj6ICrVevGp5Sk3atrUnHH3zTeZcvpmzWDuXKhSJVNOLyIikm5da3SlbIGy/Hb0N86FnmPe3nk8WOxBahWvleHPpeQpN5o0Cfz8Mu30+fKZA8rjO3sW5s0zdxL39My0pxYREUnWI76P8IjvIwDM2zuPp6o8xQdNP8jw59GA8dyoSRMoWzZLn3LbNhg7FsqVg2nTICIiS59eRETyuIRLFcx9ci6dqnTKlOdS8iQZ4rnnIDAQOnaEoUPNvYpFRESySsKlCl5c/iJLDy/NlOfK1t12ERERRMRrwggNDbVjNDnUnTtZtgtw6dIweza8+Sbkz58lTykiIgJoqQKrgIAARo8ebe8wcq7nnzfXfVqwIEufNuHuMIYBgwbBCy9Ao0ZZGoqIiOQRWqrg/yVseTp37hxVq1bVVM20CA6GH34AX19o29b22Nmz4OUFPj5ZEsrNm9C+Pbz7LjzxRJY8pYhIrqKlClKnpQr+n6urK67xVswOCcmcPWpyneBgaNkSLl+GzZttjwUFmc0/RYvC2rVZkkDlzw+bNiUuv3IFihTJ9KcXEZE8QEsVyP05dw727zenvDVqZCZQfn5xidOZM3Dpklkvi1qfEib/Fy5AxYrmAPMxY6BChSwJQ0QkT5uweQJv//k2ANte2mad1h8rJCKEUetH8fOhn7kYdpESniV4rupzjGw2Ek8X23Vobty+wdA1Q1kZuBILFtpWbMukVpMo4F4g0fN2+7kb/176lz399+Ds6Jxp16elCuT+xPbGnjljJkxbt8YlTvGP20nhwjBlCmzcaC62+eqrdg9JRCRXO3D5ACPXj8TD2SPJ4+GR4TSd15Qp26dQpXAVhjwyhMqFKzNx20Qem/8Yd6Lu2NTvsaQH3/77LR0qdaBdxXYs/HchvZb2SnTelYEr+eHgD8zuODtTE6eE/ur1F71qJo4nvsjoSEIi0t+rpeQpN/L2huLF4+6fOQMNG8YlTmAet+Nuv87O0K+fubzBxx+bQ7DS0DUtIiL34G70XXot7UWt4rV46oGnkqzz8ZaP2XtxL281fIvV3VfzUcuPWN19NW81fIud53cyZdsUa90LoRf4LfA3RjYdyeyOs/n6ya/5oMkHrDi6gothF631wiLDGPjbQF6p90qiVq7MUG5aOT79+1MAmpZpin9+f1YfW83Q1UOTrB+wKYACExK3lKVGyVNu5OtrdtUlt3Nv6dLm8Www6NDd3VwXKiDAtvzUKdDKFCIiGePDTR9y8PJB5jw5B0eLY6LjhmEwe89sPF08eb/J+zbH3m/yPp4unszeM9taFhQSBECdknWsZXVL1gWwmd32zp/vYBgG41uMz9DrSc6pm6e4eeemTdn2s9uZ9ve0DH0eJU+5lZ8fLF4Mjgl+SRwdzfJM3L4lI/TvD5062TsKEZGcb/eF3Xy46UNGNh1J1SJVk6wTeD2Q86HnaejXEA8X2249DxcPGvo15MSNEwQFm0mTn7f5HrLnwh6b54G4af/bz27n852fM6PdjETjpXI6DRjPrYKCoHNniI62LY+OhmeeMdcO+OQTs78sG/r6a3M2noiIxAkNDbWZeZ5wVnpCEVER9FzSk1rFazG84fBk6wVeCwSgYsGKSR6vWLAiq4+vJvB6IH4+fpTwKkHbim0ZuX4kJ26cwMBgwb4FdKjUgeKexbkbfZd+K/rRuVpn2lVqd49Xm32p5Sk3OnvWdnB4QufOmUuBnzyZtXGlg68v1K5tWzZ9url0VUyMfWISEbG3qlWr4uPjY70FJBzzkMAHf31A4PVA5j45F0eHxN11sYIjggHwcUt6Bra3qzlGNvhOsLVs4VML6VqjK8uOLGPF0RW88OALzO80H4CAzQGcDz3PtDbTuBR2iU7fd8JtnBsFJhRg2OphRMdEJ/k8OYVannKjkBC4GDdgj9Kl4bvvoGvXuITKwQGccs633zDMmXk//ggTJsCHH0KbNhpkLiJ5y3///UepUqWs91NqddoWtI2J2yYyqukoqhetnuGxFHQvaE2W4jt05RDjN41nZvuZFPUoSutvWnP46mEWP7eY86HnGfz7YIp5FkuxJSy7U8tTbhWbVcQODn/0UdtB5A4JvvU//2x242XT9QIsFnOo1qZN4OkJHTrA6dP2jkpEJGt5eXnh7e1tvSWXPEXFRNFraS8eLPYgbzd6O9Xz+riaLU7xW5bii53On1zLVCzDMOi3oh+N/RvTu1ZvDl89zJrjawhoEUDHyh0ZUHcAPR7swZTtU1I8T3aXc5oeJO1KlYIaNeJWGI8dHO7nZ96PXWE83qcXDh0yF9bM5k05jRrBhg1w8CCUKWPvaEREsqewyDACr5vjmFzGuSRZp8HXDQBY8vwS60Dy2MckFFue3JioWDP+mcGei3vYP3A/AEeuHgGwWeW7donazN4zm+A7wakmY/fim3+/YfvZ7db7x64fA6Dtt20T1Y09ll5KnnIjHx9z65XQ0MTLEfj5wZYtife2e++9HDOYyGKB6glaoP/+GyZPhi+/NLeDERHJy1wdXXmp9ktJHtt4eiOB1wPpWLkjRfIVoUz+MlQsWJGSXiXZErSF8Mhwmxl34ZHhbAnaQtn8ZfHzSX6m9rmQc4z4cwSjm42mXIFyNscioiISfZ2WfefuxbHrx5JMin4/9nuS9e8lDiVPuZWPT/JbryS3vlPCrrxXXjH3yHsq6QXVspMbN8xcMZtOHhQRyVLuzu7M7jg7yWO9l/Ym8HogIxqNsFm4sm/tvozZOIaxG8fyUcuPrOVjN44lLDKMdxq9k+JzDlo5iAoFKzDkkSHWsiqFqwDmKuO1S5izgFYeW0lJr5LWQegZ6eT/smYilJInSVpkpNntd/u2vSNJkzZtzFt8ISHm9n7afFhEJHXDGw5n2ZFlTNgygT0X9/BQ8YfYfXE3a46voV7Jegx+ZHCyj118cDErA1eyo+8Om1l9lQtXpk2FNozaMIrTwae5EHaBtSfWMvHxiZlyDf75/TPlvAlpwLgkzcXFnNrWtau9I7lnkydDuXIwapSZSImISPI8XDzY0HsDgx8ezKErh5i0bRKHrx5mWINh/NnzT9yd3ZN83I3bN3h91esMfWSotXUpvvmd5tOxcke+3f8t24K2MfzR4QxpMCSJM+UcFsPIptOrknD27Fn8/PwICgrCNxtsLZLnXLsGTz8Nn30GDz5o72hSdfWquazB9Ong4QHz5plrg4qI5DR6/8te1PIkaRcSAvnymTP1coDChc3VF44dg2efhfLl7R2RiIjkBhrzJGlXtiysWpW43DCy9RIHpUrBzJmJyzduNJc+SDhOXkREJCV625D7s3QpPPaYOdUtBzl6FJo2hV9+sXckIiKS06jlSe5PgQJQtaq57HcOUqmSuTZUnTr2jkRERHIatTzJ/WnaFD7/3LbbLirKfvGkQ/364Bhvn8yQEHj+edi9234xiYhI9qfkSTLeiy/C66/bO4p0O3MG9u0zW6M6d4YjR+wdkYiIZEdKniTjtWhhbkScw1SvDgcOwJw5ZpfeOykvpisiInmUxjxJxuvVK3HZ7dvgnvQCa9mJkxP06QPdukFw0puLi4hIHqeWJ8l8N29C5crw7bf2jiTNXF0TL2f10kvw6af2iUdERLIPJU+S+fLlg4EDzcHlOZRhmHvkFSxo70hERMTe1G0nmc/FBUaMSFx+44a51EEOYLHARx8lLt+/H6pUAWfnrI9JRETsQy1PYh+rV4O/PwQG2juSexYZCY8/Dg88AIsWQUyMvSMSEZGsoORJ7KN+fRgzBipUsHck98zFBf74w5yl98ILULt2jltoXURE7oG67cQ+ChSAwYNty4KDzVUrc9Bq5TVqmDvUbN9ubvvn5WXviEREJLMpeZLsY8QI2LTJXKkyh+3W+8gj5i2+kyfh2jWoW9c+MYmISOZQ8iTZx/Dh0KFDjkuckvP557B4MRw/rgHlIiK5Se54l5LcoUwZeOIJ27LAwByzV15CEybAn38qcRIRyW2UPEn2FRUFrVrBsGH2juSeODpCxYq2ZV9+aW77d+mSfWISEZH7p+RJsi8nJ/jxRxgyxN6RZJjoaFiwAMqVg3ffNRdfFxGRnEXJk2Rvdeua3Xnx/f23ueR3DjRoEJw4YbY+TZ1qbkQsIiI5i5InyVn+/dec1rZypb0juWcFC0JAAAQFQaNGtsdyaE4oIpKnKHmSnOXBB2H9+sQDy3OghPvkHT9ubvVy8GDKjwsPN7eLsVjMr0VEJGspeZKcp2lT2+UMgoNhyxb7xZNBLBazJapcOXtHIiIiKVHyJDnfrFlmS1QOH31drhx8/TW4u8eVRUbChg3m1+Hh5u3y5bjjly/HlYuISNZQ8iQ539ChsHkz5M9v70gy3M8/Q7Nm0LKluWuNp6dty1S5cnHlIiKSNZQ8Sc7n6GiOhYpv+3Y4etQ+8WSgLl1gyRK4cMHekYiISCwlT5I7vf9+4o2HcyCLBTp1MicZiohI9qC97SR3WrbMHEieSzg6pr3urVuwd685cy/hjD4REbl/anmS3ClfPihRwrZs3jy4csUu4WSlf/+Fhg2hUCEoUgSaN0+8flR0tH1iExHJDZQ8Sd5w/Tq88Qb89JO9I7lnYWHm7cSJuLITJ+LKY9WuDfv2wQ8/mCuZP/SQ2f0XyzCgWDGYOdP2/Nevw9WrmXsNIiK5gbrtJG8oWBAOH4YCBewdyT3z8LD9P/br+PcBXF3N8fMJx9DHio6GsWPh0Udty2fONPfbK1QIKleGp54y800REbGllifJOwoXth08FBYG48fD7dv2i+keJEye0svJCQYOTJxcvfACLF5sjrMvWxZiYmyP791rJlWBgbblOezlExG5b9m65SkiIoKIiAjr/dDQUDtGI7nOtm3wySfQtauZLeQQHh6Zsweev795S46nJ7Rta3b5xdexI+zebSZWlStD377mmCsRkdwqW7c8BQQE4OPjY71VrVrV3iFJbvL443DmTI5KnOypQgWYMgW8vW3Lhw0z1ymtUAH++w+uXbM9Pm+emZ+KiOQWFsPIvvu4J2x5OnfuHFWrViUoKAhfX187Ria51oYN5s68AwbY7p8n92zxYti0CT77LK4sOhr8/MyWrtgWq/79tbSCSHLOnj2Ln5+f3v+yiWz97uDq6oq3t7f15uXlZe+QJLfbuBG+/97eUeQqnTvbJk5g7tn36qtQsSIcOgQTJiQeYzV6NMyZk3VxioikVbYe8ySS5d5/H956S61OmczdHd55J+6+YdgupwBw7pztJslg7rozeHBca1WNGtChQ6aHKyJiQ8mTSEIuLrb3P/wQatWCdu3sEk5ekDBxApg1K3GZm5uZNB05AsuXg6+vbfIUEwNvvw3duye/VIOIyP1S8iSSkuhoc1aeu7uSp2ygVi2YP9/82jBsFwcFuHHD3Ej5scdsk6c5c8zhbLEtVg89pHkCInLvlDyJpMTREVasyJy1AeS+WCyQcBhkoUKJ16ECuHvXLF+xwkywXnsNPv007vjJk7BmjdlidS9rZ4lI3qKBHSKpsVhsx0Ddvg3PPgv799svJkmX/v1h61ZzGYXLl23HW4G5TtWrryZ+3Icfwrhx5q4++/dDvMm/GSY83PwRs1jMr0Uk+1PLk0h6XbpkjmZOODZKsj2LxdwsOaFnnoFbt8DZ2bZ8xw5zmYUbN8z7S5ZAp05xx/fsMR93P4uCXr5s+7W6E0WyPyVPIulVpozZjJHUKGfJsRImTgDLlpk9tlevmoPUE67T++mn5lIL27fHlUVGwscfx42vqlgx8axBiGtlunUrruzWrbhydR+KZF9KnkTuRcLEaetWCAiAhQshf367hCSZI7a1KqkWqy+/hOvXbcsuXICpU+NWWrdY4OZN25XZ9+6F2rUTn6969bivNcxOJPtS8iSSEW7fNrvxtJBrnuLiAsWL25b5+5stVdeuma1VJ04k3tKmY8fUz71pEzRuHHffMMybliATsT/9GopkhBYt4Oefzdl5sdR0kKcVKgSPPmrO4Eto7drUHz9smO39W7fAycnc7ia+xYvNvQMHDDDXd/3xR9vjt2+bLV3xuwdF5P4oeRLJLCNGwJAh9o5CsqFKlcx5B5cumYPSY+3YARcvwvHjsHKl7WMcHWHmTKhTx7b8zh3zPDt3mvn7X3/ZHj9yxOwiPHjQtnzoUKhSBR5+GFq1ils/K9bp0zB3rpl8xXfzplmmzwaSl6nbTiSz+Pubo4dFklC0qPl//OUJCheGYsWSru/mBi+/nLi8Z0/zlpxKlcykLOFg90ceMcdjBQdDSIhtoynAP//Aiy/CU0/ZDnhv3x62bDEH2Pv4mOO7Xngh7vjatbBqFUyaZHu+DRviHuPtbV5nbp2wGh4Onp7m12FhGvyfGyl5EsksAwcmLouOTvwuJXla/DfWzHiTzZcP6tVLXN65s3lLzjPPmIuLJvxx/fBDCAqKS7qqVbM9fv68mXgl9NxzcOVK3P21a83e7lgffwwHDsCCBXFl0dHmptHe3mbS5eNjPib+6xQTk/3GgcVPiMPDlTzlRkqeRLJKRAQ0aGD2lyQ1EEbypKJFs28XmFMS7xBNm6b8mORawvbsMROu2FutWrbHS5VKvAjprVswZYpZ/+5ds+z0adtkpH17M7n6/vu4srNnzQ2kYxMub2/44APbJOvgQXNcWsIB//cjNmmKnyReuRIXr5Ko3EPJk0hWiYmB1q0Tv2uI5AGlSpm35MTv+ovl5RWXiNy5YyZRhQvb1nn11cRJXkQEhIaaSVRwsPnYUaNs6zz5pNm6NmFCXNnateZq9LFJV4UK8NVXto+bM8ecCFClSlzZrVsQFWU+JiEtP5E7KXkSySru7uZaUAnduWMOaBGRZLm5Jf1r0rZt4rLy5WH16pTPt2xZ4iUkSpQwuxdjW8cSLpwaE2OOO5s50zZ5mjVLc0PyGiVPIvb0zz/Qrh38+aftR1QRyVQJx2rFln30UfKPcXAw54AkbEFq185MvLp0ydgYJfvKZsPsRPIYPz946SXbj7Eikm05OCQeRF+xIjz/vH3iEftQ8iRiT8WKwfjxtoM27twxbyKSo4SFmbcDB+LKDhyIK5fcQ8mTSHYzfjzUrRs3vUhEcgQPD/MWfx/EIkXiyiX3UPIkkt1062bus5FwtOqBA/Drr0k/5tdfbT/uiojdZPbaXWlxLuQcU7dPpdXCVpSeUhqXsS4Un1icZxY/w99n/07yMSERIQxdPRT/qf64jnOlzNQyvLnmTcIiEzeb3bh9gz7L+lBsYjGKTyzOi8te5MbtG0met9vP3aj+RXXuRueeD4QaMC6S3VSpkngM1Pr18Nhj5kjVZctsd5Zdvtycd+3gAPv2aeC5iJ15eNh/WYLPdnzGhC0TKF+gPK3Kt6JIviIEXg9k6eGlLD28lEVPL+L56nEDtcIjw2k6ryl7L+6lVflWdK3elT0X9zBx20Q2nN7Axj4bcXOKm+7YY0kP1hxfQ8+aPTEMgwX/LuDqrass77rcJo6VgSv54eAPbHlxC86OCT4Q5mBKnkSyu5gY6NMn7q/xk0/GJVCxiVNsvVOnlDyJCPVL1Wd9r/U0LWO7qumm05tosaAFA38bSKcqnXB1cgXg4y0fs/fiXt5q+BYftYybcvj22reZsGUCU7ZNYUTjEQBcCL3Ab4G/Ma75ON5t8i4AZfKX4YP1H3Ax7CLFPc2VR8Miwxj420BeqfcKj/g+khWXnWXUbSeS3Tk4mLu2Tp0aV/bkkzB8eFziBGZC1b59locnItnP0w88nShxAmjs35jmZZtz484N9l/eD4BhGMzeMxtPF0/eb/K+Tf33m7yPp4sns/fMtpYFhQQBUKdk3C7VdUvWBeBM8Blr2Tt/voNhGIxvMT7jLiybUPIkkhM0aQL/+5+ZIMX65JO4rxN25YmIJMPZwew+c3IwO58CrwdyPvQ8Df0a4uFiO0jLw8WDhn4NOXHjBEHBZtLk5+0HwJ4Le6z1dl/YDUBpn9IAbD+7nc93fs6MdjPwdPHM3AuyA3XbieQkHTvCm2/aJk5vvhmXOO3ZA8ePm/tOWCz2iVFEMk1oaCghISHW+66urri6uqb58WeCz7D2xFpKeJagRtEaAAReCwSgYsGKST6mYsGKrD6+msDrgfj5+FHCqwRtK7Zl5PqRnLhxAgODBfsW0KFSB4p7Fudu9F36rehH52qdaVep3X1cbfallieRnGT5ctvECcz7y/9/kOaSJfD224kTpx9/hIsXsyZGEck0VatWxcfHx3oLSGrLp2Tcjb5LjyU9iIiOYELLCTg6mKt9BkcEA+DjlsTmfIC3q7mPTfCdYGvZwqcW0rVGV5YdWcaKoyt44cEXmN9pPgABmwM4H3qeaW2mcSnsEp2+74TbODcKTCjAsNXDiI6Jvqdrz07U8iSSU8QfHA62LVCxg8jHjDHL47tyBTp3hp9+MlukYv33H0RHQ40amR+7iGSI//77j1LxdlhOa6tTjBFD72W92Xh6I/0e6kePmj3uK46C7gWtyVJ8h64cYvym8cxsP5OiHkVp/U1rDl89zOLnFnM+9DyDfx9MMc9iDG84/L6e396UPInkBL/+mnhweMeO0KhRXPmTT8KKFYkHjRcpAtevQ8I/sh9/DAcPws6dcWXR0bBrFzz4oDYrFsmGvLy88E64o3EqYowYXlz2Iov2L6L7g92Z2X6mzXEfV7PFKX7LUnwhEWY3YXItU7EMw6Dfin409m9M71q9OXz1MGuOr+Hbp7+lY2VzaMGu87uYsn1Kjk+e1G0nkhOUKWPOugPbweEdO8YNIndwMOslpUAByJfPtmz6dPj+e9uyo0fh4Ydh61bb8qAgCA29nysQETuIMWLos6wP8/fNp2v1rsx7ch4OFtu3/oqFzLFOgdcDkzxHbHlyY6JizfhnBnsu7uHL9l8CcOTqEQBqFa9lrVO7RG0uhl1MNlHLKdTyJJITVK9uLoB56lTilqWOHc0WpzJl0rfGk6eneYuvfHnYsQOqVrUtf+UViIiA1avjyu7eNROqggXTcyUikkViE6cF+xbwfLXnWfjUQus4p/gqFqxISa+SbAnaQnhkuM2Mu/DIcLYEbaFs/rL4+fgl+1znQs4x4s8RjG42mnIFytkci4iKSPS1JYdPaFHLk0hOUb168us4tW+fMYtjurhAvXqJ95T46CNzz734du6EQoUSbwtz+/b9xyEi9yW2q27BvgU8V/U5vnn6myQTJzATmb61+xIWGcbYjWNtjo3dOJawyDD6PdQvxecbtHIQFQpWYMgjQ6xlVQqbOyWsDFxpLVt5bCUlvUpaB6HnVGp5EpHUJWyJAqhUCRYtMv+Pr1UrqFYNZsYbVxETY84AzOGfNkVyijEbxjB/33w8XTypVKgS4zaOS1SnU5VO1i614Q2Hs+zIMiZsmcCei3t4qPhD7L64mzXH11CvZD0GPzI42edafHAxKwNXsqPvDpsErXLhyrSp0IZRG0ZxOvg0F8IusPbEWiY+PjGjLzfLKXkSkXtTuDB07Zq4fPhw8EkwsHTlSnOLmf/+s91yXkQyxambpwBzi5QPN32YZJ0y+ctYkycPFw829N7AqPWj+PnQz/x18i9KeJVgWINhjGw6Endn9yTPceP2DV5f9TpDHxlK7RK1Ex2f32k+A38byLf7v8XdyZ3hjw5nSIMhSZwpZ7EYhr23L0y7s2fP4ufnR1BQEL6+vvYOR0TS6sgR+PlnGDHCtvXp0Ufh2Wdh6FD7xSaSA+j9L3vRmCcRyXyVK8M77yTutuvUCWrVsi37/nto2NAckC4ikg0peRIR+xk+HB57zLasWDFzuQRn57gywzDHUi1dmqXhiYgkRcmTiGQvzZvD5Mm2ZZGR4OeXeCzVokUwYEDWxSYigpInEckJXF3h66/NxCq+yEjzFl9UFHTvDv/8k3XxiUieouRJRHKu3r1hzhzbsuvX4fRpc1HP+L7/Hj77LMtCE5HcS8mTiOQuRYvCpk3moPP49u+Hbdtsy0JD4f33zWRLRCSNtM6TiOQNHyax1s3p0zBrFnTubFu+dKm5MXKbNlkSmojkLGp5EpG8q3p1uHgx8dY28+fDwoW2ZVevmmXBOXtDUxG5f0qeRCRvS2rbmCVLYN4827Ldu6FnT7OrL76//oLDh9P/vAcOwK+/Jn3s118T7xkoItmGkicRkaTEX2cKzHWmbt6EUqVsy199FT7/3Lbs8mXYvt2c+ZeUAwegZk3o0AGWL7c9tny5WV6zphIokWxKyZOISFr5+CRupdqxA0aOtC1budLceub2bdvy/fshJAROnTI3SwZ48sm4BGr5cvM+mMdPncroKxCRDKDkSUTkfnh4mJskx9e1K+zbB15ecWWGAS1awKRJ0L49LFsWd+zJJ83V1mMTJzCPt2+fubGLyD1R8iQiktFcXaFGjcTlf/0FL71kft2xo20C9ckncV8vW2YmXxs32j4+IiLx+lUikuWydfIUERFBSEiI9RaacKCmiEhOYbFAtWpQunRcWceOibeXefNNs3zJksQD0b/5BtzdE2+aHBAAv/1mW3bnjtlFaBgZdw0iAmTz5CkgIAAfHx/rrWrVqvYOSUQk4yxfDjNn2pZ98olZvns3vPyy7bFmzczlEhIOZv/rLzhyxLZs5UpzjNa1a7blEyeaq63HFxFhDnKPHYclIinK1snTiBEjCA4Ott7+++8/e4ckIpIx4g8OB7PFKVb8QeTxlS8PL7yQuHzNGhg61Lbs4Yfhu++gYEHb8t27ITDQtmzrVihWDI4fty2fNs1cRDS+yEgICkp+JqFIHpCtkydXV1e8vb2tN6/4gy9FRHKqX39NPDj8448TDyJPbh2otChVCrp0AYcEf+YXLTK3pImvenWzm9DPz7b82DE4ccK27L//zK7H3btty2fMsB23BWaCFRhodiGK5CLZOnkSEcmVypSJS2qWLTPHOIHtIHIHB7NeVihSBDp1Mrekie+zz+Cjj2zLypWDVavggQdsyy9ehLNnbcvOnYNKlWDDBtvyuXPh3XdtywzDXNcqLOyeL8PugoMTvwaxzp7V6vS5iJInEZGsVr26OZtuxYq4xClWx45m+b59ibeNyQ68vc09/xL2BIwebXbzxVe0KKxbB/Xq2ZYHB5vb3cQXGmrOUEw48P2772DQoMRx7N0LN27c0yVkiuBgaN7c7C4NCrI9FhRkljdvrgQql1DyJCJiD9WrJ7+OU/v22TNxSi93dzNhSDjuavBg+PLLxHW3boXHHrMtj4iA8HDbspgYqFs38cD3pUvNrsqEdu+GS5fu5QrS7vBhM6E7fx4eeSQugQoKMu+fP28ev5etfCTbUfIkIiL25+wMDRqYXYjx9e5tbtSc0M6d8PTTtmWGkXgFeDC31pkzx7bsjz/MJDUy0rb8n38StxylhYMDODqaX8cmUFu3xiVOYB5POAZNciR9F0VEJGdxcIDatc0ZgvE99ZTZzZfQpk3Qp0/ic7i5JV72oWtXc6xXfFu3QsuWcP26bfk//8TNXKxXDzZvBicn8/7589CwYVzi5ORkHk/YhSk5kpO9AxAREclUCQe3g7lVTosWicv/+MNcIT4+JycoVAjy5bMtHzgQatWCr74y7z/8sDkYvkcP23qOjmbi9PDD93wJkr0oeRIREYmV1AzH+vXhhx8Sl69YkXgF9xIlzOQr/jY6BQpAyZIZGqbYl7rtRERE7kXx4mayFCsoCHr2TLz/4NWrtoPIJcdT8iQiInK/zp61HRzu5GRuvRN/DNQjjyS/DpTkKEqeRERE7tfRo4kHh/fvn3gQ+dGj9otRMoySJxERkftVoQJ4eMQlTrGDwx9+OC6B8vAw60mOpwHjIiIi96t0aXPfv0uXEi9H8PDDcZsvly5tn/gkQyl5EhERyQilSyefHGl9p1xF3XYiIiIi6aDkSURERCQdlDyJiIiIpIOSJxEREZF0UPIkIiIikg5KnkRERETSQcmTiIiISDooeRIRERFJByVPIiIiIumg5ElEREQkHZQ8iYiIiKSDkicRERGRdFDyJCIiIpIOSp5ERERE0kHJk4iIiEg6KHkSERERSQclTyIiIiLpoORJREREJB2UPImIiIikg5InERERkXRQ8iQiIiKSDkqeRERERNJByZOIiIhIOih5EhEREUkHJU8iIiIi6aDkSURERCQdlDyJiIjkUjvP7aTtt23J/1F+PMZ78MjsR1h8cHGielvObKHB1w3wCvDigc8fYPbu2Ume71LYJQpOKMj4TeMzO/RsTcmTiIhILvTXyb9oOKchm89spnO1zgyoM4CLYRd5/qfnmbR1krXemeAztPqmFZfCLtG/Tn8Kuhek34p+/HLol0TnfG3Va/j5+DG84fCsvJRsx8neAYiIiEjGioqJot+KfjhYHNjYZyO1itcC4IOmH1B/dn3eWfcOz1Z9Fv/8/nz777fcibrD+t7rKe1TmuiYaKp+UZVZu2bx9ANPW8+54sgKfjn0C9te2oaTQ95OH9TyJCIiksusO7mO4zeO061GN2viBODj5sM7jd4hMjqS+fvmAxAUEkSRfEUo7VMaAEcHR2oVr8WZ4DPWx4VEhDBo5SBef/h16pWql6XXkh0peRIREcll1p9aD0Cr8q0SHWtdoTUAG05vAMDP24+rt65yNuQsADFGDPsu7rMmUwBvr30bJwcnxjYfm8mR5wxKnkRERHKI0NBQQkJCrLeIiIgk6wVeDwSgYsGKiY4V9yyOp4sngdfMOt1qdMPF0YVm85rx5po3aTK3CUeuHeHlOi8D5mDyL3d9ycx2M/Fw8cikK8tZlDyJiIjkEFWrVsXHx8d6CwgISLJe8J1gwOymS4q3qzfBEWYd//z+rO6+mkL5CjHjnxlcvXWVrzp8xdMPPE1kdCT9VvSjW41utK7QmpWBK3ng8wdwGuNElelVWBW4KnMuNJvL2yO+REREcpD//vuPUqVKWe+7urpmyHkb+zfm775/Jyoft3EcV25dYUrrKZy+eZqnfniKpx94mulPTOfrPV/z1A9PcfS1ozZdfHmBWp5ERERyCC8vL7y9va235JKn2Ban2BaohEIiQvBxTbpVKtbBywf5aPNHTGk9hcL5CjPjnxm4Obkxp+McWpRrwdcdv8bVyZUZO2fc30XlQNm65SkiIsKmPzc42PwhuHDhgr1CEhERyXKx73sxMTFpqh871inweiB1StaxOXYx7CJhkWHUL1U/2cfHGDH0XdGXFuVa0P3B7gAcuXaEyoUq4+7sDoC7szuVC1Xm8LXD6b6enC5bJ08BAQGMHj06UXn9+sl/w0VERHKrS5cuUbp06l1kTf2bErA5gDXH19ClehebY6uPrbbWSc70HdPZf2k/BwYdsCmPiI5IdN+CJa3h5xrZOnkaMWIEQ4cOtd6Piori0KFD+Pn54eBw/z2OoaGhVK1alf/++w8vL6/7Pl92lVeuE/LOteo6cxddZ+6T0dcaExPDpUuXqF27dprqtyjXgnIFyrFo/yJef/h161pPwXeCGb95PC6OLvSs2TPJx54JPsO7695lbPOxlMlfxlr+QOEHWHFkBadvnsY/vz+nb57m4OWDtK/Y/n4vL8exGIZh2DsIewkJCcHHx4fg4GC8vb3tHU6mySvXCXnnWnWduYuuM/fJDtf618m/aP1Na9yc3OhSvQteLl78fOhnTgefZuLjExn26LAkH9duUTuuhF9he9/tOFjiGirOBJ+h0meVKOVdio6VOrL86HLOh54n8LVAfL19s+qysgUNGBcREcmFmpdtzuYXN9OwdEN+OPgDM/6ZQTHPYnz/zPfJJk6L9i9izfE1zO442yZxAijtU5qlXZbi7uTO5zs/x93JnWVdluW5xAmyebediIiI3Lv6peqz6oW0r8XUrUY3utXoluzxNhXa0KZCm4wILUfL0y1Prq6ujBw5MsPWyciu8sp1Qt65Vl1n7qLrzH3y0rXmRXl6zJOIiIhIeuXplicRERGR9FLyJCIiIpIOSp5ERERE0kHJk4iIiEg65Lnk6ZtvvqF///7UrVsXV1dXLBYL8+bNs3dYGe7cuXNMnTqVVq1aUbp0aVxcXChevDjPPPMMf/+deOfsnOrOnTsMHTqUJk2aULJkSdzc3ChevDgNGzZk7ty53L17194hZqoJEyZgsViwWCxs377d3uFkiDJlylivKeGtWbNm9g4vwy1ZsoTHH3+cQoUK4ebmRtmyZenatStBQUH2Di1DzJs3L9nvZ+ytRYsW9g4zQxiGwS+//ELz5s0pUaIE+fLlo3LlyvTv358TJ07YOzzJQHlunaf33nuP06dPU7hwYUqUKMHp06ftHVKm+Oyzz5gwYQLly5enVatWFClShMDAQJYuXcrSpUtZtGgRzz//vL3DvG9hYWHMmDGD+vXr065dO4oUKcKNGzdYtWoVL774It9//z2rVq3KkO18spsDBw4wcuRIPDw8CA8Pt3c4GcrHx4fBgwcnKi9TpkyWx5JZDMNgwIABzJo1i/Lly9OlSxe8vLw4f/48GzZs4PTp0/j5+dk7zPtWq1YtRo4cmeSxn376iYMHD9K6dessjipzvPHGG0yePJkSJUrQqVMnvL292bdvH1999RXfffcdW7dupXr16vYOUzKCkcf88ccfxqlTpwzDMIyAgAADMObOnWvfoDLBzz//bKxfvz5R+caNGw1nZ2ejQIECxp07d+wQWcaKjo42IiIiEpXfvXvXaNasmQEYv/76qx0iy1yRkZHGQw89ZDz88MNG9+7dDcDYtm2bvcPKEP7+/oa/v7+9w8h0U6dONQBj0KBBRlRUVKLjd+/etUNUWSciIsIoVKiQ4eTkZFy8eNHe4dy3CxcuGA4ODoa/v79x8+ZNm2OTJ082AKNPnz52ik4yWu77OJ6Kli1b4u/vb+8wMt3TTz9N06aJd8xu3LgxzZs358aNG+zfv98OkWUsBwcHXFxcEpU7OTnx1FNPAXDs2LGsDivTffjhhxw8eJA5c+bg6Oho73AknW7fvs3o0aMpV64c06ZNS/J76OSUuzsGli5dyrVr12jfvj3FihWzdzj37dSpU8TExNCwYUN8fHxsjrVvb26ce+XKFXuEJpkgd/92SpKcnZ2B3P3HOSYmht9//x0g1zWT7969mw8//JAxY8ZQtWpVe4eTKSIiIpg3bx7nz5/H29ubevXq8fDDD9s7rAyzZs0abty4QZ8+fYiOjmb58uUcPXqU/Pnz07JlSypUqGDvEDPd7NmzAejbt6+dI8kYFStWxMXFhS1bthASEmKzGfCvv/4KkGvGdomSpzznzJkzrF27lhIlSlCjRg17h5NhIiMjGT9+PIZhcO3aNf78808OHz5Mnz59ctUfrIiICHr27EmtWrUYPny4vcPJNBcvXqRPnz42ZfXq1eO7776jfPnydooq4+zatQsAR0dHHnzwQY4ePWo95uDgwJAhQ5g4caK9wst0p0+f5s8//8TX15c2bXLHPmmFChXio48+YtiwYVSpUoUnn3zSOuZp3bp1DBo0iFdffdXeYUoGUfKUh9y9e5cePXoQERHBhAkTclV3T2RkJKNHj7bet1gsvPHGGwQEBNgxqoz3wQcfEBgYyK5du3LV9y++Pn360LhxY6pXr46npydHjx5l8uTJLFy4kBYtWrB//368vLzsHeZ9uXz5MgCTJ0/moYceYseOHTzwwAPs2bOHl19+mUmTJlG+fHkGDhxo50gzx9y5c4mJiaF379656ud4yJAhlCpVir59+zJz5kxreaNGjejWrVuubu3Pc+w96MqecvOA8YSio6ONbt26GYDRr18/e4eTaaKjo42goCDjiy++MPLnz280bNjQCA4OtndYGWLr1q2Gg4ODMWbMGJvyXr165aoB48np0aOHARiTJk2ydyj3rV+/fgZguLu7G+fOnbM5tn//fsPBwcEoX768naLLXNHR0Ubp0qUNi8VinDhxwt7hZKjRo0cbzs7ORkBAgBEUFGSEhoYamzZtMurWrWs4OTkZy5Yts3eIkkHy3IDxvCgmJoYXX3yRRYsW0b17d5tPRLmNg4MDvr6+DBw4kFmzZrFlyxY+/PBDe4d136KioujVqxcPPvggb7/9tr3DsYv+/fsDsGXLFjtHcv9iBxTXrVuXkiVL2hyrXr065cqV4/jx49y8edMO0WWutWvXcubMGR577DHKli1r73AyzNq1axk5ciSvvvoqb7/9Nr6+vnh6etKoUSNWrFiBs7Mzw4YNs3eYkkHUhpjLxcTE0KdPHxYsWEDXrl2ZN29erlzzKCmtWrUCYP369fYNJAOEhYURGBgIkOTsQoAGDRoA5qKLnTp1yqrQskzhwoUBcsWaVpUrVwYgf/78SR6PLb99+3aydXKq3DZQPNaqVasAaN68eaJjxYsXp0qVKuzZs4ewsDA8PT2zOjzJYEqecrH4idPzzz/PwoULc9X4gtScP38eiJtdmJO5urry0ksvJXls48aNBAYG0rFjR4oUKZKrFpKML3Zl/NxwfbFvsIcOHUp07O7duxw7dgwPDw+KFCmS1aFlqmvXrrFs2TIKFixoXUokt4iMjASSX47gypUrODg45Iq/R4LGPJFLxzxFR0dbx8I899xzuXbBvYMHDxrh4eGJysPDw402bdoYgPHhhx/aIbKsk5vGPB06dCjJ7+ehQ4eM4sWLG4CxYcMGO0SW8Vq1amUAxldffWVTPmbMGAMwunfvbqfIMs+UKVMMwHj99dftHUqG++677wzAqFatWqJFMmfMmGEARsOGDe0UnWS0PNfyNHv2bDZv3gxgXSRy9uzZ1q6dRo0a5Yrm5DFjxjB//nw8PT2pVKkS48aNS1SnU6dO1KpVK+uDy0CLFy9m8uTJNGrUiDJlyuDt7c25c+dYtWoV165do3HjxgwZMsTeYUoaff/990yePJkmTZrg7++Ph4cHR48eZeXKldy9e5cRI0bQpEkTe4eZIb744gseffRR+vXrx9KlS63dOuvWrcPf359PPvnE3iFmuK+//hrIfV12AM899xwzZsxg48aNVKpUiY4dO5I/f352797NunXrcHd3Z/LkyfYOUzKKvbO3rBb7KT25W69evewdYoZI7TrJJS1uO3fuNPr162dUq1bNyJ8/v+Hk5GQUKlTIaN68ufHll1/m2ha3+HJTy9P69euNzp07GxUrVjS8vb0NJycno3jx4saTTz5prF692t7hZbgzZ84YvXv3NooXL244Ozsbfn5+xiuvvGJcunTJ3qFluL///tsAjPr169s7lExz584dIyAgwKhdu7aRL18+w8nJyShVqpTRvXt347///rN3eJKBLIZhGFmesYmIiIjkUHlj2pWIiIhIBlHyJCIiIpIOSp5ERERE0kHJk4iIiEg6KHkSERERSQclTyIiIiLpoORJREREJB2UPImIiIikg5InERERkXRQ8iQi96x3795YLBZOnTpl71BERLKMkieRHOrUqVNYLBYsFgutW7dOss727duxWCz07t07a4MTEcnFlDyJ5AJr1qxh3bp19g5DRCRPUPIkksOVKVMGBwcH3nrrLbTPt4hI5lPyJJLDVa5cmR49evDPP/+wePHiND3m9OnTvPTSS5QqVQoXFxd8fX156aWXOHPmTJL1Dx48SPv27fHy8sLHx4e2bdty4MCBFJ9j2bJltGjRggIFCuDm5kb16tWZOHEi0dHRNvViYmKYPXs29evXp2DBgri7u+Pr60uHDh1Yv359mq5HRCQrKXkSyQXGjBmDq6sr7733Hnfv3k2x7tGjR6lXrx5z5syhTp06DBs2jNq1azNnzhzq1q3L0aNHbeofOHCARx99lFWrVtGmTRteeeUVIiMjadiwISdOnEjyOUaMGEGnTp04cuQITz/9NIMGDcLd3Z0333yTLl26JKrbr18/rl+/Trdu3Rg8eDCPPfYYBw8eZO3atff3woiIZAZDRHKkkydPGoDRunVrwzAM44033jAA47PPPrPW2bZtmwEYvXr1spY1b97cAIwvv/zS5nyff/65ARiPPfaYTXnTpk0NwPjmm29sykeMGGEABmCcPHnSWr5mzRprXGFhYdbymJgYY8CAAQZg/PTTT9byggULGiVLljTCw8MTXeO1a9fS/oKIiGQRtTyJ5BLvvPMO+fPnZ+zYsYSFhSVZ58yZM/z1119UrVqVfv362RwbMGAAVapUYd26dQQFBVnrb9iwgQcffJAXXnghyedLaPr06QDMmjULDw8Pa7nFYuGjjz7CYrHw3Xff2TzGxcUFR0fHROcqWLBg6hcuIpLFnOwdgIhkjAIFCvD222/z9ttvM3HiREaNGpWozt69ewFo2rQpFovF5piDgwNNmjTh8OHD7N27Fz8/P/bt2wdAo0aNEp3L09OTWrVqJRqXtH37djw8PJgzZ06Scbq7u3P48GHr/S5duvDFF19QvXp1unTpQvPmzWnQoAHu7u7puHoRkayj5EkkF3n99deZPn06kyZNYtCgQYmOh4SEAFCsWLEkH1+iRAmbesHBwQAULVo0yfpJnef69etERUUxevToZOMMDw+3fj1t2jTKli3L3LlzGTduHOPGjcPNzY3OnTszadIkChcunOx5RETsQd12IrmIu7s7o0ePJiwsLMnkxdvbG4BLly4l+fiLFy/a1PPx8QHg8uXLSdZP6jze3t4UKlQIwzCSvZ08edJa38nJiTfeeIODBw9y7tw5Fi1aROPGjVmwYEGirkIRkexAyZNILtOrVy+qVavGV199xbFjx2yO1apVC4CNGzcmWhPKMAw2btxoU69mzZoAbN68OdHzhIWFWbsB43v44Ye5du0agYGB6Y69ZMmSdO3ald9//50KFSqwdu1abt++ne7ziIhkJiVPIrmMo6Mj48eP5+7du4nGPZUuXZrmzZtz8ODBRGOSZs2axaFDh3jsscfw8/Oz1m/SpAn//vsv3377rU398ePHc/PmzUTP//rrrwPw4osvcu3atUTHL168yKFDhwCIiIhg69atieqEh4cTFhaGs7MzDg76MyUi2YvGPInkQh07dqRRo0ZJthjNmDGDRo0a0a9fP1asWEHVqlU5ePAgy5cvp0iRIsyYMcOm/ueff07Dhg3p2bMnS5cupWLFiuzYsYOdO3fSuHFjNm3aZFO/TZs2vP/++4wdO5YKFSrQpk0b/P39uXbtGseOHWPTpk2MGzeOBx54gNu3b9OwYUMqVapEnTp1KF26NGFhYfz6669cvHiRN954A1dX10x9rURE0s1uiySIyH1JuM5TQlu2bLGuwxR/nSfDMIxTp04Zffr0MUqUKGE4OTkZJUqUMPr06WOcOnUqyXPt37/faNu2reHp6Wl4eXkZTzzxhLF//36jV69eidZ5ivXHH38YHTp0MIoUKWI4OzsbxYsXNxo0aGCMHTvWOHPmjGEYhhEZGWlMmDDBaNWqleHr62u4uLgYxYoVM5o0aWIsWrTIiImJua/XSEQkM1gMQ5thiYiIiKSVBhOIiIiIpIOSJxEREZF0UPIkIiIikg5KnkRERETSId1LFURHR3P37t3MiEVERESyKWdn5yQ38M6L0pw8GYbBxYsXk1wUT0RERHK//PnzU7x48UQbi+c1aU6eYhOnokWLki9fvjz/womIiOQVhmFw69Yt6z6XsZuI51VpSp6io6OtiVOhQoUyOyYRERHJZtzd3QFzo/CiRYvm6S68NA0Yjx3jlC9fvkwNRkRERLKv2Dwgr499TtdsO3XViYiI5F3KA0xaqkBEREQkHZQ85SCzds3Cb4ofDqMdmLp9apJlo9aPotbMWmk+p2W0haWHl2ZKvCIZoUyZMkydOtXeYYikm8ViYenSpfYOQzJBrk2eZv4zE68AL6JioqxlYZFhOI91ptm8ZjZ1159aj2W0hePXj1vLtgZtpe23bSkwoQBu49yoMaMGk7dNJjom2lrnkdmPMODXAYme1zLawry982zKey/tTeO5je/5ekIiQnh15au81fAtzg09x8t1Xk6y7I1H3+DPnn+m+bwXhl3giQpP3HNcknlmzpz5f+3dd1gU1/oH8O+K1F26IkWKdERsRGKJLBujIl7UWGIXLEFvsP5sN1ejeO0xapRgiUGIlcRgwRBRg7uGgEFCUSNFqqARQcHIIktZ3t8fKyNLUVCJRs/nefaBeefMzJnlsOedM2d3oa2tjZqaem1YKoWqqio8PDyUykokEvB4PGRn12vDcXHw8vKCvr4+NDQ04OLigm3btkEur9eG+/bFnDkN2vCePeDxeAgNDVWK+/r6YuDA52/DDFOnsLAQ8+bNg7W1NdTV1WFubg5vb29ERyteuyZMmABPT0+lbaKiosDj8RAQEKAUDwgIgIWFBbd84sQJ9O3bF7q6utDW1oazszMWLlzY1qfEvGXe2ORJZCWCtEqK3//8nYvF3IyBscAY8bfjIauRcXFxrhgWuhawMbABAJxIOwFhqBCddTpD7CNG+tx0LHh3Adb9sg4TwieAiLhjSPIkSscV54lhrmPeKC7Jk+B9q/ef+3zy/8pHdW01htsNh4m2CbRUtZqMCdQEMNRq+TsijQXGUG+v/tz1emv89Rdw61bT627dUqx/yUQiEaRSKX7/vV4bjomBsbEx4uPjIZPVa8NiMSwsLGBj87gNnzgBoVCIzp07QywWIz09HQsWLMC6deswYUK9NiwSQSKRKB1XLBbD3Ny8UVwikeD995+/DTMMAOTl5cHV1RUXLlzAli1bcO3aNURFRUEkEsHf3x+Aol3GxsYqXTg01y7FYjFEIhEAIDo6GuPHj8eYMWNw+fJlJCYmYv369W/95GamDVALVFRUUGpqKlVUVLSkeNPkcqLaWuVYba0i3kZMvjChjTEbueVl55aRf6Q/OX3lROJcMRd3D3EnnxM+REQkrZSS4WZDGv3d6Eb7i0iPIASAwq6FERHR2ayzhADQnbI7XJlOWzpR0OUgstxuycVySnIIAVA6ZkOlFaU089RM6vB5B9LeoE2iUBGl3EkhIqKQ5BBCAJQeTcVyS3NptXg19djdQ2nfwUnB1DWoK6mtVSPjL4zJP9KfW4cA0Im0E9xy/oN8Gvf9ONLdqEv6m/RpxNERlFuay633OeFDI4+OpC2xW8j4C2My2GxAn/z4CVXVVHFlZNUyWnZuGXXe1pnU1qqRzQ4b+ibxG6qtrSWbHTa0JXaLUv2S7yQTAkCZ9zObfX5eqQcPiPr2JbK2JsrPV16Xn6+I9+2rKPeSmZiY0MaN9drwsmXk7+9PTk5OJBaLubi7uzv5+PgQEZFUKiVDQ0MaPbqJNhwRQQAoLOxxGz57lgDQnTv12nCnThQUFESWlpZcLCcnhwAoHbMhALRnzx4aPnw4aWpqkqOjI8XFxVFmZiYJhULS0tKifv36UVZWFrdNVlYWjRgxgoyMjIjP59M777xD58+fV9qvpaUlbd++nVvet28f6erq0s8//0xERNeuXSNPT0/i8/lkZGREU6ZMoeLiYq78sWPHqFu3bqShoUEGBgY0aNAgkkqlRETk4+NDI0eOpICAAOrQoQNpa2vT7NmzqbKyktv+zJkzNGDAANLV1SUDAwMaPny40jnk5uYSAAoPDycPDw/S1NSk7t27U1xcXLPP1etEKiUCFI/HT0ubGjZsGJmZmXF/g/pKS0uJiCgjI4MA0KVLl7h1bm5uFBQURBoaGlxfVFFRQerq6hQSEkJERAsWLCAPD49W1afu7/fdd9/Re++9RxoaGvTOO+9QRkYGXb58mVxdXYnP55OnpycVFRVx212+fJk++OADMjQ0JB0dHXJ3d6fExESlfQOgEydOcMurVq0iY2NjunLlChERxcTEcMfs3LkzzZs3T+l5CQoKIltbW1JXVycjIyMaM2YMt04oFJK/vz/5+/uTjo4OGRoa0sqVK6m2Xj974MABcnV1JYFAQJ06daKJEyfS3bt3ufVisZgA0M8//0yurq6kqalJ/fr1o/T09Gafr5eSD7wB/r7kaf9+olWriEpKFMslJYrlx42+LUwKn0RDDg7hlvt83YeOXT9Gc07PoVUXVhER0aOqR6S+Vp1Ck0OJiOh46nFCACguv+kXPvtAexp5dCQRKRIt1f+p0pGrR4iI6HrRddLZqEOyahkJNggopySHiBTJi8Y6DZJVy5qt6wcHPiDvI96UcDuBbty7QYvPLibDzYZ0/9F9elT1iH7O/pkQALp86zLdKbtD0kppo1iNvKZR8rTr8i7SWKdBX176kjLuZdDlW5dp+6Xt3Pr6yVNVTRU5feVEM07OoKuFVym1KJUmhU8ih0AHqqxRdCY+J3xIZ6MOzTk9h9KK0+h0xmnSWq9FX//+NbfPj459RObbzOl46nHKLsmmn7N/5hLO9b+sp65BXZXOff5P88k9xL3Z5+aVKyhQJEiAcgJVlzjVxQsKXvqhJ02aREOG1GvDffrQsWPHaM6cObRq1eM2/OgRqaurU2jo4zZ8/DgBaLbztre3p5EjRxKRItFSVVWlI0cet+Hr10lHR4dkMhkJBALKyXnchoODSUNDg2Sy5tswADIzM6PvvvuOMjIyaNSoUWRlZUXvv/8+RUVFUWpqKvXt25c8PT25bVJSUmjPnj107do1unHjBq1cuZI0NDTo5s2bXJn6ydPmzZvJ0NCQ4uPjiUjR2Xbs2JE+/fRTSktLo6SkJBo8eDCJRCIiIvrzzz+pffv2tG3bNsrNzaWrV69SUFAQlZWVEZEieRIIBDR+/Hj6448/6Mcff6SOHTvSf//7X+74P/zwA4WHh1NmZiYlJyeTt7c3ubi4kPzxhV9d5+vo6Eg//vgjZWRk0NixY8nS0pKqq6uf9ud9LfydydP9+/eJx+PRhg0bnlnW1NSUK/fw4UNq3749FRUVkaOjI124cIGIiKKjowkA5eXlERHRxo0bqWPHjnTt2rUW16n+369+O3V1dSUPDw/69ddfKSkpiWxtbWnOnDncdtHR0XTw4EFKS0uj1NRUmjlzJnXq1IkePnzIlalLnmpra2nu3LlkZWVFmZmKi8SsrCzi8/m0fft2unHjBsXGxlKvXr3I19eXiIgSEhJIRUWFjhw5Qnl5eZSUlEQ7duzg9i0UCkkgENCCBQsoPT2dDh06RFpaWvT1109ei4ODg+mnn36i7OxsunTpEvXr14+GDRvGra9Lnt59912SSCR0/fp1GjhwIPXv37/Z54slTwovnjzdu0eUnf3snRw7RuTtTTRiBNHq1YqfI0cS1b+Srakhysggqtf4XsS+xH3EX8+nank1PZQ9pPb/a09F0iI6cvUI11lH50QTAkA3HyherDfFbCIEgEorSpvc54ijI8jpKydueUDwAPKL8CMioqDLQeR12IuIiIYcHEL7k/YTEdHU41NJFCpqtp4xN2O4pKs+mx02tPf3vUT0ZHSm/ihQU7GGyZPpVlNaEb2i2WPXT54OXjlIDoEOSlculTWVpLlOk85mnSUiRfJkud2SauQ1XJlx34+j8cfGExFRxr0MQgDofLby6EGd2w9vk8oaFYq/pej8qmqqqMPnHbjk9bXVMFGKjW06oXrJ9u3bR3w+n6qrq5U6kCNHjpC7++M2/LgDqUs4Nm3aRAC4q/iGRowYQU5O9drwgAHk5/e4DQcFkZfX4zY8ZAjt3/+4DU+dyiUkzQFAK1eu5JYvXbpEACg4OJiLHT16lDQ0NJ66H2dnZwoMDOSW65KnZcuWkYmJCf3xxx/curVr1yoll0REBQUFBIAyMjIoMTFRqXNtyMfHhwwMDKi8vJyL7d69mwQCAZccNVRcXEwAuA66rvP95ptvuDLXr18nAJSWlvbUc32VpFLF4+7dJ8nT3btP4m0hPj6eANDx48efWXby5Mnc3zYyMpK6dlVcdPn5+XEXDp999hl16dKF20YqlZKXlxcBIEtLSxo/fjwFBwc/Nelv6u939OhRAkDR0dFcbOPGjeTg4NDsfuRyOWlra9Pp06e5GAA6duwYTZo0iZycnOjWrVvcupkzZ3L/d3ViYmKoXbt2VFFRQeHh4aSjo6OUjNUnFArJyclJ6fV6+fLlSv/bDSUkJBAA7uKh/shTncjISALQbHLEkieFF5/zdPo0sHz5s8uJxYCbG2BuDly8qPgZEgLUn/gqkwGzZwOJiS9cLQDwsPJAeXU5Em4nICY/BvaG9ujI7wihlRDxtxTzniR5EljrW8NC10JpW3o8J6Qlx5DclABQzGvysFScj9BSqBQXWYma3ceVwiuQVklh+LkhBBsE3CP3Qa7SJPbWKiovwp9lf2JQl0EtKn+l8AqySrKgvVGbq4PBZgPIamRK9XA2coZKuyefLGsiMEFRueIj+1MKU6DCU4HQUtjkMUy1TTHcfjj2J+8HAJy+cRqVNZUY5zzueU/z72FuDkgkgLU1kJMDDBig+GltrYibm7fJYT08PFBeXo6EhATExMTA3t4eHTt2hFAo5OY9SSQSWFtbK02aBVrRhj08uHkkEomEm4wuFAqV4nXzSp6me/fu3O+dOnUCALi4uCjFZDIZHj58CEAxAX7JkiVwcnKCnp4eBAIB0tLSkJ+fr7TfrVu3Yt++ffj111/h7OzMxa9cuQKxWAyBQMA9HB0dAQDZ2dno0aMHBg0aBBcXF4wbNw779u1DaWmp0r579Oih9AHA/fr1g1QqRUFBAQAgMzMTEydOhLW1NXR0dGBlZQUAjepY/9zrvrqi7qssXkcCgeLx+M8EQPF7XbwttLRNAop2GRsbi+rq6ha3Sz6fj8jISGRlZWHlypUQCARYvHgx3Nzc8OjRo6ceryVtt/7f8+7du/j4449hZ2cHXV1d6OjoQCqVNmoXixYtQnx8PH755ReYmZlx8StXriA0NFSp7Q4dOhS1tbXIzc3F4MGDYWlpCWtra0ydOhWHDx9udA59+/ZV+tylfv36ITMzk3tTSGJiIry9vWFhYQFtbW0IhYrX5X96230dvHjy5O0NbN787HLLlwOzZgHz5yuW588H9PWVy2hoAHv3Aq6uL1wtALA1sFVM+s4TQ5wr5jp0U21TmOuaI64gDuI8sdJEbntDewBA2r20JveZVpzGlQEUk8Zv3L+B2w9vQ5IngdBKcQyhpRCSPAmyS7JR8LAA73dpfqKttEoKE4EJUuakKD0y5mZg6YClz33+mu01W1VeWiWFq6lro3rcmHcDk1wmceVU26kqbcfj8VBLtS0+5qxesxD2RxgqqisQkhKC8c7joaX6D/j0enNz4OBB5djBg22WOAGAra0tN+lbLBZzL36mpqYwNzdHXFwcxGKx0kRue/vHbTitmTaclsaVARSTc2/cuIHbt29DIpFwx6jrpLKzs1FQUNCiyeKqqk/aRt2LelOx2lpFe1myZAlOnDiBDRs2ICYmBikpKXBxcUFVVZXSfgcOHAi5XI7vv/9eKS6VSuHt7Y2UlBSlR2ZmJtzd3aGiooLz58/jzJkz6Nq1KwIDA+Hg4IDc3Nxnnksdb29vlJSUYN++fYiPj0d8fDwANKrj086TUbCzswOPx0N6evozy4pEIu7CoX7br7twKCkpQXx8fJPt0sbGBrNmzcI333yDpKQkpKam4rvvvnvq8VrSduv/PX18fJCSkoIdO3YgLi4OKSkpMDQ0bNQuBg8ejNu3b+Ps2bNKcalUitmzZyu12ytXriAzMxM2NjbQ1tZGUlISjh49ChMTE6xatQo9evTAgwcPnvncAUB5eTmGDh0KHR0dHD58GAkJCThx4gQA1nZfhhdPngwNFVffz2JhARgbP72Migpgbw9oa79wterUvSNOclMCDysPLu5u4Y4zmWdw+fZliLo8uXIZYjMEBpoG2Hppa6N9RWREILMkExO7TeRi/c37Q01FDbsSdkFWI4OriSLx62PWB8XlxdifvB98VT7czNyarWNvk94olBaifbv2sDWwVXp00Orw3Oeura4NKz0rROe27KMLepv0Rub9TBjxjRrVQ1dDt0X7cOnkglqqxcWbF5st42XnBb4aH7t/342orCjM6DWjRft+5QoKgKlTlWNTpyribajuHXH1r74BwN3dHWfOnMHly5eVrr6HDBkCAwMDbN3aRBuOiOBGUur0798fampq2LVrF2QyGVwfX7z06dMHxcXF2L9/P/h8Ptzcmm/Dzys2Nha+vr748MMP4eLiAmNjY+Tl5TUq5+bmhjNnzmDDhg344osvuHjv3r1x/fp1WFlZwdbWVunB5/MBKDqDAQMGYM2aNUhOToaamhrXiQCKEYCKigpu+bfffoNAIIC5uTnu37+PjIwMrFy5EoMGDYKTk1Ojkat/KqlU8bh790ns7t0n8bZgYGCAoUOHIigoCOXl5Y3W108MbGxsYG5ujoiICKSkpHDJk5mZGczMzLB161ZUVVU9c0TUysoKWlpaTR7vRcTGxmL+/Pnw8vKCs7Mz1NXVce/evUblRowYgSNHjmDWrFkICwvj4r1790Zqamqjdmtraws1NTUAQPv27fHBBx/g888/x9WrV5GXl4cLFy5w+6hL5Ov89ttvsLOzg4qKCtLT03H//n1s2rQJAwcOhKOjIxtNeone2I8qqCOyEuHX/F+RUpiidCtJaCXE3sS9qJJXKd1S46vxsfdfe3Eq/RT8Tvvh6t2ryHuQh+CkYPie9MXYrmPxkfNHXHlNVU307dwXgZcDMcBiAHc7S01FTSmuqqI8WlPfB9YfoJ95P4wKG4Vz2eeQ9yAPcQVxWBG9QumjFp5HgDAAWy9txc74nci8n4mkO0kIjA9ssuzk7pPRQasDRoaNRMzNGOSW5kKSJ8H8M/Nx62Ezb9NvwErPCj49fTDj1AycTD/J7eP7609GDFTaqcC3hy8+jf4UdgZ26Gfe74XO8W9RUKC4xVx3qy429sktPA+PNk2gRCIRfv31V6UOBFBcge/du7dRB8Ln87F3716cOnUKfn5+3ItucHAwfH19MXbsWHz0Ub02rKmJvn37IjAwEAMGDOC+7FNNTU0pXv/q9GWxs7PD8ePHuavuSZMmNXvF279/f/z0009Ys2YN96GZ/v7+KCkpwcSJE5GQkIDs7GycPXsW06dPh1wuR3x8PDZs2IDff/8d+fn5OH78OIqLi+Hk5MTtt6qqCjNnzkRqaip++uknrF69GnPnzkW7du2gr68PQ0NDfP3118jKysKFCxfwf//3fy/9eXgV+Pwnj6fFXragoCDI5XK4ubkhPDwcmZmZSEtLw86dO9Gvn/JrgUgkwq5du2Bra8vdSgMUbT8wMBD29vYwNTXl4gEBAVi2bBkkEglyc3ORnJyMGTNmoLq6GoMHD36p52FnZ4eDBw8iLS0N8fHxmDx5MvfFuQ19+OGHOHjwIKZPn44ffvgBALB8+XLExcVh7ty53GjpqVOnMHfuXADAjz/+iJ07dyIlJQU3b97EgQMHUFtbCwcHB26/+fn5+L//+z9kZGTg6NGjCAwMxIIFCwAAFhYWUFNTQ2BgIHJychAREYG1a9e+1Ofgbfb3J0+GhoCPj+Ln30DURYSKmgrYGtiik6DeP5+lEGVVZXAwdICJtonSNmO7joXYR4z8v/IxMGQgHL5ywPbftmPFwBUIGxPW6Lt9RFYilFWVcfOdGh7jafOdAMWV8U+TfoK7pTumn5oO+0B7TPhhAm7+dROd+J2euu2z+PT0wZdDv8SuhF1w3uWMfx35FzJLMpssq6WqhV+m/wILXQuM/n40nIKcMDNiJmQ1Muio67T4mLuH78bYrmPxSeQncAxyxMenP0Z5lfJV38zeM1Elr8L0ntNf6Pz+FrduKSdOEgnQv7/yHCgPj+Y/B+oFiUQiVFRUNNmBlJWVwcHBgZunUGfs2LEQi8XIz8/HwIED4eDggO3bt2PFihUIC2uiDYtEKCsra/Thm3XHaMl8p+exbds26Ovro3///vD29sbQoUPRu3fvZsu/9957iIyMxMqVKxEYGAhTU1PExsZCLpdjyJAhcHFxwcKFC6Gnp4d27dpBR0cHv/zyC7y8vGBvb4+VK1di69atGDbsyQfDDho0CHZ2dnB3d8f48eMxYsQI7oMY27Vrh7CwMCQmJqJbt25YtGgRtmzZ0ibPxdvC2toaSUlJEIlEWLx4Mbp164bBgwcjOjoau3fvVirb2nYpFAqRk5ODadOmwdHREcOGDUNhYSHOnTunlHS8DMHBwSgtLUXv3r0xdepUzJ8/H0ZGRs2WHzt2LL799ltMnToVx48fR/fu3XHx4kXcuHEDAwcORK9evbBq1SouGdTT08Px48fx/vvvw8nJCXv27MHRo0eV5vxNmzYNFRUVcHNzg7+/PxYsWAA/Pz8AQMeOHREaGopjx46ha9eu2LRpk9KoLfNieNSCGXwymQy5ubno0qULNDQ0/o56MW+4mJsxGHRgEAoWFSglta+lv/4CPD2BoqLGk8PrRqSMjICoKEC3Zbc3mdeDr68vHjx4wL5Cg/nH8fDwQM+ePf/2ry5i+YBC+1ddAebtUllTieJHxQi4GIBxzuNe/8QJUCREUVFAWRnQubPyurp3j2prs8SJYRjmLfHGz3liXi9H/zgKyy8t8UD2AJ9/8Pmrrk7L6eo2TpzqdO7MEieGYZi3CLttxzAMwzBMi7B8QIGNPDEMwzAMw7QCS54YhmEYhmFagSVPDMMwDMMwrcCSJ4ZhGIZhmFZgyRPDMAzDMEwrsOSJYRiGYRimFVjy9BoKkASg0xedwFvDw8n0k03GfE/6YlTYqBbtL+9BHnhreEgpTGmzOjP/XBKJBDwej/tS1tDQUOjp6b3SOjHMi2Btmmlrb3zyVCgtxLyf5sF6hzXU16nDfLs5vI96IzonGgAw4YcJ8DzkqbRNVFYUeGt4CJAEKMUDJAGw2G7BLZ9IO4G+3/SF7iZdaG/UhvMuZyyMWvhC9U0rTsOai2uw9197cWfxHQyzHdZkbIfnDoSOCm3RPs11zHFn8R10M+r2QnVj/n6+vr7g8XiYM2dOo3X+/v7g8Xjw9fV9qcccP348bty48VL32dCePXugra2NmpoaLiaVSqGqqtroe8zqOsLs7GwuFhcXBy8vL+jr60NDQwMuLi7Ytm0b5HI5V6Zv376Nnrc9e/aAx+MhNDRUKe7r64uBAwe+vBNkmvWmtuk6hYWFmDdvHqytraGurg5zc3N4e3sjOvpxnzNhAjw9G/Q5UVHg8XjcdyrWCQgIgIVFvT7nxAn07dsXurq60NbWhrOzMxYuXNjWp8Q04W9Pnu7fB0JDFT/bWt6DPLh+7YoLeRewZfAWXPv3NURNjoLISgT/n/wBKL7UN7YgFjW1T17ExblimOuYQ5InUdqfOE8MURfFF1FG50Rj/A/jMcZpDC7PuoxEv0Ssf389quXVL1Tn7FJFBzHSYSSMBcZQb6/eZExXQxd6Gnot2qdKOxUYC4zRvh37Np5/InNzc4SFhaGiooKLyWQyHDlyROmF9WXR1NR86hecvgwikQhSqRS///47F4uJiYGxsTHi4+Mhk8m4uFgshoWFBWxsbAAoOhChUIjOnTtDLBYjPT0dCxYswLp16zBhwgTUfe6vSCSCRCJROq5YLIa5uXmjuEQiwfvvv982J8s08ia2aQDIy8uDq6srLly4gC1btuDatWuIioqCSCSCv//jPkckQmxsrNKFQ3PtUiwWc19+HB0djfHjx2PMmDG4fPkyEhMTsX79elRXv1ifwzwnaoGKigpKTU2lioqKlhR/qowMIg8Pxc+2NuzQMDLbakbSSmmjdaUVpYr63MsgBIAuFVzi1rntc6Ogy0GksU6DKqoV51xRXUHqa9UpJDmEiIgWnFlAHqEera5T/oN8Gvf9ONLdqEv6m/RpxNERlFuaS0REq8WrCQFQejQVIyLyOeFDI4+O5PYrr5XT5l83k80OG1Jbq0bm28xp3cV1RESUW5pLCAAl30nmyl+7e408D3kSfz2fjLYY0ZTjU6i4vJhbLwwR0ryf5tHSc0tJf5M+ddrSiVaLVzd6Dv0i/MhoixGpr1Un5yBnOp1xmqSVUtLeoE3Hrh9TKn8i7QRprdeih7KHrX7e3lY+Pj40cuRI6tatGx06dIiLHz58mLp3704jR44kHx8fLi6Xy2nDhg1kZWVFGhoa1L17dzp2TPnvEBkZSXZ2dqShoUEeHh4UEhJCAKi0tJSIiEJCQkhXV5crn5WVRSNGjCAjIyPi8/n0zjvv0Pnz55X2aWlpSevXr6fp06eTQCAgc3Nz2rt371PPzcTEhDZu3MgtL1u2jPz9/cnJyYnEYjEXd3d3585RKpWSoaEhjR49utH+IiIiCACFhYUREdHZs2cJAN25c4cr06lTJwoKCiJLS0sulpOTQwCUjsm0nTe5TQ8bNozMzMxIKm2iz3lcl4yMDAJAly7V63Pc3CgoKIg0NDS4fraiooLU1dUpJCSEiIgWLFhAHh6t73NetpeZD/yTvbG37UoqShCVFQX/Pv7gq/Ebra8btbE3tIeptinEuWIAQFllGZLuJGFc13Gw0rPCpYJLAIC4gjhUyishslJcBRgLjHG96Dr+KPqjxXWqlldj6KGh0FbTRsz0GMTOiIVATQDPQ56okldhSf8lCBkZAgC4s/gO7iy+02SsKZ/+/Ck2/boJn7l/htRPUnFkzJFmv3T3gewB3v/2ffQy7oXf/X5H1OQo3JXexUfHPlIq9+2Vb8FX5SN+Vjw+H/w5/nfxfziffR4AUEu1GHZ4GGILYnHow0NI9U/Fpg82QYWnAr4aHxO6TUBISojS/kJSQjC261hoq2u3+DljFGbMmIGQkCfP5/79+zF9+vRG5TZu3IgDBw5gz549uH79OhYtWoQpU6bg4sWLAICCggKMHj0a3t7eSElJwaxZs/Cf//znqceWSqXw8vJCdHQ0kpOT4enpCW9vb+Tn5yuV27p1K9555x0kJyfjk08+wb///W9kZGQ0u1+RSASxWMwti8VieHh4QCgUcvGKigrEx8dzV9/nzp3D/fv3sWTJkkb78/b2hr29PY4ePQoAGDBgAFRVVbl9paamoqKiAjNnzsT9+/eRm5vLHVdDQwP9+vV76vPAvFxvWpsuKSlBVFQU/P39wec30ec8nnNlb28PU1NTrl2WlZUhKSkJ48aNg5WVFS5detznxMWhsrKSa/vGxsa4fv06/vij5X0O04ZakmE9LdO8d08xilT3uH376ftKT1eMPP3++9PLSaXK+83ObklNn4i/FU8IAB1PPf7MspPDJ9OQg0OIiCjyRiR1DepKRER+EX606sIqIiL67MJn1OXLLk/qVyklr8NehACQ5XZLGn9sPAUnBZOsWtbscQ5eOUgOgQ5UW1vLxSprKklznSadzTpLRIrRmbrRpTpNxeqPPD2UPST1teq0L3Ffk8dtOPK09uJa7nzrFPxVQAgAZdxTDAkKQ4T03v73lMr0+boPLT+/nIiIzmadpXZr2nHlG4q/FU8qa1Toz4d/EhHRXeldav+/9iTJlTRZ/pX580+iq1cbx5OTiQoLlWPFxUSJiY3LXr9OVFCgHPvrL0XZykrleFlZq6pXd5VeVFRE6urqlJeXR3l5eaShoUHFxcVKV+kymYy0tLQoLi5OaR8zZ86kiRMnEhHRp59+Sl27dlVav3z58qdepTfF2dmZAgMDuWVLS0uaMmUKt1xbW0tGRka0e/fuZvexb98+4vP5VF1dTQ8fPqT27dtTUVERHTlyhNzd3YmIKDo6mgDQzZs3iYho06ZNSnVtaMSIEeTk5MQtDxgwgPz8/IiIKCgoiLy8vIiIaMiQIbR//34iIpo6dSqJRKKnnu8/yZ9/Kppe3aPha+ejR4p4w6aYl6e8XRs16Te2TcfHxxMAOn68BX3O5Mk0ZMjjPicykqu/n58frVr1uM/57DPq0qVenyOVkpeXFwEgS0tLGj9+PAUHB5NM1nyf0xbYyJPCC488nT4NzJ795PH1182XLS0FAgMVv2/bplhuzvXryvtdvrx19aJnf98xx8PKA7H5saiWV0OSJ4GHpQcAQGglhOSmBAAgyZNwo04AwFfjI3JSJLLmZWGl+0oI1ARYfG4x3L5xw6PqR00e50rhFWSVZEF7ozYEGwQQbBDAYLMBZDUyZJdkN7lNS6TdS0OlvBKDugxqUfkrd69AnCvm6iDYIIDjV44AoFSP7kbdlbYz0TZBUXkRACClMAWddTrD3tC+yWO4mbnB2cgZ3175FgBw6OohWOpawt3SvdXn16b27gWGDWscd3cHDh9Wjp08Cbi6Ni47bpyiQdd36ZKibFGRcvzateeqZseOHTF8+HCEhoYiJCQEw4cPR4cOHZTKZGVl4dGjRxg8eDAEAgH3OHDgADfZOi0tDe+++67Sds8acZFKpViyZAmcnJygp6cHgUCAtLS0Rlfp3bs/aS88Hg/GxsYoanj+9Xh4eKC8vBwJCQmIiYmBvb09OnbsCKFQyM17kkgksLa2bjQPpqX/3x4eHtw8EolEwk1GFwqFSvG6q/s3wd69iqZX92j42pmTo4g3bIpr1ypv18ZN+o1r063qczw8EBsbi+rq6ha3Sz6fj8jISGRlZWHlypUQCARYvHgx3Nzc8OhR030O03ZeeAaxtzfQv/+TZYGg6XISCbB9O8DjKf4BMzMBX19g0SKgwZtrAADOzooXAa6iraypnaEdeOAh/V76M8uKrEQory5Hwp8JEOeJsbT/UgCA0FKIGadmoKSiBPG34zHbdXajbW0MbGBjYINZvWdhxcAVsP/KHt/98R2m92o8/CytksLV1BWHRx9utK6jVsfWnWA9mu01W1VeWiWFt4M3Nn+wudE6E4EJ97uqiqrSOh54qKXaFh9zVq9ZCEoIwn/e+w9CUkIwved08Hi8VtW1zc2eDYwZ0zj+yy+AiYlybNQooHfvxmWPHQN0dJRj/foBiYlAw0mqLi7PXdUZM2Zg7ty5AICgoKBG66VSKQAgMjISZmZmSuvU1dWf+7hLlizB+fPn8cUXX8DW1haampoYO3YsqqqqlMqpqjZoLzweamtrm92vra0tN+m7tLQUQqEQAGBqagpzc3PExcVBLBYrTeS2t1ck62lpaehf/4XnsbS0NHTt2pVbFolEWL9+PW7fvg2JRMLd7hMKhdi7dy+ys7NRUFDwRk0Wnz0bGDHiyXLDd+hbWyuapn2D657PPgM++eTJcsOm2wZN+o1q03Z2duDxeEhPb0GfIxJxFw5isRhLlz7uc4RCzJgxAyUlJYiPj8fs2U30OTY2sLGxwaxZs7BixQrY29vju+++a/KWJ9N2Xjh5MjRUPJ4lNxfo2RNYuBDQ11eMOn35JZCX13R5Pr/xP3drGGgaYKjtUAQlBGH+u/MbzXt6IHvAzXuyMbCBuY45IjIikFKYAqGl4kXcTMcMZjpm2Bq3FVXyKu6dds2x0rOClqoWyqvLm1zf26Q3vrv+HYz4RtBR12myzPOwM7SDZntNROdGY5b+rGeW723cG+Fp4bDSs3rud+B179Qdtx7ewo37N5odfZrSfQqW/bwMO+N3IrU4FT49fZ7rWG3KxKRxkgQoGmtDHTooHg3V66w5OjpNJ1rNXV20gKenJ6qqqsDj8TB06NAmqtEV6urqyM/P5xKRhpycnBAREaEU++2335563NjYWPj6+uLDDz8EoOjQ8pr7x22lunfElZaWch0IALi7u+PMmTO4fPky/v3vf3PxIUOGwMDAAFu3bm2UPEVERCAzMxNr167lYv3794eamhp27doFmUwG18cjh3369EFxcTH2798PPp8PNze3l3I+r4PmmnQdTc2mm6alpeLRnDZo0m9UmzYwMMDQoUMRFBSE+fPnN5r39ODBA27ek42NDczNzREREYGUlBTu3MzMzGBmZoatW7eiqqrqmSOiVlZW0NLSQnl5030O03b+tgnjPj7AmjWKxAlQ/FyzBpg2re2OGeQVBDnJ4faNG8JTw5F5PxNpxWnYGb8T/YKVh3VFXUTYlbALtga2ShOthZZCBF4O5CaW1wmQBGDZ+WWQ5EmQW5qL5DvJmBExA9Xyagy2HtxkfSZ3n4wOWh0wMmwkYm7GILc0F5I8CeafmY9bD28993lqtNfA8gHLsez8Mhy4cgDZJdn47dZvCE4KbrK8v5s/SipKMDF8IhJuJyC7JBtns85i+qnpkNfKm9ymIaGVEO6W7hjz/Riczz6P3NJcnMk8g6isKK6MvqY+RjuNxtLzSzHEZgg663R+7nNkABUVFaSlpSE1NRUqKiqN1mtra2PJkiVYtGgRvv32W2RnZyMpKQmBgYH49lvF7dM5c+YgMzMTS5cuRUZGBo4cOdLoM48asrOzw/Hjx5GSkoIrV65g0qRJTx1Rag2RSIRff/1VqQMBnowMNexA+Hw+9u7di1OnTsHPzw9Xr15FXl4egoOD4evri7Fjx+Kjj5688UFTUxN9+/ZFYGAgBgwYwD1vampqSvGGIwzM3+NNa9NBQUGQy+Vwc3NDeHg4MjMzkZaWhp07dza6lSgSibBr1y7Y2tqiU6d6fY5QiMDAQG5ieZ2AgAAsW7YMEokEubm5SE5OxowZM1BdXY3Bg5vuc5i287clT+2aOVJz8ZfBWt8aSX5JEFmJsPjcYnTb3Q2DDw5GdG40dg/frVRWZCVCWVUZN9+pjtBSiLKqMqX5TnXxnNIcTDsxDY5Bjhh2eBgKpYU4N/UcHDo4NFkfLVUt/DL9F1joWmD096PhFOSEmREzIauRvfBI1GfCz7C432KsEq+CU5ATxv8wnpuf1JCptiliZ8RCXivHkEND4LLbBQvPLoSeuh7a8Vr+Bwn/KBx9TPtgYvhEdN3VFct+XtYo+ZrZayaq5FWY0XPGC50fo6CjowOdhrcI61m7di0+++wzbNy4EU5OTvD09ERkZCS6dOkCALCwsEB4eDhOnjyJHj16YM+ePdiwYcNTj7lt2zbo6+ujf//+8Pb2xtChQ9G7qSGI5yASiVBRUdFkB1JWVgYHBweYNBhGGTt2LMRiMfLz8zFw4EA4ODhg+/btWLFiBcLCwhrdGhaJRCgrK2v04Zt1x3iT5jv9E71Jbdra2hpJSUkQiURYvHgxunXrhsGDByM6Ohq7dzfoc1rZLoVCIXJycjBt2jQ4Ojpi2LBhKCwsxLlz5+Dg0HSfw7QdHrVglptMJkNubi66dOkCDQ2Nv6NezBvi4JWDWHR2Ef5c/CfUVNRedXUYhmGYF8DyAQX2kdNMm3hU/Qh3yu5gU+wmzHadzRInhmEY5o3xxn5IJvNqfR77ORyDHGEsMManAz991dVhGIZhmJeG3bZjGIZhGKZFWD6gwEaeGIZhGIZhWoElTwzDMAzDMK3AkieGYRiGYZhWYMkTwzAMwzBMK7DkiWEYhmEYphVY8sQwDMMwDNMKb33y5BHqgYVRC1+7fRZKCzH44GDwN/Cht0mv2RhvDQ8n00+2aJ8BkgD03NPzherFMAzDMG+7N/oTxn1P+uKB7AFOTjj5qqvSatsvbcedsjtImZ0CXQ3dZmN3Ft+BvoZ+i/a5pP8SzHOb12Z1ZhiGYZi3wRudPP2TZZdmw9XUFXaGdk+NGQuMW7xPgZoAAjXBS60nwzAMw7xt3qrbduVV5Zh2YhoEGwQw2WqCrXFbG5WprKnEknNLYLbNDPwNfLz7zbuQ5Em49fcf3cfE8Ikw22YGrfVacNntgqPXjra6LqfST6H33t7QWKcB6x3WWCNZg5raGgCA1ZdWCE8Lx4ErB8Bbw4PvSd8mY0Dj23a3Ht7CxPCJMNhsAP4GPt75+h3E34oH0PRtu2+SvoFTkBM01mnA8StH7ErYxa3Le5AH3hoejqcdh+hbEbTWa6HHnh64VHBJaR+x+bHwCPWA1not6G/Wx9BDQ1FaUYoDVw7A8HNDVNZUKpUfFTYKU09MbfVzxjAMwzCvg7dq5Gnp+aW4ePMiTk04BSO+Ef574b9IupOEnsY9uTJzf5qL1HupCBsTBlNtU5xIPwHPQ5649u9rsDO0g6xGBlcTVywfsBw66jqIvBGJqSemwsbABm5mbi2qR8zNGEw7OQ07PXdioOVAZJdkw+9HPwDAao/VSPg4AdNOToOOug52eO6AZntNVMmrGsUaklZJIQwVwkzbDBETI2AsMEbSnSTUUm2T9Th89TBWiVfhK6+v0Mu4F5ILk/Hx6Y/BV+XDp6cPV27FhRX4YvAXsDO0w4oLKzAxfCKy5mehfbv2SClMwaADgzCj1wzs8NyB9u3aQ5wnhpzkGNd1HOafmY+IjAiMcx4HACgqL0JkZiTOTTnX0j8bwzAMw7xWXih5elT9COn30l9WXVrEsYMjtFS1Wr2dtEqK4ORgHPrwEAZZDwIAfDvqW3Te1pkrk/9XPkJSQpC/KB+m2qYAFPOEorKiEJISgg2DNsBMxwxL+i/htpn37jyczT6L769/3+Lkac3FNfjPgP9wCYq1vjXWitZi2fllWO2xGh35HaGuog7N9ppKt+WaitV35NoRFJcXI+HjBBhoGgAAbA1sm63HaslqbB2yFaOdRgMAuuh3QWpxKvYm7lVKnpb0W4Lh9sMVdfdYA+ddzsgqyYJjB0d8Hvs53jF9B7uGPxmxcjZy5n6f5DIJISkhXPJ06OohWOhawMPKo0XPFcMwDMO8bl4oeUq/lw7Xr11fVl1aJNEvEb1Nerd6u+ySbFTJq/Bu53e5mIGmARw6OHDL1+5eg5zksA+0V9q2Ul4JQy1DAIC8Vo4NMRvwfer3uP3wNqrkVaiUV7Yqobty9wpiC2KxPmY9F5OTHLIaGR5VP3qu5BAAUgpT0MukF5c4PU15VTmyS7MxM2ImPj79MRevqa3hJqPX6d6pO/e7icAEgGIEybGDI1IKUzCu67hmj/Nx74/RZ18f3H54G2Y6ZghNCYVvD1/weLzWnh7DMAzDvBZeKHly7OCIRL/El1WXFh+zrUirpFDhqSDRLxEq7VSU1tVNtN4StwU74nfgS88v4WLkAr4aHwujFqJKXtWq46zxWMON+NSn0f75v6W6qVt5T6sDAOzz3qeUUAKACk/53FVVVLnf65KeuluBmqpPP2Yvk17oYdwDB64cwBCbIbhefB2RPSNbXE+GYRiGed28UPKkpar1XKNAr4KNgQ1U26ki/lY8LHQtAAClFaW4cf8GhJZCAIqOXk5yFJUXYaDlwCb3E1sQi5EOIzGl+xQAiiTixv0b6Nqxa4vr0tukNzLuZTz1ltrz6N6pO75J/gYlFSXPHH3qJOgEU21T5JTmYHL3yS90zOjcaKwRrWm2zKxes/Bl/Je4XXYbH1h/AHNd8+c+HsMwDMO8am/Nu+0EagLM7DUTS88vxYXcC/ij6A/4nvJFO96Tp8De0B6TXSZj2slpOJ52HLmlubh8+zI2xmxE5A3FaImdgR3O55xHXEEc0orTMPv0bNwtv9uquqxyX4UDVw9gjWQNrhddR1pxGsL+CMPKCytf6BwnukyEscAYo8JGITY/FjmlOQhPDW/07rg6azzWYOOvG7Ezfidu3L+Ba3evISQ5BNsubWvxMT9971Mk/JmATyI/wdW7V5F+Lx27E3bj3qN7XJlJLpNw6+Et7Evahxk9Z7zQOTIMwzDMq/ZWvdtuy5AtkFZL4X3UG9pq2ljcbzH+kv2lVCZkZAjW/bIOi88txu2Ht9FBqwP6du6Lf9n/CwCw0n0lckpzMPTQUGipasGvtx9GOY5qtJ+nGWo7FD9O/BH/++V/2By7GaoqqnDs4IhZvWa90Pmpqajh3JRzWHxuMbyOeKGmtgZdO3ZFkFdQk+Vn9Z4FLVUtbInbgqXnl4KvyodLJxcsfHdhi49pb2iPc1PO4b8X/gu3fW7QVNXEu2bvYqLLRK6MroYuxjiNQWRmJEY5jnqhc2QYhmGYV41HRPSsQjKZDLm5uejSpQs0NJ5/Tg7z9hp0YBCcOzpj57Cdr7oqDMMwzHNi+YDCWzXyxPz9SitKIcmTQJInwS6vXc/egGEYhmFecyx5YtpUr729UCorxeYPNit9LATDMAzD/FOx5IlpU3kL8151FRiGYRjmpXpr3m3HMAzDMAzzMrQqeWrB3HKGYRiGYd5QLA9QaFHypKqq+ITpR48etWllGIZhGIZ5fdXlAXV5wduqRXOeVFRUoKenh6KiIgCAlpYW+24yhmEYhnlLEBEePXqEoqIi6OnpQUVF5dkbvcFa9DlPgOKJKywsxIMHD9q4SgzDMAzDvI709PRgbGz81g+gtDh5qiOXy1FdXd1W9WEYhmEY5jWkqqr61o841Wl18sQwDMMwDPM2Yx9VwDAMwzAM0woseWIYhmEYhmkFljwxDMMwDMO0AkueGIZhGIZhWoElTwzDMAzDMK3AkieGYRiGYZhWYMkTwzAMwzBMK/w/8MzjPmlH5a8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x550 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print( \"Runtime depending on the \" )\n",
    "\n",
    "small = False\n",
    "\n",
    "# Set the default figure size globally\n",
    "mpl.rcParams['figure.figsize'] = (6.4, 4.0) if small else (6.4, 5.5)\n",
    "mpl.rcParams['font.size'] = 16\n",
    "\n",
    "def plotData( results, title, color, ax, linestyle = '-', marker = 'x' ): \n",
    "    keys = []\n",
    "    values = []\n",
    "    keysMedian = []\n",
    "    median = []\n",
    "    ideal = []\n",
    "\n",
    "    # simulate missing data\n",
    "    if ( len(results[1]) == 0 ):\n",
    "        if len(results[2]) > 0:\n",
    "            results[1] = [np.median(results[2]) * 2]\n",
    "        elif len(results[4]) > 0:\n",
    "            results[1] = [np.median(results[4]) * 4]\n",
    "            results[2] = [np.median(results[4]) * 2]\n",
    "\n",
    "    valueOnOneNodeLA = np.median(results[1])\n",
    "\n",
    "    for k, v in results.items():\n",
    "        if ( len(v) > 0 ):\n",
    "            keys += ([k] * len(v))\n",
    "            values += v\n",
    "            keysMedian.append(k)\n",
    "            median.append(np.median(v))\n",
    "            ideal.append( valueOnOneNodeLA / k )\n",
    "\n",
    "    efficiency = np.array(ideal) / np.array(median)\n",
    "\n",
    "    ax.scatter(keys, np.array(values) / 1000 / 60, label=title + \" makespan\", color=color, marker=marker)\n",
    "    ax.plot( keysMedian, np.array(median) / 1000 / 60, color=color, linestyle=linestyle, linewidth=1, label='Median ' + title)\n",
    "    #ax.plot( keysMedian, np.array(ideal) / 1000 / 60, color=color, linestyle='--', linewidth=1, label='Theo. ideal ' + title)\n",
    "\n",
    "    return keysMedian, efficiency\n",
    "\n",
    "def printScalabilityAndEfficiencySeperate():\n",
    "    for workflow in [\"chipseq\"]: #, \"allIntoOne\", \"chain\", \"fork\", \"group\", \"groupMultiple\"]:\n",
    "        for filesystem in [\"ceph\", \"nfs\"]:\n",
    "            laresults = {}\n",
    "            cephResults = {}\n",
    "            for nodes in [1,2,4,6,8]:\n",
    "                results = getResultsForWorkflow( workflow, filesystem, 1, nodes, [\"la\", \"cws\"] )\n",
    "                laresults[nodes] = results[\"makespan-la\"]\n",
    "                cephResults[nodes] = results[\"makespan-cws\"]\n",
    "\n",
    "            keysLA, effLA = plotData( laresults, \"WOW\", \"red\", plt )\n",
    "            keysCeph, effCeph = plotData( cephResults, \"CWS\", \"blue\", plt )\n",
    "\n",
    "            # Adding labels and title\n",
    "            plt.xlabel('Nodes')\n",
    "            plt.ylabel('Makespan in minutes')\n",
    "            plt.title('Makespan for ' + workflowNames[workflow] + \" on \" + filesystem + \" with 1Gbit/s\")\n",
    "\n",
    "            # Adding legend\n",
    "            plt.legend()\n",
    "\n",
    "            # Display the plot\n",
    "            plt.show()\n",
    "\n",
    "            plt.plot(keysLA, np.array(effLA) * 100, label=\"WOW\", color=\"red\", linestyle='-', linewidth=1, marker='x')\n",
    "            plt.plot(keysCeph, np.array(effCeph) * 100, label=\"CWS\", color=\"blue\", linestyle='-', linewidth=1, marker='x')\n",
    "            plt.plot(keysLA, np.ones(len(keysLA)) * 100, label=\"Ideal\", color=\"green\", linestyle='--', linewidth=1)\n",
    "            plt.xlabel('Nodes')\n",
    "            #format y axis in percent\n",
    "            ax = plt.gca()\n",
    "            ax.set_yticklabels(['{:.0f}%'.format(x) for x in ax.get_yticks()])\n",
    "            \n",
    "            plt.ylabel('Efficiency')\n",
    "            plt.title('Efficiency for ' + workflowNames[workflow] + \" on \" + filesystem + \" with 1Gbit/s\")\n",
    "            plt.legend()\n",
    "            plt.show()\n",
    "\n",
    "efficiencyResults = []\n",
    "\n",
    "def printScalabilityAndEfficiencyTogether():\n",
    "    generatedLegend = False\n",
    "    for workflow in [\"chipseq\", \"allIntoOne\", \"chain\", \"fork\", \"group\", \"groupMultiple\"]:\n",
    "        maxValue = 0\n",
    "        for filesystem in [\"nfs\",\"ceph\"]:\n",
    "            plt.rcParams['font.size'] = 14\n",
    "            laresults = {}\n",
    "            cephResults = {}\n",
    "            for nodes in [1,2,4,6,8]:\n",
    "                results = getResultsForWorkflow( workflow, filesystem, 1, nodes, [\"la\", \"cws\"] )\n",
    "                laresults[nodes] = results[\"makespan-la\"]\n",
    "                cephResults[nodes] = results[\"makespan-cws\"]\n",
    "\n",
    "            fig, ax1 = plt.subplots()\n",
    "\n",
    "            keysLA, effLA = plotData( laresults, \"WOW\", \"red\", ax1, (0, (1, 3)), \"x\" )\n",
    "            keysCeph, effCeph = plotData( cephResults, \"CWS\", \"blue\", ax1, (0, (3, 3, 1, 3)), \"+\" )\n",
    "\n",
    "            \n",
    "            # Adding labels and title\n",
    "            ax1.set_xlabel('Nodes')\n",
    "            if ( filesystem == \"ceph\" ):\n",
    "                ax1.set_ylabel('Makespan')\n",
    "            #ax1.title('Makespan for ' + workflowNames[workflow] + \" on \" + filesystem + \" with 1Gbit/s\")\n",
    "            ax1.tick_params(axis='y', labelcolor=\"black\")\n",
    "\n",
    "            #nfs is always slower\n",
    "            if ( filesystem == \"nfs\" ):\n",
    "                maxValue = (np.max(cephResults[1]) / 60 / 1000) * 1.1\n",
    "            if workflow == \"allIntoOne\":\n",
    "                ax1.set_ylim( 0, maxValue * 1.07 )\n",
    "            else:\n",
    "                ax1.set_ylim( 0, maxValue * 1.05 )\n",
    "            #ax1.legend(loc=0)\n",
    "\n",
    "            ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n",
    "\n",
    "            if ( filesystem == \"nfs\" ):\n",
    "                ax2.set_ylabel('Efficiency', color=\"green\")  \n",
    "            ax2.plot(keysLA, np.array(effLA) * 100, label=\"WOW efficiency\", color=\"#ff4040\", linestyle=(0, (1, 6)), linewidth=1, marker='1')\n",
    "            ax2.plot(keysCeph, np.array(effCeph) * 100, label=\"CWS efficiency\", color=\"#4040ff\", linestyle=(0, (3, 6, 1, 6)), linewidth=1, marker='2')\n",
    "            ax2.plot(keysLA, np.ones(len(keysLA)) * 100, label=\"Ideal efficiency\", color=\"green\", linestyle='-', linewidth=1)\n",
    "            ax2.tick_params(axis='y', labelcolor=\"green\")\n",
    "            ax2.set_ylim(-1, 105)\n",
    "\n",
    "            efficiencyResults.append( [workflow, filesystem, \"wow\"] + list(np.array(effLA) * 100) )\n",
    "            efficiencyResults.append( [workflow, filesystem, \"cws\"] + list(np.array(effCeph) * 100) )\n",
    "\n",
    "\n",
    "            lines, labels = ax1.get_legend_handles_labels()\n",
    "            lines2, labels2 = ax2.get_legend_handles_labels()\n",
    "            legend = ax2.legend(lines2 + lines, labels2 + labels, loc='upper center', bbox_to_anchor=(0.5, -0.13), ncol=3)\n",
    "\n",
    "            def percent_formatter(x, pos):\n",
    "                if ( filesystem == \"nfs\" ):\n",
    "                    return f'{x:.0f}%'\n",
    "                else:\n",
    "                    return \"\"\n",
    "            \n",
    "            def minutes_to_hour_minute(minutes, pos):\n",
    "                if ( filesystem == \"nfs\" ):\n",
    "                    return \"\"\n",
    "                hours, minutes = divmod(minutes, 60)\n",
    "                return f'{int(hours)}h {int(minutes)}m'\n",
    "\n",
    "            ax1.yaxis.set_major_formatter(FuncFormatter(minutes_to_hour_minute))\n",
    "            ax2.yaxis.set_major_formatter(FuncFormatter(percent_formatter))\n",
    "\n",
    "            plt.xticks(range(1, 9))\n",
    "            \n",
    "            fig.tight_layout()\n",
    "            name = plotsFolder + \"makespan_efficiency_\" + workflow + \"_\" + filesystem + (\"\" if small else \"_large\")\n",
    "            ax2.get_legend().remove()\n",
    "            plt.savefig( name + \".pdf\", bbox_inches='tight')\n",
    "            plt.rcParams['font.size'] = original_fontsize\n",
    "            plt.title('Performance for ' + workflowNames[workflow] + \" on \" + filesystemNames[filesystem] + \" with 1Gbit/s\")\n",
    "            legend = ax2.legend(lines2 + lines, labels2 + labels, loc='upper center', bbox_to_anchor=(0.5, -0.13), ncol=3)\n",
    "\n",
    "            for text in legend.get_texts():\n",
    "                if text.get_text().endswith(\" efficiency\"):\n",
    "                    text.set_color('green')\n",
    "            fig.tight_layout()\n",
    "            plt.savefig( name + \".jpg\")\n",
    "\n",
    "\n",
    "            if ( not generatedLegend ):\n",
    "                # Save only the legend\n",
    "                fig_legend, ax_legend = plt.subplots()\n",
    "                ax_legend.axis('off')  # Turn off the axis in the legend figure\n",
    "                print(legend.legendHandles)\n",
    "                print([x for x in legend.get_texts()])\n",
    "                order = [ 3,4,5,6,0,1,2]\n",
    "                handles = [legend.legendHandles[i] for i in order]\n",
    "                labelText = [x.get_text() for x in legend.get_texts()]\n",
    "                labelText = [ labelText[i] for i in order ]\n",
    "                labelText.insert(2, None)\n",
    "                legend = ax_legend.legend(handles=handles, labels=labelText, ncol=3)\n",
    "\n",
    "                for text in legend.get_texts():\n",
    "                    if text.get_text().endswith(\" efficiency\"):\n",
    "                        text.set_color('green')\n",
    "\n",
    "                fig_legend.tight_layout()\n",
    "                fig_legend.savefig( plotsFolder + 'legend_only.pdf', bbox_inches='tight')\n",
    "                generatedLegend = True\n",
    "            \n",
    "            plt.show()\n",
    "            return\n",
    "\n",
    "printScalabilityAndEfficiencyTogether()\n",
    "\n",
    "mpl.rcParams['figure.figsize'] = (6.4, 4.8)\n",
    "mpl.rcParams['font.size'] = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>Workflow</th>\n",
       "      <th>Filesystem</th>\n",
       "      <th>Strategy</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>6</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chipseq</td>\n",
       "      <td>nfs</td>\n",
       "      <td>wow</td>\n",
       "      <td>100.0</td>\n",
       "      <td>99.202334</td>\n",
       "      <td>96.947147</td>\n",
       "      <td>92.236921</td>\n",
       "      <td>85.740452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chipseq</td>\n",
       "      <td>nfs</td>\n",
       "      <td>cws</td>\n",
       "      <td>100.0</td>\n",
       "      <td>95.782597</td>\n",
       "      <td>74.636450</td>\n",
       "      <td>59.451718</td>\n",
       "      <td>48.110088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chipseq</td>\n",
       "      <td>ceph</td>\n",
       "      <td>wow</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.495993</td>\n",
       "      <td>99.395139</td>\n",
       "      <td>96.911484</td>\n",
       "      <td>95.219938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chipseq</td>\n",
       "      <td>ceph</td>\n",
       "      <td>cws</td>\n",
       "      <td>100.0</td>\n",
       "      <td>101.084368</td>\n",
       "      <td>96.062445</td>\n",
       "      <td>92.136464</td>\n",
       "      <td>85.572963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>allIntoOne</td>\n",
       "      <td>nfs</td>\n",
       "      <td>wow</td>\n",
       "      <td>100.0</td>\n",
       "      <td>32.343925</td>\n",
       "      <td>14.468327</td>\n",
       "      <td>9.179325</td>\n",
       "      <td>6.967892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>allIntoOne</td>\n",
       "      <td>nfs</td>\n",
       "      <td>cws</td>\n",
       "      <td>100.0</td>\n",
       "      <td>52.929841</td>\n",
       "      <td>26.606887</td>\n",
       "      <td>17.721348</td>\n",
       "      <td>13.290537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>allIntoOne</td>\n",
       "      <td>ceph</td>\n",
       "      <td>wow</td>\n",
       "      <td>100.0</td>\n",
       "      <td>31.729759</td>\n",
       "      <td>14.197071</td>\n",
       "      <td>8.919281</td>\n",
       "      <td>6.760457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>allIntoOne</td>\n",
       "      <td>ceph</td>\n",
       "      <td>cws</td>\n",
       "      <td>100.0</td>\n",
       "      <td>46.934488</td>\n",
       "      <td>25.682194</td>\n",
       "      <td>17.293932</td>\n",
       "      <td>13.232049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>chain</td>\n",
       "      <td>nfs</td>\n",
       "      <td>wow</td>\n",
       "      <td>100.0</td>\n",
       "      <td>91.735153</td>\n",
       "      <td>95.998405</td>\n",
       "      <td>93.215910</td>\n",
       "      <td>90.393218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>chain</td>\n",
       "      <td>nfs</td>\n",
       "      <td>cws</td>\n",
       "      <td>100.0</td>\n",
       "      <td>58.547018</td>\n",
       "      <td>29.143573</td>\n",
       "      <td>19.291545</td>\n",
       "      <td>14.367982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>chain</td>\n",
       "      <td>ceph</td>\n",
       "      <td>wow</td>\n",
       "      <td>100.0</td>\n",
       "      <td>95.835064</td>\n",
       "      <td>100.704446</td>\n",
       "      <td>96.282004</td>\n",
       "      <td>88.278752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>chain</td>\n",
       "      <td>ceph</td>\n",
       "      <td>cws</td>\n",
       "      <td>100.0</td>\n",
       "      <td>70.735127</td>\n",
       "      <td>50.047170</td>\n",
       "      <td>39.302704</td>\n",
       "      <td>31.976107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>fork</td>\n",
       "      <td>nfs</td>\n",
       "      <td>wow</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.846852</td>\n",
       "      <td>83.491442</td>\n",
       "      <td>66.747000</td>\n",
       "      <td>50.689529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>fork</td>\n",
       "      <td>nfs</td>\n",
       "      <td>cws</td>\n",
       "      <td>100.0</td>\n",
       "      <td>55.183079</td>\n",
       "      <td>29.107117</td>\n",
       "      <td>19.379400</td>\n",
       "      <td>14.349213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>fork</td>\n",
       "      <td>ceph</td>\n",
       "      <td>wow</td>\n",
       "      <td>100.0</td>\n",
       "      <td>92.728226</td>\n",
       "      <td>79.089300</td>\n",
       "      <td>62.887881</td>\n",
       "      <td>47.141810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>fork</td>\n",
       "      <td>ceph</td>\n",
       "      <td>cws</td>\n",
       "      <td>100.0</td>\n",
       "      <td>87.274881</td>\n",
       "      <td>63.525744</td>\n",
       "      <td>43.674743</td>\n",
       "      <td>35.249459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>group</td>\n",
       "      <td>nfs</td>\n",
       "      <td>wow</td>\n",
       "      <td>100.0</td>\n",
       "      <td>87.778136</td>\n",
       "      <td>59.917273</td>\n",
       "      <td>47.762715</td>\n",
       "      <td>39.897953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>group</td>\n",
       "      <td>nfs</td>\n",
       "      <td>cws</td>\n",
       "      <td>100.0</td>\n",
       "      <td>59.382720</td>\n",
       "      <td>32.500679</td>\n",
       "      <td>20.840492</td>\n",
       "      <td>16.246179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>group</td>\n",
       "      <td>ceph</td>\n",
       "      <td>wow</td>\n",
       "      <td>100.0</td>\n",
       "      <td>86.256139</td>\n",
       "      <td>63.242064</td>\n",
       "      <td>52.399283</td>\n",
       "      <td>42.563161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>group</td>\n",
       "      <td>ceph</td>\n",
       "      <td>cws</td>\n",
       "      <td>100.0</td>\n",
       "      <td>76.567069</td>\n",
       "      <td>52.778929</td>\n",
       "      <td>39.270288</td>\n",
       "      <td>30.903540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>groupMultiple</td>\n",
       "      <td>nfs</td>\n",
       "      <td>wow</td>\n",
       "      <td>100.0</td>\n",
       "      <td>99.781107</td>\n",
       "      <td>67.563832</td>\n",
       "      <td>51.136663</td>\n",
       "      <td>44.103280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>groupMultiple</td>\n",
       "      <td>nfs</td>\n",
       "      <td>cws</td>\n",
       "      <td>100.0</td>\n",
       "      <td>67.773913</td>\n",
       "      <td>35.342158</td>\n",
       "      <td>24.026478</td>\n",
       "      <td>17.453173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>groupMultiple</td>\n",
       "      <td>ceph</td>\n",
       "      <td>wow</td>\n",
       "      <td>100.0</td>\n",
       "      <td>95.273202</td>\n",
       "      <td>66.715371</td>\n",
       "      <td>54.768015</td>\n",
       "      <td>47.524033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>groupMultiple</td>\n",
       "      <td>ceph</td>\n",
       "      <td>cws</td>\n",
       "      <td>100.0</td>\n",
       "      <td>81.253788</td>\n",
       "      <td>56.949510</td>\n",
       "      <td>42.509959</td>\n",
       "      <td>33.600157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Workflow Filesystem Strategy      1           2           4  \\\n",
       "0         chipseq        nfs      wow  100.0   99.202334   96.947147   \n",
       "1         chipseq        nfs      cws  100.0   95.782597   74.636450   \n",
       "2         chipseq       ceph      wow  100.0  100.495993   99.395139   \n",
       "3         chipseq       ceph      cws  100.0  101.084368   96.062445   \n",
       "4      allIntoOne        nfs      wow  100.0   32.343925   14.468327   \n",
       "5      allIntoOne        nfs      cws  100.0   52.929841   26.606887   \n",
       "6      allIntoOne       ceph      wow  100.0   31.729759   14.197071   \n",
       "7      allIntoOne       ceph      cws  100.0   46.934488   25.682194   \n",
       "8           chain        nfs      wow  100.0   91.735153   95.998405   \n",
       "9           chain        nfs      cws  100.0   58.547018   29.143573   \n",
       "10          chain       ceph      wow  100.0   95.835064  100.704446   \n",
       "11          chain       ceph      cws  100.0   70.735127   50.047170   \n",
       "12           fork        nfs      wow  100.0  100.846852   83.491442   \n",
       "13           fork        nfs      cws  100.0   55.183079   29.107117   \n",
       "14           fork       ceph      wow  100.0   92.728226   79.089300   \n",
       "15           fork       ceph      cws  100.0   87.274881   63.525744   \n",
       "16          group        nfs      wow  100.0   87.778136   59.917273   \n",
       "17          group        nfs      cws  100.0   59.382720   32.500679   \n",
       "18          group       ceph      wow  100.0   86.256139   63.242064   \n",
       "19          group       ceph      cws  100.0   76.567069   52.778929   \n",
       "20  groupMultiple        nfs      wow  100.0   99.781107   67.563832   \n",
       "21  groupMultiple        nfs      cws  100.0   67.773913   35.342158   \n",
       "22  groupMultiple       ceph      wow  100.0   95.273202   66.715371   \n",
       "23  groupMultiple       ceph      cws  100.0   81.253788   56.949510   \n",
       "\n",
       "            6          8  \n",
       "0   92.236921  85.740452  \n",
       "1   59.451718  48.110088  \n",
       "2   96.911484  95.219938  \n",
       "3   92.136464  85.572963  \n",
       "4    9.179325   6.967892  \n",
       "5   17.721348  13.290537  \n",
       "6    8.919281   6.760457  \n",
       "7   17.293932  13.232049  \n",
       "8   93.215910  90.393218  \n",
       "9   19.291545  14.367982  \n",
       "10  96.282004  88.278752  \n",
       "11  39.302704  31.976107  \n",
       "12  66.747000  50.689529  \n",
       "13  19.379400  14.349213  \n",
       "14  62.887881  47.141810  \n",
       "15  43.674743  35.249459  \n",
       "16  47.762715  39.897953  \n",
       "17  20.840492  16.246179  \n",
       "18  52.399283  42.563161  \n",
       "19  39.270288  30.903540  \n",
       "20  51.136663  44.103280  \n",
       "21  24.026478  17.453173  \n",
       "22  54.768015  47.524033  \n",
       "23  42.509959  33.600157  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "efficiencyResultsDf = pd.DataFrame(efficiencyResults, columns=[\"Workflow\", \"Filesystem\", \"Strategy\", \"1\", \"2\", \"4\", \"6\", \"8\"])\n",
    "efficiencyResultsDf.to_csv(\"evaluation\" + os.sep + \"efficiency.csv\")\n",
    "efficiencyResultsDf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data overhead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Fabian Lehmann\\AppData\\Local\\Temp\\ipykernel_10876\\3404570384.py:106: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels(['{:.0f}%'.format(x) for x in ax.get_yticks()])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = set()\n",
    "\n",
    "colorMarker = {\n",
    "    'nfs-2-8' : (\"4\", \"purple\"),\n",
    "    'nfs-1-8' : (\"1\", \"blue\"), \n",
    "    'nfs-1-6' : (\"*\", \"pink\"),\n",
    "    'nfs-1-4' : (\"|\", \"yellow\"),\n",
    "    'nfs-1-2' : (\"2\", \"green\"), \n",
    "    'ceph-1-2': (\"3\", \"orange\"),\n",
    "    'ceph-1-4' : (\"+\", \"black\"),\n",
    "    'ceph-1-6' : (\"8\", \"brown\"),\n",
    "    'ceph-1-8' : (\".\", \"red\"), \n",
    "    'ceph-2-8' : (\"x\", \"coral\"),\n",
    "}\n",
    "\n",
    "\n",
    "colorMarker = {\n",
    "    'nfs-2-8' : (\"x\", \"blue\"),\n",
    "    'nfs-1-8' : (\"+\", \"blue\"), \n",
    "    'nfs-1-6' : (\"o\", \"blue\"),\n",
    "    'nfs-1-4' : (\"*\", \"blue\"),\n",
    "    'nfs-1-2' : (\"1\", \"blue\"), \n",
    "    'nfs-1-1' : (\"p\", \"blue\"), \n",
    "    'ceph-2-8' : (\"x\", \"red\"),\n",
    "    'ceph-1-8' : (\"+\", \"red\"), \n",
    "    'ceph-1-6' : (\"o\", \"red\"),\n",
    "    'ceph-1-4' : (\"*\", \"red\"),\n",
    "    'ceph-1-2': (\"1\", \"red\"),\n",
    "    'ceph-1-1': (\"p\", \"red\"),\n",
    "}\n",
    "\n",
    "colorMarkerIndex = list(colorMarker.keys())\n",
    "workflowNamesIndex = list(workflowNames.keys())\n",
    "avgClusterSpeed = []\n",
    "\n",
    "sharedData = {}\n",
    "\n",
    "maxValue = 0\n",
    "\n",
    "mpl.rcParams['figure.figsize'] = (6.4, 4)\n",
    "\n",
    "for r in resultsRAW:\n",
    "    if r[\"nodes\"] == 1:\n",
    "        continue\n",
    "    if r[\"strategy\"] != \"la\":\n",
    "        if ( r[\"workflow\"] not in sharedData ):\n",
    "            sharedData[r[\"workflow\"]] = []\n",
    "        sharedData[r[\"workflow\"]].append( r[\"sharedData\"][\"workData\"] )\n",
    "for r in resultsRAW:\n",
    "    if r[\"nodes\"] == 1:\n",
    "        continue\n",
    "    if r[\"strategy\"] == \"la\":\n",
    "        localData = r[\"localData\"]\n",
    "        sumLocalData = 0\n",
    "        for node in localData:\n",
    "            sumLocalData += localData[node]\n",
    "        identifier = r[\"filesystem\"] + \"-\" + str(r[\"networkspeed\"]) + \"-\" + str(r[\"nodes\"])\n",
    "        labels.add(identifier)\n",
    "        label = str(r[\"networkspeed\"]) + \"Gbit/s \" + str(r[\"nodes\"]) + \" nodes\"\n",
    "        factor = (sumLocalData - np.median(sharedData[r[\"workflow\"]])) / (np.median(sharedData[r[\"workflow\"]])) * 100\n",
    "        avgClusterSpeed.append( {\n",
    "            \"workflow\" : r[\"workflow\"],\n",
    "            \"networkspeed\" : r[\"networkspeed\"],\n",
    "            \"nodes\" : r[\"nodes\"],\n",
    "            \"filesystem\" : r[\"filesystem\"],\n",
    "            \"avgTransferSpeed\" : (sumLocalData - np.median(sharedData[r[\"workflow\"]])) * 1024 / (r[\"trace\"][\"makespan\"] / 1000),\n",
    "            \"factor\" : factor,\n",
    "        })\n",
    "        if ( factor > maxValue ):\n",
    "            maxValue = factor\n",
    "        x = workflowNamesIndex.index(r[\"workflow\"]) + (colorMarkerIndex.index(identifier) / len(colorMarker) - 0.5) * 0.3\n",
    "        plt.scatter( x, factor, label=label, color=colorMarker[identifier][1], marker=colorMarker[identifier][0], zorder= 5)\n",
    "\n",
    "plt.hlines(100, -1, len(workflowNamesIndex), colors='lightgrey', linestyles='solid', label=\"Ceph overhead\")\n",
    "plt.xticks(range(len(workflowNamesIndex)), [workflowNames[x] for x in workflowNamesIndex], rotation=80)\n",
    "plt.xlabel('Workflow')\n",
    "plt.ylabel('Factor data generation overhead')\n",
    "plt.xticks(rotation=80)\n",
    "plt.xlim(-1, len(workflowNamesIndex))\n",
    "plt.ylim(-10, maxValue * 1.1)\n",
    "#plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.13), ncol=3)\n",
    "\n",
    "ax = plt.gca()\n",
    "legend_main = ax.legend(loc='upper right')\n",
    "\n",
    "# Filter out duplicates directly in the legend\n",
    "handles, labels = [], []\n",
    "unique_labels = set()\n",
    "\n",
    "for handle, label in zip(legend_main.legendHandles, legend_main.get_texts()):\n",
    "    if label.get_text() not in unique_labels:\n",
    "        if label.get_text() != \"Ceph overhead\":\n",
    "            handle.set_color('black')\n",
    "        handles.append(handle)\n",
    "        labels.append(label.get_text())\n",
    "        unique_labels.add(label.get_text())\n",
    "\n",
    "\n",
    "for i in workflowNamesIndex:\n",
    "    plt.vlines(i, -1000, 1000, colors='#f5f5f5', linestyles='solid', linewidth=10, zorder=0 )\n",
    "\n",
    "# Update the legend in the main plot\n",
    "legend_main.remove()\n",
    "ax.legend(handles=handles, labels=labels, loc='upper right', ncol=2)\n",
    "plt.tight_layout()\n",
    "ax.set_yticklabels(['{:.0f}%'.format(x) for x in ax.get_yticks()])\n",
    "plt.savefig( plotsFolder + \"data_generation_overhead.pdf\", bbox_inches='tight')\n",
    "plt.show()\n",
    "mpl.rcParams['figure.figsize'] = (6.4, 4.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Fabian Lehmann\\AppData\\Local\\Temp\\ipykernel_10876\\2989305818.py:70: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(['{:.0f}%'.format(x) for x in ax.get_xticks()])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# figure with x-y switch\n",
    "plt.figure(figsize=(8, 3.5))\n",
    "labels = set()\n",
    "\n",
    "avgClusterSpeed = []\n",
    "\n",
    "sharedData = {}\n",
    "\n",
    "maxValue = 0\n",
    "\n",
    "for r in resultsRAW:\n",
    "    if r[\"nodes\"] == 1:\n",
    "        continue\n",
    "    if r[\"strategy\"] != \"la\":\n",
    "        if ( r[\"workflow\"] not in sharedData ):\n",
    "            sharedData[r[\"workflow\"]] = []\n",
    "        sharedData[r[\"workflow\"]].append( r[\"sharedData\"][\"workData\"] )\n",
    "for r in resultsRAW:\n",
    "    if r[\"nodes\"] == 1:\n",
    "        continue\n",
    "    if r[\"strategy\"] == \"la\":\n",
    "        localData = r[\"localData\"]\n",
    "        sumLocalData = 0\n",
    "        for node in localData:\n",
    "            sumLocalData += localData[node]\n",
    "        identifier = r[\"filesystem\"] + \"-\" + str(r[\"networkspeed\"]) + \"-\" + str(r[\"nodes\"])\n",
    "        labels.add(identifier)\n",
    "        label = str(r[\"networkspeed\"]) + \"Gbit/s \" + str(r[\"nodes\"]) + \" nodes\"\n",
    "        factor = (sumLocalData - np.median(sharedData[r[\"workflow\"]])) / (np.median(sharedData[r[\"workflow\"]])) * 100\n",
    "        avgClusterSpeed.append( {\n",
    "            \"workflow\" : r[\"workflow\"],\n",
    "            \"networkspeed\" : r[\"networkspeed\"],\n",
    "            \"nodes\" : r[\"nodes\"],\n",
    "            \"filesystem\" : r[\"filesystem\"],\n",
    "            \"avgTransferSpeed\" : (sumLocalData - np.median(sharedData[r[\"workflow\"]])) * 1024 / (r[\"trace\"][\"makespan\"] / 1000),\n",
    "            \"factor\" : factor,\n",
    "        })\n",
    "        if ( factor > maxValue ):\n",
    "            maxValue = factor\n",
    "        x = workflowNamesIndex.index(r[\"workflow\"]) + (colorMarkerIndex.index(identifier) / len(colorMarker) - 0.5) * 0.3\n",
    "        plt.scatter( factor, x, label=label, color=colorMarker[identifier][1], marker=colorMarker[identifier][0], zorder= 5, s=15)\n",
    "\n",
    "plt.vlines(100, -1, len(workflowNamesIndex), colors='lightgrey', linestyles='solid', label=\"Ceph overhead\")\n",
    "plt.yticks(range(len(workflowNamesIndex)), [workflowNames[x] for x in workflowNamesIndex])\n",
    "plt.ylabel('Workflow')\n",
    "plt.xlabel('Factor data generation overhead')\n",
    "#plt.xticks(rotation=80)\n",
    "plt.ylim(-1, len(workflowNamesIndex))\n",
    "plt.xlim(-3, maxValue * 1.01)\n",
    "#plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.13), ncol=3)\n",
    "\n",
    "ax = plt.gca()\n",
    "legend_main = ax.legend(loc='upper right')\n",
    "\n",
    "# Filter out duplicates directly in the legend\n",
    "handles, labels = [], []\n",
    "unique_labels = set()\n",
    "\n",
    "for handle, label in zip(legend_main.legendHandles, legend_main.get_texts()):\n",
    "    if label.get_text() not in unique_labels:\n",
    "        if label.get_text() != \"Ceph overhead\":\n",
    "            handle.set_color('black')\n",
    "        handle._sizes = [80]\n",
    "        handles.append(handle)\n",
    "        labels.append(label.get_text())\n",
    "        unique_labels.add(label.get_text())\n",
    "\n",
    "\n",
    "\n",
    "ax.set_xticklabels(['{:.0f}%'.format(x) for x in ax.get_xticks()])\n",
    "\n",
    "for i in workflowNamesIndex:\n",
    "    plt.hlines(i, -1000, 1000, colors='#f5f5f5', linestyles='solid', linewidth=10, zorder=0 )\n",
    "\n",
    "# Update the legend in the main plot\n",
    "legend_main.remove()\n",
    "ax.legend(handles=handles, labels=labels, loc='upper right', ncol=2)\n",
    "plt.tight_layout()\n",
    "plt.savefig( plotsFolder + \"data_generation_overhead_inverted.pdf\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "avgClusterSpeedDf = pd.DataFrame(avgClusterSpeed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "avgClusterSpeedDf2 = avgClusterSpeedDf[(avgClusterSpeedDf[\"networkspeed\"] == 2) & (avgClusterSpeedDf[\"nodes\"] == 8)].groupby([\"workflow\", \"filesystem\"]).median()\n",
    "avgClusterSpeedDf1 = avgClusterSpeedDf[(avgClusterSpeedDf[\"networkspeed\"] == 1) & (avgClusterSpeedDf[\"nodes\"] == 8)].groupby([\"workflow\", \"filesystem\"]).median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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></th>\n",
       "      <th>1Gbit/s avgTransferSpeed</th>\n",
       "      <th>2Gbit/s avgTransferSpeed</th>\n",
       "      <th>1Gbit/s factor</th>\n",
       "      <th>2Gbit/s factor</th>\n",
       "      <th>speedDiff</th>\n",
       "      <th>factorDiff</th>\n",
       "      <th>speedDiffRelative</th>\n",
       "      <th>factorDiffRelative</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>workflow</th>\n",
       "      <th>filesystem</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">allIntoOne</th>\n",
       "      <th>ceph</th>\n",
       "      <td>160.569285</td>\n",
       "      <td>245.173584</td>\n",
       "      <td>86.073713</td>\n",
       "      <td>86.406242</td>\n",
       "      <td>84.604300</td>\n",
       "      <td>0.332529</td>\n",
       "      <td>52.690214</td>\n",
       "      <td>0.386331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nfs</th>\n",
       "      <td>161.572628</td>\n",
       "      <td>241.794431</td>\n",
       "      <td>85.370805</td>\n",
       "      <td>85.590656</td>\n",
       "      <td>80.221803</td>\n",
       "      <td>0.219851</td>\n",
       "      <td>49.650615</td>\n",
       "      <td>0.257524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">chain</th>\n",
       "      <th>ceph</th>\n",
       "      <td>275.735106</td>\n",
       "      <td>517.641053</td>\n",
       "      <td>19.704287</td>\n",
       "      <td>35.764553</td>\n",
       "      <td>241.905947</td>\n",
       "      <td>16.060265</td>\n",
       "      <td>87.731283</td>\n",
       "      <td>81.506453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nfs</th>\n",
       "      <td>312.086065</td>\n",
       "      <td>561.819301</td>\n",
       "      <td>20.878913</td>\n",
       "      <td>38.373216</td>\n",
       "      <td>249.733236</td>\n",
       "      <td>17.494303</td>\n",
       "      <td>80.020630</td>\n",
       "      <td>83.789339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">chipseq</th>\n",
       "      <th>ceph</th>\n",
       "      <td>208.139700</td>\n",
       "      <td>255.557500</td>\n",
       "      <td>291.145733</td>\n",
       "      <td>356.123738</td>\n",
       "      <td>47.417799</td>\n",
       "      <td>64.978005</td>\n",
       "      <td>22.781718</td>\n",
       "      <td>22.318034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nfs</th>\n",
       "      <td>199.889483</td>\n",
       "      <td>265.649449</td>\n",
       "      <td>308.280995</td>\n",
       "      <td>377.530982</td>\n",
       "      <td>65.759966</td>\n",
       "      <td>69.249988</td>\n",
       "      <td>32.898162</td>\n",
       "      <td>22.463269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">fork</th>\n",
       "      <th>ceph</th>\n",
       "      <td>52.243270</td>\n",
       "      <td>67.294911</td>\n",
       "      <td>6.929115</td>\n",
       "      <td>6.929111</td>\n",
       "      <td>15.051641</td>\n",
       "      <td>-0.000004</td>\n",
       "      <td>28.810679</td>\n",
       "      <td>-0.000055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nfs</th>\n",
       "      <td>55.984388</td>\n",
       "      <td>73.162046</td>\n",
       "      <td>6.929119</td>\n",
       "      <td>6.929115</td>\n",
       "      <td>17.177658</td>\n",
       "      <td>-0.000004</td>\n",
       "      <td>30.682943</td>\n",
       "      <td>-0.000055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">group</th>\n",
       "      <th>ceph</th>\n",
       "      <td>339.556333</td>\n",
       "      <td>513.788192</td>\n",
       "      <td>34.028656</td>\n",
       "      <td>39.502172</td>\n",
       "      <td>174.231859</td>\n",
       "      <td>5.473516</td>\n",
       "      <td>51.311621</td>\n",
       "      <td>16.085018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nfs</th>\n",
       "      <td>326.694300</td>\n",
       "      <td>556.057393</td>\n",
       "      <td>34.972059</td>\n",
       "      <td>38.741829</td>\n",
       "      <td>229.363093</td>\n",
       "      <td>3.769771</td>\n",
       "      <td>70.207253</td>\n",
       "      <td>10.779379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">groupMultiple</th>\n",
       "      <th>ceph</th>\n",
       "      <td>364.117389</td>\n",
       "      <td>591.035622</td>\n",
       "      <td>33.787295</td>\n",
       "      <td>39.809405</td>\n",
       "      <td>226.918233</td>\n",
       "      <td>6.022110</td>\n",
       "      <td>62.320076</td>\n",
       "      <td>17.823593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nfs</th>\n",
       "      <td>352.987687</td>\n",
       "      <td>632.036826</td>\n",
       "      <td>35.169359</td>\n",
       "      <td>41.224674</td>\n",
       "      <td>279.049139</td>\n",
       "      <td>6.055315</td>\n",
       "      <td>79.053505</td>\n",
       "      <td>17.217587</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          1Gbit/s avgTransferSpeed  2Gbit/s avgTransferSpeed  \\\n",
       "workflow      filesystem                                                       \n",
       "allIntoOne    ceph                      160.569285                245.173584   \n",
       "              nfs                       161.572628                241.794431   \n",
       "chain         ceph                      275.735106                517.641053   \n",
       "              nfs                       312.086065                561.819301   \n",
       "chipseq       ceph                      208.139700                255.557500   \n",
       "              nfs                       199.889483                265.649449   \n",
       "fork          ceph                       52.243270                 67.294911   \n",
       "              nfs                        55.984388                 73.162046   \n",
       "group         ceph                      339.556333                513.788192   \n",
       "              nfs                       326.694300                556.057393   \n",
       "groupMultiple ceph                      364.117389                591.035622   \n",
       "              nfs                       352.987687                632.036826   \n",
       "\n",
       "                          1Gbit/s factor  2Gbit/s factor   speedDiff  \\\n",
       "workflow      filesystem                                               \n",
       "allIntoOne    ceph             86.073713       86.406242   84.604300   \n",
       "              nfs              85.370805       85.590656   80.221803   \n",
       "chain         ceph             19.704287       35.764553  241.905947   \n",
       "              nfs              20.878913       38.373216  249.733236   \n",
       "chipseq       ceph            291.145733      356.123738   47.417799   \n",
       "              nfs             308.280995      377.530982   65.759966   \n",
       "fork          ceph              6.929115        6.929111   15.051641   \n",
       "              nfs               6.929119        6.929115   17.177658   \n",
       "group         ceph             34.028656       39.502172  174.231859   \n",
       "              nfs              34.972059       38.741829  229.363093   \n",
       "groupMultiple ceph             33.787295       39.809405  226.918233   \n",
       "              nfs              35.169359       41.224674  279.049139   \n",
       "\n",
       "                          factorDiff  speedDiffRelative  factorDiffRelative  \n",
       "workflow      filesystem                                                     \n",
       "allIntoOne    ceph          0.332529          52.690214            0.386331  \n",
       "              nfs           0.219851          49.650615            0.257524  \n",
       "chain         ceph         16.060265          87.731283           81.506453  \n",
       "              nfs          17.494303          80.020630           83.789339  \n",
       "chipseq       ceph         64.978005          22.781718           22.318034  \n",
       "              nfs          69.249988          32.898162           22.463269  \n",
       "fork          ceph         -0.000004          28.810679           -0.000055  \n",
       "              nfs          -0.000004          30.682943           -0.000055  \n",
       "group         ceph          5.473516          51.311621           16.085018  \n",
       "              nfs           3.769771          70.207253           10.779379  \n",
       "groupMultiple ceph          6.022110          62.320076           17.823593  \n",
       "              nfs           6.055315          79.053505           17.217587  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avgClusterSpeedDfMerged = avgClusterSpeedDf2.add_prefix(\"2Gbit/s \").join(avgClusterSpeedDf1.add_prefix(\"1Gbit/s \"))\n",
    "avgClusterSpeedDfMerged[\"speedDiff\"] = avgClusterSpeedDfMerged[\"2Gbit/s avgTransferSpeed\"] - avgClusterSpeedDfMerged[\"1Gbit/s avgTransferSpeed\"]\n",
    "avgClusterSpeedDfMerged[\"speedDiffRelative\"] = (avgClusterSpeedDfMerged[\"2Gbit/s avgTransferSpeed\"] - avgClusterSpeedDfMerged[\"1Gbit/s avgTransferSpeed\"]) / avgClusterSpeedDfMerged[\"1Gbit/s avgTransferSpeed\"] * 100\n",
    "avgClusterSpeedDfMerged[\"factorDiff\"] = avgClusterSpeedDfMerged[\"2Gbit/s factor\"] - avgClusterSpeedDfMerged[\"1Gbit/s factor\"]\n",
    "avgClusterSpeedDfMerged[\"factorDiffRelative\"] = (avgClusterSpeedDfMerged[\"2Gbit/s factor\"] - avgClusterSpeedDfMerged[\"1Gbit/s factor\"]) / avgClusterSpeedDfMerged[\"1Gbit/s factor\"] * 100\n",
    "avgClusterSpeedDfMerged[[\"1Gbit/s avgTransferSpeed\", \"2Gbit/s avgTransferSpeed\", \"1Gbit/s factor\", \"2Gbit/s factor\", \"speedDiff\", \"factorDiff\", \"speedDiffRelative\", \"factorDiffRelative\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# count\n",
    "# task gets copied\n",
    "# copy is used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = []\n",
    "for r in resultsRAW:\n",
    "    if ( r[\"strategy\"] == \"la\" and r[\"nodes\"] != 1 ):\n",
    "        data.append( [r[\"workflow\"], r[\"nodes\"], r[\"networkspeed\"], r[\"filesystem\"], r[\"copytasks\"][\"notCopied\"], r[\"copytasks\"][\"usefulCopied\"], r[\"copytasks\"][\"uselessCopied\"], r[\"copytasks\"][\"notCopied\"] / r[\"trace\"]['numberPhysicalTasks'] * 100, r[\"copytasks\"][\"usefulCopied\"] / r[\"trace\"]['numberPhysicalTasks'] * 100] )\n",
    "        tableToCSV( data, [ \"workflow\", \"nodes\", \"networkspeed\", \"filesystem\", \"notCopied\", \"usefulCopied\", \"uselessCopied\", \"notCopiedPercent\", \"usefulCopiedPercent\" ], \"evaluation\" + os.sep + \"copyOperations.csv\" )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GINI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "giniCoefficients = []\n",
    "\n",
    "def giniForDict( data, nodes ):\n",
    "    values = []\n",
    "    for key, value in data.items():\n",
    "        values.append(float(value))\n",
    "    if ( len(values) < nodes ):\n",
    "        values = values + [0] * (nodes - len(values))\n",
    "    if ( len(values) > nodes ):\n",
    "        print( \"Error: more values than nodes\" )\n",
    "        print( values )\n",
    "    return gini(np.array(values))\n",
    "\n",
    "for r in resultsRAW:\n",
    "    if ( r[\"strategy\"] == \"la\" and r[\"nodes\"] != 1 ):\n",
    "        localData = r[\"localData\"]\n",
    "        tasksByNode = r[\"trace\"][\"numberTasksByNode\"]\n",
    "        trace = pd.read_csv( r[\"folder\"] + os.sep + \"trace.csv\", sep=\",\" )\n",
    "        trace[\"allocCpuHours\"] = ((trace[\"realtime\"] / 3600000) * trace[\"cpus\"])\n",
    "        cpuHoursByNode = trace[ [\"hostname\", \"allocCpuHours\"] ].groupby(\"hostname\").sum()\n",
    "        localDataNew = {}\n",
    "        keys = list(localData.keys())\n",
    "        keys.sort()\n",
    "        i = 0\n",
    "        for k in keys:\n",
    "            localDataNew[k] = localData[k]\n",
    "            i += 1\n",
    "            if ( i == r[\"nodes\"] ):\n",
    "                break\n",
    "        giniCoefficients.append( {\n",
    "            \"workflow\": r[\"workflow\"],\n",
    "            \"nodes\": r[\"nodes\"],\n",
    "            \"networkspeed\": r[\"networkspeed\"],\n",
    "            \"filesystem\": r[\"filesystem\"],\n",
    "            \"localData\": giniForDict( localDataNew, r[\"nodes\"] ),\n",
    "            \"tasksByNode\": giniForDict( tasksByNode, r[\"nodes\"] ),\n",
    "            \"cpuHoursByNode\": giniForDict( cpuHoursByNode[\"allocCpuHours\"].to_dict(), r[\"nodes\"] )\n",
    "        } )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>workflow</th>\n",
       "      <th>filesystem</th>\n",
       "      <th>localData_1Gbit/s</th>\n",
       "      <th>tasksByNode_1Gbit/s</th>\n",
       "      <th>cpuHoursByNode_1Gbit/s</th>\n",
       "      <th>localData_2Gbit/s</th>\n",
       "      <th>tasksByNode_2Gbit/s</th>\n",
       "      <th>cpuHoursByNode_2Gbit/s</th>\n",
       "      <th>diff localData</th>\n",
       "      <th>diff tasksByNode</th>\n",
       "      <th>diff cpuHoursByNode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>allIntoOne</td>\n",
       "      <td>ceph</td>\n",
       "      <td>0.591310</td>\n",
       "      <td>0.038366</td>\n",
       "      <td>0.592982</td>\n",
       "      <td>0.589577</td>\n",
       "      <td>0.035891</td>\n",
       "      <td>0.606856</td>\n",
       "      <td>-0.001732</td>\n",
       "      <td>-0.002475</td>\n",
       "      <td>0.013873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>allIntoOne</td>\n",
       "      <td>nfs</td>\n",
       "      <td>0.601020</td>\n",
       "      <td>0.080446</td>\n",
       "      <td>0.593897</td>\n",
       "      <td>0.600509</td>\n",
       "      <td>0.080446</td>\n",
       "      <td>0.616739</td>\n",
       "      <td>-0.000511</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chain</td>\n",
       "      <td>ceph</td>\n",
       "      <td>0.037383</td>\n",
       "      <td>0.035000</td>\n",
       "      <td>0.042865</td>\n",
       "      <td>0.018599</td>\n",
       "      <td>0.035000</td>\n",
       "      <td>0.030365</td>\n",
       "      <td>-0.018784</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.012501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chain</td>\n",
       "      <td>nfs</td>\n",
       "      <td>0.044140</td>\n",
       "      <td>0.066250</td>\n",
       "      <td>0.059409</td>\n",
       "      <td>0.034069</td>\n",
       "      <td>0.057500</td>\n",
       "      <td>0.056219</td>\n",
       "      <td>-0.010070</td>\n",
       "      <td>-0.008750</td>\n",
       "      <td>-0.003190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chipseq</td>\n",
       "      <td>ceph</td>\n",
       "      <td>0.006937</td>\n",
       "      <td>0.035659</td>\n",
       "      <td>0.003389</td>\n",
       "      <td>0.008532</td>\n",
       "      <td>0.040465</td>\n",
       "      <td>0.003961</td>\n",
       "      <td>0.001594</td>\n",
       "      <td>0.004806</td>\n",
       "      <td>0.000572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>chipseq</td>\n",
       "      <td>nfs</td>\n",
       "      <td>0.013422</td>\n",
       "      <td>0.036366</td>\n",
       "      <td>0.005428</td>\n",
       "      <td>0.006636</td>\n",
       "      <td>0.049654</td>\n",
       "      <td>0.004675</td>\n",
       "      <td>-0.006785</td>\n",
       "      <td>0.013288</td>\n",
       "      <td>-0.000753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>fork</td>\n",
       "      <td>ceph</td>\n",
       "      <td>0.145833</td>\n",
       "      <td>0.164604</td>\n",
       "      <td>0.178444</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.075495</td>\n",
       "      <td>0.071335</td>\n",
       "      <td>-0.083333</td>\n",
       "      <td>-0.089109</td>\n",
       "      <td>-0.107109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>fork</td>\n",
       "      <td>nfs</td>\n",
       "      <td>0.143518</td>\n",
       "      <td>0.162129</td>\n",
       "      <td>0.157458</td>\n",
       "      <td>0.064815</td>\n",
       "      <td>0.077970</td>\n",
       "      <td>0.073191</td>\n",
       "      <td>-0.078704</td>\n",
       "      <td>-0.084158</td>\n",
       "      <td>-0.084267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>group</td>\n",
       "      <td>ceph</td>\n",
       "      <td>0.084385</td>\n",
       "      <td>0.048507</td>\n",
       "      <td>0.103380</td>\n",
       "      <td>0.064053</td>\n",
       "      <td>0.041045</td>\n",
       "      <td>0.081268</td>\n",
       "      <td>-0.020332</td>\n",
       "      <td>-0.007463</td>\n",
       "      <td>-0.022112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>group</td>\n",
       "      <td>nfs</td>\n",
       "      <td>0.105822</td>\n",
       "      <td>0.078358</td>\n",
       "      <td>0.100285</td>\n",
       "      <td>0.040014</td>\n",
       "      <td>0.054104</td>\n",
       "      <td>0.053192</td>\n",
       "      <td>-0.065808</td>\n",
       "      <td>-0.024254</td>\n",
       "      <td>-0.047093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>groupMultiple</td>\n",
       "      <td>ceph</td>\n",
       "      <td>0.086590</td>\n",
       "      <td>0.043750</td>\n",
       "      <td>0.107050</td>\n",
       "      <td>0.045724</td>\n",
       "      <td>0.029687</td>\n",
       "      <td>0.053529</td>\n",
       "      <td>-0.040866</td>\n",
       "      <td>-0.014062</td>\n",
       "      <td>-0.053520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>groupMultiple</td>\n",
       "      <td>nfs</td>\n",
       "      <td>0.045761</td>\n",
       "      <td>0.046875</td>\n",
       "      <td>0.058188</td>\n",
       "      <td>0.080686</td>\n",
       "      <td>0.045312</td>\n",
       "      <td>0.060904</td>\n",
       "      <td>0.034925</td>\n",
       "      <td>-0.001562</td>\n",
       "      <td>0.002716</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         workflow filesystem  localData_1Gbit/s  tasksByNode_1Gbit/s  \\\n",
       "0      allIntoOne       ceph           0.591310             0.038366   \n",
       "1      allIntoOne        nfs           0.601020             0.080446   \n",
       "2           chain       ceph           0.037383             0.035000   \n",
       "3           chain        nfs           0.044140             0.066250   \n",
       "4         chipseq       ceph           0.006937             0.035659   \n",
       "5         chipseq        nfs           0.013422             0.036366   \n",
       "6            fork       ceph           0.145833             0.164604   \n",
       "7            fork        nfs           0.143518             0.162129   \n",
       "8           group       ceph           0.084385             0.048507   \n",
       "9           group        nfs           0.105822             0.078358   \n",
       "10  groupMultiple       ceph           0.086590             0.043750   \n",
       "11  groupMultiple        nfs           0.045761             0.046875   \n",
       "\n",
       "    cpuHoursByNode_1Gbit/s  localData_2Gbit/s  tasksByNode_2Gbit/s  \\\n",
       "0                 0.592982           0.589577             0.035891   \n",
       "1                 0.593897           0.600509             0.080446   \n",
       "2                 0.042865           0.018599             0.035000   \n",
       "3                 0.059409           0.034069             0.057500   \n",
       "4                 0.003389           0.008532             0.040465   \n",
       "5                 0.005428           0.006636             0.049654   \n",
       "6                 0.178444           0.062500             0.075495   \n",
       "7                 0.157458           0.064815             0.077970   \n",
       "8                 0.103380           0.064053             0.041045   \n",
       "9                 0.100285           0.040014             0.054104   \n",
       "10                0.107050           0.045724             0.029687   \n",
       "11                0.058188           0.080686             0.045312   \n",
       "\n",
       "    cpuHoursByNode_2Gbit/s  diff localData  diff tasksByNode  \\\n",
       "0                 0.606856       -0.001732         -0.002475   \n",
       "1                 0.616739       -0.000511          0.000000   \n",
       "2                 0.030365       -0.018784          0.000000   \n",
       "3                 0.056219       -0.010070         -0.008750   \n",
       "4                 0.003961        0.001594          0.004806   \n",
       "5                 0.004675       -0.006785          0.013288   \n",
       "6                 0.071335       -0.083333         -0.089109   \n",
       "7                 0.073191       -0.078704         -0.084158   \n",
       "8                 0.081268       -0.020332         -0.007463   \n",
       "9                 0.053192       -0.065808         -0.024254   \n",
       "10                0.053529       -0.040866         -0.014062   \n",
       "11                0.060904        0.034925         -0.001562   \n",
       "\n",
       "    diff cpuHoursByNode  \n",
       "0              0.013873  \n",
       "1              0.022842  \n",
       "2             -0.012501  \n",
       "3             -0.003190  \n",
       "4              0.000572  \n",
       "5             -0.000753  \n",
       "6             -0.107109  \n",
       "7             -0.084267  \n",
       "8             -0.022112  \n",
       "9             -0.047093  \n",
       "10            -0.053520  \n",
       "11             0.002716  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(giniCoefficients).to_csv( \"evaluation\" + os.sep + \"gini.csv\", sep=\",\" )\n",
    "giniMedian = pd.DataFrame(giniCoefficients).groupby([\"workflow\",\"nodes\",\"networkspeed\",\"filesystem\"]).median()\n",
    "giniMedian.to_csv( \"evaluation\" + os.sep + \"giniMedian.csv\", sep=\",\" )\n",
    "giniMedian.reset_index(inplace=True)\n",
    "giniMedian1 = giniMedian[(giniMedian[\"nodes\"] == 8) & (giniMedian[\"networkspeed\"] == 1)]\n",
    "giniMedian2 = giniMedian[(giniMedian[\"nodes\"] == 8) & (giniMedian[\"networkspeed\"] == 2)]\n",
    "#giniMedianMerged = giniMedian1.add_prefix(\"1Gbit/s \").merge(giniMedian2.add_prefix(\"2Gbit/s \"), on=[\"workflow\", \"filesystem\"])\n",
    "#giniMedianMerged\n",
    "#\n",
    "giniMedian1.add_prefix(\"1Gbit/s \").merge(giniMedian2.add_prefix(\"2Gbit/s \"), left_on=[\"1Gbit/s workflow\", \"1Gbit/s filesystem\"], right_on=[\"2Gbit/s workflow\", \"2Gbit/s filesystem\"])\n",
    "giniMedianMerged = giniMedian1.merge(giniMedian2, on=[\"workflow\", \"filesystem\"], suffixes=(\"_1Gbit/s\", \"_2Gbit/s\")).drop([\"nodes_1Gbit/s\", \"networkspeed_1Gbit/s\", \"nodes_2Gbit/s\", \"networkspeed_2Gbit/s\"], axis=1)\n",
    "giniMedianMerged[\"diff localData\"] = giniMedianMerged[\"localData_2Gbit/s\"] - giniMedianMerged[\"localData_1Gbit/s\"]\n",
    "giniMedianMerged[\"diff tasksByNode\"] = giniMedianMerged[\"tasksByNode_2Gbit/s\"] - giniMedianMerged[\"tasksByNode_1Gbit/s\"]\n",
    "giniMedianMerged[\"diff cpuHoursByNode\"] = giniMedianMerged[\"cpuHoursByNode_2Gbit/s\"] - giniMedianMerged[\"cpuHoursByNode_1Gbit/s\"]\n",
    "giniMedianMerged\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "highest difference in localData: 0.08333310228362778\n",
      "highest difference in tasksByNode: 0.08910891082050777\n",
      "highest difference in cpuHoursByNode: 0.10710888464839187\n"
     ]
    }
   ],
   "source": [
    "print(\"highest difference in localData:\", giniMedianMerged[\"diff localData\"].abs().max())\n",
    "print(\"highest difference in tasksByNode:\", giniMedianMerged[\"diff tasksByNode\"].abs().max())\n",
    "print(\"highest difference in cpuHoursByNode:\", giniMedianMerged[\"diff cpuHoursByNode\"].abs().max())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average data transfer in the cluster in MB/s\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plotDistribution( data, keyValue, metric, title, minimum, maximum ):\n",
    "    maxValue = 0\n",
    "\n",
    "    for g in data:\n",
    "        identifier = g[\"filesystem\"] + \"-\" + str(g[\"networkspeed\"]) + \"-\" + str(g[\"nodes\"])\n",
    "        factor = g[keyValue]\n",
    "        label = str(g[\"networkspeed\"]) + \"Gbit/s \" + str(g[\"nodes\"]) + \" nodes\"\n",
    "        if ( factor > maxValue ):\n",
    "            maxValue = factor\n",
    "        x = workflowNamesIndex.index(g[\"workflow\"]) + (colorMarkerIndex.index(identifier) / len(colorMarker) - 0.5) * 0.3\n",
    "        plt.scatter( x, factor, label=label, color=colorMarker[identifier][1], marker=colorMarker[identifier][0], zorder= 5)\n",
    "\n",
    "    plt.xticks(range(len(workflowNamesIndex)), [workflowNames[x] for x in workflowNamesIndex], rotation=80)\n",
    "    plt.xlabel('Workflow')\n",
    "    plt.ylabel( metric )\n",
    "    plt.xticks(rotation=80)\n",
    "    plt.xlim(-1, len(workflowNamesIndex))\n",
    "    plt.ylim(minimum, maximum)\n",
    "    #plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.13), ncol=3)\n",
    "\n",
    "    ax = plt.gca()\n",
    "    legend_main = ax.legend(loc='upper right')\n",
    "\n",
    "    # Filter out duplicates directly in the legend\n",
    "    handles, labels = [], []\n",
    "    unique_labels = set()\n",
    "\n",
    "    for handle, label in zip(legend_main.legendHandles, legend_main.get_texts()):\n",
    "        if label.get_text() not in unique_labels:\n",
    "            handle.set_color('black')\n",
    "            handles.append(handle)\n",
    "            labels.append(label.get_text())\n",
    "            unique_labels.add(label.get_text())\n",
    "\n",
    "\n",
    "\n",
    "    #ax.set_yticklabels(['{:.0f}%'.format(x) for x in ax.get_yticks()])\n",
    "\n",
    "    for i in workflowNamesIndex:\n",
    "        plt.vlines(i, -1000, 1000, colors='#f5f5f5', linestyles='solid', linewidth=10, zorder=0 )\n",
    "\n",
    "    # Update the legend in the main plot\n",
    "    legend_main.remove()\n",
    "    ax.legend(handles=handles, labels=labels, loc='upper right', ncol=2)\n",
    "    plt.title( metric + ' for ' + title)\n",
    "    plt.tight_layout()\n",
    "    plt.savefig( plotsFolder + metric.replace(\" \", \"_\") + \"_\" + keyValue + \".pdf\", bbox_inches='tight')\n",
    "    plt.show()\n",
    "\n",
    "plotDistribution( giniCoefficients, \"localData\", \"Gini Coefficient\", \"local data distribution\", -0.05, 1.05 )\n",
    "plotDistribution( giniCoefficients, \"tasksByNode\", \"Gini Coefficient\", \"tasks per node distribution\", -0.05, 1.05 )\n",
    "plotDistribution( giniCoefficients, \"cpuHoursByNode\", \"Gini Coefficient\", \"CPU hours per node distribution\", -0.05, 1.05 )\n",
    "print( \"Average data transfer in the cluster in MB/s\" )\n",
    "plotDistribution( avgClusterSpeed, \"avgTransferSpeed\", \"Cluster Speed\", \"avgTransferSpeed\", 0, 800 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "avgClusterSpeedDataframe = pd.DataFrame.from_dict( avgClusterSpeed ).groupby([\"workflow\",\"nodes\",\"networkspeed\",\"filesystem\",]).median()\n",
    "avgClusterSpeedDataframe.to_csv( \"evaluation\" + os.sep + \"avgClusterSpeed.csv\", sep=\",\" )\n",
    "avgClusterSpeedDataframe = avgClusterSpeedDataframe.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>workflow</th>\n",
       "      <th>nodes</th>\n",
       "      <th>networkspeed</th>\n",
       "      <th>filesystem</th>\n",
       "      <th>avgTransferSpeed</th>\n",
       "      <th>factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>groupMultiple</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>ceph</td>\n",
       "      <td>364.117389</td>\n",
       "      <td>33.787295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>groupMultiple</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>nfs</td>\n",
       "      <td>352.987687</td>\n",
       "      <td>35.169359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>group</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>ceph</td>\n",
       "      <td>339.556333</td>\n",
       "      <td>34.028656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>group</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>nfs</td>\n",
       "      <td>326.694300</td>\n",
       "      <td>34.972059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>chain</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>nfs</td>\n",
       "      <td>312.086065</td>\n",
       "      <td>20.878913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>chain</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>ceph</td>\n",
       "      <td>275.735106</td>\n",
       "      <td>19.704287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Synthetic_Montage_200</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>ceph</td>\n",
       "      <td>259.895056</td>\n",
       "      <td>155.651110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Synthetic_Montage_200</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>nfs</td>\n",
       "      <td>257.627820</td>\n",
       "      <td>163.879993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Synthetic_Blast_200</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>ceph</td>\n",
       "      <td>246.430836</td>\n",
       "      <td>196.053254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Synthetic_Blast_200</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>nfs</td>\n",
       "      <td>223.009476</td>\n",
       "      <td>188.857560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>chipseq</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>ceph</td>\n",
       "      <td>208.139700</td>\n",
       "      <td>291.145733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Synthetic_Cycles_200</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>ceph</td>\n",
       "      <td>207.728936</td>\n",
       "      <td>77.203096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Synthetic_Cycles_200</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>nfs</td>\n",
       "      <td>203.711318</td>\n",
       "      <td>78.530416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>chipseq</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>nfs</td>\n",
       "      <td>199.889483</td>\n",
       "      <td>308.280995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Synthetic_Soykb_200</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>nfs</td>\n",
       "      <td>189.608175</td>\n",
       "      <td>123.644438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>sarek</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>ceph</td>\n",
       "      <td>180.319080</td>\n",
       "      <td>334.292353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Synthetic_Seismology_200</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>ceph</td>\n",
       "      <td>174.310791</td>\n",
       "      <td>168.680292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Synthetic_Seismology_200</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>nfs</td>\n",
       "      <td>172.175467</td>\n",
       "      <td>162.249601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>sarek</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>nfs</td>\n",
       "      <td>171.988653</td>\n",
       "      <td>348.103667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>allIntoOne</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>nfs</td>\n",
       "      <td>161.572628</td>\n",
       "      <td>85.370805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>allIntoOne</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>ceph</td>\n",
       "      <td>160.569285</td>\n",
       "      <td>86.073713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Synthetic_Genome_200</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>ceph</td>\n",
       "      <td>155.163727</td>\n",
       "      <td>64.313501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Synthetic_Genome_200</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>nfs</td>\n",
       "      <td>144.522300</td>\n",
       "      <td>73.089238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>rnaseq</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>ceph</td>\n",
       "      <td>131.206932</td>\n",
       "      <td>186.771336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Synthetic_Soykb_200</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>ceph</td>\n",
       "      <td>121.564961</td>\n",
       "      <td>60.619917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>rnaseq</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>nfs</td>\n",
       "      <td>121.472053</td>\n",
       "      <td>232.131688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Synthetic_Bwa_200</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>ceph</td>\n",
       "      <td>81.722477</td>\n",
       "      <td>44.820386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Synthetic_Bwa_200</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>nfs</td>\n",
       "      <td>80.457655</td>\n",
       "      <td>43.814344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>fork</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>nfs</td>\n",
       "      <td>55.984388</td>\n",
       "      <td>6.929119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>fork</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>ceph</td>\n",
       "      <td>52.243270</td>\n",
       "      <td>6.929115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>rangeland</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>ceph</td>\n",
       "      <td>38.339094</td>\n",
       "      <td>130.445435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>rangeland</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>nfs</td>\n",
       "      <td>35.946354</td>\n",
       "      <td>121.096690</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    workflow  nodes  networkspeed filesystem  \\\n",
       "70             groupMultiple      8             1       ceph   \n",
       "71             groupMultiple      8             1        nfs   \n",
       "60                     group      8             1       ceph   \n",
       "61                     group      8             1        nfs   \n",
       "31                     chain      8             1        nfs   \n",
       "30                     chain      8             1       ceph   \n",
       "8      Synthetic_Montage_200      8             1       ceph   \n",
       "9      Synthetic_Montage_200      8             1        nfs   \n",
       "0        Synthetic_Blast_200      8             1       ceph   \n",
       "1        Synthetic_Blast_200      8             1        nfs   \n",
       "40                   chipseq      8             1       ceph   \n",
       "4       Synthetic_Cycles_200      8             1       ceph   \n",
       "5       Synthetic_Cycles_200      8             1        nfs   \n",
       "41                   chipseq      8             1        nfs   \n",
       "13       Synthetic_Soykb_200      8             1        nfs   \n",
       "78                     sarek      8             1       ceph   \n",
       "10  Synthetic_Seismology_200      8             1       ceph   \n",
       "11  Synthetic_Seismology_200      8             1        nfs   \n",
       "79                     sarek      8             1        nfs   \n",
       "21                allIntoOne      8             1        nfs   \n",
       "20                allIntoOne      8             1       ceph   \n",
       "6       Synthetic_Genome_200      8             1       ceph   \n",
       "7       Synthetic_Genome_200      8             1        nfs   \n",
       "76                    rnaseq      8             1       ceph   \n",
       "12       Synthetic_Soykb_200      8             1       ceph   \n",
       "77                    rnaseq      8             1        nfs   \n",
       "2          Synthetic_Bwa_200      8             1       ceph   \n",
       "3          Synthetic_Bwa_200      8             1        nfs   \n",
       "51                      fork      8             1        nfs   \n",
       "50                      fork      8             1       ceph   \n",
       "74                 rangeland      8             1       ceph   \n",
       "75                 rangeland      8             1        nfs   \n",
       "\n",
       "    avgTransferSpeed      factor  \n",
       "70        364.117389   33.787295  \n",
       "71        352.987687   35.169359  \n",
       "60        339.556333   34.028656  \n",
       "61        326.694300   34.972059  \n",
       "31        312.086065   20.878913  \n",
       "30        275.735106   19.704287  \n",
       "8         259.895056  155.651110  \n",
       "9         257.627820  163.879993  \n",
       "0         246.430836  196.053254  \n",
       "1         223.009476  188.857560  \n",
       "40        208.139700  291.145733  \n",
       "4         207.728936   77.203096  \n",
       "5         203.711318   78.530416  \n",
       "41        199.889483  308.280995  \n",
       "13        189.608175  123.644438  \n",
       "78        180.319080  334.292353  \n",
       "10        174.310791  168.680292  \n",
       "11        172.175467  162.249601  \n",
       "79        171.988653  348.103667  \n",
       "21        161.572628   85.370805  \n",
       "20        160.569285   86.073713  \n",
       "6         155.163727   64.313501  \n",
       "7         144.522300   73.089238  \n",
       "76        131.206932  186.771336  \n",
       "12        121.564961   60.619917  \n",
       "77        121.472053  232.131688  \n",
       "2          81.722477   44.820386  \n",
       "3          80.457655   43.814344  \n",
       "51         55.984388    6.929119  \n",
       "50         52.243270    6.929115  \n",
       "74         38.339094  130.445435  \n",
       "75         35.946354  121.096690  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avgClusterSpeedDataframe[(avgClusterSpeedDataframe[\"nodes\"] == 8) & (avgClusterSpeedDataframe[\"networkspeed\"] == 1)].sort_values(\"avgTransferSpeed\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COPs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def countWhichCopyWasUsed ( tasksOnNode, copytasks, notCopied ):\n",
    "    copies = { 0 : notCopied, \"useless\" : 0 }\n",
    "    if ( len(copytasks) != 0 ):\n",
    "        df = copytasks.merge(tasksOnNode.rename(columns={'hostname': 'used_node', 'name':'task'}), how='left', left_on=['task'], right_on=['task'])\n",
    "        for name, group_df in df.groupby('task'):\n",
    "            group_df = group_df.sort_values(by=['starttime']).reset_index()\n",
    "            result = group_df[ group_df['node'] == group_df['used_node'] ]\n",
    "            if ( len(result) > 0 ):\n",
    "                index = result.index[0] + 1 # counting starts with zero\n",
    "                if ( index not in copies ):\n",
    "                    copies[index] = 1\n",
    "                else:\n",
    "                    copies[index] += 1\n",
    "            else:\n",
    "                copies[\"useless\"] += len(group_df)\n",
    "\n",
    "    #confirm\n",
    "    if sum(copies.values()) - copies[\"useless\"] != len(tasksOnNode):\n",
    "        print( \"Error: not all copies were counted\" )\n",
    "        print( sum(copies.values()) )\n",
    "        print( len(tasksOnNode) )\n",
    "        print( copies )\n",
    "        return None\n",
    "    return copies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "copies = []\n",
    "copytasks = None\n",
    "tasksOnNode = None\n",
    "\n",
    "for r in resultsRAW:\n",
    "    if r[\"strategy\"] != \"la\" or r[\"nodes\"] == 1 or r[\"workflow\"] in [\"sarek\", \"rangeland\"]:\n",
    "        continue\n",
    "\n",
    "    copytasks = r[\"copytasks\"][\"copytasks\"]\n",
    "    tasksOnNode = r[\"trace\"]['task-node'] \n",
    "\n",
    "    result = countWhichCopyWasUsed ( tasksOnNode, copytasks, r[\"copytasks\"][\"notCopied\"] )\n",
    "    \n",
    "    if result is None:\n",
    "        print ( r[\"workflow\"] + \" \" + r[\"filesystem\"] + \" \" + str(r[\"nodes\"]) + \" \" + str(r[\"networkspeed\"]) )\n",
    "        break\n",
    "    result[\"workflow\"] = r[\"workflow\"]\n",
    "    result[\"filesystem\"] = r[\"filesystem\"]\n",
    "    result[\"nodes\"] = r[\"nodes\"]\n",
    "    result[\"networkspeed\"] = r[\"networkspeed\"]\n",
    "    result[\"#tasks\"] = len(tasksOnNode)\n",
    "    \n",
    "    copies.append( result )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "copiesDf = pd.DataFrame(copies).fillna(0)\n",
    "allColumns = [\"useless\",1,2,3,4,5,6,7,8]\n",
    "copiesDf[\"#COPs\"] = copiesDf[allColumns].sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Fabian Lehmann\\AppData\\Local\\Temp\\ipykernel_10876\\3676337447.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  copiesDfGrouped = copiesDf.groupby([\"filesystem\", \"nodes\", \"networkspeed\"]).sum()\n"
     ]
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>useless</th>\n",
       "      <th>1</th>\n",
       "      <th>#tasks</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>7</th>\n",
       "      <th>3</th>\n",
       "      <th>6</th>\n",
       "      <th>5</th>\n",
       "      <th>...</th>\n",
       "      <th>6 relative #tasks</th>\n",
       "      <th>7 relative #tasks</th>\n",
       "      <th>8 relative #tasks</th>\n",
       "      <th>&gt;=1</th>\n",
       "      <th>&gt;=2</th>\n",
       "      <th>&gt;=3</th>\n",
       "      <th>&gt;=4</th>\n",
       "      <th>&gt;=5</th>\n",
       "      <th>&gt;=6</th>\n",
       "      <th>&gt;=7</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>filesystem</th>\n",
       "      <th>nodes</th>\n",
       "      <th>networkspeed</th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">ceph</th>\n",
       "      <th>2</th>\n",
       "      <th>1</th>\n",
       "      <td>10475.0</td>\n",
       "      <td>2741</td>\n",
       "      <td>2220.0</td>\n",
       "      <td>12699</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>44.793555</td>\n",
       "      <td>0.080564</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>1</th>\n",
       "      <td>10331.0</td>\n",
       "      <td>2864</td>\n",
       "      <td>1289.0</td>\n",
       "      <td>12699</td>\n",
       "      <td>625.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>442.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.259939</td>\n",
       "      <td>20.623089</td>\n",
       "      <td>8.677370</td>\n",
       "      <td>0.229358</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <th>1</th>\n",
       "      <td>10225.0</td>\n",
       "      <td>2450</td>\n",
       "      <td>1223.0</td>\n",
       "      <td>12699</td>\n",
       "      <td>435.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>298.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>232.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.110245</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50.243704</td>\n",
       "      <td>25.406174</td>\n",
       "      <td>16.571893</td>\n",
       "      <td>10.519903</td>\n",
       "      <td>4.995938</td>\n",
       "      <td>0.284322</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">8</th>\n",
       "      <th>1</th>\n",
       "      <td>17485.0</td>\n",
       "      <td>3222</td>\n",
       "      <td>1761.0</td>\n",
       "      <td>20658</td>\n",
       "      <td>480.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.818085</td>\n",
       "      <td>0.687385</td>\n",
       "      <td>0.053248</td>\n",
       "      <td>49.616888</td>\n",
       "      <td>22.079750</td>\n",
       "      <td>14.573886</td>\n",
       "      <td>11.071149</td>\n",
       "      <td>7.724785</td>\n",
       "      <td>5.035184</td>\n",
       "      <td>2.392494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9891.0</td>\n",
       "      <td>3195</td>\n",
       "      <td>1336.0</td>\n",
       "      <td>12699</td>\n",
       "      <td>437.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>274.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.291440</td>\n",
       "      <td>1.196945</td>\n",
       "      <td>0.078746</td>\n",
       "      <td>46.776612</td>\n",
       "      <td>24.521073</td>\n",
       "      <td>17.241379</td>\n",
       "      <td>12.676995</td>\n",
       "      <td>8.612360</td>\n",
       "      <td>5.430618</td>\n",
       "      <td>2.698651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">nfs</th>\n",
       "      <th>2</th>\n",
       "      <th>1</th>\n",
       "      <td>10423.0</td>\n",
       "      <td>2733</td>\n",
       "      <td>2271.0</td>\n",
       "      <td>12699</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.438211</td>\n",
       "      <td>0.099820</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>1</th>\n",
       "      <td>10311.0</td>\n",
       "      <td>2982</td>\n",
       "      <td>1328.0</td>\n",
       "      <td>12699</td>\n",
       "      <td>587.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>464.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>44.469274</td>\n",
       "      <td>19.739292</td>\n",
       "      <td>8.808194</td>\n",
       "      <td>0.167598</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <th>1</th>\n",
       "      <td>10211.0</td>\n",
       "      <td>2590</td>\n",
       "      <td>1268.0</td>\n",
       "      <td>12699</td>\n",
       "      <td>430.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>278.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>239.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.062997</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>48.995668</td>\n",
       "      <td>24.025207</td>\n",
       "      <td>15.557306</td>\n",
       "      <td>10.082710</td>\n",
       "      <td>4.864120</td>\n",
       "      <td>0.157542</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">8</th>\n",
       "      <th>1</th>\n",
       "      <td>17320.0</td>\n",
       "      <td>3401</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>20658</td>\n",
       "      <td>491.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>257.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.813244</td>\n",
       "      <td>0.716429</td>\n",
       "      <td>0.024204</td>\n",
       "      <td>49.532572</td>\n",
       "      <td>21.709452</td>\n",
       "      <td>14.423505</td>\n",
       "      <td>10.609883</td>\n",
       "      <td>7.553049</td>\n",
       "      <td>4.763318</td>\n",
       "      <td>2.270367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9733.0</td>\n",
       "      <td>3322</td>\n",
       "      <td>1422.0</td>\n",
       "      <td>12699</td>\n",
       "      <td>444.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>221.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.519805</td>\n",
       "      <td>1.346563</td>\n",
       "      <td>0.031499</td>\n",
       "      <td>47.169211</td>\n",
       "      <td>24.554707</td>\n",
       "      <td>17.493639</td>\n",
       "      <td>13.342875</td>\n",
       "      <td>9.367048</td>\n",
       "      <td>5.852417</td>\n",
       "      <td>2.783079</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     0  useless       1  #tasks      2      4  \\\n",
       "filesystem nodes networkspeed                                                   \n",
       "ceph       2     1             10475.0     2741  2220.0   12699    4.0    0.0   \n",
       "           4     1             10331.0     2864  1289.0   12699  625.0   12.0   \n",
       "           6     1             10225.0     2450  1223.0   12699  435.0  272.0   \n",
       "           8     1             17485.0     3222  1761.0   20658  480.0  214.0   \n",
       "                 2              9891.0     3195  1336.0   12699  437.0  244.0   \n",
       "nfs        2     1             10423.0     2733  2271.0   12699    5.0    0.0   \n",
       "           4     1             10311.0     2982  1328.0   12699  587.0    9.0   \n",
       "           6     1             10211.0     2590  1268.0   12699  430.0  265.0   \n",
       "           8     1             17320.0     3401  1875.0   20658  491.0  206.0   \n",
       "                 2              9733.0     3322  1422.0   12699  444.0  250.0   \n",
       "\n",
       "                                   7      3      6      5  ...  \\\n",
       "filesystem nodes networkspeed                              ...   \n",
       "ceph       2     1               0.0    0.0    0.0    0.0  ...   \n",
       "           4     1               0.0  442.0    0.0    0.0  ...   \n",
       "           6     1               0.0  298.0   14.0  232.0  ...   \n",
       "           8     1             142.0  224.0  169.0  172.0  ...   \n",
       "                 2             152.0  274.0  164.0  191.0  ...   \n",
       "nfs        2     1               0.0    0.0    0.0    0.0  ...   \n",
       "           4     1               0.0  464.0    0.0    0.0  ...   \n",
       "           6     1               0.0  278.0    8.0  239.0  ...   \n",
       "           8     1             148.0  257.0  168.0  188.0  ...   \n",
       "                 2             171.0  261.0  193.0  221.0  ...   \n",
       "\n",
       "                               6 relative #tasks  7 relative #tasks  \\\n",
       "filesystem nodes networkspeed                                         \n",
       "ceph       2     1                      0.000000           0.000000   \n",
       "           4     1                      0.000000           0.000000   \n",
       "           6     1                      0.110245           0.000000   \n",
       "           8     1                      0.818085           0.687385   \n",
       "                 2                      1.291440           1.196945   \n",
       "nfs        2     1                      0.000000           0.000000   \n",
       "           4     1                      0.000000           0.000000   \n",
       "           6     1                      0.062997           0.000000   \n",
       "           8     1                      0.813244           0.716429   \n",
       "                 2                      1.519805           1.346563   \n",
       "\n",
       "                               8 relative #tasks        >=1        >=2  \\\n",
       "filesystem nodes networkspeed                                            \n",
       "ceph       2     1                      0.000000  44.793555   0.080564   \n",
       "           4     1                      0.000000  45.259939  20.623089   \n",
       "           6     1                      0.000000  50.243704  25.406174   \n",
       "           8     1                      0.053248  49.616888  22.079750   \n",
       "                 2                      0.078746  46.776612  24.521073   \n",
       "nfs        2     1                      0.000000  45.438211   0.099820   \n",
       "           4     1                      0.000000  44.469274  19.739292   \n",
       "           6     1                      0.000000  48.995668  24.025207   \n",
       "           8     1                      0.024204  49.532572  21.709452   \n",
       "                 2                      0.031499  47.169211  24.554707   \n",
       "\n",
       "                                     >=3        >=4       >=5       >=6  \\\n",
       "filesystem nodes networkspeed                                             \n",
       "ceph       2     1              0.000000   0.000000  0.000000  0.000000   \n",
       "           4     1              8.677370   0.229358  0.000000  0.000000   \n",
       "           6     1             16.571893  10.519903  4.995938  0.284322   \n",
       "           8     1             14.573886  11.071149  7.724785  5.035184   \n",
       "                 2             17.241379  12.676995  8.612360  5.430618   \n",
       "nfs        2     1              0.000000   0.000000  0.000000  0.000000   \n",
       "           4     1              8.808194   0.167598  0.000000  0.000000   \n",
       "           6     1             15.557306  10.082710  4.864120  0.157542   \n",
       "           8     1             14.423505  10.609883  7.553049  4.763318   \n",
       "                 2             17.493639  13.342875  9.367048  5.852417   \n",
       "\n",
       "                                    >=7  \n",
       "filesystem nodes networkspeed            \n",
       "ceph       2     1             0.000000  \n",
       "           4     1             0.000000  \n",
       "           6     1             0.000000  \n",
       "           8     1             2.392494  \n",
       "                 2             2.698651  \n",
       "nfs        2     1             0.000000  \n",
       "           4     1             0.000000  \n",
       "           6     1             0.000000  \n",
       "           8     1             2.270367  \n",
       "                 2             2.783079  \n",
       "\n",
       "[10 rows x 37 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copiesDfGrouped = copiesDf.groupby([\"filesystem\", \"nodes\", \"networkspeed\"]).sum()\n",
    "\n",
    "for i in allColumns:\n",
    "    copiesDfGrouped[str(i) + \" relative #COPs\" ] = copiesDfGrouped[i] / copiesDfGrouped[\"#COPs\"] * 100\n",
    "\n",
    "for i in allColumns:\n",
    "    copiesDfGrouped[str(i) + \" relative #tasks\" ] = copiesDfGrouped[i] / copiesDfGrouped[\"#tasks\"] * 100\n",
    "\n",
    "for i in range(1,8):\n",
    "    copiesDfGrouped[ \">=\" + str(i) ] = copiesDfGrouped[list(range(i,9))].sum(axis=1) / copiesDfGrouped[\"#COPs\"] * 100\n",
    "    \n",
    "copiesDfGrouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>useless relative #COPs</th>\n",
       "      <th>1 relative #COPs</th>\n",
       "      <th>2 relative #COPs</th>\n",
       "      <th>3 relative #COPs</th>\n",
       "      <th>4 relative #COPs</th>\n",
       "      <th>5 relative #COPs</th>\n",
       "      <th>6 relative #COPs</th>\n",
       "      <th>7 relative #COPs</th>\n",
       "      <th>8 relative #COPs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>filesystem</th>\n",
       "      <th>nodes</th>\n",
       "      <th>networkspeed</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">ceph</th>\n",
       "      <th>2</th>\n",
       "      <th>1</th>\n",
       "      <td>55.206445</td>\n",
       "      <td>44.712991</td>\n",
       "      <td>0.080564</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>1</th>\n",
       "      <td>54.740061</td>\n",
       "      <td>24.636850</td>\n",
       "      <td>11.945719</td>\n",
       "      <td>8.448012</td>\n",
       "      <td>0.229358</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <th>1</th>\n",
       "      <td>49.756296</td>\n",
       "      <td>24.837530</td>\n",
       "      <td>8.834281</td>\n",
       "      <td>6.051990</td>\n",
       "      <td>5.523964</td>\n",
       "      <td>4.711617</td>\n",
       "      <td>0.284322</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">8</th>\n",
       "      <th>1</th>\n",
       "      <td>50.383112</td>\n",
       "      <td>27.537138</td>\n",
       "      <td>7.505864</td>\n",
       "      <td>3.502737</td>\n",
       "      <td>3.346364</td>\n",
       "      <td>2.689601</td>\n",
       "      <td>2.642690</td>\n",
       "      <td>2.220485</td>\n",
       "      <td>0.172009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53.223388</td>\n",
       "      <td>22.255539</td>\n",
       "      <td>7.279693</td>\n",
       "      <td>4.564384</td>\n",
       "      <td>4.064634</td>\n",
       "      <td>3.181742</td>\n",
       "      <td>2.731967</td>\n",
       "      <td>2.532067</td>\n",
       "      <td>0.166583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">nfs</th>\n",
       "      <th>2</th>\n",
       "      <th>1</th>\n",
       "      <td>54.561789</td>\n",
       "      <td>45.338391</td>\n",
       "      <td>0.099820</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>1</th>\n",
       "      <td>55.530726</td>\n",
       "      <td>24.729981</td>\n",
       "      <td>10.931099</td>\n",
       "      <td>8.640596</td>\n",
       "      <td>0.167598</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <th>1</th>\n",
       "      <td>51.004332</td>\n",
       "      <td>24.970461</td>\n",
       "      <td>8.467901</td>\n",
       "      <td>5.474596</td>\n",
       "      <td>5.218590</td>\n",
       "      <td>4.706577</td>\n",
       "      <td>0.157542</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">8</th>\n",
       "      <th>1</th>\n",
       "      <td>50.467428</td>\n",
       "      <td>27.823119</td>\n",
       "      <td>7.285947</td>\n",
       "      <td>3.813622</td>\n",
       "      <td>3.056833</td>\n",
       "      <td>2.789731</td>\n",
       "      <td>2.492951</td>\n",
       "      <td>2.196172</td>\n",
       "      <td>0.074195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>52.830789</td>\n",
       "      <td>22.614504</td>\n",
       "      <td>7.061069</td>\n",
       "      <td>4.150763</td>\n",
       "      <td>3.975827</td>\n",
       "      <td>3.514631</td>\n",
       "      <td>3.069338</td>\n",
       "      <td>2.719466</td>\n",
       "      <td>0.063613</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               useless relative #COPs  1 relative #COPs  \\\n",
       "filesystem nodes networkspeed                                             \n",
       "ceph       2     1                          55.206445         44.712991   \n",
       "           4     1                          54.740061         24.636850   \n",
       "           6     1                          49.756296         24.837530   \n",
       "           8     1                          50.383112         27.537138   \n",
       "                 2                          53.223388         22.255539   \n",
       "nfs        2     1                          54.561789         45.338391   \n",
       "           4     1                          55.530726         24.729981   \n",
       "           6     1                          51.004332         24.970461   \n",
       "           8     1                          50.467428         27.823119   \n",
       "                 2                          52.830789         22.614504   \n",
       "\n",
       "                               2 relative #COPs  3 relative #COPs  \\\n",
       "filesystem nodes networkspeed                                       \n",
       "ceph       2     1                     0.080564          0.000000   \n",
       "           4     1                    11.945719          8.448012   \n",
       "           6     1                     8.834281          6.051990   \n",
       "           8     1                     7.505864          3.502737   \n",
       "                 2                     7.279693          4.564384   \n",
       "nfs        2     1                     0.099820          0.000000   \n",
       "           4     1                    10.931099          8.640596   \n",
       "           6     1                     8.467901          5.474596   \n",
       "           8     1                     7.285947          3.813622   \n",
       "                 2                     7.061069          4.150763   \n",
       "\n",
       "                               4 relative #COPs  5 relative #COPs  \\\n",
       "filesystem nodes networkspeed                                       \n",
       "ceph       2     1                     0.000000          0.000000   \n",
       "           4     1                     0.229358          0.000000   \n",
       "           6     1                     5.523964          4.711617   \n",
       "           8     1                     3.346364          2.689601   \n",
       "                 2                     4.064634          3.181742   \n",
       "nfs        2     1                     0.000000          0.000000   \n",
       "           4     1                     0.167598          0.000000   \n",
       "           6     1                     5.218590          4.706577   \n",
       "           8     1                     3.056833          2.789731   \n",
       "                 2                     3.975827          3.514631   \n",
       "\n",
       "                               6 relative #COPs  7 relative #COPs  \\\n",
       "filesystem nodes networkspeed                                       \n",
       "ceph       2     1                     0.000000          0.000000   \n",
       "           4     1                     0.000000          0.000000   \n",
       "           6     1                     0.284322          0.000000   \n",
       "           8     1                     2.642690          2.220485   \n",
       "                 2                     2.731967          2.532067   \n",
       "nfs        2     1                     0.000000          0.000000   \n",
       "           4     1                     0.000000          0.000000   \n",
       "           6     1                     0.157542          0.000000   \n",
       "           8     1                     2.492951          2.196172   \n",
       "                 2                     3.069338          2.719466   \n",
       "\n",
       "                               8 relative #COPs  \n",
       "filesystem nodes networkspeed                    \n",
       "ceph       2     1                     0.000000  \n",
       "           4     1                     0.000000  \n",
       "           6     1                     0.000000  \n",
       "           8     1                     0.172009  \n",
       "                 2                     0.166583  \n",
       "nfs        2     1                     0.000000  \n",
       "           4     1                     0.000000  \n",
       "           6     1                     0.000000  \n",
       "           8     1                     0.074195  \n",
       "                 2                     0.063613  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copiesDfGrouped[[x for x in list(copiesDfGrouped.columns) if \" relative #COPs\" in str(x)]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>10.150406</td>\n",
       "      <td>4.921647</td>\n",
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       "      <td>19.292858</td>\n",
       "      <td>9.630680</td>\n",
       "      <td>3.425467</td>\n",
       "      <td>2.346641</td>\n",
       "      <td>2.141901</td>\n",
       "      <td>1.826916</td>\n",
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       "      <td>8.524543</td>\n",
       "      <td>2.323555</td>\n",
       "      <td>1.084326</td>\n",
       "      <td>1.035918</td>\n",
       "      <td>0.832607</td>\n",
       "      <td>0.818085</td>\n",
       "      <td>0.687385</td>\n",
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       "      <td>25.159461</td>\n",
       "      <td>10.520513</td>\n",
       "      <td>3.441216</td>\n",
       "      <td>2.157650</td>\n",
       "      <td>1.921411</td>\n",
       "      <td>1.504055</td>\n",
       "      <td>1.291440</td>\n",
       "      <td>1.196945</td>\n",
       "      <td>0.078746</td>\n",
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       "      <th rowspan=\"5\" valign=\"top\">nfs</th>\n",
       "      <th>2</th>\n",
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       "      <td>17.883298</td>\n",
       "      <td>0.039373</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>10.457516</td>\n",
       "      <td>4.622411</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>6</th>\n",
       "      <th>1</th>\n",
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       "      <td>9.985038</td>\n",
       "      <td>3.386093</td>\n",
       "      <td>2.189149</td>\n",
       "      <td>2.086778</td>\n",
       "      <td>1.882038</td>\n",
       "      <td>0.062997</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th rowspan=\"2\" valign=\"top\">8</th>\n",
       "      <th>1</th>\n",
       "      <td>16.463356</td>\n",
       "      <td>9.076387</td>\n",
       "      <td>2.376803</td>\n",
       "      <td>1.244070</td>\n",
       "      <td>0.997192</td>\n",
       "      <td>0.910059</td>\n",
       "      <td>0.813244</td>\n",
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      "text/plain": [
       "                               useless relative #tasks  1 relative #tasks  \\\n",
       "filesystem nodes networkspeed                                               \n",
       "ceph       2     1                           21.584377          17.481691   \n",
       "           4     1                           22.552957          10.150406   \n",
       "           6     1                           19.292858           9.630680   \n",
       "           8     1                           15.596863           8.524543   \n",
       "                 2                           25.159461          10.520513   \n",
       "nfs        2     1                           21.521380          17.883298   \n",
       "           4     1                           23.482164          10.457516   \n",
       "           6     1                           20.395307           9.985038   \n",
       "           8     1                           16.463356           9.076387   \n",
       "                 2                           26.159540          11.197732   \n",
       "\n",
       "                               2 relative #tasks  3 relative #tasks  \\\n",
       "filesystem nodes networkspeed                                         \n",
       "ceph       2     1                      0.031499           0.000000   \n",
       "           4     1                      4.921647           3.480589   \n",
       "           6     1                      3.425467           2.346641   \n",
       "           8     1                      2.323555           1.084326   \n",
       "                 2                      3.441216           2.157650   \n",
       "nfs        2     1                      0.039373           0.000000   \n",
       "           4     1                      4.622411           3.653831   \n",
       "           6     1                      3.386093           2.189149   \n",
       "           8     1                      2.376803           1.244070   \n",
       "                 2                      3.496338           2.055280   \n",
       "\n",
       "                               4 relative #tasks  5 relative #tasks  \\\n",
       "filesystem nodes networkspeed                                         \n",
       "ceph       2     1                      0.000000           0.000000   \n",
       "           4     1                      0.094496           0.000000   \n",
       "           6     1                      2.141901           1.826916   \n",
       "           8     1                      1.035918           0.832607   \n",
       "                 2                      1.921411           1.504055   \n",
       "nfs        2     1                      0.000000           0.000000   \n",
       "           4     1                      0.070872           0.000000   \n",
       "           6     1                      2.086778           1.882038   \n",
       "           8     1                      0.997192           0.910059   \n",
       "                 2                      1.968659           1.740295   \n",
       "\n",
       "                               6 relative #tasks  7 relative #tasks  \\\n",
       "filesystem nodes networkspeed                                         \n",
       "ceph       2     1                      0.000000           0.000000   \n",
       "           4     1                      0.000000           0.000000   \n",
       "           6     1                      0.110245           0.000000   \n",
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       "           8     1                      0.813244           0.716429   \n",
       "                 2                      1.519805           1.346563   \n",
       "\n",
       "                               8 relative #tasks  \n",
       "filesystem nodes networkspeed                     \n",
       "ceph       2     1                      0.000000  \n",
       "           4     1                      0.000000  \n",
       "           6     1                      0.000000  \n",
       "           8     1                      0.053248  \n",
       "                 2                      0.078746  \n",
       "nfs        2     1                      0.000000  \n",
       "           4     1                      0.000000  \n",
       "           6     1                      0.000000  \n",
       "           8     1                      0.024204  \n",
       "                 2                      0.031499  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copiesDfGrouped[[x for x in list(copiesDfGrouped.columns) if \" relative #tasks\" in str(x)]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "filesystem  nodes  networkspeed\n",
       "ceph        2      1               4965.0\n",
       "            4      1               5232.0\n",
       "            6      1               4924.0\n",
       "            8      1               6395.0\n",
       "                   2               6003.0\n",
       "nfs         2      1               5009.0\n",
       "            4      1               5370.0\n",
       "            6      1               5078.0\n",
       "            8      1               6739.0\n",
       "                   2               6288.0\n",
       "Name: #COPs, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copiesDfGrouped[\"#COPs\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "How many tasks where COPs are created needed two or less.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Fabian Lehmann\\AppData\\Local\\Temp\\ipykernel_10876\\3089026383.py:2: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  copiesDfGroupedAllFS = copiesDf.groupby([\"nodes\", \"networkspeed\"]).sum()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "nodes  networkspeed\n",
       "2      1               100.000000\n",
       "4      1                91.256367\n",
       "6      1                83.943211\n",
       "8      1                85.503274\n",
       "       2                82.629566\n",
       "dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"How many tasks where COPs are created needed two or less.\")\n",
    "copiesDfGroupedAllFS = copiesDf.groupby([\"nodes\", \"networkspeed\"]).sum()\n",
    "copiesDfGroupedAllFS[[\"useless\", 1 , 2 ]].sum(axis=1) / copiesDfGroupedAllFS[\"#COPs\"] * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Required more than four COPs\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "nodes  networkspeed\n",
       "2      1               0.000000\n",
       "4      1               0.000000\n",
       "6      1               4.929014\n",
       "8      1               7.636668\n",
       "       2               8.998454\n",
       "dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Required more than four COPs\")\n",
    "copiesDfGroupedAllFS[[ 5, 6 , 7, 8 ]].sum(axis=1) / copiesDfGroupedAllFS[\"#COPs\"] * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
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       "      <th>useless relative #tasks</th>\n",
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       "      <td>3.405780</td>\n",
       "      <td>2.267895</td>\n",
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       "      <th rowspan=\"2\" valign=\"top\">8</th>\n",
       "      <th>1</th>\n",
       "      <td>16.030109</td>\n",
       "      <td>8.800465</td>\n",
       "      <td>2.350179</td>\n",
       "      <td>1.164198</td>\n",
       "      <td>1.016555</td>\n",
       "      <td>0.871333</td>\n",
       "      <td>0.815665</td>\n",
       "      <td>0.701907</td>\n",
       "      <td>0.038726</td>\n",
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       "      <td>10.859123</td>\n",
       "      <td>3.468777</td>\n",
       "      <td>2.106465</td>\n",
       "      <td>1.945035</td>\n",
       "      <td>1.622175</td>\n",
       "      <td>1.405622</td>\n",
       "      <td>1.271754</td>\n",
       "      <td>0.055122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    useless relative #tasks  1 relative #tasks  \\\n",
       "nodes networkspeed                                               \n",
       "2     1                           21.552878          17.682495   \n",
       "4     1                           23.017560          10.303961   \n",
       "6     1                           19.844082           9.807859   \n",
       "8     1                           16.030109           8.800465   \n",
       "      2                           25.659501          10.859123   \n",
       "\n",
       "                    2 relative #tasks  3 relative #tasks  4 relative #tasks  \\\n",
       "nodes networkspeed                                                            \n",
       "2     1                      0.035436           0.000000           0.000000   \n",
       "4     1                      4.772029           3.567210           0.082684   \n",
       "6     1                      3.405780           2.267895           2.114340   \n",
       "8     1                      2.350179           1.164198           1.016555   \n",
       "      2                      3.468777           2.106465           1.945035   \n",
       "\n",
       "                    5 relative #tasks  6 relative #tasks  7 relative #tasks  \\\n",
       "nodes networkspeed                                                            \n",
       "2     1                      0.000000           0.000000           0.000000   \n",
       "4     1                      0.000000           0.000000           0.000000   \n",
       "6     1                      1.854477           0.086621           0.000000   \n",
       "8     1                      0.871333           0.815665           0.701907   \n",
       "      2                      1.622175           1.405622           1.271754   \n",
       "\n",
       "                    8 relative #tasks  \n",
       "nodes networkspeed                     \n",
       "2     1                      0.000000  \n",
       "4     1                      0.000000  \n",
       "6     1                      0.000000  \n",
       "8     1                      0.038726  \n",
       "      2                      0.055122  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copiesDfGroupedAllFS = copiesDfGrouped.groupby([\"nodes\", \"networkspeed\"])[[str(i) + \" relative #tasks\" for i in allColumns]].mean() #mean works because because both filesystems have the same number of tasks\n",
    "copiesDfGroupedAllFS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2tAjwco7MvHnzkJiYiJo1a2LLli2Ijo7GqlWroK+vr7btFOf27dtISkpCixYtAAAnT56ElZUVatWqVeR7fvrpJ1y/fh1jxozBjh078P7776Ny5cpISkrC1q1bERcXp/hdcXd3x7p16zBgwAB4eXkVuLPvw4cPsXnzZtSoUUOluq9evYoOHTogICAAtWvXhp6eHnbu3Il79+4V+3tK9MY0dbkUUXlX3F1p8/LyRI0aNUSNGjUUlx9fv35dDBo0SNjZ2Ql9fX3h4OAg3n//fbFt2zbF+wq7/PrKlSvCz89PmJqaiipVqojhw4eLCxcuCAAiODhY0e/Fixfiiy++ENbW1kKSJKVLsfGfy6/zhYeHCwBCkiSly8VfVZK6C7Nt2zbx3nvvCRsbG2FgYCCcnJzEZ599JpKTk1/7GRb2OZw4cUI0b95cGBkZiapVq4pJkyaJgwcPFujXtm1bUadOnUJrys3NFfPmzRN16tQRMplMVK5cWTRu3FjMnDlTpKWlFbs/+eOePXtW+Pj4CENDQ+Hs7CyWLFlS6u0AEKNGjSp2u/8VGhoqDA0NFZd7Dxs2THTt2vW173vx4oVYs2aNaN26tTA3Nxf6+vrC2dlZBAYGFnpp9sWLF0X//v2Fvb290NfXF3Z2dqJ///4iJiamQN+S3KH54cOHYtSoUYo7A5ubm4tmzZqJsLCwku88USlIQqjwXx4iogrK19cXDx8+VLrrMRGVf5wjQ0RERFqLQYaIiIi0FoMMERERaS3OkSEiIiKtxSMyREREpLUYZIiIiEhrVfgb4snlcty9exeVKlVS6+3EiYiIqOwIIfD06VNUrVpV8XDSwlT4IHP37l21PSyOiIiI3q5bt26hWrVqRbZX+CCT/xj6W7duFXiSKxEREZVP6enpcHR0VHyPF6XCB5n800lmZmYMMkRERFrmddNCONmXiIiItBaDDBEREWktBhkiIiLSWhV+jgwREWlWXl4enj9/rukyqJzR19eHrq7uG4/DIENERGVCCIGUlBSkpqZquhQqpywsLGBnZ/dG93ljkCEiojKRH2JsbGxgbGzMm5KSghACWVlZuH//PgDA3t6+1GMxyBARkdrl5eUpQoyVlZWmy6FyyMjICABw//592NjYlPo0Eyf7EhGR2uXPiTE2NtZwJVSe5f9+vMkcKgYZIiIqMzydRMVRx+8HgwwRERFpLQYZIiIi0loMMkRE9M568OABPv/8czg5OUEmk8HOzg4dO3bEiRMnALw89bFr1y61bCsxMRGSJCE6Olot49FLvGqJiIjeWb1790Zubi7WrVuH6tWr4969ezh8+DAePXpU4jFyc3NhYGBQhlVScXhEhoiI3kmpqak4fvw45s2bh3bt2sHZ2RlNmzbF1KlT0b17d7i4uAAAevXqBUmSFMszZsxAgwYNsGbNGri6usLQ0BAAcODAAbRq1QoWFhawsrLC+++/j+vXryu25+rqCgBo2LAhJEmCr6+vom3NmjXw9PSEoaEhPDw8sGzZMqVaT548iQYNGsDQ0BDe3t7YtWuX4uiOEAJubm748ccfld4THR0NSZKQkJCg5k+ufGGQISKid5KpqSlMTU2xa9cu5OTkFGj/559/AADBwcFITk5WLANAQkICtm/fjh07dihOFWVmZmL8+PE4e/YsDh8+DB0dHfTq1QtyuRwAcObMGQDAoUOHkJycjB07dgAANm7ciOnTp+P7779HbGwsZs+ejWnTpmHdunUAgPT0dHTr1g1eXl44d+4cvv32W0yePFlRiyRJ+OSTTxAcHKxUf3BwMNq0aQM3Nzc1fWLllKjg0tLSBACRlpam3oGDzAp/ERGRePbsmbhy5Yp49uyZpksp1rZt20TlypWFoaGhaNGihZg6daq4cOGCoh2A2Llzp9J7goKChL6+vrh//36xYz948EAAEDExMUIIIW7cuCEAiPPnzyv1q1Gjhti0aZPSum+//Vb4+PgIIYRYvny5sLKyUvosV69erTTWnTt3hK6urjh9+rQQQojc3FxRpUoVERISUuLPQhOK+z0p6fc3j8gQEdE7q3fv3rh79y52796NTp06ISIiAo0aNUJISEix73N2doa1tbXSumvXrqF///6oXr06zMzMFKeikpKSihwnMzMT169fx9ChQxVHiExNTfHdd98pTkvFx8ejXr16ilNYANC0aVOlcapWrYquXbvi119/BQDs2bMHOTk56NOnT0k/Cq3Fyb5vUayHZ5FtnnGxb7ESIiLKZ2hoCH9/f/j7+2PatGkYNmwYgoKCMGTIkCLfY2JiUmBdt27d4OzsjNWrV6Nq1aqQy+WoW7cucnNzixwnIyMDALB69Wo0a9ZMqU3VW/YPGzYMH3/8MRYuXIjg4GD07dv3nbizMoMMERHRK2rXrq245FpfXx95eXmvfc+jR48QHx+P1atXo3Xr1gCAv//+W6lP/pVNr45na2uLqlWr4t9//8WAAQMKHbtWrVrYsGEDcnJyIJPJAEBpvk6+Ll26wMTEBMuXL8eBAwdw7Nix1+9sBcBTS0RE9E569OgR2rdvjw0bNuDixYu4ceMGtm7divnz56NHjx4AABcXFxw+fBgpKSl48uRJkWNVrlwZVlZWWLVqFRISEnDkyBGMHz9eqY+NjQ2MjIxw4MAB3Lt3D2lpaQCAmTNnYs6cOVi8eDGuXr2KmJgYBAcHY8GCBQCAjz76CHK5HJ9++iliY2Nx8OBBxRVKr97iX1dXF0OGDMHUqVPh7u4OHx8ftX5e5RWDDBERvZNMTU3RrFkzLFy4EG3atEHdunUxbdo0DB8+HEuWLAEA/PTTTwgPD4ejoyMaNmxY5Fg6OjoIDQ1FVFQU6tati3HjxuGHH35Q6qOnp4fFixdj5cqVqFq1qiIsDRs2DGvWrEFwcDC8vLzQtm1bhISEKC7XNjMzw549exAdHY0GDRrg66+/xvTp0wFAad4MAAwdOhS5ubkIDAxU2+dU3klCCKHpIspSeno6zM3NkZaWBjMzM/UNPMO8iPVpRb6Fc2SI6F2RnZ2NGzduKN1nhdRn48aNCAwMRFpaGoyMjBTrjx8/jg4dOuDWrVuwtbXVYIUlU9zvSUm/vzlHhoiIqJxbv349qlevDgcHB1y4cAGTJ09GQECAIsTk5OTgwYMHmDFjBvr06aMVIUZdeGqJiIionEtJScHAgQPh6emJcePGoU+fPli1apWiffPmzXB2dkZqairmz5+vwUrfPh6RISIiKucmTZqESZMmFdk+ZMiQYi8Xr8h4RIaIiIi0FoMMERERaS0GGSIiItJaDDJERESktRhkiIiISGsxyBAREZHWYpAhIiJ6y3x9fTF27FiN1iBJkuLhmIVJTEyEJEmIjo5+azWVhkbvIzNjxgzMnDlTaV2tWrUQFxcH4OWtiydMmIDQ0FDk5OSgY8eOWLZs2Tt1x0IioorGZcret7q9xLld3+r2ysrly5cxffp0REVF4ebNm1i4cGGhYSglJQVz5szB3r17cfv2bZibm8PNzQ0DBw7E4MGDYWxsXKLtOTo6Ijk5GVWqVAEAREREoF27dnjy5AksLCwK9A8MDISDgwO+++67N9lNlWn8hnh16tTBoUOHFMt6ev+/pHHjxmHv3r3YunUrzM3NMXr0aHzwwQc4ceKEJkotEa91XkW2hb3FOoiIqGLJyspC9erV0adPH4wbN67QPv/++y9atmwJCwsLzJ49G15eXpDJZIiJicGqVavg4OCA7t27l2h7urq6sLOzK1HfvLw8/PHHH9i79+2GVKAcnFrS09ODnZ2d4pWf/NLS0rB27VosWLAA7du3R+PGjREcHIyTJ08iMjLyrdTmMmVvkS8iIqqY5HI55s+fDzc3N8hkMjg5OeH7779XtN+6dQsBAQGwsLCApaUlevTogcTEREX7kCFD0LNnT8ycORPW1tYwMzPDiBEjkJubW2A7kyZNgqWlJezs7DBjxoxi62rSpAl++OEH9OvXDzKZrNA+I0eOhJ6eHs6ePYuAgAB4enqievXq6NGjB/bu3Ytu3bop9U9OTkbnzp1hZGSE6tWrY9u2bYq2V08tJSYmol27dgCAypUrQ5IkpTsJnzx5Evr6+mjSpAlyc3MxevRo2Nvbw9DQEM7OzpgzZ06x+/YmNB5krl27hqpVq6J69eoYMGAAkpKSAABRUVF4/vw5/Pz8FH09PDzg5OSEU6dOFTleTk4O0tPTlV5EREQlNXXqVMydOxfTpk3DlStXsGnTJsWUhufPn6Njx46oVKkSjh8/jhMnTsDU1BSdOnVSCiqHDx9GbGwsIiIisHnzZuzYsaPAVIp169bBxMQEp0+fxvz58zFr1iyEh4eXuu5Hjx7hzz//xKhRo2BiYlJoH0mSlJanTZuG3r1748KFCxgwYAD69euH2NjYAu9zdHTE9u3bAQDx8fFITk7GokWLFO27d+9Gt27dIEkSFi9ejN27dyMsLAzx8fHYuHEjXFxcSr1fr6PRINOsWTOEhITgwIEDWL58OW7cuIHWrVvj6dOnSElJgYGBQYHzcLa2tkhJSSlyzDlz5sDc3FzxcnR0LOO9ICKiiuLp06dYtGgR5s+fj8GDB6NGjRpo1aoVhg0bBgDYsmUL5HI51qxZAy8vL3h6eiI4OBhJSUmIiIhQjGNgYIBff/0VderUQdeuXTFr1iwsXrwYcrlc0adevXoICgqCu7s7Bg0aBG9vbxw+fLjUtSckJEAIgVq1aimtr1KlCkxNTWFqaorJkycrtfXp0wfDhg1DzZo18e2338Lb2xu//PJLgbF1dXVhaWkJALCxsYGdnR3Mzc0V7b///rvilFVSUhLc3d3RqlUrODs7o1WrVujfv3+p9+t1NDpHpnPnzoqf69Wrh2bNmsHZ2RlhYWGKR5OraurUqRg/frxiOT09nWGGiIhKJDY2Fjk5OejQoUOh7RcuXEBCQgIqVaqktD47OxvXr19XLNevX19pUq2Pjw8yMjJw69YtODs7A3j5vfcqe3t73L9/X127onDmzBnI5XIMGDAAOTk5Sm0+Pj4FllW9Sik2NhZ3795VfGZDhgyBv78/atWqhU6dOuH999/He++990b7UByNT/Z9lYWFBWrWrImEhAT4+/sjNzcXqampSkdl7t27V+zkI5lMVuS5QyIiouK87j/RGRkZaNy4MTZu3FigzdraWqVt6evrKy1LkqR0xEZVbm5ukCQJ8fHxSuurV68O4PX7Vlq7d++Gv78/DA0NAQCNGjXCjRs3sH//fhw6dAgBAQHw8/NTmn+jThqfI/OqjIwMXL9+Hfb29mjcuDH09fWVDrPFx8cjKSmpQIIkIiJSB3d3dxgZGRV5iqdRo0a4du0abGxs4ObmpvR69VTLhQsX8OzZM8VyZGQkTE1Ny/QMgZWVFfz9/bFkyRJkZmaW6D3/vXgmMjISnp6ehfY1MDAA8PIKpVf9/vvv6NGjh9I6MzMz9O3bF6tXr8aWLVuwfft2PH78uKS7ohKNBpmJEyfi6NGjSExMxMmTJ9GrVy/o6uqif//+MDc3x9ChQzF+/Hj89ddfiIqKQmBgIHx8fNC8eXNNlk1ERBWUoaEhJk+ejEmTJmH9+vW4fv06IiMjsXbtWgDAgAEDUKVKFfTo0QPHjx/HjRs3EBERgTFjxuD27duKcXJzczF06FBcuXIF+/btQ1BQEEaPHg0dndJ/7ebm5iI6OhrR0dHIzc3FnTt3EB0djYSEBEWfZcuW4cWLF/D29saWLVsQGxuL+Ph4bNiwAXFxcdDV1VUac+vWrfj1119x9epVBAUF4cyZMxg9enSh23d2doYkSfjjjz/w4MEDZGRk4P79+zh79izef/99Rb8FCxZg8+bNiIuLw9WrV7F161bY2dkVeu8ZddDoqaXbt2+jf//+ePToEaytrdGqVStERkYqDs8tXLgQOjo66N27t9IN8YiIiMrKtGnToKenh+nTp+Pu3buwt7fHiBEjAADGxsY4duwYJk+ejA8++ABPnz6Fg4MDOnToADMzM8UYHTp0gLu7O9q0aYOcnBz079//tZdXv87du3fRsGFDxfKPP/6IH3/8EW3btlVMNK5RowbOnz+P2bNnY+rUqbh9+zZkMhlq166NiRMnYuTIkUpjzpw5E6GhoRg5ciTs7e2xefNm1K5du9DtOzg4YObMmZgyZQoCAwMxaNAgtG7dGk2bNlXcOgUAKlWqhPnz5+PatWvQ1dVFkyZNsG/fvjcKccWRhBCiTEYuJ9LT02Fubo60tDSlX7KSKO5+MYmGHxW63svVqcj3hM15UWSbZ1zBy92IiLRVdnY2bty4AVdXV8XciXfFkCFDkJqaWuzt/yuK7t27o1WrVpg0aVKp3l/c70lJv7/L1RwZIiIi0h5lfWl1SZSrq5aIiIhIe5T2SIw6McgQERGpUUhIiKZLeKfw1BIRERFpLQYZIiIi0loMMkRERKS1GGSIiIhIazHIEBERkdZikCEiIiKtxSBDRET0lvn6+mLs2LEa235iYiIkSUJ0dHSRfSIiIiBJElJTU99aXaXB+8gQEdHbNcP89X3Uur20t7u9MrJ69WqsX78ely5dAgA0btwYs2fPRtOmTZX6JSQkYPbs2Th06BDu3buHKlWqwMPDA5988gn69u0LPb2SffW3aNECycnJiqd6h4SEYOzYsUUGm3bt2mHAgAEYNmxY6XeyFHhEhoiISAtERESgf//++Ouvv3Dq1Ck4Ojrivffew507dxR9zpw5g0aNGiE2NhZLly7FpUuXEBERgWHDhmH58uW4fPlyibdnYGAAOzs7SJL02r6PHz/GiRMn0K1bt1Lt25tgkCEiInqFXC7H/Pnz4ebmBplMBicnJ3z//feK9lu3biEgIAAWFhawtLREjx49kJiYqGgfMmQIevbsiZkzZ8La2hpmZmYYMWIEcnNzC2xn0qRJsLS0hJ2d3Wufjr1x40aMHDkSDRo0gIeHB9asWQO5XI7Dhw8DAIQQGDJkCGrWrKkIFe7u7nB3d0f//v3x999/o169ekpjxsXFoUWLFjA0NETdunVx9OhRRdurp5YiIiIQGBiItLQ0SJIESZKU6t27dy8aNWoEW1tbPHnyBAMGDIC1tTWMjIzg7u6O4OBgFf8USo5BhoiI6BVTp07F3LlzMW3aNFy5cgWbNm2Cra0tAOD58+fo2LEjKlWqhOPHj+PEiRMwNTVFp06dlILK4cOHERsbi4iICGzevBk7duzAzJkzlbazbt06mJiY4PTp05g/fz5mzZqF8PDwEteZlZWF58+fw9LSEgAQHR2N2NhYTJw4ETo6hX+9//foyldffYUJEybg/Pnz8PHxQbdu3fDo0aMC72vRogV+/vlnmJmZITk5GcnJyZg4caKifffu3ejRowcAKD63/fv3IzY2FsuXL0eVKlVKvF+qYpAhIiL6P0+fPsWiRYswf/58DB48GDVq1ECrVq0U8z62bNkCuVyONWvWwMvLC56enggODkZSUhIiIiIU4xgYGODXX39FnTp10LVrV8yaNQuLFy+GXC5X9KlXrx6CgoLg7u6OQYMGwdvbW3F0pSQmT56MqlWrws/PDwBw9epVAECtWrUUfe7fvw9TU1PFa9myZUpjjB49Gr1794anpyeWL18Oc3NzrF27tsC2DAwMYG5uDkmSYGdnBzs7O5iamgIAcnJycODAAXTv3h0AkJSUhIYNG8Lb2xsuLi7w8/Mr01NODDJERET/JzY2Fjk5OejQoUOh7RcuXEBCQgIqVaqkCAeWlpbIzs7G9evXFf3q168PY2NjxbKPjw8yMjJw69Ytxbr/nuaxt7fH/fv3S1Tn3LlzERoaip07d8LQ0LDIflZWVoiOjkZ0dDQsLCwKnN7y8fFR/Kynpwdvb2/ExsaWqIZ8R44cgY2NDerUqQMA+PzzzxEaGooGDRpg0qRJOHnypErjqYpXLREREf0fIyOjYtszMjLQuHFjbNy4sUCbtbW1StvS19dXWpYkSemITVF+/PFHzJ07F4cOHVIKQ+7u7gCA+Ph4NGzYEACgq6sLNzc3ACjx1Uqq2r17t+JoDAB07twZN2/exL59+xAeHo4OHTpg1KhR+PHHH8tk+zwiQ0RE9H/c3d1hZGRU5CmeRo0a4dq1a7CxsYGbm5vSK/8yZeDlkZtnz54pliMjI2FqagpHR8c3qm/+/Pn49ttvceDAAXh7eyu1NWzYEB4eHvjxxx9LFIjy68r34sULREVFwdPTs9C+BgYGyMvLU1onhMCePXsU82PyWVtbY/DgwdiwYQN+/vlnrFq1qkT1lAaDDBER0f8xNDTE5MmTMWnSJKxfvx7Xr19HZGSkYt7IgAEDUKVKFfTo0QPHjx/HjRs3EBERgTFjxuD27duKcXJzczF06FBcuXIF+/btQ1BQEEaPHl3kJNySmDdvHqZNm4Zff/0VLi4uSElJQUpKCjIyMgC8PKITHByM+Ph4tGzZErt378a1a9dw5coVrFixAg8ePICurq7SmEuXLsXOnTsRFxeHUaNG4cmTJ/jkk08K3b6LiwsyMjJw+PBhPHz4EFlZWYiKikJWVhZatWql6Dd9+nT8/vvvSEhIwOXLl/HHH38UGY7UgaeWiIiIXjFt2jTo6elh+vTpuHv3Luzt7TFixAgAgLGxMY4dO4bJkyfjgw8+wNOnT+Hg4IAOHTrAzMxMMUaHDh3g7u6ONm3aICcnB/3793/t5dWvs3z5cuTm5uLDDz9UWh8UFKQYu3nz5oiKisLs2bMxatQopKSkwMTEBPXr18fChQsLhJS5c+di7ty5iI6OhpubG3bv3l3kFUYtWrTAiBEj0LdvXzx69AhBQUHIy8tDly5dlE5bGRgYYOrUqUhMTISRkRFat26N0NDQN9r34khCCFFmo5cD6enpMDc3R1pamtIvWUm4TNlbZFui4UeFrvdydSryPWFzXhTZ5hmn2uQqIqLyLDs7Gzdu3ICrq2uxk1EroiFDhiA1NRW7du3SdCllrl69evjmm28QEBBQqvcX93tS0u9vnloiIiIileXm5qJ3797o3LmzRuvgqSUiIiJSmYGBAYKCgjRdBoMMERGROoWEhGi6hHcKTy0RERGR1mKQISIiIq3FIENERERai0GGiIiItBaDDBEREWktBhkiIiLSWgwyREREKkpJSYG/vz9MTExgYWGh6XLg4uKCn3/+udg+kiRVyLsN8z4yRET0Vnmt83qr24sZHKP2MRcuXIjk5GRER0crPfW6pJKTkzFhwgScPXsWCQkJGDNmTKFBJD09HT/88AN27NiBf//9F8bGxqhevTr69OmD4cOHo3LlyiptM79/YmIiXF1dcf78eTRo0KBA35kzZ+LatWvYsGGDyvv2tvGIDBERkYquX7+Oxo0bw93dHTY2Niq/PycnB9bW1vjmm29Qv379Qvs8fvwYzZs3R3BwMCZOnIjTp0/j3Llz+P7773H+/Hls2rRJpW3a2dlBJpOVqO/vv/+O7t27qzS+pjDIEBERvcLX1xdjxozBpEmTYGlpCTs7O6UnV7u4uGD79u1Yv349JEnCkCFDIITAjBkz4OTkBJlMhqpVq2LMmDFFbsPFxQWLFi3CoEGDijyi87///Q9JSUk4c+YMAgMDUa9ePTg7O+O9997D5s2bMXLkSKX+T58+Rf/+/WFiYgIHBwcsXbpUqf3VU0uurq4AgIYNG0KSJPj6+ir63bp1C5cvX0anTp1U3i9NYJAhIiL6j3Xr1sHExASnT5/G/PnzMWvWLISHhwMA/vnnH3Tq1AkBAQFITk7GokWLsH37dixcuBArV67EtWvXsGvXLnh5lf4Umlwux5YtWzBw4EBUrVq10D6SJCkt//DDD6hfvz7Onz+PKVOm4Msvv1TU/F9nzpwBABw6dAjJycnYsWOHom337t3w9fWFmZmZ2verLHCODBER0X/Uq1dP8UBEd3d3LFmyBIcPH4a/vz+sra0hk8lgZGQEOzs7AEBSUhLs7Ozg5+cHfX19ODk5oWnTpqXe/oMHD5CamopatWoprW/cuDHi4+MBAN26dcPmzZsVbS1btsSUKVMAADVr1sSJEyewcOFC+Pv7Fxjf2toaAGBlZaXYh3y///47evToUSb7VRZ4RIaIiOg/6tWrp7Rsb2+P+/fvF9m/T58+ePbsGapXr47hw4dj586dePHihdrr2rlzJ6Kjo9GxY0c8e/ZMqc3Hx6fAcmxsrErjp6en4+jRo4r5MW9rv94EgwwREdF/6OvrKy1LkgS5XF5kf0dHR8THx2PZsmUwMjLCyJEj0aZNGzx//rxU27e2toaFhYXi6Es+JycnuLm5oVKlSqUa93X279+P2rVrw9HREYD696ssMMgQERGpgZGREbp164bFixcjIiICp06dQkxM6S791tHRQUBAADZs2IC7d++W6D2RkZEFlj09PQvta2BgAADIy8tTWv/qaaV86tyvssA5MkRERG8oJCQEeXl5aNasGYyNjbFhwwYYGRnB2dm5yPdER0cDADIyMvDgwQNER0fDwMAAtWvXBgDMnj0bERERaNq0KWbNmgVvb2+YmJjg4sWLOHXqFOrWras03okTJzB//nz07NkT4eHh2Lp1K/bu3Vvotm1sbGBkZIQDBw6gWrVqMDQ0hImJCfbv34+JEye+0X69bQwyRET0VpXFDeo0zcLCAnPnzsX48eORl5cHLy8v7NmzB1ZWVkW+p2HDhoqfo6KisGnTJjg7OyMxMRHAy4m4Z86cwbx58/DDDz/gxo0b0NHRgbu7O/r27YuxY8cqjZd/g72ZM2fCzMwMCxYsQMeOHQvdtp6eHhYvXoxZs2Zh+vTpaN26NaZNmwZTU1M0atTojfbrbZOEEELTRZSl9PR0mJubIy0tDWZmZiq912VK4UkWABINPyp0vZerU5HvCZtT9AQpzzjVJmQREZVn2dnZuHHjBlxdXWFoaKjpcqgExowZgxcvXmDZsmVvbZvF/Z6U9PubR2SIiIgIdevWLXDlkzZgkCEiIiJ8+umnmi6hVHjVEhEREWktBhkiIiLSWgwyREREpLUYZIiIiEhrMcgQERGR1mKQISIiIq3FIENERERai0GGiIhIRSkpKfD394eJiQksLCw0XQ4kScKuXbuKbE9MTIQkSYrnO1UkvCEeERG9VbEehT+RuayUxSNgFi5ciOTkZERHR8Pc3Fzl9+/YsQPLly9HdHQ0cnJyUKdOHcyYMaPAs5FSUlIwZ84c7N27F7dv34a5uTnc3NwwcOBADB48GMbGxiXanqOjI5KTk1GlShUAQEREBNq1a4cnT54UGsQCAwPh4OCA7777TuV9e9sYZIiIiFR0/fp1NG7cGO7u7qV6/7Fjx+Dv74/Zs2fDwsICwcHB6NatG06fPq14mOS///6Lli1bwsLCArNnz4aXlxdkMhliYmKwatUqODg4oHv37iXanq6uLuzs7ErUNy8vD3/88UeRT84ub3hqiYiI6BW+vr4YM2YMJk2aBEtLS9jZ2WHGjBmKdhcXF2zfvh3r16+HJEkYMmQIhBCYMWMGnJycIJPJULVqVYwZM6bIbfz888+YNGkSmjRpAnd3d8yePRvu7u7Ys2ePos/IkSOhp6eHs2fPIiAgAJ6enqhevTp69OiBvXv3olu3bkpjJicno3PnzjAyMkL16tWxbds2Rdurp5YSExPRrl07AEDlypUV+5Dv5MmT0NfXR5MmTZCbm4vRo0fD3t4ehoaGcHZ2xpw5c97wE1avchNk5s6dC0mSlB5Lnp2djVGjRsHKygqmpqbo3bs37t27p7kiiYjonbBu3TqYmJjg9OnTmD9/PmbNmoXw8HAAwD///INOnTohICAAycnJWLRoEbZv346FCxdi5cqVuHbtGnbt2gUvL68Sb08ul+Pp06ewtLQEADx69Ah//vknRo0aBRMTk0LfI0mS0vK0adPQu3dvXLhwAQMGDEC/fv0QG1vwtJqjoyO2b98OAIiPj1fsQ77du3ejW7dukCQJixcvxu7duxEWFob4+Hhs3LgRLi4uJd6vt6FcBJl//vkHK1euRL169ZTWjxs3Dnv27MHWrVtx9OhR3L17Fx988IGGqiQiondFvXr1EBQUBHd3dwwaNAje3t44fPgwAMDa2hoymQxGRkaws7ODubk5kpKSYGdnBz8/Pzg5OaFp06YYPnx4ibf3448/IiMjAwEBAQCAhIQECCFQq1YtpX5VqlSBqakpTE1NMXnyZKW2Pn36YNiwYahZsya+/fZbeHt745dffimwLV1dXUVgsrGxUexDvt9//11xyiopKQnu7u5o1aoVnJ2d0apVK/Tv37/E+/U2aDzIZGRkYMCAAVi9ejUqV66sWJ+Wloa1a9diwYIFaN++PRo3bozg4GCcPHkSkZGRGqyYiIgquv/+x9re3h73798vsn+fPn3w7NkzVK9eHcOHD8fOnTvx4sWLEm1r06ZNmDlzJsLCwmBjY1Ns3zNnziA6Ohp16tRBTk6OUpuPj0+B5cKOyBQnNjYWd+/eRYcOHQAAQ4YMQXR0NGrVqoUxY8bgzz//VGm8t0HjQWbUqFHo2rUr/Pz8lNZHRUXh+fPnSus9PDzg5OSEU6dOFTleTk4O0tPTlV5ERESq0NfXV1qWJAlyubzI/o6OjoiPj8eyZctgZGSEkSNHok2bNnj+/Hmx2wkNDcWwYcMQFham9H3n5uYGSZIQHx+v1L969epwc3ODkZFRKfbq9Xbv3g1/f38YGhoCABo1aoQbN27g22+/xbNnzxAQEIAPP/ywTLZdWhoNMqGhoTh37lyhE4dSUlJgYGBQ4LIwW1tbpKSkFDnmnDlzYG5urng5Ojqqu2wiIqICjIyM0K1bNyxevBgRERE4deoUYmJiiuy/efNmBAYGYvPmzejatatSm5WVFfz9/bFkyRJkZmaWaPv/PVsRGRkJT8/CL3U3MDAA8PIKpVf9/vvv6NGjh9I6MzMz9O3bF6tXr8aWLVuwfft2PH78uEQ1vQ0au/z61q1b+PLLLxEeHq5IfuowdepUjB8/XrGcnp7OMENERGUqJCQEeXl5aNasGYyNjbFhwwYYGRnB2dm50P6bNm3C4MGDsWjRIjRr1kzxH3QjIyPFfJVly5ahZcuW8Pb2xowZM1CvXj3o6Ojgn3/+QVxcHBo3bqw05tatW+Ht7Y1WrVph48aNOHPmDNauXVvo9p2dnSFJEv744w906dIFRkZGyMrKwtmzZ7F7925FvwULFsDe3h4NGzaEjo4Otm7dCjs7u3JxE8B8GgsyUVFRuH//Pho1aqRYl5eXh2PHjmHJkiU4ePAgcnNzkZqaqvSB3bt3r9hr4WUyGWQyWVmWTkREb6AsblCnaRYWFpg7dy7Gjx+PvLw8eHl5Yc+ePbCysiq0/6pVq/DixQuMGjUKo0aNUqwfPHgwQkJCAAA1atTA+fPnMXv2bEydOhW3b9+GTCZD7dq1MXHiRIwcOVJpzJkzZyI0NBQjR46Evb09Nm/ejNq1axe6fQcHB8ycORNTpkxBYGAgBg0ahNatW6Np06aKm+YBQKVKlTB//nxcu3YNurq6aNKkCfbt2wcdHY3PTFGQhBBCExt++vQpbt68qbQuMDAQHh4emDx5MhwdHWFtbY3Nmzejd+/eAF5eJubh4YFTp06hefPmJdpOeno6zM3NkZaWBjMzM5VqdJlS9M2AEg0/KnS9l6tTke8Jm1P0xK+K+BebiN5d2dnZuHHjBlxdXdV61J3KTvfu3dGqVStMmjTprW2zuN+Tkn5/a+yITKVKlVC3bl2ldSYmJrCyslKsHzp0KMaPHw9LS0uYmZnhiy++gI+PT4lDDBEREZVMeby0uiTK9SMKFi5cCB0dHfTu3Rs5OTno2LEjli1bpumyiIiIKpy3eSRGncpVkImIiFBaNjQ0xNKlS7F06VLNFERERETlWvmZrUNERESkIgYZIiIqMxq6noS0hDp+PxhkiIhI7fLvjJuVlaXhSqg8y//9+O+dlFVRrubIEBFRxaCrqwsLCwvF84mMjY0LPK2Z3l1CCGRlZeH+/fuwsLCArq5uqcdikCEiojKRf/PS4h62SO82CwuLYm9yWxIMMkREVCYkSYK9vT1sbGxe+/BEevfo6+u/0ZGYfAwyRERUpnR1ddXyhUVUGE72JSIiIq3FIENERERai0GGiIiItBaDDBEREWktBhkiIiLSWgwyREREpLUYZIiIiEhrMcgQERGR1mKQISIiIq3FIENERERai0GGiIiItBaDDBEREWktBhkiIiLSWgwyREREpLUYZIiIiEhrMcgQERGR1mKQISIiIq3FIENERERaq9RBJiEhAQcPHsSzZ88AAEIItRVFREREVBIqB5lHjx7Bz88PNWvWRJcuXZCcnAwAGDp0KCZMmKD2AomIiIiKonKQGTduHPT09JCUlARjY2PF+r59++LAgQNqLY6IiIioOHqqvuHPP//EwYMHUa1aNaX17u7uuHnzptoKIyIiInodlY/IZGZmKh2Jyff48WPIZDK1FEVERERUEioHmdatW2P9+vWKZUmSIJfLMX/+fLRr106txREREREVR+VTS/Pnz0eHDh1w9uxZ5ObmYtKkSbh8+TIeP36MEydOlEWNRERERIVS+YhM3bp1cfXqVbRq1Qo9evRAZmYmPvjgA5w/fx41atQoixqJiIiICqXSEZnnz5+jU6dOWLFiBb7++uuyqomIiIioRFQ6IqOvr4+LFy+WVS1EREREKlH51NLAgQOxdu3asqiFiIiISCUqT/Z98eIFfv31Vxw6dAiNGzeGiYmJUvuCBQvUVhwRERFRcVQOMpcuXUKjRo0AAFevXlVqkyRJPVURERERlYDKQeavv/4qizqIiIiIVFbqp18TERERaZrKR2QA4OzZswgLC0NSUhJyc3OV2nbs2KGWwoiIiIheR+UjMqGhoWjRogViY2Oxc+dOPH/+HJcvX8aRI0dgbm5eFjUSERERFUrlIDN79mwsXLgQe/bsgYGBARYtWoS4uDgEBATAycmpLGokIiIiKpTKQeb69evo2rUrAMDAwACZmZmQJAnjxo3DqlWr1F4gERERUVFUDjKVK1fG06dPAQAODg64dOkSACA1NRVZWVnqrY6IiIioGCpP9m3Tpg3Cw8Ph5eWFPn364Msvv8SRI0cQHh6ODh06lEWNRERERIVSOcgsWbIE2dnZAICvv/4a+vr6OHnyJHr37o1vvvlG7QUSERERFUXlIGNpaan4WUdHB1OmTFFrQUREREQlVar7yMjlciQkJOD+/fuQy+VKbW3atFFLYURERESvo3KQiYyMxEcffYSbN29CCKHUJkkS8vLy1FYcERERUXFUDjIjRoyAt7c39u7dC3t7ez4okoiIiDRG5SBz7do1bNu2DW5ubmVRDxEREVGJqXwfmWbNmiEhIaEsaiEiIiJSSYmOyFy8eFHx8xdffIEJEyYgJSUFXl5e0NfXV+pbr1499VZIREREVIQSBZkGDRpAkiSlyb2ffPKJ4uf8Nk72JSIiorepRKeWbty4gX///Rc3btwo9JXf9u+//6q08eXLl6NevXowMzODmZkZfHx8sH//fkV7dnY2Ro0aBSsrK5iamqJ37964d++eantIREREFVaJjsg4OzuXycarVauGuXPnwt3dHUIIrFu3Dj169MD58+dRp04djBs3Dnv37sXWrVthbm6O0aNH44MPPsCJEyfKpB4iIiLSLipP9l23bh327t2rWJ40aRIsLCzQokUL3Lx5U6WxunXrhi5dusDd3R01a9bE999/D1NTU0RGRiItLQ1r167FggUL0L59ezRu3BjBwcE4efIkIiMjVS2biIiIKiCVg8zs2bNhZGQEADh16hSWLFmC+fPno0qVKhg3blypC8nLy0NoaCgyMzPh4+ODqKgoPH/+HH5+foo+Hh4ecHJywqlTp4ocJycnB+np6UovIiIiqphUvo/MrVu3FPeQ2bVrFz788EN8+umnaNmyJXx9fVUuICYmBj4+PsjOzoapqSl27tyJ2rVrIzo6GgYGBrCwsFDqb2tri5SUlCLHmzNnDmbOnKlyHURERKR9VD4iY2pqikePHgEA/vzzT/j7+wMADA0N8ezZM5ULqFWrFqKjo3H69Gl8/vnnGDx4MK5cuaLyOPmmTp2KtLQ0xevWrVulHouIiIjKN5WPyPj7+2PYsGFo2LAhrl69ii5dugAALl++DBcXF5ULMDAwUBzhady4Mf755x8sWrQIffv2RW5uLlJTU5WOyty7dw92dnZFjieTySCTyVSug4iIiLSPykdkli5dCh8fHzx48ADbt2+HlZUVACAqKgr9+/d/44LkcjlycnLQuHFj6Ovr4/Dhw4q2+Ph4JCUlwcfH5423Q0RERNpP5SMyFhYWWLJkSYH1pZmXMnXqVHTu3BlOTk54+vQpNm3ahIiICBw8eBDm5uYYOnQoxo8fD0tLS5iZmeGLL76Aj48PmjdvrvK2iIiIqOJROcio0/379zFo0CAkJyfD3Nwc9erVw8GDBxXzbhYuXAgdHR307t0bOTk56NixI5YtW6bJkomIiKgc0WiQWbt2bbHthoaGWLp0KZYuXfqWKiIiIiJtovIcGSIiIqLygkGGiIiItFapgsyLFy9w6NAhrFy5Ek+fPgUA3L17FxkZGWotjoiIiKg4Ks+RuXnzJjp16oSkpCTk5OTA398flSpVwrx585CTk4MVK1aURZ1EREREBah8RObLL7+Et7c3njx5onjmEgD06tVL6Z4vRERERGVN5SMyx48fx8mTJ2FgYKC03sXFBXfu3FFbYURERESvo/IRGblcjry8vALrb9++jUqVKqmlKCIiIqKSUDnIvPfee/j5558Vy5IkISMjA0FBQYrnLhERERG9DSqfWvrpp5/QsWNH1K5dG9nZ2fjoo49w7do1VKlSBZs3by6LGqkc8VrnVej6mMExb7kSIiKiUgSZatWq4cKFCwgNDcXFixeRkZGBoUOHYsCAAUqTf4mIiIjKmspBJjs7G4aGhhg4cGBZ1ENvicuUvUW2Jc7t+hYrISIiKj2V58jY2Nhg8ODBCA8Ph1wuL4uaiIiIiEpE5SCzbt06ZGVloUePHnBwcMDYsWNx9uzZsqiNiIiIqFgqB5levXph69atuHfvHmbPno0rV66gefPmqFmzJmbNmlUWNRIREREVqtQPjaxUqRICAwPx559/4uLFizAxMcHMmTPVWRsRERFRsUodZLKzsxEWFoaePXuiUaNGePz4Mb766it11kZERERULJWvWjp48CA2bdqEXbt2QU9PDx9++CH+/PNPtGnTpizqIyIiIiqSykGmV69eeP/997F+/Xp06dIF+vr6ZVEXERER0WupHGTu3bvHZyoRERFRuVCiIJOeng4zMzMAgBAC6enpRfbN70dERERU1koUZCpXrozk5GTY2NjAwsICkiQV6COEgCRJhT4Zm4iIiKgslCjIHDlyBJaWlgCAv/76q0wLIiIiIiqpEgWZtm3bKn52dXWFo6NjgaMyQgjcunVLvdURERERFUPl+8i4urriwYMHBdY/fvwYrq6uaimKiIiIqCRUDjL5c2H+KyMjA4aGhmopioiIiKgkSnz59fjx4wEAkiRh2rRpMDY2VrTl5eXh9OnTaNCggdoLJCIiIipKiYPM+fPnAbw8IhMTEwMDAwNFm4GBAerXr4+JEyeqv0IiIiKiIpQ4yORfrRQYGIhFixbxfjFERESkcSrf2Tc4OLgs6iAiIiJSmcpBBgDOnj2LsLAwJCUlITc3V6ltx44daimMiIiI6HVUvmopNDQULVq0QGxsLHbu3Innz5/j8uXLOHLkCMzNzcuiRiIiIqJCqRxkZs+ejYULF2LPnj0wMDDAokWLEBcXh4CAADg5OZVFjURERESFUjnIXL9+HV27dgXw8mqlzMxMSJKEcePGYdWqVWovkIiIiKgoKgeZypUr4+nTpwAABwcHXLp0CQCQmpqKrKws9VZHREREVAyVJ/u2adMG4eHh8PLyQp8+ffDll1/iyJEjCA8PR4cOHcqiRiIiIqJCqRxklixZguzsbADA119/DX19fZw8eRK9e/fGN998o/YCiYiIiIqicpCxtLRU/Kyjo4MpU6aotSAiIiKikipRkElPTy/xgLzjLxEREb0tJQoyFhYWhT7x+lX5T8XOy8tTS2FEREREr1OiIJP/nCUiIiKi8qREQaZt27ZlXQcRERGRylS+jwwAHD9+HAMHDkSLFi1w584dAMBvv/2Gv//+W63FERERERVH5auWtm/fjo8//hgDBgzAuXPnkJOTAwBIS0vD7NmzsW/fPrUXSeVfrIdnkW2ecbFvsRIiInqXqHxE5rvvvsOKFSuwevVq6OvrK9a3bNkS586dU2txRERERMVROcjEx8ejTZs2Bdabm5sjNTVVHTURERERlYjKp5bs7OyQkJAAFxcXpfV///03qlevrq66SJNmmBfd5sonnBMRUfmh8hGZ4cOH48svv8Tp06chSRLu3r2LjRs3YuLEifj888/LokYiIiKiQql8RGbKlCmQy+Xo0KEDsrKy0KZNG8hkMkycOBFffPFFWdRIREREVCiVg4wkSfj666/x1VdfISEhARkZGahduzZMTU3x7NkzGBkZlUWdRERERAWU6j4yAGBgYIDatWujadOm0NfXx4IFC+Dq6qrO2oiIiIiKVeIgk5OTg6lTp8Lb2xstWrTArl27AADBwcFwdXXFwoULMW7cuLKqk4iIiKiAEp9amj59OlauXAk/Pz+cPHkSffr0QWBgICIjI7FgwQL06dMHurq6ZVkrERERkZISB5mtW7di/fr16N69Oy5duoR69erhxYsXuHDhwmufjE1ERERUFkp8aun27dto3LgxAKBu3bqQyWQYN24cQwwRERFpTImDTF5eHgwMDBTLenp6MDU1LZOiiIiIiEqixKeWhBAYMmQIZDIZACA7OxsjRoyAiYmJUr8dO3aUeONz5szBjh07EBcXByMjI7Ro0QLz5s1DrVq1FH2ys7MxYcIEhIaGIicnBx07dsSyZctga2tb4u0QERFRxVTiIzKDBw+GjY0NzM3NYW5ujoEDB6Jq1aqK5fyXKo4ePYpRo0YhMjIS4eHheP78Od577z1kZmYq+owbNw579uzB1q1bcfToUdy9excffPCBStshIiKiiqnER2SCg4PVvvEDBw4oLYeEhMDGxgZRUVFo06YN0tLSsHbtWmzatAnt27dX1OHp6YnIyEg0b95c7TURERGR9ij1DfHKQlpaGgDA0tISABAVFYXnz5/Dz89P0cfDwwNOTk44depUoWPk5OQgPT1d6UVEREQVU7kJMnK5HGPHjkXLli1Rt25dAEBKSgoMDAxgYWGh1NfW1hYpKSmFjjNnzhylU12Ojo5lXToRERFpSLkJMqNGjcKlS5cQGhr6RuNMnToVaWlpitetW7fUVCERERGVNyo/NLIsjB49Gn/88QeOHTuGatWqKdbb2dkhNzcXqampSkdl7t27Bzs7u0LHkslkiiuriIiIqGLT6BEZIQRGjx6NnTt34siRIwUeOtm4cWPo6+vj8OHDinXx8fFISkqCj4/P2y6XiIiIyhmNHpEZNWoUNm3ahN9//x2VKlVSzHsxNzeHkZERzM3NMXToUIwfPx6WlpYwMzPDF198AR8fH16xRERERJoNMsuXLwcA+Pr6Kq0PDg7GkCFDAAALFy6Ejo4OevfurXRDPCIiIiKNBhkhxGv7GBoaYunSpVi6dOlbqIiIiIi0Sbm5aomIiIhIVQwyREREpLUYZIiIiEhrMcgQERGR1mKQISIiIq3FIENERERai0GGiIiItBaDDBEREWktBhkiIiLSWgwyREREpLUYZIiIiEhrMcgQERGR1mKQISIiIq3FIENERERai0GGiIiItBaDDBEREWktBhkiIiLSWgwyREREpLUYZIiIiEhrMcgQERGR1mKQISIiIq3FIENERERai0GGiIiItBaDDBEREWktBhkiIiLSWgwyREREpLUYZIiIiEhrMcgQERGR1mKQISIiIq3FIENERERai0GGiIiItBaDDBEREWktBhkiIiLSWgwyREREpLUYZIiIiEhrMcgQERGR1mKQISIiIq3FIENERERai0GGiIiItBaDDBEREWktBhkiIiLSWgwyREREpLUYZIiIiEhrMcgQERGR1mKQISIiIq3FIENERERai0GGiIiItBaDDBEREWktBhkiIiLSWgwyREREpLUYZIiIiEhrMcgQERGR1mKQISIiIq3FIENERERai0GGiIiItJZGg8yxY8fQrVs3VK1aFZIkYdeuXUrtQghMnz4d9vb2MDIygp+fH65du6aZYomIiKjc0WiQyczMRP369bF06dJC2+fPn4/FixdjxYoVOH36NExMTNCxY0dkZ2e/5UqJiIioPNLT5MY7d+6Mzp07F9omhMDPP/+Mb775Bj169AAArF+/Hra2tti1axf69ev3NkslIiKicqjczpG5ceMGUlJS4Ofnp1hnbm6OZs2a4dSpUxqsjIiIiMoLjR6RKU5KSgoAwNbWVmm9ra2toq0wOTk5yMnJUSynp6eXTYFERESkceX2iExpzZkzB+bm5oqXo6OjpksiIiKiMlJug4ydnR0A4N69e0rr7927p2grzNSpU5GWlqZ43bp1q0zrJCIiIs0pt0HG1dUVdnZ2OHz4sGJdeno6Tp8+DR8fnyLfJ5PJYGZmpvQiIiKiikmjc2QyMjKQkJCgWL5x4waio6NhaWkJJycnjB07Ft999x3c3d3h6uqKadOmoWrVqujZs6fmiiYiIqJyQ6NB5uzZs2jXrp1iefz48QCAwYMHIyQkBJMmTUJmZiY+/fRTpKamolWrVjhw4AAMDQ01VTIRERGVIxoNMr6+vhBCFNkuSRJmzZqFWbNmvcWqiIiISFuU2zkyRERERK/DIENERERai0GGiIiItBaDDBEREWktBhkiIiLSWgwyREREpLUYZIiIiEhrMcgQERGR1mKQISIiIq3FIENERERaS6OPKCAqCy5T9hbZlji361ushIiIyhqPyBAREZHW4hEZerfMMC+yycvVqci2mMExZVENERG9IQYZohKI9fAsdL1nXOxbroSIiF7FU0tERESktRhkiIiISGsxyBAREZHWYpAhIiIircUgQ0RERFqLQYaIiIi0FoMMERERaS0GGSIiItJaDDJERESktRhkiIiISGsxyBAREZHWYpAhIiIircUgQ0RERFqLQYaIiIi0FoMMERERaS0GGSIiItJaepougIjUZIZ5MW1pb68OIqK3iEdkiIiISGvxiAzROy7Ww7PINs+42LdYCRGR6hhkiKj8K+VpM4Y0ooqPp5aIiIhIa/GIDNE7wGudV5FtYW+xDiIidWOQISLSQjxtRvQSgwyRFnGZsrfItkTDt1gIEVE5wSBDROUCQxoRlQaDDBGRJvFGhgXwtBmpgkGGiLQaJzITvdsYZIiIyimGNKLXY5AhIipjFXn+T/H79lHRb+RpswJ42qx0GGSIiOit0/qjTZzbVG7wzr5ERESktXhEhoiIqBClPSWo9UebtAyPyBAREZHWYpAhIiIircUgQ0RERFqLQYaIiIi0FoMMERERaS0GGSIiItJaDDJERESktRhkiIiISGsxyBAREZHW0oogs3TpUri4uMDQ0BDNmjXDmTNnNF0SERERlQPlPshs2bIF48ePR1BQEM6dO4f69eujY8eOuH//vqZLIyIiIg0r90FmwYIFGD58OAIDA1G7dm2sWLECxsbG+PXXXzVdGhEREWlYuQ4yubm5iIqKgp+fn2Kdjo4O/Pz8cOrUKQ1WRkREROVBuX769cOHD5GXlwdbW1ul9ba2toiLiyv0PTk5OcjJyVEsp6WlAQDS09NV3r48J6vItnRJFLo+71leke/JyCu6rTT1vYnS7BtQ9P5V5H0Dit6/irxvwNvdP+5bQdy3QsbkvqlN3aCDha6/ZDi0yPc0d65WZNu6n14U2VYr6mzJC/s/+Z+HEEV/1vkdyq07d+4IAOLkyZNK67/66ivRtGnTQt8TFBQkAPDFF1988cUXXxXgdevWrWKzQrk+IlOlShXo6uri3r17Suvv3bsHOzu7Qt8zdepUjB8/XrEsl8vx+PFjWFlZQZKkMq03PT0djo6OuHXrFszMzMp0W5pQkfeP+6aduG/aifumnd72vgkh8PTpU1StWrXYfuU6yBgYGKBx48Y4fPgwevbsCeBlMDl8+DBGjx5d6HtkMhlkMpnSOgsLizKuVJmZmVmF+wV+VUXeP+6bduK+aSfum3Z6m/tmbm7+2j7lOsgAwPjx4zF48GB4e3ujadOm+Pnnn5GZmYnAwEBNl0ZEREQaVu6DTN++ffHgwQNMnz4dKSkpaNCgAQ4cOFBgAjARERG9e8p9kAGA0aNHF3kqqTyRyWQICgoqcGqroqjI+8d9007cN+3EfdNO5XXfJCFed10TERERUflUrm+IR0RERFQcBhkiIiLSWgwyREREpLUYZIhIq3GaH9G7jUGGiLSaTCZDbGyspssgIg3RisuvidTt2bNniIqKgqWlJWrXrq3Ulp2djbCwMAwaNEhD1b2Z2NhYREZGwsfHBx4eHoiLi8OiRYuQk5ODgQMHon379pousVReffTIq/Ly8jB37lxYWVkBABYsWPA2yyoTmZmZCAsLQ0JCAuzt7dG/f3/F/lH58cUXXyAgIACtW7fWdCnvNrU83ZGUpKWliZ07d4orV65oupQyk5SUJAIDAzVdRqnEx8cLZ2dnIUmS0NHREW3atBF3795VtKekpAgdHR0NVlh6+/fvFwYGBsLS0lIYGhqK/fv3C2tra+Hn5yfat28vdHV1xeHDhzVdZqlIkiQaNGggfH19lV6SJIkmTZoIX19f0a5dO02XWSqenp7i0aNHQoiXf7dcXFyEubm5aNKkibC0tBQ2Njbi33//1XCVpRMVFaVU+/r160WLFi1EtWrVRMuWLcXmzZs1WN2byf83xN3dXcydO1ckJydruiS1+uWXX8THH3+s+DNav3698PT0FLVq1RJTp04Vz58/13CFLzHIqEGfPn3EL7/8IoQQIisrS7i7uwt9fX2hp6cntm3bpuHqykZ0dLTWftn37NlTdO3aVTx48EBcu3ZNdO3aVbi6uoqbN28KIbQ7yPj4+Iivv/5aCCHE5s2bReXKlcX//vc/RfuUKVOEv7+/psp7I3PmzBGurq4Fgpienp64fPmyhqpSD0mSxL1794QQQgwYMEC0aNFCpKamCiGEePr0qfDz8xP9+/fXZImlVq9ePREeHi6EEGL16tXCyMhIjBkzRixfvlyMHTtWmJqairVr12q4ytKRJEkcOnRIfPnll6JKlSpCX19fdO/eXezZs0fk5eVpurw38u2334pKlSqJ3r17Czs7OzF37lxhZWUlvvvuOzF79mxhbW0tpk+frukyhRAMMmpha2sroqOjhRBCbNy4Ubi5uYnMzEyxbNky0aBBAw1XVzq///57sa+FCxdq7Ze9jY2NuHjxomJZLpeLESNGCCcnJ3H9+nWtDjJmZmbi2rVrQggh8vLyhJ6enjh37pyiPSYmRtja2mqqvDd25swZUbNmTTFhwgSRm5srhKh4QaZ69erizz//VGo/ceKEcHR01ERpb8zIyEgkJiYKIYRo2LChWLVqlVL7xo0bRe3atTVR2ht79c8tNzdXbNmyRXTs2FHo6uqKqlWriv/973+Kv4/apkaNGmL79u1CiJf/cdXV1RUbNmxQtO/YsUO4ublpqjwlDDJqYGhoKJKSkoQQQnz88cdi8uTJQgghbt68KUxMTDRZWqnlHzKVJKnIl7Z+2VeqVKnQ036jRo0S1apVE8eOHdPafTMzMxMJCQmKZVNTU3H9+nXFcmJiojA0NNREaWrz9OlTMWjQIFGvXj0RExMj9PX1K0SQuX//vhBCiKpVq4qYmBildm3+c7OyshJnz54VQrz8T0T+f/ryJSQkCCMjI02U9sZeDTKvunnzpggKChLOzs5a+2+JkZGR4ii1EELo6+uLS5cuKZYTExOFsbGxJkorgFctqYGjoyNOnTqFzMxMHDhwAO+99x4A4MmTJzA0NNRwdaVjb2+PHTt2QC6XF/o6d+6cpkssNQ8PD5w9e7bA+iVLlqBHjx7o3r27BqpSDxcXF1y7dk2xfOrUKTg5OSmWk5KSYG9vr4nS1MbU1BTr1q3D1KlT4efnh7y8PE2XpBYdOnRAo0aNkJ6ejvj4eKW2mzdvau1k386dO2P58uUAgLZt22Lbtm1K7WFhYXBzc9NEaWXGyckJM2bMwI0bN3DgwAFNl1MqdnZ2uHLlCgDg2rVryMvLUywDwOXLl2FjY6Op8pTwqiU1GDt2LAYMGABTU1M4OzvD19cXAHDs2DF4eXlptrhSaty4MaKiotCjR49C2yVJ0tr7d/Tq1QubN2/Gxx9/XKBtyZIlkMvlWLFihQYqe3Off/650hd73bp1ldr379+vtVct/Ve/fv3QqlUrREVFwdnZWdPlvJGgoCClZVNTU6XlPXv2aO2VMfPmzUPLli3Rtm1beHt746effkJERAQ8PT0RHx+PyMhI7Ny5U9NlloqzszN0dXWLbJckCf7+/m+xIvUZMGAABg0ahB49euDw4cOYNGkSJk6ciEePHkGSJHz//ff48MMPNV0mAD40Um3Onj2LW7duwd/fX/GP0N69e2FhYYGWLVtquDrVHT9+HJmZmejUqVOh7ZmZmTh79izatm37lisjIm2TmpqKuXPnYs+ePfj3338hl8thb2+Pli1bYty4cfD29tZ0ifQfcrkcc+fOxalTp9CiRQtMmTIFW7ZswaRJk5CVlYVu3bphyZIlMDEx0XSpDDJlIS8vDzExMXB2dkblypU1XQ4REVGFxTkyajB27FisXbsWwMsQ07ZtWzRq1AiOjo6IiIjQbHFEREQVGIOMGmzbtg3169cH8PJc9o0bNxAXF4dx48bh66+/1nB1REREFReDjBo8fPgQdnZ2AIB9+/ahT58+qFmzJj755BPExMRouDoiIqKKi0FGDWxtbXHlyhXk5eXhwIEDilnqWVlZxc5oJyIiojfDy6/VIDAwEAEBAbC3t4ckSfDz8wMAnD59Gh4eHhqujoiIqOJikFGDGTNmoG7durh16xb69OkDmUwGANDV1cWUKVM0XB0REVHFxcuv1Sw7O1tr7+ZLRESkbThHRg3y8vLw7bffwsHBAaampvj3338BANOmTVNclk1ERETqxyCjBt9//z1CQkIwf/58GBgYKNbXrVsXa9as0WBlREREFRuDjBqsX78eq1atwoABA5SuUqpfvz7i4uI0WBkREVHFxiCjBnfu3Cn06a1yuRzPnz/XQEVERETvBgYZNahduzaOHz9eYP22bdvQsGFDDVRERET0buDl12owffp0DB48GHfu3IFcLseOHTsQHx+P9evX448//tB0eURERBUWL79Wk+PHj2PWrFm4cOECMjIy0KhRI0yfPh3vvfeepksjIiKqsBhkiIiISGtxjgwRERFpLc6RKaXKlStDkqQS9X38+HEZV0NERPRuYpAppZ9//lnTJRAREb3zOEeGiIiItBbnyKjJ9evX8c0336B///64f/8+AGD//v24fPmyhisjIiKquBhk1ODo0aPw8vLC6dOnsWPHDmRkZAAALly4gKCgIA1XR0REVHExyKjBlClT8N133yE8PFzpoZHt27dHZGSkBisjIiKq2Bhk1CAmJga9evUqsN7GxgYPHz7UQEVERETvBgYZNbCwsEBycnKB9efPn4eDg4MGKiIiIno3MMioQb9+/TB58mSkpKRAkiTI5XKcOHECEydOxKBBgzRdHhERUYXFy6/VIDc3F6NGjUJISAjy8vKgp6eHvLw8fPTRRwgJCYGurq6mSyQiIqqQGGTU6NatW4iJiUFGRgYaNmwId3d3TZdERERUoTHIlIG8vDzExMTA2dkZlStX1nQ5REREFRbnyKjB2LFjsXbtWgAvQ0zbtm3RqFEjODo6IiIiQrPFERERVWAMMmqwbds21K9fHwCwZ88e/Pvvv4iLi8O4cePw9ddfa7g6IiKiiotBRg0ePnwIOzs7AMC+ffsQEBCAmjVr4pNPPkFMTIyGqyMiIqq4GGTUwNbWFleuXEFeXh4OHDgAf39/AEBWVhavWCIiIipDepouoCIIDAxEQEAA7O3tIUkS/Pz8AACnT5+Gh4eHhqsjIiKquHjVkpps374dSUlJ6NOnD6pVqwYAWLduHSpXrozu3btruDoiIqKKiUFGDWbNmlVs+/Tp099SJURERO8WBhk1aNiwodLy8+fPcePGDejp6aFGjRo4d+6chiojIiKq2DhHRg3Onz9fYF16ejqGDBlS6FOxiYiISD14RKYMxcTEoFu3bkhMTNR0KURERBUSL78uQ2lpaUhLS9N0GURERBUWTy2pweLFi5WWhRBITk7Gb7/9hs6dO2uoKiIiooqPp5bUwNXVVWlZR0cH1tbWaN++PaZOnYpKlSppqDIiIqKKjUGGiIiItBbnyBAREZHWYpAhIiIircUgQ0RERFqLQYaIiIi0FoMMUQU3ZMgQ9OzZs8D6iIgISJKE1NTUt16TtkhPT8fXX38NDw8PGBoaws7ODn5+ftixYwdevU7i8uXLCAgIgLW1NWQyGWrWrInp06cjKytLaTwXFxdIkgRJkmBiYoJGjRph69atb3u3iCoUBhkiokKkpqaiRYsWWL9+PaZOnYpz587h2LFj6Nu3LyZNmqS42WVkZCSaNWuG3Nxc7N27F1evXsX333+PkJAQ+Pv7Izc3V2ncWbNmITk5GefPn0eTJk3Qt29fnDx5UhO7SFQhMMgQkcL27dtRp04dyGQyuLi44KefflK0LVmyBHXr1lUs79q1C5IkYcWKFYp1fn5++OabbwodOzExEZIkISwsDK1bt4aRkRGaNGmCq1ev4p9//oG3tzdMTU3RuXNnPHjwQPG+f/75B/7+/qhSpQrMzc3Rtm3bAg9ilSQJa9asQa9evWBsbAx3d3fs3r0bwMsbVLq5ueHHH39Uek90dDQkSUJCQkKh9f7vf/9DYmIiTp8+jcGDB6N27dqoWbMmhg8fjujoaJiamkIIgaFDh8LT0xM7duxA06ZN4ezsjD59+mDPnj04deoUFi5cqDRupUqVYGdnh5o1a2Lp0qUwMjLCnj17AADLli2Du7s7DA0NYWtriw8//LDIPysieolBhogAAFFRUQgICEC/fv0QExODGTNmYNq0aQgJCQEAtG3bFleuXFGEjKNHj6JKlSqIiIgA8PKp76dOnYKvr2+x2wkKCsI333yDc+fOQU9PDx999BEmTZqERYsW4fjx40hISMD06dMV/Z8+fYrBgwfj77//RmRkJNzd3dGlSxc8ffpUadyZM2ciICAAFy9eRJcuXTBgwAA8fvwYkiThk08+QXBwsFL/4OBgtGnTBm5ubgVqlMvlCA0NxYABA1C1atUC7aamptDT00N0dDSuXLmC8ePHQ0dH+Z/T+vXrw8/PD5s3by7ys9DT04O+vj5yc3Nx9uxZjBkzBrNmzUJ8fDwOHDiANm3aFPtZEhEAQUQV2uDBg4Wurq4wMTFRehkaGgoA4smTJ0IIIT766CPh7++v9N6vvvpK1K5dWwghhFwuF1ZWVmLr1q1CCCEaNGgg5syZI+zs7IQQQvz9999CX19fZGZmFlrHjRs3BACxZs0axbrNmzcLAOLw4cOKdXPmzBG1atUqcn/y8vJEpUqVxJ49exTrAIhvvvlGsZyRkSEAiP379wshhLhz547Q1dUVp0+fFkIIkZubK6pUqSJCQkIK3ca9e/cEALFgwYIi6xBCiNDQUAFAnD9/vtD2MWPGCCMjI8Wys7OzWLhwoRBCiJycHDF79mwBQPzxxx9i+/btwszMTKSnpxe7TSJSxiMyRO+Adu3aITo6Wum1Zs0apT6xsbFo2bKl0rqWLVvi2rVryMvLgyRJaNOmDSIiIpCamoorV65g5MiRyMnJQVxcHI4ePYomTZrA2Ni42Frq1aun+NnW1hYA4OXlpbTu/v37iuV79+5h+PDhcHd3h7m5OczMzJCRkYGkpKQixzUxMYGZmZlinKpVq6Jr16749ddfAQB79uxBTk4O+vTpU2iNQsUbnqvSf/LkyTA1NYWxsTHmzZuHuXPnomvXrvD394ezszOqV6+Ojz/+GBs3biwwWZiICmKQIXoHmJiYwM3NTenl4OCg8ji+vr6IiIjA8ePH0bBhQ5iZmSnCzdGjR9G2bdvXjqGvr6/4WZKkQtfJ5XLF8uDBgxEdHY1Fixbh5MmTiI6OhpWVVYFJtK+OUdg4w4YNQ2hoKJ49e4bg4GD07du3yNBlbW0NCwsLxMXFFbsvNWvWBPAyBBYmNjZW0SffV199hejoaNy+fRtPnjzB5MmTAbycO3Pu3Dls3rwZ9vb2mD59OurXr8+ryoheg0GGiAAAnp6eOHHihNK6EydOoGbNmtDV1QXw/+fJbN26VTEXxtfXF4cOHcKJEydeOz+mNE6cOIExY8agS5cuionIDx8+VHmcLl26wMTEBMuXL8eBAwfwySefFNlXR0cH/fr1w8aNG3H37t0C7RkZGXjx4gUaNGgADw8PLFy4UCk0AcCFCxdw6NAh9O/fX2l9lSpV4ObmBjs7O0WQy6enpwc/Pz/Mnz8fFy9eRGJiIo4cOaLyvhK9SxhkiAgAMGHCBBw+fBjffvstrl69inXr1mHJkiWYOHGiok+9evVQuXJlbNq0SSnI7Nq1Czk5OQVOTamDu7s7fvvtN8TGxuL06dMYMGAAjIyMVB5HV1cXQ4YMwdSpU+Hu7g4fH59i+3///fdwdHREs2bNsH79ely5cgXXrl3Dr7/+ioYNGyIjIwOSJGHt2rW4cuUKevfujTNnziApKQlbt25Ft27d4OPjg7Fjx5aovj/++AOLFy9GdHQ0bt68ifXr10Mul6NWrVoq7yvRu4RBhogAAI0aNUJYWBhCQ0NRt25dTJ8+HbNmzcKQIUMUfSRJQuvWrSFJElq1agXgZbgxMzODt7c3TExM1F7X2rVr8eTJEzRq1Agff/wxxowZAxsbm1KNNXToUOTm5iIwMPC1fS0tLREZGYmBAwfiu+++Q8OGDdG6dWts3rwZP/zwA8zNzQEALVq0QGRkJHR1ddG5c2e4ublh6tSpGDx4MMLDwyGTyUpUm4WFBXbs2IH27dvD09MTK1aswObNm1GnTp1S7SvRu0ISqs5qIyLSUsePH0eHDh1w69YtxURjItJuDDJEVOHl5OTgwYMHGDx4MOzs7LBx40ZNl0REasJTS0RU4W3evBnOzs5ITU3F/PnzNV0OEakRj8gQERGR1uIRGSIiItJaDDJERESktRhkiIiISGsxyBAREZHWYpAhIiIircUgQ0RERFqLQYaIiIi0FoMMERERaS0GGSIiItJa/w9vkD/c6S7/mAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>useless</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Strategy</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ceph 1Gbit/s</th>\n",
       "      <td>50.383112</td>\n",
       "      <td>27.537138</td>\n",
       "      <td>7.505864</td>\n",
       "      <td>3.502737</td>\n",
       "      <td>3.346364</td>\n",
       "      <td>2.689601</td>\n",
       "      <td>2.642690</td>\n",
       "      <td>2.220485</td>\n",
       "      <td>0.172009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ceph 2Gbit/s</th>\n",
       "      <td>53.223388</td>\n",
       "      <td>22.255539</td>\n",
       "      <td>7.279693</td>\n",
       "      <td>4.564384</td>\n",
       "      <td>4.064634</td>\n",
       "      <td>3.181742</td>\n",
       "      <td>2.731967</td>\n",
       "      <td>2.532067</td>\n",
       "      <td>0.166583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nfs 1Gbit/s</th>\n",
       "      <td>50.467428</td>\n",
       "      <td>27.823119</td>\n",
       "      <td>7.285947</td>\n",
       "      <td>3.813622</td>\n",
       "      <td>3.056833</td>\n",
       "      <td>2.789731</td>\n",
       "      <td>2.492951</td>\n",
       "      <td>2.196172</td>\n",
       "      <td>0.074195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nfs 2Gbit/s</th>\n",
       "      <td>52.830789</td>\n",
       "      <td>22.614504</td>\n",
       "      <td>7.061069</td>\n",
       "      <td>4.150763</td>\n",
       "      <td>3.975827</td>\n",
       "      <td>3.514631</td>\n",
       "      <td>3.069338</td>\n",
       "      <td>2.719466</td>\n",
       "      <td>0.063613</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                useless          1         2         3         4         5  \\\n",
       "Strategy                                                                     \n",
       "ceph 1Gbit/s  50.383112  27.537138  7.505864  3.502737  3.346364  2.689601   \n",
       "ceph 2Gbit/s  53.223388  22.255539  7.279693  4.564384  4.064634  3.181742   \n",
       "nfs 1Gbit/s   50.467428  27.823119  7.285947  3.813622  3.056833  2.789731   \n",
       "nfs 2Gbit/s   52.830789  22.614504  7.061069  4.150763  3.975827  3.514631   \n",
       "\n",
       "                     6         7         8  \n",
       "Strategy                                    \n",
       "ceph 1Gbit/s  2.642690  2.220485  0.172009  \n",
       "ceph 2Gbit/s  2.731967  2.532067  0.166583  \n",
       "nfs 1Gbit/s   2.492951  2.196172  0.074195  \n",
       "nfs 2Gbit/s   3.069338  2.719466  0.063613  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def plotRelative( df, title ):\n",
    "    copiesDfGrouped8NodesFiltered = df.reset_index()[df.reset_index()[\"nodes\"] == 8]\n",
    "    copiesDfGrouped8Nodes = pd.DataFrame({\n",
    "        'Strategy': copiesDfGrouped8NodesFiltered[\"filesystem\"] + \" \" + copiesDfGrouped8NodesFiltered[\"networkspeed\"].astype(str) + \"Gbit/s\",\n",
    "        })\n",
    "    for i in allColumns:\n",
    "        copiesDfGrouped8Nodes[ i ] = copiesDfGrouped8NodesFiltered[str(i) + \" relative \" + title ]\n",
    "    copiesDfGrouped8Nodes.set_index('Strategy', inplace=True)\n",
    "    copiesDfGrouped8Nodes_transposed = copiesDfGrouped8Nodes.T\n",
    "    copiesDfGrouped8Nodes_transposed.plot(kind='bar')\n",
    "    plt.xlabel('How many COPs')\n",
    "    plt.ylabel('Relative share')\n",
    "    plt.title('Relative share per ' + title )\n",
    "\n",
    "    plt.show()\n",
    "    return copiesDfGrouped8Nodes\n",
    "\n",
    "plotRelative( copiesDfGrouped, \"#COPs\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>useless</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Strategy</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ceph 1Gbit/s</th>\n",
       "      <td>15.596863</td>\n",
       "      <td>8.524543</td>\n",
       "      <td>2.323555</td>\n",
       "      <td>1.084326</td>\n",
       "      <td>1.035918</td>\n",
       "      <td>0.832607</td>\n",
       "      <td>0.818085</td>\n",
       "      <td>0.687385</td>\n",
       "      <td>0.053248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ceph 2Gbit/s</th>\n",
       "      <td>25.159461</td>\n",
       "      <td>10.520513</td>\n",
       "      <td>3.441216</td>\n",
       "      <td>2.157650</td>\n",
       "      <td>1.921411</td>\n",
       "      <td>1.504055</td>\n",
       "      <td>1.291440</td>\n",
       "      <td>1.196945</td>\n",
       "      <td>0.078746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nfs 1Gbit/s</th>\n",
       "      <td>16.463356</td>\n",
       "      <td>9.076387</td>\n",
       "      <td>2.376803</td>\n",
       "      <td>1.244070</td>\n",
       "      <td>0.997192</td>\n",
       "      <td>0.910059</td>\n",
       "      <td>0.813244</td>\n",
       "      <td>0.716429</td>\n",
       "      <td>0.024204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nfs 2Gbit/s</th>\n",
       "      <td>26.159540</td>\n",
       "      <td>11.197732</td>\n",
       "      <td>3.496338</td>\n",
       "      <td>2.055280</td>\n",
       "      <td>1.968659</td>\n",
       "      <td>1.740295</td>\n",
       "      <td>1.519805</td>\n",
       "      <td>1.346563</td>\n",
       "      <td>0.031499</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                useless          1         2         3         4         5  \\\n",
       "Strategy                                                                     \n",
       "ceph 1Gbit/s  15.596863   8.524543  2.323555  1.084326  1.035918  0.832607   \n",
       "ceph 2Gbit/s  25.159461  10.520513  3.441216  2.157650  1.921411  1.504055   \n",
       "nfs 1Gbit/s   16.463356   9.076387  2.376803  1.244070  0.997192  0.910059   \n",
       "nfs 2Gbit/s   26.159540  11.197732  3.496338  2.055280  1.968659  1.740295   \n",
       "\n",
       "                     6         7         8  \n",
       "Strategy                                    \n",
       "ceph 1Gbit/s  0.818085  0.687385  0.053248  \n",
       "ceph 2Gbit/s  1.291440  1.196945  0.078746  \n",
       "nfs 1Gbit/s   0.813244  0.716429  0.024204  \n",
       "nfs 2Gbit/s   1.519805  1.346563  0.031499  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "plotRelative( copiesDfGrouped, \"#tasks\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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