Plotly hlt_timings-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from plotly.offline import init_notebook_mode, iplot\n",
    "init_notebook_mode()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2020-04-21 08:47:50--  https://raw.githubusercontent.com/livcms/hlt_timing/master/sen_constPU0_numEvent100_timings.csv\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.236.133\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.236.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 6029 (5.9K) [text/plain]\n",
      "Saving to: ‘sen_constPU0_numEvent100_timings.csv.3’\n",
      "\n",
      "sen_constPU0_numEve 100%[===================>]   5.89K  --.-KB/s    in 0.001s  \n",
      "\n",
      "2020-04-21 08:47:50 (8.86 MB/s) - ‘sen_constPU0_numEvent100_timings.csv.3’ saved [6029/6029]\n",
      "\n",
      "--2020-04-21 08:47:50--  https://raw.githubusercontent.com/livcms/hlt_timing/master/sen_constPU140_numEvent100_timings.csv\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.236.133\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.236.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 6169 (6.0K) [text/plain]\n",
      "Saving to: ‘sen_constPU140_numEvent100_timings.csv.3’\n",
      "\n",
      "sen_constPU140_numE 100%[===================>]   6.02K  --.-KB/s    in 0.001s  \n",
      "\n",
      "2020-04-21 08:47:50 (4.05 MB/s) - ‘sen_constPU140_numEvent100_timings.csv.3’ saved [6169/6169]\n",
      "\n",
      "--2020-04-21 08:47:50--  https://raw.githubusercontent.com/livcms/hlt_timing/master/sen_constPU200_numEvent100_timings.csv\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.236.133\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.236.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 6216 (6.1K) [text/plain]\n",
      "Saving to: ‘sen_constPU200_numEvent100_timings.csv.3’\n",
      "\n",
      "sen_constPU200_numE 100%[===================>]   6.07K  --.-KB/s    in 0.001s  \n",
      "\n",
      "2020-04-21 08:47:51 (10.8 MB/s) - ‘sen_constPU200_numEvent100_timings.csv.3’ saved [6216/6216]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "! wget https://raw.githubusercontent.com/livcms/hlt_timing/master/sen_constPU0_numEvent100_timings.csv\n",
    "! wget https://raw.githubusercontent.com/livcms/hlt_timing/master/sen_constPU140_numEvent100_timings.csv\n",
    "! wget https://raw.githubusercontent.com/livcms/hlt_timing/master/sen_constPU200_numEvent100_timings.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "pu0=pd.read_csv(\"sen_constPU0_numEvent100_timings.csv\")\n",
    "pu140 =pd.read_csv(\"sen_constPU140_numEvent100_timings.csv\")\n",
    "pu200 = pd.read_csv(\"sen_constPU200_numEvent100_timings.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def categorise_modules(module):\n",
    "    if \"cutsReco\" in module: \n",
    "        return \"cutsReco\"\n",
    "    elif \"trackValidator\" in module: \n",
    "        return \"trackValidator\"\n",
    "    elif \"siPixel\" in module: \n",
    "        return \"siPixel\"\n",
    "    elif \"initialStep\" in module: \n",
    "        return \"initialStep\"\n",
    "    elif \"highPt\" in module: \n",
    "        return \"highPt\"\n",
    "    elif \"ecal\" in module: \n",
    "        return \"ecal\"\n",
    "    else: \n",
    "        return \"other\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#pu0.drop(\"Std (ms) at 100 events\", axis=1, inplace=True)\n",
    "allpu = pu0.copy()\n",
    "allpu[\"PU140\"] = pu140[\"Average time/event (ms) at 100 events\"]\n",
    "allpu[\"PU200\"] = pu200[\"Average time/event (ms) at 100 events\"]\n",
    "allpu[\"ModuleType\"] = allpu[\"Module\"].apply(lambda x: categorise_modules(x))\n",
    "allpu.rename(columns={'Average time/event (ms) at 100 events':'PU0'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def nlargest_three(x): \n",
    "    largestpu0 = allpu.nlargest(x, \"PU0\")[\"Module\"]\n",
    "    largestpu140 = allpu.nlargest(x, \"PU140\")[\"Module\"]\n",
    "    largestpu200 = allpu.nlargest(x, \"PU200\")[\"Module\"]\n",
    "    \n",
    "    nlongest_mod = np.unique((largestpu0.append(largestpu140)).append(largestpu200))\n",
    "    #df = dt.loc[allpu['Module'].isin(nlongest_mod)]\n",
    "    return nlongest_mod\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot1():\n",
    "    num_steps = len(np.arange(0,151,10))\n",
    "    trace_list1 = [\n",
    "        # Create subplots: use 'domain' type for Pie subplot\n",
    "        go.Pie(values=allpu.nlargest(step, \"PU0\")[\"PU0\"][5:],\n",
    "               labels=allpu.nlargest(step, \"PU0\")[\"Module\"][5:],\n",
    "               domain={'x':[0.1,0.9], 'y':[0.05,0.9]},\n",
    "               hole=0.5,\n",
    "               direction='clockwise',\n",
    "               sort=True,\n",
    "               marker=dict(line=dict(color='#000000', width=0.2))\n",
    "                  ) for step in np.arange(0,151,10)]\n",
    "\n",
    "    trace_list2 = [\n",
    "        # Create subplots: use 'domain' type for Pie subplot\n",
    "        go.Pie(values=allpu.nlargest(step, \"PU140\")[\"PU0\"][5:],\n",
    "               labels=allpu.nlargest(step, \"PU140\")[\"Module\"][5:],\n",
    "               domain={'x':[0.1,0.9], 'y':[0.05,0.9]},\n",
    "               hole=0.75,\n",
    "               direction='clockwise',\n",
    "               sort=True,\n",
    "               marker=dict(line=dict(color='#000000', width=0.2))\n",
    "                  ) for step in np.arange(0, 151, 10)]\n",
    "\n",
    "    trace_list3 = [\n",
    "        # Create subplots: use 'domain' type for Pie subplot\n",
    "        go.Pie(values=allpu.nlargest(step, \"PU200\")[\"PU0\"][5:],\n",
    "               labels=allpu.nlargest(step, \"PU200\")[\"Module\"][5:],\n",
    "               domain={'x':[0.1,0.9], 'y':[0.05,0.9]},\n",
    "               hole=0.9,\n",
    "               direction='clockwise',\n",
    "               sort=True,\n",
    "               marker=dict(line=dict(color='#000000', width=0.2))\n",
    "                  ) for step in np.arange(0, 151, 10)]\n",
    "\n",
    "\n",
    "    fig = go.Figure(data=trace_list1+trace_list2+trace_list3)\n",
    "    fig.update_traces(textposition= \"none\", textinfo=None)\n",
    "\n",
    "\n",
    "    steps = []\n",
    "    for i in range(num_steps):\n",
    "        # Hide all traces\n",
    "        step = dict(\n",
    "            method = 'restyle',  \n",
    "            args = ['visible', [False] * len(fig.data)],\n",
    "        )\n",
    "        # Enable the two traces we want to see\n",
    "        step['args'][1][i] = True\n",
    "        step['args'][1][i+num_steps] = True\n",
    "        step['args'][1][i+2*num_steps] = True\n",
    "\n",
    "        # Add step to step list\n",
    "        steps.append(step)\n",
    "\n",
    "    sliders = [dict(\n",
    "        steps = steps,\n",
    "        active=10,\n",
    "        currentvalue={\"prefix\": \"Showing the \"},\n",
    "        pad={\"t\": 50},\n",
    "    )]\n",
    "\n",
    "\n",
    "    fig.layout.sliders = sliders\n",
    "\n",
    "    fig['layout']['sliders'][0]['currentvalue']['suffix']=' longest modules in ms: '\n",
    "    for i, date in enumerate(np.arange(0,151,10)):\n",
    "        fig['layout']['sliders'][0]['steps'][i]['label']=str(date)\n",
    "\n",
    "    fig.update_layout(\n",
    "        title=\"n longest modules at PU 0,140,200\")\n",
    "    \n",
    "    iplot(fig, show_link=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import chart_studio as py\n",
    "def plot2(nmods):\n",
    "\n",
    "    nmods =20 \n",
    "\n",
    "    trace_list1 = go.Pie(values=allpu.nlargest(nmods, \"PU0\")[\"PU0\"][5:],\n",
    "           labels=allpu.nlargest(nmods, \"PU0\")[\"Module\"][5:],\n",
    "           domain={'x':[0.1,0.9], 'y':[0.05,0.9]},\n",
    "           hole=0.5,\n",
    "           direction='clockwise',\n",
    "           sort=True,\n",
    "           marker=dict(line=dict(color='#000000', width=0.2))\n",
    "              )\n",
    "\n",
    "    trace_list2 = go.Pie(values=allpu.nlargest(nmods, \"PU140\")[\"PU0\"][5:],\n",
    "           labels=allpu.nlargest(nmods, \"PU140\")[\"Module\"][5:],\n",
    "           domain={'x':[0.1,0.9], 'y':[0.05,0.9]},\n",
    "           hole=0.75,\n",
    "           direction='clockwise',\n",
    "           sort=True,\n",
    "           marker=dict(line=dict(color='#000000', width=0.2))\n",
    "              )\n",
    "\n",
    "    trace_list3 =  go.Pie(values=allpu.nlargest(nmods, \"PU200\")[\"PU0\"][5:],\n",
    "           labels=allpu.nlargest(nmods, \"PU200\")[\"Module\"][5:],\n",
    "           domain={'x':[0.1,0.9], 'y':[0.05,0.9]},\n",
    "           hole=0.9,\n",
    "           direction='clockwise',\n",
    "           sort=True,\n",
    "           marker=dict(line=dict(color='#000000', width=0.2))\n",
    "              )\n",
    "\n",
    "    data = [trace_list1, trace_list2, trace_list3]\n",
    "\n",
    "\n",
    "\n",
    "    updatemenus = list([\n",
    "    dict(type=\"buttons\",\n",
    "         active=-1,\n",
    "         buttons=list([\n",
    "            dict(label = 'PU0',\n",
    "                 method = 'update',\n",
    "                 args = [{'visible': [True, False, False]}]),\n",
    "\n",
    "            dict(label = 'PU140',\n",
    "                 method = 'update',\n",
    "                 args = [{'visible': [False, True, False]}]),\n",
    "\n",
    "            dict(label = 'PU200',\n",
    "                 method = 'update',\n",
    "                 args = [{'visible': [False, False, True]}]),\n",
    "\n",
    "            dict(label = 'PU0 and PU140',\n",
    "                 method = 'update',\n",
    "                 args = [{'visible': [True, True, False]}]),\n",
    "\n",
    "            dict(label = 'PU0 and 200',\n",
    "                 method = 'update',\n",
    "                 args = [{'visible': [True, False, True]}]),\n",
    "\n",
    "            dict(label = 'PU140 and 200',\n",
    "                 method = 'update',\n",
    "                 args = [{'visible': [False, True, True]}]),\n",
    "\n",
    "            dict(label = 'All',\n",
    "                 method = 'update',\n",
    "                 args = [{'visible': [True, True, True]}])\n",
    "        ]),\n",
    "    )\n",
    "    ])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    layout = dict(title='Compare n modules for selected pileups', showlegend=False,\n",
    "              updatemenus=updatemenus)\n",
    "\n",
    "    fig2 = dict(data=data, layout=layout)\n",
    "    \n",
    "    iplot(fig2, filename='update_button')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "itLocalReco =[\n",
    "    'siPhase2Clusters', \n",
    "    'siPixelClusters', \n",
    "    'siPixelClusterShapeCache',\n",
    "    'siPixelClustersPreSplitting', \n",
    "    'siPixelRecHits', \n",
    "    'siPixelRecHitsPreSplitting'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "caloLocalReco = [\n",
    "    'hbhereco',\n",
    "    'hfprereco',\n",
    "    'hfreco',\n",
    "    'horeco',\n",
    "    'ecalUncalibRecHitSequence',\n",
    "    'ecalRecHit'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "initialStepSequence = [\n",
    "    'initialStepSeedLayers', \n",
    "    'initialStepTrackingRegions', \n",
    "    'initialStepHitDoublets',\n",
    "    'initialStepHitQuadruplets', \n",
    "    'initialStepSeeds', \n",
    "    'initialStepTrackCandidates', \n",
    "    'initialStepTracks',\n",
    "    'initialStepPVSequence', #uses beamspot\n",
    "    'initialStepSelector']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "highPtTripletStepSequence = [\n",
    "    'highPtTripletStepClusters',\n",
    "    'highPtTripletStepSeedLayers',\n",
    "    'highPtTripletStepTrackingRegions',\n",
    "    'highPtTripletStepHitDoublets',\n",
    "    'highPtTripletStepHitTriplets',\n",
    "    'highPtTripletStepSeedLayers',\n",
    "    'highPtTripletStepSeeds',\n",
    "    'highPtTripletStepTrackCandidates',\n",
    "    'highPtTripletStepTracks',\n",
    "    'highPtTripletStepSelector',\n",
    "    'initialStepSeedClusterMask',# needed by electron, but also by highPtTripletStepSeedClusterMask\n",
    "    'highPtTripletStepSeedClusterMask']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "vertexReco = [\n",
    "    'ak4CaloJetsForTrk',\n",
    "    'unsortedOfflinePrimaryVertices',\n",
    "    'trackWithVertexRefSelectorBeforeSorting',\n",
    "    'trackRefsForJetsBeforeSorting',\n",
    "    'offlinePrimaryVertices',\n",
    "    'offlinePrimaryVerticesWithBS',\n",
    "    'inclusiveVertexFinder',\n",
    "    'vertexMerger',\n",
    "    'trackVertexArbitrator',\n",
    "    'inclusiveSecondaryVertices'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "DQM_offline = ['PV0p1',\n",
    "               'TrackMon_ckf',\n",
    "               'TrackSeedMonhighPtTripletStep',\n",
    "               'TrackSeedMoninitialStep', \n",
    "               'TrackSeedMonlowPtQuadStep', \n",
    "               'TrackSplitMonitor', \n",
    "               'TrackerCollisionSelectedTrackMonCommongeneralTracks',\n",
    "               'TrackerCollisionSelectedTrackMonCommonhighPurityPV0p1', \n",
    "               'TrackerCollisionSelectedTrackMonCommonhighPurityPt1', \n",
    "               'TrackerCollisionSelectedTrackMonCommonhighPurityPtRange0to1',\n",
    "               'dqmInfoTracking', \n",
    "               'highPurityPV0p1', \n",
    "               'highPurityPt1',\n",
    "               'highPurityPtRange0to1', \n",
    "               'primaryVertexResolution', \n",
    "               'pvMonitor', \n",
    "               'trackingDQMgoodOfflinePrimaryVertices'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "preval = [\n",
    "'trackValidatorTrackingOnly',\n",
    "'trackValidatorTPPtLess09Standalone',\n",
    "'trackValidatorFromPVStandalone',\n",
    "'trackValidatorFromPVAllTPStandalone',\n",
    "'trackValidatorAllTPEfficStandalone', \n",
    "'trackValidatorBHadronTrackingOnly', \n",
    "'trackValidatorSeedingTrackingOnly', \n",
    "'trackValidatorSeedingPreSplittingTrackingOnly', \n",
    "'trackValidatorBuilding', \n",
    "'trackValidatorBuildingPreSplitting', \n",
    "'VertexAssociatorByPositionAndTracks', \n",
    "'cutsRecoTracksBtvLike', \n",
    "'cutsRecoTracksFromPVHighPtTripletStep', \n",
    "'cutsRecoTracksFromPVHighPtTripletStepHp', \n",
    "'cutsRecoTracksFromPVHp',\n",
    "'cutsRecoTracksFromPVInitialStep', \n",
    "'cutsRecoTracksFromPVInitialStepHp', \n",
    "'cutsRecoTracksFromPVLowPtQuadStep', \n",
    "'cutsRecoTracksFromPVLowPtQuadStepHp', \n",
    "'cutsRecoTracksFromPVPt09HighPtTripletStep', \n",
    "'cutsRecoTracksFromPVPt09HighPtTripletStepHp', \n",
    "'cutsRecoTracksFromPVPt09Hp', \n",
    "'cutsRecoTracksFromPVPt09InitialStep', \n",
    "'cutsRecoTracksFromPVPt09InitialStepHp', \n",
    "'cutsRecoTracksFromPVPt09LowPtQuadStep', \n",
    "'cutsRecoTracksFromPVPt09LowPtQuadStepHp',  \n",
    "'cutsRecoTracksHighPtTripletStep', \n",
    "'cutsRecoTracksHighPtTripletStepByAlgoMask', \n",
    "'cutsRecoTracksHighPtTripletStepByAlgoMaskHp', \n",
    "'cutsRecoTracksHighPtTripletStepByOriginalAlgo', \n",
    "'cutsRecoTracksHighPtTripletStepByOriginalAlgoHp', \n",
    "'cutsRecoTracksHighPtTripletStepHp',\n",
    "'cutsRecoTracksHp',\n",
    "'cutsRecoTracksInitialStep',\n",
    "'cutsRecoTracksInitialStepByAlgoMask',\n",
    "'cutsRecoTracksInitialStepByAlgoMaskHp',\n",
    "'cutsRecoTracksInitialStepByOriginalAlgo',\n",
    "'cutsRecoTracksInitialStepByOriginalAlgoHp',\n",
    "'cutsRecoTracksInitialStepHp',\n",
    "'cutsRecoTracksLowPtQuadStep',\n",
    "'cutsRecoTracksLowPtQuadStepByAlgoMask',\n",
    "'cutsRecoTracksLowPtQuadStepByAlgoMaskHp',\n",
    "'cutsRecoTracksLowPtQuadStepByOriginalAlgo',\n",
    "'cutsRecoTracksLowPtQuadStepByOriginalAlgoHp',\n",
    "'cutsRecoTracksLowPtQuadStepHp',\n",
    "'cutsRecoTracksPt09HighPtTripletStep',\n",
    "'cutsRecoTracksPt09HighPtTripletStepHp',\n",
    "'cutsRecoTracksPt09H',\n",
    "'cutsRecoTracksPt09InitialStep',\n",
    "'cutsRecoTracksPt09InitialStepHp',\n",
    "'cutsRecoTracksPt09LowPtQuadStep', \n",
    "'cutsRecoTracksPt09LowPtQuadStepHp',\n",
    "'generalTracksFromPV',\n",
    "'generalTracksFromPVPt09',\n",
    "'generalTracksPt09',\n",
    "'quickTrackAssociatorByHits',\n",
    "'quickTrackAssociatorByHitsPreSplitting',\n",
    "'seedTrackshighPtTripletStepSeeds',\n",
    "'seedTracksinitialStepSeeds',\n",
    "'seedTrackslowPtQuadStepSeeds', \n",
    "'tpClusterProducer',\n",
    "'tpClusterProducerPreSplitting',\n",
    "'trackingParticleNumberOfLayersProducer',\n",
    "'trackingParticleRecoTrackAsssociation',\n",
    "'trackingParticlesBHadron', \n",
    "'trackingParticlesConversion', \n",
    "'trackingParticlesElectron', \n",
    "'trackingParticlesSignal',\n",
    "'selectedOfflinePrimaryVertices',\n",
    "'selectedOfflinePrimaryVerticesWithBS', \n",
    "'simHitTPAssocProducer', \n",
    "'v0Validator', \n",
    "'vertexAnalysisTrackingOnly'\n",
    "'cutsRecoTracksPt09Hp', \n",
    "'vertexAnalysisTrackingOnly'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_2digi= [\n",
    "    'ecalDigis', \n",
    "    'hcalDigis', \n",
    "    'muonCSCDigis', \n",
    "    'muonDTDigis', \n",
    "    'muonGEMDigis', \n",
    "    'muonRPCDigis', \n",
    "    'muonRPCNewDigis', \n",
    "    'scalersRawToDigi'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def categorise_modules2(module):\n",
    "    if module in itLocalReco: \n",
    "        return \"itLocalReco\"\n",
    "    elif module in caloLocalReco: \n",
    "        return \"caloLocalReco\"\n",
    "    elif module in initialStepSequence: \n",
    "        return \"initialStepSequence\"\n",
    "    elif module in highPtTripletStepSequence: \n",
    "        return \"highPtTripletStepSequence\"\n",
    "    elif module in vertexReco: \n",
    "        return \"vertexReco\"\n",
    "    elif module in DQM_offline:\n",
    "        return \"DQM_offline\"\n",
    "    elif module in preval: \n",
    "        return \"preval\"\n",
    "    elif module in raw_2digi: \n",
    "        return \"raw_2digi\"\n",
    "    else: \n",
    "        return \"other\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "allpu_mt = allpu.copy()\n",
    "\n",
    "allpu_mt[\"ModuleType\"] = allpu_mt[\"Module\"].apply(lambda x: categorise_modules2(x.strip()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pd.set_option('display.max_rows', df.shape[0]+1)\n",
    "#tmp[tmp[\"ModuleType\"]==\"other\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot3():\n",
    "\n",
    "    sumbyModuleType = allpu_mt[5:].groupby(['ModuleType']).sum().reset_index()\n",
    "\n",
    "    trace1 = [go.Pie(values=sumbyModuleType[\"PU0\"],\n",
    "               labels=sumbyModuleType[\"ModuleType\"],\n",
    "               domain={'x':[0.1,0.9], 'y':[0.05,0.9]},\n",
    "               hole=0.5,\n",
    "               direction='clockwise',\n",
    "               sort=True,\n",
    "               marker=dict(line=dict(color='#000000', width=0.2))\n",
    "                  )]\n",
    "\n",
    "\n",
    "    trace2 =   [go.Pie(values=sumbyModuleType[\"PU140\"],\n",
    "               labels=sumbyModuleType[\"ModuleType\"],\n",
    "               domain={'x':[0.1,0.9], 'y':[0.05,0.9]},\n",
    "               hole=0.75,\n",
    "               direction='clockwise',\n",
    "               sort=True,\n",
    "               marker=dict(line=dict(color='#000000', width=0.2))\n",
    "                  )]\n",
    "\n",
    "    trace3 =  [go.Pie(values=sumbyModuleType[\"PU200\"],\n",
    "               labels=sumbyModuleType[\"ModuleType\"],\n",
    "               domain={'x':[0.1,0.9], 'y':[0.05,0.9]},\n",
    "               hole=0.9,\n",
    "               direction='clockwise',\n",
    "               sort=True,\n",
    "               marker=dict(line=dict(color='#000000', width=0.2))\n",
    "                  )]\n",
    "\n",
    "    fig3 = go.Figure(data=trace1+trace2+trace3)\n",
    "\n",
    "\n",
    "    fig3.update_layout(\n",
    "        title=\"Total for module types at PU0, 140, 200\")\n",
    "\n",
    "\n",
    "    fig3.update_traces(textposition= \"none\", textinfo=None)\n",
    "\n",
    "\n",
    "    iplot(fig3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "def plot4(pu):\n",
    "    step =20 \n",
    "\n",
    "    col = \"PU\"+str(pu)\n",
    "\n",
    "    trace_list1 = go.Pie(values=allpu_mt[allpu_mt[\"ModuleType\"]==\"DQM_offline\"][col],\n",
    "            labels=allpu_mt[allpu_mt[\"ModuleType\"]==\"DQM_offline\"][\"Module\"],\n",
    "               domain={'x':[0.1,0.9], 'y':[0.05,0.9]},\n",
    "               hole=0.5,\n",
    "               direction='clockwise',\n",
    "               sort=True,\n",
    "               marker=dict(line=dict(color='#000000', width=0.2))\n",
    "                  )\n",
    "\n",
    "    trace_list2 = go.Pie(values=allpu_mt[allpu_mt[\"ModuleType\"]==\"preval\"][col],\n",
    "            labels=allpu_mt[allpu_mt[\"ModuleType\"]==\"preval\"][\"Module\"],\n",
    "               domain={'x':[0.1,0.9], 'y':[0.05,0.9]},\n",
    "               hole=0.5,\n",
    "               direction='clockwise',\n",
    "               sort=True,\n",
    "               marker=dict(line=dict(color='#000000', width=0.2))\n",
    "                  )\n",
    "\n",
    "    trace_list3 = go.Pie(values=allpu_mt[allpu_mt[\"ModuleType\"]==\"raw_2digi\"][col],\n",
    "            labels=allpu_mt[allpu_mt[\"ModuleType\"]==\"raw_2digi\"][\"Module\"],\n",
    "               domain={'x':[0.1,0.9], 'y':[0.05,0.9]},\n",
    "               hole=0.5,\n",
    "               direction='clockwise',\n",
    "               sort=True,\n",
    "               marker=dict(line=dict(color='#000000', width=0.2))\n",
    "                  )\n",
    "\n",
    "    trace_list4 = go.Pie(values=allpu_mt[allpu_mt[\"ModuleType\"]==\"caloLocalReco\"][col],\n",
    "            labels=allpu_mt[allpu_mt[\"ModuleType\"]==\"caloLocalReco\"][\"Module\"],\n",
    "               domain={'x':[0.1,0.9], 'y':[0.05,0.9]},\n",
    "               hole=0.5,\n",
    "               direction='clockwise',\n",
    "               sort=True,\n",
    "               marker=dict(line=dict(color='#000000', width=0.2))\n",
    "                  )\n",
    "\n",
    "    trace_list5 = go.Pie(values=allpu_mt[allpu_mt[\"ModuleType\"]==\"initialStepSequence\"][col],\n",
    "            labels=allpu_mt[allpu_mt[\"ModuleType\"]==\"initialStepSequence\"][\"Module\"],\n",
    "               domain={'x':[0.1,0.9], 'y':[0.05,0.9]},\n",
    "               hole=0.5,\n",
    "               direction='clockwise',\n",
    "               sort=True,\n",
    "               marker=dict(line=dict(color='#000000', width=0.2))\n",
    "                  )\n",
    "\n",
    "    trace_list6 = go.Pie(values=allpu_mt[allpu_mt[\"ModuleType\"]==\"highPtTripletStepSequence\"][col],\n",
    "            labels=allpu.nlargest(step, \"PU0\")[allpu_mt[\"ModuleType\"]==\"highPtTripletStepSequence\"][\"Module\"],\n",
    "               domain={'x':[0.1,0.9], 'y':[0.05,0.9]},\n",
    "               hole=0.5,\n",
    "               direction='clockwise',\n",
    "               sort=True,\n",
    "               marker=dict(line=dict(color='#000000', width=0.2))\n",
    "                  )\n",
    "\n",
    "    trace_list7 = go.Pie(values=allpu_mt[5:].nlargest(step, \"PU0\")[allpu_mt[5:][\"ModuleType\"]==\"other\"][col],\n",
    "            labels=allpu_mt[5:].nlargest(step, \"PU0\")[allpu_mt[5:][\"ModuleType\"]==\"other\"][\"Module\"],\n",
    "               domain={'x':[0.1,0.9], 'y':[0.05,0.9]},\n",
    "               hole=0.5,\n",
    "               direction='clockwise',\n",
    "               sort=True,\n",
    "               marker=dict(line=dict(color='#000000', width=0.2))\n",
    "                  )\n",
    "    data = [trace_list1, trace_list2, trace_list3, trace_list4, trace_list5, trace_list6, trace_list7]\n",
    "\n",
    "\n",
    "\n",
    "    updatemenus = list([\n",
    "        dict(type=\"buttons\",\n",
    "             active=-1,\n",
    "             buttons=list([\n",
    "                dict(label = 'DQM_offline',\n",
    "                     method = 'update',\n",
    "                     args = [{'visible': [True, False, False, False, False, False, False]}]),\n",
    "\n",
    "                dict(label = 'preval',\n",
    "                     method = 'update',\n",
    "                     args = [{'visible': [False, True, False, False, False, False, False]}]),\n",
    "\n",
    "                dict(label = 'raw_digi',\n",
    "                     method = 'update',\n",
    "                     args = [{'visible': [False, False, True, False, False, False, False]}]),\n",
    "\n",
    "                dict(label = 'caloLocalReco',\n",
    "                     method = 'update',\n",
    "                     args = [{'visible': [False, False, False, True, False, False, False]}]),\n",
    "\n",
    "                dict(label = 'initialStepSequence',\n",
    "                     method = 'update',\n",
    "                     args = [{'visible': [False, False, False, False, True, False, False]}]),\n",
    "\n",
    "                dict(label = 'highPtTripletStepSequence',\n",
    "                     method = 'update',\n",
    "                     args = [{'visible': [False, False, False, False, False, True, False]}]),\n",
    "\n",
    "                 dict(label = 'other',\n",
    "                     method = 'update',\n",
    "                     args = [{'visible': [False, False, False, False, False, False, True]}]),\n",
    "\n",
    "                dict(label = 'All',\n",
    "                     method = 'update',\n",
    "                     args = [{'visible': [True, True, True, True, True, True, True]}])\n",
    "            ]),\n",
    "        )\n",
    "    ])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    layout = dict(title='Modules by path for '+col, showlegend=False,\n",
    "                  updatemenus=updatemenus)\n",
    "\n",
    "    fig = dict(data=data, layout=layout)\n",
    "    iplot(fig, filename='update_button')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RESULTS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fig 1 - n longest modules at various PUs "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inner circle is no pileup, middle is PU140, outer is PU200. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
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       "            Plotly.purge(gd);\n",
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       "\n",
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     "metadata": {},
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   "source": [
    "#the argument to this function is the number of modules you want to see in each PU. Selects the n longest\n",
    "plot2(10)\n"
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fig. 3 - Sum of modules by module path "
   ]
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   "metadata": {},
   "source": [
    "Each module belongs to a specific path. The paths _initialStepSequence_, _highPtTripletStepSequence_ and most of the modules in _other_ are part of the tracking. "
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   "source": [
    "#number in the bracket is pileup\n",
    "plot4(200)"
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