{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ff26192e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code based on https://github.com/alexander-held/PyHEP-2021-cabinetry/blob/main/talk.ipynb\n",
    "import cabinetry\n",
    "cabinetry.set_logging()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ed326f80-010c-4f13-a036-ec65dfbd485e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make sure we have all the data files\n",
    "\n",
    "import os\n",
    "import requests\n",
    "\n",
    "# If any of the necessary folders don't exist, make them\n",
    "if not os.path.isdir('4lep'):\n",
    "    os.mkdir('4lep')\n",
    "if not os.path.isdir('4lep/Data'):\n",
    "    os.mkdir('4lep/Data')\n",
    "if not os.path.isdir('4lep/MC'):\n",
    "    os.mkdir('4lep/MC')\n",
    "    \n",
    "# List of all of the files we need\n",
    "filelist = ['4lep/Data/data_A.4lep.root',\n",
    "            '4lep/Data/data_B.4lep.root',\n",
    "            '4lep/Data/data_C.4lep.root',\n",
    "            '4lep/Data/data_D.4lep.root',\n",
    "            '4lep/MC/mc_361106.Zee.4lep.root',\n",
    "            '4lep/MC/mc_361107.Zmumu.4lep.root',\n",
    "            '4lep/MC/mc_410000.ttbar_lep.4lep.root',\n",
    "            '4lep/MC/mc_363490.llll.4lep.root',\n",
    "            '4lep/MC/mc_345060.ggH125_ZZ4lep.4lep.root',\n",
    "            '4lep/MC/mc_344235.VBFH125_ZZ4lep.4lep.root',\n",
    "            '4lep/MC/mc_341964.WH125_ZZ4lep.4lep.root',\n",
    "            '4lep/MC/mc_341947.ZH125_ZZ4lep.4lep.root']\n",
    "\n",
    "# For each of these files\n",
    "for filepath in filelist:\n",
    "\n",
    "    # Check if the file exists locally\n",
    "    if not os.path.isfile(filepath):\n",
    "\n",
    "        #If the file doesn't exist, download it\n",
    "        data_download = requests.get('https://atlas-opendata.web.cern.ch/atlas-opendata/samples/2020/' + filepath)\n",
    "        new_file = open(filepath, 'wb')\n",
    "        new_file.write(data_download.content)\n",
    "        new_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8f54649a",
   "metadata": {},
   "outputs": [],
   "source": [
    "config = {\n",
    "   \"General\":{\n",
    "      \"Measurement\": \"CabinetryHZZAnalysis\",\n",
    "      \"POI\": \"Signal_norm\",\n",
    "      \"InputPath\": \"4lep/{SamplePath}\",\n",
    "      \"HistogramFolder\": \"histograms/\"\n",
    "   }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d0cc7f43",
   "metadata": {},
   "outputs": [],
   "source": [
    "bin_list = list(range(80, 255, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "56a34351",
   "metadata": {},
   "outputs": [],
   "source": [
    "config.update({\n",
    "   \"Regions\":[\n",
    "      {\n",
    "         \"Name\": \"Signal_region\",\n",
    "         \"Filter\": \"(lep_charge[:,0] + lep_charge[:,1] + lep_charge[:,2] + lep_charge[:,3] == 0) & ((lep_type[:,0] + lep_type[:,1] + lep_type[:,2] + lep_type[:,3] == 44) | (lep_type[:,0] + lep_type[:,1] + lep_type[:,2] + lep_type[:,3] == 48) | (lep_type[:,0] + lep_type[:,1] + lep_type[:,2] + lep_type[:,3] == 52))\",\n",
    "         \"Variable\": \"sqrt((lep_E[:,0] + lep_E[:,1] + lep_E[:,2] + lep_E[:,3])**2 - (lep_pt[:,0]*cos(lep_phi[:,0]) + lep_pt[:,1]*cos(lep_phi[:,1]) + lep_pt[:,2]*cos(lep_phi[:,2]) + lep_pt[:,3]*cos(lep_phi[:,3]))**2 - (lep_pt[:,0]*sin(lep_phi[:,0]) + lep_pt[:,1]*sin(lep_phi[:,1]) + lep_pt[:,2]*sin(lep_phi[:,2]) + lep_pt[:,3]*sin(lep_phi[:,3]))**2 - (lep_pt[:,0]*sinh(lep_eta[:,0]) + lep_pt[:,1]*sinh(lep_eta[:,1]) + lep_pt[:,2]*sinh(lep_eta[:,2]) + lep_pt[:,3]*sinh(lep_eta[:,3]))**2)/1000\",\n",
    "         \"Binning\": bin_list\n",
    "      }\n",
    "   ]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "032cad59",
   "metadata": {},
   "outputs": [],
   "source": [
    "config.update({\n",
    "   \"Samples\":[\n",
    "      {\n",
    "         \"Name\": \"Data\",\n",
    "         \"Tree\": \"mini\",\n",
    "         \"SamplePath\": [\"Data/data_A.4lep.root\",\n",
    "                         \"Data/data_B.4lep.root\",\n",
    "                         \"Data/data_C.4lep.root\",\n",
    "                         \"Data/data_D.4lep.root\"],\n",
    "         \"Data\": True\n",
    "      },\n",
    "      {\n",
    "         \"Name\": \"Signal\",\n",
    "         \"Tree\": \"mini\",\n",
    "         \"SamplePath\": ['MC/mc_345060.ggH125_ZZ4lep.4lep.root',\n",
    "                         'MC/mc_344235.VBFH125_ZZ4lep.4lep.root',\n",
    "                         'MC/mc_341964.WH125_ZZ4lep.4lep.root',\n",
    "                         'MC/mc_341947.ZH125_ZZ4lep.4lep.root'],\n",
    "         \"Weight\": \"((channelNumber == 345060)*2.1605e-6 + (channelNumber == 344235)*1.2588e-6 + (channelNumber == 341964)*2.5228e-5 + (channelNumber == 341947)*1.4283e-7)*mcWeight*scaleFactor_PILEUP*scaleFactor_ELE*scaleFactor_MUON*scaleFactor_LepTRIGGER\"\n",
    "      },\n",
    "      {\n",
    "         \"Name\": \"Background $ZZ^{star}$\",\n",
    "         \"Tree\": \"mini\",\n",
    "         \"SamplePath\": \"MC/mc_363490.llll.4lep.root\",\n",
    "         \"Weight\": \"0.0016685*mcWeight*scaleFactor_PILEUP*scaleFactor_ELE*scaleFactor_MUON*scaleFactor_LepTRIGGER\"\n",
    "      },\n",
    "      {\n",
    "         \"Name\": \"Background $Z,tt^{bar}$\",\n",
    "         \"Tree\": \"mini\",\n",
    "         \"SamplePath\": ['MC/mc_361106.Zee.4lep.root',\n",
    "                         'MC/mc_361107.Zmumu.4lep.root',\n",
    "                         'MC/mc_410000.ttbar_lep.4lep.root'],\n",
    "         \"Weight\": \"((channelNumber == 361106)*1.2980e-4 + (channelNumber == 361107)*1.3239e-4 + (channelNumber == 410000)*0.091663)*mcWeight*scaleFactor_PILEUP*scaleFactor_ELE*scaleFactor_MUON*scaleFactor_LepTRIGGER\"\n",
    "      }\n",
    "   ]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c058e150",
   "metadata": {},
   "outputs": [],
   "source": [
    "config.update({\"Systematics\": []})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5251d431",
   "metadata": {},
   "outputs": [],
   "source": [
    "config.update({\n",
    "   \"NormFactors\":[\n",
    "      {\n",
    "         \"Name\": \"Signal_norm\",\n",
    "         \"Samples\": \"Signal\",\n",
    "         \"Nominal\": 1,\n",
    "         \"Bounds\": [0, 10]\n",
    "      },\n",
    "      {\n",
    "         \"Name\": \"ZZ_norm\",\n",
    "         \"Samples\": \"Background $ZZ^{star}$\",\n",
    "         \"Nominal\": 1,\n",
    "         \"Bounds\": [0, 10]\n",
    "      },\n",
    "      {\n",
    "         \"Name\": \"Ztt_norm\",\n",
    "         \"Samples\": \"Background $Z,tt^{bar}$\",\n",
    "         \"Nominal\": 1,\n",
    "         \"Bounds\": [0, 10]\n",
    "      }\n",
    "   ]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "90b3da0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG - cabinetry.route -   in region Signal_region\n",
      "DEBUG - cabinetry.route -     reading sample Data\n",
      "DEBUG - cabinetry.route -       variation Nominal\n",
      "DEBUG - cabinetry.histo - saving histogram to histograms/Signal_region_Data.npz\n",
      "DEBUG - cabinetry.route -     reading sample Signal\n",
      "DEBUG - cabinetry.route -       variation Nominal\n",
      "DEBUG - cabinetry.histo - saving histogram to histograms/Signal_region_Signal.npz\n",
      "DEBUG - cabinetry.route -     reading sample Background $ZZ^{star}$\n",
      "DEBUG - cabinetry.route -       variation Nominal\n",
      "DEBUG - cabinetry.histo - saving histogram to histograms/Signal_region_Background-$ZZ^{star}$.npz\n",
      "DEBUG - cabinetry.route -     reading sample Background $Z,tt^{bar}$\n",
      "DEBUG - cabinetry.route -       variation Nominal\n",
      "DEBUG - cabinetry.histo - saving histogram to histograms/Signal_region_Background-$Z,tt^{bar}$.npz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total runtime in seconds: 114.2297842502594\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "start_time = time.time()\n",
    "cabinetry.templates.build(config, method=\"uproot\")\n",
    "# cabinetry.template_builder.create_histograms(config, method=\"coffea\")\n",
    "# Using the coffea backend requires this branch of cabinetry: https://github.com/alexander-held/cabinetry/pull/216\n",
    "# It also further requires the modified coffea_wrapper.py found at https://github.com/stormsomething/CoffeaHZZAnalysis\n",
    "finish_time = time.time()\n",
    "\n",
    "print(\"Total runtime in seconds: \" + str(finish_time - start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "99a0a000",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_config = {\n",
    "   \"Regions\":[\n",
    "      {\n",
    "         \"Name\": \"Signal_region\",\n",
    "         \"Variable\": \"4-lepton invariant mass $\\mathrm{m_{4l}}$ [GeV]\",\n",
    "         \"Binning\": bin_list\n",
    "      }\n",
    "   ]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1664db43",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - cabinetry.workspace - building workspace\n",
      "WARNING - cabinetry.histo - the modified histogram histograms/Signal_region_Signal_modified.npz does not exist\n",
      "WARNING - cabinetry.histo - loading the un-modified histogram instead!\n",
      "DEBUG - cabinetry.workspace - adding NormFactor Signal_norm to sample Signal in region Signal_region\n",
      "WARNING - cabinetry.histo - the modified histogram histograms/Signal_region_Background-$ZZ^{star}$_modified.npz does not exist\n",
      "WARNING - cabinetry.histo - loading the un-modified histogram instead!\n",
      "DEBUG - cabinetry.workspace - adding NormFactor ZZ_norm to sample Background $ZZ^{star}$ in region Signal_region\n",
      "WARNING - cabinetry.histo - the modified histogram histograms/Signal_region_Background-$Z,tt^{bar}$_modified.npz does not exist\n",
      "WARNING - cabinetry.histo - loading the un-modified histogram instead!\n",
      "DEBUG - cabinetry.workspace - adding NormFactor Ztt_norm to sample Background $Z,tt^{bar}$ in region Signal_region\n",
      "WARNING - cabinetry.histo - the modified histogram histograms/Signal_region_Data_modified.npz does not exist\n",
      "WARNING - cabinetry.histo - loading the un-modified histogram instead!\n",
      "INFO - pyhf.workspace - Validating spec against schema: workspace.json\n",
      "INFO - pyhf.workspace - Validating spec against schema: workspace.json\n",
      "INFO - pyhf.pdf - Validating spec against schema: model.json\n",
      "INFO - pyhf.pdf - adding modifier staterror_Signal_region (34 new nuisance parameters)\n",
      "INFO - pyhf.pdf - adding modifier Signal_norm (1 new nuisance parameters)\n",
      "INFO - pyhf.pdf - adding modifier ZZ_norm (1 new nuisance parameters)\n",
      "INFO - pyhf.pdf - adding modifier Ztt_norm (1 new nuisance parameters)\n",
      "DEBUG - cabinetry.model_utils - total stdev is [... 0.718, 0.799, 0.552, 0.762, 0.577, 0.775, 0.65, 0.515, 0.683, 0.428, 0.585]]\n",
      "DEBUG - cabinetry.model_utils - total stdev per channel is [5.28]\n",
      "INFO - cabinetry.tabulate - yields per channel for pre-fit model prediction:\n",
      "╒═════════════════════════╤═════════════════╕\n",
      "│ sample                  │ Signal_region   │\n",
      "╞═════════════════════════╪═════════════════╡\n",
      "│ Background $Z,tt^{bar}$ │ 112.13          │\n",
      "├─────────────────────────┼─────────────────┤\n",
      "│ Background $ZZ^{star}$  │ 174.61          │\n",
      "├─────────────────────────┼─────────────────┤\n",
      "│ Signal                  │ 9.61            │\n",
      "├─────────────────────────┼─────────────────┤\n",
      "│ total                   │ 296.35 ± 5.28   │\n",
      "├─────────────────────────┼─────────────────┤\n",
      "│ data                    │ 371.00          │\n",
      "╘═════════════════════════╧═════════════════╛\n",
      "DEBUG - cabinetry.visualize.utils - saving figure as figures/Signal_region_prefit.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ws = cabinetry.workspace.build(config)\n",
    "model, data = cabinetry.model_utils.model_and_data(ws)\n",
    "model_prediction = cabinetry.model_utils.prediction(model)\n",
    "cabinetry.tabulate.yields(model_prediction, data, per_bin=False, per_channel=True)\n",
    "_ = cabinetry.visualize.data_mc(model_prediction, data, config=plot_config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3144a893",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - cabinetry.fit - performing maximum likelihood fit\n",
      "INFO - cabinetry.fit - MINUIT status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 68.27                      │             Nfcn = 2230              │\n",
      "│ EDM = 4e-05 (Goal: 0.0002)       │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "DEBUG - cabinetry.fit - -2 log(L) = 68.265587 at best-fit point\n",
      "INFO - cabinetry.fit - fit results (with symmetric uncertainties):\n",
      "INFO - cabinetry.fit - staterror_Signal_region[0]  =  1.0008 +/- 0.0581\n",
      "INFO - cabinetry.fit - staterror_Signal_region[1]  =  1.0011 +/- 0.0251\n",
      "INFO - cabinetry.fit - staterror_Signal_region[2]  =  1.0038 +/- 0.0237\n",
      "INFO - cabinetry.fit - staterror_Signal_region[3]  =  1.0069 +/- 0.1021\n",
      "INFO - cabinetry.fit - staterror_Signal_region[4]  =  0.9656 +/- 0.1642\n",
      "INFO - cabinetry.fit - staterror_Signal_region[5]  =  0.9701 +/- 0.1512\n",
      "INFO - cabinetry.fit - staterror_Signal_region[6]  =  0.9808 +/- 0.1441\n",
      "INFO - cabinetry.fit - staterror_Signal_region[7]  =  0.9370 +/- 0.1100\n",
      "INFO - cabinetry.fit - staterror_Signal_region[8]  =  1.0300 +/- 0.0981\n",
      "INFO - cabinetry.fit - staterror_Signal_region[9]  =  0.9755 +/- 0.1069\n",
      "INFO - cabinetry.fit - staterror_Signal_region[10] =  1.0413 +/- 0.1270\n",
      "INFO - cabinetry.fit - staterror_Signal_region[11] =  0.9920 +/- 0.1343\n",
      "INFO - cabinetry.fit - staterror_Signal_region[12] =  1.0356 +/- 0.1345\n",
      "INFO - cabinetry.fit - staterror_Signal_region[13] =  1.0659 +/- 0.1301\n",
      "INFO - cabinetry.fit - staterror_Signal_region[14] =  1.0041 +/- 0.1315\n",
      "INFO - cabinetry.fit - staterror_Signal_region[15] =  0.9831 +/- 0.1522\n",
      "INFO - cabinetry.fit - staterror_Signal_region[16] =  1.1054 +/- 0.1350\n",
      "INFO - cabinetry.fit - staterror_Signal_region[17] =  0.9837 +/- 0.1570\n",
      "INFO - cabinetry.fit - staterror_Signal_region[18] =  1.0149 +/- 0.1476\n",
      "INFO - cabinetry.fit - staterror_Signal_region[19] =  0.9593 +/- 0.1356\n",
      "INFO - cabinetry.fit - staterror_Signal_region[20] =  1.0376 +/- 0.0924\n",
      "INFO - cabinetry.fit - staterror_Signal_region[21] =  1.0159 +/- 0.0757\n",
      "INFO - cabinetry.fit - staterror_Signal_region[22] =  0.9820 +/- 0.0589\n",
      "INFO - cabinetry.fit - staterror_Signal_region[23] =  1.0090 +/- 0.0748\n",
      "INFO - cabinetry.fit - staterror_Signal_region[24] =  0.9818 +/- 0.0715\n",
      "INFO - cabinetry.fit - staterror_Signal_region[25] =  0.9953 +/- 0.0574\n",
      "INFO - cabinetry.fit - staterror_Signal_region[26] =  0.9823 +/- 0.0770\n",
      "INFO - cabinetry.fit - staterror_Signal_region[27] =  0.9866 +/- 0.0655\n",
      "INFO - cabinetry.fit - staterror_Signal_region[28] =  0.9564 +/- 0.0859\n",
      "INFO - cabinetry.fit - staterror_Signal_region[29] =  1.0151 +/- 0.0808\n",
      "INFO - cabinetry.fit - staterror_Signal_region[30] =  0.9894 +/- 0.0707\n",
      "INFO - cabinetry.fit - staterror_Signal_region[31] =  0.9851 +/- 0.0911\n",
      "INFO - cabinetry.fit - staterror_Signal_region[32] =  1.0057 +/- 0.0684\n",
      "INFO - cabinetry.fit - staterror_Signal_region[33] =  1.0036 +/- 0.0897\n",
      "INFO - cabinetry.fit - Signal_norm                 =  1.7923 +/- 0.8458\n",
      "INFO - cabinetry.fit - ZZ_norm                     =  1.5301 +/- 0.1292\n",
      "INFO - cabinetry.fit - Ztt_norm                    =  0.7769 +/- 0.1917\n"
     ]
    }
   ],
   "source": [
    "fit_results = cabinetry.fit.fit(model, data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4e34eba8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG - cabinetry.model_utils - total stdev is [... 1.14, 1.36, 1.31, 1.17, 1.23, 1.1, 1.2, 1.08, 0.967, 1.04, 0.849, 0.914]]\n",
      "DEBUG - cabinetry.model_utils - total stdev per channel is [19.2]\n",
      "DEBUG - cabinetry.visualize.utils - saving figure as figures/Signal_region_postfit.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_prediction_postfit = cabinetry.model_utils.prediction(model, fit_results=fit_results)\n",
    "_ = cabinetry.visualize.data_mc(model_prediction_postfit, data, config=plot_config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9f22eb11",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - cabinetry.fit - calculating discovery significance\n",
      "INFO - cabinetry.fit - observed p-value: 0.950%\n",
      "INFO - cabinetry.fit - observed significance: 2.345\n",
      "INFO - cabinetry.fit - expected p-value: 9.661%\n",
      "INFO - cabinetry.fit - expected significance: 1.301\n"
     ]
    }
   ],
   "source": [
    "significance_results = cabinetry.fit.significance(model, data)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}