ApplySPANETAfterTraining.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4fd10269-367d-4226-a924-4119e1087890",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import awkward as ak\n",
    "import numba\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import awkward as ak\n",
    "import vector\n",
    "vector.register_numba()\n",
    "vector.register_awkward()\n",
    "import os\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LogNorm\n",
    "import mplhep as hep\n",
    "hep.style.use(hep.style.ROOT)\n",
    "\n",
    "from importlib import reload\n",
    "\n",
    "import numba\n",
    "import spanet_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "316b3504-3e05-4667-adf7-67b57d8eab55",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "@numba.njit\n",
    "def assign_provenance_and_prob(t1pred, t2pred, hpred, \n",
    "                      t1prob, t2prob, hprob, njets):\n",
    "    out = np.zeros((t1pred.shape[0], njets)) -1\n",
    "    prob = np.zeros((t1pred.shape[0], njets, 3), dtype=np.float32)-np.float32(np.inf)\n",
    "    \n",
    "    #print(prob)\n",
    "    for iev, (t1, t2, h, t1p, t2p, hp) in enumerate(zip(t1pred, t2pred, hpred, t1prob, t2prob, hprob)):\n",
    "    \n",
    "        if t1[0] == -2:\n",
    "            prob_t1 = -np.inf\n",
    "        else:\n",
    "            prob_t1 = t1p[t1[0]][t1[1]][t1[2]]\n",
    "            \n",
    "        if t2[0] == -2:\n",
    "            prob_t2 = -np.inf\n",
    "        else:\n",
    "            prob_t2 = t2p[t2[0]]\n",
    "            \n",
    "        if h[0] == -2:\n",
    "            prob_h = -np.inf\n",
    "        else:\n",
    "            prob_h = hp[h[0]][h[1]]\n",
    "            \n",
    "        \n",
    "        # prob_t1 = t1p[t1[0]][t1[1]][t1[2]]\n",
    "        # prob_t2 = t2p[t2[0]][t2[1]]\n",
    "        # prob_h = hp[h[0]][h[1]]\n",
    "        # print(prob_t1, prob_t2, prob_h)\n",
    "        \n",
    "        for i in t1: \n",
    "            out[iev][i] = 1\n",
    "            prob[iev][i][0] = prob_t1\n",
    "        for i in t2: \n",
    "            out[iev][i] = 2\n",
    "            prob[iev][i][1] = prob_t2\n",
    "        for i in h: \n",
    "            out[iev][i] = 3\n",
    "            prob[iev][i][2] = prob_h\n",
    "\n",
    "    return out, prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "34969e58-80b7-456a-990f-f540a29ea141",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "basedir= \"/work/dvalsecc/ttHbb/ttHbb_jets_partons_studies/sig_bkg_30_08_2023_v2/out_forTraining_sig_bkg\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "72020e7f-30fb-45b8-bf21-74a325fec8c1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['all_jets_fullRun2_ttHTobb_forTraining_2018_v3.parquet',\n",
       " 'all_jets_fullRun2_ttHTobb_forTraining_2016_PreVFP_v3.parquet',\n",
       " 'all_jets_fullRun2_ttHTobb_forTraining_2017_v3.parquet',\n",
       " 'all_jets_fullRun2_ttHTobb_forTraining_2016_PostVFP_v3.parquet']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(basedir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7e134e3f-20a8-490f-b18a-0026d488e0d6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "AppendRowGroups requires equal schemas.\nThis schema has 41 columns, other has 45\n\nThis error occurred while calling\n\n    ak.from_parquet(\n        '/work/dvalsecc/ttHbb/ttHbb_jets_partons_studies/sig_bkg_3'...\n    )",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "File \u001b[0;32m/home/user/micromamba/lib/python3.9/site-packages/awkward/_dispatch.py:37\u001b[0m, in \u001b[0;36mnamed_high_level_function.<locals>.dispatch\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OperationErrorContext(name, args, kwargs):\n\u001b[0;32m---> 37\u001b[0m     gen_or_result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     38\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m isgenerator(gen_or_result):\n",
      "File \u001b[0;32m/home/user/micromamba/lib/python3.9/site-packages/awkward/operations/ak_from_parquet.py:58\u001b[0m, in \u001b[0;36mfrom_parquet\u001b[0;34m(path, columns, row_groups, storage_options, max_gap, max_block, footer_sample_size, generate_bitmasks, highlevel, behavior)\u001b[0m\n\u001b[1;32m     56\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mawkward\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_connect\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyarrow\u001b[39;00m  \u001b[38;5;66;03m# noqa: F401\u001b[39;00m\n\u001b[0;32m---> 58\u001b[0m parquet_columns, subform, actual_paths, fs, subrg, row_counts, meta \u001b[38;5;241m=\u001b[39m \u001b[43mmetadata\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     59\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     60\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     61\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrow_groups\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     62\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     63\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     64\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _load(\n\u001b[1;32m     65\u001b[0m     actual_paths,\n\u001b[1;32m     66\u001b[0m     parquet_columns \u001b[38;5;28;01mif\u001b[39;00m columns \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     75\u001b[0m     fs,\n\u001b[1;32m     76\u001b[0m )\n",
      "File \u001b[0;32m/home/user/micromamba/lib/python3.9/site-packages/awkward/_dispatch.py:37\u001b[0m, in \u001b[0;36mnamed_high_level_function.<locals>.dispatch\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OperationErrorContext(name, args, kwargs):\n\u001b[0;32m---> 37\u001b[0m     gen_or_result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     38\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m isgenerator(gen_or_result):\n",
      "File \u001b[0;32m/home/user/micromamba/lib/python3.9/site-packages/awkward/operations/ak_from_parquet.py:146\u001b[0m, in \u001b[0;36mmetadata\u001b[0;34m(path, storage_options, row_groups, columns, ignore_metadata, scan_files)\u001b[0m\n\u001b[1;32m    145\u001b[0m             md\u001b[38;5;241m.\u001b[39mset_file_path(apath\u001b[38;5;241m.\u001b[39mrsplit(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m1\u001b[39m)[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m])\n\u001b[0;32m--> 146\u001b[0m             \u001b[43mmetadata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mappend_row_groups\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmd\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    147\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m row_groups \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m/home/user/micromamba/lib/python3.9/site-packages/pyarrow/_parquet.pyx:805\u001b[0m, in \u001b[0;36mpyarrow._parquet.FileMetaData.append_row_groups\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: AppendRowGroups requires equal schemas.\nThis schema has 41 columns, other has 45",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mak\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_parquet\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbasedir\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/home/user/micromamba/lib/python3.9/site-packages/awkward/_dispatch.py:68\u001b[0m, in \u001b[0;36mnamed_high_level_function.<locals>.dispatch\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     64\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAssertionError\u001b[39;00m(\n\u001b[1;32m     65\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhigh-level functions should only implement a single yield statement\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     66\u001b[0m         )\n\u001b[0;32m---> 68\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m gen_or_result\n",
      "File \u001b[0;32m/home/user/micromamba/lib/python3.9/site-packages/awkward/_errors.py:67\u001b[0m, in \u001b[0;36mErrorContext.__exit__\u001b[0;34m(self, exception_type, exception_value, traceback)\u001b[0m\n\u001b[1;32m     60\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     61\u001b[0m     \u001b[38;5;66;03m# Handle caught exception\u001b[39;00m\n\u001b[1;32m     62\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m     63\u001b[0m         exception_type \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     64\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(exception_type, \u001b[38;5;167;01mException\u001b[39;00m)\n\u001b[1;32m     65\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprimary() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n\u001b[1;32m     66\u001b[0m     ):\n\u001b[0;32m---> 67\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle_exception\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexception_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexception_value\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     68\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;66;03m# `_kwargs` may hold cyclic references, that we really want to avoid\u001b[39;00m\n\u001b[1;32m     70\u001b[0m     \u001b[38;5;66;03m# as this can lead to large buffers remaining in memory for longer than absolutely necessary\u001b[39;00m\n\u001b[1;32m     71\u001b[0m     \u001b[38;5;66;03m# Let's just clear this, now.\u001b[39;00m\n\u001b[1;32m     72\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_kwargs\u001b[38;5;241m.\u001b[39mclear()\n",
      "File \u001b[0;32m/home/user/micromamba/lib/python3.9/site-packages/awkward/_errors.py:82\u001b[0m, in \u001b[0;36mErrorContext.handle_exception\u001b[0;34m(self, cls, exception)\u001b[0m\n\u001b[1;32m     80\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdecorate_exception(\u001b[38;5;28mcls\u001b[39m, exception)\n\u001b[1;32m     81\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 82\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdecorate_exception(\u001b[38;5;28mcls\u001b[39m, exception)\n",
      "\u001b[0;31mRuntimeError\u001b[0m: AppendRowGroups requires equal schemas.\nThis schema has 41 columns, other has 45\n\nThis error occurred while calling\n\n    ak.from_parquet(\n        '/work/dvalsecc/ttHbb/ttHbb_jets_partons_studies/sig_bkg_3'...\n    )"
     ]
    }
   ],
   "source": [
    "df = ak.from_parquet(basedir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7d211a6-c7a1-4adb-97c9-a6638ad00e42",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e68c183-811d-4452-8085-6c075f0be030",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "(jets,_,_,_,_,lep, met,_, weight) = ak.unzip(df)\n",
    "jets = ak.with_name(jets, name=\"Momentum4D\")\n",
    "lep = ak.with_name(lep, name=\"Momentum4D\")\n",
    "met = ak.with_name(met, name=\"Momentum4D\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8a739cfb-91af-4689-bfc3-efdebd7f0938",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(ak.num(jets))\n",
    "plt.yscale(\"log\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b99b6805-b901-4b4b-b797-b615601b7996",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source_data, ['batch_size', 'num_Source', 4]\n",
      "Source_mask, ['batch_size', 'num_Source']\n",
      "Met_data, ['batch_size', 'num_Met', 3]\n",
      "Met_mask, ['batch_size', 'num_Met']\n",
      "Lepton_data, ['batch_size', 'num_Lepton', 3]\n",
      "Lepton_mask, ['batch_size', 'num_Lepton']\n",
      "ht_data, ['batch_size', 'num_ht', 1]\n",
      "ht_mask, ['batch_size', 'num_ht']\n",
      "t1_assignment_log_probability, ['Reshapet1_assignment_log_probability_dim_0', 'Reshapet1_assignment_log_probability_dim_1', 'Reshapet1_assignment_log_probability_dim_2', 'Reshapet1_assignment_log_probability_dim_3']\n",
      "t2_assignment_log_probability, ['Reshapet2_assignment_log_probability_dim_0', 'Reshapet2_assignment_log_probability_dim_1']\n",
      "h_assignment_log_probability, ['Reshapeh_assignment_log_probability_dim_0', 'Reshapeh_assignment_log_probability_dim_1', 'Reshapeh_assignment_log_probability_dim_2']\n",
      "t1_detection_log_probability, ['Logt1_detection_log_probability_dim_0']\n",
      "t2_detection_log_probability, ['Logt1_detection_log_probability_dim_0']\n",
      "h_detection_log_probability, ['Logt1_detection_log_probability_dim_0']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-09-04 22:41:11.618771148 [W:onnxruntime:Default, onnxruntime_pybind_state.cc:640 CreateExecutionProviderInstance] Failed to create CUDAExecutionProvider. Please reference https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html#requirements to ensure all dependencies are met.\n"
     ]
    }
   ],
   "source": [
    "import onnxruntime    # to inference ONNX models, we use the ONNX Runtime\n",
    "sess_options = onnxruntime.SessionOptions()\n",
    "\n",
    "sess_options.intra_op_num_threads = 20\n",
    "sess_options.inter_op_num_threads = 2\n",
    "#sess_options.intra_op_num_threads = 15\n",
    "sess_options.execution_mode = onnxruntime.ExecutionMode.ORT_PARALLEL\n",
    "sess_options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL\n",
    "\n",
    "\n",
    "session = onnxruntime.InferenceSession(\n",
    "    \"/work/dvalsecc/ttHbb/ttHbb_jets_partons_studies/SPANET/spanet.onnx\", \n",
    "    sess_options = sess_options,\n",
    "    providers=['CUDAExecutionProvider', 'CPUExecutionProvider']\n",
    ")\n",
    "\n",
    "for input in session.get_inputs():\n",
    "    print(f\"{input.name}, {input.shape}\")\n",
    "    \n",
    "for output in session.get_outputs():\n",
    "    print(f\"{output.name}, {output.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "75384924-c88f-47d1-b157-5cfad719b693",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "jets_padded = ak.fill_none(ak.pad_none(jets, 16, clip=True), {\"btag\":0., \"pt\":0., \"phi\":0., \"eta\":0.})\n",
    "\n",
    "data = np.transpose(\n",
    "    np.stack([\n",
    "    ak.to_numpy(jets_padded.btag),\n",
    "    ak.to_numpy(jets_padded.eta),\n",
    "    ak.to_numpy(jets_padded.phi),\n",
    "    np.log(1 + ak.to_numpy(jets_padded.pt))]),\n",
    "    axes=[1,2,0]).astype(np.float32)\n",
    "\n",
    "mask = ~ak.to_numpy(jets_padded.pt == 0)\n",
    "\n",
    "met_data = np.stack([ak.to_numpy(met.eta),\n",
    "                     ak.to_numpy(met.phi),\n",
    "                     np.log(1+ ak.to_numpy(met.pt))], axis=1)[:,None,:].astype(np.float32)\n",
    "\n",
    "lep_data = np.stack([ak.to_numpy(lep[:,0].eta),\n",
    "                     ak.to_numpy(lep[:,0].phi),\n",
    "                     np.log(1+ ak.to_numpy(lep[:,0].pt))], axis=1)[:,None,:].astype(np.float32)\n",
    "\n",
    "ht_array = np.sum(ak.to_numpy(jets_padded.pt), axis=1)[:,None, None].astype(np.float32)\n",
    "\n",
    "mask_global = np.ones(shape=[met_data.shape[0], 1]) == 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7e33dda4-43d7-4aed-8945-ae4a9134b259",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nbatches=7000\n"
     ]
    }
   ],
   "source": [
    "batch_size = 512\n",
    "nbatches = data.shape[0]// batch_size\n",
    "print(f\"{nbatches=}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "320412c8-43f1-4a88-9ce6-ef11161dfb8c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5d121dae21b6443bbf4f9c009a1098f6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from rich.progress import track\n",
    "\n",
    "provenance = np.zeros((data.shape[0], 16))\n",
    "prob_assignment = np.zeros((data.shape[0], 16, 3))\n",
    "\n",
    "\n",
    "for i in track(range(nbatches), \"Inference\"):\n",
    "    start = i*batch_size\n",
    "    if i < (nbatches-1):\n",
    "        stop = start+batch_size\n",
    "    else:\n",
    "        stop = len(data)\n",
    "    outputs = session.run(input_feed={\n",
    "        \"Source_data\": data[start:stop],\n",
    "        \"Source_mask\": mask[start:stop],\n",
    "        \"Met_data\": met_data[start:stop],\n",
    "        \"Met_mask\": mask_global[start:stop],\n",
    "        \"Lepton_data\": lep_data[start:stop],\n",
    "        \"Lepton_mask\": mask_global[start:stop],\n",
    "        \"ht_data\": ht_array[start:stop], \n",
    "        \"ht_mask\": mask_global[start:stop]},\n",
    "        output_names=[\"t1_assignment_log_probability\", \"t2_assignment_log_probability\",\n",
    "                     \"h_assignment_log_probability\"]\n",
    "        )\n",
    "    preds_nomask = spanet_predictions.extract_predictions(outputs[0:3], masking=False)\n",
    "    prov, prob = assign_provenance_and_prob(*preds_nomask, outputs[0], outputs[1], outputs[2],16)\n",
    "    \n",
    "    provenance[start:stop] = prov\n",
    "    prob_assignment[start:stop]= prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "09523def-26cf-438d-b2d7-1df5f2f61c97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0mak\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhighlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbehavior\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Args:\n",
       "    base: Data containing records or tuples.\n",
       "    name (str): Name to give to the records or tuples; this assigns\n",
       "        the `\"__record__\"` parameter.\n",
       "    highlevel (bool): If True, return an #ak.Array; otherwise, return\n",
       "        a low-level #ak.layout.Content subclass.\n",
       "    behavior (None or dict): Custom #ak.behavior for the output array, if\n",
       "        high-level.\n",
       "\n",
       "Returns an #ak.Array or #ak.Record (or low-level equivalent, if\n",
       "`highlevel=False`) with a new name. This function does not change the\n",
       "array in-place.\n",
       "\n",
       "The records or tuples may be nested within multiple levels of nested lists.\n",
       "If records are nested within records, only the outermost are affected.\n",
       "\n",
       "Setting the `\"__record__\"` parameter makes it possible to add behaviors\n",
       "to the data; see #ak.Array and #ak.behavior for a more complete\n",
       "description.\n",
       "\u001b[0;31mFile:\u001b[0m      ~/micromamba/envs/pocket-coffea/lib/python3.9/site-packages/awkward/operations/structure.py\n",
       "\u001b[0;31mType:\u001b[0m      function"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ak.with_name?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "94411a3d-a722-4a3f-a2bb-0e7e7b5477b9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3584250, 16)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "provenance.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c2d4d5d-d3b1-44fc-bdcb-9b070a62d1ec",
   "metadata": {},
   "source": [
    "## Removing the padding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "244be07a-b5e3-4d89-a315-8f96497a4a06",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3584250, 16, 3)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob_assignment.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4803c658-4136-487a-a426-2d6b55f31e0c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "ak.to_parquet(ak.zip({\"spanet_prob_assignment\": ak.from_numpy(prob_assignment)}), basedir + \"/spanet.parquet\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e2dcc0bb-443d-4f8b-8cd4-dfae336767d8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3.,  3.,  1.,  2., -1., -1., -1., -1., -1., -1., -1., -1., -1.,\n",
       "       -1., -1., -1.])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "provenance[3584249]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "5034e8ec-7089-40c6-b2ad-4e4f1700bc1e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [5, 4, 4, 4, 6, 5, ... 7, 7, 5, 6, 5, 5] type='3584250 * int64'>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "njets_good"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "d6be2caa-9a3e-4cf6-963e-acb3f37b4e4e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.69542316, 0.23063106],\n",
       "       [0.16833067, 0.        , 0.        ],\n",
       "       [0.16833067, 0.        , 0.23063106],\n",
       "       [0.16833067, 0.        , 0.        ],\n",
       "       [0.        , 0.        , 0.        ],\n",
       "       [0.        , 0.        , 0.        ],\n",
       "       [0.        , 0.        , 0.        ],\n",
       "       [0.        , 0.        , 0.        ],\n",
       "       [0.        , 0.        , 0.        ],\n",
       "       [0.        , 0.        , 0.        ],\n",
       "       [0.        , 0.        , 0.        ],\n",
       "       [0.        , 0.        , 0.        ],\n",
       "       [0.        , 0.        , 0.        ],\n",
       "       [0.        , 0.        , 0.        ],\n",
       "       [0.        , 0.        , 0.        ],\n",
       "       [0.        , 0.        , 0.        ]])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob_assignment[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "01f1d18e-e857-4759-9b60-ae744fc372b2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "prob_assignment = np.exp(prob_assignment)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0ac6ac4b-215b-4ae2-8cb9-05d03759cb90",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [{jets: [, ... weight: 2.56e+03}] type='3584250 * {\"jets\": var * {\"pt\": f...'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_array = ak.zip(dict(zip(ak.fields(df), ak.unzip(df))) | {\"prov_spanet\": })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "72921f32-fee7-45f2-9aa8-2511da543340",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1344266, 16, 3)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob_assignment.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f7cf6187-783d-4d4a-9346-0115e378f876",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "njets_good = ak.sum(mask, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c6e9be8d-2c0d-463c-a3ff-8ea4d45d8a91",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [5, 4, 4, 4, 6, 5, ... 7, 7, 5, 6, 5, 5] type='3584250 * int64'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "njets_good"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "028b8aa6-91ed-4457-b68c-2d5b649d8558",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3584250, 16, 3)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob_assignment.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a631850c-ef9c-4019-ba45-5e01bea22915",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "prob_assign_reshape = np.reshape(prob_assignment, (-1, 3) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c3dd100b-552c-435b-a4be-83a4dc0c739a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "prob_masked = prob_assign_reshape[mask.flatten()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "10ac0752-e7f4-441a-8725-4067bd4c9b6d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "prob_ak = ak.unflatten(ak.from_numpy(prob_masked), njets_good)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "bb894b9c-0fad-4ab6-8d48-9aab4defdd9d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [[[0, 0.664, 0], ... [0, 0, 0]]] type='3584250 * var * 3 * float64'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob_ak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "2d80ff90-0149-498d-b6d1-574b7fe2bbb6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [5, 4, 4, 4, 6, 5, ... 7, 7, 5, 6, 5, 5] type='3584250 * int64'>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ak.num(prob_ak)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "6e7a36e3-2b6b-4f73-929a-0342ca287e7b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [5, 4, 4, 4, 6, 5, ... 7, 7, 5, 6, 5, 5] type='3584250 * int64'>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "njets_good"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "334dc466-c1ea-4a58-b094-174b3b592199",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0.0, 0.9990558011253297, 0.0],\n",
       " [0.0, 0.0, 0.48863674462167134],\n",
       " [0.34225123967733967, 0.0, 0.0],\n",
       " [0.34225123967733967, 0.0, 0.48863674462167134],\n",
       " [0.34225123967733967, 0.0, 0.0]]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob_ak[6].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "44910541-b799-46b3-b5d3-047a3eee69db",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unhashable type: 'dict'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[64], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m ak\u001b[38;5;241m.\u001b[39mto_parquet(ak\u001b[38;5;241m.\u001b[39mzip({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mspanet_prob_assignment\u001b[39m\u001b[38;5;124m\"\u001b[39m: prob_ak, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{\u001b[38;5;28mdict\u001b[39m(\u001b[38;5;28mzip\u001b[39m(ak\u001b[38;5;241m.\u001b[39mfields(df), ak\u001b[38;5;241m.\u001b[39munzip(df)))}}), basedir \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/signal_all_with_spanet.parquet\u001b[39m\u001b[38;5;124m\"\u001b[39m )\n",
      "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'dict'"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "36d50196-21f7-4636-a86a-81992b4ba5e3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [[[0, 0.664, 0], ... [0, 0, 0]]] type='3584250 * var * 3 * float64'>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob_ak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "76e1c4da-eec2-42c5-b5a4-7ecbb7558d37",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df[\"prob_ak\"] = prob_ak\n",
    "\n",
    "df[\"jets\"] = ak.zip( dict(zip(df.jets.fields,ak.unzip(df.jets))) |  {\"prov_Thad\": prob_ak[:,:,0],\"prov_Tlep\": prob_ak[:,:,1],\"prov_H\": prob_ak[:,:,2]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "929aef67-17ce-424e-8b55-252ffdbcdfc4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [[0, 0.444, 0.444, ... 0.426, 0, 0]] type='3584250 * var * float64'>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.jets.prov_Thad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "910a2e3b-5b8c-444e-a3a5-4c3e33247d56",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "ak.to_parquet(df, basedir + \"/all_jets_fullRun2_ttHbb_forTraining_allyears_spanetprov.parquet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "e87f8ee8-63df-4202-b07f-3fb73ceac0ad",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [[0, 0.444, 0.444, ... 0.426, 0, 0]] type='3584250 * var * float64'>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob_ak[:,:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "3d9a1471-e61f-4c95-a3d8-014e494320b2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [True, True, True, ... True, True, True] type='3584250 * bool'>"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "87222123-118e-4001-b08d-d466f76fcc8a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Array [[0, 0, 0, 0.335, ... 0, 0, 0, 0]] type='1784384 * var * float64'>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.jets[(njets_good >= 6)].prov_H"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "bfdb1c60-e518-4bd3-8c98-3030c3f71458",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([8.35185e+06, 0.00000e+00, 0.00000e+00, 0.00000e+00, 3.40000e+01,\n",
       "        4.40000e+02, 2.56800e+03, 8.54000e+03, 2.17300e+04, 4.11400e+04,\n",
       "        6.65880e+04, 9.42300e+04, 1.22450e+05, 1.46010e+05, 1.64298e+05,\n",
       "        1.77554e+05, 1.85120e+05, 1.89626e+05, 1.91142e+05, 1.90004e+05,\n",
       "        1.79784e+05, 1.65154e+05, 1.52148e+05, 1.38878e+05, 1.29014e+05,\n",
       "        1.17910e+05, 1.09490e+05, 1.01984e+05, 9.47940e+04, 8.77440e+04,\n",
       "        8.27880e+04, 7.72000e+04, 7.27660e+04, 6.97440e+04, 6.65360e+04,\n",
       "        6.41260e+04, 6.30320e+04, 6.24400e+04, 6.45400e+04, 6.72220e+04]),\n",
       " array([0.        , 0.01249985, 0.02499969, 0.03749954, 0.04999938,\n",
       "        0.06249923, 0.07499907, 0.08749892, 0.09999876, 0.11249861,\n",
       "        0.12499845, 0.1374983 , 0.14999814, 0.16249799, 0.17499783,\n",
       "        0.18749768, 0.19999752, 0.21249737, 0.22499721, 0.23749706,\n",
       "        0.2499969 , 0.26249675, 0.27499659, 0.28749644, 0.29999628,\n",
       "        0.31249613, 0.32499597, 0.33749582, 0.34999566, 0.36249551,\n",
       "        0.37499535, 0.3874952 , 0.39999504, 0.41249489, 0.42499473,\n",
       "        0.43749458, 0.44999442, 0.46249427, 0.47499411, 0.48749396,\n",
       "        0.4999938 ]),\n",
       " <BarContainer object of 40 artists>)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(ak.flatten(df.jets[(njets_good >= 6)].prov_H), bins=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "93b871da-54fa-43e9-924f-4057ac3e86fb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([9.0189e+05, 3.0000e+00, 7.8000e+01, 6.5000e+02, 2.7210e+03,\n",
       "        7.1340e+03, 1.4481e+04, 2.3733e+04, 3.3990e+04, 4.4253e+04,\n",
       "        5.3856e+04, 6.1846e+04, 6.8084e+04, 7.3658e+04, 7.6593e+04,\n",
       "        7.8191e+04, 7.9870e+04, 7.9699e+04, 8.0602e+04, 8.2467e+04,\n",
       "        8.0757e+04, 7.6304e+04, 7.2172e+04, 6.8407e+04, 6.5100e+04,\n",
       "        6.2020e+04, 5.9703e+04, 5.7174e+04, 5.5444e+04, 5.3355e+04,\n",
       "        5.2235e+04, 5.1278e+04, 4.9547e+04, 4.9307e+04, 4.9560e+04,\n",
       "        5.0199e+04, 5.2067e+04, 5.5967e+04, 6.5861e+04, 9.3668e+04]),\n",
       " array([0.        , 0.01249889, 0.02499778, 0.03749667, 0.04999557,\n",
       "        0.06249446, 0.07499335, 0.08749224, 0.09999113, 0.11249002,\n",
       "        0.12498891, 0.13748781, 0.1499867 , 0.16248559, 0.17498448,\n",
       "        0.18748337, 0.19998226, 0.21248115, 0.22498004, 0.23747894,\n",
       "        0.24997783, 0.26247672, 0.27497561, 0.2874745 , 0.29997339,\n",
       "        0.31247228, 0.32497118, 0.33747007, 0.34996896, 0.36246785,\n",
       "        0.37496674, 0.38746563, 0.39996452, 0.41246342, 0.42496231,\n",
       "        0.4374612 , 0.44996009, 0.46245898, 0.47495787, 0.48745676,\n",
       "        0.49995566]),\n",
       " <BarContainer object of 40 artists>)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(ak.flatten(df.jets[df.jets.prov==2].prov_Thad), bins=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "fb7930bd-baf1-4d27-8ea1-673ee0b24cba",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1.6696235e+07, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
       "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 9.0000000e+00,\n",
       "        7.3000000e+01, 5.5400000e+02, 2.8940000e+03, 1.0056000e+04,\n",
       "        2.2428000e+04, 4.5967000e+04, 7.7955000e+04, 1.0391200e+05,\n",
       "        1.2470800e+05, 1.4067600e+05, 1.5593600e+05, 1.7763600e+05,\n",
       "        2.0003100e+05, 1.8590100e+05, 1.7232900e+05, 1.5908800e+05,\n",
       "        1.4757500e+05, 1.3757800e+05, 1.2864100e+05, 1.1992100e+05,\n",
       "        1.1355500e+05, 1.0669500e+05, 1.0229600e+05, 9.8007000e+04,\n",
       "        9.4966000e+04, 9.3555000e+04, 9.3163000e+04, 9.5302000e+04,\n",
       "        1.0002700e+05, 1.1189100e+05, 1.3816600e+05, 3.2275900e+05]),\n",
       " array([0.        , 0.02499999, 0.04999998, 0.07499996, 0.09999995,\n",
       "        0.12499994, 0.14999993, 0.17499992, 0.1999999 , 0.22499989,\n",
       "        0.24999988, 0.27499987, 0.29999986, 0.32499985, 0.34999983,\n",
       "        0.37499982, 0.39999981, 0.4249998 , 0.44999979, 0.47499977,\n",
       "        0.49999976, 0.52499975, 0.54999974, 0.57499973, 0.59999971,\n",
       "        0.6249997 , 0.64999969, 0.67499968, 0.69999967, 0.72499965,\n",
       "        0.74999964, 0.77499963, 0.79999962, 0.82499961, 0.84999959,\n",
       "        0.87499958, 0.89999957, 0.92499956, 0.94999955, 0.97499954,\n",
       "        0.99999952]),\n",
       " <BarContainer object of 40 artists>)"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(ak.flatten(df.jets.prov_Tlep), bins=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0bbb7168-73ff-4f9d-b807-ef3016bdd91c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "ak.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94f4355c-9354-43d6-98fd-fa29b512cf97",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.17"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}