{
 "cells": [
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-26T12:09:23.794375Z",
     "start_time": "2025-03-26T12:09:23.764205Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# This file is for quick plotting of histograms of particle properties, to debug potential issues with cuts etc.\n",
    "import torch\n",
    "import sys\n",
    "import os.path as osp\n",
    "import os\n",
    "import sys\n",
    "import numpy as np\n",
    "from src.dataset.dataset import SimpleIterDataset, EventDataset\n",
    "from src.utils.utils import to_filelist\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "matplotlib.rc('font', size=13)\n",
    "from src.plotting.plot_event import plot_event_comparison\n",
    "from src.dataset.functions_data import concat_events\n",
    "from src.utils.paths import get_path\n",
    "from dotenv import load_dotenv\n",
    "load_dotenv()\n"
   ],
   "id": "6bae9707acf4a848",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-26T12:09:24.032716Z",
     "start_time": "2025-03-26T12:09:24.025330Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def remove_from_list(lst):\n",
    "    out = []\n",
    "    for item in lst:\n",
    "        if item in [\"hgcal\", \"data.txt\", \"test_file.root\"]:\n",
    "            continue\n",
    "        out.append(item)\n",
    "    return out\n",
    "\n",
    "#path = \"/eos/user/g/gkrzmanc/jetclustering/data/SVJ_std_UL2018_scouting_test_large/SVJ_mMed-700GeV_mDark-20GeV_rinv-0.7_alpha-peak\"\n",
    "def get_iter(path_to_ds):\n",
    "    return iter(EventDataset.from_directory(path_to_ds, aug_soft=1))\n",
    "\n"
   ],
   "id": "e7a7ef680143801e",
   "outputs": [],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-26T12:09:25.100190Z",
     "start_time": "2025-03-26T12:09:24.892228Z"
    }
   },
   "cell_type": "code",
   "source": "dataset = get_iter(get_path(\"/work/gkrzmanc/jetclustering/preprocessed_data/Feb26_2025_E1000_N500_noPartonFilter_C_F/PFNano_s-channel_mMed-1200_mDark-20_rinv-0.3_alpha-peak_13TeV-pythia8_n-1000\", \"preprocessed_data\"))",
   "id": "1549361c5b028634",
   "outputs": [],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-26T12:09:40.759101Z",
     "start_time": "2025-03-26T12:09:40.266868Z"
    }
   },
   "cell_type": "code",
   "source": "e = next(dataset)",
   "id": "f30e59b8aae67705",
   "outputs": [],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-26T12:09:49.754801Z",
     "start_time": "2025-03-26T12:09:49.730129Z"
    }
   },
   "cell_type": "code",
   "source": "e.pfcands.pt",
   "id": "2ab3b47ae54cff1",
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "        4.1711e-01, 3.4856e-01, 2.2032e-01, 2.0387e-01, 2.0870e-01, 3.0306e-01,\n",
       "        4.4315e-01, 4.6595e-01, 4.5694e-02, 1.0718e-01, 7.3868e-02, 1.1280e-01,\n",
       "        1.8637e-01, 2.6314e-01, 3.2214e-01, 3.7143e-01, 4.4725e-01, 4.9476e-01,\n",
       "        3.3817e-01, 4.2577e-01, 3.9346e-01, 1.6762e-01, 4.4033e-01, 4.0526e-02,\n",
       "        2.0176e-02, 1.5139e-01, 2.4181e-01, 3.2641e-01, 6.8850e-02, 3.4304e-01,\n",
       "        4.0487e-01, 1.0895e-01, 2.1926e-01, 2.6959e-01, 2.3687e-01, 4.0392e-01,\n",
       "        4.8105e-01, 4.0350e-01, 5.7437e-02, 4.0637e-01, 5.1966e-02, 1.3327e-01,\n",
       "        9.3487e-02, 1.1481e-01, 2.7359e-01, 3.4241e-01, 2.4575e-01, 4.8065e-01,\n",
       "        1.3534e-01, 3.8631e-01, 4.3769e-01], dtype=torch.float64)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 14
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-26T11:04:20.343152Z",
     "start_time": "2025-03-26T11:04:15.037372Z"
    }
   },
   "cell_type": "code",
   "source": [
    "\n",
    "parton_eta, parton_pt = [], []\n",
    "gen_eta, gen_pt = [], []\n",
    "pfcand_eta, pfcand_phi = [], []\n",
    "pfcand_pt = []\n",
    "parton_phi = []\n",
    "gen_phi = []\n",
    "from tqdm import tqdm\n",
    "for event in tqdm(dataset):\n",
    "    parton_eta += event.final_parton_level_particles.eta.tolist()\n",
    "    parton_pt += event.final_parton_level_particles.pt.tolist()\n",
    "    gen_eta += event.final_gen_particles.eta.tolist()\n",
    "    gen_pt += event.final_gen_particles.pt.tolist()\n",
    "    pfcand_eta += event.pfcands.eta.tolist()\n",
    "    pfcand_phi += event.pfcands.phi.tolist()\n",
    "    pfcand_pt += event.pfcands.pt.tolist()\n",
    "    parton_phi += event.final_parton_level_particles.phi.tolist()\n",
    "    gen_phi += event.final_gen_particles.phi.tolist()\n",
    "    pfcand_phi += event.pfcands.phi.tolist()\n"
   ],
   "id": "d867de5b313084ea",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "717it [00:04, 164.43it/s]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mKeyboardInterrupt\u001B[0m                         Traceback (most recent call last)",
      "Cell \u001B[0;32mIn[4], line 8\u001B[0m\n\u001B[1;32m      6\u001B[0m gen_phi \u001B[38;5;241m=\u001B[39m []\n\u001B[1;32m      7\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;21;01mtqdm\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;28;01mimport\u001B[39;00m tqdm\n\u001B[0;32m----> 8\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m event \u001B[38;5;129;01min\u001B[39;00m tqdm(dataset):\n\u001B[1;32m      9\u001B[0m     parton_eta \u001B[38;5;241m+\u001B[39m\u001B[38;5;241m=\u001B[39m event\u001B[38;5;241m.\u001B[39mfinal_parton_level_particles\u001B[38;5;241m.\u001B[39meta\u001B[38;5;241m.\u001B[39mtolist()\n\u001B[1;32m     10\u001B[0m     parton_pt \u001B[38;5;241m+\u001B[39m\u001B[38;5;241m=\u001B[39m event\u001B[38;5;241m.\u001B[39mfinal_parton_level_particles\u001B[38;5;241m.\u001B[39mpt\u001B[38;5;241m.\u001B[39mtolist()\n",
      "File \u001B[0;32m/work/gkrzmanc/1gatr/lib/python3.10/site-packages/tqdm/std.py:1181\u001B[0m, in \u001B[0;36mtqdm.__iter__\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m   1178\u001B[0m time \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_time\n\u001B[1;32m   1180\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m-> 1181\u001B[0m     \u001B[38;5;28;01mfor\u001B[39;00m obj \u001B[38;5;129;01min\u001B[39;00m iterable:\n\u001B[1;32m   1182\u001B[0m         \u001B[38;5;28;01myield\u001B[39;00m obj\n\u001B[1;32m   1183\u001B[0m         \u001B[38;5;66;03m# Update and possibly print the progressbar.\u001B[39;00m\n\u001B[1;32m   1184\u001B[0m         \u001B[38;5;66;03m# Note: does not call self.update(1) for speed optimisation.\u001B[39;00m\n",
      "File \u001B[0;32m/work/gkrzmanc/jetclustering/code/src/dataset/dataset.py:657\u001B[0m, in \u001B[0;36mEventDataset.get_iter\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m    655\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mi \u001B[38;5;241m=\u001B[39m \u001B[38;5;241m0\u001B[39m\n\u001B[1;32m    656\u001B[0m \u001B[38;5;28;01mwhile\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mi \u001B[38;5;241m<\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mn_events:\n\u001B[0;32m--> 657\u001B[0m     \u001B[38;5;28;01myield\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_idx\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mi\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    658\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mi \u001B[38;5;241m+\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;241m1\u001B[39m\n",
      "File \u001B[0;32m/work/gkrzmanc/jetclustering/code/src/dataset/dataset.py:494\u001B[0m, in \u001B[0;36mEventDataset.get_idx\u001B[0;34m(self, i)\u001B[0m\n\u001B[1;32m    492\u001B[0m end \u001B[38;5;241m=\u001B[39m {key: \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mmetadata[key \u001B[38;5;241m+\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m_batch_idx\u001B[39m\u001B[38;5;124m\"\u001B[39m][i \u001B[38;5;241m+\u001B[39m \u001B[38;5;241m1\u001B[39m] \u001B[38;5;28;01mfor\u001B[39;00m key \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mattrs}\n\u001B[1;32m    493\u001B[0m result \u001B[38;5;241m=\u001B[39m {key: \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mevents[key][start[key]:end[key]] \u001B[38;5;28;01mfor\u001B[39;00m key \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mattrs}\n\u001B[0;32m--> 494\u001B[0m result \u001B[38;5;241m=\u001B[39m {key: EventCollection\u001B[38;5;241m.\u001B[39mdeserialize(result[key], batch_number\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, \u001B[38;5;28mcls\u001B[39m\u001B[38;5;241m=\u001B[39mEvent\u001B[38;5;241m.\u001B[39mevt_collections[key]) \u001B[38;5;28;01mfor\u001B[39;00m\n\u001B[1;32m    495\u001B[0m           key \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mattrs}\n\u001B[1;32m    496\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mfinal_parton_level_particles\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01min\u001B[39;00m result:\n\u001B[1;32m    497\u001B[0m     \u001B[38;5;66;03m#print(\"i=\", i)\u001B[39;00m\n\u001B[1;32m    498\u001B[0m     \u001B[38;5;66;03m#print(\"BEFORE:\", len(result[\"final_parton_level_particles\"]))\u001B[39;00m\n\u001B[1;32m    499\u001B[0m     result[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mfinal_parton_level_particles\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m filter_pfcands(result[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mfinal_parton_level_particles\u001B[39m\u001B[38;5;124m\"\u001B[39m])\n",
      "File \u001B[0;32m/work/gkrzmanc/jetclustering/code/src/dataset/dataset.py:494\u001B[0m, in \u001B[0;36m<dictcomp>\u001B[0;34m(.0)\u001B[0m\n\u001B[1;32m    492\u001B[0m end \u001B[38;5;241m=\u001B[39m {key: \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mmetadata[key \u001B[38;5;241m+\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m_batch_idx\u001B[39m\u001B[38;5;124m\"\u001B[39m][i \u001B[38;5;241m+\u001B[39m \u001B[38;5;241m1\u001B[39m] \u001B[38;5;28;01mfor\u001B[39;00m key \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mattrs}\n\u001B[1;32m    493\u001B[0m result \u001B[38;5;241m=\u001B[39m {key: \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mevents[key][start[key]:end[key]] \u001B[38;5;28;01mfor\u001B[39;00m key \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mattrs}\n\u001B[0;32m--> 494\u001B[0m result \u001B[38;5;241m=\u001B[39m {key: \u001B[43mEventCollection\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdeserialize\u001B[49m\u001B[43m(\u001B[49m\u001B[43mresult\u001B[49m\u001B[43m[\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbatch_number\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mcls\u001B[39;49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mEvent\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mevt_collections\u001B[49m\u001B[43m[\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m \u001B[38;5;28;01mfor\u001B[39;00m\n\u001B[1;32m    495\u001B[0m           key \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mattrs}\n\u001B[1;32m    496\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mfinal_parton_level_particles\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01min\u001B[39;00m result:\n\u001B[1;32m    497\u001B[0m     \u001B[38;5;66;03m#print(\"i=\", i)\u001B[39;00m\n\u001B[1;32m    498\u001B[0m     \u001B[38;5;66;03m#print(\"BEFORE:\", len(result[\"final_parton_level_particles\"]))\u001B[39;00m\n\u001B[1;32m    499\u001B[0m     result[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mfinal_parton_level_particles\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m filter_pfcands(result[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mfinal_parton_level_particles\u001B[39m\u001B[38;5;124m\"\u001B[39m])\n",
      "File \u001B[0;32m/work/gkrzmanc/jetclustering/code/src/dataset/functions_data.py:488\u001B[0m, in \u001B[0;36mEventCollection.deserialize\u001B[0;34m(data_matrix, batch_number, cls)\u001B[0m\n\u001B[1;32m    485\u001B[0m     data[key] \u001B[38;5;241m=\u001B[39m data_matrix[:, i]\n\u001B[1;32m    486\u001B[0m     \u001B[38;5;66;03m#if key == \"pid\" and pid_filter:\u001B[39;00m\n\u001B[1;32m    487\u001B[0m     \u001B[38;5;66;03m#    filt = ~np.bool(np.abs(data[key]) >= 10000 + (np.abs(data[key]) >= 50 * np.abs(data[key]) <= 60))\u001B[39;00m\n\u001B[0;32m--> 488\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mcls\u001B[39;49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mdata\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbatch_number\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mbatch_number\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[0;32m/work/gkrzmanc/jetclustering/code/src/dataset/functions_data.py:716\u001B[0m, in \u001B[0;36mEventPFCands.__init__\u001B[0;34m(self, pt, eta, phi, mass, charge, pid, jet_idx, pfcands_idx, batch_number, offline, pf_cand_jet_idx, status, pid_filter)\u001B[0m\n\u001B[1;32m    709\u001B[0m   \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mpxyz \u001B[38;5;241m=\u001B[39m torch\u001B[38;5;241m.\u001B[39mstack(\n\u001B[1;32m    710\u001B[0m (\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mp \u001B[38;5;241m*\u001B[39m torch\u001B[38;5;241m.\u001B[39mcos(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mphi) \u001B[38;5;241m*\u001B[39m torch\u001B[38;5;241m.\u001B[39msin(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mtheta),\n\u001B[1;32m    711\u001B[0m        \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mp \u001B[38;5;241m*\u001B[39m torch\u001B[38;5;241m.\u001B[39msin(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mphi) \u001B[38;5;241m*\u001B[39m torch\u001B[38;5;241m.\u001B[39msin(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mtheta),\n\u001B[1;32m    712\u001B[0m        \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mp \u001B[38;5;241m*\u001B[39m torch\u001B[38;5;241m.\u001B[39mcos(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mtheta)),\n\u001B[1;32m    713\u001B[0m       dim\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m1\u001B[39m\n\u001B[1;32m    714\u001B[0m   )\n\u001B[1;32m    715\u001B[0m   \u001B[38;5;66;03m#assert (torch.abs(torch.norm(self.pxyz, dim=1) - self.p) < 0.1).all(), (torch.abs(torch.norm(self.pxyz, dim=1) - self.p).max())\u001B[39;00m\n\u001B[0;32m--> 716\u001B[0m   \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m (torch\u001B[38;5;241m.\u001B[39mabs(\u001B[43mtorch\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mnorm\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpxyz\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdim\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m1\u001B[39;49m\u001B[43m)\u001B[49m \u001B[38;5;241m-\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mp) \u001B[38;5;241m<\u001B[39m \u001B[38;5;241m0.05\u001B[39m)\u001B[38;5;241m.\u001B[39mall():\n\u001B[1;32m    717\u001B[0m       \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m!!!!!\u001B[39m\u001B[38;5;124m\"\u001B[39m, (torch\u001B[38;5;241m.\u001B[39mabs(torch\u001B[38;5;241m.\u001B[39mnorm(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mpxyz, dim\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m1\u001B[39m) \u001B[38;5;241m-\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mp))\u001B[38;5;241m.\u001B[39mmax())\n\u001B[1;32m    718\u001B[0m       \u001B[38;5;66;03m# argmax\u001B[39;00m\n",
      "File \u001B[0;32m/work/gkrzmanc/1gatr/lib/python3.10/site-packages/torch/functional.py:1788\u001B[0m, in \u001B[0;36mnorm\u001B[0;34m(input, p, dim, keepdim, out, dtype)\u001B[0m\n\u001B[1;32m   1781\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28minput\u001B[39m\u001B[38;5;241m.\u001B[39mlayout \u001B[38;5;241m==\u001B[39m torch\u001B[38;5;241m.\u001B[39mstrided \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28minput\u001B[39m\u001B[38;5;241m.\u001B[39mdevice\u001B[38;5;241m.\u001B[39mtype \u001B[38;5;129;01min\u001B[39;00m (\n\u001B[1;32m   1782\u001B[0m     \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcpu\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m   1783\u001B[0m     \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcuda\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m   1784\u001B[0m     \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmeta\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m   1785\u001B[0m     torch\u001B[38;5;241m.\u001B[39mutils\u001B[38;5;241m.\u001B[39mbackend_registration\u001B[38;5;241m.\u001B[39m_privateuse1_backend_name,\n\u001B[1;32m   1786\u001B[0m ):\n\u001B[1;32m   1787\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m dim \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[0;32m-> 1788\u001B[0m         \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28;43misinstance\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mdim\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mint\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtorch\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mSymInt\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m:\n\u001B[1;32m   1789\u001B[0m             _dim \u001B[38;5;241m=\u001B[39m [dim]\n\u001B[1;32m   1790\u001B[0m         \u001B[38;5;28;01melse\u001B[39;00m:\n",
      "\u001B[0;31mKeyboardInterrupt\u001B[0m: "
     ]
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-26T11:04:25.653278Z",
     "start_time": "2025-03-26T11:04:23.589153Z"
    }
   },
   "cell_type": "code",
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.hist(parton_eta, bins=100, label=\"parton level\", histtype=\"step\", density=True)\n",
    "ax.hist(gen_eta, bins=100, alpha=0.5, label=\"gen level\", histtype=\"step\",  density=True)\n",
    "ax.hist(pfcand_eta, bins=100, alpha=0.5, label=\"pfcands\", histtype=\"step\", density=True)\n",
    "ax.set_title(\"Eta distribution\")\n",
    "ax.legend()\n",
    "fig.show()\n"
   ],
   "id": "baf6c2c3905faecd",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-26T11:04:34.208397Z",
     "start_time": "2025-03-26T11:04:31.211003Z"
    }
   },
   "cell_type": "code",
   "source": [
    "fig, ax = plt.subplots()\n",
    "bins = np.linspace(0, 200, 100)\n",
    "logbins = np.geomspace(0.1, 200, 100)\n",
    "ax.hist(parton_pt, bins=logbins, label=\"parton level\", histtype=\"step\", density=True)\n",
    "ax.hist(gen_pt, bins=logbins, alpha=0.5, label=\"gen level\", histtype=\"step\",  density=True)\n",
    "ax.hist(pfcand_pt, bins=logbins, alpha=0.5, label=\"pfcands\", histtype=\"step\", density=True)\n",
    "ax.set_title(\"pt distribution\")\n",
    "ax.legend()\n",
    "ax.set_yscale(\"log\")\n",
    "ax.set_xscale(\"log\")\n",
    "ax.set_xlim([0, 200])\n",
    "fig.show()\n"
   ],
   "id": "d0d0426e2fdac522",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_59226/1458644762.py:11: UserWarning: Attempt to set non-positive xlim on a log-scaled axis will be ignored.\n",
      "  ax.set_xlim([0, 200])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-25T20:19:48.681506Z",
     "start_time": "2025-03-25T20:19:48.668899Z"
    }
   },
   "cell_type": "code",
   "source": "min(gen_pt)",
   "id": "947882371da9f90",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-25T20:19:56.114241Z",
     "start_time": "2025-03-25T20:19:56.105091Z"
    }
   },
   "cell_type": "code",
   "source": "min(parton_pt)",
   "id": "1390eb5e3c6f7307",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0814879110195774e-33"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 11
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-26T11:04:50.083990Z",
     "start_time": "2025-03-26T11:04:46.815469Z"
    }
   },
   "cell_type": "code",
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.hist(parton_phi, bins=100, label=\"parton level\", histtype=\"step\", density=True)\n",
    "ax.hist(gen_phi, bins=100, alpha=0.5, label=\"gen level\", histtype=\"step\",  density=True)\n",
    "ax.hist(pfcand_phi, bins=100, alpha=0.5, label=\"pfcands\", histtype=\"step\", density=True)\n",
    "ax.set_title(\"Phi distribution\")\n",
    "ax.legend()\n",
    "fig.show()\n"
   ],
   "id": "95a96baa38ba1190",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "1c903a13377dcb3c"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}