{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f16553be",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "signal_soln = np.load('/global/cfs/projectdirs/atlas/eligd/llm_for_analysis/solution/arrays/signal.npy')\n",
    "bkgd_soln = np.load('/global/cfs/projectdirs/atlas/eligd/llm_for_analysis/solution/arrays/bkgd.npy')\n",
    "\n",
    "signal_llm = np.load('/global/cfs/projectdirs/atlas/eligd/llm_for_analysis/results/test_new_soln/arrays/signal.npy')\n",
    "bkgd_llm = np.load('/global/cfs/projectdirs/atlas/eligd/llm_for_analysis/results/test_new_soln/arrays/bkgd.npy')\n",
    "\n",
    "signal_scores_soln= np.load('/global/cfs/projectdirs/atlas/eligd/llm_for_analysis/solution/arrays/signal_scores.npy')\n",
    "bkgd_scores_soln = np.load('/global/cfs/projectdirs/atlas/eligd/llm_for_analysis/solution/arrays/bkgd_scores.npy')\n",
    "\n",
    "signal_scores_llm = np.load('/global/cfs/projectdirs/atlas/eligd/llm_for_analysis/results/test_new_soln/arrays/signal_scores.npy')\n",
    "bkgd_scores_llm = np.load('/global/cfs/projectdirs/atlas/eligd/llm_for_analysis/results/test_new_soln/arrays/bkgd_scores.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "95c03a1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1055315, 35)\n",
      "(1055315, 35)\n",
      "\n",
      "\n",
      "\n",
      "(939354, 35)\n",
      "(939354, 35)\n"
     ]
    }
   ],
   "source": [
    "print(signal_soln.shape)\n",
    "print(signal_llm.shape)\n",
    "\n",
    "print('\\n\\n')\n",
    "\n",
    "print(bkgd_soln.shape)\n",
    "print(bkgd_llm.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4b7f1f7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1055315,)\n",
      "(1055315,)\n",
      "\n",
      "\n",
      "\n",
      "(939354,)\n",
      "(939354,)\n"
     ]
    }
   ],
   "source": [
    "print(signal_scores_soln.shape)\n",
    "print(signal_scores_llm.shape)\n",
    "\n",
    "print('\\n\\n')\n",
    "\n",
    "print(bkgd_scores_soln.shape)\n",
    "print(bkgd_scores_llm.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a73466cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0010391304540671282\n"
     ]
    }
   ],
   "source": [
    "signal_soln = np.nan_to_num(signal_soln)\n",
    "signal_llm = np.nan_to_num(signal_llm)\n",
    "print(np.max(np.abs(signal_soln[:,32] - signal_llm[:,32])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "921770c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 65113.2421875   71947.5703125  109443.96875    147763.453125\n",
      "  63255.86328125]\n",
      "[132987.203125   119780.6484375   76592.5234375   67097.6015625\n",
      "  49665.16796875]\n"
     ]
    }
   ],
   "source": [
    "print(signal_soln[0:5,0])\n",
    "print(signal_llm[0:5,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cb0c1881",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.67503306 -0.07398074 -3.00010272 ...  1.1491788   0.24779759\n",
      " -0.36138178]\n"
     ]
    }
   ],
   "source": [
    "print((signal_soln[:,33]-signal_llm[:,33])/1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4251ce9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[125145.98049331 125583.68083649 123304.16665626 ... 126510.49904401\n",
      " 125228.83675829 125038.23681329]\n",
      "[125821.01355533 125657.66158076 126304.2693749  ... 125361.32024238\n",
      " 124981.03916606 125399.61859435]\n"
     ]
    }
   ],
   "source": [
    "print(signal_soln[:,33])\n",
    "print(signal_llm[:,33])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea142ad3",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(signal_soln[:,32])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3a975052",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 8))\n",
    "\n",
    "bins = np.linspace(0, 1, 200)\n",
    "\n",
    "ax.hist(signal_scores_soln, weights=signal_soln[:,32], bins=100, histtype='step')\n",
    "ax.hist(signal_scores_llm, weights=signal_llm[:,32], bins=100, histtype='step', linestyle='--');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "634ccc33",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.11.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}