noise_1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import h5py\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.00808884 1.07195038 1.03552042 0.96796726]\n",
      "[0.0510411  0.06000206 0.04332081 0.06067377]\n"
     ]
    }
   ],
   "source": [
    "f = h5py.File('noise_1/final-errorscan-qf_2-64-4_3-3000.h5', 'r')\n",
    "\n",
    "p = np.array(f['p'])\n",
    "qfe = np.array(f['qfe'])\n",
    "qfp = np.array(f['qfp'])\n",
    "\n",
    "qfe_m = np.mean(qfe, axis=0)\n",
    "qfp_m = np.mean(qfp, axis=0)\n",
    "\n",
    "qfe_s = np.std(qfe, axis=0)\n",
    "qfp_s = np.std(qfp, axis=0)\n",
    "\n",
    "print(qfe_m)\n",
    "print(qfe_s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0771495385230985)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(p, qfe_m, marker='.', markersize=12)\n",
    "plt.ylim(bottom=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8d2339a4f0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot([-1,1],[2,0], linestyle='--', color='0.7', linewidth=4, label='Exact')\n",
    "ax.errorbar(p, qfp_m, yerr=qfp_s, marker='.', markersize=12, linewidth=2, color='tomato',label='AQT')\n",
    "\n",
    "ax.set_ybound(0.5,1.08)\n",
    "ax.set_xbound(-0.005, 0.305)\n",
    "\n",
    "ax.tick_params(axis='x', labelsize=14)\n",
    "ax.tick_params(axis='y', labelsize=14)\n",
    "ax.set_xlabel('$p$',size=14)\n",
    "ax.set_ylabel('$F_Q$',size=14)\n",
    "ax.legend(loc='lower left', fontsize=14)\n",
    "\n",
    "# fig.savefig('figures/fig2b.pdf', dpi=300, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}