Fig 5 - Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Python packages\n",
    "import pickle\n",
    "\n",
    "# Third-Party Imports\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def score_p(results, true_coeffs):\n",
    "    for result in results:\n",
    "        len_diff = int((len(true_coeffs['p'])-len(result['p']))/2)\n",
    "        diff = true_coeffs['p'][len_diff:-len_diff] - result['p']\n",
    "        score = np.linalg.norm(diff)\n",
    "        score = score/np.linalg.norm(true_coeffs['p'][len_diff:-len_diff])\n",
    "        result['score'] = score\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Figure 5a\n",
    "file_stem = \"./data/Fig5a-NLSL-\"\n",
    "results = pickle.load(open(file_stem +\"results.pickle\", \"rb\"))\n",
    "true_coeffs = pickle.load(open(\"./data/S2-NLSL-coeffs.pickle\", \"rb\"))\n",
    "\n",
    "results = score_p(results, true_coeffs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_nm = []\n",
    "plots_sc = []\n",
    "\n",
    "for result in results:\n",
    "    plot_nm.append(result['noise_mag'])\n",
    "    plots_sc.append(result['score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hMaYKYw+Z6C2lpqbi559/xtChQ9GpUyfRccgAsCATvYWnT5/C398ftWrVQmRkpOg4ZCDYsiB6C4sXL8ahQ4ewYsUK1KpVS3QcMhC8QyYqp2vXruH7779Hly5dMGjQINFxyICwIBOV05gxY5Cfn8+ZY9I4tiyIyiE+Ph7r1q3DzJkz0bhxY9FxyMBwDpmojB49eoRmzZqhWrVqOHbsGKpUqSI6EukJjc4h/+mA+goHI9JXU6dORXZ2NpKSkliMSSt4QD1RGRw/fhxz587FsGHD8PHHH4uOQwaKi3pEpXg+c1y7dm1ERESIjkMGjIt6RKVYuHAhDh8+jFWrVqFmzZqi45AB4x0y0d+4evUqJk2ahC+++AIDBgwQHYcMHAsy0d8ICgpCYWEhZ46pUrBlQfQGmzZtwm+//Ybw8HA0bNhQdBwyApxDJirBw4cP0axZM9jY2ODo0aMwNzcXHYn0GM9DJqqAH374AZcvX8bq1atZjKnSsIdM9BdHjx5FVFQUhg8fjg4dOoiOQ0aEBZnoT57PHNva2iI8PFx0HDIybFkQ/UlMTAyOHDmC1atXo0aNGqLjkJHhHTLR/12+fBmTJ09G9+7d0a9fP9FxyAixIBP9X1BQEJ4+fYr58+dz5piEYMuCCMDGjRuxceNGREREwNnZWXQcMlKcQyajl5ubC1dXV9SuXRspKSkccyON4xwyURlNmTIF165dw7p161iMSSj2kMmopaSkIDo6GiNGjED79u1FxyEjV2rLQpIkfwD+AODg4PBBVlZWZeQi0rqioiK0bdsWN27cQHp6OvgABtKWsrYsSr1DlmVZKcuyhyzLHra2tppJR6QDoqOjcezYMcybN4/FmHQCWxZklLKzszFlyhR4eXmhd+/eouMQAShjQZYkSSFJkjInJ0fbeYi0TpZljBo1CrIsIyYmhjPHpDP4kFMyOhs2bIBarcb06dPh5OQkOg7RC5xDJqPy4MEDuLq6wtbWFikpKTAz4+QnaZ9G55AlSVIAUDRu3LjCwYhECg0NxfXr17FhwwYWY9I5bFmQ0UhOTsb8+fMxatQotG3bVnQcotdwyoKMQlFREfz9/VG3bl3MnDlTdByiErFlQUbhl19+wYkTJ7B+/Xq88847ouMQlYiLemTwLl26BDc3N3Tp0gUbN27kmBtVOo3t1CPSZ7IsY+TIkZAkCdHR0SzGpNO4zEwGbd26dUhISMCcOXPg4OAgOg7R3ypTy+JPPeRh58+f134qIg3IycmBi4sL6tWrh0OHDnHMjYTRaMuCY2+kjyZNmoRbt25BqVSyGJNeYA+ZDNLBgwexcOFCjB49Gh988IHoOERlwoJMBqewsBD+/v6oX78+ZsyYIToOUZnx/3FkcObOnYuTJ09i48aNqF69uug4RGXG4zfJoGRmZmLq1Kno1asXevbsKToOUblwUY8MhizLCAwMhKmpKebNmyc6DlG5sWVBBmPt2rXYunUroqKi8O6774qOQ1RuXNQjvaZSqeDk5AQTExMMHDgQTk5OGDlypOhYRG+FBZn0lkqlgr+/P7KysiDLMoqLi3Hjxg2sXr1adDSit8KCTHorNDQUeXl5r7z25MkThIaGCkpEVDGcsiC9k5eXB7VajaysrBJ/np2dXcmJiDSDUxakFy5fvoxFixbBy8sLtWvXho+PzxtPbuMhQqSvOGVBOqm4uBiHDx9GfHw81Go1Tpw4AQBo2LAhAgICoFAocOXKFQQGBr7StrC2tkZYWJio2EQVwoJMOiM3Nxc7duyAWq1GQkICbt26BVNTU3z00UeYPXs2vL298d57771yZ2xmZobQ0FBkZ2fDwcEBYWFhGDRokMA/BdHb4xNDSKjMzEzEx8cjPj4eiYmJKCgoQI0aNdC9e3coFAp07doVtWrVEh2TqELKevwm75CpUhUVFeHgwYMvWhFpaWkAABcXFwQFBUGhUKBDhw48LpOMEv/Vk9bdv38f27ZtQ3x8PBISEnDv3j2YmZmhc+fO8Pf3h5eXF/gAXSI+dZoqQKVSvbF/e+7cuRetiKSkJBQVFaFOnTrw9vaGQqGAp6cnOLVD9Cr2kOmtPN8l9+cJBwsLC3z22We4cOECnj/qq3nz5lAoFPD29kbbtm1hamoqKjKRMGXtIbMg01txcnJ648aMbt26QaFQwMvLC46OjpWcjEj3cFGPtOLs2bN/u0tOkiRs2bKlklMRGQYWZPpbRUVF2L9/PzZt2gS1Wo1z584BAMzNzVFYWPja+7lLjujt8XAhek1OTg7Wrl2Lr776CnZ2dujcuTOio6Ph7OyMmJgYZGVlITY2FtbW1q98jrvkiCqGd8gE4NkGDbVaDbVajcTERBQVFb04M0KhUOCLL7545fl0z6cpuEuOSHO4qGekiouLkZyc/KIVcerUKQCAq6srFAoFfHx80L59e05FEGkAF/XoNY8ePcKOHTuwadMmbN68+cVZEZ06dcKcOXOgUCi4QYNIIBZkA3flypUX25R37tyJ/Px82NjYoHv37vDx8UG3bt1Qs2ZN0TGJCCzIBkeWZRw7duxFK+Lo0aMAnh1bOWLECCgUCnTs2BHm5uaCkxLRX3HrtB7665blqVOnws7O7sWi3NWrVyFJEj788EOEh4fDx8cHrq6ubzzQnYh0Axf19ExJW5afq1q1Krp27QqFQoEePXrAzs5OQEIi+isu6hmg3NxcjBkzpsRibGdnh6ysLFhaWgpIRkSawI0hOu7JkyfYsGED+vbtCzs7O9y9e7fE992+fZvFmEjPsSDroKKiImzfvh1DhgyBvb09fH19sWfPHgwdOhT29vYlfoZblon0H1sWOkKWZRw4cABxcXFYu3Ytbt26herVq8PX1xd+fn74/PPPYWZmhg8//PC1HjK3LBMZBhZkgWRZxsmTJxEXF4e4uDhkZWXBwsIC3t7eGDhwIHr06PFaG4JblokMF6csBMjIyHhRhNPS0mBqagpPT0/4+fmhV69eeOedd0RHJCIN4pSFjrl+/TrWrFmDuLg4JCcnAwA+/vhjzJ8/H3379oWtra3ghEQkGguyFt27dw/r169HXFwcEhMTIcsyWrZsiYiICAwYMIALcUT0ChZkDXv06BE2bdqEuLg4bN26FYWFhWjcuDGmTJkCPz8/uLi4iI5IRDqKY2/lpFKp4OTkBBMTEzg5OUGlUqGgoABqtRoDBw6EnZ0dBg4ciCNHjmD06NE4fPgwzp07h2nTprEYE9Hf4qJeOZS0bdnU1BQWFhbIy8tDrVq10KdPH/j5+aFjx448S5iIAHBRTytCQ0Nf27b89OlTAEB8fDw8PT1RpUoVEdGIyACwIJdRenr6G5+0/PjxY3h5eVVyIiIyNOwhlyIlJQW9e/eGm5vbG4+v5LQEEWkCC3IJZFnG7t274enpiTZt2mDnzp2YNGkSFixYwCctE5HWlNqykCTJH4A/YPh3gsXFxVCr1QgPD8ehQ4dgb2+PiIgIDB8+/MXuuerVq3PbMhFpBacs8Ox0tdWrV2PWrFk4ffo0nJycEBwcjCFDhvBISyKqME5ZlMHjx48RGxuL2bNn49KlS3Bzc8PKlSvRv39/mJkZ9aUhIgGM8pl6Dx48wMKFCzF37lzcvHkT7du3R1RUFLy9vWFiwrY6EYlRpuojy7JalmV/GxsbbefRqlu3biE0NBQODg4ICQlBixYtsHv3buzfvx8+Pj4sxkQklFH8vzw7Oxs//fQTli5diidPnsDX1xchISHw8Ci1pUNEVGkMumVx5swZREREYOXKlQCAr776ChMnTuSZEkSkkwyyZfF8M0ezZs2wZs0aBAYGIiMjA7GxsSzGRKSzDKZlIcsyEhMTER4ejh07dsDGxgaTJk3CmDFjePg7EekFvS/IxcXFiI+PR3h4OA4ePAh7e3vMmjULI0aM4KOQiEivlKllIUmSQpIkZU5OjrbzvNFfzyFesWIFVq5ciRYtWqBnz564ceMGFixYgMzMTEycOJHFmIj0jl7s1CvpHGJJkiDLMtzc3BASEoIBAwZwMwcR6SSD2qlX0jnEsizD1tYWqampnB8mIoOgFy2L7OzsEl+/c+cOizERGQydHnt79OgRgoOD8aa2iqGfPkdExkVnby8TEhLg5uaG2bNn45NPPoGVldUrP+c5xERkaHSuIF+7dg39+vWDl5cXrK2tsXfvXuzevRtLliyBo6MjJEmCo6MjlEolzyEmIoOiM1MWT58+xaJFizBp0iTk5+djypQpmDBhAh8aSkR6T6NTFto+y+L48eMICAhAcnIyPD09sWDBAujbuRlERBUldFHv0aNHmDBhAjw8PHDp0iWoVCps27aNxZiIjJKwOeTNmzcjMDAQ2dnZGDZsGGbNmoVatWqJikNEJFylLOr9edtzgwYN0K5dO3h7e6NatWpISkqCUqlkMSYio6f1O+S/bnu+evUqrl69ir59+2LlypVctCMi+j+t3yGHhIS8tu0ZAJKTk1mMiYj+RGtbp8+dO4eRI0fiypUrJf78TduhiYiMlUanLGRZxq5du6BQKODi4oKlS5eiatWqJb6X256JiF5V7pbFX88lVqlUyM/Px6+//opWrVrh888/x6FDhzBlyhRkZWVh8eLFsLa2fuU7uO2ZiOh15dqp17BhQ/nmzZuv9ITNzc1hZWWFBw8ewM3NDWPHjsWgQYNgaWn54j0qlQqhoaHIzs6Gg4MDwsLCuO2ZiIxGWXfqlasgW1hYyAUFBa+9bmlpiU2bNqFLly6QJKl8SYmIDJxWCrIkSSW+WZIkFBcXlyMeEZHxKGtBLlcP+U1jalygIyKquHKNvdWsWZMLdEREWlKusbcGDRpAqVTyXGIiIi3QmfOQiYgMlVZ6yEREpD0syEREOoIFmYhIR7AgExHpCBZkIiIdUd6derkAzmovjl6pA+CO6BA6gtfiJV6Ll3gtXnpPluXqpb2pvE8MOVuW0Q1jIElSCq/FM7wWL/FavMRr8ZIkSWWaF2bLgohIR7AgExHpiPIWZKVWUugnXouXeC1e4rV4idfipTJdi3It6hERkfawZUFEpCNYkImIdAQLMhGRjmBBJiLSESzIREQ64n/g2XWw19ZFWQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x57.6 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "mpl.rcParams[\"legend.markerscale\"] = 1.5\n",
    "mpl.rcParams[\"legend.labelspacing\"] = 1.2\n",
    "mpl.rcParams[\"legend.handlelength\"] = 3.5\n",
    "mpl.rcParams[\"legend.handletextpad\"] = 20\n",
    "\n",
    "figsize = (6,4)\n",
    "# Create figure\n",
    "plt.figure(figsize=figsize)\n",
    "# set axes\n",
    "ax1 = plt.gca()\n",
    "#ax1.autoscale(False, axis='y')\n",
    "# Plot the results\n",
    "#ax1.set_yscale('symlog')\n",
    "#loss_plot = [losses[idc] for idc in idcs]\n",
    "pltstyle=dict(linestyle=None,marker='o')\n",
    "ax1.semilogy(plot_nm, plots_sc, color='black', label = \"p(x) Error\", **pltstyle)\n",
    "# Place the legend\n",
    "lines = ax1.get_lines()\n",
    "labels = [line.get_label() for line in lines]\n",
    "labels = ['' for line in lines]\n",
    "# Turn off all the tick labels\n",
    "ax1.tick_params(labelbottom=False, labelleft=False)\n",
    "#plt.hlines(0.15, 0, 0.15, linestyles=\"--\")\n",
    "plt.xlim([0,0.1])\n",
    "plt.savefig('./Figs/5a-NLSL-pq-noise-vs-error.svg', dpi=600, transparent=True)\n",
    "\n",
    "# Create separate axes\n",
    "legend_figsize = (figsize[0]*2, figsize[1]/5)\n",
    "plt.figure(figsize=legend_figsize)\n",
    "ax = plt.gca()\n",
    "for spine in ax.spines:\n",
    "    ax.spines[spine].set_visible(False)\n",
    "ax.tick_params(labelleft=False, labelbottom=False, left=False, bottom=False)\n",
    "plt.legend(lines, labels, ncol=2, loc='center', frameon=False)\n",
    "plt.savefig('./Figs/5-legend.svg', dpi=600, transparent=True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Figure 5b\n",
    "file_stem = \"./data/Fig5b-NLSL-\"\n",
    "results = pickle.load(open(file_stem +\"results.pickle\", \"rb\"))\n",
    "true_coeffs = pickle.load(open(\"./data/S2-NLSL-coeffs.pickle\", \"rb\"))\n",
    "\n",
    "results = score_p(results, true_coeffs)\n",
    "\n",
    "plot_trials = []\n",
    "plots_sc = []\n",
    "\n",
    "for result in results:\n",
    "    plot_trials.append(result['num_trials'])\n",
    "    plots_sc.append(result['score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "mpl.rcParams[\"legend.markerscale\"] = 1.5\n",
    "mpl.rcParams[\"legend.labelspacing\"] = 1.2\n",
    "mpl.rcParams[\"legend.handlelength\"] = 3.5\n",
    "mpl.rcParams[\"legend.handletextpad\"] = 20\n",
    "pltstyle=dict(linestyle=None,marker='o')\n",
    "\n",
    "\n",
    "figsize = (6,4)\n",
    "# Create figure\n",
    "plt.figure(figsize=figsize)\n",
    "# set axes\n",
    "ax1 = plt.gca()\n",
    "# Plot the results\n",
    "#plt.hlines(0.15, 0, 200, linestyles=\"--\")\n",
    "plt.semilogy(plot_trials, plots_sc, 'o-', color='k')\n",
    "\n",
    "# Place the legend\n",
    "lines = ax1.get_lines()\n",
    "labels = [line.get_label() for line in lines]\n",
    "labels = ['' for line in lines]\n",
    "\n",
    "# Turn off all the tick labels\n",
    "ax1.tick_params(labelbottom=False, labelleft=False)\n",
    "\n",
    "# Save figure\n",
    "plt.savefig('./Figs/5b-NLSL-trials-vs-error.svg', dpi=600, transparent=True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.292884825268925, 0.21452690420673007, 0.15614801349975646, 0.1084693733963122, 0.10656189745688766, 0.10871641123509808, 0.09130282329012424, 0.07928939494973766, 0.08840986759948678, 0.08429827780858089, 0.08558348980051828, 0.08317005961161977, 0.07143127558680834, 0.0647191425895788, 0.06732586341470456, 0.07100803772497244, 0.06735583768612637, 0.06761224931472408, 0.0643614682394475, 0.060984799409459896]\n"
     ]
    }
   ],
   "source": [
    "print(plots_sc)"
   ]
  }
 ],
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