PDE_Solver_Neural/Plots of Question 3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "afd09d24",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Importing-Libraries\" data-toc-modified-id=\"Importing-Libraries-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Importing Libraries</a></span></li><li><span><a href=\"#Parameter-data\" data-toc-modified-id=\"Parameter-data-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Parameter data</a></span></li><li><span><a href=\"#Plotting-Functions\" data-toc-modified-id=\"Plotting-Functions-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Plotting Functions</a></span></li><li><span><a href=\"#Plotting-Examples\" data-toc-modified-id=\"Plotting-Examples-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Plotting Examples</a></span></li><li><span><a href=\"#Time-to-train-plots\" data-toc-modified-id=\"Time-to-train-plots-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Time to train plots</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd1c8c2f",
   "metadata": {},
   "source": [
    "# Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cbda864b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-30T11:28:11.974786Z",
     "start_time": "2022-05-30T11:28:11.965123Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['axes.facecolor'] = 'w'\n",
    "plt.rcParams['font.size'] = 12"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1f1058d",
   "metadata": {},
   "source": [
    "# Parameter data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "68e9ff33",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-30T11:28:12.112221Z",
     "start_time": "2022-05-30T11:28:12.093182Z"
    }
   },
   "outputs": [],
   "source": [
    "output_dir = 'output/'\n",
    "experiments = {}\n",
    "\n",
    "experiments[0] = [5, 5, 10, 0.001, [10], False]\n",
    "\n",
    "experiments[1] = [5, 5, 1000, 0.001, [10], False]\n",
    "experiments[2] = [5, 5, 1000, 0.01, [10], False]\n",
    "experiments[3] = [5, 5, 1000, 0.1, [10], False]\n",
    "\n",
    "experiments[4] = [5, 5, 1000, 0.001, [50], False]\n",
    "experiments[5] = [5, 5, 1000, 0.01, [50], False]\n",
    "experiments[6] = [5, 5, 1000, 0.1, [50], False]\n",
    "\n",
    "experiments[7] = [5, 5, 1000, 0.001, [100], False]\n",
    "experiments[8] = [5, 5, 1000, 0.01, [100], False]\n",
    "experiments[9] = [5, 5, 1000, 0.1, [100], False]\n",
    "\n",
    "experiments[10] = [5, 5, 1000, 0.001, [200], False]\n",
    "experiments[11] = [5, 5, 1000, 0.01, [200], False]\n",
    "experiments[12] = [5, 5, 1000, 0.1, [200], False]\n",
    "\n",
    "experiments[17] = [5, 5, 1000, 0.001, [100, 100], False]\n",
    "experiments[18] = [5, 5, 1000, 0.01, [100, 100], False]\n",
    "experiments[19] = [5, 5, 1000, 0.1, [100, 100], False]\n",
    "\n",
    "\n",
    "experiments[21] = [10, 10, 1000, 0.001, [10], False]\n",
    "experiments[22] = [10, 10, 1000, 0.01, [10], False]\n",
    "experiments[23] = [10, 10, 1000, 0.1, [10], False]\n",
    "\n",
    "experiments[24] = [10, 10, 1000, 0.001, [50], False]\n",
    "experiments[25] = [10, 10, 1000, 0.01, [50], False]\n",
    "experiments[26] = [10, 10, 1000, 0.1, [50], False]\n",
    "\n",
    "experiments[27] = [10, 10, 1000, 0.001, [100], False]\n",
    "experiments[28] = [10, 10, 1000, 0.01, [100], False]\n",
    "experiments[29] = [10, 10, 1000, 0.1, [100], False]\n",
    "\n",
    "experiments[30] = [10, 10, 1000, 0.001, [200], False]\n",
    "experiments[31] = [10, 10, 1000, 0.01, [200], False]\n",
    "experiments[32] = [10, 10, 1000, 0.1, [200], False]\n",
    "\n",
    "experiments[37] = [10, 10, 1000, 0.001, [100, 100], False]\n",
    "experiments[38] = [10, 10, 1000, 0.01, [100, 100], False]\n",
    "experiments[39] = [10, 10, 1000, 0.1, [100, 100], False]\n",
    "\n",
    "experiments[47] = [15, 15, 1000, 0.001, [100], False]\n",
    "experiments[48] = [15, 15, 1000, 0.01, [100], False]\n",
    "experiments[49] = [15, 15, 1000, 0.1, [100], False]\n",
    "\n",
    "\n",
    "experiments[101] = [10, 10, 1000, 0.001, [100], True]\n",
    "experiments[102] = [10, 10, 1000, 0.01, [100], True]\n",
    "experiments[103] = [10, 10, 1000, 0.1, [100], True]\n",
    "\n",
    "experiments[104] = [5, 5, 1000, 0.001, [100], True]\n",
    "experiments[105] = [5, 5, 1000, 0.01, [100], True]\n",
    "experiments[106] = [5, 5, 1000, 0.1, [100], True]\n",
    "\n",
    "experiments[209] = [4, 4, 1000, 0.1, [100], False]\n",
    "\n",
    "experiments[219] = [5, 5, 3000, 0.1, [100, 100], False]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2e215152",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-30T11:28:12.127547Z",
     "start_time": "2022-05-30T11:28:12.113745Z"
    }
   },
   "outputs": [],
   "source": [
    "def plot_error_field(exp):\n",
    "    g_an = np.load(output_dir + 'g_an_' + str(exp) + '.npy')\n",
    "    g_nn = np.load(output_dir + 'g_nn_' + str(exp) + '.npy')\n",
    "    error = g_an - g_nn\n",
    "    Nx = experiments[exp][0]\n",
    "    Nt = experiments[exp][1]\n",
    "    n_iter = experiments[exp][2]\n",
    "    lmb = experiments[exp][3]\n",
    "    num_neurons = experiments[exp][4]\n",
    "    SGD = experiments[exp][5]\n",
    "    \n",
    "    x = np.linspace(0, 1, Nx)\n",
    "    t = np.linspace(0, 1, Nt)\n",
    "    xx, tt = np.meshgrid(x, t)\n",
    "    \n",
    "    fig, ax = plt.subplots(facecolor = 'w')\n",
    "    cont = ax.pcolormesh(xx, tt, error, cmap = 'Spectral',vmax=0.1, vmin = -0.3)\n",
    "    ax.set_ylabel('x')\n",
    "    ax.set_xlabel('t')\n",
    "    fig.colorbar(cont, ax = ax)\n",
    "    plt.title('Error')\n",
    "def plot_absolute_error_field(exp):\n",
    "    g_an = np.load(output_dir + 'g_an_' + str(exp) + '.npy')\n",
    "    g_nn = np.load(output_dir + 'g_nn_' + str(exp) + '.npy')\n",
    "    \n",
    "    error = np.abs(g_an - g_nn)\n",
    "    Nx = experiments[exp][0]\n",
    "    Nt = experiments[exp][1]\n",
    "    n_iter = experiments[exp][2]\n",
    "    lmb = experiments[exp][3]\n",
    "    num_neurons = experiments[exp][4]\n",
    "    SGD = experiments[exp][5]\n",
    "    \n",
    "    x = np.linspace(0, 1, Nx)\n",
    "    t = np.linspace(0, 1, Nt)\n",
    "    xx, tt = np.meshgrid(x, t)\n",
    "    \n",
    "    fig, ax = plt.subplots(facecolor = 'w')\n",
    "    cont = ax.pcolormesh(xx, tt, error, cmap = 'hsv')\n",
    "    ax.set_ylabel('x')\n",
    "    ax.set_xlabel('t')\n",
    "    fig.colorbar(cont, ax = ax)\n",
    "    plt.title('Error')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4759ec5b",
   "metadata": {},
   "source": [
    "# Plotting Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1c00be2c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-30T11:28:12.249409Z",
     "start_time": "2022-05-30T11:28:12.226582Z"
    }
   },
   "outputs": [],
   "source": [
    "def plot_analytical(exp):\n",
    "    g_an = np.load(output_dir + 'g_an_' + str(exp) + '.npy')\n",
    "    Nx = experiments[exp][0]\n",
    "    Nt = experiments[exp][1]\n",
    "    n_iter = experiments[exp][2]\n",
    "    lmb = experiments[exp][3]\n",
    "    num_neurons = experiments[exp][4]\n",
    "    SGD = experiments[exp][5]\n",
    "    \n",
    "    x = np.linspace(0, 1, Nx)\n",
    "    t = np.linspace(0, 1, Nt)\n",
    "    xx, tt = np.meshgrid(x, t)\n",
    "    \n",
    "    fig, ax = plt.subplots(facecolor = 'w')\n",
    "    cont = ax.pcolormesh(xx, tt, g_an, cmap = 'hsv')\n",
    "    ax.set_ylabel('x')\n",
    "    ax.set_xlabel('t')\n",
    "    fig.colorbar(cont, ax = ax)\n",
    "    plt.title('Number of space points: ' + str(Nx) + '\\n' + \n",
    "              'Number of time points: ' + str(Nt) + '\\n')\n",
    "def plot_neural(exp):\n",
    "    g_nn = np.load(output_dir + 'g_nn_' + str(exp) + '.npy')\n",
    "    Nx = experiments[exp][0]\n",
    "    Nt = experiments[exp][1]\n",
    "    n_iter = experiments[exp][2]\n",
    "    lmb = experiments[exp][3]\n",
    "    num_neurons = experiments[exp][4]\n",
    "    SGD = experiments[exp][5]\n",
    "    \n",
    "    x = np.linspace(0, 1, Nx)\n",
    "    t = np.linspace(0, 1, Nt)\n",
    "    xx, tt = np.meshgrid(x, t)\n",
    "    \n",
    "    fig, ax = plt.subplots(facecolor = 'w')\n",
    "    cont = ax.pcolormesh(xx, tt, g_nn, cmap = 'hsv')\n",
    "    ax.set_ylabel('x')\n",
    "    ax.set_xlabel('t')\n",
    "    fig.colorbar(cont, ax = ax)\n",
    "    plt.title('Number of space points: ' + str(Nx) + '\\n' + \n",
    "              'Number of time points: ' + str(Nt) + '\\n')\n",
    "    \n",
    "def plot_solution(exp):\n",
    "    g_an = np.load(output_dir + 'g_an_' + str(exp) + '.npy')\n",
    "    g_nn = np.load(output_dir + 'g_nn_' + str(exp) + '.npy')\n",
    "    \n",
    "    Nx = experiments[exp][0]\n",
    "    Nt = experiments[exp][1]\n",
    "    n_iter = experiments[exp][2]\n",
    "    lmb = experiments[exp][3]\n",
    "    num_neurons = experiments[exp][4]\n",
    "    SGD = experiments[exp][5]\n",
    "    \n",
    "    x = np.linspace(0, 1, Nx)\n",
    "    t = np.linspace(0, 1, Nt)\n",
    "    xx, tt = np.meshgrid(x, t)\n",
    "    \n",
    "    max_value = np.max(np.array([np.max(g_an), np.max(g_nn)]))\n",
    "    min_value = np.max(np.array([np.min(g_an), np.min(g_nn)]))\n",
    "    \n",
    "    fig, ax = plt.subplots(1, 2, facecolor = 'w', figsize = (20, 8))\n",
    "    cont1 = ax[0].pcolormesh(xx, tt, g_an, cmap = 'hsv', vmax = max_value, vmin = min_value)\n",
    "    cont1 = ax[1].pcolormesh(xx, tt, g_nn, cmap = 'hsv', vmax = max_value, vmin = min_value)\n",
    "    fig.colorbar(cont1, ax = ax)\n",
    "    #plt.title('Number of space points: ' + str(Nx) + '\\n' + \n",
    "    #          'Number of time points: ' + str(Nt) + '\\n')\n",
    "    ax[0].set_title('Analytical Solution')\n",
    "    ax[1].set_title('Neural Network Solution')\n",
    "    ax[0].set_ylabel('x')\n",
    "    ax[0].set_xlabel('t')\n",
    "    ax[1].set_ylabel('x')\n",
    "    ax[1].set_xlabel('t')\n",
    "    #plt.rcParams.update({'font.size': 22})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "88bdd553",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-30T11:28:12.264981Z",
     "start_time": "2022-05-30T11:28:12.251438Z"
    }
   },
   "outputs": [],
   "source": [
    "def plot_cost_convergence(expList):\n",
    "    n_iter = 1000\n",
    "    cost_arr = np.zeros([len(expList), n_iter])\n",
    "    \n",
    "    for ind_e, exp in enumerate(expList):\n",
    "        cost_arr[ind_e] = np.load(output_dir + 'cost_arr_' + str(exp) + '.npy')\n",
    "    \n",
    "    fig, ax = plt.subplots(facecolor = 'w')\n",
    "    for ind_e, exp in enumerate(expList):\n",
    "        ax.plot(cost_arr[ind_e], label = exp)\n",
    "        ax.set_ylim(0, 100)\n",
    "    ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d235f82a",
   "metadata": {},
   "source": [
    "# Plotting Examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "731b2578",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-30T11:28:12.449762Z",
     "start_time": "2022-05-30T11:28:12.358139Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "expList = [3, 6, 9, 12]\n",
    "plot_cost_convergence(expList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b27557d3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-30T11:28:13.224644Z",
     "start_time": "2022-05-30T11:28:12.451877Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PRxS9FH4kJo6vmDBI+fv/ExLnjxPuf/dYYTtuxTqvIkeZmDDCdo+dKCjOwsli4viJ2O5X1P/IouKoBMURwJXJ7ODgYFRWVvb5mSsLkp357W9/i8ceewy+vr44evQonnvuOVRVVWHKlCkwmUwIDg526z4cURARuaEHd0+4k1MccWVBsjMzZ86EWq2Gn58fVq1ahVmzZuH48eP3fR8mCiIiN4h668mVBcmuUigUkCQJAKDT6WAwGKyfmc1mXLt2TdZ9mCiIiNwgKlG4siBZkiR0dHTg9u27UTs6OtDZ2QkAuHXrFsrKytDR0YHu7m4UFRXhzJkzmD9/PgAgKSkJ1dXVKC4uRkdHB/bs2YNp06Y5ncgGmCiIiNwi8uCivLw8tLe3IyQkBCkpKTYLklUqFYxGIwCgvr4eAQEB1lFAQECAda1EV1cXtm3bhuDgYGg0Gvzud79DSUmJ9fPg4GAUFxdj69atGDNmDM6dO4ejR4/K6isns4mI3GBZRyGHs3+RWxYk92ZZkGwRGRlpfZTUW3BwMM6fP+/wPvPmzcOlS5ecNdcOEwURkRtceevJ/0E2xAOYKIiI3MBEQUREDg2nTQGZKIiI3HAH8rfw0DzIhngAEwURkRskOF9MN1QwURARuYGPnoiIyKmeYfIbdJh0k4hILEnBREFERI4wUQxfK/GhoEgijjQBUva+LCROVd79x0gTtFW0WSsmjuM1qPI1CIoj6nn1j4I21pk4VkAQUduDD8WH+SOAntED3QjPYKIgInKHAuISqZdjoiAicgcTBREROTQCTBREROSAAsCogW6EZzBREBG5g4+eiIjIoREAVDLrinkJcsAwURARuWMYjSh4FCoRkTssk9lyihMtLS1ISkqCUqmEVqvFkSNH+qxXXV2N+fPnQ6PRQKFQ2HzW2dmJ9PR0aLVaqNVqPPHEEzhx4oT187q6OigUCqhUKmvJzs6W1VWOKIiI3CFwRJGVlQVfX180NDSgqqoKCxcuRGxsrPVsbItRo0Zh6dKlWLt2LV544QWbz7q7uzFx4kScPn0aEREROH78OJYuXYpvvvkGkZGR1nq3bt3CyJGu/epnoiAicoegRGE2m1FcXIzq6mqoVCrExcVh8eLFOHz4MHJycmzqRkdHIzo6GlevXrWLo1QqsWvXLuufFy1ahEmTJuHrr7+2SRTu4KMnIiJ3+ODuZLaM0tjYCL1eby35+fnWMLW1tfDx8UFUVJT1WmxsLGpqau6reQ0NDaitrbUblWi1WoSHh+Oll15CU1OTrFgcURARucOFEUVwcDAqKyv7/MxkMiEoKMjmWlBQENra2txuWldXF1JTU7Fq1SrExMQAADQaDc6fP4/p06ejubkZWVlZSE1NRVlZmdN4HhtRyJ2sAYDr169j0aJFUKvV0Gg02Lhxo6eaSUQkj2XBnZzigEqlQmtrq8211tZWqNVqt5rV09ODtLQ0+Pr6Ijc31+Y+er0eI0eORGhoKHJzc/HHP/7R7t598diIQu5kze3bt/HP//zPyMrKwkcffQQfHx/U1tZ6qpkA9gmKI+iQxC/E7B4rZIdUQRN3onZrFRXnB0FxJgqK41XHa3rb7rHetAutoDmKqKgodHd348qVK3j00UcBAAaDwe53oxySJCE9PR0NDQ04fvw4Ro3qP0tZ3pqSJMlpXI+MKCyTNdnZ2XaTNb0dOnQIEyZMwPr166FUKuHv749p06Z5oplERPJZEsV9vh6rVCqRnJyMHTt2wGw2o6KiAqWlpUhLS7OrK0kSOjo6cPv23YzZ0dGBzs5O6+eZmZm4ePEijh07hoCAAJvvPXfuHC5fvoyenh40Nzdj3bp1iI+Pt3vs1RePJApXJmu++uorREZGYsGCBdBoNIiPj8c333zTZ9z8/Hzr5FDjA2s9EVEfBCUKAMjLy0N7eztCQkKQkpKCAwcOQKfTwWg0QqVSwWg0AgDq6+sREBBgHW0EBAQgOjra+tnBgwdRVVWFsLAw61qJoqIiAHcf6ScmJkKtVuPxxx+Hn58fPvxQ3vk7Hnn05MpkzY0bN/DFF1/gD3/4A5555hns378fzz//PC5dugRfX9v/4hkZGcjIyAAA6HstPiEieqAsbz0JMHbsWJSUlNhdj4iIgMn0j/0/IiMj+31UpNVqHT5GSklJQUpKilvt88iIwpXJmoCAAMTFxWHBggXw9fXFhg0b0NzcjIsXL3qiqURE8ggcUXg7jySKeydrLPqbrJk2bZrd0nQiIq/DRCGWK5M1K1aswFdffYXPP/8cd+7cwVtvvQWNRoNf/OIXnmgqEZE8TBTiyZ2siY6OxgcffIBf/epXGDNmDEpLS/GHP/zBbn6CiGhACdwU0Nt5bB2F3MkaAEhOTkZycrKHWkZE5IYRAJQD3QjP4BYeRETuGEbnUTBREBG5g4mCiIgcssxRDANMFERE7uCIgoiIHGKiICIih0ZA2BYe3o6Jws53A90AW4K2VRbyDx9BPxTetuO0qH8Uiorj5PiCwUnUX5bJeRWP4RwFERE5xEdPRETkkOWEu2GAiYKIyB0cURARkUPDKFHI3hTwzJkzfV6Xe0ISEdGQMgJAwB15xYmWlhYkJSVBqVRCq9XiyJEjfdarrq7G/PnzodFo+jyOwVmc8vJyxMTEYPTo0UhISEB9fb3srsryL//yL9i0aRO6uu4e/X7r1i0sW7YMO3fulBuCiGgI6cHd17nkFMeysrLg6+uLhoYGFBUVITMzs8+jokeNGoWlS5eioKDA5ThNTU1ITk5GdnY2WlpaoNfrsWzZMlk9lZ0oDAYDqqqq8OSTT6KgoABTp07FQw89hP/+7/+WG4KIaAiRICJRmM1mFBcXIzs7GyqVCnFxcVi8eDEOHz5sVzc6Ohrp6el9HvrmLM5nn30GnU6HJUuWwN/fH7t27YLBYMClS5ec9lR2opgwYQJKSkrQ09ODjIwMLFiwAAcPHoRSOUz22SUisiEmUdTW1sLHxwdRUVHWa7GxsX2OKO4nTk1NDWJjY62fKZVKTJkyRdZ9ZCeKqqoq6PV6TJ48GaWlpTh16hRSUlJw69YtF7pCRDRUyE8UjY2N0Ov11pKfn2+NYjKZEBQUZBM5KCgIbW1tLrXGWZz7uY/st56eeeYZvP7660hPTwcAJCQkYN26dZg6dSq++87LVjMTET1wdyB3qXhwcDAqKyv7/EylUqG1tdXmWmtrK9RqtUutcRbnfu4je0Rx/vx5a5IA7g5bCgoK8M4778gNQUQ0hEgAumSW/kVFRaG7uxtXrlyxXjMYDH3OQ9xPHJ1OB4PBYP3MbDbj2rVrsu4jO1FMnjy5z+uLFy+WG4KIaAgRM0ehVCqRnJyMHTt2wGw2o6KiAqWlpUhLS7O/oySho6MDt2/fjdnR0YHOzk5ZcZKSklBdXY3i4mJ0dHRgz549mDZtGmJiYpz2VHaiICKie4lJFACQl5eH9vZ2hISEICUlBQcOHIBOp4PRaIRKpYLRaAQA1NfXIyAgwDoKCAgIQHR0tNM4wN3HX8XFxdi6dSvGjBmDc+fO4ejRo7J6ypXZdmKdV5FF0JLNX4gJEyoiiKAuiXpPTkifIK49YYLijBUUBy0CYoja9VUUx09xPMySKO7f2LFjUVJSYnc9IiICJtM/5kEiIyMhSZLLcSzmzZsn63XY3pgoiIjcYllwN/QxURARuaUH3nVAxoPDREFE5BZxj568HRMFEZFbmCiIiMghzlEQEZFDHFEQEZFDlpXZQx8TBRGRW/jWExEROTR85ig8toWH3KP+7jV37lwoFAp0d3d7oIVERK4Qt4WHt/PYiOLeI/qqqqqwcOFCxMbG9rtzYVFRERMEEXmx4TOZ7ZERhStH/QHATz/9hN27d+P111/3RPOIiNzAEYVQ/R3Rd/r06T7rv/rqq8jMzERYmONt1vLz860nRTWKay4RkQzyDy4a7DwyonDlCL7KykpUVFTglVdecRo3IyMDlZWVqKysRLCw1hIRycERhVByj+Dr6enB2rVrsX//fowcOVAvZG0UFEclJkyKmDBCNk8fJSKIuG20HxEUR9SPsaAd4aFsFhTopoAY3vY7zqv+AT981lF4ZEQh96i/1tZWVFZWYtmyZQgLC8OTTz4JAAgPD8fZs2c90VQiIpk4ohDq3iP63n33XVRVVaG0tBRffvmlTb2goCD8/e9/t/75u+++w1NPPYWvv/4awcF8uERE3oRvPQkn56g/hUKBsLAwa7Ekh9DQUPj6CjpejYhICMuCu/sfUbiyzmzfvn0ICwtDUFAQ1qxZYz0zG7j7mP/e4uPjY53vraurg0KhsPk8OztbVk89NhEg96i/ezk79o+IaOCI28JD7jqzsrIy5OTk4NSpU5gwYQKSkpKwc+dO5OTkAIDN71Kz2YzQ0FAsWbLEJsatW7dcngP22IiCiGhoETNH4co6s8LCQqSnp0On02HMmDHYvn07Dh061GfcTz/9FCEhIZg9e7b7XfwfTBRERG6RnygaGxuh1+utxbL+C+h/nVlNTY3dHWtqahAbG2tTr6GhAc3N9q/KFRYWYuXKlVAoFDbXtVotwsPD8dJLL6GpqUlWT7kpIBGRW+RvChgcHIzKyso+P3NlnVnvupav29raMG7cOOt1o9GI06dPo6CgwHpNo9Hg/PnzmD59Opqbm5GVlYXU1FSUlZU5bT8TBRGRW8S89SR3nVlfdS1f9677/vvvIy4uDpMmTbL5Xr1eD+DuC0K5ubkYP348WltbERgY6LCNfPREROQWMXMUcteZAYBOp4PBYLCpFxoaajOaAO4milWrVjm8r+WRlJwXhpgoiIjc0gPALLP07951ZmazGRUVFSgtLUVaWppd3ZUrV6KgoAAXLlzAzZs3sXfvXqxevdqmzpdffom//e1vdm87nTt3DpcvX0ZPTw+am5uxbt06xMfH2z326gsTBRGRWySMQLes4oycdWYAkJiYiI0bNyIhIQFarRZarRa7d++2iVVYWIjk5GS7x1HXr19HYmIi1Go1Hn/8cfj5+eHDDz+U1VPOURARuUWSlQTkcGWd2fr167F+/fp+Yx08eLDP6ykpKUhJcW/zOCYKIiI3KAQmCm/HRNHL/8VcIXFahEQBkHpZSJhZIoII2kVF+b2YOLHjxcQRRcgOvQBwTVCcHwTEELVbq6gdeET0SSAmCiIi6pcPhB0m4PWYKIiI3KCAuIGSt2OiICJyAxMFERE5pICwQx+9HhMFEZEbOKIgIiKHmCiIiMghV956+vlBNsQDmCiIiNzgyoiCiYKIaBjioyciInKIiYKIiBwaASYKIiJyYAS4hQcRETnABXdEROQQ5yiGsW3rBAUKExQHqUKiTLwgJIwYF8WEmen8BEeP8qkVFMjgvIosDQJidAmIITLOd4LiCDCc5ih4FCoRkRssIwo5xZmWlhYkJSVBqVRCq9XiyJEj/dbdt28fwsLCEBQUhDVr1qCzs9P6WXx8PPz9/aFSqaBSqRAdHW3zveXl5YiJicHo0aORkJCA+vp6WX1loiAicoPIRJGVlQVfX180NDSgqKgImZmZqKmpsatXVlaGnJwclJeXo66uDtevX8fOnTtt6uTm5sJkMsFkMuHy5X8cfNbU1ITk5GRkZ2ejpaUFer0ey5Ytk9VXJgoiIjdY3nqSUxwxm80oLi5GdnY2VCoV4uLisHjxYhw+fNiubmFhIdLT06HT6TBmzBhs374dhw4dktXezz77DDqdDkuWLIG/vz927doFg8GAS5cuyeorERG5yDJHIac0NjZCr9dbS35+vjVObW0tfHx8EBUVZb0WGxvb54iipqYGsbGxNvUaGhrQ3NxsvbZlyxZoNBrMmjULf/7zn/v9XqVSiSlTpvR5n944mU1E5AZX3noKDg5GZWVln5+ZTCYEBdm+mREUFIS2tjandS1ft7W1Ydy4cfjtb3+Lxx57DL6+vjh69Ciee+45VFVVYcqUKTCZTAgODpZ1n944oiAicoOoOQqVSoXW1laba62trVCr1U7rWr621J05cybUajX8/PywatUqzJo1C8ePH3f5Pr15LFHIndUvLCzEjBkzEBgYiPDwcGzcuBHd3d2eaiYRkSyiEkVUVBS6u7tx5coV6zWDwQCdTmdXV6fTwWAw2NQLDQ3FuHHj+m6jQgFJkvr8XrPZjGvXrvV5n948lijkzur//PPPeOutt9DU1IRz586hvLwcb7zxhqeaSUQki2VltpziiFKpRHJyMnbs2AGz2YyKigqUlpYiLS3Nru7KlStRUFCACxcu4ObNm9i7dy9Wr14NALh16xbKysrQ0dGB7u5uFBUV4cyZM5g/fz4AICkpCdXV1SguLkZHRwf27NmDadOmISYmxmlfPZIoXJnVz8zMxOzZs+Hr64uHH34YqampqKio8EQziYhksxxcdL9vPQFAXl4e2tvbERISgpSUFBw4cAA6nQ5GoxEqlQpGoxEAkJiYiI0bNyIhIQFarRZarRa7d+8GAHR1dWHbtm0IDg6GRqPB7373O5SUlFjXUgQHB6O4uBhbt27FmDFjcO7cORw9elRWXz0ymd3frP7p06edfu+ZM2f6HRrl5+db3x5oFNNUIiJZRG7hMXbsWJSUlNhdj4iIgMlksrm2fv16rF+/3q5ucHAwzp8/7/A+8+bNk/U6bG8eSRSuzOrf67333kNlZSXefffdPj/PyMhARkYGAECvUIhpLBGRDNzrSTB3ZttLSkqwefNmfP7559BoNA+6iURELhlOicIjcxSuzOoDwMmTJ/Hyyy/j2LFjmDp1qieaSETkElcW3A12HhlR3Dur/+6776KqqgqlpaX48ssv7eqeOnUKqamp+P3vf4+nnnrKE82ztf//CArUIiTK6/haSBwUiwkjhOPHqLL5mJzXkeW2oDiidjYVtdPvVQExRP03FkVEnwQZTgcXeez1WLmz+tnZ2fjpp5/w7LPPWndAXLBggaeaSUQki8hNAb2dx7bwkDur/8UXX3iqSUREbuMJd0RE5NBwmsxmoiAicsNwOuGOiYKIyA0cURARkUMKCfAV9cacl2OiICJyg0ICRgyTja2ZKIiI3MFEQUREDkmAomugG+EZTBRERO6QIG5Vv5djoiAicgcTBREROdQDwDzQjfAMJgoiInf0YNiMKDy2KSAR0ZBiefQkpzjR0tKCpKQkKJVKaLVaHDlypN+6+/btQ1hYGIKCgrBmzRp0dnYCADo7O5Geng6tVgu1Wo0nnngCJ06csH5fXV0dFAqFdbNVlUqF7OxsWV3liKKXCrwtJI6o7Yen/UFQoGMCYogaZos6At0gKI6ofxWK2pL7B0FxbgqI4W3bjIvayv1hATEEzlFkZWXB19cXDQ0NqKqqwsKFCxEbG2t3Zk9ZWRlycnJw6tQpTJgwAUlJSdi5cydycnLQ3d2NiRMn4vTp04iIiMDx48exdOlSfPPNN4iMjLTGuHXrFkaOdO1XP0cURETuEDSiMJvNKC4uRnZ2NlQqFeLi4rB48WIcPnzYrm5hYSHS09Oh0+kwZswYbN++HYcOHQJw99yfXbt2ITIyEiNGjMCiRYswadIkfP31/Z9pw0RBROSOO7g74pJRGhsbodfrrSU/P98apra2Fj4+PoiKirJei42NRU1Njd0ta2pqEBsba1OvoaEBzc3NdnUbGhpQW1trNyrRarUIDw/HSy+9hKamJlld5aMnIiJ3uPDoKTg4GJWVlX1+ZjKZEBQUZHMtKCgIbW1tTutavm5ra8O4ceOs17u6upCamopVq1YhJiYGAKDRaHD+/HlMnz4dzc3NyMrKQmpqKsrKypy2n4mCiMgdguYoVCoVWltbba61trZCrVY7rWv5+t66PT09SEtLg6+vL3Jzc22+V6/XAwBCQ0ORm5uL8ePHo7W1FYGBgQ7byEdPRETuEDRHERUVhe7ubly5csV6zWAw2D0yAgCdTgeDwWBTLzQ01DqakCQJ6enpaGhoQHFxMUaN6v8MPoVCYf0eZ5goiIjcIQHoklkcUCqVSE5Oxo4dO2A2m1FRUYHS0lKkpaXZ1V25ciUKCgpw4cIF3Lx5E3v37sXq1autn2dmZuLixYs4duwYAgICbL733LlzuHz5Mnp6etDc3Ix169YhPj7e7rFXX5goiIjcYVlwJ2AdRV5eHtrb2xESEoKUlBQcOHAAOp0ORqMRKpUKRqMRAJCYmIiNGzciISEBWq0WWq0Wu3fvBgDU19fj4MGDqKqqQlhYmHWtRFFREQDg+vXrSExMhFqtxuOPPw4/Pz98+OGHsrqqkOSMOwYBvUKBvqeKXPOloP8aXreOYq+AGA0CYgBArPMqsoj6j8x1FP0T1RZRxooJo394Rr+Ty7JjhCtQ+YrMup/c//0GEieziYjcwU0BiYjIISYKIiJyaBhtCshEQUTkDo4oiIjIISaK4et/KQa6BcOAcaAbQIOWqLewROwey4OLiIjIIY4oiIjIIU5mExGRQ8NoROGxLTxEHPVHROQ1BB6F6u08lijuPeqvqKgImZmZfR7MYTnqr7y8HHV1dbh+/Tp27tzpqWYSEcnjwsFFg51HEoWoo/6IiLwGRxRiPaij/oiIBgwThViijvrrLT8/33oGbaPgNhMROTSMEoVH3noSfdSfRUZGBjIyMgDc3WaciMhjXHnrafSDbMiD55ERhcij/oiIvIKgE+4GA48kCpFH/REReQW+9SSeiKP+iIi8hsA5ClHrzJzFKS8vR0xMDEaPHo2EhATU19fL7OsQMQOQJBYWFhYZZcaMGff/O0cBSfKVV5zdb/ny5dLSpUultrY26ezZs1JgYKBUXV1tV+/kyZNSSEiIVF1dLbW0tEhz5syRNm3aJCtOY2OjFBgYKH388cdSe3u7tGHDBmnmzJmy+goX/rt4NSYKFhYWuUVIohB0P5PJJI0aNUq6fPmy9dqKFStsEoBFSkqKtGXLFuufP//8cyk0NFRWnIMHD0pPP/20zX39/f2lixcvOu3rkNnrqW7cOOgjIx3WaWxsRHBwsGca9ACxH96F/fAucvpRV1d33/fRzJ8PfVOTrLrt7e3Q6/XWP9/7xmZ/68xOnz5tF6empgbPP/+8TT3LOjOj0egwTu81akqlElOmTEFNTQ1iYmIctn/IJIomGX9her0elZWVHmjNg8V+eBf2w7t4qh8nT54UEkfUOjNncUwmk10C7e8+vXlsMpuIiOyJWmfmLI4r9+mNiYKIaACJWmfmLE7v7zWbzbh27Vqf97HjdBZjCDl48OBAN0EI9sO7sB/eZTD2Y9myZdLy5cslk8kk/ed//me/bz2dOHFCCg0NlWpqaqSWlhYpISHBZtLbUZwff/xRCgwMlD799FOpvb1d2rhx4/B764mIaLBqbm6Wnn/+eWn06NHSxIkTpaKiIkmSJKm+vl5SKpVSfX29te6bb74phYSESGq1Wlq9erXU0dHhNI7Fn/70Jyk6Olry9/eX5syZI3377bey2qeQJElyZ7hERETDA+coiIjIISYKIiJyaEgliqFyLrfcfhQWFmLGjBkIDAxEeHg4Nm7ciO7ubg+3tn+u/H1YzJ07FwqFYtD24/r161i0aBHUajU0Gg02btzowZY6JrcfkiRh27ZtePjhhxEUFIT4+Pg+DxkbKLm5udDr9fDz83O6Yag3/5wPKvc5B+NVRO2XMtDk9iMvL086c+aM1NnZKd24cUP65S9/Kb322msD0OK+ye2HxQcffCDNnj1bAiB1dXV5sKWOye1HZ2enNHnyZOnNN9+UTCaT1N7eLhkMhgFocd/k9uOjjz6Sxo8fL127dk3q7u6WNm/eLD3xxBMD0OK+FRcXS7///e+lX/3qV9KqVav6reftP+eDyZBJFKL2SxlorvSjtzfffFNatGjRg2yebK7249atW9Kjjz4q/eUvf/GqROFKPw4ePCjFxcV5snmyudKPnJwcacmSJdY/V1dXS35+fh5ppyu2bt3qMFF488/5YDNkHj0NlXO5XelHb2fOnJG3eMYDXO3Hq6++iszMTISFhXmqibK40o+vvvoKkZGRWLBgATQaDeLj4/HNN994srn9cqUfy5cvx9WrV1FbW4uuri4UFhYiMTHRk80Vwpt/zgebIZMoHtS53J7mSj/u9d5776GyshIbNmx4kM2TzZV+VFZWoqKiAq+88oqnmiebK/24ceMGjh49inXr1uHvf/87Fi5ciOeffx63bw/8ocmu9GP8+PGYPXs2oqOjERAQgE8++QT79u3zVFOF8eaf88FmyCSKB3Uut6e5sx9LSUkJNm/ejBMnTkCj0TzoJsoitx89PT1Yu3Yt9u/fj5EjvW+PSlf+PgICAhAXF4cFCxbA19cXGzZsQHNzMy5evOip5vbLlX7s3r0b58+fx3fffYeOjg7s3LkTc+fOxc8//+yp5grhzT/ng82QSRRD5VxuV/oB3N3B8uWXX8axY8cwdepUTzXTKbn9aG1tRWVlJZYtW4awsDA8+eSTAIDw8HCcPXvWo23uiyt/H9OmTYNCofBk82RzpR8GgwHLli1DeHg4Ro4cidWrV+PmzZu4cOGCJ5t837z553zQGehJEpFE7Zcy0OT2o7y8XBo7dqx0+vTpAWilc3L60dPTI33//ffW8te//lUCIN24cUPq7OwcoJbbkvv3cenSJSkgIED605/+JHV3d0v//u//Lk2ePHnQ9WPXrl3SrFmzpB9++EG6c+eO9P7770ujR4+Wbt686flG96Grq0tqb2+XNm/eLK1YsUJqb2/v8+UHb/85H0yGVKIQtV/KQJPbj/j4eMnHx0dSKpXWkpiYOJBNt+HK34fFt99+61VvPUmSa/0oLi6WpkyZIqnVamnOnDkOXwf2NLn9aG9vl9auXSuFhYVJarVaeuKJJ6QTJ04MZNNt7Ny5UwJgU3bu3Dnofs4HE+71REREDg2ZOQoiInowmCiIiMghJgoiInKIiYKIiBxioiAiIoeYKIiIyCEmCiIicoiJgoasyMhIfP755wPdDKJBj4mCiIgcYqKgISktLQ1GoxHPPfccVCoVXn/99YFuEtGgxS08aMiKjIzEu+++i3nz5g10U4gGNY4oiIjIISYKIiJyiImChixvPUSIaLBhoqAhKzQ0FNevXx/oZhANekwUNGRt2bIFe/fuxUMPPYQ33nhjoJtDNGjxrSciInKIIwoiInKIiYKIiBxioiAiIoeYKIiIyCEmCiIicoiJgoiIHGKiICIih5goiIjIof8PJeQ0jwYm7BYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "expid = 49\n",
    "plot_error_field(expid)\n",
    "plot_absolute_error_field(expid)\n",
    "plot_analytical(expid)\n",
    "plot_neural(expid)\n",
    "plot_solution(expid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c0385f97",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-30T11:28:13.484618Z",
     "start_time": "2022-05-30T11:28:13.226304Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x2341270dca0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JkqQhmH/kh+Uvniv/pjbPeKrUdcdNl9+aaC7LdxEdPjRV+tqDL5R/K3v0aPn7rlk7N5L7zs/bFTcuAlhd/kc9sUxOJUmSJKli9pwWZ3IqSZIkSRVqLYg06ijGj8mpJEmSJFUp3Oe0DJNTSZIkSaqYq/UWZ3IqSZIkSRVyn9NyXENKkiRJkioUwPRU9jwqu1/EiRFxX0TMRsTeiLi4R/n3R8TjEbE/Iu6IiLUdz90fEQcjYqZ9PFxZoD2YnEqSJElSxab6OCp0K3AY2AJcAtwWEdsWKxgRFwDXAucDpwKnAR/uKva+zNzYPl5fbahLMzmVJEmSpApFtIb19jqquVdsAC4Crs/MmczcBXweuHSJSy4Dbs/M3Zn5LPAR4PJKghmQyakkSZIkVWwqeh/AyRHxYMdxVYlbnQHMZeaejnMPAYv2nLbPP9RVdktEnNRx7qMRsS8iHoiI80rEVIoLIkmSJElShQL63UpmX2ZuH/B2G4H9Xef2A8f3WX7h6+OBp4EPAt+mNUz4vcAXIuKczPzugHH2ZM+pJEmSJFUoIlk91fvor664PyJyiWMXMANs6rpsE3BgiSq7yy98fQAgM7+VmQcy81Bmfhx4AHhXn9/6QOw5lSRJkqSKVbXPaWaet9zz7Tmn0xFxemZ+p336bGD3Epfsbj//mY6yT2Tm00uFQKszeOjsOZXUOEWWQ4+I0yLiTyLiQHtuxM11xipptGwvJDXRwj6ndSyIlJmzwL3AjRGxISLOBS4E7l7ikruAKyLizIjYDFwH3AkQESdExAURsS4ipiPiEuDNwFcqCbYHe05HaMPq7t73Zjv02Ph9ljF/Yi0f8lRq1fiFPAydy6GfA3wxIh7KzGM+AYyINcC/b5f/H4E5WosCjMSR/7LUB46Dmz74/NDqHoaDLwzxn5fDw9nUfH5+OH9883PD+6OeG9L+7vM5Vg1RY9uLuR/MDLP6JU0dKnff6TXl/27nB/hdPHq0/PuLga49Uv7a6en50tfODbFNWPKeA/x8RnXtSlBVz2mfrgHuAJ6kNW/06oV2MCK20ppDemZmPpqZX25/OPc1YD3wx8AN7XpWAzcBb6DVTv4NsCMza9nr1ORUUqN0LId+VmbOALsiYmE59Gu7il8O/CAzb+k491e1BCpp5GwvJDVXEDUOUs3MZ4AdSzz3KK1FkDrP3QLcskjZp4A3DiHEvtT2ijnsRlKfiiyH/ibgkYj4UrutuD8i/lEtUUpqgqG0FxFx1cK2DrPPjteoBUnNERE9Dx2rznGancNuLgFui4iX/OPRMezmq8BPAKcAn6gxTkmjVWQ59FNoLXH++8CrgC8Cn2u3I8fwzaa0Ig2lvcjMnZm5PTO3b9g8XlNwJDVDBEzFqp6HjlVLctox7Ob6zJzJzF3AwrCbbpfTHnaTmbOZeTAzHXYjTY4iy6G/AOzKzC9l5mHgY8BJwE92F/TNprQiDaW9kKTBtYb19jp0rLpeEYfdSOrXHtrLoXecW2o59L+itby5pMlkeyGpsaKP/3SsupJTh91I6kvB5dA/AbwpIt4eEauAXwf2Af+lrngljY7thaTmCiKmeh46Vl2viMNuJBVxDa2lzZ8E7qG9HHpEbI2ImfaS6LSXNf9F4N8Cz9J6U/rP2m2HpMlgeyGpcQJ7TsuoayuZHw+7yczvtM8tN+zm3JriktRASy2HvsRS6PfS6jmRNIFsLyQ1lQseFVdLz6nDbiRJkiRNDhdEKqPOV8RhN5IkSZJWvMB9Tsuoa1ivw24kSZIkTYxJ6hmN1upO59FaO6h0p2JtyakkSZIkTYbJWvAoM+cj4nOZudhuLH2bnHRekiRJkmoygVvJfD0i3jRIBfacSpIkSVKFImISV+vdC3wpIj4H/D2QC09k5r/upwKTU0mSpCHII/MjunG5+85n7zJLmRvk2rnyQx/nB7h2EFOryn/Dqwa4dtzM5+QMa13MJM05bVsPfLb99Skd5/v+pTc5lSRJkqSKTdKcU4DM/KVB6zA5lSRJkqRKxUqcU9pTRJwO/ALwauAx4J7M/E6/10/eKyZJkiRJQxZ9/LeSRMS7gb8E3gA8A7weeDAi/lm/dZicSpIkSVKFgnpX642IEyPivoiYjYi9EXHxMmXPioivRMS+iHjJfNAidXX5HeDCzLw4M38rMy8BLmyf70vfr0hEvHmJ87/Qbx2SVj7bCkn9sr2QtHIFU6zqeVToVuAwsAW4BLgtIrYtUfYI8Bngigrq6nQK8I2uc7s4dnGkZRVJ1/84In4vIlYDRMQJEfFp4MMF6pC08tlWSOqX7YWkFSsieh4V3WcDcBFwfWbOZOYu4PPApYuVz8yHM/N2YPegdXX5T8D/0nXuN9rn+1JkQaSzgX8H/EVE/AHwIeBPgZ8qUIeklW9y24qpIc4dmRrO+nVH5g8Ppd5hmh/S6zw9PZxtPwbZcmJUBtlSpKDJbS8krXz9taUnR8SDHY93ZubOgnc6A5jLzD0d5x4C3lKwnkHruhr4QkT8Gq19Tl8DzAJ9zznt+91OZv4gInYA3wJ2Ardn5v/c7/WSJoNthaR+2V5IWtH623N4X2ZuH/BOG4H9Xef2A8fXVVe0JtC+itaHi+e0v/4B8K3MPNLvzYvMOT0HeBD4Hq2JrW+LiHsi4oR+65C08tlWSOqX7YWklStbyWmvow8RcX9E5BLHLmAG2NR12SbgQInAS9WVmfPA5xaGAmfmZ9r/7zsxhWJzTv8DcEtm7sjMP6E1FOdHwF8XuaGkFc+2QlK/bC8krUyZMH+099FXVXleZsYSx88Ce4Dp9h6jC85mkTmlfRikrq9HxJtK3PPHikxiemNmfm/hQWbOAlcU2bdG0kSwrZDUL9sLSSvX/HDWMuiWmbMRcS9wY0RcSWtY7YXAzyxWPlorMa0F1rQfr2tVk4eK1tVlL/CliPgcrTmnP551m5n/up/vpe+e085/PLrOf77fOiStfLYVkvpleyFpRatoWG+frgHWA08C9wBXZ+ZugIjYGhEzEbG1Xfa1wAu82Bv6AvBwP3X1sB74LK2k9BRaCyK9hgJbyQxn+UdJkiRJmlSZVSefPW6XzwA7lnjuUVoLHS08fgRYcun75epaSkSsotVb+tuZeajItZ1MTiVJkoYgjls9mhuvXlfqsiM17iHUadUA2y1Nry7/5n9+vvy2VINsPTU1Vf77LXvtqiHudKZl1JicjlpmzkXENbS2BCutyIJIkiRJkqSesjXntNexstwF/MtBKrDnVJIkSZKqlPS9Gu8K8t8AvxIRv8lLF0R6cz8VmJxKkiRJUqXqnXPaEH/YPrr1PR7d5FSSJEmSKpY5N+oQahERv5+Zv5qZH28/viIzb+94/o9pDfntyTmnkiRJklSlnKg5p5d3Pf7fux7/fL8V2XMqSZIkSVWbnGG93etB93q8pNp6TiPixIi4LyJmI2JvRFzcxzVfjYiMCJNoSZIkSWOiPee017EydM8p7fV4SXUmfbcCh4EtwDnAFyPioczcvVjhiLgEe3YlSZIkjZ2cpNV6pyPirbzYQ9r9eFXfFVUd2WIiYgNwEXBWZs4AuyLi88ClwLWLlH8ZcAPwL4Bv1hGjpOaIiBOB24F/CuwDfiszP9Xjmq8CbwVWZ+bE/GsgSZIaKFlJc0p7eRK4o+Px012Pn+y3orp6Js8A5jJzT8e5h4C3LFH+d4DbgMeXqzQirgKuAjjhlSdVEKakhnCkhaS++GGWpMZaOcN2l5WZp1ZVV11zTjcC+7vO7QeO7y4YEduBc4E/6FVpZu7MzO2ZuX3D5k2VBCpptDpGWlyfmTOZuQtYGGmxWPmFkRa/WV+Ukhqk88OsS4DbImLbUoX9MEtSPSZqzmll6kpOZ4Du7HETcKDzRERMAf8G+DU/yZQm1lIjLZZ6s9nXSAtJK48fZklqNJPTwupKTvfQmhh7ese5s4HuIXqbgO3ApyPiceAv2ue/HxE/N/wwJTXAUEZaRMRVEfFgRDw4++zzlQQqaeSG8mGW7YWkgWXC3NHeh45RS3KambPAvcCNEbEhIs4FLgTu7iq6H3gVrTlm5wDvap//x8C36ohV0sgNZaSF0wCkFclpQ5Kay57Twuqcc3ENrVWbnqS1gtPVmbk7IrYC3wbOzMxH6fg0MyLWtb98YiUO8/3e8y+MOoRCVm0ZdQTFrVs/N+oQCptdcb/phf14pEVmfqd9rtdIC3hxmfLvR8R7MvMbtUTbYe1PvWJodefG4Sz6dnj+h0Opd9364f0iH900nM9Vj9twZCj1rlkzvHZoVd/bmhdzeHzeL5X6MKvdZgzd9Ks2lL94qvzveazfXOq6Fw4/V/qec33vYvhS09Plf+HWrB3Nv/OD3HfN2vLf7/Tq8fnjBJgf4Pdi7GVO0mq9laktOc3MZ4Adi5x/lNYnn4td8wgv7o8jaQJk5mxELIy0uJLWKIoLgZ/pKrow0mLBa4A/pzXS4qkaQpU0emP7YZakCTDR2Xk5dc05laQirgHW0xppcQ8dIy0iYiYitmbL4wsHLyakT2Tm4VEFLqk+ThuS1Gjz872PikTEiRFxX0TMRsTeiLh4mbJnRcRXImJfRLwkg46I+yPiYPs910xEPFxZoD24lLqkxnGkhaQCnDYkqXnqH9ZbZI/4I8BnaE13+OwS9b0vM//PIcS5LJNTSZI0tvwwS1IjJXC0njnRHdtqnZWZM8CuiFjYVuval4SW+TDwcES8rpYAC3BYryRJkiRVKvsd1nvywtZV7eOqEjcruq1WPz7aHvb7QEScN0A9hdhzKkmSJElV629BpH2ZuX3AO/W9rVafPkhrWsRh4L3AFyLinMz8bvkQ+2PPqSRJkiRVKalsQaT2AkW5xLGLPrfV6jv0zG9l5oHMPJSZHwce4MWF5IbKnlNJkiRJqlRWtpVMZp633PPtOaf9bKtVOgRqmqdvz6kkSZIkVanCntOet+p/Wy0AomUdsKb9eF1ErG1/fUJEXNA+Nx0RlwBvBr5SSbA92HMqSZIkSZVKcq6e1XrbFt1WC2CRrbVeC/xdx7UvAHuBU4HVwE3AG4A54G+AHe0VfofO5FSSJEmSqrTQc1rX7ZbYVqv93DFbay23nVZmPgW8sfoI+2NyKkmSJElVqzE5XSlMTiVJkiSpUtUtiDRJTE4lSZKGIF776vIXT68qfWlu2FzqutnZUrtODGx6dfk38MdtOFL62vn58ouPrl9/tPS1a9aWn4e4uuRSplMDLIE6N0B+dXiSOw5rHta7UpicSpIkSVKVMuForQsirQgmp5IkSZJUNXtOCzM5lSRJkqSqmZwWZnIqSZIkSVVKF0Qqw+RUkiRJkqpmz2lhJqeSJEmSVCVX6y3F5FSSJEmSKuVqvWWYnEpSReKfvHFodT9z6PtDqffgXPm9FJdz/Kbyew/2snaAPQKXc9yG8vsWLmf92vH75Pyg76ckaTAJOcgmsRPK5FSSJEmSquaCSIWZnEqSJElSlTLBntPCTE4lSZIkqUIJpD2nhZmcSpIkSVKV5oHDTuAvaqquG0XEiRFxX0TMRsTeiLh4iXKXRcRfRsTzEfH9iLg5IkyiJUmSJI2JJOd7HzpWbckpcCtwGNgCXALcFhHbFil3HPDrwMnATwPnAx+oKUZJkiRJGkzSmnPa69AxaumRjIgNwEXAWZk5A+yKiM8DlwLXdpbNzNs6Hj4WEZ8E3lpHnJIkSZX5B2eUvjRWrS597f4jT5a67vnDdfZZvGjd+vLbOM2PaKemNWvK33iQ73dNyR/Rqih9y4EWnD0yfjtpVavGntGIOBG4HfinwD7gtzLzU0uUvQz4VeB04HngU8D/lplHi9ZVtbpaoTOAuczc03HuIWCxntNubwZ2L/ZERFwVEQ9GxIOzzz5fQZiSJEmSNKD2Pqe9jgr1O0oVeo9ULVJXpepKTjcC+7vO7QeOX+6iiPglYDvwscWez8ydmbk9M7dv2LypkkAlSZIkaTDZ6trvdVSgY5Tq9Zk5k5m7gIVRqi+NLPO2zPxGZh7OzMeATwLnlqmranUtNDQDdGePm4ADS10QETuA3wXenpn7hheaJEmSJFUoIfsb13xyRDzY8XhnZu4seLelRqm+pc/rO0eqDlrXQOpKTvcA0xFxemZ+p33ubJYervsO4A+B/zYz/7qmGCVJkiRpcAsLIvW2LzO3D3i3UqNU4ZiRqlcOWlcVahnWm5mzwL3AjRGxISLOBS4E7u4uGxFvo9W1fFFm/nkd8UlqFreekiRJ462PlXr7nHMaEfdHRC5x7KLEKNV2vTtojVR9Z8dI1VJ1VaXOZdmuAdYDTwL3AFdn5u6I2BoRMxGxtV3ueuBlwJ+2z89ExJdqjFPS6Ln1lCRJGl9JZfucZuZ5mRlLHD9LxyjVjsuWHKUKx4xUfXfXSNXCdVWptuQ0M5/JzB2ZuSEzty4sR5yZj2bmxsx8tP34rZk53T63cLyzrjgljVaRifjLTeiXNBkcaSGpsebmex8VKDJKFZYfqVq0rqqNZkMrSVqaW09JKsKRFpIaJyvsOe3ToqNUAUqMVF2yrmHzE0NJTTPo1lNXLvZ8e+W7nQCnbDutvl2xJQ1Nx0iLszJzBtgVEQsjLa7tLJuZt3U8fCwiPgm8tbZgJU2WTOhvtd6KbpfPADuWeO5RWu+vFh4v2/YtV9ewmZxKahq3npLUr0G2PFh2pAVwFcAJrzxp0BglTajsc8EjvchhvZKaptBE/GUm9Eta+QYdafGxxZ7PzJ2ZuT0zt2/Y3P1ZmST1IYH57H3oGPacSmqUzJyNiIWJ+FcC59CaiP8z3WU7JvT/c7eekiaSIy0kNVRWtuDRJLHnVFITufWUpH440kJSM9W/INKKYM/pCP3ZY2tHHUIh/925fsBch4Nzo45g9JaaiF90Qn/dnt68YWh1/+dnhvMP2FzGUOo96aSDQ6kX+t6zvLB1q4ZT74Yh/ks7NZwfH7NHh1Nv1Zo+0uKFTeWHBGeW73F5fPaZUtc9e2h96XsOYt368r9wU1PlG4RBrp1eXf7nM0hbU/baVQO0FYO0uRP9niYhj5h8FmXPqSRJGmeOtJDUSDkfPQ8dy55TSZI0tsZ1pIWklS0T5p1yWpjJqSRJkiRVLIc0dWYlMzmVJEmSpIoNMHV8YpmcSpIkSVKFMp1TWobJqSRJkiRVbO6oyWlRJqeSJEmSVKV0WG8ZJqeSJEmSVKHEBZHKMDmVJEmSpIrZc1qcyakkSZIkVSlh3gWRCjM5lSRJkqSK2XNanMmpJEmSJFUoE+ZdrbewqVEHIEmSJEkrS7T2Ou1xVHa3iBMj4r6ImI2IvRFx8TJlL4uIv4yI5yPi+xFxc0RMdzx/f0QcjIiZ9vFwZYH2YHIqSZIkSVVKmJ/vfVToVuAwsAW4BLgtIrYtUfY44NeBk4GfBs4HPtBV5n2ZubF9vL7SSJfhsF5JkqQh+Jvnnil97ZEBFlJ54kfrSl333OHSt2TVAB1AJ6wpf+2R6bnS1w4S89QA164ZoGto9apy1w0S71yWv/Zg+R/P2Evqm3MaERuAi4CzMnMG2BURnwcuBa59SWyZt3U8fCwiPgm8tZZge7DnVJIkSZKqlJDz0fOoyBnAXGbu6Tj3ELBUz2m3NwO7u859NCL2RcQDEXHe4CH2x55TSZIkSarYXH8LIp0cEQ92PN6ZmTsL3mojsL/r3H7g+F4XRsQvAduBKztOfxD4Nq1hwu8FvhAR52TmdwvGVZg9p5IkSZJUoczWsN5eB7AvM7d3HC9JTNsLFOUSxy5gBtjUddkm4MByMUbEDuB3gXdm5r4XY89vZeaBzDyUmR8HHgDeNcjr0a/aktOCK0i9PyIej4j9EXFHRKytK05JkiRJGlRVq/Vm5nmZGUscPwvsAaYj4vSOy87mpUN1fywi3gH8IfDuzPzrXiEAteyLU2fPaV8rSEXEBbQm7p4PnAqcBny4vjAlSZIkaTB99pwOfp/MWeBe4MaI2BAR5wIXAncvVj4i3gZ8ErgoM/+867kTIuKCiFgXEdMRcQmtOalfqSba5dWSnHasIHV9Zs5k5i5gYQWpbpcBt2fm7sx8FvgIcHkdcUqSJEnSwOpdEAngGmA98CRwD3B1Zu4GiIit7f1Kt7bLXg+8DPjTjr1Mv9R+bjVwE/AUsA/4FWBHZtay12ldCyIttYLUWxYpuw34XFe5LRFxUmY+3VkwIq4CrgI44ZUnVRuxJBV0x8Pjt8bcqhhgj4BlnNpzCQY12SRv/yBJVUgq38d0+ftlPgPsWOK5R2ktmrTweMltYzLzKeCNVcfXr7reSRVZQaq77MLXxwPHJKftCcM7AU7Zdtpw3mFJkiRJUhEJR4+YnhRVV3JaZAWp7rILXy+72pQkSZIkNUEmzM2bnBZV14JIRVaQ2t1+rrPcE91DeiVJkiSpqebneh86Vi3JacEVpO4CroiIMyNiM3AdcGcdcUqSJEnSoDJhfj57HjpWnVvJLLqCVPfqUZn5ZeBm4GvA3vZxQ41xSpIkSdJA7DktrralJZdaQap79aj2uVuAW+qJTJIkSZKqs9BzqmLGb98DSZKkMfBnj60ddQi1Wbeq/JvwdasqDESNcrjGrVQaJ9PVekuoc1ivJPUlIk6MiPsiYjYi9kbExcuUfX9EPB4R+yPijoiYnHeDkiSpkRKYm+996Fgmp5Ka6FbgMLAFuAS4LSK2dReKiAuAa4HzgVOB04AP1xemJEnSIhLm57LnoWOZnEpqlIjYAFwEXJ+ZM5m5C/g8cOkixS8Dbs/M3Zn5LPAR4PLagpUkSVpEAvPzvQ8dy+RUUtOcAcxl5p6Ocw8BL+k5bZ97qKvclog4qbtgRFwVEQ9GxIOzzz5facCSRsdpAJKayq1kijM5ldQ0G4H9Xef2A8f3UXbh65eUzcydmbk9M7dv2LypkkAlNYLTACQ1TiYcPZI9Dx3L5FRS08wA3dnjJuBAH2UXvl6srKQVxmkAkpoq0wWRyjA5ldQ0e4DpiDi949zZwO5Fyu5uP9dZ7onMfHqI8UlqjqFMA5CkKrggUnEmp5IaJTNngXuBGyNiQ0ScC1wI3L1I8buAKyLizIjYDFwH3FlbsJJGbSjTAJyjLmlg6YJIZZicSmqia4D1wJPAPcDVmbk7IrZGxExEbAXIzC8DNwNfA/a2jxtGFLOk+g1lGoBz1CUNKunda2rP6UtNjzoASeqWmc8AOxY5/yit3o/Oc7cAt9QTmaSG+fE0gMz8Tvtcr2kAn+ko5zQAScOR9oyWYc+pJEkaS04DkNRUmXDkSPY8dCyTU0mSNM6cBiCpcZJ69zktuOfzeyPi4faez09GxMcjYlOZuqrmsF5JkjS2nAYgqZES5udqvWPnns/nAF+MiIcyc7FpDg8A52bmvojYCPwfwE3Ar5aoq1L2nEqSJElShersOS245zOZ+feZua/j1BzwujJ1VS0yV8ZY54h4itYQnWE4GdjXs1RzjFu8MH4xj1u8MLyYX5uZLx9CvUNTsL1o+s+66fFB82M0vsEUiW+ltRdN/9ksMM7qjEOMsDLiHLv2olNEfJnW99fLOuBgx+Odmbmz4L1+CviPmbm+49wHgLdk5ruXuOZngS/SWrn8R8A/z8z/u0xdVVoxw3qH+csbEQ9m5vZh1V+1cYsXxi/mcYsXxjPmYSnSXjT9dWt6fND8GI1vME2Pb1DLtRfj8r0bZ3XGIUYwzibIzHfUeLsiez4D0O4RfVlEvBr4n4BHytZVJYf1SpIkSVJDRcT9EZFLHLsotufzMTLzMeDLwB+1T5WuqworpudUkiRJklaazDxvuefb80T73fN5MdPAP2x/XWT/6MrZc9qfQuO+G2Dc4oXxi3nc4oXxjLkJmv66NT0+aH6MxjeYpsc3TOPyvRtndcYhRjDOiVJwz2ci4pL2dlsREa8Ffhv4D2XqqtqKWRBJkiRJkiZRRJwI3AH8PPA0cG1mfqr93Fbg28CZmfloRPw2cBmwGXgW+FPgtzLz6V51Df37MDmVJEmSJI2aw3olSZIkSSNncipJkiRJGrmJTE4j4sSIuC8iZiNib0RcvEzZ90fE4xGxPyLuiIi1ZeqpK+aIuCwi/jIino+I70fEzREx3fH8/RFxMCJm2sfDI4738oiY64hnJiLOK1pPzTH/2654D0XEgY7n63qN3xcRD7bvf2ePso34PW6iqtqDJsTY6+9/1PF1XfPVaC2B36j4IuK0iPiTiDgQEfsi4uamxBctN0XEY+3fwfsjYlsN8VXS1owr24j64+y6pra2ouOejW4zisQ4qnajfe+JbjtUzkQmp8CtwGFgC3AJcNtif6gRcQFwLXA+cCpwGvDhovXUGTNwHPDrwMnAT7dj/0BXmfdl5sb28foRxwvwzY54Nmbm/SXrqSXmzPyXnfEC9wD/V1exOl7jHwA30ZqwvqSG/R43UVXtwchjpL+//1HGB7RWCaTercz6/RmvAf498FXgJ4BTgE80JT7gPcAvAz8HnAh8k3pWT6yqrRlXthHVaXpbsaDpbUbfMTK6dgNsO1RGZk7UAWyg9cd8Rse5u4HfXaTsp4Df6Xh8PvB40XrqjHmRa38D+ELH4/uBKxv0Gl8O7Kr6+67rNW5fdwB4S52vcVcMNwF3LvN8I36Pm3hU1R40JcZFrj3m778J8QEvo7WH2puABKabEh9wFfCNBv8OfhD4TMfjbcDBGmMt3daM62EbMbo4624rSv7Ma28zSsQ40najfc+Jazs8yh+T2HN6BjCXmXs6zj1E64+127b2c53ltkTESQXrGdQg93ozL90096PtoScPRMcQ2goVjfen2vHsiYjrO4bujMNrfBHwFPD1rvPDfo2LaMrvcRNV1R4MU9V//1UrGt/vALcBjw85rgVF4nsT8EhEfKn993t/RPyjBsX3R8DrIuKMiFhNaxuALw85viJG9TcyTLYR1Wl6W7Gg6W1G0Rib3m7Aymw7VNIkJqcbgf1d5/YDx/dRduHr4wvWM6hS94qIXwK2Ax/rOP1BWsMlXk1r4+MvRMQ/rC5UoFi8XwfOAl5BK9H7BeB/LVHPoMre6zLgrszs3JOpjte4iKb8HjdRVe3BMFX59z8MfccXEduBc4E/GHJMnYq8fqcA7wV+H3gV8EXgc+2he02I74fAN4CHgRdoDdd7/xBjK2pUfyPDZBtRnaa3FQua3mbAymo3YGW2HSppEpPTGWBT17lNtIZm9iq78PWBgvUMqvC9ImIH8LvAOzNz38L5zPxWZh7IzEOZ+XHgAeBdo4o3M7+XmX+XmfOZ+dfAjcD/ULSeCpR5jV8DvAW4q/N8Ta9xEU35PW6iqtqDYars739I+oovIqaAfwP8WmYeHXJMnYq8fi/Qmmbwpcw8TOtN+0nATzYkvhuANwKvAdbRmpP11Yg4bojxFTGqv5Fhso2oTtPbigVNbzNgZbUbsDLbDpU0icnpHmA6Ik7vOHc2iw9r2d1+rrPcE5n5dMF6BlXoXhHxDuAPgXe3E77lJBCVRPmiQV6bznga+xq3/QvgP2bm93rUPYzXuIim/B43UVXtwTAN8++/zvg20eql+XREPA78Rfv89yPi5xoQH8Bf0fp7rVOR+M4GPp2Z38/Mo5l5J7AZOHP4YfZlVH8jw2QbUZ2mtxULmt5mwMpqN2Blth0qa9STXkdx0Bp/fw+tCeXn0ho+sG2Rcu+gNdfhTFp/yF+lY7J5v/XUHPPbgKeBNy/y3AnABbQ+OZumtbrbLPD6Ecb7TmBL++s3AP8ZuKHJr3FH+YeBXx7hazzdvs9HaS2EsI5FFoxo0u9xE4+q2oOGxLjk3/+o46P1Ac1PdBxvpPWm7tXAmlHH1y73euBHwNuBVbSGvn23QfHdAOyitTrnFHBpu305YcjxVdLWjOthG1FvnKNsK0q8niNpMwrGOJJ2o33viW47PEr+3ow6gJF8062ltD/b/uN8FLi4fX4rraEFWzvK/gbwBPA88O+Atb3qGWXMwNeAo+1zC8eX2s+9nNYnkAeA54D/B/j5Ecf7sfbrOwt8j9aw3tVNfo3b5/5Ju9zxXXXU+Rp/iNY/2J3Hh5r8e9zEo6r2oAkxLvf334T4uq45lZpW4Cz4M/7vgb9t/4zvp4YPagr8fNfR2j7ih+34/l/gHTXEV0lbM66HbcRoXsuOa2prK0r+zGtvMwr+zEfSbrTvPdFth0e5I9q/EJIkSZIkjcwkzjmVJEmSJDWMyakkSZIkaeRMTiVJkiRJI2dyKkmSJEkaOZNTSZIkSdLImZxKkiRJkkbO5FSSJEmSNHImpxq5iHgkIt4+6jgkNZ/thaR+2FZI48nkVJIkSZI0cianGqmIuBvYCnwhImYi4jdHHZOkZrK9kNQP2wppfEVmjjoGTbiIeAS4MjP/bNSxSGo22wtJ/bCtkMaTPaeSJEmSpJEzOZUkSZIkjZzJqZrAseWS+mV7IakfthXSGDI5VRM8AZw26iAkjQXbC0n9sK2QxpDJqZrgo8B1EfFcRHxg1MFIajTbC0n9sK2QxpCr9UqSJEmSRs6eU0mSJEnSyJmcSpIkSZJGzuRUkiRJkjRyJqeSJEmSpJEzOZUkSZIkjZzJqSRJkiRp5ExOJUmSJEkjZ3IqSZIkSRq5/x8ztcIG7X2JHAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1224x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig ,ax = plt.subplots(1,3, figsize = (17, 4))\n",
    "exp = 9\n",
    "g_an = np.load(output_dir + 'g_an_' + str(exp) + '.npy')\n",
    "g_nn = np.load(output_dir + 'g_nn_' + str(exp) + '.npy')\n",
    "\n",
    "error = g_an - g_nn\n",
    "\n",
    "Nx = experiments[exp][0]\n",
    "Nt = experiments[exp][1]\n",
    "\n",
    "x = np.linspace(0, 1, Nx)\n",
    "t = np.linspace(0, 1, Nt)\n",
    "xx, tt = np.meshgrid(x, t)\n",
    "\n",
    "#fig, ax = plt.subplots(facecolor = 'w')\n",
    "cont = ax[0].pcolormesh(xx, tt, error, cmap = 'Spectral',vmax=0.1, vmin = -0.3)\n",
    "ax[0].set_ylabel('x')\n",
    "ax[0].set_xlabel('t')\n",
    "\n",
    "exp = 29\n",
    "g_an = np.load(output_dir + 'g_an_' + str(exp) + '.npy')\n",
    "g_nn = np.load(output_dir + 'g_nn_' + str(exp) + '.npy')\n",
    "\n",
    "error = g_an - g_nn\n",
    "\n",
    "Nx = experiments[exp][0]\n",
    "Nt = experiments[exp][1]\n",
    "\n",
    "x = np.linspace(0, 1, Nx)\n",
    "t = np.linspace(0, 1, Nt)\n",
    "xx, tt = np.meshgrid(x, t)\n",
    "\n",
    "#fig, ax = plt.subplots(facecolor = 'w')\n",
    "cont = ax[1].pcolormesh(xx, tt, error, cmap = 'Spectral',vmax=0.1, vmin = -0.3)\n",
    "ax[1].set_ylabel('x')\n",
    "ax[1].set_xlabel('t')\n",
    "\n",
    "exp = 49\n",
    "g_an = np.load(output_dir + 'g_an_' + str(exp) + '.npy')\n",
    "g_nn = np.load(output_dir + 'g_nn_' + str(exp) + '.npy')\n",
    "\n",
    "error = g_an - g_nn\n",
    "\n",
    "Nx = experiments[exp][0]\n",
    "Nt = experiments[exp][1]\n",
    "\n",
    "x = np.linspace(0, 1, Nx)\n",
    "t = np.linspace(0, 1, Nt)\n",
    "xx, tt = np.meshgrid(x, t)\n",
    "\n",
    "#fig, ax = plt.subplots(facecolor = 'w')\n",
    "cont = ax[2].pcolormesh(xx, tt, error, cmap = 'Spectral',vmax=0.1, vmin = -0.3)\n",
    "ax[2].set_ylabel('x')\n",
    "ax[2].set_xlabel('t')\n",
    "fig.colorbar(cont, ax = ax, label = 'Error')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3c64e609",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-30T11:28:14.129938Z",
     "start_time": "2022-05-30T11:28:13.486532Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\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 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting Maximum absolute error as function of lambda\n",
    "expList = [7, 8, 9]\n",
    "max_abs_error = np.zeros(len(expList))\n",
    "mean_abs_error = np.zeros(len(expList))\n",
    "rms_error = np.zeros(len(expList))\n",
    "\n",
    "lmb = np.zeros(len(expList))\n",
    "for ind_e, exp  in enumerate(expList):\n",
    "    g_an = np.load(output_dir + 'g_an_' + str(exp) + '.npy')\n",
    "    g_nn = np.load(output_dir + 'g_nn_' + str(exp) + '.npy')\n",
    "    error = g_an - g_nn\n",
    "    abs_error = np.abs(error)\n",
    "    max_abs_error[ind_e] = np.max(abs_error)\n",
    "    mean_abs_error[ind_e] = np.mean(abs_error)\n",
    "    rms_error[ind_e] = (np.mean(error**2))\n",
    "    \n",
    "    lmb[ind_e] = experiments[exp][3]\n",
    "fig, ax = plt.subplots(facecolor = 'w')\n",
    "fig2, ax2 = plt.subplots(facecolor = 'w')\n",
    "fig3, ax3 = plt.subplots(facecolor = 'w')\n",
    "\n",
    "ax.plot(lmb, max_abs_error, marker='^', label = 'Grid: 5 x 5')\n",
    "ax2.plot(lmb, mean_abs_error, marker='^', label = 'Grid: 5 x 5')\n",
    "ax3.plot(lmb, rms_error, marker='^', label = 'Grid: 5 x 5')\n",
    "\n",
    "expList = [27, 28, 29]\n",
    "max_abs_error = np.zeros(len(expList))\n",
    "mean_abs_error = np.zeros(len(expList))\n",
    "rms_error = np.zeros(len(expList))\n",
    "\n",
    "lmb = np.zeros(len(expList))\n",
    "for ind_e, exp  in enumerate(expList):\n",
    "    g_an = np.load(output_dir + 'g_an_' + str(exp) + '.npy')\n",
    "    g_nn = np.load(output_dir + 'g_nn_' + str(exp) + '.npy')\n",
    "    error = g_an - g_nn\n",
    "    abs_error = np.abs(error)\n",
    "    max_abs_error[ind_e] = np.max(abs_error)\n",
    "    mean_abs_error[ind_e] = np.mean(abs_error)\n",
    "    rms_error[ind_e] = (np.mean(error**2))\n",
    "    \n",
    "    lmb[ind_e] = experiments[exp][3]\n",
    "ax.plot(lmb, max_abs_error, marker='*', label = 'Grid: 10 x 10')\n",
    "ax2.plot(lmb, mean_abs_error, marker='*', label = 'Grid: 10 x 10')\n",
    "ax3.plot(lmb, rms_error, marker='*', label = 'Grid: 10 x 10')\n",
    "\n",
    "expList = [47, 48, 49]\n",
    "max_abs_error = np.zeros(len(expList))\n",
    "mean_abs_error = np.zeros(len(expList))\n",
    "rms_error = np.zeros(len(expList))\n",
    "\n",
    "lmb = np.zeros(len(expList))\n",
    "for ind_e, exp  in enumerate(expList):\n",
    "    g_an = np.load(output_dir + 'g_an_' + str(exp) + '.npy')\n",
    "    g_nn = np.load(output_dir + 'g_nn_' + str(exp) + '.npy')\n",
    "    error = g_an - g_nn\n",
    "    abs_error = np.abs(error)\n",
    "    max_abs_error[ind_e] = np.max(abs_error)\n",
    "    mean_abs_error[ind_e] = np.mean(abs_error)\n",
    "    rms_error[ind_e] = (np.mean(error**2))\n",
    "    \n",
    "    lmb[ind_e] = experiments[exp][3]\n",
    "ax.plot(lmb, max_abs_error, marker='o', label = 'Grid: 15 x 15')\n",
    "ax2.plot(lmb, mean_abs_error, marker='o', label = 'Grid: 15 x 15')\n",
    "ax3.plot(lmb, rms_error, marker='o', label = 'Grid: 15 x 15')\n",
    "\n",
    "expList = [104, 105, 106]\n",
    "max_abs_error = np.zeros(len(expList))\n",
    "mean_abs_error = np.zeros(len(expList))\n",
    "rms_error = np.zeros(len(expList))\n",
    "\n",
    "lmb = np.zeros(len(expList))\n",
    "for ind_e, exp  in enumerate(expList):\n",
    "    g_an = np.load(output_dir + 'g_an_' + str(exp) + '.npy')\n",
    "    g_nn = np.load(output_dir + 'g_nn_' + str(exp) + '.npy')\n",
    "    error = g_an - g_nn\n",
    "    abs_error = np.abs(error)\n",
    "    max_abs_error[ind_e] = np.max(abs_error)\n",
    "    mean_abs_error[ind_e] = np.mean(abs_error)\n",
    "    rms_error[ind_e] = (np.mean(error**2))\n",
    "    lmb[ind_e] = experiments[exp][3]\n",
    "ax.plot(lmb, max_abs_error, marker='.', label = 'Grid: 5 x 5 with SGD')\n",
    "ax2.plot(lmb, mean_abs_error, marker='.', label = 'Grid: 5 x 5 with SGD')\n",
    "ax3.plot(lmb, rms_error, marker='.', label = 'Grid: 5 x 5 with SGD')\n",
    "\n",
    "expList = [101, 102, 103]\n",
    "max_abs_error = np.zeros(len(expList))\n",
    "mean_abs_error = np.zeros(len(expList))\n",
    "rms_error = np.zeros(len(expList))\n",
    "\n",
    "lmb = np.zeros(len(expList))\n",
    "for ind_e, exp  in enumerate(expList):\n",
    "    g_an = np.load(output_dir + 'g_an_' + str(exp) + '.npy')\n",
    "    g_nn = np.load(output_dir + 'g_nn_' + str(exp) + '.npy')\n",
    "    error = g_an - g_nn\n",
    "    abs_error = np.abs(error)\n",
    "    max_abs_error[ind_e] = np.max(abs_error)\n",
    "    mean_abs_error[ind_e] = np.mean(abs_error)\n",
    "    rms_error[ind_e] = (np.mean(error**2))\n",
    "    lmb[ind_e] = experiments[exp][3]\n",
    "ax.plot(lmb, max_abs_error, marker='.', label = 'Grid: 10 x 10 with SGD')\n",
    "ax2.plot(lmb, mean_abs_error, marker='.', label = 'Grid: 10 x 10 with SGD')\n",
    "ax3.plot(lmb, rms_error, marker='.', label = 'Grid: 10 x 10 with SGD')\n",
    "\n",
    "ax.set_xlabel('$\\lambda$')\n",
    "ax.set_ylabel('Maximum absolute error')\n",
    "ax.legend()\n",
    "fig.savefig('lambdaError1.png', dpi=250)\n",
    "ax2.set_xlabel('$\\lambda$')\n",
    "ax2.set_ylabel('Mean absolute error')\n",
    "ax2.legend()\n",
    "fig2.savefig('lambdaError2.png', dpi=250)\n",
    "ax3.set_xlabel('$\\lambda$')\n",
    "ax3.set_ylabel('Mean squared error')\n",
    "ax3.legend()\n",
    "fig3.savefig('lambdaError3.png', dpi=250)\n",
    "plt.show('True')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "76e60326",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-30T11:28:14.679398Z",
     "start_time": "2022-05-30T11:28:14.131869Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\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 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#as function of number of neurons\n",
    "expList = [3, 6, 9, 12]\n",
    "max_abs_error = np.zeros(len(expList))\n",
    "mean_abs_error = np.zeros(len(expList))\n",
    "rms_error = np.zeros(len(expList))\n",
    "num_neurons = np.zeros(len(expList))\n",
    "for ind_e, exp  in enumerate(expList):\n",
    "    g_an = np.load(output_dir + 'g_an_' + str(exp) + '.npy')\n",
    "    g_nn = np.load(output_dir + 'g_nn_' + str(exp) + '.npy')\n",
    "    error = g_an - g_nn\n",
    "    abs_error = np.abs(error)\n",
    "    max_abs_error[ind_e] = np.max(abs_error)\n",
    "    mean_abs_error[ind_e] = np.mean(abs_error)\n",
    "    rms_error[ind_e] = (np.mean(error**2))\n",
    "    num_neurons[ind_e] = experiments[exp][4][0]\n",
    "fig, ax = plt.subplots(facecolor = 'w')\n",
    "fig2, ax2 = plt.subplots(facecolor = 'w')\n",
    "fig3, ax3 = plt.subplots(facecolor = 'w')\n",
    "ax.plot(num_neurons, max_abs_error, marker='^', label = 'Grid: 5 x 5')\n",
    "ax2.plot(num_neurons, mean_abs_error, marker='^', label = 'Grid: 5 x 5')\n",
    "ax3.plot(num_neurons, rms_error, marker='^', label = 'Grid: 5 x 5')\n",
    "\n",
    "expList = [23, 26, 29, 32]\n",
    "max_abs_error = np.zeros(len(expList))\n",
    "mean_abs_error = np.zeros(len(expList))\n",
    "rms_error = np.zeros(len(expList))\n",
    "num_neurons = np.zeros(len(expList))\n",
    "for ind_e, exp  in enumerate(expList):\n",
    "    g_an = np.load(output_dir + 'g_an_' + str(exp) + '.npy')\n",
    "    g_nn = np.load(output_dir + 'g_nn_' + str(exp) + '.npy')\n",
    "    error = g_an - g_nn\n",
    "    abs_error = np.abs(error)\n",
    "    max_abs_error[ind_e] = np.max(abs_error)\n",
    "    mean_abs_error[ind_e] = np.mean(abs_error)\n",
    "    rms_error[ind_e] = (np.mean(error**2))\n",
    "    num_neurons[ind_e] = experiments[exp][4][0]\n",
    "ax.plot(num_neurons, max_abs_error, marker='*', label = 'Grid: 10 x 10')\n",
    "ax2.plot(num_neurons, mean_abs_error, marker='*', label = 'Grid: 10 x 10')\n",
    "ax3.plot(num_neurons, rms_error, marker='*', label = 'Grid: 10 x 10')\n",
    "\n",
    "ax.set_xlabel('Number of neurons')\n",
    "ax.set_ylabel('Maximum absolute error')\n",
    "ax.legend()\n",
    "fig.savefig('neuronError1.png', dpi=250)\n",
    "ax2.set_xlabel('Number of neurons')\n",
    "ax2.set_ylabel('Mean absolute error')\n",
    "ax2.legend()\n",
    "fig2.savefig('neuronError2.png', dpi=250)\n",
    "ax3.set_xlabel('Number of neurons')\n",
    "ax3.set_ylabel('Mean squared error')\n",
    "ax3.legend()\n",
    "fig3.savefig('neuronError3.png', dpi=250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2574f7cb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-30T11:28:14.742637Z",
     "start_time": "2022-05-30T11:28:14.681311Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing Maximum Absolute Error and cost\n",
      "0: Max Error is 0.9866398981421757 and Mean Error is 0.3660931365537901 and cost is 37.214277680370884\n",
      "Mean squared error is 0.5367032785764208\n",
      "1: Max Error is 0.7590805477856735 and Mean Error is 0.2176315151926373 and cost is 20.788633323878976\n",
      "Mean squared error is 0.3326549094821756\n",
      "2: Max Error is 0.7454239133285969 and Mean Error is 0.20531848010723208 and cost is 18.866133586059508\n",
      "Mean squared error is 0.31744618317468976\n",
      "3: Max Error is 0.6831981500829878 and Mean Error is 0.15208323679698915 and cost is 11.7032908862972\n",
      "Mean squared error is 0.2582922483755326\n",
      "4: Max Error is 0.7756583695439139 and Mean Error is 0.17462529150699216 and cost is 18.095343788622664\n",
      "Mean squared error is 0.30050598795374894\n",
      "5: Max Error is 0.3840833669991534 and Mean Error is 0.05590363892434051 and cost is 5.594217100623661\n",
      "Mean squared error is 0.11293116140456605\n",
      "6: Max Error is 0.26266159158212216 and Mean Error is 0.0351504497443942 and cost is 4.0827822059476855\n",
      "Mean squared error is 0.07721633077259192\n",
      "7: Max Error is 0.6158086008245021 and Mean Error is 0.11844001113279572 and cost is 11.513981247089273\n",
      "Mean squared error is 0.2150747841698287\n",
      "8: Max Error is 0.3332536551275466 and Mean Error is 0.04701114532178446 and cost is 5.0530927316592305\n",
      "Mean squared error is 0.0993038568193972\n",
      "9: Max Error is 0.07432358029296754 and Mean Error is 0.018673883798991816 and cost is 2.2031224627584556\n",
      "Mean squared error is 0.030771962238141107\n",
      "10: Max Error is 0.4821695896762325 and Mean Error is 0.08013809816855959 and cost is 7.277733415562776\n",
      "Mean squared error is 0.1536052259506516\n",
      "11: Max Error is 0.2536692542001055 and Mean Error is 0.03786689837025169 and cost is 4.252765022751203\n",
      "Mean squared error is 0.07685204624375289\n",
      "12: Max Error is 0.07324828932093601 and Mean Error is 0.01769439780139191 and cost is 1.9354680602183043\n",
      "Mean squared error is 0.028555775027347522\n",
      "17: Max Error is 0.4768158174264167 and Mean Error is 0.07022251291447311 and cost is 6.70026285788349\n",
      "Mean squared error is 0.1461512086301481\n",
      "18: Max Error is 0.17099168891343447 and Mean Error is 0.024846003458595837 and cost is 3.238555379742173\n",
      "Mean squared error is 0.05071312280123505\n",
      "19: Max Error is 0.13435299895309655 and Mean Error is 0.014626517316118802 and cost is 3.189538698134023\n",
      "Mean squared error is 0.03714108678073906\n",
      "21: Max Error is 0.7932832296951399 and Mean Error is 0.34072407588291825 and cost is 24.337932723218405\n",
      "Mean squared error is 0.431573987165667\n",
      "22: Max Error is 0.5667404496583623 and Mean Error is 0.1428283948699469 and cost is 9.402876623838711\n",
      "Mean squared error is 0.22041248069913413\n",
      "23: Max Error is 0.5369824066926395 and Mean Error is 0.11727019910013749 and cost is 6.8874351693226314\n",
      "Mean squared error is 0.1974261642350203\n",
      "24: Max Error is 0.7294766897541956 and Mean Error is 0.21227144573370182 and cost is 15.81647858837509\n",
      "Mean squared error is 0.30904419784929443\n",
      "25: Max Error is 0.4019057608345234 and Mean Error is 0.08084348701727105 and cost is 4.695905762212998\n",
      "Mean squared error is 0.13955610710827257\n",
      "26: Max Error is 0.263126039166024 and Mean Error is 0.038687831196187634 and cost is 2.1020624555781566\n",
      "Mean squared error is 0.07074544877599653\n",
      "27: Max Error is 0.5689277091855132 and Mean Error is 0.14671256572070554 and cost is 8.897251794561182\n",
      "Mean squared error is 0.22278506321311153\n",
      "28: Max Error is 0.3926215311231364 and Mean Error is 0.0765124225367761 and cost is 4.402783513412354\n",
      "Mean squared error is 0.13137698723156724\n",
      "29: Max Error is 0.21217838763528113 and Mean Error is 0.031514083995226566 and cost is 1.5561902802173782\n",
      "Mean squared error is 0.05588254875387875\n",
      "30: Max Error is 0.4126720013360592 and Mean Error is 0.08617664090755506 and cost is 5.832198026942153\n",
      "Mean squared error is 0.14828843792493976\n",
      "31: Max Error is 0.33841578400122746 and Mean Error is 0.0667932228111798 and cost is 3.6575567150138837\n",
      "Mean squared error is 0.10767902355615555\n",
      "32: Max Error is 0.2143405577350162 and Mean Error is 0.034030376648251245 and cost is 1.591362455729968\n",
      "Mean squared error is 0.05820731707745433\n",
      "37: Max Error is 0.36592853639616013 and Mean Error is 0.067782789307204 and cost is 4.163093007744478\n",
      "Mean squared error is 0.12032091755516014\n",
      "38: Max Error is 0.27189013514219534 and Mean Error is 0.03177700298944764 and cost is 2.1096771749828425\n",
      "Mean squared error is 0.06957174356433493\n",
      "39: Max Error is 0.21531332483737958 and Mean Error is 0.03729861818551809 and cost is 1.8081815018014442\n",
      "Mean squared error is 0.05803275411139635\n",
      "47: Max Error is 0.6128401777424671 and Mean Error is 0.17487367023892358 and cost is 11.001813558810897\n",
      "Mean squared error is 0.25063657165053577\n",
      "48: Max Error is 0.36356096929947423 and Mean Error is 0.07576492104793354 and cost is 3.6028577507569213\n",
      "Mean squared error is 0.11665976540215642\n",
      "49: Max Error is 0.20290570144621717 and Mean Error is 0.035541322582569285 and cost is 1.3409247793835724\n",
      "Mean squared error is 0.0597649753792565\n",
      "101: Max Error is 0.4865557079501167 and Mean Error is 0.13014784723440898 and cost is 11.11505329465127\n",
      "Mean squared error is 0.1902031849128104\n",
      "102: Max Error is 0.35698439479745075 and Mean Error is 0.06795439981150335 and cost is 4.1553172169040415\n",
      "Mean squared error is 0.11436867727490634\n",
      "103: Max Error is 0.26030022987270646 and Mean Error is 0.045402390465550495 and cost is 2.1830888974426763\n",
      "Mean squared error is 0.07449391111964304\n",
      "104: Max Error is 0.5993562805577379 and Mean Error is 0.12674136706924777 and cost is 12.42961337562765\n",
      "Mean squared error is 0.21550438484938442\n",
      "105: Max Error is 0.37291717721141754 and Mean Error is 0.05320215848078114 and cost is 5.6167706178228824\n",
      "Mean squared error is 0.10908910495701675\n",
      "106: Max Error is 0.19114713419589374 and Mean Error is 0.039562469132546636 and cost is 3.479747754136143\n",
      "Mean squared error is 0.0684452886342475\n",
      "209: Max Error is 0.0371558506274811 and Mean Error is 0.0076834491192187375 and cost is 1.1075408688359574\n",
      "Mean squared error is 0.014762862419385899\n",
      "219: Max Error is 0.017598841181217156 and Mean Error is 0.0027111447054302042 and cost is 1.9820507492174608\n",
      "Mean squared error is 0.005046370578392937\n"
     ]
    }
   ],
   "source": [
    "#Printing out max error\n",
    "print('Printing Maximum Absolute Error and cost')\n",
    "for exp in experiments: \n",
    "    g_an = np.load(output_dir + 'g_an_' + str(exp) + '.npy')\n",
    "    g_nn = np.load(output_dir + 'g_nn_' + str(exp) + '.npy')\n",
    "    endcost = np.load(output_dir + 'cost_arr_' + str(exp) + '.npy')[-1]\n",
    "    abs_error = np.abs(g_an - g_nn)\n",
    "    error = g_an - g_nn\n",
    "    max_error = np.max(abs_error)\n",
    "    mean_error = np.mean(abs_error)\n",
    "    root_mean_squared_error = np.sqrt(np.mean(error**2))\n",
    "    print(str(exp) + ': Max Error is ' + str(max_error) + ' and Mean Error is ' + str(mean_error) + ' and cost is ' + str(endcost))\n",
    "    print('Mean squared error is ' + str(root_mean_squared_error))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1bde41c",
   "metadata": {},
   "source": [
    "# Time to train plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c1aeb440",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-30T11:28:14.837044Z",
     "start_time": "2022-05-30T11:28:14.744248Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\haris\\AppData\\Local\\Temp\\ipykernel_22104\\4139598717.py:7: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels([\"5x5 \\n 1 layer\",\"5x5 \\n 2 layer\",\"10x10 \\n 1 layer\",\"10x10 \\n 2 layer\"])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.axis.XTick at 0x23412f04c40>,\n",
       " <matplotlib.axis.XTick at 0x23412f04c70>,\n",
       " <matplotlib.axis.XTick at 0x23412d935e0>,\n",
       " <matplotlib.axis.XTick at 0x23412f4f250>]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "expList = [9, 19, 29, 39]\n",
    "time = np.zeros(len(expList))\n",
    "for ind_e, exp  in enumerate(expList):\n",
    "    time[ind_e] = np.load(output_dir + 'time_' + str(exp) + '.npy')\n",
    "fig, ax = plt.subplots(facecolor='w')\n",
    "ax.bar(np.arange(0, len(expList)), time)\n",
    "ax.set_xticklabels([\"5x5 \\n 1 layer\",\"5x5 \\n 2 layer\",\"10x10 \\n 1 layer\",\"10x10 \\n 2 layer\"])\n",
    "ax.set_xticks([0,1,2,3])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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