Spatial_KS_Equation_Chaotic-PySR.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "%pylab is deprecated, use %matplotlib inline and import the required libraries.\n",
      "Populating the interactive namespace from numpy and matplotlib\n",
      "Sklearn's version: 1.4.2\n",
      "mrmr is not installed in the env you are using. This may cause an error in future if you try to use the (missing) lib.\n",
      "Best-subset solvers are NOT installed.\n",
      "Detected IPython. Loading juliacall extension. See https://juliapy.github.io/PythonCall.jl/stable/compat/#IPython\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%pylab inline\n",
    "from functools import partial, reduce\n",
    "from funcy import compose\n",
    "import numpy as np\n",
    "from parametric_pde_find import *\n",
    "from scipy.integrate import odeint\n",
    "from scipy.stats import wilcoxon, ranksums, mannwhitneyu, friedmanchisquare\n",
    "from scipy.signal import savgol_filter\n",
    "from numpy.fft import fft, ifft, fftfreq\n",
    "from robust_pde_diff import DLrSR, RobustPCA, DLrSR_para\n",
    "from para_UBIC import *\n",
    "from best_subset import *\n",
    "from solvel0 import solvel0\n",
    "\n",
    "from findiff import FinDiff\n",
    "from abess.linear import LinearRegression\n",
    "from sklearn.linear_model import BayesianRidge, ARDRegression, Ridge, Lasso\n",
    "from sklearn.utils.random import sample_without_replacement\n",
    "from UBIC import fit_blr, UBIC, decreasing_values_indices\n",
    "# from skscope_tools import best_subset_all_solutions, abess_all_solutions\n",
    "\n",
    "from tqdm import tqdm\n",
    "\n",
    "from pysr import PySRRegressor\n",
    "\n",
    "fontsize = 20 # for plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def ks_rhs(u, t, params):\n",
    "    k, L, x = params\n",
    "    a = (1 + 0.25*np.sin(x*2*np.pi/L))  # uu_x\n",
    "    b = -1 + 0.25*np.exp(-(x-2)**2/5)   # u_{xx}\n",
    "    c = -1 - 0.25*np.exp(-(x+2)**2/5)   # u_{xxxx}\n",
    "    deriv = a*u*ifft(1j*k*fft(u)) + b*ifft(-k**2*fft(u)) + c*ifft(k**4*fft(u))\n",
    "    return real(deriv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set size of grid\n",
    "n = 512\n",
    "m = 512\n",
    "L = 20\n",
    "max_time = 200\n",
    "\n",
    "# Set up grid\n",
    "x = np.linspace(-L,L,n+1)[:-1]; dx = x[1]-x[0]\n",
    "t = np.linspace(0,max_time,2*m); dt = t[1]-t[0]\n",
    "k = 2*np.pi*fftfreq(n, d = dx)\n",
    "\n",
    "# Initial condition\n",
    "u0 = np.exp(-(x)**2)\n",
    "\n",
    "# Solve with time dependent uu_x term\n",
    "params = (k,L,x)\n",
    "# u_full = odeint(ks_rhs, u0, t, args=(params,)).T\n",
    "u_full = np.load(\"./Datasets/ks_u_full.npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x165f24490>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "fig = figure(figsize=(16, 4))\n",
    "X, T = np.meshgrid(x, t)\n",
    "\n",
    "subplot(1,2,1)\n",
    "pcolor(X, T, u_full.T, cmap=cm.coolwarm)\n",
    "xlabel('x', fontsize = fontsize)\n",
    "ylabel('t', fontsize = fontsize)\n",
    "xticks(fontsize = fontsize)\n",
    "yticks(fontsize = fontsize)\n",
    "\n",
    "uu_x_true = 1 + 0.25*np.sin(x*2*np.pi/L)\n",
    "u_xx_true = -1 + 0.25*np.exp(-(x-2)**2/5)\n",
    "u_4x_true = -1 - 0.25*np.exp(-(x+2)**2/5)\n",
    "\n",
    "subplot(1,2,2)\n",
    "plot(x, uu_x_true, label = r'$uu_x$')\n",
    "plot(x, u_xx_true, label = r'$u_{xx}$')\n",
    "plot(x, u_4x_true, label = r'$u_{xxxx}$')\n",
    "xlim([x[0],x[-1]])\n",
    "\n",
    "xticks(fontsize = fontsize)\n",
    "yticks(fontsize = fontsize)\n",
    "xlabel('x', fontsize = fontsize)\n",
    "legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize = fontsize+2)\n",
    "# fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# u = u_full[:,-m:]\n",
    "# t = t[-m:]\n",
    "u = u_full[:,:m]\n",
    "t = t[:m]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "noise_lv = 0.01\n",
    "np.random.seed(0)\n",
    "u = u + noise_lv*0.01*std(u)*np.random.randn(n,m)\n",
    "\n",
    "is_denoising = True\n",
    "if is_denoising and noise_lv > 0:\n",
    "    window_size = 15\n",
    "    poly_order = 2\n",
    "    u = savgol_filter(u, window_size, poly_order, axis=0)\n",
    "\n",
    "alpha = 3e-4\n",
    "diff_kwargs = {\"kind\": \"kalman\", \"alpha\": alpha}\n",
    "Ut, Theta, Ut_grouped, Theta_grouped, rhs_des = construct_group_linear_system(u, x, t, \n",
    "                                                                              diff_kwargs, \n",
    "                                                                              include_bias=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # Build linear systems\n",
    "# wx = 10\n",
    "# wt = 10\n",
    "# deg_x = 6\n",
    "# deg_t = 4\n",
    "\n",
    "# Utn, Thetan, rhs_des = build_linear_system(un, dt, dx, D=4, P=3, time_diff = 'poly',\n",
    "#                                        deg_x = deg_x, deg_t = deg_t, \n",
    "#                                        width_x = wx, width_t = wt)\n",
    "# n2 = n - 2*wx\n",
    "# m2 = m - 2*wt\n",
    "\n",
    "# Theta_grouped = np.array([real(Theta[n*np.arange(m)+j,:]) for j in range(n)])\n",
    "# Ut_grouped = np.array([real(Ut[n*np.arange(m)+j]) for j in range(n)])\n",
    "\n",
    "fft_percent = 90\n",
    "fft_Theta_grouped = np.array([remove_f(fft(Theta_grouped[:, :, k]), fft_percent) \n",
    "                              for k in range(Theta_grouped.shape[-1])])\n",
    "fft_Theta_grouped = np.moveaxis(fft_Theta_grouped, 0, -1)\n",
    "fft_Ut_grouped = remove_f(fft(Ut_grouped[:, :, 0]), fft_percent)\n",
    "fft_Ut_grouped = np.expand_dims(fft_Ut_grouped, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solve and plot\n",
    "Xi,Tol,Losses = TrainSGTRidge(Theta_grouped, Ut_grouped)\n",
    "xi = Xi[np.argmin(Losses)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 u_xx\n",
      "5 u_xxx\n",
      "6 u_xxxx\n",
      "7 uu_x\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.rcParams['figure.figsize'] = (12, 8)\n",
    "for j in range(xi.shape[0]): \n",
    "    if np.linalg.norm(xi[j,:]) > 10**-10:\n",
    "        print(j, rhs_des[j])\n",
    "        plot(x, xi[j,:], label = rhs_des[j])\n",
    "                \n",
    "title('Kuramoto Sivashinsky Equation Coefficients', fontsize = 16)\n",
    "xlabel('x', fontsize = 16)\n",
    "ylabel('Coefficient Value', fontsize = 16)\n",
    "legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize = 14)\n",
    "plt.show()\n",
    "# mse_clean_sgtr = Norm(xi-xi_true[:,wx:-wx])**2/(m*len(rhs_des))\n",
    "# print('MSE: ', mse_clean_sgtr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('u_xx', 'u_xxxx', 'uu_x')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candidates = {(0, 1, 3, 4, 6, 7, 8, 9, 11),\n",
    "              (0, 1, 3, 4, 6, 7, 10, 11),\n",
    "              (1, 2, 3, 4, 6, 7, 8, 12, 15, 16),\n",
    "              (3, 4, 6, 7),\n",
    "              (3, 4, 6, 7, 9, 10, 11, 13, 14, 15, 18),\n",
    "              (3, 4, 6, 7, 9, 12),\n",
    "              (3, 4, 6, 7, 9, 12, 18),\n",
    "              (3, 4, 6, 7, 13),\n",
    "              (4, 6, 7),\n",
    "              (6, 7),\n",
    "              (7,)}\n",
    "candidates = sorted(candidates, key=len)\n",
    "rhs_des[4], rhs_des[6], rhs_des[7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 11/11 [00:13<00:00,  1.22s/it]\n"
     ]
    }
   ],
   "source": [
    "lnorm = 1\n",
    "n_models = 20\n",
    "uncerts = [[] for _ in range(len(candidates))]\n",
    "mean_coeffs = [[] for _ in range(len(candidates))]\n",
    "std_coeffs = [[] for _ in range(len(candidates))]\n",
    "for j in trange(len(candidates)):\n",
    "    for i in range(len(Theta_grouped)):\n",
    "        np.random.seed(i)\n",
    "        coeffs = []\n",
    "        for _ in range(n_models):\n",
    "            sample_indices = np.random.choice(range(Theta_grouped[i].shape[0]), \n",
    "                                              size=int(Theta_grouped[i].shape[0]*0.8), \n",
    "                                              replace=True)\n",
    "            coeffs.append(np.linalg.lstsq(Theta_grouped[i][sample_indices, :][:, candidates[j]], \n",
    "                                          Ut_grouped[i][sample_indices, :], rcond=None)[0])\n",
    "        coeffs = np.hstack(coeffs)\n",
    "        mean_coeff = coeffs.mean(axis=1)\n",
    "        std_coeff = coeffs.std(axis=1)\n",
    "        total_std = np.sum(std_coeff)\n",
    "        \n",
    "        if lnorm > 0: uncert = total_std/np.linalg.norm(mean_coeff, lnorm)\n",
    "        else: uncert = total_std\n",
    "            \n",
    "        uncerts[j].append(uncert)\n",
    "        mean_coeffs[j].append(mean_coeff)\n",
    "        std_coeffs[j].append(std_coeff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1dbb1ea90>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YcdTXVlZW6qGHHtLKlSv17LPPqra2VosWLdKaNWuO+zWWL1+ugoKC+Ed1dfVQY8IEFotFP7v2DF1cW6re/oi+9th6fbS/y+xYAAAAADBqLMYQxzX39fWpqalJHR0dWrlypR5++GGtXr36mMX646666ipZLBa98MILx3xNMBhUMBiMf+7z+VRdXa3Ozk45nc6hxIUJAsGQvvS7t/Xunk6NL8zRs18/T+XObLNjAQAAAMBJ8/l8KigoOGEPHfJKdVZWlqZOnar58+dr+fLlmj17tn7961+f9PvPPfdcffjhh8d9jd1uj08YH/hA8sizZ+iRG87S5JJctXT06IZH18vX2292LAAAAAAYcad8T7VhGINWlU9k8+bNqqysPNUviwRXkm/XEzedo3H5dtV5fLr19xsVDIXNjgUAAAAAI2pI07/vuusuXXHFFaqurpbf79fTTz+tVatW6ZVXXpEkLVu2TC0tLXriiSckSffcc48mT56smTNnqq+vT08++aRWrlyplStXjvyfBAlnYkmuHrvxLP3v367T3z46oP/vP9/VvV+YI6vVYnY0AAAAABgRQyrV+/bt01e/+lV5PB4VFBRo1qxZeuWVV3TZZZdJkjwej5qaDt1R3NfXpzvuuEMtLS3KycnRzJkz9eKLL2rJkiUj+6dAwjp9fIEe/Oo83fjoev35PY/KHNn6wafcslgo1gAAAACS35AHlZnhZA+II3H9cXOLbv/DFknSXUtc+j8XnmZuIAAAAAA4jlEbVAYMxzVzxuuuJS5J0k9fqtdzm/eYnAgAAAAATh2lGmPmlgum6GsLayRJ//Bf72nN9v0mJwIAAACAU0OpxpixWCz63hK3rppdpVDE0N8/uVEftHSaHQsAAAAAho1SjTFltVp09+dm6bzTShToC+uGR9/R7gMBs2MBAAAAwLBQqjHm7Bk2/far8+SudKqtq0/XP/KO2rpO/q5zAAAAAEgUlGqYwpGdqcdvPEsTinK060C3vvbYegWCIbNjAQAAAMCQUKphmjJnth6/6WwV5Wbq3T2d+vq/b1J/OGJ2LAAAAAA4aZRqmOq00nw9csNZys60avX2/bpz5XtKgqvTAQAAAEASpRoJYM7EIt3/pbmyWS16dlOLfvmXBrMjAQAAAMBJoVQjISxyl2v5Z86QJP1m1Ud6/G+7zA0EAAAAACeBUo2E8fmzqvX/XTZdkvQvf9qql973mJwIAAAAAI6PUo2E8o1PTNWXz5kow5Buf3qL3t55wOxIAAAAAHBMlGokFIvFov979elaPKNcfeGIbnligxq8frNjAQAAAMBRUaqRcGxWi+794hydNblI/t6QfvmXerMjAQAAAMBRUaqRkLIzbbrzky5J0vstnSanAQAAAICjo1QjYbkqnZKkfb6gDgb6TE4DAAAAAEeiVCNh5dszNLE4V5JU7/GZnAYAAAAAjkSpRkJzVTgkSXUMKwMAAACQgCjVSGju2BbwOlaqAQAAACQgSjUSmrsyulJd76VUAwAAAEg8lGoktIGV6u37uhQKR0xOAwAAAACDUaqR0KqLcpWbZVNfKKLGtoDZcQAAAABgEEo1EprValFtbFjZNs5VAwAAAEgwlGokvIEt4PVMAAcAAACQYCjVSHjugWu1WKkGAAAAkGAo1Uh48ZVqDyvVAAAAABILpRoJb+BMtdfXq/ZAn8lpAAAAAOAQSjUSniM7U9XFOZKkOu6rBgAAAJBAKNVICq6K6BbwOraAAwAAAEgglGokhUPnqlmpBgAAAJA4KNVICvEJ4Gz/BgAAAJBAKNVICgMr1dv3dSkUjpicBgAAAACiKNVIChOLc5WbZVNfKKLGtoDZcQAAAABAEqUaScJqtcSv1qrzMqwMAAAAQGKgVCNpDEwAZ1gZAAAAgERBqUbSmFEZW6mmVAMAAABIEJRqJA3XwLVabP8GAAAAkCAo1UgaA2eqPZ296ujuMzkNAAAAAFCqkUSc2ZmaUJQjSarzsFoNAAAAwHyUaiSVgfuqOVcNAAAAIBFQqpFU3LEt4PVeSjUAAAAA81GqkVQOrVSz/RsAAACA+SjVSCoDE8C37/MrFI6YnAYAAABAuqNUI6lMLM5VTqZNwVBEuw4EzI4DAAAAIM1RqpFUbFZL/GottoADAAAAMBulGknHXTlQqhlWBgAAAMBclGoknYFhZfVeVqoBAAAAmItSjaTjquCuagAAAACJgVKNpOOKbf/2dPaqo7vP5DQAAAAA0hmlGknHmZ2p8YU5khhWBgAAAMBclGokpUPnqtkCDgAAAMA8lGokpYEJ4PWsVAMAAAAwEaUaSWlgpbqOlWoAAAAAJqJUIym5KqIr1Q1ev8IRw+Q0AAAAANIVpRpJaVJJnnIybQqGImpsC5gdBwAAAECaolQjKdmsFk2PrVYzrAwAAACAWSjVSFozYsPK6jyUagAAAADmoFQjabkqYtdqMQEcAAAAgEko1Uha8QngrFQDAAAAMAmlGkmrNnamem9nrzq7+01OAwAAACAdUaqRtApyMjW+MEcS91UDAAAAMAelGknNHRtWVs8WcAAAAAAmoFQjqR06V82wMgAAAABjj1KNpBafAM72bwAAAAAmoFQjqbli278b9vkVjhgmpwEAAACQbijVSGqTS/KUnWlVb39Euw4EzI4DAAAAIM1QqpHUbFaLasujq9XcVw0AAABgrFGqkfQGhpXVM6wMAAAAwBijVCPpuSpYqQYAAABgDko1kl58pdrLSjUAAACAsUWpRtIbuFarpaNHnT39JqcBAAAAkE4o1Uh6BbmZGl+YI0mqZws4AAAAgDFEqUZKGDhXzRZwAAAAAGOJUo2UMHCummFlAAAAAMYSpRopwVUZmwDOSjUAAACAMUSpRkoYWKlu8PoUjhgmpwEAAACQLijVSAmTS/KUnWlVb39Euw8EzI4DAAAAIE1QqpESbFaLastjW8A9bAEHAAAAMDYo1UgZA/dV13sZVgYAAABgbFCqkTLcA8PKmAAOAAAAYIxQqpEyXPFrtdj+DQAAAGBsUKqRMtyx7d8tHT3q7Ok3OQ0AAACAdECpRsooyM1UVUG2JKmB+6oBAAAAjAFKNVKKO74FnHPVAAAAAEYfpRopxRUbVsYEcAAAAABjgVKNlDJwrdY2hpUBAAAAGANDKtUPPPCAZs2aJafTKafTqQULFujll18+7ntWr16tefPmKTs7W1OmTNGDDz54SoGB4xnY/r3d61c4YpicBgAAAECqG1KpnjBhgn72s59pw4YN2rBhgz7xiU/o6quv1tatW4/6+sbGRi1ZskQXXHCBNm/erLvuukvf+ta3tHLlyhEJD3zc5JJc2TOs6ukPq+lgt9lxAAAAAKQ4i2EYp7ScV1xcrF/+8pf62te+dsRzd955p1544QXV1dXFH7v11lv17rvvat26dSf9NXw+nwoKCtTZ2Smn03kqcZEGPn3fWr23p1O/+fJcLTmj0uw4AAAAAJLQyfbQYZ+pDofDevrppxUIBLRgwYKjvmbdunVavHjxoMcuv/xybdiwQf39x75HOBgMyufzDfoATparIjasjAngAAAAAEbZkEv1+++/r/z8fNntdt1666167rnnNGPGjKO+1uv1qry8fNBj5eXlCoVCamtrO+bXWL58uQoKCuIf1dXVQ42JNDZwrpphZQAAAABG25BLdW1trbZs2aK3335bf//3f6/rr79e27ZtO+brLRbLoM8Hdpt//PHDLVu2TJ2dnfGP5ubmocZEGhuYAM61WgAAAABGW8ZQ35CVlaWpU6dKkubPn6/169fr17/+tX77298e8dqKigp5vd5Bj7W2tiojI0MlJSXH/Bp2u112u32o0QBJkjt2V/We9h75evvlzM40OREAAACAVHXK91QbhqFgMHjU5xYsWKDXXntt0GOvvvqq5s+fr8xMig5GR2FulioLsiVJDV62gAMAAAAYPUMq1XfddZfefPNN7dq1S++//76+973vadWqVfryl78sKbpt+7rrrou//tZbb9Xu3bu1dOlS1dXV6ZFHHtGKFSt0xx13jOyfAviYgXPVdQwrAwAAADCKhrT9e9++ffrqV78qj8ejgoICzZo1S6+88oouu+wySZLH41FTU1P89TU1NXrppZf0ne98R/fff7+qqqp077336tprrx3ZPwXwMa4Kh16vb1Udw8oAAAAAjKIhleoVK1Yc9/nHHnvsiMcuuugibdq0aUihgFPFSjUAAACAsXDKZ6qBRDQwrKzB61ckYpicBgAAAECqolQjJU0uyZM9w6qe/rB2H+w2Ow4AAACAFEWpRkrKsFk1vTy6Wl3PFnAAAAAAo4RSjZQ1sAWcc9UAAAAARgulGinLVREbVsZd1QAAAABGCaUaKYsJ4AAAAABGG6UaKctVEd3+vae9R77efpPTAAAAAEhFlGqkrKK8LFU4syVFr9YCAAAAgJFGqUZKGxhWxgRwAAAAAKOBUo2U5qpkWBkAAACA0UOpRkpjWBkAAACA0USpRkpzx4aVNXj9ikQMk9MAAAAASDWUaqS0mnF5ysqwqrsvrKaD3WbHAQAAAJBiKNVIaRk2q6aX50uS6r1sAQcAAAAwsijVSHnuiui56m0ehpUBAAAAGFmUaqS8gQngXKsFAAAAYKRRqpHyBu6qrmP7NwAAAIARRqlGyhvY/t18sEf+3n6T0wAAAABIJZRqpLyivCxVOLMlRa/WAgAAAICRQqlGWnDFt4BTqgEAAACMHEo10oI7NqysjmFlAAAAAEYQpRppwVURXalmAjgAAACAkUSpRlqYMXCtltevSMQwOQ0AAACAVEGpRlqoGZenrAyruvvCam7vNjsOAAAAgBRBqUZayLBZNb08XxLnqgEAAACMHEo10oarYmBYGRPAAQAAAIwMSjXShjt+rpqVagAAAAAjg1KNtOGOTQBnpRoAAADASKFUI224YivVTQe71RUMmZwGAAAAQCqgVCNtFOdlqdxplyQ1sAUcAAAAwAigVCOtMKwMAAAAwEiiVCOtDAwr41otAAAAACOBUo204q6MDiur97JSDQAAAODUUaqRVuLXanl8ikQMk9MAAAAASHaUaqSVmnF5yrJZFegLa097j9lxAAAAACQ5SjXSSqbNqmnl+ZKkbZyrBgAAAHCKKNVIOwMTwOu5VgsAAADAKaJUI+0MDCtjAjgAAACAU0WpRtqJDytjAjgAAACAU0SpRtpxVURXqncf6FZXMGRyGgAAAADJjFKNtFOSb1eZwy5JamC1GgAAAMApoFQjLQ1sAedcNQAAAIBTQalGWnLFhpUxARwAAADAqaBUIy3NiK9Us/0bAAAAwPBRqpGWBu6qbvD6FYkYJqcBAAAAkKwo1UhLU0rzlGWzqisYUktHj9lxAAAAACQpSjXSUqbNqqll+ZKkbQwrAwAAADBMlGqkrYEJ4PWcqwYAAAAwTJRqpC13bAI412oBAAAAGC5KNdLWwLAyrtUCAAAAMFyUaqStgZXq3Qe7FQiGTE4DAAAAIBlRqpG2SvLtKnXYZRhSwz7OVQMAAAAYOko10trAsDLOVQMAAAAYDko10pq7IroFnAngAAAAAIaDUo20xko1AAAAgFNBqUZac8WGldV7/TIMw+Q0AAAAAJINpRpp7bTSfGXaLOoKhrSnvcfsOAAAAACSDKUaaS3TZtXUsuhqNVvAAQAAAAwVpRppb+C+6jqGlQEAAAAYIko10p67IjqsrN7LSjUAAACAoaFUI+0xARwAAADAcFGqkfYGJoDvPtitQDBkchoAAAAAyYRSjbQ3Lt+uUoddhiFt38e5agAAAAAnj1INSHJVMKwMAAAAwNBRqgFJMyoZVgYAAABg6CjVgA6dq2ZYGQAAAIChoFQDOjQBvN7jl2EYJqcBAAAAkCwo1YCkKePylWmzyB8MaU97j9lxAAAAACQJSjUgKSvDqqll0S3g9V6GlQEAAAA4OZRqIMZdwblqAAAAAENDqQZiBoaVMQEcAAAAwMmiVAMxA8PKuKsaAAAAwMmiVAMxropoqd51IKDuvpDJaQAAAAAkA0o1EFPqsGtcvl2GITUwrAwAAADASaBUA4dxVzIBHAAAAMDJo1QDhzl0rpphZQAAAABOjFINHMYVu1arnmFlAAAAAE4CpRo4THyl2uuTYRgmpwEAAACQ6CjVwGFOK81Xps0if29ILR09ZscBAAAAkOAo1cBhsjKsOq00XxL3VQMAAAA4MUo18DEDW8DrGVYGAAAA4AQo1cDHcK0WAAAAgJNFqQY+xlXBtVoAAAAATg6lGviYge3fjQcC6ukLm5wGAAAAQCKjVAMfU+qwa1x+lgxDatjHFnAAAAAAx0apBo6CYWUAAAAATgalGjgKV0V0WBnnqgEAAAAcz5BK9fLly3XWWWfJ4XCorKxM11xzjRoaGo77nlWrVslisRzxUV9ff0rBgdE0sFJdxwRwAAAAAMcxpFK9evVq3XbbbXr77bf12muvKRQKafHixQoEAid8b0NDgzweT/xj2rRpww4NjLbDJ4AbhmFyGgAAAACJKmMoL37llVcGff7oo4+qrKxMGzdu1IUXXnjc95aVlamwsHDIAQEzTC3LV4bVIn9vSHs7ezW+MMfsSAAAAAAS0Cmdqe7s7JQkFRcXn/C1c+bMUWVlpRYtWqQ33njjuK8NBoPy+XyDPoCxlJVh1dSyfElS3V6+/wAAAAAc3bBLtWEYWrp0qRYuXKjTTz/9mK+rrKzUQw89pJUrV+rZZ59VbW2tFi1apDVr1hzzPcuXL1dBQUH8o7q6ergxgWEbGFZW76VUAwAAADg6izHMA6O33XabXnzxRa1du1YTJkwY0nuvuuoqWSwWvfDCC0d9PhgMKhgMxj/3+Xyqrq5WZ2ennE7ncOICQ/bb1R9p+cv1uvKMSt3/5blmxwEAAAAwhnw+nwoKCk7YQ4e1Uv3Nb35TL7zwgt54440hF2pJOvfcc/Xhhx8e83m73S6n0znoAxhrrvgEcFaqAQAAABzdkEq1YRj6xje+oWeffVavv/66ampqhvVFN2/erMrKymG9Fxgr7sro9u9dbQH19IVNTgMAAAAgEQ1p+vdtt92mp556Ss8//7wcDoe8Xq8kqaCgQDk50enIy5YtU0tLi5544glJ0j333KPJkydr5syZ6uvr05NPPqmVK1dq5cqVI/xHAUZWab5dJXlZOhDo0/Z9fs2uLjQ7EgAAAIAEM6RS/cADD0iSLr744kGPP/roo7rhhhskSR6PR01NTfHn+vr6dMcdd6ilpUU5OTmaOXOmXnzxRS1ZsuTUkgOjzGKxyF3p1Nodbarz+CjVAAAAAI4w7EFlY+lkD4gDI+3Hf96mh9c26obzJutfPj3T7DgAAAAAxsioDioD0oV7YFiZh2FlAAAAAI5EqQaOwxUbVlbn8SkJNnUAAAAAGGOUauA4ppblK8Nqka83JE9nr9lxAAAAACQYSjVwHPYMm04rzZfEFnAAAAAAR6JUAycwcF91vddvchIAAAAAiYZSDZyAKzasbBsr1QAAAAA+hlINnMDABPB6SjUAAACAj6FUAyfgrohu/25sC6i3P2xyGgAAAACJhFINnECpw66SvCxFDGn7Ps5VAwAAADiEUg2cgMViGXRfNQAAAAAMoFQDJ8FdET1XXedhpRoAAADAIZRq4CQMTABnpRoAAADA4SjVwElwVRy6q9owDJPTAAAAAEgUlGrgJEwrz5fNalFnT788nb1mxwEAAACQICjVwEmwZ9h0WmmeJKneyxZwAAAAAFGUauAkuSsZVgYAAABgMEo1cJJcFQwrAwAAADAYpRo4SW7uqgYAAADwMZRq4CQNbP9ubAuotz9schoAAAAAiYBSDZykModdxXlZihjSh/u6zI4DAAAAIAFQqoGTZLFY4vdVswUcAAAAgESpBoYkPgGca7UAAAAAiFINDAkr1QAAAAAOR6kGhmBgpbre65dhGCanAQAAAGA2SjUwBFPL8mWzWtTR3S+vr9fsOAAAAABMRqkGhiA706bTSvMkSfUev8lpAAAAAJiNUg0MkasiugV8G+eqAQAAgLRHqQaG6PBz1QAAAADSG6UaGCJXJRPAAQAAAERRqoEhmhFbqd65v0u9/WGT0wAAAAAwE6UaGKIyh11FuZmKGNKH+7rMjgMAAADARJRqYIgsFkv8XHWdly3gAAAAQDqjVAPDMDABnHPVAAAAQHqjVAPDMDCsjLuqAQAAgPRGqQaGYcZh278NwzA5DQAAAACzUKqBYZhali+b1aKO7n7t8wXNjgMAAADAJJRqYBiyM22aMi5PEsPKAAAAgHRGqQaGyVXJsDIAAAAg3VGqgWFyM6wMAAAASHuUamCY3FyrBQAAAKQ9SjUwTO7Y9u+dbQH19odNTgMAAADADJRqYJjKnXYV5mYqHDG0o7XL7DgAAAAATECpBobJYrGwBRwAAABIc5Rq4BS4YsPK6hhWBgAAAKQlSjVwCgbOVddzVzUAAACQlijVwCk4fPu3YRgmpwEAAAAw1ijVwCmYVp4vq0Vq7+5Xqz9odhwAAAAAY4xSDZyC7EybppTmS5K2MawMAAAASDuUauAUxc9VM6wMAAAASDuUauAUuSoGJoCzUg0AAACkG0o1cIpmMAEcAAAASFuUauAUDdxV/dH+gHr7wyanAQAAADCWKNXAKapwZqswN1PhiKEdrV1mxwEAAAAwhijVwCmyWCycqwYAAADSFKUaGAGuioFz1UwABwAAANIJpRoYAQwrAwAAANITpRoYAQPDyuo8fhmGYXIaAAAAAGOFUg2MgOnlDlkt0sFAn/b7g2bHAQAAADBGKNXACMjOtKlmXJ4kaRvDygAAAIC0QakGRoi7kmFlAAAAQLqhVAMjZKBUc60WAAAAkD4o1cAIcceGldV7WKkGAAAA0gWlGhghA3dVf7S/S8FQ2OQ0AAAAAMYCpRoYIZUF2SrIyVQoYmhHa5fZcQAAAACMAUo1MEIsFotcFYfuqwYAAACQ+ijVwAiKTwBnWBkAAACQFijVwAgaGFZW56VUAwAAAOmAUg2MoEPXavllGIbJaQAAAACMNko1MIKmlztktUgHA33a7w+aHQcAAADAKKNUAyMoO9OmmnF5kqQ6L8PKAAAAgFRHqQZGmCu+BZxz1QAAAECqo1QDI2wGE8ABAACAtEGpBkbYwF3V9Wz/BgAAAFIepRoYYQMTwHe0dikYCpucBgAAAMBoolQDI6yyIFvO7AyFIoY+ag2YHQcAAADAKKJUAyPMYrEwrAwAAABIE5RqYBTEh5V5KdUAAABAKqNUA6NgYFhZnYdhZQAAAEAqo1QDo8DNSjUAAACQFijVwCiYXu6Q1SK1dfWp1d9rdhwAAAAAo4RSDYyCnCybJo/LkyTVswUcAAAASFmUamCUuCuYAA4AAACkOko1MErcldFhZfVeVqoBAACAVEWpBkaJi5VqAAAAIOVRqoFR4q6KluodrV3qC0VMTgMAAABgNFCqgVFSVZAtZ3aGQhFDO1q7zI4DAAAAYBQMqVQvX75cZ511lhwOh8rKynTNNdeooaHhhO9bvXq15s2bp+zsbE2ZMkUPPvjgsAMDycJiscjFfdUAAABAShtSqV69erVuu+02vf3223rttdcUCoW0ePFiBQKBY76nsbFRS5Ys0QUXXKDNmzfrrrvu0re+9S2tXLnylMMDic5dER1WxrlqAAAAIGr7Pr96+sJmxxgxGUN58SuvvDLo80cffVRlZWXauHGjLrzwwqO+58EHH9TEiRN1zz33SJLcbrc2bNigu+++W9dee+3wUgNJwh1fqWYCOAAAANJbW1dQv3ptu55+p0nfuXS6vrlomtmRRsSQSvXHdXZ2SpKKi4uP+Zp169Zp8eLFgx67/PLLtWLFCvX39yszM/OI9wSDQQWDwfjnPh+rfEhOA9u/WakGAABAuurtD+uxv+3S/a/vkD8YkiTtOtBtcqqRM+xSbRiGli5dqoULF+r0008/5uu8Xq/Ky8sHPVZeXq5QKKS2tjZVVlYe8Z7ly5frhz/84XCjAQmjttwhi0Vq6+rTfn9QpQ672ZEAAACAMWEYhl5636ufvVKn5oM9kqQzxhfoB5+aobNrjr0wm2yGXaq/8Y1v6L333tPatWtP+FqLxTLoc8Mwjvr4gGXLlmnp0qXxz30+n6qrq4cbFTBNTpZNNSV52tkWUL3Xp1JHqdmRAAAAgFG3pblDP/7zNm3Y3S5JKnfa9Y+Xu/SZOeNltR69ByarYZXqb37zm3rhhRe0Zs0aTZgw4bivraiokNfrHfRYa2urMjIyVFJSctT32O122e2s6CE1uCud2tkWUJ3HpwumUaoBAACQuvZ29OgXr9Trj1v2SpJyMm36u4um6P9cOEW5Wad0+jhhDelPZRiGvvnNb+q5557TqlWrVFNTc8L3LFiwQH/6058GPfbqq69q/vz5Rz1PDaQaV4VDL77vUb2HYWUAAABITYFgSL9d/ZEeenOnevsjkqRr507QP1xeq4qCbJPTja4hlerbbrtNTz31lJ5//nk5HI74CnRBQYFycnIkRbdut7S06IknnpAk3Xrrrbrvvvu0dOlS3XLLLVq3bp1WrFih//iP/xjhPwqQmAYmgG9jWBkAAABSTCRi6JlNe3T3XxrU6o8Omz67plg/uHKGzphQYHK6sTGkUv3AAw9Iki6++OJBjz/66KO64YYbJEkej0dNTU3x52pqavTSSy/pO9/5ju6//35VVVXp3nvv5TotpA1XZfSu6o/2d6kvFFFWxpCuhwcAAAAS0rqPDujHL27T1r3RxaOJxbm6a4lLl8+sOOb8rFQ05O3fJ/LYY48d8dhFF12kTZs2DeVLASljfGGOHNkZ8veG9NH+rvjKNQAAAJCMGtsCWv5SnV7dtk+S5MjO0Lc+MU3XnTdJ9gybyenGXmqeFAcSiMVikbvCqXd2HVS910epBgAAQFLq7O7Xva9/qCfW7VJ/2JDNatGXz5moby+appL89B00TakGxoCr0qF3dh1Uncevz8wxOw0AAABw8vrDEf3727t1z18/VEd3vyTp4tpSfW+JW9PKHSanMx+lGhgDA6vTdQwrAwAAQJIwDENvNLTqJy/W6aP9AUnS9PJ8fe/KGbpoOlfFDqBUA2PAVRH9DV4d12oBAAAgCdR7ffrxn+u0dkebJKkkL0vfuWy6vnBWtTJsDN49HKUaGAO1FQ5ZLFJbV1D7/UGVOtL3zAkAAAAS135/UL96bbv+sL5JEUPKsll148LJuu2SqXJmZ5odLyFRqoExkJuVockleWpsC6je61Opg+0yAAAASBy9/WGtWNuo37yxQ4G+sCTpyjMq9d0rXKouzjU5XWKjVANjxF3piJZqj18XTKNUAwAAwHyGYejP73n0s5fr1dLRI0maNaFAP/jUDJ01udjkdMmBUg2MEVeFUy+972VYGQAAABLC5qZ2/ejP27SpqUOSVFmQrX/8ZK2unj1eVqvF3HBJhFINjJH4BHAvw8oAAABgnpaOHv3ilXo9v2WvJCkn06a/v/g03XLBFOVk2UxOl3wo1cAYGZgAvqPVr75QRFkZTE0EAADA2OkKhvTgqo/0uzd3KhiKyGKRrp07Qf9wea3Kndlmx0talGpgjEwoypHDniF/MKSdbV1yVTjNjgQAAIA0EI4YemZjs+5+dbv2+4OSpHNqivWDT83Q6eMLTE6X/CjVwBixWCxyVTq0fle76j1+SjUAAABG3d92tOlHL9bF5/pMLsnVsiVuLZ5RLouFc9MjgVINjCF3pVPrd7WrzuPTNXPGmx0HAAAAKWrn/i799KV6/XfdPkmSMztD31o0TdctmMwxxBFGqQbG0MDqNMPKAAAAMBo6uvv0679+qN+v261QxJDNatFXzpmob186XcV5WWbHS0mUamAMuSujw8q4VgsAAAAjqT8c0e/X7dav//qhOnv6JUmfcJXpriVuTS3LNzldaqNUA2OotsIhi0Xa7w+qrSuocfl2syMBAAAgiRmGof+ua9Xyl+q0sy0gSaotd+j7n3LrgmmlJqdLD5RqYAzlZmVockmeGtsCqvf4tXAapRoAAADDs22vTz9+cZv+9tEBSdK4/CwtvaxWn58/QRk2zk2PFUo1MMZcFY5oqfb6tHDaOLPjAAAAIMm0+nv1r3/Zrv/c2CzDkLIyrPrawhp9/eLT5MjONDte2qFUA2PMVeHUyx94tY1z1QAAABiC3v6wVqxt1G/e2KFAX1iS9KlZlbrzky5VF+eanC59UaqBMTYwrKzewwRwAAAAnJhhGHrh3b36xSsNaunokSTNri7UP33KrXmTik1OB0o1MMbcldFrtXa0dqk/HFEm510AAABwDBt3t+tHf96mLc0dkqSqgmzdeYVLV82qktVqMTccJFGqgTE3oShH+fYMdQVD2rk/oNoKh9mRAAAAkGD2tHfrZy/X68/veSRJuVk2/f1Fp+nmC6YoJ8tmcjocjlINjDGLxSJXhUMbdrerzuOjVAMAACDO39uvB1Z9pIfXNqovFJHFIn1u3gTdsbhWZc5ss+PhKCjVgAnclc5oqfb6dI3Gmx0HAAAAJgtHDP3nhmb966sNauvqkySdO6VY379yhk4fX2ByOhwPpRowgSs2rKyOYWUAAABpb+2Hbfrxi9tU743+bFgzLk/LrnDpshnlslg4N53oKNWACQaGldVzrRYAAEDa2tHapeUv1emv9a2SJGd2hr596XR99dxJyspgmG2yoFQDJqgtd8hikVr9QR3oCqok3252JAAAAIyR9kCffv3XD/Xk27sVihjKsFr0lXMn6duLpqkoL8vseBgiSjVggjx7hiYV52rXgW7Ve/06fyqlGgAAINX1hSJ6Yt0u3fvXD+XrDUmSLnWXadkSt04rzTc5HYaLUg2YxFXh1K4D3arz+HT+1HFmxwEAAMAoMQxDr23bp+Uv16uxLSBJclU49P0rZ2jhNH4OTHaUasAk7kqnXtnqZVgZAABACvugpVM/ebFO63YekCSNy7frjsXT9bn51bJZGUKWCijVgEkGJoDXexlWBgAAkGpafb365V8a9MymPTIMKSvDqpsX1ujrl0xVvp0alkr4fxMwyYzYBPAP93WpPxxRpo0JjwAAAMmupy+s3725Uw+u/kjdfWFJ0lWzq3TnJ2s1oSjX5HQYDZRqwCTjC3OUb89QVzCkxraAppc7zI4EAACAYYpEDL3w7l79/JV6eTp7JUlzJhbq+1fO0LxJRSanw2iiVAMmsVotclU4tGF3u+o8Pko1AABAktqw66B+9GKd3m3ukBRdPPnHT9bq07OrZLFwbjrVUaoBE7kqB0q1X1efaXYaAAAADEXzwW797JV6vfieR5KUl2XT1y+Zqq8trFF2ps3kdBgrlGrARO7Yueo6D8PKAAAAkoW/t1/3v/GRHlnbqL5wRBaL9L/nV2vp4ukqc2SbHQ9jjFINmMhVES3VTAAHAABIfKFwRH/Y0KxfvbpdBwJ9kqTzTivR96+coRlVTpPTwSyUasBEroroOep9vqAOBvpUnJdlciIAAAAczZrt+/WTF+vUsM8vSZoyLk93LXFrkbuMc9NpjlINmCjPnqFJJbnafaBb9R6fzps6zuxIAAAAiDEMQ1v3+vSvrzbojYb9kqSCnEzdfuk0feXcSVyJCkmUasB0rgqHdh/o1jZKNQAAgOkCwZDe2tGmNxr2a1VDa/x6rAyrRdctmKxvLZqqwlx2F+IQSjVgMnelU3/Zuk/1Xr/ZUQAAANKOYRja2RbQG/WtWtWwX+80HlRfOBJ/3p5h1aXucv1/i6drSmm+iUmRqCjVgMkGhpUxARwAAGBs9PaHtW7nAa2qb9UbDfvVdLB70PPVxTn6RG2ZLnaVacGUEq7HwnFRqgGTzYhdq/Xhvi6FwhFlcDYHAABgxDUf7NaqhmiJ/ttHbertP7QanWmz6JyaEl1cW6pLXGWaMi6P4WM4aZRqwGQTinKUl2VToC+snW0BTS93mB0JAAAg6fWFItqw66DeiBXpHa1dg56vLMjWxbVluqS2VOdPHac8O9UIw8N3DmAyq9UiV6VTG3e3q87jo1QDAAAMk7ezN7Ya3aq1H7Yp0BeOP2ezWjRvUpEuqS3TJa5S1ZY7WI3GiKBUAwnAVeGIlWq/rj7T7DQAAADJIRSOaHNzh96InY3++Iyacfn26Jbu2jItnDZOBTmZJiVFKqNUAwnAHTtXXe9lWBkAAMDxtHUFtbphv95oaNWa7fvl6w3Fn7NYpDOrC6Or0bVlmlnllNXKajRGF6UaSADuyuiWbyaAAwAADBaJGHqvpTN25VWr3t3TOej5wtxMXTQ9uhp94fRSFedxhzTGFqUaSAC1sWu19vmCOhjo4z8GAAAgrXV092nNh21aVd+q1dv360Cgb9Dzp4936pLaMl1cW6YzqwtlYzUaJqJUAwkg356hicW5ajrYrXqvT+edNs7sSAAAAGPGMAxt8/i0qmG/3qhv1aamdkWMQ8877Bm6YPo4XVxbpounl6rMmW1eWOBjKNVAgnBXOtR0sFt1Hj+lGgAApDx/b7/e2tGmN+r3a9X2Vu3zBQc9X1vu0MWu6LbueZOKlGmzmpQUOD5KNZAgXBVO/WXrPtVzrhoAAKQgwzC0o7Urem90/X6t33VQocOWo3MybTp/6jhd4irVxbVlGl+YY2Ja4ORRqoEEMTABvI4J4AAAIEV094W07qMD8SLd0tEz6Pkp4/J0ceze6LNrimXPsJmUFBg+SjWQIAYmgG/f16VQOKIMtjgBAIAktKstEC3RDfv19s4D6gtF4s9lZVi1YEqJLqmNrkZPHpdnYlJgZFCqgQRRXZSrvCybAn1hNbYFNK3cYXYkAACAE+rtD+udxoN6o6FVqxr2q7EtMOj58YU5+oQruhq9YMo45WSxGo3UQqkGEoTValFthUObmjpU5/VTqgEAQMJq6eiJ3xv91o4D6ukPx5/LsFp0dk2xLolt6z6tNF8WC1deIXVRqoEE4q50Rku1x6dPz64yOw4AAIAkqT8c0YZd7VrV0Ko3Glq1fV/XoOfLnfb4vdHnTy2RIzvTpKTA2KNUAwnEFRtWxgRwAABgtlZfb/Te6IZWrf2wTf5gKP6c1SLNm1QUHTJWWyZ3pYPVaKQtSjWQQGbEhpXVefwmJwEAAOkmHDG0pbldb9RHi/TWvYN/yV+Sl6WLaqP3Rl8wbZwKc7NMSgokFko1kECmx85Re329ag/0qSiP/1gBAIDRczDQpzXboyV69fb96ujujz9nsUizJhTqkliRPmN8gaxWVqOBj6NUAwnEkZ2p6uIcNR/sUZ3Xp/NOG2d2JAAAkEIiEUNb9/piV161aktzhwzj0PMFOZm6cHqpLqkt1YXTSzUu325eWCBJUKqBBOOucKr5YI/qPX5KNQAAOGWdPf1a+2Fb/Mqrtq7goOdnVDp1iSu6Gn1mdaEybFaTkgLJiVINJBhXpVOvbtunOoaVAQCAYTAMQw37/PGz0Rt3tyscObQcnW/P0MKp43SJq1QXTS9TRUG2iWmB5EepBhLMwLCyei/DygAAwImFwhHVe/3a1NSujbvb9U7jQXk6ewe9ZlpZvi5xleni2lLNn1SsrAxWo4GRQqkGEoyrInqtVsM+v0LhCFuwAADAIJ3d/drU3K5Nu6Mlektzh7r7woNek51p1fmnjdPFrjJdPL1U1cW5JqUFUh+lGkgwE4tzlZtlU3dfWLsOBDS1zGF2JAAAYBLDMLSzLaCNuw+V6A9bu454nSM7Q3MmFmnexCLNm1Sk+ZOLlJ1pMyExkH4o1UCCsVotqq1waHNTh+o8fko1AABppKcvrHf3dMRL9KamdrUfds3VgCnj8jR3UrRAz5tUpKml+Vx3BZiEUg0kIHelM1aqfbpqdpXZcQAAwCjZ29GjjbEV6E1N7dq216fQYUPFJMmeYdXs6sJogZ5YpDkTC1XCVVdAwqBUAwnIXcGwMgAAUk1/OKJte33REt0UXYn++EAxSSp32jV/UrHmTirS/ElFclc6GSwGJDBKNZCA3JXRYWVcqwUAQPI6GOiLnoOOTeV+b0+Hevsjg15js1o0o9KpeZOK4tu5qwqyZbGwlRtIFpRqIAHVxlaqPZ296ujuU2FulsmJAADA8UQihnbs7zq0lXt3u3a2BY54XWFupubGhonNnVik2dUFys3iR3IgmfFPMJCAHNmZqi7OUfPBHtV5/FpwWonZkQAAwGG6giG929wx6Dy0vzd0xOumluXHJ3LPnVSkKePyGCgGpBhKNZCgXBVONR/sUb3XR6kGAMBEhmFoT/uhgWIbd7er3uvTx+aJKSfTpjMHBopNig4UY7cZkPoo1UCCclc69dq2fZyrBgBgjAVDYX3Q4ovfC72xqV37/cEjXje+MCdeoOdNKpKrwqEMGwPFgHRDqQYSFBPAAQAYG63+Xm3a3aFNsYFi7+/pVF948ECxTJtFM6sK4gV67sQiVRRkm5QYQCKhVAMJamACeIPXr1A4wm++AQAYAeGIoQavP36l1cbd7Wo62H3E60rysuLTuOdNKtIZ4wuUnWkzITGAREepBhLUxOJc5WbZ1N0X1q4D3Zpalm92JAAAkk5nT7+2xAaKbdrdrs1N7Qr0hQe9xmKRassd0RIdGyo2qSSXa60AnBRKNZCgrFaLaisc2tzUoTqPj1INAMAJGIahXQe6B11rtb3VL+NjA8Xy7RmaM7EwfrXVmRML5czONCc0gKRHqQYSmKvCqc1NHar3+nTV7Cqz4wAAkFB6+8N6b0/noGutDgb6jnjdpJJczZtYFN/OPb3cIRvXWgEYIZRqIIHNqIwOK6vzMKwMAABPZ4827e6IT+Te2tKp0MfutcrKsGrW+IL4vdBzJxap1GE3KTGAdECpBhKYKzasrJ5rtQAAaaY/HFG9x6+Nuw9qY1OHNu1uV0tHzxGvK3XYNX9gIvekIs2scsqewUAxAGOHUg0ksNrYtVp7O3vV0d2nwtwskxMBADA62gN92tzcHt/K/W5zp3r6Bw8Us1qit2Mcfq3VhKIcBooBMBWlGkhgzuxMTSjK0Z72HtV7/Tp3SonZkQAAGDEf7e/SE3/bpbU72vTR/sARzzuzMwZN5J5dXag8Oz++Akgs/FsJSHCuCqf2tPeozuOjVAMAkp5hGFq384BWvNmov9a3DnpuSmlevEDPm1Sk00rzZWWgGIAER6kGEtyMSof+u26f6hlWBgBIYn2hiF58f68efrNRW/cemhVyqbtMn59frfmTi1WcxzEnAMmHUg0kuPiwMi/DygAAyaezu19PvdOkx/+2S15fryQpO9Oqz86boJvOr9GU0nyTEwLAqbEO9Q1r1qzRVVddpaqqKlksFv3xj3887utXrVoli8VyxEd9ff1wMwNpxR0r1Q37/Ap/7NoQAAAS1e4DAf3LC1u14Gd/1c9fqZfX16tSh113LJ6uv313kX58zRkUagApYcgr1YFAQLNnz9aNN96oa6+99qTf19DQIKfTGf+8tLR0qF8aSEsTi3OVk2lTT39Yuw4EdBo/gAAAEpRhGNq4u12/e3OnXt22T0bsd8GuCoe+trBGnz6ziuuuAKScIZfqK664QldcccWQv1BZWZkKCwuH/D4g3dmsFtVWOLSluUN1Hh+lGgCQcELhiF7Z6tXv3mzUu80d8ccvml6qWy6YovOnlnDtFYCUNWZnqufMmaPe3l7NmDFD3//+93XJJZcc87XBYFDBYDD+uc/HWVKkN3dltFTXe/z61Cyz0wAAEOXv7dcf1jfr0bd2qaWjR5KUlWHVZ84cr69dUKPp5Q6TEwLA6Bv1Ul1ZWamHHnpI8+bNUzAY1O9//3stWrRIq1at0oUXXnjU9yxfvlw//OEPRzsakDQGzlXXefgFEwDAfHvau/XYW7v09PpmdQVDkqTivCx99dxJ+sq5k1TqsJucEADGzqiX6traWtXW1sY/X7BggZqbm3X33Xcfs1QvW7ZMS5cujX/u8/lUXV092lGBhOWqGJgAzrVaAADzbGnu0MNv7tTLH3jjwzNPK83TzRdM0WfmjFd2JuelAaQfU67UOvfcc/Xkk08e83m73S67nd9wAgNcldHtcy0dPers7ldBbqbJiQAA6SIcMfTatn1asXan1u9qjz9+/tQS3XzBFF00rVRWK+elAaQvU0r15s2bVVlZacaXBpKSMztT4wtz1NLRo3qvT+dMKTE7EgAgxQWCIT2zcY8eeatRuw90S5IybRZdNbtKNy+cohlVzhP8HQAgPQy5VHd1dWnHjh3xzxsbG7VlyxYVFxdr4sSJWrZsmVpaWvTEE09Iku655x5NnjxZM2fOVF9fn5588kmtXLlSK1euHLk/BZAG3JVOtXT0qM5DqQYAjB5vZ68e+9suPfU/u+XrjZ6XLsjJ1FfOnajrFkxWuTPb5IQAkFiGXKo3bNgwaHL3wNnn66+/Xo899pg8Ho+ampriz/f19emOO+5QS0uLcnJyNHPmTL344otasmTJCMQH0oe70qH/rtvHuWoAwKj4oKVTK9Y26k/v7lUodl56ckmuvrawRtfOm6DcLFM2OAJAwrMYhmGYHeJEfD6fCgoK1NnZKaeTrUZITy+979HX/32TZk8o0PPfWGh2HABACohEDL3R0KqH32zUup0H4o+fXVOsmxfWaJG7XDbOSwNIUyfbQ/mVI5AkXBXRYWUN+/wKRwx+yAEADFtPX1jPbt6jFWsbtXN/QJJks1p05RmVuvmCGs2aUGhuQABIIpRqIElMKslTTqZNPf1h7ToQ0Gml+WZHAgAkmVZ/r55ct1u/f3u32rv7JUkOe4a+eM5E3XDeZFUV5picEACSD6UaSBI2q0XTKxx6t7lD9R4/pRoAcNIavH49/OZOPb9lr/rCEUnShKIc3XR+jT5/VrXy7fxICADDxb9BgSQyozJaqus8Pl05i2vpAADHZhiG1nzYpoff3Kk3P2yLPz5nYqFuuWCKFs8oV4bNamJCAEgNlGogibgqogMS6r0+k5MAABJVb39YL2zZq4fX7tT2fV2SJKtF+uTpFfrawimaN6nI5IQAkFoo1UASGRhWVufhWi0AwGAHuoL69/9p0hPrdqmtq0+SlJdl0+fPqtaN59VoYkmuyQkBIDVRqoEk4qqMrlS3dPSos6dfBTmZJicCAJhtR2uXVqxt1LOb9igYip6XrizI1g3nTdYXzp7IfysAYJRRqoEkUpCTqfGFOWrp6FGD16+za4rNjgQAMIFhGFq384AefrNRr9e3xh8/Y3yBbr6gRkvOqFQm56UBYExQqoEk4650qKWjR3UeH6UaANJMXyiiP7+3Vw+/2ahtnuh8DYtFutRdrpsX1ujsmmJZLBaTUwJAeqFUA0nGVeHUf9e1MqwMANJIR3efnnqnSY//bZf2+YKSpOxMqz43r1o3nj9ZU7hmEQBMQ6kGkow7dq56G8PKACDl7WoL6NG3GvWfG/aopz8sSSp12HXDeZP1pbMnqigvy+SEAABKNZBkXJXRCeDbvX6FI4ZsVrb5AUAqMQxDG3a36+E3d+rVbftkGNHHXRUO3XLBFH1qdqXsGTZzQwIA4ijVQJKZXJKn7EyrevrD2n0gwJY/AEgRoXBEL3/g1cNv7tS7ezrjj19cW6pbLpii804r4bw0ACQgSjWQZGxWi2rLHXp3T6fqvX5KNQAkOV9vv/7wTrMe+9sutXT0SJKyMqy6du543XR+jaaVO0xOCAA4Hko1kITclU69u6dTdR6flpxRaXYcAMAw7Gnv1qNv7dIf1jerKxiSJJXkZemrCybpK+dO0rh8u8kJAQAng1INJCFXRXTVoo5hZQCQdDY3tevhtY16+X2PIrHz0lPL8nXzwhpdM2e8sjM5Lw0AyYRSDSShgQngdR6u1QKAZBCOGHptm1cPv9moDbvb448vnDpOX7ugRhdNK5WVwZMAkJQo1UASclVES3VLR498vf1yZmeanAgAcDSBYEj/taFZj7y1S00HuyVJmTaLPj17vG6+oCb+S1IAQPKiVANJqCA3U+MLc9TS0aN6j19n1xSbHQkAcBhPZ48e+9su/cf/NMnXGz0vXZibqa+cM0nXLZikMme2yQkBACOFUg0kKVeFI1qqvT5KNQAkiA9aOvXwmzv15/c8CsUOTNeMy9NNC2t07dzxys3iRy8ASDX8mx1IUu5Kp/5a38q5agAwWSRi6PX6Vj28dqfe3nkw/vg5NcW6+YIpWuQq47w0AKQwSjWQpFyVTAAHADP19IW1ctMePbK2UTvbApKkDKtFV86q1M0Lp+iMCQUmJwQAjAVKNZCkBobbNHj9CkcM2VgFAYAx0erv1e/X7daTb+9We3e/JMmRnaEvnTNR1y+YrKrCHJMTAgDGEqUaSFKTS/Jkz7Cqpz+spoPdqhmXZ3YkAEhK/eGI2rv71NHdr/ZAn9q7+9Te3a+DgT51dPfpYKA/+r+x1+xp71Z/OHpeuro4RzedX6PPza9Wvp0fqwAgHfFvfyBJ2awW1VY49N6eTtV7fJRqAJAUDIXVESvE7d19ag/0x/43WpSjhfmwzwN98gdDQ/468yYV6eaFNVo8s4KdQgCQ5ijVQBJzVzj13p5O1Xl8uuKMSrPjAMCI6u0PH1mOP/bX0dXkQ6vKgb7wsL6WxSIV5mSqKDdLRXlZKso9/K9jn8f+utxp16QSfpEJAIiiVANJLD6szMuwMgCJyzAMdfeFj1KO+3SwO7a1+mPl+GB3n3r7I8P6ejarRUW5mSrMzVJxbpYKczNVnJcV/Tzv0ONFh/21MyeTFWcAwLBQqoEkNjCsjGu1AIwVwzDkD4bUEejXwY9tpf54OY6X5+5+9YWGV5AzbZZjluPoCnK0HB/66yw57BlcYQUAGDOUaiCJuSuipXpPe498vf1yZmeanAhAMolEDPl7Q0eU4/bDC3GsPB8+sCsUMYb19bIyrIPK8ZGF+NBfR8tzpvLtGbJYKMgAgMRFqQaSWEFupqoKsrW3s1cNXr/OmlxsdiQACSAYCqv5YI92tQW060BA+7uC8ZXlw1eT27v7NMx+rJxMW7z4Dqwex88hH3b++PDX5GTaKMgAgJRDqQaSnKvSqb2dvar3+CjVQBrp7Q+r+WC3dh3ojpfn3Qe61dgW0N7OHhlDKMv59ozBW6sHziPnDS7Ih68mZ2faRu8PBwBAEqFUA0nOXenQ6/Wt2uZhWBmQanpj99A3tgW0+0AgXqB3H+g+YXHOy7Jp8rg8TS7JU0VB9lHLcXFulgpyM2XPoCADADBclGogybli56rrvQwrA5JRT9/Hi3NAu9q6tftAQHs7e4/73nx7hiaPy9Xkkmh5jpboXE0qydO4/Cy2WgMAMAYo1UCSG5gA3uD1KxIxmHgLJKCevnBse3ZAjbHCPFCevb7jF2dHdoZqxuVpUkmeamKFeaBIF+dRnAEAMBulGkhyk0tyZc+wqju22jV5XJ7ZkYC0FAiGtPtAtDA3Hghod1t39H8PBLTPFzzue52x4jw5Vp4nl+TGt24X5WZSnAEASGCUaiDJZdisqq1w6L09narz+CjVwCjqCoaiq8xt3bGV5uj55l0HAmr1H784F+ZmDlptjq4+R1eci/KyxuhPAAAARhqlGkgBroFS7fXrijMqzY4DJDV/b3+8KEenanfHt223dR2/OBflZsZXmCfHtmkPrDwX5lKcAQBIRZRqIAUMnKuu8zCsDDgZvt5+7W4bXJwHzjy3dfUd970leVnRFeZYeZ5UkhtddS7OU0Fu5hj9CQAAQKKgVAMpgAngwJE6e/pjK8yxLdqH3eV8IHD84jwuPyu2wjz4fPPEklwV5FCcAQDAIZRqIAW4Kx2SpOaDPfL39suRzQ/9SA+d3f3xYWDxc86x1ef27v7jvndcvl01h23PPnzlmX+GAADAyaJUAymgMDdLlQXZ8nT2qsHr1/zJxWZHAkZMe6AvvsI8cJdzY+ycc8cJinOZwx4vyvGzzrEinW/nP4EAAODU8RMFkCLclU55OntVR6lGEopEDDUd7NbWvT417PPHVp6jZ507e45fnMud9thU7TxNit3fPFCk8yjOAABglPHTBpAiXBUOvV7fyrAyJLxgKKwP93Vp216ftu7t1DaPT3Uev7qCoWO+p8KZrcmxwhy9jiq62jypJFe5WfynDAAAmIefRIAUMTABvJ5SjQTi6+1X3V6ftsY+tnl82tHqV3/YOOK19gyrXBUOuSudsTuco3c5TyzOVU6WzYT0AAAAJ0apBlLEwLCyeq9fkYghq9ViciKkE8MwtM8X1DZPp7a2HCrQTQe7j/r6gpxMzaxyakalUzPHOzWzqkBTxuUpw2Yd4+QAAACnhlINpIjJJXnKyrCquy+s5vZuTSrJMzsSUlQ4YqixLaBtntj27b0+bdvrO+Y1VeMLczRjoEBXOTVzfIGqCrJlsfCLHwAAkPwo1UCKyLBZVVvu0Pstnarz+CjVGBG9/WE1eP3xAr11r0/1Hr96+sNHvNZmtei00jzNrCqIF+gZVU4V5maZkBwAAGBsUKqBFOKqGCjVfn3y9Eqz4yDJdHT3RVedPbHt23t92rG/S+HIkeefczJtclU6Ylu4CzSzyqnaCoeyMzn7DAAA0gulGkghA8PKmACO4zEMQ3s7e+PTtwcKdEtHz1FfX5yXFV91jq5AF6hmXJ5snNsHAACgVAOpxHXYsDJAkkLhiHa2BeJnnwcGiHV0H/3u5+riHM2sLNCMqtj556oClTvtnH8GAAA4Bko1kELcFdGV6qaD3fL39suRnWlyIoylnr6w6ry+Q+V5b6fqvX4FQ5EjXpthtWhqWX70/HOsQLsrnSrI4XsGAABgKCjVQAopystShTNbXl+vtu/za96kYrMjYZQcDPQNWn3eurdTjW0BHeX4s/KybHIfNjhsZlWBppXny57B+WcAAIBTRakGUoy70iGvr1fbPJTqVGAYhva093ysQPvk9fUe9fXj8u2xbduHCvSk4lzuLQcAABgllGogxbgqnXqjYb/qGVaWdPrDEe1o7YoPDtu6t1PbPD75e0NHff3kktz49u2BLdxljuwxTg0AAJDeKNVAimECeHIIBEOqO+zqqq2eTm33dqkvfOT550ybRdPLB66vcmrm+AK5K53Kt/OvcAAAALPxExmQYtwV0QngDV6/IhGDbb8JYL8/eOjqKk+0RO86EJBxlPPPDnuG3APbt2PXV00ty1dWhnXsgwMAAOCEKNVAiqkZl6esDKsCfWE1t3drUkme2ZHSRiRiqOlgd3xw2LbYSvR+f/Cory932jWzqmBQgZ5QlMMvQgAAAJIIpRpIMRk2q6aX5+uDFp/qPH5K9SjqCob02jav3m3u1Na9narz+NUVPPL8s8US/WXHzKqCWHmOnoEel283ITUAAABGEqUaSEHuCmesVPv0ydMrzI6TUiIRQ+t2HtAzG/folQ+86ukPD3o+K8MqV8Wh888zqgrkrnQoN4t/3QIAAKQifsoDUpArNqys3suwspGyqy2glZv26NlNLWrp6Ik/flppni6uLYtdY1Wg00rzlGHj/DMAAEC6oFQDKchdGR1WVu/1m5wkufl7+/Xiex49s3GPNuxujz/uyM7Qp2dX6bPzJujM6kJZLJyBBgAASFeUaiAFuSuiK9W7D3SrKxji6qUhCEcMrfvogJ7Z2KxXtnrV2x+94spqkS6cXqrPzpugS93lys60mZwUAAAAiYCftIEUVJSXpQpntry+XjV4/Zo3qcjsSAlv5/6u+PZuT2dv/PGpZfn67LwJ+syc8Sp3ZpuYEAAAAImIUg2kKFelQ15fr+o8Pkr1MfgO29698bDt3c7sDF195nh9dt4EzZpQwPZuAAAAHBOlGkhR7kqnVjXsZ1jZx4Qjht7a0aZnNu7RX7Z6FQwd2t590fRSfXZetRa5y9jeDQAAgJNCqQZSlKsiOqyszsOwMkn6aH+XVm6Mbu/2+g5t755Wlq/PzZ+ga84crzK2dwMAAGCIKNVAinLHrtVq8PoViRiyWtNvC3NnT7/+/N5ePbNxjzY3dcQfL8jJ1NVnRqd3nzGe7d0AAAAYPko1kKKmjMtTls2qrmBIe9p7NLEk1+xIYyIcMbT2sO3dfbHt3TarRRfHpnd/wl0mewbbuwEAAHDqKNVAisqwWTWtPF9b9/pU5/WlfKne0erXMxtb9NzmPdrnC8Yfry136LPzJujqOVUqc7C9GwAAACOLUg2kMHelM1qqPT5dPrPC7DgjrrO7X3+Kbe/e0twRf7wwN1PXxKZ3z6xysr0bAAAAo4ZSDaSwgWFl9Sk0rCwUjujN2Pbu17btG7S9+5La6PbuS1xs7wYAAMDYoFQDKWxGbFhZXQpcq/XhPr+e2bRHz21qUav/0PZuV0Vse/eZ41XqsJuYEAAAAOmIUg2kMFesVO8+0K1AMKQ8e3L9I9/R3ac/vRvd3v3uns7440W5mbqa7d0AAABIAMn1EzaAISnOy1K50659vqDqvX7Nm1RkdqQTCoUjevPDw7Z3h6PbuzOsFl3iKotu764tU1aG1eSkAAAAAKUaSHmuCqf2+far3utL6FK9fZ9fz2zco+c2t2j/Ydu73ZXO2PbuKo3LZ3s3AAAAEgulGkhx7kqnVm/frzpP4p2rbg/0xad3v3fY9u7ivCxdc+Z4XTtvvGZWFZiYEAAAADg+SjWQ4tyViTUBPBSOaPX2/Xpm4x79ta510PbuT8S2d1/M9m4AAAAkCUo1kOLcsWFl9V6/DMMwbahXvdenlRv36LnNe9XWdWh798yq6PbuT8+uUgnbuwEAAJBkKNVAiqsZl6csm1VdwZD2tPeoujh3zL72wUCfXtjSomc27dEHLYe2n5fkZemaOeN17dwJmlHlHLM8AAAAwEijVAMpLtNm1bTyfG3d61Odxzfqpbo/HNHqhtj27vp96g8bsRwWLXKV67PzJuii2lJl2tjeDQAAgORHqQbSgKvCGSvVfi2eWTEqX6POE93e/cctLWrr6os/fvp4pz47d4I+feZ4FedljcrXBgAAAMxCqQbSQHxYmXdkJ4AfDPTp+S0tembjHm3de+jvPS4/S5+ZM17XzpsgVwXbuwEAAJC6KNVAGhgYVjYS12r1hyNa1bBfz2xs1uv1rYO2d1/qjm7vvnA627sBAACQHijVQBpwVURXqncf7FYgGFKefej/6G/b69MzG/fo+S0tOhA4tL171oQCfXbeBF01q0pFbO8GAABAmqFUA2mgJN+uModdrf6gGvb5NXdi0Um970BXUM9v2atnNu7RNs/h27vt+l9zo9O7a2OFHQAAAEhHlGogTbgrnWr171e95/ilui8U0RsNrXpm4x69Ud+qUCS6vTvLZtVlM6Lbuy+YNk4ZbO8GAAAANOSfitesWaOrrrpKVVVVslgs+uMf/3jC96xevVrz5s1Tdna2pkyZogcffHA4WQGcAldsWNmxzlVv3dupH/5pq85d/lf93e836rVt+xSKGJo9oUA/unqm3vneIt3/5bm6xFVGoQYAAABihrxSHQgENHv2bN1444269tprT/j6xsZGLVmyRLfccouefPJJvfXWW/r617+u0tLSk3o/gJHhjk3hPnwCeFtXUH/cHJ3eXe/1xx8vc9j1mbnj9dm5EzStnO3dAAAAwLEMuVRfccUVuuKKK0769Q8++KAmTpyoe+65R5Lkdru1YcMG3X333ZRqYAwNTACv9/j1ygcePbOxRasaPra9e2Zse/dUtncDAAAAJ2PUz1SvW7dOixcvHvTY5ZdfrhUrVqi/v1+ZmZlHvCcYDCoYDMY/9/lG9m5dIB1NKc1Tls0qfzCkW5/cFH/8zOrC+PTugtwj/3kEAAAAcGyjXqq9Xq/Ky8sHPVZeXq5QKKS2tjZVVlYe8Z7ly5frhz/84WhHA9JKps2qs2qK9NaOAyp32vWZORP02XnjNbWM7d0AAADAcI3J9G+LxTLoc8Mwjvr4gGXLlmnp0qXxz30+n6qrq0cvIJAm7vviXDUd7Nbp4wtksx79nz8AAAAAJ2/US3VFRYW8Xu+gx1pbW5WRkaGSkpKjvsdut8tut492NCDtFOVlqSgvy+wYAAAAQMoY9UlECxYs0GuvvTbosVdffVXz588/6nlqAAAAAACSxZBLdVdXl7Zs2aItW7ZIil6ZtWXLFjU1NUmKbt2+7rrr4q+/9dZbtXv3bi1dulR1dXV65JFHtGLFCt1xxx0j8ycAAAAAAMAkQ97+vWHDBl1yySXxzwfOPl9//fV67LHH5PF44gVbkmpqavTSSy/pO9/5ju6//35VVVXp3nvv5TotAAAAAEDSsxgDU8MSmM/nU0FBgTo7O+V0Os2OAwAAAABIcSfbQ0f9TDUAAAAAAKmKUg0AAAAAwDBRqgEAAAAAGCZKNQAAAAAAw0SpBgAAAABgmCjVAAAAAAAME6UaAAAAAIBholQDAAAAADBMlGoAAAAAAIaJUg0AAAAAwDBRqgEAAAAAGCZKNQAAAAAAw0SpBgAAAABgmCjVAAAAAAAME6UaAAAAAIBholQDAAAAADBMlGoAAAAAAIaJUg0AAAAAwDBRqgEAAAAAGCZKNQAAAAAAw5RhdoCTYRiGJMnn85mcBAAAAACQDgb650AfPZakKNV+v1+SVF1dbXISAAAAAEA68fv9KigoOObzFuNEtTsBRCIR7d27Vw6HQxaLxew4x+Tz+VRdXa3m5mY5nU6z4wBH4HsUiY7vUSQ6vkeR6PgeRTJIlu9TwzDk9/tVVVUlq/XYJ6eTYqXaarVqwoQJZsc4aU6nM6G/OQC+R5Ho+B5FouN7FImO71Ekg2T4Pj3eCvUABpUBAAAAADBMlGoAAAAAAIaJUj2C7Ha7/vmf/1l2u93sKMBR8T2KRMf3KBId36NIdHyPIhmk2vdpUgwqAwAAAAAgEbFSDQAAAADAMFGqAQAAAAAYJko1AAAAAADDRKkGAAAAAGCYKNUj6De/+Y1qamqUnZ2tefPm6c033zQ7EiBJWr58uc466yw5HA6VlZXpmmuuUUNDg9mxgGNavny5LBaLbr/9drOjAHEtLS36yle+opKSEuXm5urMM8/Uxo0bzY4FSJJCoZC+//3vq6amRjk5OZoyZYr+7//9v4pEImZHQ5pas2aNrrrqKlVVVcliseiPf/zjoOcNw9C//Mu/qKqqSjk5Obr44ou1detWc8KeIkr1CPnDH/6g22+/Xd/73ve0efNmXXDBBbriiivU1NRkdjRAq1ev1m233aa3335br732mkKhkBYvXqxAIGB2NOAI69ev10MPPaRZs2aZHQWIa29v1/nnn6/MzEy9/PLL2rZtm/71X/9VhYWFZkcDJEk///nP9eCDD+q+++5TXV2dfvGLX+iXv/yl/u3f/s3saEhTgUBAs2fP1n333XfU53/xi1/oV7/6le677z6tX79eFRUVuuyyy+T3+8c46anjSq0Rcs4552ju3Ll64IEH4o+53W5dc801Wr58uYnJgCPt379fZWVlWr16tS688EKz4wBxXV1dmjt3rn7zm9/oxz/+sc4880zdc889ZscC9N3vfldvvfUWu9CQsD71qU+pvLxcK1asiD927bXXKjc3V7///e9NTAZIFotFzz33nK655hpJ0VXqqqoq3X777brzzjslScFgUOXl5fr5z3+uv/u7vzMx7dCxUj0C+vr6tHHjRi1evHjQ44sXL9bf/vY3k1IBx9bZ2SlJKi4uNjkJMNhtt92mK6+8UpdeeqnZUYBBXnjhBc2fP1+f+9znVFZWpjlz5uh3v/ud2bGAuIULF+qvf/2rtm/fLkl69913tXbtWi1ZssTkZMCRGhsb5fV6B/Unu92uiy66KCn7U4bZAVJBW1ubwuGwysvLBz1eXl4ur9drUirg6AzD0NKlS7Vw4UKdfvrpZscB4p5++mlt2rRJ69evNzsKcISdO3fqgQce0NKlS3XXXXfpnXfe0be+9S3Z7XZdd911ZscDdOedd6qzs1Mul0s2m03hcFg/+clP9MUvftHsaMARBjrS0frT7t27zYh0SijVI8hisQz63DCMIx4DzPaNb3xD7733ntauXWt2FCCuublZ3/72t/Xqq68qOzvb7DjAESKRiObPn6+f/vSnkqQ5c+Zo69ateuCBByjVSAh/+MMf9OSTT+qpp57SzJkztWXLFt1+++2qqqrS9ddfb3Y84KhSpT9RqkfAuHHjZLPZjliVbm1tPeK3L4CZvvnNb+qFF17QmjVrNGHCBLPjAHEbN25Ua2ur5s2bF38sHA5rzZo1uu+++xQMBmWz2UxMiHRXWVmpGTNmDHrM7XZr5cqVJiUCBvuHf/gHffe739UXvvAFSdIZZ5yh3bt3a/ny5ZRqJJyKigpJ0RXrysrK+OPJ2p84Uz0CsrKyNG/ePL322muDHn/ttdd03nnnmZQKOMQwDH3jG9/Qs88+q9dff101NTVmRwIGWbRokd5//31t2bIl/jF//nx9+ctf1pYtWyjUMN35559/xFWE27dv16RJk0xKBAzW3d0tq3Xwj/Y2m40rtZCQampqVFFRMag/9fX1afXq1UnZn1ipHiFLly7VV7/6Vc2fP18LFizQQw89pKamJt16661mRwN022236amnntLzzz8vh8MR31VRUFCgnJwck9MBksPhOOKMf15enkpKSjj7j4Twne98R+edd55++tOf6vOf/7zeeecdPfTQQ3rooYfMjgZIkq666ir95Cc/0cSJEzVz5kxt3rxZv/rVr3TTTTeZHQ1pqqurSzt27Ih/3tjYqC1btqi4uFgTJ07U7bffrp/+9KeaNm2apk2bpp/+9KfKzc3Vl770JRNTDw9Xao2g3/zmN/rFL34hj8ej008/Xf/v//0/ritCQjjW2ZRHH31UN9xww9iGAU7SxRdfzJVaSCh//vOftWzZMn344YeqqanR0qVLdcstt5gdC5Ak+f1+/eAHP9Bzzz2n1tZWVVVV6Ytf/KL+6Z/+SVlZWWbHQxpatWqVLrnkkiMev/766/XYY4/JMAz98Ic/1G9/+1u1t7frnHPO0f3335+Uv0ynVAMAAAAAMEycqQYAAAAAYJgo1QAAAAAADBOlGgAAAACAYaJUAwAAAAAwTJRqAAAAAACGiVINAAAAAMAwUaoBAAAAABgmSjUAAAAAAMNEqQYAAAAAYJgo1QAAAAAADBOlGgAAAACAYaJUAwAAAAAwTP8/xXCquJmuwDAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "w = 0\n",
    "cvs = np.array(uncerts)[:, w:].sum(axis=1)\n",
    "cvs = cvs/min(cvs); print(np.argmin(cvs))\n",
    "plt.plot(cvs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1dbb19b90>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in np.argsort(cvs)[:5]:\n",
    "    plt.plot(x, uncerts[i], label=f'{str(i+1)}')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ErrorbarContainer object of 3 artists>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.errorbar(x, np.array(mean_coeffs[2])[:,0], yerr=2*np.array(std_coeffs[2])[:,0], \n",
    "             fmt='-', color='blue', ecolor='lightgray', elinewidth=3, capsize=0)\n",
    "plt.errorbar(x, np.array(mean_coeffs[2])[:,1], yerr=2*np.array(std_coeffs[2])[:,1], \n",
    "             fmt='-', color='red', ecolor='lightgray', elinewidth=3, capsize=0)\n",
    "plt.errorbar(x, np.array(mean_coeffs[2])[:,2], yerr=2*np.array(std_coeffs[2])[:,2], \n",
    "             fmt='-', color='black', ecolor='lightgray', elinewidth=3, capsize=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "ard_uncerts = [[] for _ in range(len(candidates))]\n",
    "ard_mean_coeffs = [[] for _ in range(len(candidates))]\n",
    "ard_std_coeffs = [[] for _ in range(len(candidates))]\n",
    "for i, indices in enumerate(candidates):\n",
    "    for j in range(0, len(Theta_grouped), 1):        \n",
    "        # model = BayesianRidge(fit_intercept=False)\n",
    "        model = ARDRegression(threshold_lambda=2*1e4, fit_intercept=False)\n",
    "        model.fit(Theta_grouped[j][:, indices], Ut_grouped[j].flatten())\n",
    "        \n",
    "        coeff = model.coef_\n",
    "        std_coeff = np.sqrt(np.diag(model.sigma_))\n",
    "        ard_uncerts[i].append(std_coeff.sum()/np.linalg.norm(coeff, lnorm))\n",
    "        ard_mean_coeffs[i].append(coeff)\n",
    "        ard_std_coeffs[i].append(std_coeff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1dbc66110>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "w = 0 # considering from x_{5}\n",
    "ard_cvs = np.array(ard_uncerts)[:, w:].sum(axis=1)\n",
    "ard_cvs = ard_cvs/min(ard_cvs); print(np.argmin(ard_cvs))\n",
    "plt.plot(ard_cvs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fs = 12\n",
    "with plt.style.context('science'):\n",
    "    fig, ax = plt.subplots(figsize=(4, 3))\n",
    "    for i in np.argsort(ard_cvs)[:5][::-1]:\n",
    "        ax.plot(x, ard_uncerts[i], label=f'{str(i+1)}')\n",
    "    ax.set_ylabel(\"$R$\", fontsize=fs)\n",
    "    ax.set_xlabel(\"$x$\", fontsize=fs)\n",
    "    # ax.set_title(f\"$\\epsilon$ = {noise_lv}\", fontsize=fs)\n",
    "    ax.legend(title=\"$s_{k}$\")\n",
    "    fig.tight_layout()\n",
    "    # plt.savefig(f\"/Users/pongpisit/Documents/latex/UBIC-ACML/figures/ks_r_noise0-01.pdf\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cgQMHaj1YpFYQLCE3N5eDBw8yfvx4kpKS2m3JOqFlOuN10OoBH/SB3NXVFag5xFPV8uXLLdcyodPq6Im8quc9F2rX0a+D2rT6Tdu8vDxjsIf6k6fV9UEgCIIgNF2rB/z09HSys7MbdXDVoiiCIAhCy7T6kE5MTAzDhw/H3d0dDw8PVCpVrXlzJFHEXBAEwaxaPeC7urqSnp7OoUOHyM3NZfXq1URHR9fSMIlly5ZZtnWCIAidiE5n2eevc+Jv1Xq1dSVNq298XxAEQWiaVh/Dry4sLIzDhw/z6KOPcueddwLXi5i3heyZYqWtIAgdhaVX2jYY8Ddv3syCBQsYPny4sdfv6urKnDlz2LJlS8tb10KGmrazZs2ydlMEQRBaxBwBf9asWWzbtg1vb+8a+xpcy61SqdixYwcAO3furNY4SydvFgRB6DysPqQTGBhY5768vDyzNkYQBKEzs3rAz8jIMA7dVL1Ju2XLFmP6BUEQBKHlrJJaoar58+czY8YMpk+fbsyBbSiYsH37dsu2ThAEoROxesAH2LRpEyqVip07d5Kfn09oaGibmKEjCILQkbSJgA/6kmiNqXAjNEyn01FSUoKzszMFBQUkJCSgVqupqKhAq9Xi4OBAVFQUSqWSjIwMHBwc6N69O3J5gyNwQiel0+k4e/Ys58+f5+rVqzg6OrJ7925OnDiBi4sLzs7OAIwYMYLRo0c3uWSl0DraTMDftWuXscblyJEjmTp1qsUa1dGcPHmSDz74gJ07d5KRkYFarcbX15ebbrqJjIwMk3qohvskn332GZIkcfLkSfLy8lAoFPTt25fBgwfz0EMPceONN9YoOyl0DhqNhqtXr+Lk5MT69et54YUXuHr1KrpqyzSdnZ0pLi6uMZvOxcUFpVKJVqtFqVQybtw4brvtNm6++WaGDRuGQqFozf8doQpLB/waFa9qM2HCBHJzc409fJVKhUwmqzNPfmtqaxWvioqKyM7O5n//+x+nT58mIyOD7Oxs1Go1crmcbt264eLiQn5+Pu7u7vj4+ODk5ESvXr2ws7MjJyeHEydO4OjoiK2tLZmZmVy8eJGioiKT15HJZAwYMABXV1emT5/OtGnT6N27d4vaLjRPa1SwOnnyJO+88w579+7lr7/+wt3dneLiYoqKirCxsaFHjx4EBQURFBSEXC5n9uzZ3Hzzzeh0Oo4ePUplZSWFhYUcOnSIq1evYm9vz6+//sru3bvRaDSAfpp17969+e677+jatSuXL19m8ODBZvt/EBp24QL06qX/vVUqXlW3YsUKVq9ejb+/v8n2lJQUYmJiWLJkScta1UGcOnWKRx99lJ9++snYo7Kzs2P8+PGMHTuW6dOnM2bMGGxsbJr1/CUlJaSkpFBWVoZarWbfvn289957/PXXX+zdu5d58+bh4+PDihUrjPVIhfatvLyclJQUXn/9db799luTfYWFhcyYMYN58+YxcODAOtOcyOVykzQpVXPMazQakpOT+frrr0lLS+PSpUucPHmSQYMGGR9jb2/PhAkTWL58Of369TPz/6FQndV7+Js3b2batGm17luzZg2RkZEWaVhjDR8+HG9vb2bNmtWk1bbm6JWdPn2ajRs3UlJSwueff056ejo+Pj6MHDmSCRMmMHv2bBwdHRv9fE2h0WjYs2cPP/74I2lpaSQnJ3P69GmcnJz44IMP6NGjB4WFhUyePLnZHzJCw2q7jgyqXk+Nvd7UajWbNm3i3XffJS8vjytXrlBeXs6IESN49dVX8fb2pqysjL59++Lg4GD2/5+SkhJ++OEH0tLSOHbsGNu3bzeutwkLC+Pbb7/F3t7e7K8r6J07Bz4++t+bG/w3bNjAhg0bOHfunMlwMTSih19fgrS2kDzNkFqhNaWnp7NgwQK+/vprY2/e29ubxYsX8/zzz7dKMWqlUsmYMWMYM2YMoA8Uv/zyC4888ojxg6akpAQ7Ozv+/ve/8/rrr9f4lia0TH3B3rBfo9GQnp7e4PF5eXl89tlnxMfHk5+fD4CDgwPTpk3jwQcfZMyYMa1yXTk6OhIREWHSxlWrVrF06VJ27txJ7969GTVqlPF679Gjh8Xb1JmYK7XCrFmzmDJlSo19jVp4VVsR8127dtV5IXcEGo2G1NRUfv31V1JTU0lNTWXz5s10796dvn378tVXX+Hu7s7HH39MaWkpZ86c4aWXXmqVN2Vt7O3tufPOOzly5AjLli1jxowZ3HPPPchkMjZu3Ei/fv2Ii4szjtcKlpeenl7veyQ9PZ2CggIAysrK+Pzzz9FoNCiVSl544QV2797Nhx9+yLhx46x2XSmVSh5//HHOnDnDH3/8QXh4ODt37mTVqlV4e3tz2223tYl7eR2F1WfpVF14VfWmbXh4eK2FUdq7qr2uc+fOcdddd/H0008zatQoHn74YcrLywkICGDy5MksWbKELl26WLnFpjw9PY2lJ9VqNTt37uShhx4iNzeXrVu3UlhYSGlpKWFhYYwePdrKre28tm/fzrJlyygsLOT+++9nw4YNlJWVERISwssvv0zfvn0JCgqyWqCvTiaTcdNNN/Hpp5+SnJzMqlWriI+PZ/fu3dx4443MmDGDTz75RAz3tJDVx/ANDh06xMGDB8nPzyc8PNzkRpA1mXuWDmDcHhMTY7xZ1qVLF4KCgvjggw+46aab2swbsSFqtZo///yTdevWsWPHDrKysvDw8CA3N5dJkybx0ksvMXz4cGs3s82r7XoJCgpq8rfcAwcOsGrVKvbv349CocDR0ZHCwkLc3d155plnmDRpEnK53OyzfCyhsrKSr776ildeeYXU1FT+85//4Ovry9ixY7npppus3bx2KTsb/Pz0v1tllg7o8+aoVCpjOUNDPvw5c+a0rEV1SElJITIyssYNh9by119/sXz5cg4cOICzszP+/v74+Pjw3HPPceONN7b5N2JV9vb23HjjjYSGhpKWlsbHH3/MG2+8weDBgzl48CAjRoxgypQpJCQkiJu7FiZJErGxsZSUlADw6aefMmDAALKysggMDDQurGtLPfv62NjYMHXqVAYMGMB7773HBx98YFwLEBQUxOrVqxk3bpyVW9m+WD152tq1a5Ekif379xu3GeZ+r1271uwNSkhIAPRB3xo0Gg1PPvmksZD7pk2b+Pzzz9m6dSu33HJLu3gj1kapVBIcHMy//vUvFi1aRF5eHlevXuXxxx+nd+/e2NjYoNPpGl3AXqBRvfvTp0+zYMECUlNTkclkrFq1ij59+jBw4EAGDx5Mv3796Nu3r8kq6vZ0jSmVSgYNGkRcXBwnTpzghRdeYPDgwaSnpxMWFsawYcO4fPmytZvZblg94EuSxLRp09i0aZPJdldXV9zd3c3eoIiICEJDQ83+vPXJyclhyZIlZGVloVQqiYuL45FHHkGpVNKzZ8928fW6MQxvzsWLF/PVV18REBDAd999x+jRo0lMTGTFihX07duXp556iitXrli7uW1GQ7NxanPlyhWio6OZMmUKycnJxqmNJSUl/P7778YpxAqFgpCQEJOf9nqtBQUF8fLLL5OcnMyPP/7IgAEDOHz4MGFhYXz99dfk5uZau4ltntUDfn1TLzMzM83amNZWXFzMypUrmThxIt988w3Hjx8HwN/fnytXruDl5dUhl5krlUrs7OxYtmwZ+fn5zJo1iwkTJvDhhx9y//338+GHHxIYGMirr75qHH7ozJo6s+nXX3/l7rvvZs+ePXh5efHdd99x2223AfpvsG5ubtx1111A++rNN5ZSqeTOO+8kOTmZdevWUVxczNSpU+nevTsPPPAAhYWF1m5im2X1gC9JEjExMTWW9q9YsYKrV69arGGW9sMPPzBx4kQ+/vhj7rvvPuPfSqWSkJAQysvL8ff3b9c9rroY/h8nTJjAtm3b+PDDD3n55ZcpLS3lhx9+ICYmhilTpvDyyy+zfv16azfXquqbR19VeXk5x44dA2DgwIH0798f0Pf0q+a4OXbsGKNGjcLOzs4yDW5DunTpwsMPP8yhQ4d48sknsbW15bPPPqNbt2785z//Qa1WW7uJbU7VgK/V6up+YDM1GPAjIyNxd3fH1dUVT09PPD09USgUHDhwoE2kVTAUMTf8NLaYeUBAAOPGjePbb79l3rx5uLq6Atfn36elpeHi4kJqamqHnbtub2/PrbfeykMPPcT8+fP5+OOP8fb25rnnnmPr1q387W9/Y8SIEajVal5//XU2b97cqcpaNmYoR6vV8s033zB58mQee+wxKioq8PT0xN3dna5du6LVajl58qTx8efPn6+11mhH5ubmxttvv8358+d57LHHkMlkvP/++9xyyy0cOXKkw76/mqPq22vAgIFNGkUxFC83/NRWxLxRXdcFCxYQHR1tnJYZGhraZlZtNnelbf/+/XnppZfq3J+Xl0ffvn1b0LL2xd7enltuucU4z/rXX3/lo48+4s8//2Tu3Lls377dOEtpyZIlnX72hSRJ/P7777z11lucPHmS8PBwYy8W9GtVxo8fT3x8PCdOnKBfv35IksTFixfpZciO1cm4urryzjvv8Mgjj/DOO++wfv165s+fz+DBgwkLC+Puu++2dhOtqrKykrQ0FaD/dujt7YtWq2308dXTyzRrpa2Bq6srYWFhTJs2rc0Ee0uqqKjoFF+7q7K3tyckJIR//OMfPP3007z//vt06dKFp556iqeeeop169YBGN+cnb1ntmLFCnJzc/nss8946623jO8LtVrNuXPn6NevHwEBAWzdupXw8HBGjRqFVqvtdD38qpRKJaGhoaxevdo49fmNN95g4sSJ9OrViy+//LJTXlcHDx7kzjvv5JdffjVuW7HiLfwMk/LNpE1X1DDkFGlNhvFtSZLw9vbukGP4DTGcgwcffJC1a9fSu3dvFixYQHl5OYMGDeLee++lT58+DeaKae+qBx7DWpQDBw4gk8k4f/48OTk5eHp6mjzu6NGj6HQ6Bg0axMMPP8zRo0dN7oEZPhg6yuyv5rC3t2fevHmcPHmSN998kwkTJnDhwgVmzZrFjBkzKCsrs3YTW8Xx48eZMWMGI0eO5NKlS7i6uhn36cw/hN/2An5SUhILFy4EYMmSJcZ5+a2toqLC+PW8M7Ozs2P58uUUFRXx2GOPsW7dOrZu3Ur37t1JT0/ns88+Izg4mOjo6A41h7/qB5lKpWLBggX87W9/48iRI5SWlqLRaIw3HX/99VeTY/ft24ebmxt9+/Y1zsYB+Prrr/npp5867ZBObby8vHj66afZunWrMfHgd999x7x58zhz5gznz5+3dhPNznCfcO3atYSEhLB7927i4uJISUlhyJChxsdJkvmTU7a57kV4eDjh4eHExsZatR3l5eWdPuDb29sb00388MMPXLx4EVdXV959911effVVnJycGDduHEuWLCE2NpYPP/yQBx54gJiYGIKCgqzc+qZTq9U1vq0kJibyzDPP4OXlxfPPP8/f/vY3bG1tuXDhgvEGdvVj/vzzT4YOHVqjJKW3t7fINVMHe3t7Fi9ezK233spHH33EJ598wsaNGykoKGD27Nk899xzHeKeWlpaGj/99BO33XYbW7duZeTIkaxatcpYg6DqTdtO0cNvKzrjGH5tDMM7oaGhTJgwAVdXV5599lkmTZpETEwMu3fvpn///nz//fe88sorfP/99+zZswfQ34RqL6oG+yNHjvDNN9+wc+dOTp8+zfz58/n++++ZMWOGsRNgWD3q5+fHmTNnjM8jSRInTpxgwIABNV5DBPv6Gebvr1+/nm3bttG3b190Oh2ffPIJ/fr1Y+TIkRw6dMjazWyWvXv3Mm3aNG644QaWLVuGWq3m22+/Zf/+/SYpTSwd8Bvs4c+cObPerJhr164lOTmZ8ePHd6g6t2JIpyZDeoa0tDRefvllzp8/z9y5cwF45513mDRpEo8//rgxsD3wwANcvnyZJ554gsmTJ7fZRWyG6Ze//PILH3/8MYcPH6Z3796cPn0a0Af16sV1DCuRhw8fzt69e0225+bmGufhg36W29GjR1vh/6RjUCqVxm/6e/fu5auvvmLLli0cPHiQxx9/nPfee4+AgACcnZ3b7DVlUF5ezi233EJKSgo2NjZ0796dkJAQk9k3ISEhgP46rBrwLTGk02APPyoqCoCsrKwa+9asWUN8fDxRUVFkZGSwZcsWszfQGnQ6HRqNRvTwa2EI+gqFgtjYWJYsWcLtt9/OwoUL2bFjB9u3b2f37t2kpqYyadIkysrK+Pvf/05gYCCxsbHGFANthVqtZv/+/dx5553MnTsXuVzO22+/jUwmY9SoUcTGxpKVlcXhw4dNjrt8+TI2NjaEhIRw8eJF43i+4VtC1XKAs2fPZtmyZSbHt5cEadZ288038/rrr5OQkMDs2bPZt28fd999N6NGjcLPz49ly5Zx4cIFazfTRGZmJosXL+b333/npZde4sSJEwCMGjWKkJAQvv32W7755ptaj60a8CsqrBDwU1JSCAoKIiAgAIVCYZIwLS4ujtjYWIYNG8b8+fPJyMgwewOtoaKiAkD08OtgGOYZN24cc+fO5fnnn8fHx4f//ve/REREMHHiRM6cOcO0adPYs2cPBw4c4Pbbb+eVV16hvLwc0KfbLi4utkr7T506xdNPP83dd9/Njh07+OCDDxgyZAjvvvsuzz33HG+++SbZ2dk88cQT3HXXXXTt2rVGEaArV67QrVs3RowYgVwuZ/369eh0Oo4fP45CoaBnz55W+X/riJRKJUOHDuXTTz9l3759hIWFcfbsWS5dusRzzz2Hj48Pd911l9Wy6wJcvXqVjz76iJtvvpmAgACWLl1KREQEy5cv5/bbb2fz5s3s2rWLxMREhg0bVufN6KoBv6zM/CPuDXYxMjIySE5ONq5EXb58OVlZWfj5+ZGXl2csigKY/N6eGYKS6OE3TKlU0qNHD7Zs2cLmzZtxcXHh//7v//j000/x9fUlODiYESNG8Mknn/DBBx/g4OCAVqvl7rvvJj8/n3HjxjFlyhTuuecefH19LdZOjUbD+++/z+rVq405kwB+/PFH4++JiYmAvvf99ttvM3jwYADGjh3LTz/9xDPPPGPMLZWdnU337t3x8/PjzjvvNB77zjvv4OnpKXrvFjJ8+HA+/fRTfvvtN1599VV27dqFu7s7R48eJS0tDV9fXz799FMcHR2ZNGmSxa4pSZI4efIkAQEBaLVabr31VtLS0pDJZCgUCuRyOf7+/qxevZoJEyZgb29vnJ0TGBjIb7/9VsczX+/Vl5ZaIeCPGDHCGOxBP8Szc+dOY8B3cXEx7rNGjVtDaoWmFjGvj+jhN56htw8wYMAA0tLSOH36NP/73/+47777gOvzzQ1FtxUKBXv37mXr1q1s27aNxx9/nH//+9+kp6cTGBjI7t27jUNHbm5uTW5Tfn4+R44c4ZdffmHXrl14eXnRs2dP1q1bR2lpKQEBAbz44ou4urqSm5tLr169cHBw4NChQxw7doxZs2bh5eVlfL6xY8eSkJDA2bNn8fX1pbS0lN27dxMZGQlAaGgo3333nfGDpD3nmGoPlEold9xxB7fccgvvvvsu+/btY+vWrTz55JOUlpYah9cee+wxunfvzk033URMTAyjRo1CkqRmx6nff/+d5ORk9u/fz08//cT58+eJjo4mMTERlUqFr68vkydP5qGHHmLo0KF1fugHBQXVmR2gag+/uQG/ahHz6hoM+AcPHsTDw4PQ0FDy8/NZsmQJa9asAfTpB4qKinB2dgb085VbmyWKmIsefvMolUqCgoJ44IEH+Prrr/nPf/7D+PHjueeeewgODjZZaOTv78/cuXOZO3cueXl5/PLLL8ZviE899ZSxHkKPHj3o27cvL7/8MmPHjuX3338nKSnJmL+/uLiY4OBgHnzwQdLT07n55pvJyckxtkkmk9GvXz+2b9+Ora0tjz32GLNnz+aGG25AqVSazM4JCwsjLCysxv+XoaefmpqKr68vR44coayszPjY6um8Wzu9d2dlZ2fH/Pnz0Wg0fPfdd3zzzTc4Ojri4eHB//3f/+Hg4MClS5fYtm0bR44c4ZFHHiE3N5cvv/ySvn374uXlhYeHB2FhYdx3330UFBTw3nvvUVRURHFxMZcvXyYnJ4cdO3Zw4sQJpk2bxuXLl7G1tcXHxwcfHx9Wr17N6NGjiY6OZu7cufV2Eg2do6FDhxo7ldWZI+DXV8S8wYAfGxtLZGQkERERDB8+nKioKDZu3EhKSgrx8fHMmTOH6Oho4uPjmT59erMa2NaIHn7zKZVKbG1tefXVV3niiSf4+OOP+eSTT1i3bh15eXmEhobi6Ohocoy7uzt/+9vfjH//9ttvHD9+nF27dnH06FF+/PFHPv30U7p27cqhQ4f43//+R2VlJRUVFSiVShwdHXn++efp0aMHhYWFODo6cvfdd3PPPfdw77331lu3wbDWoL4kae7u7nh7e3Ps2DHGjRvHtm3bsLGxoU+fPoC+x7Zx40Z+++03hg8fXuuUzNrOk2AeSqWSe++9l3vvvdc448rZ2ZmEhAQmTZpkzNK5ePFiJEnCw8ODkpIS9u/fT3l5OXv27EGSJEpLS1mxYgWOjo5UVFSgVqvRaDTceuutXL58mUuXLtG3b1/69+/PkSNHCAoK4rXXXiM0NLRJq6a7d+9e5z5zBPz6NNhCV1fXGsVPqgoPDycuLo6IiIgOk1BL9PCbzzAUA7Br1y5kMhmzZ882FpTx8vLi4YcfZubMmRQXF7N161YAHB0dGTlyJO+88w6//fYbgwYNIjIyksOHD5OTk8NHH32Eo6Mjp0+fJicnB0mSkMvl2NnZ0bVrV0aMGMHp06d5+OGHmTZtGmPHjm30G7DqdNOq/P39jdkKQ0ND+eSTT/jiiy+orKzEwcHBZErgDTfcwA033NDga3XmdAqtwdCLDgkJYd68ecbtzzzzDFeuXGHr1q289NJLpKSk0L9/f26++WY+//xz7r//fuNjCwoKUCqV3Hbbbfj5+ZGZmcnw4cN5/PHHefLJJ1tcCrQpAT8tLc2s10yLnmXLli1MnTqV+fPnm6UxbYXo4beM4U1n6G299tprPPfcc/j6+pKTk8Orr77KunXruHr1KjqdDoVCYfyQtbW15V//+heJiYk8/PDDuLi48Nlnn7F582ZWrlyJt7c3c+fOJSQkhNtuu63VVl/OmjWLb775xriYrDOlie4ovLy8mDNnDrNmzeLo0aMMHToUe3t7VqxYwdmzZ7GxsSErK4s///yTkJAQ/Pz8LPIBXVfAVyqV9OnjZ/zbKj18gMOHD6NSqWqUKFu9enWHWmxlIAK+eVTtbU2ZMgW1Ws2pU6f46quvOHLkCKCvt+Dj40NOTg6bNm0iNDSUUaNGERUVxR9//MGwYcO46aab+Oc//0lRURFOTk4WmRxQ9eazQdUCHYMGDWL9+vX885//BDBmDhXaH0dHR0aNGmX821DnA/Tf1CZOnGix19ZoNDVupmo0GuOHitWHdBYtWkRCQgIBAQEmMyby8/OtcpO2NYghHcuwt7dn0KBBxrwh1Y0dO9bk7xEjRpj8bZgc0Fqq9+yGDBnC22+/zZAhQ+jatWurtkXoGDQaTY0OS5sK+J6ennWmv12+fLnZG9QWiB6+UJfaZvHUpeo9AEGo6rPPPmP27Nl06dLFZLvVF17Vt5iqo43dG4geviAIlmAoMhQSEkJeXh4LFiwwSapnCPhdumgt0sNv8BkDAwNrLCs3WLFihdkb1BoaqlUqeviCOSgUCuOb2/AjZugIBjY2NjUyyhoCvqOjzjpDOnPmzCE/P5/MzEyT3r4kSWRmZvLMM8+YvVFNYYmVtqKHLwiCpdna2qLVatHpdMbaCYaAb2cnUVnZvMkJLVppC/rZOB4eHibbJEli6dKlzWqQOTVnpW1dNTMNPX9DErhTp07Vu0Ra6Njqmp8vCOZgmM9fWVlp7FwaAr6NjURFhZVW2tZ1oyomJqZZDWqrDDenDV+zWrrAQhAEoS71BXxbW12ze/j1afAjpL5ZCcOGDTNrY9oKtVqNUqls88UVBEFovwz3CGvLq9OSIZ36tOiuQHu9aVufPXv2UFBQYJIhVBAEwdyq9vANqg7pWCLg1zqkM3PmTGbOnMnUqVMJCgqqdWVjW7lp21QajabOdQWHDx8mOjqaLl260KtXr1ZumdDRiHs/Qn3qC/i2tlLrzcP39/c3rqoNCAggNja2xmPayk3bpqp+w/bNN7tz5owtb711htLSUgDKyspwdXUVia4EQbCY2oZ0DHVs9WP45o89tT5j1UBuKGFYm45w0/ajjwyFLs6YBHcxpCMIgiXV1sOXy/X3Db28XLhwgRr5nVqqwe8M9eUS72g3bQ3z70H/LUf07gVDUZe6+Pv7t2JrhI5Co9Fw5swZAI4fP05qaioajabKPHyoo0ZKizQY8KOjo83/qm2QWi2jrKzM+LdOpzP+IwiCIJiboUNZ2xi+1QJ+RkYGK1asYMuWLRQWFpq/BS1kWGm7YcOGFj3PlSs2JgFfDOkIgmBJhoBv6FSmpaWh0WiBlgX8DRs2MGXKlOattE1MTDR+bd25cycqlQpPT882kwe/pTVtnZy0FBcruHRJaZL/vGoRa0EQBHOrb5ZOcXEOZWXupKb+BTStUlp9K20b7OFXHaMMCwsjMjISSZLw9PRk5syZdSZWa+skCXQ6cHHRf6Lm5ChNevjDhw+3VtOENsbe3r7OcXyxOE9ortpn6Rj2WWnhlSGgHz58mJiYGDw9PVm0aBGLFi1i6dKlDB8+nDVr1rBlyxazN84StFp92tEZMwJJTHRBqdSf4aIihUnA9/HxsVYThTaott5VcHCwsQh6bfvETX+hPk5OTgAUFRUZt2Vn62/k2tpK3FD5Jw779pv1NRu8IiMiIvD09CQ3N5eoqCiSkpJqzM6JjIwEYO3atcyZM8esDbQEBwcdDg46vvzS0/gpWlioD/jdunUjISEBEG9a4braSiAKQks4Ozsjk8lM7o1W7eFHEUfP2J1kbNlsttdsVAGU+hKoGaxdu9ZsjbIkrVY/hHPPPfm88oo3Tk7FQIWxh+/m5oa7u7sI9kKjiQ8DoTnkcjnOzs4UFBQYt0kSaFDw0/4nKScbdQ/zrvhvcEgnJiamwWBfUFDAwYMHa9Qgbct69NDfKCkuHgl4UlQkR61W1yg5JgiCYG6GmTkuLi5kZGTw+eefG/cp0BH+x9v0IZuybq0c8A8cONDgilpXV1dWrVrF0KFDzdUui6qoqCA+fgGwC0ijag+/arkxQRAESzCMHri6uvLNN9+wdOlSCgoKjKkVAH3A9/IG6q7h0VSNys4zc+bMWrdnZWWZpRGtSavVYmtrS2rqb8D16Zx5eWrKyspED18QhFbj4uJi/P3MmTP06NHT+LczxRR39Tbr6zUY8MePH09ubm6t+ww3N9ujwYOHAB8Y//7zz7/z66+/0rNnz7oPEgRBMKOqAT87O5vzZ89RABj688Ue5g34Dd6VXL16NSqVikOHDhEQEGDMotle0yMb3HjjMHbt2g44A0WUlv4BwIwZM6zaLkEQOo+qK/pPnz7NDQEVuAH/ANYDhW69MOea/wYDvkqlIiYmxhjoDSRJYtmyZWZsSvM0t4j5jBkzWLrUExhNr157uXz5Z77//gnRwxcEodVUXdCXmppKb7cLAHwOPIYNxQ5dgQrj7MLGaFER8/qmZNZWGKW1NTW1gmFlpI2NDY6OT1NSouDmm/uzefM8unT5C2j8iRUEQWiJiRMn8vrrr9OlSxf27duHr9uO6/vQsqKolEaEaRMtSq1QW7DPyspiy5YtJvNH24uqS+ENUzNvuEG/wjY93c4qbRIEoXNydXUlMTGRjRs3Ul5ezufblmAD7AMqkLFhwxtmfb1GfXQUFhaSlJRU4+ZtYmJim0mi1hx+fuVkZNgTFFSOUinx3nvdmTfvIkOGlDV8sCAIghn06NEDgLfffptdCYf4v92fMBS4jTvJLcxr0nBOQxoM+IcOHWL69OkEBASQm5tLQEAA+fn55OXlER8fb7aGtCqtlm7vv89drlPYyT1otXDffVdZv74rH33UlbffPmPtFgqC0MmEhYXhdvYGhu7+BNX4+9iRuJ5Vc86gUBSbLUlfgwE/Li7OWPTbMFPHcGd5165d+Pn5maUhValUKhISEggICEClUhEVFVXjpnFzKRQKUCjw3LCB+x5w4MyTIxg2rJSRI0s5e9YWjcb69yUEQejYlEolwcHBpKWlmWyXa/SZM09PnAmJCszYude/bkMPCA8PN/4eEBBAfHy8xROkTZ8+neTkZEAf/CMjI83+baKiVy+6XDlP5AtXjNtsbCSKi0W6W0EQLK+2HEyXPPSJ1BRdDLnyZcbHmkODN21VKhVZWVmsXbsWV1dXduzYwZEjRwD9GL65qVQqk78DAgJISkoy++tU9uqF7fnzdDl8GL+oKOxPnMDOTqK8XPTwBUGwLI1GQ2pqao0fbWkxAPJrAV+rNW88ajDgR0VFsWrVKmNwX7RoEXfccYfFCj8kJSXh4eFhss3Dw4OUlBSzvk5Fr17YnD9P95Urcdq7F5/585lz4gVC83426+sIHUNdb1BR81hojqrXjW1WFrLycgBkFfqqe/0H3wBAz569CQkJMVsPv8FnycvLY+nSpca/Q0NDyczMRKVS1ciLbw75+fm1bq8rvUNzVfbqhe2ZM9irVJSEhuKYksK9rKSL02ngNbO+liAIQm0c//gD/8hILs6bR85DDyEr188QVDrpkziauz/RYMCPjo5m+/btJttcXV0tEuzrU9cHgWGlrUFjV9wW3XEHPZcvB+DSE0+gdXdHE70M28Jys7RX6FhET16wBNcd+oVWdhkZAMgr9fFH6ahfE1Sl3G2DDCtsDZq10jYjI4MVK1bg5ubGjBkzTJL9WIKbm1uN3nxubm6ds3SaW8S8wteXk9u24fDnn5SGhoJcjsbdF9uCy6KYhWBCo9EYZ6oJgjkVZ+TjAeh+PUzg9OmEXJu1cyIrA7l8SJNmDVbv7Na20rbBgJ+YmGgsZL5z505UKhWenp6Eh4dbJPiHh4ezevXqGtstUVylwt+fiipF2rV29thqxaIrwVRDvXuNRlNjep2omCY0hjo7HwDPvGzIq7LDRolS2bQefmM0eNPWv0pADAsLIzIyEkmS8Pf359///rd5W4N+Vk5VKpWKESNGmG0efn1vwlJJhp22VNyQE1osLS1NXD9CnQxxyKksh2+5B4AKp+t5MUMGhWBjIzN7wG+wC7Jr1y7GjRvH4cOHWb16NZs2bcLDw4NFixYRFRVl3tZcEx8fz8KFCxk5ciQHDhxotRW9kp0ddpKailZ5NaGjqCuwi56/UBfDNeNWfoW0HqP58uJ9qIoD2MNoAI4fP4ZSeYPZF4I2eOVFRETg6elJbm4uUVFRHDx40KTXbwmGwumG128tOls7+kkn+WTqlwzfch8ajUa8OYV6ifF9oblkZWU4aItR9PLg84v/BOAt5vI0bwMSNjZWGNIJCAhg1apVXL16lSVLllg82FuaUqk0yUFdlWSvvzP+r1OvoTx/gfT0dPG1XKhXRYX4Pig0nVarRZmnH7TXuLvzzjvZ3HRTMfN4Exk6ZDJQKs0/LbPBgB8TE1NnPvyORmd3vYC59N3vgJiOJ9DsbIXmzHIodCwKhQLZte670tmWceOKiIjIBWSADJkM6/Twp02bZt5XbMOcu3U1/l6UVWzFlghtiQjcgiXIrl1XXZz04/QDBqiv77sW8Fu9h9+ZyB0djL9XXCoCzJe0SGifNBoNp0+frnO/+DAQmquiTAdAF2d9GPbyut6dNwzptHoPvzORO3Yx/m5T2P6qeQmtTwR8oTmUSiVlRRIA9td6+DY2knG/6OHXwZBaoeqS4uaycb4e8J01arMmLRI6Jp1OV+e+2j4MxD0hwaC8RH/t2DkYUiCb7lcomtfD37BhA1OmTKk1tUKDAT8rK6vpr9iKDKkVGpM/pyGXiwqNv8vyL4k3pyAIFqOr1Ad8ua0+DA8YEGzcFxISgq2trFk9/FmzZrFt2za8vb1r7Gsw4EdHRzf9Fdspte56EXMHdb71GiK0G5cvX65zX209/PT0dNRqdS2PFjobqfLa9aGsPdW8VcbwDcnTtmzZQmFhYUMPbxfqGqZxcbieKdOxvEAsjxcsQqzvEOB6wJfb1B7wLTEts9nJ06ZOnWrelrQBvj31Pa98G0+cKvMaeLTQGYjALFiKYUhHZlt7v9sqC6/qSp7m6enJzJkz2bVrl3lbZEUlN91EzuzZfB08F3tJDZLU8EGCIAhNpFQq6enVHYDAfkG1ThCxysIrQ0A/fPgwMTExeHp6smjRIhYtWsTSpUsZPnw4a9asYcuWLeZtmRVINjZcXLCASmd91rogPz8xS0ewCPHNQdBV6K8BhW3dQzqtXvGqevK0pKSkGtWuIiMjAVi7di1z5swxbwtbUVBQEOnp6Si76P8BMtLS6D9smAj6giCYne7aGL7Crvb4Yombtg1GMkPmyoby6axdu9ZsjbI2Q8CXiV6Y0AKV5n63Ch2KrvJaD7+OgG9jA+ae0GWW5GkFBQUcPHjQIlWprMEQ8KkUAV8QBMuQ6hnSSU1NpaQkn/z8YrMWYzJL8jRXV1dWrVrF0KFDzdGmJjHnSlsDma3+E1enFj00ofnqm6MvCIYhHaV9XUM6UrMKoLRopW19YmJiWnK4WTRnpa1SqSQ4OLjuB9hcC/jlIk9KZyaKmwiWJF0bQZDb1h7wFQqaFfDrW2lb45X69u3bqCeVJInMzEyWLFnS5Aa1VUqlkpCQEHJ89T0zfx9/ccNWEASLMMzSUdopqpTDDDHu1/fwzfuaNaKZv78/CxcuxMPDo94DJUli6dKl5m1NGyG3swFAUyaGdISW+/13J1QqO2bPvmrclp6eLurbdnKS5tqQjp0CqDmaoFBIaLUWrmkbGxtbY9plXdrCkI4lKOxFwBfM59FH/QBMAr4gSBoNGhQobWoP6jY2EvUkY22WGgG/scG+qY9tT+Qi4AuCYGFSpQYtihppkQ0WLLiIzLwd/PZ/09YSFGJIR6jDlSvKZi+Gqaw087tXaNekSi0alHUGfHMHexA3bWul7KIP+FoxLVOoQquFceP0s7veeSebceOKmnR8fr4CLy+xtkO4RqNBgxK5HLMP3dRF3LSthWEMXwR8oaoLF2yMvz/1VB+OHk012X/mjA0ffeTFsGGlTJ6cD5jm3zt92pYdO1yYNSsXebuvNSe0lGFIpzWJm7a1ED18AWomOMvMtKvjkVBSImfixP4AxMd7GAN+Scn1yP7ggwEAjBhRQv/+5TWeQ+hcJI1+SKcqS8zMqapGP6MxwT4rK4stW7aQmZlpkUY1hSVW2oqAL1RXUKDgscf86ty/cmW3WrdX/VZgcPVqzW1CJ6TRoJVVT4l8/SthcHAwISEhTa6tXd9K20Y9S2FhIUlJSeTm5ppsT0xMtHohFMNKW3OycbgW8MtFwBf0jhzpUmObJOlvrGVm2rJ+fVfjdqXy+pv2++/dcHLS0qNHJUOGlLJ5swdXrujfdmlpaWIufmem0aCrNqTzxhun+fZbtxY97axZs5g1axZTpkypsa/BK+3QoUNMnz6dgIAAcnNzCQgIID8/n7y8POLj41vUsLbK0MMXuXQ6t6o1adPSuuDsrOXnn9NYvdqLuLhu5OcrcHfXkpWlH+pZuzaTxYu9OX/ehspKGTY2Evv3O3L77UUsXXoWgB07XMnJEQFeAEmrRVOthz9mTDFjxhRb7DUbvPLi4uKM+UQOHTpEQEAArq76AiG7du3Cz8/PYo2zFmPAFz184Zq0NHsGDCjD1lbijjuKiIvrxunTtri7l3HmjC329jpuvLGEF188T3S0HxcvKvH1rSQnR8nIkSXG5/HyqjQJ+Prl9KZEr7/jup5CAUoLCtCibNViOA3OFQgPDzf+HhAQ0GF79VUZhnREwBcMzp+3wde3AoD+/ctwcNBy4IAjAGfP2uLjU4FMBgEB+gTmp07ZI0n6efteXtevo65dNQ328EU1rI6r6r+tTKtFK5O3rYCvUqnIyspi7dq1uLq6smPHDo4cOQLox/A7IhHwhequXLExzqG3sYHhw0s5eFAf8FUqO3x89B8G3btr8PDQcPx4FwoLFVRWyuna9fob2te3grS0LibTNU+csCMx0YXMTFtAn2dHBP2O7aWXenH0iF2Nm7aW1mDAj4qKYtWqVcbgvmjRIu644w4UitadP9qabO1kaFAgVYiAL+gXXOXk6HvqykuXoLISH58KLl+2ITdXwcGDjtx6q37cVVFcxJD+eRw/3sXYk+/mWY7s2vLcceMKycqy46+/7AFYs8aLiIi+zJvXmylT+rF+vSegH+oRQb/j2rzZAyUaKrStO2OrwY8XV1dXkwVWoaGhZGZmolKpOmwuHRsbqMRG9PA7kapjq9Xl5irR6WT08ConODycvMmT8ey+koICF5KTHdFqZYwbV4jyyhWCx43jg563cGvlz8bZOHf+bw7dDv5K6tGj3HxzCb17lxMb25P777/K++93o3//Mvz8ypHLITa2Jz/+6MJDD+WgVp9g6ND+Yjy/gzHMBVCioVKy8sKrxnB1de2wwR70xYNLsUUSNUkF4PJl/dukt+w0AO7ffMN/fS4Sl/8Hly8rsbXV4eVUTPCN4wDof2EPOdiQmqqfytnt4K/G57KxkXj00cs8+6wvKSn6IaETJ7qg1cr46qt07rknn6ee6sPcuX1wddXwwQfZDBpUJm7kdhBKpZKiIn2QV6K5lkun9f5dW7TAe8WKFeZqR5sik+l7+IghnU6jvuGTixf1X7t9y65Xv/I7e4CKCjlnz9rStasGx0MpNY7bvt2Vbt2uX0NZmfrnGT26GH9/NePGFfDll+ns23eMr75KN+5bubKC0NBiCgoU/OMfATz2WG/27Tslhng6AKVSSffuAwBQoG31gF/rK82cOZOZM2cydepUgoKCkNWSts2QPO2ZZ56xeCOtQYONGMNvhvqGRmoTFBSEvb29BVvUcqdP2+HRpZghCyJr7Dt1yp7+rmfxj4422e7mpiEtrQuhoSVwrbTto/d7sWXnRTw8tGzbpg/wtfXchw6FBx+U+O67DJ58sje7dzszeXJf3n03m4gI7zZ/voT6Xb1WFkFJG8ilA/oEam5uboB+KmZsbGyNx7SV5GmG1AqG1WXmopHZiCGdFpAXF+MVF0d5YCCKoiK8Vq3i8uOPU3DnnfR44w1sz52jbOBA0p9+mqD+/a0axBqqXXv6tC0TvfbC6Zr7MjLseMDnGADlffpgl52NpFAwILiMvX84U1h4/Q39zvOHcXCoPQVDdfb29kybFsS992pISjrJc8/58Mgj/rz2WjlvvHGSCRMCxBBPO6TRaEhJOQv4GYd0NBqNsbyqOWzYsIENGzY0PrVC1UBeXzK1tpA8zRKpFUAf8MWQTtMZhh16vfoqbt99Z9wuKRT0eu01er32GgBaZ2ccDx5E3a8f6QpFmx6jPn3algccDqGzsUG1YQNBERHo5ApkOh05OTb09dfnlErfsgWXpCR8Fy7kxr7n2ftHfy6orq/WHX35O9wnbUa1fj3aax2qhiiVSsaO7cPq1RlERvpx+LAjkyb1ZebMqyxYcIlBg8RN3fbC8O23oMANAGeKTAK+udSXWqHBMfzqwT4zM5MtW7aQlZXVIW/cajQaUlNTqZQpKS0sIDU1VYydNpKhp+z800+4ffcd5xYv5uS333J+0SL+2rOHnH/+k/x77iF75Ur+2rOHgvBwur/zDo579nAiJYXU1FTjT2ud88YMQWVn2zFYOkJ5UBDq/v05/dZbyHVaPNDnlvKXZVHZrRuSrS35bj4APBR+jNdeO8PX7+02Pk+PN9/ELjsb26wsQD+c1Zg3ur29PSNGDGTPHgUrV2bh4aHhyy89GTMmmC++yBbXZztTUKBgteJRJvMtXclp1dduMOBXvzHr7+/P1KlTkSSJtWvXWqxh1mJ482hktsgrK0y2CbVTq9Wkpqai+uEHZGVl9HjzTYpuvZW8qVOp6NOHzEmz2barJwfuf56zS5dSOOZ2fv/diUWa10Bdjn90NANvuokbhg+n60cftWrbG/q3LSuTcemSDb3LMyi/lkakspt+WKYP2QD4kUVFr178/LMzEc+FAeB+ScWUKQUM3fMZWkdHk+e0uXIFaPoCqy5d7PnPf/w4cULLrFm5lJfLePFFH77/XkVJibrRzyNY16VLSqK0qwEI5VCrLrRr9vcIf39/MjIyzNmWNsGQMKtM4YiuWE1hoZz09PQ2PeTQ2gy9Yvu//sJl504KJkwgaOFC7KuMg/8++1W+/9SLHj0qWbmyO9nZ+gRjNjY6XFy011IE+5E8SMWyR3bTb90Kuhw/jtt7q8mNiOCvv9K4fLwYtyBn7OykVrm5W1oq5/XXe1JUpCAjw45NmzI4e1Y/s8aj9ALqHjcAoO7XD51SyWjtHlKk4XS7+Be7K0N54ok+3HprEeXHumKvyqAQcDx0iIK77iIvIgJN1670nTgR5eXLLWqnp6c9H34I//hHOnPm+HHvvf2QySRuvbWIpUvPEBoaIG7stmG5Wdb7cK41gq1Zs4bExETjAquNGzfWeIxKpSIqKsoijUpJSSEyMpLk5GSLPD9Q500SwydtmcKJvNMVvLTAl1Wrsi3WjrZMrVaTnp6OIjcXj82bqfD2pnjUKLQeHsgLCvCdPx+77Gy6rdb3VnT29uRNncoPl2/iX6/dj06nn901eHApGzem8+673fn9d2c8PCp44YXzeHpqmDu3N3fMnQJMwZuzpGmD6Xrvg2QrAwi78CPv2P6XXp9FA+b/0K1+s/ajj7qydau78e9ff3W6Vm9UwjHvIkXduwMg2dujHjiQB8t2svnkVNxPp/GVy7MsXXqGiRMLqJwTgN21zpDyyhU0t99O2bVrTePlhU1Ojkkbqg8pNeb/097ennvu6UdamoaVK8+ybl1XfvvNmfHj+/Paa2d59NHuIui3MYaYo7ygr5R2mCEkEMF9rdmG2jZGRkYSGRlJXFwcSUlJRFebcgb62Tv+/v5mb1BCQgIBAQGkpNSc19yaeocosMvI4+TJTvamkSTIyIBevUAuR1ZWhv+cOdifOgWAxtWV3Pvvx/WHH5DnF3B00XJ8f/uWlU7P8O7B8Qw4W86ePc5Mm5bHvHkXyclR0qePPrHYqlXZxhwyMpn+W+INN5xk924nNBoZgwaV8a9HfmTGmfcIlp/kVLeRPHX5DV5fNgQW3kx8/CXc3LQMHVpqUhu2OR8E1QPtzz87s2pVN6KiLvPoo1d44AF/EhI86NGjEn/nS8iLKtBcC/hnz9pwpWQwobJD7H0rHp6G6C97Y+NbAOi/Abj8/DNotSivXqXSy+v663p5mfTw65sd1BiurkoWLuzBvfem8cUXHixb1pOnn+7Dm2+W8+KLmTz4oK/4ZtpGaDQa/vorDbL1N/kn8w1n8eU+Us1+47Yu9b5CVFQUgYGBhIWFWbwhBhEREa32WrUxnHSnHvbILp3jSrYNPXt2rOEcQ7BT5uSgXLKWgosaKrp64d6lDN8fN+GoLaLU1oXSGwbil52JvCifmEm/4igV89juaLp98AHnvW4gQrOZlLeGEhz8GEd+d2DQoFLOnLHl9tsLmT//AoMGBTbYywwNHUho6PVvE4u/daOoaDEyZx3lcsgOn82AlK/YO+syQ7XJvMyLnCAYZ2ctQ4aU8sgjV9Bo0igvl/P8896kpDiycOEFJk4sqPeDQKPRUFkp45VXevHTT85otTJuu62Ixx+/jEwGjzySw9NP9wbg/ehEWA07jgWy+nN/Dh505C3b7oS6FKHIzUVSKLDxvv7NoGzQILquX49dejoynQ5NlYBf2aMHdtdu2talqYVRDD3H11+HefPUPPpoKYcPOxAZ6c+HHxbh4qLl1VedGD5cSS1Latodw/V78YKS5I/P0+uWrjjbllGQkILyjsEMmeSKv78/jtXunRiOy/0hDeWxE7j8ezzBw0NRKpXGfbqcAnK/OIDtHYNwGdQdf39//khMI+OkDYHDbeneXYNWI5H6v7/QZV6k92M34tnPqeaQY1mZvldjb298blllJTYrPuFN9Vfo5ArO63rh4qLvuLRWfGnwVWoL9llZWcYeuLUrXpmb4c2j8/FBdvgEACkPvsZAjzN0+9/bKJ2caj2upESfI8PFpTVb23jq0lIyU1K4ELcX340fIcm60F2bjVYDpXgzgG0U48RWhwguDBlDxR8nGXv4J1KZxIfyOZw9PAxJgjcKjjDC9jA/5Y7l7rsLCMpUc/RoFxYuvMA//3nV5DWbchEbHiuXg6ur7vqOmeP5+7vL+bv2K7SOjszgG/7b6xN+cbiLw4cdeOihAAYNKqWiQsa5c7YEBJTzf//Xi6FDSwkK0r+Z0tLSSE+3Y80aL86cseUf/7jKxIkFLFrky65dLmg0MgYMKGPp0jMAXL2qoGvXSm67rRA3Ny13D9J/u3lp7TB8R0m89tpZpp3T4Ph5Puq8PLSurlStSl46aBCAvpcPJj38wttvx/fZZ7E5e5ZKHx/QaPBcvx7PL7+k+OabyXn4YSp8fZtdDatrV3vWr4eTJ9P58MOufPRRVwoLlWzfDs7OGoKCyvn3v+25/34FNjb6ZlurL3P5bAVpa37D/0YvfO8ZTGVpJceXf4ejnxdB/xoNajXodGhsbfntt3R+fuMcXUrzcOqqoDi7mFEHP2Sh7mcKvnBBjg5niina4cQ3pV9T8PMH9D64nRMP/heVXTC9SjLoNdqLSz+fZcLHT6JAR/Ive9n17yi6v/QqPSvO0FWS4aLLx4Ey8ta4sbPXfVxUFDL6zBbCULNPMYqUB/+LQ9Iv/CN7JQBnfulD0b2TOH62AM+Th7kcMAyXGTfi89yzaHVydvf8G1odlHdxZZjbKQJTfgDgyt+ms3fhCUCq5wyZn0ySpAZfsb4Sh7WN75ulYTIZjWgaU6ZMadY8/DO7s8lYvsVkm9zDlTEfPQxA1oC76Jq5nxG6/aRV9gXgcL/JpM6ah/0b67CvKKbYsSv2PZ3JL1DgciETWyq4NOZOup05ipO6CHUllJdKKCrUyPt0RxrgT+WlSoqyc/AuOIFMIScXD9ydy+kzMYSzuRpydmbhXnaRKx6BVHh6UeoZxN/mBqIsyOHAkiQq3Loh8/JEoVSAJHFn3DQUhXkk/mcTJUcz6HklDXvUnO0SAPeNBx0Erl9JQOVJ7NDPOvpZOY5KO0fKbJxIf+Rp3A/s5XKJM0UaB3yc8+iirMDxobFkXXaDA8exPXsWZ9syQMbxPB9S7Ybx2PPl2F25yJlPj6LRKbBRaHFycsK+hztT1ujn/ybM2FSjLvCY1++iZ4gnv7xxkAu/nkIjt6XCzplyGyf6hvUm/EEfTmdLvPeejqu5udhVFvP2hhCOBNyF06fz8Vm0CJdffuFYrzFsD47ie9kkfv3VGQcHHR98kM3ly0qefdaHykoZ9vYSjo5aFAp9eoQePSrRauHiRVvc3DTk5ysZMqSExx67zKBBZSxY4Mu+fY5UVuqDt0Ih8cYbp4nI/ZBer77Kb4mH8eim7yK7b9pEr9deI/e++3Dcv5/0r766/j+p03HDyJGUDh+O0969pO3ciebazB55aSn9w8PJnzyZCzEx9FyyBM8vvjAeqnV0pOiOO5Dkcpy9vFCGh8Px43DyJHTvDpWVkJ4O/v4waJB+6O2XX6CwUN/rACgvhwcfhJ49qfzhB47/fpbn/3yQg1eCuIHjuJNHGV2wd5LRoziDbrKruMiLKFc6oHV0oofuPHldenJa4Y+fexHqKwXoitTYaUvxHWRLbz8t5/ZeRZNfhsbRGUcvJfKsC/RXH0WhkDjlOZyScls8vZRoyjUUXC7HQ5NDpb0jMrUa24oS8my7M6g8mX6cQoeM38LmYX/gEDcV7gIgnUB6cxolGi4qvMkggNu013MSAZy28eds4E106d+dynKJ3GI7gvck4KfTD5lk4YcfWTXe+7tsJ3DUZRRP5bwMQJptCPtsR+MkFYFcTo7PAIacSSJIfQydJOcnjyl4OqsJydxBD+kiOmT8OOxJ7KLuwebx1xij/YVTBJEqH8yduh9woIy98pvJ9Qog+Mo+KuR2uGjz8ZIuEyNbStdn72HyfZVkvbEbx6slyKp0FnreP47+M4aQvu04Zz/czrB3H8a1t2utMawhtcXGBgN+QyUOLVXxqrEBf/jw4Xh7exv/buyK25TlOwla8Hf9a137lL1o70ffsqMAFCjccdHlc4WudOUqH/Egj6CfMnhZ1o1cyZ1enMeFIgD+kt+AwtWefnn6bz5X0Nc4VWNPPq4426jpXalCjoQOGccYiBYFnlzlqtyLQN0pnCnmKu6coTeDOIoCHXWpwAYZEjZcH8suxpHz9CIXD/zJpPu1Nf0pypHkalwoxAUXCghjF8e7jmHglV+oKK7A1tmuxvNf3H+aHiN9SQ6awfAM06I3eye9xs3fPMvhV75h6IumizuyHQfQp/g4AEUKV5x1hSb7T315kL4zh7P/xse58cD/TPb9ccvTjPr9TU58th/vf4VxUenLOYU3Gcp+pLmO4IEfRyKrrER+2yMElqQiIecJr89ItJvI/JjLjBlTwoYNHnzyiScFBQpkMtBqZTg56Zg9O4fbbivm6ad9uXTJhtJSBd27VxAYWM7772ejUMCWLe6Ulcno0aOSXr0qCQgox85OotvKlbh/9RUndu40ttXlxx/pPX8+hWPGoCgtJbPadNK+kydjm52NZGfH8X37TL4BeP3vf3Rbu5aCCRNw/fFHLj3xBK7bt2OflkbVEZfKrl2xyclBksmQVXkvSAAKBZJMhrzKdL6q75amjNxUYEMFtjhQihyJy1188Si7cG3pv9x4HZZhjxYFEjI0KCnHjm5cRo5EAS7stR+LjbMNPleP0Ut3FmeK0SLnHN7k4IWtlwPYKNCdz8WVAopxQo6WEwTzN7aiRc4G90j88vTvwTzcOYsPA2QncHPTUD5iCDckfmx8v+qQc8XWh8Dyv1Cr1ZxzHoK75hJZ+FOCA76cYb/f3+kzqTeZ35+nTJWLHeXcxffIkdjo9BC+A5TYTB3NTTH/Mj0nMju66vTTZ/90uRW/oj/RIqcAVzy4yq8Pv4Hf06NJeWw7mt+Ochu/0Z0rJBLOX/ZDGfzFdLwUuQy8924AtMjYy834cI7ypHUMuz2UZN8IBlzcZfK6h2e/wZhPI9nz5JeEvBdF4W9/4nOrX6P+HQ0rbA3OnTtXY+JLgwH/3//+Nx988AFQe4nDcePGNdiQuLi4eqdwjh8/3qSyFli+h9+glSvhv/+Ff/0LDhxASjlE6of7kZ/8i+BFf0fh7kJJbjm716bh7Kbg5sgQZDJI3XyCkjI5PW7ri0wGvr4Yx02LrlaQtfssPfq54DXgetFrnQ4+/0SD5uxF7onqzuUrJzj1SyHyy1fx7C6RXxKAGkf6jPTg3A87UaRlcOm0xJlsGzSenrj1c6HnECduf/R2fHvrpxHmnFVzZO1+vEN70P+eIGTyaiFAkvRBSJL0DahOLtc3XKOB6v8OcjkoFPoxrNrmD9td+wApK6t9n1wOFRX63mp5ORQVQXExuLqCjw+cPQtffglnzkB2Nvz5J9jbo05JIf3UKXosW0bpkCF4fv45jocPUzR6NKfffRfJxobgAQMaHAYpKdG/RP/+jVt45f3ii9ilp6Oq0hN33LMH/+hoyvv0Qd2vH2fefNPkmD7R0Tjv2UNZ//5kJCQgLy7GbetWXH75BYf9+5Ffm/5bMGECZ5Yuxf3rr0EmQ+vmhtbVFa2LC35hYSiLivTjhHI55OToz3lQkP5FdDr9DXZJ0vf+XVz0/2aSBJcv6/8nFQr9eT14UH98eDh4eur/bcrKwN7eeIOekhIoKND/ffo0xd/+TO6lSrzuDKXLkH7g6IhWq//C4esLbm5w7vAV/opPZVDkKLr7XS/0Xloi8ceP+bj7ODJkpG3Vz7saNJUSvy/fQ/dB3Qie3Lfefwuh8ZrVw9+8eTPTpk0DoKCggPj4eObMmQM0PuA3h7UCviEAuG3dis/zz1M6eDD2AwYg//JLs72G0AxqtT445eUh3XYbsmPH0Li5UTp4ME579yLZ2SEvK6Ns4EDOxMZS6ePT6DHwhoJ+n+hodA4OnHnrLeM2+2PHCLpPP6Hu6syZXHj+eZNjer38Mh7x8RSNHk32qlXIS0rod+edlA0cSNGYMVT4+qIoLKRg4kST3r9Y7yGYS22xscEry1DiMCkpiTlz5rBjxw5GjhzJkCFDSExMtFjAB8jPzzcmcWttumt3+LscP86V0aO5nJoq3ozWZJgB4e6OLDUVjh5F/vHHOKxbh87JidwZM9C6ueH52Wd4L15M1rp1Zntpm8uXKbnxRpNtuip357UeHqYHSBK6a99yHPftQ15QgM7VlRM//YRkY1rhSFxTQmtqcyUOk5KSWLhwIQBLliwhISHBIq/TEK2DAwAyjYZyC6w3EFpo0CDkb7yB8uxZlDt24LFyJQUTJlA2YABO+/fjeOBAo55GrVY3OKRjc+mSMZ2CgbZKwC/v0+f6Y8+epc+//03X9espCQnh9DvvGD8cqgd7QWhtba7EYXh4OOHh4bWmZG5N6n79jL8XjRljxZYI9XJyghEjUAJ9CwqQ/f47kkKB97PPktGjB4Hjx7eoBy0rLUVRVETltUVXBiYBv8rEhZ5Ll2KXkUH2e+9RNHZss19XECyh0e+EqnPvhw8f3iEzZVal7doV1YcfIi8vR+foKL56twPS5Mmc+vZbvF94Aee9e+k3aRJnX3+dgnvuafS/n0ytRrK3R1ZZiWRjg821VbGaagG/6gommVqNw6FDlA4bxvkXX0Tn6GgcEqyPuKaE1taoq+3RRx8lLi7OOJ5eUFBAdHQ077//viXbZnWlI0dauwlCEyiVSoLDwlCPHs2Zt9/G+6WX8Hn2WRz37+f86NGA/ttaYEgI9tdWQFZNbWB36hR9p05F6+yMrKKCtJ9/xubSJYAaPXyAc4sX0+399wl4+GEK77iD0mHDjPPtBaEtajDgL1q0CDc3N/Ly8ozTMfPz81m6dCkrVqzocCUOzVl5RrAOe3t7ej7zDMfDwuj6+ed0f/ddPLboF9mV3XADqnXr6DNoEJmZmcZjFPn5+Dz3nP73Iv3aCscDB1AUFwPUGMN3+vVXvOLiUBQVcWnuXHL++c/W+F8ThBZp8Katp6cnS5cuNQZ7ADc3N5YuXdqoaZOCYA1KpZKQkSPp8fbbaP/4g4oePdDZ2GCXnk6vl18mU6UCnQ6X7dtxj4/Hd8ECbC5cIP3LL0k9epQKHx+cf/kFh4MH9cVNquRJkVVU0Ou11ygPCODUV1+R89BD0IQbskFBQfqsiWI4R2hlDV5x9U2LDA0NNWdbBMEilDfeCOnp6F54Adnbb+P2ww9oHR1x+PNPupw8aXxc5po1qAcOBCBvyhS6XxuyvPDMM8hLS/H87DMKJk6kwtcX1fr1aLp2pUNkIxM6jQZ7+DKZjKxasvsVFhZSUFBgsq16dazWYChiXnVJsSDU4OhIxeuvk/Hll2hcXPBMSECuVnPuxRcp9/Xl7CuvUDJqlPHhufddz1KuyMuj34QJeK1ahcOhQ4A+zXFzgn1wcLDIUy9Y1IYNG5gyZUqtRcwbXGk7YcIEdu7cWaM3n5KSYrJNkiQOHTpkrBjVWiyWWkHocAwram3On8dx3z4Kw8PxXrwY559/pjQ0FHXfvmg8PSkePRr1gAF4rltHz7ffRuvgQP6UKeQ8/DCVPXs26TXFTBzBWpq10jY3N5dNmzY1uOJVkiSWLVvWogYKgiUZb8iHhMCECWg0GlRPP03p0KE4HjiA0549KK9eRevujnrAAIrHjuVMz54Ujh2LdG0hniC0Zw0G/NjY2EYXQJGJ8UyhHVEqlQSMH0+ary9XH3igxv7ywEDKAwOt0DJBsIwGx/CrB/vCwsI6Hll7sRRBEAShbagR8JcvX87MmTOZOXOmyU3YLVu24Onpibu7O3379iUmJqZVGyoIlmAY5gkypBw2o6CgIDF+L7QpNQJ+REQEkiQRFxdnXFS1c+dOpk+fTmRkJFqtllOnThEeHm6VWTmCYAm1BWZ/kTRP6GBqXOVxcXFs2rTJZFtsbCyhoaEmSdTCwsKMuXUEob2rbYW1prbiLo1Uo6i1ILQBNXr4HtVze6NPWVy9IhWIm7SCIAjtSY2AXz2I77xWx3P8+PE1DhapFQRBENqPGgFfkiQOHz4M6FMiR0dHExgYWKOy1a5duxg+fHirNLI+YqWtYClKpZLg4OA699d3o1fcrBWspb6VtjUC/vz581m1ahUeHh4EBATg5ubGjh07jPs3b97MokWLiIiIQKVSWbbljeDt7c22bduYNWuWtZsiCIJgdbNmzWLbtm14e3vX2NdgaoXqqufPqZpF0xpEagXB0tRqtUnefAND2oTaiqCLlAqCtTUrtUJ11g7wgtDaGgrcooaC0F40uNJWEDq7hsbyBaG9EAFfEAShkxABXxAEoZMQd5UEoRHEOL3QEYgeviAIQichAr4gCEIn0e4DvlhpKwiCcF2Latq2dWLhlSAIQk21xcZ238MXBEEQGkcEfEEQhE6iUwd8Me7fOOI8NZ44V40jzlPjmfNciYAvNEicp8YT56pxxHlqPBHw24CW/CM091hrvGZLWavN4ly13ddsqfZ4XbSVDzgR8JtJvDkt/7rt8diWENeU5V+3PR5rTu1+WubAgQMJDAxs1rHnzp2rtUhAWz22vbVXHNv2j21v7RXHNl5GRgbHjh0z2dbuA74gCILQOGJIRxAEoZMQAV8QBKGT6JTpkVNSUkhKSgLgwIEDrFmzBjc3NwBUKhUJCQkEBASgUqmIiooy7uusUlJSiIyMJDk52WS7OFd64jzUTVw7jdcqcUnqhGJjY01+Dw0NNf5d9feMjAwpIiKiVdvW1sTHx0vJyclSbZeKOFd64jzUTlw7TdMacanTBfzk5GTJzc3N+HdGRoYESBkZGVJGRobJiZUkyeSxnVn1N604V3riPDRMXDsNa6241OnG8ENDQ1mzZo3x7/z8fAA8PDxISkrCw8PD5PEeHh6kpKS0ZhPbBXGu9MR5aDpxzmpqrbjU6QI+QEREhPH3jRs3Eh4ejpubm/EkV5ebm9tKLWs/xLnSE+eh6cQ5q11rxKVOedPWID8/n4SEhBo3lGp7nNA44lzpifPQdOKc6VkyLnWYgB8XF0dGRkad+8ePH094eLjJtoULF5KYmGi82+3m5lbjUzM3N7fDzR5ozrmqrrOcq4aI89B04pzVz6JxqUV3Gtqx2NhYKSMjQ5IkScrLy5Py8vLqvDmSl5dnhRa2LdUvFXGu9MR5aJi4dhrP0nGpU47hJyQkEBoaSkBAAPn5+WzatAk3NzcCAgJMHqdSqRgxYoToeVxT9SukOFd64jw0jrh2GtYacanT5dJRqVQ1kq25ubmRl5dn3L969WpGjhzJgQMHiImJ6dQXYlJSEomJiSxbtowFCxYwcuRI480lca70xHmonbh2Gq+14lKnC/iCIAidVacc0hEEQeiMRMAXBEHoJETAFwRB6CREwBcEQegkRMAXBEHoJETAFwRB6CREwBcEQegkRMAXBEHoJETAFwRB6CREwBcEQegkRMAXBEHoJDpMPnxBsJaEhAQAEhMTWbhwIUlJSWRkZBAdHV0j06EgWJPo4QtCC8TFxREeHm7MArlw4UKioqJISUnp1DVahbZJ9PAFoQU8PDyMaWpVKhXR0dGAvrcvCG2NSI8sCGbi7u5OZmZmp87rLrRtYkhHEMxApVIBiGAvtGki4AuCGSQlJZkUfk9KSrJiawShdiLgC0IzJSQkMH78eEA/Zu/h4QHo67dWreEqCG2FGMMXhGZKSUkhKSmJgIAAwsPDWbhwofEDwDBrRxDaEhHwBUEQOgkxpCMIgtBJiIAvCILQSYiALwiC0EmIgC8IgtBJiIAvCILQSYiALwiC0EmIgC8IgtBJ/D9TzQmZA7BofgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.style.context('science'):\n",
    "    fig, ax = plt.subplots(figsize=(4, 3))\n",
    "    ax.errorbar(x, np.array(mean_coeffs[2])[:,0], yerr=2*np.array(std_coeffs[2])[:,0], \n",
    "                 fmt='-', color='blue', ecolor='lightgray', elinewidth=3, capsize=0, label=\"Est. $u_{xx}$\")\n",
    "    ax.errorbar(x, np.array(mean_coeffs[2])[:,1], yerr=2*np.array(std_coeffs[2])[:,1], \n",
    "                 fmt='-', color='red', ecolor='lightgray', elinewidth=3, capsize=0, label=\"Est. $u_{xxxx}$\")\n",
    "    ax.errorbar(x, np.array(mean_coeffs[2])[:,2], yerr=2*np.array(std_coeffs[2])[:,2], \n",
    "                 fmt='-', color='black', ecolor='lightgray', elinewidth=3, capsize=0, label=\"Est. $uu_{x}$\")\n",
    "    ax.plot(x, u_xx_true, '--', color='blue', label=\"$u_{xx}$\")\n",
    "    ax.plot(x, u_4x_true, '--', color='red', label=\"$u_{xxxx}$\")\n",
    "    ax.plot(x, uu_x_true, '--', color='black', label=\"$uu_{x}$\")\n",
    "    ax.set_xlabel(\"$x$\", fontsize=fs)\n",
    "    ax.set_ylabel('Spatially varying coefficients', fontsize=fs)\n",
    "    # ax.set_title(f\"$\\epsilon$ = {noise_lv}\", fontsize=fs)\n",
    "    ax.legend(loc='upper center', ncol=2)\n",
    "    fig.tight_layout()\n",
    "    plt.savefig(f\"/Users/pongpisit/Documents/latex/UBIC-ACML/figures/ks_ci_noise0-01.pdf\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f_u_xx, f_u_xxxx, f_uu_x = linear_fit_grouped_data((Theta_grouped, Ut_grouped), \n",
    "                                             considered_indices=candidates[2])\n",
    "plt.plot(f_u_xx[20:-20])\n",
    "plt.plot(f_u_xxxx[20:-20])\n",
    "plt.plot(f_uu_x[20:-20])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[ Info: Started!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Expressions evaluated per second: 2.410e+05\n",
      "Head worker occupation: 25.4%\n",
      "Progress: 523 / 1500 total iterations (34.867%)\n",
      "====================================================================================================\n",
      "Hall of Fame:\n",
      "---------------------------------------------------------------------------------------------------\n",
      "Complexity  Loss       Score     Equation\n",
      "1           1.203e+02  1.594e+01  y = x₀\n",
      "2           4.114e-02  7.980e+00  y = sin(1.2858)\n",
      "5           3.856e-02  2.161e-02  y = (-0.0046748 * x₀) + 0.95945\n",
      "7           3.855e-02  1.292e-04  y = cos(0.46683 * cos(x₀ * -0.13547))\n",
      "8           8.554e-03  1.506e+00  y = (-0.25113 * sin(x₀ * -0.32253)) + 0.95955\n",
      "10          5.243e-03  2.448e-01  y = 0.95567 + (sin((-0.31924 * x₀) + 0.3234) * -0.26367)\n",
      "11          5.057e-03  3.597e-02  y = 0.95564 + (-0.30133 * sin(sin(-0.32079 * (x₀ - 1.0576))))\n",
      "12          4.981e-03  1.518e-02  y = 0.95564 + (-0.3294 * sin(sin(sin((x₀ - 1.0475) * -0.32233)...\n",
      "                                  )))\n",
      "13          3.876e-03  2.508e-01  y = 1.1009 + (-0.35261 * sin(sin(-0.31629 * (x₀ - 1.1009)) + 0...\n",
      "                                  .59399))\n",
      "14          3.713e-03  4.299e-02  y = (sin(sin(sin((-0.31596 * x₀) + 0.34813)) - -0.82988) * -0....\n",
      "                                  48471) + 1.2487\n",
      "15          3.689e-03  6.438e-03  y = 1.7617 + sin(sin(-0.66837 * sin(sin((x₀ * -0.3151) + 0.374...\n",
      "                                  22))) - 1.0763)\n",
      "16          3.542e-03  4.078e-02  y = sin((-0.75459 * sin(sin(sin(sin(0.36694 + (-0.3161 * x₀)))...\n",
      "                                  ))) - 1.0463) + 1.7523\n",
      "17          3.529e-03  3.712e-03  y = sin(sin(-0.75459 * sin(sin(sin(sin(0.36694 + (-0.3161 * x₀...\n",
      "                                  )))))) - 1.0463) + 1.7523\n",
      "18          3.314e-03  6.275e-02  y = (-0.35261 * sin(sin((x₀ - (1.1009 - (-0.45574 * cos(x₀))))...\n",
      "                                   * -0.31629) + 0.59399)) + 1.1009\n",
      "20          3.183e-03  2.026e-02  y = 1.0495 + (sin(sin(sin(0.38652 + ((x₀ + sin(sin(x₀ + -1.120...\n",
      "                                  7))) * -0.31516)) + 0.38652)) * -0.37343)\n",
      "---------------------------------------------------------------------------------------------------\n",
      "====================================================================================================\n",
      "Press 'q' and then <enter> to stop execution early.\n",
      "\n",
      "Expressions evaluated per second: 2.490e+05\n",
      "Head worker occupation: 25.0%\n",
      "Progress: 1080 / 1500 total iterations (72.000%)\n",
      "====================================================================================================\n",
      "Hall of Fame:\n",
      "---------------------------------------------------------------------------------------------------\n",
      "Complexity  Loss       Score     Equation\n",
      "1           1.525e-01  1.594e+01  y = 0.62594\n",
      "2           4.114e-02  1.310e+00  y = sin(1.2858)\n",
      "5           3.856e-02  2.161e-02  y = (-0.0046748 * x₀) + 0.95945\n",
      "7           3.855e-02  1.292e-04  y = cos(0.46683 * cos(x₀ * -0.13547))\n",
      "8           8.554e-03  1.506e+00  y = (-0.25113 * sin(x₀ * -0.32253)) + 0.95955\n",
      "10          5.242e-03  2.448e-01  y = (-0.26368 * sin(0.32304 + (-0.31943 * x₀))) + 0.95578\n",
      "11          5.057e-03  3.599e-02  y = 0.95552 + (-0.30095 * sin(sin(0.34207 + (-0.32075 * x₀))))\n",
      "12          4.054e-03  2.211e-01  y = cos(sin(sin((x₀ * -0.31546) - -0.44477) + 0.92035)) + 0.19...\n",
      "                                  746\n",
      "13          3.859e-03  4.926e-02  y = (-0.36456 * sin(sin(-0.31629 * (x₀ - 1.1399)) + 0.57794)) ...\n",
      "                                  + 1.1009\n",
      "14          3.713e-03  3.854e-02  y = (-0.48471 * sin(sin(sin(-0.31596 * (x₀ + -1.1903))) - -0.8...\n",
      "                                  2988)) + 1.2487\n",
      "15          3.652e-03  1.665e-02  y = 1.7554 + sin(sin(-0.64752 * sin(sin(-0.31614 * (x₀ - 1.109...\n",
      "                                  3)))) - 1.05)\n",
      "16          3.033e-03  1.857e-01  y = 0.20289 + cos(sin(cos(0.41068) + sin(-0.3161 * (x₀ - (cos(...\n",
      "                                  x₀) + 1.2596)))))\n",
      "18          2.872e-03  2.733e-02  y = 0.20289 + cos(sin(cos(0.41068) + sin(-0.3161 * (x₀ - (cos(...\n",
      "                                  x₀ - -0.19398) + 1.2596)))))\n",
      "---------------------------------------------------------------------------------------------------\n",
      "====================================================================================================\n",
      "Press 'q' and then <enter> to stop execution early.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PySRRegressor.equations_ = [\n",
       "\t    pick     score                                           equation  \\\n",
       "\t0         0.000000                                          0.6259378   \n",
       "\t1         1.310062                                      sin(1.285759)   \n",
       "\t2         0.021606                      (-0.004674755 * x0) + 0.95945   \n",
       "\t3         0.005851              cos(cos(0.082310416 * x0) - 0.628859)   \n",
       "\t4         1.494141  (-0.25112787 * sin(x0 * -0.32252666)) + 0.9595501   \n",
       "\t5         0.244810  (-0.26367608 * sin(0.3230395 + (-0.31943187 * ...   \n",
       "\t6         0.035988  0.95551765 + (-0.3009507 * sin(sin(0.34207076 ...   \n",
       "\t7         0.230418  cos(sin(sin((x0 - 1.3197032) * -0.31546062) + ...   \n",
       "\t8         0.040123  (-0.36455923 * sin(sin(-0.3162855 * (x0 - 1.10...   \n",
       "\t9         0.040027  (-0.4847125 * sin(sin(sin((x0 - 1.1216884) * -...   \n",
       "\t10  &gt;&gt;&gt;&gt;  0.227548  cos(sin(-2.2354562 + sin((-0.31546062 * (x0 - ...   \n",
       "\t11        0.036314  cos(sin(-2.2354562 + sin((-0.31546062 * (x0 - ...   \n",
       "\t12        0.023154  cos(sin(-2.2354562 + sin(((x0 - (cos(x0) * 0.6...   \n",
       "\t13        0.034066  cos(sin(-2.2354562 + sin(((x0 - sin(cos(x0 + 0...   \n",
       "\t14        0.013530  0.19746058 + cos(sin(sin(sin(((x0 - 1.3197032)...   \n",
       "\t15        0.014857  0.19746058 + cos(sin(sin(sin(((x0 - 1.3197032)...   \n",
       "\t\n",
       "\t        loss  complexity  \n",
       "\t0   0.152496           1  \n",
       "\t1   0.041144           2  \n",
       "\t2   0.038562           5  \n",
       "\t3   0.038113           7  \n",
       "\t4   0.008554           8  \n",
       "\t5   0.005242          10  \n",
       "\t6   0.005057          11  \n",
       "\t7   0.004016          12  \n",
       "\t8   0.003858          13  \n",
       "\t9   0.003707          14  \n",
       "\t10  0.002953          15  \n",
       "\t11  0.002847          16  \n",
       "\t12  0.002782          17  \n",
       "\t13  0.002689          18  \n",
       "\t14  0.002653          19  \n",
       "\t15  0.002614          20  \n",
       "]</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;PySRRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>PySRRegressor.equations_ = [\n",
       "\t    pick     score                                           equation  \\\n",
       "\t0         0.000000                                          0.6259378   \n",
       "\t1         1.310062                                      sin(1.285759)   \n",
       "\t2         0.021606                      (-0.004674755 * x0) + 0.95945   \n",
       "\t3         0.005851              cos(cos(0.082310416 * x0) - 0.628859)   \n",
       "\t4         1.494141  (-0.25112787 * sin(x0 * -0.32252666)) + 0.9595501   \n",
       "\t5         0.244810  (-0.26367608 * sin(0.3230395 + (-0.31943187 * ...   \n",
       "\t6         0.035988  0.95551765 + (-0.3009507 * sin(sin(0.34207076 ...   \n",
       "\t7         0.230418  cos(sin(sin((x0 - 1.3197032) * -0.31546062) + ...   \n",
       "\t8         0.040123  (-0.36455923 * sin(sin(-0.3162855 * (x0 - 1.10...   \n",
       "\t9         0.040027  (-0.4847125 * sin(sin(sin((x0 - 1.1216884) * -...   \n",
       "\t10  &gt;&gt;&gt;&gt;  0.227548  cos(sin(-2.2354562 + sin((-0.31546062 * (x0 - ...   \n",
       "\t11        0.036314  cos(sin(-2.2354562 + sin((-0.31546062 * (x0 - ...   \n",
       "\t12        0.023154  cos(sin(-2.2354562 + sin(((x0 - (cos(x0) * 0.6...   \n",
       "\t13        0.034066  cos(sin(-2.2354562 + sin(((x0 - sin(cos(x0 + 0...   \n",
       "\t14        0.013530  0.19746058 + cos(sin(sin(sin(((x0 - 1.3197032)...   \n",
       "\t15        0.014857  0.19746058 + cos(sin(sin(sin(((x0 - 1.3197032)...   \n",
       "\t\n",
       "\t        loss  complexity  \n",
       "\t0   0.152496           1  \n",
       "\t1   0.041144           2  \n",
       "\t2   0.038562           5  \n",
       "\t3   0.038113           7  \n",
       "\t4   0.008554           8  \n",
       "\t5   0.005242          10  \n",
       "\t6   0.005057          11  \n",
       "\t7   0.004016          12  \n",
       "\t8   0.003858          13  \n",
       "\t9   0.003707          14  \n",
       "\t10  0.002953          15  \n",
       "\t11  0.002847          16  \n",
       "\t12  0.002782          17  \n",
       "\t13  0.002689          18  \n",
       "\t14  0.002653          19  \n",
       "\t15  0.002614          20  \n",
       "]</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "PySRRegressor.equations_ = [\n",
       "\t    pick     score                                           equation  \\\n",
       "\t0         0.000000                                          0.6259378   \n",
       "\t1         1.310062                                      sin(1.285759)   \n",
       "\t2         0.021606                      (-0.004674755 * x0) + 0.95945   \n",
       "\t3         0.005851              cos(cos(0.082310416 * x0) - 0.628859)   \n",
       "\t4         1.494141  (-0.25112787 * sin(x0 * -0.32252666)) + 0.9595501   \n",
       "\t5         0.244810  (-0.26367608 * sin(0.3230395 + (-0.31943187 * ...   \n",
       "\t6         0.035988  0.95551765 + (-0.3009507 * sin(sin(0.34207076 ...   \n",
       "\t7         0.230418  cos(sin(sin((x0 - 1.3197032) * -0.31546062) + ...   \n",
       "\t8         0.040123  (-0.36455923 * sin(sin(-0.3162855 * (x0 - 1.10...   \n",
       "\t9         0.040027  (-0.4847125 * sin(sin(sin((x0 - 1.1216884) * -...   \n",
       "\t10  >>>>  0.227548  cos(sin(-2.2354562 + sin((-0.31546062 * (x0 - ...   \n",
       "\t11        0.036314  cos(sin(-2.2354562 + sin((-0.31546062 * (x0 - ...   \n",
       "\t12        0.023154  cos(sin(-2.2354562 + sin(((x0 - (cos(x0) * 0.6...   \n",
       "\t13        0.034066  cos(sin(-2.2354562 + sin(((x0 - sin(cos(x0 + 0...   \n",
       "\t14        0.013530  0.19746058 + cos(sin(sin(sin(((x0 - 1.3197032)...   \n",
       "\t15        0.014857  0.19746058 + cos(sin(sin(sin(((x0 - 1.3197032)...   \n",
       "\t\n",
       "\t        loss  complexity  \n",
       "\t0   0.152496           1  \n",
       "\t1   0.041144           2  \n",
       "\t2   0.038562           5  \n",
       "\t3   0.038113           7  \n",
       "\t4   0.008554           8  \n",
       "\t5   0.005242          10  \n",
       "\t6   0.005057          11  \n",
       "\t7   0.004016          12  \n",
       "\t8   0.003858          13  \n",
       "\t9   0.003707          14  \n",
       "\t10  0.002953          15  \n",
       "\t11  0.002847          16  \n",
       "\t12  0.002782          17  \n",
       "\t13  0.002689          18  \n",
       "\t14  0.002653          19  \n",
       "\t15  0.002614          20  \n",
       "]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# y1 = (sin(x₀ * -0.32253) * -0.25117) - -0.95951\n",
    "# y1 = (-0.25113 * sin(x₀ * -0.32253)) + 0.95955\n",
    "model1 = PySRRegressor(\n",
    "    parsimony=0.01, \n",
    "    niterations=100,  \n",
    "    binary_operators=[\"+\", \"-\", \"*\"],\n",
    "    unary_operators=[\"sin\", \"cos\"], \n",
    "    denoise=False, \n",
    ")\n",
    "\n",
    "win = 16; w = 15\n",
    "A = x[win-1:-w].reshape(-1,1)\n",
    "B = pd.Series(f_uu_x).rolling(window=win).mean().iloc[win-1:].values[:-w].reshape(-1,1)\n",
    "# plt.plot(A, B); plt.show()\n",
    "model1.fit(A, B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[ Info: Started!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Expressions evaluated per second: 2.500e+05\n",
      "Head worker occupation: 28.2%\n",
      "Progress: 552 / 1500 total iterations (36.800%)\n",
      "====================================================================================================\n",
      "Hall of Fame:\n",
      "---------------------------------------------------------------------------------------------------\n",
      "Complexity  Loss       Score     Equation\n",
      "1           6.196e-02  1.594e+01  y = -0.70417\n",
      "3           1.019e-02  9.025e-01  y = -0.83082 - 0.10088\n",
      "5           1.019e-02  1.915e-04  y = -0.93192 + (0.00019302 * x₀)\n",
      "6           1.008e-02  1.075e-02  y = -0.92929 - (6.3018e-10 * exp(x₀))\n",
      "7           7.217e-03  3.340e-01  y = -0.87179 + ((-0.00056947 * x₀) * x₀)\n",
      "9           5.023e-03  1.812e-01  y = -0.95766 + exp(x₀ - exp(x₀ * 0.59029))\n",
      "10          4.215e-03  1.754e-01  y = -0.9917 + exp(-1.4843 - ((0.036295 * x₀) * x₀))\n",
      "11          1.261e-03  1.206e+00  y = exp((x₀ - exp(0.41618 * x₀)) + -0.69231) + -0.98495\n",
      "13          7.320e-04  2.721e-01  y = exp(-1.5054 - ((x₀ - exp(1.2807)) * (x₀ * 0.1463))) + -0.9...\n",
      "                                  7847\n",
      "16          7.319e-04  4.906e-05  y = -0.97847 + exp(-1.5054 - ((x₀ - (1.7509 + exp(exp(-0.48044...\n",
      "                                  )))) * (x₀ * 0.1463)))\n",
      "17          6.670e-04  9.280e-02  y = -0.9835 + exp(-1.0723 - ((0.080626 * (-1.7463 + x₀)) * (x₀...\n",
      "                                   - exp(0.82401 - x₀))))\n",
      "19          6.573e-04  7.299e-03  y = exp(-1.0723 - ((0.080626 * (-1.7463 + x₀)) * (x₀ - (exp(0....\n",
      "                                  82401 - x₀) + 0.37201)))) + -0.9835\n",
      "20          6.424e-04  2.295e-02  y = -0.9835 + exp(-1.0723 - ((0.080626 * (-1.7463 + x₀)) * ((x...\n",
      "                                  ₀ - exp(exp(-0.69002) - x₀)) + -0.69002)))\n",
      "---------------------------------------------------------------------------------------------------\n",
      "====================================================================================================\n",
      "Press 'q' and then <enter> to stop execution early.\n",
      "\n",
      "Expressions evaluated per second: 2.650e+05\n",
      "Head worker occupation: 25.0%\n",
      "Progress: 1138 / 1500 total iterations (75.867%)\n",
      "====================================================================================================\n",
      "Hall of Fame:\n",
      "---------------------------------------------------------------------------------------------------\n",
      "Complexity  Loss       Score     Equation\n",
      "1           1.019e-02  1.594e+01  y = -0.9317\n",
      "3           1.019e-02  -0.000e+00  y = -0.93192 - -0.00019579\n",
      "5           1.019e-02  1.915e-04  y = -0.93192 + (0.00019302 * x₀)\n",
      "6           1.008e-02  1.075e-02  y = (-6.3146e-10 * exp(x₀)) + -0.92929\n",
      "7           7.217e-03  3.340e-01  y = -0.87179 + ((-0.00056947 * x₀) * x₀)\n",
      "9           4.725e-03  2.118e-01  y = -0.97847 + exp(x₀ - exp(x₀ * 0.59029))\n",
      "10          4.092e-03  1.437e-01  y = exp(-2.4803 - ((x₀ + -2.5827) * x₀)) + -0.95397\n",
      "11          8.841e-04  1.532e+00  y = exp((x₀ * -0.4615) - exp(0.32764 - x₀)) + -0.97688\n",
      "12          7.458e-04  1.700e-01  y = -0.97847 + exp(-1.5054 - (x₀ * ((x₀ * 0.14551) - 0.50985))...\n",
      "                                  )\n",
      "13          7.320e-04  1.871e-02  y = exp(-1.5054 - ((x₀ - exp(1.2807)) * (x₀ * 0.1463))) + -0.9...\n",
      "                                  7847\n",
      "14          7.295e-04  3.456e-03  y = exp(-1.0864 - (((0.14627 * x₀) + -0.34919) * (x₀ + -1.2701...\n",
      "                                  ))) + -0.97806\n",
      "16          7.294e-04  1.380e-05  y = exp(-1.0864 - (((0.14627 * x₀) + -0.34919) * ((x₀ - 0.0036...\n",
      "                                  928) + -1.2701))) + -0.97806\n",
      "17          6.474e-04  1.192e-01  y = exp(-1.0864 - (((0.079651 * x₀) + -0.16996) * (x₀ - exp(0....\n",
      "                                  34431 - x₀)))) + -0.97806\n",
      "18          6.380e-04  1.476e-02  y = exp(-1.0864 - (((0.079651 * x₀) + -0.16996) * (x₀ - exp(ex...\n",
      "                                  p(-0.16996) - x₀)))) + -0.97806\n",
      "19          6.365e-04  2.287e-03  y = exp(-1.0864 - ((((0.079651 * x₀) * 1.0824) + -0.16996) * (...\n",
      "                                  x₀ - exp(0.34431 - x₀)))) + -0.97806\n",
      "---------------------------------------------------------------------------------------------------\n",
      "====================================================================================================\n",
      "Press 'q' and then <enter> to stop execution early.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-9 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-9 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-9 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-9 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-9 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-9 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-9 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-9 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-9 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-9 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-9 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-9 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PySRRegressor.equations_ = [\n",
       "\t    pick         score                                           equation  \\\n",
       "\t0         0.000000e+00                                        -0.93170226   \n",
       "\t1         4.905956e-08                       -0.93191737 - -0.00019579167   \n",
       "\t2         1.915652e-04                 -0.93192226 + (0.00019301732 * x0)   \n",
       "\t3         1.075217e-02            -0.92911786 + (-6.314627e-10 * exp(x0))   \n",
       "\t4         3.339545e-01         -0.87179476 + ((-0.00056947314 * x0) * x0)   \n",
       "\t5         2.628016e-01        -0.97846603 + exp(x0 - exp(0.5006851 * x0))   \n",
       "\t6         4.173800e-02  exp(-2.4802957 - ((x0 + -2.5827367) * x0)) + -...   \n",
       "\t7   &gt;&gt;&gt;&gt;  1.534353e+00  exp((x0 * -0.46719766) - exp(0.33867103 - x0))...   \n",
       "\t8         1.766437e-01  -0.97731245 + exp(-1.5054326 - (x0 * ((x0 * 0....   \n",
       "\t9         1.014220e-02  exp(-1.5054326 - ((x0 - exp(1.2806816)) * (x0 ...   \n",
       "\t10        3.456179e-03  exp(-1.086447 - (((0.14626744 * x0) + -0.34918...   \n",
       "\t11        1.830159e-05  exp(-1.086447 - (((0.14626744 * x0) + -0.34918...   \n",
       "\t12        1.615202e-01  exp(-1.086447 - ((-0.1699559 + (0.079651356 * ...   \n",
       "\t13        6.468012e-03  exp(-1.086447 - ((((0.079651356 * x0) * 1.0823...   \n",
       "\t\n",
       "\t        loss  complexity  \n",
       "\t0   0.010192           1  \n",
       "\t1   0.010192           3  \n",
       "\t2   0.010188           5  \n",
       "\t3   0.010079           6  \n",
       "\t4   0.007217           7  \n",
       "\t5   0.004267           9  \n",
       "\t6   0.004092          10  \n",
       "\t7   0.000882          11  \n",
       "\t8   0.000739          12  \n",
       "\t9   0.000732          13  \n",
       "\t10  0.000729          14  \n",
       "\t11  0.000729          16  \n",
       "\t12  0.000621          17  \n",
       "\t13  0.000613          19  \n",
       "]</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;PySRRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>PySRRegressor.equations_ = [\n",
       "\t    pick         score                                           equation  \\\n",
       "\t0         0.000000e+00                                        -0.93170226   \n",
       "\t1         4.905956e-08                       -0.93191737 - -0.00019579167   \n",
       "\t2         1.915652e-04                 -0.93192226 + (0.00019301732 * x0)   \n",
       "\t3         1.075217e-02            -0.92911786 + (-6.314627e-10 * exp(x0))   \n",
       "\t4         3.339545e-01         -0.87179476 + ((-0.00056947314 * x0) * x0)   \n",
       "\t5         2.628016e-01        -0.97846603 + exp(x0 - exp(0.5006851 * x0))   \n",
       "\t6         4.173800e-02  exp(-2.4802957 - ((x0 + -2.5827367) * x0)) + -...   \n",
       "\t7   &gt;&gt;&gt;&gt;  1.534353e+00  exp((x0 * -0.46719766) - exp(0.33867103 - x0))...   \n",
       "\t8         1.766437e-01  -0.97731245 + exp(-1.5054326 - (x0 * ((x0 * 0....   \n",
       "\t9         1.014220e-02  exp(-1.5054326 - ((x0 - exp(1.2806816)) * (x0 ...   \n",
       "\t10        3.456179e-03  exp(-1.086447 - (((0.14626744 * x0) + -0.34918...   \n",
       "\t11        1.830159e-05  exp(-1.086447 - (((0.14626744 * x0) + -0.34918...   \n",
       "\t12        1.615202e-01  exp(-1.086447 - ((-0.1699559 + (0.079651356 * ...   \n",
       "\t13        6.468012e-03  exp(-1.086447 - ((((0.079651356 * x0) * 1.0823...   \n",
       "\t\n",
       "\t        loss  complexity  \n",
       "\t0   0.010192           1  \n",
       "\t1   0.010192           3  \n",
       "\t2   0.010188           5  \n",
       "\t3   0.010079           6  \n",
       "\t4   0.007217           7  \n",
       "\t5   0.004267           9  \n",
       "\t6   0.004092          10  \n",
       "\t7   0.000882          11  \n",
       "\t8   0.000739          12  \n",
       "\t9   0.000732          13  \n",
       "\t10  0.000729          14  \n",
       "\t11  0.000729          16  \n",
       "\t12  0.000621          17  \n",
       "\t13  0.000613          19  \n",
       "]</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "PySRRegressor.equations_ = [\n",
       "\t    pick         score                                           equation  \\\n",
       "\t0         0.000000e+00                                        -0.93170226   \n",
       "\t1         4.905956e-08                       -0.93191737 - -0.00019579167   \n",
       "\t2         1.915652e-04                 -0.93192226 + (0.00019301732 * x0)   \n",
       "\t3         1.075217e-02            -0.92911786 + (-6.314627e-10 * exp(x0))   \n",
       "\t4         3.339545e-01         -0.87179476 + ((-0.00056947314 * x0) * x0)   \n",
       "\t5         2.628016e-01        -0.97846603 + exp(x0 - exp(0.5006851 * x0))   \n",
       "\t6         4.173800e-02  exp(-2.4802957 - ((x0 + -2.5827367) * x0)) + -...   \n",
       "\t7   >>>>  1.534353e+00  exp((x0 * -0.46719766) - exp(0.33867103 - x0))...   \n",
       "\t8         1.766437e-01  -0.97731245 + exp(-1.5054326 - (x0 * ((x0 * 0....   \n",
       "\t9         1.014220e-02  exp(-1.5054326 - ((x0 - exp(1.2806816)) * (x0 ...   \n",
       "\t10        3.456179e-03  exp(-1.086447 - (((0.14626744 * x0) + -0.34918...   \n",
       "\t11        1.830159e-05  exp(-1.086447 - (((0.14626744 * x0) + -0.34918...   \n",
       "\t12        1.615202e-01  exp(-1.086447 - ((-0.1699559 + (0.079651356 * ...   \n",
       "\t13        6.468012e-03  exp(-1.086447 - ((((0.079651356 * x0) * 1.0823...   \n",
       "\t\n",
       "\t        loss  complexity  \n",
       "\t0   0.010192           1  \n",
       "\t1   0.010192           3  \n",
       "\t2   0.010188           5  \n",
       "\t3   0.010079           6  \n",
       "\t4   0.007217           7  \n",
       "\t5   0.004267           9  \n",
       "\t6   0.004092          10  \n",
       "\t7   0.000882          11  \n",
       "\t8   0.000739          12  \n",
       "\t9   0.000732          13  \n",
       "\t10  0.000729          14  \n",
       "\t11  0.000729          16  \n",
       "\t12  0.000621          17  \n",
       "\t13  0.000613          19  \n",
       "]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# y2 = -0.97764 + (0.2091 * exp(((x₀ - 3.3079) * x₀) * -0.1777))\n",
    "# y2 = exp(-2.4803 - ((x₀ + -2.5827) * x₀)) + -0.95397\n",
    "model2 = PySRRegressor(\n",
    "    parsimony=0.01, \n",
    "    niterations=100,  \n",
    "    binary_operators=[\"+\", \"-\", \"*\"],\n",
    "    unary_operators=[\"exp\"], \n",
    "    denoise=True, \n",
    ")\n",
    "\n",
    "# win = 16; w = 15\n",
    "win = 31; w = 15\n",
    "A = x[win-1:][w:-w].reshape(-1,1)\n",
    "B = pd.Series(f_u_xx).rolling(window=win).mean().iloc[win-1:].values[w:-w].reshape(-1,1)\n",
    "# plt.plot(A, B)\n",
    "model2.fit(A, B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[ Info: Started!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Expressions evaluated per second: 3.000e+05\n",
      "Head worker occupation: 26.0%\n",
      "Progress: 648 / 1500 total iterations (43.200%)\n",
      "====================================================================================================\n",
      "Hall of Fame:\n",
      "---------------------------------------------------------------------------------------------------\n",
      "Complexity  Loss       Score     Equation\n",
      "1           6.442e-01  1.594e+01  y = -1.7795\n",
      "3           5.263e-03  2.404e+00  y = -0.56497 - 0.41513\n",
      "5           5.258e-03  4.888e-04  y = -0.97998 - (0.00021532 * x₀)\n",
      "6           5.258e-03  2.444e-06  y = exp(0.00021532 * x₀) * -0.97998\n",
      "7           5.250e-03  1.532e-03  y = (-2.7794e-05 * (x₀ * x₀)) + -0.97764\n",
      "8           4.299e-03  1.999e-01  y = exp(-1.7509 - (x₀ * x₀)) + -0.98854\n",
      "9           3.290e-03  2.674e-01  y = (x₀ * exp(x₀ - exp(x₀))) + -0.96428\n",
      "11          2.239e-03  1.925e-01  y = -0.97566 + (exp(x₀ - exp(x₀)) * (x₀ - -0.41518))\n",
      "13          1.940e-03  7.171e-02  y = -0.97562 + (exp(x₀ - exp(x₀ + 0.19875)) * (x₀ - -0.57859))\n",
      "14          1.929e-03  5.615e-03  y = (exp(x₀ - exp(x₀ - -0.50658)) * (exp(x₀) + x₀)) + -0.97566\n",
      "15          1.907e-03  1.148e-02  y = -0.97562 + (((x₀ - -0.57859) * exp(x₀ - exp(x₀ + 0.29706))...\n",
      "                                  ) * 1.2188)\n",
      "17          1.903e-03  1.106e-03  y = -0.97562 + ((x₀ - -0.57859) * (exp(x₀ - exp(x₀ + 0.29706))...\n",
      "                                   * (1.2336 - 0.060307)))\n",
      "19          1.828e-03  2.017e-02  y = exp((x₀ - 0.63638) - exp(x₀ + x₀)) + ((exp(x₀ - exp(x₀)) *...\n",
      "                                   x₀) + -0.97566)\n",
      "---------------------------------------------------------------------------------------------------\n",
      "====================================================================================================\n",
      "Press 'q' and then <enter> to stop execution early.\n",
      "\n",
      "Expressions evaluated per second: 2.990e+05\n",
      "Head worker occupation: 24.7%\n",
      "Progress: 1302 / 1500 total iterations (86.800%)\n",
      "====================================================================================================\n",
      "Hall of Fame:\n",
      "---------------------------------------------------------------------------------------------------\n",
      "Complexity  Loss       Score     Equation\n",
      "1           6.442e-01  1.594e+01  y = -1.7795\n",
      "3           5.263e-03  2.404e+00  y = -0.56497 - 0.41513\n",
      "5           5.258e-03  4.888e-04  y = -0.97998 - (0.00021532 * x₀)\n",
      "6           5.258e-03  2.444e-06  y = exp(0.00021532 * x₀) * -0.97998\n",
      "7           5.249e-03  1.822e-03  y = ((x₀ * -3.838e-05) * x₀) + -0.97584\n",
      "8           4.299e-03  1.996e-01  y = exp(-1.7509 - (x₀ * x₀)) + -0.98854\n",
      "9           3.290e-03  2.674e-01  y = (x₀ * exp(x₀ - exp(x₀))) + -0.96428\n",
      "10          3.095e-03  6.125e-02  y = -1.9666 + exp(exp(x₀ - exp(x₀)) * x₀)\n",
      "11          2.239e-03  3.237e-01  y = -0.97566 + (exp(x₀ - exp(x₀)) * (x₀ - -0.41518))\n",
      "12          2.004e-03  1.108e-01  y = -0.97668 * exp((-0.51395 - x₀) * exp(x₀ - exp(x₀)))\n",
      "13          1.936e-03  3.479e-02  y = (exp(x₀ - exp(x₀ + 0.20805)) * (x₀ + 0.55054)) + -0.9749\n",
      "14          1.890e-03  2.392e-02  y = exp(exp(x₀ - exp(x₀ + 0.13947)) * (-0.56696 - x₀)) * -0.97...\n",
      "                                  998\n",
      "16          1.888e-03  5.977e-04  y = -0.97998 * exp(exp(x₀ - exp(x₀ + 0.13947)) * (-0.56696 - (...\n",
      "                                  x₀ + 0.010185)))\n",
      "17          1.865e-03  1.210e-02  y = -0.97566 + ((x₀ - -0.62541) * exp(x₀ - exp(x₀ + exp(x₀ - e...\n",
      "                                  xp(x₀)))))\n",
      "19          1.769e-03  2.629e-02  y = (exp((x₀ * 0.67581) - exp(x₀)) * (x₀ - ((-0.15534 * (x₀ * ...\n",
      "                                  x₀)) + -0.37704))) + -0.97566\n",
      "---------------------------------------------------------------------------------------------------\n",
      "====================================================================================================\n",
      "Press 'q' and then <enter> to stop execution early.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-19 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-19 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-19 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-19 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-19 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-19 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-19 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-19 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-19 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-19 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-19 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-19 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-19 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-19 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-19 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-19 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-19 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-19 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-19 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-19 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-19 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-19 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-19 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-19 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-19 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-19 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-19 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-19 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-19 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-19 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-19 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-19 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-19 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-19 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-19 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-19 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-19 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-19 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-19 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-19 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-19 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-19 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-19\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PySRRegressor.equations_ = [\n",
       "\t    pick     score                                           equation  \\\n",
       "\t0         0.000000                                         -1.7794672   \n",
       "\t1         2.403655                           -0.56497353 - 0.41512933   \n",
       "\t2         0.000489                  -0.9799833 - (0.00021532249 * x0)   \n",
       "\t3         0.000003               exp(0.00021532249 * x0) * -0.9799833   \n",
       "\t4         0.001822           ((x0 * -3.837991e-5) * x0) + -0.97583634   \n",
       "\t5         0.199591             exp(-1.750855 - (x0 * x0)) + -0.988536   \n",
       "\t6         0.267421                (x0 * exp(x0 - exp(x0))) + -0.96428   \n",
       "\t7         0.061253           -1.9666251 + exp(exp(x0 - exp(x0)) * x0)   \n",
       "\t8   &gt;&gt;&gt;&gt;  0.323677  -0.9756581 + (exp(x0 - exp(x0)) * (x0 - -0.415...   \n",
       "\t9         0.110763  -0.9766816 * exp((-0.51394874 - x0) * exp(x0 -...   \n",
       "\t10        0.034795  (exp(x0 - exp(x0 + 0.2080543)) * (x0 + 0.55054...   \n",
       "\t11        0.023925  exp(exp(x0 - exp(x0 + 0.13947162)) * (-0.56696...   \n",
       "\t12        0.000598  -0.9799833 * exp(exp(x0 - exp(x0 + 0.13947162)...   \n",
       "\t13        0.012098  -0.9756581 + ((x0 - -0.62540776) * exp(x0 - ex...   \n",
       "\t14        0.026294  (exp((x0 * 0.6758107) - exp(x0)) * (x0 - ((-0....   \n",
       "\t\n",
       "\t        loss  complexity  \n",
       "\t0   0.644250           1  \n",
       "\t1   0.005263           3  \n",
       "\t2   0.005258           5  \n",
       "\t3   0.005258           6  \n",
       "\t4   0.005249           7  \n",
       "\t5   0.004299           8  \n",
       "\t6   0.003290           9  \n",
       "\t7   0.003095          10  \n",
       "\t8   0.002239          11  \n",
       "\t9   0.002004          12  \n",
       "\t10  0.001936          13  \n",
       "\t11  0.001890          14  \n",
       "\t12  0.001888          16  \n",
       "\t13  0.001865          17  \n",
       "\t14  0.001769          19  \n",
       "]</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" checked><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;PySRRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>PySRRegressor.equations_ = [\n",
       "\t    pick     score                                           equation  \\\n",
       "\t0         0.000000                                         -1.7794672   \n",
       "\t1         2.403655                           -0.56497353 - 0.41512933   \n",
       "\t2         0.000489                  -0.9799833 - (0.00021532249 * x0)   \n",
       "\t3         0.000003               exp(0.00021532249 * x0) * -0.9799833   \n",
       "\t4         0.001822           ((x0 * -3.837991e-5) * x0) + -0.97583634   \n",
       "\t5         0.199591             exp(-1.750855 - (x0 * x0)) + -0.988536   \n",
       "\t6         0.267421                (x0 * exp(x0 - exp(x0))) + -0.96428   \n",
       "\t7         0.061253           -1.9666251 + exp(exp(x0 - exp(x0)) * x0)   \n",
       "\t8   &gt;&gt;&gt;&gt;  0.323677  -0.9756581 + (exp(x0 - exp(x0)) * (x0 - -0.415...   \n",
       "\t9         0.110763  -0.9766816 * exp((-0.51394874 - x0) * exp(x0 -...   \n",
       "\t10        0.034795  (exp(x0 - exp(x0 + 0.2080543)) * (x0 + 0.55054...   \n",
       "\t11        0.023925  exp(exp(x0 - exp(x0 + 0.13947162)) * (-0.56696...   \n",
       "\t12        0.000598  -0.9799833 * exp(exp(x0 - exp(x0 + 0.13947162)...   \n",
       "\t13        0.012098  -0.9756581 + ((x0 - -0.62540776) * exp(x0 - ex...   \n",
       "\t14        0.026294  (exp((x0 * 0.6758107) - exp(x0)) * (x0 - ((-0....   \n",
       "\t\n",
       "\t        loss  complexity  \n",
       "\t0   0.644250           1  \n",
       "\t1   0.005263           3  \n",
       "\t2   0.005258           5  \n",
       "\t3   0.005258           6  \n",
       "\t4   0.005249           7  \n",
       "\t5   0.004299           8  \n",
       "\t6   0.003290           9  \n",
       "\t7   0.003095          10  \n",
       "\t8   0.002239          11  \n",
       "\t9   0.002004          12  \n",
       "\t10  0.001936          13  \n",
       "\t11  0.001890          14  \n",
       "\t12  0.001888          16  \n",
       "\t13  0.001865          17  \n",
       "\t14  0.001769          19  \n",
       "]</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "PySRRegressor.equations_ = [\n",
       "\t    pick     score                                           equation  \\\n",
       "\t0         0.000000                                         -1.7794672   \n",
       "\t1         2.403655                           -0.56497353 - 0.41512933   \n",
       "\t2         0.000489                  -0.9799833 - (0.00021532249 * x0)   \n",
       "\t3         0.000003               exp(0.00021532249 * x0) * -0.9799833   \n",
       "\t4         0.001822           ((x0 * -3.837991e-5) * x0) + -0.97583634   \n",
       "\t5         0.199591             exp(-1.750855 - (x0 * x0)) + -0.988536   \n",
       "\t6         0.267421                (x0 * exp(x0 - exp(x0))) + -0.96428   \n",
       "\t7         0.061253           -1.9666251 + exp(exp(x0 - exp(x0)) * x0)   \n",
       "\t8   >>>>  0.323677  -0.9756581 + (exp(x0 - exp(x0)) * (x0 - -0.415...   \n",
       "\t9         0.110763  -0.9766816 * exp((-0.51394874 - x0) * exp(x0 -...   \n",
       "\t10        0.034795  (exp(x0 - exp(x0 + 0.2080543)) * (x0 + 0.55054...   \n",
       "\t11        0.023925  exp(exp(x0 - exp(x0 + 0.13947162)) * (-0.56696...   \n",
       "\t12        0.000598  -0.9799833 * exp(exp(x0 - exp(x0 + 0.13947162)...   \n",
       "\t13        0.012098  -0.9756581 + ((x0 - -0.62540776) * exp(x0 - ex...   \n",
       "\t14        0.026294  (exp((x0 * 0.6758107) - exp(x0)) * (x0 - ((-0....   \n",
       "\t\n",
       "\t        loss  complexity  \n",
       "\t0   0.644250           1  \n",
       "\t1   0.005263           3  \n",
       "\t2   0.005258           5  \n",
       "\t3   0.005258           6  \n",
       "\t4   0.005249           7  \n",
       "\t5   0.004299           8  \n",
       "\t6   0.003290           9  \n",
       "\t7   0.003095          10  \n",
       "\t8   0.002239          11  \n",
       "\t9   0.002004          12  \n",
       "\t10  0.001936          13  \n",
       "\t11  0.001890          14  \n",
       "\t12  0.001888          16  \n",
       "\t13  0.001865          17  \n",
       "\t14  0.001769          19  \n",
       "]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# y3 = (0.19909 * (exp((x₀ * x₀) * -0.26832) * x₀)) + -0.9798\n",
    "# y3 = y = exp(-1.7509 - (x₀ * x₀)) + -0.98854\n",
    "model3 = PySRRegressor(\n",
    "    parsimony=0.01, \n",
    "    niterations=100,  \n",
    "    binary_operators=[\"+\", \"-\", \"*\"],\n",
    "    unary_operators=[\"exp\"], \n",
    "    denoise=True, \n",
    ")\n",
    "\n",
    "# win = 31; w = 15\n",
    "win = 16; w = 15\n",
    "A = x[win-1:][w:-w].reshape(-1,1)\n",
    "B = pd.Series(f_u_xxxx).rolling(window=win).mean().iloc[win-1:].values[w:-w].reshape(-1,1)\n",
    "# plt.plot(A, B)\n",
    "model3.fit(A, B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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