predictions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing for an non homogeneous Hamiltonian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import shutil\n",
    "import os\n",
    "import time\n",
    "import cv2\n",
    "import csv\n",
    "from PIL import Image\n",
    "from numba import jit\n",
    "import pickle\n",
    "from pathlib import Path\n",
    "import skimage.measure\n",
    "\n",
    "from keras.models import load_model\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# check the nth ('n') of an integer 'num'\n",
    "def check_nth_bit(num, n):\n",
    "    return (num>>n)&1\n",
    "\n",
    "# flip the bit of the integer 'int' at a position 'p'  (staring at 0)\n",
    "def bitflip(int,p):\n",
    "    mask = 1 << p\n",
    "    return (int^ mask)\n",
    "\n",
    "# count the numbers of 1 on a binarie representetion of an interger n\n",
    "def CountBits(n):\n",
    "    n = (n & 0x5555555555555555) + ((n & 0xAAAAAAAAAAAAAAAA) >> 1)\n",
    "    n = (n & 0x3333333333333333) + ((n & 0xCCCCCCCCCCCCCCCC) >> 2)\n",
    "    n = (n & 0x0F0F0F0F0F0F0F0F) + ((n & 0xF0F0F0F0F0F0F0F0) >> 4)\n",
    "    n = (n & 0x00FF00FF00FF00FF) + ((n & 0xFF00FF00FF00FF00) >> 8)\n",
    "    n = (n & 0x0000FFFF0000FFFF) + ((n & 0xFFFF0000FFFF0000) >> 16)\n",
    "    n = (n & 0x00000000FFFFFFFF) + ((n & 0xFFFFFFFF00000000) >> 32) \n",
    "    return n\n",
    "\n",
    "# defining n!\n",
    "@jit\n",
    "def factorial(n):\n",
    "    if n == 0:\n",
    "        return 1\n",
    "    else:\n",
    "        return n * factorial(n-1)\n",
    "\n",
    "# finding the states that have the maginetization \"z\"\n",
    "@jit\n",
    "def statep(y,z):\n",
    "    a = 0\n",
    "    for x in range(0,2**l):\n",
    "        if (2* CountBits(x)-l) == z:\n",
    "            y[a] = x\n",
    "            a += 1\n",
    "    return (y)\n",
    "\n",
    "# numbers of states with magnatization x\n",
    "def num_mag(x):\n",
    "    y = p = int(factorial(l)//((factorial((x+l)/2))*(factorial(l -((x+l)/2)))))\n",
    "    return y\n",
    "            \n",
    "# find the position in the list of states \"s\" that are iqual to \"b\"\n",
    "@jit\n",
    "def find_state(s,b):\n",
    "    for z in range(0,p+1):\n",
    "        if s[z] == b:\n",
    "            return(z)\n",
    "\n",
    "# power method\n",
    "@jit\n",
    "def power_iteration(A):\n",
    "    # Choose a random vector\n",
    "    y_0 = np.random.rand(A.shape[0])\n",
    "    #erro precision\n",
    "    e= 0.000000001\n",
    "    #convergence constant\n",
    "    er= 1.0\n",
    "    it = 0\n",
    "    while (er >= e):\n",
    "        # calculate the matrix-by-vector product Ab\n",
    "        z_1 = np.dot(A, y_0)\n",
    "        # calculate the norm\n",
    "        z_1_norm = np.linalg.norm(z_1)\n",
    "        # renormalize the vector\n",
    "        y_1 = z_1 / z_1_norm\n",
    "        z_2 = np.dot(A, y_1)\n",
    "        z_2_norm = np.linalg.norm(z_2)\n",
    "        lamb_1 = np.dot(z_2,y_1)/np.dot(y_1,y_1)\n",
    "        y_2 = z_2/z_2_norm\n",
    "        z_3 = np.dot(A, y_2)\n",
    "        z_3_norm = np.linalg.norm(z_3)\n",
    "        lamb_2 = np.dot(z_3,y_2)/np.dot(y_2,y_2)\n",
    "        er = abs(lamb_1 - lamb_2)/abs(lamb_2)\n",
    "        y_0 = z_3/z_3_norm\n",
    "        it += 1\n",
    "        #if( it%10 == 0):print('# of interations:',it,';','Precision:', er)\n",
    "    return (y_0, lamb_2)\n",
    "\n",
    "@jit\n",
    "def POWER_METHOD(A,dim):\n",
    "    for x in range(0,dim):\n",
    "        A[x,x] -= 2*l\n",
    "        \n",
    "    C = np.asarray(power_iteration(A))\n",
    "    C[1] += 2*l\n",
    "    return (C)\n",
    "\n",
    "def chart_regression(pred,y,sort=True):\n",
    "    t = pd.DataFrame({'pred' : pred, 'y' : y.flatten()})\n",
    "    if sort:\n",
    "        t.sort_values(by=['y'],inplace=True)\n",
    "    a = plt.plot(t['y'].tolist(),label='expected')\n",
    "    b = plt.plot(t['pred'].tolist(),label='prediction')\n",
    "    plt.ylabel('output')\n",
    "    plt.legend()\n",
    "    plt.title('RMSE ='+ str(np.round(100000*score)/100000))\n",
    "    plt.savefig('predct.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Single site external magnect field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8/8 [==============================] - 1s 164ms/step\n",
      "Score (RMSE): 0.2992966647096677\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# number of spins\n",
    "l = 8\n",
    "\n",
    "#dimension constant\n",
    "j = 1\n",
    "\n",
    "#anisotropy constant\n",
    "delt = -1\n",
    "\n",
    "path_folder = 'matrix_del/'\n",
    "ex = '.npy'\n",
    "\n",
    "if not os.path.exists(path_folder):\n",
    "    os.mkdir(path_folder)\n",
    "\n",
    "c_i = 1\n",
    "h = np.zeros(l)\n",
    "row = []\n",
    "for i in range(0,8):\n",
    "    \n",
    "    h[i] = c_i\n",
    "    \n",
    "    Hxxz = np.zeros((2**l , 2**l ))\n",
    "\n",
    "    # constructing the full xxz hamiltonian (2^L, 2^L) \n",
    "    for x in range(0,2**l):\n",
    "        for y in range(0,l):\n",
    "            k = (y+1)%l\n",
    "            if check_nth_bit(x,y) == 0:\n",
    "                Hxxz[x,x] += -j*h[y]/2\n",
    "            else:\n",
    "                Hxxz[x,x] += j*h[y]/2\n",
    "            #if y != l-1:    \n",
    "            if check_nth_bit(x,y) == check_nth_bit(x,k):\n",
    "                Hxxz[x,x] += -(j*delt)/2\n",
    "            else:\n",
    "                Hxxz[x,x] += (j*delt)/2\n",
    "                b = bitflip(x,y)\n",
    "                c = bitflip(b,k)\n",
    "                Hxxz[x,c] += -j\n",
    "    #print(Hxxz)\n",
    "       \n",
    "    \n",
    "    mag_array = np.zeros(l+1)\n",
    "    E_array = np.zeros(l+1)\n",
    "    a = 0\n",
    "    for mag in range(-l,l+1,2):\n",
    "    \n",
    "        p = num_mag(mag)\n",
    "        state = np.array(range(p))\n",
    "        s = statep(state,mag)\n",
    "        \n",
    "        Hxxz_p = np.zeros((p,p))\n",
    "        for x in range(0,p):\n",
    "            for y in range(0,l):\n",
    "                if check_nth_bit(s[x],y) == 0:\n",
    "                    Hxxz_p[x,x] += -(j*h[y])/2\n",
    "                else:\n",
    "                    Hxxz_p[x,x] += (j*h[y])/2\n",
    "                k = (y+1)%l\n",
    "                #if y != l-1:\n",
    "                if check_nth_bit(s[x],y) == check_nth_bit(s[x],k):\n",
    "                    Hxxz_p[x,x] += -(j*delt)/2\n",
    "                else:\n",
    "                    Hxxz_p[x,x] += (j*delt)/2\n",
    "                    b = bitflip(s[x],y)\n",
    "                    c = bitflip(b,k)\n",
    "                    D = np.where(s == c)\n",
    "                    Hxxz_p[x,D] = -j\n",
    "                                            \n",
    "        #print(Hxxz_p)\n",
    "        mag_array[a] = mag\n",
    "        A = POWER_METHOD(Hxxz_p,p)\n",
    "        E_array[a] = A[1] \n",
    "        psi_s = -mag_array[np.where(E_array == np.min(E_array))]\n",
    "        a += 1\n",
    "   \n",
    "    row.append([str(i+1) + ex, np.min(E_array)/l])\n",
    "    np.save(os.path.abspath(path_folder+str(i+1)), Hxxz)   \n",
    "    #print('h:',h)\n",
    "    #print('MAG_GS:', psi_s[0])\n",
    "    #print('rhoE_array:', np.min(E_array)/l)\n",
    "    \n",
    "    h[i] = 0\n",
    "row = np.asarray(row)\n",
    "\n",
    "x_test = row[:,0]\n",
    "y_test = np.array(row[:,1]).astype(np.float)\n",
    "\n",
    "test_data = []\n",
    "\n",
    "load_test_data = []\n",
    "for file in x_test:\n",
    "        path = os.path.abspath(path_folder + file)\n",
    "        H = np.load(path)\n",
    "        load_test_data.append(H)\n",
    "load_test_data = np.array(load_test_data)\n",
    "test_data = load_test_data[:,:,:,np.newaxis]\n",
    "test_data -= np.mean(test_data)\n",
    "test_data /= np.std(test_data)\n",
    "\n",
    "test_label = []\n",
    "for file in y_test:\n",
    "    test_label.append(file)\n",
    "test_label = np.array(test_label)\n",
    "\n",
    "model = load_model('my_model.h5')\n",
    "\n",
    "pred = model.predict(test_data, verbose=1)\n",
    "score = np.sqrt(metrics.mean_squared_error(pred,test_label))\n",
    "print(\"Score (RMSE): {}\".format(score))\n",
    "\n",
    "chart_regression(pred.flatten(),test_label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random local magnect field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100/100 [==============================] - 12s 116ms/step\n",
      "Score (RMSE): 0.252753747276247\n"
     ]
    },
    {
     "data": {
      "image/png": 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qSTU5mWMzk2m0YoRjlJKfn09nZydHjhzh7rvvDrQRceJHP/oRt956K7m5qu/NpZdeyq9//WuKi4uTNVzDGKPP46Onz0u/z4fb4+ODQ628ubuRtXsaOdisGvTluNKZUppDXlYGuZnpFGZnkJGuLIX8rAyqS3KoLs5hQmG2P1itAtaleZkmm2mEY4RjBOH1eklPj29lNWnSpIiiAUo4rrvuuoBwvPjii4Meo2F84/H6eHz9AX74t110uj0hjxVkZXDazDKuP30ay2pKmVtVaARgjGKEI0nU1tY6tkrXnWhffvll7rzzTk499VS++MUv0tDQQG5uLj//+c858cQT2b9/P9dccw0ej4eLL7445Ly6WNDr9fK1r32Nl156CSEEt9xyC1JKjhw5wrnnnkt5eTlr1qxh+vTpbNiwgfLych544AEeffRRAG6++Wa+9KUvUVtbyyWXXMKZZ57JunXrqK6u5rnnniMnJydVH58hxfh8kvfrWvn2c1vZeridlXMqOHtOBa50QUZaGidMzGfR5OKUt/s2JIfxKRx/uReObRnec05cAJfcF/EQp1bpoLrZvvXWW4DqafXwww8ze/Zs3n77bb7whS/w2muvcc8993DHHXdw/fXX89Of/tTx/I888gj79+9n06ZNZGRk0NzcTGlpKQ888ABr1qyhvLw85PiNGzfy2GOP8fbbbyOlZPny5axcuZKSkhJ2797N008/zc9//nM+/elP88wzz3DdddcNwwdlGKn09HnZdLCFLYfbaOvpp9OtgtX7GrrYU99JT7+XioIsfnrNyVy6YKKpfxjHjE/hSBFOrdIBrrrqKkB1x123bh1XXnll4Dlut2qFsHbtWp555hkAPvvZz/K1r31twPlfeeUVbr/9djIy1L+1tLQ04njeeustVq1aRV5eHgCXX345b775Jp/4xCeoqalh8eLFAJxyyinU1tYO9m0bUoDPJ/H4JF6fpM/rw93vpaffS3uPh2PtvRxr76W+vZfmrj5auvs43NrLtsNtgZTXjDRBvj/uML0sj6uXTQk06ivMdqX43RlSzfgUjiiWQaJwapUOBCZun89HcXFxyL4ckZ5vR0oZ1yowUp+yrKyswN/p6en09PTEfF7D8OPzSTYdauGlbcc52NRNp9tDh9uDu9+LlOCTqhiu05/WqhvyRSJNQEluJsW5LioKsrjl7Bksqynl5CklFOaYimpDeMancKQIp1bpmzZtCjxeWFhITU0Nq1ev5sorr0RKyebNm1m0aBErVqzgN7/5Dddddx1PPfWU4/kvvPBCHn74Yc4555wQV5Vur253VZ199tnceOON3HvvvUgpefbZZ3niiScS+hkYIuP2eOlye+ns9VDbpFxEu453sGZnPcfb3WSmpzGtTLXMKMpxkV2QRZoQpKVBZnoaBdku8rMzyM5IJyNdkJ4myEgT5GSmk52RTn52BlVF2UwszKYsP8sUyBkGhRGOJOLUKv2hhx4KOeapp57ijjvu4Pvf/z79/f1cffXVLFq0iB//+Mdcc801/PjHP+aKK65wPP/NN9/Mrl27WLhwIS6Xi1tuuYU777yTW2+9lUsuuYSqqirWrFkTOP7kk0/mxhtvZNmyZYHnL1myxLilhgkpJZ1uDw0dbho63DR29tHY6aa5q49+3TKj38vh1h4ONHVzqKWb3v6BlkJRjovlNaV8dGEV551YSYFxFRlSjGmrniRGU6v0eEj155pKpJS8sbuRf+xrot+jhKDL7eFoWy9HWns42tZLT7/X8bkuvzXgSk9jUlEOU8tUm4ySXBd5/j0eppTkMqsyn/L8TOM2MiQc01bdYEggvf1ent10mEff2s/u+k4y0kSgBUZuZgZVxdnMrSrkvBMrqSzMoqIgi/J8dV2Wl0VpXqZxERlGNSkRDiHElcB3gLnAMinlhjDHXQz8GEgH/ldKmZqo9jBgWqWPfnYe6+Dpdw7yh/fqaO/1MH9SIQ98ehEfWziJzAxTv2AYP6TK4tgKXA78LNwBQoh04KfAR4A64F0hxJ+klNsH+6LxZh0ZIjMW3ZxWfD7JlsNtvLrjOK/sqGf70XYy09O4ZMFErlk2lWVjYO9og2EwpEQ4pJQ7IGp66TJgj5Ryn//Y3wCXAYMSjuzsbJqamigrKzM/9mFASklTUxPZ2WNrn4Pefi9r9zTyil8sGjrcpAk4ZVoJ3/zoXC4/eTKleZmpHqbBkFJGcoyjGjhkuV0HLA93sBDiVuBWgKlTpw54fPLkydTV1dHQ0DDMwxy/ZGdnM3ny5FQPIy68PkldSzf7G7vo7vPi8Un6PT52He/g3dpmthxW24vmZ2Ww8oQKLphbyTlzKikxYmEwBEiYcAghXgEmOjz0DSnlc7GcwuG+sL4RKeUjwCOgsqrsj7tcLmpqamJ4WcNYoLffy+831vHbdw/R5W/G55WSo629jsVxmelpLJhcxE1n1nDGzHJOm1FKVoZp5W0wOJEw4ZBSXjDEU9QBUyy3JwNHhnhOwxinvqOX1RvqeGztfho7+1hQXcT86iJArUQunp/NjIo8asrzKcpxBQrkJhZlk+0yQmEwxMJIdlW9C8wWQtQAh4GrgWtSOyTDSMTnk/x12zFWbzjEG7sb8fokZ8+p4I6VMzlthglgGwzDTarScVcBDwEVwJ+FEO9LKS8SQkxCpd1eKqX0CCHuBF5CpeM+KqXclorxGkYu9e29fPl377N2TxMTC7O59ewZXHHyZGZV5qd6aAbDmCVVWVXPAs863H8EuNRy+0XA7DpkcOT1nfV89Xcf0NXn4d9XLeCqU6eYwjqDIQmMZFeVwYCUkrqWHtUKvK2Xg83dbD/SzrYjbdQ2dXPChAJ+c81pzJ5QkOqhGgzjBiMchhFLQ4ebr/zufd7c3Rhy/5TSHE6aVMS1y6fx2dOnmaC2wZBkjHAYRiRv7W7kS799n47efv7PRSdwUnUREwuzqSrONhsJGQwpxgiHIeXsbejk2fcOs/VIG919XrrcHrYfbWdmRT5P3ryMEycWpnqIBoPBghEOQ9Lo8/ho6+nnaFsP+xu7qG3sZs3Oet4/1EqagBMnFlKQncGEwmzOnF3OPefPJjfTfEUNhpGG+VUaEsqR1h6++rsP+KCule6+gXtTnDixgG9cOpfLFk+isnBs9b0yGMYqRjgMCWP93ibu/PV7uD0+rjp1CqW5mRTluqgsyKamPI9pZbkmsG0wjEKMcBiGndbuPp78xwF++Mpuppfl8rPPLjUFeQbDGMIIh2FYaO7q4539TTz3/hFe3VFPn9fHxfMncv+nF5GfZb5mBsNYwvyiDXHT3edhx9F2th1pZ0tdGxsPtrCvoQuAsrxMrjttGpefXM38SYWmT5TBMAYxwmGImbbufn6yZjePrz9An0e1Ji/Ny2TJlGI+dcpklk4rZcnUYlzpZhtVg2EsY4TDEBWP18cv19Xy0Gt7aO/t5/Ilk7n4pImcVF3IxMJsY1UYDOMMIxyGiBxq7uae32zivYOtnD2ngq9fciJzq0xBnsEwnjHCYaDT7cHrlXilxCclUoJE8va+Zv7l2S0g4cHPLOETiyaleqgGg2EEYIRjHHOktYdv/XErr35YH/aYxVOKefDqJUwty03iyAwGw0jGCMc4xOeTPPn2Af7zLx/ik/CFc2ZSUZBFmhAIAUIIBFCQncGlC6pMsNtgMIRghGMccai5m99vrOOZ9+qoa+nhrNnl/PuqBUwpNdaEwWCIHSMcY4zH19XywuYj9HslXp/E7fHS0++lp89LY2cfQsCKmeV8/ZK5XLpgosmIMhgMcWOEYwyx7Ugb33l+G7Mr85lQmI0rPQ1XuiA3M4OczHSmlOTyicWTqC7OSfVQDQbDKMYIxxhBSsl3n99OSW4mq287g6Jcs9mRwWBIDCbqOUZ4ccsx3tnfzFcvnGNEw2AwJBQjHGOA3n4v//7iDk6cWMDVp05N9XAMBsMYx7iqRjker48H/raLw609PH3LaaSnmWC3wWBILEY4Rimt3X08/c4hnlhfy5G2Xi5bPInTZ5alelgGg2EcYIRjFNLv9fGJn6zlYHM3Z8ws4zufmM/5cyekelgGg2GcYIRjFPLqjuMcbO7moc8s4eOmf5TBYEgyJjg+Cnnq7YNMKsrm0gVVqR6KwWAYhxjhGGXUNnbx5u5Grl421QTCDQZDSjDCMcp4+t2DpKcJrjp1SqqHYjAYxikpEQ4hxJVCiG1CCJ8QYmmE4x4VQtQLIbYmc3wjFbfHy+oNdVwwt5IJhdmpHo7BYBinpMri2ApcDrwR5bhfAhcnfDSjhL9uPUZzVx/XLJ+W6qEYDIZxTEqyqqSUO4ConVmllG8IIaYnYUgjGp9Psv1oO794az9TSnM4a1Z5qodkMBjGMWMmHVcIcStwK8DUqWOj7ca+hk5+8toeXt/VQHNXHwD3Xb6ANBMUNxgMKSRhwiGEeAWY6PDQN6SUzw3360kpHwEeAVi6dKkc7vMnk+auPh58dTdP/uMAWRlpXHTSRM6cVc6Zs8qpNLENg8GQYhImHFLKCxJ17rFCY6eb1z6s55Xtx/nHvibcHh8AHp9ESslnlk3lSxfMoaIgK8UjNRgMhiBjxlU12vjr1qPc+etNeHySKn8xn26HnpEm+OTiamZPKEjxKA0Gg2EgKREOIcQq4CGgAvizEOJ9KeVFQohJwP9KKS/1H/c0cA5QLoSoA/5VSvmLVIx5OPnwWDtf+d0HnFRdxPc/eRLzJxWaLVwNBsOoIVVZVc8CzzrcfwS41HL7M8kcVzJo7e7j1l9tJD8rg5999hRTj2EwGEYdxlWVRLw+yV1Pb+JYWy+/ue00IxoGg2FUYoQjiTy76TBv7m7kPy5fwMlTS1I9HIPBYBgUpldVEvnb9mNUFWVztekzZTAYRjFGOJKE2+Plrd2NnHtipQmEGwyGUY0RjiTx7v4Wuvq8nHdCZaqHYjAYDEPCCEeSePXD42RmpHHGLLMvuMFgGN0Y4UgSaz6s54yZZeRmmnwEg8EwujHCkQT2NXRS29TNeScaN5XBYBj9GOFIAq99WA/AuSa+YTAYxgBGOJLAax/WM2dCPlNKc1M9FIPBEA+bV8Or/zfVoxhxGOFIMO29/byzv5lzjZvKYBh9fPg8bHoq1aMYcRjhSDCrN9Th8UmThmswDAc+L+x5BWSSttzpbQd3e3JeaxRhhCNB9PZ7+foftvC9F7azrKaUU6aZFiMGw5DZ/TI8eQUcfT85r+fugP5u8PYn5/VGCTEJhxDinljuMyj21HfyyZ+u5el3DnL7ypk8dfNyMtKNRhsMQ6b1kLpuqU3O67k7Qq8NQOwWxw0O9904jOMYE0gp+dX6Wj720JvUd7h57HOncu8lJ+IyojH8NO+HZ24BjzvVIzEkk85j6rqtLjmvpwWjty05rzdKiFiNJoT4DHANUCOE+JPloQKgKZEDG20cbu3hX/6whb/vamDlnAr++1MLzf7giWTni7Dld3Dml2HCvFSPxpAsOo6r67bDyXk9Hd8YqXGOvm7wuiEnua7waGXM64CjQDnwA8v9HcDmRA1qNPH+oVZ+8dZ+XtxyFFe64HuXzee606aZRoaJpmmvuu7rTO04DMklYHEcSvxr+bzB71fvCBWOF/8J9r8JX3wbMpOX7h9ROKSUB4ADwOnJGc7o4qFXd/ODv+2iICuDm1ZM58YVNVQX56R6WOODpj3q2viexxfa4mhPgsVhXZREsjgOb4S1D8Kqh8GV5N9/w4fQdhDW/wRW/nPSXjamxklCiA5A579lAi6gS0pZmKiBjQZe2HyUU6aV8PhNy8jPMj2okkrzPnVtLI6RS287NO6CnlboaYFJi6F89tDOmcwYh3VREsniePlbcGAt1JwFp96c+HFZ0Z/DWz+EJZ+FwqqkvGxMUVspZYGUstB/yQauAH6S2KGNbLr7POyu72DFrHIjGsmmvzf4g3GPM+HobQdPX6pHERtPfQr+93x46gr4w83wp7uGdj6vB7oaIT0LuhrU9yCRWIUjnMVxYJ0SjYxsWPcT5d5KFh43dB6HRZ8BnyepFe6DSveRUv4ROG+YxzKq2HakHZ+ERZOLUj2U8UfLfgIG8HiyOBp2wYOL4eVvpHoksdG0F+ZcAp//G8xfpayPodBVD0ioWqhu291VRzYN78RttTLvlOhrAAAgAElEQVTCWRxv3A+55fDxB9X3csfzsZ17OAoY24+o62kr4LQ74INfq88gCcRax3G55fIpIcR9BF1X45LNdSo9b4ERjuSjA+MwfmIcbXXwxCrobkpeKupQ8HrUWKsWwZRlMGmJut3TOvhzdvrjG9VL1bX1c2jYCY+cA9ufG/z57YRYHA7puIffg72vwhl3woJPQelMWPvj6KLQ2QD3TYWdfxna+LRwFk2Gs/4J8irgr19PSlV9rBbHxy2Xi1BZVZclalCjgc11rVQVZVNZYFJuk06zFg4BfV0pHUpS6GpSouFuh6KpIzfDx0p3IyAhv0LdLp2prnVsajDowHj1KeraanEc8VeSH982+PPbcUexON78AWQXwdLPQ1q6EpAj70HtW5HPW/umOvdQRU4LZ9FkyC6E874FpTOgv2do542BmJzzUsrPJXogo43NdW0sqDbWRkpo2gs5pSB9Y99V1X4Unr4KWg/CdX+AdQ8mJ6NoqGjrIH+Cui6doa6b90H1yYM8pz8wrp9vtTjq/YLRtHtw53ZCWxyu3IExjuPb4cMXYOW9atIGFWtY8+/K6qg5K/x5D6xT1/vfUNbBYFP39fsvrFbXp9ygLkkgVlfVDCHE80KIBiFEvRDiOSHEjEQPbqTS1tPP/sYuFk0pTvVQxifN+6BsFmQVjO3g+MF/wCMroXEPfPoJmL4CsgoTa3G4O+Dlbw591drZoK4DwlGjrofD4iiaAnmVocJxfLu6btwz+PPb0cJRWD3wM9/xJ0DA8tuC97lyYNltsOdv0HIg/HkPrAORphYAVrdrvLTVqQVUEus3NLG6qn4N/A6oAiYBq4GnEzWokc7Ww/74hrE4UkPTXiibCZn50DdGYxwbH4dffgwy8+CWV2HOher+7MLEVjHv+zuse0iJ1lDQFkee31XlylET8FAmys5jaqLMyISiapvFsUNdN+8Fny/+c3/w24ExB3c7IFSKq/0z76xX1dq5paH3a0ujYafz63Q3Q/12WHiVur3/9fjHqmk/rD6HFBCrcAgp5RNSSo//8iTjODiuA+MLTWA8+fR1Q8cR5TPPyk+exeH1JK8vlrsTXvgyTDsdblkDlXODj2UXqdVvogKg3Y3+6yF2FOpSu16Sb9lOoHTG0C2Ogonq76LJQZddTyu010FJDXh6468q72qE5+9WgmnF3aGs2uzigRZHdyPklQ88V4nfsmrZ7/xah94GJCy5Tgnp/jdiG+Ohd4LiqGmrU9ZXCohVONYIIe4VQkwXQkwTQvwz8GchRKkQojTqs8cYm+tamVqaS3FuZqqHMv7QE0/ZDL/FkSTh+N1n4X9Oj9zsbv8b8NSVSmSGQvNekF4VdM2xuUOzCtVjiUoK6PILR1dDbMf3dSvLaOsfQu/vrFf/n8y84H2lMyyJDYOg81jQ9VU0RU2cUgYn1Hn+fJ144xwbHlWC090cer+73S8cDlZedzPklg08V34luPJUE04nDqyF9EyVGVazUrULicVCev4e+IutMrztcDC+kWRiFY6rgNuANcDrwB3ATcBGYENCRjaC2VzXZqyNVKEnnmRaHAffVk0Vm/eqH3C41f7+N9R+Ea0R/NuxoN05ZbMGPqYDsYlyV2lLQwtINN56QGUJ7X019P7O46HWBij34lBScq0WR2G1WjT0tgUD41o44olz9PfCO4+ov+1WlrY4sooGWhxdjc7CIQSUTA9vcRxYr7LCXNkwYyX0NMPxLdHH2dUIRz8Ifvd621WKcNHk6M9NALEKx1wpZY31Yrkv7iC5EOJKIcQ2IYRPCLE0zDFThBBrhBA7/MeOiP0/mjrdHG7tYdFkExhPCYFJdSZkFiTH4vj7farI66x/gm3Pwnu/cj5OTzxNQwzQ6vdY6vDTyvILR6IC5PFYHI274a0fqb9bbe6hznoVwLYylJRcKf1ipC0O/4TZVqcC41lFqlYkqyg+i2PL79R7rV6q/n/WRUGvxeLo7wq1JLubnIUDVCKAk8Xh7lQbUE07Q92uWamuo7mrpFQC09umsusgtIYjBcQqHOtivC9WtgKXA5E+MQ/wVSnlXOA04ItCiJT3zx5VhX+v3zf0Ng8jjea9akLKKlBukEQLx6F3YO9rsOJuOPcb6sf+l69B/YcDj9WujsYhpoQ271UraqdsmWz/926oFoeUzpZTrDEOKeHPX1WpqtPPGhhX6KwfaHFYU3LjpacFfP2hMQ5QwlG/XcWBhIDyWbF//lLC+p/ChAWqsl16Q12R7g4l1Fk2K8/nU5+PU4wD/BZH7UAXVN27qjWIFo7CKiifoxISItHXqZ4HyuqAYFv5kSgcQoiJQohTgBwhxBIhxMn+yznAoHPApJQ7pJRh0g4CxxyVUr7n/7sD2AGkxqFnYXNdG0LASaMho+rQ27B3TapHMbw07VPWBiTHVfX6fWpleerNkJYGlz+iBOv5uwceO5wWh5O1AcNncXz4Z/ivmoGfX8DiiOKq2voM7P87nP8tVVfRVhc6UXY5CccQUnI7/DUc+pwB4TikLA69J0vZ7Mif/8bHVZ3F0Q9g999Ud9nTvxgUAatgBoLjNuFwtymRyQ0jHKU1ao8MXXeiObhepeFOXha8r+ZslZ7r6VNxqw9fDKYya6yxl2Obg+8bUhbjiFYAeBFqp7/JwAOW+zuAf0nQmAYghJgOLAHejnDMrcCtAFOnTk3IOKSUvLm7gZkV+aOjsWF/r1r5DaXIaKTRvBdmf0T9nZmvVqEeN2RkDf9rHXpX+e4v+G4wyFswERZ+2tld1dOirocsHHtg3iecHwtYHEPcka7uXTXetkOhWVuBGEcEV5W3H176hnINLb1JBZe9fcqVVFilJsGelqBbSaNTcgcjHHoSzvdbHHmVkOZS78PdBpV+4SifBZt/oyZha2Ae1O/gL/+sAuEACCiogpOuUCIIapLWC5NAjMMm1l3+zyicq0pnVjXvh8JJwfsPrIOJC4NCBMqCffd/4TefUSnQfZ2w4kvwke8Gj9HfK4CjfuFoP6xEqCA53XDtRLQ4pJSPSynPBW6UUp5ruXxCSvmHSM8VQrwihNjqcImrVYkQIh94BviSlDLsMktK+YiUcqmUcmlFRUU8LxEzv37nIBsOtHD96dMScv5hp9+/O9hY2fbS3aEmJ+0rzyrw358gq2Pdj4PWhpW8cvUDtxfJ6ZXhUISjp0X5s50C4xCcdIZqcegaiI6jwfukDFoa3REsjtaDaiI/9WbVaqPY/3vQq2AtOnaLA5QlNZhaDl38p11VaWlqUt7ziro9Yb66LvO3bXf6H/R1KtE44y5Y9QgsuRYu/W9VF6LrMazvW7uqAhZHR+gxeRFiHBAaIPe4lchNWxF6bM1ZkJGjXKInXa6+bx02S6XH/70qnmqxOOqUaKSnZgEb66ueJISYb79TShm2j6+U8oJBj8qPEMKFEo2noglVoqlt7OL7L+zgrNnlXLd8lAiHXll11g9M6xyNBFJx/cKRma+u+zrC/4iHwrGtMOMc5RKzol0UXY1Q7M+jl1Kt1tNcajLWq9V4afK/Ry2OdgKr3yEuBvQkrydkUBOr162SDnrblOWQ4ZBy3lKrrvXKWn8GrQdVQ8NA8V8Y4fjwz/GPN2BxWKyYoilwwN8XSltNer+Pxt2qwaIVbU1VnAiLrlIXjbYe9DE+r/peWS0O7arqjmJxFE0BkR4aID/yvvo9TrPtiZdTAne/p65dOcqisMeXtMUx4xxl6XY2KOFIkZsKYg+OdwJd/osXuASYnqAxASDU3qu/AHZIKR+Idnwi8fokX139ARnpgv/61ELS0kaJ20eviDuPRz5utKCDnqWWGAckxuLwedWPs9jB7amroa3uHG3d6clqsBXSeqVcFkY4MvPUpDTU4LjOgrJaHNraqDhBXYcLkAeEY7q6LrIIB6iFCgx0VYE/JbcxfuHrOK4WClYR11XTBZOCe26XzgCEs8URcDE5xCbswqGTLrILg+7BgKuqMfx5ANJdSkytFseBtep6qsNmqoWTgjsH5pUPtPa0JTvjHHV97AN/8V9qAuMQ+0ZOP7Bc/g04hyEEqoUQq4QQdagtaf8shHjJf/8kIcSL/sNWAJ8FzhNCvO+/XDrY1xwKP3tjLxsPtPC9y06iqmgUbQ2rhUNX8Y5m2o/Cq99Vk7Z24wQsjgQIR8dRFT8pdrAunQKp+u8p/sCndeLyuGHz6tiqvZv3Kt+1npTtCKFWwUNxVXn6goJhXVToCbHyRP/tMHGOllpVxKb961n5qhVIwFWlhcPBZTzYzCpr8Z9GT5wTLMmWrhw1aTtlVgVcTA4Tfma+ek/6/6jdUoOxOEBZY1aL48A6ZemEy8TS5JYHBU6j6150+u7RD9ReHClqNwKxu6rs5AKDbnIopXwWeNbh/iPApf6/3wJSvrR/Zftx7n9pJx9dUMVliydFf8JIwuqqGs30tMKTV6iV1w3Pq+IpCApHIiwOvXp2tDi0q8oysepVYfUpDFjxbnoS/vwVFbidtCTy6zbtUSv4SMH+ofaraj9MoGOQ1eLQE2vFiaG37bTUKkFNs6w7i6cErZiIriq/JdW0N/pnYcVa/KfRrppKW5Z+2WznWo6ApRCmcC+33Fk4sm3uwe4mlYYcqblgaY2q+QFlvR78Byy8MvzxGieLo6dZfdfzytXnvuc1Zd2mqN0IxL7n+BaCvanSgErge4ka1Ehhc10rdz29ifmTivjvKxciBpOZJKWawJO9iT0o9wmMbldVfw88fbXaPe7a1aEtubMsMY7hRnc3dbI4rDEOjQ5gFlYPXPHu8VdVxyLgTXvDB8Y1TpXM8aAtgzRXaIwj4KryxwvsK19NS+1Ai6jI8p47G9QYXQ571ZRaMo7iofP4wJiFnjgn2MKv5bNh09sDswkjWRygBEUvAPTnm1WgRDw9KyjWXY3h3VSakhp/okOr+rz6OgYGxsONob9btXLRwtTToiw6UJ+BjhGNghjHx4DrgZ8DvwUukVI+FPkpo5tDzd3c9MsNlOZl8osbl5KbOUjjbMvv4f4TBvbBSTReT7BoyJ4XPpwc3pjY5n/vPKLy3y9/BGaeG/pYwFVl6dskpXILefuH9roBi8NhVZdVENz3WqP/v7mlobUEnr5gZXC0amwp/S3jw8Q3NNlFQ7M4tGVQtSiMxeGPcYQbb+uBgcJRPFUJUqDC28HaAEtKbpwxoE4Hi2Pa6XDyDTD7wtD7y2Yp96X1vYF/v/LM4PfGTm6pg8Xhtzas7sHupoFdce1YM6v0/htO8Q07ATeoZVHS3RxMbqlaqGpIYOTHOFC7/T0BlAMu4DEhxBgrSQ7lzqc34fZ4+eXnTh3aLn8NO1SeeaxdMIcLjyVVNFEWR/M++Pl58MFvEnN+UKu13DKVqmjHKR23bgP84Wa1yc5QaD2gfPhOLiMh/C4Fa4xDC0eZmria9qhJtO6doEUUzeLoalCCEC6jSpM9xD05tMUxean6bujYS1cjZGSriV2kO7uqelqUy6bEZokVT1Ur5e5m9T6cAuOaeFNy3Z1KCOznzCqATzw4cBK3ZlZZ6W5WlkI4z0FumUU4tMXhFw6rezBcZ1wr1lqOA2uV0MYSk3C0ZluC73GixeoaBcJxM3CalPJfpZTfRgW1b0ncsFLLsbZePjjUyhfPncXsCYNIqbSiJ4u9rw19YPHQnwTh0FtkDqXjaTS6GoJZTHYyHVxVOm3TqSXI4fdi36Co9aCzm0qTVx7649YTTnaxmrj6OtXnvudVSMvwWyhRqrEjNTe0klU4tHTc1kOqkK54qnKj9rYG30NehYpd5JY5Wxz2jCpNILPqgN/iiFBLVTlPtQnxeWMbb6ethiMa+vOzZ1Z1N0ZO2w4RDkuMA0I30OpuisFVNV1dN+9TFkcsbipwTrzoaQlmjVUtVNcZ2ZGD8wkm5v04UGm4Gi8jIHCdKNbtVT/wM2dF+XLEgp4s9q1JyibyAfQEmeZKXHD8wHp13ZbArUy7GsMLR0amcj1YLQ492TXYhKOzAf73Alj3k9het/WAc2Bck1seOrH2NCvRSM8Iupoad6sCtSnLVUV1NFeVFuCyKHkn2YVDqxxvO6hccHoFr+Mc1o6veRXOMY5wwqFdem2H/H2qIlgckxYrYY21ULLDoYYjEgVVymKyb7EbLTaRW6ZiEl7PQOGwWhxdERocarLyVXLArpfUd0P3p4qGPq89fqZjHAUT1XkLq1PaDSJW4XgMeFsI8R0hxHeAf6BqLMYk6/Y2UZzrYl5VYfSDo6FTE1sPDm0Tm3jRGVVFk9WENZhd0aKhc9OtO7FF460fwntPxH58V0Nkt4B9Tw79g2vcFXrc8S3KN7wvht5dXo8Sw0jCkVcx0A+t3Qm6evngelXpO/M8/0QcRTia9ijrpChKy5ysQjWxDXYhomsAdDqtjgVYXTB5Zc6uKi0cdmtMf1aNu9QEG07sIZhNdWRTbOPVjf1iFY60dPXe2o+E3h/NxZRbBki1wte7/2mrVlsc/T2qU24sBaelNcpVCbELhz3G4fOFWhwAJ3408p7mSSDWOo4HgM8BzUAL8Dkp5Y8SObBUIaVk3Z5GTp9RNjyFfp0NMPlU9Xcsk9ZwoTOqSqapCbNnmIPzbYfVqlykxy4cPp8SjnhiIl0NzmmdGnujQz05N+4ObYN9bKu6PvSOyliJRPth9ZnZ/fhWnFxVelVYWK3aSGx4VN2edUGMwrFX+cajtZHILgTpG1z9is8X3DlOu360K6jL4oIJN14dc8q2Laqyi1XF+eH31O1Ik3z5HJXOeuT96GN9/T546esw6eRg7CIWCicNFI6uKC6mQNuRpmDlv0451gkJsdRwaHSco6Aq+Hc0sgqVl0B/t9zt6n9tjeN8/Efw8R/Hdr4EEavFgZTyPSnlg1LKH0spY1wqjD5qm7o50tbLGcPhppJS/fimLFeryGR2qu33Wxx6ZTjccY6DfjfVzHPVVq6x7HrXvE/55mMdi6dPHR9p9RrO4vD1B1fHAMe3Be8/FGU/bb0RU0SLo9yfNunP6Oqx7AiXlqbcVR1H1UQ1caFfaBwm4r2vBYU3lowqGFjJHA9d9aohYfFUi6vKweJwKkQDlabsVJwohHJX1fn3dQuXVQXKIqhaFNnicHfAb6+F1/8DFl4Nn3tRVWTHSqHN4vC4VSws0oRvrR7Xu/9ptMURrWrcis6smnZG7G6lQOKF/3V0uxGrxTECiFk4xgs6vrFi5jAEnvo6VXZTXgXMPEdlVg11W9FYCVgc09X1cMc5DqxTk/aci9WKyN5C2onDG+IbS7S8e1BjcFuC410NajUL0Gjp3H98qwpQpmVE3/8gUvGfxp79YnVVQVAAZp2vhCSvUh1rdRnW74AnVsEP58MvLlJWUrTAOAysZI4HnYpbNEVZa1mFKsbR162+M4EYR7mKo9hTrZ1qODRFU5z3Gndi0hLlxgv3e3jjv2HXX+Hi/4RVD8dfB1VYrYRDu/O0pRAtOK6Ptfcayy5UwqPFP1pWFQStjFjdVIFxWERbewpyoqT/JhkjHDbW7WliYmE2NeV50Q+OhrVL6Ixz1Q/9yHtDP28s6BiHdrckQjimLA/+OGJxVx3eqK7dbUGLKBKBH2kEiyPLweLQbT90gNzTBw071f3VS6OnRrceVG0/CiOkO+oxdVuFwzIp6TjHzPODx0tvMIMJgkVwJ1+vvhteN1Qtjjw2GFjJHA9ttvqU/AnK4rCLtFN2j9ejgt/hss2sQhvJvQhKOPq7B8aiQP2/Nj0FJ1wKp90+uCBw4SQVi9CfUSyWQiTh0GKtrdhYXFXTV6jfyAlxdkqyxpeMxTHy8fkk6/Y2csasssFVidvRhXd5lf4GZSJ5abk6qyoRrqruZlWfMu300J3YoqHdGBBb/6zOGIQjMz+0ALCrQdUJFEyCBv+k1LhLuagmnKT2eT76fuR9r1sOqOc7dYbV5Fksjv5eNUlZf9zTz1TnmHVB6PFWAdef2Xnfgi+sh3/arfaGiEbWEFxVVosDVJyj49jAidWpnqD9sCoqDWdxWIslI/3PICiQTu6qnS+qifPkGyKfIxJ6HwztrorFerUKR2/7QIsDgmIfi3AUTYbPvxy6J0cs5FriZ91GOEY8Hx7roKW7nxUzhyG+AcHJMa9cuTEmLU5enEMLR16FCtQOp3Do+MbUM4JFTfatQ+143HBsS7CvUCwWUCxugayCYHDc5w3WIlScELQ4jvsD4xNOUjuuSV8wI8yJ1oORA+PWMXU1Bt0JVlfVzHPhqzuCrhGnjrpth1R9h56k8ytD+z+Fw74jXTy0HVIxEn2OgonKzRhw5ViC4/bxhkvF1WgxyimNLLqgXHKZ+c7C8d7jytqbdX60dxMe3Y6jwy8ckTrjalzZakzdzcG9ODT67+Z9KiEkO4HbFFiLS7XFEa1SPckY4bCg4xtnzBqmwhr7hjYzzlWbudhXin3d8Pw9w9uWRLuqXLmqGCtaRk88HFin6ieqT/E3gSuKXstxbIta9Z9wibodi5DF4qrKzA8WAHY3AzIoHI27VUzh+FY1QZfNUhluGTmR3VXRajjAsiJvCK0aD4f+DoQIR50S3ljEwspQ9uRoPRSa7jvA4rDEOCDUVRVNOPRnFi2+Aeo9Vy0eKBwtB9Tiasl1Kog+WHSqsd3iiGYp6LYjTjEOUC1Eckvj/5/FQ265WhR43MFFSSKFahAY4dBIyfR3vssNxZuHr3W6drXoSWbCfOXntvfQOfoBbPxlcCU/HGiLw5Wt/NjDbXFUnxJsYlc0JbqrSsc3Tviouo5VONKzIm+IpNNxdQYbqEmvfI5yH7UfVqm4lSeqNNeMLJh6Wnjh8PSpySaacGTmKQHqbgxOrpECmIEVvMX1M9g9FQLbxw7S4rC6lPInqkWGLj4MZFXpQjSbxZGWEb65XjzCAcoCP7YltK/YJn+Nz5LrYjtHOAYIR5OKW0Vz+ejq8QEWh/8zb94fW0bVUMizuMx6WtRrp2inv3AY4fDT75Msa3+Zi/KHuF+0la4GtVLQZrvO9um31RH0+3309vqC/l54YP7gdkzTwpGR4xeOYQqOuztV/r01U6RocnThqNugJindMiEmV1WjmoQixZsy85UYe3pDLRTdGrxhp0rFnbAg+Jyas1XLC6cxtNcBMnK7EQimTYa4qiKsZnNK1MRlje20H44cgA+HK0dN4IOJcegaDo2u5Ti+TdUPBHozFavXsApd6wH13HCTWF6FaoURLTCumbREJQRol6LXo1rQz7rAublkPGRkqnHo6vGuRiXs0SyF3DL1P+oLY3F43Ylv9WGNL1kbHI4gjHD4EYArv5T5JcNYYd1VH7r60m2SBwiEf5K3F3T1NKuJTBevxYOnR00E6RlqDMNlcRzfqiZqXdQIagUaLcZxeKNqqpfuUj+8WGMc0dIerXtydFksPN3htfZN9X+wtt6e4d8Qx8nqaImhhkOjhaPbIcZhJy09tP+Tt19ZnoOxOITwV4/HKRw9reo5xQ7CcWyrej9apJ36VUVKxdXjWvk1WHxNbOOxV5Bve1Z9JqcMIShuxVoEGEtjQlDvWadjO2VVQWK2KbZirR63NjgcQRjh8JORnkZOQSlFdEU/OFbsfZYCFoet0Z4WErslou/vjZABFI7+nuDr5VWqyW2orcYh2F+ofE7wvqLJaozhNlTqblauEL2XRqyus0gNDjXWPTn06jivwp+QUAZb/VvVTzwp+JyqxcoFpltZWNGTRrTgOAT7VXXHmGuvazlATZDSN/gOp9mDaHTYZsuogqBLp+3gQBdMXsXAGEck4QA46yuxB7VLZyg3zN7X4Lkvqq7G5XNUbdBwUFitdo6E6FXjmtyyYEDaWh1v/TvhFod2EzYNbDcyQjDCYSWnOL5Jetsf4fefh3d/AY17BvYO6qwPIxw2cdKC0We7X1sgkVJHw9HfE4xB5FcCMnp31lho3K0sGasrR09E9qZyGl27Ur00OJ5YXVXRhMNucVj92OUnBOsWJliEIy3dn7nikIyg26gUxJBCqbNfeppVu41omUTW6nHt2huscFi7tcaKTsUNiXFYWoPYV9K5ZcHvTK+/3UYsghorQsCkRcrS+OA3sOIeuGVNfBXikSisCn4no3XG1VhX91aLIyNbfe8h8TGOXKvF0Tziiv/ACEco2cXxTdIbHoWtv1fbgv7kFHj846GPd9mFwx90t1scYYVDt7NoiX1MGk+v+rJDcHIYDndV0x7VSsHq5w7UcoRxV9VtBETQNZEXg+tMSv/nF+VHGrA4/MKRWx70Y1f4raKCSQPN/dwwTfxaD6pMp1iCkQFXVRPkxrAqzKsICmZAOAbpyx/MZk4Bi8PihsvKV6IHDhaHReg2PamuJy+Lf6yROPkGmPsJuH0tfOT/Bv+fw0HhJLUQ7OsK7fwbCesxVuEQImh1xOLyGgqBeFijsThGBfFaHK0HVMHWXe/BwquUP123v9B9lkJiHP5qdLtARBOOQbmquoMWjlMq6GBp2hOsitYEajnCBMgPb1QxB/3D0xZHpO6u7nbVUymqxeH/cevJwXq8DpDbtxaF0NW0lWj7cISco1zFktrqYpuU8iqCrxmYxAe5/Wf2ILaPbT3oD17bJj4d57Dfr11Vve3w5v1Qs1JVQw8nCz4FVz2hst6Gm0LL97KnJXZXlcYa17DeTrSrKi1NWRld9Woha2IcI5x4LA6vR5n+JTWqL9G8T6r79QZCTsVr4SyOsDGOGF1VPS0OYtRrc1UxdIvD53VuxFdQpVZI4Wo56neoRn+a/Alqwo3U3dUar4iEXqG6OwYG03Ucxhrf0Nh38NO0H4l9L2c9toadsbkT8itULKbfLzY5pcHFRLwMJjiu03/tWWpaOOwTq64nePN+9Vld8J3BjTVV6Irt49tQ9T1DFI7sJAkHqLE27weksThGPDklKt0ull3i2uv8rbenq9sT/BXR9f4urAHhsFgcYdNxw2RV6eOiuaqeuhJe/ubAc2bkhI4hHuHweYMbNWlaDyorwN7eOt2lxCOcxeFuDxIU1OYAAByMSURBVP3yB1xnEeIcsTaTy7S4qrptFkfVYjX5zTh34POsu71ppPRvQhRFrDTW7JdYVoXWWo7B1nBo7NvHbnwcnvo0vPBlePMHoe1dNPZUXE3A4rBNiPr9rf8pzF8VTG4YLegFwLHN6nooripInsUB6nurt74dgTGOkVVVkmp0vnRPa/RunIEqWr9bo2gquPLg+HZ12141DmqCTXPFXscRq6uqef/AylJPT/C+zFzl0omnlmPnX1Rb65tfg8mnqPt0RpXdVQX+Wo4wMY6+rtCVtZ6YO4+HbyPuJLxOZFmD4zbhyCuDfw6zra21OlfvK64bDcZah2AVtVhdVaBcEG11se/R4IS2OHw+5dp48wfqe1KXrgKqRVPhy1tCn9N2CGZfOPBcWsidYhya8741+LGmCp0xdtQvHHFbHDbh0IWXiY5xgPruHvBvzWwsjhGOnmhjiSnY2y+kpUHlXFVYBsFJ2v4ly8yNUMcRJqvK0xu+m6zUO5Z1hN7f3xsqfrFmMmn0boX6ywvBFZBT6+/CameLw+NWrUZChCOGYH0s7UYgaHHoPRRi/VEHqnMtmVWdDmIfCetEG8uqcLgtDqRyfbXVqXjbynvha/tVLUXbodCW6B63+rwdLQ7/BOsU4wDVvTeWfUJGGpm56jcdsDhi+G5Y/4+ZtkB9si2OwN8jz+IwwmHFanFEo+XAwPYLE+Yp4Qhpf2GbhFy54es17Gm6ViEJJ2buduUys/u7+7ttwhFn9bhOYzxo2fSoaY9adTlNzkWTVXzAvkWtfg/WH2FMrqoYewulu1RNRou/a2k0odEEOqFaAuSBppRxuqogPldV0171/xqKcAT6VbWr3mEQDFyXzgBksJgRgqLuVJFdcYL6LtuTAqpPgXO/OTqtDU1htaUwNIYJPz0juJuhvcq8cJLqfqAt1ERi/W4Zi2OEoy2OWNJfW2pVdbG1EVvlPLXy7axXX9aMnIHBTyfhiJZVFWlM+n67xWFNx4Xw1ePHt8Or3xuY4aQnmoP/CIpB027lpnJqAVI0Rbl57Cmu2mqyfg45papWIlqMw9quJRJZ+cF21zELh0PbcD2eWC2OzLxg3Com4fC/pq6UHpLFYelXVfuWKqTTtSraBabFFCLXjcy6AL68bWCGV7oLVv6fEbnijRlrS/NYLQWnrXEBzvwy3Py34RlX1DEY4Rg95MTpqrKv0HTL8PrtauLLrxg4ybpyI9RxhMmqgvBWUEA47BZHT6jF4RQMBlV89eb9AydxbXH0NCvBAFXkGG7f53C1HAGLwyIcaWn+moYIrip78WQkMvPjtzicur/GGldxOk8srqrMPBUH0wWRg63hAMtmTu2qRfzU04KLGL1lqXXrXKeqcY0QwQD5WEMLR1ZRbIsQUL8Vp8aaWfmxtaIZDgKJCiK4SBhBGOGwkh2Pq6p2YPsFq3B01jtPQJm5cdRxWIQknJhpH727I9RqsAtHTok6h92VpM/beiD0/rbDar8NUN1w3Z1qb4Nwvu5ALYctJdfJVQXRYy6xVI1rsgqCAhprjMO6aY+msx4Q8fmwAxsfxfic/IpgksGQXFX+yaRptzqftb4ir0IJVLPd4hCxpxqPFbRwxNNfasoy5aZLJfp7lVM8tPbyCcIIhxWt7NEsjt52tRK3C0d+hfrRHt8evs+SKyd8HUdfZ+jk39cVHFM0V5X0BSdpn0+5jTKswlHsP6bD+fnW1amnT/n7a85WX+CDbwfbbjtlVEFwJWsPkGuryV4RHK1fVSwNDjVWUYpVbHJKABHqqupqUAIQTwtr/XqxunP08WkZsbvEnNAWx86/qOtpZwYfE0J9N0NcVYeUVRHrqnusoIUjnjYhF/0bfPJ/EjOeWAlYsiPPTQUpEg4hxJVCiG1CCJ8QYmmYY7KFEO8IIT7wH/vdhA8sLV2t5KJZHHp17tTwrXJeqKvKjisvfB2H9Ko6CU1fV7DtdjRXFViq1i17cWj0F9B+Hn3bGkjVu6YVTVYukIPrgxlV4VxVeiLusfV/cnJVQfRgfVdD7BOrFiWnmFI40tKDm/YM5jU18biqICgchZOGtpLUwfG9a5RwVi0Kfby0JtTiaD00NAtntBIQjiRkQg0nuUY4nNgKXA5E2IYNN3CelHIRsBi4WAhxWsJHllMU3eKItBNa5Ty1v0A4V4srx7mOQ/j/FVZ3VV+natSGCD8mqxBo4ei37P6nCRf417dba4P3aXdTUbUSjpb9/q1WhT9jx4FAq2+bRaM75g5wVfl3JbS7zkBV5fc0xxfjAHV8PHvF2/tVxRNX0ZTNVJk2mbnRj4Wg0AwlvgFBi8PTA1OWD7SSSqarBY7+fIea/jta0a65RLdCH260BTsCi/8gRcIhpdwhpdwZ5RgppdTRYZf/EqG50TCRUxLd4rAX/1mZME8Jg/RGiHE4pOPqFVGIcHT5t2YtjOCqsqzwdYBcWxwZDhaHXYB6HSwOHRgvrIapp6u/tzyjJrtIhZFZBQOFwymrCpTF4et3FkT7/tfRCAhHnIVZueXBvahh4P4psXD6XfCFOHZu1N+JoU7iGdlq+15w7h9VMl1l1nUeU+IRrmp8rDMYV9VIIN2lFnvG4ogfIUS6EOJ9oB74m5Ty7QjH3iqE2CCE2NDQMIRmftkxNDpsqVWxB6d/aqWloZ6jq8qWVeXtVxOoXulahaO/W024kcQsxFXlF47AtrGWVXBOFIvDKee/sFr1mMrIAXcblDsU/lnJKhiY3RXWVRWhDUqsxX+B17VYHPFgd1V1NsSXUQUqZhBPuqoe41CFQ1t4EBrf0OjMqub9yqryusencGQVwvn/qpqQjjbO/zYs/VyqR+FIwoRDCPGKEGKrw+WyWM8hpfRKKRcDk4FlQgiHbnWBYx+RUi6VUi6tqIhzArGSE0Ojw5YD4Te00TvPQRhXla2OQ/+tV8v9NldVZn5kMetpCe4TEHBVxRjjkFLdFmmq95be6Kn9iBLGrHw1MU72h6HCBcY1jhZHV/B9W4lUPR6vcGQOUjjyyoOuqr4u9dnH2qdqsARcVcPgNsouVKKu29VbsdZyOO3DMV4QQm0upXvJjSZO/XzoFs0jiIT1qpJSXjCM52oVQrwOXIyKjySOWC2OcF/ErHx/Rkut8+rVlassDG+/Mkf1JG+3OKRUf7ty/WIWxlXV3awmhOZ9luC4P8ZhzapyinG4O5RLreJEFZdpq1MrVfte2FNPUy3jnVqNhLz3goGfXV+neg/2QHCg8aJDgDzWzriB1x2Cq6q7WblyAi1ihpDpFAvadRJtJ72YzlWt/idOmVLFU9WCoKU2KKzjMcZhSAgj1lUlhKgQQhT7/84BLgA+TPgLa4sj3F4RPp8KOkbas0HXczhNfDqIaq/dCAiH/35vH/g8ysUTqd17T0twLLpbqj63NR7hylGtOawTu/67arG61rGbtrrQKuKas9W1U3tyK+EsDntgHCyuKifhCNPnKxyDtThyy5Rw9rY6N6VMBFNPh2t+BzXnDP1cn3oMVv3M+bF0lxKK5v1D3zTKYLCRqnTcVUKIOuB04M9CiJf8908SQrzoP6wKWCOE2Ay8i4pxvJDwwWUXR26t3nFUTeqRVoxTlqtJySkGoifzQH8qbXH4J0kdTLYWzuniPSd6WoIuiAFZVRZXlRADLRf99yS/cOg04/bDoa0aas5WO7TpQHk4wgqHQ4psdpESsnCuqjTXwI6/kV4XBueqgmCbGOt9iUIImHPRwD5IgyG/InJ8paRGuaraDqneSyOwAtkwOklJW3Up5bPAsw73HwEu9f+9GXBw3iYYa9sRpxTLSKm4mtPvVB1FnSYHl38S1VZBIMZREXrbGlS2WkHWdFPdGTevQrmD3DaLI8OWAWUPsmvhqJynCtJaDigh624KdVVBdGsDnNNxw1kcQoSv5Wg9qNKQY02tHXRWlT+TravRYuUk2OJIJqU1sP1Pqvut0wZOBsMgGbGuqpQRre1IpOI/TXpG+JVgYBfAMMKhBcMqHNnFKi5ib0miO+PmlIRO2jrGYU+dzbZbHP73mFeu3BitB1RgHAa3pWlWgbKYfN7gfX0d4Yvy8itUuqidhp1QMTf21520BKafFXS5xYq17UhnnAH50UBJjUrXPr5tfAbGDQnDCIedaI0OW2oBMXh/cSDGoffgiEE4wo1Ji0BOaWgqbCCrysHi6HWwOLKLVU1KS21oDUe8aJeRtTljOFcVqNiM3vdD4/VA467Q7LRoFFXDjS/EX+Rl3cGvqz72bryjBb24adlvAuOGYcUIh51oFkdLrfoRDnaC0WmpWhi0xZFdpNxFAeGwFM6FbReihaMkNL4QVjhsQXYtIjklahJvOWCpGh/ERKOFw+quiiQcE09SbinrmFr2qxhSZRwWx2Cxuqo6B1H8N9LRtRxgAuOGYcUIh51wFdYAvW2w59XgvgeDwWWzOAIZULmhfazsrioIX7yXU6Jy+u29qmKJcaRnKoEpma5W3o271GN6V7h4CCscDjEOUMWFoFwpmgZ/4lzFifG/fry4ctRn3t3sbxEzxoSjxAiHITEY4bATaRfAv/+X8oefc+/gz++ypeNqAcnMUxfHrKowrirdUj231GZx9Koc/nRX6PHZxSrmoAv9elqVmAgRbJ9yYJ1yfcXae8mKrmQOEY7OgZ1xNRMXqOtjlr2x6/3CUT4n/tcfDHllQVdVoov/kk12YdCqMjEOwzBihMNOVhGOTQUb98DbD8OS64Lpq4MhXB2Hy9/ZNeDCssY4YnFVFVrqOHqUQNmzaALWVFvw+dqaKZ6urg9vHFxgHCwWh6XtSCRXVf4EVYR33CIcDTtU8Vo4sRlucsv8rqpBtBsZDeg4h4lxGIYRIxx20tL8TQVtk/TL31Cun/O/PbTzB2IcVotDqKZ11gaIMbmq/GPMLg61ODw9oQ0ONfZ+Vb2tQTHRE4yvf2AqbqzYXVWePhWvCCccQiirw25xxJNRNVRyy1Ummbtt7FkcoNxVIl118DUYhgkjHE7Y247sfgV2/RVW/vPQA6gDXFXdQesgM39gVpUrT03IIt05qyrT308qq9CfnisH7v6nsVsuPS3B+3JLLa0phmpx+IUjEOCPYD1MXKDEwtuvMqqadseXUTVU8sqDO/KNRYtjyXVw9j/FtzmVwRAF821ywl5h/ff71D4Uy28f+rkzsgARKhzafeXKtTTd61SV1foHn+2wwVRPc7Bff1YBINXzwgmH3XLpaQsG+oVQmVX120KrxuNhgHCE6YxrZeICVanfuFvFZJKVUaXJLVNWFoy9rCqAmeeqi8EwjBiLwwlrbyh3Jxx+D066Ynhy/IVQE6m1jkNP8tYYhz024NR2pKcl6H6yTtqe3jCuKlvGmNXigGCAfLhcVbEKByh3Vf0O9XcyMqo01p3hxqLFYTAkAGNxOJFTrHpSARx5T1VnT1k+fOd35YbWceg2JJl5lhhHd6iLx6lDrnXit07a2v1lxxrj8ParDCtrP6hAIHWQrqq0dPVeBghHBFdV2WxlWR3f4k9MIHkZVRDapmQsxjgMhgRgLA4nrBbHIf/eUZNPHb7zu3JC6zhCLA6djtsZulJ36pDb3RxsbaIb2Lk7VDquy8HisBY36swqq8Wht4UdSs5/Vn58MY70DOWaOrYl+RlVYCwOg2EQGIvDCe0WkhIOvaOyfHKKoz8vVjLzgum2/T1BgbBu8uTkqmrZH3oeJ4ujt02d0zohatIzVJfUnhZLKq/lfS36jCr8c9oSN1as2V2xuKpAuat2vqgyf5KZUQXBLUWzCp3F1mAwDMBYHE7kFKsgbX+3Eo4py4b3/FaLo6/LYnHkq9f1+hsaWovw7K4q3RnXyVXlCRMch6AoauvFanFk5cPcjw3tvTkKRxQLYuJCVVhZvz25GVUQdFWNpeaGBkOCMcLhhHbpHHpHTbLDGd8Af4zDlo4LQaHo6xrYqiO7WFkTPp+6rXfvc4xx9EYQjqJQiyPWPS9iJUQ4LP22IhFo2S6Tm1EFQVefEQ6DIWaMcDih3Te7X1bXiRAOa8uRgHD4J1i9/3WIq6oYpE8FtEGl4oIlHdfS7qO/2zmrCoL9qnodLI7hwNrePVbhmDA/+HcyM6pACadIN4FxgyEOjHD8//buPsaOqozj+Pfptne3W10pSClt0YIpIOAL2JjyEiVATAukVZSISiREg39oQKMhoFFjjH8YiaLBkFRAIBLQIAhBIyovwT+UWKxRsKIICJWXFlpeWgq19PGPM2d37uyd3Z3dO3e25/4+SbN3pnN3z8nZneee85w5p5P4Kfyfd4Yb8wFv6+73bxVzGfE5jtwmT51yHJB7BmN7+/n8ch97JuhxxD05iu/vlunkOIbeNDajq5czqiBMj1501MwWrhTpM0qOdxJ7HNv+DYev6f7OafOGc7OqdrXPqoLwSb3TUBWE3sJCxt/441TYV18qfwAwXr9re265ki5vJ5rfF2T3jrBMy5yByd+39D1hWflezqiKLrgn9DpEZEoUODrJj/t3OzEOY89x7N2bJbJzz3FAeOhw9872ZzGKK+TGwJHfaXBoJHvy3CcIHNlyKru2h2Glbi9FEXsc7qEOUw0Ea77TvgFULxVXERaRCSlwdJKfotrt/AaMzaraU9hwKQaOePOfaKgqLqneNivqjbDj2fC6uBdH/vu8vjvs9NftxHgsg78e6jfRyrhFCw6ovoOfiDRCgaOTuLT6nIGwn3W3tRaEoPFaIXkcv8b9r4sPAEJugcIX2s9DFji2hNdlzyTE67c/1t1nU/JlgNDrmGgTJxHZZyk53klcWn3xO6e3odFkYg8jzoyKx3Foamd28y8uOQLtyfG4Mm7UFjhKyh17KNserylw5GZ3FZ9+F5EkKHCUOXx1eJK6DqMB4rn24xgo4nBT/qY7bzgsx/GXG8In+V3bx6biRoMj4UE6mGA6bhYsdr/c/RlV0D676zUFDpEUaaiqzFnr6/veMVC8Ugwc2ddOQ1VmsPYKuH4t/Oar2ZLqhR7D4Ajg2fecIMfR6XW3FIeqRqaxd7mIzGoKHE1oFXoc8XjuUNgrvFOPA+Cw98Pxn4M/XBGm0R5c2MI23rRhaoGjruQ4KMchkjANVTVhtMfxfPuxWZiau7NDjyM69Wuw6Oiw/MhwcagqFzjKZlXlg0XtPY4dChwiCVLgaEJZjgNCsIgJ7k6BY+4gfPhHYQ+L4j7SQyO5n1GS44jb0EIPkuMVpuOKyD5DQ1VNGJfjyPUOWsOw45nsdcmn9YOOhs/cN36r07ahqpJZVWYhYLzyfL09jl3bwpaw6nGIJEc9jiaMy3HkPpWXvS5adOQkQ1UT7C0RA0YdOY65gzDQgpdj8FOPQyQ1jQQOMzvbzB4ys71mtnKSawfMbKOZ3dGr8tUu9jBGcxy5HkdcfgQrz1OUGcwPVU3w3hgw6uhxQAhgChwiyWqqx/EgcBZw3xSuvQjYVG9xeiwGh7IcRzw3p2LzTDVwxIBRR44DssDxVHitwCGSnEYCh7tvcveHJ7vOzJYBZwBX1V+qHsr3OAYG21ePbRX25qhiykNVvexxKMchkprZnuO4HLgY2DvZhWZ2gZltMLMNW7durb9kMxF7GP76+CVN4o12JoFj7vyJl4KfvzAsYV7XTX1wZGxmWBPLpItIrWqbVWVmvwMWd/ivr7j7bVN4/5nAFnd/wMxOnux6d18PrAdYuXKlVyxub82ZE3oEe14dP/tpdMHDadxwY+Aom4obHXde2KK12/uMtJUjawINVYkkp7bA4e6nzfBbnAisNbPTgSFgxMx+4u7nzrx0s8C84c6Bo7iNbBX5HsdEFh+T2+e7BvkhMw1ViSRn1g5Vuful7r7M3ZcD5wB3JxM0YCxAFJPYo0NV01iVd85AeP9EifFeaAsc6nGIpKap6bgfMrPNwPHAL83szuz8EjP7VRNl6rmyJPhMkuMQbtoKHCJSo0aeHHf3W4FbO5x/Cji9w/l7gXtrL1gvje7BUexxzCDHASExPZsCxzwFDpHUaMmRpszLPa/R6fx0P6nPX1jP5lNVtGKuZaj7e5qLSOP0V92U4q5/UXEb2arOuGxsEcOmxB6HEuMiSVLgaEqrsHnTuPPTvOkufsf0y9Qto4FDw1QiKZq1s6qSNzqrquQBwLLVbfcF6nGIJE2BoyllgWMmz3HMFnHNrH25DiJSSoGjKcV9xqORJWFb2AOP6H2ZukVDVSJJU46jKa2SHsfw/nDJE70vTzcpcIgkTT2OppQNVaVAOQ6RpClwNCXlwNFaAJhWxhVJlAJHU8qm46bADA5fDYesarokIlID5TiaUrbIYSo+flPTJRCRmqjH0ZSUh6pEJGkKHE1ZfhKccCEsObbpkoiIVKKhqqYMjcAHvtl0KUREKlOPQ0REKlHgEBGRShQ4RESkEgUOERGpRIFDREQqUeAQEZFKFDhERKQSBQ4REanE3L3pMnSdmW0F/jPNt78ZeK6LxdkX9GOdoT/r3Y91hv6sd9U6v9XdD5zKhUkGjpkwsw3uvrLpcvRSP9YZ+rPe/Vhn6M9611lnDVWJiEglChwiIlKJAsd465suQAP6sc7Qn/XuxzpDf9a7tjorxyEiIpWoxyEiIpUocIiISCUKHBkzW21mD5vZI2Z2SdPlqYuZHWJm95jZJjN7yMwuys7vb2a/NbN/ZV8XNl3WbjOzATPbaGZ3ZMeHmtn9WZ1/amatpsvYbWa2n5ndbGb/yNr8+NTb2sy+kP1uP2hmN5rZUIptbWbXmNkWM3swd65j21rwg+z+9lczO24mP1uBg3BDAX4IrAGOAj5mZkc1W6ra7AG+6O5vB1YBn83qeglwl7uvAO7KjlNzEbApd/xt4HtZnbcDn2qkVPX6PvBrdz8SeBeh/sm2tZktBS4EVrr7McAAcA5ptvW1wOrCubK2XQOsyP5dAFw5kx+swBG8F3jE3R91993ATcC6hstUC3d/2t3/nL1+mXAjWUqo73XZZdcBH2ymhPUws2XAGcBV2bEBpwA3Z5ekWOcR4H3A1QDuvtvdXyDxtiZsiT3fzOYCw8DTJNjW7n4fsK1wuqxt1wHXe/BHYD8zO3i6P1uBI1gKPJk73pydS5qZLQeOBe4HDnL3pyEEF2BRcyWrxeXAxcDe7PgA4AV335Mdp9jmhwFbgR9nQ3RXmdkCEm5rd/8vcBnwBCFgvAg8QPptHZW1bVfvcQocgXU4l/Q8ZTN7A/Bz4PPu/lLT5amTmZ0JbHH3B/KnO1yaWpvPBY4DrnT3Y4GdJDQs1Uk2pr8OOBRYAiwgDNMUpdbWk+nq77sCR7AZOCR3vAx4qqGy1M7M5hGCxg3ufkt2+tnYdc2+bmmqfDU4EVhrZo8ThiFPIfRA9suGMyDNNt8MbHb3+7PjmwmBJOW2Pg14zN23uvv/gFuAE0i/raOytu3qPU6BI/gTsCKbedEiJNNub7hMtcjG9q8GNrn7d3P/dTtwXvb6POC2XpetLu5+qbsvc/flhLa9290/AdwDfCS7LKk6A7j7M8CTZnZEdupU4O8k3NaEIapVZjac/a7HOifd1jllbXs78MlsdtUq4MU4pDUdenI8Y2anEz6FDgDXuPu3Gi5SLczsJOD3wN8YG+//MiHP8TPgLYQ/vrPdvZh42+eZ2cnAl9z9TDM7jNAD2R/YCJzr7q81Wb5uM7N3EyYEtIBHgfMJHxiTbWsz+wbwUcIMwo3Apwnj+Um1tZndCJxMWD79WeDrwC/o0LZZEL2CMAvrFeB8d98w7Z+twCEiIlVoqEpERCpR4BARkUoUOEREpBIFDhERqUSBQ0REKlHgEBGRShQ4RESkkv8DU2K74p+Ecv8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "path_folder = 'matrix_del_rand/'\n",
    "ex = '.npy'\n",
    "\n",
    "if not os.path.exists(path_folder):\n",
    "    os.mkdir(path_folder)\n",
    "\n",
    "c_i = 0.5\n",
    "\n",
    "row = []\n",
    "for i in range(0,100):\n",
    "    \n",
    "    h = np.random.randn(l)\n",
    "    \n",
    "    Hxxz = np.zeros((2**l , 2**l ))\n",
    "\n",
    "    # constructing the full xxz hamiltonian (2^L, 2^L) \n",
    "    for x in range(0,2**l):\n",
    "        for y in range(0,l):\n",
    "            k = (y+1)%l\n",
    "            if check_nth_bit(x,y) == 0:\n",
    "                Hxxz[x,x] += -j*h[y]/2\n",
    "            else:\n",
    "                Hxxz[x,x] += j*h[y]/2\n",
    "            #if y != l-1:    \n",
    "            if check_nth_bit(x,y) == check_nth_bit(x,k):\n",
    "                Hxxz[x,x] += -(j*delt)/2\n",
    "            else:\n",
    "                Hxxz[x,x] += (j*delt)/2\n",
    "                b = bitflip(x,y)\n",
    "                c = bitflip(b,k)\n",
    "                Hxxz[x,c] += -j\n",
    "    #print(Hxxz)\n",
    "       \n",
    "    \n",
    "    mag_array = np.zeros(l+1)\n",
    "    E_array = np.zeros(l+1)\n",
    "    a = 0\n",
    "    for mag in range(-l,l+1,2):\n",
    "    \n",
    "        p = num_mag(mag)\n",
    "        state = np.array(range(p))\n",
    "        s = statep(state,mag)\n",
    "        \n",
    "        Hxxz_p = np.zeros((p,p))\n",
    "        for x in range(0,p):\n",
    "            for y in range(0,l):\n",
    "                if check_nth_bit(s[x],y) == 0:\n",
    "                    Hxxz_p[x,x] += -(j*h[y])/2\n",
    "                else:\n",
    "                    Hxxz_p[x,x] += (j*h[y])/2\n",
    "                k = (y+1)%l\n",
    "                #if y != l-1:\n",
    "                if check_nth_bit(s[x],y) == check_nth_bit(s[x],k):\n",
    "                    Hxxz_p[x,x] += -(j*delt)/2\n",
    "                else:\n",
    "                    Hxxz_p[x,x] += (j*delt)/2\n",
    "                    b = bitflip(s[x],y)\n",
    "                    c = bitflip(b,k)\n",
    "                    D = np.where(s == c)\n",
    "                    Hxxz_p[x,D] = -j\n",
    "                                            \n",
    "        #print(Hxxz_p)\n",
    "        mag_array[a] = mag\n",
    "        A = POWER_METHOD(Hxxz_p,p)\n",
    "        E_array[a] = A[1] \n",
    "        psi_s = -mag_array[np.where(E_array == np.min(E_array))]\n",
    "        a += 1\n",
    "   \n",
    "    row.append([str(i+1) + ex, np.min(E_array)/l])\n",
    "    np.save(os.path.abspath(path_folder+str(i+1)), Hxxz)   \n",
    "    #print('h:',h)\n",
    "    #print('MAG_GS:', psi_s[0])\n",
    "    #print('rhoE_array:', np.min(E_array)/l)\n",
    "    \n",
    "    \n",
    "row = np.asarray(row)\n",
    "\n",
    "x_test = row[:,0]\n",
    "y_test = np.array(row[:,1]).astype(np.float)\n",
    "\n",
    "test_data = []\n",
    "\n",
    "load_test_data = []\n",
    "for file in x_test:\n",
    "        path = os.path.abspath(path_folder + file)\n",
    "        H = np.load(path)\n",
    "        load_test_data.append(H)\n",
    "load_test_data = np.array(load_test_data)\n",
    "test_data = load_test_data[:,:,:,np.newaxis]\n",
    "test_data -= np.mean(test_data)\n",
    "test_data /= np.std(test_data)\n",
    "\n",
    "test_label = []\n",
    "for file in y_test:\n",
    "    test_label.append(file)\n",
    "test_label = np.array(test_label)\n",
    "\n",
    "model = load_model('my_model.h5')\n",
    "\n",
    "pred = model.predict(test_data, verbose=1)\n",
    "score = np.sqrt(metrics.mean_squared_error(pred,test_label))\n",
    "print(\"Score (RMSE): {}\".format(score))\n",
    "\n",
    "chart_regression(pred.flatten(),test_label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pooling the dimensions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "161/161 [==============================] - 19s 116ms/step\n",
      "Score (RMSE): 0.6090384055469993\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# number of spins\n",
    "l = 12\n",
    "\n",
    "#dimension constant\n",
    "j = 1\n",
    "\n",
    "#anisotropy constant\n",
    "delt = -1\n",
    "\n",
    "path_folder = 'matrix_pool/'\n",
    "\n",
    "if not os.path.exists(path_folder):\n",
    "    os.mkdir(path_folder)\n",
    "    \n",
    "h = -8.\n",
    "row = []\n",
    "while h <= 8:\n",
    "    \n",
    "    Hxxz = np.zeros((2**l , 2**l ))\n",
    "\n",
    "    # constructing the full xxz hamiltonian (2^L, 2^L) \n",
    "    for x in range(0,2**l):\n",
    "        for y in range(0,l):\n",
    "            k = (y+1)%l\n",
    "            if check_nth_bit(x,y) == 0:\n",
    "                Hxxz[x,x] += -j*h/2\n",
    "            else:\n",
    "                Hxxz[x,x] += j*h/2\n",
    "            #if y != l-1:    \n",
    "            if check_nth_bit(x,y) == check_nth_bit(x,k):\n",
    "                Hxxz[x,x] += -(j*delt)/2\n",
    "            else:\n",
    "                Hxxz[x,x] += (j*delt)/2\n",
    "                b = bitflip(x,y)\n",
    "                c = bitflip(b,k)\n",
    "                Hxxz[x,c] += -j\n",
    "\n",
    "    \n",
    "    \n",
    "    mag_array = np.zeros(l+1)\n",
    "    E_array = np.zeros(l+1)\n",
    "    a = 0\n",
    "    for mag in range(-l,l+1,2):\n",
    "    \n",
    "        p = num_mag(mag)\n",
    "        state = np.array(range(p))\n",
    "        s = statep(state,mag)\n",
    "        \n",
    "        Hxxz_p = np.zeros((p,p))\n",
    "        for x in range(0,p):\n",
    "            for y in range(0,l):\n",
    "                if check_nth_bit(s[x],y) == 0:\n",
    "                    Hxxz_p[x,x] += -(j*h)/2\n",
    "                else:\n",
    "                    Hxxz_p[x,x] += (j*h)/2\n",
    "                k = (y+1)%l\n",
    "                #if y != l-1:\n",
    "                if check_nth_bit(s[x],y) == check_nth_bit(s[x],k):\n",
    "                    Hxxz_p[x,x] += -(j*delt)/2\n",
    "                else:\n",
    "                    Hxxz_p[x,x] += (j*delt)/2\n",
    "                    b = bitflip(s[x],y)\n",
    "                    c = bitflip(b,k)\n",
    "                    D = np.where(s == c)\n",
    "                    Hxxz_p[x,D] = -j\n",
    "                        #if mag == 0:\n",
    "                            #print('[',x,D,']','k:',k,'s[x]:',s[x],'y:',y,'b:',b,'c:',c,'s[5]',s[5])\n",
    "                    \n",
    "        #print(Hxxz_p)\n",
    "        mag_array[a] = mag\n",
    "        A = POWER_METHOD(Hxxz_p,p)\n",
    "        E_array[a] = A[1] \n",
    "        psi_s = -mag_array[np.where(E_array == np.min(E_array))]\n",
    "        a += 1\n",
    "    ex = '.npy'\n",
    "    Hxxz_pool = skimage.measure.block_reduce(Hxxz,(16,16), np.average)\n",
    "    np.save(os.path.abspath(path_folder + str(h)), Hxxz_pool)\n",
    "    row.append([str(h) + ex, psi_s[0], np.min(E_array)/l, h])\n",
    "            \n",
    "        \n",
    "    #print('h:',h)\n",
    "    #print('MAG_GS:', psi_s[0])\n",
    "    #print('rhoE_array:', np.min(E_array)/l)\n",
    "    h += .1\n",
    "    \n",
    "row = np.asarray(row)\n",
    "\n",
    "a_h = np.array(row[:,3]).astype(np.float) #h\n",
    "b_e = np.array(row[:,2]).astype(np.float) #E_GS/l\n",
    "c_m = np.array(row[:,1]).astype(np.float) #mag_GS\n",
    "\n",
    "plt.plot(a_h,b_e,'-')\n",
    "plt.title('GS-Energy density distribution (L='+str(l)+')')\n",
    "plt.xlabel(r'$h$')\n",
    "plt\n",
    "plt.ylabel(r'$E_{GS}/L$')\n",
    "plt.grid()\n",
    "plt.savefig('GS-Energy_big.png')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "plt.plot(a_h,c_m)\n",
    "plt.title('GS-Magnetization distribution')\n",
    "plt.xlabel(r'$h$')\n",
    "plt.ylabel('GS-Mag')\n",
    "plt.grid()\n",
    "plt.savefig('GS-Mag_big.png')\n",
    "plt.show()\n",
    "\n",
    "x_test = row[:,0]\n",
    "y_test = np.array(row[:,2]).astype(np.float)\n",
    "\n",
    "test_data = []\n",
    "\n",
    "load_test_data = []\n",
    "for file in x_test:\n",
    "        path = os.path.abspath(path_folder + file)\n",
    "        H = np.load(path)\n",
    "        load_test_data.append(H)\n",
    "load_test_data = np.array(load_test_data)\n",
    "test_data = load_test_data[:,:,:,np.newaxis]\n",
    "test_data -= np.mean(test_data)\n",
    "test_data /= np.std(test_data)\n",
    "\n",
    "test_label = []\n",
    "for file in y_test:\n",
    "    test_label.append(file)\n",
    "test_label = np.array(test_label)\n",
    "\n",
    "model = load_model('my_model.h5')\n",
    "\n",
    "pred = model.predict(test_data, verbose=1)\n",
    "score = np.sqrt(metrics.mean_squared_error(pred,test_label))\n",
    "print(\"Score (RMSE): {}\".format(score))\n",
    "\n",
    "chart_regression(pred.flatten(),test_label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pooling and random local MF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100/100 [==============================] - 12s 119ms/step\n",
      "Score (RMSE): 0.8795183218527095\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# number of spins\n",
    "l = 12\n",
    "\n",
    "#dimension constant\n",
    "j = 1\n",
    "\n",
    "#anisotropy constant\n",
    "delt = -1\n",
    "\n",
    "path_folder = 'matrix_pool/'\n",
    "\n",
    "if not os.path.exists(path_folder):\n",
    "    os.mkdir(path_folder)\n",
    "    \n",
    "\n",
    "row = []\n",
    "for i in range(0,100):\n",
    "    \n",
    "    h = np.random.randn(l)\n",
    "    Hxxz = np.zeros((2**l , 2**l ))\n",
    "\n",
    "    # constructing the full xxz hamiltonian (2^L, 2^L) \n",
    "    for x in range(0,2**l):\n",
    "        for y in range(0,l):\n",
    "            k = (y+1)%l\n",
    "            if check_nth_bit(x,y) == 0:\n",
    "                Hxxz[x,x] += -j*h[y]/2\n",
    "            else:\n",
    "                Hxxz[x,x] += j*h[y]/2\n",
    "            #if y != l-1:    \n",
    "            if check_nth_bit(x,y) == check_nth_bit(x,k):\n",
    "                Hxxz[x,x] += -(j*delt)/2\n",
    "            else:\n",
    "                Hxxz[x,x] += (j*delt)/2\n",
    "                b = bitflip(x,y)\n",
    "                c = bitflip(b,k)\n",
    "                Hxxz[x,c] += -j\n",
    "\n",
    "    \n",
    "    \n",
    "    mag_array = np.zeros(l+1)\n",
    "    E_array = np.zeros(l+1)\n",
    "    a = 0\n",
    "    for mag in range(-l,l+1,2):\n",
    "    \n",
    "        p = num_mag(mag)\n",
    "        state = np.array(range(p))\n",
    "        s = statep(state,mag)\n",
    "        \n",
    "        Hxxz_p = np.zeros((p,p))\n",
    "        for x in range(0,p):\n",
    "            for y in range(0,l):\n",
    "                if check_nth_bit(s[x],y) == 0:\n",
    "                    Hxxz_p[x,x] += -(j*h[y])/2\n",
    "                else:\n",
    "                    Hxxz_p[x,x] += (j*h[y])/2\n",
    "                k = (y+1)%l\n",
    "                #if y != l-1:\n",
    "                if check_nth_bit(s[x],y) == check_nth_bit(s[x],k):\n",
    "                    Hxxz_p[x,x] += -(j*delt)/2\n",
    "                else:\n",
    "                    Hxxz_p[x,x] += (j*delt)/2\n",
    "                    b = bitflip(s[x],y)\n",
    "                    c = bitflip(b,k)\n",
    "                    D = np.where(s == c)\n",
    "                    Hxxz_p[x,D] = -j\n",
    "                        \n",
    "                    \n",
    "        #print(Hxxz_p)\n",
    "        mag_array[a] = mag\n",
    "        A = POWER_METHOD(Hxxz_p,p)\n",
    "        E_array[a] = A[1] \n",
    "        psi_s = -mag_array[np.where(E_array == np.min(E_array))]\n",
    "        a += 1\n",
    "    ex = '.npy'\n",
    "    Hxxz_pool = skimage.measure.block_reduce(Hxxz,(16,16), np.max)\n",
    "    np.save(os.path.abspath(path_folder + str(h)), Hxxz_pool)\n",
    "    row.append([str(h) + ex, psi_s[0], np.min(E_array)/l])\n",
    "            \n",
    "        \n",
    "    #print('h:',h)\n",
    "    #print('MAG_GS:', psi_s[0])\n",
    "    #print('rhoE_array:', np.min(E_array)/l)\n",
    "    \n",
    "    \n",
    "row = np.asarray(row)\n",
    "\n",
    "\n",
    "x_test = row[:,0]\n",
    "y_test = np.array(row[:,2]).astype(np.float)\n",
    "\n",
    "test_data = []\n",
    "\n",
    "load_test_data = []\n",
    "for file in x_test:\n",
    "        path = os.path.abspath(path_folder + file)\n",
    "        H = np.load(path)\n",
    "        load_test_data.append(H)\n",
    "load_test_data = np.array(load_test_data)\n",
    "test_data = load_test_data[:,:,:,np.newaxis]\n",
    "test_data -= np.mean(test_data)\n",
    "test_data /= np.std(test_data)\n",
    "\n",
    "test_label = []\n",
    "for file in y_test:\n",
    "    test_label.append(file)\n",
    "test_label = np.array(test_label)\n",
    "\n",
    "model = load_model('my_model.h5')\n",
    "\n",
    "pred = model.predict(test_data, verbose=1)\n",
    "score = np.sqrt(metrics.mean_squared_error(pred,test_label))\n",
    "print(\"Score (RMSE): {}\".format(score))\n",
    "\n",
    "chart_regression(pred.flatten(),test_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}