AI4Spec_Tutorial2.ipynb

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    "# AI4Spec 2: Predicting multiple energy levels with neural networks\n",
    "\n",
    "In these tutorials exercises, we review different AI approaches to predicting ground or excited state energy levels simply based on the atomic structure of materials. Here we focus on molecules and molecular orbitals, but the sample principles apply to crystal structures and electronic bands.\n",
    "\n",
    "We start by predicting individual states (Tutorial 1), proceed towards predicting multiple states at once (Tutorial 2) and finish by predicting entire spectral curves (Tutorial 3). The tutorials were prepared by Milica Todorović (University of Turku) and Kunal Ghosh (Aalto University).\n",
    "\n",
    "<img src=\"https://raw.githubusercontent.com/milicasan/TutorialImages/89238bfff57a38ec78ecbd08765b63bd15ff9e2c/AI4spec.png\" width=\"600px\"/>\n",
    "\n",
    "In the exercise below, our AI objective is to train a model to predict a multiple electronic energy levels, such as an energy band. This is a more complex example of supervised machine learning regression, where the objective (label) is a list of single floating-point numbers. Few AI models can address this task, because most supervised learning focuses on learning only one numerical value, and here we have several. Neural network (NN) models are well-suited for this type of task. Given the flexible NN architectures, vectors can be used as both input and output.\n",
    "\n",
    "We use a large dataset of molecular structures and their computed HOMO energies to train the AI. The HOMO energies of all molecules were pre-computed with first principles quantum mechanical methods (DFT) to obtain the target data that our model can be trained on. Our label and learning objective is now a vector of energies for 16 occupied molecular orbitals, terminating in the HOMO. We start by learning the HOMO first, and then proceed to learn multiple levels at the same time.\n"
   ]
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    "## Using neural networks to predict multiple electronic levels\n",
    "\n",
    "NN models are notoriously data-demanding, so the QM7 dataset we used before may be too small. We switch to the QM9 dataset of 132k molecules for NN work. For the purposes of this tutorial, we take a subset of 7000 molecules, but in your own time you can try the entire dataset.  \n",
    "\n",
    "Detailed descriptions and results for both QM7 and QM9 datasets can be found in [K. Ghosh, et al. \"Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra.\" Adv. Sci. 6 1801367 (2019)](https://onlinelibrary.wiley.com/doi/full/10.1002/advs.201801367). Full datasets employed is this study are available from [Zenodo record 3386508](https://zenodo.org/records/3386508).\n",
    "\n",
    "## Setup"
   ]
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   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import json\n",
    "import math, random\n",
    "from scipy.sparse import load_npz\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "import time"
   ]
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   "source": [
    "### Load and visualize data\n",
    "\n",
    "First, we load the data. The molecules are represented by their Coulomb matrices, which were computed with the [Dscribe](https://www.sciencedirect.com/science/article/pii/S0010465519303042?via%3Dihub) package. The output data y is a matrix that contains the corresponding 16 HOMO energies for all molecules."
   ]
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     "text": [
      "--2024-03-04 21:34:38--  https://zenodo.org/records/10069732/files/coulomb_7000.npz\n",
      "Resolving zenodo.org (zenodo.org)... 188.184.103.159, 188.184.98.238, 188.185.79.172, ...\n",
      "Connecting to zenodo.org (zenodo.org)|188.184.103.159|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 47096264 (45M) [application/octet-stream]\n",
      "Saving to: ‘coulomb_7000.npz.1’\n",
      "\n",
      "coulomb_7000.npz.1  100%[===================>]  44.91M  46.3MB/s    in 1.0s    \n",
      "\n",
      "2024-03-04 21:34:39 (46.3 MB/s) - ‘coulomb_7000.npz.1’ saved [47096264/47096264]\n",
      "\n",
      "--2024-03-04 21:34:40--  https://zenodo.org/records/10069732/files/energies_7000.npz\n",
      "Resolving zenodo.org (zenodo.org)... 188.184.98.238, 188.185.79.172, 188.184.103.159, ...\n",
      "Connecting to zenodo.org (zenodo.org)|188.184.98.238|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 896264 (875K) [application/octet-stream]\n",
      "Saving to: ‘energies_7000.npz.1’\n",
      "\n",
      "energies_7000.npz.1 100%[===================>] 875.26K  4.13MB/s    in 0.2s    \n",
      "\n",
      "2024-03-04 21:34:40 (4.13 MB/s) - ‘energies_7000.npz.1’ saved [896264/896264]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://zenodo.org/records/10069732/files/coulomb_7000.npz\n",
    "!wget https://zenodo.org/records/10069732/files/energies_7000.npz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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    "id": "6tTcc8Mtwrmd",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of molecules: 7000\n"
     ]
    }
   ],
   "source": [
    "# Here we load all the data\n",
    "x = np.abs(np.load(\"coulomb_7000.npz\")['arr_0'])\n",
    "energies = np.load(\"energies_7000.npz\")['arr_0']\n",
    "\n",
    "# energy labels are saved from lowest to highest value.\n",
    "# HOMO is the highest energy molecular orbital, so we pick the last column as the HOMO label.\n",
    "y = energies[:,-1]\n",
    "\n",
    "# we can also try to learn HOMO, HOMO-1 and HOMO-2 at the same time.\n",
    "y_multi = energies[:,-3:]\n",
    "\n",
    "print(\"Number of molecules:\", len(y))"
   ]
  },
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   "cell_type": "markdown",
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    "id": "yWa4pRHxwrmf"
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   "source": [
    "Let us review the Coulomb matrix for a random molecule in the dataset."
   ]
  },
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     "text": [
      "(29, 29)\n",
      "[[36.858 23.545 19.643 14.298 12.615  8.577 10.93  14.165  5.46   5.451\n",
      "   5.466  2.245  2.124  2.203  1.135  2.263  2.071  0.     0.     0.\n",
      "   0.     0.     0.     0.     0.     0.     0.     0.     0.   ]\n",
      " [23.545 36.858 33.353 23.482 19.429 13.106 16.645 23.444  2.74   2.752\n",
      "   2.774  3.039  2.741  2.759  1.571  2.759  2.788  0.     0.     0.\n",
      "   0.     0.     0.     0.     0.     0.     0.     0.     0.   ]\n",
      " [19.643 33.353 73.517 19.681 21.95  15.243 18.479 20.284  2.35   2.933\n",
      "   2.975  8.241  2.959  2.359  1.972  2.415  3.243  0.     0.     0.\n",
      "   0.     0.     0.     0.     0.     0.     0.     0.     0.   ]\n",
      " [14.298 23.482 19.681 36.858 33.39  15.238 15.014 14.537  2.16   2.15\n",
      "   1.729  2.339  5.456  5.44   1.819  2.07   1.759  0.     0.     0.\n",
      "   0.     0.     0.     0.     0.     0.     0.     0.     0.   ]\n",
      " [12.615 19.429 21.95  33.39  73.517 35.114 23.509 16.879  1.95   1.931\n",
      "   1.732  2.55   3.934  3.857  3.966  2.322  2.135  0.     0.     0.\n",
      "   0.     0.     0.     0.     0.     0.     0.     0.     0.   ]\n",
      " [ 8.577 13.106 15.243 15.238 35.114 36.858 33.114 15.338  1.343  1.247\n",
      "   1.253  1.617  1.854  2.131  5.488  2.008  1.916  0.     0.     0.\n",
      "   0.     0.     0.     0.     0.     0.     0.     0.     0.   ]\n",
      " [10.93  16.645 18.479 15.014 23.509 33.114 53.359 28.812  1.713  1.496\n",
      "   1.667  1.825  1.835  2.231  3.402  3.348  3.366  0.     0.     0.\n",
      "   0.     0.     0.     0.     0.     0.     0.     0.     0.   ]\n",
      " [14.165 23.444 20.284 14.537 16.879 15.338 28.812 36.858  2.122  1.714\n",
      "   2.161  1.863  1.738  2.159  1.784  5.445  5.453  0.     0.     0.\n",
      "   0.     0.     0.     0.     0.     0.     0.     0.     0.   ]\n",
      " [ 5.46   2.74   2.35   2.16   1.95   1.343  1.713  2.122  0.5    0.562\n",
      "   0.561  0.273  0.313  0.396  0.18   0.4    0.298  0.     0.     0.\n",
      "   0.     0.     0.     0.     0.     0.     0.     0.     0.   ]\n",
      " [ 5.451  2.752  2.933  2.15   1.931  1.247  1.496  1.714  0.562  0.5\n",
      "   0.563  0.392  0.377  0.324  0.171  0.27   0.259  0.     0.     0.\n",
      "   0.     0.     0.     0.     0.     0.     0.     0.     0.   ]\n",
      " [ 5.466  2.774  2.975  1.729  1.732  1.253  1.667  2.161  0.561  0.563\n",
      "   0.5    0.328  0.262  0.268  0.17   0.346  0.369  0.     0.     0.\n",
      "   0.     0.     0.     0.     0.     0.     0.     0.     0.   ]\n",
      " [ 2.245  3.039  8.241  2.339  2.55   1.617  1.825  1.863  0.273  0.392\n",
      "   0.328  0.5    0.415  0.28   0.22   0.245  0.293  0.     0.     0.\n",
      "   0.     0.     0.     0.     0.     0.     0.     0.     0.   ]\n",
      " [ 2.124  2.741  2.959  5.456  3.934  1.854  1.835  1.738  0.313  0.377\n",
      "   0.262  0.415  0.5    0.561  0.247  0.256  0.234  0.     0.     0.\n",
      "   0.     0.     0.     0.     0.     0.     0.     0.     0.   ]\n",
      " [ 2.203  2.759  2.359  5.44   3.857  2.131  2.231  2.159  0.396  0.324\n",
      "   0.268  0.28   0.561  0.5    0.27   0.361  0.262  0.     0.     0.\n",
      "   0.     0.     0.     0.     0.     0.     0.     0.     0.   ]\n",
      " [ 1.135  1.571  1.972  1.819  3.966  5.488  3.402  1.784  0.18   0.171\n",
      "   0.17   0.22   0.247  0.27   0.5    0.253  0.248  0.     0.     0.\n",
      "   0.     0.     0.     0.     0.     0.     0.     0.     0.   ]\n",
      " [ 2.263  2.759  2.415  2.07   2.322  2.008  3.348  5.445  0.4    0.27\n",
      "   0.346  0.245  0.256  0.361  0.253  0.5    0.567  0.     0.     0.\n",
      "   0.     0.     0.     0.     0.     0.     0.     0.     0.   ]\n",
      " [ 2.071  2.788  3.243  1.759  2.135  1.916  3.366  5.453  0.298  0.259\n",
      "   0.369  0.293  0.234  0.262  0.248  0.567  0.5    0.     0.     0.\n",
      "   0.     0.     0.     0.     0.     0.     0.     0.     0.   ]\n",
      " [ 0.     0.     0.     0.     0.     0.     0.     0.     0.     0.\n",
      "   0.     0.     0.     0.     0.     0.     0.     0.     0.     0.\n",
      "   0.     0.     0.     0.     0.     0.     0.     0.     0.   ]\n",
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      "   0.     0.     0.     0.     0.     0.     0.     0.     0.   ]\n",
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      "   0.     0.     0.     0.     0.     0.     0.     0.     0.     0.\n",
      "   0.     0.     0.     0.     0.     0.     0.     0.     0.   ]\n",
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      "   0.     0.     0.     0.     0.     0.     0.     0.     0.     0.\n",
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      "   0.     0.     0.     0.     0.     0.     0.     0.     0.   ]\n",
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      "   0.     0.     0.     0.     0.     0.     0.     0.     0.     0.\n",
      "   0.     0.     0.     0.     0.     0.     0.     0.     0.   ]\n",
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      "   0.     0.     0.     0.     0.     0.     0.     0.     0.   ]]\n"
     ]
    }
   ],
   "source": [
    "rand_mol = random.randint(0, len(y))\n",
    "\n",
    "# Consider the size of the CM - what is the size of our descriptor?\n",
    "print(x[rand_mol].shape)\n",
    "\n",
    "# Let's take a look at the matrix elements\n",
    "print(x[rand_mol])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "Note that QM9 molecules are slightly larger than the QM7 ones, so the descriptor is larger, and can contain more zero padding. Below, we visualize the Coulomb matrix of the random molecule."
   ]
  },
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\n",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shape = (29, 29)\n",
    "mat = x[rand_mol].reshape(shape)\n",
    "\n",
    "plt.figure()\n",
    "plt.figure(figsize = (6,6))\n",
    "plt.imshow(mat, origin=\"upper\", cmap='rainbow', vmin=-10, vmax=55, interpolation='nearest')\n",
    "plt.colorbar(fraction=0.046, pad=0.04).ax.tick_params(labelsize=20)\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "n434DnNSwrmi"
   },
   "source": [
    "Now, we review the energy entry for the same random molecule index. What follows is a list of 16 highest occupied molecular orbitals, ending in HOMO. In other words, you now have access to HOMO-15, HOMO-14, HOMO-13... HOMO-2, HOMO-1, HOMO energies.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "u44zKs0_4IJv",
    "outputId": "603a67c1-86f4-4d47-a578-940126f085f4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-13.97184 -12.99997 -12.46609 -11.83582 -11.2511  -10.46114 -10.41363\n",
      " -10.08263  -9.84337  -9.36033  -8.81967  -8.51355  -8.09764  -6.67936\n",
      "  -6.13557  -5.84277]\n",
      "-5.84277\n",
      "[-6.67936 -6.13557 -5.84277]\n"
     ]
    }
   ],
   "source": [
    "# Here we review the entire vector of orbital energies\n",
    "print(energies[rand_mol,:])\n",
    "\n",
    "# Here we check the HOMO only\n",
    "print(y[rand_mol])\n",
    "\n",
    "# Here we check the top 3 occupied states\n",
    "print(y_multi[rand_mol])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lBZ_Y42BcwmL"
   },
   "source": [
    "Now, let us analyse the HOMO values. We plot a histogram to review the distribution of the labels, and compute the mean value. You can compare these QM9 HOMOs to the QM7 HOMOs in the previous dataset: is there much difference?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 528
    },
    "id": "YxjygJgJwrmj",
    "outputId": "89f2f6cc-45de-4473-a771-691cfc00f932"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8vLxUsWJFhYSEZDs3IzcqV66sVq1aadCgQUpNTdWcOXN01113aciQIY4+Tz31lD777DM99NBDeuyxx3TgwAF99NFHThOkXa2ta9euat++vcaPH6/ff/9dTZo00bp16/TFF19o5MiRWcbOq6FDh+qdd97RwIEDtWPHDtWuXVufffaZvvvuO82ZM+eWk6Tzy913360PPvhA/fr1U3BwsAYPHqw6dero999/13vvvadTp05p6dKltzzuyMjIXO3vww8/VIcOHdS9e3f17dtXrVu3Vlpamj7//HNt2rRJjz/++E2DmPTH/Ju3335b/fv3V/PmzdW7d29Vq1ZNhw8f1qpVq9SyZcss4fZW2rZtq2HDhikmJkaJiYnq2LGjypUrp3379mnZsmWaO3dujnOpMrVo0UK9evXSnDlzdPr0ad1///3avHmzfvvtN0k3P4OZ22P67bff1KFDBz322GNq0KCBypYtq+XLlys1NTXbxycAkriVH7hR5i3DmYu7u7sREBBgPPjgg8bcuXOdbhnPdOOt/Bs2bDC6d+9uBAYGGu7u7kZgYKDRp08f47fffnN63RdffGE0aNDAKFu2rNOt823btjUaNmyYbX053cq/dOlSIzo62vDz8zPKly9vREREGIcOHcry+ldffdWoUaOGYbVajZYtWxo//vhjljFvVtuNt/Ibxh+3VY8aNcoIDAw0ypUrZ9x9993GrFmzjIyMDKd+koyoqKgsNeX0iIEbpaamGoMGDTKqVq1quLu7G8HBwdk+bsDVW/lz6pv53mbeym+WlJRk9OnTx6hevbpRrlw5IyAgwOjTp4+xc+fOLH1z83iIm9Vy/vx5Y/LkyUbDhg2N8uXLG15eXkbLli2NhQsXZnmPbyY+Pt4IDw83fHx8DA8PD6Nu3brGwIEDjR9//NHRJzIy0qhYsWKW1974dzzTu+++a7Ro0cJRV3BwsPH3v//dOHbs2C2PyzAM4+LFi0ZUVJRRuXJlw9PT0+jRo4eRnJxsSDJmzJjh6Hfjrfy5PaZTp04ZUVFRxj333GNUrFjR8PHxMUJCQoxPP/001+8bbj8WwygBsyABALeNxMRENWvWTB999NFNL18DBYU5RwCAIpPdE9znzJkjNze3HO/QAwoac44AAEUmNjZWO3bsUPv27VW2bFmtXr1aq1ev1tChQ1WrVq2iLg+3KS6rAQCKTFxcnKZMmaI9e/bowoULuuOOO9S/f3+NHz8+y/OXgMJCOAIAADBhzhEAAIAJ4QgAAMCEC7q5kJGRoWPHjsnLyyvfv/IBAAAUDMMwdP78eQUGBub6S78lwlGuHDt2jLsmAAAooY4cOaKaNWvmuj/hKBcyv5bgyJEjOX7LNgAAKF7sdrtq1arl8tcLEY5yIfNSmre3N+EIAIASxtUpMUzIBgAAMCEcAQAAmBCOAAAATAhHAAAAJoQjAAAAE8IRAACACeEIAADAhHAEAABgQjgCAAAwIRwBAACYEI4AAABMCEcAAAAmhCMAAAATwhEAAIAJ4QgAAMCkbFEXAAClmcVSMOMaRsGMC4AzRwAAAE4IRwAAACaEIwAAABPCEQAAgAnhCAAAwIRwBAAAYEI4AgAAMCEcAQAAmBCOAAAATAhHAAAAJkUajmJiYnTffffJy8tLfn5+6tGjh5KTk536XLlyRVFRUapSpYo8PT3Vq1cvpaamOvU5fPiwIiIiVKFCBfn5+Wns2LG6fv26U59NmzapefPmslqtuuuuu7Rw4cKCPjwAAFACFWk42rx5s6KiorRt2zbFxcXp2rVr6tixoy5evOjoM2rUKH311VdatmyZNm/erGPHjqlnz56O7enp6YqIiNDVq1e1detWffDBB1q4cKFeeuklR5+DBw8qIiJC7du3V2JiokaOHKmnnnpKa9euLdTjBQAAxZ/FMIrP1xeePHlSfn5+2rx5s9q0aSObzaZq1appyZIleuSRRyRJv/76q+rXr6+EhATdf//9Wr16tbp06aJjx47J399fkjR//nyNGzdOJ0+elLu7u8aNG6dVq1Zp165djn317t1b586d05o1a25Zl91ul4+Pj2w2m7y9vQvm4AGUSnzxLFB08vr5XazmHNlsNklS5cqVJUk7duzQtWvXFBYW5uhzzz336I477lBCQoIkKSEhQcHBwY5gJEnh4eGy2+3avXu3o495jMw+mWMAAABkKlvUBWTKyMjQyJEj1bJlSzVq1EiSlJKSInd3d/n6+jr19ff3V0pKiqOPORhlbs/cdrM+drtdly9fVvny5Z22paWlKS0tzbFut9vz4QgBAEBJUGzOHEVFRWnXrl36+OOPi7oUxcTEyMfHx7HUqlWrqEsCAACFpFiEo+HDh2vlypWKj49XzZo1He0BAQG6evWqzp0759Q/NTVVAQEBjj433r2WuX6rPt7e3lnOGklSdHS0bDabYzly5MifP0gAAFAiFGk4MgxDw4cP1/Lly7Vx40bVqVPHaXuLFi1Urlw5bdiwwdGWnJysw4cPKzQ0VJIUGhqqnTt36sSJE44+cXFx8vb2VoMGDRx9zGNk9skc40ZWq1Xe3t5OCwAAuD0U6d1qzz77rJYsWaIvvvhC9erVc7T7+Pg4zug888wz+vrrr7Vw4UJ5e3vrueeekyRt3bpV0h+38jdt2lSBgYGKjY1VSkqK+vfvr6eeekrTp0+X9Met/I0aNVJUVJSefPJJbdy4USNGjNCqVasUHh5+yzq5Ww1AXnG3GlB08vr5XaThyJLDb40FCxZo4MCBkv54COSYMWO0dOlSpaWlKTw8XG+99ZbjkpkkHTp0SM8884w2bdqkihUrKjIyUjNmzFDZsv8/33zTpk0aNWqU9uzZo5o1a2rixImOfdwK4QhAXhGOgKJTIsNRSUE4ApBXhCOg6JSK5xwBAAAUNcIRAACACeEIAADAhHAEAABgQjgCAAAwIRwBAACYEI4AAABMCEcAAAAmhCMAAAATwhEAAIAJ4QgAAMCEcAQAAGBCOAIAADAhHAEAAJgQjgAAAEwIRwAAACaEIwAAABPCEQAAgAnhCAAAwIRwBAAAYEI4AgAAMCEcAQAAmBCOAAAATAhHAAAAJoQjAAAAE8IRAACACeEIAADAhHAEAABgQjgCAAAwIRwBAACYEI4AAABMijQcbdmyRV27dlVgYKAsFotWrFjhtN1isWS7zJo1y9Gndu3aWbbPmDHDaZykpCS1bt1aHh4eqlWrlmJjYwvl+AAAQMlTpOHo4sWLatKkid58881stx8/ftxpef/992WxWNSrVy+nflOnTnXq99xzzzm22e12dezYUUFBQdqxY4dmzZqlyZMn69133y3QYwMAACVT2aLceadOndSpU6cctwcEBDitf/HFF2rfvr3uvPNOp3YvL68sfTMtXrxYV69e1fvvvy93d3c1bNhQiYmJmj17toYOHfrnDwIAAJQqJWbOUWpqqlatWqXBgwdn2TZjxgxVqVJFzZo106xZs3T9+nXHtoSEBLVp00bu7u6OtvDwcCUnJ+vs2bOFUjsAACg5ivTMkSs++OADeXl5qWfPnk7tI0aMUPPmzVW5cmVt3bpV0dHROn78uGbPni1JSklJUZ06dZxe4+/v79hWqVKlLPtKS0tTWlqaY91ut+f34QAAgGKqxISj999/X/369ZOHh4dT++jRox0/N27cWO7u7ho2bJhiYmJktVrztK+YmBhNmTLlT9ULAABKphJxWe2bb75RcnKynnrqqVv2DQkJ0fXr1/X7779L+mPeUmpqqlOfzPWc5ilFR0fLZrM5liNHjvy5AwAAACVGiQhH7733nlq0aKEmTZrcsm9iYqLc3Nzk5+cnSQoNDdWWLVt07do1R5+4uDjVq1cv20tqkmS1WuXt7e20AACA20ORhqMLFy4oMTFRiYmJkqSDBw8qMTFRhw8fdvSx2+1atmxZtmeNEhISNGfOHP3yyy/673//q8WLF2vUqFF64oknHMGnb9++cnd31+DBg7V792598sknmjt3rtPlOAAAgExFOufoxx9/VPv27R3rmYElMjJSCxculCR9/PHHMgxDffr0yfJ6q9Wqjz/+WJMnT1ZaWprq1KmjUaNGOQUfHx8frVu3TlFRUWrRooWqVq2ql156idv4AQBAtiyGYRhFXURxZ7fb5ePjI5vNxiU2AC6xWApmXH5zA7eW18/vEjHnCAAAoLAQjgAAAEwIRwAAACaEIwAAABPCEQAAgAnhCAAAwIRwBAAAYEI4AgAAMCEcAQAAmBCOAAAATAhHAAAAJoQjAAAAE8IRAACACeEIAADAhHAEAABgQjgCAAAwIRwBAACYEI4AAABMCEcAAAAmhCMAAAATwhEAAIAJ4QgAAMCEcAQAAGBCOAIAADAhHAEAAJgQjgAAAEwIRwAAACaEIwAAABPCEQAAgAnhCAAAwMTlcHTkyBEdPXrUsf79999r5MiRevfdd/O1MAAoLBZLwS0ASh6Xw1Hfvn0VHx8vSUpJSdGDDz6o77//XuPHj9fUqVNdGmvLli3q2rWrAgMDZbFYtGLFCqftAwcOlMVicVoeeughpz5nzpxRv3795O3tLV9fXw0ePFgXLlxw6pOUlKTWrVvLw8NDtWrVUmxsrKuHDQAAbhMuh6Ndu3bpr3/9qyTp008/VaNGjbR161YtXrxYCxcudGmsixcvqkmTJnrzzTdz7PPQQw/p+PHjjmXp0qVO2/v166fdu3crLi5OK1eu1JYtWzR06FDHdrvdro4dOyooKEg7duzQrFmzNHnyZM50AQCAbJV19QXXrl2T1WqVJK1fv17dunWTJN1zzz06fvy4S2N16tRJnTp1umkfq9WqgICAbLft3btXa9as0Q8//KB7771XkvT666+rc+fOeuWVVxQYGKjFixfr6tWrev/99+Xu7q6GDRsqMTFRs2fPdgpRAAAAUh7OHDVs2FDz58/XN998o7i4OMdlrmPHjqlKlSr5XuCmTZvk5+enevXq6ZlnntHp06cd2xISEuTr6+sIRpIUFhYmNzc3bd++3dGnTZs2cnd3d/QJDw9XcnKyzp49m+/1AgCAks3lcDRz5ky98847ateunfr06aMmTZpIkr788kvH5bb88tBDD+nDDz/Uhg0bNHPmTG3evFmdOnVSenq6pD/mPPn5+Tm9pmzZsqpcubJSUlIcffz9/Z36ZK5n9rlRWlqa7Ha70wIAAG4PLl9Wa9eunU6dOiW73a5KlSo52ocOHaoKFSrka3G9e/d2/BwcHKzGjRurbt262rRpkzp06JCv+zKLiYnRlClTCmx8AABQfOXpOUeGYWjHjh165513dP78eUmSu7t7voejG915552qWrWq9u/fL0kKCAjQiRMnnPpcv35dZ86cccxTCggIUGpqqlOfzPWc5jJFR0fLZrM5liNHjuT3oQAAgGLK5XB06NAhBQcHq3v37oqKitLJkycl/XG57YUXXsj3As2OHj2q06dPq3r16pKk0NBQnTt3Tjt27HD02bhxozIyMhQSEuLos2XLFl27ds3RJy4uTvXq1XM682VmtVrl7e3ttAAAgNuDy+Ho+eef17333quzZ8+qfPnyjvaHH35YGzZscGmsCxcuKDExUYmJiZKkgwcPKjExUYcPH9aFCxc0duxYbdu2Tb///rs2bNig7t2766677lJ4eLgkqX79+nrooYc0ZMgQff/99/ruu+80fPhw9e7dW4GBgZL+eC6Tu7u7Bg8erN27d+uTTz7R3LlzNXr0aFcPHQAA3A4MF1WuXNn49ddfDcMwDE9PT+PAgQOGYRjGwYMHjfLly7s0Vnx8vCEpyxIZGWlcunTJ6Nixo1GtWjWjXLlyRlBQkDFkyBAjJSXFaYzTp08bffr0MTw9PQ1vb29j0KBBxvnz5536/PLLL0arVq0Mq9Vq1KhRw5gxY4ZLddpsNkOSYbPZXHodgJJBKnkLgFvL6+e3yxOyMzIyHHeLmR09elReXl4ujdWuXTsZhpHj9rVr195yjMqVK2vJkiU37dO4cWN98803LtUGAABuTy5fVuvYsaPmzJnjWLdYLLpw4YImTZqkzp0752txAAAAhc1i3OzUTTaOHj2q8PBwGYahffv26d5779W+fftUtWpVbdmyJctzh0oDu90uHx8f2Ww2JmcDpVBJ/IIWyAPsAAAgAElEQVRY135zA7envH5+uxyOpD9ul//444+VlJSkCxcuqHnz5urXr5/TBO3ShHAElG6EI6B0yuvnt8tzjqQ/nkL9xBNP5OWlAAAAxVquwtGXX36Z6wEzv4gWAACgJMpVOOrRo0euBrNYLNneyQYAAFBS5CocZWRkFHQdAAAAxUKevlsNAACgtHJ5QvbUqVNvuv2ll17KczEAAABFzeVwtHz5cqf1a9eu6eDBgypbtqzq1q1LOAIAACWay+Ho559/ztJmt9s1cOBAPfzww/lSFAAAQFHJlzlH3t7emjJliiZOnJgfwwEAABSZfJuQbbPZZLPZ8ms4AACAIuHyZbV58+Y5rRuGoePHj2vRokXq1KlTvhUGAABQFFwOR6+99prTupubm6pVq6bIyEhFR0fnW2EAAABFweVwdPDgwYKoAwAAoFhwec6RzWbTmTNnsrSfOXNGdrs9X4oCAAAoKi6Ho969e+vjjz/O0v7pp5+qd+/e+VIUAABAUXE5HG3fvl3t27fP0t6uXTtt3749X4oCAAAoKi6Ho7S0NF2/fj1L+7Vr13T58uV8KQoAAKCouByO/vrXv+rdd9/N0j5//ny1aNEiX4oCAAAoKi7frfbyyy8rLCxMv/zyizp06CBJ2rBhg3744QetW7cu3wsEAAAoTC6fOWrZsqUSEhJUs2ZNffrpp/rqq6901113KSkpSa1bty6IGgEAAAqNxTAMo6iLKO7sdrt8fHxks9nk7e1d1OUAyGcWS1FX4Dp+cwO3ltfP7zx9t9qBAwc0YcIE9e3bVydOnJAkrV69Wrt3787LcAAAAMWGy+Fo8+bNCg4O1vbt2/Wf//xHFy5ckCT98ssvmjRpUr4XCAAAUJhcDkcvvviiXn75ZcXFxcnd3d3R/sADD2jbtm35WhwAAEBhczkc7dy5Uw8//HCWdj8/P506dSpfigIAACgqLocjX19fHT9+PEv7zz//rBo1auRLUQAAAEUlT9+tNm7cOKWkpMhisSgjI0PfffedXnjhBQ0YMKAgagQAACg0Loej6dOn65577lGtWrV04cIFNWjQQG3atNHf/vY3TZgwoSBqBAAAKDQuhyN3d3f961//0oEDB7Ry5Up99NFH+vXXX7Vo0SKVKVPGpbG2bNmirl27KjAwUBaLRStWrHBsu3btmsaNG6fg4GBVrFhRgYGBGjBggI4dO+Y0Ru3atWWxWJyWGTNmOPXJfEClh4eHatWqpdjYWFcPGwAA3CZc/vqQTHfccYfuuOOOP7XzixcvqkmTJnryySfVs2dPp22XLl3STz/9pIkTJ6pJkyY6e/asnn/+eXXr1k0//vijU9+pU6dqyJAhjnUvLy/Hz3a7XR07dlRYWJjmz5+vnTt36sknn5Svr6+GDh36p+oHAAClT67C0ejRo3M94OzZs3Pdt1OnTurUqVO223x8fBQXF+fU9sYbb+ivf/2rDh8+7BTMvLy8FBAQkO04ixcv1tWrV/X+++/L3d1dDRs2VGJiombPnk04AgAAWeQqHP3888+5GsxSwM/gt9lsslgs8vX1dWqfMWOGpk2bpjvuuEN9+/bVqFGjVLbsH4eWkJCgNm3aOD2TKTw8XDNnztTZs2dVqVKlAq0ZAACULLkKR/Hx8QVdxy1duXJF48aNU58+fZy+H2XEiBFq3ry5KleurK1btyo6OlrHjx93nMFKSUlRnTp1nMby9/d3bMsuHKWlpSktLc2xbrfbC+KQAABAMZTnOUeSdPToUUlSzZo186WYnFy7dk2PPfaYDMPQ22+/7bTNfMmvcePGcnd317BhwxQTEyOr1Zqn/cXExGjKlCl/qmYAAFAyuXy3WkZGhqZOnSofHx8FBQUpKChIvr6+mjZtmjIyMvK9wMxgdOjQIcXFxd3yW3VDQkJ0/fp1/f7775KkgIAApaamOvXJXM9pnlJ0dLRsNptjOXLkyJ8/EAAAUCK4fOZo/Pjxeu+99zRjxgy1bNlSkvTtt99q8uTJunLliv75z3/mW3GZwWjfvn2Kj49XlSpVbvmaxMREubm5yc/PT5IUGhqq8ePH69q1aypXrpwkKS4uTvXq1ctxvpHVas3zWScAAFCyuRyOPvjgA/373/9Wt27dHG2NGzdWjRo19Oyzz7oUji5cuKD9+/c71g8ePKjExERVrlxZ1atX1yOPPKKffvpJK1euVHp6ulJSUiRJlStXlru7uxISErR9+3a1b99eXl5eSkhI0KhRo/TEE084gk/fvn01ZcoUDR48WOPGjdOuXbs0d+5cvfbaa64eOgAAuB0YLrJarUZycnKW9l9//dXw8PBwaaz4+HhDUpYlMjLSOHjwYLbbJBnx8fGGYRjGjh07jJCQEMPHx8fw8PAw6tevb0yfPt24cuWK035++eUXo1WrVobVajVq1KhhzJgxw6U6bTabIcmw2WwuvQ5AySCVvAXAreX189tiGIbhSpgKCQlRSEiI5s2b59T+3HPP6YcfftC2bdv+bF4rdux2u3x8fGSz2W455wlAyVPATyEpEK795gZuT3n9/Hb5slpsbKwiIiK0fv16hYaGSvrjWUJHjhzR119/7epwAAAAxYrLd6u1bdtWv/32mx5++GGdO3dO586dU8+ePZWcnKzWrVsXRI0AAACFxuXLarcjLqsBpRuX1YDSqdAuq0l/PK06KSlJJ06cyPJsI/NdbAAAACWNy+FozZo1GjBggE6dOpVlm8ViUXp6er4UBgAAUBRcnnP03HPP6dFHH9Xx48eVkZHhtBCMAABASedyOEpNTdXo0aMdX94KAABQmrgcjh555BFt2rSpAEoBAAAoei7frXbp0iU9+uijqlatmoKDgx3fV5ZpxIgR+VpgccDdakDpxt1qQOlUaHerLV26VOvWrZOHh4c2bdoki+m3isViKZXhCAAA3D5cDkfjx4/XlClT9OKLL8rNzeWrcgAAAMWay+nm6tWrevzxxwlGAACgVHI54URGRuqTTz4piFoAAACKnMuX1dLT0xUbG6u1a9eqcePGWSZkz549O9+KAwAAKGwuh6OdO3eqWbNmkqRdu3Y5bbOUxFs+AAAATFwOR/Hx8QVRBwAAQLHArGoAAAATwhEAAIAJ4QgAAMCEcAQAAGCSq3DUvHlznT17VpI0depUXbp0qUCLAgAAKCq5Ckd79+7VxYsXJUlTpkzRhQsXCrQoAACAopKrW/mbNm2qQYMGqVWrVjIMQ6+88oo8PT2z7fvSSy/la4EAAACFyWIYhnGrTsnJyZo0aZIOHDign376SQ0aNFDZsllzlcVi0U8//VQghRYlu90uHx8f2Ww2eXt7F3U5APJZSXx+7a1/cwPI6+d3rsKRmZubm1JSUuTn5+dykSUV4Qgo3QhHQOmU189vl5+QnZGR4epLAAD5rCADHcELtzuXw5EkHThwQHPmzNHevXslSQ0aNNDzzz+vunXr5mtxAAAAhc3l5xytXbtWDRo00Pfff6/GjRurcePG2r59uxo2bKi4uLiCqBEAAKDQuDznqFmzZgoPD9eMGTOc2l988UWtW7eOCdkASpySOOeoIHFZDaVFXj+/XT5ztHfvXg0ePDhL+5NPPqk9e/a4OhwAAECx4nI4qlatmhITE7O0JyYm3lZ3sAEAgNLJ5XA0ZMgQDR06VDNnztQ333yjb775RjNmzNCwYcM0ZMgQl8basmWLunbtqsDAQFksFq1YscJpu2EYeumll1S9enWVL19eYWFh2rdvn1OfM2fOqF+/fvL29pavr68GDx6c5QneSUlJat26tTw8PFSrVi3Fxsa6etgAAOA24XI4mjhxol566SW9/vrratu2rdq2bas33nhDkydP1oQJE1wa6+LFi2rSpInefPPNbLfHxsZq3rx5mj9/vrZv366KFSsqPDxcV65ccfTp16+fdu/erbi4OK1cuVJbtmzR0KFDHdvtdrs6duyooKAg7dixQ7NmzdLkyZP17rvvunroAADgdmD8CXa73bDb7X9mCAdJxvLlyx3rGRkZRkBAgDFr1ixH27lz5wyr1WosXbrUMAzD2LNnjyHJ+OGHHxx9Vq9ebVgsFuN///ufYRiG8dZbbxmVKlUy0tLSHH3GjRtn1KtXL9e12Ww2Q5Jhs9nyfHwAiq8/piCzZC5AaZHXz2+XzxyZeXl5ycvLKz8yWhYHDx5USkqKwsLCHG0+Pj4KCQlRQkKCJCkhIUG+vr669957HX3CwsLk5uam7du3O/q0adNG7u7ujj7h4eFKTk7W2bNnC6R2AABQcuXpIZCFISUlRZLk7+/v1O7v7+/Ylt3XmJQtW1aVK1d26lOnTp0sY2Ruq1SpUpZ9p6WlKS0tzbFut9v/5NEAAICS4k+dOSqtYmJi5OPj41hq1apV1CUBAIBCUmzDUUBAgCQpNTXVqT01NdWxLSAgQCdOnHDafv36dZ05c8apT3ZjmPdxo+joaNlsNsdy5MiRP39AAACgRHApHF27dk0dOnTIcjt9QahTp44CAgK0YcMGR5vdbtf27dsVGhoqSQoNDdW5c+e0Y8cOR5+NGzcqIyNDISEhjj5btmzRtWvXHH3i4uJUr169bC+pSZLVapW3t7fTAgAAbg8uhaNy5copKSkp33Z+4cIFJSYmOh4qefDgQSUmJurw4cOyWCwaOXKkXn75ZX355ZfauXOnBgwYoMDAQPXo0UOSVL9+fT300EMaMmSIvv/+e3333XcaPny4evfurcDAQElS37595e7ursGDB2v37t365JNPNHfuXI0ePTrfjgMAAJQirt4WN3LkSGPcuHGuvixb8fHxhqQsS2RkpGEYf9zOP3HiRMPf39+wWq1Ghw4djOTkZKcxTp8+bfTp08fw9PQ0vL29jUGDBhnnz5936vPLL78YrVq1MqxWq1GjRg1jxowZLtXJrfxA6VbUt84XtwUoLfL6+e3yF88+99xz+vDDD3X33XerRYsWqlixotP22bNn509qK0b44lmgdOOLZ5259qkAFF95/fx2+Vb+Xbt2qXnz5pKk3377zWmbhd8wAACghHM5HMXHxxdEHQAAAMVCnm/l379/v9auXavLly9Lkly8OgcAAFAsuRyOTp8+rQ4dOugvf/mLOnfurOPHj0uSBg8erDFjxuR7gQAAAIXJ5XA0atQolStXTocPH1aFChUc7Y8//rjWrFmTr8UBAAAUNpfnHK1bt05r165VzZo1ndrvvvtuHTp0KN8KAwAAKAounzm6ePGi0xmjTGfOnJHVas2XogAAAIqKy+GodevW+vDDDx3rFotFGRkZio2NVfv27fO1OAAAgMLm8mW12NhYdejQQT/++KOuXr2qv//979q9e7fOnDmj7777riBqBAAAKDQunzlq1KiRfvvtN7Vq1Urdu3fXxYsX1bNnT/3888+qW7duQdQIAABQaFz++pDbEV8fApRuPNzfGZ8KKC0K7etDJOns2bN67733tHfvXklSgwYNNGjQIFWuXDkvwwEAABQbLl9W27Jli2rXrq158+bp7NmzOnv2rObNm6c6depoy5YtBVEjAABAoXH5slpwcLBCQ0P19ttvq0yZMpKk9PR0Pfvss9q6dat27txZIIUWJS6rAaUbl9WccVkNpUVeP79dPnO0f/9+jRkzxhGMJKlMmTIaPXq09u/f7+pwAAAAxYrL4ah58+aOuUZme/fuVZMmTfKlKAAAgKKSqwnZSUlJjp9HjBih559/Xvv379f9998vSdq2bZvefPNNzZgxo2CqBAAAKCS5mnPk5uYmi8WiW3W1WCxKT0/Pt+KKC+YcAaUbc46cMecIpUWB3sp/8ODBPBcGAABQkuQqHAUFBRV0HQAAAMVCnh4CeezYMX377bc6ceKEMjIynLaNGDEiXwoDAAAoCi6Ho4ULF2rYsGFyd3dXlSpVZDFdrLdYLIQjAABQorkcjiZOnKiXXnpJ0dHRcnNz+UkAAAAAxZrL6ebSpUvq3bs3wQgAAJRKLiecwYMHa9myZQVRCwAAQJFz+bvV0tPT1aVLF12+fFnBwcEqV66c0/bZs2fna4HFAc85Ako3nnPkjOccobQo0OccmcXExGjt2rWqV6+eJGWZkA0AAFCSuRyOXn31Vb3//vsaOHBgAZQDAABQtFyec2S1WtWyZcuCqAUAAKDIuRyOnn/+eb3++usFUQsAAECRc/my2vfff6+NGzdq5cqVatiwYZYJ2Z9//nm+FQcAAFDYXD5z5Ovrq549e6pt27aqWrWqfHx8nJb8Vrt2bVkslixLVFSUJKldu3ZZtj399NNOYxw+fFgRERGqUKGC/Pz8NHbsWF2/fj3fawUAACWfy2eOFixYUBB15OiHH35Qenq6Y33Xrl168MEH9eijjzrahgwZoqlTpzrWK1So4Pg5PT1dERERCggI0NatW3X8+HENGDBA5cqV0/Tp0wvnIAAAQImRpy+eLUzVqlVzWp8xY4bq1q2rtm3bOtoqVKiggICAbF+/bt067dmzR+vXr5e/v7+aNm2qadOmady4cZo8ebLc3d0LtH4AAFCyuHxZrU6dOrrzzjtzXArS1atX9dFHH+nJJ590eqbS4sWLVbVqVTVq1EjR0dG6dOmSY1tCQoKCg4Pl7+/vaAsPD5fdbtfu3bsLtF4AAFDyuHzmaOTIkU7r165d088//6w1a9Zo7Nix+VZYdlasWKFz5845PWOpb9++CgoKUmBgoJKSkjRu3DglJyc7JoanpKQ4BSNJjvWUlJRs95OWlqa0tDTHut1uz+cjAQAAxZXL4ej555/Ptv3NN9/Ujz/++KcLupn33ntPnTp1UmBgoKNt6NChjp+Dg4NVvXp1dejQQQcOHFDdunXztJ+YmBhNmTLlT9cLAABKHpcvq+WkU6dO+s9//pNfw2Vx6NAhrV+/Xk899dRN+4WEhEiS9u/fL0kKCAhQamqqU5/M9ZzmKUVHR8tmszmWI0eO/NnyAQBACZFv4eizzz5T5cqV82u4LBYsWCA/Pz9FRETctF9iYqIkqXr16pKk0NBQ7dy5UydOnHD0iYuLk7e3txo0aJDtGFarVd7e3k4LAAC4Pbh8Wa1Zs2ZOk6ENw1BKSopOnjypt956K1+Ly5SRkaEFCxYoMjJSZcv+f8kHDhzQkiVL1LlzZ1WpUkVJSUkaNWqU2rRpo8aNG0uSOnbsqAYNGqh///6KjY1VSkqKJkyYoKioKFmt1gKpFwAAlFwuh6MePXo4rbu5ualatWpq166d7rnnnnwrzGz9+vU6fPiwnnzySad2d3d3rV+/XnPmzNHFixdVq1Yt9erVSxMmTHD0KVOmjFauXKlnnnlGoaGhqlixoiIjI52eiwQAAJDJYhiGUdRFFHd2u10+Pj6y2WxcYgNKIdPJcEjiUwGlRV4/v/NtzhEAAEBpkOvLam5ubk5zjbJjsVj4zjIAAFCi5TocLV++PMdtCQkJmjdvnjIyMvKlKAAAgKKS63DUvXv3LG3Jycl68cUX9dVXX6lfv35McgYAACVenuYcHTt2TEOGDFFwcLCuX7+uxMREffDBBwoKCsrv+gAAAAqVS+HIZrNp3Lhxuuuuu7R7925t2LBBX331lRo1alRQ9QEAABSqXF9Wi42N1cyZMxUQEKClS5dme5kNAACgpMv1c47c3NxUvnx5hYWFqUyZMjn2+/zzz/OtuOKC5xwBpRvPOXLGc45QWuT18zvXZ44GDBhwy1v5AaAg8SsIQGHIdThauHBhAZYBAABQPPCEbAAAABPCEQAAgAnhCAAAwIRwBAAAYEI4AgAAMCEcAQAAmBCOAAAATAhHAAAAJoQjAAAAE8IRAACACeEIAADAhHAEAABgQjgCAAAwIRwBAACYEI4AAABMCEcAAAAmhCMAAAATwhEAAIAJ4QgAAMCEcAQAAGBCOAIAADAp1uFo8uTJslgsTss999zj2H7lyhVFRUWpSpUq8vT0VK9evZSamuo0xuHDhxUREaEKFSrIz89PY8eO1fXr1wv7UAAAQAlRtqgLuJWGDRtq/fr1jvWyZf+/5FGjRmnVqlVatmyZfHx8NHz4cPXs2VPfffedJCk9PV0REREKCAjQ1q1bdfz4cQ0YMEDlypXT9OnTC/1YAABA8Vfsw1HZsmUVEBCQpd1ms+m9997TkiVL9MADD0iSFixYoPr162vbtm26//77tW7dOu3Zs0fr16+Xv7+/mjZtqmnTpmncuHGaPHmy3N3dC/twAABAMVesL6tJ0r59+xQYGKg777xT/fr10+HDhyVJO3bs0LVr1xQWFuboe8899+iOO+5QQkKCJCkhIUHBwcHy9/d39AkPD5fdbtfu3bsL90AAAECJUKzPHIWEhGjhwoWqV6+ejh8/rilTpqh169batWuXUlJS5O7uLl9fX6fX+Pv7KyUlRZKUkpLiFIwyt2duy0laWprS0tIc63a7Pb8OCQAAFHPFOhx16tTJ8XPjxo0VEhKioKAgffrppypfvnyB7TcmJkZTpkwpsPEBAEDxVewvq5n5+vrqL3/5i/bv36+AgABdvXpV586dc+qTmprqmKMUEBCQ5e61zPXs5jFlio6Ols1mcyxHjhzJ5yMBAADFVYkKRxcuXNCBAwdUvXp1tWjRQuXKldOGDRsc25OTk3X48GGFhoZKkkJDQ7Vz506dOHHC0ScuLk7e3t5q0KBBjvuxWq3y9vZ2WgAAwO2hWF9We+GFF9S1a1cFBQXp2LFjmjRpksqUKaM+ffrIx8dHgwcP1ujRo1W5cmV5e3vrueeeU2hoqO6//35JUseOHdWgQQP1799fsbGxSklJ0YQJExQVFSWr1VrERwcAAIqjYh2Ojh49qj59+uj06dOqVq2aWrVqpW3btqlatWqSpNdee01ubm7q1auX0tLSFB4errfeesvx+jJlymjlypV65plnFBoaqooVKyoyMlJTp04tqkMCAADFnMUwDKOoiyju7Ha7fHx8ZLPZuMQGFCGLpagruD3wqYDSIq+f3yVqzhEAAEBBK9aX1QAAha8gz9BxVgolAWeOAAAATAhHAAAAJoQjAAAAE8IRAACACeEIAADAhHAEAABgQjgCAAAwIRwBAACYEI4AAABMCEcAAAAmhCMAAAATwhEAAIAJ4QgAAMCEcAQAAGBCOAIAADAhHAEAAJgQjgAAAEwIRwAAACaEIwAAABPCEQAAgAnhCAAAwIRwBAAAYEI4AgAAMCEcAQAAmJQt6gIAALcPi6VgxjWMghkXtyfOHAEAAJgQjgAAAEwIRwAAACbMOQKQrwpqTgkAFJZifeYoJiZG9913n7y8vOTn56cePXooOTnZqU+7du1ksViclqefftqpz+HDhxUREaEKFSrIz89PY8eO1fXr1wvzUAAAQAlRrM8cbd68WVFRUbrvvvt0/fp1/eMf/1DHjh21Z88eVaxY0dFvyJAhmjp1qmO9QoUKjp/T09MVERGhgIAAbd26VcePH9eAAQNUrlw5TZ8+vVCPBwAAFH8Wwyg5N0CePHlSfn5+2rx5s9q0aSPpjzNHTZs21Zw5c7J9zerVq9WlSxcdO3ZM/v7+kqT58+dr3LhxOnnypNzd3W+5X7vdLh8fH9lsNnl7e+ffAQGlEJfVUBRKzicZClNeP7+L9WW1G9lsNklS5cqVndoXL16sqlWrqlGjRoqOjtalS5cc2xISEhQcHOwIRpIUHh4uu92u3bt3F07hAACgxCjWl9XMMjIyNHLkSLVs2VKNGjVytPft21dBQUEKDAxUUlKSxo0bp+TkZH3++eeSpJSUFKdgJMmxnpKSku2+0tLSlJaW5li32+35fTgAgHxUkGcsOSt1+ykx4SgqKkq7du3St99+69Q+dOhQx8/BwcGqXr26OnTooAMHDqhu3bp52ldMTIymTJnyp+oFAAAlU4m4rDZ8+HCtXLlS8fHxqlmz5k37hoSESJL2798vSQoICFBqaqpTn8z1gICAbMeIjo6WzWZzLEeOHPmzhwAAAEqIYh2ODMPQ8OHDtXz5cm3cuFF16tS55WsSExMlSdWrV5ckhYaGaufOnTpx4oSjT1xcnLy9vdWgQYNsx7BarfL29nZaAADA7aFYX1aLiorSkiVL9MUXX8jLy8sxR8jHx0fly5fXgQMHtGTJEnXu3FlVqlRRUlKSRo0apTZt2qhx48aSpI4dO6pBgwbq37+/YmNjlZKSogkTJigqKkpWq7UoDw8AABRDxfpWfksOM+wWLFiggQMH6siRI3riiSe0a9cuXbx4UbVq1dLDDz+sCRMmOJ3tOXTokJ555hlt2rRJFStWVGRkpGbMmKGyZXOXDbmVH8g9buVHaVN8PyVxK3n9/C7W4ai4IBwBuUc4QmnDp2TJdVs85wgAAKCgEY4AAABMCEcAAAAmhCMAAAATwhEAAIAJ4QgAAMCEcAQAAGBCOAIAADAhHAEAAJgQjgAAAEwIRwAAACaEIwAAAJPcfS09AAC3qYL6MmW+0Lb44swRAACACeEIAADAhHAEAABgQjgCAAAwIRwBAACYEI4AAABMuJUfuE0V1O3JAFDSceYIAADAhHAEAABgQjgCAAAwIRwBAACYEI4AAABMCEcAAAAmhCMAAAATwhEAAIAJ4QgAAMCEJ2QDxRhPsQaAwseZIwAAAJPbKhy9+eabql27tjw8PBQSEqLvv/++qEtCKWGxFMwCACh8t004+uSTTzR69GhNmjRJP/30k5o0aaLw8HCdOHGiqEsDANyGCuo/VfzH6s+7bcLR7NmzNWTIEA0aNEgNGjTQ/PnzVaFCBb3//vtFXRoAAChGbotwdPXqVe3YsUNhYWGONjc3N4WFhSkhIaEIKwMAAMXNbXG32qlTp5Seni5/f3+ndn9/f/36669Z+qelpSktLc2xbrPZJEl2u71gC4UkycenqCsAgJKNj6s/ZH5uG4bh0utui3DkqpiYGE2ZMiVLe61atYqgGgAAXMN/Mp2dP39ePi68KbdFOKpatarKlCmj1NRUp/bU1FQFBARk6R8dHa3Ro0c71jMyMnTmzBlVqUNyZzsAAA64SURBVFJFllIy081ut6tWrVo6cuSIvL29i7qcYoX3Jme8N9njfckZ703OeG9yll/vjWEYOn/+vAIDA1163W0Rjtzd3dWiRQtt2LBBPXr0kPRH4NmwYYOGDx+epb/VapXVanVq8/X1LZRaC5u3tzf/KHPAe/N/7d17UFRlHwfw78IKK5eV5SqRQq6XyrygJopMyoZ4SQ2npEgJidWsrLSLNiMKeYtyLDNnklRMS9NGvFVKauCo6DCICuEkJd5QLpLgNHjh+nv/aNx39yUKjOXsC9/PzJnh3J7zPc+cWX8+e86eprFv/hr7pWnsm6axb5rWGn3TkhGjezpEcQQAb731FmJiYjBkyBAMHToUq1atwq1btxAbG6t0NCIiIrIhHaY4eu6551BeXo5FixahtLQUAwcORFpaWqObtImIiKhjs09MTExUOkRbGTp0KObOnYuFCxdixowZePDBB5WOpCh7e3uMGjUKanWHqZGbjX3TNPbNX2O/NI190zT2TdOU7BuVtPT5NiIiIqJ2rEP8CCQRERFRc7E4IiIiIjLD4oiIiIjIDIsjIiIiIjMsjginTp3C6NGj4ebmBg8PD8ycORNVVVVKx7IJv/76K55++ml4enpCq9UiJCQEGRkZSsdS3OHDh6FSqf5yys7OVjqe4n744QcEBQWhc+fO0Ol0ph+f7egCAgIaXS9JSUlKx7Ip1dXVGDhwIFQqFc6cOaN0HJswadIkdO/eHRqNBr6+voiOjkZxcbFVj8niqIMrLi5GWFgYevbsiaysLKSlpeHs2bOYPn260tFswoQJE1BXV4f09HTk5ORgwIABmDBhAkpLS5WOpqjg4GCUlJRYTEajEQ899BCGDBmidDxFpaamIjo6GrGxscjNzUVmZiZeeOEFpWPZjMWLF1tcN6+//rrSkWzKvHnzWvyqi/YuNDQU3377LQoKCpCamorCwkI8++yz1j2oUIeWnJws3t7eUl9fb1qWl5cnAOS3335TMJnyysvLBYAcOXLEtOyPP/4QAHLw4EEFk9mempoa8fLyksWLFysdRVG1tbXi5+cn69evVzqKTfL395dPPvlE6Rg2a9++ffLwww/L2bNnBYCcPn1a6Ug2ac+ePaJSqaSmpsZqx+DIUQdXXV0NBwcH2Nn991Lo3LkzAODYsWNKxbIJHh4e6NOnDzZv3oxbt26hrq4OycnJ8Pb2xuDBg5WOZ1P27t2LGzdudPjX8Zw6dQrXrl2DnZ0dAgMD4evri3HjxiE/P1/paDYjKSkJHh4eCAwMxIoVK1BXV6d0JJtQVlaGGTNm4KuvvoKTk5PScWxWRUUFtmzZguDgYHTq1Mlqx2Fx1MEZDAaUlpZixYoVqKmpQWVlJd577z0AQElJicLplKVSqXDo0CGcPn0arq6u0Gg0+Pjjj5GWlgadTqd0PJuyYcMGjBkzpsP/6vyFCxcAAImJiYiPj8f3338PnU6HUaNGoaKiQuF0ynvjjTewbds2ZGRk4OWXX8by5csxb948pWMpTkQwffp0zJo1q8N/Ld2U+fPnw9nZGR4eHrhy5Qr27Nlj3QNabUyKFDV//nwB8LfTL7/8IiIiW7ZsER8fH7G3txcHBwd55513xMfHR5KSkhQ+C+tobt80NDTIpEmTZNy4cXLs2DHJycmRV155Rfz8/KS4uFjp07CKllw39xQVFYmdnZ3s2LFDodTW19x+2bJliwCQ5ORk0753794VT09PWbt2rYJnYD33c83cs2HDBlGr1XL37t02Tt02mts3n376qYwYMULq6upEROTixYvt/mu1ll435eXlUlBQIAcOHJARI0bI+PHjpaGhwWr5+PqQdqq8vBw3btz422169OgBBwcH03xZWRmcnZ2hUqmg1Wqxbds2TJkyxdpR21xz++bo0aMIDw9HZWUltFqtaV2vXr0QFxdnGmFrT+7nulmyZAk+++wzXLt2zarD3Epqbr9kZmbCYDDg6NGjCAkJMa0LCgpCWFgYli1bZu2obe5+rpl7zp49i8ceewznzp1Dnz59rBVRMc3tm8jISHz33XdQqVSm5fX19bC3t8fUqVOxadMma0dtc//murl69Sq6deuG48ePY/jw4VbJxzfdtVNeXl7w8vJq0T4+Pj4AgJSUFGg0GowePdoa0RTX3L65ffs2AFjcj3VvvqGhwSrZlNbS60ZEsHHjRrz44ovttjACmt8vgwcPhqOjIwoKCkzFUW1tLS5dugR/f39rx1TE/XzW3HPmzBnY2dnB29u7lVPZhub2zerVq7F06VLTfHFxMcaMGYPt27cjKCjImhEV82+um3ufv9XV1a0ZyQKLI8KaNWsQHBwMFxcXHDx4EO+++y6SkpLg5uamdDRFDR8+HDqdDjExMVi0aBE6d+6MdevW4eLFi3jqqaeUjmcT0tPTcfHiRRiNRqWj2AStVotZs2YhISEB3bp1g7+/P1asWAEA7XIUtiVOnDiBrKwshIaGwtXVFSdOnMDcuXMxbdq0Dn8PX/fu3S3mXVxcAAB6vb7D38eXlZWF7OxshISEQKfTobCwEAsXLoRer7faqBEA3nNEItHR0eLu7i4ODg7Sv39/2bx5s9KRbEZ2draEh4eLu7u7uLq6yrBhw2Tfvn1Kx7IZUVFREhwcrHQMm1JTUyNvv/22eHt7i6urq4SFhUl+fr7SsRSXk5MjQUFB0qVLF9FoNPLII4/I8uXL2+39Rv9GR7jnqLny8vIkNDRU3N3dxdHRUQICAmTWrFly9epVqx6X9xwRERERmeGj/ERERERmWBwRERERmWFxRERERGSGxRERERGRGRZHRERERGZYHBERERGZYXFEREREZIbFERHRfUhMTIRKpYJKpcKqVatavf2AgABT+zdv3mz19omoaSyOiMjqpk+fbvqH3nwaO3as0tH+lb59+6KkpAQzZ85s1vYrV66ETqfD3bt3G627ffs2tFotVq9eDQDIzs5Gampqq+YlouZhcUREbWLs2LEoKSmxmL755hurHrOmpsaq7avVanTt2hVOTk7N2j46Ohq3bt3Czp07G63bsWMHampqMG3aNAB/vpjT3d29VfMSUfOwOCKiNuHo6IiuXbtaTOYvHFWpVFi/fj0mT54MJycn9OrVC3v37rVoIz8/H+PGjYOLiwt8fHwQHR2N33//3bR+1KhRmD17NubMmQNPT0+MGTMGAHDu3DmEhIRAo9Hg0UcfxaFDh6BSqbB7924AgMFgwOzZsy2OVV5eDgcHB/z0008tOs+bN2/CaDTCy8sLWq0WBoMBubm5AABvb29MnDgRKSkpjfZLSUlBREQECyIiG8DiiIhsxvvvv4/IyEjk5eVh/PjxmDp1KioqKgD8WXQYDAYEBgbi5MmTSEtLQ1lZGSIjIy3a2LRpExwcHJCZmYm1a9eivr4eERERcHJyQlZWFr744gssWLDAYh+j0YitW7eiurratOzrr7+Gn58fDAZDi85hypQpuH79Ovbv34+cnBwMGjQITz75pOk84uLikJ6ejsuXL5v2uXDhAo4cOYK4uLgWHYuIrMSqr7UlIhKRmJgYsbe3F2dnZ4tp2bJlpm0ASHx8vGm+qqpKAMj+/ftFRGTJkiUSHh5u0W5RUZEAkIKCAhERGTlypAQGBlpss3//flGr1VJSUmJadvDgQQEgu3btEhGRO3fuiE6nk+3bt5u26d+/vyQmJjZ5TgkJCTJgwACLZUePHhWtVtvoTfN6vV6Sk5NFRKSurk78/PwkISHBtH7hwoXSvXt3qa+vt9gvIyNDAEhlZWWTOYio9akVrcyIqMMIDQ3F559/brHsf79C6t+/v+lvZ2dnaLVaXL9+HQCQm5uLjIwMuLi4NGq7sLAQvXv3BgAMHjzYYl1BQQG6deuGrl27mpYNHTrUYhuNRoPo6GikpKQgMjISp06dQn5+fqOv9f5Jbm4uqqqq4OHhYbH8zp07KCwsBADY29sjJiYGX375JRISEiAi2LRpE2JjY2Fnx8F8IlvA4oiI2oSzszN69uz5t9t06tTJYl6lUqGhoQEAUFVVhYkTJ+LDDz9stJ+vr6/Fce6H0WjEwIEDcfXqVWzcuBEGgwH+/v4taqOqqgq+vr44fPhwo3Vubm6mv1966SV88MEHSE9PR0NDA4qKihAbG3tfuYmo9bE4IqL/C4MGDUJqaioCAgKgVjf/o6tPnz4oKipCWVkZfHx8APz5mPz/6tevH4YMGYJ169Zh69atWLNmzX1lLC0thVqtRkBAQJPb6fV6jBw5EikpKRARhIWFtbgQIyLr4RguEbWJ6upqlJaWWkzmT5r9k9deew0VFRWIiopCdnY2CgsL8eOPPyI2Nhb19fVN7jd69Gjo9XrExMQgLy8PmZmZiI+PB/DnyJQ5o9GIpKQkiAgmT57c4nMMCwvD8OHDERERgQMHDuDSpUs4fvw4FixYgJMnT1psGxcXh507d2LXrl28EZvIxrA4IqI2kZaWBl9fX4spJCSk2fs/8MADyMzMRH19PcLDw9GvXz/MmTMHbm5uf3uvjr29PXbv3o2qqio8/vjjMBqNpqfVNBqNxbZRUVFQq9WIiopqtK45VCoV9u3bhyeeeAKxsbHo3bs3nn/+eVy+fNk0anXPM888A0dHRzg5OSEiIqLFxyIi61GJiCgdgoioLWVmZiIkJATnz5+HXq83Lb906RL0ej2ys7MxaNCgv20jMTERu3fvxpkzZ6yW8/DhwwgNDUVlZaXFPUtEZF2854iI2r1du3bBxcUFvXr1wvnz5/Hmm29ixIgRpsKotrYWN27cQHx8PIYNG/aPhdE9P//8M1xcXPDRRx/h1VdfbdXMffv2xYULF1q1TSJqHo4cEVG7t3nzZixduhRXrlyBp6cnwsLCsHLlStMj9/dGaHr37o0dO3agX79+/9hmRUWF6Ycdvby80KVLl1bNfPnyZdTW1gIAevTowcf8idoQiyMiIiIiM/yvCBEREZEZFkdEREREZlgcEREREZlhcURERERkhsURERERkRkWR0RERERmWBwRERERmWFxRERERGSGxRERERGRmf8AHS903acqOnsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean value of HOMO energies in QM9 dataset: -5.75 eV\n",
      "[-9.42518 -8.12121 -6.48731 ... -5.94942 -5.47667 -5.69919]\n"
     ]
    }
   ],
   "source": [
    "plt.hist(y, bins=20, density=False, facecolor='blue')\n",
    "plt.xlabel(\"Energy [eV]\")\n",
    "plt.ylabel(\"Number of molecules\")\n",
    "plt.title(\"Distribution of HOMO energies\")\n",
    "plt.show()\n",
    "\n",
    "## mean value of distribution\n",
    "print(\"Mean value of HOMO energies in QM9 dataset: %0.2f eV\" %np.mean(y))\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QpgK9ky-wrmk"
   },
   "source": [
    "As before, we shuffle the data before dividing the dataset into training and test set, just in case there was some ordering in the input files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "6luJyqCKwrmk"
   },
   "outputs": [],
   "source": [
    "## shuffle the data\n",
    "\n",
    "c = list(zip(x, y))\n",
    "random.shuffle(c)\n",
    "\n",
    "x, y = zip(*c)\n",
    "\n",
    "x = np.array(x)\n",
    "y = np.array(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XQh-MAv5wrml"
   },
   "source": [
    "Now, we divide the data into training and test set. Note that for neural networks **we need also a validation set**. Validation set serves to check the quality of the NN learning during training and hyperparameter optimisation. After the NN is optimised, the final prediction accuracy will be evaluated on the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "wmnl8ZzAwrmm"
   },
   "outputs": [],
   "source": [
    "# decide how many samples to take from the database for training, testing and validation\n",
    "n_train = 1000\n",
    "n_test = 500\n",
    "n_val = 500\n",
    "\n",
    "# split data in training and test\n",
    "# take first n_train molecules for training\n",
    "X_train  = x[0:n_train]\n",
    "Y_train = y[0:n_train]\n",
    "\n",
    "# take the next n_test data for testing\n",
    "X_test = x[n_train:n_train + n_test]\n",
    "Y_test = y[n_train:n_train + n_test]\n",
    "\n",
    "# take the next n_val data for validation\n",
    "X_val = x[n_train + n_test: n_train + n_test+ n_val]\n",
    "Y_val = y[n_train + n_test: n_train + n_test+ n_val]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uEP7iSchwrmm"
   },
   "source": [
    "Check that the training data resemble the test data well by plotting the distribution of HOMO energies for both sets. The distributions should be centered around the same mean value and have the same shape."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 522
    },
    "id": "w_H7kOBTwrmn",
    "outputId": "2a4683d7-30dc-4419-cf0a-e064632f88c2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean value of HOMO energies in training set: -5.74 eV\n",
      "Mean value of HOMO energies in validation set: -5.72 eV\n",
      "Mean value of HOMO energies in test set: -5.77 eV\n"
     ]
    }
   ],
   "source": [
    "plt.hist(Y_test, bins=20, density=False, alpha=0.5, facecolor='red', label='test set')\n",
    "plt.hist(Y_val, bins=20, density=False, alpha=0.5, facecolor='orange', label='validation set')\n",
    "plt.hist(Y_train, bins=20, density=False, alpha=0.5, facecolor='gray', label='training set')\n",
    "plt.xlabel(\"Energy [eV]\")\n",
    "plt.ylabel(\"Number of molecules\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "## mean value of distributions\n",
    "print(\"Mean value of HOMO energies in training set: %0.2f eV\" %np.mean(Y_train))\n",
    "print(\"Mean value of HOMO energies in validation set: %0.2f eV\" %np.mean(Y_val))\n",
    "print(\"Mean value of HOMO energies in test set: %0.2f eV\" %np.mean(Y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0FxS6Kazwrmo"
   },
   "source": [
    "### Implementing the CNN\n",
    "\n",
    "Now we have a choice of NN architectures. The Multi Layer Perceptron (MLP) is the simplest deep NN architecture, but here we will review and deploy the Convolutional Neural Network (CNN). CNN is well-suited to image-like data such as the CM descriptor, and [previous work](https://onlinelibrary.wiley.com/doi/full/10.1002/advs.201801367) has established that CNNs predictive accuracy is better than that of MLPs in predicting electronic energy levels.\n",
    "\n",
    "<img src=\"https://raw.githubusercontent.com/milicasan/TutorialImages/46df4d358952ce9a1ec2037a3f622388249777dd/MLP-CNN.png\" width=\"600px\"/>\n",
    "\n",
    "Below we demonstrate how to use torch to build a simple CNN and apply it.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MmHUwC1Aorp2"
   },
   "source": [
    "PyTorch requires the user to set up the classes for the dataset and the AI model. Let's start with the dataset class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "mFuHLTSsXc_X"
   },
   "outputs": [],
   "source": [
    "from torch import nn\n",
    "from torch.optim import Adam\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "from lightning.pytorch.callbacks.early_stopping import EarlyStopping\n",
    "\n",
    "import torch\n",
    "import lightning.pytorch as pl\n",
    "import torch.nn.functional as F\n",
    "import numpy as np\n",
    "\n",
    "# Define the Dataset class\n",
    "class SimpleDataset(Dataset):\n",
    "\n",
    "    # Here we specify that the CM is a matrix with 29x29 entries.\n",
    "    # It is then necessary to flatten this into a long vector, which is input to the CNN\n",
    "    def __init__(self, features, labels):\n",
    "        self.x = torch.from_numpy(features).reshape(-1, 29, 29).unsqueeze(1).float()\n",
    "        self.y = torch.from_numpy(labels).float()\n",
    "        print(self.x.shape)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.y)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return {\"X\" : self.x[idx], \"y\" : self.y[idx]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iMmViGAzpKeu"
   },
   "source": [
    "Now we set up a simple CNN model by defining all the functional components this AI model will need, such as:\n",
    "\n",
    "*  network architecture (number of hidden layers and neurons)\n",
    "*  activation function type between hidden layers\n",
    "*  functions to load training and validation data\n",
    "*  training steps on training and validation data\n",
    "*  optimisers for NN training\n",
    "\n",
    "The model is trained by adjusting hyperparameters (weights and biases) so as to minimise the errors between current predictions and reference values. This quantity is called the **loss function**, and here we use the [mean squared error (MSE)](https://en.wikipedia.org/wiki/Mean_squared_error) to quantify it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "-Hg5y-eaoWuw"
   },
   "outputs": [],
   "source": [
    "# Parameters for NN architecture:\n",
    "# Conv2D\n",
    "output_length = 1  # this is size of output - HOMO is one number\n",
    "nConv = 20         # number of convolutional matrices\n",
    "filter = 3         # size of filter matrix (3x3)\n",
    "pad = 1            # padding for the filter scan\n",
    "\n",
    "# MaxPool2D\n",
    "maxp = 4           # size of maxpool layer\n",
    "\n",
    "# Hyperparameters for NN training:\n",
    "batch = 64            # total training set is divided into batches for training\n",
    "learning_rate = 1e-3  # the rate of updating the network weights during training\n",
    "\n",
    "\n",
    "# Define the network\n",
    "class CNN(pl.LightningModule):\n",
    "\n",
    "    # this is the main NN architecture\n",
    "    def __init__(self, output_length):\n",
    "        super(CNN, self).__init__()\n",
    "\n",
    "        # Below we define 2 hidden layers (design choice)\n",
    "        # followed by a MaxPool layer and a linear layer\n",
    "        # convolution key parameters: no. of input and output nodes\n",
    "\n",
    "        # 1st convolution, 1 = input matrix\n",
    "        self.conv1_1 = nn.Conv2d(1, nConv, filter, padding=pad)\n",
    "        # 2nd convolution\n",
    "        self.conv1_2 = nn.Conv2d(nConv, nConv, filter, padding=pad)\n",
    "        # 3rd convolution\n",
    "        self.conv1_3 = nn.Conv2d(nConv, maxp, filter, padding=pad)\n",
    "        # MaxPool - different hyperparameters apply\n",
    "        self.pool = nn.MaxPool2d(maxp,maxp)\n",
    "        # The linear layer size is approximately 4x7x7\n",
    "        self.fc1 = nn.Linear(maxp*7*7, output_length)\n",
    "\n",
    "    # this describes how the NN is propagated forward\n",
    "    def forward(self, x, labels=None):\n",
    "\n",
    "        # relu is used after the 1st layer\n",
    "        x = F.relu(self.conv1_1(x))\n",
    "        # relu is used after the 2nd layer\n",
    "        x = F.relu(self.conv1_2(x))\n",
    "        # relu is used after the 3rd layer\n",
    "        x = F.relu(self.conv1_3(x))\n",
    "        # print(x.shape)\n",
    "        x = self.pool(x)\n",
    "        x = x.view(-1, 4*7*7)\n",
    "        outputs = self.fc1(x)\n",
    "\n",
    "        # Here we set the MSE as the metric for the loss function\n",
    "        loss = 0\n",
    "        if labels is not None:\n",
    "            loss = nn.functional.mse_loss(outputs.squeeze(), labels.squeeze())\n",
    "        return loss, outputs\n",
    "\n",
    "    # note batch size below\n",
    "    def train_dataloader(self):\n",
    "        dataset = SimpleDataset(X_train, Y_train)\n",
    "        return DataLoader(dataset, batch_size=batch)\n",
    "\n",
    "    # note batch size below\n",
    "    def val_dataloader(self):\n",
    "        dataset = SimpleDataset(X_val, Y_val)\n",
    "        return DataLoader(dataset, batch_size=batch, num_workers=1)\n",
    "\n",
    "    def training_step(self, batch, batch_idx):\n",
    "        coloumb_matrices = batch[\"X\"]\n",
    "        energies = batch[\"y\"]\n",
    "        loss, outputs = self(coloumb_matrices, labels=energies)\n",
    "        self.log(\"train_loss\", loss, prog_bar=True, on_step=False, on_epoch=True)\n",
    "        # print(f\"Training loss : {loss}\")\n",
    "        return loss\n",
    "\n",
    "    def validation_step(self, batch, batch_idx):\n",
    "        coloumb_matrices = batch[\"X\"]\n",
    "        energies = batch[\"y\"]\n",
    "        loss, outputs = self(coloumb_matrices, labels=energies)\n",
    "        self.log(\"val_loss\", loss, prog_bar=True, on_step=False, on_epoch=True)\n",
    "        # print(f\"Validation loss : {loss}\")\n",
    "        return loss\n",
    "\n",
    "    # We chose the adam algorithm for backpropagation training.\n",
    "    def configure_optimizers(self):\n",
    "        optimizer = Adam(self.parameters(), lr=learning_rate)\n",
    "        return optimizer\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LOf64za-7Dog"
   },
   "source": [
    "This is how our network looks like:\n",
    "\n",
    "<img src=\"https://raw.githubusercontent.com/milicasan/TutorialImages/51cdde9d1a6daaed0e752440855fba51cf68afd9/CNNarch.png\" width=\"800px\"/>\n",
    "\n",
    "\n",
    "In this instance, we have the following layers.\n",
    "\n",
    "\n",
    "* Input layer: Coloumb matrix (29x29)\n",
    "* Hidden layer 1: 20 matrices of 29x29 (convolution before/after)\n",
    "* Hidden layer 2: 20 matrices of 29x29 (convolution before/after)\n",
    "* MaxPool layer: 4 matrices of 29x29 (convolution before/ pooling after)\n",
    "* Linear layer: 4x7x7\n",
    "* Output layer: 1\n",
    "\n",
    "The hidden layers are connected by convolution operations: there are 2 hidden layers and 3 convolutional operations. Find out more about how CNNs operate in [this useful guide](https://poloclub.github.io/cnn-explainer/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6CiTOxy1ZjQ1"
   },
   "source": [
    "## Training the CNN\n",
    "\n",
    "Now, let's create our CNN model using the class we defined above. We set *output_length to 1* to learn the HOMO only. Next, we define the NN training function, which is then applied to the CNN. The key parameter is the *number of epochs*, which we considerably reduce here in the interest of time.\n",
    "\n",
    "Ideally our model should generalise well and thus make good predictions on data that it was not training on. For this reason we monitor the loss on both the training set and validation set (data not used in training).\n",
    "We want to stop the training as soon as the validation loss does not decrease anymore and starts to depart from the training loss.\n",
    "\n",
    "This can be automated with the **EarlyStopping** callback. In the following we will continue training the above model until the validation loss does not decrease anymore and do not start from scratch. If no lower value of the validation loss is found in 3 consecutive steps (patience=3), the training will stop."
   ]
  },
  {
   "cell_type": "code",
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    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 815,
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    "id": "rQNzguz0oFUM",
    "outputId": "9f10889f-3123-4c4d-f9d6-9ec5c19c18c0"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: False, used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "\n",
      "  | Name    | Type      | Params\n",
      "--------------------------------------\n",
      "0 | conv1_1 | Conv2d    | 200   \n",
      "1 | conv1_2 | Conv2d    | 3.6 K \n",
      "2 | conv1_3 | Conv2d    | 724   \n",
      "3 | pool    | MaxPool2d | 0     \n",
      "4 | fc1     | Linear    | 197   \n",
      "--------------------------------------\n",
      "4.7 K     Trainable params\n",
      "0         Non-trainable params\n",
      "4.7 K     Total params\n",
      "0.019     Total estimated model params size (MB)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sanity Checking: 0it [00:00, ?it/s]torch.Size([500, 1, 29, 29])\n",
      "torch.Size([1000, 1, 29, 29])                                              \n",
      "Epoch 0:  67%|██████▋   | 16/24 [00:00<00:00, 31.42it/s, loss=9.1, v_num=2] \n",
      "Validation: 0it [00:00, ?it/s]\u001b[A\n",
      "Validation:   0%|          | 0/8 [00:00<?, ?it/s]\u001b[A\n",
      "Validation DataLoader 0:   0%|          | 0/8 [00:00<?, ?it/s]\u001b[A\n",
      "Epoch 0:  71%|███████   | 17/24 [00:00<00:00, 20.60it/s, loss=9.1, v_num=2]\n",
      "Epoch 0:  75%|███████▌  | 18/24 [00:00<00:00, 21.36it/s, loss=9.1, v_num=2]\n",
      "Epoch 0:  79%|███████▉  | 19/24 [00:00<00:00, 22.11it/s, loss=9.1, v_num=2]\n",
      "Epoch 0:  83%|████████▎ | 20/24 [00:00<00:00, 22.84it/s, loss=9.1, v_num=2]\n",
      "Epoch 0:  88%|████████▊ | 21/24 [00:00<00:00, 23.58it/s, loss=9.1, v_num=2]\n",
      "Epoch 0:  92%|█████████▏| 22/24 [00:00<00:00, 24.27it/s, loss=9.1, v_num=2]\n",
      "Epoch 0:  96%|█████████▌| 23/24 [00:00<00:00, 24.97it/s, loss=9.1, v_num=2]\n",
      "Epoch 0: 100%|██████████| 24/24 [00:00<00:00, 25.49it/s, loss=9.1, v_num=2, val_loss=0.998]\n",
      "Epoch 1:  67%|██████▋   | 16/24 [00:00<00:00, 31.95it/s, loss=1.64, v_num=2, val_loss=0.998, train_loss=9.300]\n",
      "Validation: 0it [00:00, ?it/s]\u001b[A\n",
      "Validation:   0%|          | 0/8 [00:00<?, ?it/s]\u001b[A\n",
      "Validation DataLoader 0:   0%|          | 0/8 [00:00<?, ?it/s]\u001b[A\n",
      "Epoch 1:  71%|███████   | 17/24 [00:00<00:00, 21.06it/s, loss=1.64, v_num=2, val_loss=0.998, train_loss=9.300]\n",
      "Epoch 1:  75%|███████▌  | 18/24 [00:00<00:00, 21.80it/s, loss=1.64, v_num=2, val_loss=0.998, train_loss=9.300]\n",
      "Epoch 1:  79%|███████▉  | 19/24 [00:00<00:00, 22.38it/s, loss=1.64, v_num=2, val_loss=0.998, train_loss=9.300]\n",
      "Epoch 1:  83%|████████▎ | 20/24 [00:00<00:00, 23.09it/s, loss=1.64, v_num=2, val_loss=0.998, train_loss=9.300]\n",
      "Epoch 1:  88%|████████▊ | 21/24 [00:00<00:00, 23.76it/s, loss=1.64, v_num=2, val_loss=0.998, train_loss=9.300]\n",
      "Epoch 1:  92%|█████████▏| 22/24 [00:00<00:00, 24.47it/s, loss=1.64, v_num=2, val_loss=0.998, train_loss=9.300]\n",
      "Epoch 1:  96%|█████████▌| 23/24 [00:00<00:00, 25.15it/s, loss=1.64, v_num=2, val_loss=0.998, train_loss=9.300]\n",
      "Epoch 1: 100%|██████████| 24/24 [00:00<00:00, 25.69it/s, loss=1.64, v_num=2, val_loss=1.490, train_loss=9.300]\n",
      "Epoch 1: 100%|██████████| 24/24 [00:00<00:00, 25.57it/s, loss=1.64, v_num=2, val_loss=1.490, train_loss=1.310]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_epochs=2` reached.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1: 100%|██████████| 24/24 [00:00<00:00, 25.33it/s, loss=1.64, v_num=2, val_loss=1.490, train_loss=1.310]\n"
     ]
    }
   ],
   "source": [
    "# This is the CNN AI model. It's created blank.\n",
    "cnn = CNN(output_length = output_length)\n",
    "\n",
    "# Here, we define the NN training function, which is then applied to the CNN.\n",
    "# The fit function returns a trained CNN.\n",
    "trainer = pl.Trainer(max_epochs=2, callbacks=[EarlyStopping(monitor=\"train_loss\", mode=\"min\", patience=3)])\n",
    "trainer.fit(cnn)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YLqHS-rNDIk0"
   },
   "source": [
    "CNN training is now complete and it's time to review prediction accuracy. We use the trained CNN model and input training and test data to compare these two results. You can follow the same syntax to check the MSE on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2BaLjWZXcNSx",
    "outputId": "37096447-a0ab-4650-aef7-222661b3503f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training loss: 1.1090996265411377\n"
     ]
    }
   ],
   "source": [
    "# Here we compute the MSE accuracy on the training set\n",
    "X_train = X_train.reshape(-1, 1, 29, 29)\n",
    "\n",
    "# Below we insert training set X and Y to obtained the MSE (loss).\n",
    "loss, _ = cnn(torch.Tensor(X_train), torch.Tensor(Y_train))\n",
    "print(f\"Training loss: {loss}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZT1r50yIwCUc",
    "outputId": "48559bde-dedc-4922-aa74-404e014a2dcc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss 1.2540472745895386\n"
     ]
    }
   ],
   "source": [
    "# Here we compute the MSE accuracy on the test set\n",
    "X_test = torch.Tensor(X_test).reshape(-1, 29, 29).unsqueeze(1).float()\n",
    "Y_test = torch.Tensor(Y_test).float()\n",
    "\n",
    "# Below we insert test set X and Y to obtained the MSE (loss).\n",
    "loss, Y_pred = cnn(X_test, Y_test)\n",
    "print(f\"Test loss {loss}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Er-GpqSrEG1K"
   },
   "source": [
    "How does the test set MSE compare to the training set MSE? Is this what you expect to find?\n",
    "\n",
    "Now, let's review the quality of HOMO predictions in a scatter plot. What do you observe?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 470
    },
    "id": "DrTsiyuDBjap",
    "outputId": "5e1d3c6e-cb53-439d-aa12-72d8c6175fd5"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lpKRYfIDg4GCcPXsWANCsWTNcuXLF7rkiIiIQERFh93YLFizAG2+8Ufr1xYsX0bdvX3zzzTfo0KGDguiJiIiokvxL0kqpP5YBJQXSsVtaSsnNbfe7LblxluIkp23btnjxxRexcuXK0gTl8uXLeOmll9CuXTsAwOnTpxEdHa1akA0bNiz3de3atQEATZo0QYMGDVR7HCIiohrlWiawZwFwYDlgKJSORbUCuk0BWvQDysygVEeKk5ylS5di0KBBaNCgQWkic+HCBcTGxmL9+vUAgPz8fLzyyivqRkpERETqyLsI7Hkf+HMFYCiSjtVvA3SfCjS7r9onN2aKa3IAwGQy4ddff8WpU6cAAC1atECfPn2g8fDhLNbkEBFRjZZzAdjzHnBwJWAslo41aAd0mwo07eWxyU2V1OSUlJQgISEBixcvRkJCAhISEhQHSlQVjCYR+89l49K1ItQL8kf7mDBoNZ755iUicrmr54Hd84BDXwKmEulYw3ukaanY7h6b3DhLUZLj6+uLo0ePuioWIlVsSsrAjA3JyMgtKj0WFeKP6QPikBAf5cbIiIiqWPY5YNe7wJFVgMkgHWvcRSoobtzFa5MbM8XzS0OHDsWyZctcEQuR0zYlZeDZLw6WS3AAIDO3CM9+cRCbkjLcFBkRURXKSgHWjQU+aAsc+lxKcGK7AyN+Akb8CMR09foEB3Cg8NhgMGD58uXYsmUL2rZti8DAwHLfnzdvnmrBESlhNImYsSEZlorMRAACgBkbktEnLpJTV0TknS6fAnbNBf76DhBN0rEmvaRpqYY1r+WK4iQnKSkJbdq0AYDSwmMzoQZkheS59p/LrjSCU5YIICO3CPvPZeOeJnWrLjAiIle7dALYOQdIWgOYP+o16ytNSzW4262huZPiJGf79u2uiIPIaZeuWU9wHLkdEZHH++cYsGM2kLwepclNi35SclO/tVtD8wQObzxx5swZpKSkoGvXrggICCjdnJPIXeoF+at6OyIij5VxFNg5Gzi+4eax2wdIG2dGtXJfXB5GcZKTlZWFRx99FNu3b4cgCDh9+jRiY2MxatQo1KlTB++++64r4iSyq31MGKJC/JGZW2SxLkcAEBkiLScnIqqWLh6SRm5O/nTjgADEPSAlN5Hxbg3NEyleXfX888/D19cXaWlpqFWrVunx//znP9i0aZOqwREpodUImD4gDoCU0JRl/nr6gDgWHRNR9fP3n8CXjwIfd7+R4AhA/L+BsYnAo58xwbFC8UjOr7/+il9++aXSnlHNmjXD+fPnVQuMyBEJ8VFYNLRNpT45keyTQ0TV0YX9wG9vAylbpa8FDdDyEaDLZCCiuXtjqwYUJzkFBQXlRnDMsrOzodPpVAmKyBkJ8VHoExfJjsdEVH2d/x3Y8Q5w9jfpa0ELtHoM6PICULeJW0OrThQnOV26dMHKlSsxc+ZMANKycZPJhNmzZ6NHjx6qB0jkCK1G4DJxIqpeRBFI3S0lN6m7pGMaH6DVYKDLJCAs1r3xVUOKk5zZs2ejV69eOHDgAIqLi/HSSy/h2LFjyM7Oxp49e1wRIxERkfcSRWnEZsdsIO136ZjGF2g9BOg8CajTyK3hVWeKk5z4+HicOnUKCxcuRFBQEPLz8/HQQw9h3LhxiIpivQMREZEsoijV2uyYDVzYJx3T+gFthgOd/guERrs3Pi8giKJoabWtV3J0q3YiIiLViCJw+ldpWir9T+mYjz/QdgTQaSIQXN+t4XkiR6/fDjUDzMnJwf79+3Hp0iWYTKZy3xs+fLgjpyQiIvJuoigt/97xDpBxRDrmEwDcPRLoNAEIinRvfF5IcZKzYcMGDBkyBPn5+QgODi7X5VgQBCY5REREZZlMwIkfpWmpf/6SjvnWAto9Bdz7HFC7nnvj82KKk5wXXngBI0eOxFtvvWVxKTkRERFBSm6Orwd2zAEuHZOO+dUG2o8G7hkPBIa7N74aQHGSk56ejgkTJjDBISIissRkBI59L+0KfvmEdEwXDHR4Bug4FqjFrWWqiuIkp2/fvjhw4ABiY7len4iIqJTRACStkZKbrNPSMV0I0PFZoOMYIKCOe+OrgRQnOf3798eLL76I5ORktGzZEr6+vuW+P3DgQNWCIyIi8njGEuDot8CuuUD2WemYf6g0JdXhacA/xL3x1WCKl5BrNNb39BQEAUaj0emgXIVLyImISDWGYuDo18Cud4GrqdKxgDDg3vFAu9GAP68zaqmyJeQVl4wTERHVKAY9cPhLYNd8IDdNOlYrXFop1e4pQFfbvfFRKYf65JgVFRXB399frViIiIg8V0kRcOhzYPd7QN7f0rHAelIDv7ufBPwC3RsfVWJ97skKo9GImTNn4tZbb0Xt2rVx9qw0//jqq69i2bJlqgdIRETkViWFwN7FwIK7gJ8mSwlO7Ugg4W3gv0el6SkmOB5JcZLz5ptvYsWKFZg9ezb8/PxKj8fHx2Pp0qWqBkdEROQ2xdeBxA+B91sBm6YA1zKA4FuBfnOBiUekVVO+Ae6OkmxQPF21cuVKfPzxx+jVqxfGjBlTerxVq1Y4ceKEqsERERFVOX0+cGAZ8PsHQMFl6VhINNBlEnDXEMBH5974SDaHmgE2bdq00nGTyYSSkhJVgiIiIqpy+mvA/k+AxIXA9SzpWGgjoMsLQKvBgI+f7fuTx1Gc5MTFxWHXrl1o1KhRueOrV69G69atVQuMiIioShTlAvs+BvZ+CBRelY7ViQG6vgjc+Sig9bV9f/JYipOc//3vf3jiiSeQnp4Ok8mEtWvX4uTJk1i5ciV+/PFHV8RIRESkvsKrwL4lwN6PpEQHAOo2lZKb+H8DWqcWIJMHUNwMEAB27dqF119/HUeOHEF+fj7atGmD//3vf7jvvvtcEaNq2AyQiIhwPVtKbPYtAfR50rHwFkC3l4A7HgQ0WvfGR5U4ev12KMmprpjkEBHVYAVZUr3N/o+B4nzpWL04aeQm7gEmNx6syjoeExERVSv5l4HfFwB/LANKCqRjt8RLIze3DQBsbFdE1RuTHCIi8k7X/rmZ3BgKpWNRrYBuU4Dm/2JyUwMwySEiIu+SlwHseQ/4cwVgKJKO1W9zI7npCwiCW8OjqsMkh4iIvEPu39K+UgdXAka9dKxBO6DbVKBpLyY3NZDDSc6VK1cAAOHh4aoFQ0REpFhOGrBrHnDoC8B0oyltdEeg+xQgtgeTmxpM0YRkTk4Oxo0bh/DwcNxyyy245ZZbEB4ejvHjxyMnJ8dVMRIRuZTRJCIxJQvrD6cjMSULRlONWXRavWWfA354DljQGvjzUynBadQZeGIDMHIT0KQnE5waTvZITnZ2Nu655x6kp6djyJAhuP322wEAycnJWLFiBbZu3Yrff/8dderUcUmgjRs3xvnz58sdmzVrFqZOneqSxyOimmFTUgZmbEhGRm5R6bGoEH9MHxCHhPgoN0ZGVmWlSCM3R1YBolE6FtNNWi3VuLN7YyOPIrtPzn//+19s3boVW7ZswS233FLue5mZmbjvvvvQq1cvzJ8/3yWBNm7cGKNGjcLo0aNLjwUFBSEwUP729uyTQ9WB0SRi/7lsXLpWhHpB/mgfEwathp9GXWFTUgae/eIgKv4RND/bi4a2YaLjSa6cBnbOBf76FhBN0rEmvaTkpmFH98ZGLuXyPjnr1q3DkiVLKiU4ABAZGYnZs2djzJgxLktyACmpiYyMdNn5idyNowpVx2gSMWNDcqUEBwBESInOjA3J6BMXySTT3S6dAHbNBZLW3Exumt0nrZZqcLd7YyOPJrsmJyMjA3fccYfV78fHxyMzM1OVoKx5++23UbduXbRu3Rpz5syBwWCweXu9Xo+8vLxy/4g8lXlUoWyCAwCZuUV49ouD2JSU4abIvNP+c9mVnuuyRAAZuUXYfy676oKi8v45Bnw3AvioI/DXd1KC06IfMHo7MOQ7Jjhkl+yRnPDwcKSmpqJBgwYWv3/u3DmEhYWpFlhFEyZMQJs2bRAWFobff/8d06ZNQ0ZGBubNm2f1PrNmzcKMGTNcFhORWjiqUPUuXbOe4DhyO1JRxlFg52zg+Iabx267X5qWimrlvrio2pE9ktO3b1+8/PLLKC4urvQ9vV6PV199FQkJCYoefOrUqRAEwea/EydOAAAmTZqE7t27484778SYMWPw7rvv4oMPPoBer7d6/mnTpiE3N7f034ULFxTFR1RVOKpQ9eoF+at6O1LBxUPAqseBJV1uJDgCEDcIGLMHeOxLJjikmOyRnNdffx133303mjVrhnHjxuG2226DKIo4fvw4PvroI+j1enz++eeKHvyFF17AiBEjbN4mNjbW4vEOHTrAYDAgNTUVLVq0sHgbnU4HnU6nKCYid+CoQtVrHxOGqBB/ZOYWWRxBEwBEhkiF3+Rif/8J7HgHOP3LjQMCEP+QtHFmvdvdGhpVb7KTnAYNGiAxMRFjx47FtGnTYF6UJQgC+vTpg4ULFyI6OlrRg0dERCAiIkJZxDccPnwYGo0G9erVc+j+RJ7E0VEFrsRynFYjYPqAODz7xUEIQLlEx/wMTh8Qx+fTlS7sl5KbM1ukrwUN0PIRoMtkIKK5e2Mjr6Co43FMTAx+/vlnXL16FadPnwYANG3a1KW1OACQmJiIffv2oUePHggKCkJiYiKef/55DB061GV9eYiqkiOjClyJ5byE+CgsGtqm0vMYyefRtc4nAjveBs7+Jn0taIE7/wN0eQEIb+rW0Mi7yO6T404HDx7E2LFjceLECej1esTExGDYsGGYNGmSouko9skhT2ZeXQVYHlUo27OF/V3UpeaIGEfXbDi3Sxq5Sd0lfa3xAVoNBrpMAsIslyYQAY5fv2UnOSNHjpR1wuXLl8t+8KrGJIc8nZzRGaNJROd3tlktVDaP+uye0pMX1yrG0TULRBE4twPYMRs4v0c6pvEFWg8BOj8P1Gns1vCoenB5M8AVK1agUaNGaN26NarB4A9RtZQQH4U+cZE2RwKUrMS6p0ndKoiaAOuja+Y+RzVudE0UgZStUnJzYZ90TOsHtB4mJTehymo4iRwhO8l59tlnsWrVKpw7dw5PPvkkhg4d6vJaHKKaSKsRbCYnXInledjnqAxRBE5vlqal0g9Ix7Q6oO0IoNNEIORWVR+O04Nki+wk58MPP8S8efOwdu1aLF++HNOmTUP//v0xatQo3HfffRC40ytRlWB/l5s85QLH0TVIyc3Jn6XkJuOwdMwnALh7JNBpAhCk/pY8nB4kexStrtLpdBg8eDAGDx6M8+fPY8WKFRg7diwMBgOOHTuG2rVruypOIrqB/V0knnSBq9GjayYTcHKjlNxk/iUd860FtBsF3DsBqO2aNh+cHiQ5ZHc8rnRHjQaCIEAURRiNRjVjIiIbzP1dgJurqcxqSn8XT9vny1Wja0aTiMSULKw/nI7ElCwYTR5UD2kyAcfWSd2JvxkqJTh+taV6m//+Bdz3hssSHHvTg4A0PehRzxe5haKRHL1eXzpdtXv3btx///1YuHAhEhISoNE4nC8RkUI1ub+LJ9a/uGJ0zZNGqsoxGYFj3wM75wCXpW134BcEdHgGuGccUMv1I4icHiS5ZCc5Y8eOxddff43o6GiMHDkSq1atQnh4uCtjIyIb5KzE8kaeeIFTu3uyR07FGA3AsbVScnPllHRMFwJ0fBboOAYIqLrGrDV6epAUkZ3kLF68GA0bNkRsbCx27NiBHTt2WLzd2rVrVQuOiGyztxLLG3nqBU6t0TWlI1UuL742GoC/vgV2zgWyU6Rj/qHSqE37p4GAUPUeSyYW35NcspOc4cOHcwUVEbmdJ1/gLI2utW1UB3+ev4r1h9NlJSFKRqpyC4tdN6VlLAGOrAJ2vQtcTZWOBYQB944H2o0G/N3XUJXF9ySXomaARETu5ukXuLKja5uSMtBtznZFSYjcEajNyZn4dE+q+lNahmLg8JfArnlAbpp0rFY4cO9z0oopXZDyc6qMm6uSXA5VC+fk5ODAgQM4cOAAcnJy1I6JiMiq6rK6zNoKsIzcIoyxsQJM7gjUusMX1V1dZNAD+z8BFrQGfvyvlOAE1pNWSf33KND5vx6R4JiZpwcjQ8o/X5Eh/lw+TqUUbdCZmpqKcePG4Zdffind2kEQBCQkJGDhwoU3iqfIAAAgAElEQVRo3Lixq+JUBfeuIvIerl59pKTWpeJt2zaqU2kEp6LQWr7485U+lc5p3pvM1khVnUBfZBeU2P0ZVo3uaL9mq6QQOLgS2P0ecO2idKx2pJTUtHkC8Ktl93EqqsomjZ7SEJJcy+UbdF64cAHt2rWDr68vxo4di9tvvx0AkJycjEWLFsFgMOCPP/5AgwYNHPsJqgCTHCLv4qoLnJIEytJtwwL9kF1QbPdxnu/dHBN7N7P4+LZ2pB/ZqTGW7Um1e/73H7sLD9xlZRuF4uvAn58Ce94H8v+RjgXVl/rctBkO+DpW0+SxS9+pWnN5kjNq1CicOXMGv/zyC/z9y7/4CwsLkZCQgGbNmmHp0qXKIq9CTHKIyJ5NSRkYcyPBKMucYJSdCrG21Fsua6M55nNbSxZCAvww+JO9ds9vcSSnuAD4Yxnw+wKg4LJ0LCRaSm5aDwV8dA7+NNafD0vPHZESLt+FfNOmTfjmm28qJTgAEBAQgJkzZ+Kxxx6T/cBERJ7GaBIxde1fFr9Xcfk2bvzfmZ66OddLrPbzsdUHyWgSlRdf669JNTeJC4HrWdKx0IZAl8lAq8GAj5/deG2NnHlik0Yi2UnOlStXbNbcxMbGIjs7W42YiIjcYuG2M8i5br3Wpezybdz4v7Nsraay1gdJ0eqiolxg/8dA4odA4VXpm3VigK6TgTv/A2h9ZcVpbxrKE5s0EslOcqKiopCcnGy15iYpKQmRkervMktEZOaqGhyjScTelCws2Zki6/ZqNhp0tJ+P3eaDTQKA394B9n4oJToAULcp0PVFIP7fgFb+rj5yOjDrDSZZ52IXYqpKsl/lgwYNwuTJk7F161ZERESU+96lS5cwZcoUDBo0SPUAiYgA+QWtShMhS+e1R41Gg2r087E4pRUpQLtvEbBhMaDPk24Y3hzo+hIQ/xCg0Sp6DLnTUHMfaSXrfOxCTFVJdpIzffp0/PTTT2jSpAmGDh2K2267DaIo4vjx4/jqq68QGRmJ//3vf66MlYi8nLUERe5eTpuSMvDaD8nIzCszshHsj9cGWl7Z40jhcGiAb2liYqsuxhY1+/mUTmkVZEmjNt98DBRfk74ZcTvQ7SUg7oFKyY3cZFDuNBRE28+Hu5s0Us0kO8mpU6cO9u3bh//7v//D119/XdoEMDQ0FI8//jjeeusthIXxxUtEjrE2UvNq/zjM3Gh/JMFkAsZ+VXlVVGae1HxvcYWVPbZGKGx5slPj0mTAWl2MPZE3fq6QAD/Z2z1YlX8ZSPwA2L8UKCmQjt0SLyU3tw0ANJV7vipZ5i13eulKgZ5diMnjKGoGaCaKIi5flpYeRkREVJs9rbiEnORgc7GqZ2vpsdw/ULV1PsjXG6x+v04tXxwos1w7MSVL1jLsskIDfPDhkLa4kq8vfW1sTs5UNN31av/bERXij5kbjzvXS+baP9Iy8APLgZLr0rHIO4FuU4AW/SwmN4DyZd5ynyfzcnX2ySFXcPkS8rIEQUC9evUcuSuRakmEK5IR/oGuevZqPuSyleAAwNXrJdh7NgudmoYDcLAAVhAwZOm+0i/Nr43dU3pi/uaTWLjdfuHyhavXMXPj8UrHzds9fPR4a/S7s771E+RlSA38/vwUMNz4Geq3BrpNBZr3BWx86HRkmbfSvcJsLX1Xgh82SA2yk5zWrVvLGrE5eLDycDGRmVpJhCuSEbl1HzVFVV1k7NV8qCkx5WaSo6QA1jyiVHF5ednXRqemEbKSnM8Sz9v8/vhVh7AQAvrdWf61Zsz5G5d/fhsRp7+B1nSjm/KtdwPdpwJNe9tMbswcWebtyGaY1pa+y2Xp/R0Z7I/B7RuicXgtJj0km6LVVWaiKGLWrFkYM2YM63BINrWSCFckI97QyEzNpKQqR7Sqdknxzd+wvREKy/eqfNz82tjxYg9Z57NXIGASpdqij9BGSnRy0pD2w5uIPLsakZBGq/4wNcfnusfQr+NgJDSzMepTgdznuuLt7C5XV/E1YfX9nVeE+VtOlX7NEVaSw6GaHAAICgrCkSNHEBsbq3ZMLsOaHPcxbzpo7VOkech795SeNi/Map2nIqV1B55GzaSkqlvzK6mNUVrgW9GXozqgU7Pw0q+tbeHgiFWjOyK3sNjinlOOaChcwhctdqPB+XXQiFJys9d0O943PIREUxyEG78RJb8PZ1/nxQYTPk9Mxfns62gUVgvD7mkMPx/LtT+OsPf+LotbRdQsjl6/1Xt1EtmgZJi8Ks5TkaOfcD2BOSmp+LyYR7Y2JWWUO240iUhMycL6w+lITMmC0SSW+569+pgZG5LL3cdZ5hEVaympAClh++jx1rgluPy+SiH+8ssKQ2v5oqMLE9RL14pKRzwiQ8pPhYUFyusqDACNhEzM9lmCbX6T0DB1NTSiAXuMd+A/+lfxWPGrSDTdAUBw6Pch97m2tMx7U1IGus3Zjpkbj2Nl4nnM3Hgc3eZsr/T6coaSqUtXvR7JuzhUeEyklFpJhKuSEbn1GZ7WyEzpNJsntuaXW/NR/siN+2rlf057+6GW5Ub3zM+dWsyvjYqFt+GBOvx+9go+tFOvEyNkYLzPOjyg2QMfQeoevNPYEgsMD+KAeJvF+yj9fThSXwNUXb2a0vctt4ogeziSQ1VCrSTCVcmIM59w3UlJUiJnxMddI1rWRkAiQ/yxaGgbAMCzXxws1+QPAK4WFNs9twDgo8crX4TVKng2vzZMJrF0dAwA7mlSFzofDSavPmIzwWkipOM934XY4jcZD2t3wUcwYZvxLjyon4HhJdOsJjhlKfl92HuuKz5PVTm65+iHCE8cYSXPIHskZ8GCBeW+NhgMWLFiBcLDw8sdnzBhgjqRkVdRugzV1eepyNFPuO4m9497Zm4hZv9y0n5r/n+7rzW/taXHAND5nW0OLzH/8PHWlVYqAepdGEUAhSVGDFlWfmn5wFZR+HjnOasxNhcu4Dmf79Ffsw8aQbrVZmMbLDA8hL9EZbWOSn8fSpZ5y02k528+hU5Nw50qeFdSDF6Wp42wkueQneTMnz+/3NeRkZH4/PPPyx0TBIFJDlmkVhLhymSkKleQqEXuH/fsgmJ5rfkF97bmt7T0ODElS9aIS1igL7ILbi7xtld4rcr+U4K0Wqri0vKM3CIs2XnO4n1uF87jOZ/v0U+7v/TYJmM7fGB4EMfExopjCA3whUkUYTSJil73cpd5y00GF24/g4Xbzzi16snW+9sSbhVB9shOcs6ds/yGJZJLrSTClcmIWo3Mqorcka2w2joL363sSr7jrfld1VdH7kX21fvvQGSwv93HN8d5MafQ6dVaStam3iGcwwSf79FXe6D02EZjeyw0PIjjYiOHY8gpLMGQpftKkwu1X79Kk0Fn63Ssvb8r8uQRVvIcDi8hr464hNwzeHLH4+rIXGsDWE5KFg1tg5AAP5e25ndlXx01l/c7suO4s+4UUjDBZy16aw8BAEyigB9NHbHQMAinxGjVHsecsIXW8i03suTs78G8rFvJFJKjrRwqPq75/Z16pQCr9qchM09f+n32yalZHL1+M8kh8gL2kgx7FypLFyW5SaQr+uqUfezw2jq88O1h/JOnlx27JY7sOO6M1sJpTPBZix7aIwAAoyjgB9O9WGgYhBTx1iqKQp1+MtYSaXvU7CvFDzU1G5McGZjkkDezdxGQM+LjSDKidnNGSwmbeXTC2hSavdiVNJlzVlvhJCb6rEVX7V8AAIOowTpTZ3xoeADnRPeMOqgxsuLIKNj4Hk3xfJ/mXpuMMPGqOkxyZGCSQzWd2tNKSqaS2seE2b0g2NuN3NGpGEd2HJcjtJYvcq+XQATQQTiOCT5r0Ul7DABQImqx1tgFHxkH4rwYqfpjO8I8suLoxdl8vz1nLsvapwvw3mklbuZbtap0F3Iiqp7ULqyWWxS8JTkTk749bPOCIKexYYCvFh+OaoMrBXpFsbuij8rEXk1xe2QQVq76AhN91qCD5gQAoFjUYrWxKz4yPoC/xXqqP64zLl0rcuribF6R1T4mDGsOpsuq0/HGDW65mW/1ISvJycvLk31CjpCQPRzidS17z6+zO0SXJXflzbI9qZWOVbwgyO3HotEIeOAuZTUt6vdREdEgOxEJaauQ4CeNEOlFH3xr7I7FhgFIR4TKj6eOXaeuYPXBvysdV3pxLrvU2x5zIvDaD8c8eoNbubxhM9+aRFaSExoaCkGQ98syGo1OBUTejUO8rlXVz6+cJeyCIO2sXVHFC4Kz3ZZtJXeONpmzFHV3zWFM9PkerY+fAQDo4YtVhh5YbBiATHj21gKWEhzAsYtzQnwUnu4aY7UfUEWZeXos3HYGE3s3UxCx53HH1ifkOFlJzvbt20v/n5qaiqlTp2LEiBG45557AACJiYn47LPPMGvWLNdEecPGjRvx+uuv4+jRo/D390e3bt2wbt06lz4mqYdDvK7ljufXXnNGEbZ7yZS9IDizZYe95E7JyIO1SHtpDmKCz/dopTkLACgSffGlsTeWGO7HJdRx8LyeQ+nF2WgS8cMRZZtzzt9yCi0ia1fr93l12cyXI+YSWUlOt27dSv//+uuvY968eRg8eHDpsYEDB6Jly5b4+OOP8cQTT6gfJYA1a9Zg9OjReOutt9CzZ08YDAYkJSW55LFIfRzidS13Pr+2mjP+Kz4Syy1MVVW0JTkT/9c/TlG3ZfMf8S3JmbKmwxLio/Dh460xbtUh2U38BJhwn+ZPPOfzPeI10mNcF3X43NgbSw39cRmh8k5UjWxJzqyU5Fi6YDq691d1f59Xh818OWJ+k+LC48TERCxevLjS8bvvvhtPPfWUKkFVZDAYMHHiRMyZMwejRo0qPR4XF2fjXuRJOMTrWu5+fq0VNC/cdlrW/b8/nI7/6x8nu9uynOXMlpK705fyZSU4AkxI0PyBCT7f43ZNGgCgQNRhpfE+LDX0QxZCZP1c1dGyPaloFxNWejG0dsFMiHdsxVh1f5+7av88tXDEvDzFu5BHR0fjk08+qXR86dKliI5Wr3tnWQcPHkR6ejo0Gg1at26NqKgo/Otf/7I7kqPX65GXl1fuH7lHdRnidSejSURiSlbpTtZKdnX2hOfXXND8wF234p4mdfFLUgbmb5GX5GQXlGD/uWxZO2Rb203dEnNy99oPSSgsNuJTO6NKGphwvyYRm/ymYpHf+7hdk4ZrYgA+MAxCZ/37eMcw2KsTHDPzruLWnuuM3CK7z6UtnvI+d+Q9Z576BG4m32bu3mqiKneMry4Uj+TMnz8fDz/8MH7++Wd06NABALB//36cPn0aa9asUT1AADh7VpoDf+211zBv3jw0btwY7777Lrp3745Tp04hLMxyxjxr1izMmDHDJTGRMtVhiNednB1e9rTn96ejGRi/6pCi+2TmFiIxJQt6g0naDV2Q9tIqW09g64+4LZ/vTcMX+9KsjuJoYcT9mkQ857MOTTUXAQB5Yi18akzAckMCclFb4SNaV3EjUU+UkVuEvSlZDj3XcnjC+9yZ95ynbubr7hFdT6Q4yenXrx9OnTqFRYsW4cQJqS/EgAEDMGbMGMUjOVOnTsU777xj8zbHjx+HyWQCALz88st4+OGHAQCffvopGjRogO+++w7PPPOMxftOmzYNkyZNKv06Ly/PZaNNZJunD/G6kxrDy1X5/MrprDz2K+UFvjM3Hkd2QXHp1+YLTtk/xo7WgQCWC6C1MOIBzR6M91mHWE0mACBHDMQyw7/wmbEv8hDo0GPZ0rJ+CHacvqL6edWWePaKSzpER3nA+1yN95wnbuabmVso63aeMpJWFRxqBhgdHY233nrL6Qd/4YUXMGLECJu3iY2NRUaGVMFftgZHp9MhNjYWaWlpVu+r0+mg08nbfZlcy94qHKBm7ibsaMGwpURD6fMrZ/VFxdtcLdBj5sbjNvfImrEh2aHnomyCA1i+4Kj1x9kHBjyo3Y3x2nVopLkEALgq1sYnhn5YabwP+ailyuNYUh0SHIlr3ovunMrZfy4bmbmFmLnxuCpF+mr2nHLWpqQMzNx4XNZtPWEkrao4lOTs2rULS5YswdmzZ/Hdd9/h1ltvxeeff46YmBh07txZ9nkiIiIQEWG/aVbbtm2h0+lw8uTJ0vOXlJQgNTUVjRo1cuRHIDfw1CFed3JkeNnWMLvc51fOUL3cvYrKJiMhAX6qffove8Hpedst+PP8VZz+J9+pc/rCgIe1OzFOux7RmssAgCwxCJ8Y+uNzYx8UIMD5wD1IaIAvcotKZK8mMxME4PSla+rGUssXbz/U0qn3uaPLopXsu1Udp3Tkbj5bE0fMFSc5a9aswbBhwzBkyBAcPHgQer0eAJCbm4u33noLP/30k+pBBgcHY8yYMZg+fTqio6PRqFEjzJkzBwDwyCOPqP545DqeOMTrTkoLhuUMs++e0lPWRp22zgFA9o7dZZORl/q2kPXzmNUJ8MHVQoPNc2fkFqHjrC1O1bH4oQSPan/Dsz4/4FYhCwBwWQzGEsMAfGnshUJ45yfb3MISh2pqRBH45dg/qsQQWssXT94bg/E9mzr1Pne0hsbR3eery5SO0jq1mjZirjjJeeONN7B48WIMHz4cX3/9denxTp064Y033lA1uLLmzJkDHx8fDBs2DIWFhejQoQO2bduGOnWqfxOumsaThnjdTUnBsJKpLWvPr5xzvPbDMQCCoouCORmpOOVkj1Hmgzia4OhQjP9ot+NZnw2IErIBAJfEUCw2DMBXxp4ogvPT2f6+GkTU9sOFq553UZTzyd7V62zeGtQS/e50bpTW0RoaRwvVgeozpSO3Ti0s0BdvPejcSFp1pDjJOXnyJLp27VrpeEhICHJyclQJyhJfX1/MnTsXc+fOddljEFU1JQXDaqyckHOOzDy9op+hrLDaOkSF+Muessorsj6K4wx/6PG4dhue8dmAWwTp71KGGIZFhgH4xtgDevip9lhFJSaPTHDkcHWCIwCYuTEZfeMdb/7nTKNLRwrVq3pKx9nOxHJHnF69/44al+AADiQ5kZGROHPmDBo3blzu+O7duxEbG6tWXEQ1gpKCbDV64bh6CD4yWJo+GOPw9gnKDbqrPtYdlpZ9B6AIQ7Rb8YzPj4gQcgEA6WJdLDIMxLfG7iiGb5XFRerUtziT3Ct9vVf1Igg1OhPLHXGKDK4eI1NqU9wMcPTo0Zg4cSL27dsHQRBw8eJFfPnll5g8eTKeffZZV8RI5NXkNMAD1OmF46oheAE3lwYnxEfh+SrchDE4wBeBKMQY7Q/YrZuIV3y/RISQiwumCEwteQrd9fPxhbEPExw3cia5dia5V/p6r/iecyVrjRbNU3CbkuTtC2YeDbaWkpV9b9ZEikdypk6dCpPJhF69euH69evo2rUrdDodJk+ejOeee84VMRJ5PTkF2Wr0wpFzjluCdQAE/JMnb8duS59+x/dshlX7LyAzT/7FLTTAFzmFN2tv6gb6IctOjU9tXMfAvFV4XrcCdQRp5dV5Uz0sNA7C98bOMFT4E1cVNShUmTPJtTPJvZzd58MCffHq/XcgMrj8e86VG1yqudecI+05atLmnYIoKl1cKCkuLsaZM2eQn5+PuLg41K6tXkdQV8nLy0NISAhyc3MRHBzs7nCIFDN/+gMs/zGT8ylUzjkAWLyNJXVq+WKWhaXBSle1fDmqAzQ3puXqBfmjbaM66DZnu8WpimAUYIT2Fzzl+zOCUQAAOGuKxELDIKw3dYIRWotx6nw0TtUcUWUaQVqNZe33XDfQD4nTesHPR/HEAQDpgtzp7a1Wf2/m5H73lJ4WL9TWXu/m+1p6z7h6g8vElCwM/mSv3dutGt1R9jSf3Jir6+adjl6/Fb/qRo4ciWvXrsHPzw9xcXFo3749ateujYKCAowcOVLp6YhIAblTW86ew3ybW2TM4+t8NOgTV3mzxtLHsXMO83B6xyZ1y+195eejwcBW5X+eEOTjeZ/V2K2biEm+q6UEJ7w5NjWfid7Fc7HW1NViggMAV6+X4N1H78Kq0R3x/mN3lU6peefn16ohABjdJab0/5ZkFRSj25ztsqdfKtqcnIkig8nq4wO2a2jMr8OQWpWnKy0dU2sayRZX7DWXEB+F3VN6lr6+V43uiN1TelZKcFz9s3kaxSM5Wq0WGRkZqFevXrnjV65cQWRkJAwG16yWUANHcshbqDHcLOcce05fwZBl++yey9YnTqNJxMJtpy1u1mlrBOqnoxcx9itp/6s6yMMon5/xhPZXBAlS6/rLAbGI6P8KEDcI649mYuLXh+3GOf/RVniwTYPSr5U0iaPyyi5Jlvs8Pt+7Gcb3bCb7tWpvNFBug8FZPyVjyc5zFr9XdjTHaBLR+Z1tVn8Oe6NGcrliJMeeqvrZXMXR67fsmpy8vDyIoghRFHHt2jX4+9/8dGY0GvHTTz9VSnyIyDXU6DUk5xxXCuRN7diqvdFqBEzs3RwtIoOsdmPuExeJxJSs0oTrSr4eE78+hDDkYbTPRgzX/opAQYrluKkhFhgewhG/zvjttl74IyUb209ckhXnzI3HEeCnLb0oVqyFCg/UwSSKeG7VoXL1QVRZ2SXJCfFR6HnbLXabNs7fchqr9l/AawPtT43I6XET4Ku1OIpY1k9HL1pNcABpCstc/1JVG1y6Yy+/mrp5p+wkJzQ0FIIgQBAENG/evNL3BUHgjt9EXkZu0efMH48hwFdT7sJVcaSoT1ykxeLqzcmZlT5hhiMXU31+xFDtFtS6kdwkmRpjgeFBbDa1hQgNkFeM1jN/RYHeKPvnuVpQXKl5nKVk7+2HW1bpMvjqKC2roNzXf56/KqtpY2aevE0w5fS4sXdRNppEvLI+yW5MGblF2Hs2C1fy5SX1zrZicMdefq6YIivLU4uZZSc527dvhyiK6NmzJ9asWYOwsJsZpp+fHxo1aoT69eu7JEgicg85q1MAqSPxmC8Olk5HbE7OlF0EWXY6oh6u4hmfHzFEuwX+gnTBPGKKxQLDg9hqaoOKlR9KEhzg5sXk/75PQs/bboFWI1j8w2wyAbV1PsjXe+70u7ut2p9WbupJycVRBPDyjd+BtYJkuefbc+ay1Qvr/nPZsrtlj/vyIJ7s1FjWbdVoxVDVe/mp0YLCGk8uZlZck3P+/Hk0bNgQguD+DE0p1uQQKWdrdYolobV8kXO98oWlYv1NscFUOr0RiSyM8dmAwdrt0N1Ibg6ZmuJ9w0P4zdQKrigP1vloEOCnLRdrVIg/4m8NxuZkedNfNV3ZmhG5dSZl2dpqwJHzVbywrj+cLqtWq6zQWr7IvW55zy9X1K1U1QiIuSbH3hSZ0p/NWt2UkhWfclTZ6qpt27Zh9erVlY5/9913+Oyzz5Sejog8nPkTZ6BO3sCvpQQHuJkgzdiQjJ+OZqDjrK3wL8jATJ/l2KF7HiN8foVOKMEBU3MMK56KB4tn4DfTXXDV+ie9wVQp1ozcIiY4CphHW4wmESZRRGiAsoaL2QUlVlf12GtyZ0nFVUJKRyUEK/8v+7WlnjOJKVlYfzgdiSlZMJqUdWUxT5eaVxU6m+BYi8c8RVb2ZzFzdIrMXr8fQHq/K31O1KR4JKd58+ZYsmQJevToUe74jh078PTTT+PkyZOqBqgmjuQQOcZoEtFm5mbkqlSM20C4jLHadfi3dif8BGnKaZ/pNrxneBiJpjhwYXf1sGp0R+QWFju1Qs3WCILSUcSK5wNgc0WRNc/3bo6v/0irdj1n5MSjZsxVuUrM5aurzNLS0hATE1PpeKNGjZCWlqb0dETkpKoY7t5/LluVBKeh8A/GadfjIe0u+N5IbvYY78ACw0PYJ97u9PnJPrWm5MICfXElX48Jqw451UXa1qoea3UrSs5nLvBVEmPj8FrYPaWnzfeVozuju4rceOR0V5fL1cXMalCc5NSrVw9Hjx6ttEHnkSNHULeu9yw7I6oOquqTpLN/pGKEDIzzWY9Bmt3wEaTGbjuNLbHA8CAOiLepESLJJIoiYsMDnT5PdkEJJn7tXIJTlrXXWMWL8ul/8rFw+xnZ53MkUaoX5G+zxYKa2zKoQWk8arSgAIDw2jpVb+cKipOcwYMHY8KECQgKCkLXrl0BSFNVEydOxGOPPaZ6gERkWVV+knR0NUkTIR3jfdZhoOZ3aAUp0u3GVvjA8CAOipVbUZDrZebpbfaNqSjQT4uCYsur2OSUWgT5++Bakf1VaqlXCuzeBpC255DjyjU91h9OL9e+YO/ZLIxeeQDXrfw85vPb608jt+fMij3nEB6kc/mSarf1wJGb4bpxwzjFSc7MmTORmpqKXr16wcdHurvJZMLw4cPx1ltvqR4gEVVW1Z8k5S4lN2suXMBzPt+jv2YfNDeSmy3GNlhgeBBHxSZOx0OuZ175JDX524psO5ulWvPagDvw5k/Jdpdyz99yGi0ig2TtI6URbCdYGkFq/GgWVabppD16K1tIlCV3ZNNSDK6YwnLXtJHcZqFyb+cKildX+fn54ZtvvsGJEyfw5ZdfYu3atUhJScHy5cvh5+fnihiJqAIln9zUYGtlRlm3CWn40Pc9/KqbggHavdAIIn4x3o3++jfxaq1XUD/uXlXiIdczdzSWmvw5luAAQP3QALz1YEu7peTmxLzsShxrey3ZG0Gq+H3z6OYHW0/bHMUBgOvFRuxNybJ5G0dGNl25P5TceMyjW46sAnPmcdXoK+QoxSM5Zs2bN7fY+ZiIXM+Vn9ysFTLbqm24Q0jFBJ+16Ks9UHpso7E9FhoexHGxEV7tfztGdIrB/M0ngWP/KI7JUQKAp7vG4JsDf1td2k6WZedLF8TT/1xz6P5ltybQagT8t3dzzN9yyurtzYn53pQsdGoWLmtbh4ojOtZGeMyjm0t3n5UVe+LZK+jULNzq95WObJaNwRW1OnLisTa65czIkju2p1BKVpIzadIkzJw5E4GBgZg0aZLN286bN0+VwIjIOld9grJXyFyxCDTvzD5EHvkAfbTSMl+TKGCjqQM+MDyIU3WY4l4AACAASURBVGJ06TnCg3RSsWNsOBZuT1EUkzOe7hqDaf3i8FLC7dh7Nkv6BGs0YdFOeRc7T2XeCsBa40VnVbwgOkIE8Fi7hqVfNw6vJet+4746iLcfbomQAD+7hcImEXi1/+0ID9LhyjW9zZhFAPmyO2TbTkBsbctgi6tqY+TEY210y5naPXdsT6GUrOmqQ4cOoaSkpPT/1v4dPqyssyQROcZeozQBUnKi5BOUtamBisPsWo2Ae/zO4t69YzAs6Un00R6EURTwvbET+hTPxnMlE8olOMDNZKtjk7oIlVk4qoYfjmTAaBKh1Qjo1DQck/u2QJgbV3qoJTLEH4uHtkG7xnWcOo+114/SmQxr17D5W06h8zvbsCkpQ3bCnVMoNQjcnJwp6/bpOYV44K5bER6k3u9VTgJiHtmMDFE+FeOKJdXW4rH2u1GrWZ+1x40M8a/yZfSWyBrJ2b59u8X/E1HVM08n/Ss+Esv3pKryCUp2IXPtVGh3zQZStiECgEHUYL2pEz40PICzYuW96yoOV2s1At5+qOo2v7T0qfmPVNv1Fp6slp8Wnwy7Gx2b1MXsTccd7nUjAHiqSwzWHEwvV29jr6DX2rkWDm6N05fyMX/L6UrfNyfJHz7eRtEUz/rDF2U9/vrDF/Fy/zjV6j5q67S4dE2PxJSsSiui7G06a280yczZWG1NKSuJR62RJTV776jN4ZocIqp6lqaTBAEo27c8MsQfr/aPQ0iAX+kSWnt/cOwVMrcTjmPi9bXQrjgGADBAgzWGrvjIOBDnRdsrViomWwnxUVg8tA1e++EYMvNurrqIDNahf8soLNuTavN8SlX81FzLr/r+2et5Wz20iwnDsp1nFS0Dr6hNo1BsOJJRLsEJ8tfiWpGyDU8FABN7NUPvuEjM3Gj5A7D5pTlzYzJe7X87xn11yO55RQBZBcWyYsoqKMb+c9kO1clYkq834vlvpFmJslO1cnpSGU0ilu4+59IaFXtxlO2Bs/5wuqxzqjGypFbvHbXJerc/9NBDsk+4du1ah4MhIuus9cUxf/Ie1akxesdF4mqBHjM3KmsQaPmPnIh7NMmY6LMWHTXSp0EjtLjS9BE8fKwj/hbr2YzX1uaL1j757T+XrXqSU/FT88NtGmCdzFECT/Pj0QxsPJrhdNuRP8/nVDqmNMEBpGTkva2nsXLvebsrsDJyi1AnUIdFQ9tg6pq/kCOjg3bbhnXw26krdm936VqRw3UytphHoZ7uGoOPd56z25PK1TUqSntjVYfVT64mqyYnJCSk9F9wcDC2bt2KAwdurqL4888/sXXrVoSEhLgsUKKazN5KEwHAT0mZuFqgx7ivDtmtq6mo/B85EZ01f+Fbv9exyu9NdNQcR7GoxReGXuhaNA8fh0ywm+AAN5cgW2NpY0JHNmW0xlpd0r1Nw1HLT6vCI7iHG/uqWSV3iXlmXhES4qPw4ZA2sm7fpVmErNuZX7/O1MlYYn6uP9lVOcEp+/2ydS2uqlEpNpjwf9//pWgzTFfU7lU3skZyPv3009L/T5kyBY8++igWL14MrVb6Q2E0GjF27FhueknkInL74ryyPsmhBoHtY8IQGuCDu/QHMMFnLdpopLb5etEXq4w9sMQwABmQhqK/PnBBVsyRwcovNGU/CTtLhOVPzVqNgHmPtqqyuiC6KTtfmp7sGFtX1tLjYfc0Vjz94+g2ENaIKD8dbOn7Feta1KhRKVt3k3rlOj5LTLXZUNFSHNVh9ZOrKZ6cXr58OXbv3l2a4ACAVqvFpEmTcO+992LOnDmqBkhE8ufMlf4RlL4h4vCWVVhhnIO7/KSl1UWiL74y9sJiwwBcQvkVPAUyluE68+nQkb2GLPHRAEH+vqWrqyo+xkePt8bL3yfhqko7q5N9YYFSw1i5F18/H41DF+my9SGJKVlOJTlyVXyPKqlRqVhILE05H3fo9V8xDmvvp0g37pZelRQnOQaDASdOnECLFi3KHT9x4gRMJvvtsIlIOTXnzEv/CIoicGIjxB3voG3mUUADFIp++NzYB58Y+uMyQh1+jMfaNXTq02HZT8KZeUU4eD4bn+9NU3QOgwkYsnQfQgN88WSnGIzv2RRGk4jPE1Ox8/QVHEy7Wm5PpYoF3KS+yJCA0v/Lvfg6e5FWqyDZHkffo5YKidWOw5NXP7ma4iTnySefxKhRo5CSkoL27dsDAPbt24e3334bTz75pOoBEpG8zqJhgX7IklEbsfvkJUSl/4o7zixB4NXjEAAUiDp8brwPnxj6IQvO19bJbfxmi7lGZ/+5bLRpWAcb/8rE1YJixReqnMISzN9yCh/9dgbFBpPV+zPBcS1Lo3tyL77OXKTtjRrJ+bXbWlrvzIopa4XEjrAXh6eufnI1xUnO3LlzERkZiXfffRcZGVIRY1RUFF588UW88MILqgdIRPL+UA9sFYV1hy/iqpUOuBqY0E+zD08d+x4tNH8DAAoQgIORj2BC6r24CvVq6tQYeVL7E66cjRdrMrVWJFk7t7XaD7kXX2cu0rZGgwpLjHa7Ro/qHIOlu6Ql+2rVtcjZtkIpb6+vcYQgio5/fsnLywOAalNwnJeXh5CQEOTm5labmInKcmhHZphwvyYRz/msQzON1DcjT6yFT40J+NSQgBzUVhRDaC1f5F4vsVkIuntKT6f+2Kr5CZfkMbcZOJl5zWJTP2fP6wm1HxVrX0wmEUOW7bN7v1WjOyK3sNhunxwlElOyMPiTvYrvZ4mtdg3ewtHrt0NdsQwGA3777TekpKTg8ccfBwBcvHgRwcHBqF1b2R9MIpKv7LD9luRMLNuTajXB0cKIBzR7MM5nPZpopFHXXLEWlhn6YYWxL/IQqOixQwN88PbDdwKA6qs1yl58wmvr8NoPx5jguFhksA6D2zdE4/DActM/feIisWr/BWTmWR9Bk9sZ+fnezTG+Z1OPGV2oOBqkpFneA3fdqmpdi1pbOwT7+2DvtN7w87HdEcZal2RvpzjJOX/+PBISEpCWlga9Xo8+ffogKCgI77zzDvR6PRYvXuyKOInoBnOtyqRvLe8V5wMDHtTuxjjtejTWSDt+XxVr4xNDP6w03od8OFYvE+DnU7r8XM3VGmpPS5FtT9zTEAnx9a1e5LQaAa8NvLmM31Iiu3BwG4QE+GLcVwetNvUTAHz9RxrG92yq7g+gIrnTqqlXrgNQt65FrcUE/27bwG6CY61L8qv941An0M+rEx/FSc7EiRNx991348iRI6hb9+Yv+8EHH8To0aNVDY6ILLPUN8cXBjys3Ylx2vWI1lwGAGSJQfjE0B+fG/ugAAGWTiVb2eXnFQtBw2vrABG4UmB5zx9rOC1V9TYczcD/BsTb/P3IWdGUmJJls2uxq3bcVlP7mDBEBuvKbS9iiTlZUzMBUGvVV58429uqWHuPZeQWYexX5XtFedLUoloUJzm7du3C77//Dj8/v3LHGzdujPR0eUN/ROScskPdfijBI9odeNbnBzQQpBb4l8VgfGy4H18Ye6MQLlh+jpufajclZWDyd0cU1yq4ovCS7MsuKJGVeNhb0SR3usUVO26rRasRMLh9Q7s1SBm5Rdh7NgsaQSj3XABQNAVUccpI7l5e1lRcsVbx/G0b1VH0HrO2PUR1pjjJMZlMMBorNwP7+++/ERQUpEpQRGRbvSB/6FCM/2i341mfDYgSsgEAl8RQLDYMwFfGniiCziWPW5bSvXTKstfFmVxHbuJha3rGW/ZFahwurzZt3Jflp+ZCa/kCQLmVWbaSe0tTRqG1fBHgp8X14vLX1Dq1fPHmoJY3dnY/VelclurfLJ0/LNDXZoPQiux1Rq+OZO1dVdZ9992H9957r/RrQRCQn5+P6dOno1+/fqoGR0QWlBSiw6VvsMv/ebzu+xmihGxkinUwveQJdNG/h+XGf0EPHSKDdfjyqQ54/7G78Hzv5hBw84+jmdw/YZb2uLE1EmNtL52yPPkTvrdTI/Fw175IRpOIxJQsrD+cjsSULKuvL7nkPhcVp+ZyrpdUWnqekVuEMV8cxPtbTpeLy/xhoGJSn3O9pFKCAwBXr5dAowH+v707j4uq3P8A/plhGdZBZERwBRX3UEBEEEW9mnbVtKwUt1RMLcUtTbvllt5y19TrGqFZqZW/1LTMvG6EJF43JBeUUFKxlIhFtoF5fn8cZ2RgljPDDGeW7/v18vVynjnLM3OYOd95lu8zo28Qto4Ohb+edbC0Hd+QAEepajejLTC4JWfNmjXo378/2rdvj9LSUowcORK3bt2CTCbDnj17zFFHQggAlD8B/vcpkLwB4id/whfAfeaDLRUv4uvKGJThWRcyA7D4xQ7o3kqmKmvj56F1jAUAzP+/qzrzhVSfNcV3PS1tXSOW/gvfVtVzczJJ4CHEukjaBtDWZhyJOTIirzuegT2pd7H4xQ7o197P4G7Zqq0p+roNzdXtays/QgwOcpo0aYIrV65g3759uHLlCoqKihAXF4dRo0bB1bV2AxsJIRqUFQHnPwHObgSKuTE3OWiADfIX8U1lDOQaPsbebk41BiTq+7Ls194PM/ZewpG0HLUvTLEIeKNHYI2biCFjMioVDL9k5iLlt8cAuC6Q8AD9WZy93Jz0Jmojhvm7WI5NJ27VmDpuqEoFg5erM8Z3D8CByw/UViI3x7pIteka1UVXsFYbDwvK8ObnFzGzb5DB3bLVfyDo6jY0V7evrfwIMSjIkcvlmDx5MhYsWIBRo0Zh1KhR5qqXmlOnTqF3794an0tNTUV4eHid1IOQOlVaAJzfAZzdBJQ8bTr2DsDttlMw4GQjVOj4+OYVax5cquvL8qdrD2sEOAC33MH2M1kIaeatdhPhP/32CcKW/aQWrGw6eRv13JwwvEsTbD+TZXS6fUvgzzNrriWpOtDWmJYQbeM/XurcGH3b+5l8KrK+rtHajiPRNpusXi2DbAYgMfmO0fvz+SFh6haX2ixTYYkMCnKcnJywf/9+LFiwwFz10SgqKkq1hITSggUL8N///hddunSp07oQYnYlfwOp24GU/wClf3Nl9VsAPecCz72KX6/+iQpozpFTlSFffsbcRPisp1XPzUnrzJW/i+XYdiYLk3sG4tCVnBrdaCPCm5o0866phDXzwoCOjSDzlMBPyt0Mfrr2EFM+v6h/ZwtkaEuIthaVvCdyfJp8B+FmyLVS265RPjS1dPLNiKyLrmn2+vD5IcH3x0Z9d2e11jZNzNXNKCSDu6uGDh2KAwcOYNasWeaoj0bOzs7w83vW9C6Xy3Hw4EHEx8dDJLKNC0EISvKAX7YCv2wByvK5MllrLrjp8DLgwH1czTGrhe9NpPo02gUD22Pql9rHZCh4rBpz6EoOTs/tjQt389S60Q6nPeBd/6o8JA4oKqs5mNNULmTn40J2vqoFxEEswoCO/ngltDG+uWh9aTQMaQkxd4uKNnU1Xb16S2elgplkvE49Vyfkl2heCkUTQ1pT+PzY8PNyqfEZy3tShqVHrpskoaclMzjICQoKwgcffIDk5GSEhYXB3V19+t306dNNVjltDh06hNzcXL2rnpeVlaGs7FmSJ+VaW4RYlOK/uFabc9uA8kKurEE7IGYu0H4oIHZQ25zvl5ohzc18bw7Vp9H6e7lgktaWmGYap79Wl5Nfigt382r8AjdmTIC7xAFbRoZhTGKqwfsaqnoLSK/WvlYZ5AD8W0LqokVFE6Gmq5tqvE50kAyH03L0bwjDW1P4DgB3dhTXuCb9O/rb/FIPBgc5CQkJqFevHi5cuIALFy6oPScSieokyElISED//v3RpEkTndt99NFHWLJkidnrQ4hRnjwGUjYBqTuA8iKuzLcDEPMO0O5FQKw5w4M5ZrUYO432YX4ptp/Jwn9GhsDbXWJ0S4ymIMuYWS+TerREVJDM5LNlNKnacqFQMMTvNT6pm6XQF+wKlQDQHIE9X9rG6/BtMXSXOOCIjgDHrVqeHGNaU/hkqNbElMtUWKparUJeW/Pnz8eKFSt0bnP9+nW0bdtW9fjevXto3rw5vvrqKwwbNkznvppacpo2bUqrkBNhFf0JnN0AnE8A5NyaOPB7DoiZB7QZqDW4qc6U02krFQzRK04YFRhoW3nckFWWZ/UNwoy+rWuUK8d/APp/Rddzc8KF9/vBQSwyaD9T4Jt0zdIHVO95o5vOmx7fa6rvOJroW0BS2zVVbmHo7CpDF6zUlE2458oTepeE8HJ1RH5Jhdbn/aQSrHmtMx4XlamOW73rlu8PFltehNPYVchrFeQodzV2XMyjR4+Qm5urc5sWLVqoLSGxdOlSbNy4Effv34eTk5NB5zP2TSLEJAofAskfA/9LBCpKuLJGIVxw03oAYMTnyJRfarUNDKrf2CoVDN2X/1fvTQDgvuiT5/9DY935LuC5tdpNztIW/pzVtzX2ns+2mPpUpS1Qre77tAeYtueS1hXI+R6nOr4BuzGBvabPyE/XHprkB4K+tdcGBfvz6qZSfnbMkQfIVtRpkJOQkIB169bh1i1u5kNQUBBmzpyJiRMnGnoogzDG0LJlS7z88stYvXq1wftTkEMEkX+fC24u7AQqn97wG3fhgpugfkYFN+aiLfU8n2m0H4/ojCGdG9c4Ht9ZR7p+/ZdXKLA75Q6Sbj3Chew8FJY+a97XdRNQ3uCSbz/CppOZvOphLh+P6IxBwY2QmvUXjv2ag51n7+oMJkUibvq+qTiKRahQMK1dnPpaQvgspiricRy+x9VWL0MCe0P+no1tEdJ0Dh93Zywd0hFyhQIz9uqfCfnxiM6QOIoNeh/MxVJbg4y9fxs8JmfhwoVYu3Yt4uPjERkZCQBISUnBrFmzkJ2djQ8++MDQQ/J24sQJZGVlmT2YIsQk/v4d+HkdcGk3UPl06mbTCC64adnHooIbpX7t/eApcVJL2gcGXtNoNY3rGdDRH3HdA5DAI1eItnEcmm4i7hIH9AySYXREALo9TZampOlLumtgfey/eN8s43T4dkH5erqoxkBwyRB9aqwCrTweAIyLCuCVY2Vo50Y4cFn/+KddE7qisFRu8LgNgF9WXbEI2BRreJeRobO1+I4j0RY8aQvYjZ0dVn3qucxDAjDg8ZMyPC7U34oJADJ3CeZ8c6XOZ61VZ4stSQYHOVu2bMGOHTsQGxurKnvxxRcRHByM+Ph4swY5CQkJiIqKUhujQ4jFybsL/LwWuPQFoHj6hdosCug1DwiMscjgBtD8Bbf/4j0sGNiuVoM++7b34xXkaAqStN2onpRV4of0P5Dy219Y/vJzamv4aPuSNkdWW/A4lrb355/B/tgq1j5Y1MvVmVeQE+Djrnegtb+XC7q14IJBXVmvteGTVVfBAG93Z53bGHpcY2drGbvUgbHnUwZeR9NzMOfrK2qvSSyC3u49iCDIrLWqzJVRWmgGBzlyuVxjAr6wsDBUVGgfXGUKX375pVmPT0it/PUbkLQGuLIXUDz9LAT0AHrNBwKiha2bHrq+4KZ+eQmTegZqzUwM6J7NZcjMmKqtMDIPCRYf+lXnjervYrnqCxiA3i9pTTNQzEnb+6N8nWUVCqx+tZPql3/VoKNSweAnlegd07Tvf79jwcB2mPql9tldL3byN7glpCpzzaoy13Fru9SBoeerVDBsOnFLY/JKXQEOwP1tPC7i1+JjrvWkhMp/VBcMDnLGjBmDLVu2YO3atWrl27dvr7NlHgixKLmZwJnVQNo+gD0dK9KiNzcVvHmUsHXjgc8X3KErOfjPyFAsPWJ4VwffKe+aBoPywQAsPvQrAJHeL+mf5/VRtWQk336MTSdvG3QuQ2l6f3S1NlUNPhzEIsR2baY363NOfim83SWY1DMQ285kadxG07IchjBXnhpzHbe2wYAh5zuanoPFh67hYYHuc1Zv0an6t5GSqXsCjjH1MoRQ+Y/qgsFBDsB1Gx07dgzdunUDAJw7dw7Z2dkYO3YsZs+erdqueiBEiE15lAEkrQaufg0wBVfWqi835qZpV2HrZgC+X3De7s74eV4fowYl6svjAWhuheFLX2tH9S/pyJY+T8fp3DPLOJ1pvVuheyuZ1mnQfLsEAmTu4OPYrzk4+usfOrepzS9xc+WpMddxjQ0GDD0fn8HYSgoGLBjYDjJPSY3PjpB5gADh8h/VBYODnPT0dISGck3DmZncbAWZTAaZTIb09HTVdrTcArFZf14HzqwC0v8PqnaJ1gOAnu8ATcIErZoxDPmCq03yMG2roANA9IoTdZI/puprNdfq0wAQ1NCjxvtkTJcA35v1/ov3UVCqfbhAbX+JmyMBpb7jKhlzXGMSSRr6OowZ9yPzlNSYgQiY7/3lS6iM0nXB4CDn5MmT5qgHIZbvYToX3Fw7CNXXUJuB3PILjUIErVpt1OUXnKYgKSUzt87GyFR/DdpamEx9HsC4LoGugfV5JRrUFeBUVZtf4sZm1eV73Pn/d7XGzCcvN8NyoSnpCxoYak4lN/R1GDPuR9dnyFzvLx9CtySZk1HdVYTYlZw04PQK4MbhZ2XtXuQWzvQPFq5eJiL0F1xtm8BFABpKJQBE+KPA8NdQtYWptuN0dJ3HmC4BB7EIL3VuzGt2Gh+1DVS1tcaZooVB09Tu/CoDywd09Dcoh4u+oKG2r8OQv1u+nyFzvr+6CN2SZE4U5BCizf2LXMvNze+fFoiADi9xwU3D9iY7jdDJt4T8gqtUMN65RDRR1mjxix0AQJWxWdM2ul6DsoXJFGMOtJ3H2BYzvlPw67s7Ie+J5pWuTRmomnq9I2W3jybV1wervmq2vhwu+oKG2rwOQwNGQxbcFGJwr5AtSeZEQQ4h1d37H9dyc+sY91gkBjoOA3rMAXxNm6PJUpJvCfEFx3fZBeUNeniXpth59o7aIqFV63c0PQdeGrLZ1nNzwkdVcunoUpuWjvruTvjwJe3nMbbFjO9+ymnk1vZLnG833lsapsjzyeFirqCB77gfP6kEi1/sYBVBglAtSeZEQQ4hStnnuOAm87/cY5EYCB4O9HgbkAWZ/HTmTL5lTOtQXX7B8Z2VojzziPBmCJC54T8jQwERVIsZKuun63h5PJakUDJmwKrSgkG6b2TGtpjx3W9AR39sEYus7pd4bVrPhMzhwmfQ9Ky+rTGtTyurChJsbWVyQVchr2u0dhXR6E4yF9xkneYeixyATrFAj9mAT0uznFK56re2X7DGLnQIWE7rkDb6XntV3m5OYFAfr1H9tXALgZ7QmqfE0PfS2IVK+a68bez14bufubo/zXVcQ1ar18WYlc9NwdI/b7ZCkFXIrQ0FOUSFMeBOEnBqBXD3Z65M7Ah0HglEzwbqB5r19Hy/2A394jZ0sUNjGXrDq7r948IyLD1yXe85XgltjG8u3q9RXv21fHw8Q2/CPMCw99KQFcyNCUiNDRiEGr9lzhu5Muitbb4iTQvE1hWhx9XZgzpboJMQq8YY8NtJ4PRKIDuFKxM7AaFjgOhZQL1mdVINcyTfqqvU7Ibe8AwJGKo6fv1PjeXqg1HBK8ABDHsvq3fd3XlcjPXHM1TnVzJ2vIuxXQJCdCWYe00jPtO9+RAyh4utdfHYErHQFSCkTjAG3DoOJDwP7H6JC3AcnIHwN4AZl4FB6+oswAHMk5vGkDwsxlLe8KqfR3nDO5qew2t7PqoOMK5O+VreP5iudZvqDL0JKm9cQzo3xrQ+rTCzb2t4uarnbfHzcrHahQv50Bc4A1ywWaltgSaelAPf/bzUr5Gflws2jwyFv5cLtIWQInBBtjXmcCHmRy05xLYxBmT8yI25efB0erGjCxA2Hug+HZA2EqRa5shNY+7U7Ia2FBm7ErQI0DhLSpO/npTzOmZtboKaWqLquTphfPcATOsTZNPdEnW5ppGuge9iMWwyhwsxP2rJIbaJMeDGEWB7DLBnOBfgOLoCkdOAGWnAC8sFC3CAZ030AGr8QjX2i9vcmYsNbSkyJiOs8tWOjzLtmChjb4LaWqLyS+RYf/wWfrr20FRVtEh1vaZR1dazyJY+qmumq6XHllvSSO1RSw6xLQoFcOM74PQq4I+rXJmTO9B1IhAZD3g0ELZ+VZg6N425MxfrW2VZSXnDM+bGVzUb7d7z2TpfizeP5Q4AbhqvMTfBuhrjZMksaU0jW8zhQsyPghxiGxSV3JpSZ1YBfz7NnursAXSdxLXeuFvmoEBTfnGbM3Px0fQcLD38K69tlTc8vjc+bSsz63sty4Z0xNIj13XOyvGTSjCtTyte9aiuLrtqLBWfvEHebk51Nh6GBvgSQ1F3FbFuikrg6jfA5kjgm/FcgCORciuCz7wK9F1ksQGOkrYmemOYo1lf2WWjr9Wk+gBQ5Q1S34DRcd0DMSiY6zo8nPYAKZm5qFQwva/ln8GNdHb5icAt92Ds+1nXXTWWSBk46xpXlVcst/luO2K9qCWHWKfKCiD9G67lJvfpgoouXkC3qUDEZMC1nrD1E5ApW4f4Dh7W1FLEt2Xpp2sPdU5J1/VaTN3lVz2fDx9VW6xsMV9Kv/Z+NVbsrsoeuu2I9aJkgMS6VMqBtK+ApNXAX79xZa7eQORUrmvKxUvY+tkYvkkLfdyd8e+XOvLOk6MMYgCYJHmhKYILTfUUiwBts6OrJwG01cy35kpcSYghKBkgsW0V5cCVPUDSGuDvu1yZmw8QFQ+ETwQknsLWz0bx7Yp5f2A7g1eCBoDoFSdMMrC3tmM1tCW80xXgAM9arsydME9I1G1HrBkFOcSyVZQBl78AktYB+dlcmXsDIGo60GUCIPEQtn42ju/gYT8vV53PawpCUjJzLWJgL58uueotOlW7w2x9FpYlzbAixFAU5BDLJC8FLu0Gfl4HFDxdv8ijIdB9BpfIz9lN2PpZidp245hzWrqltBDwyeejYNpngdn6LCxzpyYgxJwoyCGWRV4CXNgJ/LweKHo6Y8PTn1tXKnQs4KS7xYA8Y4oxIuaclm4pLQR8gyiZp0TjApCWEqyZizn/BggxN5pCTixD+RPg7CZgfTBwdD4XrLPXmgAAHQxJREFU4EgbA/9cDUy/zM2YogCHN0PXmNLFXNlm+U4xN3cLgaHBVqWCISUzFwcv30dKZi5kHhKTnscSUcZhYq2oJYcIq6wIOP8JcHYjUPyYK/NqBvSYDXQeCTjyu4GQZ8wxRsQc2WYtpYXAkO4YTa1jflIJ6rk5Ib9YbtPdOZRxmFgjCnKIMEoLgPM7uNabkqerYtdrDvScAwSPABydha2fFTPXGBFzZJs1dZ4bYxiSz0fTDKo/CspUZbbenUMZh4m1oSCH1K3SfODcdiBlE1D6N1dWvwXQcy7w3KuAg5Ow9bMB1jZGxBJaCPQFW/3a++md7u7l5gQXRwe1Nb7qMlgjhNREQQ6pGyV5wC9bgV+2AGX5XJlPEBfcdBwGONCfoqlYyoBeQ1hCC4GuYIvPdPe/i+X4Ii4UYrGIunMIsRB0ZyHmVfwX8Mtm4Nw2oKyAK2vQlgtuOrwEiB2ErZ8Noim/xtMWbPFt9Xr8pEzjDCxCiDAoyCHm8eQx1yWVugMoL+LKfDsAMXOBdkMAMU3sMxdLGdBrS6yxdYwQQkEOMbWiP7mZUucTAPkTrszvOSBmHtBmIAU3dcQSBvTaEmodI8Q6UZBDTKPwIZC8Afjfp0BFCVfm3/lpcPMCIKJWg7pmCQN6bQW1jhFinWgVclI7BQ+A5I+5LMUVT1sMGocBMfOBoH4U3BCbYqsrjRNi6WgVcoHUdm0gq5V/j1tX6uJnQGU5V9akK9BrHtDyHxTcEJtErWOEWBcKcmrBLn/V5d3lgptLnwMKOVfWLIoLbgJjKLghNs8SprsTQvihIMdIyrWBqvf1KdcGsrn1XP7KApLWAFf2AIoKriygBzfmJrCHsHUjhBBCNKAgxwjmWBvIYuVmPg1u9gKskitr0YsLbppHCVkzQgghRCcKcoxgrrWBLMqjDCBpNXD1a4ApuLJWfYGe7wDNIoStGyGEEMKD1SQtycjIwJAhQyCTySCVShEdHY2TJ08KUhdrWxvIIH/eAL6JA/7TFUjbxwU4Qf2BiSeA0fspwCGEEGI1rKYlZ9CgQQgKCsKJEyfg6uqK9evXY9CgQcjMzISfn1+d1sUms5/+8StweiVw7SBUWUDaDOQyFDcKEbRqhBBCiDGsIsh5/Pgxbt26hYSEBAQHBwMAli9fjs2bNyM9Pb3Ogxybyn6akwacWQlc/+5ZWbvBXLeUf7Bw9SKEEEJqySq6q3x8fNCmTRt89tlnePLkCSoqKrBt2zb4+voiLCyszuujzH4KPMt2qmQ12U8fXAL2xALbejwNcETcgplvngWGf04BDiGEEKtnFS05IpEIx48fx9ChQ+Hp6QmxWAxfX18cPXoU3t7eWvcrKytDWVmZ6nFBQYHJ6mS1awPduwCcXgHc+pF7LBIDHYcBPeYAvm2FrRshhBBiQoIGOfPnz8eKFSt0bnP9+nW0adMGU6dOha+vL5KSkuDq6opPPvkEgwcPxvnz5+Hvrzmg+Oijj7BkyRJzVB2AlWU//T0VOLUcyPwv91gkBp57Deg5B5AFCVs3QgghxAwEXbvq0aNHyM3N1blNixYtkJSUhOeffx55eXlqa1YEBQUhLi4O8+fP17ivppacpk2b2tfaVXfPci03v53iHoscgE6xQI/ZgE9LQatGCCGE8GGVa1c1aNAADRo00LtdcXExAEAsVh9CJBaLoVAotO4nkUggkUhqV0lrxBhw52cuuLmTxJWJHYHOI4Ho2UD9QGHrRwghhNQBqxiTExkZCW9vb7z++utYuHAhXF1dsWPHDmRlZWHgwIFCV89yMMa12JxeCWSf5crETkDIaK7lpl4zQatHCCGE1CWrCHJkMhmOHj2K9957D3369IFcLkeHDh1w8OBBdOrUSejqCY8xbqzN6ZXA7+e4MgdnIPR1IHom4NVE2PoRQgghAhB0TE5dM7ZPz2IxBtw6xnVL3b/AlTm6AGHjgO4zAGkjQatHCCGEmIJVjskhRmIMuPkDF9zkXObKHF2B8DggKh7wrNvkiIQQQogloiDHmigUwI3DXLfUH1e5Mic3IHwiEDUd8NA/iJsQQgixFxTkWAOFArh+EDi9CvjzV67M2QPoOgmInAq4y4StHyGEEGKBKMixZIpK4NdvgTOrgEc3uDKJFIiYDHR7C3CzgrWxCCGEEIFQkGOJKiuA9P1ccJN7iytz8eICm4jJgKv2pSwIIYQQwqEgx5JUyoG0r4Ck1cBfv3FlLvWAyGlAxCQu0CGEEEL0qFQw61hyyMwoyLEEFeVA2l4gaQ2Qd4crc63PzZTq+gYg8RS0eoQQQqzH0fScGotH+1v64tFmQkGOkCrKgMtfAEnrgPxsrsxNBnSfDnSJAyQewtaPEEKIVTmanoM3P7+I6gnwHuaX4s3PL2LL6FC7CnQoyBGCvBS4tBv4eT1QcI8r82jIJfALGw84uwlbP0IIIVanUsGw5LtrNQIcAGAARACWfHcN/dr72U3XFQU5dUleAlzYBSSvBwpzuDJPf6D7TCDsdcDJVdj6EUIIsVqpWX+pdVFVxwDk5JciNesvRLb0qbuKCYiCnLpQXgxcSASSPwaK/uDKpI2B6FlAyBjAyUXY+hFCCLF6fxZqD3CM2c4WUJBjTmVFwP8SgLMbgSePuDKvptyK4J1HAY4SYetHCCHEZvh68vvBzHc7W0BBjjmUFQKpO4CUTUBxLldWrznQcw4QPAJwdBa2foQQQmxO18D68PdywcP8Uo3jckQA/Ly46eT2goIcUyrNB1K3Ayn/AUryuLL6LYAec4Dg1wAHJ2HrRwghxGY5iEVYNLg93vz8IkSAWqCjHGa8aHB7uxl0DFCQYxolecC5bcAvm7lABwB8goCec4GOwwAHepsJIYSY34CO/tgyOrRGnhw/ypNDjKJQANt7A3lZ3GNZGyDmHaDDS4DYQdi6EUIIsTsDOvqjX3s/yngMCnJqTywGQscAV7/hgpt2Q7gyQojFo9T3xFY5iEV2M01cFwpyTCEyHug+i4IbQqwIpb4nxPbRXdkUHJ0pwCHEiihT31dPnKZMfX80PUegmhFCTInuzIQQu6Iv9T3Apb6vVGjaghBiTSjIIYTYFUNS3xNCrBsFOYQQu0Kp7wmxHxTkEELsCqW+J8R+UJBDCLErytT32iaKi8DNsrKn1PeE2CoKcgghdkWZ+h5AjUDHXlPfE2KrKMghhNgdZep7Py/1Lik/LxdsGR1KeXIIsRGUDJAQYpco9T0hto+CHEKI3aLU94TYNuquIoQQQohNoiCHEEIIITaJghxCCCGE2CQKcgghhBBikyjIIYQQQohNoiCHEEIIITaJghxCCCGE2CQKcgghhBBikyjIIYQQQohNsquMx4wxAEBBQYHANSGEEEIIX8r7tvI+zpddBTmFhYUAgKZNmwpcE0IIIYQYqrCwEF5eXry3FzFDwyIrplAo8ODBA3h6ekIksu9F+AoKCtC0aVP8/vvvkEqlQlfH7tH1sCx0PSwHXQvLItT1YIyhsLAQjRo1gljMf6SNXbXkiMViNGnSROhqWBSpVEpfHBaErodloethOehaWBYhrochLThKNPCYEEIIITaJghxCCCGE2CSHxYsXLxa6EkQYDg4O6NWrFxwd7arX0mLR9bAsdD0sB10Ly2JN18OuBh4TQgghxH5QdxUhhBBCbBIFOYQQQgixSRTkEEIIIcQmUZBDCCGEEJtEQQ4BAGRkZGDIkCGQyWSQSqWIjo7GyZMnha6W3Tl16hREIpHGf+fPnxe6enbryJEjiIiIgKurK7y9vTF06FChq2S3AgICanw2li9fLnS17FpZWRk6d+4MkUiEy5cvC10dNRTkEADAoEGDUFFRgRMnTuDChQvo1KkTBg0ahIcPHwpdNbsSFRWFnJwctX8TJ05EYGAgunTpInT17NL+/fsxZswYjB8/HleuXEFycjJGjhwpdLXs2gcffKD2GYmPjxe6SnbtnXfeQaNGjYSuhkaWP8mdmN3jx49x69YtJCQkIDg4GACwfPlybN68Genp6fDz8xO4hvbD2dlZ7f2Wy+U4ePAg4uPj7X69NSFUVFRgxowZWLVqFeLi4lTl7du3F7BWxNPTk76XLMQPP/yAY8eOYf/+/fjhhx+Erk4N1JJD4OPjgzZt2uCzzz7DkydPUFFRgW3btsHX1xdhYWFCV8+uHTp0CLm5uRg/frzQVbFLFy9exP379yEWixESEgJ/f3+88MILSE9PF7pqdm358uXw8fFBSEgIVq1ahYqKCqGrZJf++OMPvPHGG9i9ezfc3NyEro5G1JJDIBKJcPz4cQwdOhSenp4Qi8Xw9fXF0aNH4e3tLXT17FpCQgL69+9PC8sK5LfffgMALF68GGvXrkVAQADWrFmDXr16ISMjA/Xr1xe4hvZn+vTpCA0NRf369XH27Fm8++67yMnJwdq1a4Wuml1hjGHcuHGYMmUKunTpgjt37ghdJY2oJceGzZ8/X+sgVuW/GzdugDGGqVOnwtfXF0lJSUhNTcXQoUMxePBg5OTkCP0ybALfa1HVvXv38OOPP6p1kxDT4Hs9FAoFAOC9997DsGHDEBYWhsTERIhEInz99dcCvwrbYcjnY/bs2ejVqxeCg4MxZcoUrFmzBhs3bkRZWZnAr8I28L0WGzduRGFhId59912hq6wTLetgwx49eoTc3Fyd27Ro0QJJSUl4/vnnkZeXB6lUqnouKCgIcXFxmD9/vrmravP4XgtnZ2fV46VLl2Ljxo24f/8+nJyczF1Fu8L3eiQnJ6NPnz5ISkpCdHS06rmIiAj07dsX//73v81dVbtgzOdD6ddff0XHjh1x48YNtGnTxlxVtBt8r8Vrr72G7777Tm2sYGVlJRwcHDBq1Cjs2rXL3FXlhbqrbFiDBg3QoEEDvdsVFxcDAMRi9YY9sVis+iVLaofvtVBijCExMRFjx46lAMcM+F6PsLAwSCQS3Lx5UxXkyOVy3LlzB82bNzd3Ne2GoZ+Pqi5fvqzqYie1x/dabNiwAcuWLVM9fvDgAfr37499+/YhIiLCnFU0CAU5BJGRkfD29sbrr7+OhQsXwtXVFTt27EBWVhYGDhwodPXs0okTJ5CVlYWJEycKXRW7JpVKMWXKFCxatAhNmzZF8+bNsWrVKgDAq6++KnDt7E9KSgrOnTuH3r17w9PTEykpKZg1axZGjx5N4wfrWLNmzdQee3h4AABatmxpUWMIKcghkMlkOHr0KN577z306dMHcrkcHTp0wMGDB9GpUyehq2eXEhISEBUVhbZt2wpdFbu3atUqODo6YsyYMSgpKUFERAROnDhBN1UBSCQS7N27F4sXL0ZZWRkCAwMxa9YszJ49W+iqEQtFY3IIIYQQYpNodhUhhBBCbBIFOYQQQgixSRTkEEIIIcQmUZBDCCGEEJtEQQ4hhBBCbBIFOYQQQgixSRTkEEIIIcQmUZBDiEC2b9+Opk2bQiwWY/369UJXh9gJ5SKL9erVM/mxd+7cqTr+zJkzTX58QgxFQQ4hAigoKMC0adMwb9483L9/H5MmTRK6Sia3c+dOrTdSkUiEAwcOqJUdPnwYMTEx8PT0hJubG8LDw7Fz5061be7cuQORSAQHBwfcv39f7bmcnBw4OjpCJBLhzp07as/t2rUL4eHhcHNzg6enJ2JiYnD48OFav0ZrlZiYiIyMDF7blpeXQyaTYfny5RqfX7p0KRo2bAi5XI7hw4cjJycHkZGRpqwuIUajIIcQEyovL+e1XXZ2NuRyOQYOHAh/f3+4ubkZdT65XG7UfpZm48aNGDJkCLp3745z584hLS0NI0aMwJQpUzBnzpwa2zdu3BifffaZWtmuXbvQuHHjGtvOmTMHkydPxvDhw5GWlobU1FRER0djyJAh2LRpk9lek7EYY6ioqDDrOerVq8d7QUtnZ2eMHj0aiYmJNZ5jjGHnzp2qhWRdXV3h5+encbVwQgTBCCFGi4mJYVOnTmUzZsxgPj4+rFevXowxxvLy8lhcXByTyWTM09OT9e7dm12+fJkxxlhiYiIDoPYvKyuLMcbYgQMHWEhICJNIJCwwMJAtXryYyeVy1fkAsM2bN7PBgwczNzc3tmjRIsYYY1evXmUDBgxg7u7uzNfXl40ePZo9evRIrZ7x8fFs7ty5zNvbmzVs2FC1r1JeXh6bNGkS8/X1ZRKJhHXo0IF99913queTkpJYdHQ0c3FxYU2aNGHx8fGsqKhI63uTmJjIvLy8ND4HgH377beMMcays7OZk5MTmz17do3tNmzYwACwX375hTHGWFZWFgPA3n//fRYUFKS2bevWrdmCBQvU3s+UlBQGgG3YsKHGsWfPns2cnJxYdna21teg6zoyxtiiRYtYp06d2GeffcaaN2/OpFIpGz58OCsoKFBtU1lZyT788EMWEBDAXFxcWHBwMPv6669Vz588eZIBYN9//z0LDQ1lTk5O7OTJk4wxxpYuXcoaNGjAPDw8WFxcHJs3bx7r1KkTY4yx06dPM0dHR5aTk6NW5xkzZrDo6Gitr6nqe1+Vrr+9tLQ0BoAlJSWp7aOs+/Xr19XKY2Ji2IwZM7TWgZC6QkEOIbUQExPDPDw82Ny5c9mNGzfYjRs3GGOM9e3blw0ePJidP3+eZWRksLfffpv5+Piw3NxcVlxczI4fP84AsNTUVJaTk8MqKirYmTNnmFQqZTt37mSZmZns2LFjLCAggC1evFh1PgDM19eXffrppywzM5PdvXuX5eXlsQYNGrB3332XXb9+nV28eJH169eP9e7dW62eUqmULV68mGVkZLBdu3YxkUjEjh07xhjjbsTdunVjHTp0YMeOHWOZmZnsu+++Y99//z1jjLHbt28zd3d3tm7dOpaRkcGSk5NZSEgIGzdunNb3hm+Qs3btWgaAPXjwoMZ2ZWVlzMPDQ3XDVAY5qampTCaTqW66SUlJrEGDBiw1NVUtyJk+fTrz8PBgZWVlNY59//59BoCtW7dO62vQdR0Z44IcDw8P9vLLL7OrV6+yM2fOMD8/P/avf/1LdYxly5axtm3bsqNHj7LMzEyWmJjIJBIJO3XqFGPsWaAQHBzMjh07xm7fvs1yc3PZ559/zlxcXNinn37Kbt68yZYsWcKkUqkqyGGMC+xWrlypelxeXs5kMhn79NNPtb4mTUEOn7+98PBwNn78eLX9xo4dy6Kiomqcg4IcYikoyCGkFmJiYlhISIhaWVJSEpNKpay0tFStvGXLlmzbtm2MMcYuXbqkdjNmjLF//OMf7MMPP1TbZ/fu3czf31/1GACbOXOm2jZLly5lzz//vFrZ77//zgCwmzdvqupZ/dd9eHg4mzdvHmOMsR9//JGJxWLV9tXFxcWxSZMm1XidYrGYlZSUaNxH2WLl7u5e41/VG+2UKVO0BkOMMRYcHMxeeOEFxtizIOfSpUts5syZqpvu+PHj2axZs2q8rwMGDFALCqqTSqXszTff1Pgcn+u4aNEi5ubmptZyM3fuXBYREcEYY6y0tJS5ubmxs2fPqh0jLi6OxcbGMsaeBTkHDhxQ2yYiIoJNnTpVrax79+5qr2fFihWsXbt2qsf79+9nHh4eOlvYNAU5fP72tm7dyjw8PFhhYSFjjLGCggLm5ubGPvnkkxrnoCCHWApH83eIEWLbwsLC1B5fuXIFRUVF8PHxUSsvKSlBZmam1uNcuXIFycnJ+Pe//60qq6ysRGlpKYqLi1Xjdrp06VJjv5MnT8LDw6PGMTMzM9G6dWsAQHBwsNpz/v7++PPPPwEAly9fRpMmTVTbaqpbWloavvjiC1UZYwwKhQJZWVlo166dxv08PT1x8eLFGuVBQUEatzfEhAkTEBUVhQ8//BBff/01UlJSNI5lYYwZdXy+1zEgIACenp6qx1Xf19u3b6O4uBj9+vVTO0Z5eTlCQkLUyqpf15s3b+Ktt95SK+vatStOnDihejxu3Di8//77+OWXX9CtWzfs3LkTr732Gtzd3Q1+rfr+9mJjYzFr1ix89dVXmDBhAvbt2wexWIzhw4cbdC5C6hIFOYTUUvUbSlFREfz9/XHq1Kka2+qatltUVIQlS5bg5ZdfrvGci4uLzvMNHjwYK1asqLGfv7+/6v9OTk5qz4lEIigUCgCAq6ur1nopzzF58mRMnz69xnPNmjXTup9YLEarVq10Hrt169bIz8/HgwcP0KhRI7XnysvLkZmZid69e9fY77nnnkPbtm0RGxuLdu3aoWPHjrh8+XKNY//8888oLy+vMRj2wYMHKCgo0BrY8b2Out7XoqIiAMCRI0dqDIqWSCRqjw0NTADA19cXgwcPRmJiIgIDA/HDDz9orK8+fP72pFIpXnnlFSQmJmLChAlITEzEa6+9pjG4JsRSUJBDiImFhobi4cOHcHR0REBAgEH73bx5U29QoGm//fv3IyAgAI6Oxn2kg4ODce/ePWRkZGi86YeGhuLatWsG142PYcOGYd68eVizZg3WrFmj9tzWrVvx5MkTxMbGatx3woQJeOutt7BlyxaNz48YMQIbNmzAtm3bEB8fr/bc6tWr4eTkhGHDhmnc19jrWFX79u0hkUiQnZ2NmJgYg/Zt06YNzp8/j7Fjx6rKzp8/X2O7iRMnIjY2Fk2aNEHLli3RvXt3g+vJ928vLi4OvXr1wuHDh3H27FmsWrXK4HMRUpcoyCHExPr27YvIyEgMHToUK1euROvWrfHgwQMcOXIEL730Uo1uCaWFCxdi0KBBaNasGV555RWIxWJcuXIF6enpWLZsmdbzTZ06FTt27EBsbCzeeecd1K9fH7dv38bevXvxySefwMHBQW+dY2Ji0LNnTwwbNgxr165Fq1atcOPGDYhEIgwYMADz5s1Dt27dMG3aNEycOBHu7u64du0afvrpp1pPw27WrBlWrlyJt99+Gy4uLhgzZgycnJxw8OBB/Otf/8Lbb7+NiIgIjfu+8cYbePXVV7W2kEVGRmLGjBmYO3cuysvLMXToUMjlcnz++ef4+OOPsX79ejRt2lTjvsZex6o8PT0xZ84czJo1CwqFAtHR0cjPz0dycjKkUilef/11rfvGx8fjjTfeQJcuXRAVFYV9+/YhLS0NLVq0UNuuf//+kEqlWLZsGT744AO9ddKE799ez5490apVK4wdOxZt27ZFVFSUUecjpK5QnhxCTEwkEuH7779Hz549MX78eLRu3RojRozA3bt30bBhQ6379e/fH4cPH8axY8cQHh6Obt26Yd26dWjevLnO8zVq1AjJycmorKzE888/j+eeew4zZ85EvXr1IBbz/4jv378f4eHhiI2NRfv27fHOO++gsrISANfSc/r0aWRkZKBHjx4ICQnBwoULa3QvGWvmzJn49ttvkZSUhC5duqBjx4748ssvsWXLFqxevVrrfo6OjpDJZDpbsNavX4/Nmzdjz5496NixI7p06YIzZ87gwIEDNVp3qjL2Ola3dOlSLFiwAB999BHatWuHAQMG4MiRIwgMDNS536hRo/Duu+9izpw5CA0NRVZWFsaNG6fWdQlwXYLjxo1DZWWlWquPIfj+7YlEIkyYMAF5eXmYMGGCUecipC6JmLGj8gghhNSpfv36wc/PD7t371Yrj4uLw6NHj3Do0CG9xxCJRPj2228xdOhQc1UTvXr1QufOnWm5EiI46q4ihBALVFxcjK1bt6J///5wcHDAnj17cPz4cfz000+qbfLz83H16lV8+eWXvAIcpdjYWPj4+ODevXsmrfMXX3yByZMno6SkBJ07dzbpsQkxBrXkEEKIBSopKcHgwYNx6dIllJaWok2bNnj//ffVZkD16tULqampmDx5MtatW8fruLdv3wYAODg46O0yM1RhYSH++OMPANwMNJlMZtLjE2IoCnIIIYQQYpNo4DEhhBBCbBIFOYQQQgixSRTkEEIIIcQmUZBDCCGEEJtEQQ4hhBBCbBIFOYQQQgixSRTkEEIIIcQmUZBDCCGEEJtEQQ4hhBBCbNL/A5QNvtv2aSaNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "y_pred = Y_pred.detach().numpy().flatten()\n",
    "y_test = Y_test.detach().numpy().flatten()\n",
    "\n",
    "plt.plot(y_test, y_pred, 'o')\n",
    "plt.plot([np.min(y_test),np.max(y_test)], [np.min(y_test),np.max(y_test)], '-')\n",
    "plt.xlabel(\"reference HOMO energy [eV]\")\n",
    "plt.ylabel(\"predicted HOMO energy [eV]\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nhvA-0gp_G83"
   },
   "source": [
    "### Predicting multiple HOMO energies\n",
    "\n",
    "Now that we have successfully trained a CNN to predict single energy levels, let's repeat the process for several energy levels. Here, X vector is the same, but we will define a new Ym (multi) label that predicts 3 levels at the same time (HOMO, HOMO-1, and HOMO-2)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "K_uEzDv8_PX7"
   },
   "outputs": [],
   "source": [
    "# Generate test/training/validation sets for y_multi\n",
    "Ym_train = y_multi[0:n_train]\n",
    "Ym_test = y_multi[n_train:n_train + n_test]\n",
    "Ym_val = y_multi[n_train + n_test: n_train + n_test+ n_val]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lefGQh3oF8qM"
   },
   "source": [
    "Next, we define a new network called *CNN_multi*. We keep almost all the model exactly the same, but we insert Ym training and test data where Y used to be. The comments below indicate the changes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ORCTIpx8_Tzw"
   },
   "outputs": [],
   "source": [
    "class CNN_multi(pl.LightningModule):\n",
    "\n",
    "    # note the change in name\n",
    "    def __init__(self, output_length):\n",
    "        super(CNN_multi, self).__init__()\n",
    "\n",
    "        self.conv1_1 = nn.Conv2d(1, nConv, filter, padding=pad)\n",
    "        self.conv1_2 = nn.Conv2d(nConv, nConv, filter, padding=pad)\n",
    "        self.conv1_3 = nn.Conv2d(nConv, maxp, filter, padding=pad)\n",
    "        self.pool = nn.MaxPool2d(maxp,maxp)\n",
    "        self.fc1 = nn.Linear(maxp*7*7, output_length)\n",
    "\n",
    "    def forward(self, x, labels=None):\n",
    "\n",
    "        x = F.relu(self.conv1_1(x))\n",
    "        x = F.relu(self.conv1_2(x))\n",
    "        x = F.relu(self.conv1_3(x))\n",
    "        print(x.shape)\n",
    "        x = self.pool(x )\n",
    "        x = x.view(-1, 4*7*7)\n",
    "        outputs = self.fc1(x)\n",
    "\n",
    "        loss = 0\n",
    "        if labels is not None:\n",
    "            loss = nn.functional.mse_loss(outputs.squeeze(), labels.squeeze())\n",
    "        return loss, outputs\n",
    "\n",
    "    # Here we insert Ym training data\n",
    "    def train_dataloader(self):\n",
    "        dataset = SimpleDataset(X_train, Ym_train)\n",
    "        return DataLoader(dataset, batch_size=batch)\n",
    "\n",
    "    # Here we insert Ym validation data\n",
    "    def val_dataloader(self):\n",
    "        dataset = SimpleDataset(X_val, Ym_val)\n",
    "        return DataLoader(dataset, batch_size=batch, num_workers=1)\n",
    "\n",
    "    def training_step(self, batch, batch_idx):\n",
    "        coloumb_matrices = batch[\"X\"]\n",
    "        energies = batch[\"y\"]\n",
    "        loss, outputs = self(coloumb_matrices, labels=energies)\n",
    "        self.log(\"train_loss\", loss, prog_bar=True, on_step=False, on_epoch=True)\n",
    "        # print(f\"Training loss : {loss}\")\n",
    "        return loss\n",
    "\n",
    "    def validation_step(self, batch, batch_idx):\n",
    "        coloumb_matrices = batch[\"X\"]\n",
    "        energies = batch[\"y\"]\n",
    "        loss, outputs = self(coloumb_matrices, labels=energies)\n",
    "        self.log(\"val_loss\", loss, prog_bar=True, on_step=False, on_epoch=True)\n",
    "        # print(f\"Validation loss : {loss}\")\n",
    "        return loss\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        optimizer = Adam(self.parameters(), lr=learning_rate)\n",
    "        return optimizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hlVisYSAGTWE"
   },
   "source": [
    "That was easy! Now it's time to use our new CNN_multi. We proceed in the same way as before, but now remember that the length of the output vector is 3, for 3 energy levels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
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      "fbaaf71f2b0940299a8ac7ef09c1339b",
      "7a64bcb673124948914e268bd6f1e97a",
      "f592da6864df4478bf323d8b94a63478",
      "c744451ee27642e8a5808092a0984a00",
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     "text": [
      "INFO: GPU available: False, used: False\n",
      "INFO:lightning.pytorch.utilities.rank_zero:GPU available: False, used: False\n",
      "INFO: TPU available: False, using: 0 TPU cores\n",
      "INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
      "INFO: IPU available: False, using: 0 IPUs\n",
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      "INFO: HPU available: False, using: 0 HPUs\n",
      "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
      "INFO: \n",
      "  | Name    | Type      | Params\n",
      "--------------------------------------\n",
      "0 | conv1_1 | Conv2d    | 200   \n",
      "1 | conv1_2 | Conv2d    | 3.6 K \n",
      "2 | conv1_3 | Conv2d    | 724   \n",
      "3 | pool    | MaxPool2d | 0     \n",
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      "--------------------------------------\n",
      "5.1 K     Trainable params\n",
      "0         Non-trainable params\n",
      "5.1 K     Total params\n",
      "0.021     Total estimated model params size (MB)\n",
      "INFO:lightning.pytorch.callbacks.model_summary:\n",
      "  | Name    | Type      | Params\n",
      "--------------------------------------\n",
      "0 | conv1_1 | Conv2d    | 200   \n",
      "1 | conv1_2 | Conv2d    | 3.6 K \n",
      "2 | conv1_3 | Conv2d    | 724   \n",
      "3 | pool    | MaxPool2d | 0     \n",
      "4 | fc1     | Linear    | 591   \n",
      "--------------------------------------\n",
      "5.1 K     Trainable params\n",
      "0         Non-trainable params\n",
      "5.1 K     Total params\n",
      "0.021     Total estimated model params size (MB)\n"
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       "Training: |          | 0/? [00:00<?, ?it/s]"
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      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
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      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([52, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([40, 4, 29, 29])\n"
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      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
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      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([52, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
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      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
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      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([40, 4, 29, 29])\n"
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       "Validation: |          | 0/? [00:00<?, ?it/s]"
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     "text": [
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([52, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([40, 4, 29, 29])\n"
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       "Validation: |          | 0/? [00:00<?, ?it/s]"
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    {
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     "output_type": "stream",
     "text": [
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([52, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([40, 4, 29, 29])\n"
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       "Validation: |          | 0/? [00:00<?, ?it/s]"
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([52, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([40, 4, 29, 29])\n"
     ]
    },
    {
     "data": {
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       "model_id": "78fb634a86ce42799d1947d1476d3f37",
       "version_major": 2,
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      },
      "text/plain": [
       "Validation: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([64, 4, 29, 29])\n",
      "torch.Size([52, 4, 29, 29])\n"
     ]
    }
   ],
   "source": [
    "output_length = 3\n",
    "cnn_multi = CNN_multi(output_length = output_length)\n",
    "\n",
    "# Next, we define the NN training function, which is then applied to the CNN.\n",
    "trainer = pl.Trainer(max_epochs=10, callbacks=[EarlyStopping(monitor=\"train_loss\", mode=\"min\", patience=3)])\n",
    "trainer.fit(cnn_multi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yxZaG0QRHOig"
   },
   "source": [
    " Let us check the final prediction accuracy, this time directly on the test data. We use the trained CNN model and input the Ym test data. The result below is the joint MSE over the 3 values we are predicting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "W-5OTkrRoqUg",
    "outputId": "7352f4c6-4030-4c74-c6a8-1f1e02a3747f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([500, 4, 29, 29])\n",
      "Test loss 0.8187092542648315\n"
     ]
    }
   ],
   "source": [
    "# Here we compute the MSE accuracy on the test set\n",
    "X_test = torch.Tensor(X_test).reshape(-1, 29, 29).unsqueeze(1).float()\n",
    "Ym_test = torch.Tensor(Ym_test).float()\n",
    "\n",
    "# Below we insert test set X and Ym to obtained the MSE (loss).\n",
    "loss, Ym_pred = cnn_multi(X_test, Ym_test)\n",
    "print(f\"Test loss {loss}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PjLxX-GsH0Q3"
   },
   "source": [
    "Compare the MSE when we predict 3 electronic levels to the previous one, where we predicted only 1 level. Are the results similar or very different?\n",
    "\n",
    "Next, let us review the quality of predictions in a scatter plot. Because we predicted 3 levels simultaneously, we need to build 3 different scatter plots. How does this look?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "XA81ixv8onDV",
    "outputId": "1c882184-328b-481f-add2-d96c714e93a6"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 600x1800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# Below we define the predicted values and the reference test values.\n",
    "ym_pred = Ym_pred.detach().numpy()\n",
    "ym_test = Ym_test.detach().numpy()\n",
    "\n",
    "plt.figure(1, figsize=(6,18))\n",
    "\n",
    "idx = 0 # This is for HOMO-2\n",
    "plt.subplot(311)\n",
    "plt.plot(ym_test[:, idx], ym_pred[:, idx], 'o')\n",
    "plt.plot([np.min(ym_test[:, idx]),np.max(ym_test[:, idx])], [np.min(ym_test[:, idx]),np.max(ym_test[:, idx])], '-')\n",
    "plt.xlabel(\"reference HOMO energy [eV]\")\n",
    "plt.ylabel(\"predicted HOMO energy [eV]\")\n",
    "plt.title(\"Energy Level : HOMO -2\")\n",
    "\n",
    "idx = 1 # This is for HOMO-1\n",
    "plt.subplot(312)\n",
    "plt.plot(ym_test[:, idx], ym_pred[:, idx], 'o')\n",
    "plt.plot([np.min(ym_test[:, idx]),np.max(ym_test[:, idx])], [np.min(ym_test[:, idx]),np.max(ym_test[:, idx])], '-')\n",
    "plt.xlabel(\"reference HOMO energy [eV]\")\n",
    "plt.ylabel(\"predicted HOMO energy [eV]\")\n",
    "plt.title(\"Energy Level : HOMO -1\")\n",
    "\n",
    "idx = 2 # This is for HOMO\n",
    "plt.subplot(313)\n",
    "plt.plot(ym_test[:, idx], ym_pred[:, idx], 'o')\n",
    "plt.plot([np.min(ym_test[:, idx]),np.max(ym_test[:, idx])], [np.min(ym_test[:, idx]),np.max(ym_test[:, idx])], '-')\n",
    "plt.xlabel(\"reference HOMO energy [eV]\")\n",
    "plt.ylabel(\"predicted HOMO energy [eV]\")\n",
    "plt.title(\"Energy Level : HOMO\")\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  }
 ],
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