diff --git a/.ipynb_checkpoints/HAAI_1-checkpoint.ipynb b/.ipynb_checkpoints/HAAI_1-checkpoint.ipynb
deleted file mode 100644
index 5e3accc84990c4a4749e728ab944c8ddfe58b14d..0000000000000000000000000000000000000000
--- a/.ipynb_checkpoints/HAAI_1-checkpoint.ipynb
+++ /dev/null
@@ -1,411 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Exercise 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "IP = \"192.168.50.126\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open('ilsvrc12_synset_words.txt') as f:\n",
-    "    classes = f.read()\n",
-    "    \n",
-    "class_list = [''.join(c.split(' ')[1:]) for c in classes.split('\\n')]\n",
-    "class_list[:10]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'jetson_inference'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mjetson_inference\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mjetson_utils\u001b[39;00m\n",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'jetson_inference'"
-     ]
-    }
-   ],
-   "source": [
-    "import jetson_inference\n",
-    "import jetson_utils"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "img = jetson_utils.loadImage(\"guara_navidad.png\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "img.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.imshow(img)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = jetson_inference.imageNet(\"googlenet\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cls_idx, confid = model.Classify(img)\n",
-    "f\"Result: {class_list[cls_idx]} ({cls_idx}), with confidence {confid*100:.0f}%\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### Exercise 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set up the path\n",
-    "from pathlib import Path\n",
-    "# Import time to calculate times\n",
-    "import time\n",
-    "\n",
-    "# Set directory path and extract images\n",
-    "directory = Path('/home/jetson/hiram/task2-images/')\n",
-    "images = [str(filename) for filename in directory.iterdir() if filename.name.endswith('.jpg')]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Display image files\n",
-    "images"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# The first model\n",
-    "model_1 = jetson_inference.imageNet(\"googlenet\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# The second model\n",
-    "model_2 = jetson_inference.imageNet(\"resnet-18\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Start timing the model\n",
-    "model_start_time = time.time()\n",
-    "\n",
-    "confidences_model1 = []\n",
-    "inferences_model1 = []\n",
-    "\n",
-    "# Iterate trough images\n",
-    "for image in images:\n",
-    "    # Start timing the prediction\n",
-    "    start = time.time()\n",
-    "    \n",
-    "    # Load and classify\n",
-    "    img = jetson_utils.loadImage(image)\n",
-    "    cls_idx, confid = model_1.Classify(img)\n",
-    "    \n",
-    "    confidences_model1.append(confid)\n",
-    "    \n",
-    "    # End timing of the prediction\n",
-    "    end = time.time()\n",
-    "    inferences_model2.append(end-start)\n",
-    "    result = f\"Result: {class_list[cls_idx]} ({cls_idx}), with confidence {confid*100:.0f}%, with an inference time {end-start}s\"\n",
-    "    print(result)\n",
-    "\n",
-    "# End timing the model\n",
-    "model_end_time = time.time()\n",
-    "model_inference_time = f\"Model inference time {model_end_time-model_start_time}s\"\n",
-    "print(model_inference_time)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Start timing the model\n",
-    "model_start_time = time.time()\n",
-    "\n",
-    "confidences_model2 = []\n",
-    "inferences_model2 = []\n",
-    "\n",
-    "# Iterate trough images\n",
-    "for image in images:\n",
-    "    # Start timing the prediction\n",
-    "    start = time.time()\n",
-    "    \n",
-    "    # Load and classify\n",
-    "    img = jetson_utils.loadImage(image)\n",
-    "    cls_idx, confid = model_2.Classify(img)\n",
-    "    confidences_model2.append(confid)\n",
-    "    \n",
-    "    # End timing of the prediction\n",
-    "    end = time.time()\n",
-    "    inferences_model2.append(end-start)\n",
-    "    result = f\"Result: {class_list[cls_idx]} ({cls_idx}), with confidence {confid*100:.0f}%, with an inference time {end-start}s\"\n",
-    "    print(result)\n",
-    "\n",
-    "# End timing the model\n",
-    "model_end_time = time.time()\n",
-    "model_inference_time = f\"Model inference time {model_end_time-model_start_time}s\"\n",
-    "print(model_inference_time)\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Calculate the confusion matrix\n",
-    "def comp_confusion_matrix(actual, predicted):\n",
-    "    # extract the different classes\n",
-    "    classes = np.unique(predicted)\n",
-    "    # initialize the confusion matrix\n",
-    "    confmat = np.zeros((len(classes), len(classes)))\n",
-    "\n",
-    "    # loop across the different combinations of actual / predicted classes\n",
-    "    for i in range(len(classes)):\n",
-    "        for j in range(len(classes)):\n",
-    "           # count the number of instances in each combination of actual / predicted classes\n",
-    "           confmat[i, j] = np.sum((actual == classes[i]) & (predicted == classes[j]))\n",
-    "    return confmat"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Visualize the confusion matrix\n",
-    "def plot_confusion_matrix(cm, classes, title, cmap=plt.cm.Blues):\n",
-    "    \n",
-    "    # Creating the visualization\n",
-    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
-    "    plt.tight_layout()\n",
-    "    tick_marks = np.arange(len(classes))\n",
-    "    plt.xticks(tick_marks, classes, rotation=45)\n",
-    "    plt.yticks(tick_marks, classes)\n",
-    "    plt.title(title)\n",
-    "    plt.ylabel('True class')\n",
-    "    plt.xlabel('Predicted class')\n",
-    "    thresh = cm.max() / 2.\n",
-    "    for i in range(cm.shape[0]):\n",
-    "        for j in range(cm.shape[1]):\n",
-    "            plt.text(j, i, format(cm[i, j]),\n",
-    "                     ha=\"center\", va=\"center\",\n",
-    "                     color=\"white\" if cm[i, j] > thresh else \"black\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# googlenet results\n",
-    "actual = [0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2, 2, 1, 0]\n",
-    "predicted = [0, 0, 3, 0, 1, 1, 1, 2, 3, 3, 3, 3, 2, 2, 1, 3, 1, 3, 3, 1, 0]\n",
-    "\n",
-    "# confusion matrix for googlenet\n",
-    "classes = ['Feline', 'Canid', 'Rodent', 'Other']\n",
-    "cm_googlenet = comp_confusion_matrix(actual, predicted)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# resnet results\n",
-    "actual = [0, 1, 2, 0, 1, 1, 1, 2, 0, 2, 0, 2, 1, 1, 0, 0, 1, 2, 1, 2, 0]\n",
-    "predicted = [0, 3, 3, 0, 1, 1, 1, 2, 0, 3, 3, 2, 1, 1, 0, 0, 1, 3, 1, 3, 0]\n",
-    "\n",
-    "# confusion matrix for resnet\n",
-    "cm_resnet = comp_confusion_matrix(actual, predicted)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 350x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 350x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Show confusion matrixes\n",
-    "plt.figure(figsize=(3.5,3))\n",
-    "plot_confusion_matrix(cm_googlenet, classes=classes, title='Confusion matrix, Googlenet')\n",
-    "plt.figure(figsize=(3.5,3))\n",
-    "plot_confusion_matrix(cm_resnet, classes=classes, title='Confusion matrix, Resnet')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: title={'center': 'Inferences'}>"
-      ]
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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h0QEAAADgckh0AAAAALgcEh0AAAAALodEBwAAAIDLIdEBAAAA4HIui+fonDx5Urt375afn1+FT94FANQMY4yKiooUEhKievX4buz3mJsAoHZUdm66LBKd3bt3y2az1XYYAFBn7dixQ6GhobUdxiWFuQkAatf55qbLItHx8/OTdOrF+Pv713I0AFB3FBYWymaz2T+H8f8xNwFA7ajs3HRZJDqnLwnw9/dnMgGAWsClWeUxNwFA7Trf3MQF1wAAAABcDokOAAAAAJdDogMAAADA5VQp0Zk5c6YiIiLk6empmJgYrVq16pzt33zzTV199dXy9vZWcHCw7r77bu3fv79KAQMAAADA+Tid6CxatEhJSUmaPHmyNmzYoB49eighIUG5ubkVtl+9erWGDx+uUaNGaePGjXrnnXe0du1ajR49+oKDBwAAAICKOJ3opKena9SoURo9erSioqKUkZEhm82mWbNmVdh+zZo1Cg8P1wMPPKCIiAh1795d9913n9atW3fBwQMAAABARZxKdEpKSrR+/XrFx8c7lMfHxysrK6vCPl27dtXOnTu1ZMkSGWO0Z88evfvuu7rxxhurHjUAAAAAnINTz9HZt2+fysrKFBgY6FAeGBio/Pz8Cvt07dpVb775poYMGaLjx4+rtLRUN998s1544YWzjlNcXKzi4mL7fmFhoTNhogLHSsq0be/hCuuOnyjTzgPHFNrQS5713Sps07Kpr7w8Kq4DAKAqLmRuYl4CcD5VemDomQ/nMcac9YE9mzZt0gMPPKC//OUv6t+/v/Ly8vToo49q7NixevXVVyvsk5aWpilTplQlNJzFtr2HddMLq6vc/5MJ3RXdPKAaIwIA1HUXMjcxLwE4H4sxxlS2cUlJiby9vfXOO+9o8ODB9vIHH3xQ2dnZWrlyZbk+w4YN0/Hjx/XOO+/Yy1avXq0ePXpo9+7dCg4OLtenohUdm82mQ4cO8fTpKjrXt2ZbCw4raVG2MoZ0UKtmvhW24ZszoG4qLCxUQEAAn78V4L25cBcyNzEvAXVXZT9/nVrR8fDwUExMjDIzMx0SnczMTA0aNKjCPkePHpW7u+Mwbm6nPpjOlmNZrVZZrVZnQsN5eHm4nfebr1bNfPl2DABw0TA3AahJTl+6lpycrGHDhqlTp06KjY3V7NmzlZubq7Fjx0qSJk2apF27dmn+/PmSpIEDB2rMmDGaNWuW/dK1pKQkde7cWSEhIdX7agAAwCUnZ98RHSkudarP1oLDDn9Wlo/VXRFNfJzqA8A1OZ3oDBkyRPv379fUqVOVl5en6OhoLVmyRGFhYZKkvLw8h2fqjBw5UkVFRZoxY4YefvhhNWjQQL1799YzzzxTfa8CAABcknL2HVGvZ1dUuX/Somyn+3zxSE+SHQBVuxlBYmKiEhMTK6ybO3duubIJEyZowoQJVRkKAABcxk6v5Jzrd6AVqcwdQc90+nc9zq4eAXBNVUp0AAAAnFGV39p0Cq+ZWADUDU49MBQAAAAALgckOgAAAABcDokOAAAAAJdDogMAAADA5ZDoAAAAAHA5JDoAAAAAXA6JDgDApaSkpMhisThsQUFB9npjjFJSUhQSEiIvLy/17NlTGzdurMWIAQA1gUQHAOBy2rVrp7y8PPv2/fff2+umTZum9PR0zZgxQ2vXrlVQUJD69eunoqKiWowYAFDdSHQAAC7H3d1dQUFB9q1p06aSTq3mZGRkaPLkybr11lsVHR2tefPm6ejRo1qwYEEtRw0AqE4kOgAAl/Pzzz8rJCREERERuuOOO/TLL79IknJycpSfn6/4+Hh7W6vVqri4OGVlZZ3zmMXFxSosLHTYAACXLhIdAIBL6dKli+bPn69PP/1Ur7zyivLz89W1a1ft379f+fn5kqTAwECHPoGBgfa6s0lLS1NAQIB9s9lsNfYaAAAXjkQHAOBSEhIS9Ic//EFXXXWV+vbtq3/961+SpHnz5tnbWCwWhz7GmHJlZ5o0aZIOHTpk33bs2FH9wQMAqg2JDgDApfn4+Oiqq67Szz//bL/72pmrNwUFBeVWec5ktVrl7+/vsAEALl0kOgAAl1ZcXKzNmzcrODhYERERCgoKUmZmpr2+pKREK1euVNeuXWsxSgBAdXOv7QAAAKhOjzzyiAYOHKgWLVqooKBATz31lAoLCzVixAhZLBYlJSUpNTVVkZGRioyMVGpqqry9vTV06NDaDh0AUI1IdAAALmXnzp268847tW/fPjVt2lTXXXed1qxZo7CwMEnSxIkTdezYMSUmJurAgQPq0qWLli9fLj8/v1qOHABQnUh0AAAu5a233jpnvcViUUpKilJSUi5OQACAWsFvdAAAAAC4nColOjNnzlRERIQ8PT0VExOjVatWnbXtyJEjZbFYym3t2rWrctAAAAAAcC5OJzqLFi1SUlKSJk+erA0bNqhHjx5KSEhQbm5uhe2nT5+uvLw8+7Zjxw41atRIt9122wUHDwAAAAAVcTrRSU9P16hRozR69GhFRUUpIyNDNptNs2bNqrB9QECAgoKC7Nu6det04MAB3X333RccPAAAAABUxKlEp6SkROvXr1d8fLxDeXx8vLKysip1jFdffVV9+/a13/0GAAAAAKqbU3dd27dvn8rKyso9PTowMLDcU6YrkpeXp6VLl2rBggXnbFdcXKzi4mL7fmFhoTNhAgAAAKjjqnQzAovF4rBvjClXVpG5c+eqQYMGuuWWW87ZLi0tTQEBAfbNZrNVJUwAAAAAdZRTiU6TJk3k5uZWbvWmoKCg3CrPmYwxeu211zRs2DB5eHics+2kSZN06NAh+7Zjxw5nwgQAAABQxzmV6Hh4eCgmJkaZmZkO5ZmZmerates5+65cuVJbt27VqFGjzjuO1WqVv7+/wwYAAAAAleXUb3QkKTk5WcOGDVOnTp0UGxur2bNnKzc3V2PHjpV0ajVm165dmj9/vkO/V199VV26dFF0dHT1RA4AAAAAZ+F0ojNkyBDt379fU6dOVV5enqKjo7VkyRL7XdTy8vLKPVPn0KFDWrx4saZPn149UQMAAADAOTid6EhSYmKiEhMTK6ybO3duubKAgAAdPXq0KkMBAAAAgNOqdNc1AAAAALiUkegAAAAAcDkkOgAAAABcDokOAAAAAJdDogMAAADA5ZDoAAAAAHA5JDoAAAAAXA6JDgAAAACXQ6IDAAAAwOWQ6AAAAABwOSQ6AAAAAFyOe20HAAAAXJvFvVA5hVtUz9O3RsfJKTwsi3thjY4BnHaspEzb9h6usO74iTLtPHBMoQ295FnfrVx9y6a+8vIoX47qRaIDAABqVP0GX+uJb1Iv0lh9JA24KGOhbtu297BuemF1lfp+MqG7opsHVHNEOBOJDgAAqFEnDnbRczcOVctmNbuis63gsB54c1uNjgGc1rKprz6Z0L3Cuq0Fh5W0KFsZQzqoVQXnfcumNft3AaeQ6AAAgBplSv0V4d9abRvX7DfYJ48fkindW6NjAKd5ebidd1WmVTNfVm5qETcjAAAAAOBySHQAAAAAuBwSHQAAAAAuh0QHAAAAgMupUqIzc+ZMRUREyNPTUzExMVq1atU52xcXF2vy5MkKCwuT1WpVy5Yt9dprr1UpYAAAAAA4H6fvurZo0SIlJSVp5syZ6tatm15++WUlJCRo06ZNatGiRYV9br/9du3Zs0evvvqqWrVqpYKCApWWll5w8AAAAABQEadXdNLT0zVq1CiNHj1aUVFRysjIkM1m06xZsypsv2zZMq1cuVJLlixR3759FR4ers6dO6tr164XHDwAAOeTlpYmi8WipKQke5kxRikpKQoJCZGXl5d69uypjRs31l6QAIBq51SiU1JSovXr1ys+Pt6hPD4+XllZWRX2+eijj9SpUydNmzZNzZs315VXXqlHHnlEx44dq3rUAABUwtq1azV79my1b9/eoXzatGlKT0/XjBkztHbtWgUFBalfv34qKiqqpUgBANXNqURn3759KisrU2BgoEN5YGCg8vPzK+zzyy+/aPXq1frhhx/0/vvvKyMjQ++++67GjRt31nGKi4tVWFjosAEA4IzDhw/rrrvu0iuvvKKGDRvay40xysjI0OTJk3XrrbcqOjpa8+bN09GjR7VgwYJajBgAUJ2qdDMCi8XisG+MKVd22smTJ2WxWPTmm2+qc+fOGjBggNLT0zV37tyzruqkpaUpICDAvtlstqqECQCow8aNG6cbb7xRffv2dSjPyclRfn6+w9UJVqtVcXFxZ706QeJLOAC43DiV6DRp0kRubm7lVm8KCgrKrfKcFhwcrObNmysgIMBeFhUVJWOMdu7cWWGfSZMm6dChQ/Ztx44dzoQJAKjj3nrrLX377bdKS0srV3d6DnPm6gSJL+EA4HLjVKLj4eGhmJgYZWZmOpRnZmae9eYC3bp10+7du3X48GF72U8//aR69eopNDS0wj5Wq1X+/v4OGwAAlbFjxw49+OCDeuONN+Tp6XnWds5cnSDxJRwAXG6cvnQtOTlZ//znP/Xaa69p8+bNeuihh5Sbm6uxY8dKOjURDB8+3N5+6NChaty4se6++25t2rRJX375pR599FHdc8898vLyqr5XAgCApPXr16ugoEAxMTFyd3eXu7u7Vq5cqX/84x9yd3e3r+Q4c3WCxJdwAHC5cfo5OkOGDNH+/fs1depU5eXlKTo6WkuWLFFYWJgkKS8vT7m5ufb2vr6+yszM1IQJE9SpUyc1btxYt99+u5566qnqexUAAPxPnz599P333zuU3X333WrTpo0ee+wxXXHFFQoKClJmZqY6duwo6dRdRVeuXKlnnnmmNkIGANQApxMdSUpMTFRiYmKFdXPnzi1X1qZNm3KXuwEAUBP8/PwUHR3tUObj46PGjRvby5OSkpSamqrIyEhFRkYqNTVV3t7eGjp0aG2EDACoAVVKdAAAuJxNnDhRx44dU2Jiog4cOKAuXbpo+fLl8vPzq+3QAADVhEQHAODyVqxY4bBvsViUkpKilJSUWokHAFDzqvQcHQAAAAC4lJHoAAAAAHA5JDoAAAAAXA6JDgAAAACXQ6IDAAAAwOWQ6AAAAABwOSQ6AAAAAFwOiQ4AAAAAl0OiAwAAAMDlkOgAAAAAcDnutR0AAAAAcCnL2XdER4pLK91+a8Fhhz8ry8fqrogmPk71wdmR6AAAAABnkbPviHo9u6JKfZMWZTvd54tHepLsVBMSHQAAAOAsTq/kZAzpoFbNfCvV5/iJMu08cEyhDb3kWd+tUn22FhxW0qJsp1aOcG4kOgAAAMB5tGrmq+jmAZVu3ym85mJB5XAzAgAAAAAuh0QHAAAAgMsh0QEAAADgcqqU6MycOVMRERHy9PRUTEyMVq1adda2K1askMViKbf9+OOPVQ4aAAAAAM7F6ZsRLFq0SElJSZo5c6a6deuml19+WQkJCdq0aZNatGhx1n5btmyRv7+/fb9p06ZVixgAAFw2jp0okyT9sOuQU/2qetcqoCZY3AuVU7hF9Twrd9e1qsgpPCyLe2GNHb8ucjrRSU9P16hRozR69GhJUkZGhj799FPNmjVLaWlpZ+3XrFkzNWjQoMqBAgCAy8+2/yUfj7/3/UUb08fKTWVRveo3+FpPfJN6EcbpI2lAjY9TVzj1SVBSUqL169fr8ccfdyiPj49XVlbWOft27NhRx48fV9u2bfXnP/9ZvXr1cj5aAABwWYlvFyRJatnMV16VXJmR/v8zRZx5donEk+VRM04c7KLnbhyqlk6ci87aVnBYD7y5rcaOXxc5lejs27dPZWVlCgwMdCgPDAxUfn5+hX2Cg4M1e/ZsxcTEqLi4WK+//rr69OmjFStW6Prrr6+wT3FxsYqLi+37hYUs4zkjZ98Rpx42dXqp39klfyYTXCzHSsq0bW/F52dlLm9p2dRXXh6V/wcWgOrTyMdDd3Q++6Xt5+Pss0uAmmBK/RXh31ptG9fcuXjy+CGZ0r01dvy6qEpruxaLxWHfGFOu7LTWrVurdevW9v3Y2Fjt2LFDzz777FkTnbS0NE2ZMqUqodV5OfuOqNezK6rUN2lRttN9vnikJ8kOaty2vYd10wurq9z/kwnd+YcSAAB1jFOJTpMmTeTm5lZu9aagoKDcKs+5XHfddXrjjTfOWj9p0iQlJyfb9wsLC2Wz2ZwJtc46vZLjzFJ/VX/wmbQo26mVI6CqWjb11ScTuldYV5nLW1o2rblLDQAAwKXJqUTHw8NDMTExyszM1ODBg+3lmZmZGjRoUKWPs2HDBgUHB5+13mq1ymq1OhMazuDsUn+n8JqLBbhQXh5u5z2fubwFAAD8ntOXriUnJ2vYsGHq1KmTYmNjNXv2bOXm5mrs2LGSTq3G7Nq1S/Pnz5d06q5s4eHhateunUpKSvTGG29o8eLFWrx4cfW+EgAAAAD4H6cTnSFDhmj//v2aOnWq8vLyFB0drSVLligsLEySlJeXp9zcXHv7kpISPfLII9q1a5e8vLzUrl07/etf/9KAAdw6DwAAAEDNqNLNCBITE5WYmFhh3dy5cx32J06cqIkTJ1ZlGAAAAACoknq1HQAAAAAAVDcSHQAAAAAuh0QHAAAAgMsh0QEAAADgckh0AAAuZdasWWrfvr38/f3l7++v2NhYLV261F5vjFFKSopCQkLk5eWlnj17auPGjbUYMQCgJpDoAABcSmhoqJ5++mmtW7dO69atU+/evTVo0CB7MjNt2jSlp6drxowZWrt2rYKCgtSvXz8VFRXVcuQAgOpEogMAcCkDBw7UgAEDdOWVV+rKK6/U3/72N/n6+mrNmjUyxigjI0OTJ0/WrbfequjoaM2bN09Hjx7VggULajt0AEA1ItEBALissrIyvfXWWzpy5IhiY2OVk5Oj/Px8xcfH29tYrVbFxcUpKyurFiMFAFS3Kj0wFACAS9n333+v2NhYHT9+XL6+vnr//ffVtm1bezITGBjo0D4wMFC//vrrOY9ZXFys4uJi+35hYWH1Bw4AqDas6AAAXE7r1q2VnZ2tNWvW6P7779eIESO0adMme73FYnFob4wpV3amtLQ0BQQE2DebzVYjsQMAqgeJDgDA5Xh4eKhVq1bq1KmT0tLSdPXVV2v69OkKCgqSJOXn5zu0LygoKLfKc6ZJkybp0KFD9m3Hjh01Fj8A4MKR6AAAXJ4xRsXFxYqIiFBQUJAyMzPtdSUlJVq5cqW6du16zmNYrVb7LatPbwCASxe/0QEAuJQnnnhCCQkJstlsKioq0ltvvaUVK1Zo2bJlslgsSkpKUmpqqiIjIxUZGanU1FR5e3tr6NChtR06AKAakegAAFzKnj17NGzYMOXl5SkgIEDt27fXsmXL1K9fP0nSxIkTdezYMSUmJurAgQPq0qWLli9fLj8/v1qOHABQnUh0AAAu5dVXXz1nvcViUUpKilJSUi5OQACAWsFvdAAAAAC4HBIdAAAAAC6HRAcAAACAyyHRAQAAAOByqpTozJw5UxEREfL09FRMTIxWrVpVqX7/+c9/5O7urg4dOlRlWAAAAACoFKcTnUWLFikpKUmTJ0/Whg0b1KNHDyUkJCg3N/ec/Q4dOqThw4erT58+VQ4WAAAAACrD6UQnPT1do0aN0ujRoxUVFaWMjAzZbDbNmjXrnP3uu+8+DR06VLGxsVUOFgAAAAAqw6lEp6SkROvXr1d8fLxDeXx8vLKyss7ab86cOdq2bZuefPLJqkUJAAAAAE5w6oGh+/btU1lZmQIDAx3KAwMDlZ+fX2Gfn3/+WY8//rhWrVold/fKDVdcXKzi4mL7fmFhoTNh1nkW90LlFG5RPU/fGhsjp/CwLO78f0H1W7/zV+0u2lPp9jt+O6p6nrv05fYNyin0rnS/EL9AxYSGVSVEAABwGXAq0TnNYrE47BtjypVJUllZmYYOHaopU6boyiuvrPTx09LSNGXKlKqEBkn1G3ytJ75JvQjj9JE0oMbHQd2Rs++I7nzreVmb/tupfj4R0qytzo1VvLePPh35N0U08XGuIwAAuCw4leg0adJEbm5u5VZvCgoKyq3ySFJRUZHWrVunDRs2aPz48ZKkkydPyhgjd3d3LV++XL179y7Xb9KkSUpOTrbvFxYWymazORNqnXbiYBc9d+NQtWxWcys62woO64E3t9XY8VE3HSku1YmDXZQUe4tsjSq3OlNcelIFhcfVzN9TVvfKXY2747ej+vvPeTpSXHoh4QIAgEuYU4mOh4eHYmJilJmZqcGDB9vLMzMzNWjQoHLt/f399f333zuUzZw5U59//rneffddRUREVDiO1WqV1Wp1JjT8jin1V4R/a7VtHFBjY5w8fkimdG+NHR91lyn11/XhHRXdvObO3x92HdK00iM1dnwAAFD7nL50LTk5WcOGDVOnTp0UGxur2bNnKzc3V2PHjpV0ajVm165dmj9/vurVq6fo6GiH/s2aNZOnp2e5cgAAAACoLk4nOkOGDNH+/fs1depU5eXlKTo6WkuWLFFY2Kkf9ebl5Z33mToAAAAAUJOqdDOCxMREJSYmVlg3d+7cc/ZNSUlRSkpKVYYFAAAAgEpx+oGhAAAAAHCpI9EBAAAA4HJIdAAAAAC4HBIdAAAAAC6HRAcAAACAyyHRAQAAAOBySHQAAAAAuBwSHQAAAAAup0oPDAUAALhQx0rKtG3v4QrrthYcdvjzTC2b+srLw63GYgNOO3aiTJL0w65Dle5z/ESZdh44ptCGXvKsX7nz9GznOqqORAcAANSKbXsP66YXVp+zTdKi7ArLP5nQXdHNA2ogKsDRtv8lII+/9/1FGc/Hyj/PqwvvJAAAqBUtm/rqkwndK6w73zfiLZv61nR4gCQpvl2QJKllM195ObE6k7QoWxlDOqhVs8qfqz5Wd0U08alSnCiPRAcAANQKLw+3c67KdAq/eLEAZ9PIx0N3dG5Rpb6tmvmy8liLuBkBAAAAAJdDogMAAADA5ZDoAAAAAHA5JDoAAAAAXA6JDgAAAACXQ6IDAAAAwOWQ6AAAXEpaWpquvfZa+fn5qVmzZrrlllu0ZcsWhzbGGKWkpCgkJEReXl7q2bOnNm7cWEsRAwBqQpUSnZkzZyoiIkKenp6KiYnRqlWrztp29erV6tatmxo3biwvLy+1adNGzz//fJUDBgDgXFauXKlx48ZpzZo1yszMVGlpqeLj43XkyBF7m2nTpik9PV0zZszQ2rVrFRQUpH79+qmoqKgWIwcAVCenHxi6aNEiJSUlaebMmerWrZtefvllJSQkaNOmTWrRovzDlHx8fDR+/Hi1b99ePj4+Wr16te677z75+Pjo3nvvrZYXAQDAacuWLXPYnzNnjpo1a6b169fr+uuvlzFGGRkZmjx5sm699VZJ0rx58xQYGKgFCxbovvvuq42wAQDVzOkVnfT0dI0aNUqjR49WVFSUMjIyZLPZNGvWrArbd+zYUXfeeafatWun8PBw/elPf1L//v3PuQoEAEB1OXTokCSpUaNGkqScnBzl5+crPj7e3sZqtSouLk5ZWVm1EiMAoPo5leiUlJRo/fr1DpODJMXHx1d6ctiwYYOysrIUFxfnzNAAADjNGKPk5GR1795d0dHRkqT8/HxJUmBgoEPbwMBAe11FiouLVVhY6LABAC5dTl26tm/fPpWVlTk9OUhSaGio9u7dq9LSUqWkpGj06NFnbVtcXKzi4mL7PpNJ5R07USZJ+mHXoUr3OX6iTDsPHFNoQy951nerVJ+tBYerFB8AXEzjx4/Xd999p9WrV5ers1gsDvvGmHJlv5eWlqYpU6ZUe4wAgJrh9G90JOcnB0latWqVDh8+rDVr1ujxxx9Xq1atdOedd1bYlsmk6rb9LwF5/L3vL8p4PtYqnUIAUOMmTJigjz76SF9++aVCQ0Pt5UFBQZJOrewEBwfbywsKCsp9kfd7kyZNUnJysn2/sLBQNputBiIHAFQHp/6V2qRJE7m5uZVbvTnf5CBJERERkqSrrrpKe/bsUUpKylkTHSaTqotvd2oCb9nMV15OrM4kLcpWxpAOatXMt9Jj+VjdFdHEp0pxAkBNMcZowoQJev/997VixQr7/HNaRESEgoKClJmZqY4dO0o6dWn2ypUr9cwzz5z1uFarVVartUZjBwBUH6cSHQ8PD8XExCgzM1ODBw+2l2dmZmrQoEGVPo4xxuHStDMxmVRdIx8P3dG5/N3vKqNVM19FNw+o5ogA4OIaN26cFixYoA8//FB+fn72L+cCAgLk5eUli8WipKQkpaamKjIyUpGRkUpNTZW3t7eGDh1ay9EDAKqL09cdJScna9iwYerUqZNiY2M1e/Zs5ebmauzYsZJOrcbs2rVL8+fPlyS9+OKLatGihdq0aSPp1HN1nn32WU2YMKEaXwYAAKecvgtoz549HcrnzJmjkSNHSpImTpyoY8eOKTExUQcOHFCXLl20fPly+fn5XeRoAQA1xelEZ8iQIdq/f7+mTp2qvLw8RUdHa8mSJQoLC5Mk5eXlKTc3197+5MmTmjRpknJycuTu7q6WLVvq6aef5jkFAIAaYYw5bxuLxaKUlBSlpKTUfEAAgFpRpV+SJyYmKjExscK6uXPnOuxPmDCB1RsAAAAAF5XTDwwFAAAAgEsdiQ4AAAAAl0OiAwAAAMDlkOgAAAAAcDkkOgAAAABcDokOAAAAAJdDogMAAADA5ZDoAAAAAHA5JDoAAAAAXA6JDgAAAACXQ6IDAAAAwOWQ6AAAAABwOSQ6AAAAAFwOiQ4AAAAAl0OiAwAAAMDlkOgAAAAAcDkkOgAAAABcDokOAAAAAJdDogMAAADA5VQp0Zk5c6YiIiLk6empmJgYrVq16qxt33vvPfXr109NmzaVv7+/YmNj9emnn1Y5YAAAAAA4H6cTnUWLFikpKUmTJ0/Whg0b1KNHDyUkJCg3N7fC9l9++aX69eunJUuWaP369erVq5cGDhyoDRs2XHDwAAAAAFARpxOd9PR0jRo1SqNHj1ZUVJQyMjJks9k0a9asCttnZGRo4sSJuvbaaxUZGanU1FRFRkbq448/vuDgAQAAAKAiTiU6JSUlWr9+veLj4x3K4+PjlZWVValjnDx5UkVFRWrUqJEzQwMAAABApbk703jfvn0qKytTYGCgQ3lgYKDy8/MrdYznnntOR44c0e23337WNsXFxSouLrbvFxYWOhMmgMvUsRNlkqQfdh2qdJ/jJ8q088AxhTb0kmd9t0r12VpwuErxAQCAy4dTic5pFovFYd8YU66sIgsXLlRKSoo+/PBDNWvW7Kzt0tLSNGXKlKqEBuAytu1/Ccjj731/UcbzsVbpIxAAAFwGnJrlmzRpIjc3t3KrNwUFBeVWec60aNEijRo1Su+884769u17zraTJk1ScnKyfb+wsFA2m82ZUAFchuLbBUmSWjbzlZcTqzNJi7KVMaSDWjXzrfRYPlZ3RTTxqVKcAADg0udUouPh4aGYmBhlZmZq8ODB9vLMzEwNGjTorP0WLlyoe+65RwsXLtSNN9543nGsVqusVqszoQFwAY18PHRH5xZV6tuqma+imwdUc0QAAOBy5fR1G8nJyRo2bJg6deqk2NhYzZ49W7m5uRo7dqykU6sxu3bt0vz58yWdSnKGDx+u6dOn67rrrrOvBnl5eSkggH+UAAAAAKh+Tic6Q4YM0f79+zV16lTl5eUpOjpaS5YsUVhYmCQpLy/P4Zk6L7/8skpLSzVu3DiNGzfOXj5ixAjNnTv3wl8BAAAAAJyhSr/ETUxMVGJiYoV1ZyYvK1asqMoQAAAAAFBlTj8wFAAAAAAudSQ6AAAAAFwOiQ4AwOV8+eWXGjhwoEJCQmSxWPTBBx841BtjlJKSopCQEHl5ealnz57auHFj7QQLAKgRJDoAAJdz5MgRXX311ZoxY0aF9dOmTVN6erpmzJihtWvXKigoSP369VNRUdFFjhQAUFN4LDgAwOUkJCQoISGhwjpjjDIyMjR58mTdeuutkqR58+YpMDBQCxYs0H333XcxQwUA1BBWdAAAdUpOTo7y8/MVHx9vL7NarYqLi1NWVtZZ+xUXF6uwsNBhAwBcukh0AAB1yukHVwcGBjqUBwYG2usqkpaWpoCAAPtms9lqNE4AwIUh0QEA1EkWi8Vh3xhTruz3Jk2apEOHDtm3HTt21HSIAIALwG90AAB1SlBQkKRTKzvBwcH28oKCgnKrPL9ntVpltVprPD4AQPVgRQcAUKdEREQoKChImZmZ9rKSkhKtXLlSXbt2rcXIAADViRUdAIDLOXz4sLZu3Wrfz8nJUXZ2tho1aqQWLVooKSlJqampioyMVGRkpFJTU+Xt7a2hQ4fWYtQAgOpEogMAcDnr1q1Tr1697PvJycmSpBEjRmju3LmaOHGijh07psTERB04cEBdunTR8uXL5efnV1shAwCqGYkOAMDl9OzZU8aYs9ZbLBalpKQoJSXl4gUFALio+I0OAAAAAJdDogMAAADA5ZDoAAAAAHA5JDoAAAAAXA6JDgAAAACXQ6IDAAAAwOVUKdGZOXOmIiIi5OnpqZiYGK1ateqsbfPy8jR06FC1bt1a9erVU1JSUlVjBQAAAIBKcTrRWbRokZKSkjR58mRt2LBBPXr0UEJCgnJzcytsX1xcrKZNm2ry5Mm6+uqrLzhgAAAAADgfpxOd9PR0jRo1SqNHj1ZUVJQyMjJks9k0a9asCtuHh4dr+vTpGj58uAICAi44YAAAAAA4H6cSnZKSEq1fv17x8fEO5fHx8crKyqrWwAAAAACgqtydabxv3z6VlZUpMDDQoTwwMFD5+fnVFlRxcbGKi4vt+4WFhdV27LrqWEmZtu09XGHd1oLDDn9WpGVTX3l5uNVIbMD5cP4CAC41FzI3MS9dHE4lOqdZLBaHfWNMubILkZaWpilTplTb8SBt23tYN72w+pxtkhZln7XukwndFd2cSw9ROzh/AQCXmguZm5iXLg6nEp0mTZrIzc2t3OpNQUFBuVWeCzFp0iQlJyfb9wsLC2Wz2art+HVRy6a++mRC9wrrjp8o084DxxTa0Eue9Sv+dqFlU9+aDA84J85fAMCl5kLmJuali8OpRMfDw0MxMTHKzMzU4MGD7eWZmZkaNGhQtQVltVpltVqr7XiQvDzczvnNQafwixcL4CzOXwDApYa56dLn9KVrycnJGjZsmDp16qTY2FjNnj1bubm5Gjt2rKRTqzG7du3S/Pnz7X2ys7MlSYcPH9bevXuVnZ0tDw8PtW3btnpeBQAAAAD8jtOJzpAhQ7R//35NnTpVeXl5io6O1pIlSxQWFibp1ANCz3ymTseOHe3/vX79ei1YsEBhYWHavn37hUUPAAAAABWwGGNMbQdxPoWFhQoICNChQ4fk7+9f2+EAQJ3B5+/Z8d4AQO2o7Oev0w8MBQAAAIBLHYkOAAAAAJdDogMAAADA5VTpgaEX2+mfERUWFtZyJABQt5z+3L0Mfs550TE3AUDtqOzcdFkkOkVFRZLEQ0MBoJYUFRUpIICneP8ecxMA1K7zzU2XxV3XTp48qd27d8vPz08Wi6W2w3E5hYWFstls2rFjB3cOwmWH87dmGWNUVFSkkJAQ1avH1c6/x9xUs/i7jcsZ52/NquzcdFms6NSrV0+hoaG1HYbL8/f35y8jLlucvzWHlZyKMTddHPzdxuWM87fmVGZu4us5AAAAAC6HRAcAAACAyyHRgaxWq5588klZrdbaDgVwGucv4Jr4u43LGefvpeGyuBkBAAAAADiDFR0AAAAALodEBwAAAIDLIdEBAAAA4HJIdGC3fft2WSwWZWdn13YoAABIYm4CUHUkOpeA/Px8Pfjgg2rVqpU8PT0VGBio7t2766WXXtLRo0drO7wal5KSog4dOtR2GLgIRo4cKYvFIovFInd3d7Vo0UL333+/Dhw4UNuh2fXs2VNJSUm1HQZQ65ibmJvqAuYl1+Ze2wHUdb/88ou6deumBg0aKDU1VVdddZVKS0v1008/6bXXXlNISIhuvvnm2g4TqDY33HCD5syZo9LSUm3atEn33HOPDh48qIULF9Z2aAD+h7kJdQnzkgszqFX9+/c3oaGh5vDhwxXWnzx50hhjzK+//mpuvvlm4+PjY/z8/Mxtt91m8vPzHdrOnDnTXHHFFaZ+/frmyiuvNPPnz3eo37x5s+nWrZuxWq0mKirKZGZmGknm/fffN8YYk5OTYySZDRs22Pts3LjRJCQkGB8fH9OsWTPzpz/9yezdu9deHxcXZyZMmGAeffRR07BhQxMYGGiefPJJh3EPHjxoxowZY5o2bWr8/PxMr169THZ2tjHGmDlz5hhJDtucOXOq8E7icjBixAgzaNAgh7Lk5GTTqFEj+/5rr71m2rRpY6xWq2ndurV58cUX7XXFxcVm3LhxJigoyFitVhMWFmZSU1Pt9ZLMK6+8Ym655Rbj5eVlWrVqZT788EOH8c51To8YMaLc+ZiTk1P9bwRwiWNuYm6qK5iXXBuJTi3at2+fsVgsJi0t7ZztTp48aTp27Gi6d+9u1q1bZ9asWWOuueYaExcXZ2/z3nvvmfr165sXX3zRbNmyxTz33HPGzc3NfP7558YYY8rKykzr1q1Nv379THZ2tlm1apXp3LnzOSeT3bt3myZNmphJkyaZzZs3m2+//db069fP9OrVyz5uXFyc8ff3NykpKeann34y8+bNMxaLxSxfvtwee7du3czAgQPN2rVrzU8//WQefvhh07hxY7N//35z9OhR8/DDD5t27dqZvLw8k5eXZ44ePVp9bzIuKWdOKNu2bTNt27Y1gYGBxhhjZs+ebYKDg83ixYvNL7/8YhYvXmwaNWpk5s6da4wx5u9//7ux2Wzmyy+/NNu3bzerVq0yCxYssB9PkgkNDTULFiwwP//8s3nggQeMr6+v2b9/vzHm/Of0wYMHTWxsrBkzZoz9fCwtLb1I7w5waWBuYm6qS5iXXBuJTi1as2aNkWTee+89h/LGjRsbHx8f4+PjYyZOnGiWL19u3NzcTG5urr3Nxo0bjSTzzTffGGOM6dq1qxkzZozDcW677TYzYMAAY4wxS5cuNe7u7iYvL89ef75vzf7v//7PxMfHOxxzx44dRpLZsmWLMebUZNK9e3eHNtdee6157LHHjDHG/Pvf/zb+/v7m+PHjDm1atmxpXn75ZWOMMU8++aS5+uqrK/We4fI2YsQI4+bmZnx8fIynp6f926n09HRjjDE2m81hgjDGmL/+9a8mNjbWGGPMhAkTTO/eve3fJp9Jkvnzn/9s3z98+LCxWCxm6dKlxpjKn9MPPvhgtbxe4HLE3MTcVJcwL7k2bkZwCbBYLA7733zzjbKzs9WuXTsVFxdr8+bNstlsstls9jZt27ZVgwYNtHnzZknS5s2b1a1bN4fjdOvWzV6/ZcsW2Ww2BQUF2es7d+58zrjWr1+vL774Qr6+vvatTZs2kqRt27bZ27Vv396hX3BwsAoKCuzHOHz4sBo3buxwnJycHIdjoO7o1auXsrOz9fXXX2vChAnq37+/JkyYoL1792rHjh0aNWqUw7ny1FNP2c+VkSNHKjs7W61bt9YDDzyg5cuXlzv+789HHx8f+fn5OZyPlTmnATA3oe5gXnJd3IygFrVq1UoWi0U//vijQ/kVV1whSfLy8pIkGWPKTTgVlZ/Z5vf1ZzvGuZw8eVIDBw7UM888U64uODjY/t/169d3qLNYLDp58qT9GMHBwVqxYkW5YzRo0MCpeOAafHx81KpVK0nSP/7xD/Xq1UtTpkzR+PHjJUmvvPKKunTp4tDHzc1NknTNNdcoJydHS5cu1Weffabbb79dffv21bvvvmtve77zsTLnNFCXMTc1cCoeXP6Yl1wXiU4taty4sfr166cZM2ZowoQJ8vHxqbBd27ZtlZubqx07dti/Odu0aZMOHTqkqKgoSVJUVJRWr16t4cOH2/tlZWXZ69u0aaPc3Fzt2bNHgYGBkqS1a9eeM75rrrlGixcvVnh4uNzdq3aqXHPNNcrPz5e7u7vCw8MrbOPh4aGysrIqHR+XvyeffFIJCQm6//771bx5c/3yyy+66667ztre399fQ4YM0ZAhQ/THP/5RN9xwg3777Tc1atTovGNV5pzmfERdx9x0Cp8FdRfzkuvg0rVaNnPmTJWWlqpTp05atGiRNm/erC1btuiNN97Qjz/+KDc3N/Xt21ft27fXXXfdpW+//VbffPONhg8frri4OHXq1EmS9Oijj2ru3Ll66aWX9PPPPys9PV3vvfeeHnnkEUlSv3791LJlS40YMULfffed/vOf/2jy5MmSyn/bdtq4ceP022+/6c4779Q333yjX375RcuXL9c999xT6b9wffv2VWxsrG655RZ9+umn2r59u7KysvTnP/9Z69atkySFh4crJydH2dnZ2rdvn4qLiy/0bcVlpGfPnmrXrp1SU1OVkpKitLQ0TZ8+XT/99JO+//57zZkzR+np6ZKk559/Xm+99ZZ+/PFH/fTTT3rnnXcUFBRU6W9gK3NOh4eH6+uvv9b27du1b98++7duQF3C3MTcVJcxL7mQ2vt5EE7bvXu3GT9+vImIiDD169c3vr6+pnPnzubvf/+7OXLkiDGmem/h6eHhYdq0aWM+/vhjI8ksW7bMGFPxLTx/+uknM3jwYNOgQQPj5eVl2rRpY5KSkuw/uqvoB3KDBg0yI0aMsO8XFhaaCRMmmJCQEFO/fn1js9nMXXfdZf8B6/Hjx80f/vAH06BBA27h6eIquo2nMca8+eabxsPDw+Tm5po333zTdOjQwXh4eJiGDRua66+/3v6j6NmzZ5sOHToYHx8f4+/vb/r06WO+/fZb+3H0ux8wnxYQEOBwTp3vnN6yZYu57rrrjJeXF7fxRJ3G3MTcVBcwL7k2izHG1FaShdr1n//8R927d9fWrVvVsmXL2g4HAADmJgDVhkSnDnn//ffl6+uryMhIbd26VQ8++KAaNmyo1atX13ZoAIA6irkJQE3hZgR1SFFRkSZOnKgdO3aoSZMm6tu3r5577rnaDgsAUIcxNwGoKazoAAAAAHA53HUNAAAAgMsh0QEAAADgckh0AAAAALgcEh0AAAAALodEBwAAAIDLIdEBAAAA4HJIdAAAAAC4HBIdAAAAAC6HRAcAAACAy/l/eECDAN/zbY8AAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 1000x300 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axes = plt.subplots(1,2, figsize = (10,3))\n",
-    "confidences_model1 = [0.1, 0.3, 0.4, 0.5, 0.8]\n",
-    "confidences_model2 = [0.4, 0.1, 0.4, 0.5, 0.2]\n",
-    "\n",
-    "inferences_model1 = [53, 34, 54, 23, 43]\n",
-    "inferences_model2 = [34, 23, 36, 43, 6]\n",
-    "\n",
-    "confidences_combined = pd.DataFrame({\"Googlenet\":confidences_model1, \"Resnet\": confidences_model2})\n",
-    "inferences_combined = pd.DataFrame({\"Googlenet\":inferences_model1, \"Resnet\": inferences_model2})\n",
-    "\n",
-    "# Creating plot\n",
-    "# plt.boxplot(confidences_model1)\n",
-    "confidences_combined[['Googlenet', 'Resnet']].plot(kind='box', title='Confidences', ax=axes[0])\n",
-    "inferences_combined[['Googlenet', 'Resnet']].plot(kind='box', title='Inferences', ax=axes[1])"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/HAAI_1.ipynb b/HAAI_1.ipynb
deleted file mode 100644
index 5e3accc84990c4a4749e728ab944c8ddfe58b14d..0000000000000000000000000000000000000000
--- a/HAAI_1.ipynb
+++ /dev/null
@@ -1,411 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Exercise 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "IP = \"192.168.50.126\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open('ilsvrc12_synset_words.txt') as f:\n",
-    "    classes = f.read()\n",
-    "    \n",
-    "class_list = [''.join(c.split(' ')[1:]) for c in classes.split('\\n')]\n",
-    "class_list[:10]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'jetson_inference'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mjetson_inference\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mjetson_utils\u001b[39;00m\n",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'jetson_inference'"
-     ]
-    }
-   ],
-   "source": [
-    "import jetson_inference\n",
-    "import jetson_utils"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "img = jetson_utils.loadImage(\"guara_navidad.png\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "img.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.imshow(img)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = jetson_inference.imageNet(\"googlenet\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cls_idx, confid = model.Classify(img)\n",
-    "f\"Result: {class_list[cls_idx]} ({cls_idx}), with confidence {confid*100:.0f}%\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### Exercise 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set up the path\n",
-    "from pathlib import Path\n",
-    "# Import time to calculate times\n",
-    "import time\n",
-    "\n",
-    "# Set directory path and extract images\n",
-    "directory = Path('/home/jetson/hiram/task2-images/')\n",
-    "images = [str(filename) for filename in directory.iterdir() if filename.name.endswith('.jpg')]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Display image files\n",
-    "images"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# The first model\n",
-    "model_1 = jetson_inference.imageNet(\"googlenet\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# The second model\n",
-    "model_2 = jetson_inference.imageNet(\"resnet-18\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Start timing the model\n",
-    "model_start_time = time.time()\n",
-    "\n",
-    "confidences_model1 = []\n",
-    "inferences_model1 = []\n",
-    "\n",
-    "# Iterate trough images\n",
-    "for image in images:\n",
-    "    # Start timing the prediction\n",
-    "    start = time.time()\n",
-    "    \n",
-    "    # Load and classify\n",
-    "    img = jetson_utils.loadImage(image)\n",
-    "    cls_idx, confid = model_1.Classify(img)\n",
-    "    \n",
-    "    confidences_model1.append(confid)\n",
-    "    \n",
-    "    # End timing of the prediction\n",
-    "    end = time.time()\n",
-    "    inferences_model2.append(end-start)\n",
-    "    result = f\"Result: {class_list[cls_idx]} ({cls_idx}), with confidence {confid*100:.0f}%, with an inference time {end-start}s\"\n",
-    "    print(result)\n",
-    "\n",
-    "# End timing the model\n",
-    "model_end_time = time.time()\n",
-    "model_inference_time = f\"Model inference time {model_end_time-model_start_time}s\"\n",
-    "print(model_inference_time)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Start timing the model\n",
-    "model_start_time = time.time()\n",
-    "\n",
-    "confidences_model2 = []\n",
-    "inferences_model2 = []\n",
-    "\n",
-    "# Iterate trough images\n",
-    "for image in images:\n",
-    "    # Start timing the prediction\n",
-    "    start = time.time()\n",
-    "    \n",
-    "    # Load and classify\n",
-    "    img = jetson_utils.loadImage(image)\n",
-    "    cls_idx, confid = model_2.Classify(img)\n",
-    "    confidences_model2.append(confid)\n",
-    "    \n",
-    "    # End timing of the prediction\n",
-    "    end = time.time()\n",
-    "    inferences_model2.append(end-start)\n",
-    "    result = f\"Result: {class_list[cls_idx]} ({cls_idx}), with confidence {confid*100:.0f}%, with an inference time {end-start}s\"\n",
-    "    print(result)\n",
-    "\n",
-    "# End timing the model\n",
-    "model_end_time = time.time()\n",
-    "model_inference_time = f\"Model inference time {model_end_time-model_start_time}s\"\n",
-    "print(model_inference_time)\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Calculate the confusion matrix\n",
-    "def comp_confusion_matrix(actual, predicted):\n",
-    "    # extract the different classes\n",
-    "    classes = np.unique(predicted)\n",
-    "    # initialize the confusion matrix\n",
-    "    confmat = np.zeros((len(classes), len(classes)))\n",
-    "\n",
-    "    # loop across the different combinations of actual / predicted classes\n",
-    "    for i in range(len(classes)):\n",
-    "        for j in range(len(classes)):\n",
-    "           # count the number of instances in each combination of actual / predicted classes\n",
-    "           confmat[i, j] = np.sum((actual == classes[i]) & (predicted == classes[j]))\n",
-    "    return confmat"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Visualize the confusion matrix\n",
-    "def plot_confusion_matrix(cm, classes, title, cmap=plt.cm.Blues):\n",
-    "    \n",
-    "    # Creating the visualization\n",
-    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
-    "    plt.tight_layout()\n",
-    "    tick_marks = np.arange(len(classes))\n",
-    "    plt.xticks(tick_marks, classes, rotation=45)\n",
-    "    plt.yticks(tick_marks, classes)\n",
-    "    plt.title(title)\n",
-    "    plt.ylabel('True class')\n",
-    "    plt.xlabel('Predicted class')\n",
-    "    thresh = cm.max() / 2.\n",
-    "    for i in range(cm.shape[0]):\n",
-    "        for j in range(cm.shape[1]):\n",
-    "            plt.text(j, i, format(cm[i, j]),\n",
-    "                     ha=\"center\", va=\"center\",\n",
-    "                     color=\"white\" if cm[i, j] > thresh else \"black\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# googlenet results\n",
-    "actual = [0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2, 2, 1, 0]\n",
-    "predicted = [0, 0, 3, 0, 1, 1, 1, 2, 3, 3, 3, 3, 2, 2, 1, 3, 1, 3, 3, 1, 0]\n",
-    "\n",
-    "# confusion matrix for googlenet\n",
-    "classes = ['Feline', 'Canid', 'Rodent', 'Other']\n",
-    "cm_googlenet = comp_confusion_matrix(actual, predicted)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# resnet results\n",
-    "actual = [0, 1, 2, 0, 1, 1, 1, 2, 0, 2, 0, 2, 1, 1, 0, 0, 1, 2, 1, 2, 0]\n",
-    "predicted = [0, 3, 3, 0, 1, 1, 1, 2, 0, 3, 3, 2, 1, 1, 0, 0, 1, 3, 1, 3, 0]\n",
-    "\n",
-    "# confusion matrix for resnet\n",
-    "cm_resnet = comp_confusion_matrix(actual, predicted)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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0rVu3jgDQ6dOnv3sf5IqNjSUPDw+qX78+qaqqkqKiIhkaGlKXLl1o586dlJmZKdW/II8P0ed5pv379ydtbW1SVFSkOnXq0B9//JFnnmlUVBT16NGD1NTUSF1dnXr27En//PMPAaBjx45J+n1rJomfnx+1atWKKleuTGKxmExMTKhXr150/vx5SZ9vPa75rTMzM5NWrFhBDRo0IGVlZVJTUyMLCwv65Zdf6MmTJ5J+kZGR1LZtW1JXVy/SPFMREf86KWOlWZ8+fRAREYHg4GBZl1JkS5YswZw5cxAVFVVuv27MH/MZK8WISDJlqqxYv349AMDCwgKZmZm4cOEC1q5di4EDB5bbIAU4TBkr1UQiEeLj42VdRqGoqKhg9erViIyMREZGBoyNjTFjxgzMmTNH1qUVK/6YzxhjAuCpUYwxJgAOU8YYEwCHKWOMCYAPQBWznJwcvH79Gurq6qXqJxoYYz9GREhNTYWhoeF3v3AAcJgWu9evX+c5Iw9jrGyJjo7+4bQuDtNilnsiBZuZhyAvVpFxNSXjn4nNZV0CK2Yv3qTJuoQS8f59Klo3qlOgE6JwmBaz3I/28mIVKCiryriaklG5cmVZl8CKmVq6vKxLKFEF2UXHB6AYY0wAHKaMMSYADlPGGBMAhyljjAmAw5QxxgTAYcoYYwLgMGWMMQFwmDLGmAA4TBljTAAcpowxJgAOU8YYEwCHKWOMCYDDlDHGBMBhyhhjAuAwZYwxAXCYMsaYADhMGWNMABymjDEmAA5TxhgTAIcpY4wJgMOUMcYEwGFaxg12MELQTCdMcjb/bj9bIw34uNnh0rQW8B1tj+42BiVUoXC8Nm2ERS1TaKopo6l9Q1y5cvm7/S//7xKa2jeEppoyLGubwdtrcwlVKoyKNN6QoCsYO6Q3nOxqom41NZw/ffyHywQHXkav9s1hY6aNto71sH/n1hKo9NvKbZiKRCL4+fkBACIjIyESiXD79m2Z1iQ0S311uNoY4En8++/2M9BQxqre1rgdnYwh229ix7UoTHGpiVZ1dEqo0v/u0MEDmD51EmbMnI2g4FA0bd4Crp07ICoqKt/+kRERcO3SEU2bt0BQcCjcZ8zC1MkTcfSIbwlXXjQVbbwfPnxAnbr1MGfRygL1fxkVidGDeqKhfVP4nrmKUROmYcnc6Th70q94C/2OUh2mbm5uEIlEef6ePn1aqPUYGRkhJiYG9erVK6ZKS14lRTnM72qBpaceIzU967t9e9gaIC4lHWv8nyEy8QP+vhuL43dj0d/eqISq/e/WrlkFt6HDMXT4CFhYWmLFqjWobmQEb69N+fb33rIZRsbGWLFqDSwsLTF0+AgMcRuGNatWlHDlRVPRxvtT67b4dYYnXDp2K1D/A7v+gkG16vBYsBzmtSzQq78bevw8CNs3ry3mSr+tVIcpALRv3x4xMTFSf6ampoVah7y8PPT19aGgoFBMVZa8aW1r4eqztwh+kfTDvvWqVcb1yHdSbdcj3sJSXw3ycqJiqlA4nz59Quitm3B2aSvV7tymLYICr+W7zPWgQDi3ke7fpm073LoZgszMzGKrVQgVbbxFcfvmdTR1cpZqa96yDR7cvSWz8Zb6MBWLxdDX15f6k5eXx/Hjx9GwYUMoKyvDzMwM8+fPR1ZW/ltoX3/MDwgIgEgkgr+/Pxo1agQVFRU0bdoUjx49klquMLdRktpYVkUdPTVsCnheoP7aqkp4myb9BHublgkFeTloVlIsjhIFlZCQgOzsbOjq6km16+npIS4uNt9l4uJioacn3V9XVw9ZWVlISEgotlqFUNHGWxQJ8fHQ1tGVatPW0UVWVhaS3ibKpKZSH6b5OXPmDAYOHIiJEyciLCwMXl5e8PHxweLFiwu1ntmzZ2PlypUICQmBgoIChg0b9p9vIyMjAykpKVJ/QtJVF2NKm5qYd+IhPmVTgZcjku6buz1KKPg6ZE0kkt6KJqI8bT/qn197aVXRxltYecab+1yW0XhL/efeEydOQE1NTXK5Q4cOiIuLw8yZMzFkyBAAgJmZGRYuXAh3d3d4enoWeN2LFy+Gk5MTAGDmzJno1KkT0tPToaysjMWLFxfpNpYuXYr58+cXZagFYqGvBi1VJfi4NZS0KciJYGOkgV4Nq+GnP/6HnK/yMTHtE7TVlKTaqqgqIis7B8kfZb+l/SM6OjqQl5fPs1UWHx+fZ+stl56ePmJjpfu/eRMPBQUFaGtrF1utQqho4y0KHV1dJLyJk2p7m/AGCgoK0KyiJZOaSn2YtmrVCps2/bvTXVVVFTVr1kRwcLDUVmJ2djbS09Px4cMHqKioFGjd9evXl/zfwODzVKH4+HgYGxvj5s2bRboNDw8PTJkyRXI5JSUFRkbCHegJeZGE/luDpdrmdKqDF4kfsSsoKk+QAsD9VyloXlP6BdWkhhbCY98jO78FShklJSXY2jXEhfPn0M21u6T9gv85dO6S/wGLJg6O+Oek9PQa/3NnYdewERQVS/eujYo23qKwadgEF8/9I9V29ZI/rOrbyWy8pT5Mc8PzSzk5OZg/fz569OiRp7+ysnKB1/3lnZ77kSEnJ+c/3YZYLIZYLC5wDYX14VM2nid8kGpLz8xB8sdMSfsYJ1NUVVfCghOf9wEfCY1BL7tq+LW1OY7diUG9apXRpYE+5v4dXmx1Cm3ipCkY7jYIdg0boYmDI/7augXRUVEYMWo0AOC32R54/eoV/vLZCQAYOWo0Nm9cD/dpUzBs+EhcDwqEz/a/sGP3PlkOo8Aq2njT0t4jKuLfYwCvol4g/P5daFSpAsNqRli11BPxMa/x+1pvAMDPg4Zj73YvLJs3E70GuOH2zRvw3b8TKzZsl9UQSn+Y5sfOzg6PHj3KE7Jl7TaKi46aEvQr/xv4McnpmHLoHiY5m6OnnSES3mdg1bmnuPio7ByY6N3nZ7xNTMSSxQsQGxMDK6t68Dv+D0xMTAAAsTExiI7+dw5mDVNT+B3/B+5TJ8Nr0wYYGBpi5eq16N6jp6yGUCgVbbwP7tyCW++OksvL5s8EALj2HoAla7yQEBeLmNfRkuurG9fA5l2++H3eTOzdsQW6egaYteAPtO3kWtKlS4jo6yMTpYibmxuSkpIkk+9znTlzBp07d8bs2bPRu3dvyMnJ4e7du7h37x4WLVoE4POW5tGjR+Hq6orIyEiYmpoiNDQUNjY2CAgIQKtWrfDu3TtoamoCAG7fvg1bW1tERESgRo0aBbqNgkhJSYGGhgYaep6EgrKqUHdNqRYwzUnWJbBiFhGfJusSSsT71BTYWxgiOTkZlStX/m7fMnk0v127djhx4gTOnTuHxo0bw8HBAatWrZK8a5eV22CMlR+lesu0POAtU1Ye8ZZpXmVyy5QxxkobDlPGGBMAhyljjAmAw5QxxgTAYcoYYwLgMGWMMQFwmDLGmAA4TBljTAAcpowxJgAOU8YYEwCHKWOMCYDDlDHGBMBhyhhjAuAwZYwxAXCYMsaYADhMGWNMABymjDEmAA5TxhgTAIcpY4wJgMOUMcYEwGHKGGMC4DBljDEBcJgyxpgAFGRdQEXxz8TmP/zd7fKiSuPxsi6hRL0LXi/rElgpwFumjDEmAA5TxhgTAIcpY4wJgMOUMcYEwGHKGGMC4DBljDEBcJgyxpgAOEwZY0wAHKaMMSYADlPGGBMAhyljjAmAw5QxxgTAYcoYYwLgMGWMMQFwmDLGmAA4TBljTAAcpowxJgAOU8YYEwCHKWOMCYDDlDHGBMBhyhhjAuAwZYwxAXCYMsaYADhMGWNMABymX3Bzc4Orq+t3+7Rs2RKTJk0qkXq+x2vTRljUMoWmmjKa2jfElSuXv9v/8v8uoal9Q2iqKcOythm8vTaXUKXCMKyqgW2LBuPlxWVIvLYKQftnwtbS6LvLNG9YE1f3uONd0GqEHZ+HEb2al1C1wqhIj3FI0BWMHdIbTnY1UbeaGs6fPv7DZYIDL6NX++awMdNGW8d62L9zawlU+m1lJkxjY2MxYcIEmJmZQSwWw8jICF26dIG/v79gt/Hnn3/Cx8dHsPUVl0MHD2D61EmYMXM2goJD0bR5C7h27oCoqKh8+0dGRMC1S0c0bd4CQcGhcJ8xC1MnT8TRI74lXHnRaKpXwgWfKcjMyoHr+I2w7bkIM1cdQVLqx28uY2KoDb91Y3At9Bkc+v2O5dvOYKV7L7g625Rc4f9BRXuMP3z4gDp162HOopUF6v8yKhKjB/VEQ/um8D1zFaMmTMOSudNx9qRf8Rb6HSIiIpndegFFRkaiWbNm0NTUxPz581G/fn1kZmbizJkz2LJlCx4+fFhitbRs2RI2NjZYs2ZNgfqnpKRAQ0MDcYnJqFy5siA1tGjaBLa2dli7YZOkzcbaEl26umLh4qV5+s/2mIGTJ/7G7XvhkrYJY0fj7t07uHQlUJCavlSl8XhB17dwYlc4NjBDm+FrCrzMoond0MnJGrY9F0na1s7ui/q1q6HlkIK9YAvqXfB6QdcHlP7HOCI+TfB15qpbTQ1r/9qHNu27fLPPysW/4eLZkzhx6Zakbd6MiXgUdh/7jl8QrJb3qSmwtzBEcvKPX7+F3jK9desW7t27J7l87NgxuLq6YtasWfj06VPhqy2AsWPHQiQS4caNG+jVqxdq164NKysrTJkyBUFBQQCAVatWwdraGqqqqjAyMsLYsWPx/v17yTp8fHygqamJM2fOwNLSEmpqamjfvj1iYmIkfb7+mJ+WlobBgwdDTU0NBgYGWLlS2BdhUXz69Amht27C2aWtVLtzm7YICryW7zLXgwLh3Ea6f5u27XDrZggyMzOLrVahdHKyxq2wKOxZPgwv/JcicN8MDO3e9LvLNGlgCv+gcKm289fCYGdpDAWF0v2BrCI+xoV1++Z1NHVylmpr3rINHty9JbPxFvpZ9csvv+Dx48cAgOfPn6Nv375QUVHBoUOH4O7uLniBb9++xenTpzFu3DioqqrmuV5TUxMAICcnh7Vr1+L+/fvYsWMHLly4kKeeDx8+YMWKFdi1axf+97//ISoqCtOmTfvmbU+fPh0XL17E0aNHcfbsWQQEBODmzZuCjq+wEhISkJ2dDV1dPal2PT09xMXF5rtMXFws9PSk++vq6iErKwsJCQnFVqtQTKvpYGTvFnga9QZdx27A1sNXsNK9F/p3tv/mMnralRGXmCrVFv82FYqK8tDRVCvukv+TivgYF1ZCfDy0dXSl2rR1dJGVlYWkt4kyqUmhsAs8fvwYNjY2AIBDhw7hp59+wt69e3H16lX07du3wB9/C+rp06cgIlhYWHy335cHhUxNTbFw4UKMGTMGGzdulLRnZmZi8+bNMDc3BwCMHz8eCxYsyHd979+/x19//YWdO3fCxcUFALBjxw5Ur179u3VkZGQgIyNDcjklJeW7/YtKJBJJXSaiPG0/6p9fe2kkJyfCrbAoeK7/fFDizqOXqGtugFG9W2DviRvfXO7r/VcifB5rGdizBaBiPcZFkWe8uY+4jMZb6C1TIkJOTg4A4Pz58+jYsSMAwMjIqFjeAQv6hLh48SJcXFxQrVo1qKurY/DgwUhMTERa2r/7dlRUVCRBCgAGBgaIj4/Pd33Pnj3Dp0+f4OjoKGnT0tJCnTp1vlvH0qVLoaGhIfkzMvr+EefC0tHRgby8fJ4tlPj4+DxbMrn09PQRGyvd/82beCgoKEBbW1vQ+opDbEIKwp9L1/8wIhZG+lW+uUxcYgr0tdWl2qpqqSEzMxuJycW3v08IFfExLiwdXV0kvImTanub8AYKCgrQrKIlk5oKHaaNGjXCokWLsGvXLly6dAmdOnUCAEREROT5mCGEWrVqQSQSITw8/Jt9Xrx4gY4dO6JevXrw9fXFzZs3sWHDBgCQ2n+iqKgotZxIJPrmVkpRt148PDyQnJws+YuOji7Ser5FSUkJtnYNceH8Oan2C/7n4OCY/37EJg6OuOAv3d//3FnYNWyU5z4pjQJvP0dtE+mPdLWMdREV8/aby1y/E4HWDtKfZpwdLXErPApZWTnFUqdQKuJjXFg2DZvg2v+kDzRdveQPq/p2MhtvocN0zZo1uHXrFsaPH4/Zs2ejZs2aAIDDhw+jadPvHxQoCi0tLbRr1w4bNmyQ2srMlZSUhJCQEGRlZWHlypVwcHBA7dq18fr16/90uzVr1oSioqLkABcAvHv3TrK/+FvEYjEqV64s9Se0iZOmYPu2rdixfRsehodj+tTJiI6KwohRowEAv832wHC3wZL+I0eNRtSLF3CfNgUPw8OxY/s2+Gz/C5OmfHt/cWmybvcF2FubYvqwtjAz0sHP7RthWM9m8DrwP0mfBRO6YuvCQZLL3oevwNhAC8um9kAdUz0M7uYAN1dHrNkp3FS64lTRHuO0tPcIv38X4ffvAgBeRb1A+P27eP3q88bIqqWemDlxpKT/z4OGI+ZlNJbNm4lnTx7Cd/9O+O7fiaGjJ8qkfqAI+0zr168vdTQ/1x9//AF5eXlBivraxo0b0bRpU9jb22PBggWoX78+srKycO7cOWzatAn79u1DVlYW1q1bhy5duuDq1avYvPm/TVhWU1PD8OHDMX36dGhra0NPTw+zZ8+GnJzsjwT37vMz3iYmYsniBYiNiYGVVT34Hf8HJiYmAIDYmBhER/87H7GGqSn8jv8D96mT4bVpAwwMDbFy9Vp079FTVkMolJthUfh5qjcWTOiKWaM6IPJVIqb/4Yv9p0IkffR1KsNI/9+Pdy9eJ8J1wiYsn9oTv/RpgZg3yZi6/DD8/G/LYASFV9Ee4wd3bsGtd0fJ5WXzZwIAXHsPwJI1XkiIi0XM638/5VU3roHNu3zx+7yZ2LtjC3T1DDBrwR9o28m1pEuXKPQ80+joaIhEIsmBmBs3bmDv3r2oW7cuRo0aVSxFAkBMTAwWL16MEydOICYmBlWrVkXDhg0xefJktGzZEqtXr8Yff/yBpKQk/PTTTxgwYAAGDx6Md+/eQVNTEz4+Ppg0aRKSkpIk6/Tz80P37t0lH+nd3NyQlJQEPz8/AJ8PQo0ZMwZHjhyBuro6pk6dipMnT8p8nmlpJ/Q809KuOOaZlnbFOc+0NCnMPNNCh2mLFi0watQoDBo0CLGxsahTpw6srKzw+PFjTJw4EXPnzv1PxZc3HKblH4dp+VWsk/bv378Pe/vP8/sOHjyIevXq4dq1a9i7d2+Z+ComY4wVh0KHaWZmJsRiMYDPU6O6du0KALCwsJD6NhFjjFUkhQ5TKysrbN68GZcvX8a5c+fQvn17AMDr16/L5Xw2xhgriEKH6bJly+Dl5YWWLVuiX79+aNCgAQDg77//lnz8Z4yxiqbQU6NatmyJhIQEpKSkoEqVf7+BMmrUKKioqAhaHGOMlRWFDlMAkJeXlwpSAKhRo4YQ9TDGWJlUpDA9fPgwDh48iKioqDyn3bt169Y3lmKMsfKr0PtM165di6FDh0JXVxehoaGwt7eHtrY2nj9/jg4dOhRHjYwxVuoVOkw3btyILVu2YP369VBSUoK7uzvOnTuHiRMnIjk5uThqZIyxUq/QYRoVFSU5oUmlSpWQmvr5BLyDBg3Cvn37hK2OMcbKiEKHqb6+PhITP5/J2sTERHJWpYiIiDJz0l3GGBNaocO0devWOH788xnPhw8fjsmTJ8PFxQU///wzunfvLniBjDFWFhT6aP6WLVskZ9ofPXo0tLS0cOXKFXTp0gWjR48WvEDGGCsLCh2mcnJyUuf07NOnD/r06SNoUYwxVtYUKEzv3r1b4BXWr1+/yMUwxlhZVaAwtbGx+e7vJeUSiUTIzs4WpDDGGCtLChSmERERxV0HY4yVaQUK09zfnWGMMZa/Qk+NWrp0KbZt25anfdu2bVi2bJkgRTHGWFlT6DD18vKChYVFnvbck0YzxlhFVOgwjY2NhYGBQZ72qlWr8s+WMMYqrEKHqZGREa5evZqn/erVqzA0NBSkKMYYK2sKPWl/xIgRmDRpEjIzM9G6dWsAgL+/P9zd3TF16lTBC2SMsbKg0GHq7u6Ot2/fYuzYsZITQysrK2PGjBnw8PAQvEBW9lS035Gf+neYrEsocdsWbpR1CSWCsj/9uNP/K3SYikQiLFu2DL/99hvCw8NRqVIl1KpVS/Lzz4wxVhEV6WdLAEBNTQ2NGzcWshbGGCuzCn0AijHGWF4cpowxJgAOU8YYEwCHKWOMCaBIYbpr1y40a9YMhoaGePHiBQBgzZo1OHbsmKDFMcZYWVHoMN20aROmTJmCjh07IikpSXL+Uk1NTaxZs0bo+hhjrEwodJiuW7cO3t7emD17NuTl5SXtjRo1wr179wQtjjHGyopCh2lERARsbW3ztIvFYqSlpQlSFGOMlTWFDlNTU1Pcvn07T/upU6dQt25dIWpijLEyp9DfgJo+fTrGjRuH9PR0EBFu3LiBffv2YenSpdi6dWtx1MgYY6VeocN06NChyMrKgru7Oz58+ID+/fujWrVq+PPPP9G3b9/iqJExxkq9In03f+TIkRg5ciQSEhKQk5MDXV1doetijLEypcgnOgEAHR0doepgjLEyrdBhampqCpFI9M3rnz9//p8KYoyxsqjQYTpp0iSpy5mZmQgNDcXp06cxffp0oepijLEypdBh+uuvv+bbvmHDBoSEhPznghhjrCwS7EQnHTp0gK+vr1CrY4yxMkWwMD18+DC0tLSEWh1jjJUphf6Yb2trK3UAiogQGxuLN2/eYOPGivEjW4wx9rVCh6mrq6vUZTk5OVStWhUtW7aEhYWFUHUxxliZUqgwzcrKQo0aNdCuXTvo6+sXV02MMVbmFGqfqYKCAsaMGYOMjIziqocxxsqkQh+AatKkCUJDQ4ujFkFERkZCJBLle2ar8sRr00ZY1DKFppoymto3xJUrl7/b//L/LqGpfUNoqinDsrYZvL02l1ClwqkoY75/ej/2T+4O74H28B5oD1+P/nhx6/tjffUgGIem94ZXX1vsHtMO988cKKFqhWFYVQPbFg3Gy4vLkHhtFYL2z4StpdF3l2nesCau7nHHu6DVCDs+DyN6NS+havNX6DAdO3Yspk6divXr1yMwMBB3796V+isMNzc3iEQiiEQiKCgowNjYGGPGjMG7d+8KW1axE4lE8PPzk3UZAIBDBw9g+tRJmDFzNoKCQ9G0eQu4du6AqKiofPtHRkTAtUtHNG3eAkHBoXCfMQtTJ0/E0SNlZypbRRqzmrYeHAdORu/lB9F7+UFUq9cEp5aNx9uop/n2T4l7iZOLx8DA0g69VxyGXc+RuLJtCZ4Fni3hyotGU70SLvhMQWZWDlzHb4Rtz0WYueoIklI/fnMZE0Nt+K0bg2uhz+DQ73cs33YGK917wdXZpuQK/4qIiKggHYcNG4Y1a9ZAU1Mz70pEIhARRCKR5GdMCsLNzQ1xcXHYvn07srKyEBYWhmHDhqFFixbYt29fgdfzpcjISJiamiI0NBQ2NjZFWkd+RCIRjh49mucA3I+kpKRAQ0MDcYnJqFy5siC1tGjaBLa2dli7YZOkzcbaEl26umLh4qV5+s/2mIGTJ/7G7XvhkrYJY0fj7t07uHQlUJCailtpHvPUv8MEXV9+/hriCMdB01C3Tc881wXuWomI4AD0X3tc0hbgNR+JkY/Qc+neYqln20LhZu4snNgVjg3M0Gb4mgIvs2hiN3RysoZtz0WStrWz+6J+7WpoOWSlYLVR9idk3PNGcvKPX78F3jLdsWMH0tPTERERkefv+fPnkn8LSywWQ19fH9WrV0fbtm3x888/4+zZz++oOTk5WLBgAapXrw6xWAwbGxucPn1aavkbN27A1tYWysrKaNSoUb67IMLCwtCxY0eoqalBT08PgwYNQkJCguT6li1bYuLEiXB3d4eWlhb09fUxb948yfU1atQAAHTv3h0ikUhyWRY+ffqE0Fs34ezSVqrduU1bBAVey3eZ60GBcG4j3b9N23a4dTMEmZmZxVarUCrimHPlZGfjyZV/kJn+Efp1GuTbJ/bRHRg1aCrVZmzTDG+ePUB2Vukfaycna9wKi8Ke5cPwwn8pAvfNwNDuTb+7TJMGpvAPCpdqO38tDHaWxlBQkM2PLhf4VnM3YE1MTL779188f/4cp0+fhqKiIgDgzz//xMqVK7FixQrcvXsX7dq1Q9euXfHkyRMAQFpaGjp37ow6derg5s2bmDdvHqZNmya1zpiYGDg5OcHGxgYhISE4ffo04uLi0KdPH6l+O3bsgKqqKq5fv47ly5djwYIFOHfuHAAgODgYALB9+3bExMRILstCQkICsrOzoaurJ9Wup6eHuLjYfJeJi4uFnp50f11dPWRlZUm9qZRWFXHMiS8eY8uARvDqa4tLXgvQwX0ttIxq5tv3Q1ICVDS1pdoqaWgjJzsL6alJJVDtf2NaTQcje7fA06g36Dp2A7YevoKV7r3Qv7P9N5fR066MuMRUqbb4t6lQVJSHjqZacZecr0JNjfre2aKK6sSJE1BTU0N2djbS09MBAKtWrQIArFixAjNmzJCcdHrZsmW4ePEi1qxZgw0bNmDPnj3Izs7Gtm3boKKiAisrK7x8+RJjxoyRrH/Tpk2ws7PDkiVLJG3btm2DkZERHj9+jNq1awMA6tevD09PTwBArVq1sH79evj7+8PFxQVVq1YF8PkXWH80JSwjI0NqtkNKSsp/vYvy9fVjkbubpTD982svzSrSmDUNa+DnFb7ISEvFs6Bz8F8/C64LfL4ZqMgzpv8fa/GWKQg5ORFuhUXBc/3n3RR3Hr1EXXMDjOrdAntP3Pjmcl/vnxT9/2gLuOdScIUK09q1a//wifj27dtCFdCqVSts2rQJHz58wNatW/H48WNMmDABKSkpeP36NZo1aybVv1mzZrhz5w4AIDw8HA0aNICKiorkekdHR6n+N2/exMWLF6Gmlvfd6tmzZ1Jh+iUDAwPEx8cXaiwAsHTpUsyfP7/QyxWUjo4O5OXl82yRxcfH59lyy6Wnp4/YWOn+b97EQ0FBAdra2vkuU5pUxDHLKypBw+DzJz3dmvXw5ul93D25Gy1Hz8vTV0VTBx/eSW9tf0x+Czl5BYjVNUug2v8mNiEF4c+lH6uHEbHfPZgUl5gCfW11qbaqWmrIzMxGYrJsftizUGE6f/58aGhoCFqAqqoqatb8/G67du1atGrVCvPnz5eczu97WyMFeQfKyclBly5dsGzZsjzXGRgYSP6fu2shl0gkQk5OTuEGA8DDwwNTpkyRXE5JSYGR0feneBSGkpISbO0a4sL5c+jm2l3SfsH/HDp36ZbvMk0cHPHPyeNSbf7nzsKuYaM84y6NKuKYv0YgZGd+yvc6/ToNEBkSINUWffsaqppbQV6h9I818PZz1DaR/rWOWsa6iIr59obZ9TsR6OhUT6rN2dESt8KjkJVV+NetEAoVpn379i32nyjx9PREhw4dMGbMGBgaGuLKlSv46aefJNdfu3YN9vaf96XUrVsXu3btwsePH1GpUiUAQFBQkNT67Ozs4Ovrixo1akBBoeg/LKCoqFigmQpisRhisbjIt1MQEydNwXC3QbBr2AhNHBzx19YtiI6KwohRowEAv832wOtXr/CXz04AwMhRo7F543q4T5uCYcNH4npQIHy2/4Udu4s2Y0IWKtKYg/asgbFtC6jp6CPzYxqeXDmF1w+C0XmOFwAgcPdqpL2NR5uJn2cxWLX9GfdO7cPV7ctg6dILcY/uIPyCL1wm/SHLYRTYut0XcNFnKqYPawvfc7fQ2KoGhvVshvEL/32sFkzoCkNdDYz4bRcAwPvwFYzu+xOWTe2BbUeuokl9U7i5OmKIh4+MRlGIMC2p/UwtW7aElZUVlixZgunTp8PT0xPm5uawsbHB9u3bcfv2bezZswcA0L9/f8yePRvDhw/HnDlzEBkZiRUrVkitb9y4cfD29ka/fv0wffp06Ojo4OnTp9i/fz+8vb0hLy9foLpq1KgBf39/NGvWDGKxGFWqVBF87AXVu8/PeJuYiCWLFyA2JgZWVvXgd/wfyQHA2JgYREf/O/+yhqkp/I7/A/epk+G1aQMMDA2xcvVadO+Rd5pNaVWRxvwhKRH+a2ci7d0biFXUoW1SG53neEmO2H949wbvE2Ik/SvrVUen2Ztwdfsy3Du9D6paumg+bBbMHdt+6yZKlZthUfh5qjcWTOiKWaM6IPJVIqb/4Yv9p/49P7K+TmUY6f97VroXrxPhOmETlk/tiV/6tEDMm2RMXX4Yfv63ZTCCzwo8z1ROTg6xsbGCbpm6ubkhKSkpz2T4vXv3YujQoXj8+DF27NiBLVu2ID4+HnXr1sXvv/+O9u3bS/oGBQVh9OjRCA8PR926dfHbb7+hZ8+eUvNMnzx5ghkzZuDixYvIyMiAiYkJ2rdvj1WrVkEkEqFly5awsbHBmjVrJOt1dXWFpqYmfHx8AADHjx/HlClTEBkZiWrVqiEyMrJAYyyOeaasdCmJeaaljZDzTEuzwswzLXCYsqLhMC3/OEzLr2KZtM8YY+zbOEwZY0wAHKaMMSYADlPGGBMAhyljjAmAw5QxxgTAYcoYYwLgMGWMMQFwmDLGmAA4TBljTAAcpowxJgAOU8YYEwCHKWOMCYDDlDHGBMBhyhhjAuAwZYwxAXCYMsaYADhMGWNMABymjDEmAA5TxhgTAIcpY4wJgMOUMcYEwGHKGGMC4DBljDEBiIiIZF1EeZaSkgINDQ3EJSajcuXKsi6HMVYIKSkp0NPWQHLyj1+/vGXKGGMC4DBljDEBcJgyxpgAOEwZY0wAHKaMMSYADlPGGBMAhyljjAmAw5QxxgTAYcoYYwLgMGWMMQFwmDLGmAA4TBljTAAcpowxJgAOU8YYEwCHKWOMCYDDlDHGBMBhyhhjAuAwZYwxAXCYMsaYADhMGWNMABymjDEmAA5TxhgTAIcpY4wJgMO0jPLatBEWtUyhqaaMpvYNceXK5e/2v/y/S2hq3xCaasqwrG0Gb6/NJVSpcCramHm8ZWu85T5MfXx8oKmpKesyBHXo4AFMnzoJM2bORlBwKJo2bwHXzh0QFRWVb//IiAi4dumIps1bICg4FO4zZmHq5Ik4esS3hCsvuoo2Zh5vGRwvlRFRUVE0bNgwMjAwIEVFRTI2NqaJEydSQkKCpI+JiQmtXr1aarnt27eThoZGyRb7heTkZAJAcYnJ9DGTBPlr1NieRo4aLdVWx8KCprnPzLf/lGnuVMfCQqptxMhfyL6Jg2A1FfdfRRszj7d0jDcu8fPrNzk5+Yev9TKxZfr8+XM0atQIjx8/xr59+/D06VNs3rwZ/v7+cHR0xNu3b0u8pszMzBK/TQD49OkTQm/dhLNLW6l25zZtERR4Ld9lrgcFwrmNdP82bdvh1s0QmY2jMCramHm8n5W18ZaJMB03bhyUlJRw9uxZODk5wdjYGB06dMD58+fx6tUrzJ49Gy1btsSLFy8wefJkiEQiiEQiqXWcOXMGlpaWUFNTQ/v27RETEyN1/fbt22FpaQllZWVYWFhg48aNkusiIyMhEolw8OBBtGzZEsrKyti9e3eJjP1rCQkJyM7Ohq6unlS7np4e4uJi810mLi4WenrS/XV19ZCVlYWEhIRiq1UoFW3MPN7Pytp4FWRyq4Xw9u1bnDlzBosXL0alSpWkrtPX18eAAQNw4MABPHnyBDY2Nhg1ahRGjhwp1e/Dhw9YsWIFdu3aBTk5OQwcOBDTpk3Dnj17AADe3t7w9PTE+vXrYWtri9DQUIwcORKqqqoYMmSIZD0zZszAypUrsX37dojF4nzrzcjIQEZGhuRySkqKUHeFlK/fLIgoT9uP+ufXXppVtDHzeMvWeEt9mD558gREBEtLy3yvt7S0xLt375CdnQ15eXmoq6tDX19fqk9mZiY2b94Mc3NzAMD48eOxYMECyfULFy7EypUr0aNHDwCAqakpwsLC4OXlJRWmkyZNkvT5lqVLl2L+/PlFGmtB6OjoQF5ePs87dnx8fJ539lx6evqIjZXu/+ZNPBQUFKCtrV1stQqloo2Zx/tZWRtvmfiY/z0FeTdSUVGRBCkAGBgYID4+HgDw5s0bREdHY/jw4VBTU5P8LVq0CM+ePZNaT6NGjX5Yj4eHB5KTkyV/0dHRRRnWNykpKcHWriEunD8n1X7B/xwcHJvmu0wTB0dc8Jfu73/uLOwaNoKioqKg9RWHijZmHu9nZW68xXYYWyAJCQkkEolo8eLF+V4/cuRIqlKlCuXk5BT4aP7Ro0cpd+ixsbEEgHbv3k1PnjyR+nv+/DkREUVERBAACg0NLXT9xXE0f+ee/aSoqEibt/xFoXfDaPzESaSqqkoPn0bSx0yiae4zqf+AQZL+4Y+fk4qKCk34dTKF3g2jzVv+IkVFRdp74LDMj+LymHm8pXm8hTmaX+rDlIiobdu2VK1aNfrw4YNUe0xMDKmoqNDo0aOJiKhWrVq0YsUKqT4/ClMiomrVqtGCBQu+efulLUw/ZhKtWbuBjE1MSElJiWxt7ejchUuS6wYOGkItfnKS6n/WP4BsbGxJSUmJTGrUoLXrN8n8BcRj5vGW9vEWJkxFRP//ObkUe/LkCZo2bQpLS0ssWrQIpqamePDgAaZPn46MjAwEBQVBS0sLbdu2RaVKlbBx40aIxWLo6OjAx8cHkyZNQlJSkmR9fn5+6N69u2QXwdatWzFx4kQsXboUHTp0QEZGBkJCQvDu3TtMmTIFkZGRMDU1RWhoKGxsbApVe0pKCjQ0NBCXmIzKlSsLeK8wxopbSkoK9LQ1kJz849dvmdhnWqtWLYSEhMDc3Bw///wzzM3NMWrUKLRq1QqBgYHQ0tICACxYsACRkZEwNzdH1apVC7z+ESNGYOvWrfDx8YG1tTWcnJzg4+MDU1PT4hoSY6ycKRNbpmUZb5kyVnaVuy1Txhgr7ThMGWNMABymjDEmAA5TxhgTAIcpY4wJgMOUMcYEwGHKGGMC4DBljDEBcJgyxpgAOEwZY0wAHKaMMSYADlPGGBMAhyljjAmAw5QxxgTAYcoYYwLgMGWMMQFwmDLGmAA4TBljTAAcpowxJgAOU8YYEwCHKWOMCYDDlDHGBKAg6wLKu9xf0k5NSZFxJYyxwsp93ea+jr+Hw7SYpaamAgBqmhrJuBLGWFGlpqZCQ0Pju31EVJDIZUWWk5OD169fQ11dHSKRqMRuNyUlBUZGRoiOjkblypVL7HZlhcdbvslqvESE1NRUGBoaQk7u+3tFecu0mMnJyaF69eoyu/3KlStXiBdbLh5v+SaL8f5oizQXH4BijDEBcJgyxpgAOEzLKbFYDE9PT4jFYlmXUiJ4vOVbWRgvH4BijDEB8JYpY4wJgMOUMcYEwGHKGGMC4DBljDEBcJiyCiE5OVnWJbByjsOUlXt//fUXJk+ejOfPn8u6FJnKycnJ9//lRe7EpODgYISFhZX47XOYVmC5T77yPjsuPj4eISEhWLduXYUK1NzATEtLw6dPnyAnJ4eLFy/i7du3P/yeeVkkEolw6tQptGjRAq9evUJWVlaJ3n75u0fZD0lOC5iaiszMTLx//16qvbzx8PDAL7/8gqtXr+LPP/9ERESErEsqEXJycnj58iXs7e1x9+5d7N+/H87Ozrhx44asSysWb9++xZ07d7Bo0SK4uLhAQaFkTz3CYVrBEBFEIhFOnjyJgQMHomnTphgwYACOHz9eome1KimZmZkAgH79+sHOzg7//PMPVq9ejaioKBlXVjKqV68OfX19dOrUCQMGDMDWrVvRvn37cvfGGRYWBgMDA2zZsgW6uroyqYHDtIIRiUQ4fvw4evXqhebNm2PGjBnQ0dFBt27dZLKfqbgpKipi//79+Omnn5CQkAAlJSVs2bIFK1asKPdbqNnZ2QCA3377DW/evIGGhgYsLCyQmZlZbt44c98U6tatizFjxiAyMhIvXryQzT5hYhXKhw8fqFu3brR8+XIiInr16hWZmJjQqFGjZFxZ8QgPDyddXV3y9vamtLQ0IiJauHAhWVlZ0YQJE+jFixcyrrD4hYWF0alTp6hTp06kr69PZ86coU+fPuXpl52dLYPqiiYnJyff9nHjxpFYLKajR4+WbEFExGFawSQlJZGZmRldvnyZ4uPjqVq1alJBumPHDnr06JEMKyy6P//8k3x9faXagoODSU9Pj0JCQqTaFyxYQEpKSjRp0iR68uRJSZZZ7HKDJj4+nhISEqSuc3FxIX19fTp//rwkUH18fCgxMbHE6yyq3PFduXKFfv/9d/Lw8KDdu3dLrh89ejRVqlSJjh07VqJ18cf8CkYsFqNRo0b43//+h0aNGqFz587YuHEjACAhIQEXLlzAjRs3ytw+tZcvX+LmzZuwtraWas/JyYGioqLkIFtGRgaAzx99TUxMsH//fmzdulWyb7U8EIlEOHr0KDp16gQ7OztMmzYNly5dAgCcPXsW1tbWcHNzg5eXF6ZMmYJhw4YhISFBxlUXnEgkwpEjR9CxY0c8ePAADx8+xKJFi9CrVy8AwKZNmzB06FAMGjQIhw4dKrnCSjS6WYnKfQf/+PEjZWZmStqnTZtGIpGIOnToQB8+fJC0z5w5k+rUqUORkZElXqsQcscSGBhI+/fvl7S3bt2arKyspLa+EhMTqU+fPjRjxowyO95vuX37Nuno6NDSpUvJ09OTGjduTJ07d6bjx49L+vTp04fs7e2pXr16dOvWLRlWW3hPnz4lMzMz2rhxIxERPXz4kKpUqULjx4+X6jdgwAAyNDSk1NTUEqmLw7Scyg3S48ePk7OzM7Vv3548PT0l1/fr14/09PRo/PjxNHfuXHJzcyMNDQ0KDQ2VTcECyMnJoeTkZOrVqxfZ2trSvn37iIgoNjaWrK2tycLCgk6cOEGXLl0iDw8PsrOzo3fv3sm2aIE9fvyYFi5cSPPmzZO0BQQEUNu2baljx45SgfrixYsyOf5r165R/fr1iYgoMjKSjIyM6JdffpFcf+XKFcn/Y2JiSqwuDtNy5ssd85cuXaJKlSrRmDFjaMSIEaSiokI///yz5Pq5c+dSjx49qHHjxjRq1Ci6f/++LEoW3LVr16hfv37UokULOnDgABERvXnzhtq3b081atQgQ0NDMjU1zbMftayLiYmhxo0bk7a2Nk2YMEHquosXL5KLiwt17dpVJgdnhJD73L569Sq1bNmSbty4QUZGRjRq1CjKysoiIqLQ0FAaN24chYeHl3h9HKbl1LNnz+jkyZO0YsUKIiLKysqiCxcukJaWFvXu3VvSLzs7mzIyMiRPxrIm9wX27t07Sk9PlxyRDgwMpD59+lDz5s0lgUpEdP/+fQoLC6PY2FiZ1FscvnwDPXDgAFlbW1PDhg3pxo0bUv0uXbpE9vb21KdPH3r//n1Jl1lgOTk5kjHld9T+2bNnpKenRyKRKM8slEmTJlGrVq3yHHgrCRym5cCCBQvowYMHksuvX78mRUVFUlFRoWXLlkn1vXDhAlWpUoUGDBhQZgP0a8eOHaOGDRvSTz/9RIMHD6aMjAwi+jdQW7RoIfnIX558uU/8y8fSz8+P7OzsaNCgQRQcHCy1zJUrV0r9dLDcfd+5j+Ply5dpxYoVtHHjRnr58iURfd59paioSKNHj6YrV65QSEgITZ48mTQ0NOju3bsyqZvDtIzLyMig9u3bS4VpZmYm7dixg/T19WngwIF5lgkICCCRSEQjRowoyVIFlRskt27dImVlZZo7dy5NnjyZbG1tycrKitLT04noc6D269ePrK2ty+zH2/zkjv/06dPk6upKrVu3pk6dOkmmtfn6+lKjRo1o4MCBdPPmTVmWWig7d+4kfX19ySeHgwcPkpqaGtnY2FCtWrXIzMyMHj58SESfx2hsbEzVqlUjS0tLaty4sUz3+XOYlmFfHokn+rxf7M6dO0RE9OnTJ9q9ezeJxWKaOnVqnmWvXLkieVKWVTdv3qRTp07R77//TkSfd2UEBwdT/fr1ycLCgj5+/EhEn7dshg4dWu6O2h87doxUVFTIw8ODjh07Rra2tmRiYkKPHz8mIqJDhw6Rg4MDdevWrcwcWLx06RI5OjqStbU1RUdH0/Tp08nHx4eysrIoJCSEOnXqRJqampLn7suXL+nevXv06NEjevv2rUxr5zAtoxYvXkwTJ06U7BvKzs6mFi1akJaWFt27d4+IPofLrl27SElJKd9ALcvevHlDlpaWJBKJyMPDQ9Kek5NDISEhVL9+fapXr57kDSc3WMuD3FkLTk5OtHTpUiL6PEHf1NSURo8eLdV3x44d1Lp1a3r16pUsSi2Sq1evUrNmzahmzZrUunVrqTeCJ0+eUMeOHUlDQ6PUbQxwmJZRXl5eJBKJaM6cOZL5k6mpqdSmTRsyMTGR7DfKDVQ1NTWp6SNlXUZGBvn6+lLDhg3Jzs5O6rqcnBy6efMmGRsbk729vaStrFq2bBnt2bNHqu3t27dkZWVF0dHRFBcXR4aGhlIHY76cZ5uSklJitRZW7gHDrx+fO3fuUIcOHUhBQUGyCyu375MnT6hr164kEono2bNnJVvwd3CYlmG7du2SbJnlbqGmpqZSy5Yt8wTq1q1bSU9Pj+Li4mRZsqA+fPhAx48fJzMzM2rTpo3UdTk5ORQaGlqqXmxFNWLECJKTk5P6qmxWVhY5ODjQb7/9RjVq1KDRo0dLvh4aFxdHzs7OkkAt7W8kL168oDNnzhDR532m/fv3J6LPu6KaNGlC5ubmFB8fT0T/juXhw4fUp0+fUrV1ymFaBn354tixY8d3A/XLj/zJyckyqfe/yh1vSEgIeXt709atWyXzCD9+/EjHjx+nOnXqkIuLiyzLLFaTJ08msVhMhw8fpqysLMrOzqaZM2eSpqYmOTs7S/WdNWsWWVtbU1RUlIyqLbisrCzq0KED2dnZ0axZs0heXp42b94suf7atWvUvHlzqlu3rmRDIPf5kN/JWmSJw7Qc+Ouvv/IN1DZt2pC6urrUkf6yJveF4+vrS4aGhtSwYUNq0aIF6ejo0OXLl4no30C1srKixo0by7JcQeXk5Eg+2iYnJ5Obmxtpa2tLTuDx/Plz6tixIzVq1IgmT55MmzdvpmHDhpXJb7LVq1ePRCJRvvv2r169Si1atKD69euX6DeaCovDtIz4ciLz06dPKSgoiK5evSqZApRfoKakpFDnzp3L1FmR8vtIGhAQQDo6OuTl5UVERDdu3CCRSETKysp08uRJIvocqL6+vtS4ceNSP4+yoHLvi8OHD1Pjxo2pR48epKSkRKqqqpIvIjx79ozmzZtHNjY25ODgQH369CnV32TLfXP48OEDpaen06NHjygxMZEcHByoQYMG1KxZMzp+/Hie0wFeu3aN6tatSw4ODpSdnV0qd11wmJYRX26hWVhYUM2aNcnBwYHq168v+fiT+5F/zpw5efYxlQW5L6D4+HgKDg6WTDj39PSkuXPnEtHnqTDGxsY0dOhQGjx4MInFYrp48SIREaWnp5fYSS1KSkhICFWqVIm8vb3p1atX9ODBAxoxYgSJxWKpb3bl5ORQZmZmqfvo+6XcxzcsLIx69OhB9erVIwUFBXJ2dqaxY8dSTk4OtWrVihwcHPIN1LCwMHr+/LksSi8QDtNSKveJlHtCY6LPc/DU1NTIy8uLMjIy6Pjx4yQSiWjVqlWS0MwN1AULFpSpk/3m1vrgwQNq1qwZtW/fnrp3705EnwPl2rVrlJqaSg4ODpKj1leuXCGRSEQikUhyAKO8OXr0KFlZWUnNoczMzKRhw4aRqqoq/f3332Xicc59ft69e5c0NDRo3LhxtHXrVjp8+DB169aNRCIRubm50atXr8jZ2ZkcHR3p77//JiKiGTNm0ODBg2VZfoFwmJZCuS+OkJAQMjc3p4iICCIi+uOPPySnGYuKiiJjY2MaN26cZLncrZK9e/eWqf2kuS+0+/fvk6amJs2aNYtevHiR5+uuISEh1LhxYwoLC5P079OnD02fPl3SVt4cOHCA5OXlJbtuck+leOPGDZKTkyORSFTiJ0Euqvj4eLK1taWZM2fmaV+/fj0pKSnR+PHj6dOnT+Ti4kJ169alpk2bUpUqVejatWsyqrrgOExLmdwgvX37Nqmrq9OkSZMk1w0dOpRGjBhBr169ourVq9OoUaMkQXTo0CFasWJFmf2+fWJiIjVv3jzP2Y6+3Oo6deoUiUQiyQyFOXPmUMeOHaW23sub9+/fU+PGjalv375S52N9/vw5DRw4kKZOnVpm3khu3bpF9erVo3v37kmep7mPb1JSEi1cuJCUlJTo8uXLlJSURKtXr6aFCxfK5AxQRcFhWorkPrHu3LlDKioqNGvWLKnrV61aRd27dydDQ0PJ9+pzcnLo06dPNHbsWJo0aVKer5iWFQ8ePCBzc3MKCAjI92Nr7ji7d+9OIpGIGjduTGpqanT79m0ZVCu83DfF0NBQ8vX1pYMHD0r2D+7atYscHR2pV69e9PLlS4qOjqbZs2dTs2bNytTjvX37dlJWVpZc/np//vPnz0lDQ0Pyra6yhsO0lImKiiIdHR3q06ePVPuWLVuob9++ZG5uTlpaWpKPPampqTRr1iwyMDAoVROYC2vPnj2koKAgeYHlF6hpaWl04sQJOnr0KK1atUryHfTy4vDhw6SpqUmNGzcmJSUlatSoEf3xxx9ERLR7925q0qQJiUQiqlWrFmlra5e5M+RfvnyZlJWV6fDhw9/sY2trK/VprCzhMC1lIiIiqHHjxtS1a1fJGcOXLFlCKioq9ODBA4qOjiZTU1Oys7OjWrVqUYcOHcjAwKDMvbC+dvXq1R++0DZs2FBuJ+bfvXuXqlatSl5eXpSWlkbR0dHk7u5OdnZ2tGrVKiL6vL/05MmT5O/vXyanf0VHR5Ouri517dpVqv7cN863b99S06ZNadeuXbIq8T/hMC2FHj9+TO3bt6euXbvSyJEjSVdXV+podUxMDO3du5c8PDxoz549pXq6SEG9fPlS8kL78uxOX34UnDJlCs2YMaNMTff62reOvO/fv58sLCykfkbk9evXNGnSJHJwcCg3J7P29fUlJSUlGjx4cJ75sHPmzKEaNWqU2bN7cZiWUo8ePSIXFxeqVKmS5Gz5RCT1w3jlja+vL4nFYho0aJDUbIS0tDTy8PAgExOTMvsz1ET/BmlERAStXr2aFi1aRH5+fkT0+eCasbGxJGC+PKmHSCSiU6dOyaZogWVlZdHmzZtJQUGB6tSpQ8OGDaPZs2fTgAEDSEtLq0x/wuIwLcWePn1Kbdu2pQ4dOki+OklUtibiF0Z2drbUC23o0KE0ZswY6tq1K+nq6pbpF9qXBxeNjIyoefPmZGZmRioqKrRt2zZKTEwkHR0dmjZtmtTpAnOnE+V+MaG8CAoKoh49epCVlRU1a9ZMZr/bJCQO01Iu9yN/u3btpH51sTy7fv265BdGmzdvTjNmzCjTB5u+nqUxc+ZMysjIoNu3b5OVlRXVrVuXiD7PKRWJRPTrr7/S9evX6fXr1zRz5kwyMDCg6OhoWQ6hWGRlZX33gGNZIyIiAivVnjx5gilTpiAhIQGrV6+Gg4ODrEsqdjk5OZCTk5N1GYKJjo6GnZ0dWrVqhYMHD0ranZ2dER4ejpCQEBgaGiIgIAADBw6EkpIS5OTkkJmZiaNHj8LOzk6G1RcPIoJIJMrz/7JKQdYFsB+rVasW/vjjD/z2228wNDSUdTkl4ssXVnl4oWVnZ8PU1BQZGRm4evUqmjVrhqVLl+LixYuoX78+3NzckJ2djd69e2P16tVQV1eHmpoazMzMyu1j/uVjWtYfXwDgLdMy5NOnT1BSUpJ1GayInjx5gokTJ0JJSQm6uro4duwYNm/ejObNm+Phw4cIDw/HypUrkZ6eDlNTU1y8eLFcbZ2XdxymjJWgx48fY/z48bhy5QoWLFiAadOmSV2fmpqK+/fvQ1dXF+bm5jKqkhUFhyljJezZs2cYO3Ys5OXlMWvWLDRv3hwAkJWVBQUF3vNWVvFnCMZKmLm5OdavXw8iwqJFi3D16lUA4CAt4zhMGZOBWrVqYe3atVBUVMS0adMQFBQk65LYf8RhypiM5M7SqF69erk9Yl+R8D5TxmSMZ2mUDxymjDEmAP6YzxhjAuAwZYwxAXCYMsaYADhMGWNMABymjDEmAA5TxhgTAIcpK/fmzZsHGxsbyWU3Nze4urqWeB2RkZEQiUS4fft2kZYPCAiASCRCUlKSoHUxYXCYMplwc3ODSCSCSCSCoqIizMzMMG3aNKSlpRX7bf/555/w8fEpUN//GoCs4uAzKzCZad++PbZv347MzExcvnwZI0aMQFpaGjZt2pSnb2ZmJhQVFQW5XQ0NDUHWw9iXeMuUyYxYLIa+vj6MjIzQv39/DBgwAH5+fgD+/Wi+bds2mJmZQSwWg4iQnJyMUaNGQVdXF5UrV0br1q1x584dqfX+/vvv0NPTg7q6OoYPH4709HSp67/+mJ+Tk4Nly5ahZs2aEIvFMDY2xuLFiwEApqamAABbW1uIRCK0bNlSstz27dthaWkJZWVlWFhYYOPGjVK3c+PGDdja2kJZWRmNGjVCaGjoD++TjIwMuLu7w8jICGKxGLVq1cJff/2Vb9/ExET069cP1atXh4qKCqytrbFv3z6pPocPH4a1tTUqVaoEbW1ttGnTRrL1HxAQAHt7e6iqqkJTUxPNmjXDixcvflgjyx9vmbJSo1KlSsjMzJRcfvr0KQ4ePAhfX1/Iy8sDADp16gQtLS38888/0NDQgJeXF5ydnfH48WNoaWnh4MGD8PT0xIYNG9CiRQvs2rULa9euhZmZ2Tdv18PDA97e3li9ejWaN2+OmJgYPHz4EMDnQLS3t8f58+dhZWUl+Q69t7c3PD09sX79etja2iI0NBQjR46EqqoqhgwZgrS0NHTu3BmtW7fG7t27ERERgV9//fWH98HgwYMRGBiItWvXokGDBoiIiEBCQkK+fdPT09GwYUPMmDEDlStXxsmTJzFo0CCYmZmhSZMmiImJQb9+/bB8+XJ0794dqampuHz5MogIWVlZcHV1xciRI7Fv3z58+vQJN27cKBc/HyIzJf0LfowREQ0ZMoS6desmuXz9+nXS1tamPn36EBGRp6cnKSoqUnx8vKSPv78/Va5cmdLT06XWZW5uTl5eXkRE5OjoSKNHj5a6vkmTJtSgQYN8bzslJYXEYjF5e3vnW2dERAQBoNDQUKl2IyMj2rt3r1TbwoULydHRkYiIvLy8SEtLi9LS0iTXb9q0Kd915Xr06BEBoHPnzuV7/cWLFwkAvXv3Lt/riYg6duxIU6dOJSKimzdvEgCKjIzM0y8xMZEAUEBAwDfXxQqHt0yZzJw4cQJqamrIyspCZmYmunXrhnXr1kmuNzExQdWqVSWXb968iffv30NbW1tqPR8/fsSzZ88AAOHh4Rg9erTU9Y6Ojrh48WK+NYSHhyMjIwPOzs4FrvvNmzeIjo7G8OHDMXLkSEl7VlaWZH9seHg4GjRoABUVFak6vuf27duQl5eHk5NTgerIzs7G77//jgMHDuDVq1fIyMhARkYGVFVVAQANGjSAs7MzrK2t0a5dO7Rt2xa9evVClSpVoKWlBTc3N7Rr1w4uLi5o06YN+vTpAwMDgwLfD0wahymTmVatWmHTpk1QVFSEoaFhngNMuaGQKycnBwYGBggICMizLk1NzSLVUKlSpUIvk5OTA+DzR/0mTZpIXZe7O4KKcDK2wtaycuVKrF69GmvWrIG1tTVUVVUxadIkfPr0SVLLuXPncO3aNZw9exbr1q3D7Nmzcf36dZiammL79u2YOHEiTp8+jQMHDmDOnDk4d+5chfgp8eLAB6CYzKiqqqJmzZowMTEp0JF6Ozs7xMbGQkFBATVr1pT609HRAQBYWlrmOWv9985iX6tWLVSqVAn+/v75Xp+7jzQ7O1vSpqenh2rVquH58+d56sg9YFW3bl3cuXMHHz9+LFAdAGBtbY2cnBxcunTpu/1yXb58Gd26dcPAgQPRoEEDmJmZ4cmTJ1J9RCIRmjVrhvnz5yM0NBRKSko4evSo5HpbW1t4eHjg2rVrqFevHvbu3Vug22Z5cZiyMqNNmzZwdHSEq6srzpw5g8jISFy7dg1z5sxBSEgIAODXX3/Ftm3bsG3bNjx+/Bienp548ODBN9eprKyMGTNmwN3dHTt37sSzZ88QFBQkOYKuq6uLSpUq4fTp04iLi0NycjKAz7MNli5dij///BOPHz/GvXv3sH37dqxatQoA0L9/f8jJyWH48OEICwvDP//8gxUrVnx3fDVq1MCQIUMwbNgw+Pn5ISIiAgEBATh48GC+/WvWrCnZ8gwPD8cvv/yC2NhYyfXXr1/HkiVLEBISgqioKBw5cgRv3ryBpaUlIiIi4OHhgcDAQLx48QJnz57F48ePYWlpWfAHhEmT9U5bVjF9fQDqa56enlIHjXKlpKTQhAkTyNDQkBQVFcnIyIgGDBhAUVFRkj6LFy8mHR0dUlNToyFDhpC7u/s3D0AREWVnZ9OiRYvIxMSEFBUVydjYmJYsWSK53tvbm4yMjEhOTo6cnJwk7Xv27CEbGxtSUlKiKlWq0E8//URHjhyRXB8YGEgNGjQgJSUlsrGxIV9f3+8egCIi+vjxI02ePJkMDAxISUmJatasSdu2bSOivAegEhMTqVu3bqSmpka6uro0Z84cGjx4sGRsYWFh1K5dO6patSqJxWKqXbs2rVu3joiIYmNjydXVVXI7JiYmNHfuXMrOzv5mbez7+Ez7jDEmAP6YzxhjAuAwZYwxAXCYMsaYADhMGWNMABymjDEmAA5TxhgTAIcpY4wJgMOUMcYEwGHKGGMC4DBljDEBcJgyxpgAOEwZY0wA/wcyoLom5LxWqQAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 350x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 350x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Show confusion matrixes\n",
-    "plt.figure(figsize=(3.5,3))\n",
-    "plot_confusion_matrix(cm_googlenet, classes=classes, title='Confusion matrix, Googlenet')\n",
-    "plt.figure(figsize=(3.5,3))\n",
-    "plot_confusion_matrix(cm_resnet, classes=classes, title='Confusion matrix, Resnet')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: title={'center': 'Inferences'}>"
-      ]
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x300 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axes = plt.subplots(1,2, figsize = (10,3))\n",
-    "confidences_model1 = [0.1, 0.3, 0.4, 0.5, 0.8]\n",
-    "confidences_model2 = [0.4, 0.1, 0.4, 0.5, 0.2]\n",
-    "\n",
-    "inferences_model1 = [53, 34, 54, 23, 43]\n",
-    "inferences_model2 = [34, 23, 36, 43, 6]\n",
-    "\n",
-    "confidences_combined = pd.DataFrame({\"Googlenet\":confidences_model1, \"Resnet\": confidences_model2})\n",
-    "inferences_combined = pd.DataFrame({\"Googlenet\":inferences_model1, \"Resnet\": inferences_model2})\n",
-    "\n",
-    "# Creating plot\n",
-    "# plt.boxplot(confidences_model1)\n",
-    "confidences_combined[['Googlenet', 'Resnet']].plot(kind='box', title='Confidences', ax=axes[0])\n",
-    "inferences_combined[['Googlenet', 'Resnet']].plot(kind='box', title='Inferences', ax=axes[1])"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/README.md b/README.md
index 1c9fe12777d47c597279243bbfe5d2ebbef89de1..d54aed9d49820665b87db36d9fbd961f0f2eaccd 100644
--- a/README.md
+++ b/README.md
@@ -1 +1,5 @@
-Exercises for the course Hardware acceleration for AI.
\ No newline at end of file
+# Exercises for the Course: Hardware Acceleration for AI
+
+### Labs: The .ipynb and .pdf files can be found in each labs respective folder.
+
+### Project: Our project can be found in the project foulder
diff --git a/pdf/Lab1-task1-2.ipynb b/lab1/Lab1-task1-2.ipynb
similarity index 100%
rename from pdf/Lab1-task1-2.ipynb
rename to lab1/Lab1-task1-2.ipynb
diff --git a/pdf/Lab1-task3.ipynb b/lab1/Lab1-task3.ipynb
similarity index 100%
rename from pdf/Lab1-task3.ipynb
rename to lab1/Lab1-task3.ipynb
diff --git a/pdf/Lab1.pdf b/lab1/Lab1.pdf
similarity index 100%
rename from pdf/Lab1.pdf
rename to lab1/Lab1.pdf
diff --git a/lab1/Lab_Exercise_1.pdf b/lab1/Lab_Exercise_1.pdf
deleted file mode 100644
index 4ce52da03fdab55adcbced9694c3e8c8ca7c29cf..0000000000000000000000000000000000000000
Binary files a/lab1/Lab_Exercise_1.pdf and /dev/null differ
diff --git a/lab1/haarcascade_frontalface_default.xml b/lab1/haarcascade_frontalface_default.xml
deleted file mode 100644
index 8dff079dac798e0b84f26aad876f3323d594c8fa..0000000000000000000000000000000000000000
--- a/lab1/haarcascade_frontalface_default.xml
+++ /dev/null
@@ -1,35712 +0,0 @@
-<?xml version="1.0"?>
-<!--
-    Stump-based 24x24 discrete(?) adaboost frontal face detector.
-    Created by Rainer Lienhart.
-
-////////////////////////////////////////////////////////////////////////////////////////
-
-  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-
-  By downloading, copying, installing or using the software you agree to this license.
-  If you do not agree to this license, do not download, install,
-  copy or use the software.
-
-
-                        Intel License Agreement
-                For Open Source Computer Vision Library
-
- Copyright (C) 2000, Intel Corporation, all rights reserved.
- Third party copyrights are property of their respective owners.
-
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
-   * Redistribution's of source code must retain the above copyright notice,
-     this list of conditions and the following disclaimer.
-
-   * Redistribution's in binary form must reproduce the above copyright notice,
-     this list of conditions and the following disclaimer in the documentation
-     and/or other materials provided with the distribution.
-
-   * The name of Intel Corporation may not be used to endorse or promote products
-     derived from this software without specific prior written permission.
-
- This software is provided by the copyright holders and contributors "as is" and
- any express or implied warranties, including, but not limited to, the implied
- warranties of merchantability and fitness for a particular purpose are disclaimed.
- In no event shall the Intel Corporation or contributors be liable for any direct,
- indirect, incidental, special, exemplary, or consequential damages
- (including, but not limited to, procurement of substitute goods or services;
- loss of use, data, or profits; or business interruption) however caused
- and on any theory of liability, whether in contract, strict liability,
- or tort (including negligence or otherwise) arising in any way out of
- the use of this software, even if advised of the possibility of such damage.
--->
-<opencv_storage>
-<haarcascade_frontalface_default type_id="opencv-haar-classifier">
-  <size>24 24</size>
-  <stages>
-    <_>
-      <!-- stage 0 -->
-      <trees>
-        <_>
-          <!-- tree 0 -->
-          <_>
-            <!-- root node -->
-            <feature>
-              <rects>
-                <_>6 4 12 9 -1.</_>
-                <_>6 7 12 3 3.</_></rects>
-              <tilted>0</tilted></feature>
-            <threshold>-0.0315119996666908</threshold>
-            <left_val>2.0875380039215088</left_val>
-            <right_val>-2.2172100543975830</right_val></_></_>
-        <_>
-          <!-- tree 1 -->
-          <_>
-            <!-- root node -->
-            <feature>
-              <rects>
-                <_>6 4 12 7 -1.</_>
-                <_>10 4 4 7 3.</_></rects>
-              <tilted>0</tilted></feature>
-            <threshold>0.0123960003256798</threshold>
-            <left_val>-1.8633940219879150</left_val>
-            <right_val>1.3272049427032471</right_val></_></_>
-        <_>
-          <!-- tree 2 -->
-          <_>
-            <!-- root node -->
-            <feature>
-              <rects>
-                <_>3 9 18 9 -1.</_>
-                <_>3 12 18 3 3.</_></rects>
-              <tilted>0</tilted></feature>
-            <threshold>0.0219279993325472</threshold>
-            <left_val>-1.5105249881744385</left_val>
-            <right_val>1.0625729560852051</right_val></_></_>
-        <_>
-          <!-- tree 3 -->
-          <_>
-            <!-- root node -->
-            <feature>
-              <rects>
-                <_>8 18 9 6 -1.</_>
-                <_>8 20 9 2 3.</_></rects>
-              <tilted>0</tilted></feature>
-            <threshold>5.7529998011887074e-003</threshold>
-            <left_val>-0.8746389746665955</left_val>
-            <right_val>1.1760339736938477</right_val></_></_>
-        <_>
-          <!-- tree 4 -->
-          <_>
-            <!-- root node -->
-            <feature>
-              <rects>
-                <_>3 5 4 19 -1.</_>
-                <_>5 5 2 19 2.</_></rects>
-              <tilted>0</tilted></feature>
-            <threshold>0.0150140002369881</threshold>
-            <left_val>-0.7794569730758667</left_val>
-            <right_val>1.2608419656753540</right_val></_></_>
-        <_>
-          <!-- tree 5 -->
-          <_>
-            <!-- root node -->
-            <feature>
-              <rects>
-                <_>6 5 12 16 -1.</_>
-                <_>6 13 12 8 2.</_></rects>
-              <tilted>0</tilted></feature>
-            <threshold>0.0993710011243820</threshold>
-            <left_val>0.5575129985809326</left_val>
-            <right_val>-1.8743000030517578</right_val></_></_>
-        <_>
-          <!-- tree 6 -->
-          <_>
-            <!-- root node -->
-            <feature>
-              <rects>
-                <_>5 8 12 6 -1.</_>
-                <_>5 11 12 3 2.</_></rects>
-              <tilted>0</tilted></feature>
-            <threshold>2.7340000960975885e-003</threshold>
-            <left_val>-1.6911929845809937</left_val>
-            <right_val>0.4400970041751862</right_val></_></_>
-        <_>
-          <!-- tree 7 -->
-          <_>
-            <!-- root node -->
-            <feature>
-              <rects>
-                <_>11 14 4 10 -1.</_>
-                <_>11 19 4 5 2.</_></rects>
-              <tilted>0</tilted></feature>
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-</opencv_storage>
diff --git a/lab1/sample_oak_jetson.py b/lab1/sample_oak_jetson.py
deleted file mode 100644
index e50ab1ece9bafa5092327191dadc768469d538a8..0000000000000000000000000000000000000000
--- a/lab1/sample_oak_jetson.py
+++ /dev/null
@@ -1,55 +0,0 @@
-#!/usr/bin/env python3
-
-import cv2
-import time
-import depthai as dai
-
-import matplotlib.pyplot as plt
-
-from IPython.display import clear_output
-
-# Create pipeline
-pipeline = dai.Pipeline()
-
-# Define source and output
-camRgb = pipeline.create(dai.node.ColorCamera)
-xoutRgb = pipeline.create(dai.node.XLinkOut)
-
-xoutRgb.setStreamName("rgb")
-
-# Properties
-camRgb.setPreviewSize(300, 300)
-camRgb.setInterleaved(False)
-camRgb.setColorOrder(dai.ColorCameraProperties.ColorOrder.RGB)
-
-# Linking
-camRgb.preview.link(xoutRgb.input)
-
-# Connect to device and start pipeline
-with dai.Device(pipeline) as device:
-
-    # Output queue will be used to get the rgb frames from the output defined above
-    qRgb = device.getOutputQueue(name="rgb", maxSize=4, blocking=False)
-
-    for i in range(10) :
-        # Clear output beacause we do not have an animation here
-        # Not so fancy but breaks less often
-        clear_output(wait=True)
-        inRgb = qRgb.get()  # blocking call, will wait until a new data has arrived
-
-        # Retrieve 'bgr' (opencv format) frame
-        frame = inRgb.getCvFrame()
-
-        # Convert to RGB
-        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-
-        # Save image
-        cv2.imwrite("oak0000{}.jpeg".format(i), frame)
-        print("Saved oak000{}".format(i))
-
-        # Display image
-        plt.imshow(frame)
-        plt.show()
-        
-        # Feel free to edit
-        time.sleep(1)
diff --git a/.ipynb_checkpoints/HAAI_2-checkpoint.ipynb b/lab2/.ipynb_checkpoints/HAAI_2-checkpoint.ipynb
similarity index 100%
rename from .ipynb_checkpoints/HAAI_2-checkpoint.ipynb
rename to lab2/.ipynb_checkpoints/HAAI_2-checkpoint.ipynb
diff --git a/HAAI_2.ipynb b/lab2/HAAI_2.ipynb
similarity index 100%
rename from HAAI_2.ipynb
rename to lab2/HAAI_2.ipynb
diff --git a/lab2/Lab2.ipynb b/lab2/Lab2.ipynb
deleted file mode 100644
index 0e7d9ea06ed18abddf5ee4d781f41dadebd09148..0000000000000000000000000000000000000000
--- a/lab2/Lab2.ipynb
+++ /dev/null
@@ -1,1030 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "id": "f63ddc5b-dae2-4199-af98-78a698931ef7",
-      "metadata": {
-        "id": "f63ddc5b-dae2-4199-af98-78a698931ef7"
-      },
-      "source": [
-        "# Lab work\n",
-        "\n",
-        "Henrique Núñez"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 10,
-      "id": "a1b540ef-b205-4365-b738-07b43818261a",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "a1b540ef-b205-4365-b738-07b43818261a",
-        "outputId": "f8dd5e17-ac4f-4f96-b7e5-0e27c6ac9d28"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "<module 'numba.cuda' from '/usr/local/lib/python3.10/dist-packages/numba/cuda/__init__.py'>\n"
-          ]
-        }
-      ],
-      "source": [
-        "import numpy as np\n",
-        "import numba as nb\n",
-        "import numba.cuda\n",
-        "import matplotlib.pyplot as plt\n",
-        "from functools import partial\n",
-        "import timeit\n",
-        "\n",
-        "# For instrumentation\n",
-        "import time\n",
-        "\n",
-        "def instrumentor(num_run=1):\n",
-        "    def _instrumentor(func):\n",
-        "        def wrapper(*args, **kwargs):\n",
-        "            runs = []\n",
-        "            for _run in range(num_run):\n",
-        "                start = time.time()\n",
-        "                result = func(*args, **kwargs)\n",
-        "                end = time.time()\n",
-        "                runs.append(end - start)\n",
-        "            runtime = np.average(runs) * 10**6\n",
-        "            print(f\"Function {func.__name__} took {runtime:.2f} µs seconds to execute\")\n",
-        "            return result, runtime\n",
-        "        return wrapper\n",
-        "    return _instrumentor\n",
-        "\n",
-        "print(nb.cuda)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "dc73da03-203b-4a22-9dff-1c52296387f0",
-      "metadata": {
-        "id": "dc73da03-203b-4a22-9dff-1c52296387f0"
-      },
-      "source": [
-        "# Acceleration in CPU with numba"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "id": "f376285f-d9b1-4a2f-96e0-8e4239dc4ca9",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "f376285f-d9b1-4a2f-96e0-8e4239dc4ca9",
-        "outputId": "4b270df9-f221-4c42-fefc-6db13c79b7fa"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "4.47 ms ± 1.05 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Baseline implementation\n",
-        "\n",
-        "def monte_carlo_pi(nsamples):\n",
-        "    acc = 0\n",
-        "    for i in nb.prange(nsamples):\n",
-        "        x = np.random.random()\n",
-        "        y = np.random.random()\n",
-        "        if (x**2 + y**2) < 1.0:\n",
-        "            acc += 1\n",
-        "    return 4.0 * acc / nsamples\n",
-        "\n",
-        "%timeit monte_carlo_pi(1000)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "8a768e9b-e0e9-4f6a-9058-ab1a4d898936",
-      "metadata": {
-        "id": "8a768e9b-e0e9-4f6a-9058-ab1a4d898936"
-      },
-      "source": [
-        "### Baseline"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 11,
-      "id": "63f353e3-4a38-4324-8f12-b289523b4858",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "63f353e3-4a38-4324-8f12-b289523b4858",
-        "outputId": "eb37febb-7d8e-44f5-b1e2-b0f19f8a82f7"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "1.21 ms ± 282 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
-          ]
-        }
-      ],
-      "source": [
-        "%timeit monte_carlo_pi(1000)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "10c6cf06-d2b5-49ef-9ffc-e93f50dbd574",
-      "metadata": {
-        "id": "10c6cf06-d2b5-49ef-9ffc-e93f50dbd574"
-      },
-      "source": [
-        "### CPU accelerated"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 12,
-      "id": "a59d4f2d-b980-4538-a1ef-9083b8635180",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "a59d4f2d-b980-4538-a1ef-9083b8635180",
-        "outputId": "e5f1d799-53b8-424c-fed1-677afc633e17"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "12.3 µs ± 2.64 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
-            "13.6 µs ± 2.96 µs per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
-          ]
-        }
-      ],
-      "source": [
-        "monte_carlo_pi_jit = nb.njit()(monte_carlo_pi)\n",
-        "%timeit monte_carlo_pi_jit(1000)\n",
-        "%timeit monte_carlo_pi_jit(1000)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "cae05830-75f5-4e9a-9fa4-3bb7e3fdebb7",
-      "metadata": {
-        "id": "cae05830-75f5-4e9a-9fa4-3bb7e3fdebb7"
-      },
-      "source": [
-        "### CPU MT"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 13,
-      "id": "21eaa95e-f392-42c0-9fb5-ada210d07224",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "21eaa95e-f392-42c0-9fb5-ada210d07224",
-        "outputId": "38219d52-a247-4822-c849-24f63089e807"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "49.1 µs ± 26.4 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
-          ]
-        }
-      ],
-      "source": [
-        "monte_carlo_pi_jit_par = nb.njit(parallel=True)(monte_carlo_pi)\n",
-        "%timeit monte_carlo_pi_jit_par(1000)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "6b4ba608-c6a0-4f71-8f15-5ae39e3420b4",
-      "metadata": {
-        "id": "6b4ba608-c6a0-4f71-8f15-5ae39e3420b4"
-      },
-      "source": [
-        "## Answers\n",
-        "\n",
-        "1. Because it has the overhead of JIT'ing the code, what takes a large time.\n",
-        "2. Answer below.\n",
-        "3. `nopython` is a boolean flag for numba that makes the compiled code to execute without the python interpreter, and this leads to a better performance. This will typically be executed natively."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 14,
-      "id": "3bace93f-747d-4351-801a-d75fcd277559",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 730
-        },
-        "id": "3bace93f-747d-4351-801a-d75fcd277559",
-        "outputId": "8dda663c-3a0b-4db7-eb8c-73adc7cfa60d"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Runtime for 100: 1.80 µs.\n",
-            "Runtime for 7235: 80.69 µs.\n",
-            "Runtime for 14371: 150.93 µs.\n",
-            "Runtime for 21507: 221.42 µs.\n",
-            "Runtime for 28642: 293.57 µs.\n",
-            "Runtime for 35778: 787.27 µs.\n",
-            "Runtime for 42914: 751.27 µs.\n",
-            "Runtime for 50050: 646.66 µs.\n",
-            "Runtime for 57185: 620.98 µs.\n",
-            "Runtime for 64321: 655.55 µs.\n",
-            "Runtime for 71457: 881.66 µs.\n",
-            "Runtime for 78592: 793.74 µs.\n",
-            "Runtime for 85728: 1027.47 µs.\n",
-            "Runtime for 92864: 962.41 µs.\n",
-            "Runtime for 100000: 1163.84 µs.\n"
-          ]
-        },
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "# Answer 2\n",
-        "\n",
-        "import timeit\n",
-        "from functools import partial\n",
-        "\n",
-        "nsamples_vec = np.linspace(100, 100000, 15)\n",
-        "\n",
-        "xs = []\n",
-        "results_vec = []\n",
-        "\n",
-        "verbose=True\n",
-        "\n",
-        "for _nsamples in nsamples_vec:\n",
-        "    _nsamples = int(_nsamples)\n",
-        "    xs.append(_nsamples)\n",
-        "\n",
-        "    my_function_partial = partial(monte_carlo_pi, _nsamples)\n",
-        "\n",
-        "    run_num = 10\n",
-        "    runtime = timeit.repeat(my_function_partial, number=run_num, repeat=7)\n",
-        "    runtime_adj = np.average(runtime) * 10**3\n",
-        "\n",
-        "    if verbose:\n",
-        "        print(f\"Runtime for {_nsamples}: {runtime_adj:.2f} µs.\")\n",
-        "\n",
-        "    results_vec.append(runtime_adj)\n",
-        "\n",
-        "plt.loglog(nsamples_vec, results_vec)\n",
-        "plt.xlabel('Number of samples')\n",
-        "plt.ylabel('Execution time (µs)')\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "d72a7cce-4270-48bc-82b9-d8d24a266a16",
-      "metadata": {
-        "id": "d72a7cce-4270-48bc-82b9-d8d24a266a16"
-      },
-      "source": [
-        "# Task 2 - Accelerating in CPU and GPU with vectorize"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "4c6833cb-544e-4ddf-bb0f-89a73ab6914c",
-      "metadata": {
-        "id": "4c6833cb-544e-4ddf-bb0f-89a73ab6914c"
-      },
-      "source": [
-        "## Defining universal functions (ufunc)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 16,
-      "id": "1cc33740-fd7b-44e0-a026-17e3fac6b930",
-      "metadata": {
-        "id": "1cc33740-fd7b-44e0-a026-17e3fac6b930"
-      },
-      "outputs": [],
-      "source": [
-        "import math\n",
-        "\n",
-        "# Two functions, and 3 versions of each function\n",
-        "\n",
-        "# Simple function: multiply\n",
-        "def mul(a, b):\n",
-        "    return a * b\n",
-        "\n",
-        "@nb.vectorize([nb.float32(nb.float32, nb.float32), nb.float64(nb.float64, nb.float64)])\n",
-        "def mul_vec(a, b):\n",
-        "    return a * b\n",
-        "\n",
-        "@nb.vectorize([nb.float32(nb.float32, nb.float32), nb.float64(nb.float64, nb.float64)], target='parallel')\n",
-        "def mul_vec_par(a, b):\n",
-        "    return a * b\n",
-        "\n",
-        "@nb.vectorize([nb.float32(nb.float32, nb.float32), nb.float64(nb.float64, nb.float64)], target='cuda')\n",
-        "def mul_vec_gpu(a, b):\n",
-        "    return a * b\n",
-        "\n",
-        "# Complex function: discriminant in second degree equations\n",
-        "def discriminant(a, b, c):\n",
-        "    return np.sqrt(b ** 2 - 4 * a * c)\n",
-        "\n",
-        "@nb.vectorize(['float32(float32, float32, float32)',\n",
-        "               'float64(float64, float64, float64)'])\n",
-        "def discriminant_vec(a, b, c):\n",
-        "    return np.sqrt(b ** 2 - 4 * a * c)\n",
-        "\n",
-        "@nb.vectorize(['float32(float32, float32, float32)',\n",
-        "               'float64(float64, float64, float64)'],\n",
-        "                target='parallel')\n",
-        "def discriminant_vec_par(a, b, c):\n",
-        "    return np.sqrt(b ** 2 - 4 * a * c)\n",
-        "\n",
-        "@nb.vectorize(['float32(float32, float32, float32)',\n",
-        "               'float64(float64, float64, float64)'],\n",
-        "               target='cuda')\n",
-        "def discriminant_vec_gpu(a, b, c):\n",
-        "    return math.sqrt(b ** 2 - 4 * a * c)\n",
-        "\n",
-        "# nsamples_vec = np.linspace(100, 50000000, 50)\n",
-        "nsamples_vec = np.linspace(100, 10000000, 10)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "bf4a44f0-3a77-49b5-bae1-0ee3fffb93e7",
-      "metadata": {
-        "id": "bf4a44f0-3a77-49b5-bae1-0ee3fffb93e7"
-      },
-      "source": [
-        "## Instrument"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 17,
-      "id": "db3bbda0-9c62-48e2-9639-e9f1d71c2bf4",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 523
-        },
-        "id": "db3bbda0-9c62-48e2-9639-e9f1d71c2bf4",
-        "outputId": "16d04ec8-5aee-481b-85fe-15c6b7c296a3"
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "/usr/local/lib/python3.10/dist-packages/numba/cuda/dispatcher.py:536: NumbaPerformanceWarning: Grid size 1 will likely result in GPU under-utilization due to low occupancy.\n",
-            "  warn(NumbaPerformanceWarning(msg))\n"
-          ]
-        },
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "xs = []\n",
-        "results_vec = []\n",
-        "\n",
-        "run_num = 10\n",
-        "\n",
-        "for _nsamples in nsamples_vec:\n",
-        "    _nsamples = int(_nsamples)\n",
-        "    xs.append(_nsamples)\n",
-        "\n",
-        "    # Create synthetic data\n",
-        "    a = np.random.random(_nsamples).astype('float32')\n",
-        "    b = np.random.random(_nsamples).astype('float32')\n",
-        "\n",
-        "    # Simple function: multiply\n",
-        "    my_function_partial = partial(mul, a, b)\n",
-        "    runtime = timeit.repeat(my_function_partial, number=run_num, repeat=7)\n",
-        "    res_mul = np.average(runtime) * 10**3\n",
-        "\n",
-        "    my_function_partial = partial(mul_vec, a, b)\n",
-        "    runtime = timeit.repeat(my_function_partial, number=run_num, repeat=7)\n",
-        "    res_mul_vec = np.average(runtime) * 10**3\n",
-        "\n",
-        "    my_function_partial = partial(mul_vec_par, a, b)\n",
-        "    runtime = timeit.repeat(my_function_partial, number=run_num, repeat=7)\n",
-        "    res_mul_vec_par = np.average(runtime) * 10**3\n",
-        "\n",
-        "    my_function_partial = partial(mul_vec_gpu, a, b)\n",
-        "    runtime = timeit.repeat(my_function_partial, number=run_num, repeat=7)\n",
-        "    res_mul_vec_gpu = np.average(runtime) * 10**3\n",
-        "\n",
-        "    results_vec.append((res_mul,res_mul_vec,res_mul_vec_par,res_mul_vec_gpu))\n",
-        "\n",
-        "normal = list(zip(*results_vec))[0]\n",
-        "cpu = list(zip(*results_vec))[1]\n",
-        "par_cpu = list(zip(*results_vec))[2]\n",
-        "gpu = list(zip(*results_vec))[3]\n",
-        "\n",
-        "# fig, axs = plt.subplots(4, 1, figsize=(10, 40))\n",
-        "plt.loglog(nsamples_vec, normal, label='Baseline')\n",
-        "plt.loglog(nsamples_vec, cpu, label='CPU')\n",
-        "plt.loglog(nsamples_vec, par_cpu, label='Parallel CPU')\n",
-        "plt.loglog(nsamples_vec, gpu, label='GPU')\n",
-        "plt.legend()\n",
-        "plt.tight_layout()\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "4e867bd0-4bf1-452c-940c-01b419808af9",
-      "metadata": {
-        "id": "4e867bd0-4bf1-452c-940c-01b419808af9"
-      },
-      "source": [
-        "## Instrument discriminant function"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 18,
-      "id": "74740716-2787-48ff-b98a-c0c17e8b333b",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 707
-        },
-        "id": "74740716-2787-48ff-b98a-c0c17e8b333b",
-        "outputId": "4f7bda0e-db28-446c-8bf8-db0fb5a207af"
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "<ipython-input-16-1a8345aeb9e4>:23: RuntimeWarning: invalid value encountered in sqrt\n",
-            "  return np.sqrt(b ** 2 - 4 * a * c)\n",
-            "/usr/local/lib/python3.10/dist-packages/numba/np/ufunc/dufunc.py:190: RuntimeWarning: invalid value encountered in discriminant_vec\n",
-            "  return super().__call__(*args, **kws)\n",
-            "<timeit-src>:6: RuntimeWarning: invalid value encountered in discriminant_vec_par\n",
-            "/usr/local/lib/python3.10/dist-packages/numba/cuda/dispatcher.py:536: NumbaPerformanceWarning: Grid size 1 will likely result in GPU under-utilization due to low occupancy.\n",
-            "  warn(NumbaPerformanceWarning(msg))\n",
-            "<ipython-input-16-1a8345aeb9e4>:23: RuntimeWarning: invalid value encountered in sqrt\n",
-            "  return np.sqrt(b ** 2 - 4 * a * c)\n",
-            "/usr/local/lib/python3.10/dist-packages/numba/np/ufunc/dufunc.py:190: RuntimeWarning: invalid value encountered in discriminant_vec\n",
-            "  return super().__call__(*args, **kws)\n",
-            "<timeit-src>:6: RuntimeWarning: invalid value encountered in discriminant_vec_par\n"
-          ]
-        },
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "xs = []\n",
-        "results_vec = []\n",
-        "\n",
-        "run_num = 10\n",
-        "\n",
-        "for _nsamples in nsamples_vec:\n",
-        "    _nsamples = int(_nsamples)\n",
-        "    xs.append(_nsamples)\n",
-        "\n",
-        "    # Create synthetic data\n",
-        "    a = np.random.random(_nsamples).astype('float32')\n",
-        "    b = np.random.random(_nsamples).astype('float32')\n",
-        "    c = np.random.random(_nsamples).astype('float32')\n",
-        "\n",
-        "    # Simple function: multiply\n",
-        "    my_function_partial = partial(discriminant, a, b, c)\n",
-        "    runtime = timeit.repeat(my_function_partial, number=run_num, repeat=7)\n",
-        "    res = np.average(runtime) * 10**3\n",
-        "\n",
-        "    my_function_partial = partial(discriminant_vec, a, b, c)\n",
-        "    runtime = timeit.repeat(my_function_partial, number=run_num, repeat=7)\n",
-        "    res_vec = np.average(runtime) * 10**3\n",
-        "\n",
-        "    my_function_partial = partial(discriminant_vec_par, a, b, c)\n",
-        "    runtime = timeit.repeat(my_function_partial, number=run_num, repeat=7)\n",
-        "    res_vec_par = np.average(runtime) * 10**3\n",
-        "\n",
-        "    my_function_partial = partial(discriminant_vec_gpu, a, b, c)\n",
-        "    runtime = timeit.repeat(my_function_partial, number=run_num, repeat=7)\n",
-        "    res_vec_gpu = np.average(runtime) * 10**3\n",
-        "\n",
-        "    results_vec.append((res,res_vec,res_vec_par,res_vec_gpu))\n",
-        "\n",
-        "normal = list(zip(*results_vec))[0]\n",
-        "cpu = list(zip(*results_vec))[1]\n",
-        "par_cpu = list(zip(*results_vec))[2]\n",
-        "gpu = list(zip(*results_vec))[3]\n",
-        "\n",
-        "# fig, axs = plt.subplots(4, 1, figsize=(10, 40))\n",
-        "plt.loglog(nsamples_vec, normal, label='Baseline')\n",
-        "plt.loglog(nsamples_vec, cpu, label='CPU')\n",
-        "plt.loglog(nsamples_vec, par_cpu, label='Parallel CPU')\n",
-        "plt.loglog(nsamples_vec, gpu, label='GPU')\n",
-        "plt.legend()\n",
-        "plt.tight_layout()\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "9d580639-b3cb-478b-a849-e0625703abb1",
-      "metadata": {
-        "id": "9d580639-b3cb-478b-a849-e0625703abb1"
-      },
-      "source": [
-        "### 64-bit vectorization"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "iOZmwXGV0q-8",
-      "metadata": {
-        "id": "iOZmwXGV0q-8"
-      },
-      "source": [
-        "#### Ufunc"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 19,
-      "id": "c-lAX7Je0pYd",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 524
-        },
-        "id": "c-lAX7Je0pYd",
-        "outputId": "2e227a84-6014-44fc-8a19-3cd3c7952519"
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "/usr/local/lib/python3.10/dist-packages/numba/cuda/dispatcher.py:536: NumbaPerformanceWarning: Grid size 1 will likely result in GPU under-utilization due to low occupancy.\n",
-            "  warn(NumbaPerformanceWarning(msg))\n"
-          ]
-        },
-        {
-          "data": {
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",
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "xs = []\n",
-        "results_vec = []\n",
-        "\n",
-        "run_num = 10\n",
-        "\n",
-        "for _nsamples in nsamples_vec:\n",
-        "    _nsamples = int(_nsamples)\n",
-        "    xs.append(_nsamples)\n",
-        "\n",
-        "    # Create synthetic data\n",
-        "    a = np.random.random(_nsamples).astype('float64')\n",
-        "    b = np.random.random(_nsamples).astype('float64')\n",
-        "\n",
-        "    # Simple function: multiply\n",
-        "    my_function_partial = partial(mul, a, b)\n",
-        "    runtime = timeit.repeat(my_function_partial, number=run_num, repeat=7)\n",
-        "    res_mul = np.average(runtime) * 10**3\n",
-        "\n",
-        "    my_function_partial = partial(mul_vec, a, b)\n",
-        "    runtime = timeit.repeat(my_function_partial, number=run_num, repeat=7)\n",
-        "    res_mul_vec = np.average(runtime) * 10**3\n",
-        "\n",
-        "    my_function_partial = partial(mul_vec_par, a, b)\n",
-        "    runtime = timeit.repeat(my_function_partial, number=run_num, repeat=7)\n",
-        "    res_mul_vec_par = np.average(runtime) * 10**3\n",
-        "\n",
-        "    my_function_partial = partial(mul_vec_gpu, a, b)\n",
-        "    runtime = timeit.repeat(my_function_partial, number=run_num, repeat=7)\n",
-        "    res_mul_vec_gpu = np.average(runtime) * 10**3\n",
-        "\n",
-        "    results_vec.append((res_mul,res_mul_vec,res_mul_vec_par,res_mul_vec_gpu))\n",
-        "\n",
-        "normal = list(zip(*results_vec))[0]\n",
-        "cpu = list(zip(*results_vec))[1]\n",
-        "par_cpu = list(zip(*results_vec))[2]\n",
-        "gpu = list(zip(*results_vec))[3]\n",
-        "\n",
-        "# fig, axs = plt.subplots(4, 1, figsize=(10, 40))\n",
-        "plt.semilogx(nsamples_vec, normal, label='Baseline')\n",
-        "plt.semilogx(nsamples_vec, cpu, label='CPU')\n",
-        "plt.semilogx(nsamples_vec, par_cpu, label='Parallel CPU')\n",
-        "plt.loglog(nsamples_vec, gpu, label='GPU')\n",
-        "plt.legend()\n",
-        "plt.tight_layout()\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "rNSEPrq90vBF",
-      "metadata": {
-        "id": "rNSEPrq90vBF"
-      },
-      "source": [
-        "#### Discriminant"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 34,
-      "id": "R7scyBP80wm7",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 579
-        },
-        "id": "R7scyBP80wm7",
-        "outputId": "7f9cf223-40c3-4718-dbcb-c19cc74d0bc6"
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "<ipython-input-16-1a8345aeb9e4>:23: RuntimeWarning: invalid value encountered in sqrt\n",
-            "  return np.sqrt(b ** 2 - 4 * a * c)\n",
-            "/usr/local/lib/python3.10/dist-packages/numba/np/ufunc/dufunc.py:190: RuntimeWarning: invalid value encountered in discriminant_vec\n",
-            "  return super().__call__(*args, **kws)\n",
-            "<timeit-src>:6: RuntimeWarning: invalid value encountered in discriminant_vec_par\n"
-          ]
-        },
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "xs = []\n",
-        "results_vec = []\n",
-        "\n",
-        "run_num = 10\n",
-        "\n",
-        "for _nsamples in nsamples_vec:\n",
-        "    _nsamples = int(_nsamples)\n",
-        "    xs.append(_nsamples)\n",
-        "\n",
-        "    # Create synthetic data\n",
-        "    a = np.random.random(_nsamples).astype('float64')\n",
-        "    b = np.random.random(_nsamples).astype('float64')\n",
-        "    c = np.random.random(_nsamples).astype('float64')\n",
-        "\n",
-        "    # Simple function: multiply\n",
-        "    my_function_partial = partial(discriminant, a, b, c)\n",
-        "    runtime = timeit.repeat(my_function_partial, number=run_num, repeat=7)\n",
-        "    res = np.min(runtime) * 10**3\n",
-        "\n",
-        "    my_function_partial = partial(discriminant_vec, a, b, c)\n",
-        "    runtime = timeit.repeat(my_function_partial, number=run_num, repeat=7)\n",
-        "    res_vec = np.min(runtime) * 10**3\n",
-        "\n",
-        "    my_function_partial = partial(discriminant_vec_par, a, b, c)\n",
-        "    runtime = timeit.repeat(my_function_partial, number=run_num, repeat=7)\n",
-        "    res_vec_par = np.min(runtime) * 10**3\n",
-        "\n",
-        "    my_function_partial = partial(discriminant_vec_gpu, a, b, c)\n",
-        "    runtime = timeit.repeat(my_function_partial, number=run_num, repeat=7)\n",
-        "    res_vec_gpu = np.min(runtime) * 10**3\n",
-        "\n",
-        "    results_vec.append((res,res_vec,res_vec_par,res_vec_gpu))\n",
-        "\n",
-        "normal = list(zip(*results_vec))[0]\n",
-        "cpu = list(zip(*results_vec))[1]\n",
-        "par_cpu = list(zip(*results_vec))[2]\n",
-        "gpu = list(zip(*results_vec))[3]\n",
-        "\n",
-        "# fig, axs = plt.subplots(4, 1, figsize=(10, 40))\n",
-        "plt.semilogx(nsamples_vec, normal, label='Baseline')\n",
-        "plt.semilogx(nsamples_vec, cpu, label='CPU')\n",
-        "plt.semilogx(nsamples_vec, par_cpu, label='Parallel CPU')\n",
-        "plt.loglog(nsamples_vec, gpu, label='GPU')\n",
-        "plt.legend()\n",
-        "plt.tight_layout()\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "RcaVaPXLej3T",
-      "metadata": {
-        "id": "RcaVaPXLej3T"
-      },
-      "source": [
-        "##### Observations: \n",
-        "\n",
-        "Two universal functions were created and vectorized, a simple multiplication function, taking 2 arguments, and a more complex discriminant function, taking 3 arguments. The four versions were created (baseline, simd, parallel cpu, gpu) and the performance was evaluated using input data in the forms of arrays containing from X to Y elements each. The 32 and 64 bit execution times were computed. \n",
-        "\n",
-        "As a general trend, the latency for the GPU code is above the CPU implementations, and this is likely due to the fact that heterogeneous computing generally has the communication bottleneck. The only case where the GPU was faster than the baseline comparison was when running the 64 bit discriminant computation.\n",
-        "\n",
-        "In the other cases, specially for smaller number or values, the baseline and SIMD vectorizations were better, as it is possible to be seen from the plotted charts. The parallel CPU optimization in general is only competitive in the cases where the input size grows considerably. Even though, we can see that for the multiplication ufunc with 32 bit, the baseline performs as well as the other CPU versions.\n",
-        "\n",
-        "As for the discriminant analysis, in all evaluated cases, either the SIMD or the parallel CPU version were the best ones, with the execution time growing the fastest for the baseline. In terms of computation time growth, the GPU has the smallest angular coefficient. If the input size was large enough, the chart would likely demonstrate the performance gain.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "e186370f-ae17-4696-a27e-6bb3763afc36",
-      "metadata": {
-        "id": "e186370f-ae17-4696-a27e-6bb3763afc36"
-      },
-      "source": [
-        "## Vectorizing distance between sets of points"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 44,
-      "id": "qTTWvHaLTaxt",
-      "metadata": {
-        "id": "qTTWvHaLTaxt"
-      },
-      "outputs": [],
-      "source": [
-        "# GPU vectorization\n",
-        "@nb.cuda.jit(device=True)\n",
-        "def polar_to_cartesian_dev(rho, theta):\n",
-        "    x = rho * math.cos(theta)\n",
-        "    y = rho * math.sin(theta)\n",
-        "    return x, y\n",
-        "\n",
-        "@nb.cuda.jit(device=True)\n",
-        "def dist_rect_dev(x1, y1, x2, y2):\n",
-        "    return math.sqrt((x1 - x2) * (x1 - x2) + (y1 - y2) * (y1 - y2))\n",
-        "\n",
-        "@nb.vectorize(['float32(float32, float32, float32, float32)',\n",
-        "               'float64(float64, float64, float64, float64)'],\n",
-        "               target='cuda')\n",
-        "def dist_gpu(rho1, theta1, rho2, theta2):\n",
-        "\n",
-        "    x1, y1 = polar_to_cartesian_dev(rho1, theta1)\n",
-        "    x2, y2 = polar_to_cartesian_dev(rho2, theta2)\n",
-        "\n",
-        "    distances = dist_rect_dev(x1, y1, x2, y2)\n",
-        "\n",
-        "    return distances\n",
-        "\n",
-        "# CPU vectorization\n",
-        "@nb.jit(nopython=True)\n",
-        "def polar_to_cartesian_jit(rho, theta):\n",
-        "    x = rho * math.cos(theta)\n",
-        "    y = rho * math.sin(theta)\n",
-        "    return x, y\n",
-        "\n",
-        "@nb.jit(nopython=True)\n",
-        "def dist_rect_jit(x1, y1, x2, y2):\n",
-        "    return np.sqrt((x1 - x2) * (x1 - x2) + (y1 - y2) * (y1 - y2))\n",
-        "\n",
-        "@nb.vectorize(['float32(float32, float32, float32, float32)',\n",
-        "               'float64(float64, float64, float64, float64)'],\n",
-        "               target='parallel', nopython=True)\n",
-        "def dist_vec_par(rho1, theta1, rho2, theta2):\n",
-        "\n",
-        "    x1, y1 = polar_to_cartesian_jit(rho1, theta1)\n",
-        "    x2, y2 = polar_to_cartesian_jit(rho2, theta2)\n",
-        "\n",
-        "    distances = dist_rect_jit(x1, y1, x2, y2)\n",
-        "\n",
-        "    return distances\n",
-        "\n",
-        "@nb.vectorize(['float32(float32, float32, float32, float32)',\n",
-        "               'float64(float64, float64, float64, float64)'],\n",
-        "               nopython=True)\n",
-        "def dist_vec(rho1, theta1, rho2, theta2):\n",
-        "\n",
-        "    x1, y1 = polar_to_cartesian_jit(rho1, theta1)\n",
-        "    x2, y2 = polar_to_cartesian_jit(rho2, theta2)\n",
-        "\n",
-        "    distances = dist_rect_jit(x1, y1, x2, y2)\n",
-        "\n",
-        "    return distances\n",
-        "\n",
-        "# Baseline\n",
-        "def polar_to_cartesian(rho, theta):\n",
-        "    x = rho * np.cos(theta)\n",
-        "    y = rho * np.sin(theta)\n",
-        "    return x, y\n",
-        "\n",
-        "def dist_rect(x1, y1, x2, y2):\n",
-        "    return np.sqrt((x1 - x2) * (x1 - x2) + (y1 - y2) * (y1 - y2))\n",
-        "\n",
-        "def dist(rho1, theta1, rho2, theta2):\n",
-        "\n",
-        "    x1, y1 = polar_to_cartesian(rho1, theta1)\n",
-        "    x2, y2 = polar_to_cartesian(rho2, theta2)\n",
-        "\n",
-        "    distances = dist_rect(x1, y1, x2, y2)\n",
-        "\n",
-        "    return distances"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 32,
-      "id": "cwc2pnY_W6ht",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "cwc2pnY_W6ht",
-        "outputId": "0c82f02e-5e5e-4f84-9ae2-669bc7e67a82"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "time 0.03340315818786621, avg: 0.0\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Single run\n",
-        "n = 1000000\n",
-        "rho1 = np.random.uniform(0.5, 1.5, size=n).astype(np.float32)\n",
-        "theta1 = np.random.uniform(-np.pi, np.pi, size=n).astype(np.float32)\n",
-        "rho2 = np.random.uniform(0.5, 1.5, size=n).astype(np.float32)\n",
-        "theta2 = np.random.uniform(-np.pi, np.pi, size=n).astype(np.float32)\n",
-        "\n",
-        "# Sanity check, must be 0\n",
-        "a = time.time()\n",
-        "results = dist_vec(rho1, theta1, rho1, theta1)\n",
-        "b = time.time()\n",
-        "\n",
-        "print(f'time {b - a}, avg: {np.mean(results)}')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 46,
-      "id": "9d613271-8568-4780-a472-512b1b3c33d1",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 487
-        },
-        "id": "9d613271-8568-4780-a472-512b1b3c33d1",
-        "outputId": "8331a48a-c6a3-4a64-e829-481a5531b39c"
-      },
-      "outputs": [
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "#### Instrumentation\n",
-        "\n",
-        "# Instrument multiply function\n",
-        "nsamples_vec = np.linspace(100, 10000000, 10)\n",
-        "\n",
-        "xs = []\n",
-        "results_vec = []\n",
-        "\n",
-        "run_num = 10\n",
-        "\n",
-        "for n in nsamples_vec:\n",
-        "    n = int(n)\n",
-        "    xs.append(n)\n",
-        "\n",
-        "    rho1 = np.random.uniform(0.5, 1.5, size=n).astype(np.float32)\n",
-        "    theta1 = np.random.uniform(-np.pi, np.pi, size=n).astype(np.float32)\n",
-        "    rho2 = np.random.uniform(0.5, 1.5, size=n).astype(np.float32)\n",
-        "    theta2 = np.random.uniform(-np.pi, np.pi, size=n).astype(np.float32)\n",
-        "\n",
-        "    my_function_partial = partial(dist_vec, rho1, theta1, rho1, theta1)\n",
-        "    runtime = timeit.repeat(my_function_partial, number=run_num, repeat=7)\n",
-        "    res_vec = np.min(runtime) * 10**3\n",
-        "\n",
-        "    my_function_partial = partial(dist_vec_par, rho1, theta1, rho1, theta1)\n",
-        "    runtime = timeit.repeat(my_function_partial, number=run_num, repeat=7)\n",
-        "    res_vec_par = np.min(runtime) * 10**3\n",
-        "\n",
-        "    my_function_partial = partial(dist, rho1, theta1, rho1, theta1)\n",
-        "    runtime = timeit.repeat(my_function_partial, number=run_num, repeat=7)\n",
-        "    res = np.min(runtime) * 10**3\n",
-        "\n",
-        "    my_function_partial = partial(dist_gpu, rho1, theta1, rho1, theta1)\n",
-        "    runtime = timeit.repeat(my_function_partial, number=run_num, repeat=7)\n",
-        "    res_gpu = np.min(runtime) * 10**3\n",
-        "\n",
-        "    results_vec.append((res, res_vec, res_vec_par, res_gpu))\n",
-        "\n",
-        "normal = list(zip(*results_vec))[0]\n",
-        "cpu = list(zip(*results_vec))[1]\n",
-        "par_cpu = list(zip(*results_vec))[2]\n",
-        "gpu = list(zip(*results_vec))[3]\n",
-        "\n",
-        "# fig, axs = plt.subplots(4, 1, figsize=(10, 40))\n",
-        "plt.semilogx(nsamples_vec, normal, label='Baseline')\n",
-        "plt.semilogx(nsamples_vec, cpu, label='CPU')\n",
-        "plt.semilogx(nsamples_vec, par_cpu, label='Parallel CPU')\n",
-        "plt.loglog(nsamples_vec, gpu, label='GPU')\n",
-        "plt.legend()\n",
-        "plt.tight_layout()\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "s3Q--VlCeBbJ",
-      "metadata": {
-        "id": "s3Q--VlCeBbJ"
-      },
-      "source": [
-        "Observations: As we can see from this graph, a more complex set of function was advantageous when using GPU as an accelerator, as opposed to the baseline implementation. The parallel CPU was also faster compared to the Baseline and the CPU (SIMD) vectorizations. The baseline was probably better than the CPU because numpy also performs some vectorizations on its own."
-      ]
-    }
-  ],
-  "metadata": {
-    "accelerator": "GPU",
-    "colab": {
-      "gpuType": "T4",
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.8.10"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 5
-}
diff --git a/task5-lab2.jpg b/lab2/task5-lab2.jpg
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diff --git a/pdf/Lab4.pdf b/lab4/Lab4.pdf
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