TraceTimeCheck.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOeGh9ZrkZZoevmLDAHmWx2",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AhmedCode99/TraceOfArrayCode/blob/main/TraceTimeCheck.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import time\n",
        "\n",
        "mat = [[2,1,1],[1,3,1],[1,1,4]] # Insert a square matrix\n",
        "\n",
        "\n",
        "rows,columns = np.shape(mat) # Save the number of rows and columns\n",
        "\n",
        "if  rows == columns:\n",
        "\n",
        "  t0 = time.time()\n",
        "  trace = np.einsum('ii', mat) # Calculating Trace Using EinSum\n",
        "  print(\"Trace Using EinSum = \", trace)\n",
        "  t1 = time.time()\n",
        "\n",
        "  t2 = time.time()\n",
        "  trace = np.trace(mat) # Calculating Trace Using NumPy\n",
        "  print(\"Trace Using NumPy = \", trace)\n",
        "  t3 = time.time()\n",
        "\n",
        "  t4 = time.time()\n",
        "  trace = sum(mat[i][i] for i in range(len(mat))) # Calculating Trace Using List Comprehension\n",
        "  print(\"Trace Using List Comprehension = \", trace)\n",
        "  t5 = time.time()\n",
        "\n",
        "\n",
        "  #Calculating Trace using For Loop\n",
        "  t6 = time.time()\n",
        "  trace = 0\n",
        "  for j in range(len(mat)):\n",
        "    trace = trace + mat[j][j]\n",
        "  print(\"Trace Using For Loop = \", trace)\n",
        "  t7 = time.time()\n",
        "\n",
        "\n",
        "  #Calculating Trace using While Loop\n",
        "\n",
        "  t8 = time.time()\n",
        "  trace = 0\n",
        "  i=0\n",
        "\n",
        "  while i < len(mat):\n",
        "    trace = trace + mat[i][i]\n",
        "    i=i+1\n",
        "\n",
        "  print(\"Trace Using While Loop = \", trace)\n",
        "  t9 = time.time()\n",
        "\n",
        "\n",
        "  # Calculating Trace using SymPy\n",
        "\n",
        "  from sympy import Matrix\n",
        "\n",
        "  t10 = time.time()\n",
        "  matrix = Matrix(mat)\n",
        "\n",
        "  trace = matrix.trace()\n",
        "\n",
        "  print(\"Trace Using SymPy = \", trace)\n",
        "  t11 = time.time()\n",
        "\n",
        "else:\n",
        "    print(\"The Matrix You Inserted Is Not Square. The Trace Operator is Only Defined For Square Matrices. Please Insert A Square Matrix\")\n",
        "\n",
        "\n",
        "method = [\"EinSum\",\"NumPy\",\"List\\nComprehension\",\n",
        "          \"For Loop\", \"While Loop\",\"SymPy\"] # List of Methods Used\n",
        "\n",
        "dt = [t1-t0, t3-t2, t5-t4, t7-t6, t9-t8, t11-t10] # Time it took for each method\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Plotting Results\n",
        "\n",
        "# Create a bar graph\n",
        "plt.figure(figsize=(8, 6))  # Optional: Set the figure size\n",
        "plt.bar(method, dt)\n",
        "\n",
        "# Adding labels and title\n",
        "plt.xlabel('Method')\n",
        "plt.ylabel('Time (s)')\n",
        "plt.title('Execution Time of a Program Calculating 3x3 Matrix')\n",
        "\n",
        "# Display the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "xRU4J7AvNSqY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 688
        },
        "outputId": "767a5371-1c65-4074-d9d6-22a5dd6e0d56"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Trace Using EinSum =  9\n",
            "Trace Using NumPy =  9\n",
            "Trace Using List Comprehension =  9\n",
            "Trace Using For Loop =  9\n",
            "Trace Using While Loop =  9\n",
            "Trace Using SymPy =  9\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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Ll1aZMmUUHh6udevWKT4+XgMHDnSoFxsb67C93O7TgQMHtHXrVnXp0sWhZysyMlK1a9fOtk1t27ZV6dKlHcpcXV3tPV8ZGRk6fvy4Ll68qLCwsGz3tX379vLx8bE/Dw8PlyQ9/vjjDjdahoeHKy0tLdthHpkyh4dkN1QiJ5cfqxMnTujUqVNq2rTpNc/LwoULVbduXT388MNZXsuva/h623YtNWrUUNOmTe3PS5cuneU6WrFihSIiIhx69EuUKKHOnTvnaVvjxo3Tl19+qXfffVd33XWX0tLSdPHiRYc6fn5+9vfgpk2bavPmzZo1a5a8vb2zbfvKlSu1ePFiDRkyRJ6ennm+Aa5mzZqqU6eOPvroI0mXZodo06ZNjp/UXH4eTp8+raNHj6pp06b6559/9Ouvv+Z6u1demzlxcXHR7NmzdebMGbVq1Upvv/22hg4delve53I7YsgEbmkNGzbM1ZvN4MGDNW/ePG3YsEFjxoxRjRo17K/t3r1bkuxjS7Nz6tQppaWlKSUlRbVq1fr3Dc+jv/76S5JUtWrVLK9Vq1Yty00r7u7uWQJP8eLFdeLEiYJr5P935UfePj4+cnd3z/JRtI+Pj44dO2Z/vnv3bu3cuTNLuzNl3gyXnbwen9w6e/asxo4dq8TERP39998yl03Kc+rUqVyto02bNurXr59sNpuKFi2qmjVrZjvN25WzpeR2nzLrXTkEI7MsuxCW3cwskvTee+9pwoQJ+vXXX3XhwoWr1s/uPEtSYGBgtuVX+9nLDFCnT5/Osc6Vli5dqtGjR2vz5s0OY8yvFWr/+OMPtW3bNtfbuR7X27ZryW44yZXX9V9//aWIiIgs9bL7+biaywP1448/rvr16ysuLi7LWOhHH31UH3zwgZYtW6ZevXrpnnvuyXZ93t7e9lld2rRpYw+zP//8s8NNctfy2GOPacKECXrmmWf0/fffX3XWke3bt2vYsGH6+uuv7X90Zcrt9SvlfL1kp1KlSnrxxRc1ePBg1apVS8OHD8/1snAuAjEs4c8//7QH361btzq8lpGRIUl69dVXs4yTzOTl5ZWrG4OknH/p3cg5VJ11h3pO286pPZcHzIyMDNWuXVsTJ07Mtu6VQetG6N+/vxITEzVw4EBFRETIx8dHNptNjz76qP3n5lrKlSuXq+ndctMDlV+y29YHH3yguLg4xcTEaPDgwSpTpoxcXV01duxY/fHHH1nq53ROc3Our1S5cmUVKlQoy7WZk7Vr1+qhhx7S3Xffrbffflv+/v664447lJiYmOWmq+vxb67hgmzb9Rzb/FC4cGE99NBDGjdunM6ePevw83Ps2DFt3LhRkrRjxw5lZGTkanaURx55RE888YTmzZuXp0DcqVMnDR06VD179lTJkiV13333ZVvv5MmTioyMlLe3t0aNGqVKlSrJ3d1dP//8s/7zn//k+vqV8n5tfvnll5IufXpz7Ngx+fn55Wl5OAeBGLe9jIwMxcXFydvbWwMHDtSYMWPUrl07PfLII5L+dyPL5T0Y2SldurS8vb2vOfVT8eLFJV16Q778RrfMnrzL5bbHqEKFCpKkXbt22YcQZNq1a5f99VtZpUqVtGXLFt1zzz157kkrqOPzySefKDY21mFGjHPnzt2Qb7bL7T5l/vv7779nWUd2ZTn55JNPVLFiRS1atMjh+CckJOS57XlVpEgRtWjRQl9//bX2799/zT9+Fi5cKHd3d33xxRcON4slJiZec1uVKlXK0zV8ueyu4X/TtoIYZlWhQoV//bOQnbNnz8oYo9OnTzsExKeeekqnT5/W2LFjNXToUE2aNEnx8fHXXN/58+eVkZGRp55a6VIveePGjbVmzRr16dMnx3nQ16xZo2PHjmnRokX2G3cl2Wd3uVx+nodp06Zp5cqVevnllzV27Fg9+eST+uyzz/Jt/Sg4jCHGbW/ixIn6/vvvNX36dL300ktq1KiR+vTpY5+dIjQ0VJUqVdJrr72W7Zi2I0eOSLo0PiwmJkb//e9/7T0il8vspckM2N9++639tczpla7k6emZq3AVFhamMmXKaNq0aQ4fwS5fvlw7d+686l31t4oOHTro77//1owZM7K8dvbsWaWmpua4bEEdH1dX1yy9b2+99dYN6e3P7T4FBASoVq1amjNnjsPP7zfffJPrHlfpf72Pl+/v+vXrlZSU9G93JVcSEhJkjNETTzyR7XX4008/2a8hV1dX2Ww2h/Owd+/eXH3rW9u2bbVlyxZ9+umnWV672jWcnp6u6dOnX3P9eWlbbq//vIiOjlZSUpI2b95sLzt+/Lg+/PDDXC2f3dCkkydPauHChQoMDFSZMmXs5Z988onmz5+vcePG6bnnntOjjz6qYcOG6bfffnNY9vLhN5lmzpwpSdc1vnb06NFKSEhQ//79c6yT3c9zWlqa3n777Sx1PT098xzMs7Nnzx4NHjxYbdu21fPPP6/XXntNS5YsyXZqPNx86CHGLW358uXZ3hzRqFEjVaxYUTt37tTw4cMVFxenBx98UNKlqXhCQkLUt29fffzxx3JxcdHMmTPVqlUr1axZU127dlXZsmX1999/a/Xq1fL29tZ///tfSZfm2Pzyyy8VGRlpnxrs4MGDWrBggb777jsVK1ZM9913n8qXL6/u3btr8ODBcnV11axZs1S6dGnt27fPoZ2hoaGaOnWqRo8ercqVK6tMmTJZegMl6Y477tArr7yirl27KjIyUp06dbJPwRUUFKRnnnmmAI7ujfXEE0/o448/Vu/evbV69Wo1btxY6enp+vXXX/Xxxx/riy++yPGXZ0EdnwceeEDvv/++fHx8VKNGDSUlJemrr76yT+tXkPKyT2PGjFGbNm3UuHFjde3aVSdOnNDkyZNVq1atXN+49MADD2jRokV6+OGH1bp1a+3Zs0fTpk1TjRo1ruvbv/KqUaNGmjJlivr27atq1ao5fFPdmjVrtGTJEvtX97Zu3VoTJ05Uy5Yt9dhjj+nw4cOaMmWKKleurF9++eWq2xk8eLA++eQTtW/fXt26dVNoaKiOHz+uJUuWaNq0aapbt65q1qypu+66S0OHDtXx48dVokQJzZs3L8tNZdnJS9tCQ0P11VdfaeLEiQoICFBwcLD95sTrNWTIEH3wwQe699571b9/f/u0a+XLl9fx48ev2RvaqlUrlStXTuHh4SpTpoz27dunxMREHThwQPPnz7fXO3z4sPr06aPmzZurX79+kqTJkydr9erViouL03fffScXFxetWbNGTz/9tNq1a6cqVaooLS1Na9eu1aJFixQWFpbtl/5cS2RkpCIjI69ap1GjRipevLhiY2P19NNPy2az6f333892eEloaKjmz5+v+Ph4NWjQQF5eXvbfF7lljFG3bt3k4eGhqVOnSpKefPJJLVy4UAMGDFBUVJQCAgLytE7cYM6Y2gL4t6427Zokk5iYaC5evGgaNGhgypUr5zA9lDHGvPHGG0aSmT9/vr1s06ZN5pFHHjElS5Y0bm5upkKFCqZDhw5m1apVDsv+9ddfpkuXLqZ06dLGzc3NVKxY0Tz11FMOU1b99NNPJjw83BQuXNiUL1/eTJw4MduplJKTk03r1q1N0aJFjST7FGzZTeljjDHz58839erVM25ubqZEiRKmc+fO5v/+7/8c6mQ3/ZAxOU8HdzXXM+3akSNHctWeyMhIU7NmTYeytLQ088orr5iaNWsaNzc3U7x4cRMaGmpGjhyZ7VROV8rN8cnLtGsnTpwwXbt2NaVKlTJeXl4mOjra/Prrr1mm8sqJJPPUU09dtU7muV6wYMF175MxxsybN89Uq1bNuLm5mVq1apklS5aYtm3bmmrVqtnrXD6F1ZUyMjLMmDFjTIUKFYybm5upV6+eWbp0aZZpxXJaR077kZfjbcyla+exxx4zAQEB5o477jDFixc399xzj3nvvfdMenq6vd67775rqlSpYtzc3Ey1atVMYmJitj/j2Z2rY8eOmX79+pmyZcuawoULm3LlypnY2Fhz9OhRe50//vjDREVFGTc3N+Pr62uef/55s3LlylxNu5bbtv3666/m7rvvNh4eHkaSvZ05TbvWunXrLMcrMjIyy9SNmzZtMk2bNjVubm6mXLlyZuzYsebNN980kkxycnIOR/6SyZMnmyZNmphSpUqZQoUKmdKlS5sHH3zQYbo3Y4x55JFHTNGiRc3evXsdyj/77DMjybzyyivGGGN+//1306VLF1OxYkXj4eFh3N3dTc2aNU1CQoI5c+bMVdtizNV/Zi+X3fvMunXrzF133WU8PDxMQECAGTJkiPniiy+ynMMzZ86Yxx57zBQrVsxhqsKrXZtXvkdn/k5ZuHChQ719+/YZb29vc//9919zX+FcNmMKeDQ+AMApQkJCVLp0aa1cudLZTYGTDRw4UO+8847OnDnj1JtugZsVY4gB4BZ34cKFLB/nr1mzRlu2bLF/HTis4+zZsw7Pjx07pvfff19NmjQhDAM5oIcYAG5xe/fuVVRUlB5//HEFBATo119/1bRp0+Tj46Nt27bdkDHPuHmEhISoWbNmql69ug4dOqR3331XBw4csH89NoCsuKkOAG5xxYsXV2hoqGbOnKkjR47I09NTrVu31rhx4wjDFnT//ffrk08+0fTp02Wz2VS/fn29++67hGHgKughBgAAgKUxhhgAAACWRiAGAACApTGG+DplZGTowIEDKlq0aIF8/SYAAAD+HfP/v3I8ICBALi459wMTiK/TgQMHFBgY6OxmAAAA4Br279+vcuXK5fg6gfg6FS1aVNKlA+zt7e3k1gAAAOBKKSkpCgwMtOe2nBCIr1PmMAlvb28CMQAAwE3sWsNbuakOAAAAlkYgBgAAgKURiAEAAGBpBGIAAABYGoEYAAAAlkYgBgAAgKURiAEAAGBpBGIAAABYGoEYAAAAlkYgBgAAgKURiAEAAGBpBGIAAABYGoEYAAAAlkYgBgAAgKURiAEAAGBpBGIAAABYGoEYAAAAlkYgBgAAgKURiAEAAGBphZzdAORe0HPLnN2EW9Leca2d3QQAAHATo4cYAAAAlkYgBgAAgKURiAEAAGBpBGIAAABYGoEYAAAAlkYgBgAAgKURiAEAAGBpBGIAAABYGoEYAAAAlkYgBgAAgKURiAEAAGBpBGIAAABYGoEYAAAAlkYgBgAAgKURiAEAAGBpBGIAAABYGoEYAAAAlkYgBgAAgKURiAEAAGBpBGIAAABYGoEYAAAAlkYgBgAAgKURiAEAAGBpBGIAAABYGoEYAAAAlkYgBgAAgKURiAEAAGBpBGIAAABYGoEYAAAAlnZTBOIpU6YoKChI7u7uCg8P14YNG65af8GCBapWrZrc3d1Vu3Ztff755w6vG2M0YsQI+fv7y8PDQ1FRUdq9e3e26zp//rxCQkJks9m0efPm/NolAAAA3CKcHojnz5+v+Ph4JSQk6Oeff1bdunUVHR2tw4cPZ1v/+++/V6dOndS9e3dt2rRJMTExiomJ0bZt2+x1xo8frzfffFPTpk3T+vXr5enpqejoaJ07dy7L+oYMGaKAgIAC2z8AAADc3GzGGOPMBoSHh6tBgwaaPHmyJCkjI0OBgYHq37+/nnvuuSz1O3bsqNTUVC1dutRedtdddykkJETTpk2TMUYBAQF69tlnNWjQIEnSqVOn5Ovrq9mzZ+vRRx+1L7d8+XLFx8dr4cKFqlmzpjZt2qSQkJBs23n+/HmdP3/e/jwlJUWBgYE6deqUvL298+NQXFPQc8tuyHZuN3vHtXZ2EwAAgBOkpKTIx8fnmnnNqT3EaWlp+umnnxQVFWUvc3FxUVRUlJKSkrJdJikpyaG+JEVHR9vr79mzR8nJyQ51fHx8FB4e7rDOQ4cOqWfPnnr//fdVpEiRa7Z17Nix8vHxsT8CAwPztK8AAAC4OTk1EB89elTp6eny9fV1KPf19VVycnK2yyQnJ1+1fua/V6tjjFFcXJx69+6tsLCwXLV16NChOnXqlP2xf//+XC0HAACAm1shZzfAGd566y2dPn1aQ4cOzfUybm5ucnNzK8BWAQAAwBmc2kNcqlQpubq66tChQw7lhw4dkp+fX7bL+Pn5XbV+5r9Xq/P1118rKSlJbm5uKlSokCpXrixJCgsLU2xs7L/fMQAAANwynBqICxcurNDQUK1atcpelpGRoVWrVikiIiLbZSIiIhzqS9LKlSvt9YODg+Xn5+dQJyUlRevXr7fXefPNN7VlyxZt3rxZmzdvtk/bNn/+fL388sv5uo8AAAC4uTl9yER8fLxiY2MVFhamhg0batKkSUpNTVXXrl0lSV26dFHZsmU1duxYSdKAAQMUGRmpCRMmqHXr1po3b542btyo6dOnS5JsNpsGDhyo0aNHq0qVKgoODtbw4cMVEBCgmJgYSVL58uUd2uDl5SVJqlSpksqVK3eD9hwAAAA3A6cH4o4dO+rIkSMaMWKEkpOTFRISohUrVthvitu3b59cXP7Xkd2oUSPNnTtXw4YN0/PPP68qVapo8eLFqlWrlr3OkCFDlJqaql69eunkyZNq0qSJVqxYIXd39xu+fwAAALi5OX0e4ltVbue1y0/MQ3x9mIcYAABruiXmIQYAAACcjUAMAAAASyMQAwAAwNIIxAAAALA0AjEAAAAsjUAMAAAASyMQAwAAwNIIxAAAALA0AjEAAAAsjUAMAAAASyMQAwAAwNIIxAAAALA0AjEAAAAsjUAMAAAASyMQAwAAwNIIxAAAALA0AjEAAAAsjUAMAAAASyMQAwAAwNIIxAAAALA0AjEAAAAsjUAMAAAASyMQAwAAwNIIxAAAALA0AjEAAAAsjUAMAAAASyMQAwAAwNIIxAAAALA0AjEAAAAsjUAMAAAASyMQAwAAwNIIxAAAALA0AjEAAAAsjUAMAAAASyMQAwAAwNIIxAAAALA0AjEAAAAsjUAMAAAASyMQAwAAwNIIxAAAALA0AjEAAAAsjUAMAAAASyMQAwAAwNIIxAAAALA0AjEAAAAsjUAMAAAASyMQAwAAwNIIxAAAALA0AjEAAAAsjUAMAAAASyMQAwAAwNIIxAAAALA0AjEAAAAsjUAMAAAASyMQAwAAwNIIxAAAALA0AjEAAAAsjUAMAAAASyMQAwAAwNIIxAAAALA0AjEAAAAsjUAMAAAASyMQAwAAwNIIxAAAALA0AjEAAAAsjUAMAAAASyMQAwAAwNIIxAAAALA0AjEAAAAsjUAMAAAASyMQAwAAwNIIxAAAALA0AjEAAAAsjUAMAAAASyMQAwAAwNIIxAAAALA0AjEAAAAsjUAMAAAASyMQAwAAwNIIxAAAALA0AjEAAAAsjUAMAAAASyMQAwAAwNIIxAAAALA0AjEAAAAsjUAMAAAASyMQAwAAwNIIxAAAALA0AjEAAAAsjUAMAAAAS7spAvGUKVMUFBQkd3d3hYeHa8OGDVetv2DBAlWrVk3u7u6qXbu2Pv/8c4fXjTEaMWKE/P395eHhoaioKO3evduhzkMPPaTy5cvL3d1d/v7+euKJJ3TgwIF83zcAAADc3JweiOfPn6/4+HglJCTo559/Vt26dRUdHa3Dhw9nW//7779Xp06d1L17d23atEkxMTGKiYnRtm3b7HXGjx+vN998U9OmTdP69evl6emp6OhonTt3zl6nefPm+vjjj7Vr1y4tXLhQf/zxh9q1a1fg+wsAAICbi80YY5zZgPDwcDVo0ECTJ0+WJGVkZCgwMFD9+/fXc889l6V+x44dlZqaqqVLl9rL7rrrLoWEhGjatGkyxiggIEDPPvusBg0aJEk6deqUfH19NXv2bD366KPZtmPJkiWKiYnR+fPndccdd1yz3SkpKfLx8dGpU6fk7e19PbueZ0HPLbsh27nd7B3X2tlNAAAATpDbvObUHuK0tDT99NNPioqKspe5uLgoKipKSUlJ2S6TlJTkUF+SoqOj7fX37Nmj5ORkhzo+Pj4KDw/PcZ3Hjx/Xhx9+qEaNGuUYhs+fP6+UlBSHBwAAAG59Tg3ER48eVXp6unx9fR3KfX19lZycnO0yycnJV62f+W9u1vmf//xHnp6eKlmypPbt26fPPvssx7aOHTtWPj4+9kdgYGDudhIAAAA3NaePIXamwYMHa9OmTfryyy/l6uqqLl26KKcRJEOHDtWpU6fsj/3799/g1gIAAKAgFHLmxkuVKiVXV1cdOnTIofzQoUPy8/PLdhk/P7+r1s/899ChQ/L393eoExISkmX7pUqV0p133qnq1asrMDBQP/zwgyIiIrJs183NTW5ubnneRwAAANzcnNpDXLhwYYWGhmrVqlX2soyMDK1atSrbUCpJERERDvUlaeXKlfb6wcHB8vPzc6iTkpKi9evX57jOzO1Kl8YKAwAAwDqc2kMsSfHx8YqNjVVYWJgaNmyoSZMmKTU1VV27dpUkdenSRWXLltXYsWMlSQMGDFBkZKQmTJig1q1ba968edq4caOmT58uSbLZbBo4cKBGjx6tKlWqKDg4WMOHD1dAQIBiYmIkSevXr9ePP/6oJk2aqHjx4vrjjz80fPhwVapU6aqhGQAAALcfpwfijh076siRIxoxYoSSk5MVEhKiFStW2G+K27dvn1xc/teR3ahRI82dO1fDhg3T888/rypVqmjx4sWqVauWvc6QIUOUmpqqXr166eTJk2rSpIlWrFghd3d3SVKRIkW0aNEiJSQkKDU1Vf7+/mrZsqWGDRvGsAgAAACLcfo8xLcq5iG+dTAPMQAA1nRLzEMMAAAAOBuBGAAAAJZGIAYAAIClEYgBAABgaQRiAAAAWBqBGAAAAJZGIAYAAIClEYgBAABgaQRiAAAAWBqBGAAAAJZGIAYAAIClEYgBAABgaQRiAAAAWBqBGAAAAJZGIAYAAIClEYgBAABgaQRiAAAAWBqBGAAAAJZGIAYAAIClEYgBAABgaQRiAAAAWBqBGAAAAJZGIAYAAIClEYgBAABgaQRiAAAAWBqBGAAAAJZGIAYAAIClEYgBAABgaQRiAAAAWBqBGAAAAJZGIAYAAIClEYgBAABgaQRiAAAAWBqBGAAAAJZGIAYAAIClEYgBAABgaQRiAAAAWBqBGAAAAJZGIAYAAIClEYgBAABgaQRiAAAAWFqh61nowoULSk5O1j///KPSpUurRIkS+d0uAAAA4IbIdQ/x6dOnNXXqVEVGRsrb21tBQUGqXr26SpcurQoVKqhnz5768ccfC7KtAAAAQL7LVSCeOHGigoKClJiYqKioKC1evFibN2/Wb7/9pqSkJCUkJOjixYu677771LJlS+3evbug2w0AAADki1wNmfjxxx/17bffqmbNmtm+3rBhQ3Xr1k3Tpk1TYmKi1q5dqypVquRrQwEAAICCkKtA/NFHH+VqZW5uburdu/e/ahAAAABwI/3rWSZSUlK0ePFi7dy5Mz/aAwAAANxQeQ7EHTp00OTJkyVJZ8+eVVhYmDp06KA6depo4cKF+d5AAAAAoCDlORB/++23atq0qSTp008/lTFGJ0+e1JtvvqnRo0fnewMBAACAgpTnQHzq1Cn7vMMrVqxQ27ZtVaRIEbVu3ZrZJQAAAHDLyXMgDgwMVFJSklJTU7VixQrdd999kqQTJ07I3d093xsIAAAAFKQ8f1PdwIED1blzZ3l5ealChQpq1qyZpEtDKWrXrp3f7QMAAAAKVJ4Dcd++fRUeHq59+/bp3nvvlYvLpU7mihUrMoYYAAAAt5w8B2JJCg0NVWhoqENZ69at86VBAAAAwI2UqzHE48aN09mzZ3O1wvXr12vZsmX/qlEAAADAjZKrQLxjxw6VL19effv21fLly3XkyBH7axcvXtQvv/yit99+W40aNVLHjh1VtGjRAmswAAAAkJ9yNWRizpw52rJliyZPnqzHHntMKSkpcnV1lZubm/755x9JUr169dSjRw/FxcUx2wQAAABuGbkeQ1y3bl3NmDFD77zzjn755Rf99ddfOnv2rEqVKqWQkBCVKlWqINsJAAAAFIg831Tn4uKikJAQhYSEFEBzAAAAgBsrz1/MAQAAANxOCMQAAACwNAIxAAAALI1ADAAAAEu77kD8+++/64svvrB/YYcxJt8aBQAAANwoeQ7Ex44dU1RUlO68807df//9OnjwoCSpe/fuevbZZ/O9gQAAAEBBynMgfuaZZ1SoUCHt27dPRYoUsZd37NhRK1asyNfGAQAAAAUtz/MQf/nll/riiy9Urlw5h/IqVaror7/+yreGAQAAADdCnnuIU1NTHXqGMx0/flxubm750igAAADgRslzIG7atKnmzJljf26z2ZSRkaHx48erefPm+do4AAAAoKDlecjE+PHjdc8992jjxo1KS0vTkCFDtH37dh0/flzr1q0riDYCAAAABSbPPcS1atXSb7/9piZNmqhNmzZKTU3VI488ok2bNqlSpUoF0UYAAACgwOS5h1iSfHx89MILL+R3WwAAAIAb7roC8blz5/TLL7/o8OHDysjIcHjtoYceypeGAQAAADdCngPxihUr1KVLFx09ejTLazabTenp6fnSMAAAAOBGyPMY4v79+6t9+/Y6ePCgMjIyHB6EYQAAANxq8hyIDx06pPj4ePn6+hZEewAAAIAbKs+BuF27dlqzZk0BNAUAAAC48fI8hnjy5Mlq37691q5dq9q1a+uOO+5weP3pp5/Ot8YBAAAABS3Pgfijjz7Sl19+KXd3d61Zs0Y2m83+ms1mIxADAADglpLnQPzCCy9o5MiReu655+TikucRFwAAAMBNJc+JNi0tTR07diQMAwAA4LaQ51QbGxur+fPnF0RbAAAAgBsuz0Mm0tPTNX78eH3xxReqU6dOlpvqJk6cmG+NAwAAAApangPx1q1bVa9ePUnStm3bHF67/AY7AAAA4FaQ50C8evXqgmgHAAAA4BTcGQcAAABLy1UP8SOPPKLZs2fL29tbjzzyyFXrLlq0KF8aBgAAANwIuQrEPj4+9vHBPj4+BdogAAAA4EbKVSBOTEzUqFGjNGjQICUmJhZ0mwAAAIAbJtdjiEeOHKkzZ84UZFsAAACAGy7XgdgYU5DtAAAAAJwiT7NMFNQ8w1OmTFFQUJDc3d0VHh6uDRs2XLX+ggULVK1aNbm7u6t27dr6/PPPHV43xmjEiBHy9/eXh4eHoqKitHv3bvvre/fuVffu3RUcHCwPDw9VqlRJCQkJSktLK5D9AwAAwM0rT4H4zjvvVIkSJa76yKv58+crPj5eCQkJ+vnnn1W3bl1FR0fr8OHD2db//vvv1alTJ3Xv3l2bNm1STEyMYmJiHL4kZPz48XrzzTc1bdo0rV+/Xp6enoqOjta5c+ckSb/++qsyMjL0zjvvaPv27Xr99dc1bdo0Pf/883luPwAAAG5tNpPLsRAuLi6aNGnSNWeZiI2NzVMDwsPD1aBBA02ePFmSlJGRocDAQPXv31/PPfdclvodO3ZUamqqli5dai+76667FBISomnTpskYo4CAAD377LMaNGiQJOnUqVPy9fXV7Nmz9eijj2bbjldffVVTp07Vn3/+me3r58+f1/nz5+3PU1JSFBgYqFOnTsnb2ztP+3y9gp5bdkO2c7vZO661s5sAAACcICUlRT4+PtfMa3n6prpHH31UZcqU+deNy5SWlqaffvpJQ4cOtZe5uLgoKipKSUlJ2S6TlJSk+Ph4h7Lo6GgtXrxYkrRnzx4lJycrKirK/rqPj4/Cw8OVlJSUYyA+derUVXu4x44dq5EjR+Z21wAAAHCLyPWQiYIYP3z06FGlp6fL19fXodzX11fJycnZLpOcnHzV+pn/5mWdv//+u9566y09+eSTObZ16NChOnXqlP2xf//+q+8cAAAAbgm57iG+XWeZ+Pvvv9WyZUu1b99ePXv2zLGem5ub3NzcbmDLAAAAcCPkuoc4IyMjX4dLSFKpUqXk6uqqQ4cOOZQfOnRIfn5+2S7j5+d31fqZ/+ZmnQcOHFDz5s3VqFEjTZ8+/V/tCwAAAG5NeZplIr8VLlxYoaGhWrVqlb0sIyNDq1atUkRERLbLREREONSXpJUrV9rrBwcHy8/Pz6FOSkqK1q9f77DOv//+W82aNVNoaKgSExPl4uLUQwEAAAAnydNNdQUhPj5esbGxCgsLU8OGDTVp0iSlpqaqa9eukqQuXbqobNmyGjt2rCRpwIABioyM1IQJE9S6dWvNmzdPGzdutPfw2mw2DRw4UKNHj1aVKlUUHBys4cOHKyAgQDExMZL+F4YrVKig1157TUeOHLG3J6eeaQAAANyenB6IO3bsqCNHjmjEiBFKTk5WSEiIVqxYYb8pbt++fQ69t40aNdLcuXM1bNgwPf/886pSpYoWL16sWrVq2esMGTJEqamp6tWrl06ePKkmTZpoxYoVcnd3l3SpR/n333/X77//rnLlyjm053YdKw0AAIDs5XoeYjjK7bx2+Yl5iK8P8xADAGBNuc1rDJwFAACApRGIAQAAYGkEYgAAAFgagRgAAACWRiAGAACApRGIAQAAYGkEYgAAAFgagRgAAACWRiAGAACApRGIAQAAYGkEYgAAAFgagRgAAACWRiAGAACApRGIAQAAYGkEYgAAAFgagRgAAACWRiAGAACApRGIAQAAYGkEYgAAAFgagRgAAACWRiAGAACApRGIAQAAYGkEYgAAAFgagRgAAACWRiAGAACApRGIAQAAYGkEYgAAAFgagRgAAACWRiAGAACApRGIAQAAYGkEYgAAAFgagRgAAACWRiAGAACApRGIAQAAYGkEYgAAAFgagRgAAACWRiAGAACApRGIAQAAYGkEYgAAAFgagRgAAACWRiAGAACApRGIAQAAYGkEYgAAAFgagRgAAACWRiAGAACApRGIAQAAYGkEYgAAAFgagRgAAACWRiAGAACApRGIAQAAYGkEYgAAAFgagRgAAACWRiAGAACApRGIAQAAYGkEYgAAAFgagRgAAACWRiAGAACApRVydgMAAEDeBT23zNlNuOXsHdfa2U3ATYoeYgAAAFgagRgAAACWRiAGAACApRGIAQAAYGkEYgAAAFgagRgAAACWRiAGAACApTEPMYBbCnOvXh/mXwWAnNFDDAAAAEsjEAMAAMDSCMQAAACwNAIxAAAALI1ADAAAAEsjEAMAAMDSCMQAAACwNAIxAAAALI1ADAAAAEsjEAMAAMDSCMQAAACwNAIxAAAALI1ADAAAAEsjEAMAAMDSCMQAAACwNAIxAAAALI1ADAAAAEsjEAMAAMDSCMQAAACwNAIxAAAALI1ADAAAAEsjEAMAAMDSCMQAAACwNAIxAAAALM3pgXjKlCkKCgqSu7u7wsPDtWHDhqvWX7BggapVqyZ3d3fVrl1bn3/+ucPrxhiNGDFC/v7+8vDwUFRUlHbv3u1Q5+WXX1ajRo1UpEgRFStWLL93CQAAALcQpwbi+fPnKz4+XgkJCfr5559Vt25dRUdH6/Dhw9nW//7779WpUyd1795dmzZtUkxMjGJiYrRt2zZ7nfHjx+vNN9/UtGnTtH79enl6eio6Olrnzp2z10lLS1P79u3Vp0+fAt9HAAAA3NycGognTpyonj17qmvXrqpRo4amTZumIkWKaNasWdnWf+ONN9SyZUsNHjxY1atX10svvaT69etr8uTJki71Dk+aNEnDhg1TmzZtVKdOHc2ZM0cHDhzQ4sWL7esZOXKknnnmGdWuXftG7CYAAABuYk4LxGlpafrpp58UFRX1v8a4uCgqKkpJSUnZLpOUlORQX5Kio6Pt9ffs2aPk5GSHOj4+PgoPD89xnbl1/vx5paSkODwAAABw63NaID569KjS09Pl6+vrUO7r66vk5ORsl0lOTr5q/cx/87LO3Bo7dqx8fHzsj8DAwH+1PgAAANwcnH5T3a1i6NChOnXqlP2xf/9+ZzcJAAAA+cBpgbhUqVJydXXVoUOHHMoPHTokPz+/bJfx8/O7av3Mf/Oyztxyc3OTt7e3wwMAAAC3PqcF4sKFCys0NFSrVq2yl2VkZGjVqlWKiIjIdpmIiAiH+pK0cuVKe/3g4GD5+fk51ElJSdH69etzXCcAAACsrZAzNx4fH6/Y2FiFhYWpYcOGmjRpklJTU9W1a1dJUpcuXVS2bFmNHTtWkjRgwABFRkZqwoQJat26tebNm6eNGzdq+vTpkiSbzaaBAwdq9OjRqlKlioKDgzV8+HAFBAQoJibGvt19+/bp+PHj2rdvn9LT07V582ZJUuXKleXl5XVDjwEAAACcy6mBuGPHjjpy5IhGjBih5ORkhYSEaMWKFfab4vbt2ycXl/91Yjdq1Ehz587VsGHD9Pzzz6tKlSpavHixatWqZa8zZMgQpaamqlevXjp58qSaNGmiFStWyN3d3V5nxIgReu+99+zP69WrJ0lavXq1mjVrVsB7DQAAgJuJzRhjnN2IW1FKSop8fHx06tSpGzaeOOi5ZTdkO7ebveNaO7sJyEdcB9eH6+D2w7WQd1wH1pPbvMYsEwAAALA0AjEAAAAsjUAMAAAASyMQAwAAwNIIxAAAALA0AjEAAAAsjUAMAAAASyMQAwAAwNIIxAAAALA0AjEAAAAsjUAMAAAASyMQAwAAwNIIxAAAALA0AjEAAAAsjUAMAAAASyMQAwAAwNIIxAAAALA0AjEAAAAsjUAMAAAASyMQAwAAwNIIxAAAALA0AjEAAAAsjUAMAAAASyMQAwAAwNIIxAAAALA0AjEAAAAsjUAMAAAASyMQAwAAwNIIxAAAALA0AjEAAAAsjUAMAAAASyMQAwAAwNIIxAAAALA0AjEAAAAsjUAMAAAASyMQAwAAwNIIxAAAALA0AjEAAAAsjUAMAAAASyMQAwAAwNIIxAAAALA0AjEAAAAsjUAMAAAASyvk7AYAt5Kg55Y5uwm3pL3jWju7CQAA5IgeYgAAAFgagRgAAACWRiAGAACApRGIAQAAYGkEYgAAAFgas0wAAABcB2Yeuj4348xD9BADAADA0gjEAAAAsDQCMQAAACyNQAwAAABLIxADAADA0gjEAAAAsDQCMQAAACyNQAwAAABLIxADAADA0gjEAAAAsDQCMQAAACyNQAwAAABLIxADAADA0gjEAAAAsDQCMQAAACyNQAwAAABLIxADAADA0gjEAAAAsDQCMQAAACyNQAwAAABLIxADAADA0gjEAAAAsDQCMQAAACyNQAwAAABLIxADAADA0gjEAAAAsDQCMQAAACyNQAwAAABLIxADAADA0gjEAAAAsDQCMQAAACyNQAwAAABLIxADAADA0gjEAAAAsDQCMQAAACyNQAwAAABLIxADAADA0go5uwEAgFtL0HPLnN2EW9Leca2d3QQAOaCHGAAAAJZGIAYAAIClEYgBAABgaTdFIJ4yZYqCgoLk7u6u8PBwbdiw4ar1FyxYoGrVqsnd3V21a9fW559/7vC6MUYjRoyQv7+/PDw8FBUVpd27dzvUOX78uDp37ixvb28VK1ZM3bt315kzZ/J93wAAAHBzc3ognj9/vuLj45WQkKCff/5ZdevWVXR0tA4fPpxt/e+//16dOnVS9+7dtWnTJsXExCgmJkbbtm2z1xk/frzefPNNTZs2TevXr5enp6eio6N17tw5e53OnTtr+/btWrlypZYuXapvv/1WvXr1KvD9BQAAwM3F6YF44sSJ6tmzp7p27aoaNWpo2rRpKlKkiGbNmpVt/TfeeEMtW7bU4MGDVb16db300kuqX7++Jk+eLOlS7/CkSZM0bNgwtWnTRnXq1NGcOXN04MABLV68WJK0c+dOrVixQjNnzlR4eLiaNGmit956S/PmzdOBAwdu1K4DAADgJuDUadfS0tL0008/aejQofYyFxcXRUVFKSkpKdtlkpKSFB8f71AWHR1tD7t79uxRcnKyoqKi7K/7+PgoPDxcSUlJevTRR5WUlKRixYopLCzMXicqKkouLi5av369Hn744SzbPX/+vM6fP29/furUKUlSSkpK3nf8OmWc/+eGbet2kp/niHNwfTgHzsc5cL78/n3Becg7zsHN4UZmp8xtGWOuWs+pgfjo0aNKT0+Xr6+vQ7mvr69+/fXXbJdJTk7Otn5ycrL99cyyq9UpU6aMw+uFChVSiRIl7HWuNHbsWI0cOTJLeWBgYE67h5uEzyRntwCcA+fjHDgf58D5OAc3B2ech9OnT8vHxyfH1/lijlwaOnSoQ890RkaGjh8/rpIlS8pmszmxZc6XkpKiwMBA7d+/X97e3s5ujiVxDpyPc+B8nIObA+fB+TgH/2OM0enTpxUQEHDVek4NxKVKlZKrq6sOHTrkUH7o0CH5+fllu4yfn99V62f+e+jQIfn7+zvUCQkJsde58qa9ixcv6vjx4zlu183NTW5ubg5lxYoVu/oOWoy3t7flLzxn4xw4H+fA+TgHNwfOg/NxDi65Ws9wJqfeVFe4cGGFhoZq1apV9rKMjAytWrVKERER2S4TERHhUF+SVq5caa8fHBwsPz8/hzopKSlav369vU5ERIROnjypn376yV7n66+/VkZGhsLDw/Nt/wAAAHDzc/qQifj4eMXGxiosLEwNGzbUpEmTlJqaqq5du0qSunTporJly2rs2LGSpAEDBigyMlITJkxQ69atNW/ePG3cuFHTp0+XJNlsNg0cOFCjR49WlSpVFBwcrOHDhysgIEAxMTGSpOrVq6tly5bq2bOnpk2bpgsXLqhfv3569NFHr9mlDgAAgNuL0wNxx44ddeTIEY0YMULJyckKCQnRihUr7DfF7du3Ty4u/+vIbtSokebOnathw4bp+eefV5UqVbR48WLVqlXLXmfIkCFKTU1Vr169dPLkSTVp0kQrVqyQu7u7vc6HH36ofv366Z577pGLi4vatm2rN99888bt+G3Ezc1NCQkJWYaU4MbhHDgf58D5OAc3B86D83EO8s5mrjUPBQAAAHAbc/oXcwAAAADORCAGAACApRGIAQAAYGkEYshms9m/+hpA7nHt4FZ1rZ/dNWvWyGaz6eTJk5Kk2bNnM/c+bmsEYguIi4uTzWbL8mjZsqUk6eDBg2rVqlWe1jljxgzVrVtXXl5eKlasmOrVq2efGg85yzwX48aNcyhfvHjxDfnGw8vPv4+Pjxo3bqyvv/66wLd7K4uLi7NP2XilvFw7Vg3POb3//P777/m6nRdffNH+5UtWMW3aNBUtWlQXL160l505c0Z33HGHmjVr5lA3M+D+8ccfuVp3o0aNdPDgwVx9oUFuWfEc5caRI0fUp08flS9fXm5ubvLz81N0dLTWrVtXYNvcu3evw/VYsmRJ3Xfffdq0aVOBbfNmRyC2iJYtW+rgwYMOj48++kjSpW/uy8vULLNmzdLAgQP19NNPa/PmzVq3bp2GDBmiM2fOFFTzbyvu7u565ZVXdOLECadsPzExUQcPHtS6detUqlQpPfDAA/rzzz+d0pZbXV6vHavK7v0nODj4utaVlpaWz627dTVv3lxnzpzRxo0b7WVr166Vn5+f1q9fr3PnztnLV69erfLly6tSpUq5WnfhwoXl5+d3Q/5Qt7q2bdtq06ZNeu+99/Tbb79pyZIlatasmY4dO1bg2/7qq6908OBBffHFFzpz5oxatWpl/1TAagjEFpH5V+flj+LFi0ty7LnK/Ktx0aJFat68uYoUKaK6desqKSnJvq4lS5aoQ4cO6t69uypXrqyaNWuqU6dOevnll+11mjVrpoEDBzq0ISYmRnFxcfbnQUFBGj16tLp06SIvLy9VqFBBS5Ys0ZEjR9SmTRt5eXmpTp06Dm/2t4OoqCj5+fnl2KOeXS/KpEmTFBQUZH+e2Ws5ZswY+fr6qlixYho1apQuXryowYMHq0SJEipXrpwSExOzrL9YsWLy8/NTrVq1NHXqVJ09e1YrV67UnDlzVLJkSZ0/f96hfkxMjJ544ol/vd+3o8uvnbS0NPXr10/+/v5yd3dXhQoV7Oc489w9/PDDstlsDufSCrJ7/3F1dZUkffPNN2rYsKHc3Nzk7++v5557zqHHs1mzZurXr58GDhyoUqVKKTo6+rrasHXrVrVo0UIeHh4qWbKkevXq5fBHfEZGhkaNGqVy5crJzc3NPid+psz3xnnz5qlRo0Zyd3dXrVq19M0331znUfn3qlatKn9/f61Zs8ZetmbNGrVp00bBwcH64YcfHMqbN2/usPzRo0f18MMPq0iRIqpSpYqWLFniUP/yIRPZ+eyzz1S/fn25u7urYsWKGjlypMO5y6vb8Rxdy8mTJ7V27Vq98sorat68uSpUqKCGDRtq6NCheuihh9StWzc98MADDstcuHBBZcqU0bvvvivp0jXSv39/DRw4UMWLF5evr69mzJhh/5KzokWLqnLlylq+fHmW7ZcsWVJ+fn4KCwvTa6+9pkOHDmn9+vUaNWqUw/c7ZAoJCdHw4cML5mA4GYEY2XrhhRc0aNAgbd68WXfeeac6depkf6Pz8/PTDz/8oL/++utfb+f1119X48aNtWnTJrVu3VpPPPGEunTposcff1w///yzKlWqpC5duuh2mi7b1dVVY8aM0VtvvaX/+7//u+71fP311zpw4IC+/fZbTZw4UQkJCXrggQdUvHhxrV+/Xr1799aTTz551W14eHhIuhTm2rdvr/T0dIdfiocPH9ayZcvUrVu3626nVbz55ptasmSJPv74Y+3atUsffvihPfj++OOPkv7XO5/53Or+/vtv3X///WrQoIG2bNmiqVOn6t1339Xo0aMd6r333nsqXLiw1q1bp2nTpuV5O6mpqYqOjlbx4sX1448/asGCBfrqq6/Ur18/e5033nhDEyZM0GuvvaZffvlF0dHReuihh7R7926HdQ0ePFjPPvusNm3apIiICD344IM3pCcvJ82bN9fq1avtz1evXq1mzZopMjLSXn727FmtX78+SyAeOXKkOnTooF9++UX333+/OnfurOPHj+dqu2vXrlWXLl00YMAA7dixQ++8845mz57t0DGSF7fzOboaLy8veXl5afHixVk6IySpR48eWrFihQ4ePGgvW7p0qf755x917NjRXvbee++pVKlS2rBhg/r3768+ffqoffv2atSokX7++Wfdd999euKJJ/TPP//k2JbLfx9069ZNO3fudHiv2rRpk3755Rf7Nwnfdgxue7GxscbV1dV4eno6PF5++WVjjDGSzKeffmqMMWbPnj1Gkpk5c6Z9+e3btxtJZufOncYYYw4cOGDuuusuI8nceeedJjY21syfP9+kp6fbl4mMjDQDBgxwaEebNm1MbGys/XmFChXM448/bn9+8OBBI8kMHz7cXpaUlGQkmYMHD+bX4XCq2NhY06ZNG2OMMXfddZfp1q2bMcaYTz/91GRejgkJCaZu3boOy73++uumQoUKDuupUKGCwzGvWrWqadq0qf35xYsXjaenp/noo4/sZZef69TUVNO3b1/j6upqtmzZYowxpk+fPqZVq1b2+hMmTDAVK1Y0GRkZ/3rfb1WXn7MrXX48+/fvb1q0aJHjsbq8rpVk9/7Trl07Y4wxzz//vKlatarDMZsyZYrx8vKy/2xHRkaaevXqXXM72V03maZPn26KFy9uzpw5Yy9btmyZcXFxMcnJycYYYwICAuzviZkaNGhg+vbta4z533vjuHHj7K9fuHDBlCtXzrzyyiu5OBIFY8aMGcbT09NcuHDBpKSkmEKFCpnDhw+buXPnmrvvvtsYY8yqVauMJPPXX3/Zl5Nkhg0bZn9+5swZI8ksX77cGGPM6tWrjSRz4sQJY4wxiYmJxsfHx17/nnvuMWPGjHFoy/vvv2/8/f1zbKtVz9G1fPLJJ6Z48eLG3d3dNGrUyAwdOtT+nmyMMTVq1HBo/4MPPmji4uLszyMjI02TJk3szzPf+5944gl7Webv16SkJGPM/47Vpk2bjDHGnDhxwjz88MPGy8vLfrxbtWpl+vTpY19H//79TbNmzfJ3528i9BBbRPPmzbV582aHR+/evXOsX6dOHfv//f39JV3qLcx8npSUpK1bt2rAgAG6ePGiYmNj1bJlS2VkZOSpXZdvJ/PrumvXrp2lLHPbt5NXXnlF7733nnbu3Hldy9esWdPha819fX0djp2rq6tKliyZ5dh16tRJXl5eKlq0qBYuXKh3333Xfh569uypL7/8Un///bekS3eWZ94UhauLi4vT5s2bVbVqVT399NP68ssvnd2km8aV7z9vvvmmJGnnzp2KiIhw+Plq3Lixzpw54/DJRmho6L/a/s6dO1W3bl15eno6bCcjI0O7du1SSkqKDhw4oMaNGzss17hx4yzXZ0REhP3/hQoVUlhY2HVfw/mhWbNmSk1N1Y8//qi1a9fqzjvvVOnSpRUZGWkfR7xmzRpVrFhR5cuXd1j28vdfT09PeXt75/q9dsuWLRo1apS9h9PLy0s9e/bUwYMHr9oLmZPb+RxdS9u2bXXgwAEtWbJELVu21Jo1a1S/fn3Nnj1b0qVe4szhb4cOHdLy5cuzfGp3+bnMfO/Pze/SRo0aycvLS8WLF9eWLVs0f/58e92ePXvqo48+0rlz55SWlqa5c+fe1p8WFnJ2A3BjeHp6qnLlyrmuf8cdd9j/n/nL6sqwW6tWLdWqVUt9+/ZV79691bRpU33zzTdq3ry5XFxcsgxzuHDhQq62k5tt3w7uvvtuRUdHa+jQoQ5jq6/n2EmXjlV2ZVceu9dff11RUVHy8fFR6dKlHV6rV6+e6tatqzlz5ui+++7T9u3btWzZsuvZPcupX7++9uzZo+XLl+urr75Shw4dFBUVpU8++cTZTXO6vL7/ZLc8sle5cmWVK1dOq1ev1okTJxQZGSlJCggIUGBgoL7//nutXr1aLVq0yLJsbt4vcnLmzBmNHDlSjzzySJbX3N3dr2NPrM3d3V333nuv7r33Xg0fPlw9evRQQkKC4uLi1KVLFz333HNKSkrS999/r+DgYDVt2tRh+Wv9Psjpd+n8+fNVo0YNlSxZMsu0eg8++KDc3Nz06aefqnDhwrpw4YLatWuXj3t9c6GHGPmiRo0aki6NA5Ok0qVLO4x5Sk9P17Zt25zStpvZuHHj9N///tfhpsXSpUsrOTnZIRRv3rw537bp5+enypUrZwnDmXr06KHZs2crMTFRUVFRCgwMzLdt3+68vb3VsWNHzZgxQ/Pnz9fChQvtYzLvuOMOpaenO7mFN5fq1asrKSnJ4Wd93bp1Klq0qMqVK5ev29myZYv9/SlzOy4uLqpataq8vb0VEBCQZZqrdevW2d/bMl1+o9rFixf1008/qXr16vnW1uvRvHlzrVmzRmvWrHGYbu3uu+/W8uXLtWHDhizjh/+t+vXra9euXapcuXKWx+WfXOXW7X6O8qpGjRr2Y1GyZEnFxMQoMTFRs2fPztcxvIGBgapUqVK2c0wXKlRIsbGxSkxMVGJioh599FH7OOPbET3EFnH+/HklJyc7lBUqVEilSpXK87r69OmjgIAAtWjRQuXKldPBgwc1evRolS5d2v5RVYsWLRQfH69ly5apUqVKmjhxomWncrma2rVrq3PnzvaPkKVLH4EeOXJE48ePV7t27bRixQotX75c3t7eN6RNjz32mAYNGqQZM2Zozpw5N2SbN7tTp05l+aOkZMmSDs8nTpwof39/1atXTy4uLlqwYIH8/Pzsv2iCgoK0atUqNW7cWG5ubvZZXqysb9++mjRpkvr3769+/fpp165dSkhIUHx8/HWFqrNnz2Y5T0WLFlXnzp2VkJCg2NhYvfjiizpy5Ij69++vJ554wv7x8ODBg5WQkKBKlSopJCREiYmJ2rx5sz788EOH9U2ZMkVVqlRR9erV9frrr+vEiRNO/xi5efPmeuqpp3ThwgV7D7EkRUZGql+/fkpLS8v3QDxixAg98MADKl++vNq1aycXFxdt2bJF27Zty3JT5OWseo5ycuzYMbVv317dunVTnTp1VLRoUW3cuFHjx49XmzZt7PV69OihBx54QOnp6YqNjb1h7evRo4f9j4mCnBf5ZkAgtogVK1bYxwJnqlq1qn799dc8rysqKkqzZs3S1KlTdezYMZUqVUoRERFatWqVPSR069ZNW7ZsUZcuXVSoUCE988wz+f6GfLsYNWqU5s+fb39evXp1vf322xozZoxeeukltW3bVoMGDdL06dNvSHt8fHzUtm1bLVu2LMcvpLCaNWvWqF69eg5l3bt3d3hetGhRjR8/Xrt375arq6saNGigzz//3B7sJkyYoPj4eM2YMUNly5bV3r17b1Tzb1ply5bV559/rsGDB6tu3boqUaKEunfvrmHDhl3X+n777bcs5+mee+7RV199pS+++EIDBgxQgwYNVKRIEbVt21YTJ06013v66ad16tQpPfvsszp8+LBq1KihJUuWqEqVKg7rGzdunMaNG6fNmzercuXKWrJkyXV1LOSn5s2b6+zZs6pWrZo9PEqXAvHp06ft07Plp+joaC1dulSjRo3SK6+8ojvuuEPVqlVTjx49rrqcVc9RTry8vBQeHq7XX39df/zxhy5cuKDAwED17NlTzz//vL1eVFSU/P39VbNmTQUEBNyw9lWpUkWNGjXS8ePHFR4efsO26ww2c+VgRQCWd88996hmzZoOPdeAle3du1fBwcHatGkT37Z2k7qdz9GZM2dUtmxZJSYmZjtuu6AYY1SlShX17dtX8fHxN2y7zkAPMQC7EydO2Mcivv32285uDgBYWkZGho4ePaoJEyaoWLFieuihh27Yto8cOaJ58+YpOTn59p17+DIEYgB29erV04kTJ/TKK6+oatWqzm4OAFjavn37FBwcrHLlymn27NkqVOjGxbYyZcqoVKlSmj59uiXueWDIBAAAACyNadcAAABgaQRiAMhHe/fulc1my9e5o6+HzWbT4sWLndoGALhVEIgBOFVycrL69++vihUrys3NTYGBgXrwwQe1atUqZzftlnbw4EG1atXK2c0AgFsCN9UBcJq9e/eqcePGKlasmF599VXVrl1bFy5c0BdffKGnnnrquubJLkgXLlzI8hWpNys/Pz9nNwEAbhn0EANwmr59+8pms2nDhg1q27at7rzzTtWsWVPx8fH2r1/dt2+f2rRpIy8vL3l7e6tDhw46dOiQfR0vvviiQkJCNGvWLJUvX15eXl7q27ev0tPTNX78ePn5+alMmTJ6+eWXHbZts9k0depUtWrVSh4eHqpYsaI++eQT++uZQx/mz5+vyMhIubu7278Ra+bMmapevbrc3d1VrVq1bKeo+/PPP9W8eXMVKVJEdevWdfh6bkn67rvv1LRpU3l4eCgwMFBPP/20w9fWBgUFacyYMerWrZuKFi2q8uXLO3w5S1pamvr16yd/f3+5u7urQoUKGjt2rMP+XT5kYuvWrWrRooU8PDxUsmRJ9erVS2fOnLG/HhcXp5iYGL322mvy9/dXyZIl7d9+BgC3PQMATnDs2DFjs9nMmDFjcqyTnp5uQkJCTJMmTczGjRvNDz/8YEJDQ01kZKS9TkJCgvHy8jLt2rUz27dvN0uWLDGFCxc20dHRpn///ubXX381s2bNMpLMDz/8YF9OkilZsqSZMWOG2bVrlxk2bJhxdXU1O3bsMMYYs2fPHiPJBAUFmYULF5o///zTHDhwwHzwwQfG39/fXrZw4UJTokQJM3v2bIflqlWrZpYuXWp27dpl2rVrZypUqGAuXLhgjDHm999/N56enub11183v/32m1m3bp2pV6+eiYuLs7evQoUKpkSJEmbKlClm9+7dZuzYscbFxcX8+uuvxhhjXn31VRMYGGi+/fZbs3fvXrN27Vozd+5ch/379NNPjTHGnDlzxvj7+5tHHnnEbN261axatcoEBweb2NhYe/3Y2Fjj7e1tevfubXbu3Gn++9//miJFipjp06df3wkGgFsIgRiAU6xfv95IMosWLcqxzpdffmlcXV3Nvn377GXbt283ksyGDRuMMZcCcZEiRUxKSoq9TnR0tAkKCjLp6en2sqpVq5qxY8fan0syvXv3dtheeHi46dOnjzHmf8F20qRJDnUqVarkEDyNMeall14yERERDsvNnDkzS5t37txpjDGme/fuplevXg7rWLt2rXFxcTFnz541xlwKxI8//rj99YyMDFOmTBkzdepUY4wx/fv3Ny1atDAZGRnZHrvLA/H06dNN8eLFzZkzZ+yvL1u2zLi4uJjk5GRjzKVAXKFCBXPx4kV7nfbt25uOHTtmu34AuJ0wZAKAU5hcTIG+c+dOBQYGKjAw0F5Wo0YNFStWTDt37rSXBQUFqWjRovbnvr6+qlGjhlxcXBzKDh8+7LD+iIiILM8vX68khYWF2f+fmpqqP/74Q927d5eXl5f9MXr0aP3xxx8Oy9WpU8f+f39/f0myb3/Lli2aPXu2wzqio6OVkZGhPXv2ZLsOm80mPz8/+zri4uK0efNmVa1aVU8//bS+/PLLbI+hdOk41q1bV56envayxo0bKyMjQ7t27bKX1axZU66urg7tvvKYAcDtiJvqADhFlSpVZLPZ8uXGuStvdLPZbNmWZWRk5Hndl4fIzDG3M2bMUHh4uEO9y4PklW2y2WySZN/+mTNn9OSTT+rpp5/Osr3y5ctnu44r96F+/fras2ePli9frq+++kodOnRQVFSUwzjovMqvYwYAtxp6iAE4RYkSJRQdHa0pU6Y43EyW6eTJk6pevbr279+v/fv328t37NihkydPqkaNGv+6DZk37l3+vHr16jnW9/X1VUBAgP78809VrlzZ4REcHJzr7davX187duzIso7KlSurcOHCuV6Pt7e3OnbsqBkzZmj+/PlauHChjh8/nqVe9erVtWXLFofjvG7dOrm4uPAV3QAgeogBONGUKVPUuHFjNWzYUKNGjVKdOnV08eJFrVy5UlOnTtWOHTtUu3Ztde7cWZMmTdLFixfVt29fRUZGOgxluF4LFixQWFiYmjRpog8//FAbNmzQu+++e9VlRo4cqaefflo+Pj5q2bKlzp8/r40bN+rEiROKj4/P1Xb/85//6K677lK/fv3Uo0cPeXp6aseOHVq5cqUmT56cq3VMnDhR/v7+qlevnlxcXLRgwQL5+fmpWLFiWep27txZCQkJio2N1YsvvqgjR46of//+euKJJ+Tr65ur7QHA7YweYgBOU7FiRf38889q3ry5nn32WdWqVUv33nuvVq1apalTp8pms+mzzz5T8eLFdffddysqKkoVK1bU/Pnz82X7I0eO1Lx581SnTh3NmTNHH3300TV7nnv06KGZM2cqMTFRtWvXVmRkpGbPnp2nHuI6derom2++0W+//aamTZuqXr16GjFihAICAnK9jqJFi2r8+PEKCwtTgwYNtHfvXn3++ecO46YzFSlSRF988YWOHz+uBg0aqF27drrnnntyHb4B4HZnM7m5swUAbjM2m02ffvqpYmJinN0UAICT0UMMAAAASyMQAwAAwNK4qQ6AJTFaDACQiR5iAAAAWBqBGAAAAJZGIAYAAIClEYgBAABgaQRiAAAAWBqBGABg16xZMw0cODDf1/viiy8qJCQk39cLAPmBQAwAt4i4uDjZbDb17t07y2tPPfWUbDab4uLicrWuNWvWyGaz6eTJk/nbSAC4BRGIAeAWEhgYqHnz5uns2bP2snPnzmnu3LkqX768E1sGALcuAjEA3ELq16+vwMBALVq0yF62aNEilS9fXvXq1bOXZWRkaOzYsQoODpaHh4fq1q2rTz75RJK0d+9eNW/eXJJUvHjxLD3LGRkZGjJkiEqUKCE/Pz+9+OKLDm3Yt2+f2rRpIy8vL3l7e6tDhw46dOiQQ51x48bJ19dXRYsWVffu3XXu3Ll8PhIAkH8IxABwi+nWrZsSExPtz2fNmqWuXbs61Bk7dqzmzJmjadOmafv27XrmmWf0+OOP65tvvlFgYKAWLlwoSdq1a5cOHjyoN954w77se++9J09PT61fv17jx4/XqFGjtHLlSkmXwnKbNm10/PhxffPNN1q5cqX+/PNPdezY0b78xx9/rBdffFFjxozRxo0b5e/vr7fffrsgDwkA/Cs2w/eXAsAtIS4uTidPntSMGTMUGBioXbt2SZKqVaum/fv3q0ePHipWrJjeeecdlShRQl999ZUiIiLsy/fo0UP//POP5s6dqzVr1qh58+Y6ceKEihUrZq/TrFkzpaena+3atfayhg0bqkWLFho3bpxWrlypVq1aac+ePQoMDJQk7dixQzVr1tSGDRvUoEEDNWrUSPXq1dOUKVPs67jrrrt07tw5bd68uWAPEgBch0LObgAAIG9Kly6t1q1ba/bs2TLGqHXr1ipVqpT99d9//13//POP7r33Xofl0tLSHIZV5KROnToOz/39/XX48GFJ0s6dOxUYGGgPw5JUo0YNFStWTDt37lSDBg20c+fOLDf+RUREaPXq1XneVwC4EQjEAHAL6tatm/r16ydJDj2xknTmzBlJ0rJly1S2bFmH19zc3K657jvuuMPhuc1mU0ZGxr9pLgDc1BhDDAC3oJYtWyotLU0XLlxQdHS0w2s1atSQm5ub9u3bp8qVKzs8Mnt2CxcuLElKT0/P03arV6+u/fv3a//+/fayHTt26OTJk6pRo4a9zvr16x2W++GHH/K8jwBwo9BDDAC3IFdXV+3cudP+/8sVLVpUgwYN0jPPPKOMjAw1adJEp06d0rp16+Tt7a3Y2FhVqFBBNptNS5cu1f333y8PDw95eXldc7tRUVGqXbu2OnfurEmTJunixYvq27evIiMjFRYWJkkaMGCA4uLiFBYWpsaNG+vDDz/U9u3bVbFixfw/EACQD+ghBoBblLe3t7y9vbN97aWXXtLw4cM1duxYVa9eXS1bttSyZcsUHBwsSSpbtqxGjhyp5557Tr6+vvbhF9dis9n02WefqXjx4rr77rsVFRWlihUrav78+fY6HTt21PDhwzVkyBCFhobqr7/+Up8+ff79DgNAAWGWCQAAAFgaPcQAAACwNAIxAAAALI1ADAAAAEsjEAMAAMDSCMQAAACwNAIxAAAALI1ADAAAAEsjEAMAAMDSCMQAAACwNAIxAAAALI1ADAAAAEv7f4VMaZJAiAghAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Sample data\n",
        "categories = ['Category A', 'Category B', 'Category C', 'Category D']\n",
        "values = [15, 24, 8, 12]\n",
        "\n",
        "# Create a bar graph\n",
        "plt.figure(figsize=(8, 6))  # Optional: Set the figure size\n",
        "plt.bar(method, dt)\n",
        "\n",
        "# Adding labels and title\n",
        "plt.xlabel('Method')\n",
        "plt.ylabel('Time (s)')\n",
        "plt.title('Execution Time of a Program Calculating 3x3 Matrix')\n",
        "\n",
        "# Display the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 580
        },
        "id": "7KeGLlhnKU7E",
        "outputId": "2ffc412c-c435-49f7-ce1e-5a32a64a9397"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "iw5xn06wKhwg"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}