From 08f885136573af9492fd5e4e5bafb5490951bae0 Mon Sep 17 00:00:00 2001 From: Pedro Carneiro Marques Date: Sat, 18 Nov 2023 16:51:38 +0000 Subject: [PATCH] Lab done --- your-code/continuous.ipynb | 284 +++++++++++++++++++++++++++++++++---- your-code/discrete.ipynb | 189 +++++++++++++++++++++--- 2 files changed, 420 insertions(+), 53 deletions(-) diff --git a/your-code/continuous.ipynb b/your-code/continuous.ipynb index 32218e9..3f91b7e 100644 --- a/your-code/continuous.ipynb +++ b/your-code/continuous.ipynb @@ -35,9 +35,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2.82577736 2.45555429 2.53438668 2.39519036 2.45827368 2.41635753\n", + " 2.31707887 2.08341435 2.24635294 2.80870723]\n" + ] + } + ], "source": [ "from scipy.stats import uniform\n", "x = uniform.rvs(size=10)\n", @@ -73,11 +82,51 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "# your code here\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "def generate_uniform_random(bottom, ceiling, count):\n", + " x = np.random.uniform(low=bottom, high=ceiling, size=count)\n", + " return x\n", + "\n", + "# Generate random numbers for the given parameters\n", + "data_1 = generate_uniform_random(10, 15, 100)\n", + "data_2 = generate_uniform_random(10, 60, 1000)\n", + "\n", + "# Plot histograms\n", + "plt.figure(figsize=(12, 6))\n", + "\n", + "plt.subplot(1, 2, 1)\n", + "plt.hist(data_1, bins=10, color='skyblue')\n", + "plt.title('Uniform Distribution, 100 samples')\n", + "plt.xlabel('Value')\n", + "plt.ylabel('Count')\n", + "plt.ylim(0, 100)\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.hist(data_2, bins=10, color='blue')\n", + "plt.title('Uniform Distribution, 1000 samples')\n", + "plt.xlabel('Value')\n", + "plt.ylabel('Count')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" ] }, { @@ -93,7 +142,10 @@ "metadata": {}, "outputs": [], "source": [ - "# your answer here:\n" + "# your answer here:\n", + "\"\"\"\n", + "The distributions differ both in sample size and in size values \n", + "\"\"\"" ] }, { @@ -114,11 +166,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "# your code here\n", + "\n", + "def generate_normal_random(mean, std_dev, count):\n", + " x = np.random.normal(loc=mean, scale=std_dev, size=count)\n", + " return x\n", + "\n", + "# Generate normally distributed numbers for the given parameters\n", + "data_1 = generate_normal_random(10, 1, 1000)\n", + "data_2 = generate_normal_random(10, 50, 1000)\n", + "\n", + "# Plot distributions\n", + "plt.figure(figsize=(12, 6))\n", + "\n", + "plt.subplot(1, 2, 1)\n", + "plt.hist(data_1, bins=30, color='skyblue')\n", + "plt.title('Normal Distribution (mean=10, std=1), 1000 samples')\n", + "plt.xlabel('Value')\n", + "plt.ylabel('Frequency')\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.hist(data_2, bins=30, color='blue')\n", + "plt.title('Normal Distribution (mean=10, std=50), 1000 samples')\n", + "plt.xlabel('Value')\n", + "plt.ylabel('Frequency')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" ] }, { @@ -134,7 +223,10 @@ "metadata": {}, "outputs": [], "source": [ - "# your answer here:\n" + "# your answer here:\n", + "\"\"\"\n", + "These distribution although having diferent std's both show similar visual traits but the key difference concerns the second graph that shows a much wider distribution of values\n", + "\"\"\"" ] }, { @@ -158,11 +250,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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t21fPPvusud4yOjpaU6dO1fLly7VkyRKVl5dr48aN5nKEa6+9VsnJyXr11Vdlt9s1YMAAJSUlOX2T2V/+8hf5+fnptdde09KlS9WuXTstXLhQ06ZNu+iANG3aNJ04cULJycmaOXOm2rdvr9dee01r166t8ge8LkVsbKz8/PyUlJSke++9Vx4eHurWrZs2btyo7t271+hxf/75Z/Nn16FDBy1cuFDPP/+8U52fn5+2bNmiF198UYsXL1ZmZqZ8fHzUokUL9enTp8p3gD///HNFRUVJkjw8PNSsWTPdfPPNmjZtmq6//nqzbvXq1XriiSc0ZcoUnT59WrfccotSU1MrfOiwKqpzbhf6HX/nnXfUsmVLdenS5ZL7WrRokbp166ZFixZpwYIFKi8vV0hIiG655RbdfPPNkn57VvL777+vJ554QqNHj5anp6f69OmjDRs2qEWLFlX9kQCWYjOMi3iwJwDUAZmZmWrXrp2mT5+up59+2t3twKLat2+vgQMH6uWXX3Z3KwDOgQAMoE76+uuv9cYbb6h79+5q1KiRMjIyNHPmTBUWFmrPnj3n/CQ9AAAsgQBQJ/n5+Wnnzp1KTk7W0aNHZbfbFR0drb/85S+EXwDAeXEHGAAAAJbCY9AAAABgKQRgAAAAWAoBGAAAAJbCh+AuUnl5uX7++Wf5+/vzlZIAAAC1kGEYKioqUkhIyHm/FIkAfJF+/vlnhYaGursNAAAAXMCBAwfUvHnzc+4nAF8kf39/Sb/9QBs1auTmbgAAAHC2wsJChYaGmrntXAjAF+nMsodGjRoRgAEAAGqxCy1X5UNwAAAAsBQCMAAAACyFAAwAAABLIQADAADAUgjAAAAAsBQCMAAAACyFAAwAAABLIQADAADAUgjAAAAAsBQCMAAAACyFAAwAAABLIQADAADAUgjAAAAAsBQCMAAAACyFAAwAAABLIQADAADAUgjAAAAAsBQCMAAAACzF090NAKi7srKylJeX55K5AgMD1aJFC5fMBQDA+RCAAVRJVlaW2kVEqPjECZfM5+Prq+/S0wnBAIAaRwAGUCV5eXkqPnFCQ2csVFBYeLXmys3cp7eeeUx5eXkEYABAjSMAA6iWoLBwNYvo7O42AAC4aHwIDgAAAJZCAAYAAIClEIABAABgKW4NwK1atZLNZquwjRs3TpJkGIYSEhIUEhIiHx8fRUdHa+/evU5zlJSUaPz48QoMDJSfn58GDRqkgwcPOtXk5+crLi5OdrtddrtdcXFxOnr06OU6TQAAANQibg3AO3bsUHZ2trmlpqZKkv7nf/5HkjRz5kzNnj1b8+fP144dO+RwONS3b18VFRWZc8THx2vt2rVas2aNPvvsMx07dkwxMTEqKysza2JjY5WWlqaUlBSlpKQoLS1NcXFxl/dkAQAAUCu49SkQV111ldPrF198Uddee6169OghwzA0d+5cTZs2TUOGDJEkLV++XMHBwVq9erVGjx6tgoICJScna8WKFerTp48kaeXKlQoNDdWGDRvUv39/paenKyUlRdu2bVNkZKQkacmSJYqKilJGRobatm17eU8aAAAAblVr1gCXlpZq5cqVGjFihGw2mzIzM5WTk6N+/fqZNd7e3urRo4e2bt0qSdq1a5dOnTrlVBMSEqIOHTqYNV988YXsdrsZfiWpW7dustvtZk1lSkpKVFhY6LQBAACg7qs1Afif//ynjh49qoceekiSlJOTI0kKDg52qgsODjb35eTkyMvLS02aNDlvTVBQUIXjBQUFmTWVSUpKMtcM2+12hYaGVvncAAAAUHvUmgCcnJysgQMHKiQkxGncZrM5vTYMo8LY2c6uqaz+QvNMnTpVBQUF5nbgwIGLOQ0AAADUcrUiAO/fv18bNmzQI488Yo45HA5JqnCXNjc317wr7HA4VFpaqvz8/PPWHD58uMIxjxw5UuHu8u95e3urUaNGThsAAADqvloRgJcuXaqgoCDdcccd5lhYWJgcDof5ZAjpt3XCmzZtUvfu3SVJXbp0Uf369Z1qsrOztWfPHrMmKipKBQUF2r59u1nz5ZdfqqCgwKwBAACAdbj1KRCSVF5erqVLl2r48OHy9Pzfdmw2m+Lj45WYmKjw8HCFh4crMTFRvr6+io2NlSTZ7XaNHDlSkyZNUtOmTRUQEKDJkyerY8eO5lMhIiIiNGDAAD366KNatGiRJGnUqFGKiYnhCRAAAAAW5PYAvGHDBmVlZWnEiBEV9k2ZMkXFxcUaO3as8vPzFRkZqfXr18vf39+smTNnjjw9PTV06FAVFxerd+/eWrZsmTw8PMyaVatWacKECebTIgYNGqT58+fX/MkBAACg1rEZhmG4u4m6oLCwUHa7XQUFBawHBiR99dVX6tKlix5ftUHNIjpXa65D6V9r/gN9tGvXLt14440u6hAAYDUXm9dqxRpgAAAA4HIhAAMAAMBSCMAAAACwFAIwAAAALIUADAAAAEshAAMAAMBSCMAAAACwFLd/EQYAuFpWVpby8vKqPU9gYKBatGjhgo4AALUJARjAFSUrK0vtIiJUfOJEtefy8fXVd+nphGAAuMIQgAFcUfLy8lR84oSGzliooLDwKs+Tm7lPbz3zmPLy8gjAAHCFIQADuCIFhYVX+yuaAQBXJj4EBwAAAEshAAMAAMBSCMAAAACwFAIwAAAALIUADAAAAEshAAMAAMBSCMAAAACwFAIwAAAALIUADAAAAEshAAMAAMBSCMAAAACwFAIwAAAALIUADAAAAEshAAMAAMBSCMAAAACwFAIwAAAALIUADAAAAEshAAMAAMBSCMAAAACwFAIwAAAALIUADAAAAEshAAMAAMBSCMAAAACwFAIwAAAALIUADAAAAEshAAMAAMBSCMAAAACwFAIwAAAALIUADAAAAEshAAMAAMBSCMAAAACwFAIwAAAALIUADAAAAEshAAMAAMBSCMAAAACwFAIwAAAALIUADAAAAEshAAMAAMBSCMAAAACwFAIwAAAALMXtAfjQoUMaNmyYmjZtKl9fX11//fXatWuXud8wDCUkJCgkJEQ+Pj6Kjo7W3r17neYoKSnR+PHjFRgYKD8/Pw0aNEgHDx50qsnPz1dcXJzsdrvsdrvi4uJ09OjRy3GKAAAAqEXcGoDz8/N1yy23qH79+vrwww/17bff6uWXX1bjxo3NmpkzZ2r27NmaP3++duzYIYfDob59+6qoqMisiY+P19q1a7VmzRp99tlnOnbsmGJiYlRWVmbWxMbGKi0tTSkpKUpJSVFaWpri4uIu5+kCAACgFvB058FfeuklhYaGaunSpeZYq1atzP9tGIbmzp2radOmaciQIZKk5cuXKzg4WKtXr9bo0aNVUFCg5ORkrVixQn369JEkrVy5UqGhodqwYYP69++v9PR0paSkaNu2bYqMjJQkLVmyRFFRUcrIyFDbtm0r9FZSUqKSkhLzdWFhYU38CAAAAHCZufUO8Lp169S1a1f9z//8j4KCgnTDDTdoyZIl5v7MzEzl5OSoX79+5pi3t7d69OihrVu3SpJ27dqlU6dOOdWEhISoQ4cOZs0XX3whu91uhl9J6tatm+x2u1lztqSkJHO5hN1uV2hoqEvPHQAAAO7h1gD83//+VwsXLlR4eLg++ugjjRkzRhMmTNDrr78uScrJyZEkBQcHO70vODjY3JeTkyMvLy81adLkvDVBQUEVjh8UFGTWnG3q1KkqKCgwtwMHDlTvZAEAAFAruHUJRHl5ubp27arExERJ0g033KC9e/dq4cKFevDBB806m83m9D7DMCqMne3smsrqzzePt7e3vL29L/pcAAAAUDe49Q7w1Vdfrfbt2zuNRUREKCsrS5LkcDgkqcJd2tzcXPOusMPhUGlpqfLz889bc/jw4QrHP3LkSIW7ywAAALiyuTUA33LLLcrIyHAa+/7779WyZUtJUlhYmBwOh1JTU839paWl2rRpk7p37y5J6tKli+rXr+9Uk52drT179pg1UVFRKigo0Pbt282aL7/8UgUFBWYNAAAArMGtSyCefPJJde/eXYmJiRo6dKi2b9+uxYsXa/HixZJ+W7YQHx+vxMREhYeHKzw8XImJifL19VVsbKwkyW63a+TIkZo0aZKaNm2qgIAATZ48WR07djSfChEREaEBAwbo0Ucf1aJFiyRJo0aNUkxMTKVPgAAAAMCVy60B+KabbtLatWs1depUvfDCCwoLC9PcuXP1wAMPmDVTpkxRcXGxxo4dq/z8fEVGRmr9+vXy9/c3a+bMmSNPT08NHTpUxcXF6t27t5YtWyYPDw+zZtWqVZowYYL5tIhBgwZp/vz5l+9kAQAAUCu4NQBLUkxMjGJiYs6532azKSEhQQkJCeesadCggebNm6d58+adsyYgIEArV66sTqsAAAC4Arj9q5ABAACAy4kADAAAAEshAAMAAMBSCMAAAACwFAIwAAAALIUADAAAAEshAAMAAMBSCMAAAACwFAIwAAAALIUADAAAAEshAAMAAMBSCMAAAACwFAIwAAAALIUADAAAAEshAAMAAMBSCMAAAACwFAIwAAAALIUADAAAAEshAAMAAMBSCMAAAACwFAIwAAAALIUADAAAAEshAAMAAMBSCMAAAACwFAIwAAAALIUADAAAAEshAAMAAMBSCMAAAACwFAIwAAAALIUADAAAAEshAAMAAMBSCMAAAACwFAIwAAAALIUADAAAAEshAAMAAMBSCMAAAACwFAIwAAAALIUADAAAAEshAAMAAMBSCMAAAACwFAIwAAAALIUADAAAAEshAAMAAMBSCMAAAACwFAIwAAAALIUADAAAAEshAAMAAMBSCMAAAACwFAIwAAAALIUADAAAAEtxawBOSEiQzWZz2hwOh7nfMAwlJCQoJCREPj4+io6O1t69e53mKCkp0fjx4xUYGCg/Pz8NGjRIBw8edKrJz89XXFyc7Ha77Ha74uLidPTo0ctxigAAAKhl3H4H+LrrrlN2dra57d6929w3c+ZMzZ49W/Pnz9eOHTvkcDjUt29fFRUVmTXx8fFau3at1qxZo88++0zHjh1TTEyMysrKzJrY2FilpaUpJSVFKSkpSktLU1xc3GU9TwAAANQOnm5vwNPT6a7vGYZhaO7cuZo2bZqGDBkiSVq+fLmCg4O1evVqjR49WgUFBUpOTtaKFSvUp08fSdLKlSsVGhqqDRs2qH///kpPT1dKSoq2bdumyMhISdKSJUsUFRWljIwMtW3b9vKdLAAAANzO7XeA9+3bp5CQEIWFhem+++7Tf//7X0lSZmamcnJy1K9fP7PW29tbPXr00NatWyVJu3bt0qlTp5xqQkJC1KFDB7Pmiy++kN1uN8OvJHXr1k12u92sqUxJSYkKCwudNgAAANR9bg3AkZGRev311/XRRx9pyZIlysnJUffu3fXLL78oJydHkhQcHOz0nuDgYHNfTk6OvLy81KRJk/PWBAUFVTh2UFCQWVOZpKQkc82w3W5XaGhotc4VAAAAtYNbA/DAgQN19913q2PHjurTp4/+9a9/SfptqcMZNpvN6T2GYVQYO9vZNZXVX2ieqVOnqqCgwNwOHDhwUecEAACA2s3tSyB+z8/PTx07dtS+ffvMdcFn36XNzc017wo7HA6VlpYqPz//vDWHDx+ucKwjR45UuLv8e97e3mrUqJHTBgAAgLqvVgXgkpISpaen6+qrr1ZYWJgcDodSU1PN/aWlpdq0aZO6d+8uSerSpYvq16/vVJOdna09e/aYNVFRUSooKND27dvNmi+//FIFBQVmDQAAAKzDrU+BmDx5su688061aNFCubm5mjFjhgoLCzV8+HDZbDbFx8crMTFR4eHhCg8PV2Jionx9fRUbGytJstvtGjlypCZNmqSmTZsqICBAkydPNpdUSFJERIQGDBigRx99VIsWLZIkjRo1SjExMTwBAgAAwILcGoAPHjyo+++/X3l5ebrqqqvUrVs3bdu2TS1btpQkTZkyRcXFxRo7dqzy8/MVGRmp9evXy9/f35xjzpw58vT01NChQ1VcXKzevXtr2bJl8vDwMGtWrVqlCRMmmE+LGDRokObPn395TxYAAAC1glsD8Jo1a86732azKSEhQQkJCeesadCggebNm6d58+adsyYgIEArV66sapsAAAC4gtSqNcAAAABATSMAAwAAwFIIwAAAALAUAjAAAAAshQAMAAAASyEAAwAAwFIIwAAAALAUAjAAAAAshQAMAAAASyEAAwAAwFIIwAAAALAUAjAAAAAshQAMAAAASyEAAwAAwFIIwAAAALAUAjAAAAAshQAMAAAASyEAAwAAwFIIwAAAALAUAjAAAAAshQAMAAAASyEAAwAAwFI83d0AAFhBVlaW8vLyXDJXYGCgWrRo4ZK5AMCKCMAAUMOysrLULiJCxSdOuGQ+H19ffZeeTggGgCoiAANADcvLy1PxiRMaOmOhgsLCqzVXbuY+vfXMY8rLyyMAA0AVEYAB4DIJCgtXs4jO7m4DACyPD8EBAADAUgjAAAAAsBQCMAAAACyFAAwAAABLIQADAADAUgjAAAAAsBQCMAAAACyFAAwAAABLIQADAADAUgjAAAAAsJQqBeDMzExX9wEAAABcFlUKwK1bt1bPnj21cuVKnTx50tU9AQAAADWmSgH466+/1g033KBJkybJ4XBo9OjR2r59u6t7AwAAAFyuSgG4Q4cOmj17tg4dOqSlS5cqJydHt956q6677jrNnj1bR44ccXWfAAAAgEtU60Nwnp6euuuuu/TWW2/ppZde0o8//qjJkyerefPmevDBB5Wdne2qPgEAAACXqFYA3rlzp8aOHaurr75as2fP1uTJk/Xjjz/qk08+0aFDhzR48GBX9QkAAAC4hGdV3jR79mwtXbpUGRkZuv322/X666/r9ttvV716v+XpsLAwLVq0SO3atXNpswAAAEB1VSkAL1y4UCNGjNDDDz8sh8NRaU2LFi2UnJxcreYAAAAAV6tSAN63b98Fa7y8vDR8+PCqTA8AAADUmCqtAV66dKn+8Y9/VBj/xz/+oeXLl1e7KQAAAKCmVCkAv/jiiwoMDKwwHhQUpMTExGo3BQAAANSUKgXg/fv3KywsrMJ4y5YtlZWVVe2mAAAAgJpSpQAcFBSkb775psL4119/raZNm1a7KQAAAKCmVCkA33fffZowYYI2btyosrIylZWV6ZNPPtETTzyh++67z9U9AgAAAC5TpQA8Y8YMRUZGqnfv3vLx8ZGPj4/69eunXr16VXkNcFJSkmw2m+Lj480xwzCUkJCgkJAQ+fj4KDo6Wnv37nV6X0lJicaPH6/AwED5+flp0KBBOnjwoFNNfn6+4uLiZLfbZbfbFRcXp6NHj1apTwAAANRtVQrAXl5eevPNN/Xdd99p1apVevfdd/Xjjz/q73//u7y8vC55vh07dmjx4sXq1KmT0/jMmTM1e/ZszZ8/Xzt27JDD4VDfvn1VVFRk1sTHx2vt2rVas2aNPvvsMx07dkwxMTEqKysza2JjY5WWlqaUlBSlpKQoLS1NcXFxVTl1AAAA1HFVeg7wGW3atFGbNm2q1cCxY8f0wAMPaMmSJZoxY4Y5bhiG5s6dq2nTpmnIkCGSpOXLlys4OFirV6/W6NGjVVBQoOTkZK1YsUJ9+vSRJK1cuVKhoaHasGGD+vfvr/T0dKWkpGjbtm2KjIyUJC1ZskRRUVHKyMhQ27Ztq9U/AAAA6pYq3QEuKytTcnKyYmNj1adPH/Xq1ctpuxTjxo3THXfcYQbYMzIzM5WTk6N+/fqZY97e3urRo4e2bt0qSdq1a5dOnTrlVBMSEqIOHTqYNV988YXsdrsZfiWpW7dustvtZk1lSkpKVFhY6LQBAACg7qvSHeAnnnhCy5Yt0x133KEOHTrIZrNV6eBr1qzRV199pR07dlTYl5OTI0kKDg52Gg8ODtb+/fvNGi8vLzVp0qRCzZn35+TkKCgoqML8QUFBZk1lkpKS9Pzzz1/aCQEAAKDWq1IAXrNmjd566y3dfvvtVT7wgQMH9MQTT2j9+vVq0KDBOevODteGYVwwcJ9dU1n9heaZOnWqJk6caL4uLCxUaGjoeY8LAACA2q/KH4Jr3bp1tQ68a9cu5ebmqkuXLvL09JSnp6c2bdqkv/71r/L09DTv/J59lzY3N9fc53A4VFpaqvz8/PPWHD58uMLxjxw5UuHu8u95e3urUaNGThsAAADqvioF4EmTJumVV16RYRhVPnDv3r21e/dupaWlmVvXrl31wAMPKC0tTddcc40cDodSU1PN95SWlmrTpk3q3r27JKlLly6qX7++U012drb27Nlj1kRFRamgoEDbt283a7788ksVFBSYNQAAALCOKi2B+Oyzz7Rx40Z9+OGHuu6661S/fn2n/e++++4F5/D391eHDh2cxvz8/NS0aVNzPD4+XomJiQoPD1d4eLgSExPl6+ur2NhYSZLdbtfIkSM1adIkNW3aVAEBAZo8ebI6duxofqguIiJCAwYM0KOPPqpFixZJkkaNGqWYmBieAAEAAGBBVQrAjRs31l133eXqXiqYMmWKiouLNXbsWOXn5ysyMlLr16+Xv7+/WTNnzhx5enpq6NChKi4uVu/evbVs2TJ5eHiYNatWrdKECRPMp0UMGjRI8+fPr/H+AQAAUPtUKQAvXbrU1X1Ikj799FOn1zabTQkJCUpISDjnexo0aKB58+Zp3rx556wJCAjQypUrXdQlAAAA6rIqfxHG6dOn9emnn+rHH39UbGys/P399fPPP6tRo0Zq2LChK3sE4EJZWVnKy8ur9jzp6eku6AYAgMuvSgF4//79GjBggLKyslRSUqK+ffvK399fM2fO1MmTJ/Xaa6+5uk8ALpCVlaV2EREqPnHC3a0AAOA2Vf4ijK5du+rrr79W06ZNzfG77rpLjzzyiMuaA+BaeXl5Kj5xQkNnLFRQWHi15sr4/GOlLkhyUWcAAFw+VX4KxOeffy4vLy+n8ZYtW+rQoUMuaQxAzQkKC1eziM7VmiM3c5+LugEA4PKq0nOAy8vLVVZWVmH84MGDTk9oAAAAAGqbKgXgvn37au7cueZrm82mY8eOafr06dX6emQAAACgplVpCcScOXPUs2dPtW/fXidPnlRsbKz27dunwMBAvfHGG67uEQAAAHCZKgXgkJAQpaWl6Y033tBXX32l8vJyjRw5Ug888IB8fHxc3SMAAADgMlV+DrCPj49GjBihESNGuLIfAAAAoEZVKQC//vrr593/4IMPVqkZAAAAoKZV+TnAv3fq1CmdOHFCXl5e8vX1JQADAACg1qrSUyDy8/OdtmPHjikjI0O33norH4IDAABArValAFyZ8PBwvfjiixXuDgMAAAC1icsCsCR5eHjo559/duWUAAAAgEtVaQ3wunXrnF4bhqHs7GzNnz9ft9xyi0saAwAAAGpClQLwH//4R6fXNptNV111lXr16qWXX37ZFX0BAAAANaJKAbi8vNzVfQAAAACXhUvXAAMAAAC1XZXuAE+cOPGia2fPnl2VQwAAAAA1okoB+D//+Y+++uornT59Wm3btpUkff/99/Lw8NCNN95o1tlsNtd0CQAAALhIlQLwnXfeKX9/fy1fvlxNmjSR9NuXYzz88MP6wx/+oEmTJrm0SQAAAMBVqrQG+OWXX1ZSUpIZfiWpSZMmmjFjBk+BAAAAQK1WpQBcWFiow4cPVxjPzc1VUVFRtZsCAAAAakqVAvBdd92lhx9+WG+//bYOHjyogwcP6u2339bIkSM1ZMgQV/cIAAAAuEyV1gC/9tprmjx5soYNG6ZTp079NpGnp0aOHKlZs2a5tEEAAADAlaoUgH19fbVgwQLNmjVLP/74owzDUOvWreXn5+fq/gAAAACXqtYXYWRnZys7O1tt2rSRn5+fDMNwVV8AAABAjahSAP7ll1/Uu3dvtWnTRrfffruys7MlSY888giPQAMAAECtVqUA/OSTT6p+/frKysqSr6+vOX7vvfcqJSXFZc0BAAAArlalNcDr16/XRx99pObNmzuNh4eHa//+/S5pDAAAAKgJVboDfPz4cac7v2fk5eXJ29u72k0BAAAANaVKAfi2227T66+/br622WwqLy/XrFmz1LNnT5c1BwAAALhalZZAzJo1S9HR0dq5c6dKS0s1ZcoU7d27V7/++qs+//xzV/cIAAAAuEyV7gC3b99e33zzjW6++Wb17dtXx48f15AhQ/Sf//xH1157rat7BAAAAFzmku8Anzp1Sv369dOiRYv0/PPP10RPAAAAQI255DvA9evX1549e2Sz2WqiHwAAAKBGVWkJxIMPPqjk5GRX9wIAAADUuCp9CK60tFR/+9vflJqaqq5du8rPz89p/+zZs13SHAAAAOBqlxSA//vf/6pVq1bas2ePbrzxRknS999/71TD0ggAAADUZpcUgMPDw5Wdna2NGzdK+u2rj//6178qODi4RpoDAAAAXO2S1gAbhuH0+sMPP9Tx48dd2hAAAABQk6r0Ibgzzg7EAAAAQG13SQHYZrNVWOPLml8AAADUJZe0BtgwDD300EPy9vaWJJ08eVJjxoyp8BSId99913UdAgAAAC50SQF4+PDhTq+HDRvm0mYAAACAmnZJAXjp0qU11QcAAABwWVTrQ3AAAABAXVOlb4IDgJqQnp5eK+YAAFzZCMAA3K4o77Bs9erxuQIAwGVBAAbgdsVFhTLKyzV0xkIFhYVXa66Mzz9W6oIkF3UGALgSEYAB1BpBYeFqFtG5WnPkZu5zUTcAgCuVWz8Et3DhQnXq1EmNGjVSo0aNFBUVpQ8//NDcbxiGEhISFBISIh8fH0VHR2vv3r1Oc5SUlGj8+PEKDAyUn5+fBg0apIMHDzrV5OfnKy4uTna7XXa7XXFxcTp69OjlOEUAAADUMm4NwM2bN9eLL76onTt3aufOnerVq5cGDx5shtyZM2dq9uzZmj9/vnbs2CGHw6G+ffuqqKjInCM+Pl5r167VmjVr9Nlnn+nYsWOKiYlRWVmZWRMbG6u0tDSlpKQoJSVFaWlpiouLu+znCwAAAPdz6xKIO++80+n1X/7yFy1cuFDbtm1T+/btNXfuXE2bNk1DhgyRJC1fvlzBwcFavXq1Ro8erYKCAiUnJ2vFihXq06ePJGnlypUKDQ3Vhg0b1L9/f6WnpyslJUXbtm1TZGSkJGnJkiWKiopSRkaG2rZte3lPGgAAAG5Va54DXFZWpjVr1uj48eOKiopSZmamcnJy1K9fP7PG29tbPXr00NatWyVJu3bt0qlTp5xqQkJC1KFDB7Pmiy++kN1uN8OvJHXr1k12u92sqUxJSYkKCwudNgAAANR9bg/Au3fvVsOGDeXt7a0xY8Zo7dq1at++vXJyciRJwcHBTvXBwcHmvpycHHl5ealJkybnrQkKCqpw3KCgILOmMklJSeaaYbvdrtDQ0GqdJwAAAGoHtwfgtm3bKi0tTdu2bdNjjz2m4cOH69tvvzX322w2p3rDMCqMne3smsrqLzTP1KlTVVBQYG4HDhy42FMCAABALeb2AOzl5aXWrVura9euSkpKUufOnfXKK6/I4XBIUoW7tLm5ueZdYYfDodLSUuXn55+35vDhwxWOe+TIkQp3l3/P29vbfDrFmQ0AAAB1n9sD8NkMw1BJSYnCwsLkcDiUmppq7istLdWmTZvUvXt3SVKXLl1Uv359p5rs7Gzt2bPHrImKilJBQYG2b99u1nz55ZcqKCgwawAAAGAdbn0KxNNPP62BAwcqNDRURUVFWrNmjT799FOlpKTIZrMpPj5eiYmJCg8PV3h4uBITE+Xr66vY2FhJkt1u18iRIzVp0iQ1bdpUAQEBmjx5sjp27Gg+FSIiIkIDBgzQo48+qkWLFkmSRo0apZiYGJ4AAQAAYEFuDcCHDx9WXFycsrOzZbfb1alTJ6WkpKhv376SpClTpqi4uFhjx45Vfn6+IiMjtX79evn7+5tzzJkzR56enho6dKiKi4vVu3dvLVu2TB4eHmbNqlWrNGHCBPNpEYMGDdL8+fMv78kCAACgVnBrAE5OTj7vfpvNpoSEBCUkJJyzpkGDBpo3b57mzZt3zpqAgACtXLmyqm0CAADgClLr1gADAAAANYkADAAAAEshAAMAAMBSCMAAAACwFAIwAAAALIUADAAAAEshAAMAAMBSCMAAAACwFAIwAAAALIUADAAAAEshAAMAAMBSCMAAAACwFAIwAAAALIUADAAAAEshAAMAAMBSCMAAAACwFAIwAAAALIUADAAAAEshAAMAAMBSCMAAAACwFAIwAAAALIUADAAAAEshAAMAAMBSCMAAAACwFAIwAAAALIUADAAAAEshAAMAAMBSCMAAAACwFAIwAAAALMXT3Q0AQG2Wnp5eK+YAALgOARgAKlGUd1i2evU0bNgwd7cCAHAxAjAAVKK4qFBGebmGzliooLDwas2V8fnHSl2Q5KLOAADVRQAGgPMICgtXs4jO1ZojN3Ofi7oBALgCH4IDAACApRCAAQAAYCkEYAAAAFgKARgAAACWQgAGAACApRCAAQAAYCkEYAAAAFgKARgAAACWQgAGAACApRCAAQAAYCkEYAAAAFgKARgAAACWQgAGAACApRCAAQAAYCkEYAAAAFiKp7sbAABcuvT0dJfMExgYqBYtWrhkLgCoKwjAAFCHFOUdlq1ePQ0bNswl8/n4+uq79HRCMABLcWsATkpK0rvvvqvvvvtOPj4+6t69u1566SW1bdvWrDEMQ88//7wWL16s/Px8RUZG6tVXX9V1111n1pSUlGjy5Ml64403VFxcrN69e2vBggVq3ry5WZOfn68JEyZo3bp1kqRBgwZp3rx5aty48WU7XwCoruKiQhnl5Ro6Y6GCwsKrNVdu5j699cxjysvLIwADsBS3BuBNmzZp3Lhxuummm3T69GlNmzZN/fr107fffis/Pz9J0syZMzV79mwtW7ZMbdq00YwZM9S3b19lZGTI399fkhQfH6/3339fa9asUdOmTTVp0iTFxMRo165d8vDwkCTFxsbq4MGDSklJkSSNGjVKcXFxev/9991z8gBQDUFh4WoW0dndbQBAneTWAHwmjJ6xdOlSBQUFadeuXbrttttkGIbmzp2radOmaciQIZKk5cuXKzg4WKtXr9bo0aNVUFCg5ORkrVixQn369JEkrVy5UqGhodqwYYP69++v9PR0paSkaNu2bYqMjJQkLVmyRFFRUcrIyHC64wwAAIArW616CkRBQYEkKSAgQJKUmZmpnJwc9evXz6zx9vZWjx49tHXrVknSrl27dOrUKaeakJAQdejQwaz54osvZLfbzfArSd26dZPdbjdrzlZSUqLCwkKnDQAAAHVfrQnAhmFo4sSJuvXWW9WhQwdJUk5OjiQpODjYqTY4ONjcl5OTIy8vLzVp0uS8NUFBQRWOGRQUZNacLSkpSXa73dxCQ0Ord4IAAACoFWpNAH788cf1zTff6I033qiwz2azOb02DKPC2NnOrqms/nzzTJ06VQUFBeZ24MCBizkNAAAA1HK1IgCPHz9e69at08aNG52e3OBwOCSpwl3a3Nxc866ww+FQaWmp8vPzz1tz+PDhCsc9cuRIhbvLZ3h7e6tRo0ZOGwAAAOo+twZgwzD0+OOP691339Unn3yisLAwp/1hYWFyOBxKTU01x0pLS7Vp0yZ1795dktSlSxfVr1/fqSY7O1t79uwxa6KiolRQUKDt27ebNV9++aUKCgrMGgAAAFiDW58CMW7cOK1evVrvvfee/P39zTu9drtdPj4+stlsio+PV2JiosLDwxUeHq7ExET5+voqNjbWrB05cqQmTZqkpk2bKiAgQJMnT1bHjh3Np0JERERowIABevTRR7Vo0SJJvz0GLSYmhidAAAAAWIxbA/DChQslSdHR0U7jS5cu1UMPPSRJmjJlioqLizV27FjzizDWr19vPgNYkubMmSNPT08NHTrU/CKMZcuWmc8AlqRVq1ZpwoQJ5tMiBg0apPnz59fsCQIAAKDWcWsANgzjgjU2m00JCQlKSEg4Z02DBg00b948zZs375w1AQEBWrlyZVXaBAAAwBWkVnwIDgAAALhcCMAAAACwFLcugQBwcbKyspSXl1ftedLT013QDQAAdRsBGKjlsrKy1C4iQsUnTri7FQAArggEYKCWy8vLU/GJExo6Y6GCwsKrNVfG5x8rdUGSizoDAKBuIgADdURQWLiaRXSu1hy5mftc1A0AAHUXH4IDAACApRCAAQAAYCkEYAAAAFgKARgAAACWQgAGAACApRCAAQAAYCkEYAAAAFgKARgAAACWQgAGAACApRCAAQAAYCkEYAAAAFgKARgAAACWQgAGAACApRCAAQAAYCkEYAAAAFgKARgAAACWQgAGAACApRCAAQAAYCkEYAAAAFgKARgAAACWQgAGAACApRCAAQAAYCkEYAAAAFgKARgAAACWQgAGAACApRCAAQAAYCkEYAAAAFgKARgAAACWQgAGAACApRCAAQAAYCme7m4AAHBlyMrKUl5eXrXnCQwMVIsWLVzQEQBUjgAMAKi2rKwstYuIUPGJE9Wey8fXV9+lpxOCAdQYAjAAoNry8vJUfOKEhs5YqKCw8CrPk5u5T28985jy8vIIwABqDAEYAOAyQWHhahbR2d1tAMB58SE4AAAAWAoBGAAAAJZCAAYAAIClEIABAABgKQRgAAAAWAoBGAAAAJZCAAYAAIClEIABAABgKQRgAAAAWAoBGAAAAJbi1q9C3rx5s2bNmqVdu3YpOztba9eu1R//+Edzv2EYev7557V48WLl5+crMjJSr776qq677jqzpqSkRJMnT9Ybb7yh4uJi9e7dWwsWLFDz5s3Nmvz8fE2YMEHr1q2TJA0aNEjz5s1T48aNL9epwqKysrKUl5dXrTnS09Nd1A0AAJDcHICPHz+uzp076+GHH9bdd99dYf/MmTM1e/ZsLVu2TG3atNGMGTPUt29fZWRkyN/fX5IUHx+v999/X2vWrFHTpk01adIkxcTEaNeuXfLw8JAkxcbG6uDBg0pJSZEkjRo1SnFxcXr//fcv38nCcrKystQuIkLFJ064uxUAAPA7bg3AAwcO1MCBAyvdZxiG5s6dq2nTpmnIkCGSpOXLlys4OFirV6/W6NGjVVBQoOTkZK1YsUJ9+vSRJK1cuVKhoaHasGGD+vfvr/T0dKWkpGjbtm2KjIyUJC1ZskRRUVHKyMhQ27ZtL8/JwnLy8vJUfOKEhs5YqKCw8CrPk/H5x0pdkOTCzgAAsDa3BuDzyczMVE5Ojvr162eOeXt7q0ePHtq6datGjx6tXbt26dSpU041ISEh6tChg7Zu3ar+/fvriy++kN1uN8OvJHXr1k12u11bt249ZwAuKSlRSUmJ+bqwsLAGzhJWEBQWrmYRnav8/tzMfS7sBgAA1NoPweXk5EiSgoODncaDg4PNfTk5OfLy8lKTJk3OWxMUFFRh/qCgILOmMklJSbLb7eYWGhparfMBAABA7VBrA/AZNpvN6bVhGBXGznZ2TWX1F5pn6tSpKigoMLcDBw5cYucAAACojWptAHY4HJJU4S5tbm6ueVfY4XCotLRU+fn55605fPhwhfmPHDlS4e7y73l7e6tRo0ZOGwAAAOq+WhuAw8LC5HA4lJqaao6VlpZq06ZN6t69uySpS5cuql+/vlNNdna29uzZY9ZERUWpoKBA27dvN2u+/PJLFRQUmDUAAACwDrd+CO7YsWP64YcfzNeZmZlKS0tTQECAWrRoofj4eCUmJio8PFzh4eFKTEyUr6+vYmNjJUl2u10jR47UpEmT1LRpUwUEBGjy5Mnq2LGj+VSIiIgIDRgwQI8++qgWLVok6bfHoMXExPAECAAAAAtyawDeuXOnevbsab6eOHGiJGn48OFatmyZpkyZouLiYo0dO9b8Ioz169ebzwCWpDlz5sjT01NDhw41vwhj2bJl5jOAJWnVqlWaMGGC+bSIQYMGaf78+ZfpLAEAAFCbuDUAR0dHyzCMc+632WxKSEhQQkLCOWsaNGigefPmad68eeesCQgI0MqVK6vTKgAAAK4QtXYNMAAAAFATCMAAAACwFAIwAAAALIUADAAAAEshAAMAAMBSCMAAAACwFAIwAAAALIUADAAAAEshAAMAAMBS3PpNcAAAVCY9Pd0l8wQGBqpFixYumQvAlYMADACoNYryDstWr56GDRvmkvl8fH31XXo6IRiAEwIwAKDWKC4qlFFerqEzFiooLLxac+Vm7tNbzzymvLw8AjAAJwRgAECtExQWrmYRnd3dBoArFB+CAwAAgKUQgAEAAGApBGAAAABYCgEYAAAAlkIABgAAgKUQgAEAAGApBGAAAABYCgEYAAAAlkIABgAAgKUQgAEAAGApBGAAAABYCgEYAAAAlkIABgAAgKUQgAEAAGApnu5uAACAuiArK0t5eXkumSswMFAtWrRwyVwALh0BGACAC8jKylK7iAgVnzjhkvl8fH31XXo6IRhwEwIwAAAXkJeXp+ITJzR0xkIFhYVXa67czH1665nHlJeXRwAG3IQADADARQoKC1eziM7ubgNANfEhOAAAAFgKARgAAACWQgAGAACApRCAAQAAYCkEYAAAAFgKARgAAACWQgAGAACApfAcYOAsrvq60/T0dBd0AwAAXI0ADPyOq7/uFAAA1D4EYOB3XPl1pxmff6zUBUku6gwAALgKARiohCu+7jQ3c5+LugEAAK7Eh+AAAABgKQRgAAAAWAoBGAAAAJZCAAYAAICl8CE4AADcwFXPCg8MDFSLFi1cMhdgFQRgAAAuo6K8w7LVq6dhw4a5ZD4fX199l55OCAYuAQEYVwS+vQ2oOlf83vPPzsUrLiqUUV7ukueN52bu01vPPKa8vDwCMHAJCMCo8/j2NqBqXH0nEpfGFc8bB1A1BOBazFV3NaUre40Y394GVI0r70Tyzw6AusRSAXjBggWaNWuWsrOzdd1112nu3Ln6wx/+4O62KuXqu5pWWCPGt7cBVcM/OwCsxjIB+M0331R8fLwWLFigW265RYsWLdLAgQP17bff1spQ6Mq7mqwRAwBcTlf6XzCv9POzAssE4NmzZ2vkyJF65JFHJElz587VRx99pIULFyopqfb+2c6Va8Rc9SGVkpISeXt715p5+PANANQeV/pfMK/08zvjSg/5lgjApaWl2rVrl5566imn8X79+mnr1q2VvqekpEQlJSXm64KCAklSYWFhzTX6O8eOHZMkHUr/RqUnjldrrp++2SnZbK77oIvNJhlG7Znn/3PFz+rIT/tq3Vy1sScrzFUbe7LCXC7taf+PkqRdu3aZ/06tqoyMDNf1VUvPsV69eiovL6/WHNJvP6viEyf0hwfHqbGjWbXmOppzSFtef1UfffSR2rZtW+3eXHGOV/r5SdLhw4cV9+CDKjl5stpzSVIDHx/t3LFDoaGhLpnvfM7kNONC+cKwgEOHDhmSjM8//9xp/C9/+YvRpk2bSt8zffp0QxIbGxsbGxsbG1sd2w4cOHDebGiJO8Bn2Gw2p9eGYVQYO2Pq1KmaOHGi+bq8vFy//vqrmjZtes73uEphYaFCQ0N14MABNWrUqEaPhcuP63tl4/pe2bi+Vzaub91nGIaKiooUEhJy3jpLBODAwEB5eHgoJyfHaTw3N1fBwcGVvsfb27vC+tTGjRvXVIuVatSoEf8AXsG4vlc2ru+Vjet7ZeP61m12u/2CNfUuQx9u5+XlpS5duig1NdVpPDU1Vd27d3dTVwAAAHAHS9wBlqSJEycqLi5OXbt2VVRUlBYvXqysrCyNGTPG3a0BAADgMrJMAL733nv1yy+/6IUXXlB2drY6dOigf//732rZsqW7W6vA29tb06dPd8kjwlD7cH2vbFzfKxvX98rG9bUOm2G48DlUAAAAQC1niTXAAAAAwBkEYAAAAFgKARgAAACWQgAGAACApRCAa6EFCxYoLCxMDRo0UJcuXbRlyxZ3t4Qq2Lx5s+68806FhITIZrPpn//8p9N+wzCUkJCgkJAQ+fj4KDo6Wnv37nVPs7gkSUlJuummm+Tv76+goCD98Y9/VEZGhlMN17fuWrhwoTp16mR+GUJUVJQ+/PBDcz/X9sqRlJQkm82m+Ph4c4zraw0E4FrmzTffVHx8vKZNm6b//Oc/+sMf/qCBAwcqKyvL3a3hEh0/flydO3fW/PnzK90/c+ZMzZ49W/Pnz9eOHTvkcDjUt29fFRUVXeZOcak2bdqkcePGadu2bUpNTdXp06fVr18/HT9+3Kzh+tZdzZs314svvqidO3dq586d6tWrlwYPHmyGIK7tlWHHjh1avHixOnXq5DTO9bUIA7XKzTffbIwZM8ZprF27dsZTTz3lpo7gCpKMtWvXmq/Ly8sNh8NhvPjii+bYyZMnDbvdbrz22mtu6BDVkZuba0gyNm3aZBgG1/dK1KRJE+Nvf/sb1/YKUVRUZISHhxupqalGjx49jCeeeMIwDP7ZtRLuANcipaWl2rVrl/r16+c03q9fP23dutVNXaEmZGZmKicnx+lae3t7q0ePHlzrOqigoECSFBAQIInreyUpKyvTmjVrdPz4cUVFRXFtrxDjxo3THXfcoT59+jiNc32twzLfBFcX5OXlqaysTMHBwU7jwcHBysnJcVNXqAlnrmdl13r//v3uaAlVZBiGJk6cqFtvvVUdOnSQxPW9EuzevVtRUVE6efKkGjZsqLVr16p9+/ZmCOLa1l1r1qzRV199pR07dlTYxz+71kEAroVsNpvTa8MwKozhysC1rvsef/xxffPNN/rss88q7OP61l1t27ZVWlqajh49qnfeeUfDhw/Xpk2bzP1c27rpwIEDeuKJJ7R+/Xo1aNDgnHVc3ysfSyBqkcDAQHl4eFS425ubm1vhv0ZRtzkcDkniWtdx48eP17p167Rx40Y1b97cHOf61n1eXl5q3bq1unbtqqSkJHXu3FmvvPIK17aO27Vrl3Jzc9WlSxd5enrK09NTmzZt0l//+ld5enqa15Dre+UjANciXl5e6tKli1JTU53GU1NT1b17dzd1hZoQFhYmh8PhdK1LS0u1adMmrnUdYBiGHn/8cb377rv65JNPFBYW5rSf63vlMQxDJSUlXNs6rnfv3tq9e7fS0tLMrWvXrnrggQeUlpama665hutrESyBqGUmTpyouLg4de3aVVFRUVq8eLGysrI0ZswYd7eGS3Ts2DH98MMP5uvMzEylpaUpICBALVq0UHx8vBITExUeHq7w8HAlJibK19dXsbGxbuwaF2PcuHFavXq13nvvPfn7+5t3i+x2u3x8fMzninJ966ann35aAwcOVGhoqIqKirRmzRp9+umnSklJ4drWcf7+/uZa/TP8/PzUtGlTc5zraxHuewAFzuXVV181WrZsaXh5eRk33nij+Wgl1C0bN240JFXYhg8fbhjGb4/bmT59uuFwOAxvb2/jtttuM3bv3u3epnFRKruukoylS5eaNVzfumvEiBHmv4Ovuuoqo3fv3sb69evN/VzbK8vvH4NmGFxfq7AZhmG4KXsDAAAAlx1rgAEAAGApBGAAAABYCgEYAAAAlkIABgAAgKUQgAEAAGApBGAAAABYCgEYAAAAlkIABgAAgKUQgAHATT799FPZbDYdPXrU3a1ckrraNwCcQQAGgHN46KGHZLPZKmw//PDDZeuhVatW5nE9PDwUEhKikSNHKj8//7L14Ar79++Xt7e3Hn74YXXs2FGlpaVO+//973+rfv362rlzp5s6BGAlBGAAOI8BAwYoOzvbaQsLC7usPbzwwgvKzs5WVlaWVq1apc2bN2vChAnVmvPsACpJhmHo9OnT1Zr3XN577z1FR0frr3/9q4qKijR9+nRz39GjRzVq1ChNmzZNXbt2dfmxT5065fI5AdRtBGAAOA9vb285HA6nzcPDQw899JD++Mc/OtXGx8crOjrafG0YhmbOnKlrrrlGPj4+6ty5s95+++1L7sHf318Oh0PNmjVTz5499eCDD+qrr74y9//yyy+6//771bx5c/n6+qpjx4564403nOaIjo7W448/rokTJyowMFB9+/Y1lzJ89NFH6tq1q7y9vbVly5ZL7nv//v2688471aRJE/n5+em6667Tv//9b6ea9957T4MGDZK/v7+WLVuml19+WV9++aX5c7v66qv1zDPP6NChQ7r33nvVpEkTNW3aVIMHD9ZPP/1kzrNjxw717dtXgYGBstvt6tGjh9PPQpJsNptee+01DR48WH5+fpoxY8Yl/8wBXNkIwABQQ5555hktXbpUCxcu1N69e/Xkk09q2LBh2rRpU5XnPHTokD744ANFRkaaYydPnlSXLl30wQcfaM+ePRo1apTi4uLMgHnG8uXL5enpqc8//1yLFi0yx6dMmaKkpCSlp6erU6dOl9z3uHHjVFJSos2bN2v37t166aWX1LBhQ3P/0aNHtWXLFg0aNEjSb2F87NixGj58uP7xj3/orbfe0uuvv67S0lL17NlTDRs21ObNm/XZZ5+pYcOGGjBggHnHuqioSMOHD9eWLVu0bds2hYeH6/bbb1dRUZFTT9OnT9fgwYO1e/dujRgxoso/bwBXKAMAUKnhw4cbHh4ehp+fn7ndc8895r7Bgwc71T/xxBNGjx49DMMwjGPHjhkNGjQwtm7d6lQzcuRI4/777zcMwzA2btxoSDLy8/PP2UPLli0NLy8vw8/Pz2jQoIEhyYiMjDzvewzDMG6//XZj0qRJ5usePXoY119/vVPNmeP/85//NMeq0nfHjh2NhISEc/ayatUq48Ybb3QaO3HihNGuXTujXr16xpw5cwzDMIzk5GSjbdu2Rnl5uVlXUlJi+Pj4GB999FGlc58+fdrw9/c33n//fXNMkhEfH3/OfgDA063pGwBquZ49e2rhwoXmaz8/v4t637fffquTJ0+qb9++TuOlpaW64YYbLqmHP/3pT3rooYdkGIYOHDigp59+WnfccYc2b94sDw8PlZWV6cUXX9Sbb76pQ4cOqaSkRCUlJRV6Pdf62t+PV6XvCRMm6LHHHtP69evVp08f3X333erUqZO5/8zyh9/z8fHRpEmT9OSTT+qJJ56QJO3atUs//PCD/P39nWpPnjypH3/8UZKUm5ur5557Tp988okOHz6ssrIynThxQllZWRd1rgAgSQRgADgPPz8/tW7dusJ4vXr1ZBiG09jvP2xVXl4uSfrXv/6lZs2aOdV5e3tfUg+BgYFmD+Hh4Zo7d66ioqK0ceNG9enTRy+//LLmzJmjuXPnqmPHjvLz81N8fHyFD7qdK7z/frwqfT/yyCPq37+//vWvf2n9+vVKSkrSyy+/rPHjx+vUqVNKSUnR1KlTK7zP09NTHh4estls5rG7dOmiVatWVai96qqrJP32ZI4jR45o7ty5atmypby9vRUVFXXR5woAEgEYAKrkqquu0p49e5zG0tLSVL9+fUlS+/bt5e3traysLPXo0cOlx/bw8JAkFRcXS5K2bNmiwYMHa9iwYZJ+C5L79u1TRETEJc9d1b5DQ0M1ZswYjRkzRlOnTtWSJUs0fvx4bdy4UY0bN9b1119/wTluvPFGvfnmmwoKClKjRo0qrdmyZYsWLFig22+/XZJ04MAB5eXlXXSfACARgAGgSnr16qVZs2bp9ddfV1RUlFauXKk9e/aYywT8/f01efJkPfnkkyovL9ett96qwsJCbd26VQ0bNtTw4cMv+lhFRUXKyckxl0BMmTJFgYGB6t69uySpdevWeuedd7R161Y1adJEs2fPVk5OTpUCcFX6jo+P18CBA9WmTRvl5+frk08+MY+9bt26CssfzuWBBx7QrFmzNHjwYL3wwgtq3ry5srKy9O677+pPf/qTmjdvrtatW2vFihXq2rWrCgsL9ac//Uk+Pj6XfJ4ArI2nQABAFfTv31/PPvuspkyZoptuuklFRUV68MEHnWr+/Oc/67nnnlNSUpIiIiLUv39/vf/++5f8HOHnnntOV199tUJCQhQTEyM/Pz+lpqaqadOmkqRnn31WN954o/r376/o6Gg5HI4Kj2i7FJfad1lZmcaNG6eIiAgNGDBAbdu21YIFCyT9FoAHDx58Ucf19fXV5s2b1aJFCw0ZMkQREREaMWKEiouLzTvCf//735Wfn68bbrhBcXFxmjBhgoKCgqp8rgCsyWacvYgNAAAX+Oqrr9SrVy8dOXLEXBoCALUBd4ABADXi9OnTmjdvHuEXQK3DHWAAAABYCneAAQAAYCkEYAAAAFgKARgAAACWQgAGAACApRCAAQAAYCkEYAAAAFgKARgAAACWQgAGAACApRCAAQAAYCn/D6bTrpUpL9Y6AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "# your code here\n", + "import pandas as pd\n", + "vehicles = pd.read_csv(\"vehicles.csv\")\n", + "vehicles\n", + "\n", + "plt.figure(figsize=(8, 6))\n", + "plt.hist(vehicles[\"Fuel Barrels/Year\"], bins=30, color='skyblue', edgecolor='black')\n", + "plt.title(\"Histogram of Fuel Barrels/Year\")\n", + "plt.xlabel(\"Fuel Barrels/Year\")\n", + "plt.ylabel(\"Frequency\")\n", + "plt.show()\n" ] }, { @@ -174,11 +287,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "# your code here\n", + "plt.figure(figsize=(8, 6))\n", + "plt.hist(vehicles[\"CO2 Emission Grams/Mile\"], bins=30, color='skyblue', edgecolor='black')\n", + "plt.title(\"CO2 Emission Grams/Miler\")\n", + "plt.xlabel(\"CO2 Emission Grams/Mile\")\n", + "plt.ylabel(\"Frequency\")\n", + "plt.show()" ] }, { @@ -190,11 +320,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "# your code here\n", + "plt.figure(figsize=(8, 6))\n", + "plt.hist(vehicles[\"Combined MPG\"], bins=30, color='skyblue', edgecolor='black')\n", + "plt.title(\"Combined MPG\")\n", + "plt.xlabel(\"Combined MPG\")\n", + "plt.ylabel(\"Frequency\")\n", + "plt.show()" ] }, { @@ -210,7 +357,12 @@ "metadata": {}, "outputs": [], "source": [ - "# you answer here:\n" + "# you answer here:\n", + "\"\"\"\n", + "Both the first graphs show a normal bellshaped curved distribution meaning the majority of values are close to the mean.\n", + "The combined MPG on the other hand has a certain rightward/positive skewness.\n", + "The visuals help to understand these differences\n", + "\"\"\"\n" ] }, { @@ -237,11 +389,49 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "# your code here\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "def exponential_mean(mean, size):\n", + " return np.random.exponential(scale=mean, size=size)\n", + "\n", + "# Generate exponentially distributed numbers\n", + "data_1 = exponential_mean(1, 1000)\n", + "data_2 = exponential_mean(100, 1000)\n", + "\n", + "# Plot histograms\n", + "plt.figure(figsize=(12, 6))\n", + "\n", + "plt.subplot(1, 2, 1)\n", + "plt.hist(data_1, bins=100, color=\"blue\")\n", + "plt.title(\"Exponential Distribution (mean=1), size=1000\")\n", + "plt.xlabel(\"Value\")\n", + "plt.ylabel(\"Frequency\")\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.hist(data_2, bins=100, color=\"blue\")\n", + "plt.title(\"Exponential Distribution (mean=100), size=1000\")\n", + "plt.xlabel(\"Value\")\n", + "plt.ylabel(\"Frequency\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" ] }, { @@ -257,7 +447,11 @@ "metadata": {}, "outputs": [], "source": [ - "# your answer here:\n" + "# your answer here:\n", + "\"\"\"\n", + "Although the graps have similar visual appearence the differences lie in the scale and spread of the distributions (mean=1) is concentrated at lower values and has a smaller spread,\n", + "while the other (mean=100) is spread over a wider range, concentrating more around higher values.\n", + "\"\"\"" ] }, { @@ -273,12 +467,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The probability that a customer will spend less than fifteen minutes in the bank is approximately 0.7769\n" + ] + } + ], "source": [ "# your answer here\n", - "# Hint: This is same as saying P(x<15)\n" + "# Hint: This is same as saying P(x<15)\n", + "lambda_param = 1/10\n", + "time_limit = 15\n", + "\n", + "prob_less_than_15 = 1 - np.exp(-lambda_param * time_limit)\n", + "\n", + "print(f\"The probability that a customer will spend less than fifteen minutes in the bank is approximately {prob_less_than_15:.4f}\")" ] }, { @@ -290,11 +498,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The probability that a customer will spend more than fifteen minutes in the bank is approximately 0.2231\n" + ] + } + ], "source": [ - "# your answer here\n" + "# your answer here\n", + "lambda_param = 1/10\n", + "time_limit = 15\n", + "\n", + "prob_more_than_15 = 1 - (1 - np.exp(-lambda_param * time_limit))\n", + "\n", + "print(f\"The probability that a customer will spend more than fifteen minutes in the bank is approximately {prob_more_than_15:.4f}\")" ] } ], @@ -314,7 +536,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.11.0" } }, "nbformat": 4, diff --git a/your-code/discrete.ipynb b/your-code/discrete.ipynb index 310a3c6..082e68f 100644 --- a/your-code/discrete.ipynb +++ b/your-code/discrete.ipynb @@ -33,9 +33,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 65, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The probability that the fruit is an apple is: 0.6\n", + "The probability that the fruit is an apple is: 0.4\n" + ] + } + ], "source": [ "\"\"\"\n", "Calculate:\n", @@ -43,7 +52,21 @@ "q = probability that the fruit is an orange\n", "\"\"\"\n", "\n", - "# your code here\n" + "# your code here\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from scipy.stats import bernoulli, binom, geom, poisson, uniform, expon, norm\n", + "import random\n", + "\n", + "p = 0.6\n", + "bernoulli_dist = bernoulli(p)\n", + "print(f\"The probability that the fruit is an apple is: {bernoulli_dist.pmf(1)}\")\n", + "\n", + "q = 0.4\n", + "bernoulli_dist = bernoulli(q)\n", + "print(f\"The probability that the fruit is an apple is: {bernoulli_dist.pmf(1)}\")" ] }, { @@ -61,11 +84,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 94, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.07775999999999998\n", + "9.181209418945295e-10\n" + ] + } + ], "source": [ - "# your code here\n" + "# your code here\n", + "# 1\n", + "n = 20\n", + "p = 0.6\n", + "q = 0.4\n", + "count_apples = p**5\n", + "print(count_apples)\n", + "\n", + "# 2\n", + "n = 20\n", + "p = 0.6\n", + "q = 0.4\n", + "apples_oranges = (count_apples) * (q**(n - count_apples))\n", + "print(apples_oranges)" ] }, { @@ -83,11 +128,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 120, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Probability of the sample containing 5 apples and 15 oranges: 0.0012944935222876579\n" + ] + } + ], "source": [ - "# your code here\n" + "# your code here\n", + "#n = 20\n", + "#p = 0.6\n", + "#q = 0.4\n", + "#binomial_dist = binom(n,p)\n", + "#binomial_dist.pmf(20)\n", + "from math import comb\n", + "\n", + "p = 0.6\n", + "q = 0.4\n", + "n = 20\n", + "sample = 5\n", + "binomial_coefficient = comb(n, sample)\n", + "\n", + "# Probability of getting exactly 5 apples in a sample of 20 fruits\n", + "apples = binomial_coefficient * (p**sample) * (q**(n - sample))\n", + "\n", + "print(f\"Probability of the sample containing 5 apples and 15 oranges: {apples}\")\n" ] }, { @@ -101,11 +171,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 119, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Probability that less than 5 fruits picked are apples: 0.00031703112116863\n" + ] + } + ], "source": [ - "# your code here\n" + "p = 0.6\n", + "q = 0.4\n", + "n = 20\n", + "sample = 4\n", + "less_than_five = sum(comb(n, i) * (p**i) * (q**(n - i)) for i in range(sample + 1))\n", + "\n", + "print(f\"Probability that less than 5 fruits picked are apples: {less_than_five}\")\n" ] }, { @@ -119,12 +203,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 126, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# your code here\n", - "# Please label the axes and give a title to the plot\n" + "# Please label the axes and give a title to the plot\n", + "\n", + "p = 0.6\n", + "q = 0.4\n", + "n = 20\n", + "x = list(range(n+1))\n", + "prob = [comb(n,x) * (p**x) * (q**(n - x)) for x in x]\n", + "\n", + "# Plot the probability distribution function\n", + "plt.bar(x, prob)\n", + "plt.xlabel(\"Number of Apples\")\n", + "plt.ylabel(\"Probability\")\n", + "plt.title(\"Number of Apples in a Sample of 20 Fruits\")\n", + "plt.show()\n" ] }, { @@ -151,11 +259,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 128, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The probability of scoring exactly 5 goals in a match is: 0.0537750255819468\n" + ] + } + ], "source": [ - "# your code here\n" + "# your code here\n", + "import math\n", + "\n", + "mu = 2.3\n", + "g = 5\n", + "prob_g = (math.exp(-mu) * (mu**g)) / math.factorial(g)\n", + "\n", + "print(f\"The probability of scoring exactly 5 goals in a match is: {prob_g}\")\n" ] }, { @@ -167,12 +290,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 136, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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9de+99+rDDz/Unj17tHfvXg0aNMju9yMvAQEBat++vZYuXaqlS5fqySefzPVEjIK+j9zojz/+UGZmpvU4vLzc+Pxk7x7Lz7pQcBwDhCJTr14961lgubkx5Eh//dF/8803MgzD5vEzZ84oIyPDeixDtpSUFLsxstuy30A+/PBDtW3bVgsXLrTpl9uxC7mN6erqetPTl4vCiBEj9MEHH2jdunX6/PPPVbZs2VzPmstWrlw5lSpVSsnJyXaPZR80eePzVlivvfaali5dav0QvJG7u3uOB6ufPXu2yGr4u/y8/j4+PmrcuLHefPPNHMe48diqnH4vc1KhQgVlZGTo999/twlBhmEoJSXFesB7QYSEhKhcuXLasGGDpk6dam13cnKy/j19//33dnVIUnJyst2H66lTp6zP+/fff6/vvvtOMTEx6t+/v7VP9vF5eWnUqJFWrlwpwzB04MABxcTEaPLkyfLw8Mj1gO3b4cMPP1RQUJBiY2NtXqvcTpLIy6BBg9SnTx9lZWXZvUfcuN6CvI/cqHz58nJycsrzoHE4BjNAcLj27dvr8uXLdhfnW758ufXxv9u2bZtOnz5tvZ+ZmanY2FjVqFHD+mFgsVjsDvY9cOBAjmdcSX/9h/n3/yQvXbqkDRs2qFWrVnJycir0tkl5/xcXEhKiFi1aaPr06froo480YMCAPK+b5OnpqQceeECrV6+2GTcrK0sffvihqlSpotq1a99S3dkqVKigMWPG6L///a++/fZbu8cDAwN14MABm7b//e9/RbILLif5ef27du2q77//XjVq1FBoaKjd7WYHl99M9u/ihx9+aNO+atUqXblyJd+XGfg7V1dXjR49Wt9//731wNy8PPTQQznWsXfvXh06dMhaR3ZYuPFvoaCn1FssFjVp0kSzZs1S2bJltX///gItf6ssFotcXV1twk9KSkqBzwLL1qtXL/Xq1UuDBg2ynq2a23rz8z6S29+4h4eH2rRpo08++cRhFw9F7pgBgsP169dP8+fPV//+/XX8+HE1atRIX331laZOnapHHnlEHTp0sOnv4+Ojhx56SK+//rr1LKDDhw/bnArdtWtXTZkyRVFRUWrTpo2OHDmiyZMnKygoyOY06GxOTk7q2LGjRo0apaysLE2fPl2pqalFcuxLjRo15OHhoY8++kj16tVT6dKl5e/vb/MhPGLECEVERMhisSgyMjJf40ZHR6tjx45q166dXnnlFbm6umrBggX6/vvvtWLFinzPauTHyJEjNX/+fH322Wd2j/Xt21d9+vRRZGSkHnvsMZ04cUIzZszI1zEPhZGf13/y5MmKi4tTixYtNHz4cNWpU0fXrl3T8ePHtWnTJi1atCjfuyX+rmPHjurUqZPGjBmj1NRUtWzZ0noWWHBwsPr27VuobRozZowOHz6ssWPHaufOnYqIiFBgYKDS0tL0yy+/6L333pOTk5N1V02dOnX07LPPau7cuSpVqpQ6d+5sPQusatWqeumllyRJdevWVY0aNTR27FgZhqHy5ctrw4YNiouLy7OmjRs3asGCBerZs6eqV68uwzC0evVqXbhwQR07dizUdhZW165dtXr1akVGRurxxx/XyZMnNWXKFFWuXLlQu2bd3d313//+N1/rzc/7iJeXlwICArRu3Tq1b99e5cuXl4+PjwIDA61n3D3wwAMaO3asatasqdOnT2v9+vVavHixzTFRuMMceQQ27g7ZZzDs3bv3pv1udlbMuXPnjKFDhxqVK1c2nJ2djYCAAGPcuHHGtWvXbPpJMoYNG2YsWLDAqFGjhuHi4mLUrVvX+Oijj2z6paWlGa+88opx7733Gu7u7kazZs2MtWvX2p2dkn0W0PTp041JkyYZVapUMVxdXY3g4GBj8+bNOW5nQc8CMwzDWLFihVG3bl3DxcUlx7NF0tLSDDc3N+Phhx/O/QnMwa5du4yHHnrI8PT0NDw8PIzmzZsbGzZssOlzK2eB/d2SJUsMSXZngWVlZRkzZswwqlevbri7uxuhoaHGF198ketZYJ988onNuLn9/uR0xll+X3/D+OvMnOHDhxtBQUGGi4uLUb58eSMkJMSYMGGCcfny5QI/N9n+/PNPY8yYMUZAQIDh4uJiVK5c2Xj++eeNP/74w6Zffs8C+7v169cb3bp1M3x9fQ1nZ2fDy8vLaNq0qfHyyy8bhw8ftumbmZlpTJ8+3ahdu7bh4uJi+Pj4GH369DFOnjxp0+/HH380OnbsaHh5eRnlypUznnjiCSMpKcnu9/DG3+/Dhw8bTz31lFGjRg3Dw8PD8Pb2Nu6//34jJiYmz+3I7SywYcOG2fXN7SzCG02bNs0IDAw03NzcjHr16hnvvvuu9XckP/Xk9VrkdCZXft9HDMMwtm7dagQHBxtubm6GJJtt+vHHH40nnnjCqFChguHq6mpUq1bNGDBggPX9Lbe/gey/mS+//DLPbUTBWQzDMO5k4AJgb8OGDerevbs+/fRTPfLII44uBwDuegQgwIF+/PFHnThxQiNGjJCnp6f2799fpLuuAAA54yBowIEiIyPVvXt3lStXrsiP2wEA5I4ZIAAAYDrMAAEAANMhAAEAANMhAAEAANPhQog5yMrK0qlTp+Tl5cVBqQAAlBCGYejSpUvy9/dXqVI3n+MhAOXg1KlTqlq1qqPLAAAAhXDy5Mk8r/ZOAMpB9qXJT548affN5QAAoHhKTU1V1apV8/UVIwSgHGTv9ipTpgwBCACAEiY/h69wEDQAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdZ0cXANyKwLGfOroESdLxaV0cXQIAoACYAQIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKbj8AC0YMECBQUFyd3dXSEhIdq1a1eufVevXq2OHTuqYsWKKlOmjMLCwrR582abPjExMbJYLHa3a9eu3e5NAQAAJYRDA1BsbKxGjhypCRMmKCEhQa1atVLnzp2VlJSUY/+dO3eqY8eO2rRpk+Lj49WuXTt169ZNCQkJNv3KlCmj5ORkm5u7u/ud2CQAAFACODty5TNnztTgwYM1ZMgQSdLs2bO1efNmLVy4UNHR0Xb9Z8+ebXN/6tSpWrdunTZs2KDg4GBru8VikZ+f322tHQAAlFwOmwFKT09XfHy8wsPDbdrDw8O1e/fufI2RlZWlS5cuqXz58jbtly9fVkBAgKpUqaKuXbvazRDdKC0tTampqTY3AABw93JYADp79qwyMzPl6+tr0+7r66uUlJR8jfH222/rypUr6t27t7Wtbt26iomJ0fr167VixQq5u7urZcuWOnr0aK7jREdHy9vb23qrWrVq4TYKAACUCA4/CNpisdjcNwzDri0nK1as0MSJExUbG6tKlSpZ25s3b64+ffqoSZMmatWqlf7zn/+odu3amjt3bq5jjRs3ThcvXrTeTp48WfgNAgAAxZ7DjgHy8fGRk5OT3WzPmTNn7GaFbhQbG6vBgwfrk08+UYcOHW7at1SpUrrvvvtuOgPk5uYmNze3/BcPAABKNIfNALm6uiokJERxcXE27XFxcWrRokWuy61YsUIDBgzQxx9/rC5duuS5HsMwlJiYqMqVK99yzQAA4O7g0LPARo0apb59+yo0NFRhYWFasmSJkpKSNHToUEl/7Zr67bfftHz5ckl/hZ9+/frpnXfeUfPmza2zRx4eHvL29pYkTZo0Sc2bN1etWrWUmpqqOXPmKDExUfPnz3fMRgIAgGLHoQEoIiJC586d0+TJk5WcnKyGDRtq06ZNCggIkCQlJyfbXBNo8eLFysjI0LBhwzRs2DBre//+/RUTEyNJunDhgp599lmlpKTI29tbwcHB2rlzp+6///47um0AAKD4shiGYTi6iOImNTVV3t7eunjxosqUKePocnATgWM/dXQJkqTj0/LeHQsAuL0K8vnt8LPAAAAA7jQCEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB1nRxeA4itw7KeOLkHHp3VxdAkAgLsQM0AAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0nB1dwIIFC/Svf/1LycnJatCggWbPnq1WrVrl2Hf16tVauHChEhMTlZaWpgYNGmjixInq1KmTTb9Vq1bp9ddf188//6waNWrozTffVK9eve7E5gA5Chz7qaNL0PFpXRxdAgAUGw6dAYqNjdXIkSM1YcIEJSQkqFWrVurcubOSkpJy7L9z50517NhRmzZtUnx8vNq1a6du3bopISHB2mfPnj2KiIhQ37599d1336lv377q3bu3vvnmmzu1WQAAoJizGIZhOGrlDzzwgJo1a6aFCxda2+rVq6eePXsqOjo6X2M0aNBAERER+uc//ylJioiIUGpqqj777DNrn4cffljlypXTihUr8jVmamqqvL29dfHiRZUpU6YAW3R3KQmzFsWhRqlk1MkMEIC7XUE+vx02A5Senq74+HiFh4fbtIeHh2v37t35GiMrK0uXLl1S+fLlrW179uyxG7NTp043HTMtLU2pqak2NwAAcPdyWAA6e/asMjMz5evra9Pu6+urlJSUfI3x9ttv68qVK+rdu7e1LSUlpcBjRkdHy9vb23qrWrVqAbYEAACUNA4/C8xisdjcNwzDri0nK1as0MSJExUbG6tKlSrd0pjjxo3TxYsXrbeTJ08WYAsAAEBJ47CzwHx8fOTk5GQ3M3PmzBm7GZwbxcbGavDgwfrkk0/UoUMHm8f8/PwKPKabm5vc3NwKuAUAAKCkctgMkKurq0JCQhQXF2fTHhcXpxYtWuS63IoVKzRgwAB9/PHH6tLF/qDOsLAwuzG3bNly0zEBAIC5OPQ6QKNGjVLfvn0VGhqqsLAwLVmyRElJSRo6dKikv3ZN/fbbb1q+fLmkv8JPv3799M4776h58+bWmR4PDw95e3tLkkaMGKHWrVtr+vTp6tGjh9atW6etW7fqq6++csxGAgCAYsehxwBFRERo9uzZmjx5spo2baqdO3dq06ZNCggIkCQlJyfbXBNo8eLFysjI0LBhw1S5cmXrbcSIEdY+LVq00MqVK7Vs2TI1btxYMTExio2N1QMPPHDHtw8AABRPDr8SdGRkpCIjI3N8LCYmxub+9u3b8zXm448/rscff/wWKwMAAHcrh58FBgAAcKcRgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkUKgDFxMTo6tWrRV0LAADAHVGoADRu3Dj5+flp8ODB2r17d1HXBAAAcFsVKgD9+uuv+vDDD/XHH3+oXbt2qlu3rqZPn66UlJSirg8AAKDIFSoAOTk5qXv37lq9erVOnjypZ599Vh999JGqVaum7t27a926dcrKyirqWgEAAIrELR8EXalSJbVs2VJhYWEqVaqUDh48qAEDBqhGjRravn17EZQIAABQtAodgE6fPq233npLDRo0UNu2bZWamqqNGzfq2LFjOnXqlB599FH179+/KGsFAAAoEs6FWahbt27avHmzateurX/84x/q16+fypcvb33cw8NDL7/8smbNmlVkhQIAABSVQgWgSpUqaceOHQoLC8u1T+XKlXXs2LFCFwYAAHC7FGoXWJs2bdSsWTO79vT0dC1fvlySZLFYFBAQcGvVAQAA3AaFCkADBw7UxYsX7dovXbqkgQMH3nJRAAAAt1OhApBhGLJYLHbtv/76q7y9vW+5KAAAgNupQMcABQcHy2KxyGKxqH379nJ2/n+LZ2Zm6tixY3r44YeLvEgAAICiVKAA1LNnT0lSYmKiOnXqpNKlS1sfc3V1VWBgoB577LEiLRAAAKCoFSgARUVFSZICAwMVEREhd3f321IUAADA7VSo0+C5wCEAACjJ8n0QdPny5XX27FlJUrly5VS+fPlcbwWxYMECBQUFyd3dXSEhIdq1a1eufZOTk/X000+rTp06KlWqlEaOHGnXJyYmxnqc0t9v165dK1BdAADg7pXvGaBZs2bJy8vL+nNOZ4EVVGxsrEaOHKkFCxaoZcuWWrx4sTp37qwff/xR1apVs+uflpamihUrasKECTe9ynSZMmV05MgRmzZ21wEAgGz5DkB/3+01YMCAIln5zJkzNXjwYA0ZMkSSNHv2bG3evFkLFy5UdHS0Xf/AwEC98847kqSlS5fmOq7FYpGfn1+R1AgAAO4++Q5Aqamp+R60TJkyefZJT09XfHy8xo4da9MeHh6u3bt353tdObl8+bICAgKUmZmppk2basqUKQoODr6lMQEAwN0j3wGobNmyee72yr5AYmZmZp7jnT17VpmZmfL19bVp9/X1VUpKSn7LslO3bl3FxMSoUaNGSk1N1TvvvKOWLVvqu+++U61atXJcJi0tTWlpadb7BQl7AACg5Ml3APryyy9vSwE3hqrcrjKdX82bN1fz5s2t91u2bKlmzZpp7ty5mjNnTo7LREdHa9KkSYVeJwAAKFnyHYDatGlTpCv28fGRk5OT3WzPmTNn7GaFbkWpUqV033336ejRo7n2GTdunEaNGmW9n5qaqqpVqxZZDQAAoHjJdwA6cOCAGjZsqFKlSunAgQM37du4ceM8x3N1dVVISIji4uLUq1cva3tcXJx69OiR37LyZBiGEhMT1ahRo1z7uLm5yc3NrcjWCQAAird8B6CmTZsqJSVFlSpVUtOmTWWxWGQYhl2//B4DJEmjRo1S3759FRoaqrCwMC1ZskRJSUkaOnSopL9mZn777TctX77cukxiYqKkvw50/v3335WYmChXV1fVr19fkjRp0iQ1b95ctWrVUmpqqubMmaPExETNnz8/v5sKAADucvkOQMeOHVPFihWtPxeFiIgInTt3TpMnT1ZycrIaNmyoTZs2KSAgQNJfFz5MSkqyWebvZ3PFx8fr448/VkBAgI4fPy5JunDhgp599lmlpKTI29tbwcHB2rlzp+6///4iqRkAAJR8FiOnaRyTS01Nlbe3ty5evJivU/rvVoFjP3V0CTo+rctNHy8ONUolo868agSAkq4gn9+F+i4wSTpy5Ijmzp2rQ4cOyWKxqG7dunrxxRdVp06dwg4JAABwR+T7u8D+7r///a8aNmyo+Ph4NWnSRI0bN9b+/fvVsGFDffLJJ0VdIwAAQJEq1AzQq6++qnHjxmny5Mk27VFRURozZoyeeOKJIikOAADgdijUDFBKSor69etn196nT59buoozAADAnVCoANS2bVvt2rXLrv2rr75Sq1atbrkoAACA2ynfu8DWr19v/bl79+4aM2aM4uPjrV878fXXX+uTTz7hKyUAAECxl+8A1LNnT7u2BQsWaMGCBTZtw4YNs17IEAAAoDjKdwDKysq6nXUAAADcMYU6BggAAKAkK/SFEK9cuaIdO3YoKSlJ6enpNo8NHz78lgsDAAC4XQoVgBISEvTII4/o6tWrunLlisqXL6+zZ8/qnnvuUaVKlQhAAACgWCvULrCXXnpJ3bp10/nz5+Xh4aGvv/5aJ06cUEhIiN56662irhEAAKBIFSoAJSYm6uWXX5aTk5OcnJyUlpamqlWrasaMGRo/fnxR1wgAAFCkChWAXFxcZLFYJEm+vr5KSkqSJHl7e1t/BgAAKK4KdQxQcHCw9u3bp9q1a6tdu3b65z//qbNnz+qDDz5Qo0aNirpGAACAIlWoGaCpU6eqcuXKkqQpU6aoQoUKev7553XmzBktWbKkSAsEAAAoaoWaAQoNDbX+XLFiRW3atKnICgIAALjdCn0dIEk6c+aMjhw5IovFojp16qhixYpFVRcAAMBtU6hdYKmpqerbt6/uvfdetWnTRq1bt5a/v7/69OmjixcvFnWNAAAARapQAWjIkCH65ptvtHHjRl24cEEXL17Uxo0btW/fPv3jH/8o6hoBAACKVKF2gX366afavHmzHnzwQWtbp06d9O677+rhhx8usuIAAABuh0LNAFWoUEHe3t527d7e3ipXrtwtFwUAAHA7FSoAvfbaaxo1apSSk5OtbSkpKRo9erRef/31IisOAADgdsj3LrDg4GDr1Z8l6ejRowoICFC1atUkSUlJSXJzc9Pvv/+u5557rugrBQAAKCL5DkA9e/a8jWUAAADcOfkOQFFRUbezDgAAgDvmli6EGB8fr0OHDslisah+/foKDg4uqroAAABum0IFoDNnzujJJ5/U9u3bVbZsWRmGoYsXL6pdu3ZauXIlV4QGAADFWqHOAnvxxReVmpqqH374QefPn9cff/yh77//XqmpqRo+fHhR1wgAAFCkCjUD9Pnnn2vr1q2qV6+eta1+/fqaP3++wsPDi6w4AACA26FQM0BZWVlycXGxa3dxcVFWVtYtFwUAAHA7FSoAPfTQQxoxYoROnTplbfvtt9/00ksvqX379kVWHAAAwO1QqAA0b948Xbp0SYGBgapRo4Zq1qypoKAgXbp0SXPnzi3qGgEAAIpUoY4Bqlq1qvbv36+4uDgdPnxYhmGofv366tChQ1HXBwAAUOQKHIAyMjLk7u6uxMREdezYUR07drwddQEAANw2Bd4F5uzsrICAAGVmZt6OegAAAG67Qn8b/Lhx43T+/PmirgcAAOC2K9QxQHPmzNFPP/0kf39/BQQEyNPT0+bx/fv3F0lxAAAAt0OhAlDPnj1lsVhkGEZR1wPAgQLHfuroEnR8WhdHlwDABAoUgK5evarRo0dr7dq1un79utq3b6+5c+fKx8fndtUHAABQ5Ap0DFBUVJRiYmLUpUsXPfXUU9q6dauef/7521UbAADAbVGgGaDVq1fr/fff15NPPilJeuaZZ9SyZUtlZmbKycnpthQIAABQ1Ao0A3Ty5Em1atXKev/++++Xs7OzzVdiAAAAFHcFCkCZmZlydXW1aXN2dlZGRkaRFgUAAHA7FWgXmGEYGjBggNzc3Kxt165d09ChQ21OhV+9enXRVQgAAFDEChSA+vfvb9fWp0+fIisGAADgTihQAFq2bNntqgMAAOCOKdRXYQAAAJRkBCAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6Dg9ACxYsUFBQkNzd3RUSEqJdu3bl2jc5OVlPP/206tSpo1KlSmnkyJE59lu1apXq168vNzc31a9fX2vWrLlN1QMAgJLIoQEoNjZWI0eO1IQJE5SQkKBWrVqpc+fOSkpKyrF/WlqaKlasqAkTJqhJkyY59tmzZ48iIiLUt29ffffdd+rbt6969+6tb7755nZuCgAAKEEcGoBmzpypwYMHa8iQIapXr55mz56tqlWrauHChTn2DwwM1DvvvKN+/frJ29s7xz6zZ89Wx44dNW7cONWtW1fjxo1T+/btNXv27Nu4JQAAoCRxWABKT09XfHy8wsPDbdrDw8O1e/fuQo+7Z88euzE7dep00zHT0tKUmppqcwMAAHevAn0bfFE6e/asMjMz5evra9Pu6+urlJSUQo+bkpJS4DGjo6M1adKkQq+zoALHfnrH1pWb49O6OLoEAAAcxuEHQVssFpv7hmHYtd3uMceNG6eLFy9abydPnryl9QMAgOLNYTNAPj4+cnJyspuZOXPmjN0MTkH4+fkVeEw3Nze5ubkVep0AAKBkcdgMkKurq0JCQhQXF2fTHhcXpxYtWhR63LCwMLsxt2zZcktjAgCAu4vDZoAkadSoUerbt69CQ0MVFhamJUuWKCkpSUOHDpX0166p3377TcuXL7cuk5iYKEm6fPmyfv/9dyUmJsrV1VX169eXJI0YMUKtW7fW9OnT1aNHD61bt05bt27VV199dce3DwAAFE8ODUARERE6d+6cJk+erOTkZDVs2FCbNm1SQECApL8ufHjjNYGCg4OtP8fHx+vjjz9WQECAjh8/Lklq0aKFVq5cqddee02vv/66atSoodjYWD3wwAN3bLsAAEDx5tAAJEmRkZGKjIzM8bGYmBi7NsMw8hzz8ccf1+OPP36rpQEAgLuUw88CAwAAuNMIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHScHV0AABRU4NhPHV2Cjk/r4ugSANwCh88ALViwQEFBQXJ3d1dISIh27dp10/47duxQSEiI3N3dVb16dS1atMjm8ZiYGFksFrvbtWvXbudmAACAEsShASg2NlYjR47UhAkTlJCQoFatWqlz585KSkrKsf+xY8f0yCOPqFWrVkpISND48eM1fPhwrVq1yqZfmTJllJycbHNzd3e/E5sEAABKAIfuAps5c6YGDx6sIUOGSJJmz56tzZs3a+HChYqOjrbrv2jRIlWrVk2zZ8+WJNWrV0/79u3TW2+9pccee8zaz2KxyM/P745sAwAAKHkcNgOUnp6u+Ph4hYeH27SHh4dr9+7dOS6zZ88eu/6dOnXSvn37dP36dWvb5cuXFRAQoCpVqqhr165KSEi4aS1paWlKTU21uQEAgLuXwwLQ2bNnlZmZKV9fX5t2X19fpaSk5LhMSkpKjv0zMjJ09uxZSVLdunUVExOj9evXa8WKFXJ3d1fLli119OjRXGuJjo6Wt7e39Va1atVb3DoAAFCcOfwgaIvFYnPfMAy7trz6/729efPm6tOnj5o0aaJWrVrpP//5j2rXrq25c+fmOua4ceN08eJF6+3kyZOF3RwAAFACOOwYIB8fHzk5OdnN9pw5c8Zuliebn59fjv2dnZ1VoUKFHJcpVaqU7rvvvpvOALm5ucnNza2AWwAAAEoqh80Aubq6KiQkRHFxcTbtcXFxatGiRY7LhIWF2fXfsmWLQkND5eLikuMyhmEoMTFRlStXLprCAQBAiefQXWCjRo3Se++9p6VLl+rQoUN66aWXlJSUpKFDh0r6a9dUv379rP2HDh2qEydOaNSoUTp06JCWLl2q999/X6+88oq1z6RJk7R582b98ssvSkxM1ODBg5WYmGgdEwAAwKGnwUdEROjcuXOaPHmykpOT1bBhQ23atEkBAQGSpOTkZJtrAgUFBWnTpk166aWXNH/+fPn7+2vOnDk2p8BfuHBBzz77rFJSUuTt7a3g4GDt3LlT999//x3fPgAAUDw5/KswIiMjFRkZmeNjMTExdm1t2rTR/v37cx1v1qxZmjVrVlGVBwAA7kIOPwsMAADgTiMAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA03F2dAEAcDcKHPupo0uQJB2f1sXRJQDFEjNAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdJwdXQAAwHECx37q6BJ0fFoXR5cAE2IGCAAAmA4BCAAAmA4BCAAAmI7DA9CCBQsUFBQkd3d3hYSEaNeuXTftv2PHDoWEhMjd3V3Vq1fXokWL7PqsWrVK9evXl5ubm+rXr681a9bcrvIBAEAJ5NAAFBsbq5EjR2rChAlKSEhQq1at1LlzZyUlJeXY/9ixY3rkkUfUqlUrJSQkaPz48Ro+fLhWrVpl7bNnzx5FRESob9+++u6779S3b1/17t1b33zzzZ3aLAAAUMw59CywmTNnavDgwRoyZIgkafbs2dq8ebMWLlyo6Ohou/6LFi1StWrVNHv2bElSvXr1tG/fPr311lt67LHHrGN07NhR48aNkySNGzdOO3bs0OzZs7VixYo7s2EAgCLF2Wooag6bAUpPT1d8fLzCw8Nt2sPDw7V79+4cl9mzZ49d/06dOmnfvn26fv36TfvkNiYAADAfh80AnT17VpmZmfL19bVp9/X1VUpKSo7LpKSk5Ng/IyNDZ8+eVeXKlXPtk9uYkpSWlqa0tDTr/YsXL0qSUlNTC7RN+ZWVdvW2jFsQ+dm2klBncahRKhl13i2vuVQy6iwONUolo8675TWXpIZRm+9AJTf3/aROji7BYbJfI8Mw8uzr8AshWiwWm/uGYdi15dX/xvaCjhkdHa1JkybZtVetWjX3wks479mOriB/qLPolIQaJeosaiWhzpJQo0SdJcmlS5fk7e190z4OC0A+Pj5ycnKym5k5c+aM3QxONj8/vxz7Ozs7q0KFCjftk9uY0l/HCY0aNcp6PysrS+fPn1eFChVuGpwcJTU1VVWrVtXJkydVpkwZR5eTo5JQo0SdRa0k1FkSapSos6iVhDpLQo1S8a7TMAxdunRJ/v7+efZ1WABydXVVSEiI4uLi1KtXL2t7XFycevTokeMyYWFh2rBhg03bli1bFBoaKhcXF2ufuLg4vfTSSzZ9WrRokWstbm5ucnNzs2krW7ZsQTfpjitTpkyx++W7UUmoUaLOolYS6iwJNUrUWdRKQp0loUap+NaZ18xPNofuAhs1apT69u2r0NBQhYWFacmSJUpKStLQoUMl/TUz89tvv2n58uWSpKFDh2revHkaNWqU/vGPf2jPnj16//33bc7uGjFihFq3bq3p06erR48eWrdunbZu3aqvvvrKIdsIAACKH4cGoIiICJ07d06TJ09WcnKyGjZsqE2bNikgIECSlJycbHNNoKCgIG3atEkvvfSS5s+fL39/f82ZM8d6CrwktWjRQitXrtRrr72m119/XTVq1FBsbKweeOCBO759AACgeHL4QdCRkZGKjIzM8bGYmBi7tjZt2mj//v03HfPxxx/X448/XhTlFUtubm6Kioqy221XnJSEGiXqLGoloc6SUKNEnUWtJNRZEmqUSk6debEY+TlXDAAA4C7i8O8CAwAAuNMIQAAAwHQIQAAAwHQIQAAAwHQIQCXMggULFBQUJHd3d4WEhGjXrl2OLsnGzp071a1bN/n7+8tisWjt2rWOLilH0dHRuu++++Tl5aVKlSqpZ8+eOnLkiKPLsrFw4UI1btzYerGxsLAwffbZZ44uK0/R0dGyWCwaOXKko0uxMXHiRFksFpubn5+fo8vK0W+//aY+ffqoQoUKuueee9S0aVPFx8c7uiyrwMBAu+fSYrFo2LBhji7NRkZGhl577TUFBQXJw8ND1atX1+TJk5WVleXo0uxcunRJI0eOVEBAgDw8PNSiRQvt3bvXoTXl9X5uGIYmTpwof39/eXh4qG3btvrhhx8cU2whEIBKkNjYWI0cOVITJkxQQkKCWrVqpc6dO9tcK8nRrly5oiZNmmjevHmOLuWmduzYoWHDhunrr79WXFycMjIyFB4eritXrji6NKsqVapo2rRp2rdvn/bt26eHHnpIPXr0KNZvMHv37tWSJUvUuHFjR5eSowYNGig5Odl6O3jwoKNLsvPHH3+oZcuWcnFx0WeffaYff/xRb7/9drG6Ov3evXttnse4uDhJ0hNPPOHgymxNnz5dixYt0rx583To0CHNmDFD//rXvzR37lxHl2ZnyJAhiouL0wcffKCDBw8qPDxcHTp00G+//eawmvJ6P58xY4ZmzpypefPmae/evfLz81PHjh116dKlO1xpIRkoMe6//35j6NChNm1169Y1xo4d66CKbk6SsWbNGkeXkS9nzpwxJBk7duxwdCk3Va5cOeO9995zdBk5unTpklGrVi0jLi7OaNOmjTFixAhHl2QjKirKaNKkiaPLyNOYMWOMBx980NFlFMiIESOMGjVqGFlZWY4uxUaXLl2MQYMG2bQ9+uijRp8+fRxUUc6uXr1qODk5GRs3brRpb9KkiTFhwgQHVWXrxvfzrKwsw8/Pz5g2bZq17dq1a4a3t7exaNEiB1RYcMwAlRDp6emKj49XeHi4TXt4eLh2797toKruHhcvXpQklS9f3sGV5CwzM1MrV67UlStXFBYW5uhycjRs2DB16dJFHTp0cHQpuTp69Kj8/f0VFBSkJ598Ur/88oujS7Kzfv16hYaG6oknnlClSpUUHBysd99919Fl5So9PV0ffvihBg0aVOy+PPrBBx/Utm3b9L///U+S9N133+mrr77SI4884uDKbGVkZCgzM1Pu7u427R4eHsX2a5yOHTumlJQUm88kNzc3tWnTpsR8Jjn8StDIn7NnzyozM9PuW+19fX2VkpLioKruDoZhaNSoUXrwwQfVsGFDR5dj4+DBgwoLC9O1a9dUunRprVmzRvXr13d0WXZWrlyp/fv3O/yYhZt54IEHtHz5ctWuXVunT5/WG2+8oRYtWuiHH35QhQoVHF2e1S+//KKFCxdq1KhRGj9+vL799lsNHz5cbm5u6tevn6PLs7N27VpduHBBAwYMcHQpdsaMGaOLFy+qbt26cnJyUmZmpt5880099dRTji7NhpeXl8LCwjRlyhTVq1dPvr6+WrFihb755hvVqlXL0eXlKPtzJ6fPpBMnTjiipAIjAJUwN/6HZRhGsfuvq6R54YUXdODAgWL5n1adOnWUmJioCxcuaNWqVerfv7927NhRrELQyZMnNWLECG3ZssXuP9jipHPnztafGzVqpLCwMNWoUUP//ve/NWrUKAdWZisrK0uhoaGaOnWqJCk4OFg//PCDFi5cWCwD0Pvvv6/OnTvL39/f0aXYiY2N1YcffqiPP/5YDRo0UGJiokaOHCl/f3/179/f0eXZ+OCDDzRo0CDde++9cnJyUrNmzfT000/n+dVPjlaSP5MIQCWEj4+PnJyc7GZ7zpw5Y5fAkX8vvvii1q9fr507d6pKlSqOLseOq6uratasKUkKDQ3V3r179c4772jx4sUOruz/iY+P15kzZxQSEmJty8zM1M6dOzVv3jylpaXJycnJgRXmzNPTU40aNdLRo0cdXYqNypUr2wXcevXqadWqVQ6qKHcnTpzQ1q1btXr1akeXkqPRo0dr7NixevLJJyX9FXxPnDih6OjoYheAatSooR07dujKlStKTU1V5cqVFRERoaCgIEeXlqPsMyhTUlJUuXJla3tJ+kziGKASwtXVVSEhIdazLbLFxcWpRYsWDqqq5DIMQy+88IJWr16tL774oti+ydzIMAylpaU5ugwb7du318GDB5WYmGi9hYaG6plnnlFiYmKxDD+SlJaWpkOHDtm8eRcHLVu2tLskw//+9z8FBAQ4qKLcLVu2TJUqVVKXLl0cXUqOrl69qlKlbD/mnJyciuVp8Nk8PT1VuXJl/fHHH9q8ebN69Ojh6JJyFBQUJD8/P5vPpPT0dO3YsaPEfCYxA1SCjBo1Sn379lVoaKjCwsK0ZMkSJSUlaejQoY4uzery5cv66aefrPePHTumxMRElS9fXtWqVXNgZbaGDRumjz/+WOvWrZOXl5d1Zs3b21seHh4Oru4v48ePV+fOnVW1alVdunRJK1eu1Pbt2/X55587ujQbXl5edsdOeXp6qkKFCsXqmKpXXnlF3bp1U7Vq1XTmzBm98cYbSk1NLXYzAS+99JJatGihqVOnqnfv3vr222+1ZMkSLVmyxNGl2cjKytKyZcvUv39/OTsXz4+Sbt266c0331S1atXUoEEDJSQkaObMmRo0aJCjS7OzefNmGYahOnXq6KefftLo0aNVp04dDRw40GE15fV+PnLkSE2dOlW1atVSrVq1NHXqVN1zzz16+umnHVZzgTjyFDQU3Pz5842AgADD1dXVaNasWbE7bfvLL780JNnd+vfv7+jSbORUoyRj2bJlji7NatCgQdbXumLFikb79u2NLVu2OLqsfCmOp8FHREQYlStXNlxcXAx/f3/j0UcfNX744QdHl5WjDRs2GA0bNjTc3NyMunXrGkuWLHF0SXY2b95sSDKOHDni6FJylZqaaowYMcKoVq2a4e7ublSvXt2YMGGCkZaW5ujS7MTGxhrVq1c3XF1dDT8/P2PYsGHGhQsXHFpTXu/nWVlZRlRUlOHn52e4ubkZrVu3Ng4ePOjQmgvCYhiGccdTFwAAgANxDBAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhCAYuX48eOyWCxKTEx0dClWhw8fVvPmzeXu7q6mTZs6uhxJUmBgoGbPnu3oMoASiwAEwMaAAQNksVg0bdo0m/a1a9eWmG95LmpRUVHy9PTUkSNHtG3btlz7paSkaMSIEapZs6bc3d3l6+urBx98UIsWLdLVq1fvYMUA8lI8v8AFgEO5u7tr+vTpeu6551SuXDlHl1Mk0tPT5erqWqhlf/75Z3Xp0uWmX0j6yy+/qGXLlipbtqymTp2qRo0aKSMjQ//73/+0dOlS+fv7q3v37oUtH0ARYwYIgJ0OHTrIz89P0dHRufaZOHGi3e6g2bNnKzAw0Hp/wIAB6tmzp6ZOnSpfX1+VLVtWkyZNUkZGhkaPHq3y5curSpUqWrp0qd34hw8fVosWLeTu7q4GDRpo+/btNo//+OOPeuSRR1S6dGn5+vqqb9++Onv2rPXxtm3b6oUXXtCoUaPk4+Ojjh075rgdWVlZmjx5sqpUqSI3Nzc1bdrU5gtnLRaL4uPjNXnyZFksFk2cODHHcSIjI+Xs7Kx9+/apd+/eqlevnho1aqTHHntMn376qbp162btm5SUpB49eqh06dIqU6aMevfurdOnT1sf//nnn9WjRw/5+vqqdOnSuu+++7R169Yc15tt4sSJqlatmtzc3OTv76/hw4fftD9gdgQgAHacnJw0depUzZ07V7/++ustjfXFF1/o1KlT2rlzp2bOnKmJEyeqa9euKleunL755hsNHTpUQ4cO1cmTJ22WGz16tF5++WUlJCSoRYsW6t69u86dOydJSk5OVps2bdS0aVPt27dPn3/+uU6fPq3evXvbjPHvf/9bzs7O+r//+z8tXrw4x/reeecdvf3223rrrbd04MABderUSd27d9fRo0et62rQoIFefvllJScn65VXXrEb49y5c9qyZYuGDRsmT0/PHNeTvfvQMAz17NlT58+f144dOxQXF6eff/5ZERER1r6XL1/WI488oq1btyohIUGdOnVSt27dlJSUlOPY//3vfzVr1iwtXrxYR48e1dq1a9WoUaMc+wL4/zn4y1gBFDP9+/c3evToYRiGYTRv3twYNGiQYRiGsWbNGuPvbxlRUVFGkyZNbJadNWuWERAQYDNWQECAkZmZaW2rU6eO0apVK+v9jIwMw9PT01ixYoVhGIZx7NgxQ5Ixbdo0a5/r168bVapUMaZPn24YhmG8/vrrRnh4uM26T548afPt5G3atDGaNm2a5/b6+/sbb775pk3bfffdZ0RGRlrvN2nSxIiKisp1jK+//tqQZKxevdqmvUKFCoanp6fh6elpvPrqq4ZhGMaWLVsMJycnIykpydrvhx9+MCQZ3377ba7rqF+/vjF37lzr/YCAAGPWrFmGYRjG22+/bdSuXdtIT0/Pc3sB/IUZIAC5mj59uv7973/rxx9/LPQYDRo0UKlS/++txtfX12Z2wsnJSRUqVNCZM2dslgsLC7P+7OzsrNDQUB06dEiSFB8fry+//FKlS5e23urWrSvpr91H2UJDQ29aW2pqqk6dOqWWLVvatLds2dK6roK48SDxb7/9VomJiWrQoIHS0tIkSYcOHVLVqlVVtWpVa7/69eurbNmy1nVeuXJFr776qrW9dOnSOnz4cK4zQE888YT+/PNPVa9eXf/4xz+0Zs0aZWRkFLh+wEwIQABy1bp1a3Xq1Enjx4+3e6xUqVIyDMOm7fr163b9XFxcbO5bLJYc27KysvKsJztgZGVlqVu3bkpMTLS5HT16VK1bt7b2z213VG7jZjMMo0BnvNWsWVMWi0WHDx+2aa9evbpq1qwpDw+PPMf+e/vo0aO1atUqvfnmm9q1a5cSExPVqFEjpaen57j+qlWr6siRI5o/f748PDwUGRmp1q1b5/h6APgLAQjATU2bNk0bNmzQ7t27bdorVqyolJQUmxBUlNfu+frrr60/Z2RkKD4+3jrL06xZM/3www8KDAxUzZo1bW75DT2SVKZMGfn7++urr76yad+9e7fq1auX73EqVKigjh07at68ebpy5cpN+9avX19JSUk2xzz9+OOPunjxonWdu3bt0oABA9SrVy81atRIfn5+On78+E3H9fDwUPfu3TVnzhxt375de/bs0cGDB/O9DYDZEIAA3FSjRo30zDPPaO7cuTbtbdu21e+//64ZM2bo559/1vz58/XZZ58V2Xrnz5+vNWvW6PDhwxo2bJj++OMPDRo0SJI0bNgwnT9/Xk899ZS+/fZb/fLLL9qyZYsGDRqkzMzMAq1n9OjRmj59umJjY3XkyBGNHTtWiYmJGjFiRIHGWbBggTIyMhQaGqrY2FgdOnRIR44c0YcffqjDhw/LyclJ0l9n2DVu3FjPPPOM9u/fr2+//Vb9+vVTmzZtrLvsatasqdWrVysxMVHfffednn766ZvOkMXExOj999/X999/r19++UUffPCBPDw8bnraPmB2BCAAeZoyZYrd7q569eppwYIFmj9/vpo0aaJvv/02xzOkCmvatGmaPn26mjRpol27dmndunXy8fGRJPn7++v//u//lJmZqU6dOqlhw4YaMWKEvL29bY43yo/hw4fr5Zdf1ssvv6xGjRrp888/1/r161WrVq0CjVOjRg0lJCSoQ4cOGjdunJo0aaLQ0FDNnTtXr7zyiqZMmSLpr91ta9euVbly5dS6dWt16NBB1atXV2xsrHWsWbNmqVy5cmrRooW6deumTp06qVmzZrmuu2zZsnr33XfVsmVLNW7cWNu2bdOGDRtUoUKFAm0DYCYW48Z3NQAAgLscM0AAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0/j9Mmdt4zU0ZawAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# your code here\n", - "# Please label the axes and give a title to the plot \n" + "# Please label the axes and give a title to the plot \n", + "\n", + "mu = 2.3\n", + "x = list(range(11))\n", + "prob = [(math.exp(-mu) * (mu**x) / math.factorial(x)) for x in x]\n", + "\n", + "plt.bar(x, prob)\n", + "plt.xlabel(\"Number of Goals\")\n", + "plt.ylabel(\"Probability\")\n", + "plt.title(\"Probability of Number of Goals in a Match\")\n", + "plt.xticks(x)\n", + "plt.show()\n" ] } ], @@ -192,7 +337,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.11.0" } }, "nbformat": 4,