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hestiwahyuningsih/Enhanced_2d_scatter.ipynb

Created December 11, 2025 11:30
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Enhanced 2D Scatter Plot
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Enhanced_2d_scatter.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 15,
"id": "8eb89c50-05a9-4e4f-8fb6-1bd8f1664acf",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"x = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
"y = [1, 2, 9, 3, 8, 4, 7, 5, 6]\n",
"\n",
"plt.scatter(x, y)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "f91c7761-d55d-438c-a1f5-041d52297287",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"x = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
"y = [1, 2, 9, 3, 8, 4, 7, 5, 6]\n",
"\n",
"plt.scatter(x, y)\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "d449cf0c-8757-44e0-be13-bbf07636e170",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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cc/qtyXPmzJExY8a4WS0AAKLaXX1T9VuQ1bt7yncW6TUplzvd48mgotx77716AwAA5ojx+/SC2Xd3it67EVKM+Ah9AACAr0JQAQAAxiKoAAAAYxFUAACAsQgqAADAWAQVAABgLIIKAAAwFkEFAAAYi6ACAACMRVABAADGIqgAAABjEVQAAICxCCoAAMBYBBUAAGAsggoAADAWQQUAABiLoAIAAIxFUAEAAMYiqAAAAGMRVAAAgLEIKgAAwFgEFQAAYCyCCgAAMBZBBQAAGIugAgAAjEVQAQAAxiKoAAAAYxFUAACAsQgqAADAWAQVAABgLIIKAACNVBt2ZGPpEX1b7dUxLAwqubm54vP56mx9+vRxs0oAAPxPK7eXyZBZa2T8gmJ9rPbqWJXDwhGV66+/XsrKys5uGzZscLtKAAA0SIWRCYs2S1mwuk75oWC1LiesGBBUcnJyZP369U1WgVatWklKSsrZLSkpqcmuDQBAU1HTO3nLdkhDkzyRMnWeaaCm1aqx/0MwGJRhw4ZJt27dZNy4cTq4dOnS5ZIr8Mknn0jnzp0lLi5OBg8eLDNnzpSrrrqqwfuGQiG9RVRUVOh9TU2N3ppS5HpNfV1T0D7vow+9jz70FrUW5UjlSQnEnDkO+J06e0WdL9p9WAZ2TxQb1DTTa2FjrudzHKfRK4C++OILWbhwoSxYsEB27Nihg8vDDz8sI0eOlNjY2Iu+zooVK6SyslKuvfZaPe2Tl5cnBw4ckO3bt0v79u0bXNOi7lNfQUGBxMfHN7YZAADABVVVVZKdna0HPxISEpo+qJxr8+bNMm/ePHn11VelXbt2MnbsWHn00UelV69ejb7WsWPH9EjN7NmzdfC5mBGVtLQ0KS8vv2BDLyXtFRYWSmZmZqPCl1fQPu+jD72PPvTeiEpkAW1kJGV6RlimlvglFPadLX8tZ4BVIyqFzfBaqF6/1VKPiwkqjZ76OZcaBVENUFtMTIzcc889sm3bNklPT5cXX3xRnnzyyUZd72tf+5r07t1bdu/e3eD5QCCgt/rUg9dcYaI5r20C2ud99KH30YfeMKhnsiS2a6MXzp77F74KKaFan6ioktIhTt8vxv9lcLFBbBO/FjbmWv5LSVdvvfWW3HvvvXr044033pBJkybJwYMH9VTQ6tWr5fXXX5fnnnuusZfW00B79uyR1NTURv+/AAA0JxU+pmWl69v1Y0jkWJ23LaS4rdEjKipEhMNhefDBB2Xjxo1y0003nXefoUOH6tGRC3nqqackKytLBx4VdKZNm6ZHZtS1AQAwzV19UyV/bH/97h61cDZCjaSokKLOw+Wg8pvf/EZGjx6t36XzVVRIKS0tveC1Pv/8cx1K/vOf/0inTp1kyJAhUlRUpG8DAGAiFUYy01P0u3vKdxbpNSk2Tvd4Nqh8//vfb7J/fMmSJU12LQAAWooKJWrB7Ls7Re8JKRZ/Mi0AAMBXIagAAABjEVQAAICxCCoAAMBYBBUAAGAsggoAADAWQQUAABiLoAIAAIxFUAEAAMYiqAAAAGMRVAAAgLEIKgAAwFgEFQAAYCyCCgAAMBZBBQAAGIugAgAAjEVQAQAAxiKoAAAAYxFUAACAsQgqAADAWAQVAABgLIIKAAAwFkEFAAAYi6ACAACMRVABAADGIqgAAABjEVQAAICxCCoAAMBYBBUAAGAsggoAADAWQQUAXFAbdmRj6RF9W+3VMQCDg8oLL7wgPp9PJk2a5HZVAKBZrdxeJkNmrZHxC4r1sdqrY1UOwMCgUlxcLC+//LLccMMNblcFAJqVCiMTFm2WsmB1nfJDwWpdTlgBDAsqlZWVMmbMGHnllVekY8eOblcHAJqNmt7JW7ZDGprkiZSp80wDAV9qJS6bOHGijBgxQoYNGya//OUv/+d9Q6GQ3iIqKir0vqamRm9NKXK9pr6uKWif99GH3qPWohypPCmBmDPHAb9TZ6+o80W7D8vA7onidTxHva+mmV4LG3M9n+M4rq3gWrJkicyYMUNP/cTFxcntt98uN910k8yZM6fB++fm5kpeXt555QUFBRIfH98CNQYAAJerqqpKsrOzJRgMSkJCgplBZf/+/ZKRkSGFhYVn16ZcKKg0NKKSlpYm5eXlF2zopaQ9VbfMzEyJjY0V29A+76MPvTmiEllAGxlJmZ4RlqklfgmFfWfLX8sZYM2ICr9Hva2mmfpQvX4nJSVdVFBxbepn06ZNcvjwYenfv//ZstraWlm/fr289NJLOpDExPx3fPS/AoGA3upTD15zhYnmvLYJaJ/30YfeMahnsiS2a6MXzp77F6IKKaFan6ioktIhTt8vxv9lcPE6nqPeF9vEr4WNuZZri2nvuOMO2bZtm2zZsuXspkZY1MJadbt+SAEAr1PhY1pWur5dP4ZEjtV5m0IKcLlcG1Fp37699O3bt05Z27Zt5YorrjivHABscVffVMkf21+/u0ctnI1QIykqpKjzAAx61w8ARBsVRjLTU/S7e8p3Fuk1KbZN9wBWBpW1a9e6XQUAaBEqlKgFs+/uFL0npACGfuAbAADAVyGoAAAAYxFUAACAsQgqAADAWAQVAABgLIIKAAAwFkEFAAAYi6ACAACMRVABAADGIqgAAABjEVQAAICxCCoAAMBYBBUAAGAsggoAADAWQQUAABiLoAIAAIxFUAEAAMYiqAAAAGMRVAAAgLEIKgAAwFgEFQAAYCyCCgAAMBZBBQAAGIugAgAAjEVQAQAAxiKoAAAAYxFUAACAsQgqAADAWAQVAABgLIIKAAAwFkEF8KDasCMbS4/o22qvjgHARq4Glfz8fLnhhhskISFBb4MHD5YVK1a4WSXAeCu3l8mQWWtk/IJifaz26liVA4BtXA0qXbt2lRdeeEE2bdokJSUl8q1vfUtGjhwpH330kZvVAoylwsiERZulLFhdp/xQsFqXE1YA2MbVoJKVlSX33HOP9OrVS3r37i0zZsyQdu3aSVFRkZvVAoykpnfylu2QhiZ5ImXqPNNAAGzSSgxRW1srb7zxhpw4cUJPATUkFArpLaKiokLva2pq9NaUItdr6uuagvZ5j1qLcqTypARizhwH/E6dvaLOF+0+LAO7J4rX2f4cjYY20j7vq2mm52hjrudzHMfVVXjbtm3TwaS6ulqPphQUFOhRlobk5uZKXl7eeeXq/4mPj2+B2gIAgMtVVVUl2dnZEgwG9RpVo4PKqVOnZN++fbqyb775prz66quybt06SU9Pv6gRlbS0NCkvL79gQy8l7RUWFkpmZqbExsaKbWifN0dUIgtoIyMp0zPCMrXEL6Gw72z5azkDrBlRsflnMBraSPu8r6aZnqPq9TspKemigorrUz+tW7eWnj176tvf+MY3pLi4WH7729/Kyy+/fN59A4GA3upTD15z/ZA357VNQPu8Y1DPZEls10YvnD33rwsVUkK1PlFRJaVDnL5fjP/L4OJ1tj9Ho6GNtM/7Ypv4OdqYaxn3OSrhcLjOqAmAM1T4mJZ1ZqSxfgyJHKvzNoUUAHA1qEyZMkXWr18ve/fu1WtV1PHatWtlzJgx9AzQgLv6pkr+2P565ORc6liVq/MAYBNXp34OHz4sP/jBD6SsrEw6dOigP/xt1apVei4MQMNUGMlMT9Hv7infWaTXpNg23QMARgSVP/3pT27+84BnqVCiFsy+u1P0npACwFbGrVEBAACIIKgAAABjEVQAAICxCCoAAMBYBBUAAGAsggoAADAWQQUAABiLoAIAAIxFUAEAAMYiqAAAAGMRVAAAgLEIKgAAwFgEFQAAYCyCCgAAMBZBBQAAGIugAgAAjEVQAQAAxiKoAAAAYxFUAACAsQgqAADAWAQVAABgLIIKAAAwFkEFAAAYi6ACAACMRVABAADGIqgAAABjEVQAAICxCCoAAMBYBBUAAGAsggoAADAWQSUK1YYd2Vh6RN9We3UMAICJXA0qM2fOlAEDBkj79u0lOTlZRo0aJbt27XKzStZbub1MhsxaI+MXFOtjtVfHqhwAANO4GlTWrVsnEydOlKKiIiksLJSamhoZPny4nDhxws1qWUuFkQmLNktZsLpO+aFgtS4nrAAATNPKzX985cqVdY7nz5+vR1Y2bdokt956q2v1spGa3slbtkMamuRRZT4RfT4zPUVi/OoIAIAoDyr1BYNBvU9MTGzwfCgU0ltERUWF3quRGLU1pcj1mvq6blFrUY5UnpRAzJnjgN+ps1fU+aLdh2Vg94Yffy+xrf+isY22ty8a2kj7vK+mmZ6jjbmez3EcI1ZShsNh+fa3vy3Hjh2TDRs2NHif3NxcycvLO6+8oKBA4uPjW6CWAADgclVVVUl2drYeoEhISPBGUJkwYYKsWLFCh5SuXbte9IhKWlqalJeXX7Chl5L21LqZzMxMiY2NFRtGVCILaCMjKdMzwjK1xC+h8JdTPa/lDLBmRMWm/ovGNtrevmhoI+3zvppmeo6q1++kpKSLCipGTP089thjsnz5clm/fv1XhhQlEAjorT714DXXD3lzXrslDeqZLInt2uiFs+cmUxVSQrU+vUYlpUOcvp9Na1Rs6b9obqPt7YuGNtI+74tt4udoY67l6rt+1GCOCilLly6VNWvWSPfu3d2sjtVU+JiWla5v148hkWN13qaQAgDwPleDinpr8qJFi/QaE/VZKocOHdLbyZMn3ayWte7qmyr5Y/vrkZNzqWNVrs4DAGASV6d+8vPz9f7222+vUz5v3jx56KGHXKqV3VQYUW9BVu/uKd9ZpNek2DbdAwCwh6tBxZB1vFFHhRK1YPbdnaL3hBQAgKn4rh8AAGAsggoAADAWQQUAABiLoAIAAIxFUAEAAMYiqAAAAGMRVAAAgLEIKgAAwFgEFQAAYCyCCgAAMBZBBQAAGIugAgAAjEVQAQAAxiKoAAAAYxFUAACAsQgqAADAWAQVAABgLIIKAAAwFkEFAAAYi6ACAACMRVABAADGIqgAAABjEVQAAICxCCoAAMBYBBUAAGAsggoAADAWQQUAABiLoAIAAIxFUAEAAMYiqAAAAGMRVBpQG3ZkY+kRfVvt1TEAAIiyoLJ+/XrJysqSzp07i8/nk7ffflvctnJ7mQyZtUbGLyjWx2qvjlU5AACIoqBy4sQJufHGG+X3v/+9mECFkQmLNktZsLpO+aFgtS4nrAAA0LJaiYvuvvtuvZlATe/kLdshDU3yqDKfiD6fmZ4iMX51BAAArA4qjRUKhfQWUVFRofc1NTV6uxxqLcqRypMSiDlzHPA7dfaKOl+0+7AM7J4oXhd5vC73cTOV7e2Lhjba3r5oaCPt876aZnqONuZ6PsdxjFgpqtaoLF26VEaNGvWV98nNzZW8vLzzygsKCiQ+Pr6ZawgAAJpCVVWVZGdnSzAYlISEBHuCSkMjKmlpaVJeXn7Bhl7MiEpkAW1kJGV6RlimlvglFP5yque1nAHWjKgUFhZKZmamxMbGim1sb180tNH29kVDG2mf99U003NUvX4nJSVdVFDx1NRPIBDQW33qwbvcB3BQz2RJbNdGL5w9N7mpkBKq9ek1Kikd4vT9bFqj0hSPnclsb180tNH29kVDG2mf98U28XO0Mdfic1T+S4WPaVnp+nb9GBI5VudtCikAAJjO1aBSWVkpW7Zs0ZtSWlqqb+/bt8+V+tzVN1Xyx/bXIyfnUseqXJ0HAAAtx9Wpn5KSEhk6dOjZ48mTJ+t9Tk6OzJ8/35U6qTCi3oKs3t1TvrNIr0mxbboHAACvcDWo3H777WLIWt46VChRC2bf3Sl6T0gBAMAdrFEBAADGIqgAAABjEVQAAICxCCoAAMBYBBUAAGAsggoAADAWQQUAABiLoAIAAIxFUAEAAMby1Lcn1xf5VFv1ddHN8dXWVVVV+to2fqsp7fM++tD76ENvs73/mrONkdfti/l0ek8HlePHj+t9Wlqa21UBAACX8DreoUOH/3kfn2Pil+1cpHA4LAcPHpT27duLz9e0Xxqo0p4KQPv375eEhASxDe3zPvrQ++hDb7O9/5qzjSp6qJDSuXNn8fv99o6oqMZ17dq1Wf8N1TG2PgEV2ud99KH30YfeZnv/NVcbLzSSEsFiWgAAYCyCCgAAMBZB5SsEAgGZNm2a3tuI9nkffeh99KG32d5/prTR04tpAQCA3RhRAQAAxiKoAAAAYxFUAACAsQgqAADAWASVetavXy9ZWVn60/LUp92+/fbbYpOZM2fKgAED9Kf5Jicny6hRo2TXrl1ii/z8fLnhhhvOfjjR4MGDZcWKFWKrF154QT9PJ02aJLbIzc3VbTp369Onj9jkwIEDMnbsWLniiiukTZs20q9fPykpKRFbXH311ef1odomTpwoNqitrZWpU6dK9+7ddf/16NFDpk+fflHfW+MVx48f179XunXrptt48803S3FxsSt18fQn0zaHEydOyI033ijjx4+X++67T2yzbt06/ctChZXTp0/Ls88+K8OHD5cdO3ZI27ZtxevUJxWrF+9evXrpXxoLFiyQkSNHyr/+9S+5/vrrxSbql8bLL7+sg5ltVF+tXr367HGrVvb8qjp69KjccsstMnToUB2iO3XqJJ988ol07NhRbHpuqhfziO3bt0tmZqaMHj1abDBr1iz9R5H6/aKeqypkjhs3Tn/S6uOPPy42eOSRR3S/LVy4UP/hvmjRIhk2bJh+rejSpUvLVka9PRkNUw/P0qVLrX54Dh8+rNu5bt06x1YdO3Z0Xn31Vccmx48fd3r16uUUFhY6t912m/PEE084tpg2bZpz4403Orb62c9+5gwZMsSJJur52aNHDyccDjs2GDFihDN+/Pg6Zffdd58zZswYxwZVVVVOTEyMs3z58jrl/fv3d37+85+3eH2Y+olywWBQ7xMTE8U26i+6JUuW6FEyNQVkEzUqNmLECP0Xjo3UCIP6K+6aa66RMWPGyL59+8QW77zzjmRkZOjRBTX9+vWvf11eeeUVsdWpU6f0X+NqlLqpvzzWLWoa5P3335ePP/5YH2/dulU2bNggd999t9jg9OnT+vdnXFxcnXI1BaTa2dLsGU/FJX37tJqDVMPQffv2teYR3LZtmw4m1dXV0q5dO1m6dKmkp6eLLVT42rx5s2vzxc3tm9/8psyfP1+uvfZaKSsrk7y8PPm///s/PQyt1lZ53aeffqqnDSZPnqynXlU/qumC1q1bS05OjthGrfM7duyYPPTQQ2KLZ555Rn+rsFo7FRMTo1/UZ8yYoUO1Ddq3b69/h6p1N9ddd51ceeWVsnjxYvnwww+lZ8+eLV+hFh/D8RDbp35+8pOfON26dXP279/v2CQUCjmffPKJU1JS4jzzzDNOUlKS89FHHzk22Ldvn5OcnOxs3br1bJltUz/1HT161ElISLBm+i42NtYZPHhwnbKf/vSnzqBBgxwbDR8+3Ln33nsdmyxevNjp2rWr3v/73/92/vznPzuJiYnO/PnzHVvs3r3bufXWW/XroJoGGjBggJ7a6tOnT4vXhaASpUFl4sSJ+gft008/dWx3xx13OD/60Y8cG6jnY+QXR2RTxz6fT98+ffq0Y6OMjAwdOm1w1VVXOQ8//HCdsj/84Q9O586dHdvs3bvX8fv9zttvv+3YRP3ufOmll+qUTZ8+3bn22msd21RWVjoHDx7Ut++//37nnnvuafE6sEYlyqj89dhjj+npkDVr1ui310XDFFcoFBIb3HHHHXpqa8uWLWc3td5BDTmr22oY2jaVlZWyZ88eSU1NFRuoqdb6Hwmg1jqot4HaZt68eXodjlpPZZOqqirx++u+fKqfPfW7xjZt27bVP3vq3WqrVq3S76JsaaxRaeCX4u7du88el5aW6hcAtdj0qquuEhsWYRYUFMjf/vY3PQ956NAhXa7eVqcWSnndlClT9II21VfqcwBUW9euXat/wGyg+qz+eiL1i0R9Hoct64yeeuop/VlG6oX74MGD+ptb1YvAgw8+KDZ48skn9WLM559/Xu6//37ZuHGjzJ07V282US/aKqiodTc2vb1cUc9PtSZF/Z5Rb09WH38we/ZsvWDYFqtWrdJ/2Kq1Yuo18emnn9ZrctTbsFtci4/hGO4f//iHHkqvv+Xk5Dg2aKhtaps3b55jA/WWQbXupnXr1k6nTp30tM97773n2My2NSoPPPCAk5qaqvuwS5cu+ljNl9tk2bJlTt++fZ1AIKDn/OfOnevYZtWqVfp3y65duxzbVFRU6J85NY0XFxfnXHPNNfptu2p9nC3+8pe/6Hapn8OUlBS9XODYsWOu1MWn/tPy8QgAAODCWKMCAACMRVABAADGIqgAAABjEVQAAICxCCoAAMBYBBUAAGAsggoAADAWQQUAABiLoAIAAIxFUAEAAMYiqAAAAGMRVAAY44svvpCUlBT9zcIRH3zwgbRu3Vref/99V+sGwB18KSEAo7z77rsyatQoHVDUV8zfdNNNMnLkSJk9e7bbVQPgAoIKAONMnDhRVq9eLRkZGbJt2zYpLi6WQCDgdrUAuICgAsA4J0+elL59+8r+/ftl06ZN0q9fP7erBMAlrFEBYJw9e/bIwYMHJRwOy969e92uDgAXMaICwCinTp2SgQMH6rUpao3KnDlz9PRPcnKy21UD4AKCCgCjPP300/Lmm2/K1q1bpV27dnLbbbdJhw4dZPny5W5XDYALmPoBYIy1a9fqEZSFCxdKQkKC+P1+ffuf//yn5Ofnu109AC5gRAUAABiLERUAAGAsggoAADAWQQUAABiLoAIAAIxFUAEAAMYiqAAAAGMRVAAAgLEIKgAAwFgEFQAAYCyCCgAAMBZBBQAAiKn+H3/0Y5HY/cpmAAAAAElFTkSuQmCC",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"x = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
"y = [1, 2, 9, 3, 8, 4, 7, 5, 6]\n",
"\n",
"plt.scatter(x, y)\n",
"plt.grid()\n",
"plt.xlabel(\"x\")\n",
"plt.ylabel(\"y\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "1d9ffa61-f4d4-421a-8dc0-ebb1888e4321",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 480x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"x = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
"y = [1, 2, 9, 3, 8, 4, 7, 5, 6]\n",
"\n",
"plt.figure(figsize=(4.8, 4.8))\n",
"plt.scatter(x, y)\n",
"plt.grid()\n",
"plt.xlabel(\"x\")\n",
"plt.ylabel(\"y\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "a129eb2f-dd5c-4637-8ee9-9cae758dbf67",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 480x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"x = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
"y = [1, 2, 9, 3, 8, 4, 7, 5, 6]\n",
"\n",
"plt.figure(figsize=(4.8, 4.8))\n",
"plt.scatter(x, y)\n",
"plt.grid()\n",
"plt.xlabel(\"x\")\n",
"plt.ylabel(\"y\")\n",
"plt.xticks([0, 5, 10])\n",
"plt.yticks([0, 5, 10])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "4d77ed80-3f63-4133-8b1c-08750592577e",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 480x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"x = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
"y = [1, 2, 9, 3, 8, 4, 7, 5, 6]\n",
"\n",
"plt.figure(figsize=(4.8, 4.8))\n",
"plt.scatter(x, y, marker=\"x\")\n",
"plt.grid()\n",
"plt.xlabel(\"x\")\n",
"plt.ylabel(\"y\")\n",
"plt.xticks([0, 5, 10])\n",
"plt.yticks([0, 5, 10])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "60034e45-222b-4c6f-85af-21b9399f1836",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 480x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"x = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
"y = [1, 2, 9, 3, 8, 4, 7, 5, 6]\n",
"\n",
"plt.figure(figsize=(4.8, 4.8))\n",
"plt.scatter(x, y, marker=\"s\")\n",
"plt.grid()\n",
"plt.xlabel(\"x\")\n",
"plt.ylabel(\"y\")\n",
"plt.xticks([0, 5, 10])\n",
"plt.yticks([0, 5, 10])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "65cfb10a-054c-4782-b321-1167813b97ba",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 480x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"x = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
"y = [1, 2, 9, 3, 8, 4, 7, 5, 6]\n",
"\n",
"plt.figure(figsize=(4.8, 4.8))\n",
"plt.scatter(x, y, marker=\"s\", s=100)\n",
"plt.grid()\n",
"plt.xlabel(\"x\")\n",
"plt.ylabel(\"y\")\n",
"plt.xticks([0, 5, 10])\n",
"plt.yticks([0, 5, 10])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "b0cd60e5-aeca-4e5e-9f93-0163398ee5e7",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 480x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"x = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
"y = [1, 2, 9, 3, 8, 4, 7, 5, 6]\n",
"\n",
"plt.figure(figsize=(4.8, 4.8))\n",
"plt.scatter(x, y, marker=\"s\", s=100, c=\"#f00\")\n",
"plt.grid()\n",
"plt.xlabel(\"x\")\n",
"plt.ylabel(\"y\")\n",
"plt.xticks([0, 5, 10])\n",
"plt.yticks([0, 5, 10])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "4a8acc18-0ab8-46e6-b33e-6b8fa6638be6",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 480x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"x = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
"y = [1, 2, 9, 3, 8, 4, 7, 5, 6]\n",
"\n",
"plt.figure(figsize=(4.8, 4.8))\n",
"plt.scatter(x, y, marker=\"s\", s=100, c=\"#93c474\")\n",
"plt.grid()\n",
"plt.xlabel(\"x\")\n",
"plt.ylabel(\"y\")\n",
"plt.xticks([0, 5, 10])\n",
"plt.yticks([0, 5, 10])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "f6e3c4b2-51aa-4b3f-980e-99f3a9b388fd",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 480x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"x1 = [1, 2, 3, 4]\n",
"x2 = [5, 6, 7, 8, 9]\n",
"y1 = [1, 2, 9, 3]\n",
"y2 = [8, 4, 7, 5, 6]\n",
"\n",
"plt.figure(figsize=(4.8, 4.8))\n",
"plt.scatter(x1, y1, marker=\"s\", s=81, c=\"#93c474\")\n",
"plt.scatter(x2, y2, marker=\"o\", s=121, c=\"#9374c4\")\n",
"plt.grid()\n",
"plt.xlabel(\"x\")\n",
"plt.ylabel(\"y\")\n",
"plt.xticks([0, 5, 10])\n",
"plt.yticks([0, 5, 10])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "e0e18462-f129-4ba7-bff7-96e9a9716537",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 480x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"x = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
"y = [0, 6, 12, 15, 16, 15, 12, 6, 0]\n",
"sz= [40, 80, 120, 160, 200, 240, 280, 320, 360]\n",
"plt.figure(figsize=(4.8, 4.8))\n",
"plt.scatter(x, y, s=sz)\n",
"plt.grid()\n",
"plt.xlabel(\"x\")\n",
"plt.ylabel(\"y\")\n",
"plt.xticks([0, 2, 4, 6, 8, 10])\n",
"plt.yticks([0, 4, 8, 12, 16])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "58dcf37c-6565-4655-ac8b-d8f60bfe8dd5",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 480x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"x = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
"y = [0, 6, 12, 15, 16, 15, 12, 6, 0]\n",
"sz= [40+10*(16-i) for i in y]\n",
"plt.figure(figsize=(4.8, 4.8))\n",
"plt.scatter(x, y, s=sz)\n",
"plt.grid()\n",
"plt.xlabel(\"x\")\n",
"plt.ylabel(\"y\")\n",
"plt.xticks([0, 2, 4, 6, 8, 10])\n",
"plt.yticks([0, 4, 8, 12, 16])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "79ae162b-7da1-485d-a4f3-a6c283989b1b",
"metadata": {},
"source": [
"Sugestion for additional dimension?\n",
"# grafik kiri : nilai x makin besar, ukuran titik makin besar\n",
" dimensi yang dapat diwakili adalah : waktu (time), jarak(position), kecepatan/velocity(untuk GLBB dipercepat), temperatur, usaha(work) \n",
"\n",
"# grafik kanan : nilai y makin besar, ukuran titik mengecil\n",
" dimensi yang dapat diwakili adalah : massa jenis, temperatur udara di ruang terbuka, tekanan hidrostatis, tekanan udara di ruang terbuka, energi kinetik benda dilempar keatas dan benda jatuh bebas"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "754e1125-7fe1-4aee-8716-d323ec97d3f2",
"metadata": {},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "leading zeros in decimal integer literals are not permitted; use an 0o prefix for octal integers (2874283414.py, line 11)",
"output_type": "error",
"traceback": [
" \u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[28]\u001b[39m\u001b[32m, line 11\u001b[39m\n\u001b[31m \u001b[39m\u001b[31mTEB;Tebet;12+045;17;-6.226111;106.85611;2;2\u001b[39m\n ^\n\u001b[31mSyntaxError\u001b[39m\u001b[31m:\u001b[39m leading zeros in decimal integer literals are not permitted; use an 0o prefix for octal integers\n"
]
}
],
"source": [
"Code;Station;Pos;Elev;Lat;Long;Tracks;Platforms\n",
"JAKK;Jakarta Kota;0+000;4;-6.137579;106.814634;11;6\n",
"JAY;Jayakarta;1+400;13;-6.144167;106.8258;2;2\n",
"MGB;Mangga Besar;2+480;14;-6.153056;106.8258;2;2\n",
"SW;Sawah Besar;3+386;15;-6.162222;106.825833;2;2\n",
"JUA;Juanda;4+535;15;-6.171111;106.835;2;2\n",
"GMR;Gambir;5+540;16;-6.176716;106.830508;4;2\n",
"GDD;Gondangdia;6+550;17;-6.18556;106.83222;2;2\n",
"CKI;Cikini;8+300;20;-6.19861;106.8413;2;2\n",
"MRI;Manggarai;9+890;13;-6.207778;106.8527;7;3\n",
"TEB;Tebet;12+045;17;-6.226111;106.85611;2;2\n",
"CW;Cawang;13+730;13;-6.24444;106.8591;2;2\n",
"DRN;Duren Kalibata;15+276;26;-6.253611;106.853056;2;2\n",
"PSMB;Pasar Minggu Baru;16+100;37;-6.26278;106.85444;2;2\n",
"PSM;Pasar Minggu;18+480;36;-6.281111;106.84056;4;3\n",
"TNT;Tanjung Barat;21+300;44;-6.308333;106.83667;2;2\n",
"LNA;Lenteng Agung;23+971;57;-6.326667;106.832222;2;2\n",
"UP;Universitas Pancasila;25+000;57;-6.335833;106.834167;2;2\n",
"UI;Universitas Indonesia;27+624;74;-6.360754;106.831748;2;2\n",
"POC;Pondok Cina;28+373;74;-6.368834;106.83208;2;2\n",
"DPB;Depok Baru;31+100;93;-6.391045;106.82164;3;3\n",
"DP;Depok;32+684;93;-6.404422;106.817086;4;3\n",
"CTA;Citayam;37+768;120;-6.448879;106.802528;2;2\n",
"BJD;Bojong Gede;42+965;140;-6.4608;106.82422;2;3\n",
"CLT;Cilebut;47+296;171;-6.4642;106.8525;2;3\n",
"BOO;Bogor;54+810;246;-6.4719;106.8887;8;7"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "aa18cfd5-e004-4f99-903c-84d99129a50d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Code Station Pos Elev Lat Long Tracks Platforms\n",
"0 JAKK Jakarta Kota 0+000 4 -6.137579 106.814634 11 6\n",
"1 JAY Jayakarta 1+400 13 -6.144167 106.825800 2 2\n",
"2 MGB Mangga Besar 2+480 14 -6.153056 106.825800 2 2\n",
"3 SW Sawah Besar 3+386 15 -6.162222 106.825833 2 2\n",
"4 JUA Juanda 4+535 15 -6.171111 106.835000 2 2\n"
]
}
],
"source": [
"import pandas as pd\n",
"\n",
"df = pd.read_csv(\n",
" 'raylway_jakarta_bogor.csv',\n",
" sep=';',\n",
" skipinitialspace=True\n",
")\n",
"df.columns = df.columns.str.strip()\n",
"for col in ['Elev', 'Tracks', 'Platforms']:\n",
" df[col] = pd.to_numeric(df[col],\n",
" errors='coerce')\n",
"print(df[0:5])"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "d807f7ad-d498-4b71-bb28-f65e43a315ac",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = df['Long']\n",
"y = df['Lat']\n",
"\n",
"import matplotlib.pyplot as plt\n",
"plt.scatter(x, y)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "8ae3e843-6d7f-44ae-95c8-1354f74ce283",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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haAEAoIHotmbdJVTVjc2arvlaDpURtAAA0ED0HBbd1izJgYu913ye15IaQQsAAA1Iz2GZMeYoV5Cf2AWk95rOc1qqxsPlAABoYApMTikq4Im4tUTQAgBAI1AXUHGPDuR9LdA9BAAAgkDQAgAAgkDQAgAAgkDQAgAAgkDQAgAAgkDQAgAAgkDQAgAAgkDQAgAAgkDQAgAAgkDQAgAAgkDQAgAAgkDQAgAAgkDQAgAAgkDQAgAAgkDQAgAAgtCssVcAAIBQ7Nodc0vWbXabtla4Tm1auoHd27umTfIae7VyBkELAAA1MG9FiZs6Z5UrKauITyvMb+mmjChyw3sXkocNgO4hAABqELCMv29ZQsAipWUVfrrmo/4RtAAAsIcuIbWwxFLMs2mar+VQvwhaAACohsawJLewRClU0Xwth/pF0AIAQDU06Dady2HvEbQAAFAN3SWUzuWw9whaAACohm5r1l1CVd3YrOmar+VQvwhaAACohp7DotuaJTlwsfeaz/Na6h9BCwAAe6DnsMwYc5QryE/sAtJ7Tec5LQ2Dh8sBAFADCkxOKSrgibiNiKAFAIAaUhdQcY8O5FcjoXsIAAAEgaAFAAAEgaAFAAAEgaAFAAAEgaAFAAAEgaAFAAAEgaAFAAAEgaAFAAAEgaAFAAAEgaAFAAAEgaAFAAAEgaAFAAAEgaAFAAAEgaAFAAAEgaAFAAAEgaAFAAAEgaAFAABkZ9CyaNEiN2LECNelSxeXl5fnHn/88YT5sVjMXX/99a6wsNC1atXKDRkyxL377rsJy2zevNmNHj3atW3b1rVr186NGzfOffbZZ3XfGgAAkLVqHbRs27bN9evXz02fPj3l/FtvvdXdeeedbubMme7VV191++67rxs2bJirqKiIL6OAZeXKlW7+/Plu7ty5PhC65JJL6rYlAAAgq+XF1DSytx/Oy3OzZ892I0eO9O+VlFpgJk2a5K688ko/rayszHXu3NnNmjXLnXvuue6dd95xRUVF7rXXXnMDBgzwy8ybN8+ddtppbv369f7ze1JeXu7y8/N92mqtAQAAma+u5++0jmlZt26dKy0t9V1CRis3aNAgt3jxYv9ef9UlZAGLaPkmTZr4lplUtm/f7jc0+gIAALklrUGLAhZRy0qU3ts8/e3UqVPC/GbNmrn27dvHl0k2bdo0H/zYq2vXrulcbQAAEIAg7h6aPHmyb0qy10cffdTYqwQAAEIOWgoKCvzfjRs3JkzXe5unv5s2bUqYv3PnTn9HkS2TrEWLFr7vK/oCAAC5Ja1BS/fu3X3gsWDBgvg0jT/RWJXi4mL/Xn+3bNnili5dGl9m4cKFbvfu3X7sCwAAQCrNXC3peSpr165NGHy7fPlyPyalW7du7vLLL3c///nP3aGHHuqDmOuuu87fEWR3GPXq1csNHz7cXXzxxf626B07drgJEyb4O4tqcucQAADITbUOWl5//XV30kknxd9PnDjR/x07dqy/rfnqq6/2z3LRc1fUonLcccf5W5pbtmwZ/8z999/vA5XBgwf7u4ZGjRrln+0CAIDZtTvmlqzb7DZtrXCd2rR0A7u3d02b5DVaOgj8OS2Nhee0AEB2m7eixE2ds8qVlP3rwaSF+S3dlBFFbnjvwgZPB1n4nBYAAOpKgcb4+5YlBBpSWlbhp2t+Q6aDzEHQAgDIGOrKUctIqi4Am6b5Wq4h0kFmIWgBAGQMjT1JbhmJUoih+VquIdJBZiFoAQBkDA2WTcdy6UoHmYWgBQCQMXR3TzqWS1c6yCwELQCAjKHbkXV3T1U3JGu65mu5hkgHmYWgBQCQMfT8FN2OLMkBh73X/D09ZyVd6SCzELQAADKKnp8yY8xRriA/setG7zW9ps9XSVc6yBw8XA4AkJF4Im72Ka/jw+Vq/Rh/AAAagrpuint0yJh00PjoHgIAAEEgaAEAAEEgaAEAAEEgaAEAAEEgaAEAAEEgaAEAAEEgaAEAAEEgaAEAAEEgaAEAAEEgaAEAAEEgaAEAAEEgaAEAAEEgaAEAAEEgaAEAAEFo1tgrAABoXLt2x9ySdZvdpq0VrlOblm5g9/auaZO8Rk8LSEbQAgA5bN6KEjd1zipXUlYRn1aY39JNGVHkhvcubLS0gFToHgKAHKUgY/x9yxKCDCktq/DTNb8x0gKqQtACADlI3ThqFYmlmGfTNF/LNWRaQHUIWgAgB2ncSXKrSJTCC83Xcg2ZFlAdghYAyEEaKJuu5dKZFlAdghYAyEG6syddy6UzLaA6BC0AkIN0K7Lu7KnqZmRN13wt15BpAdUhaAGAHKRnp+hWZEkONuy95tfkGSvpTAuoDkELAOQoPTtlxpijXEF+YreN3mt6bZ6tks60gKrkxWKx4O5BKy8vd/n5+a6srMy1bdu2sVcHAILGE3ERyvmbJ+ICQI5Tt01xjw4ZlxaQjO4hAAAQBIIWAAAQBIIWAAAQBIIWAAAQBIIWAAAQBIIWAAAQBIIWAAAQBIIWAAAQBIIWAAAQBIIWAAAQBIIWAAAQBIIWAAAQBH4wEQAaCL+mDNQNQQsANIB5K0rc1DmrXElZRXxaYX5LN2VEkRveu7DR0gJCQvcQANQzBRnj71uWEGRIaVmFn675jZEWEBqCFgCo5y4htYrEUsyzaZqv5RoyLSBEBC0AUI+WrNtcqVUkSuGF5mu5hkwLCBFBCwDUo01bK9K2XDrTAkJE0AIA9ahTm5ZpWy6daQEhImgBgHo0sHt7f2dPXhXzNV3ztVxDpgWEiKAFAOpR0yZ5/lZkSQ427L3ma7mGTAsIEUELANQzPTtlxpijXEF+YreN3mt6bZ6tks60gNDkxWKxtN4bd8MNN7ipU6cmTDv88MPd6tWr/f8VFRVu0qRJ7qGHHnLbt293w4YNc3fddZfr3Llzjb+jvLzc5efnu7KyMte2bdt0rj4A1BueiItcV17H83e9PBH3iCOOcM8+++y/vqTZv77miiuucE8++aR79NFH/YpPmDDBnXXWWe6ll16qj1UBgIyhbpviHh0yLi0gFPUStChIKSgoqDRdkdXdd9/tHnjgAXfyySf7affcc4/r1auXe+WVV9wxxxxTH6sDAACyQL2MaXn33Xddly5d3MEHH+xGjx7tPvzwQz996dKlbseOHW7IkCHxZXv27Om6devmFi9eXB+rAgAAskTaW1oGDRrkZs2a5cexlJSU+PEtxx9/vFuxYoUrLS11zZs3d+3atUv4jMazaF5VNPZFr2ifGAAAyC1pD1pOPfXU+P99+/b1QcyBBx7oHnnkEdeqVau9SnPatGmVBvcCAIDcUu+3PKtV5bDDDnNr167141y+/PJLt2XLloRlNm7cmHIMjJk8ebIfD2Ovjz76qL5XGwAA5FrQ8tlnn7n33nvPFRYWuv79+7t99tnHLViwID5/zZo1fsxLcXFxlWm0aNHC3xoVfQEAgNyS9u6hK6+80o0YMcJ3CW3YsMFNmTLFNW3a1J133nn+Fudx48a5iRMnuvbt2/vg47LLLvMBC3cOAciF56sAyKCgZf369T5A+fTTT93+++/vjjvuOH87s/6X22+/3TVp0sSNGjUq4eFyAJCJ5q0ocVPnrHIlZf/65WT9vo8el8/TZ4HAn4jbEHgiLoCGCljG37fMJVeS1sbCY/OBhj1/89tDAFBFl5BaWFJd1dk0zddyABoGQQsApKAxLNEuoWQKVTRfywFoGAQtAJCCBt2mczkAdUfQAgAp6C6hmqjpcgDqjqAFAFLQbc26S6iqG5s1XfO1HICGQdACACnoOSy6rVmSAxd7r/k8rwVoOAQtAFAFPYdFtzUX5Cd2Aek9tzsDWfBwOQDIpifPKnA5pagg49YLyEUELQCy8smz6QyA9LniHh326rMA0oegBUDGPnm2tKzCT69tVwyP3geyE2NaAGTVk2ctAEp+MJwFQJoPIEwELQCy5smzPHofyG4ELQCy5smzPHofyG4ELQCy5smzPHofyG4ELQCy5smzPHofyG4ELQCy5smzPHofyG4ELQCy5smzPHofyG55sVisZvcRZpDy8nKXn5/vysrKXNu2bRt7dQCkSboeCMdzWoDsPH8TtADISpn4kwBAriuvY9DCE3EBZCUevQ9kH8a0AACAIBC0AACAINA9BKBOGDsCoKEQtADYa9ylA6Ah0T0EYK/wa8oAGhpBC4Ba49eUATQGghYAtcavKQNoDAQtAGqNX1MG0BgIWgDUGr+mDKAxELQAqDV+TRlAYyBoAaoYaLr4vU/dn5f/3f/Ve/wLv6YMoDHwnBYgCc8eqZnhvQvdjDFHualzVrmSsor49IL8lm7KiCI/HwDSiV95BlI8eyS5XcV+G1gnaU7GiXgiLoCa4leegQZ69ogCF80/pajAd4/g//FrygAaCt1DyBp1veKvzbNHint0cI2BVg0AuYygBVkhHeNQMv3ZI4y1AZDruHsIwd+pk67fwMnkZ4/wOz8AQEtLTkln10K60qpr60E6x6HYs0cU7KRKL++fd8ZouYbEWBsA+H90D+WIdHYtpCutqu7UsRaSmtypk85xKPbsEX23ApToelm4o/kNPQg3hLE2ANAQ6B7KgYecpbNrIV1ppetXgtM9DsWePaIWlSi9b6zbnTN9rA0ANBRaWupBpnSdpLv7JJ1ppav1oD7GoShvtQ2Z0pVWX2NtuBMJQGgIWtIsk7pO0t21kM600tV6UF/jUNL17JF0lIf62EbuRAIQIrqH0ijTuk7S3bWQzrTS1XqQyb+Bk67ykO5t5E4kAKEiaEmTdAYatWnRaMiuhXSmlc5fCc7EcSjpLA/p3MZ0rxcANCS6h9IkE7tO0t21kM600n2nTrrHoWTiHT/p2EbuRAIQMlpa0iQTu07S3bWQ7m6KdLeQ2DiUM4/8qv/bmL8PVF93/NR1G7kTCUDIaGlJk/roOknXwEsLDpIHhBbsxQDhdKaViS0k6ZKpT9fN1PUCgJogaEmTTO46SXdwkO5AIxt/JThTn66bqesFADVB91CaZHrXSbq7TzKpKyYTZepdTZm6XgBQE3mxWCy42wTKy8tdfn6+Kysrc23btnWZJN3Pv+ABYGHL1OehZOp6Achu5XU8fxO01AMCDYRQHjJ1vQBkr/I6Bi2MaakH2ThGA9lXHjJ1vQCgKgQtEVx5AgCQuQha/ok+fgAAMht3D/FbLAAABCHngxZ+iwUAgDDkfNCSzh8nBAAA9SfngxZ+iwUAgDDkfNDCb7EAABCGRg1apk+f7g466CDXsmVLN2jQILdkyZIGXwf7LZaqHqml6ZrPb7EAAJCjQcvDDz/sJk6c6KZMmeKWLVvm+vXr54YNG+Y2bdrUoOvBb7EAABCGRgtafvWrX7mLL77YXXTRRa6oqMjNnDnTtW7d2v3+979v8HWpjx8nBAAAWfBwuS+//NItXbrUTZ48OT6tSZMmbsiQIW7x4sWVlt++fbt/RX+7IN0UmJxSVMBvsQAAkKEaJWj55JNP3K5du1znzp0Tpuv96tWrKy0/bdo0N3Xq1HpfL36LBQCAzBXE3UNqkdEvQtrro48+auxVAgAAudDS0rFjR9e0aVO3cePGhOl6X1BQUGn5Fi1a+BcAAMhdjdLS0rx5c9e/f3+3YMGC+LTdu3f798XFxY2xSgAAIMM12q8863bnsWPHugEDBriBAwe6O+64w23bts3fTQQAAJAxQcs555zjPv74Y3f99de70tJSd+SRR7p58+ZVGpwLAAAgebFYTL8JGBTd8pyfn+8H5bZt27axVwcAADTA+TuIu4cAAAAIWgAAQBAIWgAAQBAabSBuXdgwnPp4nD8AAKgfdt7e2+G0QQYtW7du9X+7du3a2KsCAAD24jyuAbk5cfeQHkS3YcMG16ZNG5eXl5f2KFDBkH4qoC53JqUrHdIir0IoD5m4TpmaViauUy6klYnrlCtpRSnkUMDSpUsX/0PJOdHSog094IAD6vU7tJPSsaPSlQ5pkVchlIdMXKdMTSsT1ykX0srEdcqVtMzetLAYBuICAIAgELQAAIAgELQk0a9JT5kypc6/Kp2udEiLvAqhPGTiOmVqWpm4TrmQViauU66klU5BDsQFAAC5h5YWAAAQBIIWAAAQBIIWAAAQBIIWAAAQhOCDlunTp7uDDjrItWzZ0g0aNMgtWbJkr9JZtGiR+8Y3vuFHSuspux07dnS33nprpeUeffRR17NnT/99ffr0cU899VTC/Jtvvjn+pD9LRy/9v2XLloRln3/+eXfUUUf57zzkkEPcrFmzEubPmDHDffWrX42nte+++/rvbdeuXaX1uuOOO9zhhx/uWrVq5Z9ieMUVV7iKior4/DVr1rjDDjvMNW3a1Kel1xFHHOF27NhRq3RuuOGG+OeTX6+88kp8uccee8wNGDDAr6vW+8gjj3R//OMfE77r7LPPTpnOPffck3IfPfTQQ37+yJEjE6ZrLPm1117r9ttvv5Tp6XPRPE+1TGlpaXwZPa3xuOOOS5jfvn17N2HChEq/d5W8DydPnuxGjBjhy4A+9/jjj7urr77a56fea1+2bt3av9cyF1xwgX+68yOPPOLzSPMOPPBAN378+ErpRL9L61NYWOjzVk+G1mufffZJWY5UtqNp6XX55ZfH51944YVV7lOVkWh57NGjh2vWrFm8TF533XUJ36Wyoryz+VZuk/e9jtlU33fppZfGl/nss8/ct771rYQyq338xRdfJKQ1bdo0f0zacvo+TYv6zW9+4w499ND4evXu3dsvl3ws6XPdunWLL6f9NHbs2IRj4Pvf/77/XHS9dZzefvvtldLSvrS0vvKVr/j6I+qEE06olAfat/PmzUtIx+qlVPn1/vvvV7n/tC3ROkppHX300T495ZdexcXF7t13340vo2Ph29/+tq9rLB1tg8pWNK0zzzzTl1ebr/X+7W9/m5DnJ554os9nLaPyqb96MGhyXnXo0KHK7du8ebO77LLL/APOovO6d+/uVq9enZCOyqjy3MqC1u/GG2/086rLJ5Vp1Y/R7du1a5cv3/oelQNbxx//+Md+vtZL+z1VeqeffnrCer3zzjvum9/8pn+omvJDeRA9Dl977bUq1+1nP/tZQlo3pKiDtT9ff/31ao9prX/y+XHjxo1+WdUNyqvhw4cnlAWj9Yzua233P/7xjyrrGNVXyVRPX3/99b7eUn4OGTIk5XftUSxgDz30UKx58+ax3//+97GVK1fGLr744li7du1iGzduTLm8NnfdunUp5/3P//xPbN99942dcMIJfrmJEyfGWrVqFfvv//7v+DIvvfRSrGnTprFbb7011qVLl9iYMWNi++yzT+ztt9+OL3PYYYf5z915552xRx55JNaiRYtYXl6eT/Mf//hHfLm//e1vsdatW/vv0bwbbrjBpz1v3rz4MldddVWsWbNmsWnTpsX+/Oc/+3S1rNYz6v777/ffo3l/+ctfYs8880yssLAwdsUVV8SX+c///E+fvvKooKAgtt9++/nlf/SjH1VKR3817957762UztVXX+3XSfNvvvnm2FNPPeXzaPHixbEvv/wyvtxzzz0Xe+yxx/xyzz//fOyOO+6otH0jRozw80888cTYfffdF3vyySdjv/vd72LvvPNOpf2j/fbVr37Vr9+xxx6bMO+WW27x09u0aePT69WrV6xDhw4+n2666abYF198kbBeWmbNmjWxAw44IPanP/0pVlJSEtu1a1d8mWOOOcYvM3ToUJ9vRxxxhH9/0EEHxc4777xK+7Bt27axWbNmxX7961/HmjRpEjv33HPj237ZZZf5v1rnhx9+ONapUyefD1/5ylf8vho4cGDskEMO8Xk6Y8aM2HvvvRebO3eunz9s2LDYzJkz/ef118rLqlWrYmPHjvXfpbLYsmXLWO/evf0+1T5Jzmfto0svvdSno9eBBx4Y+/GPfxyfr7R03Jx11lmxBQsW+JfySeswZcqU+HJPPPFEbOrUqbHRo0fH09J3rVixIr6M8kfT+/btG7vxxhtjPXv2jLVv395v37Jly+LLbdq0ye9zrYelpZe+13zzm9/0x472p5ZTPmkZHatROmZVD1xwwQV+/qGHHur/Pv744/FlNE/rOmrUKD/P1kn7LkrrrenXXHONX077R5+bMGFCfJnvfe97fpq29Ve/+pWvC7Se2g/R+qJfv35+ue9+97ux/fffP9a1a1e/nMqN0fGlOkR5rfxSWVM5U1qWXyoHqk90HP3Hf/yHn6dyZPm1c+dOX4a1b7SdVt+ovOh4jdZRSuvss8/2dYm+8/jjj/f/q0zYcXLKKaf4fNExddRRR8XOOOOMeF4sX748vu5f+9rXYpdffrmvfzW/R48e/u+SJUv8/Ntvv93XXXYM2HGlfal1i+bVH//4R7+85r/44ovxcqS0te4qm6pvtf7f//73/TYpDeWptt/84he/iB+DyiMdd0pn9uzZ8Xz62c9+5o8V5XO3bt388tqH//u//5uwff/+7//u80HpqCxpH2rZb3zjG36+1kt5o3pS26k81P7W991zzz3xdNauXevLm+rz3/zmNz4dpVlUVBQ/DocMGRI78sgj/b7p2LGj/6t0VK61H6LHztX/3KeqN5XOq6++6s81+p7oMa36UOcjpaNtUZ7m5+fH1q9f75fZvXu33yd9+vTx5XD16tWxSy65xOfJZ599Fk9L5yalcc455/hzi+polZ3ouUN1jPLV6j3ldzLV0/p+5eWbb77pj+/u3bsn1M81EXTQooNIlbHRiUeFTwWotkHLXXfd5XfE9u3b45muiuvwww+PL/Ptb387dvrpp/v/VUBVYQwaNMgfRFYIFBD88pe/jKepdbQKORq0qOCpgoqulwqFDspU26fldbJQhaRKNUrLnHzyyQnbp5Nb9OSutBSwqEKfP3++P/CUjg7c5HSi65Scjv634OmNN96odv8k57kquWuvvTY+zw6oPaWjyubrX/+6P1gUiETXx/JcJzadHJSeToY60LXNyrMoC1q0L2wfRn3++ed+vu1nK1eqJHTSVKCTvA+j6UT3odLR8vr74YcfxtNSpaVp7777rq/g9f9pp52WsB46SemzOsFp/re+9a14eTH6Lu0/TS8rK/PLPfvss5XKkSiwsHKo5ZODFgV50Wkq/ypr77//fqX9of1paWl/aL/Ili1b/Gd0IjQKQLWcKiedVFOxtLQftT+NypmdIGw9VempcoxKPpZef/11//foo4+udCzZ/v+3f/s3H6gpAIiyY8C2UcGV/iooTE7LqPxasKQTa6rvVBkpLS31y+nEEM0vldX/+q//SsgvnZCSy64oLStTquui+WV1lAUHti6p6qgf/OAHfp1s+1S+H3zwwXi+a5ouEuxCxC4I/v73v1dap+S80v6I+u1vf+un66RodavKczSvoumIyqICBH02FZ3wNV/LR0/WVkdbnosCCStHtv0Kgg4++GAfWGo9tA9s+43SOfXUU31gZPWmAoroOUEUBCivdIzpeNA6ad8aHY+2rTq5q961OtiOOX3uD3/4gw8erCxovVXXaV2jZeHYY4/1y0e3MZkuCFW2FaRFgzbtRwVZogs3CwwtHaufovneuXPnhPOEJJ8XolIFLcnnRlEepcr3PQm2e+jLL790S5cu9U1MRs1Wer948eJap6fPqKm2efPm8WnDhg3z3SrWDKZlot9ny9j3rVu3zjetaplVq1b5Zslf/vKXVX5fdWlFt2/hwoW+++Dkk0/285IfrfP1r3/dL2v+9re/+WbO0047LSEtNcWp2VJpqold+aUm2+R0rAnxww8/TEhHlB/WpfS1r33Np6Hm2CeeeKLKvNX6LliwwH9WeWxKSkri6Vjz8bhx4yp9XvnYqVOnlPMszwcPHuy/Q9Qds3PnTt9cqibNVI8iUleMfghs0qRJ7qWXXopP//zzz/3fgQMHxqdpG9U0reZldSHWdB/K3//+d991c/fdd/v9sH37dt9dqG4OdZGUlZX55fQ+Ss2n69ev958XNYOn+i5tw0knneSb4tX03K9fv0rrIGqWrY66Lu+8807frK5mfnULKl3t2+poe9S9YE3cyuvjjz/er4P2mbpW1CytY0jpVkf70H4AddOmTb6Mav/279/fp6Uypi6joUOHJnwueT/Yr8DrGEw+lpYtW+an3Xbbba6oqMiXkyg7BpYvX+7fv/322/6vHXvJ9c62bdvcvffe6/fXBx98EC8fqeonW6+//vWv/q/mK7+07E9+8hPfZaW01HWgbsgXX3yxyh+MjXavJueFunVlzJgxVdZRylOx8te3b9/4MuoOUbrarz/84Q99uVK+y8svv1zl/rv//vvjeRg9nm666Sb/f+fOneP1k9KJHktR2u86XrQO0bSilGfWRWTba9tvea68ta5hq2ts+9Utou6gN9980z355JO+3Kv8q0vI6LtVXj755BNfb+6///4+v3TcRvfF+eef76666irflapuRHU12W/raL7S13dpP6hLTdMsP6Pf9fDDD/vP6xykYQZalx/96Ee+bEXLwpo1a/xxp/RU5nRc3XLLLQl5ru4a1VvarzJz5kxfV6jutuNQaUj0nKfPqKvJvk/HofJKLw2H0D5U3finP/0p4bywJ9Fzo1EeaUhHrc/XsUAp4tfqv/zyywnT1QSnK5zatrSoSdSugCxSVJeT/ldzvOhq5IEHHvD/29X19OnTfROddR/Zd6iZWRGtonU1MSa3tChyV1N+dL3UPaL/dbVv26fWGl2NKerWVWb//v39/8nUjWHN9fqrK6nkvNKVha66rCtJVw3Rq1FLR9tpV7/RdERXCXopj6+88kp/hWHLqgsrSpG0rZO+8+67766UlubpqkBXHHaVouZkoy4UfcfHH3/s3ye3tFie62pHLWOWV5qmKwR9r7bJ6GpPXS26GlfkP3z4cL8eS5cuTcgrtQrpf125WX7oyi7alGn7MNrSEt2Hli9q+lbTuT6vl5qKdYWntNT8rrxUU7daSXSloysgdavos+q+0l9dgVl5Meq60Txts666rVk+ug6iKxlr1UrV0qL5P/zhD333lLrxlC9absCAAZXK2VtvvRW/Etcr2nKmY8G6JdRtohYItXpaM3d13bZ6RcuHuhvtc/qrfWTdHn/9618TPp98LKmlRFfiycfS008/HW/l0rGoq+iqjiXrArUWIGNpTZo0KV5e7aVymqp+UtlQk7vqAi1j9ZO6YfX9alXRsXnbbbf5+eq60Pdr25Op687yJFWrh8qq6ohofZOqjlIXm62Tjie1PKmsi/ap5bX+ajsnT57s/4+2QFp50P62faQWzyjVqWpFtDJsx5G6wpJp/9h8/VXLQzJti32fWhKirSy2/eqG0Dwtozpz/Pjxlbbfjke1tqkuUOuU8lVdIUbHolp4rQ7T9qvFMHrsqNzp3KGWBHWdJLfwWUuH1vn888/3+1frpLRUB1ha2lfqjraypLxUC4+6rHS8RctCs3/WwSq/qktUdvQZHb+W5yeddJKvj7V/rMVXZV/LWh2mVjSVAbX46LvUy6C80/Jal+hxqPVXHthxobxLPg6ra2mxfN+wYUPC9Gi5q6msDlp0QtIBZy/LfHuviq2mQYuaV/W/ToL6rPVhqzDZgW07RumoSdCaYK251ioRrZc+o89G18sONB34tn1qald6OrB+8pOf+INSBSa6ffqcVTL6396rkIp1QagSUDeFtkvfobRUWC0t+5wFNTYeR+tpeaXPFBcXx/NN26T5qmiPO+64lHlu+aT/o82ZyWlpPIeW0UEq5eXlPl19Ppqett/e2wlUwZ0OTJ2AdaCqDOg71W9qXTrah9F1s32o9HQwaprlu/qXrbJSn6+NR1AFaOlY3lg6ti+SgxblncYaaD+oElAAoeVUmSs4UnCnJnVN0/dpfSxQsvTsBK7vUB6LBTQaY6GuMTXhKjCwoEX7WOXK1s/S0Xulb+kkUzBqy9pJQduQnOf20nulpWZtvY+O+7HxUVbOqjouo8eWvsuOJVXKGkujPnDratV4hWhaycdStAxHjyVVzjZWIlXQou+1Y8DKazTv9V2Wlk4mqrQ1rk7pqtld+80uauzEqryxvLeuXS2nMqS8UbrqVjnzzDPjQa2VGeu6iuaVBeTRY8C6fkTp27YnBy3RfRddJ01Tujqhieorqz8UzOtErhOtlrMxQFYeovVWcnnQSVNdquoS0XSNF9FfHUPar8qrVOVK22b1meqUKNVfuiBUfqu8K+i3k7Dtf+Wp7b/oBZjyycqVPqvv0JgwO3kqiFCAausTLQNaN8tXC05VJ+vCSGVC3bPKL6Wp4NNYebH9ZvWFvdf+0nppzJTKt4JJ1fn2vaovdUERLQvun3lkZdXKmv5Xt7DyfOvWrfF10LyRI0f6vxpTEy1T2i7La1tPvex4tfzSsuoyUt2qcSvK12g3cBRBSxUUFSpzkyM6nRx0ohJdfWvcgL2UmRrMZu+j/fWKglVxRDN94cKF/v/Nmzf7k4EK9E9/+lP/WRVcjZ1QYVMfp6bZSTd6VWuVkBWI66+/3q+XrmLVRx9dL12V6iSt9bLtswjXXtG0dHDo87qyGjduXML2aZ4OMl0taJCWrUtyOnrpu7ROlk50nSwdGzyoyFzLRGmallH+VJfnqhiiBT1VWnZQR8cKWGUezUv9r5YJXXnqvSoP6wu2wZmqfK3/v6Kiwu/D6LrZPtQ6KEjRtGi50mA0uzJQ5aExT0pLB66WtX1o6Wia7UMrR3a1Fx3oaydObf8nn3wSn64+Z+WfToYaJ6JlNGjNrgytvNhAOvVFa13tak2Vla78NF0nF5UjBXPJ5TCah9FBjKIrRqVjJyIb0Ku0LN+0Py0NlX0F6FonLatpNuZEAaQqRb2ifeLRMhJNS+tqx6UuFKxFw2j7dTzoij2alu0HjRvQZ/S9VR1Llg/RE6PmqZVHy9oxYJW86gA7BnTCTFXvaJpOPmrt0rEvujDQd2ibdEGkzygtpauWIJVFK7sWXOjkq32vaTox24WC5ZW2Ty0DdlVt06MtWAoGbBstXdU5Sk/L2neqxdXWSdPVOqDgV0Gq5c/gwYPj6dqYOasvouUhmldqmbDyoHKpPI7muf1VeVBepSpX+oy2T2lFx3LoIkYXOVov1SOqe7VPLVBUGlZH63jUcalpqgN0DCqfrI7W8azvUcBo9YXOG5qnfafPWV5HT+bR9xoUHd2+6HFlF2dWXmwwsy0bbcmydG1Auy4uFRQoiNEFqy5oomWhsLDQ16XRbVTZU15o3KKlmWqd1cocLVN6KR19/pVXXvHvVVYuvPBCv5yNqUsec2flJ1qvVRe0WL4nj19UvkcH9Gb1mBb1w6lf1sYxiPoK9d762HVblm7Ts5eoj97eR/vr9Rn1A0ZvAZ4/f76//Ve3KqpPXeMx3nrrLf9Z9VsqffVL67Y+TVP/qvr8ND5By8+dO9ePl9Cy8pe//MXfwqfPqY9cn42ul9I+9thj/f+2fbq9V/3reql/VX2v6nfU++9+97v+8yonGjcQ3T677VrzdBub+svPOeeceFq6HVnfEb1l09KJrpOlo1snReunPlWjvln1d2o8im5lqy7PlS9KyySnpTEc6qu2PlbdxqoxBep3tpf6dzUGRv9r7ITGXRQUFPjxApa2xgO8+uqr/nvVP679p35a7ZPoutk+VL+w9p2mRcuVbk3UNn366ad+fI/yUDRGRcvaPrR0NM32odH4FZUpG3ugfuTZs2f797o92vabaN8qHd2a+8ILL/gyqTEqor5pKy9axsqn+vPtGFD5V/qars9q+zWuQXkYvZVTty1ruvJQ3xml7127dm28v1vbX91xo/2uW2O1Tsccc4zfLpXzBx980F100UV+7Ij2gbbJRMvIc889F5+u77L0Ve7ExvQY5WX0B9yUlsrAnDlz/Ngv0XpUdSz97ne/i2+n9qPS0vGgW6u1rPJQx5n1syttOwb0N1W9o7EHeuk2aMs3jZfRcroF94033vD1gsZwaNzPKaec4sui5iv/LC3lo922qv5/jX+w/aVHEWj7tD5al+9973vxPFRa0TKUfBu3lQctq7pK47xUJrROSk+fV35pTJGN6VI+aEyQjZ+xsS/6jPJAeWXrZeNclJby08qDxukoXctzlQvR7c7aF8qrVOVK36HtU1pWd+qY1lgmfbfGNtmxrjrL8lxpWB2t79dnNU2fVZraTh3nqi+0PXYbs9UXyjeVQe07fU5lWceJplm9qXRVbvS/xmtp21UvaJyL9qXS1viWZ555xq+T1ldjQFSXKM+VF0pPeaWxJaNHj/bjFcW2SeN5dHu01kflSuNHrCwc8s99qDE6to36TtVR2j6N/9P22/pqTIwoP5WWHc/RelDpaN01vkT5uWLFCnfeeef55Wy7orc3W/lU/tT0pwst36PHjeW7na9rLBYwNc3qCki3m+rKTFGpomON0k+lujEtujLQVYr6bLWcWl50tRm9E0lNZbqi0pWXIlNd3SXf8mxdQbrFTFceaq6L9qMn3y6r7izNUx9v8q2qavlR+hpxrStu67LQ55LHNtjo/kWLFvmma12ZR/sKdVWiddeVuPpe7SpCt4Amp6MrVc1TxJ+cjt3BpHzSuAW7vVQvXeEbfY/WQ9M1Ul55pu+3UelqvtRVk6481LWhfLZ10vuqJI9pEfXDKp+07mrF0D6wW571nbrSNLqtUnlpVyfafmu1Meq/1zRtt+540XrpvVogot8dveVZ45fUDK/l9NdaiezqTVeNuvqwfn2tm5rL1eetKyzdLq07R/Q5XXmoXGssjHX1qPVC5VFNyWqyV5+zvkv5arc8q5VKyyWXI+W1pWOtNt/5zndiH3zwgZ+nK2xdoakbRtun71Z+JjfNq3tS4w7sFld76apYaYnKu11B6nhUM7O1GEXvqBA1qdv4Gb1sHIylZeMO1DyuZm21pmk5dbtGr9jsFmx1G+mvjil9LjrGSleY2qaLLrrIL6NxVHabfJRabTTfbtNVHaOrZt3BYVeM+h7lsdKyusCOy+idTWph0XSVSa27WkpUVqwOUquGWjhU76hVQi0EWkelrbETVl+oO0UtBapPVK7VgqRyY2OWjLrKo1f+Kj92e7DVUUrLupCVltVRdsuzxjmonNu4C3WhWf7qWPr5z38e/z51baoc6043zVcdo8/MmTPHz9c6aj+prGm+dRWqJUv5Hs0rlS2rd3T8673SUh2icqK6T2VFZUQtAyobeq90VOcZ5bHyT9+petdueY7eXm3dnzpetG/UcqP9oy606Papfte4P03XPlO3iPJArXFWftXioO5jTbOuaNUZ0RZM605RGda6a39rHbVN2u+W52o9Ur2p7Vbdo3VUl1W0LIj2hz6vZbR+OvatpVJ0TGsbddxYF5dadZWOWraiXUdqhVedqBYbHaPRsm7smNbt7SovylutY7T1RWlqX1u9l3wsWz2tutTG2uj8lnO3PIsqe+0Q7Rw1p6mJqyrVBS12K2TySwU3SjvZun/UxKmTQfJ3VPWKFjz7TgtEVLlH7+0XHbwq4PZ5VXgqMKpQonbs2BG/l175oLRUYKPfp8rXxmVYM6UOtmiBsXTseQs6wSano6bfVAMQH3300YR1UoVkz9XQeitA0ToYVbgaEJrcfaZgsTqpghZ1aagJNdoHre1UZaMuo2gTpp7joO2zsSyqFHQgRul5KjZ4OrqNGuibah+qQlJlpjEB9nyPVF0x1ZUNjb2wMQKqRG1Qa/JLXVT2jApVANpmnXy0vrYOyeWourKt/aAm2mjfv/JG+12VcnJ5tMChquNE5SnarF7dsaTxBNUtp5OeDSpN9TJVzY/mgw1aTn7Z+Kk9paX9YWMUdCK38Q1W1lQX6KQRLWt7Wi91NykwjOa9yokq8+gg25psnyitVMvpZLOntKIXZ+qiUndG9HhSmVDwHz0Z72m9qspzBRPJeaWypROw5qvO0zGggKW68muv6Dg5eyaOzdMxFQ1ERAGRjheVUwvyVDfqQjO6feqO0kWN6iVtv44tnWvsmT3VrVfyeUbdj6oPta/1UoAQveVZea5gIVquFJyqPkwecH3OOecknBcUuEW3Uce0XdSkWrfos5c06NzyXdumQdjq0orSeUH7IzouR7esR+vCmpw/VU9fd9118RsklKZuOqgtP3igdm0zAAAADS/YMS0AACC3ELQAAIAgELQAAIAgELQAAIAgELQAAIAgELQAAIAgELQAAIAgELQAAIAgELQAAIAgELQAAIAgELQAAIAgELQAAAAXgv8DFc3bd3NANTAAAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = df['Pos']\n",
"y = df['Elev']\n",
"\n",
"import matplotlib.pyplot as plt\n",
"plt.scatter(x, y)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "25951cb0-9afe-4aa5-b83f-095f0f931cfb",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"def kmplus_to_meters(value):\n",
" \"\"\"\n",
" Convert a 'X+YYY' string to total meters.\n",
" e.g. '2+395' -> 2395\n",
" \"\"\"\n",
" if pd.isna(value):\n",
" return None\n",
" parts = str(value).split('+')\n",
" if len(parts) == 2:\n",
" km, m = parts\n",
" return int(km) * 1000 + int(m)\n",
" else :\n",
" return pd.to_numeric(value, errors='coerce')\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "388dc873-0962-45db-b996-7f78dc7933bd",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = df['Pos'].apply(kmplus_to_meters)\n",
"y = df['Elev']\n",
"\n",
"import matplotlib.pyplot as plt\n",
"plt.scatter(x, y)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "2671bb3e-83d2-4934-bec4-a7b5964bc1c7",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"def kmplus_to_meters(value):\n",
" \"\"\"\n",
" Convert a 'X+YYY' string to total meters.\n",
" e.g. '2+395' -> 2395\n",
" \"\"\"\n",
" if pd.isna(value):\n",
" return None\n",
" parts = str(value).split('+')\n",
" if len(parts) == 2:\n",
" km, m = parts\n",
" return int(km) * 1000 + int(m)\n",
" else :\n",
" return pd.to_numeric(value, errors='coerce')\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "83b93761-2879-4a9d-b3c4-f83320e2eac1",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"df = pd.read_csv(\n",
" 'raylway_jakarta_bogor.csv',\n",
" sep=';',\n",
" skipinitialspace=True\n",
")\n",
"df.columns = df.columns.str.strip()\n",
"for col in ['Elev', 'Tracks', 'Platforms']:\n",
" df[col] = pd.to_numeric(df[col],\n",
" errors='coerce')"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "30520fab-de5e-4828-a543-7e1d6eb1b6bb",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 460x460 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = df['Long']\n",
"y = df['Lat']\n",
"t = [i*40 for i in df['Tracks']]\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"plt.figure(figsize=(4.6, 4.6))\n",
"plt.grid()\n",
"plt.xlabel('Long')\n",
"plt.ylabel('Lat')\n",
"plt.xticks(\n",
" [106.78, 106.80, 106.82, \n",
" 106.84, 106.86])\n",
"plt.ylim([106.78, 106.86])\n",
"plt.yticks([-6.8, -6.6, -6.4, \n",
" -6.2, -6])\n",
"plt.ylim([-6.8, -6.0])\n",
"plt.plot(x, y, ':k')\n",
"plt.scatter(x, y, s=t, ls='--')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "c7492fc5-303f-466d-a86e-24faa1b98590",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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Qogwtl5Tojl3MwJpD8WqQOxEVYcmNGqw2Yd6YN7odbu4ZpS5X1HolHU5kNhITKfn9umwJMjIyMGbMGPP+5ORkNGvWDJ06dbLo/v/UU09h0KBBWLZsmXlfbGwsPvvsM+zYsUP9ri8wqImbVx6IrfTfoDcYEebLZXWI7DrcVqxYgT59+sDd3R3+/v4YO3Zsucfm5+ereQHlQ8bT01NVH91xxx24cOFCnT5nsm3STf+V8Z3xxZRe8PNwRmVmyArwdMaTo9uja5Sfei26uRWFi5TaTp48iXXr1lncJioqCo0bN0bHjh3N+zZv3ozp06dj9uzZ6vd1hy/iwIU0pOz+A2vXroU+s2iQekUWbT6N4xfLHr9Xl05cysBjP+3DhA8345aPNuO5Xw/i+MX0+n5a1EDZTbXkkiVL1AfBSy+9hGHDhkGv1+PAgQPlHp+VlYVdu3bh6aefVlVB8o16zpw56lu26ZsykUkjP3dseWI4/jxyCQ98twt55dRTSmeOSH8PRFyhtKTTWb61vvnmm1LHeHl5YeTIkejcubP6fXdMCnSODkja+B3e/T0RUXcsgGN4YdufLOmTGf03XCPawrPtQPN9GHIz1WB2DxcnvHlL13r7h6bl5OOrzafx065z5jbBnWdT1Pn841+DOfUY1Tm7CDcJMgmmBQsWYNq0aeb97du3L/c2vr6+WLNmjcW+9957D71798bZs2fVt+iy5Obmqs0kLS3NXBKUrTjT7yX3U2n2cK5kxNzwNoGY2r8xFm4+be7Sr667fLljhDdeuamjek3WlHxJk810XnJy8+DioIdvm76IyD2LgpBI6J0KHz8zZi/Sty8DOgxFQIcB5vs48elMFGRn4MKLXyE/v4N53brff/8dPXv2xPXXX28+Vl7Xrq6usKbU7Dy8svIw9p1PVSuR6xyAoqGHRlxIzsBdX2zB/Os7oJG/u9VDzh5eV2Rdlf1f20W4SQns/PnzcHR0RLdu3dQClF27dlVhV7ya50pSU1NVO4g09pfn5ZdfxnPPPVdq/+rVq+HhUXYrf8kQpfLZw7lqB+C13uVdm4xD//yFQ7XwuFLu6iqP23t6sb2FPSf3ujbHbt+xqj1vSO8C8xI8N2enAQYDRoXnYuXKlWr/0qVL8eWXX2LIkCHqPWNy5513qg+GV199VVWTimPHjqnFW1u0aKHeUyYyZ2Zlpwwb5A4MKj2lZjEJiN7+F6LRsF9XZB1SK1cZDkZ5Fdu477//Hrfeeqsqbb355pto2rSpWnRSAufo0aMICCh7Xr/icnJyMGDAALRt2xbffvttuceVVXKTD4KEhAT4+FjOcCEfFPKmkqqlkgtakiWeqys7Gp+Or7ecwcp95/BCTwOe3uGIXEPFAWPQ56MgKwUv3T4E43sUBpa8JpcvX45evXqpdmYhJU1pe5a3e0xMDEJDQ9X+119/HU8++SRuv/12tUq5SZMmTcz3Jevgid27d6t2QqnmHzhwILLz9Biy4E9kVWIZIvkrmgV64pf7Blh1nk2+rhqetLQ0BAUFqcJKyc9kmym5Pf744+pbZEWka7XBYFCX582bh/Hjx6vL8kaMjIxUqyvPnDnzim+AW265Rb2xP/zwwwqPlWqbsqpuJLzKC7CKriOeq8rqEBmA+0e6Y9me8+p3CbbcgisEgYMLAkMiMbaHrBheWB943XXXqa1kG6C0O0uth3SuMpXopMOVBKD05DS9huX9IsfJ+0VWMjftX79+vXrPyvFXXXUVNp5IRnKOAec+ulsNeA+Z+IK5R6npO3PxIIu+mIULafloWoNZW8rD92DD4VzJz9p6Dbe5c+diypTSE9MW17x5c9VlumQbmwSQXCftZ5UJtjNnzqgebBUlPVF9k9KQn7u8ea9cGhIj2oXiw//rfsWJoyVkpB1atuKkTa54u5wpCM+dO6cCTr4hm7Rs2RITJkxAv379zJ1IjAV6FKTGq98dXYrmvsyK/gsZ+9ci4Jp7LYZQpGazbYzqRr2Gm3wrlO1KevToocLsyJEjqjrEFFqnT582V59UFGzSriDfOgMDA636/ImsrXmQF3o3k9dpYemtIsHernh7YpdKrYhQFRKEUrqTrTipNTHVnIieTQMwtE0o/jfjExRkJsPRvXBwugRe8p9foiD9klqh3LQ+nkwk7e1m+ZFjMBjheHl+zO/+OYuWIV5qWjEuEUQ1ZRcdSqS0NWvWLMyfP1+1f0mgSWcScfPNN5uPk/Y06RAybtw4FWzyLVM6o/z222+q8V2+iQppo3Nx4Xx8ZHvkg/6V8Z2w5o/CcCveY9PEtK9blB90Vg62qg6fuG9Ea/x1PBHO/kVB6OCkQ+ikF5H2z8/w6T1O7ZMhDr/d3x/NLldJnk3MwrvrjmHHmWR0buSDuLQcbD+drNbmaxHsiRmDm6vFaRlypOlwExJmUl0yefJkZGdnq8HcUs0og7lNpGQnjYxCeldKo7oo3gtMSClOBtsS2SJTSeyXewbg+50X0CHCRwXaEz/vV6sZ3Ng1ApP7NVGrHNQ3N52TKo2l51gOjXAOaITAUfebfx/eNhgP3X27mqps4qy5mPHdQWTmFaiQPpWQaXHbk5cy8diS/SrsFkzozIAjbYebNCJKzy7ZylO846f0qLSDjqBE5WoR4oVnxxSOXRMj2ofC00VnU8vcyNg1X3dnZF0OqrLI0710Yr+aYUiaF37Xd0KeV6gqpZXFtFtmXWHJjRrE9FtEDZmPm7NNBZsI8HTBT7P6o11YYXtb8ednujykdTC+evIONbB89uPPw+Abbg42Y0H5HUz2xKRg3eHCzipEmi25EZFtkombf71/ILadSlKdQq7tGIYP/jyBjo18cXufxugQUdhDc9SoUQjv0BdL39+kftenxiPum0fhO+BWeHW52rx+XnFP/rwfdw/KxN2Dmtf530X2jeFGRDUm1Yd9mweqTYzqGF7qmIxcPf677Yz597Qdy1GQkYjMQ3/Cq8s1Zd5vXFouftsXy3CjKmO4EVGd+OvIJfyw45z5d/+rpkLnEwz35j3NbWtlDf5Ozsrjf4iqjG1uRFQnZOWCkiue+/QaC+fASPO+9F2/IeGXV1CQk1Hu7YgqgyU3IqoT0gYnfUzK6yUpgZby91cw5mXDrWk3eHcdpTql9FGD2oGc/AKsOhBnXruuVagXhrfmxAxUNoYbEdUJmVHl2o7hWHUwrsxhA05uXgid9BIyD6w1t8HdPagZ/q9P4SxEv+y5gMeW7FMDwk2rkLcOdse9Fa5IQA0Vw42IrCYxIxcv/HYIZ5OykKs3qHkypZPJXQObwctVp8JKZiWJT8sp8/au4a3UJtqFe+Pewc0wcepsRA2+GatjjOZQM5E15ERefoGa5IHj4siEbW5EVCPSCWTLiUTEpmZj4cZTWLE/FrvOpuDghTRsOpGId9cdx9roeFVak1lVNj52FV6+qZOqoiyPk4MD2ob5YPrcp7Dqx0X44skpFY6Ju/69TViy68rzcVLDwZIbEdUo2J7/9RAWbT4Nh3La0/IKDJjz/R51nFQpfj6lF27t3Rh/H72E3w/EmefKlKwzdZIc1SkM829ojxv294Rro7bw7n4DHJzKX+okNi0bn204ifHdG7H0RgrDjYiq7c01R7Fw82l1+Uqz3SVmFnbpH//hZjwwrBXy9AbcP6wFYpKzkZVboIJNSmuTekch3NcdR+LSEaP3Ruhtr6qelcKQn4PsEzug8wuDa1hRY5s89uG4dBy7mIHWoYWzpVDDxnAjomqvHC5VjlUlbXELVh9RVY9rD19EiLcrXhzbEVd3KFr3Tew8EI3MwxvhGtEWOp/CdeWyj21Dwq8L4OTpj0b3flXqvuNScxhupLDNjYiq5YftMTWa67LgclEvPikVd7z4Be578V2L69999hE15i3nzB7zPtOYOM+Ow8qsfnTV8SONCvGVQETVMrR1cLkrAZQnP+k8Mg6sQ178yaJ9KXGI/34ePnvpCYuVPIYO6o92nbvBQVe09qJLaAs0eew3+A+9q9R9vzC2A9pH+PC/SQrDjYiq5c+jl8q9Tno2Zp3YjrR/llrslwVME1e8icwjhZMnC+eASOgCIuEU1Rmrdp9Sg7WFLDy8YdMWNO454orPpXmgJyb3bQpvt/I7nVDDwjY3IqqyzccT8OveC+Ven7jqXWQeWKcue3YYqtrIhLSfSelN5pQsvnJ3o+kfqctzlx3F3Qn5eOjqNup3WScuv8BQ4cwmItC7qHRHJFhyI6Iqk1W0L6bnlnu9W+Mu8Bt6FzzaD4FRXzQ+zavzSITd9oqaWqssEmbfbD2rAk1EBXjg27v7qgVRSzaxqaEDly8/ek1b/hfJAktuRDZK2p9ks8VZN8J93Sq83qvTcIvfMw6uh843BG6RRSuLlycpKw9rDsXjuk6Fy+Z0ivTF+oeH4qed5/DlljMY2S5Ejatr5O+BKX0jgeRDaBfOtjayxHAjsrHu9f/degrdAHR9fg2eHtMJTQM9cfBCKrzddBjTpRHcbWCW/A4RPmgW5InTCZm4UpeS3LjjSPz9P2owWtj/LYBreOsKj5eB3vL3msJN+Hm4qDXdTIuWzhvdXvXUzM/Px8qVh6zyN5G2MNyIbICU0KYs3I6/jl6ChzPQrWdhV/knft6vrje1OcksHzK7x4MjW8PTtf7evlKavGtAU8z/5WC5x0gvyPQdv8CQmw2P1v1h1OfBJazVle8bQGp2+VNtiZoMQaCGgeFGZANkGiqZn1GU1b3etEvaur7YdAqbTyTgh5n96q13oF6vx7hujeDn4Ywnl+zH+b++Q9bRLfDpMx6ebQeqYyTM0nf+CgcXd0T960egIL9SVawhPq549oYrV18SVYThRlSPpMehtCVJia2yJOjOp+SoLvO1GW5SmkxISFCBFBRUOEPI6dOnMWLECKSlpSE+Pl5VkwZ6uuDaX/+DvLhjyL94Crgcbs7+4fDpfVPhwGujwWK8WkUCPF2gc2JfN6oZvoKI6oksuvngD3vUBMJVJdV2d3+1U83EX1MFBQU4cuQIVq5cabH/vvvuQ0hICD744APzPvn95MmTuHTpktpEk0BPBHS/BsHjnoRX18J12IRMdOx/1VR4db7aPDdkZZxNysalCnpiElUGw42onny5+bTqjFG1OT6K7I1JwYBX1iHm8ppmlXHs2DEsWrQI69evN+/LyspC27ZtMXr0aCQnJ5v3N2nSRJXaTCEmPDw8sHHjRsTGxiI4uHCsWqS/B3b8ZzZCOw+GzicENSGVlqE+rgjy4rg1qhmGG1E9yMjVY/HOmCpPX1WSAxzw7bazpfYbDAa89dZbmDlzJjIyMsz7f/zxR9x1110q4Ey8vb3RunVrdO/eHYmJhe1+Yvbs2cjMzMS771rO+di/f3+EhYVZtJ8FebliwYQu6BLpW6O/R87GlP7NbHL4A9kXhhtRPdh0PAG5+YUDlWtCelR+8sNyTLj5Fjz//PPm/Y6Ojmr6qk8++QRHjx417+/RoweGDx+OTp06WdzP4cOHsXPnTrRs2dIi9Nzd3Sv9XEZ1DMNrE7rAq5pDFaQHZLC3K27sGlGt2xMVxw4lRPXgmg5haryazMhREUNuFhxdPcy/X1r2CnIvHEHIzfPhEtxU7UtPSsCSXxcjLvYCnnnmGfOxs2bNUp1C/P0Lp74So0aNUltJ1ioptQ71QpMgT0THplU4XVZJnq5OCPR0xSd39KjXIQ6kHXwVEdUDfYGhwmCTqsTzP7wNfV6Omq7KfLv0SyhIv4T8xHPmcPNp0h6T5z2P64b2s7iP4iW5uiIh+cz17TH1y+3IyTdUqtq1kZ87vpjSCy2CPdlLkqyG4UZUD15aGV3h9VKVmHliJxw9LKeVkqVepOehc1AT876po3pj3ugpsBV9mgdi+X0D8ebqo/j9QCycnRzVAqUlyThsRwcHzBneCm3CuHo2WRfDjaiWvLP2KBZtPoOsPD10jo6qZDK5X1NM6BGJxMy8Cme6l84dIdfMglO45YTAblEdSx0rkwrbmhbBXnj/9u64mJ6D4/EZqqrySGwaZny9E846R4T5uGFSryjc3DNKjWsjsjaGG5GVyJgzmY6qRYgXdp5Oxs6zSbg8ub20nmHvuVRcWBWNuNRshPu6WwSbtI1l7l8DjzYDpL+92ufX4zrkFly5LUwmGb5v2JWntaoPId5uajNVPx7797XsCUl1guFGZAUr98fi4cV7VfXb6kPx5R53KT0Pr68+Cmcny9BK+ftLpG39CZmHNyFq4tOVflwp/Q1sVTh7iD1gF3+qKxwKQFRDS3aew73f7kJ2XkGlx63lF1ge59FmoJqD0b1Zd8Ch8m9LuReZSJmILLHkRlQDMr+jdA6pzlBso0wk7FTYXuYa1hKNZn4GJw9fODgYK11qG90pXM0QQkSWWHIjquFMIzKQuqoyD2/E+U9mQp9aVIUpwVZZjQPc0atpAF4Z37nKj03UELDkRlSGPL1BTWyclpOPvs0DyzxHsqDmJ3+fREpWxWuPlWQ0FCBt2xIUpF1E2vZlCBgxU+2XVrjKxOSQ1sF497Zu8HbVsQ2LqBwMN6LLMnP1avkZ2WSGDb3BCBcnR9zRrwnCfN3ww44YtbyLtHHJVFNPLzuAPTEpVT5/Mk4teNw8ZOxdBd8Bt5r3+7g7q9n+ncqYLcQ0bEC6/T80sjV86mkdNyJ7wXAjumzLyUQ8/9shGAxGcwkqr8CAzzaeMp+jEw7A1pNJ8PdwRnIVSmz6tIvIu3gKHi37FL7xfILgN+j/LI6RYHvhxg44n5QB6E9g4ZReeG31cZxMyECrEG8VshKqrrrqzd1I1JAw3KhBi0vNwbxl++HlosMvey9c8XhTZ8iqBFt+Shzivn4YhtxMNZWWa0SbMo/TOTrgYnou5l7dBitXnlBtasvuHVD5P4aIzBhu1GCdT8nGhA83q0Cp6dIzFdH5hsC1UVvVecTJq2gS45KMl8OWiGqO4UYNtgv/5M+31UqwSUktffsyVe3o6OYFBwdHBF0/V3UZcXQpnK2jPLUXsUQNC8ONGqS3/ncUpxIyUY1e/BWSabQuLf038i+egpOnP3z7T1T7HV2uvC5aiLcrhrWt2UrWRFSI49yoQcrJK7BasOUlnIXRaDBPL+XbZzzcmvWAW5OqjUEb1CoI13UKt86TImrgWHKjBumf08lWuZ+EFW8i88A61bXfo3Xhemoe7YbAs/3QKt+XTKZMRNbBkhs1KPkFBkz/aocax2YNTt7Bqi0tL/5kjSYHdnJ0wIlLGVZ5TkTEkhs1MEfj09USMdWRfXoPUrf8iMCRs+EcFKX2+fQaC68OV8E5MLLGbXWzh7ao0X0QURGW3KhBOXA+tdq3Td/1G3LP7kPqtsXmfU7u3jUONinn3XdVS3SIqPzckkRUMba5UYMSn5arBkvL1FpVmV3EkJsF3/6ToPMJhk/vm6z2fLxcdejTPAD3D7fNxUaJ7BXDjRoUQzW6SCavX4isI5vgP3y6eZLjmpLFSh8a2QY3dW8Efw8XODuxEoXImviOogZFqv4a+VW+V6KxQA8YCtRlt6gONX586TgiZGotaWML9XGDi45vQyJrY8mNGpSR7UOx43QSPv67qHdjRRycdAge9yTyky/A2T/CvD997x+qV6Rnx+Fqlv/KcHZ0wMBWQbi9TxNcxcHaRLWK4UYNToh3xVNglaV4sBVkpyF5/Rcw5mbCwcUDnm0HVuo+ZJh3oJcrRrQPrfLjE1HVsD6EGpz1R+JVD8XqcnR2h1//SXBt3Mk8cLtSjMZanaCZiIqw5EYNyoWUbBy8kF6jCYoddM7w6T0O3r3GVmnAthzr7ca3HFFdYMmNGtRKALd/tg1pOZVfi60kfVoC4n98BvrUi1WeiUSGH7QL96n2YxNR5THcqMF4Y/URnE7MrFHVYNIf7yHn1C4k/v5OlW8rpbZOjThQm6guMNyoQdh/LhWfbThV45UAAq65F25NuyHw2vurPARg5ZyB6BDBkhtRXWADADUIrUO94OfhjOSs6ldJCpmhJHTiC1W+Xb/mgYjy96zRYxNR5bHkRponkxKvP3Kx2sGWdfwf5F44Uu3Hl3Hb55KzYGBPSaI6w5Ibad5//zmLt9ccq9Zt8y6dRsLyV2E0GBB2+6twDW9d5fuQTHv5ps5wvDw7CRHVPoYb1XupqrproFXW3pgUJGflVeu2Op8QuDXpAqM+Hy6h1VuS5tFRbdCvRWC1bktE1cNwozofZ9Yk2Bn7z6di0sdbkJVXoMacuTg5okWIJ5beMwBuzk6qCs9aJZ09MSlVWgWgOEdXD7XKtlGfV+lptoSHS+Gx9w1riXuGtqzWYxNR9THcqNZdTMvBN5tPQso9t326Da6uzjh5KdPimLwCA6Jj03HV63+ie2M/7Dufiv4tgnDXgKZoG1azHoaX0nOrVR3pEtxUlSwl1BxcKj/Zsq+7M1Y8MFAtZ+Pn4VLlxyaimmO4Ua2RYFi88xyeXX4QRoMer/QCEjJzkZtWfhVhbGoOVuyPU5d/SjmHH3fEYNaQFpgzvJUq0VVHdca1JfzyKjzaDIBLeGt4tOxdpdum5+Qj2NsVrrrqPV8iqjn2lqRak5iRh/nLD6qqx+rUCkooSZPcR3+ewJ0L/6n283j4mjZVvo17i15I3fy9WsetquRvPRqXUeXbEZH1MNzI6lKy8hCTlIVpX25Hdl7hWmg1Ibl4ODYd0bFp1br9pF6NVXf8qvDqei1CJjyLwFFVG6wtAj1dVOmNiOoPqyWp2vM0SolMMsPdxclcZShh9n+fb8OphExk5tY82ExSs/Mx6ZMt6NMsEPNGt0OTwMoPiD52MR06RwfkFRgr1daWefBP6HyC4N39+mo91w6NfNG/ZVC1bktE1sFwo0pJyMhFVm4BogLcVTvYY0v2W1zfPtwHK+cMwmt/HMbB82k1mnW/PKnZeqw9fBHxaTn4+Z4B5lWtryQlK7/MYDPkZSP33CG4hLeCk3thp5X8S6eRtu0nuIS1rHa4FRgM0BcYoHNixQhRfWG40RXnZPx0w0ms2B+rqtuy8wuQnqMvddyh2DR0eGYVMq1QDXmldri951KxcNMp3D2oeaWnvor0d0dMQrpaWdsk/vunkBd7BIHXz4VXh6vUPteoTvDsNEKNbauuTccTseZQPK7tFF7t+yCimmG4UbnVjm+uOYpP/z6pxptJqFy8Qpf62g624l5ddRhjuzVCkJfrFY89dy4GsV8/jPNnzqDRPV+aB4y7RXWAISsFMBQ9b513IIKu+1eNn19Hzv5PVK9Yb0LltlMt2nRaVS/a4urR8pR2nE4utX/37t148skn8c0335j3hYaG4sLJwyjISII+6Zx5v9/gO9Bo1ufw6jTCqs/N21WnSopEVH8YbmRBOoLsiUnG1EU71MBqWyWB28THEatXr0ZmZtGA8I0bN+Lll1/G119/bd7n6uqKn5f8jK6PfAPnwCjz/uJVlNYi7YDXdwmv1enEiOjKWC1JZudTsjH+w83IzNUjV297wWY0FFhMgTV0QF9cPHcKv//+O0aNGqX2jRw5Enfeeaf5d5Nrrx2FC96n8OJvh2qls4uJq84R/9e3SS0+AhFVBsONzLOJzP1xr+pyb2vVkPmJMUhc+R8Y8nMQMfU98/7sgJYI1OchLa1o/Fvbtm2xaNGiMu9nQo9I/Od/R5FWRocYa2jk547f7h8If09OuUVkl9WSzz//PLKyskrtz87OVteR/fl221lsPZlY78GWG3sMyX9/haxj28z7HD391Xpq0k2/ILOonS3omnsw9d0VuOWWWyo95+MLYzsiwtetVp67jL9jsBHZcbg999xzyMgoPb2QBJ5cR/YlI1ePl1ZG1/njGo0G5JyTeSeLeitmn9iOtC0/IjP6L/M+JzcvBI97AhEzP4Ojh595v8HRGWcSS3/JqsiYLhFY//BQ9GkWAGtLyqzesjpEZCPhpmZKL6PBfO/evQgIsP6HBtUuD2cndKrjrusSaBc+uwfx3z6GvLjj5v3uzXvAs8NV8Gjd3/I5tu4PZ7+wUq87GXdXFXJ7V2cnPHN9e9U+Zi3TBjbD7X0aW+3+iKhmqvTu9vf3V+ElHxCtW7dWl02br6+vasyvbBVRdaxYsQJ9+vSBu7u7ei5jx46t9G1nzZqlnvfbb79da8/PXiVm5mHX2dLd6q0pNyEG69evN/8uHUNcQprBwcUDBZkp5v2uEW0QdP1ceLYdWKn7da7mLCDNgj1rXHpzupyzDwxriadGt2MPSSJ77VAiwSCltqlTp6rqRwk0ExcXFzRt2hT9+vWrjeeJJUuWYPr06XjppZcwbNgw6PV6HDhwoFK3Xbp0KbZu3YqIiIhaeW72TCY4vmvRduRXYt7F6spPjsWFT+7Be05OaHp/b8C18HXjP3w6gkY/CAdd9TpguDs74qPbu1frth4uOiy6qze+/ees6kEpi5lWtr1RMu3qDqHo2TQAHSN8uco2kb2Hm3SxFs2aNUP//v3h7OyMuiBBNmfOHCxYsADTpk0z72/fvv0Vb3v+/Hncf//9+OOPPzB69Ohafqb2Z++5FBy/aL3lWaQUlhn9NxwcHc1zMzr7h8M1vJWq+kzIyTCHm84roMazgAT7VL9ziMy8MrlvEwxrG4KFG0+hQ4QP/jmdhLXRF0vNxiLTWErJXwJw1tDmeGxUuxo9dyKywaEAQ4YMMV/OyclBXp5lQ7qPT81WTi5p165dKqQcHR3RrVs3xMXFoWvXrirsOnbsWO7tDAYDJk+ejEceeQQdOnSo1GPl5uaqzcTUzTw/P19txZl+L7nfnhw6lwxPZ6iSizVkxB1G8tpP4OQVgMCe18LBobDasNVdr+Gp3g54eocjcq30WPEpWTgWm4KmQZVfIaAsIZ46PHZNK3X5+k6h6BHli2W7z+FCag4MRqOq+mwR5IWOjXzQNcoPvZoG1Or/XAuvq7rCc9Xw5FfyfVGtcJNekY8++ih+/PFHJCYmlrq+oMC6cwyePHlS/Xz22Wfx5ptvqurPN954A0OHDsXRo0fL7cTy6quvQqfT4YEHHqj0Y8nsFmX1+JSZMDw8PMq8zZo1a2CvZBnPl3pW77Y7d+5UA6j79u2LESMKp7DK79YFLx7rgl69euGa7nnFSveFDVQv9LTm4PBMHPrnLxyCdckzvjlU5u0qvjcdyIxF4mFg1WHUCXt+XdU1nquGI6uMYWhWCzcpCUnngA8//FCVjN5//31Vsvr444/xyiuvVPp+Hn/8cRVAFYmOjlYlMDFv3jyMHz9eXV64cCEiIyOxePFizJw5s8wP3v/85z+q1FeVqZCeeOIJPPTQQxYlt6ioKFx99dWlSqTyDULeVNKRpq6qaK3tX9/vVsvIVKYspbrsOziYS2NJW2KQsGMHDl7MxWqfay4f5QSMfhF/A/h7d9FtXR2NKtgKS24OVpnm6qZukZh/w5Wrpu2NFl5XdYXnquFJKzZpg9XD7ddff8VXX32lSk533XUXBg0ahJYtW6JJkyb49ttvcfvtt1fqfubOnYspU6ZUeEzz5s0RGxtbqo1N5guU686ePVvm7TZs2ICLFy+icePGFiVKeUzpGHP69Okybyf3K1tJ8iFT3gdNRdfZukl9m2H14YQrdihJ2fAt0vf+juAxj8KtcWe1z7XdUPjm5sKz3WDkFlQusCTYKntshQqAW/s2tdvzXhn2/LqqazxXDYdzJd8T1Qq3pKQkFSxCSjPyuxg4cCBmz55d6fsJDg5W25X06NFDBc6RI0fUY5i+sUlASaCWRUqUpqoyk2uuuUbtl0CuTQaDUa1vdiQuXQ2Qlk4Irs6OuLp9GIK9r7xES10a1CpYTRt1usRgaJkJxMnTv+j3jEQYMlOQdWSTOdx03kHwG1S5LzLWJL0kh7UNRYeIuh2bR0T2o1rhJsF26tQpVSqSufyk7a13796qRFd8eIC1SIDKOLX58+erKkIJNOlMIm6++WbzcfJcpM1s3LhxCAwMVFvJxA8LC0ObNtLSZP2VqvecTUG3xn7YHZOMWV/vUp00pEZUyinSh2LXmWT0bR6If04lqYmJb+vTWI21qs8Z5C+m58DdpWgyYqM+H/E/PIXc89FoNOsL6HyC1H7vnmPg3qov3JtVr+u9tbg4OeK/0/uieZBXvT4PItJguEnJR2YjkV6T0m52ww034L333lOlKenwURskzKRziJS8ZA5LGcy9bt06NZjbREp2qampqGuP/rQPy/fHqxAr2evQaIS5PWvJrvNqM7UZLd97Ac2CPDF9UHNM7BWl9tW1i0np2LtnD1xCW6jfHXTOql1NnnjuuQPQtR+q9rsEN1VbffvXiFbo1rjof05EZLVwe/DBB82Xperv8OHDqgNHUFCQxSKR1iSlrtdff11t5ZEB5hUpr52tOuSxPt94CuEA/jgUh6oufWYaMHw6IRPzlu1H6zAv9GxSt1OXyf9tUL9+SM8tQKN7vioMNgABI2bC0d1bVTvamsqsvE1EtiFXXwBXXWHN0H/+dwzJWXnQGwxwcXKCu4sjJvSIUl/wbXbJG6kmlE1Kc59//jk++eQTaJ0sm/LTzhjc38oUVNUrdamIMwJzvtuDl8Z1RFZeAa7tJJFpfYcOHVLto6Z2y1atWhUOb3A2oCA1FrrAws43Mi2WLTEVaGU+yITMXPxxMA6dI30R5uPGKa/qiXy5Oxqfgf3nU3HgfCrcnB0xulMEEjNyMfvbXXDWOcDfwwXdovzUYPtbezeGpytX2NK6PL0Bqw/FYfXBeOw+m6w+Jyf1ilKvE2mWKVmztTcmBf1bBuFwbLr6ot+jqb9amsoa7el8tVXRyUsZ2HEmCe+uO46Lqdmw5kKhdy7cri4/NLI1HhheOKjYWr777jvcdtttahC8DI8QTk5OauVqj4Aw9H91fb0vd1ORxgEe+GbrWbVSeMHlErqfuzP6tgjE2xO7ws25qN2QatfOM8l48Ic9OJtU2AlJ5+igPrQ++qtwPKrIzgfSsvU4l5yNX/fFIj4tByPbh+FMYiY6R/qhTZg3/00aczE9B6Pe2oCkrDzVxGL6PPn476LXRUkbjyeqzeRgbBoWbjqNLpG+mD+mA7rXoAmC4VbFAJr0yVa1tIm8mV1r6fP0zTVH4aJzxKwhhe1gVZWenq7m05SSmWmuTxmnJ6U06ZAjgyBNA9JlKjXx77Ed8du+CxYvNFsh75GSvTlFSnY+1h++iPfWHcetvaOw+UQixnSNMFeDkLU7TCWrFRU+/fukmpPUpKLZbUwfcJ9uOKU2UxD2aOKPx0a1Re9aWHqI6s6l9Fws2nRKfVn5eusZFWyiul+UTbeTkt7Hf51QX/L9PFxULU1V+yQw3CpJX2DAjK93qBn066KE88rvh9E+3AeDW195qERJpplcZIUGU7hJz1EZ9+fpWXb99qTejTGifShGvPkXUrJqb9qnEW1CsPNcGuLSCqc4C/dxRezly9UhvU7fW38cH/x5XHXe+eivE3h7Yjd0iuQwAWtZsS8WT/y8T51r2WrCFIRSZTXx4y24s39TVd0s83ySfVVL/7YvFvOW7kdmXoHVPxPl7v44GK82L1cdWoV64c1bulapfa5K4XbTTTdVeH1KStHSJVrzxaZTOHihciPjrUHe67/uvXDFcNu+fTu+/vpr3HfffWoZIiHVj7/99ht69rScV6u8YDMJ9HRBiLcrUrPzVVBYk2llmrdv7aY6Bx28kIoHv9+Do1aatNn03pJ6+xvf34h7hrZUPSt11VwSh4DkzDw8sXQ/Vh2IUy3K1nxJmP5fqw/GYfrg5mqsJdlPaW3esv2qXc3ar4uyyFjhfedScfVbf6nS/vhOlkO8rBJuVxrDJtffcccd0KLvt8cUTi1VR+TNL6tMl7cwbPFS2sqVK9W5f+GFF9S+7t27q56QVR0/J8c/Nbo9pn65XQ1Et9aXMQlqJ7V0YOGco3Lf9367CzHJ1muzNDFNtPL++uMI9XHF5H71P3zBXj344x5sOJqgLtfWB5hMTj3qrb8xqXeUWkLomg5htfRIZC0f/nVcrZwh6qqVXkqG8unx4opo7D7ua/1wk/kcG6r6qDSR5VfmLz+AGYNbIMzbRfVC/emnn7B8+XJ4eRUOYpYlgPz8/CxmY6nJoHApKX41tTemf7UDOfmGGlc3ODk4wNPVCZ9O7oa4A1vUvui4NNVuWZvVu3LP/4uOxy29otgGV8325ePx6ebOO7UpPVePzzacwmcbT+GD27rXWm9hqpnMXL3q0LV01/l67Xx2qoz297Kwza2S4tNy4eha9qoAtUVKbd9ujYG7iw6Pj2qr5sQ8duyY6iwig9lNVcVXqi6uqv4tgrBu7lA8tmSf6gDw97EE1cW3KlycpOMAMKxdCF4a1wl+bo5YeQB4ePFebDiepLoI17a/jibgtk+3omOEH567sXJLHjV0UiX96E97ER2bjnMpOXX2uGowjRG477vd+NLNGQNb2d4Yy4bscFwaJn68FXn6AmTnW3Nlj6qToSeVwXCzQfqMJKT8/RXyL51G2B1v4bttZ/HgiNZqNhiZgUVmi69toT5uWDill7qcnJWPWz/dqhY1vdI3Nunl+eDwVri1T2MkZOShRbCnKkma1vxbEx2PWuyvUsrOMylqG9QqSHWYoYo9/+tBrDkUb7Uq6aqQh5T18x74bjfWPTxE9ZKj+hebmo3bPt1mnifXXjDcbIQhLweOLoWrSju6uCPr8AYY83ORF3sUaRFtsPVkIqZOnVqnz8lUvRng6YJXbuqExTvPYfupJJy4lKE+/Ia2CcafRy6Zqx9bhnihVzN/VRUoH0zFP5xWHYxXP2sy4L36fwfw6JJ9WNeUH5gVWXc43jw9XH2RWtB8gwHpOXqGmw0wGo14ZPE+VaK3p2ATDLd6lnPuIJJWf6imugq5+VlzuPkPnwnngAi4hLdS4zukKD60TUi9PU+Zz9E0p2NOfoEKuAhfdxy/lAFPFx2aB3uWO5Ba3hgv/HoQT3VBvX1gpmTl4aWV0XhtQj09CRuXmpWPR37apzr/1PdnmATbr/suqB6vVL8W7zyHjccLOxXZG4ZbHTPkZqqZ9508/dTvTu6+qvpRnxILQ26WuV3Pu8vV5tvIB07LENuZ0UFCzDQ9Ti/PKw/C3XYyERl5td/GVhH5wJZB3jJekcMDSpNZd9Kz9fUebMUnMpAB+t/P6FcvE4pToQBP5zrp7l8bOAioDqXtWI5z701G6tbF5n3OgZEIuvFxNLrny3I7rMhCojKI0V7JdEu2oGmgB4OtgjbWvKrO/l2L9AVGbD+djD+PFHY5p7qXkZOPl1YctstgEwy3OpL4+ztI3fRfGPV5yLtYONdafkocso5ugbN/BJzcvCpc3eCfk7Y3LVZlyES632w5bRMlgk0nEvHn4Yt213ZQ247Gp+OzDeXP/1dfpMT25WbrreRBlScLLfd9eR1OJmTCXjHc6oisYG3IyUDQDQ8jdNJLal/O6T24tPTfSNn4rcWxFz6dgZj/3IrcuOPmffM+/hkzZszAxx9/bHHs5s2bsW3bNmRkWGemD2uTCaY/3XgKtkC+M0xZtB3RsXU304w9kOEZsragrZEvIRuOJeBsJcc1kfV8s/UMsvMLJ12wVwy3OuI/bBr8hk6Fe4ve5l6Ijm5ecIloA+egwqVmTAzZ6TDkpMPRuWjtMn3CaXz66adqNpLi/u///g99+/bF/v37zftkHFxkZKRaVLbkgq8yo0nxde2Sk5Oxb98+xMXFoTbIkhY1nY+wvsbJNJQptmRqI1suzP51lFWTdSkjV4/FO2PsvoaDHUrqiFfnog4iJp5tB6qtpPCp76tSns63aCqiJm06YXqv59GypWUPMpnl32AwqImRTS5duoTz58+r4Cruww8/xKlTp3DNNdegadPCaanWrFmDiRMnYvDgwfjrr7/Mx44bN07dx/vvv49evQrHu8kA8p9//hktWrTAhAkTzMdeuHABLi4ualV0WUbHRN4ch2yslCSD0mXG8Un1/URsxIELth30vh7OGNO1UX0/jQZl6a5zyK3ngdrWwHCzQTrvQEA20++ODhjStw+eHtup1LHFA8lEgqdHjx5wd7ecjFbGyUlgNW5cVFKUYAwJCVFbcbLwrARhQUFR1cTu3bvVQHIJwuLhdsMNN6g14qRUee2116p9W7ZswZy5jyA2LxABI2aYj00/vBl5uTlwa9LFvNK3sUCvNgdn11pffFRmpd97zrY/0OuSBL0tdP8vj6xQIUNPfN0LV4mnummb1gKGmx1wd3FC16jKL9oXEBCgtpKeeuqpUvsmTZqktpL++9//qhJg27ZtLUqJU6ZMMa8+YJKfXzjliMxxaRITE4PtWzbBNaqjxbFJm35AbvxJhEx41hxuOWf34+KPT8MltAXCp/zHfGzyn4ugT42HT+9xcA1vXdQJ5/BGOHn6w6vTcPOxWSe2w5CZDNfGneHsV1jiLchOQ+65Q3BwcYd7k6LxbQlnj2PzZke13l1wcOGqCzk5Oaq61tXV1bzGnUhLS4Ner1crKsh1xTv81HYQ14V8vaHw76iDOSSr655vd+Hbu/twQdo6MqV/U7UShL1jm5sdkGUeZOn1uiTteFIiKx5YsjacTJ79xBNPWBwrbXYScL179zbv69+/P7777nuMv+s+i2Pdo9rDrWk3OPkEWYz9ExJCxeWc3q1majFkFVVt6pPOI+WvRUjb8YvFsWnblqgeqXnFOuHkJ8bg0s8vIumP9y2Oddn1XwwYMMCi/TI6Ohrt2rXDwIEDS5V2pcr3888/N++T6llHR0eLqmAhyw5JCfiDDz4w74uPj1dfELp27Wpx7BtvvKFKwIsWLTLvk05BY8eOVevwFS8xL168WHUmkiphE7leStHz5s1DdnbR6gqbNm1Sa/mtX7/e4vG++uortTSSLFRrcvz4cYSmHkJ2vGWHn9zYY8iNPap69ppINXl+ciwKsixLvTI2Uzaj0VCrK3/LMitU+4xGo2bapBluNs7d2Qk3do2ArdPpdBbtbdKhZdKkiejcb4jFcSFXz0ToxBfgEly0FI1HmwGIenAxgsc9aXGsb/+J8B8+Hc7BTcz7nLwD4dlxBDxaFgWpcI1oC/cWveDkVRQ4js5uar9LSHOLY0PDwlS7oY+Pj3mflF6kzbDksk6mkLFoS7y8r2TJTdYzlNKulAJN5PKRI0fUVtzRo0exYcMGVcItfuwvv/yCZcuWWdy3VPFKZyJZu89E5up89dVX8dJLL6mSpcmqVaswd+5ciyAUUuKW5aikJFo8NG+dMA5p25dZHBv//ZOI++oh6NOLqqcyDq7HhU+mI2nNRxbHnpeevW/fgvxLZ4qO3b8WZ9+4CZeWvWx5vz/Ox4WF95uHwqi/OeYALv78Yqkew6nbliB5/RfITy7sxSmdkqRkLV8c5BwVJ8s7rV27FgkJRTNpSIhLeMfGxlocK9XwVD7pNfvWmqPQAlZLVlJ9VUBJd9ztp5MwrK39Tfobl5pTqeoN+SAvLLVZltw8WvcvdayEYtDof5Xa7z90SuljQ1sgbPLrpfYHXzcHx7/9ymKflKySkpJKHSshIWFWPGykOlNKZCU/KCVspDQVGlr0v5LL0i5a8thZs2apCbDbt29v3idVnzLUIzc3V5UMTaQdU6qZpTRsItc/+OCDKtikM49J586dcfvtt5s7AZm+jY8aNUr9HW5uhfOXirCwMPhEtYGDr+VrS+cdDEN+Nhycitq5HBwc1f/IQVfUg1cx/V0ORc/XWJCnSn1Gg2VX8vzEsyhIu6TaV030aZeQfWyrmke1uMz9/1Mlb/cWPdU4UPkb9uzZg3vvvVfVINx4443mYz/77DMVZL/++iuuv/56cwn26quvRqdOnVTNgsnw4cPx999/44cffjC3G0t78c0336z+r/LloHg1vlz30EMPmZeUOnfunPpCERQUhOeff958rCxDdfLkSQwbNkz9D0wl8f/973/w8PBQz8VEvtCkp6er829qPpDXh3y5karv4l+k6tq+c6k217u5uhhuVRhQWtf/cvk8DfJyxcCWFa/GbasCvVxwLtm2xihJ5xxv18p3TpBQk1KpxX3odKU64IhGjRqprTgJE6l+LKlbt25qK046AEn1o1TxFq8ylRAsuRKEfAhK9WNJ8iEtW8m/oeQQEiFDRZZktMSB85Y9WiPuLqpWNfHuPlptJUXe+yWMBgMcdEXn1LP9VXBv1gNwsjxvwWMeVROEOwcUVbFLW2rANffBySugVO/igowkOPmEmKd8kzAYP358qTbf8PBwdT6k5G0iYShrHprWPSxe4pUgKf4/laCRYDK1qZpISXn16tVqZXsT+VIjvY6l/bl4uEn1sgzBketM4SZBKL2O5XkV/+IkVclSRSxDcx5++GG1z9TRS1apN62gIR577DHV/i3HzZkzR+2T0vfo0aPV8/3jjz/MYfj999+rMJXrxo0bp/bJl59XXnlFfQGS25v+Rgl8KfG2adMGXboUtUfv3bUT2XEp0AU2hsPl/58hPweQDl86V4v/s61juFVSsyBPnE431unYD2njf218Z7WMjD1ydnJE2zAf1SPPVsj/r1Nk5VbybQjCfd1x6EJatXtLSunOoURBQ1a3MK1wUZxro3al9jkHNFJbSdKJyOSaDqFqZYqovn3VYr0lSTXsddddp4LBREpKElolSchLlWXxtmT5kiGTIRS/vXj00UdVsElJ0UQCVsaKSim7uEGDBqkvMsWDV+5PblsyYKUkJyU2b++i+WJNgVYyYKWqVUKyeFupXN64caP60lK8hC9/g7QNy3McdzncpDT49NNPm9uETSQIX375ZRV4sk6kkNBfMv8OdTny/m/h5FH4Pknf/gtSNnytvnAEXvuA+T7OvX+nKp2H3/kmdJe/hGQe+lNNM+jevCf8BhZ9KUhc9a4qzfsNudPckay2Mdwq6eWbOmHSor2oK9I9e1y3Rriqbf2tBGANXaP8Ls8IYhu98eRZdGrEcDPp3MgX6w5ftOnekk9f3x6ertb5qJI21ZLtqtL2WjzAildhliQl8/nz55faL1XEJUm7rgROSR999JHaipMeulIiM/U8NpEgveeee1TptPjfICEvxxavLpcOYHJcyerr6dOnq/AsHpwyznXIkCGqKtZE9QoODENObp651CbM1cjFqqlFQWYyIB2JilVJ69MT1DJdzoFRFsdmHt4IY24mfPvX3QhThlsltQ33Ub0WX/79cO1/m/ZxxbWdwjFnhGX1iz26qm0wvt56BvXYjGAhwMMF7SOKOpI0dB0jfW16JgpPFyc1ibLWSQgVL8mZSPWnbCWrr6V6tqSyqq89PDzwySeflDpWqr9lK06qLnceOIJr3t5gsd934G3w7XdLqfuImPYBjAY9nDyKSsEerQeoYCvesUv4D7lTVUmbSoN1geFWBTMGN1ezyh+NS8cPO4p6uVlTgIcz/jujn6oG1YIhrUPw/JgO+PeKA/X9VNCjiT+evaGDqi6lQn2bBeLJ69qpte5sUVZ+AdYevohpA4vGHlLtaRPmA1edo0WnElU6LKOtTVY0KbXPP1xtJXl3uw51je/yKpB/srzJXp3QGfde1UJVHVr1n+EA+Hm6oHFA2Uvf2GtHnDv6N8WglnVTz36lwalsbys9QcD47o3gbKNrpklt6eBW9f/aaSi2n05Sw4+0gOFWTbIqdlV63VX4T7j8uXJj10b4eXZ/TS7OWN4q3XVFPrzTcyzbM6hQXFoOnG2w05K8DXo19UerUNtZqFfr4tNykJKtjfeJ7b2i7USvpgH47YGi2SyqE0geLk4I83GDv6cLPrujJ96a2BV+HkVjlrRkQD2W3OQ/4+rshBHt7G+sYF1oGuhpI919LElT4JT+rI6sS5001NmKbW414H85iJbdMwDf7Tivep7tPJuCdYfjcTEtt8wPDBlnJQ34t/aOwuPXtUNmrl51c3bVaaMqoDxS9fX77/sKvwTU8TJR8n94cWxHhPiU7p5OUD0RJ/aMUh1/bKVzibxMru8cjqs78AtJXWoc4KE68WTm2fdaboLhZgUtQ7zw/I2FEwRP6BmFpbv98fOu82r8UFZeAYwwQufoiAg/N3SL8lc91KQdwcfNWW0NganLsoezE3L0BXU2C718SI5sH2oXU5jVp3uuaoElO8+ptbxsId5mDG6Bx68tmrSb6u592jnSD1tPJdry6JBKYbjVgnHdItVGpX1yR08s3HwWa6PjkVXLa0Z1ifRFt8b+6kNSCzP416YQbze8OK4jXvjtEBIyimbIqA/SW29YW/uclUcLZgxprpaGks4l9oxtblTndfrv3tYd307vCy8rDcwtr9v/dzP64tkxHeq9M4u9GNMlAn8/chXahtVfBw75CqJzckCTQG0MhbFHV7UJwbzRpWeTsTcMN6q3mUuaB3tafUJqU7+eEe1C4OHCiomqkNKth6sOT49uDxen+ivpvj6hC0LZPlqvfN2d4e9h300mDDeqtw/SxbP64b5hLVUgWWP4g+Pliaa/ntYbs4e2tMrzbIgi/N1VJ5P6GJIiVaMyOw/VLwdZLDjLvocEMNyo3kgP0blXt8Hy+waqkpZUh/VpVnoF8YpIzy5ZyDXc1w33DG2JtXOHYFArttfUhMyO89Ps/gj0dKmTgHNxcsS/RrTCaxM64/Y+RWv3Uf1pGuSpeqva85hb1ttQvevYyBcfT+6puqHLm2nNwXhM/3qHuk7eW44lOoNIY7fwdtNh8+PD4O3mrJY4YacR62kR7IVl9w7AA9/txp6YFPM5tzZPVyf8MKMvOjYqmp+QbMNzYzpgw7EEpGXn20QP2qpiuJHNMH1LHNkhFHufuRoHLqSqxRNl1gT95UUxZWog+eCVQGwd6m1eDojBZn0Rfu74cWY/HL2YjqSMPMxdvBexqUWrjNeE/KflA3No6xB2HrFRgV6uajWUOd/vRr4dTl7NcCOb5OvhrGY1qc+ZTUhmq3dQa/KJR0e1wUM/7FWl6ep+1smXkTy9QQ0WliEabF+zbdd1CkePxn6Y9tWOUova2jqGGxFViozd7NkkAP/95yy+3XoGaTl6hHq7qokK0nP15X/IODqoSQvG94iCq85B9WLt1yKQpW07Eerrjndu7YYb3tloVzOXMNyIqNKiAjzUuobSAWTH6WQ1BV27cG98s/UMnvnloKpqNFU5yjjGzpG+asaLSb2iVCcFsk9uOie4ucjsQgabmaLtShhuRFStnq7Fq4wn92uK/+vbRLXNGIxG1QNSqjRJW+2vkz7ZiqTMPLsIOA4FICKrkE490qYmM8Iw2LSnRbAXlt7TH4383K2+lmVlOBSbhL0yWHIjIqJKifT3wMo5g7Bi3wW1WvfLK6ORXctzxIo2oV5q2SpZLHpoMy+8XYnbMNyIiKjSpC11Yq/G5tLU/OUHa22VD7n/ib2i1Korzk4OqnYgLa1yvTZZLUlERNUyrnukWlDWzdlRBZGDlce8Rvq745qOYaq6u6pjWVlyIyKiapfinrmhPR4c2QpLd5/Hwk2n0TTQA2eSsnDyUmaV7ksCMtDTFRdSs9XKBFP6N0X/FoHVbr9luBERUY3IFHh39GuKyX2bqJ6UOidHfL7xJBb8cQQ5l9vkZKEJPw8XFBiNSM/RqynzTNWZs4Y0xyPXtEVKVp5qy5PemTXFcCMiIquQqkNZj09MG9gcd/VvhpMJmThwPhUJGblqmSsJuzOJWWpR2mbBnmqNR1nNwzTll7Uw3IiIqFZIlWLLEC+11TV2KCEiIs1huBERkeYw3IiISHMYbkREpDkMNyIi0hyGGxERaQ7DjYiINIfhRkREmsNwIyIizWG4ERGR5jDciIhIcxhuRESkOQw3IiLSHIYbERFpDsONiIg0h+FGRESaw3AjIiLNYbgREZHmMNyIiEhzGG5ERKQ5DDciItIchhsREWkOw42IiDSH4UZERJrDcCMiIs1huBERkeYw3IiISHPsKtxWrFiBPn36wN3dHf7+/hg7duwVbxMdHY0xY8bA19cXnp6e6NWrF86ePVsnz5eIiOqHDnZiyZIlmD59Ol566SUMGzYMer0eBw4cqPA2J06cwMCBAzFt2jQ899xz8PHxwcGDB+Hm5lZnz5uIiOqeXYSbBNmcOXOwYMECFVQm7du3r/B28+bNw3XXXYfXXnvNvK9FixYV3iY3N1dtJmlpaepnfn6+2ooz/V5yP5XGc1V5PFc8V1S+yn7eOhiNRiNs3D///KOqI7/44gu88847iIuLQ9euXVXYdezYsczbGAwGVRX56KOPYuPGjdi9ezeaNWuGJ554osLqzGeffVaV8kr673//Cw8PD6v+XUREVDVZWVm47bbbkJqaqmrj7Drcvv/+e9x6661o3Lgx3nzzTTRt2hRvvPEGVq9ejaNHjyIgIKDUbSQAw8PDVSC9+OKLuOqqq7Bq1So8+eSTWL9+PYYMGVLpkltUVBQSEhJKnUj5BrFmzRqMHDkSzs7OtfCXawfPFc8VX1dkDfKZHBQUdMVwq9dqyccffxyvvvrqFTuESCnMVM04fvx4dXnhwoWIjIzE4sWLMXPmzFK3M93mxhtvxIMPPqguS2lv8+bN+Oijj8oNN1dXV7WVJOFVXoBVdB3xXFUXX1c8V1RaZT9r6zXc5s6diylTplR4TPPmzREbG1uqjU0CSK4rr+ejJLtOpyvVLteuXTtVTUlERNpVr+EWHBystivp0aOHCrMjR46o3o+maq7Tp0+jSZMmZd7GxcVFdfuX2xQn1Zjl3YaIiLTBLnpLSr3qrFmzMH/+fNX+JeEknUnEzTffbD6ubdu2ePnllzFu3Dj1+yOPPIKJEydi8ODB5ja3X3/9FX/++We9/S1ERFT77CLchISZVDNOnjwZ2dnZqvfkunXr1GBuEymlSSOjiYSctK9J4D3wwANo06aNGi9nKv0REZE26eypEfH1119XW3nK6vg5depUtRERUcNhV9NvERERVQbDjYiINIfhRkREmsNwIyIizWG4ERGR5jDciIhIcxhuRESkOQw3IiLSHIYbERFpDsONiIg0h+FGRESaw3AjIiLNYbgREZHmMNyIiEhzGG5ERKQ5DDciItIchhsREWkOw42IiDSH4UZERJrDcCMiIs1huBERkeYw3IiISHMYbkREpDkMNyIi0hyGGxERaQ7DjYiINIfhRkREmsNwIyIizWG4ERGR5jDciIhIcxhuRESkOQw3IiLSHIYbERFpDsONiIg0h+FGRESaw3AjIiLNYbgREZHmMNyIiEhzGG5ERKQ5DDciItIchhsREWkOw42IiDSH4UZERJrDcCMiIs1huBERkeYw3IiISHMYbkREpDkMNyIi0hyGGxERaQ7DjYiINIfhRkREmsNwIyIizWG4ERGR5jDciIhIcxhuRESkOQw3IiLSHIYbERFpDsONiIg0h+FGRESaw3AjIiLNYbgREZHmMNyIiEhzGG5ERKQ5DDciItIchhsREWkOw42IiDSH4UZERJrDcCMiIs1huBERkeYw3IiISHMYbkREpDkMNyIi0hyGGxERaQ7DjYiINIfhRkREmsNwIyIizWG4ERGR5thVuK1YsQJ9+vSBu7s7/P39MXbs2AqPz8jIwH333YfIyEh1m/bt2+Ojjz6qs+dLRET1Qwc7sWTJEkyfPh0vvfQShg0bBr1ejwMHDlR4m4ceegjr1q3DN998g6ZNm2L16tW45557EBERgTFjxtTZcyciorplF+EmQTZnzhwsWLAA06ZNM++XklhFNm/ejDvvvBNDhw5Vv8+YMQMff/wx/vnnH4YbEZGG2UW47dq1C+fPn4ejoyO6deuGuLg4dO3aVYVdx44dy71d//79sXz5ckydOlWV1v78808cPXoUb731Vrm3yc3NVZtJamqq+pmUlIT8/HyLY+X3rKwsJCYmwtnZ2Sp/q1bxXPFc8XVF1pCenq5+Go3Gig802oHvvvtO/gpj48aNjT/99JNxx44dxltvvdUYGBhoTExMLPd2OTk5xjvuuEPdVqfTGV1cXIxffvllhY81f/58dTw3ngO+Bvga4GsANnsOYmJiKvwsr9eS2+OPP45XX321wmOio6NhMBjU5Xnz5mH8+PHq8sKFC1VHkcWLF2PmzJll3vbdd9/F1q1bVemtSZMm+Pvvv3HvvfeqUtyIESPKvM0TTzyh2upM5LGl1BYYGAgHBweLY9PS0hAVFYWYmBj4+PhU+e9vSHiueK74uiJrkBKblN7kc7wi9Rpuc+fOxZQpUyo8pnnz5oiNjS3Vxubq6qquO3v2bJm3y87OxpNPPomlS5di9OjRal/nzp2xZ88evP766+WGm9yvbMX5+flV+Bwl2BhulcNzVXk8VzxXVDZfX19cSb2GW3BwsNqupEePHipwjhw5goEDB5rbcE6fPq1KZGWR62WTdrrinJyczCVBIiLSJkd7+QY7a9YszJ8/X3Xnl5CbPXu2uu7mm282H9e2bVtVUjPdZsiQIXjkkUdUR5JTp05h0aJF+OqrrzBu3Lh6+1uIiKj22UVvSSE9I3U6HSZPnqyqHGUwt4xhk8HcJhJ6pt6N4vvvv1dtaLfffrtqN5NS3r///W8VlNYgpUkJ3JLVmMRzxddV3eB7kMrjIL1Kyr2WiIjIDtlFtSQREVFVMNyIiEhzGG5ERKQ5DDciItKcBhFuMjPJDTfcoEa0yywjy5Yts7he+tQ888wzCA8PV0vjyADvY8eO1cmSOzk5OWrWFJkBxcvLS83AEh8fj/piq+dKervef//9aNOmjbq+cePGeOCBByx6x9Y1Wz1XJZ/DtddeW+bzq2u2fr62bNmiVhzx9PRUQ4kGDx6semaTnTI2ACtXrjTOmzfP+PPPP6s5yZYuXWpx/SuvvGL09fU1Llu2zLh3717jmDFjjM2aNTNmZ2ebj5E5Lf39/Y0ffvih8ciRI8aDBw8af/jhhwofd/r06cYWLVoY169fbzx16pTx448/Njo5ORl/+eUX8zGzZs0yRkVFGdeuXavmzOzbt6+xf//+xvpiq+dq//79xptuusm4fPly4/Hjx9X5atWqlXH8+PHG+mKr56q4N99803jttdeW+fzqmi2fr82bNxt9fHyML7/8svHAgQPGw4cPq/uV+WnJPjWIcCuu5JvKYDAYw8LCjAsWLDDvS0lJMbq6uqoJm0V+fr6xUaNGxs8++6xKj9WhQwfj888/b7Gve/fu6g1uehxnZ2fj4sWLzddHR0er57hlyxZjfbOlc1WWH3/8UU2GLY9Z32zxXO3evVvdf2xsrE2Emy2frz59+hifeuqpGvxFZGsaRLVkRWTmEllCp/hckzJvmVR7SDVFWUvuSLWJVPVcabFU05I7clt5P69fv14tuXP11Ver63fu3KmmCCv+2DLLilS5mR7bltTnuSqLVElK9ZEM7rc19X2uZCmm2267De+//z7CwsJg6+rzfF28eBHbtm1DSEiIOjY0NFTNbrRx48Za/qupNjX4cJM3lJAXdHHyu+m6kydPqp/PPvssnnrqKfz222+qrl8WQZW2oPLIqgRSvy91/S4uLhg1apT6sJG6fCH3L/tLTsxc/LFtSX2eq5ISEhLwwgsvqAVobVF9n6sHH3xQfVDfeOONsAf1eb6K3+/06dOxatUqdO/eHcOHDy+zzY/sQ4MPt8ooueSOTOQsS+5Io7gsuVOe4kvuSCntjTfeUJ1H/ve//0Gr6uJcyfI5stKDfGDJB5K9qq1zJftlarq3334bWlJb58t0v7J01l133aVKhbKgsXRe+uKLL+roryNrs736nDpmqrKRHopSzWEiv8tq38K039pL7shj5+XlISUlxaL0Jo9ti1VJ9XmuTGQdJ/nm7e3trY631RXQ6/NcSbCdOHGiVI2ABMKgQYPUROK2pj7PV1n3K9q1a1fu/ZLta/Alt2bNmqk31tq1ay1KBlIH369fv1JL7phYY8kduV/5cC7+2PIY8oYyPbYtqc9zZXosaSeRqiX5Fu7m5gZbVZ/nShYB3rdvn/oAN21CSiNS0rFF9Xm+mjZtqoYnFL9fIe1y5d0v2QFjA5Cenq56jskmf7J0j5bLZ86cMXdB9vPzU12D9+3bZ7zxxhtLdUGeM2eO6qn1xx9/qG7C06ZNM4aEhBiTkpLMx7Rp00Z1czYZMmSI6qklXZBPnjxpXLhwodHNzc34wQcfWAwFaNy4sXHdunVqKEC/fv3UVl9s9VylpqaqHm2dOnVSQwGkB6Bp0+v1xvpgq+eqLLbQW9KWz9dbb72lhgJIz+Vjx46pnpNyjLzWyD41iHCTF7W8mUpud955p7kb8tNPP20MDQ1VXY+HDx+uxtAUl5eXZ5w7d656I3l7extHjBihxsMUJ/cpbxwT+eCdMmWKMSIiQr1R5E33xhtvqMczkTfuPffco8bueHh4GMeNG6duV19s9VyV97xkk7FL9cFWz5Wthputny8Z4xYZGaneh/IFc8OGDbV6Pqh2cckbIiLSnAbf5kZERNrDcCMiIs1huBERkeYw3IiISHMYbkREpDkMNyIi0hyGGxERaQ7DjYiINIfhRkREmsNwI7IjU6ZMwdixY+v7aRDZPIYbERFpDsONSCP++usv9O7dWy0LI2uUydI3er3efL2sWP3AAw/g0UcfRUBAgFpipuRir4cPH8bAgQPVckKyvpks6CmLgS5btqwe/iKi6mO4EWnA+fPncd1116FXr17Yu3cvPvzwQ3z++ed48cUXLY778ssv4enpqdZJe+211/D8889jzZo16rqCggJV5enh4aGu/+STT9Sq10T2qMGvxE2kBR988AGioqLw3nvvqZJW27ZtceHCBTz22GN45plnzIt1yirU8+fPV5dbtWqljpcFQkeOHKlCTlbwlpW6TStj//vf/1bXEdkbltyINCA6OlqtWC3BZjJgwABkZGTg3Llz5n0SbsVJ9eXFixfVZVmJWgLSFGxCqjmJ7BHDjagBcXZ2tvhdwtBgMNTb8yGqLQw3Ig1o164dtmzZgsKFqAtt2rQJ3t7eiIyMrNR9tGnTBjExMYiPjzfv2759e608X6LaxjY3IjuTmpqKPXv2WOybMWMG3n77bdx///247777VBWjtK099NBD5va2K5G2tRYtWuDOO+9UnU3S09Px1FNPqeuKV3cS2QOGG5GdkQ4f3bp1s9g3bdo0rFy5Eo888gi6dOmiuvrLPlM4VYaTk5Pq8n/33XerXpfNmzfHggULcMMNN6ihAUT2xMFYvB6DiKgYqdqUcW/Hjx9XpToie8FwIyKzpUuXwsvLSw0TkECbM2cO/P39sXHjRp4lsiusliQiM2lnk7FxZ8+eRVBQEEaMGIE33niDZ4jsDktuRESkORwKQEREmsNwIyIizWG4ERGR5jDciIhIcxhuRESkOQw3IiLSHIYbERFpDsONiIigNf8Po1ov2PyDVDUAAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 460x460 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = df['Long']\n",
"y = df['Lat']\n",
"t = [i*5 for i in df['Elev']]\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"plt.figure(figsize=(4.6, 4.6))\n",
"plt.grid()\n",
"plt.xlabel('Long')\n",
"plt.ylabel('Lat')\n",
"plt.xticks(\n",
" [106.78, 106.80, 106.82, \n",
" 106.84, 106.86])\n",
"plt.ylim([106.78, 106.86])\n",
"plt.yticks([-6.8, -6.6, -6.4, \n",
" -6.2, -6])\n",
"plt.ylim([-6.8, -6.0])\n",
"plt.plot(x, y, ':k')\n",
"plt.scatter(x, y, s=t, ls='--')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "08a4b2ac-274f-4617-8c14-5234b3c28e9d",
"metadata": {},
"source": [
"What are the additional dimension?\n",
"\n",
"JAWAB :\n",
"Dimensi utama pada kedua gambar adalah 'Long' dan 'Lat'. Sedangkan dimensi tambahannya ditunjukkan dengan variasi ukuran titik/bubble size. Untuk gambar sebelah kiri dimensi tambahannya adalah 'Tracks', makin besar nilai Tracks -> ukuran titik makin besar.\n",
"Sedangkan gambar sebelah kanan dimensi tambahannya adalah Elev. Makin besar nilai Elev -> makin besar ukuran titik/bubble"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "71e8b5d2-b5d2-4cf0-8018-0f30671ef923",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.cm as cm\n",
"import pandas as pd\n",
"\n",
"x = df['Long']\n",
"y = df['Lat']\n",
"e = np.array(i*5for i in df['Elev'])\n",
"n = plt.Normalize(\n",
" vmin=e.min(), vmax=e.max())\n",
"cmap = cm.seismic\n",
"colors = cmap(n(e))\n",
"import matplotlib.pyplot as plt\n"
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "195450ea-2d9d-4239-ad87-bff12f3501ee",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'colors' is not defined",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mNameError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[49]\u001b[39m\u001b[32m, line 11\u001b[39m\n\u001b[32m 9\u001b[39m plt.ylim([-\u001b[32m6.8\u001b[39m, -\u001b[32m6.0\u001b[39m])\n\u001b[32m 10\u001b[39m plt.plot(x, y, \u001b[33m'\u001b[39m\u001b[33m:k\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m11\u001b[39m plt.scatter(x, y, c=\u001b[43mcolors\u001b[49m)\n\u001b[32m 12\u001b[39m plt.show()\n",
"\u001b[31mNameError\u001b[39m: name 'colors' is not defined"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 460x460 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(4.6, 4.6))\n",
"plt.grid()\n",
"plt.xlabel('Long')\n",
"plt.ylabel('Lat')\n",
"plt.xticks(\n",
" [106.78, 106.80, 106.82, 106.84, 106.86])\n",
"plt.ylim([106.78, 106.86])\n",
"plt.yticks([-6.8, -6.6, -6.4, -6.2, -6])\n",
"plt.ylim([-6.8, -6.0])\n",
"plt.plot(x, y, ':k')\n",
"plt.scatter(x, y, c=colors)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "20e54a33-e25d-4382-8221-09f2ffa90f1b",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'colors' is not defined",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mNameError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[47]\u001b[39m\u001b[32m, line 19\u001b[39m\n\u001b[32m 17\u001b[39m plt.ylim([-\u001b[32m6.8\u001b[39m, -\u001b[32m6.0\u001b[39m])\n\u001b[32m 18\u001b[39m plt.plot(x, y, \u001b[33m'\u001b[39m\u001b[33m:k\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m19\u001b[39m plt.scatter(x, y, s=t, c=\u001b[43mcolors\u001b[49m)\n\u001b[32m 20\u001b[39m plt.show()\n",
"\u001b[31mNameError\u001b[39m: name 'colors' is not defined"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 460x460 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = df['Long']\n",
"y = df['Lat']\n",
"t = [i*40 for i in df['Tracks']]\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"plt.figure(figsize=(4.6, 4.6))\n",
"plt.grid()\n",
"plt.xlabel('Long')\n",
"plt.ylabel('Lat')\n",
"plt.xticks(\n",
" [106.78, 106.80, 106.82, \n",
" 106.84, 106.86])\n",
"plt.ylim([106.78, 106.86])\n",
"plt.yticks([-6.8, -6.6, -6.4, \n",
" -6.2, -6])\n",
"plt.ylim([-6.8, -6.0])\n",
"plt.plot(x, y, ':k')\n",
"plt.scatter(x, y, s=t, c=colors)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "a52f10c7-7f8d-4d18-9ba8-759a08ec0297",
"metadata": {},
"source": [
"It is a 2d scatter plot +2d\n",
"\n",
"1. fitur dari dataset ini :\n",
" a. Fitur dari grafik :\n",
" sumbu x = longitude\n",
" sumbu y = latitude\n",
" fitur tambahan - ukuran titik = Tracks\n",
" - warna titik = Platform atau Elev\n",
"\n",
" b. Fitur dari dataset kolom : Long, Lat, Tracks (jumlah jalur), Platform (jumlah peron), Elevation (ketinggian stasiun), Code, dan Nama Station\n",
" \n",
"2. Saran grafik 2D+2D yang lain :\n",
" x = pos\n",
" y = Elev\n",
" ukuran bubble = Tracks\n",
" warna bubble = Platform\n",
" "
]
},
{
"cell_type": "markdown",
"id": "14baf06b-cf20-4d80-a039-63216299a9ea",
"metadata": {},
"source": [
"CHALENGES\n",
"\n",
"1. Fitur ketiga kemungkinan besar adalah Tracks\n",
" \n",
"2. Track menggambarkan jumlah jalur/rel di stasiun. Jumlah Tracks banyak= stasiun besar, Tracks sedikit= stasiun kecil\n",
"\n",
"3. Jakarta kota --> Elevasi y rendah (ketinggian ~2m), Pos paling kecil nilai x (mendekati nol) karena stasiun awal, punya Tracks banyak (ukuran bubble besar)\n",
" Manggarai --> Elevasi sedikit lebih tinggi dari Jakarta kota (nilai y ~10m), terletak di tengah jalur (nilai x ~ 9000), jumlah Tracks cukup banyak (ukuran bubble lumayan besar).\n",
" Bogor --> Elevasi paling tinggi (y ~ 250m), stasiun paling akhir (Pos paling belakang x ~ 50000), jumlah Tracks cukup banyak (ukuran bubble besar)\n",
"\n",
"4. Untuk meyakinkan grafik dapat dicocokkan dengan data Elevasi, Pos dan Jumlah Tracks sesuai fakta stasiun dan Urutan pada Grafik juga sesuai urutan nyata yaitu Jakarta Kota-Manggarai-Bogor\n",
" "
]
},
{
"cell_type": "markdown",
"id": "0888583c-6f13-4c4e-9b0b-1de12b67ac7b",
"metadata": {},
"source": [
"INDEPENDENT ACTIVITY\n",
"\n",
"Reproduce Previous scatter Plot"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "c8e436db-9355-456a-87c2-9d1c1a63c9c9",
"metadata": {},
"outputs": [
{
"ename": "FileNotFoundError",
"evalue": "[Errno 2] No such file or directory: 'railway_jakarta_bogor.csv'",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mFileNotFoundError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[51]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpd\u001b[39;00m\n\u001b[32m 2\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmatplotlib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpyplot\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mplt\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m df = \u001b[43mpd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 4\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mrailway_jakarta_bogor.csv\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 5\u001b[39m \u001b[43m \u001b[49m\u001b[43msep\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43m;\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 6\u001b[39m \u001b[43m \u001b[49m\u001b[43mskipinitialspace\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[32m 7\u001b[39m \u001b[43m)\u001b[49m\n\u001b[32m 9\u001b[39m df.columns = df.columns.str.strip()\n\u001b[32m 11\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m col \u001b[38;5;129;01min\u001b[39;00m [\u001b[33m'\u001b[39m\u001b[33mElev\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mPos\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mTracks\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mPlatforms\u001b[39m\u001b[33m'\u001b[39m]:\n",
"\u001b[36mFile \u001b[39m\u001b[32mD:\\pynb\\venvs\\basic\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1026\u001b[39m, in \u001b[36mread_csv\u001b[39m\u001b[34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[39m\n\u001b[32m 1013\u001b[39m kwds_defaults = _refine_defaults_read(\n\u001b[32m 1014\u001b[39m dialect,\n\u001b[32m 1015\u001b[39m delimiter,\n\u001b[32m (...)\u001b[39m\u001b[32m 1022\u001b[39m dtype_backend=dtype_backend,\n\u001b[32m 1023\u001b[39m )\n\u001b[32m 1024\u001b[39m kwds.update(kwds_defaults)\n\u001b[32m-> \u001b[39m\u001b[32m1026\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[36mFile \u001b[39m\u001b[32mD:\\pynb\\venvs\\basic\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:620\u001b[39m, in \u001b[36m_read\u001b[39m\u001b[34m(filepath_or_buffer, kwds)\u001b[39m\n\u001b[32m 617\u001b[39m _validate_names(kwds.get(\u001b[33m\"\u001b[39m\u001b[33mnames\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[32m 619\u001b[39m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m620\u001b[39m parser = \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 622\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[32m 623\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
"\u001b[36mFile \u001b[39m\u001b[32mD:\\pynb\\venvs\\basic\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1620\u001b[39m, in \u001b[36mTextFileReader.__init__\u001b[39m\u001b[34m(self, f, engine, **kwds)\u001b[39m\n\u001b[32m 1617\u001b[39m \u001b[38;5;28mself\u001b[39m.options[\u001b[33m\"\u001b[39m\u001b[33mhas_index_names\u001b[39m\u001b[33m\"\u001b[39m] = kwds[\u001b[33m\"\u001b[39m\u001b[33mhas_index_names\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m 1619\u001b[39m \u001b[38;5;28mself\u001b[39m.handles: IOHandles | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1620\u001b[39m \u001b[38;5;28mself\u001b[39m._engine = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[36mFile \u001b[39m\u001b[32mD:\\pynb\\venvs\\basic\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1880\u001b[39m, in \u001b[36mTextFileReader._make_engine\u001b[39m\u001b[34m(self, f, engine)\u001b[39m\n\u001b[32m 1878\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[32m 1879\u001b[39m mode += \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m-> \u001b[39m\u001b[32m1880\u001b[39m \u001b[38;5;28mself\u001b[39m.handles = \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1881\u001b[39m \u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1882\u001b[39m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1883\u001b[39m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mencoding\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1884\u001b[39m \u001b[43m \u001b[49m\u001b[43mcompression\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mcompression\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1885\u001b[39m \u001b[43m \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmemory_map\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1886\u001b[39m \u001b[43m \u001b[49m\u001b[43mis_text\u001b[49m\u001b[43m=\u001b[49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1887\u001b[39m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mencoding_errors\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstrict\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1888\u001b[39m \u001b[43m \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstorage_options\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1889\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1890\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m.handles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 1891\u001b[39m f = \u001b[38;5;28mself\u001b[39m.handles.handle\n",
"\u001b[36mFile \u001b[39m\u001b[32mD:\\pynb\\venvs\\basic\\Lib\\site-packages\\pandas\\io\\common.py:873\u001b[39m, in \u001b[36mget_handle\u001b[39m\u001b[34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[39m\n\u001b[32m 868\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[32m 869\u001b[39m \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[32m 870\u001b[39m \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[32m 871\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m ioargs.encoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs.mode:\n\u001b[32m 872\u001b[39m \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m873\u001b[39m handle = \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[32m 874\u001b[39m \u001b[43m \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 875\u001b[39m \u001b[43m \u001b[49m\u001b[43mioargs\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 876\u001b[39m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m=\u001b[49m\u001b[43mioargs\u001b[49m\u001b[43m.\u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 877\u001b[39m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m=\u001b[49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 878\u001b[39m \u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 879\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 880\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 881\u001b[39m \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[32m 882\u001b[39m handle = \u001b[38;5;28mopen\u001b[39m(handle, ioargs.mode)\n",
"\u001b[31mFileNotFoundError\u001b[39m: [Errno 2] No such file or directory: 'railway_jakarta_bogor.csv'"
]
}
],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"df = pd.read_csv(\n",
" 'railway_jakarta_bogor.csv',\n",
" sep=';',\n",
" skipinitialspace=True\n",
")\n",
"\n",
"df.columns = df.columns.str.strip()\n",
"\n",
"for col in ['Elev', 'Pos', 'Tracks', 'Platforms']:\n",
" df[col] = pd.to_numeric(df[col], errors='coerce')\n",
"\n",
"x = df['Pos'] \n",
"y = df['Elev'] \n",
"size = df['Tracks'] * 40 \n",
"\n",
"plt.figure(figsize=(7,6))\n",
"plt.scatter(x, y, s=size, alpha=0.8)\n",
"plt.plot(x, y, linestyle='--')\n",
"\n",
"plt.xlabel('Pos (m)')\n",
"plt.ylabel('Elevasi (m)')\n",
"plt.title('Jakarta–Bogor Railway Elevation (+1D Feature)')\n",
"plt.grid(True)\n",
"\n",
"important_stations = ['Jakarta Kota', 'Manggarai', 'Bogor']\n",
"\n",
"for i, row in df.iterrows():\n",
" if row['Station'] in important_stations:\n",
" plt.text(row['Pos'], row['Elev'] + 5, row['Station'],\n",
" fontsize=9, weight='bold')\n",
"\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "343ce760-84d0-4913-9897-2163a2afd8ae",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "4dd61816-451a-4d64-9ee7-ddcee05bd385",
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.13.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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