CD-Ceballos/6.19 Tanque de agua.ipynb

## 6.19 Tanque de agua.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.19 Tanque de agua esferico por Newton-Raphson."
   ]
  },
  {
   "attachments": {
    "Tanque.PNG": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Tanque.PNG](attachment:Tanque.PNG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Encontrar la altura, $h$, necesaria del tanque esferico tal que  $V = 30m^3$ y  $R = 3m$, donde V es el volumen y R el radio del tanque. \n",
    "###### Formula : ######\n",
    "\n",
    "$$V = \\pi {\\color{Red} h^2}  \\frac{[3R-{\\color{Red} h}]}{3} $$\n",
    "\n",
    "**Se sustityen valores y aplicamos algebra de Baldor para obtenter nuestra funcion $f(h)$ y su derivada $f´(h)$:**\n",
    "\n",
    "$$f(x) =  3\\pi h^2 -\\frac{\\pi}{3}h^3 - 30$$\n",
    "\n",
    "$$f'(x) =  6\\pi h -\\pi h^2 $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inserte número de iteraciones: 3\n",
      "Ingrese valor inicial de h: 1\n",
      "\n",
      " Iteracion       Resultado (h)                f(h)               Ea(%)\n",
      "________________________________________________________________________\n",
      "         1         2.376525984       -21.622419590      1000.000000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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4yu5rdSyPoMIjMEY8B6fTHQWck211JK8kBSKEG9J7kjBfeRrOnHKc7+jcyyuGIylJqnZ9jKFPwt5djsudRZGTAhHCzZjrV2C+8TyUK4/xt9fkYUqFoG5pj7pjAHrNd5hrvrM6jteRy3iFcBNaa/SST9CLP3Lc3/HY32S02SKg7roPvW8P+qN/oSNrompEWR3Ja8geiBBuQOfmoue8jV78EaptZ4zRE6Q8iogyfDD++gwEVsB8bwr6/FmrI3kNKRAhLKazMjn9ylj02nhU7zjUkNEom93qWF5FlQ/EGDYOzpzCnPsOWmurI3kFKRAhLKTPncGc+neyt21EDR6Jcdd9crK8mKiaUai+98OW9egfv7c6jlfwiHMgI0aMwN/fH8Mw8PHxYcqUKZw/f55p06Zx4sQJwsPDefLJJwkIkF1+4Tn0iaOY01+E02lUGDeFczXrWx3J66lud6F/2YSe/x90VENURBWrI3k0j9kDefHFF3nttdeYMmUKAIsWLaJx48a89dZbNG7cmEWLFlmcUIiC04dSMf85Ds6fwxjzMn6tZPC/kqAMA2PIk2D3xXx/Kjo3x+pIHs1jCuSPEhMT6dSpEwCdOnUiMTHR4kRCFIzeuwvztb+BAmPsFFRt2fMoSSo4FGPwSNifgl76mdVxPJpHHMICmDRpEgDdunUjNjaWM2fOEBwcDEBwcDBnz159ZUV8fDzx8fEATJkyhbAw14eAsNlshZrfHdjtdpRSHr8dl3ji1+Ti1g2cnvYCPiFhBE94E5+KfwI8c1vy4xHb0r0PZ5I2kbX0M4K69sJevfZVb/GI7Sig4toWjyiQiRMnEhISwpkzZ3j55ZepXLlygeaLjY0lNjbW+fHJkyddzhAWFlao+d1BTk4Odrvd47fjEk/7mujN6zD/8xpERKKfnMApww6/5/e0bbkeT9kWffdg2LKB9GkvOW7Y9LlyZGNP2Y6CKMy2XO/3rUccwgoJCQEgKCiIVq1akZKSQlBQEKdOnQLg1KlTBAYGWhlRiOsy163A/Nc/oXodjGcmoQKDrY5U6qnygahBjzoOZcUvtjqOR3L7AsnKyiIzM9P5/59//plq1arRsmVLVq1aBcCqVato1aqVlTGFyJe58hv0f6dB3UYYo19ClZWrBd2FatkemrVFL/5Ihn53gdsfwjpz5gyvv/46AHl5eXTo0IFmzZpRu3Ztpk2bxvLlywkLC2PMmDEWJxXiaub3i9GfzoImrTCGPSuj6boZpRTGn4dhvjAC8+N/OQpe7sMpMLcvkEqVKvHaa69dNb18+fK88MILFiQSomDMJZ+iF30ILWIw/vqU3F3uplSFENTdf3aM2Ls5AW5pb3Ukj+H2h7CE8DRaa8zFH6EXfYhq0wnjkWekPNyc6tQTImtifjILnZVpdRyPIQUiRBHSWqMXzkN/PR/VPtYxrpWPPLfc3SkfH4z7hsGpk+gln1odx2NIgQhRRLTW6M8/QH/zOarj7ajBI1GGlIenUHUaoGK6or9fhD5y0Oo4HkEKRIgioLVGfzoL/d1C1G13oP78GMqQHy9Po+55AHz9MT/9r9VRPIJ8hwtRSFpr9Cfvo+O/RHXt43h+uZSHR1KBFVC97oVfNnFx60ar47g9+S4XohC01o6RXX/4ChV7J2rgw3IZqIdTXXpBaEXOz3kHbeZZHcetSYEI4SKtNfrjf6OXf43qdhfq3qFSHl5A2X1R9zxAbmoKet0Kq+O4NSkQIVzgLI8VS1Dd70YNGCLl4UVUyw7Y6zZEL/oQfTHL6jhuSwpEiJvkPGx1qTz6PyTl4WWUUgQ8+DicTkd/L88ayo8UiBA3wXnC/NJhKykPr+XboIljnKzvFqEvnLM6jluSAhGigJzl8cNXqNi75LBVKWDcdR9kZaKXLbQ6iluSAhGiABz3efzXUR5d+6DulfIoDVTVGqhWt6J/+Ap99rTVcdyOFIgQN+C8wzx+MapLb7lUt5RRfQZBTg76my+sjuJ2pECEuA6tNfqLOY47zDvfgYr7q5RHKaMiqqBiOqNXLkWfSrM6jluRAhEiH86BEZctQN3WEzXoUSmPUkr1jgOt0UtloMXLSYEIcQ1aa/Ti//3/wIhSHqWaCquEat8V/eP36NOyF3KJFIgQ16C/+hi95FNUh24yMKIAQPW4B0wT/Z3cF3KJ/FQI8QfmV/PRX81Hte+Kun+ElIcAQIVHoFp3RK/6Fn3urNVx3IL8ZAhxGXPJp+gvP0K16/z78zzkR0T8P9WzP+Rko+O/tDqKW5CfDiF+Zy79zPEY2ra3oR4cJQ+DEldRlatB83boFUvQGResjmM5KRAhAPObL9AL56Fad0I99ISUh8iX0WsAZF5Ar1hidRTLSYGIUs9ctgC9YA6qdUfHM8ylPMR1qGq1oVELx93pOdlWx7GUFIgo1cxlC9Cff4BqdStqyJMoHykPcWNG975w7gx6/Uqro1hKCkSUWleUx9AxUh6i4Oo3gcia6O8Xo03T6jSWkQK5AX06jby3J5KzZ4fVUUQRkvIQhaGUQnW/G44chKTNVsexjBTIjfiXgf0pnJs1rVT/peFNzG+/kPIQhaZa3goVQjFL8VDvUiA3oPzLovoNJmdPEnrjKqvjiEIyl3yK/uL3E+ZSHqIQlM2Giu0Du7ejD+y1Oo4lpEAKQLXtjK1OA8eorFmZVscRLjK/nu+4z6NNJzlhLoqEurU7+JUptcObSIEUgDIMyg8d7Xg+sjwTwONorTEXf4Re/Psd5kNGS3mIIqHKBqA6xKJ/Wos+c8rqOCVOCqSAfOs3RrW9Df3dQvSJo1bHEQWktUYvmIv++vexreQOc1HEVOdekJeLXrPM6iglTgrkJqh+D4CPD+bH/0ZrbXUccQOOx9DOQn/7BapTD9Tgx6U8RJFTlSo7bixc9S06N9fqOCVKCuQmqOBQ1J33wfafYPM6q+OI69Cmif5oJjr+S8czzGVIdlGMjM694HQ6bF1vdZQSJT9RN0l17QORNR17ITKYmlvSeXnoD95Cr/wGdXtfeYa5KH6NWkB4BGYpGx9LCuQmKR8fjPtHwtlT6EUfWh1H/IHOzUW/PxW9bjnqzvtQ9zwo5SGKnTJ8ULf1hD1J6EP7rI5TYqRAXKBqRqE690KvXIr+dbfVccTvdPZFzPcmo3/6ETXgIYw+cVIeosSo9rFg90WvWGp1lBIjBeIidfdfICgE84O3Sv2InO5AZ2ZgvvkSbP8J9ZfhjsHuhChBqlx5VJtO6PUr0ZkZVscpER5dIFu3buWJJ57g8ccfZ9Gikr2RR5Upi/HASDhyEL34fyW6bnElfe4s5tS/w96dqIefwujUw+pIopRSHXtA9kX0htIxaoXHFohpmsyaNYvx48czbdo01q5dy6FDh0o0g2p0C6rj7ejvFqGTZbBFK+i0E5ivPgu/HcAYPh6jdUfnawcO+DByZAX69w9l5MgKHDggl/CKYlajDlStWWruCfHYAklJSSEiIoJKlSphs9mIiYkhMTGxxHOoAQ9BaEXM2dNlmJMSlntwH+aUsXDmNMbol1BNWjlfO3DAh7i4EBYuLMu6dX4sXFiWuLgQKRFRrJRSqI63w4Ff0ftTrI5T7GxWB3BVeno6oaGhzo9DQ0NJTk6+7jz9+/d3eX12u52cnJxrvqYzLsDBX2F1F1REFZfXUdySkpJQShXq8+AudGYGHE4FpaBqDdT4F694PTnZxsmTV/59tH8/9OljEhXlfjd7Xe/7y9N4y7a4uh06Lw/2JsOf73eb3weF+ZokJCTk+5rHFsi17gT/4xU38fHxxMfHAzBlyhTsdrvL61NK5T9/UAVyMythnjyGT2AQRlCwy+spTpc+P4X5PLgD8+xpcg+moux2bDXqoHz9rnpPTs61r77KyTHccvuv+/3lYbxlW1zeDrud3KBgzLOnsVWOdItx14rra+KxBRIaGkpaWprz47S0NIKDr/zFHRsbS2xsrPPjjz/+2OX1hYWFcfLkyXxf17m5mK+Ph8P7MZ6fiqpY2eV1FZf+/ftjt9sL9XmwmrliKfrjf0P7XoS/OI30nLxrvm/kyAosXFj2quldumTwzjunizvmTbvR95cn8ZZtKcx26L27MKeMRQ1+GOPW7kWc7OYV19fEY8+B1K5dmyNHjnD8+HFyc3NJSEigZcuWluVRNhvGX58BwwfzX6/Kpb1FTJsm5ucfoD+aCU1aYjw16bp7emPHnqN69St32atXz2Hs2HPFHVUIqFUPKldDr/buk+keWyA+Pj4MGTKESZMm8eSTT9KuXTsiIyMtzaRCwzEeesJxAu3TWZZm8Sb64kVHKS9bgOrUA+Oxv6H8rj5sdblq1fKYPz+dvn0ziIm5SN++Gcyfn061atfeYxGiKCmlHM8KSU1GH95vdZxi47GHsABatGhBixYtrI5xBdWsDap7X/R3CzEja2J0lHsSCkOfOYU5YxKkJqMGDEF1u6vAd5dXq5bnloerROmg2nRCfz4bnbDccbWmF/LYPRB3pu4Z7Bje+aN/off8YnUcj6UP/Ir5ytOO80qP/Q2j+90yNInwGKp8EDRuid6w0nFllheSAikGyvDB+OvTEBaB+d4UdNpxqyN5HL1pLeY/nwWtMcZORjVva3UkIW6aEdMVzpyCHVusjlIspECKiSobgDHyOcjLw3x7IjrjvNWRPII2TczFH2HO/CdE1sR4biqqeh2rYwnhmsa3QEB5dMJyq5MUCymQYqQiqmI8Ng6OHsZ8+2V09kWrI7k1feGco2x/f/ys8dQklJveUyNEQSibHdXmNvTW9egL3vdHpBRIMVMNmqKGjoG9OzH//ZrXHgstLH3gV8yXx8DObY6nBz4wCuUFN6MJodp1gdxcdOJqq6MUOSmQEmC06oAa9Ahs24ie9w7aNK2O5Da01pirv3WMaZWbi/HMKxi39ZST5cJ7VKsFVap75WEsj76M15MYnXthnjuL/ur3u8AHj0QZ1g9xYCWdmYGeNwOduAaim2MMfRIVWMHqWEIUKaUUKqYL+rPZ6KOHUBFVrY5UZGQPpASpPnGoPoPQa39Az34LbZbew1l67y7MiaPRm9ai+g3GeOJFKQ/htVSrjqAUeqN3HcaSPZASpJRC3TkI0zAcD6HKy4Uho1G20nOsX+fmor+ej176OYSEYTz9Cioq2upYQhQrFRwK9RqjN6xG9xnkNYdopUAsYPQeiGmzob+Ygz572jE0R7kAq2MVO314P+bsN2F/CqpdF9SgR1Blrh7wUAhvpFp3RM99B1JToGaU1XGKRIEPYWVlZZGWlkZWVlZx5ik1jB73oIY+CSk7Mf/5LPrkMasjFRudm4P55ceYE5+EtOMYw57FGDJaykOUKuqWGLDZ0Bu953G3190DOXDgAPHx8WzevJkTJ044p1esWJFmzZrRrVs3qlWrVuwhvZXRtjM6OAzz3VcwX3ka469Poxo0tTpWkdIpOzA/fA8O70e17oSKe9gxxIMQpYwqG+AY2iRxDXrAQ15xEU2+BTJ9+nQOHTpETEwMjz/+OFWqVKFMmTJkZmZy+PBhduzYwVtvvUXVqlUZPXp0SWb2KqpeY4xxr2K+Oxlz2ouoOweh7hiAMjz7+gZ99jT68w/Q65Y7znWM/DuqaWurYwlhKaNNJ8wt62HXdohuZnWcQsu3QDp06HDN52sEBARQr1496tWrR9++fdm0aVOxBiwN1J8iMZ6bip73Lnrx/9ApOzAeGOU48eZhdE42evkS9JJPIfsiquc9qF4DUX7+VkcTwnqNW4J/GfTGVShvLpCCPpzplltuKbIwpZnyLwMPj4G6DdGfvo/54kjUgIdQHbp5xBUb2sxDb1yNXvghpJ+ARi0w7n0Y9SfvueZdiMJSvn6oFjHozevQf34MZfe1OlKhFPgqrG3btpGamnrVSfSBAwcWeajSSimF6tQD3aAJ5px30HPfQSeuwYj7K6qye55r0mYeOvFHxx7HkYNQrTbGg6O87lyOEEVFte6ITvgBkjZDM88eZbpABTJr1izWrVtHw4YN8bvBk+BE4amKlTGeehm9ehl6wVzMCaNQHWJRd96HqhBidTwA9MUs9PqV6O8Xw7HDUKU66pFnULe09/jzN0IUq3qNoVx5dOJaVGkokLVr1/Lqq68SFhZW3HnE75RhoG7riW7ZHrrVoJYAABbvSURBVP31J+iV36A3rHIc0oq9ExUeYUkuffQwes136B+/g4wLjj2Ox8ZBs7ZSHEIUgLLZUC3aoTeuQWdfRPl67h/lBSqQ8uXLU65cueLOIq5BBQSi4v6K7tLbUSSrvkWvWArN22J06AYNmqJsxXs/qD57Gr05Ab1uBfy6GwwD1SIG1bU31G7gEedohHAnqmV79JrvIGkLePDD0vL9zXPs2P/f2Na7d2/eeust+vbtS1DQldfwV6pUqfjSCSdV8U+oIaPR/e5HL/8avWoZ5uYEKB+Eatke1bgVREU7TsYXks7NhYO/ondsRf+cCPv2gNaOw1T9H0K16Yiq4HlXiAnhNuo1cTxo6qe1Hv20zXwLZNSoUVdN27x581XTPvnkk6JNJK5LVQhF9XsA3ec+SNqEXr8K/WO8Y6/Exwdq1kPVqAOVqzlOvIeGQ0DgNcfb0jnZcO4spB1HHz0ERw+h9++FfbshO9vxpup1UH0GoZq1gao1ZG9DiCKgfHxQzT3/MFa+BSLF4N6U3e4479CsLfriRdi7A73zZ/Tu7ejV30J2NvryGcqWQ+/dRTaQ9/QDkJUFFzOvXKjd11E8t97uODQVFe02J+2F8DbecBhLBlP0AsrPD6Kbo6KbA47nipN2HH47gD6dDudOw9kzsCkVwzBQTVqBnz8EBEL5QFRwGERUhdBwrxheQQiP4DyM9aPHHsbKt0Bee+01+vbtS506dfKdOSUlhUWLFvH0008XSzjhGmUYEB4B4RFcfsBJLfgem92OMXikZdmEEA7ecBgr3wK5/fbbmTVrFhkZGURHR1O5cmXnWFhHjhwhKSmJcuXKERcXV5J5hRDCazgPY+3Y4pE3FeZbIL/99huTJ08mJSWFrVu3kpycTEZGBuXKlaN69eqMHj2amjVrlmRWIYTwLnUbO85Pbl7vkTcV5lsgH3/8MT169KBOnTpMnDiROXPmlGQuIYTwespmQzVphf45EZ2Xh/LxrHOQ+RZIREQEc+fOpWrVquTm5rJixQq01le9r0uXLsUaUAghvJlq3ha9fiXs+QU8bAy5fAvkiSee4Msvv2Tt2rXk5eWxevW1HwYvBSKEEIXQsAXYfdFb1nvcIKT5FkjlypUZNmwYAP/4xz944YUXSiyUEEKUFsrPHxo2R2/dgB70iEfdrFug0e+kPIQQovio5m3h1ElITbE6yk2R4VOFEMJiqmlrMAz0lnVWR7kpUiBCCGExVa481G2E3rLe6ig3RQpECCHcgGrRzjGg6ZFDVkcpMCkQIYRwA6ppGwD0tg0WJyk4KRAhhHADKiQMImuityVaHaXApECEEMJNqKatYe8u9PmzVkcpELcezv3TTz/lhx9+IDAwEIBBgwbRokULABYuXMjy5csxDIOHHnqIZs2aWRlVCCEKTTVp7Xh09fZNqHadrY5zQ25dIAC9evXizjvvvGLaoUOHSEhI4I033uDUqVNMnDiRN998E8OQHSohhAerXhuCgmHbRvCAAvHI37iJiYnExMRgt9upWLEiERERpKR41g04QgjxR+r3B77ppM3o3Byr49yQ2++BLFu2jNWrV1OrVi0GDx5MQEAA6enpREVFOd8TEhJCenr6VfPGx8cTHx8PwJQpUwgLC3M5h81mK9T87sBut6OU8vjtuMQbviaXyLa4H6u2I6tDV86s+Y7AY4fwa9qqSJZZXNtieYFMnDiR06dPXzU9Li6O7t27079/f8DxjPa5c+cyfPjwa44KfC2xsbHExsY6Pz558qTLOcPCwgo1vzvIycnBbrd7/HZc4g1fk0tkW9yPVduhq9QCuy9n1sRjVCmaZy4VZlsqV66c72uWF8jzzz9foPd17dqVf/7znwCEhoaSlpbmfC09PZ2QkJBiySeEECVJ+flB/SbobRvRAx9268EV3focyKlTp5z/37hxI5GRkQC0bNmShIQEcnJyOH78OEeOHLnus9uFEMKTqKat4eQx+O2g1VGuy/I9kOv58MMPSU1NRSlFeHg4jzzyCACRkZG0a9eOMWPGYBgGQ4cOlSuwhBBeQzVphQb09kRUlWpWx8mXWxfI448/nu9r/fr1o1+/fiWYRgghSoYKDoWqNdC/bIYe91gdJ1/yZ7sQQrgh1egWSNmBzsywOkq+pECEEMINqUa3QF4e7NxmdZR8SYEIIYQ7ql0fypRF/7LJ6iT5kgIRQgg3pGw2aNAMvX1Tge99K2lSIEII4aZUoxZwOg0O77c6yjVJgQghhJtSjW4BcNvDWFIgQgjhpq64nNcNSYEIIYQbc+fLeaVAhBDCjanGly7n3Wp1lKtIgQghhDurVR/8y6CTpECEEELcBGWzQb3GjodMudnlvFIgQgjh5lTD5pB2HE4csTrKFaRAhBDCzano5gDoHe51GEsKRAgh3F3FP0FoRbc7DyIFIoQQbk4phYpuBrt/RufmWh3HSQpECCE8gGrYHDIzIHWP1VGcpECEEMIT1G8CynCrw1hSIEII4QFUufJQow7ajW4olAIRQggPoaKbwa970BnnrY4CSIEIIYTHUNHNQZuw62erowBSIEII4Tlq1QO/Mm5zP4gUiBBCeAhls0Hdhuhd262OAkiBCCGER1H1G8Oxw+j0k1ZHkQIRQghPouo3BUC7wXkQKRAhhPAkVWtAQHm3OJEuBSKEEB5EGYZjePfdP1s+vLsUiBBCeBhVvwmkn4Tj1g7vLgUihBAexl3Og0iBCCGEp6lUGSqEws5tlsaQAhFCCA+jlEI1aILevR1tmpblkAIRQghPVL8pnD8Lh/dbFkEKRAghPJCq3wQAbeFhLCkQIYTwQCokDCpWRu/5xbIMUiBCCOGhVL1GkJyENvMsWb8UiBBCeKq6jSDjAhxKtWT1UiBCCOGhVN1GAJYdxrJZstbLrFu3js8++4zDhw/zyiuvULt2bedrCxcuZPny5RiGwUMPPUSzZs0A2Lp1K7Nnz8Y0Tbp27crdd99tVXwhhLCMCgmD8Aj07l8g9q4SX7/leyCRkZE8/fTTNGjQ4Irphw4dIiEhgTfeeIPnnnuOWbNmYZompmkya9Ysxo8fz7Rp01i7di2HDh2yKL0QQlhL1W0Ee5IsuR/E8gKpWrUqlStXvmp6YmIiMTEx2O12KlasSEREBCkpKaSkpBAREUGlSpWw2WzExMSQmJhoQXIhhHAD9RpDxnlL7gex/BBWftLT04mKinJ+HBISQnp6OgChoaHO6aGhoSQnJ19zGfHx8cTHxwMwZcoUwsLCXM5js9kKNb87sNvtKKU8fjsu8YavySWyLe7HU7Yjr+2tnPzvNMod+pWyzVtd8z3FtS0lUiATJ07k9OnTV02Pi4ujVatrb3B+wxRfa7pS6prvjY2NJTY21vnxyZOuP8ErLCysUPO7g5ycHOx2u8dvxyXe8DW5RLbF/XjMdigbhFXi3JYNZLTres23FGZbrnWE6JISKZDnn3/+pucJDQ0lLS3N+XF6ejohISEAV0xPS0sjODi48CGFEMJDqbqN0Ns2ok3T8byQEmL5OZD8tGzZkoSEBHJycjh+/DhHjhyhTp061K5dmyNHjnD8+HFyc3NJSEigZcuWVscVQgjr1GsEF87BbwdKdLWWnwPZuHEj//3vfzl79ixTpkyhRo0aPPfcc0RGRtKuXTvGjBmDYRgMHToU4/dmHTJkCJMmTcI0TTp37kxkZKTFWyE8hdaarKwsTNPM99CnFY4dO8bFixetjgE4PkeGYeDv7+9WnyORP1W3ERrQu39BVa1RYuu1vEBat25N69atr/lav3796Nev31XTW7RoQYsWLYo7mvBCWVlZ2O12bDbLv/WvYLPZ8PHxsTqGU25uLllZWZQpU8bqKKIAVFglCAl33FDYtXeJrddtD2EJURxM03S78nBHNpsN08LnTIibp6KiHeNileBz0qVARKkih2QKTj5XHiaqIZw7U6LPSZcCEUIIL6CiogHQyUkltk4pECFKWGRkJN26dXP+O3jwYJEu/8477yzS5QkP8adICCgPyTtKbJVyMFiIEubv78/3339fbMv/8ssvi23Zwn0ppaBONDpFCkSIYmfO/w/64L4iXaaKrIkR99ebnm/+/Pls2bKFSZMmATB48GCGDRtGTEwMUVFRDB06lPj4ePz9/Zk9ezbh4eGcOHGCcePGsX+/YwykyZMn06pVK6KiokhOTkZrzcsvv8yKFStQSjFq1Cjuuusu5yClwcHB7N69myZNmvD222/LOQ8voOpEo7duQJ85hQoq/hus5RCWECUsKyvLefhq6NChN3x/RkYGLVq0ID4+nrZt2/K///0PcIzw0LZtW+Lj41m2bBn16tW7Yr6lS5eSlJTE999/z/z583n55Zc5duwYAL/88gsvvfQSK1euZP/+/TIgqZe4dB6EEjoPInsgotRyZU+hKNzsISxfX1+6desGQOPGjVmzZg0Aa9eu5c033wTAx8eHwMDAK+bbuHEjd999Nz4+PoSHh9O2bVu2bdtGQEAAzZo1c45x1LBhQw4ePJjv/VjCg1SrDb5+6OQdqJYdin11sgcihBv4430Xl9+VbrPZnIeXfHx8yM3NLdAyr3c/gK+vr/P/N7NM4d6UzQa16pXYeRApECHcQGRkJElJSZimyeHDh9m6desN5+nQoQNz584FIC8vj3Pnzl3xetu2bfnyyy/Jy8sjLS2NDRs2OJ/qKbyXqhMNB1PRmRnFvi4pECHcQOvWralWrRpdu3Zl4sSJNG7c+Ibz/OMf/yAhIYGuXbvSo0cPdu/efcXrPXv2pEGDBnTr1o17772X5557jooVKxbXJgg3oaKiQZuwd2fxr0uX5H3vFvvtt99cntdjng1wHf3798dut/Pxxx9bHaVIuPI1ycjIoGzZssWUyHU2m83tDiO5+rnyhp8V8Nzt0FmZmE8MQvXoj9H3L0DxPQ9E9kCEEMKLKP8yUK02OqX4r8SSAhFCCC+jateH1GR0Me/VSoEIIYSXUXUaQHY2FPGNsn8kBSKEEN6mVn0AdDGfSJcCEUIIL6NCwiAkHPbuKtb1SIEIIYQXUrXro6VAhPAuBw8epEuXLlbHEN6udgM4dRKdfqLYViEFIsR1HDjgw8iRFejfP5SRIytw4ID7PLdciOtRdS6dBym+vRAZTFGIfBw44ENcXAj799ud0zZvtjN/fjrVquUVatl5eXk888wz/PTTT0RERDB37lzsdvuNZxSioKrUAF8/SNkJ9C2WVcgeiBD5ePXV8leUB8D+/XZefbV8oZe9b98+HnjgAVasWEFgYCBLliwp9DKFuJyy2aBm3WLdA5ECESIfR49e+3DVsWOFP4wVGRlJo0aNAGjSpAkHDhwo9DKF+CNVuwEc/BWdlVksy5cCESIfERHXPkxVqVLhDl8B+Pn5Of/v4+NDXl7hlynEH6k69cE0yUkpnvtBpECEyMfYseeoXj3nimnVq+cwduy5fOYQws3UcjylMmfX9mJZvBSIEPmoVi2P+fPT6ds3g5iYi/Ttm1EkJ9CFKCmqXHn4UyTZu34uluXLVVhCXEe1anm8887pIl1mZGQky5cvd348bNgwtxzOXXgH1aYTdh+D4vizRwpECCG8mNHrXgLCwsgqhmebyCEsIYQQLpECEaVKKXoAZ6HJ50rciBSIKFUMw5BzDQWQm5uLYcivB3F9cg5ElCr+/v5kZWVx8eJFlFJWx3Hy8/Pj4sWLVscAHHsehmHg7+9vdRTh5qRARKmilKJMmTJWx7hKWFgYJ4vhJKcQxUn2UYUQQrhECkQIIYRLpECEEEK4RGm5Vk8IIYQLZA+kgMaNG2d1hCLhLdsBsi3uylu2xVu2A4pvW6RAhBBCuEQKRAghhEt8JkyYMMHqEJ6iVq1aVkcoEt6yHSDb4q68ZVu8ZTugeLZFTqILIYRwiRzCEkII4RIpECGEEC6RsbAKaN68eWzatAmbzUalSpUYPnw45cqVszrWTdm6dSuzZ8/GNE26du3K3XffbXUkl5w8eZIZM2Zw+vRplFLExsZyxx13WB3LZaZpMm7cOEJCQjz60tELFy4wc+ZMDh48iFKKxx57jLp161odyyVff/01y5cvRylFZGQkw4cPx9fX1+pYBfLuu++yefNmgoKCmDp1KgDnz59n2rRpnDhxgvDwcJ588kkCAgIKvzItCmTr1q06NzdXa631vHnz9Lx58yxOdHPy8vL0yJEj9dGjR3VOTo5++umn9cGDB62O5ZL09HS9d+9erbXWGRkZetSoUR67LVpr/dVXX+np06fryZMnWx2lUN5++20dHx+vtdY6JydHnz9/3uJErklLS9PDhw/XFy9e1FprPXXqVL1ixQprQ92EpKQkvXfvXj1mzBjntHnz5umFCxdqrbVeuHBhkf3+kkNYBdS0aVN8fHwAqFu3Lunp6RYnujkpKSlERERQqVIlbDYbMTExJCYmWh3LJcHBwc4rSsqUKUOVKlU87utxSVpaGps3b6Zr165WRymUjIwMdu7cSZcuXQCw2Wwet4d+OdM0yc7OJi8vj+zsbIKDg62OVGDR0dFX7V0kJibSqVMnADp16lRkP/tyCMsFy5cvJyYmxuoYNyU9PZ3Q0FDnx6GhoSQnJ1uYqGgcP36cffv2UadOHaujuOSDDz7gL3/5C5mZmVZHKZTjx48TGBjIu+++y/79+6lVqxYPPvigRz5TJCQkhD59+vDYY4/h6+tL06ZNadq0qdWxCuXMmTPOEgwODubs2bNFslwpkMtMnDiR06dPXzU9Li6OVq1aAbBgwQJ8fHy49dZbSzpeoehrXK3tTg9UckVWVhZTp07lwQcfpGzZslbHuWmbNm0iKCiIWrVqkZSUZHWcQsnLy2Pfvn0MGTKEqKgoZs+ezaJFi4iLi7M62k07f/48iYmJzJgxg7Jly/LGG2+wevVqOnbsaHU0tyMFcpnnn3/+uq+vXLmSTZs28cILL3jcL9/Q0FDS0tKcH6elpXnUbvkf5ebmMnXqVG699VbatGljdRyX7N69m59++oktW7aQnZ1NZmYmb731FqNGjbI62k0LDQ0lNDSUqKgoANq2bcuiRYssTuWa7du3U7FiRQIDAwFo06YNe/bs8egCCQoK4tSpUwQHB3Pq1CnnthWWnAMpoK1bt7J48WKeffZZ/Pz8rI5z02rXrs2RI0c4fvw4ubm5JCQk0LJlS6tjuURrzcyZM6lSpQq9e/e2Oo7L7rvvPmbOnMmMGTMYPXo0jRo18sjyAKhQoQKhoaH89ttvgOOXcNWqVS1O5ZqwsDCSk5O5ePEiWmu2b99OlSpVrI5VKC1btmTVqlUArFq1ynlEpbDkTvQCevzxx8nNzXWenIqKiuKRRx6xONXN2bx5M3PmzME0TTp37ky/fv2sjuSSXbt28cILL1CtWjXnnuCgQYNo0aKFxclcl5SUxFdffeXRl/GmpqYyc+ZMcnNzqVixIsOHDy+aS0Ut8Omnn5KQkICPjw81atRg2LBh2O12q2MVyPTp09mxYwfnzp0jKCiIe++9l1atWjFt2jROnjxJWFgYY8aMKZKvjRSIEEIIl8ghLCGEEC6RAhFCCOESKRAhhBAukQIRQgjhEikQIYQQLpECEcIiI0aM4Oeff7Y6hhAukwIRQgjhEikQIYQQLpGxsISwUGpqKnPnzuXEiRM0a9aMESNGeMyDi4SQPRAhLLRu3TrGjx/PjBkzOHDgACtXrrQ6khAFJnsgQlioZ8+ehISEAHDLLbeQmppqbSAhboLsgQhhoQoVKjj/7+vrS1ZWloVphLg5UiBCCCFcIgUihBDCJVIgQgghXCLPAxFCCOES2QMRQgjhEikQIYQQLpECEUII4RIpECGEEC6RAhFCCOESKRAhhBAukQIRQgjhEikQIYQQLvk/qfzqQEW9umMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Iteracion       Resultado (h)                f(h)               Ea(%)\n",
      "________________________________________________________________________\n",
      "         2         2.037409988         9.174150632        16.644465169\n"
     ]
    },
    {
     "data": {
      "image/png": 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kyu5tdSy3oELDMEY8D+dSHAWcnWV1JI8kBSKEC9L7EjBfGwupZx3nOzr19IjhSEqSqlUPY+jTsH+P43JnUeSkQIRwMebGVZhvvQDlymP89Q15mFIhqNvboe7uj173Hea676yO43HkMl4hXITWGr3sE/TSjxz3dzzxVxlttgioex9EH9iH/uif6PAaqNsirI7kMWQPRAgXoHNy0PPeQS/9CNWmE8boiVIeRUQZXhh/eRb8K2C+NwV98bzVkTyGFIgQFtMZ6Zx7bRx6fSyqVwxqyGiUzW51LI+iyvtjDBsPqWcx589Ea211JI8gBSKEhfSFVMypfyNrx2bUoJEY9z4oJ8uLiaoRgerzEGzbiP7xe6vjeAS3OAcyYsQIfH19MQwDLy8vpkyZwsWLF5k2bRqnT58mNDSUp59+Gj8/2eUX7kOfPoE5/SU4l0yF8VO4UKOe1ZE8nup6L/qXLeiF/0ZHNECFVbE6kltzmz2Ql156iTfeeIMpU6YAsGTJEho1asTbb79No0aNWLJkicUJhSg4ffQg5j/Gw8ULGGNewaelDP5XEpRhYAx5GuzemO9PRedkWx3JrblNgfxRfHw8HTt2BKBjx47Ex8dbnEiIgtH792C+8VdQYIybgqolex4lSQUGYwwaCYeS0Ms/szqOW3OLQ1gAr776KgBdu3YlOjqa1NRUAgMDAQgMDOT8+WuvrIiNjSU2NhaAKVOmEBLi/BAQNputUPO7ArvdjlLK7bfjMnf8nmRu38S5aS/iFRRC4MQZeFX8E+Ce25Ift9iWbr1JTdhCxvLPCOjSE3v1Wte8xS22o4CKa1vcokAmTZpEUFAQqampvPLKK1SuXLlA80VHRxMdHZ339ZkzZ5zOEBISUqj5XUF2djZ2u93tt+Myd/ue6K0bMP/9BoSFo5+eyFnDDr/nd7dtuRF32RZ93yDYtomUaS87btj0unpkY3fZjoIozLbc6PetWxzCCgoKAiAgIICWLVuSlJREQEAAZ8+eBeDs2bP4+/tbGVGIGzI3rML85z+gem2MZ19F+QdaHanUU+X9UQMfdxzKil1qdRy35PIFkpGRQXp6et7ff/75Z6pVq0aLFi1Ys2YNAGvWrKFly5ZWxhQiX+bqb9D/mQZ1GmKMfhlVVq4WdBWqRTto2ga99CMZ+t0JLn8IKzU1lTfffBOA3Nxc2rdvT9OmTalVqxbTpk1j5cqVhISEMGbMGIuTCnEt8/ul6E/nQOOWGMOek9F0XYxSCuP/hmG+OALz4386Cl7uwykwly+QSpUq8cYbb1wzvXz58rz44osWJBKiYMxln6KXfAjNozD+8ozcXe6iVIUg1H3/5xixd2sc3N7O6khuw+UPYQnhbrTWmEs/Qi/5ENW6I8Zjz0p5uDjVsQeE18D8ZA46I93qOG5DCkSIIqS1Ri9egP56IapdtGNcKy95brmrU15eGA8Og7Nn0Ms+tTqO25ACEaKIaK3Rn3+A/uZzVIe7UINGogwpD3ehatdHRXVBf78EffyI1XHcghSIEEVAa43+dA76u8WoO+9G/d8TKEN+vNyNun8wePtifvofq6O4BfkXLkQhaa3Rn7yPjv0S1aW34/nlUh5uSflXQPV8AH7ZQub2zVbHcXnyr1yIQtBaO0Z2/eErVPQ9qAGPymWgbk517gnBFbk4bybazLU6jkuTAhHCSVpr9Mf/Qq/8GtX1XtQDQ6U8PICye6PuH0zOwST0hlVWx3FpUiBCOCGvPFYtQ3W7D9V/iJSHB1Et2mOv0wC95EN0ZobVcVyWFIgQtyjvsNXl8uj3iJSHh1FK4ffwk3AuBf29PGsoP1IgQtyCvBPmlw9bSXl4LO/6jR3jZH23BH3pgtVxXJIUiBAFlFceP3yFir5XDluVAsa9D0JGOnrFYqujuCQpECEKwHGfx38c5dGlN+oBKY/SQFW9DdXyDvQPX6HPn7M6jsuRAhHiJvLuMI9diurcSy7VLWVU74GQnY3+5guro7gcKRAhbkBrjf5inuMO8053o2L+IuVRyqiwKqioTujVy9Fnk62O41KkQITIR97AiCsWoe7sgRr4uJRHKaV6xYDW6OUy0OKVpECEuA6tNXrpf/83MKKUR6mmQiqh2nVB//g9+pzshVwmBSLEdeivPkYv+xTVvqsMjCgAUN3vB9NEfyf3hVwmPxVC/IH51UL0VwtR7bqgHhoh5SEAUKFhqFYd0Gu+RV84b3UclyA/GUJcwVz2KfrLj1BtO/3+PA/5ERH/o3r0g+wsdOyXVkdxCfLTIcTvzOWfOR5D2+ZO1MOj5GFQ4hqqcjVo1ha9ahk67ZLVcSwnBSIEYH7zBXrxAlSrjqhHnpLyEPkyevaH9EvoVcusjmI5KRBR6pkrFqEXzUO16uB4hrmUh7gBVa0WNGzuuDs9O8vqOJaSAhGlmrliEfrzD1At70ANeRrlJeUhbs7o1gcupKI3rrY6iqWkQESpdVV5DB0j5SEKrl5jCK+B/n4p2jStTmMZKZCb0OeSyX1nEtn7dlkdRRQhKQ9RGEopVLf74PgRSNhqdRzLSIHcjG8ZOJTEhTnTSvX/NDyJ+e0XUh6i0FSLO6BCMGYpHupdCuQmlG9ZVN9BZO9LQG9eY3UcUUjmsk/RX/x+wlzKQxSCstlQ0b1h70704f1Wx7GEFEgBqDadsNWu7xiVNSPd6jjCSebXCx33ebTuKCfMRZFQd3QDnzKldngTKZACUIZB+aGjHc9HlmcCuB2tNebSj9BLf7/DfMhoKQ9RJFRZP1T7aPRP69GpZ62OU+KkQArIu14jVJs70d8tRp8+YXUcUUBaa/Si+eivfx/bSu4wF0VMdeoJuTnodSusjlLipEBugeo7GLy8MD/+F1prq+OIm3A8hnYO+tsvUB27owY9KeUhipyqVNlxY+Gab9E5OVbHKVFSILdABQaj7nkQdv4EWzdYHUfcgDZN9Eez0bFfOp5hLkOyi2JkdOoJ51Jg+0aro5Qo+Ym6RapLbwiv4dgLkcHUXJLOzUV/8DZ69Teou/rIM8xF8WvYHELDMEvZ+FhSILdIeXlhPDQSzp9FL/nQ6jjiD3RODvr9qegNK1H3PIi6/2EpD1HslOGFurMH7EtAHz1gdZwSIwXiBFUjAtWpJ3r1cvSve62OI36nszIx35uM/ulHVP9HMHrHSHmIEqPaRYPdG71qudVRSowUiJPUfX+GgCDMD94u9SNyugKdnoY542XY+RPqz8Mdg90JUYJUufKo1h3RG1ej09OsjlMi3LpAtm/fzlNPPcWTTz7JkiUleyOPKlMWY/BIOH4EvfS/JbpucTV94Tzm1L/B/t2oR5/B6Njd6kiilFIdukNWJnpT6Ri1wm0LxDRN5syZw4QJE5g2bRrr16/n6NGjJZpBNbwd1eEu9HdL0Iky2GJJOnzYi8GDvXi8fzbHn5mAPnYYY/gEjFYdrI4mSrPbakPVGqXmnhC3LZCkpCTCwsKoVKkSNpuNqKgo4uPjSzyH6v8IBFfEnDtdhjkpIYcPexETE8SWrw/xgs8T+GalMDJxBkcqtLE6mijllFKoDnfB4V/Rh5KsjlPsbFYHcFZKSgrBwcF5XwcHB5OYmHjDefr16+f0+ux2O9nZ2dd9TaddgiO/wtrOqLAqTq+juCUkJKCUKtTn4AoSE21knE2nXvkkntim2H0hgvTcZ9nQ2yQiwj1v5LrRvy934ynb4ux26Nxc2J8I//eQy/w+KMz3JC4uLt/X3LZArncn+B+vuImNjSU2NhaAKVOmYLfbnV6fUir/+QMqkJNeCfPMSbz8AzACAp1eT3G6/PkU5nNwBb45qdT0P0CWaWf3hQgyTR8AsrMNt922G/77cjOesi1Ob4fdTk5AIOb5c9gqh7vEuGvF9T1x2wIJDg4mOTk57+vk5GQCA6/+xR0dHU10dHTe1x9//LHT6wsJCeHMmTP5vq5zcjDfnADHDmG8MBVVsbLT6you/fr1w263F+pzsJq5ajm5H/2LHefu45GfppNp/u973rlzGjNnnrMwnfNu9u/LnXjKthRmO/T+PZhTxqEGPYpxR7ciTnbriut74rbnQGrVqsXx48c5deoUOTk5xMXF0aJFC8vyKJsN4y/PguGF+c/X5dLeIqZNE/PzD9AfzSYzogXP/jaTlKz/lUf16tmMG3fBwoRCXKFmXahcDb3Ws0+mu22BeHl5MWTIEF599VWefvpp2rZtS3h4uKWZVHAoxiNPOU6gfTrH0iyeRGdmOkp5xSJUx+6Ue+avzPs4jZiYXKKiMunTJ42FC1OoVi3X6qhCAL+fTL+jGxxMRB87ZHWcYuO2h7AAmjdvTvPmza2OcRXVtDWqWx/0d4sxw2tgdJB7EgpDp57FnPUqHExE9R+C6novSimqVctl3rxczpxJvvlChLCAat0R/flcdNxKx9WaHsht90Bcmbp/kGN454/+id73i9Vx3JY+/Cvma2Md55We+CtGt/tkaBLhNlT5AGjUAr1ptePKLA8kBVIMlOGF8ZexEBKG+d4UdPIpqyO5Hb1lPeY/ngOtMcZNRjWTezyE+zGiukDqWdi1zeooxUIKpJiosn4YI5+H3FzMdyah0y5aHcktaNPEXPoR5ux/QHgNjOenoqrXtjqWEM5pdDv4lUfHrbQ6SbGQAilGKqwqxhPj4cQxzHdeQWdlWh3JpelLFxxl+/vjZ41nXkW56D01QhSEstlRre9Eb9+IvuR5/4mUAilmqn4T1NAxsH835r/e8NhjoYWlD/+K+coY2L3D8fTAwaNQHnAzmhCqbWfIyUHHr7U6SpGTAikBRsv2qIGPwY7N6AUz0aZpdSSXobXGXPst5pRxkJOD8exrGHf2kJPlwnNUqwlVqnvkYSy3vozXnRidemJeOI/+6ve7wAeNRBnWD3FgJZ2ehl4wCx2/DiKbYQx9GuVfwepYQhQppRQqqjP6s7noE0dRYVWtjlRkZA+kBKneMajeA9Hrf0DPfRttlt7DWXr/HsxJo9Fb1qP6DsJ46iUpD+GxVMsOoBR6s2cdxpI9kBKklELdMxDTMBwPocrNgSGjUbbSc6xf5+Sgv16IXv45BIVgjH0NFRFpdSwhipUKDIa6jdCb1qJ7D/SYQ7RSIBYweg3AtNnQX8xDnz+H8cRfUeX8rI5V7PSxQ5hzZ8ChJFTbzqiBj6HKlLU6lhAlQrXqgJ4/Ew4mQY0Iq+MUiQIfwsrIyCA5OZmMjIzizFNqGN3vRw19GpJ2Y/7jOfSZk1ZHKjY6Jxvzy48xJz0Nyacwhj2HMWS0lIcoVdTtUWCzoTd7zuNub7gHcvjwYWJjY9m6dSunT5/Om16xYkWaNm1K165dqVatWrGH9FRGm07owBDMd1/DfG0sxl/Gouo3sTpWkdJJuzA/fA+OHUK16oiKedQxxIMQpYwq6+cY2iR+Hbr/Ix5xEU2+BTJ9+nSOHj1KVFQUTz75JFWqVKFMmTKkp6dz7Ngxdu3axdtvv03VqlUZPXp0SWb2KKpuI4zxr2O+Oxlz2kuoewai7u6PMtz7+gZ9/hz68w/QG1Y6znWM/BuqSSurYwlhKaN1R8xtG2HPTohsanWcQsu3QNq3b3/d52v4+flRt25d6tatS58+fdiyZUuxBiwN1J/CMZ6fil7wLnrpf9FJuzAGj3KceHMzOjsLvXIZetmnkJWJ6nE/qucAlI+v1dGEsF6jFuBbBr15DcqTC6SgD2e6/fbbiyxMaaZ8y8CjY6BOA/Sn72O+NBLV/xFU+65uccWGNnPRm9eiF38IKaehYXOMBx5F/clzrnkXorCUtw+qeRR66wb0/z2BsntbHalQCnwV1o4dOzh48OA1J9EHDBhQ5KFKK6UUqmN3dP3GmPNmoufPRMevw4j5C6qya55r0mYuOv5Hxx7H8SNQrRbGw6M87lyOEEVFteqAjvsBErZCU/ceZbpABTJnzhw2bNhAgwYN8PHxKe5MpZ6qWBnjmVfQa1egF83HnDgK1T4adc+DqApBVscDQGdmoDeuRn+/FE4egyrVUY89i7q9ndufvxGiWNVtBOXKo+PXo0pDgaxfv57XX3+dkJCQ4s4jfqcMA3VnD3SLduivP0Gv/ga9aY3jkFb0PajQMEty6RPH0Ou+Q//4HaRdcuxxPDEemraR4hCiAJTNhmreFr15HTorE87J878AABa5SURBVOXtvv8pL1CBlC9fnnLlyhV3FnEdys8fFfMXdOdejiJZ8y161XJo1gajfVeo3wRlK977QfX5c+itcegNq+DXvWAYqOZRqC69oFZ9tzhHI4QrUS3aodd9BwnbwI0flpbvb56TJ/93Y1uvXr14++236dOnDwEBV1/DX6lSpeJLJ/Koin9CDRmN7vsQeuXX6DUrMLfGQfkAVIt2qEYtISLScTK+kHRODhz5Fb1rO/rneDiwD7R2HKbq9wiqdQdUBfe7QkwIl1G3seNBUz+td+unbeZbIKNGjbpm2tatW6+Z9sknnxRtInFDqkIwqu9gdO8HIWELeuMa9I+xjr0SLy+oURd1W22oXM1x4j04FPz8rzvels7OggvnIfkU+sRROHEUfWg/HNgLWVmON1Wvjeo9ENW0NVS9TfY2hCgCyssL1cz9D2PlWyBSDK5N2e2O8w5N26AzM2H/LvTun9F7d6LXfgtZWegrZyhbDr1/D1lA7tjBkJEBmelXL9Tu7SieO+5yHJqKiHSZk/ZCeBpPOIwlgyl6AOXjA5HNUJHNAMdzxUk+Bb8dRp9LgQvn4HwqbDmIYRioxi3Bxxf8/KG8PyowBMKqQnCoRwyvIIRbyDuM9aPbHsbKt0DeeOMN+vTpQ+3atfOdOSkpiSVLljB27NhiCSecowwDQsMgNIwrDzipRd9js9sxBo20LJsQwsETDmPlWyB33XUXc+bMIS0tjcjISCpXrpw3Ftbx48dJSEigXLlyxMTElGReIYTwGHmHsXZtc8ubCvMtkN9++43JkyeTlJTE9u3bSUxMJC0tjXLlylG9enVGjx5NjRo1SjKrEEJ4ljqNHOcnt250y5sK8y2Qjz/+mO7du1O7dm0mTZrEvHnzSjKXEEJ4PGWzoRq3RP8cj87NRXm51znIfAskLCyM+fPnU7VqVXJycli1ahVa62ve17lz52INKIQQnkw1a4PeuBr2/QJuNoZcvgXy1FNP8eWXX7J+/Xpyc3NZu/b6D4OXAhFCiEJo0Bzs3uhtG91uENJ8C6Ry5coMGzYMgL///e+8+OKLJRZKCCFKC+XjCw2aobdvQg98zK1u1i3Q6HdSHkIIUXxUszZw9gwcTLI6yi2R4VOFEMJiqkkrMAz0tg1WR7klUiBCCGExVa481GmI3rbR6ii3RApECCFcgGre1jGg6fGjVkcpMCkQIYRwAapJawD0jk0WJyk4KRAhhHABKigEwmugd8RbHaXApECEEMJFqCatYP8e9MXzVkcpEJcezv3TTz/lhx9+wN/fH4CBAwfSvHlzABYvXszKlSsxDINHHnmEpk2bWhlVCCEKTTVu5Xh09c4tqLadrI5zUy5dIAA9e/bknnvuuWra0aNHiYuL46233uLs2bNMmjSJGTNmYBiyQyWEcGPVa0FAIOzYDG5QIG75Gzc+Pp6oqCjsdjsVK1YkLCyMpCT3ugFHCCH+SP3+wDedsBWdk211nJty+T2QFStWsHbtWmrWrMmgQYPw8/MjJSWFiIiIvPcEBQWRkpJyzbyxsbHExsYCMGXKFEJCQpzOYbPZCjW/K7Db7Sil3H47LvOE78llsi2ux6rtyGjfhdR13+F/8ig+TVoWyTKLa1ssL5BJkyZx7ty5a6bHxMTQrVs3+vXrBzie0T5//nyGDx9+3VGBryc6Opro6Oi8r8+cOeN0zpCQkELN7wqys7Ox2+1uvx2XecL35DLZFtdj1XboKjXB7k3quliMKkXzzKXCbEvlypXzfc3yAnnhhRcK9L4uXbrwj3/8A4Dg4GCSk5PzXktJSSEoKKhY8gkhRElSPj5QrzF6x2b0gEddenBFlz4Hcvbs2by/b968mfDwcABatGhBXFwc2dnZnDp1iuPHj9/w2e1CCOFOVJNWcOYk/HbE6ig3ZPkeyI18+OGHHDx4EKUUoaGhPPbYYwCEh4fTtm1bxowZg2EYDB06VK7AEkJ4DNW4JRrQO+NRVapZHSdfLl0gTz75ZL6v9e3bl759+5ZgGiGEKBkqMBiq3ob+ZSt0v9/qOPmS/7YLIYQLUg1vh6Rd6PQ0q6PkSwpECCFckGp4O+Tmwu4dVkfJlxSIEEK4olr1oExZ9C9brE6SLykQIYRwQcpmg/pN0Tu3FPjet5ImBSKEEC5KNWwO55Lh2CGro1yXFIgQQrgo1fB2AJc9jCUFIoQQLuqqy3ldkBSIEEK4MFe+nFcKRAghXJhqdPly3u1WR7mGFIgQQriymvXAtww6QQpECCHELVA2G9Rt5HjIlItdzisFIoQQLk41aAbJp+D0caujXEUKRAghXJyKbAaA3uVah7GkQIQQwtVV/BMEV3S58yBSIEII4eKUUqjIprD3Z3ROjtVx8kiBCCGEG1ANmkF6GhzcZ3WUPFIgQgjhDuo1BmW41GEsKRAhhHADqlx5uK022oVuKJQCEUIIN6Eim8Kv+9BpF62OAkiBCCGE21CRzUCbsOdnq6MAUiBCCOE+atYFnzIucz+IFIgQQrgJZbNBnQboPTutjgJIgQghhFtR9RrByWPolDNWR5ECEUIId6LqNQFAu8B5ECkQIYRwJ1VvA7/yLnEiXQpECCHciDIMx/Due3+2fHh3KRAhhHAzql5jSDkDp6wd3l0KRAgh3IyrnAeRAhFCCHdTqTJUCIbdOyyNIQUihBBuRimFqt8YvXcn2jQtyyEFIoQQ7qheE7h4Ho4dsiyCFIgQQrghVa8xANrCw1hSIEII4YZUUAhUrIze94tlGaRAhBDCTam6DSExAW3mWrJ+KRAhhHBXdRpC2iU4etCS1UuBCCGEm1J1GgJYdhjLZslar7BhwwY+++wzjh07xmuvvUatWrXyXlu8eDErV67EMAweeeQRmjZtCsD27duZO3cupmnSpUsX7rvvPqviCyGEZVRQCISGoff+AtH3lvj6Ld8DCQ8PZ+zYsdSvX/+q6UePHiUuLo633nqL559/njlz5mCaJqZpMmfOHCZMmMC0adNYv349R48etSi9EEJYS9VpCPsSLLkfxPICqVq1KpUrV75menx8PFFRUdjtdipWrEhYWBhJSUkkJSURFhZGpUqVsNlsREVFER8fb0FyIYRwAXUbQdpFS+4HsfwQVn5SUlKIiIjI+zooKIiUlBQAgoOD86YHBweTmJh43WXExsYSGxsLwJQpUwgJCXE6j81mK9T8rsBut6OUcvvtuMwTvieXyba4HnfZjtw2d3DmP9Mod/RXyjZred33FNe2lEiBTJo0iXPnzl0zPSYmhpYtr7/B+Q1TfL3pSqnrvjc6Opro6Oi8r8+ccf4JXiEhIYWa3xVkZ2djt9vdfjsu84TvyWWyLa7HbbZD2SCkEhe2bSKtbZfrvqUw23K9I0SXlUiBvPDCC7c8T3BwMMnJyXlfp6SkEBQUBHDV9OTkZAIDAwsfUggh3JSq0xC9YzPaNB3PCykhlp8DyU+LFi2Ii4sjOzubU6dOcfz4cWrXrk2tWrU4fvw4p06dIicnh7i4OFq0aGF1XCGEsE7dhnDpAvx2uERXa/k5kM2bN/Of//yH8+fPM2XKFG677Taef/55wsPDadu2LWPGjMEwDIYOHYrxe7MOGTKEV199FdM06dSpE+Hh4RZvhXAXWmsyMjIwTTPfQ59WOHnyJJmZmVbHAByfkWEY+Pr6utRnJPKn6jREA3rvL6iqt5XYei0vkFatWtGqVavrvta3b1/69u17zfTmzZvTvHnz4o4mPFBGRgZ2ux2bzfJ/+lex2Wx4eXlZHSNPTk4OGRkZlClTxuooogBUSCUICnXcUNilV4mt12UPYQlRHEzTdLnycEU2mw3TwudMiFunIiId42KV4HPSpUBEqSKHZApOPis3E9EALqSW6HPSpUCEEMIDqIhIAHRiQomtUwpEiBIWHh5O165d8/4cOXKkSJd/zz33FOnyhJv4Uzj4lYfEXSW2SjkYLEQJ8/X15fvvvy+25X/55ZfFtmzhupRSUDsSnSQFIkSxMxf+G33kQJEuU4XXwIj5yy3Pt3DhQrZt28arr74KwKBBgxg2bBhRUVFEREQwdOhQYmNj8fX1Ze7cuYSGhnL69GnGjx/PoUOOMZAmT55My5YtiYiIIDExEa01r7zyCqtWrUIpxahRo7j33nvzBikNDAxk7969NG7cmHfeeUfOeXgAVTsSvX0TOvUsKqD4b7CWQ1hClLCMjIy8w1dDhw696fvT0tJo3rw5sbGxtGnThv/+97+AY4SHNm3aEBsby4oVK6hbt+5V8y1fvpyEhAS+//57Fi5cyCuvvMLJkycB+OWXX3j55ZdZvXo1hw4dkgFJPcTl8yCU0HkQ2QMRpZYzewpF4VYPYXl7e9O1a1cAGjVqxLp16wBYv349M2bMAMDLywt/f/+r5tu8eTP33XcfXl5ehIaG0qZNG3bs2IGfnx9NmzbNG+OoQYMGHDlyJN/7sYQbqVYLvH3QibtQLdoX++pkD0QIF/DH+y6uvCvdZrPlHV7y8vIiJyenQMu80f0A3t7eeX+/lWUK16ZsNqhZt8TOg0iBCOECwsPDSUhIwDRNjh07xvbt2286T/v27Zk/fz4Aubm5XLhw4arX27Rpw5dffklubi7Jycls2rQp76mewnOp2pFw5CA6Pa3Y1yUFIoQLaNWqFdWqVaNLly5MmjSJRo0a3XSev//978TFxdGlSxe6d+/O3r17r3q9R48e1K9fn65du/LAAw/w/PPPU7FixeLaBOEiVEQkaBP27y7+demSvO/dYr/99pvT87rNswFuoF+/ftjtdj7++GOroxQJZ74naWlplC1btpgSOc9ms7ncYSRnPytP+FkB990OnZGO+dRAVPd+GH3+DBTf80BkD0QIITyI8i0D1Wqhk4r/SiwpECGE8DCqVj04mIgu5r1aKRAhhPAwqnZ9yMqCIr5R9o+kQIQQwtPUrAeALuYT6VIgQgjhYVRQCASFwv49xboeKRAhhPBAqlY9tBSIEJ7lyJEjdO7c2eoYwtPVqg9nz6BTThfbKqRAhLiBw4e9GDmyAv36BTNyZAUOH3ad55YLcSOq9uXzIMW3FyKDKQqRj8OHvYiJCeLQIXvetK1b7SxcmEK1armFWnZubi7PPvssP/30E2FhYcyfPx+73X7zGYUoqCq3gbcPJO0G+hTLKmQPRIh8vP56+avKA+DQITuvv16+0Ms+cOAAgwcPZtWqVfj7+7Ns2bJCL1OIKymbDWrUKdY9ECkQIfJx4sT1D1edPFn4w1jh4eE0bNgQgMaNG3P48OFCL1OIP1K16sORX9EZ6cWyfCkQIfIRFnb9w1SVKhXu8BWAj49P3t+9vLzIzS38MoX4I1W7Hpgm2UnFcz+IFIgQ+Rg37gLVq2dfNa169WzGjbuQzxxCuJiajqdUZu/ZWSyLlwIRIh/VquWycGEKffqkERWVSZ8+aUVyAl2IkqLKlYc/hZO15+diWb5chSXEDVSrlsvMmeeKdJnh4eGsXLky7+thw4a55HDuwjOo1h2xexkUx397pECEEMKDGT0fwC8khIxieLaJHMISQgjhFCkQUaqUogdwFpp8VuJmpEBEqWIYhpxrKICcnBwMQ349iBuTcyCiVPH19SUjI4PMzEyUUlbHyePj40NmZqbVMQDHnodhGPj6+lodRbg4KRBRqiilKFOmjNUxrhESEsKZYjjJKURxkn1UIYQQTpECEUII4RQpECGEEE5RWq7VE0II4QTZAymg8ePHWx2hSHjKdoBsi6vylG3xlO2A4tsWKRAhhBBOkQIRQgjhFK+JEydOtDqEu6hZs6bVEYqEp2wHyLa4Kk/ZFk/ZDiiebZGT6EIIIZwih7CEEEI4RQpECCGEU2QsrAJasGABW7ZswWazUalSJYYPH065cuWsjnVLtm/fzty5czFNky5dunDfffdZHckpZ86cYdasWZw7dw6lFNHR0dx9991Wx3KaaZqMHz+eoKAgt7509NKlS8yePZsjR46glOKJJ56gTp06Vsdyytdff83KlStRShEeHs7w4cPx9va2OlaBvPvuu2zdupWAgACmTp0KwMWLF5k2bRqnT58mNDSUp59+Gj8/v8KvTIsC2b59u87JydFaa71gwQK9YMECixPdmtzcXD1y5Eh94sQJnZ2drceOHauPHDlidSynpKSk6P3792uttU5LS9OjRo1y223RWuuvvvpKT58+XU+ePNnqKIXyzjvv6NjYWK211tnZ2frixYsWJ3JOcnKyHj58uM7MzNRaaz116lS9atUqa0PdgoSEBL1//349ZsyYvGkLFizQixcv1lprvXjx4iL7/SWHsAqoSZMmeHl5AVCnTh1SUlIsTnRrkpKSCAsLo1KlSthsNqKiooiPj7c6llMCAwPzrigpU6YMVapUcbvvx2XJycls3bqVLl26WB2lUNLS0ti9ezedO3cGwGazud0e+pVM0yQrK4vc3FyysrIIDAy0OlKBRUZGXrN3ER8fT8eOHQHo2LFjkf3syyEsJ6xcuZKoqCirY9ySlJQUgoOD874ODg4mMTHRwkRF49SpUxw4cIDatWtbHcUpH3zwAX/+859JT0+3OkqhnDp1Cn9/f959910OHTpEzZo1efjhh93ymSJBQUH07t2bJ554Am9vb5o0aUKTJk2sjlUoqampeSUYGBjI+fPni2S5UiBXmDRpEufOnbtmekxMDC1btgRg0aJFeHl5cccdd5R0vELR17la25UeqOSMjIwMpk6dysMPP0zZsmWtjnPLtmzZQkBAADVr1iQhIcHqOIWSm5vLgQMHGDJkCBEREcydO5clS5YQExNjdbRbdvHiReLj45k1axZly5blrbfeYu3atXTo0MHqaC5HCuQKL7zwwg1fX716NVu2bOHFF190u1++wcHBJCcn532dnJzsVrvlf5STk8PUqVO54447aN26tdVxnLJ3715++ukntm3bRlZWFunp6bz99tuMGjXK6mi3LDg4mODgYCIiIgBo06YNS5YssTiVc3bu3EnFihXx9/cHoHXr1uzbt8+tCyQgIICzZ88SGBjI2bNn87atsOQcSAFt376dpUuX8txzz+Hj42N1nFtWq1Ytjh8/zqlTp8jJySEuLo4WLVpYHcspWmtmz55NlSpV6NWrl9VxnPbggw8ye/ZsZs2axejRo2nYsKFblgdAhQoVCA4O5rfffgMcv4SrVq1qcSrnhISEkJiYSGZmJlprdu7cSZUqVayOVSgtWrRgzZo1AKxZsybviEphyZ3oBfTkk0+Sk5OTd3IqIiKCxx57zOJUt2br1q3MmzcP0zTp1KkTffv2tTqSU/bs2cOLL75ItWrV8vYEBw4cSPPmzS1O5ryEhAS++uort76M9+DBg8yePZucnBwqVqzI8OHDi+ZSUQt8+umnxMXF4eXlxW233cawYcOw2+1WxyqQ6dOns2vXLi5cuEBAQAAPPPAALVu2ZNq0aZw5c4aQkBDGjBlTJN8bKRAhhBBOkUNYQgghnCIFIoQQwilSIEIIIZwiBSKEEMIpUiBCCCGcIgUihEVGjBjBzz//bHUMIZwmBSKEEMIpUiBCCCGcImNhCWGhgwcPMn/+fE6fPk3Tpk0ZMWKE2zy4SAjZAxHCQhs2bGDChAnMmjWLw4cPs3r1aqsjCVFgsgcihIV69OhBUFAQALfffjsHDx60NpAQt0D2QISwUIUKFfL+7u3tTUZGhoVphLg1UiBCCCGcIgUihBDCKVIgQgghnCLPAxFCCOEU2QMRQgjhFCkQIYQQTpECEUII4RQpECGEEE6RAhFCCOEUKRAhhBBOkQIRQgjhFCkQIYQQTvl/8OnilCnJYTwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Iteracion       Resultado (h)                f(h)               Ea(%)\n",
      "________________________________________________________________________\n",
      "         3         2.026918932         0.266088835         0.517586368\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "________________________________________________________________________\n",
      "Se encontro la altura h = 2.0269189318856173, con 3 iteraciones y Ea = 0.5175863681337813\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import style\n",
    "import numpy as np\n",
    "\n",
    "def grafica(x,y,hi):\n",
    "        c = f(hi)\n",
    "        style.use(\"ggplot\")\n",
    "        plt.title(\"Gafico valor de h\")\n",
    "        plt.ylabel(\"f(h)\")\n",
    "        plt.xlabel(\"h\")\n",
    "        plt.scatter(hi,0,color='blue')\n",
    "        plt.plot(x,y, linestyle='solid')\n",
    "        plt.legend((\"Funcion\",\"h\"))\n",
    "        plt.axhline(0, color = \"black\")\n",
    "        plt.axvline(0, color = \"black\")\n",
    "\n",
    "    \n",
    "        plt.show()\n",
    "\n",
    "def f(h):\n",
    "    return ((-math.pi)*(h**3)/3) + (3*(math.pi)*(h**2)) - 30\n",
    "\n",
    "def df(h):\n",
    "    return ((-math.pi)*(h**2)) + (6*(math.pi)*h)\n",
    "\n",
    "i=0\n",
    "ea= 1000\n",
    "x = np.linspace(-2, 10, num = 100)\n",
    "y = f(x)\n",
    "\n",
    "# Permite operar sin mostrar errores (como divicion entre \"0\")\n",
    "while True:\n",
    "    try:\n",
    "        n = int(input(\"Inserte número de iteraciones: \"))\n",
    "        break\n",
    "    except ValueError:\n",
    "        input(\"Valor ingresado invalido. Presione enter para continuar...\")\n",
    "while True:\n",
    "    try:\n",
    "        h = float(input(\"Ingrese valor inicial de h: \"))\n",
    "        k= 1/h\n",
    "        break\n",
    "    except ZeroDivisionError:\n",
    "        print(\"h debe ser diferente de 0\")\n",
    "        input(\"Presione enter para continuar...\")\n",
    "#----------------------------------------------------------------------------------------------------\n",
    "while i < n:\n",
    "    print(\"\")\n",
    "    print(f\"{'Iteracion':>10s}{'Resultado (h)':>20s}{'f(h)':>20s}{'Ea(%)':>20s}\")\n",
    "    print(\"________________________________________________________________________\")\n",
    "    i += 1\n",
    "    if df(h) !=0:\n",
    "        hi = h - (f(h) / df(h))\n",
    "    else:\n",
    "        i=n\n",
    "    if i > 1:\n",
    "        ea = abs((hi-h)/hi)*100\n",
    "    print(f\"{i:10d}{hi:20.9f}{f(h):20.9f}{ea:20.9f}\")\n",
    "    h = hi\n",
    "    grafica(x,y,hi)\n",
    "print(\"________________________________________________________________________\")\n",
    "print(f\"Se encontro la altura h = {h}, con {n} iteraciones y Ea = {ea}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}