Sucesiones_competencia.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Sucesiones_competencia.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMR/5wXpBtmbcqtDcmVBZXV",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/luissolarte/calculus_colab/blob/main/Sucesiones_competencia.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Modelando batallas\n",
        "\n",
        "Modelo de Lanchester.  \n",
        "\n",
        "$A=$ número de soldados del ejercito A, $\\alpha=$ poder ofensivo del ejercito A\n",
        "\n",
        "$B=$ número de soldados del ejercito B, $\\beta=$ poder ofensivo del ejercito B\n",
        "\n",
        "\n",
        "$$ A'=-\\beta B$$\n",
        "$$ B'=-\\alpha A$$\n",
        "\n",
        "---\n",
        "Versión discreta\n",
        "\n",
        "$$ \\Delta A_n=-\\beta B_n$$\n",
        "$$ \\Delta B_n=-\\alpha A_n $$\n",
        "\n",
        "donde el operador derivada discreta $\\Delta$ se define como:\n",
        " $$ \\Delta X_n=X_{n+1}-X_n$$ \n"
      ],
      "metadata": {
        "id": "SiAI3olRzMxP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Modelando la Batalla de 300\n",
        "\n",
        "Asumiendo que un soldado espartano es 1500 veces más efectivo que sus rivales."
      ],
      "metadata": {
        "id": "U0kMwsO53mbB"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pbrcMwuOu49w"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "A=[300]\n",
        "B=[10000]\n",
        "a,b=-0.9,-0.0006\n",
        "for _ in range(100):\n",
        "  NextA,NextB=B[-1]*b+A[-1],A[-1]*a+B[-1]\n",
        "  A.append(NextA)\n",
        "  B.append(NextB)\n",
        "  if NextA<0 or NextB<0: #Salir del ciclo cuando alguno pierde\n",
        "    break"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(A)\n",
        "plt.plot(B)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "kRrShqtY1Ps1",
        "outputId": "257c7d9a-74dd-44b4-f1ca-cd3ab5340584"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f4e351a0410>]"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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YQadTYOSr0PogePpSuGsIrPk87spEUp7CQFJPo9bhXQkDR8GyBXDrITpKEKkkhYGkJjPIOzMcJbTKi44SBsPqz+KuTCQlKQwktTXaNeEo4TUYeyjMu0N3HIlsJ4WBpL4yRwkHwTOXweSB8PVHcVcmkjIUBpI+Gu0Kpz4Gg2+G5e+E9y6/crOeXhbZBgoDSS9m0PlUOH8O7H44TL8KJhwZhsoWkS1SGEh6argLnHQ/HDcBVn0Mt/8GXrgeNq6PuzKRpKQwkPRlBvseD+fPg70GwQv/A+N6wNIFcVcmknQUBpL+6ufC8RPhpAfhhzUwoTdM/ROsXxd3ZSJJQ2EgmaN9Pzh/Lhx4Bsy5JTystnhm3FWJJAWFgWSWug1h4A1w+jOQnQP3HAuPjoB1X8ddmUisFAaSmdp0g3NnQ/c/wDuPwM158OYDGtJCMpbCQDJXTl3o+Wc45yVouic8dk44Ulj1SdyVidQ4hYFI845w5jQY8E9YMj9cS3h5FBRtiLsykRqjMBAByMqCLmeHC8x79oLn/gq39wjhIJIBFAYiiXZqCcPuhWH3wY9rYEIfeOpS+PGbuCsTqVYKA5HydDgqHCUcfC4suBNuPihcaNYFZklTCgORLanTAPpfB2fNhAYt4OEzwwVmjYYqaUhhIPJLWnaGs5+H/v8ovcD84j9g409xVyZSZRQGItsiKxsOHgEXzIcOA2DWf4chsj9+Me7KRKqEwkBkezRsAUMnwcmPQPGG8KrNR86Cb5fHXZlIpVQ6DMws28xeN7Onovm2ZjbXzBab2YNmVjtqrxPNL46Wt0nYxpVR+wdm1reyNYlUu3a9YeQc6HE5vPdEuMA893Yo2hh3ZSIVUhVHBhcDCxPmrwdGufuewGpgeNQ+HFgdtY+K+mFmHYFhwN5AP+BWM8uugrpEqldOPTjiTyEUWh4Iz/4R7jhCzyZISqpUGJhZK+AoYHw0b0BP4OGoy2Tg6Gh6SDRPtLxX1H8I8IC7/+TunwCLgS6VqUukRjXdI7xuc+gkWFcYhsiecqEGv5OUUtkjgxuBPwLF0XxTYI27lxwrLwVaRtMtgSUA0fJvov6b2stZpwwzG2FmBWZWUFhYWMnSRaqQGex9TLjAfMgF8MZ9cFNnKJiodzBLSqhwGJjZQGCFu9fYa6PcfZy757l7Xm5ubk19rci2q9MA+v4dzn0ZfrUvPPU7GN9Lb1eTpFeZI4NuwGAz+xR4gHB6aDTQyMxqRX1aAcui6WVAa4Bo+U7A14nt5awjkpqa7QX5T4Z3MK/9MgTClAth3cq4KxMpV4XDwN2vdPdW7t6GcAH4eXc/GZgFHB91yweeiKanRPNEy593d4/ah0V3G7UF2gHzKlqXSNIoeQfzBfPhkPNLTx3Nu0N3HUnSqY7nDC4HLjWzxYRrAhOi9glA06j9UuAKAHd/F3gIeA+YCpzv7jrJKumjbsNw6ui8V2CXTvDMZTCuB3z2StyViWxinqIDb+Xl5XlBQUHcZYhsH3dYOAWmXQXfLIF9h0Kfa6HhLnFXJhnAzBa4e155y/QEskhNMoOOQ+D8edD9j/DeFLgpD176l8Y6klgpDETiUHsH6HkVXDAP9jgCZl4LtxwMHzyrYbIlFgoDkTg1bhNepnPqY5BdG+4fBvceD4Ufxl2ZZBiFgUgy2KMnnDcb+v4PLJkHYw+BqVfCD2virkwyhMJAJFlk54RbUC98DTqdAnPGRk8x36mnmKXaKQxEkk39XBg0Gs55EXZuD09dArf3gE9eirsySWMKA5Fk1WJ/OOMZOP5O+HENTB4ID54Cqz6JuzJJQwoDkWRmBvscG55i7vlnWPw83NIFZvwX/Lg27uokjSgMRFJBTj3o/ge4cAHsczzMHh2uJyyYpOsJUiUUBiKppGELOGYsnD0LmuwBT14Mt3eHj1+IuzJJcQoDkVTUsjOcOTW8UOentXDXELhvGKxcFHdlkqIUBiKpquSFOufPh97XwKcvw61d4dnL4ftVcVcnKUZhIJLqcurCYZfARa9D59Ng3jgYcwC8cpPGO5JtpjAQSRf1c2HgKDh3NrQ6CKb/Odx59O5jGu9IfpHCQCTdNO8IpzwCpzwKOTvCv0+HiX1hyfy4K5MkpjAQSVd79oJzX4JBY2D1pzChNzyUD6s+jrsySUIKA5F0lpUNB+aH8Y56XAGLpsPNXcIgeLrILAkUBiKZoE59OOLKEAoHnARzbwsXmWePhg0/xl2dJAGFgUgmadgCBt8ULjK3PjgMa3FzHrz5IBQXx12dxEhhIJKJmneEk/8Npz0BOzSBx0bAuB7w0ay4K5OYKAxEMtnuh8PZL8Cx48PIqHcfDXcfC8vfjrkwqWkKA5FMl5UF+w2FCwrCm9aWLYDbfgOPjoDVn8VdndQQhYGIBLXqhDetXfwmdLsY3nsiXE+YdpXuPMoACgMRKateI+hzTRgue78TYM6tMHp/eOlfsP77uKuTaqIwEJHy7dQKhtwS7jza7VCYeW3pO5mLNsZdnVQxhYGIbF3zjvDbB+GMqdBo1/BO5lu7htNIGvMobSgMRGTb7HYInDkNht0Xnmx+6DQY3ws++U/clUkVUBiIyLYzgw5HwXmvhFNI334FkwfB3cfAF6/HXZ1UgsJARLZfVjZ0OiVcZD7y7/DFGzDu8DBC6srFcVcnFaAwEJGKy6kLh14AF78B3f8IH04P71CYchF8syzu6mQ7KAxEpPLq7gQ9rwqhcNBweOM+GNMpPKOw7uu4q5NtUOEwMLPWZjbLzN4zs3fN7OKovYmZzTCzRdFn46jdzGyMmS02s7fMrHPCtvKj/ovMLL/yuyUisajfDAb8Ay4sgH2OK31G4YXr4Kdv465OtqIyRwYbgd+7e0egK3C+mXUErgBmuns7YGY0D9AfaBf9jADGQggP4GrgYKALcHVJgIhIimrcBo4ZC+e9CnscDi/8bwiFV26GDT/EXZ2Uo8Jh4O5fuvtr0fS3wEKgJTAEmBx1mwwcHU0PAe7yYA7QyMxaAH2BGe6+yt1XAzOAfhWtS0SSSLMOcOI9cPbz8Kt9YfpVMKYzFEyEog1xVycJquSagZm1AToBc4Hm7v5ltGg50DyabgksSVhtadS2pfbyvmeEmRWYWUFhYWFVlC4iNaHlgWG47Pwnw5PNT/0Obj4I3noIiovirk6ogjAws/rAI8Al7r42cZm7O1Bljyi6+zh3z3P3vNzc3KrarIjUlLbdYfh0+O1DULs+PHo2jO0GC5/U08wxq1QYmFkOIQjudfdHo+avotM/RJ8rovZlQOuE1VtFbVtqF5F0ZAa/7gvn/AeOnwjFG+DBU8JzCoueUyjEpDJ3ExkwAVjo7jckLJoClNwRlA88kdB+WnRXUVfgm+h00jTgSDNrHF04PjJqE5F0lpUV7jgaOReG3Ao/rIJ7j4M7B8Cns+OuLuNU5sigG3Aq0NPM3oh+BgDXAX3MbBHQO5oHeAb4GFgM3AGMBHD3VcDfgPnRz7VRm4hkguxa0OlkuGABDPgnrPoYJg2Au46GpQVxV5cxzFP0kCwvL88LCvSLIpJ2NvwA88fDy6Pg+6/h1/3hiD9Bi/3irizlmdkCd88rb5meQBaR5JJTDw69EC5+C3r+BT5/BW7/TRgldcX7cVeXthQGIpKc6tSH7peFUOhxOSx+PrxH4ZGzNBheNVAYiEhyq9conCa65K3wbub3n4ZbDoLHR8KqT+KuLm0oDEQkNezQJLyb+eK3oOtIeOcRuDkvjJC65vO4q0t5CgMRSS31c6Hv3+GiNyBvOLx5fxji4unfa9jsSlAYiEhqatgCBvwfXPQ6dD4VFkwOw2Y/ezl8uzzu6lKOwkBEUttOrWDgqPDWtf1PhHl3hBFSp/4Jvlvxy+sLoDAQkXTReDcYfFN4l8Lex8LcsXDjfjD9z/CdBrb8JQoDEUkvTXYP71K4oAA6DoFXb4HR+8GM/9Jb17ZCYSAi6anpHnDs7XD+POhwFMweAzfuC8/9VaFQDoWBiKS3ndvBcePh/LnQvh+8fGM4UnjuGvhew6CVUBiISGbIbR+GzB45B9odGcY+unE/mPk3hQIKAxHJNM06wNA7YeSr0K43vPQvhQIKAxHJVM32gqGT4LxXFAooDEQk0zXvuIVQuDajQkFhICICZUNhz17w0g3h7qMMCQWFgYhIouYd4YTJ0ZFCn9JQeO6atL4lVWEgIlKekiOFka8m3H20L8y4GtatjLu6KqcwEBHZmmZ7RXcfzQnPKcweHQ1z8Ze0GuZCYSAisi2adQjPKZw/FzoMgFdvDg+vTbsqLQbEUxiIiGyP3PbRE83zYK/BMOfWcKQw9U8pPXS2wkBEpCJ2bhfGPrqgAPY+BubeFobOfvZyWPtF3NVtN4WBiEhlNN0jjJJ6YQHsezzMHw+jD4CnL4NvlsZd3TZTGIiIVIUmu8OQW6KX7AyDBXeGUHjykpR4R7PCQESkKjVuA4PHlL6O8/V7wus4p1wIqz6Ju7otUhiIiFSHRruG13Fe/AYceAa8+QDcdCA8PhK+/iju6n7G3D3uGiokLy/PCwoKtnu9h+YvASCnllErK4uc7Cxyso2c7CxqZRu1s7OoldiWZVGfsDwnK3yWTGdlWVXvmoiko7VfhGcUFkyCovWw7wnQ/bJwIbqGmNkCd88rd1mmhUH7Pz/LTxuLq6yO7CzbFBi1sksCxsoGR9SWnWWbguZnbVlGdkJbSRBlR9sp+Z5aUXvYbpjffFlYP2yvZL70M+qfXX579s/6h08zhZ5Ilfj2K3hlDMyfABt/hH2Og+5/CM8xVDOFQYIVa39kfVExG4ucDUXFbNj0GaY3FheXaU/sF5Y5G4uK2Vhcul7oU/7yjYntCcuLisM6RcXOhuLQryhap8xncWgvKo733ynLKDcwshICY9OP/bxPYltiv6wt9YmWZWdROh0tz0pcbqFPVpaRZYntbKGvYdGybAshlx19j5Wsb0ZWFmF70XazSrZnpetnWemyrM22XdKvpE+2GZZFmf6J02YocDPNd4XhwbV5d8CG78P7mrv/AX61T7V95dbCoFa1fWuSatawbtwlVEhxsVPkmwdG2fmNxcUUFZMwHwVPtN7GYqeoKGovLt4UMhtLPhPWK/aS9UvDaNN2ipyi4tLtJm7jZz+b9Vm/sex6RQnfVbKPxcVs2pfiqG/i/m9qS82/Y7aoNDxCMCSGRuKyxPDYvE9iAFnCNks/Q3tJ2IU+Zb9zU58y64UaN30Pie0l/cAoXYeo36btUrptyvQtnU7cXkn9Vmbeyvz3ss37brb90n5s2ofN2zfNUPY7KbNO2X8nyqll0zTlt5cuT1D7NOp2GcS+S+9jnw8eoPZ7j/PJzkfwWtuz+LrBXmW2VaJu7WxO7brbzzdcSUkTBmbWDxgNZAPj3f26mEtKKllZRhZGTjbUzcmOu5yk4B4CoSQgSoODnwVHaR82C5TQv9hLgqi0j5e0OVF7+cs8IZwS+xW7b1pWFPVzL/tdYb60b7GH2t1L9q/0O4FN+xm+N3yfk/h9P/90SvfRKVtHsbOpraR/UXHoV/Lfx0ncVun6JdstaYPNvhugzPbDOh7VTnntlCzb7Psou87m2y5ZnrgdNvXx0nXZbBsJ241XLxpyMGfWmsqZhVM5buUsnivqxJiNx/KW71Gm587166RvGJhZNnAL0AdYCsw3synu/l68lUkyC6d1wmkZkapQEnRQNjg2Ld/UVjLvWwyT8tpLI2hLfY+DH9eyfsEd9Jo3lt4//oWNu/difbfLKG7VBaCcY4WqkRRhAHQBFrv7xwBm9gAwBFAYiEiNsYRTYgmtNVtEnSbQ83LoNhLmj6fWKzdR6+7+0LYH9Lgc2nSrlq9NlucMWgJLEuaXRm1lmNkIMysws4LCwvQZOlZE5GfqNIDDfgeXvA1H/jesWAiTBsCdR8GGH6r865IlDLaJu49z9zx3z8vNzY27HBGR6ld7Rzj0QrjkLeh3PTRpCzn1qvxrkuU00TKgdcJ8q6hNREQgBEDXc6tt88lyZDAfaGdmbc2sNjAMmBJzTSIiGSMpjgzcfaOZXQBMI9xaOtHd3425LBGRjJEUYQDg7s8Az8Rdh4hIJkqW00QiIhIjhYGIiCgMREREYSAiIigMRESEFH6fgZkVAp9VcPWdgZVVWHyiBBkAAAOXSURBVE6y0H6lnnTdt3TdL0jtfdvN3csdviFlw6AyzKxgSy94SGXar9STrvuWrvsF6btvOk0kIiIKAxERydwwGBd3AdVE+5V60nXf0nW/IE33LSOvGYiISFmZemQgIiIJFAYiIpJZYWBm/czsAzNbbGZXxF1PZZjZRDNbYWbvJLQ1MbMZZrYo+mwcZ40VYWatzWyWmb1nZu+a2cVRe0rvm5nVNbN5ZvZmtF/XRO1tzWxu9Dv5YPQ+j5RjZtlm9rqZPRXNp8t+fWpmb5vZG2ZWELWl9O/ilmRMGJhZNnAL0B/oCJxkZh3jrapSJgH9Nmu7Apjp7u2AmdF8qtkI/N7dOwJdgfOjf6dU37efgJ7uvj9wANDPzLoC1wOj3H1PYDUwPMYaK+NiYGHCfLrsF8AR7n5AwrMFqf67WK6MCQOgC7DY3T929/XAA8CQmGuqMHf/D7Bqs+YhwORoejJwdI0WVQXc/Ut3fy2a/pbwfzAtSfF98+C7aDYn+nGgJ/Bw1J5y+wVgZq2Ao4Dx0byRBvu1FSn9u7glmRQGLYElCfNLo7Z00tzdv4ymlwPN4yymssysDdAJmEsa7Ft0KuUNYAUwA/gIWOPuG6Muqfo7eSPwR6A4mm9KeuwXhMCebmYLzGxE1Jbyv4vlSZo3nUnVcnc3s5S9b9jM6gOPAJe4+9rwx2aQqvvm7kXAAWbWCHgM6BBzSZVmZgOBFe6+wMwOj7ueanCYuy8zs2bADDN7P3Fhqv4ulieTjgyWAa0T5ltFbenkKzNrARB9roi5ngoxsxxCENzr7o9GzWmxbwDuvgaYBRwCNDKzkj/KUvF3shsw2Mw+JZx67QmMJvX3CwB3XxZ9riAEeBfS6HcxUSaFwXygXXSXQ21gGDAl5pqq2hQgP5rOB56IsZYKic43TwAWuvsNCYtSet/MLDc6IsDM6gF9CNdDZgHHR91Sbr/c/Up3b+XubQj/m3re3U8mxfcLwMx2NLMGJdPAkcA7pPjv4pZk1BPIZjaAcH4zG5jo7n+PuaQKM7P7gcMJw+l+BVwNPA48BOxKGN77BHff/CJzUjOzw4CXgLcpPQf9J8J1g5TdNzPbj3CxMZvwR9hD7n6tme1O+Iu6CfA6cIq7/xRfpRUXnSa6zN0HpsN+RfvwWDRbC7jP3f9uZk1J4d/FLcmoMBARkfJl0mkiERHZAoWBiIgoDERERGEgIiIoDEREBIWBiIigMBAREeD/AbdQsXA5J9TAAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Ejercicio\n",
        "\n",
        "1. Encapsule el código anterior en una función que dependa de los parámetros del modelo.\n",
        "\n",
        "2. Modelar la batalla de los bastardos ( Incluir refuerzos)"
      ],
      "metadata": {
        "id": "n0OFdh2L4D3v"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Modelos competencia de especies\n",
        "\n",
        "ver [Wiki](https://en.wikipedia.org/wiki/Lotka%E2%80%93Volterra_equations)\n",
        "\n",
        "Construir un modelo discreto"
      ],
      "metadata": {
        "id": "30r-WSNA4OgV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def dpmodel(x0=10,y0=10,a=0.01,b1=0.9,b2=1.03,N=50):\n",
        "  shark=[x0]\n",
        "  tuna=[y0]\n",
        "  for n in range(N):\n",
        "    s=shark[-1]\n",
        "    t=tuna[-1]\n",
        "    if s>0 and t>0:\n",
        "      shark.append(a*s*t+b1*s)\n",
        "      tuna.append(-a*s*t+b2*t)\n",
        "    else:\n",
        "      break\n",
        "  return shark,tuna"
      ],
      "metadata": {
        "id": "9kUCLOuq5XWF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a,b=dpmodel(x0=20,y0=30,a=0.01,N=1000)\n",
        "plt.plot(a,\"r-\")\n",
        "plt.plot(b)\n",
        "plt.show()\n",
        "plt.plot(a,b,'.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "UbOb8gKJ5jxH",
        "outputId": "ad7c328e-1291-49f3-ffbb-4cf6f9ed7f96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f4e3510e510>]"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Proyecto\n",
        "\n",
        "Modele la técnica del uso de Wolbachia para la prevención del dengue."
      ],
      "metadata": {
        "id": "1AutRmQV68ze"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "2ei_F_Cs70Cp"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}