{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y7JgKfzlPBrr"
      },
      "source": [
        "![taller_python](https://github.com/fifabsas/talleresfifabsas/blob/master/python/2_Numerico/fig/logo_fifa.png?raw=true)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "os_60KoG9612"
      },
      "source": [
        "# Taller de Python Capítulo 3:  Práctica\n",
        "[Link al notebook en Google Colaboratory](https://drive.google.com/file/d/1a1uKTIJ19ouR6TawxHwRHZE3JKKb2M87/view?usp=sharing)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w5EORpIB961-"
      },
      "source": [
        "## Nuestra motivación para hoy\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oe2yJvZN6CXS"
      },
      "source": [
        "Vamos a ver otra función que puede serles útil para analizar datos: `find_peaks`.\n",
        "\n",
        "Una vez aprendido esto, ¡Ponemos las manos en la masa y nos peleamos mucho rato con ejercicios re divertidos!.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EhenfPIp8f2v"
      },
      "source": [
        "## `find_peaks`: *como encontrar máximos y mínimos*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "oOr1YICgFz5X"
      },
      "outputs": [],
      "source": [
        "# Importamos las librerías que vamos a usar\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DRKKliyjFsHn"
      },
      "source": [
        "Supongamos que tenemos los datos de la siguiente figura y queremos saber cuales son los máximos de la señal.\n",
        "\n",
        "¿Cómo hacemos?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "QXEnKevEFsTZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
        },
        "outputId": "d67c712b-d510-49d1-ea23-815ba969fb28"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<>:7: SyntaxWarning: invalid escape sequence '\\h'\n",
            "<>:8: SyntaxWarning: invalid escape sequence '\\h'\n",
            "<>:7: SyntaxWarning: invalid escape sequence '\\h'\n",
            "<>:8: SyntaxWarning: invalid escape sequence '\\h'\n",
            "/tmp/ipython-input-340745047.py:7: SyntaxWarning: invalid escape sequence '\\h'\n",
            "  plt.xlabel('Eje de $\\hat x$', fontsize = 14)\n",
            "/tmp/ipython-input-340745047.py:8: SyntaxWarning: invalid escape sequence '\\h'\n",
            "  plt.ylabel('Eje de $\\hat y$', fontsize = 14)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Definimos el dominio y la imagen\n",
        "x = np.linspace(0, 2*np.pi, 500)\n",
        "y = np.abs(np.sin(x)*np.sin(2.5*x))\n",
        "\n",
        "# Graficamos la función\n",
        "plt.plot(x, y)\n",
        "plt.xlabel('Eje de $\\hat x$', fontsize = 14)\n",
        "plt.ylabel('Eje de $\\hat y$', fontsize = 14)\n",
        "\n",
        "plt.grid()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dm9_5LnRJTH_"
      },
      "source": [
        "Para eso existe ```find_peaks```, una función de la librería llamada ```scipy``` que tiene un montón de cosas útiles para hacer ciencia."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "DZBCiNuVJEkx"
      },
      "outputs": [],
      "source": [
        "# De esta manera importamos solo la función y no la librería entera, que puede ser pesada\n",
        "from scipy.signal import find_peaks"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qMnGDRRHLMpd"
      },
      "source": [
        "```find_peaks``` recibe una lista/array de una dimensión y calcula los máximos locales.\n",
        "\n",
        "Apliquemos la función a nuestra tira de datos ```y``` para ver que nos devuelve."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "-X-SUoAtJlpp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "158f73b1-ce6b-4b72-f2b3-769ddaa2dc8e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 62 146 223 276 353 437]\n",
            "\n",
            "\n",
            "{}\n"
          ]
        }
      ],
      "source": [
        "picos, diccionario = find_peaks(y) # Recordemos esta forma de nombrar varias variables\n",
        "\n",
        "print(picos)\n",
        "\n",
        "print(\"\\n\")\n",
        "\n",
        "print(diccionario)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "urDOxL7eMDmW"
      },
      "source": [
        "```find_peaks``` devuelve primero un array con los índices donde encontró máximos y luego un diccionario, que está vacío, pero más adelante vamos a ver que cosas puede contener.\n",
        "\n",
        "De momento, graficamos los picos para ver que encontró ```find_peaks``` de base."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "EN0VWY1wJwgF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
        },
        "outputId": "59942695-3d58-49ad-d114-e7999b11515b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<>:7: SyntaxWarning: invalid escape sequence '\\h'\n",
            "<>:8: SyntaxWarning: invalid escape sequence '\\h'\n",
            "<>:7: SyntaxWarning: invalid escape sequence '\\h'\n",
            "<>:8: SyntaxWarning: invalid escape sequence '\\h'\n",
            "/tmp/ipython-input-1120291555.py:7: SyntaxWarning: invalid escape sequence '\\h'\n",
            "  plt.xlabel('Eje de $\\hat x$', fontsize = 14)\n",
            "/tmp/ipython-input-1120291555.py:8: SyntaxWarning: invalid escape sequence '\\h'\n",
            "  plt.ylabel('Eje de $\\hat y$', fontsize = 14)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Graficamos la función\n",
        "plt.plot(x, y)\n",
        "\n",
        "plt.plot(x[picos], y[picos],\"o\", color = \"k\")\n",
        "\n",
        "\n",
        "plt.xlabel('Eje de $\\hat x$', fontsize = 14)\n",
        "plt.ylabel('Eje de $\\hat y$', fontsize = 14)\n",
        "\n",
        "plt.grid()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fGwxx0puNcuV"
      },
      "source": [
        "¡```find_peaks``` logró hallar todos los picos! ¡Es increíble!\n",
        "\n",
        "Veamos ahora otro caso."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "PbfcCTNdN88Z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "outputId": "e1bf8012-8ee8-4d62-b4c0-f0f75ba157b7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<>:7: SyntaxWarning: invalid escape sequence '\\h'\n",
            "<>:8: SyntaxWarning: invalid escape sequence '\\h'\n",
            "<>:7: SyntaxWarning: invalid escape sequence '\\h'\n",
            "<>:8: SyntaxWarning: invalid escape sequence '\\h'\n",
            "/tmp/ipython-input-2077513170.py:7: SyntaxWarning: invalid escape sequence '\\h'\n",
            "  plt.xlabel('Eje de $\\hat x$', fontsize = 14)\n",
            "/tmp/ipython-input-2077513170.py:8: SyntaxWarning: invalid escape sequence '\\h'\n",
            "  plt.ylabel('Eje de $\\hat y$', fontsize = 14)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Definimos el dominio y la imagen\n",
        "x = np.linspace(0, 2*np.pi, 500)\n",
        "y = np.abs(np.sin(x))+np.cos(5*x)/1.2\n",
        "\n",
        "# Graficamos la función\n",
        "plt.plot(x, y)\n",
        "plt.xlabel('Eje de $\\hat x$', fontsize = 14)\n",
        "plt.ylabel('Eje de $\\hat y$', fontsize = 14)\n",
        "\n",
        "plt.grid()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AM5Ya2w8OKiI"
      },
      "source": [
        "Ahora quiero los picos más grandes nada más.\n",
        "\n",
        "¿Que parámetro los caracteriza como para que  ```find_peaks``` encuentre solo esos?\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3kCEzuC5QVuR"
      },
      "source": [
        " ### Respuesta ***(¡Animense a contestar dale!)***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2vZEUSdfQb0K"
      },
      "source": [
        "**Hay varias cosas que podríamos usar:**\n",
        "\n",
        "\n",
        "*   La altura de los picos.\n",
        "*   La distancia entre los picos (en índices)\n",
        "\n",
        "**Si utilizamos la altura de los picos obtenemos:**\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "MZbO5JqXQ09y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "outputId": "d579a1c3-f7a6-4dfe-bb19-b84c61b36f1b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<>:7: SyntaxWarning: invalid escape sequence '\\h'\n",
            "<>:8: SyntaxWarning: invalid escape sequence '\\h'\n",
            "<>:7: SyntaxWarning: invalid escape sequence '\\h'\n",
            "<>:8: SyntaxWarning: invalid escape sequence '\\h'\n",
            "/tmp/ipython-input-819487722.py:7: SyntaxWarning: invalid escape sequence '\\h'\n",
            "  plt.xlabel('Eje de $\\hat x$', fontsize = 14)\n",
            "/tmp/ipython-input-819487722.py:8: SyntaxWarning: invalid escape sequence '\\h'\n",
            "  plt.ylabel('Eje de $\\hat y$', fontsize = 14)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Calculamos los picos\n",
        "picos, diccionario = find_peaks(y, height = 1.5) # Acá agrego el parámetro\n",
        "\n",
        "# Graficamos la función\n",
        "plt.plot(x, y)\n",
        "plt.plot(x[picos], y[picos],\".\", c = \"k\", ms = 10)\n",
        "plt.xlabel('Eje de $\\hat x$', fontsize = 14)\n",
        "plt.ylabel('Eje de $\\hat y$', fontsize = 14)\n",
        "\n",
        "plt.grid()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7iEHO2yWREoa"
      },
      "source": [
        "¡Conseguimos lo que queremos! También podriamos haberle pasado la distancia u otro parámetro para que la función cuente con la mayor información posible.\n",
        "\n",
        "Podemos ver ahora que cambio el diccionario, este contiene ahora la altura de los picos."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "k9vuiphqRMfW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "650b0d16-46e6-43b6-a781-ed631299c8a3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'peak_heights': array([1.78657323, 1.78657323])}"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "diccionario"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kjs-AUxVfe_K"
      },
      "source": [
        "Veamos otro parámetro más: el ```distance```.\n",
        "\n",
        "El ```distance``` define la cantidad de datos intermedios que debe haber entre picos. Hay que tener en cuenta que ```find_peaks``` recibe un array 1D, por lo que esta distancia es en índices y no en nuestra variable ```x```."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "eugTtoz7AMe0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "outputId": "c4d115c3-1ee4-42a8-b2e0-6ddcd05fce9b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<>:7: SyntaxWarning: invalid escape sequence '\\h'\n",
            "<>:8: SyntaxWarning: invalid escape sequence '\\h'\n",
            "<>:7: SyntaxWarning: invalid escape sequence '\\h'\n",
            "<>:8: SyntaxWarning: invalid escape sequence '\\h'\n",
            "/tmp/ipython-input-3532168980.py:7: SyntaxWarning: invalid escape sequence '\\h'\n",
            "  plt.xlabel('Eje de $\\hat x$', fontsize = 14)\n",
            "/tmp/ipython-input-3532168980.py:8: SyntaxWarning: invalid escape sequence '\\h'\n",
            "  plt.ylabel('Eje de $\\hat y$', fontsize = 14)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Calculamos los picos\n",
        "picos, diccionario = find_peaks(y, distance = 100) # Acá agrego el parámetro\n",
        "\n",
        "# Graficamos la función\n",
        "plt.plot(x, y)\n",
        "plt.plot(x[picos], y[picos],\".\", c = \"k\", ms = 10)\n",
        "plt.xlabel('Eje de $\\hat x$', fontsize = 14)\n",
        "plt.ylabel('Eje de $\\hat y$', fontsize = 14)\n",
        "\n",
        "plt.grid()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BGhdZ0bAjH7k"
      },
      "source": [
        "Existen muchos otros parámetros opcionales más tales como:\n",
        "\n",
        "\n",
        "\n",
        "*   **width**: Ancho de los picos (en índices)\n",
        "*   **threshold**: Delimita de cuanto puede ser el salto del máximo respecto a los otros puntos, verticalmente.\n",
        "*   **prominence**: Prominencia de los picos (que tan picudos son los picos)\n",
        "\n",
        "\n",
        "Si no, siempre conviene leer la [documentación](https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.find_peaks.html).\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HIL8fx8PqcVt"
      },
      "source": [
        "### Muy lindo todo esto... ¿Pero cómo puedo calcular los mínimos con ```find_peaks```?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zQJemJQPry7e"
      },
      "source": [
        "Muy fácil: ¡Invertimos los datos!\n",
        "\n",
        "Así los mínimos pasan a ser los máximos y viceversa."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "UMce2R_Rrx5j",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "outputId": "deb6824c-d59f-414c-a6b1-70d63f0d5b0b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<>:12: SyntaxWarning: invalid escape sequence '\\h'\n",
            "<>:13: SyntaxWarning: invalid escape sequence '\\h'\n",
            "<>:12: SyntaxWarning: invalid escape sequence '\\h'\n",
            "<>:13: SyntaxWarning: invalid escape sequence '\\h'\n",
            "/tmp/ipython-input-2232368767.py:12: SyntaxWarning: invalid escape sequence '\\h'\n",
            "  plt.xlabel('Eje de $\\hat x$', fontsize = 14)\n",
            "/tmp/ipython-input-2232368767.py:13: SyntaxWarning: invalid escape sequence '\\h'\n",
            "  plt.ylabel('Eje de $\\hat y$', fontsize = 14)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Definimos el dominio y la imagen\n",
        "x = np.linspace(0, 2*np.pi, 500)\n",
        "y = np.abs(np.sin(x))+np.cos(5*x)/1.2\n",
        "\n",
        "# Calculamos los picos\n",
        "picos, diccionario = find_peaks(-y) # Acá agrego el parámetro\n",
        "\n",
        "# Graficamos la función\n",
        "plt.plot(x, y, label = \"Datos originales\")\n",
        "plt.plot(x, -y, label = \"Datos invertidos\")\n",
        "\n",
        "plt.xlabel('Eje de $\\hat x$', fontsize = 14)\n",
        "plt.ylabel('Eje de $\\hat y$', fontsize = 14)\n",
        "plt.legend(loc = \"upper left\", fontsize = 12)\n",
        "\n",
        "plt.grid()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JbdhDMohsj4w"
      },
      "outputs": [],
      "source": [
        "# Calculamos los picos\n",
        "picos, diccionario = find_peaks(-y) # Le paso los datos invertidos\n",
        "\n",
        "# Graficamos la función\n",
        "plt.plot(x, y)\n",
        "plt.plot(x[picos], y[picos],\".\", c = \"k\", ms = 10)\n",
        "plt.xlabel('Eje de $\\hat x$', fontsize = 14)\n",
        "plt.ylabel('Eje de $\\hat y$', fontsize = 14)\n",
        "\n",
        "plt.grid()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y4CQvLRBCDN1"
      },
      "source": [
        "Con esto termina todo lo que teníamos para enseñarles hoy, ahora les toca aprender por su cuenta, llegó la hora de elegir un problema y pensar un rato largo.\n",
        "\n",
        "## ¡Manos a la obra!\n",
        "\n",
        "## Les pedimos por favor que **lean y respeten las consignas**."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9fVj0z1tbvSz"
      },
      "source": [
        "# _Problema integrador 1 (Física)_\n",
        "\n",
        "En un laboratorio se armó el montaje que se muestra a continuación: un carrito sujeto a un resorte fijo en uno de sus extremos.\n",
        "\n",
        "![surface32779.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jJFe_UW05ZfX"
      },
      "source": [
        "Se propuso estudiar como la masa del carrito (que se podía variar agregando pesas sobre el) afectaba su frecuencia de oscilación. Con este objetivo, se realizó el siguiente procedimiento 9 veces, comenzando con el carrito vacío y agregando una pesa en cada iteración:\n",
        "\n",
        "1. Se comenzaba a grabar el carrito con una cámara lateral al montaje.\n",
        "2. El carrito se alejaba $5~\\mathrm{cm}$ de su posición de equilibrio y se soltaba.\n",
        "3. $10~\\mathrm{s}$ después de soltar el carrito, se cortaba la grabación. Esta se guardaba como `[n].mp4`, con `[n]` el número de pesas sobre el carrito.\n",
        "\n",
        "Finalmente se subieron todas las grabaciones a un software de trackeo, calibrado para poder medir la distancia del carrito a su posición de equilibrio cada $0.2~\\mathrm{s}$ desde que se soltó. Los datos de cada grabación se guardaron en archivos nombrados `[n].csv`, con `[n]` el número de pesas correspondiente.\n",
        "\n",
        "La idea es ahora hacer el análisis de las mediciones, que se pueden encontrar en este [link](https://github.com/fifabsas/talleresfifabsas/blob/master/python/3_Ejercicios/Datos_f%C3%ADsica/Datos%20Ejercicio%201%20-%20F%C3%ADsica.zip), en Python."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ccs3GpRG962T"
      },
      "source": [
        "---\n",
        "\n",
        "### Ejercicio 1 (para ir entrando en calor)\n",
        "\n",
        "Escribir una función llamada `masa_carrito(n)` que tome:\n",
        "- `n`: el número de pesas colocadas en el carro.\n",
        "\n",
        "y que devuelva:\n",
        "- `m`: la masa total del carrito.\n",
        "\n",
        "Considerar que la masa del carrito sin pesas es de $0.15~\\mathrm{kg}$ y cada una de las pesas agregadas tiene una masa de $0.10~\\mathrm{kg}$.\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "HLh912RTkU43"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Fd9nZca639_"
      },
      "source": [
        "Cada `.csv` tiene 3 columnas: \"T [s]\" para los tiempos, \"X [cm]\" para la posición, y \"X_err [cm]\" para la incerteza en la posicion. Las mediciones de tiempo también tienen incerteza, pero mucho más chica y la vamos a despreciar en el análisis. También despreciariemos las incertezas en las mediciones de masa. (Si llegan a encontrarse con un experimento similar en un labo, registren todas las incertezas y hablen con sus profes antes de no tener en cuenta incertezas en algún cálculo o ajuste).\n",
        "\n",
        "De cada archivo queremos obtener una frecuencia de oscilación. Para enfrentar esta tarea, vamos a empezar analizando solo un archivo, `0.csv`, y luego veremos como adaptar el código para poder analizar los otros .csv's sin hacer mucho más trabajo."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bHA-u8QgHhRm"
      },
      "source": [
        "---\n",
        "### Ejercicio 2: Abrir y graficar `0.csv`\n",
        "\n",
        "Queremos empezar graficando la posición en función del tiempo del carrito sin pesitas, para observar las mediciones registradas que pueden encontrar [acá](https://github.com/fifabsas/talleresfifabsas/raw/refs/heads/master/python/3_Ejercicios/Datos_f%C3%ADsica/Datos%20Ejercicio%201%20-%20F%C3%ADsica.zip).\n",
        "\n",
        "1. Importar `numpy` y `matplotlib.pyplot` para poder acceder a las funciones de Numpy y del módulo `pyplot` de Matplotlib. Usar las abreviaciones estándar `np` y `plt` (ver clase pasada).\n",
        "2. Cargar los datos del archivo `\"0.csv\"` subiendolo a Colab y usando la función `np.loadtxt`. Guardar:\n",
        "    - los _tiempos_ de las mediciones en la variable `ts`,\n",
        "    - las _posiciones_ medidas en `xs`,\n",
        "    - y las _incertezas_ de las posiciones en `xs_err`.\n",
        "    \n",
        "    _Para usar la función `np.loadtxt` tienen de referencia el material de la clase pasada o la documentación de Numpy_.\n",
        "3. Usar la función `plt.errorbar` para gráficar `xs` en función de `ts` (`ts` corresponde al eje $\\hat x$ del gráfico, mientras que `xs` corresponde al eje $\\hat y$). Incluir las incertezas en la posición.\n",
        "4. Etiquetar los ejes del gráfico \"Tiempo [s]\" (eje $\\hat x$) y \"Posición [cm]\" (eje $\\hat y$). De título poner \"0 pesas sobre el carrito\".\n",
        "5. _(opcional)_ Embellecer.\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "13C8LHpgkTbr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RjQre3h1GGPV"
      },
      "source": [
        "### UN PROBLEMA COMÚN\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ePqT8OJIHgpo"
      },
      "source": [
        "Ahora que vimos que los datos tienen un comportamiento oscilatorio, como es de esperar por la naturaleza del experimento, nuestro objetivo es medir su frecuencia de oscilación. Nos gustaría hacer un ajuste sinusoidal, pero como vimos la vez pasada, `curve_fit` no siempre devuelve los parámetros más óptimos. A veces devuelve los parámetros correspondientes a algún otro mínimo local de la función RMSE. Veamoslo con los datos de `0.csv`, ajustandolos con un coseno:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "A5Bc6FOrHdBz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "outputId": "0cd853cb-c961-4707-812a-fcc2a52d81d8",
        "cellView": "form"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#@title código secreto (solo abrir con doble-click **luego de haber intentado hasta el Ejercicio 2**)\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.optimize import curve_fit\n",
        "\n",
        "ts, xs, xs_err = np.loadtxt(\"0.csv\", delimiter = \",\", unpack = True, skiprows = 1)\n",
        "\n",
        "def sinusoidal(t, A, w, phi):\n",
        "    return A*np.cos(w*t + phi)\n",
        "\n",
        "popt, pcov = curve_fit(sinusoidal, ts, xs)\n",
        "A, w, phi = popt\n",
        "\n",
        "ts_ajuste = np.linspace(min(ts), max(ts), 1000)\n",
        "xs_ajuste = sinusoidal(ts_ajuste, A, w, phi)\n",
        "\n",
        "plt.errorbar(ts, xs, yerr = xs_err, fmt = \"s\", ms = 2, capsize = 2, color = \"k\")\n",
        "plt.plot(ts_ajuste, xs_ajuste, color = \"r\")\n",
        "plt.xlabel(\"$t$ [s]\")\n",
        "plt.ylabel(\"$x$ [cm]\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7U8MYWCSP77k"
      },
      "source": [
        "**¿Como solucionamos esto?** Como repaso, queremos que `curve_fit` empieze a buscar los parámetros óptimos _cerca de la solución que esperamos_, que se puede hacer pasandole parámetros iniciales a través del argumento `p0`:\n",
        "```python\n",
        "popt, pcov = curve_fit(sinusoidal, t, x, p0 = [5, w_aprox, 0])\n",
        "```\n",
        "De esta forma, la función va a buscar la frecuencia óptima cerca de la frecuencia aproximada `w_aprox`.\n",
        "\n",
        "La pregunta es ahora, **¿de donde sacamos estos parámetros iniciales?** Algunas posibilidades:\n",
        "- **Usando más información:** Por ejemplo, ya sabemos que la amplitud de la curva debe ser aproximadamente 5 cm por el procedimiento llevado a cabo en el laboratorio. De la misma forma, sabemos que el defasaje `phi` del coseno debe estar cerca del 0 (si no les resulta intuitivo pregunten).\n",
        "- **A ojo:** Observamos el gráfico y elegímos parámetros razonables. Se puede aproximar la frecuencia estimando primero el período $T$ de las oscilaciones. $T$ está dado por el tiempo que tarda el carrito en alejarse de y volver a un máximo, como se puede ver en el siguiente gráfico:\n",
        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        "La frecuencia aproximada se calcula entonces con $\\omega_\\text_{aprox} = 2\\pi/T$.\n",
        "\n",
        "- **Usando `find_peaks`:** Una forma de automatizar el método \"a ojo\" es midiendo el período de las oscilaciones usando `find_peaks`. Esto es lo que vamos a usar ahora, ya que tenemos muchos datos para analizar y el método \"a ojo\" puede ser tedioso.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nk-SViwj2GK1"
      },
      "source": [
        "---\n",
        "### Ejercicio 3: Estimar la frecuencia de las oscilaciones usando `find_peaks`\n",
        "\n",
        "Lo que queremos hacer es medir el intervalo de tiempo entre los dos primeros picos de amplitud. Recordemos que `find_peaks` devuelve los indices de los picos del array que le damos y un diccionario con más información.\n",
        "\n",
        "1. Importar `find_peaks` de `scipy.signal` y aplicarlo al array de posiciónes `xs` de `0.csv`. Imprimir `picos` y el diccionario.\n",
        "\n",
        "2. Graficar los datos de `0.csv` nuevamente (podés copiar y pegar!!). Luego, graficar los picos encontrados por `find_peaks` con `plt.plot(ts[picos], xs[picos], \"o\")`. ¿Coinciden los picos con lo que esperan?\n",
        "\n",
        "3. Calcular el periódo `T` e imprimir el resultado (`f'Período estimado: {T} s'`). _Ayuda: ¿cuanto vale el tiempo en el segundo pico? ¿y en el primero? Restando estos valores van a poder obtener la respuesta!_\n",
        "\n",
        "4. Calcular la frecuencia `w0` usando que está dada por  $2\\pi / T$. Imprimir el resultado (`f'Frecuencia estimada: {w0} 1/s'`).\n",
        "\n",
        "_Nota: Estamos usando este método para estimar los parámetros iniciales, pero se podría usar para medir la frecuencia. El tema es que no estamos calculando la incerteza del valor del $\\omega$ que obtenemos, y **una medición tiene incerteza**. Además, un ajuste usa todos los datos, no solo los picos, lo que está bueno para tener más información. Si queres conversar un poco más del tema, llama a un profe!_\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tlJDHJPIkWh3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cFmXk2MVCaek"
      },
      "source": [
        "---\n",
        "### Ejercicio 4: Hacer un ajuste sinusoidal usando la frecuencia estimada\n",
        "\n",
        "1. Definir una función llamada `sinusoidal` que tome la variable independiente `t` y los parámetros a determinar `A, w` y `phi` (amplitud, frecuencia, y fase), y que devuelva el resultado de $A\\cos(\\text{w}t + \\text{phi})$.\n",
        "2. Usando `curve_fit` (hay que importarlo), ajustar los datos de `xs` en función de `ts` y obtener la frecuencia óptima `w` de `popt` (los parámetros óptimos), y su error `w_err` de `pcov` (la matriz de covarianza). Imprimir el resultado usando `f'Frecuencia: ({w} ± {w_err}) 1/s'`. _Ayuda: ver clase pasada! Y configuren los parámetros iniciales (`p0`) en `curve_fit`._\n",
        "3. Graficar la función ajustada sobre los datos.\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KQLOs5sskWIR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8VNHb10jC1Zr"
      },
      "source": [
        "---\n",
        "### Ejercicio 5: Repetir para los demás archivos guardando los datos de la masa y la frecuencia.\n",
        "\n",
        "**Acá**, _esto_ es porque programar es _super útil_. Ya trabajaste un montón para analizar `0.csv`. El solo pensamiento de repetirlo 10 veces te pone los pelos de punta. ¡Pero no lo tenés que hacer! Copiando el código que ya hiciste, ordenandolo un poco y usando la mágia de las iteraciones vas a haber analizado todo en un suspiro.\n",
        "\n",
        "1. Inicializar tres listas vacías, `[]`, guardandolas en las variables `ms`, `ws`, y `ws_err`. En estas listas vamos a guardar la masa y frecuencia correspondientes a cada archivo.\n",
        "\n",
        "2. Recorrer los archivos `\"0.csv\"` a `\"8.csv\"` _(recordar subirlos a Colab)_ con un `for`, obteniendo la frecuencia de los datos y la masa total del carrito en cada uno. Si les ayuda, pueden usar la siguiente planilla. _TIP: hagan un gráfico en cada ciclo del for para verificar que find_peaks está agarrando \"bien\" los picos y vayan probando si el for funciona mientras lo van armando._\n",
        "\n",
        "```python\n",
        "for n in range(9):\n",
        "    ts, xs, xs_err = np.loadtxt(f\"{COMPLETAR}.csv\", delimiter = COMPLETAR, skiprows = COMPLETAR, unpack = COMPLETAR)\n",
        "\n",
        "    # USAR FIND_PEAKS PARA ESTIMAR LA FRECUENCIA (COPIAR CÓDIGO ANTERIOR, OJO CON LAS INDENTACIONES).\n",
        "\n",
        "    # GRAFICAR SEÑAL Y PICOS\n",
        "\n",
        "    plt.show() # para que los gráficos queden en figuras distintas\n",
        "\n",
        "    # USAR CURVE_FIT PARA CONSEGUIR EL VALOR DE LA FRECUENCIA Y SU ERROR (COPIAR CÓDIGO ANTERIOR, OJO CON LAS INDENTACIONES).\n",
        "\n",
        "    # AGREGAR FRECUENCIA OBTENIDA A LA LISTA ws\n",
        "    # AGREGAR ERROR DE LA FRECUENCIA OBTENIDA A LA LISTA ws_err\n",
        "    # AGREGAR MASA CORRESPONDIENTE A LA LISTA ms (usar función que hicieron al princípio)\n",
        "```\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "X1_YaoamkXT0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FfWUVs5eDyQL"
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      "source": [
        "---\n",
        "### Ejercicio 6: El Ajuste Final\n",
        "\n",
        "El modelo de un oscilador sin amortiguamiento cuya fuerza restitutiva está dada por:\n",
        "$$\n",
        "F_\\text{resorte} = -kx\\qquad \\text{(Ley de Hooke)}\n",
        "$$\n",
        "con $k$ una constante y $x$ el desplazamiento del oscilador del equilibrio, tiene como frecuencia natural:\n",
        "$$\n",
        "\\omega(m) = \\sqrt{\\frac{k}{m}}\n",
        "$$\n",
        "1. Graficar `ws` en función de `ms`, mostrando el error de `ws`. (`ws`en el eje $\\hat y$, `ms` en el eje $\\hat x$)\n",
        "2. Ajustar la función $\\omega(m) = \\sqrt{k/m}$ a los datos de $\\omega$ y $m$ que se guardaron en el item anterior, obteniendo el parámetro óptimo para $k$ y su error. Graficar la función ajustada encima de los datos y juzgar si el modelo es apropiado. _Nota: En los labos van a ver herramientas útiles para realizar estas evaluaciones de forma sistemática._\n",
        "\n",
        "AUTOCORRECCIÓN: Les debería dar $k = (2.000 \\pm 0.001) ~\\text{N/cm}$\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5ycFdJ1ikX1V"
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    {
      "cell_type": "markdown",
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        "id": "nEM1fRoYJkAo"
      },
      "source": [
        "# _Problema integrador 2 (Estadística)_\n",
        "\n",
        "Vamos a resolver probabilísticamente un problema simple.\n",
        "\n",
        "Si tiramos 100 monedas y las ordenamos en una secuencia como la siguiente:\n",
        "\n",
        "$$... \\text{Cara, Cruz, Cara, Cara, Cruz}...$$\n",
        "\n",
        "¿Qué es más probable? ¿Encontrar la subsecuencia \"Cara, Cara\" o encontrar la subsecuencia \"Cruz, Cara\"? ¿Son equiprobables?\n",
        "\n",
        "Rompamos nuestra cabeza."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### Ejercicio 1: Función ```generar_secuencia(largo)```\n",
        "\n",
        "Lo primero que necesitamos hacer es generar una secuencia de caras y cruces. Como tanto “cara” como “cruz” comienzan con la misma letra, vamos a representarlas con los números `0` y `1`. Es decir, estudiaremos cuántas veces aparece la subsecuencia `00` y cuántas veces aparece la subsecuencia `01`.\n",
        "\n",
        "#### Instrucciones:\n",
        "\n",
        "Escribí la función `generar_secuencia(largo)` que reciba como parámetro el largo de la secuencia a generar y devuelva una lista llena aleatoriamente con ceros y unos.\n",
        "\n",
        "Una forma de hacerlo es:\n",
        "\n",
        "1. Dentro de la función, inicializá una lista vacía llamada `secuencia`.\n",
        "2. Utilizá un bucle `for` que, en cada iteración, genere un `0` o un `1` de forma aleatoria y agregue ese resultado a la lista.  \n",
        "    *Pista: El `for` tiene que hacer tantas iteraciones como indique `largo`*  \n",
        "3. Devolvé la lista utilizando el comando `return`.\n",
        "4. Testeá la función con `largo = 1` y `largo = 10`, imprimiendo los resultados.\n",
        "\n",
        "#### AYUDA:\n",
        "\n",
        "La función `np.random.randint(0, n)` genera un número aleatorio entre `0` y `n - 1`, es decir, no incluye el valor `n` (igual que la función `range`).\n",
        "\n",
        "---"
      ],
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    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
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    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### Ejercicio 2: Función `calcular_puntajes(secuencia)`\n",
        "\n",
        "Ahora queremos calcular los puntajes de cualquier secuencia que generemos. Vamos a lograr esto definiendo la función `calcular_puntajes(secuencia)` que, usando un ciclo `for`, va a recorrer una secuencia e ir sumando sus puntajes:\n",
        "\n",
        "- **Si encuentra un `0` y, en el siguiente elemento, otro `0`, va a sumar un punto para la subsecuencia `00`.**\n",
        "- **Si encuentra un `0` y luego un `1`, va a sumar un punto para la subsecuencia `01`.**\n",
        "\n",
        "A arremangarse, porque este es un punto difícil.\n",
        "\n",
        "#### Instrucciones:\n",
        "\n",
        "Dentro de la función:\n",
        "\n",
        "1. Inicializá las variables donde vas a guardar los puntajes:  \n",
        "   ```python\n",
        "   puntaje_00 = 0\n",
        "   puntaje_01 = 0\n",
        "   ```\n",
        "   *(¿Por qué las inicializamos en 0?)*\n",
        "\n",
        "2. Escribí un ciclo `for` para recorrer la secuencia e ir sumando los puntajes. Algunas preguntas que por ahí sirven de guía:\n",
        "\n",
        "   - Lo que nos interesa es analizar **dos elementos consecutivos** de la lista. ¿Cómo se puede acceder a dos elementos consecutivos dentro del `for`?\n",
        "\n",
        "   - **Si** detectamos un `0` al recorrer la lista:  \n",
        "     ¿Cómo debe ser el siguiente elemento para que se sume un punto a la subsecuencia `00`?  \n",
        "     ¿Y cómo debe ser para sumar un punto a la subsecuencia `01`?\n",
        "\n",
        "3. Devolvé `puntaje_00` y `puntaje_01`.\n",
        "4. Calculá e imprimí los puntajes de la secuencia `[0,0]` y de la secuencia `[0,1]`. ¿El resultado es el que esperás?\n",
        "\n",
        "#### Ayudita:\n",
        "\n",
        "Recordá que se pueden anidar `for`'s dentro de `if`'s dentro de `if`'s dentro de `for`'s dentro de `if`'s...  (no es taaaaan enredado el ejercicio, peeero casi que si)\n",
        "\n",
        "Cuidado con las indentaciones!\n",
        "\n",
        "---"
      ],
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    },
    {
      "cell_type": "code",
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      "cell_type": "markdown",
      "source": [
        "---\n",
        "### Ejercicio 3: ¡A simular!\n",
        "\n",
        "Ahora nos gustaría hacer estadística para ver cuál subsecuencia es más probable según el largo de la secuencia original. Vamos a generar muchas secuencias para cada largo (entre 2 y 100), calcular cuántas veces gana cada subsecuencia (00 o 01) y ver cuál es más probable en promedio.\n",
        "\n",
        "Este inciso es la cima de la montaña; después, es todo cuesta abajo. ¡Vamos que se puede!\n",
        "\n",
        "#### Instrucciones:\n",
        "\n",
        "1. Inicializá un `np.array` con todos los largos a probar y nombralo `largos`.\n",
        "\n",
        "2. Inicializá 3 listas vacías: `probabilidad_00`, `probabilidad_01` y `probabilidad_empate`.\n",
        "\n",
        "3. Para **cada largo** *(¿qué usamos para recorrer una lista o `np.array`?)*:\n",
        "\n",
        "   (a) Inicializá las variables:\n",
        "\n",
        "    ```python\n",
        "    victorias_00 = 0  \n",
        "    victorias_01 = 0  \n",
        "    empates = 0\n",
        "    ```\n",
        "\n",
        "   (b) Procesá 1000 secuencias: *(el aire tiene un aroma sutil a más cosas anidadas, ¿o soy yo?)*:\n",
        "   \n",
        "   - Generá cada secuencia utilizando la función `generar_secuencia(largo)` y luego calculá su puntaje con `calcular_puntajes(secuencia)`.\n",
        "   - ¿Quién ganó? Sumá la victoria donde corresponda (`victorias_00`, `victorias_01` o `empates`).\n",
        "    \n",
        "   (c) Ahora que procesaste las 1000 secuencias y tenés el número de victorias para las subsecuencias `00`, `01` y los empates, podés calcular las probabilidades de que gane `00`, `01` o que haya un empate para un determinado largo. *(Dividí las victorias por 1000, cof cof)*\n",
        "\n",
        "   Añadí estas probabilidades a las tres listas de probabilidades que inicializamos al principio del ejercicio.\n",
        "\n",
        "4. Imprimí las listas de probabilidades.\n",
        "\n",
        "#### Comentario:\n",
        "\n",
        "No hagan más de 1000 secuencias por largo porque, si no, el código va a empezar a tardar en ejecutarse, y acá queremos las cosas ¡YA! Si tienen ganas de esperar más tiempo, pasen a 10000, pero solo pueden hacerlo una vez que saben que el código funciona; si no, la compu **explota**.\n",
        "\n",
        "¿Qué secuencia creés que va a ser más probable que la otra? Chan chan chan...\n",
        "\n",
        "---"
      ],
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      "cell_type": "markdown",
      "source": [
        "---\n",
        "### Ejercicio 4: ¡Es hora de graficar!\n",
        "\n",
        "Llegó el momento de responder la tan esperada pregunta, y vamos a hacerlo utilizando un gráfico.\n",
        "\n",
        "#### Instrucciones:\n",
        "\n",
        "Graficá las probabilidades de cada subsecuencia y de empatar en función del largo de la secuencia original. Es decir, que el largo de la secuencia quede en el eje $\\hat{x}$ y la probabilidad de cada subsecuencia en el eje $\\hat{y}$.\n",
        "\n",
        "¡Hacé el gráfico lo más hermoso posible! Ponele grilla, leyenda, nombres a los ejes y labels a cada curva. Si tenés ganas, jugá con los colores y preguntanos qué tan lindo te quedó el gráfico *(por contrato tenemos que responder que quedó hermoso)* *(mentira, no hay contrato)*.\n",
        "\n",
        "---"
      ],
      "metadata": {
        "id": "RL1jogCPLQXs"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-PgCVjIfkLto"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### Ejercicio 5: ¿¡Qué está pasando!?\n",
        "\n",
        "Respondimos la pregunta de qué subsecuencia tiene más chances de ganar, pero el gráfico no responde por qué ocurre esto, así que vamos a ahondar más profundo.\n",
        "\n",
        "#### Instrucciones:\n",
        "\n",
        "1. Generá 100000 secuencias de largo 4. Para cada secuencia, calculá la diferencia de puntaje entre `00` y `01`. Guardá todas las diferencias en una lista.\n",
        "\n",
        "2. Hacé un histograma de las diferencias de puntaje utilizando `plt.hist(lista)` y hacé el gráfico lo más bello que puedas. ¿Hay alguna tendencia? ¿Con cuánto puntaje máximo gana una secuencia y con cuánto gana la otra?\n",
        "\n",
        "3. Luego, **LUEGO** de hacer esto, abrí el siguiente enlace. ***¡¡LUEGO ES LUEGO!!***\n",
        "\n",
        "   [¡SOLO LUEGO DE RESOLVER EL INCISO!](https://drive.google.com/file/d/1E6mq9gDdjaWj0hhhsFbvDU-Bb1glLmP0/view?usp=sharing)\n",
        "\n",
        "Si querés, jugá a cambiar el largo de la secuencia para ver cómo cambian los histogramas.\n",
        "\n",
        "---"
      ],
      "metadata": {
        "id": "anT2IJF9MQIN"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "PBqNqX7akMlo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9hkbVPC4M59g"
      },
      "source": [
        "---\n",
        "### Ejercicio 6: *¡Felicitaciones!*\n",
        "\n",
        "Con esto hemos podido responder la pregunta, o al menos iluminar bastante la respuesta. Acá hay algunas funciones que pueden ser muy útiles para el análisis de datos:\n",
        "\n",
        "#### Instrucciones:\n",
        "\n",
        "Calculá el valor medio y la desviación estándar del histograma que calculaste recién. Utilizá las funciones de numpy `np.mean(lista)` y `np.std(lista)` y aprendé inglés para saber cual es cual. Anotate en el Laboratorio de idiomas de la UBA y... bueno paro.\n",
        "\n",
        "¿Tenes ganas de charlar un rato? Preguntanos qué significa ```ddof = 1```.\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yVwJ_u6GkNiT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oqMfNBFVoSYS"
      },
      "source": [
        "# _Problema integrador 3 (Biología)_\n",
        "\n",
        "Vamos a estudiar la dinámica de poblaciones según un modelo clásico de depredador-presa. ¡el modelo **Lotka-Volterra**!\n",
        "\n",
        "Tanto Lotka como Volterra propusieron de manera independiente un sistema de ecuaciones diferenciales para describir la dinámica de las poblaciones de dpredadores y presas, para modelar como interactúan entre sí.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### Ejercicio 1: Primero de todo, los *datos.com* (**No es necesario entrar a esta página**).\n",
        "\n",
        "Para arrancar a trabajar necesitamos los datos, sino no podemos hacer nada.\n",
        "\n",
        "#### Instrucciones:\n",
        "\n",
        "1. Bajarse los datos, que están dentro de una carpeta en el siguiente enlace:\n",
        "[Mercado Pago de la FIFA](https://github.com/fifabsas/talleresfifabsas/tree/master/python/3_Ejercicios)\n",
        "\n",
        "2. Utilizando la función `np.loadtxt()` (recordar la clase pasada) cargá los datos del archivo  `\"datos_poblaciones.csv\"`. A cada columna asignale el nombre apropiado, es decir, definí las variables `tiempo`, `pob_presas` y ```pob_depredadores```.\n",
        "\n",
        "---"
      ],
      "metadata": {
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      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
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    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### Ejercicio 2: ¡El momento de graficar!\n",
        "\n",
        "Lo primero que tenemos que hacer cuando nos dan unos datos, a parte de cargarlos, es graficarlos, así vemos cual es su pinta y podemos pensar en que análisis realizar.\n",
        "\n",
        "#### Instrucciones\n",
        "\n",
        "Utilizando lo que vimos la clase pasada de ```matplotlib```, graficá las poblaciones de presas y depredadores en función del tiempo y hacé que el gráfico sea lo más canchero posible, ponele grilla, leyenda, los colores que vos quieras, etc.*\n",
        "\n",
        "¿Son las poblaciones como esperabas? *¿No?* $\\;$ **Enseñanos algo de biología... ¡Por favor!**\n",
        "\n",
        "---"
      ],
      "metadata": {
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      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VFQcM1cYkf4S"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### Ejercicio 3: Aprendiendo un poco sobre las poblaciones\n",
        "\n",
        "Estaría bueno tener cierto conocimiento general de las poblaciones, como su promedio y cuanto se desvían las poblaciones de este.\n",
        "\n",
        "#### Instrucciones\n",
        "\n",
        "1.  Calculá el promedio de la población de presas utilizando la función de numpy `np.mean(pob_presas)`, guardalo en la variable `valor_medio`, e imprimilo. ¿Es un valor representativo de la población?\n",
        "\n",
        "2.  Graficá este promedio en la figura anterior que hiciste, utilizando\n",
        "    ```python\n",
        "    plt.axhline(valor_medio, color=\"g\", linestyle=\"--\", label=\"valor medio presas\")\n",
        "    ```\n",
        "    Probá cambiando los parámetros opcionales.\n",
        "\n",
        "3.  Calculá e imprimí también la desviación estandar, utilizando `np.std(pob_presas)`.\n",
        "\n",
        "    ¿Este valor te dice algo más respecto al promedio?\n",
        "\n",
        "---"
      ],
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    },
    {
      "cell_type": "code",
      "source": [],
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    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### Ejercicio 4: El momento de utilizar `find_peaks`\n",
        "\n",
        "Observamos que las poblaciones oscilan y, en particular, notamos varios picos.\n",
        "\n",
        "#### Instrucciones\n",
        "\n",
        "1. Utilizá la función `find_peaks` aplicada a la población de las presas para determinar la posición de los máximos. Graficá los picos en un nuevo gráfico, junto a la población de presas en función del tiempo.\n",
        "\n",
        "   ¿Cuánta población de presas hay en esos picos?\n",
        "\n",
        "2. ¿Cuánto tiempo pasa entre picos? Imprimílo.\n",
        "---"
      ],
      "metadata": {
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    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
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    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### Ejercicio 5: Ciclos poblacionales, gráficos hermosos, ¿¡el espacio de fases!?\n",
        "\n",
        "Vamos a tomarnos el laburo de hacer un gráfico bello, pero que les puede resultar complicado, así que vamos de a poco.\n",
        "\n",
        "#### Instrucciones\n",
        "\n",
        "1. Graficá la población de depredadores en función de la población de presas. ¿Qué se observa? *¿¿Alguna especie de ciclo?? Guiño guiño.*\n",
        "\n",
        "Si lo pensas, en este gráfico perdemos la noción del tiempo, no sabemos de donde parte la población ni donde termina (aunque quizá sí te des cuenta), así que vamos a tomarnos el trabajo de agregarle esto.\n",
        "\n",
        "2.  Copiá las siguientes líneas:\n",
        "\n",
        "    ```python\n",
        "    from matplotlib import colors\n",
        "    colormap = plt.cm.plasma\n",
        "    norm = colors.Normalize(vmin=min(tiempo), vmax=max(tiempo))\n",
        "    scalar_map = plt.cm.ScalarMappable(norm=norm, cmap=colormap)\n",
        "    ```\n",
        "\n",
        "    *TIP: no hace falta que las entiendas todas. Recomendamos buscar `matplotlib colormaps` en el navegador y cambiar `plasma` por el colormap que más te guste. Puede ser que el que más te guste sea `plasma`, a mí me gusta...*\n",
        "\n",
        "Ahora se viene lo lindo:\n",
        "\n",
        "3.  Realizá un ciclo `for` que recorra las listas de las poblaciones y graficá para cada `i` *(exceptuando la parte del colormap, esto si debería entenderse):*\n",
        "    ```python\n",
        "    plt.plot([pob_presas[i], pob_presas[i+1]], [pob_depredadores[i], pob_depredadores[i+1]], color=colormap(tiempo[i]/max(tiempo)))\n",
        "    ```\n",
        "    ¿Que tal el gráfico ahora?\n",
        "\n",
        "4.  Para agregar la barra de colores del tiempo, copiá:\n",
        "    ```python\n",
        "    cbar = plt.colorbar(scalar_map, ax=plt.gca())\n",
        "    cbar.set_label(\"Tiempo\")\n",
        "    ```\n",
        "\n",
        "5. ¿Te molesta como quedaron los números del eje x? Agregale al gráfico la línea `plt.xticks(rotation=45)`, mirá a los ojos a algún docente y decile que pensas que no queda mucho mejor.\n",
        "\n",
        "---"
      ],
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    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "### Ejercicio 6: ¿Qué te pareció?\n",
        "\n",
        "Este fue un ejercicio de biología pensado por una persona que no sabe de biología, así que nos re sirve tener tu opinión!\n",
        "\n",
        "Te dejamos tu última misión:\n",
        "\n",
        "*Aprendé programación por tu cuenta el tiempo que sea necesario para formar parte del plantel docente del taller de Python de la FIFA, cambiá este ejercicio y lentamente añadí gente de Biología al taller hasta que pase a ser un taller de estudiantes de Biología **para** estudiantes de Biología. **El taller de Python de la FIBA**...*\n",
        "\n",
        "---"
      ],
      "metadata": {
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      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5BAn1MvrzaSV"
      },
      "source": [
        "---\n",
        "### **Desafío:** Integración numérica\n",
        "\n",
        "El modelo de Lotka-Volterra con el que se generaron numéricamente los datos *(te pido disculpas, no fuimos a medir durante 5 años las poblaciones)* sigue las siguientes ecuaciones diferenciales, donde $u$ es la población de presas y $v$ la de depredadores:\n",
        "\n",
        "$$\n",
        "\\frac{du}{dt} = au - buv\n",
        "$$\n",
        "\n",
        "$$\n",
        "\\frac{dv}{dt} = -cv + duv\n",
        "$$\n",
        "\n",
        "Los parámetros son los siguientes:\n",
        "\n",
        "- $a$ es la tasa de nacimiento de presas  \n",
        "- $c$ es la tasa de muerte de depredadores  \n",
        "- $b$ y $d$ son los acoplamientos entre especies, que definen cómo interactúan\n",
        "\n",
        "Estas ecuaciones se pueden resolver numéricamente para jugar con distintos parámetros y entender cómo evolucionan las poblaciones. Para esto, utilizaremos la siguiente función de Scipy:\n",
        "\n",
        "```python\n",
        "from scipy.integrate import odeint\n",
        "```\n",
        "\n",
        "---\n",
        "#### (a) Intervalo de tiempo\n",
        "\n",
        "Definí un array de tiempos como vimos en la clase pasada, que vaya de 0 a 5 y tenga longitud 5000.  \n",
        "Si querés probar otro intervalo de tiempo o una cantidad distinta de puntos, ¡hacelo!\n",
        "\n",
        "---\n",
        "#### (b) ¡Definiendo los parámetros! Acá está la joda\n",
        "\n",
        "1.  Definí `a`, `b`, `c` y `d` con ávida intuición, para que pase lo que vos querés.\n",
        "\n",
        "    *Por ejemplo: si $a$ es muy grande, la población de presas crecerá muy rápido. Si $d$ es muy chico, la población de depredadores crecerá muy poco al alimentarse de las presas.*\n",
        "\n",
        "2.  Definí las poblaciones iniciales de presas y depredadores en una lista de dos elementos, llamada `X0`, que contenga:  \n",
        "    - en el primer elemento, la población inicial de depredadores.\n",
        "    - en el segundo elemento, la población inicial de presas.\n",
        "\n",
        "---\n",
        "#### (c) Ecuaciones diferenciales en código\n",
        "\n",
        "1.  Definí una función llamada `lotka_volterra`, que tome una variable `X` y los parámetros `t` (de tiempo, no la van a usar pero es necesaria) `a`, `b`, `c` y `d`.  \n",
        "   En lo que sigue, te decimos qué tiene que hacer.\n",
        "\n",
        "    *OBSERVACIÓN: la función toma la población de depredadores y la de presas en una sola variable, `X`, que es un array de dos elementos.  \n",
        "    No podemos tener `pob_depredadores` y `pob_presas` como argumentos separados de la función. Así es la vida.*\n",
        "\n",
        "2.  En la primera línea de la función, definí:\n",
        "\n",
        "        u, v = X\n",
        "\n",
        "3.  La función debe devolver una lista con la derivada temporal de cada población, usando las ecuaciones diferenciales definidas más arriba.\n",
        "\n",
        "---\n",
        "#### (d) Magia\n",
        "\n",
        "Utilizá la siguiente línea para integrar numéricamente las ecuaciones\n",
        "\n",
        "```python\n",
        "pob_presas, pob_depredadores = odeint(lotka_volterra, X0, tiempo, args=(a, b, c, d)).T\n",
        "```\n",
        "\n",
        "El `.T` del final es para transponer la matriz que nos devuelve la función; probá si hace falta o no.\n",
        "\n",
        "---\n",
        "#### (e) ¿¿Funciona o no funciona??\n",
        "\n",
        "1. Graficá las poblaciones en función del tiempo, probá todos los parámetros que te gusten y sé feliz el resto de tu vida (*esto último no es opcional*).\n",
        "\n",
        "2. Si querés chequear que funciona bien, probá que las interacciones sean nulas y que evolucione cada población por separado. *¿Qué sucede en este caso?*\n",
        "\n",
        "¡Ahora ya sabés cómo resolver ecuaciones diferenciales y podés modelar lo que vos quieras! *¿no?*\n",
        "\n",
        "---"
      ]
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      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "# _Ejercicios adicionales sueltos_"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZWmePcAzfKS3"
      },
      "source": [
        "La idea de estos ejercicios es que son cortos pero requieren conceptos que son importantes a la hora de programar o son problemas que se van a encontrar en algún momento en el futuro.\n",
        "\n",
        "Algunos son más divertidos o fáciles que otros, pero no por eso son menos importantes."
      ]
    },
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      "source": [
        "---\n",
        "## Histogramas\n",
        "\n",
        "Queremos generar numeros aleatorios con una cierta distribución de probabilidad, en este caso una Gaussiana, que es una distribución muy **normal** *(jajá que buen juego de palabras jajajajajajajajajajajajajajajajajajajajajajaja)* que aparece en todas las ciencias.\n",
        "\n",
        "Utilizando la función de `numpy` llamada `np.random.normal()` *(si, a la Gaussiana también se la llama la normal, ahora entendiste el chiste de arriba)* podemos generar números aleatorios según una distribución Gaussiana.\n",
        "\n",
        "1. *Generá 100 números aleatorios utilizando `np.random.normal()` y guardalos en una `lista`. Realizá un histograma utilizando `plt.hist(lista)` y hacé que el gráfico sea lo más canchero posible (recomendamos utilizar el parámetro opcional `edgecolor = \"k\"` para ponerle color negro a los bordes).*\n",
        "\n",
        "2. *Calcula `np.mean(lista)` y `np.std(lista)`. Estos devuelven el valor medio y la desviación estándar de los datos, respectivamente.*\n",
        "\n",
        "3. *Si conoces a la Gaussiana, sabés que esta tiene un valor medio, donde está centrada la campana, y una desviación estandar, que determina que tan dispersos están los datos. En particular `np.random.normal()` genera números aleatorios con valor medio 0 y desviación estándar 1. Probá cambiando los valores de la siguiente manera `np.random.normal(valor_medio, desviacion_estandar)` y ponele los valores que quieras. También jugá a cambiar la cantidad de números aleatorios que generas (100 por 1000 y así) y fijate como van cambiando `np.mean(lista)` y `np.std(lista)`.*\n",
        "\n",
        "4. *Si te pareció que hay demasiados chistes, dejalo en las encuestas, aprendé programación todo un cuatrimestre, vení la siguiente edición y modificá este ejercicio.*\n",
        "\n",
        "\n",
        "\n"
      ]
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      "source": [
        "---\n",
        "## Diccionarios\n",
        "\n",
        "Tenemos la siguiente lista de nombres y carreras. Armá un diccionario que asocie cada nombre con su respectiva carrera.\n",
        "\n",
        "```python\n",
        "nombres = ['Rosalind Franklin', 'Juan G. Roederer', 'Albert-László Barabási', 'Emmy Noether', 'Phyllis Nicolson',\n",
        "           'Ada Lovelace', 'Luis Caffarelli', 'Miguel Ángel Virasoro', 'Luis Federico Leloir']\n",
        "\n",
        "carreras = ['Química', 'Física', 'Física', 'Matemática', 'Física',\n",
        "            'Computación', 'Matemática', 'Física', 'Química']\n",
        "```\n",
        "\n",
        "*AYUDA: Realizá un ciclo `for` para recorrer las listas. Ayuda: Al nombre i-ésimo le corresponde la carrera i-ésima.*\n",
        "\n",
        "*Una vez hayas hecho el diccionario, recorrelo e imprimí en pantalla los nombres de las personas cuya carrera sea \"Física\" (si así no sale ya no se que más hacer, te pido perdón).*"
      ]
    },
    {
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      "metadata": {
        "id": "zGFaKEjbGGqt"
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      "source": [
        "---\n",
        "## Funciones\n",
        "\n",
        "### Factorial\n",
        "\n",
        "Definí la función `factorial(n)`, que recibe un número entero y devuelve el factorial dicho número.\n",
        "\n",
        "Expresión del factorial:\n",
        "\n",
        "$$n! = n \\cdot (n-1) \\cdot (n-2) \\cdot \\; ... \\cdot 1$$\n",
        "\n",
        "La biblioteca `math` ya tiene una función para realizar esto, llamada `math.factorial()`. Compará los resultados de tu función y la función de `math`.\n",
        "\n",
        "### Valor absoluto\n",
        "\n",
        "Definí la función `absoluto(x)` que recibe un número real (`float`) `x` y devuelve su módulo. Si $x < 0$ debería devolver $-x$ y si $x \\geq 0$ debería devolver $x$.\n",
        "\n",
        "En Python ya existe una función que hace esto, llamada `abs()`. Compará los resultados."
      ]
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      "source": [
        "---\n",
        "## Listas\n",
        "\n",
        "La idea de este ejercicio es familiarizarse con funciones super útiles aplicables a listas.\n",
        "\n",
        "```python\n",
        "numeros = [20,12,10,3,8,14,1,42]\n",
        "```\n",
        "\n",
        "*Copie la lista de arriba y aplique las funciones `max()` y `min()`.\n",
        "¿Qué devuelven?*\n",
        "\n",
        "Ahora aplicaremos `métodos` a la lista de números.\n",
        "\n",
        "*Aplique el método `pop()` a la lista de números, de la siguiente manera `numeros.pop()` (recuerde el `append()` de la primera clase).*\n",
        "\n",
        "Por último, aplicamos otro método.\n",
        "\n",
        "*Aplique el método `insert()` a la lista de números, de la siguiente manera `numeros.insert(0,77)`.*\n",
        "\n",
        "¿Qué hace cada método?"
      ]
    }
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