{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7d1a8c2e-29fd-42eb-b928-76b61cb517c7",
   "metadata": {},
   "source": [
    "## Proyecto \"Pendulo\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ac9fc79-8c97-4a49-a2a3-a0f64e0399a2",
   "metadata": {},
   "source": [
    "Vamos hacer un pendulo el cual vamos dejar oscilar 4 veces (ida y vuelta). De esas oscilasciones vamos a sacar los tiempos (t1, t2, t3, t4) de los cuales vamos a sacar el promedio (todos los tiempos divididos entre el numero de tiempos, o mean) y el margen de error (std).\n",
    "\n",
    "Esto lo vamos a hacer un total de 8 veces con longitudes de cuerda distintas, por ejemplo: 10 cm, 12 cm, 14 cm, 16 cm, 18 cm, 20 cm, 22 cm, 24 cm. Para estas longitudes vamos a hacer lo mismo, dejarlos oscilar 4 veces, por lo que en total tendriamos 32 tiempos, 4 para cada longitud, a los cuales tambien vamos a sacar el promedio y el margen de error. \n",
    "\n",
    "Vamos a usar la fórmula del período de un péndulo simple \"T=2π√(L/g)\" "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0393b97c-d33a-4c0b-9087-3e5556ea59b1",
   "metadata": {},
   "source": [
    "## Polyfit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "912b9360-e364-41b4-87c8-49ee08096cfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d969ec5b-0138-4202-9009-87e2d9049416",
   "metadata": {},
   "source": [
    "Vamos a crear unos datos ficticios para ejemplificar como funciona poplyt a partir de T=BL^(alpha)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "74f138ad-a645-474c-b731-935fdecfc946",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.20344161, 0.03912441, 0.36305887, 0.58182966, 0.00168751,\n",
       "       0.71719898, 0.03621497, 0.6338115 , 0.61341108, 0.38180788])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.rand(10) #Esto es para generar numeros aleatorios si no le pongo mas cosas, lo hace entre 0 y 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b535818b-3ee5-44be-af2e-f809d630eba7",
   "metadata": {},
   "outputs": [],
   "source": [
    "L = np.arange(10, 32, 2)    #la longitud de la cuerda\n",
    "dL = np.random.rand(len(L)) #esto es para simular datos experimentales\n",
    "B= 0.5                      # es esto ((2π)/√g)\n",
    "alpha = 0.6                 # expomemte\n",
    "T0 = B*L**alpha             #estos son los datos sin error\n",
    "T = B*(L+dL)**alpha         #estos son los datos con error (generaods aleatoriamente con np.random.rand(len(L)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "912d9cd4-09fe-4965-b496-0f1fc3d50310",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(L, T0, 'b.', label='Datos sin error')\n",
    "plt.plot(L, T, 'r.', label='Datos con error')\n",
    "plt.xlabel('Longitud')\n",
    "plt.ylabel('Periodo')\n",
    "plt.title('Longitud vs periodo', fontweight='bold')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e1e40903-f657-4170-9e49-4e8351165c73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = np.log(L) #logaritmo natural de L\n",
    "Y = np.log(T) #logaritmo natural de T\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(X, Y, 'b.', label='Datos sin error')\n",
    "plt.xlabel('Longitud')\n",
    "plt.ylabel('Periodo')\n",
    "plt.title('Longitud vs periodo con logaritmo', fontweight='bold')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5410018a-573b-4f37-a81c-6545b70b6398",
   "metadata": {},
   "source": [
    "## Ajuste lineal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8dae9bda-2169-42ed-bc38-ceeed8bd47ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5862378504559297 -0.6345564074959843\n"
     ]
    }
   ],
   "source": [
    "alpha_exp, C_exp = np.polyfit (X, Y, 1)\n",
    "print(alpha_exp, C_exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "baa381b3-f2d9-462f-90bb-c5c1664bf4b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5301706158743533"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(C_exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bee80810-84c4-4783-bfdb-3779dd18188e",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}