scipy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SciPy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importación básica de paquetes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy as sp\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "from scipy import optimize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Un ejemplo de minimización"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f(x):\n",
    "    return (2*(x**2)+2*x+1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "N=128\n",
    "x_data=np.linspace(-2.0,2.0,N)\n",
    "y_data=np.empty(N)\n",
    "for i,valor in enumerate(x_data):\n",
    "    y_data[i]=f(valor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xa4233b0>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5o3NuOfg3KbBjNceFuJfJ3JvKx5RWcUymJft/eFjiT/BJZrZXdkKrk1y4l8nK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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x56c53f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x_data, y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = optimize.minimize_scalar(f)\n",
    "#print(result.success)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "minimo de f(x) se obtiene para x: -0.5\n"
     ]
    }
   ],
   "source": [
    "print('minimo de f(x) se obtiene para x: ' + str(result.x))"
   ]
  }
 ],
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