Python/clases/1_introduccion/4_modulos_numpy_matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Notas para contenedor de docker:**\n",
    "\n",
    "Comando de docker para ejecución de la nota de forma local:\n",
    "\n",
    "nota: cambiar `dir_montar` por la ruta de directorio que se desea mapear a `/datos` dentro del contenedor de docker.\n",
    "\n",
    "```\n",
    "dir_montar=<ruta completa de mi máquina a mi directorio>#aquí colocar la ruta al directorio a montar, por ejemplo: \n",
    "#dir_montar=/Users/erick/midirectorio.\n",
    "```\n",
    "\n",
    "Ejecutar:\n",
    "\n",
    "```\n",
    "VERSION=<versión imagen de docker>\n",
    "docker run --rm -v $dir_montar:/datos --name jupyterlab_prope_r_kernel_tidyverse -p 8888:8888 -d palmoreck/jupyterlab_prope_r_kernel_tidyverse:$VERSION\n",
    "\n",
    "```\n",
    "\n",
    "Ir a `localhost:8888` y escribir el password para jupyterlab: `qwerty`\n",
    "\n",
    "\n",
    "Detener el contenedor de docker:\n",
    "\n",
    "```\n",
    "docker stop jupyterlab_prope_r_kernel_tidyverse\n",
    "```\n",
    "\n",
    "Documentación de la imagen de docker `palmoreck/jupyterlab_prope_r_kernel_tidyverse:<versión imagen de docker>` en [liga](https://github.com/palmoreck/dockerfiles/tree/master/jupyterlab/prope_r_kernel_tidyverse)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para ejecución de la nota usar:\n",
    "\n",
    "[docker](https://www.docker.com/) (instalación de forma **local** con [Get docker](https://docs.docker.com/install/)) y ejecutar comandos que están al inicio de la nota de forma **local**. \n",
    "\n",
    "O bien dar click en alguno de los botones siguientes:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/palmoreck/dockerfiles-for-binder/jupyterlab_prope_r_kernel_tidvyerse?urlpath=lab/tree/Propedeutico/Python/clases/1_introduccion/4_modulos_numpy_matplotlib.ipynb) esta opción crea una máquina individual en un servidor de Google, clona el repositorio y permite la ejecución de los notebooks de jupyter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Run on Repl.it](https://repl.it/badge/github/palmoreck/dummy)](https://repl.it/languages/python3) esta opción no clona el repositorio, no ejecuta los notebooks de jupyter pero permite ejecución de instrucciones de *Python* de forma colaborativa con [repl.it](https://repl.it/). Al dar click se crearán nuevos ***repl*** debajo de sus users de ***repl.it***.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Revisar y ejecutar los ejemplos de las secciones 1.5 a 1.8 del libro de texto \"Numerical Methods in Engineering with Python3\" de J. Kiusalaas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Temas:\n",
    "\n",
    "* Módulo *NumPy*.\n",
    "* Gráficas con `matplotlib.pyplot`.\n",
    "* Scope de variables.\n",
    "* Escritura y ejecución de programas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El paquete de [NumPy](https://numpy.org/) ayuda al cómputo numérico. Ver [SymPy](https://www.sympy.org/en/index.html) para cómputo simbólico o algebraico."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np #utilizamos un alias con la palabra reservada \"as\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Una vez hecho el `import` podemos utilizar una gran variedad de funciones en este paquete. Lo más sencillo es iniciar con formas para crear *arrays* de *NumPy*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En lo que continúa se utiliza el nombre de *array* para un *array* de *NumPy*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Crear *arrays*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Con listas de Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos usar listas en Python para crear *arrays*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = [1,2,3,4,5,6,7,8,9,10]\n",
    "v = np.array(l)\n",
    "v"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Recuerda que una lista en Python es una estructura de datos.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cada *array* tiene **atributos** como: `ndim` (número de *axes* o ejes), `shape` (tamaño de cada eje), `size` (el tamaño total del *array*) y `dtype` el **tipo** de **valor** que el *array* almacena."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "v.ndim: 1\n",
      "v.shape: (10,)\n",
      "v.size: 10\n",
      "v.dtype int64\n"
     ]
    }
   ],
   "source": [
    "print('v.ndim:', v.ndim) #usamos un punto para acceder a los atributos\n",
    "print('v.shape:', v.shape)\n",
    "print('v.size:', v.size)\n",
    "print('v.dtype', v.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "**Observación** \n",
    "\n",
    "El tipo de valor que almacena el *array* es `int64`. Si queremos crear un *array* que almacene valor tipo `float` podemos especificar el atributo `dtype` al crear el *array*.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v = np.array(l,dtype = float)\n",
    "v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "v.dtype float64\n"
     ]
    }
   ],
   "source": [
    "print('v.dtype', v.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Otra forma de crear un *array* que almacene un valor de tipo `float` es:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = [1,2,3,4,5,6,7,8,9,10.0] #el último elemento es 10.0\n",
    "v = np.array(l)\n",
    "v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "v.dtype float64\n"
     ]
    }
   ],
   "source": [
    "print('v.dtype', v.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Usamos la función `type` para ver el tipo del objeto al que hace referencia la variable `v`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "y es un tipo de la clase [ndarray](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "**Observación**\n",
    "\n",
    "Los valores de cada *array* deben ser del mismo tipo, ser **homogéneos**, esto es, hay que tener cuidado al crear un *array* como lo siguiente.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "v2 = np.array([2.0, \"hola\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2.0', 'hola'], dtype='<U32')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<U32')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v2.dtype "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pues aunque sí podemos crear un *array* con un `float` y un `string`, el tipo que resulta hace referencia a *little-endian Unicode string of 32 characters*. Ver: [liga](https://stackoverflow.com/questions/56944812/how-to-interpret-python-output-dtype-u32/56944839), [liga2](https://numpy.org/doc/stable/reference/arrays.dtypes.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Con funciones de *NumPy*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1.])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1.],\n",
       "       [1., 1., 1.],\n",
       "       [1., 1., 1.]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((3,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0.])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((3,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0.],\n",
       "       [0., 1.]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.eye(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 1.])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0,1,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0. , 0.5, 1. ])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0,1,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.11111111, 0.22222222, 0.33333333, 0.44444444,\n",
       "       0.55555556, 0.66666667, 0.77777778, 0.88888889, 1.        ])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0,1,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-2, -1,  0,  1,  2,  3,  4])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(-2,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-2,  0,  2,  4])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(-2, 6, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0. , 0.2, 0.4, 0.6, 0.8])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0, 1, .2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4, 4])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#array pseudo aleatorio\n",
    "np.random.seed(1989)\n",
    "np.random.randint(5, size=3) #números enteros pseudo aleatorios del 0 al 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 4],\n",
       "       [4, 2],\n",
       "       [3, 4]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#array pseudo aleatorio\n",
    "np.random.seed(1989)\n",
    "np.random.randint(5, size=(3,2)) #números enteros pseudo aleatorios del 0 al 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *Arrays* con dos ejes (*axes*) o 2-dimensionales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = np.array([[1,2,3],\n",
    "              [4,5,6]])\n",
    "A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`ndim` (número de dimensiones), `shape` (tamaño de cada dimensión) y `size` (el tamaño total del *array*) y `dtype` el tipo de dato del *array*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A.ndim: 2\n",
      "A.shape: (2, 3)\n",
      "A.size: 6\n",
      "A.dtype int64\n"
     ]
    }
   ],
   "source": [
    "print('A.ndim:', A.ndim)\n",
    "print('A.shape:', A.shape)\n",
    "print('A.size:', A.size)\n",
    "print('A.dtype', A.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 2., 3.],\n",
       "       [4., 5., 6.]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = np.array([[1,2,3],\n",
    "              [4,5,6.0]])\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A.ndim: 2\n",
      "A.shape: (2, 3)\n",
      "A.size: 6\n",
      "A.dtype float64\n"
     ]
    }
   ],
   "source": [
    "print('A.ndim:', A.ndim)\n",
    "print('A.shape:', A.shape)\n",
    "print('A.size:', A.size)\n",
    "print('A.dtype', A.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accesar a los valores de un *array*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accedemos con **corchetes**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "primer valor 1.0\n",
      "último valor 10.0\n",
      "segundo valor 2.0\n",
      "penúltimo valor 9.0\n",
      "del primero al 2º valor incluyendo este último [1. 2.]\n",
      "del 2º al último valor sin incluir el 2º [ 3.  4.  5.  6.  7.  8.  9. 10.]\n",
      "del 1er valor al último elemento de 2 en 2 [1. 3. 5. 7. 9.]\n"
     ]
    }
   ],
   "source": [
    "print('primer valor', v[0])\n",
    "print('último valor', v[-1])\n",
    "print('segundo valor', v[1])\n",
    "print('penúltimo valor', v[-2])\n",
    "print('del primero al 2º valor incluyendo este último', v[:2])\n",
    "print('del 2º al último valor sin incluir el 2º', v[2:])\n",
    "print('del 1er valor al último elemento de 2 en 2', v[0:10:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx = [0, 4, 9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "imprimiendo algunos índices: [ 1.  5. 10.]\n"
     ]
    }
   ],
   "source": [
    "print('imprimiendo algunos índices:', v[idx])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2-dimensionales:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 2., 3.],\n",
       "       [4., 5., 6.]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "valor en la posición (0,0): 1.0\n",
      "valor en la posición (1,2): 6.0\n",
      "valor en la posición (0,0): 1.0\n",
      "valor en la posición (1,2): 6.0\n"
     ]
    }
   ],
   "source": [
    "print('valor en la posición (0,0):', A[0][0])\n",
    "print('valor en la posición (1,2):', A[1][2])\n",
    "#también con la siguiente notación:\n",
    "print('valor en la posición (0,0):', A[0,0])\n",
    "print('valor en la posición (1,2):', A[1,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "primer columna: [1. 4.]\n",
      "tercer columna: [3. 6.]\n",
      "segundo renglón: [4. 5. 6.]\n"
     ]
    }
   ],
   "source": [
    "print('primer columna:', A[:,0])\n",
    "print('tercer columna:', A[:,2])\n",
    "print('segundo renglón:', A[1,:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reshape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "v = np.array([1,2,3,4,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5,)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3, 4, 5]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v.reshape(1,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 5)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v.reshape(1,5).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1],\n",
       "       [2],\n",
       "       [3],\n",
       "       [4],\n",
       "       [5]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v.reshape(5,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 1)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v.reshape(5,1).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import arange,sin,cos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt #plt es un submódulo que provee las funciones para crear figuras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gráfica rápida y sencilla"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ejemplo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Algunos puntos en el plano:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "point1 = (-4, 5.1)\n",
    "point2 = (-2.3, -1.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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.scatter(point1[0], point1[1], color=\"blue\") #usamos la función de scatter\n",
    "plt.scatter(point2[0], point2[1], color=\"red\")\n",
    "plt.show() #usamos show para insertar en el notebook la gráfica (aunque podríamos omitirlo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Segmentos de recta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "t1 = (np.zeros(2), point1)\n",
    "line_seg1 = np.row_stack(t1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0. ,  0. ],\n",
       "       [-4. ,  5.1]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "line_seg1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "t2 = (np.zeros(2), point2)\n",
    "line_seg2 = np.row_stack(t2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0. ,  0. ],\n",
       "       [-2.3, -1.9]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "line_seg2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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.plot(line_seg1[:,0], line_seg1[:,1], \"blue\",\n",
    "         line_seg2[:,0], line_seg2[:,1], \"red\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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.plot(line_seg1[:,0], line_seg1[:,1], \"blue\",\n",
    "         line_seg2[:,0], line_seg2[:,1], \"red\")\n",
    "plt.xlim(-4,4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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.plot(line_seg1[:,0], line_seg1[:,1], \"blue\",\n",
    "         line_seg2[:,0], line_seg2[:,1], \"red\")\n",
    "plt.ylim(-6,6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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.plot(line_seg1[:,0], line_seg1[:,1], \"blue\",\n",
    "         line_seg2[:,0], line_seg2[:,1], \"red\")\n",
    "plt.axhline(color=\"black\")\n",
    "plt.axvline(color=\"black\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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.plot(line_seg1[:,0], line_seg1[:,1], \"blue\",\n",
    "         line_seg2[:,0], line_seg2[:,1], \"red\")\n",
    "plt.axis(\"equal\")\n",
    "plt.axhline(color=\"black\")\n",
    "plt.axvline(color=\"black\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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.plot(line_seg1[:,0], line_seg1[:,1], \"blue\",\n",
    "         line_seg2[:,0], line_seg2[:,1], \"red\")\n",
    "plt.scatter(point1[0], point1[1], color=\"blue\")\n",
    "plt.scatter(point2[0], point2[1], color=\"red\")\n",
    "plt.axis(\"equal\")\n",
    "plt.axhline(color=\"black\")\n",
    "plt.axvline(color=\"black\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "plt.plot(line_seg1[:,0], line_seg1[:,1], \"blue\",\n",
    "         line_seg2[:,0], line_seg2[:,1], \"red\")\n",
    "plt.scatter(point1[0], point1[1], color=\"blue\")\n",
    "plt.scatter(point2[0], point2[1], color=\"red\")\n",
    "plt.axis(\"equal\")\n",
    "plt.axhline(color=\"black\")\n",
    "plt.axvline(color=\"black\")\n",
    "plt.title(\"Ejemplo\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ejemplo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(0.0,6.2,0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0. , 0.2, 0.4, 0.6, 0.8, 1. , 1.2, 1.4, 1.6, 1.8, 2. , 2.2, 2.4,\n",
       "       2.6, 2.8, 3. , 3.2, 3.4, 3.6, 3.8, 4. , 4.2, 4.4, 4.6, 4.8, 5. ,\n",
       "       5.2, 5.4, 5.6, 5.8, 6. ])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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.plot(x, np.sin(x),\"o-\",\n",
    "         x, np.cos(x),\"^-\")\n",
    "plt.xlabel('x') #rótulo del eje x\n",
    "plt.ylabel('y') #rótulo del eje y\n",
    "plt.legend((\"seno\", \"coseno\"),loc = 0) #localización de la legenda, 0 se utiliza para mejor localización\n",
    "plt.grid(True) #cuadrícula\n",
    "plt.title(\"Ejemplo\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure, axes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Una figura nos proporciona un lienzo, *canvas*, para la graficación. Los ejes, *axes*, proveen un sistema de coordenadas en el *canvas*. La figura y los *axes* son objetos que tienen métodos para configurar y personalizar propiedades de la gráfica."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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.subplot(2,1,1) #subplot nos ayuda a crear figuras y axes\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAACGCAYAAADQHI0rAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAALqUlEQVR4nO3df4gc933G8fcTObKpkyZKdQWjX5apUltNSuwsqkugSUksK/5DCqRtZDCRg5sDN0ohKQWXQF1kAvlBGwiota+tSFKo5cR/lCt1ECa2MYQo0Qo7jqWi5Kym1l0DViLH/yiRK/npHzPurS4n7dzd3s5pv88Llpv5znyHz33Z2+fmx87INhERUa43tF1ARES0K0EQEVG4BEFEROESBBERhUsQREQULkEQEVG4vkEg6YCklyQ9f4nlkvRlSVOSnpN0S8+yPZJ+VL/2DLLwiIgYjCZ7BF8Bdlxm+QeBLfVrHPgHAElvA+4Hfg/YBtwvac1Sio2IiMHrGwS2nwbOXGaVXcDXXDkMvFXSdcDtwOO2z9h+GXicywdKRES0YBDnCNYBp3rmp+u2S7VHRMQKclXbBQBIGqc6rMS111777htvvLHliiIirixHjx79qe2xxfQdRBDMABt65tfXbTPA++a0PzXfBmxPABMAnU7H3W53AGVFRJRD0n8vtu8gDg1NAh+trx66FXjF9k+AQ8B2SWvqk8Tb67aIiFhB+u4RSHqY6j/7tZKmqa4EeiOA7QeBx4A7gCngLPCxetkZSQ8AR+pN7bN9uZPOERHRgr5BYPvOPssNfOISyw4ABxZXWkREDEO+WRwRUbgEQURE4RIEERGFSxBERBQuQRARUbgEQURE4RIEERGFSxBERBQuQRARUbgEQURE4RIEERGFSxBERBQuQRARUbgEQURE4RIEERGFSxBERBSuURBI2iHphKQpSffNs/xLkp6tXz+U9POeZRd6lk0OsPaIiBiAJo+qXAXsB24DpoEjkiZtH399Hduf6ln/k8DNPZv4he13DaziiIgYqCZ7BNuAKdsnbb8KHAR2XWb9O4GHB1FcREQsvyZBsA441TM/Xbf9CkmbgM3AEz3N10jqSjos6UOLLTQiIpZH30NDC7QbeNT2hZ62TbZnJN0APCHpB7Zf6O0kaRwYB9i4ceOAS4qIiMtpskcwA2zomV9ft81nN3MOC9meqX+eBJ7i4vMHr68zYbtjuzM2NtagpIiIGJQmQXAE2CJps6TVVB/2v3L1j6QbgTXAd3ra1ki6up5eC7wHOD63b0REtKfvoSHb5yXtBQ4Bq4ADto9J2gd0bb8eCruBg7bd0/0m4CFJr1GFzud6rzaKiIj26eLP7fZ1Oh13u922y4iIuKJIOmq7s5i++WZxREThEgQREYVLEEREFC5BEBFRuARBREThEgQREYVLEEREFC5BEBFRuARBREThEgQREYVLEEREFC5BEBFRuARBREThEgQREYVLEEREFC5BEBFRuEZBIGmHpBOSpiTdN8/yuyWdlvRs/frTnmV7JP2ofu0ZZPEREbF0fR9VKWkVsB+4DZgGjkianOeRk4/Y3jun79uA+4EOYOBo3fflgVQfERFL1mSPYBswZfuk7VeBg8Cuhtu/HXjc9pn6w/9xYMfiSo2IiOXQJAjWAad65qfrtrk+LOk5SY9K2rCQvpLGJXUldU+fPt2w9IiIGIRBnSz+d+B6279L9V//VxfS2faE7Y7tztjY2IBKioiIJpoEwQywoWd+fd32/2z/zPa5evafgHc37RsREe1qEgRHgC2SNktaDewGJntXkHRdz+xO4D/r6UPAdklrJK0BttdtERGxQvS9asj2eUl7qT7AVwEHbB+TtA/o2p4E/lzSTuA8cAa4u+57RtIDVGECsM/2mWX4PSIiYpFku+0aLtLpdNztdtsuIyLiiiLpqO3OYvrmm8UREYVLEEREFC5BEBFRuARBREThEgQREYVLEEREFC5BEBFRuARBREThEgQREYVLEEREFC5BEBFRuARBREThEgQREYVLEEREFC5BEBFRuEZBIGmHpBOSpiTdN8/yT0s6Xj+8/luSNvUsuyDp2fo1ObdvRES0q+8TyiStAvYDtwHTwBFJk7aP96z2DNCxfVbSvcAXgI/Uy35h+12DLTsiIgalyR7BNmDK9knbrwIHgV29K9h+0vbZevYw1UPqIyLiCtAkCNYBp3rmp+u2S7kH+GbP/DWSupIOS/rQwkuMiIjl1PfQ0EJIugvoAO/tad5ke0bSDcATkn5g+4U5/caBcYCNGzcOsqSIiOijyR7BDLChZ3593XYRSR8APgPstH3u9XbbM/XPk8BTwM1z+9qesN2x3RkbG1vQLxAREUvTJAiOAFskbZa0GtgNXHT1j6SbgYeoQuClnvY1kq6up9cC7wF6TzJHRETL+h4asn1e0l7gELAKOGD7mKR9QNf2JPBF4E3ANyQBvGh7J3AT8JCk16hC53NzrjaKiIiWyXbbNVyk0+m42+22XUZExBVF0lHbncX0zTeLIyIKlyCIiChcgiAionAJgoiIwiUIIiIKlyCIiChcgiAionAJgoiIwiUIIiIKlyCIiChcgiAionAJgoiIwiUIIiIKlyCIiChcgiAionAJgoiIwjUKAkk7JJ2QNCXpvnmWXy3pkXr5dyVd37Psr+r2E5JuH2DtERExAH2DQNIqYD/wQWArcKekrXNWuwd42fZvAV8CPl/33Ur1jOPfAXYAf19vLyIiVogmewTbgCnbJ22/ChwEds1ZZxfw1Xr6UeD9qh5evAs4aPuc7f8CpurtRUTECtEkCNYBp3rmp+u2edexfR54BfiNhn0jIqJFV7VdAICkcWC8nj0n6fk261lB1gI/bbuIFSJjMStjMStjMeu3F9uxSRDMABt65tfXbfOtMy3pKuAtwM8a9sX2BDABIKlru9P0FxhlGYtZGYtZGYtZGYtZkrqL7dvk0NARYIukzZJWU538nZyzziSwp57+I+AJ267bd9dXFW0GtgDfW2yxERExeH33CGyfl7QXOASsAg7YPiZpH9C1PQn8M/AvkqaAM1RhQb3e14HjwHngE7YvLNPvEhERi9DoHIHtx4DH5rT9dc/0L4E/vkTfzwKfXUBNEwtYd9RlLGZlLGZlLGZlLGYteixUHcGJiIhS5RYTERGFay0IlnLbilHTYCw+Lem4pOckfUvSpjbqHIZ+Y9Gz3oclWdLIXjHSZCwk/Un93jgm6V+HXeOwNPgb2SjpSUnP1H8nd7RR53KTdEDSS5e6xF6VL9fj9JykWxpt2PbQX1QnnV8AbgBWA98Hts5Z58+AB+vp3cAjbdS6QsbiD4Ffq6fvLXks6vXeDDwNHAY6bdfd4vtiC/AMsKae/822625xLCaAe+vprcCP2657mcbiD4BbgOcvsfwO4JuAgFuB7zbZblt7BEu5bcWo6TsWtp+0fbaePUz1fYxR1OR9AfAA1f2sfjnM4oasyVh8HNhv+2UA2y8NucZhaTIWBn69nn4L8D9DrG9obD9NdWXmpewCvubKYeCtkq7rt922gmApt60YNQu9Dcc9VIk/ivqORb2ru8H2fwyzsBY0eV+8HXi7pG9LOixpx9CqG64mY/E3wF2SpqmucPzkcEpbcRZ1W58VcYuJaEbSXUAHeG/btbRB0huAvwPubrmUleIqqsND76PaS3xa0jtt/7zNolpyJ/AV238r6fepvtf0DtuvtV3YlaCtPYKF3LaCObetGDWNbsMh6QPAZ4Cdts8NqbZh6zcWbwbeATwl6cdUx0AnR/SEcZP3xTQwaft/Xd3d94dUwTBqmozFPcDXAWx/B7iG6j5EpWn0eTJXW0GwlNtWjJq+YyHpZuAhqhAY1ePA0GcsbL9ie63t621fT3W+ZKftRd9jZQVr8jfyb1R7A0haS3Wo6OQQaxyWJmPxIvB+AEk3UQXB6aFWuTJMAh+trx66FXjF9k/6dWrl0JCXcNuKUdNwLL4IvAn4Rn2+/EXbO1srepk0HIsiNByLQ8B2SceBC8Bf2h65veaGY/EXwD9K+hTVieO7R/EfR0kPU4X/2vp8yP3AGwFsP0h1fuQOqme/nAU+1mi7IzhWERGxAPlmcURE4RIEERGFSxBERBQuQRARUbgEQURE4RIEERGFSxBERBQuQRARUbj/AwbyyIQrqN+KAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2,1,2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2,1,1) #división de la ventana de graficación en forma 2 x 1\n",
    "plt.subplot(2,1,2) #el último argumento de subplot se refiere a la posición\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2,1,1)     \n",
    "plt.plot(x,np.sin(x),\"o-\")\n",
    "plt.xlabel('x')\n",
    "plt.ylabel(\"sin(x)\")\n",
    "plt.grid(True)\n",
    "plt.subplot(2,1,2)\n",
    "plt.plot(x,np.cos(x),\"^-\")\n",
    "plt.xlabel('x')\n",
    "plt.ylabel(\"cos(x)\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1,2,1)\n",
    "plt.plot(x,np.sin(x),\"o-\")\n",
    "plt.xlabel('x')\n",
    "plt.ylabel(\"sin(x)\")\n",
    "plt.grid(True)\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(x,np.cos(x),\"^-\")\n",
    "plt.xlabel('x')\n",
    "plt.ylabel(\"cos(x)\")\n",
    "plt.suptitle(\"Ejemplo\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ejemplo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Con la función [subplots](https://matplotlib.org/3.1.0/api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure.subplots) tenemos más control para graficar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(-5, 2, 100)\n",
    "y1 = x**3 + 5*x**2 + 10\n",
    "y2 = 3*x**2 + 10*x\n",
    "y3 = 6*x + 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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": [
    "fig, axes = plt.subplots() #sólo un subplot\n",
    "axes.plot(x, y1, color=\"blue\", label=\"y(x)\")\n",
    "axes.plot(x, y2, color=\"red\", label=\"y'(x)\")\n",
    "axes.plot(x, y3, color=\"green\", label=\"y''(x)\")\n",
    "axes.set_xlabel('x')\n",
    "axes.set_ylabel('y')\n",
    "axes.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ejemplo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(0.0,6.2,0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, sharey=True) #dos subplots que compartan los valores del eje vertical\n",
    "axes[0].plot(x,np.sin(x),\"o-\") #la posición 0 para la primer ventana de graficación\n",
    "axes[0].set_xlabel('x')\n",
    "axes[0].set_ylabel(\"sin(x)\")\n",
    "axes[0].grid(True)\n",
    "axes[1].plot(x,np.cos(x),\"^-\")\n",
    "axes[1].set_xlabel('x')\n",
    "axes[1].set_ylabel(\"cos(x)\")\n",
    "axes[1].grid(True)\n",
    "plt.suptitle(\"Ejemplo\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 1, sharex=True) #dos subplots que compartan los valores del eje horizontal\n",
    "axes[0].plot(x,np.sin(x),\"o-\") \n",
    "axes[0].set_xlabel('x')\n",
    "axes[0].set_ylabel(\"sin(x)\")\n",
    "axes[0].grid(True)\n",
    "axes[1].plot(x,np.cos(x),\"^-\") #la posición 1 para la segunda ventana de graficación\n",
    "axes[1].set_xlabel('x')\n",
    "axes[1].set_ylabel(\"cos(x)\")\n",
    "axes[1].grid(True)\n",
    "plt.suptitle(\"Ejemplo\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, sharey=True, figsize = (10,5)) \n",
    "axes[0].plot(x,np.sin(x),\"o-\") \n",
    "axes[0].set_xlabel('x')\n",
    "axes[0].set_ylabel(\"sin(x)\")\n",
    "axes[0].grid(True)\n",
    "axes[1].plot(x,np.cos(x),\"^-\")\n",
    "axes[1].set_xlabel('x')\n",
    "axes[1].set_ylabel(\"cos(x)\")\n",
    "axes[1].grid(True)\n",
    "plt.suptitle(\"Ejemplo\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Referencias\n",
    "\n",
    "* [quickstart](https://numpy.org/doc/stable/user/quickstart.html) y [Numerical Python by Robert Johansson, Apress.](https://www.apress.com/gp/book/9781484242452)\n",
    "\n",
    "* [matplotlib](https://matplotlib.org/), [pyplot tutorial](https://matplotlib.org/tutorials/introductory/pyplot.html) y [Numerical Python by Robert Johansson, Apress](https://www.apress.com/gp/book/9781484242452).\n",
    "\n",
    "* Para referencias de colores ver por ejemplo la siguiente [pregunta en stackoverflow](https://stackoverflow.com/questions/22408237/named-colors-in-matplotlib)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}