03-Matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib\n",
    "\n",
    "[Matplotlib](http://matplotlib.org/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotando com configurações padrões"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "x = np.linspace(-np.pi, np.pi, 256)\n",
    "c, s = np.cos(x), np.sin(x)\n",
    "\n",
    "plt.plot(x, s)\n",
    "plt.plot(x, c)\n",
    "#plt.plot(x, c, x, s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mudando cores e expessura das linhas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10, 6), dpi=80)\n",
    "\n",
    "plt.plot(x, c, 'b', lw=2.5)\n",
    "plt.plot(x, s, 'r--', lw=2.5)\n",
    "\n",
    "#plt.plot(x, c, color=\"blue\", linewidth=2.5,)\n",
    "#plt.plot(x, s, color=\"red\",  linewidth=2.5, linestyle=\"--\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configurando limites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10, 6), dpi=80)\n",
    "\n",
    "plt.plot(x, c, color=\"blue\", linewidth=2.5,)\n",
    "plt.plot(x, s, color=\"red\",  linewidth=2.5, linestyle=\"--\")\n",
    "\n",
    "plt.xlim(x.min(), x.max())\n",
    "plt.ylim(c.min() * 1.1, c.max() * 1.1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configurando *ticks*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10, 6), dpi=80)\n",
    "\n",
    "plt.plot(x, c, color=\"blue\", linewidth=2.5)\n",
    "plt.plot(x, s, color=\"red\",  linewidth=2.5, linestyle=\"--\")\n",
    "\n",
    "plt.xlim(x.min(), x.max())\n",
    "plt.ylim(c.min() * 1.1, c.max() * 1.1)\n",
    "\n",
    "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi])\n",
    "plt.yticks([-1, 0, +1])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configurando *ticks* labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10, 6), dpi=80)\n",
    "\n",
    "plt.plot(x, c, color=\"blue\", linewidth=2.5)\n",
    "plt.plot(x, s, color=\"red\",  linewidth=2.5, linestyle=\"--\")\n",
    "\n",
    "plt.xlim(x.min(), x.max())\n",
    "plt.ylim(c.min() * 1.1, c.max() * 1.1)\n",
    "\n",
    "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi],\n",
    "          [r'$-\\pi$', r'$-\\pi/2$', r'$0$', r'$+\\pi/2$', r'$+\\pi$'], size=20)\n",
    "\n",
    "plt.yticks([-1, 0, +1],\n",
    "          [r'$-1$', r'$0$', r'$+1$'], size=20)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adicionando legenda e título"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10, 6), dpi=80)\n",
    "\n",
    "plt.plot(x, c, color=\"blue\", linewidth=2.5, linestyle=\"-\")\n",
    "plt.plot(x, s, color=\"red\",  linewidth=2.5, linestyle=\"--\", label='seno')\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.xlim(x.min(), x.max())\n",
    "plt.ylim(c.min() * 1.1, c.max() * 1.1)\n",
    "\n",
    "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi],\n",
    "          [r'$-\\pi$', r'$-\\pi/2$', r'$0$', r'$+\\pi/2$', r'$+\\pi$'], size=20)\n",
    "\n",
    "plt.yticks([-1, 0, +1],\n",
    "          [r'$-1$', r'$0$', r'$+1$'], size=20)\n",
    "\n",
    "plt.title('cosseno(x) e seno(x)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Atividade\n",
    "\n",
    "- Plote os três gráficos como na figura abaixo.\n",
    "- Use a função `plt.subplot`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(14, 10), dpi=80)\n",
    "\n",
    "\n",
    "plt.subplot(2, 2 ,1)\n",
    "plt.plot(x, c, color=\"green\", linewidth=2.5, label='cosseno' )\n",
    "\n",
    "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi],\n",
    "          [r'$-\\pi$', r'$-\\pi/2$', r'$0$', r'$+\\pi/2$', r'$+\\pi$'], size=18)\n",
    "\n",
    "\n",
    "plt.subplot(2, 2, 2)\n",
    "plt.plot(x, s, color=\"purple\",  linewidth=2.5, linestyle=\"--\", label='seno')\n",
    "\n",
    "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi],\n",
    "          [r'$-\\pi$', r'$-\\pi/2$', r'$0$', r'$+\\pi/2$', r'$+\\pi$'], size=18)\n",
    "\n",
    "\n",
    "plt.subplot(2, 2, (3,4))\n",
    "plt.plot(x, c, color=\"green\", linewidth=2.5, label='cosseno')\n",
    "plt.plot(x, s, color=\"purple\",  linewidth=2.5, linestyle=\"--\", label='seno')\n",
    "\n",
    "\n",
    "plt.xlim(x.min(), x.max())\n",
    "plt.ylim(c.min() * 1.1, c.max() * 1.1)\n",
    "\n",
    "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi],\n",
    "          [r'$-\\pi$', r'$-\\pi/2$', r'$0$', r'$+\\pi/2$', r'$+\\pi$'], size=18)\n",
    "\n",
    "plt.yticks([-1, 0, +1],\n",
    "          [r'$-1$', r'$0$', r'$+1$'], size = 18)\n",
    "\n",
    "plt.ylabel(\"Amplitude\", size=16)\n",
    "plt.xlabel(\"Radianos\", size=16)\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exemplo da galeria do site [Matplotlib](http://matplotlib.org/gallery.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Demo of the histogram (hist) function with a few features.\n",
    "\n",
    "In addition to the basic histogram, this demo shows a few optional features:\n",
    "\n",
    "    * Setting the number of data bins\n",
    "    * The ``normed`` flag, which normalizes bin heights so that the integral of\n",
    "      the histogram is 1. The resulting histogram is a probability density.\n",
    "    * Setting the face color of the bars\n",
    "    * Setting the opacity (alpha value).\n",
    "\n",
    "\"\"\"\n",
    "import numpy as np\n",
    "import matplotlib.mlab as mlab\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# example data\n",
    "mu = 100 # mean of distribution\n",
    "sigma = 15 # standard deviation of distribution\n",
    "x = mu + sigma * np.random.randn(10000)\n",
    "\n",
    "num_bins = 50\n",
    "\n",
    "# the histogram of the data\n",
    "plt.figure(figsize=(12,6))\n",
    "n, bins, patches = plt.hist(x, num_bins, normed=1, facecolor='green', alpha=0.5)\n",
    "\n",
    "# add a 'best fit' line\n",
    "y = mlab.normpdf(bins, mu, sigma)\n",
    "plt.plot(bins, y, 'r--')\n",
    "plt.xlabel('Smarts')\n",
    "plt.ylabel('Probability')\n",
    "plt.title(r'Histogram of IQ: $\\mu=100$, $\\sigma=15$')\n",
    "\n",
    "# Tweak spacing to prevent clipping of ylabel\n",
    "plt.subplots_adjust(left=0.15)\n",
    "plt.show()"
   ]
  }
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