A-graficos com Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gráficos com o Python\n",
    "Para fazer gráficos com o Python vamos usar o módulo [matplotlib](http://www.matplotlib.org). Para carregar este módulo podemos usar a sintaxe mágica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "que carregara as funções do módulo. O parâmetro *inline* significa que as figuras aparecerão no corpo do notebook, e não serão editáveis. Acho que a função mais importante pra gente, neste módulo é o *plot*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function plot in module matplotlib.pyplot:\n",
      "\n",
      "plot(*args, **kwargs)\n",
      "    Plot lines and/or markers to the\n",
      "    :class:`~matplotlib.axes.Axes`.  *args* is a variable length\n",
      "    argument, allowing for multiple *x*, *y* pairs with an\n",
      "    optional format string.  For example, each of the following is\n",
      "    legal::\n",
      "    \n",
      "        plot(x, y)        # plot x and y using default line style and color\n",
      "        plot(x, y, 'bo')  # plot x and y using blue circle markers\n",
      "        plot(y)           # plot y using x as index array 0..N-1\n",
      "        plot(y, 'r+')     # ditto, but with red plusses\n",
      "    \n",
      "    If *x* and/or *y* is 2-dimensional, then the corresponding columns\n",
      "    will be plotted.\n",
      "    \n",
      "    An arbitrary number of *x*, *y*, *fmt* groups can be\n",
      "    specified, as in::\n",
      "    \n",
      "        a.plot(x1, y1, 'g^', x2, y2, 'g-')\n",
      "    \n",
      "    Return value is a list of lines that were added.\n",
      "    \n",
      "    By default, each line is assigned a different color specified by a\n",
      "    'color cycle'.  To change this behavior, you can edit the\n",
      "    axes.color_cycle rcParam. Alternatively, you can use\n",
      "    :meth:`~matplotlib.axes.Axes.set_default_color_cycle`.\n",
      "    \n",
      "    The following format string characters are accepted to control\n",
      "    the line style or marker:\n",
      "    \n",
      "    ================    ===============================\n",
      "    character           description\n",
      "    ================    ===============================\n",
      "    ``'-'``             solid line style\n",
      "    ``'--'``            dashed line style\n",
      "    ``'-.'``            dash-dot line style\n",
      "    ``':'``             dotted line style\n",
      "    ``'.'``             point marker\n",
      "    ``','``             pixel marker\n",
      "    ``'o'``             circle marker\n",
      "    ``'v'``             triangle_down marker\n",
      "    ``'^'``             triangle_up marker\n",
      "    ``'<'``             triangle_left marker\n",
      "    ``'>'``             triangle_right marker\n",
      "    ``'1'``             tri_down marker\n",
      "    ``'2'``             tri_up marker\n",
      "    ``'3'``             tri_left marker\n",
      "    ``'4'``             tri_right marker\n",
      "    ``'s'``             square marker\n",
      "    ``'p'``             pentagon marker\n",
      "    ``'*'``             star marker\n",
      "    ``'h'``             hexagon1 marker\n",
      "    ``'H'``             hexagon2 marker\n",
      "    ``'+'``             plus marker\n",
      "    ``'x'``             x marker\n",
      "    ``'D'``             diamond marker\n",
      "    ``'d'``             thin_diamond marker\n",
      "    ``'|'``             vline marker\n",
      "    ``'_'``             hline marker\n",
      "    ================    ===============================\n",
      "    \n",
      "    \n",
      "    The following color abbreviations are supported:\n",
      "    \n",
      "    ==========  ========\n",
      "    character   color\n",
      "    ==========  ========\n",
      "    'b'         blue\n",
      "    'g'         green\n",
      "    'r'         red\n",
      "    'c'         cyan\n",
      "    'm'         magenta\n",
      "    'y'         yellow\n",
      "    'k'         black\n",
      "    'w'         white\n",
      "    ==========  ========\n",
      "    \n",
      "    In addition, you can specify colors in many weird and\n",
      "    wonderful ways, including full names (``'green'``), hex\n",
      "    strings (``'#008000'``), RGB or RGBA tuples (``(0,1,0,1)``) or\n",
      "    grayscale intensities as a string (``'0.8'``).  Of these, the\n",
      "    string specifications can be used in place of a ``fmt`` group,\n",
      "    but the tuple forms can be used only as ``kwargs``.\n",
      "    \n",
      "    Line styles and colors are combined in a single format string, as in\n",
      "    ``'bo'`` for blue circles.\n",
      "    \n",
      "    The *kwargs* can be used to set line properties (any property that has\n",
      "    a ``set_*`` method).  You can use this to set a line label (for auto\n",
      "    legends), linewidth, anitialising, marker face color, etc.  Here is an\n",
      "    example::\n",
      "    \n",
      "        plot([1,2,3], [1,2,3], 'go-', label='line 1', linewidth=2)\n",
      "        plot([1,2,3], [1,4,9], 'rs',  label='line 2')\n",
      "        axis([0, 4, 0, 10])\n",
      "        legend()\n",
      "    \n",
      "    If you make multiple lines with one plot command, the kwargs\n",
      "    apply to all those lines, e.g.::\n",
      "    \n",
      "        plot(x1, y1, x2, y2, antialised=False)\n",
      "    \n",
      "    Neither line will be antialiased.\n",
      "    \n",
      "    You do not need to use format strings, which are just\n",
      "    abbreviations.  All of the line properties can be controlled\n",
      "    by keyword arguments.  For example, you can set the color,\n",
      "    marker, linestyle, and markercolor with::\n",
      "    \n",
      "        plot(x, y, color='green', linestyle='dashed', marker='o',\n",
      "             markerfacecolor='blue', markersize=12).\n",
      "    \n",
      "    See :class:`~matplotlib.lines.Line2D` for details.\n",
      "    \n",
      "    The kwargs are :class:`~matplotlib.lines.Line2D` properties:\n",
      "    \n",
      "      agg_filter: unknown\n",
      "      alpha: float (0.0 transparent through 1.0 opaque)         \n",
      "      animated: [True | False]         \n",
      "      antialiased or aa: [True | False]         \n",
      "      axes: an :class:`~matplotlib.axes.Axes` instance         \n",
      "      clip_box: a :class:`matplotlib.transforms.Bbox` instance         \n",
      "      clip_on: [True | False]         \n",
      "      clip_path: [ (:class:`~matplotlib.path.Path`,         :class:`~matplotlib.transforms.Transform`) |         :class:`~matplotlib.patches.Patch` | None ]         \n",
      "      color or c: any matplotlib color         \n",
      "      contains: a callable function         \n",
      "      dash_capstyle: ['butt' | 'round' | 'projecting']         \n",
      "      dash_joinstyle: ['miter' | 'round' | 'bevel']         \n",
      "      dashes: sequence of on/off ink in points         \n",
      "      drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' |                   'steps-post']         \n",
      "      figure: a :class:`matplotlib.figure.Figure` instance         \n",
      "      fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none']         \n",
      "      gid: an id string         \n",
      "      label: string or anything printable with '%s' conversion.         \n",
      "      linestyle or ls: [``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` |                   ``' '`` | ``''``]         and any drawstyle in combination with a linestyle, e.g., ``'steps--'``.         \n",
      "      linewidth or lw: float value in points         \n",
      "      lod: [True | False]         \n",
      "      marker: unknown\n",
      "      markeredgecolor or mec: any matplotlib color         \n",
      "      markeredgewidth or mew: float value in points         \n",
      "      markerfacecolor or mfc: any matplotlib color         \n",
      "      markerfacecoloralt or mfcalt: any matplotlib color         \n",
      "      markersize or ms: float         \n",
      "      markevery: None | integer | (startind, stride)\n",
      "      path_effects: unknown\n",
      "      picker: float distance in points or callable pick function         ``fn(artist, event)``         \n",
      "      pickradius: float distance in points         \n",
      "      rasterized: [True | False | None]         \n",
      "      sketch_params: unknown\n",
      "      snap: unknown\n",
      "      solid_capstyle: ['butt' | 'round' |  'projecting']         \n",
      "      solid_joinstyle: ['miter' | 'round' | 'bevel']         \n",
      "      transform: a :class:`matplotlib.transforms.Transform` instance         \n",
      "      url: a url string         \n",
      "      visible: [True | False]         \n",
      "      xdata: 1D array         \n",
      "      ydata: 1D array         \n",
      "      zorder: any number         \n",
      "    \n",
      "    kwargs *scalex* and *scaley*, if defined, are passed on to\n",
      "    :meth:`~matplotlib.axes.Axes.autoscale_view` to determine\n",
      "    whether the *x* and *y* axes are autoscaled; the default is\n",
      "    *True*.\n",
      "    \n",
      "    Additional kwargs: hold = [True|False] overrides default hold state\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xb15ca06c>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0xb1df07cc>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#teste 1\n",
    "point=[[1],[2]]\n",
    "plot(point,\"ro\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAEACAYAAACqOy3+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAFdVJREFUeJzt3XtslYX9x/FPKUzHutmxyWXS5CQHWC9UznFszQglp2B7\nHF0NDpZ5m3bK/lnSFuMuESSbUcqWjSitcX+YjEZN1LiFoJbV4sKhyELY1tbsFmEdjQWhkSXcqaXl\n+f2xtdAfl16e5/Q5z/d5v5KT+RyP53w/Ocu3Tz89lyzHcRwBAAJnit8DAAAmhgUOAAHFAgeAgGKB\nA0BAscABIKBY4AAQUK4X+MmTJ7VmzRoVFBSosLBQ+/fv92IuAMAoprq9g7q6Oq1cuVK//e1vNTAw\noHPnznkxFwBgFFlu3shz6tQpxeNx/fvf//ZyJgDAGLiqUA4fPqxbb71V3/ve93THHXfo+9//vs6f\nP+/VbACAG3C1wAcGBtTe3q4f/OAHam9v12c+8xn9/Oc/92o2AMCNOC4cO3bMiUQiw8d79+51Kisr\nR9zmS1/6kiOJCxcuXLiM4xKNRkfdwa7OwGfPnq28vDwdPHhQkvTuu++qqKhoxG0++ugjOY5j9vLT\nn/7U9xnIRzby2bt0dXWNuoNdvwqlsbFRDzzwgPr7+xWNRrVt2za3dxko3d3dfo+QVpbzWc4mkS8M\nXC/wRYsW6U9/+pMXswAAxoF3YrpUXV3t9whpZTmf5WwS+cLA1evAx/QAWVlK80MAgDlj2Z2cgbuU\nSqX8HiGtLOeznE0iXxiwwAEgoKhQACADUaEAgGEscJes93CW81nOJpEvDFjgABBQdOAAkIHowAHA\nMBa4S9Z7OMv5LGeTyBcGLHAACCg6cADIQHTgAGAYC9wl6z2c5XyWs0nkCwMWOAAElOsOPBKJ6HOf\n+5yys7M1bdo0HThwYOQD0IEDwLiNZXe6/kaerKwspVIpzZgxw+1dAfBAW3OzWhsaNPWTTzRw002q\nqK3VsspKv8dCGnhSoYT5DNt6D2c5n8Vsbc3NeqeuTs+0tiqxZ4+eaW3VO3V1amtu9ns0z1l8/sbL\n9QLPysrSnXfeqcWLF+vFF1/0YiYAE9Ta0KBN/+/bzDd1dWlXY6NPEyGdXFco+/bt05w5c/Txxx+r\nvLxc+fn5Ki0tHXGb6upqRSIRSVJubq5isZgSiYSkyz9Fg3o8dF2mzEO+sR8nEomMmseL4yO9vUpJ\nSvzv8t9/K2X39WXEfDx/1z9OpVJqamqSpOF9ORpP38jz1FNPKScnR48//vjlB+CPmMCkeTKZ1DOt\nrVddvzGZ1NMtLT5MhIlK+xt5zp8/rzNnzkiSzp07p9bWVhUXF7u5y8AZ+glqleV8FrNV1NZqQzQq\n6fLZ9/poVOU1Nb7NlC4Wn7/xclWh9Pb26p577pEkDQwM6IEHHlBFRYUngwEYv6FXm2xsbFTP8eP6\nw+zZuqumhlehGMVnoQBABuKzUADAMBa4S9Z7OMv5LGeTyBcGLHAACCg6cADIQHTgAGAYC9wl6z2c\n5XyWs0nkCwMWOAAEFB04AGQgOnAAMIwF7pL1Hs5yPsvZJPKFAQscAAKKDhwAMhAdOAAYxgJ3yXoP\nZzmf5WwS+cKABQ4AAUUHDgAZaNI68MHBQcXjcVVVVXlxdwCAMfBkgW/dulWFhYXKysry4u4CxXoP\nZzmf5WwS+cLA9QI/cuSIdu7cqbVr11KVAMAkct2Bf/vb39b69et1+vRp/epXv9Jbb7018gHowAFg\n3MayO119K/3bb7+tmTNnKh6P3/DXmerqakUiEUlSbm6uYrGYEomEpMu/BnHMMccch/k4lUqpqalJ\nkob35WhcnYGvX79eL7/8sqZOnaq+vj6dPn1aq1ev1ksvvXT5AYyfgadSqeEnwyLL+Sxnk8gXdGl/\nFUp9fb16enp0+PBhvfbaa1q+fPmI5Q0ASB/PXge+Z88ebdmyRW+++ebIBzB+Bg4A6TCW3ckbeQAg\nA/FhVpNg6I8QVlnOZzmbRL4wYIEDQEBRoQBABqJCAQDDWOAuWe/hLOeznE0iXxiwwAEgoOjAASAD\n0YEDgGEscJes93CW81nOJpEvDFjgABBQdOAAkIHowAHAMBa4S9Z7OMv5LGeTyBcGLHAACCg6cADI\nQHTgAGCYqwXe19enkpISxWIxFRYW6oknnvBqrsCw3sNZzmc5m0S+MHD1rfQ333yzdu/erenTp2tg\nYEBLly7Ve++9p6VLl3o1H3zS1tys1oYGHent1buzZqmitlbLKiv9HgvAFVwtcEmaPn26JKm/v1+D\ng4OaMWOG66GCxOK3Yrc1N+udujpt6uoavm7D//7Z0hK3+NxdiXz2ue7AL126pFgsplmzZqmsrEyF\nhYVezAUftTY0jFjekrSpq0u7Ght9mgjAtbg+A58yZYo6Ozt16tQpJZNJpVKpq34yVldXKxKJSJJy\nc3MVi8WGbzPUYwX1+LnnnjOVJ5VK6Uhvr4Y8JykmKSEpu68vI+bz6vjKDjUT5iFfuPOlUik1NTVJ\n0vC+HI2nLyN8+umn9elPf1o//OEPLz+A8ZcRpq7xAyvonkwm9UxrqyQppf8ub0namEzq6ZYWn6by\nnsXn7krkC7a0v4zwxIkTOnnypCTpwoUL2rVrl+LxuJu7DByL/weqqK3VhmhU0uXlvT4aVXlNjW8z\npYPF5+5K5LPPVYVy7NgxPfzww7p06ZIuXbqk7373u1qxYoVXs8EnQ3+o3NjYqOy+Pg3efLPuqqkx\n9QdMwALeiemS9V/jLOeznE0iX9DxTkwAMIwzcADIQJyBA4BhLHCXrnwtqkWW81nOJpEvDFjgABBQ\ndOAAkIHowAHAMBa4S9Z7OMv5LGeTyBcGLHAACCg6cADIQHTgAGAYC9wl6z2c5XyWs0nkCwMWOAAE\nFB04AGQgOnAAMMzVAu/p6VFZWZmKioq0cOFCNTQ0eDVXYFjv4Szns5xNIl8YuPpGnmnTpunZZ59V\nLBbT2bNn9ZWvfEXl5eUqKCjwaj4AwHV42oGvWrVKNTU1I75WjQ4cAMZvUjvw7u5udXR0qKSkxKu7\nBADcgCcL/OzZs1qzZo22bt2qnJwcL+4yMKz3cJbzWc4mkS8MXHXgknTx4kWtXr1aDz74oFatWnXN\n21RXVysSiUiScnNzFYvFhr+MdOhJCOpxZ2dnRs1DPo45DuZxKpVSU1OTJA3vy9G46sAdx9HDDz+s\nL3zhC3r22Wev/QB04AAwbmPZna4W+Hvvvadly5bp9ttvV1ZWliRp8+bNuuuuu8Y1BABgpLT/EXPp\n0qW6dOmSOjs71dHRoY6OjhHLOwyGfgWyynI+y9kk8oUB78QEgIDis1AAIAPxWSgAYBgL3CXrPZzl\nfJazSeQLAxY4AAQUHTgAZCA6cAAwjAXukvUeznI+y9kk8oUBCxwAAooOHAAyEB04ABjGAnfJeg9n\nOZ/lbBL5woAFDgABRQcOABmIDhwADGOBu2S9h7Ocz3I2iXxh4HqBP/LII5o1a5aKi4u9mAcAMEau\nO/C9e/cqJydHDz30kP76179e/QB04AAwbmPZna6/lb60tFTd3d1u7yZw2pqb1drQoKmffKKBm25S\nRW2tllVW+j0WgBChA5+AtuZmvVNXp2daW5XYs0fPtLbqnbo6tTU3+z2a5yz3jJazSeQLAxb4BLQ2\nNGhTV9eI6zZ1dWlXY6NPEwEII9cVylhUV1crEolIknJzcxWLxZRIJCRd/ikapOMjvb0j8qUkJSRl\n9/VlxHxeHg9dlynzeHmcSCQyah7yhTtfKpVSU1OTJA3vy9F48kae7u5uVVVVheaPmE8mk3qmtfWq\n6zcmk3q6pcWHiQBYMylv5Lnvvvu0ZMkSHTx4UHl5edq2bZvbu8x4FbW12hCNSvrv2bckrY9GVV5T\n49tM6TJ0hmCR5WwS+cLAdYXy6quvejFHoAy92mRjY6N6jh/XH2bP1l01NbwKBcCk4rNQACAD8Vko\nAGAYC9wl6z2c5XyWs0nkCwMWOAAEFB04AGQgOnAAMIwF7pL1Hs5yPsvZJPKFAQscAAKKDhwAMhAd\nOAAYxgJ3yXoPZzmf5WwS+cKABQ4AAUUHDgAZiA4cAAxjgbtkvYeznM9yNol8YcACB4CAct2Bt7S0\naN26dRocHNTatWv1k5/8ZOQD0IEDwLiNZXe6WuCDg4P68pe/rHfffVe33XabvvrVr+rVV19VQUHB\nuIYAAIyU9j9iHjhwQPPmzVMkEtG0adN07733aseOHW7uMnCs93CW81nOJpEvDFwt8KNHjyovL2/4\neO7cuTp69KjroQAAo3P1pcZZWVljul11dbUikYgkKTc3V7FYTIlEQtLln6JBPR66LlPmId/YjxOJ\nREbNQ75w50ulUmpqapKk4X05Glcd+P79+/Wzn/1MLS0tkqTNmzdrypQpI/6QSQcOAOOX9g588eLF\nOnTokLq7u9Xf36/XX39dd999t5u7DJyhn6BWWc5nOZtEvjBwVaFMnTpVzz//vJLJpAYHB/Xoo4+O\neAUKACB9+CwUAMhAfBYKABjGAnfJeg9nOZ/lbBL5woAFDgABRQcOABmIDhwADGOBu2S9h7Ocz3I2\niXxhwAIHgICiAweADEQHDgCGscBdst7DWc5nOZtEvjBggQNAQNGBA0AGogMHAMNY4C5Z7+Es57Oc\nTSJfGLDAASCg6MABIAOltQN/4403VFRUpOzsbLW3t0/0bgAAEzThBV5cXKzt27dr2bJlXs4TONZ7\nOMv5LGeTyBcGE/5OzPz8fC/nAACMk+sOvKysTFu2bNEdd9xx7QegAweAcRvL7rzhGXh5ebmOHz9+\n1fX19fWqqqoa8yDV1dWKRCKSpNzcXMViMSUSCUmXfw3imGOOOQ7zcSqVUlNTkyQN78vRcAbuUiqV\nGn4yLLKcz3I2iXxBN2nvxLS8oAEgU034DHz79u2qra3ViRMndMsttygej+v3v//91Q9g/AwcANJh\nLLuTN/IAQAbiw6wmwdAfIayynM9yNol8YcACB4CAokIBgAxEhQIAhrHAXbLew1nOZzmbRL4wYIED\nQEDRgQNABqIDBwDDWOAuWe/hLOeznE0iXxiwwAEgoOjAASAD0YEDgGEscJes93CW81nOJpEvDFjg\nABBQdOAAkIHowAHAsAkv8B/96EcqKCjQokWL9K1vfUunTp3ycq7AsN7DWc5nOZtEvjCY8AKvqKjQ\n3//+d73//vtasGCBNm/e7OVcAHBNbc3NejKZVNO6dXoymVRbc7PfI/nGkw58+/bt+t3vfqdXXnnl\n6gegAwfgkbbmZr1TV6dNXV3D122IRpXculXLKit9nMx7k9aB/+Y3v9HKlSu9uCsAuK7WhoYRy1uS\nNnV1aVdjo08T+Wvqjf5leXm5jh8/ftX19fX1qqqqkiRt2rRJn/rUp3T//fdf936qq6sViUQkSbm5\nuYrFYkokEpIu91hBPX7uuedM5QlTvis71EyYh3yjHx/p7VVKUkLS5XRSdl9fRszn9vlqamqSpOF9\nOSrHhW3btjlLlixxLly4cN3buHyIjLd7926/R0gry/ksZ3Mcm/k2VFQ4juQ4krP7f//rSM6TyaTf\no3luLLtzwh14S0uLHn/8ce3Zs0df/OIXr3s7OnAAXrlWB74+GtVdIe3AJ7zA58+fr/7+fs2YMUOS\n9PWvf10vvPDChIYAgLFqa27WrsZGZff1afDmm1VeU2NueUtpXuBeDhFkqVRquM+yyHI+y9kk8gUd\n78QEAMM4AweADMQZOAAYxgJ36crX2lpkOZ/lbBL5woAFDgABRQcOABmIDhwADGOBu2S9h7Ocz3I2\niXxhwAIHgICiAweADEQHDgCGscBdst7DWc5nOZtEvjBggQNAQNGBA0AGogMHAMMmvMA3btyoRYsW\nKRaLacWKFerp6fFyrsCw3sNZzmc5m0S+MJjwAv/xj3+s999/X52dnVq1apWeeuopL+cKjM7OTr9H\nSCvL+Sxnk8gXBhNe4J/97GeH//ns2bM3/F5My06ePOn3CGllOZ/lbBL5wmCqm/94w4YNevnllzV9\n+nTt37/fq5kAAGNwwzPw8vJyFRcXX3V56623JEmbNm3Shx9+qOrqaj322GOTMnCm6e7u9nuEtLKc\nz3I2iXxh4MnLCD/88EOtXLlSf/vb3676d/PmzVNXV5fbhwCAUIlGo/rXv/51w9tMuEI5dOiQ5s+f\nL0nasWOH4vH4NW832gAAgImZ8Bn4mjVr9MEHHyg7O1vRaFS//vWvNXPmTK/nAwBcR9rfiQkASI9J\neSfmG2+8oaKiImVnZ6u9vX0yHjLtWlpalJ+fr/nz5+sXv/iF3+N46pFHHtGsWbNUXFzs9yhp0dPT\no7KyMhUVFWnhwoVqaGjweyRP9fX1qaSkRLFYTIWFhXriiSf8Hslzg4ODisfjqqqq8nsUz0UiEd1+\n++2Kx+P62te+duMbO5Pgn//8p/PBBx84iUTC+ctf/jIZD5lWAwMDTjQadQ4fPuz09/c7ixYtcv7x\nj3/4PZZn2tranPb2dmfhwoV+j5IWx44dczo6OhzHcZwzZ844CxYsMPX8OY7jnDt3znEcx7l48aJT\nUlLi7N271+eJvLVlyxbn/vvvd6qqqvwexXORSMT5z3/+M6bbTsoZeH5+vhYsWDAZDzUpDhw4oHnz\n5ikSiWjatGm69957tWPHDr/H8kxpaak+//nP+z1G2syePVuxWEySlJOTo4KCAn300Uc+T+Wt6dOn\nS5L6+/s1ODioGTNm+DyRd44cOaKdO3dq7dq1Zj8ob6y5+DCrCTh69Kjy8vKGj+fOnaujR4/6OBEm\nqru7Wx0dHSopKfF7FE9dunRJsVhMs2bNUllZmQoLC/0eyTOPPfaYfvnLX2rKFJvrKysrS3feeacW\nL16sF1988Ya3dfVOzCuVl5fr+PHjV11fX19vrqfKysryewR44OzZs1qzZo22bt2qnJwcv8fx1JQp\nU9TZ2alTp04pmUwqlUopkUj4PZZrb7/9tmbOnKl4PG72w6z27dunOXPm6OOPP1Z5ebny8/NVWlp6\nzdt6tsB37drl1V1lvNtuu23Epy/29PRo7ty5Pk6E8bp48aJWr16tBx98UKtWrfJ7nLS55ZZbVFlZ\nqT//+c8mFvgf//hHvfnmm9q5c6f6+vp0+vRpPfTQQ3rppZf8Hs0zc+bMkSTdeuutuueee3TgwIHr\nLvBJ/x3EQme1ePFiHTp0SN3d3erv79frr7+uu+++2++xMEaO4+jRRx9VYWGh1q1b5/c4njtx4sTw\nBz1duHBBu3btuu4b7YKmvr5ePT09Onz4sF577TUtX77c1PI+f/68zpw5I0k6d+6cWltbb/hqsElZ\n4Nu3b1deXp7279+vyspKfeMb35iMh02bqVOn6vnnn1cymVRhYaG+853vqKCgwO+xPHPfffdpyZIl\nOnjwoPLy8rRt2za/R/LUvn379Morr2j37t2Kx+OKx+NqaWnxeyzPHDt2TMuXL1csFlNJSYmqqqq0\nYsUKv8dKC2t1Zm9vr0pLS4efu29+85uqqKi47u15Iw8ABJTNP+MCQAiwwAEgoFjgABBQLHAACCgW\nOAAEFAscAAKKBQ4AAcUCB4CA+j8j+pU5ofX4MAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb1e1ee0c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#teste 2: plotar os pontos de uma tabela\n",
    "T=[(0,1),(1,3),(3,5),(4,-2)]\n",
    "X=[t[0] for t in T]\n",
    "Y=[t[1] for t in T]\n",
    "plot(X,Y,\"ro\")\n",
    "axis([-1,5,-3,6]) # dá uma melhor visualização x de -1 a 5 e y de -3 a 6\n",
    "grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xb15a5fcc>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6GtbW1iInEs/Nmzdx6NAhTJ8+nTcIQvubJPVS4NnZ2ejQocOjf9vb2yM7O1sf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\nREQS9f/AkSA2WrUWNAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb157946c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# teste 3: plotar uma função\n",
    "import numpy as np\n",
    "f = lambda x: x**2+np.sin(x)*x -1\n",
    "t = np.linspace(0,5,100)\n",
    "plot(t,f(t),\"g\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FVom2Df8jfFALRUTU31PQ9lxERwszJM3MgMhI4O+/hfHczs7vF29tz0VhUC4Kh67ACVGR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb1592e0c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotar graficos simultâneos\n",
    "g = lambda x: 4*sin(3*x)\n",
    "plot(X,Y,\"ro\")\n",
    "plot(t,f(t),t,g(t))\n",
    "grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "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": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}