15-visualizando-dados.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualização de dados\n",
    "================\n",
    "\n",
    "O objetivo da computação científica é promover a compreensão do significado dos muitos números que calculamos. Precisamos de pós-processamento, análise estatística e visualização gráfica dos nossos dados. As seções seguintes descrevem\n",
    "\n",
    "- Matplotlib/Pylab - que permitem gerar gráficos de alta qualidade do tipo $y = f(x)$ (e um pouco mais).\n",
    "\n",
    "<!-- Visual Python - que é uma ferramenta muito útil para gerar rapidamente animações de processos dependentes do tempo ocorrendo no espaço 3D -->\n",
    "\n",
    "Matplotlib (Pylab): plotando $y = f(x)$ (e um pouco mais)\n",
    "-------------------------------------------------------\n",
    "\n",
    "*Matplotlib* é uma biblioteca Python para plotagem 2D que produz figuras com qualidade de publicação em uma variedade de formatos impressos e interativos. O Matplotlib tenta fazer as coisas fáceis ainda mais fáceis, e as difíceis, possíveis. Você pode gerar gráficos, histogramas, espectros de energia, gráficos de barras, de erros, de dispersão, etc, com apenas algumas linhas de código.\n",
    "\n",
    "Para informações mais detalhadas, verifique estes links\n",
    "\n",
    "- Uma introdução muito boa sobre Matplotlib orientado a objetos e um resumo de todos os modos relevantes para mudança de estilo, tamanho de figura, largura de linhas, etc. pode ser encontrada nesta útil referência:<http://nbviewer.ipython.org/urls/raw.github.com/jrjohansson/scientific-python-lectures/master/Lecture-4-Matplotlib.ipynb>\n",
    "\n",
    "- Tutoriais do Matplotlib\n",
    "\n",
    "- Homepage do Matplotlib\n",
    "\n",
    "- Lista de exemplos simples (capturas de tela) <http://matplotlib.sourceforge.net/users/screenshots.html>\n",
    "\n",
    "- Galeria de exemplos: <http://matplotlib.sourceforge.net/gallery.html>\n",
    "\n",
    "### Matplotlib e Pylab\n",
    "\n",
    "Podemos ver o Matplotlib como uma *biblioteca de plotagem orientada a objetos*. Pylab é uma interface para o mesmo conjunto de funções que imita a interface de plotagem do Matlab.\n",
    "\n",
    "Pylab é um pouco mais conveniente de usar para plotagens fáceis e o Matplotlib dá um controle muito mais detalhado sobre a forma como os plots são criados. Se você usa o Matplotlib rotineiramente para produzir figuras, aconselhamos você a aprender sobre a interface orientada a objetos do Matplotlib (em vez da interface Pylab).\n",
    "\n",
    "Este capítulo concentra-se na interface Pylab."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Primeiro exemplo\n",
    "\n",
    "A forma recomendada de usar Matplotlib em um exemplo simples é mostrada aqui (vamos chamá-lo de exemplo 1a):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x105c68dd8>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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IT41N4GRhKTOXp1kdxy7ijr3oSUlJJjk52eoYSjnNlA+3sCwlhx+nXUXrlv5Wx1Hn8fsF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\nMCillKpBC4NSSqka/h+rmznkN3y53wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105b57d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# exemplo 1a\n",
    "import numpy as np                  # obtém acesso aos arrays do numpy\n",
    "import matplotlib.pyplot as plt     # funções de plotagem\n",
    "\n",
    "x = np.arange(-3.14, 3.14, 0.01)    # cria eixo x de dados\n",
    "y = np.sin(x)                       # calcula eixo y de dados\n",
    "plt.plot(x, y)                      # cria plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Como importar tudo isso: matplotlib, pylab, pyplot, numpy \n",
    "\n",
    "O sub-módulo `matplotlib.pyplot` fornece uma interface orientada a objetos para a biblioteca de plotagem. Muitos dos exemplos na documentação do Matplotlib seguem a convenção de importação do `matplotlib.pyplot` como `plt` e o `numpy` como `np`. Evidentemente, a decisão sobre importar a biblioteca `numpy` com o mome `np` (como é frequentemente feito nos exemplos da documentação), ou com o nome `N` como feito neste texto (e nos primórdios do `Numeric`, predecessor do NumPy), ou ainda com qualquer outro nome, é uma decisão plena do usuário. Da mesma forma, é uma questão de gosto pessoal se o submódulo de plotagem `matplotlib.pyplot` deve ser importado como `plt`, como é feito na documentação do Matplotlib, ou como `plot`, se a argumentação for por uma escrita mais clara, etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como sempre, um equilíbrio deve ser encontrado entre preferências pessoais e consistência com a prática comum ao escolher esses nomes. A consistência com o uso comum é, certamente, mais importante se o código estiver propenso a ser usado por outros ou publicado.\n",
    "\n",
    "Uma plotagem quase sempre precisa de *arrays* de dados numéricos e é por esta razão que o módulo NumPy é bastante usado: ele fornece manipulação rápida e eficiente de memória em Python. \n",
    "\n",
    "Poderíamos, portanto, ter escrito o exemplo 1a acima como o exemplo 1b (que é idêntico em funcionalidade ao anterior na criação da mesma plotagem):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x105d63828>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CPt6UxS39o4kODbA6jlKW0cJwgeJaB3Fd3yjmrs0k53Sx1XFUI3h1WRqCMOVq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\nMCillKpBC4NSSqka/h+rmznkN3y53wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105ce3a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# exmplo 1b\n",
    "import pylab\n",
    "import numpy as N\n",
    "\n",
    "x = N.arange (-3.14, 3.14, 0.01)\n",
    "y = N.sin(x)\n",
    "\n",
    "pylab.plot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pelo fato de `numpy.arange` e `numpy.sin` serem objetos já importados para o espaço de nomes (conveniente) `pylab`, também poderíamos ter escrito o exemplo 1c:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x105da3828>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CPt6UxS39o4kODbA6jlKW0cJwgeJaB3Fd3yjmrs0k53Sx1XFUI3h1WRqCMOVq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\nMCillKpBC4NSSqka/h+rmznkN3y53wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105c368d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# exemplo 1c\n",
    "import pylab as p\n",
    "\n",
    "x = p.arange(-3.14, 3.14, 0.01)\n",
    "y = p.sin(x)\n",
    "\n",
    "p.plot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se realmente quisermos reduzir a digitação de caracteres, também poderíamos importar toda a funcionalidade do módulo conveniente `pylab` e reescrever o código como no exemplo 1d:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CPt6UxS39o4kODbA6jlKW0cJwgeJaB3Fd3yjmrs0k53Sx1XFUI3h1WRqCMOVq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\nMCillKpBC4NSSqka/h+rmznkN3y53wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105ea8710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# exemplo 1d\n",
    "from pylab import *  # geralmente não recomendado\n",
    "                     # OK para testes interativos\n",
    "\n",
    "x = arange(-3.14, 3.14, 0.01)\n",
    "y = sin(x)\n",
    "plot(x, y)\n",
    "show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Isto pode ser extremamente conveniente, mas vem com um grande e \"salutar\" aviso:\n",
    "\n",
    "- Enquanto usar `from pylab import *` é aceitável no prompt de comando para criar gráficos interativamente e analisar dados, isto nunca deve ser usado como regra de uso nos scripts de plotagem.\n",
    "\n",
    "- O Pylab fornece mais de 800 objetos diferentes que são todos importados para o espaço de nomes global quando executamos o comando `from pylab import *`. Isto não é uma boa prática, e pode gerar conflitos com outros objetos que já existam ou com os que serão criados mais tarde.\n",
    "\n",
    "- Como uma regra de uso: nunca invoque `from somewhere import *` em programas que salvamos. Esta é uma opção razoável no *prompt* de comando.\n",
    "\n",
    "Nos exemplos a seguir, usamos a interface do `pylab` para as rotinas de plotagem, mas isto é puramente uma questão de gosto e hábito. De nenhuma maneira, é a única opção a seguir (note que os autores do Matplotlib recomendam o estilo de importação do exemplo 1a. Veja também: [Matplotlib FAQ](http://matplotlib.sourceforge.net/faq) e [Matplotlib, pylab, and pyplot: how are they related?](http://matplotlib.sourceforge.net/faq/usage_faq.html#matplotlib-pylab-and-pyplot-how-are-they-related))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modo `inline` do  IPython\n",
    "\n",
    "Dentro do Jupyter Notebook ou Qtconsole, podemos usar o comando mágico `%matplotlib inline` para fazer novas plotagens aparecerem dentro do próprio console ou notebook. Em vez disso, para forçar janelas *pop-up*,  use `%matplotlib qt`.\n",
    "\n",
    "Há também o nome mágico `%pylab`, o qual não só irá mudar para a plotagem `inline`, mas também executará automaticamente `from pylab import *`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Salvando figuras como arquivos\n",
    "\n",
    "Depois de ter criado uma figura (usando o comando `plot`) e adicionado legendas, rótulos, etc, você tem duas opções para salvar a plotagem.\n",
    "\n",
    "1. Exibir a figura (usando `show`) e salvá-la de forma *interativa* clicando no botão de salvamento <i class=\"fa fa-floppy-o\" aria-hidden=\"true\"></i>.\n",
    "\n",
    "2. Sem exibir a figura, salvá-la do seu código Python diretamente para arquivo. O comando a usar é `savefig`. O formato é determinado pela extensão do nome do arquivo que você fornece. Aqui está um exemplo (`pylabsavefig.py`). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# pylabsavefig.py\n",
    "\n",
    "import pylab\n",
    "import numpy as N\n",
    "\n",
    "x = N.arange(-3.14, 3.14, 0.01)\n",
    "y = N.sin(x)\n",
    "\n",
    "pylab.plot(x, y, label='sin(x)')\n",
    "pylab.savefig('static/data/15-meuplot.png')  # salva arquivo png \n",
    "pylab.savefig('static/data/15-meuplot.eps')  # salva arquivo eps\n",
    "pylab.savefig('static/data/15-meuplot.pdf')  # salva arquivo pdf\n",
    "pylab.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uma nota sobre formatos de arquivo: escolha o formato de arquivo `png` se você pretende incluir o seu gráfico em um documento do Word ou em uma página web. Escolha os formatos `pdf` ou `eps` se você pretende incluir a figura em um documento LaTeX - dependendo se você for compilar usando `latex` (requer `eps`) ou `pdflatex` (pode usar `pdf` [melhor] ou `png`). Se a versão do Microsoft Word (ou outro software de processamento de texto que você usa) puder lidar com arquivos `pdf`, é melhor usar `pdf` do que `png`.\n",
    "\n",
    "Tanto `pdf` quanto `eps` são formatos vetoriais de arquivo, o que significa que podemos ampliar a imagem sem que ela perca a qualidade (as linhas ainda serão nítidas). Formatos de arquivos como `png`, `jpg`, `gif`, `tif` e `bmp` salvam a imagem em formato de mapa de bits (*bitmap*), ou seja, uma matriz de valores de cor, que aparecerá desfocada ou pixelizada ao ser ampliada ou impressa em alta resolução."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modo interativo\n",
    "\n",
    "O Matplotlib pode ser executado de dois modos:\n",
    "\n",
    "   - não interativo (padrão)\n",
    "   \n",
    "   - interativo.\n",
    "   \n",
    "No modo não interativo, nenhum plot será exibido até que o comando `show()` tenha sido chamado. Neste modo, o comando `show()` deve ser a última declaração do seu programa. \n",
    "\n",
    "No modo interativo, os plots serão imediatamente mostrados depois que o comando de plotagem tiver sido chamado. \n",
    "\n",
    "Pode-se ativar o modo interativo com o comando `pylab.ion()` e desativá-lo com o comando `pylab.ioff()`. O comando mágico `%matplotlib` do IPython também habilita o modo interativo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lidando com os detalhes de sua plotagem\n",
    "\n",
    "O Matplotlib permite-lhe fazer o \"ajuste fino\" de suas plotagens nos mínimos detalhes. Aqui está um exemplo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x105e7bd30>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8vTx4a8Nxs6U4BG04NY4jbR5UlRhuusWorVX8b/MJxvYNYVBTSwxHPwC2Ktj5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\nAW5kjIPeS9+cAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105f005c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pylab\n",
    "import numpy as N\n",
    "\n",
    "x = N.arange(-3.14, 3.14, 0.01)\n",
    "y1 = N.sin(x)\n",
    "y2 = N.cos(x)\n",
    "pylab.figure(figsize =(5 , 5))\n",
    "pylab.plot(x, y1, label='sin(x)')\n",
    "pylab.plot(x, y2, label='cos(x)')\n",
    "pylab.legend()\n",
    "pylab.grid()\n",
    "pylab.xlabel('x')\n",
    "pylab.title('Este é o titulo do gráfico')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Segue uma lista de outros comandos úteis:\n",
    "\n",
    "-   `figure(figsize=(5,5))` configura o tamanho da figura para 5x5 (polegadas)\n",
    "\n",
    "-   `plot(x,y1,label=’sin(x)’)` a palavra-chave “label” define o nome desta linha. O rótulo da linha será mostrado na legenda se o comando `legend()` for usado posteriormente.\n",
    "\n",
    "-   Note que chamando o comando `plot` repetidamente, você poderá sobrepor uma série de curvas.\n",
    "\n",
    "-   `axis([-2,2,-1,1])` restringe a área de plotagem nos limites de `xmin=-2` a `xmax=2` na direção x e de `ymin=-1` a `ymax=1` na direção y.\n",
    "\n",
    "-   `legend()` este comando mostrará uma legenda com os rótulos definidos no comando plot. Experimente `help(pylab.legend)` para aprender mais sobre o posicionamento das legendas.\n",
    "\n",
    "-   `grid()` este comando mostrará as linhas de grade em segundo plano.\n",
    "\n",
    "-   `xlabel(’...’)` e `ylabel(’...’)` adicionam legendas aos eixos.\n",
    "\n",
    "Além disso, você pode escolher diferentes estilos e espessuras de linha, cores e símbolos para os dados a serem plotados. A sintaxe é muito semelhante à do Matlab. Por exemplo:\n",
    "\n",
    "-   `plot(x,y,’og’)` plotará círculos (`o`) na cor verde (`g`)\n",
    "\n",
    "-   `plot(x,y,’-r’)` plotará uma linha (`-`) na cor vermelha (`r`)\n",
    "\n",
    "-   `plot(x,y,’-b’,linewidth=2)` plotará uma linha azul (`b`) com dois pixels de espessura `linewidth=2`, que é duas vezes mais espessa do que o padrão.\n",
    "\n",
    "A lista completa de opções pode ser encontrada ao digitar `help(pylab.plot)` no *prompt* do Python. Como esta documentação é tão útil, repetimos partes dela aqui:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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",
    "        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",
    "    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).  See\n",
    "             :class:`~matplotlib.lines.Line2D` for details."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O uso de diferentes estilos de linha e espessuras é particularmente útil quando a cor não pode ser usada para distinguir linhas (por exemplo, quando o gráfico for utilizado em um documento que será impresso apenas em preto e branco).\n",
    "\n",
    "### Plotando mais de uma curva\n",
    "\n",
    "Existem três métodos diferentes para plotar mais do que uma curva.\n",
    "\n",
    "#### Duas (ou mais) curvas em um gráfico\n",
    "\n",
    "Chamando o comando `plot` repetidamente, mais do que uma curva pode ser desenhada no mesmo gráfico. Exemplo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1062bffd0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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Zf1TNNdha6nY4sgzaPwFuDrjB1EGpxFBKamYu87cnc1+bsIqxEul6nJygywva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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105fa17f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as N\n",
    "t = N.arange(0,2*N.pi,0.01)\n",
    "\n",
    "import pylab\n",
    "pylab.plot(t,N.sin(t),label='sin(t)')\n",
    "pylab.plot(t,N.cos(t),label='cos(t)')\n",
    "pylab.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dois (ou mais gráficos) em uma janela de figura\n",
    "\n",
    "O comando `pylab.subplot` permite-lhe organizar vários gráficos dentro da mesma janela. A sintaxe geral é\n",
    "\n",
    "    subplot(numRows, numCols, plotNum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aqui está um exemplo completo de plotagem das curvas seno e cosseno em dois gráficos alinhados um embaixo do outro na mesma janela:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1063a79e8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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b9doOP4Zg38RqKkC7du3q9BwfbD7BB5tP/n/3uwmE+nvTNtSXThEBdGoZQJdW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\nFKAFQSmllJ0WBKWUUgD8Px9880jp3ItHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1060b39e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as N\n",
    "t = N.arange (0 , 2 * N . pi , 0.01)\n",
    "\n",
    "import pylab\n",
    "\n",
    "pylab.subplot(2, 1, 1)\n",
    "pylab.plot(t, N.sin(t))\n",
    "pylab.xlabel('t')\n",
    "pylab.ylabel('sen(t)')\n",
    "\n",
    "pylab.subplot(2, 1, 2)\n",
    "pylab.plot(t, N.cos(t))\n",
    "pylab.xlabel('t')\n",
    "pylab.ylabel('cos(t)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1065622b0>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x105fa81d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x105fa1dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pylab\n",
    "pylab.figure(1)\n",
    "pylab.plot(range(10),'o')\n",
    "\n",
    "pylab.figure(2)\n",
    "pylab.plot(range(100),'x')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note que você pode usar `pylab.close()` para fechar uma, algumas ou todas as janelas de figura (use `help(pylab.close)` para aprender mais)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histogramas\n",
    "\n",
    "O programa abaixo demonstra como criar histogramas a partir de dados estatísticos no Matplotlib. O plot resultante é mostrado na figura"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lIglALWBncZWq6kb33/0i8m+crrhjEoyJbb369SKw0emmaXI4hwt27eOVxvXs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\n8cT/AyLm59vKIXjmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106582198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# versão modificada de\n",
    "# http://matplotlib.sourceforge.net/plot_directive/mpl_examples/...\n",
    "#                                 /pylab_examples/histogram_demo.py\n",
    "import numpy as np\n",
    "import matplotlib.mlab as mlab\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# cria dados\n",
    "mu, sigma = 100, 15\n",
    "x = mu + sigma * np.random.randn(10000)\n",
    "\n",
    "# histograma dos dados\n",
    "n, bins, patches = plt.hist(x, 50, normed=1,\n",
    "                            facecolor='green', alpha=0.75,edgecolor='black')\n",
    "\n",
    "# alguns detalhes\n",
    "plt.xlabel('Smarts')\n",
    "plt.ylabel('Probability')\n",
    "\n",
    "# strings Latex para legendagem e títulos\n",
    "plt.title(r'$\\mathrm{Histograma\\ de\\ IQ:}\\ \\mu=100,\\ \\sigma=15$')\n",
    "plt.axis([40, 160, 0, 0.03])\n",
    "plt.grid(True)\n",
    "\n",
    "\n",
    "# adiciona linha de 'melhor ajuste' \n",
    "y = mlab.normpdf(bins, mu, sigma)\n",
    "l = plt.plot(bins, y, 'r--', linewidth=1)\n",
    "\n",
    "# salva para arquivo\n",
    "plt.savefig('static/data/15-pylabhistogram.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Não tente compreender cada comando neste arquivo: alguns são bastante especializados e não foram abordados neste texto. A intenção é fornecer alguns exemplos para mostrar o que pode - em princípio - ser feito com Matplotlib. Se você precisa de um plot como este, a expectativa é que você terá que experimentar e possivelmente aprender um pouco mais sobre Matplotlib."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizando dados de matrizes\n",
    "\n",
    "O programa abaixo demonstra como criar uma plotagem em bitmap das entradas de uma matriz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vrbdVuXbYvNZmz5HLm/oJrI9gqV9gRcu8dkkwrdfaz1X9CVZud+GyZfeXlvZa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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1067af208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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m+2SCVDfJ0kmVgSev2N7OiUHOlcN1l+RzSK47ajgjF0Y+EOU6qJR7ikEqBKKu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\npEmTRq3/CXXBiExFeNTCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105fdde48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.mlab as mlab    #  comandos compatíveis com Matlab\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# cria matriz Z que contém alguns dados interessantes\n",
    "delta = 0.1\n",
    "x = y = np.arange(-3.0, 3.0, delta)\n",
    "X, Y = np.meshgrid(x, y)\n",
    "Z = mlab.bivariate_normal(X, Y, 3.0, 1.0, 0.0, 0.0)\n",
    "\n",
    "# mostra a matriz pura de dados de Z em uma figura\n",
    "plt.figure(1)\n",
    "plt.imshow(Z, interpolation='nearest', cmap=cm.jet)\n",
    "plt.title(\"imshow examplo 1a: sem interpolação\")\n",
    "plt.savefig(\"static/data/15-pylabimshow1a.pdf\")\n",
    "\n",
    "# mostra os dados interpolados em outra figura\n",
    "plt.figure(2)\n",
    "im = plt.imshow(Z, interpolation='bilinear', cmap=cm.jet)\n",
    "plt.title(\"imshow examplo 1b: com interpolação bilinear\")\n",
    "plt.savefig(\"static/data/15-pylabimshow1b.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para usar diferentes mapas de cor, fazemos uso do módulo `matplotlib.cm` (onde `cm` significa *colormap*). O código abaixo demonstra como podemos selecionar mapas de cor no conjunto de mapas já fornecidos e como podemos modificá-los (aqui, reduzindo o número de cores no mapa). O último exemplo imita o comportamento do comando mais sofisticado `contour` que também vem com o `matplotlib`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1u9nWzTHl7+tmv7XbPLAgpCFmrqYx3bUBncGwOhDPhqF+8FWm25QnMsguQ+dy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\nZGRkZIzG/wMaZGoqsoeA2QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10606d390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.mlab as mlab    #  comandos compativeis com Matlab\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm        # submódulo de mapas de cor\n",
    "\n",
    "# cria matriz Z que contém alguns dados interessantes\n",
    "delta = 0.025\n",
    "x = y = np.arange(-3.0, 3.0, delta)\n",
    "X, Y = np.meshgrid(x, y)\n",
    "Z = mlab.bivariate_normal(X, Y, 3.0, 1.0, 0.0, 0.0)\n",
    "\n",
    "# ajuste de espaçamento dos subplots para evitar sobreposição\n",
    "aux = 1.0\n",
    "subplots_adjust(wspace=aux, hspace=aux)\n",
    "\n",
    "Nx, Ny = 2, 3\n",
    "plt.subplot(Nx, Ny, 1)  # próximo plot será mostrado no\n",
    "                        # primeiro subplot como uma \n",
    "                        # matriz Nx x Ny de subplots\n",
    "\n",
    "plt.imshow(Z, cmap=cm.jet)  # mapa de cor padrão 'jet'\n",
    "plt.title(\"mapa de cor: jet\")\n",
    "\n",
    "plt.subplot(Nx, Ny, 2)   # próximo plot para segundosubplot\n",
    "plt.imshow(Z, cmap=cm.jet_r)  # mapa de cor jet reverso\n",
    "plt.title(\"mapa de cor: jet_r\")\n",
    "\n",
    "plt.subplot(Nx, Ny, 3)\n",
    "plt.imshow(Z, cmap=cm.gray)\n",
    "plt.title(\"mapa de cor: gray\")\n",
    "\n",
    "plt.subplot(Nx, Ny, 4)\n",
    "plt.imshow(Z, cmap=cm.hsv)\n",
    "plt.title(\"mapa de cor: hsv\")\n",
    "\n",
    "plt.subplot(Nx, Ny, 5)\n",
    "plt.imshow(Z, cmap=cm.gist_earth)\n",
    "plt.title(\"mapa de cor: gist_earth\")\n",
    "\n",
    "plt.subplot(Nx, Ny, 6)\n",
    "\n",
    "# produz isolinhas reduzindo o número de cores para 10\n",
    "mycmap = cm.get_cmap('jet', 10)    # 10 cores discretas\n",
    "plt.imshow(Z, cmap=mycmap)\n",
    "plt.title(\"mapa de cor: jet\\n(10 cores apenas)\")\n",
    "\n",
    "plt.savefig(\"static/data/15-pylabimshowcm.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plots de *z = f(x,y)* e outros recursos do Matplotlib\n",
    "\n",
    "O Matplotlib tem um grande número de recursos e pode criar todo padrão de plotagens (1D e 2D): tais como histogramas, gráficos circulares, gráficos de dispersão, plotagens de intensidade 2D (i.e *z = f(x,y)* e linhas de contorno) e muito mais. O programa `contour_demo.py` é um exemplo padrão do pacote `pylab` (vide figura abaixo). O seguinte link fornece o código-fonte para produzir este tipo de plotagem: [contour\\_demo.py](http://matplotlib.org/examples/pylab_examples/contour_demo.html).\n",
    "\n",
    "Outros exemplos são:\n",
    "\n",
    "-  <http://matplotlib.org/users/screenshots.html>\n",
    "-   <http://matplotlib.org/gallery.html>\n",
    "-   Recentemente, a criação de plotagens 3D foi adicionada ao pylab: <http://matplotlib.org/examples/mplot3d/index.html#mplot3d-examples>\n",
    "\n",
    "<img src=\"static/data/contour_demo.png\" alt=\"Saída de contour_demo.py.\" style=\"width:80.0%\" />\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizando dados em dimensões superiores\n",
    "-----------------------------------\n",
    "\n",
    "Muitas vezes, precisamos entender os dados definidos em posições no espaço 3D. Os dados em si são muitas vezes um campo escalar (como uma temperatura) ou um vetor 3D (como velocidade ou campo magnético), ou ocasionalmente um tensor. Por exemplo, para um campo vetorial 3D (definido como $\\vec{f}(\\vec{x})$, onde $\\vec{x} \\in \\! \\mathbb{R}^3$ e $\\vec{f}(\\vec{x}) \\in \\! \\mathbb{R}^3$), poderíamos desenhar uma seta 3D em cada ponto (grade) no espaço. É comum que esses conjuntos de dados dependam do tempo.\n",
    "\n",
    "A biblioteca provavelmente mais comumente utilizada nas ciências e engenharia para visualizar tais conjuntos de dados é provavelmente o VTK, *Visualization ToolKit* (<http://vtk.org>). Esta é uma importante biblioteca C++ com interfaces para linguagens de alto nível, incluindo o Python.\n",
    "\n",
    "Pode-se chamar essas rotinas diretamente do código Python ou escrever os dados no disco em um formato que a biblioteca VTK pode ler (chamados arquivos *vtk*) e, em seguida, usar um programa como Mayavi, ParaView ou VisIt para ler esses arquivos de dados e manipulá-los (com uma interface gráfica). Todos estes três estão usando a biblioteca VTK internamente e podem ler arquivos *vtk*.\n",
    "\n",
    "Este pacote é muito adequado para visualizar campos estáticos e temporais 2D e 3D (campos escalares, vetoriais e tensoriais).\n",
    "\n",
    "### Mayavi, Paraview, Visit\n",
    "\n",
    "-   *Homepage* do Mayavi: <http://code.enthought.com/projects/mayavi/>\n",
    "\n",
    "-   *Homepage* do Paraview:  <http://paraview.org>\n",
    "\n",
    "-   *Homepage* do VisIt:  <https://wci.llnl.gov/simulation/computer-codes/visit/>\n",
    "\n",
    "<!-- <img src=\"static/images/mayavi/vector_field_combination.png\" alt=\"image\" style=\"width:49.0%\" /><img src=\"static/images/mayavi-samp.png\" alt=\"image\" style=\"width:49.0%\" /> \n",
    "\n",
    "Dois exemplos de visualizações do MayaVi.\n",
    "-->\n",
    "\n",
    "### Escrevendo arquivos vtk a partir do Python (pyvtk)\n",
    "\n",
    "Uma pequena, mas poderosa biblioteca Python está disponível em <https://code.google.com/p/pyvtk/>. Ela permite criar arquivos *vtk* a partir de estruturas de dados do Python muito facilmente.\n",
    "\n",
    "Dada uma malha de elementos finitos ou um conjunto de dados de diferenças finitas em Python, pode-se usar o *pyvtk* para escrever esses dados em arquivos e, em seguida, usar uma das aplicações de visualização listadas acima para carregar os arquivos *vtk* para exibi-los e investigá-los."
   ]
  }
 ],
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