4.datasci/notebooks/04.00-Introduction-To-Matplotlib.ipynb

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   "source": [
    "<!--BOOK_INFORMATION-->\n",
    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
    "\n",
    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Further Resources](03.13-Further-Resources.ipynb) | [Contents](Index.ipynb) | [Simple Line Plots](04.01-Simple-Line-Plots.ipynb) >"
   ]
  },
  {
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   "source": [
    "# Visualization with Matplotlib"
   ]
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   "source": [
    "Matplotlib is a multi-platform data visualization library built on NumPy arrays, and designed to work with the broader SciPy stack. It supports many operating systems and graphics backends.\n",
    "- operating system \n",
    "- output format\n",
    "\n",
    "\n",
    "John Hunter, 2002, originally as a patch to IPython for enabling interactive MATLAB-style plotting via gnuplot \n",
    "- IPython's creator, Fernando Perez, was at the time scrambling to finish his PhD, and let John know he wouldn’t have time to review the patch for several months.\n",
    "- John took this as a cue to set out on his own, and the Matplotlib package was born, with version 0.1 released in 2003.\n",
    "- The Space Telescope Science Institute (the folks behind the Hubble Telescope) financially supported Matplotlib’s development\n"
   ]
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    "Newer tools like ggplot2 and ggvis in the R language, along with web visualization toolkits based on D3js and HTML5 canvas, often make Matplotlib feel clunky and old-fashioned.\n",
    "\n",
    "- to set new global plotting styles (see [Customizing Matplotlib: Configurations and Style Sheets](04.11-Settings-and-Stylesheets.ipynb)), \n",
    "- new packages  build on Matplotlib\n",
    "    - Seaborn (discussed in [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)), \n",
    "    - [ggpy](http://yhat.github.io/ggpy/), \n",
    "    - [HoloViews](http://holoviews.org/), \n",
    "    - [Altair](http://altair-viz.github.io/),\n",
    "    - Pandas\n",
    "\n"
   ]
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   "source": [
    "## General Matplotlib Tips\n",
    "\n",
    "Before we dive into the details of creating visualizations with Matplotlib, there are a few useful things you should know about using the package."
   ]
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   "source": [
    "### Importing Matplotlib\n",
    "\n",
    "Just as we use the ``np`` shorthand for NumPy and the ``pd`` shorthand for Pandas, we will use some standard shorthands for Matplotlib imports:"
   ]
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   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
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   "source": [
    "The ``plt`` interface is what we will use most often, as we shall see throughout this chapter."
   ]
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    "### Setting Styles\n",
    "\n",
    "We will use the ``plt.style`` directive to choose appropriate aesthetic styles for our figures.\n",
    "Here we will set the ``classic`` style, which ensures that the plots we create use the classic Matplotlib style:"
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   "source": [
    "plt.style.use('classic')"
   ]
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   "source": [
    "Throughout this section, we will adjust this style as needed for Matplotlib version 1.5.\n",
    "\n",
    "For more information on stylesheets, see [Customizing Matplotlib: Configurations and Style Sheets](04.11-Settings-and-Stylesheets.ipynb)."
   ]
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   "source": [
    "### ``show()`` or No ``show()``? How to Display Your Plots"
   ]
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    "A visualization you can't see won't be of much use, but just how you view your Matplotlib plots depends on the context.\n",
    "The best use of Matplotlib differs depending on how you are using it; roughly, the three applicable contexts are using Matplotlib in a script, in an IPython terminal, or in an IPython notebook."
   ]
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   "source": [
    "#### Plotting from a script\n",
    "\n",
    "If you are using Matplotlib from within a script, the function ``plt.show()`` is your friend.\n",
    "``plt.show()`` starts an event loop, looks for all currently active figure objects, and opens one or more interactive windows that display your figure or figures.\n",
    "\n",
    "So, for example, you may have a file called *myplot.py* containing the following:\n",
    "\n",
    "```python\n",
    "# ------- file: myplot.py ------\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = np.linspace(0, 10, 100)\n",
    "\n",
    "plt.plot(x, np.sin(x))\n",
    "plt.plot(x, np.cos(x))\n",
    "\n",
    "plt.show()\n",
    "```\n",
    "\n",
    "You can then run this script from the command-line prompt, which will result in a window opening with your figure displayed:\n",
    "\n",
    "```\n",
    "$ python myplot.py\n",
    "```\n",
    "\n",
    "The ``plt.show()`` command does a lot under the hood, as it must interact with your system's interactive graphical backend.\n",
    "The details of this operation can vary greatly from system to system and even installation to installation, but matplotlib does its best to hide all these details from you.\n",
    "\n",
    "One thing to be aware of: the ``plt.show()`` command should be used *only once* per Python session, and is most often seen at the very end of the script.\n",
    "Multiple ``show()`` commands can lead to unpredictable backend-dependent behavior, and should mostly be avoided."
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    "#### Plotting from an IPython shell\n",
    "\n",
    "It can be very convenient to use Matplotlib interactively within an IPython shell (see [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb)).\n",
    "IPython is built to work well with Matplotlib if you specify Matplotlib mode.\n",
    "To enable this mode, you can use the ``%matplotlib`` magic command after starting ``ipython``:\n",
    "\n",
    "```ipython\n",
    "In [1]: %matplotlib\n",
    "Using matplotlib backend: TkAgg\n",
    "\n",
    "In [2]: import matplotlib.pyplot as plt\n",
    "```\n",
    "\n",
    "At this point, any ``plt`` plot command will cause a figure window to open, and further commands can be run to update the plot.\n",
    "Some changes (such as modifying properties of lines that are already drawn) will not draw automatically: to force an update, use ``plt.draw()``.\n",
    "Using ``plt.show()`` in Matplotlib mode is not required."
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    "#### Plotting from an IPython notebook\n",
    "\n",
    "The IPython notebook is a browser-based interactive data analysis tool that can combine narrative, code, graphics, HTML elements, and much more into a single executable document (see [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb)).\n",
    "\n",
    "Plotting interactively within an IPython notebook can be done with the ``%matplotlib`` command, and works in a similar way to the IPython shell.\n",
    "In the IPython notebook, you also have the option of embedding graphics directly in the notebook, with two possible options:\n",
    "\n",
    "- ``%matplotlib notebook`` will lead to *interactive* plots embedded within the notebook\n",
    "- ``%matplotlib inline`` will lead to *static* images of your plot embedded in the notebook\n",
    "\n",
    "For this book, we will generally opt for ``%matplotlib inline``:"
   ]
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   "source": [
    "%matplotlib inline"
   ]
  },
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   "source": [
    "After running this command (it needs to be done only once per kernel/session), any cell within the notebook that creates a plot will embed a PNG image of the resulting graphic:"
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "x = np.linspace(0, 10, 100)\n",
    "\n",
    "fig = plt.figure()\n",
    "plt.plot(x, np.sin(x), '-')\n",
    "plt.plot(x, np.cos(x), '--');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Saving Figures to File\n",
    "\n",
    "One nice feature of Matplotlib is the ability to save figures in a wide variety of formats.\n",
    "Saving a figure can be done using the ``savefig()`` command.\n",
    "For example, to save the previous figure as a PNG file, you can run this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "fig.savefig('my_figure.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "We now have a file called ``my_figure.png`` in the current working directory:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-r--r--  1 jakevdp  staff    16K Aug 11 10:59 my_figure.png\r\n"
     ]
    }
   ],
   "source": [
    "!ls -lh my_figure.png"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "To confirm that it contains what we think it contains, let's use the IPython ``Image`` object to display the contents of this file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TQGYm7yR8PMp6hIjECDjZiHPbHxnJ3kdqXm4V5BokVFspS0vWrmvbNt5J1MVo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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image('my_figure.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "In ``savefig()``, the file format is inferred from the extension of the given filename.\n",
    "Depending on what backends you have installed, many different file formats are available.\n",
    "The list of supported file types can be found for your system by using the following method of the figure canvas object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'eps': 'Encapsulated Postscript',\n",
       " 'jpeg': 'Joint Photographic Experts Group',\n",
       " 'jpg': 'Joint Photographic Experts Group',\n",
       " 'pdf': 'Portable Document Format',\n",
       " 'pgf': 'PGF code for LaTeX',\n",
       " 'png': 'Portable Network Graphics',\n",
       " 'ps': 'Postscript',\n",
       " 'raw': 'Raw RGBA bitmap',\n",
       " 'rgba': 'Raw RGBA bitmap',\n",
       " 'svg': 'Scalable Vector Graphics',\n",
       " 'svgz': 'Scalable Vector Graphics',\n",
       " 'tif': 'Tagged Image File Format',\n",
       " 'tiff': 'Tagged Image File Format'}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig.canvas.get_supported_filetypes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Note that when saving your figure, it's not necessary to use ``plt.show()`` or related commands discussed earlier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Two Interfaces for the Price of One\n",
    "\n",
    "A potentially confusing feature of Matplotlib is its dual interfaces: \n",
    "- a convenient MATLAB-style state-based interface, and \n",
    "- a more powerful object-oriented interface. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### MATLAB-style Interface\n",
    "\n",
    "Matplotlib was originally written as a Python alternative for MATLAB users, and much of its syntax reflects that fact.\n",
    "- The MATLAB-style tools are contained in the pyplot (``plt``) interface.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DOr4O69ertmmjWrGiaq9eqps22fPxeC02bFB95BHVffdV7d5ddfPmmF8iLtLx\nfVGQnHazSG1t5Gr7iIgWNaYxY6BNG7j0UujeHfbYI07BOZcA06ZB06bWI+/ZE/baKzHX/eor+3+0\ndq2lV088MTHXdSUnImhEB3zj6uKLYf582LABjjsO5s4NHZFzRbd5M3TuDI0bQ69eMGBA4hp+gGrV\nYNw4uP12uOACePZZGyNwqSklev55vfaaDYj17m1jBM4lg3Xr4IorYPfd4aWXYP/9w8azdKnFc+yx\n0K+fxeWiK217/nk1bgyTJ8P999tsoK1bQ0fk3I4tWAC1a9vsm9Gjwzf8YAPK06fbzLs6dWDFitAR\nuVhLucYf4J//tBlBCxfCZZf5bCAXXe++C2edBQ8+CI88AqVLh45om912s7uQli3h9NMttepSR0o2\n/gD77GMDwRUqwPnnwy+/hI7Iub8aPhyaN7fe/nXXhY4mfyJw663w2GNQvz5MnRo6IhcrKdv4A5Qt\nC4MGwfHHQ0aGzWJwLgpeeMHSkhMnWron6po0sYVhV15pH1Yu+aV04w+Ws+zdGxo2hHr1/APAhden\nj6V5pkyxFGWyqF/f0lQ33mh31S65pdxsnx158EEYMQIyM618hHOJ9uyzlkKZMgWqVg0dTfHMmmXT\nq19+2aaEuvCKM9snrRp/VbjrLisRMWlSYudQOzd4sM3jf/99OOyw0NGUzPTpdjc9eDCcd17oaJw3\n/jtBFdq3h48/tg8Bn7/sEmH0aGjVyjodqVJEbdo0m033zjs2VdWF443/TsrOtulrP/xglUKjNL3O\npZ6pU22g9N134aSTQkcTW2PG2BjABx/A4YeHjiZ9+SKvnVSqFPTvb/P/27f3Jewufr74whYeDh2a\neg0/WO7/gQegQQP47rvQ0biiSMvGH2wa6MiRln994onQ0bhUtG4dXHSRLd6qXz90NPHTqpWVUrno\nIvjtt9DRuJ2VlmmfvFatsvrmTz5ptUyci4U//oBzzrHNiLp1Cx1N/KlCs2awcaPV15IiJSBcSXnO\nv5jmzrUZC5MmWSEr50pC1TZe2bwZhg2zNGM6+OMPW0x50UW2O5hLnGA5fxFpICKLRWSJiNyZz9/X\nE5GfRWROzte9sbhurJxwgi28ufRSGwR2riR69YLPP7d58OnS8AOUL28TKPr3h1GjQkfjClPinr+I\nlAKWAPWBb4DZQBNVXZznmHpAR1W9ZCfOl/Cef6477oBPPoHx46FMLHc3dmljyhS4+mqYOTN5F3GV\n1OzZtvVpYPgEAAAUM0lEQVTklClQs2boaNJDqJ5/bWCpqq5Q1S3AMKBhfvHF4Fpx9cgj1ujfcUfo\nSFwyWrnSBj4HD07fhh/g5JNtEkWjRl5QMcpi0fgfBKzK83h1znPbqysi80TkHRE5OgbXjbnSpW1K\n3qhRNhPIuZ31xx9w+eXQsaMN9Ka766+3GU4tW/pU6qhKVEbyE+AQVT0e6AtENiO4zz42W+Gmm2DZ\nstDRuGTRsaP19jt2DB1JdPTqZZvA9O4dOhKXn1hkttcAh+R5XCXnuT+p6oY8348VkWdEZB9V/TG/\nE3bt2vXP7zMyMsjIyIhBmDvvpJOga1dblTl9ug1kOVeQESNs79s5c3yKY1677GKvzSmnWPmHU08N\nHVHqyMzMJDMzs0TniMWAb2ngC2zA91tgFnC1qi7Kc0wlVV2b831t4DVVPbSA8wUb8M1LFa66yu4E\n+vULHY2LqmXLrFEbOxZOPDF0NNE0Zgz85z82pdqr6cZHkAFfVc0C2gETgIXAMFVdJCKtRaRVzmFX\niMgCEZkL9AKuKul1400EBgyw/YCHDw8djYuiTZusg3Dffd7w78jFF1uJC8//R4sv8irEJ59YzfJZ\ns+DQQ0NH46Lk1ltths/rr3u6pzCbN9sdUvPm0LZt6GhSj6/wjZOePW3xytSpPv/fmbFjoXVr+PRT\n2Hvv0NEkh6VL7QPAV9LHnlf1jJMOHazu/3//GzoSFwXffWcpjFde8Ya/KKpXh8cft/2AN24MHY3z\nnv9O+vZbqFXLpoGecUboaFwoqpbDPu649CjYFmuqcN11UKECPPNM6GhSh/f846hyZXj+eVu8sn59\n6GhcKM88Yz3/PLORXRGIwNNP2+5fY8eGjia9ec+/iFq3tsGrgQNDR+ISbfFiu+v76CNLYbjiy8yE\na6+1MZP99gsdTfLzAd8E2LABjj8eHnvM9i916WHLFhusbNkS2rQJHU1quP12WL7cZ0vFgqd9EmCP\nPWDQICv/8L//hY7GJUq3brZAqXXr0JGkjocftkVyL70UOpL05D3/Yrr7bpg/H0aP9l5Lqvv4Y9ug\nZM4cOCi/koWu2D77zArAffxxeldCLSnv+SdQ166werXn/lPd77/bIH/v3t7wx8Oxx9pU6pYtITs7\ndDTpxXv+JZDba/nkEzjkkMKPd8mnY0f7kPcSH/GzdSucdhrccIPVAHJF5wO+ATz8sK38HT/e0z+p\nZto0q+w6f77PSIm3xYvh9NNhxgw4/PDQ0SQfT/sEcOed8NNP8NxzoSNxsfTbb1aH5tlnveFPhCOP\nhHvusdc8Kyt0NOnBe/4x8PnncOaZVvztsMNCR+NioX17+PFHK+HgEiM7GzIybPvHW28NHU1y8bRP\nQI89Bu++a0WrSvn9VFKbOtUWIH32me3n4BJn2TKoU8c2UfKFdDvP0z4BdehgM0M8/ZPcfvsNWrSw\nDXy84U+8ww+3/RE8/RN/3vOPoUWLbPn/xx977f9k1b69jeEMGhQ6kvSVm/65/HL793CF87RPBPTo\nARMmwMSJPvsn2bz/Plx9tc3u8V5/WEuXQt26nv7ZWcHSPiLSQEQWi8gSEbmzgGP6iMhSEZknIsfH\n4rpR1LGj1f/p3z90JK4oNm60dM8zz3jDHwXVq8O99/rir3iKxQbupYAl2Abu3wCzgSaqujjPMRcA\n7VT1IhE5BeitqnUKOF9S9/zBZv/Uq+dL1pPJbbdZqeYhQ0JH4nJlZdksuiZN4OabQ0cTbaF6/rWB\npaq6QlW3AMOAhtsd0xAYBKCqM4EKIlIpBteOpKOPtsbkxht9w+pk8OGHMGwY9OkTOhKXV+nS8OKL\n8MADVv3TxVYsGv+DgFV5Hq/OeW5Hx6zJ55iU0qkT/PCDvXlddP3+u6V7+va1qp0uWmrUgM6d4d//\n9vRPrEVyO/KuebZJysjIICMjI1gsxVW2rBV9q18fzj8fqlQJHZHLz/3325aMl18eOhJXkNtug5Ej\nbRzN91IwmZmZZGZmlugcscj51wG6qmqDnMedAVXVHnmO6QdMUdXhOY8XA/VUdW0+50v6nH9eDzxg\nK3/HjPHZP1EzcyY0bGiLuSpWDB2N25FFiyz/7+No+QuV858NHC4iVUWkHNAEGL3dMaOBpjlB1gF+\nzq/hT0V33WVVIX3eeLRs2mTpnt69veFPBkcdZTPpfBwtdkrc+KtqFtAOmAAsBIap6iIRaS0irXKO\neRf4SkSWAc8BaVO4tVw526moUyf45pvQ0bhcDz5o+eTGjUNH4nbW7bfbArwXXggdSWrwRV4Jcv/9\nMG8evPWWp39Cy92Z69NP4YADQkfjimLBAjjrLN9DY3te2yfC7r0XvvoKXn01dCTpbdMmaNYMnnzS\nG/5kVLOmlXxo1crTPyXlPf8E8h5nePfea73HN9/0O7BktWULnHIKtG1rK4Cd1/ZJCvfcAwsXeuMT\nQu6H77x5ULly6GhcSeRuoTpnDhx8cOhowvO0TxK4/3748ksvI5BomzZZmeDHH/eGPxUce6xt+PLv\nf3v6p7i85x/AnDnQoIH1QA88MHQ06eGuu2yuuN9xpY6tW23jl9atbQpoOvO0TxLp2hVmz/bFX4kw\nY8a2xVyVUraiVHrKnf2T7ou/PO2TRO6+2+b9v/RS6EhS2++/2+yevn294U9FNWvaLnotWnjtn6Ly\nnn9AuYNW6d5riacOHexDdtiw0JG4eNm61XbQu+aa9C397GmfJNS9+7adv3zj99iaOnXbzlxesTO1\n5e78NW0aHHlk6GgSz9M+SahTJ9i82WrMuNj55Re44QZ4/nlv+NNB9epWsuOGG+xOwBXOe/4R8OWX\ntmhl6lQ45pjQ0aSGZs2gfHno1y90JC5RVK18+pln2mK+dOJpnyTWv781VDNmWDE4V3yvv24bgMyd\nC3vsEToal0irV8OJJ8I778BJJ4WOJnE87ZPEbrzR5vzn2cfGFcO339qy/1de8YY/HVWpAk89ZYO/\nv/0WOppo855/hHz3HRx/vBV/S8LNy4LLzoYLL4STT4aHHgodjQupWTO7g+7fP3QkieE9/yRXsaLt\n+du0Kfz4Y+hokk+vXjbQe//9oSNxofXpA5Mm2Ypulz/v+UfQbbfBqlUwYoSv/t1Zc+bYYN+sWVCt\nWuhoXBRMnw6XXmrvjYMOCh1NfCW85y8ie4vIBBH5QkTGi0iFAo77WkQ+FZG5IjKrJNdMB488YvOW\nfceinbNhg83n79PHG363Td260K4dXHcdZGWFjiZ6StTzF5EewA+q+qiI3Ansraqd8zluOXCiqv60\nE+dM+54/wOefQ716MGWKLWF3BWvZ0vL9AweGjsRFTVYWnHuuTf9M5ckUIXL+DYGXc75/Gbi0gOMk\nBtdKK0cfDT17wpVXWs/W5W/QIPjwQ5vh4dz2Spe28unPPQeTJ4eOJlpK2vP/UVX3KehxnueXAz8D\nWUB/VX1+B+f0nn8eLVrYzkWDBnn+f3u5FR397sgV5r33bAbQnDmpWeCvOD3/Mjtx0veAvC+XAArk\nt4auoFb7NFX9VkT2B94TkUWqOq2ga3bNc3+WkZFBRhrPe+zbF2rXtllAvmXdNr/+CldcYXdH3vC7\nwpx7rjX+114L48fbHUEyy8zMJDMzs0TnKGnPfxGQoaprReQAYIqqHlXIz3QBflXVJwr4e+/5b2fR\nIstZTpgAJ5wQOprwVG0Rz+67w4ABoaNxyWLrVpsRVru2TapIJSFy/qOBZjnf3wC8lU9Qu4nIHjnf\n7w6cBywo4XXTylFHwdNPQ6NGsG5d6GjC69vXPhA9z++KokwZK+396qtWAiTdlbTnvw/wGnAwsAJo\nrKo/i0hl4HlVvVhEqgFvYimhMsAQVe2+g3N6z78AnTvb7l/jx9sbOR1Nnmy9/unTfVqnK56PP4YL\nLoD337eOVSrwwm4pLivLyhfUrGkbkaebr76yuduvvgpnnx06GpfMXnwRHn0UZs6ECvmuTkouXt4h\nxZUuDUOHwqhRMHhw6GgSa8MG24f3nnu84Xcl16IFnHMONG6cvvX/veefhBYutCmOI0faQHCqy8qy\nmT17722rnn3Kq4uFrVvhX/+y9OHTTyf3+8p7/mnimGMs9XHllfDFF6GjiS9Vq3X088/w7LPJ/R/U\nRUuZMjB8OHzwgZUGSTfe+Cepc86x6WoXXQTffx86mvh54gkb5H3zTdhll9DRuFSz554wZgz06AFv\nvx06msTyxj+JtWgBV11lt66pWALitdesTPPYsbDXXqGjcamqalUbR2vZ0mYApQvP+Se57GzbBezr\nr23ruvLlQ0cUGxMmWDXG996D444LHY1LBxMn2jTiceOgVq3Q0RSNT/VMU1lZtmz9t9/gjTegbNnQ\nEZVMZqbNwnjjDTj99NDRuHTyxhu2DWhmJtSoETqanecDvmmqdGnbs1Yk+WuXf/ihDWQPH+4Nv0u8\nRo1sLO3cc21PjVTmjX+KKFvWcuQ//WQbm2zeHDqiops+HS67zNYwnHVW6GhcumrWzLYCPessm1ad\nqrzxTyHly8Po0VYCumFD2LgxdEQ7b9w4uOQSePllK77lXEj//rfNADrnHJg7N3Q08eGNf4opX972\n/q1Y0RrRX34JHVHhhgyBG26At96ymivORcG111oRwQYNbC1AqvHGPwWVKWNbGp5wguXNly8PHVH+\nVG0ef+fONpf/1FNDR+TcX11+uY2nXX653ZWmEm/8U1SpUtC7N7RpY8XQJk0KHdFfbdwITZvaf6hp\n02zVsnNRdN55MHUqPPSQdVSys0NH9FcjRhTv57zxT2EiNm1t+HC7hX3ySetth/bll9t6+dOn2yIb\n56LsqKNgxgx7v15ySTRW1W/aBDffbB9IxeGNfxrIyLA37pAhllNfsyZMHKpWk+jUU21AbdAg2G23\nMLE4V1T77WeLDmvWtIWHY8eGi2X5cjjjDFi9Gj75pHjn8MY/TRx6qPVa6ta1sYDBgxN7F7B6tfWY\nHnnEaqm0a+dF2lzyKVcOune3TkybNvY+Xr8+cdffssX2Iahd26Z0v/FG8UuflKjxF5ErRGSBiGSJ\nSIELokWkgYgsFpElInJnSa7piq9sWejSxaZVdu9uC1mK22vYWZs2WcXEE06Ak06y6518cnyv6Vy8\nZWTAvHk2dlWjBjz/fPwXV06fDieeaJMjZs60arcl6kCparG/gBpAdWAyUKuAY0oBy4CqQFlgHnDk\nDs6pzkyZMiVu5968WfXZZ1UrV1Zt0kR1yZLYnn/TJtV+/VQPPlj14otV588v/rni+TokG38ttonK\na/Hxx6pnnKF67LGqo0apbt0a2/N/8IFqgwaqVaqoDh2qmp3992Ny2s0itd8l6vmr6hequhTY0edP\nbWCpqq5Q1S3AMKBhSa6bLjIzM+N27rJl7bZ1yRKbaVO3rs1qGDGiZKuDlyyB++6D6tWtDPOIEVYq\nt2bN4p8znq9DsvHXYpuovBYnnmizgbp0sbTmP/5hqZl164p/zvXrbYyuXj1bA9OoESxbBk2axC5d\nmohtwA8CVuV5vBr7QHARsMcecO+90LGjNdbPPGMzhM45x3YJq1cPjjyy4DfcunUwa5bdho4bBytW\nWC5y1ChL9TiXDkSsgW7UCGbPtp3BqlWzgeHzzrMUa82a8H//l//Pb9xopSTmzbPqvFOm2P+/m26y\nXezKxKGlLvSUIvIeUCnvU4AC96hqmm1/kLp23dXK2V5zjW2UPmWK9Wa6d4dvv4VKleCAA2wrxV9/\ntZXDP/5ob9qTToJTToEHH4T69ePzRnUuWZx8Mrz0ku0898EHVp78ppvsrnjXXW1q81572XjYH39Y\nL3/1ahs7OO44+wB56aX472ERk5LOIjIF6Kiqc/L5uzpAV1VtkPO4M5af6lHAuSIwE90555KLFrGk\ncyz7aAVdeDZwuIhUBb4FmgBXF3SSov4Czjnniq6kUz0vFZFVQB1gjIiMzXm+soiMAVDVLKAdMAFY\nCAxT1UUlC9s551xJRG4nL+ecc/EXmRW+vhDMiEgVEZksIgtFZL6I3BI6ptBEpJSIzBGR0aFjCUlE\nKojICBFZlPP+OCV0TKGIyG05C0w/E5EhIlIudEyJIiIviMhaEfksz3N7i8gEEflCRMaLSIXCzhOJ\nxl9ESgF9gfOBY4CrReTIsFEFsxXooKrHAHWBtmn8WuRqD3weOogI6A28q6pHAccBaZk+FZEDgZux\nhaXHYmOXTcJGlVADsbYyr87ARFWtgS26vauwk0Si8ccXgv1JVf+nqvNyvt+A/Qc/KGxU4YhIFeBC\nYEDoWEISkT2BM1R1IICqblXVBFaViZzSwO4iUgbYDfgmcDwJo6rTgJ+2e7ohkLvjwMvApYWdJyqN\nf34LwdK2wcslIocCxwMzw0YS1JNAJ2xtSTqrBqwTkYE5KbD+IrJr6KBCUNVvgMeBlcAa4GdVnRg2\nquAqqupasA4kULGwH4hK4++2IyJ7ACOB9jl3AGlHRC4C1ubcCQk7LiOS6soAtYCnVbUWsBG71U87\nIrIX1tOtChwI7CEi14SNKnIK7SxFpfFfAxyS53GVnOfSUs6t7EjgFVV9K3Q8AZ0GXCIiy4GhwFki\nMihwTKGsBlap6sc5j0diHwbp6Bxguar+mDOV/A0g3TcBXSsilQBE5ADgu8J+ICqN/58LwXJG7ZsA\n6Tyz40Xgc1XtHTqQkFT1blU9RFUPw94Tk1W1aei4Qsi5pV8lIkfkPFWf9B0EXwnUEZHyIiLYa5Fu\ng9/b3wmPBprlfH8DUGinMRJVWFQ1S0RyF4KVAl5I14VgInIacC0wX0TmYrdvd6vquLCRuQi4BRgi\nImWB5UDzwPEEoaqzRGQkMBfYkvNn/7BRJY6IvApkAPuKyEqgC9AdGCEiLYAVQONCz+OLvJxzLv1E\nJe3jnHMugbzxd865NOSNv3POpSFv/J1zLg154++cc2nIG3/nnEtD3vg751wa8sbfOefS0P8D6yQh\nUTSOiswAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d658438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()  # create a plot figure\n",
    "\n",
    "# create the first of two panels and set current axis\n",
    "plt.subplot(2, 1, 1) # (rows, columns, panel number)\n",
    "plt.plot(x, np.sin(x))\n",
    "\n",
    "# create the second panel and set current axis\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(x, np.cos(x));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "It is important to note that this interface is *stateful*: \n",
    "- it keeps track of the \"current\" figure and axes, which are where all ``plt`` commands are applied.\n",
    "- You can get a reference to these using \n",
    "    - the ``plt.gcf()`` (get current figure) and \n",
    "    - ``plt.gca()`` (get current axes) routines.\n",
    "\n",
    "While this stateful interface is fast and convenient for simple plots, it is easy to run into problems.\n",
    "For example, once the second panel is created, how can we go back and add something to the first?\n",
    "This is possible within the MATLAB-style interface, but a bit clunky.\n",
    "Fortunately, there is a better way."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Object-oriented interface\n",
    "\n",
    "The object-oriented interface is available for these more complicated situations, and for when you want more control over your figure.\n",
    "Rather than depending on some notion of an \"active\" figure or axes, in the object-oriented interface the plotting functions are *methods* of explicit ``Figure`` and ``Axes`` objects.\n",
    "To re-create the previous plot using this style of plotting, you might do the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d64f240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First create a grid of plots\n",
    "# ax will be an array of two Axes objects\n",
    "fig, ax = plt.subplots(2)\n",
    "\n",
    "# Call plot() method on the appropriate object\n",
    "ax[0].plot(x, np.sin(x))\n",
    "ax[1].plot(x, np.cos(x));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "For more simple plots, the choice of which style to use is largely a matter of preference, but \n",
    "- the object-oriented approach can become a necessity as plots become more complicated.\n",
    "- switching ``plt.plot()`` to ``ax.plot()``\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Further Resources](03.13-Further-Resources.ipynb) | [Contents](Index.ipynb) | [Simple Line Plots](04.01-Simple-Line-Plots.ipynb) >"
   ]
  }
 ],
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  "celltoolbar": "Slideshow",
  "kernelspec": {
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   "language": "python",
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   "hotkeys": {
    "equation": "Ctrl-E",
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  "toc": {
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   "nav_menu": {},
   "number_sections": false,
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   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
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   "toc_position": {},
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 },
 "nbformat": 4,
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}