Matplotlib_usage.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "***********\n",
    "# Usage Guide\n",
    "***********\n",
    "\n",
    "This tutorial covers some basic usage patterns and best-practices to\n",
    "help you get started with Matplotlib.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sphinx_gallery_thumbnail_number = 3\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A simple example\n",
    "================\n",
    "\n",
    "Matplotlib graphs your data on `~.figure.Figure`\\s (i.e., windows, Jupyter\n",
    "widgets, etc.), each of which can contain one or more `~.axes.Axes` (i.e., an\n",
    "area where points can be specified in terms of x-y coordinates (or theta-r\n",
    "in a polar plot, or x-y-z in a 3D plot, etc.).  The most simple way of\n",
    "creating a figure with an axes is using `.pyplot.subplots`. We can then use\n",
    "`.Axes.plot` to draw some data on the axes:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1e4ad13b0c8>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()  # Create a figure containing a single axes.\n",
    "ax.plot([1, 2, 3, 4], [1, 4, 2, 3])  # Plot some data on the axes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many other plotting libraries or languages do not require you to explicitly\n",
    "create an axes.  For example, in MATLAB, one can just do\n",
    "\n",
    ".. code-block:: matlab\n",
    "\n",
    "   plot([1, 2, 3, 4], [1, 4, 2, 3])  % MATLAB plot.\n",
    "\n",
    "and get the desired graph.\n",
    "\n",
    "In fact, you can do the same in Matplotlib: for each `~.axes.Axes` graphing\n",
    "method, there is a corresponding function in the :mod:`matplotlib.pyplot`\n",
    "module that performs that plot on the \"current\" axes, creating that axes (and\n",
    "its parent figure) if they don't exist yet.  So the previous example can be\n",
    "written more shortly as\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1e4ade79b88>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1, 2, 3, 4], [1, 4, 2, 3])  # Matplotlib plot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Parts of a Figure\n",
    "=================\n",
    "\n",
    "Now, let's have a deeper look at the components of a Matplotlib figure.\n",
    "\n",
    "![](../../_static/anatomy.png)\n",
    "\n",
    "\n",
    ":class:`~matplotlib.figure.Figure`\n",
    "----------------------------------\n",
    "\n",
    "The **whole** figure.  The figure keeps\n",
    "track of all the child :class:`~matplotlib.axes.Axes`, a smattering of\n",
    "'special' artists (titles, figure legends, etc), and the **canvas**.\n",
    "(Don't worry too much about the canvas, it is crucial as it is the\n",
    "object that actually does the drawing to get you your plot, but as the\n",
    "user it is more-or-less invisible to you).  A figure can contain any\n",
    "number of :class:`~matplotlib.axes.Axes`, but will typically have\n",
    "at least one.\n",
    "\n",
    "The easiest way to create a new figure is with pyplot::\n",
    "\n",
    "   fig = plt.figure()  # an empty figure with no Axes\n",
    "   fig, ax = plt.subplots()  # a figure with a single Axes\n",
    "   fig, axs = plt.subplots(2, 2)  # a figure with a 2x2 grid of Axes\n",
    "\n",
    "It's convenient to create the axes together with the figure, but you can\n",
    "also add axes later on, allowing for more complex axes layouts.\n",
    "\n",
    ":class:`~matplotlib.axes.Axes`\n",
    "------------------------------\n",
    "\n",
    "This is what you think of as 'a plot', it is the region of the image\n",
    "with the data space. A given figure\n",
    "can contain many Axes, but a given :class:`~matplotlib.axes.Axes`\n",
    "object can only be in one :class:`~matplotlib.figure.Figure`.  The\n",
    "Axes contains two (or three in the case of 3D)\n",
    ":class:`~matplotlib.axis.Axis` objects (be aware of the difference\n",
    "between **Axes** and **Axis**) which take care of the data limits (the\n",
    "data limits can also be controlled via the :meth:`.axes.Axes.set_xlim` and\n",
    ":meth:`.axes.Axes.set_ylim` methods).  Each :class:`~.axes.Axes` has a title\n",
    "(set via :meth:`~matplotlib.axes.Axes.set_title`), an x-label (set via\n",
    ":meth:`~matplotlib.axes.Axes.set_xlabel`), and a y-label set via\n",
    ":meth:`~matplotlib.axes.Axes.set_ylabel`).\n",
    "\n",
    "The :class:`~.axes.Axes` class and its member functions are the primary entry\n",
    "point to working with the OO interface.\n",
    "\n",
    ":class:`~matplotlib.axis.Axis`\n",
    "------------------------------\n",
    "\n",
    "These are the number-line-like objects. They take\n",
    "care of setting the graph limits and generating the ticks (the marks\n",
    "on the axis) and ticklabels (strings labeling the ticks).  The location of\n",
    "the ticks is determined by a `~matplotlib.ticker.Locator` object and the\n",
    "ticklabel strings are formatted by a `~matplotlib.ticker.Formatter`.  The\n",
    "combination of the correct `.Locator` and `.Formatter` gives very fine\n",
    "control over the tick locations and labels.\n",
    "\n",
    ":class:`~matplotlib.artist.Artist`\n",
    "----------------------------------\n",
    "\n",
    "Basically everything you can see on the figure is an artist (even the\n",
    "`.Figure`, `Axes <.axes.Axes>`, and `~.axis.Axis` objects).  This includes\n",
    "`.Text` objects, `.Line2D` objects, :mod:`.collections` objects, `.Patch`\n",
    "objects ... (you get the idea).  When the figure is rendered, all of the\n",
    "artists are drawn to the **canvas**.  Most Artists are tied to an Axes; such\n",
    "an Artist cannot be shared by multiple Axes, or moved from one to another.\n",
    "\n",
    "\n",
    "Types of inputs to plotting functions\n",
    "=====================================\n",
    "\n",
    "All of plotting functions expect `numpy.array` or `numpy.ma.masked_array` as\n",
    "input.  Classes that are 'array-like' such as `pandas` data objects\n",
    "and `numpy.matrix` may or may not work as intended.  It is best to\n",
    "convert these to `numpy.array` objects prior to plotting.\n",
    "\n",
    "For example, to convert a `pandas.DataFrame` ::\n",
    "\n",
    "  a = pandas.DataFrame(np.random.rand(4,5), columns = list('abcde'))\n",
    "  a_asarray = a.values\n",
    "\n",
    "and to convert a `numpy.matrix` ::\n",
    "\n",
    "  b = np.matrix([[1, 2], [3, 4]])\n",
    "  b_asarray = np.asarray(b)\n",
    "\n",
    "\n",
    "The object-oriented interface and the pyplot interface\n",
    "======================================================\n",
    "\n",
    "As noted above, there are essentially two ways to use Matplotlib:\n",
    "\n",
    "- Explicitly create figures and axes, and call methods on them (the\n",
    "  \"object-oriented (OO) style\").\n",
    "- Rely on pyplot to automatically create and manage the figures and axes, and\n",
    "  use pyplot functions for plotting.\n",
    "\n",
    "So one can do (OO-style)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e4adee2a08>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 2, 100)\n",
    "\n",
    "# Note that even in the OO-style, we use `.pyplot.figure` to create the figure.\n",
    "fig, ax = plt.subplots()  # Create a figure and an axes.\n",
    "ax.plot(x, x, label='linear')  # Plot some data on the axes.\n",
    "ax.plot(x, x**2, label='quadratic')  # Plot more data on the axes...\n",
    "ax.plot(x, x**3, label='cubic')  # ... and some more.\n",
    "ax.set_xlabel('x label')  # Add an x-label to the axes.\n",
    "ax.set_ylabel('y label')  # Add a y-label to the axes.\n",
    "ax.set_title(\"Simple Plot\")  # Add a title to the axes.\n",
    "ax.legend()  # Add a legend."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "or (pyplot-style)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e4acf2e5c8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 2, 100)\n",
    "\n",
    "plt.plot(x, x, label='linear')  # Plot some data on the (implicit) axes.\n",
    "plt.plot(x, x**2, label='quadratic')  # etc.\n",
    "plt.plot(x, x**3, label='cubic')\n",
    "plt.xlabel('x label')\n",
    "plt.ylabel('y label')\n",
    "plt.title(\"Simple Plot\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Actually there is a third approach, for the case where you are embedding\n",
    "Matplotlib in a GUI application, which completely drops pyplot, even for\n",
    "figure creation.  We won't discuss it here; see the corresponding section in\n",
    "the gallery for more info (`user_interfaces`).\n",
    "\n",
    "Matplotlib's documentation and examples use both the OO and the pyplot\n",
    "approaches (which are equally powerful), and you should feel free to use\n",
    "either (however, it is preferable pick one of them and stick to it, instead\n",
    "of mixing them).  In general, we suggest to restrict pyplot to interactive\n",
    "plotting (e.g., in a Jupyter notebook), and to prefer the OO-style for\n",
    "non-interactive plotting (in functions and scripts that are intended to be\n",
    "reused as part of a larger project).\n",
    "\n",
    "<div class=\"alert alert-info\"><h4>Note</h4><p>In older examples, you may find examples that instead used the so-called\n",
    "   ``pylab`` interface, via ``from pylab import *``. This star-import\n",
    "   imports everything both from pyplot and from :mod:`numpy`, so that one\n",
    "   could do ::\n",
    "\n",
    "      x = linspace(0, 2, 100)\n",
    "      plot(x, x, label='linear')\n",
    "      ...\n",
    "\n",
    "   for an even more MATLAB-like style.  This approach is strongly discouraged\n",
    "   nowadays and deprecated; it is only mentioned here because you may still\n",
    "   encounter it in the wild.</p></div>\n",
    "\n",
    "Typically one finds oneself making the same plots over and over\n",
    "again, but with different data sets, which leads to needing to write\n",
    "specialized functions to do the plotting.  The recommended function\n",
    "signature is something like:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def my_plotter(ax, data1, data2, param_dict):\n",
    "    \"\"\"\n",
    "    A helper function to make a graph\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    ax : Axes\n",
    "        The axes to draw to\n",
    "\n",
    "    data1 : array\n",
    "       The x data\n",
    "\n",
    "    data2 : array\n",
    "       The y data\n",
    "\n",
    "    param_dict : dict\n",
    "       Dictionary of kwargs to pass to ax.plot\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    out : list\n",
    "        list of artists added\n",
    "    \"\"\"\n",
    "    out = ax.plot(data1, data2, **param_dict)\n",
    "    return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "which you would then use as:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1e4adff3648>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1, data2, data3, data4 = np.random.randn(4, 100)\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "my_plotter(ax, data1, data2, {'marker': 'x'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "or if you wanted to have 2 sub-plots:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1e4ae0c4948>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "my_plotter(ax1, data1, data2, {'marker': 'x'})\n",
    "my_plotter(ax2, data3, data4, {'marker': 'o'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For these simple examples this style seems like overkill, however\n",
    "once the graphs get slightly more complex it pays off.\n",
    "\n",
    "\n",
    "\n",
    "Backends\n",
    "========\n",
    "\n",
    "\n",
    "What is a backend?\n",
    "------------------\n",
    "\n",
    "A lot of documentation on the website and in the mailing lists refers\n",
    "to the \"backend\" and many new users are confused by this term.\n",
    "matplotlib targets many different use cases and output formats.  Some\n",
    "people use matplotlib interactively from the python shell and have\n",
    "plotting windows pop up when they type commands.  Some people run\n",
    "`Jupyter <https://jupyter.org>`_ notebooks and draw inline plots for\n",
    "quick data analysis. Others embed matplotlib into graphical user\n",
    "interfaces like wxpython or pygtk to build rich applications.  Some\n",
    "people use matplotlib in batch scripts to generate postscript images\n",
    "from numerical simulations, and still others run web application\n",
    "servers to dynamically serve up graphs.\n",
    "\n",
    "To support all of these use cases, matplotlib can target different\n",
    "outputs, and each of these capabilities is called a backend; the\n",
    "\"frontend\" is the user facing code, i.e., the plotting code, whereas the\n",
    "\"backend\" does all the hard work behind-the-scenes to make the figure.\n",
    "There are two types of backends: user interface backends (for use in\n",
    "pygtk, wxpython, tkinter, qt4, or macosx; also referred to as\n",
    "\"interactive backends\") and hardcopy backends to make image files\n",
    "(PNG, SVG, PDF, PS; also referred to as \"non-interactive backends\").\n",
    "\n",
    "Selecting a backend\n",
    "-------------------\n",
    "\n",
    "There are three ways to configure your backend:\n",
    "\n",
    "1. The :rc:`backend` parameter in your ``matplotlibrc`` file\n",
    "2. The :envvar:`MPLBACKEND` environment variable\n",
    "3. The function :func:`matplotlib.use`\n",
    "\n",
    "A more detailed description is given below.\n",
    "\n",
    "If multiple of these are configurations are present, the last one from the\n",
    "list takes precedence; e.g. calling :func:`matplotlib.use()` will override\n",
    "the setting in your ``matplotlibrc``.\n",
    "\n",
    "If no backend is explicitly set, Matplotlib automatically detects a usable\n",
    "backend based on what is available on your system and on whether a GUI event\n",
    "loop is already running. On Linux, if the environment variable\n",
    ":envvar:`DISPLAY` is unset, the \"event loop\" is identified as \"headless\",\n",
    "which causes a fallback to a noninteractive backend (agg).\n",
    "\n",
    "Here is a detailed description of the configuration methods:\n",
    "\n",
    "#. Setting :rc:`backend` in your ``matplotlibrc`` file::\n",
    "\n",
    "       backend : qt5agg   # use pyqt5 with antigrain (agg) rendering\n",
    "\n",
    "   See also :doc:`/tutorials/introductory/customizing`.\n",
    "\n",
    "#. Setting the :envvar:`MPLBACKEND` environment variable:\n",
    "\n",
    "   You can set the environment variable either for your current shell or for\n",
    "   a single script.\n",
    "\n",
    "   On Unix::\n",
    "\n",
    "        > export MPLBACKEND=qt5agg\n",
    "        > python simple_plot.py\n",
    "\n",
    "        > MPLBACKEND=qt5agg python simple_plot.py\n",
    "\n",
    "   On Windows, only the former is possible::\n",
    "\n",
    "        > set MPLBACKEND=qt5agg\n",
    "        > python simple_plot.py\n",
    "\n",
    "   Setting this environment variable will override the ``backend`` parameter\n",
    "   in *any* ``matplotlibrc``, even if there is a ``matplotlibrc`` in your\n",
    "   current working directory. Therefore, setting :envvar:`MPLBACKEND`\n",
    "   globally, e.g. in your ``.bashrc`` or ``.profile``, is discouraged as it\n",
    "   might lead to counter-intuitive behavior.\n",
    "\n",
    "#. If your script depends on a specific backend you can use the function\n",
    "   :func:`matplotlib.use`::\n",
    "\n",
    "      import matplotlib\n",
    "      matplotlib.use('qt5agg')\n",
    "\n",
    "   This should be done before any figure is created; otherwise Matplotlib may\n",
    "   fail to switch the backend and raise an ImportError.\n",
    "\n",
    "   Using `~matplotlib.use` will require changes in your code if users want to\n",
    "   use a different backend.  Therefore, you should avoid explicitly calling\n",
    "   `~matplotlib.use` unless absolutely necessary.\n",
    "\n",
    "\n",
    "The builtin backends\n",
    "--------------------\n",
    "\n",
    "By default, Matplotlib should automatically select a default backend which\n",
    "allows both interactive work and plotting from scripts, with output to the\n",
    "screen and/or to a file, so at least initially you will not need to worry\n",
    "about the backend.  The most common exception is if your Python distribution\n",
    "comes without :mod:`tkinter` and you have no other GUI toolkit installed;\n",
    "this happens on certain Linux distributions, where you need to install a\n",
    "Linux package named ``python-tk`` (or similar).\n",
    "\n",
    "If, however, you want to write graphical user interfaces, or a web\n",
    "application server (`howto-webapp`), or need a better\n",
    "understanding of what is going on, read on. To make things a little\n",
    "more customizable for graphical user interfaces, matplotlib separates\n",
    "the concept of the renderer (the thing that actually does the drawing)\n",
    "from the canvas (the place where the drawing goes).  The canonical\n",
    "renderer for user interfaces is ``Agg`` which uses the `Anti-Grain\n",
    "Geometry`_ C++ library to make a raster (pixel) image of the figure; it\n",
    "is used by the ``Qt5Agg``, ``Qt4Agg``, ``GTK3Agg``, ``wxAgg``, ``TkAgg``, and\n",
    "``macosx`` backends.  An alternative renderer is based on the Cairo library,\n",
    "used by ``Qt5Cairo``, ``Qt4Cairo``, etc.\n",
    "\n",
    "For the rendering engines, one can also distinguish between `vector\n",
    "<https://en.wikipedia.org/wiki/Vector_graphics>`_ or `raster\n",
    "<https://en.wikipedia.org/wiki/Raster_graphics>`_ renderers.  Vector\n",
    "graphics languages issue drawing commands like \"draw a line from this\n",
    "point to this point\" and hence are scale free, and raster backends\n",
    "generate a pixel representation of the line whose accuracy depends on a\n",
    "DPI setting.\n",
    "\n",
    "Here is a summary of the matplotlib renderers (there is an eponymous\n",
    "backend for each; these are *non-interactive backends*, capable of\n",
    "writing to a file):\n",
    "\n",
    "========  =========  =======================================================\n",
    "Renderer  Filetypes  Description\n",
    "========  =========  =======================================================\n",
    "AGG       png        raster_ graphics -- high quality images using the\n",
    "                     `Anti-Grain Geometry`_ engine\n",
    "PS        ps,        vector_ graphics -- Postscript_ output\n",
    "          eps\n",
    "PDF       pdf        vector_ graphics -- `Portable Document Format`_\n",
    "SVG       svg        vector_ graphics -- `Scalable Vector Graphics`_\n",
    "Cairo     png, ps,   raster_ or vector_ graphics -- using the Cairo_ library\n",
    "          pdf, svg\n",
    "========  =========  =======================================================\n",
    "\n",
    "To save plots using the non-interactive backends, use the\n",
    "``matplotlib.pyplot.savefig('filename')`` method.\n",
    "\n",
    "To save plots using the non-interactive backends, use the\n",
    "``matplotlib.pyplot.savefig('filename')`` method.\n",
    "\n",
    "And here are the user interfaces and renderer combinations supported;\n",
    "these are *interactive backends*, capable of displaying to the screen\n",
    "and of using appropriate renderers from the table above to write to\n",
    "a file:\n",
    "\n",
    "========= ================================================================\n",
    "Backend   Description\n",
    "========= ================================================================\n",
    "Qt5Agg    Agg rendering in a :term:`Qt5` canvas (requires PyQt5_).  This\n",
    "          backend can be activated in IPython with ``%matplotlib qt5``.\n",
    "ipympl    Agg rendering embedded in a Jupyter widget.  (requires ipympl).\n",
    "          This backend can be enabled in a Jupyter notebook with\n",
    "          ``%matplotlib ipympl``.\n",
    "GTK3Agg   Agg rendering to a :term:`GTK` 3.x canvas (requires PyGObject_,\n",
    "          and pycairo_ or cairocffi_).  This backend can be activated in\n",
    "          IPython with ``%matplotlib gtk3``.\n",
    "macosx    Agg rendering into a Cocoa canvas in OSX.  This backend can be\n",
    "          activated in IPython with ``%matplotlib osx``.\n",
    "TkAgg     Agg rendering to a :term:`Tk` canvas (requires TkInter_). This\n",
    "          backend can be activated in IPython with ``%matplotlib tk``.\n",
    "nbAgg     Embed an interactive figure in a Jupyter classic notebook.  This\n",
    "          backend can be enabled in Jupyter notebooks via\n",
    "          ``%matplotlib notebook``.\n",
    "WebAgg    On ``show()`` will start a tornado server with an interactive\n",
    "          figure.\n",
    "GTK3Cairo Cairo rendering to a :term:`GTK` 3.x canvas (requires PyGObject_,\n",
    "          and pycairo_ or cairocffi_).\n",
    "Qt4Agg    Agg rendering to a :term:`Qt4` canvas (requires PyQt4_ or\n",
    "          ``pyside``).  This backend can be activated in IPython with\n",
    "          ``%matplotlib qt4``.\n",
    "wxAgg     Agg rendering to a :term:`wxWidgets` canvas (requires wxPython_ 4).\n",
    "          This backend can be activated in IPython with ``%matplotlib wx``.\n",
    "========= ================================================================\n",
    "\n",
    "<div class=\"alert alert-info\"><h4>Note</h4><p>The names of builtin backends case-insensitive; e.g., 'Qt5Agg' and\n",
    "   'qt5agg' are equivalent.</p></div>\n",
    "\n",
    "\n",
    "ipympl\n",
    "^^^^^^\n",
    "\n",
    "The Jupyter widget ecosystem is moving too fast to support directly in\n",
    "Matplotlib.  To install ipympl\n",
    "\n",
    ".. code-block:: bash\n",
    "\n",
    "   pip install ipympl\n",
    "   jupyter nbextension enable --py --sys-prefix ipympl\n",
    "\n",
    "or\n",
    "\n",
    ".. code-block:: bash\n",
    "\n",
    "   conda install ipympl -c conda-forge\n",
    "\n",
    "See `jupyter-matplotlib <https://github.com/matplotlib/jupyter-matplotlib>`__\n",
    "for more details.\n",
    "\n",
    "GTK and Cairo\n",
    "^^^^^^^^^^^^^\n",
    "\n",
    "``GTK3`` backends (*both* ``GTK3Agg`` and ``GTK3Cairo``) depend on Cairo\n",
    "(pycairo>=1.11.0 or cairocffi).\n",
    "\n",
    "\n",
    "How do I select PyQt4 or PySide?\n",
    "^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
    "\n",
    "The :envvar:`QT_API` environment variable can be set to either ``pyqt`` or\n",
    "``pyside`` to use ``PyQt4`` or ``PySide``, respectively.\n",
    "\n",
    "Since the default value for the bindings to be used is ``PyQt4``, Matplotlib\n",
    "first tries to import it, if the import fails, it tries to import ``PySide``.\n",
    "\n",
    "Using non-builtin backends\n",
    "--------------------------\n",
    "More generally, any importable backend can be selected by using any of the\n",
    "methods above. If ``name.of.the.backend`` is the module containing the\n",
    "backend, use ``module://name.of.the.backend`` as the backend name, e.g.\n",
    "``matplotlib.use('module://name.of.the.backend')``.\n",
    "\n",
    "\n",
    "\n",
    "What is interactive mode?\n",
    "=========================\n",
    "\n",
    "Use of an interactive backend (see `what-is-a-backend`)\n",
    "permits--but does not by itself require or ensure--plotting\n",
    "to the screen.  Whether and when plotting to the screen occurs,\n",
    "and whether a script or shell session continues after a plot\n",
    "is drawn on the screen, depends on the functions and methods\n",
    "that are called, and on a state variable that determines whether\n",
    "matplotlib is in \"interactive mode\".  The default Boolean value is set\n",
    "by the :file:`matplotlibrc` file, and may be customized like any other\n",
    "configuration parameter (see :doc:`/tutorials/introductory/customizing`).  It\n",
    "may also be set via :func:`matplotlib.interactive`, and its\n",
    "value may be queried via :func:`matplotlib.is_interactive`.  Turning\n",
    "interactive mode on and off in the middle of a stream of plotting\n",
    "commands, whether in a script or in a shell, is rarely needed\n",
    "and potentially confusing, so in the following we will assume all\n",
    "plotting is done with interactive mode either on or off.\n",
    "\n",
    "<div class=\"alert alert-info\"><h4>Note</h4><p>Major changes related to interactivity, and in particular the\n",
    "   role and behavior of :func:`~matplotlib.pyplot.show`, were made in the\n",
    "   transition to matplotlib version 1.0, and bugs were fixed in\n",
    "   1.0.1.  Here we describe the version 1.0.1 behavior for the\n",
    "   primary interactive backends, with the partial exception of\n",
    "   *macosx*.</p></div>\n",
    "\n",
    "Interactive mode may also be turned on via :func:`matplotlib.pyplot.ion`,\n",
    "and turned off via :func:`matplotlib.pyplot.ioff`.\n",
    "\n",
    "<div class=\"alert alert-info\"><h4>Note</h4><p>Interactive mode works with suitable backends in ipython and in\n",
    "   the ordinary python shell, but it does *not* work in the IDLE IDE.\n",
    "   If the default backend does not support interactivity, an interactive\n",
    "   backend can be explicitly activated using any of the methods discussed\n",
    "   in `What is a backend?`_.</p></div>\n",
    "\n",
    "\n",
    "Interactive example\n",
    "--------------------\n",
    "\n",
    "From an ordinary python prompt, or after invoking ipython with no options,\n",
    "try this::\n",
    "\n",
    "    import matplotlib.pyplot as plt\n",
    "    plt.ion()\n",
    "    plt.plot([1.6, 2.7])\n",
    "\n",
    "This will pop up a plot window. Your terminal prompt will remain active, so\n",
    "that you can type additional commands such as::\n",
    "\n",
    "    plt.title(\"interactive test\")\n",
    "    plt.xlabel(\"index\")\n",
    "\n",
    "On most interactive backends, the figure window will also be updated if you\n",
    "change it via the object-oriented interface. E.g. get a reference to the\n",
    "`~matplotlib.axes.Axes` instance, and call a method of that instance::\n",
    "\n",
    "    ax = plt.gca()\n",
    "    ax.plot([3.1, 2.2])\n",
    "\n",
    "If you are using certain backends (like ``macosx``), or an older version\n",
    "of matplotlib, you may not see the new line added to the plot immediately.\n",
    "In this case, you need to explicitly call :func:`~matplotlib.pyplot.draw`\n",
    "in order to update the plot::\n",
    "\n",
    "    plt.draw()\n",
    "\n",
    "\n",
    "Non-interactive example\n",
    "-----------------------\n",
    "\n",
    "Start a fresh session as in the previous example, but now\n",
    "turn interactive mode off::\n",
    "\n",
    "    import matplotlib.pyplot as plt\n",
    "    plt.ioff()\n",
    "    plt.plot([1.6, 2.7])\n",
    "\n",
    "Nothing happened--or at least nothing has shown up on the\n",
    "screen (unless you are using *macosx* backend, which is\n",
    "anomalous).  To make the plot appear, you need to do this::\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "Now you see the plot, but your terminal command line is\n",
    "unresponsive; the :func:`.pyplot.show()` command *blocks* the input\n",
    "of additional commands until you manually kill the plot\n",
    "window.\n",
    "\n",
    "What good is this--being forced to use a blocking function?\n",
    "Suppose you need a script that plots the contents of a file\n",
    "to the screen.  You want to look at that plot, and then end\n",
    "the script.  Without some blocking command such as ``show()``, the\n",
    "script would flash up the plot and then end immediately,\n",
    "leaving nothing on the screen.\n",
    "\n",
    "In addition, non-interactive mode delays all drawing until\n",
    "``show()`` is called; this is more efficient than redrawing\n",
    "the plot each time a line in the script adds a new feature.\n",
    "\n",
    "Prior to version 1.0, ``show()`` generally could not be called\n",
    "more than once in a single script (although sometimes one\n",
    "could get away with it); for version 1.0.1 and above, this\n",
    "restriction is lifted, so one can write a script like this::\n",
    "\n",
    "    import numpy as np\n",
    "    import matplotlib.pyplot as plt\n",
    "\n",
    "    plt.ioff()\n",
    "    for i in range(3):\n",
    "        plt.plot(np.random.rand(10))\n",
    "        plt.show()\n",
    "\n",
    "which makes three plots, one at a time. I.e. the second plot will show up,\n",
    "once the first plot is closed.\n",
    "\n",
    "Summary\n",
    "-------\n",
    "\n",
    "In interactive mode, pyplot functions automatically draw\n",
    "to the screen.\n",
    "\n",
    "When plotting interactively, if using\n",
    "object method calls in addition to pyplot functions, then\n",
    "call :func:`~matplotlib.pyplot.draw` whenever you want to\n",
    "refresh the plot.\n",
    "\n",
    "Use non-interactive mode in scripts in which you want to\n",
    "generate one or more figures and display them before ending\n",
    "or generating a new set of figures.  In that case, use\n",
    ":func:`~matplotlib.pyplot.show` to display the figure(s) and\n",
    "to block execution until you have manually destroyed them.\n",
    "\n",
    "\n",
    "Performance\n",
    "===========\n",
    "\n",
    "Whether exploring data in interactive mode or programmatically\n",
    "saving lots of plots, rendering performance can be a painful\n",
    "bottleneck in your pipeline. Matplotlib provides a couple\n",
    "ways to greatly reduce rendering time at the cost of a slight\n",
    "change (to a settable tolerance) in your plot's appearance.\n",
    "The methods available to reduce rendering time depend on the\n",
    "type of plot that is being created.\n",
    "\n",
    "Line segment simplification\n",
    "---------------------------\n",
    "\n",
    "For plots that have line segments (e.g. typical line plots,\n",
    "outlines of polygons, etc.), rendering performance can be\n",
    "controlled by the ``path.simplify`` and\n",
    "``path.simplify_threshold`` parameters in your\n",
    "``matplotlibrc`` file (see\n",
    ":doc:`/tutorials/introductory/customizing` for\n",
    "more information about the ``matplotlibrc`` file).\n",
    "The ``path.simplify`` parameter is a boolean indicating whether\n",
    "or not line segments are simplified at all. The\n",
    "``path.simplify_threshold`` parameter controls how much line\n",
    "segments are simplified; higher thresholds result in quicker\n",
    "rendering.\n",
    "\n",
    "The following script will first display the data without any\n",
    "simplification, and then display the same data with simplification.\n",
    "Try interacting with both of them::\n",
    "\n",
    "  import numpy as np\n",
    "  import matplotlib.pyplot as plt\n",
    "  import matplotlib as mpl\n",
    "\n",
    "  # Setup, and create the data to plot\n",
    "  y = np.random.rand(100000)\n",
    "  y[50000:] *= 2\n",
    "  y[np.logspace(1, np.log10(50000), 400).astype(int)] = -1\n",
    "  mpl.rcParams['path.simplify'] = True\n",
    "\n",
    "  mpl.rcParams['path.simplify_threshold'] = 0.0\n",
    "  plt.plot(y)\n",
    "  plt.show()\n",
    "\n",
    "  mpl.rcParams['path.simplify_threshold'] = 1.0\n",
    "  plt.plot(y)\n",
    "  plt.show()\n",
    "\n",
    "Matplotlib currently defaults to a conservative simplification\n",
    "threshold of ``1/9``. If you want to change your default settings\n",
    "to use a different value, you can change your ``matplotlibrc``\n",
    "file.  Alternatively, you could create a new style for\n",
    "interactive plotting (with maximal simplification) and another\n",
    "style for publication quality plotting (with minimal\n",
    "simplification) and activate them as necessary. See\n",
    ":doc:`/tutorials/introductory/customizing` for\n",
    "instructions on how to perform these actions.\n",
    "\n",
    "The simplification works by iteratively merging line segments\n",
    "into a single vector until the next line segment's perpendicular\n",
    "distance to the vector (measured in display-coordinate space)\n",
    "is greater than the ``path.simplify_threshold`` parameter.\n",
    "\n",
    "<div class=\"alert alert-info\"><h4>Note</h4><p>Changes related to how line segments are simplified were made\n",
    "  in version 2.1. Rendering time will still be improved by these\n",
    "  parameters prior to 2.1, but rendering time for some kinds of\n",
    "  data will be vastly improved in versions 2.1 and greater.</p></div>\n",
    "\n",
    "Marker simplification\n",
    "---------------------\n",
    "\n",
    "Markers can also be simplified, albeit less robustly than\n",
    "line segments. Marker simplification is only available\n",
    "to :class:`~matplotlib.lines.Line2D` objects (through the\n",
    "``markevery`` property). Wherever\n",
    ":class:`~matplotlib.lines.Line2D` construction parameters\n",
    "are passed through, such as\n",
    ":func:`matplotlib.pyplot.plot` and\n",
    ":meth:`matplotlib.axes.Axes.plot`, the ``markevery``\n",
    "parameter can be used::\n",
    "\n",
    "  plt.plot(x, y, markevery=10)\n",
    "\n",
    "The markevery argument allows for naive subsampling, or an\n",
    "attempt at evenly spaced (along the *x* axis) sampling. See the\n",
    ":doc:`/gallery/lines_bars_and_markers/markevery_demo`\n",
    "for more information.\n",
    "\n",
    "Splitting lines into smaller chunks\n",
    "-----------------------------------\n",
    "\n",
    "If you are using the Agg backend (see `what-is-a-backend`),\n",
    "then you can make use of the ``agg.path.chunksize`` rc parameter.\n",
    "This allows you to specify a chunk size, and any lines with\n",
    "greater than that many vertices will be split into multiple\n",
    "lines, each of which has no more than ``agg.path.chunksize``\n",
    "many vertices. (Unless ``agg.path.chunksize`` is zero, in\n",
    "which case there is no chunking.) For some kind of data,\n",
    "chunking the line up into reasonable sizes can greatly\n",
    "decrease rendering time.\n",
    "\n",
    "The following script will first display the data without any\n",
    "chunk size restriction, and then display the same data with\n",
    "a chunk size of 10,000. The difference can best be seen when\n",
    "the figures are large, try maximizing the GUI and then\n",
    "interacting with them::\n",
    "\n",
    "  import numpy as np\n",
    "  import matplotlib.pyplot as plt\n",
    "  import matplotlib as mpl\n",
    "  mpl.rcParams['path.simplify_threshold'] = 1.0\n",
    "\n",
    "  # Setup, and create the data to plot\n",
    "  y = np.random.rand(100000)\n",
    "  y[50000:] *= 2\n",
    "  y[np.logspace(1, np.log10(50000), 400).astype(int)] = -1\n",
    "  mpl.rcParams['path.simplify'] = True\n",
    "\n",
    "  mpl.rcParams['agg.path.chunksize'] = 0\n",
    "  plt.plot(y)\n",
    "  plt.show()\n",
    "\n",
    "  mpl.rcParams['agg.path.chunksize'] = 10000\n",
    "  plt.plot(y)\n",
    "  plt.show()\n",
    "\n",
    "Legends\n",
    "-------\n",
    "\n",
    "The default legend behavior for axes attempts to find the location\n",
    "that covers the fewest data points (``loc='best'``). This can be a\n",
    "very expensive computation if there are lots of data points. In\n",
    "this case, you may want to provide a specific location.\n",
    "\n",
    "Using the *fast* style\n",
    "----------------------\n",
    "\n",
    "The *fast* style can be used to automatically set\n",
    "simplification and chunking parameters to reasonable\n",
    "settings to speed up plotting large amounts of data.\n",
    "It can be used simply by running::\n",
    "\n",
    "  import matplotlib.style as mplstyle\n",
    "  mplstyle.use('fast')\n",
    "\n",
    "It is very light weight, so it plays nicely with other\n",
    "styles, just make sure the fast style is applied last\n",
    "so that other styles do not overwrite the settings::\n",
    "\n",
    "  mplstyle.use(['dark_background', 'ggplot', 'fast'])\n",
    "\n"
   ]
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