{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting Started With Matplotlib\n",
    "\n",
    "[back to overview page](index.ipynb)\n",
    "\n",
    "This page is about the general mechanics of plotting with [Matplotlib](http://matplotlib.org/), not so much about the plots themselves.\n",
    "For more information about those, have a look at the [overview page](index.ipynb) and especially at the [external links section](index.ipynb#External-Links)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outdated Information\n",
    "\n",
    "Matplotlib has been very actively developed in the last few years.\n",
    "Many improvements have been made and the API as well as the recommended usage patterns have changed quite a bit.\n",
    "\n",
    "Sadly, this also means that a lot of documentation out there is outdated and may actually, although well intended, be bad advice.\n",
    "In fact, this very page may be outdated at the time you are reading it!\n",
    "Have a look at this date:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-09-17\r\n"
     ]
    }
   ],
   "source": [
    "!date +%F"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the date of the last change to this page.\n",
    "If it's older then half a year or a year, it is very likely to be outdated, so don't read it!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial Setup\n",
    "\n",
    "You should create a file named `matplotlibrc`\n",
    "in the same directory as your notebook file(s),\n",
    "containing this line:\n",
    "\n",
    "    figure.dpi: 96\n",
    "\n",
    "For more details, see [Default Values for Matplotlib's \"inline\" backend](matplotlib-inline-defaults.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Step by Step\n",
    "\n",
    "There are several different ways of using Matplotlib, here we provide mainly slow-paced step-by-step instructions that show explicitly what has to be done in order to create a plot.\n",
    "\n",
    "These instructions should help you to create your own individualized plotting functions (see below).\n",
    "\n",
    "You can also use Matplotlib in a much less explicit and quicker-to-type way called \"pylab\" (see further below)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Matplotlib\n",
    "\n",
    "There are several ways to do that, some of which are *not recommended* anymore (especially `from pylab import *`).\n",
    "\n",
    "As far as I know, this is the canonical and recommended way to import Matplotlib for most use cases:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For some specialized cases, you can also import specific classes from certain `matplotlib` sub-modules, e.g.\n",
    "\n",
    "    from matplotlib.patches import Polygon, Ellipse\n",
    "    from matplotlib.colors import LinearSegmentedColormap\n",
    "\n",
    "There are lots of code examples out there that still use them, but the following imports are [not recommended anymore](https://matplotlib.org/3.6.0/api/index.html#module-pylab) and you should **never use any of these**:\n",
    "\n",
    "<pre style=\"color:gray\">\n",
    "from pylab import *\n",
    "from matplotlib.pyplot import *\n",
    "</pre>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A Quick Plot\n",
    "\n",
    "If you are in a hurry and just need to plot a few values ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = 4, 8, 15, 16, 23, 42"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... you can do it like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 614.4x460.8 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This does multiple steps at once, which is convenient for quick plots.\n",
    "For more complicated plotting needs, it is better to have more explicit control about figures, axes and so on.\n",
    "The following sections go into more detail about the different steps that are necessary to produce a plot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a `Figure` and One or More `Axes` Objects\n",
    "\n",
    "Before plotting anything, a [Figure object](http://matplotlib.org/api/figure_api.html)\n",
    "has to be created.\n",
    "Some commands handle figure creation and selection automatically (like the example above),\n",
    "but it normally makes your code easier to read if you create figures explicitly.\n",
    "\n",
    "A figure typically contains one or more\n",
    "[Axes objects](http://matplotlib.org/api/axes_api.html).\n",
    "Most plotting commands work on those.\n",
    "\n",
    "You can create a figure and a single axes object with a single command like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 614.4x460.8 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many options you can pass to\n",
    "[plt.subplots()](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.subplots)\n",
    "in order to customize things, have a look at the documentation of\n",
    "[plt.figure()](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.figure),\n",
    "[add_subplot()](https://matplotlib.org/api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure.add_subplot)\n",
    "and [GridSpec](http://matplotlib.org/api/gridspec_api.html#matplotlib.gridspec.GridSpec).\n",
    "\n",
    "If you want more than one `Axes` object, you can specify rows and columns of subplots:\n",
    "\n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "\n",
    "If you want, you can also use\n",
    "[plt.figure()](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.figure)\n",
    "to create a figure object first, and then use\n",
    "[add_axes()](https://matplotlib.org/api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure.add_axes)\n",
    "and/or\n",
    "[add_subplot()](https://matplotlib.org/api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure.add_subplot)\n",
    "to add axes objects.\n",
    "\n",
    "If you want to continue plotting on the previously used `Axes` object, you can use [plt.gca()](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.gca) (= *get current axes*).\n",
    "If no axes object exists, this command creates one for you (it also creates a figure object, if needed).\n",
    "\n",
    "Each figure has a (possibly empty) list of references to all its `Axes` objects ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<AxesSubplot: >]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig.axes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Obviously, our example figure has one `Axes` object (the one we got from `plt.subplots()` above)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig.axes[0] is ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each `Axes` object has a reference to its (exactly one) parent figure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 614.4x460.8 with 1 Axes>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.figure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So don't worry if you misplace your figure reference, you can always get it from any contained `Axes` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.figure is fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Data\n",
    "\n",
    "Once we have an `Axes` object, we can plot data onto it.\n",
    "There are many types of plots\n",
    "(have a look at the [`Axes` documentation](http://matplotlib.org/api/axes_api.html)),\n",
    "here we'll just create a very simple plot consisting of two lines.\n",
    "Note that when plotting a two-dimensional array of data,\n",
    "each column is used to create a separate line in the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f84cc47f1c0>,\n",
       " <matplotlib.lines.Line2D at 0x7f84d469a4a0>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.plot([[42, 40],\n",
    "         [22, 20],\n",
    "         [32, 30]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, this returns a list of objects (in our case it's two lines).\n",
    "You can use those objects for fine-tuning the plot and you can also get a reference to the `Axes` object they belong to:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_[0].axes is ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> In case you didn't know: The underscore \"`_`\" is a special variable name that is set by the interactive Python interpreter to the result of the last expression of the previously executed code cell.\n",
    "\n",
    "If you don't need the returned list, you can hide it by appending a semicolon (this is only necessary in the last line of a code cell).\n",
    "Let's plot again into the same axes (keeping the previous plot):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax.plot(data);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that mentioning the figure object a few cells above led to the plot being displayed in the cell output.\n",
    "The `plot()` command in the previous cell, however, didn't show the plot.\n",
    "\n",
    "Normally, you would create a figure and produce one or more plots in the same code cell.\n",
    "But if you want to show the same figure again in a later cell, just mention the figure object again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 614.4x460.8 with 1 Axes>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, you can use IPython's built-in `display()` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 614.4x460.8 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting to a File\n",
    "\n",
    "Analyzing your data in the notebook is nice,\n",
    "but you probably also want to save the plot in a separate image file.\n",
    "It's very easy to\n",
    "[save a figure to a file](https://matplotlib.org/api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure.savefig),\n",
    "e.g. a PNG file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig('my_plot.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This creates a file named [my_plot.png](my_plot.png) in the current directory.\n",
    "You can choose the file type by means of the file extension:\n",
    "\n",
    "    fig.savefig('my_plot.svg')\n",
    "    fig.savefig('my_plot.pdf')\n",
    "    fig.savefig('my_plot.eps')\n",
    "\n",
    "You can [save multiple figures to one PDF file with `PdfPages`](http://matplotlib.org/faq/howto_faq.html#save-multiple-plots-to-one-pdf-file).\n",
    "\n",
    "It is also possible to [create PGF files for use in LaTeX documents](http://matplotlib.org/users/pgf.html):\n",
    "\n",
    "    fig.savefig('my_plot.pgf')\n",
    "\n",
    "The available image formats depend on the backend, you can get a list for the currently selected backend:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Encapsulated Postscript': ['eps'],\n",
       " 'Joint Photographic Experts Group': ['jpeg', 'jpg'],\n",
       " 'Portable Document Format': ['pdf'],\n",
       " 'PGF code for LaTeX': ['pgf'],\n",
       " 'Portable Network Graphics': ['png'],\n",
       " 'Postscript': ['ps'],\n",
       " 'Raw RGBA bitmap': ['raw', 'rgba'],\n",
       " 'Scalable Vector Graphics': ['svg', 'svgz'],\n",
       " 'Tagged Image File Format': ['tif', 'tiff'],\n",
       " 'WebP Image Format': ['webp']}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig.canvas.get_supported_filetypes_grouped()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Different Backends\n",
    "\n",
    "Speaking of which, Matplotlib supports different backends.\n",
    "In the examples above, we were using the \"inline\" backend, which is the default (since `ipykernel` version 4.4, see [PR #159](https://github.com/ipython/ipykernel/pull/159)).\n",
    "This shows the plots in the notebook itself, right after the code cells that produce the plots."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you prefer a separate window for the plots,\n",
    "you can use this\n",
    "[\"magic\" command](http://ipython.org/ipython-doc/stable/interactive/magics.html#magic-matplotlib):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: <object object at 0x7f8518576170>\n"
     ]
    }
   ],
   "source": [
    "%matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(data);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After executing the above cell, the plot window should open immediately.\n",
    "Note that if you are using Matplotlib in a plain Python script,\n",
    "you'll have to call\n",
    "[plt.show()](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.show)\n",
    "in order to actually open the plot window, see also http://matplotlib.org/faq/howto_faq.html#use-show.\n",
    "\n",
    "The effects of the `Axes`'s methods are immediately visible in the plot window, e.g.:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax.plot(data[::-1]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are using Matplotlib (non-interactively) from a plain Python script,\n",
    "you'll have to use `fig.canvas.draw()` (or `plt.draw()`) to update the current figure.\n",
    "\n",
    "If you want to open a new window, just create a new figure object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig2, ax2 = plt.subplots()\n",
    "ax2.plot(data + data[::-1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you keep references to the figure/axes objects, you can update previous plots\n",
    "(as long as you don't close their window)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many plotting backends,\n",
    "some Jupyter extensions even provide their own backend.\n",
    "You can list all available backends like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available matplotlib backends: ['tk', 'gtk', 'gtk3', 'gtk4', 'wx', 'qt4', 'qt5', 'qt6', 'qt', 'osx', 'nbagg', 'notebook', 'agg', 'svg', 'pdf', 'ps', 'inline', 'ipympl', 'widget']\n"
     ]
    }
   ],
   "source": [
    "%matplotlib -l"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Switching backends might require restarting the kernel in some cases, but switching back and forth between one external backend and the \"inline\" backend should work immediately.\n",
    "\n",
    "To switch back to the \"inline\" backend, just use:\n",
    "\n",
    "    %matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Closing a Plot\n",
    "\n",
    "Matplotlib keeps references to all figures and even if you close all plot windows and delete all your own references, the figures are still kept in memory.\n",
    "\n",
    "To release all resources for the current figure, use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To close all figures, use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close('all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that [plt.clf()](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.clf)\n",
    "and [plt.cla()](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.cla)\n",
    "clear the current figure and axes, respectively,\n",
    "but they don't reset the parameters (e.g. figure size)\n",
    "and they don't free all resources.\n",
    "\n",
    "Let's switch back to the \"inline\" backend now:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Advice: Write Your Own Plotting Function!\n",
    "\n",
    "You will most likely not just plot once and then move on.\n",
    "More realistically, you'll plot many times while exploring your data and changing some parameters in your calculations.\n",
    "\n",
    "Depending on the task at hand, you'll have certain requirements regarding axis scaling, tick marks, labels etc.\n",
    "\n",
    "Instead of repeating the same commands again and again,\n",
    "you should write an individualized plotting function (or more likely: several plotting functions).\n",
    "Here's an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "def myplot(data, ax=None):\n",
    "    \"\"\"A special plotting function for my special needs.\"\"\"\n",
    "    if ax is None:\n",
    "        ax = plt.gca()\n",
    "    x = np.arange(len(data)) * 1000  # my special x-axis needs\n",
    "    lines = ax.plot(x, data)\n",
    "    ax.set_xlabel(\"my independent variable / millisomethings\")\n",
    "    ax.set_ylabel(\"very interesting unitless values\")\n",
    "    ax.grid(True)\n",
    "    return lines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that this function accepts an optional `ax` argument\n",
    "(as recommended by the [official FAQ](http://matplotlib.org/faq/usage_faq.html#coding-styles)),\n",
    "where an existing `Axes` object can be passed to be drawn on.\n",
    "By default, the current `Axes` object is used.\n",
    "If none exists, a new figure with a new `Axes` object is created automatically.\n",
    "All this is done by [plt.gca()](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.gca)\n",
    "(= *get current axes*).\n",
    "\n",
    "You should think about what is most useful to be returned from this function,\n",
    "in this example it just passes through the list of lines returned by\n",
    "[plot()](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.plot.html#matplotlib.axes.Axes.plot).\n",
    "\n",
    "Note that `show()` is not used within the function, as this might not be desired,\n",
    "e.g. if we only want to plot to a file and we don't want to show the plot window.\n",
    "If we use \"interactive\" mode (which is enabled by default in a Jupyter notebook),\n",
    "it isn't necessary anyway.\n",
    "\n",
    "The custom plotting function can be used like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 614.4x460.8 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "myplot([3, 2, 4, 1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By means of the optional `ax` argument it can also be used in more complicated settings, e.g.:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 921.6x460.8 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, sharey=True)\n",
    "fig.set_figwidth(fig.get_figwidth() * 1.5)\n",
    "fig.subplots_adjust(wspace=0.06)\n",
    "myplot([3, 2, 4, 1], ax=ax1)\n",
    "myplot([2, 3, 1, 5, 4, 0], ax=ax2)\n",
    "ax2.set_ylabel('');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of course it's also possible to fine-tune the plotting style in every detail,\n",
    "but this is beyond the scope of this little notebook.\n",
    "See https://matplotlib.org/tutorials/introductory/customizing.html for more information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finally: The Quick and Dirty Way\n",
    "\n",
    "The above examples are quite explicit (which is good) and a bit verbose (which might be tedious).\n",
    "\n",
    "If you just want to quickly fire up an IPython console and make some spontaneous throw-away plots, you can use the so-called \"pylab\" mode using [the \"magic\" command](http://ipython.org/ipython-doc/stable/interactive/magics.html#magic-pylab)\n",
    "\n",
    "    %pylab\n",
    "\n",
    "or\n",
    "\n",
    "    %pylab inline\n",
    "    \n",
    "This injects a whole lot of convenience functions into the current namespace.\n",
    "That means you don't need to write any `import` statements and you can use the functions without the `plt.` and `np.` prefixes, e.g.:\n",
    "\n",
    "    plot(arange(100)**2);\n",
    "\n",
    "This should significantly reduce the number of characters you have to type (at least on the short term).\n",
    "However, this is not recommended if you want to keep your code, because the resulting code is hard to read for others (and for future you) and [it may cause some subtle errors](http://nbviewer.ipython.org/github/Carreau/posts/blob/master/10-No-PyLab-Thanks.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p xmlns:dct=\"http://purl.org/dc/terms/\">\n",
    "  <a rel=\"license\"\n",
    "     href=\"http://creativecommons.org/publicdomain/zero/1.0/\">\n",
    "    <img src=\"http://i.creativecommons.org/p/zero/1.0/88x31.png\" style=\"border-style: none;\" alt=\"CC0\" />\n",
    "  </a>\n",
    "  <br />\n",
    "  To the extent possible under law,\n",
    "  <span rel=\"dct:publisher\" resource=\"[_:publisher]\">the person who associated CC0</span>\n",
    "  with this work has waived all copyright and related or neighboring\n",
    "  rights to this work.\n",
    "</p>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.7"
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 },
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}