.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_tutorials_text_text_intro.py>`     to download the full example code
    .. rst-class:: sphx-glr-example-title

    .. _sphx_glr_tutorials_text_text_intro.py:


========================
Text in Matplotlib Plots
========================

Introduction to plotting and working with text in Matplotlib.

Matplotlib has extensive text support, including support for
mathematical expressions, truetype support for raster and
vector outputs, newline separated text with arbitrary
rotations, and unicode support.

Because it embeds fonts directly in output documents, e.g., for postscript
or PDF, what you see on the screen is what you get in the hardcopy.
`FreeType <https://www.freetype.org/>`_ support
produces very nice, antialiased fonts, that look good even at small
raster sizes.  Matplotlib includes its own
:mod:`matplotlib.font_manager` (thanks to Paul Barrett), which
implements a cross platform, `W3C <https://www.w3.org/>`_
compliant font finding algorithm.

The user has a great deal of control over text properties (font size, font
weight, text location and color, etc.) with sensible defaults set in
the :doc:`rc file </tutorials/introductory/customizing>`.
And significantly, for those interested in mathematical
or scientific figures, Matplotlib implements a large number of TeX
math symbols and commands, supporting :doc:`mathematical expressions
</tutorials/text/mathtext>` anywhere in your figure.


Basic text commands
===================

The following commands are used to create text in the pyplot
interface and the object-oriented API:

=================== =================== ======================================
`.pyplot` API       OO API              description
=================== =================== ======================================
`~.pyplot.text`     `~.Axes.text`       Add text at an arbitrary location of
                                        the `~matplotlib.axes.Axes`.

`~.pyplot.annotate` `~.Axes.annotate`   Add an annotation, with an optional
                                        arrow, at an arbitrary location of the
                                        `~matplotlib.axes.Axes`.

`~.pyplot.xlabel`   `~.Axes.set_xlabel` Add a label to the
                                        `~matplotlib.axes.Axes`\'s x-axis.

`~.pyplot.ylabel`   `~.Axes.set_ylabel` Add a label to the
                                        `~matplotlib.axes.Axes`\'s y-axis.

`~.pyplot.title`    `~.Axes.set_title`  Add a title to the
                                        `~matplotlib.axes.Axes`.

`~.pyplot.figtext`  `~.Figure.text`     Add text at an arbitrary location of
                                        the `.Figure`.

`~.pyplot.suptitle` `~.Figure.suptitle` Add a title to the `.Figure`.
=================== =================== ======================================

All of these functions create and return a `.Text` instance, which can be
configured with a variety of font and other properties.  The example below
shows all of these commands in action, and more detail is provided in the
sections that follow.


.. code-block:: default


    import matplotlib
    import matplotlib.pyplot as plt

    fig = plt.figure()
    ax = fig.add_subplot(111)
    fig.subplots_adjust(top=0.85)

    # Set titles for the figure and the subplot respectively
    fig.suptitle('bold figure suptitle', fontsize=14, fontweight='bold')
    ax.set_title('axes title')

    ax.set_xlabel('xlabel')
    ax.set_ylabel('ylabel')

    # Set both x- and y-axis limits to [0, 10] instead of default [0, 1]
    ax.axis([0, 10, 0, 10])

    ax.text(3, 8, 'boxed italics text in data coords', style='italic',
            bbox={'facecolor': 'red', 'alpha': 0.5, 'pad': 10})

    ax.text(2, 6, r'an equation: $E=mc^2$', fontsize=15)

    ax.text(3, 2, 'unicode: Institut für Festkörperphysik')

    ax.text(0.95, 0.01, 'colored text in axes coords',
            verticalalignment='bottom', horizontalalignment='right',
            transform=ax.transAxes,
            color='green', fontsize=15)

    ax.plot([2], [1], 'o')
    ax.annotate('annotate', xy=(2, 1), xytext=(3, 4),
                arrowprops=dict(facecolor='black', shrink=0.05))

    plt.show()




.. image:: /tutorials/text/images/sphx_glr_text_intro_001.png
    :alt: bold figure suptitle, axes title
    :class: sphx-glr-single-img





Labels for x- and y-axis
========================

Specifying the labels for the x- and y-axis is straightforward, via the
`~matplotlib.axes.Axes.set_xlabel` and `~matplotlib.axes.Axes.set_ylabel`
methods.


.. code-block:: default


    import matplotlib.pyplot as plt
    import numpy as np

    x1 = np.linspace(0.0, 5.0, 100)
    y1 = np.cos(2 * np.pi * x1) * np.exp(-x1)

    fig, ax = plt.subplots(figsize=(5, 3))
    fig.subplots_adjust(bottom=0.15, left=0.2)
    ax.plot(x1, y1)
    ax.set_xlabel('time [s]')
    ax.set_ylabel('Damped oscillation [V]')

    plt.show()




.. image:: /tutorials/text/images/sphx_glr_text_intro_002.png
    :alt: text intro
    :class: sphx-glr-single-img





The x- and y-labels are automatically placed so that they clear the x- and
y-ticklabels.  Compare the plot below with that above, and note the y-label
is to the left of the one above.


.. code-block:: default


    fig, ax = plt.subplots(figsize=(5, 3))
    fig.subplots_adjust(bottom=0.15, left=0.2)
    ax.plot(x1, y1*10000)
    ax.set_xlabel('time [s]')
    ax.set_ylabel('Damped oscillation [V]')

    plt.show()




.. image:: /tutorials/text/images/sphx_glr_text_intro_003.png
    :alt: text intro
    :class: sphx-glr-single-img





If you want to move the labels, you can specify the *labelpad* keyword
argument, where the value is points (1/72", the same unit used to specify
fontsizes).


.. code-block:: default


    fig, ax = plt.subplots(figsize=(5, 3))
    fig.subplots_adjust(bottom=0.15, left=0.2)
    ax.plot(x1, y1*10000)
    ax.set_xlabel('time [s]')
    ax.set_ylabel('Damped oscillation [V]', labelpad=18)

    plt.show()




.. image:: /tutorials/text/images/sphx_glr_text_intro_004.png
    :alt: text intro
    :class: sphx-glr-single-img





Or, the labels accept all the `.Text` keyword arguments, including
*position*, via which we can manually specify the label positions.  Here we
put the xlabel to the far left of the axis.  Note, that the y-coordinate of
this position has no effect - to adjust the y-position we need to use the
*labelpad* kwarg.


.. code-block:: default


    fig, ax = plt.subplots(figsize=(5, 3))
    fig.subplots_adjust(bottom=0.15, left=0.2)
    ax.plot(x1, y1)
    ax.set_xlabel('time [s]', position=(0., 1e6), horizontalalignment='left')
    ax.set_ylabel('Damped oscillation [V]')

    plt.show()




.. image:: /tutorials/text/images/sphx_glr_text_intro_005.png
    :alt: text intro
    :class: sphx-glr-single-img





All the labelling in this tutorial can be changed by manipulating the
`matplotlib.font_manager.FontProperties` method, or by named kwargs to
`~matplotlib.axes.Axes.set_xlabel`


.. code-block:: default


    from matplotlib.font_manager import FontProperties

    font = FontProperties()
    font.set_family('serif')
    font.set_name('Times New Roman')
    font.set_style('italic')

    fig, ax = plt.subplots(figsize=(5, 3))
    fig.subplots_adjust(bottom=0.15, left=0.2)
    ax.plot(x1, y1)
    ax.set_xlabel('time [s]', fontsize='large', fontweight='bold')
    ax.set_ylabel('Damped oscillation [V]', fontproperties=font)

    plt.show()




.. image:: /tutorials/text/images/sphx_glr_text_intro_006.png
    :alt: text intro
    :class: sphx-glr-single-img





Finally, we can use native TeX rendering in all text objects and have
multiple lines:


.. code-block:: default


    fig, ax = plt.subplots(figsize=(5, 3))
    fig.subplots_adjust(bottom=0.2, left=0.2)
    ax.plot(x1, np.cumsum(y1**2))
    ax.set_xlabel('time [s] \n This was a long experiment')
    ax.set_ylabel(r'$\int\ Y^2\ dt\ \ [V^2 s]$')
    plt.show()





.. image:: /tutorials/text/images/sphx_glr_text_intro_007.png
    :alt: text intro
    :class: sphx-glr-single-img





Titles
======

Subplot titles are set in much the same way as labels, but there is
the *loc* keyword arguments that can change the position and justification
from the default value of ``loc=center``.


.. code-block:: default


    fig, axs = plt.subplots(3, 1, figsize=(5, 6), tight_layout=True)
    locs = ['center', 'left', 'right']
    for ax, loc in zip(axs, locs):
        ax.plot(x1, y1)
        ax.set_title('Title with loc at '+loc, loc=loc)
    plt.show()




.. image:: /tutorials/text/images/sphx_glr_text_intro_008.png
    :alt: Title with loc at center, Title with loc at left, Title with loc at right
    :class: sphx-glr-single-img





Vertical spacing for titles is controlled via :rc:`axes.titlepad`, which
defaults to 5 points.  Setting to a different value moves the title.


.. code-block:: default


    fig, ax = plt.subplots(figsize=(5, 3))
    fig.subplots_adjust(top=0.8)
    ax.plot(x1, y1)
    ax.set_title('Vertically offset title', pad=30)
    plt.show()





.. image:: /tutorials/text/images/sphx_glr_text_intro_009.png
    :alt: Vertically offset title
    :class: sphx-glr-single-img





Ticks and ticklabels
====================

Placing ticks and ticklabels is a very tricky aspect of making a figure.
Matplotlib does its best to accomplish the task automatically, but it also
offers a very flexible framework for determining the choices for tick
locations, and how they are labelled.

Terminology
~~~~~~~~~~~

*Axes* have an `matplotlib.axis.Axis` object for the ``ax.xaxis`` and
``ax.yaxis`` that contain the information about how the labels in the axis
are laid out.

The axis API is explained in detail in the documentation to
`~matplotlib.axis`.

An Axis object has major and minor ticks.  The Axis has
`.Axis.set_major_locator` and `.Axis.set_minor_locator` methods that use the
data being plotted to determine the location of major and minor ticks.  There
are also `.Axis.set_major_formatter` and `.Axis.set_minor_formatter` methods
that format the tick labels.

Simple ticks
~~~~~~~~~~~~

It often is convenient to simply define the
tick values, and sometimes the tick labels, overriding the default
locators and formatters.  This is discouraged because it breaks interactive
navigation of the plot.  It also can reset the axis limits: note that
the second plot has the ticks we asked for, including ones that are
well outside the automatic view limits.


.. code-block:: default


    fig, axs = plt.subplots(2, 1, figsize=(5, 3), tight_layout=True)
    axs[0].plot(x1, y1)
    axs[1].plot(x1, y1)
    axs[1].xaxis.set_ticks(np.arange(0., 8.1, 2.))
    plt.show()




.. image:: /tutorials/text/images/sphx_glr_text_intro_010.png
    :alt: text intro
    :class: sphx-glr-single-img





We can of course fix this after the fact, but it does highlight a
weakness of hard-coding the ticks.  This example also changes the format
of the ticks:


.. code-block:: default


    fig, axs = plt.subplots(2, 1, figsize=(5, 3), tight_layout=True)
    axs[0].plot(x1, y1)
    axs[1].plot(x1, y1)
    ticks = np.arange(0., 8.1, 2.)
    # list comprehension to get all tick labels...
    tickla = [f'{tick:1.2f}' for tick in ticks]
    axs[1].xaxis.set_ticks(ticks)
    axs[1].xaxis.set_ticklabels(tickla)
    axs[1].set_xlim(axs[0].get_xlim())
    plt.show()




.. image:: /tutorials/text/images/sphx_glr_text_intro_011.png
    :alt: text intro
    :class: sphx-glr-single-img





Tick Locators and Formatters
~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Instead of making a list of all the tickalbels, we could have
used `matplotlib.ticker.StrMethodFormatter` (new-style ``str.format()``
format string) or `matplotlib.ticker.FormatStrFormatter` (old-style '%'
format string) and passed it to the ``ax.xaxis``.  A
`matplotlib.ticker.StrMethodFormatter` can also be created by passing a
``str`` without having to explicitly create the formatter.


.. code-block:: default


    fig, axs = plt.subplots(2, 1, figsize=(5, 3), tight_layout=True)
    axs[0].plot(x1, y1)
    axs[1].plot(x1, y1)
    ticks = np.arange(0., 8.1, 2.)
    axs[1].xaxis.set_ticks(ticks)
    axs[1].xaxis.set_major_formatter('{x:1.1f}')
    axs[1].set_xlim(axs[0].get_xlim())
    plt.show()




.. image:: /tutorials/text/images/sphx_glr_text_intro_012.png
    :alt: text intro
    :class: sphx-glr-single-img





And of course we could have used a non-default locator to set the
tick locations.  Note we still pass in the tick values, but the
x-limit fix used above is *not* needed.


.. code-block:: default


    fig, axs = plt.subplots(2, 1, figsize=(5, 3), tight_layout=True)
    axs[0].plot(x1, y1)
    axs[1].plot(x1, y1)
    locator = matplotlib.ticker.FixedLocator(ticks)
    axs[1].xaxis.set_major_locator(locator)
    axs[1].xaxis.set_major_formatter('±{x}°')
    plt.show()




.. image:: /tutorials/text/images/sphx_glr_text_intro_013.png
    :alt: text intro
    :class: sphx-glr-single-img





The default formatter is the `matplotlib.ticker.MaxNLocator` called as
``ticker.MaxNLocator(self, nbins='auto', steps=[1, 2, 2.5, 5, 10])``
The *steps* keyword contains a list of multiples that can be used for
tick values.  i.e. in this case, 2, 4, 6 would be acceptable ticks,
as would 20, 40, 60 or 0.2, 0.4, 0.6. However, 3, 6, 9 would not be
acceptable because 3 doesn't appear in the list of steps.

``nbins=auto`` uses an algorithm to determine how many ticks will
be acceptable based on how long the axis is.  The fontsize of the
ticklabel is taken into account, but the length of the tick string
is not (because its not yet known.)  In the bottom row, the
ticklabels are quite large, so we set ``nbins=4`` to make the
labels fit in the right-hand plot.


.. code-block:: default


    fig, axs = plt.subplots(2, 2, figsize=(8, 5), tight_layout=True)
    for n, ax in enumerate(axs.flat):
        ax.plot(x1*10., y1)

    formatter = matplotlib.ticker.FormatStrFormatter('%1.1f')
    locator = matplotlib.ticker.MaxNLocator(nbins='auto', steps=[1, 4, 10])
    axs[0, 1].xaxis.set_major_locator(locator)
    axs[0, 1].xaxis.set_major_formatter(formatter)

    formatter = matplotlib.ticker.FormatStrFormatter('%1.5f')
    locator = matplotlib.ticker.AutoLocator()
    axs[1, 0].xaxis.set_major_formatter(formatter)
    axs[1, 0].xaxis.set_major_locator(locator)

    formatter = matplotlib.ticker.FormatStrFormatter('%1.5f')
    locator = matplotlib.ticker.MaxNLocator(nbins=4)
    axs[1, 1].xaxis.set_major_formatter(formatter)
    axs[1, 1].xaxis.set_major_locator(locator)

    plt.show()




.. image:: /tutorials/text/images/sphx_glr_text_intro_014.png
    :alt: text intro
    :class: sphx-glr-single-img





Finally, we can specify functions for the formatter using
`matplotlib.ticker.FuncFormatter`.  Further, like
`matplotlib.ticker.StrMethodFormatter`, passing a function will
automatically create a `matplotlib.ticker.FuncFormatter`.


.. code-block:: default



    def formatoddticks(x, pos):
        """Format odd tick positions."""
        if x % 2:
            return f'{x:1.2f}'
        else:
            return ''


    fig, ax = plt.subplots(figsize=(5, 3), tight_layout=True)
    ax.plot(x1, y1)
    locator = matplotlib.ticker.MaxNLocator(nbins=6)
    ax.xaxis.set_major_formatter(formatoddticks)
    ax.xaxis.set_major_locator(locator)

    plt.show()





.. image:: /tutorials/text/images/sphx_glr_text_intro_015.png
    :alt: text intro
    :class: sphx-glr-single-img





Dateticks
~~~~~~~~~

Matplotlib can accept `datetime.datetime` and `numpy.datetime64`
objects as plotting arguments.  Dates and times require special
formatting, which can often benefit from manual intervention. In
order to help, dates have special Locators and Formatters,
defined in the `matplotlib.dates` module.

A simple example is as follows.  Note how we have to rotate the
tick labels so that they don't over-run each other.


.. code-block:: default


    import datetime

    fig, ax = plt.subplots(figsize=(5, 3), tight_layout=True)
    base = datetime.datetime(2017, 1, 1, 0, 0, 1)
    time = [base + datetime.timedelta(days=x) for x in range(len(x1))]

    ax.plot(time, y1)
    ax.tick_params(axis='x', rotation=70)
    plt.show()




.. image:: /tutorials/text/images/sphx_glr_text_intro_016.png
    :alt: text intro
    :class: sphx-glr-single-img





We can pass a format to `matplotlib.dates.DateFormatter`.  Also note that the
29th and the next month are very close together.  We can fix this by using
the `.dates.DayLocator` class, which allows us to specify a list of days of
the month to use. Similar formatters are listed in the `matplotlib.dates`
module.


.. code-block:: default


    import matplotlib.dates as mdates

    locator = mdates.DayLocator(bymonthday=[1, 15])
    formatter = mdates.DateFormatter('%b %d')

    fig, ax = plt.subplots(figsize=(5, 3), tight_layout=True)
    ax.xaxis.set_major_locator(locator)
    ax.xaxis.set_major_formatter(formatter)
    ax.plot(time, y1)
    ax.tick_params(axis='x', rotation=70)
    plt.show()




.. image:: /tutorials/text/images/sphx_glr_text_intro_017.png
    :alt: text intro
    :class: sphx-glr-single-img





Legends and Annotations
=======================

- Legends: :doc:`/tutorials/intermediate/legend_guide`
- Annotations: :doc:`/tutorials/text/annotations`



.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  6.215 seconds)


.. _sphx_glr_download_tutorials_text_text_intro.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download sphx-glr-download-python

     :download:`Download Python source code: text_intro.py <text_intro.py>`



  .. container:: sphx-glr-download sphx-glr-download-jupyter

     :download:`Download Jupyter notebook: text_intro.ipynb <text_intro.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    Keywords: matplotlib code example, codex, python plot, pyplot
    `Gallery generated by Sphinx-Gallery
    <https://sphinx-gallery.readthedocs.io>`_