.. _howto-faq:

.. redirect-from:: /faq/howto_faq
.. redirect-from:: /users/faq/howto_faq
.. redirect-from:: /faq/index

==========================
Frequently Asked Questions
==========================

.. _how-do-no-figure:

I don't see a figure window
---------------------------

Please see :ref:`figures-not-showing`.

.. _how-to-too-many-ticks:

Why do I have so many ticks, and/or why are they out of order?
--------------------------------------------------------------

One common cause for unexpected tick behavior is passing a *list of strings
instead of numbers or datetime objects*. This can easily happen without notice
when reading in a comma-delimited text file. Matplotlib treats lists of strings
as *categorical* variables
(:doc:`/gallery/lines_bars_and_markers/categorical_variables`), and by default
puts one tick per category, and plots them in the order in which they are
supplied.

.. plot::
    :include-source:
    :align: center

    import matplotlib.pyplot as plt
    import numpy as np

    fig, ax = plt.subplots(1, 2, layout='constrained', figsize=(6, 2))

    ax[0].set_title('Ticks seem out of order / misplaced')
    x = ['5', '20', '1', '9']  # strings
    y = [5, 20, 1, 9]
    ax[0].plot(x, y, 'd')
    ax[0].tick_params(axis='x', labelcolor='red', labelsize=14)

    ax[1].set_title('Many ticks')
    x = [str(xx) for xx in np.arange(100)]  # strings
    y = np.arange(100)
    ax[1].plot(x, y)
    ax[1].tick_params(axis='x', labelcolor='red', labelsize=14)

The solution is to convert the list of strings to numbers or
datetime objects (often ``np.asarray(numeric_strings, dtype='float')`` or
``np.asarray(datetime_strings, dtype='datetime64[s]')``).

For more information see :doc:`/gallery/ticks/ticks_too_many`.

.. _howto-determine-artist-extent:

Determine the extent of Artists in the Figure
---------------------------------------------

Sometimes we want to know the extent of an Artist.  Matplotlib `.Artist` objects
have a method `.Artist.get_window_extent` that will usually return the extent of
the artist in pixels.  However, some artists, in particular text, must be
rendered at least once before their extent is known.  Matplotlib supplies
`.Figure.draw_without_rendering`, which should be called before calling
``get_window_extent``.

.. _howto-figure-empty:

Check whether a figure is empty
-------------------------------
Empty can actually mean different things. Does the figure contain any artists?
Does a figure with an empty `~.axes.Axes` still count as empty? Is the figure
empty if it was rendered pure white (there may be artists present, but they
could be outside the drawing area or transparent)?

For the purpose here, we define empty as: "The figure does not contain any
artists except it's background patch." The exception for the background is
necessary, because by default every figure contains a `.Rectangle` as it's
background patch. This definition could be checked via::

    def is_empty(figure):
        """
        Return whether the figure contains no Artists (other than the default
        background patch).
        """
        contained_artists = figure.get_children()
        return len(contained_artists) <= 1

We've decided not to include this as a figure method because this is only one
way of defining empty, and checking the above is only rarely necessary.
Usually the user or program handling the figure know if they have added
something to the figure.

The only reliable way to check whether a figure would render empty is to
actually perform such a rendering and inspect the result.

.. _howto-findobj:

Find all objects in a figure of a certain type
----------------------------------------------

Every Matplotlib artist (see :ref:`artists_tutorial`) has a method
called :meth:`~matplotlib.artist.Artist.findobj` that can be used to
recursively search the artist for any artists it may contain that meet
some criteria (e.g., match all :class:`~matplotlib.lines.Line2D`
instances or match some arbitrary filter function).  For example, the
following snippet finds every object in the figure which has a
``set_color`` property and makes the object blue::

    def myfunc(x):
        return hasattr(x, 'set_color')

    for o in fig.findobj(myfunc):
        o.set_color('blue')

You can also filter on class instances::

    import matplotlib.text as text
    for o in fig.findobj(text.Text):
        o.set_fontstyle('italic')

.. _howto-suppress_offset:

Prevent ticklabels from having an offset
----------------------------------------
The default formatter will use an offset to reduce
the length of the ticklabels.  To turn this feature
off on a per-axis basis::

   ax.xaxis.get_major_formatter().set_useOffset(False)

set :rc:`axes.formatter.useoffset`, or use a different
formatter.  See :mod:`~matplotlib.ticker` for details.

.. _howto-transparent:

Save transparent figures
------------------------

The :meth:`~matplotlib.pyplot.savefig` command has a keyword argument
*transparent* which, if 'True', will make the figure and axes
backgrounds transparent when saving, but will not affect the displayed
image on the screen.

If you need finer grained control, e.g., you do not want full transparency
or you want to affect the screen displayed version as well, you can set
the alpha properties directly.  The figure has a
:class:`~matplotlib.patches.Rectangle` instance called *patch*
and the axes has a Rectangle instance called *patch*.  You can set
any property on them directly (*facecolor*, *edgecolor*, *linewidth*,
*linestyle*, *alpha*).  e.g.::

    fig = plt.figure()
    fig.patch.set_alpha(0.5)
    ax = fig.add_subplot(111)
    ax.patch.set_alpha(0.5)

If you need *all* the figure elements to be transparent, there is
currently no global alpha setting, but you can set the alpha channel
on individual elements, e.g.::

   ax.plot(x, y, alpha=0.5)
   ax.set_xlabel('volts', alpha=0.5)

.. _howto-multipage:

Save multiple plots to one pdf file
-----------------------------------

Many image file formats can only have one image per file, but some formats
support multi-page files.  Currently, Matplotlib only provides multi-page
output to pdf files, using either the pdf or pgf backends, via the
`.backend_pdf.PdfPages` and `.backend_pgf.PdfPages` classes.

.. _howto-auto-adjust:

Make room for tick labels
-------------------------

By default, Matplotlib uses fixed percentage margins around subplots. This can
lead to labels overlapping or being cut off at the figure boundary. There are
multiple ways to fix this:

- Manually adapt the subplot parameters using `.Figure.subplots_adjust` /
  `.pyplot.subplots_adjust`.
- Use one of the automatic layout mechanisms:

  - constrained layout (:ref:`constrainedlayout_guide`)
  - tight layout (:ref:`tight_layout_guide`)

- Calculate good values from the size of the plot elements yourself
  (:doc:`/gallery/subplots_axes_and_figures/auto_subplots_adjust`)

.. _howto-align-label:

Align my ylabels across multiple subplots
-----------------------------------------

If you have multiple subplots over one another, and the y data have
different scales, you can often get ylabels that do not align
vertically across the multiple subplots, which can be unattractive.
By default, Matplotlib positions the x location of the ylabel so that
it does not overlap any of the y ticks.  You can override this default
behavior by specifying the coordinates of the label. To learn how, see
:doc:`/gallery/text_labels_and_annotations/align_ylabels`

.. _howto-set-zorder:

Control the draw order of plot elements
---------------------------------------

The draw order of plot elements, and thus which elements will be on top, is
determined by the `~.Artist.set_zorder` property.
See :doc:`/gallery/misc/zorder_demo` for a detailed description.

.. _howto-axis-equal:

Make the aspect ratio for plots equal
-------------------------------------

The Axes property :meth:`~matplotlib.axes.Axes.set_aspect` controls the
aspect ratio of the axes.  You can set it to be 'auto', 'equal', or
some ratio which controls the ratio::

  ax = fig.add_subplot(111, aspect='equal')

.. only:: html

    See :doc:`/gallery/subplots_axes_and_figures/axis_equal_demo` for a
    complete example.

.. _howto-twoscale:

Draw multiple y-axis scales
---------------------------

A frequent request is to have two scales for the left and right
y-axis, which is possible using :func:`~matplotlib.pyplot.twinx` (more
than two scales are not currently supported, though it is on the wish
list).  This works pretty well, though there are some quirks when you
are trying to interactively pan and zoom, because both scales do not get
the signals.

The approach uses :func:`~matplotlib.pyplot.twinx` (and its sister
:func:`~matplotlib.pyplot.twiny`) to use *2 different axes*,
turning the axes rectangular frame off on the 2nd axes to keep it from
obscuring the first, and manually setting the tick locs and labels as
desired.  You can use separate ``matplotlib.ticker`` formatters and
locators as desired because the two axes are independent.

.. plot::

    import numpy as np
    import matplotlib.pyplot as plt

    fig = plt.figure()
    ax1 = fig.add_subplot(111)
    t = np.arange(0.01, 10.0, 0.01)
    s1 = np.exp(t)
    ax1.plot(t, s1, 'b-')
    ax1.set_xlabel('time (s)')
    ax1.set_ylabel('exp')

    ax2 = ax1.twinx()
    s2 = np.sin(2*np.pi*t)
    ax2.plot(t, s2, 'r.')
    ax2.set_ylabel('sin')
    plt.show()


.. only:: html

    See :doc:`/gallery/subplots_axes_and_figures/two_scales` for a
    complete example.

.. _howto-batch:

Generate images without having a window appear
----------------------------------------------

Simply do not call `~matplotlib.pyplot.show`, and directly save the figure to
the desired format::

    import matplotlib.pyplot as plt
    plt.plot([1, 2, 3])
    plt.savefig('myfig.png')

.. seealso::

    :doc:`/gallery/user_interfaces/web_application_server_sgskip` for
    information about running matplotlib inside of a web application.

.. _how-to-threads:

Work with threads
-----------------

Matplotlib is not thread-safe: in fact, there are known race conditions
that affect certain artists.  Hence, if you work with threads, it is your
responsibility to set up the proper locks to serialize access to Matplotlib
artists.

You may be able to work on separate figures from separate threads.  However,
you must in that case use a *non-interactive backend* (typically Agg), because
most GUI backends *require* being run from the main thread as well.

.. _reporting-problems:
.. _get-help:

Get help
--------

There are a number of good resources for getting help with Matplotlib.
There is a good chance your question has already been asked:

- The `mailing list archive
  <https://discourse.matplotlib.org/c/community/matplotlib-users/6>`_.

- `GitHub issues <https://github.com/matplotlib/matplotlib/issues>`_.

- Stackoverflow questions tagged `matplotlib
  <https://stackoverflow.com/questions/tagged/matplotlib>`_.

If you are unable to find an answer to your question through search, please
provide the following information in your e-mail to the `mailing list
<https://mail.python.org/mailman/listinfo/matplotlib-users>`_:

* Your operating system (Linux/Unix users: post the output of ``uname -a``).

* Matplotlib version::

     python -c "import matplotlib; print(matplotlib.__version__)"

* Where you obtained Matplotlib (e.g., your Linux distribution's packages,
  GitHub, PyPI, or `Anaconda <https://www.anaconda.com/>`_).

* Any customizations to your ``matplotlibrc`` file (see
  :ref:`customizing`).

* If the problem is reproducible, please try to provide a *minimal*, standalone
  Python script that demonstrates the problem.  This is *the* critical step.
  If you can't post a piece of code that we can run and reproduce your error,
  the chances of getting help are significantly diminished.  Very often, the
  mere act of trying to minimize your code to the smallest bit that produces
  the error will help you find a bug in *your* code that is causing the
  problem.

* Matplotlib provides debugging information through the `logging` library, and
  a helper function to set the logging level: one can call ::

    plt.set_loglevel("info")  # or "debug" for more info

  to obtain this debugging information.

  Standard functions from the `logging` module are also applicable; e.g. one
  could call ``logging.basicConfig(level="DEBUG")`` even before importing
  Matplotlib (this is in particular necessary to get the logging info emitted
  during Matplotlib's import), or attach a custom handler to the "matplotlib"
  logger.  This may be useful if you use a custom logging configuration.

If you compiled Matplotlib yourself, please also provide:

* any changes you have made to ``setup.py`` or ``setupext.py``.
* the output of::

     rm -rf build
     python setup.py build

  The beginning of the build output contains lots of details about your
  platform that are useful for the Matplotlib developers to diagnose your
  problem.

* your compiler version -- e.g., ``gcc --version``.

Including this information in your first e-mail to the mailing list
will save a lot of time.

You will likely get a faster response writing to the mailing list than
filing a bug in the bug tracker.  Most developers check the bug
tracker only periodically.  If your problem has been determined to be
a bug and cannot be quickly solved, you may be asked to file a bug in
the tracker so the issue doesn't get lost.
