.. _howto-faq: ****** How-To ****** .. contents:: :backlinks: none .. _howto-plotting: Plotting: howto =============== .. _howto-datetime64: Plot `numpy.datetime64` values ------------------------------ As of Matplotlib 2.2, `numpy.datetime64` objects are handled the same way as `datetime.datetime` objects. If you prefer the pandas converters and locators, you can register their converter with the `matplotlib.units` module:: from pandas.tseries import converter as pdtc pdtc.register() If you only want to use the `pandas` converter for `datetime64` values :: from pandas.tseries import converter as pdtc import matplotlib.units as munits import numpy as np munits.registry[np.datetime64] = pdtc.DatetimeConverter() .. _howto-findobj: Find all objects in a figure of a certain type ---------------------------------------------- Every Matplotlib artist (see :doc:`/tutorials/intermediate/artists`) 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-supress_offset: How to 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.get_xaxis().get_major_formatter().set_useOffset(False) set the rcParam ``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 only the pdf backend has support for this. To make a multi-page pdf file, first initialize the file:: from matplotlib.backends.backend_pdf import PdfPages pp = PdfPages('multipage.pdf') You can give the :class:`~matplotlib.backends.backend_pdf.PdfPages` object to :func:`~matplotlib.pyplot.savefig`, but you have to specify the format:: plt.savefig(pp, format='pdf') An easier way is to call :meth:`PdfPages.savefig `:: pp.savefig() Finally, the multipage pdf object has to be closed:: pp.close() The same can be done using the pgf backend:: from matplotlib.backends.backend_pgf import PdfPages .. _howto-subplots-adjust: Move the edge of an axes to make room for tick labels ----------------------------------------------------- For subplots, you can control the default spacing on the left, right, bottom, and top as well as the horizontal and vertical spacing between multiple rows and columns using the :meth:`matplotlib.figure.Figure.subplots_adjust` method (in pyplot it is :func:`~matplotlib.pyplot.subplots_adjust`). For example, to move the bottom of the subplots up to make room for some rotated x tick labels:: fig = plt.figure() fig.subplots_adjust(bottom=0.2) ax = fig.add_subplot(111) You can control the defaults for these parameters in your :file:`matplotlibrc` file; see :doc:`/tutorials/introductory/customizing`. For example, to make the above setting permanent, you would set:: figure.subplot.bottom : 0.2 # the bottom of the subplots of the figure The other parameters you can configure are, with their defaults *left* = 0.125 the left side of the subplots of the figure *right* = 0.9 the right side of the subplots of the figure *bottom* = 0.1 the bottom of the subplots of the figure *top* = 0.9 the top of the subplots of the figure *wspace* = 0.2 the amount of width reserved for space between subplots, expressed as a fraction of the average axis width *hspace* = 0.2 the amount of height reserved for space between subplots, expressed as a fraction of the average axis height If you want additional control, you can create an :class:`~matplotlib.axes.Axes` using the :func:`~matplotlib.pyplot.axes` command (or equivalently the figure :meth:`~matplotlib.figure.Figure.add_axes` method), which allows you to specify the location explicitly:: ax = fig.add_axes([left, bottom, width, height]) where all values are in fractional (0 to 1) coordinates. See :doc:`/gallery/subplots_axes_and_figures/axes_demo` for an example of placing axes manually. .. _howto-auto-adjust: Automatically make room for tick labels --------------------------------------- .. note:: This is now easier to handle than ever before. Calling :func:`~matplotlib.pyplot.tight_layout` can fix many common layout issues. See the :doc:`/tutorials/intermediate/tight_layout_guide`. The information below is kept here in case it is useful for other purposes. In most use cases, it is enough to simply change the subplots adjust parameters as described in :ref:`howto-subplots-adjust`. But in some cases, you don't know ahead of time what your tick labels will be, or how large they will be (data and labels outside your control may be being fed into your graphing application), and you may need to automatically adjust your subplot parameters based on the size of the tick labels. Any :class:`~matplotlib.text.Text` instance can report its extent in window coordinates (a negative x coordinate is outside the window), but there is a rub. The :class:`~matplotlib.backend_bases.RendererBase` instance, which is used to calculate the text size, is not known until the figure is drawn (:meth:`~matplotlib.figure.Figure.draw`). After the window is drawn and the text instance knows its renderer, you can call :meth:`~matplotlib.text.Text.get_window_extent`. One way to solve this chicken and egg problem is to wait until the figure is draw by connecting (:meth:`~matplotlib.backend_bases.FigureCanvasBase.mpl_connect`) to the "on_draw" signal (:class:`~matplotlib.backend_bases.DrawEvent`) and get the window extent there, and then do something with it, e.g., move the left of the canvas over; see :ref:`event-handling-tutorial`. Here is an example that gets a bounding box in relative figure coordinates (0..1) of each of the labels and uses it to move the left of the subplots over so that the tick labels fit in the figure: .. figure:: ../gallery/pyplots/images/sphx_glr_auto_subplots_adjust_001.png :target: ../gallery/pyplots/auto_subplots_adjust.html :align: center :scale: 50 Auto Subplots Adjust .. _howto-ticks: Configure the tick widths ------------------------- Wherever possible, it is recommended to use the :meth:`~Axes.tick_params` or :meth:`~Axis.set_tick_params` methods to modify tick properties:: import matplotlib.pyplot as plt fig, ax = plt.subplots() ax.plot(range(10)) ax.tick_params(width=10) plt.show() For more control of tick properties that are not provided by the above methods, it is important to know that in Matplotlib, the ticks are *markers*. All :class:`~matplotlib.lines.Line2D` objects support a line (solid, dashed, etc) and a marker (circle, square, tick). The tick width is controlled by the ``"markeredgewidth"`` property, so the above effect can also be achieved by:: import matplotlib.pyplot as plt fig, ax = plt.subplots() ax.plot(range(10)) for line in ax.get_xticklines() + ax.get_yticklines(): line.set_markeredgewidth(10) plt.show() The other properties that control the tick marker, and all markers, are ``markerfacecolor``, ``markeredgecolor``, ``markeredgewidth``, ``markersize``. For more information on configuring ticks, see :ref:`axis-container` and :ref:`tick-container`. .. _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. The example below shows the default behavior in the left subplots, and the manual setting in the right subplots. .. figure:: ../gallery/pyplots/images/sphx_glr_align_ylabels_001.png :target: ../gallery/pyplots/align_ylabels.html :align: center :scale: 50 Align Ylabels .. _date-index-plots: Skip dates where there is no data --------------------------------- When plotting time series, e.g., financial time series, one often wants to leave out days on which there is no data, e.g., weekends. By passing in dates on the x-xaxis, you get large horizontal gaps on periods when there is not data. The solution is to pass in some proxy x-data, e.g., evenly sampled indices, and then use a custom formatter to format these as dates. The example below shows how to use an 'index formatter' to achieve the desired plot:: import numpy as np import matplotlib.pyplot as plt import matplotlib.mlab as mlab import matplotlib.ticker as ticker r = mlab.csv2rec('../data/aapl.csv') r.sort() r = r[-30:] # get the last 30 days N = len(r) ind = np.arange(N) # the evenly spaced plot indices def format_date(x, pos=None): thisind = np.clip(int(x+0.5), 0, N-1) return r.date[thisind].strftime('%Y-%m-%d') fig = plt.figure() ax = fig.add_subplot(111) ax.plot(ind, r.adj_close, 'o-') ax.xaxis.set_major_formatter(ticker.FuncFormatter(format_date)) fig.autofmt_xdate() plt.show() .. _howto-set-zorder: Control the depth of plot elements ---------------------------------- Within an axes, the order that the various lines, markers, text, collections, etc appear is determined by the :meth:`~matplotlib.artist.Artist.set_zorder` property. The default order is patches, lines, text, with collections of lines and collections of patches appearing at the same level as regular lines and patches, respectively:: line, = ax.plot(x, y, zorder=10) .. only:: html See :doc:`/gallery/misc/zorder_demo` for a complete example. You can also use the Axes property :meth:`~matplotlib.axes.Axes.set_axisbelow` to control whether the grid lines are placed above or below your other plot elements. .. _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: 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:: :ref:`howto-webapp` for information about running matplotlib inside of a web application. .. _howto-show: Use :func:`~matplotlib.pyplot.show` ----------------------------------- When you want to view your plots on your display, the user interface backend will need to start the GUI mainloop. This is what :func:`~matplotlib.pyplot.show` does. It tells Matplotlib to raise all of the figure windows created so far and start the mainloop. Because this mainloop is blocking by default (i.e., script execution is paused), you should only call this once per script, at the end. Script execution is resumed after the last window is closed. Therefore, if you are using Matplotlib to generate only images and do not want a user interface window, you do not need to call ``show`` (see :ref:`howto-batch` and :ref:`what-is-a-backend`). .. note:: Because closing a figure window invokes the destruction of its plotting elements, you should call :func:`~matplotlib.pyplot.savefig` *before* calling ``show`` if you wish to save the figure as well as view it. .. versionadded:: v1.0.0 ``show`` now starts the GUI mainloop only if it isn't already running. Therefore, multiple calls to ``show`` are now allowed. Having ``show`` block further execution of the script or the python interpreter depends on whether Matplotlib is set for interactive mode or not. In non-interactive mode (the default setting), execution is paused until the last figure window is closed. In interactive mode, the execution is not paused, which allows you to create additional figures (but the script won't finish until the last figure window is closed). .. note:: Support for interactive/non-interactive mode depends upon the backend. Until version 1.0.0 (and subsequent fixes for 1.0.1), the behavior of the interactive mode was not consistent across backends. As of v1.0.1, only the macosx backend differs from other backends because it does not support non-interactive mode. Because it is expensive to draw, you typically will not want Matplotlib to redraw a figure many times in a script such as the following:: plot([1,2,3]) # draw here ? xlabel('time') # and here ? ylabel('volts') # and here ? title('a simple plot') # and here ? show() However, it is *possible* to force Matplotlib to draw after every command, which might be what you want when working interactively at the python console (see :ref:`mpl-shell`), but in a script you want to defer all drawing until the call to ``show``. This is especially important for complex figures that take some time to draw. :func:`~matplotlib.pyplot.show` is designed to tell Matplotlib that you're all done issuing commands and you want to draw the figure now. .. note:: :func:`~matplotlib.pyplot.show` should typically only be called at most once per script and it should be the last line of your script. At that point, the GUI takes control of the interpreter. If you want to force a figure draw, use :func:`~matplotlib.pyplot.draw` instead. Many users are frustrated by ``show`` because they want it to be a blocking call that raises the figure, pauses the script until they close the figure, and then allow the script to continue running until the next figure is created and the next show is made. Something like this:: # WARNING : illustrating how NOT to use show for i in range(10): # make figure i show() This is not what show does and unfortunately, because doing blocking calls across user interfaces can be tricky, is currently unsupported, though we have made significant progress towards supporting blocking events. .. versionadded:: v1.0.0 As noted earlier, this restriction has been relaxed to allow multiple calls to ``show``. In *most* backends, you can now expect to be able to create new figures and raise them in a subsequent call to ``show`` after closing the figures from a previous call to ``show``. .. _howto-boxplot_violinplot: Interpreting box plots and violin plots --------------------------------------- Tukey's `box plots `_ (Robert McGill, John W. Tukey and Wayne A. Larsen: "The American Statistician" Vol. 32, No. 1, Feb., 1978, pp. 12-16) are statistical plots that provide useful information about the data distribution such as skewness. However, bar plots with error bars are still the common standard in most scientific literature, and thus, the interpretation of box plots can be challenging for the unfamiliar reader. The figure below illustrates the different visual features of a box plot. .. figure:: ../_static/boxplot_explanation.png `Violin plots `_ are closely related to box plots but add useful information such as the distribution of the sample data (density trace). Violin plots were added in Matplotlib 1.4. .. _how-to-threads: Working 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. Note that (for the case where you are working with an interactive backend) most GUI backends *require* being run from the main thread as well. .. _howto-contribute: Contributing: howto =================== .. _how-to-request-feature: Request a new feature --------------------- Is there a feature you wish Matplotlib had? Then ask! The best way to get started is to email the developer `mailing list `_ for discussion. This is an open source project developed primarily in the contributors free time, so there is no guarantee that your feature will be added. The *best* way to get the feature you need added is to contribute it your self. .. _how-to-submit-patch: Reporting a bug or submitting a patch ------------------------------------- The development of Matplotlib is organized through `github `_. If you would like to report a bug or submit a patch please use that interface. To report a bug `create an issue `_ on github (this requires having a github account). Please include a `Short, Self Contained, Correct (Compilable), Example `_ demonstrating what the bug is. Including a clear, easy to test example makes it easy for the developers to evaluate the bug. Expect that the bug reports will be a conversation. If you do not want to register with github, please email bug reports to the `mailing list `_. The easiest way to submit patches to Matplotlib is through pull requests on github. Please see the :ref:`developers-guide-index` for the details. .. _how-to-contribute-docs: Contribute to Matplotlib documentation -------------------------------------- Matplotlib is a big library, which is used in many ways, and the documentation has only scratched the surface of everything it can do. So far, the place most people have learned all these features are through studying the examples (:ref:`how-to-search-examples`), which is a recommended and great way to learn, but it would be nice to have more official narrative documentation guiding people through all the dark corners. This is where you come in. There is a good chance you know more about Matplotlib usage in some areas, the stuff you do every day, than many of the core developers who wrote most of the documentation. Just pulled your hair out compiling Matplotlib for Windows? Write a FAQ or a section for the :ref:`installing-faq` page. Are you a digital signal processing wizard? Write a tutorial on the signal analysis plotting functions like :func:`~matplotlib.pyplot.xcorr`, :func:`~matplotlib.pyplot.psd` and :func:`~matplotlib.pyplot.specgram`. Do you use Matplotlib with `django `_ or other popular web application servers? Write a FAQ or tutorial and we'll find a place for it in the :ref:`users-guide-index`. And so on... I think you get the idea. Matplotlib is documented using the `sphinx `_ extensions to restructured text `(ReST) `_. sphinx is an extensible python framework for documentation projects which generates HTML and PDF, and is pretty easy to write; you can see the source for this document or any page on this site by clicking on the *Show Source* link at the end of the page in the sidebar. The sphinx website is a good resource for learning sphinx, but we have put together a cheat-sheet at :ref:`documenting-matplotlib` which shows you how to get started, and outlines the Matplotlib conventions and extensions, e.g., for including plots directly from external code in your documents. Once your documentation contributions are working (and hopefully tested by actually *building* the docs) you can submit them as a patch against git. See :ref:`install-git` and :ref:`how-to-submit-patch`. Looking for something to do? Search for `TODO <../search.html?q=todo>`_ or look at the open issues on github. .. _howto-webapp: Matplotlib in a web application server ====================================== In general, the simplest solution when using Matplotlib in a web server is to completely avoid using pyplot (pyplot maintains references to the opened figures to make `~.matplotlib.pyplot.show` work, but this will cause memory leaks unless the figures are properly closed). Since Matplotlib 3.1, one can directly create figures using the `Figure` constructor and save them to in-memory buffers. The following example uses Flask_, but other frameworks work similarly:: import base64 from io import BytesIO from flask import Flask from matplotlib.figure import Figure app = Flask(__name__) @app.route("/") def hello(): # Generate the figure **without using pyplot**. fig = Figure() ax = fig.subplots() ax.plot([1, 2]) # Save it to a temporary buffer. buf = BytesIO() fig.savefig(buf, format="png") # Embed the result in the html output. data = base64.b64encode(buf.getbuffer()).decode("ascii") return f"" .. _Flask: http://flask.pocoo.org/ When using Matplotlib versions older than 3.1, it is necessary to explicitly instantiate an Agg canvas; see e.g. :doc:`/gallery/user_interfaces/canvasagg`. .. _howto-click-maps: Clickable images for HTML ------------------------- Andrew Dalke of `Dalke Scientific `_ has written a nice `article `_ on how to make html click maps with Matplotlib agg PNGs. We would also like to add this functionality to SVG. If you are interested in contributing to these efforts that would be great. .. _how-to-search-examples: Search examples =============== The nearly 300 code :ref:`examples-index` included with the Matplotlib source distribution are full-text searchable from the :ref:`search` page, but sometimes when you search, you get a lot of results from the :ref:`api-index` or other documentation that you may not be interested in if you just want to find a complete, free-standing, working piece of example code. To facilitate example searches, we have tagged every code example page with the keyword ``codex`` for *code example* which shouldn't appear anywhere else on this site except in the FAQ. So if you want to search for an example that uses an ellipse, :ref:`search` for ``codex ellipse``.