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Merge pull request #24526 from oscargus/tutorialspelling
[Doc] Fix spelling and grammar in tutorials
2 parents 4fda5ff + 35771d3 commit 6649511

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‎tutorials/advanced/blitting.py

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.. warning::
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This code does not work with the OSX backend (but does work with other
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GUI backends on mac).
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GUI backends on Mac).
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Minimal example
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---------------
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# pixels changes (due to either the size or dpi of the figure
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# changing) , the background will be invalid and result in incorrect
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# (but sometimes cool looking!) images. There is also a global
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# variable and a fair amount of boiler plate which suggests we should
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# variable and a fair amount of boilerplate which suggests we should
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# wrap this in a class.
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#
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# Class-based example
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Parameters
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----------
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canvas : FigureCanvasAgg
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The canvas to work with, this only works for sub-classes of the Agg
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The canvas to work with, this only works for subclasses of the Agg
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canvas which have the `~FigureCanvasAgg.copy_from_bbox` and
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`~FigureCanvasAgg.restore_region` methods.
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‎tutorials/advanced/path_tutorial.py

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the :class:`~matplotlib.path.Path`, which supports the standard set of
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moveto, lineto, curveto commands to draw simple and compound outlines
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consisting of line segments and splines. The ``Path`` is instantiated
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with a (N, 2) array of (x, y) vertices, and a N-length array of path
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with a (N, 2) array of (x, y) vertices, and an N-length array of path
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codes. For example to draw the unit rectangle from (0, 0) to (1, 1), we
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could use this code:
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"""
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# n, bins = np.histogram(data, 100)
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#
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# We'll now extract the corners of the rectangles. Each of the
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# ``left``, ``bottom``, etc, arrays below is ``len(n)``, where ``n`` is
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# ``left``, ``bottom``, etc., arrays below is ``len(n)``, where ``n`` is
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# the array of counts for each histogram bar::
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#
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# # get the corners of the rectangles for the histogram
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# series of ``MOVETO``, ``LINETO`` and ``CLOSEPOLY`` for each rectangle.
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# For each rectangle, we need five vertices: one for the ``MOVETO``,
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# three for the ``LINETO``, and one for the ``CLOSEPOLY``. As indicated
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# in the table above, the vertex for the closepoly is ignored but we still
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# in the table above, the vertex for the closepoly is ignored, but we still
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# need it to keep the codes aligned with the vertices::
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#
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# nverts = nrects*(1+3+1)

‎tutorials/advanced/transforms_tutorial.py

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system, and the *display* coordinate system. In 95% of your plotting, you
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won't need to think about this, as it happens under the hood, but as you push
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the limits of custom figure generation, it helps to have an understanding of
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these objects so you can reuse the existing transformations Matplotlib makes
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these objects, so you can reuse the existing transformations Matplotlib makes
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available to you, or create your own (see :mod:`matplotlib.transforms`). The
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table below summarizes some useful coordinate systems, a description of each
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system, and the transformation object for going from each coordinate system to
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Note that specifying the position of Artists in *display* coordinates may
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change their relative location if the ``dpi`` or size of the figure changes.
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This can cause confusion when printing or changing screen resolution, because
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the object can change location and size. Therefore it is most common for
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the object can change location and size. Therefore, it is most common for
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artists placed in an Axes or figure to have their transform set to something
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*other* than the `~.transforms.IdentityTransform()`; the default when an artist
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is added to an Axes using `~.axes.Axes.add_artist` is for the transform to be
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# coordinates is extremely useful, for example to create a horizontal
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# span which highlights some region of the y-data but spans across the
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# x-axis regardless of the data limits, pan or zoom level, etc. In fact
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# these blended lines and spans are so useful, we have built in
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# these blended lines and spans are so useful, we have built-in
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# functions to make them easy to plot (see
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# :meth:`~matplotlib.axes.Axes.axhline`,
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# :meth:`~matplotlib.axes.Axes.axvline`,
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# Another use of :class:`~matplotlib.transforms.ScaledTranslation` is to create
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# a new transformation that is
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# offset from another transformation, e.g., to place one object shifted a
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# bit relative to another object. Typically you want the shift to be in
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# bit relative to another object. Typically, you want the shift to be in
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# some physical dimension, like points or inches rather than in *data*
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# coordinates, so that the shift effect is constant at different zoom
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# levels and dpi settings.

‎tutorials/colors/colormapnorms.py

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X, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)]
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# A low hump with a spike coming out of the top right. Needs to have
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# z/colour axis on a log scale so we see both hump and spike. linear
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# z/colour axis on a log scale, so we see both hump and spike. A linear
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# scale only shows the spike.
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Z1 = np.exp(-X**2 - Y**2)
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Z2 = np.exp(-(X * 10)**2 - (Y * 10)**2)

‎tutorials/colors/colormaps.py

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# https://stackoverflow.com/q/8202605/
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if cmap_category == 'Sequential':
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# These colormaps all start at high lightness but we want them
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# These colormaps all start at high lightness, but we want them
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# reversed to look nice in the plot, so reverse the order.
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y_ = lab[0, ::-1, 0]
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c_ = x[::-1]

‎tutorials/intermediate/arranging_axes.py

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fig.suptitle('plt.subplots()')
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##############################################################################
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# We will annotate a lot of Axes, so lets encapsulate the annotation, rather
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# We will annotate a lot of Axes, so let's encapsulate the annotation, rather
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# than having that large piece of annotation code every time we need it:
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‎tutorials/intermediate/artists.py

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# :attr:`~matplotlib.axes.Axes.yaxis`. The ``XAxis`` and ``YAxis``
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# containers will be detailed below, but note that the ``Axes`` contains
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# many helper methods which forward calls on to the
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# :class:`~matplotlib.axis.Axis` instances so you often do not need to
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# :class:`~matplotlib.axis.Axis` instances, so you often do not need to
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# work with them directly unless you want to. For example, you can set
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# the font color of the ``XAxis`` ticklabels using the ``Axes`` helper
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# method::

‎tutorials/intermediate/constrainedlayout_guide.py

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# ======================
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#
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# The algorithm for the constraint is relatively straightforward, but
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# has some complexity due to the complex ways we can layout a figure.
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# has some complexity due to the complex ways we can lay out a figure.
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#
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# Layout in Matplotlib is carried out with gridspecs
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# via the `.GridSpec` class. A gridspec is a logical division of the figure
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# for each column, and ``bottom`` and ``top`` variables for each row, and
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# further it has a margin for each of left, right, bottom and top. In each
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# row, the bottom/top margins are widened until all the decorators
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# in that row are accommodated. Similarly for columns and the left/right
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# in that row are accommodated. Similarly, for columns and the left/right
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# margins.
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#
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#

‎tutorials/intermediate/tight_layout_guide.py

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# are rare cases where it is not.
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#
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# * ``pad=0`` can clip some texts by a few pixels. This may be a bug or
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# a limitation of the current algorithm and it is not clear why it
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# a limitation of the current algorithm, and it is not clear why it
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# happens. Meanwhile, use of pad larger than 0.3 is recommended.
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#
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# Use with GridSpec
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# =======================
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#
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# Pre Matplotlib 2.2, legends and annotations were excluded from the bounding
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# box calculations that decide the layout. Subsequently these artists were
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# box calculations that decide the layout. Subsequently, these artists were
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# added to the calculation, but sometimes it is undesirable to include them.
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# For instance in this case it might be good to have the axes shrink a bit
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# to make room for the legend:

‎tutorials/introductory/lifecycle.py

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* The `.Figure` is the final image, and may contain one or more `~.axes.Axes`.
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* The `~.axes.Axes` represents an individual plot (not to be confused with
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`~.axis.Axis`, which refers to the x/y axis of a plot).
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`~.axis.Axis`, which refers to the x-, y-, or z-axis of a plot).
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We call methods that do the plotting directly from the Axes, which gives
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us much more flexibility and power in customizing our plot.

‎tutorials/introductory/pyplot.py

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"""
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###############################################################################
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# Intro to pyplot
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# ===============
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# Introduction to pyplot
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# ======================
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#
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# :mod:`matplotlib.pyplot` is a collection of functions that make matplotlib
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# work like MATLAB. Each ``pyplot`` function makes some change to a figure:
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#
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# .. note::
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#
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# the implicit pyplot API is generally less verbose but also not as flexible as the
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# The implicit pyplot API is generally less verbose but also not as flexible as the
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# explicit API. Most of the function calls you see here can also be called
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# as methods from an ``Axes`` object. We recommend browsing the tutorials
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# and examples to see how this works. See :ref:`api_interfaces` for an
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# explanation of the trade off of the supported user APIs.
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# explanation of the trade-off of the supported user APIs.
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#
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# Generating visualizations with pyplot is very quick:
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# `~.pyplot.plot`, matplotlib assumes it is a
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# sequence of y values, and automatically generates the x values for
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# you. Since python ranges start with 0, the default x vector has the
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# same length as y but starts with 0. Hence the x data are
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# same length as y but starts with 0; therefore, the x data are
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# ``[0, 1, 2, 3]``.
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#
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# `~.pyplot.plot` is a versatile function, and will take an arbitrary number of
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# access particular variables with strings. For example, with
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# `numpy.recarray` or `pandas.DataFrame`.
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#
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# Matplotlib allows you provide such an object with
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# Matplotlib allows you to provide such an object with
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# the ``data`` keyword argument. If provided, then you may generate plots with
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# the strings corresponding to these variables.
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# Using mathematical expressions in text
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# --------------------------------------
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#
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# matplotlib accepts TeX equation expressions in any text expression.
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# Matplotlib accepts TeX equation expressions in any text expression.
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# For example to write the expression :math:`\sigma_i=15` in the title,
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# you can write a TeX expression surrounded by dollar signs::
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#
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# that the string is a *raw* string and not to treat backslashes as
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# python escapes. matplotlib has a built-in TeX expression parser and
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# layout engine, and ships its own math fonts -- for details see
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# :doc:`/tutorials/text/mathtext`. Thus you can use mathematical text across platforms
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# without requiring a TeX installation. For those who have LaTeX and
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# dvipng installed, you can also use LaTeX to format your text and
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# :doc:`/tutorials/text/mathtext`. Thus, you can use mathematical text across
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# platforms without requiring a TeX installation. For those who have LaTeX
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# and dvipng installed, you can also use LaTeX to format your text and
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# incorporate the output directly into your display figures or saved
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# postscript -- see :doc:`/tutorials/text/usetex`.
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#
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#
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# plt.xscale('log')
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#
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# An example of four plots with the same data and different scales for the y axis
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# An example of four plots with the same data and different scales for the y-axis
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# is shown below.
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# Fixing random state for reproducibility

‎tutorials/introductory/quick_start.py

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# Matplotlib graphs your data on `.Figure`\s (e.g., windows, Jupyter
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# widgets, etc.), each of which can contain one or more `~.axes.Axes`, an
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# area where points can be specified in terms of x-y coordinates (or theta-r
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# in a polar plot, x-y-z in a 3D plot, etc). The simplest way of
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# in a polar plot, x-y-z in a 3D plot, etc.). The simplest way of
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# creating a Figure with an Axes is using `.pyplot.subplots`. We can then use
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# `.Axes.plot` to draw some data on the Axes:
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# b_asarray = np.asarray(b)
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#
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# Most methods will also parse an addressable object like a *dict*, a
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# `numpy.recarray`, or a `pandas.DataFrame`. Matplotlib allows you provide
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# the ``data`` keyword argument and generate plots passing the strings
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# corresponding to the *x* and *y* variables.
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# `numpy.recarray`, or a `pandas.DataFrame`. Matplotlib allows you to
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# provide the ``data`` keyword argument and generate plots passing the
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# strings corresponding to the *x* and *y* variables.
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np.random.seed(19680801) # seed the random number generator.
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data = {'a': np.arange(50),
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'c': np.random.randint(0, 50, 50),

‎tutorials/provisional/mosaic.py

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make breaking changes in future versions with no warning.
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Laying out Axes in a Figure in a non uniform grid can be both tedious
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Laying out Axes in a Figure in a non-uniform grid can be both tedious
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and verbose. For dense, even grids we have `.Figure.subplots` but for
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more complex layouts, such as Axes that span multiple columns / rows
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of the layout or leave some areas of the Figure blank, you can use
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# Axes spanning multiple rows/columns
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# ===================================
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#
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# Something we can do with `.Figure.subplot_mosaic` that you can not
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# do with `.Figure.subplots` is specify that an Axes should span
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# Something we can do with `.Figure.subplot_mosaic`, that we cannot
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# do with `.Figure.subplots`, is to specify that an Axes should span
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# several rows or columns.
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# Nested list input
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# =================
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#
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# Everything we can do with the string short-hand we can also do when
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# Everything we can do with the string shorthand we can also do when
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# passing in a list (internally we convert the string shorthand to a nested
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# list), for example using spans, blanks, and *gridspec_kw*:
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‎tutorials/text/annotations.py

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#
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# The behavior of each connection style is (limitedly) demonstrated in the
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# example below. (Warning: The behavior of the ``bar`` style is currently not
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# well defined and may be changed in the future).
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# well-defined and may be changed in the future).
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#
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# .. figure:: ../../gallery/userdemo/images/sphx_glr_connectionstyle_demo_001.png
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# :target: ../../gallery/userdemo/connectionstyle_demo.html
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# pixel (radius = 10 pixel), you can utilize
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# `~mpl_toolkits.axes_grid1.anchored_artists.AnchoredDrawingArea`. The instance
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# is created with a size of the drawing area (in pixels), and arbitrary artists
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# can added to the drawing area. Note that the extents of the artists that are
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# can be added to the drawing area. Note that the extents of the artists that are
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# added to the drawing area are not related to the placement of the drawing
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# area itself. Only the initial size matters.
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#
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#
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# 3. A callable object that takes the renderer instance as single argument, and
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# returns either a `.Transform` or a `.BboxBase`. For example, the return
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# value of `.Artist.get_window_extent` is a bbox, so this method is identical to
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# value of `.Artist.get_window_extent` is a bbox, so this method is identical
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# to (2) passing in the artist:
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fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(3, 3))

‎tutorials/text/text_intro.py

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# Simple ticks
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# ~~~~~~~~~~~~
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#
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# It often is convenient to simply define the
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# It is often convenient to simply define the
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# tick values, and sometimes the tick labels, overriding the default
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# locators and formatters. This is discouraged because it breaks interactive
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# navigation of the plot. It also can reset the axis limits: note that
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# ``nbins=auto`` uses an algorithm to determine how many ticks will
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# be acceptable based on how long the axis is. The fontsize of the
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# ticklabel is taken into account, but the length of the tick string
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# is not (because its not yet known.) In the bottom row, the
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# is not (because it's not yet known.) In the bottom row, the
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# ticklabels are quite large, so we set ``nbins=4`` to make the
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# labels fit in the right-hand plot.
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