Version 3.1.3
matplotlib
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Table of Contents

Changes for 0.98.0

  • matplotlib.image.imread() now no longer always returns RGBA data---if the image is luminance or RGB, it will return a MxN or MxNx3 array if possible. Also uint8 is no longer always forced to float.
  • Rewrote the matplotlib.cm.ScalarMappable callback infrastructure to use matplotlib.cbook.CallbackRegistry rather than custom callback handling. Any users of matplotlib.cm.ScalarMappable.add_observer() of the ScalarMappable should use the matplotlib.cm.ScalarMappable.callbacks CallbackRegistry instead.
  • New axes function and Axes method provide control over the plot color cycle: matplotlib.axes.set_default_color_cycle() and matplotlib.axes.Axes.set_color_cycle().
  • Matplotlib now requires Python 2.4, so matplotlib.cbook will no longer provide set, enumerate(), reversed() or izip() compatibility functions.
  • In Numpy 1.0, bins are specified by the left edges only. The axes method matplotlib.axes.Axes.hist() now uses future Numpy 1.3 semantics for histograms. Providing binedges, the last value gives the upper-right edge now, which was implicitly set to +infinity in Numpy 1.0. This also means that the last bin doesn't contain upper outliers any more by default.
  • New axes method and pyplot function, hexbin(), is an alternative to scatter() for large datasets. It makes something like a pcolor() of a 2-D histogram, but uses hexagonal bins.
  • New kwarg, symmetric, in matplotlib.ticker.MaxNLocator allows one require an axis to be centered around zero.
  • Toolkits must now be imported from mpl_toolkits (not matplotlib.toolkits)

Notes about the transforms refactoring

A major new feature of the 0.98 series is a more flexible and extensible transformation infrastructure, written in Python/Numpy rather than a custom C extension.

The primary goal of this refactoring was to make it easier to extend matplotlib to support new kinds of projections. This is mostly an internal improvement, and the possible user-visible changes it allows are yet to come.

See matplotlib.transforms for a description of the design of the new transformation framework.

For efficiency, many of these functions return views into Numpy arrays. This means that if you hold on to a reference to them, their contents may change. If you want to store a snapshot of their current values, use the Numpy array method copy().

The view intervals are now stored only in one place -- in the matplotlib.axes.Axes instance, not in the locator instances as well. This means locators must get their limits from their matplotlib.axis.Axis, which in turn looks up its limits from the Axes. If a locator is used temporarily and not assigned to an Axis or Axes, (e.g., in matplotlib.contour), a dummy axis must be created to store its bounds. Call matplotlib.ticker.Locator.create_dummy_axis() to do so.

The functionality of Pbox has been merged with Bbox. Its methods now all return copies rather than modifying in place.

The following lists many of the simple changes necessary to update code from the old transformation framework to the new one. In particular, methods that return a copy are named with a verb in the past tense, whereas methods that alter an object in place are named with a verb in the present tense.

matplotlib.transforms

Old method New method
Bbox.get_bounds() transforms.Bbox.bounds
Bbox.width() transforms.Bbox.width
Bbox.height() transforms.Bbox.height
Bbox.intervalx().get_bounds() transforms.Bbox.intervalx
Bbox.intervalx().set_bounds() [Bbox.intervalx is now a property.]
Bbox.intervaly().get_bounds() transforms.Bbox.intervaly
Bbox.intervaly().set_bounds() [Bbox.intervaly is now a property.]
Bbox.xmin() transforms.Bbox.x0 or transforms.Bbox.xmin [1]
Bbox.ymin() transforms.Bbox.y0 or transforms.Bbox.ymin [1]
Bbox.xmax() transforms.Bbox.x1 or transforms.Bbox.xmax [1]
Bbox.ymax() transforms.Bbox.y1 or transforms.Bbox.ymax [1]
Bbox.overlaps(bboxes) Bbox.count_overlaps(bboxes)
bbox_all(bboxes) Bbox.union(bboxes) [transforms.Bbox.union() is a staticmethod.]
lbwh_to_bbox(l, b, w, h) Bbox.from_bounds(x0, y0, w, h) [transforms.Bbox.from_bounds() is a staticmethod.]
inverse_transform_bbox(trans, bbox) Bbox.inverse_transformed(trans)
Interval.contains_open(v) interval_contains_open(tuple, v)
Interval.contains(v) interval_contains(tuple, v)
identity_transform() matplotlib.transforms.IdentityTransform
blend_xy_sep_transform(xtrans, ytrans) blended_transform_factory(xtrans, ytrans)
scale_transform(xs, ys) Affine2D().scale(xs[, ys])
get_bbox_transform(boxin, boxout) BboxTransform(boxin, boxout) or BboxTransformFrom(boxin) or BboxTransformTo(boxout)
Transform.seq_xy_tup(points) Transform.transform(points)
Transform.inverse_xy_tup(points) Transform.inverted().transform(points)
[1](1, 2, 3, 4) The Bbox is bound by the points (x0, y0) to (x1, y1) and there is no defined order to these points, that is, x0 is not necessarily the left edge of the box. To get the left edge of the Bbox, use the read-only property xmin.

matplotlib.axes

Old method New method
Axes.get_position() matplotlib.axes.Axes.get_position() [2]
Axes.set_position() matplotlib.axes.Axes.set_position() [3]
Axes.toggle_log_lineary() matplotlib.axes.Axes.set_yscale() [4]
Subplot class removed.

The Polar class has moved to matplotlib.projections.polar.

[2]matplotlib.axes.Axes.get_position() used to return a list of points, now it returns a matplotlib.transforms.Bbox instance.
[3]matplotlib.axes.Axes.set_position() now accepts either four scalars or a matplotlib.transforms.Bbox instance.
[4]Since the recfactoring allows for more than two scale types ('log' or 'linear'), it no longer makes sense to have a toggle. Axes.toggle_log_lineary() has been removed.

matplotlib.artist

Old method New method
Artist.set_clip_path(path) Artist.set_clip_path(path, transform) [5]
[5]matplotlib.artist.Artist.set_clip_path() now accepts a matplotlib.path.Path instance and a matplotlib.transforms.Transform that will be applied to the path immediately before clipping.

matplotlib.collections

Old method New method
linestyle linestyles [6]
[6]Linestyles are now treated like all other collection attributes, i.e. a single value or multiple values may be provided.

matplotlib.colors

Old method New method
ColorConvertor.to_rgba_list(c) ColorConvertor.to_rgba_array(c) [matplotlib.colors.ColorConvertor.to_rgba_array() returns an Nx4 Numpy array of RGBA color quadruples.]

matplotlib.contour

Old method New method
Contour._segments matplotlib.contour.Contour.get_paths`() [Returns a list of matplotlib.path.Path instances.]

matplotlib.figure

Old method New method
Figure.dpi.get() / Figure.dpi.set() matplotlib.figure.Figure.dpi (a property)

matplotlib.patches

Old method New method
Patch.get_verts() matplotlib.patches.Patch.get_path() [Returns a matplotlib.path.Path instance]

matplotlib.backend_bases

Old method New method
GraphicsContext.set_clip_rectangle(tuple) GraphicsContext.set_clip_rectangle(bbox)
GraphicsContext.get_clip_path() GraphicsContext.get_clip_path() [7]
GraphicsContext.set_clip_path() GraphicsContext.set_clip_path() [8]

RendererBase

New methods:

Changed methods:

Removed methods:

  • draw_arc
  • draw_line_collection
  • draw_line
  • draw_lines
  • draw_point
  • draw_quad_mesh
  • draw_poly_collection
  • draw_polygon
  • draw_rectangle
  • draw_regpoly_collection
[7]matplotlib.backend_bases.GraphicsContext.get_clip_path() returns a tuple of the form (path, affine_transform), where path is a matplotlib.path.Path instance and affine_transform is a matplotlib.transforms.Affine2D instance.
[8]matplotlib.backend_bases.GraphicsContext.set_clip_path() now only accepts a matplotlib.transforms.TransformedPath instance.