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.callbacksSM 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.TickHelper.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()` `Bbox.intervalx().set_bounds()`	`transforms.Bbox.intervalx` [It is now a property.]
`Bbox.intervaly().get_bounds()` `Bbox.intervaly().set_bounds()`	`transforms.Bbox.intervaly` [It 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)` [It is a staticmethod.]
`lbwh_to_bbox(l, b, w, h)`	`Bbox.from_bounds(x0, y0, w, h)` [It 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()`	`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)`	`colors.to_rgba_array(c)` [`matplotlib.colors.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]

[7]	`matplotlib.backend_bases.GraphicsContextBase.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.GraphicsContextBase.set_clip_path()` now only accepts a `matplotlib.transforms.TransformedPath` instance.

`RendererBase`¶

New methods:

draw_path(self, gc, path, transform, rgbFace)

draw_markers(self, gc, marker_path, marker_trans, path, trans, rgbFace)

draw_path_collection(self, master_transform, cliprect, clippath, clippath_trans, paths, all_transforms, offsets, offsetTrans, facecolors, edgecolors, linewidths, linestyles, antialiaseds) [optional]

Changed methods:

draw_image(self, x, y, im, bbox) is now draw_image(self, x, y, im, bbox, clippath, clippath_trans)

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

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