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mdboommdboom
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Merge pull request #2044 from mdboom/svg-rasterize
Svg rasterize (rebased)
2 parents 22c3978 + f288b4e commit f262892

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‎CHANGELOG

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@@ -1,3 +1,11 @@
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2013-05-18 Added support for arbitrary rasterization resolutions to the
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SVG backend. Previously the resolution was hard coded to 72
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dpi. Now the backend class takes a image_dpi argument for
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its constructor, adjusts the image bounding box accordingly
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and forwards a magnification factor to the image renderer.
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The code and results now resemble those of the PDF backend.
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- MW
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2013-05-08 Changed behavior of hist when given stacked=True and normed=True.
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Histograms are now stacked first, then the sum is normalized.
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Previously, each histogram was normalized, then they were stacked.

‎doc/users/whats_new.rst

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@@ -1126,3 +1126,7 @@ Here are the 0.98.4 notes from the CHANGELOG::
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off automatically when infs or NaNs are present. Also masked
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arrays are now converted to arrays with NaNs for consistent
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handling of masks and NaNs - MGD and EF
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Added support for arbitrary rasterization resolutions to the SVG
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backend. - MW
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‎lib/matplotlib/backends/backend_svg.py

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@@ -249,10 +249,11 @@ class RendererSVG(RendererBase):
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FONT_SCALE = 100.0
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fontd = maxdict(50)
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def __init__(self, width, height, svgwriter, basename=None):
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def __init__(self, width, height, svgwriter, basename=None, image_dpi=72):
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self.width = width
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self.height = height
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self.writer = XMLWriter(svgwriter)
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self.image_dpi = image_dpi # the actual dpi we want to rasterize stuff with
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self._groupd = {}
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if not rcParams['svg.image_inline']:
@@ -747,6 +748,9 @@ def draw_gouraud_triangles(self, gc, triangles_array, colors_array,
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def option_scale_image(self):
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return True
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def get_image_magnification(self):
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return self.image_dpi / 72.0
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def draw_image(self, gc, x, y, im, dx=None, dy=None, transform=None):
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attrib = {}
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clipid = self._get_clip(gc)
@@ -769,6 +773,17 @@ def draw_image(self, gc, x, y, im, dx=None, dy=None, transform=None):
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im.resize(numcols, numrows)
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h,w = im.get_size_out()
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if dx is None:
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w = 72.0*w/self.image_dpi
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else:
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w = dx
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if dy is None:
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h = 72.0*h/self.image_dpi
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else:
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h = dy
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oid = getattr(im, '_gid', None)
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url = getattr(im, '_url', None)
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if url is not None:
@@ -1154,25 +1169,17 @@ def print_svgz(self, filename, *args, **kwargs):
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def _print_svg(self, filename, svgwriter, fh_to_close=None, **kwargs):
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try:
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image_dpi = kwargs.pop("dpi", 72)
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self.figure.set_dpi(72.0)
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width, height = self.figure.get_size_inches()
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w, h = width*72, height*72
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if rcParams['svg.image_noscale']:
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renderer = RendererSVG(w, h, svgwriter, filename)
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renderer = RendererSVG(w, h, svgwriter, filename, image_dpi)
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else:
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# setting mixed renderer dpi other than 72 results in
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# incorrect size of the rasterized image. It seems that the
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# svg internally uses fixed dpi of 72 and seems to cause
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# the problem. I hope someone who knows the svg backends
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# take a look at this problem. Meanwhile, the dpi
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# parameter is ignored and image_dpi is fixed at 72. - JJL
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#image_dpi = kwargs.pop("dpi", 72)
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image_dpi = 72
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_bbox_inches_restore = kwargs.pop("bbox_inches_restore", None)
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renderer = MixedModeRenderer(self.figure,
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width, height, image_dpi, RendererSVG(w, h, svgwriter, filename),
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width, height, image_dpi, RendererSVG(w, h, svgwriter, filename, image_dpi),
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bbox_inches_restore=_bbox_inches_restore)
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self.figure.draw(renderer)

‎lib/matplotlib/testing/compare.py

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@@ -306,6 +306,36 @@ def compare_images( expected, actual, tol, in_decorator=False ):
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expectedImage = expectedImage.astype(np.int16)
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actualImage = actualImage.astype(np.int16)
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# compare the resulting image histogram functions
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expected_version = version.LooseVersion("1.6")
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found_version = version.LooseVersion(np.__version__)
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# On Numpy 1.6, we can use bincount with minlength, which is much faster than
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# using histogram
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if found_version >= expected_version:
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rms = 0
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for i in xrange(0, 3):
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h1p = expectedImage[:,:,i]
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h2p = actualImage[:,:,i]
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h1h = np.bincount(h1p.ravel(), minlength=256)
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h2h = np.bincount(h2p.ravel(), minlength=256)
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rms += np.sum(np.power((h1h-h2h), 2))
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else:
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rms = 0
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ns = np.arange(257)
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for i in xrange(0, 3):
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h1p = expectedImage[:,:,i]
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h2p = actualImage[:,:,i]
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h1h = np.histogram(h1p, bins=ns)[0]
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h2h = np.histogram(h2p, bins=ns)[0]
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rms += np.sum(np.power((h1h-h2h), 2))
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rms = calculate_rms(expectedImage, actualImage)
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diff_image = make_test_filename(actual, 'failed-diff')

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