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Fixed pre-transform limit calculation bug for contour sets. #1265

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merged 1 commit into from Sep 18, 2012
Merged

Fixed pre-transform limit calculation bug for contour sets. #1265

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25 changes: 20 additions & 5 deletions lib/matplotlib/contour.py
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Expand Up @@ -946,6 +946,7 @@ def _process_args(self, *args, **kwargs):
min = seg.min(axis=0)
max = seg.max(axis=0)
havelimits = True

if havelimits:
self.ax.update_datalim([min, max])
self.ax.autoscale_view(tight=True)
Expand Down Expand Up @@ -1291,17 +1292,31 @@ def _process_args(self, *args, **kwargs):
self.zmax = args[0].zmax
else:
x, y, z = self._contour_args(args, kwargs)


_mask = ma.getmask(z)
if _mask is ma.nomask:
_mask = None
C = _cntr.Cntr(x, y, z.filled(), _mask)

t = self.ax.transData if self.transform is None else self.transform

# if the transform is not trans data, and some part of it
# contains transData, transform the xs and ys to data coordinates
if (t != self.ax.transData and
any(t.contains_branch_seperately(self.ax.transData))):
trans_to_data = self.transform - self.ax.transData
pts = (np.vstack([x.flat, y.flat]).T)
transformed_pts = trans_to_data.transform(pts)
x = transformed_pts[..., 0]
y = transformed_pts[..., 1]

x0 = ma.minimum(x)
x1 = ma.maximum(x)
y0 = ma.minimum(y)
y1 = ma.maximum(y)
self.ax.update_datalim([(x0,y0), (x1,y1)])
self.ax.autoscale_view(tight=True)
_mask = ma.getmask(z)
if _mask is ma.nomask:
_mask = None
C = _cntr.Cntr(x, y, z.filled(), _mask)

self.Cntr = C

def _get_allsegs_and_allkinds(self):
Expand Down
12 changes: 11 additions & 1 deletion lib/matplotlib/tests/test_transforms.py
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Expand Up @@ -16,7 +16,6 @@
import matplotlib.patches as mpatches



@cleanup
def test_non_affine_caching():
class AssertingNonAffineTransform(mtrans.Transform):
Expand Down Expand Up @@ -108,6 +107,17 @@ def test_pre_transform_plotting():
ax.quiver(x, y + 5, u, v, transform=times10 + ax.transData)

ax.barbs(x - 3, y + 5, u**2, v**2, transform=times10 + ax.transData)


@cleanup
def test_contour_pre_transform_limits():
ax = plt.axes()
xs, ys = np.meshgrid(np.linspace(15, 20, 15), np.linspace(12.4, 12.5, 20))
ax.contourf(xs, ys, np.log(xs * ys), transform=mtrans.Affine2D().scale(0.1) + ax.transData)

expected = np.array([[ 1.5 , 1.24],
[ 2. , 1.25]])
assert_almost_equal(expected, ax.dataLim.get_points())


def test_Affine2D_from_values():
Expand Down