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FIX: fix colorbars with no scales #20327

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Merged
merged 1 commit into from
Jun 10, 2021
Merged

FIX: fix colorbars with no scales #20327

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36 changes: 23 additions & 13 deletions lib/matplotlib/colorbar.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1006,24 +1006,34 @@ def _reset_locator_formatter_scale(self):
self.locator = None
self.minorlocator = None
self.formatter = None
if ((self.spacing == 'uniform') and
((self.boundaries is not None) or
isinstance(self.norm, colors.BoundaryNorm))):
funcs = (self._forward_boundaries, self._inverse_boundaries)
self.ax.set_xscale('function', functions=funcs)
self.ax.set_yscale('function', functions=funcs)
self.__scale = 'function'
elif hasattr(self.norm, '_scale') and (self.norm._scale is not None):
if (self.boundaries is not None or
isinstance(self.norm, colors.BoundaryNorm)):
if self.spacing == 'uniform':
funcs = (self._forward_boundaries, self._inverse_boundaries)
self.ax.set_xscale('function', functions=funcs)
self.ax.set_yscale('function', functions=funcs)
self.__scale = 'function'
elif self.spacing == 'proportional':
self.__scale = 'linear'
self.ax.set_xscale('linear')
self.ax.set_yscale('linear')
elif hasattr(self.norm, '_scale') and self.norm._scale is not None:
# use the norm's scale:
self.ax.set_xscale(self.norm._scale)
self.ax.set_yscale(self.norm._scale)
self.__scale = self.norm._scale.name
else:
elif type(self.norm) is colors.Normalize:
# plain Normalize:
self.ax.set_xscale('linear')
self.ax.set_yscale('linear')
if type(self.norm) is colors.Normalize:
self.__scale = 'linear'
else:
self.__scale = 'manual'
self.__scale = 'linear'
else:
# norm._scale is None or not an attr: derive the scale from
# the Norm:
funcs = (self.norm, self.norm.inverse)
self.ax.set_xscale('function', functions=funcs)
self.ax.set_yscale('function', functions=funcs)
self.__scale = 'function'

def _locate(self, x):
"""
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12 changes: 11 additions & 1 deletion lib/matplotlib/colors.py
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Expand Up @@ -1152,7 +1152,7 @@ def __init__(self, vmin=None, vmax=None, clip=False):
self.vmin = _sanitize_extrema(vmin)
self.vmax = _sanitize_extrema(vmax)
self.clip = clip
self._scale = scale.LinearScale(axis=None)
self._scale = None # will default to LinearScale for colorbar
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Note downstream libraries will likely derive from Normalize, so scale should be None so their norm gets used...

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@dstansby can you see if this version works?

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Yep, seems to work well with this version.

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Any chance of a formal review?


@staticmethod
def process_value(value):
Expand Down Expand Up @@ -1334,6 +1334,16 @@ def __call__(self, value, clip=None):
result = np.atleast_1d(result)[0]
return result

def inverse(self, value):
if not self.scaled():
raise ValueError("Not invertible until both vmin and vmax are set")
(vmin,), _ = self.process_value(self.vmin)
(vmax,), _ = self.process_value(self.vmax)
(vcenter,), _ = self.process_value(self.vcenter)

result = np.interp(value, [0, 0.5, 1.], [vmin, vcenter, vmax])
return result


class CenteredNorm(Normalize):
def __init__(self, vcenter=0, halfrange=None, clip=False):
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Binary file added lib/matplotlib/tests/baseline_images/test_colorbar/colorbar_twoslope.png
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14 changes: 14 additions & 0 deletions lib/matplotlib/tests/test_colorbar.py
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Expand Up @@ -771,3 +771,17 @@ def test_inset_colorbar_layout():
np.testing.assert_allclose(cb.ax.get_position().bounds,
[0.87, 0.342, 0.0237, 0.315], atol=0.01)
assert cb.ax.outer_ax in ax.child_axes


@image_comparison(['colorbar_twoslope.png'], remove_text=True,
style='mpl20')
def test_twoslope_colorbar():
# Note that the first tick = 20, and should be in the middle
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Adding the ticklabels to the image would be helpful here.

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Yeah, I agree, but we really try to avoid labels, because they cause image comparison problems when the font library changes...

# of the colorbar (white)
fig, ax = plt.subplots()

norm = mcolors.TwoSlopeNorm(20, 0, 100)
pc = ax.pcolormesh(np.arange(1, 11), np.arange(1, 11),
np.arange(100).reshape(10, 10),
norm=norm, cmap='RdBu_r')
fig.colorbar(pc)
3 changes: 1 addition & 2 deletions lib/matplotlib/tests/test_colors.py
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Expand Up @@ -1406,6 +1406,5 @@ def test_norm_deepcopy():
norm = mcolors.Normalize()
norm.vmin = 0.0002
norm2 = copy.deepcopy(norm)
assert isinstance(norm2._scale, mscale.LinearScale)
assert norm2._scale is None
assert norm2.vmin == norm.vmin
assert norm2._scale is not norm._scale