Bug report
When ColorbarBase is used with a custom Normalizer, if the normalizer maps the first two elements of the input to the same output value then it will raise an ValueError: Both *x_transform* and *y_transform* must be 2D affine transforms
Code for reproduction
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
plt.close('all')
fig,ax = plt.subplots(1,1)
class MyNorm(mpl.colors.Normalize):
def __call__(self, value, clip=True):
if clip is None:
clip = self.clip
result, is_scalar = self.process_value(value)
fixed_result = np.linspace(0,1, len(result))
fixed_result[1] = fixed_result[0]
#fixed_result = np.interp(fixed_result, [0,0.0001, 1], [0,0, 1])
result = np.ma.array(fixed_result)
return result
norm = MyNorm(vmax=12000, vmin=0, clip=False)
#norm = mpl.colors.Normalize(vmin=0, vmax=12000, clip=True)
cmap = plt.get_cmap('RdYlGn_r') # From red to green
sm = mpl.cm.ScalarMappable(norm=norm, cmap=cmap)
cb1 = mpl.colorbar.ColorbarBase(ax, cmap=cmap, norm=norm, orientation='vertical',
ticks=[100, 1000, 10000])
plt.show()Actual outcome
empty figure canvas
If using module://ipykernel.pylab.backend_inline then console show
# on module://ipympl.backend_nbagg
C:\appl\Anaconda3\envs\fluidprediction27\lib\site-packages\matplotlib\colorbar.py:890: RuntimeWarning: invalid value encountered in true_divide
z = np.take(y, i0) + (xn - np.take(b, i0)) * dy / db
# on module://ipykernel.pylab.backend_inline
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
C:\appl\Anaconda3\envs\fluidprediction27\lib\site-packages\IPython\core\formatters.pyc in __call__(self, obj)
332 pass
333 else:
--> 334 return printer(obj)
335 # Finally look for special method names
336 method = get_real_method(obj, self.print_method)
C:\appl\Anaconda3\envs\fluidprediction27\lib\site-packages\IPython\core\pylabtools.pyc in <lambda>(fig)
245
246 if 'png' in formats:
--> 247 png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs))
248 if 'retina' in formats or 'png2x' in formats:
249 png_formatter.for_type(Figure, lambda fig: retina_figure(fig, **kwargs))
C:\appl\Anaconda3\envs\fluidprediction27\lib\site-packages\IPython\core\pylabtools.pyc in print_figure(fig, fmt, bbox_inches, **kwargs)
129
130 bytes_io = BytesIO()
--> 131 fig.canvas.print_figure(bytes_io, **kw)
132 data = bytes_io.getvalue()
133 if fmt == 'svg':
C:\appl\Anaconda3\envs\fluidprediction27\lib\site-packages\matplotlib\backend_bases.pyc in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)
2220 bbox_filtered = []
2221 for a in bbox_artists:
-> 2222 bbox = a.get_window_extent(renderer)
2223 if a.get_clip_on():
2224 clip_box = a.get_clip_box()
C:\appl\Anaconda3\envs\fluidprediction27\lib\site-packages\matplotlib\patches.pyc in get_window_extent(self, renderer)
569
570 def get_window_extent(self, renderer=None):
--> 571 return self.get_path().get_extents(self.get_transform())
572
573
C:\appl\Anaconda3\envs\fluidprediction27\lib\site-packages\matplotlib\path.pyc in get_extents(self, transform)
531 path = self
532 if transform is not None:
--> 533 transform = transform.frozen()
534 if not transform.is_affine:
535 path = self.transformed(transform)
C:\appl\Anaconda3\envs\fluidprediction27\lib\site-packages\matplotlib\transforms.pyc in frozen(self)
2240
2241 def frozen(self):
-> 2242 return blended_transform_factory(self._x.frozen(), self._y.frozen())
2243 frozen.__doc__ = Transform.frozen.__doc__
2244
C:\appl\Anaconda3\envs\fluidprediction27\lib\site-packages\matplotlib\transforms.pyc in blended_transform_factory(x_transform, y_transform)
2387 if (isinstance(x_transform, Affine2DBase)
2388 and isinstance(y_transform, Affine2DBase)):
-> 2389 return BlendedAffine2D(x_transform, y_transform)
2390 return BlendedGenericTransform(x_transform, y_transform)
2391
C:\appl\Anaconda3\envs\fluidprediction27\lib\site-packages\matplotlib\transforms.pyc in __init__(self, x_transform, y_transform, **kwargs)
2328 is_correct = is_affine and is_separable
2329 if not is_correct:
-> 2330 raise ValueError("Both *x_transform* and *y_transform* must be 2D "
2331 "affine transforms")
2332
ValueError: Both *x_transform* and *y_transform* must be 2D affine transforms
Expected outcome
A colorbar where the bottom two lines are the same color.
In the actual case where I encountered I have a custom normalizer in which I wanted to map certain ranges of values to a single color (instead of doing a regular gradient). It works fine unless the first two map to the same color. As an example if you replace fixed_result[1] = fixed_result[0] with fixed_result[2:200] = fixed_result[1] it works.
Matplotlib version
- Operating system: Windows 7
- Matplotlib version: 2.2.2
- Matplotlib backend (
print(matplotlib.get_backend())): module://ipympl.backend_nbagg and module://ipykernel.pylab.backend_inline
- Python version: 2.7.15
- Jupyter version (if applicable): 5.'5.0
- Other libraries: module://ipympl.backend_nbagg
Installed via conda official
Bug report
When ColorbarBase is used with a custom Normalizer, if the normalizer maps the first two elements of the input to the same output value then it will raise an
ValueError: Both *x_transform* and *y_transform* must be 2D affine transformsCode for reproduction
Actual outcome
empty figure canvas
If using module://ipykernel.pylab.backend_inline then console show
Expected outcome
A colorbar where the bottom two lines are the same color.
In the actual case where I encountered I have a custom normalizer in which I wanted to map certain ranges of values to a single color (instead of doing a regular gradient). It works fine unless the first two map to the same color. As an example if you replace
fixed_result[1] = fixed_result[0]withfixed_result[2:200] = fixed_result[1]it works.Matplotlib version
print(matplotlib.get_backend())): module://ipympl.backend_nbagg and module://ipykernel.pylab.backend_inlineInstalled via conda official