Folks, still in my exploring phase of Matplotlib's ecosystem I ran into following mismatch between the APIs of BoundaryNorm and Normalize. See the following example: import matplotlib as mpl c = mpl.cm.get_cmap() bnorm = mpl.colors.BoundaryNorm([0,1,2], c.N) nnorm = mpl.colors.Normalize(0, 2) # This works: In [8]: c(nnorm(1.1)) Out[8]: (0.64199873497786197, 1.0, 0.32574320050600891, 1.0) # This doesn't: In [9]: c(bnorm(1.1)) (...) TypeError: 'numpy.int16' object does not support item assignment # But this works: In [10]: c(bnorm([1.1])) Out[10]: array([[ 0.5, 0. , 0. , 1. ]]) From the doc I would expect BoundaryNorm and Normalize to work the same way. I find the error sent by BoundaryNorm quite misleading. Should I fill a bug report for this? Thanks! Fabien
On Wed, Jul 29, 2015 at 3:18 AM, Fabien <fab...@gm...> wrote: > Folks, > > still in my exploring phase of Matplotlib's ecosystem I ran into > following mismatch between the APIs of BoundaryNorm and Normalize. > > See the following example: > > import matplotlib as mpl > c = mpl.cm.get_cmap() > bnorm = mpl.colors.BoundaryNorm([0,1,2], c.N) > nnorm = mpl.colors.Normalize(0, 2) > > # This works: > In [8]: c(nnorm(1.1)) > Out[8]: (0.64199873497786197, 1.0, 0.32574320050600891, 1.0) > > # This doesn't: > In [9]: c(bnorm(1.1)) > (...) > TypeError: 'numpy.int16' object does not support item assignment > > # But this works: > In [10]: c(bnorm([1.1])) > Out[10]: array([[ 0.5, 0. , 0. , 1. ]]) > > From the doc I would expect BoundaryNorm and Normalize to work the same > way. I find the error sent by BoundaryNorm quite misleading. > > Should I fill a bug report for this? > Fabien, What happens if your force the boundaries to floats? By that I mean: bnorm = mpl.colors.BoundaryNorm([0.0, 1.0, 2.0], c.N) -Paul
On 07/29/2015 10:34 PM, Paul Hobson wrote: > See the following example: > > import matplotlib as mpl > c = mpl.cm.get_cmap() > bnorm = mpl.colors.BoundaryNorm([0,1,2], c.N) > nnorm = mpl.colors.Normalize(0, 2) > > # This works: > In [8]: c(nnorm(1.1)) > Out[8]: (0.64199873497786197, 1.0, 0.32574320050600891, 1.0) > > # This doesn't: > In [9]: c(bnorm(1.1)) > (...) > TypeError: 'numpy.int16' object does not support item assignment > > # But this works: > In [10]: c(bnorm([1.1])) > Out[10]: array([[ 0.5, 0. , 0. , 1. ]]) > > From the doc I would expect BoundaryNorm and Normalize to work the > same > way. I find the error sent by BoundaryNorm quite misleading. > > Should I fill a bug report for this? > > > Fabien, > > What happens if your force the boundaries to floats? By that I mean: > bnorm = mpl.colors.BoundaryNorm([0.0, 1.0, 2.0], c.N) > -Paul Thanks Paul, it doesn't change anything. The problem is related to the variable iret which is of shape (): the assignment fails at L1281 in colors.py. Here is the code: def __call__(self, x, clip=None): if clip is None: clip = self.clip x = ma.asarray(x) # <--- doesnt guarantee 1D mask = ma.getmaskarray(x) xx = x.filled(self.vmax + 1) if clip: np.clip(xx, self.vmin, self.vmax) iret = np.zeros(x.shape, dtype=np.int16) # <--- x.shape = () for i, b in enumerate(self.boundaries): iret[xx >= b] = i if self._interp: scalefac = float(self.Ncmap - 1) / (self.N - 2) iret = (iret * scalefac).astype(np.int16) iret[xx < self.vmin] = -1 # <--- error iret[xx >= self.vmax] = self.Ncmap ret = ma.array(iret, mask=mask) if ret.shape == () and not mask: ret = int(ret) return ret It should be easy to fix by changing iret = np.zeros(x.shape, dtype=np.int16) to: iret = np.atleast1d(np.zeros(x.shape, dtype=np.int16)) But this would lead to an output which is never a scalar even if a scalar is given as input. Is that a problem? Cheers, Fabien
Forcing the scalar to be a 1-element array would still leave the API inconsistent with what you show for Normalize. One solution is to flag a scalar at the start, and then de-reference at the end. Would you like to submit a PR to take care of this?
On 07/30/2015 10:07 AM, Eric Firing wrote: > Forcing the scalar to be a 1-element array would still leave the API > inconsistent with what you show for Normalize. One solution is to > flag a scalar at the start, and then de-reference at the end. Would > you like to submit a PR to take care of this? Hi, my very first PR here: https://github.com/matplotlib/matplotlib/pull/4824 Thanks, Fabien
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