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
···
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
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?
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?