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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 thi
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 07/29/2015 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]: (0.64
On Wed, Jul 29, 2015 at 3:18 AM, Fabien 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.color
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)
