Re: [Matplotlib-users] [matplotlib-devel] RFC: candidates for a new default colormap

[Sourish Basu]

On 06/05/2015 03:57 PM, Joe Kington wrote: Not to plug one of my own answers to much, but here's a basic example. http://stackoverflow.com/questions/20144529/shifted-colorbar-matplotlib I've been meeting to submit a PR with a more full featured version for a few years now, but haven't. This is great, but it has a slightly bothersome side effect on the colorbar ticks. In your original example, I changed the line 'data = "" * (data - 0.8)' to 'data = "" * (data - 0.85)', so that the numbers are now in between -8.5 and +1.5. As a result, when the colorbar is drawn, you get a tick at -8, as well as one at -9 (similarly at +1 and +2). Example attached. As in, the colorbar method seems intent on adding those tick marks at -9 and +2. The result is not aesthetically pleasing. In one of my real-data example, the minimum value of the data happened to be -4.003, and as a result there was a tick label at -4 and an overlapping tick label at -5. Why does this happen only when I specify 'norm' in imshow? How do I get matplotlib to not do that? Thanks, Sourish On Jun 5, 2015 4:45 PM, "Sourish Basu" <sourish.b...@gmail.com> wrote: On 06/05/2015 01:20 PM, Eric Firing wrote: Reminder: in matplotlib, color mapping is done with the combination of a colormap and a norm. This allows one to design a norm to handle the mapping, including any nonlinearity or difference between the handling of positive and negative values. This is more general than customizing a colormap; once you have a norm to suit your purpose, you can use it with any colormap. Maybe this is actually what you are already doing, but I wanted to point it out here in case some readers are not familiar with this colormap+norm strategy. Actually, I didn't use norms because I never quite figured out how to use them or how to make my own. If there's a way to create a norm with a custom mid-point, I'd love to know/use that. -Sourish Eric ------------------------------------------------------------------------------ _______________________________________________ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users -- Q: What if you strapped C4 to a boomerang? Could this be an effective weapon, or would it be as stupid as it sounds? A: Aerodynamics aside, I’m curious what tactical advantage you’re expecting to gain by having the high explosive fly back at you if it misses the target. ------------------------------------------------------------------------------ _______________________________________________ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users -- Q: What if you strapped C4 to a boomerang? Could this be an effective weapon, or would it be as stupid as it sounds? A: Aerodynamics aside, I’m curious what tactical advantage you’re expecting to gain by having the high explosive fly back at you if it misses the target.

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import Normalize

class MidpointNormalize(Normalize):
    def __init__(self, vmin=None, vmax=None, midpoint=None, clip=False):
        self.midpoint = midpoint
        Normalize.__init__(self, vmin, vmax, clip)

    def __call__(self, value, clip=None):
        # I'm ignoring masked values and all kinds of edge cases to make a
        # simple example...
        x, y = [self.vmin, self.midpoint, self.vmax], [0, 0.5, 1]
        return np.ma.masked_array(np.interp(value, x, y))

data = np.random.random((10,10))
# generate data between -8.5 and +1.5
data = 10 * (data - 0.85)

fig, ax = plt.subplots()
norm = MidpointNormalize(midpoint=0.0)
im = ax.imshow(data, norm=norm, cmap=plt.cm.seismic, interpolation='none')
fig.colorbar(im)
plt.show()

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