I was looking at an issue where the default colormap, SymLogNorm imported from matplotlib.colors, isn't actually symmetric for my data. My data's mean and median are above zero, but I want values of zero to appear as the color white. SymLogNorm returns a colormap with center value very slightly above zero -- subtle, but definitely noticeable.
Is there any solution to this problem?
Here's my fix - it essentially uses a manual linear scale for the regions close to the center, and uses a logarithmic scale for the regions further outside. You specify lin_thres and the other arguments just as you would for SymLogNorm.
class MidpointLogNorm(colors.SymLogNorm):
"""
Normalise the colorbar so that diverging bars work there way either side from a prescribed midpoint value)
e.g. im=ax1.imshow(array, norm=MidpointNormalize(midpoint=0.,vmin=-100, vmax=100))
All arguments are the same as SymLogNorm, except for midpoint
"""
def __init__(self, lin_thres, lin_scale, midpoint=None, vmin=None, vmax=None):
self.midpoint = midpoint
self.lin_thres = lin_thres
self.lin_scale = lin_scale
#fraction of the cmap that the linear component occupies
self.linear_proportion = (lin_scale / (lin_scale + 1)) * 0.5
print(self.linear_proportion)
colors.SymLogNorm.__init__(self, lin_thres, lin_scale, vmin, vmax)
def __get_value__(self, v, log_val, clip=None):
if v < -self.lin_thres or v > self.lin_thres:
return log_val
x = [-self.lin_thres, self.midpoint, self.lin_thres]
y = [0.5 - self.linear_proportion, 0.5, 0.5 + self.linear_proportion]
interpol = np.interp(v, x, y)
return interpol
def __call__(self, value, clip=None):
log_val = colors.SymLogNorm.__call__(self, value)
out = [0] * len(value)
for i, v in enumerate(value):
out[i] = self.__get_value__(v, log_val[i])
return np.ma.masked_array(out)
I drew inspiration centering around the midpoint from here: http://chris35wills.github.io/matplotlib_diverging_colorbar/