具有公平发散色图的不对称色条 [英] Asymmetric Color Bar with Fair Diverging Color Map
问题描述
我正在尝试在散点图中绘制不对称的颜色范围.我希望使用
如果我正确理解,那么当前的问题是,以中点为中心的贴图会将颜色均匀从-2缩放为-20(蓝色),类似地(红色)从0到10.
代替缩放 [self.vmin,self.midpoint,self.vmax] = [-2、0、10]
,您应该在[code> [-v_ext,self]之间重新缩放.中点,v_ext] = [-10,0,10] 其中:
v_ext = np.max([np.abs(self.vmin),np.abs(self.vmax)])## = np.max([2,10])
完整的代码如下:
将numpy导入为np导入matplotlib.pyplot作为plt将matplotlib.colors导入为mcolorsx = np.arange(0,1,1e-1)xlen = x.shape [0]z = np.random.random(xlen ** 2)* 12-2类MidpointNormalize(mcolors.Normalize):def __init __(self,vmin = None,vmax = None,midpoint = None,clip = False):self.midpoint =中点mcolors.Normalize .__ init __(self,vmin,vmax,clip)def __call __(自身,值,剪辑=无):v_ext = np.max([np.abs(self.vmin),np.abs(self.vmax)])x,y = [-v_ext,self.midpoint,v_ext],[0,0.5,1]返回np.ma.masked_array(np.interp(value,x,y))x = np.arange(0,1,1e-1)xlen = x.shape [0]z = np.random.random(xlen ** 2)* 12-2规范= MidpointNormalize(midpoint = 0)splt = plt.scatter(np.repeat(x,xlen),np.tile(x,xlen),c = z,cmap ='地震',s = 400,规范=规范)plt.colorbar(splt)plt.show()
I'm trying to plot an asymmetric color range in a scatter plot. I want the colors to be a fair representation of the intensity using a diverging color map. I am having trouble changing the color bar to represent this.
For instance, I want to plot x-y data with range [-2, 10] in a scatter plot such that the color bar only shows the range -2 to 10 with neutral color at 0, but the 'intensity' at -2 and 2 are the same.
I've tried using ColorMap Normalization and truncating the color map, but it seems I need some combination of the two that I can't figure out.
MCV Example
x = np.arange( 0, 1, 1e-1 )
xlen = x.shape[ 0 ]
z = np.random.random( xlen**2 )*12 - 2
splt = plt.scatter(
np.repeat( x, xlen ),
np.tile( x, xlen ),
c = z, cmap = 'seismic',
s = 400
)
plt.colorbar( splt )
By using the MidpointNormalize
class MidpointNormalize(colors.Normalize):
def __init__(self, vmin=None, vmax=None, midpoint=None, clip=False):
self.midpoint = midpoint
colors.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))
x = np.arange( 0, 1, 1e-1 )
xlen = x.shape[ 0 ]
z = np.random.random( xlen**2 )*12 - 2
norm = MidpointNormalize( midpoint = 0 )
splt = plt.scatter(
np.repeat( x, xlen ),
np.tile( x, xlen ),
c = z, cmap = 'seismic', s = 400,
norm = norm
)
plt.colorbar( splt )
I can get the colorbar centered at 0, but the intensities are unfair. i.e. The intensity at -2 is much darker that at +2.
The problem I've been having with truncating the color map, is I don't know where the fair place to truncate it is.
Here is an example of the change I want to make in the color bar:
If I get you correctly, the issue at hand is that your midpoint-centered map is scaling the color evenly from -2 to 0 (blue) and similarly (red) from 0 to 10.
Instead of scaling [self.vmin, self.midpoint, self.vmax] = [-2, 0, 10]
, you should rather rescale between [-v_ext, self.midpoint, v_ext] = [-10, 0, 10]
where:
v_ext = np.max( [ np.abs(self.vmin), np.abs(self.vmax) ] ) ## = np.max( [ 2, 10 ] )
The complete code could look like:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
x = np.arange( 0, 1, 1e-1 )
xlen = x.shape[ 0 ]
z = np.random.random( xlen**2 )*12 - 2
class MidpointNormalize(mcolors.Normalize):
def __init__(self, vmin=None, vmax=None, midpoint=None, clip=False):
self.midpoint = midpoint
mcolors.Normalize.__init__(self, vmin, vmax, clip)
def __call__(self, value, clip=None):
v_ext = np.max( [ np.abs(self.vmin), np.abs(self.vmax) ] )
x, y = [-v_ext, self.midpoint, v_ext], [0, 0.5, 1]
return np.ma.masked_array(np.interp(value, x, y))
x = np.arange( 0, 1, 1e-1 )
xlen = x.shape[ 0 ]
z = np.random.random( xlen**2 )*12 - 2
norm = MidpointNormalize( midpoint = 0 )
splt = plt.scatter(
np.repeat( x, xlen ),
np.tile( x, xlen ),
c = z, cmap = 'seismic', s = 400,
norm = norm
)
plt.colorbar( splt )
plt.show()
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