非线性颜色图，matplotlib

Question

是否有任何颜色图或是否有一种简单的方法来转换 matplotlib 颜色图以提供接近 0.5 的更大颜色范围和在极端情况下更小的颜色范围？我正在创建一堆子图，其中一个的颜色值大约是其他图的 10 倍，所以它的值占主导地位，其余的图看起来都一样。对于一个简单的例子，我们有：

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(1,10,10)
y = np.linspace(1,10,10)

t1 = np.random.normal(2,0.3,10)
t2 = np.random.normal(9,0.01,10)
t2_max = max(t2)

plt.figure(figsize=(22.0, 15.50))

p = plt.subplot(1,2,1)
colors = plt.cm.Accent(t1/t2_max)
p.scatter(x, y, edgecolors=colors, s=15, linewidths=4)

p = plt.subplot(1,2,2)
colors = plt.cm.Accent(t2/t2_max)
p.scatter(x, y, edgecolors=colors, s=15, linewidths=4)

plt.subplots_adjust(left=0.2)
cbar_ax = plt.axes([0.10, 0.15, 0.05, 0.7])
sm = plt.cm.ScalarMappable(cmap=plt.cm.Accent, norm=plt.Normalize(vmin=0, vmax=t2_max))
sm._A = []
cbar = plt.colorbar(sm,cax=cbar_ax)

plt.show()

t1 中的变化比 t2 中的多得多，但是由于 t2 的值很高，所以无法看到变化。我想要的是一张地图，它将在 t1 的平均值周围提供更大的颜色渐变，而无需转换数据本身。我在这里找到了一个解决方案http://protracted-matter.blogspot.co.nz/2012/08/nonlinear-colormap-in-matplotlib.html但无法让它适用于我的散点图。

编辑：从下面的答案可以修改类以取负数和固定边界。

import numpy as np
import matplotlib.pyplot as plt

x = y = np.linspace(1, 10, 10)

t1mean, t2mean = -6, 9
sigma1, sigma2 = .3, .01
t1 = np.random.normal(t1mean, sigma1, 10)
t2 = np.random.normal(t2mean, sigma2, 10)

class nlcmap(object):
    def __init__(self, cmap, levels):
        self.cmap = cmap
        self.N = cmap.N
        self.monochrome = self.cmap.monochrome
        self.levels = np.asarray(levels, dtype='float64')
        self._x = self.levels
        self.levmax = self.levels.max()
        self.levmin = self.levels.min()
        self.transformed_levels = np.linspace(self.levmin, self.levmax,
             len(self.levels))

    def __call__(self, xi, alpha=1.0, **kw):
        yi = np.interp(xi, self._x, self.transformed_levels)
        return self.cmap(yi / (self.levmax-self.levmin)+0.5, alpha)

tmax = 10
tmin = -10
#the choice of the levels depends on the data:
levels = np.concatenate((
    [tmin, tmax],
    np.linspace(t1mean - 2 * sigma1, t1mean + 2 * sigma1, 5),
    np.linspace(t2mean - 2 * sigma2, t2mean + 2 * sigma2, 5),
    ))
levels = levels[levels <= tmax]
levels.sort()
print levels
cmap_nonlin = nlcmap(plt.cm.jet, levels)

fig, (ax1, ax2) = plt.subplots(1, 2)

ax1.scatter(x, y, edgecolors=cmap_nonlin(t1), s=15, linewidths=4)
ax2.scatter(x, y, edgecolors=cmap_nonlin(t2), s=15, linewidths=4)

fig.subplots_adjust(left=.25)
cbar_ax = fig.add_axes([0.10, 0.15, 0.05, 0.7])

#for the colorbar we map the original colormap, not the nonlinear one:
sm = plt.cm.ScalarMappable(cmap=plt.cm.jet, 
                norm=plt.Normalize(vmin=tmin, vmax=tmax))
sm._A = []

cbar = fig.colorbar(sm, cax=cbar_ax)
#here we are relabel the linear colorbar ticks to match the nonlinear ticks
cbar.set_ticks(cmap_nonlin.transformed_levels)
cbar.set_ticklabels(["%.2f" % lev for lev in levels])

plt.show()

Answer 1

您的链接为颜色图提供了一个很好的解决方案。我编辑了一点，但它包含了所有必要的内容。您需要为非线性颜色图选择一些合理的级别。我使用了两个以平均值为中心的范围，介于+- 4样本的标准偏差之间。通过将其更改为另一个数字，您可以在两个平均值周围的颜色中获得不同的局部梯度。

对于颜色条，您

• 要么将颜色与线性间隔标签保持非线性间隔
• 你有线性间隔的颜色和非线性间隔的标签。

第二个在查看数据时允许更高的分辨率，看起来更好，并在下面实现：

import numpy as np
import matplotlib.pyplot as plt

x = y = np.linspace(1, 10, 10)

t1mean, t2mean = 2, 9
sigma1, sigma2 = .3, .01
t1 = np.random.normal(t1mean, sigma1, 10)
t2 = np.random.normal(t2mean, sigma2, 10)

class nlcmap(object):
    def __init__(self, cmap, levels):
        self.cmap = cmap
        self.N = cmap.N
        self.monochrome = self.cmap.monochrome
        self.levels = np.asarray(levels, dtype='float64')
        self._x = self.levels
        self.levmax = self.levels.max()
        self.transformed_levels = np.linspace(0.0, self.levmax,
             len(self.levels))

    def __call__(self, xi, alpha=1.0, **kw):
        yi = np.interp(xi, self._x, self.transformed_levels)
        return self.cmap(yi / self.levmax, alpha)

tmax = max(t1.max(), t2.max())
#the choice of the levels depends on the data:
levels = np.concatenate((
    [0, tmax],
    np.linspace(t1mean - 4 * sigma1, t1mean + 4 * sigma1, 5),
    np.linspace(t2mean - 4 * sigma2, t2mean + 4 * sigma2, 5),
    ))

levels = levels[levels <= tmax]
levels.sort()

cmap_nonlin = nlcmap(plt.cm.jet, levels)

fig, (ax1, ax2) = plt.subplots(1, 2)

ax1.scatter(x, y, edgecolors=cmap_nonlin(t1), s=15, linewidths=4)
ax2.scatter(x, y, edgecolors=cmap_nonlin(t2), s=15, linewidths=4)

fig.subplots_adjust(left=.25)
cbar_ax = fig.add_axes([0.10, 0.15, 0.05, 0.7])

#for the colorbar we map the original colormap, not the nonlinear one:
sm = plt.cm.ScalarMappable(cmap=plt.cm.jet, 
                norm=plt.Normalize(vmin=0, vmax=tmax))
sm._A = []

cbar = fig.colorbar(sm, cax=cbar_ax)
#here we are relabel the linear colorbar ticks to match the nonlinear ticks
cbar.set_ticks(cmap_nonlin.transformed_levels)
cbar.set_ticklabels(["%.2f" % lev for lev in levels])

plt.show()

在结果中，请注意颜色条的刻度不是等距的：