多个子图中的颜色条不遵循相同的比例

Question

我正在尝试比较四个二维直方图。我需要每个直方图中的颜色条。但是，很明显，所有直方图中的色标都不相同。有没有办法让这个比例一样？

我使用的代码在这里：

plt.set_cmap('hot')


fig = plt.figure()

fig.set_size_inches(10, 10)

# Adds subplot on position 1
ax = fig.add_subplot(221,aspect='equal')
# Adds subplot on position 2
ax2 = fig.add_subplot(222, aspect='equal')

ax3 = fig.add_subplot(223, aspect='equal')

ax4 = fig.add_subplot(224, aspect='equal')


h = ax.hist2d(data,datay,bins=(100,100), rasterized=True,range=np.array([(-7.9, 7.9), (-7.9, 7.9)]))


ax.set_title('step size = 2.0 ', size= 16, fontname='Comic Sans MS')


h2 = ax2.hist2d(data2,datay2,bins=(100,100), rasterized=True,range=np.array([(-7.9, 7.9), (-7.9, 7.9)]))
ax2.set_title('step size = 5.0 ', size= 16, fontname='Comic Sans MS')



h3 = ax3.hist2d(data3,datay3,bins=(100,100), rasterized=True,range=np.array([(-7.9, 7.9), (-7.9, 7.9)]))
ax3.set_title('step size = 6.0 ', size= 16, fontname='Comic Sans MS')
    

h4 = ax4.hist2d(data4,datay4,bins=(100,100), rasterized=True,range=np.array([(-7.9, 7.9), (-7.9, 7.9)]))
ax4.set_title('step size = 8.0 ', size= 16, fontname='Comic Sans MS')


"""
DEFINING COLOR BARS
"""
divider = make_axes_locatable(ax)
cax = divider.append_axes('right', size='5%', pad=0.05)
fig.colorbar(h[3], cax=cax)


divider = make_axes_locatable(ax2)
cax = divider.append_axes('right', size='5%', pad=0.05)
fig.colorbar(h2[3], cax=cax)

divider = make_axes_locatable(ax3)
cax = divider.append_axes('right', size='5%', pad=0.05)
fig.colorbar(h3[3], cax=cax)

divider = make_axes_locatable(ax4)
cax = divider.append_axes('right', size='5%', pad=0.05)
fig.colorbar(h4[3], cax=cax)

ax2.set_yticks([])
ax4.set_yticks([])

plt.subplots_adjust(wspace=0.3)


plt.savefig('24pog.pdf') 
plt.savefig('24pog.png') 
plt.show()  

Answer 1

您必须标准化颜色范围（查看文档中的cmax、cmin。或者，您可以使用ScalarMappable，即

import matplotlib.pyplot as plt, numpy as np

norm = plt.cm.colors.Normalize(vmin = 0, vmax = 2)
cmap = 'hot'
sc = plt.cm.ScalarMappable(norm = norm, cmap =  cmap)

fig, ax = plt.subplots(2, 2)
for idx, axi in enumerate(ax.flat):
    axi.hist2d(*np.random.rand(2, 10), cmap = cmap)
    fig.colorbar(sc, ax = axi)
fig.show()

Answer 2

plt.hist2d 的问题在于，要将相同的“颜色缩放”应用于四个数据实例，您必须提前知道四个直方图的分箱值，但在绘制直方图。

一种可能的解决方案是只绘制两次直方图，第二次您将知道分箱数据的全部范围，但这似乎很浪费。

另一种方法，绘制四个直方图，读取分箱数据范围，然后更改 hist2d 返回的颜色网格的属性，但这似乎很复杂。

在我看来，最简单、最经济的方法是自己模仿hist2d，这里你可以看到它的源代码（nb：self是一个Axes实例）

def hist2d(self, x, y, bins=10, range=None, density=False, weights=None,
           cmin=None, cmax=None, **kwargs):
    ...
    h, xedges, yedges = np.histogram2d(x, y, bins=bins, range=range,
                                       density=density, weights=weights)
    ...
    pc = self.pcolormesh(xedges, yedges, h.T, **kwargs)
    self.set_xlim(xedges[0], xedges[-1])
    self.set_ylim(yedges[0], yedges[-1])

    return h, xedges, yedges, pc

好吧，没有什么特别花哨的……所以计划是在每个 x, y 实例上调用 histogram2d，将结果保存在列表中，检查分箱数据以找到正确的规范化，最后使用我们保存的数据histogram2d，调用 pcolormesh — 请注意，如果需要，您还可以对所有绘图使用相同的轴限制。

这是我用过的代码

In [49]: import matplotlib.pyplot as plt
    ...: from matplotlib.cm import ScalarMappable
    ...: from numpy.random import rand
    ...: from numpy import histogram2d
    ...: 
    ...: # generate data - I have not your data…
    ...: data = ((rand(10000+500*i), rand(10000+500*i)) for i in range(4))
    ...: 
    ...: # generate histograms (for you it's for x, y in ((x1,x2),(x2,y2),...)
    ...: hist = [histogram2d(x,y) for x, y in data]
    ...: # find min and max for each histogram
    ...: min_, max_ = zip(*((h.min(), h.max()) for h, xedges, yedges in hist))
    ...: 
    ...: norm = plt.Normalize(min(min_), max(max_))
    ...: sm = ScalarMappable(norm, 'hot')
    ...: 
    ...: fig, sp = plt.subplots(2, 2, constrained_layout=1)
    ...: sp = sp.flatten()
    ...: for ax, (h, xedges, yedges) in zip(sp, hist):
    ...:     ax.pcolormesh(xedges, yedges, h.T, norm=norm, cmap='hot')
    ...: plt.colorbar(sm, ax=sp, aspect=30)