在 imshow 的边缘添加条形图，保持条形与单元格对齐

Question

我正在尝试在 imshow 图的顶部添加一个条形图，并在右侧添加另一个条形图，条形图与 imshow“单元格”对齐。

我已尝试使用此示例中使用的方法adding histograms at the margins of a scatterplot) 和make_axes_locatable。

我得到的结果如图所示。有两个问题我无法解决：

1. imshow 图的实际大小小于我在其中绘制它的轴的大小，因为我想保持矩阵纵横比，因此实际图将严格包含在轴中
2. 即使这不是问题（参见上图），条形也不会与 imshow 单元格对齐。

这是我的代码

# from mpl_toolkits.axes_grid1 import make_axes_locatable

plt.style.use('dark_background')

m = np.random.rand(25, 200)

# definitions for the axes
left, width = 0.1, 0.65
bottom, height = 0.1, 0.65
spacing = 0.005

rect0 = [left, bottom, width, height]
rect1 = [left, bottom + height + spacing, width, 0.2]
rect2 = [left + width + spacing, bottom, 0.2, height]

# start with a rectangular Figure
fig = plt.figure(figsize=(20, 8))

ax0 = plt.axes(rect0)
ax0.tick_params(direction='in', top=True, right=True)
ax1 = plt.axes(rect1)
ax1.tick_params(direction='in', labelbottom=False)
ax2 = plt.axes(rect2)
ax2.tick_params(direction='in', labelleft=False)

ax0.matshow(m, norm=matplotlib.colors.LogNorm())

# divider = make_axes_locatable(ax)
# cax = divider.append_axes('right', size='95%', pad=0)
ax1.bar(np.arange(m.shape[1]), np.apply_along_axis(scipy.stats.entropy, 0, m))

# divider = make_axes_locatable(ax)
# cax = divider.append_axes('bottom', size='95%', pad=0)
ax2.barh(np.arange(m.shape[0]), np.apply_along_axis(scipy.stats.entropy, 1, m), orientation='horizontal')
plt.savefig('/data/l989o/a/so.png')
plt.style.use('default')

编辑 尝试向情节添加细节，如轴标签或 colobar，我发现一般情况可能更加复杂。我为添加其他绘图元素的更一般情况添加了代码，以及代码。

注意，我注意到我必须反转右侧的条形图，因为在使用orientation=horizontal 时，条形图的顺序与图像的其中一行相反。

# from mpl_toolkits.axes_grid1 import make_axes_locatable
import functools

plt.style.use('dark_background')
m = np.random.rand(58, 226) * 20
# definitions for the axes
left, width = 0.1, 0.65
bottom, height = 0.1, 0.65
spacing = 0.005

rect0 = [left, bottom, width, height]
rect1 = [left, bottom + height + spacing, width, 0.2]
rect2 = [left + width + spacing, bottom, 0.2, height]

# start with a rectangular Figure
fig = plt.figure(figsize=(20, 8))

ax0 = plt.axes(rect0)
ax0.tick_params(direction='in', top=True, right=True)
ax1 = plt.axes(rect1)
ax1.tick_params(direction='in', labelbottom=False)
ax2 = plt.axes(rect2)
ax2.tick_params(direction='in', labelleft=False)

t = 10
n = 2
cmap = matplotlib.colors.LinearSegmentedColormap.from_list(None, plt.cm.Set1(range(0, n)), n)
im = ax0.imshow(m > t, cmap=cmap)
ax0.set_xlabel('image')
ax0.set_ylabel('cluster label')

divider = make_axes_locatable(ax0)
cax = divider.append_axes('left', size='1%', pad=1)
cbar = fig.colorbar(im, ticks=range(n), cax=cax)
# cbar.set_lim(-0.5, n - 0.5)
cbar.ax.tick_params(length=0)
cbar.set_ticks([0.25, 0.75])
cbar.set_ticklabels([f'<= {t}', f'> {t}'])
cbar.ax.set_title('# cells')

# divider = make_axes_locatable(ax)
# cax = divider.append_axes('right', size='95%', pad=0)
def sum_treshold(v, threshold):
    return np.sum(v > threshold)
ax1.bar(np.arange(m.shape[1]), np.apply_along_axis(functools.partial(sum_treshold, threshold=t), 0, m))
ax1.set_xlim([0, m.shape[1]])

# divider = make_axes_locatable(ax)
# cax = divider.append_axes('bottom', size='95%', pad=0)
ax2.barh(np.arange(m.shape[0])[::-1], np.apply_along_axis(functools.partial(sum_treshold, threshold=t), 1, m), orientation='horizontal')
ax2.set_ylim([0, m.shape[0]])
plt.savefig('/data/l989o/a/so.png')
plt.style.use('default')

编辑 2 这是最终输出应该是什么样子的示例。为了获得这一点，我进行了疯狂的二进制搜索并设置了硬编码坐标（这当然只适用于我拥有的特定数据矩阵，而不是一般情况下）。

Answer 1

我不确定这是否会为您提供所需的确切布局，但也许这里的一些内容会有所帮助。

此答案使用gridspec 定义子图的相对比率，使用inset_axes 和transform 添加颜色条。 @Marc 的答案here 是一个很好的简单示例，说明如何使用gridspec，如果该部分令人困惑。

import matplotlib.pyplot as plt
%matplotlib inline
import matplotlib.gridspec as gridspec
import matplotlib.colors as mcolors
import numpy as np

m = np.random.rand(58, 226) * 20

fig = plt.figure(figsize=(20,8), constrained_layout=True)
gs = fig.add_gridspec(2, 3)
ax1 = fig.add_subplot(gs[0, 0:2])
ax2 = fig.add_subplot(gs[1, 0:2]) 
## can add these if you need to share axes:, sharex = ax1, sharey = ax1)
ax3 = fig.add_subplot(gs[:, -1])

ax1.bar(np.arange(m.shape[1]), np.arange(m.shape[1]))
vals = ax2.imshow(np.random.random((20,10)), cmap='rainbow', aspect='auto') 
## aspect = 'auto' follows the established gridpec space
## default for imshow is equal axis
ax3.barh(np.arange(m.shape[1]), np.arange(m.shape[1]))

cbax2=ax2.inset_axes([1.05,0,0.03,1], transform=ax2.transAxes)
## the inset axes inputs are x,y,width,height
## the transform "anchors" these relative to the ax2 axis
## so here we are saying start at 5% past the ax2 width; start at the bottom of ax2 (y=0);
### make the inset axis 3% as wide as the ax2 axis; and make it 100% as tall as the ax2 axis
cbar2=fig.colorbar(vals, cax=cbax2, format = '%1.2g', orientation='vertical')

根据评论更新： 这是否更接近您需要的答案？

import matplotlib.pyplot as plt
%matplotlib inline
import matplotlib.gridspec as gridspec
import matplotlib.colors as mcolors
import numpy as np
import functools

def sum_treshold(v, threshold):
    return np.sum(v > threshold)

m = np.random.rand(58, 226) * 20
t = 10
n = 2
cmap = mcolors.LinearSegmentedColormap.from_list(None, plt.cm.Set1(range(0, n)), n)

fig = plt.figure(figsize=(20,8), constrained_layout=True)
gs = fig.add_gridspec(2, 3)
ax1 = fig.add_subplot(gs[0, 0:2])
ax2 = fig.add_subplot(gs[1, 0:2], sharex = ax1, sharey = ax1)
ax3 = fig.add_subplot(gs[1, -1], sharey = ax1)

ax1.bar(np.arange(m.shape[1]), 
        np.apply_along_axis(functools.partial(sum_treshold, threshold=t), 0, m))
ax1.set_xlim([0, m.shape[1]])
im = ax2.imshow(m > t, cmap=cmap)
ax3.barh(np.arange(m.shape[0])[::-1], 
         np.apply_along_axis(functools.partial(sum_treshold, threshold=t), 1, m), 
         orientation='horizontal')
ax3.set_ylim([0, m.shape[0]])


cbax2=ax2.inset_axes([-0.10,0,0.03,1], transform=ax2.transAxes)
cbar2=fig.colorbar(im, cax=cbax2, format = '%1.2g', orientation='vertical')

Answer 2

由于您已经计算了轴的所有尺寸，您可以调整它们以遵循图像纵横比施加的限制。您需要通过更改height 或更改图形高度来更改ax0 的高度。如果纵横比在其他方面有误，则需要对宽度进行类似的处理。

要添加颜色条，您要么需要从一开始就预留一些空间，要么将其放置在右上角的空白处。

这是一个示例，现在包括左侧颜色栏的空间，底部移动到图的中心。子图之间的间距现在是相等的。

import matplotlib.pyplot as plt
import matplotlib
import numpy as np

plt.style.use('dark_background')

m = np.random.randn(25, 200).cumsum(axis=0).cumsum(axis=1)
m -= m.min()
m *= 20 / m.max()

# definitions for the axes
left, width = 0.1, 0.65
bottom, height = 0.1, 0.65
spacing = 0.005
width_2 = 0.2
height_1 = 0.2

fig_width, fig_height = 20, 8
aspect_m = m.shape[0] / m.shape[1]
aspect_rect = fig_height * height / (fig_width * width)
if aspect_m < aspect_rect:  # either reduce the fig_height, or reduce adapt rectangle height
    new_height = aspect_m * (fig_width * width) / fig_height
    # optionally increase height_1 and/or increase bottom
    bottom += (height - new_height) / 2
    height = new_height
else:  # similar for the width
    width = fig_height * height / fig_width / aspect_m
    # optionally increase width_2 and/or increase left
rect0 = [left, bottom, width, height]
rect1 = [left, bottom + height + spacing, width, height_1]
rect2 = [left + width + spacing * fig_height / fig_width, bottom, width_2, height]
rectcbar = [left - 0.06, bottom, 0.01, height]

fig = plt.figure(figsize=(fig_width, fig_height))

ax0 = plt.axes(rect0)
ax0.tick_params(direction='in', top=True, right=True)
ax1 = plt.axes(rect1)
ax1.tick_params(direction='in', labelbottom=False)
ax2 = plt.axes(rect2)
ax2.tick_params(direction='in', labelleft=False)
cbarax = plt.axes(rectcbar)

t = 10
cmap = matplotlib.colors.ListedColormap(['red', 'dodgerblue'])
im = ax0.imshow(m > t, cmap=cmap, origin='lower')
ax0.set_xlabel('image')
ax0.set_ylabel('cluster label')

cbar = fig.colorbar(im, cax=cbarax)
cbar.ax.tick_params(length=0)
cbar.set_ticks([0.25, 0.75])
cbar.ax.set_yticklabels([f'≤ {t}', f'> {t}'])
cbar.ax.set_title('# cells')

ax1.bar(np.arange(m.shape[1]), np.sum(m > t, axis=0))

ax2.barh(np.arange(m.shape[0]), np.sum(m > t, axis=1))

ax0.set_aspect('equal')
ax1.get_shared_x_axes().join(ax1, ax0)
ax2.get_shared_y_axes().join(ax2, ax0)
plt.show()