缩放图形以显示长注释

Question

我在带有明显名称的分类热图上标注了颜色栏。绘制时，这些名称并不完全可见。 plt.tight_layout()破坏了元素的排列，有时不显示热图刻度线或不按比例缩放颜色条和热图。我如何在不引入其他问题的情况下自动增大身材？

这里是一个示例：

import numpy as np

from matplotlib import pyplot as plt
import seaborn as sns
import matplotlib

n_labs = 4
labels = np.floor(np.random.rand(10, 10) * n_labs)
names = np.array(['a'*20] * n_labs)

bounds = np.linspace(-0.5, n_labs - 0.5, n_labs + 1)
norm = matplotlib.colors.BoundaryNorm(bounds, n_labs)
fmt = matplotlib.ticker.FuncFormatter(
    lambda z, pos: names[norm(z)]
)

plt.figure()
plt.suptitle('Title')
sns.heatmap(
    labels, cmap=plt.get_cmap('copper', n_labs), square=True, linewidths=1, vmax=n_labs,
    cbar_kws=dict(
        ticks=np.arange(n_labs), format=fmt,
        boundaries=bounds, drawedges=True
    ),
)
plt.tight_layout()
plt.show()

不带tight_layout的结果：

Answer 1

在我看来，seaborn正在对边界框进行一些奇怪的计算；无论如何，我以前在plt.tight_layout()的“聪明猜测”中也遇到了很多问题，现在我通常使用gridspec对图形布局进行更细粒度的控制（控制多个图形的好处更多）子图）：

from matplotlib import gridspec

####
omitting your data code here  for simplicity...
####

fig=plt.figure(figsize=(3,3),dpi=96,) ##### figure size in inch
gs = gridspec.GridSpec(1, 1, ##### a 1x1 subplot grid
    left=0.1,right=0.7,     #####<—— play with these values, 
    bottom=0.0,top=1.0,     #####    they give the margins as percentage of the figure
    wspace=0.0,hspace=0.0,  #####<—- this controls additional padding around the subplot
)
ax = fig.add_subplot(gs[0]) ##### add the gridspec subplot gs[0]

ax.set_title('Title')
sns.heatmap(
    labels, cmap=plt.get_cmap('copper', n_labs), square=True, linewidths=1, vmax=n_labs,
    cbar_kws=dict(
        ticks=np.arange(n_labs), format=fmt,
        boundaries=bounds, drawedges=True,
        shrink=0.6
    ),

    ax=ax #####<—— it is important to let seaborn know which subplot to use
)

——————————

为了使事情或多或少地自动，请尝试像这样绘制：

fig,axes=plt.subplots(1,2, gridspec_kw = {'width_ratios':[20, 1]},figsize=(3,3))
cbar_ax=axes[1]
ax=axes[0]

ax.set_title('Title')
sns.heatmap(
    labels, cmap=plt.get_cmap('copper', n_labs), square=True, linewidths=1, vmax=n_labs,
    cbar_kws=dict(
        ticks=np.arange(n_labs), format=fmt,
        boundaries=bounds, drawedges=True,
        shrink=0.1,
    ),
    cbar_ax=cbar_ax, #####< it is important to let seaborn know which subplot to use
    ax=ax #####< it is important to let seaborn know which subplot to use
)


plt.tight_layout()    
plt.show()    

这里您要创建两个子图（ax和cbar_ax），其中width_ratio为20:1，然后告诉sns.heatmap实际使用这些轴。 plt.tight_layout()似乎工作得更好，并且尽可能自动运行，但是仍然会遇到问题（例如，通过设置figsize=(2,2)会抛出ValueError: left cannot be >= right