问题描述
我目前正在尝试使用GridSpec在Matplotlib(Python 3.6,Matplotlib 2.0.0)中绘制许多子图.这是最小的工作示例:
I am currently trying to plot many subplots in Matplotlib (Python 3.6, Matplotlib 2.0.0) using GridSpec. Here is the minimal working example:
import matplotlib.pyplot as plt
from matplotlib.gridspec import *
# Color vector for scatter plot points
preds = np.random.randint(2, size=100000)
# Setup the scatter plots
fig = plt.figure(figsize=(8,8))
grid = GridSpec(9, 9)
# Create the scatter plots
for ii in np.arange(0, 9):
for jj in np.arange(0, 9):
if (ii > jj):
ax = fig.add_subplot(grid[ii, jj])
x = np.random.rand(100000)*2000
y = np.random.rand(100000)*2000
ax.scatter(x, y, c=preds)
这是未经任何修改的结果:
This is the result without any modifications:
当然子图之间的间距并不令人满意,所以我做了我通常做的事情并使用了tight_layout()
.但如下图所示,tight_layout()
无法接受地挤压了图的宽度:
Of course the spacing between subplots is unsatisfactory so I did what I usually do and used tight_layout()
. But as can be seen in the figure below, tight_layout()
squeezes the width of the plots unacceptably:
我认为我应该只使用 subplots_adjust()
手动调整子图,而不是使用 tight_layout()
.下图为subplots_adjust(hspace=1.0, wspace=1.0)
.
Instead of using tight_layout()
, I figured I should just adjust the subplots manually using subplots_adjust()
. Below is the figure with subplots_adjust(hspace=1.0, wspace=1.0)
.
结果几乎是正确的,再稍微调整一下子图之间的空间就完美了.然而,子图似乎太小而无法充分传达信息.
The result is almost correct, and with a little more tweaking the space between subplots would be perfect. However the subplots appear too small to adequately convey information.
在保持纵横比和足够大的子图尺寸的同时,是否有更好的方法在子图之间保持适当的间距?我能想到的唯一可能的解决方案是使用 subplots_adjust()
和更大的 figsize
,但这会导致图形边缘和子图.
Is there a better way to get proper spacing between subplots while still maintaining aspect ratio and a large enough subplot size? The only possible solution I could come up with was to use subplots_adjust()
with a larger figsize
, but this results in a very large space between the edges of the figure and the subplots.
任何解决方案都值得赞赏.
Any solutions are appreciated.
推荐答案
由于您所有的轴都具有相同的 x
和 y
范围,因此我选择显示对勾标签仅在外部 Axes
上.对于相同大小的子图网格,使用 plt.subplots()
的 sharex
和 sharey
关键字可以轻松实现自动化.当然,如果您设置9x9子图的网格,则可以提供比您想要的更多的图,但是您可以使冗余图不可见(例如,使用 Axes.set_visible
或完全删除它们).在下面的示例中,我将使用后者.
As all your axes have the same x
and y
ranges, I would choose to show the tick labels only on the outer Axes
. For a grid of equally-sized subplots, this is easily automated with the sharex
and sharey
keywords of plt.subplots()
. Of course, if you set up a grid of 9x9 subplots, that gives you more plots than you want, but you can either make the redundant plots invisible (for instance with Axes.set_visible
or remove them entirely. In the example below I go with the latter.
from matplotlib import pyplot as plt
import numpy as np
fig, axes = plt.subplots(
nrows=9, ncols=9, sharex=True, sharey=True, figsize = (8,8)
)
# Color vector for scatter plot points
preds = np.random.randint(2, size=1000)
# Create the scatter plots
for ii in np.arange(0, 9):
for jj in np.arange(0, 9):
if (ii > jj):
ax = axes[ii,jj]
x = np.random.rand(1000)*100
y = np.random.rand(1000)*2000
ax.scatter(x, y, c=preds)
else:
axes[ii,jj].remove() ##remove Axes from fig
axes[ii,jj] = None ##make sure that there are no 'dangling' references.
plt.show()
结果图形如下:
当然可以使用 subplots_adjust()
之类的东西进一步调整.希望这会有所帮助.
This can be of course adjusted further with something like subplots_adjust()
. Hope this helps.
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