Python 在 matplotlib imshow 中调整网格线和刻度线
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Adjusting gridlines and ticks in matplotlib imshow
提问by Joe Bathelt
I'm trying to plot a matrix of values and would like to add gridlines to make the boundary between values clearer. Unfortunately, imshow decided to locate the tick marks in the middle of each voxel. Is it possible to
我正在尝试绘制一个值矩阵，并希望添加网格线以使值之间的边界更清晰。不幸的是，imshow 决定在每个体素的中间定位刻度线。是否有可能
a) remove the ticks but leave the label in the same location and
b) add gridlines between the pixel boundaries?
a) 删除刻度但将标签保留在同一位置和
b) 在像素边界之间添加网格线？
import matplotlib.pyplot as plt
import numpy as np
im = plt.imshow(np.reshape(np.random.rand(100), newshape=(10,10)),
interpolation='none', vmin=0, vmax=1, aspect='equal');
ax = plt.gca();
ax.set_xticks(np.arange(0, 10, 1));
ax.set_yticks(np.arange(0, 10, 1));
ax.set_xticklabels(np.arange(1, 11, 1));
ax.set_yticklabels(np.arange(1, 11, 1));
Image without the gridline and with tick marks in the wrong location
没有网格线且在错误位置有刻度线的图像
ax.grid(color='w', linestyle='-', linewidth=2)
Image with gridlines in the wrong location:
网格线位置错误的图像：
采纳答案by Serenity
Try to shift axes ticks:
尝试移动轴刻度：
ax = plt.gca()
ax.set_xticks(np.arange(-.5, 10, 1))
ax.set_yticks(np.arange(-.5, 10, 1))
ax.set_xticklabels(np.arange(1, 12, 1))
ax.set_yticklabels(np.arange(1, 12, 1))
回答by Joe Bathelt
Code for solution as suggested by Serenity:
Serenity 建议的解决方案代码：
plt.figure()
im = plt.imshow(np.reshape(np.random.rand(100), newshape=(10,10)),
interpolation='none', vmin=0, vmax=1, aspect='equal');
ax = plt.gca();
# Major ticks
ax.set_xticks(np.arange(0, 10, 1));
ax.set_yticks(np.arange(0, 10, 1));
# Labels for major ticks
ax.set_xticklabels(np.arange(1, 11, 1));
ax.set_yticklabels(np.arange(1, 11, 1));
# Minor ticks
ax.set_xticks(np.arange(-.5, 10, 1), minor=True);
ax.set_yticks(np.arange(-.5, 10, 1), minor=True);
# Gridlines based on minor ticks
ax.grid(which='minor', color='w', linestyle='-', linewidth=2)
Resulting image:
结果图像：
回答by Georgy
One can find it easier to use plt.pcoloror plt.pcolormesh:
人们可以发现它更易于使用plt.pcolor或plt.pcolormesh：
data = np.random.rand(10, 10)
plt.pcolormesh(data, edgecolors='k', linewidth=2)
ax = plt.gca()
ax.set_aspect('equal')
Though, there are some differences among them and plt.imshow, the most obvious being that the image is swapped by the Y-axis (you can reversed it back easily by adding ax.invert_yaxis()though). For further discussion see here: When to use imshow over pcolormesh?
但是，它们之间存在一些差异plt.imshow，最明显的是图像由 Y 轴交换（ax.invert_yaxis()尽管添加，您可以轻松地将其反转回来）。有关进一步讨论，请参见此处：何时在 pcolormesh 上使用 imshow？
回答by drammock
You can shift the pixels by passing the extentargument to imshow. extentis a 4-element list of scalars (left, right, bottom, top):
您可以通过将extent参数传递给 来移动像素imshow。extent是标量的 4 元素列表（左、右、下、上）：
foo = np.random.rand(35).reshape(5, 7)
# This keeps the default orientation (origin at top left):
extent = (0, foo.shape[1], foo.shape[0], 0)
_, ax = plt.subplots()
ax.imshow(foo, extent=extent)
ax.grid(color='w', linewidth=2)
ax.set_frame_on(False)
