如何在一个图形中正确显示多个图像? [英] How to display multiple images in one figure correctly?
问题描述
我正在尝试在单个图形上显示20个随机图像.图像确实已显示，但是它们被覆盖了.我正在使用:
I am trying to display 20 random images on a single Figure. The images are indeed displayed, but they are overlaid. I am using:
import numpy as np
import matplotlib.pyplot as plt
w=10
h=10
fig=plt.figure()
for i in range(1,20):
img = np.random.randint(10, size=(h,w))
fig.add_subplot(i,2,1)
plt.imshow(img)
plt.show()
我希望它们自然出现在网格布局(例如4x5)中，并且每个都具有相同的大小.问题的部分原因是我不知道add_subplot的参数是什么意思.文档指出，参数是行数，列数和图号.没有定位参数.另外，地块号只能是1或2.如何实现?
I would like them to appear naturally in a grid layout (say 4x5), each with the same size. Part of the problem is that I do not know what the arguments to add_subplot mean. The documentation states that the arguments are the number of rows, number of columns, and plot number. There is no positioning argument. Additionally, the plot number can only be 1 or 2. How can I achieve this?
推荐答案
以下是我可以尝试的方法:
Here is my approach that you may try:
import numpy as np
import matplotlib.pyplot as plt
w=10
h=10
fig=plt.figure(figsize=(8, 8))
columns = 4
rows = 5
for i in range(1, columns*rows +1):
img = np.random.randint(10, size=(h,w))
fig.add_subplot(rows, columns, i)
plt.imshow(img)
plt.show()
结果图像:
(原始答复日期:17年10月7日，4:20)
(Original answer date: Oct 7 '17 at 4:20)
编辑1
由于这个答案很受欢迎，超出了我的预期.而且，我看到需要进行一些小的更改以使各个图的操作具有灵活性.因此，我将这个新版本提供给原始代码. 本质上，它提供:-
Since this answer is popular beyond my expectation. And I see that a small change is needed to enable flexibility for the manipulation of the individual plots. So that I offer this new version to the original code. In essence, it provides:-
- 访问子图的各个轴
- 可以在选定的轴/子图上绘制更多特征
新代码:
import numpy as np
import matplotlib.pyplot as plt
w = 10
h = 10
fig = plt.figure(figsize=(9, 13))
columns = 4
rows = 5
# prep (x,y) for extra plotting
xs = np.linspace(0, 2*np.pi, 60) # from 0 to 2pi
ys = np.abs(np.sin(xs)) # absolute of sine
# ax enables access to manipulate each of subplots
ax = []
for i in range(columns*rows):
img = np.random.randint(10, size=(h,w))
# create subplot and append to ax
ax.append( fig.add_subplot(rows, columns, i+1) )
ax[-1].set_title("ax:"+str(i)) # set title
plt.imshow(img, alpha=0.25)
# do extra plots on selected axes/subplots
# note: index starts with 0
ax[2].plot(xs, 3*ys)
ax[19].plot(ys**2, xs)
plt.show() # finally, render the plot
结果图:
编辑2
在前面的示例中，该代码提供了对具有单个索引的子图的访问，当图形具有许多行/列的子图时，这很不方便.这是它的替代方案.下面的代码使用[row_index][column_index]提供对子图的访问，这更适合于处理许多子图的数组.
In the previous example, the code provides access to the sub-plots with single index, which is inconvenient when the figure has many rows/columns of sub-plots. Here is an alternative of it. The code below provides access to the sub-plots with [row_index][column_index], which is more suitable for manipulation of array of many sub-plots.
import matplotlib.pyplot as plt
import numpy as np
# settings
h, w = 10, 10 # for raster image
nrows, ncols = 5, 4 # array of sub-plots
figsize = [6, 8] # figure size, inches
# prep (x,y) for extra plotting on selected sub-plots
xs = np.linspace(0, 2*np.pi, 60) # from 0 to 2pi
ys = np.abs(np.sin(xs)) # absolute of sine
# create figure (fig), and array of axes (ax)
fig, ax = plt.subplots(nrows=nrows, ncols=ncols, figsize=figsize)
# plot simple raster image on each sub-plot
for i, axi in enumerate(ax.flat):
# i runs from 0 to (nrows*ncols-1)
# axi is equivalent with ax[rowid][colid]
img = np.random.randint(10, size=(h,w))
axi.imshow(img, alpha=0.25)
# get indices of row/column
rowid = i // ncols
colid = i % ncols
# write row/col indices as axes' title for identification
axi.set_title("Row:"+str(rowid)+", Col:"+str(colid))
# one can access the axes by ax[row_id][col_id]
# do additional plotting on ax[row_id][col_id] of your choice
ax[0][2].plot(xs, 3*ys, color='red', linewidth=3)
ax[4][3].plot(ys**2, xs, color='green', linewidth=3)
plt.tight_layout(True)
plt.show()
结果图:
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