问题:我如何告诉Matplotlib创建第二个(新的)图,然后在旧的图上进行更新?
我想绘制数据,然后创建一个新图形并绘制数据2,最后回到原始绘制并绘制数据3,有点像这样:
import numpy as np
import matplotlib as plt
x = arange(5)
y = np.exp(5)
plt.figure()
plt.plot(x, y)
z = np.sin(x)
plt.figure()
plt.plot(x, z)
w = np.cos(x)
plt.figure("""first figure""") # Here's the part I need
plt.plot(x, w)
仅供参考,我如何告诉matplotlib我已经完成了一个情节?做类似的事情,但不完全相同!它并不允许我访问该原始图。
I want to plot data, then create a new figure and plot data2, and finally come back to the original plot and plot data3, kinda like this:
import numpy as np
import matplotlib as plt
x = arange(5)
y = np.exp(5)
plt.figure()
plt.plot(x, y)
z = np.sin(x)
plt.figure()
plt.plot(x, z)
w = np.cos(x)
plt.figure("""first figure""") # Here's the part I need
plt.plot(x, w)
FYI How do I tell matplotlib that I am done with a plot? does something similar, but not quite! It doesn’t let me get access to that original plot.
回答 0
如果您发现自己定期执行此类操作,则可能值得研究matplotlib的面向对象的接口。在您的情况下:
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(5)
y = np.exp(x)
fig1, ax1 = plt.subplots()
ax1.plot(x, y)
ax1.set_title("Axis 1 title")
ax1.set_xlabel("X-label for axis 1")
z = np.sin(x)
fig2, (ax2, ax3) = plt.subplots(nrows=2, ncols=1) # two axes on figure
ax2.plot(x, z)
ax3.plot(x, -z)
w = np.cos(x)
ax1.plot(x, w) # can continue plotting on the first axis
它稍微冗长一些,但是更容易跟踪,尤其是在几个具有多个子图的图形上。
If you find yourself doing things like this regularly it may be worth investigating the object-oriented interface to matplotlib. In your case:
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(5)
y = np.exp(x)
fig1, ax1 = plt.subplots()
ax1.plot(x, y)
ax1.set_title("Axis 1 title")
ax1.set_xlabel("X-label for axis 1")
z = np.sin(x)
fig2, (ax2, ax3) = plt.subplots(nrows=2, ncols=1) # two axes on figure
ax2.plot(x, z)
ax3.plot(x, -z)
w = np.cos(x)
ax1.plot(x, w) # can continue plotting on the first axis
It is a little more verbose but it’s much clearer and easier to keep track of, especially with several figures each with multiple subplots.
回答 1
调用时figure
,只需为图编号即可。
x = arange(5)
y = np.exp(5)
plt.figure(0)
plt.plot(x, y)
z = np.sin(x)
plt.figure(1)
plt.plot(x, z)
w = np.cos(x)
plt.figure(0) # Here's the part I need
plt.plot(x, w)
编辑:请注意,您可以根据需要对图进行编号(从此处开始0
),但是如果在创建新图形时根本不提供图形编号,则自动编号将以1
(“ Matlab Style”到文档)。
When you call figure
, simply number the plot.
x = arange(5)
y = np.exp(5)
plt.figure(0)
plt.plot(x, y)
z = np.sin(x)
plt.figure(1)
plt.plot(x, z)
w = np.cos(x)
plt.figure(0) # Here's the part I need
plt.plot(x, w)
Edit: Note that you can number the plots however you want (here, starting from 0
) but if you don’t provide figure with a number at all when you create a new one, the automatic numbering will start at 1
(“Matlab Style” according to the docs).
回答 2
但是,编号从开始1
,因此:
x = arange(5)
y = np.exp(5)
plt.figure(1)
plt.plot(x, y)
z = np.sin(x)
plt.figure(2)
plt.plot(x, z)
w = np.cos(x)
plt.figure(1) # Here's the part I need, but numbering starts at 1!
plt.plot(x, w)
同样,如果图形上有多个轴(例如子图),请使用axes(h)
命令where h
是所需轴对象的句柄来集中于该轴。
(尚无评论权限,对不起,新答案!)
However, numbering starts at 1
, so:
x = arange(5)
y = np.exp(5)
plt.figure(1)
plt.plot(x, y)
z = np.sin(x)
plt.figure(2)
plt.plot(x, z)
w = np.cos(x)
plt.figure(1) # Here's the part I need, but numbering starts at 1!
plt.plot(x, w)
Also, if you have multiple axes on a figure, such as subplots, use the axes(h)
command where h
is the handle of the desired axes object to focus on that axes.
(don’t have comment privileges yet, sorry for new answer!)
回答 3
经过一番努力后,我发现的一种方法是创建一个函数,该函数以data_plot矩阵,文件名和顺序作为参数,以根据顺序图中的给定数据(不同的顺序=不同的图)创建箱形图并将其保存在给定的file_name下。
def plotFigure(data_plot,file_name,order):
fig = plt.figure(order, figsize=(9, 6))
ax = fig.add_subplot(111)
bp = ax.boxplot(data_plot)
fig.savefig(file_name, bbox_inches='tight')
plt.close()
One way I found after some struggling is creating a function which gets data_plot matrix, file name and order as parameter to create boxplots from the given data in the ordered figure (different orders = different figures) and save it under the given file_name.
def plotFigure(data_plot,file_name,order):
fig = plt.figure(order, figsize=(9, 6))
ax = fig.add_subplot(111)
bp = ax.boxplot(data_plot)
fig.savefig(file_name, bbox_inches='tight')
plt.close()