import numpy as np
import matplotlib.pyplot as plt
t= np.arange(1000)/100.
x = np.sin(2*np.pi*10*t)
y = np.cos(2*np.pi*10*t)
fig=plt.figure()
ax1 = plt.subplot(211)
plt.plot(t,x)
ax2 = plt.subplot(212)
plt.plot(t,y)
# some code to share both x axis
plt.show()
I’m trying to share two subplots axis, but I need to share x axis after the figure was created.
So, for instance, I create this figure:
import numpy as np
import matplotlib.pyplot as plt
t= np.arange(1000)/100.
x = np.sin(2*np.pi*10*t)
y = np.cos(2*np.pi*10*t)
fig=plt.figure()
ax1 = plt.subplot(211)
plt.plot(t,x)
ax2 = plt.subplot(212)
plt.plot(t,y)
# some code to share both x axis
plt.show()
Instead of the comment I would insert some code to share both x axis.
I didn’t find any clue how i can do that. There are some attributes
_shared_x_axes and _shared_x_axes when i check to figure axis (fig.get_axes()) but I don’t know how to link them.
Sharing the axes after they have been created should therefore not be necessary.
However if for any reason, you need to share axes after they have been created (actually, using a different library which creates some subplots, like here, or sharing an inset axes might be a reason), there would still be a solution:
Using
ax1.get_shared_x_axes().join(ax1, ax2)
creates a link between the two axes, ax1 and ax2. In contrast to the sharing at creation time, you will have to set the xticklabels off manually for one of the axes (in case that is wanted).
A complete example:
import numpy as np
import matplotlib.pyplot as plt
t= np.arange(1000)/100.
x = np.sin(2*np.pi*10*t)
y = np.cos(2*np.pi*10*t)
fig=plt.figure()
ax1 = plt.subplot(211)
ax2 = plt.subplot(212)
ax1.plot(t,x)
ax2.plot(t,y)
ax1.get_shared_x_axes().join(ax1, ax2)
ax1.set_xticklabels([])
# ax2.autoscale() ## call autoscale if needed
plt.show()
How can I set the y axis range of the second subplot to e.g. [0,1000] ?
The FFT plot of my data (a column in a text file) results in a (inf.?) spike so that the actual data is not visible.
pylab.ylim([0,1000])
has no effect, unfortunately. This is the whole script:
# based on http://www.swharden.com/blog/2009-01-21-signal-filtering-with-python/
import numpy, scipy, pylab, random
xs = []
rawsignal = []
with open("test.dat", 'r') as f:
for line in f:
if line[0] != '#' and len(line) > 0:
xs.append( int( line.split()[0] ) )
rawsignal.append( int( line.split()[1] ) )
h, w = 3, 1
pylab.figure(figsize=(12,9))
pylab.subplots_adjust(hspace=.7)
pylab.subplot(h,w,1)
pylab.title("Signal")
pylab.plot(xs,rawsignal)
pylab.subplot(h,w,2)
pylab.title("FFT")
fft = scipy.fft(rawsignal)
#~ pylab.axis([None,None,0,1000])
pylab.ylim([0,1000])
pylab.plot(abs(fft))
pylab.savefig("SIG.png",dpi=200)
pylab.show()
Using axes objects is a great approach for this. It helps if you want to interact with multiple figures and sub-plots. To add and manipulate the axes objects directly:
Sometimes you really want to set the axes limits before you plot the data. In that case, you can set the “autoscaling” feature of the Axes or AxesSubplot object. The functions of interest are set_autoscale_on, set_autoscalex_on, and set_autoscaley_on.
In your case, you want to freeze the y axis’ limits, but allow the x axis to expand to accommodate your data. Therefore, you want to change the autoscaley_on property to False. Here is a modified version of the FFT subplot snippet from your code:
works as answered previously. But if you want a more flexible axis to fit your exact data, as I did when I found this question, then set axis limit to be the length of your dataset. If your dataset is fft as in the question, then add this after your plot command:
I have a plot with two y-axes, using twinx(). I also give labels to the lines, and want to show them with legend(), but I only succeed to get the labels of one axis in the legend:
I’m not sure if this functionality is new, but you can also use the get_legend_handles_labels() method rather than keeping track of lines and labels yourself:
From matplotlib version 2.1 onwards, you may use a figure legend. Instead of ax.legend(), which produces a legend with the handles from the axes ax, one can create a figure legend
fig.legend(loc="upper right")
which will gather all handles from all subplots in the figure. Since it is a figure legend, it will be placed at the corner of the figure, and the loc argument is relative to the figure.
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0,10)
y = np.linspace(0,10)
z = np.sin(x/3)**2*98
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(x,y, '-', label = 'Quantity 1')
ax2 = ax.twinx()
ax2.plot(x,z, '-r', label = 'Quantity 2')
fig.legend(loc="upper right")
ax.set_xlabel("x [units]")
ax.set_ylabel(r"Quantity 1")
ax2.set_ylabel(r"Quantity 2")
plt.show()
In order to place the legend back into the axes, one would supply a bbox_to_anchor and a bbox_transform. The latter would be the axes transform of the axes the legend should reside in. The former may be the coordinates of the edge defined by loc given in axes coordinates.
You can easily get what you want by adding the line in ax:
ax.plot([], [], '-r', label = 'temp')
or
ax.plot(np.nan, '-r', label = 'temp')
This would plot nothing but add a label to legend of ax.
I think this is a much easier way.
It’s not necessary to track lines automatically when you have only a few lines in the second axes, as fixing by hand like above would be quite easy. Anyway, it depends on what you need.
The whole code is as below:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rc
rc('mathtext', default='regular')
time = np.arange(22.)
temp = 20*np.random.rand(22)
Swdown = 10*np.random.randn(22)+40
Rn = 40*np.random.rand(22)
fig = plt.figure()
ax = fig.add_subplot(111)
ax2 = ax.twinx()
#---------- look at below -----------
ax.plot(time, Swdown, '-', label = 'Swdown')
ax.plot(time, Rn, '-', label = 'Rn')
ax2.plot(time, temp, '-r') # The true line in ax2
ax.plot(np.nan, '-r', label = 'temp') # Make an agent in ax
ax.legend(loc=0)
#---------------done-----------------
ax.grid()
ax.set_xlabel("Time (h)")
ax.set_ylabel(r"Radiation ($MJ\,m^{-2}\,d^{-1}$)")
ax2.set_ylabel(r"Temperature ($^\circ$C)")
ax2.set_ylim(0, 35)
ax.set_ylim(-20,100)
plt.show()
The plot is as below:
Update: add a better version:
ax.plot(np.nan, '-r', label = 'temp')
This will do nothing while plot(0, 0) may change the axis range.
One extra example for scatter
ax.scatter([], [], s=100, label = 'temp') # Make an agent in ax
ax2.scatter(time, temp, s=10) # The true scatter in ax2
ax.legend(loc=1, framealpha=1)
回答 4
可能适合您需求的快速技巧。
取下盒子的框架，然后手动将两个图例彼此相邻放置。像这样
ax1.legend(loc =(.75,.1), frameon =False)
ax2.legend( loc =(.75,.05), frameon =False)
I found an following official matplotlib example that uses host_subplot to display multiple y-axes and all the different labels in one legend. No workaround necessary. Best solution I found so far.
http://matplotlib.org/examples/axes_grid/demo_parasite_axes2.html
