matplotlib: overlay plots with different scales?

问题:

So far I have the following code:

colors = ('k','r','b') ax = [] for i in range(3):     ax.append(plt.axes())     plt.plot(datamatrix[:,0],datamatrix[:,i],colors[i]+'o')     ax[i].set(autoscale_on=True) 

With the autoscale_on=True option for each axis, I thought each plot should have its own y-axis limits, but it appears they all share the same value (even if they share different axes). How do I set them to scale to show the range of each datamatrix[:,i] (just an explicit call to .set_ylim()?) And also, how can I create an offset y-axis for the third variable (datamatrix[:,2]) that might be required above? Thanks all.

回答1:

It sounds like what you're wanting is subplots... What you're doing now doesn't make much sense (Or I'm very confused by your code snippet, at any rate...).

Try something more like this:

import matplotlib.pyplot as plt import numpy as np  fig, axes = plt.subplots(nrows=3)  colors = ('k', 'r', 'b') for ax, color in zip(axes, colors):     data = np.random.random(1) * np.random.random(10)     ax.plot(data, marker='o', linestyle='none', color=color)  plt.show() 

Edit:

If you don't want subplots, your code snippet makes a lot more sense.

You're trying to add three axes right on top of each other. Matplotlib is recognizing that there's already a subplot in that exactly size and location on the figure, and so it's returning the same axes object each time. In other words, if you look at your list ax, you'll see that they're all the same object.

If you really want to do that, you'll need to reset fig._seen to an empty dict each time you add an axes. You probably don't really want to do that, however.

Instead of putting three independent plots over each other, have a look at using twinx instead.

E.g.

import matplotlib.pyplot as plt import numpy as np # To make things reproducible... np.random.seed(1977)  fig, ax = plt.subplots()  # Twin the x-axis twice to make independent y-axes. axes = [ax, ax.twinx(), ax.twinx()]  # Make some space on the right side for the extra y-axis. fig.subplots_adjust(right=0.75)  # Move the last y-axis spine over to the right by 20% of the width of the axes axes[-1].spines['right'].set_position(('axes', 1.2))  # To make the border of the right-most axis visible, we need to turn the frame # on. This hides the other plots, however, so we need to turn its fill off. axes[-1].set_frame_on(True) axes[-1].patch.set_visible(False)  # And finally we get to plot things... colors = ('Green', 'Red', 'Blue') for ax, color in zip(axes, colors):     data = np.random.random(1) * np.random.random(10)     ax.plot(data, marker='o', linestyle='none', color=color)     ax.set_ylabel('%s Thing' % color, color=color)     ax.tick_params(axis='y', colors=color) axes[0].set_xlabel('X-axis')  plt.show() 

回答2:

Bootstrapping something fast to chart multiple y-axes sharing an x-axis using @joe-kington's answer:

# d = Pandas Dataframe,  # ys = [ [cols in the same y], [cols in the same y], [cols in the same y], .. ]  def chart(d,ys):      from itertools import cycle     fig, ax = plt.subplots()      axes = [ax]     for y in ys[1:]:         # Twin the x-axis twice to make independent y-axes.         axes.append(ax.twinx())      extra_ys =  len(axes[2:])      # Make some space on the right side for the extra y-axes.     if extra_ys>0:         temp = 0.85         if extra_ys5:             print 'you are being ridiculous'         fig.subplots_adjust(right=temp)         right_additive = (0.98-temp)/float(extra_ys)     # Move the last y-axis spine over to the right by x% of the width of the axes     i = 1.     for ax in axes[2:]:         ax.spines['right'].set_position(('axes', 1.+right_additive*i))         ax.set_frame_on(True)         ax.patch.set_visible(False)         ax.yaxis.set_major_formatter(matplotlib.ticker.OldScalarFormatter())         i +=1.     # To make the border of the right-most axis visible, we need to turn the frame     # on. This hides the other plots, however, so we need to turn its fill off.      cols = []     lines = []     line_styles = cycle(['-','-','-', '--', '-.', ':', '.', ',', 'o', 'v', '^', '',                '1', '2', '3', '4', 's', 'p', '*', 'h', 'H', '+', 'x', 'D', 'd', '|', '_'])     colors = cycle(matplotlib.rcParams['axes.color_cycle'])     for ax,y in zip(axes,ys):         ls=line_styles.next()         if len(y)==1:             col = y[0]             cols.append(col)             color = colors.next()             lines.append(ax.plot(d[col],linestyle =ls,label = col,color=color))             ax.set_ylabel(col,color=color)             #ax.tick_params(axis='y', colors=color)             ax.spines['right'].set_color(color)         else:             for col in y:                 color = colors.next()                 lines.append(ax.plot(d[col],linestyle =ls,label = col,color=color))                 cols.append(col)             ax.set_ylabel(', '.join(y))             #ax.tick_params(axis='y')     axes[0].set_xlabel(d.index.name)     lns = lines[0]     for l in lines[1:]:         lns +=l     labs = [l.get_label() for l in lns]     axes[0].legend(lns, labs, loc=0)      plt.show() 

回答3:

Thanks to Joe Kington's answer I could come up with a solution for my requirement that all additional y-axis are on the left hand side of the graph.

I still would like to know how to do it correct, because it's just a work around:

import matplotlib.pyplot as plt import numpy as np # To make things reproducible... np.random.seed(1977)  fig, ax = plt.subplots()  # Twin the x-axis twice to make independent y-axes. axes = [ax, ax.twinx(), ax.twinx()]  # Make some space on the right side for the extra y-axis. fig.subplots_adjust(right=0.75)  # Move the last y-axis spine over to the right by 20% of the width of the axes axes[1].spines['right'].set_position(('axes', -0.25)) axes[2].spines['right'].set_position(('axes', -0.5))  # To make the border of the right-most axis visible, we need to turn the frame # on. This hides the other plots, however, so we need to turn its fill off. axes[-1].set_frame_on(True) axes[-1].patch.set_visible(False)  # And finally we get to plot things... colors = ('Green', 'Red', 'Blue') intAxNo = 0 for ax, color in zip(axes, colors):     intAxNo += 1     data = np.random.random(1) * np.random.random(10)     ax.plot(data, marker='o', linestyle='none', color=color)     if (intAxNo > 1):         if (intAxNo == 2):             ax.set_ylabel('%s Thing' % color, color=color, labelpad = -40 )         elif (intAxNo == 3):             ax.set_ylabel('%s Thing' % color, color=color, labelpad = -45 )         ax.get_yaxis().set_tick_params(direction='out')     else:         ax.set_ylabel('%s Thing' % color, color=color, labelpad = +0 )      ax.tick_params(axis='y', colors=color) axes[0].set_xlabel('X-axis')   plt.show() 

文章来源: matplotlib: overlay plots with different scales?