Multiple imshow-subplots, each with colorbar

Question

I want to have a figure consisting of, let's say, four subplots. Two of them are usual line-plots, two of them imshow-images.

I can format the imshow-images to proper plots itself, because every single one of them needs its own colorbar, a modified axis and the other axis removed. This, however, seems to be absolutely useless for the subplotting. Can anyone help me with that?

I use this for displaying the data of the "regular" plots above as a colormap (by scaling the input-array i to [ i, i, i, i, i, i ] for 2D and calling imshow() with it).

The following code first displays what I need as a subplot and the second one shows all I can do, which is not sufficient.

#!/usr/bin/env python  import matplotlib.pyplot as plt from matplotlib.colors import LogNorm  s = { 't':1, 'x':[1,2,3,4,5,6,7,8], 'D':[0.3,0.5,0.2,0.3,0.5,0.5,0.3,0.4] } width = 40  # how I do it in just one plot tot = [] for i in range(width):     tot.append(s['D'])  plt.imshow(tot, norm=LogNorm(vmin=0.001, vmax=1)) plt.colorbar() plt.axes().axes.get_xaxis().set_visible(False) plt.yticks([0, 2, 4, 6], [s['x'][0], s['x'][2], s['x'][4], s['x'][6]])  plt.show()   f = plt.figure(figsize=(20,20))  plt.subplot(211) plt.plot(s['x'], s['D']) plt.ylim([0, 1])  #colorplot sp = f.add_subplot(212)  #reshape (just necessary to see something) tot = [] for i in range(width):     tot.append(s['D'])  sp.imshow(tot, norm=LogNorm(vmin=0.001, vmax=1))      #what I can't do now but needs to be done:     #sp.colorbar() #sp.axes().axes.get_xaxis().set_visible(False) #sp.yticks([0, 200, 400, 600, 800, 1000], [s['x'][0], s['x'][200], s['x'][400], s['x'][600], s['x'][800], s['x'][1000]])  plt.show() 

Answer 1

You can make use of matplotlibs object oriented interface rather than the state-machine interace in order to get better control over each axes. Also, to get control over the height/width of the colorbar you can make use of the AxesGrid toolkit of matplotlib.

For example:

import matplotlib.pyplot as plt import numpy as np from mpl_toolkits.axes_grid1 import make_axes_locatable from matplotlib.colors import LogNorm from matplotlib.ticker import MultipleLocator  s = {'t': 1,      'x': [1, 2, 3, 4, 5, 6, 7, 8],      'T': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8],      'D': [0.3, 0.5, 0.2, 0.3, 0.5, 0.5, 0.3, 0.4]}  width = 40  tot = np.repeat(s['D'],width).reshape(len(s['D']), width) tot2 = np.repeat(s['T'],width).reshape(len(s['D']), width)  fig, (ax1, ax2, ax3, ax4) = plt.subplots(1,4)  fig.suptitle('Title of figure', fontsize=20)  # Line plots ax1.set_title('Title of ax1') ax1.plot(s['x'], s['T']) ax1.set_ylim(0,1)  ax2.set_title('Title of ax2') ax2.plot(s['x'], s['D']) # Set locations of ticks on y-axis (at every multiple of 0.25) ax2.yaxis.set_major_locator(MultipleLocator(0.25)) # Set locations of ticks on x-axis (at every multiple of 2) ax2.xaxis.set_major_locator(MultipleLocator(2)) ax2.set_ylim(0,1)  ax3.set_title('Title of ax3') # Display image, `aspect='auto'` makes it fill the whole `axes` (ax3) im3 = ax3.imshow(tot, norm=LogNorm(vmin=0.001, vmax=1), aspect='auto') # Create divider for existing axes instance divider3 = make_axes_locatable(ax3) # Append axes to the right of ax3, with 20% width of ax3 cax3 = divider3.append_axes("right", size="20%", pad=0.05) # Create colorbar in the appended axes # Tick locations can be set with the kwarg `ticks` # and the format of the ticklabels with kwarg `format` cbar3 = plt.colorbar(im3, cax=cax3, ticks=MultipleLocator(0.2), format="%.2f") # Remove xticks from ax3 ax3.xaxis.set_visible(False) # Manually set ticklocations ax3.set_yticks([0.0, 2.5, 3.14, 4.0, 5.2, 7.0])  ax4.set_title('Title of ax4') im4 = ax4.imshow(tot2, norm=LogNorm(vmin=0.001, vmax=1), aspect='auto') divider4 = make_axes_locatable(ax4) cax4 = divider4.append_axes("right", size="20%", pad=0.05) cbar4 = plt.colorbar(im4, cax=cax4) ax4.xaxis.set_visible(False) # Manually set ticklabels (not ticklocations, they remain unchanged) ax4.set_yticklabels([0, 50, 30, 'foo', 'bar', 'baz'])  plt.tight_layout() # Make space for title plt.subplots_adjust(top=0.85) plt.show() 

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You can change the locations and labels of the ticks on either axis with the set_ticks and set_ticklabels methods as in the example above.

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As for what the make_axes_locatable function does, from the matplotlib site about the AxesGrid toolkit:

The axes_divider module provides a helper function make_axes_locatable, which can be useful. It takes a existing axes instance and create a divider for it.

ax = subplot(1,1,1) divider = make_axes_locatable(ax) 

make_axes_locatable returns an instance of the AxesLocator class, derived from the Locator. It provides append_axes method that creates a new axes on the given side of (“top”, “right”, “bottom” and “left”) of the original axes.