Combine matplotlib subtitles with a common x axis

Question

I have two graphs in which both have the same X axis, but with different scaling along the Y axis.

A plot with regular axes is data with a trend line depicting decay, while y-semi-logarithmic scaling displays fit accuracy.

fig1 = plt.figure(figsize=(15,6)) ax1 = fig1.add_subplot(111) # Plot of the decay model ax1.plot(FreqTime1,DecayCount1, '.', color='mediumaquamarine') # Plot of the optimized fit ax1.plot(x1, y1M, '-k', label='Fitting Function: $f(t) = %.3f e^{%.3f\t} \ %+.3f$' % (aR1,kR1,bR1)) ax1.set_xlabel('Time (sec)') ax1.set_ylabel('Count') ax1.set_title('Run 1 of Cesium-137 Decay') # Allows me to change scales # ax1.set_yscale('log') ax1.legend(bbox_to_anchor=(1.0, 1.0), prop={'size':15}, fancybox=True, shadow=True) 

Now, I'm trying to understand how to implement both close ones, like the examples provided by this link http://matplotlib.org/examples/pylab_examples/subplots_demo.html

In particular, this

When looking at the code for an example, I got a little confused about how to implant 3 things:

1) Axis scaling in different ways

2) Saving the size of the figure is the same for the exponential decay graph, but with a line graph they have a smaller size y and the same size x.

For instance:

3) The storage of the function label appears only in the decay graph.

Any help would be most appreciated.

Answer 1

Look at the code and comments in it:

 import matplotlib.pyplot as plt import numpy as np from matplotlib import gridspec # Simple data to display in various forms x = np.linspace(0, 2 * np.pi, 400) y = np.sin(x ** 2) fig = plt.figure() # set height ratios for sublots gs = gridspec.GridSpec(2, 1, height_ratios=[2, 1]) # the fisrt subplot ax0 = plt.subplot(gs[0]) # log scale for axis Y of the first subplot ax0.set_yscale("log") line0, = ax0.plot(x, y, color='r') #the second subplot # shared axis X ax1 = plt.subplot(gs[1], sharex = ax0) line1, = ax1.plot(x, y, color='b', linestyle='--') plt.setp(ax0.get_xticklabels(), visible=False) # remove last tick label for the second subplot yticks = ax1.yaxis.get_major_ticks() yticks[-1].label1.set_visible(False) # put lened on first subplot ax0.legend((line0, line1), ('red line', 'blue line'), loc='lower left') # remove vertical gap between subplots plt.subplots_adjust(hspace=.0) plt.show()