Merge matplotlib subplots with shared x-axis

Question

I have two graphs to where both have the same x-axis, but with different y-axis scalings.

The plot with regular axes is the data with a trend line depicting a decay while the y semi-log scaling depicts the accuracy of the fit.

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 figure out to implement both close together like the examples supplied by this link http://matplotlib.org/examples/pylab_examples/subplots_demo.html

In particular, this one

When looking at the code for the example, i'm a bit confused on how to implant 3 things:

1) Scaling the axes differently

2) Keeping the figure size the same for the exponential decay graph but having a the line graph have a smaller y size and same x size.

For example:

3) Keeping the label of the function to appear in just only 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 subplots gs = gridspec.GridSpec(2, 1, height_ratios=[2, 1])   # the first 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 legend 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()