How to plot arrays of different lengths

Question

How do you plot arrays of different lengths but extend properly on the x-axis? The code below generates 2 data sets, the second one being shorter. I run an interpolation over each set resampling the data with multiple samples per data point. When I plot all of the data the data sets that are shorter don't extend to the end of the plot. I don't want subplots, I need to overlay the data onto each other.

#!/usr/bin/env python3

from scipy import interpolate
import matplotlib.pyplot as plt
import numpy as np

num_points = 100

# Generate an array of data, interpolate, re-sample and graph
x1 = np.arange(0, num_points)
y1 = np.cos(x1)
f1 = interpolate.interp1d(x1, y1, kind='cubic')
xnew1 = np.arange(0, num_points - 1, 0.2)
ynew1 = f1(xnew1)  

plt.plot(x1, y1, color='g', label='input 1')
plt.plot(x1, y1, 'o', color='g')
plt.plot(xnew1, ynew1, color='m', label='interp 1')
plt.plot(xnew1, ynew1, '+', color='m')

# Generate ana array different size of data, interpolate, re-sample and graph
x2 = np.arange(0, num_points/2)
y2 = np.sin(x2)
f2 = interpolate.interp1d(x2, y2, kind='cubic')
xnew2 = np.arange(0, (num_points/2) - 1, 0.2)
ynew2 = f2(xnew2)  

plt.plot(x2, y2, color='k', label='input 2')
plt.plot(x2, y2, 'o', color='k')
plt.plot(xnew2, ynew2, color='r', label='interp 2')
plt.plot(xnew2, ynew2, '+', color='r')

plt.legend(loc='upper left')
plt.show()

Answer 1

If I am understanding correctly, this can be done by using two different axes which share the same y-axis, as outlined in this matplotlib example.

In your case you can accomplish this by making the following modifications:

from scipy import interpolate
import matplotlib.pyplot as plt
import numpy as np
num_points = 100

# Generate an array of data, interpolate, re-sample and graph
x1 = np.arange(0, num_points)
y1 = np.cos(x1)
f1 = interpolate.interp1d(x1, y1, kind='cubic')
xnew1 = np.arange(0, num_points - 1, 0.2)
ynew1 = f1(xnew1)  

fig, ax1 = plt.subplots()  # Create the first axis

ax1.plot(x1, y1, color='g', label='input 1')
ax1.plot(x1, y1, 'o', color='g')
ax1.plot(xnew1, ynew1, color='m', label='interp 1')
ax1.plot(xnew1, ynew1, '+', color='m')

ax2 = ax1.twiny()          # Create a twin which shares the y-axis

# Generate an array different size of data, interpolate, re-sample and graph
x2 = np.arange(0, num_points/2)
y2 = np.sin(x2)
f2 = interpolate.interp1d(x2, y2, kind='cubic')
xnew2 = np.arange(0, (num_points/2) - 1, 0.2)
ynew2 = f2(xnew2)  

ax2.plot(x2, y2, color='k', label='input 2')
ax2.plot(x2, y2, 'o', color='k')
ax2.plot(xnew2, ynew2, color='r', label='interp 2')
ax2.plot(xnew2, ynew2, '+', color='r')
plt.figlegend(loc='upper left', bbox_to_anchor=(0.065, 0.3, 0.5, 0.5))
plt.show()

This will give you something that looks like

Edit

In order to properly display the legend you can construct one legend for all the subplots, as outlined in this demo. Note that using this method will require some manhandling of the bounding box for the legend, and there are much cleaner ways to do this than specifying a 4-tuple of floats as I have in the line

plt.figlegend(loc='upper left', bbox_to_anchor=(0.065, 0.3, 0.5, 0.5))