1.6.12.10. A demo of 1D interpolation

# Generate data
import numpy as np
np.random.seed(0)
measured_time = np.linspace(0, 1, 10)
noise = 1e-1 * (np.random.random(10)*2 - 1)
measures = np.sin(2 * np.pi * measured_time) + noise

# Interpolate it to new time points
from scipy.interpolate import interp1d
linear_interp = interp1d(measured_time, measures)
interpolation_time = np.linspace(0, 1, 50)
linear_results = linear_interp(interpolation_time)
cubic_interp = interp1d(measured_time, measures, kind='cubic')
cubic_results = cubic_interp(interpolation_time)

# Plot the data and the interpolation
from matplotlib import pyplot as plt
plt.figure(figsize=(6, 4))
plt.plot(measured_time, measures, 'o', ms=6, label='measures')
plt.plot(interpolation_time, linear_results, label='linear interp')
plt.plot(interpolation_time, cubic_results, label='cubic interp')
plt.legend()
plt.show()

Total running time of the script: ( 0 minutes 0.018 seconds)