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1.5.12.10. A demo of 1D interpolationΒΆ

plot 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

import scipy as sp



linear_interp = sp.interpolate.interp1d(measured_time, measures)

interpolation_time = np.linspace(0, 1, 50)

linear_results = linear_interp(interpolation_time)

cubic_interp = sp.interpolate.interp1d(measured_time, measures, kind="cubic")

cubic_results = cubic_interp(interpolation_time)



# Plot the data and the interpolation

import matplotlib.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 0.071 seconds)

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