Example: Fourier Series¶
Suppose we want to fit a Fourier series to a dataset. As an example, let’s take a step function:
\[\begin{split}f(x) = \begin{cases} 0 & \text{if}\quad - \pi < x \leq 0 \\
1 & \text{if}\quad 0 < x < \pi
\end{cases}\end{split}\]
In the example below, we will attempt to fit this with a Fourier Series of order \(n=3\).
\[y(x) = a_0 + \sum_{i=1}^n a_i cos(i \omega x)
+ \sum_{i=1}^n b_i sin(i \omega x)\]
from symfit import parameters, variables, sin, cos, Fit
import numpy as np
import matplotlib.pyplot as plt
def fourier_series(x, f, n=0):
"""
Returns a symbolic fourier series of order `n`.
:param n: Order of the fourier series.
:param x: Independent variable
:param f: Frequency of the fourier series
"""
# Make the parameter objects for all the terms
a0, *cos_a = parameters(','.join(['a{}'.format(i) for i in range(0, n + 1)]))
sin_b = parameters(','.join(['b{}'.format(i) for i in range(1, n + 1)]))
# Construct the series
series = a0 + sum(ai * cos(i * f * x) + bi * sin(i * f * x)
for i, (ai, bi) in enumerate(zip(cos_a, sin_b), start=1))
return series
x, y = variables('x, y')
w, = parameters('w')
model_dict = {y: fourier_series(x, f=w, n=3)}
print(model_dict)
# Make step function data
xdata = np.linspace(-np.pi, np.pi)
ydata = np.zeros_like(xdata)
ydata[xdata > 0] = 1
# Define a Fit object for this model and data
fit = Fit(model_dict, x=xdata, y=ydata)
fit_result = fit.execute()
print(fit_result)
# Plot the result
plt.plot(xdata, ydata)
plt.plot(xdata, fit.model(x=xdata, **fit_result.params).y, ls=':')
plt.xlabel('x')
plt.ylabel('y')
plt.show()
This code prints:
{y: a0 + a1*cos(w*x) + a2*cos(2*w*x) + a3*cos(3*w*x) + b1*sin(w*x) + b2*sin(2*w*x) + b3*sin(3*w*x)}
Parameter Value Standard Deviation
a0 5.000000e-01 2.075395e-02
a1 -4.903805e-12 3.277426e-02
a2 5.325068e-12 3.197889e-02
a3 -4.857033e-12 3.080979e-02
b1 6.267589e-01 2.546980e-02
b2 1.986491e-02 2.637273e-02
b3 1.846406e-01 2.725019e-02
w 8.671471e-01 3.132108e-02
Fitting status message: Optimization terminated successfully.
Number of iterations: 44
Regression Coefficient: 0.9401712713086535