hiperwalk.ContinuousTime.fit_sin_squared#

static ContinuousTime.fit_sin_squared(x, y)#

Fit data to the squared sine function.

Parameters:

xnumpy.ndarray: The domain values. It is assumed that the entries are evenly spaced. That is, x[i + 1] - x[i] == x[j + 1] - x[j] for any valid i and j.
ynumpy.ndarray: The image values evaluated at each x[i]. It is required that y[i] corresponds to the evaluation at x[i].

Returns:

ddict

It contains information about the best fit found. The dictionary keys are

fit function: A pointer to the obtained sine squared function. Issuing d["fit function"](theta) evaluates the function at point theta.
amplitude:: Amplitude of the obtained fit function.
angular frequency: Angular frequency of the obtained fit function.
phase shift: Phase shift of the obtained fit function.
vertical offset: Vertical offset of the obtained fit function.
frequency: Frequency of the obtained fit function.
period: Period of the obtained fit function.

Notes

The returned d["fit function"] is a pointer to

def fit_func(theta):
    return d["amplitude"]*np.sin(
               d["angular frequency"]*theta +
               d["phase shift"]
           )**2 + d["vertical offset"]

The code was adapted from https://stackoverflow.com/questions/16716302/how-do-i-fit-a-sine-curve-to-my-data-with-pylab-and-numpy