scipy.signal.chirp

scipy.signal.chirp(t, f0, t1, f1, method='linear', phi=0, vertex_zero=True, qshape=None)

Frequency-swept cosine generator.

In the following, ‘Hz’ should be interpreted as ‘cycles per time unit’; there is no assumption here that the time unit is one second. The important distinction is that the units of rotation are cycles, not radians.

Parameters :

t : ndarray

Times at which to evaluate the waveform.

f0 : float

Frequency (in Hz) at time t=0.

t1 : float

Time at which f1 is specified.

f1 : float

Frequency (in Hz) of the waveform at time t1.

method : {‘linear’, ‘quadratic’, ‘logarithmic’, ‘hyperbolic’}, optional

Kind of frequency sweep. If not given, linear is assumed. See Notes below for more details.

phi : float, optional

Phase offset, in degrees. Default is 0.

vertex_zero : bool, optional

This parameter is only used when method is ‘quadratic’. It determines whether the vertex of the parabola that is the graph of the frequency is at t=0 or t=t1.

qshape : str (deprecated)

If method is quadratic and qshape is not None, chirp() will use scipy.signal.waveforms.old_chirp to compute the wave form. This parameter is deprecated, and will be removed in SciPy 0.9.

Returns :

A numpy array containing the signal evaluated at ‘t’ with the requested :

time-varying frequency. More precisely, the function returns: :

cos(phase + (pi/180)*phi)

where `phase` is the integral (from 0 to t) of ``2*pi*f(t)``. :

``f(t)`` is defined below. :

See also

scipy.signal.waveforms.sweep_poly

Notes

There are four options for the method. The following formulas give the instantaneous frequency (in Hz) of the signal generated by chirp(). For convenience, the shorter names shown below may also be used.

linear, lin, li:

f(t) = f0 + (f1 - f0) * t / t1

quadratic, quad, q:

The graph of the frequency f(t) is a parabola through (0, f0) and (t1, f1). By default, the vertex of the parabola is at (0, f0). If vertex_zero is False, then the vertex is at (t1, f1). The formula is:

if vertex_zero is True:

f(t) = f0 + (f1 - f0) * t**2 / t1**2

else:

f(t) = f1 - (f1 - f0) * (t1 - t)**2 / t1**2

To use a more general quadratic function, or an arbitrary polynomial, use the function scipy.signal.waveforms.sweep_poly.

logarithmic, log, lo:

f(t) = f0 * (f1/f0)**(t/t1)

f0 and f1 must be nonzero and have the same sign.

This signal is also known as a geometric or exponential chirp.

hyperbolic, hyp:

f(t) = f0*f1*t1 / ((f0 - f1)*t + f1*t1)

f1 must be positive, and f0 must be greater than f1.