mtuq.Wavelet¶

class mtuq.Wavelet[source]¶

Bases: object

Source wavelet/source-time function base class

Most easily, an analytical expression can be used to define the wavelet, but a user-supplied time series or on-the-fly numerical procedure can also be used. By inheriting from this class and implementing the evaluate method, all of these differents types of wavelets can be defined.

Example

We can implement a Gaussian function with unit standard deviation as follows:

class SimpleGaussian(Wavelet):
    def evaluate(self, t):
        return ((2*np.pi)**0.5)**(-1.)*np.exp(-0.5*(t)**2.)

We can now evaluate SimpleGaussian on any given set, say, on the interval [-5, +5]:

wavelet = SimpleGaussian()

t = np.linspace(-5,. +5., 101)
y = wavelet.evaluate(t)

Or we can convolve it with an ObsPy trace:

from obspy import read
trace = read('http://examples.obspy.org/exaple_data.sac')[0]
convolved_trace = wavelet.convolve(trace)

Public Methods

`convolve`	Convolves ObsPy trace with given wavelet
`evaluate`	Evaluates wavelet at chosen points

Private Methods

Warning

Private methods are mainly for internal/developer use and their API might change without notice.

`_convolve_array`	Convolves NumPy array with given wavelet
`_evaluate_on_interval`	Evaluates wavelet on an interval about zero