Signal Processing Utilities

Functions

• signals.corrvis() … Visualization for cross- and auto-correlation

• signals.pSpect() … Simple power spectrum from FFT

• signals.savgol() … Savitzky-Golay filter

• signals.show_se() … Show mean and standard error, of a dataset in column form.

Details

Utilities for signal processing

signals.corrvis(x, y)

Visualize correlation, by calculating the cross-correlation of two signals. The aligned signals and the resulting cross correlation value are shown, and advanced when the user hits a key or clicks with the mouse.

Parameters

Xarray (N,)

Comparison signal

Yarray (M,)

Reference signal

Notes

Based on an idea from dpwe@ee.columbia.edu

Examples

>>> x = np.r_[0:2*pi:10j]
>>> y = np.sin(x)
>>> corrvis(y,y)

signals.pSpect(data, rate)

Power spectrum and frequency

Parameters

dataarray, shape (N,)

measurement data

ratefloat

sampling rate [Hz]

Returns

powerspectrumarray, shape (N,)

frequencyarray, shape (N,)

signals.show_se(raw)

Show mean and standard error, of a dataset in column form.

Parameters

rawarray (N,M)

input data, M sets of N data points

Returns

avgarray (N,)

average value

searray (N,)

standard error

Notes

Examples

>>> t = np.arange(0,20,0.1)
>>> x = np.sin(t)
>>> data = []
>>> for ii in range(10):
>>>     data.append(x + np.random.randn(len(t)))
>>> show_se(np.array(data).T)