Filter Plugins

Kst has a number of built-in filters.

Generic Filter

This plugin filters a set of data by using the bilinear transformation formula.

Inputs

Y (vector): The array of data to filter
Sampling interval(s) (scalar): The time periods for sampling of the input vector, reciprocal of the sampling frequency
Numerator/Denominator(increasing order) (string): A string of numbers separated by commas to indicate the coefficients of the polynomial as numerator/denominator in the transform formula. For example, if you have a polynomial: 2*z^2 + 3*z + 5, you will need to specify the string: 2,3,5. To input the string, click on the icon to construct a string with value 2,3,5

Outputs

Filtered (vector): an array of filtered data

Band-pass filter

The Butterworth band-pass plugin filters a set of data by calculating the Fourier transform of the data and scaling the spectral amplitudes using the following function G(f)

Bandpass formula

where f is the frequency, f_c is the central frequency to filter around, b is the bandwidth to pass, and n is the order of the Butterworth filter. The inverse Fourier transform is then calculated using the scaled spectral amplitudes to give a filtered dataset.

Inputs

Y (vector): The array of values to filter.
Order (scalar): The order of the Butterworth filter to use.
Central frequency/sample rate (scalar): The frequency to filter around.
Band width (scalar): The width of the band to pass. This should be the difference between the desired high cutoff frequency and the low cutoff frequency.

Outputs

Filtered (vector): The array of filtered data values.

Band-stop filter

The Butterworth band-stop effectively removes signals in a given frequency range from an input vector. The options are the same as for the Band-pass filter except that the scaling function G(f) which is used is now given by:

Bandstop formula

High-pass filter

The Butterworth high-pass plugin effectively removes signals below a given cutoff frequency from a data vector. It does this by by calculating the Fourier transform of the data and rescaling the the spectral amplitudes using the following following function G(f)

Highpass formula

where f is the frequency, f_c is the high frequency cutoff, and n is the order of the Butterworth filter. The inverse Fourier transform is then calculated using the new filtered frequency responses.

Inputs

Y (vector): The array of values to filter.
Order (scalar): The order of the Butterworth filter to use.
Cutoff frequency/sample rate (scalar): The cutoff frequency (f_c).

Outputs

Filtered (vector): The array of filtered data values.

Low-pass filter

The Butterworth low-pass filter effectively removes signals above a given frequency cutoff from an input vector. The options are the same as for the High-pass filter except that the scaling function G(f) which is used is now given by:

Autocorrelation formula

Despike Filter

This plugin is used to find and remove spikes in a data set.

Inputs

Y (vector): Select the data vector needed to be smoothed.
Spacing (scalar): Specify the width of the spikes.
NSigma (scalar): If a data point value varies from its neighboring data points by NSigma times more, it will be counted as a spike.

Outputs

Despiked (vector): This plugin outputs a smoothed data set with all specified spikes removed.