Post-processing filters

Post-processing filters are handled by the terapy.filters package.

TeraPy comes with the following post-processing filter modules:

ApodizationWindow

• Function: apodization window
• Dimension: 1
• Filter type: pre-transform

Properties	Type	Default	Description
type	int	0	type: 0.Boxcar, 1.Bartlett, 2.Blackmann, 3.Hamming, 4.Hanning, 5.Blackman-Harris, 6.Lanczos, 7.Custom
custom	str	exp(-4x**2)	custom window function (defined on [-1, 1])

Centering

• Function: center maximum value by zero-padding
• Dimension: 1
• Filter type: generic

Properties	Type	Default	Description

FourierTransform

• Function: Fourier transform
• Dimension: 1
• Filter type: transform

Properties	Type	Default	Description

Harminv

• Function: filter data by harmonic inversion
• Dimension: 1
• Filter type: generic

Properties	Type	Default	Description

HighPass

• Function: high pass window
• Dimension: 1
• Filter type: pre-transform

Properties	Type	Default	Description
type	int	0	type (see ApodizationWindow)
size	float	11.0	filter spread (%)
position	float	25.0	position offset (%)
relative	bool	True	if True, offset is relative to data maximum
custom	str	exp(-4x**2)	custom window function (defined on [-1, 1])

LombScargle

• Function: Lomb-Scargle periodogram
• Dimension: 1
• Filter type: transform

Properties	Type	Default	Description

LowPass

• Function: low pass window
• Dimension: 1
• Filter type: pre-transform

Properties	Type	Default	Description
type	int	0	type (see ApodizationWindow)
size	float	11.0	filter spread (%)
position	float	25.0	position offset (%)
relative	bool	True	if True, offset is relative to data maximum
custom	str	exp(-4x**2)	custom window function (defined on [-1, 1])

Multiply

• Function: multiply data by given factor
• Dimension: 1
• Filter type: generic

Properties	Type	Default	Description
factor	float	1.0	multiplication factor

Normalize

• Function: normalize data with reference data (interpolate if necessary)
• Dimension: 1
• Filter type: reference

Properties	Type	Default	Description
imethod	int	1	interpolation method: 0. nearest neighbor, 1. linear, 2. quadratic, 3. cubic

Offset

• Function: offset correction
• Dimension: 1
• Filter type: generic

Properties	Type	Default	Description
position	float	3.0	distance before main peak (%)
width	float	0.1	area on which to average (%)

Power

• Function: raise data to given power
• Dimension: 1
• Filter type: generic

Properties	Type	Default	Description
power	float	2.0	exponent

SavitzkyGolay

• Function: Savitzky-Golay smoothing
• Dimension: 1
• Filter type: generic

Properties	Type	Default	Description
size	int	11	filter size (number of data points)
order	int	5	polynomial order

UniformSampling

• Function: uniform resampling of data
• Dimension: 1
• Filter type: generic

Properties	Type	Default	Description
imethod	int	1	interpolation method: 0. nearest neighbor, 1. linear, 2. quadratic, 3. cubic

Welch

• Function: Welch periodogram
• Dimension: 1
• Filter type: transform

Properties	Type	Default	Description
type	int	0	window type: 0. Boxcar, 1. Triangular, 2. Blackman, 3. Hamming, 4. Hann, 5. Bartlett, 6. Flat top, 7. Parzen, 8. Bohman, 9. Blackman-Harris, 10. Nuttall, 11. Bartlett-Hann
length	float	20.0	window size (%)
overlap	float	50.0	overlap between windows (%)
scaling	int	0	scaling type: 0. density, 1. spectrum

WaveletDenoise

• Function: wavelet denoising
• Dimension: 1
• Filter type: generic
• Dependencies: pyWavelets, statsmodels, pandas

Properties	Type	Default	Description
type	str	bior6.8	wavelet code (see list in pywt.wavelist())
threshold	float	0.1	filtering threshold
auto_threshold	bool	True	if True, compute threshold automatically
thresholding	int	0	thresholding type: 0.Hard, 1.Soft

ZeroPadding

• Function: extend data to the next power of 2
• Dimension: 1
• Filter type: generic

Properties	Type	Default	Description