LTPDA Toolbox™ | contents | ![]() ![]() |
WHITEN1D whitens the input time-series. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% DESCRIPTION: WHITEN1D whitens the input time-series. The filter is built by fitting to the model provided. If no model is provided, a fit is made to a spectral-density estimate of the time-series (made using psd+bin_data or lpsd). Note: The function assumes that the input model corresponds to the one-sided psd of the data to be whitened. ALGORITHM: 1) If no model provided, make psd+bin_data or lpsd of time-series and take it as a model for the data power spectral density 2) Fit a set of partial fraction z-domain filters using utils.math.psd2wf. The fit is automatically stopped when the accuracy tolerance is reached. 3) Convert to array of MIIR filters 4) Assemble into a parallel filterbank object 5) Filter time-series in parallel CALL: b = whiten1D(a, pl) [b1,b2,...,bn] = whiten1D(a1,a2,...,an, pl); INPUT: - as are time-series analysis objects or a vector of analysis objects - pl is a plist with the input parameters OUTPUT: - bs "whitened" time-series AOs. The whitening filters used are stored in the objects procinfo field under the parameter 'Filter'. Parameters Description VERSION: $Id: whiten1D.m,v 1.43 2011/04/08 08:56:12 hewitson Exp $ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Method Details | |
---|---|
Access | public |
Defining Class | ao |
Sealed | 0 |
Static | 0 |
Sets for this method … |
---|
Default |
PSD |
LPSD |
Default |
|||
---|---|---|---|
no description | |||
Key | Default Value | Options | Description |
FLIM | [0.001 0.029999999999999999] | none | Band to calculate the scaling power |
SCALEOUT | 0 |
|
Scale your output by the inband power |
MODEL | [] | none | A frequency-series AO describing the model response to build the filter from. As an alternative, the user can choose a model estimation technique: |
RANGE | [] | none | The frequency range to evaluate the fitting. An empty value or [-inf inf] will include the whole range. The remaining part of the model will be completed according to the option chosen in the 'complete' parameter. |
COMPLETE_HF | 'flat' |
|
Choose how to complete the frequency range up to fs/2.
|
FS | [] | none | The sampling frequency to design the output filter on. If it is not a positive number, it will be taken from the model |
MAXITER | 30 | none | Maximum number of iterations in fit routine. |
POLETYPE | 1 |
|
Choose the pole type for fitting:
|
MINORDER | 2 | none | Minimum order to fit with. |
MAXORDER | 25 | none | Maximum order to fit with. |
WEIGHTS | '1/abs' |
|
Choose weighting method:
|
PLOT | 0 |
|
Plot results of each fitting step. |
DISP | 0 |
|
Display the progress of the fitting iteration. |
MSEVARTOL | 0.10000000000000001 | none | Mean Squared Error Variation - Check if the relative variation of the mean squared error is smaller than the value specified. This option is useful for finding the minimum of Chi-squared. |
FITTOL | 0.01 | none | Mean Squared Error Value - Check if the mean squared error value is lower than the value specified. |
PSD |
|||
---|---|---|---|
no description | |||
Key | Default Value | Options | Description |
FLIM | [0.001 0.029999999999999999] | none | Band to calculate the scaling power |
SCALEOUT | 0 |
|
Scale your output by the inband power |
MODEL | 'PSD' | none | A model estimation technique in the case of tsdata input: |
RANGE | [] | none | The frequency range to evaluate the fitting. An empty value or [-inf inf] will include the whole range. The remaining part of the model will be completed according to the option chosen in the 'complete' parameter. |
COMPLETE_HF | 'flat' |
|
Choose how to complete the frequency range up to fs/2.
|
FS | [] | none | The sampling frequency to design the output filter on. If it is not a positive number, it will be taken from the model |
MAXITER | 30 | none | Maximum number of iterations in fit routine. |
POLETYPE | 1 |
|
Choose the pole type for fitting:
|
MINORDER | 2 | none | Minimum order to fit with. |
MAXORDER | 25 | none | Maximum order to fit with. |
WEIGHTS | '1/abs' |
|
Choose weighting method:
|
PLOT | 0 |
|
Plot results of each fitting step. |
DISP | 0 |
|
Display the progress of the fitting iteration. |
MSEVARTOL | 0.10000000000000001 | none | Mean Squared Error Variation - Check if the relative variation of the mean squared error is smaller than the value specified. This option is useful for finding the minimum of Chi-squared. |
FITTOL | 0.01 | none | Mean Squared Error Value - Check if the mean squared error value is lower than the value specified. |
NFFT | -1 | none | The number of samples in each fft [default: length of input data]. A string value containing the variable 'fs' can also be used, e.g., plist('Nfft', '2*fs') |
WIN | 'BH92' |
|
The window to be applied to the data to remove the discontinuities at edges of segments. [default: taken from user prefs] Only the design parameters of the window object are used. Enter a string value containing the window name e.g. plist('Win', 'Kaiser', 'psll', 200) plist('Win', 'BH92') |
PSLL | 200 | none | The peak sidelobe level for Kaiser windows. Note: it is ignored for all other windows |
OLAP | 50 | none | The segment percent overlap [-1 == take from window function] |
ORDER | 1 | none | The order of segment detrending:
|
NAVS | 16 | none | Force number of averages. If set, and if Nfft was set to 0 or -1, the number of points for each window will be calculated to match the request. |
TIMES | [] | none | The time range to analyze. If not empty, sets the time interval to operate on. As in ao/split, the interval can be specified by:
|
SPLIT | [] | none | The time range to analyze. If not empty, sets the time interval to operate on. As in ao/split, the interval can be specified by:
|
SCALE | 'PSD' |
|
The scaling of output. Choose from:
|
METHOD | 'MEAN' | none | method for binning. Choose from:
|
XSCALE | 'LOG' |
|
scaling of binning. Choose from:
|
RESOLUTION | 50 | none | When setting logaritmic x scale, it sets the number of points per decade. When setting linear x scale, it sets the number of points. |
XVALS | [] | none | List of x values to evaluate the binning between. It may be a vector or an ao, in which case it will take the x field |
INHERIT_DY | 1 |
|
Choose what to do in the case of mean, and bins with only one point. Choose from:
|
LPSD |
|||
---|---|---|---|
no description | |||
Key | Default Value | Options | Description |
FLIM | [0.001 0.029999999999999999] | none | Band to calculate the scaling power |
SCALEOUT | 0 |
|
Scale your output by the inband power |
MODEL | 'LPSD' | none | A model estimation technique in the case of tsdata input: |
RANGE | [] | none | The frequency range to evaluate the fitting. An empty value or [-inf inf] will include the whole range. The remaining part of the model will be completed according to the option chosen in the 'complete' parameter. |
COMPLETE_HF | 'flat' |
|
Choose how to complete the frequency range up to fs/2.
|
FS | [] | none | The sampling frequency to design the output filter on. If it is not a positive number, it will be taken from the model |
MAXITER | 30 | none | Maximum number of iterations in fit routine. |
POLETYPE | 1 |
|
Choose the pole type for fitting:
|
MINORDER | 2 | none | Minimum order to fit with. |
MAXORDER | 25 | none | Maximum order to fit with. |
WEIGHTS | '1/abs' |
|
Choose weighting method:
|
PLOT | 0 |
|
Plot results of each fitting step. |
DISP | 0 |
|
Display the progress of the fitting iteration. |
MSEVARTOL | 0.10000000000000001 | none | Mean Squared Error Variation - Check if the relative variation of the mean squared error is smaller than the value specified. This option is useful for finding the minimum of Chi-squared. |
FITTOL | 0.01 | none | Mean Squared Error Value - Check if the mean squared error value is lower than the value specified. |
KDES | 100 | none | The desired number of averages. |
JDES | 1000 | none | The desired number of spectral frequencies to compute. |
LMIN | 0 | none | The minimum segment length. |
WIN | 'BH92' |
|
The window to be applied to the data to remove the discontinuities at edges of segments. [default: taken from user prefs] Only the design parameters of the window object are used. Enter a string value containing the window name e.g. plist('Win', 'Kaiser', 'psll', 200) plist('Win', 'BH92') |
PSLL | 200 | none | The peak sidelobe level for Kaiser windows. Note: it is ignored for all other windows |
OLAP | -1 | none | The segment percent overlap [-1 == take from window function] |
ORDER | 0 |
|
The order of segment detrending:
|
TIMES | [] | none | The time range to analyze. If not empty, sets the time interval to operate on. As in ao/split, the interval can be specified by:
|
SPLIT | [] | none | The time range to analyze. If not empty, sets the time interval to operate on. As in ao/split, the interval can be specified by:
|
SCALE | 'PSD' |
|
The scaling of output. Choose from:
|
Some information of the method ao/whiten1D are listed below: | |
---|---|
Class name | ao |
Method name | whiten1D |
Category | Signal Processing |
CVS Version | $Id: whiten1D.m,v 1.43 2011/04/08 08:56:12 hewitson Exp $ |
Min input args | 1 |
Max input args | -1 |
Min output args | 1 |
Max output args | -1 |
![]() |
Method: ao/upsample | Method: ao/whiten2D | ![]() |
©LTP Team