public class MinimumPhaseFilter
extends java.lang.Object
Minimum-phase filters are generalized to multi-dimensional arrays as in Claerbout, J., 1998, Multidimensional recursive filters via a helix: Geophysics, v. 63, n. 5, p. 1532-1541.
Filter constructors do not ensure that specified lags and coefficients correspond to minimum-phase filters. If not minimum-phase, then the causal inverse and inverse-transpose filters are unstable.
Minimum-phase filters may be obtained through Wilson-Burg factorization of specified auto-correlations.
Constructor and Description |
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MinimumPhaseFilter(int[] lag1)
Constructs a unit-impulse filter for specified lag1.
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MinimumPhaseFilter(int[] lag1,
float[] a)
Constructs a minimum-phase filter for specified lag1.
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MinimumPhaseFilter(int[] lag1,
int[] lag2)
Constructs a unit-impulse filter for specified lag1 and lag2.
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MinimumPhaseFilter(int[] lag1,
int[] lag2,
float[] a)
Constructs a minimum-phase filter for specified lag1 and lag2.
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MinimumPhaseFilter(int[] lag1,
int[] lag2,
float[][] a)
Constructs indexed minimum-phase filters for specified lag1 and lag2.
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MinimumPhaseFilter(int[] lag1,
int[] lag2,
int[] lag3)
Constructs a unit-impulse filter for specified lag1, lag2, and lag3.
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MinimumPhaseFilter(int[] lag1,
int[] lag2,
int[] lag3,
float[] a)
Constructs a minimum-phase filter for specified lag1, lag2, and lag3.
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Modifier and Type | Method and Description |
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void |
apply(float[][][] x,
float[][][] y)
Applies this filter.
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void |
apply(float[][] x,
float[][] y)
Applies this filter.
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void |
apply(float[] x,
float[] y)
Applies this filter.
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void |
apply(int[][] i,
float[][] x,
float[][] y)
Applies this indexed filter.
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void |
applyInverse(float[][][] x,
float[][][] y)
Applies the inverse of this filter.
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void |
applyInverse(float[][] x,
float[][] y)
Applies the inverse of this filter.
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void |
applyInverse(float[] x,
float[] y)
Applies the inverse of this filter.
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void |
applyInverse(int[][] i,
float[][] x,
float[][] y)
Applies the inverse of this indexed filter.
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void |
applyInverseTranspose(float[][][] x,
float[][][] y)
Applies the inverse transpose of this filter.
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void |
applyInverseTranspose(float[][] x,
float[][] y)
Applies the inverse transpose of this filter.
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void |
applyInverseTranspose(float[] x,
float[] y)
Applies the inverse transpose of this filter.
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void |
applyInverseTranspose(int[][] i,
float[][] x,
float[][] y)
Applies the inverse transpose of this indexed filter.
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void |
applyTranspose(float[][][] x,
float[][][] y)
Applies the transpose of this filter.
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void |
applyTranspose(float[][] x,
float[][] y)
Applies the transpose of this filter.
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void |
applyTranspose(float[] x,
float[] y)
Applies the transpose of this filter.
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void |
applyTranspose(int[][] i,
float[][] x,
float[][] y)
Applies the transpose of this indexed filter.
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void |
factorWilsonBurg(int maxiter,
float epsilon,
float[] r)
Wilson-Burg factorization for the specified 1-D auto-correlation.
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void |
factorWilsonBurg(int maxiter,
float epsilon,
float[][] r)
Wilson-Burg factorization for the specified 2-D auto-correlation.
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void |
factorWilsonBurg(int maxiter,
float epsilon,
float[][][] r)
Wilson-Burg factorization for the specified 3-D auto-correlation.
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float[] |
getA()
Gets a copy of the filter coefficients.
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int[] |
getLag1()
Gets a copy of the lags in the 1st dimension.
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int[] |
getLag2()
Gets a copy of the lags in the 2nd dimension.
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int[] |
getLag3()
Gets a copy of the lags in the 3rd dimension.
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public MinimumPhaseFilter(int[] lag1)
For j=0 only, lag1[j] is zero. All lag1[j] must be non-negative.
lag1
- array of lags.public MinimumPhaseFilter(int[] lag1, int[] lag2)
For j=0 only, lag1[j] and lag2[j] are zero. All lag2[j] must be non-negative. If lag2[j] is zero, then lag1[j] must be non-negative.
lag1
- array of lags in 1st dimension.lag2
- array of lags in 2nd dimension.public MinimumPhaseFilter(int[] lag1, int[] lag2, int[] lag3)
For j=0 only, lag1[j] and lag2[j] and lag3[j] are zero. All lag3[j] must be non-negative. If lag3[j] is zero, then lag2[j] must be non-negative. If lag3[j] and lag2[j] are zero, then lag1[j] must be non-negative.
lag1
- array of lags in 1st dimension.lag2
- array of lags in 2nd dimension.lag3
- array of lags in 3rd dimension.public MinimumPhaseFilter(int[] lag1, float[] a)
For j=0 only, lag1[j] is zero. All lag1[j] must be non-negative.
lag1
- array of lags.a
- array of filter coefficients for each lag.public MinimumPhaseFilter(int[] lag1, int[] lag2, float[] a)
For j=0 only, lag1[j] and lag2[j] are zero. All lag2[j] must be non-negative. If lag2[j] is zero, then lag1[j] must be non-negative.
lag1
- array of lags in 1st dimension.lag2
- array of lags in 2nd dimension.a
- array of filter coefficients for each lag.public MinimumPhaseFilter(int[] lag1, int[] lag2, float[][] a)
For j=0 only, lag1[j] and lag2[j] are zero. All lag2[j] must be non-negative. If lag2[j] is zero, then lag1[j] must be non-negative.
lag1
- array of lags in 1st dimension.lag2
- array of lags in 2nd dimension.a
- array of filter coefficients for each index and lag.public MinimumPhaseFilter(int[] lag1, int[] lag2, int[] lag3, float[] a)
For j=0 only, lag1[j] and lag2[j] and lag3[j] are zero. All lag3[j] must be non-negative. If lag3[j] is zero, then lag2[j] must be non-negative. If lag3[j] and lag2[j] are zero, then lag1[j] must be non-negative.
lag1
- array of lags in 1st dimension.lag2
- array of lags in 2nd dimension.lag3
- array of lags in 3rd dimension.a
- array of filter coefficients for each lag.public int[] getLag1()
public int[] getLag2()
public int[] getLag3()
public float[] getA()
public void factorWilsonBurg(int maxiter, float epsilon, float[] r)
maxiter
- maximum number of Wilson-Burg iterations.epsilon
- tolerance for convergence. Iterations have converged
when the change in all filter coefficients is less than this factor
times the square root of the zero-lag of the auto correlation.r
- the auto-correlation. This 1-D array must have odd length.
The middle array element is the zero-lag of the auto-correlation,
and other elements are symmetric about the middle element.java.lang.IllegalStateException
- if Wilson-Burg iterations do not
converge within the specified maximum number of iterations.public void factorWilsonBurg(int maxiter, float epsilon, float[][] r)
maxiter
- maximum number of Wilson-Burg iterations.epsilon
- tolerance for convergence. Iterations have converged
when the change in all filter coefficients is less than this factor
times the square root of the zero-lag of the auto correlation.r
- the auto-correlation. This 2-D array must have odd lengths.
The middle array element is the zero-lag of the auto-correlation,
and other elements are symmetric about the middle element.java.lang.IllegalStateException
- if Wilson-Burg iterations do not
converge within the specified maximum number of iterations.public void factorWilsonBurg(int maxiter, float epsilon, float[][][] r)
maxiter
- maximum number of Wilson-Burg iterations.epsilon
- tolerance for convergence. Iterations have converged
when the change in all filter coefficients is less than this factor
times the square root of the zero-lag of the auto correlation.r
- the auto-correlation. This 3-D array must have odd lengths.
The middle array element is the zero-lag of the auto-correlation,
and other elements are symmetric about the middle element.java.lang.IllegalStateException
- if Wilson-Burg iterations do not
converge within the specified maximum number of iterations.public void apply(float[] x, float[] y)
x
- input array.y
- output array.public void apply(float[][] x, float[][] y)
x
- input array.y
- output array.public void apply(float[][][] x, float[][][] y)
x
- input array.y
- output array.public void apply(int[][] i, float[][] x, float[][] y)
i
- index array.x
- input array.y
- output array.public void applyTranspose(float[] x, float[] y)
x
- input array.y
- output array.public void applyTranspose(float[][] x, float[][] y)
x
- input array.y
- output array.public void applyTranspose(float[][][] x, float[][][] y)
x
- input array.y
- output array.public void applyTranspose(int[][] i, float[][] x, float[][] y)
i
- index array.x
- input array.y
- output array.public void applyInverse(float[] x, float[] y)
x
- input array.y
- output array.public void applyInverse(float[][] x, float[][] y)
x
- input array.y
- output array.public void applyInverse(float[][][] x, float[][][] y)
x
- input array.y
- output array.public void applyInverse(int[][] i, float[][] x, float[][] y)
i
- index array.x
- input array.y
- output array.public void applyInverseTranspose(float[] x, float[] y)
x
- input array.y
- output array.public void applyInverseTranspose(float[][] x, float[][] y)
x
- input array.y
- output array.public void applyInverseTranspose(float[][][] x, float[][][] y)
x
- input array.y
- output array.public void applyInverseTranspose(int[][] i, float[][] x, float[][] y)
i
- index array.x
- input array.y
- output array.