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java.lang.Objectedu.mines.jtk.dsp.CausalFilter
public class CausalFilter
A multi-dimensional causal filter that is linear and shift-invariant. The output samples of a causal filter depend only on present and past input samples. In two dimensions, causal filters are also called non-symmetric half-plane (NSHP) filters, and this notion of causal can be extended to higher dimensions.
A causal filter is a linear operator with a corresponding anti-causal transpose (adjoint) operator. A causal filter may have a causal inverse, and its transpose may have an anti-causal inverse.
A causal filter is a stable all-zero filter that may or may not be minimum-phase; that is, it may or may not have a causal stable inverse. That inverse is a recursive all-pole filter, as described by Claerbout, J., 1998, Multidimensional recursive filters via a helix: Geophysics, v. 63, n. 5, p. 1532-1541.
The filter and its transpose, inverse, and inverse-transpose may all be applied in-place; that is, the input and output arrays may be the same array.
| Constructor Summary | |
|---|---|
CausalFilter(int[] lag1)
Constructs a unit-impulse filter for specified lag1. |
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CausalFilter(int[] lag1,
float[] a)
Constructs a causal filter for specified lag1. |
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CausalFilter(int[] lag1,
int[] lag2)
Constructs a unit-impulse filter for specified lag1 and lag2. |
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CausalFilter(int[] lag1,
int[] lag2,
float[] a)
Constructs a causal filter for specified lag1 and lag2. |
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CausalFilter(int[] lag1,
int[] lag2,
int[] lag3)
Constructs a unit-impulse filter for specified lag1, lag2, and lag3. |
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CausalFilter(int[] lag1,
int[] lag2,
int[] lag3,
float[] a)
Constructs a causal filter for specified lag1, lag2, and lag3. |
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| Method Summary | |
|---|---|
void |
apply(float[][][] x,
float[][][] y)
Applies this filter. |
void |
apply(float[][] x,
float[][] y)
Applies this filter. |
void |
apply(float[] x,
float[] y)
Applies this filter. |
void |
applyInverse(float[][][] y,
float[][][] x)
Applies the inverse of this filter. |
void |
applyInverse(float[][] y,
float[][] x)
Applies the inverse of this filter. |
void |
applyInverse(float[] y,
float[] x)
Applies the inverse of this filter. |
void |
applyInverseTranspose(float[][][] y,
float[][][] x)
Applies the inverse transpose of this filter. |
void |
applyInverseTranspose(float[][] y,
float[][] x)
Applies the inverse transpose of this filter. |
void |
applyInverseTranspose(float[] y,
float[] x)
Applies the inverse transpose of this filter. |
void |
applyTranspose(float[][][] x,
float[][][] y)
Applies the transpose of this filter. |
void |
applyTranspose(float[][] x,
float[][] y)
Applies the transpose of this filter. |
void |
applyTranspose(float[] x,
float[] y)
Applies the transpose of this filter. |
void |
factorWilsonBurg(int maxiter,
float epsilon,
float[] r)
Wilson-Burg factorization for the specified 1-D auto-correlation. |
void |
factorWilsonBurg(int maxiter,
float epsilon,
float[][] r)
Wilson-Burg factorization for the specified 2-D auto-correlation. |
void |
factorWilsonBurg(int maxiter,
float epsilon,
float[][][] r)
Wilson-Burg factorization for the specified 3-D auto-correlation. |
float[] |
getA()
Gets a copy of the filter coefficients. |
int[] |
getLag1()
Gets a copy of the lags in the 1st dimension. |
int[] |
getLag2()
Gets a copy of the lags in the 2nd dimension. |
int[] |
getLag3()
Gets a copy of the lags in the 3rd dimension. |
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Constructor Detail |
|---|
public CausalFilter(int[] lag1)
For j=0 only, lag1[j] is zero. All lag1[j] must be non-negative.
lag1 - array of lags.
public CausalFilter(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 CausalFilter(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 CausalFilter(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 CausalFilter(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 CausalFilter(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.| Method Detail |
|---|
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)
May be applied in-place; input and output arrays may be the same.
x - input array.y - output array.
public void applyTranspose(float[] x,
float[] y)
May be applied in-place; input and output arrays may be the same.
x - input array.y - output array.
public void applyInverse(float[] y,
float[] x)
May be applied in-place; input and output arrays may be the same.
y - input array.x - output array.
public void applyInverseTranspose(float[] y,
float[] x)
May be applied in-place; input and output arrays may be the same.
y - input array.x - output array.
public void apply(float[][] x,
float[][] y)
May be applied in-place; input and output arrays may be the same.
x - input array.y - output array.
public void applyTranspose(float[][] x,
float[][] y)
May be applied in-place; input and output arrays may be the same.
x - input array.y - output array.
public void applyInverse(float[][] y,
float[][] x)
May be applied in-place; input and output arrays may be the same.
y - input array.x - output array.
public void applyInverseTranspose(float[][] y,
float[][] x)
May be applied in-place; input and output arrays may be the same.
y - input array.x - output array.
public void apply(float[][][] x,
float[][][] y)
May be applied in-place; input and output arrays may be the same.
x - input array.y - output array.
public void applyTranspose(float[][][] x,
float[][][] y)
May be applied in-place; input and output arrays may be the same.
x - input array.y - output array.
public void applyInverse(float[][][] y,
float[][][] x)
y - output array.x - input array.
May be applied in-place; input and output arrays may be the same.
public void applyInverseTranspose(float[][][] y,
float[][][] x)
May be applied in-place; input and output arrays may be the same.
y - output array.x - input array.
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