Perform a nlevel DTCWT decompostion on a 1D column vector X (or on the columns of a matrix X).
Parameters:  

Returns Yl:  The real lowpass image from the final level 
Returns Yh:  A tuple containing the (N, M, 6) shape complex highpass subimages for each level. 
Returns Yscale:  If include_scale is True, a tuple containing real lowpass coefficients for every scale. 
If biort or qshift are strings, they are used as an argument to the biort() or qshift() functions. Otherwise, they are interpreted as tuples of vectors giving filter coefficients. In the biort case, this should be (h0o, g0o, h1o, g1o). In the qshift case, this should be (h0a, h0b, g0a, g0b, h1a, h1b, g1a, g1b).
Example:
# Performs a 5level transform on the real image X using the 13,19tap
# filters for level 1 and the Qshift 14tap filters for levels >= 2.
Yl, Yh = dtwavexfm(X,5,'near_sym_b','qshift_b')
Perform an nlevel dualtree complex wavelet (DTCWT) 1D reconstruction.
Parameters:  

Returns Z:  Reconstructed real signal vector (or matrix). 
The lth element of gain_mask is gain for wavelet subband at level l. If gain_mask[l] == 0, no computation is performed for band l. Default gain_mask is all ones. Note that l is 0indexed.
If biort or qshift are strings, they are used as an argument to the biort() or qshift() functions. Otherwise, they are interpreted as tuples of vectors giving filter coefficients. In the biort case, this should be (h0o, g0o, h1o, g1o). In the qshift case, this should be (h0a, h0b, g0a, g0b, h1a, h1b, g1a, g1b).
Example:
# Performs a reconstruction from Yl,Yh using the 13,19tap filters
# for level 1 and the Qshift 14tap filters for levels >= 2.
Z = dtwaveifm(Yl, Yh, 'near_sym_b', 'qshift_b')
Perform a nlevel DTCWT2D decompostion on a 2D matrix X.
Parameters:  

Returns Yl:  The real lowpass image from the final level 
Returns Yh:  A tuple containing the (N, M, 6) shape complex highpass subimages for each level. 
Returns Yscale:  If include_scale is True, a tuple containing real lowpass coefficients for every scale. 
If biort or qshift are strings, they are used as an argument to the biort() or qshift() functions. Otherwise, they are interpreted as tuples of vectors giving filter coefficients. In the biort case, this should be (h0o, g0o, h1o, g1o). In the qshift case, this should be (h0a, h0b, g0a, g0b, h1a, h1b, g1a, g1b).
Example:
# Performs a 3level transform on the real image X using the 13,19tap
# filters for level 1 and the Qshift 14tap filters for levels >= 2.
Yl, Yh = dtwavexfm2(X, 3, 'near_sym_b', 'qshift_b')
Perform an nlevel dualtree complex wavelet (DTCWT) 2D reconstruction.
Parameters:  

Returns Z:  Reconstructed real image matrix. 
The (d, l)th element of gain_mask is gain for subband with direction d at level l. If gain_mask[d,l] == 0, no computation is performed for band (d,l). Default gain_mask is all ones. Note that both d and l are zeroindexed.
If biort or qshift are strings, they are used as an argument to the biort() or qshift() functions. Otherwise, they are interpreted as tuples of vectors giving filter coefficients. In the biort case, this should be (h0o, g0o, h1o, g1o). In the qshift case, this should be (h0a, h0b, g0a, g0b, h1a, h1b, g1a, g1b).
Example:
# Performs a 3level reconstruction from Yl,Yh using the 13,19tap
# filters for level 1 and the Qshift 14tap filters for levels >= 2.
Z = dtwaveifm2(Yl, Yh, 'near_sym_b', 'qshift_b')
Load level 1 wavelet by name.
Parameters:  name – a string specifying the wavelet family name 

Returns:  a tuple of vectors giving filter coefficients 
Name  Wavelet 

antonini  Antonini 9,7 tap filters. 
legall  LeGall 5,3 tap filters. 
near_sym_a  NearSymmetric 5,7 tap filters. 
near_sym_b  NearSymmetric 13,19 tap filters. 
Return a tuple whose elements are a vector specifying the h0o, g0o, h1o and g1o coefficients.
Raises: 


Load level >=2 wavelet by name,
Parameters:  name – a string specifying the wavelet family name 

Returns:  a tuple of vectors giving filter coefficients 
Name  Wavelet 

qshift_06  Quarter Sample Shift Orthogonal (QShift) 10,10 tap filters, (only 6,6 nonzero taps). 
qshift_a  Qshift 10,10 tap filters, (with 10,10 nonzero taps, unlike qshift_06). 
qshift_b  QShift 14,14 tap filters. 
qshift_c  QShift 16,16 tap filters. 
qshift_d  QShift 18,18 tap filters. 
Return a tuple whose elements are a vector specifying the h0a, h0b, g0a, g0b, h1a, h1b, g1a and g1b coefficients.
Raises: 


A normal user should not need to call these functions but they are documented here just in case you do.
Return v as a column vector with shape (N,1).
Filter the columns of image X using the two filters ha and hb = reverse(ha). ha operates on the odd samples of X and hb on the even samples. Both filters should be even length, and h should be approx linear phase with a quarter sample advance from its mid pt (i.e. \(h(m/2) > h(m/2 + 1)\)).
ext top edge bottom edge ext
Level 1: !  !  !
odd filt on . b b b b a a a a a a a a b b b b
odd filt on . a a a a b b b b b b b b a a a a
Level 2: !  !  !
+q filt on x b b a a a a b b
q filt on o a a b b b b a a
The output is decimated by two from the input sample rate and the results from the two filters, Ya and Yb, are interleaved to give Y. Symmetric extension with repeated end samples is used on the composite X columns before each filter is applied.
Raises ValueError if the number of rows in X is not a multiple of 4, the length of ha does not match hb or the lengths of ha or hb are noneven.
Filter the columns of image X using filter vector h, without decimation. If len(h) is odd, each output sample is aligned with each input sample and Y is the same size as X. If len(h) is even, each output sample is aligned with the mid point of each pair of input samples, and Y.shape = X.shape + [1 0].
Parameters: 


Returns Y:  the filtered image. 
Filter the columns of image X using the two filters ha and hb = reverse(ha). ha operates on the odd samples of X and hb on the even samples. Both filters should be even length, and h should be approx linear phase with a quarter sample advance from its mid pt (i.e :math:`h(m/2) > h(m/2 + 1)).
ext left edge right edge ext
Level 2: !  !  !
+q filt on x b b a a a a b b
q filt on o a a b b b b a a
Level 1: !  !  !
odd filt on . b b b b a a a a a a a a b b b b
odd filt on . a a a a b b b b b b b b a a a a
The output is interpolated by two from the input sample rate and the results from the two filters, Ya and Yb, are interleaved to give Y. Symmetric extension with repeated end samples is used on the composite X columns before each filter is applied.
Reflect the values in matrix x about the scalar values minx and maxx. Hence a vector x containing a long linearly increasing series is converted into a waveform which ramps linearly up and down between minx and maxx. If x contains integers and minx and maxx are (integers + 0.5), the ramps will have repeated max and min samples.