i,zdZddlmZddlZddlmZddlmZm Z ddl m Z m Z m Z gdZdd Zdd Zdd Zd Zdd Zy)zP 2D and nD Discrete Wavelet Transforms and Inverse Discrete Wavelet Transforms. )productN)_have_c99_complex)dwt_axis idwt_axis) AxisError_modes_per_axis_wavelets_per_axis)dwt2idwt2dwtnidwtnc"t|}tj|}t|dk7r t d|j ttj |kr t dt||||}|d|d|d|dffS)a 2D Discrete Wavelet Transform. Parameters ---------- data : array_like 2D array with input data wavelet : Wavelet object or name string, or 2-tuple of wavelets Wavelet to use. This can also be a tuple containing a wavelet to apply along each axis in ``axes``. mode : str or 2-tuple of strings, optional Signal extension mode, see :ref:`Modes `. This can also be a tuple of modes specifying the mode to use on each axis in ``axes``. axes : 2-tuple of ints, optional Axes over which to compute the DWT. Repeated elements mean the DWT will be performed multiple times along these axes. Returns ------- (cA, (cH, cV, cD)) : tuple Approximation, horizontal detail, vertical detail and diagonal detail coefficients respectively. Horizontal refers to array axis 0 (or ``axes[0]`` for user-specified ``axes``). Examples -------- >>> import numpy as np >>> import pywt >>> data = np.ones((4,4), dtype=np.float64) >>> coeffs = pywt.dwt2(data, 'haar') >>> cA, (cH, cV, cD) = coeffs >>> cA array([[ 2., 2.], [ 2., 2.]]) >>> cV array([[ 0., 0.], [ 0., 0.]]) Expected 2 axesz8Input array has fewer dimensions than the specified axesaadaaddd)tuplenpasarraylen ValueErrorndimuniquer )datawaveletmodeaxescoefss J/var/www/html/BatchJob/venv/lib/python3.12/site-packages/pywt/_multidim.pyr r sR ;D ::d D 4yA~*++ yy3ryy'' ! ! wd +E ;teDk5;? ??c|\}\}}}t|}t|dk7r td||||d}t||||S)a 2-D Inverse Discrete Wavelet Transform. Reconstructs data from coefficient arrays. Parameters ---------- coeffs : tuple (cA, (cH, cV, cD)) A tuple with approximation coefficients and three details coefficients 2D arrays like from ``dwt2``. If any of these components are set to ``None``, it will be treated as zeros. wavelet : Wavelet object or name string, or 2-tuple of wavelets Wavelet to use. This can also be a tuple containing a wavelet to apply along each axis in ``axes``. mode : str or 2-tuple of strings, optional Signal extension mode, see :ref:`Modes `. This can also be a tuple of modes specifying the mode to use on each axis in ``axes``. axes : 2-tuple of ints, optional Axes over which to compute the IDWT. Repeated elements mean the IDWT will be performed multiple times along these axes. Examples -------- >>> import numpy as np >>> import pywt >>> data = np.array([[1,2], [3,4]], dtype=np.float64) >>> coeffs = pywt.dwt2(data, 'haar') >>> pywt.idwt2(coeffs, 'haar') array([[ 1., 2.], [ 3., 4.]]) rr)rrrr)rrrr)coeffsrrr LLHLLHHHs r"r r KsTFB R ;D 4yA~*++b" 5F $ --r#ctj|}tsbtj|rMt |j |||}t |j |||}|Dcic]}|||d||zzc}S|jtjdk(r td|jdkr td|t|j}|Dcgc]}|dkr||jzn|}}t||}t||} d|fg} t|| |D]B\} } }g} | D]3\}}t|| || \}}| j!|dz|f|d z|fg5| } Dt#| Scc}wcc}w) aO Single-level n-dimensional Discrete Wavelet Transform. Parameters ---------- data : array_like n-dimensional array with input data. wavelet : Wavelet object or name string, or tuple of wavelets Wavelet to use. This can also be a tuple containing a wavelet to apply along each axis in ``axes``. mode : str or tuple of string, optional Signal extension mode used in the decomposition, see :ref:`Modes `. This can also be a tuple of modes specifying the mode to use on each axis in ``axes``. axes : sequence of ints, optional Axes over which to compute the DWT. Repeated elements mean the DWT will be performed multiple times along these axes. A value of ``None`` (the default) selects all axes. Axes may be repeated, but information about the original size may be lost if it is not divisible by ``2 ** nrepeats``. The reconstruction will be larger, with additional values derived according to the ``mode`` parameter. ``pywt.wavedecn`` should be used for multilevel decomposition. Returns ------- coeffs : dict Results are arranged in a dictionary, where key specifies the transform type on each dimension and value is a n-dimensional coefficients array. For example, for a 2D case the result will look something like this:: {'aa': # A(LL) - approx. on 1st dim, approx. on 2nd dim 'ad': # V(LH) - approx. on 1st dim, det. on 2nd dim 'da': # H(HL) - det. on 1st dim, approx. on 2nd dim 'dd': # D(HH) - det. on 1st dim, det. on 2nd dim } For user-specified ``axes``, the order of the characters in the dictionary keys map to the specified ``axes``. ?objectz"Input must be a numeric array-likerzInput data must be at least 1Drad)rrr iscomplexobjr realimagdtype TypeErrorrrranger r ziprextenddict)rrrr r1r2kr.modeswaveletsr%axiswav new_coeffssubbandxcAcDs r"r r wsZ ::d D !6DIIwd3DIIwd3378a47R$q'\))88 zzRXXh''<== yy1}9:: |TYY37 8aQUA M ) 8D 8 D$ 'E!'40H4j\FtXu5c4   5JGQadD1FB   # r2 '# r24 5 5  <-9 9s %E9 E>c&|jDcgc] \}}| | }}}|rtd|d|jDcgc]\}}t|tdks| }}}|rtd|d|Dcgc] }t|}}tt j |dkDr td|jDcic]\}}|t j |c}}Scc}}wcc}}wcc}wcc}}w)Nz4The following detail coefficients were set to None: zr For multilevel transforms, rather than setting coeffs[key] = None use coeffs[key] = np.zeros_like(coeffs[key]) rzLThe following invalid keys were found in the detail coefficient dictionary: .rz4All detail coefficient names must have equal length.)itemsrsetrrrr)r%r9v missing_keys invalid_keys key_lengthss r" _fix_coeffsrKs"(,,.>$!QAIA>L> Cn; ;< <#),,.-$!QA#d)+-L- ''3nA 78 8$**a3q6*K* 299[ !"Q& BD D*0 8AArzz!}  88-?-+ 9s C<C< #DD D c h|jDcic] \}}| || }}}|jDcic] \}}| || }}}t|}tstd|j Drz|jDcic]\}}||j }}}|jDcic]\}}||j }}}t||||dt||||zzStd|D fd|jD}t|tfd|Dr td|t}} n t} |D cgc]} | d kr| | zn| }} t||} t||} t!t#t%t'|| | D]-\} \}}}|d ks|| k\r t)d i}t+d | Dcgc]}d j-|}}|D]}|j/|dzd}|j/|dzd}|||j0|j0k7r|j0j2dk(s|j0j2dk(rt4j6}nt4j8}t5j:||}t5j:||}t=|||||||<|}0|d Scc}}wcc}}wcc}}wcc}}w#t$r tdwxYwcc} wcc}w)aV Single-level n-dimensional Inverse Discrete Wavelet Transform. Parameters ---------- coeffs: dict Dictionary as in output of ``dwtn``. Missing or ``None`` items will be treated as zeros. wavelet : Wavelet object or name string, or tuple of wavelets Wavelet to use. This can also be a tuple containing a wavelet to apply along each axis in ``axes``. mode : str or list of string, optional Signal extension mode used in the decomposition, see :ref:`Modes `. This can also be a tuple of modes specifying the mode to use on each axis in ``axes``. axes : sequence of ints, optional Axes over which to compute the IDWT. Repeated elements mean the IDWT will be performed multiple times along these axes. A value of ``None`` (the default) selects all axes. For the most accurate reconstruction, the axes should be provided in the same order as they were provided to ``dwtn``. Returns ------- data: ndarray Original signal reconstructed from input data. Nc3FK|]}tj|ywN)rr0).0rGs r" zidwtn..s. s4cS4sc3\K|]#\}}|t|k(r|j%ywrN)rshape)rOr9rGndim_transforms r"rPzidwtn..s2GDAq=SV~-EGs),z8`coeffs` must contain at least one non-null wavelet bandc3(K|] }|k7 ywrN)rOs coeff_shapes r"rPzidwtn..s 21  2sz,`coeffs` must all be of equal size (or None)rz!Axis greater than data dimensionsr)repeatr-r.r/c)r3)rErKranyvaluesr1r2rmaxnext StopIterationrr5rr r reversedlist enumerater6rrjoingetr3kindr complex128float64rr)r%rrr r9rG real_coeffs imag_coeffs coeff_shapesrr.r:r; key_lengthr<r=r>coefnew_keysrRLHr3rYrUs @@r"rrs@ &||~ ?tq!ad ?F ? &||~ ?tq!ad ?F ? F  4V44N!GFLLNG <(  2\ 22GHH |^$;.2 3AAH1 $ 3D 3 D$ 'E!'40H)1 3tXu56 7*9% %T3 !8tt|?@ @ .5d:.NOdBGGDMOO ?C 39d+A 39d+A}77agg%ww||s*aggllc.A "  "  1E2A 1E2A'1c4>JsO ?+. ":y@@=< ! ! !! 4Ps9 K:K: LLLL  $L/L*5L/L') symmetric))rqN)__doc__ itertoolsrnumpyr _c99_configr_extensions._dwtrr_utilsrr r __all__r r r rKrrWr#r"r{sB  *1BB ,2@j).XGT94\r#