!Цi,SrSSKJr SSKrSSKJr SSKJrJ r SSK J r J r J r /SQrSS jrSS jrSS jrS rSS jrg)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&[U5n[R"U5n[U5S:wa [ S5eUR [[R "U55:a [ S5e[XX#5nUSUSUSUS44$)ae 2D Discrete Wavelet Transform. Parameters ---------- data : array_like 2D array with input data wavelet : Wavelet object or name string, or 2-tuple of wavelets Wavelet to use. This can also be a tuple containing a wavelet to apply along each axis in ``axes``. mode : str or 2-tuple of strings, optional Signal extension mode, see :ref:`Modes `. This can also be a tuple of modes specifying the mode to use on each axis in ``axes``. axes : 2-tuple of ints, optional Axes over which to compute the DWT. Repeated elements mean the DWT will be performed multiple times along these axes. Returns ------- (cA, (cH, cV, cD)) : tuple Approximation, horizontal detail, vertical detail and diagonal detail coefficients respectively. Horizontal refers to array axis 0 (or ``axes[0]`` for user-specified ``axes``). Examples -------- >>> import numpy as np >>> import pywt >>> data = np.ones((4,4), dtype=np.float64) >>> coeffs = pywt.dwt2(data, 'haar') >>> cA, (cH, cV, cD) = coeffs >>> cA array([[ 2., 2.], [ 2., 2.]]) >>> cV array([[ 0., 0.], [ 0., 0.]]) Expected 2 axesz8Input array has fewer dimensions than the specified axesaadaaddd)tuplenpasarraylen ValueErrorndimuniquer )datawaveletmodeaxescoefss M/var/www/html/ai-image-ml/venv/lib/python3.13/site-packages/pywt/_multidim.pyr r sR ;D ::d D 4yA~*++ yy3ryy'' ! !  +E ;teDk5;? ??c~UunupVn[U5n[U5S:wa [S5eXEXgS.n[XX#5$)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 KsJFB  ;D 4yA~*++ 5F $ --r#c [R"U5n[(df[R"U5(aK[ UR XU5n[ UR XU5nUVs0sHofXFSXV--_M sn$UR[R"S5:Xa [S5eURS:a [S5eUc[UR5nUVs/sHowS:aXpR-OUPM nn[X#5n[X5n SU4/n [X9U5HAupn/n U H1up[XX+5unnU R!US-U4US -U4/5 M3 U n MC [#U 5$s snfs snf) a 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 wszZ ::d D  !6!6DIIwd3DIIwd33784a47R$'\))488 zzRXXh''<== yy1}9:: |TYY37 84aUA M )4D 8 D 'E!'0H4j\Ftu54  JGad1FB   # r2 '# r24 5! 6 <-9 9s /F.F c\UR5VVs/sH upUbM UPM nnnU(a[SUS35eUR5VVs/sH!up[U5[S5::aMUPM# nnnU(a[SUS35eUVs/sHn[U5PM nn[[R "U55S:a [S5eUR5VVs0sHupU[R "U5_M snn$s snnfs snnfs snfs snnf)Nz4The following detail coefficients were set to None: zr For multilevel transforms, rather than setting coeffs[key] = None use coeffs[key] = np.zeros_like(coeffs[key]) rzLThe following invalid keys were found in the detail coefficient dictionary: .rz4All detail coefficient names must have equal length.)itemsrsetrrrr)r%r9v missing_keys invalid_keys key_lengthss r" _fix_coeffsrKs"(,,.>.$!AA.L> Cn; ;< <#),,.-.$!A#d)+.L- ''3nA 78 8$**6a3q66K* 299[ !"Q& BD D*0 8Arzz!}  88-?-+ 9s" DDD5DD#0#D(c ^^UR5VVs0sH upEUcM XE_M nnnUR5VVs0sH upEUcM XE_M nnn[U5n[(d[SUR 555(axUR5VVs0sHupEXER _M nnnUR5VVs0sHupEXER _M nnn[XaX#5S[XqX#5--$[SU55mU4SjUR55n[U5m[U4SjU55(a [S5eUc[T5nTn O [T5n UV s/sHoS :aX-OU PM nn [X#5n [X5n [![#[%['X`. This can also be a tuple of modes specifying the mode to use on each axis in ``axes``. axes : sequence of ints, optional Axes over which to compute the IDWT. Repeated elements mean the IDWT will be performed multiple times along these axes. A value of ``None`` (the default) selects all axes. For the most accurate reconstruction, the axes should be provided in the same order as they were provided to ``dwtn``. Returns ------- data: ndarray Original signal reconstructed from input data. Nc3N# UHn[R"U5v M g7fN)rr0).0rGs r" idwtn..s# UH)upUcM [U5T:XdMURv M+ g7frN)rshape)rOr9rGndim_transforms r"rPrQs3GNDA -0V~-E Ns 4 44z8`coeffs` must contain at least one non-null wavelet bandc3,># UH oT:gv M g7frN)rOs coeff_shapes r"rPrQs 2\ \sz,`coeffs` must all be of equal size (or None)rz!Axis greater than data dimensionsr)repeatr-r.r/c)r3)rErKranyvaluesr1r2rmaxnext StopIterationrr5rr r reversedlist enumerater6rrjoingetr3kindr complex128float64rr)r%rrr r9rG real_coeffs imag_coeffs coeff_shapesrr.r:r; key_lengthr<r=r>coefnew_keysrSLHr3rZrVs @@r"rrs@ &||~ ?~tqdad~F ? &||~ ?~tqdad~F ? F   4V44N!GFLLNG <(  2\ 222GHH |^$;.2 3dAAH1 $dD 3 D 'E!'0H)1 3tu56 7*9% %T !8t|?@ @ .5d:.NO.NdBGGDM.NOC 39d+A 39d+A}77agg%ww||s*aggllc.A "  "  1E2A 1E2A'1c>JsO+*9. ":y@@=< ! ! !! 4Ps9 LL L L#LL%%L+MM +M) symmetric))rrN)__doc__ itertoolsrnumpyr _c99_configr_extensions._dwtrr_utilsrr r __all__r r r rKrrXr#r"r|sB  *1BB ,2@j).XGT94\r#