Χga[BddlmZmZddlZddlmZddlmZddlmZm Z m Z gdZ e e dedd iZ d Z d ed ed ejdejddf dZe ddjdEie ddddejdddd edeededed eejdejdeejdedefdZe ddjdEie ddejdddd eded eejdejdeejdedefd Ze d!d"jdEie dddejddd#d ed$eded eejdejdeejdedefd%Ze d&d'jdEie d(ddejddd)d ed*eded eejdejdeejdedefd+Ze d,d-jdEie ddejdddd eded eejdejdeejdedefd.Ze d/d0jdEie ddejdddd eded eejdejdeejdedefd1Ze d2d3jdEie ddejdddd eded eejdejdeejdedefd4Ze d5d6jdEie ddejdddd eded eejdejdeejdedefd7Ze d8d9jdEie ddejdddd:eded eejdejdeejdedefd;Z e d<d=jdEie d>ddejddd?d@eded eejdejdeejdedefdAZ!e dBdCjdEie ddejdddd eded eejdejdeejdedefdDZ"dS)F)OptionalIterableN)sqrt)Tensor)factory_common_args parse_kwargs merge_dicts) bartlettblackmancosine exponentialgaussiangeneral_cosinegeneral_hamminghamminghannkaisernuttalla6 M (int): the length of the window. In other words, the number of points of the returned window. sym (bool, optional): If `False`, returns a periodic window suitable for use in spectral analysis. If `True`, returns a symmetric window suitable for use in filter design. Default: `True`. normalizationzThe window is normalized to 1 (maximum value is 1). However, the 1 doesn't appear if :attr:`M` is even and :attr:`sym` is `True`.cfd}|S)a8Adds docstrings to a given decorated function. Specially useful when then docstrings needs string interpolation, e.g., with str.format(). REMARK: Do not use this function if the docstring doesn't need string interpolation, just write a conventional docstring. Args: args (str): c<d|_|S)N)join__doc__)oargss X/var/www/html/ai-engine/env/lib/python3.11/site-packages/torch/signal/windows/windows.py decoratorz_add_docstr..decorator6sGGDMM )rrs` r _add_docstrr!*s$ r function_nameMdtypelayoutreturnc|dkrt|d||tjurt|d||tjtjfvrt|d|dS)aPerforms common checks for all the defined windows. This function should be called before computing any window. Args: function_name (str): name of the window function. M (int): length of the window. dtype (:class:`torch.dtype`): the desired data type of returned tensor. layout (:class:`torch.layout`): the desired layout of returned tensor. rz, requires non-negative window length, got M=z/ is implemented for strided tensors only, got: z) expects float32 or float64 dtypes, got: N) ValueErrortorchstridedfloat32float64)r"r#r$r%s r_window_function_checksr-=s 1uuMZZWXZZ[[[ U]""MbbZ`bbccc U]EM222M[[TY[[\\\32rz Computes a window with an exponential waveform. Also known as Poisson window. The exponential window is defined as follows: .. math:: w_n = \exp{\left(-\frac{|n - c|}{\tau}\right)} where `c` is the ``center`` of the window. aF {normalization} Args: {M} Keyword args: center (float, optional): where the center of the window will be located. Default: `M / 2` if `sym` is `False`, else `(M - 1) / 2`. tau (float, optional): the decay value. Tau is generally associated with a percentage, that means, that the value should vary within the interval (0, 100]. If tau is 100, it is considered the uniform window. Default: 1.0. {sym} {dtype} {layout} {device} {requires_grad} Examples:: >>> # Generates a symmetric exponential window of size 10 and with a decay value of 1.0. >>> # The center will be at (M - 1) / 2, where M is 10. >>> torch.signal.windows.exponential(10) tensor([0.0111, 0.0302, 0.0821, 0.2231, 0.6065, 0.6065, 0.2231, 0.0821, 0.0302, 0.0111]) >>> # Generates a periodic exponential window and decay factor equal to .5 >>> torch.signal.windows.exponential(10, sym=False,tau=.5) tensor([4.5400e-05, 3.3546e-04, 2.4788e-03, 1.8316e-02, 1.3534e-01, 1.0000e+00, 1.3534e-01, 1.8316e-02, 2.4788e-03, 3.3546e-04]) ?TF)centertausymr$r%device requires_gradr/r0r1r2r3c |tj}td||||dkrtd|d|r|td|dkrtjd||||S||s|dkr|n|dz d z }d|z }tj| |z| |dz z|z||||| } tjtj|  S) Nr rzTau must be positive, got: instead.z)Center must be None for symmetric windowsrr$r%r2r3@startendstepsr$r%r2r3)r)get_default_dtyper-r(emptylinspaceexpabs) r#r/r0r1r$r%r2r3constantks rr r Osr }'))M1eV<<< axxEsEEEFFF Fv!DEEEAvv{4uVFZghhhh ~31q55!!a!es:3wH fWx/#Gq1u-9"$$%2  4 4 4A 9eill] # ##ra Computes a window with a simple cosine waveform, following the same implementation as SciPy. This window is also known as the sine window. The cosine window is defined as follows: .. math:: w_n = \sin\left(\frac{\pi (n + 0.5)}{M}\right) This formula differs from the typical cosine window formula by incorporating a 0.5 term in the numerator, which shifts the sample positions. This adjustment results in a window that starts and ends with non-zero values. a {normalization} Args: {M} Keyword args: {sym} {dtype} {layout} {device} {requires_grad} Examples:: >>> # Generates a symmetric cosine window. >>> torch.signal.windows.cosine(10) tensor([0.1564, 0.4540, 0.7071, 0.8910, 0.9877, 0.9877, 0.8910, 0.7071, 0.4540, 0.1564]) >>> # Generates a periodic cosine window. >>> torch.signal.windows.cosine(10, sym=False) tensor([0.1423, 0.4154, 0.6549, 0.8413, 0.9595, 1.0000, 0.9595, 0.8413, 0.6549, 0.4154]) r1r$r%r2r3c @|tj}td||||dkrtjd||||Sd}tj|s |dkr|dzn|z }tj||z||dz z|z|||||}tj|S)Nr rr6r7?r8r:)r)r>r-r?pir@sin r#r1r$r%r2r3r;rCrDs rr r sd }'))Ha777Avv{4uVFZghhhh Ex<A1q551=H UX-!QUOx7"$$%2  4 4 4A 9Q<<rz Computes a window with a gaussian waveform. The gaussian window is defined as follows: .. math:: w_n = \exp{\left(-\left(\frac{n}{2\sigma}\right)^2\right)} a  {normalization} Args: {M} Keyword args: std (float, optional): the standard deviation of the gaussian. It controls how narrow or wide the window is. Default: 1.0. {sym} {dtype} {layout} {device} {requires_grad} Examples:: >>> # Generates a symmetric gaussian window with a standard deviation of 1.0. >>> torch.signal.windows.gaussian(10) tensor([4.0065e-05, 2.1875e-03, 4.3937e-02, 3.2465e-01, 8.8250e-01, 8.8250e-01, 3.2465e-01, 4.3937e-02, 2.1875e-03, 4.0065e-05]) >>> # Generates a periodic gaussian window and standard deviation equal to 0.9. >>> torch.signal.windows.gaussian(10, sym=False,std=0.9) tensor([1.9858e-07, 5.1365e-05, 3.8659e-03, 8.4658e-02, 5.3941e-01, 1.0000e+00, 5.3941e-01, 8.4658e-02, 3.8659e-03, 5.1365e-05]) )stdr1r$r%r2r3rKc |tj}td||||dkrtd|d|dkrtjd||||S|s|dkr|n|dz dz }d|t d zz }tj||z||dz z|z||||| } tj| d z S) Nrrz*Standard deviation must be positive, got: r5r6r7r8r9r:)r)r>r-r(r?rr@rA) r#rKr1r$r%r2r3r;rCrDs rrrs` }'))J5&999 axxTcTTTUUUAvv{4uVFZghhhh/q1uuaa!a% 03 6EC$q''M"H UX-!QUOx7"$$%2  4 4 4A 9a1fW  raK Computes the Kaiser window. The Kaiser window is defined as follows: .. math:: w_n = I_0 \left( \beta \sqrt{1 - \left( {\frac{n - N/2}{N/2}} \right) ^2 } \right) / I_0( \beta ) where ``I_0`` is the zeroth order modified Bessel function of the first kind (see :func:`torch.special.i0`), and ``N = M - 1 if sym else M``. a {normalization} Args: {M} Keyword args: beta (float, optional): shape parameter for the window. Must be non-negative. Default: 12.0 {sym} {dtype} {layout} {device} {requires_grad} Examples:: >>> # Generates a symmetric gaussian window with a standard deviation of 1.0. >>> torch.signal.windows.kaiser(5) tensor([4.0065e-05, 2.1875e-03, 4.3937e-02, 3.2465e-01, 8.8250e-01, 8.8250e-01, 3.2465e-01, 4.3937e-02, 2.1875e-03, 4.0065e-05]) >>> # Generates a periodic gaussian window and standard deviation equal to 0.9. >>> torch.signal.windows.kaiser(5, sym=False,std=0.9) tensor([1.9858e-07, 5.1365e-05, 3.8659e-03, 8.4658e-02, 5.3941e-01, 1.0000e+00, 5.3941e-01, 8.4658e-02, 3.8659e-03, 5.1365e-05]) g(@)betar1r$r%r2r3rNc j|tj}td||||dkrtd|d|dkrtjd||||S|dkrtjd||||Stj||| }| }d |z|s|n|dz z }tj|||dz |zz} tj|| ||||| } tj tj ||ztj | d z tj |z S) Nrrz beta must be non-negative, got: r5r6r7r8r8)r$r2r9r:rM) r)r>r-r(r?onestensorminimumr@i0rpow) r#rNr1r$r%r2r3r;rCr<rDs rrr8sXb }'))Ha777 axxKDKKKLLLAvv{4uVFZghhhhAvvz$eF6Yfgggg <E& 9 9 9D EETzc4QQq1u5H -eq1u&88 9 9C U"$$%2  4 4 4A 8EJtd{UYq!__<== > >$ OOrz Computes the Hamming window. The Hamming window is defined as follows: .. math:: w_n = \alpha - \beta\ \cos \left( \frac{2 \pi n}{M - 1} \right) a {normalization} Arguments: {M} Keyword args: {sym} alpha (float, optional): The coefficient :math:`\alpha` in the equation above. beta (float, optional): The coefficient :math:`\beta` in the equation above. {dtype} {layout} {device} {requires_grad} Examples:: >>> # Generates a symmetric Hamming window. >>> torch.signal.windows.hamming(10) tensor([0.0800, 0.1876, 0.4601, 0.7700, 0.9723, 0.9723, 0.7700, 0.4601, 0.1876, 0.0800]) >>> # Generates a periodic Hamming window. >>> torch.signal.windows.hamming(10, sym=False) tensor([0.0800, 0.1679, 0.3979, 0.6821, 0.9121, 1.0000, 0.9121, 0.6821, 0.3979, 0.1679]) c,t||||||S)NrErr#r1r$r%r2r3s rrrs!Z 1#U6&`m n n nnrz Computes the Hann window. The Hann window is defined as follows: .. math:: w_n = \frac{1}{2}\ \left[1 - \cos \left( \frac{2 \pi n}{M - 1} \right)\right] = \sin^2 \left( \frac{\pi n}{M - 1} \right) a {normalization} Arguments: {M} Keyword args: {sym} {dtype} {layout} {device} {requires_grad} Examples:: >>> # Generates a symmetric Hann window. >>> torch.signal.windows.hann(10) tensor([0.0000, 0.1170, 0.4132, 0.7500, 0.9698, 0.9698, 0.7500, 0.4132, 0.1170, 0.0000]) >>> # Generates a periodic Hann window. >>> torch.signal.windows.hann(10, sym=False) tensor([0.0000, 0.0955, 0.3455, 0.6545, 0.9045, 1.0000, 0.9045, 0.6545, 0.3455, 0.0955]) c .t|d|||||S)NrGalphar1r$r%r2r3rWrXs rrrs0X 1!$"!&"("()6  8 8 88rz Computes the Blackman window. The Blackman window is defined as follows: .. math:: w_n = 0.42 - 0.5 \cos \left( \frac{2 \pi n}{M - 1} \right) + 0.08 \cos \left( \frac{4 \pi n}{M - 1} \right) a {normalization} Arguments: {M} Keyword args: {sym} {dtype} {layout} {device} {requires_grad} Examples:: >>> # Generates a symmetric Blackman window. >>> torch.signal.windows.blackman(5) tensor([-1.4901e-08, 3.4000e-01, 1.0000e+00, 3.4000e-01, -1.4901e-08]) >>> # Generates a periodic Blackman window. >>> torch.signal.windows.blackman(5, sym=False) tensor([-1.4901e-08, 2.0077e-01, 8.4923e-01, 8.4923e-01, 2.0077e-01]) c |tj}td|||t|gd|||||S)Nr )gzG?rGg{Gz?ar1r$r%r2r3)r)r>r-rrXs rr r sZV }'))J5&999 !000cv^d(5 7 7 77ra4 Computes the Bartlett window. The Bartlett window is defined as follows: .. math:: w_n = 1 - \left| \frac{2n}{M - 1} - 1 \right| = \begin{cases} \frac{2n}{M - 1} & \text{if } 0 \leq n \leq \frac{M - 1}{2} \\ 2 - \frac{2n}{M - 1} & \text{if } \frac{M - 1}{2} < n < M \\ \end{cases} a {normalization} Arguments: {M} Keyword args: {sym} {dtype} {layout} {device} {requires_grad} Examples:: >>> # Generates a symmetric Bartlett window. >>> torch.signal.windows.bartlett(10) tensor([0.0000, 0.2222, 0.4444, 0.6667, 0.8889, 0.8889, 0.6667, 0.4444, 0.2222, 0.0000]) >>> # Generates a periodic Bartlett window. >>> torch.signal.windows.bartlett(10, sym=False) tensor([0.0000, 0.2000, 0.4000, 0.6000, 0.8000, 1.0000, 0.8000, 0.6000, 0.4000, 0.2000]) c ^|tj}td||||dkrtjd||||S|dkrtjd||||Sd}d|s|n|dz z }tj|||dz |zz||||| }dtj|z S) Nr rr6r7r8rPrMr:)r)r>r-r?rQr@rBrJs rr r "sZ }'))J5&999Avv{4uVFZghhhhAvvz$eF6Yfgggg ES+AAa!e,H U AEX#55"$$%2  4 4 4A uy|| rz Computes the general cosine window. The general cosine window is defined as follows: .. math:: w_n = \sum^{M-1}_{i=0} (-1)^i a_i \cos{ \left( \frac{2 \pi i n}{M - 1}\right)} a {normalization} Arguments: {M} Keyword args: a (Iterable): the coefficients associated to each of the cosine functions. {sym} {dtype} {layout} {device} {requires_grad} Examples:: >>> # Generates a symmetric general cosine window with 3 coefficients. >>> torch.signal.windows.general_cosine(10, a=[0.46, 0.23, 0.31], sym=True) tensor([0.5400, 0.3376, 0.1288, 0.4200, 0.9136, 0.9136, 0.4200, 0.1288, 0.3376, 0.5400]) >>> # Generates a periodic general cosine window wit 2 coefficients. >>> torch.signal.windows.general_cosine(10, a=[0.5, 1 - 0.5], sym=False) tensor([0.0000, 0.0955, 0.3455, 0.6545, 0.9045, 1.0000, 0.9045, 0.6545, 0.3455, 0.0955]) r^c |tj}td||||dkrtjd||||S|dkrtjd||||St |t std|stdd tj z|s|n|dz z }tj d|dz |z||||| }tj d t|D||| } tj | jd| j| j| j } | dtj| d|zzdS)Nrrr6r7r8rPz!Coefficients must be a list/tuplezCoefficients cannot be emptyrMr:c$g|] \}}d|z|zS)r`r ).0iws r z"general_cosine..s$???$!Q A ???r)r2r$r3)r$r2r3r`)r)r>r-r?rQ isinstancer TypeErrorr(rHr@rR enumeratearangeshaper$r2r3 unsqueezecossum) r#r^r1r$r%r2r3rCrDa_irds rrrhsX }')),a???Avv{4uVFZghhhhAvvz$eF6Yfgggg a " "=;<<< 9788858|6qqQ7H QEX-"$$%2  4 4 4A ,??)A,,???V[kx y y yC SYq\3:UXUfgggA MM"   !++b//A*= > > > C CA F FFrz Computes the general Hamming window. The general Hamming window is defined as follows: .. math:: w_n = \alpha - (1 - \alpha) \cos{ \left( \frac{2 \pi n}{M-1} \right)} a {normalization} Arguments: {M} Keyword args: alpha (float, optional): the window coefficient. Default: 0.54. {sym} {dtype} {layout} {device} {requires_grad} Examples:: >>> # Generates a symmetric Hamming window with the general Hamming window. >>> torch.signal.windows.general_hamming(10, sym=True) tensor([0.0800, 0.1876, 0.4601, 0.7700, 0.9723, 0.9723, 0.7700, 0.4601, 0.1876, 0.0800]) >>> # Generates a periodic Hann window with the general Hamming window. >>> torch.signal.windows.general_hamming(10, alpha=0.5, sym=False) tensor([0.0000, 0.0955, 0.3455, 0.6545, 0.9045, 1.0000, 0.9045, 0.6545, 0.3455, 0.0955]) gHzG?rZr[c 8t||d|z g|||||S)Nr.r]r)r#r[r1r$r%r2r3s rrrs9Z !"BJ/! %!'!'(5  7 7 77rz Computes the minimum 4-term Blackman-Harris window according to Nuttall. .. math:: w_n = 1 - 0.36358 \cos{(z_n)} + 0.48917 \cos{(2z_n)} - 0.13659 \cos{(3z_n)} + 0.01064 \cos{(4z_n)} where :math:`z_n = \frac{2 \pi n}{M}`. a {normalization} Arguments: {M} Keyword args: {sym} {dtype} {layout} {device} {requires_grad} References:: - A. Nuttall, "Some windows with very good sidelobe behavior," IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 29, no. 1, pp. 84-91, Feb 1981. https://doi.org/10.1109/TASSP.1981.1163506 - Heinzel G. et al., "Spectrum and spectral density estimation by the Discrete Fourier transform (DFT), including a comprehensive list of window functions and some new flat-top windows", February 15, 2002 https://holometer.fnal.gov/GH_FFT.pdf Examples:: >>> # Generates a symmetric Nutall window. >>> torch.signal.windows.general_hamming(5, sym=True) tensor([3.6280e-04, 2.2698e-01, 1.0000e+00, 2.2698e-01, 3.6280e-04]) >>> # Generates a periodic Nuttall window. >>> torch.signal.windows.general_hamming(5, sym=False) tensor([3.6280e-04, 1.1052e-01, 7.9826e-01, 7.9826e-01, 1.1052e-01]) c 2t|gd|||||S)N)gzD?g;%N?g1|?gC˅?r]rqrXs rrrs6n !HHH! %!'!'(5  7 7 77rr )#typingrrr)mathrrtorch._torch_docsrrr __all__window_common_argsr!strintr$r%r-formatr*floatboolr2r r rrrrr r rrrr rrr}sd &%%%%%%% LLLLLLLLLL   ![L H"&]3]3]u{]TYT`]ei]]]]$  < =  > ?  --b#''+$})-#&$&$&$ &$&$ &$  &$  $ &$ &$&&$&$ &$&$&$]--\&$R   . /0 1((X'+$})-#  $    &  S((R>  2 34 5%%R'+$})-#!!! !! !  $ !  !&!! !!!M%%L!H   . /0 1&&T'+$})-#'P'P'P 'P'P 'P  $ 'P  'P&'P'P 'P'P'PO&&N'PT  2 34 5%%P+/#(=-1"' ooosooEK(oL o U\* o  o -3 oooM%%Lo  . /0 1$$N(, % *.$ 8 8 8C 8 8% 8 8 %,' 8  8 *0 8 8 8K$$J 8   . /0 1##L,0$)M.2#( 7 7 7 7 7U[) 7\ 7 el+ 7 ! 7 .4 7 7 7I##H 7    . /0 1%%P,0$)M.2#( U[)\  el+  !  .4 M%%L@  0 12 3$$N $26*/-48). $G$G$G$G$G#5;/$G!< $G $EL1 $G #' $G 4: $G$G$GK$$J$GN  0 12 3$$N$( $37+0=59*/777 77$EK0 7 "L 7 %U\2 7$(75;777K$$J7" ! !B C##D E##--b'+$})-#777 77 $ 7  7 & 77 777]--\777r