!ЦinSSKJrJr SSKJrJrJrJr SSKJ r J r SSK J r S/r SSKrSrS S S .S jjrg) )ceilfloor)ContinuousWaveletDiscreteContinuousWaveletWavelet _check_dtype)integrate_waveletscale2frequency) AxisErrorcwtNcFS[[R"U55-$)zRound up size to the nearest power of two. Given a number of samples `n`, returns the next power of two following this number to take advantage of FFT speedup. )rnplog2)ns H/var/www/html/ai-image-ml/venv/lib/python3.13/site-packages/pywt/_cwt.py next_fast_lenrs d2771:   precisionc [U5n[R"XS9n[R"U[R5n[ U[ [45(d [U5n[R"U5n[R"US:*5(a [S5e[R"U5(d [S5eUR(aUOUn [R"[R "U54UR"-U S9n [%X&S9upUR(a[R&"U 5OU n U R(R*S:XaUOUn [R"XS9n [R"XR,R(S9n US:XaSnS nOUS :wa [S 5eUR.S :a>UR1SU5nUR"nUR3SUR"S45n[5U5GHunnU S U S- n[R6"UU SU S- -S -5UU-- nUR9[:5nUSU R :a$[R<"UU R :U5nU US S S2nUS :XaUR.S :Xa[R>"UU5nGOE[AUR"5nUS==UR S - - ss'[CU5n[R"UU S9n[EUR"S5H$n[R>"UUU5UUS S 24'M& O[GUR"SUR -S - 5nUW:wa[RHRIUUSS 9nUn[RHRIUUSS 9n[RHRKUW-SS 9nUSS UR"SUR -S - 24n[RL"U5*[RN"USS 9-nU R(R*S:wa UR,nUR"SUR"S- S- nUS:aUS[QU5[SU5*24nOUS:a[SUS35eUR.S :a#UR3W5nUR1US5nUU US4'GM [UX!U5n[R"U5(a[RV"U/5nUU-nU U4$)a One dimensional Continuous Wavelet Transform. Parameters ---------- data : array_like Input signal scales : array_like The wavelet scales to use. One can use ``f = scale2frequency(wavelet, scale)/sampling_period`` to determine what physical frequency, ``f``. Here, ``f`` is in hertz when the ``sampling_period`` is given in seconds. wavelet : Wavelet object or name Wavelet to use sampling_period : float Sampling period for the frequencies output (optional). The values computed for ``coefs`` are independent of the choice of ``sampling_period`` (i.e. ``scales`` is not scaled by the sampling period). method : {'conv', 'fft'}, optional The method used to compute the CWT. Can be any of: - ``conv`` uses ``numpy.convolve``. - ``fft`` uses frequency domain convolution. - ``auto`` uses automatic selection based on an estimate of the computational complexity at each scale. The ``conv`` method complexity is ``O(len(scale) * len(data))``. The ``fft`` method is ``O(N * log2(N))`` with ``N = len(scale) + len(data) - 1``. It is well suited for large size signals but slightly slower than ``conv`` on small ones. axis: int, optional Axis over which to compute the CWT. If not given, the last axis is used. precision: int, optional Length of wavelet (``2 ** precision``) used to compute the CWT. Greater will increase resolution, especially for higher scales, but will compute a bit slower. Too low will distort coefficients and their norms, with a zipper-like effect. The default is 12, it's recommended to use >=12. Returns ------- coefs : array_like Continuous wavelet transform of the input signal for the given scales and wavelet. The first axis of ``coefs`` corresponds to the scales. The remaining axes match the shape of ``data``. frequencies : array_like If the unit of sampling period are seconds and given, then frequencies are in hertz. Otherwise, a sampling period of 1 is assumed. Notes ----- Size of coefficients arrays depends on the length of the input array and the length of given scales. Examples -------- >>> import pywt >>> import numpy as np >>> import matplotlib.pyplot as plt >>> x = np.exp(np.linspace(0, 2, 512)) >>> y = np.cos(2*np.pi*x) # exponential chirp >>> scales = np.logspace(np.log10(1), np.log10(128), 128) >>> coef, freqs = pywt.cwt(y, scales, 'gaus1') >>> plt.matshow(coef) >>> plt.show() >>> import pywt >>> import numpy as np >>> import matplotlib.pyplot as plt >>> t = np.linspace(-1, 1, 200, endpoint=False) >>> sig = np.cos(2 * np.pi * 7 * t) + np.real(np.exp(-7*(t-0.4)**2)*np.exp(1j*2*np.pi*2*(t-0.4))) >>> widths = np.logspace(np.log10(1), np.log10(30), 30) >>> cwtmatr, freqs = pywt.cwt(sig, widths, 'mexh') >>> plt.imshow(cwtmatr, extent=[-1, 1, 1, 31], cmap='PRGn', aspect='auto', ... vmax=abs(cwtmatr).max(), vmin=-abs(cwtmatr).max()) >>> plt.show() )dtyperz*`scales` must only include positive valueszaxis must be a scalar.rcfftNconvzmethod must be 'conv' or 'fft'r)axis.g@zSelected scale of z too small.),r rasarray result_type complex64 isinstancerrr atleast_1dany ValueErrorisscalarr complex_cwtemptysizeshaper conjrkindrealndimswapaxesreshape enumeratearangeastypeintextractconvolvelisttuplerangerrifftsqrtdiffrrr array)datascaleswaveletsampling_periodmethodrrdtdt_cplxdt_outoutint_psixdt_psi size_scale0fft_datadata_shape_preiscalestepj int_psi_scaler conv_shaper size_scalefft_wavcoefd frequenciess rr r sd d B ::d %DnnR.G g 17; < <+G4 ]]6 "F vvfkEFF ;;t  011++WF ((BGGFO% 2& AC"7@JG")"5"5bggg7G **c1WrFjj/G 1IIOO,A   6 9:: yy1}}}R&||RB01f%5tad{ IIequqt|,q0 1UT\ B HHSM R5GLL  1w||+Q/A 4R4( V yyA~{{47"$**- 2-"4"4q"88":. xx &9tzz!}-A!#T!Wm!DDAJ.' 2!3!33a7J[(66::dJR:@$Kffjj jDG66;;w1;;DEdjjn}/A/AAAEEEFD"''$R"88 99>>S 99D ZZ^djjn , 2 q5U1XtAwh../D U$UG;79 9 99q=<</D==r*DAsF c&f"'9=K {{;hh }- ?"K  r)g?rr)mathrr_extensions._pywtrrrr _functionsr r _utilsr __all__numpyrrr rrr`s: ; 'oor