+ [j|#p^RIHtHt^RIHtHtHtHt^RIH t H t ^RI H t R.t ^RItRtR R^ /R lltR#) )ceilfloor)ContinuousWaveletDiscreteContinuousWaveletWavelet _check_dtype)integrate_waveletscale2frequency) AxisErrorcwtNcN^\\P!V44,#)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&J/home/luca/screen-checking/.venv/lib/python3.14/site-packages/pywt/_cwt.py next_fast_lenrs d2771:  precisionc \V4p\P!WR7p\P!V\P4p\ V\ \34'g \V4p\ V\ 4'g\RVP: R24h\P!V4p\P!V^8*4'd \R4h\P!V4'g \R4hVP'dTMTp \P !\P"!V43VP$,V R7p \'W&R7wrVP'd\P(!V 4MT p V P*P,R8XdTMTp \P!WR7p \P!WP.P*R7p VR8Xd@RpR pVP*P,R8g;'dV P*P,R8gpMVR 8wd \R 4hVP0^8dCVP3RV4pVP$pVP5RVP$R,34p\7V4EFwppV ^,V ^,, p\P8!VV R,V ^,, ,^,4VV,, pVP;\<4pVR,V P"8d%\P>!VV P"8V4pV V,R R R1,pVR 8XdVP0^8Xd\P@!VV4pEM\CVP$4pVR;;,VP"^, , uu&\EV4p\P !VV R7p\GVP$^,4F&p\P@!VV,V4VVR 3&K( EM^\IVP$R,VP",^, 4pVX8wdNX'd$\PJPMVVRR 7pM"\PJPKVVRR 7pTpX'dM\PJPMVVRR 7p\PJPOVX,VRR7pMJ\PJPKVVRR 7p\PJPQVX,RR 7pVRR VP$R,VP",^, 13,p\PR!V4)\PT!VRR 7,pV P*P,R8wd VP.pVP$R,VP$R,, R, pV^8d"VR\WV4\YV4)13,pMV^8d\RV R24hVP0^8d$VP5X4pVP3VR4pVV VR3&EK \[W!V4p\P!V4'd\P\!V.4pVV,pV V3#)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. The transform is computed entirely in units of samples. With the scale ``a`` and the translation ``b`` both expressed in samples, the returned coefficients are:: C[a, b] = 1/sqrt(a) * sum_n data[n] * conj(psi((n - b)/a)) No sampling interval enters this expression, which is why ``coefs`` does not depend on ``sampling_period``. To express the coefficients in physical-time units instead, multiply them by ``sqrt(dt)`` (equivalently, divide by ``sqrt(fs)``); that factor comes from approximating the integral of the continuous-time transform by a Riemann sum. See the :ref:`CWT normalization` section of the documentation for details. 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() )dtypez)cwt() requires a continuous wavelet, but z is a discrete wavelet. Use a continuous wavelet such as those returned by pywt.wavelist(kind='continuous') (e.g. 'morl', 'mexh', 'cmor').z*`scales` must only include positive valueszaxis must be a scalar.)rcfftNconvzmethod must be 'conv' or 'fft':NNN)axis)rr.g@zSelected scale of z too small.)/rrasarray result_type complex64 isinstancerrr ValueErrorname atleast_1danyisscalarr complex_cwtemptysizeshaper conjrkindrealndimswapaxesreshape enumeratearangeastypeintextractconvolvelisttuplerangerrrfftirfftifftsqrtdiffrrr array)datascaleswaveletsampling_periodmethodrrdtdt_cplxdt_outoutint_psixdt_psi size_scale0fft_data use_real_fftdata_shape_preiscalestepj int_psi_scaler conv_shaper size_scalefft_wavcoefd frequenciess&&&&&&$ rr r sH~ d B ::d %DnnR.G g 17; < <+G4 g0 1 17 7GHQ R  ]]6 "F vvfkEFF ;;t  011+++WF ((BGGFO% 2& AC"7@JG")"5"5"5bggg7G **c1WrFjj/G 1IIOO,A  zz#-KK'--2D2D2K 6 9:: yy1}}}R&||RB01f%5tad{ IIequqt|,q0 1UT\ B HHSM R5GLL  1w||+Q/A 4R4( V yyA~{{47"$**- 2-"4"4q"88":. xx &9tzz!}-A!#T!Wm!DDAJ.' 2!3!33a7J[(!vv{{4"{EH!vvzz$ zDH$K&&++mZb+Ivv||h&*2$&&**]JR*Hvv{{7X#5B{?Edjjn}/A/AAAEEEFD"''$R"88 99>>S 99D ZZ^djjn , 2 q5U1XtAwh../D U$UG;79 9 99q=<</D==r*DAsF u&x"'9=K {{;hh }- ?"K  r)g?rr)mathrr_extensions._pywtrrrr _functionsr r _utilsr __all__numpyrrr rrr`s: ; 'LLr