ijddlmZmZddlmZmZmZmZddlm Z m Z ddl m Z dgZ ddlZdZd d d d Zy) )ceilfloor)ContinuousWaveletDiscreteContinuousWaveletWavelet _check_dtype)integrate_waveletscale2frequency) AxisErrorcwtNcDdttj|zS)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 E/var/www/html/BatchJob/venv/lib/python3.12/site-packages/pywt/_cwt.py next_fast_lenrs d2771:   precisioncz t|}tj||}tj|tj}t |t tfs t|}tj|}tj|dkr tdtj|s td|jr|n|} tjtj |f|j"z| } t%||\} } |jrtj&| n| } | j(j*dk(r|n|} tj| | } tj| |j,j(} |dk(rd}d }n|d k7r td |j.d kDr>|j1d|}|j"}|j3d|j"df}t5|D]\}}| d | dz }tj6|| d| dz zd z||zz }|j9t:}|d| j k\r#tj<|| j k|}| |d d d}|d k(r|j.d k(rtj>||}nLtA|j"}|dxx|j d z z cc<tC|}tj|| }tE|j"dD]"}tj>|||||d d f<$ntG|j"d|j zd z }|k7r"tjHjI||d }|}tjHjI||d }tjHjK|zd }|dd |j"d|j zd z f}tjL| tjN|d z}| j(j*dk7r |j,}|j"d|j"dz dz }|dkDr|dtQ|tS| f}n|dkrtd|d|j.d kDr#|j3}|j1|d}|| |df<tU|||}tj|rtjV|g}||z}| |fS)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"7i@JGQ")"5"5bggg7G **c1WrFjj/G 1DIIOO,A   6 9:: yy1}}}R&||RB01f%15tad{ IIequqt|,q0 1UT\ B HHSM R5GLL  1w||+Q/A 4R4( V yyA~{{47"$**- 2-"4"4q"88":. xx &9tzz!}-EA!#T!Wm!DDAJE' 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 c1f"'69=K {{;hh }- ?"K  r)g?rr)mathrr_extensions._pywtrrrr _functionsr r _utilsr __all__numpyrrr rrr`s: ; 'oor