bMhddlmZmZddlmZmZmZmZddlm Z m Z ddl m Z dgZ ddlZ ddlZejZejZn#e$r ejZdZYnwxYwd d ZdS))ceilfloor)ContinuousWaveletDiscreteContinuousWaveletWavelet _check_dtype)integrate_waveletscale2frequency) AxisErrorcwtNcJdttj|zS)aRound up size to the nearest power of two. Given a number of samples `n`, returns the next power of two following this number to take advantage of FFT speedup. This fallback is less efficient than `scipy.fftpack.next_fast_len` )rnplog2)ns I/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/pywt/_cwt.py next_fast_lenrs$rwqzz""""?convcV t|}tj||}tj|tj}t |t tfst|}tj |}tj |dkrtdtj |std|jr|n|}tjtj|f|jz|} d} t%|| \} } |jrtj| n| } | jjdkr|n|} tj| | } tj| |jj} |dkrd }d }n|d krtd |jd kr?|d |}|j}|d |jd f}t5|D]\}}| d | dz }tj|| d | dz zd z||zz }|t:}|d | jkrtj|| jk|}| |d d d }|d kr|jd krtj||}n9tA|j}|d xx|jd z z cc<tC|}tj||}tE|jdD]$}tj|||||d d f<%ntG|jd |jzd z }||krtH%||d }|}tH%||d }tH&||zd }|dd |jd |jzd z f}tj'| tj(|d z}| jjdkr|j}|jd |jd z dz }|dkr(|dtS|tU| f}n|dkrtd|d|jd kr+||}||d }|| |df<tW||| }tj |rtj,|g}||z}| |fS)a cwt(data, scales, wavelet) 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. 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.arange(512) >>> y = np.sin(2*np.pi*x/32) >>> coef, freqs=pywt.cwt(y,np.arange(1,129),'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.arange(1, 31) >>> 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. ) precisioncfftrNrzmethod 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 enumeratearangeastypeintextractconvolvelisttupleranger fftmodulerifftsqrtdiffrrr array)datascaleswaveletsampling_periodmethodrdtdt_cplxdt_outoutrint_psixdt_psi size_scale0fft_datadata_shape_preiscalestepj int_psi_scaler conv_shaper size_scalefft_wavcoefd frequenciess rr r %sX d  B :d" % % %DnR..G g 17; < <5+G44 ]6 " "F vfkGEFFF ;t  20111+ 3WWF (BGFOO% 2& A A ACI"7i@@@JGQ")"5Bbgg7G *c11WWrFj///G 1DIO,,,A   6  9::: y1}}}}R&&||RB011f%%115tad{ Iequqt|,q0 1 1UT\ B HHSMM R5GL  1w|+Q//A 44R4( V  yA~~{477"$*-- 2-"4q"88":.. x &999tz!}--EEA!#T!Wm!D!DDAAAJJE' 2!33a7J[(($==z=CC$KmmM:BmGGG>>'H"42>>>DEdjn}/AAAEEEFD"'$R"8"8"88 9>S 9D Z^djn , 2 q55U1XXtAwwh../DD UU7U77799 9 9q==<<//D==r**DAsF !'69==K {;.h }-- ?"K  r)rrr)mathrr_extensions._pywtrrrr _functionsr r _utilsr __all__numpyrscipyrr;r ImportErrorr rrrcs ;::::::: '#LLL I+MM # # #I ##### # iiiiiis>AA