U ?h# @sddlmZmZddlmZmZmZmZddlm Z m Z dgZ ddl Z zddlZejZejZWnPek rzddlZejZejZWn"ek re jZddZYnXYnXd d dZdS))floorceil)DiscreteContinuousWaveletContinuousWaveletWavelet _check_dtype)integrate_waveletscale2frequencycwtNcCsdtt|S)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)nr;/var/www/html/venv/lib/python3.8/site-packages/pywt/_cwt.py next_fast_lensr?convcCst|}tj||d}t|tj}t|ttfs:t|}t |rPt |g}t |sdt d|j rn|n|}tj t|f|j|d} d} t|| d\} } |j rt| n| } | jjdkr|n|} tj| | d} tj| |jjd} |dkrd}d}n|d kstd |jd kr@|d|}|j}|d|jdf}t|D]6\}}| d | d }t|| d| d d ||}|t}|d| jkrt|| jk|}| |ddd}|d krT|jd krt||}nht|j}|d|jd 7<t |}tj ||d}t!|jd D]"}t|||||ddf<q.nxt"|jd|jd }||krt#j$||dd }|}t#j$||dd }t#j%||dd }|dd|jd|jd f}t&| tj'|dd }| jjdkr|j}|jd|jdd}|d kr8|dt(|t)| f}n|d krPtd*||jd krr||}||d}|| |df<qHt+||| }t |rt |g}||}| |fS)aH 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, than 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) # doctest: +SKIP >>> plt.show() # doctest: +SKIP ---------- >>> 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()) # doctest: +SKIP >>> plt.show() # doctest: +SKIP )dtypezaxis must be a scalar. ) precisioncfftrNrzmethod must be 'conv' or 'fft'rr)axis.g@zSelected scale of {} too small.),rr ZasarrayZ result_typeZ complex64 isinstancerrrZisscalararrayZ AxisErrorZ complex_cwtemptysizeshaper Zconjrkindreal ValueErrorndimZswapaxesZreshape enumerateZarangeZastypeintextractZconvolvelisttupleranger fftmodulerZifftsqrtdiffrrformatr )datascalesZwaveletZsampling_periodmethodrdtZdt_cplxZdt_outoutrZint_psixZdt_psiZ size_scale0Zfft_dataZdata_shape_preiscalestepjZ int_psi_scalerZ conv_shaperZ size_scaleZfft_wavZcoefdZ frequenciesrrrr %sL       &    "          )rrr)mathrrZ_extensions._pywtrrrrZ _functionsr r __all__numpyr Z scipy.fftZscipyrr+r ImportErrorZ scipy.fftpackZfftpackr rrrrs"