 Cannot retrieve contributors at this time
 # -*- coding: utf-8 -*- """ Seismic wavelets. :copyright: 2015 Agile Geoscience :license: Apache 2.0 """ from collections import namedtuple import numpy as np import scipy.signal def generic(func, duration, dt, f, return_t=False, taper='blackman'): """ Generic wavelet generator: applies a window to a continuous function. Args: func (function): The continuous function, taking t, f as arguments. duration (float): The length in seconds of the wavelet. dt (float): The sample interval in seconds (often one of 0.001, 0.002, or 0.004). f (ndarray): Dominant frequency of the wavelet in Hz. If a sequence is passed, you will get a 2D array in return, one row per frequency. return_t (bool): If True, then the function returns a tuple of wavelet, time-basis, where time is the range from -duration/2 to duration/2 in steps of dt. taper (str or function): The window or tapering function to apply. To use one of NumPy's functions, pass 'bartlett', 'blackman' (the default), 'hamming', or 'hanning'; to apply no tapering, pass 'none'. To apply your own function, pass a function taking only the length of the window and returning the window function. Returns: ndarray. wavelet(s) with centre frequency f sampled on t. """ f = np.asanyarray(f).reshape(-1, 1) t = np.arange(-duration/2., duration/2., dt) t[t == 0] = 1e-12 # Avoid division by zero. f[f == 0] = 1e-12 # Avoid division by zero. w = np.squeeze(func(t, f)) if taper: tapers = { 'bartlett': np.bartlett, 'blackman': np.blackman, 'hamming': np.hamming, 'hanning': np.hanning, 'none': lambda _: 1, } taper = tapers.get(taper, taper) w *= taper(t.size) if return_t: Wavelet = namedtuple('Wavelet', ['amplitude', 'time']) return Wavelet(w, t) else: return w def sinc(duration, dt, f, return_t=False, taper='blackman'): """ sinc function centered on t=0, with a dominant frequency of f Hz. If you pass a 1D array of frequencies, you get a wavelet bank in return. Args: duration (float): The length in seconds of the wavelet. dt (float): The sample interval in seconds (often one of 0.001, 0.002, or 0.004). f (ndarray): Dominant frequency of the wavelet in Hz. If a sequence is passed, you will get a 2D array in return, one row per frequency. return_t (bool): If True, then the function returns a tuple of wavelet, time-basis, where time is the range from -duration/2 to duration/2 in steps of dt. taper (str or function): The window or tapering function to apply. To use one of NumPy's functions, pass 'bartlett', 'blackman' (the default), 'hamming', or 'hanning'; to apply no tapering, pass 'none'. To apply your own function, pass a function taking only the length of the window and returning the window function. Returns: ndarray. sinc wavelet(s) with centre frequency f sampled on t. """ def func(t_, f_): return np.sin(2*np.pi*f_*t_) / (2*np.pi*f_*t_) return generic(func, duration, dt, f, return_t, taper) def cosine(duration, dt, f, return_t=False, taper='gaussian', sigma=None): """ With the default Gaussian window, equivalent to a 'modified Morlet' also sometimes called a 'Gabor' wavelet. The bruges.filters.gabor function returns a similar shape, but with a higher mean frequancy, somewhere between a Ricker and a cosine (pure tone). If you pass a 1D array of frequencies, you get a wavelet bank in return. Args: duration (float): The length in seconds of the wavelet. dt (float): The sample interval in seconds (often one of 0.001, 0.002, or 0.004). f (ndarray): Dominant frequency of the wavelet in Hz. If a sequence is passed, you will get a 2D array in return, one row per frequency. return_t (bool): If True, then the function returns a tuple of wavelet, time-basis, where time is the range from -duration/2 to duration/2 in steps of dt. taper (str or function): The window or tapering function to apply. To use one of NumPy's functions, pass 'bartlett', 'blackman' (the default), 'hamming', or 'hanning'; to apply no tapering, pass 'none'. To apply your own function, pass a function taking only the length of the window and returning the window function. sigma (float): Width of the default Gaussian window, in seconds. Defaults to 1/8 of the duration. Returns: ndarray. sinc wavelet(s) with centre frequency f sampled on t. """ if sigma is None: sigma = duration / 8 def func(t_, f_): return np.cos(2 * np.pi * f_ * t_) def taper(length): return scipy.signal.gaussian(length, sigma/dt) return generic(func, duration, dt, f, return_t, taper) def gabor(duration, dt, f, return_t=False): """ Generates a Gabor wavelet with a peak frequency f0 at time t. https://en.wikipedia.org/wiki/Gabor_wavelet If you pass a 1D array of frequencies, you get a wavelet bank in return. Args: duration (float): The length in seconds of the wavelet. dt (float): The sample interval in seconds (often one of 0.001, 0.002, or 0.004). f (ndarray): Centre frequency of the wavelet in Hz. If a sequence is passed, you will get a 2D array in return, one row per frequency. return_t (bool): If True, then the function returns a tuple of wavelet, time-basis, where time is the range from -duration/2 to duration/2 in steps of dt. Returns: ndarray. Gabor wavelet(s) with centre frequency f sampled on t. """ def func(t_, f_): return np.exp(-2 * f_**2 * t_**2) * np.cos(2 * np.pi * f_ * t_) return generic(func, duration, dt, f, return_t) def ricker(duration, dt, f, return_t=False): """ Also known as the mexican hat wavelet, models the function: .. math:: A = (1 - 2 \pi^2 f^2 t^2) e^{-\pi^2 f^2 t^2} If you pass a 1D array of frequencies, you get a wavelet bank in return. Args: duration (float): The length in seconds of the wavelet. dt (float): The sample interval in seconds (often one of 0.001, 0.002, or 0.004). f (ndarray): Centre frequency of the wavelet in Hz. If a sequence is passed, you will get a 2D array in return, one row per frequency. return_t (bool): If True, then the function returns a tuple of wavelet, time-basis, where time is the range from -duration/2 to duration/2 in steps of dt. Returns: ndarray. Ricker wavelet(s) with centre frequency f sampled on t. .. plot:: plt.plot(bruges.filters.ricker(.5, 0.002, 40)) """ f = np.asanyarray(f).reshape(-1, 1) t = np.arange(-duration/2, duration/2, dt) pft2 = (np.pi * f * t)**2 w = np.squeeze((1 - (2 * pft2)) * np.exp(-pft2)) if return_t: RickerWavelet = namedtuple('RickerWavelet', ['amplitude', 'time']) return RickerWavelet(w, t) else: return w def sweep(duration, dt, f, autocorrelate=True, return_t=False, taper='blackman', **kwargs): """ Generates a linear frequency modulated wavelet (sweep). Wraps scipy.signal.chirp, adding dimensions as necessary. Args: duration (float): The length in seconds of the wavelet. dt (float): is the sample interval in seconds (usually 0.001, 0.002, or 0.004) f (ndarray): Any sequence like (f1, f2). A list of lists will create a wavelet bank. autocorrelate (bool): Whether to autocorrelate the sweep(s) to create a wavelet. Default is True. return_t (bool): If True, then the function returns a tuple of wavelet, time-basis, where time is the range from -duration/2 to duration/2 in steps of dt. taper (str or function): The window or tapering function to apply. To use one of NumPy's functions, pass 'bartlett', 'blackman' (the default), 'hamming', or 'hanning'; to apply no tapering, pass 'none'. To apply your own function, pass a function taking only the length of the window and returning the window function. **kwargs: Further arguments are passed to scipy.signal.chirp. They are method ('linear','quadratic','logarithmic'), phi (phase offset in degrees), and vertex_zero. Returns: ndarray: The waveform. """ t0, t1 = -duration/2, duration/2 t = np.arange(t0, t1, dt) f = np.asanyarray(f).reshape(-1, 1) f1, f2 = f c = [scipy.signal.chirp(t, f1_+(f2_-f1_)/2., t1, f2_, **kwargs) for f1_, f2_ in zip(f1, f2)] if autocorrelate: w = [np.correlate(c_, c_, mode='same') for c_ in c] w = np.squeeze(w) / np.amax(w) if taper: funcs = { 'bartlett': np.bartlett, 'blackman': np.blackman, 'hamming': np.hamming, 'hanning': np.hanning, 'none': lambda x: x, } func = funcs.get(taper, taper) w *= func(t.size) if return_t: Sweep = namedtuple('Sweep', ['amplitude', 'time']) return Sweep(w, t) else: return w def ormsby(duration, dt, f, return_t=False): """ The Ormsby wavelet requires four frequencies which together define a trapezoid shape in the spectrum. The Ormsby wavelet has several sidelobes, unlike Ricker wavelets. Args: duration (float): The length in seconds of the wavelet. dt (float): The sample interval in seconds (usually 0.001, 0.002, or 0.004). f (ndarray): Sequence of form (f1, f2, f3, f4), or list of lists of frequencies, which will return a 2D wavelet bank. Returns: ndarray: A vector containing the Ormsby wavelet, or a bank of them. """ f = np.asanyarray(f).reshape(-1, 1) try: f1, f2, f3, f4 = f except ValueError: raise ValueError("The last dimension must be 4") def numerator(f, t): return (np.sinc(f * t)**2) * ((np.pi * f) ** 2) pf43 = (np.pi * f4) - (np.pi * f3) pf21 = (np.pi * f2) - (np.pi * f1) t = np.arange(-duration/2, duration/2, dt) w = ((numerator(f4, t)/pf43) - (numerator(f3, t)/pf43) - (numerator(f2, t)/pf21) + (numerator(f1, t)/pf21)) w = np.squeeze(w) / np.amax(w) if return_t: OrmsbyWavelet = namedtuple('OrmsbyWavelet', ['amplitude', 'time']) return OrmsbyWavelet(w, t) else: return w def rotate_phase(w, phi, degrees=False): """ Performs a phase rotation of wavelet or wavelet bank using: The analytic signal can be written in the form S(t) = A(t)exp(j*theta(t)) where A(t) = magnitude(hilbert(w(t))) and theta(t) = angle(hilbert(w(t)) then a constant phase rotation phi would produce the analytic signal S(t) = A(t)exp(j*(theta(t) + phi)). To get the non analytic signal we take real(S(t)) == A(t)cos(theta(t) + phi) == A(t)(cos(theta(t))cos(phi) - sin(theta(t))sin(phi)) <= trig idenity == w(t)cos(phi) - h(t)sin(phi) A = w(t)Cos(phi) - h(t)Sin(phi) Where w(t) is the wavelet and h(t) is its Hilbert transform. Args: w (ndarray): The wavelet vector, can be a 2D wavelet bank. phi (float): The phase rotation angle (in radians) to apply. degrees (bool): If phi is in degrees not radians. Returns: The phase rotated signal (or bank of signals). """ if degrees: phi = phi * np.pi / 180.0 a = scipy.signal.hilbert(w, axis=0) w = (np.real(a) * np.cos(phi) - np.imag(a) * np.sin(phi)) return w