Source code for PyDynamic.misc.testsignals

"""A collection of test signals which can be used to simulate dynamic measurements

This module contains the following functions:

* :func:`GaussianPulse`: Generate a Gaussian pulse at t0 with height m0 and std sigma
* :func:`multi_sine`: Generate a multi-sine signal as summation of single sine signals
* :func:`rect`: Rectangular signal of given height and width :math:`t_1 - t_0`
* :func:`shocklikeGaussian`: Generate a signal that resembles a shock excitation as a
  Gaussian
* :func:`sine`: Generate a sine signal
* :func:`squarepulse`: Generates a series of rect functions to represent a square
  pulse signal
"""

__all__ = [
    "shocklikeGaussian",
    "GaussianPulse",
    "rect",
    "squarepulse",
    "corr_noise",
    "sine",
    "multi_sine",
]

import itertools

import numpy as np
from numpy import array, corrcoef, diff, sqrt, sum
from scipy.signal import periodogram
from scipy.special import comb

from .noise import white_gaussian


[docs] def shocklikeGaussian(time, t0, m0, sigma, noise=0.0): """Generate a signal that resembles a shock excitation as a Gaussian The main shock is followed by a smaller Gaussian of opposite sign. Parameters ---------- time : np.ndarray of shape (N,) time instants (equidistant) t0 : float time instant of signal maximum m0 : float signal maximum sigma : float std of main pulse noise : float, optional std of simulated signal noise Returns ------- x : np.ndarray of shape (N,) signal amplitudes at time instants """ x = ( -m0 * (time - t0) / sigma * np.exp(0.5) * np.exp(-((time - t0) ** 2) / (2 * sigma**2)) ) if noise > 0: x = white_gaussian(len(x), x, noise) return x
[docs] def GaussianPulse(time, t0, m0, sigma, noise=0.0): """Generate a Gaussian pulse at t0 with height m0 and std sigma Parameters ---------- time : np.ndarray of shape (N,) time instants (equidistant) t0 : float time instant of signal maximum m0 : float signal maximum sigma : float std of pulse noise : float, optional std of simulated signal noise Returns ------- x : np.ndarray of shape (N,) signal amplitudes at time instants """ x = m0 * np.exp(-((time - t0) ** 2) / (2 * sigma**2)) if noise > 0: x = white_gaussian(len(x), x, noise) return x
[docs] def rect(time, t0, t1, height=1, noise=0.0): """Rectangular signal of given height and width t1-t0 Parameters ---------- time : np.ndarray of shape (N,) time instants (equidistant) t0 : float time instant of rect lhs t1 : float time instant of rect rhs height : float signal maximum noise :float or numpy.ndarray of shape (N,), optional float: standard deviation of additive white gaussian noise ndarray: user-defined additive noise Returns ------- x : np.ndarray of shape (N,) signal amplitudes at time instants """ x = np.zeros((len(time),)) x[np.nonzero(time > t0)] = height x[np.nonzero(time > t1)] = 0.0 # add the noise if isinstance(noise, float): if noise > 0: x = white_gaussian(len(x), x, noise) elif isinstance(noise, np.ndarray): if x.size == noise.size: x += noise else: raise ValueError("Mismatching sizes of x and noise.") else: raise NotImplementedError( "The given noise is neither of type float nor numpy.ndarray. " ) return x
[docs] def squarepulse(time, height, numpulse=4, noise=0.0): """Generates a series of rect functions to represent a square pulse signal Parameters ---------- time : np.ndarray of shape (N,) time instants height : float height of the rectangular pulses numpulse : int number of pulses noise : float, optional std of simulated signal noise Returns ------- x : np.ndarray of shape (N,) signal amplitude at time instants """ width = (time[-1] - time[0]) / (2 * numpulse + 1) # width of each individual rect x = np.zeros_like(time) for k in range(numpulse): x += rect(time, (2 * k + 1) * width, (2 * k + 2) * width, height) if noise > 0: x = white_gaussian(len(x), x, noise) return x
[docs] def sine(time, amp=1.0, freq=1.0, noise=0.0): r"""Generate a batch of a sine signal with normally distributed noise Parameters ---------- time : np.ndarray of shape (N,) time instants amp : float, optional amplitude of the sine (default = 1.0) freq : float, optional frequency of the sine in Hz (default = 1.0) noise : float, optional std of simulated signal noise (default = 0.0) Returns ------- x : np.ndarray of shape (N,) signal amplitude at time instants """ # Design the sine signal according to e.g. # https://de.wikipedia.org/wiki/Sinuston#Mathematischer_Hintergrund x = amp * np.sin(2 * np.pi * freq * time) if noise: # noise = 0.0 (default) is equivalent to noise = False here x = white_gaussian(len(x), x, noise) return x
[docs] def multi_sine(time, amps, freqs, noise=0.0): r"""Generate a batch of a summation of sine signals with normally distributed noise Parameters ---------- time : np.ndarray of shape (N,) time instants amps : list or np.ndarray of shape (M,) of floating point values amplitudes of the sine signals freqs : list or np.ndarray of shape (M,) of floating point values frequencies of the sine signals in Hz noise : float, optional std of simulated signal noise (default = 0.0) Returns ------- x : np.ndarray of shape (N,) signal amplitude at time instants """ x = np.zeros_like(time) for amp, freq in zip(amps, freqs): x += sine(time=time, amp=amp, freq=freq, noise=0.0) if noise: x = white_gaussian(len(x), x, noise) return x
[docs] class corr_noise: """Base class for generation of a correlated noise process""" def __init__(self, w, sigma, seed=None): self.w = w self.sigma = sigma self.rst = np.random.RandomState(seed) self.Cw = None self.noise = None self.Sigma = None def calc_noise(self, N=100): z = self.rst.standard_normal(N + 4) noise = ( diff( diff( diff(diff(z * self.w**4) - 4 * z[1:] * self.w**3) + 6 * z[2:] * self.w**2 ) - 4 * z[3:] * self.w ) + z[4:] ) self.Cw = sqrt(sum([comb(4, el) ** 2 * self.w ** (2 * el) for el in range(5)])) self.noise = noise * self.sigma / self.Cw return self.noise def calc_noise2(self, N=100): P = np.ceil(1.5 * N) NT = self.rst.standard_normal(P) * self.sigma STD = np.zeros(21) STD[10] = 1.0 for _ in itertools.repeat(None, 5): NTtmp = NT.copy() NT[:-1] = NT[:-1] + self.w * NTtmp[1:] NT[-1] = NT[-1] + self.w * NTtmp[-1] NT[1:] = NT[1:] + self.w * NTtmp[:-1] NT[0] = NT[0] + self.w * NTtmp[-1] STDtmp = STD.copy() STD[1:] = STD[1:] + self.w * STDtmp[:-1] STD[:-1] = STD[:-1] + self.w * STDtmp[1:] NT /= np.linalg.norm(STD) self.noise = NT[:N] self.Cw = sqrt(sum([comb(4, el) ** 2 * self.w ** (2 * el) for el in range(5)])) return self.noise def calc_autocorr(self, lag=10): return array( [1] + [corrcoef(self.noise[:-i], self.noise[i:])[0, 1] for i in range(1, lag)] ) def calc_cov(self): def cw(k): if np.abs(k) > 4: return 0 c = sum( [ comb(4, el) * comb(4, np.abs(k) + el) * self.w ** (np.abs(k) + 2 * el) for el in range(5 - np.abs(k)) ] ) return c / self.Cw**2 N = len(self.noise) Sigma = np.zeros((N, N)) for m in range(N): for n in range(m, N): Sigma[m, n] = self.sigma**2 * cw(n - m) self.Sigma = Sigma + Sigma.T - np.diag(np.diag(Sigma)) return self.Sigma def calc_psd(self, noise=None, Fs=1.0, **kwargs): if isinstance(noise, np.ndarray): return periodogram(noise, fs=Fs, **kwargs) return periodogram(self.noise, fs=Fs, **kwargs)