| import numpy as np | |
| from scipy.signal import convolve | |
| def _ricker(points, a): | |
| A = 2 / (np.sqrt(3 * a) * (np.pi**0.25)) | |
| wsq = a**2 | |
| vec = np.arange(0, points) - (points - 1.0) / 2 | |
| xsq = vec**2 | |
| mod = (1 - xsq / wsq) | |
| gauss = np.exp(-xsq / (2 * wsq)) | |
| total = A * mod * gauss | |
| return total | |
| def _cwt(data, wavelet, widths, dtype=None, **kwargs): | |
| # Determine output type | |
| if dtype is None: | |
| if np.asarray(wavelet(1, widths[0], **kwargs)).dtype.char in 'FDG': | |
| dtype = np.complex128 | |
| else: | |
| dtype = np.float64 | |
| output = np.empty((len(widths), len(data)), dtype=dtype) | |
| for ind, width in enumerate(widths): | |
| N = np.min([10 * width, len(data)]) | |
| wavelet_data = np.conj(wavelet(N, width, **kwargs)[::-1]) | |
| output[ind] = convolve(data, wavelet_data, mode='same') | |
| return output | |