pytomography.utils.fourier_filters
#
Module Contents#
Classes#
Implementation of the Ramp filter \(\Pi(\omega) = |\omega|\) |
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Implementation of the Hamming filter given by \(\Pi(\omega) = \frac{1}{2}\left(1+\cos\left(\frac{\pi(|\omega|-\omega_L)}{\omega_H-\omega_L} \right)\right)\) for \(\omega_L \leq |\omega| < \omega_H\) and \(\Pi(\omega) = 1\) for \(|\omega| \leq \omega_L\) and \(\Pi(\omega) = 0\) for \(|\omega|>\omega_H\). Arguments |
- class pytomography.utils.fourier_filters.RampFilter[source]#
Implementation of the Ramp filter \(\Pi(\omega) = |\omega|\)
- class pytomography.utils.fourier_filters.HammingFilter(wl, wh)[source]#
Implementation of the Hamming filter given by \(\Pi(\omega) = \frac{1}{2}\left(1+\cos\left(\frac{\pi(|\omega|-\omega_L)}{\omega_H-\omega_L} \right)\right)\) for \(\omega_L \leq |\omega| < \omega_H\) and \(\Pi(\omega) = 1\) for \(|\omega| \leq \omega_L\) and \(\Pi(\omega) = 0\) for \(|\omega|>\omega_H\). Arguments
wl
andwh
should be expressed as fractions of the Nyquist frequency (i.e.wh=0.93
represents 93% the Nyquist frequency).