pytomography.utils.fourier_filters#

Module Contents#

Classes#

RampFilter

Implementation of the Ramp filter \(\Pi(\omega) = |\omega|\)

HammingFilter

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 and wh should be expressed as fractions of the Nyquist frequency (i.e. wh=0.93 represents 93% the Nyquist frequency).

class pytomography.utils.fourier_filters.RampFilter[source]#

Implementation of the Ramp filter \(\Pi(\omega) = |\omega|\)

__call__(w)[source]#
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 and wh should be expressed as fractions of the Nyquist frequency (i.e. wh=0.93 represents 93% the Nyquist frequency).

__call__(w)[source]#