CSVD 0.1.6

Fast and Scalable Water Removal in MR Spectroscopic Data using Casorati Lanczos Singular Value Decomposition

CSVD

Fast and Scalable Water Removal in MR Spectroscopic Data using Casorati Lanczos Singular Value Decomposition

Example code:

import numpy as np
import matplotlib.pyplot as plt
from numpy.fft import fft, fftshift

from CSVD import CSVD

t=np.arange(0,1024)*.01
ampl = np.random.normal(1,0.2,(1000,1))
fr = np.random.normal(-15,0.1,(1000,1))
sig1 = ampl * np.exp(-2*t) *np.exp(2*np.pi*fr*t*1j)

ampl2 = np.random.normal(1,0.2,(1000,1))
fr2 = np.random.normal(0,0.1,(1000,1))
sig2 = ampl2 * np.exp(-2*t) *np.exp(2*np.pi*fr2*t*1j)

ampl3 = np.random.normal(1,0.2,(1000,1))
fr3 = np.random.normal(15,0.1,(1000,1))
sig3 = ampl3 * np.exp(-2*t) *np.exp(2*np.pi*fr3*t*1j)

sig = sig1 + sig2 +sig3
noise = np.random.normal(0,1,(sig.shape)) + 1j*np.random.normal(0,1,(sig.shape))
sig = sig + 0.1*noise

csvd = CSVD(sig.T, 0.01)

sig_ = csvd.remove('auto',([-5,-20],[5,-10]),3)
plt.plot(fftshift(fft(sig[0,:])).T)
plt.plot(fftshift(fft(sig_[:,0])).T)
plt.legend(['Orginal signal', 'Water-removed signal'])
plt.savefig('example.jpg')
plt.show()

output:

Acknowledgments

This project has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 813120.