Metadata-Version: 2.1
Name: CSVD
Version: 0.1.4
Summary: Fast and Scalable Water Removal in MR Spectroscopic Data using Casorati Lanczos Singular Value Decomposition
Home-page: https://github.com/pypa/sampleproject
Author: amir shamaei
Author-email: amirshamaei@isibrno.cz
Project-URL: Bug Tracker, https://github.com/amirshamaei/CSVD
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE

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

**Example code:**
```python

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

from src.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)

sig = sig1 + sig2
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',[-20,-10],3)
plt.plot(fftshift(fft(sig[0,:])).T)
plt.plot(fftshift(fft(sig_[:,0])).T)
plt.legend(['Orginal signal', 'Water-removed signal'])
plt.show()
```
**output:**
![example](/test/example.jpg)


**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.
