def denoise_ornlm(data_in, v=3, f=1, h=0.05):
from commands import getstatusoutput
from sys import path
# append python path for importing module
# N.B. PYTHONPATH variable should take care of it, but this is only used for Travis.
status, path_sct = getstatusoutput('echo $SCT_DIR')
path.append(path_sct + '/external/denoise/ornlm')
from ornlm import ornlm
from numpy import array, max, float64
dat = data_in.astype(float64)
denoised = array(ornlm(dat, v, f, max(dat)*h))
return denoised
import numpy as np
from dipy.data import fetch_stanford_hardi
from dipy.data import read_stanford_hardi
from ornlm import ornlm
from hsm import hsm
from ascm import ascm
if __name__=='__main__':
fetch_stanford_hardi()
img, gtab = read_stanford_hardi()
data = img.get_data()
S0 = data[..., 0].astype(np.float64)
mv=np.max(S0)
#Note: in P. Coupe et al. the rician noise was simulated as
#sqrt((f+x)^2 + (y)^2) where f is the pixel value and x and y are
#independent realizations of a random variable with Normal distribution,
#with mean=0 and standard deviation=h. The user must tune the 'h'
#parameter taking that into consideration
h=0.01*mv
f1=np.array(ornlm(S0, 3, 1, h))
f2=np.array(ornlm(S0, 3, 2, h))
fhsm=hsm(f1,f2)
filterd=ascm(S0,f1,f2,h)#this is reported to have the top performer
