Getting started with Dipy
=========================
In diffusion MRI (dMRI) usually we use three types of files, a Nifti file with the
diffusion weighted data, and two text files one with b-values and
one with the b-vectors.
In Dipy we provide tools to load and process these files and we also provide
access to publically available datasets for those who haven't acquired yet
their own datasets.
With the following commands we can download a dMRI dataset
"""
from dipy.data import fetch_sherbrooke_3shell
fetch_sherbrooke_3shell()
"""
By default these datasets will go in the .dipy folder inside your home directory.
Here is how you can access them.
"""
from os.path import expanduser, join
home = expanduser('~')
"""
``dname`` holds the directory name where the 3 files are in.
"""
dname = join(home, '.dipy', 'sherbrooke_3shell')
Using the non-local means filter [Coupe08]_ and [Coupe11]_ and you can denoise
3D or 4D images and boost the SNR of your datasets. You can also decide between
modeling the noise as Gaussian or Rician (default).
"""
import numpy as np
import nibabel as nib
import matplotlib.pyplot as plt
from time import time
from dipy.denoise.non_local_means import non_local_means
from dipy.denoise.nlmeans import nlmeans
from dipy.denoise.noise_estimate import estimate_sigma
from dipy.data import fetch_sherbrooke_3shell, read_sherbrooke_3shell
fetch_sherbrooke_3shell()
img, gtab = read_sherbrooke_3shell()
data = img.get_data()
affine = img.get_affine()
mask = data[..., 0] > 80
# We select only one volume for the example to run quickly.
data = data[..., 1]
print("vol size", data.shape)
# lets create a noisy data with gaussian data
"""
In order to call ``non_local_means`` first you need to estimate the standard
