To get started we'll need to have a set of streamlines to work with. We'll use
EuDX along with the CsaOdfModel to make some streamlines. Let's import the
modules and download the data we'll be using.
"""
from dipy.tracking.eudx import EuDX
from dipy.reconst import peaks, shm
from dipy.tracking import utils
from dipy.data import read_stanford_labels, fetch_stanford_t1, read_stanford_t1
hardi_img, gtab, labels_img = read_stanford_labels()
data = hardi_img.get_data()
labels = labels_img.get_data()
fetch_stanford_t1()
t1 = read_stanford_t1()
t1_data = t1.get_data()
"""
We've loaded an image called ``labels_img`` which is a map of tissue types such
that every integer value in the array ``labels`` represents an anatomical
structure or tissue type [#]_. For this example, the image was created so that
white matter voxels have values of either 1 or 2. We'll use
``peaks_from_model`` to apply the ``CsaOdfModel`` to each white matter voxel
and estimate fiber orientations which we can use for tracking.
"""
white_matter = (labels == 1) | (labels == 2)
csamodel = shm.CsaOdfModel(gtab, 6)
csapeaks = peaks.peaks_from_model(model=csamodel,
data=data,
(150 orientations, b=2000s/mm^2) which is one of the standard example datasets
in DIPY.
"""
import numpy as np
from dipy.data import (read_stanford_labels, fetch_stanford_t1,
read_stanford_t1)
# Fix seed
np.random.seed(1)
# Read data
hardi_img, gtab, labels_img = read_stanford_labels()
data = hardi_img.get_data()
labels = labels_img.get_data()
affine = hardi_img.get_affine()
fetch_stanford_t1()
t1 = read_stanford_t1()
t1_data = t1.get_data()
# Select a relevant part of the data (left hemisphere)
# Coordinates given in x bounds, y bounds, z bounds
dshape = data.shape[:-1]
xa, xb, ya, yb, za, zb = [15, 42, 10, 65, 18, 65]
data_small = data[xa:xb, ya:yb, za:zb]
selectionmask = np.zeros(dshape, 'bool')
selectionmask[xa:xb, ya:yb, za:zb] = True
"""
The data is first fitted to Constant Solid Angle (CDA) ODF Model. CSA is a
good choice to estimate general fractional anisotropy (GFA), which the tissue
classifier can use to restrict fiber tracking to those areas where the ODF
