from pyfat.algorithm.fiber_clustering import FibClustering
from pyfat.viz.colormap import create_random_colormap
from pyfat.viz.fiber_simple_viz_advanced import fiber_simple_3d_show_advanced
fib = '/home/brain/workingdir/data/dwi/hcp/preprocessed/' \
'response_dhollander/101107/Diffusion/1M_20_01_20dynamic250_SD_Stream_rhemi_occipital5.tck'
# fib = '/home/brain/workingdir/data/dwi/hcp/preprocessed/' \
# 'response_dhollander/101107/Diffusion/1M_20_01_20dynamic250_SD_Stream_occipital8_lr5.tck'
img_path = '/home/brain/workingdir/data/dwi/hcp/preprocessed/' \
'response_dhollander/101107/Structure/T1w_acpc_dc_restore_brain1.25.nii.gz'
img = nib.load(img_path)
fa = Fasciculus(fib)
streamlines = fa.get_data()
length_t = fa.get_lengths()
ind = length_t > 10
streamlines = streamlines[ind]
fa.set_data(streamlines)
fibcluster = FibClustering(fa)
print len(streamlines)
# 1
qb = QuickBundles(streamlines, 2)
clusters = qb.clusters()
print qb.clusters_sizes()
indexs = []
for i in range(len(clusters)):
if clusters[i]['N'] >= 400:
indexs += clusters[i]['indices']
# !/usr/bin/python
# -*- coding: utf-8 -*-
from __future__ import division
from pyfat.io.load import load_tck, load_trk
from pyfat.viz.visualization import show
from pyfat.core.dataobject import Fasciculus
# tck
tck_path = '/home/brain/workingdir/data/dwi/hcp/preprocessed/' \
'response_dhollander/101107/Diffusion/1M_20_01_20dynamic250_SD_Stream_lh_occipital8.tck'
fas = Fasciculus(tck_path)
lengths = fas.get_lengths()
show(lengths)
R = fas.get_lr_ratio()
show(R, title='Rat histogram', xlabel='Rat')
