comparison linear_fascile_evaluation.py @ 1:84a2e30b5404 draft

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0:cbfa8c336751

import matplotlib

from dipy.viz.colormap import line_colors
from dipy.viz import fvtk
from mpl_toolkits.axes_grid1 import AxesGrid

parser = argparse.ArgumentParser()
parser.add_argument('--candidates', dest='candidates', help='Candidates selection')
parser.add_argument('--output_life_candidates', dest='output_life_candidates', help='Output life candidates')
parser.add_argument('--output_life_optimized', dest='output_life_optimized', help='Output life optimized streamlines')

parser.add_argument('--output_error_histograms', dest='output_error_histograms', help='Output error histograms')
parser.add_argument('--output_spatial_errors', dest='output_spatial_errors', help='Output spatial errors')

args = parser.parse_args()

if not op.exists(args.candidates):
    from streamline_tools import *
else:
    # We'll need to know where the corpus callosum is from these variables:
    from dipy.data import (read_stanford_labels, fetch_stanford_t1, read_stanford_t1)
    hardi_img, gtab, labels_img = read_stanford_labels()
    labels = labels_img.get_data()
    cc_slice = labels == 2
    fetch_stanford_t1()
    t1 = read_stanford_t1()
    t1_data = t1.get_data()
    data = hardi_img.get_data()

# Read the candidates from file in voxel space:
candidate_sl = [s[0] for s in nib.trackvis.read(args.candidates, points_space='voxel')[0]]
# Visualize the initial candidate group of streamlines
# in 3D, relative to the anatomical structure of this brain.

1:84a2e30b5404

import matplotlib

from dipy.viz.colormap import line_colors
from dipy.viz import fvtk
from mpl_toolkits.axes_grid1 import AxesGrid
from dipy.data import read_stanford_labels, fetch_stanford_t1, read_stanford_t1

parser = argparse.ArgumentParser()
parser.add_argument('--candidates', dest='candidates', help='Candidates selection')
parser.add_argument('--output_life_candidates', dest='output_life_candidates', help='Output life candidates')
parser.add_argument('--output_life_optimized', dest='output_life_optimized', help='Output life optimized streamlines')

parser.add_argument('--output_error_histograms', dest='output_error_histograms', help='Output error histograms')
parser.add_argument('--output_spatial_errors', dest='output_spatial_errors', help='Output spatial errors')

args = parser.parse_args()

# We'll need to know where the corpus callosum is from these variables.
hardi_img, gtab, labels_img = read_stanford_labels()
labels = labels_img.get_data()
cc_slice = labels == 2
fetch_stanford_t1()
t1 = read_stanford_t1()
t1_data = t1.get_data()
data = hardi_img.get_data()

# Read the candidates from file in voxel space:
candidate_sl = [s[0] for s in nib.trackvis.read(args.candidates, points_space='voxel')[0]]
# Visualize the initial candidate group of streamlines
# in 3D, relative to the anatomical structure of this brain.