python-dipy 0.10.1-1 / usr / lib / python2.7 / dist-packages / dipy / tracking / tests / test_distances.py

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from __future__ import division, print_function, absolute_import

import numpy as np
import nose
from nose.tools import assert_true, assert_false, assert_equal, assert_almost_equal
from numpy.testing import assert_array_equal, assert_array_almost_equal
from dipy.tracking import metrics as tm
from dipy.tracking import distances as pf


def test_LSCv2():
    xyz1=np.array([[1,0,0],[2,0,0],[3,0,0]],dtype='float32')
    xyz2=np.array([[1,0,0],[1,2,0],[1,3,0]],dtype='float32')
    xyz3=np.array([[1.1,0,0],[1,2,0],[1,3,0]],dtype='float32')
    xyz4=np.array([[1,0,0],[2.1,0,0],[3,0,0]],dtype='float32')
    
    xyz5=np.array([[100,0,0],[200,0,0],[300,0,0]],dtype='float32')
    xyz6=np.array([[0,20,0],[0,40,0],[300,50,0]],dtype='float32')
    
    T=[xyz1,xyz2,xyz3,xyz4,xyz5,xyz6]
    C=pf.local_skeleton_clustering(T,0.2)
    
    #print C
    #print len(C)
    
    C2=pf.local_skeleton_clustering_3pts(T,0.2)
    
    #print C2
    #print len(C2)
            
    #"""
    
    for i in range(40):
        xyz=np.random.rand(3,3).astype('f4')
        T.append(xyz)
            
    from time import time
    t1=time()
    C3=pf.local_skeleton_clustering(T,.5)
    t2=time()
    print(t2-t1)
    print(len(C3))
    
    t1=time()
    C4=pf.local_skeleton_clustering_3pts(T,.5)
    t2=time()
    print(t2-t1)
    print(len(C4))

    for c in C3:
        assert_equal(np.sum(C3[c]['hidden']-C4[c]['hidden']),0)
    
    T2=[]
    for i in range(10**4):
        xyz=np.random.rand(10,3).astype('f4')
        T2.append(xyz)
    t1=time()
    C5=pf.local_skeleton_clustering(T2,.5)
    t2=time()
    print(t2-t1)
    print(len(C5))
    
    from dipy.data import get_data
    from nibabel import trackvis as tv
    try:
        from dipy.viz import fvtk
    except ImportError as e:
        raise nose.plugins.skip.SkipTest(
            'Fails to import dipy.viz due to %s' % str(e))
    
    streams,hdr=tv.read(get_data('fornix'))
    T3=[tm.downsample(s[0],6) for s in streams]    
    

    print('lenT3',len(T3))
    
    C=pf.local_skeleton_clustering(T3,10.)
    
    print('lenC',len(C))
    
    """
    
    r=fvtk.ren()
    colors=np.zeros((len(C),3))
    for c in C:
        color=np.random.rand(3)
        for i in C[c]['indices']:
            fvtk.add(r,fvtk.line(T3[i],color))
        colors[c]=color
    fvtk.show(r)
    fvtk.clear(r)
    skeleton=[]
    
    def width(w):
        if w<1:
            return 1
        else:
            return w
    
    for c in C:
    
        bundle=[T3[i] for i in C[c]['indices']]
        si,s=pf.most_similar_track_mam(bundle,'avg')    
        skeleton.append(bundle[si])
        fvtk.label(r,text=str(len(bundle)),pos=(bundle[si][-1]),scale=(2,2,2))
        fvtk.add(r,fvtk.line(skeleton,colors,opacity=1,linewidth=width(len(bundle)/10.)))
    
    fvtk.show(r)
    
    """
    
def test_bundles_distances_mam():
    xyz1A = np.array([[0,0,0],[1,0,0],[2,0,0],[3,0,0]],dtype='float32')
    xyz2A = np.array([[0,1,1],[1,0,1],[2,3,-2]],dtype='float32')
    xyz1B = np.array([[-1,0,0],[2,0,0],[2,3,0],[3,0,0]],dtype='float32')
    tracksA = [xyz1A, xyz2A]
    tracksB = [xyz1B, xyz1A, xyz2A]
    for metric in ('avg', 'min', 'max'):       
        DM2 = pf.bundles_distances_mam(tracksA, tracksB, metric=metric)
        
def test_bundles_distances_mdf():
    xyz1A = np.array([[0,0,0],[1,0,0],[2,0,0]],dtype='float32')    
    xyz2A = np.array([[0,1,1],[1,0,1],[2,3,-2]],dtype='float32')
    xyz3A = np.array([[0,0,0],[1,0,0],[3,0,0]],dtype='float32')    
    xyz1B = np.array([[-1,0,0],[2,0,0],[2,3,0]],dtype='float32')
       
    tracksA = [xyz1A,xyz2A]
    tracksB = [xyz1B, xyz1A, xyz2A]           
    DM2 = pf.bundles_distances_mdf(tracksA, tracksB)
    
    tracksA = [xyz1A,xyz1A]
    tracksB = [xyz1A,xyz1A]
    
    DM2 = pf.bundles_distances_mdf(tracksA, tracksB)
    assert_array_almost_equal(DM2,np.zeros((2,2)))
    
    tracksA = [xyz1A,xyz3A]
    tracksB = [xyz2A]
    
    DM2 = pf.bundles_distances_mdf(tracksA, tracksB)
    print(DM2)
        
    #assert_array_almost_equal(DM2,np.zeros((2,2)))    
    DM=np.zeros(DM2.shape)
    for (a,ta) in enumerate(tracksA):
        for (b,tb) in enumerate(tracksB):
            md=np.sum(np.sqrt(np.sum((ta-tb)**2,axis=1)))/3.
            md2=np.sum(np.sqrt(np.sum((ta-tb[::-1])**2,axis=1)))/3.
            DM[a,b]=np.min((md,md2))
    print(DM)
    
    print('--------------')
    for t in tracksA:
        print(t)
    print('--------------')
    for t in tracksB:
        print(t)
        
    assert_array_almost_equal(DM,DM2,4)
    
def test_mam_distances():
    xyz1 = np.array([[0,0,0],[1,0,0],[2,0,0],[3,0,0]])
    xyz2 = np.array([[0,1,1],[1,0,1],[2,3,-2]])
    # dm=array([[ 2,  2, 17], [ 3,  1, 14], [6,  2, 13], [11,  5, 14]])
    # this is the distance matrix between points of xyz1
    # and points of xyz2
    xyz1=xyz1.astype('float32')
    xyz2=xyz2.astype('float32')
    zd2 = pf.mam_distances(xyz1,xyz2)
    assert_almost_equal( zd2[0], 1.76135602742)

def test_approx_ei_traj():
    
    segs=100
    t=np.linspace(0,1.75*2*np.pi,segs)    
    x =t 
    y=5*np.sin(5*t)
    z=np.zeros(x.shape)    
    xyz=np.vstack((x,y,z)).T
    xyza=pf.approx_polygon_track(xyz)
    assert_equal(len(xyza), 27)

def test_approx_mdl_traj():
    
    t=np.linspace(0,1.75*2*np.pi,100)
    x = np.sin(t)
    y = np.cos(t)
    z = t    
    xyz=np.vstack((x,y,z)).T     
    xyza1 = pf.approximate_mdl_trajectory(xyz,alpha=1.)
    xyza2 = pf.approximate_mdl_trajectory(xyz,alpha=2.)    
    assert_equal(len(xyza1), 10)
    assert_equal(len(xyza2), 8)
    assert_array_almost_equal( xyza1, np.array([[  0.00000000e+00,   1.00000000e+00,   0.00000000e+00],
       [  9.39692621e-01,   3.42020143e-01,   1.22173048e+00],
       [  6.42787610e-01,  -7.66044443e-01,   2.44346095e+00],
       [ -5.00000000e-01,  -8.66025404e-01,   3.66519143e+00],
       [ -9.84807753e-01,   1.73648178e-01,   4.88692191e+00],
       [ -1.73648178e-01,   9.84807753e-01,   6.10865238e+00],
       [  8.66025404e-01,   5.00000000e-01,   7.33038286e+00],
       [  7.66044443e-01,  -6.42787610e-01,   8.55211333e+00],
       [ -3.42020143e-01,  -9.39692621e-01,   9.77384381e+00],
       [ -1.00000000e+00,  -4.28626380e-16,   1.09955743e+01]]))
    
    assert_array_almost_equal(xyza2, np.array([[  0.00000000e+00,   1.00000000e+00,   0.00000000e+00],
       [  9.95471923e-01,  -9.50560433e-02,   1.66599610e+00],
       [ -1.89251244e-01,  -9.81928697e-01,   3.33199221e+00],
       [ -9.59492974e-01,   2.81732557e-01,   4.99798831e+00],
       [  3.71662456e-01,   9.28367933e-01,   6.66398442e+00],
       [  8.88835449e-01,  -4.58226522e-01,   8.32998052e+00],
       [ -5.40640817e-01,  -8.41253533e-01,   9.99597663e+00],
       [ -1.00000000e+00,  -4.28626380e-16,   1.09955743e+01]]))
    
    
    
def test_point_track_sq_distance():
    
    t=np.array([[0,0,0],[1,1,1],[2,2,2]],dtype='f4')
    p=np.array([-1,-1.,-1],dtype='f4')
    assert_equal( pf.point_track_sq_distance_check(t,p,.2**2), False)    
    pf.point_track_sq_distance_check(t,p,2**2), True
    t=np.array([[0,0,0],[1,0,0],[2,2,0]],dtype='f4')
    p=np.array([.5,0,0],dtype='f4')
    assert_equal( pf.point_track_sq_distance_check(t,p,.2**2), True)
    p=np.array([.5,1,0],dtype='f4')
    assert_equal( pf.point_track_sq_distance_check(t,p,.2**2), False)
    
def test_track_roi_intersection_check():    
    roi=np.array([[0,0,0],[1,0,0],[2,0,0]],dtype='f4')    
    t=np.array([[0,0,0],[1,1,1],[2,2,2]],dtype='f4')
    assert_equal( pf.track_roi_intersection_check(t,roi,1), True)
    t=np.array([[0,0,0],[1,0,0],[2,2,2]],dtype='f4')
    assert_equal(pf.track_roi_intersection_check(t,roi,1), True)
    t=np.array([[1,1,0],[1,0,0],[1,-1,0]],dtype='f4')
    assert_equal( pf.track_roi_intersection_check(t,roi,1), True)    
    t=np.array([[4,0,0],[4,1,1],[4,2,0]],dtype='f4')
    assert_equal(pf.track_roi_intersection_check(t,roi,1), False)
    
    
def test_minimum_distance():    
    xyz1=np.array([[1,0,0],[2,0,0]],dtype='float32')
    xyz2=np.array([[3,0,0],[4,0,0]],dtype='float32')
    assert_equal(pf.minimum_closest_distance(xyz1,xyz2), 1.0)
    
    
def test_most_similar_mam():
    xyz1 = np.array([[0,0,0],[1,0,0],[2,0,0],[3,0,0]],dtype='float32')
    xyz2 = np.array([[0,1,1],[1,0,1],[2,3,-2]],dtype='float32')
    xyz3 = np.array([[-1,0,0],[2,0,0],[2,3,0],[3,0,0]],dtype='float32')
    tracks=[xyz1,xyz2,xyz3]
    for metric in ('avg', 'min', 'max'):        
        #pf should be much faster and the results equivalent
        si2,s2=pf.most_similar_track_mam(tracks,metric=metric)       
    
    


def test_cut_plane():
    dt = np.dtype(np.float32)
    refx = np.array([[0,0,0],[1,0,0],[2,0,0],[3,0,0]],dtype=dt)
    bundlex = [np.array([[0.5,1,0],[1.5,2,0],[2.5,3,0]],dtype=dt), 
               np.array([[0.5,2,0],[1.5,3,0],[2.5,4,0]],dtype=dt),
               np.array([[0.5,1,1],[1.5,2,2],[2.5,3,3]],dtype=dt),
               np.array([[-0.5,2,-1],[-1.5,3,-2],[-2.5,4,-3]],dtype=dt)]
    expected_hit0 = [
        [ 1.        ,  1.5       ,  0.        ,  0.70710683,  0.        ],
        [ 1.        ,  2.5       ,  0.        ,  0.70710677,  1.        ],
        [ 1.        ,  1.5       ,  1.5       ,  0.81649661,  2.        ]]
    expected_hit1 = [
        [ 2.        ,  2.5       ,  0.        ,  0.70710677,  0.        ],
        [ 2.        ,  3.5       ,  0.        ,  0.70710677,  1.        ],
        [ 2.        ,  2.5       ,  2.5       ,  0.81649655,  2.        ]]
    hitx=pf.cut_plane(bundlex,refx)
    assert_array_almost_equal(hitx[0], expected_hit0)
    assert_array_almost_equal(hitx[1], expected_hit1)
    # check that algorithm allows types other than float32
    bundlex[0] = np.asarray(bundlex[0], dtype=np.float64)
    hitx=pf.cut_plane(bundlex,refx)
    assert_array_almost_equal(hitx[0], expected_hit0)
    assert_array_almost_equal(hitx[1], expected_hit1)
    refx = np.asarray(refx, dtype=np.float64)
    hitx=pf.cut_plane(bundlex,refx)
    assert_array_almost_equal( hitx[0], expected_hit0)
    assert_array_almost_equal( hitx[1], expected_hit1)

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