/usr/share/pyshared/dipy/tracking/learning.py is in python-dipy 0.5.0-3.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 | ''' Learning algorithms for tractography'''
import numpy as np
import dipy.tracking.distances as pf
def detect_corresponding_tracks(indices,tracks1,tracks2):
''' Detect corresponding tracks from list tracks1 to list tracks2
where tracks1 & tracks2 are lists of tracks
Parameters
------------
indices : sequence
of indices of tracks1 that are to be detected in tracks2
tracks1 : sequence
of tracks as arrays, shape (N1,3) .. (Nm,3)
tracks2 : sequence
of tracks as arrays, shape (M1,3) .. (Mm,3)
Returns
---------
track2track : array (N,2) where N is len(indices) of int
it shows the correspondance in the following way:
the first column is the current index in tracks1
the second column is the corresponding index in tracks2
Examples
----------
>>> import numpy as np
>>> import dipy.tracking.learning as tl
>>> A=np.array([[0,0,0],[1,1,1],[2,2,2]])
>>> B=np.array([[1,0,0],[2,0,0],[3,0,0]])
>>> C=np.array([[0,0,-1],[0,0,-2],[0,0,-3]])
>>> bundle1=[A,B,C]
>>> bundle2=[B,A]
>>> indices=[0,1]
>>> arr=tl.detect_corresponding_tracks(indices,bundle1,bundle2)
Notes
-------
To find the corresponding tracks we use mam_distances with 'avg' option. Then we calculate the
argmin of all the calculated distances and return it for every index. (See 3rd column of arr
in the example given below.
'''
li=len(indices)
track2track=np.zeros((li,2))
cnt=0
for i in indices:
rt=[pf.mam_distances(tracks1[i],t,'avg') for t in tracks2]
rt=np.array(rt)
track2track[cnt]=np.array([i,rt.argmin()])
cnt+=1
return track2track.astype(int)
def detect_corresponding_tracks_plus(indices,tracks1,indices2,tracks2):
''' Detect corresponding tracks from 1 to 2 where tracks1 & tracks2 are sequences of tracks
Parameters
------------
indices : sequence
of indices of tracks1 that are to be detected in tracks2
tracks1 : sequence
of tracks as arrays, shape (N1,3) .. (Nm,3)
indices2 : sequence
of indices of tracks2 in the initial brain
tracks2 : sequence
of tracks as arrays, shape (M1,3) .. (Mm,3)
Returns
---------
track2track : array (N,2) where N is len(indices)
of int showing the correspondance in th following way
the first colum is the current index of tracks1
the second column is the corresponding index in tracks2
Examples
----------
>>> import numpy as np
>>> import dipy.tracking.learning as tl
>>> A=np.array([[0,0,0],[1,1,1],[2,2,2]])
>>> B=np.array([[1,0,0],[2,0,0],[3,0,0]])
>>> C=np.array([[0,0,-1],[0,0,-2],[0,0,-3]])
>>> bundle1=[A,B,C]
>>> bundle2=[B,A]
>>> indices=[0,1]
>>> indices2=indices
>>> arr=tl.detect_corresponding_tracks_plus(indices,bundle1,indices2,bundle2)
Notes
-------
To find the corresponding tracks we use mam_distances with 'avg' option. Then we calculate the
argmin of all the calculated distances and return it for every index. (See 3rd column of arr
in the example given below.
See also
----------
distances.mam_distances
'''
li=len(indices)
track2track=np.zeros((li,2))
cnt=0
for i in indices:
rt=[pf.mam_distances(tracks1[i],t,'avg') for t in tracks2]
rt=np.array(rt)
track2track[cnt]=np.array([i,indices2[rt.argmin()]])
cnt+=1
return track2track.astype(int)
|