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
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
|
"""
Module containing several functions to compute SPIKE profiles and distances
Copyright 2014-2015, Mario Mulansky <mario.mulansky@gmx.net>
Distributed under the BSD License
"""
from pyspike import PieceWiseLinFunc
from pyspike.generic import _generic_profile_multi, _generic_distance_matrix
############################################################
# spike_profile
############################################################
def spike_profile(spikes1, spikes2):
""" Computes the spike-distance profile S_spike(t) of the two given spike
trains. Returns the profile as a PieceWiseLinFunc object. The S_spike
values are defined positive S_spike(t)>=0. The spike trains are expected to
have auxiliary spikes at the beginning and end of the interval. Use the
function add_auxiliary_spikes to add those spikes to the spike train.
:param spikes1: ordered array of spike times with auxiliary spikes.
:param spikes2: ordered array of spike times with auxiliary spikes.
:returns: The spike-distance profile :math:`S_{spike}(t)`.
:rtype: :class:`pyspike.function.PieceWiseLinFunc`
"""
# check for auxiliary spikes - first and last spikes should be identical
assert spikes1[0] == spikes2[0], \
"Given spike trains seems not to have auxiliary spikes!"
assert spikes1[-1] == spikes2[-1], \
"Given spike trains seems not to have auxiliary spikes!"
# cython implementation
try:
from cython.cython_distance import spike_distance_cython \
as spike_distance_impl
except ImportError:
print("Warning: spike_distance_cython not found. Make sure that \
PySpike is installed by running\n 'python setup.py build_ext --inplace'!\n \
Falling back to slow python backend.")
# use python backend
from cython.python_backend import spike_distance_python \
as spike_distance_impl
times, y_starts, y_ends = spike_distance_impl(spikes1, spikes2)
return PieceWiseLinFunc(times, y_starts, y_ends)
############################################################
# spike_distance
############################################################
def spike_distance(spikes1, spikes2, interval=None):
""" Computes the spike-distance S of the given spike trains. The
spike-distance is the integral over the isi distance profile S_spike(t):
.. math:: S = \int_{T_0}^{T_1} S_{spike}(t) dt.
:param spikes1: ordered array of spike times with auxiliary spikes.
:param spikes2: ordered array of spike times with auxiliary spikes.
:param interval: averaging interval given as a pair of floats (T0, T1),
if None the average over the whole function is computed.
:type interval: Pair of floats or None.
:returns: The spike-distance.
:rtype: double
"""
return spike_profile(spikes1, spikes2).avrg(interval)
############################################################
# spike_profile_multi
############################################################
def spike_profile_multi(spike_trains, indices=None):
""" Computes the multi-variate spike distance profile for a set of spike
trains. That is the average spike-distance of all pairs of spike-trains:
:math:`S_spike(t) = 2/((N(N-1)) sum_{<i,j>} S_{spike}^{i, j}`,
where the sum goes over all pairs <i,j>
:param spike_trains: list of spike trains
:param indices: list of indices defining which spike trains to use,
if None all given spike trains are used (default=None)
:type indices: list or None
:returns: The averaged spike profile :math:`<S_{spike}>(t)`
:rtype: :class:`pyspike.function.PieceWiseLinFunc`
"""
average_dist, M = _generic_profile_multi(spike_trains, spike_profile,
indices)
average_dist.mul_scalar(1.0/M) # normalize
return average_dist
############################################################
# spike_distance_multi
############################################################
def spike_distance_multi(spike_trains, indices=None, interval=None):
""" Computes the multi-variate spike distance for a set of spike trains.
That is the time average of the multi-variate spike profile:
S_{spike} = \int_0^T 2/((N(N-1)) sum_{<i,j>} S_{spike}^{i, j} dt
where the sum goes over all pairs <i,j>
:param spike_trains: list of spike trains
:param indices: list of indices defining which spike trains to use,
if None all given spike trains are used (default=None)
:type indices: list or None
:param interval: averaging interval given as a pair of floats, if None
the average over the whole function is computed.
:type interval: Pair of floats or None.
:returns: The averaged spike distance S.
:rtype: double
"""
return spike_profile_multi(spike_trains, indices).avrg(interval)
############################################################
# spike_distance_matrix
############################################################
def spike_distance_matrix(spike_trains, indices=None, interval=None):
""" Computes the time averaged spike-distance of all pairs of spike-trains.
:param spike_trains: list of spike trains
:param indices: list of indices defining which spike trains to use,
if None all given spike trains are used (default=None)
:type indices: list or None
:param interval: averaging interval given as a pair of floats, if None
the average over the whole function is computed.
:type interval: Pair of floats or None.
:returns: 2D array with the pair wise time average spike distances
:math:`S_{ij}`
:rtype: np.array
"""
return _generic_distance_matrix(spike_trains, spike_distance,
indices, interval)
|
