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"""
Generic functions to compute multi-variate profiles and distance matrices.
Copyright 2015, Mario Mulansky <mario.mulansky@gmx.net>
Distributed under the BSD License
"""
import numpy as np
############################################################
# _generic_profile_multi
############################################################
def _generic_profile_multi(spike_trains, pair_distance_func, indices=None):
""" Internal implementation detail, don't call this function directly,
use isi_profile_multi or spike_profile_multi instead.
Computes the multi-variate distance for a set of spike-trains using the
pair_dist_func to compute pair-wise distances. That is it computes the
average distance of all pairs of spike-trains:
:math:`S(t) = 2/((N(N-1)) sum_{<i,j>} S_{i,j}`,
where the sum goes over all pairs <i,j>.
Args:
- spike_trains: list of spike trains
- pair_distance_func: function computing the distance of two spike trains
- indices: list of indices defining which spike trains to use,
if None all given spike trains are used (default=None)
Returns:
- The averaged multi-variate distance of all pairs
"""
if indices is None:
indices = np.arange(len(spike_trains))
indices = np.array(indices)
# check validity of indices
assert (indices < len(spike_trains)).all() and (indices >= 0).all(), \
"Invalid index list."
# generate a list of possible index pairs
pairs = [(indices[i], j) for i in range(len(indices))
for j in indices[i+1:]]
# start with first pair
(i, j) = pairs[0]
average_dist = pair_distance_func(spike_trains[i], spike_trains[j])
for (i, j) in pairs[1:]:
current_dist = pair_distance_func(spike_trains[i], spike_trains[j])
average_dist.add(current_dist) # add to the average
return average_dist, len(pairs)
############################################################
# generic_distance_matrix
############################################################
def _generic_distance_matrix(spike_trains, dist_function,
indices=None, interval=None):
""" Internal implementation detail. Don't use this function directly.
Instead use isi_distance_matrix or spike_distance_matrix.
Computes the time averaged distance of all pairs of spike-trains.
Args:
- spike_trains: list of spike trains
- indices: list of indices defining which spike-trains to use
if None all given spike-trains are used (default=None)
Return:
- a 2D array of size len(indices)*len(indices) containing the average
pair-wise distance
"""
if indices is None:
indices = np.arange(len(spike_trains))
indices = np.array(indices)
# check validity of indices
assert (indices < len(spike_trains)).all() and (indices >= 0).all(), \
"Invalid index list."
# generate a list of possible index pairs
pairs = [(indices[i], j) for i in range(len(indices))
for j in indices[i+1:]]
distance_matrix = np.zeros((len(indices), len(indices)))
for i, j in pairs:
d = dist_function(spike_trains[i], spike_trains[j], interval)
distance_matrix[i, j] = d
distance_matrix[j, i] = d
return distance_matrix
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