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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
"""
from __future__ import division
from pyspike.isi_lengths import default_thresh
from pyspike.spikes import reconcile_spike_trains, reconcile_spike_trains_bi
import numpy as np
def resolve_keywords(**kwargs):
""" resolve keywords
In: kwargs - dictionary of keywords
out: MRTS - Minimum Relevant Time Scale, default 0.
RI - Rate Independent Adaptive distance, default False
"""
if 'MRTS' in kwargs:
MRTS = kwargs['MRTS']
else:
MRTS = 0. # default
if 'RI' in kwargs:
RI = kwargs['RI']
else:
RI = False # default
return MRTS, RI
############################################################
# _generic_profile_multi
############################################################
def _generic_profile_multi(spike_trains, pair_distance_func, indices=None, **kwargs):
""" 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 kwargs.get('Reconcile', True):
spike_trains = reconcile_spike_trains(spike_trains)
kwargs['Reconcile'] = False
MRTS, RI = resolve_keywords(**kwargs)
if isinstance(MRTS, str):
kwargs['MRTS'] = default_thresh(spike_trains)
def divide_and_conquer(pairs1, pairs2):
""" recursive calls by splitting the two lists in half.
"""
L1 = len(pairs1)
if L1 > 1:
dist_prof1 = divide_and_conquer(pairs1[:L1//2],
pairs1[L1//2:])
else:
dist_prof1 = pair_distance_func(spike_trains[pairs1[0][0]],
spike_trains[pairs1[0][1]],
**kwargs)
L2 = len(pairs2)
if L2 > 1:
dist_prof2 = divide_and_conquer(pairs2[:L2//2],
pairs2[L2//2:])
else:
dist_prof2 = pair_distance_func(spike_trains[pairs2[0][0]],
spike_trains[pairs2[0][1]],
**kwargs)
dist_prof1.add(dist_prof2)
return dist_prof1
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:]]
L = len(pairs)
if L > 1:
# recursive iteration through the list of pairs to get average profile
avrg_dist = divide_and_conquer(pairs[:len(pairs)//2],
pairs[len(pairs)//2:])
else:
avrg_dist = pair_distance_func(spike_trains[pairs[0][0]],
spike_trains[pairs[0][1]],
**kwargs)
return avrg_dist, L
############################################################
# _generic_distance_multi
############################################################
def _generic_distance_multi(spike_trains, pair_distance_func,
indices=None, interval=None, **kwargs):
""" Internal implementation detail, don't call this function directly,
use isi_distance_multi or spike_distance_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>} D_{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 kwargs.get('Reconcile', True):
spike_trains = reconcile_spike_trains(spike_trains)
kwargs['Reconcile'] = False
MRTS, RI = resolve_keywords(**kwargs)
if isinstance(MRTS, str):
kwargs['MRTS'] = default_thresh(spike_trains)
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:]]
avrg_dist = 0.0
for (i, j) in pairs:
one_dist = pair_distance_func(spike_trains[i], spike_trains[j],
interval, **kwargs)
avrg_dist += one_dist
return avrg_dist/len(pairs)
############################################################
# generic_distance_matrix
############################################################
def _generic_distance_matrix(spike_trains, dist_function,
indices=None, interval=None, **kwargs):
""" 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 kwargs.get('Reconcile', True):
spike_trains = reconcile_spike_trains(spike_trains)
kwargs['Reconcile'] = False
MRTS, RI = resolve_keywords(**kwargs)
if isinstance(MRTS, str):
kwargs['MRTS'] = default_thresh(spike_trains)
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 = [(i, j) for i in range(len(indices))
for j in range(i+1, len(indices))]
distance_matrix = np.zeros((len(indices), len(indices)))
for i, j in pairs:
d = dist_function(spike_trains[indices[i]], spike_trains[indices[j]],
interval, **kwargs)
distance_matrix[i, j] = d
distance_matrix[j, i] = d
return distance_matrix
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