From be9eeb2e48115134b93ec0cd9035d97117bd019e Mon Sep 17 00:00:00 2001
From: Mario Mulansky <mario.mulansky@gmx.net>
Date: Tue, 3 Feb 2015 10:45:32 +0100
Subject: split distance.py into 3 separate modules

---
 pyspike/generic.py | 83 ++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 83 insertions(+)
 create mode 100644 pyspike/generic.py

(limited to 'pyspike/generic.py')

diff --git a/pyspike/generic.py b/pyspike/generic.py
new file mode 100644
index 0000000..4f278d2
--- /dev/null
+++ b/pyspike/generic.py
@@ -0,0 +1,83 @@
+"""
+
+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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