From 99730806c22f79089d4cdaf2a1ce713712ad557b Mon Sep 17 00:00:00 2001
From: Mario Mulansky <mario.mulansky@gmx.net>
Date: Wed, 1 Oct 2014 18:21:36 +0200
Subject: added multithreaded version of multi_distance (slow)

---
 pyspike/cython_distance.pyx | 215 ++++++++++++++++++++--------------------
 pyspike/distances.py        | 233 ++++++++++----------------------------------
 2 files changed, 162 insertions(+), 286 deletions(-)

(limited to 'pyspike')

diff --git a/pyspike/cython_distance.pyx b/pyspike/cython_distance.pyx
index 6edcc01..23ffc37 100644
--- a/pyspike/cython_distance.pyx
+++ b/pyspike/cython_distance.pyx
@@ -54,38 +54,41 @@ def isi_distance_cython(double[:] s1,
     spike_events[0] = s1[0]
     # the values have one entry less - the number of intervals between events
     isi_values = np.empty(N1+N2-1)
-    isi_values[0] = (nu1-nu2)/max(nu1,nu2)
-    index1 = 0
-    index2 = 0
-    index = 1
-    while True:
-        # check which spike is next - from s1 or s2
-        if s1[index1+1] < s2[index2+1]:
-            index1 += 1
-            # break condition relies on existence of spikes at T_end
-            if index1 >= N1:
-                break
-            spike_events[index] = s1[index1]
-            nu1 = s1[index1+1]-s1[index1]
-        elif s1[index1+1] > s2[index2+1]:
-            index2 += 1
-            if index2 >= N2:
-                break
-            spike_events[index] = s2[index2]
-            nu2 = s2[index2+1]-s2[index2]
-        else: # s1[index1+1] == s2[index2+1]
-            index1 += 1
-            index2 += 1
-            if (index1 >= N1) or (index2 >= N2):
-                break
-            spike_events[index] = s1[index1]
-            nu1 = s1[index1+1]-s1[index1]
-            nu2 = s2[index2+1]-s2[index2]            
-        # compute the corresponding isi-distance
-        isi_values[index] = (nu1 - nu2) / max(nu1, nu2)
-        index += 1
-    # the last event is the interval end
-    spike_events[index] = s1[N1]
+
+    with nogil: # release the interpreter to allow multithreading
+        isi_values[0] = (nu1-nu2)/max(nu1,nu2)
+        index1 = 0
+        index2 = 0
+        index = 1
+        while True:
+            # check which spike is next - from s1 or s2
+            if s1[index1+1] < s2[index2+1]:
+                index1 += 1
+                # break condition relies on existence of spikes at T_end
+                if index1 >= N1:
+                    break
+                spike_events[index] = s1[index1]
+                nu1 = s1[index1+1]-s1[index1]
+            elif s1[index1+1] > s2[index2+1]:
+                index2 += 1
+                if index2 >= N2:
+                    break
+                spike_events[index] = s2[index2]
+                nu2 = s2[index2+1]-s2[index2]
+            else: # s1[index1+1] == s2[index2+1]
+                index1 += 1
+                index2 += 1
+                if (index1 >= N1) or (index2 >= N2):
+                    break
+                spike_events[index] = s1[index1]
+                nu1 = s1[index1+1]-s1[index1]
+                nu2 = s2[index2+1]-s2[index2]            
+            # compute the corresponding isi-distance
+            isi_values[index] = (nu1 - nu2) / max(nu1, nu2)
+            index += 1
+        # the last event is the interval end
+        spike_events[index] = s1[N1]
+    # end nogil
 
     return spike_events[:index+1], isi_values[:index]
 
@@ -98,7 +101,7 @@ cdef inline double get_min_dist_cython(double spike_time,
                                        # use memory view to ensure inlining
                                        # np.ndarray[DTYPE_t,ndim=1] spike_train,
                                        int N,
-                                       int start_index=0):
+                                       int start_index=0) nogil:
     """ Returns the minimal distance |spike_time - spike_train[i]| 
     with i>=start_index.
     """
@@ -136,78 +139,80 @@ def spike_distance_cython(double[:] t1,
     y_starts = np.empty(len(spike_events)-1)
     y_ends = np.empty(len(spike_events)-1)
 
-    index1 = 0
-    index2 = 0
-    index = 1
-    dt_p1 = 0.0
-    dt_f1 = get_min_dist_cython(t1[1], t2, N2, 0)
-    dt_p2 = 0.0
-    dt_f2 = get_min_dist_cython(t2[1], t1, N1, 0)
-    isi1 = max(t1[1]-t1[0], t1[2]-t1[1])
-    isi2 = max(t2[1]-t2[0], t2[2]-t2[1])
-    s1 = dt_f1*(t1[1]-t1[0])/isi1
-    s2 = dt_f2*(t2[1]-t2[0])/isi2
-    y_starts[0] = (s1*isi2 + s2*isi1) / ((isi1+isi2)**2/2)
-    while True:
-        # print(index, index1, index2)
-        if t1[index1+1] < t2[index2+1]:
-            index1 += 1
-            # break condition relies on existence of spikes at T_end
-            if index1+1 >= N1:
-                break
-            spike_events[index] = t1[index1]
-            # first calculate the previous interval end value
-            dt_p1 = dt_f1 # the previous time now was the following time before
-            s1 = dt_p1
-            s2 = (dt_p2*(t2[index2+1]-t1[index1]) + 
-                  dt_f2*(t1[index1]-t2[index2])) / isi2
-            y_ends[index-1] = (s1*isi2 + s2*isi1)/(0.5*(isi1+isi2)*(isi1+isi2))
-            # now the next interval start value
-            dt_f1 = get_min_dist_cython(t1[index1+1], t2, N2, index2)
-            isi1 = t1[index1+1]-t1[index1]
-            # s2 is the same as above, thus we can compute y2 immediately
-            y_starts[index] = (s1*isi2 + s2*isi1)/(0.5*(isi1+isi2)*(isi1+isi2))
-        elif t1[index1+1] > t2[index2+1]:
-            index2 += 1
-            if index2+1 >= N2:
-                break
-            spike_events[index] = t2[index2]
-            # first calculate the previous interval end value
-            dt_p2 = dt_f2 # the previous time now was the following time before
-            s1 = (dt_p1*(t1[index1+1]-t2[index2]) + 
-                  dt_f1*(t2[index2]-t1[index1])) / isi1
-            s2 = dt_p2
-            y_ends[index-1] = (s1*isi2 + s2*isi1) / (0.5*(isi1+isi2)*(isi1+isi2))
-            # now the next interval start value
-            dt_f2 = get_min_dist_cython(t2[index2+1], t1, N1, index1)
-            #s2 = dt_f2
-            isi2 = t2[index2+1]-t2[index2]
-            # s2 is the same as above, thus we can compute y2 immediately
-            y_starts[index] = (s1*isi2 + s2*isi1)/(0.5*(isi1+isi2)*(isi1+isi2))
-        else: # t1[index1+1] == t2[index2+1] - generate only one event
-            index1 += 1
-            index2 += 1
-            if (index1+1 >= N1) or (index2+1 >= N2):
-                break
-            spike_events[index] = t1[index1]
-            y_ends[index-1] = 0.0
-            y_starts[index] = 0.0
-            dt_p1 = 0.0
-            dt_p2 = 0.0
-            dt_f1 = get_min_dist_cython(t1[index1+1], t2, N2, index2)
-            dt_f2 = get_min_dist_cython(t2[index2+1], t1, N1, index1)
-            isi1 = t1[index1+1]-t1[index1]
-            isi2 = t2[index2+1]-t2[index2]
-        index += 1
-    # the last event is the interval end
-    spike_events[index] = t1[N1-1]
-    # the ending value of the last interval
-    isi1 = max(t1[N1-1]-t1[N1-2], t1[N1-2]-t1[N1-3])
-    isi2 = max(t2[N2-1]-t2[N2-2], t2[N2-2]-t2[N2-3])
-    s1 = dt_p1*(t1[N1-1]-t1[N1-2])/isi1
-    s2 = dt_p2*(t2[N2-1]-t2[N2-2])/isi2
-    y_ends[index-1] = (s1*isi2 + s2*isi1) / (0.5*(isi1+isi2)*(isi1+isi2))
+    with nogil: # release the interpreter to allow multithreading
+        index1 = 0
+        index2 = 0
+        index = 1
+        dt_p1 = 0.0
+        dt_f1 = get_min_dist_cython(t1[1], t2, N2, 0)
+        dt_p2 = 0.0
+        dt_f2 = get_min_dist_cython(t2[1], t1, N1, 0)
+        isi1 = max(t1[1]-t1[0], t1[2]-t1[1])
+        isi2 = max(t2[1]-t2[0], t2[2]-t2[1])
+        s1 = dt_f1*(t1[1]-t1[0])/isi1
+        s2 = dt_f2*(t2[1]-t2[0])/isi2
+        y_starts[0] = (s1*isi2 + s2*isi1) / ((isi1+isi2)**2/2)
+        while True:
+            # print(index, index1, index2)
+            if t1[index1+1] < t2[index2+1]:
+                index1 += 1
+                # break condition relies on existence of spikes at T_end
+                if index1+1 >= N1:
+                    break
+                spike_events[index] = t1[index1]
+                # first calculate the previous interval end value
+                dt_p1 = dt_f1 # the previous time now was the following time before
+                s1 = dt_p1
+                s2 = (dt_p2*(t2[index2+1]-t1[index1]) + 
+                      dt_f2*(t1[index1]-t2[index2])) / isi2
+                y_ends[index-1] = (s1*isi2 + s2*isi1)/(0.5*(isi1+isi2)*(isi1+isi2))
+                # now the next interval start value
+                dt_f1 = get_min_dist_cython(t1[index1+1], t2, N2, index2)
+                isi1 = t1[index1+1]-t1[index1]
+                # s2 is the same as above, thus we can compute y2 immediately
+                y_starts[index] = (s1*isi2 + s2*isi1)/(0.5*(isi1+isi2)*(isi1+isi2))
+            elif t1[index1+1] > t2[index2+1]:
+                index2 += 1
+                if index2+1 >= N2:
+                    break
+                spike_events[index] = t2[index2]
+                # first calculate the previous interval end value
+                dt_p2 = dt_f2 # the previous time now was the following time before
+                s1 = (dt_p1*(t1[index1+1]-t2[index2]) + 
+                      dt_f1*(t2[index2]-t1[index1])) / isi1
+                s2 = dt_p2
+                y_ends[index-1] = (s1*isi2 + s2*isi1) / (0.5*(isi1+isi2)*(isi1+isi2))
+                # now the next interval start value
+                dt_f2 = get_min_dist_cython(t2[index2+1], t1, N1, index1)
+                #s2 = dt_f2
+                isi2 = t2[index2+1]-t2[index2]
+                # s2 is the same as above, thus we can compute y2 immediately
+                y_starts[index] = (s1*isi2 + s2*isi1)/(0.5*(isi1+isi2)*(isi1+isi2))
+            else: # t1[index1+1] == t2[index2+1] - generate only one event
+                index1 += 1
+                index2 += 1
+                if (index1+1 >= N1) or (index2+1 >= N2):
+                    break
+                spike_events[index] = t1[index1]
+                y_ends[index-1] = 0.0
+                y_starts[index] = 0.0
+                dt_p1 = 0.0
+                dt_p2 = 0.0
+                dt_f1 = get_min_dist_cython(t1[index1+1], t2, N2, index2)
+                dt_f2 = get_min_dist_cython(t2[index2+1], t1, N1, index1)
+                isi1 = t1[index1+1]-t1[index1]
+                isi2 = t2[index2+1]-t2[index2]
+            index += 1
+        # the last event is the interval end
+        spike_events[index] = t1[N1-1]
+        # the ending value of the last interval
+        isi1 = max(t1[N1-1]-t1[N1-2], t1[N1-2]-t1[N1-3])
+        isi2 = max(t2[N2-1]-t2[N2-2], t2[N2-2]-t2[N2-3])
+        s1 = dt_p1*(t1[N1-1]-t1[N1-2])/isi1
+        s2 = dt_p2*(t2[N2-1]-t2[N2-2])/isi2
+        y_ends[index-1] = (s1*isi2 + s2*isi1) / (0.5*(isi1+isi2)*(isi1+isi2))
+    # end nogil
+
     # use only the data added above 
     # could be less than original length due to equal spike times
-    
     return spike_events[:index+1], y_starts[:index], y_ends[:index]
diff --git a/pyspike/distances.py b/pyspike/distances.py
index 79278b4..35650f7 100644
--- a/pyspike/distances.py
+++ b/pyspike/distances.py
@@ -6,6 +6,7 @@ Copyright 2014, Mario Mulansky <mario.mulansky@gmx.net>
 """
 
 import numpy as np
+import threading
 
 from pyspike import PieceWiseConstFunc, PieceWiseLinFunc
 
@@ -126,6 +127,57 @@ def multi_distance(spike_trains, pair_distance_func, indices=None):
     return average_dist
 
 
+############################################################
+# multi_distance_par
+############################################################
+def multi_distance_par(spike_trains, pair_distance_func, indices=None):
+    """ parallel implementation of the multi-distance. Not currently used as
+    it does not improve the performance.
+    """
+
+    num_threads = 2
+
+    lock = threading.Lock()
+    def run(spike_trains, index_pairs, average_dist):
+        (i,j) = index_pairs[0]
+        # print(i,j)
+        this_avrg = pair_distance_func(spike_trains[i], spike_trains[j])
+        for (i,j) in index_pairs[1:]:
+            # print(i,j)
+            current_dist = pair_distance_func(spike_trains[i], spike_trains[j])
+            this_avrg.add(current_dist)
+        with lock:
+            average_dist.add(this_avrg)    
+
+    if indices==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 indices for j in indices[i+1:]]
+    num_pairs = len(pairs)
+
+    # start with first pair
+    (i,j) = pairs[0]
+    average_dist = pair_distance_func(spike_trains[i], spike_trains[j])
+    # remove the one we already computed
+    pairs = pairs[1:]
+    # distribute the rest into num_threads pieces
+    clustered_pairs = [ pairs[i::num_threads] for i in xrange(num_threads) ]
+
+    threads = []
+    for pairs in clustered_pairs:
+        t = threading.Thread(target=run, args=(spike_trains, pairs, average_dist))
+        threads.append(t)
+        t.start()
+    for t in threads:
+        t.join()
+    average_dist.mul_scalar(1.0/num_pairs) # normalize
+    return average_dist
+
+
 ############################################################
 # isi_distance_multi
 ############################################################
@@ -161,184 +213,3 @@ def spike_distance_multi(spike_trains, indices=None):
     """
     return multi_distance(spike_trains, spike_distance, indices)
 
-
-############################################################
-############################################################
-# VANILLA PYTHON IMPLEMENTATIONS OF ISI AND SPIKE DISTANCE
-############################################################
-############################################################
-
-
-############################################################
-# isi_distance_python
-############################################################
-def isi_distance_python(s1, s2):
-    """ Plain Python implementation of the isi distance.
-    """
-    # compute the interspike interval
-    nu1 = s1[1:]-s1[:-1]
-    nu2 = s2[1:]-s2[:-1]
-    
-    # compute the isi-distance
-    spike_events = np.empty(len(nu1)+len(nu2))
-    spike_events[0] = s1[0]
-    # the values have one entry less - the number of intervals between events
-    isi_values = np.empty(len(spike_events)-1)
-    # add the distance of the first events
-    # isi_values[0] = nu1[0]/nu2[0] - 1.0 if nu1[0] <= nu2[0] \
-    #                 else 1.0 - nu2[0]/nu1[0]
-    isi_values[0] = (nu1[0]-nu2[0])/max(nu1[0],nu2[0])
-    index1 = 0
-    index2 = 0
-    index = 1
-    while True:
-        # check which spike is next - from s1 or s2
-        if s1[index1+1] < s2[index2+1]:
-            index1 += 1
-            # break condition relies on existence of spikes at T_end
-            if index1 >= len(nu1):
-                break
-            spike_events[index] = s1[index1]
-        elif s1[index1+1] > s2[index2+1]:
-            index2 += 1
-            if index2 >= len(nu2):
-                break
-            spike_events[index] = s2[index2]
-        else: # s1[index1+1] == s2[index2+1]
-            index1 += 1
-            index2 += 1
-            if (index1 >= len(nu1)) or (index2 >= len(nu2)):
-                break
-            spike_events[index] = s1[index1]
-        # compute the corresponding isi-distance
-        isi_values[index] = (nu1[index1]-nu2[index2]) / \
-                            max(nu1[index1], nu2[index2])
-        index += 1
-    # the last event is the interval end
-    spike_events[index] = s1[-1]
-    # use only the data added above 
-    # could be less than original length due to equal spike times
-    return PieceWiseConstFunc(spike_events[:index+1], isi_values[:index])
-
-
-############################################################
-# get_min_dist
-############################################################
-def get_min_dist(spike_time, spike_train, start_index=0):
-    """ Returns the minimal distance |spike_time - spike_train[i]| 
-    with i>=start_index.
-    """
-    d = abs(spike_time - spike_train[start_index])
-    start_index += 1
-    while start_index < len(spike_train):
-        d_temp = abs(spike_time - spike_train[start_index])
-        if d_temp > d:
-            break
-        else:
-            d = d_temp
-        start_index += 1
-    return d
-
-
-############################################################
-# spike_distance_python
-############################################################
-def spike_distance_python(spikes1, spikes2):
-    """ Computes the instantaneous spike-distance S_spike (t) of the two given
-    spike trains. 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.
-    Args:
-    - spikes1, spikes2: ordered arrays of spike times with auxiliary spikes.
-    Returns:
-    - PieceWiseLinFunc describing the spike-distance.
-    """
-    # 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!"
-    # shorter variables
-    t1 = spikes1
-    t2 = spikes2
-
-    spike_events = np.empty(len(t1)+len(t2)-2)
-    spike_events[0] = t1[0]
-    y_starts = np.empty(len(spike_events)-1)
-    y_ends = np.empty(len(spike_events)-1)
-
-    index1 = 0
-    index2 = 0
-    index = 1
-    dt_p1 = 0.0
-    dt_f1 = get_min_dist(t1[1], t2, 0)
-    dt_p2 = 0.0
-    dt_f2 = get_min_dist(t2[1], t1, 0)
-    isi1 = max(t1[1]-t1[0], t1[2]-t1[1])
-    isi2 = max(t2[1]-t2[0], t2[2]-t2[1])
-    s1 = dt_f1*(t1[1]-t1[0])/isi1
-    s2 = dt_f2*(t2[1]-t2[0])/isi2
-    y_starts[0] = (s1*isi2 + s2*isi1) / ((isi1+isi2)**2/2)
-    while True:
-        # print(index, index1, index2)
-        if t1[index1+1] < t2[index2+1]:
-            index1 += 1
-            # break condition relies on existence of spikes at T_end
-            if index1+1 >= len(t1):
-                break
-            spike_events[index] = t1[index1]
-            # first calculate the previous interval end value
-            dt_p1 = dt_f1 # the previous time now was the following time before
-            s1 = dt_p1
-            s2 = (dt_p2*(t2[index2+1]-t1[index1]) + dt_f2*(t1[index1]-t2[index2])) / isi2
-            y_ends[index-1] = (s1*isi2 + s2*isi1) / ((isi1+isi2)**2/2)
-            # now the next interval start value
-            dt_f1 = get_min_dist(t1[index1+1], t2, index2)
-            isi1 = t1[index1+1]-t1[index1]
-            # s2 is the same as above, thus we can compute y2 immediately
-            y_starts[index] = (s1*isi2 + s2*isi1) / ((isi1+isi2)**2/2)
-        elif t1[index1+1] > t2[index2+1]:
-            index2 += 1
-            if index2+1 >= len(t2):
-                break
-            spike_events[index] = t2[index2]
-            # first calculate the previous interval end value
-            dt_p2 = dt_f2 # the previous time now was the following time before
-            s1 = (dt_p1*(t1[index1+1]-t2[index2]) + dt_f1*(t2[index2]-t1[index1])) / isi1
-            s2 = dt_p2
-            y_ends[index-1] = (s1*isi2 + s2*isi1) / ((isi1+isi2)**2/2)
-            # now the next interval start value
-            dt_f2 = get_min_dist(t2[index2+1], t1, index1)
-            #s2 = dt_f2
-            isi2 = t2[index2+1]-t2[index2]
-            # s2 is the same as above, thus we can compute y2 immediately
-            y_starts[index] = (s1*isi2 + s2*isi1) / ((isi1+isi2)**2/2)
-        else: # t1[index1+1] == t2[index2+1] - generate only one event
-            index1 += 1
-            index2 += 1
-            if (index1+1 >= len(t1)) or (index2+1 >= len(t2)):
-                break
-            assert dt_f2 == 0.0
-            assert dt_f1 == 0.0
-            spike_events[index] = t1[index1]
-            y_ends[index-1] = 0.0
-            y_starts[index] = 0.0
-            dt_p1 = 0.0
-            dt_p2 = 0.0
-            dt_f1 = get_min_dist(t1[index1+1], t2, index2)
-            dt_f2 = get_min_dist(t2[index2+1], t1, index1)
-            isi1 = t1[index1+1]-t1[index1]
-            isi2 = t2[index2+1]-t2[index2]
-        index += 1
-    # the last event is the interval end
-    spike_events[index] = t1[-1]
-    # the ending value of the last interval
-    isi1 = max(t1[-1]-t1[-2], t1[-2]-t1[-3])
-    isi2 = max(t2[-1]-t2[-2], t2[-2]-t2[-3])
-    s1 = dt_p1*(t1[-1]-t1[-2])/isi1
-    s2 = dt_p2*(t2[-1]-t2[-2])/isi2
-    y_ends[index-1] = (s1*isi2 + s2*isi1) / ((isi1+isi2)**2/2)
-    # use only the data added above 
-    # could be less than original length due to equal spike times
-    return PieceWiseLinFunc(spike_events[:index+1], 
-                            y_starts[:index], y_ends[:index])
-- 
cgit v1.2.3