added multithreaded version of multi_distance (slow)

1 files changed, 110 insertions, 105 deletions

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]