Edited sparse random connectivity method to not build full adj mat

jaxley/connection.py

@@ -50,22 +50,22 @@ def fc(self, pre_cell_inds, post_cell_inds):
                 rand_loc = np.random.rand()
                 conns.append(Connection(pre_ind, 0, 0, post_ind, rand_branch, rand_loc))
         return conns
-    
+
     def sparse_random(self, pre_cell_inds, post_cell_inds, p):
-        """Returns a list of `Connection`s which build a sparse, randomly 
-         connected layer. The presence of each connection is determined by a 
-         Bernoulli trial with probability p.
+        """Returns a list of `Connection`s which build a sparse, randomly
+         connected layer.
 
-        Connections are from branch 0 location 0 to a randomly chosen branch 
+        Connections are from branch 0 location 0 to a randomly chosen branch
         and loc.
-
-        NOTE: autapses are allowed.
         """
+        num_pre = len(pre_cell_inds)
+        num_post = len(post_cell_inds)
+        num_connections = np.random.binomial(num_pre * num_post, p)
+        pre_syn_neurons = np.random.choice(pre_cell_inds, size=num_connections)
+        post_syn_neurons = np.random.choice(post_cell_inds, size=num_connections)
+
         conns = []
-        adj_mat = np.random.binomial(1, p, size=(len(pre_cell_inds), 
-                                                 len(post_cell_inds)))
-        cxn_inds = np.where(adj_mat)
-        for pre_ind, post_ind in zip(cxn_inds[0], cxn_inds[1]):
+        for pre_ind, post_ind in zip(pre_syn_neurons, post_syn_neurons):
             num_branches_post = self.nbranches_per_submodule[post_ind]
             rand_branch = np.random.randint(0, num_branches_post)
             rand_loc = np.random.rand()