Removed dependence on CSA (#123)

* new functions to create random connectivy matrices

* removed csa dependence

* travisCI not ready for 3.7....

* removed some commented out code

LFPy/network.py

@@ -19,12 +19,10 @@
 import numpy as np
 import os
 import scipy.stats as stats
-import json
 import h5py
 from mpi4py import MPI
 import neuron
 from .templatecell import TemplateCell
-import csa
 import scipy.sparse as ss
 
 # set up MPI environment
@@ -622,16 +620,12 @@ class defining a Cell-like object, see class NetworkCell
     def get_connectivity_rand(self, pre='L5PC', post='L5PC', connprob = 0.2):
         """
         Dummy function creating a (boolean) cell to cell connectivity matrix
-        between pre and postsynaptic populations relying on the use of the
-        'Connection Set Algebra (CSA)' implementation in Python; see
-        https://github.com/INCF/csa, Mikael Djurfeldt (2012) "The Connection-set
-        Algebra---A Novel Formalism for the Representation of Connectivity
-        Structure in Neuronal Network Models" Neuroinformatics 10(3), 1539-2791,
-        http://dx.doi.org/10.1007/s12021-012-9146-1
-
+        between pre and postsynaptic populations.
+        
         Connections are drawn randomly between presynaptic cell gids in
         population 'pre' and postsynaptic cell gids in 'post' on this RANK with
-        a fixed connection probability.
+        a fixed connection probability. self-connections are disabled if
+        presynaptic and postsynaptic populations are the same.
 
         Parameters
         ----------
@@ -650,30 +644,35 @@ def get_connectivity_rand(self, pre='L5PC', post='L5PC', connprob = 0.2):
             with True denotes a connection.        
         """                
         n_pre = self.populations[pre].POP_SIZE
-        n_post = self.populations[post].POP_SIZE
-
-        first_gid = self.populations[post].first_gid
         gids = np.array(self.populations[post].gids).astype(int)
 
         # first check if there are any postsyn cells on this RANK
         if gids.size > 0:
             # define incoming connections for cells on this RANK
+            C = np.random.rand(n_pre, gids.size) < connprob
             if pre == post:
-                # avoid self connections
-                c = np.array([x for x in csa.cross(range(n_pre), range(gids.size)) *
-                              (csa.random(connprob) - csa.oneToOne)])
-            else:
-                c = np.array([x for x in csa.cross(range(n_pre), range(gids.size)) *
-                              csa.random(connprob)])
-            if c.ndim == 2:
-                # construct sparse boolean array
-                C = ss.csr_matrix((np.ones(c.shape[0], dtype=bool), (c[:, 0], c[:, 1])),
-                                  shape=(n_pre, gids.size), dtype=bool)                
+                # avoid self connections.
+                gids_pre, gids_post = np.where(C)
+                gids_pre += self.populations[pre].first_gid
+                gids_post *= SIZE # asssume round-robin distribution of gids
+                gids_post += self.populations[post].gids[0]
+                inds = gids_pre == gids_post
+                gids_pre = gids_pre[inds == False]
+                gids_pre -= self.populations[pre].first_gid
+                gids_post = gids_post[inds == False]
+                gids_post -= self.populations[post].gids[0]
+                gids_post //= SIZE
+                c = np.c_[gids_pre, gids_post]
+                # create boolean matrix
+                C = ss.csr_matrix((np.ones(gids_pre.shape[0], dtype=bool),
+                                   (c[:, 0], c[:, 1])),
+                                  shape=(n_pre, gids.size), dtype=bool)    
                 return C.toarray()
             else:
-                return np.zeros((n_pre, gids.size), dtype=bool)
+                return C
         else:
             return np.zeros((n_pre, 0), dtype=bool)
+
 
     def connect(self, pre, post, connectivity,
                 syntype=neuron.h.ExpSyn,
@@ -747,10 +746,6 @@ def connect(self, pre, post, connectivity,
         # gids of presynaptic neurons:
         pre_gids = np.arange(n0, n0 + self.populations[pre].POP_SIZE)
 
-        # global population sizes
-        n_pre = self.populations[pre].POP_SIZE
-        n_post = self.populations[post].POP_SIZE
-
         # count connections and synapses made on this RANK
         conncount = connectivity.astype(int).sum()
         syncount = 0
@@ -763,6 +758,10 @@ def connect(self, pre, post, connectivity,
         for i, (post_gid, cell) in enumerate(zip(self.populations[post].gids, self.populations[post].cells)):
             # do NOT iterate over all possible presynaptic neurons
             for pre_gid in pre_gids[connectivity[:, i]]:
+                # throw a warning if sender neuron is identical to receiving neuron
+                if post_gid == pre_gid:
+                    print('connecting cell w. gid {} to itself (RANK {})'.format(post_gid, RANK))
+
                 # assess number of synapses
                 if multapsefun is None:
                     nidx = 1
@@ -775,8 +774,6 @@ def connect(self, pre, post, connectivity,
                     if j == 1000:
                         raise Exception('change multapseargs as no positive synapse count was found in 1000 trials')
 
-                # print('connecting cell {} to cell {} with {} synapses on RANK {}'.format(pre_gid, post_gid, nidx, RANK))
-
                 # find synapse locations and corresponding section names
                 idxs = cell.get_rand_idx_area_and_distribution_norm(nidx=nidx, **syn_pos_args)
                 secs = cell.get_idx_name(idxs)
@@ -857,7 +854,6 @@ def connect(self, pre, post, connectivity,
         return COMM.bcast([conncount, syncount])
 
 
-
     def simulate(self, electrode=None, rec_imem=False, rec_vmem=False,
                  rec_ipas=False, rec_icap=False,
                  rec_isyn=False, rec_vmemsyn=False, rec_istim=False,

requirements.txt

@@ -6,7 +6,6 @@ Cython>=0.20
 h5py>=2.5
 mpi4py>=1.2
 matplotlib>=2.0
-csa >= 0.1.8
 # essential, not automatically installable:
 # neuron>=7.6.4
 # optional, for running tests:

setup.py

@@ -96,7 +96,6 @@
         'Cython>=0.20',
         'h5py>=2.5',
         'mpi4py>=1.2',
-        'csa>=0.1.8',
         ],
     extras_require = {'tests': ['nose>=1.3.3']},
     dependency_links = [],