diff --git a/python/moose/neuroml/NetworkML.py b/python/moose/neuroml/NetworkML.py index e2e283f4950291bb4dd42237ae246ba6ce5c4bbb..b96eeec884f13d3b2b2c4aa5aa541e13c666ce54 100644 --- a/python/moose/neuroml/NetworkML.py +++ b/python/moose/neuroml/NetworkML.py @@ -156,7 +156,7 @@ class NetworkML(): cell_name = self.populationDict[population][0] segment_path = self.populationDict[population][1][int(cell_id)].path+'/'+\ self.cellSegmentDict[cell_name][0][segment_id][0] - compartment = moose.Compartment(segment_path) + compartment = moose.element(segment_path) _logger.debug("Adding pulse at {0}: {1}".format( segment_path, pulsegen.firstLevel ) ) @@ -226,7 +226,7 @@ class NetworkML(): if childobj.className in ['Compartment','SymCompartment']: ## SymCompartment inherits from Compartment, ## so below wrapping by Compartment() is fine for both Compartment and SymCompartment - child = moose.Compartment(childId) + child = moose.element(childId) x0 = child.x0 y0 = child.y0 x0new = x0*cos(ztheta)-y0*sin(ztheta) @@ -316,7 +316,7 @@ class NetworkML(): weight_override, threshold, delay_override) def connect(self, syn_name, pre_path, post_path, weight, threshold, delay): - postcomp = moose.Compartment(post_path) + postcomp = moose.element(post_path) ## We usually try to reuse an existing SynChan & SynHandler - ## event based SynHandlers have an array of weights and delays and can represent multiple synapses, ## so a new element of the weights and delays array is created @@ -349,7 +349,7 @@ class NetworkML(): if gradedchild is not None and gradedchild.value=='True': # graded synapse interpol = moose.element(syn.path+"/graded_table") #### always connect source to input - else 'cannot create message' error. - precomp = moose.Compartment(pre_path) + precomp = moose.element(pre_path) moose.connect(precomp,"VmOut",interpol,"input") try: tau_table = moose.element(syn.path+'/tau_table')