[NEST Users] MPI error when using GetConnections

Hello, I am trying to normalize connection weights using a code similar to the one on the Documentation (see code below). I receive an error when assigning the modified weights on the last line of the for loop below (only when I use several MPI processes). That is, when using mpirun -np 1 python3 test_DumpLayerConnections.py I do not receive any error, but when using 4 MPI processes: mpirun -np 4 python3 test_DumpLayerConnections.py I receive the following error when trying to start a Simulation: Mar 16 11:13:53 NodeManager::prepare_nodes [Info]: Preparing 675 nodes for simulation. [dcccluster:747534] *** An error occurred in MPI_Allgather [dcccluster:747534] *** reported by process [2696478721,0] [dcccluster:747534] *** on communicator MPI_COMM_WORLD [dcccluster:747534] *** MPI_ERR_TRUNCATE: message truncated [dcccluster:747534] *** MPI_ERRORS_ARE_FATAL (processes in this communicator will now abort, [dcccluster:747534] *** and potentially your MPI job) I can avoid this error by commenting the last for loop assignament (the normalized weights assignment operation). I am using NEST 3.3 (compiled with MPI option) and Python 3.8.12. I guess it could be related to the DumpLayerConnections bug I told you some days ago. Thanks a lot in advance! Xavier import nest import numpy as np pos = nest.spatial.grid(shape = [30,30] ) input = nest.Create('iaf_psc_alpha', positions=pos) layer_0 = nest.Create('iaf_psc_alpha', positions=pos) layer_1 = nest.Create('iaf_psc_alpha', positions=pos) conn_neur = {'rule':'pairwise_bernoulli', 'use_on_source': True, 'mask': {'grid':{'shape':[9,9]}} } nest.CopyModel('static_synapse', 'syn_1_model') syn_0 = {'synapse_model': 'static_synapse'} syn_1 = {'synapse_model': 'syn_1_model'} nest.Connect(input, layer_0, conn_neur, syn_0) nest.Connect(input, layer_1, conn_neur, syn_1) nest.DumpLayerConnections(input, layer_0, 'static_synapse', 'conn.txt') nest.DumpLayerConnections(input, layer_1, 'syn_1_model', 'conn.txt') for neuron in layer_0: conn = nest.GetConnections(target=neuron, synapse_model='static_synapse') w = np.array(conn.weight) if (w.size>1): w_normed = w / sum(abs(w)) # L1-norm w_nparray = 2. * w_normed conn.weight = w_nparray.tolist() nest.Simulate(100)