Blackened code

.github/workflows/static_tests.yml

@@ -57,4 +57,4 @@ jobs:
 
       - name: "Run flake8..."
         run: |
-          flake8 . 
+          flake8 .

.gitignore

@@ -37,4 +37,4 @@ libtool
 /.settings/
 doxygen
 python/test/log.txt
-.vscode/
+.vscode/

Makefile.am

@@ -175,7 +175,7 @@ libnestgpu.la: $(OBJS)
 
 lib_LTLIBRARIES = libnestgpu.la
 
-%.cu: 
+%.cu:
 
 clean-local:
 	rm -f *.so obj/*.o

README.md

@@ -5,7 +5,7 @@
 NEST GPU is a GPU-MPI library for simulation of large-scale networks of spiking neurons.
 Can be used in Python, in C++ and in C.
 
-With this library it is possible to run relatively fast simulations of large-scale networks of spiking neurons. For instance, on a single Nvidia GeForce RTX 2080 Ti GPU board it is possible to simulate the activity of 1 million multisynapse AdEx neurons with 1000 synapse per neurons, for a total of 1 billion synapse, using the fifth-order Runge-Kutta method with adaptive stepsize as differential equations solver, in little more than 70 seconds per second of neural activity. The MPI communication is also very efficient. 
+With this library it is possible to run relatively fast simulations of large-scale networks of spiking neurons. For instance, on a single Nvidia GeForce RTX 2080 Ti GPU board it is possible to simulate the activity of 1 million multisynapse AdEx neurons with 1000 synapse per neurons, for a total of 1 billion synapse, using the fifth-order Runge-Kutta method with adaptive stepsize as differential equations solver, in little more than 70 seconds per second of neural activity. The MPI communication is also very efficient.
 The Python interface is very similar to that of the NEST simulator: the most used commands are practically identical, dictionaries are used to define neurons, connections and synapses properties in the same way.
 
 ## Documentation

VERSION

@@ -1 +1 @@
-   1.08  
+   1.08

c++/examples/brunel_mpi.cpp

@@ -49,7 +49,7 @@ int main(int argc, char *argv[])
   cout << "Building on host " << mpi_id << " ..." <<endl;
 
   ngpu.SetRandomSeed(1234ULL + mpi_id); // seed for GPU random numbers
- 
+
   //////////////////////////////////////////////////////////////////////
   // WRITE HERE COMMANDS THAT ARE EXECUTED ON ALL HOSTS
   //////////////////////////////////////////////////////////////////////
@@ -82,7 +82,7 @@ int main(int argc, char *argv[])
 					   n_receptors);
   NodeSeq exc_neuron = neuron.Subseq(0,NE-1); // excitatory neuron group
   NodeSeq inh_neuron = neuron.Subseq(NE, n_neurons-1); //inhibitory neuron group
-  
+
   // the following parameters are set to the same values on all hosts
   float E_rev[] = {0.0, -85.0};
   float tau_decay[] = {1.0, 1.0};

c++/examples/brunel_mpi_nx.cpp.old

@@ -29,12 +29,12 @@ int main(int argc, char *argv[])
     cerr << "Usage: mpirun -np <number-of-mpi-hosts> brunel_mpi_nx\n";
     return -1;
   }
-  
+
   int mpi_id = neural_gpu.MpiId();
   cout << "Building on host " << mpi_id << " ..." <<endl;
-  
+
   neural_gpu.max_spike_buffer_num_=10; //reduce it to save GPU memory
-  
+
   //////////////////////////////////////////////////////////////////////
   // WRITE HERE COMMANDS THAT ARE EXECUTED ON ALL HOSTS
   //////////////////////////////////////////////////////////////////////
@@ -62,7 +62,7 @@ int main(int argc, char *argv[])
   int neuron = neural_gpu.CreateNeuron(n_neurons, n_receptors);
   int exc_neuron = neuron;      // excitatory neuron id
   int inh_neuron = neuron + NE; // inhibitory neuron id
-  
+
   // the following parameters are set to the same values on all hosts
   float E_rev[] = {0.0, -85.0};
   float tau_decay[] = {1.0, 1.0};
@@ -95,13 +95,13 @@ int main(int argc, char *argv[])
   // connect poisson generator to port 0 of all neurons
   neural_gpu.ConnectAllToAll(pg, n_pg, neuron, n_neurons, 0, poiss_weight,
 			     poiss_delay);
-  
+
   char filename[100];
   sprintf(filename, "test_brunel_mpi_nx_%d.dat", mpi_id);
   int i_neurons[] = {2000, 8000, 9999}; // any set of neuron indexes
   // create multimeter record of V_m
   neural_gpu.CreateRecord(string(filename), "V_m", i_neurons, 3);
-  
+
   //////////////////////////////////////////////////////////////////////
   // WRITE HERE REMOTE CONNECTIONS
   //////////////////////////////////////////////////////////////////////
@@ -120,8 +120,8 @@ int main(int argc, char *argv[])
     neural_gpu.RemoteConnectFixedIndegree(ish2, exc_neuron+NE-NEext/2,
 					  NEext/2, ith, neuron, n_neurons,
 					  0, Wex, delay, CE/4);
-    
-    
+
+
     // Inhibitory remote connections
     // connect inhibitory neurons to port 1 of all neurons
     // weight Win and fixed indegree CI-CI*3/4
@@ -133,9 +133,9 @@ int main(int argc, char *argv[])
     neural_gpu.RemoteConnectFixedIndegree(ish2, inh_neuron+NI-NIext/2,
 					  NIext/2, ith, neuron, n_neurons,
 					  1, Win, delay, CI/4);
-    
+
   }
-    
+
   neural_gpu.SetRandomSeed(1234ULL); // just to have same results in different simulations
   neural_gpu.Simulate();
 

c++/examples/brunel_net.cpp

@@ -44,7 +44,7 @@ int main(int argc, char *argv[])
   cout << "Building ...\n";
 
   ngpu.SetRandomSeed(1234ULL); // seed for GPU random numbers
-  
+
   int n_receptors = 2;
 
   int order = arg1/5;
@@ -80,7 +80,7 @@ int main(int argc, char *argv[])
   ngpu.SetNeuronParam(neuron, "E_rev", E_rev, 2);
   ngpu.SetNeuronParam(neuron, "tau_decay", tau_decay, 2);
   ngpu.SetNeuronParam(neuron, "tau_rise", tau_rise, 2);
-  
+
   float mean_delay = 0.5;
   float std_delay = 0.25;
   float min_delay = 0.1;
@@ -90,7 +90,7 @@ int main(int argc, char *argv[])
   float *exc_delays = ngpu.RandomNormalClipped(CE*n_neurons, mean_delay,
   						     std_delay, min_delay,
   						     mean_delay+3*std_delay);
-  
+
   ConnSpec conn_spec1(FIXED_INDEGREE, CE);
   SynSpec syn_spec1;
   syn_spec1.SetParam("receptor", 0);

c++/examples/brunel_outdegree.cpp

@@ -44,7 +44,7 @@ int main(int argc, char *argv[])
   cout << "Building ...\n";
 
   ngpu.SetRandomSeed(12345ULL); // seed for GPU random numbers
-  
+
   int n_receptors = 2;
 
   int order = arg1/5;
@@ -53,7 +53,7 @@ int main(int argc, char *argv[])
   int n_neurons = NE + NI; // number of neurons in total
 
   int CPN = 1000; // number of output connections per neuron
-  
+
   float Wex = 0.05;
   float Win = 0.35;
 
@@ -79,7 +79,7 @@ int main(int argc, char *argv[])
   ngpu.SetNeuronParam(neuron, "E_rev", E_rev, 2);
   ngpu.SetNeuronParam(neuron, "tau_decay", tau_decay, 2);
   ngpu.SetNeuronParam(neuron, "tau_rise", tau_rise, 2);
-  
+
   float mean_delay = 0.5;
   float std_delay = 0.25;
   float min_delay = 0.1;
@@ -89,7 +89,7 @@ int main(int argc, char *argv[])
   float *exc_delays = ngpu.RandomNormalClipped(CPN*NE, mean_delay,
   						     std_delay, min_delay,
   						     mean_delay+3*std_delay);
-  
+
   ConnSpec conn_spec1(FIXED_OUTDEGREE, CPN);
   SynSpec syn_spec1;
   syn_spec1.SetParam("receptor", 0);

c++/examples/brunel_outdegree_mpi.cpp

@@ -44,12 +44,12 @@ int main(int argc, char *argv[])
   }
   int arg1;
   sscanf(argv[1], "%d", &arg1);
-  
+
   int mpi_id = ngpu.MpiId();
   cout << "Building on host " << mpi_id << " ..." <<endl;
 
   ngpu.SetRandomSeed(12345ULL + mpi_id); // seed for GPU random numbers
-  
+
   //////////////////////////////////////////////////////////////////////
   // WRITE HERE COMMANDS THAT ARE EXECUTED ON ALL HOSTS
   //////////////////////////////////////////////////////////////////////
@@ -68,7 +68,7 @@ int main(int argc, char *argv[])
   // send their output to neurons of another mpi host
   int NEext = (int)(fext*NE);
   int NEint = NE - NEext;
-  
+
   float Wex = 0.05;
   float Win = 0.35;
 
@@ -88,9 +88,9 @@ int main(int argc, char *argv[])
   // of neurons that project internally
   NodeSeq excext_neuron = neuron.Subseq(NEint,NE-1); // excitatory group
   // of neurons that project externally
-  
+
   NodeSeq inh_neuron = neuron.Subseq(NE, n_neurons-1); //inhibitory neuron group
-  
+
   // the following parameters are set to the same values on all hosts
   float E_rev[] = {0.0, -85.0};
   float tau_decay[] = {1.0, 1.0};

c++/examples/brunel_vect.cpp

@@ -44,7 +44,7 @@ int main(int argc, char *argv[])
   cout << "Building ...\n";
 
   ngpu.SetRandomSeed(1234ULL); // seed for GPU random numbers
-  
+
   int n_receptors = 2;
 
   int order = arg1/5;
@@ -84,7 +84,7 @@ int main(int argc, char *argv[])
   ngpu.SetNeuronParam(neuron_vect, "E_rev", E_rev, 2);
   ngpu.SetNeuronParam(neuron_vect, "tau_decay", tau_decay, 2);
   ngpu.SetNeuronParam(neuron_vect, "tau_rise", tau_rise, 2);
-  
+
   float mean_delay = 0.5;
   float std_delay = 0.25;
   float min_delay = 0.1;
@@ -94,7 +94,7 @@ int main(int argc, char *argv[])
   float *exc_delays = ngpu.RandomNormalClipped(CE*n_neurons, mean_delay,
   						     std_delay, min_delay,
   						     mean_delay+3*std_delay);
-  
+
   ConnSpec conn_spec1(FIXED_INDEGREE, CE);
   SynSpec syn_spec1;
   syn_spec1.SetParam("receptor", 0);
@@ -124,7 +124,7 @@ int main(int argc, char *argv[])
   // connect poisson generator to port 0 of all neurons
   ngpu.Connect(pg_vect, neuron_vect, conn_spec3, syn_spec3);
   char filename[] = "test_brunel_vect.dat";
-  
+
   int i_neuron_arr[] = {neuron[0], neuron[rand()%n_neurons],
 		     neuron[n_neurons-1]}; // any set of neuron indexes
   // create multimeter record of V_m

c++/examples/make_brunel_net.sh

@@ -1,2 +1 @@
 g++ -Wall -I ../../src -o bin/brunel_net brunel_net.cpp -lm -lnestgpu
-

c++/examples/make_brunel_outdegree.sh

@@ -1,2 +1 @@
 g++ -Wall -I ../../src -o bin/brunel_outdegree brunel_outdegree.cpp -lm -lnestgpu
-

c++/examples/make_brunel_vect.sh

@@ -1,2 +1 @@
 g++ -Wall -I ../../src -o bin/brunel_vect brunel_vect.cpp -lm -lnestgpu
-

c++/examples/make_constcurr.sh

@@ -1,2 +1 @@
 g++ -Wall -I ../../src -o bin/test_constcurr test_constcurr.cpp -lm -lnestgpu
-

c++/examples/make_setvar.sh

@@ -1,2 +1 @@
 g++ -Wall -I ../../src -o bin/test_setvar test_setvar.cpp -lm -lnestgpu
-

c++/examples/make_test_connect.sh

@@ -1,2 +1 @@
 g++ -Wall -I ../../src -o bin/test_connect test_connect.cpp -lm -lnestgpu
-

c++/examples/test_aeif_cond_beta.cpp

@@ -36,10 +36,10 @@ int main(int argc, char *argv[])
 {
   NESTGPU ngpu;
   cout << "Building ...\n";
-  
+
   srand(12345);
   int n_neurons = 10000;
-  
+
   // create n_neurons neurons with 3 receptor ports
   NodeSeq neuron = ngpu.Create("aeif_cond_beta", n_neurons, 3);
 
@@ -63,7 +63,7 @@ int main(int argc, char *argv[])
   // set spike times and height
   ngpu.SetNeuronParam(sg, "spike_times", spike_times, n_spikes);
   ngpu.SetNeuronParam(sg, "spike_heights", spike_heights, n_spikes);
-  
+
   float delay[] = {1.0, 100.0, 130.0};
   float weight[] = {0.1, 0.2, 0.5};
 

c++/examples/test_connect.cpp

@@ -33,7 +33,7 @@
 int main(int argc, char *argv[])
 {
   const int N = 5;
-  
+
   NESTGPU ngpu;
 
   NodeSeq neuron = ngpu.Create("aeif_cond_beta", 2*N);
@@ -67,7 +67,7 @@ int main(int argc, char *argv[])
   SynSpec odd_to_even_syn_spec;
   odd_to_even_syn_spec.SetParam("weight_array", odd_to_even_weight);
   odd_to_even_syn_spec.SetParam("delay_array", odd_to_even_delay);
-  
+
   ngpu.Connect(neuron_even, neuron_odd, conn_spec, even_to_odd_syn_spec);
   ngpu.Connect(neuron_odd, neuron_even, conn_spec, odd_to_even_syn_spec);
 
@@ -95,7 +95,7 @@ int main(int argc, char *argv[])
   }
   std::cout << "########################################\n";
 
-  
+
   // All to odd
   conn_id = ngpu.GetConnections(neuron, neuron_odd);
   conn_stat_vect = ngpu.GetConnectionStatus(conn_id);
@@ -118,7 +118,7 @@ int main(int argc, char *argv[])
   }
   std::cout << "########################################\n";
 
-  
+
   // Even to 3,4,5,6
   NodeSeq neuron_3_6 = neuron.Subseq(3,6);
   conn_id = ngpu.GetConnections(neuron_even, neuron_3_6);
@@ -142,7 +142,7 @@ int main(int argc, char *argv[])
   }
   std::cout << "########################################\n";
 
-  
+
   // 3,4,5,6 to odd
   conn_id = ngpu.GetConnections(neuron_3_6, neuron_odd);
   conn_stat_vect = ngpu.GetConnectionStatus(conn_id);
@@ -165,6 +165,6 @@ int main(int argc, char *argv[])
   }
   std::cout << "########################################\n";
 
-  
+
   return 0;
 }

c++/examples/test_constcurr.cpp

@@ -36,10 +36,10 @@ int main(int argc, char *argv[])
 {
   NESTGPU ngpu;
   cout << "Building ...\n";
-  
+
   srand(12345);
   int n_neurons = 10000;
-  
+
   // create n_neurons neurons with 1 receptor ports
   NodeSeq neuron = ngpu.Create("aeif_cond_beta", n_neurons, 1);
 

c++/examples/test_error.cpp

@@ -46,7 +46,7 @@ int main(int argc, char *argv[])
   cout << "Building ...\n";
 
   ngpu.SetRandomSeed(1234ULL); // seed for GPU random numbers
-  
+
   int n_receptors = 2;
 
   int order = arg1/5;
@@ -83,7 +83,7 @@ int main(int argc, char *argv[])
   ngpu.SetNeuronParam(neuron, "Non-existent", E_rev, 2);
   ngpu.SetNeuronParam(neuron, "tau_decay", tau_decay, 2);
   ngpu.SetNeuronParam(neuron, "tau_rise", tau_rise, 2);
-  
+
   float mean_delay = 0.5;
   float std_delay = 0.25;
   float min_delay = 0.1;
@@ -93,7 +93,7 @@ int main(int argc, char *argv[])
   float *exc_delays = ngpu.RandomNormalClipped(CE*n_neurons, mean_delay,
   						     std_delay, min_delay,
   						     mean_delay+3*std_delay);
-  
+
   ConnSpec conn_spec1(FIXED_INDEGREE, CE);
   SynSpec syn_spec1;
   syn_spec1.SetParam("receptor", 0);