/**
* "FNS" (Firnet NeuroScience), ver.3.x
*
* FNS is an event-driven Spiking Neural Network framework, oriented
* to data-driven neural simulations.
*
* (c) 2020, Gianluca Susi, Emanuele Paracone, Mario Salerno,
* Alessandro Cristini, Fernando Maestú.
*
* CITATION:
* When using FNS for scientific publications, cite us as follows:
*
* Gianluca Susi, Pilar Garcés, Alessandro Cristini, Emanuele Paracone,
* Mario Salerno, Fernando Maestú, Ernesto Pereda (2020).
* "FNS: an event-driven spiking neural network simulator based on the
* LIFL neuron model".
* Laboratory of Cognitive and Computational Neuroscience, UPM-UCM
* Centre for Biomedical Technology, Technical University of Madrid;
* University of Rome "Tor Vergata".
* Paper under review.
*
* FNS is free software: you can redistribute it and/or modify it
* under the terms of the GNU General Public License version 3 as
* published by the Free Software Foundation.
*
* FNS is distributed in the hope that it will be useful, but WITHOUT
* ANY WARRANTY; without even the implied warranty of MERCHANTABILITY
* or FITNESS FOR A PARTICULAR PURPOSE.
* See the GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with FNS. If not, see <http://www.gnu.org/licenses/>.
*
* -----------------------------------------------------------
*
* Website: http://www.fnsneuralsimulator.org
*
* Contacts: fnsneuralsimulator (at) gmail.com
* gianluca.susi82 (at) gmail.com
* emanuele.paracone (at) gmail.com
*
*
* -----------------------------------------------------------
* -----------------------------------------------------------
**/
package spiking.simulator;
import java.io.File;
import java.util.ArrayList;
import java.util.Scanner;
import java.util.HashMap;
import connectivity.nodes.ConnectivityPackageManager;
import utils.tools.NiceQueue;
import spiking.controllers.node.NodeThread;
import spiking.node.NodesInterconnection;
import spiking.node.NodesManager;
import spiking.node.external_inputs.ExternalInput;
import utils.configuration.NeuManCfg;
import utils.configuration.NodeCfg;
import utils.configuration.SpikingConfigManager;
import utils.configuration.SpikingSimulatorCfg;
import utils.constants.Constants;
import utils.exceptions.BadParametersException;
import utils.experiment.Experiment;
import utils.statistics.StatisticsCollector;
import org.apache.commons.cli.*;
public class SpikingNeuralSimulator extends Thread{
private final static String TAG = "[Spiking Neural Simulator] ";
private final static int SERIALIZE_AFTER=1000000;
private Boolean verbose = false;
private Boolean debug = false;
private NodesManager nMan;
private Double minQueuesValue = Double.MAX_VALUE;
private NiceQueue minQueue=null;
// the nodes of interest NOI
private HashMap <Integer,Boolean> NOI;
private volatile Boolean checkall=false;
private double total_time=10;
private double cycle_time;
public final static long simulationNeurons=100l;
public final static int externalNeurons=10;
private Boolean initialized=false;
private int debNum=27;
private int [] debugCases = new int[debNum];
private int completing;
private double currentTime=0.0;
private long splitCount = 0;
//the number neuron Ids must be divided to allow the graphical compression
//for plotting the neuron fires.
private double compressionFactor;
private Boolean keep_running=true;
private Double avgNeuronalSignalSpeed = 5.1;
private Boolean badCurve=false;
//standard parameters for testing, never use for other purposes
public static final Double excitProportion4Test = 0.8;
public static final Integer k4Test = 20;
public static final Double prew4Test = 0.5;
public static final Double d4Test = 0.04;
public static final Double ld4Test = 0.001;
public static final Double kr4Test = 0.3;
public static final Double internalAmplitude4Test = 1.0;
public static final Integer BnTest=1;
public static final Double IBITest=0.001;
private static final Double epsilon = 0.00000001;
long[] times= new long[10];
/* the coefficient for which multiply the BOP to obtain a cycle:
* 1. if it is less than 1, than we fall into the optimistic
* simulation
* 2. if it is greater or equal to 1, than we have a conservative
behavior
* Note: using a gamma distribution with shape parameter
* (alpha_lambda) for the connectivity topology lengths, we
* need to re-calculate such factor in order to controll
* lossess between nodes under the bop_conservative_p
* probability.
*/
private static final Double bop_to_cycle_factor = 1.0;
private static final Double bop_conservative_p = 0.9999;
private ArrayList<StatisticsCollector> scs =
new ArrayList <StatisticsCollector>();
public SpikingNeuralSimulator (){
nMan = new NodesManager(this, bop_conservative_p);
<<<<<<< HEAD
=======
//sc.start();
>>>>>>> 48bb69b3e778691a096596083425b528609c0729
}
private void addNodeThread(NodeThread node){
nMan.addNodeThread(node);
}
public void init(){
if (nMan.getnSms()<=0){
//debprintln(" no node added to the simulator");
return;
}
initialized=true;
String minTractLengthStr=(nMan.getMinTractLength()!=NodesManager.MAX_TRACT_LENGTH)?
(""+nMan.getMinTractLength()):" there are no connection betwen regions.";
println("min tract length:"+minTractLengthStr);
println("avg neuronal signal speed:"+avgNeuronalSignalSpeed);
cycle_time=(nMan.getMinTractLength()+epsilon)/
(avgNeuronalSignalSpeed*bop_to_cycle_factor);
}
private void setTotalTime(double total_time){
this.total_time=total_time;
}
/**
* Starts all node threads
*/
public void startAll(){
completing = nMan.getNodeThreadsNum()-1;
nMan.startAll();
}
/**
* Awakes all node threads
* @param newStopTime the stop time for the next "split"
*
*/
public void runNewSplit(double newStopTime){
double stopTime=(newStopTime>total_time)?total_time:newStopTime;
if (verbose||((splitCount%100)==0))
println(
"running new split "
+splitCount
+" with new stop simulated time:"
+stopTime);
nMan.runNewSplit(stopTime);
}
/**
* Kills all node threads
*/
public void killAll(){
println("stopping node threads...");
nMan.killAll();
try {
Thread.sleep(1000);
} catch (InterruptedException e) {
e.printStackTrace();
}
keep_running=false;
println("node threads stopped.");
println("killing statistics collector...");
killscs();
println("statistics collector stopped.");
}
private void killscs(){
for (int i=0; i<scs.size(); ++i)
scs.get(i).kill();
}
/**
* This function performs the spike routine for
* a fixed number of fires (nstop).
*
* The main characters are:
* 1. the active neurons: neurons which are over the spike
* threshold and are going to "shoot"
* at a certain time. This fire time may be deleted by
* effect of the arrival of
* a negative input or anticipated by the arrival
* of a new excitatory signal.
* 2. the synapsys queues: synapsys are modeled by a queue for
* each couple of connected neurons.
* Each queue holds the signals generated by the
* source neuron but not still "delivered"
* at the target neuron.
*
*/
public void run(){
long startTime = System.currentTimeMillis();
if (!initialized)
return;
println("starting nodes...");
startAll();
println("nodes started.");
while (keep_running){
try {
Thread.sleep(100);
} catch (InterruptedException e) {
e.printStackTrace();
}
}
println(
"end of simulator run, "+
(System.currentTimeMillis()-startTime)+
" ms elapsed.");
println("effective simulation time: "+
(System.currentTimeMillis()-startTime-
times[0]-times[1]-times[2]-times[3]-times[4])+
" ms.");
println(
"init phases:\n\t conn pckg read:\t\t"+
times[0]+" ms\n\t config file read:\t\t"+
times[1]+
" ms\n\t node init:\t\t\t"+
times[2]+
" ms\n\t inter-node connections init:\t"+
times[3]+
" ms\n\t simulator init:\t\t"+
times[4]+
" ms\n\t total init time:\t\t"+
(times[0]+times[1]+times[2]+times[3]+times[4])+
" ms");
println("avg neuronal signal speed:"+avgNeuronalSignalSpeed);
println("cycle time:"+cycle_time);
println(
"total inter node axonal connections:"+
nMan.getTotalInterNodeConnectionsNumber());
}
public Boolean splitComplete(Integer nodeId){
if (completing>0)
--completing;
else{
++splitCount;
if (currentTime>=total_time)
killAll();
else{
//printBreakLine();
try {
sleep(5);
} catch (InterruptedException e) {
e.printStackTrace();
}
currentTime+=cycle_time;
runNewSplit(currentTime);
completing=nMan.getNodeThreadsNum()-1;
}
return false;
}
return true;
}
private void setMinMaxNe_xn_ratiosScs(
Double minNe_xn_ratio,
Double maxNe_xn_ratio){
for (int i=0; i<scs.size(); ++i)
scs.get(i).setMinMaxNe_xn_ratios(
minNe_xn_ratio,
maxNe_xn_ratio);
}
private void setSerializeAfterScs(int serializeAfter){
for (int i=0; i<scs.size(); ++i)
scs.get(i).setSerializeAfter(serializeAfter);
}
private void startScs(){
for (int i=0; i<scs.size(); ++i)
scs.get(i).start();
}
private void setFilenameScs(String filename){
for (int i=0; i<scs.size(); ++i)
scs.get(i).setFilename(filename+"-"+i);
}
private void setMatlabScs(){
for (int i=0; i<scs.size(); ++i)
scs.get(i).setMatlab();
}
private void setReducedOutputScs(){
for (int i=0; i<scs.size(); ++i)
scs.get(i).setReducedOutput();
}
private void setSuperReducedOutputScs(){
for (int i=0; i<scs.size(); ++i)
scs.get(i).setSuperReducedOutput();
}
private void setGephiScs(){
for (int i=0; i<scs.size(); ++i)
scs.get(i).setGephi();
}
private void printResultsScs(){
for (int i=0; i<scs.size(); ++i){
System.out.println(
"\n==============| node "+
i+
"|=============");
scs.get(i).PrintResults();
}
}
/**
* Read the config files for nodes and connectivity topology,
* creates a thread for each network node with the corersponding
* parameters and parametrizes the inter-node synapses
*
* @param configPath the path of the config package
* @param connPkgPath the path of che connectivity package (the
* folder in which all the topology parameters
* are stored
* @param do_fast if true, fastest algorithms are used
* (with some aproximations)
*/
public void initFromConfigFileAndConnectivityPackage(
String configPath,
String connPkgPath,
Boolean do_fast)
throws BadParametersException{
long startTime = System.currentTimeMillis();
long lastTime = startTime;
println("reading connectivity package file:"+connPkgPath);
ConnectivityPackageManager cpm = new ConnectivityPackageManager();
cpm.readConnectivityPackage(connPkgPath);
for (int i=0; i<cpm.getNodesNum();++i)
scs.add(new StatisticsCollector());
setMinMaxNe_xn_ratiosScs(
cpm.getMinNe_xn_ratio(),
cpm.getMaxNe_xn_ratio());
ArrayList<NodesInterconnection> conns =
cpm.getInterNodeConnections();
times[0]=System.currentTimeMillis()-lastTime;
lastTime+=times[0];
System.out.println("reading config file:"+configPath);
SpikingSimulatorCfg ssc =
SpikingConfigManager.readConfigFile(configPath);
setTotalTime(new Double(ssc.getStop()));
HashMap <Integer, NodeCfg> nodeCs = ssc.getNodesMap();
avgNeuronalSignalSpeed=ssc.getAvg_neuronal_signal_speed();
Integer serializeAfter=ssc.getSerialize_after();
if (serializeAfter==null)
serializeAfter=SERIALIZE_AFTER;
<<<<<<< HEAD
setSerializeAfterScs(serializeAfter);
=======
sc.setSerializeAfter(serializeAfter);
>>>>>>> 48bb69b3e778691a096596083425b528609c0729
times[1]=System.currentTimeMillis()-lastTime;
lastTime+=times[1];
System.out.println("creating and adding nodes...\n");
NodeCfg tmp;
Boolean tmpPlasticity;
Long tmpN;
Integer tmpK, tmpExternal, tmpBn;
Double tmpExcitRatio;
Double tmp_mu_w_exc;
Double tmp_mu_w_inh;
Double tmp_sigma_w_exc;
Double tmp_sigma_w_inh;
Double tmpW_pre_exc;
Double tmpW_pre_inh;
Double tmpRewiringP;
Double tmpIBI;
Double tmpEtap;
Double tmpEtam;
Double tmpTaup;
Double tmpTaum;
Double tmpWMax;
Double tmpTo;
NeuManCfg nmcfg;
Boolean lif=new Boolean(ssc.getLif());
Boolean exp_decay=new Boolean(ssc.getExp_decay());
for (int i=0; i<cpm.getNodesNum();++i){
tmp = ( nodeCs.get(i) != null )?nodeCs.get(i):null;
tmpN=((tmp!=null)&&(tmp.getN()!=null))?
tmp.getN():ssc.getGlob_local_n();
tmpExcitRatio = ((tmp!=null)&&(tmp.getExcitatory_inhibitory_ratio()!=null))?
tmp.getExcitatory_inhibitory_ratio():
ssc.getR();
tmp_mu_w_exc=((tmp!=null)&&(tmp.getMu_w_exc()!=null))?
tmp.getMu_w_exc():
ssc.getGlob_mu_w_exc();
tmp_mu_w_inh=((tmp!=null)&&(tmp.getMu_w_inh()!=null))?
tmp.getMu_w_inh():
ssc.getGlob_mu_w_inh();
tmp_sigma_w_exc=((tmp!=null)&&(tmp.getSigma_w_exc()!=null))?
tmp.getSigma_w_exc():
ssc.getGlob_sigma_w_exc();
tmp_sigma_w_inh=((tmp!=null)&&(tmp.getSigma_w_inh()!=null))?
tmp.getSigma_w_inh():
ssc.getGlob_sigma_w_inh();
tmpW_pre_exc=((tmp!=null)&&(tmp.getW_pre_exc()!=null))?
tmp.getW_pre_exc():
ssc.getGlob_w_pre_exc();
tmpW_pre_inh=((tmp!=null)&&(tmp.getW_pre_inh()!=null))?
tmp.getW_pre_inh():
ssc.getGlob_w_pre_inh();
tmpK = ((tmp!=null)&&(tmp.getK()!=null))?
tmp.getK(): ssc.getGlob_k();
tmpRewiringP = ((tmp!=null)&&(tmp.get_rewiring_P()!=null))?
tmp.get_rewiring_P() : ssc.getGlob_rewiring_P();
tmpExternal = ((tmp!=null)&&(tmp.getExternal_inputs_number()!=null))?
tmp.getExternal_inputs_number() : ssc.getGlob_external_inputs_number();
tmpBn=((tmp!=null)&&(tmp.getBn()!=null))?
tmp.getBn() : ssc.getGlob_Bn();
if (tmpBn==0)
tmpBn=1;
tmpIBI=((tmp!=null)&&(tmp.getIBI()!=null))?
tmp.getIBI() : ssc.getGlob_IBI();
nmcfg=((tmp!=null)&&(tmp.getNeuron_manager()!=null))?
tmp.getNeuron_manager() : ssc.getGlobal_neuron_manager();
tmpPlasticity=((tmp!=null)&&(tmp.getPlasticity()!=null))?
tmp.getPlasticity() : ssc.getPlasticity();
tmpEtap=((tmp!=null)&&(tmp.getEtap()!=null))?
tmp.getEtap() : ssc.getGlob_etap();
tmpEtam=((tmp!=null)&&(tmp.getEtam()!=null))?
tmp.getEtam() : ssc.getGlob_etam();
tmpTaup=((tmp!=null)&&(tmp.getTaup()!=null))?
tmp.getTaup() : ssc.getGlob_taup();
tmpTaum=((tmp!=null)&&(tmp.getTaum()!=null))?
tmp.getTaum() : ssc.getGlob_taum();
tmpWMax=((tmp!=null)&&(tmp.getW_max()!=null))?
tmp.getW_max() : ssc.getGlob_w_max();
tmpTo=((tmp!=null)&&(tmp.getTo()!=null))?
tmp.getTo() : ssc.getGlob_to();
if (tmpK>=tmpN){
throw new BadParametersException("bad parameters exception, "
+ "n has to be greater than k (now n is "+tmpN+
" and k is +"+tmpK+")");
}
//add a new node with or without external inputs
println("adding node:"+i);
if (tmpExternal.equals(0)) {
addNodeThread(
new NodeThread(
nMan,
cpm.getNode(i).getId(),
tmpN,
tmpExcitRatio,
tmpK,
tmpRewiringP,
tmpBn,
tmpIBI,
nmcfg.getC(),
nmcfg.getD_exc(),
nmcfg.getD_inh(),
nmcfg.getT_arp(),
tmp_mu_w_exc,
tmp_mu_w_inh,
tmp_sigma_w_exc,
tmp_sigma_w_inh,
tmpW_pre_exc,
tmpW_pre_inh,
Constants.EXTERNAL_SOURCES_PRESYNAPTIC_DEF_VAL,
tmpPlasticity,
tmpEtap,
tmpEtam,
tmpTaup,
tmpTaum,
tmpWMax,
tmpTo,
avgNeuronalSignalSpeed,
lif,
exp_decay,
do_fast,
(checkall||NOI.get(cpm.getNode(i).getId())),
<<<<<<< HEAD
scs.get(i)));
=======
sc));
>>>>>>> 48bb69b3e778691a096596083425b528609c0729
}
else{
Integer tmpExternalType;
Double tmpExternalTimestep;
Integer tmpExternalFireDuration;
Integer tmpExternalFireOutdegree;
Double tmpExternalFireAmplitude;
Double tmpExternalInputsTimeOffset;
tmpExternalType=
((tmp!=null)&&(tmp.getExternal_inputs_type()!=null))?
tmp.getExternal_inputs_type():
ssc.getGlob_external_inputs_type();
tmpExternalInputsTimeOffset=
((tmp!=null)&&(tmp.getExternal_inputs_time_offset()!=null))?
tmp.getExternal_inputs_time_offset():
ssc.getGlob_external_inputs_time_offset();
tmpExternalTimestep=
((tmp!=null)&&(tmp.getExternal_inputs_timestep()!=null))?
tmp.getExternal_inputs_timestep():
ssc.getGlob_external_inputs_timestep();
tmpExternalFireDuration=
((tmp!=null)&&(tmp.getExternal_inputs_fireduration()!=null))?
tmp.getExternal_inputs_fireduration():
ssc.getGlob_external_inputs_fireduration();
tmpExternalFireOutdegree=
((tmp!=null)&&(tmp.getExternal_inputs_outdegree()!=null))?
tmp.getExternal_inputs_outdegree():
ssc.getGlob_external_inputs_outdegree();
tmpExternalFireAmplitude=
((tmp!=null)&&(tmp.getExternal_inputs_amplitude()!=null))?
tmp.getExternal_inputs_amplitude():
ssc.getGlob_external_inputs_amplitude();
addNodeThread(
new NodeThread(
nMan,
cpm.getNode(i).getId(),
tmpN,
tmpExternal,
tmpExternalType,
tmpExternalInputsTimeOffset,
tmpExternalTimestep,
tmpExternalFireDuration,
tmpExternalFireAmplitude,
tmpExternalFireOutdegree,
tmpExcitRatio,
tmpK,
tmpRewiringP,
tmpBn,
tmpIBI,
nmcfg.getC(),
nmcfg.getD_exc(),
nmcfg.getD_inh(),
nmcfg.getT_arp(),
tmp_mu_w_exc,
tmp_mu_w_inh,
tmp_sigma_w_exc,
tmp_sigma_w_inh,
tmpW_pre_exc,
tmpW_pre_inh,
Constants.EXTERNAL_SOURCES_PRESYNAPTIC_DEF_VAL,
tmpPlasticity,
tmpEtap,
tmpEtam,
tmpTaup,
tmpTaum,
tmpWMax,
tmpTo,
avgNeuronalSignalSpeed,
lif,
exp_decay,
do_fast,
(checkall||NOI.get(cpm.getNode(i).getId())),
<<<<<<< HEAD
scs.get(i)));
=======
sc));
>>>>>>> 48bb69b3e778691a096596083425b528609c0729
}
}
calculateCompressionFactor();
times[2]=System.currentTimeMillis()-lastTime;
lastTime+=times[2];
println("adding inter-node connection ...");
for (int i=0; i<conns.size();++i){
println("adding connection bundle: "+conns.get(i).getSrc()+"-"+conns.get(i).getDst());
addInterNodeThreadConnection(
nMan.getNodeThread(conns.get(i).getSrc()),
nMan.getNodeThread(conns.get(i).getDst()),
conns.get(i).getNe_xn_ratio(),
conns.get(i).getMu_omega(),
conns.get(i).getSigma_w(),
conns.get(i).getLength(),
conns.get(i).getLengthShapeParameter(),
conns.get(i).getType());
}
times[3]=System.currentTimeMillis()-lastTime;
lastTime+=times[3];
System.out.println("initializing simulator...\n");
init();
times[4]=System.currentTimeMillis()-lastTime;
lastTime+=times[4];
<<<<<<< HEAD
startScs();
=======
sc.start();
>>>>>>> 48bb69b3e778691a096596083425b528609c0729
}
/**
* Creates a new inter-node connection
* Each connection is to be intended as a couple of boundle of
* synapsis between two nodes, one per each direction
*
* @param nd1 one node of the connection
* @param nd2 the other node of the connection
* @param Ne_xn_ratio the ratio of excitatory connections
* @param mu_omega the mean of postsynaptic weight
* distribution
* @param sigma_omega the std dev of postsynaptic weights
* distribution
* @param mu_lambda the mean of synapsis length
* distribution
* @param alpha_lambda the shape factor of the synapsys
* length distribution
* @param inter_node_conn_type the type of inter node connection
*
*/
public void addInterNodeThreadConnection(
NodeThread nd1,
NodeThread nd2,
Double Ne_xn_ratio,
Double mu_omega,
Double sigma_omega,
Double mu_lambda,
Double alpha_lambda,
Integer inter_node_conn_type){
nMan.addInterNodeConnection(
nd1,
nd2,
Ne_xn_ratio,
mu_omega,
sigma_omega,
mu_lambda,
alpha_lambda,
inter_node_conn_type);
}
/**
* Allows debug print to the stdout
*/
public void setDebug(Boolean debug){
this.debug=debug;
nMan.setDebug(debug);
}
/**
* Set the name for the simulation
* The name is the same of the configuration path
*/
public void setExperimentName(String expName) {
Experiment.setExperimentName(expName);
File expDir = new File (Experiment.getExperimentDir());
if (!expDir.exists())
expDir.mkdirs();
}
private void calculateCompressionFactor(){
if (nMan.getTotalN()>1000000)
compressionFactor =
new Double(Integer.MAX_VALUE) / nMan.getTotalN();
else
compressionFactor = 1.0;
nMan.setCompressionFactor(compressionFactor);
}
public double getReducingFactor(){
return compressionFactor;
}
public void setNOI(HashMap <Integer, Boolean> NOI){
//sc.setNOI(NOI);
this.NOI=NOI;
}
public void checkAll(){
this.checkall=true;
}
public void setVerbose(){
verbose=true;
}
public void setBadCurve(){
badCurve=true;
}
//================ printing functions ============================
private void println(String s){
System.out.println(TAG+s);
}
private void debprintln(String s){
if (verbose&&debug)
System.out.println(TAG+"[debug] "+s);
}
public void printDebug(){
System.out.println("debug variables:");
for(int i=0; i<debNum;++i)
System.out.print("\n\t"+i+". "+debugCases[i]);
System.out.println();
}
public void printNodeFields(){
nMan.printNodeFields();
}
public void printBreakLine(){
if (verbose)
System.out.println("\n\n----------------------------------------"
+splitCount
+"-----------------------------------------------------\n");
}
//========================== main function ========================
public static void main(String[] args) {
System.out.println("\n\n\n\t\t\t\t\t"
+"=================================");
System.out.println("\t\t\t\t\t=\t F N S\t\t=");
System.out.println("\t\t\t\t\t=\tNeural Simulator\t=");
System.out.println("\t\t\t\t\t"
+"=================================\n\n");
// options parsing and management
Options options= new Options();
options.addOption("n", "nodes-list", true, " followed by the "
+"list of the node of interest (NOI) "
+ "for which to store the output data. "
+ "The format for such list is like this exmaple: [3,25,13,12]. "
+ "If this switch is not present, the entire set of nodes"
+ "will be considered for the generation of output data.");
options.addOption("f", "fast", false, "enables faster "
+"algorithms at different levels, "
+ "in return for some approximations "
+"(i.e., plasticity exponentials, etc.)");
options.addOption("m", "matlab", false, "provides with a set "
+"of matlab-compliant "
+ "CSV files, in addition to the output CSVs.");
options.addOption("r", "reduced-output", false,
"enables reduced CSV files, i.e., outputs that indicates "
+"only spiking events and inner states of the neurons");
options.addOption("R", "super-reduced-output", false,
"enables super-reduced CSV files, i.e., outputs that indicates "
+"only spiking events and inner states of the neurons"
+" with reduced precision");
options.addOption("g", "gephi", false,
"produce also a csv for Gephi");
options.addOption("v", "verbose", false, "print verbose output");
options.addOption("h", "help", false, "shows this help");
CommandLineParser parser = new DefaultParser();
HelpFormatter formatter = new HelpFormatter();
CommandLine cmd;
try {
cmd = parser.parse(options, args);
if (cmd.hasOption("help")){
formatter.printHelp("FNS", options);
System.out.println("\nExamples:");
System.out.println("[Windows] \t> .\\start.bat"
+" exp01 -f -n [3,25,13,12] -m -r");
System.out.println("[Linux] \t$ ./start"
+" exp01 -f -n [3,25,13,12] -m -r\n");
System.exit(0);
return;
}
} catch (ParseException e) {
System.out.println(e.getMessage());
formatter.printHelp("FNS", options);
System.out.println("\nExamples:");
System.out.println(
"[Windows] > .\\start.bat "
+"exp01 -f -n [3,25,13,12] -p -m -r" );
System.out.println(
"[Linux] > ./start exp01 -f -n [3,25,13,12] -p -m -r\n");
System.exit(1);
return;
}
// intializing simulator
System.out.println("initializing simulator");
SpikingNeuralSimulator sns = new SpikingNeuralSimulator();
sns.setExperimentName(args[0]);
String filename=null;
HashMap<Integer, Boolean> NOI=new HashMap<Integer,Boolean>();
Boolean do_plot=cmd.hasOption("plot");
Boolean do_fast=cmd.hasOption("fast");
String nodeListString=cmd.getOptionValue("nodes-list","[]")
.replaceAll("\\s+","")
.substring(1,cmd.getOptionValue("nodes-list","[]").length()-1);
if(nodeListString.length()>0){
String [] nodesStr=nodeListString.split(",");
for (int i=0; i<nodesStr.length; ++i)
NOI.put(Integer.parseInt(nodesStr[i]),true);
filename = Experiment.getExperimentDir()
+"nodes_"
+ nodeListString.replaceAll(",","-")
+"_";
sns.setNOI(NOI);
}
else {
filename = Experiment.getExperimentDir()+"all_nodes_";
sns.checkAll();
//sns.setCheckalle;
}
sns.setFilenameScs(filename);
if (cmd.hasOption("matlab"))
sns.setMatlabScs();
if (cmd.hasOption("reduced-output"))
<<<<<<< HEAD
sns.setReducedOutputScs();
if (cmd.hasOption("super-reduced-output"))
sns.setSuperReducedOutputScs();
if (cmd.hasOption("gephi"))
sns.setGephiScs();
=======
sns.sc.setReducedOutput();
if (cmd.hasOption("super-reduced-output"))
sns.sc.setSuperReducedOutput();
if (cmd.hasOption("gephi"))
sns.sc.setGephi();
>>>>>>> 48bb69b3e778691a096596083425b528609c0729
if (cmd.hasOption("verbose"))
sns.setVerbose();
try {
sns.initFromConfigFileAndConnectivityPackage(
(new File(args[0]+"/config.xml")).getAbsolutePath(),
(new File(args[0]+"/connectivity")).getAbsolutePath(),
do_fast);
} catch (BadParametersException e) {
e.printStackTrace();
}
System.out.println("nodes to check mask:"+nodeListString);
System.out.println("running simulator...\n");
sns.start();
try {
sns.join();
} catch (InterruptedException e1) {
e1.printStackTrace();
}
<<<<<<< HEAD
sns.printResultsScs();
sns.killscs();
=======
sns.sc.PrintResults();
sns.sc.kill();
>>>>>>> 48bb69b3e778691a096596083425b528609c0729
try{
//sns.sc.makeCsv(filename);
System.out.println("done.");
//if (do_plot){
// sns.sc.printFirePlot(filename);
// System.out.println("Press enter twice to quit:");
// System.in.read();
// System.in.read();
// System.in.read();
//}
} catch (Exception e){
e.printStackTrace();
}
System.out.println("bye!");
System.exit(0);
}
}