Merge branch remote 'release-v0.7'

of https://github.com/fzenke/auryn into release-v0.7

README.md

@@ -10,11 +10,23 @@ to simulate recurrent spiking neural networks with spike timing dependent
 plasticity (STDP). It comes with the GPLv3 (please see COPYING).
 
 
+Quick start
+-----------
+
+To download and compile the examples try:
+
+```
+sudo apt-get install cmake git build-essential libboost-all-dev
+git clone https://github.com/fzenke/auryn.git && cd auryn 
+git checkout -b develop origin/develop
+mkdir build && cd build
+cmake ../ -DCMAKE_BUILD_TYPE=Release && make
+```
+
 Documentation & Installation/Use
 --------------------------------
 
-Please visit http://www.fzenke.net/auryn/
-
+Please visit the wiki at http://www.fzenke.net/auryn/
 
 
 Requirements

tools/aube.cpp

@@ -101,6 +101,8 @@ int main(int ac, char* av[])
 	double to_time   = -1.0;
 	double seconds_to_extract_from_end = -1.0; // negative means disabled
 	NeuronID maxid = std::numeric_limits<NeuronID>::max();
+	// one more decimal than neede to show values are not rounded
+	int decimal_places = -std::log(dt)/std::log(10)+2; 
 
 	try {
 		po::options_description desc("Allowed options");
@@ -267,7 +269,10 @@ int main(int ac, char* av[])
  	if( !output_file_name.empty() ) {
 		write_to_stdout = false;
  		of.open( output_file_name.c_str(), std::ofstream::out );
+		of << std::fixed << std::setprecision(decimal_places);
 	}
+	// sets output format to right number of decimal places
+	std::cout << std::fixed << std::setprecision(decimal_places);
 
 	while ( true ) {
 		// find smallest time reference