settings['CLASS_ACTIVATION_SPIKE_INJECTION_POP_LABEL'] = sys.argv[7] if numArgumentsProvided >=8 : settings['NUM_OBSERVATIONS'] = int(sys.argv[8]) if numArgumentsProvided >=9 : settings['OBSERVATION_EXPOSURE_TIME_MS'] = int(sys.argv[9]) if numArgumentsProvided >=10 : settings['OUTPUT_DIR'] = sys.argv[10] if numArgumentsProvided >=11 : params['CLUSTER_SIZE'] = int(sys.argv[11]) if numArgumentsProvided >=12 : settings['STARTUP_WAIT_SECS'] = int(sys.argv[12]) print 'updated STARTUP_WAIT_SECS to ', settings['STARTUP_WAIT_SECS'] classifier.printParameters('Model Parameters',params) classifier.printParameters('Classifier Settings',settings) utils.writeObjectToFile(params,(settings['OUTPUT_DIR'] + '/Spynnaker-ModelParams.txt')) utils.writeObjectToFile(settings,(settings['OUTPUT_DIR'] + '/Spynnaker-Settings.txt')) populationsInput = list() populationsNoiseSource = list() populationsRN = list() populationsPN = list() populationsAN = list() projectionsPNAN = list() #keep handle to these for saving learnt weights totalSimulationTimeMs = settings['OBSERVATION_EXPOSURE_TIME_MS'] * settings['NUM_OBSERVATIONS'] + (1000 * settings['STARTUP_WAIT_SECS']) + 1000 print 'Total Simulation Time will be', totalSimulationTimeMs startEverything = time.time() DT = 1.0 #ms Integration timestep for simulation