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src/n/e/NeuroTools-0.2.0/examples/sfn2008/sfn_example_stgen.py   NeuroTools(Download)
rate_shared = 10. #Hz, 10 % correlation
 
st1 = sg.poisson_generator(rate=rate_independent, t_stop = duration) 
print "Spiketrain 1:"
print "mean rate: %f" % st1.mean_rate()
print "coefficient of variation: %f" % st1.cv_isi()
print "fano factor: %f" % st1.fano_factor_isi()
 
st2 = sg.poisson_generator(rate=rate_independent, t_stop = duration) 
 
# inject correlation into st1 and st2 
st3 = sg.poisson_generator(rate = rate_shared, t_stop = duration) 
st1.merge(st3)
st2.merge(st3)

src/n/e/NeuroTools-0.2.0/examples/sfn2008/sfn_example_parameterspace.py   NeuroTools(Download)
    rate_shared = p.c*p.nu
 
    st1 = sg.poisson_generator(rate=rate_independent, t_stop = p.duration) 
    st2 = sg.poisson_generator(rate=rate_independent, t_stop = p.duration)
    if p.c > 0.:
        st3 = sg.poisson_generator(rate=rate_shared, t_stop = p.duration) 

src/p/y/PyNN-HEAD/examples/iaf_sfa_relref/backend_comparison.py   PyNN(Download)
    #poissonI_params = {'rate': rateI, 'start': 0.0, 'duration': tsim}
 
    spike_times_E = stg.poisson_generator(rateE*connectionsE, 0.0, tsim, array=True)
    spike_times_I = stg.poisson_generator(rateI*connectionsI, 0.0, tsim, array=True)
 

src/n/e/NeuroTools-0.2.0/examples/parameter_search/parameter_search_example.py   NeuroTools(Download)
    import NeuroTools.stgen as stgen
    stgen = stgen.StGen()
    spiketrain = stgen.poisson_generator(param_dict['rate'], t_stop = 1000.)
    source = sim.Population(1, sim.SpikeSourceArray,  
                            {'spike_times':spiketrain.spike_times})

src/n/e/NeuroTools-0.2.0/test/test_stgen.py   NeuroTools(Download)
        rate = 100.0 #Hz
        t_stop = 1000.0 # milliseconds
        st = stg.poisson_generator(rate,0.0,t_stop)
 
        assert isinstance(st,signals.SpikeTrain)
 
        st = stg.poisson_generator(rate,0.0,t_stop,array=True)
        assert isinstance(st, numpy.ndarray)
 
        st = stg.poisson_generator(rate,0.0,t_stop,array=True,debug=True)
 
        assert isinstance(st[0], numpy.ndarray)
        assert isinstance(st[1], list)
 
        st = stg.poisson_generator(rate,0.0,t_stop,debug=True)
            N = rate*dt/1000.0
 
            st = stg.poisson_generator(rate,t_start=t_start,t_stop=t_stop,array=True)
 
            if len(st) in (0,1,2,3):

src/p/y/PyNN-HEAD/test/unsorted/test_stdp.py   PyNN(Download)
parameters = ParameterSet({
    'system': { 'timestep': 0.01, 'min_delay': 0.1, 'max_delay': 10.0 },
    'input_spike_times': stgen.poisson_generator(rate=1000.0/spike_interval, t_stop=sim_time, array=True),
    'trigger_spike_times': stgen.poisson_generator(rate=1000.0/spike_interval, t_stop=sim_time, array=True),
    'cell_type': 'IF_curr_exp',

src/p/y/PyNN-HEAD/test/unsorted/test_synaptic_integration.py   PyNN(Download)
    model_parameters = ParameterSet({
        'system': parameters.system,
        'input_spike_times': stgen.poisson_generator(1000.0/spike_interval, t_stop=sim_time, array=True),
        'cell_type': parameters.cell.type,
        'cell_parameters': parameters.cell.params,