def test_new_sim():
#src_files = sorted( glob.glob("/home/michael/hw_to_come//NeuroUnits/src/test_data/l4-9ml/std/*.9ml" ))
src_files = Locations.get_default_9ml_locations()
print src_files
obj_cache_file = 'obj_cache.txt'
if not os.path.exists(obj_cache_file):
library_manager = neurounits.NeuroUnitParser.Parse9MLFiles( src_files)
general_neuron_with_step_inj = library_manager.get('general_neuron_with_step_inj')
comp = NineMLComponent.build_compound_component(
component_name = 'Test1',
instantiate={
'o1': general_neuron_with_step_inj,
'o2': general_neuron_with_step_inj,
'o3': general_neuron_with_step_inj,
'o4': general_neuron_with_step_inj,
},
event_connections= [
('o1/syntrigger/spike', 'o2/nrn/syn_excit/event'),
('o1/syntrigger/spike', 'o2/nrn/syn_inhib/event'),
('o1/syntrigger/spike', 'o3/nrn/syn_excit/event'),
('o2/syntrigger/spike', 'o3/nrn/syn_excit/event'),
('o4/syntrigger/spike', 'o2/nrn/syn_excit/event'),
('o3/syntrigger/spike', 'o4/nrn/syn_excit/event'),
]
)
else:
with open(obj_cache_file) as fin:
comp = cPickle.load(fin)
# Write the cache:
with open(obj_cache_file,'w') as fout:
cPickle.dump(comp, fout)
# Old version:
#res = general_neuron_with_step_inj.simulate( times = np.arange(0, 0.1,0.00001),)
#res = comp.simulate( times = np.arange(0, 0.1,0.00001),)
# New versions:
#res = neurounits.codegen.python.simulate(comp, times = np.arange(0, 0.5,0.0001),)
res = neurounits.codegen.cpp1.simulate(comp, times = np.arange(0, 0.5,0.0001),)
res.auto_plot()
def test_new_sim():
#src_files = sorted( glob.glob("/home/michael/hw_to_come//NeuroUnits/src/test_data/l4-9ml/std/*.9ml" ))
src_files = Locations.get_default_9ml_locations()
print src_files
obj_cache_file = 'obj_cache.txt'
if not os.path.exists(obj_cache_file):
library_manager = neurounits.NeuroUnitParser.Parse9MLFiles(src_files)
general_neuron_with_step_inj = library_manager.get(
'general_neuron_with_step_inj')
comp = NineMLComponent.build_compound_component(
component_name='Test1',
instantiate={
'o1': general_neuron_with_step_inj,
'o2': general_neuron_with_step_inj,
'o3': general_neuron_with_step_inj,
'o4': general_neuron_with_step_inj,
},
event_connections=[
('o1/syntrigger/spike', 'o2/nrn/syn_excit/event'),
('o1/syntrigger/spike', 'o2/nrn/syn_inhib/event'),
('o1/syntrigger/spike', 'o3/nrn/syn_excit/event'),
('o2/syntrigger/spike', 'o3/nrn/syn_excit/event'),
('o4/syntrigger/spike', 'o2/nrn/syn_excit/event'),
('o3/syntrigger/spike', 'o4/nrn/syn_excit/event'),
])
else:
with open(obj_cache_file) as fin:
comp = cPickle.load(fin)
# Write the cache:
with open(obj_cache_file, 'w') as fout:
cPickle.dump(comp, fout)
# Old version:
#res = general_neuron_with_step_inj.simulate( times = np.arange(0, 0.1,0.00001),)
#res = comp.simulate( times = np.arange(0, 0.1,0.00001),)
# New versions:
#res = neurounits.codegen.python.simulate(comp, times = np.arange(0, 0.5,0.0001),)
res = neurounits.codegen.cpp1.simulate(
comp,
times=np.arange(0, 0.5, 0.0001),
)
res.auto_plot()
def test1():
library_manager = neurounits.NeuroUnitParser.Parse9MLFile( test_text )
chlstd_leak = library_manager.get('chlstd_leak')
std_neuron = library_manager.get('std_neuron')
step_current = library_manager.get('step_current')
square_current = library_manager.get('i_squarewave')
simple_syn1 = library_manager.get('simple_syn')
evt_gen = library_manager.get('evt_gen')
evt_syn = library_manager.get('evt_syn')
c1 = NineMLComponent.build_compound_component(
component_name = 'Neuron1',
instantiate = { 'lk': chlstd_leak, 'nrn': std_neuron, 'i_inj':step_current, 'i_square':square_current },
event_connections = [],
analog_connections = [
('i_inj/i', 'nrn/i_sum'),
('lk/i', 'nrn/i_sum'),
('i_square/i', 'nrn/i_sum'),
('nrn/V', 'lk/V'),
],
)
c2 = NineMLComponent.build_compound_component(
component_name = 'Neuron2',
instantiate = { 'lk': chlstd_leak, 'nrn': std_neuron,},
event_connections = [],
analog_connections = [
('lk/i', 'nrn/i_sum'),
('nrn/V', 'lk/V'),
],
)
c3 = NineMLComponent.build_compound_component(
component_name = 'driven_synapse',
instantiate = { 'spike_gen': evt_gen, 'syn': evt_syn,},
event_connections = [
('spike_gen/myevent', 'syn/myevent' ),
],
analog_connections = [
],
)
c = NineMLComponent.build_compound_component(
component_name = 'network',
instantiate = { 'nrn1': c1, 'nrn2': c2, 'syn1':simple_syn1, 'syn2':c3},
event_connections = [],
analog_connections = [
('syn1/i','nrn2/nrn/i_sum'),
('nrn1/nrn/V','syn1/V_pre'),
('nrn2/nrn/V','syn1/V_post'),
('syn2/syn/i','nrn2/nrn/i_sum'),
('nrn2/nrn/V','syn2/syn/V_post'),
],
)
res = c.simulate(
times = np.linspace(0,1,num=1000),
close_reduce_ports=True,
parameters={
'nrn1/i_inj/i_amp':'5pA',
'nrn1/lk/g': '0.1pS/um2',
'nrn1/nrn/C': '0.5pF',
'nrn1/lk/erev': '-60mV',
'nrn1/i_inj/t_start': '500ms',
'nrn1/i_square/t_on': '100ms',
'nrn1/i_square/t_off': '50ms',
'nrn1/i_square/i_amp': '2pA',
'nrn2/lk/g': '0.1pS/um2',
'nrn2/nrn/C': '0.5pF',
'nrn2/lk/erev': '-55mV',
'syn1/t_open': '3ms',
'syn1/t_close': '10ms',
'syn1/g_bar': '100pS',
'syn1/e_syn': '0mV',
'syn2/syn/t_close':'80ms',
'syn2/syn/t_open':'4ms',
'syn2/syn/g_bar': '10pS',
'syn2/syn/e_syn': '0mV',
},
initial_state_values={
'nrn1/nrn/V': '-50mV',
'nrn2/nrn/V': '-60mV',
'syn1/A':'0',
'syn1/B':'0',
'syn2/syn/A':'0',
'syn2/syn/B':'0',
},
initial_regimes={
'nrn1/i_inj/':'OFF',
}
)
f = pylab.figure()
ax1 = f.add_subplot(3,1,1)
ax2 = f.add_subplot(3,1,2)
ax3 = f.add_subplot(3,1,3)
ax1.set_ylim((-70e-3,50e-3))
ax1.plot( res.get_time(), res.state_variables['nrn1/nrn/V'] )
ax1.plot( res.get_time(), res.state_variables['nrn2/nrn/V'] )
ax1.set_ylabel('nrn1/nrn/V %s' %('??'))
ax2.plot( res.get_time(), res.state_variables['nrn1/i_square/t_last'] )
ax3.plot( res.get_time(), res.rt_regimes['nrn1/nrn/']+0.0 , label='nrn1/nrn')
ax3.plot( res.get_time(), res.rt_regimes['nrn1/i_inj/']+0.1, label='nrn1/i_inj')
ax3.plot( res.get_time(), res.rt_regimes['nrn1/i_square/']+0.2,label='nrn1/i_square' )
ax3.legend()
