file_name = None
screen = False
if args.log.lower() in ['file', 'both']:
file_name = 'neurokernel.log'
if args.log.lower() in ['screen', 'both']:
screen = True
logger = base.setup_logger(file_name, screen)
if args.port_data is None and args.port_ctrl is None:
port_data = get_random_port()
port_ctrl = get_random_port()
else:
port_data = args.port_data
port_ctrl = args.port_ctrl
man = core.Manager(port_data, port_ctrl)
man.add_brok()
(n_dict, s_dict) = LPU_rev.lpu_parser('./data/antennallobe.gexf.gz')
al = LPU_rev(dt, n_dict, s_dict, input_file='./data/olfactory_input.h5',
output_file='olfactory_output.h5', port_ctrl=port_ctrl,
port_data=port_data,
device=args.al_dev, id='al',
debug=args.debug)
man.add_mod(al)
man.start(steps=args.steps)
man.stop()
else:
port_data = args.port_data
port_ctrl = args.port_ctrl
for i, neu_num in neu_dict.iteritems():
lpu_entry = {}
if i == 0:
in_file_name = in_file_name_0
else:
in_file_name = None
lpu_file_name = 'generic_lpu_%s.gexf.gz' % i
out_file_name = 'generic_output_%s.h5' % i
g.create_lpu(lpu_file_name, *neu_num)
(n_dict, s_dict) = LPU_rev.lpu_parser(lpu_file_name)
id = 'lpu_%s' % i
lpu = LPU_rev(dt, n_dict, s_dict, input_file=in_file_name,
output_file=out_file_name,
port_ctrl=port_ctrl, port_data=port_data,
device=i, id=id,
debug=args.debug)
lpu_entry['lpu_file_name'] = lpu_file_name
lpu_entry['in_file_name'] = in_file_name
lpu_entry['out_file_name'] = out_file_name
lpu_entry['lpu'] = lpu
lpu_entry['id'] = id
lpu_dict[i] = lpu_entry
steps= int(args.steps)
else:
steps=10000
dt = 1e-4
dur = 1.0
Nt = 10000#int(dur/dt)
logger = base.setup_logger()
man = core.Manager(port_data=data_port, port_ctrl=ctrl_port)
man.add_brok()
(n_dict_lam, s_dict_lam) = lpu_parser('./config_files/lamina.gexf')
(n_dict, s_dict) = LPU_rev.lpu_parser( './config_files/lamina.gexf')
lam_org = LPU( dt, n_dict_lam, s_dict_lam,
#input_file='videos/flicker_stripe_same6.h5',
output_file='lamina_output_org.h5', port_ctrl= man.port_ctrl,
port_data=man.port_data, device=dev1, id='lamina',
debug=args.debug)
lam = LPU_rev( dt, n_dict, s_dict,\
#input_file='videos/flciker_stripe_same6.h5',
output_file='lamina_output.h5', port_ctrl=man.port_ctrl,
port_data=man.port_data, device=dev1, id='lamina',
debug=args.debug)
file_name = None
screen = False
if args.log.lower() in ['file', 'both']:
file_name = 'neurokernel.log'
if args.log.lower() in ['screen', 'both']:
screen = True
logger = base.setup_logger(file_name, screen)
if args.port_data is None and args.port_ctrl is None:
port_data = get_random_port()
port_ctrl = get_random_port()
else:
port_data = args.port_data
port_ctrl = args.port_ctrl
man = core.Manager(port_data, port_ctrl)
man.add_brok()
(n_dict, s_dict) = LPU_rev.lpu_parser('./data/generic_lpu.gexf.gz')
ge = LPU_rev(dt, n_dict, s_dict,
input_file='./data/generic_input.h5',
output_file='generic_output.h5', port_ctrl=port_ctrl,
port_data=port_data,
device=args.gpu_dev, id='ge',
debug=args.debug)
man.add_mod(ge)
man.start(steps=args.steps)
man.stop()
def run(connected):
if args.port_data is None and args.port_ctrl is None:
port_data = get_random_port()
port_ctrl = get_random_port()
else:
port_data = args.port_data
port_ctrl = args.port_ctrl
out_name = 'un' if not connected else 'co'
man = core.Manager(port_data, port_ctrl)
man.add_brok()
lpu_file_0 = './data/generic_lpu_0.gexf.gz'
lpu_file_1 = './data/generic_lpu_1.gexf.gz'
(n_dict_0, s_dict_0) = LPU_rev.lpu_parser(lpu_file_0)
(n_dict_1, s_dict_1) = LPU_rev.lpu_parser(lpu_file_1)
ge_0_id = 'ge_0'
ge_0 = LPU_rev(dt, n_dict_0, s_dict_0,
input_file='./data/generic_input_0.h5',
output_file='generic_output_0_%s.h5' % out_name,
port_ctrl=port_ctrl, port_data=port_data,
device=args.gpu_dev[0], id=ge_0_id,
debug=args.debug)
man.add_mod(ge_0)
ge_1_id = 'ge_1'
ge_1 = LPU_rev(dt, n_dict_1, s_dict_1,
input_file='./data/generic_input_1.h5',
output_file='generic_output_1_%s.h5' % out_name,
port_ctrl=port_ctrl, port_data=port_data,
device=args.gpu_dev[1], id=ge_1_id,
debug=args.debug)
man.add_mod(ge_1)
# Connect the public neurons in the two LPUs:
df_neu_0, df_syn_0 = neurokernel.tools.graph.graph_to_df(nx.read_gexf(lpu_file_0))
df_neu_1, df_syn_1 = neurokernel.tools.graph.graph_to_df(nx.read_gexf(lpu_file_1))
# Number of public neurons in each LPU:
N_spike_0 = len(df_neu_0[(df_neu_0['spiking']==True)&(df_neu_0['public']==True)])
N_gpot_0 = len(df_neu_0[(df_neu_0['spiking']==False)&(df_neu_0['public']==True)])
N_spike_1 = len(df_neu_1[(df_neu_1['spiking']==True)&(df_neu_1['public']==True)])
N_gpot_1 = len(df_neu_1[(df_neu_1['spiking']==False)&(df_neu_1['public']==True)])
# Alpha function synaptic parameters:
alphasynapse_type_params = {'AlphaSynapse': ['ad', 'ar', 'gmax', 'id', 'class', 'conductance',
'reverse']}
if connected:
conn = core.Connectivity(N_gpot_0, N_spike_0, N_gpot_1, N_spike_1, 1,
ge_0.id, ge_1.id, alphasynapse_type_params)
for id, (i, j) in enumerate(itertools.product(xrange(N_spike_0), xrange(N_spike_1))):
conn[ge_0_id, 'spike', i, ge_1_id, 'spike', j] = 1
conn[ge_0_id, 'spike', i, ge_1_id, 'spike', j, 0, 'name'] = 'int_0to1_%s_%s' % (i, j)
conn[ge_0_id, 'spike', i, ge_1_id, 'spike', j, 0, 'model'] = 'AlphaSynapse'
conn[ge_0_id, 'spike', i, ge_1_id, 'spike', j, 0, 'ad'] = 0.19*1000
conn[ge_0_id, 'spike', i, ge_1_id, 'spike', j, 0, 'ar'] = 1.1*100
conn[ge_0_id, 'spike', i, ge_1_id, 'spike', j, 0, 'class'] = 0
conn[ge_0_id, 'spike', i, ge_1_id, 'spike', j, 0, 'conductance'] = True
conn[ge_0_id, 'spike', i, ge_1_id, 'spike', j, 0, 'gmax'] = 0.003
conn[ge_0_id, 'spike', i, ge_1_id, 'spike', j, 0, 'id'] = id
conn[ge_0_id, 'spike', i, ge_1_id, 'spike', j, 0, 'reverse'] = 0.065
man.connect(ge_0, ge_1, conn)
man.start(steps=args.steps)
man.stop()