def test_dest_mask_conn(self):
c = core.Connectivity(2, 2, 3, 3)
c['A', 'gpot', 0, 'B', 'gpot', 1] = 1
assert all(c.dest_mask(src_type='gpot', dest_type='gpot') == [False, True, False])
assert all(c.dest_mask(src_type='gpot', dest_type='spike') == [False, False, False])
assert all(c.dest_mask(src_type='spike', dest_type='gpot') == [False, False, False])
assert all(c.dest_mask(src_type='spike', dest_type='spike') == [False, False, False])
def test_dest_mask_no_conn(self):
c = core.Connectivity(2, 2, 3, 3)
all(c.dest_mask() == [False, False, False, False, False, False])
assert all(c.dest_mask(src_type='gpot', dest_type='gpot') == [False, False, False])
assert all(c.dest_mask(src_type='gpot', dest_type='spike') == [False, False, False])
assert all(c.dest_mask(src_type='spike', dest_type='gpot') == [False, False, False])
assert all(c.dest_mask(src_type='spike', dest_type='gpot') == [False, False, False])
def test_src_mask_conn_slice(self):
c = core.Connectivity(2, 2, 3, 3)
c['A', 'all', :, 'B', 'gpot', 1] = 1
assert all(c.src_mask(src_type='gpot', dest_type='gpot') == [True, True])
assert all(c.src_mask(src_type='gpot', dest_type='spike') == [False, False])
assert all(c.src_mask(src_type='spike', dest_type='gpot') == [True, True])
assert all(c.src_mask(src_type='spike', dest_type='spike') == [False, False])
def test_src_mask_no_conn_no_spike_A(self):
c = core.Connectivity(2, 0, 3, 3)
all(c.src_mask() == [False, False])
assert all(c.src_mask(src_type='gpot', dest_type='gpot') == [False, False])
assert all(c.src_mask(src_type='gpot', dest_type='spike') == [False, False])
assert all(c.src_mask(src_type='spike', dest_type='gpot') == [])
assert all(c.src_mask(src_type='spike', dest_type='gpot') == [])
N_int = 8 # number of public integration neurons
N_al = 1540 # number of public antennal lobe neurons
N_al_pn = 165 # number of antennal lobe projection neurons
int_id = 'integrate'
med_id = 'medulla'
al_id = 'antennallobe'
alphasynapse_type_params = {
'AlphaSynapse':
['reverse', 'gmax', 'id', 'ar', 'ad', 'class', 'conductance']
}
power_gpot_type_params = {'power_gpot_gpot': ['id','class','slope','threshold','power',\
'saturation','delay','reverse','conductance']}
conn_med_int = core.Connectivity(N_med_gpot, 0, 0, N_int, 1, med_id, int_id,
power_gpot_type_params)
for id, i in enumerate(range(N_med_gpot - 8, N_med_gpot)):
conn_med_int[med_id, 'gpot', i, int_id, 'spike', i - N_med_gpot + 8] = 1
conn_med_int[med_id, 'gpot', i, int_id, 'spike', i - N_med_gpot + 8, 0,
'id'] = id
conn_med_int[med_id, 'gpot', i, int_id, 'spike', i - N_med_gpot + 8, 0,
'name'] = 'med_int_%s_%s' % (i, i)
conn_med_int[med_id, 'gpot', i, int_id, 'spike', i - N_med_gpot + 8, 0,
'model'] = 'power_gpot_gpot'
conn_med_int[med_id, 'gpot', i, int_id, 'spike', i - N_med_gpot + 8, 0,
'class'] = 2
conn_med_int[med_id, 'gpot', i, int_id, 'spike', i - N_med_gpot + 8, 0,
'conductance'] = True
conn_med_int[med_id, 'gpot', i, int_id, 'spike', i - N_med_gpot + 8, 0,
'slope'] = 4e9
import neurokernel.core as core
import neurokernel.tools.graph as graph
# Create files containing module connectivity info:
N_A_gpot = 4
N_A_spike = 3
g = nx.MultiDiGraph()
for i in xrange(N_A_gpot + N_A_spike):
g.add_node('%s' % i)
for i in xrange(0, N_A_gpot):
g.node['%s' % i]['neuron_type'] = 'gpot'
for i in xrange(N_A_gpot, N_A_gpot + N_A_spike):
g.node['%s' % i]['neuron_type'] = 'spike'
nx.write_gexf(g, 'A.gexf')
N_B_gpot = 3
N_B_spike = 2
g = nx.MultiDiGraph()
for i in xrange(N_B_gpot + N_B_spike):
g.add_node('%s' % i)
for i in xrange(0, N_B_gpot):
g.node['%s' % i]['neuron_type'] = 'gpot'
for i in xrange(N_B_gpot, N_B_gpot + N_B_spike):
g.node['%s' % i]['neuron_type'] = 'spike'
nx.write_gexf(g, 'B.gexf')
# Create files containing inter-module connectivity info:
c = core.Connectivity(N_A_gpot, N_A_spike, N_B_gpot, N_B_spike)
g = graph.conn_to_graph(c)
nx.write_gexf(g, 'A_B.gexf')
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.lpu_parser(lpu_file_0)
(n_dict_1, s_dict_1) = LPU.lpu_parser(lpu_file_1)
ge_0_id = 'ge_0'
ge_0 = LPU(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(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()
