def main(argv = None):
if argv is None:
argv = sys.argv
parser = argparse.ArgumentParser(prog="genPreBotBot_gamma",
description = 'Generates Graph based on Block Model with varying amounts of intra inhibition')
parser.add_argument('n0', type = int, help='number of nodes in pop1')
parser.add_argument('n1', type = int, help='number of nodes in pop2')
parser.add_argument('gamma', type = float, help='the percentage of unifrom inhbiiton')
parser.add_argument('output', help='output filename')
parser.add_argument('-pI', type = float,
help = 'probability of inhibitory neuron',
default = .2)
parser.add_argument('-gE', type=float,
help='conductance of excitatory (E) synapses, nS',
default = 2.5)
parser.add_argument('-gI', type=float,
help='conductance of inhibitory (I) synapses, nS',
default = 2.5)
args = parser.parse_args(argv[1:])
n0 = args.n0
n1 = args.n1
#Multiply by a factor of 3/2 to maintain an average degree of 6 for each neuron
gamma = 3*float(args.gamma) / 2
output = args.output
gE = args.gE
gI = args.gI
pI = args.pI
#Each column of a matrix is responsible for the probability that index 1 connects to index 2
#Excitatory connection probability matrix
pMatE = np.array([ (3.0/(n0-1), 0.05/n1),
(0.05/n0, 3.0/(n1-1)) ])
#Inhibitory connection probaility matrix
pMatI = np.array([ (gamma/(n0-1), (3.0-gamma)/n1),
((3.0-gamma)/n0, gamma/(n1-1)) ])
#Probability distribution for neuron types (?,Bursting,Tonic,Quiescent)
pTypes = [0, 0.25, 0.45, 0.3]
print pMatE
print pMatI
g = respirnet.er_prebot_bot(n0,n1,pMatI, pMatE, pTypes, pI, gE, gI)
nx.write_gml(g, output + '.gml')
def main(argv = None):
if argv is None:
argv = sys.argv
parser = argparse.ArgumentParser(prog="genPreBotBot_gamma",
description = 'Generates Graph based on Block Model with varying amounts of intra inhibition')
parser.add_argument('n0', type = int, help='number of nodes in pop1')
parser.add_argument('n1', type = int, help='number of nodes in pop2')
parser.add_argument('intraDegree', type = float, help='the average degree for a population to itself')
parser.add_argument('interDegree',type = float, help='the average degree for a population to the other')
parser.add_argument('output', help='output filename')
parser.add_argument('-pI', type = float,
help = 'probability of inhibitory neuron',
default = .2)
parser.add_argument('-gE', type=float,
help='conductance of excitatory (E) synapses, nS',
default = 2.5)
parser.add_argument('-gI', type=float,
help='conductance of inhibitory (I) synapses, nS',
default = 2.5)
args = parser.parse_args(argv[1:])
n0 = args.n0
n1 = args.n1
intraDegree = args.intraDegree
interDegree = args.interDegree
output = args.output
gE = args.gE
gI = args.gI
pI = args.pI
#Each column of a matrix is responsible for the probability that index 1 connects to index 2
#Excitatory connection probability matrix
pMatE = np.array([ (3.0/(n0-1), 0.00/n1),
(0.00/n0, 3.0/(n1-1)) ])
#Inhibitory connection probaility matrix
pMatI = np.array([ (intraDegree/(n0-1), interDegree/n1),
(interDegree/n0, intraDegree/(n1-1)) ])
#Probability distribution for neuron types (?,Bursting,Tonic,Quiescent)
pTypes = [0, 0.25, 0.45, 0.3]
g = respirnet.er_prebot_bot(n0,n1,pMatI, pMatE, pTypes, pI, gE, gI)
nx.write_gml(g, output)
def main(argv=None):
if argv is None:
argv = sys.argv
parser = argparse.ArgumentParser(
prog="genPreBotBot",
description='Generates Graph based on Block Model')
parser.add_argument('n', type=int, help='number of nodes')
parser.add_argument('idegree',
type=float,
help='probabilty of inhibtory connection degree')
parser.add_argument('output', help='output filename')
parser.add_argument('-pI',
type=float,
help='probability of inhibitory neuron',
default=.2)
parser.add_argument('-gE',
type=float,
help='conductance of excitatory (E) synapses, nS',
default=2.5)
parser.add_argument('-gI',
type=float,
help='conductance of inhibitory (I) synapses, nS',
default=2.5)
args = parser.parse_args(argv[1:])
n = args.n
idegree = float(args.idegree)
output = args.output
gE = args.gE
gI = args.gI
pI = args.pI
pMatE = np.array([(3.0 / (n - 1), 0.05 / (n - 1)),
(0.05 / (n - 1), 3.0 / (n - 1))])
###Idegree normally all the same, however changing to sweep block structure
pMatI = np.array([(idegree / (n - 1), (3.0 - idegree) / (n - 1)),
((3.0 - idegree) / (n - 1), idegree / (n - 1))])
pTypes = [0, 0.25, 0.45, 0.3]
g = respirnet.er_prebot_bot(n, pMatI, pMatE, pTypes, pI, gE, gI)
nx.write_gml(g, output)
def main(argv = None):
if argv is None:
argv = sys.argv
parser = argparse.ArgumentParser(prog="genPreBotBot",
description = 'Generates Graph based on Block Model')
parser.add_argument('n', type = int, help='number of nodes')
parser.add_argument('idegree', type = float, help='probabilty of inhibtory connection degree')
parser.add_argument('output', help='output filename')
parser.add_argument('-pI', type = float,
help = 'probability of inhibitory neuron',
default = .2)
parser.add_argument('-gE', type=float,
help='conductance of excitatory (E) synapses, nS',
default = 2.5)
parser.add_argument('-gI', type=float,
help='conductance of inhibitory (I) synapses, nS',
default = 2.5)
args = parser.parse_args(argv[1:])
n = args.n
idegree = float(args.idegree)
output = args.output
gE = args.gE
gI = args.gI
pI = args.pI
pMatE = np.array([ (3.0/(n-1), 0.05/(n-1)),
(0.05/(n-1), 3.0/(n-1)) ])
###Idegree normally all the same, however changing to sweep block structure
pMatI = np.array([ (idegree/(n-1), (3.0-idegree)/(n-1)),
((3.0-idegree)/(n-1), idegree/(n-1)) ])
pTypes = [0, 0.25, 0.45, 0.3]
g = respirnet.er_prebot_bot(n, pMatI, pMatE, pTypes, pI, gE, gI)
nx.write_gml(g, output)
import networkx as nx
pTypes = [0, 0.25, 0.45, 0.3]
n0 = 100
n1 = 100
# pI = 0.2
# gE = 2.0
# gI = 5.0
# a = 2.5
# b = 0.5
# c = 0.5
# d = 2.5
# these work:
# n0 = 200
# n1 = 100
pI = 0.2
gE = 2.5
gI = 5.0
a = 3.0
b = 0.5
c = 0.5
d = 3.0
pMatE = np.array([(a/(n0-1), b/(n1-1)),
(b/(n0-1), a/(n1-1))])
pMatI = np.array([(c/(n0-1), d/(n1-1)),
(d/(n0-1), c/(n1-1))])
g = respirnet.er_prebot_bot(n0, n1, pMatI, pMatE, pTypes, pI, gE, gI)
nx.write_gml(g, 'test_prebot_bot.gml')