def main_rst(data_path, nn):
'''
This is the main function that applies the cox method on the rst data prodcued by ELIF simulator that is based on \
ELIF model (the software can be found from the following web-site: http://www.tech.plymouth.ac.uk/infovis.) This \
function plots the resulted connectivity map.
:param data_path: The path to the .rst data file.
:param nn: Total number of neurons in the network.
Main internal variables:
* p: Number of reference neurons in the network.
* betahats: Matrix of betahat values in the network.
* betacis: Matrix of confidence interval of betahat values in the network.
* target: Target spike train
* maxi: The length of reference spike trains varies. This parameter defines the length of the longest reference spike train.
* tsp: The matrix of reference spike trains. The size of this matrix is (maxi x nn-1) where each column corresponds\
to a reference spike train and in case the length of the spike train is shorter than "maxi", it should be padded by zeros.
* to_r: The output matrix of the main_rst function expressing the connections and corresponding reference and target neurons.
'''
p = nn - 1 # Number of reference neurons in the network
betahats = zeros((nn, nn)) # Matrix of betahat values in the network
betacis = zeros((
nn * 3 - 1,
nn)) # Matrix of confidence interval of betahat values in the network.
for neuron in range(0, nn):
with open(data_path, 'rb') as f:
a = f.read().split()
a = map(int, a)
each = (len(a) + 1) / nn
target = a[neuron * each:neuron * each +
each] # Target spike train
target = nonzero(target)[0]
target = target + 1
lengths = zeros(nn)
lengths_b = zeros(nn)
for i in range(0, nn):
lengths[i] = len(nonzero(a[(i * each):(i + 1) * each])[0])
if (neuron == 0):
print("Length of each train in average: ")
print(lengths.astype(int))
print(average(lengths))
if (neuron == 0):
maxi = max(lengths[1:])
elif (neuron == p):
maxi = max(lengths[0:nn])
else:
maxi = max([max(lengths[0:neuron]), max(lengths[neuron + 1:])])
tsp = zeros(
(maxi, nn - 1)
) - 1 #The matrix of reference spike trains. The size of this matrix is (maxi x nn-1) where each column corresponds to a reference spike train and in case the length of the spike train is shorter than "maxi", it should be padded by zeros.
if (neuron == 0):
for i in range(0, p):
tsp[0:lengths[i + 1],
i] = nonzero(a[((i + 1) * each):(i + 2) * each])[0] + 1
elif (neuron == p):
for i in range(0, p):
tsp[0:lengths[i],
i] = nonzero(a[(i * each):(i + 1) * each])[0] + 1
else:
for i in range(0, neuron):
tsp[0:lengths[i],
i] = nonzero(a[(i * each):(i + 1) * each])[0] + 1
for i in range(neuron + 1, nn):
tsp[0:lengths[i],
i - 1] = nonzero(a[(i * each):(i + 1) * each])[0] + 1
delta = zeros([p])
cox = Cox_Method(nn, maxi, target, int_(tsp), delta)
betahat, betaci = cox.alg2()
if (neuron == 0):
betahats[0, 1:] = betahat
betacis[0:2, 1:] = betaci.T
elif (neuron == p):
betahats[neuron, 0:neuron] = betahat
betacis[nn * 3 - 3:, 0:neuron] = betaci.T
else:
betahats[neuron, 0:neuron] = betahat[0:neuron]
betahats[neuron, neuron + 1:] = betahat[neuron:]
ind_temp = 3 * (neuron + 1) - 3
betacis[ind_temp:ind_temp + 2, 0:neuron] = betaci.T[:, 0:neuron]
betacis[ind_temp:ind_temp + 2, neuron + 1:] = betaci.T[:, neuron:]
print "Neuron " + str(neuron + 1) + " out of " + str(
nn) + " finished at %s." % datetime.now()
for row in betahats:
row = list(row)
print(list(betahats))
print("\n\n\n\n\n")
print(betacis)
p = 0
fro = []
to = []
thickness = []
for i in range(0, nn):
for j in range(0, nn):
if (betahats[i, j] > 0):
if ((betacis[p + 1, j] > 0.005 and betacis[p, j] > 0.005) or
(betacis[p + 1, j] < -0.005 and betacis[p, j] < -0.005)):
fro = append(fro, j + 1)
to = append(to, i + 1)
thickness = append(thickness, betahats[i, j])
p = p + 3
to_r = append(append([fro], [to], axis=0), [thickness], axis=0)
to_file = to_r.T
print("\n\n\n\n\n")
print(to_file)
def main_rst(data_path,nn):
'''
This is the main function that applies the cox method on the rst data prodcued by ELIF simulator that is based on \
ELIF model (the software can be found from the following web-site: http://www.tech.plymouth.ac.uk/infovis.) This \
function plots the resulted connectivity map.
:param data_path: The path to the .rst data file.
:param nn: Total number of neurons in the network.
Main internal variables:
* p: Number of reference neurons in the network.
* betahats: Matrix of betahat values in the network.
* betacis: Matrix of confidence interval of betahat values in the network.
* target: Target spike train
* maxi: The length of reference spike trains varies. This parameter defines the length of the longest reference spike train.
* tsp: The matrix of reference spike trains. The size of this matrix is (maxi x nn-1) where each column corresponds\
to a reference spike train and in case the length of the spike train is shorter than "maxi", it should be padded by zeros.
* to_r: The output matrix of the main_rst function expressing the connections and corresponding reference and target neurons.
'''
p = nn - 1 # Number of reference neurons in the network
betahats = zeros((nn,nn)) # Matrix of betahat values in the network
betacis = zeros ((nn*3-1 , nn)) # Matrix of confidence interval of betahat values in the network.
for neuron in range (0,nn):
with open (data_path ,'rb') as f:
a = f.read().split()
a = map(int,a)
each = (len(a)+1)/nn
target = a[neuron*each:neuron*each+each] # Target spike train
target = nonzero(target)[0]
target = target + 1
lengths = zeros (nn)
lengths_b = zeros (nn)
for i in range(0,nn):
lengths[i] = len(nonzero(a[(i*each):(i+1)*each])[0])
if (neuron == 0):
print ("Length of each train in average: ")
print(lengths.astype(int))
print(average(lengths))
if (neuron == 0):
maxi = max(lengths[1:])
elif (neuron == p):
maxi = max(lengths[0:nn])
else:
maxi = max([max(lengths[0:neuron]),max(lengths[neuron+1:])])
tsp = zeros((maxi,nn-1))-1 #The matrix of reference spike trains. The size of this matrix is (maxi x nn-1) where each column corresponds to a reference spike train and in case the length of the spike train is shorter than "maxi", it should be padded by zeros.
if (neuron==0):
for i in range (0,p):
tsp[0:lengths[i+1],i] = nonzero(a[((i+1)*each):(i+2)*each])[0]+1
elif (neuron == p):
for i in range (0,p):
tsp[0:lengths[i],i] = nonzero(a[(i*each):(i+1)*each])[0]+1
else:
for i in range (0,neuron):
tsp[0:lengths[i],i] = nonzero(a[(i*each):(i+1)*each])[0]+1
for i in range (neuron+1,nn):
tsp[0:lengths[i],i-1] = nonzero(a[(i*each):(i+1)*each])[0]+1
delta = zeros ([p])
cox = Cox_Method(nn,maxi,target,int_(tsp),delta)
betahat,betaci = cox.alg2()
if (neuron == 0):
betahats[0,1:] = betahat
betacis[0:2,1:] = betaci.T
elif (neuron == p):
betahats [neuron,0:neuron] = betahat
betacis[nn*3-3:,0:neuron] = betaci.T
else:
betahats [neuron,0:neuron] = betahat[0:neuron]
betahats [neuron,neuron+1:] = betahat [neuron:]
ind_temp = 3*(neuron+1) - 3
betacis [ind_temp:ind_temp+2 , 0:neuron] = betaci.T [:,0:neuron]
betacis [ind_temp:ind_temp+2 , neuron+1:] = betaci.T [:, neuron:]
print "Neuron " + str(neuron+1) + " out of " + str(nn) + " finished at %s." %datetime.now()
for row in betahats:
row = list(row)
print (list(betahats))
print("\n\n\n\n\n")
print (betacis)
p = 0
fro = []
to = []
thickness = []
for i in range (0,nn):
for j in range (0,nn):
if (betahats[i,j]>0):
if ((betacis[p+1,j]> 0.005 and betacis[p,j]>0.005) or (betacis[p+1,j]< -0.005 and betacis[p,j]< -0.005)):
fro = append(fro,j+1)
to = append(to,i+1)
thickness = append(thickness, betahats[i,j])
p = p + 3
to_r = append (append([fro], [to],axis=0), [thickness], axis = 0)
to_file = to_r.T
print("\n\n\n\n\n")
print (to_file)
