class Main():
def __init__(self, numproc):
self.numproc = numproc
def simulate(self, process):
# parallel processing on each setting value
self.pid = os.getpid()
self.progress_co = 0
self.nr = Neuron(**self.parm[process + self.multiproc_co])
self.nr.parm_dict = self.parm[process + self.multiproc_co]
for i in range(0, self.nr.allsteps - 1):
if (self.nr.curstep * self.nr.dt) > 200:
for j in range(self.nr.numneu):
self.nr.cnct[j, j + 1] = 1.0
self.nr.cnct[j + 1, j] = 1.0
pass
self.nr.propagation()
if self.progress_co % 1000 == 0:
logging.warning('process id : %d : %4d steps', self.pid,
self.progress_co)
self.progress_co += 1
return self.nr
def form_parm(self):
self.parm = []
self.multiproc_co = 0
self.parm_counter = 0
"""
for i, j, k, l in itertools.product(range(12), range(1), range(1),
range(1)):
self.parm.append({})
self.parm[self.parm_counter] = {"numneu": +10, "Syncp": 4,
"Iext": round(i*0.1+1.5, 1)}
self.parm_counter += 1
"""
# i * j * k * l = 6n!!
for i, j, k, l in itertools.product(range(12), range(6), range(1),
range(1)):
self.parm.append({})
self.parm[self.parm_counter] = {
"numneu": 20,
"Syncp": 5,
"Iext_amp": 20 + 10 * j,
"Iext_width": 10,
"Iext_duty": 0,
"Iext_num": 1000,
"gcmp": 0.03 * i
}
self.parm_counter += 1
self.cycle_multiproc = int(self.parm_counter / 6)
class Main():
def __init__(self, numproc):
self.numproc = numproc
def simulate(self, process):
# parallel processing on each setting value
self.pid = os.getpid()
self.progress_co = 0
self.nr = Neuron(**self.parm[process+self.multiproc_co])
self.nr.parm_dict = self.parm[process+self.multiproc_co]
for i in range(0, self.nr.allsteps-1):
self.nr.propagation()
if self.progress_co % 100000 == 0:
logging.warning('process id : %d : %4d steps',
self.pid, self.progress_co)
self.progress_co += 1
return self.nr
def form_parm(self):
self.parm = []
self.cycle_multiproc = int(6 / 6)
self.multiproc_co = 0
self.parm_counter = 0
"""
for i, j, k, l in itertools.product(range(12), range(1), range(1),
range(1)):
self.parm.append({})
self.parm[self.parm_counter] = {"numneu": 10, "Syncp": 4,
"Iext": round(i*0.1+1.5, 1)}
self.parm_counter += 1
"""
for i, j, k, l in itertools.product(range(4), range(12), range(1),
range(1)):
self.parm.append({})
self.parm[self.parm_counter] = {"numneu": 1,
"b": 3.3,
"r":0.01,
"xr":-3,
"Syncp": 4,
"syn_delay": 10,
"noise":2,
"alpha": round(0.3+i*0.1, 1),
"D": round(1+2*j, 1)}
self.parm_counter += 1
def simulate(self, process):
# parallel processing on each setting value
self.pid = os.getpid()
self.progress_co = 0
self.nr = Neuron(**self.parm[process + self.multiproc_co])
self.nr.parm_dict = self.parm[process + self.multiproc_co]
for i in range(0, self.nr.allsteps - 1):
self.nr.propagation()
if self.progress_co % 100000 == 0:
logging.warning('process id : %d : %4d steps', self.pid,
self.progress_co)
self.progress_co += 1
return self.nr
def simulate(self, process):
# parallel processing on each setting value
self.pid = os.getpid()
self.progress_co = 0
self.nr = Neuron(**self.parm[process + self.multiproc_co])
self.nr.parm_dict = self.parm[process + self.multiproc_co]
for i in range(0, self.nr.allsteps - 1):
if (self.nr.curstep * self.nr.dt) > 200:
for j in range(self.nr.numneu):
self.nr.cnct[j, j + 1] = 1.0
self.nr.cnct[j + 1, j] = 1.0
pass
self.nr.propagation()
if self.progress_co % 1000 == 0:
logging.warning('process id : %d : %4d steps', self.pid,
self.progress_co)
self.progress_co += 1
return self.nr
class Main():
def __init__(self, numproc):
self.numproc = numproc
def simulate(self, process):
# parallel processing on each setting value
self.pid = os.getpid()
self.progress_co = 0
self.nr = Neuron(**self.parm[process + self.multiproc_co])
self.nr.parm_dict = self.parm[process + self.multiproc_co]
for i in range(0, self.nr.allsteps - 1):
"""
if (self.nr.curstep * self.nr.dt) > 200:
for j in range(19):
self.nr.cnct[j, j+1] = 1.0
self.nr.cnct[j+1, j] = 1.0
pass
"""
self.nr.propagation()
if self.progress_co % 1000 == 0:
logging.warning('process id : %d : %4d steps', self.pid,
self.progress_co)
self.progress_co += 1
return self.nr
def form_parm(self):
self.parm = []
self.multiproc_co = 0
self.parm_counter = 0
"""
for i, j, k, l in itertools.product(range(12), range(1), range(1),
range(1)):
self.parm.append({})
self.parm[self.parm_counter] = {"numneu": +10, "Syncp": 4,
"Iext": round(i*0.1+1.5, 1)}
self.parm_counter += 1
"""
# i * j * k * l = 6n!!
for i, j, k, l in itertools.product(range(8), range(12), range(1),
range(1)):
self.parm.append({})
self.parm[self.parm_counter] = {
"numneu": 1,
"b": 3.3,
"r": 0.01,
"xr": -3,
"Syncp": 4,
"syn_delay": 10,
"noise": 2,
"alpha": 0.5,
"D": round(1 + 2 * j, 1),
"Pmax": round(1 + i, 1)
}
"""
"noise": 2,
"alpha": round(0.1+0.2*i, 1),
"D": round(0.5+j, 1)}
"""
self.parm_counter += 1
self.cycle_multiproc = int(self.parm_counter / 6)
def plot(self, process):
#parallel processing on each setting value
if process == 0:
self.pid = os.getpid()
self.progress_co = 0
self.nr = Neuron(**palm1)
for i in range(0, self.nr.allsteps - 1):
self.nr.propagation()
if self.progress_co % 100000 == 0:
logging.warning('process id : %d : %4d steps', self.pid,
self.progress_co)
self.progress_co += 1
return self.nr
elif process == 1:
self.pid = os.getpid()
self.progress_co = 0
self.nr = Neuron(**palm2)
for i in range(0, self.nr.allsteps - 1):
self.nr.propagation()
if self.progress_co % 100000 == 0:
logging.warning('process id : %d : %d steps', self.pid,
self.progress_co)
self.progress_co += 1
return self.nr
elif process == 2:
self.pid = os.getpid()
self.progress_co = 0
self.nr = Neuron(**palm3)
for i in range(0, self.nr.allsteps - 1):
self.nr.propagation()
if self.progress_co % 100000 == 0:
logging.warning('process id : %d : %d steps', self.pid,
self.progress_co)
self.progress_co += 1
return self.nr
elif process == 3:
self.pid = os.getpid()
self.progress_co = 0
self.nr = Neuron(**palm4)
for i in range(0, self.nr.allsteps - 1):
self.nr.propagation()
if self.progress_co % 100000 == 0:
logging.warning('process id : %d : %d steps', self.pid,
self.progress_co)
self.progress_co += 1
return self.nr
elif process == 4:
self.pid = os.getpid()
self.progress_co = 0
self.nr = Neuron(**palm5)
for i in range(0, self.nr.allsteps - 1):
self.nr.propagation()
if self.progress_co % 100000 == 0:
logging.warning('process id : %d : %d steps', self.pid,
self.progress_co)
self.progress_co += 1
return self.nr
elif process == 5:
self.pid = os.getpid()
self.progress_co = 0
self.nr = Neuron(**palm6)
for i in range(0, self.nr.allsteps - 1):
self.nr.propagation()
if self.progress_co % 100000 == 0:
logging.warning('process id : %d : %d steps', self.pid,
self.progress_co)
self.progress_co += 1
return self.nr
else:
pass