def main():
radius, D = 5.0e-3, 1.0
N_A = 60
U = 0.5
ka_factor = 10
number_of_samples = 20
kD = 4 * np.pi * (radius * 2) * (D * 2)
ka = kD * ka_factor
kd = ka * N_A * U * U / (1 - U)
kon = ka * kD / (ka + kD)
koff = kd * kon / ka
with species_attributes():
A | B | C | {'radius': str(radius), 'D': str(D)}
with reaction_rules():
A + B == C | (kon, koff)
m = get_model()
rng = GSLRandomNumberGenerator()
rng.seed(0)
y0 = {'A': N_A, 'B': N_A}
duration = 0.35
T = np.linspace(0, duration, 21)
obs = run_simulation(np.linspace(0, duration, 101),
y0,
model=ode.ODENetworkModel(m),
return_type='observer',
solver='ode')
with species_attributes():
A | B | C | {'radius': str(radius), 'D': str(D)}
with reaction_rules():
A + B == C | (ka, kd)
m = get_model()
ensemble_simulations(T,
y0,
model=m,
return_type='matplotlib',
opt_args=('o', obs, '-'),
solver=('spatiocyte', radius),
n=number_of_samples)
raise ValueError('An invald value for "return_type" was given [{}].'.
format(str(return_type)) +
'Use "none" if you need nothing to be returned.')
if __name__ == "__main__":
# def myrun(job, job_id=0, task_id=0):
# import ecell4
# print("Hi, I'm in local!")
# print("My job id is {:d}, and my task id is {:d}.".format(job_id, task_id))
# print("My job is {:s}.".format(str(job)))
# return job['x'] + job['y']
# jobs = [{'x': i, 'y': i ** 2} for i in range(1, 4)]
# print(run_serial(myrun, jobs, n=2))
# print(run_multiprocessing(myrun, jobs, n=2))
# # print(run_sge(myrun, jobs, n=2, delete=False))
# print(run_sge(myrun, jobs, n=2))
from ecell4 import *
from ecell4.extra import ensemble
with reaction_rules():
A + B == C | (0.01, 0.3)
ensemble.ensemble_simulations(10.0, {'C': 60},
solver='gillespie',
return_type='matplotlib',
n=30,
method='multiprocessing')
# jobs = [{'x': i, 'y': i ** 2} for i in range(1, 4)]
# print(run_serial(myrun, jobs, n=2))
# print(run_multiprocessing(myrun, jobs, n=2))
# # environ = {'LD_LIBRARY_PATH': '/home/kaizu/lily_kaizu/src/ecell4/local/lib', 'PYTHONPATH': '/home/kaizu/lily_kaizu/src/ecell4/local/lib/python3.4/site-packages'}
# environ = {}
# # print(run_sge(myrun, jobs, n=2, delete=False, environ=environ))
# print(run_sge(myrun, jobs, n=2, environ=environ))
from ecell4 import *
from ecell4.extra import ensemble
with reaction_rules():
A + B == C | (0.01, 0.3)
environ = {'LD_LIBRARY_PATH': '/home/kaizu/lily_kaizu/src/ecell4/local/lib',
'PYTHONPATH': '/home/kaizu/lily_kaizu/src/ecell4/local/lib/python3.4/site-packages'}
retval = ensemble.ensemble_simulations(
10.0, {'C': 60}, solver='gillespie', return_type='matplotlib',
n=5, method='multiprocessing', environ=environ)
# import numpy
# def concatenate(results):
# return sum([numpy.array(data) for data in results]) / len(results)
# retval = [concatenate(results) for results in retval]
# print(retval)
# numpy.savetxt('ens.dat', retval[0])
for data in retval[0]]
else:
raise ValueError(
'An invald value for "return_type" was given [{}].'.format(str(return_type))
+ 'Use "none" if you need nothing to be returned.')
if __name__ == "__main__":
# def myrun(job, job_id=0, task_id=0):
# import ecell4_base
# print("Hi, I'm in local!")
# print("My job id is {:d}, and my task id is {:d}.".format(job_id, task_id))
# print("My job is {:s}.".format(str(job)))
# return job['x'] + job['y']
# jobs = [{'x': i, 'y': i ** 2} for i in range(1, 4)]
# print(run_serial(myrun, jobs, n=2))
# print(run_multiprocessing(myrun, jobs, n=2))
# # print(run_sge(myrun, jobs, n=2, delete=False))
# print(run_sge(myrun, jobs, n=2))
from ecell4 import *
from ecell4.extra import ensemble
with reaction_rules():
A + B == C | (0.01, 0.3)
ensemble.ensemble_simulations(
10.0, {'C': 60}, solver='gillespie', return_type='matplotlib',
n=30, method='multiprocessing')
radius, D = 5.0e-3, 1.0
with species_attributes():
A | {'radius': str(radius), 'D': str(D)}
with reaction_rules():
~A > A | 45.0
A > ~A | 1.5
model = get_model()
rng = GSLRandomNumberGenerator()
rng.seed(0)
number_of_samples = 20
y0 = {}
duration = 3
T = np.linspace(0, duration, 21)
V = 8
obs = run_simulation(np.linspace(0, duration, 101), y0, volume=V,
model=ode.ODENetworkModel(model),
return_type='observer',
solver='ode')
ensemble_simulations(T, y0, volume=V, model=model,
return_type='matplotlib',
opt_args=('o', obs, '-'),
solver=('spatiocyte', radius),
n=number_of_samples)