In [1]:
%matplotlib inline
import numpy
from ecell4 import *
from ecell4.extra.ensemble import ensemble_simulations
from ecell4_base.core import GSLRandomNumberGenerator, Integer3
Parameters are given as follows. D and radius mean a diffusion constant and a radius of molecules, respectively. Dimensions of length and time are assumed to be micro-meter and second.
In [2]:
D = 1 # 0.01
radius = 0.005
In [3]:
N = 20 # a number of samples
rng = GSLRandomNumberGenerator()
rng.seed(0)
In [4]:
y0 = {} # {'A': 60}
duration = 3
T = numpy.linspace(0, duration, 21)
V = 8
Make a model for all algorithms. No birth reaction with more than one product is accepted.
In [5]:
with species_attributes():
A | {'radius': radius, 'D': D}
with reaction_rules():
~A > A | 45.0
A > ~A | 1.5
m = get_model()
Save a result with ode as obs, and plot it:
In [6]:
obs = run_simulation(numpy.linspace(0, duration, 101), y0, volume=V, model=m,
return_type='observer', solver='ode')
viz.plot_number_observer(obs)
Simulating with gillespie (Bars represent standard error of the mean):
In [7]:
ensemble_simulations(T, y0, volume=V, model=m, opt_args=('o', obs, '-'),
solver='gillespie', n=N)
Simulating with meso:
In [8]:
ensemble_simulations(T, y0, volume=V, model=m, opt_args=('o', obs, '-'),
solver=('meso', Integer3(1, 1, 1), 0.25), n=N)
Simulating with spatiocyte:
In [9]:
ensemble_simulations(T, y0, volume=V, model=m, opt_args=('o', obs, '-'),
solver=('spatiocyte', radius), n=N)
Simulating with egfrd:
In [10]:
ensemble_simulations(T, y0, volume=V, model=m, opt_args=('o', obs, '-'),
solver=('egfrd', Integer3(8, 8, 8)), n=N)
In [11]:
ensemble_simulations(T, y0, volume=V, model=m, opt_args=('o', obs, '-'),
solver=('bd', Integer3(8, 8, 8), 0.1), n=N)