In sections 2 and 3, we explained the way to build a model and to setup the intial state. Now, it is the time to run a simulation. Corresponding to World classes, six Simulator classes are there: spatiocyte.SpatiocyteSimulator, egfrd.EGFRDSimulator, bd.BDSimulator, meso.MesoscopicSimulator, gillespie.GillespieSimulator, and ode.ODESimulator. Each Simulator class only accepts the corresponding type of World, but all of them allow the same Model.
In [1]:
from ecell4_base.core import *
In [2]:
from ecell4_base import *
Before constructing a Simulator, parepare a Model and a World corresponding to the type of Simulator.
In [3]:
from ecell4 import species_attributes, reaction_rules, get_model
with species_attributes():
A | B | C | {'D': 1, 'radius': 0.005}
with reaction_rules():
A + B == C | (0.01, 0.3)
m = get_model()
In [4]:
w1 = gillespie.World()
w2 = ode.World()
w3 = spatiocyte.World()
w4 = bd.World()
w5 = meso.World()
w6 = egfrd.World()
Simulator requires both Model and World in this order at the construction.
In [5]:
sim1 = gillespie.Simulator(w1, m)
sim2 = ode.Simulator(w2, m)
sim3 = spatiocyte.Simulator(w3, m)
sim4 = bd.Simulator(w4, m)
sim5 = meso.Simulator(w5, m)
sim6 = egfrd.Simulator(w6, m)
If you bind the Model to the World, you need only the World to create a Simulator.
In [6]:
w1.bind_to(m)
w2.bind_to(m)
w3.bind_to(m)
w4.bind_to(m)
w5.bind_to(m)
w6.bind_to(m)
In [7]:
sim1 = gillespie.Simulator(w1)
sim2 = ode.Simulator(w2)
sim3 = spatiocyte.Simulator(w3)
sim4 = bd.Simulator(w4)
sim5 = meso.Simulator(w5)
sim6 = egfrd.Simulator(w6)
Of course, the Model and World bound to a Simulator can be drawn from Simulator in the way below:
In [8]:
print(sim1.model(), sim1.world())
print(sim2.model(), sim2.world())
print(sim3.model(), sim3.world())
print(sim4.model(), sim4.world())
print(sim5.model(), sim5.world())
print(sim6.model(), sim6.world())
After updating the World by yourself, you must initialize the internal state of a Simulator before running simulation.
In [9]:
w1.add_molecules(Species('C'), 60)
w2.add_molecules(Species('C'), 60)
w3.add_molecules(Species('C'), 60)
w4.add_molecules(Species('C'), 60)
w5.add_molecules(Species('C'), 60)
w6.add_molecules(Species('C'), 60)
In [10]:
sim1.initialize()
sim2.initialize()
sim3.initialize()
sim4.initialize()
sim5.initialize()
sim6.initialize()
For algorithms with a fixed step interval, the Simulator also requires dt.
In [11]:
sim2.set_dt(1e-6) # ode.Simulator. This is optional
sim4.set_dt(1e-6) # bd.Simulator
In [12]:
print(sim1.t(), sim1.next_time(), sim1.dt())
print(sim2.t(), sim2.next_time(), sim2.dt()) # => (0.0, 1e-6, 1e-6)
print(sim3.t(), sim3.next_time(), sim3.dt())
print(sim4.t(), sim4.next_time(), sim4.dt()) # => (0.0, 1e-6, 1e-6)
print(sim5.t(), sim5.next_time(), sim5.dt())
print(sim6.t(), sim6.next_time(), sim6.dt()) # => (0.0, 0.0, 0.0)
In [13]:
sim1.step()
sim2.step()
sim3.step()
sim4.step()
sim5.step()
sim6.step()
In [14]:
print(sim1.t())
print(sim2.t()) # => 1e-6
print(sim3.t())
print(sim4.t()) # => 1e-6
print(sim5.t())
print(sim6.t()) # => 0.0
last_reactions() returns a list of pairs of ReactionRule and ReactionInfo which occurs at the last step. Each algorithm have its own implementation of ReactionInfo. See help(module.ReactionInfo) for details.
In [15]:
print(sim1.last_reactions())
# print(sim2.last_reactions())
print(sim3.last_reactions())
print(sim4.last_reactions())
print(sim5.last_reactions())
print(sim6.last_reactions())
step(upto) advances a simulation for next_time if next_time is less than upto, or for upto otherwise. step(upto) returns whether the time does NOT reach the limit, upto.
In [16]:
print(sim1.step(1.0), sim1.t())
print(sim2.step(1.0), sim2.t())
print(sim3.step(1.0), sim3.t())
print(sim4.step(1.0), sim4.t())
print(sim5.step(1.0), sim5.t())
print(sim6.step(1.0), sim6.t())
To run a simulation just until the time, upto, call step(upto) while it returns True.
In [17]:
while sim1.step(1.0): pass
while sim2.step(0.001): pass
while sim3.step(0.001): pass
while sim4.step(0.001): pass
while sim5.step(1.0): pass
while sim6.step(0.001): pass
In [18]:
print(sim1.t()) # => 1.0
print(sim2.t()) # => 0.001
print(sim3.t()) # => 0.001
print(sim4.t()) # => 0.001
print(sim5.t()) # => 1.0
print(sim6.t()) # => 0.001
This is just what run does. run(tau) advances a simulation upto t()+tau.
In [19]:
sim1.run(1.0)
sim2.run(0.001)
sim3.run(0.001)
sim4.run(0.001)
sim5.run(1.0)
sim6.run(0.001)
In [20]:
print(sim1.t()) # => 2.0
print(sim2.t()) # => 0.002
print(sim3.t()) # => 0.002
print(sim4.t()) # => 0.002
print(sim5.t()) # => 2.0
print(sim6.t()) # => 0.02
num_steps returns the number of steps during the simulation.
In [21]:
print(sim1.num_steps())
print(sim2.num_steps())
print(sim3.num_steps())
print(sim4.num_steps())
print(sim5.num_steps())
print(sim6.num_steps())
Owing to the portability of a Model and consistent APIs of Worlds and Simulators, it is very easy to write a script common in algorithms. However, when switching the algorithm, still we have to rewrite the name of classes in the code, one by one.
To avoid the trouble, E-Cell4 also provides a Factory class for each algorithm. Factory encapsulates World and Simulator with their arguments needed for the construction. By using Factory class, your script could be portable and robust agaist changes in the algorithm.
Factory just provides two functions, world and simulator.
In [22]:
def singlerun(f, m):
w = f.world(Real3(1, 1, 1))
w.bind_to(m)
w.add_molecules(Species('C'), 60)
sim = f.simulator(w)
sim.run(0.01)
print(sim.t(), w.num_molecules(Species('C')))
singlerun above is free from the algorithm. Thus, by just switching Factory, you can easily compare the results.
In [23]:
singlerun(gillespie.Factory(), m)
singlerun(ode.Factory(), m)
singlerun(spatiocyte.Factory(), m)
singlerun(bd.Factory(bd_dt_factor=1), m)
singlerun(meso.Factory(), m)
singlerun(egfrd.Factory(), m)
When you need to provide several parameters to initialize World or Simulator, run_simulation also accepts Factory instead of solver.
In [24]:
from ecell4.util import run_simulation
print(run_simulation(0.01, model=m, y0={'C': 60}, return_type='array', solver=gillespie.Factory())[-1])
print(run_simulation(0.01, model=m, y0={'C': 60}, return_type='array', solver=ode.Factory())[-1])
print(run_simulation(0.01, model=m, y0={'C': 60}, return_type='array', solver=spatiocyte.Factory())[-1])
print(run_simulation(0.01, model=m, y0={'C': 60}, return_type='array', solver=bd.Factory(bd_dt_factor=1))[-1])
print(run_simulation(0.01, model=m, y0={'C': 60}, return_type='array', solver=meso.Factory())[-1])
print(run_simulation(0.01, model=m, y0={'C': 60}, return_type='array', solver=egfrd.Factory())[-1])