See also
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4. How to Run a Simulation¶
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.
[1]:
from ecell4_base.core import *
4.1. How to Setup a Simulator¶
Except for the initialization (so-called constructor function) with arguments specific to the algorithm, all Simulators have the same APIs.
[2]:
from ecell4_base import *
Before constructing a Simulator, parepare a Model and a World corresponding to the type of Simulator.
[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()
[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.
[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.
[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)
[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:
[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())
<ecell4_base.core.NetworkModel object at 0x14882cdd8730> <ecell4_base.gillespie.GillespieWorld object at 0x14882cdae768>
<ecell4_base.core.NetworkModel object at 0x14882cdd8730> <ecell4_base.ode.ODEWorld object at 0x14882cdae6f8>
<ecell4_base.core.NetworkModel object at 0x14882cdd8730> <ecell4_base.spatiocyte.SpatiocyteWorld object at 0x14882cdae688>
<ecell4_base.core.NetworkModel object at 0x14882cdd8730> <ecell4_base.bd.BDWorld object at 0x14882cdae650>
<ecell4_base.core.NetworkModel object at 0x14882cdd8730> <ecell4_base.meso.MesoscopicWorld object at 0x14882cdae7a0>
<ecell4_base.core.NetworkModel object at 0x14882cdd8730> <ecell4_base.egfrd.EGFRDWorld object at 0x14882cdae7d8>
After updating the World by yourself, you must initialize the internal state of a Simulator before running simulation.
[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)
[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.
[11]:
sim2.set_dt(1e-6) # ode.Simulator. This is optional
sim4.set_dt(1e-6) # bd.Simulator
4.2. Running Simulations¶
For running simulations, Simulator provides two APIs, step and run.
step() advances a simulation for the time that the Simulator expects, next_time().
[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)
0.0 0.03782982587603311 0.03782982587603311
0.0 1e-06 1e-06
0.0 6.666666666666667e-05 6.666666666666667e-05
0.0 1e-06 1e-06
0.0 0.0025188519074043213 0.0025188519074043213
0.0 0.0 0.0
[13]:
sim1.step()
sim2.step()
sim3.step()
sim4.step()
sim5.step()
sim6.step()
[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
0.03782982587603311
1e-06
6.666666666666667e-05
1e-06
0.0025188519074043213
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.
[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())
[(<ecell4_base.core.ReactionRule object at 0x148858eaf5e0>, <ecell4_base.gillespie.ReactionInfo object at 0x148858eaf618>)]
[]
[]
[]
[]
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.
[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())
True 0.1531251186177207
True 2e-06
True 0.00013333333333333334
True 2e-06
True 0.003502192717675309
True 0.0
To run a simulation just until the time, upto, call step(upto) while it returns True.
[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
[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
1.0
0.001
0.001
0.001
1.0
0.001
This is just what run does. run(tau) advances a simulation upto t()+tau.
[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)
[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
2.0
0.002
0.002
0.002
2.0
0.002
num_steps returns the number of steps during the simulation.
[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())
28
2001
28
2001
835
738
4.3. Capsulizing Algorithm into a Factory Class¶
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.
[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.
[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)
0.01 60
0.01 59
0.01 60
0.01 60
0.01 60
0.01 60
When you need to provide several parameters to initialize World or Simulator, run_simulation also accepts Factory instead of solver.
[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])
[0.01, 0.0, 0.0, 60.0]
[0.01, 0.17972919304002502, 0.17972919304002502, 59.820270806960046]
[0.01, 1.0, 1.0, 59.0]
[0.01, 0.0, 0.0, 60.0]
[0.01, 1.0, 1.0, 59.0]
[0.01, 1.0, 1.0, 59.0]