def get_solver(solver_type, mdl, geom):
r = srng.create('r123', 1000)
if solver_type == "Wmrk4":
solver = ssolv.Wmrk4(mdl, geom)
solver.setDT(1e-5)
return solver
elif solver_type == "Wmdirect":
solver = ssolv.Wmdirect(mdl, geom, r)
return solver
if solver_type == "Wmrssa":
solver = ssolv.Wmrssa(mdl, geom, r)
return solver
elif solver_type == "Tetexact":
solver = ssolv.Tetexact(mdl, geom, r)
return solver
else:
assert (False)
def runSBMLmod(sbmlFile,
time_end,
time_dt,
iter_n=1,
solver='Wmdirect',
specs_plot={},
vol_units=1.0e-3,
vol_def=False):
iSbml = ssbml.Interface(sbmlFile,
volunits_def=vol_units,
volume_def=vol_def)
mdl = iSbml.getModel()
mesh = iSbml.getGeom()
comp_specs = {}
if not specs_plot: got_sp = False
else: got_sp = True
for comp in mesh.getAllComps():
comp_specs[comp.getID()] = []
volsys_strings = comp.getVolsys()
for vsys_str in volsys_strings:
vsys = mdl.getVolsys(vsys_str)
specs = vsys.getAllSpecs()
for spec in specs:
comp_specs[comp.getID()].append(spec.getID())
if (got_sp == False): specs_plot.update({spec.getID(): ''})
comp_specs_n = 0
for comp in comp_specs:
comp_specs[comp].sort()
comp_specs_n += len(comp_specs[comp])
time_pnts = numpy.arange(0.0, time_end, time_dt)
points_n = int(round(time_end / time_dt))
if (len(time_pnts) == (points_n + 1)):
time_pnts = numpy.delete(time_pnts, len(time_pnts) - 1)
res = numpy.zeros([iter_n, points_n, comp_specs_n])
r = srng.create('mt19937', 256)
r.initialize(7233)
if (solver == 'Wmdirect'):
sim = ssolver.Wmdirect(mdl, mesh, r)
elif (solver == 'Wmrk4'):
sim = ssolver.Wmrk4(mdl, mesh, r)
sim.setDT(0.0001)
else:
raise NameError(
"Unsupported solver. SBML importer accepts well-mixed solvers 'Wmrk4' and 'Wmdirect'"
)
for it in range(0, iter_n):
sim.reset()
iSbml.reset()
iSbml.setupSim(sim)
for tp in range(0, points_n):
sim.run(time_pnts[tp])
i = 0
for comp in comp_specs:
for spec in comp_specs[comp]:
res[it, tp, i] = sim.getCompConc(comp, spec)
i += 1
iSbml.updateSim(sim, time_dt)
mean_res = numpy.mean(res, 0)
i = 0
for comp in comp_specs:
for spec in comp_specs[comp]:
if (spec in specs_plot):
if (specs_plot[spec]):
plot(time_pnts,
mean_res[:, i],
label=spec + ", " + comp,
color=specs_plot[spec])
else:
plot(time_pnts, mean_res[:, i], label=spec + ", " + comp)
i += 1
legend(loc='best', numpoints=1)
xlabel('Time (s)')
ylabel('Conc (M)')
show()
def run(model_string,
initial_condition_string,
stop_time,
event_string='',
seed=23412,
solver='wmdirect',
dt=1,
rk4dt=1e-5,
realisations=1,
file=False):
"""
Run a Matlab Simbiology model in STEPS
using the Wmdriect or Wmrk4 solver.
This is the entry point called by Matlab via the main code.
Input parameters:
- STEPS model as a string,
- initial condition part of the STEPS model as a string,
- stop time
- pseudo random number generator seed,
- solver: wmdirect or wmrk4,
- dt: time increment betwen sampling points
- realisations: number of times the simulation is to be run
- file: flag to indicate if the generated model should be
written to file. The file name carries a timestamp.
"""
if (not solver.upper() == 'WMDIRECT' and not solver.upper() == 'WMRK4'):
logger.error('run unknown solver string: ' + solver)
raise Exception('run unknown solver string: ' + solver)
if (file):
if (not os.path.exists(steps_path + '/.logs')):
os.makedirs(steps_path + '/.logs')
outfile = open(
steps_path + '/.logs/matlab_steps_model_' +
str(datetime.datetime.now()), 'w')
outfile.write(model_string)
outfile.write(initial_condition_string)
outfile.close()
exec(model_string.encode('ascii').decode('unicode-escape'), globals())
# setup the solver, only two solvers are supported,
# so a simple if construct is sufficient.
global sim
if (solver.upper() == 'WMDIRECT'):
r = srng.create('mt19937', 256)
r.initialize(seed)
sim = ssolver.Wmdirect(model, geometry, r)
elif (solver.upper() == 'WMRK4'):
sim = ssolver.Wmrk4(model, geometry)
sim.setRk4DT(rk4dt)
else:
logger.error('run unknown solver string: ' + solver)
raise Exception('run unknown solver string: ' + solver)
# steup the return data structures
nSpecies = len(model.getAllSpecs())
list_of_species = model.getAllSpecs()
specie_names = []
for s in list_of_species:
specie_names.append(s.getID())
tvector = numpy.arange(0, stop_time, dt)
# get a list of the compartment names
compartment_names = []
compartments = geometry.getAllComps()
for comp in compartments:
compartment_names.append(str(comp.getID()))
res = numpy.zeros(
[realisations, len(compartments),
len(tvector), nSpecies])
# parse the events and
# store them in a dictionary
event_string = json.loads(event_string.replace('][', ', '))
logger.debug('run got events: ' + str(event_string))
# iterate the names of the reaction rates
rate_names = event_string[1]
# iterate the events
event_string = event_string[0]
# number of events, each event is a dict with trigger and event entry
nevents = len(event_string)
event_dict = {}
# iterate all the dictionaries
for i in range(nevents):
# extract the trigger and event
event_dict.update(event_string[i])
event_times = {}
nevents = len(event_dict)
if (nevents % 2 != 0):
logger.error(
'run internal error, number of triggers and events do not match: '
+ str(event_dict))
raise RuntimeError(
'run internal error, number of triggers and events do not match: '
+ str(event_dict))
for i in range(nevents // 2):
trigger = event_dict['trigger_' + str(i)]
if (trigger.find('>=') >= 0):
tmp = trigger.split('>=')
elif (trigger.find('>') >= 0):
tmp = trigger.split('>')
else:
logger.error('run invalid event trigger: ' + e)
raise RuntimeError('run invalid event trigger: ' + e)
#if(event_times.has_key(float(tmp[1]))):
if (float(tmp[1]) in event_times):
event_times[float(tmp[1])].append(event_dict['event_' + str(i)])
else:
event_times[float(tmp[1])] = [event_dict['event_' + str(i)]]
# build the event queue - sort the events
event_queue = sorted(event_times.keys())
if (not event_queue):
# run without event queue
for i in range(realisations): # this is an index
sim.reset()
exec(
initial_condition_string.encode('ascii').decode(
'unicode-escape'), globals())
for t in range(len(tvector)): # this is an index
for c in range(len(compartment_names)): # this is an index
for s in range(len(specie_names)):
res[i, c, t,
s] = sim.getCompCount(compartment_names[c],
specie_names[s])
sim.run(tvector[t])
else:
# run with event queue
logger.debug('run event times: ' + str(event_times))
logger.debug('run event queue ' + str(event_queue))
logger.debug('run event dict: ' + str(event_dict))
for i in range(realisations):
# copy the event queue for this realisation
# FIXME: replace copying the queue with a counter
eq = list(event_queue)
t_event = float(eq.pop(0))
# initial conditions
sim.reset()
exec(
initial_condition_string.encode('ascii').decode(
'unicode-escape'), globals())
for t in range(len(tvector)):
if tvector[t] >= float(t_event):
logger.debug('run time of next event: ' +
str((float(t_event))))
sim.run(float(t_event))
# handle all events event queued for this t_event
for e in event_times[t_event]:
tmp = handleEvent(e, sim, model, spec_names,
comp_names, rate_names, reac_to_comp,
sreac_to_patch, kcst_si_factor)
exec(tmp)
# get next event
if eq:
t_event = eq.pop(0)
else:
t_event = float('inf')
# end if
sim.run(tvector[t])
# get molecule counts
for c in range(len(compartment_names)): # this is an index
#sim.run(tvector[t])
for s in range(len(specie_names)):
res[i, c, t,
s] = sim.getCompCount(compartment_names[c],
specie_names[s])
# return the data json encoded
data = {
'species': specie_names,
'compartments': compartment_names,
'data': res.tolist()
}
sys.stdout.write(json.dumps(data))
sys.stdout.flush()
sys.exit(0)
memb.setArea(0.21544368e-12)
print 'Inner compartment to memb is', memb.getIComp().getID()
print 'Outer compartment to memb is', memb.getOComp().getID()
#RNG setup
import steps.rng as srng
import pylab
import numpy
r = srng.create('mt19937', 1000)
r.initialize(7233)
#solver setup
import steps.solver as ssolover
if deterministic: sim = ssolover.Wmrk4(mdl, wmgeom, r)
else: sim = ssolover.Wmdirect(mdl, wmgeom, r)
tpnt = numpy.arange(0.0, 20.01, 0.01)
res = numpy.zeros([NITER, 2001, 4])
res_std = numpy.zeros([2001, 4])
res_std1 = numpy.zeros([2001, 4])
res_std2 = numpy.zeros([2001, 4])
prob = []
ca = 0.1e-6
ip = 2e-6
for i in range(0, NITER):
sim.reset()
print "Sim Time: %e" % sim.getTime()
print "Sim Steps: %e" % sim.getNSteps()
# or step, without checkpointing
print "Run 1 SSA step"
sim.step()
print "Sim Time: %e" % sim.getTime()
print "Sim Steps: %e" % sim.getNSteps()
###################################
# Restore from checkpoint file
###################################
print "Restore from file"
sim.restore("manual.checkpoint")
print "Sim Time: %e" % sim.getTime()
print "Sim Steps: %e" % sim.getNSteps()
###################################
# RK4 solver
###################################
print "Well Mixed RK4"
rk4sim = ssolver.Wmrk4(mdl, wmgeom, r)
rk4sim.reset()
rk4sim.setDT(1e-4)
rk4sim.setCompConc('comp', 'molA', 31.4e-6)
rk4sim.setCompConc('comp', 'molB', 22.3e-6)
# Notice: No checkpointing for RK4 yet
rk4sim.run(1e-3)
print "Sim Time: %e" % rk4sim.getTime()
def run_sim():
# Set up and run the simulations once, before the tests
# analyze the results.
##################### First order irreversible #########################
global KCST_foi, N_foi, tolerance_foi
KCST_foi = 5 # The reaction constant
N_foi = 50 # Can set count or conc
NITER_foi = 1 # The number of iterations
# Tolerance for the comparison.
tolerance_foi = 1e-3/100
####################### First order reversible #########################
global KCST_f_for, KCST_b_for, COUNT_for, tolerance_for
KCST_f_for = 10.0 # The reaction constant
KCST_b_for = 2.0
COUNT_for = 100000 # Can set count or conc
NITER_for = 1 # The number of iterations
# Tolerance for the comparison. The only test where tolerance must
# be relatively high
tolerance_for = 0.5/100
####################### Second order irreversible A2 ###################
global KCST_soA2, CONCA_soA2, tolerance_soA2
KCST_soA2 = 10.0e6 # The reaction constant
CONCA_soA2 = 10.0e-6
NITER_soA2 = 1 # The number of iterations
tolerance_soA2 = 1.0e-3/100
####################### Second order irreversible AA ###################
global KCST_soAA, CONCA_soAA, CONCB_soAA, tolerance_soAA
KCST_soAA = 50.0e6 # The reaction constant
CONCA_soAA = 20.0e-6
CONCB_soAA = CONCA_soAA
NITER_soAA = 1 # The number of iterations
tolerance_soAA = 1.0e-3/100
####################### Second order irreversible AB ###################
global KCST_soAB, CONCA_soAB, CONCB_soAB, tolerance_soAB
KCST_soAB = 5.0e6 # The reaction constant
CONCA_soAB = 1.0e-6
n_soAB = 2
CONCB_soAB = CONCA_soAB/n_soAB
NITER_soAB = 1 # The number of iterations
tolerance_soAB = 1.0e-3/100
####################### Third order irreversible A3 ###################
global KCST_toA3, CONCA_toA3, tolerance_toA3
KCST_toA3 = 1.0e12 # The reaction constant
CONCA_toA3 = 100.0e-6
NITER_toA3 = 1 # The number of iterations
tolerance_toA3 = 1.0e-3/100
####################### Third order irreversible A2B ###################
global KCST_toA2B, CONCA_toA2B, CONCB_toA2B, tolerance_toA2B
KCST_toA2B = 0.1e12 # The reaction constant
CONCA_toA2B = 30.0e-6
CONCB_toA2B = 20.0e-6
NITER_toA2B = 1 # The number of iterations
tolerance_toA2B = 1.0e-3/100
####################### Second order irreversible 2D ###################
global COUNTA_so2d, COUNTB_so2d, CCST_so2d, tolerance_so2d
COUNTA_so2d = 100.0
n_so2d=2.0
COUNTB_so2d = COUNTA_so2d/n_so2d
KCST_so2d = 10.0e10 # The reaction constant
AREA_so2d = 10.0e-12
CCST_so2d = KCST_so2d/(6.02214179e23*AREA_so2d)
NITER_so2d = 1 # The number of iterations
tolerance_so2d = 1.0e-3/100
############################ Common parameters ########################
global VOL
DT = 0.1 # Sampling time-step
INT = 1.1 # Sim endtime
VOL = 9.0e-18
NITER_max = 1
########################################################################
mdl = smod.Model()
volsys = smod.Volsys('vsys',mdl)
surfsys = smod.Surfsys('ssys',mdl)
# First order irreversible
A_foi = smod.Spec('A_foi', mdl)
R1_foi = smod.Reac('R1_foi', volsys, lhs = [A_foi], rhs = [], kcst = KCST_foi)
# First order reversible
A_for = smod.Spec('A_for', mdl)
B_for = smod.Spec('B_for', mdl)
R1_for = smod.Reac('R1_for', volsys, lhs = [A_for], rhs = [B_for], kcst = KCST_f_for)
R2_for = smod.Reac('R2_for', volsys, lhs = [B_for], rhs = [A_for], kcst = KCST_b_for)
# Second order irreversible A2
A_soA2 = smod.Spec('A_soA2', mdl)
C_soA2 = smod.Spec('C_soA2', mdl)
R1_soA2 = smod.Reac('R1_soA2', volsys, lhs = [A_soA2, A_soA2], rhs = [C_soA2], kcst = KCST_soA2)
# Second order irreversible AA
A_soAA = smod.Spec('A_soAA', mdl)
B_soAA = smod.Spec('B_soAA', mdl)
C_soAA = smod.Spec('C_soAA', mdl)
R1_soAA = smod.Reac('R1_soAA', volsys, lhs = [A_soAA, B_soAA], rhs = [C_soAA], kcst = KCST_soAA)
# Second order irreversible AB
A_soAB = smod.Spec('A_soAB', mdl)
B_soAB = smod.Spec('B_soAB', mdl)
C_soAB = smod.Spec('C_soAB', mdl)
R1_soAB = smod.Reac('R1_soAB', volsys, lhs = [A_soAB, B_soAB], rhs = [C_soAB], kcst = KCST_soAB)
# Third order irreversible A3
A_toA3 = smod.Spec('A_toA3', mdl)
C_toA3 = smod.Spec('C_toA3', mdl)
R1_toA3 = smod.Reac('R1_toA3', volsys, lhs = [A_toA3, A_toA3, A_toA3], rhs = [C_toA3], kcst = KCST_toA3)
# Third order irreversible A2B
A_toA2B = smod.Spec('A_toA2B', mdl)
B_toA2B = smod.Spec('B_toA2B', mdl)
C_toA2B = smod.Spec('C_toA2B', mdl)
R1_toA3 = smod.Reac('R1_toA2B', volsys, lhs = [A_toA2B, A_toA2B, B_toA2B], rhs = [C_toA2B], kcst = KCST_toA2B)
# Second order irreversible 2D
A_so2d = smod.Spec('A_so2d', mdl)
B_so2d = smod.Spec('B_so2d', mdl)
C_so2d = smod.Spec('C_so2d', mdl)
SR1_so2d = smod.SReac('SR1_so2d', surfsys, slhs = [A_so2d, B_so2d], srhs = [C_so2d], kcst = KCST_so2d)
geom = sgeom.Geom()
comp1 = sgeom.Comp('comp1', geom, VOL)
comp1.addVolsys('vsys')
patch1 = sgeom.Patch('patch1', geom, comp1, area = AREA_so2d)
patch1.addSurfsys('ssys')
rng = srng.create('r123', 512)
rng.initialize(1000)
sim = ssolv.Wmrk4(mdl, geom, rng)
sim.setDT(0.00001)
global tpnts, ntpnts
tpnts = numpy.arange(0.0, INT, DT)
ntpnts = tpnts.shape[0]
res_m_foi = numpy.zeros([NITER_foi, ntpnts, 1])
res_m_for = numpy.zeros([NITER_for, ntpnts, 2])
res_m_soA2 = numpy.zeros([NITER_soA2, ntpnts, 2])
res_m_soAA = numpy.zeros([NITER_soAA, ntpnts, 3])
res_m_soAB = numpy.zeros([NITER_soAB, ntpnts, 3])
res_m_toA3 = numpy.zeros([NITER_toA3, ntpnts, 2])
res_m_toA2B = numpy.zeros([NITER_toA2B, ntpnts, 3])
res_m_so2d = numpy.zeros([NITER_so2d, ntpnts, 3])
for i in range (0, NITER_max):
if i < NITER_foi:
sim.setCompCount('comp1', 'A_foi', N_foi)
if i < NITER_for:
sim.setCompCount('comp1', 'A_for', COUNT_for)
sim.setCompCount('comp1', 'B_for', 0.0)
if i < NITER_soA2:
sim.setCompConc('comp1', 'A_soA2', CONCA_soA2)
if i < NITER_soAA:
sim.setCompConc('comp1', 'A_soAA', CONCA_soAA)
sim.setCompConc('comp1', 'B_soAA', CONCB_soAA)
if i < NITER_soAB:
sim.setCompConc('comp1', 'A_soAB', CONCA_soAB)
sim.setCompConc('comp1', 'B_soAB', CONCB_soAB)
if i < NITER_toA3:
sim.setCompConc('comp1', 'A_toA3', CONCA_toA3)
if i < NITER_toA2B:
sim.setCompConc('comp1', 'A_toA2B', CONCA_toA2B)
sim.setCompConc('comp1', 'B_toA2B', CONCB_toA2B)
if i < NITER_so2d:
sim.setPatchCount('patch1', 'A_so2d', COUNTA_so2d)
sim.setPatchCount('patch1', 'B_so2d', COUNTB_so2d)
for t in range(0, ntpnts):
sim.run(tpnts[t])
if i < NITER_foi:
res_m_foi[i, t, 0] = sim.getCompCount('comp1', 'A_foi')
if i < NITER_for:
res_m_for[i, t, 0] = sim.getCompConc('comp1', 'A_for')*1e6
res_m_for[i, t, 1] = sim.getCompConc('comp1', 'B_for')*1e6
if i < NITER_soA2:
res_m_soA2[i, t, 0] = sim.getCompConc('comp1', 'A_soA2')
if i < NITER_soAA:
res_m_soAA[i, t, 0] = sim.getCompConc('comp1', 'A_soAA')
res_m_soAA[i, t, 1] = sim.getCompConc('comp1', 'B_soAA')
if i < NITER_soAB:
res_m_soAB[i, t, 0] = sim.getCompConc('comp1', 'A_soAB')
res_m_soAB[i, t, 1] = sim.getCompConc('comp1', 'B_soAB')
if i < NITER_toA3:
res_m_toA3[i, t, 0] = sim.getCompConc('comp1', 'A_toA3')
if i < NITER_toA2B:
res_m_toA2B[i, t, 0] = sim.getCompConc('comp1', 'A_toA2B')
res_m_toA2B[i, t, 1] = sim.getCompConc('comp1', 'B_toA2B')
res_m_toA2B[i, t, 2] = sim.getCompConc('comp1', 'C_toA2B')
if i < NITER_so2d:
res_m_so2d[i, t, 0] = sim.getPatchCount('patch1', 'A_so2d')
res_m_so2d[i, t, 1] = sim.getPatchCount('patch1', 'B_so2d')
global mean_res_foi
mean_res_foi = numpy.mean(res_m_foi, 0)
global mean_res_for
mean_res_for = numpy.mean(res_m_for, 0)
global mean_res_soA2
mean_res_soA2 = numpy.mean(res_m_soA2, 0)
global mean_res_soAA
mean_res_soAA = numpy.mean(res_m_soAA, 0)
global mean_res_soAB
mean_res_soAB = numpy.mean(res_m_soAB, 0)
global mean_res_toA3
mean_res_toA3 = numpy.mean(res_m_toA3, 0)
global mean_res_toA2B
mean_res_toA2B = numpy.mean(res_m_toA2B, 0)
global mean_res_so2d
mean_res_so2d = numpy.mean(res_m_so2d, 0)
global ran_sim
ran_sim = True
