def run(n_sim, model, tspan, simulator='cuda'):
v = False
if 'cl' in simulator:
sim = OpenCLSSASimulator(model,
tspan=tspan,
verbose=v,
precision=np.float64)
elif simulator == 'cuda':
sim = CudaSSASimulator(model,
tspan=tspan,
verbose=v,
precision=np.float64)
elif simulator == 'bng':
sim = BngSimulator(model, tspan=tspan, verbose=v)
elif simulator == 'stochkit':
sim = StochKitSimulator(model, tspan=tspan, verbose=v)
else:
return
st = time.time()
if simulator in ('bng', 'stochkit'):
sim.run(tspan, n_runs=n_sim)
else:
# sim.run(tspan, number_sim=n_sim)
traj = sim.run(tspan, number_sim=n_sim)
# print(traj.dataframe.min())
# quit()
total_time = time.time() - st
return total_time, sim._time
def main(self):
observables = self.make_model()
write_columns(observables)
write_model_attributes(self.model.rules, "rules")
write_model_attributes(self.model.parameters, "parameters")
write_model_attributes(self.model.observables, "observables")
np.savetxt("time", self.tspan, fmt='%f')
self.model.parameters['Ls_0'].value = self.ls
sim = BngSimulator(self.model, tspan=self.tspan, cleanup=False)
simulation_results = sim.run(n_runs=self.runs,
method='ssa',
cleanup=False,
output_dir=os.getcwd())
# sim = StochKitSimulator(model, tspan=self.tspan)
# simulation_results = sim.run(n_runs=self.runs)
if len(observables) > 1:
ls_results = []
lf_results = []
for i in range(self.runs):
ls_results.append(
simulation_results.observables[i][observables[0]])
lf_results.append(
simulation_results.observables[i][observables[1]])
output = np.mean(ls_results, axis=0) + np.mean(lf_results, axis=0)
else:
results = []
for i in range(self.runs):
results.append(
simulation_results.observables[i][observables[0]])
output = np.mean(results, axis=0)
print(results)
print(output)
np.savetxt("output", [output[-1]], fmt='%f')
def bngNF(model,
start=0,
finish=10,
points=10,
n_runs=20,
path='/opt/conda/bin/'):
set_path('bng', path)
sims = BngSimulator(model,
linspace(start, finish,
points + 1)).run(method='nf',
n_runs=n_runs).dataframe
sims = modes(sims, n_runs)
return {'sims': sims['sims'], 'avrg': sims['avrg'], 'stdv': sims['stdv']}
def produceGraph(model, filename, methods, observe, parameters):
# make a graph
# parameters is list will run multiple times and put them on the same graph
model=model.reload()
for graph in observe:
for name, monomer in graph.items():
model.add_component(Observable(f'obs{name}', monomer))
t = np.linspace(0, 100)
graphs = len(observe)
fig, axs = plt.subplots(2, 2, constrained_layout=True, figsize=(10,6))
fig.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.6, hspace=0.3)
# fig.tight_layout(pad=5.0)
# hack for when there is only one subplot
if graphs == 1:
axs = [axs]
for i, parameter_set in enumerate(parameters):
for method in methods:
# test without glutathionine and sHSP enabled
simulationResult = BngSimulator(model).run(
tspan=t,
method=method,
param_values=parameter_set
)
for i, name in enumerate(simulationResult.observables.dtype.names):
num = getGraph(observe, name)
if (num == 0):
axs[0][0].plot(t, simulationResult.all[name], label=f'{method.upper()} {name} High Temp')
elif (num == 1):
axs[0][1].plot(t, simulationResult.all[name], label=f'{method.upper()} {name} High Temp')
elif (num == 2):
axs[1][0].plot(t, simulationResult.all[name], label=f'{method.upper()} {name} High Temp')
else:
axs[1][1].plot(t, simulationResult.all[name], label=f'{method.upper()} {name} High Temp')
for i in range(0, 2):
for j in range(0,2):
axs[i][j].set_xlabel('Time in seconds')
axs[i][j].set_ylabel('Number of molecules')
axs[i][j].legend()
axs[i][j].set_ylim(bottom=0)
axs[0][0].set_ylim(ymin=0, ymax=10**7)
fig.savefig(f'export/{filename}.png')
n_runs = n_genes
len_tspan = len(tspan)
HO_tot = np.array(len_tspan * [0])
skew = np.empty((len_tspan, n_runs), dtype=float)
color = np.empty((len_tspan, n_runs), dtype=str)
# batch_size = int(n_runs/n_levels)
np.random.seed(44)
# for batch in range(n_levels):
start = 0
for batch in range(len(N_GENES)):
# size = batch_size if batch < n_levels-1 else n_runs-(n_levels-1)*batch_size
size = N_GENES[batch]
model = create_model(N_STATES[batch], K_SKEW[batch])
sim = BngSimulator(model, tspan, verbose=True)
x = sim.run(
n_runs=size,
netgen_args={'max_iter': 1000000},
seed=np.random.choice(int(1e6), size=size, replace=False) #,
# param_values={ k.name : 0 for k in kf_skew[len(kf_skew)-k_remove[batch]:] }
# if k_remove[batch] > 0 else None
)
observables = [x.observables] if size == 1 else x.observables
species = [x.species] if size == 1 else x.species
for t in range(len_tspan):
for n in range(size):
# Total HO genes
HO_tot[t] += observables[n][t][0]
# Skew and color
idx = np.where(species[n][t] == species[n][t].max())[0][0]
def test_save_load():
tspan = np.linspace(0, 100, 101)
model = tyson_oscillator.model
test_unicode_name = u'Hello \u2603 and \U0001f4a9!'
model.name = test_unicode_name
sim = ScipyOdeSimulator(model, integrator='lsoda')
simres = sim.run(tspan=tspan, param_values={'k6': 1.0})
sim_rob = ScipyOdeSimulator(robertson.model, integrator='lsoda')
simres_rob = sim_rob.run(tspan=tspan)
# Reset equations from any previous network generation
robertson.model.reset_equations()
A = robertson.model.monomers['A']
# NFsim without expressions
nfsim1 = BngSimulator(robertson.model)
nfres1 = nfsim1.run(n_runs=2, method='nf', tspan=np.linspace(0, 1))
# Test attribute saving (text, float, list)
nfres1.custom_attrs['note'] = 'NFsim without expressions'
nfres1.custom_attrs['pi'] = 3.14
nfres1.custom_attrs['some_list'] = [1, 2, 3]
# NFsim with expressions
nfsim2 = BngSimulator(expression_observables.model)
nfres2 = nfsim2.run(n_runs=1, method='nf', tspan=np.linspace(0, 100, 11))
with tempfile.NamedTemporaryFile() as tf:
# Cannot have two file handles on Windows
tf.close()
simres.save(tf.name, dataset_name='test', append=True)
# Try to reload when file contains only one dataset and group
SimulationResult.load(tf.name)
simres.save(tf.name, append=True)
# Trying to overwrite an existing dataset gives a ValueError
assert_raises(ValueError, simres.save, tf.name, append=True)
# Trying to write to an existing file without append gives an IOError
assert_raises(IOError, simres.save, tf.name)
# Trying to write a SimulationResult to the same group with a
# different model name results in a ValueError
assert_raises(ValueError, simres_rob.save, tf.name,
dataset_name='robertson', group_name=model.name,
append=True)
simres_rob.save(tf.name, append=True)
# Trying to load from a file with more than one group without
# specifying group_name should raise a ValueError
assert_raises(ValueError, SimulationResult.load, tf.name)
# Trying to load from a group with more than one dataset without
# specifying a dataset_name should raise a ValueError
assert_raises(ValueError, SimulationResult.load, tf.name,
group_name=model.name)
# Load should succeed when specifying group_name and dataset_name
simres_load = SimulationResult.load(tf.name, group_name=model.name,
dataset_name='test')
assert simres_load._model.name == test_unicode_name
# Saving network free results requires include_obs_exprs=True,
# otherwise a warning should be raised
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
nfres1.save(tf.name, dataset_name='nfsim_no_obs', append=True)
assert len(w) == 1
assert issubclass(w[-1].category, UserWarning)
nfres1.save(tf.name, include_obs_exprs=True,
dataset_name='nfsim test', append=True)
# NFsim load
nfres1_load = SimulationResult.load(tf.name,
group_name=nfres1._model.name,
dataset_name='nfsim test')
# NFsim with expression
nfres2.save(tf.name, include_obs_exprs=True, append=True)
nfres2_load = SimulationResult.load(tf.name,
group_name=nfres2._model.name)
_check_resultsets_equal(simres, simres_load)
_check_resultsets_equal(nfres1, nfres1_load)
_check_resultsets_equal(nfres2, nfres2_load)
# make a graph without any text to display on the wiki
from pysb import *
from pysb.simulator import BngSimulator
import matplotlib.pyplot as plt
import numpy as np
from base_model import *
t = np.linspace(0, 100)
fig, axs = plt.subplots(1, 1)
model.add_component(Observable(f'obsATP', ATP()))
model.add_component(Observable(f'obsADP', ADP()))
simulationResult = BngSimulator(model).run(
tspan=t,
method='ssa',
param_values={**tempValues(1), **{'ROS'}
)
for name in simulationResult.observables.dtype.names:
axs.plot(t, simulationResult.all[name])
ax
fig.savefig(f'export/blank_graph.png')
def bngODE(model, start=0, finish=10, points=10, path='/opt/conda/bin/'):
set_path('bng', path)
return BngSimulator(model,
linspace(start, finish,
points + 1)).run(method='ode').dataframe
def test_save_load():
tspan = np.linspace(0, 100, 101)
# Make a copy of model so other tests etc. don't see the changed name.
model = copy.deepcopy(tyson_oscillator.model)
test_unicode_name = u'Hello \u2603 and \U0001f4a9!'
model.name = test_unicode_name
sim = ScipyOdeSimulator(model, integrator='lsoda')
simres = sim.run(tspan=tspan, param_values={'k6': 1.0})
sim_rob = ScipyOdeSimulator(robertson.model, integrator='lsoda')
simres_rob = sim_rob.run(tspan=tspan)
# Reset equations from any previous network generation
robertson.model.reset_equations()
A = robertson.model.monomers['A']
# NFsim without expressions
nfsim1 = BngSimulator(robertson.model)
nfres1 = nfsim1.run(n_runs=2, method='nf', tspan=np.linspace(0, 1))
# Test attribute saving (text, float, list)
nfres1.custom_attrs['note'] = 'NFsim without expressions'
nfres1.custom_attrs['pi'] = 3.14
nfres1.custom_attrs['some_list'] = [1, 2, 3]
# NFsim with expressions
nfsim2 = BngSimulator(expression_observables.model)
nfres2 = nfsim2.run(n_runs=1, method='nf', tspan=np.linspace(0, 100, 11))
with tempfile.NamedTemporaryFile() as tf:
# Cannot have two file handles on Windows
tf.close()
simres.save(tf.name, dataset_name='test', append=True)
# Try to reload when file contains only one dataset and group
SimulationResult.load(tf.name)
simres.save(tf.name, append=True)
# Trying to overwrite an existing dataset gives a ValueError
assert_raises(ValueError, simres.save, tf.name, append=True)
# Trying to write to an existing file without append gives an IOError
assert_raises(IOError, simres.save, tf.name)
# Trying to write a SimulationResult to the same group with a
# different model name results in a ValueError
assert_raises(ValueError, simres_rob.save, tf.name,
dataset_name='robertson', group_name=model.name,
append=True)
simres_rob.save(tf.name, append=True)
# Trying to load from a file with more than one group without
# specifying group_name should raise a ValueError
assert_raises(ValueError, SimulationResult.load, tf.name)
# Trying to load from a group with more than one dataset without
# specifying a dataset_name should raise a ValueError
assert_raises(ValueError, SimulationResult.load, tf.name,
group_name=model.name)
# Load should succeed when specifying group_name and dataset_name
simres_load = SimulationResult.load(tf.name, group_name=model.name,
dataset_name='test')
assert simres_load._model.name == test_unicode_name
# Saving network free results requires include_obs_exprs=True,
# otherwise a warning should be raised
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
nfres1.save(tf.name, dataset_name='nfsim_no_obs', append=True)
assert len(w) == 1
assert issubclass(w[-1].category, UserWarning)
nfres1.save(tf.name, include_obs_exprs=True,
dataset_name='nfsim test', append=True)
# NFsim load
nfres1_load = SimulationResult.load(tf.name,
group_name=nfres1._model.name,
dataset_name='nfsim test')
# NFsim with expression
nfres2.save(tf.name, include_obs_exprs=True, append=True)
nfres2_load = SimulationResult.load(tf.name,
group_name=nfres2._model.name)
_check_resultsets_equal(simres, simres_load)
_check_resultsets_equal(nfres1, nfres1_load)
_check_resultsets_equal(nfres2, nfres2_load)
plt.ylabel('Population of %s' % name)
PARP, CPARP, Mito, mCytoC = [
model.monomers[x] for x in ['PARP', 'CPARP', 'Mito', 'mCytoC']
]
klump = Parameter('klump', 10000, _export=False)
model.add_component(klump)
population_maps = [
PopulationMap(PARP(b=None), klump),
PopulationMap(CPARP(b=None), klump),
PopulationMap(Mito(b=None), klump),
PopulationMap(mCytoC(b=None), klump)
]
sim = BngSimulator(model, tspan=np.linspace(0, 20000, 101))
simres = sim.run(n_runs=20, method='nf', population_maps=population_maps)
trajectories = simres.all
tout = simres.tout
plot_mean_min_max('Bid_unbound')
plot_mean_min_max('PARP_unbound')
plot_mean_min_max('mSmac_unbound')
plot_mean_min_max('tBid_total')
plot_mean_min_max('CPARP_total')
plot_mean_min_max('cSmac_total')
plt.show()
plt.legend(loc=0)
plt.xlabel('Time')
plt.ylabel('Population of %s' % name)
PARP, CPARP, Mito, mCytoC = [model.monomers[x] for x in
['PARP', 'CPARP', 'Mito', 'mCytoC']]
klump = Parameter('klump', 10000, _export=False)
model.add_component(klump)
population_maps = [
PopulationMap(PARP(b=None), klump),
PopulationMap(CPARP(b=None), klump),
PopulationMap(Mito(b=None), klump),
PopulationMap(mCytoC(b=None), klump)
]
sim = BngSimulator(model, tspan=np.linspace(0, 20000, 101))
simres = sim.run(n_runs=20, method='nf', population_maps=population_maps)
trajectories = simres.all
tout = simres.tout
plot_mean_min_max('Bid_unbound')
plot_mean_min_max('PARP_unbound')
plot_mean_min_max('mSmac_unbound')
plot_mean_min_max('tBid_total')
plot_mean_min_max('CPARP_total')
plot_mean_min_max('cSmac_total')
plt.show()