def test_stochkit_earm_multi_initials():
model = earm_1_0.model
tspan = np.linspace(0, 1000, 10)
sim = StochKitSimulator(model, tspan=tspan)
unbound_L = model.monomers['L'](b=None)
simres = sim.run(initials={unbound_L: [3000, 1500]},
n_runs=2, seed=_STOCHKIT_SEED, algorithm="ssa")
df = simres.dataframe
unbound_L_index = model.get_species_index(as_complex_pattern(unbound_L))
# Check we have two repeats of each initial
assert np.allclose(df.loc[(slice(None), 0), '__s%d' % unbound_L_index],
[3000, 3000, 1500, 1500])
def test_stochkit_earm_multi_initials():
model = earm_1_0.model
tspan = np.linspace(0, 1000, 10)
sim = StochKitSimulator(model, tspan=tspan)
unbound_L = model.monomers['L'](b=None)
simres = sim.run(initials={unbound_L: [3000, 1500]},
n_runs=2, seed=_STOCHKIT_SEED, algorithm="ssa")
df = simres.dataframe
unbound_L_index = model.get_species_index(as_complex_pattern(unbound_L))
# Check we have two repeats of each initial
assert np.allclose(df.loc[(slice(None), 0), '__s%d' % unbound_L_index],
[3000, 3000, 1500, 1500])
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 stochkit(model,
start=0,
finish=10,
points=10,
n_runs=20,
path='/opt/conda/bin/'):
set_path('stochkit_ssa', path)
sims = StochKitSimulator(model,
linspace(start, finish,
points + 1)).run(n_runs=n_runs).dataframe
sims = modes(sims, n_runs)
return {'sims': sims['sims'], 'avrg': sims['avrg'], 'stdv': sims['stdv']}
def plot_mean_min_max(name, title=None):
x = np.array([tr[:][name] for tr in trajectories]).T
if not title:
title = name
plt.figure(title)
plt.plot(tout.T, x, '0.5', lw=2, alpha=0.25) # individual trajectories
plt.plot(tout[0], x.mean(1), 'k--', lw=3, label="Mean")
plt.plot(tout[0], x.min(1), 'b--', lw=3, label="Minimum")
plt.plot(tout[0], x.max(1), 'r--', lw=3, label="Maximum")
plt.legend(loc=0)
plt.xlabel('Time')
plt.ylabel('Population of %s' % name)
tspan = np.linspace(0, 20000, 1000)
sim = StochKitSimulator(model, tspan)
simres = sim.run(n_runs=20, seed=None, algorithm="ssa")
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()
#turn off graphs showing up
plt.ioff()
#Set Path to save figures
# path = '/home/asasla/main/ComplexII/'
path = '/Users/ariella/PycharmProjects/ComplexII/'
#RUN THROUGH EACH AMOUNT OF TNF: HOW DOES THAT AFFECT SSA VS ODE
# TNF_LOOP = [('100 ng/ml TNF', 9390), ('30 ng/ml TNF', 2817), ('10 ng/ml TNF', 939), ('1 ng/ml TNF', 94), ('.1 ng/ml TNF', 9)]
TNF_LOOP = [('1 ng/ml TNF', 94), ('.1 ng/ml TNF', 9)]
for tnf_title, dose in TNF_LOOP:
#RUN STOCHASTIC SIMULATION ALGORITHM (SSA)
ssa_sim = StochKitSimulator(model, tspan=tspan, verbose=True)
ssa_sim_res = ssa_sim.run(initials={TNF(tnfr=None): dose},
n_runs=NUM_SSA_RUNS)
df = ssa_sim_res.dataframe
#FOR EACH OBSERVABLE AVERAGE THE SSA RUNS AT EACH TIME POINT
avg = df.groupby(level='time').mean()
#RUN ODE SIMULATION
ode_sim = ScipyOdeSimulator(model, tspan=tspan)
ode_sim_res = ode_sim.run(initials={TNF(tnfr=None): dose})
#PLOT STOCHASTIC SIMULATION ALGORITHM (SSA) WITH AVG SSA (YELLOW) AND ODE (BLACK)
# Array: [(Observable name, number to start y axis at, number to end y axis at)]
obs_y_range = [('obsComplexI', float(dose), 0, .02),
('obsComplexIIa', 8030.0, 0, 1),
cont = readlines(f)
pop = popdict(cont, pop)
enterdata(cont, i)
i += 1
#Length of sim
tspan = np.linspace(0, 1440, 1441)
#Run ODE
print('simulating')
ode_sim = ScipyOdeSimulator(model, tspan=tspan)
ode_sim_res = ode_sim.run()
yout = ode_sim_res.all
#SSA Simulation
ssa_sim = StochKitSimulator(model, tspan=tspan, verbose=True)
ssa_sim_res = ssa_sim.run(n_runs=NUM_SSA_RUNS)
#ssa_sim_res = ssa_sim.run(initials={TNF(tnfr=None): dose}, n_runs=NUM_SSA_RUNS)
df = ssa_sim_res.dataframe
#FOR EACH OBSERVABLE AVERAGE THE SSA RUNS AT EACH TIME POINT
ssa_avg = df.groupby(level='time').mean()
for key in keys:
print(key)
if key in avgs:
#if key != 'time':
color = iter(plt.cm.rainbow(np.linspace(0, 1, len(datafiles) + 1)))
f = plt.figure()
ax = f.add_subplot(1, 1, 1)
'''
def test_stochkit_expressions():
model = expression_observables.model
tspan = np.linspace(0, 100, 11)
sim = StochKitSimulator(model, tspan=tspan)
assert np.allclose(sim.run().tout, tspan)
def test_stochkit_earm():
tspan = np.linspace(0, 1000, 10)
sim = StochKitSimulator(earm_1_0.model, tspan=tspan)
simres = sim.run(n_runs=2, seed=_STOCHKIT_SEED, algorithm="ssa")
simres_tl = sim.run(n_runs=2, seed=_STOCHKIT_SEED, algorithm="tau_leaping")
def plot_mean_min_max(name, title=None):
x = np.array([tr[:][name] for tr in trajectories]).T
if not title:
title = name
plt.figure(title)
plt.plot(tout.T, x, '0.5', lw=2, alpha=0.25) # individual trajectories
plt.plot(tout[0], x.mean(1), 'k--', lw=3, label="Mean")
plt.plot(tout[0], x.min(1), 'b--', lw=3, label="Minimum")
plt.plot(tout[0], x.max(1), 'r--', lw=3, label="Maximum")
plt.legend(loc=0)
plt.xlabel('Time')
plt.ylabel('Population of %s' % name)
tspan = np.linspace(0, 20000, 1000)
sim = StochKitSimulator(model, tspan)
simres = sim.run(n_runs=20, seed=None, algorithm="ssa")
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()
import numpy as np
from pysb.simulator import StochKitSimulator
from tyson_oscillator import model
def plot_mean_min_max(name, title=None):
x = np.array([tr[:][name] for tr in trajectories]).T
if not title:
title = name
plt.figure(title)
plt.plot(tout.T, x, '0.5', lw=2, alpha=0.25) # individual trajectories
plt.plot(tout[0], x.mean(1), 'k--', lw=3, label="Mean")
plt.plot(tout[0], x.min(1), 'b--', lw=3, label="Minimum")
plt.plot(tout[0], x.max(1), 'r--', lw=3, label="Maximum")
plt.legend(loc=0)
plt.xlabel('Time')
plt.ylabel('Population of %s' % name)
tspan = np.linspace(0, 50, 5001)
sim = StochKitSimulator(model, verbose=True)
simres = sim.run(tspan=tspan, n_runs=5, seed=None, algorithm="ssa")
tout = simres.tout
trajectories = simres.all
plot_mean_min_max('__s0', str(model.species[0]))
plot_mean_min_max('YT')
plot_mean_min_max('M')
plt.show()
def test_stochkit_expressions():
model = expression_observables.model
tspan = np.linspace(0, 100, 11)
sim = StochKitSimulator(model, tspan=tspan)
assert np.allclose(sim.run().tout, tspan)
def test_stochkit_earm():
tspan = np.linspace(0, 1000, 10)
sim = StochKitSimulator(earm_1_0.model, tspan=tspan)
simres = sim.run(n_runs=2, seed=_STOCHKIT_SEED, algorithm="ssa")
simres_tl = sim.run(n_runs=2, seed=_STOCHKIT_SEED, algorithm="tau_leaping")
from pysb.simulator import StochKitSimulator
from tyson_oscillator import model
def plot_mean_min_max(name, title=None):
x = np.array([tr[:][name] for tr in trajectories]).T
if not title:
title = name
plt.figure(title)
plt.plot(tout.T, x, '0.5', lw=2, alpha=0.25) # individual trajectories
plt.plot(tout[0], x.mean(1), 'k--', lw=3, label="Mean")
plt.plot(tout[0], x.min(1), 'b--', lw=3, label="Minimum")
plt.plot(tout[0], x.max(1), 'r--', lw=3, label="Maximum")
plt.legend(loc=0)
plt.xlabel('Time')
plt.ylabel('Population of %s' % name)
tspan = np.linspace(0, 50, 5001)
sim = StochKitSimulator(model, verbose=True)
simres = sim.run(tspan=tspan, n_runs=5, seed=None, algorithm="ssa")
tout = simres.tout
trajectories = simres.all
plot_mean_min_max('__s0', str(model.species[0]))
plot_mean_min_max('YT')
plot_mean_min_max('M')
plt.show()
def test_stochkit_invalid_init_kwarg():
StochKitSimulator(earm_1_0.model, tspan=range(100), spam='eggs')