def plot_bng_and_kappa_sims(model, t_end, n_steps, title):
# BNG
x = bng.run_ssa(model, t_end=t_end, n_steps=n_steps)
plt.figure()
for name in x.dtype.names:
if not name == 'time':
plt.plot(x['time'], x[name], label="B:"+name)
# Kappa
x = kappa.run_simulation(model, time=t_end, points=n_steps)
for name in x.dtype.names:
if not name == 'time':
plt.plot(x['time'], x[name], label="K:"+name)
plt.title(title)
plt.ylabel("Number")
plt.xlabel("Time")
plt.legend(loc='upper right')
def run_site_cpt(self):
"""Run a set of simulations using the site_cpt implementation.
Builds the model using the :py:meth:`Job.build` method, then runs
the number of simulations specified in ``self.num_sims`` using
`pysb.kappa.run_simulation` and returns the results.
Returns
-------
list of numpy.recarrays
List of record arrays, each one containing the results of a
single stochastic simulation. The entries in the record array
correspond to observables in the model.
"""
b = self.site_cpt_builder()
xrecs = []
for i in range(self.num_sims):
xrecs.append(kappa.run_simulation(b.model, time=self.tmax,
points=self.n_steps,
output_dir='.'))
return xrecs
def run_model(self, tmax=12000, num_sims=1, use_kappa=True,
figure_ids=[0, 1]):
xrecs = [] # The array to store the simulation data
dr_all = [] # TODO: Delete this
# Run multiple simulations and collect data
for i in range(0, num_sims):
# Run simulation using Kappa:
if use_kappa:
ssa_result = kappa.run_simulation(self.model, time=tmax,
points=100,
output_dir='simdata')
xrecs.append(ssa_result)
# Run simulation using BNG SSA implementation:
else:
ssa_result = bng.run_ssa(self.model, t_end=tmax, n_steps=100,
cleanup=True)
xrecs.append(ssa_result)
#dr_all.append(get_dye_release(model, 'pores', ssa_result))
# Convert the multiple simulations in an array...
xall = array([x.tolist() for x in xrecs])
# ...and calculate the Mean and SD across the simulations
x_std = recarray(xrecs[0].shape, dtype=xrecs[0].dtype, buf=std(xall, 0))
x_avg = recarray(xrecs[0].shape, dtype=xrecs[0].dtype,
buf=mean(xall, 0))
# Plotting parameters, aliases
ci = color_iter()
marker = 'x'
linestyle = '-'
tBid_0 = self['tBid_0']
Bax_0 = self['Bax_0']
# Translocation: plot cyto/mito tBid, and cyto/mito Bax
plt.ion()
plt.figure(figure_ids[0])
plt.errorbar(x_avg['time'], x_avg['ctBid']/tBid_0.value,
yerr=x_std['ctBid']/tBid_0.value,
color=ci.next(), marker=marker, linestyle=linestyle)
plt.errorbar(x_avg['time'], x_avg['mtBid']/tBid_0.value,
yerr=x_std['mtBid']/tBid_0.value,
color=ci.next(), marker=marker, linestyle=linestyle)
plt.errorbar(x_avg['time'], x_avg['cBax']/Bax_0.value,
yerr=x_std['cBax']/Bax_0.value,
color=ci.next(), marker=marker, linestyle=linestyle)
plt.errorbar(x_avg['time'], x_avg['mBax']/Bax_0.value,
yerr=x_std['mBax']/Bax_0.value,
color=ci.next(), marker=marker, linestyle=linestyle)
# Activation: plot iBax and tBidBax
plt.errorbar(x_avg['time'], x_avg['iBax']/Bax_0.value,
yerr=x_std['iBax']/Bax_0.value, label='iBax',
color=ci.next(), marker=marker, linestyle=linestyle)
plt.errorbar(x_avg['time'], x_avg['tBidBax']/tBid_0.value,
yerr=x_std['tBidBax']/tBid_0.value,
color=ci.next(), marker=marker, linestyle=linestyle)
# Dye release calculated exactly ----------
#dr_avg = mean(dr_all, 0)
#dr_std = std(dr_all, 0)
#errorbar(x_avg['time'], dr_avg,
# yerr=dr_std, label='dye_release',
# color=ci.next(), marker=marker, linestyle=linestyle)
# Pore Formation
#plot(x['time'], x['pBax']/Bax_0.value, label='pBax')
#leg = legend()
#ltext = leg.get_texts()
#setp(ltext, fontsize='small')
#xlabel("Time (seconds)")
#ylabel("Normalized Concentration")
#ci = color_iter()
# Plot pores/vesicle in a new figure ------
#figure(2)
#errorbar(x_avg['time'], x_avg['pores'] / float(NUM_COMPARTMENTS),
# yerr=x_std['pores']/float(NUM_COMPARTMENTS), label='pores',
# color=ci.next(), marker=marker, linestyle=linestyle)
#F_t = 1 - dr_avg
#pores_poisson = -log(F_t)
#plot(x_avg['time'], pores_poisson, color=ci.next(), label='-ln F(t),
# stoch',
# marker=marker, linestyle=linestyle)
#xlabel("Time (seconds)")
#ylabel("Pores/vesicle")
#title("Pores/vesicle")
#legend()
#xlabel("Time (seconds)")
#ylabel("Dye Release")
#title("Dye release calculated via compartmental model")
return xrecs[0]
