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]