def run():
# factors to multiply the values of the initial conditions
multipliers = np.linspace(0.8, 1.2, 11)
# 2D array of initial concentrations
initial_concentrations = [
multipliers * ic.value.value for ic in model.initials
]
# Cartesian product of initial concentrations
cartesian_product = itertools.product(*initial_concentrations)
# the Cartesian product object must be cast to a list, then to a numpy array
# and transposed to give a (n_species x n_vals) matrix of initial concentrations
initials_matrix = np.array(list(cartesian_product)).T
# we can now construct the initials dictionary
initials = {
ic.pattern: initials_matrix[i] for i, ic in enumerate(model.initials)
}
# simulation time span and output points
tspan = np.linspace(0, 50, 501)
# run_cupsoda returns a 3D array of species and observables trajectories
trajectories = run_cupsoda(model, tspan, initials=initials,
integrator_options={'atol': 1e-10, 'rtol': 1e-4},
verbose=True)
# extract the trajectories for the 'Product' into a numpy array and
# transpose to aid in plotting
x = np.array([tr['Product'] for tr in trajectories]).T
# plot the mean, minimum, and maximum concentrations at each time point
plt.plot(tspan, x.mean(axis=1), 'b', lw=3, label="Product")
plt.plot(tspan, x.max(axis=1), 'b--', lw=2, label="min/max")
plt.plot(tspan, x.min(axis=1), 'b--', lw=2)
# define the axis labels and legend
plt.xlabel('time')
plt.ylabel('concentration')
plt.legend(loc='upper left')
# show the plot
plt.show()
def run():
# factors to multiply the values of the initial conditions
multipliers = np.linspace(0.8, 1.2, 11)
# 2D array of initial concentrations
initial_concentrations = [multipliers*ic[1].value for ic in model.initial_conditions]
# Cartesian product of initial concentrations
cartesian_product = itertools.product(*initial_concentrations)
# the Cartesian product object must be cast to a list, then to a numpy array
# and transposed to give a (n_species x n_vals) matrix of initial concentrations
initials_matrix = np.array(list(cartesian_product)).T
# we can now construct the initials dictionary
initials = { ic[0] : initials_matrix[i] for i,ic in enumerate(model.initial_conditions) }
# simulation time span and output points
tspan = np.linspace(0, 50, 501)
# run_cupsoda returns a 3D array of species and observables trajectories
trajectories = run_cupsoda(model, tspan, initials=initials,
atol=1e-10, rtol=1e-4, verbose=True)
# extract the trajectories for the 'Product' into a numpy array and
# transpose to aid in plotting
x = np.array([tr['Product'] for tr in trajectories]).T
# plot the mean, minimum, and maximum concentrations at each time point
plt.plot(tspan, x.mean(axis=1), 'b', lw=3, label="Product")
plt.plot(tspan, x.max(axis=1), 'b--', lw=2, label="min/max")
plt.plot(tspan, x.min(axis=1), 'b--', lw=2)
# define the axis labels and legend
plt.xlabel('time')
plt.ylabel('concentration')
plt.legend(loc='upper left')
# show the plot
plt.show()
def test_run_cupsoda_instance(self):
run_cupsoda(model, tspan=self.tspan)
def test_run_cupsoda_instance(self):
run_cupsoda(model, tspan=self.tspan)