def TPR_simulationFunctionWrapperPiecewise(discreteParameterVector):
global times
discreteParameterVectorList = list(discreteParameterVector) #converting to list so can use list expansion in arguments.
tpr_theta_Arguments = [tprequation, initial_concentrations_array, times, (*discreteParameterVectorList,beta_dTdt,T_0) ]
tpr_theta = odeint(*tpr_theta_Arguments) # [0.5, 0.5] are the initial theta's.
simulationInputArguments = [tpr_theta, times, *discreteParameterVectorList, beta_dTdt,T_0]
simulationOutput = tprequation(*simulationInputArguments)
return simulationOutput
def TPR_simulationFunctionWrapperRatioFirst(discreteParameterVectorRatioFirst):
global times
discreteParameterVectorList = list(
discreteParameterVectorRatioFirst
) #converting to list so can use pop and also list expansion in arguments.
initial_concentrations_array[0] = 1 - discreteParameterVectorRatioFirst[0]
initial_concentrations_array[1] = discreteParameterVectorRatioFirst[0]
discreteParameterVectorList.pop(0)
tpr_theta_Arguments = [
tprequation, initial_concentrations_array, times,
(*discreteParameterVectorList, beta_dTdt, T_0)
]
tpr_theta = odeint(
*tpr_theta_Arguments) # [0.5, 0.5] are the initial theta's.
simulationInputArguments = [
tpr_theta, times, *discreteParameterVectorList, beta_dTdt, T_0
]
simulationOutput = tprequation(*simulationInputArguments)
return simulationOutput
######################## Import experimental data
experiments_df = pd.read_csv(
'../ExperimentalDataAcetaldehydeTPDCeO2111MullinsTruncatedLargerErrors.csv'
)
dT = experiments_df['dT'][0] # assuming dT and dt are constant throughout
dt = experiments_df['dt'][0]
start_T = experiments_df['AcH - T'][0]
theta_1_t0 = 0.5 # half of the surface initially coverered with theta_1
theta_2_t0 = 0.5
tpr_theta = odeint(tprequation, [theta_1_t0, theta_2_t0],
experiments_df['time'].to_numpy(),
args=(Ea1_mean, Ea2_mean, log_A1_mean, log_A2_mean,
gamma_1_mean, gamma_2_mean, dT, dt, start_T))
rate = tprequation(tpr_theta, experiments_df['time'].to_numpy(), Ea1_mean,
Ea2_mean, log_A1_mean, log_A2_mean, gamma_1_mean,
gamma_2_mean, dT, dt, start_T)
print(rate)
rate = np.asarray(rate)
print(rate.shape)
rate_tot = -np.sum(rate, axis=0)
fig1, ax1 = plt.subplots()
ax1.plot(experiments_df['AcH - T'].to_numpy(), rate_tot, 'r')
ax1.set_xlabel('T (K)')
ax1.set_ylabel(r'$rate (s^{-1})$')
ax1.legend(['model prior'])
fig1.tight_layout()
fig1.savefig('twocomponent.png', dpi=220)
fig2, ax2 = plt.subplots()
ax2.plot(experiments_df['AcH - T'].to_numpy(),
