'product_concentration', 'substrate_concentration'
]
# Filter the data frame for specific condition
this_volume_fraction = 0.0
this_mu = 31.9
this_sigma = 0.825
this_df = df[(df['sigma_mass'] == this_sigma) & (df['mu_mass'] == this_mu) &
(df['volume_fraction'] == this_volume_fraction)]
# Extract the GEEK parameters from Linear regression
k1_fwd_params = geek_regression(this_df,
concentrations,
reference_concentrations,
'k1_fwd_relative',
verbose=False)
k1_bwd_params = geek_regression(this_df,
concentrations,
reference_concentrations,
'k1_bwd_relative',
verbose=False)
k2_fwd_params = geek_regression(this_df,
concentrations,
reference_concentrations,
'k1_fwd_relative',
verbose=False)
k2_bwd_params = geek_regression(this_df,
def geek_simulations_crwderfree(parameters, sim_type, phi= 0.0, seed=1):
from geek.analysis import geek_regression
if sim_type == 'diff':
df = read_csv('../data/validation_diffusion_lim_crowderfree.csv')
elif sim_type == 'react':
df = read_csv('../data/validation_reaction_lim_crowderfree.csv')
else:
raise ValueError('{} is not a valid input'.format(sim_type))
# Reference concentrations
reference_concentrations = [50e-6,]*3
concentrations = ['A_concentration',
'B_concentration',
'C_concentration',]
this_df = df[(df['volume_fraction'] == phi)]
# Extract the GEEK parameters from Linear regression
k1_fwd_params = geek_regression(this_df,
concentrations,
reference_concentrations,
'k1_fwd_relative',
verbose=True)
k1_bwd_params = geek_regression(this_df,
concentrations,
reference_concentrations,
'k1_bwd_relative',
verbose=True)
random.seed(seed)
#Map to parameter dict
param_dict = {
'k_1f0': parameters['k_fwd'],
'k_1b0': parameters['k_fwd']*parameters['K_eq'],
'beta_1f': k1_fwd_params['beta_lb'] + (k1_fwd_params['beta_ub'] - k1_fwd_params['beta_lb']) * random.random(),
'alpha_A_1f': k1_fwd_params['alpha_A_concentration_lb'] + (
k1_fwd_params['alpha_A_concentration_ub'] - k1_fwd_params[
'alpha_A_concentration_lb']) * random.random(),
'alpha_B_1f': k1_fwd_params['alpha_B_concentration_lb'] + (
k1_fwd_params['alpha_B_concentration_ub'] - k1_fwd_params[
'alpha_B_concentration_lb']) * random.random(),
'alpha_C_1f': k1_fwd_params['alpha_C_concentration_lb'] + (
k1_fwd_params['alpha_C_concentration_ub'] - k1_fwd_params[
'alpha_C_concentration_lb']) * random.random(),
'beta_1b': k1_bwd_params['beta_lb'] + (k1_bwd_params['beta_ub'] - k1_bwd_params['beta_lb']) * random.random(),
'alpha_A_1b': k1_bwd_params['alpha_A_concentration_lb'] + (
k1_bwd_params['alpha_A_concentration_ub'] - k1_bwd_params[
'alpha_A_concentration_lb']) * random.random(),
'alpha_B_1b': k1_bwd_params['alpha_B_concentration_lb'] + (
k1_bwd_params['alpha_B_concentration_ub'] - k1_bwd_params[
'alpha_B_concentration_lb']) * random.random(),
'alpha_C_1b': k1_bwd_params['alpha_C_concentration_lb'] + (
k1_bwd_params['alpha_C_concentration_ub'] - k1_bwd_params[
'alpha_C_concentration_lb']) * random.random(),
'A0': reference_concentrations[0],
'B0': reference_concentrations[1],
'C0': reference_concentrations[2],
}
"""
Declare ODE-Problem
"""
from sympy import symbols
from sympy import exp as sym_exp
# Variables
A, B, C = symbols(['A', 'B', 'C'])
variables = [A, B, C,]
# Parameters
k_1f0, k_1b0, = symbols(['k_1f0', 'k_1b0',] )
# Define symbols for the GEEK parameters
beta_1f, beta_1b, = symbols(['beta_1f', 'beta_1b',] )
alpha_A_1f, alpha_A_1b, = symbols(['alpha_A_1f', 'alpha_A_1b',])
alpha_B_1f, alpha_B_1b, = symbols(['alpha_B_1f', 'alpha_B_1b',])
alpha_C_1f, alpha_C_1b, = symbols(['alpha_C_1f', 'alpha_C_1b',])
A0,B0,C0 = symbols(['A0', 'B0', 'C0'])
ode_params = [k_1f0, k_1b0,
beta_1f, beta_1b ,
alpha_A_1b, alpha_A_1f ,
alpha_B_1b, alpha_B_1f,
alpha_C_1f, alpha_C_1b,
A0, B0, C0]
# Reactions
geek_reactions = {
'r_1f': k_1f0 * A * B * sym_exp(beta_1f) * (A / A0) ** alpha_A_1f * (B / B0) ** alpha_B_1f * (
C / C0) ** alpha_C_1f,
'r_1b': k_1b0 * C * sym_exp(beta_1b) * (A / A0) ** alpha_A_1b * (B / B0) ** alpha_B_1b * (
C / C0) ** alpha_C_1b
}
#Expressions
expressions = {
A: geek_reactions['r_1b'] - geek_reactions['r_1f'],
B: geek_reactions['r_1b'] - geek_reactions['r_1f'],
C: geek_reactions['r_1f'] - geek_reactions['r_1b'],
}
from geek.analysis.ode_function import OdeFun
fun = OdeFun(variables,ode_params,expressions)
from scipy.integrate import ode
r = ode(fun).set_integrator('vode', method='bdf')
eps = 1e-3
A0 = round(parameters['A_0']*AVOGADRO_NUMBER*parameters['volume'])/AVOGADRO_NUMBER/parameters['volume']
B0 = round(parameters['B_0']*AVOGADRO_NUMBER*parameters['volume'])/AVOGADRO_NUMBER/parameters['volume']
C0 = round(parameters['C_0']*AVOGADRO_NUMBER*parameters['volume'])/AVOGADRO_NUMBER/parameters['volume']
print(A0,B0,C0)
y0 = [A0 * (1. - eps),
B0 * (1. - eps),
A0 * eps]
t0 = 0.0
r.set_initial_value(y0, t0).set_f_params(param_dict)
data = []
scale = parameters['volume']*AVOGADRO_NUMBER
while r.successful() and r.t < parameters['t_max']:
data.append( np.append(r.t + parameters['t_max']/1000.0,
r.integrate(r.t + parameters['t_max']/1000.0) * scale))
data = np.array(data)
df = DataFrame(data=data, columns = ['time', 'A', 'B', 'C'])
return df