# Add the model parameters
k1 = r1.parameter('k1', value=2.0, bounds=(0.0, 5.0), fixed=False)
k2 = r1.parameter('k2', value=0.2, bounds=(0.0, 2.0), fixed=False)
# Declare the components and give the initial values
A = r1.component('A',
value=0.001,
variance=1e-10,
known=False,
bounds=(0.0, 3))
B = r1.component('B', value=0.0, variance=1e-11)
C = r1.component('C', value=0.0, variance=1e-8)
# Use this function to replace the old filename set-up
filename = 'data/Ex_1_C_data.txt'
full_data = kipet.read_data(filename)
r1.add_data(data=full_data.iloc[::10, :], remove_negatives=True)
# Define the reaction model
r1.add_ode('A', -k1 * A)
r1.add_ode('B', k1 * A - k2 * B)
r1.add_ode('C', k2 * B)
# Settings
r1.settings.collocation.nfe = 60
r1.settings.parameter_estimator.covairance = 'k_aug'
# Run KIPET
r1.run_opt()
# Display the results
# Z_in = dict()
# Z_in["t=5"] = [0,0,5]
r1.unwanted_contribution('time_invariant_G')
# Run KIPET
r1.run_opt()
r1.results.show_parameters
if with_plots:
r1.report()
"""We can now compare the results with the known profiles"""
# Read the true S to compare with results
S_true_filename = 'data/S_True_for_unwanted_G.csv'
S_True = kipet.read_data(S_true_filename)
# In this example, we know the magnitude of unwanted contribution.
# Therefore, we can calculate the matched S according to "" to compare the results.
index = list(S_True.index)
column = list(S_True.columns)
data = []
for i in index:
sgi = 2.5E-6 * i / 0.01
row = [sgi, sgi, sgi]
data.append(row)
Sg = pd.DataFrame(data, columns=column, index=index)
S_matched = S_True + Sg
# Make sure the columns have the same names as in the original
S_matched.columns = ['A', 'B', 'C']
r1 = kipet.ReactionModel('reaction-1')
# Add the model parameters
k1 = r1.parameter('k1', value=4.0, bounds=(0.0, 5.0))
k2 = r1.parameter('k2', value=0.5, bounds=(0.0, 2.0))
# Declare the components and give the initial values
A = r1.component('A', value=1e-3)
B = r1.component('B', value=0.0)
C = r1.component('C', value=0.0)
# Input data
file_name = 'data/Dij.txt'
r1.add_data(category='spectral', file=file_name)
a_data = kipet.read_data('data/uplc.csv')
a_data = a_data[['A']]
a_data.columns = ['y']
#r1.add_data('y_data', data=a_data)
#r1.add_data(category='uplc', file='data/uplc.csv')
# Input the reactions as expressions
rA = r1.add_reaction('rA', k1*A)
rB = r1.add_reaction('rB', k2*B)
# Input the ODEs
r1.add_ode('A', -rA )
r1.add_ode('B', rA - rB )
r1.add_ode('C', rB )
"""
import sys
import kipet
if __name__ == "__main__":
with_plots = True
if len(sys.argv) == 2 and int(sys.argv[1]):
with_plots = False
r1 = kipet.ReactionModel('reaction-1')
r1.unit_base.time = 's'
full_data = kipet.read_data('data/ratios.csv')
# Add the model parameters
k1 = r1.parameter('k1', value=2, bounds=(0.0, 10.0), units='1/s')
k2 = r1.parameter('k2', value=0.2, bounds=(0.0, 10.0), units='1/s')
# Declare the components and give the valueial values
A = r1.component('A', value=1.0, units='mol/l')
B = r1.component('B', value=0.0, units='mol/l')
C = r1.component('C', value=0.0, units='mol/l')
# The following has been automated throught the add_expression method
#y = r1.algebraic('y', description='Ratio of B to B + C')
r1.add_data('C_data', data=full_data[['A']], remove_negatives=True)
r1.add_data('y_data', data=full_data[['y']])