with_plots = True
if len(sys.argv) == 2:
if int(sys.argv[1]):
with_plots = False
# =========================================================================
# USER INPUT SECTION - MODEL BUILDING - See user guide for how this section functions
# =========================================================================
# read S matrix
wl_span = np.arange(180, 230, 1) #1599,1852, 5)
S_parameters = Lorentzian_parameters()
S_frame = generate_absorbance_data(wl_span, S_parameters)
# create template model
builder = TemplateBuilder()
# components
components = dict()
components['A'] = 0.395555
components['B'] = 0.0351202
components['C'] = 0.0
components['D'] = 0.0
components['E'] = 0.0
components['F'] = 0.0
components['G'] = 0.0
builder.add_mixture_component(components)
# add algebraics
#USER INPUT SECTION - REQUIRED MODEL BUILDING ACTIONS
#=========================================================================
# Load spectral data from the relevant file location. As described in section 4.3.1
#################################################################################
dataDirectory = os.path.abspath(
os.path.join( os.path.dirname( os.path.abspath( inspect.getfile(
inspect.currentframe() ) ) ), 'data_sets'))
filename = os.path.join(dataDirectory,'Dij.txt')
D_frame = read_spectral_data_from_txt(filename)
# Then we build dae block for as described in the section 4.2.1. Note the addition
# of the data using .add_spectral_data
#################################################################################
builder = TemplateBuilder()
components = {'A':1e-3,'B':0,'C':0}
builder.add_mixture_component(components)
builder.add_parameter('k1', init=2.0, bounds=(1.0,5.0))
#There is also the option of providing initial values: Just add init=... as additional argument as above.
builder.add_parameter('k2',init = 0.2, bounds=(0.0,1))
builder.add_spectral_data(D_frame)
# define explicit system of ODEs
def rule_odes(m,t):
exprs = dict()
exprs['A'] = -m.P['k1']*m.Z[t,'A']
exprs['B'] = m.P['k1']*m.Z[t,'A']-m.P['k2']*m.Z[t,'B']
exprs['C'] = m.P['k2']*m.Z[t,'B']
return exprs
if len(sys.argv) == 2:
if int(sys.argv[1]):
with_plots = False
# read 300x100 spectra matrix D_{i,j}
# this defines the measurement points t_i and l_j as well
dataDirectory = os.path.abspath(
os.path.join(
os.path.dirname(
os.path.abspath(inspect.getfile(inspect.currentframe()))),
'..', '..', 'data_sets'))
filename = os.path.join(dataDirectory, 'Dij.txt')
D_frame = read_spectral_data_from_txt(filename)
######################################
builder = TemplateBuilder()
components = {'A': 1e-3, 'B': 0, 'C': 0}
builder.add_mixture_component(components)
# note the parameter is not fixed
builder.add_parameter('k1', bounds=(0.0, 5.0))
builder.add_parameter('k2', bounds=(0.0, 1.0))
builder.add_spectral_data(D_frame)
# define explicit system of ODEs
def rule_odes(m, t):
exprs = dict()
exprs['A'] = -m.P['k1'] * m.Z[t, 'A']
exprs['B'] = m.P['k1'] * m.Z[t, 'A'] - m.P['k2'] * m.Z[t, 'B']
exprs['C'] = m.P['k2'] * m.Z[t, 'B']
return exprs
from kipet.library.TemplateBuilder import *
from kipet.library.CasadiSimulator import *
import matplotlib.pyplot as plt
import sys
import os
if __name__ == "__main__":
with_plots = True
if len(sys.argv) == 2:
if int(sys.argv[1]):
with_plots = False
# create template model
builder = TemplateBuilder()
components = {'A': 1, 'B': 0.8, 'C': 0, 'D': 0}
builder.add_mixture_component(components)
builder.add_parameter('k1', 2.0)
builder.add_parameter('k2', 1.0)
# define explicit system of ODEs
def rule_odes(m, t):
exprs = dict()
exprs['A'] = -m.P['k1'] * m.Z[t, 'A'] * m.Z[t, 'B']
exprs['B'] = -m.P['k1'] * m.Z[t, 'A'] * m.Z[t, 'B']
exprs['C'] = m.P['k1'] * m.Z[t, 'A'] * m.Z[
t, 'B'] - 2 * m.P['k2'] * m.Z[t, 'C']**2
exprs['D'] = m.P['k2'] * m.Z[t, 'C']**2
return exprs
#=========================================================================
# Load spectral data from the relevant file location. As described in section 4.3.1
#################################################################################
dataDirectory = os.path.abspath(
os.path.join(
os.path.dirname(
os.path.abspath(inspect.getfile(inspect.currentframe()))),
'data_sets'))
filename = os.path.join(dataDirectory, 'Dij.txt')
D_frame = read_spectral_data_from_txt(filename)
# Then we build dae block for as described in the section 4.2.1. Note the addition
# of the data using .add_spectral_data
#################################################################################
builder = TemplateBuilder()
components = {'A': 1e-3, 'B': 0, 'C': 0}
builder.add_mixture_component(components)
builder.add_parameter('k1', 4.0) #init=4.0, bounds=(0.0,5.0))
#There is also the option of providing initial values: Just add init=... as additional argument as above.
builder.add_parameter('k2', bounds=(0.0, 1.0))
builder.add_spectral_data(D_frame)
# define explicit system of ODEs
def rule_odes(m, t):
exprs = dict()
exprs['A'] = -m.P['k1'] * m.Z[t, 'A']
exprs['B'] = m.P['k1'] * m.Z[t, 'A'] - m.P['k2'] * m.Z[t, 'B']
exprs['C'] = m.P['k2'] * m.Z[t, 'B']
return exprs
filename2 = os.path.join(dataDirectory, 'Dij_exp3_reduced.txt')
D_frame1 = read_spectral_data_from_txt(filename1)
D_frame2 = read_spectral_data_from_txt(filename2)
#This function can be used to remove a certain number of wavelengths from data
# in this case only every 2nd wavelength is included
D_frame1 = decrease_wavelengths(D_frame1, A_set=2)
#Here we add noise to datasets in order to make our data differenct between experiments
D_frame2 = add_noise_to_signal(D_frame2, 0.00001)
D_frame2 = decrease_wavelengths(D_frame2, A_set=2)
#D_frame3 = add_noise_to_signal(D_frame2, 0.0004)
#################################################################################
builder = TemplateBuilder()
components = {'A': 1e-3, 'B': 0, 'C': 0}
builder.add_mixture_component(components)
builder.add_parameter('k1', init=1.0, bounds=(0.00, 10))
#There is also the option of providing initial values: Just add init=... as additional argument as above.
builder.add_parameter('k2', init=0.224, bounds=(0.0, 10))
# If you have multiple experiments, you need to add your experimental datasets to a dictionary:
datasets = {'Exp1': D_frame1, 'Exp2': D_frame2}
#, 'Exp3': D_frame3}
# Additionally, we do not add the spectral data to the TemplateBuilder, rather supplying the
# TemplateBuilder before data is added as an argument into the function
# define explicit system of ODEs
def rule_odes(m, t):
if __name__ == "__main__":
with_plots = True
if len(sys.argv) == 2:
if int(sys.argv[1]):
with_plots = False
# read 500x2 S matrix
wl_span = np.arange(180, 230, 10)
S_parameters = Lorentzian_parameters()
S_frame = generate_absorbance_data(wl_span, S_parameters)
# components
concentrations = {'A': 1, 'B': 0}
# create template model
builder = TemplateBuilder()
builder.add_mixture_component(concentrations)
builder.add_parameter('k', 0.1)
builder.add_absorption_data(S_frame)
builder.add_measurement_times([i for i in range(0, 50, 10)])
dataDirectory = os.path.abspath(
os.path.join(
os.path.dirname(
os.path.abspath(inspect.getfile(inspect.currentframe()))),
'..', 'data_sets'))
# filename = os.path.join(dataDirectory,'Dij_case52a.txt')
filename = os.path.join(dataDirectory, 'Sij_small.txt')
write_absorption_data_to_txt(filename, S_frame)
#USER INPUT SECTION - REQUIRED MODEL BUILDING ACTIONS
#=========================================================================
# Load spectral data from the relevant file location. As described in section 4.3.1
#################################################################################
dataDirectory = os.path.abspath(
os.path.join( os.path.dirname( os.path.abspath( inspect.getfile(
inspect.currentframe() ) ) ), 'data_sets'))
filename = os.path.join(dataDirectory,'new_estim_problem_conc.csv')
D_frame = read_concentration_data_from_csv(filename)
# Then we build dae block for as described in the section 4.2.1. Note the addition
# of the data using .add_concentration_data
#################################################################################
builder = TemplateBuilder()
builder.add_mixture_component('A',0.3)
builder.add_mixture_component('B',0.0)
builder.add_mixture_component('C',0.0)
builder.add_mixture_component('D',0.01)
builder.add_mixture_component('E',0.0)
#Following this we add the kinetic parameters
builder.add_parameter('k1',bounds=(0.1,2))
builder.add_parameter('k2',bounds=(0.0,2))
builder.add_parameter('k3',bounds=(0.0,2))
builder.add_parameter('k4',bounds=(0.0,2))
# define explicit system of ODEs
def rule_odes(m,t):
exprs = dict()
exprs['A'] = -m.P['k1']*m.Z[t,'A']-m.P['k4']*m.Z[t,'A']
with_plots = True
if len(sys.argv)==2:
if int(sys.argv[1]):
with_plots = False
#=========================================================================
#USER INPUT SECTION - REQUIRED MODEL BUILDING ACTIONS
#=========================================================================
######################################
# create template model
species = {'A':6.7, 'B':20.2, 'C':0.0}
params = {'k_p':3.734e7}
builder = TemplateBuilder()
builder = TemplateBuilder(concentrations=species,
parameters=params)
#extra_states=dict()
#extra_states['T']=290.0
builder.add_complementary_state_variable('T',290.0)
# define explicit system of ODEs
def rule_odes(m,t):
r = m.P['k_p']*exp(-15400.0/(1.987*m.X[t,'T']))*m.Z[t,'A']*m.Z[t,'B']
T1 = 45650.0*(r*0.01)/28.0
T2 = 1+(328.0-m.X[t,'T'])/((328.0-m.X[t,'T'])**2+1e-5**2)**0.5
exprs = dict()
exprs['A'] = -r
exprs['B'] = -r
from kipet.library.TemplateBuilder import *
from kipet.library.PyomoSimulator import *
import matplotlib.pyplot as plt
from pyomo.core import *
import sys
if __name__ == "__main__":
with_plots = True
if len(sys.argv) == 2:
if int(sys.argv[1]):
with_plots = False
# create template model
builder = TemplateBuilder()
builder.add_mixture_component('A', 1.0)
builder.add_mixture_component('B', 0.0)
builder.add_mixture_component('C', 0.0)
builder.add_complementary_state_variable('T', 290.0)
builder.add_complementary_state_variable('V', 100.0)
# define explicit system of ODEs
def rule_odes(m, t):
k1 = 1.25 * exp((9500 / 1.987) * (1 / 320.0 - 1 / m.X[t, 'T']))
k2 = 0.08 * exp((7000 / 1.987) * (1 / 290.0 - 1 / m.X[t, 'T']))
ra = -k1 * m.Z[t, 'A']
rb = 0.5 * k1 * m.Z[t, 'A'] - k2 * m.Z[t, 'B']
rc = 3 * k2 * m.Z[t, 'B']
cao = 4.0
# \frac{dZ_b}{dt} = k*Z_a Z_b(0) = 0
from kipet.library.TemplateBuilder import *
from kipet.library.PyomoSimulator import *
import matplotlib.pyplot as plt
import sys
if __name__ == "__main__":
with_plots = True
if len(sys.argv) == 2:
if int(sys.argv[1]):
with_plots = False
# create template model
builder = TemplateBuilder()
builder.add_mixture_component('A', 1)
builder.add_mixture_component('B', 0)
builder.add_parameter('k', 0.01)
# define explicit system of ODEs
def rule_odes(m, t):
exprs = dict()
exprs['A'] = -m.P['k'] * m.Z[t, 'A']
exprs['B'] = m.P['k'] * m.Z[t, 'A']
return exprs
builder.set_odes_rule(rule_odes)
# create an instance of a pyomo model template
# the template includes
import os
from itertools import count, takewhile
if __name__ == "__main__":
with_plots = True
if len(sys.argv) == 2:
if int(sys.argv[1]):
with_plots = False
# =========================================================================
# USER INPUT SECTION - MODEL BUILDING - See user guide for how this section functions
# =========================================================================
# create template model
builder = TemplateBuilder()
# components
components = dict()
components['A'] = 0.395555
components['B'] = 0.0351202
components['C'] = 0.0
components['D'] = 0.0
components['E'] = 0.0
components['F'] = 0.0
components['G'] = 0.0
builder.add_mixture_component(components)
# add algebraics
algebraics = ['0', '1', '2', '3', '4', '5']#, 'Temp'] # the indices of the rate rxns
#USER INPUT SECTION - REQUIRED MODEL BUILDING ACTIONS
#=========================================================================
# Load concentration data from the relevant file location. As described in section 4.3.1
#################################################################################
dataDirectory = os.path.abspath(
os.path.join(
os.path.dirname(
os.path.abspath(inspect.getfile(inspect.currentframe()))),
'data_sets'))
filename = os.path.join(dataDirectory, 'Ex_1_C_data_withoutA.csv')
C_frame1 = read_concentration_data_from_csv(filename)
C_frame2 = add_noise_to_signal(C_frame1, 0.0001)
#################################################################################
builder = TemplateBuilder()
builder2 = TemplateBuilder()
components = {'B': 0, 'C': 0}
builder.add_mixture_component(components)
builder.add_parameter('k1', init=1.0, bounds=(0.00, 10))
#There is also the option of providing initial values: Just add init=... as additional argument as above.
builder.add_parameter('k2', init=0.224, bounds=(0.0, 10))
builder2.add_mixture_component(components)
builder2.add_parameter('k1', init=1.0, bounds=(0.00, 10))
# There is also the option of providing initial values: Just add init=... as additional argument as above.
builder2.add_parameter('k2', init=0.224, bounds=(0.0, 10))
#A and A2 are the states that we want to estimate the initial condition for, they are not measured:
#add complementary state variable:
extra_states1 = dict()
#=========================================================================
# Load spectral data from the relevant file location. As described in section 4.3.1
#################################################################################
dataDirectory = os.path.abspath(
os.path.join(
os.path.dirname(
os.path.abspath(inspect.getfile(inspect.currentframe()))),
'data_sets'))
filename = os.path.join(dataDirectory, 'Dij.txt')
D_frame = read_spectral_data_from_txt(filename)
# Then we build dae block for as described in the section 4.2.1. Note the addition
# of the data using .add_spectral_data
#################################################################################
builder = TemplateBuilder()
components = {'A': 1e-3, 'B': 0, 'C': 0}
builder.add_mixture_component(components)
builder.add_parameter('k1', init=4.0, bounds=(0.0, 5.0))
#There is also the option of providing initial values: Just add init=... as additional argument as above.
builder.add_parameter('k2', bounds=(0.0, 1.0))
builder.add_spectral_data(D_frame)
# define explicit system of ODEs
def rule_odes(m, t):
exprs = dict()
exprs['A'] = -m.P['k1'] * m.Z[t, 'A']
exprs['B'] = m.P['k1'] * m.Z[t, 'A'] - m.P['k2'] * m.Z[t, 'B']
exprs['C'] = m.P['k2'] * m.Z[t, 'B']
return exprs
with_plots = True
if len(sys.argv) == 2:
if int(sys.argv[1]):
with_plots = False
# =========================================================================
# USER INPUT SECTION - MODEL BUILDING - See user guide for how this section functions
# =========================================================================
# read S matrix
wl_span = np.arange(180,230, 1)#1599,1852, 5)
S_parameters = Lorentzian_parameters()
S_frame = generate_absorbance_data(wl_span, S_parameters)
# create template model
builder = TemplateBuilder()
# components
components = dict()
components['A'] = 1e-3
components['B'] = 0.0
components['C'] = 0.0
components['D'] = 1e-3
components['E'] = 0
builder.add_mixture_component(components)
params = dict()
params['k1'] = 1.5
params['k2'] = 0.2
with_plots = True
if len(sys.argv)==2:
if int(sys.argv[1]):
with_plots = False
# read 200x500 spectra matrix D_{i,j}
# this defines the measurement points t_i and l_j as well
dataDirectory = os.path.abspath(
os.path.join( os.path.dirname( os.path.abspath( inspect.getfile(
inspect.currentframe() ) ) ), '..','..','data_sets'))
filename = os.path.join(dataDirectory,'Dijsawall.txt')
D_frame = read_spectral_data_from_txt(filename)
##########################################################
builder2 = TemplateBuilder()
builder2.add_mixture_component({'A':1,'B':0})
# note the parameter is not fixed
builder2.add_parameter('k',bounds=(0.0,1))
builder2.add_spectral_data(D_frame)
# define explicit system of ODEs
def rule_odes(m,t):
exprs = dict()
exprs['A'] = -m.P['k']*m.Z[t,'A']
exprs['B'] = m.P['k']*m.Z[t,'A']
return exprs
builder2.set_odes_rule(rule_odes)
if len(sys.argv) == 2:
if int(sys.argv[1]):
with_plots = False
# read 100x3 S matrix
# this defines the measurement lambdas l_j but the t_i still need to be passed
dataDirectory = os.path.abspath(
os.path.join(
os.path.dirname(
os.path.abspath(inspect.getfile(inspect.currentframe()))),
'..', '..', 'data_sets'))
filename = os.path.join(dataDirectory, 'Slk_case51a.txt')
S_frame = read_absorption_data_from_txt(filename)
# create template model
builder = TemplateBuilder()
builder.add_mixture_component('A', 1)
builder.add_mixture_component('B', 0)
builder.add_mixture_component('C', 0)
builder.add_parameter('k1', 2.0)
builder.add_parameter('k2', 0.2)
# includes absorption data in the template and defines measurement sets
builder.add_absorption_data(S_frame)
builder.add_measurement_times([i * 0.0333 for i in range(300)])
# define explicit system of ODEs
def rule_odes(m, t):
exprs = dict()
exprs['A'] = -m.P['k1'] * m.Z[t, 'A']
exprs['B'] = m.P['k1'] * m.Z[t, 'A'] - m.P['k2'] * m.Z[t, 'B']
exprs['C'] = m.P['k2'] * m.Z[t, 'B']
if __name__ == "__main__":
with_plots = True
if len(sys.argv)==2:
if int(sys.argv[1]):
with_plots = False
#=========================================================================
#USER INPUT SECTION - REQUIRED MODEL BUILDING ACTIONS
#=========================================================================
# Then we build dae block for as described in the section 4.2.1. Note the addition
# of the data using .add_spectral_data
#################################################################################
builder = TemplateBuilder()
components = {'A':1e-3,'B':0,'C':0}
builder.add_mixture_component(components)
algebraics=['1','2','3','k2T','Temp']
builder.add_algebraic_variable(algebraics)
# Load Temp data:
dataDirectory = os.path.abspath(
os.path.join(os.path.dirname(os.path.abspath(inspect.getfile(
inspect.currentframe()))), 'data_sets'))
Ttraj = os.path.join(dataDirectory, 'Tempvalues.csv')
fixed_Ttraj = read_absorption_data_from_csv(Ttraj)
params = dict()
params['k1'] = 1.0
# \frac{dZ_c}{dt} = k_2*Z_b Z_c(0) = 0
from kipet.library.TemplateBuilder import *
from kipet.library.PyomoSimulator import *
import matplotlib.pyplot as plt
import sys
if __name__ == "__main__":
with_plots = True
if len(sys.argv) == 2:
if int(sys.argv[1]):
with_plots = False
# create template model
builder = TemplateBuilder()
builder.add_mixture_component('A', 1)
builder.add_mixture_component('B', 0)
builder.add_mixture_component('C', 0)
builder.add_parameter('k1', 2.0)
builder.add_parameter('k2', 0.2)
# define explicit system of ODEs
def rule_odes(m, t):
exprs = dict()
exprs['A'] = -m.P['k1'] * m.Z[t, 'A']
exprs['B'] = m.P['k1'] * m.Z[t, 'A'] - m.P['k2'] * m.Z[t, 'B']
exprs['C'] = m.P['k2'] * m.Z[t, 'B']
return exprs
builder.set_odes_rule(rule_odes)
#=========================================================================
# Load spectral data from the relevant file location. As described in section 4.3.1
#################################################################################
dataDirectory = os.path.abspath(
os.path.join(
os.path.dirname(
os.path.abspath(inspect.getfile(inspect.currentframe()))),
'data_sets'))
filename = os.path.join(dataDirectory, 'Dij.txt')
D_frame = read_spectral_data_from_txt(filename)
# Then we build dae block for as described in the section 4.2.1. Note the addition
# of the data using .add_spectral_data
#################################################################################
builder = TemplateBuilder()
components = {'A': 1e-3, 'B': 0, 'C': 0}
builder.add_mixture_component(components)
builder.add_parameter('k1', init=4.0, bounds=(0.0, 5.0))
#There is also the option of providing initial values: Just add init=... as additional argument as above.
builder.add_parameter('k2', bounds=(0.0, 1.0))
builder.add_spectral_data(D_frame)
# define explicit system of ODEs
def rule_odes(m, t):
exprs = dict()
exprs['A'] = -m.P['k1'] * m.Z[t, 'A']
exprs['B'] = m.P['k1'] * m.Z[t, 'A'] - m.P['k2'] * m.Z[t, 'B']
exprs['C'] = m.P['k2'] * m.Z[t, 'B']
return exprs
# \frac{dC_b}{dt} = k*C_a C_b(0) = 0
from kipet.library.TemplateBuilder import *
from kipet.library.CasadiSimulator import *
import matplotlib.pyplot as plt
import sys
if __name__ == "__main__":
with_plots = True
if len(sys.argv) == 2:
if int(sys.argv[1]):
with_plots = False
# create template model
builder = TemplateBuilder()
builder.add_mixture_component('A', 1)
builder.add_mixture_component('B', 0)
builder.add_algebraic_variable('ra')
builder.add_parameter('k', 0.01)
# define explicit system of ODEs
def rule_odes(m, t):
exprs = dict()
exprs['A'] = -m.Y[t, 'ra']
exprs['B'] = m.Y[t, 'ra']
return exprs
builder.set_odes_rule(rule_odes)
def rule_algebraics(m, t):
#USER INPUT SECTION - REQUIRED MODEL BUILDING ACTIONS
#=========================================================================
# Load spectral data
#################################################################################
dataDirectory = os.path.abspath(
os.path.join(
os.path.dirname(
os.path.abspath(inspect.getfile(inspect.currentframe()))),
'data_sets'))
filename = os.path.join(dataDirectory, 'Dij.txt')
D_frame = read_spectral_data_from_txt(filename)
# build dae block for optimization problems
#################################################################################
builder = TemplateBuilder()
components = {'A': 1e-3, 'B': 0, 'C': 0}
builder.add_mixture_component(components)
builder.add_parameter('k1', bounds=(0.1, 4.0))
builder.add_parameter('k2', bounds=(0.01, 1.0))
builder.add_spectral_data(D_frame)
# define explicit system of ODEs
def rule_odes(m, t):
exprs = dict()
exprs['A'] = -m.P['k1'] * m.Z[t, 'A']
exprs['B'] = m.P['k1'] * m.Z[t, 'A'] - m.P['k2'] * m.Z[t, 'B']
exprs['C'] = m.P['k2'] * m.Z[t, 'B']
return exprs
builder.set_odes_rule(rule_odes)
from kipet.library.TemplateBuilder import *
from kipet.library.CasadiSimulator import *
import matplotlib.pyplot as plt
import casadi as ca
import sys
if __name__ == "__main__":
with_plots = True
if len(sys.argv) == 2:
if int(sys.argv[1]):
with_plots = False
# create template model
builder = TemplateBuilder()
builder.add_mixture_component('A', 6.7)
builder.add_mixture_component('B', 20.0)
builder.add_mixture_component('C', 0.0)
builder.add_complementary_state_variable('T', 290.0)
builder.add_parameter('k_p', 3.734e7)
# define explicit system of ODEs
def rule_odes(m, t):
r = -m.P['k_p'] * ca.exp(
-15400.0 / (1.987 * m.X[t, 'T'])) * m.Z[t, 'A'] * m.Z[t, 'B']
T1 = 45650.0 * (-r * 0.01) / 28.0
#T2 = ca.if_else(m.X[t,'T']>328.0,0.0,2.0)
T2 = 1 + (328.0 - m.X[t, 'T']) / (
filename1 = os.path.join(dataDirectory, 'multexp4D.csv')
filename2 = os.path.join(dataDirectory, 'multexp5D.csv')
D_frame1 = read_spectral_data_from_csv(filename1, negatives_to_zero=True)
D_frame2 = read_spectral_data_from_csv(filename2, negatives_to_zero=True)
# We wish to add a third dataset that is just a more noisey version of 2
D_frame3 = add_noise_to_signal(D_frame2, 0.00001)
# If you have multiple experiments, you need to add your experimental datasets to a dictionary:
datasets = {'Exp1': D_frame1, 'Exp2': D_frame2, 'Exp3': D_frame3}
#=========================================================================
# EXPERIMENT 1
#=========================================================================
#Initial conditions
builder1 = TemplateBuilder()
components1 = dict()
components1['A'] = 1e-3
components1['B'] = 0.0
components1['C'] = 0.0
components1['D'] = 0.0
components1['E'] = 0
builder1.add_mixture_component(components1)
builder1.add_parameter('k1', init=1.5, bounds=(0.001, 10))
builder1.add_parameter('k2', init=0.2, bounds=(0.0001, 5))
builder1.add_parameter('k3', init=0.4, bounds=(0.3, 2))
# Notice that, although we will not have any reaction for D and E, we still add this equation
# This model acts as the main model for all experimental datasets
#=========================================================================
# Load spectral data from the relevant file location. As described in section 4.3.1
#################################################################################
dataDirectory = os.path.abspath(
os.path.join(
os.path.dirname(
os.path.abspath(inspect.getfile(inspect.currentframe()))),
'data_sets'))
filename = os.path.join(dataDirectory, 'Ex_1_C_data.txt')
C_frame = read_concentration_data_from_txt(filename)
# Then we build dae block for as described in the section 4.2.1. Note the addition
# of the data using .add_spectral_data
#################################################################################
builder = TemplateBuilder()
components = {'A': 1e-3, 'B': 0, 'C': 0}
builder.add_mixture_component(components)
builder.add_parameter('k1', bounds=(0.0, 5.0))
builder.add_parameter('k2', bounds=(0.0, 1.0))
builder.add_concentration_data(C_frame)
# define explicit system of ODEs
def rule_odes(m, t):
exprs = dict()
exprs['A'] = -m.P['k1'] * m.Z[t, 'A']
exprs['B'] = m.P['k1'] * m.Z[t, 'A'] - m.P['k2'] * m.Z[t, 'B']
exprs['C'] = m.P['k2'] * m.Z[t, 'B']
return exprs
builder.set_odes_rule(rule_odes)
if len(sys.argv) == 2:
if int(sys.argv[1]):
with_plots = False
# read 200x500 D matrix
# this defines the measurement points t_i and l_j as well
dataDirectory = os.path.abspath(
os.path.join(
os.path.dirname(
os.path.abspath(inspect.getfile(inspect.currentframe()))),
'..', '..', 'data_sets'))
filename = os.path.join(dataDirectory, 'Slk_sawall.txt')
S_frame = read_absorption_data_from_txt(filename)
# create template model
builder = TemplateBuilder()
builder.add_mixture_component({'A': 1, 'B': 0})
builder.add_parameter('k', 0.01)
# includes spectra data in the template and defines measurement sets
builder.add_absorption_data(S_frame)
builder.add_measurement_times([i for i in range(200)])
# define explicit system of ODEs
def rule_odes(m, t):
exprs = dict()
exprs['A'] = -m.P['k'] * m.Z[t, 'A']
exprs['B'] = m.P['k'] * m.Z[t, 'A']
return exprs
builder.set_odes_rule(rule_odes)
from kipet.library.TemplateBuilder import *
from kipet.library.PyomoSimulator import *
import matplotlib.pyplot as plt
import sys
import os
if __name__ == "__main__":
with_plots = True
if len(sys.argv) == 2:
if int(sys.argv[1]):
with_plots = False
# create template model
builder = TemplateBuilder()
components = {'A': 217.324e-3, 'B': 167.35e-3, 'C': 2.452e-3}
builder.add_mixture_component(components)
builder.add_parameter('k1', 0.00539048)
# define explicit system of ODEs
def rule_odes(m, t):
exprs = dict()
exprs['A'] = -m.P['k1'] * m.Z[t, 'A'] * m.Z[t, 'B']
exprs['B'] = -m.P['k1'] * m.Z[t, 'A'] * m.Z[t, 'B']
exprs['C'] = m.P['k1'] * m.Z[t, 'A'] * m.Z[t, 'B']
return exprs
builder.set_odes_rule(rule_odes)
# create an instance of a pyomo model template
import matplotlib.pyplot as plt
from pyomo.core import *
import sys
if __name__ == "__main__":
with_plots = True
if len(sys.argv) == 2:
if int(sys.argv[1]):
with_plots = False
# create template model
species = {'A': 6.7, 'B': 20.2, 'C': 0.0}
params = {'k_p': 3.734e7}
builder = TemplateBuilder(concentrations=species, parameters=params)
builder.add_complementary_state_variable('T', 290.0)
# define explicit system of ODEs
def rule_odes(m, t):
r = m.P['k_p'] * exp(-15400.0 /
(1.987 * m.X[t, 'T'])) * m.Z[t, 'A'] * m.Z[t, 'B']
T1 = 45650.0 * (r * 0.01) / 28.0
#T2 = Expr_if(IF=m.X[t,'T']>328.0, THEN=0.0, ELSE=2.0)
T2 = 1 + (328.0 - m.X[t, 'T']) / (
(328.0 - m.X[t, 'T'])**2 + 1e-5**2)**0.5
exprs = dict()
exprs['A'] = -r
exprs['B'] = -r
exprs['C'] = r
import os
import six
if __name__ == "__main__":
with_plots = True
if len(sys.argv) == 2:
if int(sys.argv[1]):
with_plots = False
#=========================================================================
#USER INPUT SECTION - REQUIRED MODEL BUILDING ACTIONS
#=========================================================================
# create template model
builder = TemplateBuilder()
# components
components = dict()
components['AH'] = 0.395555
components['B'] = 0.0351202
components['C'] = 0.0
components['BH+'] = 0.0
components['A-'] = 0.0
components['AC-'] = 0.0
components['P'] = 0.0
builder.add_mixture_component(components)
# add algebraics
algebraics = ['0', '1', '2', '3', '4', '5', 'k4T',
import inspect
import six
import pandas as pd
if __name__ == "__main__":
with_plots = True
if len(sys.argv)==2:
if int(sys.argv[1]):
with_plots = False
#=========================================================================
# SIMULATION MODEL
#=========================================================================
# First we will look to generate our data for each of our components in order
# to build the C-matrix that we desire. We will look to give our species different
# noise levels in order to also test whether the scaling for multiple levels works
builder = TemplateBuilder()
builder.add_mixture_component('A',0.5)
builder.add_mixture_component('B',0.0)
builder.add_mixture_component('C',0.0)
builder.add_mixture_component('D',0.01)
builder.add_mixture_component('E',0.0)
builder.add_mixture_component('F',0.3)
builder.add_mixture_component('G',0.5)
builder.add_mixture_component('H',0.0)
#Following this we add the kinetic parameters
builder.add_parameter('k1',0.3)
builder.add_parameter('k2',0.1)
builder.add_parameter('k3',0.1)
builder.add_parameter('k4',0.4)
builder.add_parameter('k5',0.02)
