# SPDX-License-Identifier: MIT
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from symfit import variables, Parameter, Fit, D, ODEModel
import numpy as np
from symfit.contrib.interactive_guess import InteractiveGuess
# First order reaction kinetics. Data taken from
# http://chem.libretexts.org/Core/Physical_Chemistry/Kinetics/Rate_Laws/The_Rate_Law
tdata = np.array([0, 0.9184, 9.0875, 11.2485, 17.5255, 23.9993, 27.7949,
31.9783, 35.2118, 42.973, 46.6555, 50.3922, 55.4747, 61.827,
65.6603, 70.0939])
concentration = np.array([0.906, 0.8739, 0.5622, 0.5156, 0.3718, 0.2702, 0.2238,
0.1761, 0.1495, 0.1029, 0.086, 0.0697, 0.0546, 0.0393,
0.0324, 0.026])
# Define our ODE model
A, t = variables('A, t')
k = Parameter('k')
model = ODEModel({D(A, t): - k * A}, initial={t: tdata[0], A: concentration[0]})
guess = InteractiveGuess(model, A=concentration, t=tdata, n_points=250)
guess.execute()
print(guess)
fit = Fit(model, A=concentration, t=tdata)
fit_result = fit.execute()
print(fit_result)
Created on Mon Dec 7 11:28:58 2015
@author: peterkroon
"""
from symfit import Variable, Parameter, exp, Fit, Model
from symfit.contrib.interactive_guess import InteractiveGuess
import numpy as np
def distr(x, k, x0):
kbT = 4.11
return exp(-k * (x - x0)**2 / kbT)
x = Variable('x')
y = Variable('y')
k = Parameter('k', 900)
x0 = Parameter('x0', 1.5)
model = Model({y: distr(x, k, x0)})
x_data = np.linspace(0, 2.5, 50)
y_data = model(x=x_data, k=1000, x0=1).y
guess = InteractiveGuess(model, x=x_data, y=y_data, n_points=150)
guess.execute()
print(guess)
fit = Fit(model, x=x_data, y=y_data)
fit_result = fit.execute(maxiter=1000)
print(fit_result)
from symfit import variables, Parameter, exp, Fit, Model
from symfit.distributions import Gaussian
from symfit.contrib.interactive_guess import InteractiveGuess
import numpy as np
x, y, z = variables('x, y, z')
mu_x = Parameter('mu_x', 10)
mu_y = Parameter('mu_y', 10)
sig_x = Parameter('sig_x', 1)
sig_y = Parameter('sig_y', 1)
model = Model({z: Gaussian(x, mu_x, sig_x) * Gaussian(y, mu_y, sig_y)})
x_data = np.linspace(0, 25, 50)
y_data = np.linspace(0, 25, 50)
x_data, y_data = np.meshgrid(x_data, y_data)
x_data = x_data.flatten()
y_data = y_data.flatten()
z_data = model(x=x_data, y=y_data, mu_x=5, sig_x=0.3, mu_y=10, sig_y=1).z
guess = InteractiveGuess(model, x=x_data, y=y_data, z=z_data)
guess.execute()
print(guess)
fit = Fit(model, x=x_data, y=y_data, z=z_data)
fit_result = fit.execute()
print(fit_result)
sigma: sigma.value
}),
modules=[{
'ImmutableMatrix': ndarray
}, 'numpy', 'scipy'])
x_finer = r_[d.getaxis('$B_0$')[0]:d.getaxis('$B_0$')[-1]:500j]
result = model_lambda(x_finer)
guess_nddata = nddata(result, [-1],
['$B_0$']).setaxis('$B_0$', x_finer).set_units(
'$B_0$', d.get_units('$B_0$'))
plot(d, label='data')
plot(guess_nddata, ':', label='guess')
model = s.Model({y_var: dVoigt})
if interactive:
guess = InteractiveGuess(model,
y=d.data.real,
B=d.getaxis('$B_0$'),
n_points=500)
guess.execute()
logger.info(strm(guess))
y_var = s.Variable('y')
logger.info(strm("about to run fit"))
fit = s.Fit(
model, d.getaxis('$B_0$'), d.data.real
) # really want to use minpack here, but gives "not proper array of floats
fit_result = fit.execute()
logger.info(strm("fit is done"))
plt.figure()
plt.title('data with fit')
plot(d, '.', label='data')
fit_nddata = nddata(
fit.model(B=x_finer, **fit_result.params).y, [-1],
# -*- coding: utf-8 -*-
from symfit import variables, Parameter, exp, Fit, Model
from symfit.distributions import Gaussian
from symfit.contrib.interactive_guess import InteractiveGuess
import numpy as np
x, y, z = variables('x, y, z')
mu_x = Parameter('mu_x', 10)
mu_y = Parameter('mu_y', 10)
sig_x = Parameter('sig_x', 1)
sig_y = Parameter('sig_y', 1)
model = Model({z: Gaussian(x, mu_x, sig_x) * Gaussian(y, mu_y, sig_y)})
x_data = np.linspace(0, 25, 50)
y_data = np.linspace(0, 25, 50)
x_data, y_data = np.meshgrid(x_data, y_data)
x_data = x_data.flatten()
y_data = y_data.flatten()
z_data = model(x=x_data, y=y_data, mu_x=5, sig_x=0.3, mu_y=10, sig_y=1).z
guess = InteractiveGuess(model, x=x_data, y=y_data, z=z_data)
guess.execute()
print(guess)
fit = Fit(model, x=x_data, y=y_data, z=z_data)
fit_result = fit.execute()
print(fit_result)
#
# SPDX-License-Identifier: MIT
# -*- coding: utf-8 -*-
from symfit import Variable, Parameter, Fit, Model
from symfit.contrib.interactive_guess import InteractiveGuess
import numpy as np
x = Variable('x')
y1 = Variable('y1')
y2 = Variable('y2')
k = Parameter('k', 900)
x0 = Parameter('x0', 1.5)
model = {y1: k * (x - x0)**2, y2: x - x0}
model = Model(model)
# Generate example data
x_data = np.linspace(0, 2.5, 50)
data = model(x=x_data, k=1000, x0=1)
y1_data = data.y1
y2_data = data.y2
guess = InteractiveGuess(model, x=x_data, y1=y1_data, y2=y2_data, n_points=250)
guess.execute()
print(guess)
fit = Fit(model, x=x_data, y1=y1_data, y2=y2_data)
fit_result = fit.execute()
print(fit_result)