SlimFit Documentation

slimfit is inspired by symfit and internally also depends on sympy but has some differences in API and functionality.

Currently, slimfit is very barebones and supports only basic fitting, but advanced features such as analytical calculation of jacobians, error estimation or constraints are not included.

Quick Start

from sympy import symbols
from slimfit import Model, Fit, Parameter
import numpy as np

# Generate some data
xdata = np.linspace(0, 11, 25)
ydata = 0.5*xdata + 2.5
ydata += np.random.normal(0, scale= ydata / 10.0 + 0.2)
data = {'x': xdata, 'y': ydata}

# Define model and parameters
y, a, x, b = symbols('y a x b')
model = Model({y: a*x + b})
parameters = [
    Parameter(a, guess=2.5),
    Parameter(b, guess=1, lower_bound=0.)
]

# Fit the model
fit = Fit(model, parameters, data)
result = fit.execute()

print(result.parameters)