Metadata-Version: 1.1
Name: symfit
Version: 0.3.3
Summary: Symbolic Fitting; fitting as it should be.
Home-page: https://github.com/tBuLi/symfit
Author: Martin Roelfs
Author-email: m.roelfs@student.rug.nl
License: MIT
Description: 
        Documentation: http://symfit.readthedocs.org/
        
        This project aims to marry the power of ``scipy.optimize`` with the readability of ``SymPy`` to create a highly readable and easy to use fitting package which works for projects of any scale.
        
        ``symfit`` is designed to be very readable::
        
        	x = variables('x')
        	A, sig, x0 = parameters('A, sig, x0')
        
        	# Gaussian distribution
        	gaussian = A * exp(-(x - x0)**2 / (2 * sig**2))
        
        	fit = Fit(gaussian, xdata, ydata)
        	fit_result = fit.execute()
        
        You can also name dependent variables, allowing for sexy assignment of data::
        
        	x, y = variables('x, y')
        	model = {y: a * x**2}
        
        	fit = Fit(model, x=xdata, y=ydata, sigma_y=sigma)
        	fit.execute()
        
        Constraint maximization has never been this easy::
        
        	x, y = parameters('x, y')
        	model = 2*x*y + 2*x - x**2 -2*y**2
        	constraints = [
        	    Eq(x**3 - y, 0),
        	    Ge(y - 1, 0),
        	]
        
        	fit = Maximize(model, constraints=constraints)
        	fit_result = fit.execute()
        
        And evaluating a model with the best fit parameters is easy since ``symfit`` expressions are callable::
        
        	y = gaussian(x=xdata, **fit_result.params)
        
        .. figure:: http://symfit.readthedocs.org/en/latest/_images/gaussian_intro.png
           :width: 500px
           :alt: Gaussian Data
        
        For many more features such as bounds on ``Parameter``'s, maximum-likelihood fitting, and much more check the docs at http://symfit.readthedocs.org/.
        
        You can find ``symfit`` on github at https://github.com/tBuLi/symfit.
        
Keywords: fit fitting symbolic
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
