Metadata-Version: 1.1
Name: symfit
Version: 0.3.3.dev155
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
        
        Project Goals
        =============
        
        The goal of this project is simple: to make fitting in Python sexy and pythonic. What does pythonic fitting look like? 
        Well, there's a simple test. 
        If I can give you pieces of example code and don't have to use any additional words to explain what it does, it's pythonic.
        
        ::
        
            from symfit import parameters, variables, Fit
            
            xdata = [1.0, 2.0, 3.0, 4.0, 5.0]
            ydata = [2.3, 3.3, 4.1, 5.5, 6.7]
            yerr = [0.1, 0.1, 0.1, 0.1, 0.1]
            
            a, b = parameters('a, b')
            x, y = variables('x, y')
            model = {y: a * x + b}
            
            fit = Fit(model, x=xdata, y=ydata, sigma_y=yerr)
            fit_result = fit.execute()
        
        Cool right? So now that we have done a fit, how do can we use the results?
        
        ::
        
            import matplotlib.pyplot as plt
            
            y = model(x=xdata, **fit_result.params)
            plt.plot(xdata, y)
            plt.show()
        
        .. figure:: http://symfit.readthedocs.org/en/latest/_images/linear_model_fit.png
            :width: 600px
            :alt: Linear Fit
        
        Need I say more? How about I let another code example do the talking?
        
        ::
        
            from symfit import parameters, Maximize, Equality, GreaterThan
            
            x, y = parameters('x, y')
            model = 2 * x * y + 2 * x - x**2 - 2 * y**2
            constraints = [
                Equality(x**3, y),
                GreaterThan(y, 1),
            ]
            
            fit = Maximize(model, constraints=constraints)
            fit_result = fit.execute()
        
        "But what if I need to fit to an ODE?"
        
        ::
        
            from symfit import variables, Parameter, ODEModel, Fit, D
            
            tdata = np.array([10, 26, 44, 70, 120])
            adata = 10e-4 * np.array([44, 34, 27, 20, 14])
                    
            a, b, t = variables('a, b, t')
            k = Parameter(0.1)
            
            model_dict = {
                D(a, t): - k * a**2,
                D(b, t): k * a**2,
            }
            
            ode_model = ODEModel(model_dict, initial={t: 0.0, a: 54 * 10e-4, b: 0.0})
            
            fit = Fit(ode_model, t=tdata, a=adata, b=None)
            fit_result = fit.execute()
        
        For more fitting delight, check the docs at http://symfit.readthedocs.org.
        
        
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
Classifier: Programming Language :: Python :: 3.5
