.. include:: links.inc

Basics
======

The `astropy.modeling` package defines a number of models that are collected
under a single namespace as ``astropy.modeling.models``.  Models behave like
parametrized functions::

    >>> import numpy as np
    >>> from astropy.modeling import models
    >>> g = models.Gaussian1D(amplitude=1.2, mean=0.9, stddev=0.5)
    >>> print(g)
    Model: Gaussian1D
    Inputs: ('x',)
    Outputs: ('y',)
    Model set size: 1
    Parameters:
        amplitude mean stddev
        --------- ---- ------
              1.2  0.9    0.5

Model parameters can be accessed as attributes::

    >>> g.amplitude
    Parameter('amplitude', value=1.2)
    >>> g.mean
    Parameter('mean', value=0.9)
    >>> g.stddev  # doctest: +FLOAT_CMP
    Parameter('stddev', value=0.5, bounds=(1.1754943508222875e-38, None))

and can also be updated via those attributes::

    >>> g.amplitude = 0.8
    >>> g.amplitude
    Parameter('amplitude', value=0.8)

Models can be evaluated by calling them as functions::

    >>> g(0.1)
    0.22242984036255528
    >>> g(np.linspace(0.5, 1.5, 7))  # doctest: +FLOAT_CMP
    array([0.58091923, 0.71746405, 0.7929204 , 0.78415894, 0.69394278,
           0.54952605, 0.3894018 ])

As the above example demonstrates, in general most models evaluate array-like
inputs according to the standard `Numpy broadcasting rules`_ for arrays.

Models can therefore already be useful to evaluate common functions,
independently of the fitting features of the package.