lsst.gauss2d.fit¶
gauss2d_fit is a submodule for gauss2d that implements a 2D Gaussian mixture model class, along with its constituent parts, including parameters and data. The Model class can evaluate the likelihood and gradients thereof. gauss2d_fit does not yet provide optimizers, although support for GSL fitters is planned. Users should turn to MultiProFit for access to Python (scipy) optimizers.
Gaussian mixture approximations to the Sersic profile are provided. Use of the GSL library is strongly recommended as it enables nonlinear interpolation of the profile weights, which is necessary to compute accurate derivatives of the likelihood and model for the Sersic index parameter.
Using lsst.gauss2d.fit¶
Example usage can be found in the unit tests and also in dependent packages, particularly MultiProFit.
Contributing¶
lsst.gauss2d.fit is developed at https://github.com/lsst-dm/gauss2d_fit.
You can find Jira issues for this module under the
gauss2d_fit
component.
Python API reference¶
lsst.gauss2d.fit has Python bindings for classes using numpy-based single
and double precision arrays. Support for GSL arrays is forthcoming with
DM-38617.
lsst.gauss2d.fit Package¶
Functions¶
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Classes¶
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