oimFitter
model fitting
Classes:
|
|
|
|
|
A multinested fitter that has generally a better coverage of the global (than MCMC) parameter space. |
|
|
|
Functions:
|
- class oimodeler.oimFitter.oimFitter(*args, **kwargs)
- params = {}
- description = 'Abstract class for model-fitting'
- _eval(**kwargs)
- prepare(**kwargs)
- run(**kwargs)
- getResults(**kwargs)
- printResults(format: str = '.5f', **kwargs)
- _prepare(**kwargs)
- _run(**kwargs)
- class oimodeler.oimFitter.oimFitterEmcee(*args, **kwargs)
- description = 'MCMC sampler (based on the emcee python package)'
- _prepare(**kwargs)
- _initGaussian()
- _initRandom()
- _initFixed()
- _run(**kwargs)
- _logProbability(theta)
- getResults(mode: str = 'best', discard: int = 0, thin: int = 1, chi2limfact: int | float = 20, **kwargs)
- cornerPlot(discard: int = 0, thin: int = 1, chi2limfact: int | float = 20, savefig: str | Path | None = None, **kwargs)
- walkersPlot(discard: int = 0, thin: int = 1, chi2limfact: int | float = 20, savefig: str | Path | None = None, labelsize: int = 10, ncolors: int = 128, **kwargs)
- class oimodeler.oimFitter.oimFitterDynesty(*args, **kwargs)
A multinested fitter that has generally a better coverage of the global (than MCMC) parameter space.
- description = 'Dynamic/static nested sampler (based on the the dynesty package)'
- _prepare(**kwargs)
Prepares the dynesty fitter.
- _run(**kwargs)
- _ptform(uniform_samples: ndarray) ndarray
The transformation for uniform sampled values to the uniform parameter space.
- _logProbability(theta: ndarray) float
The log probability.
- getResults(mode: str = 'median', **kwargs)
- cornerPlot(savefig: str | Path | None = None, **kwargs)
- walkersPlot(savefig: str | Path | None = None, **kwargs)
- class oimodeler.oimFitter.oimFitterMinimize(*args, **kwargs)
- description = 'a simple :math:`\\chi^2` minimizer using the numpy Minimize function'
- _prepare(**kwargs)
- _getChi2r(theta: ndarray) float
- _run(**kwargs)
- getResults(**kwargs)
- printResults(format: str = '.5f', **kwargs)
- class oimodeler.oimFitter.oimFitterRegularGrid(*args, **kwargs)
- description = 'regular grid with :math:`\\chi^2` explorer'
- _prepare(**kwargs)
- _run(**kwargs)
- getResults(**kwargs)
- printResults(format: str = '.5f', **kwargs)
- plotMap(params=None, fixedValues: str = 'best', plotContour: bool = False, plotMinLines: bool = False, plotMin: bool = True, minLines_kwargs: Dict = {}, contour_kwargs: Dict = {}, clabel_kwargs: Dict = {}, min_kwargs: Dict = {}, axe: Axes | None = None, **kwargs)
- oimodeler.oimFitter.oimComputeChi2PlusOneUncertainties(fit, factErr: int = 500, npts: int = 100, plot: bool = False, dataTypes=None)