NormalizerSimultan

class ompy.NormalizerSimultan(*, gsf=None, nld=None, normalizer_nld=None, normalizer_gsf=None, path='saved_run/normalizers', regenerate=False)

Bases: AbstractNormalizer

Simultaneous normalization of nld and gsf. Composed of Normalizer and NormalizerGSF as input, so read more on the normalization there

Variables:

• extractor (Extractor) – Extractor instance

• gsf (Optional[Vector], optional) – gsf to normalize

• multinest_path (Path, optional) – Default path where multinest saves files

• multinest_kwargs (dict) – Additional keywords to multinest. Defaults to {“seed”: 65498, “resume”: False}

• nld (Optional[Vector], optional) – nld to normalize

• normalizer_nld (NormalizerNLD) – NormalizerNLD instance to get the normalization paramters

• normalizer_gsf (NormalizerGSF) – NormalizerGSF instance to get the normalization paramters

• res (ResultsNormalized) – Results

• std_fake_gsf (bool) – Whether the std. deviation is faked (see normalize)

• std_fake_nld (bool) – Whether the std. deviation is faked (see normalize)

• path (Path) – The path save the results.

Todo

Work with more general models, too, not just CT for nld

Todo

• currently have to set arguments here, an cannot set them in “normalize”

Parameters:

• gsf (optional) – see above

• nld (optional) – see above

• normalizer_nld (optional) – see above

• normalizer_gsf (optional) – see above

Attributes Summary

LOG

Methods Summary

initial_guess()	Find an inital guess for normalization parameters
lnlike(x, args_nld)	Compute log likelihood of the normalization fitting
load([path])	Loads (pickeled) instance.
normalize(*[, num, gsf, nld, ...])	Perform normalization and saves results to self.res
optimize(num, args_nld, guess)	Find parameters given model constraints and an initial guess
plot([ax, add_label, add_figlegend])	Plots nld and gsf
save([path, overwrite])	Save (pickels) the instance
save_results_txt([path, nld, gsf, samples, ...])	Save results as txt
self_if_none(*args, **kwargs)	wrapper for lib.self_if_none

Attributes Documentation

LOG = <Logger ompy.normalizer_simultan (WARNING)>

Methods Documentation

initial_guess()

Find an inital guess for normalization parameters

Uses guess of normalizer_nld and corresponding normalization of gsf

Return type:

None

Returns:

The arguments used for chi^2 minimization and the minimizer.

lnlike(x, args_nld)

Compute log likelihood of the normalization fitting

This is the result up to the constant, which is irrelevant for the maximization

Parameters:

• x (Tuple[float, float, float, float, float]) – The arguments ordered as A, alpha, T and Eshift, B

• args_nld (TYPE) – Additional arguments for the nld lnlike

Returns:

log likelihood

Return type:

lnlike

load(path=None)

Loads (pickeled) instance.

Such that it can be loaded if regenerate = False. Note that if any modifications of the __getstate__ method are present, these will effect what attributes are pickeled.

Parameters:

path (Union[str, Path, None]) – The path to the directoryto load file. If the value is None, ‘self.path’ will be used.

Raises:

FileNotFoundError – If file is not found

normalize(*, num=0, gsf=None, nld=None, normalizer_nld=None, normalizer_gsf=None)

Perform normalization and saves results to self.res

Parameters:

• num (int, optional) – Loop number

• gsf (Optional[Vector], optional) – gsf before normalization

• nld (Optional[Vector], optional) – nld before normalization

• normalizer_nld (Optional[NormalizerNLD], optional) – NormalizerNLD instance

• normalizer_gsf (Optional[NormalizerGSF], optional) – NormalizerGSF instance

Return type:

None

optimize(num, args_nld, guess)

Find parameters given model constraints and an initial guess

Employs Multinest.

Parameters:

• num (int) – Loop number

• args_nld (Iterable) – Additional arguments for the nld lnlike

• guess (Dict[str, float]) – The initial guess of the parameters

Returns:

• popt (Dict[str, Tuple[float, float]]): Median and 1sigma of the

  parameters

• samples (Dict[str, List[float]]): Multinest samplesø.

  Note: They are still importance weighted, not random draws from the posterior.

Return type:

Tuple

Raises:

ValueError – Invalid parameters for automatix prior

Note

You might want to adjust the priors for your specific case! Here we just propose a general solution that might often work out of the box.

plot(ax=None, add_label=True, add_figlegend=True, **kwargs)

Plots nld and gsf

Parameters:

• ax (optional) – The matplotlib axis to plot onto. Creates axis is not provided

• add_label (bool, optional) – Defaults to True.

• add_figlegend (bool, optional) – Defaults to True.

• Optional[ResultsNormalized] (results) – If provided, gsf and model are taken from here instead.

• **kwargs – kwargs for plot

Return type:

Tuple[Any, Any]

Returns:

fig, ax

save(path=None, overwrite=True)

Save (pickels) the instance

Such that it can be loaded, and enabling the regenerate later.

Parameters:

• path (Union[str, Path, None]) – The path to the save directory. If the value is None, ‘self.path’ will be used.

• overwrite (bool) – Overwrite file if existent

save_results_txt(path=None, nld=None, gsf=None, samples=None, suffix=None)

Save results as txt

Uses a folder to save nld, gsf, and the samples (converted to an array)

Parameters:

• path (Union[str, Path, None]) – The path to the save directory. If the value is None, ‘self.path’ will be used.

• nld (Optional[Vector]) – (unnormalized) NLD to save

• gsf (Optional[Vector]) – (unnormalized) gSF to save

• samples (Optional[dict]) – samples to use for normalization

• suffix (Optional[str]) – suffix to append to filename, eg. iteration number

self_if_none(*args, **kwargs)

wrapper for lib.self_if_none