twopc package

Submodules

twopc.ppc module

twopc.ppc.compute_postpc(analysis: threeML.bayesian.bayesian_analysis.BayesianAnalysis, result: threeML.analysis_results.BayesianResults, n_sims: int, file_name: str, overwrite: bool = False, return_ppc: bool = False) → Optional[twopc.ppc.PPC]

Compute a posterior predictive check from a 3ML DispersionLike Plugin. The resulting posterior data simulations are stored in an HDF5 file which can be read by the PPC class

Parameters

• analysis – 3ML bayesian analysis object

• result – 3ML analysis result

• n_sims – the number of posterior simulations to create

• file_name – the filename to save to

• overwrite – to overwrite an existsing file

• return_ppc – if true, PPC object will be return directy

Returns

None

Return type

twopc.ppc.compute_priorpc(analysis: threeML.bayesian.bayesian_analysis.BayesianAnalysis, n_sims: int, file_name: str, parameters: Optional[List[str]] = None, overwrite: bool = False, return_ppc: bool = False) → Optional[twopc.ppc.PPC]

Compute a prior predictive check from a 3ML DispersionLike Plugin. The resulting prior data simulations are stored in an HDF5 file which can be read by the PPC class

Parameters

• analysis – 3ML bayesian analysis object

• n_sims – the number of posterior simulations to create

• file_name – the filename to save to

• parameters – optional parameter paths to only be used

• overwrite – to overwrite an existsing file

• return_ppc – if true, PPC object will be return directy

Returns

None

Return type

class twopc.ppc.PPC(file_name: str)

Bases: object

__init__(file_name: str)

Reads a PPC HDF5 created by compute_ppc. This applies to DispersionLike data types only. Each detector is read from the file and an associated detector attribute is added to the class allowing the user to access the observed and PPC information of the detector

param filename

the file name to read

returns

rtype

>

property n_sims: int

property detectors: List[str]

class twopc.ppc.PPCDetector(name: str, obs_counts: numpy.ndarray, obs_background: numpy.ndarray, mask: numpy.ndarray, ebounds: numpy.ndarray, exposure: float, scale_factor: float, ppc_counts, ppc_background)

Bases: object

__init__(name: str, obs_counts: numpy.ndarray, obs_background: numpy.ndarray, mask: numpy.ndarray, ebounds: numpy.ndarray, exposure: float, scale_factor: float, ppc_counts, ppc_background)

This is simply a container object that stores the observed and PPC information of each detector for examination

Parameters

• name –

• obs_counts –

• obs_background –

• mask –

• ebounds –

• exposure –

• ppc_counts –

• ppc_background –

Returns

Return type

property name: str

property obs_counts: numpy.ndarray

property obs_background: numpy.ndarray

property mask: numpy.ndarray

property ebounds: numpy.ndarray

property channel_width: numpy.ndarray

property exposure: float

property scale_factor: float

property ppc_counts: numpy.ndarray

property ppc_background: numpy.ndarray

get_integral_ppc(ene_min: float, ene_max: float) → Dict[str, Any]

check_ppc_qq(level) → bool

plot_qq(levels: List[float] = [95, 75, 55], colors: List[str] = ['#ABB2B9', '#566573', '#17202A'], center_color: str = '#FFD100', channel_energies: Optional[List[float]] = [30.0, 300.0, 500.0], level_check: Optional[float] = None, bad_color: str = '#FF6E3F', ax=None, **kwargs)

plot(bkg_subtract: bool = False, ax=None, levels: List[float] = [95, 75, 55], colors: List[str] = ['#ABB2B9', '#566573', '#17202A'], lc: str = '#FFD100', lw: float = 0.9, **kwargs)

FIXME! briefly describe function

Parameters

• bkg_subtract –

• ax –

• levels –

• 75 –

• 55] –

• colors –

• '#566573' –

• '#17202A'] –

• lc –

• lw –

Returns

Return type

twopc.ppc.slice_disjoint(arr)

Returns an array of disjoint indices from a bool array :param arr: and array of bools

Module contents