lsst.meas.base.diaCalculationPlugins Namespace Reference

Classes
class	Chi2DiaPsfFlux
class	Chi2DiaPsfFluxConfig
class	ErrMeanDiaPsfFlux
class	ErrMeanDiaPsfFluxConfig
class	HTMIndexDiaPosition
class	HTMIndexDiaPositionConfig
class	LinearFitDiaPsfFlux
class	LinearFitDiaPsfFluxConfig
class	LombScarglePeriodogram
class	LombScarglePeriodogramConfig
class	LombScarglePeriodogramMulti
class	LombScarglePeriodogramMultiConfig
class	MadDiaPsfFlux
class	MadDiaPsfFluxConfig
class	MaxSlopeDiaPsfFlux
class	MaxSlopeDiaPsfFluxConfig
class	MeanDiaPosition
class	MeanDiaPositionConfig
class	MinMaxDiaPsfFlux
class	MinMaxDiaPsfFluxConfig
class	NumDiaSourcesDiaPlugin
class	NumDiaSourcesDiaPluginConfig
class	PercentileDiaPsfFlux
class	PercentileDiaPsfFluxConfig
class	SigmaDiaPsfFlux
class	SigmaDiaPsfFluxConfig
class	SigmaDiaTotFlux
class	SigmaDiaTotFluxConfig
class	SimpleSourceFlagDiaPlugin
class	SimpleSourceFlagDiaPluginConfig
class	SkewDiaPsfFlux
class	SkewDiaPsfFluxConfig
class	StetsonJDiaPsfFlux
class	StetsonJDiaPsfFluxConfig
class	WeightedMeanDiaPsfFlux
class	WeightedMeanDiaPsfFluxConfig
class	WeightedMeanDiaTotFlux
class	WeightedMeanDiaTotFluxConfig

Functions
	catchWarnings (_func=None, *, warns=[])
	compute_optimized_periodogram_grid (x0, oversampling_factor=5, nyquist_factor=100)

Detailed Description

Plugins for use in DiaSource summary statistics.

Output columns must be
as defined in the schema of the Apdb both in name and units.

Function Documentation

catchWarnings()

lsst.meas.base.diaCalculationPlugins.catchWarnings	(		_func = None,
		*	,
			warns = [] )

Decorator for generically catching numpy warnings.

Definition at line 71 of file diaCalculationPlugins.py.

def catchWarnings(_func=None, *, warns=[]):
    """Decorator for generically catching numpy warnings.
    """
    def decoratorCatchWarnings(func):
        @functools.wraps(func)
        def wrapperCatchWarnings(*args, **kwargs):
            with warnings.catch_warnings():
                for val in warns:
                    warnings.filterwarnings("ignore", val)
                return func(*args, **kwargs)
        return wrapperCatchWarnings
 
    if _func is None:
        return decoratorCatchWarnings
    else:
        return decoratorCatchWarnings(_func)
 
 

compute_optimized_periodogram_grid()

lsst.meas.base.diaCalculationPlugins.compute_optimized_periodogram_grid	(		x0,
			oversampling_factor = 5,
			nyquist_factor = 100 )

Computes an optimized periodogram frequency grid for a given time series.

Parameters
----------
x0 : `array`
    The input time axis.
oversampling_factor : `int`, optional
    The oversampling factor for frequency grid.
nyquist_factor : `int`, optional
    The Nyquist factor for frequency grid.

Returns
-------
frequencies : `array`
    The computed optimized periodogram frequency grid.

Definition at line 89 of file diaCalculationPlugins.py.

def compute_optimized_periodogram_grid(x0, oversampling_factor=5, nyquist_factor=100):
    """
    Computes an optimized periodogram frequency grid for a given time series.
 
    Parameters
    ----------
    x0 : `array`
        The input time axis.
    oversampling_factor : `int`, optional
        The oversampling factor for frequency grid.
    nyquist_factor : `int`, optional
        The Nyquist factor for frequency grid.
 
    Returns
    -------
    frequencies : `array`
        The computed optimized periodogram frequency grid.
    """
 
    num_points = len(x0)
    baseline = np.max(x0) - np.min(x0)
 
    # Calculate the frequency resolution based on oversampling factor and baseline
    frequency_resolution = 1. / baseline / oversampling_factor
 
    num_frequencies = int(
        0.5 * oversampling_factor * nyquist_factor * num_points)
    frequencies = frequency_resolution + \
        frequency_resolution * np.arange(num_frequencies)
 
    return frequencies