Inheritance diagram for lsst.meas.base.diaCalculationPlugins.StetsonJDiaPsfFlux:

Public Member Functions
	getExecutionOrder (cls)

	calculate (self, diaObjects, diaSources, filterDiaSources, band, **kwargs)

Static Public Attributes
	ConfigClass = LinearFitDiaPsfFluxConfig

list	inputCols = ["psfFluxMean"]

list	outputCols = ["psfFluxStetsonJ"]

str	plugType = "multi"

bool	needsFilter = True

Protected Member Functions
	_stetson_J (self, fluxes, errors, mean=None)

	_stetson_mean (self, values, errors, mean=None, alpha=2., beta=2., n_iter=20, tol=1e-6)

Detailed Description

Compute the StetsonJ statistic on the DIA point source fluxes.

Definition at line 887 of file diaCalculationPlugins.py.

Member Function Documentation

◆ _stetson_J()

lsst.meas.base.diaCalculationPlugins.StetsonJDiaPsfFlux._stetson_J	(	self,
		fluxes,
		errors,
		mean = None )

protected

Compute the single band stetsonJ statistic.

Parameters
----------
fluxes : `numpy.ndarray` (N,)
    Calibrated lightcurve flux values.
errors : `numpy.ndarray` (N,)
    Errors on the calibrated lightcurve fluxes.
mean : `float`
    Starting mean from previous plugin.

Returns
-------
stetsonJ : `float`
    stetsonJ statistic for the input fluxes and errors.

References
----------
.. [1] Stetson, P. B., "On the Automatic Determination of Light-Curve
   Parameters for Cepheid Variables", PASP, 108, 851S, 1996

Definition at line 949 of file diaCalculationPlugins.py.

    def _stetson_J(self, fluxes, errors, mean=None):
        """Compute the single band stetsonJ statistic.
 
        Parameters
        ----------
        fluxes : `numpy.ndarray` (N,)
            Calibrated lightcurve flux values.
        errors : `numpy.ndarray` (N,)
            Errors on the calibrated lightcurve fluxes.
        mean : `float`
            Starting mean from previous plugin.
 
        Returns
        -------
        stetsonJ : `float`
            stetsonJ statistic for the input fluxes and errors.
 
        References
        ----------
        .. [1] Stetson, P. B., "On the Automatic Determination of Light-Curve
           Parameters for Cepheid Variables", PASP, 108, 851S, 1996
        """
        n_points = len(fluxes)
        flux_mean = self._stetson_mean(fluxes, errors, mean)
        delta_val = (
            np.sqrt(n_points / (n_points - 1)) * (fluxes - flux_mean) / errors)
        p_k = delta_val ** 2 - 1
 
        return np.mean(np.sign(p_k) * np.sqrt(np.fabs(p_k)))
 

◆ _stetson_mean()

lsst.meas.base.diaCalculationPlugins.StetsonJDiaPsfFlux._stetson_mean	(	self,
		values,
		errors,
		mean = None,
		alpha = 2.,
		beta = 2.,
		n_iter = 20,
		tol = 1e-6 )

protected

Compute the stetson mean of the fluxes which down-weights outliers.

Weighted biased on an error weighted difference scaled by a constant
(1/``a``) and raised to the power beta. Higher betas more harshly
penalize outliers and ``a`` sets the number of sigma where a weighted
difference of 1 occurs.

Parameters
----------
values : `numpy.dnarray`, (N,)
    Input values to compute the mean of.
errors : `numpy.ndarray`, (N,)
    Errors on the input values.
mean : `float`
    Starting mean value or None.
alpha : `float`
    Scalar down-weighting of the fractional difference. lower->more
    clipping. (Default value is 2.)
beta : `float`
    Power law slope of the used to down-weight outliers. higher->more
    clipping. (Default value is 2.)
n_iter : `int`
    Number of iterations of clipping.
tol : `float`
    Fractional and absolute tolerance goal on the change in the mean
    before exiting early. (Default value is 1e-6)

Returns
-------
mean : `float`
    Weighted stetson mean result.

References
----------
.. [1] Stetson, P. B., "On the Automatic Determination of Light-Curve
   Parameters for Cepheid Variables", PASP, 108, 851S, 1996

Definition at line 979 of file diaCalculationPlugins.py.

                      tol=1e-6):
        """Compute the stetson mean of the fluxes which down-weights outliers.
 
        Weighted biased on an error weighted difference scaled by a constant
        (1/``a``) and raised to the power beta. Higher betas more harshly
        penalize outliers and ``a`` sets the number of sigma where a weighted
        difference of 1 occurs.
 
        Parameters
        ----------
        values : `numpy.dnarray`, (N,)
            Input values to compute the mean of.
        errors : `numpy.ndarray`, (N,)
            Errors on the input values.
        mean : `float`
            Starting mean value or None.
        alpha : `float`
            Scalar down-weighting of the fractional difference. lower->more
            clipping. (Default value is 2.)
        beta : `float`
            Power law slope of the used to down-weight outliers. higher->more
            clipping. (Default value is 2.)
        n_iter : `int`
            Number of iterations of clipping.
        tol : `float`
            Fractional and absolute tolerance goal on the change in the mean
            before exiting early. (Default value is 1e-6)
 
        Returns
        -------
        mean : `float`
            Weighted stetson mean result.
 
        References
        ----------
        .. [1] Stetson, P. B., "On the Automatic Determination of Light-Curve
           Parameters for Cepheid Variables", PASP, 108, 851S, 1996
        """
        n_points = len(values)
        n_factor = np.sqrt(n_points / (n_points - 1))
        inv_var = 1 / errors ** 2
 
        if mean is None:
            mean = np.average(values, weights=inv_var)
        for iter_idx in range(n_iter):
            chi = np.fabs(n_factor * (values - mean) / errors)
            tmp_mean = np.average(
                values,
                weights=inv_var / (1 + (chi / alpha) ** beta))
            diff = np.fabs(tmp_mean - mean)
            mean = tmp_mean
            if diff / mean < tol and diff < tol:
                break
        return mean
 
 

◆ calculate()

lsst.meas.base.diaCalculationPlugins.StetsonJDiaPsfFlux.calculate	(		self,
			diaObjects,
			diaSources,
			filterDiaSources,
			band,
		**	kwargs )

Compute the StetsonJ statistic on the DIA point source fluxes.

Parameters
----------
diaObject : `dict`
    Summary object to store values in.
diaSources : `pandas.DataFrame`
    DataFrame representing all diaSources associated with this
    diaObject.
filterDiaSources : `pandas.DataFrame`
    DataFrame representing diaSources associated with this
    diaObject that are observed in the band pass ``band``.
band : `str`
    Simple, string name of the filter for the flux being calculated.
**kwargs
    Any additional keyword arguments that may be passed to the plugin.

Reimplemented from lsst.meas.base.diaCalculation.DiaObjectCalculationPlugin.

Definition at line 904 of file diaCalculationPlugins.py.

                  **kwargs):
        """Compute the StetsonJ statistic on the DIA point source fluxes.
 
        Parameters
        ----------
        diaObject : `dict`
            Summary object to store values in.
        diaSources : `pandas.DataFrame`
            DataFrame representing all diaSources associated with this
            diaObject.
        filterDiaSources : `pandas.DataFrame`
            DataFrame representing diaSources associated with this
            diaObject that are observed in the band pass ``band``.
        band : `str`
            Simple, string name of the filter for the flux being calculated.
        **kwargs
            Any additional keyword arguments that may be passed to the plugin.
        """
        meanName = "{}_psfFluxMean".format(band)
 
        def _stetsonJ(df):
            tmpDf = df[~np.logical_or(np.isnan(df["psfFlux"]),
                                      np.isnan(df["psfFluxErr"]))]
            if len(tmpDf) < 2:
                return np.nan
            fluxes = tmpDf["psfFlux"].to_numpy()
            errors = tmpDf["psfFluxErr"].to_numpy()
 
            return self._stetson_J(
                fluxes,
                errors,
                diaObjects.at[tmpDf.diaObjectId.iat[0], meanName])
 
        column = "{}_psfFluxStetsonJ".format(band)
        if column in diaObjects:
            dtype = diaObjects[column].dtype
            diaObjects.loc[:, column] = filterDiaSources.apply(_stetsonJ).astype(dtype)
        else:
            diaObjects.loc[:, column] = filterDiaSources.apply(_stetsonJ)
 

◆ getExecutionOrder()

lsst.meas.base.diaCalculationPlugins.StetsonJDiaPsfFlux.getExecutionOrder ( cls )

Used to set the relative order of plugin execution.

    The values returned by `getExecutionOrder` are compared across all
    plugins, and smaller numbers run first.

    Notes
    -----
    `CatalogCalculationPlugin`\s must run with
    `BasePlugin.DEFAULT_CATALOGCALCULATION` or higher.

    All plugins must implement this method with an appropriate run level

Reimplemented from lsst.meas.base.catalogCalculation.CatalogCalculationPlugin.

Definition at line 901 of file diaCalculationPlugins.py.

    def getExecutionOrder(cls):
        return cls.FLUX_MOMENTS_CALCULATED
 

Member Data Documentation

◆ ConfigClass

lsst.meas.base.diaCalculationPlugins.StetsonJDiaPsfFlux.ConfigClass = LinearFitDiaPsfFluxConfig

static

Definition at line 891 of file diaCalculationPlugins.py.

◆ inputCols

list lsst.meas.base.diaCalculationPlugins.StetsonJDiaPsfFlux.inputCols = ["psfFluxMean"]

static

Definition at line 894 of file diaCalculationPlugins.py.

◆ needsFilter

bool lsst.meas.base.diaCalculationPlugins.StetsonJDiaPsfFlux.needsFilter = True

static

Definition at line 898 of file diaCalculationPlugins.py.

◆ outputCols

list lsst.meas.base.diaCalculationPlugins.StetsonJDiaPsfFlux.outputCols = ["psfFluxStetsonJ"]

static

Definition at line 896 of file diaCalculationPlugins.py.

◆ plugType

str lsst.meas.base.diaCalculationPlugins.StetsonJDiaPsfFlux.plugType = "multi"

static

Definition at line 897 of file diaCalculationPlugins.py.

The documentation for this class was generated from the following file:

/j/snowflake/release/lsstsw/stack/lsst-scipipe-9.0.0/Linux64/meas_base/gd397e13551+64039f84ea/python/lsst/meas/base/diaCalculationPlugins.py

Public Member Functions

Static Public Attributes

Protected Member Functions

Detailed Description

Member Function Documentation

◆ _stetson_J()

◆ _stetson_mean()

◆ calculate()

◆ getExecutionOrder()

Member Data Documentation

◆ ConfigClass

◆ inputCols

◆ needsFilter

◆ outputCols

◆ plugType