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=[])
	typeSafePandasAssignment (target, source, columns, default_dtype=np.float64, int_fill_value=0, force_int_to_float=False)
	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 150 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
 
 

typeSafePandasAssignment()

lsst.meas.base.diaCalculationPlugins.typeSafePandasAssignment	(		target,
			source,
			columns,
			default_dtype = np.float64,
			int_fill_value = 0,
			force_int_to_float = False )

Assign from a source dataframe to a target dataframe in a type safe way.

Parameters
----------
target : `pd.DataFrame`
    Target pandas dataframe.
source : `pd.DataFrame` or `pd.Series`
    Grouped source dataframe.
columns : `list` [`str`]
    List of columns to transfer.
default_dtype : `np.dtype`, optional
    Default datatype (if not in target).
int_fill_value : `int`, optional
    Fill value for integer columns to avoid pandas insisting
    that everything should be float-ified as nans.
force_int_to_float : `bool`, optional
    Force integer columns to float columns? Use this option
    for backwards compatibility for old pandas misfeatures which
    are expected by some downstream processes.

Definition at line 89 of file diaCalculationPlugins.py.

):
    """
    Assign from a source dataframe to a target dataframe in a type safe way.
 
    Parameters
    ----------
    target : `pd.DataFrame`
        Target pandas dataframe.
    source : `pd.DataFrame` or `pd.Series`
        Grouped source dataframe.
    columns : `list` [`str`]
        List of columns to transfer.
    default_dtype : `np.dtype`, optional
        Default datatype (if not in target).
    int_fill_value : `int`, optional
        Fill value for integer columns to avoid pandas insisting
        that everything should be float-ified as nans.
    force_int_to_float : `bool`, optional
        Force integer columns to float columns? Use this option
        for backwards compatibility for old pandas misfeatures which
        are expected by some downstream processes.
    """
    is_series = isinstance(source, pd.Series)
    for col in columns:
        if is_series:
            source_col = source
        else:
            source_col = source[col]
 
        matched_length = False
        if col in target.columns:
            target_dtype = target[col].dtype
            matched_length = len(target) == len(source)
        else:
            target_dtype = default_dtype
 
        if (matched_length or pd.api.types.is_float_dtype(target_dtype)) and not force_int_to_float:
            # If we have a matched length or float, we can do a
            # straight assignment here.
            target.loc[:, col] = source_col.astype(target_dtype)
        else:
            # With mis-matched integers, we must do this dance to preserve types.
            # Note that this may lose precision with very large numbers.
 
            # Convert to float
            target[col] = target[col].astype(np.float64)
            # Set the column, casting to float.
            target.loc[:, col] = source_col.astype(np.float64)
            if not force_int_to_float:
                # Convert back to integer
                target[col] = target[col].fillna(int_fill_value).astype(target_dtype)