lsst.pipe.tasks.functors Namespace Reference

Classes
class	Color
class	Column
class	CompositeFunctor
class	ComputePixelScale
class	ConvertPixelToArcseconds
class	CoordColumn
class	CustomFunctor
class	DecColumn
class	DeconvolvedMoments
class	E1
class	E2
class	FootprintNPix
class	Functor
class	HsmFwhm
class	HsmTraceSize
class	IDColumn
class	Index
class	Labeller
class	LocalMagnitude
class	LocalMagnitudeErr
class	LocalNanojansky
class	LocalNanojanskyErr
class	LocalPhotometry
class	LocalWcs
class	Mag
class	MagDiff
class	MagErr
class	Magnitude
class	MagnitudeErr
class	NanoJansky
class	NanoJanskyErr
class	NanoMaggie
class	NumStarLabeller
class	Photometry
class	PsfHsmTraceSizeDiff
class	PsfSdssTraceSizeDiff
class	RAColumn
class	RadiusFromQuadrupole
class	ReferenceBand
class	SdssTraceSize
class	StarGalaxyLabeller

Functions
def	init_fromDict (initDict, basePath='lsst.pipe.tasks.functors', typeKey='functor', name=None)
def	mag_aware_eval (df, expr)
def	fluxName (col)
def	fluxErrName (col)

Function Documentation

fluxErrName()

def lsst.pipe.tasks.functors.fluxErrName

(

col

)

Definition at line 500 of file functors.py.

def fluxErrName(col):
    if not col.endswith('_instFluxErr'):
        col += '_instFluxErr'
    return col
 
 

fluxName()

def lsst.pipe.tasks.functors.fluxName

(

col

)

Definition at line 494 of file functors.py.

def fluxName(col):
    if not col.endswith('_instFlux'):
        col += '_instFlux'
    return col
 
 

init_fromDict()

def lsst.pipe.tasks.functors.init_fromDict	(		initDict,
			basePath = 'lsst.pipe.tasks.functors',
			typeKey = 'functor',
			name = None
	)

Initialize an object defined in a dictionary

The object needs to be importable as
    f'{basePath}.{initDict[typeKey]}'
The positional and keyword arguments (if any) are contained in
"args" and "kwargs" entries in the dictionary, respectively.
This is used in `functors.CompositeFunctor.from_yaml` to initialize
a composite functor from a specification in a YAML file.

Parameters
----------
initDict : dictionary
    Dictionary describing object's initialization.  Must contain
    an entry keyed by ``typeKey`` that is the name of the object,
    relative to ``basePath``.
basePath : str
    Path relative to module in which ``initDict[typeKey]`` is defined.
typeKey : str
    Key of ``initDict`` that is the name of the object
    (relative to `basePath`).

Definition at line 12 of file functors.py.

def init_fromDict(initDict, basePath='lsst.pipe.tasks.functors',
                  typeKey='functor', name=None):
    """Initialize an object defined in a dictionary
 
    The object needs to be importable as
        f'{basePath}.{initDict[typeKey]}'
    The positional and keyword arguments (if any) are contained in
    "args" and "kwargs" entries in the dictionary, respectively.
    This is used in `functors.CompositeFunctor.from_yaml` to initialize
    a composite functor from a specification in a YAML file.
 
    Parameters
    ----------
    initDict : dictionary
        Dictionary describing object's initialization.  Must contain
        an entry keyed by ``typeKey`` that is the name of the object,
        relative to ``basePath``.
    basePath : str
        Path relative to module in which ``initDict[typeKey]`` is defined.
    typeKey : str
        Key of ``initDict`` that is the name of the object
        (relative to `basePath`).
    """
    initDict = initDict.copy()
    # TO DO: DM-21956 We should be able to define functors outside this module
    pythonType = doImport(f'{basePath}.{initDict.pop(typeKey)}')
    args = []
    if 'args' in initDict:
        args = initDict.pop('args')
        if isinstance(args, str):
            args = [args]
    try:
        element = pythonType(*args, **initDict)
    except Exception as e:
        message = f'Error in constructing functor "{name}" of type {pythonType.__name__} with args: {args}'
        raise type(e)(message, e.args)
    return element
 
 

mag_aware_eval()

def lsst.pipe.tasks.functors.mag_aware_eval	(		df,
			expr
	)

Evaluate an expression on a DataFrame, knowing what the 'mag' function means

Builds on `pandas.DataFrame.eval`, which parses and executes math on dataframes.

Parameters
----------
df : pandas.DataFrame
    Dataframe on which to evaluate expression.

expr : str
    Expression.

Definition at line 347 of file functors.py.

def mag_aware_eval(df, expr):
    """Evaluate an expression on a DataFrame, knowing what the 'mag' function means
 
    Builds on `pandas.DataFrame.eval`, which parses and executes math on dataframes.
 
    Parameters
    ----------
    df : pandas.DataFrame
        Dataframe on which to evaluate expression.
 
    expr : str
        Expression.
    """
    try:
        expr_new = re.sub(r'mag\‍((\w+)\‍)', r'-2.5*log(\g<1>)/log(10)', expr)
        val = df.eval(expr_new, truediv=True)
    except Exception:  # Should check what actually gets raised
        expr_new = re.sub(r'mag\‍((\w+)\‍)', r'-2.5*log(\g<1>_instFlux)/log(10)', expr)
        val = df.eval(expr_new, truediv=True)
    return val