lsst.meas.algorithms.testUtils Namespace Reference

Classes
class	MockRefcatDataId
class	MockReferenceObjectLoaderFromFiles
class	MockReferenceObjectLoaderFromMemory

Functions
	plantSources (bbox, kwid, sky, coordList, addPoissonNoise=True)
	makeRandomTransmissionCurve (rng, minWavelength=4000.0, maxWavelength=7000.0, nWavelengths=200, maxRadius=80.0, nRadii=30, perturb=0.05)
	makeDefectList ()

Function Documentation

makeDefectList()

lsst.meas.algorithms.testUtils.makeDefectList

(

)

Create a list of defects that can be used for testing.

Returns
-------
defectList = `list` [`lsst.meas.algorithms.Defect`]
    The list of defects.

Definition at line 151 of file testUtils.py.

makeRandomTransmissionCurve()

lsst.meas.algorithms.testUtils.makeRandomTransmissionCurve	(		rng,
			minWavelength = 4000.0,
			maxWavelength = 7000.0,
			nWavelengths = 200,
			maxRadius = 80.0,
			nRadii = 30,
			perturb = 0.05 )

Create a random TransmissionCurve with nontrivial spatial and
wavelength variation.

Parameters
----------
rng : numpy.random.RandomState
    Random number generator.
minWavelength : float
    Average minimum wavelength for generated TransmissionCurves (will be
    randomly perturbed).
maxWavelength : float
    Average maximum wavelength for generated TransmissionCurves (will be
    randomly perturbed).
nWavelengths : int
    Number of samples in the wavelength dimension.
maxRadius : float
    Average maximum radius for spatial variation (will be perturbed).
nRadii : int
    Number of samples in the radial dimension.
perturb: float
    Fraction by which wavelength and radius bounds should be randomly
    perturbed.

Definition at line 107 of file testUtils.py.

plantSources()

lsst.meas.algorithms.testUtils.plantSources	(		bbox,
			kwid,
			sky,
			coordList,
			addPoissonNoise = True )

Make an exposure with stars (modelled as Gaussians)

Parameters
----------
bbox : `lsst.geom.Box2I`
    Parent bbox of exposure
kwid : `int`
    Kernal width (and height; kernal is square)
sky : `float`
    Amount of sky background (counts)
coordList : `list [tuple]`
    A list of [x, y, counts, sigma] where:
        * x,y are relative to exposure origin
        * counts is the integrated counts for the star
        * sigma is the Gaussian sigma in pixels
addPoissonNoise : `bool`
    If True: add Poisson noise to the exposure

Definition at line 42 of file testUtils.py.