lsst.meas.algorithms gf5d53e8f6c+82a853f15c
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Classes | |
class | MockRefcatDataId |
class | MockRefcatDeferredDatasetHandle |
class | MockReferenceObjectLoaderFromFiles |
Functions | |
def | plantSources (bbox, kwid, sky, coordList, addPoissonNoise=True) |
def | makeRandomTransmissionCurve (rng, minWavelength=4000.0, maxWavelength=7000.0, nWavelengths=200, maxRadius=80.0, nRadii=30, perturb=0.05) |
def | makeDefectList () |
def 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 148 of file testUtils.py.
def lsst.meas.algorithms.testUtils.makeRandomTransmissionCurve | ( | rng, | |
minWavelength = 4000.0 , |
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maxWavelength = 7000.0 , |
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nWavelengths = 200 , |
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maxRadius = 80.0 , |
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nRadii = 30 , |
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perturb = 0.05 |
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) |
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 104 of file testUtils.py.
def lsst.meas.algorithms.testUtils.plantSources | ( | bbox, | |
kwid, | |||
sky, | |||
coordList, | |||
addPoissonNoise = True |
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) |
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 40 of file testUtils.py.