LSSTApplications
20.0.0
LSSTDataManagementBasePackage
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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 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 88 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) @param bbox: parent bbox of exposure @param kwid: kernel width (and height; kernel is square) @param sky: amount of sky background (counts) @param coordList: 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 @param addPoissonNoise: add Poisson noise to the exposure?
Definition at line 31 of file testUtils.py.