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lsst.meas.algorithms g511c235543+401ba1f5a0
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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.