LSSTApplications
19.0.0-14-gb0260a2+72efe9b372,20.0.0+7927753e06,20.0.0+8829bf0056,20.0.0+995114c5d2,20.0.0+b6f4b2abd1,20.0.0+bddc4f4cbe,20.0.0-1-g253301a+8829bf0056,20.0.0-1-g2b7511a+0d71a2d77f,20.0.0-1-g5b95a8c+7461dd0434,20.0.0-12-g321c96ea+23efe4bbff,20.0.0-16-gfab17e72e+fdf35455f6,20.0.0-2-g0070d88+ba3ffc8f0b,20.0.0-2-g4dae9ad+ee58a624b3,20.0.0-2-g61b8584+5d3db074ba,20.0.0-2-gb780d76+d529cf1a41,20.0.0-2-ged6426c+226a441f5f,20.0.0-2-gf072044+8829bf0056,20.0.0-2-gf1f7952+ee58a624b3,20.0.0-20-geae50cf+e37fec0aee,20.0.0-25-g3dcad98+544a109665,20.0.0-25-g5eafb0f+ee58a624b3,20.0.0-27-g64178ef+f1f297b00a,20.0.0-3-g4cc78c6+e0676b0dc8,20.0.0-3-g8f21e14+4fd2c12c9a,20.0.0-3-gbd60e8c+187b78b4b8,20.0.0-3-gbecbe05+48431fa087,20.0.0-38-ge4adf513+a12e1f8e37,20.0.0-4-g97dc21a+544a109665,20.0.0-4-gb4befbc+087873070b,20.0.0-4-gf910f65+5d3db074ba,20.0.0-5-gdfe0fee+199202a608,20.0.0-5-gfbfe500+d529cf1a41,20.0.0-6-g64f541c+d529cf1a41,20.0.0-6-g9a5b7a1+a1cd37312e,20.0.0-68-ga3f3dda+5fca18c6a4,20.0.0-9-g4aef684+e18322736b,w.2020.45
LSSTDataManagementBasePackage
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Classes | |
class | BrighterFatterGain |
class | BrighterFatterKernel |
class | BrighterFatterKernelTaskDataIdContainer |
class | MakeBrighterFatterKernelTask |
class | MakeBrighterFatterKernelTaskConfig |
Functions | |
def | calcBiasCorr (fluxLevels, imageShape, repeats=1, seed=0, addCorrelations=False, correlationStrength=0.1, maxLag=10, nSigmaClip=5, border=10, logger=None) |
def lsst.cp.pipe.makeBrighterFatterKernel.calcBiasCorr | ( | fluxLevels, | |
imageShape, | |||
repeats = 1 , |
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seed = 0 , |
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addCorrelations = False , |
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correlationStrength = 0.1 , |
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maxLag = 10 , |
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nSigmaClip = 5 , |
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border = 10 , |
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logger = None |
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) |
Calculate the bias induced when sigma-clipping non-Gaussian distributions. Fill image-pairs of the specified size with Poisson-distributed values, adding correlations as necessary. Then calculate the cross correlation, and calculate the bias induced using the cross-correlation image and the image means. Parameters: ----------- fluxLevels : `list` of `int` The mean flux levels at which to simulate. Nominal values might be something like [70000, 90000, 110000] imageShape : `tuple` of `int` The shape of the image array to simulate, nx by ny pixels. repeats : `int`, optional Number of repeats to perform so that results can be averaged to improve SNR. seed : `int`, optional The random seed to use for the Poisson points. addCorrelations : `bool`, optional Whether to add brighter-fatter-like correlations to the simulated images If true, a correlation between x_{i,j} and x_{i+1,j+1} is introduced by adding a*x_{i,j} to x_{i+1,j+1} correlationStrength : `float`, optional The strength of the correlations. This is the value of the coefficient `a` in the above definition. maxLag : `int`, optional The maximum lag to work to in pixels nSigmaClip : `float`, optional Number of sigma to clip to when calculating the sigma-clipped mean. border : `int`, optional Number of border pixels to mask logger : `lsst.log.Log`, optional Logger to use. Instantiated anew if not provided. Returns: -------- biases : `dict` [`float`, `list` of `float`] A dictionary, keyed by flux level, containing a list of the biases for each repeat at that flux level means : `dict` [`float`, `list` of `float`] A dictionary, keyed by flux level, containing a list of the average mean fluxes (average of the mean of the two images) for the image pairs at that flux level xcorrs : `dict` [`float`, `list` of `np.ndarray`] A dictionary, keyed by flux level, containing a list of the xcorr images for the image pairs at that flux level
Definition at line 1615 of file makeBrighterFatterKernel.py.