LSSTApplications
17.0+11,17.0+34,17.0+56,17.0+57,17.0+59,17.0+7,17.0-1-g377950a+33,17.0.1-1-g114240f+2,17.0.1-1-g4d4fbc4+28,17.0.1-1-g55520dc+49,17.0.1-1-g5f4ed7e+52,17.0.1-1-g6dd7d69+17,17.0.1-1-g8de6c91+11,17.0.1-1-gb9095d2+7,17.0.1-1-ge9fec5e+5,17.0.1-1-gf4e0155+55,17.0.1-1-gfc65f5f+50,17.0.1-1-gfc6fb1f+20,17.0.1-10-g87f9f3f+1,17.0.1-11-ge9de802+16,17.0.1-16-ga14f7d5c+4,17.0.1-17-gc79d625+1,17.0.1-17-gdae4c4a+8,17.0.1-2-g26618f5+29,17.0.1-2-g54f2ebc+9,17.0.1-2-gf403422+1,17.0.1-20-g2ca2f74+6,17.0.1-23-gf3eadeb7+1,17.0.1-3-g7e86b59+39,17.0.1-3-gb5ca14a,17.0.1-3-gd08d533+40,17.0.1-30-g596af8797,17.0.1-4-g59d126d+4,17.0.1-4-gc69c472+5,17.0.1-6-g5afd9b9+4,17.0.1-7-g35889ee+1,17.0.1-7-gc7c8782+18,17.0.1-9-gc4bbfb2+3,w.2019.22
LSSTDataManagementBasePackage
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Classes | |
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) |
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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) |
Calculate the bias induced when sigma-clipping non-Gassian 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 simiulate. 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 Returns: -------- biases : `dict` of `list` of `float` A dictionary, keyed by flux level, containing a list of the biases for each repeat at that flux level means : `dict` of `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` of `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 1248 of file makeBrighterFatterKernel.py.