LSSTApplications
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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 1218 of file makeBrighterFatterKernel.py.