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 1609 of file makeBrighterFatterKernel.py.