LSSTApplications
18.1.0
LSSTDataManagementBasePackage
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Functions | |
def | clean (srcMatch, wcs, order=3, nsigma=3) |
def | indicesOfGoodPoints (x, y, s, order=1, nsigma=3, maxiter=100) |
def | chooseRx (x, idx, order) |
def | chooseRy (y, idx, order) |
def lsst.meas.extensions.astrometryNet.cleanBadPoints.chooseRx | ( | x, | |
idx, | |||
order | |||
) |
Create order+1 values of the ordinate based on the median of groups of elements of x
Definition at line 111 of file cleanBadPoints.py.
def lsst.meas.extensions.astrometryNet.cleanBadPoints.chooseRy | ( | y, | |
idx, | |||
order | |||
) |
Create order+1 values of the ordinate based on the median of groups of elements of y
Definition at line 122 of file cleanBadPoints.py.
def lsst.meas.extensions.astrometryNet.cleanBadPoints.clean | ( | srcMatch, | |
wcs, | |||
order = 3 , |
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nsigma = 3 |
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) |
Remove bad points from srcMatch Input: srcMatch : list of det::SourceMatch order: Order of polynomial to use in robust fitting nsigma: Sources more than this far away from the robust best fit polynomial are removed Return: list of det::SourceMatch of the good data points
Definition at line 31 of file cleanBadPoints.py.
def lsst.meas.extensions.astrometryNet.cleanBadPoints.indicesOfGoodPoints | ( | x, | |
y, | |||
s, | |||
order = 1 , |
|||
nsigma = 3 , |
|||
maxiter = 100 |
|||
) |
Return a list of indices in the range [0, len(x)] of points that lie less than nsigma away from the robust best fit polynomial
Definition at line 66 of file cleanBadPoints.py.