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def | runQuantum (self, butlerQC, inputRefs, outputRefs) |
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def | run (self, inputPtc, camera=None, inputDims=None) |
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def | debugFit (self, stepname, timeVector, meanVector, linearizer, ampName) |
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Fit the linearity from the PTC dataset.
Definition at line 89 of file linearity.py.
◆ debugFit()
def lsst.cp.pipe.linearity.LinearitySolveTask.debugFit |
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self, |
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stepname, |
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timeVector, |
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meanVector, |
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linearizer, |
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ampName |
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Debug method for linearity fitting.
Parameters
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stepname : `str`
A label to use to check if we care to debug at a given
line of code.
timeVector : `numpy.array`
The values to use as the independent variable in the
linearity fit.
meanVector : `numpy.array`
The values to use as the dependent variable in the
linearity fit.
linearizer : `lsst.ip.isr.Linearizer`
The linearity correction to compare.
ampName : `str`
Amplifier name to lookup linearity correction values.
Definition at line 223 of file linearity.py.
◆ run()
def lsst.cp.pipe.linearity.LinearitySolveTask.run |
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self, |
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inputPtc, |
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camera = None , |
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inputDims = None |
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Fit non-linearity to PTC data, returning the correct Linearizer
object.
Parameters
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inputPtc : `lsst.cp.pipe.PtcDataset`
Pre-measured PTC dataset.
camera : `lsst.afw.cameraGeom.Camera`, optional
Camera geometry.
inputDims : `lsst.daf.butler.DataCoordinate` or `dict`, optional
DataIds to use to populate the output calibration.
Returns
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results : `lsst.pipe.base.Struct`
The results struct containing:
``outputLinearizer`` : `lsst.ip.isr.Linearizer`
Final linearizer calibration.
``outputProvenance`` : `lsst.ip.isr.IsrProvenance`
Provenance data for the new calibration.
Notes
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This task currently fits only polynomial-defined corrections,
where the correction coefficients are defined such that:
corrImage = uncorrImage + sum_i c_i uncorrImage^(2 + i)
These `c_i` are defined in terms of the direct polynomial fit:
meanVector ~ P(x=timeVector) = sum_j k_j x^j
such that c_(j-2) = -k_j/(k_1^j) in units of DN^(1-j) (c.f.,
Eq. 37 of 2003.05978). The `config.polynomialOrder` defines
the maximum order of x^j to fit. As k_0 and k_1 are
degenerate with bias level and gain, they are not included in
the non-linearity correction.
Definition at line 115 of file linearity.py.
◆ runQuantum()
def lsst.cp.pipe.linearity.LinearitySolveTask.runQuantum |
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self, |
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butlerQC, |
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inputRefs, |
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outputRefs |
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Ensure that the input and output dimensions are passed along.
Parameters
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butlerQC : `lsst.daf.butler.butlerQuantumContext.ButlerQuantumContext`
Butler to operate on.
inputRefs : `lsst.pipe.base.connections.InputQuantizedConnection`
Input data refs to load.
ouptutRefs : `lsst.pipe.base.connections.OutputQuantizedConnection`
Output data refs to persist.
Definition at line 95 of file linearity.py.
◆ ConfigClass
The documentation for this class was generated from the following file: