InverseVariance¶

class
astropy.nddata.
InverseVariance
(array=None, copy=True, unit=None)[source] [edit on github]¶ Bases:
astropy.nddata.nduncertainty._VariancePropagationMixin
,astropy.nddata.NDUncertainty
Inverse variance uncertainty assuming first order Gaussian error propagation.
This class implements uncertainty propagation for
addition
,subtraction
,multiplication
anddivision
with other instances ofInverseVariance
. The class can handle if the uncertainty has a unit that differs from (but is convertible to) the parentsNDData
unit. The unit of the resulting uncertainty will the inverse square of the unit of the resulting data. Also support for correlation is possible but requires the correlation as input. It cannot handle correlation determination itself.Parameters:  args, kwargs :
see
NDUncertainty
Examples
Compare this example to that in
StdDevUncertainty
; the uncertainties in the examples below are equivalent to the uncertainties inStdDevUncertainty
.InverseVariance
should always be associated with anNDData
like instance, either by creating it during initialization:>>> from astropy.nddata import NDData, InverseVariance >>> ndd = NDData([1,2,3], unit='m', ... uncertainty=InverseVariance([100, 100, 100])) >>> ndd.uncertainty InverseVariance([100, 100, 100])
or by setting it manually on the
NDData
instance:>>> ndd.uncertainty = InverseVariance([25], unit='1/m^2', copy=True) >>> ndd.uncertainty InverseVariance([25])
the uncertainty
array
can also be set directly:>>> ndd.uncertainty.array = 0.25 >>> ndd.uncertainty InverseVariance(0.25)
Note
The unit will not be displayed.
Attributes Summary
supports_correlated
True
:InverseVariance
allows to propagate correlated uncertainties.uncertainty_type
"ivar"
:InverseVariance
implements inverse variance.Attributes Documentation
True
:InverseVariance
allows to propagate correlated uncertainties.correlation
must be given, this class does not implement computing it by itself.

uncertainty_type
¶ "ivar"
:InverseVariance
implements inverse variance.