NDUncertainty#
- class astropy.nddata.NDUncertainty(array=None, copy=True, unit=None)[source]#
Bases:
objectThis is the metaclass for uncertainty classes used with
NDData.- Parameters:
- array
anytype, optional The array or value (the parameter name is due to historical reasons) of the uncertainty.
numpy.ndarray,QuantityorNDUncertaintysubclasses are recommended. If thearrayislist-like ornumpy.ndarray-like it will be cast to a plainnumpy.ndarray. Default isNone.- unitastropy:unit-like, optional
Unit for the uncertainty
array. Strings that can be converted to aUnitare allowed. Default isNone.- copy
bool, optional Indicates whether to save the
arrayas a copy.Truecopies it before saving, whileFalsetries to save every parameter as reference. Note however that it is not always possible to save the input as reference. Default isTrue.
- array
- Raises:
IncompatibleUncertaintiesExceptionIf given another
NDUncertainty-like class asarrayif theiruncertainty_typeis different.
Attributes Summary
numpy.ndarray: the uncertainty's value.NDData: reference toNDDatainstance with this uncertainty.This uncertainty as an
Quantityobject.bool: Supports uncertainty propagation with correlated uncertainties?str: Short description of the type of uncertainty.Unit: The unit of the uncertainty, if any.Methods Summary
propagate(operation, other_nddata, ...[, axis])Calculate the resulting uncertainty given an operation on the data.
represent_as(other_uncert)Convert this uncertainty to a different uncertainty type.
Attributes Documentation
- array#
numpy.ndarray: the uncertainty’s value.
- parent_nddata#
NDData: reference toNDDatainstance with this uncertainty.In case the reference is not set uncertainty propagation will not be possible since propagation might need the uncertain data besides the uncertainty.
bool: Supports uncertainty propagation with correlated uncertainties?New in version 1.2.
- uncertainty_type#
str: Short description of the type of uncertainty.Defined as abstract property so subclasses have to override this.
Methods Documentation
- propagate(operation, other_nddata, result_data, correlation, axis=None)[source]#
Calculate the resulting uncertainty given an operation on the data.
New in version 1.2.
- Parameters:
- operation
callable() The operation that is performed on the
NDData. Supported arenumpy.add,numpy.subtract,numpy.multiplyandnumpy.true_divide(ornumpy.divide).- other_nddata
NDDatainstance The second operand in the arithmetic operation.
- result_data
Quantityorndarray The result of the arithmetic operations on the data.
- correlation
numpy.ndarrayor number The correlation (rho) is defined between the uncertainties in sigma_AB = sigma_A * sigma_B * rho. A value of
0means uncorrelated operands.- axis
intortupleofint, optional Axis over which to perform a collapsing operation.
- operation
- Returns:
- resulting_uncertainty
NDUncertaintyinstance Another instance of the same
NDUncertaintysubclass containing the uncertainty of the result.
- resulting_uncertainty
- Raises:
ValueErrorIf the
operationis not supported or if correlation is not zero but the subclass does not support correlated uncertainties.
Notes
First this method checks if a correlation is given and the subclass implements propagation with correlated uncertainties. Then the second uncertainty is converted (or an Exception is raised) to the same class in order to do the propagation. Then the appropriate propagation method is invoked and the result is returned.
- represent_as(other_uncert)[source]#
Convert this uncertainty to a different uncertainty type.
- Parameters:
- other_uncert
NDUncertaintysubclass The
NDUncertaintysubclass to convert to.
- other_uncert
- Returns:
- resulting_uncertainty
NDUncertaintyinstance An instance of
other_uncertsubclass containing the uncertainty converted to the new uncertainty type.
- resulting_uncertainty
- Raises: