# Licensed under a 3-clause BSD style license - see LICENSE.rst
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import abc
import numpy as np
from ..utils.compat import ignored
from ..units import Quantity
from ..extern import six
__all__ = ['MissingDataAssociationException',
'IncompatibleUncertaintiesException', 'NDUncertainty',
'StdDevUncertainty']
[docs]class IncompatibleUncertaintiesException(Exception):
"""
This exception should be used to indicate cases in which uncertainties
with two different classes can not be propagated.
"""
[docs]class MissingDataAssociationException(Exception):
"""
This exception should be used to indicate that an uncertainty instance has
not been associated with a parent `~astropy.nddata.NDData` object.
"""
[docs]class NDUncertainty(object):
"""
This is the base class for uncertainty classes used with NDData. It is
implemented as an abstract class and should never be directly
instantiated.
Classes inheriting from NDData should overload the ``propagate_*``
methods, keeping the call signature the same. The propagate methods can
assume that a `parent_nddata` attribute is present which links to the
parent_nddata dataset, and take an `~astropy.nddata.NDData` instance as
the positional argument, *not* an `~astropy.nddata.NDUncertainty`
instance, because the `~astropy.nddata.NDData` instance can be used to
access both the data and the uncertainties (some propagations require the
data values).
"""
__metaclass__ = abc.ABCMeta
# Indicates whether the class supports the propagation of correlated
# uncertainties
supports_correlated = False
@property
def parent_nddata(self):
if self._parent_nddata is None:
message = "uncertainty is not associated with an NDData object"
raise MissingDataAssociationException(message)
else:
return self._parent_nddata
@parent_nddata.setter
def parent_nddata(self, value):
self._parent_nddata = value
@property
def uncertainty_type(self):
return self._uncertainty_type
@uncertainty_type.setter
def uncertainty_type(self, value):
if not isinstance(value, six.string_types):
raise ValueError('uncertainty_type must be a string.')
self._uncertainty_type = value
@abc.abstractmethod
[docs] def propagate_add(self, other_nddata, result_data):
"""
Propagate uncertainties for addition.
Parameters
----------
other_nddata : NDData instance
The data for the second other_nddata in a + b
result_data : `~numpy.ndarray` instance
The data array that is the result of the addition
Returns
-------
result_uncertainty : NDUncertainty instance
The resulting uncertainty
Raises
------
IncompatibleUncertaintiesException
Raised if the method does not know how to add the uncertainties
"""
@abc.abstractmethod
[docs] def propagate_subtract(self, other_nddata, result_data):
"""
Propagate uncertainties for subtraction.
Parameters
----------
other_nddata : NDData instance
The data for the second other_nddata in a + b
result_data : `~numpy.ndarray` instance
The data array that is the result of the addition
Returns
-------
result_uncertainty : NDUncertainty instance
The resulting uncertainty
Raises
------
IncompatibleUncertaintiesException
Raised if the method does not know how to add the uncertainties
"""
@abc.abstractmethod
[docs] def propagate_multiply(self, other_nddata, result_data):
"""
Propagate uncertainties for multiplication.
Parameters
----------
other_nddata : NDData instance
The data for the second other_nddata in a + b
result_data : `~numpy.ndarray` instance
The data array that is the result of the addition
Returns
-------
result_uncertainty : NDUncertainty instance
The resulting uncertainty
"""
@abc.abstractmethod
[docs] def propagate_divide(self, other_nddata, result_data):
"""
Propagate uncertainties for division.
Parameters
----------
other_nddata : NDData instance
The data for the second other_nddata in a + b
result_data : `~numpy.ndarray` instance
The data array that is the result of the addition
Returns
-------
result_uncertainty : NDUncertainty instance
The resulting uncertainty
"""
[docs]class StdDevUncertainty(NDUncertainty):
"""
A class for standard deviation uncertainties
"""
support_correlated = False
def __init__(self, array=None, copy=True):
self._unit = None
self.uncertainty_type = 'std'
if array is None:
self.array = None
elif isinstance(array, StdDevUncertainty):
self.array = np.array(array.array, copy=copy, subok=True)
elif isinstance(array, Quantity):
self.array = np.array(array.value, copy=copy, subok=True)
self._unit = array.unit
else:
self.array = np.array(array, copy=copy, subok=True)
@property
def parent_nddata(self):
message = "Uncertainty is not associated with an NDData object"
try:
if self._parent_nddata is None:
raise MissingDataAssociationException(message)
else:
return self._parent_nddata
except AttributeError:
raise MissingDataAssociationException(message)
@parent_nddata.setter
def parent_nddata(self, value):
if self.array is None or value is None:
self._parent_nddata = value
else:
if value.data.shape != self.array.shape:
raise ValueError("parent shape does not match "
"array data shape")
self._parent_nddata = value
@property
def array(self):
return self._array
@array.setter
def array(self, value):
if value is not None:
with ignored(MissingDataAssociationException):
if value.shape != self.parent_nddata.data.shape:
raise ValueError("array shape does not match "
"parent data shape")
self._array = value
[docs] def propagate_add(self, other_nddata, result_data):
"""
Propagate uncertainties for addition.
Parameters
----------
other_nddata : NDData instance
The data for the second other_nddata in a + b
result_data : `~numpy.ndarray` instance
The data array that is the result of the addition
Returns
-------
result_uncertainty : NDUncertainty instance
The resulting uncertainty
Raises
------
IncompatibleUncertaintiesException
Raised if the method does not know how to propagate the
uncertainties.
"""
if not isinstance(other_nddata.uncertainty, StdDevUncertainty):
raise IncompatibleUncertaintiesException
if self.array is None:
raise ValueError("standard deviation values are not set")
if other_nddata.uncertainty.array is None:
raise ValueError("standard deviation values are not set "
"in other_nddata")
result_uncertainty = StdDevUncertainty()
result_uncertainty.array = np.sqrt(self.array**2 +
other_nddata.uncertainty.array**2)
return result_uncertainty
def __getitem__(self, item):
"""
Slice the standard deviation array using standard numpy slicing
"""
new_array = self.array[item]
return self.__class__(new_array, copy=False)
[docs] def propagate_subtract(self, other_nddata, result_data):
"""
Propagate uncertainties for subtraction.
Parameters
----------
other_nddata : NDData instance
The data for the second other_nddata in a + b
result_data : `~numpy.ndarray` instance
The data array that is the result of the addition
Returns
-------
result_uncertainty : NDUncertainty instance
The resulting uncertainty
Raises
------
IncompatibleUncertaintiesException
Raised if the method does not know how to propagate the
uncertainties.
"""
if not isinstance(other_nddata.uncertainty, StdDevUncertainty):
raise IncompatibleUncertaintiesException
if self.array is None:
raise ValueError("standard deviation values are not set")
if other_nddata.uncertainty.array is None:
raise ValueError("standard deviation values are not set "
"in other_nddata")
result_uncertainty = StdDevUncertainty()
result_uncertainty.array = np.sqrt(self.array**2 +
other_nddata.uncertainty.array**2)
return result_uncertainty
[docs] def propagate_multiply(self, other_nddata, result_data):
"""
Propagate uncertainties for multiplication.
Parameters
----------
other_nddata : NDData instance
The data for the second other_nddata in a + b
result_data : `~numpy.ndarray` instance
The data array that is the result of the addition
Returns
-------
result_uncertainty : NDUncertainty instance
The resulting uncertainty
Raises
------
IncompatibleUncertaintiesException
Raised if the method does not know how to propagate the
uncertainties.
"""
if not isinstance(other_nddata.uncertainty, StdDevUncertainty):
raise IncompatibleUncertaintiesException
if self.array is None:
raise ValueError("standard deviation values are not set")
if other_nddata.uncertainty.array is None:
raise ValueError("standard deviation values are not set in "
"other_nddata")
result_uncertainty = StdDevUncertainty()
result_uncertainty.array = \
(np.sqrt((self.array/self.parent_nddata.data)**2
+ (other_nddata.uncertainty.array/other_nddata.data)**2) *
result_data)
return result_uncertainty
[docs] def propagate_divide(self, other_nddata, result_data):
"""
Propagate uncertainties for division.
Parameters
----------
other_nddata : NDData instance
The data for the second other_nddata in a + b
result_data : `~numpy.ndarray` instance
The data array that is the result of the addition
Returns
-------
result_uncertainty : NDUncertainty instance
The resulting uncertainty
Raises
------
IncompatibleUncertaintiesException
Raised if the method does not know how to propagate the
uncertainties.
"""
if not isinstance(other_nddata.uncertainty, StdDevUncertainty):
raise IncompatibleUncertaintiesException
if self.array is None:
raise ValueError("standard deviation values are not set")
if other_nddata.uncertainty.array is None:
raise ValueError("standard deviation values are not set "
"in other_nddata")
result_uncertainty = StdDevUncertainty()
result_uncertainty.array = \
(np.sqrt((self.array/self.parent_nddata.data)**2
+ (other_nddata.uncertainty.array/other_nddata.data)**2) *
result_data)
return result_uncertainty