__all__ = ["Instrument"]
from xpsi.global_imports import *
from xpsi.utils import make_verbose
from xpsi.ParameterSubspace import ParameterSubspace
[docs]class ResponseError(xpsiError):
""" Raised if there is a problem with the input response matrix. """
[docs]class EdgesError(xpsiError):
""" Raised if there is a problem with the input energy edges. """
[docs]class ChannelError(xpsiError):
""" Raised if there is a problem with the input channel numbers. """
[docs]class Instrument(ParameterSubspace):
""" Base class for astronomical X-ray instruments on-board space telescopes.
The body of the initialiser must not be changed to ensure inter-module
compatibility, but can be extended if appropriate using a call to
``super().__init__``. Specialist constructors can be defined in a subclass
using the ``@classmethod`` decorator.
:param ndarray[p,q] matrix:
A :math:`p \\times q` matrix which is the product of a redistribution
matrix and effective area vector. The input energy intervals must
increase along the columns of :attr:`matrix`, and the output channels
must increase along the rows of :attr:`matrix`. The *units* of the
elements must be that of an *effective* area (:math:`cm^2`). Generally
there will be some available calibration product, and deviations from
this nominal response model will be parametrised. So here load some
nominal response matrix.
:param ndarray[q+1] energy_edges:
Energy edges in keV of the instrument energy intervals which must be
congruent to the first dimension of the :attr:`matrix`: the number of
edges must be :math:`q + 1`. The edges must be monotonically increasing.
These edges will correspond to the nominal response matrix and any
deviation from this matrix (see above).
:param ndarray[p] channels:
Instrument channel numbers which must be equal in number to the number
of rows of the :attr:`matrix`. The number of channels must therefore be
:math:`p`. These channels will correspond to the nominal response
matrix and any deviation from this matrix (see above). In common usage
patterns, the channel numbers will increase monotonically with row
number, and usually increment by one (but this is not necessary).
.. note::
That these channel numbers are not used to index the loaded instrument
(sub)matrix. The :attr:`xpsi.Data.index_range` property returns
bounding row numbers that index the loaded instrument response
(sub)matrix in order to operate on an incident signal flux. The
channel array contained in :attr:`xpsi.Data.channels` must be a
contiguous (ordered) subset of the channel array loaded here.
.. note::
The dimensions of the response matrix need not be equal, but it is
required that the number of input intervals be greater than or equal to
the number of output channels -- i.e., :math:`p \leq q`. If :math:`p <
q` then it is implied that subsets of adjacent output channels are
effectively grouped together.
:param ndarray[p+1] channel_edges:
The channel (energy) edges of the instrument, in keV. The array must
be congruent to the zeroth dimension of the :attr:`matrix`: the number
of edges must be :math:`p + 1`. The edges must be monotonically
increasing. These edges will correspond to the nominal response matrix
and any deviation from this matrix (see above).
:param tuple args:
Container of parameter instances.
:param dict kwargs:
If you want to prefix parameters of an instance of this instrument
subspace with an identifier, pass it as keyword argument and it will
find its way to the base class.
"""
def __init__(self, matrix, energy_edges, channels, channel_edges=None,
*args, **kwargs):
self.matrix = matrix
self.energy_edges = energy_edges
self.channels = channels
if channel_edges is not None:
self.channel_edges = channel_edges
super(Instrument, self).__init__(*args, **kwargs)
@property
def matrix(self):
""" Get the reference response matrix.
In common usage patterns there will be some fiducial or nominal
response matrix that either defines fixed instrument operation or
is a basis for parametrised deviations. This matrix is usually a
calibration product distributed by an instrument calibration team.
A matrix of dimension :math:`p \\times q`. Here :math:`p` must be the
number of input energy intervals, and :math:`q \geq p` the number of
output channels.
.. note::
The attribute :attr:`matrix` must be assigned, and it must be
a :class:`numpy.ndarray` for use with :func:`numpy.dot` (even
if the matrix is sparse to some degree).
"""
return self._matrix
@matrix.setter
def matrix(self, matrix):
""" Set the matrix. """
try:
assert isinstance(matrix, _np.ndarray)
assert matrix.ndim == 2
assert matrix.shape[0] <= matrix.shape[1]
assert (matrix >= 0.0).all()
except AssertionError:
raise ResponseError('Input matrix must be a two-dimensional ndarray, with number of channel intervals'
'(dimension 0) smaller than the number of energy intervals (dimension 1),'
' and with all matrix elements that are zero or positive.')
try:
for i in range(matrix.shape[0]):
assert matrix[i,:].any()
for j in range(matrix.shape[1]):
assert matrix[:,j].any()
except AssertionError:
raise ResponseError('Each row and column of the matrix must contain at least one positive number.'
' Try reducing the \"gap\" between the minimum input energy '
'and minimum input channel.')
self._matrix = matrix
[docs] def construct_matrix(self):
""" Construct the response matrix if it is parameterised.
If customising, do operations to calculate a matrix, and return it.
You can access parameters (free, fixed, and derived) via the container
access ``self[<name>]``.
If the instrument operation is fixed, you might not need to subclass,
because the default behaviour is to return the nominal response you
loaded. If for some reason the matrix you loaded is to be modified in
some fixed manner, possibly as a function of some custom fixed
parameters that you defined, you would also have to subclass and
provide the correct implementation of this method.
"""
return self.matrix
[docs] def __call__(self, signal, irange, orange):
""" Register an incident signal.
:param ndarray[m,n] signal:
An :math:`m \\times n` matrix, where input energy interval
increments along rows, and phase increases along columns. The
number of rows, :math:`m`, must equal the number of columns of
:attr:`matrix`: :math:`m=q`.
:param array-like irange:
Indexable object with two elements respectively denoting the
indices of the first and last *input* intervals. The response
matrix :attr:`matrix` must be indexable with these numbers, i.e.,
they must satisfy :math:`indx < q`.
:param array-like orange:
Indexable object with two elements respectively denoting the
indices of the first and last *output* channels. The response
matrix :attr:`matrix` must be indexable with these numbers, i.e.,
they must satisfy :math:`indx < p`.
:return: *ndarray[p,n]* containing the registered signal.
.. note::
The product of the most recent operation is stored as the property
:attr:`cached_signal`.
"""
matrix = self.construct_matrix()
self._cached_signal = _np.dot(matrix[orange[0]:orange[1],
irange[0]:irange[1]], signal)
return self._cached_signal
@property
def cached_signal(self):
""" Get the cached registered signal. """
return self._cached_signal
@property
def energy_edges(self):
""" Get the energy edges of the instrument, in keV.
A :class:`numpy.ndarray` of edges of the input energy intervals which
map to channels defined in the data space.
"""
return self._energy_edges
@energy_edges.setter
def energy_edges(self, energy_edges):
""" Set the energy edges in keV. """
if not isinstance(energy_edges, _np.ndarray):
try:
energy_edges = _np.array(energy_edges)
except TypeError:
raise EdgesError('Energy edges must be in a one-dimensional array of positive increasing values.')
try:
assert energy_edges.ndim == 1
assert (energy_edges >= 0.0).all()
assert energy_edges.shape[0] == self._matrix.shape[1] + 1
assert not (energy_edges[1:] <= energy_edges[:-1]).any()
except AssertionError:
raise EdgesError('Energy edges must be in a one-dimensional array of positive increasing values, with a '
'length equal to number of energy intervals in the matrix + 1.')
self._energy_edges = energy_edges
@property
def channel_edges(self):
""" Get the channel (energy) edges of the instrument, in keV.
A :class:`numpy.ndarray` of edges of the registered energy intervals
labelled as channels defined in the data space. This is relevant when
there is a detector-by-detector gain scale applied to event data (such
as for NICER instrument calibration products), meaning that the
redistribution matrix is effectively shared by detectors and the
channels across detectors can share an energy scale definition.
An incident photon of given energy then has a registered-energy
distribution that generally peaks in the vicinity of the true photon
energy. The resdistribution matrix will have some energy resolution
(along with other features such as shelves). With thanks to Paul S.
Ray for explaining the choice to calibrate in this manner.
.. note::
If you made a channel cut that results in a non-contiguous subset
of channels, you will need to overwrite the setter method because
the checks will fail.
"""
return self._channel_edges
@channel_edges.setter
def channel_edges(self, channel_edges):
""" Set the channel (energy) edges in keV. """
if not isinstance(channel_edges, _np.ndarray):
try:
channel_edges = _np.array(channel_edges)
except TypeError:
raise EdgesError('Channel edges must be in a one-dimensional array of positive increasing values.')
try:
assert channel_edges.ndim == 1
assert (channel_edges >= 0.0).all()
assert channel_edges.shape[0] == self._matrix.shape[0] + 1
assert not (channel_edges[1:] <= channel_edges[:-1]).any()
except AssertionError:
raise EdgesError('Channel edges must be in a one-dimensional array of positive increasing values, with a '
'length equal to the number of channel intervals in the matrix + 1.')
self._channel_edges = channel_edges
@property
def channels(self):
""" Get the array of channels corresponding to rows of the matrix.
The matrix being the loaded instrument response (sub)matrix.
"""
return self._channels
@channels.setter
@make_verbose('Setting channels for loaded instrument response (sub)matrix',
'Channels set')
def channels(self, channel_array):
if not isinstance(channel_array, _np.ndarray):
try:
channel_array = _np.array(channel_array)
except TypeError:
raise ChannelError('Channel numbers must be in a one-dimensional array of positive integers'
'(including zero).')
try:
assert channel_array.ndim == 1
assert (channel_array >= 0).all()
assert channel_array.shape[0] == self._matrix.shape[0]
except AssertionError:
raise ChannelError('Channel numbers must be in a one-dimensional array of positive integers'
'(including zero).')
if (channel_array[1:] - channel_array[:-1] != 1).any():
yield ('Warning: Channel numbers do not uniformly increment by one.\n'
' Please check for correctness.')
self._channels = channel_array
yield