BORGForwardModel

class aquila_borg.forward.BORGForwardModel

Base class for all forward models.

All forward models derive from this class. However python implemented forward models must use BaseForwardModel.

Members

accumulateAdjoint(self: aquila_borg._borg.forward.BORGForwardModel, do_accumulate: bool) → None

Request the model to accumulate adjoint vectors instead of resetting at each call.

This changes the default behavior of adjointModel_v2. Use with care.

Parameters: do_accumulate (bool) – If true, request the change of behavior.

adjointModel_v2(self: aquila_borg._borg.forward.BORGForwardModel, adjoint_gradient: object) → None

Pushes the adjoint gradient from a deeper part of the computation.

This function allows to push the adjoint gradient vector for a deeper part of the computation (like the likelihood) to the forward model. It will not return the result which has to be query with getAdjointModel().

Parameters

adjoint_gradient (numpy.array) – A 3d numpy array either real or complex. If
it must use the RFFT half-complex representation (complex) –

Returns

None

clearAdjointGradient(self: aquila_borg._borg.forward.BORGForwardModel) → None

Clear accumulated information to compute the adjoint gradient vector.

This is automaticalled by getAdjointModelOutput.

forwardModel(self: aquila_borg._borg.forward.BORGForwardModel, arg0: numpy.ndarray[numpy.complex128], arg1: numpy.ndarray[numpy.float64]) → None

forwardModel_v2(self: aquila_borg._borg.forward.BORGForwardModel, arg0: numpy.ndarray) → None

Run the first part of the forward model (v2 API).

Parameters: input_array (numpy.array) – A 3d numpy array either real or complex. If complex it must use the RFFT half-complex representation
Returns: None

getAdjointModel(self: aquila_borg._borg.forward.BORGForwardModel, arg0: numpy.ndarray) → None

getBoxModel(self: aquila_borg._borg.forward.BORGForwardModel) → aquila_borg._borg.forward.BoxModel: Return the box on which is defined the input of the model is defined.

getCommunicator(self: aquila_borg._borg.forward.BORGForwardModel) → object

Build and return an MPI4PY communicator object that is linked to the internal MPI communicator of that object.

Returns: mpi4py.MPI.Comm

getDensityFinal(self: aquila_borg._borg.forward.BORGForwardModel, arg0: numpy.ndarray) → None: Obtain the density field produced by the forward model (part 2 of the evaluation, v2 API).

getMPISlice(self: aquila_borg._borg.forward.BORGForwardModel) → tuple

Returns a tuple of integer indicating the way the slab is distributed among the node.

Returns: (startN0, localN0, N1, N2)
Return type: tuple of int

getModelParam(self: aquila_borg._borg.forward.BORGForwardModel, model: str, keyname: str) → object

This queries the current state of the parameters ‘keyname’ in model ‘model’.

Parameters: model (str) – sub model name keyname (str): key of the parameter to query
Returns: the value of the parameter
Return type: object

getOutputBoxModel(self: aquila_borg._borg.forward.BORGForwardModel) → aquila_borg._borg.forward.BoxModel

Return the box on which is defined the output of the model is defined.

Returns: the box model representing the shape and size of the output of that forward model.
Return type: BoxModel

getOutputMPISlice(self: aquila_borg._borg.forward.BORGForwardModel) → tuple: Return a tuple indicating what is the expected output MPI slicing (startN0,localN0,N1,N2) (Warning! unstable API)

getPreferredInput(self: aquila_borg._borg.forward.BORGForwardModel) → aquila_borg._borg.forward.PreferredIO

Returns the preferred output format (i.e. the one that incurs less transforms) for that model.

Returns: the preferred format
Return type: PreferredIO

getPreferredOutput(self: aquila_borg._borg.forward.BORGForwardModel) → aquila_borg._borg.forward.PreferredIO

Returns the preferred output format (i.e. the one that incurs less transforms) for that model.

Returns: the preferred format
Return type: PreferredIO

holdParticles(self: aquila_borg._borg.forward.BORGForwardModel) → None

setAdjointRequired(self: aquila_borg._borg.forward.BORGForwardModel, arg0: bool) → None

Indicate whether the caller require the adjoint gradient to be computed later.

Parameters: flag (bool) – if True, tape is recorded to compute adjoint

setCosmoParams(self: aquila_borg._borg.forward.BORGForwardModel, cosmo_params: aquila_borg._borg.cosmo.CosmologicalParameters) → None

Setup the cosmological parameters that this model requires.

Parameters: cosmo_params (aquila_borg.cosmo.CosmologicalParameters) – Cosmological parameters

setModelParams(self: aquila_borg._borg.forward.BORGForwardModel, params: dict) → None: Allow changing model parameters for different model indexed by the dictionnary key, each item is another dictionnary with key/value pairs.

setName(self: aquila_borg._borg.forward.BORGForwardModel, name: str) → None

Give a to localize more easily a model instance.

Parameters: name (str) – string name of the instance

Methods

`__init__`	Initialize self.
`accumulateAdjoint`(self, do_accumulate)	Request the model to accumulate adjoint vectors instead of resetting at each call.
`adjointModel_v2`(self, adjoint_gradient)	Pushes the adjoint gradient from a deeper part of the computation.
`clearAdjointGradient`(self)	Clear accumulated information to compute the adjoint gradient vector.
`forwardModel`(self, arg0, arg1)
`forwardModel_v2`(self, arg0)	Run the first part of the forward model (v2 API).
`getAdjointModel`(self, arg0)
`getBoxModel`(self)	Return the box on which is defined the input of the model is defined.
`getCommunicator`(self)	Build and return an MPI4PY communicator object that is linked to the internal MPI communicator of that object.
`getDensityFinal`(self, arg0)	Obtain the density field produced by the forward model (part 2 of the evaluation, v2 API).
`getMPISlice`(self)	Returns a tuple of integer indicating the way the slab is distributed among the node.
`getModelParam`(self, model, keyname)	This queries the current state of the parameters ‘keyname’ in model ‘model’.
`getOutputBoxModel`(self)	Return the box on which is defined the output of the model is defined.
`getOutputMPISlice`(self)	Return a tuple indicating what is the expected output MPI slicing (startN0,localN0,N1,N2) (Warning! unstable API)
`getPreferredInput`(self)	Returns the preferred output format (i.e.
`getPreferredOutput`(self)	Returns the preferred output format (i.e.
`holdParticles`(self)
`setAdjointRequired`(self, arg0)	Indicate whether the caller require the adjoint gradient to be computed later.
`setCosmoParams`(self, cosmo_params)	Setup the cosmological parameters that this model requires.
`setModelParams`(self, params)	Allow changing model parameters for different model indexed by the dictionnary key, each item is another dictionnary with key/value pairs.
`setName`(self, name)	Give a to localize more easily a model instance.