BorgLpt

class aquila_borg.forward.models.BorgLpt

This class binds the borg LPT forward model (CIC mass assignment).

Parameters

box (BoxModel) – Input box comoving size

Keyword Arguments

  • box_out (BoxModel) – Output box (default is equal to input).

  • rsd (bool) – inject redshift space distortions, default false.

  • supersampling (int) – supersampling factor, i.e. by multiplicative factor, for each side, between particle array and input array [default 1].

  • particle_factor (float) – allocation ratio of the particle arrays (must be > 1) [default 1.1]

  • ai (float) – scale factor used for the input density field (default 0.1)

  • af (float) – scale factor requested for the output (default 1.0)

  • lightcone (bool) – if true, add lightcone effect at order 0, (default false)

  • lightcone_boost (float) – make the structures goes quicker, useful for art drawing of lightcones (default 1.0)

Methods

__init__(self, box, box_out, rsd, …)

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’.

getNumberOfParticles(self)

Return the number of particles present on the current MPI task

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)

getParticlePositions(self, positions)

Return the positions of the particles in the provided numpy array

getParticleVelocities(self, velocities)

Return the velocities of the particles in the provided numpy array

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.

setStepNotifier(self, arg0)

Setup a callback when a new step is being computed for particles.