core

Core module for generating models and their gradients.

model = jax.jit(model, static_argnums=model_static) module-attribute

Generically create models with substructure.

Parameters:

Name	Type	Description	Default
xyz	tuple[Array, Array, Array, float, float]	Grid to compute model on. See containers.Model.xyz for details.	required
n_struct	tuple[int, ...]	Number of each structure to use. Should be in the same order as order.	required
dz	float	Factor to scale by while integrating. Should at least include the pixel size along the LOS.	required
beam	Array	Beam to convolve by, should be a 2d array.	required
*pars	Unpack[tuple[float, ...]]	1D container of model parameters.	required

Returns:

Name	Type	Description
model	Array	The model with the specified substructure evaluated on the grid.

model_grad = jax.jit(model_grad, static_argnums=model_grad_static) module-attribute

A wrapper around model that also returns the gradients of the model. Only the additional arguments are described here, see model for the others. Note that the additional arguments are passed before the *params argument.

Parameters:

Name	Type	Description	Default
argnums	tuple[int, ...]	The indices of the arguments to evaluate the gradient at.	required

Returns:

Name	Type	Description
model	Array	The model with the specified substructure evaluated on the grid.
grad	Array	The gradient of the model with respect to the model parameters. Has shape (len(pars),) + model.shape).