pyissm.model.classes.sampling

class pyissm.model.classes.sampling(other=None)

Bases: manage_state

Sampling parameters class for ISSM.

This class encapsulates parameters for stochastic sampling in the ISSM (Ice Sheet System Model) framework. It configures parameters for generating random fields using PDE-based operators and autoregressive processes, useful for uncertainty quantification and stochastic forcing applications.

Parameters:: other (any, optional) – Any other class object that contains common fields to inherit from. If values in other differ from default values, they will override the default values.

kappa

Coefficient of the identity operator in PDE operator (kappa^2 I - Laplacian)^(alpha/2)(tau).

Type:: float, default=nan

tau

Scaling coefficient of the solution.

Type:: float, default=0

beta

Coefficient in Robin boundary conditions (to be defined for robin = 1).

Type:: float, default=nan

phi

Temporal correlation factor for first-order autoregressive process X_t = phi X_{t-1} + noise (|phi|<1 for stationary process, phi = 1 for random walk process).

Type:: float, default=nan

alpha

Exponent in PDE operator (default: 2.0, BiLaplacian covariance operator).

Type:: float, default=2

robin

Apply Robin boundary conditions (1 if applied and 0 for homogeneous Neumann boundary conditions).

Type:: int, default=0

seed

Seed for pseudorandom number generator (given seed if >=0 and random seed if <0).

Type:: int, default=-1

requested_outputs

Additional outputs requested (not implemented yet).

Type:: list, default=[]

__init__(self, other=None): Initializes the sampling parameters, optionally inheriting from another instance.

__repr__(self): Returns a detailed string representation of the sampling parameters.

__str__(self): Returns a short string identifying the class.

_process_outputs(self, md=None, return_default_outputs=False): Process requested outputs, expanding ‘default’ to appropriate outputs.

marshall_class(self, fid, prefix, md=None): Marshall parameters to a binary file

Examples

md.sampling = pyissm.model.classes.sampling() md.sampling.kappa = 0.1 md.sampling.alpha = 2.0 md.sampling.phi = 0.9 md.sampling.robin = 1 md.sampling.beta = 0.5

__init__(other=None)

Initialize the instance with optional field inheritance.

Parameters:: other (object, optional) – Another instance to inherit field values from. If provided, any fields in the current instance that differ from the default values and exist in ‘other’ will be replaced with the values from ‘other’.

Notes

The inheritance process:

Iterates through all attributes of the current instance

Checks if the same attribute exists in the ‘other’ instance

Compares field values using _fields_equal()

If different, replaces the current value with the ‘other’ value

This enables the common ISSM pattern where classes can inherit configurations from existing instances while maintaining their default values for unspecified fields.

Methods

`__init__`([other])	Initialize the instance with optional field inheritance.
`check_consistency`(md, solution, analyses)	Check consistency of the [sampling.sampling] parameters.
`issm_enum_string`()	Default ISSM C++ enum string used during marshalling.
`marshall_class`(fid, prefix[, md])	Marshall [sampling.sampling] parameters to a binary file.

check_consistency(md, solution, analyses)

Check consistency of the [sampling.sampling] parameters.

Parameters:

md (pyissm.model.Model) – The model object to check.
solution (str) – The solution name to check.
analyses (list of str) – List of analyses to check consistency for.

Returns:

md – The model object with any consistency errors noted.

Return type:

pyissm.model.Model

marshall_class(fid, prefix, md=None)

Marshall [sampling.sampling] parameters to a binary file.

Parameters:

fid (file object) – The file object to write the binary data to.
prefix (str) – Prefix string used for data identification in the binary file.
md (ISSM model object, optional.) – ISSM model object needed in some cases.

Return type:

None