hysop.backend.host.python.operator.derivative module

class hysop.backend.host.python.operator.derivative.PythonFiniteDifferencesSpaceDerivative(**kwds)

Bases: FiniteDifferencesSpaceDerivativeBase, HostOperator

Compute a derivative of a scalar field in a given direction using explicit finite differences.

Initialize a FiniteDifferencesSpaceDerivative operator on the python backend.

See hysop.operator.base.derivative.FiniteDifferencesSpaceDerivativeBase for more information.

Parameters:

kwds (dict, optional) – Base class arguments.

apply(**kwds)

Abstract method that should be implemented. Applies this node (operator, computational graph operator…).

discretize()

By default, an operator discretize all its variables. For each input continuous field that is also an output field, input topology may be different from the output topology.

After this call, one can access self.input_discrete_fields and self.output_discrete_fields, which contains input and output dicretised fields mapped by continuous fields.

self.discrete_fields will be a tuple containing all input and output discrete fields.

Discrete tensor fields are built back from discretized scalar fields and are accessible from self.input_tensor_fields, self.output_tensor_fields and self.discrete_tensor_fields like their scalar counterpart.

get_field_requirements()

Called just after handle_method(), ie self.method has been set. Field requirements are:

1. required local and global transposition state, if any.

2. required memory ordering (either C or Fortran)

Default is Backend.HOST, no min or max ghosts, MemoryOrdering.ANY and no specific default transposition state for each input and output variables.

handle_method(method)

Method automatically called during initialization. This allow to extract method values after method preprocessing. Method preprocessing means:

1. complete user input with compatible top graph user inputs

2. complete the resulting dictionnary with the node default_method

3. check method against available_methods.

The result of this process is fed as argument of this function.

setup(work)

Setup temporary buffer that have been requested in get_work_properties(). This function may be used to execute post allocation routines. This sets self.ready flag to True. Once this flag is set one may call ComputationalGraphNode.apply() and ComputationalGraphNode.finalize().

Automatically honour temporary field memory requests.

class hysop.backend.host.python.operator.derivative.PythonSpectralSpaceDerivative(testing=False, **kwds)

Bases: SpectralSpaceDerivativeBase, HostOperator

Compute a derivative of a scalar field in a given direction using spectral methods.

Initialize a spectral operator base. kwds: dict

Base class keyword arguments.

apply(**kwds)

Abstract method that should be implemented. Applies this node (operator, computational graph operator…).

compute_derivative()

scale_derivative()

setup(work)

Setup temporary buffer that have been requested in get_work_properties(). This function may be used to execute post allocation routines. This sets self.ready flag to True. Once this flag is set one may call ComputationalGraphNode.apply() and ComputationalGraphNode.finalize().

Automatically honour temporary field memory requests.