qimpy.grid.FieldH

class FieldH(grid, *, shape_batch=(), data=None)

Bases: Field[FieldH]

Real fields in (half) reciprocal space. Note that the underlying data is complex in reciprocal space, but reduced to one half of reciprocal space using Hermitian symmetry.

Parameters

grid (Grid) –
shape_batch (Sequence[int]) –
data (Optional[torch.Tensor]) –

__init__(grid, *, shape_batch=(), data=None)

Initialize to zeros or specified data.

Parameters

grid (Grid) – Associated grid, which determines last three dimensions of data
shape_batch (Sequence[int]) – Optional preceding batch dimensions for vector fields, arrays of scalar fields etc. Not used if data is provided.
data (Optional[Tensor]) – Initial data if provided; initialize to zero otherwise

Return type

None

Methods

`__init__`	Initialize to zeros or specified data.
`add_`	Add in-place with optional scale factor (Mirroring torch.Tensor.add_).
`clone`	Create field with cloned data (deep copy).
`convolve`	Convolve scalar field by reciprocal-space kernel_tilde.
`divergence`	Divergence of field.
`dot`	Compute broadcasted inner product \(\int a^\dagger b\).
`dtype`	Data type for the Field type
`get_origin_index`	Return index into local data of the spatial index = 0 component(s), which corresponds to r = 0 for real-space fields and to the G = 0 component for reciprocal-space fields.
`gradient`	Gradient of field.
`integral`	Compute integral over unit cell, retaining batch dimensions in output.
`laplacian`	Laplacian of field.
`norm`	Norm of a field, defined by \(\sqrt{\int \|a\|^2}\).
`offset_grid_mine`	Offset of local grid dimensions into global grid for Field type
`read`	Read field from cp_path.
`requires_grad_`	Set whether gradient with respect to this object is needed.
`shape_grid`	Global grid shape for the Field type
`shape_grid_mine`	Local grid shape (last 3 data dimensions) for the Field type
`symmetrize`	Symmetrize field in-place.
`to`	Switch field to another grid with possibly different shape.
`vdot`	Vector-space dot product of data summed over all dimensions.
`write`	Write field to cp_path.
`zeros_like`	Create zero Field with same grid and batch dimensions.

Attributes

`is_complex`	Whether this represents a complex scalar field.
`is_tilde`	Whether this field is in reciprocal space.
`o`	Slice of data corresponding to `get_origin_index()`.
`requires_grad`	Return whether gradient with respect to this object is needed.

dtype()

Data type for the Field type

Return type: dtype

offset_grid_mine()

Offset of local grid dimensions into global grid for Field type

Return type: tuple[int, …]

shape_grid()

Global grid shape for the Field type

Return type: tuple[int, …]

shape_grid_mine()

Local grid shape (last 3 data dimensions) for the Field type

Return type: tuple[int, …]

symmetrize()

Symmetrize field in-place.

Return type: None

to(grid)

Switch field to another grid with possibly different shape. This routine will perform Fourier resampling and MPI rearrangements, as necessary.

Parameters: grid (Grid) –
Return type: FieldH

data: torch.Tensor: Underlying data, with last three dimensions on grid

grad: GradientType: optional gradient (of energy) with respect to this object.

grid: Grid: Associated grid that determines dimensions of field

property is_complex: bool: Whether this represents a complex scalar field. Note that a real scalar field has complex Fourier transform coefficients, but would still be considered real here (i.e. this is False for FieldH).

property is_tilde: bool: Whether this field is in reciprocal space. (Corresponding variable names are typically suffixed by ‘_tilde’.)