gt4py_utils

`make_storage_data(data, shape=None, origin=origin, *, backend, dtype=Float, mask=None, start=(0, 0, 0), dummy=None, axis=2, max_dim=3, read_only=True)`

Create a new gt4py storage from the given data.

Parameters:

Name	Type	Description	Default
`data`	`NDArray`	Data array for new storage	required
`shape`	`tuple[int, ...] \| None`	Shape of the new storage. Number of indices should be equal to number of unmasked axes	`None`
`origin`	`tuple[int, ...]`	Default origin for gt4py stencil calls	`origin`
`backend`	`Backend`	current backend in use	required
`dtype`	`DTypeLike`	Data type	`Float`
`mask`	`tuple[bool, ...] \| None`	Tuple indicating the axes used when initializing the storage. True indicates a masked axis, False is a used axis.	`None`
`start`	`tuple[int, ...]`	Starting points for slices in data copies	`(0, 0, 0)`
`dummy`	`tuple[int, ...] \| None`	Dummy axes	`None`
`axis`	`int`	Axis for 2D to 3D arrays	`2`
`max_dim`	`int`	Number of cartesian dimensions. Those will be index-aligned, while additional "data" dimensions are considered "en block".	`3`
`read_only`	`bool`	?	`True`

Returns:

Type	Description
`NDArray`	Field[..., dtype]: New storage

Example

ptop = utils.make_storage_data(top_p, q4_1.shape)

Example

ws3 = utils.make_storage_data(ws3[:, :, -1], shape, origin=(0, 0, 0))

Example

data_dict[names[i]] = make_storage_data(
       data[:, :, :, i],
       shape,
       origin=origin,
       start=start,
       dummy=dummy,
       axis=axis,
   )

`make_storage_from_shape(shape, origin=origin, *, backend, dtype=Float, mask=None)`

Create a new gt4py storage of a given shape filled with zeros.

Parameters:

Name	Type	Description	Default
`shape`	`tuple[int, ...]`	Shape of the new storage	required
`origin`	`tuple[int, ...]`	Default origin for gt4py stencil calls	`origin`
`dtype`	`DTypeLike`	Data type	`Float`
`mask`	`tuple[bool, ...] \| None`	Tuple indicating the axes used when initializing the storage	`None`
`backend`	`Backend`	gt4py backend to use when making the storage	required

Returns:

Type	Description
`NDArray`	Field[..., dtype]: New storage

Example

utmp = utils.make_storage_from_shape(ua.shape)

Example

qx = utils.make_storage_from_shape(
       qin.shape, origin=(grid().is_, grid().jsd, kstart)
   )

Example

q_out = utils.make_storage_from_shape(q_in.shape, origin)

`run_once(f)`

Python trick to enforce function is only called once

`split_cartesian_into_storages(var)`

Provided a storage of dims [I_DIM, J_DIM, CARTESIAN_DIM] or [I_INTERFACE_DIM, J_INTERFACE_DIM, CARTESIAN_DIM]. Split it into separate 2D storages for each cartesian dimension, and return these in a list.