seaduck.get_masks

seaduck.get_masks#

seaduck.get_masks.abandon_stuck(p)#

Abandon those stucked in mud.

seaduck.get_masks.get_mask_arrays(od)#

Mask all staggered valocity points.

A wrapper around mask_u_node, mask_v_node, mask_w_node. If there is no maskC in the dataset, just return nothing is masked.

Parameters:

  • od (OceData object) – The dataset to compute masks on.

  • tp (Topology object) – The Topology of the datset

Returns:

maskC,maskU,maskV,maskW – masks at center points, U-walls, V-walls, W-walls respectively.

Return type:

numpy.ndarray

seaduck.get_masks.get_masked(od, ind, cuvwg='C')#

Return whether points are masked.

Return whether the indexes of intersts are masked or not.

Parameters:

  • od (OceData object) – Dataset to find mask values from.

  • ind (tuple of numpy.ndarray) – Indexes of grid points.

  • cuvwg (str) – Whether the indexes is for points at center points or on the walls. Options are: [‘C’,’U’,’V’,’Wvel’].

seaduck.get_masks.mask_u_node(maskC, tp)#

Mask out U-points.

for MITgcm indexing, U is defined on the left of the cell, When the C grid is dry, U are either: a. dry; b. on the interface, where the cell to the left is wet. if b is the case, we need to unmask the udata, because it makes some physical sense.

Parameters:

  • maskC (numpy.ndarray) – numpy array with the same shape as the model spacial coordinates. 1 for wet cells (center points), 0 for dry ones.

  • tp (Topology object) – The Topology object for the dataset.

Returns:

maskU – numpy array with the same shape as the model spacial coordinates. 1 for wet U-walls (including interface between wet and dry), 0 for dry ones.

Return type:

numpy.ndarray

seaduck.get_masks.mask_v_node(maskC, tp)#

Mask out v-points.

for MITgcm indexing, V is defined on the “south” side of the cell, When the C grid is dry, V are either: a. dry; b. on the interface, where the cell to the downside is wet. if b is the case, we need to unmask the vdata.

Parameters:

  • maskC (numpy.ndarray) – numpy array with the same shape as the model spacial coordinates. 1 for wet cells (center points), 0 for dry ones.

  • tp (Topology object) – The Topology object for the dataset.

Returns:

+ maskV – numpy array with the same shape as the model spacial coordinates. 1 for wet W-walls (including interface between wet and dry), 0 for dry ones.

Return type:

numpy.ndarray

seaduck.get_masks.mask_w_node(maskC, tp=None)#

Mask out W-points.

for MITgcm indexing, W is defined on the top of the cell, When the C grid is dry, W are either: a. dry; b. on the interface, where the cell above is wet. if b is the case, we need to unmask the wdata.

Parameters:

  • maskC (numpy.ndarray) – numpy array with the same shape as the model spacial coordinates. 1 for wet cells (center points), 0 for dry ones.

  • tp (Topology object) – The Topology object for the dataset.

Returns:

+ maskWvel – numpy array with the same shape as the model spacial coordinates. 1 for wet W-walls (including interface between wet and dry), 0 for dry ones.

Return type:

numpy.ndarray

seaduck.get_masks.which_not_stuck(p)#

Investigate which points are in land mask.