Horizontal re-gridding (regrid / std)¶
Description¶
Re-projects from one domain to another horizontally.
Note
This transform is based on ad-hoc calculations and not on external libraries
for two reasons:
to avoid compatibility issues
to be able to compute both the forward and adjoint of the operations.
A matrix of weights from the original domain to the target one is saved to enable the forward and adjoint operations. It can be copied in a reference folder and re-used for another pyCIF computation to avoid re-computing weights for equivalent re-gridding.
Yaml arguments¶
The following arguments are used to configure the plugin. pyCIF will return an exception at the initialization if mandatory arguments are not specified, or if any argument does not fit accepted values or type:
Mandatory arguments¶
parameter: (mandatory)
Component to reproject
accepted type: str
component: (mandatory)
Component to reproject
accepted type: str
method: (mandatory)
Method by which the original data is spatially interpolated onto the output grid
accepted values:
mass-conservation: area-weighted interpolation. Can be heavy to compute. Suitable when mass needs to be conserved
bilinear: Linear interpolation from the 4 closest corners. For unstructured domains, a Delaunay triangularisation is used.
gridcell: Finds in which grid cell from the original domain are grid cell centers in the target domain.
Optional arguments¶
orig_parameter_plg: (optional)
Plugin object on which the transform works on
accepted type: Plugin
orig_component_plg: (optional)
Corresponding component object on which the transform works on
accepted type: Plugin
successor: (optional)
Name of the successor transform
accepted type: str
precursor: (optional)
Name of the precursor transform
accepted type: str
dir_wgt: (optional):
Directory where pre-computed interpolation weights are available
accepted type: str
min_weight: (optional): 1e-10
Minimum weight to consider when reprojecting. If sensitivity to original dataset is smaller to min_weight, then crops
accepted type: float
orig_regular: (optional): True
Set to FALSE if irregular original grid when reprojecting, True by default
accepted type: bool
target_lbc: (optional): False
Interpolate to the LBC of a domain
accepted type: bool
sparse_in: (optional)
True if the input has the shape of sparse data, i.e., a pandas.DataFrame
accepted type: str
sparse_out: (optional)
True if the output has the shape of sparse data, i.e., a pandas.DataFrame
accepted type: str
rounding_domain: (optional): 8
Number of digits to keep when rounding deltas in the domain to check whether the domain is regular or not. Use with the method mass-conservation
accepted type: int
Yaml template¶
Please find below a template for a Yaml configuration:
1transform:
2 plugin:
3 name: regrid
4 version: std
5 type: transform
6
7 # Mandatory arguments
8 parameter: XXXXX # str
9 component: XXXXX # str
10 method: XXXXX # mass-conservation|bilinear|gridcell
11
12 # Optional arguments
13 orig_parameter_plg: XXXXX # Plugin
14 orig_component_plg: XXXXX # Plugin
15 successor: XXXXX # str
16 precursor: XXXXX # str
17 dir_wgt: XXXXX # str
18 min_weight: XXXXX # float
19 orig_regular: XXXXX # bool
20 target_lbc: XXXXX # bool
21 sparse_in: XXXXX # str
22 sparse_out: XXXXX # str
23 rounding_domain: XXXXX # int
