apply_mask#
- echopype.mask.apply_mask(source_ds: Union[Dataset, str, Path], mask: Union[DataArray, str, Path, List[Union[DataArray, str, Path]]], var_name: str = 'Sv', fill_value: Union[int, float, ndarray, DataArray] = nan, storage_options_ds: dict = {}, storage_options_mask: Union[dict, List[dict]] = {}) Dataset #
Applies the provided mask(s) to the Sv variable
var_name
in the provided Datasetsource_ds
.- Parameters
- source_ds: xr.Dataset, str, or pathlib.Path
Points to a Dataset that contains the variable the mask should be applied to
- mask: xr.DataArray, str, pathlib.Path, or a list of these datatypes
The mask(s) to be applied. Can be a single input or list that corresponds to a DataArray or a path. Each entry in the list must have dimensions
('ping_time', 'range_sample')
. Multi-channel masks are not currently supported. If a path is provided this should point to a zarr or netcdf file with only one data variable in it. If the inputmask
is a list, a logical AND will be used to produce the final mask that will be applied tovar_name
.- var_name: str, default=”Sv”
The Sv variable name in
source_ds
that the mask should be applied to. This variable needs to have coordinatesping_time
andrange_sample
, and can optionally also have coordinatechannel
. In the case of a multi-channel Sv data variable, themask
will be broadcast to all channels.- fill_value: int, float, np.ndarray, or xr.DataArray, default=np.nan
Value(s) at masked indices. If
fill_value
is of typenp.ndarray
orxr.DataArray
, it must have the same shape as each entry ofmask
.- storage_options_ds: dict, default={}
Any additional parameters for the storage backend, corresponding to the path provided for
source_ds
- storage_options_mask: dict or list of dict, default={}
Any additional parameters for the storage backend, corresponding to the path provided for
mask
. Ifmask
is a list, then this input should either be a list of dictionaries or a single dictionary with storage options that correspond to all elements inmask
that are paths.
- Returns
- xr.Dataset
A Dataset with the same format of
source_ds
with the mask(s) applied tovar_name