rompy.core.boundary.BoundaryWaveStation#
- pydantic model rompy.core.boundary.BoundaryWaveStation[source]#
Wave boundary data from station datasets.
Note
The tolerance behaves differently with sel_methods idw and nearest; in idw sites with no enough neighbours within tolerance are masked whereas in nearest an exception is raised (see wavespectra documentation for more details).
Note
Be aware that when using idw missing values will be returned for sites with less than 2 neighbours within tolerance in the original dataset. This is okay for land mask areas but could cause boundary issues when on an open boundary location. To avoid this either use nearest or increase tolerance to include more neighbours.
Show JSON schema
{ "title": "BoundaryWaveStation", "description": "Wave boundary data from station datasets.\n\nNote\n----\nThe `tolerance` behaves differently with sel_methods `idw` and `nearest`; in `idw`\nsites with no enough neighbours within `tolerance` are masked whereas in `nearest`\nan exception is raised (see wavespectra documentation for more details).\n\nNote\n----\nBe aware that when using `idw` missing values will be returned for sites with less\nthan 2 neighbours within `tolerance` in the original dataset. This is okay for land\nmask areas but could cause boundary issues when on an open boundary location. To\navoid this either use `nearest` or increase `tolerance` to include more neighbours.", "type": "object", "properties": { "model_type": { "const": "boundary", "default": "data_boundary", "description": "Model type discriminator", "title": "Model Type", "type": "string" }, "id": { "description": "Unique identifier for this data source", "title": "Id", "type": "string" }, "source": { "description": "Dataset source reader, must return a wavespectra-enabled xarray dataset in the open method", "discriminator": { "mapping": { "csv": "#/$defs/SourceTimeseriesCSV", "datamesh": "#/$defs/SourceDatamesh", "file": "#/$defs/SourceFile", "intake": "#/$defs/SourceIntake", "wavespectra": "#/$defs/SourceWavespectra" }, "propertyName": "model_type" }, "oneOf": [ { "$ref": "#/$defs/SourceTimeseriesCSV" }, { "$ref": "#/$defs/SourceDatamesh" }, { "$ref": "#/$defs/SourceFile" }, { "$ref": "#/$defs/SourceIntake" }, { "$ref": "#/$defs/SourceWavespectra" } ], "title": "Source" }, "link": { "default": false, "description": "Whether to create a symbolic link instead of copying the file", "title": "Link", "type": "boolean" }, "filter": { "anyOf": [ { "$ref": "#/$defs/Filter" }, { "type": "null" } ], "description": "Optional filter specification to apply to the dataset" }, "variables": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "default": [], "description": "Subset of variables to extract from the dataset", "title": "Variables" }, "coords": { "anyOf": [ { "$ref": "#/$defs/DatasetCoords" }, { "type": "null" } ], "default": { "t": "time", "x": "longitude", "y": "latitude", "z": "depth", "s": "site" }, "description": "Names of the coordinates in the dataset" }, "crop_data": { "default": true, "description": "Update crop filter from Time object if passed to get method", "title": "Crop Data", "type": "boolean" }, "buffer": { "default": 2.0, "description": "Space to buffer the grid bounding box if `filter_grid` is True", "title": "Buffer", "type": "number" }, "time_buffer": { "default": [ 0, 0 ], "description": "Number of source data timesteps to buffer the time range if `filter_time` is True", "items": { "type": "integer" }, "title": "Time Buffer", "type": "array" }, "spacing": { "anyOf": [ { "type": "number" }, { "const": "parent", "type": "string" }, { "type": "null" } ], "default": null, "description": "Spacing between points along the grid boundary to retrieve data for. If None (default), points are defined from the the actual grid object passed to the `get` method. If 'parent', the resolution of the parent dataset is used to define the spacing.", "title": "Spacing" }, "sel_method": { "default": "idw", "description": "Wavespectra method to use for selecting boundary points from the dataset", "enum": [ "idw", "nearest" ], "title": "Sel Method", "type": "string" }, "sel_method_kwargs": { "additionalProperties": true, "default": {}, "description": "Keyword arguments for sel_method", "title": "Sel Method Kwargs", "type": "object" }, "grid_type": { "const": "boundary_wave_station", "default": "boundary_wave_station", "description": "Model type discriminator", "title": "Grid Type", "type": "string" } }, "$defs": { "DatasetCoords": { "additionalProperties": false, "description": "Coordinates representation.", "properties": { "t": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": "time", "description": "Name of the time coordinate", "title": "T" }, "x": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": "longitude", "description": "Name of the x coordinate", "title": "X" }, "y": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": "latitude", "description": "Name of the y coordinate", "title": "Y" }, "z": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": "depth", "description": "Name of the z coordinate", "title": "Z" }, "s": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": "site", "description": "Name of the site coordinate", "title": "S" } }, "title": "DatasetCoords", "type": "object" }, "Filter": { "additionalProperties": false, "properties": { "sort": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "default": {}, "title": "Sort" }, "subset": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "default": {}, "title": "Subset" }, "crop": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "default": {}, "title": "Crop" }, "timenorm": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "default": {}, "title": "Timenorm" }, "rename": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "default": {}, "title": "Rename" }, "derived": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "default": {}, "title": "Derived" } }, "title": "Filter", "type": "object" }, "SourceDatamesh": { "additionalProperties": false, "description": "Source dataset from Datamesh.\n\nDatamesh documentation: https://docs.oceanum.io/datamesh/index.html", "properties": { "model_type": { "const": "datamesh", "default": "datamesh", "description": "Model type discriminator", "title": "Model Type", "type": "string" }, "datasource": { "description": "The id of the datasource on Datamesh", "title": "Datasource", "type": "string" }, "token": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Datamesh API token, taken from the environment if not provided", "title": "Token" }, "kwargs": { "additionalProperties": true, "default": {}, "description": "Keyword arguments to pass to `oceanum.datamesh.Connector`", "title": "Kwargs", "type": "object" } }, "required": [ "datasource", "token" ], "title": "SourceDatamesh", "type": "object" }, "SourceFile": { "additionalProperties": false, "description": "Source dataset from file to open with xarray.open_dataset.", "properties": { "model_type": { "const": "file", "default": "file", "description": "Model type discriminator", "title": "Model Type", "type": "string" }, "uri": { "anyOf": [ { "type": "string" }, { "format": "path", "type": "string" } ], "description": "Path to the dataset", "title": "Uri" }, "kwargs": { "additionalProperties": true, "default": {}, "description": "Keyword arguments to pass to xarray.open_dataset", "title": "Kwargs", "type": "object" } }, "required": [ "uri" ], "title": "SourceFile", "type": "object" }, "SourceIntake": { "additionalProperties": false, "description": "Source dataset from intake catalog.\n\nnote\n----\nThe intake catalog can be prescribed either by the URI of an existing catalog file\nor by a YAML string defining the catalog. The YAML string can be obtained from\ncalling the `yaml()` method on an intake dataset instance.", "properties": { "model_type": { "const": "intake", "default": "intake", "description": "Model type discriminator", "title": "Model Type", "type": "string" }, "dataset_id": { "description": "The id of the dataset to read in the catalog", "title": "Dataset Id", "type": "string" }, "catalog_uri": { "anyOf": [ { "type": "string" }, { "format": "path", "type": "string" }, { "type": "null" } ], "default": null, "description": "The URI of the catalog to read from", "title": "Catalog Uri" }, "catalog_yaml": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "The YAML string of the catalog to read from", "title": "Catalog Yaml" }, "kwargs": { "additionalProperties": true, "default": {}, "description": "Keyword arguments to define intake dataset parameters", "title": "Kwargs", "type": "object" } }, "required": [ "dataset_id" ], "title": "SourceIntake", "type": "object" }, "SourceTimeseriesCSV": { "additionalProperties": false, "description": "Timeseries source class from CSV file.\n\nThis class should return a timeseries from a CSV file. The dataset variables are\ndefined from the column headers, therefore the appropriate read_csv kwargs must be\npassed to allow defining the columns. The time index is defined from column name\nidentified by the tcol field.", "properties": { "model_type": { "const": "csv", "default": "csv", "description": "Model type discriminator", "title": "Model Type", "type": "string" }, "filename": { "anyOf": [ { "type": "string" }, { "format": "path", "type": "string" } ], "description": "Path to the csv file", "title": "Filename" }, "tcol": { "default": "time", "description": "Name of the column containing the time data", "title": "Tcol", "type": "string" }, "read_csv_kwargs": { "additionalProperties": true, "default": {}, "description": "Keyword arguments to pass to pandas.read_csv", "title": "Read Csv Kwargs", "type": "object" } }, "required": [ "filename" ], "title": "SourceTimeseriesCSV", "type": "object" }, "SourceWavespectra": { "additionalProperties": false, "description": "Wavespectra dataset from wavespectra reader.", "properties": { "model_type": { "const": "wavespectra", "default": "wavespectra", "description": "Model type discriminator", "title": "Model Type", "type": "string" }, "uri": { "anyOf": [ { "type": "string" }, { "format": "path", "type": "string" } ], "description": "Path to the dataset", "title": "Uri" }, "reader": { "description": "Name of the wavespectra reader to use, e.g., read_swan", "title": "Reader", "type": "string" }, "kwargs": { "additionalProperties": true, "default": {}, "description": "Keyword arguments to pass to the wavespectra reader", "title": "Kwargs", "type": "object" } }, "required": [ "uri", "reader" ], "title": "SourceWavespectra", "type": "object" } }, "additionalProperties": false, "required": [ "id", "source" ] }
- Fields:
- Validators:
- field buffer: float = 2.0#
Space to buffer the grid bounding box if filter_grid is True
- field grid_type: Literal['boundary_wave_station'] = 'boundary_wave_station'#
Model type discriminator
- field sel_method: Literal['idw', 'nearest'] = 'idw'#
Wavespectra method to use for selecting boundary points from the dataset
- field source: SourceTimeseriesCSV | SourceDatamesh | SourceFile | SourceIntake | SourceWavespectra [Required]#
Dataset source reader, must return a wavespectra-enabled xarray dataset in the open method
- get(destdir: str | Path, grid: RegularGrid, time: TimeRange | None = None) str[source]#
Write the selected boundary data to a netcdf file.
- Parameters:
destdir (str | Path) – Destination directory for the netcdf file.
grid (RegularGrid) – Grid instance to use for selecting the boundary points.
time (TimeRange, optional) – The times to filter the data to, only used if self.crop_data is True.
- Returns:
outfile – Path to the netcdf file.
- Return type:
Path
- model_post_init(_BoundaryWaveStation__context)[source]#
This function is meant to behave like a BaseModel method to initialise private attributes.
It takes context as an argument since that’s what pydantic-core passes when calling it.
- Parameters:
self – The BaseModel instance.
context – The context.
- property ds#
Return the filtered xarray dataset instance.