rompy.schism.data.SCHISMDataWave#
- pydantic model rompy.schism.data.SCHISMDataWave[source]#
This class is used to write wave spectral boundary data. Spectral data is extracted from the nearest points along the grid boundary
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{ "title": "SCHISMDataWave", "description": "This class is used to write wave spectral boundary data. Spectral data is extracted\nfrom the nearest points along the grid boundary", "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": "#/$defs/DatasetCoords" }, { "type": "null" } ], "default": { "t": "time", "x": "longitude", "y": "latitude", "z": "depth", "s": "site" }, "description": "Names of the coordinates in the dataset" }, "crop_data": { "default": true, "description": "Update crop filter from Time object if passed to get method", "title": "Crop Data", "type": "boolean" }, "buffer": { "default": 2.0, "description": "Space to buffer the grid bounding box if `filter_grid` is True", "title": "Buffer", "type": "number" }, "time_buffer": { "default": [ 0, 1 ], "description": "Number of source data timesteps to buffer the time range if `filter_time` is True", "items": { "type": "integer" }, "title": "Time Buffer", "type": "array" }, "spacing": { "anyOf": [ { "type": "number" }, { "const": "parent", "type": "string" }, { "type": "null" } ], "default": null, "description": "Spacing between points along the grid boundary to retrieve data for. If None (default), points are defined from the the actual grid object passed to the `get` method. If 'parent', the resolution of the parent dataset is used to define the spacing.", "title": "Spacing" }, "sel_method": { "additionalProperties": true, "default": "nearest", "description": "Keyword arguments for sel_method", "title": "Sel Method", "type": "object" }, "sel_method_kwargs": { "additionalProperties": true, "default": { "unique": true }, "description": "Keyword arguments for sel_method", "title": "Sel Method Kwargs", "type": "object" }, "grid_type": { "const": "boundary_wave_station", "default": "boundary_wave_station", "description": "Model type discriminator", "title": "Grid Type", "type": "string" }, "data_type": { "const": "wave", "default": "wave", "description": "Model type discriminator", "title": "Data Type", "type": "string" } }, "$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 data_type: Literal['wave'] = 'wave'#
Model type discriminator
- field sel_method: dict = 'nearest'#
Keyword arguments for sel_method
- field sel_method_kwargs: dict = {'unique': True}#
Keyword arguments for sel_method
- field time_buffer: list[int] = [0, 1]#
Number of source data timesteps to buffer the time range if filter_time is True
- get(destdir: str | Path, grid: SCHISMGrid, time: TimeRange | None = None) str[source]#
Write the selected boundary data to a netcdf file. :param destdir: Destination directory for the netcdf file. :type destdir: str | Path :param grid: Grid instance to use for selecting the boundary points. :type grid: SCHISMGrid :param time: The times to filter the data to, only used if self.crop_data is True. :type time: TimeRange, optional
- Returns:
outfile – Path to the netcdf file.
- Return type:
Path
- model_post_init(_BoundaryWaveStation__context)#
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.