capytaine.io.xarray module

Tools to use xarray Datasets as inputs and outputs.

Todo

This module could be tidied up a bit and some methods merged or uniformized.

capytaine.io.xarray.assemble_dataframe(results, wavenumber=True, wavelength=True)

capytaine.io.xarray.assemble_dataset(results, omega=True, freq=True, wavenumber=True, wavelength=True, period=True, mesh=False, hydrostatics=True, attrs=None) → Dataset

Transform a list of LinearPotentialFlowResult into a xarray.Dataset.

Todo

The mesh option to store information on the mesh could be improved. It could store the full mesh in the dataset to ensure the reproducibility of the results.

Parameters:

• results (list of LinearPotentialFlowResult or BEMIO dataset) – The results that will be read.

• omega (bool, optional) – If True, the coordinate ‘omega’ will be added to the output dataset.

• freq (bool, optional) – If True, the coordinate ‘freq’ will be added to the output dataset.

• wavenumber (bool, optional) – If True, the coordinate ‘wavenumber’ will be added to the output dataset.

• wavelength (bool, optional) – If True, the coordinate ‘wavelength’ will be added to the output dataset.

• period (bool, optional) – If True, the coordinate ‘period’ will be added to the output dataset.

• mesh (bool, optional) – If True, store some infos on the mesh in the output dataset.

• hydrostatics (bool, optional) – If True, store the hydrostatic data in the output dataset if they exist.

• attrs (dict, optional) – Attributes that should be added to the output dataset.

capytaine.io.xarray.assemble_matrices(results)

Simplified version of assemble_dataset, returning only bare matrices. Meant mainly for teaching without introducing Xarray to beginers.

Parameters:

results (list of LinearPotentialFlowResult) – The results that will be read.

Returns:

The added mass matrix, the radiation damping matrix and the excitation force. If the data are no available in the results, returns None instead.

Return type:

3-ple of (np.arrays or None)

capytaine.io.xarray.export_dataset(filename, dataset, format=None, **kwargs)

Save dataset into a format, provided by the format argument or inferred by the filename.

Parameters:

• filename (str or Path) – Where to store the data

• dataset (xarray.Dataset) – Dataset, which is assumed to have been computed by Capytaine

• format (str, optional) – Format of output. Accepted values: “netcdf”

• **kwargs (optional) – Remaining argument are passed to the specific export function, such as save_dataset_as_netcdf, export_to_wamit or write_dataset_as_tecplot_files.

Return type:

None

capytaine.io.xarray.hydrostatics_dataset(bodies: Sequence[FloatingBody]) → Dataset

Create a dataset by looking for ‘inertia_matrix’ and ‘hydrostatic_stiffness’ for each of the bodies in the list passed as argument.

capytaine.io.xarray.kochin_data_array(results: Sequence[LinearPotentialFlowResult], theta_range: Sequence[float], **kwargs) → Dataset

Compute the Kochin function for a list of results and fills a dataset.

See also

compute_kochin()

The present function is just a wrapper around compute_kochin.

capytaine.io.xarray.merge_complex_values(ds: Dataset) → Dataset

Return a new Dataset where real-valued arrays of shape (2, …) have been replaced by complex-valued arrays of shape (…).

See also

separate_complex_values()

The invert operation

capytaine.io.xarray.problems_from_dataset(dataset: Dataset, bodies: FloatingBody | Sequence[FloatingBody]) → List[LinearPotentialFlowProblem]

Generate a list of problems from a test matrix.

Parameters:

• dataset (xarray Dataset) – Test matrix containing the problems parameters.

• bodies (FloatingBody or list of FloatingBody) – The bodies on which the computations of the test matrix will be applied. They should all have different names.

Return type:

list of LinearPotentialFlowProblem

Raises:

ValueError – if required fields are missing in the dataset

capytaine.io.xarray.save_dataset_as_netcdf(filename, dataset)

Save dataset as a NetCDF file with name (or path) filename

capytaine.io.xarray.separate_complex_values(ds: Dataset) → Dataset

Return a new Dataset where complex-valued arrays of shape (…) have been replaced by real-valued arrays of shape (2, …).

See also

merge_complex_values()

The invert operation