adam_core.photometry.rotation.jax_backend module¶

class adam_core.photometry.rotation.jax_backend.JAXBatchFitResult(scores: 'npt.NDArray[np.float64]', best_valid: 'bool', best_coeffs: 'npt.NDArray[np.float64]', best_mask: 'npt.NDArray[np.bool_]', best_sigma: 'float', best_rss: 'float', best_df: 'int', best_n_fit: 'int', best_n_clipped: 'int')[source]¶

Bases: object

scores: ndarray[tuple[Any, ...], dtype[float64]]¶

best_valid: bool¶

best_coeffs: ndarray[tuple[Any, ...], dtype[float64]]¶

best_mask: ndarray[tuple[Any, ...], dtype[bool]]¶

best_sigma: float¶

best_rss: float¶

best_df: int¶

best_n_fit: int¶

best_n_clipped: int¶

adam_core.photometry.rotation.jax_backend.evaluate_frequency_indices_jax(*, time_rel: ndarray[tuple[Any, ...], dtype[float64]], y: ndarray[tuple[Any, ...], dtype[float64]], fixed: ndarray[tuple[Any, ...], dtype[float64]], weights: ndarray[tuple[Any, ...], dtype[float64]] | None, prior_rows: ndarray[tuple[Any, ...], dtype[float64]], prior_target: ndarray[tuple[Any, ...], dtype[float64]], prior_weights: ndarray[tuple[Any, ...], dtype[float64]], frequencies: ndarray[tuple[Any, ...], dtype[float64]], sample_indices: ndarray[tuple[Any, ...], dtype[int64]], fourier_order: int, clip_sigma: float, jax_batch_size: int, row_pad_multiple: int, max_clip_iterations: int) → JAXBatchFitResult[source]¶