punchbowl.levelq.pca#

Functions#

`pca_filter`(→ None)	Run PCA-based filtering.
`load_files`(→ tuple[numpy.ndarray, numpy.ndarray, ...)	Load files.
`pca_filter_one_stride`(→ tuple[numpy.ndarray, ...)	Run PCA-based filtering for one stride position.
`run_pca_filtering`(→ numpy.ndarray)	Run PCA filtering.
`find_bodies_in_image_quarters`(→ list)	Find celestial bodies in image.

Module Contents#

punchbowl.levelq.pca.pca_filter(input_cubes: list[ndcube.NDCube], files_to_fit: list[ndcube.NDCube | punchbowl.util.DataLoader | str], n_components: int = 50, med_filt: int = 5, n_strides: int = 8, blend_size: int = 70) → None[source]#: Run PCA-based filtering.

punchbowl.levelq.pca.load_files(input_cubes: list[ndcube.NDCube], files_to_fit: list[ndcube.NDCube | str | punchbowl.util.DataLoader], blend_size: int = 70) → tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray][source]#: Load files.

punchbowl.levelq.pca.pca_filter_one_stride(all_files_to_fit: numpy.ndarray, stride: int, n_strides: int, bodies_in_quarter: numpy.ndarray, input_list_indices: numpy.ndarray, n_components: int, med_filt: int, blend_size: int, good_data_mask: numpy.ndarray, is_outlier: numpy.ndarray, logger: logging.Logger) → tuple[numpy.ndarray, numpy.ndarray][source]#: Run PCA-based filtering for one stride position.

punchbowl.levelq.pca.run_pca_filtering(images_to_subtract: numpy.ndarray, images_to_fit: numpy.ndarray, n_components: int, med_filt: int, tag: str, good_data_mask: numpy.ndarray, logger: logging.Logger) → numpy.ndarray[source]#: Run PCA filtering.

punchbowl.levelq.pca.find_bodies_in_image_quarters(frame: str | ndcube.NDCube | tuple[punchbowl.data.NormalizedMetadata, astropy.wcs.WCS], extra_padding: int = 0) → list[source]#: Find celestial bodies in image.