punchbowl.levelq.flow#

Attributes#

ORDER_QP

SPACECRAFT_OBSCODE

Functions#

levelq_CNN_core_flow(→ list[ndcube.NDCube])

Run the LQ CNN flow.

levelq_CQM_core_flow(→ list[ndcube.NDCube])

Level quickPUNCH core flow.

levelq_CTM_core_flow(→ list[ndcube.NDCube])

Level Q CTM flow.

Module Contents#

punchbowl.levelq.flow.ORDER_QP = ['QR1', 'QR2', 'QR3', 'CNN']#
punchbowl.levelq.flow.SPACECRAFT_OBSCODE#
punchbowl.levelq.flow.levelq_CNN_core_flow(data_list: list[str] | list[ndcube.NDCube], output_filename: list[str] | None = None, files_to_fit: list[str | ndcube.NDCube | punchbowl.util.DataLoader] | None = None, data_root: str | None = None) → list[ndcube.NDCube][source]#

Run the LQ CNN flow.

This flow is designed to run on a batch of input CR4 images to facilitate more efficient PCA fitting.

Parameters:

  • data_list (list[str | NDCube]) – The input images, either as paths or NDCubes

  • output_filename (list[str]) – Optional output paths at which the CNN files should be written

  • files_to_fit (list[str | NDCube | DataLoader]) – Additional files to use for the PCA fitting, but not to actually be filtered or output

  • data_root (str) – The root directory which the paths in data_list are relative to

Returns:

output_cubes – The CNN data cubes

Return type:

list[NDCube]

punchbowl.levelq.flow.levelq_CQM_core_flow(data_list: list[str] | list[ndcube.NDCube], output_filename: list[str] | None = None, trim_edges_px: int = 0, alphas_file: str | None = None, trefoil_wcs: astropy.wcs.WCS | None = None, trefoil_shape: tuple[int, int] | None = None) → list[ndcube.NDCube][source]#

Level quickPUNCH core flow.

punchbowl.levelq.flow.levelq_CTM_core_flow(data_list: list[str] | list[ndcube.NDCube], before_f_corona_model_path: str, after_f_corona_model_path: str, output_filename: str | None = None, reference_time: datetime.datetime | None = None) → list[ndcube.NDCube][source]#

Level Q CTM flow.