nabu.stitching.config module

nabu.stitching.config.str_to_shifts(my_str: str | None) → str | tuple

class nabu.stitching.config.SlurmConfig(partition: str = '', mem: str = '128', n_jobs: int = 1, other_options: str = '', preprocessing_command: str = '', clean_script: bool = '', n_tasks: int = 1, n_cpu_per_task: int = 4)

Bases: object

configuration for slurm jobs

partition: str = ''

mem: str = '128'

n_jobs: int = 1

other_options: str = ''

preprocessing_command: str = ''

clean_script: bool = ''

n_tasks: int = 1

n_cpu_per_task: int = 4

to_dict() → dict

dump configuration to dict

static from_dict(config: dict)

class nabu.stitching.config.StitchingType(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)

Bases: Enum

Z_PREPROC = 'z-preproc'

Z_POSTPROC = 'z-postproc'

class nabu.stitching.config.StitchingConfiguration(axis_0_pos_px: tuple | str | None, axis_1_pos_px: tuple | str | None, axis_2_pos_px: tuple | str | None, axis_0_pos_mm: tuple | str | None = None, axis_1_pos_mm: tuple | str | None = None, axis_2_pos_mm: tuple | str | None = None, axis_0_params: dict = None, axis_1_params: dict = None, axis_2_params: dict = None, slurm_config: SlurmConfig = None, flip_lr: tuple | bool = False, flip_ud: tuple | bool = False, overwrite_results: bool = False, stitching_strategy: OverlapStitchingStrategy = OverlapStitchingStrategy.COSINUS_WEIGHTS, stitching_kernels_extra_params: dict = None, slice_for_cross_correlation: str | int = 'middle', rescale_frames: bool = False, rescale_params: dict = None)

Bases: object

bass class to define stitching configuration

axis_0_pos_px: tuple | str | None

px

Type:

position along axis 0 in absolute. unit

axis_1_pos_px: tuple | str | None

px

Type:

position along axis 1 in absolute. unit

axis_2_pos_px: tuple | str | None

px

Type:

position along axis 2 in absolute. unit

axis_0_pos_mm: tuple | str | None = None

mm

Type:

position along axis 0 in absolute. unit

axis_1_pos_mm: tuple | str | None = None

mm

Type:

position along axis 0 in absolute. unit

axis_2_pos_mm: tuple | str | None = None

mm

Type:

position along axis 0 in absolute. unit

axis_0_params: dict = None

axis_1_params: dict = None

axis_2_params: dict = None

slurm_config: SlurmConfig = None

flip_lr: tuple | bool = False

flip frame left-right. For scan this will happen after possible flip of NXtomo metadata x_flipped field (also know as lr_flipped)

flip_ud: tuple | bool = False

flip frame up-down. For scan this will happen after possible flip of NXtomo metadata y_flipped field (also know as ud_flipped)

overwrite_results: bool = False

stitching_strategy: OverlapStitchingStrategy = 'cosinus weights'

stitching_kernels_extra_params: dict = None

slice_for_cross_correlation: str | int = 'middle'

rescale_frames: bool = False

rescale_params: dict = None

property stitching_type

static get_description_dict() → dict

to_dict()

dump configuration to a dict. Must be serializable because might be dump to HDF5 file

class nabu.stitching.config.ZStitchingConfiguration(axis_0_pos_px: tuple | str | None, axis_1_pos_px: tuple | str | None, axis_2_pos_px: tuple | str | None, axis_0_pos_mm: tuple | str | None = None, axis_1_pos_mm: tuple | str | None = None, axis_2_pos_mm: tuple | str | None = None, axis_0_params: dict = None, axis_1_params: dict = None, axis_2_params: dict = None, slurm_config: SlurmConfig = None, flip_lr: tuple | bool = False, flip_ud: tuple | bool = False, overwrite_results: bool = False, stitching_strategy: OverlapStitchingStrategy = OverlapStitchingStrategy.COSINUS_WEIGHTS, stitching_kernels_extra_params: dict = None, slice_for_cross_correlation: str | int = 'middle', rescale_frames: bool = False, rescale_params: dict = None, slices: slice | tuple | None = None)

Bases: StitchingConfiguration

base class to define z-stitching parameters

slices: slice | tuple | None = None

settle_inputs() → None

settle_slices() → tuple

get_output_object()

to_dict()

dump configuration to a dict. Must be serializable because might be dump to HDF5 file

class nabu.stitching.config.PreProcessedZStitchingConfiguration(axis_0_pos_px: tuple | str | None, axis_1_pos_px: tuple | str | None, axis_2_pos_px: tuple | str | None, axis_0_pos_mm: tuple | str | None = None, axis_1_pos_mm: tuple | str | None = None, axis_2_pos_mm: tuple | str | None = None, axis_0_params: dict = None, axis_1_params: dict = None, axis_2_params: dict = None, slurm_config: SlurmConfig = None, flip_lr: tuple | bool = False, flip_ud: tuple | bool = False, overwrite_results: bool = False, stitching_strategy: OverlapStitchingStrategy = OverlapStitchingStrategy.COSINUS_WEIGHTS, stitching_kernels_extra_params: dict = None, slice_for_cross_correlation: str | int = 'middle', rescale_frames: bool = False, rescale_params: dict = None, slices: slice | tuple | None = None, input_scans: tuple = (), output_file_path: str = '', output_data_path: str = '', output_nexus_version: float | None = None, pixel_size: float | None = None)

Bases: ZStitchingConfiguration

base class to define z-stitching parameters

input_scans: tuple = ()

output_file_path: str = ''

output_data_path: str = ''

output_nexus_version: float | None = None

pixel_size: float | None = None

property stitching_type: StitchingType

get_output_object()

settle_inputs() → None

settle_input_scans()

slice_idx_from_str_to_int(index)

settle_slices() → tuple

interpret the slices to be stitched if needed

Nore: if slices is an instance of slice will redefine start and stop to avoid having negative indexes

Returns:

(slices:[slice,Iterable], n_proj:int)

Return type:

tuple

to_dict()

dump configuration to a dict. Must be serializable because might be dump to HDF5 file

static get_description_dict() → dict

static from_dict(config: dict)

class nabu.stitching.config.PostProcessedZStitchingConfiguration(axis_0_pos_px: tuple | str | None, axis_1_pos_px: tuple | str | None, axis_2_pos_px: tuple | str | None, axis_0_pos_mm: tuple | str | None = None, axis_1_pos_mm: tuple | str | None = None, axis_2_pos_mm: tuple | str | None = None, axis_0_params: dict = None, axis_1_params: dict = None, axis_2_params: dict = None, slurm_config: SlurmConfig = None, flip_lr: tuple | bool = False, flip_ud: tuple | bool = False, overwrite_results: bool = False, stitching_strategy: OverlapStitchingStrategy = OverlapStitchingStrategy.COSINUS_WEIGHTS, stitching_kernels_extra_params: dict = None, slice_for_cross_correlation: str | int = 'middle', rescale_frames: bool = False, rescale_params: dict = None, slices: slice | tuple | None = None, input_volumes: tuple = (), output_volume: VolumeIdentifier | None = None, voxel_size: float | None = None)

Bases: ZStitchingConfiguration

base class to define z-stitching parameters

input_volumes: tuple = ()

output_volume: VolumeIdentifier | None = None

voxel_size: float | None = None

property stitching_type: StitchingType

get_output_object()

settle_inputs() → None

settle_input_volumes()

slice_idx_from_str_to_int(index)

settle_slices() → tuple

interpret the slices to be stitched if needed

Nore: if slices is an instance of slice will redefine start and stop to avoid having negative indexes

Returns:

(slices:[slice,Iterable], n_proj:int)

Return type:

tuple

static from_dict(config: dict)

to_dict()

dump configuration to a dict. Must be serializable because might be dump to HDF5 file

static get_description_dict() → dict

nabu.stitching.config.identifiers_as_str_to_instances(list_identifiers_as_str: str) → tuple

nabu.stitching.config.dict_to_config_obj(config: dict)

nabu.stitching.config.get_default_stitching_config(stitching_type: StitchingType | str | None) → tuple

Return a default configuration for doing stitching.

Parameters:

stitching_type – if None then return a configuration were use can provide inputs for any of the stitching. Else return config dict dedicated to a particular stitching

Returns:

(config, section comments)