neuralib.scanner.czi
- final class neuralib.scanner.czi.CziConfocalScanner[source]
czi confocal image data
- czifile: CziFile
aicspylibczi.CziFile
- filepath: Path
- meta: dict[str, Any]
metadata dict
- n_scenes: int
positions scan
- consistent_scan_configs: bool
whether same configs (i.e., X, Y, C …) in different scenes
- classmethod load(file)[source]
- Parameters:
file (str | Path | bytes | BinaryIO | BufferedIOBase | BufferedReader)
- Return type:
- tile_info: dict[str, Any]
- property tile_ncols: int
how many tiles for each column
- property tile_nrows: int
how many tiles for each row
- get_dim_code()[source]
get the DimCode
Dimension parameters (DimCode):
V - view
H - phase
I - illumination
S - scene
R - rotation
T - time
C - channel
Z - z plane (height)
M - mosaic tile, mosaic images only
Y - image height
X - image width
A - samples, BGR/RGB images only
- Return type:
DIMCODE
- property width: dict[int, int]
X
- property height: dict[int, int]
Y
- property n_phases: dict[int, int]
how many scanning face
- property n_channels: dict[int, int]
number of fluorescence channels. C
- property n_zstacks: dict[int, int]
number of stacks in z axis. Z
- property n_tiles: dict[int, int]
- property is_mosaic: bool
- get_image(channel, scene=None, depth=None, zproj_type='max', norm=True)[source]
Get the image array
- Parameters:
channel (int) – channel index
scene (SceneIdx | None) – scanning position
depth (int | slice | np.ndarray | None) – z stacks index, if None, use all stacks
zproj_type (ZPROJ_TYPE) – which z projection type, refer to fiji
norm (bool) – normalization, for visualization
- Returns:
(Y, X)
- Return type:
np.ndarray
- imshow(scene=None, add_scale_bar=True, output=None, position_only=False)[source]
Simple plot for specific config
- Parameters:
scene (SceneIdx | None)
add_scale_bar (bool)
output (PathLike | None)
- foreach_tif_output(output=None, combine_channels=False, combine_tiles=True)[source]
- Parameters:
output (PathLike | None) – output directory
combine_channels (bool)
combine_tiles (bool) – TODO overlap compensation?
- Returns:
- __init__(czifile, filepath, meta, n_scenes=Field(name=None, type=None, default=1, default_factory=<dataclasses._MISSING_TYPE object>, init=False, repr=True, hash=None, compare=True, metadata=mappingproxy({}), _field_type=None), consistent_scan_configs=Field(name=None, type=None, default=<dataclasses._MISSING_TYPE object>, default_factory=<dataclasses._MISSING_TYPE object>, init=False, repr=True, hash=None, compare=True, metadata=mappingproxy({}), _field_type=None))
Method generated by attrs for class CziConfocalScanner.
- Parameters:
czifile (CziFile)
filepath (Path)
meta (dict[str, Any])
n_scenes (int)
consistent_scan_configs (bool)
- Return type:
None