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webknossos.dataset.layer

Layer 

Layer(dataset: Dataset, properties: LayerProperties)

A Layer consists of multiple MagViews, which store the same data in different magnifications.

Do not use this constructor manually. Instead use Dataset.add_layer() to create a Layer.

bounding_box property writable 

bounding_box: NDBoundingBox

category property 

category: LayerCategoryType

data_format property 

data_format: DataFormat

dataset property 

dataset: Dataset

default_view_configuration property writable 

default_view_configuration: Optional[LayerViewConfiguration]

dtype_per_channel property 

dtype_per_channel: dtype

dtype_per_layer property 

dtype_per_layer: str

mags property 

mags: Dict[Mag, MagView]

Getter for dictionary containing all mags.

name property writable 

name: str

num_channels property 

num_channels: int

path property 

path: Path

read_only property 

read_only: bool

add_copy_mag 

add_copy_mag(foreign_mag_view_or_path: Union[PathLike, str, MagView], extend_layer_bounding_box: bool = True, chunk_shape: Optional[Union[Vec3IntLike, int]] = None, chunks_per_shard: Optional[Union[Vec3IntLike, int]] = None, compress: Optional[bool] = None, executor: Optional[Executor] = None) -> MagView

Copies the data at foreign_mag_view_or_path which can belong to another dataset to the current dataset. Additionally, the relevant information from the datasource-properties.json of the other dataset are copied, too.

add_fs_copy_mag 

add_fs_copy_mag(foreign_mag_view_or_path: Union[PathLike, str, MagView], extend_layer_bounding_box: bool = True) -> MagView

Copies the data at foreign_mag_view_or_path which belongs to another dataset to the current dataset via the filesystem. Additionally, the relevant information from the datasource-properties.json of the other dataset are copied, too.

add_mag 

add_mag(mag: Union[int, str, list, tuple, ndarray, Mag], chunk_shape: Optional[Union[Vec3IntLike, int]] = None, chunks_per_shard: Optional[Union[int, Vec3IntLike]] = None, compress: bool = False, *, chunk_size: Optional[Union[Vec3IntLike, int]] = None, block_len: Optional[int] = None, file_len: Optional[int] = None) -> MagView

Creates a new mag called and adds it to the layer. The parameter chunk_shape, chunks_per_shard and compress can be specified to adjust how the data is stored on disk. Note that writing compressed data which is not aligned with the blocks on disk may result in diminished performance, as full blocks will automatically be read to pad the write actions. Alternatively, you can call mag.compress() after all the data was written

The return type is webknossos.dataset.mag_view.MagView.

Raises an IndexError if the specified mag already exists.

add_mag_for_existing_files 

add_mag_for_existing_files(mag: Union[int, str, list, tuple, ndarray, Mag]) -> MagView

Creates a new mag based on already existing files.

Raises an IndexError if the specified mag does not exists.

add_symlink_mag(foreign_mag_view_or_path: Union[PathLike, str, MagView], make_relative: bool = False, extend_layer_bounding_box: bool = True) -> MagView

Creates a symlink to the data at foreign_mag_view_or_path which belongs to another dataset. The relevant information from the datasource-properties.json of the other dataset is copied to this dataset. Note: If the other dataset modifies its bounding box afterwards, the change does not affect this properties (or vice versa). If make_relative is True, the symlink is made relative to the current dataset path. Symlinked mags can only be added to layers on local file systems.

delete_mag 

delete_mag(mag: Union[int, str, list, tuple, ndarray, Mag]) -> None

Deletes the MagView from the datasource-properties.json and the data from disk.

This function raises an IndexError if the specified mag does not exist.

downsample 

downsample(from_mag: Optional[Mag] = None, coarsest_mag: Optional[Mag] = None, interpolation_mode: str = 'default', compress: bool = True, sampling_mode: Union[str, SamplingModes] = SamplingModes.ANISOTROPIC, align_with_other_layers: Union[bool, Dataset] = True, buffer_shape: Optional[Vec3Int] = None, force_sampling_scheme: bool = False, args: Optional[Namespace] = None, allow_overwrite: bool = False, only_setup_mags: bool = False, executor: Optional[Executor] = None) -> None

Downsamples the data starting from from_mag until a magnification is >= max(coarsest_mag). There are three different sampling_modes:

  • 'anisotropic' - The next magnification is chosen so that the width, height and depth of a downsampled voxel assimilate. For example, if the z resolution is worse than the x/y resolution, z won't be downsampled in the first downsampling step(s). As a basis for this method, the voxel_size from the datasource-properties.json is used.
  • 'isotropic' - Each dimension is downsampled equally.
  • 'constant_z' - The x and y dimensions are downsampled equally, but the z dimension remains the same.

See downsample_mag for more information.

Example: 

from webknossos import SamplingModes

# ...
# let 'layer' be a `Layer` with only `Mag(1)`
assert "1" in self.mags.keys()

layer.downsample(
    coarsest_mag=Mag(4),
    sampling_mode=SamplingModes.ISOTROPIC
)

assert "2" in self.mags.keys()
assert "4" in self.mags.keys()

downsample_mag 

downsample_mag(from_mag: Mag, target_mag: Mag, interpolation_mode: str = 'default', compress: bool = True, buffer_shape: Optional[Vec3Int] = None, args: Optional[Namespace] = None, allow_overwrite: bool = False, only_setup_mag: bool = False, executor: Optional[Executor] = None) -> None

Performs a single downsampling step from from_mag to target_mag.

The supported interpolation_modes are:

  • "median"
  • "mode"
  • "nearest"
  • "bilinear"
  • "bicubic"

If allow_overwrite is True, an existing Mag may be overwritten.

If only_setup_mag is True, the magnification is created, but left empty. This parameter can be used to prepare for parallel downsampling of multiple layers while avoiding parallel writes with outdated updates to the datasource-properties.json file.

executor can be passed to allow distributed computation, parallelizing across chunks. args is deprecated.

downsample_mag_list 

downsample_mag_list(from_mag: Mag, target_mags: List[Mag], interpolation_mode: str = 'default', compress: bool = True, buffer_shape: Optional[Vec3Int] = None, args: Optional[Namespace] = None, allow_overwrite: bool = False, only_setup_mags: bool = False, executor: Optional[Executor] = None) -> None

Downsamples the data starting at from_mag to each magnification in target_mags iteratively.

See downsample_mag for more information.

get_best_mag 

get_best_mag() -> MagView

Deprecated, please use get_finest_mag.

get_finest_mag 

get_finest_mag() -> MagView

get_mag 

get_mag(mag: Union[int, str, list, tuple, ndarray, Mag]) -> MagView

Returns the MagView called mag of this layer. The return type is webknossos.dataset.mag_view.MagView.

This function raises an IndexError if the specified mag does not exist.

get_or_add_mag 

get_or_add_mag(mag: Union[int, str, list, tuple, ndarray, Mag], chunk_shape: Optional[Union[Vec3IntLike, int]] = None, chunks_per_shard: Optional[Union[Vec3IntLike, int]] = None, compress: Optional[bool] = None, *, chunk_size: Optional[Union[Vec3IntLike, int]] = None, block_len: Optional[int] = None, file_len: Optional[int] = None) -> MagView

Creates a new mag and adds it to the dataset, in case it did not exist before. Then, returns the mag.

See add_mag for more information.

redownsample 

redownsample(interpolation_mode: str = 'default', compress: bool = True, buffer_shape: Optional[Vec3Int] = None, args: Optional[Namespace] = None, executor: Optional[Executor] = None) -> None

Use this method to recompute downsampled magnifications after mutating data in the base magnification.

upsample 

upsample(from_mag: Mag, finest_mag: Mag = Mag(1), compress: bool = False, sampling_mode: Union[str, SamplingModes] = SamplingModes.ANISOTROPIC, align_with_other_layers: Union[bool, Dataset] = True, buffer_shape: Optional[Vec3Int] = None, buffer_edge_len: Optional[int] = None, args: Optional[Namespace] = None, executor: Optional[Executor] = None, *, min_mag: Optional[Mag] = None) -> None

Upsamples the data starting from from_mag as long as the magnification is >= finest_mag. There are three different sampling_modes:

  • 'anisotropic' - The next magnification is chosen so that the width, height and depth of a downsampled voxel assimilate. For example, if the z resolution is worse than the x/y resolution, z won't be downsampled in the first downsampling step(s). As a basis for this method, the voxel_size from the datasource-properties.json is used.
  • 'isotropic' - Each dimension is downsampled equally.
  • 'constant_z' - The x and y dimensions are downsampled equally, but the z dimension remains the same.

min_mag is deprecated, please use finest_mag instead.

SegmentationLayer 

SegmentationLayer(dataset: Dataset, properties: LayerProperties)

Bases: Layer

Do not use this constructor manually. Instead use Dataset.add_layer() to create a Layer.

bounding_box property writable 

bounding_box: NDBoundingBox

category property 

category: LayerCategoryType

data_format property 

data_format: DataFormat

dataset property 

dataset: Dataset

default_view_configuration property writable 

default_view_configuration: Optional[LayerViewConfiguration]

dtype_per_channel property 

dtype_per_channel: dtype

dtype_per_layer property 

dtype_per_layer: str

largest_segment_id property writable 

largest_segment_id: Optional[int]

mags property 

mags: Dict[Mag, MagView]

Getter for dictionary containing all mags.

name property writable 

name: str

num_channels property 

num_channels: int

path property 

path: Path

read_only property 

read_only: bool

add_copy_mag 

add_copy_mag(foreign_mag_view_or_path: Union[PathLike, str, MagView], extend_layer_bounding_box: bool = True, chunk_shape: Optional[Union[Vec3IntLike, int]] = None, chunks_per_shard: Optional[Union[Vec3IntLike, int]] = None, compress: Optional[bool] = None, executor: Optional[Executor] = None) -> MagView

Copies the data at foreign_mag_view_or_path which can belong to another dataset to the current dataset. Additionally, the relevant information from the datasource-properties.json of the other dataset are copied, too.

add_fs_copy_mag 

add_fs_copy_mag(foreign_mag_view_or_path: Union[PathLike, str, MagView], extend_layer_bounding_box: bool = True) -> MagView

Copies the data at foreign_mag_view_or_path which belongs to another dataset to the current dataset via the filesystem. Additionally, the relevant information from the datasource-properties.json of the other dataset are copied, too.

add_mag 

add_mag(mag: Union[int, str, list, tuple, ndarray, Mag], chunk_shape: Optional[Union[Vec3IntLike, int]] = None, chunks_per_shard: Optional[Union[int, Vec3IntLike]] = None, compress: bool = False, *, chunk_size: Optional[Union[Vec3IntLike, int]] = None, block_len: Optional[int] = None, file_len: Optional[int] = None) -> MagView

Creates a new mag called and adds it to the layer. The parameter chunk_shape, chunks_per_shard and compress can be specified to adjust how the data is stored on disk. Note that writing compressed data which is not aligned with the blocks on disk may result in diminished performance, as full blocks will automatically be read to pad the write actions. Alternatively, you can call mag.compress() after all the data was written

The return type is webknossos.dataset.mag_view.MagView.

Raises an IndexError if the specified mag already exists.

add_mag_for_existing_files 

add_mag_for_existing_files(mag: Union[int, str, list, tuple, ndarray, Mag]) -> MagView

Creates a new mag based on already existing files.

Raises an IndexError if the specified mag does not exists.

add_symlink_mag(foreign_mag_view_or_path: Union[PathLike, str, MagView], make_relative: bool = False, extend_layer_bounding_box: bool = True) -> MagView

Creates a symlink to the data at foreign_mag_view_or_path which belongs to another dataset. The relevant information from the datasource-properties.json of the other dataset is copied to this dataset. Note: If the other dataset modifies its bounding box afterwards, the change does not affect this properties (or vice versa). If make_relative is True, the symlink is made relative to the current dataset path. Symlinked mags can only be added to layers on local file systems.

delete_mag 

delete_mag(mag: Union[int, str, list, tuple, ndarray, Mag]) -> None

Deletes the MagView from the datasource-properties.json and the data from disk.

This function raises an IndexError if the specified mag does not exist.

downsample 

downsample(from_mag: Optional[Mag] = None, coarsest_mag: Optional[Mag] = None, interpolation_mode: str = 'default', compress: bool = True, sampling_mode: Union[str, SamplingModes] = SamplingModes.ANISOTROPIC, align_with_other_layers: Union[bool, Dataset] = True, buffer_shape: Optional[Vec3Int] = None, force_sampling_scheme: bool = False, args: Optional[Namespace] = None, allow_overwrite: bool = False, only_setup_mags: bool = False, executor: Optional[Executor] = None) -> None

Downsamples the data starting from from_mag until a magnification is >= max(coarsest_mag). There are three different sampling_modes:

  • 'anisotropic' - The next magnification is chosen so that the width, height and depth of a downsampled voxel assimilate. For example, if the z resolution is worse than the x/y resolution, z won't be downsampled in the first downsampling step(s). As a basis for this method, the voxel_size from the datasource-properties.json is used.
  • 'isotropic' - Each dimension is downsampled equally.
  • 'constant_z' - The x and y dimensions are downsampled equally, but the z dimension remains the same.

See downsample_mag for more information.

Example: 

from webknossos import SamplingModes

# ...
# let 'layer' be a `Layer` with only `Mag(1)`
assert "1" in self.mags.keys()

layer.downsample(
    coarsest_mag=Mag(4),
    sampling_mode=SamplingModes.ISOTROPIC
)

assert "2" in self.mags.keys()
assert "4" in self.mags.keys()

downsample_mag 

downsample_mag(from_mag: Mag, target_mag: Mag, interpolation_mode: str = 'default', compress: bool = True, buffer_shape: Optional[Vec3Int] = None, args: Optional[Namespace] = None, allow_overwrite: bool = False, only_setup_mag: bool = False, executor: Optional[Executor] = None) -> None

Performs a single downsampling step from from_mag to target_mag.

The supported interpolation_modes are:

  • "median"
  • "mode"
  • "nearest"
  • "bilinear"
  • "bicubic"

If allow_overwrite is True, an existing Mag may be overwritten.

If only_setup_mag is True, the magnification is created, but left empty. This parameter can be used to prepare for parallel downsampling of multiple layers while avoiding parallel writes with outdated updates to the datasource-properties.json file.

executor can be passed to allow distributed computation, parallelizing across chunks. args is deprecated.

downsample_mag_list 

downsample_mag_list(from_mag: Mag, target_mags: List[Mag], interpolation_mode: str = 'default', compress: bool = True, buffer_shape: Optional[Vec3Int] = None, args: Optional[Namespace] = None, allow_overwrite: bool = False, only_setup_mags: bool = False, executor: Optional[Executor] = None) -> None

Downsamples the data starting at from_mag to each magnification in target_mags iteratively.

See downsample_mag for more information.

get_best_mag 

get_best_mag() -> MagView

Deprecated, please use get_finest_mag.

get_finest_mag 

get_finest_mag() -> MagView

get_mag 

get_mag(mag: Union[int, str, list, tuple, ndarray, Mag]) -> MagView

Returns the MagView called mag of this layer. The return type is webknossos.dataset.mag_view.MagView.

This function raises an IndexError if the specified mag does not exist.

get_or_add_mag 

get_or_add_mag(mag: Union[int, str, list, tuple, ndarray, Mag], chunk_shape: Optional[Union[Vec3IntLike, int]] = None, chunks_per_shard: Optional[Union[Vec3IntLike, int]] = None, compress: Optional[bool] = None, *, chunk_size: Optional[Union[Vec3IntLike, int]] = None, block_len: Optional[int] = None, file_len: Optional[int] = None) -> MagView

Creates a new mag and adds it to the dataset, in case it did not exist before. Then, returns the mag.

See add_mag for more information.

redownsample 

redownsample(interpolation_mode: str = 'default', compress: bool = True, buffer_shape: Optional[Vec3Int] = None, args: Optional[Namespace] = None, executor: Optional[Executor] = None) -> None

Use this method to recompute downsampled magnifications after mutating data in the base magnification.

refresh_largest_segment_id 

refresh_largest_segment_id(chunk_shape: Optional[Vec3Int] = None, executor: Optional[Executor] = None) -> None

Sets the largest segment id to the highest value in the data. largest_segment_id is set to None if the data is empty.

upsample 

upsample(from_mag: Mag, finest_mag: Mag = Mag(1), compress: bool = False, sampling_mode: Union[str, SamplingModes] = SamplingModes.ANISOTROPIC, align_with_other_layers: Union[bool, Dataset] = True, buffer_shape: Optional[Vec3Int] = None, buffer_edge_len: Optional[int] = None, args: Optional[Namespace] = None, executor: Optional[Executor] = None, *, min_mag: Optional[Mag] = None) -> None

Upsamples the data starting from from_mag as long as the magnification is >= finest_mag. There are three different sampling_modes:

  • 'anisotropic' - The next magnification is chosen so that the width, height and depth of a downsampled voxel assimilate. For example, if the z resolution is worse than the x/y resolution, z won't be downsampled in the first downsampling step(s). As a basis for this method, the voxel_size from the datasource-properties.json is used.
  • 'isotropic' - Each dimension is downsampled equally.
  • 'constant_z' - The x and y dimensions are downsampled equally, but the z dimension remains the same.

min_mag is deprecated, please use finest_mag instead.