sample

Classes:

Name	Description
LinearSampleAxisTransform
Sample	A dataset sample.
SampleBlock	A block of a dataset sample
SampleBlockBase	base class for SampleBlockMeta and SampleBlock
SampleBlockMeta	Meta data of a dataset sample block
SampleBlockWithOrigin	A SampleBlock with a reference (origin) to the whole Sample

Functions:

Name	Description
sample_block_generator
sample_block_meta_generator

Attributes:

Name	Type	Description
BlockT

BlockT module-attribute

BlockT = TypeVar('BlockT', bound=BlockMeta)

Subclassed by:

• API Reference  sample  SampleBlockBase

LinearSampleAxisTransform dataclass

LinearSampleAxisTransform(axis: AxisId, scale: float, offset: int, member: MemberId)

Bases: LinearAxisTransform

              flowchart TD
              bioimageio.core.sample.LinearSampleAxisTransform[LinearSampleAxisTransform]
              bioimageio.core.block_meta.LinearAxisTransform[LinearAxisTransform]

                              bioimageio.core.block_meta.LinearAxisTransform --> bioimageio.core.sample.LinearSampleAxisTransform
                


              click bioimageio.core.sample.LinearSampleAxisTransform href "" "bioimageio.core.sample.LinearSampleAxisTransform"
              click bioimageio.core.block_meta.LinearAxisTransform href "" "bioimageio.core.block_meta.LinearAxisTransform"
            

Returned by:

• API Reference  digest_spec  get_block_transform

Methods:

Name	Description
compute

Attributes:

Name	Type	Description
axis	AxisId
member	MemberId
offset	int
scale	float

axis instance-attribute

axis: AxisId

member instance-attribute

member: MemberId

offset instance-attribute

offset: int

scale instance-attribute

scale: float

compute

compute(s: int, round: Callable[[float], int] = floor) -> int

Source code in src/bioimageio/core/block_meta.py

def compute(self, s: int, round: Callable[[float], int] = floor) -> int:
    return round(s * self.scale) + self.offset

Sample dataclass

Sample(members: Dict[MemberId, Tensor], stat: Stat, id: SampleId)

A dataset sample.

A Sample has members, which allows to combine multiple tensors into a single sample. For example a Sample from a dataset with masked images may contain a MemberId("raw") and MemberId("mask") image.

Returned by:

• API Reference
  •  core
    •  predict
    •  predict_many
  •  digest_spec
    •  create_sample_for_model
    •  get_test_input_sample
    •  get_test_output_sample
    •  transpose_sample_for_model
  •  prediction
    •  predict
    •  predict_many

Used by:

• API Reference
  •  core
    •  compute_dataset_measures
    •  compute_measures
    •  compute_sample_measures
    •  create_prediction_pipeline
    •  predict
  •  dataset  Dataset
  •  digest_spec
    •  split_sample_into_blocks_for_model
    •  transpose_sample_for_model
  •  io  save_sample
  •  prediction  predict
  •  proc_setup
    •  get_pre_and_postprocessing
    •  setup_pre_and_postprocessing
  •  sample
    •  SampleBlockWithOrigin
    •  sample_block_generator
  •  stat_calculators
    •  compute_dataset_measures
    •  compute_measures
    •  compute_sample_measures

Methods:

Name	Description
__getitem__
as_arrays	Return sample as dictionary of arrays.
as_single_block
assign_batch_multi_index	Return a new sample with the batch multi-index assigned to all sample members.
from_blocks	Create a Sample from an iterable of SampleBlocks.
from_blocks_yield_intermediates	Create a Sample from an iterable of SampleBlocks, yielding the intermediate sample after each block.
pad	Convenience method to pad sample members.
set_block	Set values of block.
split_into_blocks
transpose	Return a new sample with transposed sample members.
unstack_batch_multi_index	Unstack the batch multi-index of all sample members.

Attributes:

Name	Type	Description
batch_multi_index	Optional['pd.MultiIndex']	Return the batch multi-index of the sample, if it has one.
id	SampleId	Identifies the Sample within the dataset -- typically a number or a string.
members	Dict[MemberId, Tensor]	The sample's tensors
shape	PerMember[PerAxis[int]]
stat	Stat	Sample and dataset statistics

batch_multi_index property

batch_multi_index: Optional['pd.MultiIndex']

Return the batch multi-index of the sample, if it has one.

Returns:

Type	Description
Optional['pd.MultiIndex']	The batch multi-index of the sample, or None if the sample does not have a batch dimension.

id instance-attribute

id: SampleId

Identifies the Sample within the dataset -- typically a number or a string.

members instance-attribute

members: Dict[MemberId, Tensor]

The sample's tensors

shape property

shape: PerMember[PerAxis[int]]

stat instance-attribute

stat: Stat

Sample and dataset statistics

__getitem__

__getitem__(key: PerMember[Union[SliceInfo, slice, int, PerAxis[Union[SliceInfo, slice, int]], Tensor, xr.DataArray]]) -> Self

Source code in src/bioimageio/core/sample.py

def __getitem__(
    self,
    key: PerMember[
        Union[
            SliceInfo,
            slice,
            int,
            PerAxis[Union[SliceInfo, slice, int]],
            Tensor,
            xr.DataArray,
        ]
    ],
) -> Self:
    return self.__class__(
        members={m: t[key[m]] for m, t in self.members.items() if m in key},
        stat=self.stat,
        id=self.id,
    )

as_arrays

as_arrays() -> Dict[MemberId, NDArray[Any]]

Return sample as dictionary of arrays.

Source code in src/bioimageio/core/sample.py

def as_arrays(self) -> Dict[MemberId, NDArray[Any]]:
    """Return sample as dictionary of arrays."""
    return {m: t.to_numpy() for m, t in self.members.items()}

as_single_block

as_single_block(halo: Optional[PerMember[PerAxis[Halo]]] = None)

Source code in src/bioimageio/core/sample.py

def as_single_block(self, halo: Optional[PerMember[PerAxis[Halo]]] = None):
    if halo is None:
        halo = {}
    return SampleBlockWithOrigin(
        sample_shape=self.shape,
        sample_id=self.id,
        blocks={
            m: Block(
                sample_shape=self.shape[m],
                data=data,
                inner_slice={
                    a: SliceInfo(0, s) for a, s in data.tagged_shape.items()
                },
                halo=halo.get(m, {}),
                block_index=0,
                blocks_in_sample=1,
            )
            for m, data in self.members.items()
        },
        stat=self.stat,
        origin=self,
        block_index=0,
        blocks_in_sample=1,
    )

assign_batch_multi_index

assign_batch_multi_index(multi_index: 'pd.MultiIndex') -> Self

Return a new sample with the batch multi-index assigned to all sample members.

Raises:

Type	Description
ValueError	If not all sample members have a batch dimension.

Source code in src/bioimageio/core/sample.py

def assign_batch_multi_index(self, multi_index: "pd.MultiIndex") -> Self:
    """Return a new sample with the batch multi-index assigned to all sample members.

    Raises:
        ValueError: If not all sample members have a batch dimension.
    """
    if len(
        no_batch := [
            m for m, t in self.members.items() if AxisId("batch") not in t.dims
        ]
    ) == len(self.members):
        raise ValueError(f"No member has a batch dimension: {no_batch}")

    return self.__class__(
        members={
            m: t
            if AxisId("batch") not in t.dims
            else t.assign_batch_multi_index(multi_index)
            for m, t in self.members.items()
        },
        stat=dict(self.stat),
        id=self.id,
    )

from_blocks classmethod

from_blocks(sample_blocks: Iterable[SampleBlock], *, fill_value: float = float('nan')) -> Self

Create a Sample from an iterable of SampleBlocks.

Note

All sample blocks must have the same sample_id.

Parameters:

Name	Type	Description	Default
sample_blocks	Iterable[SampleBlock]	The blocks to create the sample from.	required
fill_value	float	The value to fill missing values with (default: nan).	float('nan')

Source code in src/bioimageio/core/sample.py

@classmethod
def from_blocks(
    cls,
    sample_blocks: Iterable[SampleBlock],
    *,
    fill_value: float = float("nan"),
) -> Self:
    """Create a `Sample` from an iterable of `SampleBlock`s.

    Note:
        All sample blocks must have the same `sample_id`.

    Args:
        sample_blocks: The blocks to create the sample from.
        fill_value: The value to fill missing values with (default: `nan`).
    """
    output = None
    for output in cls.from_blocks_yield_intermediates(
        sample_blocks, fill_value=fill_value
    ):
        pass

    if output is None:
        raise ValueError("no sample blocks provided")

    return output

from_blocks_yield_intermediates classmethod

from_blocks_yield_intermediates(sample_blocks: Iterable[SampleBlock], *, fill_value: float = float('nan'))

Create a Sample from an iterable of SampleBlocks, yielding the intermediate sample after each block.

Parameters:

Name	Type	Description	Default
sample_blocks	Iterable[SampleBlock]	The blocks to create the sample from.	required
fill_value	float	The value to fill missing values with (default: nan).	float('nan')

Source code in src/bioimageio/core/sample.py

@classmethod
def from_blocks_yield_intermediates(
    cls,
    sample_blocks: Iterable[SampleBlock],
    *,
    fill_value: float = float("nan"),
):
    """Create a `Sample` from an iterable of `SampleBlock`s, yielding the intermediate sample after each block.

    Args:
        sample_blocks: The blocks to create the sample from.
        fill_value: The value to fill missing values with (default: `nan`).
    """
    output = cls(members={}, stat={}, id=None)
    for sample_block in sample_blocks:
        if output.id is None:
            output.id = sample_block.sample_id
        else:
            assert output.id == sample_block.sample_id, (
                "sample id changed between sample blocks"
            )

        output.stat = sample_block.stat

        for m, block in sample_block.blocks.items():
            if m not in output.members:
                if -1 in block.sample_shape.values():
                    raise NotImplementedError(
                        "merging blocks with data dependent axis not yet implemented"
                    )

                output.members[m] = Tensor(
                    np.full(
                        tuple(block.sample_shape[a] for a in block.data.dims),
                        fill_value,
                        dtype=block.data.dtype,
                    ),
                    dims=block.data.dims,
                )

            output.members[m][block.inner_slice] = block.inner_data
        yield output

    yield output

pad

pad(pad_width: PerMember[PerAxis[Union[int, PadWidthLike]]], mode: Union[PerMember[PadMode], PadMode]) -> Self

Convenience method to pad sample members.

Source code in src/bioimageio/core/sample.py

def pad(
    self,
    pad_width: PerMember[PerAxis[Union[int, PadWidthLike]]],
    mode: Union[PerMember[PadMode], PadMode],
) -> Self:
    """Convenience method to pad sample members."""
    default_mode = "symmetric"
    if isinstance(mode, collections.abc.Mapping):
        mode_per_member = mode
    else:
        mode_per_member: Mapping[MemberId, PadMode] = {}
        default_mode = mode

    return self.__class__(
        members={
            m: t.pad(
                pad_width=pad_width.get(m, {}),
                mode=mode_per_member.get(m, default_mode),
            )
            for m, t in self.members.items()
        },
        stat=self.stat,
        id=self.id,
    )

set_block

set_block(block: SampleBlock) -> None

Set values of block.

Note

• Updates only existing sample members (extra block members are ignored)
• Ignores missing block members (i.e. members in the sample but not in the block are not modified)

Source code in src/bioimageio/core/sample.py

def set_block(self, block: SampleBlock) -> None:
    """Set values of `block`.

    Note:
        - Updates only existing sample members (extra block members are ignored)
        - Ignores missing block members (i.e. members in the sample but not in the block are not modified)

    Raises:
        ValueError if block and sample members do not overlap at all.
    """
    no_overlap = True
    for m in self.members:
        if m not in block.blocks:
            continue
        b = block.blocks[m]
        self.members[m][b.inner_slice] = b.inner_data
        no_overlap = False

    if no_overlap:
        raise ValueError(
            f"block with members {list(block.blocks)} does not overlap with sample members {list(self.members)}"
        )

split_into_blocks

split_into_blocks(block_shapes: PerMember[PerAxis[int]], halo: PerMember[PerAxis[HaloLike]], pad_mode: Union[PadMode, PerMember[PadMode]], broadcast: bool = False) -> Tuple[TotalNumberOfBlocks, Iterable[SampleBlockWithOrigin]]

Source code in src/bioimageio/core/sample.py

def split_into_blocks(
    self,
    block_shapes: PerMember[PerAxis[int]],
    halo: PerMember[PerAxis[HaloLike]],
    pad_mode: Union[PadMode, PerMember[PadMode]],
    broadcast: bool = False,
) -> Tuple[TotalNumberOfBlocks, Iterable[SampleBlockWithOrigin]]:
    assert not (missing := [m for m in block_shapes if m not in self.members]), (
        f"`block_shapes` specified for unknown members: {missing}"
    )
    assert not (missing := [m for m in halo if m not in block_shapes]), (
        f"`halo` specified for members without `block_shape`: {missing}"
    )

    n_blocks, blocks = split_multiple_shapes_into_blocks(
        shapes=self.shape,
        block_shapes=block_shapes,
        halo=halo,
        broadcast=broadcast,
    )
    return n_blocks, sample_block_generator(blocks, origin=self, pad_mode=pad_mode)

transpose

transpose(axes: PerMember[Sequence[AxisId]], *, extra_dims: Literal['raise', 'squeeze', 'stack', 'squeeze_or_stack'] = 'raise', missing_dims: Literal['raise', 'expand', 'unstack', 'unstack_or_expand'] = 'raise') -> Self

Return a new sample with transposed sample members.

Raises:

Type	Description
ValueError	If not all batch dimensions have the same length after transposition (and possibly stacking/unstacking extra dimensions).

Source code in src/bioimageio/core/sample.py

def transpose(
    self,
    axes: PerMember[Sequence[AxisId]],
    *,
    extra_dims: Literal["raise", "squeeze", "stack", "squeeze_or_stack"] = "raise",
    missing_dims: Literal[
        "raise", "expand", "unstack", "unstack_or_expand"
    ] = "raise",
) -> Self:
    """Return a new sample with transposed sample members.

    Raises:
        ValueError: If not all batch dimensions have the same length after transposition (and possibly stacking/unstacking extra dimensions).

    """
    if any((unknown := [m not in self.members for m in axes])):
        raise ValueError(f"Axes specified for unknown members: {unknown}")

    members = {
        m: t
        if m not in axes
        else t.transpose(
            axes=axes[m],
            extra_dims=extra_dims,
            missing_dims=missing_dims,
        )
        for m, t in self.members.items()
    }

    if (
        len(
            (
                batch_lengths := {
                    t.sizes[AxisId("batch")]
                    for t in members.values()
                    if AxisId("batch") in t.dims
                }
            )
        )
        > 1
    ):
        raise ValueError(
            f"Transposed sample members have incompatible batch lengths: {batch_lengths}."
        )

    return self.__class__(members=members, stat=dict(self.stat), id=self.id)

unstack_batch_multi_index

unstack_batch_multi_index(*, errors: Literal['raise', 'ignore'] = 'raise') -> Self

Unstack the batch multi-index of all sample members.

Parameters:

Name	Type	Description	Default
errors	Literal['raise', 'ignore']	Whether to raise an error if a member does not have a batch multi-index. Default is "raise".	'raise'

Returns:

Type	Description
Self	A new Sample with unstacked batch multi-index for all members.

Source code in src/bioimageio/core/sample.py

def unstack_batch_multi_index(
    self, *, errors: Literal["raise", "ignore"] = "raise"
) -> Self:
    """Unstack the batch multi-index of all sample members.

    Args:
        errors: Whether to raise an error if a member does not have a batch multi-index. Default is "raise".

    Returns:
        A new `Sample` with unstacked batch multi-index for all members.
    """
    if (
        len(
            no_batch := [
                m for m, t in self.members.items() if AxisId("batch") not in t.dims
            ]
        )
        == len(self.members)
        and errors == "raise"
    ):
        raise ValueError(f"No member has a batch dimension: {no_batch}")

    members = {
        m: t
        if AxisId("batch") not in t.dims
        else t.unstack_batch_multi_index(errors=errors)
        for m, t in self.members.items()
    }
    if (
        len(
            batch_lengths := {
                t.sizes.get(AxisId("batch"))
                for t in members.values()
                if AxisId("batch") in t.dims
            }
        )
        > 1
    ):
        raise ValueError(
            f"Different batch lengths after unstacking: {batch_lengths}"
        )

    stat: Stat = {
        k: v.unstack_batch_multi_index(errors="ignore")
        if isinstance(v, Tensor)
        else float(v)
        for k, v in self.stat.items()
    }
    return self.__class__(
        members=members,
        stat=stat,
        id=self.id,
    )

SampleBlock dataclass

SampleBlock(blocks: Dict[MemberId, Block], stat: Stat)

Bases: SampleBlockBase[Block]

              flowchart TD
              bioimageio.core.sample.SampleBlock[SampleBlock]
              bioimageio.core.sample.SampleBlockBase[SampleBlockBase]

                              bioimageio.core.sample.SampleBlockBase --> bioimageio.core.sample.SampleBlock
                


              click bioimageio.core.sample.SampleBlock href "" "bioimageio.core.sample.SampleBlock"
              click bioimageio.core.sample.SampleBlockBase href "" "bioimageio.core.sample.SampleBlockBase"
            

A block of a dataset sample

Subclassed by:

• API Reference  sample  SampleBlockWithOrigin

Methods:

Name	Description
as_sample	Convert this sample block to a Sample with the shape of this block.
from_meta
get_meta
get_transformed_meta

Attributes:

Name	Type	Description
block_index	BlockIndex	the n-th block of the sample
blocks	Dict[MemberId, Block]	Individual tensor blocks comprising this sample block
blocks_in_sample	TotalNumberOfBlocks	total number of blocks in the sample
inner_shape	PerMember[PerAxis[int]]
members	PerMember[Tensor]	the sample block's tensors
sample_id	SampleId	identifier for the sample within its dataset
sample_shape	PerMemberAnno[PerAxis[int]]	the sample shape this block represents a part of
shape	PerMember[PerAxis[int]]
stat	Stat	computed statistics

block_index instance-attribute

block_index: BlockIndex

the n-th block of the sample

blocks instance-attribute

blocks: Dict[MemberId, Block]

Individual tensor blocks comprising this sample block

blocks_in_sample instance-attribute

blocks_in_sample: TotalNumberOfBlocks

total number of blocks in the sample

inner_shape property

inner_shape: PerMember[PerAxis[int]]

members property

members: PerMember[Tensor]

the sample block's tensors

sample_id instance-attribute

sample_id: SampleId

identifier for the sample within its dataset

sample_shape instance-attribute

sample_shape: PerMemberAnno[PerAxis[int]]

the sample shape this block represents a part of

shape property

shape: PerMember[PerAxis[int]]

stat instance-attribute

stat: Stat

computed statistics

as_sample

as_sample() -> Sample

Convert this sample block to a Sample with the shape of this block.

Note

If you want to convert one or more sample block to a sample with the shape of the original, whole sample, use Sample.from_blocks() instead.

Source code in src/bioimageio/core/sample.py

def as_sample(self) -> Sample:
    """Convert this sample block to a `Sample` with the shape of this block.

    Note:
        If you want to convert one or more sample block to a sample with the shape of the original, whole sample,
        use `Sample.from_blocks()` instead.
    """
    return Sample(
        members=dict(self.members),
        stat=dict(self.stat),
        id=self.sample_id,
    )

from_meta classmethod

from_meta(meta: SampleBlockMeta, data: PerMember[Tensor], stat: Stat) -> Self

Source code in src/bioimageio/core/sample.py

@classmethod
def from_meta(
    cls, meta: SampleBlockMeta, data: PerMember[Tensor], stat: Stat
) -> Self:
    return cls(
        sample_shape=meta.sample_shape,
        sample_id=meta.sample_id,
        blocks={
            m: Block.from_meta(b, data=data[m]) for m, b in meta.blocks.items()
        },
        stat=stat,
        block_index=meta.block_index,
        blocks_in_sample=meta.blocks_in_sample,
    )

get_meta

get_meta() -> SampleBlockMeta

Source code in src/bioimageio/core/sample.py

def get_meta(self) -> SampleBlockMeta:
    return SampleBlockMeta(
        sample_id=self.sample_id,
        blocks={m: b.get_meta() for m, b in self.blocks.items()},
        sample_shape=self.sample_shape,
        block_index=self.block_index,
        blocks_in_sample=self.blocks_in_sample,
    )

get_transformed_meta

get_transformed_meta(new_axes: PerMember[PerAxis[Union[LinearSampleAxisTransform, int]]]) -> SampleBlockMeta

Source code in src/bioimageio/core/sample.py

def get_transformed_meta(
    self, new_axes: PerMember[PerAxis[Union[LinearSampleAxisTransform, int]]]
) -> SampleBlockMeta:
    return SampleBlockMeta(
        sample_id=self.sample_id,
        blocks=dict(self.blocks),
        sample_shape=self.sample_shape,
        block_index=self.block_index,
        blocks_in_sample=self.blocks_in_sample,
    ).get_transformed(new_axes)

SampleBlockBase

Bases: Generic[BlockT]

              flowchart TD
              bioimageio.core.sample.SampleBlockBase[SampleBlockBase]

              

              click bioimageio.core.sample.SampleBlockBase href "" "bioimageio.core.sample.SampleBlockBase"
            

base class for SampleBlockMeta and SampleBlock

Subclassed by:

• API Reference
  •  core
    •  SampleBlock
    •  SampleBlockMeta
  •  sample
    •  SampleBlock
    •  SampleBlockMeta

Attributes:

Name	Type	Description
block_index	BlockIndex	the n-th block of the sample
blocks	PerMemberAnno[BlockT]	Individual tensor blocks comprising this sample block
blocks_in_sample	TotalNumberOfBlocks	total number of blocks in the sample
inner_shape	PerMember[PerAxis[int]]
sample_id	SampleId	identifier for the sample within its dataset
sample_shape	PerMemberAnno[PerAxis[int]]	the sample shape this block represents a part of
shape	PerMember[PerAxis[int]]

block_index instance-attribute

block_index: BlockIndex

the n-th block of the sample

blocks instance-attribute

blocks: PerMemberAnno[BlockT]

Individual tensor blocks comprising this sample block

blocks_in_sample instance-attribute

blocks_in_sample: TotalNumberOfBlocks

total number of blocks in the sample

inner_shape property

inner_shape: PerMember[PerAxis[int]]

sample_id instance-attribute

sample_id: SampleId

identifier for the sample within its dataset

sample_shape instance-attribute

sample_shape: PerMemberAnno[PerAxis[int]]

the sample shape this block represents a part of

shape property

shape: PerMember[PerAxis[int]]

SampleBlockMeta

Bases: SampleBlockBase[BlockMeta]

              flowchart TD
              bioimageio.core.sample.SampleBlockMeta[SampleBlockMeta]
              bioimageio.core.sample.SampleBlockBase[SampleBlockBase]

                              bioimageio.core.sample.SampleBlockBase --> bioimageio.core.sample.SampleBlockMeta
                


              click bioimageio.core.sample.SampleBlockMeta href "" "bioimageio.core.sample.SampleBlockMeta"
              click bioimageio.core.sample.SampleBlockBase href "" "bioimageio.core.sample.SampleBlockBase"
            

Meta data of a dataset sample block

Methods:

Name	Description
get_transformed
with_data

Attributes:

Name	Type	Description
block_index	BlockIndex	the n-th block of the sample
blocks	PerMemberAnno[BlockT]	Individual tensor blocks comprising this sample block
blocks_in_sample	TotalNumberOfBlocks	total number of blocks in the sample
inner_shape	PerMember[PerAxis[int]]
sample_id	SampleId	identifier for the sample within its dataset
sample_shape	PerMemberAnno[PerAxis[int]]	the sample shape this block represents a part of
shape	PerMember[PerAxis[int]]

block_index instance-attribute

block_index: BlockIndex

the n-th block of the sample

blocks instance-attribute

blocks: PerMemberAnno[BlockT]

Individual tensor blocks comprising this sample block

blocks_in_sample instance-attribute

blocks_in_sample: TotalNumberOfBlocks

total number of blocks in the sample

inner_shape property

inner_shape: PerMember[PerAxis[int]]

sample_id instance-attribute

sample_id: SampleId

identifier for the sample within its dataset

sample_shape instance-attribute

sample_shape: PerMemberAnno[PerAxis[int]]

the sample shape this block represents a part of

shape property

shape: PerMember[PerAxis[int]]

get_transformed

get_transformed(new_axes: PerMember[PerAxis[Union[LinearSampleAxisTransform, int]]]) -> Self

Source code in src/bioimageio/core/sample.py

def get_transformed(
    self, new_axes: PerMember[PerAxis[Union[LinearSampleAxisTransform, int]]]
) -> Self:
    sample_shape = {
        m: {
            a: (
                trf
                if isinstance(trf, int)
                else trf.compute(self.sample_shape[trf.member][trf.axis])
            )
            for a, trf in new_axes[m].items()
        }
        for m in new_axes
    }

    def get_member_halo(m: MemberId, round: Callable[[float], int]):
        return {
            a: (
                Halo(0, 0)
                if isinstance(trf, int)
                or trf.axis not in self.blocks[trf.member].halo
                else Halo(
                    round(self.blocks[trf.member].halo[trf.axis].left * trf.scale),
                    round(self.blocks[trf.member].halo[trf.axis].right * trf.scale),
                )
            )
            for a, trf in new_axes[m].items()
        }

    halo: Dict[MemberId, Dict[AxisId, Halo]] = {}
    for m in new_axes:
        halo[m] = get_member_halo(m, floor)
        if halo[m] != get_member_halo(m, ceil):
            raise ValueError(
                f"failed to unambiguously scale halo {halo[m]} with {new_axes[m]}"
                + f" for {m}."
            )

    inner_slice = {
        m: {
            a: (
                SliceInfo(0, trf)
                if isinstance(trf, int)
                else SliceInfo(
                    trf.compute(
                        self.blocks[trf.member].inner_slice[trf.axis].start
                    ),
                    trf.compute(self.blocks[trf.member].inner_slice[trf.axis].stop),
                )
            )
            for a, trf in new_axes[m].items()
        }
        for m in new_axes
    }
    return self.__class__(
        blocks={
            m: BlockMeta(
                sample_shape=sample_shape[m],
                inner_slice=inner_slice[m],
                halo=halo[m],
                block_index=self.block_index,
                blocks_in_sample=self.blocks_in_sample,
            )
            for m in new_axes
        },
        sample_shape=sample_shape,
        sample_id=self.sample_id,
        block_index=self.block_index,
        blocks_in_sample=self.blocks_in_sample,
    )

with_data

with_data(data: PerMember[Tensor], *, stat: Stat) -> SampleBlock

Source code in src/bioimageio/core/sample.py

def with_data(self, data: PerMember[Tensor], *, stat: Stat) -> SampleBlock:
    return SampleBlock(
        sample_shape={
            m: {
                a: data[m].tagged_shape[a] if s == -1 else s
                for a, s in member_shape.items()
            }
            for m, member_shape in self.sample_shape.items()
        },
        sample_id=self.sample_id,
        blocks={
            m: Block.from_meta(b, data=data[m]) for m, b in self.blocks.items()
        },
        stat=stat,
        block_index=self.block_index,
        blocks_in_sample=self.blocks_in_sample,
    )

SampleBlockWithOrigin dataclass

SampleBlockWithOrigin(blocks: Dict[MemberId, Block], stat: Stat, origin: Sample)

Bases: SampleBlock

              flowchart TD
              bioimageio.core.sample.SampleBlockWithOrigin[SampleBlockWithOrigin]
              bioimageio.core.sample.SampleBlock[SampleBlock]
              bioimageio.core.sample.SampleBlockBase[SampleBlockBase]

                              bioimageio.core.sample.SampleBlock --> bioimageio.core.sample.SampleBlockWithOrigin
                                bioimageio.core.sample.SampleBlockBase --> bioimageio.core.sample.SampleBlock
                



              click bioimageio.core.sample.SampleBlockWithOrigin href "" "bioimageio.core.sample.SampleBlockWithOrigin"
              click bioimageio.core.sample.SampleBlock href "" "bioimageio.core.sample.SampleBlock"
              click bioimageio.core.sample.SampleBlockBase href "" "bioimageio.core.sample.SampleBlockBase"
            

A SampleBlock with a reference (origin) to the whole Sample

Returned by:

• API Reference
  •  digest_spec  split_sample_into_blocks_for_model
  •  sample  sample_block_generator

Methods:

Name	Description
as_sample	Convert this sample block to a Sample with the shape of this block.
from_meta
get_meta
get_transformed_meta

Attributes:

Name	Type	Description
block_index	BlockIndex	the n-th block of the sample
blocks	Dict[MemberId, Block]	Individual tensor blocks comprising this sample block
blocks_in_sample	TotalNumberOfBlocks	total number of blocks in the sample
inner_shape	PerMember[PerAxis[int]]
members	PerMember[Tensor]	the sample block's tensors
origin	Sample	the sample this sample block was taken from
sample_id	SampleId	identifier for the sample within its dataset
sample_shape	PerMemberAnno[PerAxis[int]]	the sample shape this block represents a part of
shape	PerMember[PerAxis[int]]
stat	Stat	computed statistics

block_index instance-attribute

block_index: BlockIndex

the n-th block of the sample

blocks instance-attribute

blocks: Dict[MemberId, Block]

Individual tensor blocks comprising this sample block

blocks_in_sample instance-attribute

blocks_in_sample: TotalNumberOfBlocks

total number of blocks in the sample

inner_shape property

inner_shape: PerMember[PerAxis[int]]

members property

members: PerMember[Tensor]

the sample block's tensors

origin instance-attribute

origin: Sample

the sample this sample block was taken from

sample_id instance-attribute

sample_id: SampleId

identifier for the sample within its dataset

sample_shape instance-attribute

sample_shape: PerMemberAnno[PerAxis[int]]

the sample shape this block represents a part of

shape property

shape: PerMember[PerAxis[int]]

stat instance-attribute

stat: Stat

computed statistics

as_sample

as_sample() -> Sample

Convert this sample block to a Sample with the shape of this block.

Note

If you want to convert one or more sample block to a sample with the shape of the original, whole sample, use Sample.from_blocks() instead.

Source code in src/bioimageio/core/sample.py

def as_sample(self) -> Sample:
    """Convert this sample block to a `Sample` with the shape of this block.

    Note:
        If you want to convert one or more sample block to a sample with the shape of the original, whole sample,
        use `Sample.from_blocks()` instead.
    """
    return Sample(
        members=dict(self.members),
        stat=dict(self.stat),
        id=self.sample_id,
    )

from_meta classmethod

from_meta(meta: SampleBlockMeta, data: PerMember[Tensor], stat: Stat) -> Self

Source code in src/bioimageio/core/sample.py

@classmethod
def from_meta(
    cls, meta: SampleBlockMeta, data: PerMember[Tensor], stat: Stat
) -> Self:
    return cls(
        sample_shape=meta.sample_shape,
        sample_id=meta.sample_id,
        blocks={
            m: Block.from_meta(b, data=data[m]) for m, b in meta.blocks.items()
        },
        stat=stat,
        block_index=meta.block_index,
        blocks_in_sample=meta.blocks_in_sample,
    )

get_meta

get_meta() -> SampleBlockMeta

Source code in src/bioimageio/core/sample.py

def get_meta(self) -> SampleBlockMeta:
    return SampleBlockMeta(
        sample_id=self.sample_id,
        blocks={m: b.get_meta() for m, b in self.blocks.items()},
        sample_shape=self.sample_shape,
        block_index=self.block_index,
        blocks_in_sample=self.blocks_in_sample,
    )

get_transformed_meta

get_transformed_meta(new_axes: PerMember[PerAxis[Union[LinearSampleAxisTransform, int]]]) -> SampleBlockMeta

Source code in src/bioimageio/core/sample.py

def get_transformed_meta(
    self, new_axes: PerMember[PerAxis[Union[LinearSampleAxisTransform, int]]]
) -> SampleBlockMeta:
    return SampleBlockMeta(
        sample_id=self.sample_id,
        blocks=dict(self.blocks),
        sample_shape=self.sample_shape,
        block_index=self.block_index,
        blocks_in_sample=self.blocks_in_sample,
    ).get_transformed(new_axes)

sample_block_generator

sample_block_generator(blocks: Iterable[PerMember[BlockMeta]], *, origin: Sample, pad_mode: Union[PadMode, PerMember[PadMode]]) -> Iterable[SampleBlockWithOrigin]

Source code in src/bioimageio/core/sample.py

def sample_block_generator(
    blocks: Iterable[PerMember[BlockMeta]],
    *,
    origin: Sample,
    pad_mode: Union[PadMode, PerMember[PadMode]],
) -> Iterable[SampleBlockWithOrigin]:
    for member_blocks in blocks:
        cons = _ConsolidatedMemberBlocks(member_blocks)
        yield SampleBlockWithOrigin(
            blocks={
                m: Block.from_sample_member(
                    origin.members[m],
                    block=member_blocks[m],
                    pad_mode=pad_mode.get(m, "symmetric")
                    if isinstance(pad_mode, collections.abc.Mapping)
                    else pad_mode,
                )
                for m in origin.members
            },
            sample_shape=origin.shape,
            origin=origin,
            stat=origin.stat,
            sample_id=origin.id,
            block_index=cons.block_index,
            blocks_in_sample=cons.blocks_in_sample,
        )

sample_block_meta_generator

sample_block_meta_generator(blocks: Iterable[PerMember[BlockMeta]], *, sample_shape: PerMember[PerAxis[int]], sample_id: SampleId)

Source code in src/bioimageio/core/sample.py

def sample_block_meta_generator(
    blocks: Iterable[PerMember[BlockMeta]],
    *,
    sample_shape: PerMember[PerAxis[int]],
    sample_id: SampleId,
):
    for member_blocks in blocks:
        cons = _ConsolidatedMemberBlocks(member_blocks)
        yield SampleBlockMeta(
            blocks=dict(member_blocks),
            sample_shape=sample_shape,
            sample_id=sample_id,
            block_index=cons.block_index,
            blocks_in_sample=cons.blocks_in_sample,
        )