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Generic Component Implementations

Generic "ready-to-use" implementations of common components.

Other generic and largely reusable components can be added to this submodule.

Note

Numpy (and jaxtyping) are the only dependencies; to keep the abstract_dataloader's dependencies lightweight and flexible, components should only be added here if they do not require any additional dependencies.

abstract_dataloader.generic.ComposedPipeline

Bases: Pipeline[TRaw, TTransformed, TCollated, TProcessed], Generic[TRaw, TRawInner, TTransformed, TCollated, TProcessedInner, TProcessed]

Compose pipeline sequentially with pre and post transforms.

Type Parameters
  • TRaw: initial input type.
  • TRawInner: output of the pre-composed transform, and input to the provided Pipeline.
  • TCollated, TProcessed: intermediate values for the provided Pipeline.
  • TProcessedInner: output of the transforms, and input to the post-composed transform.
  • TProcessed: output type.

Parameters:

Name Type Description Default
pipeline Pipeline[TRawInner, TTransformed, TCollated, TProcessedInner]

pipeline to compose.

 required
pre Transform[TRaw, TRawInner] | None

pre-transform to apply on the CPU side; skipped if None.

 None
post Transform[TProcessedInner, TProcessed] | None

post-transform to apply on the GPU side; skipped if None.

 None
Source code in src/abstract_dataloader/generic/composition.py 
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class ComposedPipeline(
    spec.Pipeline[TRaw, TTransformed, TCollated, TProcessed],
    Generic[
        TRaw, TRawInner, TTransformed,
        TCollated, TProcessedInner, TProcessed]
):
    """Compose pipeline sequentially with pre and post transforms.

    Type Parameters:
        - `TRaw`: initial input type.
        - `TRawInner`: output of the pre-composed transform, and input to the
            provided [`Pipeline`][abstract_dataloader.spec].
        - `TCollated`, `TProcessed`: intermediate values for the provided
            [`Pipeline`][abstract_dataloader.spec].
        - `TProcessedInner`: output of the transforms, and input to the
            post-composed transform.
        - `TProcessed`: output type.

    Args:
        pipeline: pipeline to compose.
        pre: pre-transform to apply on the CPU side; skipped if `None`.
        post: post-transform to apply on the GPU side; skipped if `None`.
    """

    def __init__(
        self, pipeline: spec.Pipeline[
            TRawInner, TTransformed, TCollated, TProcessedInner],
        pre: spec.Transform[TRaw, TRawInner] | None = None,
        post: spec.Transform[TProcessedInner, TProcessed] | None = None
    ) -> None:
        self.pipeline = pipeline
        self.pre = pre
        self.post = post

        self.collate = pipeline.collate

    def sample(self, data: TRaw) -> TTransformed:
        """Transform single samples.

        Args:
            data: A single `TRaw` data sample.

        Returns:
            A single `TTransformed` data sample.
        """
        if self.pre is None:
            transformed = cast(TRawInner, data)
        else:
            transformed = self.pre(data)
        return self.pipeline.sample(transformed)

    def batch(self, data: TCollated) -> TProcessed:
        """Transform data batch.

        Args:
            data: A `TCollated` batch of data, nominally already sent to the
                GPU.

        Returns:
            The `TProcessed` output, ready for the downstream model.
        """
        transformed = self.pipeline.batch(data)
        if self.post is None:
            return cast(TProcessed, transformed)
        else:
            return self.post(transformed)

batch 

batch(data: TCollated) -> TProcessed

Transform data batch.

Parameters:

Name Type Description Default
data TCollated

A TCollated batch of data, nominally already sent to the GPU.

 required

Returns:

Type Description
TProcessed

The TProcessed output, ready for the downstream model.
Source code in src/abstract_dataloader/generic/composition.py 
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def batch(self, data: TCollated) -> TProcessed:
    """Transform data batch.

    Args:
        data: A `TCollated` batch of data, nominally already sent to the
            GPU.

    Returns:
        The `TProcessed` output, ready for the downstream model.
    """
    transformed = self.pipeline.batch(data)
    if self.post is None:
        return cast(TProcessed, transformed)
    else:
        return self.post(transformed)

sample 

sample(data: TRaw) -> TTransformed

Transform single samples.

Parameters:

Name Type Description Default
data TRaw

A single TRaw data sample.

 required

Returns:

Type Description
TTransformed

A single TTransformed data sample.
Source code in src/abstract_dataloader/generic/composition.py 
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def sample(self, data: TRaw) -> TTransformed:
    """Transform single samples.

    Args:
        data: A single `TRaw` data sample.

    Returns:
        A single `TTransformed` data sample.
    """
    if self.pre is None:
        transformed = cast(TRawInner, data)
    else:
        transformed = self.pre(data)
    return self.pipeline.sample(transformed)

abstract_dataloader.generic.Empty

Bases: Synchronization

Dummy synchronization which does not synchronize sensor pairs.

No samples will be registered, and the trace can only be used as a collection of sensors.

Source code in src/abstract_dataloader/generic/sync.py 
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class Empty(spec.Synchronization):
    """Dummy synchronization which does not synchronize sensor pairs.

    No samples will be registered, and the trace can only be used as a
    collection of sensors.
    """

    def __call__(
        self, timestamps: dict[str, Float64[np.ndarray, "_N"]]
    ) -> dict[str, UInt32[np.ndarray, "M"]]:
        """Apply synchronization protocol.

        Args:
            timestamps: input sensor timestamps.

        Returns:
            Synchronized index map.
        """
        return {k: np.array([], dtype=np.uint32) for k in timestamps}

__call__ 

__call__(
    timestamps: dict[str, Float64[ndarray, _N]],
) -> dict[str, UInt32[ndarray, M]]

Apply synchronization protocol.

Parameters:

Name Type Description Default
timestamps dict[str, Float64[ndarray, _N]]

input sensor timestamps.

 required

Returns:

Type Description
dict[str, UInt32[ndarray, M]]

Synchronized index map.
Source code in src/abstract_dataloader/generic/sync.py 
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def __call__(
    self, timestamps: dict[str, Float64[np.ndarray, "_N"]]
) -> dict[str, UInt32[np.ndarray, "M"]]:
    """Apply synchronization protocol.

    Args:
        timestamps: input sensor timestamps.

    Returns:
        Synchronized index map.
    """
    return {k: np.array([], dtype=np.uint32) for k in timestamps}

abstract_dataloader.generic.Metadata dataclass

Bases: Metadata

Generic metadata with timestamps.

Attributes:

Name Type Description
timestamps Float64[ndarray, N]

epoch timestamps.
Source code in src/abstract_dataloader/generic/sequence.py 
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@dataclass
class Metadata(spec.Metadata):
    """Generic metadata with timestamps.

    Attributes:
        timestamps: epoch timestamps.
    """

    timestamps: Float64[np.ndarray, "N"]

abstract_dataloader.generic.Nearest

Bases: Synchronization

Nearest sample synchronization, with respect to a reference sensor.

Applies the following:

  • Compute the midpoints between observations between each sensor.
  • Find which bin the reference sensor timestamps fall into.
  • Calculate the resulting time delta between timestamps. If this exceeds tol for any sensor-reference pair, remove this match.

See Synchronization for protocol details.

Parameters:

Name Type Description Default
reference str

reference sensor to synchronize to.

 required
tol float

synchronization time tolerance, in seconds. Setting tol = np.inf works to disable this check altogether.

 0.1
Source code in src/abstract_dataloader/generic/sync.py 
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class Nearest(spec.Synchronization):
    """Nearest sample synchronization, with respect to a reference sensor.

    Applies the following:

    - Compute the midpoints between observations between each sensor.
    - Find which bin the reference sensor timestamps fall into.
    - Calculate the resulting time delta between timestamps. If this exceeds
      `tol` for any sensor-reference pair, remove this match.

    See [`Synchronization`][abstract_dataloader.spec.] for protocol details.

    Args:
        reference: reference sensor to synchronize to.
        tol: synchronization time tolerance, in seconds. Setting `tol = np.inf`
            works to disable this check altogether.
    """

    def __init__(self, reference: str, tol: float = 0.1) -> None:
        if tol < 0:
            raise ValueError(
                f"Synchronization tolerance must be positive: {tol} < 0")

        self.tol = tol
        self.reference = reference

    def __call__(
        self, timestamps: dict[str, Float64[np.ndarray, "_N"]]
    ) -> dict[str, UInt32[np.ndarray, "M"]]:
        """Apply synchronization protocol.

        Args:
            timestamps: input sensor timestamps.

        Returns:
            Synchronized index map.
        """
        try:
            t_ref = timestamps[self.reference]
        except KeyError:
            raise KeyError(
                f"Reference sensor {self.reference} was not provided in "
                f"timestamps, with keys: {list(timestamps.keys())}")

        indices = {
            k: np.searchsorted(
                (t_sensor[:-1] + t_sensor[1:]) / 2, t_ref
            ).astype(np.uint32)
            for k, t_sensor in timestamps.items()}
        valid = np.all(np.array([
           np.abs(timestamps[k][i_nearest] - t_ref) < self.tol
        for k, i_nearest in indices.items()]), axis=0)

        return {k: v[valid] for k, v in indices.items()}

__call__ 

__call__(
    timestamps: dict[str, Float64[ndarray, _N]],
) -> dict[str, UInt32[ndarray, M]]

Apply synchronization protocol.

Parameters:

Name Type Description Default
timestamps dict[str, Float64[ndarray, _N]]

input sensor timestamps.

 required

Returns:

Type Description
dict[str, UInt32[ndarray, M]]

Synchronized index map.
Source code in src/abstract_dataloader/generic/sync.py 
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def __call__(
    self, timestamps: dict[str, Float64[np.ndarray, "_N"]]
) -> dict[str, UInt32[np.ndarray, "M"]]:
    """Apply synchronization protocol.

    Args:
        timestamps: input sensor timestamps.

    Returns:
        Synchronized index map.
    """
    try:
        t_ref = timestamps[self.reference]
    except KeyError:
        raise KeyError(
            f"Reference sensor {self.reference} was not provided in "
            f"timestamps, with keys: {list(timestamps.keys())}")

    indices = {
        k: np.searchsorted(
            (t_sensor[:-1] + t_sensor[1:]) / 2, t_ref
        ).astype(np.uint32)
        for k, t_sensor in timestamps.items()}
    valid = np.all(np.array([
       np.abs(timestamps[k][i_nearest] - t_ref) < self.tol
    for k, i_nearest in indices.items()]), axis=0)

    return {k: v[valid] for k, v in indices.items()}

abstract_dataloader.generic.Next

Bases: Synchronization

Next sample synchronization, with respect to a reference sensor.

Applies the following:

  • Find the start time, defined by the earliest time which is observed by all sensors, and the end time, defined by the last time which is observed by all sensors.
  • Truncate the reference sensor's timestamps to this start and end time, and use this as the query timestamps.
  • For each time in the query, find the first sample from each sensor which is after this time.

See Synchronization for protocol details.

Parameters:

Name Type Description Default
reference str

reference sensor to synchronize to.

 required
Source code in src/abstract_dataloader/generic/sync.py 
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class Next(spec.Synchronization):
    """Next sample synchronization, with respect to a reference sensor.

    Applies the following:

    - Find the start time, defined by the earliest time which is observed by
      all sensors, and the end time, defined by the last time which is observed
      by all sensors.
    - Truncate the reference sensor's timestamps to this start and end time,
      and use this as the query timestamps.
    - For each time in the query, find the first sample from each sensor which
      is after this time.

    See [`Synchronization`][abstract_dataloader.spec.] for protocol details.

    Args:
        reference: reference sensor to synchronize to.
    """

    def __init__(self, reference: str) -> None:
        self.reference = reference

    def __call__(
        self, timestamps: dict[str, Float64[np.ndarray, "_N"]]
    ) -> dict[str, UInt32[np.ndarray, "M"]]:
        """Apply synchronization protocol.

        Args:
            timestamps: input sensor timestamps.

        Returns:
            Synchronized index map.
        """
        try:
            ref_time_all = timestamps[self.reference]
        except KeyError:
            raise KeyError(
                f"Reference sensor {self.reference} was not provided in "
                f"timestamps, with keys: {list(timestamps.keys())}")

        start_time = max(t[0] for t in timestamps.values())
        end_time = min(t[-1] for t in timestamps.values())

        start_idx = np.searchsorted(ref_time_all, start_time)
        end_idx = np.searchsorted(ref_time_all, end_time)
        ref_time = ref_time_all[start_idx:end_idx]
        return {
            k: np.searchsorted(v, ref_time).astype(np.uint32)
            for k, v in timestamps.items()}

__call__ 

__call__(
    timestamps: dict[str, Float64[ndarray, _N]],
) -> dict[str, UInt32[ndarray, M]]

Apply synchronization protocol.

Parameters:

Name Type Description Default
timestamps dict[str, Float64[ndarray, _N]]

input sensor timestamps.

 required

Returns:

Type Description
dict[str, UInt32[ndarray, M]]

Synchronized index map.
Source code in src/abstract_dataloader/generic/sync.py 
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def __call__(
    self, timestamps: dict[str, Float64[np.ndarray, "_N"]]
) -> dict[str, UInt32[np.ndarray, "M"]]:
    """Apply synchronization protocol.

    Args:
        timestamps: input sensor timestamps.

    Returns:
        Synchronized index map.
    """
    try:
        ref_time_all = timestamps[self.reference]
    except KeyError:
        raise KeyError(
            f"Reference sensor {self.reference} was not provided in "
            f"timestamps, with keys: {list(timestamps.keys())}")

    start_time = max(t[0] for t in timestamps.values())
    end_time = min(t[-1] for t in timestamps.values())

    start_idx = np.searchsorted(ref_time_all, start_time)
    end_idx = np.searchsorted(ref_time_all, end_time)
    ref_time = ref_time_all[start_idx:end_idx]
    return {
        k: np.searchsorted(v, ref_time).astype(np.uint32)
        for k, v in timestamps.items()}

abstract_dataloader.generic.ParallelPipelines

Bases: Pipeline[PRaw, PTransformed, PCollated, PProcessed]

Compose multiple transforms in parallel.

For example, with transforms {"radar": radar_tf, "lidar": lidar_tf, ...}, the composed transform performs:

{
    "radar": radar_tf.transform(data["radar"]),
    "lidar": lidar_tf.transform(data["lidar"]),
    ...
}

Note

This implies that the type parameters must be dict[str, Any], so this class is parameterized by a separate set of Composed(Raw|Transformed|Collated|Processed) types with this bound.

Tip

See torch.ParallelPipelines for an implementation which is compatible with nn.Module-based pipelines.

Type Parameters
  • PRaw, PTransformed, PCollated, PProcessed: see Pipeline.

Parameters:

Name Type Description Default
transforms Pipeline

transforms to compose. The key indicates the subkey to apply each transform to.

 {}
Source code in src/abstract_dataloader/generic/composition.py 
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class ParallelPipelines(
    spec.Pipeline[PRaw, PTransformed, PCollated, PProcessed],
):
    """Compose multiple transforms in parallel.

    For example, with transforms `{"radar": radar_tf, "lidar": lidar_tf, ...}`,
    the composed transform performs:

    ```python
    {
        "radar": radar_tf.transform(data["radar"]),
        "lidar": lidar_tf.transform(data["lidar"]),
        ...
    }
    ```

    !!! note

        This implies that the type parameters must be `dict[str, Any]`, so this
        class is parameterized by a separate set of
        `Composed(Raw|Transformed|Collated|Processed)` types with this bound.

    !!! tip

        See [`torch.ParallelPipelines`][abstract_dataloader.] for an
        implementation which is compatible with [`nn.Module`][torch.]-based
        pipelines.

    Type Parameters:
        - `PRaw`, `PTransformed`, `PCollated`, `PProcessed`: see
          [`Pipeline`][abstract_dataloader.spec.].

    Args:
        transforms: transforms to compose. The key indicates the subkey to
            apply each transform to.
    """

    def __init__(self, **transforms: spec.Pipeline) -> None:
        self.transforms = transforms

    def sample(self, data: PRaw) -> PTransformed:
        return cast(
            PTransformed,
            {k: v.sample(data[k]) for k, v in self.transforms.items()})

    def collate(self, data: Sequence[PTransformed]) -> PCollated:
        return cast(PCollated, {
            k: v.collate([x[k] for x in data])
            for k, v in self.transforms.items()
        })

    def batch(self, data: PCollated) -> PProcessed:
        return cast(
            PProcessed,
            {k: v.batch(data[k]) for k, v in self.transforms.items()})

abstract_dataloader.generic.SequencePipeline

Bases: Pipeline[Sequence[TRaw], Sequence[TTransformed], Sequence[TCollated], Sequence[TProcessed]], Generic[TRaw, TTransformed, TCollated, TProcessed]

Transform which passes an additional sequence axis through.

The given Pipeline is modified to accept Sequence[...] for each data type in its pipeline, and return a list[...] across the additional axis, thus "passing through" the axis.

For example, suppose a sequence dataloader reads

[
    [Raw[s=0, t=0], Raw[s=0, t=1], ... Raw[s=0, t=n]]
    [Raw[s=1, t=0], Raw[s=1, t=1], ... Raw[s=1, t=n]]
    ...
    [Raw[s=b, t=0], Raw[s=b, t=1], ... Raw[s=b, t=n]
]

for sequence length t = 0...n and batch sample s = 0...b. For sequence length t, the output of the transforms will be batched with the sequence on the outside:

[
    Processed[s=0...b] [t=0],
    Processed[s=0...b] [t=1],
    ...
    Processed[s=0...b] [t=n]
]
Type Parameters
  • TRaw, TTransformed, TCollated, TProcessed: see Pipeline.

Parameters:

Name Type Description Default
pipeline Pipeline[TRaw, TTransformed, TCollated, TProcessed]

input pipeline.

 required
Source code in src/abstract_dataloader/generic/sequence.py 
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class SequencePipeline(
    spec.Pipeline[
        Sequence[TRaw], Sequence[TTransformed],
        Sequence[TCollated], Sequence[TProcessed]],
    Generic[TRaw, TTransformed, TCollated, TProcessed]
):
    """Transform which passes an additional sequence axis through.

    The given `Pipeline` is modified to accept `Sequence[...]` for each
    data type in its pipeline, and return a `list[...]` across the additional
    axis, thus "passing through" the axis.

    For example, suppose a sequence dataloader reads

    ```
    [
        [Raw[s=0, t=0], Raw[s=0, t=1], ... Raw[s=0, t=n]]
        [Raw[s=1, t=0], Raw[s=1, t=1], ... Raw[s=1, t=n]]
        ...
        [Raw[s=b, t=0], Raw[s=b, t=1], ... Raw[s=b, t=n]
    ]
    ```

    for sequence length `t = 0...n` and batch sample `s = 0...b`. For sequence
    length `t`, the output of the transforms will be batched with the sequence
    on the outside:

    ```
    [
        Processed[s=0...b] [t=0],
        Processed[s=0...b] [t=1],
        ...
        Processed[s=0...b] [t=n]
    ]
    ```

    Type Parameters:
        - `TRaw`, `TTransformed`, `TCollated`, `TProcessed`: see
          [`Pipeline`][abstract_dataloader.spec.].

    Args:
        pipeline: input pipeline.
    """

    def __init__(
        self, pipeline: spec.Pipeline[
            TRaw, TTransformed, TCollated, TProcessed]
    ) -> None:
        self.pipeline = pipeline

    def sample(self, data: Sequence[TRaw]) -> list[TTransformed]:
        return [self.pipeline.sample(x) for x in data]

    def collate(
        self, data: Sequence[Sequence[TTransformed]]
    ) -> list[TCollated]:
        return [self.pipeline.collate(x) for x in zip(*data)]

    def batch(self, data: Sequence[TCollated]) -> list[TProcessed]:
        return [self.pipeline.batch(x) for x in data]

abstract_dataloader.generic.Window

Bases: Sensor[SampleStack, Metadata], Generic[SampleStack, Sample, TMetadata]

Load sensor data across a time window using a sensor transform.

Use this class as a generic transform to give time history to any sensor:

sensor =  ... # implements spec.Sensor
with_history = generic.Window(sensor, past=5, future=1, parallel=7)

In this example, 5 past samples, the current sample, and 1 future sample are loaded on every index:

with_history[i] = [
    sensor[i], sensor[i + 1], ... sensor[i + 5], sensor[i + 6]]
                                        ^
                            # timestamp for synchronization

Parameters:

Name Type Description Default
sensor Sensor[Sample, TMetadata]

sensor to wrap.

 required
collate_fn Callable[[list[Sample]], SampleStack] | None

collate function for aggregating a list of samples; if not specified, the samples are simply returned as a list.

 None
past int

number of past samples, in addition to the current sample. Set to 0 to disable.

 0
future int

number of future samples, in addition to the current sample. Set to 0 to disable.

 0
parallel int | None

maximum number of samples to load in parallel; if None, all samples are loaded sequentially.

 None
Type Parameters
  • SampleStack: a collated series of consecutive samples. Can simply be list[Sample].
  • Sample: single observation sample type.
  • TMetadata: metadata type for the underlying sensor. Note that the Window wrapper doesn't actually have metadata type TMetadata; this type is just passed through from the sensor which is wrapped.
Source code in src/abstract_dataloader/generic/sequence.py 
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class Window(
    abstract.Sensor[SampleStack, Metadata],
    Generic[SampleStack, Sample, TMetadata]
):
    """Load sensor data across a time window using a sensor transform.

    Use this class as a generic transform to give time history to any sensor:

    ```python
    sensor =  ... # implements spec.Sensor
    with_history = generic.Window(sensor, past=5, future=1, parallel=7)
    ```

    In this example, 5 past samples, the current sample, and 1 future sample
    are loaded on every index:

    ```python
    with_history[i] = [
        sensor[i], sensor[i + 1], ... sensor[i + 5], sensor[i + 6]]
                                            ^
                                # timestamp for synchronization
    ```

    Args:
        sensor: sensor to wrap.
        collate_fn: collate function for aggregating a list of samples; if not
            specified, the samples are simply returned as a list.
        past: number of past samples, in addition to the current sample. Set
            to `0` to disable.
        future: number of future samples, in addition to the current sample.
            Set to `0` to disable.
        parallel: maximum number of samples to load in parallel; if `None`, all
            samples are loaded sequentially.

    Type Parameters:
        - `SampleStack`: a collated series of consecutive samples. Can simply be
            `list[Sample]`.
        - `Sample`: single observation sample type.
        - `TMetadata`: metadata type for the underlying sensor. Note that the
            `Window` wrapper doesn't actually have metadata type `TMetadata`;
            this type is just passed through from the sensor which is wrapped.
    """

    def __init__(
        self, sensor: spec.Sensor[Sample, TMetadata],
        collate_fn: Callable[[list[Sample]], SampleStack] | None = None,
        past: int = 0, future: int = 0, parallel: int | None = None
    ) -> None:
        self.sensor = sensor
        self.past = past
        self.future = future
        self.parallel = parallel

        if collate_fn is None:
            collate_fn = cast(
                Callable[[list[Sample]], SampleStack], lambda x: x)
        self.collate_fn = collate_fn

        # hack for negative indexing
        _future = None if future == 0 else -future
        self.metadata = Metadata(
            timestamps=sensor.metadata.timestamps[past:_future])

    @classmethod
    def from_partial_sensor(
        cls, sensor: Callable[[str], spec.Sensor[Sample, TMetadata]],
        collate_fn: Callable[[list[Sample]], SampleStack] | None = None,
        past: int = 0, future: int = 0, parallel: int | None = None
    ) -> Callable[[str], "Window[SampleStack, Sample, TMetadata]"]:
        """Partially initialize from partially initialized sensor.

        Use this to create windowed sensor constructors which can be
        applied to different traces to construct a dataset. For example,
        if you have a `sensor_constructor`:

        ```python
        sensor_constructor = ...
        windowed_sensor_constructor = Window.from_partial_sensor(
            sensor_constructor, ...)

        # ... somewhere inside the dataset constructor
        sensor_instance = windowed_sensor_constructor(path_to_trace)
        ```

        Args:
            sensor: sensor *constructor* to wrap.
            collate_fn: collate function for aggregating a list of samples; if
                not specified, the samples are simply returned as a list.
            past: number of past samples, in addition to the current sample.
                Set to `0` to disable.
            future: number of future samples, in addition to the current
                sample. Set to `0` to disable.
            parallel: maximum number of samples to load in parallel; if `None`,
                all samples are loaded sequentially.
        """
        def create_wrapped_sensor(
            path: str
        ) -> Window[SampleStack, Sample, TMetadata]:
            return cls(
                sensor(path), collate_fn=collate_fn, past=past,
                future=future, parallel=parallel)

        return create_wrapped_sensor

    def __getitem__(self, index: int | np.integer) -> SampleStack:
        """Fetch measurements from this sensor, by index.

        Args:
            index: sample index; note that `past` samples are lost at the
                beginning, and `future` at the end to account for the window
                size.

        Returns:
            A set of `past + 1 + future` consecutives samples. Note that there
                is a `past` offset of indices between the wrapped `Window` and
                the underlying sensor!
        """
        window = list(range(index, index + self.past + self.future + 1))

        if self.parallel is not None:
            with ThreadPool(min(len(window), self.parallel)) as p:
                return self.collate_fn(p.map(self.sensor.__getitem__, window))
        else:
            return self.collate_fn(list(map(self.sensor.__getitem__, window)))

    def __repr__(self) -> str:
        """Get friendly name (passing through to the underlying sensor)."""
        return f"{repr(self.sensor)} x [-{self.past}:+{self.future}]"

__getitem__ 

__getitem__(index: int | integer) -> SampleStack

Fetch measurements from this sensor, by index.

Parameters:

Name Type Description Default
index int | integer

sample index; note that past samples are lost at the beginning, and future at the end to account for the window size.

 required

Returns:

Type Description
SampleStack

A set of past + 1 + future consecutives samples. Note that there is a past offset of indices between the wrapped Window and the underlying sensor!
Source code in src/abstract_dataloader/generic/sequence.py 
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def __getitem__(self, index: int | np.integer) -> SampleStack:
    """Fetch measurements from this sensor, by index.

    Args:
        index: sample index; note that `past` samples are lost at the
            beginning, and `future` at the end to account for the window
            size.

    Returns:
        A set of `past + 1 + future` consecutives samples. Note that there
            is a `past` offset of indices between the wrapped `Window` and
            the underlying sensor!
    """
    window = list(range(index, index + self.past + self.future + 1))

    if self.parallel is not None:
        with ThreadPool(min(len(window), self.parallel)) as p:
            return self.collate_fn(p.map(self.sensor.__getitem__, window))
    else:
        return self.collate_fn(list(map(self.sensor.__getitem__, window)))

__repr__ 

__repr__() -> str

Get friendly name (passing through to the underlying sensor).

Source code in src/abstract_dataloader/generic/sequence.py 
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def __repr__(self) -> str:
    """Get friendly name (passing through to the underlying sensor)."""
    return f"{repr(self.sensor)} x [-{self.past}:+{self.future}]"

from_partial_sensor classmethod 

from_partial_sensor(
    sensor: Callable[[str], Sensor[Sample, TMetadata]],
    collate_fn: Callable[[list[Sample]], SampleStack] | None = None,
    past: int = 0,
    future: int = 0,
    parallel: int | None = None,
) -> Callable[[str], Window[SampleStack, Sample, TMetadata]]

Partially initialize from partially initialized sensor.

Use this to create windowed sensor constructors which can be applied to different traces to construct a dataset. For example, if you have a sensor_constructor:

sensor_constructor = ...
windowed_sensor_constructor = Window.from_partial_sensor(
    sensor_constructor, ...)

# ... somewhere inside the dataset constructor
sensor_instance = windowed_sensor_constructor(path_to_trace)

Parameters:

Name Type Description Default
sensor Callable[[str], Sensor[Sample, TMetadata]]

sensor constructor to wrap.

 required
collate_fn Callable[[list[Sample]], SampleStack] | None

collate function for aggregating a list of samples; if not specified, the samples are simply returned as a list.

 None
past int

number of past samples, in addition to the current sample. Set to 0 to disable.

 0
future int

number of future samples, in addition to the current sample. Set to 0 to disable.

 0
parallel int | None

maximum number of samples to load in parallel; if None, all samples are loaded sequentially.

 None
Source code in src/abstract_dataloader/generic/sequence.py 
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@classmethod
def from_partial_sensor(
    cls, sensor: Callable[[str], spec.Sensor[Sample, TMetadata]],
    collate_fn: Callable[[list[Sample]], SampleStack] | None = None,
    past: int = 0, future: int = 0, parallel: int | None = None
) -> Callable[[str], "Window[SampleStack, Sample, TMetadata]"]:
    """Partially initialize from partially initialized sensor.

    Use this to create windowed sensor constructors which can be
    applied to different traces to construct a dataset. For example,
    if you have a `sensor_constructor`:

    ```python
    sensor_constructor = ...
    windowed_sensor_constructor = Window.from_partial_sensor(
        sensor_constructor, ...)

    # ... somewhere inside the dataset constructor
    sensor_instance = windowed_sensor_constructor(path_to_trace)
    ```

    Args:
        sensor: sensor *constructor* to wrap.
        collate_fn: collate function for aggregating a list of samples; if
            not specified, the samples are simply returned as a list.
        past: number of past samples, in addition to the current sample.
            Set to `0` to disable.
        future: number of future samples, in addition to the current
            sample. Set to `0` to disable.
        parallel: maximum number of samples to load in parallel; if `None`,
            all samples are loaded sequentially.
    """
    def create_wrapped_sensor(
        path: str
    ) -> Window[SampleStack, Sample, TMetadata]:
        return cls(
            sensor(path), collate_fn=collate_fn, past=past,
            future=future, parallel=parallel)

    return create_wrapped_sensor