wehub-resource-sync
89 lines
2.9 KiB
Python
89 lines
2.9 KiB
Python
from __future__ import annotations
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import itertools
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from typing import TYPE_CHECKING, cast
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import numpy as np
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import rerun as rr
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import rerun.blueprint as rrb
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from .common_arrays import none_empty_or_value
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if TYPE_CHECKING:
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from rerun.blueprint.datatypes import TensorDimensionIndexSliderArrayLike
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def test_tensor_slice_selection() -> None:
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widths = [
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None,
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2,
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rr.datatypes.TensorDimensionSelection(dimension=2, invert=False),
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rr.components.TensorWidthDimension(dimension=2, invert=False),
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]
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heights = [
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None,
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3,
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rr.datatypes.TensorDimensionSelection(dimension=3, invert=False),
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rr.components.TensorHeightDimension(dimension=3, invert=False),
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]
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indices_arrays = [
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[
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rr.components.TensorDimensionIndexSelection(dimension=1, index=3),
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rr.components.TensorDimensionIndexSelection(dimension=2, index=2),
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rr.components.TensorDimensionIndexSelection(dimension=3, index=1),
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],
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None,
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]
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slider_arrays = [
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None,
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[1, 2, 3],
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[
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rrb.components.TensorDimensionIndexSlider(1),
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rrb.components.TensorDimensionIndexSlider(2),
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rrb.components.TensorDimensionIndexSlider(3),
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],
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np.array([1, 2, 3]),
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]
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all_arrays = itertools.zip_longest(
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widths,
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heights,
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indices_arrays,
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slider_arrays,
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)
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for width, height, indices, slider in all_arrays:
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width = cast("rr.datatypes.TensorDimensionSelectionLike | None", width)
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height = cast("rr.datatypes.TensorDimensionSelectionLike | None", height)
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indices = cast("rr.datatypes.TensorDimensionIndexSelectionArrayLike | None", indices)
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slider = cast("TensorDimensionIndexSliderArrayLike | None", slider)
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print(
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f"rr.TensorSliceSelection(\n"
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f" width={width!r}\n"
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f" height={height!r}\n"
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f" indices={indices!r}\n"
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f" slider={slider!r}\n"
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f")",
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)
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arch = rrb.TensorSliceSelection(
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width=width,
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height=height,
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indices=indices,
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slider=slider,
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)
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print(f"{arch}\n")
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assert arch.width == rr.components.TensorWidthDimensionBatch._converter(
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none_empty_or_value(width, rr.components.TensorWidthDimension(dimension=2, invert=False)),
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)
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assert arch.height == rr.components.TensorHeightDimensionBatch._converter(
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none_empty_or_value(height, rr.components.TensorHeightDimension(dimension=3, invert=False)),
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)
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assert arch.indices == rr.components.TensorDimensionIndexSelectionBatch._converter(
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none_empty_or_value(indices, indices_arrays[0]),
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)
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assert arch.slider == rrb.components.TensorDimensionIndexSliderBatch._converter(
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none_empty_or_value(slider, [1, 2, 3]),
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)
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