pymif.microscope_manager.utils.downsampling

Attributes

`SpatialFactor`
`SpatialScale`
`SpatialIntFactor`
`SPATIAL_AXES`

Functions

`normalize_spatial_factor`(→ SpatialScale \| SpatialIntFactor)	Normalize scalar or ZYX factor to a 3-tuple.
`normalize_spatial_factor_for_axes`(...)	Normalize a factor to the spatial axes present in `axes`.
`spatial_factor_power`(→ SpatialScale)
`spatial_factor_power_for_axes`(→ SpatialScale)
`multiply_spatial_scales`(→ tuple[float, float, float])
`relative_level_factors`(→ list[SpatialScale])	Return level scale ratios relative to ref_level.
`relative_level_factors_for_axes`(→ list[SpatialScale])	Return axis-aware level scale ratios relative to `ref_level`.
`axis_names_from_multiscales`(→ list[str])
`_dataset_scale_vector`(...)
`level_scale_ratios_from_multiscales`(...)	Infer relative spatial level factors from NGFF multiscales metadata.

Module Contents

type pymif.microscope_manager.utils.downsampling.SpatialFactor = int | float | Sequence[int | float][source]

type pymif.microscope_manager.utils.downsampling.SpatialScale = tuple[float, ...][source]

type pymif.microscope_manager.utils.downsampling.SpatialIntFactor = tuple[int, ...][source]

pymif.microscope_manager.utils.downsampling.SPATIAL_AXES[source]

pymif.microscope_manager.utils.downsampling.normalize_spatial_factor(value: SpatialFactor, *, name: str = 'downscale_factor', allow_float: bool = True) → SpatialScale | SpatialIntFactor[source]

Normalize scalar or ZYX factor to a 3-tuple.

This helper keeps the old public behavior. Axis-aware code should use normalize_spatial_factor_for_axes().

Scalar 2 -> (2, 2, 2) Tuple (1,2,2) -> (1, 2, 2)

pymif.microscope_manager.utils.downsampling.normalize_spatial_factor_for_axes(value: SpatialFactor, axes: str | collections.abc.Sequence[str], *, name: str = 'downscale_factor', allow_float: bool = True) → SpatialScale | SpatialIntFactor[source]: Normalize a factor to the spatial axes present in axes.

pymif.microscope_manager.utils.downsampling.spatial_factor_power(factor: SpatialFactor, exponent: int) → SpatialScale[source]

pymif.microscope_manager.utils.downsampling.spatial_factor_power_for_axes(factor: SpatialFactor, axes: str | collections.abc.Sequence[str], exponent: int) → SpatialScale[source]

pymif.microscope_manager.utils.downsampling.multiply_spatial_scales(scales: collections.abc.Sequence[int | float], factors: SpatialFactor) → tuple[float, float, float][source]

pymif.microscope_manager.utils.downsampling.relative_level_factors(total_levels: int, ref_level: int, downscale_factor: SpatialFactor = 2) → list[SpatialScale][source]

Return level scale ratios relative to ref_level.

Example with downscale_factor=(1,2,2), ref_level=0:

level 0 -> (1, 1, 1) level 1 -> (1, 2, 2) level 2 -> (1, 4, 4)

Example with ref_level=1:

level 0 -> (1, 0.5, 0.5) level 1 -> (1, 1, 1) level 2 -> (1, 2, 2)

pymif.microscope_manager.utils.downsampling.relative_level_factors_for_axes(total_levels: int, ref_level: int, axes: str | collections.abc.Sequence[str], downscale_factor: SpatialFactor = 2) → list[SpatialScale][source]: Return axis-aware level scale ratios relative to ref_level.

pymif.microscope_manager.utils.downsampling.axis_names_from_multiscales(multiscales: dict[str, Any]) → list[str][source]

pymif.microscope_manager.utils.downsampling._dataset_scale_vector(dataset: dict[str, Any]) → collections.abc.Sequence[int | float] | None[source]

pymif.microscope_manager.utils.downsampling.level_scale_ratios_from_multiscales(multiscales: dict[str, Any], datasets: collections.abc.Sequence[dict[str, Any]], ref_level: int) → list[SpatialScale] | None[source]

Infer relative spatial level factors from NGFF multiscales metadata.

Returned tuples are ordered like the spatial axes in the metadata, not necessarily legacy ZYX.