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ngio.core

ngio.core

Core classes for the ngio library.

NGFFImage

ngio.core.NgffImage

A class to handle OME-NGFF images.

Source code in ngio/core/ngff_image.py 
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class NgffImage:
    """A class to handle OME-NGFF images."""

    def __init__(
        self, store: StoreLike, cache: bool = False, mode: AccessModeLiteral = "r+"
    ) -> None:
        """Initialize the NGFFImage in read mode."""
        self.store = store
        self._mode = mode
        self._group = open_group_wrapper(store=store, mode=self._mode)

        if self._group.read_only:
            self._mode = "r"

        self._image_meta = get_ngff_image_meta_handler(
            self._group, meta_mode="image", cache=cache
        )
        self._metadata_cache = cache
        self.tables = TableGroup(self._group, mode=self._mode)
        self.labels = LabelGroup(
            self._group, image_ref=self.get_image(), mode=self._mode
        )

        ngio_logger.info(f"Opened image located in store: {store}")
        ngio_logger.info(f"- Image number of levels: {self.num_levels}")

    def __repr__(self) -> str:
        """Get the string representation of the image."""
        name = "NGFFImage("
        len_name = len(name)
        return (
            f"{name}"
            f"group_path={self.group_path}, \n"
            f"{' ':>{len_name}}paths={self.levels_paths}, \n"
            f"{' ':>{len_name}}labels={self.labels.list()}, \n"
            f"{' ':>{len_name}}tables={self.tables.list()}, \n"
            ")"
        )

    @property
    def group(self) -> zarr.Group:
        """Get the group of the image."""
        return self._group

    @property
    def root_path(self) -> str:
        """Get the root path of the image."""
        return str(self._group.store.path)

    @property
    def group_path(self) -> str:
        """Get the path of the group."""
        root = self.root_path
        if root.endswith("/"):
            root = root[:-1]
        return f"{root}/{self._group.path}"

    @property
    def image_meta(self) -> ImageMeta:
        """Get the image metadata."""
        meta = self._image_meta.load_meta()
        assert isinstance(meta, ImageMeta)
        return meta

    @property
    def num_levels(self) -> int:
        """Get the number of levels in the image."""
        return self.image_meta.num_levels

    @property
    def levels_paths(self) -> list[str]:
        """Get the paths of the levels in the image."""
        return self.image_meta.levels_paths

    def get_image(
        self,
        *,
        path: str | None = None,
        pixel_size: PixelSize | None = None,
        highest_resolution: bool = True,
    ) -> Image:
        """Get an image handler for the given level.

        Args:
            path (str | None, optional): The path to the level.
            pixel_size (tuple[float, ...] | list[float] | None, optional): The pixel
                size of the level.
            highest_resolution (bool, optional): Whether to get the highest
                resolution level

        Returns:
            ImageHandler: The image handler.
        """
        if path is not None or pixel_size is not None:
            highest_resolution = False

        image = Image(
            store=self._group,
            path=path,
            pixel_size=pixel_size,
            highest_resolution=highest_resolution,
            label_group=LabelGroup(self._group, image_ref=None, mode=self._mode),
            cache=self._metadata_cache,
            mode=self._mode,
        )
        ngio_logger.info(f"Opened image at path: {image.path}")
        ngio_logger.info(f"- {image.dimensions}")
        ngio_logger.info(f"- {image.pixel_size}")
        return image

    def _compute_percentiles(
        self, start_percentile: float, end_percentile: float
    ) -> tuple[list[float], list[float]]:
        """Compute the percentiles for the window.

        This will setup percentiles based values for the window of each channel.

        Args:
            start_percentile (int): The start percentile.
            end_percentile (int): The end percentile

        """
        meta = self.image_meta

        lowest_res_image = self.get_image(highest_resolution=True)
        lowest_res_shape = lowest_res_image.shape
        for path in self.levels_paths:
            image = self.get_image(path=path)
            if np.prod(image.shape) < np.prod(lowest_res_shape):
                lowest_res_shape = image.shape
                lowest_res_image = image

        num_c = lowest_res_image.dimensions.get("c", 1)

        if meta.omero is None:
            raise NotImplementedError(
                "OMERO metadata not found. " " Please add OMERO metadata to the image."
            )

        channel_list = meta.omero.channels
        if len(channel_list) != num_c:
            raise ValueError("The number of channels does not match the image.")

        starts, ends = [], []
        for c in range(num_c):
            data = lowest_res_image.get_array(c=c, mode="dask").ravel()
            _start_percentile, _end_percentile = da.percentile(
                data, [start_percentile, end_percentile], method="nearest"
            ).compute()

            starts.append(_start_percentile)
            ends.append(_end_percentile)

        return starts, ends

    def lazy_init_omero(
        self,
        labels: list[str] | int | None = None,
        wavelength_ids: list[str] | None = None,
        colors: list[str] | None = None,
        active: list[bool] | None = None,
        start_percentile: float | None = 1,
        end_percentile: float | None = 99,
        data_type: Any = np.uint16,
        consolidate: bool = True,
    ) -> None:
        """Set the OMERO metadata for the image.

        Args:
            labels (list[str] | int | None): The labels of the channels.
            wavelength_ids (list[str] | None): The wavelengths of the channels.
            colors (list[str] | None): The colors of the channels.
            active (list[bool] | None): Whether the channels are active.
            start_percentile (float | None): The start percentile for computing the data
                range. If None, the start is the same as the min value of the data type.
            end_percentile (float | None): The end percentile for for computing the data
                range. If None, the start is the same as the max value of the data type.
            data_type (Any): The data type of the image.
            consolidate (bool): Whether to consolidate the metadata.
        """
        if labels is None:
            ref = self.get_image()
            labels = ref.num_channels

        if start_percentile is not None and end_percentile is not None:
            start, end = self._compute_percentiles(
                start_percentile=start_percentile, end_percentile=end_percentile
            )
        elif start_percentile is None and end_percentile is None:
            raise ValueError("Both start and end percentiles cannot be None.")
        elif end_percentile is None and start_percentile is not None:
            raise ValueError(
                "End percentile cannot be None if start percentile is not."
            )
        else:
            start, end = None, None

        self.image_meta.lazy_init_omero(
            labels=labels,
            wavelength_ids=wavelength_ids,
            colors=colors,
            start=start,
            end=end,
            active=active,
            data_type=data_type,
        )

        if consolidate:
            self._image_meta.write_meta(self.image_meta)

    def update_omero_window(
        self,
        start_percentile: int = 1,
        end_percentile: int = 99,
        min_value: int | float | None = None,
        max_value: int | float | None = None,
    ) -> None:
        """Update the OMERO window.

        This will setup percentiles based values for the window of each channel.

        Args:
            start_percentile (int): The start percentile.
            end_percentile (int): The end percentile
            min_value (int | float | None): The minimum value of the window.
            max_value (int | float | None): The maximum value of the window.

        """
        start, ends = self._compute_percentiles(
            start_percentile=start_percentile, end_percentile=end_percentile
        )
        meta = self.image_meta
        ref_image = self.get_image()

        for func in [np.iinfo, np.finfo]:
            try:
                type_max = func(ref_image.on_disk_array.dtype).max
                type_min = func(ref_image.on_disk_array.dtype).min
                break
            except ValueError:
                continue
        else:
            raise ValueError("Data type not recognized.")

        if min_value is None:
            min_value = type_min
        if max_value is None:
            max_value = type_max

        num_c = ref_image.dimensions.get("c", 1)

        if meta.omero is None:
            raise NotImplementedError(
                "OMERO metadata not found. " " Please add OMERO metadata to the image."
            )

        channel_list = meta.omero.channels
        if len(channel_list) != num_c:
            raise ValueError("The number of channels does not match the image.")

        if len(channel_list) != len(start):
            raise ValueError("The number of channels does not match the image.")

        for c, (channel, s, e) in enumerate(
            zip(channel_list, start, ends, strict=True)
        ):
            channel.channel_visualisation.start = s
            channel.channel_visualisation.end = e
            channel.channel_visualisation.min = min_value
            channel.channel_visualisation.max = max_value

            ngio_logger.info(
                f"Updated window for channel {channel.label}. "
                f"Start: {start_percentile}, End: {end_percentile}"
            )
            meta.omero.channels[c] = channel

        self._image_meta.write_meta(meta)

    def derive_new_image(
        self,
        store: StoreLike,
        name: str,
        overwrite: bool = True,
        copy_labels: bool = False,
        copy_tables: bool = False,
        **kwargs: dict,
    ) -> "NgffImage":
        """Derive a new image from the current image.

        Args:
            store (StoreLike): The store to create the new image in.
            name (str): The name of the new image.
            overwrite (bool): Whether to overwrite the image if it exists
            copy_labels (bool): Whether to copy the labels from the current image
                to the new image.
            copy_tables (bool): Whether to copy the tables from the current image
                to the new image.
            **kwargs: Additional keyword arguments.
                Follow the same signature as `create_empty_ome_zarr_image`.

        Returns:
            NgffImage: The new image.
        """
        image_0 = self.get_image(highest_resolution=True)

        # Get the channel information if it exists
        omero = self.image_meta.omero
        if omero is not None:
            channels = omero.channels
            omero_kwargs = omero.extra_fields
        else:
            channels = []
            omero_kwargs = {}

        default_kwargs = {
            "store": store,
            "on_disk_shape": image_0.on_disk_shape,
            "chunks": image_0.on_disk_array.chunks,
            "dtype": image_0.on_disk_array.dtype,
            "on_disk_axis": image_0.dataset.on_disk_axes_names,
            "pixel_sizes": image_0.pixel_size,
            "xy_scaling_factor": self.image_meta.xy_scaling_factor,
            "z_scaling_factor": self.image_meta.z_scaling_factor,
            "time_spacing": image_0.dataset.time_spacing,
            "time_units": image_0.dataset.time_axis_unit,
            "levels": self.num_levels,
            "name": name,
            "channel_labels": image_0.channel_labels,
            "channel_wavelengths": [ch.wavelength_id for ch in channels],
            "channel_visualization": [ch.channel_visualisation for ch in channels],
            "omero_kwargs": omero_kwargs,
            "overwrite": overwrite,
            "version": self.image_meta.version,
        }

        default_kwargs.update(kwargs)

        create_empty_ome_zarr_image(
            **default_kwargs,
        )

        new_image = NgffImage(store=store)

        if copy_tables:
            # TODO: to be refactored when the table location is changed in the spec
            source_tables_group = self.tables._table_group

            if source_tables_group is None:
                raise ValueError("No tables group found in the source image.")

            zarr.copy(source=source_tables_group, dest=new_image.group)

            # Reopen the image to get the new tables
            new_image = NgffImage(store=store)

        if copy_labels:
            # TODO: to be refactored when the label location is changed in the spec
            source_labels_group = self.labels._label_group

            if source_labels_group is None:
                raise ValueError("No labels group found in the source image.")

            zarr.copy(source=source_labels_group, dest=new_image.group)

            # Reopen the image to get the new labels
            new_image = NgffImage(store=store)

        return new_image

group: zarr.Group property

Get the group of the image.

group_path: str property

Get the path of the group.

image_meta: ImageMeta property

Get the image metadata.

levels_paths: list[str] property

Get the paths of the levels in the image.

num_levels: int property

Get the number of levels in the image.

root_path: str property

Get the root path of the image.

__init__(store: StoreLike, cache: bool = False, mode: AccessModeLiteral = 'r+') -> None

Initialize the NGFFImage in read mode.

Source code in ngio/core/ngff_image.py 
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def __init__(
    self, store: StoreLike, cache: bool = False, mode: AccessModeLiteral = "r+"
) -> None:
    """Initialize the NGFFImage in read mode."""
    self.store = store
    self._mode = mode
    self._group = open_group_wrapper(store=store, mode=self._mode)

    if self._group.read_only:
        self._mode = "r"

    self._image_meta = get_ngff_image_meta_handler(
        self._group, meta_mode="image", cache=cache
    )
    self._metadata_cache = cache
    self.tables = TableGroup(self._group, mode=self._mode)
    self.labels = LabelGroup(
        self._group, image_ref=self.get_image(), mode=self._mode
    )

    ngio_logger.info(f"Opened image located in store: {store}")
    ngio_logger.info(f"- Image number of levels: {self.num_levels}")

__repr__() -> str

Get the string representation of the image.

Source code in ngio/core/ngff_image.py 
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def __repr__(self) -> str:
    """Get the string representation of the image."""
    name = "NGFFImage("
    len_name = len(name)
    return (
        f"{name}"
        f"group_path={self.group_path}, \n"
        f"{' ':>{len_name}}paths={self.levels_paths}, \n"
        f"{' ':>{len_name}}labels={self.labels.list()}, \n"
        f"{' ':>{len_name}}tables={self.tables.list()}, \n"
        ")"
    )

derive_new_image(store: StoreLike, name: str, overwrite: bool = True, copy_labels: bool = False, copy_tables: bool = False, **kwargs: dict) -> NgffImage

Derive a new image from the current image.

Parameters:

  • store (StoreLike) –

    The store to create the new image in.

  • name (str) –

    The name of the new image.

  • overwrite (bool, default: True ) –

    Whether to overwrite the image if it exists

  • copy_labels (bool, default: False ) –

    Whether to copy the labels from the current image to the new image.

  • copy_tables (bool, default: False ) –

    Whether to copy the tables from the current image to the new image.

  • **kwargs (dict, default: {} ) –

    Additional keyword arguments. Follow the same signature as create_empty_ome_zarr_image.

Returns:

Source code in ngio/core/ngff_image.py 
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def derive_new_image(
    self,
    store: StoreLike,
    name: str,
    overwrite: bool = True,
    copy_labels: bool = False,
    copy_tables: bool = False,
    **kwargs: dict,
) -> "NgffImage":
    """Derive a new image from the current image.

    Args:
        store (StoreLike): The store to create the new image in.
        name (str): The name of the new image.
        overwrite (bool): Whether to overwrite the image if it exists
        copy_labels (bool): Whether to copy the labels from the current image
            to the new image.
        copy_tables (bool): Whether to copy the tables from the current image
            to the new image.
        **kwargs: Additional keyword arguments.
            Follow the same signature as `create_empty_ome_zarr_image`.

    Returns:
        NgffImage: The new image.
    """
    image_0 = self.get_image(highest_resolution=True)

    # Get the channel information if it exists
    omero = self.image_meta.omero
    if omero is not None:
        channels = omero.channels
        omero_kwargs = omero.extra_fields
    else:
        channels = []
        omero_kwargs = {}

    default_kwargs = {
        "store": store,
        "on_disk_shape": image_0.on_disk_shape,
        "chunks": image_0.on_disk_array.chunks,
        "dtype": image_0.on_disk_array.dtype,
        "on_disk_axis": image_0.dataset.on_disk_axes_names,
        "pixel_sizes": image_0.pixel_size,
        "xy_scaling_factor": self.image_meta.xy_scaling_factor,
        "z_scaling_factor": self.image_meta.z_scaling_factor,
        "time_spacing": image_0.dataset.time_spacing,
        "time_units": image_0.dataset.time_axis_unit,
        "levels": self.num_levels,
        "name": name,
        "channel_labels": image_0.channel_labels,
        "channel_wavelengths": [ch.wavelength_id for ch in channels],
        "channel_visualization": [ch.channel_visualisation for ch in channels],
        "omero_kwargs": omero_kwargs,
        "overwrite": overwrite,
        "version": self.image_meta.version,
    }

    default_kwargs.update(kwargs)

    create_empty_ome_zarr_image(
        **default_kwargs,
    )

    new_image = NgffImage(store=store)

    if copy_tables:
        # TODO: to be refactored when the table location is changed in the spec
        source_tables_group = self.tables._table_group

        if source_tables_group is None:
            raise ValueError("No tables group found in the source image.")

        zarr.copy(source=source_tables_group, dest=new_image.group)

        # Reopen the image to get the new tables
        new_image = NgffImage(store=store)

    if copy_labels:
        # TODO: to be refactored when the label location is changed in the spec
        source_labels_group = self.labels._label_group

        if source_labels_group is None:
            raise ValueError("No labels group found in the source image.")

        zarr.copy(source=source_labels_group, dest=new_image.group)

        # Reopen the image to get the new labels
        new_image = NgffImage(store=store)

    return new_image

get_image(*, path: str | None = None, pixel_size: PixelSize | None = None, highest_resolution: bool = True) -> Image

Get an image handler for the given level.

Parameters:

  • path (str | None, default: None ) –

    The path to the level.

  • pixel_size (tuple[float, ...] | list[float] | None, default: None ) –

    The pixel size of the level.

  • highest_resolution (bool, default: True ) –

    Whether to get the highest resolution level

Returns:

  • ImageHandler ( Image ) –

    The image handler.

Source code in ngio/core/ngff_image.py 
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def get_image(
    self,
    *,
    path: str | None = None,
    pixel_size: PixelSize | None = None,
    highest_resolution: bool = True,
) -> Image:
    """Get an image handler for the given level.

    Args:
        path (str | None, optional): The path to the level.
        pixel_size (tuple[float, ...] | list[float] | None, optional): The pixel
            size of the level.
        highest_resolution (bool, optional): Whether to get the highest
            resolution level

    Returns:
        ImageHandler: The image handler.
    """
    if path is not None or pixel_size is not None:
        highest_resolution = False

    image = Image(
        store=self._group,
        path=path,
        pixel_size=pixel_size,
        highest_resolution=highest_resolution,
        label_group=LabelGroup(self._group, image_ref=None, mode=self._mode),
        cache=self._metadata_cache,
        mode=self._mode,
    )
    ngio_logger.info(f"Opened image at path: {image.path}")
    ngio_logger.info(f"- {image.dimensions}")
    ngio_logger.info(f"- {image.pixel_size}")
    return image

lazy_init_omero(labels: list[str] | int | None = None, wavelength_ids: list[str] | None = None, colors: list[str] | None = None, active: list[bool] | None = None, start_percentile: float | None = 1, end_percentile: float | None = 99, data_type: Any = np.uint16, consolidate: bool = True) -> None

Set the OMERO metadata for the image.

Parameters:

  • labels (list[str] | int | None, default: None ) –

    The labels of the channels.

  • wavelength_ids (list[str] | None, default: None ) –

    The wavelengths of the channels.

  • colors (list[str] | None, default: None ) –

    The colors of the channels.

  • active (list[bool] | None, default: None ) –

    Whether the channels are active.

  • start_percentile (float | None, default: 1 ) –

    The start percentile for computing the data range. If None, the start is the same as the min value of the data type.

  • end_percentile (float | None, default: 99 ) –

    The end percentile for for computing the data range. If None, the start is the same as the max value of the data type.

  • data_type (Any, default: uint16 ) –

    The data type of the image.

  • consolidate (bool, default: True ) –

    Whether to consolidate the metadata.

Source code in ngio/core/ngff_image.py 
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def lazy_init_omero(
    self,
    labels: list[str] | int | None = None,
    wavelength_ids: list[str] | None = None,
    colors: list[str] | None = None,
    active: list[bool] | None = None,
    start_percentile: float | None = 1,
    end_percentile: float | None = 99,
    data_type: Any = np.uint16,
    consolidate: bool = True,
) -> None:
    """Set the OMERO metadata for the image.

    Args:
        labels (list[str] | int | None): The labels of the channels.
        wavelength_ids (list[str] | None): The wavelengths of the channels.
        colors (list[str] | None): The colors of the channels.
        active (list[bool] | None): Whether the channels are active.
        start_percentile (float | None): The start percentile for computing the data
            range. If None, the start is the same as the min value of the data type.
        end_percentile (float | None): The end percentile for for computing the data
            range. If None, the start is the same as the max value of the data type.
        data_type (Any): The data type of the image.
        consolidate (bool): Whether to consolidate the metadata.
    """
    if labels is None:
        ref = self.get_image()
        labels = ref.num_channels

    if start_percentile is not None and end_percentile is not None:
        start, end = self._compute_percentiles(
            start_percentile=start_percentile, end_percentile=end_percentile
        )
    elif start_percentile is None and end_percentile is None:
        raise ValueError("Both start and end percentiles cannot be None.")
    elif end_percentile is None and start_percentile is not None:
        raise ValueError(
            "End percentile cannot be None if start percentile is not."
        )
    else:
        start, end = None, None

    self.image_meta.lazy_init_omero(
        labels=labels,
        wavelength_ids=wavelength_ids,
        colors=colors,
        start=start,
        end=end,
        active=active,
        data_type=data_type,
    )

    if consolidate:
        self._image_meta.write_meta(self.image_meta)

update_omero_window(start_percentile: int = 1, end_percentile: int = 99, min_value: int | float | None = None, max_value: int | float | None = None) -> None

Update the OMERO window.

This will setup percentiles based values for the window of each channel.

Parameters:

  • start_percentile (int, default: 1 ) –

    The start percentile.

  • end_percentile (int, default: 99 ) –

    The end percentile

  • min_value (int | float | None, default: None ) –

    The minimum value of the window.

  • max_value (int | float | None, default: None ) –

    The maximum value of the window.

Source code in ngio/core/ngff_image.py 
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def update_omero_window(
    self,
    start_percentile: int = 1,
    end_percentile: int = 99,
    min_value: int | float | None = None,
    max_value: int | float | None = None,
) -> None:
    """Update the OMERO window.

    This will setup percentiles based values for the window of each channel.

    Args:
        start_percentile (int): The start percentile.
        end_percentile (int): The end percentile
        min_value (int | float | None): The minimum value of the window.
        max_value (int | float | None): The maximum value of the window.

    """
    start, ends = self._compute_percentiles(
        start_percentile=start_percentile, end_percentile=end_percentile
    )
    meta = self.image_meta
    ref_image = self.get_image()

    for func in [np.iinfo, np.finfo]:
        try:
            type_max = func(ref_image.on_disk_array.dtype).max
            type_min = func(ref_image.on_disk_array.dtype).min
            break
        except ValueError:
            continue
    else:
        raise ValueError("Data type not recognized.")

    if min_value is None:
        min_value = type_min
    if max_value is None:
        max_value = type_max

    num_c = ref_image.dimensions.get("c", 1)

    if meta.omero is None:
        raise NotImplementedError(
            "OMERO metadata not found. " " Please add OMERO metadata to the image."
        )

    channel_list = meta.omero.channels
    if len(channel_list) != num_c:
        raise ValueError("The number of channels does not match the image.")

    if len(channel_list) != len(start):
        raise ValueError("The number of channels does not match the image.")

    for c, (channel, s, e) in enumerate(
        zip(channel_list, start, ends, strict=True)
    ):
        channel.channel_visualisation.start = s
        channel.channel_visualisation.end = e
        channel.channel_visualisation.min = min_value
        channel.channel_visualisation.max = max_value

        ngio_logger.info(
            f"Updated window for channel {channel.label}. "
            f"Start: {start_percentile}, End: {end_percentile}"
        )
        meta.omero.channels[c] = channel

    self._image_meta.write_meta(meta)