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Reference

This is the reference material for the project API.

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API

load_sample_data()

Load sample data.

Source code in src/napari_workshop_plugin/_sample_data.py 
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def load_sample_data() -> List[tuple]:
    """Load sample data."""
    path = str(here / "data" / "sample.npy")
    return read_numpy_file(path)

napari_get_reader(path)

Find the reader contribution for a path.

If path is recognised as a readable extension, return the appropriate function to read from that path.

Parameters:

Name Type Description Default
path str or list of str

Path to file, or list of paths.

 required

Returns:

Type Description
function or None

If the path is a recognized format, return a function that accepts the same path or list of paths, and returns a list of layer data tuples.
Source code in src/napari_workshop_plugin/_reader.py 
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def napari_get_reader(
    path: Union[str, List[str]]) -> Union[None, callable]:
    """Find the reader contribution for a path.

    If path is recognised as a readable extension, return the appropriate function to read from that path.

    Parameters
    ----------
    path : str or list of str
        Path to file, or list of paths.

    Returns
    -------
    function or None
        If the path is a recognized format,
        return a function that accepts the same path or
        list of paths, and returns a list of layer data tuples.
    """
    if isinstance(path, list):
        # reader plugins may be handed single path, or a list of paths.
        # if it is a list, it is assumed to be an image stack...
        # so we are only going to look at the first file.
        path = path[0]

    # if we know we cannot read the file, we immediately return None.
    if not path.endswith(".npy"):
        return None

    # otherwise we return the *function* that can read ``path``.
    return read_numpy_file

read_numpy_file(path)

Take a path or list of paths and return a list of LayerData tuples.

Readers are expected to return data as a list of tuples, where each tuple is (data, [add_kwargs, [layer_type]]), "add_kwargs" and "layer_type" are both optional.

Parameters:

Name Type Description Default
path str or list of str

Path to file, or list of paths.

 required

Returns:

Name Type Description
layer_data list of tuples

A list of LayerData tuples where each tuple in the list contains (data, metadata, layer_type), where data is a numpy array, metadata is a dict of keyword arguments for the corresponding viewer.add_* method in napari, and layer_type is a lower-case string naming the type of layer. Both "meta", and "layer_type" are optional. napari will default to layer_type=="image" if not provided
Source code in src/napari_workshop_plugin/_reader.py 
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def read_numpy_file(path: Union[str, List[str]]) -> List[tuple]:
    """Take a path or list of paths and return a list of LayerData tuples.

    Readers are expected to return data as a list of tuples, where each tuple
    is (data, [add_kwargs, [layer_type]]), "add_kwargs" and "layer_type" are
    both optional.

    Parameters
    ----------
    path : str or list of str
        Path to file, or list of paths.

    Returns
    -------
    layer_data : list of tuples
        A list of LayerData tuples where each tuple in the list contains
        (data, metadata, layer_type), where data is a numpy array, metadata is
        a dict of keyword arguments for the corresponding viewer.add_* method
        in napari, and layer_type is a lower-case string naming the type of
        layer. Both "meta", and "layer_type" are optional. napari will
        default to layer_type=="image" if not provided
    """
    # handle both a string and a list of strings
    paths = [path] if isinstance(path, str) else path
    # load all files into array
    arrays = [np.load(_path) for _path in paths]
    # stack arrays into single array
    data = np.squeeze(np.stack(arrays))

    # optional kwargs for the corresponding viewer.add_* method
    add_kwargs = {}

    layer_type = "image"  # optional, default is "image"
    return [(data, add_kwargs, layer_type)]

segmentation_widget(viewer, image, blur_sigma=2.0, disk_size=4, classes=3)

Segment cells from calcium image max projection.

  1. Grayscale and blur the image.
  2. Perform a multiotsu to separate the image into three classes: background, non-cell tissue, and cells.
  3. Smooth the multiotsu segmentation via disks.

Parameters:

Name Type Description Default
viewer napari.Viewer

A viewer instance from napari

 required
image napari.layers.Image

The image to segment from. Can be one channel or more. If more than one channel, is converted to grayscale.

 required
blur_sigma float

The standard deviation of the gaussian blur to be applied. A higher sigma indicates more blur. By default 2.0.

 2.0
disk_size int

The size of the disk in pixels for smoothing. A higher disk_size indicates more smoothing. By default 4.

 4
classes int

The number of classes to segment into. One of the classes will be background. By default 3. Going past 5 classes is not recommended for speed.

 3
Source code in src/napari_workshop_plugin/_widget.py 
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@magic_factory(persist=True)
def segmentation_widget(
    viewer: "napari.Viewer",
    image: "napari.layers.Image",
    blur_sigma: float = 2.0,
    disk_size: int = 4,
    classes: int = 3,
):
    """Segment cells from calcium image max projection.

    1. Grayscale and blur the image.
    2. Perform a multiotsu to separate the image into three classes:
        background, non-cell tissue, and cells.
    3. Smooth the multiotsu segmentation via disks.

    Parameters
    ----------
    viewer : napari.Viewer
        A viewer instance from napari
    image : napari.layers.Image
        The image to segment from. Can be one channel or more.
        If more than one channel, is converted to grayscale.
    blur_sigma : float, optional
        The standard deviation of the gaussian blur to be applied.
        A higher sigma indicates more blur.
        By default 2.0.
    disk_size : int, optional
        The size of the disk in pixels for smoothing.
        A higher disk_size indicates more smoothing.
        By default 4.
    classes : int, optional
        The number of classes to segment into.
        One of the classes will be background.
        By default 3. Going past 5 classes is not recommended for speed.

    """
    def inner_display_layer(args) -> None:
        display_layer(viewer, args)


    worker = segment(image, blur_sigma, disk_size, classes)
    worker.yielded.connect(inner_display_layer)
    worker.start()

    return worker