Using the vectors layer#

In this document, you will learn about the napari vectors layer, including how to display many vectors simultaneously and adjust their properties.

For more information about layers, refer to Layers at a glance.

Note

Vector layers can be added only programmatically, i.e., in the console, or using a script, not from the GUI. Please refer to A simple example and use the code there to add a vectors layer first, then explore the GUI controls.

When to use the vectors layer#

The vectors layer allows you to display many vectors with defined starting points and directions. It is particularly useful for people who want to visualize large vector fields, for example if you are doing polarization microscopy. You can adjust the color, width, and length of all the vectors from the console or from the GUI.

A simple example#

You can create a new viewer and add vectors in one go using the napari.view_vectors() method, or if you already have an existing viewer, you can add vectors to it using viewer.add_vectors. The API of both methods is the same. In these examples we’ll mainly use add_vectors to overlay vectors onto an existing image.

In this example, we will overlay vectors on the image of a photographer:

import napari
import numpy as np
from skimage import data

# create vector data
n = 250
vectors = np.zeros((n, 2, 2), dtype=np.float32)
phi_space = np.linspace(0, 4 * np.pi, n)
radius_space = np.linspace(0, 100, n)
# assign x-y projection
vectors[:, 1, 0] = radius_space * np.cos(phi_space)
vectors[:, 1, 1] = radius_space * np.sin(phi_space)
# assign x-y position
vectors[:, 0] = vectors[:, 1] + 256

# add the image
viewer = napari.view_image(data.camera(), name='photographer')
# add the vectors
vectors_layer = viewer.add_vectors(vectors, edge_width=3)
Hide code cell source 
from napari.utils import nbscreenshot

nbscreenshot(viewer, alt_text="Vectors overlaid on an image")
Vectors overlaid on an image

Using the GUI to manipulate vectors#

Before you can use the GUI to manipulate vectors, you must load a vector layer. Please refer to A simple example to add a vectors layer first, then explore the editing options the GUI provides.

  • Opacity - click and hold the circle on the opacity slider bar and adjust it to any value between 0.00 (clear) and 1.00 (completely opaque).

  • Width - adjusting the width makes the vectors appear thicker or thinner. Use the + and - buttons on either side of the width bar to adjust width or click on the number in the middle of the bar and enter a value. The minimum value is 0.10.

  • Length - adjusting the length makes the vector longer or shorter. Use the + and - buttons on either side of the length bar to adjust length or click on the number in the middle of the bar and enter a value. The minimum value is 0.10.

  • Blending - blending has the options of opaque, translucent, translucent no depth, minimum, or additive in the dropdown. Refer to the Blending layers section of Layers at a glance for an explanation of each type of blending.

  • Edge color mode - select direct, cycle, or colormap from the dropdown.

    • Direct (default mode) allows each vector to be set arbitrarily.

    • Cycle allows the color to be set via a color cycle over an attribute.

    • Colormap allows the color to be set via a color map over an attribute.

  • Edge color - click the thumbnail next to edge color: to select or create a color from the pallette.

  • Out of slice - if this box is checked, out of slice is on or true. If this box is not checked, out of slice is off or false. If it is on or true, vectors slightly out of slice are rendered.

Vector starting position#

The starting position of vectors cannot be edited from the GUI. Nor is it possible to draw vectors from the GUI. If you want to draw lines using the GUI you should use the Lines shape inside a shapes layer.

3D rendering#

All layers can be rendered in both 2D and 3D. One of the viewer buttons at the bottom of the left panel can toggle between these 2 modes. When in 2D, the button looks like this: image: 2D/3D button, ready to switch to 3D mode. When in 3D, the button looks like this: image: 2D/3D button, ready to switch to 2D mode.

The number of dimensions sliders will be 2 or 3 less than the total number of dimensions of the layer, allowing you to browse volumetric timeseries data and other high dimensional data. See nD vectors to see shapes in both 2D and 3D:

Arguments of view_vectors and add_vectors#

view_vectors() and add_vectors() accept the same layer-creation parameters.

help(napari.view_vectors)
Hide code cell output 
Help on function view_vectors in module napari.view_layers:

view_vectors(data=None, *, affine=None, blending='translucent', cache=True, edge_color='red', edge_color_cycle=None, edge_colormap='viridis', edge_contrast_limits=None, edge_width=1, experimental_clipping_planes=None, feature_defaults=None, features=None, length=1, metadata=None, name=None, ndim=None, opacity=0.7, out_of_slice_display=False, projection_mode='none', properties=None, property_choices=None, rotate=None, scale=None, shear=None, translate=None, vector_style='triangle', visible=True, title='napari', ndisplay=2, order=(), axis_labels=(), show=True, camera: napari.components.camera.Camera = None, cursor: napari.components.cursor.Cursor = None, dims: napari.components.dims.Dims = None, grid: napari.components.grid.GridCanvas = None, layers: napari.components.layerlist.LayerList = None, help: str = '', status: Union[str, dict] = 'Ready', tooltip: napari.components.tooltip.Tooltip = None, theme: str = None, mouse_over_canvas: bool = False) -> napari.viewer.Viewer
    Create a viewer and add a vectors layer.
    
    Parameters
    ----------
    data : (N, 2, D) or (N1, N2, ..., ND, D) array
        An (N, 2, D) array is interpreted as "coordinate-like" data and a
        list of N vectors with start point and projections of the vector in
        D dimensions. An (N1, N2, ..., ND, D) array is interpreted as
        "image-like" data where there is a length D vector of the
        projections at each pixel.
    affine : n-D array or napari.utils.transforms.Affine
        (N+1, N+1) affine transformation matrix in homogeneous coordinates.
        The first (N, N) entries correspond to a linear transform and
        the final column is a length N translation vector and a 1 or a napari
        `Affine` transform object. Applied as an extra transform on top of the
        provided scale, rotate, and shear values.
    blending : str
        One of a list of preset blending modes that determines how RGB and
        alpha values of the layer visual get mixed. Allowed values are
        {'opaque', 'translucent', and 'additive'}.
    cache : bool
        Whether slices of out-of-core datasets should be cached upon retrieval.
        Currently, this only applies to dask arrays.
    edge_color : str
        Color of all of the vectors.
    edge_color_cycle : np.ndarray, list
        Cycle of colors (provided as string name, RGB, or RGBA) to map to edge_color if a
        categorical attribute is used color the vectors.
    edge_colormap : str, napari.utils.Colormap
        Colormap to set vector color if a continuous attribute is used to set edge_color.
    edge_contrast_limits : None, (float, float)
        clims for mapping the property to a color map. These are the min and max value
        of the specified property that are mapped to 0 and 1, respectively.
        The default value is None. If set the none, the clims will be set to
        (property.min(), property.max())
    edge_width : float
        Width for all vectors in pixels.
    experimental_clipping_planes : list of dicts, list of ClippingPlane, or ClippingPlaneList
        Each dict defines a clipping plane in 3D in data coordinates.
        Valid dictionary keys are {'position', 'normal', and 'enabled'}.
        Values on the negative side of the normal are discarded if the plane is enabled.
    feature_defaults : dict[str, Any] or DataFrame
        The default value of each feature in a table with one row.
    features : dict[str, array-like] or DataFrame
        Features table where each row corresponds to a vector and each column
        is a feature.
    length : float
        Multiplicative factor on projections for length of all vectors.
    metadata : dict
        Layer metadata.
    name : str
        Name of the layer.
    ndim : int
        Number of dimensions for vectors. When data is not None, ndim must be D.
        An empty vectors layer can be instantiated with arbitrary ndim.
    opacity : float
        Opacity of the layer visual, between 0.0 and 1.0.
    out_of_slice_display : bool
        If True, renders vectors not just in central plane but also slightly out of slice
        according to specified point marker size.
    projection_mode : str
        How data outside the viewed dimensions but inside the thick Dims slice will
        be projected onto the viewed dimenions.
    properties : dict {str: array (N,)}, DataFrame
        Properties for each vector. Each property should be an array of length N,
        where N is the number of vectors.
    property_choices : dict {str: array (N,)}
        possible values for each property.
    rotate : float, 3-tuple of float, or n-D array.
        If a float convert into a 2D rotation matrix using that value as an
        angle. If 3-tuple convert into a 3D rotation matrix, using a yaw,
        pitch, roll convention. Otherwise assume an nD rotation. Angles are
        assumed to be in degrees. They can be converted from radians with
        np.degrees if needed.
    scale : tuple of float
        Scale factors for the layer.
    shear : 1-D array or n-D array
        Either a vector of upper triangular values, or an nD shear matrix with
        ones along the main diagonal.
    translate : tuple of float
        Translation values for the layer.
    vector_style : str
        One of a list of preset display modes that determines how vectors are displayed.
        Allowed values are {'line', 'triangle', and 'arrow'}.
    visible : bool
        Whether the layer visual is currently being displayed.
        title : string, optional
        The title of the viewer window. By default 'napari'.
    ndisplay : {2, 3}, optional
        Number of displayed dimensions. By default 2.
    order : tuple of int, optional
        Order in which dimensions are displayed where the last two or last
        three dimensions correspond to row x column or plane x row x column if
        ndisplay is 2 or 3. By default None
    axis_labels : list of str, optional
        Dimension names. By default they are labeled with sequential numbers
    show : bool, optional
        Whether to show the viewer after instantiation. By default True.
    
    
    Returns
    -------
    viewer : :class:`napari.Viewer`
        The newly-created viewer.

Changing vector length, width, and color#

Scale the length of all the vectors on a layer using the layer.length property.

Set the width of all the vectors in a layer using the layer.width property.

Set the color of all the vectors in a layer using the layer.edge_color property.

Pan and zoom are not available on the vectors layer.

Vectors data#

The input data to the vectors layer must be an Nx2xD NumPy array representing N vectors with start position and projection values in D dimensions, or it must be an N1xN2 ... xNDxD array where each of the first D dimensions corresponds to the voxel of the location of the vector, and the last dimension contains the D values of the projection of that vector. The former representation is useful when you have vectors that can start in arbitrary positions in the canvas. The latter representation is useful when your vectors are defined on a grid, say corresponding to the voxels of an image, and you have one vector per grid.

Here is an example from Add vectors image of a grid of vectors defined over a random image:

image: add vectors overlaid on an image

Regardless of how the data is passed, we convert it to the Nx2xD representation internally. This representation is accessible through the layer.data property.