"""Methods to create a new viewer instance then add a particular layer type.
All functions follow this pattern, (where <layer_type> is replaced with one
of the layer types, like "image", "points", etc...):
.. code-block:: python
def view_<layer_type>(*args, **kwargs):
# ... pop all of the viewer kwargs out of kwargs into viewer_kwargs
viewer = Viewer(**viewer_kwargs)
add_method = getattr(viewer, f"add_{<layer_type>}")
add_method(*args, **kwargs)
return viewer
"""
import inspect
from typing import Any, Optional
from numpydoc.docscrape import NumpyDocString as _NumpyDocString
from napari.components.dims import Dims
from napari.layers import Image
from napari.viewer import Viewer
__all__ = [
'view_image',
'view_labels',
'view_path',
'view_points',
'view_shapes',
'view_surface',
'view_tracks',
'view_vectors',
'imshow',
]
_doc_template = """Create a viewer and add a{n} {layer_string} layer.
{params}
Returns
-------
viewer : :class:`napari.Viewer`
The newly-created viewer.
"""
_VIEW_DOC = _NumpyDocString(Viewer.__doc__)
_VIEW_PARAMS = ' ' + '\n'.join(_VIEW_DOC._str_param_list('Parameters')[2:])
def _merge_docstrings(add_method, layer_string):
# create combined docstring with parameters from add_* and Viewer methods
import textwrap
add_method_doc = _NumpyDocString(add_method.__doc__)
# this ugliness is because the indentation of the parsed numpydocstring
# is different for the first parameter :(
lines = add_method_doc._str_param_list('Parameters')
lines = lines[:3] + textwrap.dedent('\n'.join(lines[3:])).splitlines()
params = '\n'.join(lines) + '\n' + textwrap.dedent(_VIEW_PARAMS)
n = 'n' if layer_string.startswith(tuple('aeiou')) else ''
return _doc_template.format(n=n, layer_string=layer_string, params=params)
def _merge_layer_viewer_sigs_docs(func):
"""Make combined signature, docstrings, and annotations for `func`.
This is a decorator that combines information from `Viewer.__init__`,
and one of the `viewer.add_*` methods. It updates the docstring,
signature, and type annotations of the decorated function with the merged
versions.
Parameters
----------
func : callable
`view_<layer_type>` function to modify
Returns
-------
func : callable
The same function, with merged metadata.
"""
from napari.utils.misc import _combine_signatures
# get the `Viewer.add_*` method
layer_string = func.__name__.replace('view_', '')
if layer_string == 'path':
add_method = Viewer.open
else:
add_method = getattr(Viewer, f'add_{layer_string}')
# merge the docstrings of Viewer and viewer.add_*
func.__doc__ = _merge_docstrings(add_method, layer_string)
# merge the signatures of Viewer and viewer.add_*
func.__signature__ = _combine_signatures(
add_method,
Viewer,
return_annotation=Viewer,
exclude=('self', 'axis_labels'),
)
# merge the __annotations__
func.__annotations__ = {
**add_method.__annotations__,
**Viewer.__init__.__annotations__,
'return': Viewer,
}
# _forwardrefns_ is used by stubgen.py to populate the globals
# when evaluate forward references with get_type_hints
func._forwardrefns_ = {**add_method.__globals__}
return func
_viewer_params = inspect.signature(Viewer).parameters
_dims_params = Dims.__fields__
def _make_viewer_then(
add_method: str,
/,
*args,
viewer: Optional[Viewer] = None,
**kwargs,
) -> tuple[Viewer, Any]:
"""Create a viewer, call given add_* method, then return viewer and layer.
This function will be deprecated soon (See #4693)
Parameters
----------
add_method : str
Which ``add_`` method to call on the viewer, e.g. `add_image`,
or `add_labels`.
*args : list
Positional arguments for the ``add_`` method.
viewer : Viewer, optional
A pre-existing viewer, which will be used provided, rather than
creating a new one.
**kwargs : dict
Keyword arguments for either the `Viewer` constructor or for the
``add_`` method.
Returns
-------
viewer : napari.Viewer
The created viewer, or the same one that was passed in, if given.
layer(s): napari.layers.Layer or List[napari.layers.Layer]
The value returned by the add_method. Can be a list of layers if
``add_image`` is called with a ``channel_axis=`` keyword
argument.
"""
vkwargs = {
k: kwargs.pop(k)
for k in list(kwargs)
if k in _viewer_params
if k != 'axis_labels'
}
if 'axis_labels' in kwargs:
vkwargs['axis_labels'] = (
kwargs['axis_labels'] if kwargs['axis_labels'] is not None else ()
)
# separate dims kwargs because we want to set those after adding data
dims_kwargs = {
k: vkwargs.pop(k) for k in list(vkwargs) if k in _dims_params
}
if viewer is None:
viewer = Viewer(**vkwargs)
kwargs.update(kwargs.pop('kwargs', {}))
method = getattr(viewer, add_method)
added = method(*args, **kwargs)
if isinstance(added, list):
added = tuple(added)
for arg_name, arg_val in dims_kwargs.items():
setattr(viewer.dims, arg_name, arg_val)
return viewer, added
# Each of the following functions will have this pattern:
#
# def view_image(*args, **kwargs):
# # ... pop all of the viewer kwargs out of kwargs into viewer_kwargs
# viewer = Viewer(**viewer_kwargs)
# viewer.add_image(*args, **kwargs)
# return viewer
[docs]
@_merge_layer_viewer_sigs_docs
def view_image(*args, **kwargs):
return _make_viewer_then('add_image', *args, **kwargs)[0]
[docs]
@_merge_layer_viewer_sigs_docs
def view_labels(*args, **kwargs):
return _make_viewer_then('add_labels', *args, **kwargs)[0]
[docs]
@_merge_layer_viewer_sigs_docs
def view_points(*args, **kwargs):
return _make_viewer_then('add_points', *args, **kwargs)[0]
[docs]
@_merge_layer_viewer_sigs_docs
def view_shapes(*args, **kwargs):
return _make_viewer_then('add_shapes', *args, **kwargs)[0]
[docs]
@_merge_layer_viewer_sigs_docs
def view_surface(*args, **kwargs):
return _make_viewer_then('add_surface', *args, **kwargs)[0]
[docs]
@_merge_layer_viewer_sigs_docs
def view_tracks(*args, **kwargs):
return _make_viewer_then('add_tracks', *args, **kwargs)[0]
[docs]
@_merge_layer_viewer_sigs_docs
def view_vectors(*args, **kwargs):
return _make_viewer_then('add_vectors', *args, **kwargs)[0]
[docs]
@_merge_layer_viewer_sigs_docs
def view_path(*args, **kwargs):
return _make_viewer_then('open', *args, **kwargs)[0]
[docs]
def imshow(
data,
*,
channel_axis=None,
affine=None,
axis_labels=None,
attenuation=0.05,
blending=None,
cache=True,
colormap=None,
contrast_limits=None,
custom_interpolation_kernel_2d=None,
depiction='volume',
experimental_clipping_planes=None,
gamma=1.0,
interpolation2d='nearest',
interpolation3d='linear',
iso_threshold=None,
metadata=None,
multiscale=None,
name=None,
opacity=1.0,
plane=None,
projection_mode='none',
rendering='mip',
rgb=None,
rotate=None,
scale=None,
shear=None,
translate=None,
units=None,
visible=True,
viewer=None,
title='napari',
ndisplay=2,
order=(),
show=True,
) -> tuple[Viewer, list['Image']]:
"""Load data into an Image layer and return the Viewer and Layer.
Parameters
----------
data : array or list of array
Image data. Can be N >= 2 dimensional. If the last dimension has length
3 or 4 can be interpreted as RGB or RGBA if rgb is `True`. If a
list and arrays are decreasing in shape then the data is treated as
a multiscale image. Please note multiscale rendering is only
supported in 2D. In 3D, only the lowest resolution scale is
displayed.
channel_axis : int, optional
Axis to expand image along. If provided, each channel in the data
will be added as an individual image layer. In channel_axis mode,
other parameters MAY be provided as lists. The Nth value of the list
will be applied to the Nth channel in the data. If a single value
is provided, it will be broadcast to all Layers.
All parameters except data, rgb, and multiscale can be provided as
list of values. If a list is provided, it must be the same length as
the axis that is being expanded as channels.
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.
attenuation : float or list of float
Attenuation rate for attenuated maximum intensity projection.
blending : str or list of 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
{'translucent', 'translucent_no_depth', 'additive', 'minimum', 'opaque'}.
cache : bool or list of bool
Whether slices of out-of-core datasets should be cached upon
retrieval. Currently, this only applies to dask arrays.
colormap : str, napari.utils.Colormap, tuple, dict, list or list of these types
Colormaps to use for luminance images. If a string, it can be the name
of a supported colormap from vispy or matplotlib or the name of
a vispy color or a hexadecimal RGB color representation.
If a tuple, the first value must be a string to assign as a name to a
colormap and the second item must be a Colormap. If a dict, the key must
be a string to assign as a name to a colormap and the value must be a
Colormap.
contrast_limits : list (2,)
Intensity value limits to be used for determining the minimum and maximum colormap bounds for
luminance images. If not passed, they will be calculated as the min and max intensity value of
the image.
custom_interpolation_kernel_2d : np.ndarray
Convolution kernel used with the 'custom' interpolation mode in 2D rendering.
depiction : str or list of str
3D Depiction mode. Must be one of {'volume', 'plane'}.
The default value is 'volume'.
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.
gamma : float or list of float
Gamma correction for determining colormap linearity; defaults to 1.
interpolation2d : str or list of str
Interpolation mode used by vispy for rendering 2d data.
Must be one of our supported modes.
(for list of supported modes see Interpolation enum)
'custom' is a special mode for 2D interpolation in which a regular grid
of samples is taken from the texture around a position using 'linear'
interpolation before being multiplied with a custom interpolation kernel
(provided with 'custom_interpolation_kernel_2d').
interpolation3d : str or list of str
Same as 'interpolation2d' but for 3D rendering.
iso_threshold : float or list of float
Threshold for isosurface.
metadata : dict or list of dict
Layer metadata.
multiscale : bool
Whether the data is a multiscale image or not. Multiscale data is
represented by a list of array-like image data. If not specified by
the user and if the data is a list of arrays that decrease in shape,
then it will be taken to be multiscale. The first image in the list
should be the largest. Please note multiscale rendering is only
supported in 2D. In 3D, only the lowest resolution scale is
displayed.
name : str or list of str
Name of the layer.
opacity : float or list
Opacity of the layer visual, between 0.0 and 1.0.
plane : dict or SlicingPlane
Properties defining plane rendering in 3D. Properties are defined in
data coordinates. Valid dictionary keys are
{'position', 'normal', 'thickness', and 'enabled'}.
projection_mode : str
How data outside the viewed dimensions, but inside the thick Dims slice will
be projected onto the viewed dimensions. Must fit to cls._projectionclass
rendering : str or list of str
Rendering mode used by vispy. Must be one of our supported
modes. If a list then must be same length as the axis that is being
expanded as channels.
rgb : bool, optional
Whether the image is RGB or RGBA if rgb. If not
specified by user, but the last dimension of the data has length 3 or 4,
it will be set as `True`. If `False`, the image is interpreted as a
luminance image.
rotate : float, 3-tuple of float, n-D array or list.
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 or list of tuple of float
Scale factors for the layer.
shear : 1-D array or list.
A vector of shear values for an upper triangular n-D shear matrix.
translate : tuple of float or list of tuple of float
Translation values for the layer.
visible : bool or list of bool
Whether the layer visual is currently being displayed.
viewer : Viewer object, optional, by default None.
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 : napari.Viewer
The created or passed viewer.
layer(s) : napari.layers.Image or List[napari.layers.Image]
The added layer(s). (May be more than one if the ``channel_axis`` keyword
argument is given.
"""
return _make_viewer_then(
'add_image',
data,
viewer=viewer,
channel_axis=channel_axis,
axis_labels=axis_labels,
rgb=rgb,
colormap=colormap,
contrast_limits=contrast_limits,
gamma=gamma,
interpolation2d=interpolation2d,
interpolation3d=interpolation3d,
rendering=rendering,
depiction=depiction,
iso_threshold=iso_threshold,
attenuation=attenuation,
name=name,
metadata=metadata,
scale=scale,
translate=translate,
rotate=rotate,
shear=shear,
affine=affine,
opacity=opacity,
blending=blending,
visible=visible,
multiscale=multiscale,
cache=cache,
plane=plane,
units=units,
experimental_clipping_planes=experimental_clipping_planes,
custom_interpolation_kernel_2d=custom_interpolation_kernel_2d,
projection_mode=projection_mode,
title=title,
ndisplay=ndisplay,
order=order,
show=show,
)
