import warnings
from copy import copy
from typing import Any, Dict, Tuple, Union
import numpy as np
import pandas as pd
from napari.layers.base import Layer
from napari.layers.utils._color_manager_constants import ColorMode
from napari.layers.utils._slice_input import _SliceInput, _ThickNDSlice
from napari.layers.utils.color_manager import ColorManager
from napari.layers.utils.color_transformations import ColorType
from napari.layers.utils.layer_utils import _FeatureTable
from napari.layers.vectors._slice import (
_VectorSliceRequest,
_VectorSliceResponse,
)
from napari.layers.vectors._vector_utils import fix_data_vectors
from napari.layers.vectors._vectors_constants import (
VectorsProjectionMode,
VectorStyle,
)
from napari.utils.colormaps import Colormap, ValidColormapArg
from napari.utils.events import Event
from napari.utils.events.custom_types import Array
from napari.utils.translations import trans
[docs]class Vectors(Layer):
"""
Vectors layer renders lines onto the canvas.
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.
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.
features : dict[str, array-like] or DataFrame
Features table where each row corresponds to a vector and each column
is a feature.
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.
edge_width : float
Width for all vectors in pixels.
vector_style : str
One of a list of preset display modes that determines how vectors are displayed.
Allowed values are {'line', 'triangle', and 'arrow'}.
length : float
Multiplicative factor on projections for length of all vectors.
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())
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.
name : str
Name of the layer.
metadata : dict
Layer metadata.
scale : tuple of float
Scale factors for the layer.
translate : tuple of float
Translation values for the layer.
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.
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.
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.
opacity : float
Opacity of the layer visual, between 0.0 and 1.0.
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'}.
visible : bool
Whether the layer visual is currently being displayed.
cache : bool
Whether slices of out-of-core datasets should be cached upon retrieval.
Currently, this only applies to dask arrays.
Attributes
----------
data : (N, 2, D) array
The start point and projections of N vectors in D dimensions.
features : Dataframe-like
Features table where each row corresponds to a vector and each column
is a feature.
feature_defaults : DataFrame-like
Stores the default value of each feature in a table with one row.
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.
edge_width : float
Width for all vectors in pixels.
vector_style : VectorStyle
Determines how vectors are displayed.
* ``VectorStyle.LINE``:
Vectors are displayed as lines.
* ``VectorStyle.TRIANGLE``:
Vectors are displayed as triangles.
* ``VectorStyle.ARROW``:
Vectors are displayed as arrows.
length : float
Multiplicative factor on projections for length of all vectors.
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())
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.
Notes
-----
_view_data : (M, 2, 2) array
The start point and projections of N vectors in 2D for vectors whose
start point is in the currently viewed slice.
_view_face_color : (M, 4) np.ndarray
colors for the M in view vectors
_view_indices : (1, M) array
indices for the M in view vectors
_view_alphas : (M,) or float
relative opacity for the M in view vectors
_property_choices : dict {str: array (N,)}
Possible values for the properties in Vectors.properties.
_max_vectors_thumbnail : int
The maximum number of vectors that will ever be used to render the
thumbnail. If more vectors are present then they are randomly
subsampled.
"""
_projectionclass = VectorsProjectionMode
# The max number of vectors that will ever be used to render the thumbnail
# If more vectors are present then they are randomly subsampled
_max_vectors_thumbnail = 1024
def __init__(
self,
data=None,
*,
ndim=None,
features=None,
feature_defaults=None,
properties=None,
property_choices=None,
edge_width=1,
vector_style='triangle',
edge_color='red',
edge_color_cycle=None,
edge_colormap='viridis',
edge_contrast_limits=None,
out_of_slice_display=False,
length=1,
name=None,
metadata=None,
scale=None,
translate=None,
rotate=None,
shear=None,
affine=None,
opacity=0.7,
blending='translucent',
visible=True,
cache=True,
experimental_clipping_planes=None,
projection_mode='none',
) -> None:
if ndim is None and scale is not None:
ndim = len(scale)
data, ndim = fix_data_vectors(data, ndim)
super().__init__(
data,
ndim,
name=name,
metadata=metadata,
scale=scale,
translate=translate,
rotate=rotate,
shear=shear,
affine=affine,
opacity=opacity,
blending=blending,
visible=visible,
cache=cache,
experimental_clipping_planes=experimental_clipping_planes,
projection_mode=projection_mode,
)
# events for non-napari calculations
self.events.add(
length=Event,
edge_width=Event,
edge_color=Event,
vector_style=Event,
edge_color_mode=Event,
properties=Event,
out_of_slice_display=Event,
features=Event,
feature_defaults=Event,
)
# Save the vector style params
self._vector_style = VectorStyle(vector_style)
self._edge_width = edge_width
self._out_of_slice_display = out_of_slice_display
self._length = float(length)
self._data = data
self._feature_table = _FeatureTable.from_layer(
features=features,
feature_defaults=feature_defaults,
properties=properties,
property_choices=property_choices,
num_data=len(self.data),
)
self._edge = ColorManager._from_layer_kwargs(
n_colors=len(self.data),
colors=edge_color,
continuous_colormap=edge_colormap,
contrast_limits=edge_contrast_limits,
categorical_colormap=edge_color_cycle,
properties=self.properties
if self._data.size > 0
else self._feature_table.currents(),
)
# Data containing vectors in the currently viewed slice
self._view_data = np.empty((0, 2, 2))
self._view_indices = np.array([], dtype=int)
self._view_alphas: Union[float, np.ndarray] = 1.0
# now that everything is set up, make the layer visible (if set to visible)
self.refresh()
self.visible = visible
@property
def data(self) -> np.ndarray:
"""(N, 2, D) array: start point and projections of vectors."""
return self._data
@data.setter
def data(self, vectors: np.ndarray):
previous_n_vectors = len(self.data)
self._data, _ = fix_data_vectors(vectors, self.ndim)
n_vectors = len(self.data)
# Adjust the props/color arrays when the number of vectors has changed
with self.events.blocker_all(), self._edge.events.blocker_all():
self._feature_table.resize(n_vectors)
if n_vectors < previous_n_vectors:
# If there are now fewer points, remove the size and colors of the
# extra ones
if len(self._edge.colors) > n_vectors:
self._edge._remove(
np.arange(n_vectors, len(self._edge.colors))
)
elif n_vectors > previous_n_vectors:
# If there are now more points, add the size and colors of the
# new ones
adding = n_vectors - previous_n_vectors
self._edge._update_current_properties(
self._feature_table.currents()
)
self._edge._add(n_colors=adding)
self._update_dims()
self.events.data(value=self.data)
self._reset_editable()
@property
def features(self):
"""Dataframe-like features table.
It is an implementation detail that this is a `pandas.DataFrame`. In the future,
we will target the currently-in-development Data API dataframe protocol [1].
This will enable us to use alternate libraries such as xarray or cuDF for
additional features without breaking existing usage of this.
If you need to specifically rely on the pandas API, please coerce this to a
`pandas.DataFrame` using `features_to_pandas_dataframe`.
References
----------
.. [1]: https://data-apis.org/dataframe-protocol/latest/API.html
"""
return self._feature_table.values
@features.setter
def features(
self,
features: Union[Dict[str, np.ndarray], pd.DataFrame],
) -> None:
self._feature_table.set_values(features, num_data=len(self.data))
if self._edge.color_properties is not None:
if self._edge.color_properties.name not in self.features:
self._edge.color_mode = ColorMode.DIRECT
self._edge.color_properties = None
warnings.warn(
trans._(
'property used for edge_color dropped',
deferred=True,
),
RuntimeWarning,
)
else:
edge_color_name = self._edge.color_properties.name
property_values = self.features[edge_color_name].to_numpy()
self._edge.color_properties = {
'name': edge_color_name,
'values': property_values,
'current_value': self.feature_defaults[edge_color_name][0],
}
self.events.properties()
self.events.features()
@property
def properties(self) -> Dict[str, np.ndarray]:
"""dict {str: array (N,)}, DataFrame: Annotations for each point"""
return self._feature_table.properties()
@properties.setter
def properties(self, properties: Dict[str, Array]):
self.features = properties
@property
def feature_defaults(self):
"""Dataframe-like with one row of feature default values.
See `features` for more details on the type of this property.
"""
return self._feature_table.defaults
@feature_defaults.setter
def feature_defaults(
self, defaults: Union[Dict[str, Any], pd.DataFrame]
) -> None:
self._feature_table.set_defaults(defaults)
self.events.feature_defaults()
@property
def property_choices(self) -> Dict[str, np.ndarray]:
return self._feature_table.choices()
def _get_state(self):
"""Get dictionary of layer state.
Returns
-------
state : dict
Dictionary of layer state.
"""
state = self._get_base_state()
state.update(
{
'length': self.length,
'edge_width': self.edge_width,
'vector_style': self.vector_style,
'edge_color': self.edge_color
if self.data.size
else [self._edge.current_color],
'edge_color_cycle': self.edge_color_cycle,
'edge_colormap': self.edge_colormap.dict(),
'edge_contrast_limits': self.edge_contrast_limits,
'data': self.data,
'properties': self.properties,
'property_choices': self.property_choices,
'ndim': self.ndim,
'features': self.features,
'feature_defaults': self.feature_defaults,
'out_of_slice_display': self.out_of_slice_display,
}
)
return state
def _get_ndim(self) -> int:
"""Determine number of dimensions of the layer."""
return self.data.shape[2]
@property
def _extent_data(self) -> np.ndarray:
"""Extent of layer in data coordinates.
Returns
-------
extent_data : array, shape (2, D)
"""
if len(self.data) == 0:
extrema = np.full((2, self.ndim), np.nan)
else:
# Convert from projections to endpoints using the current length
data = copy(self.data)
data[:, 1, :] = data[:, 0, :] + self.length * data[:, 1, :]
maxs = np.max(data, axis=(0, 1))
mins = np.min(data, axis=(0, 1))
extrema = np.vstack([mins, maxs])
return extrema
@property
def out_of_slice_display(self) -> bool:
"""bool: renders vectors slightly out of slice."""
return self._out_of_slice_display
@out_of_slice_display.setter
def out_of_slice_display(self, out_of_slice_display: bool) -> None:
self._out_of_slice_display = out_of_slice_display
self.events.out_of_slice_display()
self.refresh()
@property
def edge_width(self) -> float:
"""float: Width for all vectors in pixels."""
return self._edge_width
@edge_width.setter
def edge_width(self, edge_width: float):
self._edge_width = edge_width
self.events.edge_width()
self.refresh()
@property
def vector_style(self) -> str:
"""Vectors display mode: Determines how vectors are displayed.
VectorStyle.LINE
Displays vectors as rectangular lines.
VectorStyle.TRIANGLE
Displays vectors as triangles.
VectorStyle.ARROW
Displays vectors as arrows.
"""
return str(self._vector_style)
@vector_style.setter
def vector_style(self, vector_style: str):
old_vector_style = self._vector_style
self._vector_style = VectorStyle(vector_style)
if self._vector_style != old_vector_style:
self.events.vector_style()
self.refresh()
@property
def length(self) -> float:
"""float: Multiplicative factor for length of all vectors."""
return self._length
@length.setter
def length(self, length: float):
self._length = float(length)
self.events.length()
self.refresh()
@property
def edge_color(self) -> np.ndarray:
"""(1 x 4) np.ndarray: Array of RGBA edge colors (applied to all vectors)"""
return self._edge.colors
@edge_color.setter
def edge_color(self, edge_color: ColorType):
self._edge._set_color(
color=edge_color,
n_colors=len(self.data),
properties=self.properties,
current_properties=self._feature_table.currents(),
)
self.events.edge_color()
[docs] def refresh_colors(self, update_color_mapping: bool = False):
"""Calculate and update edge colors if using a cycle or color map
Parameters
----------
update_color_mapping : bool
If set to True, the function will recalculate the color cycle map
or colormap (whichever is being used). If set to False, the function
will use the current color cycle map or color map. For example, if you
are adding/modifying vectors and want them to be colored with the same
mapping as the other vectors (i.e., the new vectors shouldn't affect
the color cycle map or colormap), set update_color_mapping=False.
Default value is False.
"""
self._edge._refresh_colors(self.properties, update_color_mapping)
@property
def edge_color_mode(self) -> ColorMode:
"""str: Edge color setting mode
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 color to be set via a color map over an attribute
"""
return self._edge.color_mode
@edge_color_mode.setter
def edge_color_mode(self, edge_color_mode: Union[str, ColorMode]):
edge_color_mode = ColorMode(edge_color_mode)
if edge_color_mode == ColorMode.DIRECT:
self._edge_color_mode = edge_color_mode
elif edge_color_mode in (ColorMode.CYCLE, ColorMode.COLORMAP):
if self._edge.color_properties is not None:
color_property = self._edge.color_properties.name
else:
color_property = ''
if color_property == '':
if self.properties:
color_property = next(iter(self.properties))
self._edge.color_properties = {
'name': color_property,
'values': self.features[color_property].to_numpy(),
'current_value': self.feature_defaults[color_property][
0
],
}
warnings.warn(
trans._(
'edge_color property was not set, setting to: {color_property}',
deferred=True,
color_property=color_property,
),
RuntimeWarning,
)
else:
raise ValueError(
trans._(
'There must be a valid Points.properties to use {edge_color_mode}',
deferred=True,
edge_color_mode=edge_color_mode,
)
)
# ColorMode.COLORMAP can only be applied to numeric properties
if (edge_color_mode == ColorMode.COLORMAP) and not issubclass(
self.properties[color_property].dtype.type,
np.number,
):
raise TypeError(
trans._(
'selected property must be numeric to use ColorMode.COLORMAP',
deferred=True,
)
)
self._edge.color_mode = edge_color_mode
self.events.edge_color_mode()
@property
def edge_color_cycle(self) -> np.ndarray:
"""list, np.ndarray : Color cycle for edge_color.
Can be a list of colors defined by name, RGB or RGBA
"""
return self._edge.categorical_colormap.fallback_color.values
@edge_color_cycle.setter
def edge_color_cycle(self, edge_color_cycle: Union[list, np.ndarray]):
self._edge.categorical_colormap = edge_color_cycle
@property
def edge_colormap(self) -> Tuple[str, Colormap]:
"""Return the colormap to be applied to a property to get the edge color.
Returns
-------
colormap : napari.utils.Colormap
The Colormap object.
"""
return self._edge.continuous_colormap
@edge_colormap.setter
def edge_colormap(self, colormap: ValidColormapArg):
self._edge.continuous_colormap = colormap
@property
def edge_contrast_limits(self) -> Tuple[float, float]:
"""None, (float, float): contrast limits for mapping
the edge_color colormap property to 0 and 1
"""
return self._edge.contrast_limits
@edge_contrast_limits.setter
def edge_contrast_limits(
self, contrast_limits: Union[None, Tuple[float, float]]
):
self._edge.contrast_limits = contrast_limits
@property
def _view_face_color(self) -> np.ndarray:
"""(Mx4) np.ndarray : colors for the M in view triangles"""
# Create as many colors as there are visible vectors.
# Using fancy array indexing implicitly creates a new
# array rather than creating a view of the original one
# in ColorManager
face_color = self.edge_color[self._view_indices]
face_color[:, -1] *= self._view_alphas
# Generally, several triangles are drawn for each vector,
# so we need to duplicate the colors accordingly
if self.vector_style == 'line':
# Line vectors are drawn with 2 triangles
face_color = np.repeat(face_color, 2, axis=0)
elif self.vector_style == 'triangle':
# Triangle vectors are drawn with 1 triangle
pass # No need to duplicate colors
elif self.vector_style == 'arrow':
# Arrow vectors are drawn with 3 triangles
face_color = np.repeat(face_color, 3, axis=0)
if self._slice_input.ndisplay == 3 and self.ndim > 2:
face_color = np.vstack([face_color, face_color])
return face_color
def _set_view_slice(self):
"""Sets the view given the indices to slice with."""
# The new slicing code makes a request from the existing state and
# executes the request on the calling thread directly.
# For async slicing, the calling thread will not be the main thread.
request = self._make_slice_request_internal(
self._slice_input, self._data_slice
)
response = request()
self._update_slice_response(response)
def _make_slice_request(self, dims) -> _VectorSliceRequest:
"""Make a Vectors slice request based on the given dims and these data."""
slice_input = self._make_slice_input(dims)
# TODO: [see Image]
# For the existing sync slicing, slice_indices is passed through
# to avoid some performance issues related to the evaluation of the
# data-to-world transform and its inverse. Async slicing currently
# absorbs these performance issues here, but we can likely improve
# things either by caching the world-to-data transform on the layer
# or by lazily evaluating it in the slice task itself.
slice_indices = slice_input.data_slice(self._data_to_world.inverse)
return self._make_slice_request_internal(slice_input, slice_indices)
def _make_slice_request_internal(
self, slice_input: _SliceInput, data_slice: _ThickNDSlice
):
return _VectorSliceRequest(
slice_input=slice_input,
data=self.data,
data_slice=data_slice,
projection_mode=self.projection_mode,
out_of_slice_display=self.out_of_slice_display,
length=self.length,
)
def _update_slice_response(self, response: _VectorSliceResponse):
"""Handle a slicing response."""
self._slice_input = response.slice_input
indices = response.indices
alphas = response.alphas
disp = self._slice_input.displayed
self._view_indices = indices
self._view_alphas = alphas
self._view_data = self.data[np.ix_(list(indices), [0, 1], disp)]
def _update_thumbnail(self):
"""Update thumbnail with current vectors and colors."""
# Set the default thumbnail to black, opacity 1
colormapped = np.zeros(self._thumbnail_shape)
colormapped[..., 3] = 1
if len(self.data) == 0:
self.thumbnail = colormapped
else:
# calculate min vals for the vertices and pad with 0.5
# the offset is needed to ensure that the top left corner of the
# vectors corresponds to the top left corner of the thumbnail
de = self._extent_data
offset = (
np.array([de[0, d] for d in self._slice_input.displayed]) + 0.5
)[-2:]
# calculate range of values for the vertices and pad with 1
# padding ensures the entire vector can be represented in the thumbnail
# without getting clipped
shape = np.ceil(
[de[1, d] - de[0, d] + 1 for d in self._slice_input.displayed]
).astype(int)[-2:]
zoom_factor = np.divide(self._thumbnail_shape[:2], shape).min()
if self._view_data.shape[0] > self._max_vectors_thumbnail:
thumbnail_indices = np.random.randint(
0, self._view_data.shape[0], self._max_vectors_thumbnail
)
vectors = copy(self._view_data[thumbnail_indices, :, -2:])
thumbnail_color_indices = self._view_indices[thumbnail_indices]
else:
vectors = copy(self._view_data[:, :, -2:])
thumbnail_color_indices = self._view_indices
vectors[:, 1, :] = (
vectors[:, 0, :] + vectors[:, 1, :] * self.length
)
downsampled = (vectors - offset) * zoom_factor
downsampled = np.clip(
downsampled, 0, np.subtract(self._thumbnail_shape[:2], 1)
)
edge_colors = self._edge.colors[thumbnail_color_indices]
for v, ec in zip(downsampled, edge_colors):
start = v[0]
stop = v[1]
step = int(np.ceil(np.max(abs(stop - start))))
x_vals = np.linspace(start[0], stop[0], step)
y_vals = np.linspace(start[1], stop[1], step)
for x, y in zip(x_vals, y_vals):
colormapped[int(x), int(y), :] = ec
colormapped[..., 3] *= self.opacity
self.thumbnail = colormapped
def _get_value(self, position):
"""Value of the data at a position in data coordinates.
Parameters
----------
position : tuple
Position in data coordinates.
Returns
-------
value : None
Value of the data at the coord.
"""
return