# from napari.layers.base.base import Layer
# from napari.utils.events import Event
# from napari.utils.colormaps import AVAILABLE_COLORMAPS
from typing import Dict, List, Union
from warnings import warn
import numpy as np
import pandas as pd
from ...utils.colormaps import AVAILABLE_COLORMAPS, Colormap
from ...utils.events import Event
from ...utils.translations import trans
from ..base import Layer
from ._track_utils import TrackManager
[docs]class Tracks(Layer):
"""Tracks layer.
Parameters
----------
data : array (N, D+1)
Coordinates for N points in D+1 dimensions. ID,T,(Z),Y,X. The first
axis is the integer ID of the track. D is either 3 or 4 for planar
or volumetric timeseries respectively.
features : Dataframe-like
Features table where each row corresponds to a point and each column
is a feature.
properties : dict {str: array (N,)}, DataFrame
Properties for each point. Each property should be an array of length N,
where N is the number of points.
graph : dict {int: list}
Graph representing associations between tracks. Dictionary defines the
mapping between a track ID and the parents of the track. This can be
one (the track has one parent, and the parent has >=1 child) in the
case of track splitting, or more than one (the track has multiple
parents, but only one child) in the case of track merging.
See examples/tracks_3d_with_graph.py
color_by : str
Track property (from property keys) by which to color vertices.
tail_width : float
Width of the track tails in pixels.
tail_length : float
Length of the positive (backward in time) tails in units of time.
head_length : float
Length of the positive (forward in time) tails in units of time.
colormap : str
Default colormap to use to set vertex colors. Specialized colormaps,
relating to specified properties can be passed to the layer via
colormaps_dict.
colormaps_dict : dict {str: napari.utils.Colormap}
Optional dictionary mapping each property to a colormap for that
property. This allows each property to be assigned a specific colormap,
rather than having a global colormap for everything.
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.
"""
# The max number of tracks that will ever be used to render the thumbnail
# If more tracks are present then they are randomly subsampled
_max_tracks_thumbnail = 1024
def __init__(
self,
data,
*,
features=None,
properties=None,
graph=None,
tail_width=2,
tail_length=30,
head_length=0,
name=None,
metadata=None,
scale=None,
translate=None,
rotate=None,
shear=None,
affine=None,
opacity=1,
blending='additive',
visible=True,
colormap='turbo',
color_by='track_id',
colormaps_dict=None,
cache=True,
experimental_clipping_planes=None,
):
# if not provided with any data, set up an empty layer in 2D+t
if data is None:
data = np.empty((0, 4))
else:
# convert data to a numpy array if it is not already one
data = np.asarray(data)
# set the track data dimensions (remove ID from data)
ndim = data.shape[1] - 1
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,
)
self.events.add(
tail_width=Event,
tail_length=Event,
head_length=Event,
display_id=Event,
display_tail=Event,
display_graph=Event,
color_by=Event,
colormap=Event,
properties=Event,
rebuild_tracks=Event,
rebuild_graph=Event,
)
# track manager deals with data slicing, graph building and properties
self._manager = TrackManager()
self._track_colors = None
self._colormaps_dict = colormaps_dict or {} # additional colormaps
self._color_by = color_by # default color by ID
self._colormap = colormap
# use this to update shaders when the displayed dims change
self._current_displayed_dims = None
# track display default limits
self._max_length = 300
self._max_width = 20
# track display properties
self.tail_width = tail_width
self.tail_length = tail_length
self.head_length = head_length
self.display_id = False
self.display_tail = True
self.display_graph = True
# set the data, features, and graph
self.data = data
if properties is not None:
self.properties = properties
else:
self.features = features
self.graph = graph or {}
self.color_by = color_by
self.colormap = colormap
self._update_dims()
# reset the display before returning
self._current_displayed_dims = None
@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:
maxs = np.max(self.data, axis=0)
mins = np.min(self.data, axis=0)
extrema = np.vstack([mins, maxs])
return extrema[:, 1:]
def _get_ndim(self) -> int:
"""Determine number of dimensions of the layer."""
return self._manager.ndim
def _get_state(self):
"""Get dictionary of layer state.
Returns
-------
state : dict
Dictionary of layer state.
"""
state = self._get_base_state()
state.update(
{
'data': self.data,
'properties': self.properties,
'graph': self.graph,
'color_by': self.color_by,
'colormap': self.colormap,
'colormaps_dict': self.colormaps_dict,
'tail_width': self.tail_width,
'tail_length': self.tail_length,
'head_length': self.head_length,
'features': self.features,
}
)
return state
def _set_view_slice(self):
"""Sets the view given the indices to slice with."""
# if the displayed dims have changed, update the shader data
if self._dims_displayed != self._current_displayed_dims:
# store the new dims
self._current_displayed_dims = self._dims_displayed
# fire the events to update the shaders
self.events.rebuild_tracks()
self.events.rebuild_graph()
return
def _get_value(self, position) -> int:
"""Value of the data at a position in data coordinates.
Use a kd-tree to lookup the ID of the nearest tree.
Parameters
----------
position : tuple
Position in data coordinates.
Returns
-------
value : int or None
Index of track that is at the current coordinate if any.
"""
return self._manager.get_value(np.array(position))
def _update_thumbnail(self):
"""Update thumbnail with current points and colors."""
colormapped = np.zeros(self._thumbnail_shape)
colormapped[..., 3] = 1
if self._view_data is not None and self.track_colors is not None:
de = self._extent_data
min_vals = [de[0, i] for i in self._dims_displayed]
shape = np.ceil(
[de[1, i] - de[0, i] + 1 for i in self._dims_displayed]
).astype(int)
zoom_factor = np.divide(
self._thumbnail_shape[:2], shape[-2:]
).min()
if len(self._view_data) > self._max_tracks_thumbnail:
thumbnail_indices = np.random.randint(
0, len(self._view_data), self._max_tracks_thumbnail
)
points = self._view_data[thumbnail_indices]
else:
points = self._view_data
thumbnail_indices = range(len(self._view_data))
# get the track coords here
coords = np.floor(
(points[:, :2] - min_vals[1:] + 0.5) * zoom_factor
).astype(int)
coords = np.clip(
coords, 0, np.subtract(self._thumbnail_shape[:2], 1)
)
# modulate track colors as per colormap/current_time
colors = self.track_colors[thumbnail_indices]
times = self.track_times[thumbnail_indices]
alpha = (self.head_length + self.current_time - times) / (
self.tail_length + self.head_length
)
alpha[times > self.current_time] = 1.0
colors[:, -1] = np.clip(1.0 - alpha, 0.0, 1.0)
colormapped[coords[:, 1], coords[:, 0]] = colors
colormapped[..., 3] *= self.opacity
self.thumbnail = colormapped
@property
def _view_data(self):
"""return a view of the data"""
return self._pad_display_data(self._manager.track_vertices)
@property
def _view_graph(self):
"""return a view of the graph"""
return self._pad_display_data(self._manager.graph_vertices)
def _pad_display_data(self, vertices):
"""pad display data when moving between 2d and 3d"""
if vertices is None:
return
data = vertices[:, self._dims_displayed]
# if we're only displaying two dimensions, then pad the display dim
# with zeros
if self._ndisplay == 2:
data = np.pad(data, ((0, 0), (0, 1)), 'constant')
return data[:, (1, 0, 2)] # y, x, z -> x, y, z
else:
return data[:, (2, 1, 0)] # z, y, x -> x, y, z
@property
def current_time(self):
"""current time according to the first dimension"""
# TODO(arl): get the correct index here
time_step = self._slice_indices[0]
if isinstance(time_step, slice):
# if we are visualizing all time, then just set to the maximum
# timestamp of the dataset
return self._manager.max_time
return time_step
@property
def use_fade(self) -> bool:
"""toggle whether we fade the tail of the track, depending on whether
the time dimension is displayed"""
return 0 in self._dims_not_displayed
@property
def data(self) -> np.ndarray:
"""array (N, D+1): Coordinates for N points in D+1 dimensions."""
return self._manager.data
@data.setter
def data(self, data: np.ndarray):
"""set the data and build the vispy arrays for display"""
# set the data and build the tracks
self._manager.data = data
self._manager.build_tracks()
# reset the properties and recolor the tracks
self.features = {}
self._recolor_tracks()
# reset the graph
self._manager.graph = {}
self._manager.build_graph()
# fire events to update shaders
self._update_dims()
self.events.rebuild_tracks()
self.events.rebuild_graph()
self.events.data(value=self.data)
self._set_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._manager.features
@features.setter
def features(
self,
features: Union[Dict[str, np.ndarray], pd.DataFrame],
) -> None:
self._manager.features = features
self.events.properties()
self._check_color_by_in_features()
@property
def properties(self) -> Dict[str, np.ndarray]:
"""dict {str: np.ndarray (N,)}: Properties for each track."""
return self._manager.properties
@property
def properties_to_color_by(self) -> List[str]:
"""track properties that can be used for coloring etc..."""
return list(self.properties.keys())
@properties.setter
def properties(self, properties: Dict[str, np.ndarray]):
"""set track properties"""
self.features = properties
@property
def graph(self) -> Dict[int, Union[int, List[int]]]:
"""dict {int: list}: Graph representing associations between tracks."""
return self._manager.graph
@graph.setter
def graph(self, graph: Dict[int, Union[int, List[int]]]):
"""Set the track graph."""
self._manager.graph = graph
self._manager.build_graph()
self.events.rebuild_graph()
@property
def tail_width(self) -> Union[int, float]:
"""float: Width for all vectors in pixels."""
return self._tail_width
@tail_width.setter
def tail_width(self, tail_width: Union[int, float]):
self._tail_width = np.clip(tail_width, 0.5, self._max_width)
self.events.tail_width()
@property
def tail_length(self) -> Union[int, float]:
"""float: Width for all vectors in pixels."""
return self._tail_length
@tail_length.setter
def tail_length(self, tail_length: Union[int, float]):
if tail_length > self._max_length:
self._max_length = tail_length
self._tail_length = tail_length
self.events.tail_length()
@property
def head_length(self) -> Union[int, float]:
return self._head_length
@head_length.setter
def head_length(self, head_length: Union[int, float]):
if head_length > self._max_length:
self._max_length = head_length
self._head_length = head_length
self.events.head_length()
@property
def display_id(self) -> bool:
"""display the track id"""
return self._display_id
@display_id.setter
def display_id(self, value: bool):
self._display_id = value
self.events.display_id()
self.refresh()
@property
def display_tail(self) -> bool:
"""display the track tail"""
return self._display_tail
@display_tail.setter
def display_tail(self, value: bool):
self._display_tail = value
self.events.display_tail()
@property
def display_graph(self) -> bool:
"""display the graph edges"""
return self._display_graph
@display_graph.setter
def display_graph(self, value: bool):
self._display_graph = value
self.events.display_graph()
@property
def color_by(self) -> str:
return self._color_by
@color_by.setter
def color_by(self, color_by: str):
"""set the property to color vertices by"""
if color_by not in self.properties_to_color_by:
raise ValueError(
trans._(
'{color_by} is not a valid property key',
deferred=True,
color_by=color_by,
)
)
self._color_by = color_by
self._recolor_tracks()
self.events.color_by()
@property
def colormap(self) -> str:
return self._colormap
@colormap.setter
def colormap(self, colormap: str):
"""set the default colormap"""
if colormap not in AVAILABLE_COLORMAPS:
raise ValueError(
trans._(
'Colormap {colormap} not available',
deferred=True,
colormap=colormap,
)
)
self._colormap = colormap
self._recolor_tracks()
self.events.colormap()
@property
def colormaps_dict(self) -> Dict[str, Colormap]:
return self._colormaps_dict
@colormaps_dict.setter
def colomaps_dict(self, colormaps_dict: Dict[str, Colormap]):
# validate the dictionary entries?
self._colormaps_dict = colormaps_dict
def _recolor_tracks(self):
"""recolor the tracks"""
# this catch prevents a problem coloring the tracks if the data is
# updated before the properties are. properties should always contain
# a track_id key
if self.color_by not in self.properties_to_color_by:
self._color_by = 'track_id'
self.events.color_by()
# if we change the coloring, rebuild the vertex colors array
vertex_properties = self._manager.vertex_properties(self.color_by)
def _norm(p):
return (p - np.min(p)) / np.max([1e-10, np.ptp(p)])
if self.color_by in self.colormaps_dict:
colormap = self.colormaps_dict[self.color_by]
else:
# if we don't have a colormap, get one and scale the properties
colormap = AVAILABLE_COLORMAPS[self.colormap]
vertex_properties = _norm(vertex_properties)
# actually set the vertex colors
self._track_colors = colormap.map(vertex_properties)
@property
def track_connex(self) -> np.ndarray:
"""vertex connections for drawing track lines"""
return self._manager.track_connex
@property
def track_colors(self) -> np.ndarray:
"""return the vertex colors according to the currently selected
property"""
return self._track_colors
@property
def graph_connex(self) -> np.ndarray:
"""vertex connections for drawing the graph"""
return self._manager.graph_connex
@property
def track_times(self) -> np.ndarray:
"""time points associated with each track vertex"""
return self._manager.track_times
@property
def graph_times(self) -> np.ndarray:
"""time points associated with each graph vertex"""
return self._manager.graph_times
@property
def track_labels(self) -> tuple:
"""return track labels at the current time"""
labels, positions = self._manager.track_labels(self.current_time)
# if there are no labels, return empty for vispy
if not labels:
return None, (None, None)
padded_positions = self._pad_display_data(positions)
return labels, padded_positions
def _check_color_by_in_features(self):
if self._color_by not in self.features.columns:
warn(
(
trans._(
"Previous color_by key {key!r} not present in features. Falling back to track_id",
deferred=True,
key=self._color_by,
)
),
UserWarning,
)
self._color_by = 'track_id'
self.events.color_by()