"""Behaviors for recording the state of the ``ipyforcegraph`` graphs."""
# Copyright (c) 2023 ipyforcegraph contributors.
# Distributed under the terms of the Modified BSD License.
from typing import Any, Optional, Tuple
import ipywidgets as W
import traitlets as T
from ..sources.dataframe import DataFrameSource
from ..trait_utils import JSON_TYPES, coerce
from ._base import Behavior, TFeature, _make_trait
[docs]@W.register
class GraphImage(Behavior):
"""Captures multiple subsequent frames of a canvas, each as an :class:`~ipywidgets.widgets.widget_media.Image`."""
_model_name: str = T.Unicode("GraphImageModel").tag(sync=True)
capturing = T.Bool(False, help="whether the frame capture is currently active").tag(
sync=True
)
frame_count = T.Int(1, help="the number of frames to capture").tag(sync=True)
frames: Tuple[W.Image, ...] = W.TypedTuple(
T.Instance(W.Image),
help="a tuple of :class:`~ipywidgets.widgets.widget_media.Image` to populate with frames of the graph",
).tag(sync=True, **W.widget_serialization)
def _get_frames(self) -> Tuple[W.Image, ...]:
return tuple(
[W.Image(description=f"frame {i}") for i in range(self.frame_count)]
)
@T.default("frames")
def _default_frames(self) -> Tuple[W.Image, ...]:
return self._get_frames()
@T.observe("frame_count")
def _on_frame_count(self, change: T.Bunch) -> None:
frames = self.frames
self.frames = tuple()
for frame in frames:
frame.close()
self.frames = self._get_frames()
[docs]@W.register
class GraphData(Behavior):
"""Captures multiple subsequent ticks of a graph simulation, each as a :class:`~pandas.DataFrame`."""
_model_name: str = T.Unicode("GraphDataModel").tag(sync=True)
capturing: bool = T.Bool(
False, help="whether the dataframe capture is currently active"
).tag(sync=True)
source_count = T.Int(1, help="the number of sources to capture").tag(sync=True)
sources: Tuple[DataFrameSource, ...] = W.TypedTuple(
T.Instance(DataFrameSource),
help="a tuple of :class:`~ipyforcegraph.sources.dataframe.DataFrameSource` to be populated with data of the graph",
).tag(sync=True, **W.widget_serialization)
def _get_sources(self) -> Tuple[DataFrameSource, ...]:
return tuple([DataFrameSource() for i in range(self.source_count)])
@T.default("sources")
def _default_sources(self) -> Tuple[DataFrameSource, ...]:
return self._get_sources()
@T.observe("source_count")
def _on_source_count(self, change: T.Bunch) -> None:
sources = self.sources
self.sources = tuple()
for source in sources:
source.close()
self.sources = self._get_sources()
[docs]@W.register
class GraphCamera(Behavior):
"""Captures the current center and zoom of the graph viewport."""
_model_name: str = T.Unicode("GraphCameraModel").tag(sync=True)
zoom: float = T.Float(
None, allow_none=True, help="the current 2D zoom level of the viewport"
).tag(sync=True)
center: Tuple[float, ...] = W.TypedTuple(
T.Float(), allow_none=True, help="the center of the viewport as `[x, y, z?]`"
).tag(sync=True)
look_at: Tuple[float, ...] = W.TypedTuple(
T.Float(), allow_none=True, help="the direction of a 3D camera as `[x, y, z?]`"
).tag(sync=True)
visible: Tuple[int, ...] = W.TypedTuple(
T.Int(), help="the indices of all visible nodes"
).tag(sync=True)
capturing: bool = T.Bool(
False, help="whether visible nodes should be captured as ``visible``"
).tag(sync=True)
[docs]@W.register
class GraphDirector(Behavior):
"""Set a desired center and zoom of the graph viewport."""
_model_name: str = T.Unicode("GraphDirectorModel").tag(sync=True)
zoom: Optional[float] = T.Float(
None, allow_none=True, help="the desired 2D zoom level of the viewport"
).tag(sync=True)
center: Optional[Tuple[float, ...]] = W.TypedTuple(
T.Float(),
allow_none=True,
help="the desired center of the viewport as `[x, y, z?]`",
).tag(sync=True)
look_at: Tuple[float, ...] = W.TypedTuple(
T.Float(), allow_none=True, help="the direction of a 3d camera as `[x, y, z]`"
).tag(sync=True)
zoom_first: bool = T.Bool(
False, help="whether to zoom the viewport before panning"
).tag(sync=True)
visible: TFeature = _make_trait(
"fit nodes in viewport for which this column/template is truthy",
by_template=True,
by_column=True,
)
zoom_duration: float = T.Float(0.2, help="seconds to animate a zoom").tag(sync=True)
pan_duration: float = T.Float(0.2, help="seconds to animate a pan").tag(sync=True)
fit_duration: float = T.Float(0.2, help="seconds to animate a fit").tag(sync=True)
fit_padding: float = T.Float(
10, help="pixels of padding between visible nodes and viewport"
).tag(sync=True)
@T.validate("visible")
def _validate_scale_bools(self, proposal: T.Bunch) -> Any:
return coerce(proposal, JSON_TYPES.boolean)
