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Interactive visualization¤

interactive_plot(data: np.ndarray | Sequence[np.ndarray] | Tensor | Sequence[Tensor], labels: Optional[str | Sequence[str]] = None, point_size: int = 3, opacity: float = 0.8, color: Optional[np.ndarray] = None, cmap: Optional[str] = None, constraint_x: bool = False, constraint_y: bool = False, constraint_z: bool = False, return_fig: bool = False, width: int = 500, height: int = 500) -> Optional[go.Figure] ¤

Interactive plot of point cloud(s) based on Plotly. Can display N pointcloud(s).

Parameters:

  • data (ndarray | Sequence[ndarray] | Tensor | Sequence[Tensor]) –

    description

  • labels (Optional[str | Sequence[str]], default: None ) –

    description. Defaults to None.

  • point_size (int, default: 3 ) –

    description. Defaults to 3.

  • opacity (float, default: 0.8 ) –

    description. Defaults to 0.8.

  • color (Optional[ndarray], default: None ) –

    description. Defaults to None.

  • cmap (Optional[str], default: None ) –

    description. Defaults to None.

  • constraint_x (bool, default: False ) –

    description. Defaults to False.

  • constraint_y (bool, default: False ) –

    description. Defaults to False.

  • constraint_z (bool, default: False ) –

    description. Defaults to False.

  • return_fig (bool, default: False ) –

    description. Defaults to False.

  • width (int, default: 500 ) –

    description. Defaults to 500.

  • height (int, default: 500 ) –

    description. Defaults to 500.

Raises:

  • ValueError –

    description

Returns:

  • Optional[Figure] –

    Optional[go.Figure]: description

Source code in src/polar/example/utils.py 
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def interactive_plot(
    data: np.ndarray | Sequence[np.ndarray] | Tensor | Sequence[Tensor],
    labels: Optional[str | Sequence[str]] = None,
    point_size: int = 3,
    opacity: float = 0.8,
    color: Optional[np.ndarray] = None,
    cmap: Optional[str] = None,
    constraint_x: bool = False,
    constraint_y: bool = False,
    constraint_z: bool = False,
    return_fig: bool = False,
    width: int = 500,
    height: int = 500,
) -> Optional[go.Figure]:
    """ Interactive plot of point cloud(s) based on Plotly. Can display N pointcloud(s).

    Args:
        data (np.ndarray | Sequence[np.ndarray] | Tensor | Sequence[Tensor]): _description_
        labels (Optional[str  |  Sequence[str]], optional): _description_. Defaults to None.
        point_size (int, optional): _description_. Defaults to 3.
        opacity (float, optional): _description_. Defaults to 0.8.
        color (Optional[np.ndarray], optional): _description_. Defaults to None.
        cmap (Optional[str], optional): _description_. Defaults to None.
        constraint_x (bool, optional): _description_. Defaults to False.
        constraint_y (bool, optional): _description_. Defaults to False.
        constraint_z (bool, optional): _description_. Defaults to False.
        return_fig (bool, optional): _description_. Defaults to False.
        width (int, optional): _description_. Defaults to 500.
        height (int, optional): _description_. Defaults to 500.

    Raises:
        ValueError: _description_

    Returns:
        Optional[go.Figure]: _description_
    """
    if not isinstance(data, (list, tuple)):
        data = [data]
    if isinstance(data, tuple):
        data = list(data)
    for i, x in enumerate(data):
        if isinstance(x, Tensor) and x.is_cuda:
            data[i] = x.cpu()
    N = len(data)
    if labels is None:
        labels = [f"pointcloud {i + 1}" for i in range(N)]
    if not isinstance(labels, (list, tuple)):
        labels = (labels, )
    if labels is not None and not len(data) == len(labels):
        raise ValueError(f"You gave {len(data)} pointclouds but {len(labels)} labels.")
    all_cmaps = get_all_named_cmaps()
    if isinstance(cmap, str):
        cmaps = N * [cmap]
    elif not (isinstance(cmap, (tuple, list)) and len(cmap) == len(data)):
        cmaps = all_cmaps[:N]
    else:
        cmaps = cmap
    traces = list()
    for pointcloud, label, cmap in zip(data, labels, cmaps):
        x, y, z = pointcloud[:, 0], pointcloud[:, 1], pointcloud[:, 2]
        c = color if color is not None else z
        marker_kwargs = dict(size=point_size, opacity=opacity, color=c, colorscale=cmap)
        scatter_kwargs = dict(visible=True, mode='markers', name=label, marker=marker_kwargs)
        traces.append(go.Scatter3d(x=x, y=y, z=z, **scatter_kwargs))
    layout = dict(
        width=width, height=height,
        xaxis=dict(range=[0, 1]), yaxis=dict(range=[0, 1]), margin=dict(t=50)
    )
    if constraint_x:
        layout['scene'] = dict(xaxis=dict(nticks=4, range=[-1, 1]))
    if constraint_y:
        layout['scene'] = dict(yaxis=dict(nticks=4, range=[-1, 1]))
    if constraint_z:
        layout['scene'] = dict(zaxis=dict(nticks=4, range=[-1, 1]))
    fig = go.Figure(data=traces)
    fig.update_layout(**layout)
    if return_fig:
        return fig
    fig.show()