SplineDraw API

Bases: AnyWidget

Draw scatter points and see a spline curve fitted through them.

This widget is built on the same D3/SVG canvas as ScatterWidget. The spline is computed by a Python callback function. Pass any callable with signature (x, y) -> (x_curve, y_curve) where inputs and outputs are 1-D numpy arrays.

Examples:

from wigglystuff import SplineDraw
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import SplineTransformer
from sklearn.linear_model import LinearRegression

pipe = make_pipeline(SplineTransformer(), LinearRegression())

def spline_fn(x, y):
    pipe.fit(x.reshape(-1, 1), y)
    x_curve = np.linspace(x.min(), x.max(), 200)
    return x_curve, pipe.predict(x_curve.reshape(-1, 1))

widget = SplineDraw(spline_fn=spline_fn)
widget

Create a SplineDraw widget.

Parameters:

Name	Type	Description	Default
spline_fn	Callable	A callable (x, y) -> (x_curve, y_curve) where inputs are 1-D numpy arrays of drawn point coordinates and outputs are 1-D numpy arrays defining the fitted curve.	required
n_classes	int	Number of point classes (1-4). Defaults to 1.	1
brushsize	int	Initial brush radius in pixels.	40
width	int	SVG viewBox width in pixels.	600
height	int	SVG viewBox height in pixels.	400
**kwargs	Any	Forwarded to anywidget.AnyWidget.	{}

Source code in wigglystuff/spline_draw.py

def __init__(
    self,
    spline_fn: Callable,
    *,
    n_classes: int = 1,
    brushsize: int = 40,
    width: int = 600,
    height: int = 400,
    **kwargs: Any,
) -> None:
    """Create a SplineDraw widget.

    Args:
        spline_fn: A callable ``(x, y) -> (x_curve, y_curve)`` where
            inputs are 1-D numpy arrays of drawn point coordinates and
            outputs are 1-D numpy arrays defining the fitted curve.
        n_classes: Number of point classes (1-4). Defaults to 1.
        brushsize: Initial brush radius in pixels.
        width: SVG viewBox width in pixels.
        height: SVG viewBox height in pixels.
        **kwargs: Forwarded to ``anywidget.AnyWidget``.
    """
    if not 1 <= n_classes <= 4:
        raise ValueError("n_classes must be between 1 and 4")
    self._spline_fn = spline_fn
    super().__init__(
        n_classes=n_classes,
        brushsize=brushsize,
        width=width,
        height=height,
        **kwargs,
    )
    self.observe(self._recompute_curve, names=["data"])

curve_as_numpy property

curve_as_numpy: dict[str, tuple[ndarray, ndarray]]

Return fitted curves as a dict keyed by color.

Each value is an (x_array, y_array) tuple of numpy arrays. With a single class the dict has one entry.

data_as_X_y property

data_as_X_y

Return the drawn points as a (X, y) tuple of numpy arrays.

With n_classes=1 returns X of shape (n, 1) with x values and y as target. With n_classes>1 returns X of shape (n, 2) and y as color strings.

data_as_numpy property

data_as_numpy: tuple[ndarray, ndarray]

Return the drawn points as (x_array, y_array) numpy arrays.

data_as_pandas property

data_as_pandas

Return the drawn points as a :class:pandas.DataFrame.

data_as_polars property

data_as_polars

Return the drawn points as a :class:polars.DataFrame.

Synced traitlets

Traitlet	Type	Notes
data	list	Drawn scatter points (list of dicts with x, y, color).
curve	list	Fitted curve data computed by the spline function.
curve_error	str	Error message from the last spline computation, or empty string on success.
brushsize	int	Brush radius in pixels.
n_classes	int	Number of point classes (1–4).
width	int	SVG viewBox width in pixels.
height	int	SVG viewBox height in pixels.