Function _compute_grid

plotly/figure_factory/_ternary_contour.py:187–222

Transform data points with Cartesian or ILR mapping, then Compute interpolation on a regular grid. Parameters ========== coordinates : array-like Barycentric coordinates of data points. values : 1-d array-like Data points, field to be represented as contour

(coordinates, values, interp_mode="ilr")

def _compute_grid(coordinates, values, interp_mode="ilr"):
    """
    Transform data points with Cartesian or ILR mapping, then Compute
    interpolation on a regular grid.

    Parameters
    ==========

    coordinates : array-like
        Barycentric coordinates of data points.
    values : 1-d array-like
        Data points, field to be represented as contours.
    interp_mode : 'ilr' (default) or 'cartesian'
        Defines how data are interpolated to compute contours.
    """
    if interp_mode == "cartesian":
        M, invM = _transform_barycentric_cartesian()
        coord_points = np.einsum("ik, kj -> ij", M, coordinates)
    elif interp_mode == "ilr":
        coordinates = _replace_zero_coords(coordinates)
        coord_points = _ilr_transform(coordinates)
    else:
        raise ValueError("interp_mode should be cartesian or ilr")
    xx, yy = coord_points[:2]
    x_min, x_max = xx.min(), xx.max()
    y_min, y_max = yy.min(), yy.max()
    n_interp = max(200, int(np.sqrt(len(values))))
    gr_x = np.linspace(x_min, x_max, n_interp)
    gr_y = np.linspace(y_min, y_max, n_interp)
    grid_x, grid_y = np.meshgrid(gr_x, gr_y)
    # We use cubic interpolation, except outside of the convex hull
    # of data points where we use nearest neighbor values.
    grid_z = scipy_interp.griddata(
        coord_points[:2].T, values, (grid_x, grid_y), method="cubic"
    )
    return grid_z, gr_x, gr_y


# ----------------------- Contour traces ----------------------