def intersect_curves(nodes1, nodes2):
r"""Intersect two parametric B |eacute| zier curves.
Args:
nodes1 (numpy.ndarray): The nodes in the first curve.
nodes2 (numpy.ndarray): The nodes in the second curve.
Returns:
numpy.ndarray: ``Nx2`` array of intersection parameters.
Each row contains a pair of values :math:`s` and :math:`t`
(each in :math:`\left[0, 1\right]`) such that the curves
intersect: :math:`B_1(s) = B_2(t)`.
Raises:
NotImplementedError: If the "intersection polynomial" is
all zeros -- which indicates coincident curves.
"""
nodes1 = _curve_helpers.full_reduce(nodes1)
nodes2 = _curve_helpers.full_reduce(nodes2)
num_nodes1, _ = nodes1.shape
num_nodes2, _ = nodes2.shape
swapped = False
if num_nodes1 > num_nodes2:
nodes1, nodes2 = nodes2, nodes1
swapped = True
coeffs = normalize_polynomial(to_power_basis(nodes1, nodes2))
if np.all(coeffs == 0.0):
raise NotImplementedError(_COINCIDENT_ERR)
_check_non_simple(coeffs)
t_vals = roots_in_unit_interval(coeffs)
final_s = []
final_t = []
for t_val in t_vals:
(x_val,
y_val), = _curve_helpers.evaluate_multi(nodes2,
np.asfortranarray([t_val]))
s_val = locate_point(nodes1, x_val, y_val)
if s_val is not None:
_resolve_and_add(nodes1, s_val, final_s, nodes2, t_val, final_t)
result = np.zeros((len(final_s), 2), order='F')
if swapped:
final_s, final_t = final_t, final_s
result[:, 0] = final_s
result[:, 1] = final_t
return result
def locate_point(nodes, x_val, y_val):
r"""Find the parameter corresponding to a point on a curve.
.. note::
This assumes that the curve :math:`B(s, t)` defined by ``nodes``
lives in :math:`\mathbf{R}^2`.
Args:
nodes (numpy.ndarray): The nodes defining a B |eacute| zier curve.
x_val (float): The :math:`x`-coordinate of the point.
y_val (float): The :math:`y`-coordinate of the point.
Returns:
Optional[float]: The parameter on the curve (if it exists).
"""
# First, reduce to the true degree of x(s) and y(s).
zero1 = _curve_helpers.full_reduce(nodes[:, [0]]) - x_val
zero2 = _curve_helpers.full_reduce(nodes[:, [1]]) - y_val
# Make sure we have the lowest degree in front, to make the polynomial
# solve have the fewest number of roots.
if zero1.shape[0] > zero2.shape[0]:
zero1, zero2 = zero2, zero1
# If the "smallest" is a constant, we can't find any roots from it.
if zero1.shape[0] == 1:
# NOTE: We assume that callers won't pass ``nodes`` that are
# degree 0, so if ``zero1`` is a constant, ``zero2`` won't be.
zero1, zero2 = zero2, zero1
power_basis1 = poly_to_power_basis(zero1[:, 0])
all_roots = roots_in_unit_interval(power_basis1)
if all_roots.size == 0:
return
# NOTE: We normalize ``power_basis2`` because we want to check for
# "zero" values, i.e. f2(s) == 0.
power_basis2 = normalize_polynomial(poly_to_power_basis(zero2[:, 0]))
near_zero = np.abs(polynomial.polyval(all_roots, power_basis2))
index = np.argmin(near_zero)
if near_zero[index] < _ZERO_THRESHOLD:
return all_roots[index]
def _call_function_under_test(nodes):
from bezier import _curve_helpers
return _curve_helpers.full_reduce(nodes)
