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dhermes/bezier | docs/make_images.py | surface_subdivide1 | def surface_subdivide1():
"""Image for :meth`.Surface.subdivide` docstring."""
if NO_IMAGES:
return
surface = bezier.Surface.from_nodes(
np.asfortranarray([[0.0, 1.0, 0.0], [0.0, 0.0, 1.0]])
)
surf_a, surf_b, surf_c, surf_d = surface.subdivide()
figure, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)
for ax in (ax1, ax2, ax3, ax4):
surface.plot(2, ax=ax)
surf_a.plot(2, ax=ax1)
ax1.text(
1.0 / 6.0,
1.0 / 6.0,
r"$A$",
fontsize=20,
verticalalignment="center",
horizontalalignment="center",
)
surf_b.plot(2, ax=ax2)
ax2.text(
1.0 / 3.0,
1.0 / 3.0,
r"$B$",
fontsize=20,
verticalalignment="center",
horizontalalignment="center",
)
surf_c.plot(2, ax=ax3)
ax3.text(
2.0 / 3.0,
1.0 / 6.0,
r"$C$",
fontsize=20,
verticalalignment="center",
horizontalalignment="center",
)
surf_d.plot(2, ax=ax4)
ax4.text(
1.0 / 6.0,
2.0 / 3.0,
r"$D$",
fontsize=20,
verticalalignment="center",
horizontalalignment="center",
)
for ax in (ax1, ax2, ax3, ax4):
ax.axis("scaled")
save_image(figure, "surface_subdivide1") | python | def surface_subdivide1():
"""Image for :meth`.Surface.subdivide` docstring."""
if NO_IMAGES:
return
surface = bezier.Surface.from_nodes(
np.asfortranarray([[0.0, 1.0, 0.0], [0.0, 0.0, 1.0]])
)
surf_a, surf_b, surf_c, surf_d = surface.subdivide()
figure, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)
for ax in (ax1, ax2, ax3, ax4):
surface.plot(2, ax=ax)
surf_a.plot(2, ax=ax1)
ax1.text(
1.0 / 6.0,
1.0 / 6.0,
r"$A$",
fontsize=20,
verticalalignment="center",
horizontalalignment="center",
)
surf_b.plot(2, ax=ax2)
ax2.text(
1.0 / 3.0,
1.0 / 3.0,
r"$B$",
fontsize=20,
verticalalignment="center",
horizontalalignment="center",
)
surf_c.plot(2, ax=ax3)
ax3.text(
2.0 / 3.0,
1.0 / 6.0,
r"$C$",
fontsize=20,
verticalalignment="center",
horizontalalignment="center",
)
surf_d.plot(2, ax=ax4)
ax4.text(
1.0 / 6.0,
2.0 / 3.0,
r"$D$",
fontsize=20,
verticalalignment="center",
horizontalalignment="center",
)
for ax in (ax1, ax2, ax3, ax4):
ax.axis("scaled")
save_image(figure, "surface_subdivide1") | [
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dhermes/bezier | docs/make_images.py | surface_subdivide2 | def surface_subdivide2(surface, sub_surface_b):
"""Image for :meth`.Surface.subdivide` docstring."""
if NO_IMAGES:
return
# Plot set-up.
figure = plt.figure()
ax = figure.gca()
colors = seaborn.husl_palette(6)
N = 128
s_vals = np.linspace(0.0, 1.0, N + 1)
# Add edges from surface.
add_edges(ax, surface, s_vals, colors[4])
# Now do the same for surface B.
add_edges(ax, sub_surface_b, s_vals, colors[0])
# Add the control points polygon for the original surface.
nodes = surface._nodes[:, (0, 2, 4, 5, 0)]
add_patch(ax, nodes, colors[2], with_nodes=False)
# Add the control points polygon for the sub-surface.
nodes = sub_surface_b._nodes[:, (0, 1, 2, 5, 3, 0)]
add_patch(ax, nodes, colors[1], with_nodes=False)
# Plot **all** the nodes.
sub_nodes = sub_surface_b._nodes
ax.plot(
sub_nodes[0, :],
sub_nodes[1, :],
color="black",
linestyle="None",
marker="o",
)
# Take those same points and add the boundary.
ax.plot(nodes[0, :], nodes[1, :], color="black", linestyle="dashed")
ax.axis("scaled")
ax.set_xlim(-1.125, 2.125)
ax.set_ylim(-0.125, 4.125)
save_image(ax.figure, "surface_subdivide2") | python | def surface_subdivide2(surface, sub_surface_b):
"""Image for :meth`.Surface.subdivide` docstring."""
if NO_IMAGES:
return
# Plot set-up.
figure = plt.figure()
ax = figure.gca()
colors = seaborn.husl_palette(6)
N = 128
s_vals = np.linspace(0.0, 1.0, N + 1)
# Add edges from surface.
add_edges(ax, surface, s_vals, colors[4])
# Now do the same for surface B.
add_edges(ax, sub_surface_b, s_vals, colors[0])
# Add the control points polygon for the original surface.
nodes = surface._nodes[:, (0, 2, 4, 5, 0)]
add_patch(ax, nodes, colors[2], with_nodes=False)
# Add the control points polygon for the sub-surface.
nodes = sub_surface_b._nodes[:, (0, 1, 2, 5, 3, 0)]
add_patch(ax, nodes, colors[1], with_nodes=False)
# Plot **all** the nodes.
sub_nodes = sub_surface_b._nodes
ax.plot(
sub_nodes[0, :],
sub_nodes[1, :],
color="black",
linestyle="None",
marker="o",
)
# Take those same points and add the boundary.
ax.plot(nodes[0, :], nodes[1, :], color="black", linestyle="dashed")
ax.axis("scaled")
ax.set_xlim(-1.125, 2.125)
ax.set_ylim(-0.125, 4.125)
save_image(ax.figure, "surface_subdivide2") | [
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dhermes/bezier | docs/make_images.py | curved_polygon_constructor1 | def curved_polygon_constructor1(curved_poly):
"""Image for :class`.CurvedPolygon` docstring."""
if NO_IMAGES:
return
ax = curved_poly.plot(256)
ax.axis("scaled")
ax.set_xlim(-0.125, 2.125)
ax.set_ylim(-0.625, 1.625)
save_image(ax.figure, "curved_polygon_constructor1.png") | python | def curved_polygon_constructor1(curved_poly):
"""Image for :class`.CurvedPolygon` docstring."""
if NO_IMAGES:
return
ax = curved_poly.plot(256)
ax.axis("scaled")
ax.set_xlim(-0.125, 2.125)
ax.set_ylim(-0.625, 1.625)
save_image(ax.figure, "curved_polygon_constructor1.png") | [
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dhermes/bezier | docs/make_images.py | curve_specialize | def curve_specialize(curve, new_curve):
"""Image for :meth`.Curve.specialize` docstring."""
if NO_IMAGES:
return
ax = curve.plot(256)
interval = r"$\left[0, 1\right]$"
line = ax.lines[-1]
line.set_label(interval)
color1 = line.get_color()
new_curve.plot(256, ax=ax)
interval = r"$\left[-\frac{1}{4}, \frac{3}{4}\right]$"
line = ax.lines[-1]
line.set_label(interval)
ax.plot(
curve._nodes[0, (0, -1)],
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color=color1,
linestyle="None",
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ax.plot(
new_curve._nodes[0, (0, -1)],
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color=line.get_color(),
linestyle="None",
marker="o",
)
ax.legend(loc="lower right", fontsize=12)
ax.axis("scaled")
ax.set_xlim(-0.375, 1.125)
ax.set_ylim(-0.75, 0.625)
save_image(ax.figure, "curve_specialize.png") | python | def curve_specialize(curve, new_curve):
"""Image for :meth`.Curve.specialize` docstring."""
if NO_IMAGES:
return
ax = curve.plot(256)
interval = r"$\left[0, 1\right]$"
line = ax.lines[-1]
line.set_label(interval)
color1 = line.get_color()
new_curve.plot(256, ax=ax)
interval = r"$\left[-\frac{1}{4}, \frac{3}{4}\right]$"
line = ax.lines[-1]
line.set_label(interval)
ax.plot(
curve._nodes[0, (0, -1)],
curve._nodes[1, (0, -1)],
color=color1,
linestyle="None",
marker="o",
)
ax.plot(
new_curve._nodes[0, (0, -1)],
new_curve._nodes[1, (0, -1)],
color=line.get_color(),
linestyle="None",
marker="o",
)
ax.legend(loc="lower right", fontsize=12)
ax.axis("scaled")
ax.set_xlim(-0.375, 1.125)
ax.set_ylim(-0.75, 0.625)
save_image(ax.figure, "curve_specialize.png") | [
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dhermes/bezier | docs/make_images.py | newton_refine_surface | def newton_refine_surface(surface, x_val, y_val, s, t, new_s, new_t):
"""Image for :func:`._surface_helpers.newton_refine` docstring."""
if NO_IMAGES:
return
figure, (ax1, ax2) = plt.subplots(1, 2)
# Plot features of the parameter space in ax1.
tri_surf = bezier.Surface.from_nodes(
np.asfortranarray([[0.0, 1.0, 0.0], [0.0, 0.0, 1.0]])
)
tri_surf.plot(2, ax=ax1)
ax1.plot([0.25], [0.5], marker="H")
ax1.plot([s], [t], color="black", linestyle="None", marker="o")
ax1.plot(
[new_s],
[new_t],
color="black",
linestyle="None",
marker="o",
markeredgewidth=1,
markerfacecolor="None",
)
# Plot the equivalent output in ax2.
surface.plot(256, ax=ax2)
points = surface.evaluate_cartesian_multi(
np.asfortranarray([[s, t], [new_s, new_t]])
)
ax2.plot([x_val], [y_val], marker="H")
ax2.plot(
points[0, [0]],
points[1, [0]],
color="black",
linestyle="None",
marker="o",
)
ax2.plot(
points[0, [1]],
points[1, [1]],
color="black",
linestyle="None",
marker="o",
markeredgewidth=1,
markerfacecolor="None",
)
# Set the axis bounds / scaling.
ax1.axis("scaled")
ax1.set_xlim(-0.0625, 1.0625)
ax1.set_ylim(-0.0625, 1.0625)
ax2.axis("scaled")
ax2.set_xlim(-0.125, 2.125)
ax2.set_ylim(-0.125, 2.125)
save_image(figure, "newton_refine_surface.png") | python | def newton_refine_surface(surface, x_val, y_val, s, t, new_s, new_t):
"""Image for :func:`._surface_helpers.newton_refine` docstring."""
if NO_IMAGES:
return
figure, (ax1, ax2) = plt.subplots(1, 2)
# Plot features of the parameter space in ax1.
tri_surf = bezier.Surface.from_nodes(
np.asfortranarray([[0.0, 1.0, 0.0], [0.0, 0.0, 1.0]])
)
tri_surf.plot(2, ax=ax1)
ax1.plot([0.25], [0.5], marker="H")
ax1.plot([s], [t], color="black", linestyle="None", marker="o")
ax1.plot(
[new_s],
[new_t],
color="black",
linestyle="None",
marker="o",
markeredgewidth=1,
markerfacecolor="None",
)
# Plot the equivalent output in ax2.
surface.plot(256, ax=ax2)
points = surface.evaluate_cartesian_multi(
np.asfortranarray([[s, t], [new_s, new_t]])
)
ax2.plot([x_val], [y_val], marker="H")
ax2.plot(
points[0, [0]],
points[1, [0]],
color="black",
linestyle="None",
marker="o",
)
ax2.plot(
points[0, [1]],
points[1, [1]],
color="black",
linestyle="None",
marker="o",
markeredgewidth=1,
markerfacecolor="None",
)
# Set the axis bounds / scaling.
ax1.axis("scaled")
ax1.set_xlim(-0.0625, 1.0625)
ax1.set_ylim(-0.0625, 1.0625)
ax2.axis("scaled")
ax2.set_xlim(-0.125, 2.125)
ax2.set_ylim(-0.125, 2.125)
save_image(figure, "newton_refine_surface.png") | [
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dhermes/bezier | docs/make_images.py | classify_intersection1 | def classify_intersection1(s, curve1, tangent1, curve2, tangent2):
"""Image for :func:`._surface_helpers.classify_intersection` docstring."""
if NO_IMAGES:
return
surface1 = bezier.Surface.from_nodes(
np.asfortranarray(
[[1.0, 1.75, 2.0, 1.0, 1.5, 1.0], [0.0, 0.25, 1.0, 1.0, 1.5, 2.0]]
)
)
surface2 = bezier.Surface.from_nodes(
np.asfortranarray(
[
[0.0, 1.6875, 2.0, 0.25, 1.25, 0.5],
[0.0, 0.0625, 0.5, 1.0, 1.25, 2.0],
]
)
)
ax = classify_help(s, curve1, surface1, curve2, surface2, 0)
(int_x,), (int_y,) = curve1.evaluate(s)
# Remove the alpha from the color
color1 = ax.patches[0].get_facecolor()[:3]
color2 = ax.patches[1].get_facecolor()[:3]
ax.plot(
[int_x, int_x + tangent1[0, 0]],
[int_y, int_y + tangent1[1, 0]],
color=color1,
linestyle="dashed",
)
ax.plot(
[int_x, int_x + tangent2[0, 0]],
[int_y, int_y + tangent2[1, 0]],
color=color2,
linestyle="dashed",
)
ax.plot([int_x], [int_y], color=color1, linestyle="None", marker="o")
ax.axis("scaled")
ax.set_xlim(-0.125, 2.125)
ax.set_ylim(-0.125, 1.125)
save_image(ax.figure, "classify_intersection1.png") | python | def classify_intersection1(s, curve1, tangent1, curve2, tangent2):
"""Image for :func:`._surface_helpers.classify_intersection` docstring."""
if NO_IMAGES:
return
surface1 = bezier.Surface.from_nodes(
np.asfortranarray(
[[1.0, 1.75, 2.0, 1.0, 1.5, 1.0], [0.0, 0.25, 1.0, 1.0, 1.5, 2.0]]
)
)
surface2 = bezier.Surface.from_nodes(
np.asfortranarray(
[
[0.0, 1.6875, 2.0, 0.25, 1.25, 0.5],
[0.0, 0.0625, 0.5, 1.0, 1.25, 2.0],
]
)
)
ax = classify_help(s, curve1, surface1, curve2, surface2, 0)
(int_x,), (int_y,) = curve1.evaluate(s)
# Remove the alpha from the color
color1 = ax.patches[0].get_facecolor()[:3]
color2 = ax.patches[1].get_facecolor()[:3]
ax.plot(
[int_x, int_x + tangent1[0, 0]],
[int_y, int_y + tangent1[1, 0]],
color=color1,
linestyle="dashed",
)
ax.plot(
[int_x, int_x + tangent2[0, 0]],
[int_y, int_y + tangent2[1, 0]],
color=color2,
linestyle="dashed",
)
ax.plot([int_x], [int_y], color=color1, linestyle="None", marker="o")
ax.axis("scaled")
ax.set_xlim(-0.125, 2.125)
ax.set_ylim(-0.125, 1.125)
save_image(ax.figure, "classify_intersection1.png") | [
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dhermes/bezier | docs/make_images.py | classify_intersection2 | def classify_intersection2(s, curve1, curve2):
"""Image for :func:`._surface_helpers.classify_intersection` docstring."""
if NO_IMAGES:
return
surface1 = bezier.Surface.from_nodes(
np.asfortranarray(
[[1.0, 1.5, 2.0, 1.25, 1.75, 1.5], [0.0, 1.0, 0.0, 1.0, 1.0, 2.0]]
)
)
surface2 = bezier.Surface.from_nodes(
np.asfortranarray(
[[0.0, 1.5, 3.0, 0.75, 2.25, 1.5], [0.0, 1.0, 0.0, 2.0, 2.0, 4.0]]
)
)
ax = classify_help(s, curve1, surface1, curve2, surface2, 1)
ax.set_xlim(-0.0625, 3.0625)
ax.set_ylim(-0.0625, 0.5625)
save_image(ax.figure, "classify_intersection2.png") | python | def classify_intersection2(s, curve1, curve2):
"""Image for :func:`._surface_helpers.classify_intersection` docstring."""
if NO_IMAGES:
return
surface1 = bezier.Surface.from_nodes(
np.asfortranarray(
[[1.0, 1.5, 2.0, 1.25, 1.75, 1.5], [0.0, 1.0, 0.0, 1.0, 1.0, 2.0]]
)
)
surface2 = bezier.Surface.from_nodes(
np.asfortranarray(
[[0.0, 1.5, 3.0, 0.75, 2.25, 1.5], [0.0, 1.0, 0.0, 2.0, 2.0, 4.0]]
)
)
ax = classify_help(s, curve1, surface1, curve2, surface2, 1)
ax.set_xlim(-0.0625, 3.0625)
ax.set_ylim(-0.0625, 0.5625)
save_image(ax.figure, "classify_intersection2.png") | [
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dhermes/bezier | docs/make_images.py | classify_intersection5 | def classify_intersection5(s, curve1, curve2):
"""Image for :func:`._surface_helpers.classify_intersection` docstring."""
if NO_IMAGES:
return
surface1 = bezier.Surface.from_nodes(
np.asfortranarray(
[
[1.0, 1.5, 2.0, 1.25, 1.75, 1.5],
[0.0, 1.0, 0.0, 0.9375, 0.9375, 1.875],
]
)
)
surface2 = bezier.Surface.from_nodes(
np.asfortranarray(
[
[3.0, 1.5, 0.0, 2.25, 0.75, 1.5],
[0.0, 1.0, 0.0, -2.0, -2.0, -4.0],
]
)
)
figure, (ax1, ax2) = plt.subplots(2, 1)
classify_help(s, curve1, surface1, curve2, surface2, 0, ax=ax1)
classify_help(s, curve1, surface1, curve2, surface2, 1, ax=ax2)
# Remove the alpha from the color
color1 = ax1.patches[0].get_facecolor()[:3]
color2 = ax1.patches[1].get_facecolor()[:3]
# Now add the "degenerate" intersection polygons. The first
# comes from specializing to
# left1(0.5, 1.0)-left2(0.0, 0.25)-right1(0.375, 0.5)
surface3 = bezier.Surface.from_nodes(
np.asfortranarray(
[
[1.5, 1.75, 2.0, 1.6875, 1.9375, 1.875],
[0.5, 0.5, 0.0, 0.5, 0.234375, 0.46875],
]
)
)
# NOTE: We don't require the intersection polygon be valid.
surface3.plot(256, ax=ax1)
# The second comes from specializing to
# left1(0.0, 0.5)-right1(0.5, 0.625)-left3(0.75, 1.0)
surface4 = bezier.Surface.from_nodes(
np.asfortranarray(
[
[1.0, 1.25, 1.5, 1.0625, 1.3125, 1.125],
[0.0, 0.5, 0.5, 0.234375, 0.5, 0.46875],
]
)
)
# NOTE: We don't require the intersection polygon be valid.
surface4.plot(256, ax=ax2)
(int_x,), (int_y,) = curve1.evaluate(s)
ax1.plot([int_x], [int_y], color=color1, linestyle="None", marker="o")
ax2.plot([int_x], [int_y], color=color2, linestyle="None", marker="o")
for ax in (ax1, ax2):
ax.axis("scaled")
ax.set_xlim(-0.0625, 3.0625)
ax.set_ylim(-0.0625, 0.5625)
plt.setp(ax1.get_xticklabels(), visible=False)
figure.tight_layout(h_pad=-7.0)
save_image(figure, "classify_intersection5.png") | python | def classify_intersection5(s, curve1, curve2):
"""Image for :func:`._surface_helpers.classify_intersection` docstring."""
if NO_IMAGES:
return
surface1 = bezier.Surface.from_nodes(
np.asfortranarray(
[
[1.0, 1.5, 2.0, 1.25, 1.75, 1.5],
[0.0, 1.0, 0.0, 0.9375, 0.9375, 1.875],
]
)
)
surface2 = bezier.Surface.from_nodes(
np.asfortranarray(
[
[3.0, 1.5, 0.0, 2.25, 0.75, 1.5],
[0.0, 1.0, 0.0, -2.0, -2.0, -4.0],
]
)
)
figure, (ax1, ax2) = plt.subplots(2, 1)
classify_help(s, curve1, surface1, curve2, surface2, 0, ax=ax1)
classify_help(s, curve1, surface1, curve2, surface2, 1, ax=ax2)
# Remove the alpha from the color
color1 = ax1.patches[0].get_facecolor()[:3]
color2 = ax1.patches[1].get_facecolor()[:3]
# Now add the "degenerate" intersection polygons. The first
# comes from specializing to
# left1(0.5, 1.0)-left2(0.0, 0.25)-right1(0.375, 0.5)
surface3 = bezier.Surface.from_nodes(
np.asfortranarray(
[
[1.5, 1.75, 2.0, 1.6875, 1.9375, 1.875],
[0.5, 0.5, 0.0, 0.5, 0.234375, 0.46875],
]
)
)
# NOTE: We don't require the intersection polygon be valid.
surface3.plot(256, ax=ax1)
# The second comes from specializing to
# left1(0.0, 0.5)-right1(0.5, 0.625)-left3(0.75, 1.0)
surface4 = bezier.Surface.from_nodes(
np.asfortranarray(
[
[1.0, 1.25, 1.5, 1.0625, 1.3125, 1.125],
[0.0, 0.5, 0.5, 0.234375, 0.5, 0.46875],
]
)
)
# NOTE: We don't require the intersection polygon be valid.
surface4.plot(256, ax=ax2)
(int_x,), (int_y,) = curve1.evaluate(s)
ax1.plot([int_x], [int_y], color=color1, linestyle="None", marker="o")
ax2.plot([int_x], [int_y], color=color2, linestyle="None", marker="o")
for ax in (ax1, ax2):
ax.axis("scaled")
ax.set_xlim(-0.0625, 3.0625)
ax.set_ylim(-0.0625, 0.5625)
plt.setp(ax1.get_xticklabels(), visible=False)
figure.tight_layout(h_pad=-7.0)
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dhermes/bezier | docs/make_images.py | classify_intersection7 | def classify_intersection7(s, curve1a, curve1b, curve2):
"""Image for :func:`._surface_helpers.classify_intersection` docstring."""
if NO_IMAGES:
return
surface1 = bezier.Surface.from_nodes(
np.asfortranarray(
[
[0.0, 4.5, 9.0, 0.0, 4.5, 0.0],
[0.0, 0.0, 2.25, 1.25, 2.375, 2.5],
]
)
)
surface2 = bezier.Surface.from_nodes(
np.asfortranarray(
[
[11.25, 9.0, 2.75, 8.125, 3.875, 5.0],
[0.0, 4.5, 1.0, -0.75, -0.25, -1.5],
]
)
)
figure, (ax1, ax2) = plt.subplots(2, 1)
classify_help(s, curve1a, surface1, curve2, surface2, None, ax=ax1)
surface1._nodes = np.asfortranarray(surface1._nodes[:, (2, 4, 5, 1, 3, 0)])
surface1._edges = None
classify_help(0.0, curve1b, surface1, curve2, surface2, 0, ax=ax2)
for ax in (ax1, ax2):
ax.set_xlim(-0.125, 11.5)
ax.set_ylim(-0.125, 2.625)
plt.setp(ax1.get_xticklabels(), visible=False)
figure.tight_layout(h_pad=-5.0)
save_image(figure, "classify_intersection7.png") | python | def classify_intersection7(s, curve1a, curve1b, curve2):
"""Image for :func:`._surface_helpers.classify_intersection` docstring."""
if NO_IMAGES:
return
surface1 = bezier.Surface.from_nodes(
np.asfortranarray(
[
[0.0, 4.5, 9.0, 0.0, 4.5, 0.0],
[0.0, 0.0, 2.25, 1.25, 2.375, 2.5],
]
)
)
surface2 = bezier.Surface.from_nodes(
np.asfortranarray(
[
[11.25, 9.0, 2.75, 8.125, 3.875, 5.0],
[0.0, 4.5, 1.0, -0.75, -0.25, -1.5],
]
)
)
figure, (ax1, ax2) = plt.subplots(2, 1)
classify_help(s, curve1a, surface1, curve2, surface2, None, ax=ax1)
surface1._nodes = np.asfortranarray(surface1._nodes[:, (2, 4, 5, 1, 3, 0)])
surface1._edges = None
classify_help(0.0, curve1b, surface1, curve2, surface2, 0, ax=ax2)
for ax in (ax1, ax2):
ax.set_xlim(-0.125, 11.5)
ax.set_ylim(-0.125, 2.625)
plt.setp(ax1.get_xticklabels(), visible=False)
figure.tight_layout(h_pad=-5.0)
save_image(figure, "classify_intersection7.png") | [
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dhermes/bezier | docs/make_images.py | get_curvature | def get_curvature(nodes, s, tangent_vec, curvature):
"""Image for :func:`get_curvature` docstring."""
if NO_IMAGES:
return
curve = bezier.Curve.from_nodes(nodes)
# Find the center of the circle along the direction
# perpendicular to the tangent vector (90 degree left turn).
radius_dir = np.asfortranarray([[-tangent_vec[1, 0]], [tangent_vec[0, 0]]])
radius_dir /= np.linalg.norm(radius_dir, ord=2)
point = curve.evaluate(s)
circle_center = point + radius_dir / curvature
# Add the curve.
ax = curve.plot(256)
# Add the circle.
circle_center = circle_center.ravel(order="F")
circle = plt.Circle(circle_center, 1.0 / abs(curvature), alpha=0.25)
ax.add_artist(circle)
# Add the point.
ax.plot(
point[0, :], point[1, :], color="black", marker="o", linestyle="None"
)
ax.axis("scaled")
ax.set_xlim(-0.0625, 1.0625)
ax.set_ylim(-0.0625, 0.625)
save_image(ax.figure, "get_curvature.png") | python | def get_curvature(nodes, s, tangent_vec, curvature):
"""Image for :func:`get_curvature` docstring."""
if NO_IMAGES:
return
curve = bezier.Curve.from_nodes(nodes)
# Find the center of the circle along the direction
# perpendicular to the tangent vector (90 degree left turn).
radius_dir = np.asfortranarray([[-tangent_vec[1, 0]], [tangent_vec[0, 0]]])
radius_dir /= np.linalg.norm(radius_dir, ord=2)
point = curve.evaluate(s)
circle_center = point + radius_dir / curvature
# Add the curve.
ax = curve.plot(256)
# Add the circle.
circle_center = circle_center.ravel(order="F")
circle = plt.Circle(circle_center, 1.0 / abs(curvature), alpha=0.25)
ax.add_artist(circle)
# Add the point.
ax.plot(
point[0, :], point[1, :], color="black", marker="o", linestyle="None"
)
ax.axis("scaled")
ax.set_xlim(-0.0625, 1.0625)
ax.set_ylim(-0.0625, 0.625)
save_image(ax.figure, "get_curvature.png") | [
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dhermes/bezier | docs/make_images.py | curve_locate | def curve_locate(curve, point1, point2, point3):
"""Image for :meth`.Curve.locate` docstring."""
if NO_IMAGES:
return
ax = curve.plot(256)
points = np.hstack([point1, point2, point3])
ax.plot(
points[0, :], points[1, :], color="black", linestyle="None", marker="o"
)
ax.axis("scaled")
ax.set_xlim(-0.8125, 0.0625)
ax.set_ylim(0.75, 2.0625)
save_image(ax.figure, "curve_locate.png") | python | def curve_locate(curve, point1, point2, point3):
"""Image for :meth`.Curve.locate` docstring."""
if NO_IMAGES:
return
ax = curve.plot(256)
points = np.hstack([point1, point2, point3])
ax.plot(
points[0, :], points[1, :], color="black", linestyle="None", marker="o"
)
ax.axis("scaled")
ax.set_xlim(-0.8125, 0.0625)
ax.set_ylim(0.75, 2.0625)
save_image(ax.figure, "curve_locate.png") | [
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dhermes/bezier | docs/make_images.py | newton_refine_curve | def newton_refine_curve(curve, point, s, new_s):
"""Image for :func:`._curve_helpers.newton_refine` docstring."""
if NO_IMAGES:
return
ax = curve.plot(256)
ax.plot(point[0, :], point[1, :], marker="H")
wrong_points = curve.evaluate_multi(np.asfortranarray([s, new_s]))
ax.plot(
wrong_points[0, [0]],
wrong_points[1, [0]],
color="black",
linestyle="None",
marker="o",
)
ax.plot(
wrong_points[0, [1]],
wrong_points[1, [1]],
color="black",
linestyle="None",
marker="o",
markeredgewidth=1,
markerfacecolor="None",
)
# Set the axis bounds / scaling.
ax.axis("scaled")
ax.set_xlim(-0.125, 3.125)
ax.set_ylim(-0.125, 1.375)
save_image(ax.figure, "newton_refine_curve.png") | python | def newton_refine_curve(curve, point, s, new_s):
"""Image for :func:`._curve_helpers.newton_refine` docstring."""
if NO_IMAGES:
return
ax = curve.plot(256)
ax.plot(point[0, :], point[1, :], marker="H")
wrong_points = curve.evaluate_multi(np.asfortranarray([s, new_s]))
ax.plot(
wrong_points[0, [0]],
wrong_points[1, [0]],
color="black",
linestyle="None",
marker="o",
)
ax.plot(
wrong_points[0, [1]],
wrong_points[1, [1]],
color="black",
linestyle="None",
marker="o",
markeredgewidth=1,
markerfacecolor="None",
)
# Set the axis bounds / scaling.
ax.axis("scaled")
ax.set_xlim(-0.125, 3.125)
ax.set_ylim(-0.125, 1.375)
save_image(ax.figure, "newton_refine_curve.png") | [
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dhermes/bezier | docs/make_images.py | newton_refine_curve_cusp | def newton_refine_curve_cusp(curve, s_vals):
"""Image for :func:`._curve_helpers.newton_refine` docstring."""
if NO_IMAGES:
return
ax = curve.plot(256)
ax.lines[-1].zorder = 1
points = curve.evaluate_multi(np.asfortranarray(s_vals))
colors = seaborn.dark_palette("blue", 6)
ax.scatter(
points[0, :], points[1, :], c=colors, s=20, alpha=0.75, zorder=2
)
# Set the axis bounds / scaling.
ax.axis("scaled")
ax.set_xlim(-0.125, 6.125)
ax.set_ylim(-3.125, 3.125)
save_image(ax.figure, "newton_refine_curve_cusp.png") | python | def newton_refine_curve_cusp(curve, s_vals):
"""Image for :func:`._curve_helpers.newton_refine` docstring."""
if NO_IMAGES:
return
ax = curve.plot(256)
ax.lines[-1].zorder = 1
points = curve.evaluate_multi(np.asfortranarray(s_vals))
colors = seaborn.dark_palette("blue", 6)
ax.scatter(
points[0, :], points[1, :], c=colors, s=20, alpha=0.75, zorder=2
)
# Set the axis bounds / scaling.
ax.axis("scaled")
ax.set_xlim(-0.125, 6.125)
ax.set_ylim(-3.125, 3.125)
save_image(ax.figure, "newton_refine_curve_cusp.png") | [
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dhermes/bezier | docs/make_images.py | classify_intersection8 | def classify_intersection8(s, curve1, surface1, curve2, surface2):
"""Image for :func:`._surface_helpers.classify_intersection` docstring."""
if NO_IMAGES:
return
ax = classify_help(s, curve1, surface1, curve2, surface2, None)
ax.set_xlim(-1.125, 1.125)
ax.set_ylim(-0.125, 1.125)
save_image(ax.figure, "classify_intersection8.png") | python | def classify_intersection8(s, curve1, surface1, curve2, surface2):
"""Image for :func:`._surface_helpers.classify_intersection` docstring."""
if NO_IMAGES:
return
ax = classify_help(s, curve1, surface1, curve2, surface2, None)
ax.set_xlim(-1.125, 1.125)
ax.set_ylim(-0.125, 1.125)
save_image(ax.figure, "classify_intersection8.png") | [
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dhermes/bezier | docs/make_images.py | _edges_classify_intersection9 | def _edges_classify_intersection9():
"""The edges for the curved polygon intersection used below.
Helper for :func:`classify_intersection9`.
"""
edges1 = (
bezier.Curve.from_nodes(
np.asfortranarray([[32.0, 30.0], [20.0, 25.0]])
),
bezier.Curve.from_nodes(
np.asfortranarray([[30.0, 25.0, 20.0], [25.0, 20.0, 20.0]])
),
bezier.Curve.from_nodes(
np.asfortranarray([[20.0, 25.0, 30.0], [20.0, 20.0, 15.0]])
),
bezier.Curve.from_nodes(
np.asfortranarray([[30.0, 32.0], [15.0, 20.0]])
),
)
edges2 = (
bezier.Curve.from_nodes(
np.asfortranarray([[8.0, 10.0], [20.0, 15.0]])
),
bezier.Curve.from_nodes(
np.asfortranarray([[10.0, 15.0, 20.0], [15.0, 20.0, 20.0]])
),
bezier.Curve.from_nodes(
np.asfortranarray([[20.0, 15.0, 10.0], [20.0, 20.0, 25.0]])
),
bezier.Curve.from_nodes(
np.asfortranarray([[10.0, 8.0], [25.0, 20.0]])
),
)
return edges1, edges2 | python | def _edges_classify_intersection9():
"""The edges for the curved polygon intersection used below.
Helper for :func:`classify_intersection9`.
"""
edges1 = (
bezier.Curve.from_nodes(
np.asfortranarray([[32.0, 30.0], [20.0, 25.0]])
),
bezier.Curve.from_nodes(
np.asfortranarray([[30.0, 25.0, 20.0], [25.0, 20.0, 20.0]])
),
bezier.Curve.from_nodes(
np.asfortranarray([[20.0, 25.0, 30.0], [20.0, 20.0, 15.0]])
),
bezier.Curve.from_nodes(
np.asfortranarray([[30.0, 32.0], [15.0, 20.0]])
),
)
edges2 = (
bezier.Curve.from_nodes(
np.asfortranarray([[8.0, 10.0], [20.0, 15.0]])
),
bezier.Curve.from_nodes(
np.asfortranarray([[10.0, 15.0, 20.0], [15.0, 20.0, 20.0]])
),
bezier.Curve.from_nodes(
np.asfortranarray([[20.0, 15.0, 10.0], [20.0, 20.0, 25.0]])
),
bezier.Curve.from_nodes(
np.asfortranarray([[10.0, 8.0], [25.0, 20.0]])
),
)
return edges1, edges2 | [
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dhermes/bezier | docs/make_images.py | classify_intersection9 | def classify_intersection9(s, curve1, curve2):
"""Image for :func:`._surface_helpers.classify_intersection` docstring."""
if NO_IMAGES:
return
surface1 = bezier.Surface.from_nodes(
np.asfortranarray(
[
[0.0, 20.0, 40.0, 10.0, 30.0, 20.0],
[0.0, 40.0, 0.0, 25.0, 25.0, 50.0],
]
)
)
surface2 = bezier.Surface.from_nodes(
np.asfortranarray(
[
[40.0, 20.0, 0.0, 30.0, 10.0, 20.0],
[40.0, 0.0, 40.0, 15.0, 15.0, -10.0],
]
)
)
figure, (ax1, ax2) = plt.subplots(1, 2)
classify_help(s, curve1, surface1, curve2, surface2, 0, ax=ax1)
classify_help(s, curve1, surface1, curve2, surface2, 1, ax=ax2)
# Remove the alpha from the color
color1 = ax1.patches[0].get_facecolor()[:3]
color2 = ax1.patches[1].get_facecolor()[:3]
# Now add the "degenerate" intersection polygons.
cp_edges1, cp_edges2 = _edges_classify_intersection9()
curved_polygon1 = bezier.CurvedPolygon(*cp_edges1)
curved_polygon1.plot(256, ax=ax1)
curved_polygon2 = bezier.CurvedPolygon(*cp_edges2)
curved_polygon2.plot(256, ax=ax2)
(int_x,), (int_y,) = curve1.evaluate(s)
ax1.plot([int_x], [int_y], color=color1, linestyle="None", marker="o")
ax2.plot([int_x], [int_y], color=color2, linestyle="None", marker="o")
for ax in (ax1, ax2):
ax.axis("scaled")
ax.set_xlim(-2.0, 42.0)
ax.set_ylim(-12.0, 52.0)
plt.setp(ax2.get_yticklabels(), visible=False)
figure.tight_layout(w_pad=1.0)
save_image(figure, "classify_intersection9.png") | python | def classify_intersection9(s, curve1, curve2):
"""Image for :func:`._surface_helpers.classify_intersection` docstring."""
if NO_IMAGES:
return
surface1 = bezier.Surface.from_nodes(
np.asfortranarray(
[
[0.0, 20.0, 40.0, 10.0, 30.0, 20.0],
[0.0, 40.0, 0.0, 25.0, 25.0, 50.0],
]
)
)
surface2 = bezier.Surface.from_nodes(
np.asfortranarray(
[
[40.0, 20.0, 0.0, 30.0, 10.0, 20.0],
[40.0, 0.0, 40.0, 15.0, 15.0, -10.0],
]
)
)
figure, (ax1, ax2) = plt.subplots(1, 2)
classify_help(s, curve1, surface1, curve2, surface2, 0, ax=ax1)
classify_help(s, curve1, surface1, curve2, surface2, 1, ax=ax2)
# Remove the alpha from the color
color1 = ax1.patches[0].get_facecolor()[:3]
color2 = ax1.patches[1].get_facecolor()[:3]
# Now add the "degenerate" intersection polygons.
cp_edges1, cp_edges2 = _edges_classify_intersection9()
curved_polygon1 = bezier.CurvedPolygon(*cp_edges1)
curved_polygon1.plot(256, ax=ax1)
curved_polygon2 = bezier.CurvedPolygon(*cp_edges2)
curved_polygon2.plot(256, ax=ax2)
(int_x,), (int_y,) = curve1.evaluate(s)
ax1.plot([int_x], [int_y], color=color1, linestyle="None", marker="o")
ax2.plot([int_x], [int_y], color=color2, linestyle="None", marker="o")
for ax in (ax1, ax2):
ax.axis("scaled")
ax.set_xlim(-2.0, 42.0)
ax.set_ylim(-12.0, 52.0)
plt.setp(ax2.get_yticklabels(), visible=False)
figure.tight_layout(w_pad=1.0)
save_image(figure, "classify_intersection9.png") | [
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dhermes/bezier | docs/make_images.py | curve_elevate | def curve_elevate(curve, elevated):
"""Image for :meth:`.curve.Curve.elevate` docstring."""
if NO_IMAGES:
return
figure, (ax1, ax2) = plt.subplots(1, 2)
curve.plot(256, ax=ax1)
color = ax1.lines[-1].get_color()
add_patch(ax1, curve._nodes, color)
elevated.plot(256, ax=ax2)
color = ax2.lines[-1].get_color()
add_patch(ax2, elevated._nodes, color)
ax1.axis("scaled")
ax2.axis("scaled")
_plot_helpers.add_plot_boundary(ax1)
ax2.set_xlim(*ax1.get_xlim())
ax2.set_ylim(*ax1.get_ylim())
save_image(figure, "curve_elevate.png") | python | def curve_elevate(curve, elevated):
"""Image for :meth:`.curve.Curve.elevate` docstring."""
if NO_IMAGES:
return
figure, (ax1, ax2) = plt.subplots(1, 2)
curve.plot(256, ax=ax1)
color = ax1.lines[-1].get_color()
add_patch(ax1, curve._nodes, color)
elevated.plot(256, ax=ax2)
color = ax2.lines[-1].get_color()
add_patch(ax2, elevated._nodes, color)
ax1.axis("scaled")
ax2.axis("scaled")
_plot_helpers.add_plot_boundary(ax1)
ax2.set_xlim(*ax1.get_xlim())
ax2.set_ylim(*ax1.get_ylim())
save_image(figure, "curve_elevate.png") | [
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dhermes/bezier | docs/make_images.py | unit_triangle | def unit_triangle():
"""Image for :class:`.surface.Surface` docstring."""
if NO_IMAGES:
return
nodes = np.asfortranarray([[0.0, 1.0, 0.0], [0.0, 0.0, 1.0]])
surface = bezier.Surface(nodes, degree=1)
ax = surface.plot(256)
ax.axis("scaled")
_plot_helpers.add_plot_boundary(ax)
save_image(ax.figure, "unit_triangle.png") | python | def unit_triangle():
"""Image for :class:`.surface.Surface` docstring."""
if NO_IMAGES:
return
nodes = np.asfortranarray([[0.0, 1.0, 0.0], [0.0, 0.0, 1.0]])
surface = bezier.Surface(nodes, degree=1)
ax = surface.plot(256)
ax.axis("scaled")
_plot_helpers.add_plot_boundary(ax)
save_image(ax.figure, "unit_triangle.png") | [
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dhermes/bezier | docs/make_images.py | curve_reduce | def curve_reduce(curve, reduced):
"""Image for :meth:`.curve.Curve.reduce` docstring."""
if NO_IMAGES:
return
figure, (ax1, ax2) = plt.subplots(1, 2, sharex=True, sharey=True)
curve.plot(256, ax=ax1)
color = ax1.lines[-1].get_color()
add_patch(ax1, curve._nodes, color)
reduced.plot(256, ax=ax2)
color = ax2.lines[-1].get_color()
add_patch(ax2, reduced._nodes, color)
ax1.axis("scaled")
ax2.axis("scaled")
_plot_helpers.add_plot_boundary(ax2)
save_image(figure, "curve_reduce.png") | python | def curve_reduce(curve, reduced):
"""Image for :meth:`.curve.Curve.reduce` docstring."""
if NO_IMAGES:
return
figure, (ax1, ax2) = plt.subplots(1, 2, sharex=True, sharey=True)
curve.plot(256, ax=ax1)
color = ax1.lines[-1].get_color()
add_patch(ax1, curve._nodes, color)
reduced.plot(256, ax=ax2)
color = ax2.lines[-1].get_color()
add_patch(ax2, reduced._nodes, color)
ax1.axis("scaled")
ax2.axis("scaled")
_plot_helpers.add_plot_boundary(ax2)
save_image(figure, "curve_reduce.png") | [
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dhermes/bezier | docs/make_images.py | curve_reduce_approx | def curve_reduce_approx(curve, reduced):
"""Image for :meth:`.curve.Curve.reduce` docstring."""
if NO_IMAGES:
return
ax = curve.plot(256)
color = ax.lines[-1].get_color()
add_patch(ax, curve._nodes, color, alpha=0.25, node_color=color)
reduced.plot(256, ax=ax)
color = ax.lines[-1].get_color()
add_patch(ax, reduced._nodes, color, alpha=0.25, node_color=color)
ax.axis("scaled")
_plot_helpers.add_plot_boundary(ax)
save_image(ax.figure, "curve_reduce_approx.png") | python | def curve_reduce_approx(curve, reduced):
"""Image for :meth:`.curve.Curve.reduce` docstring."""
if NO_IMAGES:
return
ax = curve.plot(256)
color = ax.lines[-1].get_color()
add_patch(ax, curve._nodes, color, alpha=0.25, node_color=color)
reduced.plot(256, ax=ax)
color = ax.lines[-1].get_color()
add_patch(ax, reduced._nodes, color, alpha=0.25, node_color=color)
ax.axis("scaled")
_plot_helpers.add_plot_boundary(ax)
save_image(ax.figure, "curve_reduce_approx.png") | [
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dhermes/bezier | src/bezier/_surface_intersection.py | newton_refine_solve | def newton_refine_solve(jac_both, x_val, surf_x, y_val, surf_y):
r"""Helper for :func:`newton_refine`.
We have a system:
.. code-block:: rest
[A C][ds] = [E]
[B D][dt] [F]
This is not a typo, ``A->B->C->D`` matches the data in ``jac_both``.
We solve directly rather than using a linear algebra utility:
.. code-block:: rest
ds = (D E - C F) / (A D - B C)
dt = (A F - B E) / (A D - B C)
Args:
jac_both (numpy.ndarray): A ``4 x 1`` matrix of entries in a Jacobian.
x_val (float): An ``x``-value we are trying to reach.
surf_x (float): The actual ``x``-value we are currently at.
y_val (float): An ``y``-value we are trying to reach.
surf_y (float): The actual ``x``-value we are currently at.
Returns:
Tuple[float, float]: The pair of values the solve the
linear system.
"""
a_val, b_val, c_val, d_val = jac_both[:, 0]
# and
e_val = x_val - surf_x
f_val = y_val - surf_y
# Now solve:
denom = a_val * d_val - b_val * c_val
delta_s = (d_val * e_val - c_val * f_val) / denom
delta_t = (a_val * f_val - b_val * e_val) / denom
return delta_s, delta_t | python | def newton_refine_solve(jac_both, x_val, surf_x, y_val, surf_y):
r"""Helper for :func:`newton_refine`.
We have a system:
.. code-block:: rest
[A C][ds] = [E]
[B D][dt] [F]
This is not a typo, ``A->B->C->D`` matches the data in ``jac_both``.
We solve directly rather than using a linear algebra utility:
.. code-block:: rest
ds = (D E - C F) / (A D - B C)
dt = (A F - B E) / (A D - B C)
Args:
jac_both (numpy.ndarray): A ``4 x 1`` matrix of entries in a Jacobian.
x_val (float): An ``x``-value we are trying to reach.
surf_x (float): The actual ``x``-value we are currently at.
y_val (float): An ``y``-value we are trying to reach.
surf_y (float): The actual ``x``-value we are currently at.
Returns:
Tuple[float, float]: The pair of values the solve the
linear system.
"""
a_val, b_val, c_val, d_val = jac_both[:, 0]
# and
e_val = x_val - surf_x
f_val = y_val - surf_y
# Now solve:
denom = a_val * d_val - b_val * c_val
delta_s = (d_val * e_val - c_val * f_val) / denom
delta_t = (a_val * f_val - b_val * e_val) / denom
return delta_s, delta_t | [
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surf_x (float): The actual ``x``-value we are currently at.
y_val (float): An ``y``-value we are trying to reach.
surf_y (float): The actual ``x``-value we are currently at.
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Tuple[float, float]: The pair of values the solve the
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dhermes/bezier | src/bezier/_surface_intersection.py | _newton_refine | def _newton_refine(nodes, degree, x_val, y_val, s, t):
r"""Refine a solution to :math:`B(s, t) = p` using Newton's method.
.. note::
There is also a Fortran implementation of this function, which
will be used if it can be built.
Computes updates via
.. math::
\left[\begin{array}{c}
0 \\ 0 \end{array}\right] \approx
\left(B\left(s_{\ast}, t_{\ast}\right) -
\left[\begin{array}{c} x \\ y \end{array}\right]\right) +
\left[\begin{array}{c c}
B_s\left(s_{\ast}, t_{\ast}\right) &
B_t\left(s_{\ast}, t_{\ast}\right) \end{array}\right]
\left[\begin{array}{c}
\Delta s \\ \Delta t \end{array}\right]
For example, (with weights
:math:`\lambda_1 = 1 - s - t, \lambda_2 = s, \lambda_3 = t`)
consider the surface
.. math::
B(s, t) =
\left[\begin{array}{c} 0 \\ 0 \end{array}\right] \lambda_1^2 +
\left[\begin{array}{c} 1 \\ 0 \end{array}\right]
2 \lambda_1 \lambda_2 +
\left[\begin{array}{c} 2 \\ 0 \end{array}\right] \lambda_2^2 +
\left[\begin{array}{c} 2 \\ 1 \end{array}\right]
2 \lambda_1 \lambda_3 +
\left[\begin{array}{c} 2 \\ 2 \end{array}\right]
2 \lambda_2 \lambda_1 +
\left[\begin{array}{c} 0 \\ 2 \end{array}\right] \lambda_3^2
and the point
:math:`B\left(\frac{1}{4}, \frac{1}{2}\right) =
\frac{1}{4} \left[\begin{array}{c} 5 \\ 5 \end{array}\right]`.
Starting from the **wrong** point
:math:`s = \frac{1}{2}, t = \frac{1}{4}`, we have
.. math::
\begin{align*}
\left[\begin{array}{c} x \\ y \end{array}\right] -
B\left(\frac{1}{2}, \frac{1}{4}\right) &= \frac{1}{4}
\left[\begin{array}{c} -1 \\ 2 \end{array}\right] \\
DB\left(\frac{1}{2}, \frac{1}{4}\right) &= \frac{1}{2}
\left[\begin{array}{c c} 3 & 2 \\ 1 & 6 \end{array}\right] \\
\Longrightarrow \left[\begin{array}{c}
\Delta s \\ \Delta t \end{array}\right] &= \frac{1}{32}
\left[\begin{array}{c} -10 \\ 7 \end{array}\right]
\end{align*}
.. image:: ../images/newton_refine_surface.png
:align: center
.. testsetup:: newton-refine-surface
import numpy as np
import bezier
from bezier._surface_intersection import newton_refine
.. doctest:: newton-refine-surface
>>> nodes = np.asfortranarray([
... [0.0, 1.0, 2.0, 2.0, 2.0, 0.0],
... [0.0, 0.0, 0.0, 1.0, 2.0, 2.0],
... ])
>>> surface = bezier.Surface(nodes, degree=2)
>>> surface.is_valid
True
>>> (x_val,), (y_val,) = surface.evaluate_cartesian(0.25, 0.5)
>>> x_val, y_val
(1.25, 1.25)
>>> s, t = 0.5, 0.25
>>> new_s, new_t = newton_refine(nodes, 2, x_val, y_val, s, t)
>>> 32 * (new_s - s)
-10.0
>>> 32 * (new_t - t)
7.0
.. testcleanup:: newton-refine-surface
import make_images
make_images.newton_refine_surface(
surface, x_val, y_val, s, t, new_s, new_t)
Args:
nodes (numpy.ndarray): Array of nodes in a surface.
degree (int): The degree of the surface.
x_val (float): The :math:`x`-coordinate of a point
on the surface.
y_val (float): The :math:`y`-coordinate of a point
on the surface.
s (float): Approximate :math:`s`-value to be refined.
t (float): Approximate :math:`t`-value to be refined.
Returns:
Tuple[float, float]: The refined :math:`s` and :math:`t` values.
"""
lambda1 = 1.0 - s - t
(surf_x,), (surf_y,) = _surface_helpers.evaluate_barycentric(
nodes, degree, lambda1, s, t
)
if surf_x == x_val and surf_y == y_val:
# No refinement is needed.
return s, t
# NOTE: This function assumes ``dimension==2`` (i.e. since ``x, y``).
jac_nodes = _surface_helpers.jacobian_both(nodes, degree, 2)
# The degree of the jacobian is one less.
jac_both = _surface_helpers.evaluate_barycentric(
jac_nodes, degree - 1, lambda1, s, t
)
# The first row of the jacobian matrix is B_s (i.e. the
# top-most values in ``jac_both``).
delta_s, delta_t = newton_refine_solve(
jac_both, x_val, surf_x, y_val, surf_y
)
return s + delta_s, t + delta_t | python | def _newton_refine(nodes, degree, x_val, y_val, s, t):
r"""Refine a solution to :math:`B(s, t) = p` using Newton's method.
.. note::
There is also a Fortran implementation of this function, which
will be used if it can be built.
Computes updates via
.. math::
\left[\begin{array}{c}
0 \\ 0 \end{array}\right] \approx
\left(B\left(s_{\ast}, t_{\ast}\right) -
\left[\begin{array}{c} x \\ y \end{array}\right]\right) +
\left[\begin{array}{c c}
B_s\left(s_{\ast}, t_{\ast}\right) &
B_t\left(s_{\ast}, t_{\ast}\right) \end{array}\right]
\left[\begin{array}{c}
\Delta s \\ \Delta t \end{array}\right]
For example, (with weights
:math:`\lambda_1 = 1 - s - t, \lambda_2 = s, \lambda_3 = t`)
consider the surface
.. math::
B(s, t) =
\left[\begin{array}{c} 0 \\ 0 \end{array}\right] \lambda_1^2 +
\left[\begin{array}{c} 1 \\ 0 \end{array}\right]
2 \lambda_1 \lambda_2 +
\left[\begin{array}{c} 2 \\ 0 \end{array}\right] \lambda_2^2 +
\left[\begin{array}{c} 2 \\ 1 \end{array}\right]
2 \lambda_1 \lambda_3 +
\left[\begin{array}{c} 2 \\ 2 \end{array}\right]
2 \lambda_2 \lambda_1 +
\left[\begin{array}{c} 0 \\ 2 \end{array}\right] \lambda_3^2
and the point
:math:`B\left(\frac{1}{4}, \frac{1}{2}\right) =
\frac{1}{4} \left[\begin{array}{c} 5 \\ 5 \end{array}\right]`.
Starting from the **wrong** point
:math:`s = \frac{1}{2}, t = \frac{1}{4}`, we have
.. math::
\begin{align*}
\left[\begin{array}{c} x \\ y \end{array}\right] -
B\left(\frac{1}{2}, \frac{1}{4}\right) &= \frac{1}{4}
\left[\begin{array}{c} -1 \\ 2 \end{array}\right] \\
DB\left(\frac{1}{2}, \frac{1}{4}\right) &= \frac{1}{2}
\left[\begin{array}{c c} 3 & 2 \\ 1 & 6 \end{array}\right] \\
\Longrightarrow \left[\begin{array}{c}
\Delta s \\ \Delta t \end{array}\right] &= \frac{1}{32}
\left[\begin{array}{c} -10 \\ 7 \end{array}\right]
\end{align*}
.. image:: ../images/newton_refine_surface.png
:align: center
.. testsetup:: newton-refine-surface
import numpy as np
import bezier
from bezier._surface_intersection import newton_refine
.. doctest:: newton-refine-surface
>>> nodes = np.asfortranarray([
... [0.0, 1.0, 2.0, 2.0, 2.0, 0.0],
... [0.0, 0.0, 0.0, 1.0, 2.0, 2.0],
... ])
>>> surface = bezier.Surface(nodes, degree=2)
>>> surface.is_valid
True
>>> (x_val,), (y_val,) = surface.evaluate_cartesian(0.25, 0.5)
>>> x_val, y_val
(1.25, 1.25)
>>> s, t = 0.5, 0.25
>>> new_s, new_t = newton_refine(nodes, 2, x_val, y_val, s, t)
>>> 32 * (new_s - s)
-10.0
>>> 32 * (new_t - t)
7.0
.. testcleanup:: newton-refine-surface
import make_images
make_images.newton_refine_surface(
surface, x_val, y_val, s, t, new_s, new_t)
Args:
nodes (numpy.ndarray): Array of nodes in a surface.
degree (int): The degree of the surface.
x_val (float): The :math:`x`-coordinate of a point
on the surface.
y_val (float): The :math:`y`-coordinate of a point
on the surface.
s (float): Approximate :math:`s`-value to be refined.
t (float): Approximate :math:`t`-value to be refined.
Returns:
Tuple[float, float]: The refined :math:`s` and :math:`t` values.
"""
lambda1 = 1.0 - s - t
(surf_x,), (surf_y,) = _surface_helpers.evaluate_barycentric(
nodes, degree, lambda1, s, t
)
if surf_x == x_val and surf_y == y_val:
# No refinement is needed.
return s, t
# NOTE: This function assumes ``dimension==2`` (i.e. since ``x, y``).
jac_nodes = _surface_helpers.jacobian_both(nodes, degree, 2)
# The degree of the jacobian is one less.
jac_both = _surface_helpers.evaluate_barycentric(
jac_nodes, degree - 1, lambda1, s, t
)
# The first row of the jacobian matrix is B_s (i.e. the
# top-most values in ``jac_both``).
delta_s, delta_t = newton_refine_solve(
jac_both, x_val, surf_x, y_val, surf_y
)
return s + delta_s, t + delta_t | [
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will be used if it can be built.
Computes updates via
.. math::
\left[\begin{array}{c}
0 \\ 0 \end{array}\right] \approx
\left(B\left(s_{\ast}, t_{\ast}\right) -
\left[\begin{array}{c} x \\ y \end{array}\right]\right) +
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\Delta s \\ \Delta t \end{array}\right]
For example, (with weights
:math:`\lambda_1 = 1 - s - t, \lambda_2 = s, \lambda_3 = t`)
consider the surface
.. math::
B(s, t) =
\left[\begin{array}{c} 0 \\ 0 \end{array}\right] \lambda_1^2 +
\left[\begin{array}{c} 1 \\ 0 \end{array}\right]
2 \lambda_1 \lambda_2 +
\left[\begin{array}{c} 2 \\ 0 \end{array}\right] \lambda_2^2 +
\left[\begin{array}{c} 2 \\ 1 \end{array}\right]
2 \lambda_1 \lambda_3 +
\left[\begin{array}{c} 2 \\ 2 \end{array}\right]
2 \lambda_2 \lambda_1 +
\left[\begin{array}{c} 0 \\ 2 \end{array}\right] \lambda_3^2
and the point
:math:`B\left(\frac{1}{4}, \frac{1}{2}\right) =
\frac{1}{4} \left[\begin{array}{c} 5 \\ 5 \end{array}\right]`.
Starting from the **wrong** point
:math:`s = \frac{1}{2}, t = \frac{1}{4}`, we have
.. math::
\begin{align*}
\left[\begin{array}{c} x \\ y \end{array}\right] -
B\left(\frac{1}{2}, \frac{1}{4}\right) &= \frac{1}{4}
\left[\begin{array}{c} -1 \\ 2 \end{array}\right] \\
DB\left(\frac{1}{2}, \frac{1}{4}\right) &= \frac{1}{2}
\left[\begin{array}{c c} 3 & 2 \\ 1 & 6 \end{array}\right] \\
\Longrightarrow \left[\begin{array}{c}
\Delta s \\ \Delta t \end{array}\right] &= \frac{1}{32}
\left[\begin{array}{c} -10 \\ 7 \end{array}\right]
\end{align*}
.. image:: ../images/newton_refine_surface.png
:align: center
.. testsetup:: newton-refine-surface
import numpy as np
import bezier
from bezier._surface_intersection import newton_refine
.. doctest:: newton-refine-surface
>>> nodes = np.asfortranarray([
... [0.0, 1.0, 2.0, 2.0, 2.0, 0.0],
... [0.0, 0.0, 0.0, 1.0, 2.0, 2.0],
... ])
>>> surface = bezier.Surface(nodes, degree=2)
>>> surface.is_valid
True
>>> (x_val,), (y_val,) = surface.evaluate_cartesian(0.25, 0.5)
>>> x_val, y_val
(1.25, 1.25)
>>> s, t = 0.5, 0.25
>>> new_s, new_t = newton_refine(nodes, 2, x_val, y_val, s, t)
>>> 32 * (new_s - s)
-10.0
>>> 32 * (new_t - t)
7.0
.. testcleanup:: newton-refine-surface
import make_images
make_images.newton_refine_surface(
surface, x_val, y_val, s, t, new_s, new_t)
Args:
nodes (numpy.ndarray): Array of nodes in a surface.
degree (int): The degree of the surface.
x_val (float): The :math:`x`-coordinate of a point
on the surface.
y_val (float): The :math:`y`-coordinate of a point
on the surface.
s (float): Approximate :math:`s`-value to be refined.
t (float): Approximate :math:`t`-value to be refined.
Returns:
Tuple[float, float]: The refined :math:`s` and :math:`t` values. | [
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dhermes/bezier | src/bezier/_surface_intersection.py | update_locate_candidates | def update_locate_candidates(candidate, next_candidates, x_val, y_val, degree):
"""Update list of candidate surfaces during geometric search for a point.
.. note::
This is used **only** as a helper for :func:`locate_point`.
Checks if the point ``(x_val, y_val)`` is contained in the ``candidate``
surface. If not, this function does nothing. If the point is contaned,
the four subdivided surfaces from ``candidate`` are added to
``next_candidates``.
Args:
candidate (Tuple[float, float, float, numpy.ndarray]): A 4-tuple
describing a surface and its centroid / width. Contains
* Three times centroid ``x``-value
* Three times centroid ``y``-value
* "Width" of parameter space for the surface
* Control points for the surface
next_candidates (list): List of "candidate" sub-surfaces that may
contain the point being located.
x_val (float): The ``x``-coordinate being located.
y_val (float): The ``y``-coordinate being located.
degree (int): The degree of the surface.
"""
centroid_x, centroid_y, width, candidate_nodes = candidate
point = np.asfortranarray([x_val, y_val])
if not _helpers.contains_nd(candidate_nodes, point):
return
nodes_a, nodes_b, nodes_c, nodes_d = _surface_helpers.subdivide_nodes(
candidate_nodes, degree
)
half_width = 0.5 * width
next_candidates.extend(
(
(
centroid_x - half_width,
centroid_y - half_width,
half_width,
nodes_a,
),
(centroid_x, centroid_y, -half_width, nodes_b),
(centroid_x + width, centroid_y - half_width, half_width, nodes_c),
(centroid_x - half_width, centroid_y + width, half_width, nodes_d),
)
) | python | def update_locate_candidates(candidate, next_candidates, x_val, y_val, degree):
"""Update list of candidate surfaces during geometric search for a point.
.. note::
This is used **only** as a helper for :func:`locate_point`.
Checks if the point ``(x_val, y_val)`` is contained in the ``candidate``
surface. If not, this function does nothing. If the point is contaned,
the four subdivided surfaces from ``candidate`` are added to
``next_candidates``.
Args:
candidate (Tuple[float, float, float, numpy.ndarray]): A 4-tuple
describing a surface and its centroid / width. Contains
* Three times centroid ``x``-value
* Three times centroid ``y``-value
* "Width" of parameter space for the surface
* Control points for the surface
next_candidates (list): List of "candidate" sub-surfaces that may
contain the point being located.
x_val (float): The ``x``-coordinate being located.
y_val (float): The ``y``-coordinate being located.
degree (int): The degree of the surface.
"""
centroid_x, centroid_y, width, candidate_nodes = candidate
point = np.asfortranarray([x_val, y_val])
if not _helpers.contains_nd(candidate_nodes, point):
return
nodes_a, nodes_b, nodes_c, nodes_d = _surface_helpers.subdivide_nodes(
candidate_nodes, degree
)
half_width = 0.5 * width
next_candidates.extend(
(
(
centroid_x - half_width,
centroid_y - half_width,
half_width,
nodes_a,
),
(centroid_x, centroid_y, -half_width, nodes_b),
(centroid_x + width, centroid_y - half_width, half_width, nodes_c),
(centroid_x - half_width, centroid_y + width, half_width, nodes_d),
)
) | [
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surface. If not, this function does nothing. If the point is contaned,
the four subdivided surfaces from ``candidate`` are added to
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Args:
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describing a surface and its centroid / width. Contains
* Three times centroid ``x``-value
* Three times centroid ``y``-value
* "Width" of parameter space for the surface
* Control points for the surface
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contain the point being located.
x_val (float): The ``x``-coordinate being located.
y_val (float): The ``y``-coordinate being located.
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dhermes/bezier | src/bezier/_surface_intersection.py | mean_centroid | def mean_centroid(candidates):
"""Take the mean of all centroids in set of reference triangles.
.. note::
This is used **only** as a helper for :func:`locate_point`.
Args:
candidates (List[Tuple[float, float, float, numpy.ndarray]): List of
4-tuples, each of which has been produced by :func:`locate_point`.
Each 4-tuple contains
* Three times centroid ``x``-value
* Three times centroid ``y``-value
* "Width" of a parameter space for a surface
* Control points for a surface
We only use the first two values, which are triple the desired
value so that we can put off division by three until summing in
our average. We don't use the other two values, they are just an
artifact of the way ``candidates`` is constructed by the caller.
Returns:
Tuple[float, float]: The mean of all centroids.
"""
sum_x = 0.0
sum_y = 0.0
for centroid_x, centroid_y, _, _ in candidates:
sum_x += centroid_x
sum_y += centroid_y
denom = 3.0 * len(candidates)
return sum_x / denom, sum_y / denom | python | def mean_centroid(candidates):
"""Take the mean of all centroids in set of reference triangles.
.. note::
This is used **only** as a helper for :func:`locate_point`.
Args:
candidates (List[Tuple[float, float, float, numpy.ndarray]): List of
4-tuples, each of which has been produced by :func:`locate_point`.
Each 4-tuple contains
* Three times centroid ``x``-value
* Three times centroid ``y``-value
* "Width" of a parameter space for a surface
* Control points for a surface
We only use the first two values, which are triple the desired
value so that we can put off division by three until summing in
our average. We don't use the other two values, they are just an
artifact of the way ``candidates`` is constructed by the caller.
Returns:
Tuple[float, float]: The mean of all centroids.
"""
sum_x = 0.0
sum_y = 0.0
for centroid_x, centroid_y, _, _ in candidates:
sum_x += centroid_x
sum_y += centroid_y
denom = 3.0 * len(candidates)
return sum_x / denom, sum_y / denom | [
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* Three times centroid ``x``-value
* Three times centroid ``y``-value
* "Width" of a parameter space for a surface
* Control points for a surface
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Returns:
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dhermes/bezier | src/bezier/_surface_intersection.py | _locate_point | def _locate_point(nodes, degree, x_val, y_val):
r"""Locate a point on a surface.
.. note::
There is also a Fortran implementation of this function, which
will be used if it can be built.
Does so by recursively subdividing the surface and rejecting
sub-surfaces with bounding boxes that don't contain the point.
After the sub-surfaces are sufficiently small, uses Newton's
method to narrow in on the pre-image of the point.
Args:
nodes (numpy.ndarray): Control points for B |eacute| zier surface
(assumed to be two-dimensional).
degree (int): The degree of the surface.
x_val (float): The :math:`x`-coordinate of a point
on the surface.
y_val (float): The :math:`y`-coordinate of a point
on the surface.
Returns:
Optional[Tuple[float, float]]: The :math:`s` and :math:`t`
values corresponding to ``x_val`` and ``y_val`` or
:data:`None` if the point is not on the ``surface``.
"""
# We track the centroid rather than base_x/base_y/width (by storing triple
# the centroid -- to avoid division by three until needed). We also need
# to track the width (or rather, just the sign of the width).
candidates = [(1.0, 1.0, 1.0, nodes)]
for _ in six.moves.xrange(MAX_LOCATE_SUBDIVISIONS + 1):
next_candidates = []
for candidate in candidates:
update_locate_candidates(
candidate, next_candidates, x_val, y_val, degree
)
candidates = next_candidates
if not candidates:
return None
# We take the average of all centroids from the candidates
# that may contain the point.
s_approx, t_approx = mean_centroid(candidates)
s, t = newton_refine(nodes, degree, x_val, y_val, s_approx, t_approx)
actual = _surface_helpers.evaluate_barycentric(
nodes, degree, 1.0 - s - t, s, t
)
expected = np.asfortranarray([x_val, y_val])
if not _helpers.vector_close(
actual.ravel(order="F"), expected, eps=LOCATE_EPS
):
s, t = newton_refine(nodes, degree, x_val, y_val, s, t)
return s, t | python | def _locate_point(nodes, degree, x_val, y_val):
r"""Locate a point on a surface.
.. note::
There is also a Fortran implementation of this function, which
will be used if it can be built.
Does so by recursively subdividing the surface and rejecting
sub-surfaces with bounding boxes that don't contain the point.
After the sub-surfaces are sufficiently small, uses Newton's
method to narrow in on the pre-image of the point.
Args:
nodes (numpy.ndarray): Control points for B |eacute| zier surface
(assumed to be two-dimensional).
degree (int): The degree of the surface.
x_val (float): The :math:`x`-coordinate of a point
on the surface.
y_val (float): The :math:`y`-coordinate of a point
on the surface.
Returns:
Optional[Tuple[float, float]]: The :math:`s` and :math:`t`
values corresponding to ``x_val`` and ``y_val`` or
:data:`None` if the point is not on the ``surface``.
"""
# We track the centroid rather than base_x/base_y/width (by storing triple
# the centroid -- to avoid division by three until needed). We also need
# to track the width (or rather, just the sign of the width).
candidates = [(1.0, 1.0, 1.0, nodes)]
for _ in six.moves.xrange(MAX_LOCATE_SUBDIVISIONS + 1):
next_candidates = []
for candidate in candidates:
update_locate_candidates(
candidate, next_candidates, x_val, y_val, degree
)
candidates = next_candidates
if not candidates:
return None
# We take the average of all centroids from the candidates
# that may contain the point.
s_approx, t_approx = mean_centroid(candidates)
s, t = newton_refine(nodes, degree, x_val, y_val, s_approx, t_approx)
actual = _surface_helpers.evaluate_barycentric(
nodes, degree, 1.0 - s - t, s, t
)
expected = np.asfortranarray([x_val, y_val])
if not _helpers.vector_close(
actual.ravel(order="F"), expected, eps=LOCATE_EPS
):
s, t = newton_refine(nodes, degree, x_val, y_val, s, t)
return s, t | [
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.. note::
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Does so by recursively subdividing the surface and rejecting
sub-surfaces with bounding boxes that don't contain the point.
After the sub-surfaces are sufficiently small, uses Newton's
method to narrow in on the pre-image of the point.
Args:
nodes (numpy.ndarray): Control points for B |eacute| zier surface
(assumed to be two-dimensional).
degree (int): The degree of the surface.
x_val (float): The :math:`x`-coordinate of a point
on the surface.
y_val (float): The :math:`y`-coordinate of a point
on the surface.
Returns:
Optional[Tuple[float, float]]: The :math:`s` and :math:`t`
values corresponding to ``x_val`` and ``y_val`` or
:data:`None` if the point is not on the ``surface``. | [
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dhermes/bezier | src/bezier/_surface_intersection.py | same_intersection | def same_intersection(intersection1, intersection2, wiggle=0.5 ** 40):
"""Check if two intersections are close to machine precision.
.. note::
This is a helper used only by :func:`verify_duplicates`, which in turn
is only used by :func:`generic_intersect`.
Args:
intersection1 (.Intersection): The first intersection.
intersection2 (.Intersection): The second intersection.
wiggle (Optional[float]): The amount of relative error allowed
in parameter values.
Returns:
bool: Indicates if the two intersections are the same to
machine precision.
"""
if intersection1.index_first != intersection2.index_first:
return False
if intersection1.index_second != intersection2.index_second:
return False
return np.allclose(
[intersection1.s, intersection1.t],
[intersection2.s, intersection2.t],
atol=0.0,
rtol=wiggle,
) | python | def same_intersection(intersection1, intersection2, wiggle=0.5 ** 40):
"""Check if two intersections are close to machine precision.
.. note::
This is a helper used only by :func:`verify_duplicates`, which in turn
is only used by :func:`generic_intersect`.
Args:
intersection1 (.Intersection): The first intersection.
intersection2 (.Intersection): The second intersection.
wiggle (Optional[float]): The amount of relative error allowed
in parameter values.
Returns:
bool: Indicates if the two intersections are the same to
machine precision.
"""
if intersection1.index_first != intersection2.index_first:
return False
if intersection1.index_second != intersection2.index_second:
return False
return np.allclose(
[intersection1.s, intersection1.t],
[intersection2.s, intersection2.t],
atol=0.0,
rtol=wiggle,
) | [
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dhermes/bezier | src/bezier/_surface_intersection.py | verify_duplicates | def verify_duplicates(duplicates, uniques):
"""Verify that a set of intersections had expected duplicates.
.. note::
This is a helper used only by :func:`generic_intersect`.
Args:
duplicates (List[.Intersection]): List of intersections
corresponding to duplicates that were filtered out.
uniques (List[.Intersection]): List of "final" intersections
with duplicates filtered out.
Raises:
ValueError: If the ``uniques`` are not actually all unique.
ValueError: If one of the ``duplicates`` does not correspond to
an intersection in ``uniques``.
ValueError: If a duplicate occurs only once but does not have
exactly one of ``s`` and ``t`` equal to ``0.0``.
ValueError: If a duplicate occurs three times but does not have
exactly both ``s == t == 0.0``.
ValueError: If a duplicate occurs a number other than one or three
times.
"""
for uniq1, uniq2 in itertools.combinations(uniques, 2):
if same_intersection(uniq1, uniq2):
raise ValueError("Non-unique intersection")
counter = collections.Counter()
for dupe in duplicates:
matches = []
for index, uniq in enumerate(uniques):
if same_intersection(dupe, uniq):
matches.append(index)
if len(matches) != 1:
raise ValueError("Duplicate not among uniques", dupe)
matched = matches[0]
counter[matched] += 1
for index, count in six.iteritems(counter):
uniq = uniques[index]
if count == 1:
if (uniq.s, uniq.t).count(0.0) != 1:
raise ValueError("Count == 1 should be a single corner", uniq)
elif count == 3:
if (uniq.s, uniq.t) != (0.0, 0.0):
raise ValueError("Count == 3 should be a double corner", uniq)
else:
raise ValueError("Unexpected duplicate count", count) | python | def verify_duplicates(duplicates, uniques):
"""Verify that a set of intersections had expected duplicates.
.. note::
This is a helper used only by :func:`generic_intersect`.
Args:
duplicates (List[.Intersection]): List of intersections
corresponding to duplicates that were filtered out.
uniques (List[.Intersection]): List of "final" intersections
with duplicates filtered out.
Raises:
ValueError: If the ``uniques`` are not actually all unique.
ValueError: If one of the ``duplicates`` does not correspond to
an intersection in ``uniques``.
ValueError: If a duplicate occurs only once but does not have
exactly one of ``s`` and ``t`` equal to ``0.0``.
ValueError: If a duplicate occurs three times but does not have
exactly both ``s == t == 0.0``.
ValueError: If a duplicate occurs a number other than one or three
times.
"""
for uniq1, uniq2 in itertools.combinations(uniques, 2):
if same_intersection(uniq1, uniq2):
raise ValueError("Non-unique intersection")
counter = collections.Counter()
for dupe in duplicates:
matches = []
for index, uniq in enumerate(uniques):
if same_intersection(dupe, uniq):
matches.append(index)
if len(matches) != 1:
raise ValueError("Duplicate not among uniques", dupe)
matched = matches[0]
counter[matched] += 1
for index, count in six.iteritems(counter):
uniq = uniques[index]
if count == 1:
if (uniq.s, uniq.t).count(0.0) != 1:
raise ValueError("Count == 1 should be a single corner", uniq)
elif count == 3:
if (uniq.s, uniq.t) != (0.0, 0.0):
raise ValueError("Count == 3 should be a double corner", uniq)
else:
raise ValueError("Unexpected duplicate count", count) | [
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uniques (List[.Intersection]): List of "final" intersections
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Raises:
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dhermes/bezier | src/bezier/_surface_intersection.py | verify_edge_segments | def verify_edge_segments(edge_infos):
"""Verify that the edge segments in an intersection are valid.
.. note::
This is a helper used only by :func:`generic_intersect`.
Args:
edge_infos (Optional[list]): List of "edge info" lists. Each list
represents a curved polygon and contains 3-tuples of edge index,
start and end (see the output of :func:`ends_to_curve`).
Raises:
ValueError: If two consecutive edge segments lie on the same edge
index.
ValueError: If the start and end parameter are "invalid" (they should
be between 0 and 1 and start should be strictly less than end).
"""
if edge_infos is None:
return
for edge_info in edge_infos:
num_segments = len(edge_info)
for index in six.moves.xrange(-1, num_segments - 1):
index1, start1, end1 = edge_info[index]
# First, verify the start and end parameters for the current
# segment.
if not 0.0 <= start1 < end1 <= 1.0:
raise ValueError(BAD_SEGMENT_PARAMS, edge_info[index])
# Then, verify that the indices are not the same.
index2, _, _ = edge_info[index + 1]
if index1 == index2:
raise ValueError(
SEGMENTS_SAME_EDGE, edge_info[index], edge_info[index + 1]
) | python | def verify_edge_segments(edge_infos):
"""Verify that the edge segments in an intersection are valid.
.. note::
This is a helper used only by :func:`generic_intersect`.
Args:
edge_infos (Optional[list]): List of "edge info" lists. Each list
represents a curved polygon and contains 3-tuples of edge index,
start and end (see the output of :func:`ends_to_curve`).
Raises:
ValueError: If two consecutive edge segments lie on the same edge
index.
ValueError: If the start and end parameter are "invalid" (they should
be between 0 and 1 and start should be strictly less than end).
"""
if edge_infos is None:
return
for edge_info in edge_infos:
num_segments = len(edge_info)
for index in six.moves.xrange(-1, num_segments - 1):
index1, start1, end1 = edge_info[index]
# First, verify the start and end parameters for the current
# segment.
if not 0.0 <= start1 < end1 <= 1.0:
raise ValueError(BAD_SEGMENT_PARAMS, edge_info[index])
# Then, verify that the indices are not the same.
index2, _, _ = edge_info[index + 1]
if index1 == index2:
raise ValueError(
SEGMENTS_SAME_EDGE, edge_info[index], edge_info[index + 1]
) | [
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dhermes/bezier | src/bezier/_surface_intersection.py | add_edge_end_unused | def add_edge_end_unused(intersection, duplicates, intersections):
"""Add intersection that is ``COINCIDENT_UNUSED`` but on an edge end.
This is a helper for :func:`~._surface_intersection.add_intersection`.
It assumes that
* ``intersection`` will have at least one of ``s == 0.0`` or ``t == 0.0``
* A "misclassified" intersection in ``intersections`` that matches
``intersection`` will be the "same" if it matches both ``index_first``
and ``index_second`` and if it matches the start index exactly
Args:
intersection (.Intersection): An intersection to be added.
duplicates (List[.Intersection]): List of duplicate intersections.
intersections (List[.Intersection]): List of "accepted" (i.e.
non-duplicate) intersections.
"""
found = None
for other in intersections:
if (
intersection.index_first == other.index_first
and intersection.index_second == other.index_second
):
if intersection.s == 0.0 and other.s == 0.0:
found = other
break
if intersection.t == 0.0 and other.t == 0.0:
found = other
break
if found is not None:
intersections.remove(found)
duplicates.append(found)
intersections.append(intersection) | python | def add_edge_end_unused(intersection, duplicates, intersections):
"""Add intersection that is ``COINCIDENT_UNUSED`` but on an edge end.
This is a helper for :func:`~._surface_intersection.add_intersection`.
It assumes that
* ``intersection`` will have at least one of ``s == 0.0`` or ``t == 0.0``
* A "misclassified" intersection in ``intersections`` that matches
``intersection`` will be the "same" if it matches both ``index_first``
and ``index_second`` and if it matches the start index exactly
Args:
intersection (.Intersection): An intersection to be added.
duplicates (List[.Intersection]): List of duplicate intersections.
intersections (List[.Intersection]): List of "accepted" (i.e.
non-duplicate) intersections.
"""
found = None
for other in intersections:
if (
intersection.index_first == other.index_first
and intersection.index_second == other.index_second
):
if intersection.s == 0.0 and other.s == 0.0:
found = other
break
if intersection.t == 0.0 and other.t == 0.0:
found = other
break
if found is not None:
intersections.remove(found)
duplicates.append(found)
intersections.append(intersection) | [
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dhermes/bezier | src/bezier/_surface_intersection.py | check_unused | def check_unused(intersection, duplicates, intersections):
"""Check if a "valid" ``intersection`` is already in ``intersections``.
This assumes that
* ``intersection`` will have at least one of ``s == 0.0`` or ``t == 0.0``
* At least one of the intersections in ``intersections`` is classified as
``COINCIDENT_UNUSED``.
Args:
intersection (.Intersection): An intersection to be added.
duplicates (List[.Intersection]): List of duplicate intersections.
intersections (List[.Intersection]): List of "accepted" (i.e.
non-duplicate) intersections.
Returns:
bool: Indicates if the ``intersection`` is a duplicate.
"""
for other in intersections:
if (
other.interior_curve == UNUSED_T
and intersection.index_first == other.index_first
and intersection.index_second == other.index_second
):
if intersection.s == 0.0 and other.s == 0.0:
duplicates.append(intersection)
return True
if intersection.t == 0.0 and other.t == 0.0:
duplicates.append(intersection)
return True
return False | python | def check_unused(intersection, duplicates, intersections):
"""Check if a "valid" ``intersection`` is already in ``intersections``.
This assumes that
* ``intersection`` will have at least one of ``s == 0.0`` or ``t == 0.0``
* At least one of the intersections in ``intersections`` is classified as
``COINCIDENT_UNUSED``.
Args:
intersection (.Intersection): An intersection to be added.
duplicates (List[.Intersection]): List of duplicate intersections.
intersections (List[.Intersection]): List of "accepted" (i.e.
non-duplicate) intersections.
Returns:
bool: Indicates if the ``intersection`` is a duplicate.
"""
for other in intersections:
if (
other.interior_curve == UNUSED_T
and intersection.index_first == other.index_first
and intersection.index_second == other.index_second
):
if intersection.s == 0.0 and other.s == 0.0:
duplicates.append(intersection)
return True
if intersection.t == 0.0 and other.t == 0.0:
duplicates.append(intersection)
return True
return False | [
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intersection (.Intersection): An intersection to be added.
duplicates (List[.Intersection]): List of duplicate intersections.
intersections (List[.Intersection]): List of "accepted" (i.e.
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dhermes/bezier | src/bezier/_surface_intersection.py | add_intersection | def add_intersection( # pylint: disable=too-many-arguments
index1,
s,
index2,
t,
interior_curve,
edge_nodes1,
edge_nodes2,
duplicates,
intersections,
):
"""Create an :class:`Intersection` and append.
The intersection will be classified as either a duplicate or a valid
intersection and appended to one of ``duplicates`` or ``intersections``
depending on that classification.
Args:
index1 (int): The index (among 0, 1, 2) of the first edge in the
intersection.
s (float): The parameter along the first curve of the intersection.
index2 (int): The index (among 0, 1, 2) of the second edge in the
intersection.
t (float): The parameter along the second curve of the intersection.
interior_curve (Optional[.IntersectionClassification]): The
classification of the intersection, if known. If :data:`None`,
the classification will be computed below.
edge_nodes1 (Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]): The
nodes of the three edges of the first surface being intersected.
edge_nodes2 (Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]): The
nodes of the three edges of the second surface being intersected.
duplicates (List[.Intersection]): List of duplicate intersections.
intersections (List[.Intersection]): List of "accepted" (i.e.
non-duplicate) intersections.
"""
# NOTE: There is no corresponding "enable", but the disable only applies
# in this lexical scope.
# pylint: disable=too-many-locals
edge_end, is_corner, intersection_args = _surface_helpers.handle_ends(
index1, s, index2, t
)
if edge_end:
intersection = _intersection_helpers.Intersection(*intersection_args)
intersection.interior_curve = interior_curve
if interior_curve == UNUSED_T:
add_edge_end_unused(intersection, duplicates, intersections)
else:
duplicates.append(intersection)
else:
intersection = _intersection_helpers.Intersection(index1, s, index2, t)
if is_corner:
is_duplicate = check_unused(
intersection, duplicates, intersections
)
if is_duplicate:
return
# Classify the intersection.
if interior_curve is None:
interior_curve = _surface_helpers.classify_intersection(
intersection, edge_nodes1, edge_nodes2
)
intersection.interior_curve = interior_curve
intersections.append(intersection) | python | def add_intersection( # pylint: disable=too-many-arguments
index1,
s,
index2,
t,
interior_curve,
edge_nodes1,
edge_nodes2,
duplicates,
intersections,
):
"""Create an :class:`Intersection` and append.
The intersection will be classified as either a duplicate or a valid
intersection and appended to one of ``duplicates`` or ``intersections``
depending on that classification.
Args:
index1 (int): The index (among 0, 1, 2) of the first edge in the
intersection.
s (float): The parameter along the first curve of the intersection.
index2 (int): The index (among 0, 1, 2) of the second edge in the
intersection.
t (float): The parameter along the second curve of the intersection.
interior_curve (Optional[.IntersectionClassification]): The
classification of the intersection, if known. If :data:`None`,
the classification will be computed below.
edge_nodes1 (Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]): The
nodes of the three edges of the first surface being intersected.
edge_nodes2 (Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]): The
nodes of the three edges of the second surface being intersected.
duplicates (List[.Intersection]): List of duplicate intersections.
intersections (List[.Intersection]): List of "accepted" (i.e.
non-duplicate) intersections.
"""
# NOTE: There is no corresponding "enable", but the disable only applies
# in this lexical scope.
# pylint: disable=too-many-locals
edge_end, is_corner, intersection_args = _surface_helpers.handle_ends(
index1, s, index2, t
)
if edge_end:
intersection = _intersection_helpers.Intersection(*intersection_args)
intersection.interior_curve = interior_curve
if interior_curve == UNUSED_T:
add_edge_end_unused(intersection, duplicates, intersections)
else:
duplicates.append(intersection)
else:
intersection = _intersection_helpers.Intersection(index1, s, index2, t)
if is_corner:
is_duplicate = check_unused(
intersection, duplicates, intersections
)
if is_duplicate:
return
# Classify the intersection.
if interior_curve is None:
interior_curve = _surface_helpers.classify_intersection(
intersection, edge_nodes1, edge_nodes2
)
intersection.interior_curve = interior_curve
intersections.append(intersection) | [
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edge_nodes2 (Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]): The
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dhermes/bezier | src/bezier/_surface_intersection.py | classify_coincident | def classify_coincident(st_vals, coincident):
r"""Determine if coincident parameters are "unused".
.. note::
This is a helper for :func:`surface_intersections`.
In the case that ``coincident`` is :data:`True`, then we'll have two
sets of parameters :math:`(s_1, t_1)` and :math:`(s_2, t_2)`.
If one of :math:`s1 < s2` or :math:`t1 < t2` is not satisfied, the
coincident segments will be moving in opposite directions, hence don't
define an interior of an intersection.
.. warning::
In the "coincident" case, this assumes, but doesn't check, that
``st_vals`` is ``2 x 2``.
Args:
st_vals (numpy.ndarray): ``2 X N`` array of intersection parameters.
coincident (bool): Flag indicating if the intersections are the
endpoints of coincident segments of two curves.
Returns:
Optional[.IntersectionClassification]: The classification of the
intersections.
"""
if not coincident:
return None
if st_vals[0, 0] >= st_vals[0, 1] or st_vals[1, 0] >= st_vals[1, 1]:
return UNUSED_T
else:
return CLASSIFICATION_T.COINCIDENT | python | def classify_coincident(st_vals, coincident):
r"""Determine if coincident parameters are "unused".
.. note::
This is a helper for :func:`surface_intersections`.
In the case that ``coincident`` is :data:`True`, then we'll have two
sets of parameters :math:`(s_1, t_1)` and :math:`(s_2, t_2)`.
If one of :math:`s1 < s2` or :math:`t1 < t2` is not satisfied, the
coincident segments will be moving in opposite directions, hence don't
define an interior of an intersection.
.. warning::
In the "coincident" case, this assumes, but doesn't check, that
``st_vals`` is ``2 x 2``.
Args:
st_vals (numpy.ndarray): ``2 X N`` array of intersection parameters.
coincident (bool): Flag indicating if the intersections are the
endpoints of coincident segments of two curves.
Returns:
Optional[.IntersectionClassification]: The classification of the
intersections.
"""
if not coincident:
return None
if st_vals[0, 0] >= st_vals[0, 1] or st_vals[1, 0] >= st_vals[1, 1]:
return UNUSED_T
else:
return CLASSIFICATION_T.COINCIDENT | [
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dhermes/bezier | src/bezier/_surface_intersection.py | should_use | def should_use(intersection):
"""Check if an intersection can be used as part of a curved polygon.
Will return :data:`True` if the intersection is classified as
:attr:`~.IntersectionClassification.FIRST`,
:attr:`~.IntersectionClassification.SECOND` or
:attr:`~.IntersectionClassification.COINCIDENT` or if the intersection
is classified is a corner / edge end which is classified as
:attr:`~.IntersectionClassification.TANGENT_FIRST` or
:attr:`~.IntersectionClassification.TANGENT_SECOND`.
Args:
intersection (.Intersection): An intersection to be added.
Returns:
bool: Indicating if the intersection will be used.
"""
if intersection.interior_curve in ACCEPTABLE_CLASSIFICATIONS:
return True
if intersection.interior_curve in TANGENT_CLASSIFICATIONS:
return intersection.s == 0.0 or intersection.t == 0.0
return False | python | def should_use(intersection):
"""Check if an intersection can be used as part of a curved polygon.
Will return :data:`True` if the intersection is classified as
:attr:`~.IntersectionClassification.FIRST`,
:attr:`~.IntersectionClassification.SECOND` or
:attr:`~.IntersectionClassification.COINCIDENT` or if the intersection
is classified is a corner / edge end which is classified as
:attr:`~.IntersectionClassification.TANGENT_FIRST` or
:attr:`~.IntersectionClassification.TANGENT_SECOND`.
Args:
intersection (.Intersection): An intersection to be added.
Returns:
bool: Indicating if the intersection will be used.
"""
if intersection.interior_curve in ACCEPTABLE_CLASSIFICATIONS:
return True
if intersection.interior_curve in TANGENT_CLASSIFICATIONS:
return intersection.s == 0.0 or intersection.t == 0.0
return False | [
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dhermes/bezier | src/bezier/_surface_intersection.py | surface_intersections | def surface_intersections(edge_nodes1, edge_nodes2, all_intersections):
"""Find all intersections among edges of two surfaces.
This treats intersections which have ``s == 1.0`` or ``t == 1.0``
as duplicates. The duplicates may be checked by the caller, e.g.
by :func:`verify_duplicates`.
Args:
edge_nodes1 (Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]): The
nodes of the three edges of the first surface being intersected.
edge_nodes2 (Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]): The
nodes of the three edges of the second surface being intersected.
all_intersections (Callable): A helper that intersects B |eacute| zier
curves. Takes the nodes of each curve as input and returns an
array (``2 x N``) of intersections.
Returns:
Tuple[list, list, list, set]: 4-tuple of
* The actual "unique" :class:`Intersection`-s
* Duplicate :class:`Intersection`-s encountered (these will be
corner intersections)
* Intersections that won't be used, such as a tangent intersection
along an edge
* All the intersection classifications encountered
"""
# NOTE: There is no corresponding "enable", but the disable only applies
# in this lexical scope.
# pylint: disable=too-many-locals
intersections = []
duplicates = []
for index1, nodes1 in enumerate(edge_nodes1):
for index2, nodes2 in enumerate(edge_nodes2):
st_vals, coincident = all_intersections(nodes1, nodes2)
interior_curve = classify_coincident(st_vals, coincident)
for s, t in st_vals.T:
add_intersection(
index1,
s,
index2,
t,
interior_curve,
edge_nodes1,
edge_nodes2,
duplicates,
intersections,
)
all_types = set()
to_keep = []
unused = []
for intersection in intersections:
all_types.add(intersection.interior_curve)
# Only keep the intersections which are "acceptable".
if should_use(intersection):
to_keep.append(intersection)
else:
unused.append(intersection)
return to_keep, duplicates, unused, all_types | python | def surface_intersections(edge_nodes1, edge_nodes2, all_intersections):
"""Find all intersections among edges of two surfaces.
This treats intersections which have ``s == 1.0`` or ``t == 1.0``
as duplicates. The duplicates may be checked by the caller, e.g.
by :func:`verify_duplicates`.
Args:
edge_nodes1 (Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]): The
nodes of the three edges of the first surface being intersected.
edge_nodes2 (Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]): The
nodes of the three edges of the second surface being intersected.
all_intersections (Callable): A helper that intersects B |eacute| zier
curves. Takes the nodes of each curve as input and returns an
array (``2 x N``) of intersections.
Returns:
Tuple[list, list, list, set]: 4-tuple of
* The actual "unique" :class:`Intersection`-s
* Duplicate :class:`Intersection`-s encountered (these will be
corner intersections)
* Intersections that won't be used, such as a tangent intersection
along an edge
* All the intersection classifications encountered
"""
# NOTE: There is no corresponding "enable", but the disable only applies
# in this lexical scope.
# pylint: disable=too-many-locals
intersections = []
duplicates = []
for index1, nodes1 in enumerate(edge_nodes1):
for index2, nodes2 in enumerate(edge_nodes2):
st_vals, coincident = all_intersections(nodes1, nodes2)
interior_curve = classify_coincident(st_vals, coincident)
for s, t in st_vals.T:
add_intersection(
index1,
s,
index2,
t,
interior_curve,
edge_nodes1,
edge_nodes2,
duplicates,
intersections,
)
all_types = set()
to_keep = []
unused = []
for intersection in intersections:
all_types.add(intersection.interior_curve)
# Only keep the intersections which are "acceptable".
if should_use(intersection):
to_keep.append(intersection)
else:
unused.append(intersection)
return to_keep, duplicates, unused, all_types | [
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by :func:`verify_duplicates`.
Args:
edge_nodes1 (Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]): The
nodes of the three edges of the first surface being intersected.
edge_nodes2 (Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]): The
nodes of the three edges of the second surface being intersected.
all_intersections (Callable): A helper that intersects B |eacute| zier
curves. Takes the nodes of each curve as input and returns an
array (``2 x N``) of intersections.
Returns:
Tuple[list, list, list, set]: 4-tuple of
* The actual "unique" :class:`Intersection`-s
* Duplicate :class:`Intersection`-s encountered (these will be
corner intersections)
* Intersections that won't be used, such as a tangent intersection
along an edge
* All the intersection classifications encountered | [
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dhermes/bezier | src/bezier/_surface_intersection.py | generic_intersect | def generic_intersect(
nodes1, degree1, nodes2, degree2, verify, all_intersections
):
r"""Find all intersections among edges of two surfaces.
This treats intersections which have ``s == 1.0`` or ``t == 1.0``
as duplicates. The duplicates will be checked by :func:`verify_duplicates`
if ``verify`` is :data:`True`.
Args:
nodes1 (numpy.ndarray): The nodes defining the first surface in
the intersection (assumed in :math:\mathbf{R}^2`).
degree1 (int): The degree of the surface given by ``nodes1``.
nodes2 (numpy.ndarray): The nodes defining the second surface in
the intersection (assumed in :math:\mathbf{R}^2`).
degree2 (int): The degree of the surface given by ``nodes2``.
verify (Optional[bool]): Indicates if duplicate intersections
should be checked.
all_intersections (Callable): A helper that intersects B |eacute| zier
curves. Takes the nodes of each curve as input and returns an
array (``2 x N``) of intersections.
Returns:
Tuple[Optional[list], Optional[bool], tuple]: 3-tuple of
* List of "edge info" lists. Each list represents a curved polygon
and contains 3-tuples of edge index, start and end (see the
output of :func:`ends_to_curve`).
* "Contained" boolean. If not :data:`None`, indicates
that one of the surfaces is contained in the other.
* The nodes of three edges of the first surface being intersected
followed by the nodes of the three edges of the second.
"""
bbox_int = _geometric_intersection.bbox_intersect(nodes1, nodes2)
if bbox_int != INTERSECTION_T:
return [], None, ()
# We need **all** edges (as nodes).
edge_nodes1 = _surface_helpers.compute_edge_nodes(nodes1, degree1)
edge_nodes2 = _surface_helpers.compute_edge_nodes(nodes2, degree2)
# Run through **all** pairs of edges.
intersections, duplicates, unused, all_types = surface_intersections(
edge_nodes1, edge_nodes2, all_intersections
)
edge_infos, contained = _surface_helpers.combine_intersections(
intersections, nodes1, degree1, nodes2, degree2, all_types
)
# Verify the duplicates and intersection if need be.
if verify:
verify_duplicates(duplicates, intersections + unused)
verify_edge_segments(edge_infos)
if edge_infos is None or edge_infos == []:
return edge_infos, contained, ()
return edge_infos, contained, edge_nodes1 + edge_nodes2 | python | def generic_intersect(
nodes1, degree1, nodes2, degree2, verify, all_intersections
):
r"""Find all intersections among edges of two surfaces.
This treats intersections which have ``s == 1.0`` or ``t == 1.0``
as duplicates. The duplicates will be checked by :func:`verify_duplicates`
if ``verify`` is :data:`True`.
Args:
nodes1 (numpy.ndarray): The nodes defining the first surface in
the intersection (assumed in :math:\mathbf{R}^2`).
degree1 (int): The degree of the surface given by ``nodes1``.
nodes2 (numpy.ndarray): The nodes defining the second surface in
the intersection (assumed in :math:\mathbf{R}^2`).
degree2 (int): The degree of the surface given by ``nodes2``.
verify (Optional[bool]): Indicates if duplicate intersections
should be checked.
all_intersections (Callable): A helper that intersects B |eacute| zier
curves. Takes the nodes of each curve as input and returns an
array (``2 x N``) of intersections.
Returns:
Tuple[Optional[list], Optional[bool], tuple]: 3-tuple of
* List of "edge info" lists. Each list represents a curved polygon
and contains 3-tuples of edge index, start and end (see the
output of :func:`ends_to_curve`).
* "Contained" boolean. If not :data:`None`, indicates
that one of the surfaces is contained in the other.
* The nodes of three edges of the first surface being intersected
followed by the nodes of the three edges of the second.
"""
bbox_int = _geometric_intersection.bbox_intersect(nodes1, nodes2)
if bbox_int != INTERSECTION_T:
return [], None, ()
# We need **all** edges (as nodes).
edge_nodes1 = _surface_helpers.compute_edge_nodes(nodes1, degree1)
edge_nodes2 = _surface_helpers.compute_edge_nodes(nodes2, degree2)
# Run through **all** pairs of edges.
intersections, duplicates, unused, all_types = surface_intersections(
edge_nodes1, edge_nodes2, all_intersections
)
edge_infos, contained = _surface_helpers.combine_intersections(
intersections, nodes1, degree1, nodes2, degree2, all_types
)
# Verify the duplicates and intersection if need be.
if verify:
verify_duplicates(duplicates, intersections + unused)
verify_edge_segments(edge_infos)
if edge_infos is None or edge_infos == []:
return edge_infos, contained, ()
return edge_infos, contained, edge_nodes1 + edge_nodes2 | [
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as duplicates. The duplicates will be checked by :func:`verify_duplicates`
if ``verify`` is :data:`True`.
Args:
nodes1 (numpy.ndarray): The nodes defining the first surface in
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degree1 (int): The degree of the surface given by ``nodes1``.
nodes2 (numpy.ndarray): The nodes defining the second surface in
the intersection (assumed in :math:\mathbf{R}^2`).
degree2 (int): The degree of the surface given by ``nodes2``.
verify (Optional[bool]): Indicates if duplicate intersections
should be checked.
all_intersections (Callable): A helper that intersects B |eacute| zier
curves. Takes the nodes of each curve as input and returns an
array (``2 x N``) of intersections.
Returns:
Tuple[Optional[list], Optional[bool], tuple]: 3-tuple of
* List of "edge info" lists. Each list represents a curved polygon
and contains 3-tuples of edge index, start and end (see the
output of :func:`ends_to_curve`).
* "Contained" boolean. If not :data:`None`, indicates
that one of the surfaces is contained in the other.
* The nodes of three edges of the first surface being intersected
followed by the nodes of the three edges of the second. | [
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] | train | https://github.com/dhermes/bezier/blob/4f941f82637a8e70a5b159a9203132192e23406b/src/bezier/_surface_intersection.py#L756-L810 |
dhermes/bezier | src/bezier/_surface_intersection.py | _geometric_intersect | def _geometric_intersect(nodes1, degree1, nodes2, degree2, verify):
r"""Find all intersections among edges of two surfaces.
.. note::
There is also a Fortran implementation of this function, which
will be used if it can be built.
Uses :func:`generic_intersect` with the
:attr:`~.IntersectionStrategy.GEOMETRIC` intersection strategy.
Args:
nodes1 (numpy.ndarray): The nodes defining the first surface in
the intersection (assumed in :math:\mathbf{R}^2`).
degree1 (int): The degree of the surface given by ``nodes1``.
nodes2 (numpy.ndarray): The nodes defining the second surface in
the intersection (assumed in :math:\mathbf{R}^2`).
degree2 (int): The degree of the surface given by ``nodes2``.
verify (Optional[bool]): Indicates if duplicate intersections
should be checked.
Returns:
Tuple[Optional[list], Optional[bool], tuple]: 3-tuple of
* List of "edge info" lists. Each list represents a curved polygon
and contains 3-tuples of edge index, start and end (see the
output of :func:`ends_to_curve`).
* "Contained" boolean. If not :data:`None`, indicates
that one of the surfaces is contained in the other.
* The nodes of three edges of the first surface being intersected
followed by the nodes of the three edges of the second.
"""
all_intersections = _geometric_intersection.all_intersections
return generic_intersect(
nodes1, degree1, nodes2, degree2, verify, all_intersections
) | python | def _geometric_intersect(nodes1, degree1, nodes2, degree2, verify):
r"""Find all intersections among edges of two surfaces.
.. note::
There is also a Fortran implementation of this function, which
will be used if it can be built.
Uses :func:`generic_intersect` with the
:attr:`~.IntersectionStrategy.GEOMETRIC` intersection strategy.
Args:
nodes1 (numpy.ndarray): The nodes defining the first surface in
the intersection (assumed in :math:\mathbf{R}^2`).
degree1 (int): The degree of the surface given by ``nodes1``.
nodes2 (numpy.ndarray): The nodes defining the second surface in
the intersection (assumed in :math:\mathbf{R}^2`).
degree2 (int): The degree of the surface given by ``nodes2``.
verify (Optional[bool]): Indicates if duplicate intersections
should be checked.
Returns:
Tuple[Optional[list], Optional[bool], tuple]: 3-tuple of
* List of "edge info" lists. Each list represents a curved polygon
and contains 3-tuples of edge index, start and end (see the
output of :func:`ends_to_curve`).
* "Contained" boolean. If not :data:`None`, indicates
that one of the surfaces is contained in the other.
* The nodes of three edges of the first surface being intersected
followed by the nodes of the three edges of the second.
"""
all_intersections = _geometric_intersection.all_intersections
return generic_intersect(
nodes1, degree1, nodes2, degree2, verify, all_intersections
) | [
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dhermes/bezier | src/bezier/_surface_intersection.py | algebraic_intersect | def algebraic_intersect(nodes1, degree1, nodes2, degree2, verify):
r"""Find all intersections among edges of two surfaces.
Uses :func:`generic_intersect` with the
:attr:`~.IntersectionStrategy.ALGEBRAIC` intersection strategy.
Args:
nodes1 (numpy.ndarray): The nodes defining the first surface in
the intersection (assumed in :math:\mathbf{R}^2`).
degree1 (int): The degree of the surface given by ``nodes1``.
nodes2 (numpy.ndarray): The nodes defining the second surface in
the intersection (assumed in :math:\mathbf{R}^2`).
degree2 (int): The degree of the surface given by ``nodes2``.
verify (Optional[bool]): Indicates if duplicate intersections
should be checked.
Returns:
Tuple[Optional[list], Optional[bool], tuple]: 3-tuple of
* List of "edge info" lists. Each list represents a curved polygon
and contains 3-tuples of edge index, start and end (see the
output of :func:`ends_to_curve`).
* "Contained" boolean. If not :data:`None`, indicates
that one of the surfaces is contained in the other.
* The nodes of three edges of the first surface being intersected
followed by the nodes of the three edges of the second.
"""
all_intersections = _algebraic_intersection.all_intersections
return generic_intersect(
nodes1, degree1, nodes2, degree2, verify, all_intersections
) | python | def algebraic_intersect(nodes1, degree1, nodes2, degree2, verify):
r"""Find all intersections among edges of two surfaces.
Uses :func:`generic_intersect` with the
:attr:`~.IntersectionStrategy.ALGEBRAIC` intersection strategy.
Args:
nodes1 (numpy.ndarray): The nodes defining the first surface in
the intersection (assumed in :math:\mathbf{R}^2`).
degree1 (int): The degree of the surface given by ``nodes1``.
nodes2 (numpy.ndarray): The nodes defining the second surface in
the intersection (assumed in :math:\mathbf{R}^2`).
degree2 (int): The degree of the surface given by ``nodes2``.
verify (Optional[bool]): Indicates if duplicate intersections
should be checked.
Returns:
Tuple[Optional[list], Optional[bool], tuple]: 3-tuple of
* List of "edge info" lists. Each list represents a curved polygon
and contains 3-tuples of edge index, start and end (see the
output of :func:`ends_to_curve`).
* "Contained" boolean. If not :data:`None`, indicates
that one of the surfaces is contained in the other.
* The nodes of three edges of the first surface being intersected
followed by the nodes of the three edges of the second.
"""
all_intersections = _algebraic_intersection.all_intersections
return generic_intersect(
nodes1, degree1, nodes2, degree2, verify, all_intersections
) | [
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dhermes/bezier | src/bezier/__config__.py | modify_path | def modify_path():
"""Modify the module search path."""
# Only modify path on Windows.
if os.name != "nt":
return
path = os.environ.get("PATH")
if path is None:
return
try:
extra_dll_dir = pkg_resources.resource_filename("bezier", "extra-dll")
if os.path.isdir(extra_dll_dir):
os.environ["PATH"] = path + os.pathsep + extra_dll_dir
except ImportError:
pass | python | def modify_path():
"""Modify the module search path."""
# Only modify path on Windows.
if os.name != "nt":
return
path = os.environ.get("PATH")
if path is None:
return
try:
extra_dll_dir = pkg_resources.resource_filename("bezier", "extra-dll")
if os.path.isdir(extra_dll_dir):
os.environ["PATH"] = path + os.pathsep + extra_dll_dir
except ImportError:
pass | [
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dhermes/bezier | src/bezier/__config__.py | handle_import_error | def handle_import_error(caught_exc, name):
"""Allow or re-raise an import error.
This is to distinguish between expected and unexpected import errors.
If the module is not found, it simply means the Cython / Fortran speedups
were not built with the package. If the error message is different, e.g.
``... undefined symbol: __curve_intersection_MOD_all_intersections``, then
the import error **should** be raised.
Args:
caught_exc (ImportError): An exception caught when trying to import
a Cython module.
name (str): The name of the module. For example, for the module
``bezier._curve_speedup``, the name is ``"_curve_speedup"``.
Raises:
ImportError: If the error message is different than the basic
"missing module" error message.
"""
for template in TEMPLATES:
expected_msg = template.format(name)
if caught_exc.args == (expected_msg,):
return
raise caught_exc | python | def handle_import_error(caught_exc, name):
"""Allow or re-raise an import error.
This is to distinguish between expected and unexpected import errors.
If the module is not found, it simply means the Cython / Fortran speedups
were not built with the package. If the error message is different, e.g.
``... undefined symbol: __curve_intersection_MOD_all_intersections``, then
the import error **should** be raised.
Args:
caught_exc (ImportError): An exception caught when trying to import
a Cython module.
name (str): The name of the module. For example, for the module
``bezier._curve_speedup``, the name is ``"_curve_speedup"``.
Raises:
ImportError: If the error message is different than the basic
"missing module" error message.
"""
for template in TEMPLATES:
expected_msg = template.format(name)
if caught_exc.args == (expected_msg,):
return
raise caught_exc | [
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dhermes/bezier | setup_helpers_macos.py | is_macos_gfortran | def is_macos_gfortran(f90_compiler):
"""Checks if the current build is ``gfortran`` on macOS.
Args:
f90_compiler (numpy.distutils.fcompiler.FCompiler): A Fortran compiler
instance.
Returns:
bool: Only :data:`True` if
* Current OS is macOS (checked via ``sys.platform``).
* ``f90_compiler`` corresponds to ``gfortran``.
"""
# NOTE: NumPy may not be installed, but we don't want **this** module to
# cause an import failure.
from numpy.distutils.fcompiler import gnu
# Only macOS.
if sys.platform != MAC_OS:
return False
# Only ``gfortran``.
if not isinstance(f90_compiler, gnu.Gnu95FCompiler):
return False
return True | python | def is_macos_gfortran(f90_compiler):
"""Checks if the current build is ``gfortran`` on macOS.
Args:
f90_compiler (numpy.distutils.fcompiler.FCompiler): A Fortran compiler
instance.
Returns:
bool: Only :data:`True` if
* Current OS is macOS (checked via ``sys.platform``).
* ``f90_compiler`` corresponds to ``gfortran``.
"""
# NOTE: NumPy may not be installed, but we don't want **this** module to
# cause an import failure.
from numpy.distutils.fcompiler import gnu
# Only macOS.
if sys.platform != MAC_OS:
return False
# Only ``gfortran``.
if not isinstance(f90_compiler, gnu.Gnu95FCompiler):
return False
return True | [
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dhermes/bezier | setup_helpers_macos.py | patch_f90_compiler | def patch_f90_compiler(f90_compiler):
"""Patch up ``f90_compiler.library_dirs``.
On macOS, a Homebrew installed ``gfortran`` needs some help. The
``numpy.distutils`` "default" constructor for ``Gnu95FCompiler`` only has
a single library search path, but there are many library paths included in
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Args:
f90_compiler (numpy.distutils.fcompiler.FCompiler): A Fortran compiler
instance.
"""
if not is_macos_gfortran(f90_compiler):
return
library_dirs = f90_compiler.library_dirs
# ``library_dirs`` is a list (i.e. mutable), so we can update in place.
library_dirs[:] = setup_helpers.gfortran_search_path(library_dirs) | python | def patch_f90_compiler(f90_compiler):
"""Patch up ``f90_compiler.library_dirs``.
On macOS, a Homebrew installed ``gfortran`` needs some help. The
``numpy.distutils`` "default" constructor for ``Gnu95FCompiler`` only has
a single library search path, but there are many library paths included in
the full ``gcc`` install.
Args:
f90_compiler (numpy.distutils.fcompiler.FCompiler): A Fortran compiler
instance.
"""
if not is_macos_gfortran(f90_compiler):
return
library_dirs = f90_compiler.library_dirs
# ``library_dirs`` is a list (i.e. mutable), so we can update in place.
library_dirs[:] = setup_helpers.gfortran_search_path(library_dirs) | [
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dhermes/bezier | src/bezier/surface.py | _make_intersection | def _make_intersection(edge_info, all_edge_nodes):
"""Convert a description of edges into a curved polygon.
.. note::
This is a helper used only by :meth:`.Surface.intersect`.
Args:
edge_info (Tuple[Tuple[int, float, float], ...]): Information
describing each edge in the curved polygon by indicating which
surface / edge on the surface and then start and end parameters
along that edge. (See :func:`.ends_to_curve`.)
all_edge_nodes (Tuple[numpy.ndarray, ...]): The nodes of three edges
of the first surface being intersected followed by the nodes of
the three edges of the second.
Returns:
.CurvedPolygon: The intersection corresponding to ``edge_info``.
"""
edges = []
for index, start, end in edge_info:
nodes = all_edge_nodes[index]
new_nodes = _curve_helpers.specialize_curve(nodes, start, end)
degree = new_nodes.shape[1] - 1
edge = _curve_mod.Curve(new_nodes, degree, _copy=False)
edges.append(edge)
return curved_polygon.CurvedPolygon(
*edges, metadata=edge_info, _verify=False
) | python | def _make_intersection(edge_info, all_edge_nodes):
"""Convert a description of edges into a curved polygon.
.. note::
This is a helper used only by :meth:`.Surface.intersect`.
Args:
edge_info (Tuple[Tuple[int, float, float], ...]): Information
describing each edge in the curved polygon by indicating which
surface / edge on the surface and then start and end parameters
along that edge. (See :func:`.ends_to_curve`.)
all_edge_nodes (Tuple[numpy.ndarray, ...]): The nodes of three edges
of the first surface being intersected followed by the nodes of
the three edges of the second.
Returns:
.CurvedPolygon: The intersection corresponding to ``edge_info``.
"""
edges = []
for index, start, end in edge_info:
nodes = all_edge_nodes[index]
new_nodes = _curve_helpers.specialize_curve(nodes, start, end)
degree = new_nodes.shape[1] - 1
edge = _curve_mod.Curve(new_nodes, degree, _copy=False)
edges.append(edge)
return curved_polygon.CurvedPolygon(
*edges, metadata=edge_info, _verify=False
) | [
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dhermes/bezier | src/bezier/surface.py | Surface.from_nodes | def from_nodes(cls, nodes, _copy=True):
"""Create a :class:`.Surface` from nodes.
Computes the ``degree`` based on the shape of ``nodes``.
Args:
nodes (numpy.ndarray): The nodes in the surface. The columns
represent each node while the rows are the dimension
of the ambient space.
_copy (bool): Flag indicating if the nodes should be copied before
being stored. Defaults to :data:`True` since callers may
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Returns:
Surface: The constructed surface.
"""
_, num_nodes = nodes.shape
degree = cls._get_degree(num_nodes)
return cls(nodes, degree, _copy=_copy) | python | def from_nodes(cls, nodes, _copy=True):
"""Create a :class:`.Surface` from nodes.
Computes the ``degree`` based on the shape of ``nodes``.
Args:
nodes (numpy.ndarray): The nodes in the surface. The columns
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_copy (bool): Flag indicating if the nodes should be copied before
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Returns:
Surface: The constructed surface.
"""
_, num_nodes = nodes.shape
degree = cls._get_degree(num_nodes)
return cls(nodes, degree, _copy=_copy) | [
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dhermes/bezier | src/bezier/surface.py | Surface._get_degree | def _get_degree(num_nodes):
"""Get the degree of the current surface.
Args:
num_nodes (int): The number of control points for a
B |eacute| zier surface.
Returns:
int: The degree :math:`d` such that :math:`(d + 1)(d + 2)/2`
equals ``num_nodes``.
Raises:
ValueError: If ``num_nodes`` isn't a triangular number.
"""
# 8 * num_nodes = 4(d + 1)(d + 2)
# = 4d^2 + 12d + 8
# = (2d + 3)^2 - 1
d_float = 0.5 * (np.sqrt(8.0 * num_nodes + 1.0) - 3.0)
d_int = int(np.round(d_float))
if (d_int + 1) * (d_int + 2) == 2 * num_nodes:
return d_int
else:
raise ValueError(num_nodes, "not a triangular number") | python | def _get_degree(num_nodes):
"""Get the degree of the current surface.
Args:
num_nodes (int): The number of control points for a
B |eacute| zier surface.
Returns:
int: The degree :math:`d` such that :math:`(d + 1)(d + 2)/2`
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Raises:
ValueError: If ``num_nodes`` isn't a triangular number.
"""
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# = 4d^2 + 12d + 8
# = (2d + 3)^2 - 1
d_float = 0.5 * (np.sqrt(8.0 * num_nodes + 1.0) - 3.0)
d_int = int(np.round(d_float))
if (d_int + 1) * (d_int + 2) == 2 * num_nodes:
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else:
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dhermes/bezier | src/bezier/surface.py | Surface.area | def area(self):
r"""The area of the current surface.
For surfaces in :math:`\mathbf{R}^2`, this computes the area via
Green's theorem. Using the vector field :math:`\mathbf{F} =
\left[-y, x\right]^T`, since :math:`\partial_x(x) - \partial_y(-y) = 2`
Green's theorem says twice the area is equal to
.. math::
\int_{B\left(\mathcal{U}\right)} 2 \, d\mathbf{x} =
\int_{\partial B\left(\mathcal{U}\right)} -y \, dx + x \, dy.
This relies on the assumption that the current surface is valid, which
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.. math::
\int_C -y \, dx + x \, dy = \int_0^1 (x y' - y x') \, dr
= \sum_{i < j} (x_i y_j - y_i x_j) \int_0^1 b_{i, d}
b'_{j, d} \, dr
where :math:`b_{i, d}, b_{j, d}` are Bernstein basis polynomials.
Returns:
float: The area of the current surface.
Raises:
NotImplementedError: If the current surface isn't in
:math:`\mathbf{R}^2`.
"""
if self._dimension != 2:
raise NotImplementedError(
"2D is the only supported dimension",
"Current dimension",
self._dimension,
)
edge1, edge2, edge3 = self._get_edges()
return _surface_helpers.compute_area(
(edge1._nodes, edge2._nodes, edge3._nodes)
) | python | def area(self):
r"""The area of the current surface.
For surfaces in :math:`\mathbf{R}^2`, this computes the area via
Green's theorem. Using the vector field :math:`\mathbf{F} =
\left[-y, x\right]^T`, since :math:`\partial_x(x) - \partial_y(-y) = 2`
Green's theorem says twice the area is equal to
.. math::
\int_{B\left(\mathcal{U}\right)} 2 \, d\mathbf{x} =
\int_{\partial B\left(\mathcal{U}\right)} -y \, dx + x \, dy.
This relies on the assumption that the current surface is valid, which
implies that the image of the unit triangle under the B |eacute| zier
map --- :math:`B\left(\mathcal{U}\right)` --- has the edges of the
surface as its boundary.
Note that for a given edge :math:`C(r)` with control points
:math:`x_j, y_j`, the integral can be simplified:
.. math::
\int_C -y \, dx + x \, dy = \int_0^1 (x y' - y x') \, dr
= \sum_{i < j} (x_i y_j - y_i x_j) \int_0^1 b_{i, d}
b'_{j, d} \, dr
where :math:`b_{i, d}, b_{j, d}` are Bernstein basis polynomials.
Returns:
float: The area of the current surface.
Raises:
NotImplementedError: If the current surface isn't in
:math:`\mathbf{R}^2`.
"""
if self._dimension != 2:
raise NotImplementedError(
"2D is the only supported dimension",
"Current dimension",
self._dimension,
)
edge1, edge2, edge3 = self._get_edges()
return _surface_helpers.compute_area(
(edge1._nodes, edge2._nodes, edge3._nodes)
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.. math::
\int_C -y \, dx + x \, dy = \int_0^1 (x y' - y x') \, dr
= \sum_{i < j} (x_i y_j - y_i x_j) \int_0^1 b_{i, d}
b'_{j, d} \, dr
where :math:`b_{i, d}, b_{j, d}` are Bernstein basis polynomials.
Returns:
float: The area of the current surface.
Raises:
NotImplementedError: If the current surface isn't in
:math:`\mathbf{R}^2`. | [
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] | train | https://github.com/dhermes/bezier/blob/4f941f82637a8e70a5b159a9203132192e23406b/src/bezier/surface.py#L240-L286 |
dhermes/bezier | src/bezier/surface.py | Surface._compute_edges | def _compute_edges(self):
"""Compute the edges of the current surface.
Returns:
Tuple[~curve.Curve, ~curve.Curve, ~curve.Curve]: The edges of
the surface.
"""
nodes1, nodes2, nodes3 = _surface_helpers.compute_edge_nodes(
self._nodes, self._degree
)
edge1 = _curve_mod.Curve(nodes1, self._degree, _copy=False)
edge2 = _curve_mod.Curve(nodes2, self._degree, _copy=False)
edge3 = _curve_mod.Curve(nodes3, self._degree, _copy=False)
return edge1, edge2, edge3 | python | def _compute_edges(self):
"""Compute the edges of the current surface.
Returns:
Tuple[~curve.Curve, ~curve.Curve, ~curve.Curve]: The edges of
the surface.
"""
nodes1, nodes2, nodes3 = _surface_helpers.compute_edge_nodes(
self._nodes, self._degree
)
edge1 = _curve_mod.Curve(nodes1, self._degree, _copy=False)
edge2 = _curve_mod.Curve(nodes2, self._degree, _copy=False)
edge3 = _curve_mod.Curve(nodes3, self._degree, _copy=False)
return edge1, edge2, edge3 | [
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dhermes/bezier | src/bezier/surface.py | Surface._get_edges | def _get_edges(self):
"""Get the edges for the current surface.
If they haven't been computed yet, first compute and store them.
This is provided as a means for internal calls to get the edges
without copying (since :attr:`.edges` copies before giving to
a user to keep the stored data immutable).
Returns:
Tuple[~bezier.curve.Curve, ~bezier.curve.Curve, \
~bezier.curve.Curve]: The edges of
the surface.
"""
if self._edges is None:
self._edges = self._compute_edges()
return self._edges | python | def _get_edges(self):
"""Get the edges for the current surface.
If they haven't been computed yet, first compute and store them.
This is provided as a means for internal calls to get the edges
without copying (since :attr:`.edges` copies before giving to
a user to keep the stored data immutable).
Returns:
Tuple[~bezier.curve.Curve, ~bezier.curve.Curve, \
~bezier.curve.Curve]: The edges of
the surface.
"""
if self._edges is None:
self._edges = self._compute_edges()
return self._edges | [
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If they haven't been computed yet, first compute and store them.
This is provided as a means for internal calls to get the edges
without copying (since :attr:`.edges` copies before giving to
a user to keep the stored data immutable).
Returns:
Tuple[~bezier.curve.Curve, ~bezier.curve.Curve, \
~bezier.curve.Curve]: The edges of
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dhermes/bezier | src/bezier/surface.py | Surface.edges | def edges(self):
"""The edges of the surface.
.. doctest:: surface-edges
:options: +NORMALIZE_WHITESPACE
>>> nodes = np.asfortranarray([
... [0.0, 0.5 , 1.0, 0.1875, 0.625, 0.0],
... [0.0, -0.1875, 0.0, 0.5 , 0.625, 1.0],
... ])
>>> surface = bezier.Surface(nodes, degree=2)
>>> edge1, _, _ = surface.edges
>>> edge1
<Curve (degree=2, dimension=2)>
>>> edge1.nodes
array([[ 0. , 0.5 , 1. ],
[ 0. , -0.1875, 0. ]])
Returns:
Tuple[~bezier.curve.Curve, ~bezier.curve.Curve, \
~bezier.curve.Curve]: The edges of
the surface.
"""
edge1, edge2, edge3 = self._get_edges()
# NOTE: It is crucial that we return copies here. Since the edges
# are cached, if they were mutable, callers could
# inadvertently mutate the cached value.
edge1 = edge1._copy() # pylint: disable=protected-access
edge2 = edge2._copy() # pylint: disable=protected-access
edge3 = edge3._copy() # pylint: disable=protected-access
return edge1, edge2, edge3 | python | def edges(self):
"""The edges of the surface.
.. doctest:: surface-edges
:options: +NORMALIZE_WHITESPACE
>>> nodes = np.asfortranarray([
... [0.0, 0.5 , 1.0, 0.1875, 0.625, 0.0],
... [0.0, -0.1875, 0.0, 0.5 , 0.625, 1.0],
... ])
>>> surface = bezier.Surface(nodes, degree=2)
>>> edge1, _, _ = surface.edges
>>> edge1
<Curve (degree=2, dimension=2)>
>>> edge1.nodes
array([[ 0. , 0.5 , 1. ],
[ 0. , -0.1875, 0. ]])
Returns:
Tuple[~bezier.curve.Curve, ~bezier.curve.Curve, \
~bezier.curve.Curve]: The edges of
the surface.
"""
edge1, edge2, edge3 = self._get_edges()
# NOTE: It is crucial that we return copies here. Since the edges
# are cached, if they were mutable, callers could
# inadvertently mutate the cached value.
edge1 = edge1._copy() # pylint: disable=protected-access
edge2 = edge2._copy() # pylint: disable=protected-access
edge3 = edge3._copy() # pylint: disable=protected-access
return edge1, edge2, edge3 | [
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.. doctest:: surface-edges
:options: +NORMALIZE_WHITESPACE
>>> nodes = np.asfortranarray([
... [0.0, 0.5 , 1.0, 0.1875, 0.625, 0.0],
... [0.0, -0.1875, 0.0, 0.5 , 0.625, 1.0],
... ])
>>> surface = bezier.Surface(nodes, degree=2)
>>> edge1, _, _ = surface.edges
>>> edge1
<Curve (degree=2, dimension=2)>
>>> edge1.nodes
array([[ 0. , 0.5 , 1. ],
[ 0. , -0.1875, 0. ]])
Returns:
Tuple[~bezier.curve.Curve, ~bezier.curve.Curve, \
~bezier.curve.Curve]: The edges of
the surface. | [
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] | train | https://github.com/dhermes/bezier/blob/4f941f82637a8e70a5b159a9203132192e23406b/src/bezier/surface.py#L322-L352 |
dhermes/bezier | src/bezier/surface.py | Surface._verify_barycentric | def _verify_barycentric(lambda1, lambda2, lambda3):
"""Verifies that weights are barycentric and on the reference triangle.
I.e., checks that they sum to one and are all non-negative.
Args:
lambda1 (float): Parameter along the reference triangle.
lambda2 (float): Parameter along the reference triangle.
lambda3 (float): Parameter along the reference triangle.
Raises:
ValueError: If the weights are not valid barycentric
coordinates, i.e. they don't sum to ``1``.
ValueError: If some weights are negative.
"""
weights_total = lambda1 + lambda2 + lambda3
if not np.allclose(weights_total, 1.0, atol=0.0):
raise ValueError(
"Weights do not sum to 1", lambda1, lambda2, lambda3
)
if lambda1 < 0.0 or lambda2 < 0.0 or lambda3 < 0.0:
raise ValueError(
"Weights must be positive", lambda1, lambda2, lambda3
) | python | def _verify_barycentric(lambda1, lambda2, lambda3):
"""Verifies that weights are barycentric and on the reference triangle.
I.e., checks that they sum to one and are all non-negative.
Args:
lambda1 (float): Parameter along the reference triangle.
lambda2 (float): Parameter along the reference triangle.
lambda3 (float): Parameter along the reference triangle.
Raises:
ValueError: If the weights are not valid barycentric
coordinates, i.e. they don't sum to ``1``.
ValueError: If some weights are negative.
"""
weights_total = lambda1 + lambda2 + lambda3
if not np.allclose(weights_total, 1.0, atol=0.0):
raise ValueError(
"Weights do not sum to 1", lambda1, lambda2, lambda3
)
if lambda1 < 0.0 or lambda2 < 0.0 or lambda3 < 0.0:
raise ValueError(
"Weights must be positive", lambda1, lambda2, lambda3
) | [
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I.e., checks that they sum to one and are all non-negative.
Args:
lambda1 (float): Parameter along the reference triangle.
lambda2 (float): Parameter along the reference triangle.
lambda3 (float): Parameter along the reference triangle.
Raises:
ValueError: If the weights are not valid barycentric
coordinates, i.e. they don't sum to ``1``.
ValueError: If some weights are negative. | [
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dhermes/bezier | src/bezier/surface.py | Surface.evaluate_barycentric | def evaluate_barycentric(self, lambda1, lambda2, lambda3, _verify=True):
r"""Compute a point on the surface.
Evaluates :math:`B\left(\lambda_1, \lambda_2, \lambda_3\right)`.
.. image:: ../../images/surface_evaluate_barycentric.png
:align: center
.. testsetup:: surface-barycentric, surface-barycentric-fail1,
surface-barycentric-fail2, surface-barycentric-no-verify
import numpy as np
import bezier
nodes = np.asfortranarray([
[0.0, 0.5, 1.0 , 0.125, 0.375, 0.25],
[0.0, 0.0, 0.25, 0.5 , 0.375, 1.0 ],
])
surface = bezier.Surface(nodes, degree=2)
.. doctest:: surface-barycentric
:options: +NORMALIZE_WHITESPACE
>>> nodes = np.asfortranarray([
... [0.0, 0.5, 1.0 , 0.125, 0.375, 0.25],
... [0.0, 0.0, 0.25, 0.5 , 0.375, 1.0 ],
... ])
>>> surface = bezier.Surface(nodes, degree=2)
>>> point = surface.evaluate_barycentric(0.125, 0.125, 0.75)
>>> point
array([[0.265625 ],
[0.73046875]])
.. testcleanup:: surface-barycentric
import make_images
make_images.surface_evaluate_barycentric(surface, point)
However, this can't be used for points **outside** the
reference triangle:
.. doctest:: surface-barycentric-fail1
>>> surface.evaluate_barycentric(-0.25, 0.75, 0.5)
Traceback (most recent call last):
...
ValueError: ('Weights must be positive', -0.25, 0.75, 0.5)
or for non-barycentric coordinates;
.. doctest:: surface-barycentric-fail2
>>> surface.evaluate_barycentric(0.25, 0.25, 0.25)
Traceback (most recent call last):
...
ValueError: ('Weights do not sum to 1', 0.25, 0.25, 0.25)
However, these "invalid" inputs can be used if ``_verify`` is
:data:`False`.
.. doctest:: surface-barycentric-no-verify
:options: +NORMALIZE_WHITESPACE
>>> surface.evaluate_barycentric(-0.25, 0.75, 0.5, _verify=False)
array([[0.6875 ],
[0.546875]])
>>> surface.evaluate_barycentric(0.25, 0.25, 0.25, _verify=False)
array([[0.203125],
[0.1875 ]])
Args:
lambda1 (float): Parameter along the reference triangle.
lambda2 (float): Parameter along the reference triangle.
lambda3 (float): Parameter along the reference triangle.
_verify (Optional[bool]): Indicates if the barycentric coordinates
should be verified as summing to one and all non-negative (i.e.
verified as barycentric). Can either be used to evaluate at
points outside the domain, or to save time when the caller
already knows the input is verified. Defaults to :data:`True`.
Returns:
numpy.ndarray: The point on the surface (as a two dimensional
NumPy array with a single column).
Raises:
ValueError: If the weights are not valid barycentric
coordinates, i.e. they don't sum to ``1``. (Won't raise if
``_verify=False``.)
ValueError: If some weights are negative. (Won't raise if
``_verify=False``.)
"""
if _verify:
self._verify_barycentric(lambda1, lambda2, lambda3)
return _surface_helpers.evaluate_barycentric(
self._nodes, self._degree, lambda1, lambda2, lambda3
) | python | def evaluate_barycentric(self, lambda1, lambda2, lambda3, _verify=True):
r"""Compute a point on the surface.
Evaluates :math:`B\left(\lambda_1, \lambda_2, \lambda_3\right)`.
.. image:: ../../images/surface_evaluate_barycentric.png
:align: center
.. testsetup:: surface-barycentric, surface-barycentric-fail1,
surface-barycentric-fail2, surface-barycentric-no-verify
import numpy as np
import bezier
nodes = np.asfortranarray([
[0.0, 0.5, 1.0 , 0.125, 0.375, 0.25],
[0.0, 0.0, 0.25, 0.5 , 0.375, 1.0 ],
])
surface = bezier.Surface(nodes, degree=2)
.. doctest:: surface-barycentric
:options: +NORMALIZE_WHITESPACE
>>> nodes = np.asfortranarray([
... [0.0, 0.5, 1.0 , 0.125, 0.375, 0.25],
... [0.0, 0.0, 0.25, 0.5 , 0.375, 1.0 ],
... ])
>>> surface = bezier.Surface(nodes, degree=2)
>>> point = surface.evaluate_barycentric(0.125, 0.125, 0.75)
>>> point
array([[0.265625 ],
[0.73046875]])
.. testcleanup:: surface-barycentric
import make_images
make_images.surface_evaluate_barycentric(surface, point)
However, this can't be used for points **outside** the
reference triangle:
.. doctest:: surface-barycentric-fail1
>>> surface.evaluate_barycentric(-0.25, 0.75, 0.5)
Traceback (most recent call last):
...
ValueError: ('Weights must be positive', -0.25, 0.75, 0.5)
or for non-barycentric coordinates;
.. doctest:: surface-barycentric-fail2
>>> surface.evaluate_barycentric(0.25, 0.25, 0.25)
Traceback (most recent call last):
...
ValueError: ('Weights do not sum to 1', 0.25, 0.25, 0.25)
However, these "invalid" inputs can be used if ``_verify`` is
:data:`False`.
.. doctest:: surface-barycentric-no-verify
:options: +NORMALIZE_WHITESPACE
>>> surface.evaluate_barycentric(-0.25, 0.75, 0.5, _verify=False)
array([[0.6875 ],
[0.546875]])
>>> surface.evaluate_barycentric(0.25, 0.25, 0.25, _verify=False)
array([[0.203125],
[0.1875 ]])
Args:
lambda1 (float): Parameter along the reference triangle.
lambda2 (float): Parameter along the reference triangle.
lambda3 (float): Parameter along the reference triangle.
_verify (Optional[bool]): Indicates if the barycentric coordinates
should be verified as summing to one and all non-negative (i.e.
verified as barycentric). Can either be used to evaluate at
points outside the domain, or to save time when the caller
already knows the input is verified. Defaults to :data:`True`.
Returns:
numpy.ndarray: The point on the surface (as a two dimensional
NumPy array with a single column).
Raises:
ValueError: If the weights are not valid barycentric
coordinates, i.e. they don't sum to ``1``. (Won't raise if
``_verify=False``.)
ValueError: If some weights are negative. (Won't raise if
``_verify=False``.)
"""
if _verify:
self._verify_barycentric(lambda1, lambda2, lambda3)
return _surface_helpers.evaluate_barycentric(
self._nodes, self._degree, lambda1, lambda2, lambda3
) | [
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Evaluates :math:`B\left(\lambda_1, \lambda_2, \lambda_3\right)`.
.. image:: ../../images/surface_evaluate_barycentric.png
:align: center
.. testsetup:: surface-barycentric, surface-barycentric-fail1,
surface-barycentric-fail2, surface-barycentric-no-verify
import numpy as np
import bezier
nodes = np.asfortranarray([
[0.0, 0.5, 1.0 , 0.125, 0.375, 0.25],
[0.0, 0.0, 0.25, 0.5 , 0.375, 1.0 ],
])
surface = bezier.Surface(nodes, degree=2)
.. doctest:: surface-barycentric
:options: +NORMALIZE_WHITESPACE
>>> nodes = np.asfortranarray([
... [0.0, 0.5, 1.0 , 0.125, 0.375, 0.25],
... [0.0, 0.0, 0.25, 0.5 , 0.375, 1.0 ],
... ])
>>> surface = bezier.Surface(nodes, degree=2)
>>> point = surface.evaluate_barycentric(0.125, 0.125, 0.75)
>>> point
array([[0.265625 ],
[0.73046875]])
.. testcleanup:: surface-barycentric
import make_images
make_images.surface_evaluate_barycentric(surface, point)
However, this can't be used for points **outside** the
reference triangle:
.. doctest:: surface-barycentric-fail1
>>> surface.evaluate_barycentric(-0.25, 0.75, 0.5)
Traceback (most recent call last):
...
ValueError: ('Weights must be positive', -0.25, 0.75, 0.5)
or for non-barycentric coordinates;
.. doctest:: surface-barycentric-fail2
>>> surface.evaluate_barycentric(0.25, 0.25, 0.25)
Traceback (most recent call last):
...
ValueError: ('Weights do not sum to 1', 0.25, 0.25, 0.25)
However, these "invalid" inputs can be used if ``_verify`` is
:data:`False`.
.. doctest:: surface-barycentric-no-verify
:options: +NORMALIZE_WHITESPACE
>>> surface.evaluate_barycentric(-0.25, 0.75, 0.5, _verify=False)
array([[0.6875 ],
[0.546875]])
>>> surface.evaluate_barycentric(0.25, 0.25, 0.25, _verify=False)
array([[0.203125],
[0.1875 ]])
Args:
lambda1 (float): Parameter along the reference triangle.
lambda2 (float): Parameter along the reference triangle.
lambda3 (float): Parameter along the reference triangle.
_verify (Optional[bool]): Indicates if the barycentric coordinates
should be verified as summing to one and all non-negative (i.e.
verified as barycentric). Can either be used to evaluate at
points outside the domain, or to save time when the caller
already knows the input is verified. Defaults to :data:`True`.
Returns:
numpy.ndarray: The point on the surface (as a two dimensional
NumPy array with a single column).
Raises:
ValueError: If the weights are not valid barycentric
coordinates, i.e. they don't sum to ``1``. (Won't raise if
``_verify=False``.)
ValueError: If some weights are negative. (Won't raise if
``_verify=False``.) | [
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] | train | https://github.com/dhermes/bezier/blob/4f941f82637a8e70a5b159a9203132192e23406b/src/bezier/surface.py#L381-L475 |
dhermes/bezier | src/bezier/surface.py | Surface.evaluate_barycentric_multi | def evaluate_barycentric_multi(self, param_vals, _verify=True):
r"""Compute multiple points on the surface.
Assumes ``param_vals`` has three columns of barycentric coordinates.
See :meth:`evaluate_barycentric` for more details on how each row of
parameter values is evaluated.
.. image:: ../../images/surface_evaluate_barycentric_multi.png
:align: center
.. doctest:: surface-eval-multi2
:options: +NORMALIZE_WHITESPACE
>>> nodes = np.asfortranarray([
... [0.0, 1.0 , 2.0, -1.5, -0.5, -3.0],
... [0.0, 0.75, 1.0, 1.0, 1.5, 2.0],
... ])
>>> surface = bezier.Surface(nodes, degree=2)
>>> surface
<Surface (degree=2, dimension=2)>
>>> param_vals = np.asfortranarray([
... [0. , 0.25, 0.75 ],
... [1. , 0. , 0. ],
... [0.25 , 0.5 , 0.25 ],
... [0.375, 0.25, 0.375],
... ])
>>> points = surface.evaluate_barycentric_multi(param_vals)
>>> points
array([[-1.75 , 0. , 0.25 , -0.625 ],
[ 1.75 , 0. , 1.0625 , 1.046875]])
.. testcleanup:: surface-eval-multi2
import make_images
make_images.surface_evaluate_barycentric_multi(surface, points)
Args:
param_vals (numpy.ndarray): Array of parameter values (as a
``N x 3`` array).
_verify (Optional[bool]): Indicates if the coordinates should be
verified. See :meth:`evaluate_barycentric`. Defaults to
:data:`True`. Will also double check that ``param_vals``
is the right shape.
Returns:
numpy.ndarray: The points on the surface.
Raises:
ValueError: If ``param_vals`` is not a 2D array and
``_verify=True``.
"""
if _verify:
if param_vals.ndim != 2:
raise ValueError("Parameter values must be 2D array")
for lambda1, lambda2, lambda3 in param_vals:
self._verify_barycentric(lambda1, lambda2, lambda3)
return _surface_helpers.evaluate_barycentric_multi(
self._nodes, self._degree, param_vals, self._dimension
) | python | def evaluate_barycentric_multi(self, param_vals, _verify=True):
r"""Compute multiple points on the surface.
Assumes ``param_vals`` has three columns of barycentric coordinates.
See :meth:`evaluate_barycentric` for more details on how each row of
parameter values is evaluated.
.. image:: ../../images/surface_evaluate_barycentric_multi.png
:align: center
.. doctest:: surface-eval-multi2
:options: +NORMALIZE_WHITESPACE
>>> nodes = np.asfortranarray([
... [0.0, 1.0 , 2.0, -1.5, -0.5, -3.0],
... [0.0, 0.75, 1.0, 1.0, 1.5, 2.0],
... ])
>>> surface = bezier.Surface(nodes, degree=2)
>>> surface
<Surface (degree=2, dimension=2)>
>>> param_vals = np.asfortranarray([
... [0. , 0.25, 0.75 ],
... [1. , 0. , 0. ],
... [0.25 , 0.5 , 0.25 ],
... [0.375, 0.25, 0.375],
... ])
>>> points = surface.evaluate_barycentric_multi(param_vals)
>>> points
array([[-1.75 , 0. , 0.25 , -0.625 ],
[ 1.75 , 0. , 1.0625 , 1.046875]])
.. testcleanup:: surface-eval-multi2
import make_images
make_images.surface_evaluate_barycentric_multi(surface, points)
Args:
param_vals (numpy.ndarray): Array of parameter values (as a
``N x 3`` array).
_verify (Optional[bool]): Indicates if the coordinates should be
verified. See :meth:`evaluate_barycentric`. Defaults to
:data:`True`. Will also double check that ``param_vals``
is the right shape.
Returns:
numpy.ndarray: The points on the surface.
Raises:
ValueError: If ``param_vals`` is not a 2D array and
``_verify=True``.
"""
if _verify:
if param_vals.ndim != 2:
raise ValueError("Parameter values must be 2D array")
for lambda1, lambda2, lambda3 in param_vals:
self._verify_barycentric(lambda1, lambda2, lambda3)
return _surface_helpers.evaluate_barycentric_multi(
self._nodes, self._degree, param_vals, self._dimension
) | [
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Assumes ``param_vals`` has three columns of barycentric coordinates.
See :meth:`evaluate_barycentric` for more details on how each row of
parameter values is evaluated.
.. image:: ../../images/surface_evaluate_barycentric_multi.png
:align: center
.. doctest:: surface-eval-multi2
:options: +NORMALIZE_WHITESPACE
>>> nodes = np.asfortranarray([
... [0.0, 1.0 , 2.0, -1.5, -0.5, -3.0],
... [0.0, 0.75, 1.0, 1.0, 1.5, 2.0],
... ])
>>> surface = bezier.Surface(nodes, degree=2)
>>> surface
<Surface (degree=2, dimension=2)>
>>> param_vals = np.asfortranarray([
... [0. , 0.25, 0.75 ],
... [1. , 0. , 0. ],
... [0.25 , 0.5 , 0.25 ],
... [0.375, 0.25, 0.375],
... ])
>>> points = surface.evaluate_barycentric_multi(param_vals)
>>> points
array([[-1.75 , 0. , 0.25 , -0.625 ],
[ 1.75 , 0. , 1.0625 , 1.046875]])
.. testcleanup:: surface-eval-multi2
import make_images
make_images.surface_evaluate_barycentric_multi(surface, points)
Args:
param_vals (numpy.ndarray): Array of parameter values (as a
``N x 3`` array).
_verify (Optional[bool]): Indicates if the coordinates should be
verified. See :meth:`evaluate_barycentric`. Defaults to
:data:`True`. Will also double check that ``param_vals``
is the right shape.
Returns:
numpy.ndarray: The points on the surface.
Raises:
ValueError: If ``param_vals`` is not a 2D array and
``_verify=True``. | [
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] | train | https://github.com/dhermes/bezier/blob/4f941f82637a8e70a5b159a9203132192e23406b/src/bezier/surface.py#L477-L536 |
dhermes/bezier | src/bezier/surface.py | Surface._verify_cartesian | def _verify_cartesian(s, t):
"""Verifies that a point is in the reference triangle.
I.e., checks that they sum to <= one and are each non-negative.
Args:
s (float): Parameter along the reference triangle.
t (float): Parameter along the reference triangle.
Raises:
ValueError: If the point lies outside the reference triangle.
"""
if s < 0.0 or t < 0.0 or s + t > 1.0:
raise ValueError("Point lies outside reference triangle", s, t) | python | def _verify_cartesian(s, t):
"""Verifies that a point is in the reference triangle.
I.e., checks that they sum to <= one and are each non-negative.
Args:
s (float): Parameter along the reference triangle.
t (float): Parameter along the reference triangle.
Raises:
ValueError: If the point lies outside the reference triangle.
"""
if s < 0.0 or t < 0.0 or s + t > 1.0:
raise ValueError("Point lies outside reference triangle", s, t) | [
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I.e., checks that they sum to <= one and are each non-negative.
Args:
s (float): Parameter along the reference triangle.
t (float): Parameter along the reference triangle.
Raises:
ValueError: If the point lies outside the reference triangle. | [
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dhermes/bezier | src/bezier/surface.py | Surface.evaluate_cartesian | def evaluate_cartesian(self, s, t, _verify=True):
r"""Compute a point on the surface.
Evaluates :math:`B\left(1 - s - t, s, t\right)` by calling
:meth:`evaluate_barycentric`:
This method acts as a (partial) inverse to :meth:`locate`.
.. testsetup:: surface-cartesian
import numpy as np
import bezier
.. doctest:: surface-cartesian
:options: +NORMALIZE_WHITESPACE
>>> nodes = np.asfortranarray([
... [0.0, 0.5, 1.0 , 0.0, 0.5, 0.25],
... [0.0, 0.5, 0.625, 0.5, 0.5, 1.0 ],
... ])
>>> surface = bezier.Surface(nodes, degree=2)
>>> point = surface.evaluate_cartesian(0.125, 0.375)
>>> point
array([[0.16015625],
[0.44726562]])
>>> surface.evaluate_barycentric(0.5, 0.125, 0.375)
array([[0.16015625],
[0.44726562]])
Args:
s (float): Parameter along the reference triangle.
t (float): Parameter along the reference triangle.
_verify (Optional[bool]): Indicates if the coordinates should be
verified inside of the reference triangle. Defaults to
:data:`True`.
Returns:
numpy.ndarray: The point on the surface (as a two dimensional
NumPy array).
"""
if _verify:
self._verify_cartesian(s, t)
return _surface_helpers.evaluate_barycentric(
self._nodes, self._degree, 1.0 - s - t, s, t
) | python | def evaluate_cartesian(self, s, t, _verify=True):
r"""Compute a point on the surface.
Evaluates :math:`B\left(1 - s - t, s, t\right)` by calling
:meth:`evaluate_barycentric`:
This method acts as a (partial) inverse to :meth:`locate`.
.. testsetup:: surface-cartesian
import numpy as np
import bezier
.. doctest:: surface-cartesian
:options: +NORMALIZE_WHITESPACE
>>> nodes = np.asfortranarray([
... [0.0, 0.5, 1.0 , 0.0, 0.5, 0.25],
... [0.0, 0.5, 0.625, 0.5, 0.5, 1.0 ],
... ])
>>> surface = bezier.Surface(nodes, degree=2)
>>> point = surface.evaluate_cartesian(0.125, 0.375)
>>> point
array([[0.16015625],
[0.44726562]])
>>> surface.evaluate_barycentric(0.5, 0.125, 0.375)
array([[0.16015625],
[0.44726562]])
Args:
s (float): Parameter along the reference triangle.
t (float): Parameter along the reference triangle.
_verify (Optional[bool]): Indicates if the coordinates should be
verified inside of the reference triangle. Defaults to
:data:`True`.
Returns:
numpy.ndarray: The point on the surface (as a two dimensional
NumPy array).
"""
if _verify:
self._verify_cartesian(s, t)
return _surface_helpers.evaluate_barycentric(
self._nodes, self._degree, 1.0 - s - t, s, t
) | [
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Evaluates :math:`B\left(1 - s - t, s, t\right)` by calling
:meth:`evaluate_barycentric`:
This method acts as a (partial) inverse to :meth:`locate`.
.. testsetup:: surface-cartesian
import numpy as np
import bezier
.. doctest:: surface-cartesian
:options: +NORMALIZE_WHITESPACE
>>> nodes = np.asfortranarray([
... [0.0, 0.5, 1.0 , 0.0, 0.5, 0.25],
... [0.0, 0.5, 0.625, 0.5, 0.5, 1.0 ],
... ])
>>> surface = bezier.Surface(nodes, degree=2)
>>> point = surface.evaluate_cartesian(0.125, 0.375)
>>> point
array([[0.16015625],
[0.44726562]])
>>> surface.evaluate_barycentric(0.5, 0.125, 0.375)
array([[0.16015625],
[0.44726562]])
Args:
s (float): Parameter along the reference triangle.
t (float): Parameter along the reference triangle.
_verify (Optional[bool]): Indicates if the coordinates should be
verified inside of the reference triangle. Defaults to
:data:`True`.
Returns:
numpy.ndarray: The point on the surface (as a two dimensional
NumPy array). | [
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] | train | https://github.com/dhermes/bezier/blob/4f941f82637a8e70a5b159a9203132192e23406b/src/bezier/surface.py#L554-L598 |
dhermes/bezier | src/bezier/surface.py | Surface.evaluate_cartesian_multi | def evaluate_cartesian_multi(self, param_vals, _verify=True):
r"""Compute multiple points on the surface.
Assumes ``param_vals`` has two columns of Cartesian coordinates.
See :meth:`evaluate_cartesian` for more details on how each row of
parameter values is evaluated.
.. image:: ../../images/surface_evaluate_cartesian_multi.png
:align: center
.. doctest:: surface-eval-multi1
:options: +NORMALIZE_WHITESPACE
>>> nodes = np.asfortranarray([
... [0.0, 2.0, -3.0],
... [0.0, 1.0, 2.0],
... ])
>>> surface = bezier.Surface(nodes, degree=1)
>>> surface
<Surface (degree=1, dimension=2)>
>>> param_vals = np.asfortranarray([
... [0.0 , 0.0 ],
... [0.125, 0.625],
... [0.5 , 0.5 ],
... ])
>>> points = surface.evaluate_cartesian_multi(param_vals)
>>> points
array([[ 0. , -1.625, -0.5 ],
[ 0. , 1.375, 1.5 ]])
.. testcleanup:: surface-eval-multi1
import make_images
make_images.surface_evaluate_cartesian_multi(surface, points)
Args:
param_vals (numpy.ndarray): Array of parameter values (as a
``N x 2`` array).
_verify (Optional[bool]): Indicates if the coordinates should be
verified. See :meth:`evaluate_cartesian`. Defaults to
:data:`True`. Will also double check that ``param_vals``
is the right shape.
Returns:
numpy.ndarray: The points on the surface.
Raises:
ValueError: If ``param_vals`` is not a 2D array and
``_verify=True``.
"""
if _verify:
if param_vals.ndim != 2:
raise ValueError("Parameter values must be 2D array")
for s, t in param_vals:
self._verify_cartesian(s, t)
return _surface_helpers.evaluate_cartesian_multi(
self._nodes, self._degree, param_vals, self._dimension
) | python | def evaluate_cartesian_multi(self, param_vals, _verify=True):
r"""Compute multiple points on the surface.
Assumes ``param_vals`` has two columns of Cartesian coordinates.
See :meth:`evaluate_cartesian` for more details on how each row of
parameter values is evaluated.
.. image:: ../../images/surface_evaluate_cartesian_multi.png
:align: center
.. doctest:: surface-eval-multi1
:options: +NORMALIZE_WHITESPACE
>>> nodes = np.asfortranarray([
... [0.0, 2.0, -3.0],
... [0.0, 1.0, 2.0],
... ])
>>> surface = bezier.Surface(nodes, degree=1)
>>> surface
<Surface (degree=1, dimension=2)>
>>> param_vals = np.asfortranarray([
... [0.0 , 0.0 ],
... [0.125, 0.625],
... [0.5 , 0.5 ],
... ])
>>> points = surface.evaluate_cartesian_multi(param_vals)
>>> points
array([[ 0. , -1.625, -0.5 ],
[ 0. , 1.375, 1.5 ]])
.. testcleanup:: surface-eval-multi1
import make_images
make_images.surface_evaluate_cartesian_multi(surface, points)
Args:
param_vals (numpy.ndarray): Array of parameter values (as a
``N x 2`` array).
_verify (Optional[bool]): Indicates if the coordinates should be
verified. See :meth:`evaluate_cartesian`. Defaults to
:data:`True`. Will also double check that ``param_vals``
is the right shape.
Returns:
numpy.ndarray: The points on the surface.
Raises:
ValueError: If ``param_vals`` is not a 2D array and
``_verify=True``.
"""
if _verify:
if param_vals.ndim != 2:
raise ValueError("Parameter values must be 2D array")
for s, t in param_vals:
self._verify_cartesian(s, t)
return _surface_helpers.evaluate_cartesian_multi(
self._nodes, self._degree, param_vals, self._dimension
) | [
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Assumes ``param_vals`` has two columns of Cartesian coordinates.
See :meth:`evaluate_cartesian` for more details on how each row of
parameter values is evaluated.
.. image:: ../../images/surface_evaluate_cartesian_multi.png
:align: center
.. doctest:: surface-eval-multi1
:options: +NORMALIZE_WHITESPACE
>>> nodes = np.asfortranarray([
... [0.0, 2.0, -3.0],
... [0.0, 1.0, 2.0],
... ])
>>> surface = bezier.Surface(nodes, degree=1)
>>> surface
<Surface (degree=1, dimension=2)>
>>> param_vals = np.asfortranarray([
... [0.0 , 0.0 ],
... [0.125, 0.625],
... [0.5 , 0.5 ],
... ])
>>> points = surface.evaluate_cartesian_multi(param_vals)
>>> points
array([[ 0. , -1.625, -0.5 ],
[ 0. , 1.375, 1.5 ]])
.. testcleanup:: surface-eval-multi1
import make_images
make_images.surface_evaluate_cartesian_multi(surface, points)
Args:
param_vals (numpy.ndarray): Array of parameter values (as a
``N x 2`` array).
_verify (Optional[bool]): Indicates if the coordinates should be
verified. See :meth:`evaluate_cartesian`. Defaults to
:data:`True`. Will also double check that ``param_vals``
is the right shape.
Returns:
numpy.ndarray: The points on the surface.
Raises:
ValueError: If ``param_vals`` is not a 2D array and
``_verify=True``. | [
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dhermes/bezier | src/bezier/surface.py | Surface.plot | def plot(self, pts_per_edge, color=None, ax=None, with_nodes=False):
"""Plot the current surface.
Args:
pts_per_edge (int): Number of points to plot per edge.
color (Optional[Tuple[float, float, float]]): Color as RGB profile.
ax (Optional[matplotlib.artist.Artist]): matplotlib axis object
to add plot to.
with_nodes (Optional[bool]): Determines if the control points
should be added to the plot. Off by default.
Returns:
matplotlib.artist.Artist: The axis containing the plot. This
may be a newly created axis.
Raises:
NotImplementedError: If the surface's dimension is not ``2``.
"""
if self._dimension != 2:
raise NotImplementedError(
"2D is the only supported dimension",
"Current dimension",
self._dimension,
)
if ax is None:
ax = _plot_helpers.new_axis()
_plot_helpers.add_patch(ax, color, pts_per_edge, *self._get_edges())
if with_nodes:
ax.plot(
self._nodes[0, :],
self._nodes[1, :],
color="black",
marker="o",
linestyle="None",
)
return ax | python | def plot(self, pts_per_edge, color=None, ax=None, with_nodes=False):
"""Plot the current surface.
Args:
pts_per_edge (int): Number of points to plot per edge.
color (Optional[Tuple[float, float, float]]): Color as RGB profile.
ax (Optional[matplotlib.artist.Artist]): matplotlib axis object
to add plot to.
with_nodes (Optional[bool]): Determines if the control points
should be added to the plot. Off by default.
Returns:
matplotlib.artist.Artist: The axis containing the plot. This
may be a newly created axis.
Raises:
NotImplementedError: If the surface's dimension is not ``2``.
"""
if self._dimension != 2:
raise NotImplementedError(
"2D is the only supported dimension",
"Current dimension",
self._dimension,
)
if ax is None:
ax = _plot_helpers.new_axis()
_plot_helpers.add_patch(ax, color, pts_per_edge, *self._get_edges())
if with_nodes:
ax.plot(
self._nodes[0, :],
self._nodes[1, :],
color="black",
marker="o",
linestyle="None",
)
return ax | [
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ax (Optional[matplotlib.artist.Artist]): matplotlib axis object
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with_nodes (Optional[bool]): Determines if the control points
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Returns:
matplotlib.artist.Artist: The axis containing the plot. This
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Raises:
NotImplementedError: If the surface's dimension is not ``2``. | [
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] | train | https://github.com/dhermes/bezier/blob/4f941f82637a8e70a5b159a9203132192e23406b/src/bezier/surface.py#L660-L696 |
dhermes/bezier | src/bezier/surface.py | Surface.subdivide | def subdivide(self):
r"""Split the surface into four sub-surfaces.
Does so by taking the unit triangle (i.e. the domain
of the surface) and splitting it into four sub-triangles
.. image:: ../../images/surface_subdivide1.png
:align: center
Then the surface is re-parameterized via the map to / from the
given sub-triangles and the unit triangle.
For example, when a degree two surface is subdivided:
.. image:: ../../images/surface_subdivide2.png
:align: center
.. doctest:: surface-subdivide
:options: +NORMALIZE_WHITESPACE
>>> nodes = np.asfortranarray([
... [-1.0, 0.5, 2.0, 0.25, 2.0, 0.0],
... [ 0.0, 0.5, 0.0, 1.75, 3.0, 4.0],
... ])
>>> surface = bezier.Surface(nodes, degree=2)
>>> _, sub_surface_b, _, _ = surface.subdivide()
>>> sub_surface_b
<Surface (degree=2, dimension=2)>
>>> sub_surface_b.nodes
array([[ 1.5 , 0.6875, -0.125 , 1.1875, 0.4375, 0.5 ],
[ 2.5 , 2.3125, 1.875 , 1.3125, 1.3125, 0.25 ]])
.. testcleanup:: surface-subdivide
import make_images
make_images.surface_subdivide1()
make_images.surface_subdivide2(surface, sub_surface_b)
Returns:
Tuple[Surface, Surface, Surface, Surface]: The lower left, central,
lower right and upper left sub-surfaces (in that order).
"""
nodes_a, nodes_b, nodes_c, nodes_d = _surface_helpers.subdivide_nodes(
self._nodes, self._degree
)
return (
Surface(nodes_a, self._degree, _copy=False),
Surface(nodes_b, self._degree, _copy=False),
Surface(nodes_c, self._degree, _copy=False),
Surface(nodes_d, self._degree, _copy=False),
) | python | def subdivide(self):
r"""Split the surface into four sub-surfaces.
Does so by taking the unit triangle (i.e. the domain
of the surface) and splitting it into four sub-triangles
.. image:: ../../images/surface_subdivide1.png
:align: center
Then the surface is re-parameterized via the map to / from the
given sub-triangles and the unit triangle.
For example, when a degree two surface is subdivided:
.. image:: ../../images/surface_subdivide2.png
:align: center
.. doctest:: surface-subdivide
:options: +NORMALIZE_WHITESPACE
>>> nodes = np.asfortranarray([
... [-1.0, 0.5, 2.0, 0.25, 2.0, 0.0],
... [ 0.0, 0.5, 0.0, 1.75, 3.0, 4.0],
... ])
>>> surface = bezier.Surface(nodes, degree=2)
>>> _, sub_surface_b, _, _ = surface.subdivide()
>>> sub_surface_b
<Surface (degree=2, dimension=2)>
>>> sub_surface_b.nodes
array([[ 1.5 , 0.6875, -0.125 , 1.1875, 0.4375, 0.5 ],
[ 2.5 , 2.3125, 1.875 , 1.3125, 1.3125, 0.25 ]])
.. testcleanup:: surface-subdivide
import make_images
make_images.surface_subdivide1()
make_images.surface_subdivide2(surface, sub_surface_b)
Returns:
Tuple[Surface, Surface, Surface, Surface]: The lower left, central,
lower right and upper left sub-surfaces (in that order).
"""
nodes_a, nodes_b, nodes_c, nodes_d = _surface_helpers.subdivide_nodes(
self._nodes, self._degree
)
return (
Surface(nodes_a, self._degree, _copy=False),
Surface(nodes_b, self._degree, _copy=False),
Surface(nodes_c, self._degree, _copy=False),
Surface(nodes_d, self._degree, _copy=False),
) | [
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Does so by taking the unit triangle (i.e. the domain
of the surface) and splitting it into four sub-triangles
.. image:: ../../images/surface_subdivide1.png
:align: center
Then the surface is re-parameterized via the map to / from the
given sub-triangles and the unit triangle.
For example, when a degree two surface is subdivided:
.. image:: ../../images/surface_subdivide2.png
:align: center
.. doctest:: surface-subdivide
:options: +NORMALIZE_WHITESPACE
>>> nodes = np.asfortranarray([
... [-1.0, 0.5, 2.0, 0.25, 2.0, 0.0],
... [ 0.0, 0.5, 0.0, 1.75, 3.0, 4.0],
... ])
>>> surface = bezier.Surface(nodes, degree=2)
>>> _, sub_surface_b, _, _ = surface.subdivide()
>>> sub_surface_b
<Surface (degree=2, dimension=2)>
>>> sub_surface_b.nodes
array([[ 1.5 , 0.6875, -0.125 , 1.1875, 0.4375, 0.5 ],
[ 2.5 , 2.3125, 1.875 , 1.3125, 1.3125, 0.25 ]])
.. testcleanup:: surface-subdivide
import make_images
make_images.surface_subdivide1()
make_images.surface_subdivide2(surface, sub_surface_b)
Returns:
Tuple[Surface, Surface, Surface, Surface]: The lower left, central,
lower right and upper left sub-surfaces (in that order). | [
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] | train | https://github.com/dhermes/bezier/blob/4f941f82637a8e70a5b159a9203132192e23406b/src/bezier/surface.py#L698-L748 |
dhermes/bezier | src/bezier/surface.py | Surface._compute_valid | def _compute_valid(self):
r"""Determines if the current surface is "valid".
Does this by checking if the Jacobian of the map from the
reference triangle is everywhere positive.
Returns:
bool: Flag indicating if the current surface is valid.
Raises:
NotImplementedError: If the surface is in a dimension other
than :math:`\mathbf{R}^2`.
.UnsupportedDegree: If the degree is not 1, 2 or 3.
"""
if self._dimension != 2:
raise NotImplementedError("Validity check only implemented in R^2")
poly_sign = None
if self._degree == 1:
# In the linear case, we are only invalid if the points
# are collinear.
first_deriv = self._nodes[:, 1:] - self._nodes[:, :-1]
poly_sign = _SIGN(np.linalg.det(first_deriv))
elif self._degree == 2:
bernstein = _surface_helpers.quadratic_jacobian_polynomial(
self._nodes
)
poly_sign = _surface_helpers.polynomial_sign(bernstein, 2)
elif self._degree == 3:
bernstein = _surface_helpers.cubic_jacobian_polynomial(self._nodes)
poly_sign = _surface_helpers.polynomial_sign(bernstein, 4)
else:
raise _helpers.UnsupportedDegree(self._degree, supported=(1, 2, 3))
return poly_sign == 1 | python | def _compute_valid(self):
r"""Determines if the current surface is "valid".
Does this by checking if the Jacobian of the map from the
reference triangle is everywhere positive.
Returns:
bool: Flag indicating if the current surface is valid.
Raises:
NotImplementedError: If the surface is in a dimension other
than :math:`\mathbf{R}^2`.
.UnsupportedDegree: If the degree is not 1, 2 or 3.
"""
if self._dimension != 2:
raise NotImplementedError("Validity check only implemented in R^2")
poly_sign = None
if self._degree == 1:
# In the linear case, we are only invalid if the points
# are collinear.
first_deriv = self._nodes[:, 1:] - self._nodes[:, :-1]
poly_sign = _SIGN(np.linalg.det(first_deriv))
elif self._degree == 2:
bernstein = _surface_helpers.quadratic_jacobian_polynomial(
self._nodes
)
poly_sign = _surface_helpers.polynomial_sign(bernstein, 2)
elif self._degree == 3:
bernstein = _surface_helpers.cubic_jacobian_polynomial(self._nodes)
poly_sign = _surface_helpers.polynomial_sign(bernstein, 4)
else:
raise _helpers.UnsupportedDegree(self._degree, supported=(1, 2, 3))
return poly_sign == 1 | [
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Does this by checking if the Jacobian of the map from the
reference triangle is everywhere positive.
Returns:
bool: Flag indicating if the current surface is valid.
Raises:
NotImplementedError: If the surface is in a dimension other
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.UnsupportedDegree: If the degree is not 1, 2 or 3. | [
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] | train | https://github.com/dhermes/bezier/blob/4f941f82637a8e70a5b159a9203132192e23406b/src/bezier/surface.py#L750-L784 |
dhermes/bezier | src/bezier/surface.py | Surface.locate | def locate(self, point, _verify=True):
r"""Find a point on the current surface.
Solves for :math:`s` and :math:`t` in :math:`B(s, t) = p`.
This method acts as a (partial) inverse to :meth:`evaluate_cartesian`.
.. warning::
A unique solution is only guaranteed if the current surface is
valid. This code assumes a valid surface, but doesn't check.
.. image:: ../../images/surface_locate.png
:align: center
.. doctest:: surface-locate
>>> nodes = np.asfortranarray([
... [0.0, 0.5 , 1.0, 0.25, 0.75, 0.0],
... [0.0, -0.25, 0.0, 0.5 , 0.75, 1.0],
... ])
>>> surface = bezier.Surface(nodes, degree=2)
>>> point = np.asfortranarray([
... [0.59375],
... [0.25 ],
... ])
>>> s, t = surface.locate(point)
>>> s
0.5
>>> t
0.25
.. testcleanup:: surface-locate
import make_images
make_images.surface_locate(surface, point)
Args:
point (numpy.ndarray): A (``D x 1``) point on the surface,
where :math:`D` is the dimension of the surface.
_verify (Optional[bool]): Indicates if extra caution should be
used to verify assumptions about the inputs. Can be
disabled to speed up execution time. Defaults to :data:`True`.
Returns:
Optional[Tuple[float, float]]: The :math:`s` and :math:`t`
values corresponding to ``point`` or :data:`None` if the point
is not on the surface.
Raises:
NotImplementedError: If the surface isn't in :math:`\mathbf{R}^2`.
ValueError: If the dimension of the ``point`` doesn't match the
dimension of the current surface.
"""
if _verify:
if self._dimension != 2:
raise NotImplementedError("Only 2D surfaces supported.")
if point.shape != (self._dimension, 1):
point_dimensions = " x ".join(
str(dimension) for dimension in point.shape
)
msg = _LOCATE_ERROR_TEMPLATE.format(
self._dimension, self._dimension, point, point_dimensions
)
raise ValueError(msg)
return _surface_intersection.locate_point(
self._nodes, self._degree, point[0, 0], point[1, 0]
) | python | def locate(self, point, _verify=True):
r"""Find a point on the current surface.
Solves for :math:`s` and :math:`t` in :math:`B(s, t) = p`.
This method acts as a (partial) inverse to :meth:`evaluate_cartesian`.
.. warning::
A unique solution is only guaranteed if the current surface is
valid. This code assumes a valid surface, but doesn't check.
.. image:: ../../images/surface_locate.png
:align: center
.. doctest:: surface-locate
>>> nodes = np.asfortranarray([
... [0.0, 0.5 , 1.0, 0.25, 0.75, 0.0],
... [0.0, -0.25, 0.0, 0.5 , 0.75, 1.0],
... ])
>>> surface = bezier.Surface(nodes, degree=2)
>>> point = np.asfortranarray([
... [0.59375],
... [0.25 ],
... ])
>>> s, t = surface.locate(point)
>>> s
0.5
>>> t
0.25
.. testcleanup:: surface-locate
import make_images
make_images.surface_locate(surface, point)
Args:
point (numpy.ndarray): A (``D x 1``) point on the surface,
where :math:`D` is the dimension of the surface.
_verify (Optional[bool]): Indicates if extra caution should be
used to verify assumptions about the inputs. Can be
disabled to speed up execution time. Defaults to :data:`True`.
Returns:
Optional[Tuple[float, float]]: The :math:`s` and :math:`t`
values corresponding to ``point`` or :data:`None` if the point
is not on the surface.
Raises:
NotImplementedError: If the surface isn't in :math:`\mathbf{R}^2`.
ValueError: If the dimension of the ``point`` doesn't match the
dimension of the current surface.
"""
if _verify:
if self._dimension != 2:
raise NotImplementedError("Only 2D surfaces supported.")
if point.shape != (self._dimension, 1):
point_dimensions = " x ".join(
str(dimension) for dimension in point.shape
)
msg = _LOCATE_ERROR_TEMPLATE.format(
self._dimension, self._dimension, point, point_dimensions
)
raise ValueError(msg)
return _surface_intersection.locate_point(
self._nodes, self._degree, point[0, 0], point[1, 0]
) | [
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Solves for :math:`s` and :math:`t` in :math:`B(s, t) = p`.
This method acts as a (partial) inverse to :meth:`evaluate_cartesian`.
.. warning::
A unique solution is only guaranteed if the current surface is
valid. This code assumes a valid surface, but doesn't check.
.. image:: ../../images/surface_locate.png
:align: center
.. doctest:: surface-locate
>>> nodes = np.asfortranarray([
... [0.0, 0.5 , 1.0, 0.25, 0.75, 0.0],
... [0.0, -0.25, 0.0, 0.5 , 0.75, 1.0],
... ])
>>> surface = bezier.Surface(nodes, degree=2)
>>> point = np.asfortranarray([
... [0.59375],
... [0.25 ],
... ])
>>> s, t = surface.locate(point)
>>> s
0.5
>>> t
0.25
.. testcleanup:: surface-locate
import make_images
make_images.surface_locate(surface, point)
Args:
point (numpy.ndarray): A (``D x 1``) point on the surface,
where :math:`D` is the dimension of the surface.
_verify (Optional[bool]): Indicates if extra caution should be
used to verify assumptions about the inputs. Can be
disabled to speed up execution time. Defaults to :data:`True`.
Returns:
Optional[Tuple[float, float]]: The :math:`s` and :math:`t`
values corresponding to ``point`` or :data:`None` if the point
is not on the surface.
Raises:
NotImplementedError: If the surface isn't in :math:`\mathbf{R}^2`.
ValueError: If the dimension of the ``point`` doesn't match the
dimension of the current surface. | [
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] | train | https://github.com/dhermes/bezier/blob/4f941f82637a8e70a5b159a9203132192e23406b/src/bezier/surface.py#L877-L946 |
dhermes/bezier | src/bezier/surface.py | Surface.intersect | def intersect(self, other, strategy=_STRATEGY.GEOMETRIC, _verify=True):
"""Find the common intersection with another surface.
Args:
other (Surface): Other surface to intersect with.
strategy (Optional[~bezier.curve.IntersectionStrategy]): The
intersection algorithm to use. Defaults to geometric.
_verify (Optional[bool]): Indicates if extra caution should be
used to verify assumptions about the algorithm as it
proceeds. Can be disabled to speed up execution time.
Defaults to :data:`True`.
Returns:
List[Union[~bezier.curved_polygon.CurvedPolygon, \
~bezier.surface.Surface]]: List of intersections (possibly empty).
Raises:
TypeError: If ``other`` is not a surface (and ``_verify=True``).
NotImplementedError: If at least one of the surfaces
isn't two-dimensional (and ``_verify=True``).
ValueError: If ``strategy`` is not a valid
:class:`.IntersectionStrategy`.
"""
if _verify:
if not isinstance(other, Surface):
raise TypeError(
"Can only intersect with another surface",
"Received",
other,
)
if self._dimension != 2 or other._dimension != 2:
raise NotImplementedError(
"Intersection only implemented in 2D"
)
if strategy == _STRATEGY.GEOMETRIC:
do_intersect = _surface_intersection.geometric_intersect
elif strategy == _STRATEGY.ALGEBRAIC:
do_intersect = _surface_intersection.algebraic_intersect
else:
raise ValueError("Unexpected strategy.", strategy)
edge_infos, contained, all_edge_nodes = do_intersect(
self._nodes, self._degree, other._nodes, other._degree, _verify
)
if edge_infos is None:
if contained:
return [self]
else:
return [other]
else:
return [
_make_intersection(edge_info, all_edge_nodes)
for edge_info in edge_infos
] | python | def intersect(self, other, strategy=_STRATEGY.GEOMETRIC, _verify=True):
"""Find the common intersection with another surface.
Args:
other (Surface): Other surface to intersect with.
strategy (Optional[~bezier.curve.IntersectionStrategy]): The
intersection algorithm to use. Defaults to geometric.
_verify (Optional[bool]): Indicates if extra caution should be
used to verify assumptions about the algorithm as it
proceeds. Can be disabled to speed up execution time.
Defaults to :data:`True`.
Returns:
List[Union[~bezier.curved_polygon.CurvedPolygon, \
~bezier.surface.Surface]]: List of intersections (possibly empty).
Raises:
TypeError: If ``other`` is not a surface (and ``_verify=True``).
NotImplementedError: If at least one of the surfaces
isn't two-dimensional (and ``_verify=True``).
ValueError: If ``strategy`` is not a valid
:class:`.IntersectionStrategy`.
"""
if _verify:
if not isinstance(other, Surface):
raise TypeError(
"Can only intersect with another surface",
"Received",
other,
)
if self._dimension != 2 or other._dimension != 2:
raise NotImplementedError(
"Intersection only implemented in 2D"
)
if strategy == _STRATEGY.GEOMETRIC:
do_intersect = _surface_intersection.geometric_intersect
elif strategy == _STRATEGY.ALGEBRAIC:
do_intersect = _surface_intersection.algebraic_intersect
else:
raise ValueError("Unexpected strategy.", strategy)
edge_infos, contained, all_edge_nodes = do_intersect(
self._nodes, self._degree, other._nodes, other._degree, _verify
)
if edge_infos is None:
if contained:
return [self]
else:
return [other]
else:
return [
_make_intersection(edge_info, all_edge_nodes)
for edge_info in edge_infos
] | [
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] | Find the common intersection with another surface.
Args:
other (Surface): Other surface to intersect with.
strategy (Optional[~bezier.curve.IntersectionStrategy]): The
intersection algorithm to use. Defaults to geometric.
_verify (Optional[bool]): Indicates if extra caution should be
used to verify assumptions about the algorithm as it
proceeds. Can be disabled to speed up execution time.
Defaults to :data:`True`.
Returns:
List[Union[~bezier.curved_polygon.CurvedPolygon, \
~bezier.surface.Surface]]: List of intersections (possibly empty).
Raises:
TypeError: If ``other`` is not a surface (and ``_verify=True``).
NotImplementedError: If at least one of the surfaces
isn't two-dimensional (and ``_verify=True``).
ValueError: If ``strategy`` is not a valid
:class:`.IntersectionStrategy`. | [
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] | train | https://github.com/dhermes/bezier/blob/4f941f82637a8e70a5b159a9203132192e23406b/src/bezier/surface.py#L948-L1005 |
dhermes/bezier | src/bezier/surface.py | Surface.elevate | def elevate(self):
r"""Return a degree-elevated version of the current surface.
Does this by converting the current nodes
:math:`\left\{v_{i, j, k}\right\}_{i + j + k = d}` to new nodes
:math:`\left\{w_{i, j, k}\right\}_{i + j + k = d + 1}`. Does so
by re-writing
.. math::
E\left(\lambda_1, \lambda_2, \lambda_3\right) =
\left(\lambda_1 + \lambda_2 + \lambda_3\right)
B\left(\lambda_1, \lambda_2, \lambda_3\right) =
\sum_{i + j + k = d + 1} \binom{d + 1}{i \, j \, k}
\lambda_1^i \lambda_2^j \lambda_3^k \cdot w_{i, j, k}
In this form, we must have
.. math::
\begin{align*}
\binom{d + 1}{i \, j \, k} \cdot w_{i, j, k} &=
\binom{d}{i - 1 \, j \, k} \cdot v_{i - 1, j, k} +
\binom{d}{i \, j - 1 \, k} \cdot v_{i, j - 1, k} +
\binom{d}{i \, j \, k - 1} \cdot v_{i, j, k - 1} \\
\Longleftrightarrow (d + 1) \cdot w_{i, j, k} &=
i \cdot v_{i - 1, j, k} + j \cdot v_{i, j - 1, k} +
k \cdot v_{i, j, k - 1}
\end{align*}
where we define, for example, :math:`v_{i, j, k - 1} = 0`
if :math:`k = 0`.
.. image:: ../../images/surface_elevate.png
:align: center
.. doctest:: surface-elevate
:options: +NORMALIZE_WHITESPACE
>>> nodes = np.asfortranarray([
... [0.0, 1.5, 3.0, 0.75, 2.25, 0.0],
... [0.0, 0.0, 0.0, 1.5 , 2.25, 3.0],
... ])
>>> surface = bezier.Surface(nodes, degree=2)
>>> elevated = surface.elevate()
>>> elevated
<Surface (degree=3, dimension=2)>
>>> elevated.nodes
array([[0. , 1. , 2. , 3. , 0.5 , 1.5 , 2.5 , 0.5 , 1.5 , 0. ],
[0. , 0. , 0. , 0. , 1. , 1.25, 1.5 , 2. , 2.5 , 3. ]])
.. testcleanup:: surface-elevate
import make_images
make_images.surface_elevate(surface, elevated)
Returns:
Surface: The degree-elevated surface.
"""
_, num_nodes = self._nodes.shape
# (d + 1)(d + 2)/2 --> (d + 2)(d + 3)/2
num_new = num_nodes + self._degree + 2
new_nodes = np.zeros((self._dimension, num_new), order="F")
# NOTE: We start from the index triples (i, j, k) for the current
# nodes and map them onto (i + 1, j, k), etc. This index
# tracking is also done in :func:`.de_casteljau_one_round`.
index = 0
# parent_i1 = index + k
# parent_i2 = index + k + 1
# parent_i3 = index + degree + 2
parent_i1 = 0
parent_i2 = 1
parent_i3 = self._degree + 2
for k in six.moves.xrange(self._degree + 1):
for j in six.moves.xrange(self._degree + 1 - k):
i = self._degree - j - k
new_nodes[:, parent_i1] += (i + 1) * self._nodes[:, index]
new_nodes[:, parent_i2] += (j + 1) * self._nodes[:, index]
new_nodes[:, parent_i3] += (k + 1) * self._nodes[:, index]
# Update all the indices.
parent_i1 += 1
parent_i2 += 1
parent_i3 += 1
index += 1
# Update the indices that depend on k.
parent_i1 += 1
parent_i2 += 1
# Hold off on division until the end, to (attempt to) avoid round-off.
denominator = self._degree + 1.0
new_nodes /= denominator
return Surface(new_nodes, self._degree + 1, _copy=False) | python | def elevate(self):
r"""Return a degree-elevated version of the current surface.
Does this by converting the current nodes
:math:`\left\{v_{i, j, k}\right\}_{i + j + k = d}` to new nodes
:math:`\left\{w_{i, j, k}\right\}_{i + j + k = d + 1}`. Does so
by re-writing
.. math::
E\left(\lambda_1, \lambda_2, \lambda_3\right) =
\left(\lambda_1 + \lambda_2 + \lambda_3\right)
B\left(\lambda_1, \lambda_2, \lambda_3\right) =
\sum_{i + j + k = d + 1} \binom{d + 1}{i \, j \, k}
\lambda_1^i \lambda_2^j \lambda_3^k \cdot w_{i, j, k}
In this form, we must have
.. math::
\begin{align*}
\binom{d + 1}{i \, j \, k} \cdot w_{i, j, k} &=
\binom{d}{i - 1 \, j \, k} \cdot v_{i - 1, j, k} +
\binom{d}{i \, j - 1 \, k} \cdot v_{i, j - 1, k} +
\binom{d}{i \, j \, k - 1} \cdot v_{i, j, k - 1} \\
\Longleftrightarrow (d + 1) \cdot w_{i, j, k} &=
i \cdot v_{i - 1, j, k} + j \cdot v_{i, j - 1, k} +
k \cdot v_{i, j, k - 1}
\end{align*}
where we define, for example, :math:`v_{i, j, k - 1} = 0`
if :math:`k = 0`.
.. image:: ../../images/surface_elevate.png
:align: center
.. doctest:: surface-elevate
:options: +NORMALIZE_WHITESPACE
>>> nodes = np.asfortranarray([
... [0.0, 1.5, 3.0, 0.75, 2.25, 0.0],
... [0.0, 0.0, 0.0, 1.5 , 2.25, 3.0],
... ])
>>> surface = bezier.Surface(nodes, degree=2)
>>> elevated = surface.elevate()
>>> elevated
<Surface (degree=3, dimension=2)>
>>> elevated.nodes
array([[0. , 1. , 2. , 3. , 0.5 , 1.5 , 2.5 , 0.5 , 1.5 , 0. ],
[0. , 0. , 0. , 0. , 1. , 1.25, 1.5 , 2. , 2.5 , 3. ]])
.. testcleanup:: surface-elevate
import make_images
make_images.surface_elevate(surface, elevated)
Returns:
Surface: The degree-elevated surface.
"""
_, num_nodes = self._nodes.shape
# (d + 1)(d + 2)/2 --> (d + 2)(d + 3)/2
num_new = num_nodes + self._degree + 2
new_nodes = np.zeros((self._dimension, num_new), order="F")
# NOTE: We start from the index triples (i, j, k) for the current
# nodes and map them onto (i + 1, j, k), etc. This index
# tracking is also done in :func:`.de_casteljau_one_round`.
index = 0
# parent_i1 = index + k
# parent_i2 = index + k + 1
# parent_i3 = index + degree + 2
parent_i1 = 0
parent_i2 = 1
parent_i3 = self._degree + 2
for k in six.moves.xrange(self._degree + 1):
for j in six.moves.xrange(self._degree + 1 - k):
i = self._degree - j - k
new_nodes[:, parent_i1] += (i + 1) * self._nodes[:, index]
new_nodes[:, parent_i2] += (j + 1) * self._nodes[:, index]
new_nodes[:, parent_i3] += (k + 1) * self._nodes[:, index]
# Update all the indices.
parent_i1 += 1
parent_i2 += 1
parent_i3 += 1
index += 1
# Update the indices that depend on k.
parent_i1 += 1
parent_i2 += 1
# Hold off on division until the end, to (attempt to) avoid round-off.
denominator = self._degree + 1.0
new_nodes /= denominator
return Surface(new_nodes, self._degree + 1, _copy=False) | [
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Does this by converting the current nodes
:math:`\left\{v_{i, j, k}\right\}_{i + j + k = d}` to new nodes
:math:`\left\{w_{i, j, k}\right\}_{i + j + k = d + 1}`. Does so
by re-writing
.. math::
E\left(\lambda_1, \lambda_2, \lambda_3\right) =
\left(\lambda_1 + \lambda_2 + \lambda_3\right)
B\left(\lambda_1, \lambda_2, \lambda_3\right) =
\sum_{i + j + k = d + 1} \binom{d + 1}{i \, j \, k}
\lambda_1^i \lambda_2^j \lambda_3^k \cdot w_{i, j, k}
In this form, we must have
.. math::
\begin{align*}
\binom{d + 1}{i \, j \, k} \cdot w_{i, j, k} &=
\binom{d}{i - 1 \, j \, k} \cdot v_{i - 1, j, k} +
\binom{d}{i \, j - 1 \, k} \cdot v_{i, j - 1, k} +
\binom{d}{i \, j \, k - 1} \cdot v_{i, j, k - 1} \\
\Longleftrightarrow (d + 1) \cdot w_{i, j, k} &=
i \cdot v_{i - 1, j, k} + j \cdot v_{i, j - 1, k} +
k \cdot v_{i, j, k - 1}
\end{align*}
where we define, for example, :math:`v_{i, j, k - 1} = 0`
if :math:`k = 0`.
.. image:: ../../images/surface_elevate.png
:align: center
.. doctest:: surface-elevate
:options: +NORMALIZE_WHITESPACE
>>> nodes = np.asfortranarray([
... [0.0, 1.5, 3.0, 0.75, 2.25, 0.0],
... [0.0, 0.0, 0.0, 1.5 , 2.25, 3.0],
... ])
>>> surface = bezier.Surface(nodes, degree=2)
>>> elevated = surface.elevate()
>>> elevated
<Surface (degree=3, dimension=2)>
>>> elevated.nodes
array([[0. , 1. , 2. , 3. , 0.5 , 1.5 , 2.5 , 0.5 , 1.5 , 0. ],
[0. , 0. , 0. , 0. , 1. , 1.25, 1.5 , 2. , 2.5 , 3. ]])
.. testcleanup:: surface-elevate
import make_images
make_images.surface_elevate(surface, elevated)
Returns:
Surface: The degree-elevated surface. | [
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] | train | https://github.com/dhermes/bezier/blob/4f941f82637a8e70a5b159a9203132192e23406b/src/bezier/surface.py#L1007-L1097 |
dhermes/bezier | src/bezier/_intersection_helpers.py | _newton_refine | def _newton_refine(s, nodes1, t, nodes2):
r"""Apply one step of 2D Newton's method.
.. note::
There is also a Fortran implementation of this function, which
will be used if it can be built.
We want to use Newton's method on the function
.. math::
F(s, t) = B_1(s) - B_2(t)
to refine :math:`\left(s_{\ast}, t_{\ast}\right)`. Using this,
and the Jacobian :math:`DF`, we "solve"
.. math::
\left[\begin{array}{c}
0 \\ 0 \end{array}\right] \approx
F\left(s_{\ast} + \Delta s, t_{\ast} + \Delta t\right) \approx
F\left(s_{\ast}, t_{\ast}\right) +
\left[\begin{array}{c c}
B_1'\left(s_{\ast}\right) &
- B_2'\left(t_{\ast}\right) \end{array}\right]
\left[\begin{array}{c}
\Delta s \\ \Delta t \end{array}\right]
and refine with the component updates :math:`\Delta s` and
:math:`\Delta t`.
.. note::
This implementation assumes the curves live in
:math:`\mathbf{R}^2`.
For example, the curves
.. math::
\begin{align*}
B_1(s) &= \left[\begin{array}{c} 0 \\ 0 \end{array}\right] (1 - s)^2
+ \left[\begin{array}{c} 2 \\ 4 \end{array}\right] 2s(1 - s)
+ \left[\begin{array}{c} 4 \\ 0 \end{array}\right] s^2 \\
B_2(t) &= \left[\begin{array}{c} 2 \\ 0 \end{array}\right] (1 - t)
+ \left[\begin{array}{c} 0 \\ 3 \end{array}\right] t
\end{align*}
intersect at the point
:math:`B_1\left(\frac{1}{4}\right) = B_2\left(\frac{1}{2}\right) =
\frac{1}{2} \left[\begin{array}{c} 2 \\ 3 \end{array}\right]`.
However, starting from the wrong point we have
.. math::
\begin{align*}
F\left(\frac{3}{8}, \frac{1}{4}\right) &= \frac{1}{8}
\left[\begin{array}{c} 0 \\ 9 \end{array}\right] \\
DF\left(\frac{3}{8}, \frac{1}{4}\right) &=
\left[\begin{array}{c c}
4 & 2 \\ 2 & -3 \end{array}\right] \\
\Longrightarrow \left[\begin{array}{c} \Delta s \\ \Delta t
\end{array}\right] &= \frac{9}{64} \left[\begin{array}{c}
-1 \\ 2 \end{array}\right].
\end{align*}
.. image:: ../images/newton_refine1.png
:align: center
.. testsetup:: newton-refine1, newton-refine2, newton-refine3
import numpy as np
import bezier
from bezier._intersection_helpers import newton_refine
machine_eps = np.finfo(np.float64).eps
def cuberoot(value):
return np.cbrt(value)
.. doctest:: newton-refine1
>>> nodes1 = np.asfortranarray([
... [0.0, 2.0, 4.0],
... [0.0, 4.0, 0.0],
... ])
>>> nodes2 = np.asfortranarray([
... [2.0, 0.0],
... [0.0, 3.0],
... ])
>>> s, t = 0.375, 0.25
>>> new_s, new_t = newton_refine(s, nodes1, t, nodes2)
>>> 64.0 * (new_s - s)
-9.0
>>> 64.0 * (new_t - t)
18.0
.. testcleanup:: newton-refine1
import make_images
curve1 = bezier.Curve(nodes1, degree=2)
curve2 = bezier.Curve(nodes2, degree=1)
make_images.newton_refine1(s, new_s, curve1, t, new_t, curve2)
For "typical" curves, we converge to a solution quadratically.
This means that the number of correct digits doubles every
iteration (until machine precision is reached).
.. image:: ../images/newton_refine2.png
:align: center
.. doctest:: newton-refine2
>>> nodes1 = np.asfortranarray([
... [0.0, 0.25, 0.5, 0.75, 1.0],
... [0.0, 2.0 , -2.0, 2.0 , 0.0],
... ])
>>> nodes2 = np.asfortranarray([
... [0.0, 0.25, 0.5, 0.75, 1.0],
... [1.0, 0.5 , 0.5, 0.5 , 0.0],
... ])
>>> # The expected intersection is the only real root of
>>> # 28 s^3 - 30 s^2 + 9 s - 1.
>>> omega = cuberoot(28.0 * np.sqrt(17.0) + 132.0) / 28.0
>>> expected = 5.0 / 14.0 + omega + 1 / (49.0 * omega)
>>> s_vals = [0.625, None, None, None, None]
>>> t = 0.625
>>> np.log2(abs(expected - s_vals[0]))
-4.399...
>>> s_vals[1], t = newton_refine(s_vals[0], nodes1, t, nodes2)
>>> np.log2(abs(expected - s_vals[1]))
-7.901...
>>> s_vals[2], t = newton_refine(s_vals[1], nodes1, t, nodes2)
>>> np.log2(abs(expected - s_vals[2]))
-16.010...
>>> s_vals[3], t = newton_refine(s_vals[2], nodes1, t, nodes2)
>>> np.log2(abs(expected - s_vals[3]))
-32.110...
>>> s_vals[4], t = newton_refine(s_vals[3], nodes1, t, nodes2)
>>> np.allclose(s_vals[4], expected, rtol=machine_eps, atol=0.0)
True
.. testcleanup:: newton-refine2
import make_images
curve1 = bezier.Curve(nodes1, degree=4)
curve2 = bezier.Curve(nodes2, degree=4)
make_images.newton_refine2(s_vals, curve1, curve2)
However, when the intersection occurs at a point of tangency,
the convergence becomes linear. This means that the number of
correct digits added each iteration is roughly constant.
.. image:: ../images/newton_refine3.png
:align: center
.. doctest:: newton-refine3
>>> nodes1 = np.asfortranarray([
... [0.0, 0.5, 1.0],
... [0.0, 1.0, 0.0],
... ])
>>> nodes2 = np.asfortranarray([
... [0.0, 1.0],
... [0.5, 0.5],
... ])
>>> expected = 0.5
>>> s_vals = [0.375, None, None, None, None, None]
>>> t = 0.375
>>> np.log2(abs(expected - s_vals[0]))
-3.0
>>> s_vals[1], t = newton_refine(s_vals[0], nodes1, t, nodes2)
>>> np.log2(abs(expected - s_vals[1]))
-4.0
>>> s_vals[2], t = newton_refine(s_vals[1], nodes1, t, nodes2)
>>> np.log2(abs(expected - s_vals[2]))
-5.0
>>> s_vals[3], t = newton_refine(s_vals[2], nodes1, t, nodes2)
>>> np.log2(abs(expected - s_vals[3]))
-6.0
>>> s_vals[4], t = newton_refine(s_vals[3], nodes1, t, nodes2)
>>> np.log2(abs(expected - s_vals[4]))
-7.0
>>> s_vals[5], t = newton_refine(s_vals[4], nodes1, t, nodes2)
>>> np.log2(abs(expected - s_vals[5]))
-8.0
.. testcleanup:: newton-refine3
import make_images
curve1 = bezier.Curve(nodes1, degree=2)
curve2 = bezier.Curve(nodes2, degree=1)
make_images.newton_refine3(s_vals, curve1, curve2)
Unfortunately, the process terminates with an error that is not close
to machine precision :math:`\varepsilon` when
:math:`\Delta s = \Delta t = 0`.
.. testsetup:: newton-refine3-continued
import numpy as np
import bezier
from bezier._intersection_helpers import newton_refine
nodes1 = np.asfortranarray([
[0.0, 0.5, 1.0],
[0.0, 1.0, 0.0],
])
nodes2 = np.asfortranarray([
[0.0, 1.0],
[0.5, 0.5],
])
.. doctest:: newton-refine3-continued
>>> s1 = t1 = 0.5 - 0.5**27
>>> np.log2(0.5 - s1)
-27.0
>>> s2, t2 = newton_refine(s1, nodes1, t1, nodes2)
>>> s2 == t2
True
>>> np.log2(0.5 - s2)
-28.0
>>> s3, t3 = newton_refine(s2, nodes1, t2, nodes2)
>>> s3 == t3 == s2
True
Due to round-off near the point of tangency, the final error
resembles :math:`\sqrt{\varepsilon}` rather than machine
precision as expected.
.. note::
The following is not implemented in this function. It's just
an exploration on how the shortcomings might be addressed.
However, this can be overcome. At the point of tangency, we want
:math:`B_1'(s) \parallel B_2'(t)`. This can be checked numerically via
.. math::
B_1'(s) \times B_2'(t) = 0.
For the last example (the one that converges linearly), this is
.. math::
0 = \left[\begin{array}{c} 1 \\ 2 - 4s \end{array}\right] \times
\left[\begin{array}{c} 1 \\ 0 \end{array}\right] = 4 s - 2.
With this, we can modify Newton's method to find a zero of the
over-determined system
.. math::
G(s, t) = \left[\begin{array}{c} B_0(s) - B_1(t) \\
B_1'(s) \times B_2'(t) \end{array}\right] =
\left[\begin{array}{c} s - t \\ 2 s (1 - s) - \frac{1}{2} \\
4 s - 2\end{array}\right].
Since :math:`DG` is :math:`3 \times 2`, we can't invert it. However,
we can find a least-squares solution:
.. math::
\left(DG^T DG\right) \left[\begin{array}{c}
\Delta s \\ \Delta t \end{array}\right] = -DG^T G.
This only works if :math:`DG` has full rank. In this case, it does
since the submatrix containing the first and last rows has rank two:
.. math::
DG = \left[\begin{array}{c c} 1 & -1 \\
2 - 4 s & 0 \\
4 & 0 \end{array}\right].
Though this avoids a singular system, the normal equations have a
condition number that is the square of the condition number of the matrix.
Starting from :math:`s = t = \frac{3}{8}` as above:
.. testsetup:: newton-refine4
import numpy as np
from bezier import _helpers
def modified_update(s, t):
minus_G = np.asfortranarray([
[t - s],
[0.5 - 2.0 * s * (1.0 - s)],
[2.0 - 4.0 * s],
])
DG = np.asfortranarray([
[1.0, -1.0],
[2.0 - 4.0 * s, 0.0],
[4.0, 0.0],
])
DG_t = np.asfortranarray(DG.T)
LHS = _helpers.matrix_product(DG_t, DG)
RHS = _helpers.matrix_product(DG_t, minus_G)
delta_params = np.linalg.solve(LHS, RHS)
delta_s, delta_t = delta_params.flatten()
return s + delta_s, t + delta_t
.. doctest:: newton-refine4
>>> s0, t0 = 0.375, 0.375
>>> np.log2(0.5 - s0)
-3.0
>>> s1, t1 = modified_update(s0, t0)
>>> s1 == t1
True
>>> 1040.0 * s1
519.0
>>> np.log2(0.5 - s1)
-10.022...
>>> s2, t2 = modified_update(s1, t1)
>>> s2 == t2
True
>>> np.log2(0.5 - s2)
-31.067...
>>> s3, t3 = modified_update(s2, t2)
>>> s3 == t3 == 0.5
True
Args:
s (float): Parameter of a near-intersection along the first curve.
nodes1 (numpy.ndarray): Nodes of first curve forming intersection.
t (float): Parameter of a near-intersection along the second curve.
nodes2 (numpy.ndarray): Nodes of second curve forming intersection.
Returns:
Tuple[float, float]: The refined parameters from a single Newton
step.
Raises:
ValueError: If the Jacobian is singular at ``(s, t)``.
"""
# NOTE: We form -F(s, t) since we want to solve -DF^{-1} F(s, t).
func_val = _curve_helpers.evaluate_multi(
nodes2, np.asfortranarray([t])
) - _curve_helpers.evaluate_multi(nodes1, np.asfortranarray([s]))
if np.all(func_val == 0.0):
# No refinement is needed.
return s, t
# NOTE: This assumes the curves are 2D.
jac_mat = np.empty((2, 2), order="F")
jac_mat[:, :1] = _curve_helpers.evaluate_hodograph(s, nodes1)
jac_mat[:, 1:] = -_curve_helpers.evaluate_hodograph(t, nodes2)
# Solve the system.
singular, delta_s, delta_t = _helpers.solve2x2(jac_mat, func_val[:, 0])
if singular:
raise ValueError("Jacobian is singular.")
else:
return s + delta_s, t + delta_t | python | def _newton_refine(s, nodes1, t, nodes2):
r"""Apply one step of 2D Newton's method.
.. note::
There is also a Fortran implementation of this function, which
will be used if it can be built.
We want to use Newton's method on the function
.. math::
F(s, t) = B_1(s) - B_2(t)
to refine :math:`\left(s_{\ast}, t_{\ast}\right)`. Using this,
and the Jacobian :math:`DF`, we "solve"
.. math::
\left[\begin{array}{c}
0 \\ 0 \end{array}\right] \approx
F\left(s_{\ast} + \Delta s, t_{\ast} + \Delta t\right) \approx
F\left(s_{\ast}, t_{\ast}\right) +
\left[\begin{array}{c c}
B_1'\left(s_{\ast}\right) &
- B_2'\left(t_{\ast}\right) \end{array}\right]
\left[\begin{array}{c}
\Delta s \\ \Delta t \end{array}\right]
and refine with the component updates :math:`\Delta s` and
:math:`\Delta t`.
.. note::
This implementation assumes the curves live in
:math:`\mathbf{R}^2`.
For example, the curves
.. math::
\begin{align*}
B_1(s) &= \left[\begin{array}{c} 0 \\ 0 \end{array}\right] (1 - s)^2
+ \left[\begin{array}{c} 2 \\ 4 \end{array}\right] 2s(1 - s)
+ \left[\begin{array}{c} 4 \\ 0 \end{array}\right] s^2 \\
B_2(t) &= \left[\begin{array}{c} 2 \\ 0 \end{array}\right] (1 - t)
+ \left[\begin{array}{c} 0 \\ 3 \end{array}\right] t
\end{align*}
intersect at the point
:math:`B_1\left(\frac{1}{4}\right) = B_2\left(\frac{1}{2}\right) =
\frac{1}{2} \left[\begin{array}{c} 2 \\ 3 \end{array}\right]`.
However, starting from the wrong point we have
.. math::
\begin{align*}
F\left(\frac{3}{8}, \frac{1}{4}\right) &= \frac{1}{8}
\left[\begin{array}{c} 0 \\ 9 \end{array}\right] \\
DF\left(\frac{3}{8}, \frac{1}{4}\right) &=
\left[\begin{array}{c c}
4 & 2 \\ 2 & -3 \end{array}\right] \\
\Longrightarrow \left[\begin{array}{c} \Delta s \\ \Delta t
\end{array}\right] &= \frac{9}{64} \left[\begin{array}{c}
-1 \\ 2 \end{array}\right].
\end{align*}
.. image:: ../images/newton_refine1.png
:align: center
.. testsetup:: newton-refine1, newton-refine2, newton-refine3
import numpy as np
import bezier
from bezier._intersection_helpers import newton_refine
machine_eps = np.finfo(np.float64).eps
def cuberoot(value):
return np.cbrt(value)
.. doctest:: newton-refine1
>>> nodes1 = np.asfortranarray([
... [0.0, 2.0, 4.0],
... [0.0, 4.0, 0.0],
... ])
>>> nodes2 = np.asfortranarray([
... [2.0, 0.0],
... [0.0, 3.0],
... ])
>>> s, t = 0.375, 0.25
>>> new_s, new_t = newton_refine(s, nodes1, t, nodes2)
>>> 64.0 * (new_s - s)
-9.0
>>> 64.0 * (new_t - t)
18.0
.. testcleanup:: newton-refine1
import make_images
curve1 = bezier.Curve(nodes1, degree=2)
curve2 = bezier.Curve(nodes2, degree=1)
make_images.newton_refine1(s, new_s, curve1, t, new_t, curve2)
For "typical" curves, we converge to a solution quadratically.
This means that the number of correct digits doubles every
iteration (until machine precision is reached).
.. image:: ../images/newton_refine2.png
:align: center
.. doctest:: newton-refine2
>>> nodes1 = np.asfortranarray([
... [0.0, 0.25, 0.5, 0.75, 1.0],
... [0.0, 2.0 , -2.0, 2.0 , 0.0],
... ])
>>> nodes2 = np.asfortranarray([
... [0.0, 0.25, 0.5, 0.75, 1.0],
... [1.0, 0.5 , 0.5, 0.5 , 0.0],
... ])
>>> # The expected intersection is the only real root of
>>> # 28 s^3 - 30 s^2 + 9 s - 1.
>>> omega = cuberoot(28.0 * np.sqrt(17.0) + 132.0) / 28.0
>>> expected = 5.0 / 14.0 + omega + 1 / (49.0 * omega)
>>> s_vals = [0.625, None, None, None, None]
>>> t = 0.625
>>> np.log2(abs(expected - s_vals[0]))
-4.399...
>>> s_vals[1], t = newton_refine(s_vals[0], nodes1, t, nodes2)
>>> np.log2(abs(expected - s_vals[1]))
-7.901...
>>> s_vals[2], t = newton_refine(s_vals[1], nodes1, t, nodes2)
>>> np.log2(abs(expected - s_vals[2]))
-16.010...
>>> s_vals[3], t = newton_refine(s_vals[2], nodes1, t, nodes2)
>>> np.log2(abs(expected - s_vals[3]))
-32.110...
>>> s_vals[4], t = newton_refine(s_vals[3], nodes1, t, nodes2)
>>> np.allclose(s_vals[4], expected, rtol=machine_eps, atol=0.0)
True
.. testcleanup:: newton-refine2
import make_images
curve1 = bezier.Curve(nodes1, degree=4)
curve2 = bezier.Curve(nodes2, degree=4)
make_images.newton_refine2(s_vals, curve1, curve2)
However, when the intersection occurs at a point of tangency,
the convergence becomes linear. This means that the number of
correct digits added each iteration is roughly constant.
.. image:: ../images/newton_refine3.png
:align: center
.. doctest:: newton-refine3
>>> nodes1 = np.asfortranarray([
... [0.0, 0.5, 1.0],
... [0.0, 1.0, 0.0],
... ])
>>> nodes2 = np.asfortranarray([
... [0.0, 1.0],
... [0.5, 0.5],
... ])
>>> expected = 0.5
>>> s_vals = [0.375, None, None, None, None, None]
>>> t = 0.375
>>> np.log2(abs(expected - s_vals[0]))
-3.0
>>> s_vals[1], t = newton_refine(s_vals[0], nodes1, t, nodes2)
>>> np.log2(abs(expected - s_vals[1]))
-4.0
>>> s_vals[2], t = newton_refine(s_vals[1], nodes1, t, nodes2)
>>> np.log2(abs(expected - s_vals[2]))
-5.0
>>> s_vals[3], t = newton_refine(s_vals[2], nodes1, t, nodes2)
>>> np.log2(abs(expected - s_vals[3]))
-6.0
>>> s_vals[4], t = newton_refine(s_vals[3], nodes1, t, nodes2)
>>> np.log2(abs(expected - s_vals[4]))
-7.0
>>> s_vals[5], t = newton_refine(s_vals[4], nodes1, t, nodes2)
>>> np.log2(abs(expected - s_vals[5]))
-8.0
.. testcleanup:: newton-refine3
import make_images
curve1 = bezier.Curve(nodes1, degree=2)
curve2 = bezier.Curve(nodes2, degree=1)
make_images.newton_refine3(s_vals, curve1, curve2)
Unfortunately, the process terminates with an error that is not close
to machine precision :math:`\varepsilon` when
:math:`\Delta s = \Delta t = 0`.
.. testsetup:: newton-refine3-continued
import numpy as np
import bezier
from bezier._intersection_helpers import newton_refine
nodes1 = np.asfortranarray([
[0.0, 0.5, 1.0],
[0.0, 1.0, 0.0],
])
nodes2 = np.asfortranarray([
[0.0, 1.0],
[0.5, 0.5],
])
.. doctest:: newton-refine3-continued
>>> s1 = t1 = 0.5 - 0.5**27
>>> np.log2(0.5 - s1)
-27.0
>>> s2, t2 = newton_refine(s1, nodes1, t1, nodes2)
>>> s2 == t2
True
>>> np.log2(0.5 - s2)
-28.0
>>> s3, t3 = newton_refine(s2, nodes1, t2, nodes2)
>>> s3 == t3 == s2
True
Due to round-off near the point of tangency, the final error
resembles :math:`\sqrt{\varepsilon}` rather than machine
precision as expected.
.. note::
The following is not implemented in this function. It's just
an exploration on how the shortcomings might be addressed.
However, this can be overcome. At the point of tangency, we want
:math:`B_1'(s) \parallel B_2'(t)`. This can be checked numerically via
.. math::
B_1'(s) \times B_2'(t) = 0.
For the last example (the one that converges linearly), this is
.. math::
0 = \left[\begin{array}{c} 1 \\ 2 - 4s \end{array}\right] \times
\left[\begin{array}{c} 1 \\ 0 \end{array}\right] = 4 s - 2.
With this, we can modify Newton's method to find a zero of the
over-determined system
.. math::
G(s, t) = \left[\begin{array}{c} B_0(s) - B_1(t) \\
B_1'(s) \times B_2'(t) \end{array}\right] =
\left[\begin{array}{c} s - t \\ 2 s (1 - s) - \frac{1}{2} \\
4 s - 2\end{array}\right].
Since :math:`DG` is :math:`3 \times 2`, we can't invert it. However,
we can find a least-squares solution:
.. math::
\left(DG^T DG\right) \left[\begin{array}{c}
\Delta s \\ \Delta t \end{array}\right] = -DG^T G.
This only works if :math:`DG` has full rank. In this case, it does
since the submatrix containing the first and last rows has rank two:
.. math::
DG = \left[\begin{array}{c c} 1 & -1 \\
2 - 4 s & 0 \\
4 & 0 \end{array}\right].
Though this avoids a singular system, the normal equations have a
condition number that is the square of the condition number of the matrix.
Starting from :math:`s = t = \frac{3}{8}` as above:
.. testsetup:: newton-refine4
import numpy as np
from bezier import _helpers
def modified_update(s, t):
minus_G = np.asfortranarray([
[t - s],
[0.5 - 2.0 * s * (1.0 - s)],
[2.0 - 4.0 * s],
])
DG = np.asfortranarray([
[1.0, -1.0],
[2.0 - 4.0 * s, 0.0],
[4.0, 0.0],
])
DG_t = np.asfortranarray(DG.T)
LHS = _helpers.matrix_product(DG_t, DG)
RHS = _helpers.matrix_product(DG_t, minus_G)
delta_params = np.linalg.solve(LHS, RHS)
delta_s, delta_t = delta_params.flatten()
return s + delta_s, t + delta_t
.. doctest:: newton-refine4
>>> s0, t0 = 0.375, 0.375
>>> np.log2(0.5 - s0)
-3.0
>>> s1, t1 = modified_update(s0, t0)
>>> s1 == t1
True
>>> 1040.0 * s1
519.0
>>> np.log2(0.5 - s1)
-10.022...
>>> s2, t2 = modified_update(s1, t1)
>>> s2 == t2
True
>>> np.log2(0.5 - s2)
-31.067...
>>> s3, t3 = modified_update(s2, t2)
>>> s3 == t3 == 0.5
True
Args:
s (float): Parameter of a near-intersection along the first curve.
nodes1 (numpy.ndarray): Nodes of first curve forming intersection.
t (float): Parameter of a near-intersection along the second curve.
nodes2 (numpy.ndarray): Nodes of second curve forming intersection.
Returns:
Tuple[float, float]: The refined parameters from a single Newton
step.
Raises:
ValueError: If the Jacobian is singular at ``(s, t)``.
"""
# NOTE: We form -F(s, t) since we want to solve -DF^{-1} F(s, t).
func_val = _curve_helpers.evaluate_multi(
nodes2, np.asfortranarray([t])
) - _curve_helpers.evaluate_multi(nodes1, np.asfortranarray([s]))
if np.all(func_val == 0.0):
# No refinement is needed.
return s, t
# NOTE: This assumes the curves are 2D.
jac_mat = np.empty((2, 2), order="F")
jac_mat[:, :1] = _curve_helpers.evaluate_hodograph(s, nodes1)
jac_mat[:, 1:] = -_curve_helpers.evaluate_hodograph(t, nodes2)
# Solve the system.
singular, delta_s, delta_t = _helpers.solve2x2(jac_mat, func_val[:, 0])
if singular:
raise ValueError("Jacobian is singular.")
else:
return s + delta_s, t + delta_t | [
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.. note::
There is also a Fortran implementation of this function, which
will be used if it can be built.
We want to use Newton's method on the function
.. math::
F(s, t) = B_1(s) - B_2(t)
to refine :math:`\left(s_{\ast}, t_{\ast}\right)`. Using this,
and the Jacobian :math:`DF`, we "solve"
.. math::
\left[\begin{array}{c}
0 \\ 0 \end{array}\right] \approx
F\left(s_{\ast} + \Delta s, t_{\ast} + \Delta t\right) \approx
F\left(s_{\ast}, t_{\ast}\right) +
\left[\begin{array}{c c}
B_1'\left(s_{\ast}\right) &
- B_2'\left(t_{\ast}\right) \end{array}\right]
\left[\begin{array}{c}
\Delta s \\ \Delta t \end{array}\right]
and refine with the component updates :math:`\Delta s` and
:math:`\Delta t`.
.. note::
This implementation assumes the curves live in
:math:`\mathbf{R}^2`.
For example, the curves
.. math::
\begin{align*}
B_1(s) &= \left[\begin{array}{c} 0 \\ 0 \end{array}\right] (1 - s)^2
+ \left[\begin{array}{c} 2 \\ 4 \end{array}\right] 2s(1 - s)
+ \left[\begin{array}{c} 4 \\ 0 \end{array}\right] s^2 \\
B_2(t) &= \left[\begin{array}{c} 2 \\ 0 \end{array}\right] (1 - t)
+ \left[\begin{array}{c} 0 \\ 3 \end{array}\right] t
\end{align*}
intersect at the point
:math:`B_1\left(\frac{1}{4}\right) = B_2\left(\frac{1}{2}\right) =
\frac{1}{2} \left[\begin{array}{c} 2 \\ 3 \end{array}\right]`.
However, starting from the wrong point we have
.. math::
\begin{align*}
F\left(\frac{3}{8}, \frac{1}{4}\right) &= \frac{1}{8}
\left[\begin{array}{c} 0 \\ 9 \end{array}\right] \\
DF\left(\frac{3}{8}, \frac{1}{4}\right) &=
\left[\begin{array}{c c}
4 & 2 \\ 2 & -3 \end{array}\right] \\
\Longrightarrow \left[\begin{array}{c} \Delta s \\ \Delta t
\end{array}\right] &= \frac{9}{64} \left[\begin{array}{c}
-1 \\ 2 \end{array}\right].
\end{align*}
.. image:: ../images/newton_refine1.png
:align: center
.. testsetup:: newton-refine1, newton-refine2, newton-refine3
import numpy as np
import bezier
from bezier._intersection_helpers import newton_refine
machine_eps = np.finfo(np.float64).eps
def cuberoot(value):
return np.cbrt(value)
.. doctest:: newton-refine1
>>> nodes1 = np.asfortranarray([
... [0.0, 2.0, 4.0],
... [0.0, 4.0, 0.0],
... ])
>>> nodes2 = np.asfortranarray([
... [2.0, 0.0],
... [0.0, 3.0],
... ])
>>> s, t = 0.375, 0.25
>>> new_s, new_t = newton_refine(s, nodes1, t, nodes2)
>>> 64.0 * (new_s - s)
-9.0
>>> 64.0 * (new_t - t)
18.0
.. testcleanup:: newton-refine1
import make_images
curve1 = bezier.Curve(nodes1, degree=2)
curve2 = bezier.Curve(nodes2, degree=1)
make_images.newton_refine1(s, new_s, curve1, t, new_t, curve2)
For "typical" curves, we converge to a solution quadratically.
This means that the number of correct digits doubles every
iteration (until machine precision is reached).
.. image:: ../images/newton_refine2.png
:align: center
.. doctest:: newton-refine2
>>> nodes1 = np.asfortranarray([
... [0.0, 0.25, 0.5, 0.75, 1.0],
... [0.0, 2.0 , -2.0, 2.0 , 0.0],
... ])
>>> nodes2 = np.asfortranarray([
... [0.0, 0.25, 0.5, 0.75, 1.0],
... [1.0, 0.5 , 0.5, 0.5 , 0.0],
... ])
>>> # The expected intersection is the only real root of
>>> # 28 s^3 - 30 s^2 + 9 s - 1.
>>> omega = cuberoot(28.0 * np.sqrt(17.0) + 132.0) / 28.0
>>> expected = 5.0 / 14.0 + omega + 1 / (49.0 * omega)
>>> s_vals = [0.625, None, None, None, None]
>>> t = 0.625
>>> np.log2(abs(expected - s_vals[0]))
-4.399...
>>> s_vals[1], t = newton_refine(s_vals[0], nodes1, t, nodes2)
>>> np.log2(abs(expected - s_vals[1]))
-7.901...
>>> s_vals[2], t = newton_refine(s_vals[1], nodes1, t, nodes2)
>>> np.log2(abs(expected - s_vals[2]))
-16.010...
>>> s_vals[3], t = newton_refine(s_vals[2], nodes1, t, nodes2)
>>> np.log2(abs(expected - s_vals[3]))
-32.110...
>>> s_vals[4], t = newton_refine(s_vals[3], nodes1, t, nodes2)
>>> np.allclose(s_vals[4], expected, rtol=machine_eps, atol=0.0)
True
.. testcleanup:: newton-refine2
import make_images
curve1 = bezier.Curve(nodes1, degree=4)
curve2 = bezier.Curve(nodes2, degree=4)
make_images.newton_refine2(s_vals, curve1, curve2)
However, when the intersection occurs at a point of tangency,
the convergence becomes linear. This means that the number of
correct digits added each iteration is roughly constant.
.. image:: ../images/newton_refine3.png
:align: center
.. doctest:: newton-refine3
>>> nodes1 = np.asfortranarray([
... [0.0, 0.5, 1.0],
... [0.0, 1.0, 0.0],
... ])
>>> nodes2 = np.asfortranarray([
... [0.0, 1.0],
... [0.5, 0.5],
... ])
>>> expected = 0.5
>>> s_vals = [0.375, None, None, None, None, None]
>>> t = 0.375
>>> np.log2(abs(expected - s_vals[0]))
-3.0
>>> s_vals[1], t = newton_refine(s_vals[0], nodes1, t, nodes2)
>>> np.log2(abs(expected - s_vals[1]))
-4.0
>>> s_vals[2], t = newton_refine(s_vals[1], nodes1, t, nodes2)
>>> np.log2(abs(expected - s_vals[2]))
-5.0
>>> s_vals[3], t = newton_refine(s_vals[2], nodes1, t, nodes2)
>>> np.log2(abs(expected - s_vals[3]))
-6.0
>>> s_vals[4], t = newton_refine(s_vals[3], nodes1, t, nodes2)
>>> np.log2(abs(expected - s_vals[4]))
-7.0
>>> s_vals[5], t = newton_refine(s_vals[4], nodes1, t, nodes2)
>>> np.log2(abs(expected - s_vals[5]))
-8.0
.. testcleanup:: newton-refine3
import make_images
curve1 = bezier.Curve(nodes1, degree=2)
curve2 = bezier.Curve(nodes2, degree=1)
make_images.newton_refine3(s_vals, curve1, curve2)
Unfortunately, the process terminates with an error that is not close
to machine precision :math:`\varepsilon` when
:math:`\Delta s = \Delta t = 0`.
.. testsetup:: newton-refine3-continued
import numpy as np
import bezier
from bezier._intersection_helpers import newton_refine
nodes1 = np.asfortranarray([
[0.0, 0.5, 1.0],
[0.0, 1.0, 0.0],
])
nodes2 = np.asfortranarray([
[0.0, 1.0],
[0.5, 0.5],
])
.. doctest:: newton-refine3-continued
>>> s1 = t1 = 0.5 - 0.5**27
>>> np.log2(0.5 - s1)
-27.0
>>> s2, t2 = newton_refine(s1, nodes1, t1, nodes2)
>>> s2 == t2
True
>>> np.log2(0.5 - s2)
-28.0
>>> s3, t3 = newton_refine(s2, nodes1, t2, nodes2)
>>> s3 == t3 == s2
True
Due to round-off near the point of tangency, the final error
resembles :math:`\sqrt{\varepsilon}` rather than machine
precision as expected.
.. note::
The following is not implemented in this function. It's just
an exploration on how the shortcomings might be addressed.
However, this can be overcome. At the point of tangency, we want
:math:`B_1'(s) \parallel B_2'(t)`. This can be checked numerically via
.. math::
B_1'(s) \times B_2'(t) = 0.
For the last example (the one that converges linearly), this is
.. math::
0 = \left[\begin{array}{c} 1 \\ 2 - 4s \end{array}\right] \times
\left[\begin{array}{c} 1 \\ 0 \end{array}\right] = 4 s - 2.
With this, we can modify Newton's method to find a zero of the
over-determined system
.. math::
G(s, t) = \left[\begin{array}{c} B_0(s) - B_1(t) \\
B_1'(s) \times B_2'(t) \end{array}\right] =
\left[\begin{array}{c} s - t \\ 2 s (1 - s) - \frac{1}{2} \\
4 s - 2\end{array}\right].
Since :math:`DG` is :math:`3 \times 2`, we can't invert it. However,
we can find a least-squares solution:
.. math::
\left(DG^T DG\right) \left[\begin{array}{c}
\Delta s \\ \Delta t \end{array}\right] = -DG^T G.
This only works if :math:`DG` has full rank. In this case, it does
since the submatrix containing the first and last rows has rank two:
.. math::
DG = \left[\begin{array}{c c} 1 & -1 \\
2 - 4 s & 0 \\
4 & 0 \end{array}\right].
Though this avoids a singular system, the normal equations have a
condition number that is the square of the condition number of the matrix.
Starting from :math:`s = t = \frac{3}{8}` as above:
.. testsetup:: newton-refine4
import numpy as np
from bezier import _helpers
def modified_update(s, t):
minus_G = np.asfortranarray([
[t - s],
[0.5 - 2.0 * s * (1.0 - s)],
[2.0 - 4.0 * s],
])
DG = np.asfortranarray([
[1.0, -1.0],
[2.0 - 4.0 * s, 0.0],
[4.0, 0.0],
])
DG_t = np.asfortranarray(DG.T)
LHS = _helpers.matrix_product(DG_t, DG)
RHS = _helpers.matrix_product(DG_t, minus_G)
delta_params = np.linalg.solve(LHS, RHS)
delta_s, delta_t = delta_params.flatten()
return s + delta_s, t + delta_t
.. doctest:: newton-refine4
>>> s0, t0 = 0.375, 0.375
>>> np.log2(0.5 - s0)
-3.0
>>> s1, t1 = modified_update(s0, t0)
>>> s1 == t1
True
>>> 1040.0 * s1
519.0
>>> np.log2(0.5 - s1)
-10.022...
>>> s2, t2 = modified_update(s1, t1)
>>> s2 == t2
True
>>> np.log2(0.5 - s2)
-31.067...
>>> s3, t3 = modified_update(s2, t2)
>>> s3 == t3 == 0.5
True
Args:
s (float): Parameter of a near-intersection along the first curve.
nodes1 (numpy.ndarray): Nodes of first curve forming intersection.
t (float): Parameter of a near-intersection along the second curve.
nodes2 (numpy.ndarray): Nodes of second curve forming intersection.
Returns:
Tuple[float, float]: The refined parameters from a single Newton
step.
Raises:
ValueError: If the Jacobian is singular at ``(s, t)``. | [
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dhermes/bezier | src/bezier/_intersection_helpers.py | newton_iterate | def newton_iterate(evaluate_fn, s, t):
r"""Perform a Newton iteration.
In this function, we assume that :math:`s` and :math:`t` are nonzero,
this makes convergence easier to detect since "relative error" at
``0.0`` is not a useful measure.
There are several tolerance / threshold quantities used below:
* :math:`10` (:attr:`MAX_NEWTON_ITERATIONS`) iterations will be done before
"giving up". This is based on the assumption that we are already starting
near a root, so quadratic convergence should terminate quickly.
* :math:`\tau = \frac{1}{4}` is used as the boundary between linear
and superlinear convergence. So if the current error
:math:`\|p_{n + 1} - p_n\|` is not smaller than :math:`\tau` times
the previous error :math:`\|p_n - p_{n - 1}\|`, then convergence
is considered to be linear at that point.
* :math:`\frac{2}{3}` of all iterations must be converging linearly
for convergence to be stopped (and moved to the next regime). This
will only be checked after 4 or more updates have occurred.
* :math:`\tau = 2^{-42}` (:attr:`NEWTON_ERROR_RATIO`) is used to
determine that an update is sufficiently small to stop iterating. So if
the error :math:`\|p_{n + 1} - p_n\|` smaller than :math:`\tau` times
size of the term being updated :math:`\|p_n\|`, then we
exit with the "correct" answer.
It is assumed that ``evaluate_fn`` will use a Jacobian return value of
:data:`None` to indicate that :math:`F(s, t)` is exactly ``0.0``. We
**assume** that if the function evaluates to exactly ``0.0``, then we are
at a solution. It is possible however, that badly parameterized curves
can evaluate to exactly ``0.0`` for inputs that are relatively far away
from a solution (see issue #21).
Args:
evaluate_fn (Callable[Tuple[float, float], tuple]): A callable
which takes :math:`s` and :math:`t` and produces an evaluated
function value and the Jacobian matrix.
s (float): The (first) parameter where the iteration will start.
t (float): The (second) parameter where the iteration will start.
Returns:
Tuple[bool, float, float]: The triple of
* Flag indicating if the iteration converged.
* The current :math:`s` value when the iteration stopped.
* The current :math:`t` value when the iteration stopped.
"""
# Several quantities will be tracked throughout the iteration:
# * norm_update_prev: ||p{n} - p{n-1}|| = ||dp{n-1}||
# * norm_update : ||p{n+1} - p{n} || = ||dp{n} ||
# * linear_updates : This is a count on the number of times that
# ``dp{n}`` "looks like" ``dp{n-1}`` (i.e.
# is within a constant factor of it).
norm_update_prev = None
norm_update = None
linear_updates = 0 # Track the number of "linear" updates.
current_s = s
current_t = t
for index in six.moves.xrange(MAX_NEWTON_ITERATIONS):
jacobian, func_val = evaluate_fn(current_s, current_t)
if jacobian is None:
return True, current_s, current_t
singular, delta_s, delta_t = _helpers.solve2x2(
jacobian, func_val[:, 0]
)
if singular:
break
norm_update_prev = norm_update
norm_update = np.linalg.norm([delta_s, delta_t], ord=2)
# If ||p{n} - p{n-1}|| > 0.25 ||p{n-1} - p{n-2}||, then that means
# our convergence is acting linear at the current step.
if index > 0 and norm_update > 0.25 * norm_update_prev:
linear_updates += 1
# If ``>=2/3`` of the updates have been linear, we are near a
# non-simple root. (Make sure at least 5 updates have occurred.)
if index >= 4 and 3 * linear_updates >= 2 * index:
break
# Determine the norm of the "old" solution before updating.
norm_soln = np.linalg.norm([current_s, current_t], ord=2)
current_s -= delta_s
current_t -= delta_t
if norm_update < NEWTON_ERROR_RATIO * norm_soln:
return True, current_s, current_t
return False, current_s, current_t | python | def newton_iterate(evaluate_fn, s, t):
r"""Perform a Newton iteration.
In this function, we assume that :math:`s` and :math:`t` are nonzero,
this makes convergence easier to detect since "relative error" at
``0.0`` is not a useful measure.
There are several tolerance / threshold quantities used below:
* :math:`10` (:attr:`MAX_NEWTON_ITERATIONS`) iterations will be done before
"giving up". This is based on the assumption that we are already starting
near a root, so quadratic convergence should terminate quickly.
* :math:`\tau = \frac{1}{4}` is used as the boundary between linear
and superlinear convergence. So if the current error
:math:`\|p_{n + 1} - p_n\|` is not smaller than :math:`\tau` times
the previous error :math:`\|p_n - p_{n - 1}\|`, then convergence
is considered to be linear at that point.
* :math:`\frac{2}{3}` of all iterations must be converging linearly
for convergence to be stopped (and moved to the next regime). This
will only be checked after 4 or more updates have occurred.
* :math:`\tau = 2^{-42}` (:attr:`NEWTON_ERROR_RATIO`) is used to
determine that an update is sufficiently small to stop iterating. So if
the error :math:`\|p_{n + 1} - p_n\|` smaller than :math:`\tau` times
size of the term being updated :math:`\|p_n\|`, then we
exit with the "correct" answer.
It is assumed that ``evaluate_fn`` will use a Jacobian return value of
:data:`None` to indicate that :math:`F(s, t)` is exactly ``0.0``. We
**assume** that if the function evaluates to exactly ``0.0``, then we are
at a solution. It is possible however, that badly parameterized curves
can evaluate to exactly ``0.0`` for inputs that are relatively far away
from a solution (see issue #21).
Args:
evaluate_fn (Callable[Tuple[float, float], tuple]): A callable
which takes :math:`s` and :math:`t` and produces an evaluated
function value and the Jacobian matrix.
s (float): The (first) parameter where the iteration will start.
t (float): The (second) parameter where the iteration will start.
Returns:
Tuple[bool, float, float]: The triple of
* Flag indicating if the iteration converged.
* The current :math:`s` value when the iteration stopped.
* The current :math:`t` value when the iteration stopped.
"""
# Several quantities will be tracked throughout the iteration:
# * norm_update_prev: ||p{n} - p{n-1}|| = ||dp{n-1}||
# * norm_update : ||p{n+1} - p{n} || = ||dp{n} ||
# * linear_updates : This is a count on the number of times that
# ``dp{n}`` "looks like" ``dp{n-1}`` (i.e.
# is within a constant factor of it).
norm_update_prev = None
norm_update = None
linear_updates = 0 # Track the number of "linear" updates.
current_s = s
current_t = t
for index in six.moves.xrange(MAX_NEWTON_ITERATIONS):
jacobian, func_val = evaluate_fn(current_s, current_t)
if jacobian is None:
return True, current_s, current_t
singular, delta_s, delta_t = _helpers.solve2x2(
jacobian, func_val[:, 0]
)
if singular:
break
norm_update_prev = norm_update
norm_update = np.linalg.norm([delta_s, delta_t], ord=2)
# If ||p{n} - p{n-1}|| > 0.25 ||p{n-1} - p{n-2}||, then that means
# our convergence is acting linear at the current step.
if index > 0 and norm_update > 0.25 * norm_update_prev:
linear_updates += 1
# If ``>=2/3`` of the updates have been linear, we are near a
# non-simple root. (Make sure at least 5 updates have occurred.)
if index >= 4 and 3 * linear_updates >= 2 * index:
break
# Determine the norm of the "old" solution before updating.
norm_soln = np.linalg.norm([current_s, current_t], ord=2)
current_s -= delta_s
current_t -= delta_t
if norm_update < NEWTON_ERROR_RATIO * norm_soln:
return True, current_s, current_t
return False, current_s, current_t | [
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In this function, we assume that :math:`s` and :math:`t` are nonzero,
this makes convergence easier to detect since "relative error" at
``0.0`` is not a useful measure.
There are several tolerance / threshold quantities used below:
* :math:`10` (:attr:`MAX_NEWTON_ITERATIONS`) iterations will be done before
"giving up". This is based on the assumption that we are already starting
near a root, so quadratic convergence should terminate quickly.
* :math:`\tau = \frac{1}{4}` is used as the boundary between linear
and superlinear convergence. So if the current error
:math:`\|p_{n + 1} - p_n\|` is not smaller than :math:`\tau` times
the previous error :math:`\|p_n - p_{n - 1}\|`, then convergence
is considered to be linear at that point.
* :math:`\frac{2}{3}` of all iterations must be converging linearly
for convergence to be stopped (and moved to the next regime). This
will only be checked after 4 or more updates have occurred.
* :math:`\tau = 2^{-42}` (:attr:`NEWTON_ERROR_RATIO`) is used to
determine that an update is sufficiently small to stop iterating. So if
the error :math:`\|p_{n + 1} - p_n\|` smaller than :math:`\tau` times
size of the term being updated :math:`\|p_n\|`, then we
exit with the "correct" answer.
It is assumed that ``evaluate_fn`` will use a Jacobian return value of
:data:`None` to indicate that :math:`F(s, t)` is exactly ``0.0``. We
**assume** that if the function evaluates to exactly ``0.0``, then we are
at a solution. It is possible however, that badly parameterized curves
can evaluate to exactly ``0.0`` for inputs that are relatively far away
from a solution (see issue #21).
Args:
evaluate_fn (Callable[Tuple[float, float], tuple]): A callable
which takes :math:`s` and :math:`t` and produces an evaluated
function value and the Jacobian matrix.
s (float): The (first) parameter where the iteration will start.
t (float): The (second) parameter where the iteration will start.
Returns:
Tuple[bool, float, float]: The triple of
* Flag indicating if the iteration converged.
* The current :math:`s` value when the iteration stopped.
* The current :math:`t` value when the iteration stopped. | [
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dhermes/bezier | src/bezier/_intersection_helpers.py | full_newton_nonzero | def full_newton_nonzero(s, nodes1, t, nodes2):
r"""Perform a Newton iteration until convergence to a solution.
This is the "implementation" for :func:`full_newton`. In this
function, we assume that :math:`s` and :math:`t` are nonzero.
Args:
s (float): The parameter along the first curve where the iteration
will start.
nodes1 (numpy.ndarray): Control points of the first curve.
t (float): The parameter along the second curve where the iteration
will start.
nodes2 (numpy.ndarray): Control points of the second curve.
Returns:
Tuple[float, float]: The pair of :math:`s` and :math:`t` values that
Newton's method converged to.
Raises:
NotImplementedError: If Newton's method doesn't converge in either the
multiplicity 1 or 2 cases.
"""
# NOTE: We somewhat replicate code in ``evaluate_hodograph()``
# here. This is so we don't re-compute the nodes for the first
# (and possibly second) derivatives every time they are evaluated.
_, num_nodes1 = np.shape(nodes1)
first_deriv1 = (num_nodes1 - 1) * (nodes1[:, 1:] - nodes1[:, :-1])
_, num_nodes2 = np.shape(nodes2)
first_deriv2 = (num_nodes2 - 1) * (nodes2[:, 1:] - nodes2[:, :-1])
evaluate_fn = NewtonSimpleRoot(nodes1, first_deriv1, nodes2, first_deriv2)
converged, current_s, current_t = newton_iterate(evaluate_fn, s, t)
if converged:
return current_s, current_t
# If Newton's method did not converge, then assume the root is not simple.
second_deriv1 = (num_nodes1 - 2) * (
first_deriv1[:, 1:] - first_deriv1[:, :-1]
)
second_deriv2 = (num_nodes2 - 2) * (
first_deriv2[:, 1:] - first_deriv2[:, :-1]
)
evaluate_fn = NewtonDoubleRoot(
nodes1,
first_deriv1,
second_deriv1,
nodes2,
first_deriv2,
second_deriv2,
)
converged, current_s, current_t = newton_iterate(
evaluate_fn, current_s, current_t
)
if converged:
return current_s, current_t
raise NotImplementedError(NEWTON_NO_CONVERGE) | python | def full_newton_nonzero(s, nodes1, t, nodes2):
r"""Perform a Newton iteration until convergence to a solution.
This is the "implementation" for :func:`full_newton`. In this
function, we assume that :math:`s` and :math:`t` are nonzero.
Args:
s (float): The parameter along the first curve where the iteration
will start.
nodes1 (numpy.ndarray): Control points of the first curve.
t (float): The parameter along the second curve where the iteration
will start.
nodes2 (numpy.ndarray): Control points of the second curve.
Returns:
Tuple[float, float]: The pair of :math:`s` and :math:`t` values that
Newton's method converged to.
Raises:
NotImplementedError: If Newton's method doesn't converge in either the
multiplicity 1 or 2 cases.
"""
# NOTE: We somewhat replicate code in ``evaluate_hodograph()``
# here. This is so we don't re-compute the nodes for the first
# (and possibly second) derivatives every time they are evaluated.
_, num_nodes1 = np.shape(nodes1)
first_deriv1 = (num_nodes1 - 1) * (nodes1[:, 1:] - nodes1[:, :-1])
_, num_nodes2 = np.shape(nodes2)
first_deriv2 = (num_nodes2 - 1) * (nodes2[:, 1:] - nodes2[:, :-1])
evaluate_fn = NewtonSimpleRoot(nodes1, first_deriv1, nodes2, first_deriv2)
converged, current_s, current_t = newton_iterate(evaluate_fn, s, t)
if converged:
return current_s, current_t
# If Newton's method did not converge, then assume the root is not simple.
second_deriv1 = (num_nodes1 - 2) * (
first_deriv1[:, 1:] - first_deriv1[:, :-1]
)
second_deriv2 = (num_nodes2 - 2) * (
first_deriv2[:, 1:] - first_deriv2[:, :-1]
)
evaluate_fn = NewtonDoubleRoot(
nodes1,
first_deriv1,
second_deriv1,
nodes2,
first_deriv2,
second_deriv2,
)
converged, current_s, current_t = newton_iterate(
evaluate_fn, current_s, current_t
)
if converged:
return current_s, current_t
raise NotImplementedError(NEWTON_NO_CONVERGE) | [
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This is the "implementation" for :func:`full_newton`. In this
function, we assume that :math:`s` and :math:`t` are nonzero.
Args:
s (float): The parameter along the first curve where the iteration
will start.
nodes1 (numpy.ndarray): Control points of the first curve.
t (float): The parameter along the second curve where the iteration
will start.
nodes2 (numpy.ndarray): Control points of the second curve.
Returns:
Tuple[float, float]: The pair of :math:`s` and :math:`t` values that
Newton's method converged to.
Raises:
NotImplementedError: If Newton's method doesn't converge in either the
multiplicity 1 or 2 cases. | [
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dhermes/bezier | src/bezier/_intersection_helpers.py | full_newton | def full_newton(s, nodes1, t, nodes2):
r"""Perform a Newton iteration until convergence to a solution.
This assumes :math:`s` and :math:`t` are sufficiently close to an
intersection. It **does not** govern the maximum distance away
that the solution can lie, though the subdivided intervals that contain
:math:`s` and :math:`t` could be used.
To avoid round-off issues near ``0.0``, this reverses the direction
of a curve and replaces the parameter value :math:`\nu` with
:math:`1 - \nu` whenever :math:`\nu < \tau` (here we use a threshold
:math:`\tau` equal to :math:`2^{-10}`, i.e. ``ZERO_THRESHOLD``).
Args:
s (float): The parameter along the first curve where the iteration
will start.
nodes1 (numpy.ndarray): Control points of the first curve.
t (float): The parameter along the second curve where the iteration
will start.
nodes2 (numpy.ndarray): Control points of the second curve.
Returns:
Tuple[float, float]: The pair of :math:`s` and :math:`t` values that
Newton's method converged to.
"""
if s < ZERO_THRESHOLD:
reversed1 = np.asfortranarray(nodes1[:, ::-1])
if t < ZERO_THRESHOLD:
reversed2 = np.asfortranarray(nodes2[:, ::-1])
refined_s, refined_t = full_newton_nonzero(
1.0 - s, reversed1, 1.0 - t, reversed2
)
return 1.0 - refined_s, 1.0 - refined_t
else:
refined_s, refined_t = full_newton_nonzero(
1.0 - s, reversed1, t, nodes2
)
return 1.0 - refined_s, refined_t
else:
if t < ZERO_THRESHOLD:
reversed2 = np.asfortranarray(nodes2[:, ::-1])
refined_s, refined_t = full_newton_nonzero(
s, nodes1, 1.0 - t, reversed2
)
return refined_s, 1.0 - refined_t
else:
return full_newton_nonzero(s, nodes1, t, nodes2) | python | def full_newton(s, nodes1, t, nodes2):
r"""Perform a Newton iteration until convergence to a solution.
This assumes :math:`s` and :math:`t` are sufficiently close to an
intersection. It **does not** govern the maximum distance away
that the solution can lie, though the subdivided intervals that contain
:math:`s` and :math:`t` could be used.
To avoid round-off issues near ``0.0``, this reverses the direction
of a curve and replaces the parameter value :math:`\nu` with
:math:`1 - \nu` whenever :math:`\nu < \tau` (here we use a threshold
:math:`\tau` equal to :math:`2^{-10}`, i.e. ``ZERO_THRESHOLD``).
Args:
s (float): The parameter along the first curve where the iteration
will start.
nodes1 (numpy.ndarray): Control points of the first curve.
t (float): The parameter along the second curve where the iteration
will start.
nodes2 (numpy.ndarray): Control points of the second curve.
Returns:
Tuple[float, float]: The pair of :math:`s` and :math:`t` values that
Newton's method converged to.
"""
if s < ZERO_THRESHOLD:
reversed1 = np.asfortranarray(nodes1[:, ::-1])
if t < ZERO_THRESHOLD:
reversed2 = np.asfortranarray(nodes2[:, ::-1])
refined_s, refined_t = full_newton_nonzero(
1.0 - s, reversed1, 1.0 - t, reversed2
)
return 1.0 - refined_s, 1.0 - refined_t
else:
refined_s, refined_t = full_newton_nonzero(
1.0 - s, reversed1, t, nodes2
)
return 1.0 - refined_s, refined_t
else:
if t < ZERO_THRESHOLD:
reversed2 = np.asfortranarray(nodes2[:, ::-1])
refined_s, refined_t = full_newton_nonzero(
s, nodes1, 1.0 - t, reversed2
)
return refined_s, 1.0 - refined_t
else:
return full_newton_nonzero(s, nodes1, t, nodes2) | [
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:math:`s` and :math:`t` could be used.
To avoid round-off issues near ``0.0``, this reverses the direction
of a curve and replaces the parameter value :math:`\nu` with
:math:`1 - \nu` whenever :math:`\nu < \tau` (here we use a threshold
:math:`\tau` equal to :math:`2^{-10}`, i.e. ``ZERO_THRESHOLD``).
Args:
s (float): The parameter along the first curve where the iteration
will start.
nodes1 (numpy.ndarray): Control points of the first curve.
t (float): The parameter along the second curve where the iteration
will start.
nodes2 (numpy.ndarray): Control points of the second curve.
Returns:
Tuple[float, float]: The pair of :math:`s` and :math:`t` values that
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dhermes/bezier | setup_helpers.py | gfortran_search_path | def gfortran_search_path(library_dirs):
"""Get the library directory paths for ``gfortran``.
Looks for ``libraries: =`` in the output of ``gfortran -print-search-dirs``
and then parses the paths. If this fails for any reason, this method will
print an error and return ``library_dirs``.
Args:
library_dirs (List[str]): Existing library directories.
Returns:
List[str]: The library directories for ``gfortran``.
"""
cmd = ("gfortran", "-print-search-dirs")
process = subprocess.Popen(cmd, stdout=subprocess.PIPE)
return_code = process.wait()
# Bail out if the command failed.
if return_code != 0:
return library_dirs
cmd_output = process.stdout.read().decode("utf-8")
# Find single line starting with ``libraries: ``.
search_lines = cmd_output.strip().split("\n")
library_lines = [
line[len(FORTRAN_LIBRARY_PREFIX) :]
for line in search_lines
if line.startswith(FORTRAN_LIBRARY_PREFIX)
]
if len(library_lines) != 1:
msg = GFORTRAN_MISSING_LIBS.format(cmd_output)
print(msg, file=sys.stderr)
return library_dirs
# Go through each library in the ``libraries: = ...`` line.
library_line = library_lines[0]
accepted = set(library_dirs)
for part in library_line.split(os.pathsep):
full_path = os.path.abspath(part.strip())
if os.path.isdir(full_path):
accepted.add(full_path)
else:
# Ignore anything that isn't a directory.
msg = GFORTRAN_BAD_PATH.format(full_path)
print(msg, file=sys.stderr)
return sorted(accepted) | python | def gfortran_search_path(library_dirs):
"""Get the library directory paths for ``gfortran``.
Looks for ``libraries: =`` in the output of ``gfortran -print-search-dirs``
and then parses the paths. If this fails for any reason, this method will
print an error and return ``library_dirs``.
Args:
library_dirs (List[str]): Existing library directories.
Returns:
List[str]: The library directories for ``gfortran``.
"""
cmd = ("gfortran", "-print-search-dirs")
process = subprocess.Popen(cmd, stdout=subprocess.PIPE)
return_code = process.wait()
# Bail out if the command failed.
if return_code != 0:
return library_dirs
cmd_output = process.stdout.read().decode("utf-8")
# Find single line starting with ``libraries: ``.
search_lines = cmd_output.strip().split("\n")
library_lines = [
line[len(FORTRAN_LIBRARY_PREFIX) :]
for line in search_lines
if line.startswith(FORTRAN_LIBRARY_PREFIX)
]
if len(library_lines) != 1:
msg = GFORTRAN_MISSING_LIBS.format(cmd_output)
print(msg, file=sys.stderr)
return library_dirs
# Go through each library in the ``libraries: = ...`` line.
library_line = library_lines[0]
accepted = set(library_dirs)
for part in library_line.split(os.pathsep):
full_path = os.path.abspath(part.strip())
if os.path.isdir(full_path):
accepted.add(full_path)
else:
# Ignore anything that isn't a directory.
msg = GFORTRAN_BAD_PATH.format(full_path)
print(msg, file=sys.stderr)
return sorted(accepted) | [
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dhermes/bezier | setup_helpers.py | _update_flags | def _update_flags(compiler_flags, remove_flags=()):
"""Update a given set of compiler flags.
Args:
compiler_flags (List[str]): Existing flags associated with a compiler.
remove_flags (Optional[Container[str]]): A container of flags to remove
that will override any of the defaults.
Returns:
List[str]: The modified list (i.e. some flags added and some removed).
"""
for flag in GFORTRAN_SHARED_FLAGS:
if flag not in compiler_flags:
compiler_flags.append(flag)
if DEBUG_ENV in os.environ:
to_add = GFORTRAN_DEBUG_FLAGS
to_remove = GFORTRAN_OPTIMIZE_FLAGS
else:
to_add = GFORTRAN_OPTIMIZE_FLAGS
if os.environ.get(WHEEL_ENV) is None:
to_add += (GFORTRAN_NATIVE_FLAG,)
to_remove = GFORTRAN_DEBUG_FLAGS
for flag in to_add:
if flag not in compiler_flags:
compiler_flags.append(flag)
return [
flag
for flag in compiler_flags
if not (flag in to_remove or flag in remove_flags)
] | python | def _update_flags(compiler_flags, remove_flags=()):
"""Update a given set of compiler flags.
Args:
compiler_flags (List[str]): Existing flags associated with a compiler.
remove_flags (Optional[Container[str]]): A container of flags to remove
that will override any of the defaults.
Returns:
List[str]: The modified list (i.e. some flags added and some removed).
"""
for flag in GFORTRAN_SHARED_FLAGS:
if flag not in compiler_flags:
compiler_flags.append(flag)
if DEBUG_ENV in os.environ:
to_add = GFORTRAN_DEBUG_FLAGS
to_remove = GFORTRAN_OPTIMIZE_FLAGS
else:
to_add = GFORTRAN_OPTIMIZE_FLAGS
if os.environ.get(WHEEL_ENV) is None:
to_add += (GFORTRAN_NATIVE_FLAG,)
to_remove = GFORTRAN_DEBUG_FLAGS
for flag in to_add:
if flag not in compiler_flags:
compiler_flags.append(flag)
return [
flag
for flag in compiler_flags
if not (flag in to_remove or flag in remove_flags)
] | [
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dhermes/bezier | setup_helpers.py | patch_f90_compiler | def patch_f90_compiler(f90_compiler):
"""Patch up ``f90_compiler``.
For now, only updates the flags for ``gfortran``. In this case, it add
any of ``GFORTRAN_SHARED_FLAGS`` that are missing. In debug mode, it also
adds any flags in ``GFORTRAN_DEBUG_FLAGS`` and makes sure none of the flags
in ``GFORTRAN_OPTIMIZE_FLAGS`` are present. In standard mode ("OPTIMIZE"),
makes sure flags in ``GFORTRAN_OPTIMIZE_FLAGS`` are present and flags in
``GFORTRAN_DEBUG_FLAGS`` are not.
Args:
f90_compiler (numpy.distutils.fcompiler.FCompiler): A Fortran compiler
instance.
"""
# NOTE: NumPy may not be installed, but we don't want **this** module to
# cause an import failure in ``setup.py``.
from numpy.distutils.fcompiler import gnu
# Only ``gfortran``.
if not isinstance(f90_compiler, gnu.Gnu95FCompiler):
return False
f90_compiler.compiler_f77[:] = _update_flags(
f90_compiler.compiler_f77, remove_flags=("-Werror",)
)
f90_compiler.compiler_f90[:] = _update_flags(f90_compiler.compiler_f90) | python | def patch_f90_compiler(f90_compiler):
"""Patch up ``f90_compiler``.
For now, only updates the flags for ``gfortran``. In this case, it add
any of ``GFORTRAN_SHARED_FLAGS`` that are missing. In debug mode, it also
adds any flags in ``GFORTRAN_DEBUG_FLAGS`` and makes sure none of the flags
in ``GFORTRAN_OPTIMIZE_FLAGS`` are present. In standard mode ("OPTIMIZE"),
makes sure flags in ``GFORTRAN_OPTIMIZE_FLAGS`` are present and flags in
``GFORTRAN_DEBUG_FLAGS`` are not.
Args:
f90_compiler (numpy.distutils.fcompiler.FCompiler): A Fortran compiler
instance.
"""
# NOTE: NumPy may not be installed, but we don't want **this** module to
# cause an import failure in ``setup.py``.
from numpy.distutils.fcompiler import gnu
# Only ``gfortran``.
if not isinstance(f90_compiler, gnu.Gnu95FCompiler):
return False
f90_compiler.compiler_f77[:] = _update_flags(
f90_compiler.compiler_f77, remove_flags=("-Werror",)
)
f90_compiler.compiler_f90[:] = _update_flags(f90_compiler.compiler_f90) | [
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any of ``GFORTRAN_SHARED_FLAGS`` that are missing. In debug mode, it also
adds any flags in ``GFORTRAN_DEBUG_FLAGS`` and makes sure none of the flags
in ``GFORTRAN_OPTIMIZE_FLAGS`` are present. In standard mode ("OPTIMIZE"),
makes sure flags in ``GFORTRAN_OPTIMIZE_FLAGS`` are present and flags in
``GFORTRAN_DEBUG_FLAGS`` are not.
Args:
f90_compiler (numpy.distutils.fcompiler.FCompiler): A Fortran compiler
instance. | [
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dhermes/bezier | setup_helpers.py | BuildFortranThenExt.start_journaling | def start_journaling(self):
"""Capture calls to the system by compilers.
See: https://github.com/numpy/numpy/blob/v1.15.2/\
numpy/distutils/ccompiler.py#L154
Intercepts all calls to ``CCompiler.spawn`` and keeps the
arguments around to be stored in the local ``commands``
instance attribute.
"""
import numpy.distutils.ccompiler
if self.journal_file is None:
return
def journaled_spawn(patched_self, cmd, display=None):
self.commands.append(cmd)
return numpy.distutils.ccompiler.CCompiler_spawn(
patched_self, cmd, display=None
)
numpy.distutils.ccompiler.replace_method(
distutils.ccompiler.CCompiler, "spawn", journaled_spawn
) | python | def start_journaling(self):
"""Capture calls to the system by compilers.
See: https://github.com/numpy/numpy/blob/v1.15.2/\
numpy/distutils/ccompiler.py#L154
Intercepts all calls to ``CCompiler.spawn`` and keeps the
arguments around to be stored in the local ``commands``
instance attribute.
"""
import numpy.distutils.ccompiler
if self.journal_file is None:
return
def journaled_spawn(patched_self, cmd, display=None):
self.commands.append(cmd)
return numpy.distutils.ccompiler.CCompiler_spawn(
patched_self, cmd, display=None
)
numpy.distutils.ccompiler.replace_method(
distutils.ccompiler.CCompiler, "spawn", journaled_spawn
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dhermes/bezier | setup_helpers.py | BuildFortranThenExt.save_journal | def save_journal(self):
"""Save journaled commands to file.
If there is no active journal, does nothing.
If saving the commands to a file fails, a message will be printed to
STDERR but the failure will be swallowed so that the extension can
be built successfully.
"""
if self.journal_file is None:
return
try:
as_text = self._commands_to_text()
with open(self.journal_file, "w") as file_obj:
file_obj.write(as_text)
except Exception as exc:
msg = BAD_JOURNAL.format(exc)
print(msg, file=sys.stderr) | python | def save_journal(self):
"""Save journaled commands to file.
If there is no active journal, does nothing.
If saving the commands to a file fails, a message will be printed to
STDERR but the failure will be swallowed so that the extension can
be built successfully.
"""
if self.journal_file is None:
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try:
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with open(self.journal_file, "w") as file_obj:
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msg = BAD_JOURNAL.format(exc)
print(msg, file=sys.stderr) | [
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dhermes/bezier | setup_helpers.py | BuildFortranThenExt._default_static_lib | def _default_static_lib(self, obj_files):
"""Create a static library (i.e. a ``.a`` / ``.lib`` file).
Args:
obj_files (List[str]): List of paths of compiled object files.
"""
c_compiler = self.F90_COMPILER.c_compiler
static_lib_dir = os.path.join(self.build_lib, "bezier", "lib")
if not os.path.exists(static_lib_dir):
os.makedirs(static_lib_dir)
c_compiler.create_static_lib(
obj_files, "bezier", output_dir=static_lib_dir
)
# NOTE: We must "modify" the paths for the ``extra_objects`` in
# each extension since they were compiled with
# ``output_dir=self.build_temp``.
for extension in self.extensions:
extension.extra_objects[:] = [
os.path.join(self.build_temp, rel_path)
for rel_path in extension.extra_objects
] | python | def _default_static_lib(self, obj_files):
"""Create a static library (i.e. a ``.a`` / ``.lib`` file).
Args:
obj_files (List[str]): List of paths of compiled object files.
"""
c_compiler = self.F90_COMPILER.c_compiler
static_lib_dir = os.path.join(self.build_lib, "bezier", "lib")
if not os.path.exists(static_lib_dir):
os.makedirs(static_lib_dir)
c_compiler.create_static_lib(
obj_files, "bezier", output_dir=static_lib_dir
)
# NOTE: We must "modify" the paths for the ``extra_objects`` in
# each extension since they were compiled with
# ``output_dir=self.build_temp``.
for extension in self.extensions:
extension.extra_objects[:] = [
os.path.join(self.build_temp, rel_path)
for rel_path in extension.extra_objects
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dhermes/bezier | setup_helpers.py | BuildFortranThenExt._default_cleanup | def _default_cleanup(self):
"""Default cleanup after :meth:`run`.
For in-place builds, moves the built shared library into the source
directory.
"""
if not self.inplace:
return
shutil.move(
os.path.join(self.build_lib, "bezier", "lib"),
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"""Default cleanup after :meth:`run`.
For in-place builds, moves the built shared library into the source
directory.
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if not self.inplace:
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dhermes/bezier | src/bezier/curved_polygon.py | CurvedPolygon._verify_pair | def _verify_pair(prev, curr):
"""Verify a pair of sides share an endpoint.
.. note::
This currently checks that edge endpoints match **exactly**
but allowing some roundoff may be desired.
Args:
prev (.Curve): "Previous" curve at piecewise junction.
curr (.Curve): "Next" curve at piecewise junction.
Raises:
ValueError: If the previous side is not in 2D.
ValueError: If consecutive sides don't share an endpoint.
"""
if prev._dimension != 2:
raise ValueError("Curve not in R^2", prev)
end = prev._nodes[:, -1]
start = curr._nodes[:, 0]
if not _helpers.vector_close(end, start):
raise ValueError(
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prev,
curr,
) | python | def _verify_pair(prev, curr):
"""Verify a pair of sides share an endpoint.
.. note::
This currently checks that edge endpoints match **exactly**
but allowing some roundoff may be desired.
Args:
prev (.Curve): "Previous" curve at piecewise junction.
curr (.Curve): "Next" curve at piecewise junction.
Raises:
ValueError: If the previous side is not in 2D.
ValueError: If consecutive sides don't share an endpoint.
"""
if prev._dimension != 2:
raise ValueError("Curve not in R^2", prev)
end = prev._nodes[:, -1]
start = curr._nodes[:, 0]
if not _helpers.vector_close(end, start):
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dhermes/bezier | src/bezier/curved_polygon.py | CurvedPolygon._verify | def _verify(self):
"""Verify that the edges define a curved polygon.
This may not be entirely comprehensive, e.g. won't check
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.. note::
This currently checks that edge endpoints match **exactly**
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# Now we check that the final edge wraps around.
prev = self._edges[-1]
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self._verify_pair(prev, curr) | python | def _verify(self):
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.. note::
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self._verify_pair(prev, curr) | [
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dhermes/bezier | src/bezier/curved_polygon.py | CurvedPolygon.area | def area(self):
r"""The area of the current curved polygon.
This assumes, but does not check, that the current curved polygon
is valid (i.e. it is bounded by the edges).
This computes the area via Green's theorem. Using the vector field
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\int_{\mathcal{P}} 2 \, d\textbf{x} =
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where :math:`b_{i, d}, b_{j, d}` are Bernstein basis polynomials.
Returns:
float: The area of the current curved polygon.
"""
edges = tuple(edge._nodes for edge in self._edges)
return _surface_helpers.compute_area(edges) | python | def area(self):
r"""The area of the current curved polygon.
This assumes, but does not check, that the current curved polygon
is valid (i.e. it is bounded by the edges).
This computes the area via Green's theorem. Using the vector field
:math:`\mathbf{F} = \left[-y, x\right]^T`, since
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.. math::
\int_{\mathcal{P}} 2 \, d\textbf{x} =
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(where :math:`\mathcal{P}` is the current curved polygon).
Note that for a given edge :math:`C(r)` with control points
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.. math::
\int_C -y \, dx + x \, dy = \int_0^1 (x y' - y x') \, dr
= \sum_{i < j} (x_i y_j - y_i x_j) \int_0^1 b_{i, d}
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where :math:`b_{i, d}, b_{j, d}` are Bernstein basis polynomials.
Returns:
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"""
edges = tuple(edge._nodes for edge in self._edges)
return _surface_helpers.compute_area(edges) | [
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dhermes/bezier | src/bezier/curved_polygon.py | CurvedPolygon.plot | def plot(self, pts_per_edge, color=None, ax=None):
"""Plot the current curved polygon.
Args:
pts_per_edge (int): Number of points to plot per curved edge.
color (Optional[Tuple[float, float, float]]): Color as RGB profile.
ax (Optional[matplotlib.artist.Artist]): matplotlib axis object
to add plot to.
Returns:
matplotlib.artist.Artist: The axis containing the plot. This
may be a newly created axis.
"""
if ax is None:
ax = _plot_helpers.new_axis()
_plot_helpers.add_patch(ax, color, pts_per_edge, *self._edges)
return ax | python | def plot(self, pts_per_edge, color=None, ax=None):
"""Plot the current curved polygon.
Args:
pts_per_edge (int): Number of points to plot per curved edge.
color (Optional[Tuple[float, float, float]]): Color as RGB profile.
ax (Optional[matplotlib.artist.Artist]): matplotlib axis object
to add plot to.
Returns:
matplotlib.artist.Artist: The axis containing the plot. This
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"""
if ax is None:
ax = _plot_helpers.new_axis()
_plot_helpers.add_patch(ax, color, pts_per_edge, *self._edges)
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Args:
pts_per_edge (int): Number of points to plot per curved edge.
color (Optional[Tuple[float, float, float]]): Color as RGB profile.
ax (Optional[matplotlib.artist.Artist]): matplotlib axis object
to add plot to.
Returns:
matplotlib.artist.Artist: The axis containing the plot. This
may be a newly created axis. | [
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dhermes/bezier | src/bezier/_clipping.py | compute_implicit_line | def compute_implicit_line(nodes):
"""Compute the implicit form of the line connecting curve endpoints.
.. note::
This assumes, but does not check, that the first and last nodes
in ``nodes`` are different.
Computes :math:`a, b` and :math:`c` in the normalized implicit equation
for the line
.. math::
ax + by + c = 0
where :math:`a^2 + b^2 = 1` (only unique up to sign).
Args:
nodes (numpy.ndarray): ``2 x N`` array of nodes in a curve.
The line will be (directed) from the first to last
node in ``nodes``.
Returns:
Tuple[float, float, float]: The triple of
* The :math:`x` coefficient :math:`a`
* The :math:`y` coefficient :math:`b`
* The constant :math:`c`
"""
delta = nodes[:, -1] - nodes[:, 0]
length = np.linalg.norm(delta, ord=2)
# Normalize and rotate 90 degrees to the "left".
coeff_a = -delta[1] / length
coeff_b = delta[0] / length
# c = - ax - by = (delta[1] x - delta[0] y) / L
# NOTE: We divide by ``length`` at the end to "put off" rounding.
coeff_c = (delta[1] * nodes[0, 0] - delta[0] * nodes[1, 0]) / length
return coeff_a, coeff_b, coeff_c | python | def compute_implicit_line(nodes):
"""Compute the implicit form of the line connecting curve endpoints.
.. note::
This assumes, but does not check, that the first and last nodes
in ``nodes`` are different.
Computes :math:`a, b` and :math:`c` in the normalized implicit equation
for the line
.. math::
ax + by + c = 0
where :math:`a^2 + b^2 = 1` (only unique up to sign).
Args:
nodes (numpy.ndarray): ``2 x N`` array of nodes in a curve.
The line will be (directed) from the first to last
node in ``nodes``.
Returns:
Tuple[float, float, float]: The triple of
* The :math:`x` coefficient :math:`a`
* The :math:`y` coefficient :math:`b`
* The constant :math:`c`
"""
delta = nodes[:, -1] - nodes[:, 0]
length = np.linalg.norm(delta, ord=2)
# Normalize and rotate 90 degrees to the "left".
coeff_a = -delta[1] / length
coeff_b = delta[0] / length
# c = - ax - by = (delta[1] x - delta[0] y) / L
# NOTE: We divide by ``length`` at the end to "put off" rounding.
coeff_c = (delta[1] * nodes[0, 0] - delta[0] * nodes[1, 0]) / length
return coeff_a, coeff_b, coeff_c | [
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Args:
nodes (numpy.ndarray): ``2 x N`` array of nodes in a curve.
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Returns:
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* The :math:`y` coefficient :math:`b`
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dhermes/bezier | src/bezier/_clipping.py | compute_fat_line | def compute_fat_line(nodes):
"""Compute the "fat line" around a B |eacute| zier curve.
Both computes the implicit (normalized) form
.. math::
ax + by + c = 0
for the line connecting the first and last node in ``nodes``.
Also computes the maximum and minimum distances to that line
from each control point.
Args:
nodes (numpy.ndarray): ``2 x N`` array of nodes in a curve.
Returns:
Tuple[float, float, float, float, float]: The 5-tuple of
* The :math:`x` coefficient :math:`a`
* The :math:`y` coefficient :math:`b`
* The constant :math:`c`
* The "minimum" distance to the fat line among the control points.
* The "maximum" distance to the fat line among the control points.
"""
coeff_a, coeff_b, coeff_c = compute_implicit_line(nodes)
# NOTE: This assumes, but does not check, that there are two rows.
_, num_nodes = nodes.shape
d_min = 0.0
d_max = 0.0
for index in six.moves.xrange(1, num_nodes - 1): # Only interior nodes.
curr_dist = (
coeff_a * nodes[0, index] + coeff_b * nodes[1, index] + coeff_c
)
if curr_dist < d_min:
d_min = curr_dist
elif curr_dist > d_max:
d_max = curr_dist
return coeff_a, coeff_b, coeff_c, d_min, d_max | python | def compute_fat_line(nodes):
"""Compute the "fat line" around a B |eacute| zier curve.
Both computes the implicit (normalized) form
.. math::
ax + by + c = 0
for the line connecting the first and last node in ``nodes``.
Also computes the maximum and minimum distances to that line
from each control point.
Args:
nodes (numpy.ndarray): ``2 x N`` array of nodes in a curve.
Returns:
Tuple[float, float, float, float, float]: The 5-tuple of
* The :math:`x` coefficient :math:`a`
* The :math:`y` coefficient :math:`b`
* The constant :math:`c`
* The "minimum" distance to the fat line among the control points.
* The "maximum" distance to the fat line among the control points.
"""
coeff_a, coeff_b, coeff_c = compute_implicit_line(nodes)
# NOTE: This assumes, but does not check, that there are two rows.
_, num_nodes = nodes.shape
d_min = 0.0
d_max = 0.0
for index in six.moves.xrange(1, num_nodes - 1): # Only interior nodes.
curr_dist = (
coeff_a * nodes[0, index] + coeff_b * nodes[1, index] + coeff_c
)
if curr_dist < d_min:
d_min = curr_dist
elif curr_dist > d_max:
d_max = curr_dist
return coeff_a, coeff_b, coeff_c, d_min, d_max | [
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* The :math:`y` coefficient :math:`b`
* The constant :math:`c`
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dhermes/bezier | src/bezier/_clipping.py | _update_parameters | def _update_parameters(s_min, s_max, start0, end0, start1, end1):
"""Update clipped parameter range.
.. note::
This is a helper for :func:`clip_range`.
Does so by intersecting one of the two fat lines with an edge
of the convex hull of the distance polynomial of the curve being
clipped.
If both of ``s_min`` and ``s_max`` are "unset", then any :math:`s`
value that is valid for ``s_min`` would also be valid for ``s_max``.
Rather than adding a special case to handle this scenario, **only**
``s_min`` will be updated.
In cases where a given parameter :math:`s` would be a valid update
for both ``s_min`` and ``s_max``
This function **only** updates ``s_min``
Args:
s_min (float): Current start of clipped interval. If "unset", this
value will be ``DEFAULT_S_MIN``.
s_max (float): Current end of clipped interval. If "unset", this
value will be ``DEFAULT_S_MAX``.
start0 (numpy.ndarray): A 1D NumPy ``2``-array that is the start
vector of one of the two fat lines.
end0 (numpy.ndarray): A 1D NumPy ``2``-array that is the end
vector of one of the two fat lines.
start1 (numpy.ndarray): A 1D NumPy ``2``-array that is the start
vector of an edge of the convex hull of the distance
polynomial :math:`d(t)` as an explicit B |eacute| zier curve.
end1 (numpy.ndarray): A 1D NumPy ``2``-array that is the end
vector of an edge of the convex hull of the distance
polynomial :math:`d(t)` as an explicit B |eacute| zier curve.
Returns:
Tuple[float, float]: The (possibly updated) start and end
of the clipped parameter range.
Raises:
NotImplementedError: If the two line segments are parallel. (This
case will be supported at some point, just not now.)
"""
s, t, success = _geometric_intersection.segment_intersection(
start0, end0, start1, end1
)
if not success:
raise NotImplementedError(NO_PARALLEL)
if _helpers.in_interval(t, 0.0, 1.0):
if _helpers.in_interval(s, 0.0, s_min):
return s, s_max
elif _helpers.in_interval(s, s_max, 1.0):
return s_min, s
return s_min, s_max | python | def _update_parameters(s_min, s_max, start0, end0, start1, end1):
"""Update clipped parameter range.
.. note::
This is a helper for :func:`clip_range`.
Does so by intersecting one of the two fat lines with an edge
of the convex hull of the distance polynomial of the curve being
clipped.
If both of ``s_min`` and ``s_max`` are "unset", then any :math:`s`
value that is valid for ``s_min`` would also be valid for ``s_max``.
Rather than adding a special case to handle this scenario, **only**
``s_min`` will be updated.
In cases where a given parameter :math:`s` would be a valid update
for both ``s_min`` and ``s_max``
This function **only** updates ``s_min``
Args:
s_min (float): Current start of clipped interval. If "unset", this
value will be ``DEFAULT_S_MIN``.
s_max (float): Current end of clipped interval. If "unset", this
value will be ``DEFAULT_S_MAX``.
start0 (numpy.ndarray): A 1D NumPy ``2``-array that is the start
vector of one of the two fat lines.
end0 (numpy.ndarray): A 1D NumPy ``2``-array that is the end
vector of one of the two fat lines.
start1 (numpy.ndarray): A 1D NumPy ``2``-array that is the start
vector of an edge of the convex hull of the distance
polynomial :math:`d(t)` as an explicit B |eacute| zier curve.
end1 (numpy.ndarray): A 1D NumPy ``2``-array that is the end
vector of an edge of the convex hull of the distance
polynomial :math:`d(t)` as an explicit B |eacute| zier curve.
Returns:
Tuple[float, float]: The (possibly updated) start and end
of the clipped parameter range.
Raises:
NotImplementedError: If the two line segments are parallel. (This
case will be supported at some point, just not now.)
"""
s, t, success = _geometric_intersection.segment_intersection(
start0, end0, start1, end1
)
if not success:
raise NotImplementedError(NO_PARALLEL)
if _helpers.in_interval(t, 0.0, 1.0):
if _helpers.in_interval(s, 0.0, s_min):
return s, s_max
elif _helpers.in_interval(s, s_max, 1.0):
return s_min, s
return s_min, s_max | [
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s_max (float): Current end of clipped interval. If "unset", this
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end0 (numpy.ndarray): A 1D NumPy ``2``-array that is the end
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Returns:
Tuple[float, float]: The (possibly updated) start and end
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Raises:
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dhermes/bezier | src/bezier/_clipping.py | _check_parameter_range | def _check_parameter_range(s_min, s_max):
r"""Performs a final check on a clipped parameter range.
.. note::
This is a helper for :func:`clip_range`.
If both values are unchanged from the "unset" default, this returns
the whole interval :math:`\left[0.0, 1.0\right]`.
If only one of the values is set to some parameter :math:`s`, this
returns the "degenerate" interval :math:`\left[s, s\right]`. (We rely
on the fact that ``s_min`` must be the only set value, based on how
:func:`_update_parameters` works.)
Otherwise, this simply returns ``[s_min, s_max]``.
Args:
s_min (float): Current start of clipped interval. If "unset", this
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s_max (float): Current end of clipped interval. If "unset", this
value will be ``DEFAULT_S_MAX``.
Returns:
Tuple[float, float]: The (possibly updated) start and end
of the clipped parameter range.
"""
if s_min == DEFAULT_S_MIN:
# Based on the way ``_update_parameters`` works, we know
# both parameters must be unset if ``s_min``.
return 0.0, 1.0
if s_max == DEFAULT_S_MAX:
return s_min, s_min
return s_min, s_max | python | def _check_parameter_range(s_min, s_max):
r"""Performs a final check on a clipped parameter range.
.. note::
This is a helper for :func:`clip_range`.
If both values are unchanged from the "unset" default, this returns
the whole interval :math:`\left[0.0, 1.0\right]`.
If only one of the values is set to some parameter :math:`s`, this
returns the "degenerate" interval :math:`\left[s, s\right]`. (We rely
on the fact that ``s_min`` must be the only set value, based on how
:func:`_update_parameters` works.)
Otherwise, this simply returns ``[s_min, s_max]``.
Args:
s_min (float): Current start of clipped interval. If "unset", this
value will be ``DEFAULT_S_MIN``.
s_max (float): Current end of clipped interval. If "unset", this
value will be ``DEFAULT_S_MAX``.
Returns:
Tuple[float, float]: The (possibly updated) start and end
of the clipped parameter range.
"""
if s_min == DEFAULT_S_MIN:
# Based on the way ``_update_parameters`` works, we know
# both parameters must be unset if ``s_min``.
return 0.0, 1.0
if s_max == DEFAULT_S_MAX:
return s_min, s_min
return s_min, s_max | [
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s_max (float): Current end of clipped interval. If "unset", this
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dhermes/bezier | src/bezier/_clipping.py | clip_range | def clip_range(nodes1, nodes2):
r"""Reduce the parameter range where two curves can intersect.
Does so by using the "fat line" for ``nodes1`` and computing the
distance polynomial against ``nodes2``.
.. note::
This assumes, but does not check that the curves being considered
will only have one intersection in the parameter ranges
:math:`s \in \left[0, 1\right]`, :math:`t \in \left[0, 1\right]`.
This assumption is based on the fact that B |eacute| zier clipping
is meant to be used to find tangent intersections for already
subdivided (i.e. sufficiently zoomed in) curve segments.
Args:
nodes1 (numpy.ndarray): ``2 x N1`` array of nodes in a curve which
will define the clipping region.
nodes2 (numpy.ndarray): ``2 x N2`` array of nodes in a curve which
will be clipped.
Returns:
Tuple[float, float]: The pair of
* The start parameter of the clipped range.
* The end parameter of the clipped range.
"""
# NOTE: There is no corresponding "enable", but the disable only applies
# in this lexical scope.
# pylint: disable=too-many-locals
coeff_a, coeff_b, coeff_c, d_min, d_max = compute_fat_line(nodes1)
# NOTE: This assumes, but does not check, that there are two rows.
_, num_nodes2 = nodes2.shape
polynomial = np.empty((2, num_nodes2), order="F")
denominator = float(num_nodes2 - 1)
for index in six.moves.xrange(num_nodes2):
polynomial[0, index] = index / denominator
polynomial[1, index] = (
coeff_a * nodes2[0, index] + coeff_b * nodes2[1, index] + coeff_c
)
# Define segments for the top and the bottom of the region
# bounded by the fat line.
start_bottom = np.asfortranarray([0.0, d_min])
end_bottom = np.asfortranarray([1.0, d_min])
start_top = np.asfortranarray([0.0, d_max])
end_top = np.asfortranarray([1.0, d_max])
s_min = DEFAULT_S_MIN
s_max = DEFAULT_S_MAX
# NOTE: We avoid computing the convex hull and just compute where
# all segments connecting two control points intersect the
# fat lines.
for start_index in six.moves.xrange(num_nodes2 - 1):
for end_index in six.moves.xrange(start_index + 1, num_nodes2):
s_min, s_max = _update_parameters(
s_min,
s_max,
start_bottom,
end_bottom,
polynomial[:, start_index],
polynomial[:, end_index],
)
s_min, s_max = _update_parameters(
s_min,
s_max,
start_top,
end_top,
polynomial[:, start_index],
polynomial[:, end_index],
)
return _check_parameter_range(s_min, s_max) | python | def clip_range(nodes1, nodes2):
r"""Reduce the parameter range where two curves can intersect.
Does so by using the "fat line" for ``nodes1`` and computing the
distance polynomial against ``nodes2``.
.. note::
This assumes, but does not check that the curves being considered
will only have one intersection in the parameter ranges
:math:`s \in \left[0, 1\right]`, :math:`t \in \left[0, 1\right]`.
This assumption is based on the fact that B |eacute| zier clipping
is meant to be used to find tangent intersections for already
subdivided (i.e. sufficiently zoomed in) curve segments.
Args:
nodes1 (numpy.ndarray): ``2 x N1`` array of nodes in a curve which
will define the clipping region.
nodes2 (numpy.ndarray): ``2 x N2`` array of nodes in a curve which
will be clipped.
Returns:
Tuple[float, float]: The pair of
* The start parameter of the clipped range.
* The end parameter of the clipped range.
"""
# NOTE: There is no corresponding "enable", but the disable only applies
# in this lexical scope.
# pylint: disable=too-many-locals
coeff_a, coeff_b, coeff_c, d_min, d_max = compute_fat_line(nodes1)
# NOTE: This assumes, but does not check, that there are two rows.
_, num_nodes2 = nodes2.shape
polynomial = np.empty((2, num_nodes2), order="F")
denominator = float(num_nodes2 - 1)
for index in six.moves.xrange(num_nodes2):
polynomial[0, index] = index / denominator
polynomial[1, index] = (
coeff_a * nodes2[0, index] + coeff_b * nodes2[1, index] + coeff_c
)
# Define segments for the top and the bottom of the region
# bounded by the fat line.
start_bottom = np.asfortranarray([0.0, d_min])
end_bottom = np.asfortranarray([1.0, d_min])
start_top = np.asfortranarray([0.0, d_max])
end_top = np.asfortranarray([1.0, d_max])
s_min = DEFAULT_S_MIN
s_max = DEFAULT_S_MAX
# NOTE: We avoid computing the convex hull and just compute where
# all segments connecting two control points intersect the
# fat lines.
for start_index in six.moves.xrange(num_nodes2 - 1):
for end_index in six.moves.xrange(start_index + 1, num_nodes2):
s_min, s_max = _update_parameters(
s_min,
s_max,
start_bottom,
end_bottom,
polynomial[:, start_index],
polynomial[:, end_index],
)
s_min, s_max = _update_parameters(
s_min,
s_max,
start_top,
end_top,
polynomial[:, start_index],
polynomial[:, end_index],
)
return _check_parameter_range(s_min, s_max) | [
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.. note::
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will only have one intersection in the parameter ranges
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This assumption is based on the fact that B |eacute| zier clipping
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Args:
nodes1 (numpy.ndarray): ``2 x N1`` array of nodes in a curve which
will define the clipping region.
nodes2 (numpy.ndarray): ``2 x N2`` array of nodes in a curve which
will be clipped.
Returns:
Tuple[float, float]: The pair of
* The start parameter of the clipped range.
* The end parameter of the clipped range. | [
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dhermes/bezier | scripts/post_process_journal.py | post_process_travis_macos | def post_process_travis_macos(journal_filename):
"""Post-process a generated journal file on Travis macOS.
Args:
journal_filename (str): The name of the journal file.
"""
travis_build_dir = os.environ.get("TRAVIS_BUILD_DIR", "")
with open(journal_filename, "r") as file_obj:
content = file_obj.read()
processed = content.replace(travis_build_dir, "${TRAVIS_BUILD_DIR}")
with open(journal_filename, "w") as file_obj:
file_obj.write(processed) | python | def post_process_travis_macos(journal_filename):
"""Post-process a generated journal file on Travis macOS.
Args:
journal_filename (str): The name of the journal file.
"""
travis_build_dir = os.environ.get("TRAVIS_BUILD_DIR", "")
with open(journal_filename, "r") as file_obj:
content = file_obj.read()
processed = content.replace(travis_build_dir, "${TRAVIS_BUILD_DIR}")
with open(journal_filename, "w") as file_obj:
file_obj.write(processed) | [
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dhermes/bezier | scripts/check_doc_templates.py | mod_replace | def mod_replace(match, sphinx_modules):
"""Convert Sphinx ``:mod:`` to plain reST link.
Args:
match (_sre.SRE_Match): A match (from ``re``) to be used
in substitution.
sphinx_modules (list): List to be track the modules that have been
encountered.
Returns:
str: The ``match`` converted to a link.
"""
sphinx_modules.append(match.group("module"))
return "`{}`_".format(match.group("value")) | python | def mod_replace(match, sphinx_modules):
"""Convert Sphinx ``:mod:`` to plain reST link.
Args:
match (_sre.SRE_Match): A match (from ``re``) to be used
in substitution.
sphinx_modules (list): List to be track the modules that have been
encountered.
Returns:
str: The ``match`` converted to a link.
"""
sphinx_modules.append(match.group("module"))
return "`{}`_".format(match.group("value")) | [
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sphinx_modules (list): List to be track the modules that have been
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Returns:
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dhermes/bezier | scripts/check_doc_templates.py | doc_replace | def doc_replace(match, sphinx_docs):
"""Convert Sphinx ``:doc:`` to plain reST link.
Args:
match (_sre.SRE_Match): A match (from ``re``) to be used
in substitution.
sphinx_docs (list): List to be track the documents that have been
encountered.
Returns:
str: The ``match`` converted to a link.
"""
sphinx_docs.append(match.group("path"))
return "`{}`_".format(match.group("value")) | python | def doc_replace(match, sphinx_docs):
"""Convert Sphinx ``:doc:`` to plain reST link.
Args:
match (_sre.SRE_Match): A match (from ``re``) to be used
in substitution.
sphinx_docs (list): List to be track the documents that have been
encountered.
Returns:
str: The ``match`` converted to a link.
"""
sphinx_docs.append(match.group("path"))
return "`{}`_".format(match.group("value")) | [
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dhermes/bezier | scripts/check_doc_templates.py | get_diff | def get_diff(value1, value2, name1, name2):
"""Get a diff between two strings.
Args:
value1 (str): First string to be compared.
value2 (str): Second string to be compared.
name1 (str): Name of the first string.
name2 (str): Name of the second string.
Returns:
str: The full diff.
"""
lines1 = [line + "\n" for line in value1.splitlines()]
lines2 = [line + "\n" for line in value2.splitlines()]
diff_lines = difflib.context_diff(
lines1, lines2, fromfile=name1, tofile=name2
)
return "".join(diff_lines) | python | def get_diff(value1, value2, name1, name2):
"""Get a diff between two strings.
Args:
value1 (str): First string to be compared.
value2 (str): Second string to be compared.
name1 (str): Name of the first string.
name2 (str): Name of the second string.
Returns:
str: The full diff.
"""
lines1 = [line + "\n" for line in value1.splitlines()]
lines2 = [line + "\n" for line in value2.splitlines()]
diff_lines = difflib.context_diff(
lines1, lines2, fromfile=name1, tofile=name2
)
return "".join(diff_lines) | [
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str: The full diff. | [
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dhermes/bezier | scripts/check_doc_templates.py | populate_readme | def populate_readme(revision, rtd_version, **extra_kwargs):
"""Populate README template with values.
Args:
revision (str): The branch, commit, etc. being referred to (e.g.
``master``).
rtd_version (str): The version to use for RTD (Read the Docs) links
(e.g. ``latest``).
extra_kwargs (Dict[str, str]): Over-ride for template arguments.
Returns:
str: The populated README contents.
Raises:
ValueError: If the ``sphinx_modules`` encountered are not as expected.
ValueError: If the ``sphinx_docs`` encountered are not as expected.
"""
with open(TEMPLATE_FILE, "r") as file_obj:
template = file_obj.read()
img_prefix = IMG_PREFIX.format(revision=revision)
extra_links = EXTRA_LINKS.format(
rtd_version=rtd_version, revision=revision
)
docs_img = DOCS_IMG.format(rtd_version=rtd_version)
bernstein_basis = BERNSTEIN_BASIS_PLAIN.format(img_prefix=img_prefix)
bezier_defn = BEZIER_DEFN_PLAIN.format(img_prefix=img_prefix)
sum_to_unity = SUM_TO_UNITY_PLAIN.format(img_prefix=img_prefix)
template_kwargs = {
"code_block1": PLAIN_CODE_BLOCK,
"code_block2": PLAIN_CODE_BLOCK,
"code_block3": PLAIN_CODE_BLOCK,
"testcleanup": "",
"toctree": "",
"bernstein_basis": bernstein_basis,
"bezier_defn": bezier_defn,
"sum_to_unity": sum_to_unity,
"img_prefix": img_prefix,
"extra_links": extra_links,
"docs": "|docs| ",
"docs_img": docs_img,
"pypi": "\n\n|pypi| ",
"pypi_img": PYPI_IMG,
"versions": "|versions|\n\n",
"versions_img": VERSIONS_IMG,
"rtd_version": rtd_version,
"revision": revision,
"circleci_badge": CIRCLECI_BADGE,
"circleci_path": "",
"travis_badge": TRAVIS_BADGE,
"travis_path": "",
"appveyor_badge": APPVEYOR_BADGE,
"appveyor_path": "",
"coveralls_badge": COVERALLS_BADGE,
"coveralls_path": COVERALLS_PATH,
"zenodo": "|zenodo|",
"zenodo_img": ZENODO_IMG,
"joss": " |JOSS|",
"joss_img": JOSS_IMG,
}
template_kwargs.update(**extra_kwargs)
readme_contents = template.format(**template_kwargs)
# Apply regular expressions to convert Sphinx "roles" to plain reST.
readme_contents = INLINE_MATH_EXPR.sub(inline_math, readme_contents)
sphinx_modules = []
to_replace = functools.partial(mod_replace, sphinx_modules=sphinx_modules)
readme_contents = MOD_EXPR.sub(to_replace, readme_contents)
if sphinx_modules != ["bezier.curve", "bezier.surface"]:
raise ValueError("Unexpected sphinx_modules", sphinx_modules)
sphinx_docs = []
to_replace = functools.partial(doc_replace, sphinx_docs=sphinx_docs)
readme_contents = DOC_EXPR.sub(to_replace, readme_contents)
if sphinx_docs != ["python/reference/bezier", "development"]:
raise ValueError("Unexpected sphinx_docs", sphinx_docs)
return readme_contents | python | def populate_readme(revision, rtd_version, **extra_kwargs):
"""Populate README template with values.
Args:
revision (str): The branch, commit, etc. being referred to (e.g.
``master``).
rtd_version (str): The version to use for RTD (Read the Docs) links
(e.g. ``latest``).
extra_kwargs (Dict[str, str]): Over-ride for template arguments.
Returns:
str: The populated README contents.
Raises:
ValueError: If the ``sphinx_modules`` encountered are not as expected.
ValueError: If the ``sphinx_docs`` encountered are not as expected.
"""
with open(TEMPLATE_FILE, "r") as file_obj:
template = file_obj.read()
img_prefix = IMG_PREFIX.format(revision=revision)
extra_links = EXTRA_LINKS.format(
rtd_version=rtd_version, revision=revision
)
docs_img = DOCS_IMG.format(rtd_version=rtd_version)
bernstein_basis = BERNSTEIN_BASIS_PLAIN.format(img_prefix=img_prefix)
bezier_defn = BEZIER_DEFN_PLAIN.format(img_prefix=img_prefix)
sum_to_unity = SUM_TO_UNITY_PLAIN.format(img_prefix=img_prefix)
template_kwargs = {
"code_block1": PLAIN_CODE_BLOCK,
"code_block2": PLAIN_CODE_BLOCK,
"code_block3": PLAIN_CODE_BLOCK,
"testcleanup": "",
"toctree": "",
"bernstein_basis": bernstein_basis,
"bezier_defn": bezier_defn,
"sum_to_unity": sum_to_unity,
"img_prefix": img_prefix,
"extra_links": extra_links,
"docs": "|docs| ",
"docs_img": docs_img,
"pypi": "\n\n|pypi| ",
"pypi_img": PYPI_IMG,
"versions": "|versions|\n\n",
"versions_img": VERSIONS_IMG,
"rtd_version": rtd_version,
"revision": revision,
"circleci_badge": CIRCLECI_BADGE,
"circleci_path": "",
"travis_badge": TRAVIS_BADGE,
"travis_path": "",
"appveyor_badge": APPVEYOR_BADGE,
"appveyor_path": "",
"coveralls_badge": COVERALLS_BADGE,
"coveralls_path": COVERALLS_PATH,
"zenodo": "|zenodo|",
"zenodo_img": ZENODO_IMG,
"joss": " |JOSS|",
"joss_img": JOSS_IMG,
}
template_kwargs.update(**extra_kwargs)
readme_contents = template.format(**template_kwargs)
# Apply regular expressions to convert Sphinx "roles" to plain reST.
readme_contents = INLINE_MATH_EXPR.sub(inline_math, readme_contents)
sphinx_modules = []
to_replace = functools.partial(mod_replace, sphinx_modules=sphinx_modules)
readme_contents = MOD_EXPR.sub(to_replace, readme_contents)
if sphinx_modules != ["bezier.curve", "bezier.surface"]:
raise ValueError("Unexpected sphinx_modules", sphinx_modules)
sphinx_docs = []
to_replace = functools.partial(doc_replace, sphinx_docs=sphinx_docs)
readme_contents = DOC_EXPR.sub(to_replace, readme_contents)
if sphinx_docs != ["python/reference/bezier", "development"]:
raise ValueError("Unexpected sphinx_docs", sphinx_docs)
return readme_contents | [
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Args:
revision (str): The branch, commit, etc. being referred to (e.g.
``master``).
rtd_version (str): The version to use for RTD (Read the Docs) links
(e.g. ``latest``).
extra_kwargs (Dict[str, str]): Over-ride for template arguments.
Returns:
str: The populated README contents.
Raises:
ValueError: If the ``sphinx_modules`` encountered are not as expected.
ValueError: If the ``sphinx_docs`` encountered are not as expected. | [
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dhermes/bezier | scripts/check_doc_templates.py | readme_verify | def readme_verify():
"""Populate the template and compare to ``README``.
Raises:
ValueError: If the current README doesn't agree with the expected
value computed from the template.
"""
expected = populate_readme(REVISION, RTD_VERSION)
# Actually get the stored contents.
with open(README_FILE, "r") as file_obj:
contents = file_obj.read()
if contents != expected:
err_msg = "\n" + get_diff(
contents, expected, "README.rst.actual", "README.rst.expected"
)
raise ValueError(err_msg)
else:
print("README contents are as expected.") | python | def readme_verify():
"""Populate the template and compare to ``README``.
Raises:
ValueError: If the current README doesn't agree with the expected
value computed from the template.
"""
expected = populate_readme(REVISION, RTD_VERSION)
# Actually get the stored contents.
with open(README_FILE, "r") as file_obj:
contents = file_obj.read()
if contents != expected:
err_msg = "\n" + get_diff(
contents, expected, "README.rst.actual", "README.rst.expected"
)
raise ValueError(err_msg)
else:
print("README contents are as expected.") | [
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dhermes/bezier | scripts/check_doc_templates.py | release_readme_verify | def release_readme_verify():
"""Specialize the template to a PyPI release template.
Once populated, compare to ``README.rst.release.template``.
Raises:
ValueError: If the current template doesn't agree with the expected
value specialized from the template.
"""
version = "{version}"
expected = populate_readme(
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versions="\n\n",
versions_img="",
circleci_badge=CIRCLECI_BADGE_RELEASE,
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travis_badge=TRAVIS_BADGE_RELEASE,
travis_path="/builds/{travis_build}",
appveyor_badge=APPVEYOR_BADGE_RELEASE,
appveyor_path="/build/{appveyor_build}",
coveralls_badge=COVERALLS_BADGE_RELEASE,
coveralls_path="builds/{coveralls_build}",
)
with open(RELEASE_README_FILE, "r") as file_obj:
contents = file_obj.read()
if contents != expected:
err_msg = "\n" + get_diff(
contents,
expected,
"README.rst.release.actual",
"README.rst.release.expected",
)
raise ValueError(err_msg)
else:
print("README.rst.release.template contents are as expected.") | python | def release_readme_verify():
"""Specialize the template to a PyPI release template.
Once populated, compare to ``README.rst.release.template``.
Raises:
ValueError: If the current template doesn't agree with the expected
value specialized from the template.
"""
version = "{version}"
expected = populate_readme(
version,
version,
pypi="",
pypi_img="",
versions="\n\n",
versions_img="",
circleci_badge=CIRCLECI_BADGE_RELEASE,
circleci_path="/{circleci_build}",
travis_badge=TRAVIS_BADGE_RELEASE,
travis_path="/builds/{travis_build}",
appveyor_badge=APPVEYOR_BADGE_RELEASE,
appveyor_path="/build/{appveyor_build}",
coveralls_badge=COVERALLS_BADGE_RELEASE,
coveralls_path="builds/{coveralls_build}",
)
with open(RELEASE_README_FILE, "r") as file_obj:
contents = file_obj.read()
if contents != expected:
err_msg = "\n" + get_diff(
contents,
expected,
"README.rst.release.actual",
"README.rst.release.expected",
)
raise ValueError(err_msg)
else:
print("README.rst.release.template contents are as expected.") | [
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dhermes/bezier | scripts/check_doc_templates.py | _index_verify | def _index_verify(index_file, **extra_kwargs):
"""Populate the template and compare to documentation index file.
Used for both ``docs/index.rst`` and ``docs/index.rst.release.template``.
Args:
index_file (str): Filename to compare against.
extra_kwargs (Dict[str, str]): Over-ride for template arguments.
One **special** keyword is ``side_effect``, which can be used
to update the template output after the fact.
Raises:
ValueError: If the current ``index.rst`` doesn't agree with the
expected value computed from the template.
"""
side_effect = extra_kwargs.pop("side_effect", None)
with open(TEMPLATE_FILE, "r") as file_obj:
template = file_obj.read()
template_kwargs = {
"code_block1": SPHINX_CODE_BLOCK1,
"code_block2": SPHINX_CODE_BLOCK2,
"code_block3": SPHINX_CODE_BLOCK3,
"testcleanup": TEST_CLEANUP,
"toctree": TOCTREE,
"bernstein_basis": BERNSTEIN_BASIS_SPHINX,
"bezier_defn": BEZIER_DEFN_SPHINX,
"sum_to_unity": SUM_TO_UNITY_SPHINX,
"img_prefix": "",
"extra_links": "",
"docs": "",
"docs_img": "",
"pypi": "\n\n|pypi| ",
"pypi_img": PYPI_IMG,
"versions": "|versions|\n\n",
"versions_img": VERSIONS_IMG,
"rtd_version": RTD_VERSION,
"revision": REVISION,
"circleci_badge": CIRCLECI_BADGE,
"circleci_path": "",
"travis_badge": TRAVIS_BADGE,
"travis_path": "",
"appveyor_badge": APPVEYOR_BADGE,
"appveyor_path": "",
"coveralls_badge": COVERALLS_BADGE,
"coveralls_path": COVERALLS_PATH,
"zenodo": "|zenodo|",
"zenodo_img": ZENODO_IMG,
"joss": " |JOSS|",
"joss_img": JOSS_IMG,
}
template_kwargs.update(**extra_kwargs)
expected = template.format(**template_kwargs)
if side_effect is not None:
expected = side_effect(expected)
with open(index_file, "r") as file_obj:
contents = file_obj.read()
if contents != expected:
err_msg = "\n" + get_diff(
contents,
expected,
index_file + ".actual",
index_file + ".expected",
)
raise ValueError(err_msg)
else:
rel_name = os.path.relpath(index_file, _ROOT_DIR)
msg = "{} contents are as expected.".format(rel_name)
print(msg) | python | def _index_verify(index_file, **extra_kwargs):
"""Populate the template and compare to documentation index file.
Used for both ``docs/index.rst`` and ``docs/index.rst.release.template``.
Args:
index_file (str): Filename to compare against.
extra_kwargs (Dict[str, str]): Over-ride for template arguments.
One **special** keyword is ``side_effect``, which can be used
to update the template output after the fact.
Raises:
ValueError: If the current ``index.rst`` doesn't agree with the
expected value computed from the template.
"""
side_effect = extra_kwargs.pop("side_effect", None)
with open(TEMPLATE_FILE, "r") as file_obj:
template = file_obj.read()
template_kwargs = {
"code_block1": SPHINX_CODE_BLOCK1,
"code_block2": SPHINX_CODE_BLOCK2,
"code_block3": SPHINX_CODE_BLOCK3,
"testcleanup": TEST_CLEANUP,
"toctree": TOCTREE,
"bernstein_basis": BERNSTEIN_BASIS_SPHINX,
"bezier_defn": BEZIER_DEFN_SPHINX,
"sum_to_unity": SUM_TO_UNITY_SPHINX,
"img_prefix": "",
"extra_links": "",
"docs": "",
"docs_img": "",
"pypi": "\n\n|pypi| ",
"pypi_img": PYPI_IMG,
"versions": "|versions|\n\n",
"versions_img": VERSIONS_IMG,
"rtd_version": RTD_VERSION,
"revision": REVISION,
"circleci_badge": CIRCLECI_BADGE,
"circleci_path": "",
"travis_badge": TRAVIS_BADGE,
"travis_path": "",
"appveyor_badge": APPVEYOR_BADGE,
"appveyor_path": "",
"coveralls_badge": COVERALLS_BADGE,
"coveralls_path": COVERALLS_PATH,
"zenodo": "|zenodo|",
"zenodo_img": ZENODO_IMG,
"joss": " |JOSS|",
"joss_img": JOSS_IMG,
}
template_kwargs.update(**extra_kwargs)
expected = template.format(**template_kwargs)
if side_effect is not None:
expected = side_effect(expected)
with open(index_file, "r") as file_obj:
contents = file_obj.read()
if contents != expected:
err_msg = "\n" + get_diff(
contents,
expected,
index_file + ".actual",
index_file + ".expected",
)
raise ValueError(err_msg)
else:
rel_name = os.path.relpath(index_file, _ROOT_DIR)
msg = "{} contents are as expected.".format(rel_name)
print(msg) | [
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dhermes/bezier | scripts/check_doc_templates.py | release_docs_side_effect | def release_docs_side_effect(content):
"""Updates the template so that curly braces are escaped correctly.
Args:
content (str): The template for ``docs/index.rst.release.template``.
Returns:
str: The updated template with properly escaped curly braces.
"""
# First replace **all** curly braces.
result = content.replace("{", "{{").replace("}", "}}")
# Then reset the actual template arguments.
result = result.replace("{{version}}", "{version}")
result = result.replace("{{circleci_build}}", "{circleci_build}")
result = result.replace("{{travis_build}}", "{travis_build}")
result = result.replace("{{appveyor_build}}", "{appveyor_build}")
result = result.replace("{{coveralls_build}}", "{coveralls_build}")
return result | python | def release_docs_side_effect(content):
"""Updates the template so that curly braces are escaped correctly.
Args:
content (str): The template for ``docs/index.rst.release.template``.
Returns:
str: The updated template with properly escaped curly braces.
"""
# First replace **all** curly braces.
result = content.replace("{", "{{").replace("}", "}}")
# Then reset the actual template arguments.
result = result.replace("{{version}}", "{version}")
result = result.replace("{{circleci_build}}", "{circleci_build}")
result = result.replace("{{travis_build}}", "{travis_build}")
result = result.replace("{{appveyor_build}}", "{appveyor_build}")
result = result.replace("{{coveralls_build}}", "{coveralls_build}")
return result | [
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dhermes/bezier | scripts/check_doc_templates.py | release_docs_index_verify | def release_docs_index_verify():
"""Populate template and compare to ``docs/index.rst.release.template``.
Raises:
ValueError: If the current ``index.rst.release.template`` doesn't
agree with the expected value computed from the template.
"""
version = "{version}"
_index_verify(
RELEASE_INDEX_FILE,
side_effect=release_docs_side_effect,
pypi="",
pypi_img="",
versions="\n\n",
versions_img="",
rtd_version=version,
revision=version,
circleci_badge=CIRCLECI_BADGE_RELEASE,
circleci_path="/{circleci_build}",
travis_badge=TRAVIS_BADGE_RELEASE,
travis_path="/builds/{travis_build}",
appveyor_badge=APPVEYOR_BADGE_RELEASE,
appveyor_path="/build/{appveyor_build}",
coveralls_badge=COVERALLS_BADGE_RELEASE,
coveralls_path="builds/{coveralls_build}",
) | python | def release_docs_index_verify():
"""Populate template and compare to ``docs/index.rst.release.template``.
Raises:
ValueError: If the current ``index.rst.release.template`` doesn't
agree with the expected value computed from the template.
"""
version = "{version}"
_index_verify(
RELEASE_INDEX_FILE,
side_effect=release_docs_side_effect,
pypi="",
pypi_img="",
versions="\n\n",
versions_img="",
rtd_version=version,
revision=version,
circleci_badge=CIRCLECI_BADGE_RELEASE,
circleci_path="/{circleci_build}",
travis_badge=TRAVIS_BADGE_RELEASE,
travis_path="/builds/{travis_build}",
appveyor_badge=APPVEYOR_BADGE_RELEASE,
appveyor_path="/build/{appveyor_build}",
coveralls_badge=COVERALLS_BADGE_RELEASE,
coveralls_path="builds/{coveralls_build}",
) | [
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dhermes/bezier | scripts/check_doc_templates.py | development_verify | def development_verify():
"""Populate template and compare to ``DEVELOPMENT.rst``
Raises:
ValueError: If the current ``DEVELOPMENT.rst`` doesn't
agree with the expected value computed from the template.
"""
with open(DEVELOPMENT_TEMPLATE, "r") as file_obj:
template = file_obj.read()
expected = template.format(revision=REVISION, rtd_version=RTD_VERSION)
with open(DEVELOPMENT_FILE, "r") as file_obj:
contents = file_obj.read()
if contents != expected:
err_msg = "\n" + get_diff(
contents,
expected,
"DEVELOPMENT.rst.actual",
"DEVELOPMENT.rst.expected",
)
raise ValueError(err_msg)
else:
print("DEVELOPMENT.rst contents are as expected.") | python | def development_verify():
"""Populate template and compare to ``DEVELOPMENT.rst``
Raises:
ValueError: If the current ``DEVELOPMENT.rst`` doesn't
agree with the expected value computed from the template.
"""
with open(DEVELOPMENT_TEMPLATE, "r") as file_obj:
template = file_obj.read()
expected = template.format(revision=REVISION, rtd_version=RTD_VERSION)
with open(DEVELOPMENT_FILE, "r") as file_obj:
contents = file_obj.read()
if contents != expected:
err_msg = "\n" + get_diff(
contents,
expected,
"DEVELOPMENT.rst.actual",
"DEVELOPMENT.rst.expected",
)
raise ValueError(err_msg)
else:
print("DEVELOPMENT.rst contents are as expected.") | [
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dhermes/bezier | scripts/check_doc_templates.py | native_libraries_verify | def native_libraries_verify():
"""Populate the template and compare to ``binary-extension.rst``.
Raises:
ValueError: If the current ``docs/python/binary-extension.rst`` doesn't
agree with the expected value computed from the template.
"""
with open(BINARY_EXT_TEMPLATE, "r") as file_obj:
template = file_obj.read()
expected = template.format(revision=REVISION)
with open(BINARY_EXT_FILE, "r") as file_obj:
contents = file_obj.read()
if contents != expected:
err_msg = "\n" + get_diff(
contents,
expected,
"docs/python/binary-extension.rst.actual",
"docs/python/binary-extension.rst.expected",
)
raise ValueError(err_msg)
else:
print("docs/python/binary-extension.rst contents are as expected.") | python | def native_libraries_verify():
"""Populate the template and compare to ``binary-extension.rst``.
Raises:
ValueError: If the current ``docs/python/binary-extension.rst`` doesn't
agree with the expected value computed from the template.
"""
with open(BINARY_EXT_TEMPLATE, "r") as file_obj:
template = file_obj.read()
expected = template.format(revision=REVISION)
with open(BINARY_EXT_FILE, "r") as file_obj:
contents = file_obj.read()
if contents != expected:
err_msg = "\n" + get_diff(
contents,
expected,
"docs/python/binary-extension.rst.actual",
"docs/python/binary-extension.rst.expected",
)
raise ValueError(err_msg)
else:
print("docs/python/binary-extension.rst contents are as expected.") | [
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dhermes/bezier | src/bezier/_algebraic_intersection.py | _evaluate3 | def _evaluate3(nodes, x_val, y_val):
"""Helper for :func:`evaluate` when ``nodes`` is degree 3.
Args:
nodes (numpy.ndarray): ``2 x 4`` array of nodes in a curve.
x_val (float): ``x``-coordinate for evaluation.
y_val (float): ``y``-coordinate for evaluation.
Returns:
float: The computed value of :math:`f(x, y)`.
"""
# NOTE: This may be (a) slower and (b) less precise than
# hard-coding the determinant.
sylvester_mat = np.zeros((6, 6), order="F")
delta = nodes - np.asfortranarray([[x_val], [y_val]])
delta[:, 1:3] *= 3.0
# Swap rows/columns so that x-y are right next to each other.
# This will only change the determinant up to a sign.
sylvester_mat[:2, :4] = delta
sylvester_mat[2:4, 1:5] = delta
sylvester_mat[4:, 2:] = delta
return np.linalg.det(sylvester_mat) | python | def _evaluate3(nodes, x_val, y_val):
"""Helper for :func:`evaluate` when ``nodes`` is degree 3.
Args:
nodes (numpy.ndarray): ``2 x 4`` array of nodes in a curve.
x_val (float): ``x``-coordinate for evaluation.
y_val (float): ``y``-coordinate for evaluation.
Returns:
float: The computed value of :math:`f(x, y)`.
"""
# NOTE: This may be (a) slower and (b) less precise than
# hard-coding the determinant.
sylvester_mat = np.zeros((6, 6), order="F")
delta = nodes - np.asfortranarray([[x_val], [y_val]])
delta[:, 1:3] *= 3.0
# Swap rows/columns so that x-y are right next to each other.
# This will only change the determinant up to a sign.
sylvester_mat[:2, :4] = delta
sylvester_mat[2:4, 1:5] = delta
sylvester_mat[4:, 2:] = delta
return np.linalg.det(sylvester_mat) | [
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dhermes/bezier | src/bezier/_algebraic_intersection.py | evaluate | def evaluate(nodes, x_val, y_val):
r"""Evaluate the implicitized bivariate polynomial containing the curve.
Assumes `algebraic curve`_ containing :math:`B(s, t)` is given by
:math:`f(x, y) = 0`. This function evaluates :math:`f(x, y)`.
.. note::
This assumes, but doesn't check, that ``nodes`` has 2 rows.
.. note::
This assumes, but doesn't check, that ``nodes`` is not degree-elevated.
If it were degree-elevated, then the Sylvester matrix will always
have zero determinant.
Args:
nodes (numpy.ndarray): ``2 x N`` array of nodes in a curve.
x_val (float): ``x``-coordinate for evaluation.
y_val (float): ``y``-coordinate for evaluation.
Returns:
float: The computed value of :math:`f(x, y)`.
Raises:
ValueError: If the curve is a point.
.UnsupportedDegree: If the degree is not 1, 2 or 3.
"""
_, num_nodes = nodes.shape
if num_nodes == 1:
raise ValueError("A point cannot be implicitized")
elif num_nodes == 2:
# x(s) - x = (x0 - x) (1 - s) + (x1 - x) s
# y(s) - y = (y0 - y) (1 - s) + (y1 - y) s
# Modified Sylvester: [x0 - x, x1 - x]
# [y0 - y, y1 - y]
return (nodes[0, 0] - x_val) * (nodes[1, 1] - y_val) - (
nodes[0, 1] - x_val
) * (nodes[1, 0] - y_val)
elif num_nodes == 3:
# x(s) - x = (x0 - x) (1 - s)^2 + 2 (x1 - x) s(1 - s) + (x2 - x) s^2
# y(s) - y = (y0 - y) (1 - s)^2 + 2 (y1 - y) s(1 - s) + (y2 - y) s^2
# Modified Sylvester: [x0 - x, 2(x1 - x), x2 - x, 0] = A|B|C|0
# [ 0, x0 - x, 2(x1 - x), x2 - x] 0|A|B|C
# [y0 - y, 2(y1 - y), y2 - y, 0] D|E|F|0
# [ 0, y0 - y, 2(y1 - y), y2 - y] 0|D|E|F
val_a, val_b, val_c = nodes[0, :] - x_val
val_b *= 2
val_d, val_e, val_f = nodes[1, :] - y_val
val_e *= 2
# [A, B, C] [E, F, 0]
# det [E, F, 0] = - det [A, B, C] = -E (BF - CE) + F(AF - CD)
# [D, E, F] [D, E, F]
sub1 = val_b * val_f - val_c * val_e
sub2 = val_a * val_f - val_c * val_d
sub_det_a = -val_e * sub1 + val_f * sub2
# [B, C, 0]
# det [A, B, C] = B (BF - CE) - C (AF - CD)
# [D, E, F]
sub_det_d = val_b * sub1 - val_c * sub2
return val_a * sub_det_a + val_d * sub_det_d
elif num_nodes == 4:
return _evaluate3(nodes, x_val, y_val)
else:
raise _helpers.UnsupportedDegree(num_nodes - 1, supported=(1, 2, 3)) | python | def evaluate(nodes, x_val, y_val):
r"""Evaluate the implicitized bivariate polynomial containing the curve.
Assumes `algebraic curve`_ containing :math:`B(s, t)` is given by
:math:`f(x, y) = 0`. This function evaluates :math:`f(x, y)`.
.. note::
This assumes, but doesn't check, that ``nodes`` has 2 rows.
.. note::
This assumes, but doesn't check, that ``nodes`` is not degree-elevated.
If it were degree-elevated, then the Sylvester matrix will always
have zero determinant.
Args:
nodes (numpy.ndarray): ``2 x N`` array of nodes in a curve.
x_val (float): ``x``-coordinate for evaluation.
y_val (float): ``y``-coordinate for evaluation.
Returns:
float: The computed value of :math:`f(x, y)`.
Raises:
ValueError: If the curve is a point.
.UnsupportedDegree: If the degree is not 1, 2 or 3.
"""
_, num_nodes = nodes.shape
if num_nodes == 1:
raise ValueError("A point cannot be implicitized")
elif num_nodes == 2:
# x(s) - x = (x0 - x) (1 - s) + (x1 - x) s
# y(s) - y = (y0 - y) (1 - s) + (y1 - y) s
# Modified Sylvester: [x0 - x, x1 - x]
# [y0 - y, y1 - y]
return (nodes[0, 0] - x_val) * (nodes[1, 1] - y_val) - (
nodes[0, 1] - x_val
) * (nodes[1, 0] - y_val)
elif num_nodes == 3:
# x(s) - x = (x0 - x) (1 - s)^2 + 2 (x1 - x) s(1 - s) + (x2 - x) s^2
# y(s) - y = (y0 - y) (1 - s)^2 + 2 (y1 - y) s(1 - s) + (y2 - y) s^2
# Modified Sylvester: [x0 - x, 2(x1 - x), x2 - x, 0] = A|B|C|0
# [ 0, x0 - x, 2(x1 - x), x2 - x] 0|A|B|C
# [y0 - y, 2(y1 - y), y2 - y, 0] D|E|F|0
# [ 0, y0 - y, 2(y1 - y), y2 - y] 0|D|E|F
val_a, val_b, val_c = nodes[0, :] - x_val
val_b *= 2
val_d, val_e, val_f = nodes[1, :] - y_val
val_e *= 2
# [A, B, C] [E, F, 0]
# det [E, F, 0] = - det [A, B, C] = -E (BF - CE) + F(AF - CD)
# [D, E, F] [D, E, F]
sub1 = val_b * val_f - val_c * val_e
sub2 = val_a * val_f - val_c * val_d
sub_det_a = -val_e * sub1 + val_f * sub2
# [B, C, 0]
# det [A, B, C] = B (BF - CE) - C (AF - CD)
# [D, E, F]
sub_det_d = val_b * sub1 - val_c * sub2
return val_a * sub_det_a + val_d * sub_det_d
elif num_nodes == 4:
return _evaluate3(nodes, x_val, y_val)
else:
raise _helpers.UnsupportedDegree(num_nodes - 1, supported=(1, 2, 3)) | [
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Assumes `algebraic curve`_ containing :math:`B(s, t)` is given by
:math:`f(x, y) = 0`. This function evaluates :math:`f(x, y)`.
.. note::
This assumes, but doesn't check, that ``nodes`` has 2 rows.
.. note::
This assumes, but doesn't check, that ``nodes`` is not degree-elevated.
If it were degree-elevated, then the Sylvester matrix will always
have zero determinant.
Args:
nodes (numpy.ndarray): ``2 x N`` array of nodes in a curve.
x_val (float): ``x``-coordinate for evaluation.
y_val (float): ``y``-coordinate for evaluation.
Returns:
float: The computed value of :math:`f(x, y)`.
Raises:
ValueError: If the curve is a point.
.UnsupportedDegree: If the degree is not 1, 2 or 3. | [
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dhermes/bezier | src/bezier/_algebraic_intersection.py | eval_intersection_polynomial | def eval_intersection_polynomial(nodes1, nodes2, t):
r"""Evaluates a parametric curve **on** an implicitized algebraic curve.
Uses :func:`evaluate` to evaluate :math:`f_1(x, y)`, the implicitization
of ``nodes1``. Then plugs ``t`` into the second parametric curve to
get an ``x``- and ``y``-coordinate and evaluate the
**intersection polynomial**:
.. math::
g(t) = f_1\left(x_2(t), y_2(t)right)
Args:
nodes1 (numpy.ndarray): The nodes in the first curve.
nodes2 (numpy.ndarray): The nodes in the second curve.
t (float): The parameter along ``nodes2`` where we evaluate
the function.
Returns:
float: The computed value of :math:`f_1(x_2(t), y_2(t))`.
"""
(x_val,), (y_val,) = _curve_helpers.evaluate_multi(
nodes2, np.asfortranarray([t])
)
return evaluate(nodes1, x_val, y_val) | python | def eval_intersection_polynomial(nodes1, nodes2, t):
r"""Evaluates a parametric curve **on** an implicitized algebraic curve.
Uses :func:`evaluate` to evaluate :math:`f_1(x, y)`, the implicitization
of ``nodes1``. Then plugs ``t`` into the second parametric curve to
get an ``x``- and ``y``-coordinate and evaluate the
**intersection polynomial**:
.. math::
g(t) = f_1\left(x_2(t), y_2(t)right)
Args:
nodes1 (numpy.ndarray): The nodes in the first curve.
nodes2 (numpy.ndarray): The nodes in the second curve.
t (float): The parameter along ``nodes2`` where we evaluate
the function.
Returns:
float: The computed value of :math:`f_1(x_2(t), y_2(t))`.
"""
(x_val,), (y_val,) = _curve_helpers.evaluate_multi(
nodes2, np.asfortranarray([t])
)
return evaluate(nodes1, x_val, y_val) | [
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get an ``x``- and ``y``-coordinate and evaluate the
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.. math::
g(t) = f_1\left(x_2(t), y_2(t)right)
Args:
nodes1 (numpy.ndarray): The nodes in the first curve.
nodes2 (numpy.ndarray): The nodes in the second curve.
t (float): The parameter along ``nodes2`` where we evaluate
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Returns:
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dhermes/bezier | src/bezier/_algebraic_intersection.py | _to_power_basis11 | def _to_power_basis11(nodes1, nodes2):
r"""Compute the coefficients of an **intersection polynomial**.
Helper for :func:`to_power_basis` in the case that each curve is
degree one. In this case, B |eacute| zout's `theorem`_ tells us
that the **intersection polynomial** is degree :math:`1 \cdot 1`
hence we return two coefficients.
Args:
nodes1 (numpy.ndarray): The nodes in the first curve.
nodes2 (numpy.ndarray): The nodes in the second curve.
Returns:
numpy.ndarray: ``2``-array of coefficients.
"""
# We manually invert the Vandermonde matrix:
# [1 0][c0] = [n0]
# [1 1][c1] [n1]
val0 = eval_intersection_polynomial(nodes1, nodes2, 0.0)
val1 = eval_intersection_polynomial(nodes1, nodes2, 1.0)
# [c0] = [ 1 0][n0]
# [c1] = [-1 1][n1]
return np.asfortranarray([val0, -val0 + val1]) | python | def _to_power_basis11(nodes1, nodes2):
r"""Compute the coefficients of an **intersection polynomial**.
Helper for :func:`to_power_basis` in the case that each curve is
degree one. In this case, B |eacute| zout's `theorem`_ tells us
that the **intersection polynomial** is degree :math:`1 \cdot 1`
hence we return two coefficients.
Args:
nodes1 (numpy.ndarray): The nodes in the first curve.
nodes2 (numpy.ndarray): The nodes in the second curve.
Returns:
numpy.ndarray: ``2``-array of coefficients.
"""
# We manually invert the Vandermonde matrix:
# [1 0][c0] = [n0]
# [1 1][c1] [n1]
val0 = eval_intersection_polynomial(nodes1, nodes2, 0.0)
val1 = eval_intersection_polynomial(nodes1, nodes2, 1.0)
# [c0] = [ 1 0][n0]
# [c1] = [-1 1][n1]
return np.asfortranarray([val0, -val0 + val1]) | [
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Helper for :func:`to_power_basis` in the case that each curve is
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that the **intersection polynomial** is degree :math:`1 \cdot 1`
hence we return two coefficients.
Args:
nodes1 (numpy.ndarray): The nodes in the first curve.
nodes2 (numpy.ndarray): The nodes in the second curve.
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numpy.ndarray: ``2``-array of coefficients. | [
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dhermes/bezier | src/bezier/_algebraic_intersection.py | _to_power_basis12 | def _to_power_basis12(nodes1, nodes2):
r"""Compute the coefficients of an **intersection polynomial**.
Helper for :func:`to_power_basis` in the case that the first curve is
degree one and the second is degree two. In this case, B |eacute|
zout's `theorem`_ tells us that the **intersection polynomial** is
degree :math:`1 \cdot 2` hence we return three coefficients.
Args:
nodes1 (numpy.ndarray): The nodes in the first curve.
nodes2 (numpy.ndarray): The nodes in the second curve.
Returns:
numpy.ndarray: ``3``-array of coefficients.
"""
# We manually invert the Vandermonde matrix:
# [1 0 0 ][c0] = [n0]
# [1 1/2 1/4][c1] [n1]
# [1 1 1 ][c2] [n2]
val0 = eval_intersection_polynomial(nodes1, nodes2, 0.0)
val1 = eval_intersection_polynomial(nodes1, nodes2, 0.5)
val2 = eval_intersection_polynomial(nodes1, nodes2, 1.0)
# [c0] = [ 1 0 0][n0]
# [c1] = [-3 4 -1][n1]
# [c2] = [ 2 -4 2][n2]
return np.asfortranarray(
[
val0,
-3.0 * val0 + 4.0 * val1 - val2,
2.0 * val0 - 4.0 * val1 + 2.0 * val2,
]
) | python | def _to_power_basis12(nodes1, nodes2):
r"""Compute the coefficients of an **intersection polynomial**.
Helper for :func:`to_power_basis` in the case that the first curve is
degree one and the second is degree two. In this case, B |eacute|
zout's `theorem`_ tells us that the **intersection polynomial** is
degree :math:`1 \cdot 2` hence we return three coefficients.
Args:
nodes1 (numpy.ndarray): The nodes in the first curve.
nodes2 (numpy.ndarray): The nodes in the second curve.
Returns:
numpy.ndarray: ``3``-array of coefficients.
"""
# We manually invert the Vandermonde matrix:
# [1 0 0 ][c0] = [n0]
# [1 1/2 1/4][c1] [n1]
# [1 1 1 ][c2] [n2]
val0 = eval_intersection_polynomial(nodes1, nodes2, 0.0)
val1 = eval_intersection_polynomial(nodes1, nodes2, 0.5)
val2 = eval_intersection_polynomial(nodes1, nodes2, 1.0)
# [c0] = [ 1 0 0][n0]
# [c1] = [-3 4 -1][n1]
# [c2] = [ 2 -4 2][n2]
return np.asfortranarray(
[
val0,
-3.0 * val0 + 4.0 * val1 - val2,
2.0 * val0 - 4.0 * val1 + 2.0 * val2,
]
) | [
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Helper for :func:`to_power_basis` in the case that the first curve is
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zout's `theorem`_ tells us that the **intersection polynomial** is
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Args:
nodes1 (numpy.ndarray): The nodes in the first curve.
nodes2 (numpy.ndarray): The nodes in the second curve.
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numpy.ndarray: ``3``-array of coefficients. | [
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dhermes/bezier | src/bezier/_algebraic_intersection.py | _to_power_basis13 | def _to_power_basis13(nodes1, nodes2):
r"""Compute the coefficients of an **intersection polynomial**.
Helper for :func:`to_power_basis` in the case that the first curve is
degree one and the second is degree three. In this case, B |eacute|
zout's `theorem`_ tells us that the **intersection polynomial** is
degree :math:`1 \cdot 3` hence we return four coefficients.
Args:
nodes1 (numpy.ndarray): The nodes in the first curve.
nodes2 (numpy.ndarray): The nodes in the second curve.
Returns:
numpy.ndarray: ``4``-array of coefficients.
"""
# We manually invert the Vandermonde matrix:
# Use exact f.p. numbers to avoid round-off wherever possible.
# [1 0 0 0 ][c0] = [n0]
# [1 1/4 1/16 1/64 ][c1] [n1]
# [1 3/4 9/16 27/64][c2] [n2]
# [1 1 1 1 ][c3] [n3]
val0 = eval_intersection_polynomial(nodes1, nodes2, 0.0)
val1 = eval_intersection_polynomial(nodes1, nodes2, 0.25)
val2 = eval_intersection_polynomial(nodes1, nodes2, 0.75)
val3 = eval_intersection_polynomial(nodes1, nodes2, 1.0)
# [c0] = [ 3 0 0 0][n0]
# [c1] = 1 / 3 [-19 24 -8 3][n1]
# [c2] = [ 32 -56 40 -16][n2]
# [c3] = [-16 32 -32 16][n3]
# Since polynomial coefficients, we don't need to divide by 3
# to get the same polynomial. Avoid the division to avoid round-off.
return np.asfortranarray(
[
3.0 * val0,
-19.0 * val0 + 24.0 * val1 - 8.0 * val2 + 3.0 * val3,
32.0 * val0 - 56.0 * val1 + 40.0 * val2 - 16.0 * val3,
-16.0 * val0 + 32.0 * val1 - 32.0 * val2 + 16.0 * val3,
]
) | python | def _to_power_basis13(nodes1, nodes2):
r"""Compute the coefficients of an **intersection polynomial**.
Helper for :func:`to_power_basis` in the case that the first curve is
degree one and the second is degree three. In this case, B |eacute|
zout's `theorem`_ tells us that the **intersection polynomial** is
degree :math:`1 \cdot 3` hence we return four coefficients.
Args:
nodes1 (numpy.ndarray): The nodes in the first curve.
nodes2 (numpy.ndarray): The nodes in the second curve.
Returns:
numpy.ndarray: ``4``-array of coefficients.
"""
# We manually invert the Vandermonde matrix:
# Use exact f.p. numbers to avoid round-off wherever possible.
# [1 0 0 0 ][c0] = [n0]
# [1 1/4 1/16 1/64 ][c1] [n1]
# [1 3/4 9/16 27/64][c2] [n2]
# [1 1 1 1 ][c3] [n3]
val0 = eval_intersection_polynomial(nodes1, nodes2, 0.0)
val1 = eval_intersection_polynomial(nodes1, nodes2, 0.25)
val2 = eval_intersection_polynomial(nodes1, nodes2, 0.75)
val3 = eval_intersection_polynomial(nodes1, nodes2, 1.0)
# [c0] = [ 3 0 0 0][n0]
# [c1] = 1 / 3 [-19 24 -8 3][n1]
# [c2] = [ 32 -56 40 -16][n2]
# [c3] = [-16 32 -32 16][n3]
# Since polynomial coefficients, we don't need to divide by 3
# to get the same polynomial. Avoid the division to avoid round-off.
return np.asfortranarray(
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3.0 * val0,
-19.0 * val0 + 24.0 * val1 - 8.0 * val2 + 3.0 * val3,
32.0 * val0 - 56.0 * val1 + 40.0 * val2 - 16.0 * val3,
-16.0 * val0 + 32.0 * val1 - 32.0 * val2 + 16.0 * val3,
]
) | [
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Helper for :func:`to_power_basis` in the case that the first curve is
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Args:
nodes1 (numpy.ndarray): The nodes in the first curve.
nodes2 (numpy.ndarray): The nodes in the second curve.
Returns:
numpy.ndarray: ``4``-array of coefficients. | [
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dhermes/bezier | src/bezier/_algebraic_intersection.py | _to_power_basis_degree4 | def _to_power_basis_degree4(nodes1, nodes2):
r"""Compute the coefficients of an **intersection polynomial**.
Helper for :func:`to_power_basis` in the case that B |eacute| zout's
`theorem`_ tells us the **intersection polynomial** is degree
:math:`4`. This happens if the two curves have degrees two and two
or have degrees one and four.
Args:
nodes1 (numpy.ndarray): The nodes in the first curve.
nodes2 (numpy.ndarray): The nodes in the second curve.
Returns:
numpy.ndarray: ``5``-array of coefficients.
"""
# We manually invert the Vandermonde matrix:
# [1 0 0 0 0 ][c0] = [n0]
# [1 1/4 1/16 1/64 1/256 ][c1] [n1]
# [1 1/2 1/4 1/8 1/16 ][c2] [n2]
# [1 3/4 9/16 27/64 81/256][c3] [n3]
# [1 1 1 1 1 ][c4] [n4]
val0 = eval_intersection_polynomial(nodes1, nodes2, 0.0)
val1 = eval_intersection_polynomial(nodes1, nodes2, 0.25)
val2 = eval_intersection_polynomial(nodes1, nodes2, 0.5)
val3 = eval_intersection_polynomial(nodes1, nodes2, 0.75)
val4 = eval_intersection_polynomial(nodes1, nodes2, 1.0)
# [c0] = [ 3 0 0 0 0 ][n0]
# [c1] = 1 / 3 [-25 48 -36 16 -3 ][n1]
# [c2] = [ 70 -208 228 -112 22][n2]
# [c3] = [-80 288 -384 224 -48][n3]
# [c4] = [ 32 -128 192 -128 32][n4]
# Since polynomial coefficients, we don't need to divide by 3
# to get the same polynomial. Avoid the division to avoid round-off.
return np.asfortranarray(
[
3.0 * val0,
-25.0 * val0
+ 48.0 * val1
- 36.0 * val2
+ 16.0 * val3
- 3.0 * val4,
70.0 * val0
- 208.0 * val1
+ 228.0 * val2
- 112.0 * val3
+ 22.0 * val4,
(
-80.0 * val0
+ 288.0 * val1
- 384.0 * val2
+ 224.0 * val3
- 48.0 * val4
),
32.0 * val0
- 128.0 * val1
+ 192.0 * val2
- 128.0 * val3
+ 32.0 * val4,
]
) | python | def _to_power_basis_degree4(nodes1, nodes2):
r"""Compute the coefficients of an **intersection polynomial**.
Helper for :func:`to_power_basis` in the case that B |eacute| zout's
`theorem`_ tells us the **intersection polynomial** is degree
:math:`4`. This happens if the two curves have degrees two and two
or have degrees one and four.
Args:
nodes1 (numpy.ndarray): The nodes in the first curve.
nodes2 (numpy.ndarray): The nodes in the second curve.
Returns:
numpy.ndarray: ``5``-array of coefficients.
"""
# We manually invert the Vandermonde matrix:
# [1 0 0 0 0 ][c0] = [n0]
# [1 1/4 1/16 1/64 1/256 ][c1] [n1]
# [1 1/2 1/4 1/8 1/16 ][c2] [n2]
# [1 3/4 9/16 27/64 81/256][c3] [n3]
# [1 1 1 1 1 ][c4] [n4]
val0 = eval_intersection_polynomial(nodes1, nodes2, 0.0)
val1 = eval_intersection_polynomial(nodes1, nodes2, 0.25)
val2 = eval_intersection_polynomial(nodes1, nodes2, 0.5)
val3 = eval_intersection_polynomial(nodes1, nodes2, 0.75)
val4 = eval_intersection_polynomial(nodes1, nodes2, 1.0)
# [c0] = [ 3 0 0 0 0 ][n0]
# [c1] = 1 / 3 [-25 48 -36 16 -3 ][n1]
# [c2] = [ 70 -208 228 -112 22][n2]
# [c3] = [-80 288 -384 224 -48][n3]
# [c4] = [ 32 -128 192 -128 32][n4]
# Since polynomial coefficients, we don't need to divide by 3
# to get the same polynomial. Avoid the division to avoid round-off.
return np.asfortranarray(
[
3.0 * val0,
-25.0 * val0
+ 48.0 * val1
- 36.0 * val2
+ 16.0 * val3
- 3.0 * val4,
70.0 * val0
- 208.0 * val1
+ 228.0 * val2
- 112.0 * val3
+ 22.0 * val4,
(
-80.0 * val0
+ 288.0 * val1
- 384.0 * val2
+ 224.0 * val3
- 48.0 * val4
),
32.0 * val0
- 128.0 * val1
+ 192.0 * val2
- 128.0 * val3
+ 32.0 * val4,
]
) | [
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Helper for :func:`to_power_basis` in the case that B |eacute| zout's
`theorem`_ tells us the **intersection polynomial** is degree
:math:`4`. This happens if the two curves have degrees two and two
or have degrees one and four.
Args:
nodes1 (numpy.ndarray): The nodes in the first curve.
nodes2 (numpy.ndarray): The nodes in the second curve.
Returns:
numpy.ndarray: ``5``-array of coefficients. | [
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dhermes/bezier | src/bezier/_algebraic_intersection.py | _to_power_basis23 | def _to_power_basis23(nodes1, nodes2):
r"""Compute the coefficients of an **intersection polynomial**.
Helper for :func:`to_power_basis` in the case that the first curve is
degree two and the second is degree three. In this case, B |eacute|
zout's `theorem`_ tells us that the **intersection polynomial** is
degree :math:`2 \cdot 3` hence we return seven coefficients.
.. note::
This uses a least-squares fit to the function evaluated at the
Chebyshev nodes (scaled and shifted onto ``[0, 1]``). Hence, the
coefficients may be less stable than those produced for smaller
degrees.
Args:
nodes1 (numpy.ndarray): The nodes in the first curve.
nodes2 (numpy.ndarray): The nodes in the second curve.
Returns:
numpy.ndarray: ``7``-array of coefficients.
"""
evaluated = [
eval_intersection_polynomial(nodes1, nodes2, t_val) for t_val in _CHEB7
]
return polynomial.polyfit(_CHEB7, evaluated, 6) | python | def _to_power_basis23(nodes1, nodes2):
r"""Compute the coefficients of an **intersection polynomial**.
Helper for :func:`to_power_basis` in the case that the first curve is
degree two and the second is degree three. In this case, B |eacute|
zout's `theorem`_ tells us that the **intersection polynomial** is
degree :math:`2 \cdot 3` hence we return seven coefficients.
.. note::
This uses a least-squares fit to the function evaluated at the
Chebyshev nodes (scaled and shifted onto ``[0, 1]``). Hence, the
coefficients may be less stable than those produced for smaller
degrees.
Args:
nodes1 (numpy.ndarray): The nodes in the first curve.
nodes2 (numpy.ndarray): The nodes in the second curve.
Returns:
numpy.ndarray: ``7``-array of coefficients.
"""
evaluated = [
eval_intersection_polynomial(nodes1, nodes2, t_val) for t_val in _CHEB7
]
return polynomial.polyfit(_CHEB7, evaluated, 6) | [
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Helper for :func:`to_power_basis` in the case that the first curve is
degree two and the second is degree three. In this case, B |eacute|
zout's `theorem`_ tells us that the **intersection polynomial** is
degree :math:`2 \cdot 3` hence we return seven coefficients.
.. note::
This uses a least-squares fit to the function evaluated at the
Chebyshev nodes (scaled and shifted onto ``[0, 1]``). Hence, the
coefficients may be less stable than those produced for smaller
degrees.
Args:
nodes1 (numpy.ndarray): The nodes in the first curve.
nodes2 (numpy.ndarray): The nodes in the second curve.
Returns:
numpy.ndarray: ``7``-array of coefficients. | [
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Subsets and Splits