Files
fluencyCAD/sdf/dn.py

367 lines
9.8 KiB
Python

import itertools
from functools import reduce, partial
import warnings
from . import ease
import numpy as np
_min = np.minimum
_max = np.maximum
def distance_to_plane(p, origin, normal):
"""
Calculate the distance of a point ``p`` to the plane around ``origin`` with
normal ``normal``. This is dimension-independent, so e.g. the z-coordinate
can be omitted.
Args:
p (array): either [x,y,z] or [[x,y,z],[x,y,z],...]
origin (vector): a point on the plane
normal (vector): normal vector of the plane
Returns:
int: distance to plane
"""
normal = normal / np.linalg.norm(normal)
return abs((p - origin) @ normal)
def minimum(a, b, r=0):
if r:
Δ = b - a
h = np.clip(0.5 + 0.5 * Δ / r, 0, 1)
return b - Δ * h - r * h * (1 - h)
else:
return np.minimum(a, b)
def maximum(a, b, r=0):
if r:
Δ = b - a
h = np.clip(0.5 - 0.5 * Δ / r, 0, 1)
return b - Δ * h + r * h * (1 - h)
else:
return np.maximum(a, b)
def union(*sdfs, chamfer=0, c=0, radius=0, r=0, fillet=0, f=0):
c = max(chamfer, c)
r = max(radius, r, fillet, f)
sqrt05 = np.sqrt(0.5)
def f(p):
sdfs_ = iter(sdfs)
d1 = next(sdfs_)(p)
for sdf in sdfs_:
d2 = sdf(p)
R = r or getattr(sdf, "_r", 0)
C = c or getattr(sdf, "_c", 0)
parts = (d1, d2)
if C:
parts = (minimum(d1, d2), (d1 + d2 - C) * sqrt05)
d1 = minimum(*parts, R)
return d1
return f
def intersection(*sdfs, chamfer=0, c=0, radius=0, r=0, fillet=0, f=0):
c = max(chamfer, c)
r = max(radius, r, fillet, f)
sqrt05 = np.sqrt(0.5)
def f(p):
sdfs_ = iter(sdfs)
d1 = next(sdfs_)(p)
for sdf in sdfs_:
d2 = sdf(p)
R = r or getattr(sdf, "_r", 0)
C = c or getattr(sdf, "_c", 0)
parts = (d1, d2)
if C:
parts = (maximum(d1, d2), (d1 + d2 + C) * sqrt05)
d1 = maximum(*parts, R)
return d1
return f
def difference(*sdfs, chamfer=0, c=0, radius=0, r=0, fillet=0, f=0):
c = max(chamfer, c)
r = max(radius, r, fillet, f)
sqrt05 = np.sqrt(0.5)
def f(p):
sdfs_ = iter(sdfs)
d1 = next(sdfs_)(p)
for sdf in sdfs_:
d2 = sdf(p)
R = r or getattr(sdf, "_r", 0)
C = c or getattr(sdf, "_c", 0)
parts = (d1, -d2)
if C:
parts = (maximum(d1, -d2), (d1 - d2 + C) * sqrt05)
d1 = maximum(*parts, R)
return d1
return f
def union_legacy(a, *bs, r=None):
def f(p):
d1 = a(p)
for b in bs:
d2 = b(p)
K = k or getattr(b, "_r", None)
if K is None:
d1 = _min(d1, d2)
else:
h = np.clip(0.5 + 0.5 * (d2 - d1) / K, 0, 1)
m = d2 + (d1 - d2) * h
d1 = m - K * h * (1 - h)
return d1
return f
def difference_legacy(a, *bs, r=None):
def f(p):
d1 = a(p)
for b in bs:
d2 = b(p)
K = k or getattr(b, "_r", None)
if K is None:
d1 = _max(d1, -d2)
else:
h = np.clip(0.5 - 0.5 * (d2 + d1) / K, 0, 1)
m = d1 + (-d2 - d1) * h
d1 = m + K * h * (1 - h)
return d1
return f
def intersection_legacy(a, *bs, r=None):
def f(p):
d1 = a(p)
for b in bs:
d2 = b(p)
K = k or getattr(b, "_r", None)
if K is None:
d1 = _max(d1, d2)
else:
h = np.clip(0.5 - 0.5 * (d2 - d1) / K, 0, 1)
m = d2 + (d1 - d2) * h
d1 = m + K * h * (1 - h)
return d1
return f
def blend(a, *bs, r=0.5):
def f(p):
d1 = a(p)
for b in bs:
d2 = b(p)
K = k or getattr(b, "_r", None)
d1 = K * d2 + (1 - K) * d1
return d1
return f
def negate(other):
def f(p):
return -other(p)
return f
def dilate(other, r):
def f(p):
return other(p) - r
return f
def erode(other, r):
def f(p):
return other(p) + r
return f
def shell(other, thickness=1, type="center"):
"""
Keep only a margin of a given thickness around the object's boundary.
Args:
thickness (float): the resulting thickness
type (str): what kind of shell to generate.
``"center"`` (default)
shell is spaced symmetrically around boundary
``"outer"``
the resulting shell will be ``thickness`` larger than before
``"inner"``
the resulting shell will be as large as before
"""
return dict(
center=lambda p: np.abs(other(p)) - thickness / 2,
inner=other - other.erode(thickness),
outer=other.dilate(thickness) - other,
)[type]
def modulate_between(sdf, a, b, e=ease.in_out_cubic):
"""
Apply a distance offset transition between two control points
(e.g. make a rod thicker or thinner at some point or add a bump)
Args:
a, b (vectors): the two control points
e (scalar function): the distance offset function, will be called with
values between 0 (at control point ``a``) and 1 (at control point
``b``). Its result will be subtracted from the given SDF, thus
enlarging the object by that value.
"""
# unit vector from control point a to b
ab = (ab := b - a) / (L := np.linalg.norm(ab))
def f(p):
# project current point onto control direction, clip and apply easing
offset = e(np.clip((p - a) @ ab / L, 0, 1))
return (dist := sdf(p)) - offset.reshape(dist.shape)
return f
def stretch(sdf, a, b, symmetric=False, e=ease.linear):
"""
Grab the object at point ``a`` and stretch the entire plane to ``b``.
Args:
a, b (point vectors): the control points
symmetric (bool): also stretch the same into the other direction.
e (Easing): easing to apply
Examples
========
.. code-block:: python
# make a capsule
sphere(5).stretch(ORIGIN, 10*Z).save() # same as capsule(ORIGIN, 10*Z, 5)
# make an egg
sphere(5).stretch(ORIGIN, 10*Z, e=ease.smoothstep[:0.44]).save()
"""
ab = (ab := b - a) / (L := np.linalg.norm(ab))
def f(p):
# s = ”how far are we between a and b as fraction?”
# if symmetric=True this also goes into the negative direction
s = np.clip((p - a) @ ab / L, -1 if symmetric else 0, 1)
# we return the sdf at a point 'behind' (p minus ...)
# the current point, but we go only as far back as the stretch distance
# at max
return sdf(p - (np.sign(s) * e(abs(s)) * L * ab[:, np.newaxis]).T)
return f
def shear(sdf, fix, grab, move, e=ease.linear):
"""
Grab the object at point ``grab`` and shear the entire plane in direction
``move``, keeping point ``fix`` in place. If ``move`` is orthogonal to the
direction ``fix``->``grab``, then this operation is a shear.
Args:
fix, grab (point vectors): the control points
move (point vector): direction to shear to
e (Easing): easing to apply
Examples
========
.. code-block:: python
# make a capsule
box([20,10,50]).shear(fix=-15*Z, grab=15*Z, move=-5*X, e=ease.smoothstep)
"""
ab = (ab := grab - fix) / (L := np.linalg.norm(ab))
def f(p):
# s = ”how far are we between a and b as fraction?”
s = (p - fix) @ ab / L
return sdf(p - move * np.expand_dims(e(np.clip(s, 0, 1)), axis=1))
return f
def mirror(other, direction, at=0):
"""
Mirror around a given plane defined by ``origin`` reference point and
``direction``.
Args:
direction (vector): direction to mirror to (e.g. :any:`X` to mirror along X axis)
at (3D vector): point to mirror at. Default is the origin.
"""
direction = direction / np.linalg.norm(direction)
def f(p):
projdir = np.expand_dims((p - at) @ direction, axis=1) * direction
# mirrored point:
# - project 'p' onto 'direction' (result goes into 'projdir' direction)
# - projected point is at 'at + projdir'
# - remember direction from projected point to the original point (p - (at + projdir))
# - from origin 'at' go backwards the projected direction (at - projdir)
# - from that target, move along the remembered direction (p - (at + projdir))
# - pmirr = at - projdir + (p - (at + projdir))
# - the 'at' cancels out, the projdir is subtracted twice from the point
return other(p - 2 * projdir)
return f
def repeat(other, spacing, count=None, padding=0):
count = np.array(count) if count is not None else None
spacing = np.array(spacing)
def neighbors(dim, padding, spacing):
try:
padding = [padding[i] for i in range(dim)]
except Exception:
padding = [padding] * dim
try:
spacing = [spacing[i] for i in range(dim)]
except Exception:
spacing = [spacing] * dim
for i, s in enumerate(spacing):
if s == 0:
padding[i] = 0
axes = [list(range(-p, p + 1)) for p in padding]
return list(itertools.product(*axes))
def f(p):
q = np.divide(p, spacing, out=np.zeros_like(p), where=spacing != 0)
if count is None:
index = np.round(q)
else:
index = np.clip(np.round(q), -count, count)
indexes = [index + n for n in neighbors(p.shape[-1], padding, spacing)]
A = [other(p - spacing * i) for i in indexes]
a = A[0]
for b in A[1:]:
a = _min(a, b)
return a
return f