def test_distance2():
p1 = pgf.var_vec2()
p2 = pgf.var_vec2()
dist = pgf.distance(p1, p2)
for _ in range(20):
val1 = (random.uniform(1.2, 12.5), random.uniform(2.5, 15.6))
val2 = (random.uniform(1.26, 22.5), random.uniform(1.5, 13.9))
dval = math.sqrt(
math.pow(val1[0] - val2[0], 2.) + math.pow(val1[1] - val2[1], 2.))
pgf.assign(p1, val1)
pgf.assign(p2, val2)
assert tu.equalf(pgf.read(dist), dval)
def test_cachedOffsetDataRegression():
# This tests for a regression (lack of) happened while working on PR # 53.
pt = pgf.var_vec3((0., 0., 0.))
norm = pgf.var_vec3((0., 0., 1.))
plane = pgf.plane(pt, norm)
minpt = pgf.var_vec3((-.5, -.5, 0.))
maxpt = pgf.var_vec3((.5, .5, 0.))
box2 = pgf.box2(pgf.var_vec2((-.5, -.5)), pgf.var_vec2((.5, .5)))
box3 = pgf.box3(minpt, maxpt)
npts = pgf.var_int(4)
cloud = pgf.randomPointsInBox(box3, npts)
edgeLen = pgf.var_float(1)
rect = pgf.rectangleMesh(plane, box2, edgeLen)
distances = pgf.distance(pgf.graft(pgf.vertices(rect)), cloud)
pdists = pgf.read(pgf.flatten(distances))
assert len(pdists) == 16
pt = pgf.var_vec3((0., 0., 0.))
norm = pgf.var_vec3((0., 0., 1.))
plane = pgf.plane(pt, norm)
minpt = pgf.var_vec3((-.5, -.5, 0.))
maxpt = pgf.var_vec3((.5, .5, 0.))
box2 = pgf.box2(pgf.var_vec2((-.5, -.5)), pgf.var_vec2((.5, .5)))
box3 = pgf.box3(minpt, maxpt)
npts = pgv.slideri32("Point count", 5, 50, 25)
cloud = pgf.randomPointsInBox(box3, npts)
edgeLen = pgf.var_float(.01)
rect = pgf.rectangleMesh(plane, box2, edgeLen)
distances = pgf.distance(pgf.graft(pgf.vertices(rect)), cloud)
sortedDists = pgf.sort(distances, distances)
maxDist = pgf.listItem(sortedDists, pgf.sub(pgf.listLength(sortedDists), pgf.var_int(1)))
scheme = pgf.var_vec3([
(0., 0., 1.),
(0., 1., 0.),
(1., 1., 0.),
(1., 0., 0.)
])
colors = pgf.mapValueToColor(maxDist, pgf.var_vec2((.5, 1.2)), scheme)
colored = pgf.meshWithVertexColors(rect, colors)
circ, *_ = pgf.boundingCircle(cloud)
# pgv.show("rectangle", rect)