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)