def remove_cells(roundRadius, Circles, w1, grid1, cols1, rows1, depths1): for i in range(len(Circles)): col_sub = math.floor(Circles[i].x / w1) row_sub = math.floor(Circles[i].y / w1) depth_sub = math.floor(Circles[i].z / w1) if Circles[i].r > w1: range_circles = math.ceil((Circles[i].r + 2) / w1) nums = list(range(-range_circles, range_circles + 1)) num1s = list(range(-range_circles, range_circles + 1)) num2s = list(range(-range_circles, range_circles + 1)) for num in nums: for num1 in num1s: for num2 in num2s: if 0 <= col_sub + num1 < cols1 and 0 <= row_sub + num < rows1 and 0 <= depth_sub + num2 < depths1: index = (col_sub + num1) * depths1 + ( row_sub + num) * cols1 * depths1 + (depth_sub + num2) y_grid = math.floor(index / cols1 / depths1) x_grid = math.floor( (index - y_grid * cols1 * depths1) / depths1) z_grid = index - x_grid * depths1 - y_grid * cols1 * depths1 y1 = y_grid * w1 x1 = x_grid * w1 z1 = z_grid * w1 # distances of the eight corners of the cell and the particle center d1 = _distance3d.dist(x1, y1, z1, Circles[i].x, Circles[i].y, Circles[i].z) d2 = _distance3d.dist((x1 + w1), y1, z1, Circles[i].x, Circles[i].y, Circles[i].z) d3 = _distance3d.dist(x1, (y1 + w1), z1, Circles[i].x, Circles[i].y, Circles[i].z) d4 = _distance3d.dist(x1, y1, (z1 + w1), Circles[i].x, Circles[i].y, Circles[i].z) d5 = _distance3d.dist((x1 + w1), (y1 + w1), z1, Circles[i].x, Circles[i].y, Circles[i].z) d6 = _distance3d.dist(x1, (y1 + w1), (z1 + w1), Circles[i].x, Circles[i].y, Circles[i].z) d7 = _distance3d.dist((x1 + w1), y1, (z1 + w1), Circles[i].x, Circles[i].y, Circles[i].z) d8 = _distance3d.dist((x1 + w1), (y1 + w1), (z1 + w1), Circles[i].x, Circles[i].y, Circles[i].z) distance_around = [d1, d2, d3, d4, d5, d6, d7, d8] distance_around.sort() distance_max = distance_around[6] # if the maximum distance is smaller than the particle radius plus the minimun value of the radius range for the next round # mark this cell as minus one if distance_max <= Circles[i].r + roundRadius: grid1[index] = -1 else: # if the particle radius is smaller than the cell size, this cell will be marked as occupied directly index = col_sub * depths1 + row_sub * cols1 * depths1 + depth_sub grid1[index] = -1 print("filter the non-filled cells")
def generateCircles_p(mindis, maxi, Circles): growing = [] for k in range(len(Circles)): if Circles[k].g is True: growing.append([Circles[k].x, Circles[k].y, Circles[k].z]) while len(growing) > 0: for i in range(len(Circles)): if Circles[i].g is True: if Circles[i].r > maxi: Circles[i].g = None growing.remove([Circles[i].x, Circles[i].y, Circles[i].z]) break elif Circles[i].edge() is None: growing.remove([Circles[i].x, Circles[i].y, Circles[i].z]) break else: for j in range(len(Circles)): if (j != i) and ((Circles[i].x - mindis) < Circles[j].x < (Circles[i].x + mindis)) and ((Circles[i].y - mindis) < Circles[j].y < (Circles[i].y + mindis)) and ((Circles[i].z - mindis) < Circles[j].z < (Circles[i].z + mindis)): dis = _distance3d.dist(Circles[i].x, Circles[i].y, Circles[i].z, Circles[j].x, Circles[j].y, Circles[j].z) if dis < (Circles[i].r + Circles[j].r - 1): Circles[i].g = None growing.remove([Circles[i].x, Circles[i].y, Circles[i].z]) break Circles[i].grow()
def point_poisson(): min_radius = roundRadius[0] global occupation_poisson while len(active) > 0: check = np.random.randint(0, len(active)) found = None for n in range(k): s = _random_vector_function.random_vector_function(r, math.floor(r * 1.5)) sample = [] for i in range(0, len(s)): sample.append(s[i] + active[check][i]) gcol = math.floor(sample[0] / w) grow = math.floor(sample[1] / w) gdepth = math.floor(sample[2] / w) ggrid = gdepth + gcol * depths + grow * cols * depths if gcol > -1 and grow > -1 and gdepth > -1 and gcol < cols and grow < rows and gdepth < depths: ok = True # check distances between selected point and points in the around grids nums = [-1, 0, 1] num1s = [-1, 0, 1] num2s = [-1, 0, 1] for num in nums: for num1 in num1s: for num2 in num2s: if 0 <= gcol + num1 < cols and 0 <= grow + num < rows and 0 <= gdepth + num2 < depths: index = (gcol + num1) * depths + (grow + num) * cols * depths + (gdepth + num2) if grid[index] is None: pass else: d = _distance3d.dist(grid[index][0], grid[index][1], grid[index][2], sample[0], sample[1], sample[2]) if d < r: ok = None if ok is True: found = True grid[gdepth + gcol * depths + grow * depths * cols] = sample active.append(sample) break if found is None: del active [check : (check + 1)] for i in range(len(grid)): if grid[i] is None: pass else: #delete points which are too close to the border if (grid[i][0] < width - min_radius) and (grid[i][0] > min_radius) and (grid[i][1] < height - min_radius) and (grid[i][1] > min_radius) and (grid[i][2] < depth - min_radius) and (grid[i][2] > min_radius): Circles.append(_Sphere.Sphere(grid[i][0], grid[i][1], grid[i][2], min_radius, width, height, depth)) occupation_poisson = occupation_poisson + 1 # enlarge the particles _generateParticle.generateCircles_p(ranges[0], (maximums[0] - 2), Circles) print('poisson disk sampling', occupation_poisson)
def single_radius(q, initial_radius, range1, maximum1, Circles, cols1, depths1, w1, width, height, depth): valid = True yaxis = math.floor(q / cols1 / depths1) xaxis = math.floor((q - yaxis * cols1 * depths1) / depths1) zaxis = q - xaxis * depths1 - yaxis * cols1 * depths1 w_use = w1 x = np.random.randint(math.floor(xaxis * w_use), math.floor((xaxis + 1) * w_use)) y = np.random.randint(math.floor(yaxis * w_use), math.floor((yaxis + 1) * w_use)) z = np.random.randint(math.floor(zaxis * w_use), math.floor((zaxis + 1) * w_use)) #if the randomly selected point in the void grid is too close to borders, it will be deleted. m = initial_radius min_boundary = [x, y, z, (width - x), (height - y), (depth - z)] min_b = np.amin(min_boundary) if min_b < m: return 0 #in the reseaching sphere area with 'mindis' radius min_around = [] for j in range(len(Circles)): if ((x - range1) < Circles[j].x < (x + range1)) and ((y - range1) < Circles[j].y < (y + range1)) and ((z - range1) < Circles[j].z < (z + range1)): k = _distance3d.dist(x, y, z, Circles[j].x, Circles[j].y, Circles[j].z) - Circles[j].r if (k < m): return 0 #generate particles Circles.append(_Sphere.Sphere(x, y, z, m, width, height, depth)) return m