import numpy as np import unittest import astra import math import pylab # Display sinograms with mismatch on test failure DISPLAY=False NONUNITDET=False OBLIQUE=False FLEXVOL=False NONSQUARE=False # non-square pixels not supported yet by most projectors # Round interpolation weight to 8 bits to emulate CUDA texture unit precision CUDA_8BIT_LINEAR=True CUDA_TOL=2e-2 nloops = 50 seed = 123 # KNOWN FAILURES: # fan/strip relatively high numerical errors around 45 degrees # return length of intersection of the line through points src = (x,y) # and det (x,y), and the rectangle defined by xmin, ymin, xmax, ymax def intersect_line_rectangle(src, det, xmin, xmax, ymin, ymax): EPS = 1e-5 if np.abs(src[0] - det[0]) < EPS: if src[0] >= xmin and src[0] < xmax: return ymax - ymin else: return 0.0 if np.abs(src[1] - det[1]) < EPS: if src[1] >= ymin and src[1] < ymax: return xmax - xmin else: return 0.0 n = np.sqrt((det[0] - src[0]) ** 2 + (det[1] - src[1]) ** 2) check = [ (-(xmin - src[0]), -(det[0] - src[0]) / n ), (xmax - src[0], (det[0] - src[0]) / n ), (-(ymin - src[1]), -(det[1] - src[1]) / n ), (ymax - src[1], (det[1] - src[1]) / n ) ] pre = [ -np.Inf ] post = [ np.Inf ] for p, q in check: r = p / (1.0 * q) if q > 0: post.append(r) # exiting half-plane else: pre.append(r) # entering half-plane end_r = np.min(post) start_r = np.max(pre) if end_r > start_r: return end_r - start_r else: return 0.0 def intersect_line_rectangle_feather(src, det, xmin, xmax, ymin, ymax, feather): return intersect_line_rectangle(src, det, xmin-feather, xmax+feather, ymin-feather, ymax+feather) def intersect_line_rectangle_interval(src, det, xmin, xmax, ymin, ymax, f): a = intersect_line_rectangle_feather(src, det, xmin, xmax, ymin, ymax, -f) b = intersect_line_rectangle(src, det, xmin, xmax, ymin, ymax) c = intersect_line_rectangle_feather(src, det, xmin, xmax, ymin, ymax, f) return (a,b,c) # x-coord of intersection of the line (src, det) with the horizontal line at y def intersect_line_horizontal(src, det, y): EPS = 1e-5 if np.abs(src[1] - det[1]) < EPS: return np.nan t = (y - src[1]) / (det[1] - src[1]) return src[0] + t * (det[0] - src[0]) # y-coord of intersection of the line (src, det) with the vertical line at x def intersect_line_vertical(src, det, x): src = ( src[1], src[0] ) det = ( det[1], det[0] ) return intersect_line_horizontal(src, det, x) # length of the intersection of the strip with boundaries edge1, edge2 with the horizontal # segment at y, with horizontal extent x_seg def intersect_ray_horizontal_segment(edge1, edge2, y, x_seg): e1 = intersect_line_horizontal(edge1[0], edge1[1], y) e2 = intersect_line_horizontal(edge2[0], edge2[1], y) if not (np.isfinite(e1) and np.isfinite(e2)): return np.nan (e1, e2) = np.sort([e1, e2]) (x1, x2) = np.sort(x_seg) l = np.max([e1, x1]) r = np.min([e2, x2]) return np.max([r-l, 0.0]) def intersect_ray_vertical_segment(edge1, edge2, x, y_seg): # mirror edge1 and edge2 edge1 = [ (a[1], a[0]) for a in edge1 ] edge2 = [ (a[1], a[0]) for a in edge2 ] return intersect_ray_horizontal_segment(edge1, edge2, x, y_seg) # weight of the intersection of line with the horizontal segment at y, with horizontal extent x_seg # using linear interpolation def intersect_line_horizontal_segment_linear(src, det, y, x_seg, inter_width): EPS = 1e-5 x = intersect_line_horizontal(src, det, y) assert(x_seg[1] - x_seg[0] + EPS >= inter_width) if x < x_seg[0] - 0.5*inter_width: return 0.0 elif x < x_seg[0] + 0.5*inter_width: return (x - (x_seg[0] - 0.5*inter_width)) / inter_width elif x < x_seg[1] - 0.5*inter_width: return 1.0 elif x < x_seg[1] + 0.5*inter_width: return (x_seg[1] + 0.5*inter_width - x) / inter_width else: return 0.0 def intersect_line_vertical_segment_linear(src, det, x, y_seg, inter_height): src = ( src[1], src[0] ) det = ( det[1], det[0] ) return intersect_line_horizontal_segment_linear(src, det, x, y_seg, inter_height) def area_signed(a, b): return a[0] * b[1] - a[1] * b[0] # is c to the left of ab def is_left_of(a, b, c): EPS = 1e-5 return area_signed( (b[0] - a[0], b[1] - a[1]), (c[0] - a[0], c[1] - a[1]) ) > EPS # compute area of rect on left side of line def halfarea_rect_line(src, det, xmin, xmax, ymin, ymax): pts = ( (xmin,ymin), (xmin,ymax), (xmax,ymin), (xmax,ymax) ) pts_left = list(filter( lambda p: is_left_of(src, det, p), pts )) npts_left = len(pts_left) if npts_left == 0: return 0.0 elif npts_left == 1: # triangle p = pts_left[0] xd = intersect_line_horizontal(src, det, p[1]) - p[0] yd = intersect_line_vertical(src, det, p[0]) - p[1] ret = 0.5 * abs(xd) * abs(yd) return ret elif npts_left == 2: p = pts_left[0] q = pts_left[1] if p[0] == q[0]: # vertical intersection x1 = intersect_line_horizontal(src, det, p[1]) - p[0] x2 = intersect_line_horizontal(src, det, q[1]) - q[0] ret = 0.5 * (ymax - ymin) * (abs(x1) + abs(x2)) return ret else: assert(p[1] == q[1]) # horizontal intersection y1 = intersect_line_vertical(src, det, p[0]) - p[1] y2 = intersect_line_vertical(src, det, q[0]) - q[1] ret = 0.5 * (xmax - xmin) * (abs(y1) + abs(y2)) return ret else: # mirror and invert ret = ((xmax - xmin) * (ymax - ymin)) - halfarea_rect_line(det, src, xmin, xmax, ymin, ymax) return ret # area of intersection of the strip with boundaries edge1, edge2 with rectangle def intersect_ray_rect(edge1, edge2, xmin, xmax, ymin, ymax): s1 = halfarea_rect_line(edge1[0], edge1[1], xmin, xmax, ymin, ymax) s2 = halfarea_rect_line(edge2[0], edge2[1], xmin, xmax, ymin, ymax) return abs(s1 - s2) # width of projection of detector orthogonal to ray direction # i.e., effective detector width def effective_detweight(src, det, u): ray = np.array(det) - np.array(src) ray = ray / np.linalg.norm(ray, ord=2) return abs(area_signed(ray, u)) # LINE GENERATORS # --------------- # # Per ray these yield three lines, at respectively the center and two edges of the detector pixel. # Each line is given by two points on the line. # ( ( (p0x, p0y), (q0x, q0y) ), ( (p1x, p1y), (q1x, q1y) ), ( (p2x, p2y), (q2x, q2y) ) ) def gen_lines_fanflat(proj_geom): angles = proj_geom['ProjectionAngles'] for theta in angles: #theta = -theta src = ( math.sin(theta) * proj_geom['DistanceOriginSource'], -math.cos(theta) * proj_geom['DistanceOriginSource'] ) detc= (-math.sin(theta) * proj_geom['DistanceOriginDetector'], math.cos(theta) * proj_geom['DistanceOriginDetector'] ) detu= ( math.cos(theta) * proj_geom['DetectorWidth'], math.sin(theta) * proj_geom['DetectorWidth'] ) src = np.array(src, dtype=np.float64) detc= np.array(detc, dtype=np.float64) detu= np.array(detu, dtype=np.float64) detb= detc + (0.5 - 0.5*proj_geom['DetectorCount']) * detu for i in range(proj_geom['DetectorCount']): yield ((src, detb + i * detu), (src, detb + (i - 0.5) * detu), (src, detb + (i + 0.5) * detu)) def gen_lines_fanflat_vec(proj_geom): v = proj_geom['Vectors'] for i in range(v.shape[0]): src = v[i,0:2] detc = v[i,2:4] detu = v[i,4:6] detb = detc + (0.5 - 0.5*proj_geom['DetectorCount']) * detu for i in range(proj_geom['DetectorCount']): yield ((src, detb + i * detu), (src, detb + (i - 0.5) * detu), (src, detb + (i + 0.5) * detu)) def gen_lines_parallel(proj_geom): angles = proj_geom['ProjectionAngles'] for theta in angles: ray = ( math.sin(theta), -math.cos(theta) ) detc= (0, 0 ) detu= ( math.cos(theta) * proj_geom['DetectorWidth'], math.sin(theta) * proj_geom['DetectorWidth'] ) ray = np.array(ray, dtype=np.float64) detc= np.array(detc, dtype=np.float64) detu= np.array(detu, dtype=np.float64) detb= detc + (0.5 - 0.5*proj_geom['DetectorCount']) * detu for i in range(proj_geom['DetectorCount']): yield ((detb + i * detu - ray, detb + i * detu), (detb + (i - 0.5) * detu - ray, detb + (i - 0.5) * detu), (detb + (i + 0.5) * detu - ray, detb + (i + 0.5) * detu)) def gen_lines_parallel_vec(proj_geom): v = proj_geom['Vectors'] for i in range(v.shape[0]): ray = v[i,0:2] detc = v[i,2:4] detu = v[i,4:6] detb = detc + (0.5 - 0.5*proj_geom['DetectorCount']) * detu for i in range(proj_geom['DetectorCount']): yield ((detb + i * detu - ray, detb + i * detu), (detb + (i - 0.5) * detu - ray, detb + (i - 0.5) * detu), (detb + (i + 0.5) * detu - ray, detb + (i + 0.5) * detu)) def gen_lines(proj_geom): g = { 'fanflat': gen_lines_fanflat, 'fanflat_vec': gen_lines_fanflat_vec, 'parallel': gen_lines_parallel, 'parallel_vec': gen_lines_parallel_vec } for l in g[proj_geom['type']](proj_geom): yield l range2d = ( 8, 64 ) def gen_random_geometry_fanflat(): if not NONUNITDET: w = 1.0 else: w = 0.6 + 0.8 * np.random.random() pg = astra.create_proj_geom('fanflat', w, np.random.randint(*range2d), np.linspace(0, 2*np.pi, np.random.randint(*range2d), endpoint=False), 256 * (0.5 + np.random.random()), 256 * np.random.random()) return pg def gen_random_geometry_parallel(): if not NONUNITDET: w = 1.0 else: w = 0.8 + 0.4 * np.random.random() pg = astra.create_proj_geom('parallel', w, np.random.randint(*range2d), np.linspace(0, 2*np.pi, np.random.randint(*range2d), endpoint=False)) return pg def gen_random_geometry_fanflat_vec(): Vectors = np.zeros([16,6]) # We assume constant detector width in these tests if not NONUNITDET: w = 1.0 else: w = 0.6 + 0.8 * np.random.random() for i in range(Vectors.shape[0]): angle1 = 2*np.pi*np.random.random() if OBLIQUE: angle2 = angle1 + 0.5 * np.random.random() else: angle2 = angle1 dist1 = 256 * (0.5 + np.random.random()) detc = 10 * np.random.random(size=2) detu = [ math.cos(angle1) * w, math.sin(angle1) * w ] src = [ math.sin(angle2) * dist1, -math.cos(angle2) * dist1 ] Vectors[i, :] = [ src[0], src[1], detc[0], detc[1], detu[0], detu[1] ] pg = astra.create_proj_geom('fanflat_vec', np.random.randint(*range2d), Vectors) return pg def gen_random_geometry_parallel_vec(): Vectors = np.zeros([16,6]) # We assume constant detector width in these tests if not NONUNITDET: w = 1.0 else: w = 0.6 + 0.8 * np.random.random() for i in range(Vectors.shape[0]): l = 0.6 + 0.8 * np.random.random() angle1 = 2*np.pi*np.random.random() if OBLIQUE: angle2 = angle1 + 0.5 * np.random.random() else: angle2 = angle1 detc = 10 * np.random.random(size=2) detu = [ math.cos(angle1) * w, math.sin(angle1) * w ] ray = [ math.sin(angle2) * l, -math.cos(angle2) * l ] Vectors[i, :] = [ ray[0], ray[1], detc[0], detc[1], detu[0], detu[1] ] pg = astra.create_proj_geom('parallel_vec', np.random.randint(*range2d), Vectors) return pg def proj_type_to_fan(t): if t == 'cuda': return t else: return t + '_fanflat' def display_mismatch(data, sinogram, a): pylab.gray() pylab.imshow(data) pylab.figure() pylab.imshow(sinogram) pylab.figure() pylab.imshow(a) pylab.figure() pylab.imshow(sinogram-a) pylab.show() def display_mismatch_triple(data, sinogram, a, b, c): pylab.gray() pylab.imshow(data) pylab.figure() pylab.imshow(sinogram) pylab.figure() pylab.imshow(b) pylab.figure() pylab.imshow(a) pylab.figure() pylab.imshow(c) pylab.figure() pylab.imshow(sinogram-a) pylab.figure() pylab.imshow(c-sinogram) pylab.show() class Test2DKernel(unittest.TestCase): def single_test(self, type, proj_type): shape = np.random.randint(*range2d, size=2) # these rectangles are biased, but that shouldn't matter rect_min = [ np.random.randint(0, a) for a in shape ] rect_max = [ np.random.randint(rect_min[i]+1, shape[i]+1) for i in range(len(shape))] if FLEXVOL: if not NONSQUARE: pixsize = np.array([0.5, 0.5]) + np.random.random() else: pixsize = 0.5 + np.random.random(size=2) origin = 10 * np.random.random(size=2) else: pixsize = (1.,1.) origin = (0.,0.) vg = astra.create_vol_geom(shape[1], shape[0], origin[0] - 0.5 * shape[0] * pixsize[0], origin[0] + 0.5 * shape[0] * pixsize[0], origin[1] - 0.5 * shape[1] * pixsize[1], origin[1] + 0.5 * shape[1] * pixsize[1]) if type == 'parallel': pg = gen_random_geometry_parallel() projector_id = astra.create_projector(proj_type, pg, vg) elif type == 'parallel_vec': pg = gen_random_geometry_parallel_vec() projector_id = astra.create_projector(proj_type, pg, vg) elif type == 'fanflat': pg = gen_random_geometry_fanflat() projector_id = astra.create_projector(proj_type_to_fan(proj_type), pg, vg) elif type == 'fanflat_vec': pg = gen_random_geometry_fanflat_vec() projector_id = astra.create_projector(proj_type_to_fan(proj_type), pg, vg) data = np.zeros((shape[1], shape[0]), dtype=np.float32) data[rect_min[1]:rect_max[1],rect_min[0]:rect_max[0]] = 1 sinogram_id, sinogram = astra.create_sino(data, projector_id) self.assertTrue(np.all(np.isfinite(sinogram))) #print(pg) #print(vg) astra.data2d.delete(sinogram_id) astra.projector.delete(projector_id) # NB: Flipped y-axis here, since that is how astra interprets 2D volumes xmin = origin[0] + (-0.5 * shape[0] + rect_min[0]) * pixsize[0] xmax = origin[0] + (-0.5 * shape[0] + rect_max[0]) * pixsize[0] ymin = origin[1] + (+0.5 * shape[1] - rect_max[1]) * pixsize[1] ymax = origin[1] + (+0.5 * shape[1] - rect_min[1]) * pixsize[1] if proj_type == 'line': a = np.zeros(np.prod(astra.functions.geom_size(pg)), dtype=np.float32) b = np.zeros(np.prod(astra.functions.geom_size(pg)), dtype=np.float32) c = np.zeros(np.prod(astra.functions.geom_size(pg)), dtype=np.float32) for i, (center, edge1, edge2) in enumerate(gen_lines(pg)): (src, det) = center # We compute line intersections with slightly bigger (cw) and # smaller (aw) rectangles, and see if the kernel falls # between these two values. (aw,bw,cw) = intersect_line_rectangle_interval(src, det, xmin, xmax, ymin, ymax, 1e-3) a[i] = aw b[i] = bw c[i] = cw a = a.reshape(astra.functions.geom_size(pg)) b = b.reshape(astra.functions.geom_size(pg)) c = c.reshape(astra.functions.geom_size(pg)) if not np.all(np.isfinite(a)): raise RuntimeError("Invalid value in reference sinogram") if not np.all(np.isfinite(b)): raise RuntimeError("Invalid value in reference sinogram") if not np.all(np.isfinite(c)): raise RuntimeError("Invalid value in reference sinogram") self.assertTrue(np.all(np.isfinite(sinogram))) # Check if sinogram lies between a and c y = np.min(sinogram-a) z = np.min(c-sinogram) if DISPLAY and (z < 0 or y < 0): display_mismatch_triple(data, sinogram, a, b, c) self.assertFalse(z < 0 or y < 0) elif proj_type == 'linear' or proj_type == 'cuda': a = np.zeros(np.prod(astra.functions.geom_size(pg)), dtype=np.float32) for i, (center, edge1, edge2) in enumerate(gen_lines(pg)): (src, det) = center (xd, yd) = det - src l = 0.0 if np.abs(xd) > np.abs(yd): # horizontal ray length = math.sqrt(1.0 + abs(yd/xd)**2) * pixsize[0] y_seg = (ymin, ymax) for j in range(rect_min[0], rect_max[0]): x = origin[0] + (-0.5 * shape[0] + j + 0.5) * pixsize[0] w = intersect_line_vertical_segment_linear(center[0], center[1], x, y_seg, pixsize[1]) # limited interpolation precision with cuda if CUDA_8BIT_LINEAR and proj_type == 'cuda': w = np.round(w * 256.0) / 256.0 l += w * length else: length = math.sqrt(1.0 + abs(xd/yd)**2) * pixsize[1] x_seg = (xmin, xmax) for j in range(rect_min[1], rect_max[1]): y = origin[1] + (+0.5 * shape[1] - j - 0.5) * pixsize[1] w = intersect_line_horizontal_segment_linear(center[0], center[1], y, x_seg, pixsize[0]) # limited interpolation precision with cuda if CUDA_8BIT_LINEAR and proj_type == 'cuda': w = np.round(w * 256.0) / 256.0 l += w * length a[i] = l a = a.reshape(astra.functions.geom_size(pg)) if not np.all(np.isfinite(a)): raise RuntimeError("Invalid value in reference sinogram") x = np.max(np.abs(sinogram-a)) TOL = 2e-3 if proj_type != 'cuda' else CUDA_TOL if DISPLAY and x > TOL: display_mismatch(data, sinogram, a) self.assertFalse(x > TOL) elif proj_type == 'distance_driven' and 'par' in type: a = np.zeros(np.prod(astra.functions.geom_size(pg)), dtype=np.float32) for i, (center, edge1, edge2) in enumerate(gen_lines(pg)): (src, det) = center try: detweight = pg['DetectorWidth'] except KeyError: detweight = effective_detweight(src, det, pg['Vectors'][i//pg['DetectorCount'],4:6]) (xd, yd) = det - src l = 0.0 if np.abs(xd) > np.abs(yd): # horizontal ray y_seg = (ymin, ymax) for j in range(rect_min[0], rect_max[0]): x = origin[0] + (-0.5 * shape[0] + j + 0.5) * pixsize[0] l += intersect_ray_vertical_segment(edge1, edge2, x, y_seg) * pixsize[0] / detweight else: x_seg = (xmin, xmax) for j in range(rect_min[1], rect_max[1]): y = origin[1] + (+0.5 * shape[1] - j - 0.5) * pixsize[1] l += intersect_ray_horizontal_segment(edge1, edge2, y, x_seg) * pixsize[1] / detweight a[i] = l a = a.reshape(astra.functions.geom_size(pg)) if not np.all(np.isfinite(a)): raise RuntimeError("Invalid value in reference sinogram") x = np.max(np.abs(sinogram-a)) TOL = 2e-3 if DISPLAY and x > TOL: display_mismatch(data, sinogram, a) self.assertFalse(x > TOL) elif proj_type == 'strip' and 'fan' in type: a = np.zeros(np.prod(astra.functions.geom_size(pg)), dtype=np.float32) for i, (center, edge1, edge2) in enumerate(gen_lines(pg)): (src, det) = center detweight = effective_detweight(src, det, edge2[1] - edge1[1]) det_dist = np.linalg.norm(src-det, ord=2) l = 0.0 for j in range(rect_min[0], rect_max[0]): xmin = origin[0] + (-0.5 * shape[0] + j) * pixsize[0] xmax = origin[0] + (-0.5 * shape[0] + j + 1) * pixsize[0] xcen = 0.5 * (xmin + xmax) for k in range(rect_min[1], rect_max[1]): ymin = origin[1] + (+0.5 * shape[1] - k - 1) * pixsize[1] ymax = origin[1] + (+0.5 * shape[1] - k) * pixsize[1] ycen = 0.5 * (ymin + ymax) scale = det_dist / (np.linalg.norm( src - np.array((xcen,ycen)), ord=2 ) * detweight) w = intersect_ray_rect(edge1, edge2, xmin, xmax, ymin, ymax) l += w * scale a[i] = l a = a.reshape(astra.functions.geom_size(pg)) if not np.all(np.isfinite(a)): raise RuntimeError("Invalid value in reference sinogram") x = np.max(np.abs(sinogram-a)) # BUG: Known bug in fan/strip code around 45 degree projections causing larger errors than desirable TOL = 4e-2 if DISPLAY and x > TOL: display_mismatch(data, sinogram, a) self.assertFalse(x > TOL) elif proj_type == 'strip': a = np.zeros(np.prod(astra.functions.geom_size(pg)), dtype=np.float32) for i, (center, edge1, edge2) in enumerate(gen_lines(pg)): (src, det) = center try: detweight = pg['DetectorWidth'] except KeyError: detweight = effective_detweight(src, det, pg['Vectors'][i//pg['DetectorCount'],4:6]) a[i] = intersect_ray_rect(edge1, edge2, xmin, xmax, ymin, ymax) / detweight a = a.reshape(astra.functions.geom_size(pg)) if not np.all(np.isfinite(a)): raise RuntimeError("Invalid value in reference sinogram") x = np.max(np.abs(sinogram-a)) TOL = 8e-3 if DISPLAY and x > TOL: display_mismatch(data, sinogram, a) self.assertFalse(x > TOL) else: raise RuntimeError("Unsupported projector") def single_test_adjoint(self, type, proj_type): shape = np.random.randint(*range2d, size=2) if FLEXVOL: if not NONSQUARE: pixsize = np.array([0.5, 0.5]) + np.random.random() else: pixsize = 0.5 + np.random.random(size=2) origin = 10 * np.random.random(size=2) else: pixsize = (1.,1.) origin = (0.,0.) vg = astra.create_vol_geom(shape[1], shape[0], origin[0] - 0.5 * shape[0] * pixsize[0], origin[0] + 0.5 * shape[0] * pixsize[0], origin[1] - 0.5 * shape[1] * pixsize[1], origin[1] + 0.5 * shape[1] * pixsize[1]) if type == 'parallel': pg = gen_random_geometry_parallel() projector_id = astra.create_projector(proj_type, pg, vg) elif type == 'parallel_vec': pg = gen_random_geometry_parallel_vec() projector_id = astra.create_projector(proj_type, pg, vg) elif type == 'fanflat': pg = gen_random_geometry_fanflat() projector_id = astra.create_projector(proj_type_to_fan(proj_type), pg, vg) elif type == 'fanflat_vec': pg = gen_random_geometry_fanflat_vec() projector_id = astra.create_projector(proj_type_to_fan(proj_type), pg, vg) for i in range(5): X = np.random.random((shape[1], shape[0])) Y = np.random.random(astra.geom_size(pg)) sinogram_id, fX = astra.create_sino(X, projector_id) bp_id, fTY = astra.create_backprojection(Y, projector_id) astra.data2d.delete(sinogram_id) astra.data2d.delete(bp_id) da = np.dot(fX.ravel(), Y.ravel()) db = np.dot(X.ravel(), fTY.ravel()) m = np.abs(da - db) TOL = 1e-3 if 'cuda' not in proj_type else 1e-1 if m / da >= TOL: print(vg) print(pg) print(m/da, da/db, da, db) self.assertTrue(m / da < TOL) astra.projector.delete(projector_id) def multi_test(self, type, proj_type): np.random.seed(seed) for _ in range(nloops): self.single_test(type, proj_type) def multi_test_adjoint(self, type, proj_type): np.random.seed(seed) for _ in range(nloops): self.single_test_adjoint(type, proj_type) __combinations = { 'parallel': [ 'line', 'linear', 'distance_driven', 'strip', 'cuda' ], 'parallel_vec': [ 'line', 'linear', 'distance_driven', 'strip', 'cuda' ], 'fanflat': [ 'line', 'strip', 'cuda' ], 'fanflat_vec': [ 'line', 'cuda' ] } for k, l in __combinations.items(): for v in l: def f(k,v): return lambda self: self.multi_test(k, v) def f_adj(k,v): return lambda self: self.multi_test_adjoint(k, v) setattr(Test2DKernel, 'test_' + k + '_' + v, f(k,v)) setattr(Test2DKernel, 'test_' + k + '_' + v + '_adjoint', f_adj(k,v)) if __name__ == '__main__': unittest.main()