= x[0]))\n y_logical = np.logical_and((places[:, 1] < y[1]), (places[:, 1] >= y[0]))\n z_logical = np.logical_and((places[:, 2] < z[1]), (places[:, 2] >= z[0]))\n xyz_logical = np.logical_and(x_logical, np.logical_and(y_logical, z_logical))\n center = places.copy()\n center[:, 2] = center[:, 2] + size[:, 0] / 2.\n sphere_center = ((center[xyz_logical] - min_value) / (resolution * scale)).astype(np.int32)\n return sphere_center","def sphere_to_center(p_sphere, resolution=0.5, scale=4, min_value=np.array([0., -50., -4.5])):\n center = p_sphere * (resolution*scale) + min_value\n return center","def Center(self, *args):\n return _Bnd.Bnd_Sphere_Center(self, *args)","def on_sphere():\n vec = np.random.standard_normal(3)\n return vec / np.linalg.norm(vec)","def normalize(x: float, y: float, z: float) -> Point3D:\n mag = math.sqrt(x*x + y*y + z*z)\n return x/mag, y/mag, z/mag","def fix_sphere_m (center_x, center_y, center_z, radius, centers, radii, len_points):\n \n g_x = []\n g_y = []\n g_z = []\n points = [hydrogen_coord_gen(center_x, center_y, center_z, radius) for i in range(0, len_points)] \n x = [points[i][0] for i in range(0, len(points))] \n y = [points[i][1] for i in range(0, len(points))]\n z = [points[i][2] for i in range(0, len(points))]\n\n for i in range(0, len(points)):\n check = 0\n j = 0\n while (j <= (len(centers) - 1) and (check == 0)): \n if (calculate_3D_distance_2_centers(x[i], y[i], z[i], centers[j][0], centers[j][1], centers[j][2]) < radii[j]):\n check += 1\n j += 1\n if (check == 0):\n g_x.append(x[i])\n g_y.append(y[i])\n g_z.append(z[i])\n\n return g_x, g_y, g_z","def sphere_centers(r_x, r_y, r_z):\n a_ccs_p_trans_m = hom_translation_matrix(\n t_x=0.265, t_y=0, t_z=0.014)\n a_ccs_p_rot_m = hom_rotation(x_axis_rotation_matrix(r_x) @\n y_axis_rotation_matrix(r_y) @\n z_axis_rotation_matrix(r_z))\n a_p_sph_1_2 = hom_translation_matrix(\n t_x=0.015, t_y=0.029, t_z=-0.0965)\n a_p_sph_2_2 = hom_translation_matrix(\n t_x=0.015, t_y=-0.029, t_z=-0.0965)\n\n a_ccs_ = a_ccs_p_trans_m @ a_ccs_p_rot_m\n a_c1 = a_ccs_ @ a_p_sph_1_2\n a_c2 = a_ccs_ @ a_p_sph_2_2\n\n return get_translation(a_c1), get_translation(a_c2)","def center(self):\n try: \n return self._center\n except AttributeError:\n self._center = vector(ZZ, [0]*self.ambient_dim())\n for v in self.vertex_generator(): self._center += v.vector()\n self._center /= self.n_vertices()\n return self._center","def center_normalize(x):\n return (x - K.mean(x)) / K.std(x)","def center_normalize(x):\n return (x - K.mean(x)) / K.std(x)","def centroid(self): # -> BaseGeometry:\n ...","def center(self):\n points = set()\n for face in self._points:\n points.update(face)\n x_points = [point[0] for point in points]\n y_points = [point[1] for point in points]\n z_points = [point[2] for point in points]\n return \\\n (np.average(x_points), np.average(y_points), np.average(z_points))","def center(self):\n if not hasattr(self, '_center'):\n self._center = np.unique(self.points, axis=0).mean(axis=0)\n return self._center","def normal(self, point):\n point = self._center - np.array(point)\n # if abs(point.dot(point) - self._radius**2) > 1e-15:\n # raise RayTraceError(\n # 'Cannot compute normal. Point is too far from surface ({}).'.format(\n # (abs(point.dot(point) - self._radius**2))))\n return normalize(point / self._radius)","def nearest_sphere_surface(x_input, y_input, z_input):\n\n vm = math.sqrt(sum([x_input**2, y_input**2, z_input**2]))\n return (x_input/vm, y_input/vm, z_input/vm)","def _find_centroid_sphere(ch_pos, group_names):\n cartesian_positions = np.array([ch_pos[ch_name] for ch_name in group_names])\n sphere_positions = _cart_to_sph(cartesian_positions)\n cartesian_pos_centroid = np.average(cartesian_positions, axis=0)\n sphere_pos_centroid = _cart_to_sph(cartesian_pos_centroid)\n # average the radius and overwrite it\n avg_radius = np.average(sphere_positions, axis=0)[0]\n sphere_pos_centroid[0, 0] = avg_radius\n # convert back to cartesian\n pos_centroid = _sph_to_cart(sphere_pos_centroid)[0, :]\n return pos_centroid","def sphere_norm_by_layer(M):\r\n M_inner = M\r\n shape = np.shape(M_inner)\r\n if len(shape) == 3:\r\n for i in range(shape[0]):\r\n norm = np.sqrt(np.sum(M_inner[i, :, :] ** 2))\r\n M_inner[i, :, :] = M_inner[i, :, :] / norm\r\n return M_inner\r\n if len(shape) == 2:\r\n norm = np.sqrt(np.sum(M_inner ** 2))\r\n M_inner = M_inner / norm\r\n return M_inner","def center(x):\n return x - x.mean()","def distance_from_sphere(self, points, params, sqrt=False):\n center, radius = params\n center = center.reshape((1, 3))\n distance = (torch.norm(points - center, p=2, dim=1) - radius) ** 2\n if sqrt:\n distance = guard_sqrt(distance)\n\n if self.reduce:\n distance = torch.mean(distance)\n return distance","def computeCenters3d(self, data):\n\n\n for i in range(self.nPoints):\n print(\"Label of point \", i, \" is \", self.labels[i])\n for j in range(3):\n self.centers[self.labels[i]][j] += data[i][j]\n\n for c in range(self.n):\n for j in range(3):\n self.centers[c][j] /= self.tots[c]","def GetParametricCenter(self, p_float=..., p_float=..., p_float=...):\n ...","def center(self) -> Point:\n return Point(*np.sum(self.normalized_array[:, :-1], axis=0))","def objectCenter(*args, gl: bool=True, local: bool=True, x: bool=True, y: bool=True, z:\n bool=True, **kwargs)->List[float]:\n pass","def normalise(self):\n for at in self.atoms:\n if at.x < 0. :\n at.x = self.coordx + at.x\n if at.y < 0. :\n at.y = self.coordy + at.y\n if at.z < 0. :\n at.z = self.coordz + at.z","def insphere(network,\n geometry,\n **kwargs):\n import warnings\n try:\n import pulp as pu\n Np = geometry.num_pores()\n value = _sp.zeros(Np)\n pore_map = geometry.map_pores(geometry.pores(),geometry._net)\n for geom_pore,net_pore in pore_map:\n net_throats = geometry._net.find_neighbor_throats(net_pore)\n geom_throats = geometry._net.map_throats(net_throats,geometry)[:,1]\n verts = geometry['throat.offset_vertices'][geom_throats]\n if len(verts) > 1:\n try:\n pts = np.vstack((i for i in verts if len(i)>0))\n except ValueError:\n pts = []\n if len(pts) > 4:\n \"Work out central point to use as initial guess\"\n c0 = np.mean(pts,axis=0)\n \"Compute convex hull to find points lying on the hull in order\"\n hull = ConvexHull(pts, qhull_options='QJ Pp')\n \"For each simplex making up the hull collect the end points\"\n A = pts[hull.simplices[:,0]]\n B = pts[hull.simplices[:,1]]\n C = pts[hull.simplices[:,2]]\n #I = np.array([[0,1],[-1,0]])\n \"Normal of the simplices\"\n #N = np.dot((B-A),I)\n N = np.cross((B-A),(C-A),axis=1)\n #L = np.sqrt(np.sum(np.square(N),axis=1))\n \"Normalize the normal vector\"\n L = np.linalg.norm(N,axis=1)\n F = np.vstack((L,L,L)).T\n N /= F\n \"If normals point out of hull change sign to point in\"\n pointing_out = (np.sum((A-c0)*N,axis=1)>0)\n N[pointing_out]*= -1\n \"Define Linear Program Variables\"\n \"The centre of the incircle adjustment\"\n cx = pu.LpVariable(\"cx\",None,None,pu.LpContinuous)\n cy = pu.LpVariable(\"cy\",None,None,pu.LpContinuous)\n cz = pu.LpVariable(\"cz\",None,None,pu.LpContinuous)\n \"Radius of the incircle\"\n R = pu.LpVariable(\"R\",0,None,pu.LpContinuous)\n \"Slack variables for shortest distance between centre and simplices\" \n S = pu.LpVariable.dict(\"SlackVariable\",range(len(A)),0,None,pu.LpContinuous)\n \"Set up LP problem\"\n prob = pu.LpProblem(\"FindInRadius\",pu.LpMaximize)\n \"Objective Function\"\n prob += R\n for i in range(len(A)):\n \" Ni.(C-Ai)-Si = 0\"\n prob += N[i][0]*(c0[0]+cx) + N[i][1]*(c0[1]+cy) + N[i][2]*(c0[2]+cz)- N[i][0]*A[i][0] - N[i][1]*A[i][1] - N[i][2]*A[i][2]- S[i] == 0\n \"Si >= R\"\n prob += S[i] >= R\n \"Solve the LP\"\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\")\n prob.solve()\n \"As the radius is the objective function we can get it from the objective or as R.value()\"\n rad = prob.objective.value()\n #cen = c0 + np.array([cx.value(),cy.value(),cz.value()])\n value[geom_pore]=rad*2\n \n \n return value\n except ImportError:\n print(\"Cannot use insphere method without installing pulp package\")","def Icosphere(radius=1.0, center=(0.0, 0.0, 0.0), nsub=3):\n mesh = Icosahedron()\n mesh.clear_data()\n mesh = mesh.subdivide(nsub=nsub)\n\n # scale to desired radius and translate origin\n dist = np.linalg.norm(mesh.points, axis=1, keepdims=True) # distance from origin\n mesh.points = mesh.points * (radius / dist) + center\n return mesh","def generate_sphere_full():\n \n num_voxels = 31\n c = (15.0, 15.0, 15.0)\n\n data_x = []\n data_y = []\n data_z = []\n data_intensity = []\n\n volume = numpy.zeros((num_voxels, num_voxels, num_voxels))\n\n for x in range(num_voxels):\n for y in range(num_voxels):\n for z in range(num_voxels):\n\n if numpy.sqrt((x-c[0])**2 + (y-c[1])**2 + (z-c[2])**2) - 7.5 < 1.5:\n data_x.append(x)\n data_y.append(y)\n data_z.append(z)\n data_intensity.append(200.0)\n\n volume[x,y,z] = 200.0\n\n\n return data_x, data_y, data_z, data_intensity, volume","def normal_filter(pointcloud, target_orient, threshold):\n\n xyz_points = np.asarray(pointcloud.points)\n xyz_normals = np.asarray(pointcloud.normals)\n projection = xyz_normals.dot(target_orient)\n projection_mask = projection > threshold #\n target_points = xyz_points[projection_mask]\n other_points = xyz_points[~projection_mask]\n target_normals = xyz_normals[projection_mask]\n other_normals = xyz_normals[~projection_mask]\n\n target_points_ = o3d.geometry.PointCloud()\n other_points_ = o3d.geometry.PointCloud()\n target_points_.points = o3d.utility.Vector3dVector(target_points)\n target_points_.normals = o3d.utility.Vector3dVector(target_normals)\n other_points_.points = o3d.utility.Vector3dVector(other_points)\n other_points_.normals = o3d.utility.Vector3dVector(other_normals)\n\n return target_points_, other_points_","def center_normalize(M_t, dim=0):\n N_t = M_t - M_t.mean(dim=dim, keepdim=True)\n return N_t / torch.sqrt(torch.pow(N_t, 2).sum(dim=dim, keepdim=True))","def GetModelCenterOfMass(self, model):\n polyData = model.GetPolyData()\n comFilter = vtk.vtkCenterOfMass()\n comFilter.SetInputData(polyData)\n comFilter.SetUseScalarsAsWeights(False)\n comFilter.Update()\n comPoint = comFilter.GetCenter()\n return comPoint","def plane_sphere(p, s):\n\n p.normalize()\n\n d = dot(s.o-p.o, p.n)\n\n if d > s.r:\n return False\n else:\n return (s.o - d*p.n, sqrt(s.r*s.r - d*d))","def fix_sphere_h (center_x, center_y, center_z, radius, centers, radii, len_points, list_of_a):\n g_x = []\n g_y = []\n g_z = []\n points = [hydrogen_coord_gen(center_x, center_y, center_z, radius) for i in range(0, len_points)] \n x = [points[i][0] for i in range(0, len(points))] \n y = [points[i][1] for i in range(0, len(points))]\n z = [points[i][2] for i in range(0, len(points))]\n for i in range(0, len(points)):\n check = 0\n check_b = 0\n j = 0\n while (j <= (len(centers) - 1) and (check == 0)): \n if (calculate_3D_distance_2_centers(x[i], y[i], z[i], centers[j][0], centers[j][1], centers[j][2]) < radii[j]):\n check += 1\n j += 1\n h = 0\n while ((check_b == 0) and (h <= len(list_of_a) -1)):\n if (calculate_3D_distance_2_centers(x[i], y[i], z[i], list_of_a[h].x, list_of_a[h].y, list_of_a[h].z) <= 1.50): \n check_b += 1\n h += 1\n if ((check == 0) and (check_b == 0)):\n g_x.append(x[i])\n g_y.append(y[i])\n g_z.append(z[i])\n return g_x, g_y, g_z","def _update_surface_normals(self):\n\n # This is the case if there are too few points to\n # compute normals so there can be values to remove\n\n #can be important for parallel\n self.swarm.shadow_particles_fetch()\n\n if self.empty:\n self.director.data[...] = 0.0\n else:\n\n particle_coords = self.swarm.particleCoordinates.data\n\n Nx = np.empty(self.swarm.particleLocalCount)\n Ny = np.empty(self.swarm.particleLocalCount)\n Nz = np.empty(self.swarm.particleLocalCount)\n\n for i, xyz in enumerate(particle_coords):\n r, neighbours = self.kdtree.query(particle_coords[i], k=4)\n\n # this point is neighbour[0] and neighbour points are neighbours[(1,2,3)]\n XYZ1 = self.kdtree.data[neighbours[1]]\n XYZ2 = self.kdtree.data[neighbours[2]]\n XYZ3 = self.kdtree.data[neighbours[3]]\n\n dXYZ1 = XYZ2 - XYZ1\n dXYZ2 = XYZ3 - XYZ1\n\n # Cross product of those 2 vectors can be use as the local normal (perhaps)\n\n Nx[i], Ny[i], Nz[i] = np.cross(dXYZ1, dXYZ2)\n #if i == 0:\n # print(Nx, Ny, Nz)\n # print(xyz[0], xyz[1],xyz[2])\n # print((self.insidePt[0] - xyz[0]) * Nx[i] )\n\n if (self.insidePt):\n sign = np.sign( (self.insidePt[0] - xyz[0]) * Nx[i] +\n (self.insidePt[1] - xyz[1]) * Ny[i] +\n (self.insidePt[2] - xyz[2]) * Nz[i] )\n Nx[i] *= sign\n Ny[i] *= sign\n Nz[i] *= sign\n\n\n for i in range(0, self.swarm.particleLocalCount):\n scale = 1.0 / np.sqrt(Nx[i]**2 + Ny[i]**2 + Nz[i]**2)\n Nx[i] *= scale\n Ny[i] *= scale\n Nz[i] *= scale\n\n\n self.director.data[:,0] = Nx[:]\n self.director.data[:,1] = Ny[:]\n self.director.data[:,2] = Nz[:]\n\n print(\"Surf Norms\")\n\n return","def centroid(self, method: str = 'median') -> Vector:\n\n\t\toriginal_origins = deepcopy(self.origin)\n\t\tself.origin_to_centroid(method=method)\n\t\tcentroid = deepcopy(self.origin)\n\t\tself.origin = original_origins\n\n\t\tif len(self._meshes) > 1:\n\t\t\treturn sum(centroid) / len(self._meshes)\n\n\t\treturn centroid","def sphere(self, x):\r\n # return np.random.rand(1)[0]**0 * sum(x**2) + 1 * np.random.rand(1)[0]\r\n return sum((x+0)**2)","def test_one_center(self):\n sv=system_vars_c().init_xyzlike([ [8, [0.0, 0.0, 0.0]]])\n atom2rcut=np.array([5.0])\n g = dft.gen_grid.Grids(sv)\n g.level = 1 # precision as implemented in pyscf\n g.radi_method=leggauss_ab\n g.build(atom2rcut=atom2rcut)\n\n #print( max( np.linalg.norm(g.coords, axis=1) ) )\n #print( g.weights.sum(), 4.0 *np.pi*5.0**3 / 3.0 )\n self.assertAlmostEqual(max( np.linalg.norm(g.coords, axis=1) ), 4.9955942742763986)\n self.assertAlmostEqual(g.weights.sum(), 4.0 *np.pi*5.0**3 / 3.0)\n self.assertEqual(len(g.weights), 6248)","def project_to_sphere(points):\n # for uv, the sphere: r=1, azimuth(phi): 2*pi*u, elevation(theta): 2*pi*v\n # theta is elevation, phi is azimuth\n r, theta, phi = cs.cart2sp(x=points[:, 0], y=points[:, 1], z=points[:, 2])\n # logger.info(f\"number of zero points in r: {np.sum(r==0)}\")\n assert np.sum(r == 0) == 0, \"points contains zeros\"\n points_sphere = points / r.reshape(-1, 1)\n return points_sphere, r, theta, phi\n\n # r, theta, phi = cs.cart2sp(x=1, y=1, z=1)\n\n # # spherical to cartesian\n # x, y, z = cs.sp2cart(r=1, theta=np.pi/4, phi=np.pi/4)\n\n # # cartesian to cylindrical\n # r, phi, z = cs.cart2cyl(x=1, y=1, z=1)","def getCentroid(self):\n centroid = 0.0\n sumMagnitude = 0.0\n\n for i in range(0,self.nUniquePoints):\n freq,magnitude = self.fDomain[i]\n\n centroid += freq*magnitude\n sumMagnitude += magnitude\n \n centroid /= sumMagnitude\n return centroid","def centre(self):\n n = len(self.point)\n return Point(\n sum(map(lambda p: p.x, self.point)) / n,\n sum(map(lambda p: p.y, self.point)) / n\n )","def scale_and_translate_points(points):\n x = points[0]\n y = points[1]\n center = points.mean(axis=1) # mean of each row\n cx = x - center[0] # center the points\n cy = y - center[1]\n dist = np.sqrt(np.power(cx, 2) + np.power(cy, 2))\n scale = np.sqrt(2) / dist.mean()\n norm3d = np.array([\n [scale, 0, -scale * center[0]],\n [0, scale, -scale * center[1]],\n [0, 0, 1]\n ])\n\n return np.dot(norm3d, points), norm3d","def center(self, center_mass=False):\n if center_mass:\n com = self.center_of_mass\n self.xyz -= com\n else:\n self.xyz -= self.xyz.mean(0)","def onsphere(size=None):\n xy = oncircle(size)\n z = 2.*random(xy.shape[:-1] + (1,)) - 1.\n xy *= sqrt(1. - z*z)\n return concatenate((xy, z), axis=-1)","def centerPoint(featureCollection):\n features = featureCollection[\"features\"]\n center = [0, 0]\n for feature in features:\n geometry = feature[\"geometry\"]\n if geometry[\"type\"] == \"Point\":\n point = feature[\"geometry\"][\"coordinates\"]\n center[0] += point[0]\n center[1] += point[1]\n else:\n raise ValueError(\"expected a point but got a {}\".format(geometry[\"type\"]))\n center[0] /= len(features) \n center[1] /= len(features) \n\n return geojson.Point(coordinates=center)","def getCenterOfMass(self, filtered = True):\n n_time = len(self.pos)\n x_mean = [0.0,]*n_time\n y_mean = [0.0,]*n_time\n z_mean = [0.0,]*n_time \n for frame in range(n_time):\n # get all the positions of the filtered points\n x,y,z = self.getAllPositions(frame, filtered)\n x_mean[frame] = np.asarray(x).mean() if len(x) > 0 else None\n y_mean[frame] = np.asarray(y).mean() if len(y) > 0 else None\n z_mean[frame] = np.asarray(z).mean() if len(z) > 0 else None\n\n return x_mean, y_mean, z_mean","def plot_points_on_sphere(points_x, points_y, points_z, center_x, center_y, center_z, radius):\n from mayavi import mlab\n mlab.figure(1, bgcolor=(1,1,1), fgcolor=(0,0,0), size=(800,800))\n return mlab.points3d(points_x, points_y, points_z, scale_factor=0.05, color=(0.25, 0.75, 0.77))","def center_of_mass(points):\n # break into many triangles\n # each point is part of two triangles\n cor = [sum(points) / len(points)]\n mass_points = []\n area = 0\n for i in range(len(points) - 1):\n triangle = cor + points[i:i + 2]\n # print(triangle)\n mass_points.append(build_triangle_point_mass(triangle))\n area += shoelace_area(triangle)\n # print(triangle, area)\n mass_points.append(build_triangle_point_mass(cor + [points[-1], points[0]]))\n area += shoelace_area(cor + [points[-1], points[0]])\n return Vector2D(*find_com(*zip(*mass_points))), area","def centers_displacement(p1, p2):\n # geometric center\n c1 = np.mean(np.array(p1), axis=0)\n c2 = np.mean(np.array(p2), axis=0)\n return c2 - c1","def surfaceIntSphere(r: float) -> float:\n return 4.0 * np.pi * r * r","def compute_center(self, mole_object):\r\n if mole_object.plugin_type == \"PyMOL\":\r\n sel = PymolPlugin.PymolPlugin().get_model('all')\r\n cnt = len(sel.atom)\r\n\r\n else:\r\n sel = ChimeraPlugin.ChimeraPlugin().select()\r\n cnt = len(ChimeraPlugin.ChimeraPlugin().current_atoms())\r\n\r\n cent_x = 0\r\n cent_y = 0\r\n cent_z = 0\r\n\r\n if cnt == 0:\r\n return 0, 0, 0\r\n\r\n if mole_object.plugin_type == \"PyMOL\":\r\n\r\n for a in sel.atom:\r\n cent_x += a.coord[0]\r\n cent_y += a.coord[1]\r\n cent_z += a.coord[2]\r\n\r\n else:\r\n\r\n for a in ChimeraPlugin.ChimeraPlugin().current_atoms():\r\n cent_x += a.coord()[0]\r\n cent_y += a.coord()[1]\r\n cent_z += a.coord()[2]\r\n\r\n cent_x /= cnt\r\n cent_y /= cnt\r\n cent_z /= cnt\r\n\r\n self.point_x.component('entryfield').setentry(cent_x)\r\n self.point_y.component('entryfield').setentry(cent_y)\r\n self.point_z.component('entryfield').setentry(cent_z)\r\n\r\n self.show_crisscross(mole_object)","def sphere(\n network,\n pore_diameter='pore.diameter'\n):\n return 4/3*_pi*(network[pore_diameter]/2)**3","def getAfinityCenter(width, height, point, center, radius=7, img_affinity=None):\n tensor = torch.zeros(2, height, width).float()\n\n # Create the canvas for the affinity output\n imgAffinity = Image.new(\"RGB\", (width, height), \"black\")\n totensor = transforms.Compose([transforms.ToTensor()])\n\n draw = ImageDraw.Draw(imgAffinity)\n r1 = radius\n p = point\n draw.ellipse((p[0] - r1, p[1] - r1, p[0] + r1, p[1] + r1), (255, 255, 255))\n\n del draw\n\n # Compute the array to add the affinity\n array = (np.array(imgAffinity) / 255)[:, :, 0]\n\n angle_vector = np.array(center) - np.array(point)\n angle_vector = normalize(angle_vector)\n affinity = np.concatenate([[array * angle_vector[0]], [array * angle_vector[1]]])\n\n # print (tensor)\n if not img_affinity is None:\n # Find the angle vector\n # print (angle_vector)\n if length(angle_vector) > 0:\n angle = py_ang(angle_vector)\n else:\n angle = 0\n # print(angle)\n c = np.array(colorsys.hsv_to_rgb(angle / 360, 1, 1)) * 255\n draw = ImageDraw.Draw(img_affinity)\n draw.ellipse((p[0] - r1, p[1] - r1, p[0] + r1, p[1] + r1), fill=(int(c[0]), int(c[1]), int(c[2])))\n del draw\n re = torch.from_numpy(affinity).float() + tensor\n return re, img_affinity","def centroid(self) -> Point:\n points = self.normalized_array\n centroids = [np.average(points[[0, i, i + 1], :-1], axis=0) for i in range(1, points.shape[0] - 1)]\n weights = [det(self._normalized_projection()[[0, i, i + 1]]) / 2 for i in range(1, points.shape[0] - 1)]\n return Point(*np.average(centroids, weights=weights, axis=0))","def U(self, *args):\n return _Bnd.Bnd_Sphere_U(self, *args)","def CenterOfMass(points):\n A = AreaOfPolygon(points)\n N = len(points)\n cx = 0\n cy = 0\n for i in xrange(0, N):\n x_i = points[i][0]\n y_i = points[i][1]\n x_ip1 = points[(i+1) % N][0]\n y_ip1 = points[(i+1) % N][1]\n part = (x_i * y_ip1 - x_ip1 * y_i)\n cx += ((x_i + x_ip1) * part)\n cy += ((y_i + y_ip1) * part)\n return (cx/(6*A), cy/(6*A), abs(A))","def normalize_particles(self):\n tot_weight = sum([particle.w for particle in self.particle_cloud]) or 1\n for particle in self.particle_cloud:\n particle.w = particle.w / tot_weight;","def normalize(coords, radius):\n co = coords.copy()\n # calculate current distance\n dist = (coords[0] ** 2 + coords[1] ** 2 + coords[2] ** 2) ** 0.5\n # normalize\n for axis in range(3):\n co[axis] = coords[axis] / dist * radius\n return co","def surface_norm(self, pt):\n\n return (pt - self.origin).normalize()","def fit_plane_to_point_cloud(pc: np.ndarray) -> Tuple[Any, Any, Any, Any]:\n center = pc.sum(axis=0) / pc.shape[0]\n u, s, vh = np.linalg.svd(pc - center)\n\n # Get the unitary normal vector\n u_norm = vh[2, :]\n d = -np.dot(u_norm, center)\n a, b, c = u_norm\n return a, b, c, d","def centerOfMass(data):\r\n dd = []\r\n for d in data:\r\n dd.append(d.coordinate)\r\n\r\n data = dd\r\n data = np.array(data)\r\n n = len(data)\r\n x = sum(data[:,0])\r\n y = sum(data[:,1])\r\n z = sum(data[:,2])\r\n x/=n\r\n y/=n\r\n z/=n\r\n return x,y,z,n","def center(self, obj):\n mn0 = self.master.xy >= obj.center\n mn1 = self.master.xy <= obj.center\n\n point_list = [self.master.xy[mn0], self.master.xy[mn1], self.master.xy[mn0[0], mn1[1]], self.master.xy[mn1[0], mn0[1]]] # 4 physical points near the center coordinate.\n dist_list = []\n idx = 0\n for point in point_list:\n dist_list.append([idx, np.linalg.norm(point - obj.center)]) # Calculate Euclidean distances.\n idx += 1\n dist_sorted = sorted(dist_list, key=lambda distance : distance[1]) # Sort distances in ascending order.\n return self.master.mn(point_list[dist_sorted[0][0]]) # Convert the closest point to abstract coordinate and then return.","def center_of_mass_polyhedron():\n raise NotImplementedError","def unit_to_sphere(v):\n return (math.acos(v[2]), math.atan2(v[1], v[0]))","def surface_norm(self, pt):\n\n return self.normal.normalize()","def __call__(self, x):\n v = vector(RDF,x)\n if v.is_zero():\n raise ValueError, \"The origin must not be a vertex.\"\n v = v/norm(v) # normalize vertices to unit sphere\n v = self.house*v # reflect so self.projection_dir is at \"north pole\"\n denom = self.height-v[self.dim-1]\n if denom.is_zero():\n raise ValueError, 'Point cannot coincide with ' \\\n 'coordinate singularity at ' + repr(x)\n return vector(RDF, [ v[i]/denom for i in range(self.dim-1) ])","def compute_centrifugal(self):\r\n # update the coordinates\r\n self.get_coords()\r\n\r\n # compute the centrifugal force\r\n self.centrifugal.assign(project(\r\n -1*self.rho*cross(self.omega, cross(self.omega, self.r)), self.V))","def centers_cartesian(self):\n polar_centers, azimuthal_centers = self.centers()\n x_centers, y_centers, z_centers = \\\n starwinds_magnetogram.coordinate_transforms.rectangular_coordinates_from_spherical(\n np.ones(polar_centers.shape),\n polar_centers,\n azimuthal_centers)\n\n return x_centers, y_centers, z_centers","def getCenter(self):\n return Point.average(self.points)","def worldToCameraCentricXform(self):\n return self.rotateAlignXform().dot(self.translateToOriginXform())","def centroid(sign, FS):\n\n time = compute_time(sign, FS)\n\n energy, time_energy=signal_energy(sign, time)\n\n total_energy = np.dot(np.array(time_energy),np.array(energy))\n energy_sum = np.sum(energy)\n\n if energy_sum == 0 or total_energy == 0:\n centroid = 0\n else:\n centroid = total_energy / energy_sum\n return centroid","def pointcenter(x):\n return point(x)","def norm(self):\r\n old_origin = np.array(self.origin)\r\n self.origin = [0, 0, 0]\r\n old_origin[0] = old_origin[0] / self.x[0]\r\n old_origin[1] = old_origin[1] / self.y[1]\r\n old_origin[2] = old_origin[2] / self.z[2]\r\n self.data = ndimage.shift(self.data, -old_origin, mode='wrap')","def getSphereRadius(self):\n return 1.5","def center_of_gravity(self):\n weights = [self.stabilizer_h.weight, self.stabilizer_vright.weight, self.stabilizer_vleft.weight]\n cgs = [self.stabilizer_h.center_of_gravity, self.stabilizer_vright.center_of_gravity,\n self.stabilizer_vleft.center_of_gravity]\n total_weight = sum(weights)\n cg_x = sum([weights[i] * cgs[i].x for i in range(0, len(weights))]) / total_weight\n cg_y = sum([weights[i] * cgs[i].y for i in range(0, len(weights))]) / total_weight\n cg_z = sum([weights[i] * cgs[i].z for i in range(0, len(weights))]) / total_weight\n\n return Point(cg_x, cg_y, cg_z)","def getAfinityCenter(width, height, point, center, radius=7, img_affinity=None):\n tensor = torch.zeros(2,height,width).float()\n\n # Create the canvas for the afinity output\n imgAffinity = Image.new(\"RGB\", (width,height), \"black\")\n totensor = transforms.Compose([transforms.ToTensor()])\n \n draw = ImageDraw.Draw(imgAffinity) \n r1 = radius\n p = point\n draw.ellipse((p[0]-r1,p[1]-r1,p[0]+r1,p[1]+r1),(255,255,255))\n\n del draw\n\n # Compute the array to add the afinity\n array = (np.array(imgAffinity)/255)[:,:,0]\n\n angle_vector = np.array(center) - np.array(point)\n angle_vector = normalize(angle_vector)\n affinity = np.concatenate([[array*angle_vector[0]],[array*angle_vector[1]]])\n\n # print (tensor)\n if not img_affinity is None:\n # Find the angle vector\n # print (angle_vector)\n if length(angle_vector) >0:\n angle=py_ang(angle_vector)\n else:\n angle = 0\n # print(angle)\n c = np.array(colorsys.hsv_to_rgb(angle/360,1,1)) * 255\n draw = ImageDraw.Draw(img_affinity) \n draw.ellipse((p[0]-r1,p[1]-r1,p[0]+r1,p[1]+r1),fill=(int(c[0]),int(c[1]),int(c[2])))\n del draw\n re = torch.from_numpy(affinity).float() + tensor\n return re, img_affinity","def centroid(self, unit='spatial'):\n com = ndimage.center_of_mass(self.data)\n if unit != 'spatial':\n return com\n else:\n # tuple - cast from generator\n # sample spacing - indices to units\n # x-c -- index shifted from center\n return tuple(self.sample_spacing * (x-c) for x, c in zip(com, (self.center_y, self.center_x)))","def center(self):\n return self.centralizer(self)","def GetCentroid(self, p_float=..., p_float=..., p_float=...):\n ...","def get_sphere_info(points):\n rib = np.sum(points, axis=0)\n rib3d = proj3d(rib)\n pts3d = np.asarray([proj3d(p) for p in points])\n face_size = np.linalg.norm(pts3d[0] - rib3d)\n\n M = np.ones((4, 4), dtype=np.float)\n M[:3, :3] = pts3d[:3]\n M[3, :3] = rib3d\n b = [-sum(x*x) for x in M[:, :3]]\n # if this is a plane\n if abs(np.linalg.det(M)) < 1e-4:\n center = rib3d\n return True, center, None, face_size\n else:\n T = np.linalg.solve(M, b)\n D, E, F, G = T\n center = -0.5 * T[:3]\n radius = 0.5 * np.sqrt(D*D + E*E + F*F - 4*G)\n return False, center, radius, face_size","def correct(point_cloud, axis_order=[0, 1, 2], dist_from_center=0):\n \n if type(axis_order) != list:\n axis_order = list(axis_order)\n\n # Adding the fixed distance to x and y.\n x = point_cloud[:, axis_order[0]] + dist_from_center\n y = point_cloud[:, axis_order[1]] + dist_from_center\n z = point_cloud[:, axis_order[2]]\n\n return np.vstack((x, y, z)).T","def corner_to_train(corners, sphere_center, resolution=0.50, x=(0, 90), y=(-50, 50), z=(-4.5, 5.5), scale=4, min_value=np.array([0., -50., -4.5])):\n x_logical = np.logical_and((corners[:, :, 0] < x[1]), (corners[:, :, 0] >= x[0]))\n y_logical = np.logical_and((corners[:, :, 1] < y[1]), (corners[:, :, 1] >= y[0]))\n z_logical = np.logical_and((corners[:, :, 2] < z[1]), (corners[:, :, 2] >= z[0]))\n xyz_logical = np.logical_and(x_logical, np.logical_and(y_logical, z_logical)).all(axis=1)\n train_corners = corners[xyz_logical].copy()\n sphere_center = sphere_to_center(sphere_center, resolution=resolution, scale=scale, min_value=min_value) #sphere to center\n for index, (corner, center) in enumerate(zip(corners[xyz_logical], sphere_center)):\n train_corners[index] = corner - center\n return train_corners","def _fit_plane_to_point_cloud(\n points_xyz: NDArrayFloat,\n) -> Tuple[float, float, float, float]:\n center_xyz: NDArrayFloat = np.mean(points_xyz, axis=0)\n out: Tuple[NDArrayFloat, NDArrayFloat, NDArrayFloat] = np.linalg.svd(\n points_xyz - center_xyz\n )\n vh = out[2]\n\n # Get the unitary normal vector\n a, b, c = float(vh[2, 0]), float(vh[2, 1]), float(vh[2, 2])\n d: float = -np.dot([a, b, c], center_xyz)\n return (a, b, c, d)","def createSphere( position=(0,0,0), radius=1, colour=(0.6,0.6,0.6), samplesY = 20, samplesXZ = 20 ):\r\n return createEllipsoid( position, (radius,radius,radius), colour, samplesY, samplesXZ )","def compute_centers_of_hypercubes(self):\n for hc in self.hypercubes.flatten():\n for i in range(self.dims - 1, -1, -1):\n index = self.dims - (i + 1)\n hc.center[i] = (hc.coords[index] + 0.5) * self.hypercube_measurements[index]","def sphere(indiv):\n return sum([ x ** 2 for x in indiv])","def sphere(geometry,\n psd_name,psd_shape,psd_loc,psd_scale,\n pore_seed='pore.seed',\n psd_offset=0,\n **kwargs):\n import scipy.stats as spst\n prob_fn = getattr(spst,psd_name)\n P = prob_fn(psd_shape,loc=psd_loc,scale=psd_scale)\n value = P.ppf(geometry[pore_seed])+psd_offset\n return value","def center(box):\n x_center = box[:, 0] + (box[:, 2] - box[:, 0]) // 2\n y_center = box[:, 1] + (box[:, 3] - box[:, 1]) // 2\n return torch.stack((x_center, y_center)).t().to(box.device)","def normal_polygon(points, unitized=True):\n p = len(points)\n assert p > 2, \"At least three points required\"\n nx = 0\n ny = 0\n nz = 0\n o = centroid_points(points)\n a = subtract_vectors(points[-1], o)\n for i in range(p):\n b = subtract_vectors(points[i], o)\n n = cross_vectors(a, b)\n a = b\n nx += n[0]\n ny += n[1]\n nz += n[2]\n if not unitized:\n return nx, ny, nz\n l = length_vector([nx, ny, nz])\n return nx / l, ny / l, nz / l","def normalise(self):\n s = self._sum()\n if s != 0:\n for element, value in self.focals.items():\n self.focals[element] /= s","def center_of_coor(coordinates):\n return (np.sum(coordinates, axis=0) / coordinates.shape[0])","def Sphere_ExactSerendipityLagrangeQuad():\n\n mesh = Sphere_CubeToSerendipityLagrangeQuad(1)\n \n ################\n # Modifications for exact sphere\n ################\n # x=+1 side\n def posXvals(xi1,xi2):\n xi1=np.asarray(xi1,np.float).reshape(-1,)\n xi2=np.asarray(xi2,np.float).reshape(-1,)\n xb=np.ones(xi1.shape);yb=np.array(-xi1);zb=np.array(xi2);\n xx=xb**2;yy=yb**2;zz=zb**2;\n x = xb*np.sqrt(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)\n y = yb*np.sqrt(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)\n z = zb*np.sqrt(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)\n return np.vstack([x,y,z])\n def posXnormals(xi1,xi2):\n xi1=np.asarray(xi1,np.float).reshape(-1,)\n xi2=np.asarray(xi2,np.float).reshape(-1,)\n xb=np.ones(xi1.shape);yb=np.array(-xi1);zb=np.array(xi2);\n xx=xb**2;yy=yb**2;zz=zb**2;\n dxdxi1 = -1.0 * xb*0.5*(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)**-0.5 * (-yb+2.0*yb*zz/3.0)\n dxdxi2 = 1.0 * xb*0.5*(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)**-0.5 * (-zb+2.0*yy*zb/3.0)\n dydxi1 = -1.0 * np.sqrt(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)\n dydxi2 = 1.0 * yb*0.5*(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)**-0.5 * (-zb+2.0*xx*zb/3.0)\n dzdxi1 = -1.0 * zb*0.5*(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)**-0.5 * (-yb+2.0*xx*yb/3.0)\n dzdxi2 = 1.0 * np.sqrt(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)\n J = np.array([[dxdxi1,dxdxi2],[dydxi1,dydxi2],[dzdxi1,dzdxi2]]).T\n J = np.cross(J[:,0,:],J[:,1,:])\n magnitude = np.sqrt(np.sum(J**2,axis=1))\n return J.T/magnitude\n def posXJ(xi1,xi2):\n xi1=np.asarray(xi1,np.float).reshape(-1,)\n xi2=np.asarray(xi2,np.float).reshape(-1,)\n xb=np.ones(xi1.shape);yb=np.array(-xi1);zb=np.array(xi2);\n xx=xb**2;yy=yb**2;zz=zb**2;\n dxdxi1 = -1.0 * xb*0.5*(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)**-0.5 * (-yb+2.0*yb*zz/3.0)\n dxdxi2 = 1.0 * xb*0.5*(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)**-0.5 * (-zb+2.0*yy*zb/3.0)\n dydxi1 = -1.0 * np.sqrt(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)\n dydxi2 = 1.0 * yb*0.5*(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)**-0.5 * (-zb+2.0*xx*zb/3.0)\n dzdxi1 = -1.0 * zb*0.5*(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)**-0.5 * (-yb+2.0*xx*yb/3.0)\n dzdxi2 = 1.0 * np.sqrt(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)\n J = np.array([[dxdxi1,dxdxi2],[dydxi1,dydxi2],[dzdxi1,dzdxi2]]).T\n J = np.cross(J[:,0,:],J[:,1,:])\n return np.sqrt(np.sum(J**2,axis=1))\n mesh.eList[0].vals = posXvals\n mesh.eList[0].normals = posXnormals\n mesh.eList[0].J = posXJ\n \n def posYvals(xi1,xi2):\n xi1=np.asarray(xi1,np.float).reshape(-1,)\n xi2=np.asarray(xi2,np.float).reshape(-1,)\n xb=np.array(xi1);yb=np.ones(xi1.shape);zb=np.array(xi2);\n xx=xb**2;yy=yb**2;zz=zb**2;\n x = xb*np.sqrt(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)\n y = yb*np.sqrt(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)\n z = zb*np.sqrt(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)\n return np.vstack([x,y,z])\n def posYnormals(xi1,xi2):\n xi1=np.asarray(xi1,np.float).reshape(-1,)\n xi2=np.asarray(xi2,np.float).reshape(-1,)\n xb=np.array(xi1);yb=np.ones(xi1.shape);zb=np.array(xi2);\n xx=xb**2;yy=yb**2;zz=zb**2;\n dxdxi1 = 1.0*np.sqrt(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)\n dxdxi2 = 1.0*xb*0.5*(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)**-0.5 * (-zb+2.0*yy*zb/3.0)\n dydxi1 = 1.0*yb*0.5*(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)**-0.5 * (-xb+2.0*xb*zz/3.0)\n dydxi2 = 1.0*yb*0.5*(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)**-0.5 * (-zb+2.0*xx*zb/3.0)\n dzdxi1 = 1.0*zb*0.5*(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)**-0.5 * (-xb+2.0*xb*yy/3.0)\n dzdxi2 = 1.0*np.sqrt(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)\n J = np.array([[dxdxi1,dxdxi2],[dydxi1,dydxi2],[dzdxi1,dzdxi2]]).T\n J = np.cross(J[:,0,:],J[:,1,:])\n magnitude = np.sqrt(np.sum(J**2,axis=1))\n return J.T/magnitude\n def posYJ(xi1,xi2):\n xi1=np.asarray(xi1,np.float).reshape(-1,)\n xi2=np.asarray(xi2,np.float).reshape(-1,)\n xb=np.array(xi1);yb=np.ones(xi1.shape);zb=np.array(xi2);\n xx=xb**2;yy=yb**2;zz=zb**2;\n dxdxi1 = 1.0*np.sqrt(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)\n dxdxi2 = 1.0*xb*0.5*(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)**-0.5 * (-zb+2.0*yy*zb/3.0)\n dydxi1 = 1.0*yb*0.5*(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)**-0.5 * (-xb+2.0*xb*zz/3.0)\n dydxi2 = 1.0*yb*0.5*(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)**-0.5 * (-zb+2.0*xx*zb/3.0)\n dzdxi1 = 1.0*zb*0.5*(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)**-0.5 * (-xb+2.0*xb*yy/3.0)\n dzdxi2 = 1.0*np.sqrt(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)\n J = np.array([[dxdxi1,dxdxi2],[dydxi1,dydxi2],[dzdxi1,dzdxi2]]).T\n J = np.cross(J[:,0,:],J[:,1,:])\n return np.sqrt(np.sum(J**2,axis=1))\n mesh.eList[1].vals = posYvals\n mesh.eList[1].normals = posYnormals\n mesh.eList[1].J = posYJ\n \n # x=-1 side\n def negXvals(xi1,xi2):\n xi1=np.asarray(xi1,np.float).reshape(-1,)\n xi2=np.asarray(xi2,np.float).reshape(-1,)\n xb=-np.ones(xi1.shape);yb=np.array(xi1);zb=np.array(xi2);\n xx=xb**2;yy=yb**2;zz=zb**2;\n x = xb*np.sqrt(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)\n y = yb*np.sqrt(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)\n z = zb*np.sqrt(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)\n return np.vstack([x,y,z])\n def negXnormals(xi1,xi2):\n xi1=np.asarray(xi1,np.float).reshape(-1,)\n xi2=np.asarray(xi2,np.float).reshape(-1,)\n xb=-np.ones(xi1.shape);yb=np.array(xi1);zb=np.array(xi2);\n xx=xb**2;yy=yb**2;zz=zb**2;\n dxdxi1 = 1.0*xb*0.5*(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)**-0.5 * (-yb+2.0*yb*zz/3.0)\n dxdxi2 = 1.0*xb*0.5*(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)**-0.5 * (-zb+2.0*yy*zb/3.0)\n dydxi1 = 1.0*np.sqrt(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)\n dydxi2 = 1.0*yb*0.5*(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)**-0.5 * (-zb+2.0*xx*zb/3.0)\n dzdxi1 = 1.0*zb*0.5*(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)**-0.5 * (-yb+2.0*xx*yb/3.0)\n dzdxi2 = 1.0*np.sqrt(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)\n J = np.array([[dxdxi1,dxdxi2],[dydxi1,dydxi2],[dzdxi1,dzdxi2]]).T\n J = np.cross(J[:,0,:],J[:,1,:])\n magnitude = np.sqrt(np.sum(J**2,axis=1))\n return J.T/magnitude\n def negXJ(xi1,xi2):\n xi1=np.asarray(xi1,np.float).reshape(-1,)\n xi2=np.asarray(xi2,np.float).reshape(-1,)\n xb=-np.ones(xi1.shape);yb=np.array(xi1);zb=np.array(xi2);\n xx=xb**2;yy=yb**2;zz=zb**2;\n dxdxi1 = 1.0*xb*0.5*(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)**-0.5 * (-yb+2.0*yb*zz/3.0)\n dxdxi2 = 1.0*xb*0.5*(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)**-0.5 * (-zb+2.0*yy*zb/3.0)\n dydxi1 = 1.0*np.sqrt(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)\n dydxi2 = 1.0*yb*0.5*(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)**-0.5 * (-zb+2.0*xx*zb/3.0)\n dzdxi1 = 1.0*zb*0.5*(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)**-0.5 * (-yb+2.0*xx*yb/3.0)\n dzdxi2 = 1.0*np.sqrt(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)\n J = np.array([[dxdxi1,dxdxi2],[dydxi1,dydxi2],[dzdxi1,dzdxi2]]).T\n J = np.cross(J[:,0,:],J[:,1,:])\n return np.sqrt(np.sum(J**2,axis=1))\n mesh.eList[2].vals = negXvals\n mesh.eList[2].normals = negXnormals\n mesh.eList[2].J = negXJ\n\n # y=-1 side\n def negYvals(xi1,xi2):\n xi1=np.asarray(xi1,np.float).reshape(-1,)\n xi2=np.asarray(xi2,np.float).reshape(-1,)\n xb=np.array(-xi1);yb=-np.ones(xi1.shape);zb=np.array(xi2);\n xx=xb**2;yy=yb**2;zz=zb**2;\n x = xb*np.sqrt(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)\n y = yb*np.sqrt(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)\n z = zb*np.sqrt(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)\n return np.vstack([x,y,z])\n def negYnormals(xi1,xi2):\n xi1=np.asarray(xi1,np.float).reshape(-1,)\n xi2=np.asarray(xi2,np.float).reshape(-1,)\n xb=np.array(-xi1);yb=-np.ones(xi1.shape);zb=np.array(xi2);\n xx=xb**2;yy=yb**2;zz=zb**2; \n dxdxi1 = -1.0*np.sqrt(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)\n dxdxi2 = 1.0*0.5*xb*(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)**-0.5*(-zb+2.0*yy*zb/3.0)\n dydxi1 = -1.0*0.5*yb*(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)**-0.5*(-xb+2.0*xb*zz/3.0)\n dydxi2 = 1.0*0.5*yb*(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)**-0.5*(-zb+2.0*xx*zb/3.0)\n dzdxi1 = -1.0*0.5*zb*(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)**-0.5*(-xb+2.0*xb*yy/3.0)\n dzdxi2 = 1.0*np.sqrt(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0) \n J = np.array([[dxdxi1,dxdxi2],[dydxi1,dydxi2],[dzdxi1,dzdxi2]]).T\n J = np.cross(J[:,0,:],J[:,1,:])\n magnitude = np.sqrt(np.sum(J**2,axis=1))\n return J.T/magnitude\n def negYJ(xi1,xi2):\n xi1=np.asarray(xi1,np.float).reshape(-1,)\n xi2=np.asarray(xi2,np.float).reshape(-1,)\n xb=np.array(-xi1);yb=-np.ones(xi1.shape);zb=np.array(xi2);\n xx=xb**2;yy=yb**2;zz=zb**2; \n dxdxi1 = -1.0*np.sqrt(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)\n dxdxi2 = 1.0*0.5*xb*(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)**-0.5*(-zb+2.0*yy*zb/3.0)\n dydxi1 = -1.0*0.5*yb*(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)**-0.5*(-xb+2.0*xb*zz/3.0)\n dydxi2 = 1.0*0.5*yb*(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)**-0.5*(-zb+2.0*xx*zb/3.0)\n dzdxi1 = -1.0*0.5*zb*(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)**-0.5*(-xb+2.0*xb*yy/3.0)\n dzdxi2 = 1.0*np.sqrt(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0) \n J = np.array([[dxdxi1,dxdxi2],[dydxi1,dydxi2],[dzdxi1,dzdxi2]]).T\n J = np.cross(J[:,0,:],J[:,1,:])\n return np.sqrt(np.sum(J**2,axis=1))\n mesh.eList[3].vals = negYvals\n mesh.eList[3].normals = negYnormals\n mesh.eList[3].J = negYJ\n \n # z=+1 side\n def posZvals(xi1,xi2):\n xi1=np.asarray(xi1,np.float).reshape(-1,)\n xi2=np.asarray(xi2,np.float).reshape(-1,)\n xb=np.array(xi1)\n yb=np.array(-xi2)\n zb=np.ones(xi1.shape)\n xx=xb**2;yy=yb**2;zz=zb**2;\n x = xb*np.sqrt(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)\n y = yb*np.sqrt(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)\n z = zb*np.sqrt(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)\n return np.vstack([x,y,z])\n def posZnormals(xi1,xi2):\n xi1=np.asarray(xi1,np.float).reshape(-1,)\n xi2=np.asarray(xi2,np.float).reshape(-1,)\n xb=np.array(xi1);yb=np.array(-xi2);zb=np.ones(xi1.shape);\n xx=xb**2;yy=yb**2;zz=zb**2;\n dxdxi1 = 1.0*np.sqrt(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)\n dxdxi2 = -1.0*xb*0.5*(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)**-0.5 * (-yb+2.0*yb*zz/3.0)\n dydxi1 = 1.0*yb*0.5*(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)**-0.5 * (-xb+2.0*xb*zz/3.0)\n dydxi2 = -1.0*np.sqrt(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)\n dzdxi1 = 1.0*zb*0.5*(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)**-0.5 * (-xb+2.0*xb*yy/3.0)\n dzdxi2 = -1.0*zb*0.5*(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)**-0.5 * (-yb+2.0*xx*yb/3.0)\n J = np.array([[dxdxi1,dxdxi2],[dydxi1,dydxi2],[dzdxi1,dzdxi2]]).T\n J = np.cross(J[:,0,:],J[:,1,:])\n magnitude = np.sqrt(np.sum(J**2,axis=1))\n return J.T/magnitude\n def posZJ(xi1,xi2):\n xi1=np.asarray(xi1,np.float).reshape(-1,)\n xi2=np.asarray(xi2,np.float).reshape(-1,)\n xb=np.array(xi1);yb=np.array(-xi2);zb=np.ones(xi1.shape);\n xx=xb**2;yy=yb**2;zz=zb**2;\n dxdxi1 = 1.0*np.sqrt(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)\n dxdxi2 = -1.0*xb*0.5*(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)**-0.5 * (-yb+2.0*yb*zz/3.0)\n dydxi1 = 1.0*yb*0.5*(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)**-0.5 * (-xb+2.0*xb*zz/3.0)\n dydxi2 = -1.0*np.sqrt(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)\n dzdxi1 = 1.0*zb*0.5*(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)**-0.5 * (-xb+2.0*xb*yy/3.0)\n dzdxi2 = -1.0*zb*0.5*(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)**-0.5 * (-yb+2.0*xx*yb/3.0)\n J = np.array([[dxdxi1,dxdxi2],[dydxi1,dydxi2],[dzdxi1,dzdxi2]]).T\n J = np.cross(J[:,0,:],J[:,1,:])\n return np.sqrt(np.sum(J**2,axis=1))\n mesh.eList[4].vals = posZvals\n mesh.eList[4].normals = posZnormals\n mesh.eList[4].J = posZJ\n \n # z=-1 side\n def negZvals(xi1,xi2):\n xi1=np.asarray(xi1,np.float).reshape(-1,)\n xi2=np.asarray(xi2,np.float).reshape(-1,)\n xb=np.array(xi1);yb=np.array(xi2);zb=-np.ones(xi1.shape);\n xx=xb**2;yy=yb**2;zz=zb**2;\n x = xb*np.sqrt(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)\n y = yb*np.sqrt(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)\n z = zb*np.sqrt(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)\n return np.vstack([x,y,z])\n def negZnormals(xi1,xi2):\n xi1=np.asarray(xi1,np.float).reshape(-1,)\n xi2=np.asarray(xi2,np.float).reshape(-1,)\n xb=np.array(xi1);yb=np.array(xi2);zb=-np.ones(xi1.shape);\n xx=xb**2;yy=yb**2;zz=zb**2;\n dxdxi1 = 1.0*np.sqrt(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)\n dxdxi2 = 1.0*xb*0.5*(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)**-0.5 * (-yb+2.0*yb*zz/3.0)\n dydxi1 = 1.0*yb*0.5*(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)**-0.5 * (-xb+2.0*xb*zz/3.0)\n dydxi2 = 1.0*np.sqrt(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)\n dzdxi1 = 1.0*zb*0.5*(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)**-0.5 * (-xb+2.0*xb*yy/3.0)\n dzdxi2 = 1.0*zb*0.5*(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)**-0.5 * (-yb+2.0*xx*yb/3.0)\n J = np.array([[dxdxi1,dxdxi2],[dydxi1,dydxi2],[dzdxi1,dzdxi2]]).T\n J = np.cross(J[:,0,:],J[:,1,:])\n magnitude = np.sqrt(np.sum(J**2,axis=1))\n return J.T/magnitude\n def negZJ(xi1,xi2):\n xi1=np.asarray(xi1,np.float).reshape(-1,)\n xi2=np.asarray(xi2,np.float).reshape(-1,)\n xb=np.array(xi1);yb=np.array(xi2);zb=-np.ones(xi1.shape);\n xx=xb**2;yy=yb**2;zz=zb**2;\n dxdxi1 = 1.0*np.sqrt(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)\n dxdxi2 = 1.0*xb*0.5*(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)**-0.5 * (-yb+2.0*yb*zz/3.0)\n dydxi1 = 1.0*yb*0.5*(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)**-0.5 * (-xb+2.0*xb*zz/3.0)\n dydxi2 = 1.0*np.sqrt(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)\n dzdxi1 = 1.0*zb*0.5*(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)**-0.5 * (-xb+2.0*xb*yy/3.0)\n dzdxi2 = 1.0*zb*0.5*(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)**-0.5 * (-yb+2.0*xx*yb/3.0)\n J = np.array([[dxdxi1,dxdxi2],[dydxi1,dydxi2],[dzdxi1,dzdxi2]]).T\n J = np.cross(J[:,0,:],J[:,1,:])\n return np.sqrt(np.sum(J**2,axis=1))\n mesh.eList[5].vals = negZvals\n mesh.eList[5].normals = negZnormals\n mesh.eList[5].J = negZJ\n \n for e in mesh.eList:\n e.ExactElement = True\n \n return mesh","def get_center_of_mass_allies(self,obs):","def center(self):\n return self.map_.geom.center_skydir","def unit_normals(self):\n return np.stack(self.centers_cartesian(), axis=-1)","def calc_centroid(self):\n num = 0\n centroid = numpy.zeros(3, float)\n for atm in self:\n if atm.position is not None:\n centroid += atm.position\n num += 1\n return centroid / num","def centre_of_points(list_of_points):\n\n cp = np.average(list_of_points, axis=0)\n return cp","def fit_hypersphere(data, method=\"Hyper\"):\n num_points = len(data)\n# print >>stderr, \"DEBUG: num_points=\", num_points\n \n if num_points==0:\n return (0,None)\n if num_points==1:\n return (0,data[0])\n dimen = len(data[0]) # dimensionality of hypersphere\n# print >>stderr, \"DEBUG: dimen=\", dimen\n \n if num_points<dimen+1:\n raise ValueError(\\\n \"Error: fit_hypersphere needs at least {} points to fit {}-dimensional sphere, but only given {}\".format(dimen+1,dimen,num_points))\n \n # central dimen columns of matrix (data - centroid)\n central = np.matrix(data, dtype=float) # copy the data\n centroid = np.mean(central, axis=0)\n for row in central:\n row -= centroid\n# print >>stderr, \"DEBUG: central=\", repr(central)\n\n # squared magnitude for each centered point, as a column vector\n square_mag= [sum(a*a for a in row.flat) for row in central] \n square_mag = np.matrix(square_mag).transpose()\n# print >>stderr, \"DEBUG: square_mag=\", square_mag\n \n if method==\"Taubin\":\n # matrix of normalized squared magnitudes, data\n mean_square = square_mag.mean()\n data_Z = np.bmat( [[(square_mag-mean_square)/(2*sqrt(mean_square)), central]])\n # print >> stderr, \"DEBUG: data_Z=\",data_Z\n u,s,v = linalg.svd(data_Z, full_matrices=False)\n param_vect= v[-1,:]\n params = [ x for x in np.asarray(param_vect)[0]] # convert from (dimen+1) x 1 matrix to list\n params[0] /= 2*sqrt(mean_square)\n params.append(-mean_square*params[0])\n params=np.array(params)\n \n else:\n # matrix of squared magnitudes, data, 1s\n data_Z = np.bmat( [[square_mag, central, np.ones((num_points,1))]])\n # print >> stderr, \"DEBUG: data_Z=\",data_Z\n\n # SVD of data_Z\n # Note: numpy's linalg.svd returns data_Z = u * s * v\n # not u*s*v.H as the Release 1.4.1 documentation claims.\n # Newer documentation is correct.\n u,s,v = linalg.svd(data_Z, full_matrices=False)\n # print >>stderr, \"DEBUG: u=\",repr(u)\n # print >>stderr, \"DEBUG: s=\",repr(s)\n # print >>stderr, \"DEBUG: v=\",repr(v)\n # print >>stderr, \"DEBUG: v.I=\",repr(v.I)\n\n if s[-1]/s[0] < 1e-12:\n # singular case\n # param_vect as (dimen+2) x 1 matrix\n param_vect = v[-1,:]\n # Note: I get last ROW of v, while Chernov claims last COLUMN,\n # because of difference in definition of SVD for MATLAB and numpy\n\n # print >> stderr, \"DEBUG: singular, param_vect=\", repr(param_vect)\n # print >> stderr, \"DEBUG: data_Z*V=\", repr(data_Z*v)\n # print >> stderr, \"DEBUG: data_Z*VI=\", repr(data_Z*v.I)\n # print >> stderr, \"DEBUG: data_Z*A=\", repr(data_Z*v[:,-1])\n else: \n Y = v.H*np.diag(s)*v\n Y_inv = v.H*np.diag([1./x for x in s])*v\n # print >>stderr, \"DEBUG: Y=\",repr(Y)\n # print >>stderr, \"DEBUG: Y.I=\",repr(Y.I), \"\\nY_inv=\",repr(Y_inv)\n #Ninv is the inverse of the constraint matrix, after centroid has been removed\n Ninv = np.asmatrix(np.identity(dimen+2, dtype=float))\n if method==\"Hyper\":\n Ninv[0,0] = 0\n Ninv[0,-1]=0.5\n Ninv[-1,0]=0.5\n Ninv[-1,-1] = -2*square_mag.mean()\n elif method==\"Pratt\":\n Ninv[0,0] = 0\n Ninv[0,-1]=-0.5\n Ninv[-1,0]=-0.5\n Ninv[-1,-1]=0\n else: \n raise ValueError(\"Error: unknown method: {} should be 'Hyper', 'Pratt', or 'Taubin'\")\n # print >> stderr, \"DEBUG: Ninv=\", repr(Ninv)\n\n # get the eigenvector for the smallest positive eigenvalue\n matrix_for_eigen = Y*Ninv*Y\n # print >> stderr, \"DEBUG: {} matrix_for_eigen=\\n{}\".format(method, repr(matrix_for_eigen))\n eigen_vals,eigen_vects = linalg.eigh(matrix_for_eigen)\n # print >> stderr, \"DEBUG: eigen_vals=\", repr(eigen_vals)\n # print >> stderr, \"DEBUG: eigen_vects=\", repr(eigen_vects)\n\n positives = [x for x in eigen_vals if x>0]\n if len(positives)+1 != len(eigen_vals):\n # raise ValueError(\"Error: for method {} exactly one eigenvalue should be negative: {}\".format(method,eigen_vals))\n print>>stderr, \"Warning: for method {} exactly one eigenvalue should be negative: {}\".format(method,eigen_vals)\n smallest_positive = min(positives)\n # print >> stderr, \"DEBUG: smallest_positive=\", smallest_positive\n # chosen eigenvector as 1 x (dimen+2) matrix\n A_colvect =eigen_vects[:,list(eigen_vals).index(smallest_positive)]\n # print >> stderr, \"DEBUG: A_colvect=\", repr(A_colvect)\n # now have to multiply by Y inverse\n param_vect = (Y_inv*A_colvect).transpose()\n # print >> stderr, \"DEBUG: nonsingular, param_vect=\", repr(param_vect) \n params = np.asarray(param_vect)[0] # convert from (dimen+2) x 1 matrix to array of (dimen+2)\n\n \n# print >> stderr, \"DEBUG: params=\", repr(params)\n radius = 0.5* sqrt( sum(a*a for a in params[1:-1])- 4*params[0]*params[-1])/abs(params[0])\n center = -0.5*params[1:-1]/params[0]\n#y print >> stderr, \"DEBUG: center=\", repr(center), \"centroid=\", repr(centroid)\n center += np.asarray(centroid)[0]\n return (radius,center)","def norm(point):\n return np.sqrt(norm2(point))","def center(emg_data: np.ndarray, center_value: float = None) -> np.ndarray:\n center_value = center_value if center_value else emg_data.mean(axis=1)\n emg_centered = np.copy(emg_data)\n for i in range(emg_data.shape[0]):\n emg_centered[i, :] = emg_data[i, :] - center_value[i]\n return emg_centered","def filled_sphere(shape, radius, center=None):\n\tr2 = radius*radius\n\tif center is None:\n\t\t### set to center of array\n\t\tcenter = (shape[0]-1)/2.0,(shape[1]-1)/2.0,(shape[2]-1)/2.0\n\tdef func(i0, i1, i2):\n\t\tii0 = i0 - center[0]\n\t\tii1 = i1 - center[1]\n\t\tii2 = i2 - center[2]\n\t\trr2 = ii0**2 + ii1**2 + ii2**2\n\t\tc = numpy.where(rr2<r2, 0.0, 1.0)\n\t\treturn c\n\treturn numpy.fromfunction(func, shape)","def vincenty_direct_solution(begin_lat, begin_lon, begin_azimuth, distance, a, b, f):\n # Convert latitude, longitude, azimuth of the begining point to radians\n lat1 = math.radians(begin_lat)\n lon1 = math.radians(begin_lon)\n alfa1 = math.radians(begin_azimuth)\n\n sinAlfa1 = math.sin(alfa1)\n cosAlfa1 = math.cos(alfa1)\n\n # U1 - reduced latitude\n tanU1 = (1 - f) * math.tan(lat1)\n cosU1 = 1 / math.sqrt(1 + tanU1 * tanU1)\n sinU1 = tanU1 * cosU1\n\n # sigma1 - angular distance on the sphere from the equator to begining point\n sigma1 = math.atan2(tanU1, math.cos(alfa1))\n\n # sinAlfa - azimuth of the geodesic at the equator\n sinAlfa = cosU1 * sinAlfa1\n cosSqAlfa = 1 - sinAlfa * sinAlfa\n uSq = cosSqAlfa * (a * a - b * b) / (b * b)\n A = 1 + uSq / 16384 * (4096 + uSq * (-768 + uSq * (320 - 175 * uSq)))\n B = uSq / 1024 * (256 + uSq * (-128 + uSq * (74 - 47 * uSq)))\n\n sigma = distance / (b * A)\n sigmap = 1\n\n while (math.fabs(sigma - sigmap) > 1e-12):\n cos2sigmaM = math.cos(2 * sigma1 + sigma)\n sinSigma = math.sin(sigma)\n cosSigma = math.cos(sigma)\n dSigma = B * sinSigma * (cos2sigmaM + B / 4 * (\n cosSigma * (-1 + 2 * cos2sigmaM * cos2sigmaM) - B / 6 * cos2sigmaM * (\n -3 + 4 * sinSigma * sinSigma) * (-3 + 4 * cos2sigmaM * cos2sigmaM)))\n sigmap = sigma\n sigma = distance / (b * A) + dSigma\n\n var_aux = sinU1 * sinSigma - cosU1 * cosSigma * cosAlfa1 # Auxiliary variable\n\n # Latitude of the end point in radians\n lat2 = math.atan2(sinU1 * cosSigma + cosU1 * sinSigma * cosAlfa1,\n (1 - f) * math.sqrt(sinAlfa * sinAlfa + var_aux * var_aux))\n\n lamb = math.atan2(sinSigma * sinAlfa1, cosU1 * cosSigma - sinU1 * sinSigma * cosAlfa1)\n C = f / 16 * cosSqAlfa * (4 + f * (4 - 3 * cosSqAlfa))\n L = lamb - (1 - C) * f * sinAlfa * (\n sigma + C * sinSigma * (cos2sigmaM + C * cosSigma * (-1 + 2 * cos2sigmaM * cos2sigmaM)))\n # Longitude of the second point in radians\n lon2 = (lon1 + L + 3 * math.pi) % (2 * math.pi) - math.pi\n\n # Convert to decimal degrees\n lat2_dd = math.degrees(lat2)\n lon2_dd = math.degrees(lon2)\n\n return lat2_dd, lon2_dd","def uniform_sphere(batch_size, dim, epsilon=1, ord=2):\r\n\r\n random = numpy.random.randn(batch_size, dim)\r\n random /= numpy.repeat(numpy.linalg.norm(random, ord=ord, axis=1).reshape(-1, 1), axis=1, repeats=dim)\r\n random *= epsilon\r\n\r\n return random"],"string":"[\n \"def center_to_sphere(places, size, resolution=0.50, min_value=np.array([0., -50., -4.5]), scale=4, x=(0, 90), y=(-50, 50), z=(-4.5, 5.5)):\\n x_logical = np.logical_and((places[:, 0] < x[1]), (places[:, 0] >= x[0]))\\n y_logical = np.logical_and((places[:, 1] < y[1]), (places[:, 1] >= y[0]))\\n z_logical = np.logical_and((places[:, 2] < z[1]), (places[:, 2] >= z[0]))\\n xyz_logical = np.logical_and(x_logical, np.logical_and(y_logical, z_logical))\\n center = places.copy()\\n center[:, 2] = center[:, 2] + size[:, 0] / 2.\\n sphere_center = ((center[xyz_logical] - min_value) / (resolution * scale)).astype(np.int32)\\n return sphere_center\",\n \"def sphere_to_center(p_sphere, resolution=0.5, scale=4, min_value=np.array([0., -50., -4.5])):\\n center = p_sphere * (resolution*scale) + min_value\\n return center\",\n \"def Center(self, *args):\\n return _Bnd.Bnd_Sphere_Center(self, *args)\",\n \"def on_sphere():\\n vec = np.random.standard_normal(3)\\n return vec / np.linalg.norm(vec)\",\n \"def normalize(x: float, y: float, z: float) -> Point3D:\\n mag = math.sqrt(x*x + y*y + z*z)\\n return x/mag, y/mag, z/mag\",\n \"def fix_sphere_m (center_x, center_y, center_z, radius, centers, radii, len_points):\\n \\n g_x = []\\n g_y = []\\n g_z = []\\n points = [hydrogen_coord_gen(center_x, center_y, center_z, radius) for i in range(0, len_points)] \\n x = [points[i][0] for i in range(0, len(points))] \\n y = [points[i][1] for i in range(0, len(points))]\\n z = [points[i][2] for i in range(0, len(points))]\\n\\n for i in range(0, len(points)):\\n check = 0\\n j = 0\\n while (j <= (len(centers) - 1) and (check == 0)): \\n if (calculate_3D_distance_2_centers(x[i], y[i], z[i], centers[j][0], centers[j][1], centers[j][2]) < radii[j]):\\n check += 1\\n j += 1\\n if (check == 0):\\n g_x.append(x[i])\\n g_y.append(y[i])\\n g_z.append(z[i])\\n\\n return g_x, g_y, g_z\",\n \"def sphere_centers(r_x, r_y, r_z):\\n a_ccs_p_trans_m = hom_translation_matrix(\\n t_x=0.265, t_y=0, t_z=0.014)\\n a_ccs_p_rot_m = hom_rotation(x_axis_rotation_matrix(r_x) @\\n y_axis_rotation_matrix(r_y) @\\n z_axis_rotation_matrix(r_z))\\n a_p_sph_1_2 = hom_translation_matrix(\\n t_x=0.015, t_y=0.029, t_z=-0.0965)\\n a_p_sph_2_2 = hom_translation_matrix(\\n t_x=0.015, t_y=-0.029, t_z=-0.0965)\\n\\n a_ccs_ = a_ccs_p_trans_m @ a_ccs_p_rot_m\\n a_c1 = a_ccs_ @ a_p_sph_1_2\\n a_c2 = a_ccs_ @ a_p_sph_2_2\\n\\n return get_translation(a_c1), get_translation(a_c2)\",\n \"def center(self):\\n try: \\n return self._center\\n except AttributeError:\\n self._center = vector(ZZ, [0]*self.ambient_dim())\\n for v in self.vertex_generator(): self._center += v.vector()\\n self._center /= self.n_vertices()\\n return self._center\",\n \"def center_normalize(x):\\n return (x - K.mean(x)) / K.std(x)\",\n \"def center_normalize(x):\\n return (x - K.mean(x)) / K.std(x)\",\n \"def centroid(self): # -> BaseGeometry:\\n ...\",\n \"def center(self):\\n points = set()\\n for face in self._points:\\n points.update(face)\\n x_points = [point[0] for point in points]\\n y_points = [point[1] for point in points]\\n z_points = [point[2] for point in points]\\n return \\\\\\n (np.average(x_points), np.average(y_points), np.average(z_points))\",\n \"def center(self):\\n if not hasattr(self, '_center'):\\n self._center = np.unique(self.points, axis=0).mean(axis=0)\\n return self._center\",\n \"def normal(self, point):\\n point = self._center - np.array(point)\\n # if abs(point.dot(point) - self._radius**2) > 1e-15:\\n # raise RayTraceError(\\n # 'Cannot compute normal. Point is too far from surface ({}).'.format(\\n # (abs(point.dot(point) - self._radius**2))))\\n return normalize(point / self._radius)\",\n \"def nearest_sphere_surface(x_input, y_input, z_input):\\n\\n vm = math.sqrt(sum([x_input**2, y_input**2, z_input**2]))\\n return (x_input/vm, y_input/vm, z_input/vm)\",\n \"def _find_centroid_sphere(ch_pos, group_names):\\n cartesian_positions = np.array([ch_pos[ch_name] for ch_name in group_names])\\n sphere_positions = _cart_to_sph(cartesian_positions)\\n cartesian_pos_centroid = np.average(cartesian_positions, axis=0)\\n sphere_pos_centroid = _cart_to_sph(cartesian_pos_centroid)\\n # average the radius and overwrite it\\n avg_radius = np.average(sphere_positions, axis=0)[0]\\n sphere_pos_centroid[0, 0] = avg_radius\\n # convert back to cartesian\\n pos_centroid = _sph_to_cart(sphere_pos_centroid)[0, :]\\n return pos_centroid\",\n \"def sphere_norm_by_layer(M):\\r\\n M_inner = M\\r\\n shape = np.shape(M_inner)\\r\\n if len(shape) == 3:\\r\\n for i in range(shape[0]):\\r\\n norm = np.sqrt(np.sum(M_inner[i, :, :] ** 2))\\r\\n M_inner[i, :, :] = M_inner[i, :, :] / norm\\r\\n return M_inner\\r\\n if len(shape) == 2:\\r\\n norm = np.sqrt(np.sum(M_inner ** 2))\\r\\n M_inner = M_inner / norm\\r\\n return M_inner\",\n \"def center(x):\\n return x - x.mean()\",\n \"def distance_from_sphere(self, points, params, sqrt=False):\\n center, radius = params\\n center = center.reshape((1, 3))\\n distance = (torch.norm(points - center, p=2, dim=1) - radius) ** 2\\n if sqrt:\\n distance = guard_sqrt(distance)\\n\\n if self.reduce:\\n distance = torch.mean(distance)\\n return distance\",\n \"def computeCenters3d(self, data):\\n\\n\\n for i in range(self.nPoints):\\n print(\\\"Label of point \\\", i, \\\" is \\\", self.labels[i])\\n for j in range(3):\\n self.centers[self.labels[i]][j] += data[i][j]\\n\\n for c in range(self.n):\\n for j in range(3):\\n self.centers[c][j] /= self.tots[c]\",\n \"def GetParametricCenter(self, p_float=..., p_float=..., p_float=...):\\n ...\",\n \"def center(self) -> Point:\\n return Point(*np.sum(self.normalized_array[:, :-1], axis=0))\",\n \"def objectCenter(*args, gl: bool=True, local: bool=True, x: bool=True, y: bool=True, z:\\n bool=True, **kwargs)->List[float]:\\n pass\",\n \"def normalise(self):\\n for at in self.atoms:\\n if at.x < 0. :\\n at.x = self.coordx + at.x\\n if at.y < 0. :\\n at.y = self.coordy + at.y\\n if at.z < 0. :\\n at.z = self.coordz + at.z\",\n \"def insphere(network,\\n geometry,\\n **kwargs):\\n import warnings\\n try:\\n import pulp as pu\\n Np = geometry.num_pores()\\n value = _sp.zeros(Np)\\n pore_map = geometry.map_pores(geometry.pores(),geometry._net)\\n for geom_pore,net_pore in pore_map:\\n net_throats = geometry._net.find_neighbor_throats(net_pore)\\n geom_throats = geometry._net.map_throats(net_throats,geometry)[:,1]\\n verts = geometry['throat.offset_vertices'][geom_throats]\\n if len(verts) > 1:\\n try:\\n pts = np.vstack((i for i in verts if len(i)>0))\\n except ValueError:\\n pts = []\\n if len(pts) > 4:\\n \\\"Work out central point to use as initial guess\\\"\\n c0 = np.mean(pts,axis=0)\\n \\\"Compute convex hull to find points lying on the hull in order\\\"\\n hull = ConvexHull(pts, qhull_options='QJ Pp')\\n \\\"For each simplex making up the hull collect the end points\\\"\\n A = pts[hull.simplices[:,0]]\\n B = pts[hull.simplices[:,1]]\\n C = pts[hull.simplices[:,2]]\\n #I = np.array([[0,1],[-1,0]])\\n \\\"Normal of the simplices\\\"\\n #N = np.dot((B-A),I)\\n N = np.cross((B-A),(C-A),axis=1)\\n #L = np.sqrt(np.sum(np.square(N),axis=1))\\n \\\"Normalize the normal vector\\\"\\n L = np.linalg.norm(N,axis=1)\\n F = np.vstack((L,L,L)).T\\n N /= F\\n \\\"If normals point out of hull change sign to point in\\\"\\n pointing_out = (np.sum((A-c0)*N,axis=1)>0)\\n N[pointing_out]*= -1\\n \\\"Define Linear Program Variables\\\"\\n \\\"The centre of the incircle adjustment\\\"\\n cx = pu.LpVariable(\\\"cx\\\",None,None,pu.LpContinuous)\\n cy = pu.LpVariable(\\\"cy\\\",None,None,pu.LpContinuous)\\n cz = pu.LpVariable(\\\"cz\\\",None,None,pu.LpContinuous)\\n \\\"Radius of the incircle\\\"\\n R = pu.LpVariable(\\\"R\\\",0,None,pu.LpContinuous)\\n \\\"Slack variables for shortest distance between centre and simplices\\\" \\n S = pu.LpVariable.dict(\\\"SlackVariable\\\",range(len(A)),0,None,pu.LpContinuous)\\n \\\"Set up LP problem\\\"\\n prob = pu.LpProblem(\\\"FindInRadius\\\",pu.LpMaximize)\\n \\\"Objective Function\\\"\\n prob += R\\n for i in range(len(A)):\\n \\\" Ni.(C-Ai)-Si = 0\\\"\\n prob += N[i][0]*(c0[0]+cx) + N[i][1]*(c0[1]+cy) + N[i][2]*(c0[2]+cz)- N[i][0]*A[i][0] - N[i][1]*A[i][1] - N[i][2]*A[i][2]- S[i] == 0\\n \\\"Si >= R\\\"\\n prob += S[i] >= R\\n \\\"Solve the LP\\\"\\n with warnings.catch_warnings():\\n warnings.simplefilter(\\\"ignore\\\")\\n prob.solve()\\n \\\"As the radius is the objective function we can get it from the objective or as R.value()\\\"\\n rad = prob.objective.value()\\n #cen = c0 + np.array([cx.value(),cy.value(),cz.value()])\\n value[geom_pore]=rad*2\\n \\n \\n return value\\n except ImportError:\\n print(\\\"Cannot use insphere method without installing pulp package\\\")\",\n \"def Icosphere(radius=1.0, center=(0.0, 0.0, 0.0), nsub=3):\\n mesh = Icosahedron()\\n mesh.clear_data()\\n mesh = mesh.subdivide(nsub=nsub)\\n\\n # scale to desired radius and translate origin\\n dist = np.linalg.norm(mesh.points, axis=1, keepdims=True) # distance from origin\\n mesh.points = mesh.points * (radius / dist) + center\\n return mesh\",\n \"def generate_sphere_full():\\n \\n num_voxels = 31\\n c = (15.0, 15.0, 15.0)\\n\\n data_x = []\\n data_y = []\\n data_z = []\\n data_intensity = []\\n\\n volume = numpy.zeros((num_voxels, num_voxels, num_voxels))\\n\\n for x in range(num_voxels):\\n for y in range(num_voxels):\\n for z in range(num_voxels):\\n\\n if numpy.sqrt((x-c[0])**2 + (y-c[1])**2 + (z-c[2])**2) - 7.5 < 1.5:\\n data_x.append(x)\\n data_y.append(y)\\n data_z.append(z)\\n data_intensity.append(200.0)\\n\\n volume[x,y,z] = 200.0\\n\\n\\n return data_x, data_y, data_z, data_intensity, volume\",\n \"def normal_filter(pointcloud, target_orient, threshold):\\n\\n xyz_points = np.asarray(pointcloud.points)\\n xyz_normals = np.asarray(pointcloud.normals)\\n projection = xyz_normals.dot(target_orient)\\n projection_mask = projection > threshold #\\n target_points = xyz_points[projection_mask]\\n other_points = xyz_points[~projection_mask]\\n target_normals = xyz_normals[projection_mask]\\n other_normals = xyz_normals[~projection_mask]\\n\\n target_points_ = o3d.geometry.PointCloud()\\n other_points_ = o3d.geometry.PointCloud()\\n target_points_.points = o3d.utility.Vector3dVector(target_points)\\n target_points_.normals = o3d.utility.Vector3dVector(target_normals)\\n other_points_.points = o3d.utility.Vector3dVector(other_points)\\n other_points_.normals = o3d.utility.Vector3dVector(other_normals)\\n\\n return target_points_, other_points_\",\n \"def center_normalize(M_t, dim=0):\\n N_t = M_t - M_t.mean(dim=dim, keepdim=True)\\n return N_t / torch.sqrt(torch.pow(N_t, 2).sum(dim=dim, keepdim=True))\",\n \"def GetModelCenterOfMass(self, model):\\n polyData = model.GetPolyData()\\n comFilter = vtk.vtkCenterOfMass()\\n comFilter.SetInputData(polyData)\\n comFilter.SetUseScalarsAsWeights(False)\\n comFilter.Update()\\n comPoint = comFilter.GetCenter()\\n return comPoint\",\n \"def plane_sphere(p, s):\\n\\n p.normalize()\\n\\n d = dot(s.o-p.o, p.n)\\n\\n if d > s.r:\\n return False\\n else:\\n return (s.o - d*p.n, sqrt(s.r*s.r - d*d))\",\n \"def fix_sphere_h (center_x, center_y, center_z, radius, centers, radii, len_points, list_of_a):\\n g_x = []\\n g_y = []\\n g_z = []\\n points = [hydrogen_coord_gen(center_x, center_y, center_z, radius) for i in range(0, len_points)] \\n x = [points[i][0] for i in range(0, len(points))] \\n y = [points[i][1] for i in range(0, len(points))]\\n z = [points[i][2] for i in range(0, len(points))]\\n for i in range(0, len(points)):\\n check = 0\\n check_b = 0\\n j = 0\\n while (j <= (len(centers) - 1) and (check == 0)): \\n if (calculate_3D_distance_2_centers(x[i], y[i], z[i], centers[j][0], centers[j][1], centers[j][2]) < radii[j]):\\n check += 1\\n j += 1\\n h = 0\\n while ((check_b == 0) and (h <= len(list_of_a) -1)):\\n if (calculate_3D_distance_2_centers(x[i], y[i], z[i], list_of_a[h].x, list_of_a[h].y, list_of_a[h].z) <= 1.50): \\n check_b += 1\\n h += 1\\n if ((check == 0) and (check_b == 0)):\\n g_x.append(x[i])\\n g_y.append(y[i])\\n g_z.append(z[i])\\n return g_x, g_y, g_z\",\n \"def _update_surface_normals(self):\\n\\n # This is the case if there are too few points to\\n # compute normals so there can be values to remove\\n\\n #can be important for parallel\\n self.swarm.shadow_particles_fetch()\\n\\n if self.empty:\\n self.director.data[...] = 0.0\\n else:\\n\\n particle_coords = self.swarm.particleCoordinates.data\\n\\n Nx = np.empty(self.swarm.particleLocalCount)\\n Ny = np.empty(self.swarm.particleLocalCount)\\n Nz = np.empty(self.swarm.particleLocalCount)\\n\\n for i, xyz in enumerate(particle_coords):\\n r, neighbours = self.kdtree.query(particle_coords[i], k=4)\\n\\n # this point is neighbour[0] and neighbour points are neighbours[(1,2,3)]\\n XYZ1 = self.kdtree.data[neighbours[1]]\\n XYZ2 = self.kdtree.data[neighbours[2]]\\n XYZ3 = self.kdtree.data[neighbours[3]]\\n\\n dXYZ1 = XYZ2 - XYZ1\\n dXYZ2 = XYZ3 - XYZ1\\n\\n # Cross product of those 2 vectors can be use as the local normal (perhaps)\\n\\n Nx[i], Ny[i], Nz[i] = np.cross(dXYZ1, dXYZ2)\\n #if i == 0:\\n # print(Nx, Ny, Nz)\\n # print(xyz[0], xyz[1],xyz[2])\\n # print((self.insidePt[0] - xyz[0]) * Nx[i] )\\n\\n if (self.insidePt):\\n sign = np.sign( (self.insidePt[0] - xyz[0]) * Nx[i] +\\n (self.insidePt[1] - xyz[1]) * Ny[i] +\\n (self.insidePt[2] - xyz[2]) * Nz[i] )\\n Nx[i] *= sign\\n Ny[i] *= sign\\n Nz[i] *= sign\\n\\n\\n for i in range(0, self.swarm.particleLocalCount):\\n scale = 1.0 / np.sqrt(Nx[i]**2 + Ny[i]**2 + Nz[i]**2)\\n Nx[i] *= scale\\n Ny[i] *= scale\\n Nz[i] *= scale\\n\\n\\n self.director.data[:,0] = Nx[:]\\n self.director.data[:,1] = Ny[:]\\n self.director.data[:,2] = Nz[:]\\n\\n print(\\\"Surf Norms\\\")\\n\\n return\",\n \"def centroid(self, method: str = 'median') -> Vector:\\n\\n\\t\\toriginal_origins = deepcopy(self.origin)\\n\\t\\tself.origin_to_centroid(method=method)\\n\\t\\tcentroid = deepcopy(self.origin)\\n\\t\\tself.origin = original_origins\\n\\n\\t\\tif len(self._meshes) > 1:\\n\\t\\t\\treturn sum(centroid) / len(self._meshes)\\n\\n\\t\\treturn centroid\",\n \"def sphere(self, x):\\r\\n # return np.random.rand(1)[0]**0 * sum(x**2) + 1 * np.random.rand(1)[0]\\r\\n return sum((x+0)**2)\",\n \"def test_one_center(self):\\n sv=system_vars_c().init_xyzlike([ [8, [0.0, 0.0, 0.0]]])\\n atom2rcut=np.array([5.0])\\n g = dft.gen_grid.Grids(sv)\\n g.level = 1 # precision as implemented in pyscf\\n g.radi_method=leggauss_ab\\n g.build(atom2rcut=atom2rcut)\\n\\n #print( max( np.linalg.norm(g.coords, axis=1) ) )\\n #print( g.weights.sum(), 4.0 *np.pi*5.0**3 / 3.0 )\\n self.assertAlmostEqual(max( np.linalg.norm(g.coords, axis=1) ), 4.9955942742763986)\\n self.assertAlmostEqual(g.weights.sum(), 4.0 *np.pi*5.0**3 / 3.0)\\n self.assertEqual(len(g.weights), 6248)\",\n \"def project_to_sphere(points):\\n # for uv, the sphere: r=1, azimuth(phi): 2*pi*u, elevation(theta): 2*pi*v\\n # theta is elevation, phi is azimuth\\n r, theta, phi = cs.cart2sp(x=points[:, 0], y=points[:, 1], z=points[:, 2])\\n # logger.info(f\\\"number of zero points in r: {np.sum(r==0)}\\\")\\n assert np.sum(r == 0) == 0, \\\"points contains zeros\\\"\\n points_sphere = points / r.reshape(-1, 1)\\n return points_sphere, r, theta, phi\\n\\n # r, theta, phi = cs.cart2sp(x=1, y=1, z=1)\\n\\n # # spherical to cartesian\\n # x, y, z = cs.sp2cart(r=1, theta=np.pi/4, phi=np.pi/4)\\n\\n # # cartesian to cylindrical\\n # r, phi, z = cs.cart2cyl(x=1, y=1, z=1)\",\n \"def getCentroid(self):\\n centroid = 0.0\\n sumMagnitude = 0.0\\n\\n for i in range(0,self.nUniquePoints):\\n freq,magnitude = self.fDomain[i]\\n\\n centroid += freq*magnitude\\n sumMagnitude += magnitude\\n \\n centroid /= sumMagnitude\\n return centroid\",\n \"def centre(self):\\n n = len(self.point)\\n return Point(\\n sum(map(lambda p: p.x, self.point)) / n,\\n sum(map(lambda p: p.y, self.point)) / n\\n )\",\n \"def scale_and_translate_points(points):\\n x = points[0]\\n y = points[1]\\n center = points.mean(axis=1) # mean of each row\\n cx = x - center[0] # center the points\\n cy = y - center[1]\\n dist = np.sqrt(np.power(cx, 2) + np.power(cy, 2))\\n scale = np.sqrt(2) / dist.mean()\\n norm3d = np.array([\\n [scale, 0, -scale * center[0]],\\n [0, scale, -scale * center[1]],\\n [0, 0, 1]\\n ])\\n\\n return np.dot(norm3d, points), norm3d\",\n \"def center(self, center_mass=False):\\n if center_mass:\\n com = self.center_of_mass\\n self.xyz -= com\\n else:\\n self.xyz -= self.xyz.mean(0)\",\n \"def onsphere(size=None):\\n xy = oncircle(size)\\n z = 2.*random(xy.shape[:-1] + (1,)) - 1.\\n xy *= sqrt(1. - z*z)\\n return concatenate((xy, z), axis=-1)\",\n \"def centerPoint(featureCollection):\\n features = featureCollection[\\\"features\\\"]\\n center = [0, 0]\\n for feature in features:\\n geometry = feature[\\\"geometry\\\"]\\n if geometry[\\\"type\\\"] == \\\"Point\\\":\\n point = feature[\\\"geometry\\\"][\\\"coordinates\\\"]\\n center[0] += point[0]\\n center[1] += point[1]\\n else:\\n raise ValueError(\\\"expected a point but got a {}\\\".format(geometry[\\\"type\\\"]))\\n center[0] /= len(features) \\n center[1] /= len(features) \\n\\n return geojson.Point(coordinates=center)\",\n \"def getCenterOfMass(self, filtered = True):\\n n_time = len(self.pos)\\n x_mean = [0.0,]*n_time\\n y_mean = [0.0,]*n_time\\n z_mean = [0.0,]*n_time \\n for frame in range(n_time):\\n # get all the positions of the filtered points\\n x,y,z = self.getAllPositions(frame, filtered)\\n x_mean[frame] = np.asarray(x).mean() if len(x) > 0 else None\\n y_mean[frame] = np.asarray(y).mean() if len(y) > 0 else None\\n z_mean[frame] = np.asarray(z).mean() if len(z) > 0 else None\\n\\n return x_mean, y_mean, z_mean\",\n \"def plot_points_on_sphere(points_x, points_y, points_z, center_x, center_y, center_z, radius):\\n from mayavi import mlab\\n mlab.figure(1, bgcolor=(1,1,1), fgcolor=(0,0,0), size=(800,800))\\n return mlab.points3d(points_x, points_y, points_z, scale_factor=0.05, color=(0.25, 0.75, 0.77))\",\n \"def center_of_mass(points):\\n # break into many triangles\\n # each point is part of two triangles\\n cor = [sum(points) / len(points)]\\n mass_points = []\\n area = 0\\n for i in range(len(points) - 1):\\n triangle = cor + points[i:i + 2]\\n # print(triangle)\\n mass_points.append(build_triangle_point_mass(triangle))\\n area += shoelace_area(triangle)\\n # print(triangle, area)\\n mass_points.append(build_triangle_point_mass(cor + [points[-1], points[0]]))\\n area += shoelace_area(cor + [points[-1], points[0]])\\n return Vector2D(*find_com(*zip(*mass_points))), area\",\n \"def centers_displacement(p1, p2):\\n # geometric center\\n c1 = np.mean(np.array(p1), axis=0)\\n c2 = np.mean(np.array(p2), axis=0)\\n return c2 - c1\",\n \"def surfaceIntSphere(r: float) -> float:\\n return 4.0 * np.pi * r * r\",\n \"def compute_center(self, mole_object):\\r\\n if mole_object.plugin_type == \\\"PyMOL\\\":\\r\\n sel = PymolPlugin.PymolPlugin().get_model('all')\\r\\n cnt = len(sel.atom)\\r\\n\\r\\n else:\\r\\n sel = ChimeraPlugin.ChimeraPlugin().select()\\r\\n cnt = len(ChimeraPlugin.ChimeraPlugin().current_atoms())\\r\\n\\r\\n cent_x = 0\\r\\n cent_y = 0\\r\\n cent_z = 0\\r\\n\\r\\n if cnt == 0:\\r\\n return 0, 0, 0\\r\\n\\r\\n if mole_object.plugin_type == \\\"PyMOL\\\":\\r\\n\\r\\n for a in sel.atom:\\r\\n cent_x += a.coord[0]\\r\\n cent_y += a.coord[1]\\r\\n cent_z += a.coord[2]\\r\\n\\r\\n else:\\r\\n\\r\\n for a in ChimeraPlugin.ChimeraPlugin().current_atoms():\\r\\n cent_x += a.coord()[0]\\r\\n cent_y += a.coord()[1]\\r\\n cent_z += a.coord()[2]\\r\\n\\r\\n cent_x /= cnt\\r\\n cent_y /= cnt\\r\\n cent_z /= cnt\\r\\n\\r\\n self.point_x.component('entryfield').setentry(cent_x)\\r\\n self.point_y.component('entryfield').setentry(cent_y)\\r\\n self.point_z.component('entryfield').setentry(cent_z)\\r\\n\\r\\n self.show_crisscross(mole_object)\",\n \"def sphere(\\n network,\\n pore_diameter='pore.diameter'\\n):\\n return 4/3*_pi*(network[pore_diameter]/2)**3\",\n \"def getAfinityCenter(width, height, point, center, radius=7, img_affinity=None):\\n tensor = torch.zeros(2, height, width).float()\\n\\n # Create the canvas for the affinity output\\n imgAffinity = Image.new(\\\"RGB\\\", (width, height), \\\"black\\\")\\n totensor = transforms.Compose([transforms.ToTensor()])\\n\\n draw = ImageDraw.Draw(imgAffinity)\\n r1 = radius\\n p = point\\n draw.ellipse((p[0] - r1, p[1] - r1, p[0] + r1, p[1] + r1), (255, 255, 255))\\n\\n del draw\\n\\n # Compute the array to add the affinity\\n array = (np.array(imgAffinity) / 255)[:, :, 0]\\n\\n angle_vector = np.array(center) - np.array(point)\\n angle_vector = normalize(angle_vector)\\n affinity = np.concatenate([[array * angle_vector[0]], [array * angle_vector[1]]])\\n\\n # print (tensor)\\n if not img_affinity is None:\\n # Find the angle vector\\n # print (angle_vector)\\n if length(angle_vector) > 0:\\n angle = py_ang(angle_vector)\\n else:\\n angle = 0\\n # print(angle)\\n c = np.array(colorsys.hsv_to_rgb(angle / 360, 1, 1)) * 255\\n draw = ImageDraw.Draw(img_affinity)\\n draw.ellipse((p[0] - r1, p[1] - r1, p[0] + r1, p[1] + r1), fill=(int(c[0]), int(c[1]), int(c[2])))\\n del draw\\n re = torch.from_numpy(affinity).float() + tensor\\n return re, img_affinity\",\n \"def centroid(self) -> Point:\\n points = self.normalized_array\\n centroids = [np.average(points[[0, i, i + 1], :-1], axis=0) for i in range(1, points.shape[0] - 1)]\\n weights = [det(self._normalized_projection()[[0, i, i + 1]]) / 2 for i in range(1, points.shape[0] - 1)]\\n return Point(*np.average(centroids, weights=weights, axis=0))\",\n \"def U(self, *args):\\n return _Bnd.Bnd_Sphere_U(self, *args)\",\n \"def CenterOfMass(points):\\n A = AreaOfPolygon(points)\\n N = len(points)\\n cx = 0\\n cy = 0\\n for i in xrange(0, N):\\n x_i = points[i][0]\\n y_i = points[i][1]\\n x_ip1 = points[(i+1) % N][0]\\n y_ip1 = points[(i+1) % N][1]\\n part = (x_i * y_ip1 - x_ip1 * y_i)\\n cx += ((x_i + x_ip1) * part)\\n cy += ((y_i + y_ip1) * part)\\n return (cx/(6*A), cy/(6*A), abs(A))\",\n \"def normalize_particles(self):\\n tot_weight = sum([particle.w for particle in self.particle_cloud]) or 1\\n for particle in self.particle_cloud:\\n particle.w = particle.w / tot_weight;\",\n \"def normalize(coords, radius):\\n co = coords.copy()\\n # calculate current distance\\n dist = (coords[0] ** 2 + coords[1] ** 2 + coords[2] ** 2) ** 0.5\\n # normalize\\n for axis in range(3):\\n co[axis] = coords[axis] / dist * radius\\n return co\",\n \"def surface_norm(self, pt):\\n\\n return (pt - self.origin).normalize()\",\n \"def fit_plane_to_point_cloud(pc: np.ndarray) -> Tuple[Any, Any, Any, Any]:\\n center = pc.sum(axis=0) / pc.shape[0]\\n u, s, vh = np.linalg.svd(pc - center)\\n\\n # Get the unitary normal vector\\n u_norm = vh[2, :]\\n d = -np.dot(u_norm, center)\\n a, b, c = u_norm\\n return a, b, c, d\",\n \"def centerOfMass(data):\\r\\n dd = []\\r\\n for d in data:\\r\\n dd.append(d.coordinate)\\r\\n\\r\\n data = dd\\r\\n data = np.array(data)\\r\\n n = len(data)\\r\\n x = sum(data[:,0])\\r\\n y = sum(data[:,1])\\r\\n z = sum(data[:,2])\\r\\n x/=n\\r\\n y/=n\\r\\n z/=n\\r\\n return x,y,z,n\",\n \"def center(self, obj):\\n mn0 = self.master.xy >= obj.center\\n mn1 = self.master.xy <= obj.center\\n\\n point_list = [self.master.xy[mn0], self.master.xy[mn1], self.master.xy[mn0[0], mn1[1]], self.master.xy[mn1[0], mn0[1]]] # 4 physical points near the center coordinate.\\n dist_list = []\\n idx = 0\\n for point in point_list:\\n dist_list.append([idx, np.linalg.norm(point - obj.center)]) # Calculate Euclidean distances.\\n idx += 1\\n dist_sorted = sorted(dist_list, key=lambda distance : distance[1]) # Sort distances in ascending order.\\n return self.master.mn(point_list[dist_sorted[0][0]]) # Convert the closest point to abstract coordinate and then return.\",\n \"def center_of_mass_polyhedron():\\n raise NotImplementedError\",\n \"def unit_to_sphere(v):\\n return (math.acos(v[2]), math.atan2(v[1], v[0]))\",\n \"def surface_norm(self, pt):\\n\\n return self.normal.normalize()\",\n \"def __call__(self, x):\\n v = vector(RDF,x)\\n if v.is_zero():\\n raise ValueError, \\\"The origin must not be a vertex.\\\"\\n v = v/norm(v) # normalize vertices to unit sphere\\n v = self.house*v # reflect so self.projection_dir is at \\\"north pole\\\"\\n denom = self.height-v[self.dim-1]\\n if denom.is_zero():\\n raise ValueError, 'Point cannot coincide with ' \\\\\\n 'coordinate singularity at ' + repr(x)\\n return vector(RDF, [ v[i]/denom for i in range(self.dim-1) ])\",\n \"def compute_centrifugal(self):\\r\\n # update the coordinates\\r\\n self.get_coords()\\r\\n\\r\\n # compute the centrifugal force\\r\\n self.centrifugal.assign(project(\\r\\n -1*self.rho*cross(self.omega, cross(self.omega, self.r)), self.V))\",\n \"def centers_cartesian(self):\\n polar_centers, azimuthal_centers = self.centers()\\n x_centers, y_centers, z_centers = \\\\\\n starwinds_magnetogram.coordinate_transforms.rectangular_coordinates_from_spherical(\\n np.ones(polar_centers.shape),\\n polar_centers,\\n azimuthal_centers)\\n\\n return x_centers, y_centers, z_centers\",\n \"def getCenter(self):\\n return Point.average(self.points)\",\n \"def worldToCameraCentricXform(self):\\n return self.rotateAlignXform().dot(self.translateToOriginXform())\",\n \"def centroid(sign, FS):\\n\\n time = compute_time(sign, FS)\\n\\n energy, time_energy=signal_energy(sign, time)\\n\\n total_energy = np.dot(np.array(time_energy),np.array(energy))\\n energy_sum = np.sum(energy)\\n\\n if energy_sum == 0 or total_energy == 0:\\n centroid = 0\\n else:\\n centroid = total_energy / energy_sum\\n return centroid\",\n \"def pointcenter(x):\\n return point(x)\",\n \"def norm(self):\\r\\n old_origin = np.array(self.origin)\\r\\n self.origin = [0, 0, 0]\\r\\n old_origin[0] = old_origin[0] / self.x[0]\\r\\n old_origin[1] = old_origin[1] / self.y[1]\\r\\n old_origin[2] = old_origin[2] / self.z[2]\\r\\n self.data = ndimage.shift(self.data, -old_origin, mode='wrap')\",\n \"def getSphereRadius(self):\\n return 1.5\",\n \"def center_of_gravity(self):\\n weights = [self.stabilizer_h.weight, self.stabilizer_vright.weight, self.stabilizer_vleft.weight]\\n cgs = [self.stabilizer_h.center_of_gravity, self.stabilizer_vright.center_of_gravity,\\n self.stabilizer_vleft.center_of_gravity]\\n total_weight = sum(weights)\\n cg_x = sum([weights[i] * cgs[i].x for i in range(0, len(weights))]) / total_weight\\n cg_y = sum([weights[i] * cgs[i].y for i in range(0, len(weights))]) / total_weight\\n cg_z = sum([weights[i] * cgs[i].z for i in range(0, len(weights))]) / total_weight\\n\\n return Point(cg_x, cg_y, cg_z)\",\n \"def getAfinityCenter(width, height, point, center, radius=7, img_affinity=None):\\n tensor = torch.zeros(2,height,width).float()\\n\\n # Create the canvas for the afinity output\\n imgAffinity = Image.new(\\\"RGB\\\", (width,height), \\\"black\\\")\\n totensor = transforms.Compose([transforms.ToTensor()])\\n \\n draw = ImageDraw.Draw(imgAffinity) \\n r1 = radius\\n p = point\\n draw.ellipse((p[0]-r1,p[1]-r1,p[0]+r1,p[1]+r1),(255,255,255))\\n\\n del draw\\n\\n # Compute the array to add the afinity\\n array = (np.array(imgAffinity)/255)[:,:,0]\\n\\n angle_vector = np.array(center) - np.array(point)\\n angle_vector = normalize(angle_vector)\\n affinity = np.concatenate([[array*angle_vector[0]],[array*angle_vector[1]]])\\n\\n # print (tensor)\\n if not img_affinity is None:\\n # Find the angle vector\\n # print (angle_vector)\\n if length(angle_vector) >0:\\n angle=py_ang(angle_vector)\\n else:\\n angle = 0\\n # print(angle)\\n c = np.array(colorsys.hsv_to_rgb(angle/360,1,1)) * 255\\n draw = ImageDraw.Draw(img_affinity) \\n draw.ellipse((p[0]-r1,p[1]-r1,p[0]+r1,p[1]+r1),fill=(int(c[0]),int(c[1]),int(c[2])))\\n del draw\\n re = torch.from_numpy(affinity).float() + tensor\\n return re, img_affinity\",\n \"def centroid(self, unit='spatial'):\\n com = ndimage.center_of_mass(self.data)\\n if unit != 'spatial':\\n return com\\n else:\\n # tuple - cast from generator\\n # sample spacing - indices to units\\n # x-c -- index shifted from center\\n return tuple(self.sample_spacing * (x-c) for x, c in zip(com, (self.center_y, self.center_x)))\",\n \"def center(self):\\n return self.centralizer(self)\",\n \"def GetCentroid(self, p_float=..., p_float=..., p_float=...):\\n ...\",\n \"def get_sphere_info(points):\\n rib = np.sum(points, axis=0)\\n rib3d = proj3d(rib)\\n pts3d = np.asarray([proj3d(p) for p in points])\\n face_size = np.linalg.norm(pts3d[0] - rib3d)\\n\\n M = np.ones((4, 4), dtype=np.float)\\n M[:3, :3] = pts3d[:3]\\n M[3, :3] = rib3d\\n b = [-sum(x*x) for x in M[:, :3]]\\n # if this is a plane\\n if abs(np.linalg.det(M)) < 1e-4:\\n center = rib3d\\n return True, center, None, face_size\\n else:\\n T = np.linalg.solve(M, b)\\n D, E, F, G = T\\n center = -0.5 * T[:3]\\n radius = 0.5 * np.sqrt(D*D + E*E + F*F - 4*G)\\n return False, center, radius, face_size\",\n \"def correct(point_cloud, axis_order=[0, 1, 2], dist_from_center=0):\\n \\n if type(axis_order) != list:\\n axis_order = list(axis_order)\\n\\n # Adding the fixed distance to x and y.\\n x = point_cloud[:, axis_order[0]] + dist_from_center\\n y = point_cloud[:, axis_order[1]] + dist_from_center\\n z = point_cloud[:, axis_order[2]]\\n\\n return np.vstack((x, y, z)).T\",\n \"def corner_to_train(corners, sphere_center, resolution=0.50, x=(0, 90), y=(-50, 50), z=(-4.5, 5.5), scale=4, min_value=np.array([0., -50., -4.5])):\\n x_logical = np.logical_and((corners[:, :, 0] < x[1]), (corners[:, :, 0] >= x[0]))\\n y_logical = np.logical_and((corners[:, :, 1] < y[1]), (corners[:, :, 1] >= y[0]))\\n z_logical = np.logical_and((corners[:, :, 2] < z[1]), (corners[:, :, 2] >= z[0]))\\n xyz_logical = np.logical_and(x_logical, np.logical_and(y_logical, z_logical)).all(axis=1)\\n train_corners = corners[xyz_logical].copy()\\n sphere_center = sphere_to_center(sphere_center, resolution=resolution, scale=scale, min_value=min_value) #sphere to center\\n for index, (corner, center) in enumerate(zip(corners[xyz_logical], sphere_center)):\\n train_corners[index] = corner - center\\n return train_corners\",\n \"def _fit_plane_to_point_cloud(\\n points_xyz: NDArrayFloat,\\n) -> Tuple[float, float, float, float]:\\n center_xyz: NDArrayFloat = np.mean(points_xyz, axis=0)\\n out: Tuple[NDArrayFloat, NDArrayFloat, NDArrayFloat] = np.linalg.svd(\\n points_xyz - center_xyz\\n )\\n vh = out[2]\\n\\n # Get the unitary normal vector\\n a, b, c = float(vh[2, 0]), float(vh[2, 1]), float(vh[2, 2])\\n d: float = -np.dot([a, b, c], center_xyz)\\n return (a, b, c, d)\",\n \"def createSphere( position=(0,0,0), radius=1, colour=(0.6,0.6,0.6), samplesY = 20, samplesXZ = 20 ):\\r\\n return createEllipsoid( position, (radius,radius,radius), colour, samplesY, samplesXZ )\",\n \"def compute_centers_of_hypercubes(self):\\n for hc in self.hypercubes.flatten():\\n for i in range(self.dims - 1, -1, -1):\\n index = self.dims - (i + 1)\\n hc.center[i] = (hc.coords[index] + 0.5) * self.hypercube_measurements[index]\",\n \"def sphere(indiv):\\n return sum([ x ** 2 for x in indiv])\",\n \"def sphere(geometry,\\n psd_name,psd_shape,psd_loc,psd_scale,\\n pore_seed='pore.seed',\\n psd_offset=0,\\n **kwargs):\\n import scipy.stats as spst\\n prob_fn = getattr(spst,psd_name)\\n P = prob_fn(psd_shape,loc=psd_loc,scale=psd_scale)\\n value = P.ppf(geometry[pore_seed])+psd_offset\\n return value\",\n \"def center(box):\\n x_center = box[:, 0] + (box[:, 2] - box[:, 0]) // 2\\n y_center = box[:, 1] + (box[:, 3] - box[:, 1]) // 2\\n return torch.stack((x_center, y_center)).t().to(box.device)\",\n \"def normal_polygon(points, unitized=True):\\n p = len(points)\\n assert p > 2, \\\"At least three points required\\\"\\n nx = 0\\n ny = 0\\n nz = 0\\n o = centroid_points(points)\\n a = subtract_vectors(points[-1], o)\\n for i in range(p):\\n b = subtract_vectors(points[i], o)\\n n = cross_vectors(a, b)\\n a = b\\n nx += n[0]\\n ny += n[1]\\n nz += n[2]\\n if not unitized:\\n return nx, ny, nz\\n l = length_vector([nx, ny, nz])\\n return nx / l, ny / l, nz / l\",\n \"def normalise(self):\\n s = self._sum()\\n if s != 0:\\n for element, value in self.focals.items():\\n self.focals[element] /= s\",\n \"def center_of_coor(coordinates):\\n return (np.sum(coordinates, axis=0) / coordinates.shape[0])\",\n \"def Sphere_ExactSerendipityLagrangeQuad():\\n\\n mesh = Sphere_CubeToSerendipityLagrangeQuad(1)\\n \\n ################\\n # Modifications for exact sphere\\n ################\\n # x=+1 side\\n def posXvals(xi1,xi2):\\n xi1=np.asarray(xi1,np.float).reshape(-1,)\\n xi2=np.asarray(xi2,np.float).reshape(-1,)\\n xb=np.ones(xi1.shape);yb=np.array(-xi1);zb=np.array(xi2);\\n xx=xb**2;yy=yb**2;zz=zb**2;\\n x = xb*np.sqrt(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)\\n y = yb*np.sqrt(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)\\n z = zb*np.sqrt(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)\\n return np.vstack([x,y,z])\\n def posXnormals(xi1,xi2):\\n xi1=np.asarray(xi1,np.float).reshape(-1,)\\n xi2=np.asarray(xi2,np.float).reshape(-1,)\\n xb=np.ones(xi1.shape);yb=np.array(-xi1);zb=np.array(xi2);\\n xx=xb**2;yy=yb**2;zz=zb**2;\\n dxdxi1 = -1.0 * xb*0.5*(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)**-0.5 * (-yb+2.0*yb*zz/3.0)\\n dxdxi2 = 1.0 * xb*0.5*(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)**-0.5 * (-zb+2.0*yy*zb/3.0)\\n dydxi1 = -1.0 * np.sqrt(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)\\n dydxi2 = 1.0 * yb*0.5*(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)**-0.5 * (-zb+2.0*xx*zb/3.0)\\n dzdxi1 = -1.0 * zb*0.5*(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)**-0.5 * (-yb+2.0*xx*yb/3.0)\\n dzdxi2 = 1.0 * np.sqrt(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)\\n J = np.array([[dxdxi1,dxdxi2],[dydxi1,dydxi2],[dzdxi1,dzdxi2]]).T\\n J = np.cross(J[:,0,:],J[:,1,:])\\n magnitude = np.sqrt(np.sum(J**2,axis=1))\\n return J.T/magnitude\\n def posXJ(xi1,xi2):\\n xi1=np.asarray(xi1,np.float).reshape(-1,)\\n xi2=np.asarray(xi2,np.float).reshape(-1,)\\n xb=np.ones(xi1.shape);yb=np.array(-xi1);zb=np.array(xi2);\\n xx=xb**2;yy=yb**2;zz=zb**2;\\n dxdxi1 = -1.0 * xb*0.5*(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)**-0.5 * (-yb+2.0*yb*zz/3.0)\\n dxdxi2 = 1.0 * xb*0.5*(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)**-0.5 * (-zb+2.0*yy*zb/3.0)\\n dydxi1 = -1.0 * np.sqrt(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)\\n dydxi2 = 1.0 * yb*0.5*(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)**-0.5 * (-zb+2.0*xx*zb/3.0)\\n dzdxi1 = -1.0 * zb*0.5*(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)**-0.5 * (-yb+2.0*xx*yb/3.0)\\n dzdxi2 = 1.0 * np.sqrt(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)\\n J = np.array([[dxdxi1,dxdxi2],[dydxi1,dydxi2],[dzdxi1,dzdxi2]]).T\\n J = np.cross(J[:,0,:],J[:,1,:])\\n return np.sqrt(np.sum(J**2,axis=1))\\n mesh.eList[0].vals = posXvals\\n mesh.eList[0].normals = posXnormals\\n mesh.eList[0].J = posXJ\\n \\n def posYvals(xi1,xi2):\\n xi1=np.asarray(xi1,np.float).reshape(-1,)\\n xi2=np.asarray(xi2,np.float).reshape(-1,)\\n xb=np.array(xi1);yb=np.ones(xi1.shape);zb=np.array(xi2);\\n xx=xb**2;yy=yb**2;zz=zb**2;\\n x = xb*np.sqrt(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)\\n y = yb*np.sqrt(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)\\n z = zb*np.sqrt(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)\\n return np.vstack([x,y,z])\\n def posYnormals(xi1,xi2):\\n xi1=np.asarray(xi1,np.float).reshape(-1,)\\n xi2=np.asarray(xi2,np.float).reshape(-1,)\\n xb=np.array(xi1);yb=np.ones(xi1.shape);zb=np.array(xi2);\\n xx=xb**2;yy=yb**2;zz=zb**2;\\n dxdxi1 = 1.0*np.sqrt(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)\\n dxdxi2 = 1.0*xb*0.5*(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)**-0.5 * (-zb+2.0*yy*zb/3.0)\\n dydxi1 = 1.0*yb*0.5*(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)**-0.5 * (-xb+2.0*xb*zz/3.0)\\n dydxi2 = 1.0*yb*0.5*(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)**-0.5 * (-zb+2.0*xx*zb/3.0)\\n dzdxi1 = 1.0*zb*0.5*(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)**-0.5 * (-xb+2.0*xb*yy/3.0)\\n dzdxi2 = 1.0*np.sqrt(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)\\n J = np.array([[dxdxi1,dxdxi2],[dydxi1,dydxi2],[dzdxi1,dzdxi2]]).T\\n J = np.cross(J[:,0,:],J[:,1,:])\\n magnitude = np.sqrt(np.sum(J**2,axis=1))\\n return J.T/magnitude\\n def posYJ(xi1,xi2):\\n xi1=np.asarray(xi1,np.float).reshape(-1,)\\n xi2=np.asarray(xi2,np.float).reshape(-1,)\\n xb=np.array(xi1);yb=np.ones(xi1.shape);zb=np.array(xi2);\\n xx=xb**2;yy=yb**2;zz=zb**2;\\n dxdxi1 = 1.0*np.sqrt(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)\\n dxdxi2 = 1.0*xb*0.5*(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)**-0.5 * (-zb+2.0*yy*zb/3.0)\\n dydxi1 = 1.0*yb*0.5*(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)**-0.5 * (-xb+2.0*xb*zz/3.0)\\n dydxi2 = 1.0*yb*0.5*(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)**-0.5 * (-zb+2.0*xx*zb/3.0)\\n dzdxi1 = 1.0*zb*0.5*(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)**-0.5 * (-xb+2.0*xb*yy/3.0)\\n dzdxi2 = 1.0*np.sqrt(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)\\n J = np.array([[dxdxi1,dxdxi2],[dydxi1,dydxi2],[dzdxi1,dzdxi2]]).T\\n J = np.cross(J[:,0,:],J[:,1,:])\\n return np.sqrt(np.sum(J**2,axis=1))\\n mesh.eList[1].vals = posYvals\\n mesh.eList[1].normals = posYnormals\\n mesh.eList[1].J = posYJ\\n \\n # x=-1 side\\n def negXvals(xi1,xi2):\\n xi1=np.asarray(xi1,np.float).reshape(-1,)\\n xi2=np.asarray(xi2,np.float).reshape(-1,)\\n xb=-np.ones(xi1.shape);yb=np.array(xi1);zb=np.array(xi2);\\n xx=xb**2;yy=yb**2;zz=zb**2;\\n x = xb*np.sqrt(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)\\n y = yb*np.sqrt(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)\\n z = zb*np.sqrt(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)\\n return np.vstack([x,y,z])\\n def negXnormals(xi1,xi2):\\n xi1=np.asarray(xi1,np.float).reshape(-1,)\\n xi2=np.asarray(xi2,np.float).reshape(-1,)\\n xb=-np.ones(xi1.shape);yb=np.array(xi1);zb=np.array(xi2);\\n xx=xb**2;yy=yb**2;zz=zb**2;\\n dxdxi1 = 1.0*xb*0.5*(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)**-0.5 * (-yb+2.0*yb*zz/3.0)\\n dxdxi2 = 1.0*xb*0.5*(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)**-0.5 * (-zb+2.0*yy*zb/3.0)\\n dydxi1 = 1.0*np.sqrt(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)\\n dydxi2 = 1.0*yb*0.5*(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)**-0.5 * (-zb+2.0*xx*zb/3.0)\\n dzdxi1 = 1.0*zb*0.5*(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)**-0.5 * (-yb+2.0*xx*yb/3.0)\\n dzdxi2 = 1.0*np.sqrt(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)\\n J = np.array([[dxdxi1,dxdxi2],[dydxi1,dydxi2],[dzdxi1,dzdxi2]]).T\\n J = np.cross(J[:,0,:],J[:,1,:])\\n magnitude = np.sqrt(np.sum(J**2,axis=1))\\n return J.T/magnitude\\n def negXJ(xi1,xi2):\\n xi1=np.asarray(xi1,np.float).reshape(-1,)\\n xi2=np.asarray(xi2,np.float).reshape(-1,)\\n xb=-np.ones(xi1.shape);yb=np.array(xi1);zb=np.array(xi2);\\n xx=xb**2;yy=yb**2;zz=zb**2;\\n dxdxi1 = 1.0*xb*0.5*(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)**-0.5 * (-yb+2.0*yb*zz/3.0)\\n dxdxi2 = 1.0*xb*0.5*(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)**-0.5 * (-zb+2.0*yy*zb/3.0)\\n dydxi1 = 1.0*np.sqrt(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)\\n dydxi2 = 1.0*yb*0.5*(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)**-0.5 * (-zb+2.0*xx*zb/3.0)\\n dzdxi1 = 1.0*zb*0.5*(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)**-0.5 * (-yb+2.0*xx*yb/3.0)\\n dzdxi2 = 1.0*np.sqrt(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)\\n J = np.array([[dxdxi1,dxdxi2],[dydxi1,dydxi2],[dzdxi1,dzdxi2]]).T\\n J = np.cross(J[:,0,:],J[:,1,:])\\n return np.sqrt(np.sum(J**2,axis=1))\\n mesh.eList[2].vals = negXvals\\n mesh.eList[2].normals = negXnormals\\n mesh.eList[2].J = negXJ\\n\\n # y=-1 side\\n def negYvals(xi1,xi2):\\n xi1=np.asarray(xi1,np.float).reshape(-1,)\\n xi2=np.asarray(xi2,np.float).reshape(-1,)\\n xb=np.array(-xi1);yb=-np.ones(xi1.shape);zb=np.array(xi2);\\n xx=xb**2;yy=yb**2;zz=zb**2;\\n x = xb*np.sqrt(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)\\n y = yb*np.sqrt(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)\\n z = zb*np.sqrt(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)\\n return np.vstack([x,y,z])\\n def negYnormals(xi1,xi2):\\n xi1=np.asarray(xi1,np.float).reshape(-1,)\\n xi2=np.asarray(xi2,np.float).reshape(-1,)\\n xb=np.array(-xi1);yb=-np.ones(xi1.shape);zb=np.array(xi2);\\n xx=xb**2;yy=yb**2;zz=zb**2; \\n dxdxi1 = -1.0*np.sqrt(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)\\n dxdxi2 = 1.0*0.5*xb*(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)**-0.5*(-zb+2.0*yy*zb/3.0)\\n dydxi1 = -1.0*0.5*yb*(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)**-0.5*(-xb+2.0*xb*zz/3.0)\\n dydxi2 = 1.0*0.5*yb*(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)**-0.5*(-zb+2.0*xx*zb/3.0)\\n dzdxi1 = -1.0*0.5*zb*(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)**-0.5*(-xb+2.0*xb*yy/3.0)\\n dzdxi2 = 1.0*np.sqrt(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0) \\n J = np.array([[dxdxi1,dxdxi2],[dydxi1,dydxi2],[dzdxi1,dzdxi2]]).T\\n J = np.cross(J[:,0,:],J[:,1,:])\\n magnitude = np.sqrt(np.sum(J**2,axis=1))\\n return J.T/magnitude\\n def negYJ(xi1,xi2):\\n xi1=np.asarray(xi1,np.float).reshape(-1,)\\n xi2=np.asarray(xi2,np.float).reshape(-1,)\\n xb=np.array(-xi1);yb=-np.ones(xi1.shape);zb=np.array(xi2);\\n xx=xb**2;yy=yb**2;zz=zb**2; \\n dxdxi1 = -1.0*np.sqrt(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)\\n dxdxi2 = 1.0*0.5*xb*(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)**-0.5*(-zb+2.0*yy*zb/3.0)\\n dydxi1 = -1.0*0.5*yb*(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)**-0.5*(-xb+2.0*xb*zz/3.0)\\n dydxi2 = 1.0*0.5*yb*(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)**-0.5*(-zb+2.0*xx*zb/3.0)\\n dzdxi1 = -1.0*0.5*zb*(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)**-0.5*(-xb+2.0*xb*yy/3.0)\\n dzdxi2 = 1.0*np.sqrt(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0) \\n J = np.array([[dxdxi1,dxdxi2],[dydxi1,dydxi2],[dzdxi1,dzdxi2]]).T\\n J = np.cross(J[:,0,:],J[:,1,:])\\n return np.sqrt(np.sum(J**2,axis=1))\\n mesh.eList[3].vals = negYvals\\n mesh.eList[3].normals = negYnormals\\n mesh.eList[3].J = negYJ\\n \\n # z=+1 side\\n def posZvals(xi1,xi2):\\n xi1=np.asarray(xi1,np.float).reshape(-1,)\\n xi2=np.asarray(xi2,np.float).reshape(-1,)\\n xb=np.array(xi1)\\n yb=np.array(-xi2)\\n zb=np.ones(xi1.shape)\\n xx=xb**2;yy=yb**2;zz=zb**2;\\n x = xb*np.sqrt(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)\\n y = yb*np.sqrt(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)\\n z = zb*np.sqrt(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)\\n return np.vstack([x,y,z])\\n def posZnormals(xi1,xi2):\\n xi1=np.asarray(xi1,np.float).reshape(-1,)\\n xi2=np.asarray(xi2,np.float).reshape(-1,)\\n xb=np.array(xi1);yb=np.array(-xi2);zb=np.ones(xi1.shape);\\n xx=xb**2;yy=yb**2;zz=zb**2;\\n dxdxi1 = 1.0*np.sqrt(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)\\n dxdxi2 = -1.0*xb*0.5*(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)**-0.5 * (-yb+2.0*yb*zz/3.0)\\n dydxi1 = 1.0*yb*0.5*(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)**-0.5 * (-xb+2.0*xb*zz/3.0)\\n dydxi2 = -1.0*np.sqrt(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)\\n dzdxi1 = 1.0*zb*0.5*(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)**-0.5 * (-xb+2.0*xb*yy/3.0)\\n dzdxi2 = -1.0*zb*0.5*(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)**-0.5 * (-yb+2.0*xx*yb/3.0)\\n J = np.array([[dxdxi1,dxdxi2],[dydxi1,dydxi2],[dzdxi1,dzdxi2]]).T\\n J = np.cross(J[:,0,:],J[:,1,:])\\n magnitude = np.sqrt(np.sum(J**2,axis=1))\\n return J.T/magnitude\\n def posZJ(xi1,xi2):\\n xi1=np.asarray(xi1,np.float).reshape(-1,)\\n xi2=np.asarray(xi2,np.float).reshape(-1,)\\n xb=np.array(xi1);yb=np.array(-xi2);zb=np.ones(xi1.shape);\\n xx=xb**2;yy=yb**2;zz=zb**2;\\n dxdxi1 = 1.0*np.sqrt(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)\\n dxdxi2 = -1.0*xb*0.5*(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)**-0.5 * (-yb+2.0*yb*zz/3.0)\\n dydxi1 = 1.0*yb*0.5*(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)**-0.5 * (-xb+2.0*xb*zz/3.0)\\n dydxi2 = -1.0*np.sqrt(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)\\n dzdxi1 = 1.0*zb*0.5*(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)**-0.5 * (-xb+2.0*xb*yy/3.0)\\n dzdxi2 = -1.0*zb*0.5*(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)**-0.5 * (-yb+2.0*xx*yb/3.0)\\n J = np.array([[dxdxi1,dxdxi2],[dydxi1,dydxi2],[dzdxi1,dzdxi2]]).T\\n J = np.cross(J[:,0,:],J[:,1,:])\\n return np.sqrt(np.sum(J**2,axis=1))\\n mesh.eList[4].vals = posZvals\\n mesh.eList[4].normals = posZnormals\\n mesh.eList[4].J = posZJ\\n \\n # z=-1 side\\n def negZvals(xi1,xi2):\\n xi1=np.asarray(xi1,np.float).reshape(-1,)\\n xi2=np.asarray(xi2,np.float).reshape(-1,)\\n xb=np.array(xi1);yb=np.array(xi2);zb=-np.ones(xi1.shape);\\n xx=xb**2;yy=yb**2;zz=zb**2;\\n x = xb*np.sqrt(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)\\n y = yb*np.sqrt(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)\\n z = zb*np.sqrt(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)\\n return np.vstack([x,y,z])\\n def negZnormals(xi1,xi2):\\n xi1=np.asarray(xi1,np.float).reshape(-1,)\\n xi2=np.asarray(xi2,np.float).reshape(-1,)\\n xb=np.array(xi1);yb=np.array(xi2);zb=-np.ones(xi1.shape);\\n xx=xb**2;yy=yb**2;zz=zb**2;\\n dxdxi1 = 1.0*np.sqrt(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)\\n dxdxi2 = 1.0*xb*0.5*(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)**-0.5 * (-yb+2.0*yb*zz/3.0)\\n dydxi1 = 1.0*yb*0.5*(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)**-0.5 * (-xb+2.0*xb*zz/3.0)\\n dydxi2 = 1.0*np.sqrt(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)\\n dzdxi1 = 1.0*zb*0.5*(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)**-0.5 * (-xb+2.0*xb*yy/3.0)\\n dzdxi2 = 1.0*zb*0.5*(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)**-0.5 * (-yb+2.0*xx*yb/3.0)\\n J = np.array([[dxdxi1,dxdxi2],[dydxi1,dydxi2],[dzdxi1,dzdxi2]]).T\\n J = np.cross(J[:,0,:],J[:,1,:])\\n magnitude = np.sqrt(np.sum(J**2,axis=1))\\n return J.T/magnitude\\n def negZJ(xi1,xi2):\\n xi1=np.asarray(xi1,np.float).reshape(-1,)\\n xi2=np.asarray(xi2,np.float).reshape(-1,)\\n xb=np.array(xi1);yb=np.array(xi2);zb=-np.ones(xi1.shape);\\n xx=xb**2;yy=yb**2;zz=zb**2;\\n dxdxi1 = 1.0*np.sqrt(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)\\n dxdxi2 = 1.0*xb*0.5*(1.0 - yy/2.0 - zz/2.0 + yy*zz/3.0)**-0.5 * (-yb+2.0*yb*zz/3.0)\\n dydxi1 = 1.0*yb*0.5*(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)**-0.5 * (-xb+2.0*xb*zz/3.0)\\n dydxi2 = 1.0*np.sqrt(1.0 - xx/2.0 - zz/2.0 + xx*zz/3.0)\\n dzdxi1 = 1.0*zb*0.5*(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)**-0.5 * (-xb+2.0*xb*yy/3.0)\\n dzdxi2 = 1.0*zb*0.5*(1.0 - xx/2.0 - yy/2.0 + xx*yy/3.0)**-0.5 * (-yb+2.0*xx*yb/3.0)\\n J = np.array([[dxdxi1,dxdxi2],[dydxi1,dydxi2],[dzdxi1,dzdxi2]]).T\\n J = np.cross(J[:,0,:],J[:,1,:])\\n return np.sqrt(np.sum(J**2,axis=1))\\n mesh.eList[5].vals = negZvals\\n mesh.eList[5].normals = negZnormals\\n mesh.eList[5].J = negZJ\\n \\n for e in mesh.eList:\\n e.ExactElement = True\\n \\n return mesh\",\n \"def get_center_of_mass_allies(self,obs):\",\n \"def center(self):\\n return self.map_.geom.center_skydir\",\n \"def unit_normals(self):\\n return np.stack(self.centers_cartesian(), axis=-1)\",\n \"def calc_centroid(self):\\n num = 0\\n centroid = numpy.zeros(3, float)\\n for atm in self:\\n if atm.position is not None:\\n centroid += atm.position\\n num += 1\\n return centroid / num\",\n \"def centre_of_points(list_of_points):\\n\\n cp = np.average(list_of_points, axis=0)\\n return cp\",\n \"def fit_hypersphere(data, method=\\\"Hyper\\\"):\\n num_points = len(data)\\n# print >>stderr, \\\"DEBUG: num_points=\\\", num_points\\n \\n if num_points==0:\\n return (0,None)\\n if num_points==1:\\n return (0,data[0])\\n dimen = len(data[0]) # dimensionality of hypersphere\\n# print >>stderr, \\\"DEBUG: dimen=\\\", dimen\\n \\n if num_points<dimen+1:\\n raise ValueError(\\\\\\n \\\"Error: fit_hypersphere needs at least {} points to fit {}-dimensional sphere, but only given {}\\\".format(dimen+1,dimen,num_points))\\n \\n # central dimen columns of matrix (data - centroid)\\n central = np.matrix(data, dtype=float) # copy the data\\n centroid = np.mean(central, axis=0)\\n for row in central:\\n row -= centroid\\n# print >>stderr, \\\"DEBUG: central=\\\", repr(central)\\n\\n # squared magnitude for each centered point, as a column vector\\n square_mag= [sum(a*a for a in row.flat) for row in central] \\n square_mag = np.matrix(square_mag).transpose()\\n# print >>stderr, \\\"DEBUG: square_mag=\\\", square_mag\\n \\n if method==\\\"Taubin\\\":\\n # matrix of normalized squared magnitudes, data\\n mean_square = square_mag.mean()\\n data_Z = np.bmat( [[(square_mag-mean_square)/(2*sqrt(mean_square)), central]])\\n # print >> stderr, \\\"DEBUG: data_Z=\\\",data_Z\\n u,s,v = linalg.svd(data_Z, full_matrices=False)\\n param_vect= v[-1,:]\\n params = [ x for x in np.asarray(param_vect)[0]] # convert from (dimen+1) x 1 matrix to list\\n params[0] /= 2*sqrt(mean_square)\\n params.append(-mean_square*params[0])\\n params=np.array(params)\\n \\n else:\\n # matrix of squared magnitudes, data, 1s\\n data_Z = np.bmat( [[square_mag, central, np.ones((num_points,1))]])\\n # print >> stderr, \\\"DEBUG: data_Z=\\\",data_Z\\n\\n # SVD of data_Z\\n # Note: numpy's linalg.svd returns data_Z = u * s * v\\n # not u*s*v.H as the Release 1.4.1 documentation claims.\\n # Newer documentation is correct.\\n u,s,v = linalg.svd(data_Z, full_matrices=False)\\n # print >>stderr, \\\"DEBUG: u=\\\",repr(u)\\n # print >>stderr, \\\"DEBUG: s=\\\",repr(s)\\n # print >>stderr, \\\"DEBUG: v=\\\",repr(v)\\n # print >>stderr, \\\"DEBUG: v.I=\\\",repr(v.I)\\n\\n if s[-1]/s[0] < 1e-12:\\n # singular case\\n # param_vect as (dimen+2) x 1 matrix\\n param_vect = v[-1,:]\\n # Note: I get last ROW of v, while Chernov claims last COLUMN,\\n # because of difference in definition of SVD for MATLAB and numpy\\n\\n # print >> stderr, \\\"DEBUG: singular, param_vect=\\\", repr(param_vect)\\n # print >> stderr, \\\"DEBUG: data_Z*V=\\\", repr(data_Z*v)\\n # print >> stderr, \\\"DEBUG: data_Z*VI=\\\", repr(data_Z*v.I)\\n # print >> stderr, \\\"DEBUG: data_Z*A=\\\", repr(data_Z*v[:,-1])\\n else: \\n Y = v.H*np.diag(s)*v\\n Y_inv = v.H*np.diag([1./x for x in s])*v\\n # print >>stderr, \\\"DEBUG: Y=\\\",repr(Y)\\n # print >>stderr, \\\"DEBUG: Y.I=\\\",repr(Y.I), \\\"\\\\nY_inv=\\\",repr(Y_inv)\\n #Ninv is the inverse of the constraint matrix, after centroid has been removed\\n Ninv = np.asmatrix(np.identity(dimen+2, dtype=float))\\n if method==\\\"Hyper\\\":\\n Ninv[0,0] = 0\\n Ninv[0,-1]=0.5\\n Ninv[-1,0]=0.5\\n Ninv[-1,-1] = -2*square_mag.mean()\\n elif method==\\\"Pratt\\\":\\n Ninv[0,0] = 0\\n Ninv[0,-1]=-0.5\\n Ninv[-1,0]=-0.5\\n Ninv[-1,-1]=0\\n else: \\n raise ValueError(\\\"Error: unknown method: {} should be 'Hyper', 'Pratt', or 'Taubin'\\\")\\n # print >> stderr, \\\"DEBUG: Ninv=\\\", repr(Ninv)\\n\\n # get the eigenvector for the smallest positive eigenvalue\\n matrix_for_eigen = Y*Ninv*Y\\n # print >> stderr, \\\"DEBUG: {} matrix_for_eigen=\\\\n{}\\\".format(method, repr(matrix_for_eigen))\\n eigen_vals,eigen_vects = linalg.eigh(matrix_for_eigen)\\n # print >> stderr, \\\"DEBUG: eigen_vals=\\\", repr(eigen_vals)\\n # print >> stderr, \\\"DEBUG: eigen_vects=\\\", repr(eigen_vects)\\n\\n positives = [x for x in eigen_vals if x>0]\\n if len(positives)+1 != len(eigen_vals):\\n # raise ValueError(\\\"Error: for method {} exactly one eigenvalue should be negative: {}\\\".format(method,eigen_vals))\\n print>>stderr, \\\"Warning: for method {} exactly one eigenvalue should be negative: {}\\\".format(method,eigen_vals)\\n smallest_positive = min(positives)\\n # print >> stderr, \\\"DEBUG: smallest_positive=\\\", smallest_positive\\n # chosen eigenvector as 1 x (dimen+2) matrix\\n A_colvect =eigen_vects[:,list(eigen_vals).index(smallest_positive)]\\n # print >> stderr, \\\"DEBUG: A_colvect=\\\", repr(A_colvect)\\n # now have to multiply by Y inverse\\n param_vect = (Y_inv*A_colvect).transpose()\\n # print >> stderr, \\\"DEBUG: nonsingular, param_vect=\\\", repr(param_vect) \\n params = np.asarray(param_vect)[0] # convert from (dimen+2) x 1 matrix to array of (dimen+2)\\n\\n \\n# print >> stderr, \\\"DEBUG: params=\\\", repr(params)\\n radius = 0.5* sqrt( sum(a*a for a in params[1:-1])- 4*params[0]*params[-1])/abs(params[0])\\n center = -0.5*params[1:-1]/params[0]\\n#y print >> stderr, \\\"DEBUG: center=\\\", repr(center), \\\"centroid=\\\", repr(centroid)\\n center += np.asarray(centroid)[0]\\n return (radius,center)\",\n \"def norm(point):\\n return np.sqrt(norm2(point))\",\n \"def center(emg_data: np.ndarray, center_value: float = None) -> np.ndarray:\\n center_value = center_value if center_value else emg_data.mean(axis=1)\\n emg_centered = np.copy(emg_data)\\n for i in range(emg_data.shape[0]):\\n emg_centered[i, :] = emg_data[i, :] - center_value[i]\\n return emg_centered\",\n \"def filled_sphere(shape, radius, center=None):\\n\\tr2 = radius*radius\\n\\tif center is None:\\n\\t\\t### set to center of array\\n\\t\\tcenter = (shape[0]-1)/2.0,(shape[1]-1)/2.0,(shape[2]-1)/2.0\\n\\tdef func(i0, i1, i2):\\n\\t\\tii0 = i0 - center[0]\\n\\t\\tii1 = i1 - center[1]\\n\\t\\tii2 = i2 - center[2]\\n\\t\\trr2 = ii0**2 + ii1**2 + ii2**2\\n\\t\\tc = numpy.where(rr2<r2, 0.0, 1.0)\\n\\t\\treturn c\\n\\treturn numpy.fromfunction(func, shape)\",\n \"def vincenty_direct_solution(begin_lat, begin_lon, begin_azimuth, distance, a, b, f):\\n # Convert latitude, longitude, azimuth of the begining point to radians\\n lat1 = math.radians(begin_lat)\\n lon1 = math.radians(begin_lon)\\n alfa1 = math.radians(begin_azimuth)\\n\\n sinAlfa1 = math.sin(alfa1)\\n cosAlfa1 = math.cos(alfa1)\\n\\n # U1 - reduced latitude\\n tanU1 = (1 - f) * math.tan(lat1)\\n cosU1 = 1 / math.sqrt(1 + tanU1 * tanU1)\\n sinU1 = tanU1 * cosU1\\n\\n # sigma1 - angular distance on the sphere from the equator to begining point\\n sigma1 = math.atan2(tanU1, math.cos(alfa1))\\n\\n # sinAlfa - azimuth of the geodesic at the equator\\n sinAlfa = cosU1 * sinAlfa1\\n cosSqAlfa = 1 - sinAlfa * sinAlfa\\n uSq = cosSqAlfa * (a * a - b * b) / (b * b)\\n A = 1 + uSq / 16384 * (4096 + uSq * (-768 + uSq * (320 - 175 * uSq)))\\n B = uSq / 1024 * (256 + uSq * (-128 + uSq * (74 - 47 * uSq)))\\n\\n sigma = distance / (b * A)\\n sigmap = 1\\n\\n while (math.fabs(sigma - sigmap) > 1e-12):\\n cos2sigmaM = math.cos(2 * sigma1 + sigma)\\n sinSigma = math.sin(sigma)\\n cosSigma = math.cos(sigma)\\n dSigma = B * sinSigma * (cos2sigmaM + B / 4 * (\\n cosSigma * (-1 + 2 * cos2sigmaM * cos2sigmaM) - B / 6 * cos2sigmaM * (\\n -3 + 4 * sinSigma * sinSigma) * (-3 + 4 * cos2sigmaM * cos2sigmaM)))\\n sigmap = sigma\\n sigma = distance / (b * A) + dSigma\\n\\n var_aux = sinU1 * sinSigma - cosU1 * cosSigma * cosAlfa1 # Auxiliary variable\\n\\n # Latitude of the end point in radians\\n lat2 = math.atan2(sinU1 * cosSigma + cosU1 * sinSigma * cosAlfa1,\\n (1 - f) * math.sqrt(sinAlfa * sinAlfa + var_aux * var_aux))\\n\\n lamb = math.atan2(sinSigma * sinAlfa1, cosU1 * cosSigma - sinU1 * sinSigma * cosAlfa1)\\n C = f / 16 * cosSqAlfa * (4 + f * (4 - 3 * cosSqAlfa))\\n L = lamb - (1 - C) * f * sinAlfa * (\\n sigma + C * sinSigma * (cos2sigmaM + C * cosSigma * (-1 + 2 * cos2sigmaM * cos2sigmaM)))\\n # Longitude of the second point in radians\\n lon2 = (lon1 + L + 3 * math.pi) % (2 * math.pi) - math.pi\\n\\n # Convert to decimal degrees\\n lat2_dd = math.degrees(lat2)\\n lon2_dd = math.degrees(lon2)\\n\\n return lat2_dd, lon2_dd\",\n \"def uniform_sphere(batch_size, dim, epsilon=1, ord=2):\\r\\n\\r\\n random = numpy.random.randn(batch_size, dim)\\r\\n random /= numpy.repeat(numpy.linalg.norm(random, ord=ord, axis=1).reshape(-1, 1), axis=1, repeats=dim)\\r\\n random *= epsilon\\r\\n\\r\\n return random\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6750035","0.67149276","0.6667598","0.65624475","0.65602094","0.650648","0.64572716","0.6323207","0.624639","0.624639","0.62402195","0.6174017","0.61365646","0.6134152","0.6077822","0.6074374","0.606006","0.6051627","0.5994066","0.59825337","0.59711474","0.596547","0.59578013","0.59503067","0.59282684","0.5908275","0.5882229","0.5876924","0.5861842","0.5858708","0.58315206","0.5817533","0.579772","0.57943046","0.57912725","0.57907605","0.57656056","0.5759009","0.5755694","0.5749876","0.57477117","0.5736047","0.57045406","0.56901556","0.5689317","0.5689229","0.56818956","0.56794477","0.5676896","0.56753474","0.5675127","0.566972","0.5669659","0.5666684","0.5651398","0.56329715","0.56231517","0.56117934","0.5597711","0.559396","0.5591241","0.55907357","0.5586705","0.5585959","0.5582967","0.5574001","0.55666506","0.55568236","0.5555132","0.5550074","0.55499166","0.5549557","0.5546174","0.55415225","0.55404","0.5540202","0.5533881","0.55321836","0.55130255","0.55017823","0.5495183","0.5494943","0.5492517","0.549167","0.5490024","0.5488546","0.54807895","0.5480068","0.54782116","0.54646206","0.54615265","0.5459193","0.54569286","0.5455671","0.5453627","0.5452008","0.54500943","0.5447486","0.5437895","0.54357004","0.543567"],"string":"[\n \"0.6750035\",\n \"0.67149276\",\n \"0.6667598\",\n \"0.65624475\",\n \"0.65602094\",\n \"0.650648\",\n \"0.64572716\",\n \"0.6323207\",\n \"0.624639\",\n \"0.624639\",\n \"0.62402195\",\n \"0.6174017\",\n \"0.61365646\",\n \"0.6134152\",\n \"0.6077822\",\n \"0.6074374\",\n \"0.606006\",\n \"0.6051627\",\n \"0.5994066\",\n \"0.59825337\",\n \"0.59711474\",\n \"0.596547\",\n \"0.59578013\",\n \"0.59503067\",\n \"0.59282684\",\n \"0.5908275\",\n \"0.5882229\",\n \"0.5876924\",\n \"0.5861842\",\n \"0.5858708\",\n \"0.58315206\",\n \"0.5817533\",\n \"0.579772\",\n \"0.57943046\",\n \"0.57912725\",\n \"0.57907605\",\n \"0.57656056\",\n \"0.5759009\",\n \"0.5755694\",\n \"0.5749876\",\n \"0.57477117\",\n \"0.5736047\",\n \"0.57045406\",\n \"0.56901556\",\n \"0.5689317\",\n \"0.5689229\",\n \"0.56818956\",\n \"0.56794477\",\n \"0.5676896\",\n \"0.56753474\",\n \"0.5675127\",\n \"0.566972\",\n \"0.5669659\",\n \"0.5666684\",\n \"0.5651398\",\n \"0.56329715\",\n \"0.56231517\",\n \"0.56117934\",\n \"0.5597711\",\n \"0.559396\",\n \"0.5591241\",\n \"0.55907357\",\n \"0.5586705\",\n \"0.5585959\",\n \"0.5582967\",\n \"0.5574001\",\n \"0.55666506\",\n \"0.55568236\",\n \"0.5555132\",\n \"0.5550074\",\n \"0.55499166\",\n \"0.5549557\",\n \"0.5546174\",\n \"0.55415225\",\n \"0.55404\",\n \"0.5540202\",\n \"0.5533881\",\n \"0.55321836\",\n \"0.55130255\",\n \"0.55017823\",\n \"0.5495183\",\n \"0.5494943\",\n \"0.5492517\",\n \"0.549167\",\n \"0.5490024\",\n \"0.5488546\",\n \"0.54807895\",\n \"0.5480068\",\n \"0.54782116\",\n \"0.54646206\",\n \"0.54615265\",\n \"0.5459193\",\n \"0.54569286\",\n \"0.5455671\",\n \"0.5453627\",\n \"0.5452008\",\n \"0.54500943\",\n \"0.5447486\",\n \"0.5437895\",\n \"0.54357004\",\n \"0.543567\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":21197,"cells":{"query":{"kind":"string","value":"Show a point cloud in 2d"},"document":{"kind":"string","value":"def show_point_set_curvature(points, curvatures, title, thresh = 0.9, save_path = None):\n dim = points.shape[1]\n assert(dim == 2), \"Only data in 2 dimension space can be visualized.\"\n selected_pts = points[np.abs(curvatures) < thresh]\n \n fig = plt.figure(figsize = (6, 6))\n plt.title(title)\n plt.scatter(points[:, 0], points[:, 1], c = 'b', s = 5, label = \"point cloud\")\n plt.scatter(selected_pts[:, 0], selected_pts[:, 1], c = 'r', s = 5, label = \"selected point cloud\")\n plt.xlabel(\"x\")\n plt.ylabel(\"y\")\n plt.axis('equal')\n plt.grid()\n plt.legend()\n plt.show()\n \n if type(save_path) != type(None):\n fig.savefig(save_path)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def visualize_point_cloud(points, colors=None, normals=None,\n show_frame=False, frame_size=1.0, frame_origin=(0, 0, 0)):\n pc = np2pcd(points, colors, normals)\n geometries = [pc]\n if show_frame:\n coord_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=frame_size, origin=frame_origin)\n geometries.append(coord_frame)\n o3d.visualization.draw_geometries(geometries)","def render_point_cloud(frame_id):\n point_cloud_world_coordinates = get_point_cloud_world_coordinates(frame_id)\n # pptk\n v = pptk.viewer(point_cloud_world_coordinates)\n v.set(point_size=0.0001)\n v.color_map('cool', scale=[0, 5])","def create_point_cloud(self):\n pixels = []\n colors = []\n my_pixels = []\n for j in range(self.height):\n for i in range(self.width):\n depth = self.depth[j, i]\n pixels.append(\n [i * depth, j * depth, depth]\n )\n my_pixels.append(\n [i, j, 1]\n )\n # make rgb with flip()\n colors.append(np.flip(self.bgr[j, i, :]))\n # colors.append(self.bgr[j, i, :])\n self.my_pixels = my_pixels\n pixels = np.array(pixels)\n\n # project pixels to camera space\n self.xyz_points = self.intrinsics_inv @ np.transpose(pixels)\n self.color_points = colors\n\n # now add 1s to the points for homogenous coordinates\n num_points = self.get_num_xyz_points()\n ones = np.ones((1, num_points))\n self.xyzw_points = np.concatenate((self.xyz_points, ones), axis=0)\n\n self.scene = None\n self.camera_pose = None\n self.nm = None\n self.nl = None\n self.nc = None\n self.create_mesh()","def draw_pointcloud(ax, example):\n points = example['points'].cpu().detach().numpy()\n points_num = len(points)\n xs = np.empty([points_num])\n ys = np.empty([points_num])\n zs = np.empty([points_num])\n intensity = np.empty([len(points)])\n for j, point in enumerate(points):\n xs[j] = point[1]\n ys[j] = point[2]\n zs[j] = point[3]\n intensity[j] = point[4]\n\n intensity = intensity\n ax.scatter3D(xs, ys, zs, c=intensity, marker='.', s=0.3, cmap=plt.get_cmap('jet'))","def show_pointclouds(points, colors, text=[], title=\"Default\", png_path=\"\", interactive=True, orientation='horizontal'):\n\n # make sure pointclouds is a list\n assert isinstance(points, type([])), \\\n \"Pointclouds argument must be a list\"\n\n # make sure colors is a list\n assert isinstance(colors, type([])), \\\n \"Colors argument must be a list\"\n\n # make sure number of pointclouds and colors are the same\n assert len(points) == len(colors), \\\n \"Number of pointclouds (%d) is different then number of colors (%d)\" % (len(points), len(colors))\n\n while len(text) < len(points):\n text.append(\"\")\n\n # Number of pointclouds to be displayed in this window\n num_pointclouds = len(points)\n\n point_size = 10\n pointclouds = [VtkPointCloud(point_size) for _ in range(num_pointclouds)]\n renderers = [vtk.vtkRenderer() for _ in range(num_pointclouds)]\n\n height = 1.0 / max(num_pointclouds, 1)\n viewports = [(i*height, (i+1)*height) for i in range(num_pointclouds)]\n #print(viewports)\n\n # iterate over all point clouds\n for i, pc in enumerate(points):\n pc = pc.squeeze()\n co = colors[i].squeeze()\n assert pc.shape[0] == co.shape[0], \\\n \"expected same number of points (%d) then colors (%d), cloud index = %d\" % (pc.shape[0], co.shape[0], i)\n assert pc.shape[1] == 3, \"expected points to be N x 3, got N x %d\" % pc.shape[1]\n assert co.shape[1] == 3, \"expected colors to be N x 3, got N x %d\" % co.shape[1]\n\n # for each point cloud iterate over all points\n for j in range(pc.shape[0]):\n point = pc[j, :]\n color = co[j, :]\n pointclouds[i].add_point(point, color)\n\n renderers[i].AddActor(pointclouds[i].vtkActor)\n # renderers[i].AddActor(vtk.vtkAxesActor())\n renderers[i].SetBackground(1.0, 1.0, 1.0)\n if orientation == 'horizontal':\n print(viewports[i][0])\n renderers[i].SetViewport(viewports[i][0], 0.0, viewports[i][1], 1.0)\n elif orientation == 'vertical':\n renderers[i].SetViewport(0.0, viewports[i][0], 1.0, viewports[i][1])\n else:\n raise Exception('Not a valid orientation!')\n renderers[i].ResetCamera()\n\n # Add circle to first render\n renderers[0].AddActor(getActorCircle())\n renderers[0].AddActor(getActorCircle(50, 49, color=(0, 1, 0)))\n\n # Text actors\n text_actors = [vtk.vtkTextActor() for _ in text]\n for i, ta in enumerate(text_actors):\n if orientation == 'horizontal':\n ta.SetInput(' ' + text[i])\n elif orientation == 'vertical':\n ta.SetInput(text[i] + '\\n\\n\\n\\n\\n\\n')\n else:\n raise Exception('Not a valid orientation!')\n txtprop = ta.GetTextProperty()\n txtprop.SetFontFamilyToArial()\n txtprop.SetFontSize(0)\n txtprop.SetColor(0, 0, 0)\n # txtprop.SetJustificationToCentered()\n # ta.SetDisplayPosition(500, 10)\n # ta.SetAlignmentPoint()\n renderers[i].AddActor(ta)\n\n # Render Window\n render_window = vtk.vtkRenderWindow()\n for renderer in renderers:\n render_window.AddRenderer(renderer)\n\n render_window_interactor = vtk.vtkRenderWindowInteractor()\n render_window_interactor.SetInteractorStyle(vtk.vtkInteractorStyleTrackballCamera())\n render_window_interactor.SetRenderWindow(render_window)\n\n [center_x, center_y, center_z] = np.mean(points[0].squeeze(), axis=0)\n camera = vtk.vtkCamera()\n # d = 10\n # camera.SetViewUp(0, -1, 0)\n\n # camera.SetPosition(center_x + d, center_y + d, center_z + d / 2)\n # camera.SetFocalPoint(center_x, center_y, center_z)\n # camera.SetFocalPoint(0, 0, 0)\n\n camera.SetViewUp(0, 0, 1)\n if orientation == 'horizontal':\n camera.SetPosition(3, -10, 2)\n camera.SetFocalPoint(3, 1.5, 1.5)\n elif orientation == 'vertical':\n camera.SetPosition(1.5, -6, 2)\n camera.SetFocalPoint(1.5, 1.5, 1.5)\n else:\n raise Exception('Not a valid orientation!')\n\n camera.SetClippingRange(0.002, 1000)\n for renderer in renderers:\n renderer.SetActiveCamera(camera)\n\n # Begin Interaction\n render_window.Render()\n render_window.SetWindowName(title)\n if orientation == 'horizontal':\n render_window.SetSize(1940, 720)\n elif orientation == 'vertical':\n render_window.SetSize(600, 1388)\n else:\n raise Exception('Not a valid orientation!')\n\n if interactive:\n render_window_interactor.Start()\n\n if png_path:\n # screenshot code:\n w2if = vtk.vtkWindowToImageFilter()\n w2if.SetInput(render_window)\n w2if.Update()\n\n writer = vtk.vtkPNGWriter()\n writer.SetFileName(png_path)\n writer.SetInputConnection(w2if.GetOutputPort())\n writer.Write()","def make_point_cloud(self):\r\n\r\n self.pointCloud = VtkPointCloud()\r\n for k in range(np.size(self.pos, 0)):\r\n self.pointCloud.addPoint(self.pos[k, :])\r\n\r\n # Renderer\r\n renderer = vtk.vtkRenderer()\r\n renderer.AddActor(self.pointCloud.vtkActor)\r\n renderer.SetBackground(.2, .3, .4)\r\n renderer.SetBackground(0.0, 0.0, 0.0)\r\n renderer.ResetCamera()\r\n\r\n # Render Window\r\n renderWindow = vtk.vtkRenderWindow()\r\n renderWindow.AddRenderer(renderer)\r\n\r\n # Interactor\r\n renderWindowInteractor = vtk.vtkRenderWindowInteractor()\r\n renderWindowInteractor.SetRenderWindow(renderWindow)\r\n\r\n # Begin Interaction\r\n renderWindow.Render()\r\n renderWindow.SetWindowName(\"XYZ Data Viewer: \")\r\n renderWindowInteractor.Start()","def publish_point_cloud(self):\n all_points = [np.zeros((0, 2), np.float32)]\n all_keys = []\n for key in range(len(self.keyframes)):\n pose = self.keyframes[key].pose\n transf_points = self.keyframes[key].transf_points\n all_points.append(transf_points)\n all_keys.append(key * np.ones((len(transf_points), 1)))\n\n all_points = np.concatenate(all_points)\n all_keys = np.concatenate(all_keys)\n sampled_points, sampled_keys = pcl.downsample(\n all_points, all_keys, self.point_resolution\n )\n sampled_xyzi = np.c_[sampled_points, np.zeros_like(sampled_keys), sampled_keys]\n if len(sampled_xyzi) == 0:\n return\n\n if self.save_fig:\n plt.figure()\n plt.scatter(\n sampled_xyzi[:, 0], sampled_xyzi[:, 1], c=sampled_xyzi[:, 3], s=1\n )\n plt.axis(\"equal\")\n plt.gca().invert_yaxis()\n plt.savefig(\"step-{}-map.png\".format(self.current_key - 1), dpi=100)\n plt.close(\"all\")\n\n cloud_msg = n2r(sampled_xyzi, \"PointCloudXYZI\")\n cloud_msg.header.stamp = self.current_keyframe.time\n cloud_msg.header.frame_id = \"map\"\n self.cloud_pub.publish(cloud_msg)","def showPoints(surface, points):\n for point in points:\n point.show(surface)","def as_point_cloud(self):\n far = 1000.0 # max depth in meters.\n intrinsic_mat = self.camera_setup.get_intrinsic_matrix()\n width, height = self.camera_setup.width, self.camera_setup.height\n # 2d pixel coordinates\n pixel_length = width * height\n u_coord = repmat(np.r_[0:width:1], height, 1).reshape(pixel_length)\n v_coord = repmat(np.c_[0:height:1], 1, width).reshape(pixel_length)\n normalized_depth = np.reshape(self.frame, pixel_length)\n\n # p2d = [u,v,1]\n p2d = np.array([u_coord, v_coord, np.ones_like(u_coord)])\n\n # P = [X,Y,Z]\n p3d = np.dot(inv(intrinsic_mat), p2d)\n p3d *= normalized_depth * far\n\n # [[X1,Y1,Z1],[X2,Y2,Z2], ... [Xn,Yn,Zn]]\n locations = np.asarray(np.transpose(p3d))\n # Transform the points in 3D world coordinates.\n to_world_transform = self.camera_setup.get_unreal_transform()\n point_cloud = to_world_transform.transform_points(locations)\n return point_cloud","def render_point_cloud(self, point_cloud, extrinsics=Pose(), color=GRAY):\n\n combined_transform = self._bev_rotation * extrinsics\n\n pointcloud_in_bev = combined_transform * point_cloud\n point_cloud2d = pointcloud_in_bev[:, :2]\n\n point_cloud2d[:, 0] = (self._center_pixel[0] + point_cloud2d[:, 0] * self._pixels_per_meter)\n point_cloud2d[:, 1] = (self._center_pixel[1] + point_cloud2d[:, 1] * self._pixels_per_meter)\n\n H, W = self.data.shape[:2]\n uv = point_cloud2d.astype(np.int32)\n in_view = np.logical_and.reduce([\n (point_cloud2d >= 0).all(axis=1),\n point_cloud2d[:, 0] < W,\n point_cloud2d[:, 1] < H,\n ])\n uv = uv[in_view]\n self.data[uv[:, 1], uv[:, 0], :] = color","def showCrossPoints(self, surface):\n for point in self.cross_points:\n point.show(surface)","def drawPointCloud(points, ax, color=None):\n if len(points.shape) != 2 or points.shape[0] != 3:\n raise ValueError(\"'points' must be 3xN\")\n if color == None:\n color = __color_cycle.next()\n elif color in (0, 1, 2):\n color = points[color, :]\n ax.scatter(points[0,:].T, points[1,:].T, points[2,:].T, c=color)","def create_pointcloud(pts):\n depths = PointCloud()\n depths.header = std_msgs.msg.Header()\n depths.header.stamp = rospy.Time.now()\n depths.header.frame_id = \"view_zero\"\n depths.points = [None] * len(pts)\n for p in xrange(len(pts)):\n #Giving point the same orientation as the robot\n y = pts[p,0]\n z = - pts[p,1] #y in images is down\n x = pts[p,2]\n depths.points[p] = Point(x, y, z)\n return depths","def generate_pointcloud(rgb_file, mask_file,depth_file,ply_file):\n rgb = Image.open(rgb_file)\n # depth = Image.open(depth_file)\n depth = Image.open(depth_file).convert('I')\n mask = Image.open(mask_file).convert('I')\n\n # if rgb.size != depth.size:\n # raise Exception(\"Color and depth image do not have the same resolution.\")\n # if rgb.mode != \"RGB\":\n # raise Exception(\"Color image is not in RGB format\")\n # if depth.mode != \"I\":\n # raise Exception(\"Depth image is not in intensity format\")\n\n\n points = [] \n for v in range(rgb.size[1]):\n for u in range(rgb.size[0]):\n color = rgb.getpixel((u,v))\n # Z = depth.getpixel((u,v)) / scalingFactor\n # if Z==0: continue\n # X = (u - centerX) * Z / focalLength\n # Y = (v - centerY) * Z / focalLength\n if (mask.getpixel((u,v))<55):\n Z = depth.getpixel((u, v))*.22 \n if Z == 0: continue\n Y = .22 * v\n X = .22 * u\n points.append(\"%f %f %f %d %d %d 0\\n\"%(X,Y,Z,color[0],color[1],color[2]))\n file = open(ply_file,\"w\")\n file.write('''ply\nformat ascii 1.0\nelement vertex %d\nproperty float x\nproperty float y\nproperty float z\nproperty uchar red\nproperty uchar green\nproperty uchar blue\nproperty uchar alpha\nend_header\n%s\n'''%(len(points),\"\".join(points)))\n file.close()","def point_cloud(self):\n\t\tgen = self.loop(point_cloud=True)\n\t\tpoint_cloud = next(gen)\n\t\treturn point_cloud","def debug_filter_points(self, points):\n cloud_msg = PointCloud2()\n cloud_msg.header.frame_id = \"map\"\n cloud_msg.header.stamp = rospy.Time.now() \n xyz = [[p.pose.position.x, p.pose.position.y, p.pose.position.z] for p in points] \n point_cloud = pc2.create_cloud_xyz32(cloud_msg.header, xyz)\n self._points_publisher.publish(point_cloud)","def visualize_scan(self):\n fig = plt.figure()\n ax = fig.add_subplot(111, projection='3d')\n ax.scatter(self.p1_points[:, 0], self.p1_points[:, 1], self.p1_points[:, 2], c='r')\n ax.scatter(self.p2_points[:, 0], self.p2_points[:, 1], self.p2_points[:, 2], c='g')\n ax.scatter(self.p3_points[:, 0], self.p3_points[:, 1], self.p3_points[:, 2], c='b')\n ax.scatter(self.p4_points[:, 0], self.p4_points[:, 1], self.p4_points[:, 2])\n\n ax.set_xlabel('x')\n ax.set_ylabel('y')\n ax.set_zlabel('z')\n plt.show()","def visualize_in_2d(self):\n ae = Autoencoder_test(self.data)\n self.code = ae.encode(n_dimension=2, learning_rate=0.01, training_epochs=10, batch_size=400)\n for i in range(len(self.cluster)):\n list_x = []\n list_y = []\n for j in self.cluster[i]:\n list_x.append(self.code[0][j,0])\n list_y.append(self.code[0][j,1])\n plt.scatter(list_x,list_y)\n plt.show()\n return","def convertcloud(points):\n pcd = open3d.geometry.PointCloud()\n pcd.points = open3d.utility.Vector3dVector(points)\n return pcd","def voxelize(points,leaf = 0.1):\n if (type(points) == pclpy.pcl.PointCloud.PointXYZRGB):\n cloud = points\n voxel_filter = pclpy.pcl.filters.VoxelGrid.PointXYZRGB()\n filtered_pointcloud = pclpy.pcl.PointCloud.PointXYZRGB()\n else:\n cloud = pclpy.pcl.PointCloud.PointXYZ(points)\n voxel_filter = pclpy.pcl.filters.VoxelGrid.PointXYZ()\n filtered_pointcloud = pclpy.pcl.PointCloud.PointXYZ()\n \n voxel_filter.setLeafSize(leaf,leaf,leaf)\n voxel_filter.setInputCloud(cloud)\n \n voxel_filter.filter(filtered_pointcloud)\n if type(points) == pclpy.pcl.PointCloud.PointXYZRGB:\n return filtered_pointcloud\n else:\n return filtered_pointcloud.xyz","def plot(self, subsample=None, valid_instance_types=None):\n pt = self.sample_indices(subsample, valid_instance_types)\n\n x, y, z = self.pc[pt, 0], self.pc[pt, 1], self.pc[pt, 2]\n color = self.color[pt]\n\n return plot_pointcloud(x, y, z, color=color)","def show(data_set, number_points: int):\n print(f'info: Showing {number_points} as maximum.')\n sub_set_points = np.random.choice(range(data_set.shape[0]), size=min(data_set.shape[0], number_points))\n x = data_set[sub_set_points, 0]\n y = data_set[sub_set_points, 1]\n z = data_set[sub_set_points, 2]\n\n fig = plt.figure(figsize=(8, 8))\n ax = mplot3d.Axes3D(fig)\n ax.set_title('NMSLIB index 3D representation', fontsize=20)\n ax.scatter(xs=x, ys=y, zs=z)\n plt.show()","def _project_pointcloud(self, cloud):\n\n assert isinstance(cloud, PointCloud2)\n\n pc1 = PointCloud()\n pc1.header = cloud.header\n # hack the time! dont move the robot :-0\n pc1.header.stamp = rospy.Time.now()\n \n \n pc1.points = [Point32(*p) for p in pc2.read_points(cloud)]\n\n self._tf_listener.waitForTransform(pc1.header.frame_id,\n self._image_info.tf_frame, \n rospy.Time(0), \n rospy.Duration(4))\n\n image_frame_cloud = self._tf_listener.transformPointCloud (\n self._image_info.tf_frame, \n pc1)\n min_x, max_x, min_y, max_y = 640, 0, 480, 0 # TODO: remove hard coded image size!\n for pt in image_frame_cloud.points:\n u, v = self._image_info.project3dToPixel((pt.x, pt.y, pt.z))\n if v < min_y:\n min_y = int(v)\n if v > max_y:\n max_y = int(v)\n if u < min_x:\n min_x = int(u)\n if u > max_x:\n max_x = int(u)\n location = (((min_x, min_y), (max_x, max_y)))\n rospy.loginfo(\"Transformed cloud into image plane\")\n return location","def visualize(self):\n colors = {'outline': (220, 220, 220),\n 'inlier': (0, 255, 0),\n 'outlier': (0, 0, 255),\n 'lines': (128, 220, 128)}\n # Create output image for visualization\n gap = 5\n h1, w1 = self.target.image.shape[:2]\n h2, w2 = self.image.shape[:2]\n vis = np.zeros((max(h1, h2), w1 + w2 + gap, 3), np.uint8)\n vis[:h1, :w1, :] = self.target.image\n w1 += gap\n vis[:h2, w1:w1+w2, :] = self.image\n \n # Draw the located object \n quad = np.float32(self.quad) + np.float32([w1, 0])\n self.draw(vis, colors['outline'], 2, quad)\n \n # draw point details\n inliers = [(x0, y0, x1 + w1, y1) for (x0, y0), (x1, y1) in self.inliers]\n outliers = [(x0, y0, x1 + w1, y1) for (x0, y0), (x1, y1) in self.outliers]\n if colors['outlier'] is not None: # draw x on each point\n r = 2 # radius\n thickness = 2\n for x0, y0, x1, y1 in outliers:\n cv2.line(vis, (x0 - r, y0 - r), (x0 + r, y0 + r), colors['outlier'], thickness)\n cv2.line(vis, (x0 + r, y0 - r), (x0 - r, y0 + r), colors['outlier'], thickness)\n cv2.line(vis, (x1 - r, y1 - r), (x1 + r, y1 + r), colors['outlier'], thickness)\n cv2.line(vis, (x1 + r, y1 - r), (x1 - r, y1 + r), colors['outlier'], thickness)\n if colors['lines'] is not None:\n for x0, y0, x1, y1 in inliers:\n cv2.line(vis, (x0, y0), (x1, y1), colors['lines'], 1)\n if colors['inlier'] is not None:\n for x0, y0, x1, y1 in inliers:\n cv2.circle(vis, (x0, y0), 2, colors['inlier'], -1)\n cv2.circle(vis, (x1, y1), 2, colors['inlier'], -1)\n return vis","def point_cloud(self, X, Y, Z, size=1, color='#FF3232', bordercolor='#FF3232', legend='', width=0.5, opacity=1.0):\n point_cloud = go.Scatter3d(\n x=X,\n y=Y,\n z=Z,\n # showlegend=False,\n name=legend,\n mode='markers',\n marker=dict(\n size=size,\n color=color,\n line=dict(\n color=bordercolor,\n width=width\n ),\n # opacity=opacity\n )\n )\n\n return point_cloud","def visualize(self):\n self.octree.updateInnerOccupancy()\n print(\"Start Octomap Visualization\")\n\n # define parameters\n data = imgviz.data.arc2017()\n camera_info = data['camera_info']\n K = np.array(camera_info['K']).reshape(3, 3)\n width=camera_info['width']\n height=camera_info['height']\n\n # get free and occupied grid\n occupied, _ = self.octree.extractPointCloud()\n #frontier = self.gen_frontier()\n \n print(\"load point cloud\")\n window = pyglet.window.Window(\n width=int(1280), height=int(960)\n )\n\n @window.event\n def on_key_press(symbol, modifiers):\n if modifiers == 0:\n if symbol == pyglet.window.key.Q:\n window.on_close()\n\n gui = glooey.Gui(window)\n hbox = glooey.HBox()\n hbox.set_padding(5)\n\n camera = trimesh.scene.Camera(\n resolution=(width, height), focal=(K[0, 0], K[1, 1])\n )\n\n # initial camera pose\n camera_transform = np.array(\n [\n [1, 0, 0, 0],\n [0, -1, 0, 0],\n [0, 0, -1, -5],\n [0.0, 0.0, 0.0, 1.0],\n ],\n )\n\n \n\n occupied_geom = trimesh.voxel.ops.multibox(\n occupied, pitch=self.resolution, colors=[0.0, 0.0, 0.0, 0.5]\n )\n\n # frontier_geom = trimesh.voxel.ops.multibox(\n # frontier, pitch=self.resolution, colors=[1.0, 0, 0, 0.5]\n # )\n scene = trimesh.Scene(camera=camera, geometry=[occupied_geom])#, frontier_geom])\n scene.camera_transform = camera_transform\n hbox.add(self.labeled_scene_widget(scene, label='octomap'))\n\n\n gui.add(hbox)\n pyglet.app.run()","def display(self):\n scatter_plot(self.points, self.hull_points, self.color, self.title)","def vis_points(data,f1,f2):\n if np.isnan(data).any():\n return\n \n plt.scatter(data[:,f1], data[:,f2], alpha=0.2, c='b')\n plt.xlim(lims)\n plt.ylim(lims)","def show_pair_points(points, pairs, title, save_path = None):\n\n fig = plt.figure(figsize = (6, 6))\n plt.title(title)\n plt.scatter(points[:, 0], points[:, 1], c = 'b', s = 5, label = \"point cloud\")\n for pair in pairs:\n print(pair)\n point_1 = points[pair[0]]\n point_2 = points[pair[1]]\n plt.scatter([point_1[0], point_2[0]], [point_1[1], point_2[1]], s = 10)\n plt.xlabel(\"x\")\n plt.ylabel(\"y\")\n plt.axis('equal')\n plt.grid()\n plt.legend()\n plt.show()\n \n if type(save_path) != type(None):\n fig.savefig(save_path)","def show(self):\n from matplotlib import pyplot as plt\n from mpl_toolkits.mplot3d import Axes3D\n\n fig = plt.figure()\n ax = Axes3D(fig)\n pos = self.cluster.get_positions()\n from itertools import combinations\n for tri in self.mesh.simplices:\n for comb in combinations(tri, 2):\n x1 = pos[comb[0], 0]\n x2 = pos[comb[1], 0]\n y1 = pos[comb[0], 1]\n y2 = pos[comb[1], 1]\n z1 = pos[comb[0], 2]\n z2 = pos[comb[1], 2]\n ax.plot([x1, x2], [y1, y2], zs=[z1, z2], color=\"black\")\n plt.show()","def __show_source(self):\n pcd = o3d.io.read_point_cloud(\n self.source_cloud\n )\n if np.asarray(pcd.points).shape[0] != 0:\n pcd.paint_uniform_color([0, 1, 0])\n pcd.estimate_normals()\n self.source_point_cloud_view.load_cloud(pcd)\n try:\n self.source_point_cloud_view.show_window()\n except RuntimeError:\n pass\n else:\n QtWidgets.QMessageBox.warning(self, \"Error\",\n f\"Source point cloud is no longer available\"\n )\n self.source_cloud = \"\"\n self.__update_clickability()\n self.__save_context()","def open3dpaint(nppoints, color_map = 'jet', reduce_for_vis = False, voxel_size = 0.1, pointsize = 0.1):\n assert (type(nppoints) == pclpy.pcl.PointCloud.PointXYZRGB) or (type(nppoints) == pclpy.pcl.PointCloud.PointXYZ) or (type(nppoints) == np.ndarray) or (type(nppoints) is list) or (type(nppoints) is tuple), 'Not valid point_cloud'\n \n if (type(nppoints) is not list) & (type(nppoints) is not tuple):\n nppoints = [nppoints]\n try:\n visualizer = open3d.visualization.Visualizer()\n visualizer.create_window()\n options = visualizer.get_render_option()\n options.background_color = np.asarray([0, 0, 0])\n options.point_size = pointsize\n\n if len(nppoints) > 1:\n for n,i in enumerate(nppoints):\n workpoints = i\n if (type(workpoints) == pclpy.pcl.PointCloud.PointXYZRGB) or (type(workpoints) == pclpy.pcl.PointCloud.PointXYZ):\n workpoints = workpoints.xyz\n\n if reduce_for_vis:\n workpoints = seg_tree.voxelize(workpoints,voxel_size)\n\n points = convertcloud(workpoints)\n color_coef = n/len(nppoints)/2 + n%2*.5\n if type(color_map) == np.ndarray:\n color = color_map\n elif color_map == 'jet':\n color=cm.jet(color_coef)[:3]\n else:\n color=cm.Set1(color_coef)[:3]\n points.colors = open3d.utility.Vector3dVector(np.ones_like(workpoints)*color)\n #points.colors = open3d.utility.Vector3dVector(color)\n visualizer.add_geometry(points)\n else:\n workpoints = nppoints[0]\n if (type(workpoints) == pclpy.pcl.PointCloud.PointXYZRGB) or (type(workpoints) == pclpy.pcl.PointCloud.PointXYZ):\n workpoints = workpoints.xyz\n \n if reduce_for_vis:\n workpoints = seg_tree.voxelize(workpoints,voxel_size)\n points = convertcloud(workpoints)\n visualizer.add_geometry(points)\n visualizer.run()\n visualizer.destroy_window()\n \n except Exception as e:\n print(type(e))\n print(e.args)\n print(e)\n visualizer.destroy_window()","def plot(self):\n # Find only unmasked data :\n # xyz, sData, sColor, _ = self._select_unmasked()\n xyz, sData, sColor = self.xyz, self.sData, self.sColor\n\n # Render as cloud points :\n self.mesh = visu.Markers(name='Sources')\n self.mesh.set_data(xyz, edge_color=self.edgecolor, face_color=sColor,\n size=sData, scaling=self.scaling,\n edge_width=self.edgewidth, symbol=self.symbol)\n self.mesh.set_gl_state('translucent')","def show_2d_projections(self):\n\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\")\n fig, axs = plt.subplots(1, 3)\n for i in range(3):\n plt.sca(axs[i])\n io.imshow(self.image.max(i), cmap='viridis')","def show_keypoints(image, key_pts):\n plt.imshow(image)\n plt.scatter(key_pts[:, 0], key_pts[:, 1], s=20, marker='.', c='m')","def visualize(vis, features, label):\n if vis == 'PCA':\n #n_components = st.sidebar.slider(\"n_components\", 2, 10)\n #alpha = st.sidebar.slider(\"alpha\", 0.8, 2.0)\n #pca = PCA(n_components)\n pca = PCA(2)\n\n X_projected = pca.fit_transform(features)\n \n x1 = X_projected[:, 0]\n x2 = X_projected[:, 1]\n\n\n fig = plt.figure()\n plt.scatter(x1, x2, c=label, alpha=0.8, cmap='viridis')\n plt.xlabel(\"Principal Component 1\")\n plt.ylabel(\"Principal Component 2\")\n plt.colorbar()\n\n st.pyplot()","def show_feat(feat_map):\n for i in range(feat_map.shape[0]):\n plt.imshow(feat_map[i])\n plt.show()","def show_points(self, imsize):\n mask = np.zeros(imsize)\n for t in self.points:\n mask[t] = 1\n toimage(mask).show()","def visualize_clouds(clouds, paths):\n\n for cloud, path in zip(clouds, paths):\n print(\"Visualizing scans generated from:\", path)\n for scan in cloud:\n draw_geometries([scan])","def sample_and_plot(self):\n fig = plt.figure()\n ax = plt.axes(projection = '3d')\n ax.plot_surface(self.X, self.Y, self.sample(), cmap = plt.cm.jet, rstride = 2, cstride = 2, linewidth = 1)\n plt.show()","def save_3d_render(\r\n self, points: List[np.ndarray], colors: List[np.ndarray]\r\n ) -> None:\r\n pcd = o3d.geometry.PointCloud()\r\n pcd.points = o3d.utility.Vector3dVector(np.vstack(points).astype(np.float64))\r\n pcd.colors = o3d.utility.Vector3dVector(np.vstack(colors))\r\n if self.debug:\r\n o3d.visualization.draw_geometries([pcd])\r\n if not self.debug:\r\n o3d.io.write_point_cloud(f\"results/{self.filename[:-4]}.ply\", pcd)","def plotrgcloud(self):\n print self.kpunten\n for i in range(len(self.kpunten[0])):\n self.writetext('sen ='+ self.kpunten[0][i][0], (0.65,0.85), axnum = 0, hor = None ,ver = None , rot = None ,fs =14 , transform = self.fig.axes[0].transAxes)\n if i == len(self.kpunten[0]) -1 :\n end = None\n else:\n end = self.kpunten[0][i+1][1] + 1\n print end\n self.plotrgwrap( self.rgindex,2*self.reader.npair+self.rgindex,'real part of rgvars (a.u)' , 'imaginary part of rgvars (a.u.)', tit ='RG vars g = %f all states'%(self.chardata) , begin = self.kpunten[0][i][1] , stop = end , name = 'cpcloud'+ self.kpunten[0][i][0] , filenum = 0)","def __show_target(self):\n pcd = o3d.io.read_point_cloud(\n self.target_cloud\n )\n if np.asarray(pcd.points).shape[0] != 0:\n pcd.paint_uniform_color([0, 0, 1])\n pcd.estimate_normals()\n self.target_point_cloud_view.load_cloud(pcd)\n try:\n self.target_point_cloud_view.show_window()\n except RuntimeError:\n pass\n else:\n QtWidgets.QMessageBox.warning(self, \"Error\",\n f\"Target point cloud is no longer available\"\n )\n self.target_cloud = \"\"\n self.__update_clickability()\n self.__save_context()","def scatter(self, points):\n assert points.shape[0] == 3\n self.points = points\n self.remove_scatter_plot()\n x, y, z = zip(*points.T)\n self.base_fig.add_trace(go.Scatter3d(\n x=x, y=y, z=z,\n mode='markers',\n marker=dict(\n size=2,\n color=np.zeros(points.shape[1]),\n colorscale=\"RdBu\",\n colorbar=dict(\n title=\"Samples\",\n x=-0.2\n ),\n )\n ))","def mri_point_plot(self, vcol=1):\n img = self.voxels\n points = self.point_position \n ax = []\n fig = plt.figure(figsize=(9, 8))\n # TODO make this setable in the function call\n columns = 3\n rows = 2\n\n for i in range(points.shape[0]):\n im_slice = int(np.round(points[i, vcol]))\n if vcol == 0:\n im = img[im_slice, :, :]\n elif vcol == 1:\n im = img[:, im_slice, :]\n else:\n im = img[:, :, im_slice]\n ax.append( fig.add_subplot(rows, columns, i+1))\n ax[-1].set_title(\"Image depth: \"+str(im_slice)) # set title\n plt.imshow(im)\n plot_cols = np.array([0, 1, 2])\n plot_cols = plot_cols[plot_cols != vcol]\n plt.plot(points[i, min(plot_cols)], points[i, max(plot_cols)], 'ro')\n\n plt.show()","def render_radar_point_cloud(self, point_cloud, extrinsics=Pose(), color=RED, velocity=None, velocity_scale=10):\n combined_transform = self._bev_rotation * extrinsics\n\n pointcloud_in_bev = combined_transform * point_cloud\n point_cloud2d = pointcloud_in_bev[:, :2]\n\n point_cloud2d[:, 0] = (self._center_pixel[0] + point_cloud2d[:, 0] * self._pixels_per_meter)\n point_cloud2d[:, 1] = (self._center_pixel[1] + point_cloud2d[:, 1] * self._pixels_per_meter)\n\n H, W = self.data.shape[:2]\n uv = point_cloud2d.astype(np.int32)\n in_view = np.logical_and.reduce([\n (point_cloud2d >= 0).all(axis=1),\n point_cloud2d[:, 0] < W,\n point_cloud2d[:, 1] < H,\n ])\n uv = uv[in_view]\n\n for row in uv:\n cx, cy = row\n cv2.circle(self.data, (cx, cy), 7, RED, thickness=1)\n\n def clip_norm(v, x):\n M = np.linalg.norm(v)\n if M == 0:\n return v\n\n return np.clip(M, 0, x) * v / M\n\n if velocity is not None:\n tail = point_cloud + velocity_scale * velocity\n pointcloud_in_bev_tail = combined_transform * tail\n point_cloud2d_tail = pointcloud_in_bev_tail[:, :2]\n point_cloud2d_tail[:, 0] = (self._center_pixel[0] + point_cloud2d_tail[:, 0] * self._pixels_per_meter)\n point_cloud2d_tail[:, 1] = (self._center_pixel[1] + point_cloud2d_tail[:, 1] * self._pixels_per_meter)\n uv_tail = point_cloud2d_tail.astype(np.int32)\n uv_tail = uv_tail[in_view]\n for row, row_tail in zip(uv, uv_tail):\n v_2d = row_tail - row\n v_2d = clip_norm(v_2d, .025 * W)\n\n cx, cy = row\n cx2, cy2 = row + v_2d.astype(np.int)\n\n cx2 = np.clip(cx2, 0, W - 1)\n cy2 = np.clip(cy2, 0, H - 1)\n color = GREEN\n # If moving away from vehicle change the color (not strictly correct because radar is not a (0,0))\n # TODO: calculate actual radar sensor position\n if np.dot(row - np.array([W / 2, H / 2]), v_2d) > 0:\n color = (255, 110, 199)\n cv2.arrowedLine(self.data, (cx, cy), (cx2, cy2), color, thickness=1, line_type=cv2.LINE_AA)","def plot_wordcloud(word_list, file_name=\"\"):\n plt.figure(figsize=plt.figaspect(0.8), dpi=100)\n all_words = ' '.join([text for text in word_list])\n wordcloud = WordCloud(width=800, height=600, random_state=21, max_font_size=110).generate(all_words)\n plt.imshow(wordcloud, interpolation=\"bilinear\")\n plt.axis('off')\n if file_name:\n plt.savefig(file_name, bbox_inches='tight')\n plt.show()\n return","def get_point_cloud(self):\n tsdf_vol, color_vol = self.get_volume()\n\n # Marching cubes\n verts = measure.marching_cubes_lewiner(tsdf_vol, level=0)[0]\n verts_ind = np.round(verts).astype(int)\n verts = verts * self._voxel_size + self._vol_origin\n\n # Get vertex colors\n rgb_vals = color_vol[verts_ind[:, 0], verts_ind[:, 1], verts_ind[:, 2]]\n colors_b = np.floor(rgb_vals / self._color_const)\n colors_g = np.floor((rgb_vals - colors_b * self._color_const) / 256)\n colors_r = rgb_vals - colors_b * self._color_const - colors_g * 256\n colors = np.floor(np.asarray([colors_r, colors_g, colors_b])).T\n colors = colors.astype(np.uint8)\n\n pc = np.hstack([verts, colors])\n return pc","def show_point_set(points, title, save_path = None):\n dim = points.shape[1]\n assert(dim == 2 or dim == 3), \"Only data in 2 or 3 dimension space can be visualized.\"\n \n if dim == 2:\n fig = plt.figure(figsize = (6, 6))\n plt.title(title)\n plt.scatter(points[:, 0], points[:, 1], c = 'b', s = 5)\n plt.xlabel(\"x\")\n plt.ylabel(\"y\")\n plt.axis('equal')\n plt.grid()\n plt.show()\n elif dim == 3:\n fig = plt.figure(figsize = (6, 6))\n ax = fig.add_subplot(111, projection='3d')\n ax.set_title(title)\n ax.scatter(points[:, 0], points[:, 1], points[:, 2], c = 'b', s = 5)\n ax.set_xlabel(\"x\")\n ax.set_ylabel(\"y\")\n ax.set_zlabel(\"z\")\n plt.show()\n\n if type(save_path) != type(None):\n fig.savefig(save_path)","def render_points_2d(self, **kwds):\n return point2d(self.coordinates_of(self.points), **kwds)","def draw_clouds(a):\n small_cloud(0 + a, 0, 0)\n small_cloud(200 + a, -150, -10)\n big_cloud(350 + a, 0, -3)\n small_cloud(600 + a, -90, 3)\n small_cloud(800 + a, 0, 0)\n small_cloud(1000 + a, -150, -10)\n big_cloud(1150 + a, 0, -3)\n small_cloud(1400 + a, -90, 3)\n small_cloud(-800 + a, 0, 0)\n small_cloud(-600 + a, -150, -10)\n big_cloud(-450 + a, 0, -3)\n small_cloud(-200 + a, -90, 3)","def plotsite(self):\n if self.dimension==1 or self.dimension==2:\n for site in self.sites.flatten():\n plt.scatter(site.coordinate[0],site.coordinate[1],marker=site.marker,s=site.markersize,c=site.color)\n elif self.dimension==3:\n from mpl_toolkits.mplot3d import Axes3D\n fig = plt.figure()\n ax = Axes3D(fig)\n for site in self.sites.flatten():\n ax.scatter(site.coordinate[0],site.coordinate[1],site.coordinate[2],marker=site.marker,s=site.markersize,c=site.color)","def imshow_surface(self):\n plt.imshow(self.z)\n plt.colorbar()\n plt.show()","def _build_point_cloud_graph(self):\n depths = self.depths_placeholder[tf.newaxis]\n images = self.images_placeholder[tf.newaxis]\n poses = self.poses_placeholder[tf.newaxis]\n intrinsics = self.intrinsics_placeholder[tf.newaxis]\n intrinsics = intrinsics_vec_to_matrix(intrinsics)\n\n depths_pad = tf.pad(depths, [[0,0],[0,0],[0,1],[0,1]], \"CONSTANT\")\n\n depths_grad = \\\n (depths_pad[:, :, 1:, :-1] - depths_pad[:, :, :-1, :-1])**2 + \\\n (depths_pad[:, :, :-1, 1:] - depths_pad[:, :, :-1, :-1])**2\n\n # don't use large depths for point cloud and ignore boundary regions\n valid = (depths < 3.0) & (depths_grad < 0.001)\n\n batch, num, ht, wd = tf.unstack(tf.shape(depths), num=4)\n Ts = VideoSE3Transformation(matrix=poses)\n X0 = projective_ops.backproject(depths, intrinsics)\n \n # transform point cloud into world coordinates\n X1 = Ts.inv()(X0)\n\n crop_h = 12\n crop_w = 32\n\n X1 = X1[:, :, crop_h:-crop_h, crop_w:-crop_w]\n valid = valid[:, :, crop_h:-crop_h, crop_w:-crop_w]\n images = images[:, :, crop_h:-crop_h, crop_w:-crop_w, ::-1]\n \n X1 = tf.reshape(X1, [-1, 3])\n colors = tf.reshape(images, [-1, 3])\n\n valid_inds = tf.where(tf.reshape(valid, [-1]))\n valid_inds = tf.reshape(valid_inds, [-1])\n\n X1 = tf.gather(X1, valid_inds, axis=0)\n colors = tf.gather(colors, valid_inds, axis=0)\n\n self.outputs['point_cloud'] = (X1, colors)","def show_points_on_img(mask,img):\n labeled, num_objects = ndi.label(mask)\n slices = ndi.find_objects(labeled)\n x, y = [], []\n for dy,dx in slices:\n x_center = (dx.start + dx.stop - 1)/2\n x.append(x_center)\n y_center = (dy.start + dy.stop - 1)/2 \n y.append(y_center)\n plt.figure()\n plt.imshow(img)\n plt.autoscale(False)\n plt.plot(x,y, \"o\")","def show_transformed_point_set(points_1, points_2, points_12, loss, save_path = None):\n\n fig = plt.figure(figsize = (6, 6))\n plt.title(\"Loss = %f\" % loss)\n plt.scatter(points_1[:, 0], points_1[:, 1], s = 5, label = \"point cloud 1\")\n plt.scatter(points_2[:, 0], points_2[:, 1], s = 5, label = \"point cloud 2\")\n plt.scatter(points_12[:, 0], points_12[:, 1], s = 5, label = \"point cloud 1 to 2\")\n plt.xlabel(\"x\")\n plt.ylabel(\"y\")\n plt.axis('equal')\n plt.grid()\n plt.legend()\n plt.show()\n \n if type(save_path) != type(None):\n fig.savefig(save_path)","def draw_features(self, image):\n \n for x,y in self.new_points.reshape(-1,2):\n cv2.circle(image, (x,y), 2, (255,0,255), 2)\n return image","def show_orbits(points, nb_pairs, title, save_path = None):\n\n fig = plt.figure(figsize = (6, 6))\n plt.title(title)\n plt.scatter(points[:, 0], points[:, 1], c = 'b', s = 5, label = \"point cloud\")\n for i in range(len(nb_pairs)):\n plt.scatter(nb_pairs[i][0][:, 0], nb_pairs[i][0][:, 1], s = 5, label = \"%d_1\" % i)\n plt.scatter(nb_pairs[i][1][:, 0], nb_pairs[i][1][:, 1], s = 5, label = \"%d_2\" % i)\n plt.xlabel(\"x\")\n plt.ylabel(\"y\")\n plt.axis('equal')\n plt.grid()\n plt.legend()\n plt.show()\n \n if type(save_path) != type(None):\n fig.savefig(save_path)","def point_from_rays(self):\n print \"generating the 3d point from given clicked points\"\n \n #gather cams and points clicked \n uvs = []\n cams = []\n for iFrame in self.frames:\n if iFrame.lastClick : \n uv = numpy.multiply(iFrame.lastClick,self.reduceFactor)\n uvs.append(uv)\n cam = load_perspective_camera(self.camList[iFrame.currImg])\n cams.append(cam)\n point = get_3d_from_cams(cams, uvs)\n self.point3d = point;\n self.pointLabel.set(\"3d Point: \" + str(self.point3d))\n\n # project 3d point into each image, and gather intensities \n values = []\n ims = []\n for idx, img in enumerate(self.imgList):\n cam = load_perspective_camera(self.camList[idx])\n imgPoint = project_point(cam, point[0], point[1], point[2])\n imgPoint = numpy.divide(imgPoint, self.reduceFactor)\n self.allUVs.append(imgPoint)\n \n #grab float intensity value at this point \n imgView,ni,nj = load_image(img)\n val = pixel(imgView, imgPoint)\n if val > 0.0:\n values.append(val)\n ims.append(idx)\n \n #cleanup\n remove_from_db([imgView, cam])\n \n\n #write mean/std of intensities \n self.meanLabel.set(\"Mean: \" + str(numpy.mean(values)) )\n self.stdLabel.set(\"Std Dev: \" + str(numpy.std(values)) )\n #plot the intensities by image number \n self.f.clf();\n self.a = self.f.add_subplot(311)\n self.a.set_xlabel(\"img #\")\n self.a.set_ylabel(\"intensity\")\n self.a.plot(ims, values)\n #plot the histogram of intensities by image number \n pdf, bins, patches = plt.hist(values)\n self.b = self.f.add_subplot(313)\n self.b.set_xlabel(\"bin val\")\n self.b.set_ylabel(\"freq\")\n self.b.hist(values, 15, normed=1, facecolor=\"green\" )\n self.canvas.show();","def showAsPoints(self): \n MonkeyPatchMayaVi()\n #import enthought.mayavi.mlab as mlab\n from mayavi import mlab\n \n @mlab.show\n def _showSimple():\n morphPts = [ SVVisitorFactory.Array4AllPoints(morph)() for morph in self.morphs ]\n pts = numpy.concatenate( morphPts)\n return mlab.points3d(pts[:, 0], pts[:, 1], pts[:, 2], pts[:, 3], colormap=self.colormap, scale_factor=self.scale_factor)\n _showSimple()","def plot_surface(self):\n X, Y = np.meshgrid(self.x, self.y)\n fig = plt.figure()\n ax = fig.add_subplot(111, projection='3d')\n ax.plot_surface(X=X, Y=Y, Z=self.z)\n plt.show()","def show_trace_2d(f, results): #@save\n set_figsize()\n plt.plot(*zip(*results), '-o', color='#ff7f0e')\n x1, x2 = torch.meshgrid(torch.arange(-5.5, 1.0, 0.1),torch.arange(-3.0, 1.0, 0.1))\n plt.contour(x1, x2, f(x1, x2), colors='#1f77b4')\n plt.xlabel('x1')","def plot_2d(vector_array, save_plot_dir):\n principal_df = pd.DataFrame(data = vector_array, columns = ['pc1', 'pc2'])\n fig = plt.figure()\n ax = fig.add_subplot(111)\n\n xs = principal_df['pc1']\n ys = principal_df['pc2']\n ax.scatter(xs, ys, s=50, alpha=0.6, edgecolors='w')\n\n ax.set_xlabel('pc1')\n ax.set_ylabel('pc2')\n\n plt.savefig(save_plot_dir + '/2D_scatter.png')\n plt.close()","def show_skeletons(self, skel_2d, z_out, z_gt=None):\n fig = plt.figure(figsize=(20, 20))\n ax1 = fig.add_subplot(1, 2, 1)\n ax2 = fig.add_subplot(1, 2, 2, projection='3d')\n edges = np.array([[1, 0], [0, 2],[2, 3], [3, 4], [0, 5], [5, 6], [6, 7]])\n\n ax_2d = ax1\n ax_3d = ax2\n\n # draw 3d\n for edge in edges:\n ax_3d.plot(skel_2d[0, edge], z_out[edge], skel_2d[1, edge], color='r')\n if z_gt is not None:\n ax_3d.plot(skel_2d[0, edge], z_gt[edge], skel_2d[1, edge], color='g')\n\n ax_3d.set_aspect('equal')\n ax_3d.set_xlabel(\"x\"), ax_3d.set_ylabel(\"z\"), ax_3d.set_zlabel(\"y\")\n ax_3d.set_xlim3d([-2, 2]), ax_3d.set_ylim3d([2, -2]), ax_3d.set_zlim3d([2, -2])\n ax_3d.view_init(elev=10, azim=-45)\n\n # draw 2d\n for edge in edges:\n ax_2d.plot(skel_2d[0, edge], skel_2d[1, edge], color='r')\n\n ax_2d.set_aspect('equal')\n ax_2d.set_xlabel(\"x\"), ax_2d.set_ylabel(\"y\")\n ax_2d.set_xlim([-2, 2]), ax_2d.set_ylim([2, -2])\n\n plt.show()","def show_wordcloud(dictionary, title, min_font = 10):\n wordcloud = WordCloud(min_font_size=min_font).generate_from_frequencies(dictionary)\n plt.figure(figsize = (8, 8), facecolor = None) \n plt.imshow(wordcloud) \n plt.axis(\"off\")\n if title:\n plt.title(title)\n else:\n plt.title(\"Word Cloud\")\n plt.tight_layout(pad = 0) \n\n plt.show()","def show_point_set_normal(points, normals, title, save_path = None):\n dim = points.shape[1]\n assert(dim == 2), \"Only data in 2 dimension space can be visualized.\"\n \n fig = plt.figure(figsize = (6, 6))\n plt.title(title)\n plt.scatter(points[:, 0], points[:, 1], c = 'b', s = 5, label = \"point cloud\")\n plt.quiver(points[:, 0], points[:, 1], normals[:, 0], normals[:, 1])\n plt.xlabel(\"x\")\n plt.ylabel(\"y\")\n plt.axis('equal')\n plt.grid()\n plt.legend()\n plt.show()\n \n if type(save_path) != type(None):\n fig.savefig(save_path)","def view_point_cloud_model_and_affordance(number_of_object_per_category=5):\n list_pc_paths = [f for f in glob.glob('./dataset/*.npy', recursive=True)]\n set_objects = set([os.path.basename(pc_path).split('_')[0] for pc_path in list_pc_paths])\n\n for obj in set_objects:\n try:\n # load point cloud models\n pc_models = np.load('./dataset/{}_point_cloud_models.npy'.format(obj))[\n :number_of_object_per_category]\n # load point cloud grasp affordance\n pc_affordance = np.load('./dataset/{}_point_cloud_grasp_affordance.npy'.format(obj))[\n :number_of_object_per_category]\n\n # visualization\n for i, m in enumerate(pc_models):\n fig = plt.figure()\n ax = fig.add_subplot(projection='3d')\n list_model_x, list_model_y, list_model_z = [], [], []\n list_affordance_x, list_affordance_y, list_affordance_z = [], [], []\n\n for x in range(32):\n for y in range(32):\n for z in range(32):\n if pc_affordance[i, x, y, z] == 1:\n list_affordance_x.append(x)\n list_affordance_y.append(y)\n list_affordance_z.append(z)\n elif m[x, y, z] == 1:\n list_model_x.append(x)\n list_model_y.append(y)\n list_model_z.append(z)\n\n ax.scatter(list_model_x, list_model_y, list_model_z, c='#0c457d')\n ax.scatter(list_affordance_x, list_affordance_y, list_affordance_z, c='#e8702a', alpha=0.35)\n ax.set_xlim(0, 32)\n ax.set_ylim(0, 32)\n ax.set_zlim(0, 32)\n plt.show()\n\n except FileNotFoundError:\n print('Some point cloud npy files are not found.')\n continue","def show_scatter(self):\n plt.scatter(self.a1[:, 0], self.a1[:, 1], c=\"red\", alpha=0.5, s=10)\n plt.scatter(self.a2[:, 0], self.a2[:, 1], c=\"blue\", alpha=0.5, s=10)\n plt.scatter(0, 0, marker=\"D\", c=\"black\", alpha=0.8)\n plt.scatter(2, 2, marker=\"D\", c=\"black\", alpha=0.8)\n plt.show()","def image_to_point_cloud(depth):\n cx, cy, fx, fy = 5.8818670481438744*100, 3.1076280589210484*100, 5.8724220649505514*100, 2.2887144980135292*100\n depth = depth[:,:,0]\n\n rows, cols = depth.shape\n c, r = np.meshgrid(np.arange(cols), np.arange(rows), sparse=True)\n valid = (depth > 0) & (depth < 255)\n # print(valid)\n z = np.where(valid, depth / 256.0, np.nan)\n x = np.where(valid, z * (c - cx) / fx, 0)\n y = np.where(valid, z * (r - cy) / fy, 0)\n return np.dstack((x, y, z))","def generate_image(self,true_dist):\n N_POINTS = 128\n RANGE = 3\n\n points = np.zeros((N_POINTS, N_POINTS, 2), dtype='float32')\n points[:, :, 0] = np.linspace(-RANGE, RANGE, N_POINTS)[:, None]\n points[:, :, 1] = np.linspace(-RANGE, RANGE, N_POINTS)[None, :]\n points = points.reshape((-1, 2))\n\n plt.clf()\n\n #true_dist = true_dist.cpu().data.numpy()\n samples = self.G(self.sample_z_)\n print('generate size is',samples.size())\n samples = samples.cpu().data.numpy()\n\n x = y = np.linspace(-RANGE, RANGE, N_POINTS)\n # plt.contour(x, y, disc_map.reshape((len(x), len(y))).transpose())\n\n plt.scatter(true_dist[:, 0], true_dist[:, 1], c='orange', marker='+')\n plt.scatter(samples[:, 0], samples[:, 1], c='green', marker='+')\n plt.show()","def showAllPoints(self, surface, **kwargs):\n if not \"point_color\" in kwargs:\n kwargs[\"point_color\"] = self.point_color\n if not \"point_radius\" in kwargs:\n kwargs[\"point_radius\"] = self.point_radius\n if not \"point_mode\" in kwargs:\n kwargs[\"point_mode\"] = self.point_mode\n if not \"point_size\" in kwargs:\n kwargs[\"point_size\"] = self.point_size\n if not \"point_width\" in kwargs:\n kwargs[\"point_width\"] = self.point_width\n if not \"point_fill\" in kwargs:\n kwargs[\"point_fill\"] = self.point_fill\n for point in self.points:\n point.show(surface,\n color=kwargs[\"point_color\"],\n mode=kwargs[\"point_mode\"],\n fill=kwargs[\"point_fill\"],\n radius=kwargs[\"point_radius\"],\n size=kwargs[\"point_size\"],\n width=kwargs[\"point_width\"])","def visualize_output(self, keyframe_index):\n\n self.vis_counter += 1\n keyframe_image = self.images[keyframe_index]\n keyframe_depth = self.depths[keyframe_index]\n keyframe_pose = self.poses[keyframe_index]\n\n feed_dict = {\n self.images_placeholder: keyframe_image[np.newaxis],\n self.depths_placeholder: keyframe_depth[np.newaxis],\n self.poses_placeholder: keyframe_pose[np.newaxis],\n self.intrinsics_placeholder: self.intrinsics}\n\n keyframe_point_cloud, keyframe_point_colors = \\\n self.sess.run(self.outputs['point_cloud'], feed_dict=feed_dict)\n\n pointcloud = (keyframe_point_cloud, keyframe_point_colors)\n\n # only add the point cloud once in every 5 frames\n if self.vis_counter % 4 == 0:\n self.queue.put((pointcloud, keyframe_pose))\n \n else:\n self.queue.put((None, keyframe_pose))","def format_point_cloud(self, pc, num_points):\n resized_pc = np.zeros([num_points, 3])\n if len(pc) > num_points:\n # Randomly sample point cloud to reduce size\n resized_pc = pc[np.random.choice(pc.shape[0], num_points,\n replace=False), :]\n elif len(pc) < num_points:\n # Duplicate last point to fill point cloud. Because of the max function\n # the duplicated points will not affect the output\n resized_pc[:len(pc)] = pc\n resized_pc[len(pc):] = pc[-1]\n else:\n resized_pc = pc\n\n return resized_pc","def test_get_mesh_grid_as_point_cloud_single_pt() -> None:\n min_x = -3 # integer, minimum x-coordinate of 2D grid\n max_x = -3 # integer, maximum x-coordinate of 2D grid\n min_y = 2 # integer, minimum y-coordinate of 2D grid\n max_y = 2 # integer, maximum y-coordinate of 2D grid\n\n # return pts, a Numpy array of shape (N,2)\n pts = mesh_grid_utils.get_mesh_grid_as_point_cloud(\n min_x, max_x, min_y, max_y, downsample_factor=1.0\n )\n\n assert pts.shape == (1, 2)\n gt_pts: NDArrayFloat = np.array([[-3.0, 2.0]])\n\n assert np.allclose(gt_pts, pts)","def test_get_mesh_grid_as_point_cloud_3x2rect() -> None:\n min_x = -3 # integer, minimum x-coordinate of 2D grid\n max_x = -1 # integer, maximum x-coordinate of 2D grid\n min_y = 2 # integer, minimum y-coordinate of 2D grid\n max_y = 3 # integer, maximum y-coordinate of 2D grid\n\n # return pts, a Numpy array of shape (N,2)\n pts = mesh_grid_utils.get_mesh_grid_as_point_cloud(\n min_x, max_x, min_y, max_y, downsample_factor=1.0\n )\n\n assert pts.shape == (6, 2)\n # fmt: off\n gt_pts: NDArrayFloat = np.array(\n [\n [-3.0, 2.0],\n [-2.0, 2.0],\n [-1.0, 2.0],\n [-3.0, 3.0],\n [-2.0, 3.0],\n [-1.0, 3.0]\n ])\n # fmt: on\n assert np.allclose(gt_pts, pts)","def show_points(self, **kwargs):\n\n if hasattr(self, 'method') and hasattr(self.method, 'show_points'):\n self.method.show_points(self, **kwargs)\n else:\n figsize = kwargs.get('figsize', (15, 5))\n cmap = kwargs.get('cmap', 'gray')\n marker = kwargs.get('marker', '.')\n color = kwargs.get('color', 'r')\n fig, ax = plt.subplots(figsize=figsize)\n ax.imshow(self.mraw[0].astype(float), cmap=cmap)\n ax.scatter(self.points[:, 1], self.points[:, 0], \n marker=marker, color=color)\n plt.grid(False)\n plt.show()","def partial_visualize_in_2d(self, cluster_index=[5,12,35,44,64,75,81]):\n for i in cluster_index:\n list_x = []\n list_y = []\n for j in self.cluster[i]:\n list_x.append(self.code[0][j,0])\n list_y.append(self.code[0][j,1])\n plt.scatter(list_x,list_y, label=self.skill[self.present_skill[i]])\n plt.legend()\n plt.show()\n return","def plot_scatter_points(self):\n self.plot(1)","def view(config_file):\n import open3d as o3d\n with open(config_file) as f:\n config = json.load(f)\n scenes = get_realsense_scenes(config['realsense_dir'])\n for scene in scenes:\n # if scene['scene_name'] != \"Scene_004\":\n # continue\n scene_data = get_data_from_scene(scene)\n logger.info(\"Visualizing - %s\", scene['scene_name'])\n pcd = o3d.geometry.PointCloud(o3d.utility.Vector3dVector(scene_data['points3d']))\n o3d.visualization.draw_geometries_with_editing([pcd])\n pcd = o3d.geometry.PointCloud(o3d.utility.Vector3dVector(scene_data['points3d_segmented']))\n o3d.visualization.draw_geometries([pcd])","def plot_data(self):\n if hasattr(self,'data'):\n plt.scatter(*self.data.T)\n plt.show()\n else:\n raise Exception('No 2d data of the instance has been loaded')","def pangolin_draw(points):\n display.setup()\n\n while not display.should_quit():\n display.init_frame()\n for idx, pose in enumerate(CAMERA_POSES):\n is_last_pose = idx == len(CAMERA_POSES) - 1\n homogenous_pose = np.vstack((pose, [0, 0, 0, 1]))\n display.draw_camera(homogenous_pose, (0.0, 1.0, 1.0 if is_last_pose else 0.0))\n\n display.draw_points(points)\n\n display.finish_frame()","def plot_data(x):\n if DATA_2D:\n plt.scatter(x[:, 0], x[:, 1])\n plt.show()\n else:\n fig = plt.figure()\n ax = Axes3D(fig)\n ax.scatter(x[:, 0], x[:, 1], x[:, 2])\n ax.set_xlabel('X Label')\n ax.set_ylabel('Y Label')\n ax.set_zlabel('Z Label')\n plt.show()","def PointCloudfromStructOutput(self,file):\n print(\"Creating Structure Point Cloud\")\n xyz = self.readStructOutput(file)\n pc = np.zeros((int(len(xyz)/2.0),3))\n pc[:,0] = xyz[::2,0]*1000\n pc[:,1] = xyz[::2,1]*1000\n pc[:,2] = xyz[::2,2]*1000\n head = \"\"\"X,Y,Z\"\"\"\n np.savetxt(file, pc, delimiter=',',fmt='%.10f', header=head)\n return","def showFastCrossPoints(self, surface, color=None, mode=None, radius=None, width=None, size=None):\n points = self.crossSelf()\n if not color:\n color = self.cross_point_color\n if not mode:\n mode = self.cross_point_mode\n if not radius:\n radius = self.cross_point_radius\n if not width:\n width = self.cross_point_width\n if not size:\n size = self.cross_point_size\n for point in points:\n point.show(surface, color=color, mode=mode, radius=radius, width=width, size=size)","def plot_samples(s):\r\n assert len(s[0, :]) >= 2, ('The Phase space dimensions are less than two.', ' Need at least two to plot.')\r\n fig = plt.figure(1)\r\n if len(s[0, :]) >= 3:\r\n ax = fig.add_subplot(111, projection='3d')\r\n ax.scatter(s[:, 0], s[:, 1], s[:, 2])\r\n fig = plt.figure(2)\r\n plt.scatter(s[:, 0], s[:, 1])\r\n plt.show()","def viz1(img, interest_points, color='r'):\n\n\tx = [a[1] for a in interest_points] #blob detection x axis\n\ty = [a[0] for a in interest_points] #blob detection y axis\n\ts = [a[2] for a in interest_points] #blob detected at sigma \n \n\tplt.imshow(img, cmap='gray') #adding the input image to plot \n\tfor x, y, s in zip(x, y, s):\n\t\tplt.scatter(x, y, alpha=1, facecolors='none', edgecolors='r', s=s**2) #plotting the input interest points\n\tplt.axis('off')\n\t#plt.show() #showing the image // can be changed to saving the image locally \n\n\treturn","def show_grid(self):\n\n if not os.path.exists(self.path_to_results):\n os.mkdir(self.path_to_results)\n\n fig = plt.figure()\n\n if self.show_points == 1:\n plt.scatter(self.x_list_grid, self.y_list_grid, c='blue')\n\n plt.plot(self.x_list_main, self.y_list_main,\n 'green', label='straight path')\n plt.plot(self.x_list, self.y_list, 'red', label='first path')\n plt.plot(self.x_list_filtered, self.y_list_filtered,\n 'blue', label='filtered path')\n plt.title('Paths')\n plt.ylabel('Latitude')\n plt.xlabel('Longitude')\n # plt.legend()\n\n fig.savefig(os.path.join(self.path_to_results, 'Paths.png'))","def draw_birdseye_points(self, image, points):\n point_image = np.copy(image)\n for point in points:\n cv2.circle(point_image, point, 5, [0, 0, 255], cv2.FILLED)\n\n cv2.imshow('image1', point_image)\n cv2.waitKey(1)","def plt3dpaint(nppoints, color_map = 'jet', reduce_for_vis = True, voxel_size = 0.2, pointsize = 0.1, subplots = 5):\n assert (type(nppoints) == pclpy.pcl.PointCloud.PointXYZRGB) or (type(nppoints) == pclpy.pcl.PointCloud.PointXYZ) or (type(nppoints) == np.ndarray) or (type(nppoints) is list) or (type(nppoints) is tuple), 'Not valid point_cloud'\n cloudlist = []\n cloudcolors = []\n if (type(nppoints) is not list) & (type(nppoints) is not tuple):\n nppoints = [nppoints]\n \n if len(nppoints) > 1:\n for n,i in enumerate(nppoints):\n workpoints = i\n if (type(workpoints) == pclpy.pcl.PointCloud.PointXYZRGB) or (type(workpoints) == pclpy.pcl.PointCloud.PointXYZ):\n workpoints = workpoints.xyz\n\n if reduce_for_vis:\n workpoints = seg_tree.voxelize(workpoints,voxel_size)\n\n \n cloudmin = np.min(workpoints[:,2])\n cloudmax = np.max(workpoints[:,2])\n \n points = workpoints\n color_coef = n/len(nppoints)/2 + n%2*.5\n if type(color_map) == np.ndarray:\n color = color_map\n elif color_map == 'jet':\n color=cm.jet(color_coef)[:3]\n else:\n color=cm.Set1(color_coef)[:3]\n cloudcolors.append(np.ones_like(workpoints)*color + 0.4*(np.ones_like(workpoints) * ((workpoints[:,2] - cloudmin)/(cloudmax - cloudmin)).reshape(-1,1)-0.5) )\n cloudlist.append(points)\n else:\n workpoints = nppoints[0]\n if (type(workpoints) == pclpy.pcl.PointCloud.PointXYZRGB) or (type(workpoints) == pclpy.pcl.PointCloud.PointXYZ):\n workpoints = workpoints.xyz\n\n if reduce_for_vis:\n workpoints = seg_tree.voxelize(workpoints,voxel_size)\n cloudcolors.append(workpoints[:,2])\n cloudlist.append(workpoints)\n\n plt_pointcloud = np.concatenate(cloudlist)\n plt_colors = np.concatenate(cloudcolors)\n if len(nppoints) > 1:\n plt_colors = np.minimum(plt_colors,np.ones_like(plt_colors))\n plt_colors = np.maximum(plt_colors,np.zeros_like(plt_colors))\n fig = plt.figure(figsize=(30,16) )\n for i in range(subplots):\n ax = fig.add_subplot(1, subplots, i+1, projection='3d')\n ax.view_init(30, 360*i/subplots)\n ax.scatter3D(plt_pointcloud[:,0], plt_pointcloud[:,1], plt_pointcloud[:,2], c=plt_colors, s=pointsize)","def plot(self):\n\t\tif (2 <= len(self.X) <= 3):\n\t\t\tvDataFrame(self.name, self.cursor).scatter(columns = self.X, catcol = \"dbscan_cluster\", max_cardinality = 100, max_nb_points = 10000)\n\t\telse:\n\t\t\traise ValueError(\"Clustering Plots are only available in 2D or 3D\")","def plot(self):\n y = self.projection\n mpl.scatter(y[:, 0], y[:, 1], c=self.data_class)\n mpl.show()","def imshow(self, depth):\n layer = self.cube[depth]\n img = []\n for i in range(self.height):\n img.append([layer[i][j].value for j in range(self.width)])\n plt.imshow(img, cmap='gray')\n plt.show()","def show2d(self, data):\n self.__plot2d.clear()\n self.__plot2d.addImage(legend=\"data\", data=data)\n self.setCurrentIndex(self.__index2d)","def visualise_cppn(self, resolution=(64, 64)):\n import matplotlib.pyplot as plt\n from matplotlib.pyplot import imshow\n data = np.empty([resolution[0], resolution[1]])\n x_linspace = np.linspace(-1, 1, resolution[0])\n y_linspace = np.linspace(-1, 1, resolution[1])\n for row, x in enumerate(x_linspace):\n for col, y in enumerate(y_linspace):\n data[row, col] = self.graph(np.array([x, y, 0, 0], dtype=np.float32))[0]\n #plt.axis([-1, 1, -1, 1])\n print(data.min(), \" \", data.max())\n imshow(data, cmap='Greys', vmin=-1, vmax=1)\n plt.show()","def plot_imshow(data, col_name):\n city_x,city_y = get_city_base()\n fig = plt.figure(figsize = FIGURE_SIZE)\n plt.imshow(data[['lat_ind','lng_ind',col_name]], cmap=CONTOUR_PLOT_COLOUR)\n plt.scatter(city_x,city_y, color = CITY_MAP_COLOR, s = SCATTER_SIZE_OF_CHICAGO_CITY, zorder = CITY_MAP_ORDER)","def draw_point_gt(img,point_gt,width=3):\n point_img=copy.deepcopy(img)\n\n for i in range(point_gt.shape[0]):\n x=int(point_gt[i,0])\n y=int(point_gt[i,1])\n point_img=cv2.circle(point_img,center=(x,y),radius=width//2+1,color=(0,0,255),thickness=width)\n\n return point_img","def displayDensePts(dense_pts, dense_slices, origin, transform, dense_displacement_all=False, dense_plot_quiver=0, timepoint=-1, ax=None):\n\t# If no axes were passed, generate axes\n\tif not ax:\n\t\tfig = mplt.figure()\n\t\tax = fig.add_subplot(111, projection='3d')\n\t\n\t# Pull appropriate timepoint (or set to False if displacement is undesired)\n\tif timepoint >= 0:\n\t\ttry:\n\t\t\tdense_displacement = dense_displacement_all[timepoint]\n\t\texcept(IndexError):\n\t\t\twarnings.warn('Timepoint not in range of values! Displacement will be ignored.')\n\t\t\ttimepoint = -1\n\t\t\tdense_displacement = False\n\t\texcept(TypeError):\n\t\t\twarnings.warn('Displacement not passed, but timepoint requested! Displacement will be ignored.')\n\t\t\tdense_displacement = False\n\telse:\n\t\tdense_displacement = False\n\t\n\tfor i in range(len(dense_slices)):\n\t\tcur_slice = dense_slices[i]\n\t\tcur_dense_pts = np.column_stack((dense_pts[i], [cur_slice - origin[2]]*dense_pts[i].shape[0]))\n\t\tdata_dense = np.dot(cur_dense_pts, np.transpose(transform))\n\t\tx = data_dense[:, 2]\n\t\ty = data_dense[:, 1]\n\t\tz = data_dense[:, 0]\n\t\tax.scatter(x, y, -z, ',')\n\t\t\n\t\tif dense_displacement:\n\t\t\tdense_displacement_slice = dense_displacement[i]\n\t\t\tif dense_plot_quiver == 1:\n\t\t\t\tax.quiver(x, y, -z, dense_displacement_slice[:, 0], dense_displacement_slice[:, 1], [0]*dense_displacement_slice.shape[0])\n\treturn(ax)","def trimesh_from_point_cloud(cloud):\n points = np.asarray(cloud)\n hull = scipy.spatial.ConvexHull(points)\n hull = scipy.spatial.ConvexHull(points[hull.vertices])\n ru.transforms.counterclockwise_hull(hull)\n vertices = hull.points\n faces = hull.simplices\n return vertices, faces","def occupancy_grid(self, point_cloud=None, ss_pred=None, *, return_image=False, show=False, keep_showing=False):\n\t\tif point_cloud is None or ss_pred is None:\n\t\t\tgen = self.loop(point_cloud=True, ss_pred=True)\n\t\t\tpoint_cloud, ss_pred = next(gen)\n\t\t#TODO: Check that this function works with ZED camera\n\t\tpass","def plot(self):\n\n fig = plt.figure()\n ax = fig.add_subplot(111, projection=Axes3D.name)\n\n # TODO Use numpy to rotate esp_points matrix for faster variable access.\n ax.scatter(\n xs=[i[0][0] for i in self.esp_points],\n ys=[i[0][1] for i in self.esp_points],\n zs=[i[0][2] for i in self.esp_points],\n c=[i[1] for i in self.esp_points],\n marker='o',\n s=2,\n alpha=0.5\n )\n\n ax.scatter(\n xs=[i[0][0] for i in self.atom_points],\n ys=[i[0][1] for i in self.atom_points],\n zs=[i[0][2] for i in self.atom_points],\n c=[i[1] for i in self.atom_points],\n marker='X',\n s=100\n )\n\n plt.show()"],"string":"[\n \"def visualize_point_cloud(points, colors=None, normals=None,\\n show_frame=False, frame_size=1.0, frame_origin=(0, 0, 0)):\\n pc = np2pcd(points, colors, normals)\\n geometries = [pc]\\n if show_frame:\\n coord_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=frame_size, origin=frame_origin)\\n geometries.append(coord_frame)\\n o3d.visualization.draw_geometries(geometries)\",\n \"def render_point_cloud(frame_id):\\n point_cloud_world_coordinates = get_point_cloud_world_coordinates(frame_id)\\n # pptk\\n v = pptk.viewer(point_cloud_world_coordinates)\\n v.set(point_size=0.0001)\\n v.color_map('cool', scale=[0, 5])\",\n \"def create_point_cloud(self):\\n pixels = []\\n colors = []\\n my_pixels = []\\n for j in range(self.height):\\n for i in range(self.width):\\n depth = self.depth[j, i]\\n pixels.append(\\n [i * depth, j * depth, depth]\\n )\\n my_pixels.append(\\n [i, j, 1]\\n )\\n # make rgb with flip()\\n colors.append(np.flip(self.bgr[j, i, :]))\\n # colors.append(self.bgr[j, i, :])\\n self.my_pixels = my_pixels\\n pixels = np.array(pixels)\\n\\n # project pixels to camera space\\n self.xyz_points = self.intrinsics_inv @ np.transpose(pixels)\\n self.color_points = colors\\n\\n # now add 1s to the points for homogenous coordinates\\n num_points = self.get_num_xyz_points()\\n ones = np.ones((1, num_points))\\n self.xyzw_points = np.concatenate((self.xyz_points, ones), axis=0)\\n\\n self.scene = None\\n self.camera_pose = None\\n self.nm = None\\n self.nl = None\\n self.nc = None\\n self.create_mesh()\",\n \"def draw_pointcloud(ax, example):\\n points = example['points'].cpu().detach().numpy()\\n points_num = len(points)\\n xs = np.empty([points_num])\\n ys = np.empty([points_num])\\n zs = np.empty([points_num])\\n intensity = np.empty([len(points)])\\n for j, point in enumerate(points):\\n xs[j] = point[1]\\n ys[j] = point[2]\\n zs[j] = point[3]\\n intensity[j] = point[4]\\n\\n intensity = intensity\\n ax.scatter3D(xs, ys, zs, c=intensity, marker='.', s=0.3, cmap=plt.get_cmap('jet'))\",\n \"def show_pointclouds(points, colors, text=[], title=\\\"Default\\\", png_path=\\\"\\\", interactive=True, orientation='horizontal'):\\n\\n # make sure pointclouds is a list\\n assert isinstance(points, type([])), \\\\\\n \\\"Pointclouds argument must be a list\\\"\\n\\n # make sure colors is a list\\n assert isinstance(colors, type([])), \\\\\\n \\\"Colors argument must be a list\\\"\\n\\n # make sure number of pointclouds and colors are the same\\n assert len(points) == len(colors), \\\\\\n \\\"Number of pointclouds (%d) is different then number of colors (%d)\\\" % (len(points), len(colors))\\n\\n while len(text) < len(points):\\n text.append(\\\"\\\")\\n\\n # Number of pointclouds to be displayed in this window\\n num_pointclouds = len(points)\\n\\n point_size = 10\\n pointclouds = [VtkPointCloud(point_size) for _ in range(num_pointclouds)]\\n renderers = [vtk.vtkRenderer() for _ in range(num_pointclouds)]\\n\\n height = 1.0 / max(num_pointclouds, 1)\\n viewports = [(i*height, (i+1)*height) for i in range(num_pointclouds)]\\n #print(viewports)\\n\\n # iterate over all point clouds\\n for i, pc in enumerate(points):\\n pc = pc.squeeze()\\n co = colors[i].squeeze()\\n assert pc.shape[0] == co.shape[0], \\\\\\n \\\"expected same number of points (%d) then colors (%d), cloud index = %d\\\" % (pc.shape[0], co.shape[0], i)\\n assert pc.shape[1] == 3, \\\"expected points to be N x 3, got N x %d\\\" % pc.shape[1]\\n assert co.shape[1] == 3, \\\"expected colors to be N x 3, got N x %d\\\" % co.shape[1]\\n\\n # for each point cloud iterate over all points\\n for j in range(pc.shape[0]):\\n point = pc[j, :]\\n color = co[j, :]\\n pointclouds[i].add_point(point, color)\\n\\n renderers[i].AddActor(pointclouds[i].vtkActor)\\n # renderers[i].AddActor(vtk.vtkAxesActor())\\n renderers[i].SetBackground(1.0, 1.0, 1.0)\\n if orientation == 'horizontal':\\n print(viewports[i][0])\\n renderers[i].SetViewport(viewports[i][0], 0.0, viewports[i][1], 1.0)\\n elif orientation == 'vertical':\\n renderers[i].SetViewport(0.0, viewports[i][0], 1.0, viewports[i][1])\\n else:\\n raise Exception('Not a valid orientation!')\\n renderers[i].ResetCamera()\\n\\n # Add circle to first render\\n renderers[0].AddActor(getActorCircle())\\n renderers[0].AddActor(getActorCircle(50, 49, color=(0, 1, 0)))\\n\\n # Text actors\\n text_actors = [vtk.vtkTextActor() for _ in text]\\n for i, ta in enumerate(text_actors):\\n if orientation == 'horizontal':\\n ta.SetInput(' ' + text[i])\\n elif orientation == 'vertical':\\n ta.SetInput(text[i] + '\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n')\\n else:\\n raise Exception('Not a valid orientation!')\\n txtprop = ta.GetTextProperty()\\n txtprop.SetFontFamilyToArial()\\n txtprop.SetFontSize(0)\\n txtprop.SetColor(0, 0, 0)\\n # txtprop.SetJustificationToCentered()\\n # ta.SetDisplayPosition(500, 10)\\n # ta.SetAlignmentPoint()\\n renderers[i].AddActor(ta)\\n\\n # Render Window\\n render_window = vtk.vtkRenderWindow()\\n for renderer in renderers:\\n render_window.AddRenderer(renderer)\\n\\n render_window_interactor = vtk.vtkRenderWindowInteractor()\\n render_window_interactor.SetInteractorStyle(vtk.vtkInteractorStyleTrackballCamera())\\n render_window_interactor.SetRenderWindow(render_window)\\n\\n [center_x, center_y, center_z] = np.mean(points[0].squeeze(), axis=0)\\n camera = vtk.vtkCamera()\\n # d = 10\\n # camera.SetViewUp(0, -1, 0)\\n\\n # camera.SetPosition(center_x + d, center_y + d, center_z + d / 2)\\n # camera.SetFocalPoint(center_x, center_y, center_z)\\n # camera.SetFocalPoint(0, 0, 0)\\n\\n camera.SetViewUp(0, 0, 1)\\n if orientation == 'horizontal':\\n camera.SetPosition(3, -10, 2)\\n camera.SetFocalPoint(3, 1.5, 1.5)\\n elif orientation == 'vertical':\\n camera.SetPosition(1.5, -6, 2)\\n camera.SetFocalPoint(1.5, 1.5, 1.5)\\n else:\\n raise Exception('Not a valid orientation!')\\n\\n camera.SetClippingRange(0.002, 1000)\\n for renderer in renderers:\\n renderer.SetActiveCamera(camera)\\n\\n # Begin Interaction\\n render_window.Render()\\n render_window.SetWindowName(title)\\n if orientation == 'horizontal':\\n render_window.SetSize(1940, 720)\\n elif orientation == 'vertical':\\n render_window.SetSize(600, 1388)\\n else:\\n raise Exception('Not a valid orientation!')\\n\\n if interactive:\\n render_window_interactor.Start()\\n\\n if png_path:\\n # screenshot code:\\n w2if = vtk.vtkWindowToImageFilter()\\n w2if.SetInput(render_window)\\n w2if.Update()\\n\\n writer = vtk.vtkPNGWriter()\\n writer.SetFileName(png_path)\\n writer.SetInputConnection(w2if.GetOutputPort())\\n writer.Write()\",\n \"def make_point_cloud(self):\\r\\n\\r\\n self.pointCloud = VtkPointCloud()\\r\\n for k in range(np.size(self.pos, 0)):\\r\\n self.pointCloud.addPoint(self.pos[k, :])\\r\\n\\r\\n # Renderer\\r\\n renderer = vtk.vtkRenderer()\\r\\n renderer.AddActor(self.pointCloud.vtkActor)\\r\\n renderer.SetBackground(.2, .3, .4)\\r\\n renderer.SetBackground(0.0, 0.0, 0.0)\\r\\n renderer.ResetCamera()\\r\\n\\r\\n # Render Window\\r\\n renderWindow = vtk.vtkRenderWindow()\\r\\n renderWindow.AddRenderer(renderer)\\r\\n\\r\\n # Interactor\\r\\n renderWindowInteractor = vtk.vtkRenderWindowInteractor()\\r\\n renderWindowInteractor.SetRenderWindow(renderWindow)\\r\\n\\r\\n # Begin Interaction\\r\\n renderWindow.Render()\\r\\n renderWindow.SetWindowName(\\\"XYZ Data Viewer: \\\")\\r\\n renderWindowInteractor.Start()\",\n \"def publish_point_cloud(self):\\n all_points = [np.zeros((0, 2), np.float32)]\\n all_keys = []\\n for key in range(len(self.keyframes)):\\n pose = self.keyframes[key].pose\\n transf_points = self.keyframes[key].transf_points\\n all_points.append(transf_points)\\n all_keys.append(key * np.ones((len(transf_points), 1)))\\n\\n all_points = np.concatenate(all_points)\\n all_keys = np.concatenate(all_keys)\\n sampled_points, sampled_keys = pcl.downsample(\\n all_points, all_keys, self.point_resolution\\n )\\n sampled_xyzi = np.c_[sampled_points, np.zeros_like(sampled_keys), sampled_keys]\\n if len(sampled_xyzi) == 0:\\n return\\n\\n if self.save_fig:\\n plt.figure()\\n plt.scatter(\\n sampled_xyzi[:, 0], sampled_xyzi[:, 1], c=sampled_xyzi[:, 3], s=1\\n )\\n plt.axis(\\\"equal\\\")\\n plt.gca().invert_yaxis()\\n plt.savefig(\\\"step-{}-map.png\\\".format(self.current_key - 1), dpi=100)\\n plt.close(\\\"all\\\")\\n\\n cloud_msg = n2r(sampled_xyzi, \\\"PointCloudXYZI\\\")\\n cloud_msg.header.stamp = self.current_keyframe.time\\n cloud_msg.header.frame_id = \\\"map\\\"\\n self.cloud_pub.publish(cloud_msg)\",\n \"def showPoints(surface, points):\\n for point in points:\\n point.show(surface)\",\n \"def as_point_cloud(self):\\n far = 1000.0 # max depth in meters.\\n intrinsic_mat = self.camera_setup.get_intrinsic_matrix()\\n width, height = self.camera_setup.width, self.camera_setup.height\\n # 2d pixel coordinates\\n pixel_length = width * height\\n u_coord = repmat(np.r_[0:width:1], height, 1).reshape(pixel_length)\\n v_coord = repmat(np.c_[0:height:1], 1, width).reshape(pixel_length)\\n normalized_depth = np.reshape(self.frame, pixel_length)\\n\\n # p2d = [u,v,1]\\n p2d = np.array([u_coord, v_coord, np.ones_like(u_coord)])\\n\\n # P = [X,Y,Z]\\n p3d = np.dot(inv(intrinsic_mat), p2d)\\n p3d *= normalized_depth * far\\n\\n # [[X1,Y1,Z1],[X2,Y2,Z2], ... [Xn,Yn,Zn]]\\n locations = np.asarray(np.transpose(p3d))\\n # Transform the points in 3D world coordinates.\\n to_world_transform = self.camera_setup.get_unreal_transform()\\n point_cloud = to_world_transform.transform_points(locations)\\n return point_cloud\",\n \"def render_point_cloud(self, point_cloud, extrinsics=Pose(), color=GRAY):\\n\\n combined_transform = self._bev_rotation * extrinsics\\n\\n pointcloud_in_bev = combined_transform * point_cloud\\n point_cloud2d = pointcloud_in_bev[:, :2]\\n\\n point_cloud2d[:, 0] = (self._center_pixel[0] + point_cloud2d[:, 0] * self._pixels_per_meter)\\n point_cloud2d[:, 1] = (self._center_pixel[1] + point_cloud2d[:, 1] * self._pixels_per_meter)\\n\\n H, W = self.data.shape[:2]\\n uv = point_cloud2d.astype(np.int32)\\n in_view = np.logical_and.reduce([\\n (point_cloud2d >= 0).all(axis=1),\\n point_cloud2d[:, 0] < W,\\n point_cloud2d[:, 1] < H,\\n ])\\n uv = uv[in_view]\\n self.data[uv[:, 1], uv[:, 0], :] = color\",\n \"def showCrossPoints(self, surface):\\n for point in self.cross_points:\\n point.show(surface)\",\n \"def drawPointCloud(points, ax, color=None):\\n if len(points.shape) != 2 or points.shape[0] != 3:\\n raise ValueError(\\\"'points' must be 3xN\\\")\\n if color == None:\\n color = __color_cycle.next()\\n elif color in (0, 1, 2):\\n color = points[color, :]\\n ax.scatter(points[0,:].T, points[1,:].T, points[2,:].T, c=color)\",\n \"def create_pointcloud(pts):\\n depths = PointCloud()\\n depths.header = std_msgs.msg.Header()\\n depths.header.stamp = rospy.Time.now()\\n depths.header.frame_id = \\\"view_zero\\\"\\n depths.points = [None] * len(pts)\\n for p in xrange(len(pts)):\\n #Giving point the same orientation as the robot\\n y = pts[p,0]\\n z = - pts[p,1] #y in images is down\\n x = pts[p,2]\\n depths.points[p] = Point(x, y, z)\\n return depths\",\n \"def generate_pointcloud(rgb_file, mask_file,depth_file,ply_file):\\n rgb = Image.open(rgb_file)\\n # depth = Image.open(depth_file)\\n depth = Image.open(depth_file).convert('I')\\n mask = Image.open(mask_file).convert('I')\\n\\n # if rgb.size != depth.size:\\n # raise Exception(\\\"Color and depth image do not have the same resolution.\\\")\\n # if rgb.mode != \\\"RGB\\\":\\n # raise Exception(\\\"Color image is not in RGB format\\\")\\n # if depth.mode != \\\"I\\\":\\n # raise Exception(\\\"Depth image is not in intensity format\\\")\\n\\n\\n points = [] \\n for v in range(rgb.size[1]):\\n for u in range(rgb.size[0]):\\n color = rgb.getpixel((u,v))\\n # Z = depth.getpixel((u,v)) / scalingFactor\\n # if Z==0: continue\\n # X = (u - centerX) * Z / focalLength\\n # Y = (v - centerY) * Z / focalLength\\n if (mask.getpixel((u,v))<55):\\n Z = depth.getpixel((u, v))*.22 \\n if Z == 0: continue\\n Y = .22 * v\\n X = .22 * u\\n points.append(\\\"%f %f %f %d %d %d 0\\\\n\\\"%(X,Y,Z,color[0],color[1],color[2]))\\n file = open(ply_file,\\\"w\\\")\\n file.write('''ply\\nformat ascii 1.0\\nelement vertex %d\\nproperty float x\\nproperty float y\\nproperty float z\\nproperty uchar red\\nproperty uchar green\\nproperty uchar blue\\nproperty uchar alpha\\nend_header\\n%s\\n'''%(len(points),\\\"\\\".join(points)))\\n file.close()\",\n \"def point_cloud(self):\\n\\t\\tgen = self.loop(point_cloud=True)\\n\\t\\tpoint_cloud = next(gen)\\n\\t\\treturn point_cloud\",\n \"def debug_filter_points(self, points):\\n cloud_msg = PointCloud2()\\n cloud_msg.header.frame_id = \\\"map\\\"\\n cloud_msg.header.stamp = rospy.Time.now() \\n xyz = [[p.pose.position.x, p.pose.position.y, p.pose.position.z] for p in points] \\n point_cloud = pc2.create_cloud_xyz32(cloud_msg.header, xyz)\\n self._points_publisher.publish(point_cloud)\",\n \"def visualize_scan(self):\\n fig = plt.figure()\\n ax = fig.add_subplot(111, projection='3d')\\n ax.scatter(self.p1_points[:, 0], self.p1_points[:, 1], self.p1_points[:, 2], c='r')\\n ax.scatter(self.p2_points[:, 0], self.p2_points[:, 1], self.p2_points[:, 2], c='g')\\n ax.scatter(self.p3_points[:, 0], self.p3_points[:, 1], self.p3_points[:, 2], c='b')\\n ax.scatter(self.p4_points[:, 0], self.p4_points[:, 1], self.p4_points[:, 2])\\n\\n ax.set_xlabel('x')\\n ax.set_ylabel('y')\\n ax.set_zlabel('z')\\n plt.show()\",\n \"def visualize_in_2d(self):\\n ae = Autoencoder_test(self.data)\\n self.code = ae.encode(n_dimension=2, learning_rate=0.01, training_epochs=10, batch_size=400)\\n for i in range(len(self.cluster)):\\n list_x = []\\n list_y = []\\n for j in self.cluster[i]:\\n list_x.append(self.code[0][j,0])\\n list_y.append(self.code[0][j,1])\\n plt.scatter(list_x,list_y)\\n plt.show()\\n return\",\n \"def convertcloud(points):\\n pcd = open3d.geometry.PointCloud()\\n pcd.points = open3d.utility.Vector3dVector(points)\\n return pcd\",\n \"def voxelize(points,leaf = 0.1):\\n if (type(points) == pclpy.pcl.PointCloud.PointXYZRGB):\\n cloud = points\\n voxel_filter = pclpy.pcl.filters.VoxelGrid.PointXYZRGB()\\n filtered_pointcloud = pclpy.pcl.PointCloud.PointXYZRGB()\\n else:\\n cloud = pclpy.pcl.PointCloud.PointXYZ(points)\\n voxel_filter = pclpy.pcl.filters.VoxelGrid.PointXYZ()\\n filtered_pointcloud = pclpy.pcl.PointCloud.PointXYZ()\\n \\n voxel_filter.setLeafSize(leaf,leaf,leaf)\\n voxel_filter.setInputCloud(cloud)\\n \\n voxel_filter.filter(filtered_pointcloud)\\n if type(points) == pclpy.pcl.PointCloud.PointXYZRGB:\\n return filtered_pointcloud\\n else:\\n return filtered_pointcloud.xyz\",\n \"def plot(self, subsample=None, valid_instance_types=None):\\n pt = self.sample_indices(subsample, valid_instance_types)\\n\\n x, y, z = self.pc[pt, 0], self.pc[pt, 1], self.pc[pt, 2]\\n color = self.color[pt]\\n\\n return plot_pointcloud(x, y, z, color=color)\",\n \"def show(data_set, number_points: int):\\n print(f'info: Showing {number_points} as maximum.')\\n sub_set_points = np.random.choice(range(data_set.shape[0]), size=min(data_set.shape[0], number_points))\\n x = data_set[sub_set_points, 0]\\n y = data_set[sub_set_points, 1]\\n z = data_set[sub_set_points, 2]\\n\\n fig = plt.figure(figsize=(8, 8))\\n ax = mplot3d.Axes3D(fig)\\n ax.set_title('NMSLIB index 3D representation', fontsize=20)\\n ax.scatter(xs=x, ys=y, zs=z)\\n plt.show()\",\n \"def _project_pointcloud(self, cloud):\\n\\n assert isinstance(cloud, PointCloud2)\\n\\n pc1 = PointCloud()\\n pc1.header = cloud.header\\n # hack the time! dont move the robot :-0\\n pc1.header.stamp = rospy.Time.now()\\n \\n \\n pc1.points = [Point32(*p) for p in pc2.read_points(cloud)]\\n\\n self._tf_listener.waitForTransform(pc1.header.frame_id,\\n self._image_info.tf_frame, \\n rospy.Time(0), \\n rospy.Duration(4))\\n\\n image_frame_cloud = self._tf_listener.transformPointCloud (\\n self._image_info.tf_frame, \\n pc1)\\n min_x, max_x, min_y, max_y = 640, 0, 480, 0 # TODO: remove hard coded image size!\\n for pt in image_frame_cloud.points:\\n u, v = self._image_info.project3dToPixel((pt.x, pt.y, pt.z))\\n if v < min_y:\\n min_y = int(v)\\n if v > max_y:\\n max_y = int(v)\\n if u < min_x:\\n min_x = int(u)\\n if u > max_x:\\n max_x = int(u)\\n location = (((min_x, min_y), (max_x, max_y)))\\n rospy.loginfo(\\\"Transformed cloud into image plane\\\")\\n return location\",\n \"def visualize(self):\\n colors = {'outline': (220, 220, 220),\\n 'inlier': (0, 255, 0),\\n 'outlier': (0, 0, 255),\\n 'lines': (128, 220, 128)}\\n # Create output image for visualization\\n gap = 5\\n h1, w1 = self.target.image.shape[:2]\\n h2, w2 = self.image.shape[:2]\\n vis = np.zeros((max(h1, h2), w1 + w2 + gap, 3), np.uint8)\\n vis[:h1, :w1, :] = self.target.image\\n w1 += gap\\n vis[:h2, w1:w1+w2, :] = self.image\\n \\n # Draw the located object \\n quad = np.float32(self.quad) + np.float32([w1, 0])\\n self.draw(vis, colors['outline'], 2, quad)\\n \\n # draw point details\\n inliers = [(x0, y0, x1 + w1, y1) for (x0, y0), (x1, y1) in self.inliers]\\n outliers = [(x0, y0, x1 + w1, y1) for (x0, y0), (x1, y1) in self.outliers]\\n if colors['outlier'] is not None: # draw x on each point\\n r = 2 # radius\\n thickness = 2\\n for x0, y0, x1, y1 in outliers:\\n cv2.line(vis, (x0 - r, y0 - r), (x0 + r, y0 + r), colors['outlier'], thickness)\\n cv2.line(vis, (x0 + r, y0 - r), (x0 - r, y0 + r), colors['outlier'], thickness)\\n cv2.line(vis, (x1 - r, y1 - r), (x1 + r, y1 + r), colors['outlier'], thickness)\\n cv2.line(vis, (x1 + r, y1 - r), (x1 - r, y1 + r), colors['outlier'], thickness)\\n if colors['lines'] is not None:\\n for x0, y0, x1, y1 in inliers:\\n cv2.line(vis, (x0, y0), (x1, y1), colors['lines'], 1)\\n if colors['inlier'] is not None:\\n for x0, y0, x1, y1 in inliers:\\n cv2.circle(vis, (x0, y0), 2, colors['inlier'], -1)\\n cv2.circle(vis, (x1, y1), 2, colors['inlier'], -1)\\n return vis\",\n \"def point_cloud(self, X, Y, Z, size=1, color='#FF3232', bordercolor='#FF3232', legend='', width=0.5, opacity=1.0):\\n point_cloud = go.Scatter3d(\\n x=X,\\n y=Y,\\n z=Z,\\n # showlegend=False,\\n name=legend,\\n mode='markers',\\n marker=dict(\\n size=size,\\n color=color,\\n line=dict(\\n color=bordercolor,\\n width=width\\n ),\\n # opacity=opacity\\n )\\n )\\n\\n return point_cloud\",\n \"def visualize(self):\\n self.octree.updateInnerOccupancy()\\n print(\\\"Start Octomap Visualization\\\")\\n\\n # define parameters\\n data = imgviz.data.arc2017()\\n camera_info = data['camera_info']\\n K = np.array(camera_info['K']).reshape(3, 3)\\n width=camera_info['width']\\n height=camera_info['height']\\n\\n # get free and occupied grid\\n occupied, _ = self.octree.extractPointCloud()\\n #frontier = self.gen_frontier()\\n \\n print(\\\"load point cloud\\\")\\n window = pyglet.window.Window(\\n width=int(1280), height=int(960)\\n )\\n\\n @window.event\\n def on_key_press(symbol, modifiers):\\n if modifiers == 0:\\n if symbol == pyglet.window.key.Q:\\n window.on_close()\\n\\n gui = glooey.Gui(window)\\n hbox = glooey.HBox()\\n hbox.set_padding(5)\\n\\n camera = trimesh.scene.Camera(\\n resolution=(width, height), focal=(K[0, 0], K[1, 1])\\n )\\n\\n # initial camera pose\\n camera_transform = np.array(\\n [\\n [1, 0, 0, 0],\\n [0, -1, 0, 0],\\n [0, 0, -1, -5],\\n [0.0, 0.0, 0.0, 1.0],\\n ],\\n )\\n\\n \\n\\n occupied_geom = trimesh.voxel.ops.multibox(\\n occupied, pitch=self.resolution, colors=[0.0, 0.0, 0.0, 0.5]\\n )\\n\\n # frontier_geom = trimesh.voxel.ops.multibox(\\n # frontier, pitch=self.resolution, colors=[1.0, 0, 0, 0.5]\\n # )\\n scene = trimesh.Scene(camera=camera, geometry=[occupied_geom])#, frontier_geom])\\n scene.camera_transform = camera_transform\\n hbox.add(self.labeled_scene_widget(scene, label='octomap'))\\n\\n\\n gui.add(hbox)\\n pyglet.app.run()\",\n \"def display(self):\\n scatter_plot(self.points, self.hull_points, self.color, self.title)\",\n \"def vis_points(data,f1,f2):\\n if np.isnan(data).any():\\n return\\n \\n plt.scatter(data[:,f1], data[:,f2], alpha=0.2, c='b')\\n plt.xlim(lims)\\n plt.ylim(lims)\",\n \"def show_pair_points(points, pairs, title, save_path = None):\\n\\n fig = plt.figure(figsize = (6, 6))\\n plt.title(title)\\n plt.scatter(points[:, 0], points[:, 1], c = 'b', s = 5, label = \\\"point cloud\\\")\\n for pair in pairs:\\n print(pair)\\n point_1 = points[pair[0]]\\n point_2 = points[pair[1]]\\n plt.scatter([point_1[0], point_2[0]], [point_1[1], point_2[1]], s = 10)\\n plt.xlabel(\\\"x\\\")\\n plt.ylabel(\\\"y\\\")\\n plt.axis('equal')\\n plt.grid()\\n plt.legend()\\n plt.show()\\n \\n if type(save_path) != type(None):\\n fig.savefig(save_path)\",\n \"def show(self):\\n from matplotlib import pyplot as plt\\n from mpl_toolkits.mplot3d import Axes3D\\n\\n fig = plt.figure()\\n ax = Axes3D(fig)\\n pos = self.cluster.get_positions()\\n from itertools import combinations\\n for tri in self.mesh.simplices:\\n for comb in combinations(tri, 2):\\n x1 = pos[comb[0], 0]\\n x2 = pos[comb[1], 0]\\n y1 = pos[comb[0], 1]\\n y2 = pos[comb[1], 1]\\n z1 = pos[comb[0], 2]\\n z2 = pos[comb[1], 2]\\n ax.plot([x1, x2], [y1, y2], zs=[z1, z2], color=\\\"black\\\")\\n plt.show()\",\n \"def __show_source(self):\\n pcd = o3d.io.read_point_cloud(\\n self.source_cloud\\n )\\n if np.asarray(pcd.points).shape[0] != 0:\\n pcd.paint_uniform_color([0, 1, 0])\\n pcd.estimate_normals()\\n self.source_point_cloud_view.load_cloud(pcd)\\n try:\\n self.source_point_cloud_view.show_window()\\n except RuntimeError:\\n pass\\n else:\\n QtWidgets.QMessageBox.warning(self, \\\"Error\\\",\\n f\\\"Source point cloud is no longer available\\\"\\n )\\n self.source_cloud = \\\"\\\"\\n self.__update_clickability()\\n self.__save_context()\",\n \"def open3dpaint(nppoints, color_map = 'jet', reduce_for_vis = False, voxel_size = 0.1, pointsize = 0.1):\\n assert (type(nppoints) == pclpy.pcl.PointCloud.PointXYZRGB) or (type(nppoints) == pclpy.pcl.PointCloud.PointXYZ) or (type(nppoints) == np.ndarray) or (type(nppoints) is list) or (type(nppoints) is tuple), 'Not valid point_cloud'\\n \\n if (type(nppoints) is not list) & (type(nppoints) is not tuple):\\n nppoints = [nppoints]\\n try:\\n visualizer = open3d.visualization.Visualizer()\\n visualizer.create_window()\\n options = visualizer.get_render_option()\\n options.background_color = np.asarray([0, 0, 0])\\n options.point_size = pointsize\\n\\n if len(nppoints) > 1:\\n for n,i in enumerate(nppoints):\\n workpoints = i\\n if (type(workpoints) == pclpy.pcl.PointCloud.PointXYZRGB) or (type(workpoints) == pclpy.pcl.PointCloud.PointXYZ):\\n workpoints = workpoints.xyz\\n\\n if reduce_for_vis:\\n workpoints = seg_tree.voxelize(workpoints,voxel_size)\\n\\n points = convertcloud(workpoints)\\n color_coef = n/len(nppoints)/2 + n%2*.5\\n if type(color_map) == np.ndarray:\\n color = color_map\\n elif color_map == 'jet':\\n color=cm.jet(color_coef)[:3]\\n else:\\n color=cm.Set1(color_coef)[:3]\\n points.colors = open3d.utility.Vector3dVector(np.ones_like(workpoints)*color)\\n #points.colors = open3d.utility.Vector3dVector(color)\\n visualizer.add_geometry(points)\\n else:\\n workpoints = nppoints[0]\\n if (type(workpoints) == pclpy.pcl.PointCloud.PointXYZRGB) or (type(workpoints) == pclpy.pcl.PointCloud.PointXYZ):\\n workpoints = workpoints.xyz\\n \\n if reduce_for_vis:\\n workpoints = seg_tree.voxelize(workpoints,voxel_size)\\n points = convertcloud(workpoints)\\n visualizer.add_geometry(points)\\n visualizer.run()\\n visualizer.destroy_window()\\n \\n except Exception as e:\\n print(type(e))\\n print(e.args)\\n print(e)\\n visualizer.destroy_window()\",\n \"def plot(self):\\n # Find only unmasked data :\\n # xyz, sData, sColor, _ = self._select_unmasked()\\n xyz, sData, sColor = self.xyz, self.sData, self.sColor\\n\\n # Render as cloud points :\\n self.mesh = visu.Markers(name='Sources')\\n self.mesh.set_data(xyz, edge_color=self.edgecolor, face_color=sColor,\\n size=sData, scaling=self.scaling,\\n edge_width=self.edgewidth, symbol=self.symbol)\\n self.mesh.set_gl_state('translucent')\",\n \"def show_2d_projections(self):\\n\\n with warnings.catch_warnings():\\n warnings.simplefilter(\\\"ignore\\\")\\n fig, axs = plt.subplots(1, 3)\\n for i in range(3):\\n plt.sca(axs[i])\\n io.imshow(self.image.max(i), cmap='viridis')\",\n \"def show_keypoints(image, key_pts):\\n plt.imshow(image)\\n plt.scatter(key_pts[:, 0], key_pts[:, 1], s=20, marker='.', c='m')\",\n \"def visualize(vis, features, label):\\n if vis == 'PCA':\\n #n_components = st.sidebar.slider(\\\"n_components\\\", 2, 10)\\n #alpha = st.sidebar.slider(\\\"alpha\\\", 0.8, 2.0)\\n #pca = PCA(n_components)\\n pca = PCA(2)\\n\\n X_projected = pca.fit_transform(features)\\n \\n x1 = X_projected[:, 0]\\n x2 = X_projected[:, 1]\\n\\n\\n fig = plt.figure()\\n plt.scatter(x1, x2, c=label, alpha=0.8, cmap='viridis')\\n plt.xlabel(\\\"Principal Component 1\\\")\\n plt.ylabel(\\\"Principal Component 2\\\")\\n plt.colorbar()\\n\\n st.pyplot()\",\n \"def show_feat(feat_map):\\n for i in range(feat_map.shape[0]):\\n plt.imshow(feat_map[i])\\n plt.show()\",\n \"def show_points(self, imsize):\\n mask = np.zeros(imsize)\\n for t in self.points:\\n mask[t] = 1\\n toimage(mask).show()\",\n \"def visualize_clouds(clouds, paths):\\n\\n for cloud, path in zip(clouds, paths):\\n print(\\\"Visualizing scans generated from:\\\", path)\\n for scan in cloud:\\n draw_geometries([scan])\",\n \"def sample_and_plot(self):\\n fig = plt.figure()\\n ax = plt.axes(projection = '3d')\\n ax.plot_surface(self.X, self.Y, self.sample(), cmap = plt.cm.jet, rstride = 2, cstride = 2, linewidth = 1)\\n plt.show()\",\n \"def save_3d_render(\\r\\n self, points: List[np.ndarray], colors: List[np.ndarray]\\r\\n ) -> None:\\r\\n pcd = o3d.geometry.PointCloud()\\r\\n pcd.points = o3d.utility.Vector3dVector(np.vstack(points).astype(np.float64))\\r\\n pcd.colors = o3d.utility.Vector3dVector(np.vstack(colors))\\r\\n if self.debug:\\r\\n o3d.visualization.draw_geometries([pcd])\\r\\n if not self.debug:\\r\\n o3d.io.write_point_cloud(f\\\"results/{self.filename[:-4]}.ply\\\", pcd)\",\n \"def plotrgcloud(self):\\n print self.kpunten\\n for i in range(len(self.kpunten[0])):\\n self.writetext('sen ='+ self.kpunten[0][i][0], (0.65,0.85), axnum = 0, hor = None ,ver = None , rot = None ,fs =14 , transform = self.fig.axes[0].transAxes)\\n if i == len(self.kpunten[0]) -1 :\\n end = None\\n else:\\n end = self.kpunten[0][i+1][1] + 1\\n print end\\n self.plotrgwrap( self.rgindex,2*self.reader.npair+self.rgindex,'real part of rgvars (a.u)' , 'imaginary part of rgvars (a.u.)', tit ='RG vars g = %f all states'%(self.chardata) , begin = self.kpunten[0][i][1] , stop = end , name = 'cpcloud'+ self.kpunten[0][i][0] , filenum = 0)\",\n \"def __show_target(self):\\n pcd = o3d.io.read_point_cloud(\\n self.target_cloud\\n )\\n if np.asarray(pcd.points).shape[0] != 0:\\n pcd.paint_uniform_color([0, 0, 1])\\n pcd.estimate_normals()\\n self.target_point_cloud_view.load_cloud(pcd)\\n try:\\n self.target_point_cloud_view.show_window()\\n except RuntimeError:\\n pass\\n else:\\n QtWidgets.QMessageBox.warning(self, \\\"Error\\\",\\n f\\\"Target point cloud is no longer available\\\"\\n )\\n self.target_cloud = \\\"\\\"\\n self.__update_clickability()\\n self.__save_context()\",\n \"def scatter(self, points):\\n assert points.shape[0] == 3\\n self.points = points\\n self.remove_scatter_plot()\\n x, y, z = zip(*points.T)\\n self.base_fig.add_trace(go.Scatter3d(\\n x=x, y=y, z=z,\\n mode='markers',\\n marker=dict(\\n size=2,\\n color=np.zeros(points.shape[1]),\\n colorscale=\\\"RdBu\\\",\\n colorbar=dict(\\n title=\\\"Samples\\\",\\n x=-0.2\\n ),\\n )\\n ))\",\n \"def mri_point_plot(self, vcol=1):\\n img = self.voxels\\n points = self.point_position \\n ax = []\\n fig = plt.figure(figsize=(9, 8))\\n # TODO make this setable in the function call\\n columns = 3\\n rows = 2\\n\\n for i in range(points.shape[0]):\\n im_slice = int(np.round(points[i, vcol]))\\n if vcol == 0:\\n im = img[im_slice, :, :]\\n elif vcol == 1:\\n im = img[:, im_slice, :]\\n else:\\n im = img[:, :, im_slice]\\n ax.append( fig.add_subplot(rows, columns, i+1))\\n ax[-1].set_title(\\\"Image depth: \\\"+str(im_slice)) # set title\\n plt.imshow(im)\\n plot_cols = np.array([0, 1, 2])\\n plot_cols = plot_cols[plot_cols != vcol]\\n plt.plot(points[i, min(plot_cols)], points[i, max(plot_cols)], 'ro')\\n\\n plt.show()\",\n \"def render_radar_point_cloud(self, point_cloud, extrinsics=Pose(), color=RED, velocity=None, velocity_scale=10):\\n combined_transform = self._bev_rotation * extrinsics\\n\\n pointcloud_in_bev = combined_transform * point_cloud\\n point_cloud2d = pointcloud_in_bev[:, :2]\\n\\n point_cloud2d[:, 0] = (self._center_pixel[0] + point_cloud2d[:, 0] * self._pixels_per_meter)\\n point_cloud2d[:, 1] = (self._center_pixel[1] + point_cloud2d[:, 1] * self._pixels_per_meter)\\n\\n H, W = self.data.shape[:2]\\n uv = point_cloud2d.astype(np.int32)\\n in_view = np.logical_and.reduce([\\n (point_cloud2d >= 0).all(axis=1),\\n point_cloud2d[:, 0] < W,\\n point_cloud2d[:, 1] < H,\\n ])\\n uv = uv[in_view]\\n\\n for row in uv:\\n cx, cy = row\\n cv2.circle(self.data, (cx, cy), 7, RED, thickness=1)\\n\\n def clip_norm(v, x):\\n M = np.linalg.norm(v)\\n if M == 0:\\n return v\\n\\n return np.clip(M, 0, x) * v / M\\n\\n if velocity is not None:\\n tail = point_cloud + velocity_scale * velocity\\n pointcloud_in_bev_tail = combined_transform * tail\\n point_cloud2d_tail = pointcloud_in_bev_tail[:, :2]\\n point_cloud2d_tail[:, 0] = (self._center_pixel[0] + point_cloud2d_tail[:, 0] * self._pixels_per_meter)\\n point_cloud2d_tail[:, 1] = (self._center_pixel[1] + point_cloud2d_tail[:, 1] * self._pixels_per_meter)\\n uv_tail = point_cloud2d_tail.astype(np.int32)\\n uv_tail = uv_tail[in_view]\\n for row, row_tail in zip(uv, uv_tail):\\n v_2d = row_tail - row\\n v_2d = clip_norm(v_2d, .025 * W)\\n\\n cx, cy = row\\n cx2, cy2 = row + v_2d.astype(np.int)\\n\\n cx2 = np.clip(cx2, 0, W - 1)\\n cy2 = np.clip(cy2, 0, H - 1)\\n color = GREEN\\n # If moving away from vehicle change the color (not strictly correct because radar is not a (0,0))\\n # TODO: calculate actual radar sensor position\\n if np.dot(row - np.array([W / 2, H / 2]), v_2d) > 0:\\n color = (255, 110, 199)\\n cv2.arrowedLine(self.data, (cx, cy), (cx2, cy2), color, thickness=1, line_type=cv2.LINE_AA)\",\n \"def plot_wordcloud(word_list, file_name=\\\"\\\"):\\n plt.figure(figsize=plt.figaspect(0.8), dpi=100)\\n all_words = ' '.join([text for text in word_list])\\n wordcloud = WordCloud(width=800, height=600, random_state=21, max_font_size=110).generate(all_words)\\n plt.imshow(wordcloud, interpolation=\\\"bilinear\\\")\\n plt.axis('off')\\n if file_name:\\n plt.savefig(file_name, bbox_inches='tight')\\n plt.show()\\n return\",\n \"def get_point_cloud(self):\\n tsdf_vol, color_vol = self.get_volume()\\n\\n # Marching cubes\\n verts = measure.marching_cubes_lewiner(tsdf_vol, level=0)[0]\\n verts_ind = np.round(verts).astype(int)\\n verts = verts * self._voxel_size + self._vol_origin\\n\\n # Get vertex colors\\n rgb_vals = color_vol[verts_ind[:, 0], verts_ind[:, 1], verts_ind[:, 2]]\\n colors_b = np.floor(rgb_vals / self._color_const)\\n colors_g = np.floor((rgb_vals - colors_b * self._color_const) / 256)\\n colors_r = rgb_vals - colors_b * self._color_const - colors_g * 256\\n colors = np.floor(np.asarray([colors_r, colors_g, colors_b])).T\\n colors = colors.astype(np.uint8)\\n\\n pc = np.hstack([verts, colors])\\n return pc\",\n \"def show_point_set(points, title, save_path = None):\\n dim = points.shape[1]\\n assert(dim == 2 or dim == 3), \\\"Only data in 2 or 3 dimension space can be visualized.\\\"\\n \\n if dim == 2:\\n fig = plt.figure(figsize = (6, 6))\\n plt.title(title)\\n plt.scatter(points[:, 0], points[:, 1], c = 'b', s = 5)\\n plt.xlabel(\\\"x\\\")\\n plt.ylabel(\\\"y\\\")\\n plt.axis('equal')\\n plt.grid()\\n plt.show()\\n elif dim == 3:\\n fig = plt.figure(figsize = (6, 6))\\n ax = fig.add_subplot(111, projection='3d')\\n ax.set_title(title)\\n ax.scatter(points[:, 0], points[:, 1], points[:, 2], c = 'b', s = 5)\\n ax.set_xlabel(\\\"x\\\")\\n ax.set_ylabel(\\\"y\\\")\\n ax.set_zlabel(\\\"z\\\")\\n plt.show()\\n\\n if type(save_path) != type(None):\\n fig.savefig(save_path)\",\n \"def render_points_2d(self, **kwds):\\n return point2d(self.coordinates_of(self.points), **kwds)\",\n \"def draw_clouds(a):\\n small_cloud(0 + a, 0, 0)\\n small_cloud(200 + a, -150, -10)\\n big_cloud(350 + a, 0, -3)\\n small_cloud(600 + a, -90, 3)\\n small_cloud(800 + a, 0, 0)\\n small_cloud(1000 + a, -150, -10)\\n big_cloud(1150 + a, 0, -3)\\n small_cloud(1400 + a, -90, 3)\\n small_cloud(-800 + a, 0, 0)\\n small_cloud(-600 + a, -150, -10)\\n big_cloud(-450 + a, 0, -3)\\n small_cloud(-200 + a, -90, 3)\",\n \"def plotsite(self):\\n if self.dimension==1 or self.dimension==2:\\n for site in self.sites.flatten():\\n plt.scatter(site.coordinate[0],site.coordinate[1],marker=site.marker,s=site.markersize,c=site.color)\\n elif self.dimension==3:\\n from mpl_toolkits.mplot3d import Axes3D\\n fig = plt.figure()\\n ax = Axes3D(fig)\\n for site in self.sites.flatten():\\n ax.scatter(site.coordinate[0],site.coordinate[1],site.coordinate[2],marker=site.marker,s=site.markersize,c=site.color)\",\n \"def imshow_surface(self):\\n plt.imshow(self.z)\\n plt.colorbar()\\n plt.show()\",\n \"def _build_point_cloud_graph(self):\\n depths = self.depths_placeholder[tf.newaxis]\\n images = self.images_placeholder[tf.newaxis]\\n poses = self.poses_placeholder[tf.newaxis]\\n intrinsics = self.intrinsics_placeholder[tf.newaxis]\\n intrinsics = intrinsics_vec_to_matrix(intrinsics)\\n\\n depths_pad = tf.pad(depths, [[0,0],[0,0],[0,1],[0,1]], \\\"CONSTANT\\\")\\n\\n depths_grad = \\\\\\n (depths_pad[:, :, 1:, :-1] - depths_pad[:, :, :-1, :-1])**2 + \\\\\\n (depths_pad[:, :, :-1, 1:] - depths_pad[:, :, :-1, :-1])**2\\n\\n # don't use large depths for point cloud and ignore boundary regions\\n valid = (depths < 3.0) & (depths_grad < 0.001)\\n\\n batch, num, ht, wd = tf.unstack(tf.shape(depths), num=4)\\n Ts = VideoSE3Transformation(matrix=poses)\\n X0 = projective_ops.backproject(depths, intrinsics)\\n \\n # transform point cloud into world coordinates\\n X1 = Ts.inv()(X0)\\n\\n crop_h = 12\\n crop_w = 32\\n\\n X1 = X1[:, :, crop_h:-crop_h, crop_w:-crop_w]\\n valid = valid[:, :, crop_h:-crop_h, crop_w:-crop_w]\\n images = images[:, :, crop_h:-crop_h, crop_w:-crop_w, ::-1]\\n \\n X1 = tf.reshape(X1, [-1, 3])\\n colors = tf.reshape(images, [-1, 3])\\n\\n valid_inds = tf.where(tf.reshape(valid, [-1]))\\n valid_inds = tf.reshape(valid_inds, [-1])\\n\\n X1 = tf.gather(X1, valid_inds, axis=0)\\n colors = tf.gather(colors, valid_inds, axis=0)\\n\\n self.outputs['point_cloud'] = (X1, colors)\",\n \"def show_points_on_img(mask,img):\\n labeled, num_objects = ndi.label(mask)\\n slices = ndi.find_objects(labeled)\\n x, y = [], []\\n for dy,dx in slices:\\n x_center = (dx.start + dx.stop - 1)/2\\n x.append(x_center)\\n y_center = (dy.start + dy.stop - 1)/2 \\n y.append(y_center)\\n plt.figure()\\n plt.imshow(img)\\n plt.autoscale(False)\\n plt.plot(x,y, \\\"o\\\")\",\n \"def show_transformed_point_set(points_1, points_2, points_12, loss, save_path = None):\\n\\n fig = plt.figure(figsize = (6, 6))\\n plt.title(\\\"Loss = %f\\\" % loss)\\n plt.scatter(points_1[:, 0], points_1[:, 1], s = 5, label = \\\"point cloud 1\\\")\\n plt.scatter(points_2[:, 0], points_2[:, 1], s = 5, label = \\\"point cloud 2\\\")\\n plt.scatter(points_12[:, 0], points_12[:, 1], s = 5, label = \\\"point cloud 1 to 2\\\")\\n plt.xlabel(\\\"x\\\")\\n plt.ylabel(\\\"y\\\")\\n plt.axis('equal')\\n plt.grid()\\n plt.legend()\\n plt.show()\\n \\n if type(save_path) != type(None):\\n fig.savefig(save_path)\",\n \"def draw_features(self, image):\\n \\n for x,y in self.new_points.reshape(-1,2):\\n cv2.circle(image, (x,y), 2, (255,0,255), 2)\\n return image\",\n \"def show_orbits(points, nb_pairs, title, save_path = None):\\n\\n fig = plt.figure(figsize = (6, 6))\\n plt.title(title)\\n plt.scatter(points[:, 0], points[:, 1], c = 'b', s = 5, label = \\\"point cloud\\\")\\n for i in range(len(nb_pairs)):\\n plt.scatter(nb_pairs[i][0][:, 0], nb_pairs[i][0][:, 1], s = 5, label = \\\"%d_1\\\" % i)\\n plt.scatter(nb_pairs[i][1][:, 0], nb_pairs[i][1][:, 1], s = 5, label = \\\"%d_2\\\" % i)\\n plt.xlabel(\\\"x\\\")\\n plt.ylabel(\\\"y\\\")\\n plt.axis('equal')\\n plt.grid()\\n plt.legend()\\n plt.show()\\n \\n if type(save_path) != type(None):\\n fig.savefig(save_path)\",\n \"def point_from_rays(self):\\n print \\\"generating the 3d point from given clicked points\\\"\\n \\n #gather cams and points clicked \\n uvs = []\\n cams = []\\n for iFrame in self.frames:\\n if iFrame.lastClick : \\n uv = numpy.multiply(iFrame.lastClick,self.reduceFactor)\\n uvs.append(uv)\\n cam = load_perspective_camera(self.camList[iFrame.currImg])\\n cams.append(cam)\\n point = get_3d_from_cams(cams, uvs)\\n self.point3d = point;\\n self.pointLabel.set(\\\"3d Point: \\\" + str(self.point3d))\\n\\n # project 3d point into each image, and gather intensities \\n values = []\\n ims = []\\n for idx, img in enumerate(self.imgList):\\n cam = load_perspective_camera(self.camList[idx])\\n imgPoint = project_point(cam, point[0], point[1], point[2])\\n imgPoint = numpy.divide(imgPoint, self.reduceFactor)\\n self.allUVs.append(imgPoint)\\n \\n #grab float intensity value at this point \\n imgView,ni,nj = load_image(img)\\n val = pixel(imgView, imgPoint)\\n if val > 0.0:\\n values.append(val)\\n ims.append(idx)\\n \\n #cleanup\\n remove_from_db([imgView, cam])\\n \\n\\n #write mean/std of intensities \\n self.meanLabel.set(\\\"Mean: \\\" + str(numpy.mean(values)) )\\n self.stdLabel.set(\\\"Std Dev: \\\" + str(numpy.std(values)) )\\n #plot the intensities by image number \\n self.f.clf();\\n self.a = self.f.add_subplot(311)\\n self.a.set_xlabel(\\\"img #\\\")\\n self.a.set_ylabel(\\\"intensity\\\")\\n self.a.plot(ims, values)\\n #plot the histogram of intensities by image number \\n pdf, bins, patches = plt.hist(values)\\n self.b = self.f.add_subplot(313)\\n self.b.set_xlabel(\\\"bin val\\\")\\n self.b.set_ylabel(\\\"freq\\\")\\n self.b.hist(values, 15, normed=1, facecolor=\\\"green\\\" )\\n self.canvas.show();\",\n \"def showAsPoints(self): \\n MonkeyPatchMayaVi()\\n #import enthought.mayavi.mlab as mlab\\n from mayavi import mlab\\n \\n @mlab.show\\n def _showSimple():\\n morphPts = [ SVVisitorFactory.Array4AllPoints(morph)() for morph in self.morphs ]\\n pts = numpy.concatenate( morphPts)\\n return mlab.points3d(pts[:, 0], pts[:, 1], pts[:, 2], pts[:, 3], colormap=self.colormap, scale_factor=self.scale_factor)\\n _showSimple()\",\n \"def plot_surface(self):\\n X, Y = np.meshgrid(self.x, self.y)\\n fig = plt.figure()\\n ax = fig.add_subplot(111, projection='3d')\\n ax.plot_surface(X=X, Y=Y, Z=self.z)\\n plt.show()\",\n \"def show_trace_2d(f, results): #@save\\n set_figsize()\\n plt.plot(*zip(*results), '-o', color='#ff7f0e')\\n x1, x2 = torch.meshgrid(torch.arange(-5.5, 1.0, 0.1),torch.arange(-3.0, 1.0, 0.1))\\n plt.contour(x1, x2, f(x1, x2), colors='#1f77b4')\\n plt.xlabel('x1')\",\n \"def plot_2d(vector_array, save_plot_dir):\\n principal_df = pd.DataFrame(data = vector_array, columns = ['pc1', 'pc2'])\\n fig = plt.figure()\\n ax = fig.add_subplot(111)\\n\\n xs = principal_df['pc1']\\n ys = principal_df['pc2']\\n ax.scatter(xs, ys, s=50, alpha=0.6, edgecolors='w')\\n\\n ax.set_xlabel('pc1')\\n ax.set_ylabel('pc2')\\n\\n plt.savefig(save_plot_dir + '/2D_scatter.png')\\n plt.close()\",\n \"def show_skeletons(self, skel_2d, z_out, z_gt=None):\\n fig = plt.figure(figsize=(20, 20))\\n ax1 = fig.add_subplot(1, 2, 1)\\n ax2 = fig.add_subplot(1, 2, 2, projection='3d')\\n edges = np.array([[1, 0], [0, 2],[2, 3], [3, 4], [0, 5], [5, 6], [6, 7]])\\n\\n ax_2d = ax1\\n ax_3d = ax2\\n\\n # draw 3d\\n for edge in edges:\\n ax_3d.plot(skel_2d[0, edge], z_out[edge], skel_2d[1, edge], color='r')\\n if z_gt is not None:\\n ax_3d.plot(skel_2d[0, edge], z_gt[edge], skel_2d[1, edge], color='g')\\n\\n ax_3d.set_aspect('equal')\\n ax_3d.set_xlabel(\\\"x\\\"), ax_3d.set_ylabel(\\\"z\\\"), ax_3d.set_zlabel(\\\"y\\\")\\n ax_3d.set_xlim3d([-2, 2]), ax_3d.set_ylim3d([2, -2]), ax_3d.set_zlim3d([2, -2])\\n ax_3d.view_init(elev=10, azim=-45)\\n\\n # draw 2d\\n for edge in edges:\\n ax_2d.plot(skel_2d[0, edge], skel_2d[1, edge], color='r')\\n\\n ax_2d.set_aspect('equal')\\n ax_2d.set_xlabel(\\\"x\\\"), ax_2d.set_ylabel(\\\"y\\\")\\n ax_2d.set_xlim([-2, 2]), ax_2d.set_ylim([2, -2])\\n\\n plt.show()\",\n \"def show_wordcloud(dictionary, title, min_font = 10):\\n wordcloud = WordCloud(min_font_size=min_font).generate_from_frequencies(dictionary)\\n plt.figure(figsize = (8, 8), facecolor = None) \\n plt.imshow(wordcloud) \\n plt.axis(\\\"off\\\")\\n if title:\\n plt.title(title)\\n else:\\n plt.title(\\\"Word Cloud\\\")\\n plt.tight_layout(pad = 0) \\n\\n plt.show()\",\n \"def show_point_set_normal(points, normals, title, save_path = None):\\n dim = points.shape[1]\\n assert(dim == 2), \\\"Only data in 2 dimension space can be visualized.\\\"\\n \\n fig = plt.figure(figsize = (6, 6))\\n plt.title(title)\\n plt.scatter(points[:, 0], points[:, 1], c = 'b', s = 5, label = \\\"point cloud\\\")\\n plt.quiver(points[:, 0], points[:, 1], normals[:, 0], normals[:, 1])\\n plt.xlabel(\\\"x\\\")\\n plt.ylabel(\\\"y\\\")\\n plt.axis('equal')\\n plt.grid()\\n plt.legend()\\n plt.show()\\n \\n if type(save_path) != type(None):\\n fig.savefig(save_path)\",\n \"def view_point_cloud_model_and_affordance(number_of_object_per_category=5):\\n list_pc_paths = [f for f in glob.glob('./dataset/*.npy', recursive=True)]\\n set_objects = set([os.path.basename(pc_path).split('_')[0] for pc_path in list_pc_paths])\\n\\n for obj in set_objects:\\n try:\\n # load point cloud models\\n pc_models = np.load('./dataset/{}_point_cloud_models.npy'.format(obj))[\\n :number_of_object_per_category]\\n # load point cloud grasp affordance\\n pc_affordance = np.load('./dataset/{}_point_cloud_grasp_affordance.npy'.format(obj))[\\n :number_of_object_per_category]\\n\\n # visualization\\n for i, m in enumerate(pc_models):\\n fig = plt.figure()\\n ax = fig.add_subplot(projection='3d')\\n list_model_x, list_model_y, list_model_z = [], [], []\\n list_affordance_x, list_affordance_y, list_affordance_z = [], [], []\\n\\n for x in range(32):\\n for y in range(32):\\n for z in range(32):\\n if pc_affordance[i, x, y, z] == 1:\\n list_affordance_x.append(x)\\n list_affordance_y.append(y)\\n list_affordance_z.append(z)\\n elif m[x, y, z] == 1:\\n list_model_x.append(x)\\n list_model_y.append(y)\\n list_model_z.append(z)\\n\\n ax.scatter(list_model_x, list_model_y, list_model_z, c='#0c457d')\\n ax.scatter(list_affordance_x, list_affordance_y, list_affordance_z, c='#e8702a', alpha=0.35)\\n ax.set_xlim(0, 32)\\n ax.set_ylim(0, 32)\\n ax.set_zlim(0, 32)\\n plt.show()\\n\\n except FileNotFoundError:\\n print('Some point cloud npy files are not found.')\\n continue\",\n \"def show_scatter(self):\\n plt.scatter(self.a1[:, 0], self.a1[:, 1], c=\\\"red\\\", alpha=0.5, s=10)\\n plt.scatter(self.a2[:, 0], self.a2[:, 1], c=\\\"blue\\\", alpha=0.5, s=10)\\n plt.scatter(0, 0, marker=\\\"D\\\", c=\\\"black\\\", alpha=0.8)\\n plt.scatter(2, 2, marker=\\\"D\\\", c=\\\"black\\\", alpha=0.8)\\n plt.show()\",\n \"def image_to_point_cloud(depth):\\n cx, cy, fx, fy = 5.8818670481438744*100, 3.1076280589210484*100, 5.8724220649505514*100, 2.2887144980135292*100\\n depth = depth[:,:,0]\\n\\n rows, cols = depth.shape\\n c, r = np.meshgrid(np.arange(cols), np.arange(rows), sparse=True)\\n valid = (depth > 0) & (depth < 255)\\n # print(valid)\\n z = np.where(valid, depth / 256.0, np.nan)\\n x = np.where(valid, z * (c - cx) / fx, 0)\\n y = np.where(valid, z * (r - cy) / fy, 0)\\n return np.dstack((x, y, z))\",\n \"def generate_image(self,true_dist):\\n N_POINTS = 128\\n RANGE = 3\\n\\n points = np.zeros((N_POINTS, N_POINTS, 2), dtype='float32')\\n points[:, :, 0] = np.linspace(-RANGE, RANGE, N_POINTS)[:, None]\\n points[:, :, 1] = np.linspace(-RANGE, RANGE, N_POINTS)[None, :]\\n points = points.reshape((-1, 2))\\n\\n plt.clf()\\n\\n #true_dist = true_dist.cpu().data.numpy()\\n samples = self.G(self.sample_z_)\\n print('generate size is',samples.size())\\n samples = samples.cpu().data.numpy()\\n\\n x = y = np.linspace(-RANGE, RANGE, N_POINTS)\\n # plt.contour(x, y, disc_map.reshape((len(x), len(y))).transpose())\\n\\n plt.scatter(true_dist[:, 0], true_dist[:, 1], c='orange', marker='+')\\n plt.scatter(samples[:, 0], samples[:, 1], c='green', marker='+')\\n plt.show()\",\n \"def showAllPoints(self, surface, **kwargs):\\n if not \\\"point_color\\\" in kwargs:\\n kwargs[\\\"point_color\\\"] = self.point_color\\n if not \\\"point_radius\\\" in kwargs:\\n kwargs[\\\"point_radius\\\"] = self.point_radius\\n if not \\\"point_mode\\\" in kwargs:\\n kwargs[\\\"point_mode\\\"] = self.point_mode\\n if not \\\"point_size\\\" in kwargs:\\n kwargs[\\\"point_size\\\"] = self.point_size\\n if not \\\"point_width\\\" in kwargs:\\n kwargs[\\\"point_width\\\"] = self.point_width\\n if not \\\"point_fill\\\" in kwargs:\\n kwargs[\\\"point_fill\\\"] = self.point_fill\\n for point in self.points:\\n point.show(surface,\\n color=kwargs[\\\"point_color\\\"],\\n mode=kwargs[\\\"point_mode\\\"],\\n fill=kwargs[\\\"point_fill\\\"],\\n radius=kwargs[\\\"point_radius\\\"],\\n size=kwargs[\\\"point_size\\\"],\\n width=kwargs[\\\"point_width\\\"])\",\n \"def visualize_output(self, keyframe_index):\\n\\n self.vis_counter += 1\\n keyframe_image = self.images[keyframe_index]\\n keyframe_depth = self.depths[keyframe_index]\\n keyframe_pose = self.poses[keyframe_index]\\n\\n feed_dict = {\\n self.images_placeholder: keyframe_image[np.newaxis],\\n self.depths_placeholder: keyframe_depth[np.newaxis],\\n self.poses_placeholder: keyframe_pose[np.newaxis],\\n self.intrinsics_placeholder: self.intrinsics}\\n\\n keyframe_point_cloud, keyframe_point_colors = \\\\\\n self.sess.run(self.outputs['point_cloud'], feed_dict=feed_dict)\\n\\n pointcloud = (keyframe_point_cloud, keyframe_point_colors)\\n\\n # only add the point cloud once in every 5 frames\\n if self.vis_counter % 4 == 0:\\n self.queue.put((pointcloud, keyframe_pose))\\n \\n else:\\n self.queue.put((None, keyframe_pose))\",\n \"def format_point_cloud(self, pc, num_points):\\n resized_pc = np.zeros([num_points, 3])\\n if len(pc) > num_points:\\n # Randomly sample point cloud to reduce size\\n resized_pc = pc[np.random.choice(pc.shape[0], num_points,\\n replace=False), :]\\n elif len(pc) < num_points:\\n # Duplicate last point to fill point cloud. Because of the max function\\n # the duplicated points will not affect the output\\n resized_pc[:len(pc)] = pc\\n resized_pc[len(pc):] = pc[-1]\\n else:\\n resized_pc = pc\\n\\n return resized_pc\",\n \"def test_get_mesh_grid_as_point_cloud_single_pt() -> None:\\n min_x = -3 # integer, minimum x-coordinate of 2D grid\\n max_x = -3 # integer, maximum x-coordinate of 2D grid\\n min_y = 2 # integer, minimum y-coordinate of 2D grid\\n max_y = 2 # integer, maximum y-coordinate of 2D grid\\n\\n # return pts, a Numpy array of shape (N,2)\\n pts = mesh_grid_utils.get_mesh_grid_as_point_cloud(\\n min_x, max_x, min_y, max_y, downsample_factor=1.0\\n )\\n\\n assert pts.shape == (1, 2)\\n gt_pts: NDArrayFloat = np.array([[-3.0, 2.0]])\\n\\n assert np.allclose(gt_pts, pts)\",\n \"def test_get_mesh_grid_as_point_cloud_3x2rect() -> None:\\n min_x = -3 # integer, minimum x-coordinate of 2D grid\\n max_x = -1 # integer, maximum x-coordinate of 2D grid\\n min_y = 2 # integer, minimum y-coordinate of 2D grid\\n max_y = 3 # integer, maximum y-coordinate of 2D grid\\n\\n # return pts, a Numpy array of shape (N,2)\\n pts = mesh_grid_utils.get_mesh_grid_as_point_cloud(\\n min_x, max_x, min_y, max_y, downsample_factor=1.0\\n )\\n\\n assert pts.shape == (6, 2)\\n # fmt: off\\n gt_pts: NDArrayFloat = np.array(\\n [\\n [-3.0, 2.0],\\n [-2.0, 2.0],\\n [-1.0, 2.0],\\n [-3.0, 3.0],\\n [-2.0, 3.0],\\n [-1.0, 3.0]\\n ])\\n # fmt: on\\n assert np.allclose(gt_pts, pts)\",\n \"def show_points(self, **kwargs):\\n\\n if hasattr(self, 'method') and hasattr(self.method, 'show_points'):\\n self.method.show_points(self, **kwargs)\\n else:\\n figsize = kwargs.get('figsize', (15, 5))\\n cmap = kwargs.get('cmap', 'gray')\\n marker = kwargs.get('marker', '.')\\n color = kwargs.get('color', 'r')\\n fig, ax = plt.subplots(figsize=figsize)\\n ax.imshow(self.mraw[0].astype(float), cmap=cmap)\\n ax.scatter(self.points[:, 1], self.points[:, 0], \\n marker=marker, color=color)\\n plt.grid(False)\\n plt.show()\",\n \"def partial_visualize_in_2d(self, cluster_index=[5,12,35,44,64,75,81]):\\n for i in cluster_index:\\n list_x = []\\n list_y = []\\n for j in self.cluster[i]:\\n list_x.append(self.code[0][j,0])\\n list_y.append(self.code[0][j,1])\\n plt.scatter(list_x,list_y, label=self.skill[self.present_skill[i]])\\n plt.legend()\\n plt.show()\\n return\",\n \"def plot_scatter_points(self):\\n self.plot(1)\",\n \"def view(config_file):\\n import open3d as o3d\\n with open(config_file) as f:\\n config = json.load(f)\\n scenes = get_realsense_scenes(config['realsense_dir'])\\n for scene in scenes:\\n # if scene['scene_name'] != \\\"Scene_004\\\":\\n # continue\\n scene_data = get_data_from_scene(scene)\\n logger.info(\\\"Visualizing - %s\\\", scene['scene_name'])\\n pcd = o3d.geometry.PointCloud(o3d.utility.Vector3dVector(scene_data['points3d']))\\n o3d.visualization.draw_geometries_with_editing([pcd])\\n pcd = o3d.geometry.PointCloud(o3d.utility.Vector3dVector(scene_data['points3d_segmented']))\\n o3d.visualization.draw_geometries([pcd])\",\n \"def plot_data(self):\\n if hasattr(self,'data'):\\n plt.scatter(*self.data.T)\\n plt.show()\\n else:\\n raise Exception('No 2d data of the instance has been loaded')\",\n \"def pangolin_draw(points):\\n display.setup()\\n\\n while not display.should_quit():\\n display.init_frame()\\n for idx, pose in enumerate(CAMERA_POSES):\\n is_last_pose = idx == len(CAMERA_POSES) - 1\\n homogenous_pose = np.vstack((pose, [0, 0, 0, 1]))\\n display.draw_camera(homogenous_pose, (0.0, 1.0, 1.0 if is_last_pose else 0.0))\\n\\n display.draw_points(points)\\n\\n display.finish_frame()\",\n \"def plot_data(x):\\n if DATA_2D:\\n plt.scatter(x[:, 0], x[:, 1])\\n plt.show()\\n else:\\n fig = plt.figure()\\n ax = Axes3D(fig)\\n ax.scatter(x[:, 0], x[:, 1], x[:, 2])\\n ax.set_xlabel('X Label')\\n ax.set_ylabel('Y Label')\\n ax.set_zlabel('Z Label')\\n plt.show()\",\n \"def PointCloudfromStructOutput(self,file):\\n print(\\\"Creating Structure Point Cloud\\\")\\n xyz = self.readStructOutput(file)\\n pc = np.zeros((int(len(xyz)/2.0),3))\\n pc[:,0] = xyz[::2,0]*1000\\n pc[:,1] = xyz[::2,1]*1000\\n pc[:,2] = xyz[::2,2]*1000\\n head = \\\"\\\"\\\"X,Y,Z\\\"\\\"\\\"\\n np.savetxt(file, pc, delimiter=',',fmt='%.10f', header=head)\\n return\",\n \"def showFastCrossPoints(self, surface, color=None, mode=None, radius=None, width=None, size=None):\\n points = self.crossSelf()\\n if not color:\\n color = self.cross_point_color\\n if not mode:\\n mode = self.cross_point_mode\\n if not radius:\\n radius = self.cross_point_radius\\n if not width:\\n width = self.cross_point_width\\n if not size:\\n size = self.cross_point_size\\n for point in points:\\n point.show(surface, color=color, mode=mode, radius=radius, width=width, size=size)\",\n \"def plot_samples(s):\\r\\n assert len(s[0, :]) >= 2, ('The Phase space dimensions are less than two.', ' Need at least two to plot.')\\r\\n fig = plt.figure(1)\\r\\n if len(s[0, :]) >= 3:\\r\\n ax = fig.add_subplot(111, projection='3d')\\r\\n ax.scatter(s[:, 0], s[:, 1], s[:, 2])\\r\\n fig = plt.figure(2)\\r\\n plt.scatter(s[:, 0], s[:, 1])\\r\\n plt.show()\",\n \"def viz1(img, interest_points, color='r'):\\n\\n\\tx = [a[1] for a in interest_points] #blob detection x axis\\n\\ty = [a[0] for a in interest_points] #blob detection y axis\\n\\ts = [a[2] for a in interest_points] #blob detected at sigma \\n \\n\\tplt.imshow(img, cmap='gray') #adding the input image to plot \\n\\tfor x, y, s in zip(x, y, s):\\n\\t\\tplt.scatter(x, y, alpha=1, facecolors='none', edgecolors='r', s=s**2) #plotting the input interest points\\n\\tplt.axis('off')\\n\\t#plt.show() #showing the image // can be changed to saving the image locally \\n\\n\\treturn\",\n \"def show_grid(self):\\n\\n if not os.path.exists(self.path_to_results):\\n os.mkdir(self.path_to_results)\\n\\n fig = plt.figure()\\n\\n if self.show_points == 1:\\n plt.scatter(self.x_list_grid, self.y_list_grid, c='blue')\\n\\n plt.plot(self.x_list_main, self.y_list_main,\\n 'green', label='straight path')\\n plt.plot(self.x_list, self.y_list, 'red', label='first path')\\n plt.plot(self.x_list_filtered, self.y_list_filtered,\\n 'blue', label='filtered path')\\n plt.title('Paths')\\n plt.ylabel('Latitude')\\n plt.xlabel('Longitude')\\n # plt.legend()\\n\\n fig.savefig(os.path.join(self.path_to_results, 'Paths.png'))\",\n \"def draw_birdseye_points(self, image, points):\\n point_image = np.copy(image)\\n for point in points:\\n cv2.circle(point_image, point, 5, [0, 0, 255], cv2.FILLED)\\n\\n cv2.imshow('image1', point_image)\\n cv2.waitKey(1)\",\n \"def plt3dpaint(nppoints, color_map = 'jet', reduce_for_vis = True, voxel_size = 0.2, pointsize = 0.1, subplots = 5):\\n assert (type(nppoints) == pclpy.pcl.PointCloud.PointXYZRGB) or (type(nppoints) == pclpy.pcl.PointCloud.PointXYZ) or (type(nppoints) == np.ndarray) or (type(nppoints) is list) or (type(nppoints) is tuple), 'Not valid point_cloud'\\n cloudlist = []\\n cloudcolors = []\\n if (type(nppoints) is not list) & (type(nppoints) is not tuple):\\n nppoints = [nppoints]\\n \\n if len(nppoints) > 1:\\n for n,i in enumerate(nppoints):\\n workpoints = i\\n if (type(workpoints) == pclpy.pcl.PointCloud.PointXYZRGB) or (type(workpoints) == pclpy.pcl.PointCloud.PointXYZ):\\n workpoints = workpoints.xyz\\n\\n if reduce_for_vis:\\n workpoints = seg_tree.voxelize(workpoints,voxel_size)\\n\\n \\n cloudmin = np.min(workpoints[:,2])\\n cloudmax = np.max(workpoints[:,2])\\n \\n points = workpoints\\n color_coef = n/len(nppoints)/2 + n%2*.5\\n if type(color_map) == np.ndarray:\\n color = color_map\\n elif color_map == 'jet':\\n color=cm.jet(color_coef)[:3]\\n else:\\n color=cm.Set1(color_coef)[:3]\\n cloudcolors.append(np.ones_like(workpoints)*color + 0.4*(np.ones_like(workpoints) * ((workpoints[:,2] - cloudmin)/(cloudmax - cloudmin)).reshape(-1,1)-0.5) )\\n cloudlist.append(points)\\n else:\\n workpoints = nppoints[0]\\n if (type(workpoints) == pclpy.pcl.PointCloud.PointXYZRGB) or (type(workpoints) == pclpy.pcl.PointCloud.PointXYZ):\\n workpoints = workpoints.xyz\\n\\n if reduce_for_vis:\\n workpoints = seg_tree.voxelize(workpoints,voxel_size)\\n cloudcolors.append(workpoints[:,2])\\n cloudlist.append(workpoints)\\n\\n plt_pointcloud = np.concatenate(cloudlist)\\n plt_colors = np.concatenate(cloudcolors)\\n if len(nppoints) > 1:\\n plt_colors = np.minimum(plt_colors,np.ones_like(plt_colors))\\n plt_colors = np.maximum(plt_colors,np.zeros_like(plt_colors))\\n fig = plt.figure(figsize=(30,16) )\\n for i in range(subplots):\\n ax = fig.add_subplot(1, subplots, i+1, projection='3d')\\n ax.view_init(30, 360*i/subplots)\\n ax.scatter3D(plt_pointcloud[:,0], plt_pointcloud[:,1], plt_pointcloud[:,2], c=plt_colors, s=pointsize)\",\n \"def plot(self):\\n\\t\\tif (2 <= len(self.X) <= 3):\\n\\t\\t\\tvDataFrame(self.name, self.cursor).scatter(columns = self.X, catcol = \\\"dbscan_cluster\\\", max_cardinality = 100, max_nb_points = 10000)\\n\\t\\telse:\\n\\t\\t\\traise ValueError(\\\"Clustering Plots are only available in 2D or 3D\\\")\",\n \"def plot(self):\\n y = self.projection\\n mpl.scatter(y[:, 0], y[:, 1], c=self.data_class)\\n mpl.show()\",\n \"def imshow(self, depth):\\n layer = self.cube[depth]\\n img = []\\n for i in range(self.height):\\n img.append([layer[i][j].value for j in range(self.width)])\\n plt.imshow(img, cmap='gray')\\n plt.show()\",\n \"def show2d(self, data):\\n self.__plot2d.clear()\\n self.__plot2d.addImage(legend=\\\"data\\\", data=data)\\n self.setCurrentIndex(self.__index2d)\",\n \"def visualise_cppn(self, resolution=(64, 64)):\\n import matplotlib.pyplot as plt\\n from matplotlib.pyplot import imshow\\n data = np.empty([resolution[0], resolution[1]])\\n x_linspace = np.linspace(-1, 1, resolution[0])\\n y_linspace = np.linspace(-1, 1, resolution[1])\\n for row, x in enumerate(x_linspace):\\n for col, y in enumerate(y_linspace):\\n data[row, col] = self.graph(np.array([x, y, 0, 0], dtype=np.float32))[0]\\n #plt.axis([-1, 1, -1, 1])\\n print(data.min(), \\\" \\\", data.max())\\n imshow(data, cmap='Greys', vmin=-1, vmax=1)\\n plt.show()\",\n \"def plot_imshow(data, col_name):\\n city_x,city_y = get_city_base()\\n fig = plt.figure(figsize = FIGURE_SIZE)\\n plt.imshow(data[['lat_ind','lng_ind',col_name]], cmap=CONTOUR_PLOT_COLOUR)\\n plt.scatter(city_x,city_y, color = CITY_MAP_COLOR, s = SCATTER_SIZE_OF_CHICAGO_CITY, zorder = CITY_MAP_ORDER)\",\n \"def draw_point_gt(img,point_gt,width=3):\\n point_img=copy.deepcopy(img)\\n\\n for i in range(point_gt.shape[0]):\\n x=int(point_gt[i,0])\\n y=int(point_gt[i,1])\\n point_img=cv2.circle(point_img,center=(x,y),radius=width//2+1,color=(0,0,255),thickness=width)\\n\\n return point_img\",\n \"def displayDensePts(dense_pts, dense_slices, origin, transform, dense_displacement_all=False, dense_plot_quiver=0, timepoint=-1, ax=None):\\n\\t# If no axes were passed, generate axes\\n\\tif not ax:\\n\\t\\tfig = mplt.figure()\\n\\t\\tax = fig.add_subplot(111, projection='3d')\\n\\t\\n\\t# Pull appropriate timepoint (or set to False if displacement is undesired)\\n\\tif timepoint >= 0:\\n\\t\\ttry:\\n\\t\\t\\tdense_displacement = dense_displacement_all[timepoint]\\n\\t\\texcept(IndexError):\\n\\t\\t\\twarnings.warn('Timepoint not in range of values! Displacement will be ignored.')\\n\\t\\t\\ttimepoint = -1\\n\\t\\t\\tdense_displacement = False\\n\\t\\texcept(TypeError):\\n\\t\\t\\twarnings.warn('Displacement not passed, but timepoint requested! Displacement will be ignored.')\\n\\t\\t\\tdense_displacement = False\\n\\telse:\\n\\t\\tdense_displacement = False\\n\\t\\n\\tfor i in range(len(dense_slices)):\\n\\t\\tcur_slice = dense_slices[i]\\n\\t\\tcur_dense_pts = np.column_stack((dense_pts[i], [cur_slice - origin[2]]*dense_pts[i].shape[0]))\\n\\t\\tdata_dense = np.dot(cur_dense_pts, np.transpose(transform))\\n\\t\\tx = data_dense[:, 2]\\n\\t\\ty = data_dense[:, 1]\\n\\t\\tz = data_dense[:, 0]\\n\\t\\tax.scatter(x, y, -z, ',')\\n\\t\\t\\n\\t\\tif dense_displacement:\\n\\t\\t\\tdense_displacement_slice = dense_displacement[i]\\n\\t\\t\\tif dense_plot_quiver == 1:\\n\\t\\t\\t\\tax.quiver(x, y, -z, dense_displacement_slice[:, 0], dense_displacement_slice[:, 1], [0]*dense_displacement_slice.shape[0])\\n\\treturn(ax)\",\n \"def trimesh_from_point_cloud(cloud):\\n points = np.asarray(cloud)\\n hull = scipy.spatial.ConvexHull(points)\\n hull = scipy.spatial.ConvexHull(points[hull.vertices])\\n ru.transforms.counterclockwise_hull(hull)\\n vertices = hull.points\\n faces = hull.simplices\\n return vertices, faces\",\n \"def occupancy_grid(self, point_cloud=None, ss_pred=None, *, return_image=False, show=False, keep_showing=False):\\n\\t\\tif point_cloud is None or ss_pred is None:\\n\\t\\t\\tgen = self.loop(point_cloud=True, ss_pred=True)\\n\\t\\t\\tpoint_cloud, ss_pred = next(gen)\\n\\t\\t#TODO: Check that this function works with ZED camera\\n\\t\\tpass\",\n \"def plot(self):\\n\\n fig = plt.figure()\\n ax = fig.add_subplot(111, projection=Axes3D.name)\\n\\n # TODO Use numpy to rotate esp_points matrix for faster variable access.\\n ax.scatter(\\n xs=[i[0][0] for i in self.esp_points],\\n ys=[i[0][1] for i in self.esp_points],\\n zs=[i[0][2] for i in self.esp_points],\\n c=[i[1] for i in self.esp_points],\\n marker='o',\\n s=2,\\n alpha=0.5\\n )\\n\\n ax.scatter(\\n xs=[i[0][0] for i in self.atom_points],\\n ys=[i[0][1] for i in self.atom_points],\\n zs=[i[0][2] for i in self.atom_points],\\n c=[i[1] for i in self.atom_points],\\n marker='X',\\n s=100\\n )\\n\\n plt.show()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72636724","0.72375584","0.71609396","0.7132317","0.6933942","0.6919783","0.6877211","0.68398106","0.65854543","0.6562634","0.65615726","0.6518098","0.64886963","0.64688116","0.64599943","0.64297676","0.64172477","0.6347307","0.6338426","0.6316377","0.6249169","0.6247689","0.61805177","0.6168697","0.61648315","0.6162763","0.61433035","0.61228466","0.6120385","0.60887784","0.60840714","0.6073814","0.606181","0.60497266","0.60478836","0.60273826","0.60230863","0.6011479","0.5949034","0.5944727","0.5941591","0.59129876","0.5882682","0.58782804","0.58763915","0.58751667","0.58735824","0.58648884","0.58637315","0.58447856","0.5813793","0.5810271","0.58056587","0.5803301","0.57765025","0.57596034","0.57574964","0.57572305","0.57341605","0.5723177","0.57149345","0.57101357","0.57069534","0.5700627","0.56989515","0.56848264","0.5670351","0.5662748","0.5656756","0.56466407","0.5642417","0.5637249","0.56292343","0.5626862","0.56252885","0.5621153","0.5601514","0.55969167","0.5594165","0.5589445","0.55824107","0.55757415","0.55727154","0.5560376","0.5551333","0.5550308","0.55499405","0.5545071","0.5526621","0.55255795","0.55196786","0.55180466","0.5517805","0.551735","0.55166","0.55158687","0.55143934","0.55120367","0.55085087","0.5502027"],"string":"[\n \"0.72636724\",\n \"0.72375584\",\n \"0.71609396\",\n \"0.7132317\",\n \"0.6933942\",\n \"0.6919783\",\n \"0.6877211\",\n \"0.68398106\",\n \"0.65854543\",\n \"0.6562634\",\n \"0.65615726\",\n \"0.6518098\",\n \"0.64886963\",\n \"0.64688116\",\n \"0.64599943\",\n \"0.64297676\",\n \"0.64172477\",\n \"0.6347307\",\n \"0.6338426\",\n \"0.6316377\",\n \"0.6249169\",\n \"0.6247689\",\n \"0.61805177\",\n \"0.6168697\",\n \"0.61648315\",\n \"0.6162763\",\n \"0.61433035\",\n \"0.61228466\",\n \"0.6120385\",\n \"0.60887784\",\n \"0.60840714\",\n \"0.6073814\",\n \"0.606181\",\n \"0.60497266\",\n \"0.60478836\",\n \"0.60273826\",\n \"0.60230863\",\n \"0.6011479\",\n \"0.5949034\",\n \"0.5944727\",\n \"0.5941591\",\n \"0.59129876\",\n \"0.5882682\",\n \"0.58782804\",\n \"0.58763915\",\n \"0.58751667\",\n \"0.58735824\",\n \"0.58648884\",\n \"0.58637315\",\n \"0.58447856\",\n \"0.5813793\",\n \"0.5810271\",\n \"0.58056587\",\n \"0.5803301\",\n \"0.57765025\",\n \"0.57596034\",\n \"0.57574964\",\n \"0.57572305\",\n \"0.57341605\",\n \"0.5723177\",\n \"0.57149345\",\n \"0.57101357\",\n \"0.57069534\",\n \"0.5700627\",\n \"0.56989515\",\n \"0.56848264\",\n \"0.5670351\",\n \"0.5662748\",\n \"0.5656756\",\n \"0.56466407\",\n \"0.5642417\",\n \"0.5637249\",\n \"0.56292343\",\n \"0.5626862\",\n \"0.56252885\",\n \"0.5621153\",\n \"0.5601514\",\n \"0.55969167\",\n \"0.5594165\",\n \"0.5589445\",\n \"0.55824107\",\n \"0.55757415\",\n \"0.55727154\",\n \"0.5560376\",\n \"0.5551333\",\n \"0.5550308\",\n \"0.55499405\",\n \"0.5545071\",\n \"0.5526621\",\n \"0.55255795\",\n \"0.55196786\",\n \"0.55180466\",\n \"0.5517805\",\n \"0.551735\",\n \"0.55166\",\n \"0.55158687\",\n \"0.55143934\",\n \"0.55120367\",\n \"0.55085087\",\n \"0.5502027\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.60903704"},"document_rank":{"kind":"string","value":"29"}}},{"rowIdx":21198,"cells":{"query":{"kind":"string","value":"Show a point cloud in 2d"},"document":{"kind":"string","value":"def show_point_set_normal(points, normals, title, save_path = None):\n dim = points.shape[1]\n assert(dim == 2), \"Only data in 2 dimension space can be visualized.\"\n \n fig = plt.figure(figsize = (6, 6))\n plt.title(title)\n plt.scatter(points[:, 0], points[:, 1], c = 'b', s = 5, label = \"point cloud\")\n plt.quiver(points[:, 0], points[:, 1], normals[:, 0], normals[:, 1])\n plt.xlabel(\"x\")\n plt.ylabel(\"y\")\n plt.axis('equal')\n plt.grid()\n plt.legend()\n plt.show()\n \n if type(save_path) != type(None):\n fig.savefig(save_path)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def visualize_point_cloud(points, colors=None, normals=None,\n show_frame=False, frame_size=1.0, frame_origin=(0, 0, 0)):\n pc = np2pcd(points, colors, normals)\n geometries = [pc]\n if show_frame:\n coord_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=frame_size, origin=frame_origin)\n geometries.append(coord_frame)\n o3d.visualization.draw_geometries(geometries)","def render_point_cloud(frame_id):\n point_cloud_world_coordinates = get_point_cloud_world_coordinates(frame_id)\n # pptk\n v = pptk.viewer(point_cloud_world_coordinates)\n v.set(point_size=0.0001)\n v.color_map('cool', scale=[0, 5])","def create_point_cloud(self):\n pixels = []\n colors = []\n my_pixels = []\n for j in range(self.height):\n for i in range(self.width):\n depth = self.depth[j, i]\n pixels.append(\n [i * depth, j * depth, depth]\n )\n my_pixels.append(\n [i, j, 1]\n )\n # make rgb with flip()\n colors.append(np.flip(self.bgr[j, i, :]))\n # colors.append(self.bgr[j, i, :])\n self.my_pixels = my_pixels\n pixels = np.array(pixels)\n\n # project pixels to camera space\n self.xyz_points = self.intrinsics_inv @ np.transpose(pixels)\n self.color_points = colors\n\n # now add 1s to the points for homogenous coordinates\n num_points = self.get_num_xyz_points()\n ones = np.ones((1, num_points))\n self.xyzw_points = np.concatenate((self.xyz_points, ones), axis=0)\n\n self.scene = None\n self.camera_pose = None\n self.nm = None\n self.nl = None\n self.nc = None\n self.create_mesh()","def draw_pointcloud(ax, example):\n points = example['points'].cpu().detach().numpy()\n points_num = len(points)\n xs = np.empty([points_num])\n ys = np.empty([points_num])\n zs = np.empty([points_num])\n intensity = np.empty([len(points)])\n for j, point in enumerate(points):\n xs[j] = point[1]\n ys[j] = point[2]\n zs[j] = point[3]\n intensity[j] = point[4]\n\n intensity = intensity\n ax.scatter3D(xs, ys, zs, c=intensity, marker='.', s=0.3, cmap=plt.get_cmap('jet'))","def show_pointclouds(points, colors, text=[], title=\"Default\", png_path=\"\", interactive=True, orientation='horizontal'):\n\n # make sure pointclouds is a list\n assert isinstance(points, type([])), \\\n \"Pointclouds argument must be a list\"\n\n # make sure colors is a list\n assert isinstance(colors, type([])), \\\n \"Colors argument must be a list\"\n\n # make sure number of pointclouds and colors are the same\n assert len(points) == len(colors), \\\n \"Number of pointclouds (%d) is different then number of colors (%d)\" % (len(points), len(colors))\n\n while len(text) < len(points):\n text.append(\"\")\n\n # Number of pointclouds to be displayed in this window\n num_pointclouds = len(points)\n\n point_size = 10\n pointclouds = [VtkPointCloud(point_size) for _ in range(num_pointclouds)]\n renderers = [vtk.vtkRenderer() for _ in range(num_pointclouds)]\n\n height = 1.0 / max(num_pointclouds, 1)\n viewports = [(i*height, (i+1)*height) for i in range(num_pointclouds)]\n #print(viewports)\n\n # iterate over all point clouds\n for i, pc in enumerate(points):\n pc = pc.squeeze()\n co = colors[i].squeeze()\n assert pc.shape[0] == co.shape[0], \\\n \"expected same number of points (%d) then colors (%d), cloud index = %d\" % (pc.shape[0], co.shape[0], i)\n assert pc.shape[1] == 3, \"expected points to be N x 3, got N x %d\" % pc.shape[1]\n assert co.shape[1] == 3, \"expected colors to be N x 3, got N x %d\" % co.shape[1]\n\n # for each point cloud iterate over all points\n for j in range(pc.shape[0]):\n point = pc[j, :]\n color = co[j, :]\n pointclouds[i].add_point(point, color)\n\n renderers[i].AddActor(pointclouds[i].vtkActor)\n # renderers[i].AddActor(vtk.vtkAxesActor())\n renderers[i].SetBackground(1.0, 1.0, 1.0)\n if orientation == 'horizontal':\n print(viewports[i][0])\n renderers[i].SetViewport(viewports[i][0], 0.0, viewports[i][1], 1.0)\n elif orientation == 'vertical':\n renderers[i].SetViewport(0.0, viewports[i][0], 1.0, viewports[i][1])\n else:\n raise Exception('Not a valid orientation!')\n renderers[i].ResetCamera()\n\n # Add circle to first render\n renderers[0].AddActor(getActorCircle())\n renderers[0].AddActor(getActorCircle(50, 49, color=(0, 1, 0)))\n\n # Text actors\n text_actors = [vtk.vtkTextActor() for _ in text]\n for i, ta in enumerate(text_actors):\n if orientation == 'horizontal':\n ta.SetInput(' ' + text[i])\n elif orientation == 'vertical':\n ta.SetInput(text[i] + '\\n\\n\\n\\n\\n\\n')\n else:\n raise Exception('Not a valid orientation!')\n txtprop = ta.GetTextProperty()\n txtprop.SetFontFamilyToArial()\n txtprop.SetFontSize(0)\n txtprop.SetColor(0, 0, 0)\n # txtprop.SetJustificationToCentered()\n # ta.SetDisplayPosition(500, 10)\n # ta.SetAlignmentPoint()\n renderers[i].AddActor(ta)\n\n # Render Window\n render_window = vtk.vtkRenderWindow()\n for renderer in renderers:\n render_window.AddRenderer(renderer)\n\n render_window_interactor = vtk.vtkRenderWindowInteractor()\n render_window_interactor.SetInteractorStyle(vtk.vtkInteractorStyleTrackballCamera())\n render_window_interactor.SetRenderWindow(render_window)\n\n [center_x, center_y, center_z] = np.mean(points[0].squeeze(), axis=0)\n camera = vtk.vtkCamera()\n # d = 10\n # camera.SetViewUp(0, -1, 0)\n\n # camera.SetPosition(center_x + d, center_y + d, center_z + d / 2)\n # camera.SetFocalPoint(center_x, center_y, center_z)\n # camera.SetFocalPoint(0, 0, 0)\n\n camera.SetViewUp(0, 0, 1)\n if orientation == 'horizontal':\n camera.SetPosition(3, -10, 2)\n camera.SetFocalPoint(3, 1.5, 1.5)\n elif orientation == 'vertical':\n camera.SetPosition(1.5, -6, 2)\n camera.SetFocalPoint(1.5, 1.5, 1.5)\n else:\n raise Exception('Not a valid orientation!')\n\n camera.SetClippingRange(0.002, 1000)\n for renderer in renderers:\n renderer.SetActiveCamera(camera)\n\n # Begin Interaction\n render_window.Render()\n render_window.SetWindowName(title)\n if orientation == 'horizontal':\n render_window.SetSize(1940, 720)\n elif orientation == 'vertical':\n render_window.SetSize(600, 1388)\n else:\n raise Exception('Not a valid orientation!')\n\n if interactive:\n render_window_interactor.Start()\n\n if png_path:\n # screenshot code:\n w2if = vtk.vtkWindowToImageFilter()\n w2if.SetInput(render_window)\n w2if.Update()\n\n writer = vtk.vtkPNGWriter()\n writer.SetFileName(png_path)\n writer.SetInputConnection(w2if.GetOutputPort())\n writer.Write()","def make_point_cloud(self):\r\n\r\n self.pointCloud = VtkPointCloud()\r\n for k in range(np.size(self.pos, 0)):\r\n self.pointCloud.addPoint(self.pos[k, :])\r\n\r\n # Renderer\r\n renderer = vtk.vtkRenderer()\r\n renderer.AddActor(self.pointCloud.vtkActor)\r\n renderer.SetBackground(.2, .3, .4)\r\n renderer.SetBackground(0.0, 0.0, 0.0)\r\n renderer.ResetCamera()\r\n\r\n # Render Window\r\n renderWindow = vtk.vtkRenderWindow()\r\n renderWindow.AddRenderer(renderer)\r\n\r\n # Interactor\r\n renderWindowInteractor = vtk.vtkRenderWindowInteractor()\r\n renderWindowInteractor.SetRenderWindow(renderWindow)\r\n\r\n # Begin Interaction\r\n renderWindow.Render()\r\n renderWindow.SetWindowName(\"XYZ Data Viewer: \")\r\n renderWindowInteractor.Start()","def publish_point_cloud(self):\n all_points = [np.zeros((0, 2), np.float32)]\n all_keys = []\n for key in range(len(self.keyframes)):\n pose = self.keyframes[key].pose\n transf_points = self.keyframes[key].transf_points\n all_points.append(transf_points)\n all_keys.append(key * np.ones((len(transf_points), 1)))\n\n all_points = np.concatenate(all_points)\n all_keys = np.concatenate(all_keys)\n sampled_points, sampled_keys = pcl.downsample(\n all_points, all_keys, self.point_resolution\n )\n sampled_xyzi = np.c_[sampled_points, np.zeros_like(sampled_keys), sampled_keys]\n if len(sampled_xyzi) == 0:\n return\n\n if self.save_fig:\n plt.figure()\n plt.scatter(\n sampled_xyzi[:, 0], sampled_xyzi[:, 1], c=sampled_xyzi[:, 3], s=1\n )\n plt.axis(\"equal\")\n plt.gca().invert_yaxis()\n plt.savefig(\"step-{}-map.png\".format(self.current_key - 1), dpi=100)\n plt.close(\"all\")\n\n cloud_msg = n2r(sampled_xyzi, \"PointCloudXYZI\")\n cloud_msg.header.stamp = self.current_keyframe.time\n cloud_msg.header.frame_id = \"map\"\n self.cloud_pub.publish(cloud_msg)","def showPoints(surface, points):\n for point in points:\n point.show(surface)","def as_point_cloud(self):\n far = 1000.0 # max depth in meters.\n intrinsic_mat = self.camera_setup.get_intrinsic_matrix()\n width, height = self.camera_setup.width, self.camera_setup.height\n # 2d pixel coordinates\n pixel_length = width * height\n u_coord = repmat(np.r_[0:width:1], height, 1).reshape(pixel_length)\n v_coord = repmat(np.c_[0:height:1], 1, width).reshape(pixel_length)\n normalized_depth = np.reshape(self.frame, pixel_length)\n\n # p2d = [u,v,1]\n p2d = np.array([u_coord, v_coord, np.ones_like(u_coord)])\n\n # P = [X,Y,Z]\n p3d = np.dot(inv(intrinsic_mat), p2d)\n p3d *= normalized_depth * far\n\n # [[X1,Y1,Z1],[X2,Y2,Z2], ... [Xn,Yn,Zn]]\n locations = np.asarray(np.transpose(p3d))\n # Transform the points in 3D world coordinates.\n to_world_transform = self.camera_setup.get_unreal_transform()\n point_cloud = to_world_transform.transform_points(locations)\n return point_cloud","def render_point_cloud(self, point_cloud, extrinsics=Pose(), color=GRAY):\n\n combined_transform = self._bev_rotation * extrinsics\n\n pointcloud_in_bev = combined_transform * point_cloud\n point_cloud2d = pointcloud_in_bev[:, :2]\n\n point_cloud2d[:, 0] = (self._center_pixel[0] + point_cloud2d[:, 0] * self._pixels_per_meter)\n point_cloud2d[:, 1] = (self._center_pixel[1] + point_cloud2d[:, 1] * self._pixels_per_meter)\n\n H, W = self.data.shape[:2]\n uv = point_cloud2d.astype(np.int32)\n in_view = np.logical_and.reduce([\n (point_cloud2d >= 0).all(axis=1),\n point_cloud2d[:, 0] < W,\n point_cloud2d[:, 1] < H,\n ])\n uv = uv[in_view]\n self.data[uv[:, 1], uv[:, 0], :] = color","def showCrossPoints(self, surface):\n for point in self.cross_points:\n point.show(surface)","def drawPointCloud(points, ax, color=None):\n if len(points.shape) != 2 or points.shape[0] != 3:\n raise ValueError(\"'points' must be 3xN\")\n if color == None:\n color = __color_cycle.next()\n elif color in (0, 1, 2):\n color = points[color, :]\n ax.scatter(points[0,:].T, points[1,:].T, points[2,:].T, c=color)","def create_pointcloud(pts):\n depths = PointCloud()\n depths.header = std_msgs.msg.Header()\n depths.header.stamp = rospy.Time.now()\n depths.header.frame_id = \"view_zero\"\n depths.points = [None] * len(pts)\n for p in xrange(len(pts)):\n #Giving point the same orientation as the robot\n y = pts[p,0]\n z = - pts[p,1] #y in images is down\n x = pts[p,2]\n depths.points[p] = Point(x, y, z)\n return depths","def generate_pointcloud(rgb_file, mask_file,depth_file,ply_file):\n rgb = Image.open(rgb_file)\n # depth = Image.open(depth_file)\n depth = Image.open(depth_file).convert('I')\n mask = Image.open(mask_file).convert('I')\n\n # if rgb.size != depth.size:\n # raise Exception(\"Color and depth image do not have the same resolution.\")\n # if rgb.mode != \"RGB\":\n # raise Exception(\"Color image is not in RGB format\")\n # if depth.mode != \"I\":\n # raise Exception(\"Depth image is not in intensity format\")\n\n\n points = [] \n for v in range(rgb.size[1]):\n for u in range(rgb.size[0]):\n color = rgb.getpixel((u,v))\n # Z = depth.getpixel((u,v)) / scalingFactor\n # if Z==0: continue\n # X = (u - centerX) * Z / focalLength\n # Y = (v - centerY) * Z / focalLength\n if (mask.getpixel((u,v))<55):\n Z = depth.getpixel((u, v))*.22 \n if Z == 0: continue\n Y = .22 * v\n X = .22 * u\n points.append(\"%f %f %f %d %d %d 0\\n\"%(X,Y,Z,color[0],color[1],color[2]))\n file = open(ply_file,\"w\")\n file.write('''ply\nformat ascii 1.0\nelement vertex %d\nproperty float x\nproperty float y\nproperty float z\nproperty uchar red\nproperty uchar green\nproperty uchar blue\nproperty uchar alpha\nend_header\n%s\n'''%(len(points),\"\".join(points)))\n file.close()","def point_cloud(self):\n\t\tgen = self.loop(point_cloud=True)\n\t\tpoint_cloud = next(gen)\n\t\treturn point_cloud","def debug_filter_points(self, points):\n cloud_msg = PointCloud2()\n cloud_msg.header.frame_id = \"map\"\n cloud_msg.header.stamp = rospy.Time.now() \n xyz = [[p.pose.position.x, p.pose.position.y, p.pose.position.z] for p in points] \n point_cloud = pc2.create_cloud_xyz32(cloud_msg.header, xyz)\n self._points_publisher.publish(point_cloud)","def visualize_scan(self):\n fig = plt.figure()\n ax = fig.add_subplot(111, projection='3d')\n ax.scatter(self.p1_points[:, 0], self.p1_points[:, 1], self.p1_points[:, 2], c='r')\n ax.scatter(self.p2_points[:, 0], self.p2_points[:, 1], self.p2_points[:, 2], c='g')\n ax.scatter(self.p3_points[:, 0], self.p3_points[:, 1], self.p3_points[:, 2], c='b')\n ax.scatter(self.p4_points[:, 0], self.p4_points[:, 1], self.p4_points[:, 2])\n\n ax.set_xlabel('x')\n ax.set_ylabel('y')\n ax.set_zlabel('z')\n plt.show()","def visualize_in_2d(self):\n ae = Autoencoder_test(self.data)\n self.code = ae.encode(n_dimension=2, learning_rate=0.01, training_epochs=10, batch_size=400)\n for i in range(len(self.cluster)):\n list_x = []\n list_y = []\n for j in self.cluster[i]:\n list_x.append(self.code[0][j,0])\n list_y.append(self.code[0][j,1])\n plt.scatter(list_x,list_y)\n plt.show()\n return","def convertcloud(points):\n pcd = open3d.geometry.PointCloud()\n pcd.points = open3d.utility.Vector3dVector(points)\n return pcd","def voxelize(points,leaf = 0.1):\n if (type(points) == pclpy.pcl.PointCloud.PointXYZRGB):\n cloud = points\n voxel_filter = pclpy.pcl.filters.VoxelGrid.PointXYZRGB()\n filtered_pointcloud = pclpy.pcl.PointCloud.PointXYZRGB()\n else:\n cloud = pclpy.pcl.PointCloud.PointXYZ(points)\n voxel_filter = pclpy.pcl.filters.VoxelGrid.PointXYZ()\n filtered_pointcloud = pclpy.pcl.PointCloud.PointXYZ()\n \n voxel_filter.setLeafSize(leaf,leaf,leaf)\n voxel_filter.setInputCloud(cloud)\n \n voxel_filter.filter(filtered_pointcloud)\n if type(points) == pclpy.pcl.PointCloud.PointXYZRGB:\n return filtered_pointcloud\n else:\n return filtered_pointcloud.xyz","def plot(self, subsample=None, valid_instance_types=None):\n pt = self.sample_indices(subsample, valid_instance_types)\n\n x, y, z = self.pc[pt, 0], self.pc[pt, 1], self.pc[pt, 2]\n color = self.color[pt]\n\n return plot_pointcloud(x, y, z, color=color)","def show(data_set, number_points: int):\n print(f'info: Showing {number_points} as maximum.')\n sub_set_points = np.random.choice(range(data_set.shape[0]), size=min(data_set.shape[0], number_points))\n x = data_set[sub_set_points, 0]\n y = data_set[sub_set_points, 1]\n z = data_set[sub_set_points, 2]\n\n fig = plt.figure(figsize=(8, 8))\n ax = mplot3d.Axes3D(fig)\n ax.set_title('NMSLIB index 3D representation', fontsize=20)\n ax.scatter(xs=x, ys=y, zs=z)\n plt.show()","def _project_pointcloud(self, cloud):\n\n assert isinstance(cloud, PointCloud2)\n\n pc1 = PointCloud()\n pc1.header = cloud.header\n # hack the time! dont move the robot :-0\n pc1.header.stamp = rospy.Time.now()\n \n \n pc1.points = [Point32(*p) for p in pc2.read_points(cloud)]\n\n self._tf_listener.waitForTransform(pc1.header.frame_id,\n self._image_info.tf_frame, \n rospy.Time(0), \n rospy.Duration(4))\n\n image_frame_cloud = self._tf_listener.transformPointCloud (\n self._image_info.tf_frame, \n pc1)\n min_x, max_x, min_y, max_y = 640, 0, 480, 0 # TODO: remove hard coded image size!\n for pt in image_frame_cloud.points:\n u, v = self._image_info.project3dToPixel((pt.x, pt.y, pt.z))\n if v < min_y:\n min_y = int(v)\n if v > max_y:\n max_y = int(v)\n if u < min_x:\n min_x = int(u)\n if u > max_x:\n max_x = int(u)\n location = (((min_x, min_y), (max_x, max_y)))\n rospy.loginfo(\"Transformed cloud into image plane\")\n return location","def visualize(self):\n colors = {'outline': (220, 220, 220),\n 'inlier': (0, 255, 0),\n 'outlier': (0, 0, 255),\n 'lines': (128, 220, 128)}\n # Create output image for visualization\n gap = 5\n h1, w1 = self.target.image.shape[:2]\n h2, w2 = self.image.shape[:2]\n vis = np.zeros((max(h1, h2), w1 + w2 + gap, 3), np.uint8)\n vis[:h1, :w1, :] = self.target.image\n w1 += gap\n vis[:h2, w1:w1+w2, :] = self.image\n \n # Draw the located object \n quad = np.float32(self.quad) + np.float32([w1, 0])\n self.draw(vis, colors['outline'], 2, quad)\n \n # draw point details\n inliers = [(x0, y0, x1 + w1, y1) for (x0, y0), (x1, y1) in self.inliers]\n outliers = [(x0, y0, x1 + w1, y1) for (x0, y0), (x1, y1) in self.outliers]\n if colors['outlier'] is not None: # draw x on each point\n r = 2 # radius\n thickness = 2\n for x0, y0, x1, y1 in outliers:\n cv2.line(vis, (x0 - r, y0 - r), (x0 + r, y0 + r), colors['outlier'], thickness)\n cv2.line(vis, (x0 + r, y0 - r), (x0 - r, y0 + r), colors['outlier'], thickness)\n cv2.line(vis, (x1 - r, y1 - r), (x1 + r, y1 + r), colors['outlier'], thickness)\n cv2.line(vis, (x1 + r, y1 - r), (x1 - r, y1 + r), colors['outlier'], thickness)\n if colors['lines'] is not None:\n for x0, y0, x1, y1 in inliers:\n cv2.line(vis, (x0, y0), (x1, y1), colors['lines'], 1)\n if colors['inlier'] is not None:\n for x0, y0, x1, y1 in inliers:\n cv2.circle(vis, (x0, y0), 2, colors['inlier'], -1)\n cv2.circle(vis, (x1, y1), 2, colors['inlier'], -1)\n return vis","def point_cloud(self, X, Y, Z, size=1, color='#FF3232', bordercolor='#FF3232', legend='', width=0.5, opacity=1.0):\n point_cloud = go.Scatter3d(\n x=X,\n y=Y,\n z=Z,\n # showlegend=False,\n name=legend,\n mode='markers',\n marker=dict(\n size=size,\n color=color,\n line=dict(\n color=bordercolor,\n width=width\n ),\n # opacity=opacity\n )\n )\n\n return point_cloud","def visualize(self):\n self.octree.updateInnerOccupancy()\n print(\"Start Octomap Visualization\")\n\n # define parameters\n data = imgviz.data.arc2017()\n camera_info = data['camera_info']\n K = np.array(camera_info['K']).reshape(3, 3)\n width=camera_info['width']\n height=camera_info['height']\n\n # get free and occupied grid\n occupied, _ = self.octree.extractPointCloud()\n #frontier = self.gen_frontier()\n \n print(\"load point cloud\")\n window = pyglet.window.Window(\n width=int(1280), height=int(960)\n )\n\n @window.event\n def on_key_press(symbol, modifiers):\n if modifiers == 0:\n if symbol == pyglet.window.key.Q:\n window.on_close()\n\n gui = glooey.Gui(window)\n hbox = glooey.HBox()\n hbox.set_padding(5)\n\n camera = trimesh.scene.Camera(\n resolution=(width, height), focal=(K[0, 0], K[1, 1])\n )\n\n # initial camera pose\n camera_transform = np.array(\n [\n [1, 0, 0, 0],\n [0, -1, 0, 0],\n [0, 0, -1, -5],\n [0.0, 0.0, 0.0, 1.0],\n ],\n )\n\n \n\n occupied_geom = trimesh.voxel.ops.multibox(\n occupied, pitch=self.resolution, colors=[0.0, 0.0, 0.0, 0.5]\n )\n\n # frontier_geom = trimesh.voxel.ops.multibox(\n # frontier, pitch=self.resolution, colors=[1.0, 0, 0, 0.5]\n # )\n scene = trimesh.Scene(camera=camera, geometry=[occupied_geom])#, frontier_geom])\n scene.camera_transform = camera_transform\n hbox.add(self.labeled_scene_widget(scene, label='octomap'))\n\n\n gui.add(hbox)\n pyglet.app.run()","def display(self):\n scatter_plot(self.points, self.hull_points, self.color, self.title)","def vis_points(data,f1,f2):\n if np.isnan(data).any():\n return\n \n plt.scatter(data[:,f1], data[:,f2], alpha=0.2, c='b')\n plt.xlim(lims)\n plt.ylim(lims)","def show_pair_points(points, pairs, title, save_path = None):\n\n fig = plt.figure(figsize = (6, 6))\n plt.title(title)\n plt.scatter(points[:, 0], points[:, 1], c = 'b', s = 5, label = \"point cloud\")\n for pair in pairs:\n print(pair)\n point_1 = points[pair[0]]\n point_2 = points[pair[1]]\n plt.scatter([point_1[0], point_2[0]], [point_1[1], point_2[1]], s = 10)\n plt.xlabel(\"x\")\n plt.ylabel(\"y\")\n plt.axis('equal')\n plt.grid()\n plt.legend()\n plt.show()\n \n if type(save_path) != type(None):\n fig.savefig(save_path)","def show_point_set_curvature(points, curvatures, title, thresh = 0.9, save_path = None):\n dim = points.shape[1]\n assert(dim == 2), \"Only data in 2 dimension space can be visualized.\"\n selected_pts = points[np.abs(curvatures) < thresh]\n \n fig = plt.figure(figsize = (6, 6))\n plt.title(title)\n plt.scatter(points[:, 0], points[:, 1], c = 'b', s = 5, label = \"point cloud\")\n plt.scatter(selected_pts[:, 0], selected_pts[:, 1], c = 'r', s = 5, label = \"selected point cloud\")\n plt.xlabel(\"x\")\n plt.ylabel(\"y\")\n plt.axis('equal')\n plt.grid()\n plt.legend()\n plt.show()\n \n if type(save_path) != type(None):\n fig.savefig(save_path)","def show(self):\n from matplotlib import pyplot as plt\n from mpl_toolkits.mplot3d import Axes3D\n\n fig = plt.figure()\n ax = Axes3D(fig)\n pos = self.cluster.get_positions()\n from itertools import combinations\n for tri in self.mesh.simplices:\n for comb in combinations(tri, 2):\n x1 = pos[comb[0], 0]\n x2 = pos[comb[1], 0]\n y1 = pos[comb[0], 1]\n y2 = pos[comb[1], 1]\n z1 = pos[comb[0], 2]\n z2 = pos[comb[1], 2]\n ax.plot([x1, x2], [y1, y2], zs=[z1, z2], color=\"black\")\n plt.show()","def __show_source(self):\n pcd = o3d.io.read_point_cloud(\n self.source_cloud\n )\n if np.asarray(pcd.points).shape[0] != 0:\n pcd.paint_uniform_color([0, 1, 0])\n pcd.estimate_normals()\n self.source_point_cloud_view.load_cloud(pcd)\n try:\n self.source_point_cloud_view.show_window()\n except RuntimeError:\n pass\n else:\n QtWidgets.QMessageBox.warning(self, \"Error\",\n f\"Source point cloud is no longer available\"\n )\n self.source_cloud = \"\"\n self.__update_clickability()\n self.__save_context()","def open3dpaint(nppoints, color_map = 'jet', reduce_for_vis = False, voxel_size = 0.1, pointsize = 0.1):\n assert (type(nppoints) == pclpy.pcl.PointCloud.PointXYZRGB) or (type(nppoints) == pclpy.pcl.PointCloud.PointXYZ) or (type(nppoints) == np.ndarray) or (type(nppoints) is list) or (type(nppoints) is tuple), 'Not valid point_cloud'\n \n if (type(nppoints) is not list) & (type(nppoints) is not tuple):\n nppoints = [nppoints]\n try:\n visualizer = open3d.visualization.Visualizer()\n visualizer.create_window()\n options = visualizer.get_render_option()\n options.background_color = np.asarray([0, 0, 0])\n options.point_size = pointsize\n\n if len(nppoints) > 1:\n for n,i in enumerate(nppoints):\n workpoints = i\n if (type(workpoints) == pclpy.pcl.PointCloud.PointXYZRGB) or (type(workpoints) == pclpy.pcl.PointCloud.PointXYZ):\n workpoints = workpoints.xyz\n\n if reduce_for_vis:\n workpoints = seg_tree.voxelize(workpoints,voxel_size)\n\n points = convertcloud(workpoints)\n color_coef = n/len(nppoints)/2 + n%2*.5\n if type(color_map) == np.ndarray:\n color = color_map\n elif color_map == 'jet':\n color=cm.jet(color_coef)[:3]\n else:\n color=cm.Set1(color_coef)[:3]\n points.colors = open3d.utility.Vector3dVector(np.ones_like(workpoints)*color)\n #points.colors = open3d.utility.Vector3dVector(color)\n visualizer.add_geometry(points)\n else:\n workpoints = nppoints[0]\n if (type(workpoints) == pclpy.pcl.PointCloud.PointXYZRGB) or (type(workpoints) == pclpy.pcl.PointCloud.PointXYZ):\n workpoints = workpoints.xyz\n \n if reduce_for_vis:\n workpoints = seg_tree.voxelize(workpoints,voxel_size)\n points = convertcloud(workpoints)\n visualizer.add_geometry(points)\n visualizer.run()\n visualizer.destroy_window()\n \n except Exception as e:\n print(type(e))\n print(e.args)\n print(e)\n visualizer.destroy_window()","def plot(self):\n # Find only unmasked data :\n # xyz, sData, sColor, _ = self._select_unmasked()\n xyz, sData, sColor = self.xyz, self.sData, self.sColor\n\n # Render as cloud points :\n self.mesh = visu.Markers(name='Sources')\n self.mesh.set_data(xyz, edge_color=self.edgecolor, face_color=sColor,\n size=sData, scaling=self.scaling,\n edge_width=self.edgewidth, symbol=self.symbol)\n self.mesh.set_gl_state('translucent')","def show_2d_projections(self):\n\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\")\n fig, axs = plt.subplots(1, 3)\n for i in range(3):\n plt.sca(axs[i])\n io.imshow(self.image.max(i), cmap='viridis')","def show_keypoints(image, key_pts):\n plt.imshow(image)\n plt.scatter(key_pts[:, 0], key_pts[:, 1], s=20, marker='.', c='m')","def visualize(vis, features, label):\n if vis == 'PCA':\n #n_components = st.sidebar.slider(\"n_components\", 2, 10)\n #alpha = st.sidebar.slider(\"alpha\", 0.8, 2.0)\n #pca = PCA(n_components)\n pca = PCA(2)\n\n X_projected = pca.fit_transform(features)\n \n x1 = X_projected[:, 0]\n x2 = X_projected[:, 1]\n\n\n fig = plt.figure()\n plt.scatter(x1, x2, c=label, alpha=0.8, cmap='viridis')\n plt.xlabel(\"Principal Component 1\")\n plt.ylabel(\"Principal Component 2\")\n plt.colorbar()\n\n st.pyplot()","def show_feat(feat_map):\n for i in range(feat_map.shape[0]):\n plt.imshow(feat_map[i])\n plt.show()","def show_points(self, imsize):\n mask = np.zeros(imsize)\n for t in self.points:\n mask[t] = 1\n toimage(mask).show()","def visualize_clouds(clouds, paths):\n\n for cloud, path in zip(clouds, paths):\n print(\"Visualizing scans generated from:\", path)\n for scan in cloud:\n draw_geometries([scan])","def sample_and_plot(self):\n fig = plt.figure()\n ax = plt.axes(projection = '3d')\n ax.plot_surface(self.X, self.Y, self.sample(), cmap = plt.cm.jet, rstride = 2, cstride = 2, linewidth = 1)\n plt.show()","def save_3d_render(\r\n self, points: List[np.ndarray], colors: List[np.ndarray]\r\n ) -> None:\r\n pcd = o3d.geometry.PointCloud()\r\n pcd.points = o3d.utility.Vector3dVector(np.vstack(points).astype(np.float64))\r\n pcd.colors = o3d.utility.Vector3dVector(np.vstack(colors))\r\n if self.debug:\r\n o3d.visualization.draw_geometries([pcd])\r\n if not self.debug:\r\n o3d.io.write_point_cloud(f\"results/{self.filename[:-4]}.ply\", pcd)","def plotrgcloud(self):\n print self.kpunten\n for i in range(len(self.kpunten[0])):\n self.writetext('sen ='+ self.kpunten[0][i][0], (0.65,0.85), axnum = 0, hor = None ,ver = None , rot = None ,fs =14 , transform = self.fig.axes[0].transAxes)\n if i == len(self.kpunten[0]) -1 :\n end = None\n else:\n end = self.kpunten[0][i+1][1] + 1\n print end\n self.plotrgwrap( self.rgindex,2*self.reader.npair+self.rgindex,'real part of rgvars (a.u)' , 'imaginary part of rgvars (a.u.)', tit ='RG vars g = %f all states'%(self.chardata) , begin = self.kpunten[0][i][1] , stop = end , name = 'cpcloud'+ self.kpunten[0][i][0] , filenum = 0)","def __show_target(self):\n pcd = o3d.io.read_point_cloud(\n self.target_cloud\n )\n if np.asarray(pcd.points).shape[0] != 0:\n pcd.paint_uniform_color([0, 0, 1])\n pcd.estimate_normals()\n self.target_point_cloud_view.load_cloud(pcd)\n try:\n self.target_point_cloud_view.show_window()\n except RuntimeError:\n pass\n else:\n QtWidgets.QMessageBox.warning(self, \"Error\",\n f\"Target point cloud is no longer available\"\n )\n self.target_cloud = \"\"\n self.__update_clickability()\n self.__save_context()","def scatter(self, points):\n assert points.shape[0] == 3\n self.points = points\n self.remove_scatter_plot()\n x, y, z = zip(*points.T)\n self.base_fig.add_trace(go.Scatter3d(\n x=x, y=y, z=z,\n mode='markers',\n marker=dict(\n size=2,\n color=np.zeros(points.shape[1]),\n colorscale=\"RdBu\",\n colorbar=dict(\n title=\"Samples\",\n x=-0.2\n ),\n )\n ))","def mri_point_plot(self, vcol=1):\n img = self.voxels\n points = self.point_position \n ax = []\n fig = plt.figure(figsize=(9, 8))\n # TODO make this setable in the function call\n columns = 3\n rows = 2\n\n for i in range(points.shape[0]):\n im_slice = int(np.round(points[i, vcol]))\n if vcol == 0:\n im = img[im_slice, :, :]\n elif vcol == 1:\n im = img[:, im_slice, :]\n else:\n im = img[:, :, im_slice]\n ax.append( fig.add_subplot(rows, columns, i+1))\n ax[-1].set_title(\"Image depth: \"+str(im_slice)) # set title\n plt.imshow(im)\n plot_cols = np.array([0, 1, 2])\n plot_cols = plot_cols[plot_cols != vcol]\n plt.plot(points[i, min(plot_cols)], points[i, max(plot_cols)], 'ro')\n\n plt.show()","def render_radar_point_cloud(self, point_cloud, extrinsics=Pose(), color=RED, velocity=None, velocity_scale=10):\n combined_transform = self._bev_rotation * extrinsics\n\n pointcloud_in_bev = combined_transform * point_cloud\n point_cloud2d = pointcloud_in_bev[:, :2]\n\n point_cloud2d[:, 0] = (self._center_pixel[0] + point_cloud2d[:, 0] * self._pixels_per_meter)\n point_cloud2d[:, 1] = (self._center_pixel[1] + point_cloud2d[:, 1] * self._pixels_per_meter)\n\n H, W = self.data.shape[:2]\n uv = point_cloud2d.astype(np.int32)\n in_view = np.logical_and.reduce([\n (point_cloud2d >= 0).all(axis=1),\n point_cloud2d[:, 0] < W,\n point_cloud2d[:, 1] < H,\n ])\n uv = uv[in_view]\n\n for row in uv:\n cx, cy = row\n cv2.circle(self.data, (cx, cy), 7, RED, thickness=1)\n\n def clip_norm(v, x):\n M = np.linalg.norm(v)\n if M == 0:\n return v\n\n return np.clip(M, 0, x) * v / M\n\n if velocity is not None:\n tail = point_cloud + velocity_scale * velocity\n pointcloud_in_bev_tail = combined_transform * tail\n point_cloud2d_tail = pointcloud_in_bev_tail[:, :2]\n point_cloud2d_tail[:, 0] = (self._center_pixel[0] + point_cloud2d_tail[:, 0] * self._pixels_per_meter)\n point_cloud2d_tail[:, 1] = (self._center_pixel[1] + point_cloud2d_tail[:, 1] * self._pixels_per_meter)\n uv_tail = point_cloud2d_tail.astype(np.int32)\n uv_tail = uv_tail[in_view]\n for row, row_tail in zip(uv, uv_tail):\n v_2d = row_tail - row\n v_2d = clip_norm(v_2d, .025 * W)\n\n cx, cy = row\n cx2, cy2 = row + v_2d.astype(np.int)\n\n cx2 = np.clip(cx2, 0, W - 1)\n cy2 = np.clip(cy2, 0, H - 1)\n color = GREEN\n # If moving away from vehicle change the color (not strictly correct because radar is not a (0,0))\n # TODO: calculate actual radar sensor position\n if np.dot(row - np.array([W / 2, H / 2]), v_2d) > 0:\n color = (255, 110, 199)\n cv2.arrowedLine(self.data, (cx, cy), (cx2, cy2), color, thickness=1, line_type=cv2.LINE_AA)","def plot_wordcloud(word_list, file_name=\"\"):\n plt.figure(figsize=plt.figaspect(0.8), dpi=100)\n all_words = ' '.join([text for text in word_list])\n wordcloud = WordCloud(width=800, height=600, random_state=21, max_font_size=110).generate(all_words)\n plt.imshow(wordcloud, interpolation=\"bilinear\")\n plt.axis('off')\n if file_name:\n plt.savefig(file_name, bbox_inches='tight')\n plt.show()\n return","def get_point_cloud(self):\n tsdf_vol, color_vol = self.get_volume()\n\n # Marching cubes\n verts = measure.marching_cubes_lewiner(tsdf_vol, level=0)[0]\n verts_ind = np.round(verts).astype(int)\n verts = verts * self._voxel_size + self._vol_origin\n\n # Get vertex colors\n rgb_vals = color_vol[verts_ind[:, 0], verts_ind[:, 1], verts_ind[:, 2]]\n colors_b = np.floor(rgb_vals / self._color_const)\n colors_g = np.floor((rgb_vals - colors_b * self._color_const) / 256)\n colors_r = rgb_vals - colors_b * self._color_const - colors_g * 256\n colors = np.floor(np.asarray([colors_r, colors_g, colors_b])).T\n colors = colors.astype(np.uint8)\n\n pc = np.hstack([verts, colors])\n return pc","def show_point_set(points, title, save_path = None):\n dim = points.shape[1]\n assert(dim == 2 or dim == 3), \"Only data in 2 or 3 dimension space can be visualized.\"\n \n if dim == 2:\n fig = plt.figure(figsize = (6, 6))\n plt.title(title)\n plt.scatter(points[:, 0], points[:, 1], c = 'b', s = 5)\n plt.xlabel(\"x\")\n plt.ylabel(\"y\")\n plt.axis('equal')\n plt.grid()\n plt.show()\n elif dim == 3:\n fig = plt.figure(figsize = (6, 6))\n ax = fig.add_subplot(111, projection='3d')\n ax.set_title(title)\n ax.scatter(points[:, 0], points[:, 1], points[:, 2], c = 'b', s = 5)\n ax.set_xlabel(\"x\")\n ax.set_ylabel(\"y\")\n ax.set_zlabel(\"z\")\n plt.show()\n\n if type(save_path) != type(None):\n fig.savefig(save_path)","def render_points_2d(self, **kwds):\n return point2d(self.coordinates_of(self.points), **kwds)","def draw_clouds(a):\n small_cloud(0 + a, 0, 0)\n small_cloud(200 + a, -150, -10)\n big_cloud(350 + a, 0, -3)\n small_cloud(600 + a, -90, 3)\n small_cloud(800 + a, 0, 0)\n small_cloud(1000 + a, -150, -10)\n big_cloud(1150 + a, 0, -3)\n small_cloud(1400 + a, -90, 3)\n small_cloud(-800 + a, 0, 0)\n small_cloud(-600 + a, -150, -10)\n big_cloud(-450 + a, 0, -3)\n small_cloud(-200 + a, -90, 3)","def plotsite(self):\n if self.dimension==1 or self.dimension==2:\n for site in self.sites.flatten():\n plt.scatter(site.coordinate[0],site.coordinate[1],marker=site.marker,s=site.markersize,c=site.color)\n elif self.dimension==3:\n from mpl_toolkits.mplot3d import Axes3D\n fig = plt.figure()\n ax = Axes3D(fig)\n for site in self.sites.flatten():\n ax.scatter(site.coordinate[0],site.coordinate[1],site.coordinate[2],marker=site.marker,s=site.markersize,c=site.color)","def imshow_surface(self):\n plt.imshow(self.z)\n plt.colorbar()\n plt.show()","def _build_point_cloud_graph(self):\n depths = self.depths_placeholder[tf.newaxis]\n images = self.images_placeholder[tf.newaxis]\n poses = self.poses_placeholder[tf.newaxis]\n intrinsics = self.intrinsics_placeholder[tf.newaxis]\n intrinsics = intrinsics_vec_to_matrix(intrinsics)\n\n depths_pad = tf.pad(depths, [[0,0],[0,0],[0,1],[0,1]], \"CONSTANT\")\n\n depths_grad = \\\n (depths_pad[:, :, 1:, :-1] - depths_pad[:, :, :-1, :-1])**2 + \\\n (depths_pad[:, :, :-1, 1:] - depths_pad[:, :, :-1, :-1])**2\n\n # don't use large depths for point cloud and ignore boundary regions\n valid = (depths < 3.0) & (depths_grad < 0.001)\n\n batch, num, ht, wd = tf.unstack(tf.shape(depths), num=4)\n Ts = VideoSE3Transformation(matrix=poses)\n X0 = projective_ops.backproject(depths, intrinsics)\n \n # transform point cloud into world coordinates\n X1 = Ts.inv()(X0)\n\n crop_h = 12\n crop_w = 32\n\n X1 = X1[:, :, crop_h:-crop_h, crop_w:-crop_w]\n valid = valid[:, :, crop_h:-crop_h, crop_w:-crop_w]\n images = images[:, :, crop_h:-crop_h, crop_w:-crop_w, ::-1]\n \n X1 = tf.reshape(X1, [-1, 3])\n colors = tf.reshape(images, [-1, 3])\n\n valid_inds = tf.where(tf.reshape(valid, [-1]))\n valid_inds = tf.reshape(valid_inds, [-1])\n\n X1 = tf.gather(X1, valid_inds, axis=0)\n colors = tf.gather(colors, valid_inds, axis=0)\n\n self.outputs['point_cloud'] = (X1, colors)","def show_points_on_img(mask,img):\n labeled, num_objects = ndi.label(mask)\n slices = ndi.find_objects(labeled)\n x, y = [], []\n for dy,dx in slices:\n x_center = (dx.start + dx.stop - 1)/2\n x.append(x_center)\n y_center = (dy.start + dy.stop - 1)/2 \n y.append(y_center)\n plt.figure()\n plt.imshow(img)\n plt.autoscale(False)\n plt.plot(x,y, \"o\")","def show_transformed_point_set(points_1, points_2, points_12, loss, save_path = None):\n\n fig = plt.figure(figsize = (6, 6))\n plt.title(\"Loss = %f\" % loss)\n plt.scatter(points_1[:, 0], points_1[:, 1], s = 5, label = \"point cloud 1\")\n plt.scatter(points_2[:, 0], points_2[:, 1], s = 5, label = \"point cloud 2\")\n plt.scatter(points_12[:, 0], points_12[:, 1], s = 5, label = \"point cloud 1 to 2\")\n plt.xlabel(\"x\")\n plt.ylabel(\"y\")\n plt.axis('equal')\n plt.grid()\n plt.legend()\n plt.show()\n \n if type(save_path) != type(None):\n fig.savefig(save_path)","def draw_features(self, image):\n \n for x,y in self.new_points.reshape(-1,2):\n cv2.circle(image, (x,y), 2, (255,0,255), 2)\n return image","def show_orbits(points, nb_pairs, title, save_path = None):\n\n fig = plt.figure(figsize = (6, 6))\n plt.title(title)\n plt.scatter(points[:, 0], points[:, 1], c = 'b', s = 5, label = \"point cloud\")\n for i in range(len(nb_pairs)):\n plt.scatter(nb_pairs[i][0][:, 0], nb_pairs[i][0][:, 1], s = 5, label = \"%d_1\" % i)\n plt.scatter(nb_pairs[i][1][:, 0], nb_pairs[i][1][:, 1], s = 5, label = \"%d_2\" % i)\n plt.xlabel(\"x\")\n plt.ylabel(\"y\")\n plt.axis('equal')\n plt.grid()\n plt.legend()\n plt.show()\n \n if type(save_path) != type(None):\n fig.savefig(save_path)","def point_from_rays(self):\n print \"generating the 3d point from given clicked points\"\n \n #gather cams and points clicked \n uvs = []\n cams = []\n for iFrame in self.frames:\n if iFrame.lastClick : \n uv = numpy.multiply(iFrame.lastClick,self.reduceFactor)\n uvs.append(uv)\n cam = load_perspective_camera(self.camList[iFrame.currImg])\n cams.append(cam)\n point = get_3d_from_cams(cams, uvs)\n self.point3d = point;\n self.pointLabel.set(\"3d Point: \" + str(self.point3d))\n\n # project 3d point into each image, and gather intensities \n values = []\n ims = []\n for idx, img in enumerate(self.imgList):\n cam = load_perspective_camera(self.camList[idx])\n imgPoint = project_point(cam, point[0], point[1], point[2])\n imgPoint = numpy.divide(imgPoint, self.reduceFactor)\n self.allUVs.append(imgPoint)\n \n #grab float intensity value at this point \n imgView,ni,nj = load_image(img)\n val = pixel(imgView, imgPoint)\n if val > 0.0:\n values.append(val)\n ims.append(idx)\n \n #cleanup\n remove_from_db([imgView, cam])\n \n\n #write mean/std of intensities \n self.meanLabel.set(\"Mean: \" + str(numpy.mean(values)) )\n self.stdLabel.set(\"Std Dev: \" + str(numpy.std(values)) )\n #plot the intensities by image number \n self.f.clf();\n self.a = self.f.add_subplot(311)\n self.a.set_xlabel(\"img #\")\n self.a.set_ylabel(\"intensity\")\n self.a.plot(ims, values)\n #plot the histogram of intensities by image number \n pdf, bins, patches = plt.hist(values)\n self.b = self.f.add_subplot(313)\n self.b.set_xlabel(\"bin val\")\n self.b.set_ylabel(\"freq\")\n self.b.hist(values, 15, normed=1, facecolor=\"green\" )\n self.canvas.show();","def showAsPoints(self): \n MonkeyPatchMayaVi()\n #import enthought.mayavi.mlab as mlab\n from mayavi import mlab\n \n @mlab.show\n def _showSimple():\n morphPts = [ SVVisitorFactory.Array4AllPoints(morph)() for morph in self.morphs ]\n pts = numpy.concatenate( morphPts)\n return mlab.points3d(pts[:, 0], pts[:, 1], pts[:, 2], pts[:, 3], colormap=self.colormap, scale_factor=self.scale_factor)\n _showSimple()","def plot_surface(self):\n X, Y = np.meshgrid(self.x, self.y)\n fig = plt.figure()\n ax = fig.add_subplot(111, projection='3d')\n ax.plot_surface(X=X, Y=Y, Z=self.z)\n plt.show()","def show_trace_2d(f, results): #@save\n set_figsize()\n plt.plot(*zip(*results), '-o', color='#ff7f0e')\n x1, x2 = torch.meshgrid(torch.arange(-5.5, 1.0, 0.1),torch.arange(-3.0, 1.0, 0.1))\n plt.contour(x1, x2, f(x1, x2), colors='#1f77b4')\n plt.xlabel('x1')","def plot_2d(vector_array, save_plot_dir):\n principal_df = pd.DataFrame(data = vector_array, columns = ['pc1', 'pc2'])\n fig = plt.figure()\n ax = fig.add_subplot(111)\n\n xs = principal_df['pc1']\n ys = principal_df['pc2']\n ax.scatter(xs, ys, s=50, alpha=0.6, edgecolors='w')\n\n ax.set_xlabel('pc1')\n ax.set_ylabel('pc2')\n\n plt.savefig(save_plot_dir + '/2D_scatter.png')\n plt.close()","def show_skeletons(self, skel_2d, z_out, z_gt=None):\n fig = plt.figure(figsize=(20, 20))\n ax1 = fig.add_subplot(1, 2, 1)\n ax2 = fig.add_subplot(1, 2, 2, projection='3d')\n edges = np.array([[1, 0], [0, 2],[2, 3], [3, 4], [0, 5], [5, 6], [6, 7]])\n\n ax_2d = ax1\n ax_3d = ax2\n\n # draw 3d\n for edge in edges:\n ax_3d.plot(skel_2d[0, edge], z_out[edge], skel_2d[1, edge], color='r')\n if z_gt is not None:\n ax_3d.plot(skel_2d[0, edge], z_gt[edge], skel_2d[1, edge], color='g')\n\n ax_3d.set_aspect('equal')\n ax_3d.set_xlabel(\"x\"), ax_3d.set_ylabel(\"z\"), ax_3d.set_zlabel(\"y\")\n ax_3d.set_xlim3d([-2, 2]), ax_3d.set_ylim3d([2, -2]), ax_3d.set_zlim3d([2, -2])\n ax_3d.view_init(elev=10, azim=-45)\n\n # draw 2d\n for edge in edges:\n ax_2d.plot(skel_2d[0, edge], skel_2d[1, edge], color='r')\n\n ax_2d.set_aspect('equal')\n ax_2d.set_xlabel(\"x\"), ax_2d.set_ylabel(\"y\")\n ax_2d.set_xlim([-2, 2]), ax_2d.set_ylim([2, -2])\n\n plt.show()","def show_wordcloud(dictionary, title, min_font = 10):\n wordcloud = WordCloud(min_font_size=min_font).generate_from_frequencies(dictionary)\n plt.figure(figsize = (8, 8), facecolor = None) \n plt.imshow(wordcloud) \n plt.axis(\"off\")\n if title:\n plt.title(title)\n else:\n plt.title(\"Word Cloud\")\n plt.tight_layout(pad = 0) \n\n plt.show()","def view_point_cloud_model_and_affordance(number_of_object_per_category=5):\n list_pc_paths = [f for f in glob.glob('./dataset/*.npy', recursive=True)]\n set_objects = set([os.path.basename(pc_path).split('_')[0] for pc_path in list_pc_paths])\n\n for obj in set_objects:\n try:\n # load point cloud models\n pc_models = np.load('./dataset/{}_point_cloud_models.npy'.format(obj))[\n :number_of_object_per_category]\n # load point cloud grasp affordance\n pc_affordance = np.load('./dataset/{}_point_cloud_grasp_affordance.npy'.format(obj))[\n :number_of_object_per_category]\n\n # visualization\n for i, m in enumerate(pc_models):\n fig = plt.figure()\n ax = fig.add_subplot(projection='3d')\n list_model_x, list_model_y, list_model_z = [], [], []\n list_affordance_x, list_affordance_y, list_affordance_z = [], [], []\n\n for x in range(32):\n for y in range(32):\n for z in range(32):\n if pc_affordance[i, x, y, z] == 1:\n list_affordance_x.append(x)\n list_affordance_y.append(y)\n list_affordance_z.append(z)\n elif m[x, y, z] == 1:\n list_model_x.append(x)\n list_model_y.append(y)\n list_model_z.append(z)\n\n ax.scatter(list_model_x, list_model_y, list_model_z, c='#0c457d')\n ax.scatter(list_affordance_x, list_affordance_y, list_affordance_z, c='#e8702a', alpha=0.35)\n ax.set_xlim(0, 32)\n ax.set_ylim(0, 32)\n ax.set_zlim(0, 32)\n plt.show()\n\n except FileNotFoundError:\n print('Some point cloud npy files are not found.')\n continue","def show_scatter(self):\n plt.scatter(self.a1[:, 0], self.a1[:, 1], c=\"red\", alpha=0.5, s=10)\n plt.scatter(self.a2[:, 0], self.a2[:, 1], c=\"blue\", alpha=0.5, s=10)\n plt.scatter(0, 0, marker=\"D\", c=\"black\", alpha=0.8)\n plt.scatter(2, 2, marker=\"D\", c=\"black\", alpha=0.8)\n plt.show()","def image_to_point_cloud(depth):\n cx, cy, fx, fy = 5.8818670481438744*100, 3.1076280589210484*100, 5.8724220649505514*100, 2.2887144980135292*100\n depth = depth[:,:,0]\n\n rows, cols = depth.shape\n c, r = np.meshgrid(np.arange(cols), np.arange(rows), sparse=True)\n valid = (depth > 0) & (depth < 255)\n # print(valid)\n z = np.where(valid, depth / 256.0, np.nan)\n x = np.where(valid, z * (c - cx) / fx, 0)\n y = np.where(valid, z * (r - cy) / fy, 0)\n return np.dstack((x, y, z))","def generate_image(self,true_dist):\n N_POINTS = 128\n RANGE = 3\n\n points = np.zeros((N_POINTS, N_POINTS, 2), dtype='float32')\n points[:, :, 0] = np.linspace(-RANGE, RANGE, N_POINTS)[:, None]\n points[:, :, 1] = np.linspace(-RANGE, RANGE, N_POINTS)[None, :]\n points = points.reshape((-1, 2))\n\n plt.clf()\n\n #true_dist = true_dist.cpu().data.numpy()\n samples = self.G(self.sample_z_)\n print('generate size is',samples.size())\n samples = samples.cpu().data.numpy()\n\n x = y = np.linspace(-RANGE, RANGE, N_POINTS)\n # plt.contour(x, y, disc_map.reshape((len(x), len(y))).transpose())\n\n plt.scatter(true_dist[:, 0], true_dist[:, 1], c='orange', marker='+')\n plt.scatter(samples[:, 0], samples[:, 1], c='green', marker='+')\n plt.show()","def showAllPoints(self, surface, **kwargs):\n if not \"point_color\" in kwargs:\n kwargs[\"point_color\"] = self.point_color\n if not \"point_radius\" in kwargs:\n kwargs[\"point_radius\"] = self.point_radius\n if not \"point_mode\" in kwargs:\n kwargs[\"point_mode\"] = self.point_mode\n if not \"point_size\" in kwargs:\n kwargs[\"point_size\"] = self.point_size\n if not \"point_width\" in kwargs:\n kwargs[\"point_width\"] = self.point_width\n if not \"point_fill\" in kwargs:\n kwargs[\"point_fill\"] = self.point_fill\n for point in self.points:\n point.show(surface,\n color=kwargs[\"point_color\"],\n mode=kwargs[\"point_mode\"],\n fill=kwargs[\"point_fill\"],\n radius=kwargs[\"point_radius\"],\n size=kwargs[\"point_size\"],\n width=kwargs[\"point_width\"])","def visualize_output(self, keyframe_index):\n\n self.vis_counter += 1\n keyframe_image = self.images[keyframe_index]\n keyframe_depth = self.depths[keyframe_index]\n keyframe_pose = self.poses[keyframe_index]\n\n feed_dict = {\n self.images_placeholder: keyframe_image[np.newaxis],\n self.depths_placeholder: keyframe_depth[np.newaxis],\n self.poses_placeholder: keyframe_pose[np.newaxis],\n self.intrinsics_placeholder: self.intrinsics}\n\n keyframe_point_cloud, keyframe_point_colors = \\\n self.sess.run(self.outputs['point_cloud'], feed_dict=feed_dict)\n\n pointcloud = (keyframe_point_cloud, keyframe_point_colors)\n\n # only add the point cloud once in every 5 frames\n if self.vis_counter % 4 == 0:\n self.queue.put((pointcloud, keyframe_pose))\n \n else:\n self.queue.put((None, keyframe_pose))","def format_point_cloud(self, pc, num_points):\n resized_pc = np.zeros([num_points, 3])\n if len(pc) > num_points:\n # Randomly sample point cloud to reduce size\n resized_pc = pc[np.random.choice(pc.shape[0], num_points,\n replace=False), :]\n elif len(pc) < num_points:\n # Duplicate last point to fill point cloud. Because of the max function\n # the duplicated points will not affect the output\n resized_pc[:len(pc)] = pc\n resized_pc[len(pc):] = pc[-1]\n else:\n resized_pc = pc\n\n return resized_pc","def test_get_mesh_grid_as_point_cloud_single_pt() -> None:\n min_x = -3 # integer, minimum x-coordinate of 2D grid\n max_x = -3 # integer, maximum x-coordinate of 2D grid\n min_y = 2 # integer, minimum y-coordinate of 2D grid\n max_y = 2 # integer, maximum y-coordinate of 2D grid\n\n # return pts, a Numpy array of shape (N,2)\n pts = mesh_grid_utils.get_mesh_grid_as_point_cloud(\n min_x, max_x, min_y, max_y, downsample_factor=1.0\n )\n\n assert pts.shape == (1, 2)\n gt_pts: NDArrayFloat = np.array([[-3.0, 2.0]])\n\n assert np.allclose(gt_pts, pts)","def test_get_mesh_grid_as_point_cloud_3x2rect() -> None:\n min_x = -3 # integer, minimum x-coordinate of 2D grid\n max_x = -1 # integer, maximum x-coordinate of 2D grid\n min_y = 2 # integer, minimum y-coordinate of 2D grid\n max_y = 3 # integer, maximum y-coordinate of 2D grid\n\n # return pts, a Numpy array of shape (N,2)\n pts = mesh_grid_utils.get_mesh_grid_as_point_cloud(\n min_x, max_x, min_y, max_y, downsample_factor=1.0\n )\n\n assert pts.shape == (6, 2)\n # fmt: off\n gt_pts: NDArrayFloat = np.array(\n [\n [-3.0, 2.0],\n [-2.0, 2.0],\n [-1.0, 2.0],\n [-3.0, 3.0],\n [-2.0, 3.0],\n [-1.0, 3.0]\n ])\n # fmt: on\n assert np.allclose(gt_pts, pts)","def show_points(self, **kwargs):\n\n if hasattr(self, 'method') and hasattr(self.method, 'show_points'):\n self.method.show_points(self, **kwargs)\n else:\n figsize = kwargs.get('figsize', (15, 5))\n cmap = kwargs.get('cmap', 'gray')\n marker = kwargs.get('marker', '.')\n color = kwargs.get('color', 'r')\n fig, ax = plt.subplots(figsize=figsize)\n ax.imshow(self.mraw[0].astype(float), cmap=cmap)\n ax.scatter(self.points[:, 1], self.points[:, 0], \n marker=marker, color=color)\n plt.grid(False)\n plt.show()","def partial_visualize_in_2d(self, cluster_index=[5,12,35,44,64,75,81]):\n for i in cluster_index:\n list_x = []\n list_y = []\n for j in self.cluster[i]:\n list_x.append(self.code[0][j,0])\n list_y.append(self.code[0][j,1])\n plt.scatter(list_x,list_y, label=self.skill[self.present_skill[i]])\n plt.legend()\n plt.show()\n return","def plot_scatter_points(self):\n self.plot(1)","def view(config_file):\n import open3d as o3d\n with open(config_file) as f:\n config = json.load(f)\n scenes = get_realsense_scenes(config['realsense_dir'])\n for scene in scenes:\n # if scene['scene_name'] != \"Scene_004\":\n # continue\n scene_data = get_data_from_scene(scene)\n logger.info(\"Visualizing - %s\", scene['scene_name'])\n pcd = o3d.geometry.PointCloud(o3d.utility.Vector3dVector(scene_data['points3d']))\n o3d.visualization.draw_geometries_with_editing([pcd])\n pcd = o3d.geometry.PointCloud(o3d.utility.Vector3dVector(scene_data['points3d_segmented']))\n o3d.visualization.draw_geometries([pcd])","def plot_data(self):\n if hasattr(self,'data'):\n plt.scatter(*self.data.T)\n plt.show()\n else:\n raise Exception('No 2d data of the instance has been loaded')","def pangolin_draw(points):\n display.setup()\n\n while not display.should_quit():\n display.init_frame()\n for idx, pose in enumerate(CAMERA_POSES):\n is_last_pose = idx == len(CAMERA_POSES) - 1\n homogenous_pose = np.vstack((pose, [0, 0, 0, 1]))\n display.draw_camera(homogenous_pose, (0.0, 1.0, 1.0 if is_last_pose else 0.0))\n\n display.draw_points(points)\n\n display.finish_frame()","def plot_data(x):\n if DATA_2D:\n plt.scatter(x[:, 0], x[:, 1])\n plt.show()\n else:\n fig = plt.figure()\n ax = Axes3D(fig)\n ax.scatter(x[:, 0], x[:, 1], x[:, 2])\n ax.set_xlabel('X Label')\n ax.set_ylabel('Y Label')\n ax.set_zlabel('Z Label')\n plt.show()","def PointCloudfromStructOutput(self,file):\n print(\"Creating Structure Point Cloud\")\n xyz = self.readStructOutput(file)\n pc = np.zeros((int(len(xyz)/2.0),3))\n pc[:,0] = xyz[::2,0]*1000\n pc[:,1] = xyz[::2,1]*1000\n pc[:,2] = xyz[::2,2]*1000\n head = \"\"\"X,Y,Z\"\"\"\n np.savetxt(file, pc, delimiter=',',fmt='%.10f', header=head)\n return","def showFastCrossPoints(self, surface, color=None, mode=None, radius=None, width=None, size=None):\n points = self.crossSelf()\n if not color:\n color = self.cross_point_color\n if not mode:\n mode = self.cross_point_mode\n if not radius:\n radius = self.cross_point_radius\n if not width:\n width = self.cross_point_width\n if not size:\n size = self.cross_point_size\n for point in points:\n point.show(surface, color=color, mode=mode, radius=radius, width=width, size=size)","def plot_samples(s):\r\n assert len(s[0, :]) >= 2, ('The Phase space dimensions are less than two.', ' Need at least two to plot.')\r\n fig = plt.figure(1)\r\n if len(s[0, :]) >= 3:\r\n ax = fig.add_subplot(111, projection='3d')\r\n ax.scatter(s[:, 0], s[:, 1], s[:, 2])\r\n fig = plt.figure(2)\r\n plt.scatter(s[:, 0], s[:, 1])\r\n plt.show()","def viz1(img, interest_points, color='r'):\n\n\tx = [a[1] for a in interest_points] #blob detection x axis\n\ty = [a[0] for a in interest_points] #blob detection y axis\n\ts = [a[2] for a in interest_points] #blob detected at sigma \n \n\tplt.imshow(img, cmap='gray') #adding the input image to plot \n\tfor x, y, s in zip(x, y, s):\n\t\tplt.scatter(x, y, alpha=1, facecolors='none', edgecolors='r', s=s**2) #plotting the input interest points\n\tplt.axis('off')\n\t#plt.show() #showing the image // can be changed to saving the image locally \n\n\treturn","def show_grid(self):\n\n if not os.path.exists(self.path_to_results):\n os.mkdir(self.path_to_results)\n\n fig = plt.figure()\n\n if self.show_points == 1:\n plt.scatter(self.x_list_grid, self.y_list_grid, c='blue')\n\n plt.plot(self.x_list_main, self.y_list_main,\n 'green', label='straight path')\n plt.plot(self.x_list, self.y_list, 'red', label='first path')\n plt.plot(self.x_list_filtered, self.y_list_filtered,\n 'blue', label='filtered path')\n plt.title('Paths')\n plt.ylabel('Latitude')\n plt.xlabel('Longitude')\n # plt.legend()\n\n fig.savefig(os.path.join(self.path_to_results, 'Paths.png'))","def draw_birdseye_points(self, image, points):\n point_image = np.copy(image)\n for point in points:\n cv2.circle(point_image, point, 5, [0, 0, 255], cv2.FILLED)\n\n cv2.imshow('image1', point_image)\n cv2.waitKey(1)","def plt3dpaint(nppoints, color_map = 'jet', reduce_for_vis = True, voxel_size = 0.2, pointsize = 0.1, subplots = 5):\n assert (type(nppoints) == pclpy.pcl.PointCloud.PointXYZRGB) or (type(nppoints) == pclpy.pcl.PointCloud.PointXYZ) or (type(nppoints) == np.ndarray) or (type(nppoints) is list) or (type(nppoints) is tuple), 'Not valid point_cloud'\n cloudlist = []\n cloudcolors = []\n if (type(nppoints) is not list) & (type(nppoints) is not tuple):\n nppoints = [nppoints]\n \n if len(nppoints) > 1:\n for n,i in enumerate(nppoints):\n workpoints = i\n if (type(workpoints) == pclpy.pcl.PointCloud.PointXYZRGB) or (type(workpoints) == pclpy.pcl.PointCloud.PointXYZ):\n workpoints = workpoints.xyz\n\n if reduce_for_vis:\n workpoints = seg_tree.voxelize(workpoints,voxel_size)\n\n \n cloudmin = np.min(workpoints[:,2])\n cloudmax = np.max(workpoints[:,2])\n \n points = workpoints\n color_coef = n/len(nppoints)/2 + n%2*.5\n if type(color_map) == np.ndarray:\n color = color_map\n elif color_map == 'jet':\n color=cm.jet(color_coef)[:3]\n else:\n color=cm.Set1(color_coef)[:3]\n cloudcolors.append(np.ones_like(workpoints)*color + 0.4*(np.ones_like(workpoints) * ((workpoints[:,2] - cloudmin)/(cloudmax - cloudmin)).reshape(-1,1)-0.5) )\n cloudlist.append(points)\n else:\n workpoints = nppoints[0]\n if (type(workpoints) == pclpy.pcl.PointCloud.PointXYZRGB) or (type(workpoints) == pclpy.pcl.PointCloud.PointXYZ):\n workpoints = workpoints.xyz\n\n if reduce_for_vis:\n workpoints = seg_tree.voxelize(workpoints,voxel_size)\n cloudcolors.append(workpoints[:,2])\n cloudlist.append(workpoints)\n\n plt_pointcloud = np.concatenate(cloudlist)\n plt_colors = np.concatenate(cloudcolors)\n if len(nppoints) > 1:\n plt_colors = np.minimum(plt_colors,np.ones_like(plt_colors))\n plt_colors = np.maximum(plt_colors,np.zeros_like(plt_colors))\n fig = plt.figure(figsize=(30,16) )\n for i in range(subplots):\n ax = fig.add_subplot(1, subplots, i+1, projection='3d')\n ax.view_init(30, 360*i/subplots)\n ax.scatter3D(plt_pointcloud[:,0], plt_pointcloud[:,1], plt_pointcloud[:,2], c=plt_colors, s=pointsize)","def plot(self):\n\t\tif (2 <= len(self.X) <= 3):\n\t\t\tvDataFrame(self.name, self.cursor).scatter(columns = self.X, catcol = \"dbscan_cluster\", max_cardinality = 100, max_nb_points = 10000)\n\t\telse:\n\t\t\traise ValueError(\"Clustering Plots are only available in 2D or 3D\")","def plot(self):\n y = self.projection\n mpl.scatter(y[:, 0], y[:, 1], c=self.data_class)\n mpl.show()","def imshow(self, depth):\n layer = self.cube[depth]\n img = []\n for i in range(self.height):\n img.append([layer[i][j].value for j in range(self.width)])\n plt.imshow(img, cmap='gray')\n plt.show()","def show2d(self, data):\n self.__plot2d.clear()\n self.__plot2d.addImage(legend=\"data\", data=data)\n self.setCurrentIndex(self.__index2d)","def visualise_cppn(self, resolution=(64, 64)):\n import matplotlib.pyplot as plt\n from matplotlib.pyplot import imshow\n data = np.empty([resolution[0], resolution[1]])\n x_linspace = np.linspace(-1, 1, resolution[0])\n y_linspace = np.linspace(-1, 1, resolution[1])\n for row, x in enumerate(x_linspace):\n for col, y in enumerate(y_linspace):\n data[row, col] = self.graph(np.array([x, y, 0, 0], dtype=np.float32))[0]\n #plt.axis([-1, 1, -1, 1])\n print(data.min(), \" \", data.max())\n imshow(data, cmap='Greys', vmin=-1, vmax=1)\n plt.show()","def plot_imshow(data, col_name):\n city_x,city_y = get_city_base()\n fig = plt.figure(figsize = FIGURE_SIZE)\n plt.imshow(data[['lat_ind','lng_ind',col_name]], cmap=CONTOUR_PLOT_COLOUR)\n plt.scatter(city_x,city_y, color = CITY_MAP_COLOR, s = SCATTER_SIZE_OF_CHICAGO_CITY, zorder = CITY_MAP_ORDER)","def draw_point_gt(img,point_gt,width=3):\n point_img=copy.deepcopy(img)\n\n for i in range(point_gt.shape[0]):\n x=int(point_gt[i,0])\n y=int(point_gt[i,1])\n point_img=cv2.circle(point_img,center=(x,y),radius=width//2+1,color=(0,0,255),thickness=width)\n\n return point_img","def displayDensePts(dense_pts, dense_slices, origin, transform, dense_displacement_all=False, dense_plot_quiver=0, timepoint=-1, ax=None):\n\t# If no axes were passed, generate axes\n\tif not ax:\n\t\tfig = mplt.figure()\n\t\tax = fig.add_subplot(111, projection='3d')\n\t\n\t# Pull appropriate timepoint (or set to False if displacement is undesired)\n\tif timepoint >= 0:\n\t\ttry:\n\t\t\tdense_displacement = dense_displacement_all[timepoint]\n\t\texcept(IndexError):\n\t\t\twarnings.warn('Timepoint not in range of values! Displacement will be ignored.')\n\t\t\ttimepoint = -1\n\t\t\tdense_displacement = False\n\t\texcept(TypeError):\n\t\t\twarnings.warn('Displacement not passed, but timepoint requested! Displacement will be ignored.')\n\t\t\tdense_displacement = False\n\telse:\n\t\tdense_displacement = False\n\t\n\tfor i in range(len(dense_slices)):\n\t\tcur_slice = dense_slices[i]\n\t\tcur_dense_pts = np.column_stack((dense_pts[i], [cur_slice - origin[2]]*dense_pts[i].shape[0]))\n\t\tdata_dense = np.dot(cur_dense_pts, np.transpose(transform))\n\t\tx = data_dense[:, 2]\n\t\ty = data_dense[:, 1]\n\t\tz = data_dense[:, 0]\n\t\tax.scatter(x, y, -z, ',')\n\t\t\n\t\tif dense_displacement:\n\t\t\tdense_displacement_slice = dense_displacement[i]\n\t\t\tif dense_plot_quiver == 1:\n\t\t\t\tax.quiver(x, y, -z, dense_displacement_slice[:, 0], dense_displacement_slice[:, 1], [0]*dense_displacement_slice.shape[0])\n\treturn(ax)","def trimesh_from_point_cloud(cloud):\n points = np.asarray(cloud)\n hull = scipy.spatial.ConvexHull(points)\n hull = scipy.spatial.ConvexHull(points[hull.vertices])\n ru.transforms.counterclockwise_hull(hull)\n vertices = hull.points\n faces = hull.simplices\n return vertices, faces","def occupancy_grid(self, point_cloud=None, ss_pred=None, *, return_image=False, show=False, keep_showing=False):\n\t\tif point_cloud is None or ss_pred is None:\n\t\t\tgen = self.loop(point_cloud=True, ss_pred=True)\n\t\t\tpoint_cloud, ss_pred = next(gen)\n\t\t#TODO: Check that this function works with ZED camera\n\t\tpass","def plot(self):\n\n fig = plt.figure()\n ax = fig.add_subplot(111, projection=Axes3D.name)\n\n # TODO Use numpy to rotate esp_points matrix for faster variable access.\n ax.scatter(\n xs=[i[0][0] for i in self.esp_points],\n ys=[i[0][1] for i in self.esp_points],\n zs=[i[0][2] for i in self.esp_points],\n c=[i[1] for i in self.esp_points],\n marker='o',\n s=2,\n alpha=0.5\n )\n\n ax.scatter(\n xs=[i[0][0] for i in self.atom_points],\n ys=[i[0][1] for i in self.atom_points],\n zs=[i[0][2] for i in self.atom_points],\n c=[i[1] for i in self.atom_points],\n marker='X',\n s=100\n )\n\n plt.show()"],"string":"[\n \"def visualize_point_cloud(points, colors=None, normals=None,\\n show_frame=False, frame_size=1.0, frame_origin=(0, 0, 0)):\\n pc = np2pcd(points, colors, normals)\\n geometries = [pc]\\n if show_frame:\\n coord_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=frame_size, origin=frame_origin)\\n geometries.append(coord_frame)\\n o3d.visualization.draw_geometries(geometries)\",\n \"def render_point_cloud(frame_id):\\n point_cloud_world_coordinates = get_point_cloud_world_coordinates(frame_id)\\n # pptk\\n v = pptk.viewer(point_cloud_world_coordinates)\\n v.set(point_size=0.0001)\\n v.color_map('cool', scale=[0, 5])\",\n \"def create_point_cloud(self):\\n pixels = []\\n colors = []\\n my_pixels = []\\n for j in range(self.height):\\n for i in range(self.width):\\n depth = self.depth[j, i]\\n pixels.append(\\n [i * depth, j * depth, depth]\\n )\\n my_pixels.append(\\n [i, j, 1]\\n )\\n # make rgb with flip()\\n colors.append(np.flip(self.bgr[j, i, :]))\\n # colors.append(self.bgr[j, i, :])\\n self.my_pixels = my_pixels\\n pixels = np.array(pixels)\\n\\n # project pixels to camera space\\n self.xyz_points = self.intrinsics_inv @ np.transpose(pixels)\\n self.color_points = colors\\n\\n # now add 1s to the points for homogenous coordinates\\n num_points = self.get_num_xyz_points()\\n ones = np.ones((1, num_points))\\n self.xyzw_points = np.concatenate((self.xyz_points, ones), axis=0)\\n\\n self.scene = None\\n self.camera_pose = None\\n self.nm = None\\n self.nl = None\\n self.nc = None\\n self.create_mesh()\",\n \"def draw_pointcloud(ax, example):\\n points = example['points'].cpu().detach().numpy()\\n points_num = len(points)\\n xs = np.empty([points_num])\\n ys = np.empty([points_num])\\n zs = np.empty([points_num])\\n intensity = np.empty([len(points)])\\n for j, point in enumerate(points):\\n xs[j] = point[1]\\n ys[j] = point[2]\\n zs[j] = point[3]\\n intensity[j] = point[4]\\n\\n intensity = intensity\\n ax.scatter3D(xs, ys, zs, c=intensity, marker='.', s=0.3, cmap=plt.get_cmap('jet'))\",\n \"def show_pointclouds(points, colors, text=[], title=\\\"Default\\\", png_path=\\\"\\\", interactive=True, orientation='horizontal'):\\n\\n # make sure pointclouds is a list\\n assert isinstance(points, type([])), \\\\\\n \\\"Pointclouds argument must be a list\\\"\\n\\n # make sure colors is a list\\n assert isinstance(colors, type([])), \\\\\\n \\\"Colors argument must be a list\\\"\\n\\n # make sure number of pointclouds and colors are the same\\n assert len(points) == len(colors), \\\\\\n \\\"Number of pointclouds (%d) is different then number of colors (%d)\\\" % (len(points), len(colors))\\n\\n while len(text) < len(points):\\n text.append(\\\"\\\")\\n\\n # Number of pointclouds to be displayed in this window\\n num_pointclouds = len(points)\\n\\n point_size = 10\\n pointclouds = [VtkPointCloud(point_size) for _ in range(num_pointclouds)]\\n renderers = [vtk.vtkRenderer() for _ in range(num_pointclouds)]\\n\\n height = 1.0 / max(num_pointclouds, 1)\\n viewports = [(i*height, (i+1)*height) for i in range(num_pointclouds)]\\n #print(viewports)\\n\\n # iterate over all point clouds\\n for i, pc in enumerate(points):\\n pc = pc.squeeze()\\n co = colors[i].squeeze()\\n assert pc.shape[0] == co.shape[0], \\\\\\n \\\"expected same number of points (%d) then colors (%d), cloud index = %d\\\" % (pc.shape[0], co.shape[0], i)\\n assert pc.shape[1] == 3, \\\"expected points to be N x 3, got N x %d\\\" % pc.shape[1]\\n assert co.shape[1] == 3, \\\"expected colors to be N x 3, got N x %d\\\" % co.shape[1]\\n\\n # for each point cloud iterate over all points\\n for j in range(pc.shape[0]):\\n point = pc[j, :]\\n color = co[j, :]\\n pointclouds[i].add_point(point, color)\\n\\n renderers[i].AddActor(pointclouds[i].vtkActor)\\n # renderers[i].AddActor(vtk.vtkAxesActor())\\n renderers[i].SetBackground(1.0, 1.0, 1.0)\\n if orientation == 'horizontal':\\n print(viewports[i][0])\\n renderers[i].SetViewport(viewports[i][0], 0.0, viewports[i][1], 1.0)\\n elif orientation == 'vertical':\\n renderers[i].SetViewport(0.0, viewports[i][0], 1.0, viewports[i][1])\\n else:\\n raise Exception('Not a valid orientation!')\\n renderers[i].ResetCamera()\\n\\n # Add circle to first render\\n renderers[0].AddActor(getActorCircle())\\n renderers[0].AddActor(getActorCircle(50, 49, color=(0, 1, 0)))\\n\\n # Text actors\\n text_actors = [vtk.vtkTextActor() for _ in text]\\n for i, ta in enumerate(text_actors):\\n if orientation == 'horizontal':\\n ta.SetInput(' ' + text[i])\\n elif orientation == 'vertical':\\n ta.SetInput(text[i] + '\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n')\\n else:\\n raise Exception('Not a valid orientation!')\\n txtprop = ta.GetTextProperty()\\n txtprop.SetFontFamilyToArial()\\n txtprop.SetFontSize(0)\\n txtprop.SetColor(0, 0, 0)\\n # txtprop.SetJustificationToCentered()\\n # ta.SetDisplayPosition(500, 10)\\n # ta.SetAlignmentPoint()\\n renderers[i].AddActor(ta)\\n\\n # Render Window\\n render_window = vtk.vtkRenderWindow()\\n for renderer in renderers:\\n render_window.AddRenderer(renderer)\\n\\n render_window_interactor = vtk.vtkRenderWindowInteractor()\\n render_window_interactor.SetInteractorStyle(vtk.vtkInteractorStyleTrackballCamera())\\n render_window_interactor.SetRenderWindow(render_window)\\n\\n [center_x, center_y, center_z] = np.mean(points[0].squeeze(), axis=0)\\n camera = vtk.vtkCamera()\\n # d = 10\\n # camera.SetViewUp(0, -1, 0)\\n\\n # camera.SetPosition(center_x + d, center_y + d, center_z + d / 2)\\n # camera.SetFocalPoint(center_x, center_y, center_z)\\n # camera.SetFocalPoint(0, 0, 0)\\n\\n camera.SetViewUp(0, 0, 1)\\n if orientation == 'horizontal':\\n camera.SetPosition(3, -10, 2)\\n camera.SetFocalPoint(3, 1.5, 1.5)\\n elif orientation == 'vertical':\\n camera.SetPosition(1.5, -6, 2)\\n camera.SetFocalPoint(1.5, 1.5, 1.5)\\n else:\\n raise Exception('Not a valid orientation!')\\n\\n camera.SetClippingRange(0.002, 1000)\\n for renderer in renderers:\\n renderer.SetActiveCamera(camera)\\n\\n # Begin Interaction\\n render_window.Render()\\n render_window.SetWindowName(title)\\n if orientation == 'horizontal':\\n render_window.SetSize(1940, 720)\\n elif orientation == 'vertical':\\n render_window.SetSize(600, 1388)\\n else:\\n raise Exception('Not a valid orientation!')\\n\\n if interactive:\\n render_window_interactor.Start()\\n\\n if png_path:\\n # screenshot code:\\n w2if = vtk.vtkWindowToImageFilter()\\n w2if.SetInput(render_window)\\n w2if.Update()\\n\\n writer = vtk.vtkPNGWriter()\\n writer.SetFileName(png_path)\\n writer.SetInputConnection(w2if.GetOutputPort())\\n writer.Write()\",\n \"def make_point_cloud(self):\\r\\n\\r\\n self.pointCloud = VtkPointCloud()\\r\\n for k in range(np.size(self.pos, 0)):\\r\\n self.pointCloud.addPoint(self.pos[k, :])\\r\\n\\r\\n # Renderer\\r\\n renderer = vtk.vtkRenderer()\\r\\n renderer.AddActor(self.pointCloud.vtkActor)\\r\\n renderer.SetBackground(.2, .3, .4)\\r\\n renderer.SetBackground(0.0, 0.0, 0.0)\\r\\n renderer.ResetCamera()\\r\\n\\r\\n # Render Window\\r\\n renderWindow = vtk.vtkRenderWindow()\\r\\n renderWindow.AddRenderer(renderer)\\r\\n\\r\\n # Interactor\\r\\n renderWindowInteractor = vtk.vtkRenderWindowInteractor()\\r\\n renderWindowInteractor.SetRenderWindow(renderWindow)\\r\\n\\r\\n # Begin Interaction\\r\\n renderWindow.Render()\\r\\n renderWindow.SetWindowName(\\\"XYZ Data Viewer: \\\")\\r\\n renderWindowInteractor.Start()\",\n \"def publish_point_cloud(self):\\n all_points = [np.zeros((0, 2), np.float32)]\\n all_keys = []\\n for key in range(len(self.keyframes)):\\n pose = self.keyframes[key].pose\\n transf_points = self.keyframes[key].transf_points\\n all_points.append(transf_points)\\n all_keys.append(key * np.ones((len(transf_points), 1)))\\n\\n all_points = np.concatenate(all_points)\\n all_keys = np.concatenate(all_keys)\\n sampled_points, sampled_keys = pcl.downsample(\\n all_points, all_keys, self.point_resolution\\n )\\n sampled_xyzi = np.c_[sampled_points, np.zeros_like(sampled_keys), sampled_keys]\\n if len(sampled_xyzi) == 0:\\n return\\n\\n if self.save_fig:\\n plt.figure()\\n plt.scatter(\\n sampled_xyzi[:, 0], sampled_xyzi[:, 1], c=sampled_xyzi[:, 3], s=1\\n )\\n plt.axis(\\\"equal\\\")\\n plt.gca().invert_yaxis()\\n plt.savefig(\\\"step-{}-map.png\\\".format(self.current_key - 1), dpi=100)\\n plt.close(\\\"all\\\")\\n\\n cloud_msg = n2r(sampled_xyzi, \\\"PointCloudXYZI\\\")\\n cloud_msg.header.stamp = self.current_keyframe.time\\n cloud_msg.header.frame_id = \\\"map\\\"\\n self.cloud_pub.publish(cloud_msg)\",\n \"def showPoints(surface, points):\\n for point in points:\\n point.show(surface)\",\n \"def as_point_cloud(self):\\n far = 1000.0 # max depth in meters.\\n intrinsic_mat = self.camera_setup.get_intrinsic_matrix()\\n width, height = self.camera_setup.width, self.camera_setup.height\\n # 2d pixel coordinates\\n pixel_length = width * height\\n u_coord = repmat(np.r_[0:width:1], height, 1).reshape(pixel_length)\\n v_coord = repmat(np.c_[0:height:1], 1, width).reshape(pixel_length)\\n normalized_depth = np.reshape(self.frame, pixel_length)\\n\\n # p2d = [u,v,1]\\n p2d = np.array([u_coord, v_coord, np.ones_like(u_coord)])\\n\\n # P = [X,Y,Z]\\n p3d = np.dot(inv(intrinsic_mat), p2d)\\n p3d *= normalized_depth * far\\n\\n # [[X1,Y1,Z1],[X2,Y2,Z2], ... [Xn,Yn,Zn]]\\n locations = np.asarray(np.transpose(p3d))\\n # Transform the points in 3D world coordinates.\\n to_world_transform = self.camera_setup.get_unreal_transform()\\n point_cloud = to_world_transform.transform_points(locations)\\n return point_cloud\",\n \"def render_point_cloud(self, point_cloud, extrinsics=Pose(), color=GRAY):\\n\\n combined_transform = self._bev_rotation * extrinsics\\n\\n pointcloud_in_bev = combined_transform * point_cloud\\n point_cloud2d = pointcloud_in_bev[:, :2]\\n\\n point_cloud2d[:, 0] = (self._center_pixel[0] + point_cloud2d[:, 0] * self._pixels_per_meter)\\n point_cloud2d[:, 1] = (self._center_pixel[1] + point_cloud2d[:, 1] * self._pixels_per_meter)\\n\\n H, W = self.data.shape[:2]\\n uv = point_cloud2d.astype(np.int32)\\n in_view = np.logical_and.reduce([\\n (point_cloud2d >= 0).all(axis=1),\\n point_cloud2d[:, 0] < W,\\n point_cloud2d[:, 1] < H,\\n ])\\n uv = uv[in_view]\\n self.data[uv[:, 1], uv[:, 0], :] = color\",\n \"def showCrossPoints(self, surface):\\n for point in self.cross_points:\\n point.show(surface)\",\n \"def drawPointCloud(points, ax, color=None):\\n if len(points.shape) != 2 or points.shape[0] != 3:\\n raise ValueError(\\\"'points' must be 3xN\\\")\\n if color == None:\\n color = __color_cycle.next()\\n elif color in (0, 1, 2):\\n color = points[color, :]\\n ax.scatter(points[0,:].T, points[1,:].T, points[2,:].T, c=color)\",\n \"def create_pointcloud(pts):\\n depths = PointCloud()\\n depths.header = std_msgs.msg.Header()\\n depths.header.stamp = rospy.Time.now()\\n depths.header.frame_id = \\\"view_zero\\\"\\n depths.points = [None] * len(pts)\\n for p in xrange(len(pts)):\\n #Giving point the same orientation as the robot\\n y = pts[p,0]\\n z = - pts[p,1] #y in images is down\\n x = pts[p,2]\\n depths.points[p] = Point(x, y, z)\\n return depths\",\n \"def generate_pointcloud(rgb_file, mask_file,depth_file,ply_file):\\n rgb = Image.open(rgb_file)\\n # depth = Image.open(depth_file)\\n depth = Image.open(depth_file).convert('I')\\n mask = Image.open(mask_file).convert('I')\\n\\n # if rgb.size != depth.size:\\n # raise Exception(\\\"Color and depth image do not have the same resolution.\\\")\\n # if rgb.mode != \\\"RGB\\\":\\n # raise Exception(\\\"Color image is not in RGB format\\\")\\n # if depth.mode != \\\"I\\\":\\n # raise Exception(\\\"Depth image is not in intensity format\\\")\\n\\n\\n points = [] \\n for v in range(rgb.size[1]):\\n for u in range(rgb.size[0]):\\n color = rgb.getpixel((u,v))\\n # Z = depth.getpixel((u,v)) / scalingFactor\\n # if Z==0: continue\\n # X = (u - centerX) * Z / focalLength\\n # Y = (v - centerY) * Z / focalLength\\n if (mask.getpixel((u,v))<55):\\n Z = depth.getpixel((u, v))*.22 \\n if Z == 0: continue\\n Y = .22 * v\\n X = .22 * u\\n points.append(\\\"%f %f %f %d %d %d 0\\\\n\\\"%(X,Y,Z,color[0],color[1],color[2]))\\n file = open(ply_file,\\\"w\\\")\\n file.write('''ply\\nformat ascii 1.0\\nelement vertex %d\\nproperty float x\\nproperty float y\\nproperty float z\\nproperty uchar red\\nproperty uchar green\\nproperty uchar blue\\nproperty uchar alpha\\nend_header\\n%s\\n'''%(len(points),\\\"\\\".join(points)))\\n file.close()\",\n \"def point_cloud(self):\\n\\t\\tgen = self.loop(point_cloud=True)\\n\\t\\tpoint_cloud = next(gen)\\n\\t\\treturn point_cloud\",\n \"def debug_filter_points(self, points):\\n cloud_msg = PointCloud2()\\n cloud_msg.header.frame_id = \\\"map\\\"\\n cloud_msg.header.stamp = rospy.Time.now() \\n xyz = [[p.pose.position.x, p.pose.position.y, p.pose.position.z] for p in points] \\n point_cloud = pc2.create_cloud_xyz32(cloud_msg.header, xyz)\\n self._points_publisher.publish(point_cloud)\",\n \"def visualize_scan(self):\\n fig = plt.figure()\\n ax = fig.add_subplot(111, projection='3d')\\n ax.scatter(self.p1_points[:, 0], self.p1_points[:, 1], self.p1_points[:, 2], c='r')\\n ax.scatter(self.p2_points[:, 0], self.p2_points[:, 1], self.p2_points[:, 2], c='g')\\n ax.scatter(self.p3_points[:, 0], self.p3_points[:, 1], self.p3_points[:, 2], c='b')\\n ax.scatter(self.p4_points[:, 0], self.p4_points[:, 1], self.p4_points[:, 2])\\n\\n ax.set_xlabel('x')\\n ax.set_ylabel('y')\\n ax.set_zlabel('z')\\n plt.show()\",\n \"def visualize_in_2d(self):\\n ae = Autoencoder_test(self.data)\\n self.code = ae.encode(n_dimension=2, learning_rate=0.01, training_epochs=10, batch_size=400)\\n for i in range(len(self.cluster)):\\n list_x = []\\n list_y = []\\n for j in self.cluster[i]:\\n list_x.append(self.code[0][j,0])\\n list_y.append(self.code[0][j,1])\\n plt.scatter(list_x,list_y)\\n plt.show()\\n return\",\n \"def convertcloud(points):\\n pcd = open3d.geometry.PointCloud()\\n pcd.points = open3d.utility.Vector3dVector(points)\\n return pcd\",\n \"def voxelize(points,leaf = 0.1):\\n if (type(points) == pclpy.pcl.PointCloud.PointXYZRGB):\\n cloud = points\\n voxel_filter = pclpy.pcl.filters.VoxelGrid.PointXYZRGB()\\n filtered_pointcloud = pclpy.pcl.PointCloud.PointXYZRGB()\\n else:\\n cloud = pclpy.pcl.PointCloud.PointXYZ(points)\\n voxel_filter = pclpy.pcl.filters.VoxelGrid.PointXYZ()\\n filtered_pointcloud = pclpy.pcl.PointCloud.PointXYZ()\\n \\n voxel_filter.setLeafSize(leaf,leaf,leaf)\\n voxel_filter.setInputCloud(cloud)\\n \\n voxel_filter.filter(filtered_pointcloud)\\n if type(points) == pclpy.pcl.PointCloud.PointXYZRGB:\\n return filtered_pointcloud\\n else:\\n return filtered_pointcloud.xyz\",\n \"def plot(self, subsample=None, valid_instance_types=None):\\n pt = self.sample_indices(subsample, valid_instance_types)\\n\\n x, y, z = self.pc[pt, 0], self.pc[pt, 1], self.pc[pt, 2]\\n color = self.color[pt]\\n\\n return plot_pointcloud(x, y, z, color=color)\",\n \"def show(data_set, number_points: int):\\n print(f'info: Showing {number_points} as maximum.')\\n sub_set_points = np.random.choice(range(data_set.shape[0]), size=min(data_set.shape[0], number_points))\\n x = data_set[sub_set_points, 0]\\n y = data_set[sub_set_points, 1]\\n z = data_set[sub_set_points, 2]\\n\\n fig = plt.figure(figsize=(8, 8))\\n ax = mplot3d.Axes3D(fig)\\n ax.set_title('NMSLIB index 3D representation', fontsize=20)\\n ax.scatter(xs=x, ys=y, zs=z)\\n plt.show()\",\n \"def _project_pointcloud(self, cloud):\\n\\n assert isinstance(cloud, PointCloud2)\\n\\n pc1 = PointCloud()\\n pc1.header = cloud.header\\n # hack the time! dont move the robot :-0\\n pc1.header.stamp = rospy.Time.now()\\n \\n \\n pc1.points = [Point32(*p) for p in pc2.read_points(cloud)]\\n\\n self._tf_listener.waitForTransform(pc1.header.frame_id,\\n self._image_info.tf_frame, \\n rospy.Time(0), \\n rospy.Duration(4))\\n\\n image_frame_cloud = self._tf_listener.transformPointCloud (\\n self._image_info.tf_frame, \\n pc1)\\n min_x, max_x, min_y, max_y = 640, 0, 480, 0 # TODO: remove hard coded image size!\\n for pt in image_frame_cloud.points:\\n u, v = self._image_info.project3dToPixel((pt.x, pt.y, pt.z))\\n if v < min_y:\\n min_y = int(v)\\n if v > max_y:\\n max_y = int(v)\\n if u < min_x:\\n min_x = int(u)\\n if u > max_x:\\n max_x = int(u)\\n location = (((min_x, min_y), (max_x, max_y)))\\n rospy.loginfo(\\\"Transformed cloud into image plane\\\")\\n return location\",\n \"def visualize(self):\\n colors = {'outline': (220, 220, 220),\\n 'inlier': (0, 255, 0),\\n 'outlier': (0, 0, 255),\\n 'lines': (128, 220, 128)}\\n # Create output image for visualization\\n gap = 5\\n h1, w1 = self.target.image.shape[:2]\\n h2, w2 = self.image.shape[:2]\\n vis = np.zeros((max(h1, h2), w1 + w2 + gap, 3), np.uint8)\\n vis[:h1, :w1, :] = self.target.image\\n w1 += gap\\n vis[:h2, w1:w1+w2, :] = self.image\\n \\n # Draw the located object \\n quad = np.float32(self.quad) + np.float32([w1, 0])\\n self.draw(vis, colors['outline'], 2, quad)\\n \\n # draw point details\\n inliers = [(x0, y0, x1 + w1, y1) for (x0, y0), (x1, y1) in self.inliers]\\n outliers = [(x0, y0, x1 + w1, y1) for (x0, y0), (x1, y1) in self.outliers]\\n if colors['outlier'] is not None: # draw x on each point\\n r = 2 # radius\\n thickness = 2\\n for x0, y0, x1, y1 in outliers:\\n cv2.line(vis, (x0 - r, y0 - r), (x0 + r, y0 + r), colors['outlier'], thickness)\\n cv2.line(vis, (x0 + r, y0 - r), (x0 - r, y0 + r), colors['outlier'], thickness)\\n cv2.line(vis, (x1 - r, y1 - r), (x1 + r, y1 + r), colors['outlier'], thickness)\\n cv2.line(vis, (x1 + r, y1 - r), (x1 - r, y1 + r), colors['outlier'], thickness)\\n if colors['lines'] is not None:\\n for x0, y0, x1, y1 in inliers:\\n cv2.line(vis, (x0, y0), (x1, y1), colors['lines'], 1)\\n if colors['inlier'] is not None:\\n for x0, y0, x1, y1 in inliers:\\n cv2.circle(vis, (x0, y0), 2, colors['inlier'], -1)\\n cv2.circle(vis, (x1, y1), 2, colors['inlier'], -1)\\n return vis\",\n \"def point_cloud(self, X, Y, Z, size=1, color='#FF3232', bordercolor='#FF3232', legend='', width=0.5, opacity=1.0):\\n point_cloud = go.Scatter3d(\\n x=X,\\n y=Y,\\n z=Z,\\n # showlegend=False,\\n name=legend,\\n mode='markers',\\n marker=dict(\\n size=size,\\n color=color,\\n line=dict(\\n color=bordercolor,\\n width=width\\n ),\\n # opacity=opacity\\n )\\n )\\n\\n return point_cloud\",\n \"def visualize(self):\\n self.octree.updateInnerOccupancy()\\n print(\\\"Start Octomap Visualization\\\")\\n\\n # define parameters\\n data = imgviz.data.arc2017()\\n camera_info = data['camera_info']\\n K = np.array(camera_info['K']).reshape(3, 3)\\n width=camera_info['width']\\n height=camera_info['height']\\n\\n # get free and occupied grid\\n occupied, _ = self.octree.extractPointCloud()\\n #frontier = self.gen_frontier()\\n \\n print(\\\"load point cloud\\\")\\n window = pyglet.window.Window(\\n width=int(1280), height=int(960)\\n )\\n\\n @window.event\\n def on_key_press(symbol, modifiers):\\n if modifiers == 0:\\n if symbol == pyglet.window.key.Q:\\n window.on_close()\\n\\n gui = glooey.Gui(window)\\n hbox = glooey.HBox()\\n hbox.set_padding(5)\\n\\n camera = trimesh.scene.Camera(\\n resolution=(width, height), focal=(K[0, 0], K[1, 1])\\n )\\n\\n # initial camera pose\\n camera_transform = np.array(\\n [\\n [1, 0, 0, 0],\\n [0, -1, 0, 0],\\n [0, 0, -1, -5],\\n [0.0, 0.0, 0.0, 1.0],\\n ],\\n )\\n\\n \\n\\n occupied_geom = trimesh.voxel.ops.multibox(\\n occupied, pitch=self.resolution, colors=[0.0, 0.0, 0.0, 0.5]\\n )\\n\\n # frontier_geom = trimesh.voxel.ops.multibox(\\n # frontier, pitch=self.resolution, colors=[1.0, 0, 0, 0.5]\\n # )\\n scene = trimesh.Scene(camera=camera, geometry=[occupied_geom])#, frontier_geom])\\n scene.camera_transform = camera_transform\\n hbox.add(self.labeled_scene_widget(scene, label='octomap'))\\n\\n\\n gui.add(hbox)\\n pyglet.app.run()\",\n \"def display(self):\\n scatter_plot(self.points, self.hull_points, self.color, self.title)\",\n \"def vis_points(data,f1,f2):\\n if np.isnan(data).any():\\n return\\n \\n plt.scatter(data[:,f1], data[:,f2], alpha=0.2, c='b')\\n plt.xlim(lims)\\n plt.ylim(lims)\",\n \"def show_pair_points(points, pairs, title, save_path = None):\\n\\n fig = plt.figure(figsize = (6, 6))\\n plt.title(title)\\n plt.scatter(points[:, 0], points[:, 1], c = 'b', s = 5, label = \\\"point cloud\\\")\\n for pair in pairs:\\n print(pair)\\n point_1 = points[pair[0]]\\n point_2 = points[pair[1]]\\n plt.scatter([point_1[0], point_2[0]], [point_1[1], point_2[1]], s = 10)\\n plt.xlabel(\\\"x\\\")\\n plt.ylabel(\\\"y\\\")\\n plt.axis('equal')\\n plt.grid()\\n plt.legend()\\n plt.show()\\n \\n if type(save_path) != type(None):\\n fig.savefig(save_path)\",\n \"def show_point_set_curvature(points, curvatures, title, thresh = 0.9, save_path = None):\\n dim = points.shape[1]\\n assert(dim == 2), \\\"Only data in 2 dimension space can be visualized.\\\"\\n selected_pts = points[np.abs(curvatures) < thresh]\\n \\n fig = plt.figure(figsize = (6, 6))\\n plt.title(title)\\n plt.scatter(points[:, 0], points[:, 1], c = 'b', s = 5, label = \\\"point cloud\\\")\\n plt.scatter(selected_pts[:, 0], selected_pts[:, 1], c = 'r', s = 5, label = \\\"selected point cloud\\\")\\n plt.xlabel(\\\"x\\\")\\n plt.ylabel(\\\"y\\\")\\n plt.axis('equal')\\n plt.grid()\\n plt.legend()\\n plt.show()\\n \\n if type(save_path) != type(None):\\n fig.savefig(save_path)\",\n \"def show(self):\\n from matplotlib import pyplot as plt\\n from mpl_toolkits.mplot3d import Axes3D\\n\\n fig = plt.figure()\\n ax = Axes3D(fig)\\n pos = self.cluster.get_positions()\\n from itertools import combinations\\n for tri in self.mesh.simplices:\\n for comb in combinations(tri, 2):\\n x1 = pos[comb[0], 0]\\n x2 = pos[comb[1], 0]\\n y1 = pos[comb[0], 1]\\n y2 = pos[comb[1], 1]\\n z1 = pos[comb[0], 2]\\n z2 = pos[comb[1], 2]\\n ax.plot([x1, x2], [y1, y2], zs=[z1, z2], color=\\\"black\\\")\\n plt.show()\",\n \"def __show_source(self):\\n pcd = o3d.io.read_point_cloud(\\n self.source_cloud\\n )\\n if np.asarray(pcd.points).shape[0] != 0:\\n pcd.paint_uniform_color([0, 1, 0])\\n pcd.estimate_normals()\\n self.source_point_cloud_view.load_cloud(pcd)\\n try:\\n self.source_point_cloud_view.show_window()\\n except RuntimeError:\\n pass\\n else:\\n QtWidgets.QMessageBox.warning(self, \\\"Error\\\",\\n f\\\"Source point cloud is no longer available\\\"\\n )\\n self.source_cloud = \\\"\\\"\\n self.__update_clickability()\\n self.__save_context()\",\n \"def open3dpaint(nppoints, color_map = 'jet', reduce_for_vis = False, voxel_size = 0.1, pointsize = 0.1):\\n assert (type(nppoints) == pclpy.pcl.PointCloud.PointXYZRGB) or (type(nppoints) == pclpy.pcl.PointCloud.PointXYZ) or (type(nppoints) == np.ndarray) or (type(nppoints) is list) or (type(nppoints) is tuple), 'Not valid point_cloud'\\n \\n if (type(nppoints) is not list) & (type(nppoints) is not tuple):\\n nppoints = [nppoints]\\n try:\\n visualizer = open3d.visualization.Visualizer()\\n visualizer.create_window()\\n options = visualizer.get_render_option()\\n options.background_color = np.asarray([0, 0, 0])\\n options.point_size = pointsize\\n\\n if len(nppoints) > 1:\\n for n,i in enumerate(nppoints):\\n workpoints = i\\n if (type(workpoints) == pclpy.pcl.PointCloud.PointXYZRGB) or (type(workpoints) == pclpy.pcl.PointCloud.PointXYZ):\\n workpoints = workpoints.xyz\\n\\n if reduce_for_vis:\\n workpoints = seg_tree.voxelize(workpoints,voxel_size)\\n\\n points = convertcloud(workpoints)\\n color_coef = n/len(nppoints)/2 + n%2*.5\\n if type(color_map) == np.ndarray:\\n color = color_map\\n elif color_map == 'jet':\\n color=cm.jet(color_coef)[:3]\\n else:\\n color=cm.Set1(color_coef)[:3]\\n points.colors = open3d.utility.Vector3dVector(np.ones_like(workpoints)*color)\\n #points.colors = open3d.utility.Vector3dVector(color)\\n visualizer.add_geometry(points)\\n else:\\n workpoints = nppoints[0]\\n if (type(workpoints) == pclpy.pcl.PointCloud.PointXYZRGB) or (type(workpoints) == pclpy.pcl.PointCloud.PointXYZ):\\n workpoints = workpoints.xyz\\n \\n if reduce_for_vis:\\n workpoints = seg_tree.voxelize(workpoints,voxel_size)\\n points = convertcloud(workpoints)\\n visualizer.add_geometry(points)\\n visualizer.run()\\n visualizer.destroy_window()\\n \\n except Exception as e:\\n print(type(e))\\n print(e.args)\\n print(e)\\n visualizer.destroy_window()\",\n \"def plot(self):\\n # Find only unmasked data :\\n # xyz, sData, sColor, _ = self._select_unmasked()\\n xyz, sData, sColor = self.xyz, self.sData, self.sColor\\n\\n # Render as cloud points :\\n self.mesh = visu.Markers(name='Sources')\\n self.mesh.set_data(xyz, edge_color=self.edgecolor, face_color=sColor,\\n size=sData, scaling=self.scaling,\\n edge_width=self.edgewidth, symbol=self.symbol)\\n self.mesh.set_gl_state('translucent')\",\n \"def show_2d_projections(self):\\n\\n with warnings.catch_warnings():\\n warnings.simplefilter(\\\"ignore\\\")\\n fig, axs = plt.subplots(1, 3)\\n for i in range(3):\\n plt.sca(axs[i])\\n io.imshow(self.image.max(i), cmap='viridis')\",\n \"def show_keypoints(image, key_pts):\\n plt.imshow(image)\\n plt.scatter(key_pts[:, 0], key_pts[:, 1], s=20, marker='.', c='m')\",\n \"def visualize(vis, features, label):\\n if vis == 'PCA':\\n #n_components = st.sidebar.slider(\\\"n_components\\\", 2, 10)\\n #alpha = st.sidebar.slider(\\\"alpha\\\", 0.8, 2.0)\\n #pca = PCA(n_components)\\n pca = PCA(2)\\n\\n X_projected = pca.fit_transform(features)\\n \\n x1 = X_projected[:, 0]\\n x2 = X_projected[:, 1]\\n\\n\\n fig = plt.figure()\\n plt.scatter(x1, x2, c=label, alpha=0.8, cmap='viridis')\\n plt.xlabel(\\\"Principal Component 1\\\")\\n plt.ylabel(\\\"Principal Component 2\\\")\\n plt.colorbar()\\n\\n st.pyplot()\",\n \"def show_feat(feat_map):\\n for i in range(feat_map.shape[0]):\\n plt.imshow(feat_map[i])\\n plt.show()\",\n \"def show_points(self, imsize):\\n mask = np.zeros(imsize)\\n for t in self.points:\\n mask[t] = 1\\n toimage(mask).show()\",\n \"def visualize_clouds(clouds, paths):\\n\\n for cloud, path in zip(clouds, paths):\\n print(\\\"Visualizing scans generated from:\\\", path)\\n for scan in cloud:\\n draw_geometries([scan])\",\n \"def sample_and_plot(self):\\n fig = plt.figure()\\n ax = plt.axes(projection = '3d')\\n ax.plot_surface(self.X, self.Y, self.sample(), cmap = plt.cm.jet, rstride = 2, cstride = 2, linewidth = 1)\\n plt.show()\",\n \"def save_3d_render(\\r\\n self, points: List[np.ndarray], colors: List[np.ndarray]\\r\\n ) -> None:\\r\\n pcd = o3d.geometry.PointCloud()\\r\\n pcd.points = o3d.utility.Vector3dVector(np.vstack(points).astype(np.float64))\\r\\n pcd.colors = o3d.utility.Vector3dVector(np.vstack(colors))\\r\\n if self.debug:\\r\\n o3d.visualization.draw_geometries([pcd])\\r\\n if not self.debug:\\r\\n o3d.io.write_point_cloud(f\\\"results/{self.filename[:-4]}.ply\\\", pcd)\",\n \"def plotrgcloud(self):\\n print self.kpunten\\n for i in range(len(self.kpunten[0])):\\n self.writetext('sen ='+ self.kpunten[0][i][0], (0.65,0.85), axnum = 0, hor = None ,ver = None , rot = None ,fs =14 , transform = self.fig.axes[0].transAxes)\\n if i == len(self.kpunten[0]) -1 :\\n end = None\\n else:\\n end = self.kpunten[0][i+1][1] + 1\\n print end\\n self.plotrgwrap( self.rgindex,2*self.reader.npair+self.rgindex,'real part of rgvars (a.u)' , 'imaginary part of rgvars (a.u.)', tit ='RG vars g = %f all states'%(self.chardata) , begin = self.kpunten[0][i][1] , stop = end , name = 'cpcloud'+ self.kpunten[0][i][0] , filenum = 0)\",\n \"def __show_target(self):\\n pcd = o3d.io.read_point_cloud(\\n self.target_cloud\\n )\\n if np.asarray(pcd.points).shape[0] != 0:\\n pcd.paint_uniform_color([0, 0, 1])\\n pcd.estimate_normals()\\n self.target_point_cloud_view.load_cloud(pcd)\\n try:\\n self.target_point_cloud_view.show_window()\\n except RuntimeError:\\n pass\\n else:\\n QtWidgets.QMessageBox.warning(self, \\\"Error\\\",\\n f\\\"Target point cloud is no longer available\\\"\\n )\\n self.target_cloud = \\\"\\\"\\n self.__update_clickability()\\n self.__save_context()\",\n \"def scatter(self, points):\\n assert points.shape[0] == 3\\n self.points = points\\n self.remove_scatter_plot()\\n x, y, z = zip(*points.T)\\n self.base_fig.add_trace(go.Scatter3d(\\n x=x, y=y, z=z,\\n mode='markers',\\n marker=dict(\\n size=2,\\n color=np.zeros(points.shape[1]),\\n colorscale=\\\"RdBu\\\",\\n colorbar=dict(\\n title=\\\"Samples\\\",\\n x=-0.2\\n ),\\n )\\n ))\",\n \"def mri_point_plot(self, vcol=1):\\n img = self.voxels\\n points = self.point_position \\n ax = []\\n fig = plt.figure(figsize=(9, 8))\\n # TODO make this setable in the function call\\n columns = 3\\n rows = 2\\n\\n for i in range(points.shape[0]):\\n im_slice = int(np.round(points[i, vcol]))\\n if vcol == 0:\\n im = img[im_slice, :, :]\\n elif vcol == 1:\\n im = img[:, im_slice, :]\\n else:\\n im = img[:, :, im_slice]\\n ax.append( fig.add_subplot(rows, columns, i+1))\\n ax[-1].set_title(\\\"Image depth: \\\"+str(im_slice)) # set title\\n plt.imshow(im)\\n plot_cols = np.array([0, 1, 2])\\n plot_cols = plot_cols[plot_cols != vcol]\\n plt.plot(points[i, min(plot_cols)], points[i, max(plot_cols)], 'ro')\\n\\n plt.show()\",\n \"def render_radar_point_cloud(self, point_cloud, extrinsics=Pose(), color=RED, velocity=None, velocity_scale=10):\\n combined_transform = self._bev_rotation * extrinsics\\n\\n pointcloud_in_bev = combined_transform * point_cloud\\n point_cloud2d = pointcloud_in_bev[:, :2]\\n\\n point_cloud2d[:, 0] = (self._center_pixel[0] + point_cloud2d[:, 0] * self._pixels_per_meter)\\n point_cloud2d[:, 1] = (self._center_pixel[1] + point_cloud2d[:, 1] * self._pixels_per_meter)\\n\\n H, W = self.data.shape[:2]\\n uv = point_cloud2d.astype(np.int32)\\n in_view = np.logical_and.reduce([\\n (point_cloud2d >= 0).all(axis=1),\\n point_cloud2d[:, 0] < W,\\n point_cloud2d[:, 1] < H,\\n ])\\n uv = uv[in_view]\\n\\n for row in uv:\\n cx, cy = row\\n cv2.circle(self.data, (cx, cy), 7, RED, thickness=1)\\n\\n def clip_norm(v, x):\\n M = np.linalg.norm(v)\\n if M == 0:\\n return v\\n\\n return np.clip(M, 0, x) * v / M\\n\\n if velocity is not None:\\n tail = point_cloud + velocity_scale * velocity\\n pointcloud_in_bev_tail = combined_transform * tail\\n point_cloud2d_tail = pointcloud_in_bev_tail[:, :2]\\n point_cloud2d_tail[:, 0] = (self._center_pixel[0] + point_cloud2d_tail[:, 0] * self._pixels_per_meter)\\n point_cloud2d_tail[:, 1] = (self._center_pixel[1] + point_cloud2d_tail[:, 1] * self._pixels_per_meter)\\n uv_tail = point_cloud2d_tail.astype(np.int32)\\n uv_tail = uv_tail[in_view]\\n for row, row_tail in zip(uv, uv_tail):\\n v_2d = row_tail - row\\n v_2d = clip_norm(v_2d, .025 * W)\\n\\n cx, cy = row\\n cx2, cy2 = row + v_2d.astype(np.int)\\n\\n cx2 = np.clip(cx2, 0, W - 1)\\n cy2 = np.clip(cy2, 0, H - 1)\\n color = GREEN\\n # If moving away from vehicle change the color (not strictly correct because radar is not a (0,0))\\n # TODO: calculate actual radar sensor position\\n if np.dot(row - np.array([W / 2, H / 2]), v_2d) > 0:\\n color = (255, 110, 199)\\n cv2.arrowedLine(self.data, (cx, cy), (cx2, cy2), color, thickness=1, line_type=cv2.LINE_AA)\",\n \"def plot_wordcloud(word_list, file_name=\\\"\\\"):\\n plt.figure(figsize=plt.figaspect(0.8), dpi=100)\\n all_words = ' '.join([text for text in word_list])\\n wordcloud = WordCloud(width=800, height=600, random_state=21, max_font_size=110).generate(all_words)\\n plt.imshow(wordcloud, interpolation=\\\"bilinear\\\")\\n plt.axis('off')\\n if file_name:\\n plt.savefig(file_name, bbox_inches='tight')\\n plt.show()\\n return\",\n \"def get_point_cloud(self):\\n tsdf_vol, color_vol = self.get_volume()\\n\\n # Marching cubes\\n verts = measure.marching_cubes_lewiner(tsdf_vol, level=0)[0]\\n verts_ind = np.round(verts).astype(int)\\n verts = verts * self._voxel_size + self._vol_origin\\n\\n # Get vertex colors\\n rgb_vals = color_vol[verts_ind[:, 0], verts_ind[:, 1], verts_ind[:, 2]]\\n colors_b = np.floor(rgb_vals / self._color_const)\\n colors_g = np.floor((rgb_vals - colors_b * self._color_const) / 256)\\n colors_r = rgb_vals - colors_b * self._color_const - colors_g * 256\\n colors = np.floor(np.asarray([colors_r, colors_g, colors_b])).T\\n colors = colors.astype(np.uint8)\\n\\n pc = np.hstack([verts, colors])\\n return pc\",\n \"def show_point_set(points, title, save_path = None):\\n dim = points.shape[1]\\n assert(dim == 2 or dim == 3), \\\"Only data in 2 or 3 dimension space can be visualized.\\\"\\n \\n if dim == 2:\\n fig = plt.figure(figsize = (6, 6))\\n plt.title(title)\\n plt.scatter(points[:, 0], points[:, 1], c = 'b', s = 5)\\n plt.xlabel(\\\"x\\\")\\n plt.ylabel(\\\"y\\\")\\n plt.axis('equal')\\n plt.grid()\\n plt.show()\\n elif dim == 3:\\n fig = plt.figure(figsize = (6, 6))\\n ax = fig.add_subplot(111, projection='3d')\\n ax.set_title(title)\\n ax.scatter(points[:, 0], points[:, 1], points[:, 2], c = 'b', s = 5)\\n ax.set_xlabel(\\\"x\\\")\\n ax.set_ylabel(\\\"y\\\")\\n ax.set_zlabel(\\\"z\\\")\\n plt.show()\\n\\n if type(save_path) != type(None):\\n fig.savefig(save_path)\",\n \"def render_points_2d(self, **kwds):\\n return point2d(self.coordinates_of(self.points), **kwds)\",\n \"def draw_clouds(a):\\n small_cloud(0 + a, 0, 0)\\n small_cloud(200 + a, -150, -10)\\n big_cloud(350 + a, 0, -3)\\n small_cloud(600 + a, -90, 3)\\n small_cloud(800 + a, 0, 0)\\n small_cloud(1000 + a, -150, -10)\\n big_cloud(1150 + a, 0, -3)\\n small_cloud(1400 + a, -90, 3)\\n small_cloud(-800 + a, 0, 0)\\n small_cloud(-600 + a, -150, -10)\\n big_cloud(-450 + a, 0, -3)\\n small_cloud(-200 + a, -90, 3)\",\n \"def plotsite(self):\\n if self.dimension==1 or self.dimension==2:\\n for site in self.sites.flatten():\\n plt.scatter(site.coordinate[0],site.coordinate[1],marker=site.marker,s=site.markersize,c=site.color)\\n elif self.dimension==3:\\n from mpl_toolkits.mplot3d import Axes3D\\n fig = plt.figure()\\n ax = Axes3D(fig)\\n for site in self.sites.flatten():\\n ax.scatter(site.coordinate[0],site.coordinate[1],site.coordinate[2],marker=site.marker,s=site.markersize,c=site.color)\",\n \"def imshow_surface(self):\\n plt.imshow(self.z)\\n plt.colorbar()\\n plt.show()\",\n \"def _build_point_cloud_graph(self):\\n depths = self.depths_placeholder[tf.newaxis]\\n images = self.images_placeholder[tf.newaxis]\\n poses = self.poses_placeholder[tf.newaxis]\\n intrinsics = self.intrinsics_placeholder[tf.newaxis]\\n intrinsics = intrinsics_vec_to_matrix(intrinsics)\\n\\n depths_pad = tf.pad(depths, [[0,0],[0,0],[0,1],[0,1]], \\\"CONSTANT\\\")\\n\\n depths_grad = \\\\\\n (depths_pad[:, :, 1:, :-1] - depths_pad[:, :, :-1, :-1])**2 + \\\\\\n (depths_pad[:, :, :-1, 1:] - depths_pad[:, :, :-1, :-1])**2\\n\\n # don't use large depths for point cloud and ignore boundary regions\\n valid = (depths < 3.0) & (depths_grad < 0.001)\\n\\n batch, num, ht, wd = tf.unstack(tf.shape(depths), num=4)\\n Ts = VideoSE3Transformation(matrix=poses)\\n X0 = projective_ops.backproject(depths, intrinsics)\\n \\n # transform point cloud into world coordinates\\n X1 = Ts.inv()(X0)\\n\\n crop_h = 12\\n crop_w = 32\\n\\n X1 = X1[:, :, crop_h:-crop_h, crop_w:-crop_w]\\n valid = valid[:, :, crop_h:-crop_h, crop_w:-crop_w]\\n images = images[:, :, crop_h:-crop_h, crop_w:-crop_w, ::-1]\\n \\n X1 = tf.reshape(X1, [-1, 3])\\n colors = tf.reshape(images, [-1, 3])\\n\\n valid_inds = tf.where(tf.reshape(valid, [-1]))\\n valid_inds = tf.reshape(valid_inds, [-1])\\n\\n X1 = tf.gather(X1, valid_inds, axis=0)\\n colors = tf.gather(colors, valid_inds, axis=0)\\n\\n self.outputs['point_cloud'] = (X1, colors)\",\n \"def show_points_on_img(mask,img):\\n labeled, num_objects = ndi.label(mask)\\n slices = ndi.find_objects(labeled)\\n x, y = [], []\\n for dy,dx in slices:\\n x_center = (dx.start + dx.stop - 1)/2\\n x.append(x_center)\\n y_center = (dy.start + dy.stop - 1)/2 \\n y.append(y_center)\\n plt.figure()\\n plt.imshow(img)\\n plt.autoscale(False)\\n plt.plot(x,y, \\\"o\\\")\",\n \"def show_transformed_point_set(points_1, points_2, points_12, loss, save_path = None):\\n\\n fig = plt.figure(figsize = (6, 6))\\n plt.title(\\\"Loss = %f\\\" % loss)\\n plt.scatter(points_1[:, 0], points_1[:, 1], s = 5, label = \\\"point cloud 1\\\")\\n plt.scatter(points_2[:, 0], points_2[:, 1], s = 5, label = \\\"point cloud 2\\\")\\n plt.scatter(points_12[:, 0], points_12[:, 1], s = 5, label = \\\"point cloud 1 to 2\\\")\\n plt.xlabel(\\\"x\\\")\\n plt.ylabel(\\\"y\\\")\\n plt.axis('equal')\\n plt.grid()\\n plt.legend()\\n plt.show()\\n \\n if type(save_path) != type(None):\\n fig.savefig(save_path)\",\n \"def draw_features(self, image):\\n \\n for x,y in self.new_points.reshape(-1,2):\\n cv2.circle(image, (x,y), 2, (255,0,255), 2)\\n return image\",\n \"def show_orbits(points, nb_pairs, title, save_path = None):\\n\\n fig = plt.figure(figsize = (6, 6))\\n plt.title(title)\\n plt.scatter(points[:, 0], points[:, 1], c = 'b', s = 5, label = \\\"point cloud\\\")\\n for i in range(len(nb_pairs)):\\n plt.scatter(nb_pairs[i][0][:, 0], nb_pairs[i][0][:, 1], s = 5, label = \\\"%d_1\\\" % i)\\n plt.scatter(nb_pairs[i][1][:, 0], nb_pairs[i][1][:, 1], s = 5, label = \\\"%d_2\\\" % i)\\n plt.xlabel(\\\"x\\\")\\n plt.ylabel(\\\"y\\\")\\n plt.axis('equal')\\n plt.grid()\\n plt.legend()\\n plt.show()\\n \\n if type(save_path) != type(None):\\n fig.savefig(save_path)\",\n \"def point_from_rays(self):\\n print \\\"generating the 3d point from given clicked points\\\"\\n \\n #gather cams and points clicked \\n uvs = []\\n cams = []\\n for iFrame in self.frames:\\n if iFrame.lastClick : \\n uv = numpy.multiply(iFrame.lastClick,self.reduceFactor)\\n uvs.append(uv)\\n cam = load_perspective_camera(self.camList[iFrame.currImg])\\n cams.append(cam)\\n point = get_3d_from_cams(cams, uvs)\\n self.point3d = point;\\n self.pointLabel.set(\\\"3d Point: \\\" + str(self.point3d))\\n\\n # project 3d point into each image, and gather intensities \\n values = []\\n ims = []\\n for idx, img in enumerate(self.imgList):\\n cam = load_perspective_camera(self.camList[idx])\\n imgPoint = project_point(cam, point[0], point[1], point[2])\\n imgPoint = numpy.divide(imgPoint, self.reduceFactor)\\n self.allUVs.append(imgPoint)\\n \\n #grab float intensity value at this point \\n imgView,ni,nj = load_image(img)\\n val = pixel(imgView, imgPoint)\\n if val > 0.0:\\n values.append(val)\\n ims.append(idx)\\n \\n #cleanup\\n remove_from_db([imgView, cam])\\n \\n\\n #write mean/std of intensities \\n self.meanLabel.set(\\\"Mean: \\\" + str(numpy.mean(values)) )\\n self.stdLabel.set(\\\"Std Dev: \\\" + str(numpy.std(values)) )\\n #plot the intensities by image number \\n self.f.clf();\\n self.a = self.f.add_subplot(311)\\n self.a.set_xlabel(\\\"img #\\\")\\n self.a.set_ylabel(\\\"intensity\\\")\\n self.a.plot(ims, values)\\n #plot the histogram of intensities by image number \\n pdf, bins, patches = plt.hist(values)\\n self.b = self.f.add_subplot(313)\\n self.b.set_xlabel(\\\"bin val\\\")\\n self.b.set_ylabel(\\\"freq\\\")\\n self.b.hist(values, 15, normed=1, facecolor=\\\"green\\\" )\\n self.canvas.show();\",\n \"def showAsPoints(self): \\n MonkeyPatchMayaVi()\\n #import enthought.mayavi.mlab as mlab\\n from mayavi import mlab\\n \\n @mlab.show\\n def _showSimple():\\n morphPts = [ SVVisitorFactory.Array4AllPoints(morph)() for morph in self.morphs ]\\n pts = numpy.concatenate( morphPts)\\n return mlab.points3d(pts[:, 0], pts[:, 1], pts[:, 2], pts[:, 3], colormap=self.colormap, scale_factor=self.scale_factor)\\n _showSimple()\",\n \"def plot_surface(self):\\n X, Y = np.meshgrid(self.x, self.y)\\n fig = plt.figure()\\n ax = fig.add_subplot(111, projection='3d')\\n ax.plot_surface(X=X, Y=Y, Z=self.z)\\n plt.show()\",\n \"def show_trace_2d(f, results): #@save\\n set_figsize()\\n plt.plot(*zip(*results), '-o', color='#ff7f0e')\\n x1, x2 = torch.meshgrid(torch.arange(-5.5, 1.0, 0.1),torch.arange(-3.0, 1.0, 0.1))\\n plt.contour(x1, x2, f(x1, x2), colors='#1f77b4')\\n plt.xlabel('x1')\",\n \"def plot_2d(vector_array, save_plot_dir):\\n principal_df = pd.DataFrame(data = vector_array, columns = ['pc1', 'pc2'])\\n fig = plt.figure()\\n ax = fig.add_subplot(111)\\n\\n xs = principal_df['pc1']\\n ys = principal_df['pc2']\\n ax.scatter(xs, ys, s=50, alpha=0.6, edgecolors='w')\\n\\n ax.set_xlabel('pc1')\\n ax.set_ylabel('pc2')\\n\\n plt.savefig(save_plot_dir + '/2D_scatter.png')\\n plt.close()\",\n \"def show_skeletons(self, skel_2d, z_out, z_gt=None):\\n fig = plt.figure(figsize=(20, 20))\\n ax1 = fig.add_subplot(1, 2, 1)\\n ax2 = fig.add_subplot(1, 2, 2, projection='3d')\\n edges = np.array([[1, 0], [0, 2],[2, 3], [3, 4], [0, 5], [5, 6], [6, 7]])\\n\\n ax_2d = ax1\\n ax_3d = ax2\\n\\n # draw 3d\\n for edge in edges:\\n ax_3d.plot(skel_2d[0, edge], z_out[edge], skel_2d[1, edge], color='r')\\n if z_gt is not None:\\n ax_3d.plot(skel_2d[0, edge], z_gt[edge], skel_2d[1, edge], color='g')\\n\\n ax_3d.set_aspect('equal')\\n ax_3d.set_xlabel(\\\"x\\\"), ax_3d.set_ylabel(\\\"z\\\"), ax_3d.set_zlabel(\\\"y\\\")\\n ax_3d.set_xlim3d([-2, 2]), ax_3d.set_ylim3d([2, -2]), ax_3d.set_zlim3d([2, -2])\\n ax_3d.view_init(elev=10, azim=-45)\\n\\n # draw 2d\\n for edge in edges:\\n ax_2d.plot(skel_2d[0, edge], skel_2d[1, edge], color='r')\\n\\n ax_2d.set_aspect('equal')\\n ax_2d.set_xlabel(\\\"x\\\"), ax_2d.set_ylabel(\\\"y\\\")\\n ax_2d.set_xlim([-2, 2]), ax_2d.set_ylim([2, -2])\\n\\n plt.show()\",\n \"def show_wordcloud(dictionary, title, min_font = 10):\\n wordcloud = WordCloud(min_font_size=min_font).generate_from_frequencies(dictionary)\\n plt.figure(figsize = (8, 8), facecolor = None) \\n plt.imshow(wordcloud) \\n plt.axis(\\\"off\\\")\\n if title:\\n plt.title(title)\\n else:\\n plt.title(\\\"Word Cloud\\\")\\n plt.tight_layout(pad = 0) \\n\\n plt.show()\",\n \"def view_point_cloud_model_and_affordance(number_of_object_per_category=5):\\n list_pc_paths = [f for f in glob.glob('./dataset/*.npy', recursive=True)]\\n set_objects = set([os.path.basename(pc_path).split('_')[0] for pc_path in list_pc_paths])\\n\\n for obj in set_objects:\\n try:\\n # load point cloud models\\n pc_models = np.load('./dataset/{}_point_cloud_models.npy'.format(obj))[\\n :number_of_object_per_category]\\n # load point cloud grasp affordance\\n pc_affordance = np.load('./dataset/{}_point_cloud_grasp_affordance.npy'.format(obj))[\\n :number_of_object_per_category]\\n\\n # visualization\\n for i, m in enumerate(pc_models):\\n fig = plt.figure()\\n ax = fig.add_subplot(projection='3d')\\n list_model_x, list_model_y, list_model_z = [], [], []\\n list_affordance_x, list_affordance_y, list_affordance_z = [], [], []\\n\\n for x in range(32):\\n for y in range(32):\\n for z in range(32):\\n if pc_affordance[i, x, y, z] == 1:\\n list_affordance_x.append(x)\\n list_affordance_y.append(y)\\n list_affordance_z.append(z)\\n elif m[x, y, z] == 1:\\n list_model_x.append(x)\\n list_model_y.append(y)\\n list_model_z.append(z)\\n\\n ax.scatter(list_model_x, list_model_y, list_model_z, c='#0c457d')\\n ax.scatter(list_affordance_x, list_affordance_y, list_affordance_z, c='#e8702a', alpha=0.35)\\n ax.set_xlim(0, 32)\\n ax.set_ylim(0, 32)\\n ax.set_zlim(0, 32)\\n plt.show()\\n\\n except FileNotFoundError:\\n print('Some point cloud npy files are not found.')\\n continue\",\n \"def show_scatter(self):\\n plt.scatter(self.a1[:, 0], self.a1[:, 1], c=\\\"red\\\", alpha=0.5, s=10)\\n plt.scatter(self.a2[:, 0], self.a2[:, 1], c=\\\"blue\\\", alpha=0.5, s=10)\\n plt.scatter(0, 0, marker=\\\"D\\\", c=\\\"black\\\", alpha=0.8)\\n plt.scatter(2, 2, marker=\\\"D\\\", c=\\\"black\\\", alpha=0.8)\\n plt.show()\",\n \"def image_to_point_cloud(depth):\\n cx, cy, fx, fy = 5.8818670481438744*100, 3.1076280589210484*100, 5.8724220649505514*100, 2.2887144980135292*100\\n depth = depth[:,:,0]\\n\\n rows, cols = depth.shape\\n c, r = np.meshgrid(np.arange(cols), np.arange(rows), sparse=True)\\n valid = (depth > 0) & (depth < 255)\\n # print(valid)\\n z = np.where(valid, depth / 256.0, np.nan)\\n x = np.where(valid, z * (c - cx) / fx, 0)\\n y = np.where(valid, z * (r - cy) / fy, 0)\\n return np.dstack((x, y, z))\",\n \"def generate_image(self,true_dist):\\n N_POINTS = 128\\n RANGE = 3\\n\\n points = np.zeros((N_POINTS, N_POINTS, 2), dtype='float32')\\n points[:, :, 0] = np.linspace(-RANGE, RANGE, N_POINTS)[:, None]\\n points[:, :, 1] = np.linspace(-RANGE, RANGE, N_POINTS)[None, :]\\n points = points.reshape((-1, 2))\\n\\n plt.clf()\\n\\n #true_dist = true_dist.cpu().data.numpy()\\n samples = self.G(self.sample_z_)\\n print('generate size is',samples.size())\\n samples = samples.cpu().data.numpy()\\n\\n x = y = np.linspace(-RANGE, RANGE, N_POINTS)\\n # plt.contour(x, y, disc_map.reshape((len(x), len(y))).transpose())\\n\\n plt.scatter(true_dist[:, 0], true_dist[:, 1], c='orange', marker='+')\\n plt.scatter(samples[:, 0], samples[:, 1], c='green', marker='+')\\n plt.show()\",\n \"def showAllPoints(self, surface, **kwargs):\\n if not \\\"point_color\\\" in kwargs:\\n kwargs[\\\"point_color\\\"] = self.point_color\\n if not \\\"point_radius\\\" in kwargs:\\n kwargs[\\\"point_radius\\\"] = self.point_radius\\n if not \\\"point_mode\\\" in kwargs:\\n kwargs[\\\"point_mode\\\"] = self.point_mode\\n if not \\\"point_size\\\" in kwargs:\\n kwargs[\\\"point_size\\\"] = self.point_size\\n if not \\\"point_width\\\" in kwargs:\\n kwargs[\\\"point_width\\\"] = self.point_width\\n if not \\\"point_fill\\\" in kwargs:\\n kwargs[\\\"point_fill\\\"] = self.point_fill\\n for point in self.points:\\n point.show(surface,\\n color=kwargs[\\\"point_color\\\"],\\n mode=kwargs[\\\"point_mode\\\"],\\n fill=kwargs[\\\"point_fill\\\"],\\n radius=kwargs[\\\"point_radius\\\"],\\n size=kwargs[\\\"point_size\\\"],\\n width=kwargs[\\\"point_width\\\"])\",\n \"def visualize_output(self, keyframe_index):\\n\\n self.vis_counter += 1\\n keyframe_image = self.images[keyframe_index]\\n keyframe_depth = self.depths[keyframe_index]\\n keyframe_pose = self.poses[keyframe_index]\\n\\n feed_dict = {\\n self.images_placeholder: keyframe_image[np.newaxis],\\n self.depths_placeholder: keyframe_depth[np.newaxis],\\n self.poses_placeholder: keyframe_pose[np.newaxis],\\n self.intrinsics_placeholder: self.intrinsics}\\n\\n keyframe_point_cloud, keyframe_point_colors = \\\\\\n self.sess.run(self.outputs['point_cloud'], feed_dict=feed_dict)\\n\\n pointcloud = (keyframe_point_cloud, keyframe_point_colors)\\n\\n # only add the point cloud once in every 5 frames\\n if self.vis_counter % 4 == 0:\\n self.queue.put((pointcloud, keyframe_pose))\\n \\n else:\\n self.queue.put((None, keyframe_pose))\",\n \"def format_point_cloud(self, pc, num_points):\\n resized_pc = np.zeros([num_points, 3])\\n if len(pc) > num_points:\\n # Randomly sample point cloud to reduce size\\n resized_pc = pc[np.random.choice(pc.shape[0], num_points,\\n replace=False), :]\\n elif len(pc) < num_points:\\n # Duplicate last point to fill point cloud. Because of the max function\\n # the duplicated points will not affect the output\\n resized_pc[:len(pc)] = pc\\n resized_pc[len(pc):] = pc[-1]\\n else:\\n resized_pc = pc\\n\\n return resized_pc\",\n \"def test_get_mesh_grid_as_point_cloud_single_pt() -> None:\\n min_x = -3 # integer, minimum x-coordinate of 2D grid\\n max_x = -3 # integer, maximum x-coordinate of 2D grid\\n min_y = 2 # integer, minimum y-coordinate of 2D grid\\n max_y = 2 # integer, maximum y-coordinate of 2D grid\\n\\n # return pts, a Numpy array of shape (N,2)\\n pts = mesh_grid_utils.get_mesh_grid_as_point_cloud(\\n min_x, max_x, min_y, max_y, downsample_factor=1.0\\n )\\n\\n assert pts.shape == (1, 2)\\n gt_pts: NDArrayFloat = np.array([[-3.0, 2.0]])\\n\\n assert np.allclose(gt_pts, pts)\",\n \"def test_get_mesh_grid_as_point_cloud_3x2rect() -> None:\\n min_x = -3 # integer, minimum x-coordinate of 2D grid\\n max_x = -1 # integer, maximum x-coordinate of 2D grid\\n min_y = 2 # integer, minimum y-coordinate of 2D grid\\n max_y = 3 # integer, maximum y-coordinate of 2D grid\\n\\n # return pts, a Numpy array of shape (N,2)\\n pts = mesh_grid_utils.get_mesh_grid_as_point_cloud(\\n min_x, max_x, min_y, max_y, downsample_factor=1.0\\n )\\n\\n assert pts.shape == (6, 2)\\n # fmt: off\\n gt_pts: NDArrayFloat = np.array(\\n [\\n [-3.0, 2.0],\\n [-2.0, 2.0],\\n [-1.0, 2.0],\\n [-3.0, 3.0],\\n [-2.0, 3.0],\\n [-1.0, 3.0]\\n ])\\n # fmt: on\\n assert np.allclose(gt_pts, pts)\",\n \"def show_points(self, **kwargs):\\n\\n if hasattr(self, 'method') and hasattr(self.method, 'show_points'):\\n self.method.show_points(self, **kwargs)\\n else:\\n figsize = kwargs.get('figsize', (15, 5))\\n cmap = kwargs.get('cmap', 'gray')\\n marker = kwargs.get('marker', '.')\\n color = kwargs.get('color', 'r')\\n fig, ax = plt.subplots(figsize=figsize)\\n ax.imshow(self.mraw[0].astype(float), cmap=cmap)\\n ax.scatter(self.points[:, 1], self.points[:, 0], \\n marker=marker, color=color)\\n plt.grid(False)\\n plt.show()\",\n \"def partial_visualize_in_2d(self, cluster_index=[5,12,35,44,64,75,81]):\\n for i in cluster_index:\\n list_x = []\\n list_y = []\\n for j in self.cluster[i]:\\n list_x.append(self.code[0][j,0])\\n list_y.append(self.code[0][j,1])\\n plt.scatter(list_x,list_y, label=self.skill[self.present_skill[i]])\\n plt.legend()\\n plt.show()\\n return\",\n \"def plot_scatter_points(self):\\n self.plot(1)\",\n \"def view(config_file):\\n import open3d as o3d\\n with open(config_file) as f:\\n config = json.load(f)\\n scenes = get_realsense_scenes(config['realsense_dir'])\\n for scene in scenes:\\n # if scene['scene_name'] != \\\"Scene_004\\\":\\n # continue\\n scene_data = get_data_from_scene(scene)\\n logger.info(\\\"Visualizing - %s\\\", scene['scene_name'])\\n pcd = o3d.geometry.PointCloud(o3d.utility.Vector3dVector(scene_data['points3d']))\\n o3d.visualization.draw_geometries_with_editing([pcd])\\n pcd = o3d.geometry.PointCloud(o3d.utility.Vector3dVector(scene_data['points3d_segmented']))\\n o3d.visualization.draw_geometries([pcd])\",\n \"def plot_data(self):\\n if hasattr(self,'data'):\\n plt.scatter(*self.data.T)\\n plt.show()\\n else:\\n raise Exception('No 2d data of the instance has been loaded')\",\n \"def pangolin_draw(points):\\n display.setup()\\n\\n while not display.should_quit():\\n display.init_frame()\\n for idx, pose in enumerate(CAMERA_POSES):\\n is_last_pose = idx == len(CAMERA_POSES) - 1\\n homogenous_pose = np.vstack((pose, [0, 0, 0, 1]))\\n display.draw_camera(homogenous_pose, (0.0, 1.0, 1.0 if is_last_pose else 0.0))\\n\\n display.draw_points(points)\\n\\n display.finish_frame()\",\n \"def plot_data(x):\\n if DATA_2D:\\n plt.scatter(x[:, 0], x[:, 1])\\n plt.show()\\n else:\\n fig = plt.figure()\\n ax = Axes3D(fig)\\n ax.scatter(x[:, 0], x[:, 1], x[:, 2])\\n ax.set_xlabel('X Label')\\n ax.set_ylabel('Y Label')\\n ax.set_zlabel('Z Label')\\n plt.show()\",\n \"def PointCloudfromStructOutput(self,file):\\n print(\\\"Creating Structure Point Cloud\\\")\\n xyz = self.readStructOutput(file)\\n pc = np.zeros((int(len(xyz)/2.0),3))\\n pc[:,0] = xyz[::2,0]*1000\\n pc[:,1] = xyz[::2,1]*1000\\n pc[:,2] = xyz[::2,2]*1000\\n head = \\\"\\\"\\\"X,Y,Z\\\"\\\"\\\"\\n np.savetxt(file, pc, delimiter=',',fmt='%.10f', header=head)\\n return\",\n \"def showFastCrossPoints(self, surface, color=None, mode=None, radius=None, width=None, size=None):\\n points = self.crossSelf()\\n if not color:\\n color = self.cross_point_color\\n if not mode:\\n mode = self.cross_point_mode\\n if not radius:\\n radius = self.cross_point_radius\\n if not width:\\n width = self.cross_point_width\\n if not size:\\n size = self.cross_point_size\\n for point in points:\\n point.show(surface, color=color, mode=mode, radius=radius, width=width, size=size)\",\n \"def plot_samples(s):\\r\\n assert len(s[0, :]) >= 2, ('The Phase space dimensions are less than two.', ' Need at least two to plot.')\\r\\n fig = plt.figure(1)\\r\\n if len(s[0, :]) >= 3:\\r\\n ax = fig.add_subplot(111, projection='3d')\\r\\n ax.scatter(s[:, 0], s[:, 1], s[:, 2])\\r\\n fig = plt.figure(2)\\r\\n plt.scatter(s[:, 0], s[:, 1])\\r\\n plt.show()\",\n \"def viz1(img, interest_points, color='r'):\\n\\n\\tx = [a[1] for a in interest_points] #blob detection x axis\\n\\ty = [a[0] for a in interest_points] #blob detection y axis\\n\\ts = [a[2] for a in interest_points] #blob detected at sigma \\n \\n\\tplt.imshow(img, cmap='gray') #adding the input image to plot \\n\\tfor x, y, s in zip(x, y, s):\\n\\t\\tplt.scatter(x, y, alpha=1, facecolors='none', edgecolors='r', s=s**2) #plotting the input interest points\\n\\tplt.axis('off')\\n\\t#plt.show() #showing the image // can be changed to saving the image locally \\n\\n\\treturn\",\n \"def show_grid(self):\\n\\n if not os.path.exists(self.path_to_results):\\n os.mkdir(self.path_to_results)\\n\\n fig = plt.figure()\\n\\n if self.show_points == 1:\\n plt.scatter(self.x_list_grid, self.y_list_grid, c='blue')\\n\\n plt.plot(self.x_list_main, self.y_list_main,\\n 'green', label='straight path')\\n plt.plot(self.x_list, self.y_list, 'red', label='first path')\\n plt.plot(self.x_list_filtered, self.y_list_filtered,\\n 'blue', label='filtered path')\\n plt.title('Paths')\\n plt.ylabel('Latitude')\\n plt.xlabel('Longitude')\\n # plt.legend()\\n\\n fig.savefig(os.path.join(self.path_to_results, 'Paths.png'))\",\n \"def draw_birdseye_points(self, image, points):\\n point_image = np.copy(image)\\n for point in points:\\n cv2.circle(point_image, point, 5, [0, 0, 255], cv2.FILLED)\\n\\n cv2.imshow('image1', point_image)\\n cv2.waitKey(1)\",\n \"def plt3dpaint(nppoints, color_map = 'jet', reduce_for_vis = True, voxel_size = 0.2, pointsize = 0.1, subplots = 5):\\n assert (type(nppoints) == pclpy.pcl.PointCloud.PointXYZRGB) or (type(nppoints) == pclpy.pcl.PointCloud.PointXYZ) or (type(nppoints) == np.ndarray) or (type(nppoints) is list) or (type(nppoints) is tuple), 'Not valid point_cloud'\\n cloudlist = []\\n cloudcolors = []\\n if (type(nppoints) is not list) & (type(nppoints) is not tuple):\\n nppoints = [nppoints]\\n \\n if len(nppoints) > 1:\\n for n,i in enumerate(nppoints):\\n workpoints = i\\n if (type(workpoints) == pclpy.pcl.PointCloud.PointXYZRGB) or (type(workpoints) == pclpy.pcl.PointCloud.PointXYZ):\\n workpoints = workpoints.xyz\\n\\n if reduce_for_vis:\\n workpoints = seg_tree.voxelize(workpoints,voxel_size)\\n\\n \\n cloudmin = np.min(workpoints[:,2])\\n cloudmax = np.max(workpoints[:,2])\\n \\n points = workpoints\\n color_coef = n/len(nppoints)/2 + n%2*.5\\n if type(color_map) == np.ndarray:\\n color = color_map\\n elif color_map == 'jet':\\n color=cm.jet(color_coef)[:3]\\n else:\\n color=cm.Set1(color_coef)[:3]\\n cloudcolors.append(np.ones_like(workpoints)*color + 0.4*(np.ones_like(workpoints) * ((workpoints[:,2] - cloudmin)/(cloudmax - cloudmin)).reshape(-1,1)-0.5) )\\n cloudlist.append(points)\\n else:\\n workpoints = nppoints[0]\\n if (type(workpoints) == pclpy.pcl.PointCloud.PointXYZRGB) or (type(workpoints) == pclpy.pcl.PointCloud.PointXYZ):\\n workpoints = workpoints.xyz\\n\\n if reduce_for_vis:\\n workpoints = seg_tree.voxelize(workpoints,voxel_size)\\n cloudcolors.append(workpoints[:,2])\\n cloudlist.append(workpoints)\\n\\n plt_pointcloud = np.concatenate(cloudlist)\\n plt_colors = np.concatenate(cloudcolors)\\n if len(nppoints) > 1:\\n plt_colors = np.minimum(plt_colors,np.ones_like(plt_colors))\\n plt_colors = np.maximum(plt_colors,np.zeros_like(plt_colors))\\n fig = plt.figure(figsize=(30,16) )\\n for i in range(subplots):\\n ax = fig.add_subplot(1, subplots, i+1, projection='3d')\\n ax.view_init(30, 360*i/subplots)\\n ax.scatter3D(plt_pointcloud[:,0], plt_pointcloud[:,1], plt_pointcloud[:,2], c=plt_colors, s=pointsize)\",\n \"def plot(self):\\n\\t\\tif (2 <= len(self.X) <= 3):\\n\\t\\t\\tvDataFrame(self.name, self.cursor).scatter(columns = self.X, catcol = \\\"dbscan_cluster\\\", max_cardinality = 100, max_nb_points = 10000)\\n\\t\\telse:\\n\\t\\t\\traise ValueError(\\\"Clustering Plots are only available in 2D or 3D\\\")\",\n \"def plot(self):\\n y = self.projection\\n mpl.scatter(y[:, 0], y[:, 1], c=self.data_class)\\n mpl.show()\",\n \"def imshow(self, depth):\\n layer = self.cube[depth]\\n img = []\\n for i in range(self.height):\\n img.append([layer[i][j].value for j in range(self.width)])\\n plt.imshow(img, cmap='gray')\\n plt.show()\",\n \"def show2d(self, data):\\n self.__plot2d.clear()\\n self.__plot2d.addImage(legend=\\\"data\\\", data=data)\\n self.setCurrentIndex(self.__index2d)\",\n \"def visualise_cppn(self, resolution=(64, 64)):\\n import matplotlib.pyplot as plt\\n from matplotlib.pyplot import imshow\\n data = np.empty([resolution[0], resolution[1]])\\n x_linspace = np.linspace(-1, 1, resolution[0])\\n y_linspace = np.linspace(-1, 1, resolution[1])\\n for row, x in enumerate(x_linspace):\\n for col, y in enumerate(y_linspace):\\n data[row, col] = self.graph(np.array([x, y, 0, 0], dtype=np.float32))[0]\\n #plt.axis([-1, 1, -1, 1])\\n print(data.min(), \\\" \\\", data.max())\\n imshow(data, cmap='Greys', vmin=-1, vmax=1)\\n plt.show()\",\n \"def plot_imshow(data, col_name):\\n city_x,city_y = get_city_base()\\n fig = plt.figure(figsize = FIGURE_SIZE)\\n plt.imshow(data[['lat_ind','lng_ind',col_name]], cmap=CONTOUR_PLOT_COLOUR)\\n plt.scatter(city_x,city_y, color = CITY_MAP_COLOR, s = SCATTER_SIZE_OF_CHICAGO_CITY, zorder = CITY_MAP_ORDER)\",\n \"def draw_point_gt(img,point_gt,width=3):\\n point_img=copy.deepcopy(img)\\n\\n for i in range(point_gt.shape[0]):\\n x=int(point_gt[i,0])\\n y=int(point_gt[i,1])\\n point_img=cv2.circle(point_img,center=(x,y),radius=width//2+1,color=(0,0,255),thickness=width)\\n\\n return point_img\",\n \"def displayDensePts(dense_pts, dense_slices, origin, transform, dense_displacement_all=False, dense_plot_quiver=0, timepoint=-1, ax=None):\\n\\t# If no axes were passed, generate axes\\n\\tif not ax:\\n\\t\\tfig = mplt.figure()\\n\\t\\tax = fig.add_subplot(111, projection='3d')\\n\\t\\n\\t# Pull appropriate timepoint (or set to False if displacement is undesired)\\n\\tif timepoint >= 0:\\n\\t\\ttry:\\n\\t\\t\\tdense_displacement = dense_displacement_all[timepoint]\\n\\t\\texcept(IndexError):\\n\\t\\t\\twarnings.warn('Timepoint not in range of values! Displacement will be ignored.')\\n\\t\\t\\ttimepoint = -1\\n\\t\\t\\tdense_displacement = False\\n\\t\\texcept(TypeError):\\n\\t\\t\\twarnings.warn('Displacement not passed, but timepoint requested! Displacement will be ignored.')\\n\\t\\t\\tdense_displacement = False\\n\\telse:\\n\\t\\tdense_displacement = False\\n\\t\\n\\tfor i in range(len(dense_slices)):\\n\\t\\tcur_slice = dense_slices[i]\\n\\t\\tcur_dense_pts = np.column_stack((dense_pts[i], [cur_slice - origin[2]]*dense_pts[i].shape[0]))\\n\\t\\tdata_dense = np.dot(cur_dense_pts, np.transpose(transform))\\n\\t\\tx = data_dense[:, 2]\\n\\t\\ty = data_dense[:, 1]\\n\\t\\tz = data_dense[:, 0]\\n\\t\\tax.scatter(x, y, -z, ',')\\n\\t\\t\\n\\t\\tif dense_displacement:\\n\\t\\t\\tdense_displacement_slice = dense_displacement[i]\\n\\t\\t\\tif dense_plot_quiver == 1:\\n\\t\\t\\t\\tax.quiver(x, y, -z, dense_displacement_slice[:, 0], dense_displacement_slice[:, 1], [0]*dense_displacement_slice.shape[0])\\n\\treturn(ax)\",\n \"def trimesh_from_point_cloud(cloud):\\n points = np.asarray(cloud)\\n hull = scipy.spatial.ConvexHull(points)\\n hull = scipy.spatial.ConvexHull(points[hull.vertices])\\n ru.transforms.counterclockwise_hull(hull)\\n vertices = hull.points\\n faces = hull.simplices\\n return vertices, faces\",\n \"def occupancy_grid(self, point_cloud=None, ss_pred=None, *, return_image=False, show=False, keep_showing=False):\\n\\t\\tif point_cloud is None or ss_pred is None:\\n\\t\\t\\tgen = self.loop(point_cloud=True, ss_pred=True)\\n\\t\\t\\tpoint_cloud, ss_pred = next(gen)\\n\\t\\t#TODO: Check that this function works with ZED camera\\n\\t\\tpass\",\n \"def plot(self):\\n\\n fig = plt.figure()\\n ax = fig.add_subplot(111, projection=Axes3D.name)\\n\\n # TODO Use numpy to rotate esp_points matrix for faster variable access.\\n ax.scatter(\\n xs=[i[0][0] for i in self.esp_points],\\n ys=[i[0][1] for i in self.esp_points],\\n zs=[i[0][2] for i in self.esp_points],\\n c=[i[1] for i in self.esp_points],\\n marker='o',\\n s=2,\\n alpha=0.5\\n )\\n\\n ax.scatter(\\n xs=[i[0][0] for i in self.atom_points],\\n ys=[i[0][1] for i in self.atom_points],\\n zs=[i[0][2] for i in self.atom_points],\\n c=[i[1] for i in self.atom_points],\\n marker='X',\\n s=100\\n )\\n\\n plt.show()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72636724","0.72375584","0.71609396","0.7132317","0.6933942","0.6919783","0.6877211","0.68398106","0.65854543","0.6562634","0.65615726","0.6518098","0.64886963","0.64688116","0.64599943","0.64297676","0.64172477","0.6347307","0.6338426","0.6316377","0.6249169","0.6247689","0.61805177","0.6168697","0.61648315","0.6162763","0.61433035","0.61228466","0.6120385","0.60903704","0.60887784","0.60840714","0.6073814","0.606181","0.60497266","0.60478836","0.60273826","0.60230863","0.6011479","0.5949034","0.5944727","0.5941591","0.59129876","0.5882682","0.58782804","0.58763915","0.58751667","0.58735824","0.58648884","0.58637315","0.58447856","0.5813793","0.5810271","0.58056587","0.5803301","0.57765025","0.57596034","0.57574964","0.57572305","0.57341605","0.5723177","0.57149345","0.57101357","0.57069534","0.5700627","0.56989515","0.5670351","0.5662748","0.5656756","0.56466407","0.5642417","0.5637249","0.56292343","0.5626862","0.56252885","0.5621153","0.5601514","0.55969167","0.5594165","0.5589445","0.55824107","0.55757415","0.55727154","0.5560376","0.5551333","0.5550308","0.55499405","0.5545071","0.5526621","0.55255795","0.55196786","0.55180466","0.5517805","0.551735","0.55166","0.55158687","0.55143934","0.55120367","0.55085087","0.5502027"],"string":"[\n \"0.72636724\",\n \"0.72375584\",\n \"0.71609396\",\n \"0.7132317\",\n \"0.6933942\",\n \"0.6919783\",\n \"0.6877211\",\n \"0.68398106\",\n \"0.65854543\",\n \"0.6562634\",\n \"0.65615726\",\n \"0.6518098\",\n \"0.64886963\",\n \"0.64688116\",\n \"0.64599943\",\n \"0.64297676\",\n \"0.64172477\",\n \"0.6347307\",\n \"0.6338426\",\n \"0.6316377\",\n \"0.6249169\",\n \"0.6247689\",\n \"0.61805177\",\n \"0.6168697\",\n \"0.61648315\",\n \"0.6162763\",\n \"0.61433035\",\n \"0.61228466\",\n \"0.6120385\",\n \"0.60903704\",\n \"0.60887784\",\n \"0.60840714\",\n \"0.6073814\",\n \"0.606181\",\n \"0.60497266\",\n \"0.60478836\",\n \"0.60273826\",\n \"0.60230863\",\n \"0.6011479\",\n \"0.5949034\",\n \"0.5944727\",\n \"0.5941591\",\n \"0.59129876\",\n \"0.5882682\",\n \"0.58782804\",\n \"0.58763915\",\n \"0.58751667\",\n \"0.58735824\",\n \"0.58648884\",\n \"0.58637315\",\n \"0.58447856\",\n \"0.5813793\",\n \"0.5810271\",\n \"0.58056587\",\n \"0.5803301\",\n \"0.57765025\",\n \"0.57596034\",\n \"0.57574964\",\n \"0.57572305\",\n \"0.57341605\",\n \"0.5723177\",\n \"0.57149345\",\n \"0.57101357\",\n \"0.57069534\",\n \"0.5700627\",\n \"0.56989515\",\n \"0.5670351\",\n \"0.5662748\",\n \"0.5656756\",\n \"0.56466407\",\n \"0.5642417\",\n \"0.5637249\",\n \"0.56292343\",\n \"0.5626862\",\n \"0.56252885\",\n \"0.5621153\",\n \"0.5601514\",\n \"0.55969167\",\n \"0.5594165\",\n \"0.5589445\",\n \"0.55824107\",\n \"0.55757415\",\n \"0.55727154\",\n \"0.5560376\",\n \"0.5551333\",\n \"0.5550308\",\n \"0.55499405\",\n \"0.5545071\",\n \"0.5526621\",\n \"0.55255795\",\n \"0.55196786\",\n \"0.55180466\",\n \"0.5517805\",\n \"0.551735\",\n \"0.55166\",\n \"0.55158687\",\n \"0.55143934\",\n \"0.55120367\",\n \"0.55085087\",\n \"0.5502027\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.56848264"},"document_rank":{"kind":"string","value":"66"}}},{"rowIdx":21199,"cells":{"query":{"kind":"string","value":"Show a point cloud in 2d"},"document":{"kind":"string","value":"def show_transformed_point_set(points_1, points_2, points_12, loss, save_path = None):\n\n fig = plt.figure(figsize = (6, 6))\n plt.title(\"Loss = %f\" % loss)\n plt.scatter(points_1[:, 0], points_1[:, 1], s = 5, label = \"point cloud 1\")\n plt.scatter(points_2[:, 0], points_2[:, 1], s = 5, label = \"point cloud 2\")\n plt.scatter(points_12[:, 0], points_12[:, 1], s = 5, label = \"point cloud 1 to 2\")\n plt.xlabel(\"x\")\n plt.ylabel(\"y\")\n plt.axis('equal')\n plt.grid()\n plt.legend()\n plt.show()\n \n if type(save_path) != type(None):\n fig.savefig(save_path)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def visualize_point_cloud(points, colors=None, normals=None,\n show_frame=False, frame_size=1.0, frame_origin=(0, 0, 0)):\n pc = np2pcd(points, colors, normals)\n geometries = [pc]\n if show_frame:\n coord_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=frame_size, origin=frame_origin)\n geometries.append(coord_frame)\n o3d.visualization.draw_geometries(geometries)","def render_point_cloud(frame_id):\n point_cloud_world_coordinates = get_point_cloud_world_coordinates(frame_id)\n # pptk\n v = pptk.viewer(point_cloud_world_coordinates)\n v.set(point_size=0.0001)\n v.color_map('cool', scale=[0, 5])","def create_point_cloud(self):\n pixels = []\n colors = []\n my_pixels = []\n for j in range(self.height):\n for i in range(self.width):\n depth = self.depth[j, i]\n pixels.append(\n [i * depth, j * depth, depth]\n )\n my_pixels.append(\n [i, j, 1]\n )\n # make rgb with flip()\n colors.append(np.flip(self.bgr[j, i, :]))\n # colors.append(self.bgr[j, i, :])\n self.my_pixels = my_pixels\n pixels = np.array(pixels)\n\n # project pixels to camera space\n self.xyz_points = self.intrinsics_inv @ np.transpose(pixels)\n self.color_points = colors\n\n # now add 1s to the points for homogenous coordinates\n num_points = self.get_num_xyz_points()\n ones = np.ones((1, num_points))\n self.xyzw_points = np.concatenate((self.xyz_points, ones), axis=0)\n\n self.scene = None\n self.camera_pose = None\n self.nm = None\n self.nl = None\n self.nc = None\n self.create_mesh()","def draw_pointcloud(ax, example):\n points = example['points'].cpu().detach().numpy()\n points_num = len(points)\n xs = np.empty([points_num])\n ys = np.empty([points_num])\n zs = np.empty([points_num])\n intensity = np.empty([len(points)])\n for j, point in enumerate(points):\n xs[j] = point[1]\n ys[j] = point[2]\n zs[j] = point[3]\n intensity[j] = point[4]\n\n intensity = intensity\n ax.scatter3D(xs, ys, zs, c=intensity, marker='.', s=0.3, cmap=plt.get_cmap('jet'))","def show_pointclouds(points, colors, text=[], title=\"Default\", png_path=\"\", interactive=True, orientation='horizontal'):\n\n # make sure pointclouds is a list\n assert isinstance(points, type([])), \\\n \"Pointclouds argument must be a list\"\n\n # make sure colors is a list\n assert isinstance(colors, type([])), \\\n \"Colors argument must be a list\"\n\n # make sure number of pointclouds and colors are the same\n assert len(points) == len(colors), \\\n \"Number of pointclouds (%d) is different then number of colors (%d)\" % (len(points), len(colors))\n\n while len(text) < len(points):\n text.append(\"\")\n\n # Number of pointclouds to be displayed in this window\n num_pointclouds = len(points)\n\n point_size = 10\n pointclouds = [VtkPointCloud(point_size) for _ in range(num_pointclouds)]\n renderers = [vtk.vtkRenderer() for _ in range(num_pointclouds)]\n\n height = 1.0 / max(num_pointclouds, 1)\n viewports = [(i*height, (i+1)*height) for i in range(num_pointclouds)]\n #print(viewports)\n\n # iterate over all point clouds\n for i, pc in enumerate(points):\n pc = pc.squeeze()\n co = colors[i].squeeze()\n assert pc.shape[0] == co.shape[0], \\\n \"expected same number of points (%d) then colors (%d), cloud index = %d\" % (pc.shape[0], co.shape[0], i)\n assert pc.shape[1] == 3, \"expected points to be N x 3, got N x %d\" % pc.shape[1]\n assert co.shape[1] == 3, \"expected colors to be N x 3, got N x %d\" % co.shape[1]\n\n # for each point cloud iterate over all points\n for j in range(pc.shape[0]):\n point = pc[j, :]\n color = co[j, :]\n pointclouds[i].add_point(point, color)\n\n renderers[i].AddActor(pointclouds[i].vtkActor)\n # renderers[i].AddActor(vtk.vtkAxesActor())\n renderers[i].SetBackground(1.0, 1.0, 1.0)\n if orientation == 'horizontal':\n print(viewports[i][0])\n renderers[i].SetViewport(viewports[i][0], 0.0, viewports[i][1], 1.0)\n elif orientation == 'vertical':\n renderers[i].SetViewport(0.0, viewports[i][0], 1.0, viewports[i][1])\n else:\n raise Exception('Not a valid orientation!')\n renderers[i].ResetCamera()\n\n # Add circle to first render\n renderers[0].AddActor(getActorCircle())\n renderers[0].AddActor(getActorCircle(50, 49, color=(0, 1, 0)))\n\n # Text actors\n text_actors = [vtk.vtkTextActor() for _ in text]\n for i, ta in enumerate(text_actors):\n if orientation == 'horizontal':\n ta.SetInput(' ' + text[i])\n elif orientation == 'vertical':\n ta.SetInput(text[i] + '\\n\\n\\n\\n\\n\\n')\n else:\n raise Exception('Not a valid orientation!')\n txtprop = ta.GetTextProperty()\n txtprop.SetFontFamilyToArial()\n txtprop.SetFontSize(0)\n txtprop.SetColor(0, 0, 0)\n # txtprop.SetJustificationToCentered()\n # ta.SetDisplayPosition(500, 10)\n # ta.SetAlignmentPoint()\n renderers[i].AddActor(ta)\n\n # Render Window\n render_window = vtk.vtkRenderWindow()\n for renderer in renderers:\n render_window.AddRenderer(renderer)\n\n render_window_interactor = vtk.vtkRenderWindowInteractor()\n render_window_interactor.SetInteractorStyle(vtk.vtkInteractorStyleTrackballCamera())\n render_window_interactor.SetRenderWindow(render_window)\n\n [center_x, center_y, center_z] = np.mean(points[0].squeeze(), axis=0)\n camera = vtk.vtkCamera()\n # d = 10\n # camera.SetViewUp(0, -1, 0)\n\n # camera.SetPosition(center_x + d, center_y + d, center_z + d / 2)\n # camera.SetFocalPoint(center_x, center_y, center_z)\n # camera.SetFocalPoint(0, 0, 0)\n\n camera.SetViewUp(0, 0, 1)\n if orientation == 'horizontal':\n camera.SetPosition(3, -10, 2)\n camera.SetFocalPoint(3, 1.5, 1.5)\n elif orientation == 'vertical':\n camera.SetPosition(1.5, -6, 2)\n camera.SetFocalPoint(1.5, 1.5, 1.5)\n else:\n raise Exception('Not a valid orientation!')\n\n camera.SetClippingRange(0.002, 1000)\n for renderer in renderers:\n renderer.SetActiveCamera(camera)\n\n # Begin Interaction\n render_window.Render()\n render_window.SetWindowName(title)\n if orientation == 'horizontal':\n render_window.SetSize(1940, 720)\n elif orientation == 'vertical':\n render_window.SetSize(600, 1388)\n else:\n raise Exception('Not a valid orientation!')\n\n if interactive:\n render_window_interactor.Start()\n\n if png_path:\n # screenshot code:\n w2if = vtk.vtkWindowToImageFilter()\n w2if.SetInput(render_window)\n w2if.Update()\n\n writer = vtk.vtkPNGWriter()\n writer.SetFileName(png_path)\n writer.SetInputConnection(w2if.GetOutputPort())\n writer.Write()","def make_point_cloud(self):\r\n\r\n self.pointCloud = VtkPointCloud()\r\n for k in range(np.size(self.pos, 0)):\r\n self.pointCloud.addPoint(self.pos[k, :])\r\n\r\n # Renderer\r\n renderer = vtk.vtkRenderer()\r\n renderer.AddActor(self.pointCloud.vtkActor)\r\n renderer.SetBackground(.2, .3, .4)\r\n renderer.SetBackground(0.0, 0.0, 0.0)\r\n renderer.ResetCamera()\r\n\r\n # Render Window\r\n renderWindow = vtk.vtkRenderWindow()\r\n renderWindow.AddRenderer(renderer)\r\n\r\n # Interactor\r\n renderWindowInteractor = vtk.vtkRenderWindowInteractor()\r\n renderWindowInteractor.SetRenderWindow(renderWindow)\r\n\r\n # Begin Interaction\r\n renderWindow.Render()\r\n renderWindow.SetWindowName(\"XYZ Data Viewer: \")\r\n renderWindowInteractor.Start()","def publish_point_cloud(self):\n all_points = [np.zeros((0, 2), np.float32)]\n all_keys = []\n for key in range(len(self.keyframes)):\n pose = self.keyframes[key].pose\n transf_points = self.keyframes[key].transf_points\n all_points.append(transf_points)\n all_keys.append(key * np.ones((len(transf_points), 1)))\n\n all_points = np.concatenate(all_points)\n all_keys = np.concatenate(all_keys)\n sampled_points, sampled_keys = pcl.downsample(\n all_points, all_keys, self.point_resolution\n )\n sampled_xyzi = np.c_[sampled_points, np.zeros_like(sampled_keys), sampled_keys]\n if len(sampled_xyzi) == 0:\n return\n\n if self.save_fig:\n plt.figure()\n plt.scatter(\n sampled_xyzi[:, 0], sampled_xyzi[:, 1], c=sampled_xyzi[:, 3], s=1\n )\n plt.axis(\"equal\")\n plt.gca().invert_yaxis()\n plt.savefig(\"step-{}-map.png\".format(self.current_key - 1), dpi=100)\n plt.close(\"all\")\n\n cloud_msg = n2r(sampled_xyzi, \"PointCloudXYZI\")\n cloud_msg.header.stamp = self.current_keyframe.time\n cloud_msg.header.frame_id = \"map\"\n self.cloud_pub.publish(cloud_msg)","def showPoints(surface, points):\n for point in points:\n point.show(surface)","def as_point_cloud(self):\n far = 1000.0 # max depth in meters.\n intrinsic_mat = self.camera_setup.get_intrinsic_matrix()\n width, height = self.camera_setup.width, self.camera_setup.height\n # 2d pixel coordinates\n pixel_length = width * height\n u_coord = repmat(np.r_[0:width:1], height, 1).reshape(pixel_length)\n v_coord = repmat(np.c_[0:height:1], 1, width).reshape(pixel_length)\n normalized_depth = np.reshape(self.frame, pixel_length)\n\n # p2d = [u,v,1]\n p2d = np.array([u_coord, v_coord, np.ones_like(u_coord)])\n\n # P = [X,Y,Z]\n p3d = np.dot(inv(intrinsic_mat), p2d)\n p3d *= normalized_depth * far\n\n # [[X1,Y1,Z1],[X2,Y2,Z2], ... [Xn,Yn,Zn]]\n locations = np.asarray(np.transpose(p3d))\n # Transform the points in 3D world coordinates.\n to_world_transform = self.camera_setup.get_unreal_transform()\n point_cloud = to_world_transform.transform_points(locations)\n return point_cloud","def render_point_cloud(self, point_cloud, extrinsics=Pose(), color=GRAY):\n\n combined_transform = self._bev_rotation * extrinsics\n\n pointcloud_in_bev = combined_transform * point_cloud\n point_cloud2d = pointcloud_in_bev[:, :2]\n\n point_cloud2d[:, 0] = (self._center_pixel[0] + point_cloud2d[:, 0] * self._pixels_per_meter)\n point_cloud2d[:, 1] = (self._center_pixel[1] + point_cloud2d[:, 1] * self._pixels_per_meter)\n\n H, W = self.data.shape[:2]\n uv = point_cloud2d.astype(np.int32)\n in_view = np.logical_and.reduce([\n (point_cloud2d >= 0).all(axis=1),\n point_cloud2d[:, 0] < W,\n point_cloud2d[:, 1] < H,\n ])\n uv = uv[in_view]\n self.data[uv[:, 1], uv[:, 0], :] = color","def showCrossPoints(self, surface):\n for point in self.cross_points:\n point.show(surface)","def drawPointCloud(points, ax, color=None):\n if len(points.shape) != 2 or points.shape[0] != 3:\n raise ValueError(\"'points' must be 3xN\")\n if color == None:\n color = __color_cycle.next()\n elif color in (0, 1, 2):\n color = points[color, :]\n ax.scatter(points[0,:].T, points[1,:].T, points[2,:].T, c=color)","def create_pointcloud(pts):\n depths = PointCloud()\n depths.header = std_msgs.msg.Header()\n depths.header.stamp = rospy.Time.now()\n depths.header.frame_id = \"view_zero\"\n depths.points = [None] * len(pts)\n for p in xrange(len(pts)):\n #Giving point the same orientation as the robot\n y = pts[p,0]\n z = - pts[p,1] #y in images is down\n x = pts[p,2]\n depths.points[p] = Point(x, y, z)\n return depths","def generate_pointcloud(rgb_file, mask_file,depth_file,ply_file):\n rgb = Image.open(rgb_file)\n # depth = Image.open(depth_file)\n depth = Image.open(depth_file).convert('I')\n mask = Image.open(mask_file).convert('I')\n\n # if rgb.size != depth.size:\n # raise Exception(\"Color and depth image do not have the same resolution.\")\n # if rgb.mode != \"RGB\":\n # raise Exception(\"Color image is not in RGB format\")\n # if depth.mode != \"I\":\n # raise Exception(\"Depth image is not in intensity format\")\n\n\n points = [] \n for v in range(rgb.size[1]):\n for u in range(rgb.size[0]):\n color = rgb.getpixel((u,v))\n # Z = depth.getpixel((u,v)) / scalingFactor\n # if Z==0: continue\n # X = (u - centerX) * Z / focalLength\n # Y = (v - centerY) * Z / focalLength\n if (mask.getpixel((u,v))<55):\n Z = depth.getpixel((u, v))*.22 \n if Z == 0: continue\n Y = .22 * v\n X = .22 * u\n points.append(\"%f %f %f %d %d %d 0\\n\"%(X,Y,Z,color[0],color[1],color[2]))\n file = open(ply_file,\"w\")\n file.write('''ply\nformat ascii 1.0\nelement vertex %d\nproperty float x\nproperty float y\nproperty float z\nproperty uchar red\nproperty uchar green\nproperty uchar blue\nproperty uchar alpha\nend_header\n%s\n'''%(len(points),\"\".join(points)))\n file.close()","def point_cloud(self):\n\t\tgen = self.loop(point_cloud=True)\n\t\tpoint_cloud = next(gen)\n\t\treturn point_cloud","def debug_filter_points(self, points):\n cloud_msg = PointCloud2()\n cloud_msg.header.frame_id = \"map\"\n cloud_msg.header.stamp = rospy.Time.now() \n xyz = [[p.pose.position.x, p.pose.position.y, p.pose.position.z] for p in points] \n point_cloud = pc2.create_cloud_xyz32(cloud_msg.header, xyz)\n self._points_publisher.publish(point_cloud)","def visualize_scan(self):\n fig = plt.figure()\n ax = fig.add_subplot(111, projection='3d')\n ax.scatter(self.p1_points[:, 0], self.p1_points[:, 1], self.p1_points[:, 2], c='r')\n ax.scatter(self.p2_points[:, 0], self.p2_points[:, 1], self.p2_points[:, 2], c='g')\n ax.scatter(self.p3_points[:, 0], self.p3_points[:, 1], self.p3_points[:, 2], c='b')\n ax.scatter(self.p4_points[:, 0], self.p4_points[:, 1], self.p4_points[:, 2])\n\n ax.set_xlabel('x')\n ax.set_ylabel('y')\n ax.set_zlabel('z')\n plt.show()","def visualize_in_2d(self):\n ae = Autoencoder_test(self.data)\n self.code = ae.encode(n_dimension=2, learning_rate=0.01, training_epochs=10, batch_size=400)\n for i in range(len(self.cluster)):\n list_x = []\n list_y = []\n for j in self.cluster[i]:\n list_x.append(self.code[0][j,0])\n list_y.append(self.code[0][j,1])\n plt.scatter(list_x,list_y)\n plt.show()\n return","def convertcloud(points):\n pcd = open3d.geometry.PointCloud()\n pcd.points = open3d.utility.Vector3dVector(points)\n return pcd","def voxelize(points,leaf = 0.1):\n if (type(points) == pclpy.pcl.PointCloud.PointXYZRGB):\n cloud = points\n voxel_filter = pclpy.pcl.filters.VoxelGrid.PointXYZRGB()\n filtered_pointcloud = pclpy.pcl.PointCloud.PointXYZRGB()\n else:\n cloud = pclpy.pcl.PointCloud.PointXYZ(points)\n voxel_filter = pclpy.pcl.filters.VoxelGrid.PointXYZ()\n filtered_pointcloud = pclpy.pcl.PointCloud.PointXYZ()\n \n voxel_filter.setLeafSize(leaf,leaf,leaf)\n voxel_filter.setInputCloud(cloud)\n \n voxel_filter.filter(filtered_pointcloud)\n if type(points) == pclpy.pcl.PointCloud.PointXYZRGB:\n return filtered_pointcloud\n else:\n return filtered_pointcloud.xyz","def plot(self, subsample=None, valid_instance_types=None):\n pt = self.sample_indices(subsample, valid_instance_types)\n\n x, y, z = self.pc[pt, 0], self.pc[pt, 1], self.pc[pt, 2]\n color = self.color[pt]\n\n return plot_pointcloud(x, y, z, color=color)","def show(data_set, number_points: int):\n print(f'info: Showing {number_points} as maximum.')\n sub_set_points = np.random.choice(range(data_set.shape[0]), size=min(data_set.shape[0], number_points))\n x = data_set[sub_set_points, 0]\n y = data_set[sub_set_points, 1]\n z = data_set[sub_set_points, 2]\n\n fig = plt.figure(figsize=(8, 8))\n ax = mplot3d.Axes3D(fig)\n ax.set_title('NMSLIB index 3D representation', fontsize=20)\n ax.scatter(xs=x, ys=y, zs=z)\n plt.show()","def _project_pointcloud(self, cloud):\n\n assert isinstance(cloud, PointCloud2)\n\n pc1 = PointCloud()\n pc1.header = cloud.header\n # hack the time! dont move the robot :-0\n pc1.header.stamp = rospy.Time.now()\n \n \n pc1.points = [Point32(*p) for p in pc2.read_points(cloud)]\n\n self._tf_listener.waitForTransform(pc1.header.frame_id,\n self._image_info.tf_frame, \n rospy.Time(0), \n rospy.Duration(4))\n\n image_frame_cloud = self._tf_listener.transformPointCloud (\n self._image_info.tf_frame, \n pc1)\n min_x, max_x, min_y, max_y = 640, 0, 480, 0 # TODO: remove hard coded image size!\n for pt in image_frame_cloud.points:\n u, v = self._image_info.project3dToPixel((pt.x, pt.y, pt.z))\n if v < min_y:\n min_y = int(v)\n if v > max_y:\n max_y = int(v)\n if u < min_x:\n min_x = int(u)\n if u > max_x:\n max_x = int(u)\n location = (((min_x, min_y), (max_x, max_y)))\n rospy.loginfo(\"Transformed cloud into image plane\")\n return location","def visualize(self):\n colors = {'outline': (220, 220, 220),\n 'inlier': (0, 255, 0),\n 'outlier': (0, 0, 255),\n 'lines': (128, 220, 128)}\n # Create output image for visualization\n gap = 5\n h1, w1 = self.target.image.shape[:2]\n h2, w2 = self.image.shape[:2]\n vis = np.zeros((max(h1, h2), w1 + w2 + gap, 3), np.uint8)\n vis[:h1, :w1, :] = self.target.image\n w1 += gap\n vis[:h2, w1:w1+w2, :] = self.image\n \n # Draw the located object \n quad = np.float32(self.quad) + np.float32([w1, 0])\n self.draw(vis, colors['outline'], 2, quad)\n \n # draw point details\n inliers = [(x0, y0, x1 + w1, y1) for (x0, y0), (x1, y1) in self.inliers]\n outliers = [(x0, y0, x1 + w1, y1) for (x0, y0), (x1, y1) in self.outliers]\n if colors['outlier'] is not None: # draw x on each point\n r = 2 # radius\n thickness = 2\n for x0, y0, x1, y1 in outliers:\n cv2.line(vis, (x0 - r, y0 - r), (x0 + r, y0 + r), colors['outlier'], thickness)\n cv2.line(vis, (x0 + r, y0 - r), (x0 - r, y0 + r), colors['outlier'], thickness)\n cv2.line(vis, (x1 - r, y1 - r), (x1 + r, y1 + r), colors['outlier'], thickness)\n cv2.line(vis, (x1 + r, y1 - r), (x1 - r, y1 + r), colors['outlier'], thickness)\n if colors['lines'] is not None:\n for x0, y0, x1, y1 in inliers:\n cv2.line(vis, (x0, y0), (x1, y1), colors['lines'], 1)\n if colors['inlier'] is not None:\n for x0, y0, x1, y1 in inliers:\n cv2.circle(vis, (x0, y0), 2, colors['inlier'], -1)\n cv2.circle(vis, (x1, y1), 2, colors['inlier'], -1)\n return vis","def point_cloud(self, X, Y, Z, size=1, color='#FF3232', bordercolor='#FF3232', legend='', width=0.5, opacity=1.0):\n point_cloud = go.Scatter3d(\n x=X,\n y=Y,\n z=Z,\n # showlegend=False,\n name=legend,\n mode='markers',\n marker=dict(\n size=size,\n color=color,\n line=dict(\n color=bordercolor,\n width=width\n ),\n # opacity=opacity\n )\n )\n\n return point_cloud","def visualize(self):\n self.octree.updateInnerOccupancy()\n print(\"Start Octomap Visualization\")\n\n # define parameters\n data = imgviz.data.arc2017()\n camera_info = data['camera_info']\n K = np.array(camera_info['K']).reshape(3, 3)\n width=camera_info['width']\n height=camera_info['height']\n\n # get free and occupied grid\n occupied, _ = self.octree.extractPointCloud()\n #frontier = self.gen_frontier()\n \n print(\"load point cloud\")\n window = pyglet.window.Window(\n width=int(1280), height=int(960)\n )\n\n @window.event\n def on_key_press(symbol, modifiers):\n if modifiers == 0:\n if symbol == pyglet.window.key.Q:\n window.on_close()\n\n gui = glooey.Gui(window)\n hbox = glooey.HBox()\n hbox.set_padding(5)\n\n camera = trimesh.scene.Camera(\n resolution=(width, height), focal=(K[0, 0], K[1, 1])\n )\n\n # initial camera pose\n camera_transform = np.array(\n [\n [1, 0, 0, 0],\n [0, -1, 0, 0],\n [0, 0, -1, -5],\n [0.0, 0.0, 0.0, 1.0],\n ],\n )\n\n \n\n occupied_geom = trimesh.voxel.ops.multibox(\n occupied, pitch=self.resolution, colors=[0.0, 0.0, 0.0, 0.5]\n )\n\n # frontier_geom = trimesh.voxel.ops.multibox(\n # frontier, pitch=self.resolution, colors=[1.0, 0, 0, 0.5]\n # )\n scene = trimesh.Scene(camera=camera, geometry=[occupied_geom])#, frontier_geom])\n scene.camera_transform = camera_transform\n hbox.add(self.labeled_scene_widget(scene, label='octomap'))\n\n\n gui.add(hbox)\n pyglet.app.run()","def display(self):\n scatter_plot(self.points, self.hull_points, self.color, self.title)","def vis_points(data,f1,f2):\n if np.isnan(data).any():\n return\n \n plt.scatter(data[:,f1], data[:,f2], alpha=0.2, c='b')\n plt.xlim(lims)\n plt.ylim(lims)","def show_pair_points(points, pairs, title, save_path = None):\n\n fig = plt.figure(figsize = (6, 6))\n plt.title(title)\n plt.scatter(points[:, 0], points[:, 1], c = 'b', s = 5, label = \"point cloud\")\n for pair in pairs:\n print(pair)\n point_1 = points[pair[0]]\n point_2 = points[pair[1]]\n plt.scatter([point_1[0], point_2[0]], [point_1[1], point_2[1]], s = 10)\n plt.xlabel(\"x\")\n plt.ylabel(\"y\")\n plt.axis('equal')\n plt.grid()\n plt.legend()\n plt.show()\n \n if type(save_path) != type(None):\n fig.savefig(save_path)","def show_point_set_curvature(points, curvatures, title, thresh = 0.9, save_path = None):\n dim = points.shape[1]\n assert(dim == 2), \"Only data in 2 dimension space can be visualized.\"\n selected_pts = points[np.abs(curvatures) < thresh]\n \n fig = plt.figure(figsize = (6, 6))\n plt.title(title)\n plt.scatter(points[:, 0], points[:, 1], c = 'b', s = 5, label = \"point cloud\")\n plt.scatter(selected_pts[:, 0], selected_pts[:, 1], c = 'r', s = 5, label = \"selected point cloud\")\n plt.xlabel(\"x\")\n plt.ylabel(\"y\")\n plt.axis('equal')\n plt.grid()\n plt.legend()\n plt.show()\n \n if type(save_path) != type(None):\n fig.savefig(save_path)","def show(self):\n from matplotlib import pyplot as plt\n from mpl_toolkits.mplot3d import Axes3D\n\n fig = plt.figure()\n ax = Axes3D(fig)\n pos = self.cluster.get_positions()\n from itertools import combinations\n for tri in self.mesh.simplices:\n for comb in combinations(tri, 2):\n x1 = pos[comb[0], 0]\n x2 = pos[comb[1], 0]\n y1 = pos[comb[0], 1]\n y2 = pos[comb[1], 1]\n z1 = pos[comb[0], 2]\n z2 = pos[comb[1], 2]\n ax.plot([x1, x2], [y1, y2], zs=[z1, z2], color=\"black\")\n plt.show()","def __show_source(self):\n pcd = o3d.io.read_point_cloud(\n self.source_cloud\n )\n if np.asarray(pcd.points).shape[0] != 0:\n pcd.paint_uniform_color([0, 1, 0])\n pcd.estimate_normals()\n self.source_point_cloud_view.load_cloud(pcd)\n try:\n self.source_point_cloud_view.show_window()\n except RuntimeError:\n pass\n else:\n QtWidgets.QMessageBox.warning(self, \"Error\",\n f\"Source point cloud is no longer available\"\n )\n self.source_cloud = \"\"\n self.__update_clickability()\n self.__save_context()","def open3dpaint(nppoints, color_map = 'jet', reduce_for_vis = False, voxel_size = 0.1, pointsize = 0.1):\n assert (type(nppoints) == pclpy.pcl.PointCloud.PointXYZRGB) or (type(nppoints) == pclpy.pcl.PointCloud.PointXYZ) or (type(nppoints) == np.ndarray) or (type(nppoints) is list) or (type(nppoints) is tuple), 'Not valid point_cloud'\n \n if (type(nppoints) is not list) & (type(nppoints) is not tuple):\n nppoints = [nppoints]\n try:\n visualizer = open3d.visualization.Visualizer()\n visualizer.create_window()\n options = visualizer.get_render_option()\n options.background_color = np.asarray([0, 0, 0])\n options.point_size = pointsize\n\n if len(nppoints) > 1:\n for n,i in enumerate(nppoints):\n workpoints = i\n if (type(workpoints) == pclpy.pcl.PointCloud.PointXYZRGB) or (type(workpoints) == pclpy.pcl.PointCloud.PointXYZ):\n workpoints = workpoints.xyz\n\n if reduce_for_vis:\n workpoints = seg_tree.voxelize(workpoints,voxel_size)\n\n points = convertcloud(workpoints)\n color_coef = n/len(nppoints)/2 + n%2*.5\n if type(color_map) == np.ndarray:\n color = color_map\n elif color_map == 'jet':\n color=cm.jet(color_coef)[:3]\n else:\n color=cm.Set1(color_coef)[:3]\n points.colors = open3d.utility.Vector3dVector(np.ones_like(workpoints)*color)\n #points.colors = open3d.utility.Vector3dVector(color)\n visualizer.add_geometry(points)\n else:\n workpoints = nppoints[0]\n if (type(workpoints) == pclpy.pcl.PointCloud.PointXYZRGB) or (type(workpoints) == pclpy.pcl.PointCloud.PointXYZ):\n workpoints = workpoints.xyz\n \n if reduce_for_vis:\n workpoints = seg_tree.voxelize(workpoints,voxel_size)\n points = convertcloud(workpoints)\n visualizer.add_geometry(points)\n visualizer.run()\n visualizer.destroy_window()\n \n except Exception as e:\n print(type(e))\n print(e.args)\n print(e)\n visualizer.destroy_window()","def plot(self):\n # Find only unmasked data :\n # xyz, sData, sColor, _ = self._select_unmasked()\n xyz, sData, sColor = self.xyz, self.sData, self.sColor\n\n # Render as cloud points :\n self.mesh = visu.Markers(name='Sources')\n self.mesh.set_data(xyz, edge_color=self.edgecolor, face_color=sColor,\n size=sData, scaling=self.scaling,\n edge_width=self.edgewidth, symbol=self.symbol)\n self.mesh.set_gl_state('translucent')","def show_2d_projections(self):\n\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\")\n fig, axs = plt.subplots(1, 3)\n for i in range(3):\n plt.sca(axs[i])\n io.imshow(self.image.max(i), cmap='viridis')","def show_keypoints(image, key_pts):\n plt.imshow(image)\n plt.scatter(key_pts[:, 0], key_pts[:, 1], s=20, marker='.', c='m')","def visualize(vis, features, label):\n if vis == 'PCA':\n #n_components = st.sidebar.slider(\"n_components\", 2, 10)\n #alpha = st.sidebar.slider(\"alpha\", 0.8, 2.0)\n #pca = PCA(n_components)\n pca = PCA(2)\n\n X_projected = pca.fit_transform(features)\n \n x1 = X_projected[:, 0]\n x2 = X_projected[:, 1]\n\n\n fig = plt.figure()\n plt.scatter(x1, x2, c=label, alpha=0.8, cmap='viridis')\n plt.xlabel(\"Principal Component 1\")\n plt.ylabel(\"Principal Component 2\")\n plt.colorbar()\n\n st.pyplot()","def show_feat(feat_map):\n for i in range(feat_map.shape[0]):\n plt.imshow(feat_map[i])\n plt.show()","def show_points(self, imsize):\n mask = np.zeros(imsize)\n for t in self.points:\n mask[t] = 1\n toimage(mask).show()","def visualize_clouds(clouds, paths):\n\n for cloud, path in zip(clouds, paths):\n print(\"Visualizing scans generated from:\", path)\n for scan in cloud:\n draw_geometries([scan])","def sample_and_plot(self):\n fig = plt.figure()\n ax = plt.axes(projection = '3d')\n ax.plot_surface(self.X, self.Y, self.sample(), cmap = plt.cm.jet, rstride = 2, cstride = 2, linewidth = 1)\n plt.show()","def save_3d_render(\r\n self, points: List[np.ndarray], colors: List[np.ndarray]\r\n ) -> None:\r\n pcd = o3d.geometry.PointCloud()\r\n pcd.points = o3d.utility.Vector3dVector(np.vstack(points).astype(np.float64))\r\n pcd.colors = o3d.utility.Vector3dVector(np.vstack(colors))\r\n if self.debug:\r\n o3d.visualization.draw_geometries([pcd])\r\n if not self.debug:\r\n o3d.io.write_point_cloud(f\"results/{self.filename[:-4]}.ply\", pcd)","def plotrgcloud(self):\n print self.kpunten\n for i in range(len(self.kpunten[0])):\n self.writetext('sen ='+ self.kpunten[0][i][0], (0.65,0.85), axnum = 0, hor = None ,ver = None , rot = None ,fs =14 , transform = self.fig.axes[0].transAxes)\n if i == len(self.kpunten[0]) -1 :\n end = None\n else:\n end = self.kpunten[0][i+1][1] + 1\n print end\n self.plotrgwrap( self.rgindex,2*self.reader.npair+self.rgindex,'real part of rgvars (a.u)' , 'imaginary part of rgvars (a.u.)', tit ='RG vars g = %f all states'%(self.chardata) , begin = self.kpunten[0][i][1] , stop = end , name = 'cpcloud'+ self.kpunten[0][i][0] , filenum = 0)","def __show_target(self):\n pcd = o3d.io.read_point_cloud(\n self.target_cloud\n )\n if np.asarray(pcd.points).shape[0] != 0:\n pcd.paint_uniform_color([0, 0, 1])\n pcd.estimate_normals()\n self.target_point_cloud_view.load_cloud(pcd)\n try:\n self.target_point_cloud_view.show_window()\n except RuntimeError:\n pass\n else:\n QtWidgets.QMessageBox.warning(self, \"Error\",\n f\"Target point cloud is no longer available\"\n )\n self.target_cloud = \"\"\n self.__update_clickability()\n self.__save_context()","def scatter(self, points):\n assert points.shape[0] == 3\n self.points = points\n self.remove_scatter_plot()\n x, y, z = zip(*points.T)\n self.base_fig.add_trace(go.Scatter3d(\n x=x, y=y, z=z,\n mode='markers',\n marker=dict(\n size=2,\n color=np.zeros(points.shape[1]),\n colorscale=\"RdBu\",\n colorbar=dict(\n title=\"Samples\",\n x=-0.2\n ),\n )\n ))","def mri_point_plot(self, vcol=1):\n img = self.voxels\n points = self.point_position \n ax = []\n fig = plt.figure(figsize=(9, 8))\n # TODO make this setable in the function call\n columns = 3\n rows = 2\n\n for i in range(points.shape[0]):\n im_slice = int(np.round(points[i, vcol]))\n if vcol == 0:\n im = img[im_slice, :, :]\n elif vcol == 1:\n im = img[:, im_slice, :]\n else:\n im = img[:, :, im_slice]\n ax.append( fig.add_subplot(rows, columns, i+1))\n ax[-1].set_title(\"Image depth: \"+str(im_slice)) # set title\n plt.imshow(im)\n plot_cols = np.array([0, 1, 2])\n plot_cols = plot_cols[plot_cols != vcol]\n plt.plot(points[i, min(plot_cols)], points[i, max(plot_cols)], 'ro')\n\n plt.show()","def render_radar_point_cloud(self, point_cloud, extrinsics=Pose(), color=RED, velocity=None, velocity_scale=10):\n combined_transform = self._bev_rotation * extrinsics\n\n pointcloud_in_bev = combined_transform * point_cloud\n point_cloud2d = pointcloud_in_bev[:, :2]\n\n point_cloud2d[:, 0] = (self._center_pixel[0] + point_cloud2d[:, 0] * self._pixels_per_meter)\n point_cloud2d[:, 1] = (self._center_pixel[1] + point_cloud2d[:, 1] * self._pixels_per_meter)\n\n H, W = self.data.shape[:2]\n uv = point_cloud2d.astype(np.int32)\n in_view = np.logical_and.reduce([\n (point_cloud2d >= 0).all(axis=1),\n point_cloud2d[:, 0] < W,\n point_cloud2d[:, 1] < H,\n ])\n uv = uv[in_view]\n\n for row in uv:\n cx, cy = row\n cv2.circle(self.data, (cx, cy), 7, RED, thickness=1)\n\n def clip_norm(v, x):\n M = np.linalg.norm(v)\n if M == 0:\n return v\n\n return np.clip(M, 0, x) * v / M\n\n if velocity is not None:\n tail = point_cloud + velocity_scale * velocity\n pointcloud_in_bev_tail = combined_transform * tail\n point_cloud2d_tail = pointcloud_in_bev_tail[:, :2]\n point_cloud2d_tail[:, 0] = (self._center_pixel[0] + point_cloud2d_tail[:, 0] * self._pixels_per_meter)\n point_cloud2d_tail[:, 1] = (self._center_pixel[1] + point_cloud2d_tail[:, 1] * self._pixels_per_meter)\n uv_tail = point_cloud2d_tail.astype(np.int32)\n uv_tail = uv_tail[in_view]\n for row, row_tail in zip(uv, uv_tail):\n v_2d = row_tail - row\n v_2d = clip_norm(v_2d, .025 * W)\n\n cx, cy = row\n cx2, cy2 = row + v_2d.astype(np.int)\n\n cx2 = np.clip(cx2, 0, W - 1)\n cy2 = np.clip(cy2, 0, H - 1)\n color = GREEN\n # If moving away from vehicle change the color (not strictly correct because radar is not a (0,0))\n # TODO: calculate actual radar sensor position\n if np.dot(row - np.array([W / 2, H / 2]), v_2d) > 0:\n color = (255, 110, 199)\n cv2.arrowedLine(self.data, (cx, cy), (cx2, cy2), color, thickness=1, line_type=cv2.LINE_AA)","def plot_wordcloud(word_list, file_name=\"\"):\n plt.figure(figsize=plt.figaspect(0.8), dpi=100)\n all_words = ' '.join([text for text in word_list])\n wordcloud = WordCloud(width=800, height=600, random_state=21, max_font_size=110).generate(all_words)\n plt.imshow(wordcloud, interpolation=\"bilinear\")\n plt.axis('off')\n if file_name:\n plt.savefig(file_name, bbox_inches='tight')\n plt.show()\n return","def get_point_cloud(self):\n tsdf_vol, color_vol = self.get_volume()\n\n # Marching cubes\n verts = measure.marching_cubes_lewiner(tsdf_vol, level=0)[0]\n verts_ind = np.round(verts).astype(int)\n verts = verts * self._voxel_size + self._vol_origin\n\n # Get vertex colors\n rgb_vals = color_vol[verts_ind[:, 0], verts_ind[:, 1], verts_ind[:, 2]]\n colors_b = np.floor(rgb_vals / self._color_const)\n colors_g = np.floor((rgb_vals - colors_b * self._color_const) / 256)\n colors_r = rgb_vals - colors_b * self._color_const - colors_g * 256\n colors = np.floor(np.asarray([colors_r, colors_g, colors_b])).T\n colors = colors.astype(np.uint8)\n\n pc = np.hstack([verts, colors])\n return pc","def show_point_set(points, title, save_path = None):\n dim = points.shape[1]\n assert(dim == 2 or dim == 3), \"Only data in 2 or 3 dimension space can be visualized.\"\n \n if dim == 2:\n fig = plt.figure(figsize = (6, 6))\n plt.title(title)\n plt.scatter(points[:, 0], points[:, 1], c = 'b', s = 5)\n plt.xlabel(\"x\")\n plt.ylabel(\"y\")\n plt.axis('equal')\n plt.grid()\n plt.show()\n elif dim == 3:\n fig = plt.figure(figsize = (6, 6))\n ax = fig.add_subplot(111, projection='3d')\n ax.set_title(title)\n ax.scatter(points[:, 0], points[:, 1], points[:, 2], c = 'b', s = 5)\n ax.set_xlabel(\"x\")\n ax.set_ylabel(\"y\")\n ax.set_zlabel(\"z\")\n plt.show()\n\n if type(save_path) != type(None):\n fig.savefig(save_path)","def render_points_2d(self, **kwds):\n return point2d(self.coordinates_of(self.points), **kwds)","def draw_clouds(a):\n small_cloud(0 + a, 0, 0)\n small_cloud(200 + a, -150, -10)\n big_cloud(350 + a, 0, -3)\n small_cloud(600 + a, -90, 3)\n small_cloud(800 + a, 0, 0)\n small_cloud(1000 + a, -150, -10)\n big_cloud(1150 + a, 0, -3)\n small_cloud(1400 + a, -90, 3)\n small_cloud(-800 + a, 0, 0)\n small_cloud(-600 + a, -150, -10)\n big_cloud(-450 + a, 0, -3)\n small_cloud(-200 + a, -90, 3)","def plotsite(self):\n if self.dimension==1 or self.dimension==2:\n for site in self.sites.flatten():\n plt.scatter(site.coordinate[0],site.coordinate[1],marker=site.marker,s=site.markersize,c=site.color)\n elif self.dimension==3:\n from mpl_toolkits.mplot3d import Axes3D\n fig = plt.figure()\n ax = Axes3D(fig)\n for site in self.sites.flatten():\n ax.scatter(site.coordinate[0],site.coordinate[1],site.coordinate[2],marker=site.marker,s=site.markersize,c=site.color)","def imshow_surface(self):\n plt.imshow(self.z)\n plt.colorbar()\n plt.show()","def _build_point_cloud_graph(self):\n depths = self.depths_placeholder[tf.newaxis]\n images = self.images_placeholder[tf.newaxis]\n poses = self.poses_placeholder[tf.newaxis]\n intrinsics = self.intrinsics_placeholder[tf.newaxis]\n intrinsics = intrinsics_vec_to_matrix(intrinsics)\n\n depths_pad = tf.pad(depths, [[0,0],[0,0],[0,1],[0,1]], \"CONSTANT\")\n\n depths_grad = \\\n (depths_pad[:, :, 1:, :-1] - depths_pad[:, :, :-1, :-1])**2 + \\\n (depths_pad[:, :, :-1, 1:] - depths_pad[:, :, :-1, :-1])**2\n\n # don't use large depths for point cloud and ignore boundary regions\n valid = (depths < 3.0) & (depths_grad < 0.001)\n\n batch, num, ht, wd = tf.unstack(tf.shape(depths), num=4)\n Ts = VideoSE3Transformation(matrix=poses)\n X0 = projective_ops.backproject(depths, intrinsics)\n \n # transform point cloud into world coordinates\n X1 = Ts.inv()(X0)\n\n crop_h = 12\n crop_w = 32\n\n X1 = X1[:, :, crop_h:-crop_h, crop_w:-crop_w]\n valid = valid[:, :, crop_h:-crop_h, crop_w:-crop_w]\n images = images[:, :, crop_h:-crop_h, crop_w:-crop_w, ::-1]\n \n X1 = tf.reshape(X1, [-1, 3])\n colors = tf.reshape(images, [-1, 3])\n\n valid_inds = tf.where(tf.reshape(valid, [-1]))\n valid_inds = tf.reshape(valid_inds, [-1])\n\n X1 = tf.gather(X1, valid_inds, axis=0)\n colors = tf.gather(colors, valid_inds, axis=0)\n\n self.outputs['point_cloud'] = (X1, colors)","def show_points_on_img(mask,img):\n labeled, num_objects = ndi.label(mask)\n slices = ndi.find_objects(labeled)\n x, y = [], []\n for dy,dx in slices:\n x_center = (dx.start + dx.stop - 1)/2\n x.append(x_center)\n y_center = (dy.start + dy.stop - 1)/2 \n y.append(y_center)\n plt.figure()\n plt.imshow(img)\n plt.autoscale(False)\n plt.plot(x,y, \"o\")","def draw_features(self, image):\n \n for x,y in self.new_points.reshape(-1,2):\n cv2.circle(image, (x,y), 2, (255,0,255), 2)\n return image","def show_orbits(points, nb_pairs, title, save_path = None):\n\n fig = plt.figure(figsize = (6, 6))\n plt.title(title)\n plt.scatter(points[:, 0], points[:, 1], c = 'b', s = 5, label = \"point cloud\")\n for i in range(len(nb_pairs)):\n plt.scatter(nb_pairs[i][0][:, 0], nb_pairs[i][0][:, 1], s = 5, label = \"%d_1\" % i)\n plt.scatter(nb_pairs[i][1][:, 0], nb_pairs[i][1][:, 1], s = 5, label = \"%d_2\" % i)\n plt.xlabel(\"x\")\n plt.ylabel(\"y\")\n plt.axis('equal')\n plt.grid()\n plt.legend()\n plt.show()\n \n if type(save_path) != type(None):\n fig.savefig(save_path)","def point_from_rays(self):\n print \"generating the 3d point from given clicked points\"\n \n #gather cams and points clicked \n uvs = []\n cams = []\n for iFrame in self.frames:\n if iFrame.lastClick : \n uv = numpy.multiply(iFrame.lastClick,self.reduceFactor)\n uvs.append(uv)\n cam = load_perspective_camera(self.camList[iFrame.currImg])\n cams.append(cam)\n point = get_3d_from_cams(cams, uvs)\n self.point3d = point;\n self.pointLabel.set(\"3d Point: \" + str(self.point3d))\n\n # project 3d point into each image, and gather intensities \n values = []\n ims = []\n for idx, img in enumerate(self.imgList):\n cam = load_perspective_camera(self.camList[idx])\n imgPoint = project_point(cam, point[0], point[1], point[2])\n imgPoint = numpy.divide(imgPoint, self.reduceFactor)\n self.allUVs.append(imgPoint)\n \n #grab float intensity value at this point \n imgView,ni,nj = load_image(img)\n val = pixel(imgView, imgPoint)\n if val > 0.0:\n values.append(val)\n ims.append(idx)\n \n #cleanup\n remove_from_db([imgView, cam])\n \n\n #write mean/std of intensities \n self.meanLabel.set(\"Mean: \" + str(numpy.mean(values)) )\n self.stdLabel.set(\"Std Dev: \" + str(numpy.std(values)) )\n #plot the intensities by image number \n self.f.clf();\n self.a = self.f.add_subplot(311)\n self.a.set_xlabel(\"img #\")\n self.a.set_ylabel(\"intensity\")\n self.a.plot(ims, values)\n #plot the histogram of intensities by image number \n pdf, bins, patches = plt.hist(values)\n self.b = self.f.add_subplot(313)\n self.b.set_xlabel(\"bin val\")\n self.b.set_ylabel(\"freq\")\n self.b.hist(values, 15, normed=1, facecolor=\"green\" )\n self.canvas.show();","def showAsPoints(self): \n MonkeyPatchMayaVi()\n #import enthought.mayavi.mlab as mlab\n from mayavi import mlab\n \n @mlab.show\n def _showSimple():\n morphPts = [ SVVisitorFactory.Array4AllPoints(morph)() for morph in self.morphs ]\n pts = numpy.concatenate( morphPts)\n return mlab.points3d(pts[:, 0], pts[:, 1], pts[:, 2], pts[:, 3], colormap=self.colormap, scale_factor=self.scale_factor)\n _showSimple()","def plot_surface(self):\n X, Y = np.meshgrid(self.x, self.y)\n fig = plt.figure()\n ax = fig.add_subplot(111, projection='3d')\n ax.plot_surface(X=X, Y=Y, Z=self.z)\n plt.show()","def show_trace_2d(f, results): #@save\n set_figsize()\n plt.plot(*zip(*results), '-o', color='#ff7f0e')\n x1, x2 = torch.meshgrid(torch.arange(-5.5, 1.0, 0.1),torch.arange(-3.0, 1.0, 0.1))\n plt.contour(x1, x2, f(x1, x2), colors='#1f77b4')\n plt.xlabel('x1')","def plot_2d(vector_array, save_plot_dir):\n principal_df = pd.DataFrame(data = vector_array, columns = ['pc1', 'pc2'])\n fig = plt.figure()\n ax = fig.add_subplot(111)\n\n xs = principal_df['pc1']\n ys = principal_df['pc2']\n ax.scatter(xs, ys, s=50, alpha=0.6, edgecolors='w')\n\n ax.set_xlabel('pc1')\n ax.set_ylabel('pc2')\n\n plt.savefig(save_plot_dir + '/2D_scatter.png')\n plt.close()","def show_skeletons(self, skel_2d, z_out, z_gt=None):\n fig = plt.figure(figsize=(20, 20))\n ax1 = fig.add_subplot(1, 2, 1)\n ax2 = fig.add_subplot(1, 2, 2, projection='3d')\n edges = np.array([[1, 0], [0, 2],[2, 3], [3, 4], [0, 5], [5, 6], [6, 7]])\n\n ax_2d = ax1\n ax_3d = ax2\n\n # draw 3d\n for edge in edges:\n ax_3d.plot(skel_2d[0, edge], z_out[edge], skel_2d[1, edge], color='r')\n if z_gt is not None:\n ax_3d.plot(skel_2d[0, edge], z_gt[edge], skel_2d[1, edge], color='g')\n\n ax_3d.set_aspect('equal')\n ax_3d.set_xlabel(\"x\"), ax_3d.set_ylabel(\"z\"), ax_3d.set_zlabel(\"y\")\n ax_3d.set_xlim3d([-2, 2]), ax_3d.set_ylim3d([2, -2]), ax_3d.set_zlim3d([2, -2])\n ax_3d.view_init(elev=10, azim=-45)\n\n # draw 2d\n for edge in edges:\n ax_2d.plot(skel_2d[0, edge], skel_2d[1, edge], color='r')\n\n ax_2d.set_aspect('equal')\n ax_2d.set_xlabel(\"x\"), ax_2d.set_ylabel(\"y\")\n ax_2d.set_xlim([-2, 2]), ax_2d.set_ylim([2, -2])\n\n plt.show()","def show_wordcloud(dictionary, title, min_font = 10):\n wordcloud = WordCloud(min_font_size=min_font).generate_from_frequencies(dictionary)\n plt.figure(figsize = (8, 8), facecolor = None) \n plt.imshow(wordcloud) \n plt.axis(\"off\")\n if title:\n plt.title(title)\n else:\n plt.title(\"Word Cloud\")\n plt.tight_layout(pad = 0) \n\n plt.show()","def show_point_set_normal(points, normals, title, save_path = None):\n dim = points.shape[1]\n assert(dim == 2), \"Only data in 2 dimension space can be visualized.\"\n \n fig = plt.figure(figsize = (6, 6))\n plt.title(title)\n plt.scatter(points[:, 0], points[:, 1], c = 'b', s = 5, label = \"point cloud\")\n plt.quiver(points[:, 0], points[:, 1], normals[:, 0], normals[:, 1])\n plt.xlabel(\"x\")\n plt.ylabel(\"y\")\n plt.axis('equal')\n plt.grid()\n plt.legend()\n plt.show()\n \n if type(save_path) != type(None):\n fig.savefig(save_path)","def view_point_cloud_model_and_affordance(number_of_object_per_category=5):\n list_pc_paths = [f for f in glob.glob('./dataset/*.npy', recursive=True)]\n set_objects = set([os.path.basename(pc_path).split('_')[0] for pc_path in list_pc_paths])\n\n for obj in set_objects:\n try:\n # load point cloud models\n pc_models = np.load('./dataset/{}_point_cloud_models.npy'.format(obj))[\n :number_of_object_per_category]\n # load point cloud grasp affordance\n pc_affordance = np.load('./dataset/{}_point_cloud_grasp_affordance.npy'.format(obj))[\n :number_of_object_per_category]\n\n # visualization\n for i, m in enumerate(pc_models):\n fig = plt.figure()\n ax = fig.add_subplot(projection='3d')\n list_model_x, list_model_y, list_model_z = [], [], []\n list_affordance_x, list_affordance_y, list_affordance_z = [], [], []\n\n for x in range(32):\n for y in range(32):\n for z in range(32):\n if pc_affordance[i, x, y, z] == 1:\n list_affordance_x.append(x)\n list_affordance_y.append(y)\n list_affordance_z.append(z)\n elif m[x, y, z] == 1:\n list_model_x.append(x)\n list_model_y.append(y)\n list_model_z.append(z)\n\n ax.scatter(list_model_x, list_model_y, list_model_z, c='#0c457d')\n ax.scatter(list_affordance_x, list_affordance_y, list_affordance_z, c='#e8702a', alpha=0.35)\n ax.set_xlim(0, 32)\n ax.set_ylim(0, 32)\n ax.set_zlim(0, 32)\n plt.show()\n\n except FileNotFoundError:\n print('Some point cloud npy files are not found.')\n continue","def show_scatter(self):\n plt.scatter(self.a1[:, 0], self.a1[:, 1], c=\"red\", alpha=0.5, s=10)\n plt.scatter(self.a2[:, 0], self.a2[:, 1], c=\"blue\", alpha=0.5, s=10)\n plt.scatter(0, 0, marker=\"D\", c=\"black\", alpha=0.8)\n plt.scatter(2, 2, marker=\"D\", c=\"black\", alpha=0.8)\n plt.show()","def image_to_point_cloud(depth):\n cx, cy, fx, fy = 5.8818670481438744*100, 3.1076280589210484*100, 5.8724220649505514*100, 2.2887144980135292*100\n depth = depth[:,:,0]\n\n rows, cols = depth.shape\n c, r = np.meshgrid(np.arange(cols), np.arange(rows), sparse=True)\n valid = (depth > 0) & (depth < 255)\n # print(valid)\n z = np.where(valid, depth / 256.0, np.nan)\n x = np.where(valid, z * (c - cx) / fx, 0)\n y = np.where(valid, z * (r - cy) / fy, 0)\n return np.dstack((x, y, z))","def generate_image(self,true_dist):\n N_POINTS = 128\n RANGE = 3\n\n points = np.zeros((N_POINTS, N_POINTS, 2), dtype='float32')\n points[:, :, 0] = np.linspace(-RANGE, RANGE, N_POINTS)[:, None]\n points[:, :, 1] = np.linspace(-RANGE, RANGE, N_POINTS)[None, :]\n points = points.reshape((-1, 2))\n\n plt.clf()\n\n #true_dist = true_dist.cpu().data.numpy()\n samples = self.G(self.sample_z_)\n print('generate size is',samples.size())\n samples = samples.cpu().data.numpy()\n\n x = y = np.linspace(-RANGE, RANGE, N_POINTS)\n # plt.contour(x, y, disc_map.reshape((len(x), len(y))).transpose())\n\n plt.scatter(true_dist[:, 0], true_dist[:, 1], c='orange', marker='+')\n plt.scatter(samples[:, 0], samples[:, 1], c='green', marker='+')\n plt.show()","def showAllPoints(self, surface, **kwargs):\n if not \"point_color\" in kwargs:\n kwargs[\"point_color\"] = self.point_color\n if not \"point_radius\" in kwargs:\n kwargs[\"point_radius\"] = self.point_radius\n if not \"point_mode\" in kwargs:\n kwargs[\"point_mode\"] = self.point_mode\n if not \"point_size\" in kwargs:\n kwargs[\"point_size\"] = self.point_size\n if not \"point_width\" in kwargs:\n kwargs[\"point_width\"] = self.point_width\n if not \"point_fill\" in kwargs:\n kwargs[\"point_fill\"] = self.point_fill\n for point in self.points:\n point.show(surface,\n color=kwargs[\"point_color\"],\n mode=kwargs[\"point_mode\"],\n fill=kwargs[\"point_fill\"],\n radius=kwargs[\"point_radius\"],\n size=kwargs[\"point_size\"],\n width=kwargs[\"point_width\"])","def visualize_output(self, keyframe_index):\n\n self.vis_counter += 1\n keyframe_image = self.images[keyframe_index]\n keyframe_depth = self.depths[keyframe_index]\n keyframe_pose = self.poses[keyframe_index]\n\n feed_dict = {\n self.images_placeholder: keyframe_image[np.newaxis],\n self.depths_placeholder: keyframe_depth[np.newaxis],\n self.poses_placeholder: keyframe_pose[np.newaxis],\n self.intrinsics_placeholder: self.intrinsics}\n\n keyframe_point_cloud, keyframe_point_colors = \\\n self.sess.run(self.outputs['point_cloud'], feed_dict=feed_dict)\n\n pointcloud = (keyframe_point_cloud, keyframe_point_colors)\n\n # only add the point cloud once in every 5 frames\n if self.vis_counter % 4 == 0:\n self.queue.put((pointcloud, keyframe_pose))\n \n else:\n self.queue.put((None, keyframe_pose))","def format_point_cloud(self, pc, num_points):\n resized_pc = np.zeros([num_points, 3])\n if len(pc) > num_points:\n # Randomly sample point cloud to reduce size\n resized_pc = pc[np.random.choice(pc.shape[0], num_points,\n replace=False), :]\n elif len(pc) < num_points:\n # Duplicate last point to fill point cloud. Because of the max function\n # the duplicated points will not affect the output\n resized_pc[:len(pc)] = pc\n resized_pc[len(pc):] = pc[-1]\n else:\n resized_pc = pc\n\n return resized_pc","def test_get_mesh_grid_as_point_cloud_single_pt() -> None:\n min_x = -3 # integer, minimum x-coordinate of 2D grid\n max_x = -3 # integer, maximum x-coordinate of 2D grid\n min_y = 2 # integer, minimum y-coordinate of 2D grid\n max_y = 2 # integer, maximum y-coordinate of 2D grid\n\n # return pts, a Numpy array of shape (N,2)\n pts = mesh_grid_utils.get_mesh_grid_as_point_cloud(\n min_x, max_x, min_y, max_y, downsample_factor=1.0\n )\n\n assert pts.shape == (1, 2)\n gt_pts: NDArrayFloat = np.array([[-3.0, 2.0]])\n\n assert np.allclose(gt_pts, pts)","def test_get_mesh_grid_as_point_cloud_3x2rect() -> None:\n min_x = -3 # integer, minimum x-coordinate of 2D grid\n max_x = -1 # integer, maximum x-coordinate of 2D grid\n min_y = 2 # integer, minimum y-coordinate of 2D grid\n max_y = 3 # integer, maximum y-coordinate of 2D grid\n\n # return pts, a Numpy array of shape (N,2)\n pts = mesh_grid_utils.get_mesh_grid_as_point_cloud(\n min_x, max_x, min_y, max_y, downsample_factor=1.0\n )\n\n assert pts.shape == (6, 2)\n # fmt: off\n gt_pts: NDArrayFloat = np.array(\n [\n [-3.0, 2.0],\n [-2.0, 2.0],\n [-1.0, 2.0],\n [-3.0, 3.0],\n [-2.0, 3.0],\n [-1.0, 3.0]\n ])\n # fmt: on\n assert np.allclose(gt_pts, pts)","def show_points(self, **kwargs):\n\n if hasattr(self, 'method') and hasattr(self.method, 'show_points'):\n self.method.show_points(self, **kwargs)\n else:\n figsize = kwargs.get('figsize', (15, 5))\n cmap = kwargs.get('cmap', 'gray')\n marker = kwargs.get('marker', '.')\n color = kwargs.get('color', 'r')\n fig, ax = plt.subplots(figsize=figsize)\n ax.imshow(self.mraw[0].astype(float), cmap=cmap)\n ax.scatter(self.points[:, 1], self.points[:, 0], \n marker=marker, color=color)\n plt.grid(False)\n plt.show()","def partial_visualize_in_2d(self, cluster_index=[5,12,35,44,64,75,81]):\n for i in cluster_index:\n list_x = []\n list_y = []\n for j in self.cluster[i]:\n list_x.append(self.code[0][j,0])\n list_y.append(self.code[0][j,1])\n plt.scatter(list_x,list_y, label=self.skill[self.present_skill[i]])\n plt.legend()\n plt.show()\n return","def plot_scatter_points(self):\n self.plot(1)","def view(config_file):\n import open3d as o3d\n with open(config_file) as f:\n config = json.load(f)\n scenes = get_realsense_scenes(config['realsense_dir'])\n for scene in scenes:\n # if scene['scene_name'] != \"Scene_004\":\n # continue\n scene_data = get_data_from_scene(scene)\n logger.info(\"Visualizing - %s\", scene['scene_name'])\n pcd = o3d.geometry.PointCloud(o3d.utility.Vector3dVector(scene_data['points3d']))\n o3d.visualization.draw_geometries_with_editing([pcd])\n pcd = o3d.geometry.PointCloud(o3d.utility.Vector3dVector(scene_data['points3d_segmented']))\n o3d.visualization.draw_geometries([pcd])","def plot_data(self):\n if hasattr(self,'data'):\n plt.scatter(*self.data.T)\n plt.show()\n else:\n raise Exception('No 2d data of the instance has been loaded')","def pangolin_draw(points):\n display.setup()\n\n while not display.should_quit():\n display.init_frame()\n for idx, pose in enumerate(CAMERA_POSES):\n is_last_pose = idx == len(CAMERA_POSES) - 1\n homogenous_pose = np.vstack((pose, [0, 0, 0, 1]))\n display.draw_camera(homogenous_pose, (0.0, 1.0, 1.0 if is_last_pose else 0.0))\n\n display.draw_points(points)\n\n display.finish_frame()","def plot_data(x):\n if DATA_2D:\n plt.scatter(x[:, 0], x[:, 1])\n plt.show()\n else:\n fig = plt.figure()\n ax = Axes3D(fig)\n ax.scatter(x[:, 0], x[:, 1], x[:, 2])\n ax.set_xlabel('X Label')\n ax.set_ylabel('Y Label')\n ax.set_zlabel('Z Label')\n plt.show()","def PointCloudfromStructOutput(self,file):\n print(\"Creating Structure Point Cloud\")\n xyz = self.readStructOutput(file)\n pc = np.zeros((int(len(xyz)/2.0),3))\n pc[:,0] = xyz[::2,0]*1000\n pc[:,1] = xyz[::2,1]*1000\n pc[:,2] = xyz[::2,2]*1000\n head = \"\"\"X,Y,Z\"\"\"\n np.savetxt(file, pc, delimiter=',',fmt='%.10f', header=head)\n return","def showFastCrossPoints(self, surface, color=None, mode=None, radius=None, width=None, size=None):\n points = self.crossSelf()\n if not color:\n color = self.cross_point_color\n if not mode:\n mode = self.cross_point_mode\n if not radius:\n radius = self.cross_point_radius\n if not width:\n width = self.cross_point_width\n if not size:\n size = self.cross_point_size\n for point in points:\n point.show(surface, color=color, mode=mode, radius=radius, width=width, size=size)","def plot_samples(s):\r\n assert len(s[0, :]) >= 2, ('The Phase space dimensions are less than two.', ' Need at least two to plot.')\r\n fig = plt.figure(1)\r\n if len(s[0, :]) >= 3:\r\n ax = fig.add_subplot(111, projection='3d')\r\n ax.scatter(s[:, 0], s[:, 1], s[:, 2])\r\n fig = plt.figure(2)\r\n plt.scatter(s[:, 0], s[:, 1])\r\n plt.show()","def viz1(img, interest_points, color='r'):\n\n\tx = [a[1] for a in interest_points] #blob detection x axis\n\ty = [a[0] for a in interest_points] #blob detection y axis\n\ts = [a[2] for a in interest_points] #blob detected at sigma \n \n\tplt.imshow(img, cmap='gray') #adding the input image to plot \n\tfor x, y, s in zip(x, y, s):\n\t\tplt.scatter(x, y, alpha=1, facecolors='none', edgecolors='r', s=s**2) #plotting the input interest points\n\tplt.axis('off')\n\t#plt.show() #showing the image // can be changed to saving the image locally \n\n\treturn","def show_grid(self):\n\n if not os.path.exists(self.path_to_results):\n os.mkdir(self.path_to_results)\n\n fig = plt.figure()\n\n if self.show_points == 1:\n plt.scatter(self.x_list_grid, self.y_list_grid, c='blue')\n\n plt.plot(self.x_list_main, self.y_list_main,\n 'green', label='straight path')\n plt.plot(self.x_list, self.y_list, 'red', label='first path')\n plt.plot(self.x_list_filtered, self.y_list_filtered,\n 'blue', label='filtered path')\n plt.title('Paths')\n plt.ylabel('Latitude')\n plt.xlabel('Longitude')\n # plt.legend()\n\n fig.savefig(os.path.join(self.path_to_results, 'Paths.png'))","def draw_birdseye_points(self, image, points):\n point_image = np.copy(image)\n for point in points:\n cv2.circle(point_image, point, 5, [0, 0, 255], cv2.FILLED)\n\n cv2.imshow('image1', point_image)\n cv2.waitKey(1)","def plt3dpaint(nppoints, color_map = 'jet', reduce_for_vis = True, voxel_size = 0.2, pointsize = 0.1, subplots = 5):\n assert (type(nppoints) == pclpy.pcl.PointCloud.PointXYZRGB) or (type(nppoints) == pclpy.pcl.PointCloud.PointXYZ) or (type(nppoints) == np.ndarray) or (type(nppoints) is list) or (type(nppoints) is tuple), 'Not valid point_cloud'\n cloudlist = []\n cloudcolors = []\n if (type(nppoints) is not list) & (type(nppoints) is not tuple):\n nppoints = [nppoints]\n \n if len(nppoints) > 1:\n for n,i in enumerate(nppoints):\n workpoints = i\n if (type(workpoints) == pclpy.pcl.PointCloud.PointXYZRGB) or (type(workpoints) == pclpy.pcl.PointCloud.PointXYZ):\n workpoints = workpoints.xyz\n\n if reduce_for_vis:\n workpoints = seg_tree.voxelize(workpoints,voxel_size)\n\n \n cloudmin = np.min(workpoints[:,2])\n cloudmax = np.max(workpoints[:,2])\n \n points = workpoints\n color_coef = n/len(nppoints)/2 + n%2*.5\n if type(color_map) == np.ndarray:\n color = color_map\n elif color_map == 'jet':\n color=cm.jet(color_coef)[:3]\n else:\n color=cm.Set1(color_coef)[:3]\n cloudcolors.append(np.ones_like(workpoints)*color + 0.4*(np.ones_like(workpoints) * ((workpoints[:,2] - cloudmin)/(cloudmax - cloudmin)).reshape(-1,1)-0.5) )\n cloudlist.append(points)\n else:\n workpoints = nppoints[0]\n if (type(workpoints) == pclpy.pcl.PointCloud.PointXYZRGB) or (type(workpoints) == pclpy.pcl.PointCloud.PointXYZ):\n workpoints = workpoints.xyz\n\n if reduce_for_vis:\n workpoints = seg_tree.voxelize(workpoints,voxel_size)\n cloudcolors.append(workpoints[:,2])\n cloudlist.append(workpoints)\n\n plt_pointcloud = np.concatenate(cloudlist)\n plt_colors = np.concatenate(cloudcolors)\n if len(nppoints) > 1:\n plt_colors = np.minimum(plt_colors,np.ones_like(plt_colors))\n plt_colors = np.maximum(plt_colors,np.zeros_like(plt_colors))\n fig = plt.figure(figsize=(30,16) )\n for i in range(subplots):\n ax = fig.add_subplot(1, subplots, i+1, projection='3d')\n ax.view_init(30, 360*i/subplots)\n ax.scatter3D(plt_pointcloud[:,0], plt_pointcloud[:,1], plt_pointcloud[:,2], c=plt_colors, s=pointsize)","def plot(self):\n\t\tif (2 <= len(self.X) <= 3):\n\t\t\tvDataFrame(self.name, self.cursor).scatter(columns = self.X, catcol = \"dbscan_cluster\", max_cardinality = 100, max_nb_points = 10000)\n\t\telse:\n\t\t\traise ValueError(\"Clustering Plots are only available in 2D or 3D\")","def plot(self):\n y = self.projection\n mpl.scatter(y[:, 0], y[:, 1], c=self.data_class)\n mpl.show()","def imshow(self, depth):\n layer = self.cube[depth]\n img = []\n for i in range(self.height):\n img.append([layer[i][j].value for j in range(self.width)])\n plt.imshow(img, cmap='gray')\n plt.show()","def show2d(self, data):\n self.__plot2d.clear()\n self.__plot2d.addImage(legend=\"data\", data=data)\n self.setCurrentIndex(self.__index2d)","def visualise_cppn(self, resolution=(64, 64)):\n import matplotlib.pyplot as plt\n from matplotlib.pyplot import imshow\n data = np.empty([resolution[0], resolution[1]])\n x_linspace = np.linspace(-1, 1, resolution[0])\n y_linspace = np.linspace(-1, 1, resolution[1])\n for row, x in enumerate(x_linspace):\n for col, y in enumerate(y_linspace):\n data[row, col] = self.graph(np.array([x, y, 0, 0], dtype=np.float32))[0]\n #plt.axis([-1, 1, -1, 1])\n print(data.min(), \" \", data.max())\n imshow(data, cmap='Greys', vmin=-1, vmax=1)\n plt.show()","def plot_imshow(data, col_name):\n city_x,city_y = get_city_base()\n fig = plt.figure(figsize = FIGURE_SIZE)\n plt.imshow(data[['lat_ind','lng_ind',col_name]], cmap=CONTOUR_PLOT_COLOUR)\n plt.scatter(city_x,city_y, color = CITY_MAP_COLOR, s = SCATTER_SIZE_OF_CHICAGO_CITY, zorder = CITY_MAP_ORDER)","def draw_point_gt(img,point_gt,width=3):\n point_img=copy.deepcopy(img)\n\n for i in range(point_gt.shape[0]):\n x=int(point_gt[i,0])\n y=int(point_gt[i,1])\n point_img=cv2.circle(point_img,center=(x,y),radius=width//2+1,color=(0,0,255),thickness=width)\n\n return point_img","def displayDensePts(dense_pts, dense_slices, origin, transform, dense_displacement_all=False, dense_plot_quiver=0, timepoint=-1, ax=None):\n\t# If no axes were passed, generate axes\n\tif not ax:\n\t\tfig = mplt.figure()\n\t\tax = fig.add_subplot(111, projection='3d')\n\t\n\t# Pull appropriate timepoint (or set to False if displacement is undesired)\n\tif timepoint >= 0:\n\t\ttry:\n\t\t\tdense_displacement = dense_displacement_all[timepoint]\n\t\texcept(IndexError):\n\t\t\twarnings.warn('Timepoint not in range of values! Displacement will be ignored.')\n\t\t\ttimepoint = -1\n\t\t\tdense_displacement = False\n\t\texcept(TypeError):\n\t\t\twarnings.warn('Displacement not passed, but timepoint requested! Displacement will be ignored.')\n\t\t\tdense_displacement = False\n\telse:\n\t\tdense_displacement = False\n\t\n\tfor i in range(len(dense_slices)):\n\t\tcur_slice = dense_slices[i]\n\t\tcur_dense_pts = np.column_stack((dense_pts[i], [cur_slice - origin[2]]*dense_pts[i].shape[0]))\n\t\tdata_dense = np.dot(cur_dense_pts, np.transpose(transform))\n\t\tx = data_dense[:, 2]\n\t\ty = data_dense[:, 1]\n\t\tz = data_dense[:, 0]\n\t\tax.scatter(x, y, -z, ',')\n\t\t\n\t\tif dense_displacement:\n\t\t\tdense_displacement_slice = dense_displacement[i]\n\t\t\tif dense_plot_quiver == 1:\n\t\t\t\tax.quiver(x, y, -z, dense_displacement_slice[:, 0], dense_displacement_slice[:, 1], [0]*dense_displacement_slice.shape[0])\n\treturn(ax)","def trimesh_from_point_cloud(cloud):\n points = np.asarray(cloud)\n hull = scipy.spatial.ConvexHull(points)\n hull = scipy.spatial.ConvexHull(points[hull.vertices])\n ru.transforms.counterclockwise_hull(hull)\n vertices = hull.points\n faces = hull.simplices\n return vertices, faces","def occupancy_grid(self, point_cloud=None, ss_pred=None, *, return_image=False, show=False, keep_showing=False):\n\t\tif point_cloud is None or ss_pred is None:\n\t\t\tgen = self.loop(point_cloud=True, ss_pred=True)\n\t\t\tpoint_cloud, ss_pred = next(gen)\n\t\t#TODO: Check that this function works with ZED camera\n\t\tpass","def plot(self):\n\n fig = plt.figure()\n ax = fig.add_subplot(111, projection=Axes3D.name)\n\n # TODO Use numpy to rotate esp_points matrix for faster variable access.\n ax.scatter(\n xs=[i[0][0] for i in self.esp_points],\n ys=[i[0][1] for i in self.esp_points],\n zs=[i[0][2] for i in self.esp_points],\n c=[i[1] for i in self.esp_points],\n marker='o',\n s=2,\n alpha=0.5\n )\n\n ax.scatter(\n xs=[i[0][0] for i in self.atom_points],\n ys=[i[0][1] for i in self.atom_points],\n zs=[i[0][2] for i in self.atom_points],\n c=[i[1] for i in self.atom_points],\n marker='X',\n s=100\n )\n\n plt.show()"],"string":"[\n \"def visualize_point_cloud(points, colors=None, normals=None,\\n show_frame=False, frame_size=1.0, frame_origin=(0, 0, 0)):\\n pc = np2pcd(points, colors, normals)\\n geometries = [pc]\\n if show_frame:\\n coord_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=frame_size, origin=frame_origin)\\n geometries.append(coord_frame)\\n o3d.visualization.draw_geometries(geometries)\",\n \"def render_point_cloud(frame_id):\\n point_cloud_world_coordinates = get_point_cloud_world_coordinates(frame_id)\\n # pptk\\n v = pptk.viewer(point_cloud_world_coordinates)\\n v.set(point_size=0.0001)\\n v.color_map('cool', scale=[0, 5])\",\n \"def create_point_cloud(self):\\n pixels = []\\n colors = []\\n my_pixels = []\\n for j in range(self.height):\\n for i in range(self.width):\\n depth = self.depth[j, i]\\n pixels.append(\\n [i * depth, j * depth, depth]\\n )\\n my_pixels.append(\\n [i, j, 1]\\n )\\n # make rgb with flip()\\n colors.append(np.flip(self.bgr[j, i, :]))\\n # colors.append(self.bgr[j, i, :])\\n self.my_pixels = my_pixels\\n pixels = np.array(pixels)\\n\\n # project pixels to camera space\\n self.xyz_points = self.intrinsics_inv @ np.transpose(pixels)\\n self.color_points = colors\\n\\n # now add 1s to the points for homogenous coordinates\\n num_points = self.get_num_xyz_points()\\n ones = np.ones((1, num_points))\\n self.xyzw_points = np.concatenate((self.xyz_points, ones), axis=0)\\n\\n self.scene = None\\n self.camera_pose = None\\n self.nm = None\\n self.nl = None\\n self.nc = None\\n self.create_mesh()\",\n \"def draw_pointcloud(ax, example):\\n points = example['points'].cpu().detach().numpy()\\n points_num = len(points)\\n xs = np.empty([points_num])\\n ys = np.empty([points_num])\\n zs = np.empty([points_num])\\n intensity = np.empty([len(points)])\\n for j, point in enumerate(points):\\n xs[j] = point[1]\\n ys[j] = point[2]\\n zs[j] = point[3]\\n intensity[j] = point[4]\\n\\n intensity = intensity\\n ax.scatter3D(xs, ys, zs, c=intensity, marker='.', s=0.3, cmap=plt.get_cmap('jet'))\",\n \"def show_pointclouds(points, colors, text=[], title=\\\"Default\\\", png_path=\\\"\\\", interactive=True, orientation='horizontal'):\\n\\n # make sure pointclouds is a list\\n assert isinstance(points, type([])), \\\\\\n \\\"Pointclouds argument must be a list\\\"\\n\\n # make sure colors is a list\\n assert isinstance(colors, type([])), \\\\\\n \\\"Colors argument must be a list\\\"\\n\\n # make sure number of pointclouds and colors are the same\\n assert len(points) == len(colors), \\\\\\n \\\"Number of pointclouds (%d) is different then number of colors (%d)\\\" % (len(points), len(colors))\\n\\n while len(text) < len(points):\\n text.append(\\\"\\\")\\n\\n # Number of pointclouds to be displayed in this window\\n num_pointclouds = len(points)\\n\\n point_size = 10\\n pointclouds = [VtkPointCloud(point_size) for _ in range(num_pointclouds)]\\n renderers = [vtk.vtkRenderer() for _ in range(num_pointclouds)]\\n\\n height = 1.0 / max(num_pointclouds, 1)\\n viewports = [(i*height, (i+1)*height) for i in range(num_pointclouds)]\\n #print(viewports)\\n\\n # iterate over all point clouds\\n for i, pc in enumerate(points):\\n pc = pc.squeeze()\\n co = colors[i].squeeze()\\n assert pc.shape[0] == co.shape[0], \\\\\\n \\\"expected same number of points (%d) then colors (%d), cloud index = %d\\\" % (pc.shape[0], co.shape[0], i)\\n assert pc.shape[1] == 3, \\\"expected points to be N x 3, got N x %d\\\" % pc.shape[1]\\n assert co.shape[1] == 3, \\\"expected colors to be N x 3, got N x %d\\\" % co.shape[1]\\n\\n # for each point cloud iterate over all points\\n for j in range(pc.shape[0]):\\n point = pc[j, :]\\n color = co[j, :]\\n pointclouds[i].add_point(point, color)\\n\\n renderers[i].AddActor(pointclouds[i].vtkActor)\\n # renderers[i].AddActor(vtk.vtkAxesActor())\\n renderers[i].SetBackground(1.0, 1.0, 1.0)\\n if orientation == 'horizontal':\\n print(viewports[i][0])\\n renderers[i].SetViewport(viewports[i][0], 0.0, viewports[i][1], 1.0)\\n elif orientation == 'vertical':\\n renderers[i].SetViewport(0.0, viewports[i][0], 1.0, viewports[i][1])\\n else:\\n raise Exception('Not a valid orientation!')\\n renderers[i].ResetCamera()\\n\\n # Add circle to first render\\n renderers[0].AddActor(getActorCircle())\\n renderers[0].AddActor(getActorCircle(50, 49, color=(0, 1, 0)))\\n\\n # Text actors\\n text_actors = [vtk.vtkTextActor() for _ in text]\\n for i, ta in enumerate(text_actors):\\n if orientation == 'horizontal':\\n ta.SetInput(' ' + text[i])\\n elif orientation == 'vertical':\\n ta.SetInput(text[i] + '\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n')\\n else:\\n raise Exception('Not a valid orientation!')\\n txtprop = ta.GetTextProperty()\\n txtprop.SetFontFamilyToArial()\\n txtprop.SetFontSize(0)\\n txtprop.SetColor(0, 0, 0)\\n # txtprop.SetJustificationToCentered()\\n # ta.SetDisplayPosition(500, 10)\\n # ta.SetAlignmentPoint()\\n renderers[i].AddActor(ta)\\n\\n # Render Window\\n render_window = vtk.vtkRenderWindow()\\n for renderer in renderers:\\n render_window.AddRenderer(renderer)\\n\\n render_window_interactor = vtk.vtkRenderWindowInteractor()\\n render_window_interactor.SetInteractorStyle(vtk.vtkInteractorStyleTrackballCamera())\\n render_window_interactor.SetRenderWindow(render_window)\\n\\n [center_x, center_y, center_z] = np.mean(points[0].squeeze(), axis=0)\\n camera = vtk.vtkCamera()\\n # d = 10\\n # camera.SetViewUp(0, -1, 0)\\n\\n # camera.SetPosition(center_x + d, center_y + d, center_z + d / 2)\\n # camera.SetFocalPoint(center_x, center_y, center_z)\\n # camera.SetFocalPoint(0, 0, 0)\\n\\n camera.SetViewUp(0, 0, 1)\\n if orientation == 'horizontal':\\n camera.SetPosition(3, -10, 2)\\n camera.SetFocalPoint(3, 1.5, 1.5)\\n elif orientation == 'vertical':\\n camera.SetPosition(1.5, -6, 2)\\n camera.SetFocalPoint(1.5, 1.5, 1.5)\\n else:\\n raise Exception('Not a valid orientation!')\\n\\n camera.SetClippingRange(0.002, 1000)\\n for renderer in renderers:\\n renderer.SetActiveCamera(camera)\\n\\n # Begin Interaction\\n render_window.Render()\\n render_window.SetWindowName(title)\\n if orientation == 'horizontal':\\n render_window.SetSize(1940, 720)\\n elif orientation == 'vertical':\\n render_window.SetSize(600, 1388)\\n else:\\n raise Exception('Not a valid orientation!')\\n\\n if interactive:\\n render_window_interactor.Start()\\n\\n if png_path:\\n # screenshot code:\\n w2if = vtk.vtkWindowToImageFilter()\\n w2if.SetInput(render_window)\\n w2if.Update()\\n\\n writer = vtk.vtkPNGWriter()\\n writer.SetFileName(png_path)\\n writer.SetInputConnection(w2if.GetOutputPort())\\n writer.Write()\",\n \"def make_point_cloud(self):\\r\\n\\r\\n self.pointCloud = VtkPointCloud()\\r\\n for k in range(np.size(self.pos, 0)):\\r\\n self.pointCloud.addPoint(self.pos[k, :])\\r\\n\\r\\n # Renderer\\r\\n renderer = vtk.vtkRenderer()\\r\\n renderer.AddActor(self.pointCloud.vtkActor)\\r\\n renderer.SetBackground(.2, .3, .4)\\r\\n renderer.SetBackground(0.0, 0.0, 0.0)\\r\\n renderer.ResetCamera()\\r\\n\\r\\n # Render Window\\r\\n renderWindow = vtk.vtkRenderWindow()\\r\\n renderWindow.AddRenderer(renderer)\\r\\n\\r\\n # Interactor\\r\\n renderWindowInteractor = vtk.vtkRenderWindowInteractor()\\r\\n renderWindowInteractor.SetRenderWindow(renderWindow)\\r\\n\\r\\n # Begin Interaction\\r\\n renderWindow.Render()\\r\\n renderWindow.SetWindowName(\\\"XYZ Data Viewer: \\\")\\r\\n renderWindowInteractor.Start()\",\n \"def publish_point_cloud(self):\\n all_points = [np.zeros((0, 2), np.float32)]\\n all_keys = []\\n for key in range(len(self.keyframes)):\\n pose = self.keyframes[key].pose\\n transf_points = self.keyframes[key].transf_points\\n all_points.append(transf_points)\\n all_keys.append(key * np.ones((len(transf_points), 1)))\\n\\n all_points = np.concatenate(all_points)\\n all_keys = np.concatenate(all_keys)\\n sampled_points, sampled_keys = pcl.downsample(\\n all_points, all_keys, self.point_resolution\\n )\\n sampled_xyzi = np.c_[sampled_points, np.zeros_like(sampled_keys), sampled_keys]\\n if len(sampled_xyzi) == 0:\\n return\\n\\n if self.save_fig:\\n plt.figure()\\n plt.scatter(\\n sampled_xyzi[:, 0], sampled_xyzi[:, 1], c=sampled_xyzi[:, 3], s=1\\n )\\n plt.axis(\\\"equal\\\")\\n plt.gca().invert_yaxis()\\n plt.savefig(\\\"step-{}-map.png\\\".format(self.current_key - 1), dpi=100)\\n plt.close(\\\"all\\\")\\n\\n cloud_msg = n2r(sampled_xyzi, \\\"PointCloudXYZI\\\")\\n cloud_msg.header.stamp = self.current_keyframe.time\\n cloud_msg.header.frame_id = \\\"map\\\"\\n self.cloud_pub.publish(cloud_msg)\",\n \"def showPoints(surface, points):\\n for point in points:\\n point.show(surface)\",\n \"def as_point_cloud(self):\\n far = 1000.0 # max depth in meters.\\n intrinsic_mat = self.camera_setup.get_intrinsic_matrix()\\n width, height = self.camera_setup.width, self.camera_setup.height\\n # 2d pixel coordinates\\n pixel_length = width * height\\n u_coord = repmat(np.r_[0:width:1], height, 1).reshape(pixel_length)\\n v_coord = repmat(np.c_[0:height:1], 1, width).reshape(pixel_length)\\n normalized_depth = np.reshape(self.frame, pixel_length)\\n\\n # p2d = [u,v,1]\\n p2d = np.array([u_coord, v_coord, np.ones_like(u_coord)])\\n\\n # P = [X,Y,Z]\\n p3d = np.dot(inv(intrinsic_mat), p2d)\\n p3d *= normalized_depth * far\\n\\n # [[X1,Y1,Z1],[X2,Y2,Z2], ... [Xn,Yn,Zn]]\\n locations = np.asarray(np.transpose(p3d))\\n # Transform the points in 3D world coordinates.\\n to_world_transform = self.camera_setup.get_unreal_transform()\\n point_cloud = to_world_transform.transform_points(locations)\\n return point_cloud\",\n \"def render_point_cloud(self, point_cloud, extrinsics=Pose(), color=GRAY):\\n\\n combined_transform = self._bev_rotation * extrinsics\\n\\n pointcloud_in_bev = combined_transform * point_cloud\\n point_cloud2d = pointcloud_in_bev[:, :2]\\n\\n point_cloud2d[:, 0] = (self._center_pixel[0] + point_cloud2d[:, 0] * self._pixels_per_meter)\\n point_cloud2d[:, 1] = (self._center_pixel[1] + point_cloud2d[:, 1] * self._pixels_per_meter)\\n\\n H, W = self.data.shape[:2]\\n uv = point_cloud2d.astype(np.int32)\\n in_view = np.logical_and.reduce([\\n (point_cloud2d >= 0).all(axis=1),\\n point_cloud2d[:, 0] < W,\\n point_cloud2d[:, 1] < H,\\n ])\\n uv = uv[in_view]\\n self.data[uv[:, 1], uv[:, 0], :] = color\",\n \"def showCrossPoints(self, surface):\\n for point in self.cross_points:\\n point.show(surface)\",\n \"def drawPointCloud(points, ax, color=None):\\n if len(points.shape) != 2 or points.shape[0] != 3:\\n raise ValueError(\\\"'points' must be 3xN\\\")\\n if color == None:\\n color = __color_cycle.next()\\n elif color in (0, 1, 2):\\n color = points[color, :]\\n ax.scatter(points[0,:].T, points[1,:].T, points[2,:].T, c=color)\",\n \"def create_pointcloud(pts):\\n depths = PointCloud()\\n depths.header = std_msgs.msg.Header()\\n depths.header.stamp = rospy.Time.now()\\n depths.header.frame_id = \\\"view_zero\\\"\\n depths.points = [None] * len(pts)\\n for p in xrange(len(pts)):\\n #Giving point the same orientation as the robot\\n y = pts[p,0]\\n z = - pts[p,1] #y in images is down\\n x = pts[p,2]\\n depths.points[p] = Point(x, y, z)\\n return depths\",\n \"def generate_pointcloud(rgb_file, mask_file,depth_file,ply_file):\\n rgb = Image.open(rgb_file)\\n # depth = Image.open(depth_file)\\n depth = Image.open(depth_file).convert('I')\\n mask = Image.open(mask_file).convert('I')\\n\\n # if rgb.size != depth.size:\\n # raise Exception(\\\"Color and depth image do not have the same resolution.\\\")\\n # if rgb.mode != \\\"RGB\\\":\\n # raise Exception(\\\"Color image is not in RGB format\\\")\\n # if depth.mode != \\\"I\\\":\\n # raise Exception(\\\"Depth image is not in intensity format\\\")\\n\\n\\n points = [] \\n for v in range(rgb.size[1]):\\n for u in range(rgb.size[0]):\\n color = rgb.getpixel((u,v))\\n # Z = depth.getpixel((u,v)) / scalingFactor\\n # if Z==0: continue\\n # X = (u - centerX) * Z / focalLength\\n # Y = (v - centerY) * Z / focalLength\\n if (mask.getpixel((u,v))<55):\\n Z = depth.getpixel((u, v))*.22 \\n if Z == 0: continue\\n Y = .22 * v\\n X = .22 * u\\n points.append(\\\"%f %f %f %d %d %d 0\\\\n\\\"%(X,Y,Z,color[0],color[1],color[2]))\\n file = open(ply_file,\\\"w\\\")\\n file.write('''ply\\nformat ascii 1.0\\nelement vertex %d\\nproperty float x\\nproperty float y\\nproperty float z\\nproperty uchar red\\nproperty uchar green\\nproperty uchar blue\\nproperty uchar alpha\\nend_header\\n%s\\n'''%(len(points),\\\"\\\".join(points)))\\n file.close()\",\n \"def point_cloud(self):\\n\\t\\tgen = self.loop(point_cloud=True)\\n\\t\\tpoint_cloud = next(gen)\\n\\t\\treturn point_cloud\",\n \"def debug_filter_points(self, points):\\n cloud_msg = PointCloud2()\\n cloud_msg.header.frame_id = \\\"map\\\"\\n cloud_msg.header.stamp = rospy.Time.now() \\n xyz = [[p.pose.position.x, p.pose.position.y, p.pose.position.z] for p in points] \\n point_cloud = pc2.create_cloud_xyz32(cloud_msg.header, xyz)\\n self._points_publisher.publish(point_cloud)\",\n \"def visualize_scan(self):\\n fig = plt.figure()\\n ax = fig.add_subplot(111, projection='3d')\\n ax.scatter(self.p1_points[:, 0], self.p1_points[:, 1], self.p1_points[:, 2], c='r')\\n ax.scatter(self.p2_points[:, 0], self.p2_points[:, 1], self.p2_points[:, 2], c='g')\\n ax.scatter(self.p3_points[:, 0], self.p3_points[:, 1], self.p3_points[:, 2], c='b')\\n ax.scatter(self.p4_points[:, 0], self.p4_points[:, 1], self.p4_points[:, 2])\\n\\n ax.set_xlabel('x')\\n ax.set_ylabel('y')\\n ax.set_zlabel('z')\\n plt.show()\",\n \"def visualize_in_2d(self):\\n ae = Autoencoder_test(self.data)\\n self.code = ae.encode(n_dimension=2, learning_rate=0.01, training_epochs=10, batch_size=400)\\n for i in range(len(self.cluster)):\\n list_x = []\\n list_y = []\\n for j in self.cluster[i]:\\n list_x.append(self.code[0][j,0])\\n list_y.append(self.code[0][j,1])\\n plt.scatter(list_x,list_y)\\n plt.show()\\n return\",\n \"def convertcloud(points):\\n pcd = open3d.geometry.PointCloud()\\n pcd.points = open3d.utility.Vector3dVector(points)\\n return pcd\",\n \"def voxelize(points,leaf = 0.1):\\n if (type(points) == pclpy.pcl.PointCloud.PointXYZRGB):\\n cloud = points\\n voxel_filter = pclpy.pcl.filters.VoxelGrid.PointXYZRGB()\\n filtered_pointcloud = pclpy.pcl.PointCloud.PointXYZRGB()\\n else:\\n cloud = pclpy.pcl.PointCloud.PointXYZ(points)\\n voxel_filter = pclpy.pcl.filters.VoxelGrid.PointXYZ()\\n filtered_pointcloud = pclpy.pcl.PointCloud.PointXYZ()\\n \\n voxel_filter.setLeafSize(leaf,leaf,leaf)\\n voxel_filter.setInputCloud(cloud)\\n \\n voxel_filter.filter(filtered_pointcloud)\\n if type(points) == pclpy.pcl.PointCloud.PointXYZRGB:\\n return filtered_pointcloud\\n else:\\n return filtered_pointcloud.xyz\",\n \"def plot(self, subsample=None, valid_instance_types=None):\\n pt = self.sample_indices(subsample, valid_instance_types)\\n\\n x, y, z = self.pc[pt, 0], self.pc[pt, 1], self.pc[pt, 2]\\n color = self.color[pt]\\n\\n return plot_pointcloud(x, y, z, color=color)\",\n \"def show(data_set, number_points: int):\\n print(f'info: Showing {number_points} as maximum.')\\n sub_set_points = np.random.choice(range(data_set.shape[0]), size=min(data_set.shape[0], number_points))\\n x = data_set[sub_set_points, 0]\\n y = data_set[sub_set_points, 1]\\n z = data_set[sub_set_points, 2]\\n\\n fig = plt.figure(figsize=(8, 8))\\n ax = mplot3d.Axes3D(fig)\\n ax.set_title('NMSLIB index 3D representation', fontsize=20)\\n ax.scatter(xs=x, ys=y, zs=z)\\n plt.show()\",\n \"def _project_pointcloud(self, cloud):\\n\\n assert isinstance(cloud, PointCloud2)\\n\\n pc1 = PointCloud()\\n pc1.header = cloud.header\\n # hack the time! dont move the robot :-0\\n pc1.header.stamp = rospy.Time.now()\\n \\n \\n pc1.points = [Point32(*p) for p in pc2.read_points(cloud)]\\n\\n self._tf_listener.waitForTransform(pc1.header.frame_id,\\n self._image_info.tf_frame, \\n rospy.Time(0), \\n rospy.Duration(4))\\n\\n image_frame_cloud = self._tf_listener.transformPointCloud (\\n self._image_info.tf_frame, \\n pc1)\\n min_x, max_x, min_y, max_y = 640, 0, 480, 0 # TODO: remove hard coded image size!\\n for pt in image_frame_cloud.points:\\n u, v = self._image_info.project3dToPixel((pt.x, pt.y, pt.z))\\n if v < min_y:\\n min_y = int(v)\\n if v > max_y:\\n max_y = int(v)\\n if u < min_x:\\n min_x = int(u)\\n if u > max_x:\\n max_x = int(u)\\n location = (((min_x, min_y), (max_x, max_y)))\\n rospy.loginfo(\\\"Transformed cloud into image plane\\\")\\n return location\",\n \"def visualize(self):\\n colors = {'outline': (220, 220, 220),\\n 'inlier': (0, 255, 0),\\n 'outlier': (0, 0, 255),\\n 'lines': (128, 220, 128)}\\n # Create output image for visualization\\n gap = 5\\n h1, w1 = self.target.image.shape[:2]\\n h2, w2 = self.image.shape[:2]\\n vis = np.zeros((max(h1, h2), w1 + w2 + gap, 3), np.uint8)\\n vis[:h1, :w1, :] = self.target.image\\n w1 += gap\\n vis[:h2, w1:w1+w2, :] = self.image\\n \\n # Draw the located object \\n quad = np.float32(self.quad) + np.float32([w1, 0])\\n self.draw(vis, colors['outline'], 2, quad)\\n \\n # draw point details\\n inliers = [(x0, y0, x1 + w1, y1) for (x0, y0), (x1, y1) in self.inliers]\\n outliers = [(x0, y0, x1 + w1, y1) for (x0, y0), (x1, y1) in self.outliers]\\n if colors['outlier'] is not None: # draw x on each point\\n r = 2 # radius\\n thickness = 2\\n for x0, y0, x1, y1 in outliers:\\n cv2.line(vis, (x0 - r, y0 - r), (x0 + r, y0 + r), colors['outlier'], thickness)\\n cv2.line(vis, (x0 + r, y0 - r), (x0 - r, y0 + r), colors['outlier'], thickness)\\n cv2.line(vis, (x1 - r, y1 - r), (x1 + r, y1 + r), colors['outlier'], thickness)\\n cv2.line(vis, (x1 + r, y1 - r), (x1 - r, y1 + r), colors['outlier'], thickness)\\n if colors['lines'] is not None:\\n for x0, y0, x1, y1 in inliers:\\n cv2.line(vis, (x0, y0), (x1, y1), colors['lines'], 1)\\n if colors['inlier'] is not None:\\n for x0, y0, x1, y1 in inliers:\\n cv2.circle(vis, (x0, y0), 2, colors['inlier'], -1)\\n cv2.circle(vis, (x1, y1), 2, colors['inlier'], -1)\\n return vis\",\n \"def point_cloud(self, X, Y, Z, size=1, color='#FF3232', bordercolor='#FF3232', legend='', width=0.5, opacity=1.0):\\n point_cloud = go.Scatter3d(\\n x=X,\\n y=Y,\\n z=Z,\\n # showlegend=False,\\n name=legend,\\n mode='markers',\\n marker=dict(\\n size=size,\\n color=color,\\n line=dict(\\n color=bordercolor,\\n width=width\\n ),\\n # opacity=opacity\\n )\\n )\\n\\n return point_cloud\",\n \"def visualize(self):\\n self.octree.updateInnerOccupancy()\\n print(\\\"Start Octomap Visualization\\\")\\n\\n # define parameters\\n data = imgviz.data.arc2017()\\n camera_info = data['camera_info']\\n K = np.array(camera_info['K']).reshape(3, 3)\\n width=camera_info['width']\\n height=camera_info['height']\\n\\n # get free and occupied grid\\n occupied, _ = self.octree.extractPointCloud()\\n #frontier = self.gen_frontier()\\n \\n print(\\\"load point cloud\\\")\\n window = pyglet.window.Window(\\n width=int(1280), height=int(960)\\n )\\n\\n @window.event\\n def on_key_press(symbol, modifiers):\\n if modifiers == 0:\\n if symbol == pyglet.window.key.Q:\\n window.on_close()\\n\\n gui = glooey.Gui(window)\\n hbox = glooey.HBox()\\n hbox.set_padding(5)\\n\\n camera = trimesh.scene.Camera(\\n resolution=(width, height), focal=(K[0, 0], K[1, 1])\\n )\\n\\n # initial camera pose\\n camera_transform = np.array(\\n [\\n [1, 0, 0, 0],\\n [0, -1, 0, 0],\\n [0, 0, -1, -5],\\n [0.0, 0.0, 0.0, 1.0],\\n ],\\n )\\n\\n \\n\\n occupied_geom = trimesh.voxel.ops.multibox(\\n occupied, pitch=self.resolution, colors=[0.0, 0.0, 0.0, 0.5]\\n )\\n\\n # frontier_geom = trimesh.voxel.ops.multibox(\\n # frontier, pitch=self.resolution, colors=[1.0, 0, 0, 0.5]\\n # )\\n scene = trimesh.Scene(camera=camera, geometry=[occupied_geom])#, frontier_geom])\\n scene.camera_transform = camera_transform\\n hbox.add(self.labeled_scene_widget(scene, label='octomap'))\\n\\n\\n gui.add(hbox)\\n pyglet.app.run()\",\n \"def display(self):\\n scatter_plot(self.points, self.hull_points, self.color, self.title)\",\n \"def vis_points(data,f1,f2):\\n if np.isnan(data).any():\\n return\\n \\n plt.scatter(data[:,f1], data[:,f2], alpha=0.2, c='b')\\n plt.xlim(lims)\\n plt.ylim(lims)\",\n \"def show_pair_points(points, pairs, title, save_path = None):\\n\\n fig = plt.figure(figsize = (6, 6))\\n plt.title(title)\\n plt.scatter(points[:, 0], points[:, 1], c = 'b', s = 5, label = \\\"point cloud\\\")\\n for pair in pairs:\\n print(pair)\\n point_1 = points[pair[0]]\\n point_2 = points[pair[1]]\\n plt.scatter([point_1[0], point_2[0]], [point_1[1], point_2[1]], s = 10)\\n plt.xlabel(\\\"x\\\")\\n plt.ylabel(\\\"y\\\")\\n plt.axis('equal')\\n plt.grid()\\n plt.legend()\\n plt.show()\\n \\n if type(save_path) != type(None):\\n fig.savefig(save_path)\",\n \"def show_point_set_curvature(points, curvatures, title, thresh = 0.9, save_path = None):\\n dim = points.shape[1]\\n assert(dim == 2), \\\"Only data in 2 dimension space can be visualized.\\\"\\n selected_pts = points[np.abs(curvatures) < thresh]\\n \\n fig = plt.figure(figsize = (6, 6))\\n plt.title(title)\\n plt.scatter(points[:, 0], points[:, 1], c = 'b', s = 5, label = \\\"point cloud\\\")\\n plt.scatter(selected_pts[:, 0], selected_pts[:, 1], c = 'r', s = 5, label = \\\"selected point cloud\\\")\\n plt.xlabel(\\\"x\\\")\\n plt.ylabel(\\\"y\\\")\\n plt.axis('equal')\\n plt.grid()\\n plt.legend()\\n plt.show()\\n \\n if type(save_path) != type(None):\\n fig.savefig(save_path)\",\n \"def show(self):\\n from matplotlib import pyplot as plt\\n from mpl_toolkits.mplot3d import Axes3D\\n\\n fig = plt.figure()\\n ax = Axes3D(fig)\\n pos = self.cluster.get_positions()\\n from itertools import combinations\\n for tri in self.mesh.simplices:\\n for comb in combinations(tri, 2):\\n x1 = pos[comb[0], 0]\\n x2 = pos[comb[1], 0]\\n y1 = pos[comb[0], 1]\\n y2 = pos[comb[1], 1]\\n z1 = pos[comb[0], 2]\\n z2 = pos[comb[1], 2]\\n ax.plot([x1, x2], [y1, y2], zs=[z1, z2], color=\\\"black\\\")\\n plt.show()\",\n \"def __show_source(self):\\n pcd = o3d.io.read_point_cloud(\\n self.source_cloud\\n )\\n if np.asarray(pcd.points).shape[0] != 0:\\n pcd.paint_uniform_color([0, 1, 0])\\n pcd.estimate_normals()\\n self.source_point_cloud_view.load_cloud(pcd)\\n try:\\n self.source_point_cloud_view.show_window()\\n except RuntimeError:\\n pass\\n else:\\n QtWidgets.QMessageBox.warning(self, \\\"Error\\\",\\n f\\\"Source point cloud is no longer available\\\"\\n )\\n self.source_cloud = \\\"\\\"\\n self.__update_clickability()\\n self.__save_context()\",\n \"def open3dpaint(nppoints, color_map = 'jet', reduce_for_vis = False, voxel_size = 0.1, pointsize = 0.1):\\n assert (type(nppoints) == pclpy.pcl.PointCloud.PointXYZRGB) or (type(nppoints) == pclpy.pcl.PointCloud.PointXYZ) or (type(nppoints) == np.ndarray) or (type(nppoints) is list) or (type(nppoints) is tuple), 'Not valid point_cloud'\\n \\n if (type(nppoints) is not list) & (type(nppoints) is not tuple):\\n nppoints = [nppoints]\\n try:\\n visualizer = open3d.visualization.Visualizer()\\n visualizer.create_window()\\n options = visualizer.get_render_option()\\n options.background_color = np.asarray([0, 0, 0])\\n options.point_size = pointsize\\n\\n if len(nppoints) > 1:\\n for n,i in enumerate(nppoints):\\n workpoints = i\\n if (type(workpoints) == pclpy.pcl.PointCloud.PointXYZRGB) or (type(workpoints) == pclpy.pcl.PointCloud.PointXYZ):\\n workpoints = workpoints.xyz\\n\\n if reduce_for_vis:\\n workpoints = seg_tree.voxelize(workpoints,voxel_size)\\n\\n points = convertcloud(workpoints)\\n color_coef = n/len(nppoints)/2 + n%2*.5\\n if type(color_map) == np.ndarray:\\n color = color_map\\n elif color_map == 'jet':\\n color=cm.jet(color_coef)[:3]\\n else:\\n color=cm.Set1(color_coef)[:3]\\n points.colors = open3d.utility.Vector3dVector(np.ones_like(workpoints)*color)\\n #points.colors = open3d.utility.Vector3dVector(color)\\n visualizer.add_geometry(points)\\n else:\\n workpoints = nppoints[0]\\n if (type(workpoints) == pclpy.pcl.PointCloud.PointXYZRGB) or (type(workpoints) == pclpy.pcl.PointCloud.PointXYZ):\\n workpoints = workpoints.xyz\\n \\n if reduce_for_vis:\\n workpoints = seg_tree.voxelize(workpoints,voxel_size)\\n points = convertcloud(workpoints)\\n visualizer.add_geometry(points)\\n visualizer.run()\\n visualizer.destroy_window()\\n \\n except Exception as e:\\n print(type(e))\\n print(e.args)\\n print(e)\\n visualizer.destroy_window()\",\n \"def plot(self):\\n # Find only unmasked data :\\n # xyz, sData, sColor, _ = self._select_unmasked()\\n xyz, sData, sColor = self.xyz, self.sData, self.sColor\\n\\n # Render as cloud points :\\n self.mesh = visu.Markers(name='Sources')\\n self.mesh.set_data(xyz, edge_color=self.edgecolor, face_color=sColor,\\n size=sData, scaling=self.scaling,\\n edge_width=self.edgewidth, symbol=self.symbol)\\n self.mesh.set_gl_state('translucent')\",\n \"def show_2d_projections(self):\\n\\n with warnings.catch_warnings():\\n warnings.simplefilter(\\\"ignore\\\")\\n fig, axs = plt.subplots(1, 3)\\n for i in range(3):\\n plt.sca(axs[i])\\n io.imshow(self.image.max(i), cmap='viridis')\",\n \"def show_keypoints(image, key_pts):\\n plt.imshow(image)\\n plt.scatter(key_pts[:, 0], key_pts[:, 1], s=20, marker='.', c='m')\",\n \"def visualize(vis, features, label):\\n if vis == 'PCA':\\n #n_components = st.sidebar.slider(\\\"n_components\\\", 2, 10)\\n #alpha = st.sidebar.slider(\\\"alpha\\\", 0.8, 2.0)\\n #pca = PCA(n_components)\\n pca = PCA(2)\\n\\n X_projected = pca.fit_transform(features)\\n \\n x1 = X_projected[:, 0]\\n x2 = X_projected[:, 1]\\n\\n\\n fig = plt.figure()\\n plt.scatter(x1, x2, c=label, alpha=0.8, cmap='viridis')\\n plt.xlabel(\\\"Principal Component 1\\\")\\n plt.ylabel(\\\"Principal Component 2\\\")\\n plt.colorbar()\\n\\n st.pyplot()\",\n \"def show_feat(feat_map):\\n for i in range(feat_map.shape[0]):\\n plt.imshow(feat_map[i])\\n plt.show()\",\n \"def show_points(self, imsize):\\n mask = np.zeros(imsize)\\n for t in self.points:\\n mask[t] = 1\\n toimage(mask).show()\",\n \"def visualize_clouds(clouds, paths):\\n\\n for cloud, path in zip(clouds, paths):\\n print(\\\"Visualizing scans generated from:\\\", path)\\n for scan in cloud:\\n draw_geometries([scan])\",\n \"def sample_and_plot(self):\\n fig = plt.figure()\\n ax = plt.axes(projection = '3d')\\n ax.plot_surface(self.X, self.Y, self.sample(), cmap = plt.cm.jet, rstride = 2, cstride = 2, linewidth = 1)\\n plt.show()\",\n \"def save_3d_render(\\r\\n self, points: List[np.ndarray], colors: List[np.ndarray]\\r\\n ) -> None:\\r\\n pcd = o3d.geometry.PointCloud()\\r\\n pcd.points = o3d.utility.Vector3dVector(np.vstack(points).astype(np.float64))\\r\\n pcd.colors = o3d.utility.Vector3dVector(np.vstack(colors))\\r\\n if self.debug:\\r\\n o3d.visualization.draw_geometries([pcd])\\r\\n if not self.debug:\\r\\n o3d.io.write_point_cloud(f\\\"results/{self.filename[:-4]}.ply\\\", pcd)\",\n \"def plotrgcloud(self):\\n print self.kpunten\\n for i in range(len(self.kpunten[0])):\\n self.writetext('sen ='+ self.kpunten[0][i][0], (0.65,0.85), axnum = 0, hor = None ,ver = None , rot = None ,fs =14 , transform = self.fig.axes[0].transAxes)\\n if i == len(self.kpunten[0]) -1 :\\n end = None\\n else:\\n end = self.kpunten[0][i+1][1] + 1\\n print end\\n self.plotrgwrap( self.rgindex,2*self.reader.npair+self.rgindex,'real part of rgvars (a.u)' , 'imaginary part of rgvars (a.u.)', tit ='RG vars g = %f all states'%(self.chardata) , begin = self.kpunten[0][i][1] , stop = end , name = 'cpcloud'+ self.kpunten[0][i][0] , filenum = 0)\",\n \"def __show_target(self):\\n pcd = o3d.io.read_point_cloud(\\n self.target_cloud\\n )\\n if np.asarray(pcd.points).shape[0] != 0:\\n pcd.paint_uniform_color([0, 0, 1])\\n pcd.estimate_normals()\\n self.target_point_cloud_view.load_cloud(pcd)\\n try:\\n self.target_point_cloud_view.show_window()\\n except RuntimeError:\\n pass\\n else:\\n QtWidgets.QMessageBox.warning(self, \\\"Error\\\",\\n f\\\"Target point cloud is no longer available\\\"\\n )\\n self.target_cloud = \\\"\\\"\\n self.__update_clickability()\\n self.__save_context()\",\n \"def scatter(self, points):\\n assert points.shape[0] == 3\\n self.points = points\\n self.remove_scatter_plot()\\n x, y, z = zip(*points.T)\\n self.base_fig.add_trace(go.Scatter3d(\\n x=x, y=y, z=z,\\n mode='markers',\\n marker=dict(\\n size=2,\\n color=np.zeros(points.shape[1]),\\n colorscale=\\\"RdBu\\\",\\n colorbar=dict(\\n title=\\\"Samples\\\",\\n x=-0.2\\n ),\\n )\\n ))\",\n \"def mri_point_plot(self, vcol=1):\\n img = self.voxels\\n points = self.point_position \\n ax = []\\n fig = plt.figure(figsize=(9, 8))\\n # TODO make this setable in the function call\\n columns = 3\\n rows = 2\\n\\n for i in range(points.shape[0]):\\n im_slice = int(np.round(points[i, vcol]))\\n if vcol == 0:\\n im = img[im_slice, :, :]\\n elif vcol == 1:\\n im = img[:, im_slice, :]\\n else:\\n im = img[:, :, im_slice]\\n ax.append( fig.add_subplot(rows, columns, i+1))\\n ax[-1].set_title(\\\"Image depth: \\\"+str(im_slice)) # set title\\n plt.imshow(im)\\n plot_cols = np.array([0, 1, 2])\\n plot_cols = plot_cols[plot_cols != vcol]\\n plt.plot(points[i, min(plot_cols)], points[i, max(plot_cols)], 'ro')\\n\\n plt.show()\",\n \"def render_radar_point_cloud(self, point_cloud, extrinsics=Pose(), color=RED, velocity=None, velocity_scale=10):\\n combined_transform = self._bev_rotation * extrinsics\\n\\n pointcloud_in_bev = combined_transform * point_cloud\\n point_cloud2d = pointcloud_in_bev[:, :2]\\n\\n point_cloud2d[:, 0] = (self._center_pixel[0] + point_cloud2d[:, 0] * self._pixels_per_meter)\\n point_cloud2d[:, 1] = (self._center_pixel[1] + point_cloud2d[:, 1] * self._pixels_per_meter)\\n\\n H, W = self.data.shape[:2]\\n uv = point_cloud2d.astype(np.int32)\\n in_view = np.logical_and.reduce([\\n (point_cloud2d >= 0).all(axis=1),\\n point_cloud2d[:, 0] < W,\\n point_cloud2d[:, 1] < H,\\n ])\\n uv = uv[in_view]\\n\\n for row in uv:\\n cx, cy = row\\n cv2.circle(self.data, (cx, cy), 7, RED, thickness=1)\\n\\n def clip_norm(v, x):\\n M = np.linalg.norm(v)\\n if M == 0:\\n return v\\n\\n return np.clip(M, 0, x) * v / M\\n\\n if velocity is not None:\\n tail = point_cloud + velocity_scale * velocity\\n pointcloud_in_bev_tail = combined_transform * tail\\n point_cloud2d_tail = pointcloud_in_bev_tail[:, :2]\\n point_cloud2d_tail[:, 0] = (self._center_pixel[0] + point_cloud2d_tail[:, 0] * self._pixels_per_meter)\\n point_cloud2d_tail[:, 1] = (self._center_pixel[1] + point_cloud2d_tail[:, 1] * self._pixels_per_meter)\\n uv_tail = point_cloud2d_tail.astype(np.int32)\\n uv_tail = uv_tail[in_view]\\n for row, row_tail in zip(uv, uv_tail):\\n v_2d = row_tail - row\\n v_2d = clip_norm(v_2d, .025 * W)\\n\\n cx, cy = row\\n cx2, cy2 = row + v_2d.astype(np.int)\\n\\n cx2 = np.clip(cx2, 0, W - 1)\\n cy2 = np.clip(cy2, 0, H - 1)\\n color = GREEN\\n # If moving away from vehicle change the color (not strictly correct because radar is not a (0,0))\\n # TODO: calculate actual radar sensor position\\n if np.dot(row - np.array([W / 2, H / 2]), v_2d) > 0:\\n color = (255, 110, 199)\\n cv2.arrowedLine(self.data, (cx, cy), (cx2, cy2), color, thickness=1, line_type=cv2.LINE_AA)\",\n \"def plot_wordcloud(word_list, file_name=\\\"\\\"):\\n plt.figure(figsize=plt.figaspect(0.8), dpi=100)\\n all_words = ' '.join([text for text in word_list])\\n wordcloud = WordCloud(width=800, height=600, random_state=21, max_font_size=110).generate(all_words)\\n plt.imshow(wordcloud, interpolation=\\\"bilinear\\\")\\n plt.axis('off')\\n if file_name:\\n plt.savefig(file_name, bbox_inches='tight')\\n plt.show()\\n return\",\n \"def get_point_cloud(self):\\n tsdf_vol, color_vol = self.get_volume()\\n\\n # Marching cubes\\n verts = measure.marching_cubes_lewiner(tsdf_vol, level=0)[0]\\n verts_ind = np.round(verts).astype(int)\\n verts = verts * self._voxel_size + self._vol_origin\\n\\n # Get vertex colors\\n rgb_vals = color_vol[verts_ind[:, 0], verts_ind[:, 1], verts_ind[:, 2]]\\n colors_b = np.floor(rgb_vals / self._color_const)\\n colors_g = np.floor((rgb_vals - colors_b * self._color_const) / 256)\\n colors_r = rgb_vals - colors_b * self._color_const - colors_g * 256\\n colors = np.floor(np.asarray([colors_r, colors_g, colors_b])).T\\n colors = colors.astype(np.uint8)\\n\\n pc = np.hstack([verts, colors])\\n return pc\",\n \"def show_point_set(points, title, save_path = None):\\n dim = points.shape[1]\\n assert(dim == 2 or dim == 3), \\\"Only data in 2 or 3 dimension space can be visualized.\\\"\\n \\n if dim == 2:\\n fig = plt.figure(figsize = (6, 6))\\n plt.title(title)\\n plt.scatter(points[:, 0], points[:, 1], c = 'b', s = 5)\\n plt.xlabel(\\\"x\\\")\\n plt.ylabel(\\\"y\\\")\\n plt.axis('equal')\\n plt.grid()\\n plt.show()\\n elif dim == 3:\\n fig = plt.figure(figsize = (6, 6))\\n ax = fig.add_subplot(111, projection='3d')\\n ax.set_title(title)\\n ax.scatter(points[:, 0], points[:, 1], points[:, 2], c = 'b', s = 5)\\n ax.set_xlabel(\\\"x\\\")\\n ax.set_ylabel(\\\"y\\\")\\n ax.set_zlabel(\\\"z\\\")\\n plt.show()\\n\\n if type(save_path) != type(None):\\n fig.savefig(save_path)\",\n \"def render_points_2d(self, **kwds):\\n return point2d(self.coordinates_of(self.points), **kwds)\",\n \"def draw_clouds(a):\\n small_cloud(0 + a, 0, 0)\\n small_cloud(200 + a, -150, -10)\\n big_cloud(350 + a, 0, -3)\\n small_cloud(600 + a, -90, 3)\\n small_cloud(800 + a, 0, 0)\\n small_cloud(1000 + a, -150, -10)\\n big_cloud(1150 + a, 0, -3)\\n small_cloud(1400 + a, -90, 3)\\n small_cloud(-800 + a, 0, 0)\\n small_cloud(-600 + a, -150, -10)\\n big_cloud(-450 + a, 0, -3)\\n small_cloud(-200 + a, -90, 3)\",\n \"def plotsite(self):\\n if self.dimension==1 or self.dimension==2:\\n for site in self.sites.flatten():\\n plt.scatter(site.coordinate[0],site.coordinate[1],marker=site.marker,s=site.markersize,c=site.color)\\n elif self.dimension==3:\\n from mpl_toolkits.mplot3d import Axes3D\\n fig = plt.figure()\\n ax = Axes3D(fig)\\n for site in self.sites.flatten():\\n ax.scatter(site.coordinate[0],site.coordinate[1],site.coordinate[2],marker=site.marker,s=site.markersize,c=site.color)\",\n \"def imshow_surface(self):\\n plt.imshow(self.z)\\n plt.colorbar()\\n plt.show()\",\n \"def _build_point_cloud_graph(self):\\n depths = self.depths_placeholder[tf.newaxis]\\n images = self.images_placeholder[tf.newaxis]\\n poses = self.poses_placeholder[tf.newaxis]\\n intrinsics = self.intrinsics_placeholder[tf.newaxis]\\n intrinsics = intrinsics_vec_to_matrix(intrinsics)\\n\\n depths_pad = tf.pad(depths, [[0,0],[0,0],[0,1],[0,1]], \\\"CONSTANT\\\")\\n\\n depths_grad = \\\\\\n (depths_pad[:, :, 1:, :-1] - depths_pad[:, :, :-1, :-1])**2 + \\\\\\n (depths_pad[:, :, :-1, 1:] - depths_pad[:, :, :-1, :-1])**2\\n\\n # don't use large depths for point cloud and ignore boundary regions\\n valid = (depths < 3.0) & (depths_grad < 0.001)\\n\\n batch, num, ht, wd = tf.unstack(tf.shape(depths), num=4)\\n Ts = VideoSE3Transformation(matrix=poses)\\n X0 = projective_ops.backproject(depths, intrinsics)\\n \\n # transform point cloud into world coordinates\\n X1 = Ts.inv()(X0)\\n\\n crop_h = 12\\n crop_w = 32\\n\\n X1 = X1[:, :, crop_h:-crop_h, crop_w:-crop_w]\\n valid = valid[:, :, crop_h:-crop_h, crop_w:-crop_w]\\n images = images[:, :, crop_h:-crop_h, crop_w:-crop_w, ::-1]\\n \\n X1 = tf.reshape(X1, [-1, 3])\\n colors = tf.reshape(images, [-1, 3])\\n\\n valid_inds = tf.where(tf.reshape(valid, [-1]))\\n valid_inds = tf.reshape(valid_inds, [-1])\\n\\n X1 = tf.gather(X1, valid_inds, axis=0)\\n colors = tf.gather(colors, valid_inds, axis=0)\\n\\n self.outputs['point_cloud'] = (X1, colors)\",\n \"def show_points_on_img(mask,img):\\n labeled, num_objects = ndi.label(mask)\\n slices = ndi.find_objects(labeled)\\n x, y = [], []\\n for dy,dx in slices:\\n x_center = (dx.start + dx.stop - 1)/2\\n x.append(x_center)\\n y_center = (dy.start + dy.stop - 1)/2 \\n y.append(y_center)\\n plt.figure()\\n plt.imshow(img)\\n plt.autoscale(False)\\n plt.plot(x,y, \\\"o\\\")\",\n \"def draw_features(self, image):\\n \\n for x,y in self.new_points.reshape(-1,2):\\n cv2.circle(image, (x,y), 2, (255,0,255), 2)\\n return image\",\n \"def show_orbits(points, nb_pairs, title, save_path = None):\\n\\n fig = plt.figure(figsize = (6, 6))\\n plt.title(title)\\n plt.scatter(points[:, 0], points[:, 1], c = 'b', s = 5, label = \\\"point cloud\\\")\\n for i in range(len(nb_pairs)):\\n plt.scatter(nb_pairs[i][0][:, 0], nb_pairs[i][0][:, 1], s = 5, label = \\\"%d_1\\\" % i)\\n plt.scatter(nb_pairs[i][1][:, 0], nb_pairs[i][1][:, 1], s = 5, label = \\\"%d_2\\\" % i)\\n plt.xlabel(\\\"x\\\")\\n plt.ylabel(\\\"y\\\")\\n plt.axis('equal')\\n plt.grid()\\n plt.legend()\\n plt.show()\\n \\n if type(save_path) != type(None):\\n fig.savefig(save_path)\",\n \"def point_from_rays(self):\\n print \\\"generating the 3d point from given clicked points\\\"\\n \\n #gather cams and points clicked \\n uvs = []\\n cams = []\\n for iFrame in self.frames:\\n if iFrame.lastClick : \\n uv = numpy.multiply(iFrame.lastClick,self.reduceFactor)\\n uvs.append(uv)\\n cam = load_perspective_camera(self.camList[iFrame.currImg])\\n cams.append(cam)\\n point = get_3d_from_cams(cams, uvs)\\n self.point3d = point;\\n self.pointLabel.set(\\\"3d Point: \\\" + str(self.point3d))\\n\\n # project 3d point into each image, and gather intensities \\n values = []\\n ims = []\\n for idx, img in enumerate(self.imgList):\\n cam = load_perspective_camera(self.camList[idx])\\n imgPoint = project_point(cam, point[0], point[1], point[2])\\n imgPoint = numpy.divide(imgPoint, self.reduceFactor)\\n self.allUVs.append(imgPoint)\\n \\n #grab float intensity value at this point \\n imgView,ni,nj = load_image(img)\\n val = pixel(imgView, imgPoint)\\n if val > 0.0:\\n values.append(val)\\n ims.append(idx)\\n \\n #cleanup\\n remove_from_db([imgView, cam])\\n \\n\\n #write mean/std of intensities \\n self.meanLabel.set(\\\"Mean: \\\" + str(numpy.mean(values)) )\\n self.stdLabel.set(\\\"Std Dev: \\\" + str(numpy.std(values)) )\\n #plot the intensities by image number \\n self.f.clf();\\n self.a = self.f.add_subplot(311)\\n self.a.set_xlabel(\\\"img #\\\")\\n self.a.set_ylabel(\\\"intensity\\\")\\n self.a.plot(ims, values)\\n #plot the histogram of intensities by image number \\n pdf, bins, patches = plt.hist(values)\\n self.b = self.f.add_subplot(313)\\n self.b.set_xlabel(\\\"bin val\\\")\\n self.b.set_ylabel(\\\"freq\\\")\\n self.b.hist(values, 15, normed=1, facecolor=\\\"green\\\" )\\n self.canvas.show();\",\n \"def showAsPoints(self): \\n MonkeyPatchMayaVi()\\n #import enthought.mayavi.mlab as mlab\\n from mayavi import mlab\\n \\n @mlab.show\\n def _showSimple():\\n morphPts = [ SVVisitorFactory.Array4AllPoints(morph)() for morph in self.morphs ]\\n pts = numpy.concatenate( morphPts)\\n return mlab.points3d(pts[:, 0], pts[:, 1], pts[:, 2], pts[:, 3], colormap=self.colormap, scale_factor=self.scale_factor)\\n _showSimple()\",\n \"def plot_surface(self):\\n X, Y = np.meshgrid(self.x, self.y)\\n fig = plt.figure()\\n ax = fig.add_subplot(111, projection='3d')\\n ax.plot_surface(X=X, Y=Y, Z=self.z)\\n plt.show()\",\n \"def show_trace_2d(f, results): #@save\\n set_figsize()\\n plt.plot(*zip(*results), '-o', color='#ff7f0e')\\n x1, x2 = torch.meshgrid(torch.arange(-5.5, 1.0, 0.1),torch.arange(-3.0, 1.0, 0.1))\\n plt.contour(x1, x2, f(x1, x2), colors='#1f77b4')\\n plt.xlabel('x1')\",\n \"def plot_2d(vector_array, save_plot_dir):\\n principal_df = pd.DataFrame(data = vector_array, columns = ['pc1', 'pc2'])\\n fig = plt.figure()\\n ax = fig.add_subplot(111)\\n\\n xs = principal_df['pc1']\\n ys = principal_df['pc2']\\n ax.scatter(xs, ys, s=50, alpha=0.6, edgecolors='w')\\n\\n ax.set_xlabel('pc1')\\n ax.set_ylabel('pc2')\\n\\n plt.savefig(save_plot_dir + '/2D_scatter.png')\\n plt.close()\",\n \"def show_skeletons(self, skel_2d, z_out, z_gt=None):\\n fig = plt.figure(figsize=(20, 20))\\n ax1 = fig.add_subplot(1, 2, 1)\\n ax2 = fig.add_subplot(1, 2, 2, projection='3d')\\n edges = np.array([[1, 0], [0, 2],[2, 3], [3, 4], [0, 5], [5, 6], [6, 7]])\\n\\n ax_2d = ax1\\n ax_3d = ax2\\n\\n # draw 3d\\n for edge in edges:\\n ax_3d.plot(skel_2d[0, edge], z_out[edge], skel_2d[1, edge], color='r')\\n if z_gt is not None:\\n ax_3d.plot(skel_2d[0, edge], z_gt[edge], skel_2d[1, edge], color='g')\\n\\n ax_3d.set_aspect('equal')\\n ax_3d.set_xlabel(\\\"x\\\"), ax_3d.set_ylabel(\\\"z\\\"), ax_3d.set_zlabel(\\\"y\\\")\\n ax_3d.set_xlim3d([-2, 2]), ax_3d.set_ylim3d([2, -2]), ax_3d.set_zlim3d([2, -2])\\n ax_3d.view_init(elev=10, azim=-45)\\n\\n # draw 2d\\n for edge in edges:\\n ax_2d.plot(skel_2d[0, edge], skel_2d[1, edge], color='r')\\n\\n ax_2d.set_aspect('equal')\\n ax_2d.set_xlabel(\\\"x\\\"), ax_2d.set_ylabel(\\\"y\\\")\\n ax_2d.set_xlim([-2, 2]), ax_2d.set_ylim([2, -2])\\n\\n plt.show()\",\n \"def show_wordcloud(dictionary, title, min_font = 10):\\n wordcloud = WordCloud(min_font_size=min_font).generate_from_frequencies(dictionary)\\n plt.figure(figsize = (8, 8), facecolor = None) \\n plt.imshow(wordcloud) \\n plt.axis(\\\"off\\\")\\n if title:\\n plt.title(title)\\n else:\\n plt.title(\\\"Word Cloud\\\")\\n plt.tight_layout(pad = 0) \\n\\n plt.show()\",\n \"def show_point_set_normal(points, normals, title, save_path = None):\\n dim = points.shape[1]\\n assert(dim == 2), \\\"Only data in 2 dimension space can be visualized.\\\"\\n \\n fig = plt.figure(figsize = (6, 6))\\n plt.title(title)\\n plt.scatter(points[:, 0], points[:, 1], c = 'b', s = 5, label = \\\"point cloud\\\")\\n plt.quiver(points[:, 0], points[:, 1], normals[:, 0], normals[:, 1])\\n plt.xlabel(\\\"x\\\")\\n plt.ylabel(\\\"y\\\")\\n plt.axis('equal')\\n plt.grid()\\n plt.legend()\\n plt.show()\\n \\n if type(save_path) != type(None):\\n fig.savefig(save_path)\",\n \"def view_point_cloud_model_and_affordance(number_of_object_per_category=5):\\n list_pc_paths = [f for f in glob.glob('./dataset/*.npy', recursive=True)]\\n set_objects = set([os.path.basename(pc_path).split('_')[0] for pc_path in list_pc_paths])\\n\\n for obj in set_objects:\\n try:\\n # load point cloud models\\n pc_models = np.load('./dataset/{}_point_cloud_models.npy'.format(obj))[\\n :number_of_object_per_category]\\n # load point cloud grasp affordance\\n pc_affordance = np.load('./dataset/{}_point_cloud_grasp_affordance.npy'.format(obj))[\\n :number_of_object_per_category]\\n\\n # visualization\\n for i, m in enumerate(pc_models):\\n fig = plt.figure()\\n ax = fig.add_subplot(projection='3d')\\n list_model_x, list_model_y, list_model_z = [], [], []\\n list_affordance_x, list_affordance_y, list_affordance_z = [], [], []\\n\\n for x in range(32):\\n for y in range(32):\\n for z in range(32):\\n if pc_affordance[i, x, y, z] == 1:\\n list_affordance_x.append(x)\\n list_affordance_y.append(y)\\n list_affordance_z.append(z)\\n elif m[x, y, z] == 1:\\n list_model_x.append(x)\\n list_model_y.append(y)\\n list_model_z.append(z)\\n\\n ax.scatter(list_model_x, list_model_y, list_model_z, c='#0c457d')\\n ax.scatter(list_affordance_x, list_affordance_y, list_affordance_z, c='#e8702a', alpha=0.35)\\n ax.set_xlim(0, 32)\\n ax.set_ylim(0, 32)\\n ax.set_zlim(0, 32)\\n plt.show()\\n\\n except FileNotFoundError:\\n print('Some point cloud npy files are not found.')\\n continue\",\n \"def show_scatter(self):\\n plt.scatter(self.a1[:, 0], self.a1[:, 1], c=\\\"red\\\", alpha=0.5, s=10)\\n plt.scatter(self.a2[:, 0], self.a2[:, 1], c=\\\"blue\\\", alpha=0.5, s=10)\\n plt.scatter(0, 0, marker=\\\"D\\\", c=\\\"black\\\", alpha=0.8)\\n plt.scatter(2, 2, marker=\\\"D\\\", c=\\\"black\\\", alpha=0.8)\\n plt.show()\",\n \"def image_to_point_cloud(depth):\\n cx, cy, fx, fy = 5.8818670481438744*100, 3.1076280589210484*100, 5.8724220649505514*100, 2.2887144980135292*100\\n depth = depth[:,:,0]\\n\\n rows, cols = depth.shape\\n c, r = np.meshgrid(np.arange(cols), np.arange(rows), sparse=True)\\n valid = (depth > 0) & (depth < 255)\\n # print(valid)\\n z = np.where(valid, depth / 256.0, np.nan)\\n x = np.where(valid, z * (c - cx) / fx, 0)\\n y = np.where(valid, z * (r - cy) / fy, 0)\\n return np.dstack((x, y, z))\",\n \"def generate_image(self,true_dist):\\n N_POINTS = 128\\n RANGE = 3\\n\\n points = np.zeros((N_POINTS, N_POINTS, 2), dtype='float32')\\n points[:, :, 0] = np.linspace(-RANGE, RANGE, N_POINTS)[:, None]\\n points[:, :, 1] = np.linspace(-RANGE, RANGE, N_POINTS)[None, :]\\n points = points.reshape((-1, 2))\\n\\n plt.clf()\\n\\n #true_dist = true_dist.cpu().data.numpy()\\n samples = self.G(self.sample_z_)\\n print('generate size is',samples.size())\\n samples = samples.cpu().data.numpy()\\n\\n x = y = np.linspace(-RANGE, RANGE, N_POINTS)\\n # plt.contour(x, y, disc_map.reshape((len(x), len(y))).transpose())\\n\\n plt.scatter(true_dist[:, 0], true_dist[:, 1], c='orange', marker='+')\\n plt.scatter(samples[:, 0], samples[:, 1], c='green', marker='+')\\n plt.show()\",\n \"def showAllPoints(self, surface, **kwargs):\\n if not \\\"point_color\\\" in kwargs:\\n kwargs[\\\"point_color\\\"] = self.point_color\\n if not \\\"point_radius\\\" in kwargs:\\n kwargs[\\\"point_radius\\\"] = self.point_radius\\n if not \\\"point_mode\\\" in kwargs:\\n kwargs[\\\"point_mode\\\"] = self.point_mode\\n if not \\\"point_size\\\" in kwargs:\\n kwargs[\\\"point_size\\\"] = self.point_size\\n if not \\\"point_width\\\" in kwargs:\\n kwargs[\\\"point_width\\\"] = self.point_width\\n if not \\\"point_fill\\\" in kwargs:\\n kwargs[\\\"point_fill\\\"] = self.point_fill\\n for point in self.points:\\n point.show(surface,\\n color=kwargs[\\\"point_color\\\"],\\n mode=kwargs[\\\"point_mode\\\"],\\n fill=kwargs[\\\"point_fill\\\"],\\n radius=kwargs[\\\"point_radius\\\"],\\n size=kwargs[\\\"point_size\\\"],\\n width=kwargs[\\\"point_width\\\"])\",\n \"def visualize_output(self, keyframe_index):\\n\\n self.vis_counter += 1\\n keyframe_image = self.images[keyframe_index]\\n keyframe_depth = self.depths[keyframe_index]\\n keyframe_pose = self.poses[keyframe_index]\\n\\n feed_dict = {\\n self.images_placeholder: keyframe_image[np.newaxis],\\n self.depths_placeholder: keyframe_depth[np.newaxis],\\n self.poses_placeholder: keyframe_pose[np.newaxis],\\n self.intrinsics_placeholder: self.intrinsics}\\n\\n keyframe_point_cloud, keyframe_point_colors = \\\\\\n self.sess.run(self.outputs['point_cloud'], feed_dict=feed_dict)\\n\\n pointcloud = (keyframe_point_cloud, keyframe_point_colors)\\n\\n # only add the point cloud once in every 5 frames\\n if self.vis_counter % 4 == 0:\\n self.queue.put((pointcloud, keyframe_pose))\\n \\n else:\\n self.queue.put((None, keyframe_pose))\",\n \"def format_point_cloud(self, pc, num_points):\\n resized_pc = np.zeros([num_points, 3])\\n if len(pc) > num_points:\\n # Randomly sample point cloud to reduce size\\n resized_pc = pc[np.random.choice(pc.shape[0], num_points,\\n replace=False), :]\\n elif len(pc) < num_points:\\n # Duplicate last point to fill point cloud. Because of the max function\\n # the duplicated points will not affect the output\\n resized_pc[:len(pc)] = pc\\n resized_pc[len(pc):] = pc[-1]\\n else:\\n resized_pc = pc\\n\\n return resized_pc\",\n \"def test_get_mesh_grid_as_point_cloud_single_pt() -> None:\\n min_x = -3 # integer, minimum x-coordinate of 2D grid\\n max_x = -3 # integer, maximum x-coordinate of 2D grid\\n min_y = 2 # integer, minimum y-coordinate of 2D grid\\n max_y = 2 # integer, maximum y-coordinate of 2D grid\\n\\n # return pts, a Numpy array of shape (N,2)\\n pts = mesh_grid_utils.get_mesh_grid_as_point_cloud(\\n min_x, max_x, min_y, max_y, downsample_factor=1.0\\n )\\n\\n assert pts.shape == (1, 2)\\n gt_pts: NDArrayFloat = np.array([[-3.0, 2.0]])\\n\\n assert np.allclose(gt_pts, pts)\",\n \"def test_get_mesh_grid_as_point_cloud_3x2rect() -> None:\\n min_x = -3 # integer, minimum x-coordinate of 2D grid\\n max_x = -1 # integer, maximum x-coordinate of 2D grid\\n min_y = 2 # integer, minimum y-coordinate of 2D grid\\n max_y = 3 # integer, maximum y-coordinate of 2D grid\\n\\n # return pts, a Numpy array of shape (N,2)\\n pts = mesh_grid_utils.get_mesh_grid_as_point_cloud(\\n min_x, max_x, min_y, max_y, downsample_factor=1.0\\n )\\n\\n assert pts.shape == (6, 2)\\n # fmt: off\\n gt_pts: NDArrayFloat = np.array(\\n [\\n [-3.0, 2.0],\\n [-2.0, 2.0],\\n [-1.0, 2.0],\\n [-3.0, 3.0],\\n [-2.0, 3.0],\\n [-1.0, 3.0]\\n ])\\n # fmt: on\\n assert np.allclose(gt_pts, pts)\",\n \"def show_points(self, **kwargs):\\n\\n if hasattr(self, 'method') and hasattr(self.method, 'show_points'):\\n self.method.show_points(self, **kwargs)\\n else:\\n figsize = kwargs.get('figsize', (15, 5))\\n cmap = kwargs.get('cmap', 'gray')\\n marker = kwargs.get('marker', '.')\\n color = kwargs.get('color', 'r')\\n fig, ax = plt.subplots(figsize=figsize)\\n ax.imshow(self.mraw[0].astype(float), cmap=cmap)\\n ax.scatter(self.points[:, 1], self.points[:, 0], \\n marker=marker, color=color)\\n plt.grid(False)\\n plt.show()\",\n \"def partial_visualize_in_2d(self, cluster_index=[5,12,35,44,64,75,81]):\\n for i in cluster_index:\\n list_x = []\\n list_y = []\\n for j in self.cluster[i]:\\n list_x.append(self.code[0][j,0])\\n list_y.append(self.code[0][j,1])\\n plt.scatter(list_x,list_y, label=self.skill[self.present_skill[i]])\\n plt.legend()\\n plt.show()\\n return\",\n \"def plot_scatter_points(self):\\n self.plot(1)\",\n \"def view(config_file):\\n import open3d as o3d\\n with open(config_file) as f:\\n config = json.load(f)\\n scenes = get_realsense_scenes(config['realsense_dir'])\\n for scene in scenes:\\n # if scene['scene_name'] != \\\"Scene_004\\\":\\n # continue\\n scene_data = get_data_from_scene(scene)\\n logger.info(\\\"Visualizing - %s\\\", scene['scene_name'])\\n pcd = o3d.geometry.PointCloud(o3d.utility.Vector3dVector(scene_data['points3d']))\\n o3d.visualization.draw_geometries_with_editing([pcd])\\n pcd = o3d.geometry.PointCloud(o3d.utility.Vector3dVector(scene_data['points3d_segmented']))\\n o3d.visualization.draw_geometries([pcd])\",\n \"def plot_data(self):\\n if hasattr(self,'data'):\\n plt.scatter(*self.data.T)\\n plt.show()\\n else:\\n raise Exception('No 2d data of the instance has been loaded')\",\n \"def pangolin_draw(points):\\n display.setup()\\n\\n while not display.should_quit():\\n display.init_frame()\\n for idx, pose in enumerate(CAMERA_POSES):\\n is_last_pose = idx == len(CAMERA_POSES) - 1\\n homogenous_pose = np.vstack((pose, [0, 0, 0, 1]))\\n display.draw_camera(homogenous_pose, (0.0, 1.0, 1.0 if is_last_pose else 0.0))\\n\\n display.draw_points(points)\\n\\n display.finish_frame()\",\n \"def plot_data(x):\\n if DATA_2D:\\n plt.scatter(x[:, 0], x[:, 1])\\n plt.show()\\n else:\\n fig = plt.figure()\\n ax = Axes3D(fig)\\n ax.scatter(x[:, 0], x[:, 1], x[:, 2])\\n ax.set_xlabel('X Label')\\n ax.set_ylabel('Y Label')\\n ax.set_zlabel('Z Label')\\n plt.show()\",\n \"def PointCloudfromStructOutput(self,file):\\n print(\\\"Creating Structure Point Cloud\\\")\\n xyz = self.readStructOutput(file)\\n pc = np.zeros((int(len(xyz)/2.0),3))\\n pc[:,0] = xyz[::2,0]*1000\\n pc[:,1] = xyz[::2,1]*1000\\n pc[:,2] = xyz[::2,2]*1000\\n head = \\\"\\\"\\\"X,Y,Z\\\"\\\"\\\"\\n np.savetxt(file, pc, delimiter=',',fmt='%.10f', header=head)\\n return\",\n \"def showFastCrossPoints(self, surface, color=None, mode=None, radius=None, width=None, size=None):\\n points = self.crossSelf()\\n if not color:\\n color = self.cross_point_color\\n if not mode:\\n mode = self.cross_point_mode\\n if not radius:\\n radius = self.cross_point_radius\\n if not width:\\n width = self.cross_point_width\\n if not size:\\n size = self.cross_point_size\\n for point in points:\\n point.show(surface, color=color, mode=mode, radius=radius, width=width, size=size)\",\n \"def plot_samples(s):\\r\\n assert len(s[0, :]) >= 2, ('The Phase space dimensions are less than two.', ' Need at least two to plot.')\\r\\n fig = plt.figure(1)\\r\\n if len(s[0, :]) >= 3:\\r\\n ax = fig.add_subplot(111, projection='3d')\\r\\n ax.scatter(s[:, 0], s[:, 1], s[:, 2])\\r\\n fig = plt.figure(2)\\r\\n plt.scatter(s[:, 0], s[:, 1])\\r\\n plt.show()\",\n \"def viz1(img, interest_points, color='r'):\\n\\n\\tx = [a[1] for a in interest_points] #blob detection x axis\\n\\ty = [a[0] for a in interest_points] #blob detection y axis\\n\\ts = [a[2] for a in interest_points] #blob detected at sigma \\n \\n\\tplt.imshow(img, cmap='gray') #adding the input image to plot \\n\\tfor x, y, s in zip(x, y, s):\\n\\t\\tplt.scatter(x, y, alpha=1, facecolors='none', edgecolors='r', s=s**2) #plotting the input interest points\\n\\tplt.axis('off')\\n\\t#plt.show() #showing the image // can be changed to saving the image locally \\n\\n\\treturn\",\n \"def show_grid(self):\\n\\n if not os.path.exists(self.path_to_results):\\n os.mkdir(self.path_to_results)\\n\\n fig = plt.figure()\\n\\n if self.show_points == 1:\\n plt.scatter(self.x_list_grid, self.y_list_grid, c='blue')\\n\\n plt.plot(self.x_list_main, self.y_list_main,\\n 'green', label='straight path')\\n plt.plot(self.x_list, self.y_list, 'red', label='first path')\\n plt.plot(self.x_list_filtered, self.y_list_filtered,\\n 'blue', label='filtered path')\\n plt.title('Paths')\\n plt.ylabel('Latitude')\\n plt.xlabel('Longitude')\\n # plt.legend()\\n\\n fig.savefig(os.path.join(self.path_to_results, 'Paths.png'))\",\n \"def draw_birdseye_points(self, image, points):\\n point_image = np.copy(image)\\n for point in points:\\n cv2.circle(point_image, point, 5, [0, 0, 255], cv2.FILLED)\\n\\n cv2.imshow('image1', point_image)\\n cv2.waitKey(1)\",\n \"def plt3dpaint(nppoints, color_map = 'jet', reduce_for_vis = True, voxel_size = 0.2, pointsize = 0.1, subplots = 5):\\n assert (type(nppoints) == pclpy.pcl.PointCloud.PointXYZRGB) or (type(nppoints) == pclpy.pcl.PointCloud.PointXYZ) or (type(nppoints) == np.ndarray) or (type(nppoints) is list) or (type(nppoints) is tuple), 'Not valid point_cloud'\\n cloudlist = []\\n cloudcolors = []\\n if (type(nppoints) is not list) & (type(nppoints) is not tuple):\\n nppoints = [nppoints]\\n \\n if len(nppoints) > 1:\\n for n,i in enumerate(nppoints):\\n workpoints = i\\n if (type(workpoints) == pclpy.pcl.PointCloud.PointXYZRGB) or (type(workpoints) == pclpy.pcl.PointCloud.PointXYZ):\\n workpoints = workpoints.xyz\\n\\n if reduce_for_vis:\\n workpoints = seg_tree.voxelize(workpoints,voxel_size)\\n\\n \\n cloudmin = np.min(workpoints[:,2])\\n cloudmax = np.max(workpoints[:,2])\\n \\n points = workpoints\\n color_coef = n/len(nppoints)/2 + n%2*.5\\n if type(color_map) == np.ndarray:\\n color = color_map\\n elif color_map == 'jet':\\n color=cm.jet(color_coef)[:3]\\n else:\\n color=cm.Set1(color_coef)[:3]\\n cloudcolors.append(np.ones_like(workpoints)*color + 0.4*(np.ones_like(workpoints) * ((workpoints[:,2] - cloudmin)/(cloudmax - cloudmin)).reshape(-1,1)-0.5) )\\n cloudlist.append(points)\\n else:\\n workpoints = nppoints[0]\\n if (type(workpoints) == pclpy.pcl.PointCloud.PointXYZRGB) or (type(workpoints) == pclpy.pcl.PointCloud.PointXYZ):\\n workpoints = workpoints.xyz\\n\\n if reduce_for_vis:\\n workpoints = seg_tree.voxelize(workpoints,voxel_size)\\n cloudcolors.append(workpoints[:,2])\\n cloudlist.append(workpoints)\\n\\n plt_pointcloud = np.concatenate(cloudlist)\\n plt_colors = np.concatenate(cloudcolors)\\n if len(nppoints) > 1:\\n plt_colors = np.minimum(plt_colors,np.ones_like(plt_colors))\\n plt_colors = np.maximum(plt_colors,np.zeros_like(plt_colors))\\n fig = plt.figure(figsize=(30,16) )\\n for i in range(subplots):\\n ax = fig.add_subplot(1, subplots, i+1, projection='3d')\\n ax.view_init(30, 360*i/subplots)\\n ax.scatter3D(plt_pointcloud[:,0], plt_pointcloud[:,1], plt_pointcloud[:,2], c=plt_colors, s=pointsize)\",\n \"def plot(self):\\n\\t\\tif (2 <= len(self.X) <= 3):\\n\\t\\t\\tvDataFrame(self.name, self.cursor).scatter(columns = self.X, catcol = \\\"dbscan_cluster\\\", max_cardinality = 100, max_nb_points = 10000)\\n\\t\\telse:\\n\\t\\t\\traise ValueError(\\\"Clustering Plots are only available in 2D or 3D\\\")\",\n \"def plot(self):\\n y = self.projection\\n mpl.scatter(y[:, 0], y[:, 1], c=self.data_class)\\n mpl.show()\",\n \"def imshow(self, depth):\\n layer = self.cube[depth]\\n img = []\\n for i in range(self.height):\\n img.append([layer[i][j].value for j in range(self.width)])\\n plt.imshow(img, cmap='gray')\\n plt.show()\",\n \"def show2d(self, data):\\n self.__plot2d.clear()\\n self.__plot2d.addImage(legend=\\\"data\\\", data=data)\\n self.setCurrentIndex(self.__index2d)\",\n \"def visualise_cppn(self, resolution=(64, 64)):\\n import matplotlib.pyplot as plt\\n from matplotlib.pyplot import imshow\\n data = np.empty([resolution[0], resolution[1]])\\n x_linspace = np.linspace(-1, 1, resolution[0])\\n y_linspace = np.linspace(-1, 1, resolution[1])\\n for row, x in enumerate(x_linspace):\\n for col, y in enumerate(y_linspace):\\n data[row, col] = self.graph(np.array([x, y, 0, 0], dtype=np.float32))[0]\\n #plt.axis([-1, 1, -1, 1])\\n print(data.min(), \\\" \\\", data.max())\\n imshow(data, cmap='Greys', vmin=-1, vmax=1)\\n plt.show()\",\n \"def plot_imshow(data, col_name):\\n city_x,city_y = get_city_base()\\n fig = plt.figure(figsize = FIGURE_SIZE)\\n plt.imshow(data[['lat_ind','lng_ind',col_name]], cmap=CONTOUR_PLOT_COLOUR)\\n plt.scatter(city_x,city_y, color = CITY_MAP_COLOR, s = SCATTER_SIZE_OF_CHICAGO_CITY, zorder = CITY_MAP_ORDER)\",\n \"def draw_point_gt(img,point_gt,width=3):\\n point_img=copy.deepcopy(img)\\n\\n for i in range(point_gt.shape[0]):\\n x=int(point_gt[i,0])\\n y=int(point_gt[i,1])\\n point_img=cv2.circle(point_img,center=(x,y),radius=width//2+1,color=(0,0,255),thickness=width)\\n\\n return point_img\",\n \"def displayDensePts(dense_pts, dense_slices, origin, transform, dense_displacement_all=False, dense_plot_quiver=0, timepoint=-1, ax=None):\\n\\t# If no axes were passed, generate axes\\n\\tif not ax:\\n\\t\\tfig = mplt.figure()\\n\\t\\tax = fig.add_subplot(111, projection='3d')\\n\\t\\n\\t# Pull appropriate timepoint (or set to False if displacement is undesired)\\n\\tif timepoint >= 0:\\n\\t\\ttry:\\n\\t\\t\\tdense_displacement = dense_displacement_all[timepoint]\\n\\t\\texcept(IndexError):\\n\\t\\t\\twarnings.warn('Timepoint not in range of values! Displacement will be ignored.')\\n\\t\\t\\ttimepoint = -1\\n\\t\\t\\tdense_displacement = False\\n\\t\\texcept(TypeError):\\n\\t\\t\\twarnings.warn('Displacement not passed, but timepoint requested! Displacement will be ignored.')\\n\\t\\t\\tdense_displacement = False\\n\\telse:\\n\\t\\tdense_displacement = False\\n\\t\\n\\tfor i in range(len(dense_slices)):\\n\\t\\tcur_slice = dense_slices[i]\\n\\t\\tcur_dense_pts = np.column_stack((dense_pts[i], [cur_slice - origin[2]]*dense_pts[i].shape[0]))\\n\\t\\tdata_dense = np.dot(cur_dense_pts, np.transpose(transform))\\n\\t\\tx = data_dense[:, 2]\\n\\t\\ty = data_dense[:, 1]\\n\\t\\tz = data_dense[:, 0]\\n\\t\\tax.scatter(x, y, -z, ',')\\n\\t\\t\\n\\t\\tif dense_displacement:\\n\\t\\t\\tdense_displacement_slice = dense_displacement[i]\\n\\t\\t\\tif dense_plot_quiver == 1:\\n\\t\\t\\t\\tax.quiver(x, y, -z, dense_displacement_slice[:, 0], dense_displacement_slice[:, 1], [0]*dense_displacement_slice.shape[0])\\n\\treturn(ax)\",\n \"def trimesh_from_point_cloud(cloud):\\n points = np.asarray(cloud)\\n hull = scipy.spatial.ConvexHull(points)\\n hull = scipy.spatial.ConvexHull(points[hull.vertices])\\n ru.transforms.counterclockwise_hull(hull)\\n vertices = hull.points\\n faces = hull.simplices\\n return vertices, faces\",\n \"def occupancy_grid(self, point_cloud=None, ss_pred=None, *, return_image=False, show=False, keep_showing=False):\\n\\t\\tif point_cloud is None or ss_pred is None:\\n\\t\\t\\tgen = self.loop(point_cloud=True, ss_pred=True)\\n\\t\\t\\tpoint_cloud, ss_pred = next(gen)\\n\\t\\t#TODO: Check that this function works with ZED camera\\n\\t\\tpass\",\n \"def plot(self):\\n\\n fig = plt.figure()\\n ax = fig.add_subplot(111, projection=Axes3D.name)\\n\\n # TODO Use numpy to rotate esp_points matrix for faster variable access.\\n ax.scatter(\\n xs=[i[0][0] for i in self.esp_points],\\n ys=[i[0][1] for i in self.esp_points],\\n zs=[i[0][2] for i in self.esp_points],\\n c=[i[1] for i in self.esp_points],\\n marker='o',\\n s=2,\\n alpha=0.5\\n )\\n\\n ax.scatter(\\n xs=[i[0][0] for i in self.atom_points],\\n ys=[i[0][1] for i in self.atom_points],\\n zs=[i[0][2] for i in self.atom_points],\\n c=[i[1] for i in self.atom_points],\\n marker='X',\\n s=100\\n )\\n\\n plt.show()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72636724","0.72375584","0.71609396","0.7132317","0.6933942","0.6919783","0.6877211","0.68398106","0.65854543","0.6562634","0.65615726","0.6518098","0.64886963","0.64688116","0.64599943","0.64297676","0.64172477","0.6347307","0.6338426","0.6316377","0.6249169","0.6247689","0.61805177","0.6168697","0.61648315","0.6162763","0.61433035","0.61228466","0.6120385","0.60903704","0.60887784","0.60840714","0.6073814","0.606181","0.60497266","0.60478836","0.60273826","0.60230863","0.6011479","0.5949034","0.5944727","0.5941591","0.59129876","0.5882682","0.58782804","0.58763915","0.58751667","0.58735824","0.58648884","0.58637315","0.58447856","0.5813793","0.5810271","0.58056587","0.5803301","0.57765025","0.57574964","0.57572305","0.57341605","0.5723177","0.57149345","0.57101357","0.57069534","0.5700627","0.56989515","0.56848264","0.5670351","0.5662748","0.5656756","0.56466407","0.5642417","0.5637249","0.56292343","0.5626862","0.56252885","0.5621153","0.5601514","0.55969167","0.5594165","0.5589445","0.55824107","0.55757415","0.55727154","0.5560376","0.5551333","0.5550308","0.55499405","0.5545071","0.5526621","0.55255795","0.55196786","0.55180466","0.5517805","0.551735","0.55166","0.55158687","0.55143934","0.55120367","0.55085087","0.5502027"],"string":"[\n \"0.72636724\",\n \"0.72375584\",\n \"0.71609396\",\n \"0.7132317\",\n \"0.6933942\",\n \"0.6919783\",\n \"0.6877211\",\n \"0.68398106\",\n \"0.65854543\",\n \"0.6562634\",\n \"0.65615726\",\n \"0.6518098\",\n \"0.64886963\",\n \"0.64688116\",\n \"0.64599943\",\n \"0.64297676\",\n \"0.64172477\",\n \"0.6347307\",\n \"0.6338426\",\n \"0.6316377\",\n \"0.6249169\",\n \"0.6247689\",\n \"0.61805177\",\n \"0.6168697\",\n \"0.61648315\",\n \"0.6162763\",\n \"0.61433035\",\n \"0.61228466\",\n \"0.6120385\",\n \"0.60903704\",\n \"0.60887784\",\n \"0.60840714\",\n \"0.6073814\",\n \"0.606181\",\n \"0.60497266\",\n \"0.60478836\",\n \"0.60273826\",\n \"0.60230863\",\n \"0.6011479\",\n \"0.5949034\",\n \"0.5944727\",\n \"0.5941591\",\n \"0.59129876\",\n \"0.5882682\",\n \"0.58782804\",\n \"0.58763915\",\n \"0.58751667\",\n \"0.58735824\",\n \"0.58648884\",\n \"0.58637315\",\n \"0.58447856\",\n \"0.5813793\",\n \"0.5810271\",\n \"0.58056587\",\n \"0.5803301\",\n \"0.57765025\",\n \"0.57574964\",\n \"0.57572305\",\n \"0.57341605\",\n \"0.5723177\",\n \"0.57149345\",\n \"0.57101357\",\n \"0.57069534\",\n \"0.5700627\",\n \"0.56989515\",\n \"0.56848264\",\n \"0.5670351\",\n \"0.5662748\",\n \"0.5656756\",\n \"0.56466407\",\n \"0.5642417\",\n \"0.5637249\",\n \"0.56292343\",\n \"0.5626862\",\n \"0.56252885\",\n \"0.5621153\",\n \"0.5601514\",\n \"0.55969167\",\n \"0.5594165\",\n \"0.5589445\",\n \"0.55824107\",\n \"0.55757415\",\n \"0.55727154\",\n \"0.5560376\",\n \"0.5551333\",\n \"0.5550308\",\n \"0.55499405\",\n \"0.5545071\",\n \"0.5526621\",\n \"0.55255795\",\n \"0.55196786\",\n \"0.55180466\",\n \"0.5517805\",\n \"0.551735\",\n \"0.55166\",\n \"0.55158687\",\n \"0.55143934\",\n \"0.55120367\",\n \"0.55085087\",\n \"0.5502027\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.57596034"},"document_rank":{"kind":"string","value":"56"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":211,"numItemsPerPage":100,"numTotalItems":95772,"offset":21100,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjQ1ODE4NCwianRpIjoiODc4MzA4MTItMjgyMi00NmFjLWIzMzItNTZlYTc4ZmQyMTFmIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODY0NjE3ODQsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.ynYR_MS5NEp8mwrK0sCryAUpq3_jDaqucaLq98qDZv82G1GhkB6nfLhVvQ-6LwN0bUelXLZwUbeF15ybY6DDBA","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
Check if production env is activated. | def test_is_production_env(self) -> None:
os.environ.update({"NC_MODE": "production"})
is_develop = is_development_env()
self.assertFalse(is_develop) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_production_environment(self):\n return self.get_current_environment() == Environment.PRODUCTION",
"def is_production_mode(self):\n return getattr(self.env, 'mode', None) == 'production'",
"def prod(environment):\n return environment == 'live' or environment == 'debug' or environment == ... | [
"0.81930846",
"0.81518",
"0.8027799",
"0.7621694",
"0.7604779",
"0.7513769",
"0.7450725",
"0.7421038",
"0.70685047",
"0.69504565",
"0.6856602",
"0.68520004",
"0.67687356",
"0.6757052",
"0.6719974",
"0.6677296",
"0.6666935",
"0.6606695",
"0.6603931",
"0.65954214",
"0.65905064"... | 0.7997026 | 3 |
Check if development env is activated. | def test_is_empty_nc_mode_env(self) -> None:
if os.environ.get("NC_MODE", None) is not None:
del os.environ["NC_MODE"]
is_develop = is_development_env()
self.assertFalse(is_develop) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_development():\n return os.environ.get('SERVER_SOFTWARE', '').startswith('Development')",
"def test_is_development_env(self) -> None:\n os.environ.update({\"NC_MODE\": \"development\"})\n is_develop = is_development_env()\n self.assertTrue(is_develop)",
"def get_site_env(self):\n ... | [
"0.8337604",
"0.81877184",
"0.7692659",
"0.7504923",
"0.74659204",
"0.7465296",
"0.7377704",
"0.7323249",
"0.72394073",
"0.72125643",
"0.71630734",
"0.7148706",
"0.70545775",
"0.70386916",
"0.7021576",
"0.69120103",
"0.68301886",
"0.68271375",
"0.6818852",
"0.67625374",
"0.67... | 0.59749204 | 57 |
Test release tag building. | def test_release_tag(self) -> None:
self.assertEqual("v3.14.15", release_tag()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_release_deployment_run(self):\n pass",
"def test_release_tag_for_dev_version(self) -> None:\n self.assertEqual(\"v42.12\", release_tag())",
"def test_release_version():\n pkg_vars = {}\n with open(VERSION_FILE) as f:\n exec(f.read(), pkg_vars) # nosec\n project_version =... | [
"0.7284068",
"0.70147276",
"0.6728345",
"0.6709383",
"0.66933805",
"0.66797566",
"0.6608459",
"0.64857477",
"0.64158696",
"0.6389684",
"0.6376367",
"0.6313048",
"0.6291171",
"0.6285542",
"0.6277399",
"0.627431",
"0.6255137",
"0.6237298",
"0.62290806",
"0.6162094",
"0.6159651"... | 0.7737768 | 0 |
Test release tag building. | def test_release_tag_for_dev_version(self) -> None:
self.assertEqual("v42.12", release_tag()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_release_tag(self) -> None:\n self.assertEqual(\"v3.14.15\", release_tag())",
"def test_release_deployment_run(self):\n pass",
"def test_release_version():\n pkg_vars = {}\n with open(VERSION_FILE) as f:\n exec(f.read(), pkg_vars) # nosec\n project_version = pkg_vars[\"__... | [
"0.7737768",
"0.7284068",
"0.6728345",
"0.6709383",
"0.66933805",
"0.66797566",
"0.6608459",
"0.64857477",
"0.64158696",
"0.6389684",
"0.6376367",
"0.6313048",
"0.6291171",
"0.6285542",
"0.6277399",
"0.627431",
"0.6255137",
"0.6237298",
"0.62290806",
"0.6162094",
"0.6159651",... | 0.70147276 | 2 |
Test release tag building. | def test_release_tag_for_empty(self) -> None:
with self.assertRaisesRegexp(ValueError, "Unable to parse version "):
release_tag() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_release_tag(self) -> None:\n self.assertEqual(\"v3.14.15\", release_tag())",
"def test_release_deployment_run(self):\n pass",
"def test_release_tag_for_dev_version(self) -> None:\n self.assertEqual(\"v42.12\", release_tag())",
"def test_release_version():\n pkg_vars = {}\n ... | [
"0.7737768",
"0.7284068",
"0.70147276",
"0.6728345",
"0.6709383",
"0.66933805",
"0.66797566",
"0.6608459",
"0.64857477",
"0.6389684",
"0.6376367",
"0.6313048",
"0.6291171",
"0.6285542",
"0.6277399",
"0.627431",
"0.6255137",
"0.6237298",
"0.62290806",
"0.6162094",
"0.6159651",... | 0.64158696 | 9 |
Test release tag building. | def test_release_tag_for_invalid_version(self) -> None:
with self.assertRaisesRegexp(ValueError, "Unable to parse version foo.bar.ba"):
release_tag() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_release_tag(self) -> None:\n self.assertEqual(\"v3.14.15\", release_tag())",
"def test_release_deployment_run(self):\n pass",
"def test_release_tag_for_dev_version(self) -> None:\n self.assertEqual(\"v42.12\", release_tag())",
"def test_release_version():\n pkg_vars = {}\n ... | [
"0.7737768",
"0.7284068",
"0.70147276",
"0.6728345",
"0.6709383",
"0.66933805",
"0.66797566",
"0.6608459",
"0.64857477",
"0.64158696",
"0.6389684",
"0.6376367",
"0.6313048",
"0.6291171",
"0.6285542",
"0.6277399",
"0.627431",
"0.6255137",
"0.6237298",
"0.62290806",
"0.6162094"... | 0.5718554 | 38 |
Turn URL into PIL image. Can throw a timout error. | def url2img(url : str, timeout = 1) -> Image:
response = requests.get(url, timeout = timeout)
return Image.open(BytesIO(response.content)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def urlToImage(url):\n\n response = requests.get(url)\n image = Image.open(BytesIO(response.content))\n return image",
"def _url_to_image(url: str) -> Image.Image:\n assert url.lower().startswith(\"http\"), \"invalid url, must start with http\"\n content = requests.get(url).content\n image = Im... | [
"0.7684103",
"0.7520165",
"0.74877226",
"0.7116487",
"0.7027549",
"0.7010515",
"0.6980545",
"0.6932684",
"0.68663347",
"0.680157",
"0.6707649",
"0.649833",
"0.64743775",
"0.64194036",
"0.62737274",
"0.62376577",
"0.62025154",
"0.61823267",
"0.61713475",
"0.61658984",
"0.61528... | 0.8368085 | 0 |
Do not return anything, modify board inplace instead. | def gameOfLife(self, board: List[List[int]]) -> None:
self.board = board
self.l = len(board)
self.w = len(board[0])
status = [[0] * self.w for _ in range(self.l)]
for i in range(self.l):
for j in range(self.w):
status[i][j] = self.statusUpdate(b... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def applyMove(self, (from_row,from_col), (to_row,to_col)):\n newboard = deepcopy(self)\n piece = newboard.board[from_row][from_col]\n newboard.board[from_row][from_col] = None\n newboard.board[to_row][to_col] = piece\n newboard.toplay = 'BLACK' if self.toplay == 'WHITE' else 'WHI... | [
"0.7664789",
"0.70971596",
"0.70476234",
"0.7024398",
"0.69861203",
"0.6829364",
"0.6778242",
"0.6727011",
"0.66938925",
"0.6692377",
"0.6625323",
"0.6625323",
"0.65893996",
"0.6565318",
"0.6564258",
"0.65587217",
"0.6539339",
"0.65249294",
"0.65223134",
"0.6520972",
"0.65202... | 0.0 | -1 |
Add user if not exists | def AddUser(effective_user, effective_chat):
chatId = effective_chat.id
chatType = effective_chat.type
chatTitle = effective_chat.title
userId = effective_user.id
if chatId == userId:
exists = session.query(User).filter_by(user_id=userId, group_id=None).one_or_none()
group_exists = ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def addUser(User):\n # check if user already exists #\n user_exists = run('id -u %s >/dev/null 2>&1 || echo \"no\"' % (User))\n if user_exists == \"no\":\n sudo('useradd -m -c \"%s\" -s /bin/bash %s' % (agUsers[User], User))\n else:\n print \"[Info] User '%s' already exists on host '%s'\"... | [
"0.7453931",
"0.74400806",
"0.7315973",
"0.72034574",
"0.71068114",
"0.70417374",
"0.7041168",
"0.6952933",
"0.6949969",
"0.6912825",
"0.6909413",
"0.68748516",
"0.68633693",
"0.6849981",
"0.68408054",
"0.6829215",
"0.6801972",
"0.68012327",
"0.68012327",
"0.68012327",
"0.678... | 0.0 | -1 |
Called after the image sticher performs some work. | def post_work(self, frames_rec: int, frames_proc: int, nooo: int):
raise NotImplementedError | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def process_image(self):\n pass",
"def process(self, image):",
"def do_stuff(self):\n self.create_tourism_raster()",
"def post_process(self):\n pass",
"def post_process(self):\n pass",
"def post_process(self):\n pass",
"def post_process(self):\n pass",
"def p... | [
"0.7214914",
"0.7196402",
"0.6725398",
"0.6696196",
"0.6696196",
"0.6696196",
"0.6696196",
"0.6696196",
"0.6660734",
"0.65854293",
"0.6560694",
"0.6550989",
"0.65413284",
"0.65371954",
"0.6455175",
"0.63938737",
"0.63936543",
"0.6379982",
"0.6379982",
"0.63412863",
"0.6324683... | 0.0 | -1 |
Goes through and removes all values in frames_rec and frames_proc that are outside of the window | def clean_window(self) -> None:
prune_before = time.time() - self.window_size
while self.frames_rec:
left = self.frames_rec.popleft()
if left[1] >= prune_before:
self.frames_rec.appendleft(left)
break
self.sum_frames_rec -= left[... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clear_restriction_details(self):\n\t\n\t\tif getattr(self,'new_seq_win_objs',None):\n\t\t\tfor obj in self.new_seq_win_objs.keys():\n\t\t\t\tself.seqframe.delete(obj)\n\t\t\tself.new_seq_win_objs={}\n\t\t\tself.donepos={}\n\t\t\t#\n\t\t\tfor obj in self.temp_objs.keys():\n\t\t\t\tself.seqframe.delete(obj)\n\t\... | [
"0.6719497",
"0.6220488",
"0.6218328",
"0.59831357",
"0.5953131",
"0.58991516",
"0.58938974",
"0.58484584",
"0.58360916",
"0.57718647",
"0.5765755",
"0.5718492",
"0.56914794",
"0.563131",
"0.56236684",
"0.56055874",
"0.55914086",
"0.5591337",
"0.55780524",
"0.5515344",
"0.546... | 0.7736172 | 0 |
Returns True if we have a full window of information available, False otherwise | def have_window(self) -> bool:
return (
self._first_enc_at is not None
and (time.time() > self._first_enc_at + self.window_size)
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def exists(self):\n if self.attributes[AT.GARBAGE]:\n return False\n if get_ticks() < self.attributes[AT.TIME_TO_BE_SHOWN]:\n return False\n return True",
"def is_full(self) -> bool:",
"def _can_render_now(self):\n # First check that no update events are pendin... | [
"0.6700973",
"0.66572267",
"0.65650487",
"0.65073746",
"0.644696",
"0.6444967",
"0.6371296",
"0.63529104",
"0.632829",
"0.6322513",
"0.629695",
"0.6292452",
"0.6283709",
"0.6269726",
"0.62444526",
"0.62417144",
"0.6239692",
"0.6218333",
"0.6218333",
"0.62145",
"0.6200358",
... | 0.7614796 | 0 |
Returns the number of frames received per second and the number of frames processed per second in the current window. | def mean(self) -> typing.Tuple[float, float]:
self.clean_window()
return (
(self.sum_frames_rec / self.window_size),
(self.sum_frames_proc / self.window_size)
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_total_frames(self) -> int:\n return self.num_frames",
"def frameTimes(self):\n sr = self.sampleRate\n offset = self.activeOffset\n stride = self.activeStride\n nf = self.numFrames\n t = np.arange(nf) * (stride[0] / sr) + (offset / sr)\n return t",
"def F... | [
"0.78337723",
"0.7294236",
"0.713848",
"0.7087923",
"0.7029475",
"0.7021391",
"0.70037943",
"0.6973596",
"0.6899183",
"0.68941",
"0.6872302",
"0.6869656",
"0.6807797",
"0.68056464",
"0.6759291",
"0.6744807",
"0.6681289",
"0.66297203",
"0.66244495",
"0.66077125",
"0.65771705",... | 0.0 | -1 |
Spawns the ffmpeg process | def _spawn_ffmpeg(self) -> None:
if self.ffmpeg_proc is not None:
raise RuntimeError('_spawn_ffmpeg called when ffmpeg_proc is '
+ f'{self.ffmpeg_proc} (not None)')
args = ['ffmpeg', '-f', 'rawvideo', '-vcodec', 'rawvideo',
'-s', f'{self.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def start(self) -> None:\r\n self._spawn_ffmpeg()",
"def ffmpeg(*options):\n\tffmpeg_command = [\"ffmpeg\"] + list(options)\n\tprint(\"Calling FFMPEG:\", \" \".join(ffmpeg_command))\n\n\tprocess = subprocess.Popen(ffmpeg_command, stdout=subprocess.PIPE)\n\t(cout, cerr) = process.communicate()\n\texit_code... | [
"0.80590403",
"0.70434517",
"0.6731381",
"0.6610697",
"0.65816295",
"0.638432",
"0.62684786",
"0.61771494",
"0.61691076",
"0.61160266",
"0.60946435",
"0.6022187",
"0.60099894",
"0.5973808",
"0.59619623",
"0.5928561",
"0.59079623",
"0.5861136",
"0.5848352",
"0.582267",
"0.5821... | 0.81389457 | 0 |
Cleans up the ffmpeg process. This will wait for it to terminate | def _cleanup_ffmpeg(self) -> None:
self.ffmpeg_proc.communicate()
self.ffmpeg_proc = None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def async_stop_ffmpeg(self):\n return self.ffmpeg.close()",
"def _cleanup_proc(self):\n logger.debug(\"{}: Cleaning up and waiting for process to exit\".format(\n self))\n try:\n self._loop.remove_reader(self.... | [
"0.731078",
"0.6837441",
"0.6784892",
"0.6526165",
"0.6473467",
"0.6470982",
"0.632042",
"0.63029844",
"0.6262408",
"0.6260875",
"0.61485505",
"0.61473036",
"0.60400724",
"0.60322404",
"0.6029492",
"0.6027344",
"0.6026851",
"0.6022948",
"0.5987162",
"0.59848726",
"0.59642464"... | 0.86087924 | 0 |
Prepares this image sticher to begin. Receive queues may still be added or removed at any time. | def start(self) -> None:
self._spawn_ffmpeg() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def prepare_images(self):\n\n qt_original_image = self.convert_image_to_QTformat(self.original_image)\n self.send_original_photo_to_gui.emit(qt_original_image)\n\n self.processed_image = self.procces_image(self.original_image)\n qt_processed_image = self.convert_image_to_QTformat(self.p... | [
"0.634032",
"0.607448",
"0.60480696",
"0.6037353",
"0.6015708",
"0.5867834",
"0.5867834",
"0.5867834",
"0.5867066",
"0.58492875",
"0.5788658",
"0.56946063",
"0.5655614",
"0.562711",
"0.5624617",
"0.56243294",
"0.5615637",
"0.55929005",
"0.5559435",
"0.5555809",
"0.55425805",
... | 0.0 | -1 |
Registers the specified queuelike object as something frames can be received from. Must have a get_nowait and empty member. | def register_queue(self, queue) -> None:
if queue is None:
raise ValueError('queue is None')
if not hasattr(queue, 'empty'):
raise ValueError(f'queue {queue} is missing empty member')
if not hasattr(queue, 'get_nowait'):
raise ValueError(f'queue {queue} ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def on_queue_declared(frame):\n start_consuming(frame)",
"def example(example_object, queue):\n queue.put(example_object)",
"def example(example_object, queue_object):\n queue_object.put(example_object)",
"def on_queue_declared(frame):\n channel.basic_consume(handle_delivery, queue='test')",
"d... | [
"0.66028094",
"0.629318",
"0.61060864",
"0.597517",
"0.580639",
"0.5723007",
"0.5670818",
"0.565421",
"0.55929124",
"0.5555419",
"0.55552125",
"0.5430858",
"0.5422504",
"0.54204226",
"0.5383923",
"0.5375583",
"0.5371588",
"0.5342425",
"0.5279118",
"0.5275986",
"0.5268184",
... | 0.69833153 | 0 |
Removes the given queuelike object from this. Uses the same comparison as list.remove. Raises ValueError if the queue is not currently being used to fetch frames. | def remove_queue(self, queue) -> None:
self.receive_queues.remove(queue) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove(self) -> T:\n if not self.is_empty():\n return self._queue.pop()",
"def remove_queue(self, queue):\n with self.mutex:\n self.queues.remove(queue)",
"def remove(self, index):\n if index < 0 or index >= len(self):\n raise AttributeError(\"i must be... | [
"0.7187264",
"0.6991843",
"0.69270307",
"0.6874676",
"0.66540396",
"0.65354115",
"0.6491751",
"0.6465907",
"0.64182615",
"0.6388193",
"0.6382077",
"0.63798213",
"0.6345612",
"0.632236",
"0.63211536",
"0.6297915",
"0.62840116",
"0.62811065",
"0.6269877",
"0.62553895",
"0.62484... | 0.6955731 | 2 |
Checks for items from each of the receive queues and pushes them onto the local memory dict. Returns the number of frames received | def check_queues(self) -> int:
nframes = 0
for queue in self.receive_queues:
if not queue.empty():
nframes += 1
frame, img_bytes = queue.get_nowait()
if frame < self.next_frame:
raise ValueError('received frame ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def calculate_queues(self):\n\t\t#queues = [get_queue(lane) for lane in self.Vissim_Lanes]\n\t\t\n\t\tqueues = [0. if queue.AttValue('QLen(Current, Last)') is None else queue.AttValue('QLen(Current, Last)') for queue in self.queues_counters]\n\t\treturn queues",
"def queue_lengths(self):\r\n out = []\r\n ... | [
"0.58479637",
"0.58138037",
"0.5686759",
"0.55891025",
"0.5548044",
"0.55331",
"0.5531125",
"0.55247325",
"0.5522078",
"0.55212706",
"0.55021536",
"0.5491394",
"0.54898417",
"0.5464607",
"0.54142845",
"0.54142845",
"0.54142845",
"0.54142845",
"0.54142845",
"0.54142845",
"0.54... | 0.7559541 | 0 |
Processes the next frame to the ffmpeg process if it is available. Returns True if we processed a frame, False if we did not. | def process_frame(self) -> bool:
if self.next_frame not in self.ooo_frames:
return False
img_bytes = self.ooo_frames.pop(self.next_frame)
for kb_start in range(0, len(img_bytes), self.block_size):
self.ffmpeg_proc.stdin.write(
img_bytes[kb_start:... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def process(self, frame, metadata):\n self.log.debug(f\"In process() method...\")\n\n # Publishing metadata & frames\n self.publisher.publish((metadata, frame.tobytes(),))\n metadata, frame = self.subscriber.recv()\n\n if \"jpnb_frame_drop\" in metadata:\n del metadata... | [
"0.681551",
"0.62653166",
"0.62197095",
"0.62151057",
"0.6016715",
"0.5888307",
"0.585658",
"0.5855879",
"0.58241194",
"0.57367325",
"0.5663488",
"0.565733",
"0.561935",
"0.5575768",
"0.55190694",
"0.54909426",
"0.5479278",
"0.547769",
"0.54572225",
"0.54553086",
"0.54294866"... | 0.8323678 | 0 |
A catchall function to do some work. Returns True if some work was done, False otherwise | def do_work(self):
recv = self.check_queues()
proc = 1 if self.process_frame() else 0
for perf in self.perfs:
perf.post_work(recv, proc, len(self.ooo_frames))
return recv > 0 or proc > 0 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _doWork(self) -> bool:\n # grab a job from queur\n self.lock.acquire ()\n jobId = self.nextJob\n self.nextJob = jobId + 1\n self.lock.release ()\n\n # abort if no jobs are left\n if jobId >= len (self.jobs):\n return False\n\n # execute job\n ... | [
"0.6764182",
"0.6484873",
"0.63052315",
"0.6302114",
"0.6237362",
"0.6228252",
"0.6161876",
"0.6097608",
"0.5983474",
"0.5931103",
"0.5929668",
"0.5917428",
"0.5846048",
"0.58357775",
"0.58357775",
"0.58357775",
"0.5820937",
"0.581694",
"0.5782128",
"0.57802105",
"0.5778865",... | 0.5947233 | 9 |
redirect to login page | def login():
login_page = Login()
login_page.login_main_page() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def login_redirect(request):\n return HttpResponseRedirect('/login')",
"def login():\n return redirect(build_authorize_url())",
"def login(request):\n return HttpResponseRedirect('/')",
"def login(self) -> redirect:\n\t\tif self.is_authorized:\n\t\t\tflash(\"You are already logged in.\")\n\t\t\tretu... | [
"0.80341816",
"0.7834743",
"0.7738441",
"0.75973696",
"0.7556122",
"0.7555812",
"0.7506934",
"0.74773985",
"0.7468347",
"0.7450045",
"0.74444264",
"0.743553",
"0.7387644",
"0.73819023",
"0.7376208",
"0.73413944",
"0.72765326",
"0.72681993",
"0.7236252",
"0.7228724",
"0.722632... | 0.6780875 | 90 |
Register the new user | def registration():
registration_page = Registration()
registration_page.registration_main_page() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def register_user():\n pass",
"def register_user(self):\n User.add_user(User(self.email.data, self.password.data))",
"def register():\n insert_user(json_body())\n try:\n db.session.commit()\n except IntegrityError:\n raise exc.CouldNotCreateEntry()\n\n return jsonify({'messa... | [
"0.8750097",
"0.8606665",
"0.820681",
"0.79145604",
"0.7867718",
"0.779165",
"0.77669674",
"0.7759361",
"0.77407837",
"0.77339166",
"0.7713933",
"0.7711798",
"0.77113533",
"0.7702676",
"0.7689713",
"0.7675305",
"0.7671336",
"0.7657973",
"0.7636853",
"0.7630487",
"0.7598397",
... | 0.0 | -1 |
get the params value | def paramValue(self, key):
return self.options[key] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getParam(self, params, name):\n return params.get(name)",
"def getParameter(self, name):",
"def get_params(self):",
"def get_param(self, param):\n return self.params.get(param, None)",
"def get(self):\n return self._params",
"def getParam(self,param):\n if param in self.params... | [
"0.77904546",
"0.7687604",
"0.7684831",
"0.76296043",
"0.75449836",
"0.7464194",
"0.7336356",
"0.7303772",
"0.7257967",
"0.7243042",
"0.71784574",
"0.7160648",
"0.71456605",
"0.71456605",
"0.7134405",
"0.7133184",
"0.7111483",
"0.7111483",
"0.7111483",
"0.7085192",
"0.7066033... | 0.6697429 | 46 |
get the param object | def param(self, *args, **kwargs):
return self.options.param(*args,**kwargs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getParam(self,param):\n if param in self.params.keys():\n return self.params[param]\n else:\n return None",
"def get_params(self):",
"def get_param(self, param):\n return self.params.get(param, None)",
"def param(self):\n return self._param",
"def getPa... | [
"0.76784503",
"0.7652682",
"0.7514661",
"0.75113237",
"0.7407263",
"0.7306728",
"0.71759325",
"0.7116908",
"0.71134484",
"0.70567626",
"0.7050655",
"0.6953516",
"0.69525486",
"0.6947529",
"0.6942389",
"0.6929406",
"0.6917862",
"0.6917862",
"0.6917862",
"0.6898828",
"0.6887106... | 0.74724686 | 4 |
Helping function to get the color of the dock | def _getDockColor(self, plane):
color = (0,0,0)
if plane.zAxis != -1:
color = self.globalAxis[plane.zAxis].color[0:3]
return color | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def color(self):\n return self.container['color']",
"def _get_color(self):\n return self.__color",
"def _get_color(self):\n return self.__color",
"def _get_color(self):\n return self.__color",
"def _get_color(self):\n return self.__color",
"def get_color(self):\n self.view.prese... | [
"0.7055499",
"0.6641315",
"0.6641315",
"0.6641315",
"0.6641315",
"0.662588",
"0.6576148",
"0.6518216",
"0.65073246",
"0.65073246",
"0.65073246",
"0.650731",
"0.64849645",
"0.6471284",
"0.64579517",
"0.6433993",
"0.6423269",
"0.64182955",
"0.639525",
"0.6385216",
"0.63695",
... | 0.7151222 | 0 |
Helping function to place the docks | def _placeDock(self, dock, pos=None, otherDock=None):
if otherDock is not None and pos is not None:
self.area.addDock(dock,pos,otherDock)
elif pos is not None:
self.area.addDock(dock,pos,otherDock)
else:
self.area.addDock(dock)
return dock | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def placeOrder(self, dish):\n # Get a random waiter\n waiter = choice(self.waiters)\n # Place the order, returning the result\n food = waiter.takeOrder(dish, self.chefs)\n return food",
"def place_dungeon_items(self):\r\n self.place_entrance()\r\n self.place_exit(... | [
"0.53520143",
"0.5322772",
"0.5258693",
"0.521382",
"0.50301754",
"0.50091743",
"0.5002837",
"0.49934033",
"0.4981615",
"0.49582452",
"0.49508584",
"0.49172708",
"0.49121922",
"0.49081165",
"0.48742324",
"0.48737693",
"0.4866753",
"0.48548388",
"0.48385",
"0.48325017",
"0.482... | 0.4883966 | 14 |
Called to return the list of groups to automatically add/remove this connection to/from. | def connection_groups(self, **kwargs):
return ["listener-%s" % self.path[1:]] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def groups(self):\n return []",
"def get_groups_connection(self):\n return self.m_connection.groups",
"def getGroups(self):\n return [g[0] for g in grp.getgrall()]",
"def get_pingroups(self):\n return self.groups[:]",
"def getGroups():\r\n return Group.getGroups()",
"de... | [
"0.7407197",
"0.73968863",
"0.7091112",
"0.70297974",
"0.6961423",
"0.68978125",
"0.68090963",
"0.6806716",
"0.6790244",
"0.6771468",
"0.6655087",
"0.6637095",
"0.66344905",
"0.66344905",
"0.66344905",
"0.66318434",
"0.6562618",
"0.6541859",
"0.64721674",
"0.6461446",
"0.6457... | 0.7185008 | 2 |
Perform things on connection start | def connect(self, message, **kwargs):
Group("radio-listeners").add(message.reply_channel)
message.reply_channel.send({"accept": True})
# immidiately send events for song status
track_name, track_author, track_comment = get_current_track_info()
events = [
{
't... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def start(self):\n self.conn.start()",
"def init_connection(self, connection):",
"def on_connection_start(self) -> None:\r\n print(\r\n \"Connected with: {}:{}\\n\".format(\r\n self.connection_info[\"host\"], self.connection_info[\"port\"]\r\n )\r\n )",... | [
"0.7554287",
"0.75064856",
"0.74878454",
"0.74447787",
"0.73129517",
"0.73129517",
"0.73129517",
"0.73129517",
"0.73129517",
"0.73129517",
"0.73129517",
"0.73129517",
"0.7308945",
"0.72731483",
"0.72653943",
"0.72329885",
"0.72232544",
"0.71871233",
"0.71843165",
"0.7163643",
... | 0.0 | -1 |
Called when a message is received with decoded JSON content | def receive(self, content, **kwargs):
print(content)
dispatch(content, self.path[1:]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _on_message(self, message):\n print(\"RECEIVED on \" + self.session_name + \":\")\n message_json = json.loads(message)\n print(json.dumps(message_json, sort_keys=True, indent=2, separators=(',', ':')))\n\n for singleMsg in message_json:\n self._process_message(singleMsg)"... | [
"0.7854412",
"0.76435155",
"0.76435155",
"0.7386805",
"0.7192146",
"0.71768695",
"0.7144816",
"0.7113034",
"0.7106444",
"0.7081956",
"0.7069198",
"0.7044991",
"0.69177085",
"0.69148946",
"0.69056135",
"0.68867594",
"0.68732697",
"0.6862182",
"0.6853316",
"0.6838448",
"0.68328... | 0.61205995 | 88 |
Perform things on connection close | def disconnect(self, message, **kwargs):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def on_connection_closed(self):",
"def close(self): \n\t\tself.connection = None",
"def end(self):\n if self.conn:\n self.conn.close()\n self.conn = None\n #print(\"closed.\")",
"def _on_connection_close(self):\n print(\"connection was closed, reconnecting...\")... | [
"0.8287754",
"0.82687026",
"0.8102429",
"0.80203164",
"0.80165076",
"0.7942211",
"0.7918457",
"0.7875925",
"0.78517574",
"0.78462976",
"0.781964",
"0.7806219",
"0.7805812",
"0.78015393",
"0.77674127",
"0.7762862",
"0.7762862",
"0.77322",
"0.77268296",
"0.7720061",
"0.7715943"... | 0.0 | -1 |
Simple exercise of the commandline mode | def test_script(self):
f1 = self.write_file("foobar")
f2 = self.write_file("foobarbaz")
out = io.BytesIO()
ghdiff.main([f1, f2], stdout=out)
output = out.getvalue()
self.assertTrue(b"-foobar" in output)
self.assertTrue(b'+foobar<span class="highlight">baz</span>' ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cli() -> None:",
"def cli() -> None:",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():"... | [
"0.7687243",
"0.7687243",
"0.7510942",
"0.7510942",
"0.7510942",
"0.7510942",
"0.7510942",
"0.7510942",
"0.7510942",
"0.7510942",
"0.7510942",
"0.7510942",
"0.7510942",
"0.7510942",
"0.7510942",
"0.7510942",
"0.7510942",
"0.7510942",
"0.7510942",
"0.7510942",
"0.7510942",
"... | 0.0 | -1 |
Simple test for nocss option | def test_no_css_option(self):
f1 = self.write_file("foobar")
f2 = self.write_file("foobarbaz")
out = io.BytesIO()
ghdiff.main([f1, f2, "--no-css"], stdout=out)
output = out.getvalue()
self.assertFalse(b"<style" in output) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_checkCustoms(self):\n self.failUnlessEqual(self.nice.opts['myflag'], \"PONY!\")\n self.failUnlessEqual(self.nice.opts['myparam'], \"Tofu WITH A PONY!\")",
"def nox(c, s=''):\n if not s:\n c.run('nox --list')\n else:\n c.run(f'nox -s {s}')",
"def test_get_option_exist(... | [
"0.6366742",
"0.627073",
"0.61997247",
"0.6189137",
"0.60918605",
"0.60011894",
"0.5984865",
"0.5972409",
"0.5784358",
"0.57673573",
"0.57223225",
"0.5683754",
"0.5679617",
"0.567664",
"0.56709033",
"0.56357384",
"0.5614824",
"0.5604445",
"0.5579641",
"0.55726093",
"0.5570491... | 0.0 | -1 |
Base `class` for all spiders. It implements a bunch of methods that are reused by child classes. Also, defines an `abstractmethod` that needs to implemented. | def __init__(
self,
timestamp: datetime,
start_urls: Optional[Union[str, List[str]]] = None,
category: Optional[str] = None,
gender: Optional[str] = None,
consumer_lifestage: Optional[str] = None,
search_term: Optional[str] = None,
meta_data: Optional[Unio... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def abstract(f):\r\n\tdef raiser(*args,**kargs):\r\n\t\traise Exception('Abstract function must be overridden by a child class before being called')\r\n\treturn raiser",
"def __init__(self):\n self.SPIDER = \"spider\"",
"def use_abstract_base_class_one():\r\n print(\"* use_abstract_base_class_one() *... | [
"0.5925785",
"0.5669243",
"0.55507463",
"0.54713047",
"0.53880554",
"0.5317189",
"0.5303386",
"0.52768236",
"0.52768236",
"0.52768236",
"0.52768236",
"0.52768236",
"0.52768236",
"0.52768236",
"0.52768236",
"0.52768236",
"0.52768236",
"0.52768236",
"0.52768236",
"0.52768236",
... | 0.0 | -1 |
Helper method to parse start_urls. | def parse_urls(start_urls: Union[str, List[str]]) -> List[str]:
if not (
(type(start_urls) == str)
or ((type(start_urls) == list) and (all([type(x) == str for x in start_urls])))
):
error_message = (
"Argument 'start_urls' need to be of type list or (c... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_start_url(self, response):\n self.parse_obj(response)",
"def start_requests(self):\n for url in self.start_urls:\n yield scrapy.Request(url, callback=self.parse)",
"def start_requests(self):\n url = self.start_urls[0]\n yield scrapy.Request(url=url, callback=sel... | [
"0.7345545",
"0.66011286",
"0.653537",
"0.6299785",
"0.61055857",
"0.60630804",
"0.60070723",
"0.59906334",
"0.5949757",
"0.5919338",
"0.5888575",
"0.5851758",
"0.58476037",
"0.5839682",
"0.5839238",
"0.5828793",
"0.58075947",
"0.57936287",
"0.57855034",
"0.5766114",
"0.57447... | 0.638832 | 3 |
Helper method to parse meta_data. | def parse_meta_data(meta_data: Union[Dict[str, str], str, None]) -> Union[dict, None]:
if meta_data:
meta_data = json.loads(meta_data) if isinstance(meta_data, str) else meta_data # type: ignore # noqa
if isinstance(meta_data, dict):
return meta_data
else:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _parse_metadata(self, meta):\r\n output = {}\r\n for name, value in meta.items():\r\n name = name.lower()\r\n if name == \"summary\":\r\n # handle summary metadata as markdown\r\n # summary metadata is special case and join all list values\r\n ... | [
"0.746068",
"0.6892583",
"0.6836341",
"0.66852564",
"0.66316384",
"0.6485698",
"0.647439",
"0.64668316",
"0.636508",
"0.6314219",
"0.62637264",
"0.6242225",
"0.6163834",
"0.61158377",
"0.60986596",
"0.60902125",
"0.60852194",
"0.6082936",
"0.60725725",
"0.6046314",
"0.6040117... | 0.6533727 | 5 |
The `Scrapy` framework executes this method. If start_urls are set, the spider will just crawl the specified urls. If no start_urls are specified (default) the spider will read all settings from its respective start_script file and generate start_requests. | def start_requests(self) -> Iterator[SplashRequest]:
def get_request_specific_parameters() -> dict:
if self.StartRequest == SplashRequest:
return {
"endpoint": "execute",
"args": { # passed to Splash HTTP API
"wait": s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def start_requests(self):\n for url in self.start_urls:\n yield scrapy.Request(url, callback=self.parse)",
"def start_requests(self):\r\n try:\r\n\r\n for url in self.start_urls:\r\n yield scrapy.Request(url,\r\n callback=self... | [
"0.7262331",
"0.7171378",
"0.71610796",
"0.69475484",
"0.67612314",
"0.6699213",
"0.6567548",
"0.65054625",
"0.6427605",
"0.6386813",
"0.63609964",
"0.63548535",
"0.61784303",
"0.616039",
"0.6143129",
"0.6100814",
"0.5984085",
"0.5961182",
"0.59141284",
"0.58525497",
"0.58159... | 0.6329376 | 12 |
Helper method for child classes. Simply instantiates a `SrapedPage` object and enqueues this to the scraping `Queue`. | def _save_SERP(
self, response: Union[SplashJsonResponse, ScrapyHttpResponse, ScrapyTextResponse]
) -> None:
scraped_page = ScrapedPage(
timestamp=self.timestamp,
source=self.source,
merchant=self.merchant,
country=self.country,
url=respon... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _enqueue(self, page):\n self._queue.append(page)",
"def get_page_queue(url, queue):\n queue.put(urlopen(url).read())\n return None",
"def _add_page_to_rank_queue(self, page):\n with Spider.processed_urls_lock:\n if page and page.url not in self.processed_urls:\n ... | [
"0.71564823",
"0.6373749",
"0.6046783",
"0.59380674",
"0.59113616",
"0.5907949",
"0.5791702",
"0.5763333",
"0.56091076",
"0.55656135",
"0.555843",
"0.5536511",
"0.55028975",
"0.54806",
"0.5454422",
"0.54259396",
"0.5412631",
"0.54105043",
"0.5390186",
"0.5362956",
"0.53565854... | 0.59851176 | 3 |
Helper method for child classes. Simply instantiates a `SrapedPage` object and enqueues this to the scraping `Queue`. | def parse_PRODUCT(self, response: Union[SplashJsonResponse, ScrapyHttpResponse]) -> None:
if meta_information := response.meta.get("meta_data"):
meta_information |= response.meta.get("request_meta_information", {})
else:
meta_information = response.meta.get("request_meta_informa... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _enqueue(self, page):\n self._queue.append(page)",
"def get_page_queue(url, queue):\n queue.put(urlopen(url).read())\n return None",
"def _add_page_to_rank_queue(self, page):\n with Spider.processed_urls_lock:\n if page and page.url not in self.processed_urls:\n ... | [
"0.7156211",
"0.63737756",
"0.6048151",
"0.59868485",
"0.5938351",
"0.591402",
"0.59112746",
"0.5795364",
"0.5764445",
"0.5610664",
"0.55691767",
"0.5559017",
"0.5537731",
"0.55032164",
"0.5480359",
"0.54554003",
"0.5425501",
"0.541318",
"0.5411602",
"0.53905827",
"0.5363092"... | 0.0 | -1 |
Abstract method forces child classes to implement this. It is responsible for yielding new `SplashRequest` for each product on the `response` page and use pagination to the new result page (a.k.a `SERP`). | def parse_SERP(self, response: SplashJsonResponse) -> Iterator[SplashRequest]: | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse(self, response):\n product_urls = response.css(\n '.product-li .product-image a::attr(href)'\n ).getall()\n for product_url in product_urls:\n yield scrapy.Request(response.urljoin(product_url), self.parse_product)\n\n\n next_page_number = 2\n if '... | [
"0.69253975",
"0.6723404",
"0.6673714",
"0.66428447",
"0.66276383",
"0.6357844",
"0.6292618",
"0.6221962",
"0.6189304",
"0.6114769",
"0.61069465",
"0.6071867",
"0.59653133",
"0.5960233",
"0.5958122",
"0.59552586",
"0.59454393",
"0.5938231",
"0.5937393",
"0.59161866",
"0.59145... | 0.6265049 | 7 |
Helper method to create default request meta. All 'SERP' and 'PRODUCT' requests need to " "implement this. It will propagate the meta information from the original/ parent request " "to the child requests. | def create_default_request_meta(
response: Union[ScrapyTextResponse, ScrapyHttpResponse], original_url: Optional[str] = None
) -> Dict:
return {
"original_URL": original_url if original_url else response.url,
"category": response.meta.get("category"),
"gender": r... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def request_info(self, request):\n\n\t\t# We have to re-resolve the request path here, because the information\n\t\t# is not stored on the request.\n\t\tview, args, kwargs = resolve(request.path)\n\t\tfor i, arg in enumerate(args):\n\t\t\tkwargs[i] = arg\n\n\t\tparameters = {}\n\t\tparameters.update(kwargs)\n\t\tp... | [
"0.5762792",
"0.56348234",
"0.56074893",
"0.55480593",
"0.55469114",
"0.5497104",
"0.5468277",
"0.54309773",
"0.54052013",
"0.5381905",
"0.537283",
"0.53606224",
"0.5316467",
"0.5299309",
"0.5298441",
"0.5293145",
"0.52903724",
"0.5282184",
"0.52806866",
"0.5275312",
"0.52701... | 0.7019134 | 0 |
Check if a user is stored in the database with the given ID. | def userIDExists(self, id : int) -> bool:
return id in self.users.keys() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def hasUser(self, id):\n try:\n self.getUser(id)\n return True\n except KeyError:\n return False",
"def checkIfUserExists(self, userID):\n return self.db.select_user(userID)",
"def exists(cls, user_id):\n user_id = int(user_id)\n user = DB_... | [
"0.7826322",
"0.7779238",
"0.7474107",
"0.7306266",
"0.7229806",
"0.7166336",
"0.6960701",
"0.68470055",
"0.680666",
"0.66621155",
"0.664354",
"0.6635361",
"0.6557324",
"0.6557324",
"0.65509564",
"0.65507483",
"0.6542062",
"0.650547",
"0.6498227",
"0.64851344",
"0.646647",
... | 0.76288635 | 2 |
Check if a given bbUser object is stored in the database. Currently only checks if a user with the same ID is stored in the database, not if the objects are the same. | def userObjExists(self, user : bbUser.bbUser) -> bool:
return self.userIDExists(user.id) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def exists_in_db(self) -> bool:\n query = \"\"\"SELECT * \n FROM Users \n WHERE Username=?;\"\"\"\n return len(self.db.fetchall(query, values=(self.username,))) > 0",
"def isOwner(id, userId):\n db = core.connect()\n return db[id][\"createdBy\"] == userId",... | [
"0.66454977",
"0.6629096",
"0.6502776",
"0.64873165",
"0.6384",
"0.63549405",
"0.634741",
"0.6321379",
"0.63175",
"0.63037634",
"0.62980324",
"0.6297806",
"0.6245325",
"0.62262326",
"0.62138486",
"0.62071496",
"0.6190167",
"0.6176963",
"0.616143",
"0.615577",
"0.6141846",
"... | 0.7575999 | 0 |
Internal function to assert the type of and, potentially cast, an ID. | def validateID(self, id : int) -> int:
# If ID is a string, ensure it can be casted to an int before casting and returning.
if type(id) == str:
if not lib.stringTyping.isInt(id):
raise TypeError("user ID must be either int or string of digits")
return int(id)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_id_type(self):\n\n base_model = BaseModel()\n self.assertTrue(base_model.id, str)",
"def check_id(self, id):",
"def test_data_type_id(self):\n self.assertTrue(self.tester.data_type(ret_id=True), 2)",
"def test_convert_id():",
"def test_init_id_identity(self):\n self.ass... | [
"0.6917475",
"0.6664331",
"0.65523475",
"0.6467995",
"0.64658165",
"0.6458434",
"0.62941015",
"0.62888676",
"0.6277681",
"0.62713206",
"0.61878324",
"0.6169987",
"0.61562884",
"0.61032397",
"0.59731686",
"0.5968201",
"0.5962869",
"0.5944459",
"0.59211767",
"0.59169257",
"0.59... | 0.5899975 | 21 |
Reset the stats for the user with the specified ID. | def reinitUser(self, id : int):
id = self.validateID(id)
# ensure the ID exists in the database
if not self.userIDExists(id):
raise KeyError("user not found: " + str(id))
# Reset the user
self.users[id].resetUser() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def stats_reset(self):\n self.stats.reset()",
"def stats_reset(self):\n self.stats.reset()",
"def reset_stats(self):\n print(\"Reseting stats\")\n self.player_lives = self.ai_stts.player_lives\n self.score = 0\n self.level = 1",
"def resetUser(self):\n\t\turl = \"htt... | [
"0.6416",
"0.6416",
"0.63771105",
"0.6241124",
"0.6069007",
"0.6018874",
"0.6008657",
"0.5996576",
"0.59836346",
"0.59318155",
"0.5928619",
"0.5910059",
"0.5876897",
"0.58712983",
"0.58664316",
"0.58137023",
"0.5787853",
"0.5773324",
"0.5766223",
"0.5684285",
"0.5558619",
"... | 0.694401 | 0 |
Create a new bbUser object with the specified ID and add it to the database | def addUser(self, id : int) -> bbUser.bbUser:
id = self.validateID(id)
# Ensure no user exists with the specified ID in the database
if self.userIDExists(id):
raise KeyError("Attempted to add a user that is already in this bbUserDB")
# Create and return a new user
new... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_user_by_id(cls, m_id):",
"def saveNewUser(self, userID):\n self.db.insert_new_user(userID)",
"def fusion_api_add_user(self, body, api=None, headers=None):\n return self.user.create(body, api, headers)",
"def create_user():\n new_user = User(id=login_session['gplus_id'],\n ... | [
"0.71949327",
"0.7119717",
"0.6967095",
"0.6960353",
"0.69133216",
"0.6906858",
"0.6867741",
"0.68506837",
"0.684258",
"0.682456",
"0.6823309",
"0.6796022",
"0.67648846",
"0.67618465",
"0.6743965",
"0.6695233",
"0.6691359",
"0.6680211",
"0.6674689",
"0.66608787",
"0.6653089",... | 0.78019196 | 0 |
Store the given bbUser object in the database | def addUserObj(self, userObj : bbUser.bbUser):
# Ensure no bbUser exists in the db with the same ID as the given bbUser
if self.userIDExists(userObj.id):
raise KeyError("Attempted to add a user that is already in this bbUserDB: " + str(userObj))
# Store the passed bbUser
self... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_user(self):\n db.session.add(self)\n db.session.commit()",
"def save_user(user):\n User.save_user(user)",
"def write_user(self, _user):\n try:\n self.conn_cursor.execute(\"INSERT INTO users (id,bank) VALUES (?, ?)\", (_user.id, _user.bank))\n except sqlite3.In... | [
"0.778756",
"0.7689327",
"0.76267",
"0.7370468",
"0.7265316",
"0.7023805",
"0.6962059",
"0.6888581",
"0.6867369",
"0.6859548",
"0.6841464",
"0.6841464",
"0.68113154",
"0.67926407",
"0.67926407",
"0.67617935",
"0.6744484",
"0.6736218",
"0.6716842",
"0.6694753",
"0.66884285",
... | 0.7119752 | 5 |
If a bbUser exists in the database with the requested ID, return it. If not, create and store a new bbUser and return it. | def getOrAddID(self, id : int) -> bbUser.bbUser:
return self.getUser(id) if self.userIDExists(id) else self.addUser(id) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getUser(self, id : int) -> bbUser.bbUser:\n id = self.validateID(id)\n return self.users[id]",
"def addUser(self, id : int) -> bbUser.bbUser:\n id = self.validateID(id)\n # Ensure no user exists with the specified ID in the database\n if self.userIDExists(id):\n ... | [
"0.71380436",
"0.7094037",
"0.70649517",
"0.6900218",
"0.68044686",
"0.67403007",
"0.6731143",
"0.67079204",
"0.6696172",
"0.6682262",
"0.6681719",
"0.6681719",
"0.6681719",
"0.6681719",
"0.6681203",
"0.666396",
"0.6660379",
"0.66603625",
"0.6650168",
"0.6632992",
"0.6603484"... | 0.797189 | 0 |
Fetch the bbUser from the database with the given ID. | def getUser(self, id : int) -> bbUser.bbUser:
id = self.validateID(id)
return self.users[id] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def fetch_user(self, id: utils.Intable) -> User | None:\n id64 = make_id64(id=id, type=Type.Individual)\n return await self._connection.fetch_user(id64)",
"def get_user_by_id(self, id):\n\t\treturn self.users.get(id)",
"def load_user(id):\n return User.query.get(int(id))",
"def load_us... | [
"0.8142412",
"0.7948193",
"0.79404026",
"0.79404026",
"0.79404026",
"0.79404026",
"0.7924561",
"0.7899446",
"0.7896432",
"0.7849403",
"0.78431547",
"0.78220767",
"0.78131187",
"0.7770938",
"0.7769073",
"0.77454466",
"0.77377176",
"0.7650907",
"0.7642752",
"0.7618635",
"0.7615... | 0.8525421 | 0 |
Get a list of all bbUser objects stored in the database | def getUsers(self) -> List[bbUser.bbUser]:
return list(self.users.values()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_all_users(db):\n return list(db['user'].find())",
"def get_all_users():\n db = api.db.get_conn()\n return list(db.users.find({}, {\"_id\": 0, \"password_hash\": 0}))",
"def get_users():\n\n return User.query.all() # [<User user_id=1 fname=Alice lname=Apple>]",
"def get_all_users():",
"def... | [
"0.8300631",
"0.77812666",
"0.77371156",
"0.7678607",
"0.76764804",
"0.7638404",
"0.75855684",
"0.75751007",
"0.75272286",
"0.7521709",
"0.75214297",
"0.74901927",
"0.7472128",
"0.74240303",
"0.7348471",
"0.7340735",
"0.7311398",
"0.72926855",
"0.72752255",
"0.72612697",
"0.7... | 0.75356483 | 8 |
Get a list of all user IDs stored in the database | def getIds(self) -> List[int]:
return list(self.users.keys()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def user_ids(self):\n return list(self.get_users())",
"def get_id_users(self):\n return self.execute(TABELLE['id_users']['select']['all'])",
"def get_uids():\n DB_NAME = 'cloud_storage.db'\n DB_DIRECTORY = 'server_side_storage/'\n db = sqlite3.connect('{}{}'.format(DB_DIRECTORY, DB_NAME)... | [
"0.83350194",
"0.83074826",
"0.8069338",
"0.7976568",
"0.78300416",
"0.7765191",
"0.7542464",
"0.7480183",
"0.74640286",
"0.7435473",
"0.74341965",
"0.74210936",
"0.7396314",
"0.7283571",
"0.71906507",
"0.7168802",
"0.7152981",
"0.71111584",
"0.71046853",
"0.70904654",
"0.708... | 0.8086009 | 2 |
Serialise this bbUserDB into dictionary format. | def toDict(self, **kwargs) -> dict:
data = {}
# Iterate over all user IDs in the database
for id in self.getIds():
# Serialise each bbUser in the database and save it, along with its ID to dict
# JSON stores properties as strings, so ids must be converted to str first.
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def user2dict(self):\n d = {}\n d['username'] = self.username\n d['level'] = self.level\n d['name'] = self.name\n d['email'] = self.email\n d['creation'] = self.creation\n d['update'] = self.update\n d['nsentences'] = self.nsentences\n d['nsessions'] =... | [
"0.73725003",
"0.72896534",
"0.71920073",
"0.71753246",
"0.716274",
"0.7151962",
"0.71234363",
"0.7105887",
"0.7040066",
"0.69651514",
"0.6952894",
"0.6938799",
"0.6933408",
"0.6907724",
"0.68980014",
"0.68346447",
"0.6777401",
"0.67636013",
"0.6733848",
"0.67159617",
"0.6711... | 0.7482245 | 0 |
Get summarising information about this bbUserDB in string format. Currently only the number of users stored. | def __str__(self) -> str:
return "<bbUserDB: " + str(len(self.users)) + " users>" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def stats_get_str(self):\n return self.stats.get_all_str()",
"def stats_get_str(self):\n return self.stats.get_all_str()",
"def to_string(self):\n return \"User: {} Description: {} Ratings: {}\".format(self.id_user, self.description, self.ratings)",
"def get_stats(self):\n result ... | [
"0.6607278",
"0.6607278",
"0.65238464",
"0.6518738",
"0.6509178",
"0.6467798",
"0.641684",
"0.6371659",
"0.62588465",
"0.6195195",
"0.61339843",
"0.6110665",
"0.6098782",
"0.6087525",
"0.60621756",
"0.60452694",
"0.60246444",
"0.60194707",
"0.6006507",
"0.6000051",
"0.5972194... | 0.76016575 | 0 |
Construct a bbUserDB from a dictionaryserialised representation the reverse of bbUserDB.toDict() | def fromDict(cls, userDBDict : dict, **kwargs) -> bbUserDB:
# Instance the new bbUserDB
newDB = bbUserDB()
# iterate over all user IDs to spawn
for id in userDBDict.keys():
# Construct new bbUsers for each ID in the database
# JSON stores properties as strings, so... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def from_dict(cls, dikt: dict) -> 'UserBase':\n return util.deserialize_model(dikt, cls)",
"def toDict(self, **kwargs) -> dict:\n data = {}\n # Iterate over all user IDs in the database\n for id in self.getIds():\n # Serialise each bbUser in the database and save it, along ... | [
"0.67769945",
"0.6622561",
"0.65516293",
"0.63922143",
"0.63320786",
"0.62116504",
"0.6042782",
"0.6004884",
"0.5900546",
"0.5864641",
"0.5853948",
"0.578413",
"0.5688235",
"0.5666956",
"0.56637603",
"0.5660747",
"0.5650265",
"0.5648268",
"0.56327444",
"0.5618494",
"0.560434"... | 0.71973324 | 0 |
set up for test | def setUpClass(cls):
cls.user = User()
cls.user.first_name = "Kev"
cls.user.last_name = "Yo"
cls.user.email = "1234@yahoo.com"
cls.storage = db_storage() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setup( self ):",
"def setup(self) -> None:",
"def setUp(self):\n logging.debug('setting up')",
"def setUp(self):\n logging.debug('setting up')",
"def _setup(self):",
"def _setup(self):",
"def setUp(self):\r\n # nothing to do, all tests use different things\r\n pass",
"... | [
"0.8239149",
"0.81545687",
"0.7943907",
"0.7943907",
"0.7942225",
"0.7942225",
"0.79307884",
"0.7923598",
"0.7908888",
"0.7908888",
"0.7908888",
"0.7908888",
"0.7908888",
"0.7908888",
"0.7908888",
"0.7908888",
"0.7908888",
"0.7908888",
"0.7908888",
"0.7908888",
"0.7908776",
... | 0.0 | -1 |
at the end of the test this will tear it down | def teardown(cls):
del cls.user | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def teardown(self) -> None:",
"def teardown(self) -> None:",
"def teardown(self) -> None:",
"def teardown(self):",
"def teardown(self):",
"def teardown(self):",
"def teardown(self):\n pass",
"def teardown(self):\n pass",
"def teardown(self):\n pass",
"def teardown(self) -> No... | [
"0.91463774",
"0.91463774",
"0.91463774",
"0.9129943",
"0.9129943",
"0.9129943",
"0.90080214",
"0.90080214",
"0.90080214",
"0.8812721",
"0.8812721",
"0.86614734",
"0.8510095",
"0.8510095",
"0.8370365",
"0.8370365",
"0.8370365",
"0.83670706",
"0.83619934",
"0.83339196",
"0.831... | 0.0 | -1 |
Screen with calendar for one month | def create_month_scr(self, month, toogle_today=False):
scr = Screen()
m = self.month_names_eng[self.active_date[1] - 1]
scr.name = "%s-%s" % (m, self.active_date[2]) # like march-2015
# Grid for days
grid_layout = GridLayout(cols=7, rows=7, size_hint=(1, 1), pos_hint={"top": 1... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def show_cal(request, year=None, month=None):\n if year == None:\n # get the current comic as a starting point\n lToday = Comic.objects.filter(published=True).order_by('-date')[0].date\n year = lToday.year\n month = lToday.month\n\n return calendar(request, year, month)",
"def m... | [
"0.73538274",
"0.6901004",
"0.6769055",
"0.6733737",
"0.6690152",
"0.65959895",
"0.65230334",
"0.6518159",
"0.64170593",
"0.63540894",
"0.6350945",
"0.6336885",
"0.6195404",
"0.6162957",
"0.61168915",
"0.60795885",
"0.6076546",
"0.60162735",
"0.59980357",
"0.59725446",
"0.595... | 0.7408204 | 0 |
Prepare data for showing on widget loading | def prepare_data(self):
# Get days abbrs and month names lists
self.month_names = cal_data.get_month_names()
self.month_names_eng = cal_data.get_month_names_eng()
self.days_abrs = cal_data.get_days_abbrs()
# Today date
self.active_date = cal_data.today_date_list()
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def prepare_data(self):",
"def prepare_UI(self):",
"def populate_widget(self):\n self.temp_dropdown.addItems(self.units_data[\"Choices\"][\"Temperature\"])\n self.volume_dropdown.addItems(self.units_data[\"Choices\"][\"Volume\"])\n self.press_dropdown.addItems(self.units_data[\"Choices\"][... | [
"0.73985404",
"0.66375476",
"0.6256399",
"0.6207288",
"0.61352146",
"0.60843796",
"0.60673505",
"0.59987247",
"0.59987247",
"0.5973385",
"0.59121627",
"0.587666",
"0.5833557",
"0.57790965",
"0.5755037",
"0.57352716",
"0.57310146",
"0.5705217",
"0.5695651",
"0.56943774",
"0.56... | 0.6518631 | 2 |
Get caledar and months/years nums for quarter | def get_quarter(self):
self.quarter_nums = cal_data.calc_quarter(self.active_date[2],
self.active_date[1])
self.quarter = cal_data.get_quarter(self.active_date[2],
self.active_date[1]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def quarter_from_month(month: int) -> int:\n return ((month - 1) // 3) + 1",
"def _quarter_number(self, dategiven):\n return RetailDate(dategiven).quarter",
"def next_quarter_start(start_year:int, start_month:int, logger:lg.Logger = None) -> (int, int):\n if logger:\n logger.debug(F\"start ... | [
"0.71771586",
"0.69877535",
"0.69545823",
"0.68355507",
"0.65251267",
"0.62602466",
"0.6153807",
"0.60453457",
"0.59756655",
"0.5955437",
"0.58934164",
"0.58909696",
"0.5877462",
"0.5877462",
"0.5867307",
"0.5819606",
"0.5772608",
"0.57516044",
"0.5712844",
"0.57028717",
"0.5... | 0.67201173 | 4 |
Get day value from pressed button | def get_btn_value(self, inst):
self.active_date[0] = int(inst.text)
selected = [self.active_date[0], self.active_date[1], self.active_date[2]]
global selectedDates
if selected in selectedDates:
selectedDates.remove(selected)
else:
selectedDates.append(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_day():\n return handle_invalid_inputs(question_4, days)",
"def day(sym, date):\n return get(sym, date, date)[0][1]",
"def on_Calendar_day_selected_double_click(self, widget):\n try:\n agno, mes, dia = variables.calendar.get_date()\n fecha = \"%02d/\" % dia + \"%02d/\"... | [
"0.66065156",
"0.6411967",
"0.62181884",
"0.61390465",
"0.6137295",
"0.61261487",
"0.6031694",
"0.6031341",
"0.6031341",
"0.6031341",
"0.60150236",
"0.60150236",
"0.5965724",
"0.59653014",
"0.5939489",
"0.59322006",
"0.58828443",
"0.587401",
"0.58704",
"0.58308136",
"0.581959... | 0.6740263 | 0 |
Go to screen with previous month | def go_prev(self, inst):
# Change active date
self.active_date = [self.active_date[0], self.quarter_nums[0][1],
self.quarter_nums[0][0]]
# Name of prev screen
n = self.quarter_nums[0][1] - 1
prev_scr_name = "%s-%s" % (self.month_names_eng[n],
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def click_previous_month(self):\n self.action.click(self.calendar_previous)\n time.sleep(3)",
"def showPreviousMonth(self):\n pass",
"def _prev_month(self):\r\n self._canvas.place_forget()\r\n\r\n self._date = self._date - self.timedelta(days=1)\r\n self._date = self.d... | [
"0.77513474",
"0.7437955",
"0.73504657",
"0.73097664",
"0.66519666",
"0.6488268",
"0.6330314",
"0.6143109",
"0.6109163",
"0.6047146",
"0.6028771",
"0.6025365",
"0.6016673",
"0.5978298",
"0.5845772",
"0.5841132",
"0.5791068",
"0.57260734",
"0.5630533",
"0.5602191",
"0.5602191"... | 0.71048 | 4 |
Go to screen with next month | def go_next(self, inst):
# Change active date
self.active_date = [self.active_date[0], self.quarter_nums[2][1],
self.quarter_nums[2][0]]
# Name of prev screen
n = self.quarter_nums[2][1] - 1
next_scr_name = "%s-%s" % (self.month_names_eng[n],
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def click_next_month(self):\n self.action.click(self.calendar_next)\n time.sleep(3)",
"def showNextMonth(self):\n pass",
"def _next_month(self):\r\n self._canvas.place_forget()\r\n\r\n year, month = self._date.year, self._date.month\r\n self._date = self._date + self.t... | [
"0.8026814",
"0.7535266",
"0.73783004",
"0.73252517",
"0.6710318",
"0.64836913",
"0.6130388",
"0.59935415",
"0.5949899",
"0.5932306",
"0.591973",
"0.5908469",
"0.58657205",
"0.58533937",
"0.5806184",
"0.5803234",
"0.5780424",
"0.5727439",
"0.5727259",
"0.5713089",
"0.5697559"... | 0.7162475 | 4 |
Switch months pages by touch move | def on_touch_move(self, touch):
if self.touch_switch:
# Left - prev
if touch.dpos[0] < -30:
self.go_prev(None)
# Right - next
elif touch.dpos[0] > 30:
self.go_next(None) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def changeDisplayedMonth(self):\n #ho bisogno di sapere qual è il mese mostrato\n currentMonth = self.indexMonth\n currentYear = self.currentYear\n\n sender = self.sender().objectName()\n if sender == 'bot_next':\n # if currentMonth < 11:\n if self.indexMont... | [
"0.6470862",
"0.6099905",
"0.5709868",
"0.5687509",
"0.56433487",
"0.56086636",
"0.5535471",
"0.5520773",
"0.54963934",
"0.5425755",
"0.5371083",
"0.53478676",
"0.5297901",
"0.52765054",
"0.5259761",
"0.525924",
"0.52506715",
"0.52460074",
"0.51859444",
"0.51507366",
"0.51237... | 0.5567337 | 6 |
Project a vector onto an Linf ball. | def project_Linf_ball(x: "fasta.linalg.Vector", t: float) -> "fasta.linalg.Vector":
N = len(x)
xabs = np.abs(x)
# Reverse sort the absolute values of z
flipped = xabs.copy()
flipped[::-1].sort()
# Magic
alpha = np.max((np.cumsum(flipped) - t) / np.arange(1, N+1))
if alpha > 0:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def project_L1_ball(x: \"fasta.linalg.Vector\", t: float) -> \"fasta.linalg.Vector\":\n # By Moreau's identity, we convert to proximal of dual problem (L-inf norm)\n return x - project_Linf_ball(x, t)",
"def translate(self, vector):\n if self.blender_object:\n self.blender_object.location... | [
"0.660014",
"0.6530639",
"0.62310714",
"0.6171093",
"0.61017925",
"0.6068066",
"0.59561163",
"0.59489536",
"0.5879308",
"0.58778346",
"0.5838586",
"0.58375114",
"0.581667",
"0.58055514",
"0.5805013",
"0.5804504",
"0.5783989",
"0.5782783",
"0.57660913",
"0.5748762",
"0.5747332... | 0.6179265 | 3 |
Project a vector onto an L1 ball. | def project_L1_ball(x: "fasta.linalg.Vector", t: float) -> "fasta.linalg.Vector":
# By Moreau's identity, we convert to proximal of dual problem (L-inf norm)
return x - project_Linf_ball(x, t) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def translate(self, vector):\n if self.blender_object:\n self.blender_object.location = vector",
"def project_vector(self, vector: array_like) -> Vector:\n point_in_space = self.point + vector\n point_on_plane = self.project_point(point_in_space)\n\n return Vector.from_poin... | [
"0.66520876",
"0.6209839",
"0.6140181",
"0.6015735",
"0.5979458",
"0.5940027",
"0.5939712",
"0.59238505",
"0.5875229",
"0.5867069",
"0.5799263",
"0.5797262",
"0.57569957",
"0.5726671",
"0.57266325",
"0.5723105",
"0.5687589",
"0.56815106",
"0.56803626",
"0.5629013",
"0.5628878... | 0.7311037 | 0 |
Project a matrix onto a ball induced by the nuclear norm. | def project_Lnuc_ball(X: "fasta.linalg.Matrix", t: float) -> "fasta.linalg.Matrix":
U, s, V = la.svd(X)
# Construct the diagonal matrix of singular values, S, as a shrunken version of the original signal values
S = np.zeros(X.shape)
S[:len(s),:len(s)] = np.diag(shrink(s, t))
return U @ S @ V | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def normalize(self):\n det = self._mat[0][0]*self._mat[1][1] - self._mat[0][1]*self._mat[1][0]\n for i in range(2):\n for j in range(2):\n self._mat[i][j] = (self._mat[i][j])/(np.sqrt(det))",
"def normalize(self):\r\n max = np.amax(self.matrix)\r\n min = np.a... | [
"0.63313407",
"0.60166395",
"0.59255046",
"0.5873462",
"0.58486867",
"0.5786831",
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"0.56968874",
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"0.5478803",
"0.54655546",
"0.545859",
"0.544245",
"0.5396391",
"0.539067... | 0.57607806 | 7 |
The shrink (softthresholding) operator, which is also the proximal operator for the L1norm. The shrink operator reducing the magnitudes of all entries in x by t, leaving them at zero if they're already less than t. | def shrink(x: np.ndarray, t: float) -> np.ndarray:
return np.sign(x) * np.maximum(np.abs(x) - t, 0) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def convert_softshrink(g, op, block):\n\n x = g.get_node(op.input(\"X\")[0])\n dtype = infer_type(x).checked_type.dtype\n threshold = _expr.const(op.attr(\"lambda\"), dtype=dtype)\n zeros = _op.zeros_like(x)\n out = _op.where(x < -threshold, x + threshold, zeros) + _op.where(\n x > threshold,... | [
"0.65450245",
"0.6496893",
"0.64630055",
"0.6456143",
"0.6226922",
"0.61587155",
"0.5946745",
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"0.5629461",
"0.5480745",
"0.543062",
"0.5420821",
"0.54135394",
"0.5374806",
"0.53720266... | 0.78240633 | 0 |
build resnet backbone and position embedding according to config | def build_backbone(config):
assert config.MODEL.BACKBONE in ['resnet50', 'resnet101'], "backbone name is not supported!"
backbone_name = config.MODEL.BACKBONE
dilation = False
train_backbone = not config.EVAL
return_interm_layers = False #TODO: impl case True for segmentation
position_embedding... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def backbone_generator(params):\n if params.architecture.backbone == 'resnet':\n resnet_params = params.resnet\n backbone_fn = resnet.Resnet(\n resnet_depth=resnet_params.resnet_depth,\n dropblock=dropblock_generator(params.dropblock),\n activation=params.batch_norm_activation.activatio... | [
"0.6338696",
"0.62638956",
"0.62531716",
"0.6237589",
"0.6195611",
"0.6154484",
"0.60957676",
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"0.59420633",
"0.59154165",
"0.58285683",
"0.58096284",
"0.58037734",
"0.5793... | 0.66462153 | 0 |
Implementation of the identity block as defined in Figure 3 | def identity_block(X, f, filters, stage, block):
# defining name basis
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
# Retrieve Filters
F1, F2, F3 = filters
# Save the input value. You'll need this later to add back to... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def makeIdentity(self) -> None:\n ...",
"def identity(cls):\n return super().identity(4, 4)",
"def identity_block(self, input_tensor, middle_filter_size, filters, stage, block):\n\n # Set the naming convention\n conv_name_base = 'res' + str(stage) + block + '_branch'\n batch_... | [
"0.6719558",
"0.64335775",
"0.64263827",
"0.6325968",
"0.62210673",
"0.6205097",
"0.61472464",
"0.61263555",
"0.5965766",
"0.5961469",
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"0.59137326",
"0.58742166",
"0.585961",
"0.585453",
"0.5833093",
"0.58320105",
"0.5832010... | 0.5725329 | 35 |
Implementation of the convolutional block as defined in Figure 4 | def convolutional_block(X, f, filters, stage, block, s = 2):
# defining name basis
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
# Retrieve Filters
F1, F2, F3 = filters
# Save the input value
X_shortcut = X
#... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _conv_block( inputs, filters, kernel, strides, nl):\n channel_axis = 1 if K.image_data_format() == 'channels_first' else -1\n x = Conv2D(filters, kernel, padding='same', strides=strides)(inputs)\n x = BatchNormalization(axis=channel_axis)(x)\n return _return_activati... | [
"0.75467706",
"0.7491661",
"0.7307114",
"0.72310984",
"0.7224572",
"0.7169003",
"0.71397346",
"0.7121883",
"0.7079463",
"0.70640475",
"0.70419514",
"0.7027111",
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"0.7014711",
"0.7007952",
"0.70026505",
"0.6999438",
"0.69759107",
"0.6945785",
"0.6941995",
"0.69197... | 0.6933016 | 20 |
a simple data filter for On and Off data set | def simple(onArray, offArray):
Larray = len(onArray)
Larray2 = len(offArray)
assert Larray == Larray2, "both arrays should have the same size"
#onFiltered = numpy.array(onArray)[:,OnOff.misc.constants.dataRange]
#offFiltered = numpy.array(offArray)[:,OnOff.misc.constants.dataRange]
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def do_data_filter(self, arg):\n if arg:\n if arg.upper() == \"ON\":\n self.data_only = True\n print(\"Filtering for data\")\n elif arg.upper() == \"OFF\":\n self.data_only = False\n print(\"Raw data, no filtering\")\n ... | [
"0.790279",
"0.68484104",
"0.6469736",
"0.6187022",
"0.60985696",
"0.6086896",
"0.60415226",
"0.60381603",
"0.5973669",
"0.59631866",
"0.595",
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"0.58900917",
"0.5875511",
"0.5874126",
"0.5817856",
"0.5786788",
"0.5784326",
"0.5749742",
... | 0.64173406 | 3 |
For a single channel single sample, we compute Pois(d | fac(pars) (delta(pars) + nom) ) Gaus(a | pars[is_gaus], sigmas) Pois(a cfac | pars[is_poi] cfac) | def expected_actualdata(self, pars):
tensorlib, _ = get_backend()
pars = tensorlib.astensor(pars)
deltas, factors = self._modifications(pars)
allsum = tensorlib.concatenate(deltas + [tensorlib.astensor(self.thenom)])
nom_plus_delta = tensorlib.sum(allsum, axis=0)
nom_p... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def G_MP9401(ps, **kw) -> MAG:\n g, i = kw.get(\"g\", \"g\"), kw.get(\"i\", \"i\")\n gmi = kw.get(\"gmi\", \"g-i\")\n\n if gmi in ps.colnames:\n gmi = ps[gmi]\n else:\n gmi = ps[g] - ps[i]\n\n ind = (-1.0 * MAG < gmi) & (gmi < 4 * MAG)\n if not all(ind):\n warnings.warn(\"MCg... | [
"0.5365971",
"0.53142685",
"0.5280462",
"0.5248588",
"0.5151607",
"0.5151149",
"0.50696945",
"0.5016467",
"0.5013076",
"0.49877918",
"0.4887354",
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"0.4853184",
"0.48526874",
"0.4847001",
"0.48311242",
"0.48219612",
"0.48190698",
"0.4817454",
"0.4801044",
"0.479936... | 0.0 | -1 |
Create a model object with/without patches applied. | def model(self, **config_kwargs):
measurement = self.get_measurement(**config_kwargs)
log.debug(
'model being created for measurement {0:s}'.format(measurement['name'])
)
patches = config_kwargs.get('patches', [])
modelspec = {
'channels': self.spec['cha... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_model(self):\n self.model = None\n pass",
"def create_model(self):\n pass",
"def create_model(self):\n pass",
"def MakeModel(self):\n pass",
"def new(self, **kwargs):\n return self.__model__(**self._preprocess_params(kwargs))",
"def make_model():\n ... | [
"0.6639623",
"0.64880705",
"0.64880705",
"0.63333046",
"0.63234615",
"0.62291735",
"0.61787397",
"0.6168018",
"0.6163506",
"0.61584336",
"0.61175513",
"0.61146307",
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"0.6030808",
"0.6009319",
"0.59917843",
"0.5973701",
"0.5952992",
"0.59278697",
"0.5... | 0.60898846 | 13 |
Return the data for the supplied model with or without auxiliary data from the model. The model is needed as the order of the data depends on the order of the channels in the model. | def data(self, model, with_aux=True):
try:
observed_data = sum(
(self.observations[c] for c in model.config.channels), []
)
except KeyError:
log.error(
"Invalid channel: the workspace does not have observation data for one of the chann... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_input_data_for_model(self, extra_data=None):\n extra_data = {} if extra_data is None else extra_data\n if self.metadata['sample_rate'] is not None:\n if self.audio_signal.sample_rate != self.metadata['sample_rate']:\n self.audio_signal.resample(self.metadata['sample... | [
"0.6538062",
"0.6490415",
"0.62376046",
"0.6171267",
"0.6053518",
"0.58562785",
"0.580033",
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"0.57479113",
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"0.56002873",
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"0.54566306",
"0.54521996",
"0.54192096",
"0.5412202",
"0.53936917",
"0.5341618",
"0.5324312... | 0.72179353 | 0 |
Main web page, displays summary statistics of all switches | def switch_inventory():
lastupdate = getLastUpdate()
switchdata = getSwitchInfo()
return render_template("main.html", switches=switchdata, lastupdate=lastupdate) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def statistics():\n return render_template('statistics.html'), 200",
"def index():\r\n\r\n title = \"Global Statistics\"\r\n\r\n n_auth = n_auth_users()\r\n\r\n n_anon = n_anon_users()\r\n\r\n n_total_users = n_anon + n_auth\r\n\r\n n_published_apps = cached_apps.n_published()\r\n n_draft_ap... | [
"0.69233876",
"0.6406892",
"0.6289335",
"0.61874783",
"0.6130209",
"0.61086214",
"0.60472643",
"0.6045767",
"0.6032195",
"0.5959054",
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"0.5834708",
"0.5812339",
"0.58045405",
"0.5771641",
"0.5763239",
"0.57630... | 0.5535049 | 51 |
This page shows detailed stats on an individual switch queried by serial number | def switch_info(serial):
detail = getSwitchDetail(serial)
intdetail = getInterfaceDetail(serial)
try:
raw_data = open(f"raw_output/{serial}.txt", "r").read().splitlines()
except:
raw_data = "None collected yet"
return render_template(
"detail.html",
title=serial,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getSwitchDetail(serial):\n swDB = switchdb.DB()\n raw_info = swDB.getSwitchDetail(serial)\n switch = {}\n for row in raw_info:\n switch[\"name\"] = row[0]\n switch[\"serial\"] = row[1]\n switch[\"model\"] = row[2]\n switch[\"swver\"] = row[3]\n switch[\"ip\"] = ro... | [
"0.747413",
"0.6891839",
"0.5926545",
"0.58273053",
"0.57952845",
"0.5792791",
"0.57800347",
"0.56501704",
"0.564042",
"0.5636971",
"0.56230223",
"0.54754305",
"0.5463351",
"0.5437777",
"0.5394672",
"0.53712904",
"0.536182",
"0.53433543",
"0.53223366",
"0.52944213",
"0.527366... | 0.7308758 | 1 |
This page shows a summary of all port counts, etc across the entire network | def network_wide():
network = getNetworkWide()
return render_template("network-wide.html", network=network) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def list_ports(state):\n\tstate.report()",
"def PortStatistics(self):\n return self._get_attribute('portStatistics')",
"def get_port_counts(ssh):\r\n cmd02='netstat -na'\r\n retry_number=3\r\n try:\r\n while True:\r\n if retry_number == 0:\r\n logger.writeLog(\"... | [
"0.71391034",
"0.7044147",
"0.7031719",
"0.6976165",
"0.6814074",
"0.6660063",
"0.665406",
"0.6509906",
"0.6460758",
"0.6430918",
"0.6304462",
"0.6292888",
"0.62819636",
"0.6264129",
"0.62447363",
"0.62227875",
"0.61759615",
"0.61522573",
"0.6141049",
"0.60898095",
"0.6084936... | 0.0 | -1 |
Check DB for last runtime of backend script This is published on the main page to see when stats were last updated | def getLastUpdate():
swDB = switchdb.DB()
lastupdate = swDB.getLastUpdate()
swDB.close()
return lastupdate | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_last_init() -> str:\n return db[\"last_init\"]",
"def test_log_last_started_datetime(self):\n initial_count = CostUsageReportStatus.objects.count()\n saver = ReportStatsDBAccessor(\"myreport\", self.manifest_id)\n saver.log_last_started_datetime()\n self.assertIsNotNone(sav... | [
"0.575571",
"0.5727403",
"0.56392384",
"0.5554743",
"0.5544511",
"0.5508971",
"0.5502992",
"0.54756486",
"0.54550827",
"0.5454201",
"0.54325056",
"0.54227924",
"0.5381801",
"0.5367871",
"0.53669065",
"0.5365385",
"0.536471",
"0.5316883",
"0.53158337",
"0.53126395",
"0.5308993... | 0.6065848 | 0 |
Query DB for summary info on all switches currently monitored | def getSwitchInfo():
swDB = switchdb.DB()
raw_info = swDB.getAllSummary()
switchList = []
for row in raw_info:
row = list(row)
switch = {}
switch["name"] = row[0]
switch["serial"] = row[1]
switch["swver"] = row[2]
switch["ip"] = row[3]
switch["chec... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_switches_stats(self, site_id: str) -> List:\n try:\n stats = self.api.get(host=self.host, endpoint=f\"/api/v1/sites/{site_id}/stats/devices?type=switch\")\n except Exception as e:\n logger.error(f\"{TextColors.FAIL}Error getting switch stats:{TextColors.ENDC} {e}\")\n ... | [
"0.59377617",
"0.58443993",
"0.58337426",
"0.569104",
"0.5689093",
"0.56877404",
"0.5678661",
"0.56559193",
"0.56524736",
"0.5558809",
"0.5531664",
"0.55013895",
"0.5465066",
"0.5456181",
"0.54546607",
"0.54425275",
"0.5420958",
"0.5404441",
"0.53917074",
"0.53812945",
"0.536... | 0.71028024 | 0 |
Query DB for details on one specific device by serial number | def getSwitchDetail(serial):
swDB = switchdb.DB()
raw_info = swDB.getSwitchDetail(serial)
switch = {}
for row in raw_info:
switch["name"] = row[0]
switch["serial"] = row[1]
switch["model"] = row[2]
switch["swver"] = row[3]
switch["ip"] = row[4]
switch["che... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def db_get_device_info(db_path, rec_name):\n path_exist = os.path.exists(db_path)\n if path_exist is False:\n print '!!!Error, database does not exist.'\n return\n\n try:\n with db.connect(db_path) as conn:\n cursor = conn.cursor()\n # Prepare and execute SQL sta... | [
"0.6281861",
"0.62783605",
"0.6202396",
"0.6042647",
"0.596461",
"0.5964163",
"0.5939326",
"0.59231377",
"0.5860751",
"0.585554",
"0.57786566",
"0.57782006",
"0.5775878",
"0.57700396",
"0.57700396",
"0.5758909",
"0.5742942",
"0.57343465",
"0.5723668",
"0.57158846",
"0.5696746... | 0.5622504 | 27 |
Query DB for interface details on one specific device by management IP | def getInterfaceDetail(serial):
swDB = switchdb.DB()
raw_info = swDB.getInterfaceDetail(serial)
interfaceList = []
for row in raw_info:
row = list(row)
interface = {}
interface["name"] = row[0]
interface["description"] = row[1]
interface["physical_address"] = row[... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pull_info(task):\n\n interface_result = task.run(task=send_command, command=\"show interfaces\")\n task.host[\"facts\"] = interface_result.scrapli_response.genie_parse_output()\n interfaces = task.host[\"facts\"]\n for interface in interfaces:\n try:\n mac_addr = interfaces[interf... | [
"0.63337666",
"0.62210196",
"0.6105885",
"0.6033438",
"0.5927044",
"0.58544815",
"0.5841657",
"0.5808452",
"0.58042765",
"0.57559174",
"0.57140326",
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"0.56404054",
"0.5636814",
"0.5565979",
"0.55621296",
"0.5549179",
"0.55420583",
"0.54... | 0.6122693 | 2 |
Query DB for all switch statistcs, then tally results & return to web page | def getNetworkWide():
swDB = switchdb.DB()
result = swDB.getNetworkWideStats()
swDB.close()
network = {
"models": [],
"swvers": [],
"total": 0,
"up": 0,
"down": 0,
"disabled": 0,
"int10m": 0,
"int100m": 0,
"int1g": 0,
"int10... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def index():\n\twith get_db().cursor() as cursor :\n\t\tcursor.execute(\"SELECT *, (SELECT COUNT(*) FROM `cheer` WHERE ask_id = ask.id) AS cheer_cnt FROM `ask`\")\n\t\tresult = cursor.fetchall()\n\n\treturn render_template('main.html',\n\t\tdataset=result,\n\t)",
"def getSwitchInfo():\n swDB = switchdb.DB()\n... | [
"0.58005536",
"0.579169",
"0.5744348",
"0.5681391",
"0.5491771",
"0.54005426",
"0.53476256",
"0.5337913",
"0.533657",
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"0.5244099",
"0.5227676",
"0.5225523",
"0.5219429",
"0.5215352",
"0.51971906",
"0.51844835",... | 0.0 | -1 |
Call to DB to delete a device by serial number | def deleteDevice(serial):
swDB = switchdb.DB()
swDB.deleteBySerial(serial)
swDB.close() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def db_delete_device_record(db_path, rec_name):\n path_exist = os.path.exists(db_path)\n if path_exist is False:\n print '!!!Error, database does not exist.'\n return\n\n try:\n with db.connect(db_path) as conn:\n cursor = conn.cursor()\n # Prepare and execute SQ... | [
"0.71832097",
"0.70881724",
"0.6902768",
"0.6834857",
"0.67007685",
"0.67007685",
"0.66466284",
"0.6552417",
"0.65387774",
"0.6451662",
"0.64388514",
"0.6282438",
"0.6280378",
"0.62574196",
"0.6221847",
"0.621336",
"0.61614496",
"0.61176556",
"0.6114799",
"0.6113734",
"0.6084... | 0.82801366 | 0 |
Does the user have the permission to publish a data. | def has_perm_publish_data(user):
has_perm_publish(user, rights.PUBLISH_DATA) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def canPublish(id, userId):\n db = core.connect()\n theShift = db[id]\n return user.isAdmin(userId) or (userId == theShift['createdBy'])",
"def has_add_permission(self, request):\n if not settings.PUBLISHER_CODE:\n return False\n return super().has_add_permission(request)",
"d... | [
"0.7401715",
"0.68930787",
"0.6854572",
"0.68086797",
"0.6761256",
"0.67495507",
"0.6707519",
"0.6702206",
"0.6684865",
"0.66546744",
"0.6624943",
"0.66060966",
"0.6596229",
"0.65667605",
"0.6543968",
"0.6537075",
"0.6512933",
"0.65074724",
"0.6503338",
"0.64965105",
"0.64941... | 0.889468 | 0 |
Can read list of data. | def can_read_list_data_id(func, list_data_id, user):
if user.is_superuser:
return func(list_data_id, user)
# check anonymous access
_check_anonymous_access(user)
list_data = func(list_data_id, user)
check_can_read_list(list_data, user)
return list_data | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_list(self, register, length):\n raise NotImplementedError",
"def _read_data(self):",
"def canread(self):\n return False",
"def storage_can_read(self):\n return True",
"def read_data(self) -> List[BaseRecord]:\n pass",
"def read(self) -> List[str]:\n pass",
"d... | [
"0.662519",
"0.6616855",
"0.6610192",
"0.6583547",
"0.6576495",
"0.64457273",
"0.6304789",
"0.62909526",
"0.6290033",
"0.6213072",
"0.616153",
"0.61129534",
"0.60768044",
"0.59920454",
"0.5950494",
"0.5942837",
"0.59417313",
"0.593891",
"0.59161913",
"0.59137505",
"0.5912139"... | 0.6868034 | 0 |
Can read a data, given a query. | def can_read_data_query(
func,
query,
user,
workspace_filter=None,
user_filter=None,
order_by_field=DATA_SORTING_FIELDS,
):
# check anonymous access
_check_anonymous_access(user)
# update the query
query = _update_can_read_query(query, user, workspace_filter, user_filter)
# g... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read(self, query):\r\n t1 = time.time()\r\n if self.database in ['redshift', 'postgres']:\r\n ret = postgres_helper.fetchall(config=self.conf, sql=query)\r\n else:\r\n raise Exception(\"database not supported yet: '{}'\"\r\n .format(self.dat... | [
"0.64174247",
"0.6388573",
"0.6367381",
"0.6172934",
"0.5943755",
"0.59167004",
"0.5904258",
"0.5697214",
"0.5670645",
"0.56312513",
"0.5612141",
"0.555337",
"0.55376184",
"0.5506616",
"0.550447",
"0.54996186",
"0.5492912",
"0.5484353",
"0.5476029",
"0.5465419",
"0.5450928",
... | 0.71578825 | 0 |
Can read a data, given an aggregate query. | def can_read_aggregate_query(func, query, user):
if user.is_superuser:
return func(query, user)
# check anonymous access
_check_anonymous_access(user)
# update the query
query = _update_can_read_aggregate_query(query, user)
# get list of data
data = func(query, user)
return da... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def can_read_data_query(\n func,\n query,\n user,\n workspace_filter=None,\n user_filter=None,\n order_by_field=DATA_SORTING_FIELDS,\n):\n # check anonymous access\n _check_anonymous_access(user)\n # update the query\n query = _update_can_read_query(query, user, workspace_filter, user... | [
"0.68077844",
"0.6013364",
"0.5804927",
"0.57963204",
"0.5774573",
"0.5536843",
"0.5486568",
"0.54431725",
"0.534709",
"0.53435445",
"0.52316743",
"0.5172882",
"0.5158935",
"0.5156636",
"0.51558",
"0.5118686",
"0.5110658",
"0.507409",
"0.50682276",
"0.5029962",
"0.50261956",
... | 0.7802571 | 0 |
Update query with access control parameters. | def _update_can_read_query(
query, user, workspace_filter=None, user_filter=None
):
accessible_workspaces = _get_read_accessible_workspaces_by_user(user)
# update query with workspace criteria
query = django_raw_query.add_access_criteria(
query, accessible_workspaces, user, workspace_filter, us... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update(self, params):",
"def update(self, query: str, *args, **kwargs):\n cursor = self._cursor()\n try:\n self._execute(cursor, query, args, kwargs)\n return cursor.rowcount\n finally:\n cursor.close()",
"def _update(self, values):\n if self.que... | [
"0.6119067",
"0.6095496",
"0.60731256",
"0.6058203",
"0.58836794",
"0.58246744",
"0.5813785",
"0.58044815",
"0.5776203",
"0.57282144",
"0.57191473",
"0.5662358",
"0.5660843",
"0.5659255",
"0.5619666",
"0.560143",
"0.5595878",
"0.55811363",
"0.55611885",
"0.5551484",
"0.554431... | 0.65168345 | 0 |
Update query with access control parameters. | def _update_can_read_aggregate_query(query, user):
accessible_workspaces = _get_read_accessible_workspaces_by_user(user)
# update query with workspace criteria
query = mongo_raw_query.add_aggregate_access_criteria(
query, accessible_workspaces, user
)
return query | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _update_can_read_query(\n query, user, workspace_filter=None, user_filter=None\n):\n\n accessible_workspaces = _get_read_accessible_workspaces_by_user(user)\n # update query with workspace criteria\n query = django_raw_query.add_access_criteria(\n query, accessible_workspaces, user, workspac... | [
"0.65168345",
"0.6119067",
"0.6095496",
"0.60731256",
"0.6058203",
"0.58836794",
"0.58246744",
"0.5813785",
"0.58044815",
"0.5776203",
"0.57282144",
"0.57191473",
"0.5662358",
"0.5660843",
"0.5659255",
"0.5619666",
"0.560143",
"0.5595878",
"0.55811363",
"0.55611885",
"0.55514... | 0.534593 | 59 |
Get read accessible workspaces by user. | def _get_read_accessible_workspaces_by_user(user):
if not settings.CAN_ANONYMOUS_ACCESS_PUBLIC_DOCUMENT and user.is_anonymous:
accessible_workspaces = []
else:
# workspace case
# list accessible workspaces
accessible_workspaces = [
workspace.id
for workspa... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_accessible_spaces(user):\n if not user:\n return []\n obj_list = get_objects_for_user(user, 'access_space',Space)\\\n .order_by('-created_at')\n return obj_list",
"def get_own_spaces(user):\n if not user:\n return []\n own_spaces = []\n accessible_spacs = ge... | [
"0.6719666",
"0.66262877",
"0.6177913",
"0.61746544",
"0.61572975",
"0.59542626",
"0.5788981",
"0.5735323",
"0.5701356",
"0.5671635",
"0.5643736",
"0.5525086",
"0.55015767",
"0.54937226",
"0.549314",
"0.54608184",
"0.5457025",
"0.54482085",
"0.54317015",
"0.54259753",
"0.5395... | 0.82568294 | 0 |
Can user write data in workspace. | def can_write_data_workspace(func, data, workspace, user):
return can_write_in_workspace(
func, data, workspace, user, rights.PUBLISH_DATA
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def can_write(self, auth_param: str) -> bool:\n perms = self._get_workspace_permissions([auth_param])\n return self._has_write_perm(perms.get(auth_param, WorkspacePermission.NONE))",
"def canwrite(self):\n return False",
"def set_data_writable(self):\n pass",
"def isWriteable(self... | [
"0.72511923",
"0.70142853",
"0.6588434",
"0.6476887",
"0.6420118",
"0.63587517",
"0.6332469",
"0.63128155",
"0.62503433",
"0.6213865",
"0.6140603",
"0.61385965",
"0.60728014",
"0.59627616",
"0.59163034",
"0.5911914",
"0.5841909",
"0.5827193",
"0.5817602",
"0.57675314",
"0.575... | 0.81769687 | 0 |
Can read from object id. | def can_read_blob(func, data, user):
if user.is_superuser:
return func(data, user)
if data._blob is not None:
_check_can_read(data._blob, user)
return func(data, user) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_id(self, id):",
"def get_object(id):",
"def read_object(self, object_id: str) -> Any:\n return self.objects[object_id]",
"def has_object_permission(self , request , view , obj):\n if request.method in permissions.SAFE_METHODS:\n return True\n \n return o... | [
"0.68517625",
"0.6691061",
"0.65946645",
"0.6365738",
"0.6347586",
"0.6273723",
"0.62640613",
"0.6212131",
"0.61415297",
"0.6131172",
"0.6058819",
"0.60123235",
"0.5980215",
"0.59708816",
"0.5950038",
"0.5917729",
"0.5897726",
"0.5897726",
"0.5895458",
"0.5891156",
"0.5870854... | 0.0 | -1 |
Prompts the user to create a question | def get_question():
last_line = '\n'
question = ""
print("Type in your question here:")
while last_line != "":
current_line = input()
if current_line == "":
break
question += current_line + "\n"
variables = {}
# Special syntax variables
rand = random.ra... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ask_question():\n title_question = request.form.get(\"title\")\n question = request.form.get(\"question\")\n\n date_string = datetime.today().strftime('%Y-%m-%d')\n \n ask = Question(user_id = session[\"user_id\"],question_created=date_string, title_question = title_question, question = question... | [
"0.7486152",
"0.6696017",
"0.6658708",
"0.66517705",
"0.6646394",
"0.6635777",
"0.6586772",
"0.65711033",
"0.64845",
"0.6458855",
"0.64416367",
"0.6407239",
"0.63839",
"0.63815075",
"0.63770765",
"0.6374164",
"0.6351017",
"0.6281378",
"0.62183434",
"0.62095314",
"0.62069523",... | 0.0 | -1 |
Read data from pslg file | def read_pslg(file_name):
with open(file_name, "r") as fp:
components = fp.read().split("component: ")[1:]
lpoints, lconnect_left, lconnect_right = [], [], []
index = 0
for component in components:
raw_points = component.split("\n")[1:-1]
points = [list(map(floa... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _load_spgfile(casename=None):\n\n data={}\n \n if casename==None:\n print('_load_spgfile requires a filename to load.')\n return\n try:\n fp=open(casename+'_spg.dat','r')\n except IOError:\n print('_load_spgfile: invalid case name.')\n return data\n\n spg_st... | [
"0.6585657",
"0.652411",
"0.6342616",
"0.62879246",
"0.6264648",
"0.6150379",
"0.6124599",
"0.6101262",
"0.61011523",
"0.60455316",
"0.60414195",
"0.6040804",
"0.6021278",
"0.6021278",
"0.6021278",
"0.60139185",
"0.600449",
"0.59920204",
"0.5962291",
"0.59514767",
"0.5944652"... | 0.62928504 | 3 |
Generate symmetric figure by reflection. | def generate_symmetry(points, pts_left, pts_right, mode = 1, offset = 0.2):
assert(mode == 1 or mode == 2), "The selected mode (%d) is not defined!" % mode
# horizental
box_min, box_max = points.min(0), points.max(0)
hor_axis = box_max[0] + offset
new_comp = deepcopy(points)
new_comp[:, 0... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate(self, diagram):",
"def show_symmetry(points, nb_1, nb_2, title, save_path = None):\n\n fig = plt.figure(figsize = (6, 6))\n plt.title(title)\n plt.scatter(points[:, 0], points[:, 1], c = 'b', s = 5, label = \"point cloud\")\n plt.scatter(nb_1[:, 0], nb_1[:, 1], c = 'r', s = 5, label = \"... | [
"0.5937008",
"0.54731977",
"0.54227936",
"0.5377924",
"0.53774554",
"0.5328629",
"0.53062236",
"0.5270452",
"0.51984763",
"0.5187913",
"0.5158385",
"0.51435167",
"0.5114701",
"0.5114701",
"0.5091624",
"0.5076573",
"0.5064932",
"0.5064614",
"0.5055809",
"0.5052378",
"0.4979211... | 0.49154073 | 29 |
Show a point cloud in 2d or 3d | def show_point_set(points, title, save_path = None):
dim = points.shape[1]
assert(dim == 2 or dim == 3), "Only data in 2 or 3 dimension space can be visualized."
if dim == 2:
fig = plt.figure(figsize = (6, 6))
plt.title(title)
plt.scatter(points[:, 0], points[:, 1], c = 'b', s =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def visualize_point_cloud(points, colors=None, normals=None,\n show_frame=False, frame_size=1.0, frame_origin=(0, 0, 0)):\n pc = np2pcd(points, colors, normals)\n geometries = [pc]\n if show_frame:\n coord_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=frame... | [
"0.7614337",
"0.7583234",
"0.7297616",
"0.71946776",
"0.71542764",
"0.71168196",
"0.6915885",
"0.6814755",
"0.6801549",
"0.6792311",
"0.6709529",
"0.6671749",
"0.6652905",
"0.6646663",
"0.65614176",
"0.6532126",
"0.64922553",
"0.6464758",
"0.6449736",
"0.63894534",
"0.6383050... | 0.61476123 | 30 |
Normalize and centralize a point cloud in a unit sphere | def data_preprocessing(points):
mean_coords = points.mean(0)
points -= mean_coords
max_norm = np.max(np.linalg.norm(points, axis = 1))
points /= max_norm
return points, mean_coords, max_norm | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def center_to_sphere(places, size, resolution=0.50, min_value=np.array([0., -50., -4.5]), scale=4, x=(0, 90), y=(-50, 50), z=(-4.5, 5.5)):\n x_logical = np.logical_and((places[:, 0] < x[1]), (places[:, 0] >= x[0]))\n y_logical = np.logical_and((places[:, 1] < y[1]), (places[:, 1] >= y[0]))\n z_logical = n... | [
"0.6750035",
"0.67149276",
"0.6667598",
"0.65624475",
"0.65602094",
"0.650648",
"0.64572716",
"0.6323207",
"0.624639",
"0.624639",
"0.62402195",
"0.6174017",
"0.61365646",
"0.6134152",
"0.6077822",
"0.6074374",
"0.606006",
"0.6051627",
"0.5994066",
"0.59825337",
"0.59711474",... | 0.0 | -1 |
Show a point cloud in 2d | def show_point_set_curvature(points, curvatures, title, thresh = 0.9, save_path = None):
dim = points.shape[1]
assert(dim == 2), "Only data in 2 dimension space can be visualized."
selected_pts = points[np.abs(curvatures) < thresh]
fig = plt.figure(figsize = (6, 6))
plt.title(title)
plt.sca... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def visualize_point_cloud(points, colors=None, normals=None,\n show_frame=False, frame_size=1.0, frame_origin=(0, 0, 0)):\n pc = np2pcd(points, colors, normals)\n geometries = [pc]\n if show_frame:\n coord_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=frame... | [
"0.72636724",
"0.72375584",
"0.71609396",
"0.7132317",
"0.6933942",
"0.6919783",
"0.6877211",
"0.68398106",
"0.65854543",
"0.6562634",
"0.65615726",
"0.6518098",
"0.64886963",
"0.64688116",
"0.64599943",
"0.64297676",
"0.64172477",
"0.6347307",
"0.6338426",
"0.6316377",
"0.62... | 0.60903704 | 29 |
Show a point cloud in 2d | def show_point_set_normal(points, normals, title, save_path = None):
dim = points.shape[1]
assert(dim == 2), "Only data in 2 dimension space can be visualized."
fig = plt.figure(figsize = (6, 6))
plt.title(title)
plt.scatter(points[:, 0], points[:, 1], c = 'b', s = 5, label = "point cloud")
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def visualize_point_cloud(points, colors=None, normals=None,\n show_frame=False, frame_size=1.0, frame_origin=(0, 0, 0)):\n pc = np2pcd(points, colors, normals)\n geometries = [pc]\n if show_frame:\n coord_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=frame... | [
"0.72636724",
"0.72375584",
"0.71609396",
"0.7132317",
"0.6933942",
"0.6919783",
"0.6877211",
"0.68398106",
"0.65854543",
"0.6562634",
"0.65615726",
"0.6518098",
"0.64886963",
"0.64688116",
"0.64599943",
"0.64297676",
"0.64172477",
"0.6347307",
"0.6338426",
"0.6316377",
"0.62... | 0.56848264 | 66 |
Show a point cloud in 2d | def show_transformed_point_set(points_1, points_2, points_12, loss, save_path = None):
fig = plt.figure(figsize = (6, 6))
plt.title("Loss = %f" % loss)
plt.scatter(points_1[:, 0], points_1[:, 1], s = 5, label = "point cloud 1")
plt.scatter(points_2[:, 0], points_2[:, 1], s = 5, label = "point cloud 2")... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def visualize_point_cloud(points, colors=None, normals=None,\n show_frame=False, frame_size=1.0, frame_origin=(0, 0, 0)):\n pc = np2pcd(points, colors, normals)\n geometries = [pc]\n if show_frame:\n coord_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=frame... | [
"0.72636724",
"0.72375584",
"0.71609396",
"0.7132317",
"0.6933942",
"0.6919783",
"0.6877211",
"0.68398106",
"0.65854543",
"0.6562634",
"0.65615726",
"0.6518098",
"0.64886963",
"0.64688116",
"0.64599943",
"0.64297676",
"0.64172477",
"0.6347307",
"0.6338426",
"0.6316377",
"0.62... | 0.57596034 | 56 |
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