voxel_map = np.zeros((100, 100), dtype=np.int) voxel_map[rad > 50.] = -1 # removing the area outside the circle for i in range(50): voxel_map[(rad < i + 1.) * (rad > i)] = i # mapping multiple grid cells to a single light source rtc.voxel_map = voxel_map # applying a voxel map # now we have only 50 light sources # creating ray transfer pipeline pipeline = RayTransferPipeline2D() # setting up the camera camera = PinholeCamera((256, 256), pipelines=[pipeline], frame_sampler=FullFrameSampler2D(), transform=translate(219., 0, 0) * rotate(90., 0., -90.), parent=world) camera.fov = 90 camera.pixel_samples = 500 camera.min_wavelength = 500. camera.max_wavelength = camera.min_wavelength + 1. camera.spectral_bins = rtc.bins # starting ray tracing camera.observe() # uncomment this to save ray transfer matrix to file # np.save('ray_transfer_map.npy', pipeline.matrix) # let's collapse the ray transfer matrix with some emission profiles # obtaining 30 images for 30 emission profiles images = [] vmax = 0 rad_inside = np.linspace(0.5, 49.5, 50) # we have only 50 light sources now
# Process the ray-traced spectra with the RGB pipeline. rgb = RGBPipeline2D() # camera camera = PinholeCamera((512, 512), pipelines=[rgb], transform=translate(0, 10, -10) * rotate(0, -45, 0)) # camera - pixel sampling settings camera.fov = 45 camera.pixel_samples = 250 # camera - ray sampling settings camera.spectral_rays = 1 camera.spectral_bins = 20 camera.ray_max_depth = 100 camera.ray_extinction_prob = 0.1 camera.min_wavelength = 375.0 camera.max_wavelength = 740.0 # 3. Build Scenegraph # ------------------- world = World() sphere.parent = world ground.parent = world emitter.parent = world camera.parent = world # 4. Observe()
def make_cherab_image(self): """ run cherab to generate the synthetic spectral cube :return: """ if self.radiance is not NotImplemented: self.radiance.close() if self.spectral_radiance is not NotImplemented: self.spectral_radiance.close() import_mastu_mesh(self.world, ) # first, define camera, calculate view vectors and calculate ray lengths pipeline_spectral = SpectralPowerPipeline2D() pipeline_spectral_rad = SpectralRadiancePipeline2D() pipelines = [pipeline_spectral, pipeline_spectral_rad, ] camera = PinholeCamera(self.sensor_format_ds, fov=self.fov, pipelines=pipelines, parent=self.world) # orient and position the camera init_view_vector, init_up_vector = Vector3D(0, 0, 1), Vector3D(0, 1, 0) axle_1 = init_view_vector.cross(self.view_vector) angle = init_view_vector.angle(self.view_vector) t_1 = rotate_vector(angle, axle_1) final_up_vector = rotate_vector(-90, axle_1) * self.view_vector intermediate_up_vector = t_1 * init_up_vector angle_between = intermediate_up_vector.angle(final_up_vector) t_2 = rotate_vector(-angle_between, self.view_vector) camera.transform = translate(self.pupil_point[0], self.pupil_point[1], self.pupil_point[2], ) * t_2 * t_1 vector_xyz = np.arange(3) vector_xyz = xr.DataArray(vector_xyz, coords=(vector_xyz, ), dims=('vector_xyz',), name='vector_xyz', ) # calculating the pixel view directions view_vectors = xr.combine_nested( [xr.zeros_like(self.x_pixel_ds + self.y_pixel_ds) + self.view_vector[i] for i in [0, 1, 2, ]], concat_dim=(vector_xyz,), ) view_vectors = view_vectors.rename('view_vectors') def v3d2da(v3d): """ raysect Vector3D to xarray DataArray :param v3d: :return: """ da = np.array([v3d.x, v3d.y, v3d.z, ]) da = xr.DataArray(da, coords=(np.arange(3),), dims=('vector_xyz',), ) return da # basis unit vectors defining camera view -- v_z is forward and v_y is up v_y = final_up_vector.normalise() v_x = self.view_vector.cross(v_y).normalise() v_z = self.view_vector.normalise() v_x, v_y, v_z = [v3d2da(i) for i in [v_x, v_y, v_z, ]] # FOV defines the widest view, with pixels defined as square. sensor_aspect = self.sensor_format[1] / self.sensor_format[0] if sensor_aspect > 1: fov_v = self.fov fov_h = self.fov / sensor_aspect elif sensor_aspect == 1: fov_v = fov_h = self.fov elif sensor_aspect < 1: fov_h = self.fov fov_v = self.fov * sensor_aspect else: raise Exception() pixel_projection = 2 * np.tan(fov_h * np.pi / 360) / self.sensor_format[0] view_vectors = view_vectors + (v_x * (self.x_pixel_ds - self.sensor_format[0] / 2 + 0.5) * pixel_projection) + \ (v_y * (self.y_pixel_ds - self.sensor_format[1] / 2 + 0.5) * pixel_projection) if self.verbose: print('--status: calculating ray lengths') # TODO there has to be a better way of doing this?! ray_lengths = xr.DataArray(np.zeros(self.sensor_format_ds), dims=('x', 'y', ), coords=(self.x_ds, self.y_ds, )) for idx_x, x_pixel in enumerate(self.x_pixel_ds.values): if self.verbose and idx_x % 10 == 0: print('x =', str(x_pixel)) for idx_y, y_pixel in enumerate(self.y_pixel_ds.values): direction = Vector3D(*list(view_vectors.isel(x=idx_x, y=idx_y, ).values)) intersections = [] for p in self.world.primitives: intersection = p.hit(CoreRay(self.pupil_point, direction, )) if intersection is not None: intersections.append(intersection) # find the intersection corresponding to the shortest ray length no_intersections = len(intersections) if no_intersections == 0: ray_lengths.values[idx_x, idx_y] = 3 else: ray_lengths.values[idx_x, idx_y] = min([i.ray_distance for i in intersections if i.primitive.name != 'Plasma Geometry']) camera.spectral_bins = 40 camera.pixel_samples = 10 camera.min_wavelength = self.wl_min_nm camera.max_wavelength = self.wl_max_nm camera.quiet = not self.verbose camera.observe() # output to netCDF via xarray wl = pipeline_spectral.wavelengths wl = xr.DataArray(wl, coords=(wl, ), dims=('wavelength', )) * 1e-9 # ( m ) spec_power_ds = pipeline_spectral.frame.mean * 1e9 # converting units from (W/nm) --> (W/m) spec_radiance_ds = pipeline_spectral_rad.frame.mean * 1e9 coords = (self.x_ds, self.y_ds, wl, ) dims = ('x', 'y', 'wavelength', ) name = 'spec_power' attrs = {'units': 'W/m^2/str/m'} spec_power_ds = xr.DataArray(np.flip(spec_power_ds, axis=1), coords=coords, dims=dims, name=name, attrs=attrs, ) spec_radiance_ds = xr.DataArray(np.flip(spec_radiance_ds, axis=1, ), coords=coords, dims=dims, name=name, attrs=attrs, ) # calculate the centre-of-mass wavelength radiance_ds = spec_power_ds.integrate(dim='wavelength').assign_attrs({'units': 'W/m^2/str', }) ds_ds = xr.Dataset({'spectral_radiance_ds': spec_radiance_ds, 'radiance_ds': radiance_ds, 'view_vectors_ds': view_vectors, 'ray_lengths_ds': ray_lengths }) x_p_y = self.x + self.y spec_power = spec_power_ds.interp_like(x_p_y) / self.cherab_down_sample # to conserve power ds = xr.Dataset({'spectral_radiance': spec_power, }) ds_ds.to_netcdf(self.fpath_ds, mode='w', ) ds.to_netcdf(self.fpath, mode='w', )