# Give the prism a high importance to ensure adequate sampling
prism.material.importance = 9
rgb = RGBPipeline2D()
rgb.display_sensitivity = 2.0
sampler = RGBAdaptiveSampler2D(rgb, min_samples=500)
# create and setup the camera
camera = PinholeCamera((1920, 1080),
fov=45,
parent=world,
pipelines=[rgb],
frame_sampler=sampler)
camera.transform = translate(0, 0.075, -0.05) * rotate(
180, -45, 0) * translate(0, 0, -0.75)
camera.ray_importance_sampling = True
camera.ray_important_path_weight = 0.75
camera.ray_max_depth = 500
camera.ray_extinction_prob = 0.01
camera.spectral_bins = 32
camera.spectral_rays = 32
camera.pixel_samples = 250
# start ray tracing
plt.ion()
for p in range(0, 1000):
print("Rendering pass {}".format(p + 1))
camera.observe()
transform=rotate(-35.5, 0, 0) * translate(0.10, 0, 0) * rotate(90, 0, 0))
# background light source
top_light = Sphere(0.5, parent=world, transform=translate(0, 2, -1),
material=UniformSurfaceEmitter(d65_white, scale=2))
# Give the prism a high importance to ensure adequate sampling
prism.material.importance = 9
rgb = RGBPipeline2D()
# create and setup the camera
camera = PinholeCamera((512, 256), fov=45, parent=world, pipelines=[rgb])
camera.transform = translate(0, 0.05, -0.05) * rotate(180, -65, 0) * translate(0, 0, -0.75)
camera.ray_importance_sampling = True
camera.ray_important_path_weight = 0.75
camera.ray_max_depth = 500
camera.ray_extinction_prob = 0.01
camera.spectral_bins = 32
camera.spectral_rays = 32
camera.pixel_samples = 100
# start ray tracing
plt.ion()
for p in range(0, 1000):
print("Rendering pass {}".format(p+1))
camera.observe()
rgb.save("prisms_{}.png".format(p+1))
plt.colorbar()
plt.axis('equal')
plt.xlabel('r axis')
plt.ylabel('z axis')
plt.title("Neutral Density profile in r-z plane")
plt.figure()
xrange = np.linspace(0, 4, 200)
yrange = np.linspace(-2, 2, 200)
d_alpha_rz_intensity = np.zeros((200, 200))
direction = Vector3D(0, 1, 0)
for i, x in enumerate(xrange):
for j, y in enumerate(yrange):
emission = d_alpha_excit.emission(Point3D(x, 0.0, y), direction, Spectrum(650, 660, 1))
d_alpha_rz_intensity[j, i] = emission.total()
plt.imshow(d_alpha_rz_intensity, extent=[0, 4, -2, 2], origin='lower')
plt.colorbar()
plt.xlabel('r axis')
plt.ylabel('z axis')
plt.title("D-alpha emission in R-Z")
camera = PinholeCamera((256, 256), pipelines=[PowerPipeline2D()], parent=world)
camera.transform = translate(2.5, -4.5, 0)*rotate_basis(Vector3D(0, 1, 0), Vector3D(0, 0, 1))
camera.pixel_samples = 1
plt.ion()
camera.observe()
plt.ioff()
plt.show()
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', )
yrange = np.linspace(-2, 2, 200)
d_alpha_rz_intensity = np.zeros((200, 200))
direction = Vector3D(0, 1, 0)
for i, x in enumerate(xrange):
for j, y in enumerate(yrange):
emission = d_alpha_excit.emission(Point3D(x, y, 0.0), direction,
Spectrum(650, 660, 1))
d_alpha_rz_intensity[j, i] = emission.total()
plt.imshow(d_alpha_rz_intensity, extent=[-2, 2, -2, 2], origin='lower')
plt.colorbar()
plt.xlabel('x axis')
plt.ylabel('y axis')
plt.title("D-alpha emission in x-y")
camera = PinholeCamera((256, 256), pipelines=[PowerPipeline2D()], parent=world)
camera.transform = translate(-3, 0, 0) * rotate_basis(Vector3D(1, 0, 0),
Vector3D(0, 0, 1))
camera.pixel_samples = 1
plt.ion()
camera.observe()
plt.ioff()
plt.show()
# this code can be used to plot the mesh, but it's quite slow
# for tri_index in range(triangles.shape[0]):
# v1, v2, v3 = triangles[tri_index]
# plt.plot([vertex_coords[v1, 0], vertex_coords[v2, 0], vertex_coords[v3, 0], vertex_coords[v1, 0]],
# [vertex_coords[v1, 1], vertex_coords[v2, 1], vertex_coords[v3, 1], vertex_coords[v1, 1]], 'k')
# plt.plot([vertex_coords[v1, 0], vertex_coords[v2, 0], vertex_coords[v3, 0], vertex_coords[v1, 0]],
# [vertex_coords[v1, 1], vertex_coords[v2, 1], vertex_coords[v3, 1], vertex_coords[v1, 1]], '.b')