class CherabMASTU(object):
"""
demo class for modelling Cherab images of MAST-U
"""
def __init__(self, solps_ref_no, view, line, camera, optics='MAST_CI_mod', cherab_down_sample=50, verbose=True,
display=False, overwrite=False):
"""
:param solps_ref_no: int
:param view: str
:param line: str
:param camera: str
:param optics: str
:param cherab_down_sample: int downsample the image by ths factor in both dimensions, for speed / testing.
:param verbose: bool
:param display: bool
:param overwrite: bool
"""
self.world = World()
self.solps_ref_no = solps_ref_no
self.line = line
self.view = view
self.camera = camera
self.optics = optics
self.cherab_down_sample = cherab_down_sample
self.verbose = verbose
self.display = display
self.overwrite = overwrite
self.radiance = NotImplemented
self.spectral_radiance = NotImplemented
self.pupil_point = settings.view[self.view]['pupil_point']
self.view_vector = settings.view[self.view]['view_vector']
self.sensor_format = settings.camera[self.camera]['sensor_format']
for s in self.sensor_format:
assert s % cherab_down_sample == 0
self.sensor_format_ds = tuple((np.array(self.sensor_format) / cherab_down_sample).astype(np.int))
self.pixel_size = settings.camera[self.camera]['pixel_size']
self.pixel_size_ds = self.pixel_size * cherab_down_sample
self.x, self.y, = self.calc_pixel_position(self.pixel_size, self.sensor_format)
self.x_pixel = self.x.x_pixel
self.y_pixel = self.y.y_pixel
self.x_ds = self.x.isel(x=slice(0, self.sensor_format[0], cherab_down_sample, ))
self.y_ds = self.y.isel(y=slice(0, self.sensor_format[1], cherab_down_sample, ))
self.x_pixel_ds = self.x_ds.x_pixel
self.y_pixel_ds = self.y_ds.y_pixel
self.chunks = {'x': 800, 'y': 800}
# calculate field of view (FOV) (horizontal) using sensor geometry and lens focal lengths
f_1, f_2, f_3 = settings.optics[self.optics]
sensor_dim = self.sensor_format[0] * self.pixel_size
self.fov = 2 * np.arctan((sensor_dim / 2) * f_2 / (f_3 * f_1)) * 180 / np.pi # deg
#
# file and directory paths
self.dir_name = str(solps_ref_no) + '_' + view + '_' + line + '_' + camera
self.dir_path = os.path.join(path_saved_data, self.dir_name)
fname = 'spec_power.nc'
fname_ds = 'spec_power_ds.nc'
self.fpath_ds = os.path.join(self.dir_path, fname_ds, )
self.fpath = os.path.join(self.dir_path, fname, )
# load SOLPS plasma and set emission line model
self.sim = load_solps_from_mdsplus(mds_server, self.solps_ref_no)
self.plasma = self.sim.create_plasma(parent=self.world)
self.plasma.atomic_data = OpenADAS(permit_extrapolation=True)
emission_line = settings.line[self.line]
element, charge, transition = [emission_line[s] for s in ['element', 'charge', 'transition', ]]
line_obj = Line(element, charge, transition, )
self._line_excit = ExcitationLine(line_obj, lineshape=StarkBroadenedLine)
self._line_recom = RecombinationLine(line_obj, lineshape=StarkBroadenedLine)
self.plasma.models = [Bremsstrahlung(), self._line_excit, self._line_recom]
wl_0_nm = self._line_excit.atomic_data.wavelength(element, charge, transition, )
self.wl_0 = wl_0_nm * 1e-9 # [m]
self.wl_min_nm, self.wl_max_nm = wl_0_nm - 0.2, wl_0_nm + 0.2 # [m]
# ugly, but I want to convert the density units to 10^{20} m^{-3}
def get_ne(x, y, z, ):
return 1e-20 * self.plasma.electron_distribution.density(x, y, z, )
def get_ni(x, y, z, ):
return 1e-20 * self.plasma.composition.get(deuterium, 0).distribution.density(x, y, z, )
def get_b_field_mag(x, y, z, ):
"""
magnitude of the magnetic field at x, y, z
:param x:
:param y:
:param z:
:return:
"""
return self.plasma.b_field(x, y, z, ).length
def get_emiss_excit(x, y, z, ):
return self._line_excit.emission(Point3D(x, y, z, ), Vector3D(1, 1, 1), Spectrum(380, 700, 100)).total()
def get_emiss_recom(x, y, z, ):
return self._line_recom.emission(Point3D(x, y, z, ), Vector3D(1, 1, 1), Spectrum(380, 700, 100)).total()
self.valid_param_get_fns = {'ne': get_ne,
'ni': get_ni,
'te': self.plasma.electron_distribution.effective_temperature,
'ti': self.plasma.composition.get(deuterium, 0).distribution.effective_temperature,
'tn': self.plasma.composition.get(deuterium, 1).distribution.effective_temperature,
'b_field_mag': get_b_field_mag,
'emiss_excit': get_emiss_excit,
'emiss_recom': get_emiss_recom,
}
self.valid_params = list(self.valid_param_get_fns.keys())
# load / make the cherab image
if os.path.isdir(self.dir_path) and self.overwrite is False:
if os.path.isfile(self.fpath) and os.path.isfile(self.fpath_ds):
pass
else:
if not os.path.isdir(self.dir_path):
os.mkdir(self.dir_path)
self.make_cherab_image()
ds, ds_ds = self.load_cherab_image()
self.spectral_radiance = ds['spectral_radiance']
self.radiance = self.spectral_radiance.integrate(dim='wavelength')
self.radiance = xr.where(xr.ufuncs.isnan(self.radiance), 0, self.radiance, )
self.spectral_radiance_ds = ds_ds['spectral_radiance_ds']
self.radiance_ds = ds_ds['radiance_ds']
self.view_vectors = ds_ds['view_vectors_ds']
self.ray_lengths = ds_ds['ray_lengths_ds']
ds.close()
# self.mask_ds = self.make_mask_ds()
def load_cherab_image(self):
ds = xr.open_dataset(self.fpath, chunks=self.chunks)
ds_ds = xr.load_dataset(self.fpath_ds, )
return ds, ds_ds,
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', )
def plot_line_of_sight(self, x_pixel, y_pixel, ax, **kwargs):
"""
:param x_pixel:
:param y_pixel:
:param ax:
:return:
"""
direction = Vector3D(*list(self.view_vectors.isel(x=x_pixel, y=y_pixel, ).values)).normalise()
ray_length = float(self.ray_lengths.isel(x=x_pixel, y=y_pixel, ).values)
n_steps = 50
increment = (ray_length / n_steps) * direction
rs, zs = np.zeros(n_steps), np.zeros(n_steps)
point_i = self.pupil_point # initialise at pupil
for i in range(n_steps):
point_i += increment
rs[i], zs[i], _ = cart2cyl(*[point_i[j] for j in range(3)])
ax.plot(rs, zs, **kwargs)
def calc_pixel_position(self, pixel_size, sensor_format, ):
"""
Calculate pixel positions (in m) on the camera's sensor plane (the x-y plane).
The origin of the x-y coordinate system is the centre of the sensor. Pixel positions correspond to the pixel
centres. If x_pixel and y_pixel are specified then only returns the position of that pixel.
:param float pixel_size: in m
:param tuple sensor_format: (num_pix_x, num_pix_y)
:return:
"""
centre_pos = pixel_size * np.array(sensor_format) / 2
x = (np.arange(sensor_format[0]) + 0.5) * pixel_size - centre_pos[0]
y = (np.arange(sensor_format[1]) + 0.5) * pixel_size - centre_pos[1]
x = xr.DataArray(x, dims=('x',), coords=(x,), )
y = xr.DataArray(y, dims=('y',), coords=(y,), )
x_pixel_coord = xr.DataArray(np.arange(sensor_format[0], ), dims=('x',), coords=(x,), )
y_pixel_coord = xr.DataArray(np.arange(sensor_format[1], ), dims=('y',), coords=(y,), )
x = x.assign_coords({'x_pixel': ('x', x_pixel_coord), }, )
y = y.assign_coords({'y_pixel': ('y', y_pixel_coord), }, )
return x, y
# Get new sample point location and log distance
x = start_point.x + parametric_vector.x * t
y = start_point.y + parametric_vector.y * t
z = start_point.z + parametric_vector.z * t
sample_point = Point3D(x, y, z)
ray_r_points.append(np.sqrt(x**2 + y**2))
ray_z_points.append(z)
distance.append(start_point.distance_to(sample_point))
# Sample plasma conditions
te.append(electrons.effective_temperature(x, y, z))
ne.append(electrons.density(x, y, z))
Spectrum(350, 700, 8000)
# Log magnitude of emission
dalpha[i] = d_alpha_excit.emission(sample_point, forward_vector, Spectrum(350, 700, 8000)).total() + \
d_alpha_recom.emission(sample_point, forward_vector, Spectrum(350, 700, 8000)).total()
dgamma[i] = d_gamma_excit.emission(sample_point, forward_vector, Spectrum(350, 700, 8000)).total() + \
d_gamma_recom.emission(sample_point, forward_vector, Spectrum(350, 700, 8000)).total()
dbeta[i] = d_beta_excit.emission(sample_point, forward_vector, Spectrum(350, 700, 8000)).total() + \
d_beta_recom.emission(sample_point, forward_vector, Spectrum(350, 700, 8000)).total()
ddelta[i] = d_delta_excit.emission(sample_point, forward_vector, Spectrum(350, 700, 8000)).total() + \
d_delta_recom.emission(sample_point, forward_vector, Spectrum(350, 700, 8000)).total()
depsilon[i] = d_epsilon_excit.emission(sample_point, forward_vector, Spectrum(350, 700, 8000)).total() + \
d_epsilon_recom.emission(sample_point, forward_vector, Spectrum(350, 700, 8000)).total()
# Normalise the emission arrays
dalpha /= dalpha.sum()
dgamma /= dgamma.sum()
dbeta /= dbeta.sum()
r, _, z, t_samples = sample3d(d0_density, (0, 4, 200), (0, 0, 1), (-2, 2, 200))
plt.imshow(np.transpose(np.squeeze(t_samples)), extent=[0, 4, -2, 2])
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()