def read_temperature_UDA(shot,time,**properties):
"""
notes:
needs NaN checking (replace with interpolated values)
this script heavily relies on the structure of UDA - please contant Stuart Henderson for updates
"""
print("reading temperature from UDA")
try:
import pyuda
udaClient=pyuda.Client()
getdata=udaClient.get
import numpy as np
except:
raise ImportError("ERROR: read_temperature_UDA could not import pyuda!\nreturning\n")
equilibrium=classes.input_classes.equilibrium.Equilibrium(ID='',data_format='UDA',shot=shot,time=time) #needs to read corresponding equilibrium
input_data={}
if properties['species']=='electrons':
if shot<23000:
signal_T='atm_te'
signal_R='atm_R'
else:
signal_T='ayc_te'
signal_R='ayc_r'
T=getdata(signal_T,shot)
R_data=getdata(signal_R,shot) #electron and ion temperature signals are stored against different radial bases - must access another dataset for electrons
time_grid=T.dims[0].data
time_index=np.abs(time_grid-time).argmin()[0] #figure out what time we are wanting to output (pick closest)
R_grid=R_data.data[time_index]
elif properties['species']=='ions':
T=getdata('act_ss_temperature',shot)
time_grid=T.dims[0].data
time_index=np.abs(time_grid-time).argmin()[0] #figure out what time we are wanting to output (pick closest)
R_grid=T.dims[1].data
T=T.data[time_index,:]
psi_grid=processing.utils.RZ_to_Psi(R_grid,np.full(len(R_grid),0.0),equilibrium) #assume measurements are at along Z=0
interpolator_temperature=processing.utils.interpolate_1D(psi_grid,T)
input_data['flux_pol']=np.linspace(np.min(psi_grid),np.max(psi_grid),200)
input_data['flux_pol_norm']=(input_data['flux_pol']-np.min(input_data['flux_pol']))/(np.max(input_data['flux_pol']-np.min(input_data['flux_pol'])))
input_data['T']=interpolator_temperature(input_data['flux_pol'])
print("finished reading temperature from UDA")
return input_data
def __init__(self, pulse):
self.client = pyuda.Client() # get the pyuda client
# Poloidal magnetic flux per toroidal radian as a function of (Z,R) and timebase
self.psi = self.client.get("efm_psi(r,z)", pulse)
self.time_slices = self.psi.dims[0].data
# Psi grid axes f(nr), f(nz)
self.r = self.client.get("efm_grid(r)", pulse)
self.z = self.client.get("efm_grid(z)", pulse)
# f profile
self.f = self.client.get(
"efm_f(psi)_(c)",
pulse) # Poloidal current flux function, f=R*Bphi; f(psin, C)
self.psi_r = self.client.get(
"efm_psi(r)", pulse
) #poloidal magnetic flux per toroidal radian as a function of radius at Z=0
# Poloidal magnetic flux per toroidal radian at the plasma boundary and magnetic axis
self.psi_lcfs = self.client.get("efm_psi_boundary", pulse)
self.psi_axis = self.client.get("efm_psi_axis", pulse)
# Plasma current
self.plasma_current = self.client.get("efm_plasma_curr(C)", pulse)
# Reference vaccuum toroidal B field at R = efm_bvac_r
self.b_vacuum_magnitude = self.client.get("efm_bvac_val", pulse)
self.b_vacuum_radius = self.client.get("efm_bvac_r", pulse)
# Magnetic axis co-ordinates
self.axis_coord_r = self.client.get("efm_magnetic_axis_r", pulse)
self.axis_coord_z = self.client.get("efm_magnetic_axis_z", pulse)
#minor radius
self.minor_radius = self.client.get("EFM_MINOR_RADIUS", pulse)
#lcfs boundary polygon
self.lcfs_poly_r = self.client.get("efm_lcfs(r)_(c)", pulse)
self.lcfs_poly_z = self.client.get("efm_lcfs(z)_(c)", pulse)
# Number of LCFS co-ordinates
self.nlcfs = self.client.get("EFM_LCFS(N)_(C)", pulse)
# time slices when plasma is present
self.plasma_times = self.client.get("EFM_IP_TIMES", pulse)
self.time_range = self.time_slices.min(), self.time_slices.max()
"""
This script demonstrates loading the reconstruction grid for the lower
divertor bolometers, and plotting it alongside the MAST-U passive
structures.
"""
import matplotlib.pyplot as plt
import pyuda
from cherab.mastu.bolometry import load_standard_voxel_grid
grid = load_standard_voxel_grid('sxdl')
client = pyuda.Client()
passive_structures = client.geometry('/passive/efit', 50000)
fig, ax = plt.subplots()
grid.plot(ax=ax)
passive_structures.plot(ax_2d=ax, color='k')
ax.set_ylim(top=-1.5)
# Reduce the line width of the grid edges to make it clearer
for patch_collection in ax.collections:
patch_collection.set_linewidth(0.7)
plt.show()
def __init__(self, pulse):
self.client = pyuda.Client() # get the pyuda client
# Poloidal magnetic flux per toroidal radian as a function of (Z,R) and timebase
self.psi = self.client.get("efm_psi(r,z)", pulse)
self.time_slices = self.psi.dims[0].data
# Psi grid axes f(nr), f(nz)
self.r = self.client.get("efm_grid(r)", pulse)
self.z = self.client.get("efm_grid(z)", pulse)
# f profile
self.f = self.client.get("efm_f(psi)_(c)", pulse)# Poloidal current flux function, f=R*Bphi; f(psin, C)
# q profile
self.q = self.client.get("efm_q(psi)_(c)", pulse)
self.psi_r = self.client.get("efm_psi(r)", pulse) #poloidal magnetic flux per toroidal radian as a function of radius at Z=0
# Poloidal magnetic flux per toroidal radian at the plasma boundary and magnetic axis
self.psi_lcfs = self.client.get("efm_psi_boundary", pulse)
self.psi_axis = self.client.get("efm_psi_axis", pulse)
# Plasma current
self.plasma_current = self.client.get("efm_plasma_curr(C)", pulse)
# Reference vaccuum toroidal B field at R = efm_bvac_r
self.b_vacuum_magnitude = self.client.get("efm_bvac_val", pulse)
self.b_vacuum_radius = self.client.get("efm_bvac_r", pulse)
# Magnetic axis co-ordinates
self.axis_coord_r = self.client.get("efm_magnetic_axis_r", pulse)
self.axis_coord_z = self.client.get("efm_magnetic_axis_z", pulse)
# X point coordinates
xpoint1r = self.client.get("efm_xpoint1_r(c)", pulse).data
xpoint1z = self.client.get("efm_xpoint1_z(c)", pulse).data
xpoint2r = self.client.get("efm_xpoint2_r(c)", pulse).data
xpoint2z = self.client.get("efm_xpoint2_z(c)", pulse).data
self.xpoints = [
(Point2D(r1, z1), Point2D(r2, z2))
for (r1, z1, r2, z2) in zip(xpoint1r, xpoint1z, xpoint2r, xpoint2z)
]
#minor radius
self.minor_radius = self.client.get("efm_minor_radius", pulse)
#lcfs boundary polygon
self.lcfs_poly_r = self.client.get("efm_lcfs(r)_(c)", pulse)
self.lcfs_poly_z = self.client.get("efm_lcfs(z)_(c)", pulse)
# Number of LCFS co-ordinates
self.nlcfs = self.client.get("efm_lcfs(n)_(c)", pulse)
# limiter boundary polygon
self.limiter_poly_r = self.client.get("efm_limiter(r)", pulse)
self.limiter_poly_z = self.client.get("efm_limiter(z)", pulse)
# Number of limiter co-ordinates
self.nlimiter = self.client.get("efm_limiter(n)", pulse)
# time slices when plasma is present
self.plasma_times = self.client.get("efm_ip_times", pulse)
self.time_range = self.time_slices.min(), self.time_slices.max()
def read_equilibrium_UDA(shot,time):
"""
reads equilibrium from analysed EFIT signals from MAST UDA database
notes:
args:
shot - target shot number to read
time - fetch equilibrium at this time
"""
print("reading equilibrium from UDA")
input_data={}
try:
import pyuda
udaClient=pyuda.Client()
getdata=udaClient.get
import numpy as np
except:
raise ImportError("ERROR: read_equilibrium_UDA could not import pyuda!\nreturning\n")
#time dependent data first
input_data['psirz']=getdata('efm_psi(r,z)', shot)
input_data['simag']=getdata('efm_psi_axis', shot)
input_data['sibry']=getdata('efm_psi_boundary', shot)
input_data['current']=getdata('efm_plasma_curr(C)', shot)
input_data['bcentr']=getdata('efm_bvac_val', shot)
input_data['rcentr']=getdata('efm_bvac_r', shot)
input_data['pres']=getdata('efm_p(psi)_(c)',shot)
input_data['fpol']=getdata('efm_f(psi)_(c)',shot)
input_data['rmaxis']=getdata('efm_magnetic_axis_r',shot)
input_data['zmaxis']=getdata('efm_magnetic_axis_z',shot)
input_data['ffprime']=getdata('efm_ffprime',shot)
input_data['pprime']=getdata('efm_pprime',shot)
input_data['qpsi']=getdata('efm_q(psi)_(c)',shot)
input_data['lcfs_n']=getdata('efm_lcfs(n)_(c)',shot)
input_data['lcfs_r']=getdata('efm_lcfs(r)_(c)',shot)
input_data['lcfs_z']=getdata('efm_lcfs(z)_(c)',shot)
input_data['limitr']=getdata('efm_limiter(n)',shot) #limiter data not on time base but contains single time coordinate, so read in here
input_data['rlim']=getdata('efm_limiter(r)',shot)
input_data['zlim']=getdata('efm_limiter(z)',shot)
#redefine read time based on available converged EFIT equilibria constructions
time_index=np.abs(input_data['psirz'].time.data-time).argmin()
time_new=input_data['psirz'].time.data[time_index]
#go through time dependent data and pick closest times
for key in input_data:
#print(key) #to check times
time_index=np.abs(input_data[key].time.data-time_new).argmin()
#print(input_data[key].time.data[time_index]) #to check times
input_data[key]=np.array(input_data[key].data[time_index])
input_data['lcfs_r']=input_data['lcfs_r'][0:input_data['lcfs_n']] #LCFS data seems to have junk on the end, so crop accordingly
input_data['lcfs_z']=input_data['lcfs_z'][0:input_data['lcfs_n']]
input_data['psirz']=input_data['psirz'].T
input_data['R_1D']=np.asarray(getdata('efm_grid(r)',shot).data[0,:])
input_data['Z_1D']=np.asarray(getdata('efm_grid(z)',shot).data[0,:])
input_data['nR_1D']=np.asarray(getdata('efm_nw',shot).data)
input_data['nZ_1D']=np.asarray(getdata('efm_nh',shot).data)
input_data['rdim']=input_data['R_1D'][-1]-input_data['R_1D'][0]
input_data['rleft']=input_data['R_1D'][0]
input_data['zdim']=input_data['Z_1D'][-1]-input_data['Z_1D'][0]
input_data['zmid']=np.asarray((input_data['Z_1D'][-1]+input_data['Z_1D'][0])*.5)
input_data['flux_pol']=np.linspace(input_data['simag'],input_data['sibry'],100)
'''
input_data['rlim'] = np.array([
0.195, 0.195, 0.280, 0.280, 0.280, 0.175,
0.175, 0.190, 0.190, 0.330, 0.330, 0.535,
0.535, 0.755, 0.755, 0.755, 1.110, 1.655,
1.655, 1.655, 2.000, 2.000, 2.000, 2.000,
2.000, 1.655, 1.655, 1.655, 1.110, 0.755,
0.755, 0.755, 0.535, 0.535, 0.330, 0.330,
0.190, 0.190, 0.175, 0.175, 0.280, 0.280,
0.280, 0.195, 0.195])
input_data['zlim'] = np.array([
0.000, 1.080, 1.220, 1.450, 1.670, 1.670,
1.720, 1.820, 1.905, 2.000, 2.000, 2.000,
2.000, 2.000, 1.975, 1.826, 1.826, 1.826,
1.975, 2.000, 2.000, 0.300, 0.000,-0.300,
-2.000,-2.000,-1.975,-1.826,-1.826,-1.826,
-1.975,-2.000,-2.000,-2.000,-2.000,-2.000,
-1.905,-1.820,-1.720,-1.670,-1.670,-1.450,
-1.220,-1.080, 0.000])'''
print("finished reading equilibrium from UDA")
return input_data
def load_omas_uda(server=None,
port=None,
pulse=None,
run=0,
paths=None,
imas_version=os.environ.get(
'IMAS_VERSION', omas_rcparams['default_imas_version']),
skip_uncertainties=False,
skip_ggd=True,
verbose=True):
'''
Load UDA data to OMAS
:param server: UDA server
:param port: UDA port
:param pulse: UDA pulse
:param run: UDA run
:param paths: list of paths to load from IMAS
:param imas_version: IMAS version
:param skip_uncertainties: do not load uncertain data
:param skip_ggd: do not load ggd structure
:param verbose: print loading progress
:return: OMAS data set
'''
if pulse is None or run is None:
raise Exception('`pulse` and `run` must be specified')
if server is not None:
pyuda.Client.server = server
elif not os.environ['UDA_HOST']:
raise pyuda.UDAException('Must set UDA_HOST environmental variable')
if port is not None:
pyuda.Client.port = port
elif not os.environ['UDA_PORT']:
raise pyuda.UDAException('Must set UDA_PORT environmental variable')
# set this to get pyuda metadata (maybe of interest for future use):
# pyuda.Client.set_property(pyuda.Properties.PROP_META, True)
client = pyuda.Client()
# if paths is None then figure out what IDS are available and get ready to retrieve everything
if paths is None:
requested_paths = [[
structure
] for structure in list_structures(imas_version=imas_version)]
else:
requested_paths = map(p2l, paths)
available_ds = []
for ds in numpy.unique([p[0] for p in requested_paths]):
if ds in add_datastructures.keys():
continue
if uda_get(client, [ds, 'ids_properties', 'homogeneous_time'], pulse,
run) is None:
if verbose:
print('- ', ds)
continue
if verbose:
print('* ', ds)
available_ds.append(ds)
ods = ODS(consistency_check=False)
for k, ds in enumerate(available_ds):
filled_paths_in_uda(ods,
client,
pulse,
run,
load_structure(ds, imas_version=imas_version)[1],
path=[],
paths=[],
requested_paths=requested_paths,
skip_uncertainties=skip_uncertainties,
skip_ggd=skip_ggd,
perc=[
float(k) / len(available_ds) * 100,
float(k + 1) / len(available_ds) * 100,
float(k) / len(available_ds) * 100
])
ods.consistency_check = True
ods.prune()
if verbose:
print()
return ods
def __init__(self, shot):
"""
Pulls some useful parameters from the database for the MSE diagnostic. Requires a shotnumber as an input.
:param shot: (int) Shot number.
"""
self.client = pyuda.Client() #client to retrieve data
self.beam_present = self.client.get('ams_beam_ok', shot)
if self.beam_present.data == 0.:
print('Beam not present - no MSE data available!')
return
else:
print('Beam present - MSE data available!')
pass
self.channels = self.client.get('ams_ch', shot)
# Fibers connected to each channel
self.fibers = self.client.get('ams_md', shot)
# Polarization angle (proportional to pitch angle in absence of Er)
self.gamma = self.client.get('ams_gamma', shot)
self.gamma_noise = self.client.get('ams_gammanoise', shot)
# Magnetic Pitch angle tan(gamma)*(A5/A0) = tan(pitch_angle) where A0 and A5 are geometry coefficients
self.pitch_angle = self.client.get('ams_pitcha', shot)
self.pitch_angle_noise = self.client.get('ams_pitchanoise', shot)
# Major radius and Time axis
self.time_axis = self.gamma.dims[0].data
self.time_range = self.time_axis.min(), self.time_axis.max()
self.major_radius = self.client.get('ams_rpos', shot)
# Stokes vectors and errors
self.S0 = self.client.get('ams_s0', shot)
self.S0_noise = self.client.get('ams_s0noise', shot)
self.S1 = self.client.get('ams_s1noise', shot)
self.S1_noise = self.client.get('ams_s1noise', shot)
self.S2 = self.client.get('ams_s2noise', shot)
self.S2_noise = self.client.get('ams_s2noise', shot)
self.S3 = self.client.get('ams_s3noise', shot)
self.S3_noise = self.client.get('ams_s3noise', shot)
# View geometry data
self.view_geometry = self.client.get('ams_viewstr', shot)
# Horizontal co-ordinates of the MSE fibres on the focal plane of the collection lens
self.vx0 = self.client.get('ams_vx0', shot)
self.vy0 = self.client.get('ams_vy0', shot)
# Cosine between the line of sight and beam direction for each sightline
self.cosbeam = self.client.get('ams_cosbeam', shot)
# Photoelastic Modulator parameters (driving frequency and their retardance)
self.PEM1_frequency = self.client.get('ams_pem1_freq', shot)
self.PEM1_retardance = self.client.get('ams_retar1', shot)
self.PEM2_frequency = self.client.get('ams_pem2_freq', shot)
self.PEM2_retardance = self.client.get('ams_retar2', shot)
self.A_coefficients = self.client.get('AMS_ACOEFF', shot)
#A coefficients related to the geometry of the diagnostic. There is a separate A coefficient for each line of sight, so 36x6 coefficients
self.A0 = self.A_coefficients.data[0,0,:]
self.A1 = self.A_coefficients.data[0,1,:]
self.A2 = self.A_coefficients.data[0,2,:]
self.A3 = self.A_coefficients.data[0,3,:]
self.A4 = self.A_coefficients.data[0,4,:]
self.A5 = self.A_coefficients.data[0,5,:]
def load_default_bolometer_config(bolometer_id, parent=None, shot=50000):
"""
Load the bolometer confituration for a given bolometer ID.
:param bolometer_id: the name of the bolometer camera,
in the form "chamber - camera"
:param parent: the parent node of the resulting bolometer node
:param shot: read the bolometer state at the given shot
<shot> can be either a shot number, in which case the geometry is
looked up in pyuda, or a string containing the path to a local
netCDF file, e.g. '/home/jlovell/Bolometer/bolo_geom_fromscript.nc',
in which case the geometry is loaded from that file. The file must
conform to the same specification as that in the machine
description, described in CD/MU/05410.
:return bolometer: the BolometerCamera object for the requested camera.
"""
try:
chamber, camera_name = [
elem.strip() for elem in bolometer_id.split('-')
]
except KeyError:
raise ValueError(
"Bolometer camera ID '{}' not recognised.".format(bolometer_id))
if chamber.lower() not in ('sxdl', 'core'):
raise ValueError('Chamber must be "SXDL" or "CORE"')
client = pyuda.Client()
try:
bolometers = client.geometry('/bolo/{}'.format(chamber.lower()),
shot,
cartesian_coords=True).data
except AttributeError:
raise pyuda.UDAException(
"Couldn't retrieve bolometer geometry data from UDA.")
camera_id = 'MAST-U {} - {} Bolometer'.format(chamber, camera_name)
bolometer_camera = BolometerCamera(name=camera_id, parent=parent)
# FIXME: When reading from local files with UDA, need an extra data child.
# This is due to a bug in the pyuda geometry wrapper for local files.
try:
slits = bolometers['/slits/data']
except KeyError:
slits = bolometers['/data/slits/data']
slit_objects = {}
for slit in slits:
try:
curvature_radius = slit.curvature_radius
except AttributeError:
curvature_radius = 0
# Only include slits in the requested camera
# Slit IDs are of the form "MAST-U <chamber> - <camera> Slit <N>"
slit_camera = slit.id[0].split('-')[2].split()[0]
if slit_camera != camera_name:
continue
slit_objects[slit.id[0]] = BolometerSlit(
basis_x=uda_to_vector(slit['basis_1']),
basis_y=uda_to_vector(slit['basis_2']),
centre_point=uda_to_point(slit['centre_point']),
csg_aperture=True,
dx=slit.width,
dy=slit.height,
curvature_radius=curvature_radius,
slit_id=slit.id[0],
parent=bolometer_camera,
)
try:
foils = bolometers['/foils/data']
except KeyError:
foils = bolometers['/data/foils/data']
for foil in foils:
# Only include foils in the requested camera
# Foil IDs are of the form "MAST-U <chamber> - <camera> CH<N>"
foil_camera = foil.id[0].split('-')[2].split()[0]
if foil_camera != camera_name:
continue
foil = BolometerFoil(
basis_x=uda_to_vector(foil['basis_1']),
basis_y=uda_to_vector(foil['basis_2']),
centre_point=uda_to_point(foil['centre_point']),
dx=foil.width,
dy=foil.height,
curvature_radius=foil.curvature_radius,
detector_id=foil.id[0],
slit=slit_objects[foil.slit_id[0]],
parent=bolometer_camera,
)
bolometer_camera.add_foil_detector(foil)
return bolometer_camera
'''
This code is used to take the basic EFIT run used for a shot on MAST. Take the necessary parameters in order to
calculate B field components"
NB: To load OLD MAST data with pyIDAM. in the terminal: module load idam/1.6.3
To load MAST-U data using pyuda: module unload idam/1.6.3, module load uda
'''
import pyuda
import numpy as np
import matplotlib.pyplot as plt
client = pyuda.Client() #get the pyuda client
class EFit:
def __init__(self, shot_number, time, load_mast=True):
self.shot_number = shot_number
self.time = time
self.Bphi_Rgeom = None
self.Bphi_Rmag = None
self.Bvac_R = None
self.Bvac_Rgeom = None
self.Bvac_Rmag = None
self.Bvac_val = None
self.chisq_magnetic = None
self.chisq_total = None
