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1654 | class D3DPhysicsMethods:
"""
A class to retrieve and calculate physics-related data for DIII-D.
"""
@staticmethod
@physics_method(columns=["time_until_disrupt"], tokamak=Tokamak.D3D)
def get_time_until_disrupt(params: PhysicsMethodParams):
"""
Calculate the time until the disruption for a given shot. If the shot does
not disrupt, return NaN.
Parameters
----------
params : PhysicsMethodParams
Parameters containing MDS connection and shot information.
Returns
-------
dict
A dictionary containing the time until disruption. If the shot does
not disrupt, return NaN.
"""
if params.disrupted:
return {"time_until_disrupt": params.disruption_time - params.times}
return {"time_until_disrupt": [np.nan]}
@staticmethod
@physics_method(columns=["h98"], tokamak=Tokamak.D3D)
def get_h98(params: PhysicsMethodParams):
"""
Get the H98y2 energy confinement time parameter
Reference
-------
https://github.com/MIT-PSFC/disruption-py/blob/matlab/DIII-D/get_H98_d3d.m
Last major update by William Wei on 7/31/2024
"""
output = {
"h98": [np.nan],
}
try:
h_98, t_h_98 = params.mds_conn.get_data_with_dims(
r"\H_THH98Y2", tree_name="transport"
)
t_h_98 /= 1e3 # [ms] -> [s]
h_98 = interp1(t_h_98, h_98, params.times, "linear")
output["h98"] = h_98
except ValueError:
params.logger.info(
"[Shot %s]: Failed to get H98 signal. Returning NaNs.", params.shot_id
)
params.logger.debug("[Shot %s]: %s", params.shot_id, traceback.format_exc())
return output
@staticmethod
@physics_method(columns=["h_alpha"], tokamak=Tokamak.D3D)
def get_h_alpha(params: PhysicsMethodParams):
"""
Get the H_alpha line emission intensity.
Reference
-------
https://github.com/MIT-PSFC/disruption-py/blob/matlab/DIII-D/get_H98_d3d.m
Last major update by William Wei on 7/31/2024
"""
output = {
"h_alpha": [np.nan],
}
try:
h_alpha, t_h_alpha = params.mds_conn.get_data_with_dims(
r"\fs04", tree_name="d3d"
)
t_h_alpha /= 1e3 # [ms] -> [s]
h_alpha = interp1(t_h_alpha, h_alpha, params.times, "linear")
output["h_alpha"] = h_alpha
except ValueError:
params.logger.info(
"[Shot %s]: Failed to get H_alpha signal. Returning NaNs.",
params.shot_id,
)
params.logger.debug("[Shot %s]: %s", params.shot_id, traceback.format_exc())
return output
@staticmethod
@physics_method(
columns=["p_rad", "p_nbi", "p_ech", "radiated_fraction"],
tokamak=Tokamak.D3D,
)
def get_power_parameters(params: PhysicsMethodParams):
"""
Compute the input NBI, ECH powers, radiated power measured by the bolometer array,
and the radiated fraction for a DIII-D shot.
References:
-------
- https://github.com/MIT-PSFC/disruption-py/blob/matlab/DIII-D/get_power_d3d.m
Last major update by William Wei on 8/1/2024
"""
# Get neutral beam injected power
try:
p_nbi, t_nbi = params.mds_conn.get_data_with_dims(
r"\d3d::top.nb:pinj", tree_name="d3d", astype="float64"
)
t_nbi /= 1e3 # [ms] -> [s]
p_nbi *= 1e3 # [KW] -> [W]
if len(t_nbi) > 2:
p_nbi = interp1(
t_nbi,
p_nbi,
params.times,
"linear",
bounds_error=False,
fill_value=0.0,
)
else:
params.logger.info(
"[Shot %s]: No NBI power data found in this shot.", params.shot_id
)
p_nbi = np.zeros(len(params.times))
except mdsExceptions.MdsException:
p_nbi = np.zeros(len(params.times))
params.logger.info("[Shot %s]: Failed to open NBI node", params.shot_id)
params.logger.debug("[Shot %s]: %s", params.shot_id, traceback.format_exc())
# Get electron cyclotron heating (ECH) power. It's point data, so it's not
# stored in an MDSplus tree
try:
p_ech, t_ech = params.mds_conn.get_data_with_dims(
r"\top.ech.total:echpwrc", tree_name="rf"
)
t_ech /= 1e3 # [ms] -> [s]
if len(t_ech) > 2:
# Sometimes, t_ech has an extra "0" value tacked on to the end.
# This must be removed before the interpolation.
if t_ech[-1] == 0:
t_ech, p_ech = t_ech[:-1], p_ech[:-1]
p_ech = interp1(
t_ech,
p_ech,
params.times,
"linear",
bounds_error=False,
fill_value=0.0,
)
else:
params.logger.info(
"[Shot %s]: No ECH power data found in this shot. Setting to zeros",
params.shot_id,
)
p_ech = np.zeros(len(params.times))
except mdsExceptions.MdsException:
p_ech = np.zeros(len(params.times))
params.logger.info(
"[Shot %s]: Failed to open ECH node. Setting to zeros", params.shot_id
)
params.logger.debug("[Shot %s]: %s", params.shot_id, traceback.format_exc())
# Get ohmic power and loop voltage
ohmic_parameters = D3DPhysicsMethods.get_ohmic_parameters(params)
p_ohm = ohmic_parameters["p_ohm"]
# Radiated power
# We had planned to use the standard signal r'\bolom::prad_tot' for this
# parameter. However, the processing involved in calculating \prad_tot
# from the arrays of bolometry channels involves non-causal filtering with
# a 50 ms window. This is not acceptable for our purposes. Tony Leonard
# provided us with the two IDL routines that are used to do the automatic
# processing that generates the \prad_tot signal in the tree (getbolo.pro
# and powers.pro). I converted them into Matlab routines, and modified the
# analysis so that the smoothing is causal, and uses a shorter window.
smoothing_window = 0.010 # [s]
try:
bol_prm, _ = params.mds_conn.get_data_with_dims(
r"\bol_prm", tree_name="bolom"
)
except mdsExceptions.MdsException:
params.logger.info("[Shot %s]: Failed to open bolom tree.", params.shot_id)
params.logger.debug("[Shot %s]: %s", params.shot_id, traceback.format_exc())
upper_channels = [f"bol_u{i+1:02d}_v" for i in range(24)]
lower_channels = [f"bol_l{i+1:02d}_v" for i in range(24)]
bol_channels = upper_channels + lower_channels
bol_signals = []
for i in range(48):
bol_signal = params.mds_conn.get_data(
rf"\top.raw:{bol_channels[i]}", tree_name="bolom"
)
bol_signals.append(bol_signal)
bol_time = params.mds_conn.get_dims(
rf"\top.raw:{bol_channels[0]}", tree_name="bolom"
)[0]
bol_time /= 1e3 # [ms] -> [s]
a_struct = matlab_get_bolo(
shot_id=params.shot_id,
bol_channels=bol_channels,
bol_prm=bol_prm,
bol_top=bol_signals,
bol_time=bol_time,
drtau=smoothing_window * 1e3,
)
ier = 0
for j in range(48):
# TODO: Ask about how many valid channels are needed for proper calculation
if a_struct.channels[j].ier == 1:
ier = 1
p_rad = np.full(len(params.times), np.nan)
break
if ier == 0:
b_struct = matlab_power(a_struct)
p_rad = b_struct.pwrmix # [W]
p_rad = interp1(a_struct.raw_time, p_rad, params.times, "linear")
# Remove any negative values from the power data
# TODO: Could p_ohm be negative?
p_rad[np.isinf(p_rad)] = np.nan
p_rad[p_rad < 0] = 0
p_nbi[p_nbi < 0] = 0
p_ech[p_ech < 0] = 0
p_input = p_ohm + p_nbi + p_ech # [W]
rad_fraction = p_rad / p_input
rad_fraction[np.isinf(rad_fraction)] = np.nan
output = {
"p_rad": p_rad,
"p_nbi": p_nbi,
"p_ech": p_ech,
"radiated_fraction": rad_fraction,
}
return output
@staticmethod
@physics_method(
columns=["p_ohm", "v_loop"],
tokamak=Tokamak.D3D,
)
def get_ohmic_parameters(params: PhysicsMethodParams):
"""
Compute ohmic heating power and loop voltage for a DIII-D shot
References:
-------
- https://github.com/MIT-PSFC/disruption-py/blob/matlab/DIII-D/get_P_ohm_d3d.m
Last major update by William Wei on 8/1/2024
"""
# Get edge loop voltage and smooth it a bit with a median filter
v_loop, t_v_loop = params.mds_conn.get_data_with_dims(
f'ptdata("vloopb", {params.shot_id})', tree_name="d3d"
)
t_v_loop /= 1e3 # [ms] -> [s]
v_loop = scipy.signal.medfilt(v_loop, 11)
v_loop = interp1(t_v_loop, v_loop, params.times, "linear")
# Get plasma current
ip, t_ip = params.mds_conn.get_data_with_dims(
f"ptdata('ip', {params.shot_id})", tree_name="d3d"
)
t_ip /= 1e3 # [ms] -> [s]
# Alessandro Pau (JET & AUG) has given Cristina a robust routine that
# performs time differentiation with smoothing, while preserving causality.
# It can be useful for differentiating numerous signals such as Ip, Vloop,
# etc. It is called 'GSASTD'. We will use this routine in place of Matlab's
# 'gradient' and smoothing/filtering routines for certain signals.
# We choose a 20-point width for gsastd. This means a 10ms window for
# ip smoothing
dipdt_smoothed = matlab_gsastd(
x=t_ip,
y=ip,
derivative_mode=1,
width=20,
smooth_type=3,
ends_type=1,
slew_rate=0,
)
li, t_li = params.mds_conn.get_data_with_dims(
r"\efit_a_eqdsk:li", tree_name="_efit_tree"
)
t_li /= 1e3
# Use chisq to determine which time slices are invalid
chisq = params.mds_conn.get_data(r"\efit_a_eqdsk:chisq", tree_name="_efit_tree")
# Filter out invalid indices of efit reconstruction
(invalid_indices,) = np.where(chisq > 50)
li[invalid_indices] = np.nan
r_0, t_r0 = params.mds_conn.get_data_with_dims(
r"\top.results.geqdsk:rmaxis", tree_name="_efit_tree"
) # [m], [ms]
t_r0 /= 1e3 # [ms] -> [s]
li = interp1(t_li, li, params.times, "linear")
r_0 = interp1(t_r0, r_0, params.times, "linear")
inductance = 4.0 * np.pi * 1e-7 * r_0 * li / 2 # [H]
ip = interp1(t_ip, ip, params.times, "linear")
dipdt_smoothed = interp1(t_ip, dipdt_smoothed, params.times, "linear")
v_inductive = inductance * dipdt_smoothed # [V]
v_resistive = v_loop - v_inductive # [V]
p_ohm = ip * v_resistive # [W]
output = {"p_ohm": p_ohm, "v_loop": v_loop}
return output
@staticmethod
@physics_method(
columns=["n_e", "greenwald_fraction", "dn_dt"],
tokamak=Tokamak.D3D,
)
def get_density_parameters(params: PhysicsMethodParams):
"""
Get electron density from EFIT, then compute dn_dt and Greenwald_fraction.
References
-------
https://github.com/MIT-PSFC/disruption-py/blob/matlab/DIII-D/get_density_parameters.m
https://github.com/MIT-PSFC/disruption-py/issues/238
https://github.com/MIT-PSFC/disruption-py/pull/249
Last major update by William Wei on 8/2/2024
"""
ne, t_ne = params.mds_conn.get_data_with_dims(
r"\density", tree_name="_efit_tree"
)
# If EFIT disruption tree does not contain density data,
# then read density from BCI subtree of D3D main tree
# TODO: Find a shot to test this logic
if len(~np.isnan(ne)) == 0:
ne, t_ne = params.mds_conn.get_data_with_dims(r"\denv2", tree_name="d3d")
ne = ne * 1.0e6 # [cm^3] -> [m^3]
t_ne = t_ne / 1.0e3 # [ms] -> [s]
dne_dt = np.gradient(ne, t_ne)
# NOTE: t_ne has higher resolution than efit_time so t_ne[0] < efit_time[0]
# because of rounding, meaning we need to allow extrapolation
ne = interp1(
t_ne,
ne,
params.times,
"linear",
bounds_error=False,
)
dne_dt = interp1(
t_ne,
dne_dt,
params.times,
"linear",
bounds_error=False,
)
try:
ip, t_ip = params.mds_conn.get_data_with_dims(
f"ptdata('ip', {params.shot_id})", tree_name="_efit_tree"
) # [A], [ms]
t_ip = t_ip / 1.0e3 # [ms] -> [s]
ipsign = np.sign(np.sum(ip))
ip = interp1(t_ip, ip * ipsign, params.times, "linear") # positive definite
a_minor, t_a = params.mds_conn.get_data_with_dims(
r"\efit_a_eqdsk:aminor", tree_name="_efit_tree"
) # [m], [ms]
t_a = t_a / 1.0e3 # [ms] -> [s]
a_minor = interp1(t_a, a_minor, params.times, "linear")
with np.errstate(divide="ignore"):
n_g = ip / 1.0e6 / (np.pi * a_minor**2) # [MA/m^2]
g_f = ne / n_g * 1e-20
except (mdsExceptions.MdsException, ValueError) as e:
# TODO: Confirm that there is a separate exception if ptdata name doesn't exist
params.logger.info(
"[Shot %s]: Failed to compute Greenwald fraction.", params.shot_id
)
params.logger.debug("[Shot %s]: %s", params.shot_id, traceback.format_exc())
err = "operands could not be broadcast together with shapes"
if isinstance(ValueError, e) and err not in e.args:
raise
g_f = [np.nan]
return {
"n_e": ne,
"greenwald_fraction": g_f,
"dn_dt": dne_dt,
}
@staticmethod
@physics_method(
columns=["n_e_rt", "greenwald_fraction_rt", "dn_dt_rt"],
tokamak=Tokamak.D3D,
)
def get_rt_density_parameters(params: PhysicsMethodParams):
"""
Get real-time electron density from EFIT, then compute the
real-time dn_dt and Greenwald_fraction.
References
-------
https://github.com/MIT-PSFC/disruption-py/blob/matlab/DIII-D/get_density_parameters_RT.m
https://github.com/MIT-PSFC/disruption-py/pull/251
Last major update by William Wei on 8/2/2024
"""
ne_rt, t_ne_rt = params.mds_conn.get_data_with_dims(
f"ptdata('dssdenest', {params.shot_id})", tree_name="_efit_tree"
) # [10^19 m^-3]
t_ne_rt = t_ne_rt / 1.0e3 # [ms] to [s]
ne_rt = ne_rt * 1.0e19 # [10^19 m^-3] -> [m^-3]
dne_dt_rt = np.gradient(ne_rt, t_ne_rt) # [m^-3/s]
ne_rt = interp1(t_ne_rt, ne_rt, params.times, "linear")
dne_dt_rt = interp1(t_ne_rt, dne_dt_rt, params.times, "linear")
# Get real time ip to calculate the Greenwald density
try:
ip_rt, t_ip_rt = params.mds_conn.get_data_with_dims(
f"ptdata('ipsip', {params.shot_id})"
) # [MA], [ms]
t_ip_rt = t_ip_rt / 1.0e3 # [ms] to [s]
except mdsExceptions.MdsException:
ip_rt, t_ip_rt = params.mds_conn.get_data_with_dims(
f"ptdata('ipspr15v', {params.shot_id})"
) # [volts; 2 V/MA], [ms]
t_ip_rt = t_ip_rt / 1.0e3 # [ms] to [s]
ip_rt /= 2 # [volts] to [MA]
ip_sign = np.sign(np.sum(ip_rt))
ip_rt = interp1(t_ip_rt, ip_rt * ip_sign, params.times, "linear")
# Read in EFIT minor radius and timebase. This is also needed to calculate
# the Greenwald density limit. However, if the minor radius data is not
# available, use a default fixed value of 0.59 m. (We surveyed several
# hundred shots to determine this default value.) Note that the efit
# timebase data is in a node called "atime" instead of "time" (where "time"
# does not work).
# For the real-time (RT) signals, read from the EFITRT1 tree
try:
a_minor_rt, t_a_rt = params.mds_conn.get_data_with_dims(
r"\efit_a_eqdsk:aminor", tree_name="efitrt1"
) # [m], [ms]
t_a_rt = t_a_rt / 1.0e3 # [ms] -> [s]
a_minor_rt = interp1(t_a_rt, a_minor_rt, params.times, "linear")
except mdsExceptions.MdsException:
a_minor_rt = 0.59 * np.ones(len(params.times))
try:
with np.errstate(divide="ignore"):
n_g_rt = ip_rt / (np.pi * a_minor_rt**2) # [MA/m^2]
g_f_rt = ne_rt / 1.0e20 / n_g_rt
except ValueError as e:
params.logger.info(
"[Shot %s]: Failed to compute Greenwald fraction rt.", params.shot_id
)
params.logger.debug("[Shot %s]: %s", params.shot_id, traceback.format_exc())
err = "operands could not be broadcast together with shapes"
if err not in e.args:
raise
g_f_rt = [np.nan]
return {"n_e_rt": ne_rt, "greenwald_fraction_rt": g_f_rt, "dn_dt_rt": dne_dt_rt}
@staticmethod
@physics_method(
columns=["ip", "ip_error", "dip_dt", "dipprog_dt", "power_supply_railed"],
tokamak=Tokamak.D3D,
)
def get_ip_parameters(params: PhysicsMethodParams):
"""
Retrieve plasma current parameters including measured and programmed values.
Parameters
----------
params : PhysicsMethodParams
Parameters containing MDS connection and shot information
Returns
-------
dict
A dictionary containing the following keys:
- 'ip' : array
Measured plasma current values interpolated to the specified times.
- 'ip_error' : array
Error in plasma current, defined where feedback is active.
- 'dip_dt' : array
Time derivative of the measured plasma current.
- 'dipprog_dt' : array
Time derivative of the programmed plasma current.
- 'power_supply_railed' : array
Indicator of whether the power supply has railed at the specified times.
"""
ip = [np.nan]
ip_prog = [np.nan]
dip_dt = [np.nan]
dipprog_dt = [np.nan]
# Fill with nans instead of using a single nan because indices are used
ip_error = np.full(len(params.times), np.nan)
# Get measured plasma current parameters
try:
ip, t_ip = params.mds_conn.get_data_with_dims(
f"ptdata('ip', {params.shot_id})", tree_name="d3d"
) # [A], [ms]
t_ip = t_ip / 1.0e3 # [ms] -> [s]
dip_dt = np.gradient(ip, t_ip)
ip = interp1(t_ip, ip, params.times, "linear")
dip_dt = interp1(t_ip, dip_dt, params.times, "linear")
except mdsExceptions.MdsException:
params.logger.info(
"[Shot %s]: Failed to get measured plasma current parameters",
params.shot_id,
)
params.logger.debug("[Shot %s]: %s", params.shot_id, traceback.format_exc())
# Get programmed plasma current parameters
try:
ip_prog, t_ip_prog = params.mds_conn.get_data_with_dims(
f"ptdata('iptipp', {params.shot_id})", tree_name="d3d"
) # [A], [ms]
t_ip_prog = t_ip_prog / 1.0e3 # [ms] -> [s]
polarity = np.unique(
params.mds_conn.get_data(
f"ptdata('iptdirect', {params.shot_id})", tree_name="d3d"
)
)
if len(polarity) > 1:
params.logger.info(
(
"[Shot %s]: Polarity of Ip target is not constant. "
"Using value at first timestep."
),
params.shot_id,
)
params.logger.debug(
"[Shot %s]: Polarity array %s", params.shot_id, polarity
)
polarity = polarity[0]
ip_prog = ip_prog * polarity
dipprog_dt = np.gradient(ip_prog, t_ip_prog)
ip_prog = interp1(t_ip_prog, ip_prog, params.times, "linear")
dipprog_dt = interp1(t_ip_prog, dipprog_dt, params.times, "linear")
except mdsExceptions.MdsException:
params.logger.info(
"[Shot %s]: Failed to get programmed plasma current parameters",
params.shot_id,
)
params.logger.debug("[Shot %s]: %s", params.shot_id, traceback.format_exc())
# Now get the signal pointname 'ipimode'. This PCS signal denotes whether
# or not PCS is actually feedback controlling the plasma current. There
# are times when feedback of Ip is purposely turned off, such as during
# electron cyclotron current drive experiments. Here is how to interpret
# the value of 'ipimode':
# 0: normal Ip feedback to E-coils supplies
# 3: almost normal Ip feedback, except that abs(Ip) > 2.5 MA
# Anything else: not in normal Ip feedback mode. In this case, the
# 'ip_prog' signal is irrelevant, and therefore 'ip_error' is not defined.
try:
ipimode, t_ipimode = params.mds_conn.get_data_with_dims(
f"ptdata('ipimode', {params.shot_id})", tree_name="d3d"
)
t_ipimode = t_ipimode / 1.0e3 # [ms] -> [s]
ipimode = interp1(t_ipimode, ipimode, params.times, "linear")
except mdsExceptions.MdsException:
params.logger.info(
"[Shot %s]: Failed to get ipimode signal. Setting to NaN.",
params.shot_id,
)
params.logger.debug("[Shot %s]: %s", params.shot_id, traceback.format_exc())
ipimode = np.full(len(params.times), np.nan)
feedback_on_indices = np.where((ipimode == 0) | (ipimode == 3))
ip_error[feedback_on_indices] = (
ip[feedback_on_indices] - ip_prog[feedback_on_indices]
)
# Finally, get 'epsoff' to determine if/when the E-coil power supplies have railed
# Times at which power_supply_railed ~=0 (i.e. epsoff ~=0) mean that
# PCS feedback control of Ip is not being applied. Therefore the
# 'ip_error' parameter is undefined for these times.
try:
epsoff, t_epsoff = params.mds_conn.get_data_with_dims(
f"ptdata('epsoff', {params.shot_id})", tree_name="d3d"
)
t_epsoff = t_epsoff / 1.0e3 # [ms] -> [s]
# Avoid problem with simultaneity of epsoff being triggered exactly
# on the last time sample
t_epsoff += 0.001
epsoff = interp1(t_epsoff, epsoff, params.times, "linear")
railed_indices = np.where(np.abs(epsoff) > 0.5)
power_supply_railed = np.zeros(len(params.times))
power_supply_railed[railed_indices] = 1
ip_error[railed_indices] = np.nan
except mdsExceptions.MdsException:
params.logger.info(
"[Shot %s]: Failed to get epsoff signal. Setting to NaN.",
params.shot_id,
)
params.logger.debug("[Shot %s]: %s", params.shot_id, traceback.format_exc())
power_supply_railed = [np.nan]
# 'ip_prog': ip_prog,
output = {
"ip": ip,
"ip_error": ip_error,
"dip_dt": dip_dt,
"dipprog_dt": dipprog_dt,
"power_supply_railed": power_supply_railed,
}
return output
@staticmethod
@physics_method(
columns=[
"ip_rt",
"ip_prog_rt",
"ip_error_rt",
"dip_dt_rt",
"dipprog_dt_rt",
],
tokamak=Tokamak.D3D,
)
def get_rt_ip_parameters(params: PhysicsMethodParams):
"""
Get the real-time plasma current and programmed plasma current from EFIT,
then compute the real-time ip_error and the derivatives of all of the above signals.
References
-------
https://github.com/MIT-PSFC/disruption-py/blob/matlab/DIII-D/get_Ip_parameters_RT.m
https://github.com/MIT-PSFC/disruption-py/pull/254
Last major update by William Wei on 8/5/2024
"""
ip_rt = [np.nan]
ip_prog_rt = [np.nan]
ip_error_rt = [np.nan]
dip_dt_rt = [np.nan]
dipprog_dt_rt = [np.nan]
# Get measured plasma current parameters
# TODO: Why open d3d and not the rt efit tree?
try:
ip_rt, t_ip_rt = params.mds_conn.get_data_with_dims(
f"ptdata('ipsip', {params.shot_id})", tree_name="d3d"
) # [MA], [ms]
t_ip_rt = t_ip_rt / 1.0e3 # [ms] -> [s]
ip_rt = ip_rt * 1.0e6 # [MA] -> [A]
dip_dt_rt = np.gradient(ip_rt, t_ip_rt)
ip_rt = interp1(t_ip_rt, ip_rt, params.times, "linear")
dip_dt_rt = interp1(t_ip_rt, dip_dt_rt, params.times, "linear")
except mdsExceptions.MdsException:
params.logger.info(
"[Shot %s]: Failed to get measured plasma current parameters",
params.shot_id,
)
params.logger.debug("[Shot %s]: %s", params.shot_id, traceback.format_exc())
# Get programmed plasma current parameters
try:
ip_prog_rt, t_ip_prog_rt = params.mds_conn.get_data_with_dims(
f"ptdata('ipsiptargt', {params.shot_id})", tree_name="d3d"
) # [MA], [ms]
t_ip_prog_rt = t_ip_prog_rt / 1.0e3 # [ms] -> [s]
ip_prog_rt = ip_prog_rt * 1.0e6 * 0.5 # [MA] -> [A]
polarity = np.unique(
params.mds_conn.get_data(
f"ptdata('iptdirect', {params.shot_id})", tree_name="d3d"
)
)
if len(polarity) > 1:
params.logger.info(
"[Shot %s]: Polarity of Ip target is not constant."
" Setting to first value in array.",
params.shot_id,
)
params.logger.debug(
"[Shot %s]: Polarity array: %s", params.shot_id, polarity
)
polarity = polarity[0]
ip_prog_rt = ip_prog_rt * polarity
dipprog_dt_rt = np.gradient(ip_prog_rt, t_ip_prog_rt)
ip_prog_rt = interp1(t_ip_prog_rt, ip_prog_rt, params.times, "linear")
dipprog_dt_rt = interp1(t_ip_prog_rt, dipprog_dt_rt, params.times, "linear")
except mdsExceptions.MdsException:
params.logger.info(
"[Shot %s]: Failed to get programmed plasma current parameters",
params.shot_id,
)
params.logger.debug("[Shot %s]: %s", params.shot_id, traceback.format_exc())
try:
ip_error_rt, t_ip_error_rt = params.mds_conn.get_data_with_dims(
f"ptdata('ipeecoil', {params.shot_id})", tree_name="d3d"
) # [MA], [ms]
t_ip_error_rt = t_ip_error_rt / 1.0e3 # [ms] to [s]
ip_error_rt = ip_error_rt * 1.0e6 * 0.5 # [MA] -> [A]
ip_error_rt = interp1(t_ip_error_rt, ip_error_rt, params.times, "linear")
except mdsExceptions.MdsException:
params.logger.info(
"[Shot %s]: Failed to get ipeecoil signal. Setting to NaN.",
params.shot_id,
)
params.logger.debug("[Shot %s]: %s", params.shot_id, traceback.format_exc())
# Now get the signal pointname 'ipimode'. This PCS signal denotes whether
# or not PCS is actually feedback controlling the plasma current. There
# are times when feedback of Ip is purposely turned off, such as during
# electron cyclotron current drive experiments. Here is how to interpret
# the value of 'ipimode':
# 0: normal Ip feedback to E-coils supplies
# 3: almost normal Ip feedback, except that abs(Ip) > 2.5 MA
# Anything else: not in normal Ip feedback mode. In this case, the
# 'ip_prog' signal is irrelevant, and therefore 'ip_error' is not defined.
try:
ipimode, t_ipimode = params.mds_conn.get_data_with_dims(
f"ptdata('ipimode', {params.shot_id})", tree_name="d3d"
)
t_ipimode = t_ipimode / 1.0e3 # [ms] -> [s]
ipimode = interp1(t_ipimode, ipimode, params.times, "linear")
except mdsExceptions.MdsException:
params.logger.info(
"[Shot %s]: Failed to get ipimode signal. Setting to NaN.",
params.shot_id,
)
params.logger.debug("[Shot %s]: %s", params.shot_id, traceback.format_exc())
ipimode = np.full(len(params.times), np.nan)
(feedback_off_indices,) = np.where((ipimode != 0) & (ipimode == 3))
ip_error_rt[feedback_off_indices] = np.nan
# Finally, get 'epsoff' to determine if/when the E-coil power supplies have railed
# Times at which power_supply_railed ~=0 (i.e. epsoff ~=0) mean that
# PCS feedback control of Ip is not being applied. Therefore the
# 'ip_error' parameter is undefined for these times.
try:
epsoff, t_epsoff = params.mds_conn.get_data_with_dims(
f"ptdata('epsoff', {params.shot_id})", tree_name="d3d"
)
t_epsoff = t_epsoff / 1.0e3 # [ms] -> [s]
# Avoid problem with simultaneity of epsoff being triggered exactly on
# the last time sample
t_epsoff += 0.001
epsoff = interp1(t_epsoff, epsoff, params.times, "linear")
power_supply_railed = np.zeros(len(params.times))
(railed_indices,) = np.where(np.abs(epsoff) > 0.5)
power_supply_railed[railed_indices] = 1
# Times at which power_supply_railed ~=0 (i.e. epsoff ~=0) mean that
# PCS feedback control of Ip is not being applied. Therefore the
# 'ip_error' parameter is undefined for these times.
(ps_railed_indices,) = np.where(power_supply_railed != 0)
ip_error_rt[ps_railed_indices] = np.nan
except mdsExceptions.MdsException:
params.logger.info(
(
"[Shot %s]: Failed to get epsoff signal. "
"power_supply_railed will be NaN."
),
params.shot_id,
)
params.logger.debug("[Shot %s]: %s", params.shot_id, traceback.format_exc())
# 'dip_dt_RT': dip_dt_rt,
output = {
"ip_rt": ip_rt,
"ip_prog_rt": ip_prog_rt,
"ip_error_rt": ip_error_rt,
"dip_dt_rt": dip_dt_rt,
"dipprog_dt_rt": dipprog_dt_rt,
}
return output
@staticmethod
@physics_method(
columns=["zcur", "zcur_normalized"],
tokamak=Tokamak.D3D,
)
def get_z_parameters(params: PhysicsMethodParams):
"""
Get the vertical position of the plasma current centroid, then
compute the normalized values with respect to the plasma minor radius.
References
-------
https://github.com/MIT-PSFC/disruption-py/blob/matlab/DIII-D/get_Z_error_d3d.m
https://github.com/MIT-PSFC/disruption-py/pull/255
Last major update by William Wei on 9/4/2024
"""
nominal_flattop_radius = 0.59
# Get z_cur
z_cur, t_z_cur = params.mds_conn.get_data_with_dims(
f"ptdata('vpszp', {params.shot_id})", tree_name="d3d"
)
t_z_cur = t_z_cur / 1.0e3 # [ms] -> [s]
z_cur = z_cur / 1.0e2 # [cm] -> [m]
z_cur = interp1(t_z_cur, z_cur, params.times, "linear")
# Compute z_cur_norm
try:
a_minor, t_a = params.mds_conn.get_data_with_dims(
r"\efit_a_eqdsk:aminor", tree_name="_efit_tree"
) # [m], [ms]
t_a = t_a / 1.0e3 # [ms] -> [s]
chisq = params.mds_conn.get_data(
r"\efit_a_eqdsk:chisq", tree_name="_efit_tree"
)
(invalid_indices,) = np.where(chisq > 50)
a_minor[invalid_indices] = np.nan
a_minor = interp1(t_a, a_minor, params.times, "linear")
z_cur_norm = z_cur / a_minor
except mdsExceptions.MdsException:
params.logger.info(
"[Shot %s]: Failed to get efit parameters", params.shot_id
)
params.logger.debug("[Shot %s]: %s", params.shot_id, traceback.format_exc())
z_cur_norm = z_cur / nominal_flattop_radius
return {"zcur": z_cur, "zcur_normalized": z_cur_norm}
@staticmethod
@physics_method(columns=["n1rms", "n1rms_normalized"], tokamak=Tokamak.D3D)
def get_n1rms_parameters(params: PhysicsMethodParams):
"""
Get the n1rms data, then compute n1rms_normalized = n1rms / btor
References
-------
https://github.com/MIT-PSFC/disruption-py/blob/matlab/DIII-D/get_n1rms_d3d.m
https://github.com/MIT-PSFC/disruption-py/pull/257
Last major update by William Wei on 8/6/2024
"""
# Get n1rms signal from d3d tree
n1rms, t_n1rms = params.mds_conn.get_data_with_dims(r"\n1rms", tree_name="d3d")
n1rms *= 1.0e-4 # Gauss -> Tesla
t_n1rms /= 1e3 # [ms] -> [s]
n1rms = interp1(t_n1rms, n1rms, params.times)
# Calculate n1rms_norm
try:
b_tor, t_b_tor = params.mds_conn.get_data_with_dims(
f"ptdata('bt', {params.shot_id})", tree_name="d3d"
)
t_b_tor /= 1e3 # [ms] -> [s]
b_tor = interp1(t_b_tor, b_tor, params.times) # [T]
n1rms_norm = n1rms / np.abs(b_tor)
except mdsExceptions.MdsException:
params.logger.info(
"[Shot %s]: Failed to get b_tor signal to compute n1rms_normalized",
params.shot_id,
)
params.logger.debug("[Shot %s]: %s", params.shot_id, traceback.format_exc())
n1rms_norm = [np.nan]
return {"n1rms": n1rms, "n1rms_normalized": n1rms_norm}
# TODO: Need to test and unblock recalculating peaking factors
# By default get_peaking_factors should grab the data from MDSPlus as opposed
# to recalculate. See DPP v4 document for details:
# https://docs.google.com/document/d/1R7fI7mCOkMQGt8xX2nS6ZmNNkcyvPQ7NmBfRPICFaFs/edit?usp=sharing
@staticmethod
@physics_method(
columns=[
"te_peaking_cva_rt",
"ne_peaking_cva_rt",
"prad_peaking_cva_rt",
"prad_peaking_xdiv_rt",
],
tokamak=Tokamak.D3D,
)
def get_peaking_factors(params: PhysicsMethodParams):
"""
This function calculates peaking factors for the shot number
given by the user corresponding to the times in the given timebase.
Electron temperature (Te_PF) and density (ne_PF) profile peaking
factors are taken from Thomson scattering measurements, and the peaking
factors describing radiated power distributions (Rad_CVA and Rad_XDIV)
are taken from the 2pi foil bolometer system.
The Thomson-based peaking factors are computed by first mapping the channel
locations to the EFIT grid (rhovn: normalized rho, psin: normalized poloidal
flux) and then determining the core channels through a threshold on rhovn.
For the bolometer-based peaking factors, a subset of 12 chords from the lower
fan array (fan = 'custom') are selected for the calculation. The core chords
are determined through a threshold from the magnetic axis. The divertor chords
preselected and consist of 5 chords from the 12-chord array.
Returns
-------
te_peaking_cva_rt: np.ndarray
Te peaking factor, core vs all channels
ne_peaking_cva_rt: np.ndarray
ne peaking factor, core vs all channels
prad_peaking_cva_rt: np.ndarray
bolometer peaking factor, core vs all-but-divertor channels
prad_peaking_xdiv_rt: np.ndarray
bolometer peaking factor, divertor vs all-but-core channels
Reference
-------
https://github.com/MIT-PSFC/disruption-py/blob/matlab/DIII-D/get_peaking_factors_d3d.m
https://github.com/MIT-PSFC/disruption-py/pull/265
https://github.com/MIT-PSFC/disruption-py/pull/328
Last major update by William Wei on 10/01/2024
"""
## Thomson parameters
ts_data_type = "blessed" # either 'blessed', 'unblessed', or 'ptdata'
# metric to use for core/edge binning (either 'psin' or 'rhovn')
ts_radius = "rhovn"
# ts_radius value defining boundary of 'core' region (between 0 and 1)
ts_core_margin = 0.3
# All data outside this range excluded. For example, psin=0 at magnetic axis
# and 1 at separatrix.
ts_radial_range = (0, 1)
# set to true to interpolate ts_channel data onto equispaced radial grid
ts_equispaced = False
## Bolometer parameters
# fan to use for P_rad peaking factors (either 'lower', 'upper', or 'custom')
bolometer_fan = "custom"
# array of bolometer fan channel numbers covering divertor
# (upper fan: 0->23, lower fan: 24:47)
div_channels = np.arange(26, 31)
# time window for filtering raw bolometer signal in [ms]
smoothing_window = 40
p_rad_core_def = (
0.06 # percentage of DIII-D veritcal extent defining the core margin
)
# 'brightness'; % either 'brightness' or 'power' ('z')
p_rad_metric = "brightness"
## Additional parameters (not in MATLAB script)
# Ts options
ts_options = ["combined", "core", "tangential"]
# vertical range of the DIII-D cross section in meters (for p_rad)
vert_range = 3.0
ne_pf = [np.nan]
te_pf = [np.nan]
rad_cva = [np.nan]
rad_xdiv = [np.nan]
# Get precomputed rad_cva & rad_xdiv data stored in ptdata tree
calculate_prad_pf = False
try:
rad_cva, t_rad_cva = params.mds_conn.get_data_with_dims(
f"ptdata('dpsrrdcva', {params.shot_id})", tree_name="d3d"
) # [], [ms]
t_rad_cva /= 1e3 # [ms] -> [s]
rad_cva = interp1(t_rad_cva, rad_cva, params.times)
rad_xdiv, t_rad_xdiv = params.mds_conn.get_data_with_dims(
f"ptdata('dpsrrdxdiv', {params.shot_id})", tree_name="d3d"
) # [], [ms]
t_rad_xdiv /= 1e3 # [ms] -> [s]
rad_xdiv = interp1(t_rad_xdiv, rad_xdiv, params.times)
except mdsExceptions.MdsException:
calculate_prad_pf = True
params.logger.debug("[Shot %s]: %s", params.shot_id, traceback.format_exc())
params.logger.info(
(
"[Shot %s]: Failed to get rad_cva and rad_xdiv from MDSplus."
" Calculating using raw bolometer data."
),
params.shot_id,
)
# Get raw Thomson data
try:
ts = D3DPhysicsMethods._get_ne_te(params, data_source=ts_data_type)
for option in ts_options:
if option in ts:
ts = ts[option]
break
efit_dict = D3DPhysicsMethods._get_efit_dict(params)
except (NotImplementedError, CalculationError, mdsExceptions.MdsException):
ts = {}
params.logger.info("[Shot %s]: Failed to get TS data", params.shot_id)
params.logger.debug("[Shot %s]: %s", params.shot_id, traceback.format_exc())
if ts:
ts["psin"], ts["rhovn"] = D3DPhysicsMethods.efit_rz_interp(ts, efit_dict)
ts["rhovn"] = ts["rhovn"].T
ts["psin"] = ts["psin"].T
# Get P_rad data
p_rad = {}
if calculate_prad_pf:
try:
p_rad = D3DPhysicsMethods._get_p_rad(
params, fan=bolometer_fan, smoothing_window=smoothing_window
)
except mdsExceptions.MdsException:
params.logger.info(
"[Shot %s]: Failed to get bolometer data", params.shot_id
)
params.logger.debug(
"[Shot %s]: %s", params.shot_id, traceback.format_exc()
)
# Calculate te_pf & ne_pf
if ts_radius in ts:
# Drop data outside of valid range
invalid_indices = np.where(
(ts[ts_radius] < ts_radial_range[0])
| (ts[ts_radius] > ts_radial_range[1])
)
ts["te"][invalid_indices] = np.nan
ts["ne"][invalid_indices] = np.nan
ts["te"][np.isnan(ts[ts_radius])] = np.nan
ts["ne"][np.isnan(ts[ts_radius])] = np.nan
# Interpolate onto uniform radial base if needed
if ts_equispaced:
for i in range(len(ts["time"])):
(no_nans,) = np.where(
~np.isnan(ts["te"][:, i]) & ~np.isnan(ts["ne"][:, i])
)
if len(no_nans) <= 1:
continue
radii = ts[ts_radius][no_nans, i]
if len(radii) <= 2:
continue
rad_coord_interp = np.linspace(min(radii), max(radii), len(radii))
# MATLAB used interp1(kind='pchip') which isn't available in disruption-py
ts["te"][no_nans, i] = interp1(
radii,
ts["te"][no_nans, i],
rad_coord_interp,
"linear",
)
ts["ne"][no_nans, i] = interp1(
radii,
ts["ne"][no_nans, i],
rad_coord_interp,
"linear",
)
ts[ts_radius][no_nans, i] = rad_coord_interp
# Find core bin for Thomson and calculate Te, ne peaking factors
core_mask = ts[ts_radius] < ts_core_margin
te_core = ts["te"].copy()
te_core[~core_mask] = np.nan
ne_core = ts["ne"].copy()
ne_core[~core_mask] = np.nan
te_pf = np.full(len(ts["time"]), np.nan)
ne_pf = np.full(len(ts["time"]), np.nan)
# pylint: disable-next=consider-using-enumerate
for i in range(len(te_pf)):
if (
~np.isnan(te_core[:, i]).all()
and ~np.isnan(ts["te"][:, i]).all()
and np.nanmean(ts["te"][:, i]) != 0
):
te_pf[i] = np.nanmean(te_core[:, i]) / np.nanmean(ts["te"][:, i])
if (
~np.isnan(ne_core[:, i]).all()
and ~np.isnan(ts["ne"][:, i]).all()
and np.nanmean(ts["ne"][:, i]) != 0
):
ne_pf[i] = np.nanmean(ne_core[:, i]) / np.nanmean(ts["ne"][:, i])
te_pf = interp1(ts["time"], te_pf, params.times)
ne_pf = interp1(ts["time"], ne_pf, params.times)
# Calculate prad_cva, prad_xdiv
if calculate_prad_pf and p_rad:
# Interpolate zmaxis and channel intersects x onto the bolometer timebase
z_m_axis = interp1(efit_dict["time"], efit_dict["zmaxis"], p_rad["t"])
z_m_axis = np.repeat(z_m_axis[:, np.newaxis], p_rad["x"].shape[1], axis=1)
# NOTE: MATLAB uses extrapolation in p_rad["xinterp"] computation.
p_rad["xinterp"] = interp1(p_rad["xtime"], p_rad["x"], p_rad["t"], axis=0)
# Determine the bolometer channels falling in the 'core' bin
core_indices = (
p_rad["xinterp"] < z_m_axis + p_rad_core_def * vert_range
) & (p_rad["xinterp"] > z_m_axis - p_rad_core_def * vert_range)
# Designate the divertor bin and find all 'other' channels not in that bin
div_indices = np.full(len(p_rad["ch_avail"]), False)
for div_channel in div_channels:
div_indices[p_rad["ch_avail"].index(div_channel)] = True
# Grab p_rad measurements for each needed set of channels
p_rad_core = np.array(p_rad[p_rad_metric]).T
p_rad_all_but_core = p_rad_core.copy()
p_rad_div = p_rad_core.copy()
p_rad_all_but_div = p_rad_core.copy()
p_rad_core[~core_indices] = np.nan
p_rad_all_but_core[core_indices] = np.nan
p_rad_div[:, ~div_indices] = np.nan
p_rad_all_but_div[:, div_indices] = np.nan
# Calculate the peaking factors
rad_cva = np.full(len(p_rad["t"]), np.nan)
rad_xdiv = np.full(len(p_rad["t"]), np.nan)
# pylint: disable-next=consider-using-enumerate
for i in range(len(rad_cva)):
if (
~np.isnan(p_rad_core[i, :]).all()
and ~np.isnan(p_rad_all_but_div[i, :]).all()
and np.nanmean(p_rad_all_but_div[i, :]) != 0
):
# NOTE: How is this core vs all?
rad_cva[i] = np.nanmean(p_rad_core[i, :]) / np.nanmean(
p_rad_all_but_div[i, :]
)
if (
~np.isnan(p_rad_div[i, :]).all()
and ~np.isnan(p_rad_all_but_core[i, :]).all()
and np.nanmean(p_rad_all_but_core[i, :]) != 0
):
# NOTE: How is this div vs all?
rad_xdiv[i] = np.nanmean(p_rad_div[i, :]) / np.nanmean(
p_rad_all_but_core[i, :]
)
rad_cva = interp1(p_rad["t"], rad_cva, params.times)
rad_xdiv = interp1(p_rad["t"], rad_xdiv, params.times)
output = {
"te_peaking_cva_rt": te_pf,
"ne_peaking_cva_rt": ne_pf,
"prad_peaking_cva_rt": rad_cva,
"prad_peaking_xdiv_rt": rad_xdiv,
}
return output
@staticmethod
def efit_rz_interp(ts, efit_dict):
"""
Interpolate the efit data to the given timebase and project onto the
poloidal plane.
Parameters
----------
ts: dict
Thomson scattering data returned by D3DPhysicsMethods._get_ne_te(...)
efit_dict: dict
Dictionary with the efit data. Keys are 'time', 'r', 'z', 'psin', 'rhovn'
Returns
-------
psin: np.ndarray
Array of plasma normalized flux
rho_vn_diag: np.ndarray
Array of normalized minor radius
Reference
-------
https://github.com/MIT-PSFC/disruption-py/blob/matlab/DIII-D/sorting/efit_Rz_interp.m
https://github.com/MIT-PSFC/disruption-py/pull/265#issuecomment-2318294825
Last major update by William Wei on 8/29/2024
"""
t = np.tile(ts["time"], [len(ts["r"]), 1]).transpose()
r = np.tile(ts["r"], [len(ts["time"]), 1])
z = np.tile(ts["z"], [len(ts["time"]), 1])
# Implement a 3D (time,radial,vertical) gridded interpolation
# efit_dict['psin'] has the dimensions (time, z, r)
interp = scipy.interpolate.RegularGridInterpolator(
[efit_dict["time"], efit_dict["z"], efit_dict["r"]],
efit_dict["psin"],
method="linear",
bounds_error=False,
fill_value=np.nan,
)
# Apply interpolant to diagnostic data and return outputs as a new structure field
psin = interp((t, z, r))
# Get rhovn using the interpolant stored in EFIT, then save this as another field in 'data'
rho_vn_diag_almost = interp1(
efit_dict["time"], efit_dict["rhovn"], ts["time"], axis=0
)
rho_vn_diag = np.empty(psin.shape[:2])
# Ger the implied psin grid for rhovn
psin_interp = np.linspace(0, 1, efit_dict["rhovn"].shape[1])
# Interpolate again to get rhovn on same psin base
for i in range(psin.shape[0]):
rho_vn_diag[i] = interp1(psin_interp, rho_vn_diag_almost[i, :], psin[i, :])
return psin, rho_vn_diag
@staticmethod
@physics_method(columns=["z_eff"], tokamak=Tokamak.D3D)
def get_zeff_parameters(params: PhysicsMethodParams):
"""
Retrieve the effective charge (Z_eff) parameters for a given shot.
Parameters
----------
params : PhysicsMethodParams
Parameters containing MDS connection and shot information
Returns
-------
dict
A dictionary containing the following key:
- 'z_eff' : array
Effective charge values interpolated to the specified times.
"""
# Get Zeff
zeff, t_zeff = params.mds_conn.get_data_with_dims(
r"\d3d::top.spectroscopy.vb.zeff:zeff", tree_name="d3d"
)
t_zeff = t_zeff / 1.0e3 # [ms] -> [s]
if len(t_zeff) > 2:
zeff = interp1(
t_zeff,
zeff,
params.times,
"linear",
bounds_error=False,
fill_value=0.0,
)
else:
zeff = np.zeros(len(params.times))
params.logger.info(
"[Shot %s]: No zeff data found in this shot.", params.shot_id
)
return {"z_eff": zeff}
@staticmethod
@physics_method(columns=["kappa_area"], tokamak=Tokamak.D3D)
def get_kappa_area(params: PhysicsMethodParams):
"""
Compute kappa_area (elongation parameter) defined as
plasma area / (pi * aminor**2)
Note: the EFIT-computed kappa is retrieved in D3DEfitMethods.
References
-------
https://github.com/MIT-PSFC/disruption-py/blob/matlab/DIII-D/get_kappa_area.m
https://github.com/MIT-PSFC/disruption-py/pull/256
Last major update by William Wei on 8/6/2024
"""
a_minor = params.mds_conn.get_data(
r"\efit_a_eqdsk:aminor", tree_name="_efit_tree"
)
area = params.mds_conn.get_data(r"\efit_a_eqdsk:area", tree_name="_efit_tree")
chisq = params.mds_conn.get_data(r"\efit_a_eqdsk:chisq", tree_name="_efit_tree")
t = params.mds_conn.get_data(r"\efit_a_eqdsk:atime", tree_name="_efit_tree")
t /= 1e3 # [ms] -> [s]
kappa_area = area / (np.pi * a_minor**2)
invalid_indices = np.where(chisq > 50)
kappa_area[invalid_indices] = np.nan
kappa_area = interp1(t, kappa_area, params.times)
return {"kappa_area": kappa_area}
@staticmethod
@physics_method(
columns=["delta", "squareness", "aminor"],
tokamak=Tokamak.D3D,
)
def get_shape_parameters(params: PhysicsMethodParams):
"""
Get the plasma triangularity (delta), squareness, and minor radius [m] from EFIT.
References
-------
https://github.com/MIT-PSFC/disruption-py/blob/matlab/DIII-D/get_shape_parameters.m
https://github.com/MIT-PSFC/disruption-py/pull/258
Last major update by William Wei on 8/6/2024
"""
# Get efit_time
efit_time = params.mds_conn.get_data(
r"\efit_a_eqdsk:atime", tree_name="_efit_tree"
)
efit_time /= 1e3 # [ms] -> [s]
# Compute triangularity
try:
tritop = params.mds_conn.get_data(
r"\efit_a_eqdsk:tritop", tree_name="_efit_tree"
) # meters
tribot = params.mds_conn.get_data(
r"\efit_a_eqdsk:tribot", tree_name="_efit_tree"
) # meters
delta = (tritop + tribot) / 2.0
except mdsExceptions.MdsException:
params.logger.info(
"[Shot %s]: Failed to obtain triangularity signals", params.shot_id
)
params.logger.debug("[Shot %s]: %s", params.shot_id, traceback.format_exc())
delta = [np.nan]
# Compute squareness
try:
sqfod = params.mds_conn.get_data(
r"\efit_a_eqdsk:sqfod", tree_name="_efit_tree"
)
sqfou = params.mds_conn.get_data(
r"\efit_a_eqdsk:sqfou", tree_name="_efit_tree"
)
squareness = (sqfod + sqfou) / 2.0
except mdsExceptions.MdsException:
params.logger.info(
"[Shot %s]: Failed to obtain squareness signals", params.shot_id
)
params.logger.debug("[Shot %s]: %s", params.shot_id, traceback.format_exc())
squareness = [np.nan]
# Get aminor
try:
aminor = params.mds_conn.get_data(
r"\efit_a_eqdsk:aminor", tree_name="_efit_tree"
)
except mdsExceptions.MdsException:
params.logger.info(
"[Shot %s]: Failed to obtain aminor signals", params.shot_id
)
params.logger.debug("[Shot %s]: %s", params.shot_id, traceback.format_exc())
aminor = [np.nan]
# Remove invalid indices
try:
chisq = params.mds_conn.get_data(
r"\efit_a_eqdsk:chisq", tree_name="_efit_tree"
)
invalid_indices = np.where(chisq > 50)
if ~np.isnan(delta[0]):
delta[invalid_indices] = np.nan
if ~np.isnan(squareness[0]):
squareness[invalid_indices] = np.nan
if ~np.isnan(aminor[0]):
aminor[invalid_indices] = np.nan
except mdsExceptions.MdsException:
params.logger.info(
"[Shot %s]: Failed to obtain chisq to remove unreliable time points.",
params.shot_id,
)
params.logger.debug("[Shot %s]: %s", params.shot_id, traceback.format_exc())
# Interpolate to the requested time basis
if ~np.isnan(delta[0]):
delta = interp1(efit_time, delta, params.times, "linear")
if ~np.isnan(squareness[0]):
squareness = interp1(efit_time, squareness, params.times, "linear")
if ~np.isnan(aminor[0]):
aminor = interp1(efit_time, aminor, params.times, "linear")
return {"delta": delta, "squareness": squareness, "aminor": aminor}
@staticmethod
@cache_method
def _get_ne_te(
params: PhysicsMethodParams,
data_source="blessed",
ts_systems=None,
):
"""
Retrieves DIII-D Thomson scattering data
Inputs
-------
data_source: string
"blessed", "unblessed", or "ptdata'
("blessed" by Thomson group)
ts_systems: list
default: ["core", "tangential"]
Returns
-------
lasers: dict
References
-------
https://github.com/MIT-PSFC/disruption-py/blob/matlab/DIII-D/utils/load_ne_Te.m
NOTE: data_source="ptdata" has not been fully implemented; however, for now this
option isn't used in any of the methods.
Original method by Kevin Montes on March 2019
Last major update by William Wei on 8/8/2024
"""
if ts_systems is None:
ts_systems = ["core", "tangential"]
if data_source == "blessed": # 'blessed' by Thomson group
mds_path = r"\top.ts.blessed."
elif data_source == "unblessed":
mds_path = r"\top.ts.revisions.revision00."
elif data_source == "ptdata":
mds_path = r"\top.ts.blessed." # Don't ask...I don't have the answer
raise NotImplementedError("ptdata case not fully implemented yet") # TODO
else:
raise CalculationError(f"Invalid data_source: {data_source}")
# Account for pointname formatting change in 2017 (however using ptdata is unimplemented)
# NOTE: "suffix" is only used if data_source="ptdata" which isn't implemented yet
suffix = {"core": "cor", "tangential": "tan"}
if params.shot_id < 172749: # First shot on Sep 19, 2017
suffix["tangential"] = "hor"
lasers = {}
for laser in ts_systems:
lasers[laser] = {}
sub_tree = f"{mds_path}{laser}"
try:
(t_sub_tree,) = params.mds_conn.get_dims(
f"{sub_tree}:temp", tree_name="electrons"
)
# lasers[laser]['time'] gets overwritten in the loop later
lasers[laser]["time"] = t_sub_tree / 1.0e3 # [ms] -> [s]
except mdsExceptions.MdsException:
lasers[laser] = None
params.logger.info(
"[Shot %s]: Failed to get %s time. Setting laser data to None.",
params.shot_id,
laser,
)
params.logger.debug(
"[Shot %s]: %s", params.shot_id, traceback.format_exc()
)
continue
child_nodes = {
"r": "r",
"z": "z",
"te": "temp",
"ne": "density",
"time": "time",
"te_error": "temp_e",
"ne_error": "density_e",
}
for node, name in child_nodes.items():
try:
lasers[laser][node] = params.mds_conn.get_data(
f"{sub_tree}:{name}", tree_name="electrons"
)
except mdsExceptions.MdsException:
lasers[laser][node] = np.full(lasers[laser]["time"].shape, np.nan)
params.logger.info(
"[Shot %s]: Failed to get %s:%s(%s) data, Setting to all NaNs.",
params.shot_id,
laser,
name,
node,
)
params.logger.debug(
"[Shot %s]: %s", params.shot_id, traceback.format_exc()
)
# Place NaNs for broken channels
lasers[laser]["te"][lasers[laser]["te"] == 0] = np.nan
lasers[laser]["ne"][lasers[laser]["ne"] == 0] = np.nan
lasers[laser]["time"] /= 1e3 # [ms] -> [s]
# If both systems/lasers available, combine them and interpolate the data
# from the tangential system onto the finer (core) timebase
if "tangential" in lasers and lasers["tangential"] is not None:
if "core" in lasers and lasers["core"] is not None:
lasers["combined"] = {}
# Interpolate tangential data onto core timebase
for key in lasers["tangential"]:
if key not in ["time", "r", "z"]:
lasers["tangential"][key] = interp1(
lasers["tangential"]["time"],
lasers["tangential"][key],
lasers["core"]["time"],
)
lasers["combined"][key] = np.concatenate(
(lasers["core"][key], lasers["tangential"][key])
)
lasers["tangential"]["time"] = lasers["core"]["time"]
lasers["combined"]["time"] = lasers["core"]["time"]
lasers["combined"]["r"] = np.concatenate(
(lasers["core"]["r"], lasers["tangential"]["r"])
)
lasers["combined"]["z"] = np.concatenate(
(lasers["core"]["z"], lasers["tangential"]["z"])
)
return lasers
@staticmethod
@cache_method
def _get_p_rad(params: PhysicsMethodParams, fan="custom", smoothing_window=50):
"""
Retrieves DIII-D radiation data from the bolometer MDSplus tree
Note: a_struct.channels[i].pwr does not exactly match the results from MATLAB
due to the use of different filtering functions (lfilter & medfilt in Python).
However the differences are close enough so that this isn't a major problem.
Inputs:
-------
fan: str
'upper', 'lower', or 'custom' (default)
References:
-------
https://github.com/MIT-PSFC/disruption-py/blob/matlab/DIII-D/sorting/load_Prad.m
Original author: Kevin Montes. Date: March 2019
Last major update by William Wei on 8/30/2024
"""
if fan == "upper":
fan_chans = np.arange(0, 24)
elif fan == "lower":
fan_chans = np.arange(24, 48)
elif fan == "custom":
# 1st choice (heavily cover divertor and core)
fan_chans = np.array([3, 4, 5, 6, 7, 8, 9, 12, 14, 15, 16, 22]) + 23
else:
return False
# Get bolometry data
bol_prm, _ = params.mds_conn.get_data_with_dims(r"\bol_prm", tree_name="bolom")
upper_channels = [f"bol_u{i+1:02d}_v" for i in range(24)]
lower_channels = [f"bol_l{i+1:02d}_v" for i in range(24)]
bol_channels = upper_channels + lower_channels
bol_signals = []
bol_times = (
[]
) # TODO: Decide whether to actually use all bol_times instead of just first one
for i in range(48):
bol_signal, bol_time = params.mds_conn.get_data_with_dims(
rf"\top.raw:{bol_channels[i]}", tree_name="bolom"
)
bol_time /= 1e3 # [ms] -> [s]
bol_signals.append(bol_signal)
bol_times.append(bol_time)
a_struct = matlab_get_bolo(
params.shot_id,
bol_channels,
bol_prm,
bol_signals,
bol_times[0],
smoothing_window,
)
b_struct = matlab_power(a_struct)
r_major_axis, efit_time = params.mds_conn.get_data_with_dims(
r"\top.results.geqdsk:rmaxis", tree_name="_efit_tree"
)
efit_time /= 1e3 # [ms] -> [s]
output = {
"ch_avail": [],
"z": [],
"brightness": [],
"power": [],
"x": np.full((len(efit_time), len(fan_chans)), np.nan),
"xtime": efit_time,
"t": a_struct.raw_time,
}
if fan != "custom":
for i, ichan in enumerate(fan_chans):
if a_struct.channels[ichan].ier == 0:
output["ch_avail"].append(ichan)
output["x"][:, i] = a_struct.channels[ichan].z + np.tan(
a_struct.channels[ichan].angle * np.pi / 180.0
) * (r_major_axis - a_struct.channels[ichan].r)
b_struct.chan[ichan].chanpwr[
np.where(b_struct.chan[ichan].chanpwr < 0)
] = 0
b_struct.chan[ichan].brightness[
np.where(b_struct.chan[ichan].brightness < 0)
] = 0
output["z"].append(b_struct.chan[ichan].chanpwr)
output["brightness"].append(b_struct.chan[ichan].brightness)
output["power"] = output["z"]
else:
# All custom channels are in the lower array
lower_fan_chans = np.arange(24, 48)
j = 0
for i, lower_fan_chan in enumerate(lower_fan_chans):
# Why include these extra channels in output['power']?
output["power"].append(b_struct.chan[lower_fan_chan].chanpwr)
if lower_fan_chan in fan_chans:
ichan = fan_chans[j]
if a_struct.channels[ichan].ier == 0:
output["ch_avail"].append(ichan)
output["x"][:, j] = a_struct.channels[ichan].z + np.tan(
a_struct.channels[ichan].angle * np.pi / 180.0
) * (r_major_axis - a_struct.channels[ichan].r)
b_struct.chan[ichan].chanpwr[
np.where(b_struct.chan[ichan].chanpwr < 0)
] = 0
b_struct.chan[ichan].brightness[
np.where(b_struct.chan[ichan].brightness < 0)
] = 0
output["z"].append(b_struct.chan[ichan].chanpwr)
output["brightness"].append(b_struct.chan[ichan].brightness)
j += 1
return output
# TODO: Replace all instances of efit_dict with a dataclass
@staticmethod
@cache_method
def _get_efit_dict(params: PhysicsMethodParams):
"""
Retrieve the EFIT data dictionary for a given shot.
Parameters
----------
params : PhysicsMethodParams
Parameters containing MDS connection and shot information
Returns
-------
dict
A dictionary containing the following keys:
- 'time' : array
Time corresponding to the EFIT data in seconds.
- 'z' : array
Elevation coordinates of the grid from the EFIT data.
- 'r' : array
Radial coordinates of the grid from the EFIT data.
- 'rhovn' : array
Normalized radius from the EFIT data.
- 'psirz' : array
Poloidal flux on the rectangular grid points from the EFIT data.
- 'zmaxis' : array
Z of magnetic axis from the EFIT data.
- 'ssimag' : array
Poloidal flux at magnetic axis from the EFIT data.
- 'ssibry' : array
Poloidal flux at the plasma boundary from the EFIT data.
- 'psin' : array
Normalized poloidal flux values.
"""
efit_dict = {}
path = r"\top.results.geqdsk:"
nodes = ["z", "r", "rhovn", "psirz", "zmaxis", "ssimag", "ssibry"]
(efit_dict_time,) = params.mds_conn.get_dims(
f"{path}psirz", tree_name="_efit_tree", dim_nums=[2]
)
efit_dict["time"] = efit_dict_time / 1e3 # [ms] -> [s]
for node in nodes:
try:
efit_dict[node] = params.mds_conn.get_data(
f"{path}{node}", tree_name="_efit_tree"
)
except mdsExceptions.MdsException:
efit_dict[node] = np.full(len(efit_dict["time"]), np.nan)
params.logger.info(
"[Shot %s]: Failed to get %s from efit, Setting to all NaNs.",
params.shot_id,
node,
)
params.logger.debug(
"[Shot %s]: %s", params.shot_id, traceback.format_exc()
)
# Normalize the poloidal flux grid (0=magnetic axis, 1=boundary)
# [Translated from D. Eldon's OMFITeqdsk read_basic_eq_from_mds() function]
psi_norm_f = efit_dict["ssibry"] - efit_dict["ssimag"]
(problems,) = np.where(psi_norm_f == 0)
# Prevent divide by 0 error by replacing 0s in the denominator
psi_norm_f[problems] = 1
efit_dict["psin"] = (
efit_dict["psirz"] - efit_dict["ssimag"][:, np.newaxis, np.newaxis]
) / psi_norm_f[:, np.newaxis, np.newaxis]
efit_dict["psin"][problems, :, :] = 0
return efit_dict
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