def verify_CECE_LOS(ECRad_results_file):
res = ECRadResults()
res.load(ECRad_results_file)
x,y = plot1DECE(project="toroidal" , freq=res.Scenario["diagnostic"]["f"][0][0]/1.e9,doPlot=False, verb=False)
R,z = plot1DECE(project="poloidal" , freq=res.Scenario["diagnostic"]["f"][0][0]/1.e9,doPlot=False, verb=False)
fig_top = plt.figure()
ax_top = fig_top.add_subplot(111)
fig_side = plt.figure()
ax_side = fig_side.add_subplot(111)
label_Pedro = "Pedro"
for i in [0, 2, 4]:
ax_top.plot(x, y.T[i], "--", label=label_Pedro)
ax_side.plot(R, z.T[i], "--", label=label_Pedro)
label_Pedro =None
label_ECRad = "ECRad"
for ir in range(res.Config["Physics"]["N_ray"]):
phi = np.arctan2(np.array(res["ray"]["y"][0][0][0][ir],dtype=np.float),
np.array(res["ray"]["x"][0][0][0][ir],dtype=np.float))
phi_rot = 22.5 * 8.5
x_ECRad = res["ray"]["R"][0][0][0][ir] * np.cos(phi - np.deg2rad(phi_rot))
y_ECRad = res["ray"]["R"][0][0][0][ir] * np.sin(phi - np.deg2rad(phi_rot))
ax_top.plot(x_ECRad, y_ECRad, "-", label=label_ECRad)
ax_side.plot(res["ray"]["R"][0][0][0][ir], res["ray"]["z"][0][0][0][ir], "-", label=label_ECRad)
label_ECRad = None
ax_top.set_xlabel(r"$x$ [m]")
ax_top.set_xlim(1,3)
ax_top.set_ylim(-0.1,0.2)
ax_top.set_ylabel(r"$y$ [m]")
ax_side.set_xlabel(r"$R$ [m]")
ax_side.set_ylabel(r"$z$ [m]")
ax_side.set_xlim(1,3)
ax_side.set_ylim(-0.1,0.2)
ax_top.legend()
ax_side.legend()
plt.show()
def get_BPD_width(Results, itime=0, logfile=None):
# Gaussian fit of all BPD
plt.figure()
for ich in range(len(Results.Scenario["diagnostic"]["f"][itime])):
params = np.zeros(3)
params[0] = np.max(Results["BPD"]["BPD"][itime][ich][0])
params[1] = Results["resonance"]["rhop_warm"][0][itime][ich]
params[2] = 0.05
fitted_params, unc = make_fit("gauss", Results["BPD"]["rhop"][itime][ich][0], Results["BPD"]["BPD"][itime][ich][0],
p_est = params)
plt.plot(Results["BPD"]["rhop"][itime][ich][0], Results["BPD"]["BPD"][itime][ich][0], label=f"original ch. {ich+1}")
plt.plot(Results["BPD"]["rhop"][itime][ich][0],
gauss_fit_func(Results["BPD"]["rhop"][itime][ich][0], fitted_params[0],
fitted_params[1], fitted_params[2]), linestyle="--", label=f"fit ch. {ich+1}")
if(logfile is not None):
logfile.write("Channel {0:d} rho_pol position: {1:1.3f}\n".format(ich + 1, fitted_params[1]))
logfile.write("Channel {0:d} rho_pol width: {1:1.3f}\n".format(ich + 1, fitted_params[2]))
else:
print("Channel {0:d} rho_pol width: {1:1.3f}".format(ich + 1, fitted_params[2]))
mask = Results["ray"]["rhop"][itime][ich][0][0] > 0.0
R_center_spl = InterpolatedUnivariateSpline(Results["ray"]["s"][itime][ich][0][0][mask],
Results["ray"]["rhop"][itime][ich][0][0][mask]
- fitted_params[1])
R_in_spl = InterpolatedUnivariateSpline(Results["ray"]["s"][itime][ich][0][0][mask],
Results["ray"]["rhop"][itime][ich][0][0][mask]
- fitted_params[1] + fitted_params[2]/2.0)
R_out_spl = InterpolatedUnivariateSpline(Results["ray"]["s"][itime][ich][0][0][mask],
Results["ray"]["rhop"][itime][ich][0][0][mask]
- fitted_params[1] - fitted_params[2]/2.0)
R_spl = InterpolatedUnivariateSpline(Results["ray"]["s"][itime][ich][0][0],
Results["ray"]["R"][itime][ich][0][0])
s_center_roots = R_center_spl.roots()
s_center = s_center_roots[np.argmin(np.abs(s_center_roots - Results["resonance"]["s_cold"][0][itime][ich]))]
s_in_roots = R_in_spl.roots()
s_in = s_in_roots[np.argmin(np.abs(s_in_roots - Results["resonance"]["s_cold"][0][itime][ich]))]
s_out_roots = R_out_spl.roots()
s_out = s_out_roots[np.argmin(np.abs(s_out_roots - Results["resonance"]["s_cold"][0][itime][ich]))]
width = np.abs(R_spl(s_in)-R_spl(s_out))
if(logfile is not None):
logfile.write("Channel {0:d} R position: {1:1.3f} cm\n".format(ich + 1, R_spl(s_center)))
logfile.write("Channel {0:d} R width: {1:1.3f} cm\n".format(ich + 1, width*1.e2))
else:
print("Channel {0:d} R width: {1:1.3f} cm\n".format(ich + 1, width*1.e2))
plt.legend()
plt.gca().set_xlabel(r"$\rho_\mathrm{pol}$")
plt.gca().set_ylabel(r"$D_\omega$")
plt.suptitle(f"\# {Results.Scenario['shot']}")
def plot_harmonics_and_frequencies(res_file):
Results = ECRadResults()
Results.load(res_file)
fig = plt.figure()
pc_obj = PlottingCore(fig=fig)
ich_list = np.arange(0,
len(Results.Scenario["diagnostic"]["f"][0]),
3,
dtype=np.int)
imode_list = np.zeros(ich_list.shape, dtype=np.int)
ir_list = np.zeros(ich_list.shape, dtype=np.int)
pc_obj.B_plot(Results, 0, ich_list, imode_list, ir_list)
plt.show()
def plot_Te_ne(self):
from Plotting_Configuration import plt
fig = plt.figure()
ax = fig.add_subplot(211)
ax_ne = fig.add_subplot(212, sharex=ax)
ax.plot(self.rhop_1D_profs, self.Te_init/1.e3, label=r"$T_\mathrm{e,init}$")
ax.plot(self.rhop, self.Te/1.e3, label=r"$T_\mathrm{e}$")
ax_ne.plot(self.rhop_1D_profs, self.ne_init / 1.e19, label=r"$n_\mathrm{e,init}$")
ax_ne.plot(self.rhop, self.ne / 1.e19, label=r"$n_\mathrm{e}$")
ax_ne.set_xlabel(r"$\rho_\mathrm{pol}$")
ax.set_ylabel(r"$T_\mathrm{e}$ [keV]")
ax_ne.set_ylabel(r"$n_\mathrm{e}$ [10$^{19}\times$ m$^{-3}$]")
ax.legend()
ax_ne.legend()
def __init__(self, parent, figure_width=12.0, figure_height=8.5):
wx.Panel.__init__(self, parent, wx.ID_ANY)
self.sizer = wx.BoxSizer(wx.VERTICAL)
self.SetSizer(self.sizer)
self.ctrl_sizer = wx.BoxSizer(wx.HORIZONTAL)
self.fig = plt.figure(figsize=(figure_width, figure_height),
tight_layout=True,
frameon=False)
self.fig.clf()
self.linestyles = ["-", "--", ":", "-."]
self.markers = ["^", "v", "+", "d", "o"]
self.cmap = plt.cm.ScalarMappable(plt.Normalize(0, 1), "rainbow")
self.canvas = FigureCanvas(self, -1, self.fig)
self.axlist = []
self.canvas.mpl_connect('motion_notify_event', self.UpdateCoords)
self.Bind(EVT_DONE_PLOTTING, self.OnDonePlotting)
self.plot_toolbar = NavigationToolbar2Wx(self.canvas)
fw, th = self.plot_toolbar.GetSize().Get()
self.plot_toolbar.SetSize(wx.Size(fw, th))
self.plot_toolbar.Realize()
self.ctrl_sizer.Add(self.plot_toolbar, 0, wx.ALL | \
wx.ALIGN_CENTER_VERTICAL , 5)
self.lower_lim_tc = simple_label_tc(self, "lower limit", 0.0, "real")
self.upper_lim_tc = simple_label_tc(self, "upper limit", 0.0, "real")
self.axis_choice = wx.Choice(self, wx.ID_ANY)
self.axis_choice.Append("x")
self.axis_choice.Append("y1")
self.axis_choice.Append("y2")
self.axis_choice.Select(0)
self.set_lim_button = wx.Button(self, wx.ID_ANY, "Set axis limits")
self.set_lim_button.Bind(wx.EVT_BUTTON, self.OnSetLim)
self.auto_lim_button = wx.Button(self, wx.ID_ANY, "Auto limits")
self.auto_lim_button.Bind(wx.EVT_BUTTON, self.OnAutoLim)
self.ctrl_sizer.Add(self.lower_lim_tc, 0, wx.ALL | \
wx.ALIGN_CENTER_VERTICAL , 5)
self.ctrl_sizer.Add(self.upper_lim_tc, 0, wx.ALL | \
wx.ALIGN_CENTER_VERTICAL , 5)
self.ctrl_sizer.Add(self.axis_choice, 0, wx.ALL | \
wx.ALIGN_CENTER_VERTICAL , 5)
self.ctrl_sizer.Add(self.set_lim_button, 0, wx.ALL | \
wx.ALIGN_CENTER_VERTICAL , 5)
self.ctrl_sizer.Add(self.auto_lim_button, 0, wx.ALL | \
wx.ALIGN_CENTER_VERTICAL , 5)
self.sizer.Add(self.ctrl_sizer, 0, wx.ALL | wx.LEFT, 5)
self.sizer.Add(self.canvas, 0, wx.ALL | \
wx.LEFT, 5)
def current_weight(DistWaveFile,
shot,
time,
EQ_exp,
EQ_diag,
EQ_ed,
fig=None,
ax=None):
dist_obj = load_f_from_mat(DistWaveFile, True)
dist_mat = loadmat(DistWaveFile, squeeze_me=True)
waves = read_dist_mat_to_beam(dist_mat, True)
ECCD_weight = np.zeros(dist_obj.f_cycl[0].shape)
j_spl = InterpolatedUnivariateSpline(waves.rhop, waves.j)
j_tot = j_spl.integral(waves.rhop[0], waves.rhop[-1])
EQ_aug_obj = EQData(shot, EQ_exp=EQ_exp, EQ_diag=EQ_diag, EQ_ed=EQ_ed)
EQ_aug_obj.init_read_from_shotfile()
Vol = EQ_aug_obj.getQuantity(dist_obj.rhop, "Vol", time)
Vol_spl = InterpolatedUnivariateSpline(dist_obj.rhop, Vol)
for irhop, rhop in enumerate(dist_obj.rhop):
weight = np.abs(waves.j[irhop] / j_tot) * Vol_spl(rhop, nu=1)
for i, u_par in enumerate(dist_obj.ull):
for j, u_perp in enumerate(dist_obj.uxx):
f_diff = dist_obj.f_cycl[irhop][i, j] - Juettner2D(
u_par, u_perp, dist_obj.Te[irhop])
ECCD_weight[i,
j] += weight * f_diff * u_par / np.sqrt(1.0 +
u_par**2 +
u_perp**2)
ECCD_weight /= np.max(np.abs(ECCD_weight.flatten()))
if (fig is None):
fig = plt.figure()
if (ax is None):
ax = fig.add_subplot(111)
ax.contourf(dist_obj.uxx, dist_obj.ull, ECCD_weight, \
levels = np.linspace(-1,1,30), cmap = plt.get_cmap("coolwarm"))
ax.set_ylabel(r"$u_\parallel$")
ax.set_xlabel(r"$u_\perp$")
m = cm.ScalarMappable(cmap=plt.cm.get_cmap("coolwarm"))
m.set_array(np.linspace(-1.0, 1.0, 30))
cb_j = fig.colorbar(m, pad=0.15, ticks=[-1.0, 0.0, 1.0])
cb_j.set_label(r"$(f - f_0) \beta_\parallel [\si{{a.u.}}]$")
ax.set_aspect("equal")
def torbeam_interface(working_dir, shot, time, itime, plasma_dict, eq_slice, ece_launch, R_res, z_res,
wg=8, dtoECESI=0.055, corr=0.85, logfile=None, TB_plot=True, only_TB=False):
cece_launches = []
for ich in range(len(ece_launch["f"])):
cece_launches.append(launch())
cece_launches[-1].parse_custom(ece_launch["f"][ich], ece_launch["R"][ich] * np.cos(np.deg2rad(ece_launch["phi"][ich])),
ece_launch["R"][ich] * np.sin(np.deg2rad(ece_launch["phi"][ich])), ece_launch["z"][ich],
ece_launch["phi_tor"][ich], ece_launch["theta_pol"][ich], ece_launch["width"][ich], ece_launch["dist_focus"][ich])
make_TORBEAM_no_data_load(working_dir, shot, time, plasma_dict["rhop_prof"][itime], plasma_dict["Te"][itime], plasma_dict["ne"][itime],
eq_slice.R, eq_slice.z, eq_slice.Psi, eq_slice.Br, eq_slice.Bt, eq_slice.Bz,
eq_slice.Psi_ax, eq_slice.Psi_sep, cece_launches, ITM=False,
ITER=False, mode = -1)
for ich in range(len(ece_launch["f"])):
TB_failed = False
Rz_data = np.loadtxt(os.path.join(working_dir, "{0:d}_{1:1.3f}_rays".format(shot, time),
"Rz_beam_{0:1d}.dat".format(ich + 1).replace(",", "")))
R_center = Rz_data.T[0] * 1.e-2
mask = np.logical_and(R_center > np.min(eq_slice.R), R_center < np.max(eq_slice.R))
z_center = Rz_data.T[1] * 1.e-2
mask[np.logical_and(z_center < np.min(eq_slice.z), z_center > np.max(eq_slice.z))] = False
x = None
z = None
try:
if(not np.any(mask)):
raise ValueError("No points inside flux Matrix!")
if(TB_plot):
plt.figure()
plt.plot(R_center, z_center)
plt.plot(Rz_data.T[2]*1.e-2, Rz_data.T[3]*1.e-2, "--")
plt.plot(Rz_data.T[4]*1.e-2, Rz_data.T[5]*1.e-2, "--")
x, z = plot1DECE(wgIn=wg, freq=ece_launch["f"][ich]/1.e9, dtoECE=dtoECESI*1.e3, project='poloidal', doPlot=False)
plt.plot(R_center, z_center)
plt.plot(x, z.T[0], ":")
plt.plot(x, z.T[1], ":")
plt.plot(x, z.T[2], "-.")
plt.plot(x, z.T[3], ":")
plt.plot(x, z.T[4], ":")
v_min = min(np.min(z), np.min(Rz_data.T[1:][::2]*1.e-2))
v_max = max(np.max(z), np.max(Rz_data.T[1:][::2]*1.e-2))
plt.vlines([R_res[ich]], v_min, v_max, linestyles="--", colors="k")
plt.gca().set_xlabel(r"$R$ [m]")
plt.gca().set_ylabel(r"$z$ [m]")
plt.gca().set_aspect("equal")
i_min = np.argmin(np.abs((R_center[mask]-R_res[ich])**2 + (z_center[mask]-z_res[ich])**2))
width = np.sqrt((Rz_data.T[2][i_min]*1.e-2 - R_center[i_min])**2
+ (Rz_data.T[3][i_min]*1.e-2 - z_center[i_min])**2)
if(logfile is not None):
logfile.write("TORBEAM width channel {0:d}: {1:1.3f} cm\n".format(ich+1, width*1.e2))
else:
print("TORBEAM width channel {0:d}: {1:1.3f} cm\n".format(ich+1, width*1.e2))
if only_TB:
return
except ValueError:
print("Failed to run TORBEAM. Using vacuum values!")
TB_failed = True
if(not only_TB or TB_failed):
if(x is None or z is None):
x, z = plot1DECE(wgIn=wg, freq=ece_launch["f"][ich]/1.e9, dtoECE=dtoECESI*1.e3, project='poloidal', doPlot=False)
waist = np.zeros(len(x))
waist = np.sqrt( ((z[:,2]-z[:,0])**2)+((z[:,2]-z[:, 1])**2) )
waist += np.sqrt( ((z[:,2]-z[:,3])**2)+((z[:,2]-z[:, 4])**2) )
waist /= 2
R_center = x
mask = np.logical_and(R_center > np.min(eq_slice.R), R_center < np.max(eq_slice.R))
z_center = z.T[2]
mask[np.logical_or(z_center < np.min(eq_slice.z), z_center > np.max(eq_slice.z))] = False
if(not np.any(mask)):
raise ValueError("No resonance found!")
i_min = np.argmin(np.abs((R_center-R_res[ich])**2 + (z_center-z_res[ich])**2))
if(logfile is not None):
logfile.write("Vacuum width channel {0:d}: {1:1.3f} cm\n".format(ich + 1, waist[i_min] * 100.0))
else:
print("Vacuum width channel {0:d}: {1:1.3f} cm\n".format(ich + 1, waist[i_min] * 100.0))
ax = fig.add_subplot(111)
ax.contourf(dist_obj.uxx, dist_obj.ull, ECCD_weight, \
levels = np.linspace(-1,1,30), cmap = plt.get_cmap("coolwarm"))
ax.set_ylabel(r"$u_\parallel$")
ax.set_xlabel(r"$u_\perp$")
m = cm.ScalarMappable(cmap=plt.cm.get_cmap("coolwarm"))
m.set_array(np.linspace(-1.0, 1.0, 30))
cb_j = fig.colorbar(m, pad=0.15, ticks=[-1.0, 0.0, 1.0])
cb_j.set_label(r"$(f - f_0) \beta_\parallel [\si{{a.u.}}]$")
ax.set_aspect("equal")
# plt.show()
if (__name__ == "__main__"):
fig = plt.figure(figsize=(12.5, 8.5))
ax = fig.add_subplot(111)
current_weight(
"/tokp/work/sdenk/DRELAX_35662_rdiff_prof/ECRad_35662_ECECTCCTA_run3204.mat",
35662, 3.84, "AUGD", "IDE", 0, fig, ax)
diag_weight_stand_alone(fig, ax, "/tokp/work/sdenk/DRELAX_35662_rdiff_prof/ECRad_35662_ECECTCCTA_run3204.mat", 3.84, 88, \
"/tokp/work/sdenk/DRELAX_35662_rdiff_prof/ECRad_35662_ECECTCCTA_run3204.mat")
diag_weight_stand_alone(fig, ax, "/tokp/work/sdenk/DRELAX_35662_rdiff_prof/ECRad_35662_ECECTCCTA_run3204.mat", 3.84, 136, \
"/tokp/work/sdenk/DRELAX_35662_rdiff_prof/ECRad_35662_ECECTCCTA_run3204.mat")
#
# plt.pause(-1)
# # plt.hold(True)
# current_weight("/tokp/work/sdenk/DRELAX_35662_rdiff_prof/ECRad_35662_ECECTCCTA_run3224.mat", 35662, 3.84, "AUGD", "IDE", 0, fig, ax)
# ECRH_weight(fig, "/tokp/work/sdenk/DRELAX_35662_rdiff_prof/ECRad_35662_ECECTCCTA_run3224.mat", 3.84, 0, \
# # "/tokp/work/sdenk/DRELAX_35662_rdiff_prof/ECRad_35662_ECECTCCTA_run3224.mat", beam_freq=105.e9, ax=ax)