def get_nesep(shot, tmin=2500, tmax=4000, tstep=250):
ts_dict = ts.run_script(shot, 'core', tmin=tmin, tmax=tmax, tstep=tstep)
# Plot the data to make sure its not grabbing during an ELM.
all_psins = ts_dict['psins']['all_psins']
all_nes = ts_dict['psins']['all_nes']
all_tes = ts_dict['psins']['all_Tes']
plt.style.use('seaborn')
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.plot(all_psins, all_tes, '.')
ax1.set_xlabel('Psin')
ax1.set_ylabel('Te (eV)')
ax1.set_title(str(shot))
# Plot the avg values over it.
ax1.plot(ts_dict['psins']['avg_psins'], ts_dict['psins']['avg_Tes'])
plt.show()
# Assuming it's a good fit, get the first point inside and outside sep.
inside = np.min(np.where(ts_dict['psins']['avg_psins']<1.0))
outside = inside - 1
# Get avg ne between the two as ne_sep.
ne_sep = np.mean([ts_dict['psins']['avg_nes'][inside], ts_dict['psins']['avg_nes'][outside]])
Te_sep = np.mean([ts_dict['psins']['avg_Tes'][inside], ts_dict['psins']['avg_Tes'][outside]])
print('ne sep = {:.4e}'.format(ne_sep))
print('Te sep = {:.4e}'.format(Te_sep))
return Te_sep
def heat_flux(shot, start_time=2000, end_time=4500):
ts_dict = ts.run_script(shot, 'divertor', tmin=start_time, tmax=end_time)
time = ts_dict['temp']['X']
temp_0 = ts_dict['temp']['Y'][0] # Right above DiMES.
temp_7 = ts_dict['temp']['Y'][
-1] # Topmost chord near separatrix (167196, 167536).
# Get the data in the time range.
low = np.where(time > start_time)[0].min()
high = np.where(time < end_time)[0].max()
time = time[low:high]
temp_0 = temp_0[low:high]
temp_7 = temp_7[low:high]
avg_temp_0 = np.mean(temp_0)
avg_temp_7 = np.mean(temp_7)
print("Chord 0: {:.2f}\nChord 7: {:.2f}".format(avg_temp_0, avg_temp_7))
dens_0 = ts_dict['density']['Y'][0] # Right above DiMES.
dens_7 = ts_dict['density']['Y'][
-1] # Topmost chord near separatrix (167196, 167536).
dens_0 = dens_0[low:high]
dens_7 = dens_7[low:high]
avg_dens_0 = np.mean(dens_0)
avg_dens_7 = np.mean(dens_7)
print("Chord 0: {:.2f}\nChord 7: {:.2f}".format(avg_dens_0, avg_dens_7))
# Plot temp and density at each chord location.
fig = plt.figure()
ax1 = fig.add_subplot(211) # Chord 7
ax1.plot(ts_dict['temp']['X'], ts_dict['temp']['Y'][-1], 'r.')
ax1.set_xlim([0, 6000])
ax1.set_ylim([0, 500])
ax1.set_ylabel('Te (eV)')
ax2 = fig.add_subplot(212) # Chord 0
ax2.plot(ts_dict['temp']['X'], ts_dict['temp']['Y'][0], 'r.')
ax2.set_xlim([0, 6000])
ax2.set_ylim([0, 50])
ax2.set_ylabel('Te (eV)')
ax2.set_xlabel('Time (ms)')
fig.tight_layout()
fig.show()
fig = plt.figure()
ax1 = fig.add_subplot(211) # Chord 7
ax1.plot(ts_dict['density']['X'], ts_dict['density']['Y'][-1], 'r.')
ax1.set_xlim([0, 6000])
#ax1.set_ylim([0,500])
ax1.set_ylabel('ne (m-3)')
ax2 = fig.add_subplot(212) # Chord 0
ax2.plot(ts_dict['density']['X'], ts_dict['density']['Y'][0], 'r.')
ax2.set_xlim([0, 6000])
#ax2.set_ylim([0,50])
ax2.set_ylabel('ne (m-3)')
ax2.set_xlabel('Time (ms)')
fig.tight_layout()
fig.show()
return ts_dict
def fit_ne(shot, lowlim=None, uplim=None, tmin=2000, tmax=4000):
ts_dict = ts.run_script(shot, 'core', tmin=tmin, tmax=tmax)
x_ts = ts_dict['psins']['avg_omps'] * 100
y_te = ts_dict['psins']['avg_Tes']
y_ne = ts_dict['psins']['avg_nes'] * 10e-18
x_ts_err = ts_dict['psins']['avg_omps_err'] * 100
y_te_err = ts_dict['psins']['avg_Tes_err']
y_ne_err = ts_dict['psins']['avg_nes_err'] * 10e-18
x_ts = x_ts[np.where(x_ts > 0)[0]][lowlim:uplim]
y_te = y_te[np.where(x_ts > 0)[0]]
y_ne = y_ne[np.where(x_ts > 0)[0]]
x_ts_err = x_ts_err[np.where(x_ts > 0)[0]]
y_te_err = y_te_err[np.where(x_ts > 0)[0]]
y_ne_err = y_ne_err[np.where(x_ts > 0)[0]]
def exp_fit(x, a, b):
return a * np.exp(-x * b)
popt_te, pcov_te = curve_fit(exp_fit, x_ts, y_te)
popt_ne, pcov_ne = curve_fit(exp_fit, x_ts, y_ne)
x_fit = np.linspace(0, 14, 100)
y_fit_te = exp_fit(x_fit, *popt_te)
y_fit_ne = exp_fit(x_fit, *popt_ne)
print("ne fall off length: {:.3f}".format(1 / popt_ne[1]))
errs = np.sqrt(np.diag(pcov_ne))
print("ne fall off length error: {:.4f}".format(
(errs[1] / popt_ne[1]) * (1 / popt_ne[1])))
print("Te fall off length: {:.3f}".format(1 / popt_te[1]))
errs = np.sqrt(np.diag(pcov_te))
print("Te fall off length error: {:.4f}".format(
(errs[1] / popt_te[1]) * (1 / popt_te[1])))
font = {'fontsize': 24, 'weight': 'bold'}
plt.style.use('seaborn')
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.errorbar(x_ts, y_ne, y_ne_err, 0.5, 'k.', ms=20, capsize=5, capthick=1)
ax1.plot(x_fit, y_fit_ne, 'k--', lw=3)
ax1.set_xlabel('R-Rsep omp (cm)', font)
ax1.set_ylabel(r'$\mathrm{\mathbf{Density\ (1e18\ m^{-3}}}$)', font)
ax1.tick_params(labelsize=22)
fig.tight_layout()
plt.show()
import get_lp
import get_ts
from scipy.signal import lfilter
# Grab ts_data.
ts_dict = get_ts.run_script(167196, 'core')
ts_psins = ts_dict['psins']['avg_psins']
# Grab lp data.
lp_dict = get_lp.get_dict_of_lps(167196)
for p in lp_dict.keys():
min_idx = np.where(lp_dict[p]['time'] > 2500)[0][0]
max_idx = np.where(lp_dict[p]['time'] > 5000)[0][0]
avg_psin = lp_dict[p]['psin'][min_idx:max_idx].mean()
lp_dict[p]['avg_psin'] = avg_psin
# probe 23 1.019 --> chord 8 1.022
# probe 25 1.028 --> chord 7 1.029
# probe 29 1.050 --> chord 5 1.056
# probe 31 1.063 --> chord 4 1.070
# probe 33 1.077 --> chord 4 1.070
# probe 35 1.092 --> chord 3 1.087
# Plot the time series of matching probes.
lp_min_idx = np.where(lp_dict['probe 25']['time'] > 2500)[0][0]
lp_max_idx = np.where(lp_dict['probe 25']['time'] > 5000)[0][0]
ts_min_idx = np.where(ts_dict['density']['X'] > 2500)[0][0]
ts_max_idx = np.where(ts_dict['density']['X'] > 5000)[0][0]
lp25_time = lp_dict['probe 25']['time'][lp_min_idx:lp_max_idx]
import get_ts as ts from scipy.interpolate import Rbf from scipy.signal import savgol_filter import numpy as np import matplotlib.pyplot as plt shot = 167279 tmin = 2500 tmax = 4000 # Get core TS data (i.e. the dat at the top of the device). core = ts.run_script(shot, 'core', tmin=2500, tmax=4500, tstep=300) # Get the divertor TS data (i.e. close to the target). div = ts.run_script(shot, 'divertor', tmin=2500, tmax=4500, tstep=300) # Get average psin of divertor TS closest to target. div_psin = div['psins']['avg_psins'][0] # Get the raw TS data for the divertor TS. div_raw_time = div['temp']['X'] div_raw_temp = div['temp']['Y'][0] # Filter out the spikes from ELMs by just removing data above a threshold. thresh = 40 div_noelm_time = div_raw_time[np.where(div_raw_temp < thresh)] div_noelm_temp = div_raw_temp[np.where(div_raw_temp < thresh)] # Filter the data using a savgol filter. div_filt_temp = savgol_filter(div_noelm_temp, 51, 3)
167463 : None, None
167481 : None, -5, [2000, 5000]
167530 : 5, None, [2000, 4500]
167531 : 3, None
167534 : None, None
167536 : None, -4
167618 :
"""
lowlim = None # Furthest points out to ignore. Must be positive.
uplim = None # Closest points to ignore. Must be negative.
r_tip = 6.84 # Tip coordinate as distance from sep at omp.
if True:
ts_dict = ts.run_script(167537, 'core', tmin=3700, tmax=5400)
x_ts = ts_dict['psins']['avg_omps'] * 100
y_te = ts_dict['psins']['avg_Tes']
y_ne = ts_dict['psins']['avg_nes'] * 1e-18
x_ts_err = ts_dict['psins']['avg_omps_err'] * 100
y_te_err = ts_dict['psins']['avg_Tes_err']
y_ne_err = ts_dict['psins']['avg_nes_err'] * 1e-18
x_ts = x_ts[np.where(x_ts > 0)[0]][lowlim:uplim]
y_te = y_te[np.where(x_ts > 0)[0]]
y_ne = y_ne[np.where(x_ts > 0)[0]]
x_ts_err = x_ts_err[np.where(x_ts > 0)[0]]
y_te_err = y_te_err[np.where(x_ts > 0)[0]]
import sys
sys.path.append("/home/shawn/DIII-D/ORNL-Fusion/Collector-Probes")
import get_ts as ts
import matplotlib.pyplot as plt
import numpy as np
from scipy.signal import savgol_filter
from scipy.interpolate import interp1d
ts192 = ts.run_script(167192, "core", tmin=1900, tmax=3000, tstep=150)
ts193 = ts.run_script(167193, "core", tmin=1900, tmax=3000, tstep=150)
ts194 = ts.run_script(167194, "core", tmin=1900, tmax=3000, tstep=150)
ts195 = ts.run_script(167195, "core", tmin=1900, tmax=3000, tstep=150)
all_psin = np.array([])
all_Te = np.array([])
all_Te_err = np.array([])
for ts_dict in [ts192, ts193, ts194, ts195]:
psin = ts_dict["psins"]["avg_psins"][:-1]
all_psin = np.append(all_psin, psin)
Te = ts_dict["psins"]["avg_Tes"][:-1]
Te_err = ts_dict["psins"]["avg_Tes_err"][:-1]
all_Te = np.append(all_Te, Te)
all_Te_err = np.append(all_Te_err, Te_err)
shot = ts_dict["shot"]
#plt.plot(psin, Te, label=str(shot))
# Sort all_psin and all_Te.
idx = np.argsort(all_psin)
all_psin = all_psin[idx]
all_Te = all_Te[idx]
all_Te_err = all_Te_err[idx]
# Only R's on the right half.
Rs_trunc = Rs > R_axis
# Interpolation functions of psin(R, Z) and R(psin, Z).
f_psin = scinter.Rbf(Rs[Rs_trunc], Zs[Rs_trunc], gfile['psiRZn'][Rs_trunc])
# No we have the function we want, psin(R, Z). Let's get those psins.
lp_psins = np.array([f_psin(r, -0.18) for r in lp_r])
# Plot it just to check it looks good.
#plt.plot(lp_psins, lp_te)
#plt.show()
# Great looks good. Let's get the TS data now.
ts_dict = ts.run_script(167192, "core", tmin=2930, tmax=2990, tstep=20)
ts_psins = ts_dict["psins"]["avg_psins"]
ts_te = ts_dict["psins"]["avg_Tes"]
def exp_fit(x, a, b, c):
return a * np.exp(-b * (x - 1.0)) + c
last_sol_index = np.argmax(ts_psins < 1.0)
popt, pcov = curve_fit(exp_fit,
ts_psins[:last_sol_index],
ts_te[:last_sol_index],
maxfev=5000)
fit_to_ts = exp_fit(np.arange(0.9, 1.5, 0.01), *popt)
# And now let's plot them together.
plt.rcParams.update({'font.size': 22})