Inner Stabilization¶
The mission of this script is to process data from Mirnov coils, determine plasma position and thus help with plasma stabilization. Mirnov coils are type of magnetic diagnostics and are used to the plasma position determination on GOLEM. 4 Mirnov coils are placed inside the tokamak 93 mm from the center of the chamber. The effective area of each coil is $A_{eff}=3.8\cdot10^{-3}$ m. Coils mc9 and mc1 are used to determination the horizontal plasma position and coils mc5 and mc13 to determination vertical plasma position.
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Procedure (This notebook to download)
In [1]:
import os
import glob
#remove files from the previous discharge
def remove_old(name):
if os.path.exists(name):
os.remove(name)
names = ['analysis.html','icon-fig.png', 'plasma_position.png']
for name in names:
remove_old(name)
if os.path.exists('Results/'):
file=glob.glob('Results/*')
for f in file:
os.remove(f)
import numpy as np
import matplotlib.pyplot as plt
from scipy import integrate, signal, interpolate
import pandas as pd
import holoviews as hv
hv.extension('bokeh')
import hvplot.pandas
import requests
from IPython.display import Markdown
#execute time
from timeit import default_timer as timer
start_execute = timer()
In [2]:
BDdata_URL = "http://golem.fjfi.cvut.cz/shots/{shot_no}/Diagnostics/BasicDiagnostics/{identifier}.csv"
data_URL = "http://golem.fjfi.cvut.cz/shots/{shot_no}/Diagnostics/LimiterMirnovCoils/DAS_raw_data_dir/NIdata_6133.lvm" #NI Standart (DAS) new address
shot_no = 35989 # to be replaced by the actual discharge number
# shot_no = 35940
vacuum_shot = 35984 # to be replaced by the discharge command line paramater "vacuum_shot"
# vacuum_shot = 35937 #number of the vacuum shot or 'False'
ds = np.DataSource(destpath='')
In [3]:
def open_remote(shot_no, identifier, url_template=data_URL):
return ds.open(url_template.format(shot_no=shot_no, identifier=identifier))
def read_signal(shot_no, identifier, url = data_URL, data_type='csv'):
file = open_remote(shot_no, identifier, url)
if data_type == 'lvm':
channels = ['Time', 'mc1', 'mc5', 'mc9', 'mc13', '5', 'RogQuadr', '7', '8', '9', '10', '11', '12']
return pd.read_table(file, sep = '\t', names=channels)
else:
return pd.read_csv(file, names=['Time', identifier],
index_col = 'Time', squeeze=True)
Data integration and $B_t$ elimination¶
The coils measure voltage induced by changes in poloidal magnetic field. In order to obtain measured quantity (poloidal magnetic field), it is necessary to integrate the measured voltage and multiply by constant: $B(t)=\frac{1}{A_{eff}}\int_{0}^{t} U_{sig}(\tau)d\tau$
Ideally axis of the coils is perpendicular to the toroidal magnetic field, but in fact they are slightly deflected and measure toroidal magnetic field too. To determine the position of the plasma, we must eliminate this additional signal. For this we use vacuum discharge with the same parameters.
In [4]:
def elimination (shot_no, identifier, vacuum_shot = False):
#load data
mc = (read_signal(shot_no, identifier, data_URL, 'lvm'))
mc = mc.set_index('Time')
mc = mc.replace([np.inf, -np.inf, np.nan], value = 0)
mc = mc[identifier]
konst = 1/(3.8e-03)
if vacuum_shot == False:
signal_start = mc.index[0]
length = len(mc)
Bt = read_signal(shot_no, 'BtCoil_integrated', BDdata_URL).loc[signal_start:signal_start+length*1e-06]
if len(Bt)>len(mc):
Bt = Bt.iloc[:length]
if len(Bt)<len(mc):
mc = mc.iloc[:len(Bt)]
if identifier == 'mc1':
k=300
elif identifier == 'mc5':
k= 14
elif identifier == 'mc9':
k = 31
elif identifier == 'mc13':
k = -100
mc_vacuum = Bt/k
else:
mc_vacuum = (read_signal(vacuum_shot, identifier, data_URL, 'lvm'))
mc_vacuum = mc_vacuum.set_index('Time')
mc_vacuum = mc_vacuum[identifier]
mc_vacuum -= mc_vacuum.loc[:0.9e-3].mean() #remove offset
mc_vacuum = mc_vacuum.replace([np.inf, -np.inf], value = 0)
mc_vacuum = pd.Series(integrate.cumtrapz(mc_vacuum, x=mc_vacuum.index, initial=0) * konst,
index=mc_vacuum.index*1000, name= identifier) #integration
mc -= mc.loc[:0.9e-3].mean() #remove offset
mc = pd.Series(integrate.cumtrapz(mc, x=mc.index, initial=0) * konst,
index=mc.index*1000, name= identifier) #integration
#Bt elimination
mc_vacuum = np.array(mc_vacuum)
mc_elim = mc - mc_vacuum
return mc_elim
Plasma life time¶
In [5]:
loop_voltage = read_signal(shot_no, 'U_Loop', BDdata_URL)
dIpch = read_signal(shot_no, 'U_RogCoil', BDdata_URL)
dIpch -= dIpch.loc[:0.9e-3].mean()
Ipch = pd.Series(integrate.cumtrapz(dIpch, x=dIpch.index, initial=0) * (-5.3*1e06),
index=dIpch.index, name='Ipch')
U_l_func = interpolate.interp1d(loop_voltage.index, loop_voltage)
def dIch_dt(t, Ich):
return (U_l_func(t) - 0.0097 * Ich) / (1.2e-6/2)
t_span = loop_voltage.index[[0, -1]]
solution = integrate.solve_ivp(dIch_dt, t_span, [0], t_eval=loop_voltage.index, )
Ich = pd.Series(solution.y[0], index=loop_voltage.index, name='Ich')
Ip = Ipch - Ich
Ip.name = 'Ip'
Ip_detect = Ip.loc[0.0025:]
dt = (Ip_detect.index[-1] - Ip_detect.index[0]) / (Ip_detect.index.size)
window_length = int(0.5e-3/dt)
if window_length % 2 == 0:
window_length += 1
dIp = pd.Series(signal.savgol_filter(Ip_detect, window_length, polyorder=3, deriv=1, delta=dt),
name='dIp', index=Ip_detect.index) / 1e6
threshold = 0.05
CD = requests.get("http://golem.fjfi.cvut.cz/shots/%i/Production/Parameters/CD_orientation" % shot_no)
CD_orientation = CD.text
if "ACW" in CD_orientation:
plasma_start = dIp[dIp < dIp.min()*threshold].index[0]*1e3
plasma_end = dIp.idxmax()*1e3
else:
plasma_start = dIp[dIp > dIp.max()*threshold].index[0]*1e3
plasma_end = dIp.idxmin()*1e3 + 0.5
print ('Plasma start =', round(plasma_start, 3), 'ms')
print ('Plasma end =', round(plasma_end, 3), 'ms')
# print (CD_orientation)
Plasma Position¶
To calculate displacement of plasma column, we can use approximation of the straight conductor. If we measure the poloidal field on the two opposite sides of the column, its displacement can be expressed as: $\Delta=\frac{B_{\Theta=0}-B_{\Theta=\pi}}{B_{\Theta=0}+B_{\Theta=\pi}}\cdot b$
Horizontal plasma position $\Delta r$ calculation¶
In [6]:
def horpol(shot_no, vacuum_shot=False):
mc1 = elimination(shot_no, 'mc1', vacuum_shot)
mc9 = elimination (shot_no, 'mc9', vacuum_shot)
b = 93
r = ((mc1-mc9)/(mc1+mc9))*b
r = r.replace([np.nan], value = 0)
# return r.loc[plasma_start:]
return r.loc[plasma_start:plasma_end]
# return r
In [7]:
r = horpol(shot_no, vacuum_shot)
ax = r.plot()
ax.set(ylim=(-85,85), xlim=(plasma_start,plasma_end), xlabel= 'Time [ms]', ylabel = '$\Delta$r [mm]', title = 'Horizontal plasma position #{}'.format(shot_no))
ax.axhline(y=0, color='k', ls='--', lw=1, alpha=0.4)
Out[7]:
Vertical plasma position $\Delta z$ calculation¶
In [8]:
def vertpol(shot_no, vacuum_shot = False):
mc5 = elimination(shot_no, 'mc5', vacuum_shot)
mc13 = elimination (shot_no, 'mc13', vacuum_shot)
b = 93
z = ((mc5-mc13)/(mc5+mc13))*b
z = z.replace([np.nan], value = 0)
# return z.loc[plasma_start:]
return z.loc[plasma_start:plasma_end]
# return z
In [9]:
z = vertpol (shot_no, vacuum_shot)
ax = z.plot()
ax.set(ylim=(-85, 85), xlim=(plasma_start,plasma_end), xlabel= 'Time [ms]', ylabel = '$\Delta$z [mm]', title = 'Vertical plasma position #{}'.format(shot_no))
ax.axhline(y=0, color='k', ls='--', lw=1, alpha=0.4)
Out[9]:
Plasma column radius $a$ calculation¶
In [10]:
def plasma_radius(shot_no, vacuum_shot=False):
r = horpol(shot_no, vacuum_shot)
z = vertpol(shot_no, vacuum_shot)
a0 = 85
a = a0 - np.sqrt((r**2)+(z**2))
a = a.replace([np.nan], value = 0)
# return a.loc[plasma_start:]
return a.loc[plasma_start:plasma_end]
# return a
In [11]:
a = plasma_radius(shot_no,vacuum_shot)
ax = a.plot()
ax.set(ylim=(0,85), xlim=(plasma_start,plasma_end), xlabel= 'Time [ms]', ylabel = '$a$ [mm]', title = 'Plasma column radius #{}'.format(shot_no))
Out[11]:
In [12]:
plasma_time = []
t = 0
for i in a:
if i>85 or i <0:
a = a.replace(i, value = 0)
else:
plasma_time.append(a.index[t])
t+=1
start = plasma_time[0]-1e-03
end = plasma_time[-1]-1e-03
print('start =', round(start, 3), 'ms')
print('end =', round(end, 3), 'ms')
Data¶
In [13]:
r_cut = r.loc[start:end]
a_cut = a.loc[start:end]
z_cut = z.loc[start:end]
df_processed = pd.concat([r_cut.rename('r'), z_cut.rename('z'), a_cut.rename('a')], axis= 'columns')
df_processed
Out[13]:
In [14]:
savedata = 'plasma_position.csv'
df_processed.to_csv(savedata)
In [15]:
Markdown("[Plasma position data - $r, z, a$ ](./{})".format(savedata))
Out[15]:
Graphs¶
In [16]:
hline = hv.HLine(0)
hline.opts(color='k',line_dash='dashed', alpha = 0.4,line_width=1.0)
layout = hv.Layout([df_processed[v].hvplot.line(
xlabel='', ylabel=l,ylim=(-85,85), xlim=(start,end),legend=False, title='', grid=True, group_label=v)
for (v, l) in [('r', ' r [mm]'), ('z', 'z [mm]'), ('a', 'a [mm]')] ])*hline
plot=layout.cols(1).opts(hv.opts.Curve(width=600, height=200), hv.opts.Curve('a', xlabel='time [ms]'))
plot
Out[16]:
Results¶
In [17]:
df_processed.describe()
Out[17]:
In [18]:
z_end=round(z_cut.iat[-25],2)
z_start=round(z_cut.iat[25],2)
r_end=round(r_cut.iat[-25],2)
r_start=round(r_cut.iat[25],2)
name=('z_start', 'z_end', 'r_start', 'r_end')
values=(z_start, z_end, r_start, r_end)
data={'Values':[z_start, z_end, r_start, r_end]}
results=pd.DataFrame(data, index=name)
dictionary='Results/'
j=0
for i in values :
with open(dictionary+name[j], 'w') as f:
f.write(str(i))
j+=1
results
Out[18]:
Icon fig¶
In [19]:
responsestab = requests.get("http://golem.fjfi.cvut.cz/shots/%i/Production/Parameters/PowSup4InnerStab"%shot_no)
stab = responsestab.text
if not '-1' in stab or 'Not Found' in stab and exStab==False:
fig, axs = plt.subplots(4, 1, sharex=True, dpi=200)
dataNI = (read_signal(shot_no, 'Rog', data_URL, 'lvm')) #data from NI
dataNI = dataNI.set_index('Time')
dataNI = dataNI.set_index(dataNI.index*1e3)
Irog = dataNI['RogQuadr']
# Irog=abs(Irog)
Irog*=1/5e-3
r.plot(grid=True, ax=axs[0])
z.plot(grid=True, ax=axs[1])
a.plot(grid=True, ax=axs[2])
Irog.plot(grid=True, ax=axs[3])
axs[3].set(xlim=(start,end), ylabel = 'I [A]')
axs[2].set(ylim=(0,85), xlim=(start,end), xlabel= 'Time [ms]', ylabel = '$a$ [mm]')
axs[1].set(ylim=(-85,85), xlim=(start,end), xlabel= 'Time [ms]', ylabel = '$\Delta$z [mm]')
axs[0].set(ylim=(-85,85), xlim=(start,end), xlabel= 'Time [ms]', ylabel = '$\Delta$r [mm]',
title = 'Horizontal, vertical plasma position and radius #{}'.format(shot_no))
else:
fig, axs = plt.subplots(3, 1, sharex=True, dpi=200)
r.plot(grid=True, ax=axs[0])
z.plot(grid=True, ax=axs[1])
a.plot(grid=True, ax=axs[2])
axs[2].set(ylim=(0,85), xlim=(start,end), xlabel= 'Time [ms]', ylabel = '$a$ [mm]')
axs[1].set(ylim=(-85,85), xlim=(start,end), xlabel= 'Time [ms]', ylabel = '$\Delta$z [mm]')
axs[0].set(ylim=(-85,85), xlim=(start,end), xlabel= 'Time [ms]', ylabel = '$\Delta$r [mm]',
title = 'Horizontal, vertical plasma position and radius #{}'.format(shot_no))
plt.savefig('icon-fig')
Plasma position during discharge¶
In [20]:
tms = np.arange(np.round(plasma_start),np.round(plasma_end)-1,1)
circle1 = plt.Circle((0, 0), 100, color='black', fill = False, lw = 5)
circle2 = plt.Circle((0, 0), 85, color='black', fill = False, lw = 3)
fig, ax = plt.subplots(figsize = (10,10))
ax.axvline(x=0, color = 'black', alpha = 0.5)
ax.axhline(y=0, color = 'black', alpha = 0.5)
ax.set_xlim(-100,100)
ax.set_ylim(-100,100)
ax.add_patch(circle1)
ax.add_patch(circle2)
plt.annotate('vessel',xy=(27-100, 170-100), xytext=(2-100, 190-100),
arrowprops=dict(facecolor='black', shrink=0.05), fontsize = 12)
plt.annotate('limiter',xy=(175-100, 140-100), xytext=(170-100, 190-100),
arrowprops=dict(facecolor='black', shrink=0.05), fontsize = 12)
color_idx = np.linspace(0, 1, tms.size)
a = 0
for i in tms:
mask_pos = ((df_processed.index>i) & (df_processed.index<i+1))
a_mean = df_processed['a'][mask_pos].mean()
r_mean = df_processed['r'][mask_pos].mean()
z_mean = df_processed['z'][mask_pos].mean()
circle3 = plt.Circle((r_mean, z_mean), a_mean, fill = False,
color=plt.cm.jet(color_idx[a]),label = str(i) +'-' + str(i+1) + ' ms')
ax.add_patch(circle3)
a+=1
plt.grid(True)
plt.title('Estimated plasma position during discharge', fontweight = 'bold', fontsize = 20)
leg=plt.legend(bbox_to_anchor = (1,1))
for text in leg.get_texts():
plt.setp(text, fontsize = 16)
ax.set_xlabel('r [mm]',fontweight = 'bold', fontsize = 15)
ax.set_ylabel('z [mm]', fontweight = 'bold', fontsize = 15)
plt.xticks( fontweight = 'bold', fontsize = 14)
plt.yticks( fontweight = 'bold', fontsize = 14)
plt.savefig('plasma_position.png', bbox_inches='tight')
Fast Cameras¶
In [21]:
from PIL import Image
from io import BytesIO
from IPython.display import Image as DispImage
FastCamera = True
try:
url = "http://golem.fjfi.cvut.cz/shots/%i/Diagnostics/FastExCameras/plasma_film.png"%(shot_no)
image = requests.get(url)
img = Image.open(BytesIO(image.content)).convert('P') #'L','1'
data = np.asanyarray(img)
r = []
# z = []
for i in range(data.shape[1]):
a=0
b=0
for j in range(336):
a += data[j,i]*j
b += data[j,i]
r.append((a/b)*(170/336))
Tcd = 3 #delay
camera_time = np.linspace(start+abs(start-Tcd), end, len(r))
r_camera = pd.Series(r, index = camera_time)-85
r_mirnov = horpol(shot_no, vacuum_shot).loc[start:end]
fig = plt.figure()
ax = r_camera.plot(label = 'Fast Camera')
ax = r_mirnov.plot(label = 'Mirnov coils')
plt.legend()
ax.set(ylim=(-85, 85), title='#%i'%shot_no,ylabel='$\Delta$r visible radiation')
ax.axhline(y=0, color='k', ls='--', lw=1, alpha=0.4)
fig.savefig('FastCameras_deltaR')
except OSError:
FastCamera = False
In [22]:
if FastCamera == True:
plt.imshow(img)
In [23]:
if not '-1' in stab and not 'Not Found' in stab:
dataNI = (read_signal(shot_no, 'Rog', data_URL, 'lvm')) #data from NI
dataNI = dataNI.set_index('Time')
dataNI=dataNI.set_index(dataNI.index*1e3)
Irog = dataNI['RogQuadr']
Irog*=1/5e-3
Irog=Irog.loc[start:end]
data = pd.concat([df_processed, Irog], axis='columns')
data.to_csv('plasma_position.csv')
data['RogQuadr'].plot()