def __init__(self, shot, i_diag, v_diag, dev_name="W7X", debug=debug, plot=1, verbose=0, params=None):
self.dev = pyfusion.getDevice(dev_name)
self.shot = shot
self.verbose = verbose
self.i_diag = i_diag
self.v_diag = v_diag
self.debug = debug
self.plot = plot
self.select = None
self.t_comp = (0.1,0.2)
self.params = params
self.figs = []
self.suffix = '' # this gets put at the end of the fig name (title bar)
self.imeasfull = self.dev.acq.getdata(shot, i_diag)
self.vmeasfull = self.dev.acq.getdata(shot, v_diag)
comlen = min(len(self.vmeasfull.timebase), len(self.imeasfull.timebase))
FFT_size = nice_FFT_size(comlen-2, -1)
# the minus 2 is a fudge to hide small inconsistencies in reduce_time
# e.g. 20160310 9 W7X_L5_LPALLI
self.imeasfull = self.imeasfull.reduce_time([self.imeasfull.timebase[0], self.imeasfull.timebase[FFT_size]])
self.vmeasfull = self.vmeasfull.reduce_time([self.vmeasfull.timebase[0], self.vmeasfull.timebase[FFT_size]])
if self.params is not None:
self.process_swept_Langmuir(**self.params)
def get_iprobe(self, leakage=None, t_comp=None):
""" main purpose is to subtract leakage currents
Will use the full data, as the t_range is meant to be plasma interval
returns a copy of the measured courremt, overwritten with the
corrected iprobe
"""
# obtain leakage estimate
if t_comp is None:
t_comp = self.t_comp
FFT_size = nice_FFT_size(len(self.imeasfull.timebase), -1)
self.iprobefull = self.imeasfull.copy()
self.sweepQ = [] # will keep these for synchronous sampling
input_leakage = leakage
for (c, chan) in enumerate(self.imeasfull.channels):
if self.select is not None and c not in self.select:
continue
leakage = input_leakage
cname = chan.config_name
sweepV = self.vcorrfull.signal[self.vlookup[self.vassoc[c]]][0:FFT_size]
sweepQ = hilbert(sweepV)
self.sweepQ.append(sweepQ) # save for synchronising segments (it is smoothed)
# these attempts to make it accept a single channel are only partial
imeas = self.imeasfull.signal[c] # len(self.imeasfull.channels) >1 else self.imeasfull.signal
tb = self.imeasfull.timebase
w_comp = np.where((tb>=t_comp[0]) & (tb<=t_comp[1]))[0]
if len(w_comp) < 2000:
raise ValueError('Not enough points {wc} t_comp - try {tt}'
.format(tt=np.round([tb[0], tb[0] + t_comp[1]-t_comp[0]],3),
wc=len(w_comp)))
ns = len(w_comp)
wind = np.blackman(ns)
offset = np.mean(wind * imeas[w_comp])/np.mean(wind)
sweepVFT = np.fft.fft(AC(sweepV[w_comp]) * wind)
imeasFT = np.fft.fft(AC(imeas[w_comp]) * wind)
ipk = np.argmax(np.abs(sweepVFT)[0:ns//2]) # avoid the upper one
comp = imeasFT[ipk]/sweepVFT[ipk]
#print('leakage compensation factor = {r:.2e} + j{i:.2e}'
# .format(r=np.real(comp), i=np.imag(comp)))
print('{u}sing computed leakage comp factor = {m:.2e} e^{p:.2f}j'
.format(u = ["Not u", "U"][leakage is None],
m=np.abs(comp), p=np.angle(comp)))
if leakage is None:
leakage = [np.real(comp), np.imag(comp)]
# find the common length - assuming they start at the same time????
comlen = min(len(self.imeasfull.timebase),len(self.vmeasfull.timebase),len(sweepQ))
# put signals back into rdata (original was copied by reduce_time)
# overwrite - is this OK?
self.iprobefull.signal[c] = self.iprobefull.signal[c]*0. # clear it
# sweepV has a DC component! beware
self.iprobefull.signal[c][0:comlen] = self.imeasfull.signal[c][0:comlen]-offset \
- sweepV[0:comlen] * leakage[0] - sweepQ[0:comlen] * leakage[1]
# remove DC cpt (including that from the compensation sweepV)
offset = np.mean(wind * self.iprobefull.signal[c][w_comp])/np.mean(wind)
self.iprobefull.signal[c][0:comlen] -= offset
print('data length is ', len(data.timebase))
if add_corruption:
for sig in data.signal: # small dislocation for 0309_52
sig[100000:140000] = sig[140001:
180001] # small disloc (depends on fsw)
sig[200000:201450] = sig[201450:202900] # big dislocation 1.5 cycles
sig[300000:300450] = sig[300450:300900] # small 0.5 cycles
fd = data
# this is a workaround for clipped sweep signals, but it slows time response
if fft:
# in this context (data with errors), many weird FFT sizes are likely, so limit planning time
if pyfusion.fft_type == 'fftw3':
pyfusion.fftw3_args.update(dict(planning_timelimit=1.0))
FFT_size = nice_FFT_size(len(data.timebase) - 1,
-1) # 1 less to allow rounding err in reduce time
data = data.reduce_time([data.timebase[0], data.timebase[FFT_size]])
fd = data.filter_fourier_bandpass(passband=[100, 900], stopband=[50, 950])
print('filtered data length is ', len(fd.timebase))
phc = analytic_phase(data[data.keys()[0]]) - fsamp * 2 * pi * data.timebase
ends = len(phc) / 10
#fsamp += np.diff(phc)[ends:-ends].mean()/np.diff(data.timebase).mean()/(2*pi)
if iterate: # this correction looks just at data between 30% and 70% to avoid hiccups from shot with no plasma
dfsamp = (phc[-3 * ends:-2 * ends].mean() -
phc[2 * ends:3 * ends].mean()) / (
(data.timebase[-3 * ends:-2 * ends].mean() -
data.timebase[2 * ends:3 * ends].mean())) / (2 * np.pi)
if np.isnan(dfsamp):
print(
