def __init__(self, shot, trange=None):
self.shot = shot
try:
LiBD = dd.shotfile('LIN', shot, experiment='AUGD')
except:
LiBD = dd.shotfile('LIN', shot, experiment='LIBE')
ne = LiBD('ne').data
time = LiBD('ne').time
rhoP = LiBD('ne').area
LiBD.close
# limit to the part in trange if given
if trange:
_idx = np.where(((time >= trange[0]) & (time <= trange[1])))[0]
ne = ne[_idx, :]
time = time[_idx]
rhoP = rhoP[_idx, :]
# load the equilibrium
self.eq = eqtools.AUGDDData(self.shot)
self.eq.remapLCFS()
# now build your own basis on the same rhoPoloidal
# outsize the separatrix
self.neraw = ne[:, ::-1]
self.rhoraw = rhoP.data[:, ::-1]
self.rho = np.linspace(0.9, 1.1, 50)
self.ne = np.zeros((time.size, self.rho.size))
self.neNorm = np.zeros((time.size, self.rho.size))
self.time = time
# now compute the UnivariateSpline
for t in range(time.size):
S = UnivariateSpline(self.rhoraw[t, :], self.neraw[t, :], s=0)
self.ne[t, :] = S(self.rho)
self.neNorm[t, :] = S(self.rho) / S(1)
def plotSIFGI():
a = eqtools.EqdskReader(gfile='/afs/ipp-garching.mpg.de/home/i/ianf/codes/python/general/g33669.3000')
a.remapLCFS()
b = eqtools.AUGDDData(33669)
tok = TRIPPy.Tokamak(b)
cont = (pow(scipy.linspace(0,1,11),2)*(a.getFluxLCFS()-a.getFluxAxis())+a.getFluxAxis())
print(a.getFluxAxis())
print(a.getFluxLCFS())
print(cont)
temp = b.getMachineCrossSectionFull()
plotEq.plot(a,33669,lims=temp,contours=-1*cont)
GI = genGRW(tok)
r = GI.r()[0][-2:]
z = GI.r()[2][-2:]
print(r,z)
plt.plot(r,z,color='#0E525A',linewidth=2.)
SIF = genSIF(tok)
r = SIF.r()[0][-2:]
z = SIF.r()[2][-2:]
print(r,z)
plt.plot(r,z,color='#228B22',linewidth=2.)
plt.subplots_adjust(left=.2,right=.95)
plt.gca().set_xlim([scipy.nanmin(temp[0].astype(float)),scipy.nanmax(temp[0].astype(float))])
plt.gca().set_ylim([scipy.nanmin(temp[1].astype(float)),scipy.nanmax(temp[1].astype(float))])
plt.text(1.4,.9,'CSXR',fontsize=22,zorder=10)
plt.text(2.25,.1,'GI',fontsize=22,zorder=10)
limy = plt.gca().get_ylim()
limx = plt.gca().get_xlim()
plt.gca().text(1.0075*(limx[1]-limx[0])+limx[0],.97*(limy[1]-limy[0])+limy[0],str(33669),rotation=90,fontsize=14)
def __init__(self, shot, Probe='HFF', Xprobe=None, angle=80):
self.shot = shot
self.angle = angle
# load the equilibria
try:
start = time.time()
self.Eq = eqtools.AUGDDData(self.shot)
print('Equilibrium loaded in %5.4f' % (time.time() - start) + ' s')
except ImportError:
print('Equilibrium not loaded')
self.Xprobe = float(Xprobe)
# open the shot file
self.Probe = Probe
if self.Probe == 'HFF':
self._HHFGeometry(angle=self.angle)
start = time.time()
self._loadHHF()
print('Probe signal loaded in %5.4f' % (time.time() - start) + ' s')
elif self.Probe == '14Pin':
self._14PGeometry(angle=self.angle)
self._loadHHF()
else:
logging.warning('Other probe head not implemented yet')
# load the data from Langmuir probes
self.Target = langmuir.Target(self.shot)
# load the profile of the Li-Beam so that
# we can evaluate the Efolding length
try:
start = time.time()
self.LiB = libes.Libes(self.shot)
self._tagLiB = True
print('LiB loaded in %5.4f' % (time.time() - start) + ' s')
except ImportWarning:
self._tagLiB = False
logging.warning('Li-Beam not found for shot %5i' % shot)
def genInp(num=1e-3,shot=34228):
tok = TRIPPy.Tokamak(eqtools.AUGDDData(34228))
ray = genSIF(tok)
print(ray.norm.s)
#inp = scipy.linspace(ray.norm.s[-2],ray.norm.s[-1],num)
inp = scipy.mgrid[ray.norm.s[-2]:ray.norm.s[-1]:num]
r = ray(inp).r()[0]
z = ray(inp).r()[2]
l = inp - inp[0]
return [r,z,l]
def plotCross(shot,time):
eq = eqtools.AUGDDData(shot)
eq.remapLCFS() #get that pretty boundary
rl = eq.getRLCFS()[eq._getNearestIdx(eq.getTimeBase(),time)]
zl = eq.getZLCFS()[eq._getNearestIdx(eq.getTimeBase(),time)]
out = eq.getMachineCrossSectionFull()
plt.plot(rl,zl,lw=2)
plt.plot(out[0],out[1],'k')
plt.gca().set_aspect('equal')
plt.gca().autoscale(tight=True)
plt.show()
def weights2(inp, te, shot, time):
""" pull out data vs time necessary to calculate the weights"""
#condition initialized values
output = scipy.zeros((len(time), 2))
r = inp[0]
z = inp[1]
l = inp[2]
#load GIW ne, Te data
tped = goodGIW(time, shot, name="t_e_ped")
tcore = goodGIW(time, shot, name="t_e_core")
nped = goodGIW(time, shot, name="n_e_ped")
ncore = goodGIW(time, shot, name="n_e_core")
good = scipy.arange(len(time))[scipy.logical_and(
ncore != 0, tcore != 0)] #take only good data
#use spline of the GIW data to solve for the proper Te, otherwise dont evaluate
logte = scipy.log(te)
halfte = scipy.exp(logte[1:] / 2. +
logte[:-1] / 2.) #not going to worry about endpoints
# because I trust te is big enough to be larger than the range of the profile
#step 1, use array of r,z values to solve for rho
eq = eqtools.AUGDDData(shot)
rho = eq.rz2rho('psinorm', r, z, time[good], each_t=True,
sqrt=True) #solve at each time
rhomin = scipy.nanmin(rho, axis=1)
temax = GIWprofiles.te(rhomin, tcore[good], tped[good])
idx = 0
#step 2, construct 2 splines of l(rho)
for i in good:
temp = calcArealDens(l, te, halfte, rho[idx], tped[i], tcore[i],
nped[i], ncore[i])
temp[scipy.logical_not(scipy.isfinite(temp))] = 0.
temp = scipy.sum(temp)
output[i, 0] = temp
output[i, 1] = temax[idx]
idx += 1
#print(idx),
#step 8, return values for storage
return output
both the parallel connection length from midplane to LFS and from
Xpoint height to the LFS. We will also save the distance as R-Rsep and
rho_p
"""
from cyfieldlineTracer import get_fieldline_tracer
from copy import deepcopy as copy
import numpy as np
import eqtools
import matplotlib as mpl
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
shotList = (34102, 34103, 34104, 34105, 34106)
for shot in shotList:
Eq = eqtools.AUGDDData(shot)
Eq.remapLCFS()
# load now the tracer for the same shot all at 3s
myTracer = get_fieldline_tracer('RK4', machine='AUG', shot=shot, time=3, interp='quintic', rev_bt=True)
# height of magnetic axis
zMAxis = myTracer.eq.axis.__dict__['z']
# height of Xpoint
zXPoint = myTracer.eq.xpoints['xpl'].__dict__['z']
rXPoint = myTracer.eq.xpoints['xpl'].__dict__['r']
# now determine at the height of the zAxis the R of the LCFS
_idTime = np.argmin(np.abs(Eq.getTimeBase()-3))
RLcfs = Eq.getRLCFS()[_idTime, :]
ZLcfs = Eq.getZLCFS()[_idTime, :]
# onlye the part greater then xaxis
ZLcfs = ZLcfs[RLcfs > myTracer.eq.axis.__dict__['r']]
def plotSIF2(num=1e-4):
a = eqtools.EqdskReader(gfile='/afs/ipp-garching.mpg.de/home/i/ianf/codes/python/general/g33669.3000')
a.remapLCFS()
b = eqtools.AUGDDData(33669)
tok = TRIPPy.Tokamak(b)
cont = (pow(scipy.linspace(0,1,11),2)*(a.getFluxLCFS()-a.getFluxAxis())+a.getFluxAxis())
print(a.getFluxAxis())
print(a.getFluxLCFS())
print(cont)
temp = b.getMachineCrossSectionFull()
plotEq.plot(a,33669,lims=temp,contours=-1*cont[[0,10]],alpha=.15)
ray = genSIF(tok)
print(ray.norm.s)
#inp = scipy.linspace(ray.norm.s[-2],ray.norm.s[-1],num)
inp = scipy.mgrid[ray.norm.s[-2]:ray.norm.s[-1]:num]
r = ray(inp).r()[0]
z = ray(inp).r()[2]
l = inp - inp[0]
print(r.shape,z.shape,l.shape)
plt.plot(r,z,color='#228B22',linewidth=2.)
plt.subplots_adjust(left=.2,right=.95)
plt.gca().set_xlim([scipy.nanmin(temp[0].astype(float)),scipy.nanmax(temp[0].astype(float))])
plt.gca().set_ylim([scipy.nanmin(temp[1].astype(float)),scipy.nanmax(temp[1].astype(float))])
limy = plt.gca().get_ylim()
limx = plt.gca().get_xlim()
plt.gca().text(1.0075*(limx[1]-limx[0])+limx[0],.97*(limy[1]-limy[0])+limy[0],str(33669),rotation=90,fontsize=14)
ax1 = plt.gca()
ax1.arrow(1.8,-.608,.025,0.0,head_width=.05,head_length=.1,fc='k',ec='k',zorder=10)
ax1.arrow(1.8,0.728,.025,0.0,head_width=.05,head_length=.1,fc='k',ec='k',zorder=10)
iax = plt.axes([0,0,1,1])
x0 = (r.min()-limx[0])/(limx[1]-limx[0])
y0 = (z.min()-limy[0])/(limy[1]-limy[0])
y1 = (z.max()-limy[0])/(limy[1]-limy[0])
print(limx)
print(limy)
print([x0+.05,y0,.2,y1-y0])
ip = InsetPosition(ax1, [x0+.1,y0,.3,y1-y0])
#ip = plt.axes([.05,.05,.02,.02])
iax.set_axes_locator(ip)
tempData = scipy.squeeze(GIWprofiles.te(b.rz2psinorm(r,z,[3.0],sqrt=True,each_t=True),4e3,1e3))
print(tempData.shape)
iax.semilogx(tempData,l,color='#0E525A',linewidth=2.)
iax.set_ylim(l.max(),l.min())
iax.set_xlim(1e2,1e4)
iax.set_xlabel('$T_e$ [eV]')
iax.text(2e2,1.0,'$l(T_e)$')
iax.yaxis.tick_right()
iax.set_axis_bgcolor('#eaeaea')