def __init__(self, shot):
self.shot = shot
# load the geometry
self._geometry()
# open the shotfile
self.Ioc = dd.shotfile('IOC', self.shot)
# read and create the attribute dictionary
self._read()
# close all the stuff
self.Ioc.close()
# open and read available gas information
self.Uvs = dd.shotfile('UVS', self.shot)
self._valves()
self._readGas()
self.Uvs.close()
# now the directory where eventually the neutrals resides
try:
_path = os.path.abspath(
os.path.join(
os.path.join(__file__, '../../../..'),
'Experiments/AUG/analysis/data/neutrals/%5i' % self.shot))
data = numpy.load(_path + '/n0_avg.npy')
self.n0 = data[:, 0]
self.n0Err = data[:, 1]
self.n0Time = numpy.loadtxt(_path + '/time_brillanza.txt')
except:
logging.warning('File not found')
pass
# now also the equilibrium since it will be useful for
# the plot of gauges and valves location
self.Eq = equilibrium.equilibrium(device='AUG', time=2, shot=self.shot)
self.rg, self.zg = map_equ.get_gc()
def plotGeometry(self, time=3):
"""
Plot the geometry of the different LOS in the divertor region
with the corresponding equilibrium.
"""
self.Eq.set_time(time)
rVessel, zVessel = map_equ.get_gc()
fig, ax = mpl.pylab.subplots(figsize=(10, 8), nrows=1, ncols=1)
fig.subplots_adjust(right=0.8)
for key in rVessel.iterkeys():
ax.plot(rVessel[key], zVessel[key], 'k', lw=1.5)
ax.contour(self.Eq.R,
self.Eq.Z,
self.Eq.psiN,
np.linspace(0.1, 0.95, num=10),
colors='grey',
linestyles='-')
ax.contour(self.Eq.R,
self.Eq.Z,
self.Eq.psiN, [1],
colors='r',
linewidths=2)
ax.contour(self.Eq.R,
self.Eq.Z,
self.Eq.psiN,
np.linspace(1.01, 1.05, num=4),
colors='grey',
linestyles='--')
# adess facciamo il ciclo sulle LoS salvate e
# costruiamo una legenda a dx
ax.set_color_cycle(palettable.tableau.Tableau_20.mpl_colors)
for name in self.Los.iterkeys():
ax.plot([self.Los[name]['R1'], self.Los[name]['R2']],
[self.Los[name]['Z1'], self.Los[name]['Z2']],
label=name,
lw=2)
ax.legend(loc='upper center',
numpoints=1,
frameon=False,
bbox_to_anchor=(0.5, 1.2),
fontsize=14,
ncol=3)
ax.set_aspect('equal')
ax.set_xlim([0.7, 2.])
ax.set_ylim([-1.5, -0.5])
def _loadeq(self):
# loading the equilibrium
self.Eqm = map_equ.equ_map()
status = self.Eqm.Open(self.shot, diag='EQH')
self.Eqm._read_scalars()
self.Eqm._read_profiles()
self.Eqm._read_pfm()
# load the wall for aug
self.rg, self.zg = map_equ.get_gc()
self._psi = self.Eqm.pfm.transpose()
self._time_array = self.Eqm.t_eq
nr = self._psi.shape[0]
nz = self._psi.shape[1]
self._r = self.Eqm.Rmesh
self._z = self.Eqm.Zmesh
self._psi_axis = self.Eqm.psi0
self._psi_bnd = self.Eqm.psix
# get the fpol in similar way
# as done in eqtools
self._jpol = self.Eqm.jpol
# these are the lower xpoints
self._rxpl = self.Eqm.ssq['Rxpu']
self._zxpl = self.Eqm.ssq['Zxpu']
# read also the upper xpoint
self._rxpu = self.Eqm.ssq['Rxpo']
self._zxpu = self.Eqm.ssq['Zxpo']
# R magnetic axis
self._axisr = self.Eqm.ssq['Rmag']
# Z magnetic axis
self._axisz = self.Eqm.ssq['Zmag']
# eqm does not load the RBphi on axis
Mai = dd.shotfile('MAI', self.shot)
self.Rcent = 1.65
# we want to interpolate on the same time basis
Spl = UnivariateSpline(Mai('BTF').time, Mai('BTF').data, s=0)
self._bphi = Spl(self._time_array) * self.Rcent
Mai.close()
Mag = dd.shotfile('MAG', self.shot)
Spl = UnivariateSpline(Mag('Ipa').time, Mag('Ipa').data, s=0)
self._cplasma = Spl(self._time_array)
# we want to load also the plasma curent
# now define the psiN
self._psiN = (self._psi-self._psi_axis[:, np.newaxis, np.newaxis])/ \
(self._psi_bnd[:, np.newaxis, np.newaxis]-self._psi_axis[:, np.newaxis, np.newaxis])
def plot_flux(self,
col_levels=None,
Nlines=20,
axes=None,
show=True,
title=None,
colorbar=True):
from copy import deepcopy as copy
"""
Contour plot of the equilibrium poloidal flux
keywords:
t = int time index to plot flux at
col_levels = [] array storing contour levels for color plot
Nlines = int number of contour lines to display
"""
import matplotlib.pyplot as plt
#Set contour levels
if self.psi is not None:
if col_levels is None:
col_levels = np.linspace(np.min(self.psi), np.max(self.psi),
100)
if axes is None:
axes = plt.gca()
else:
show = False
im = axes.contourf(self.R, self.Z, self.psi, levels=col_levels)
if colorbar: cbar = plt.colorbar(im, format='%.3f', ax=axes)
axes.contour(self.R, self.Z, self.psi, Nlines, colors='k')
axes.contour(self.R,
self.Z,
self.psi, [self.psi_bnd],
colors='r',
linewidth=1.5)
axes.set_xlim(np.min(self.R), np.max(self.R))
axes.set_ylim(np.min(self.Z), np.max(self.Z))
if self.wall is not None:
axes.plot(self.wall['R'], self.wall['Z'], '-g', linewidth=2)
if self.xpoints:
for xpoint in self.xpoints:
axes.plot(self.xpoints[xpoint].r, self.xpoints[xpoint].z,
'rx')
if self.axis is not None:
axes.plot(self.axis.r, self.axis.z, 'ro')
if self.machine == 'AUG':
rg, zg = map_equ.get_gc()
for key in rg.iterkeys():
axes.plot(rg[key], zg[key], 'k')
axes.set_xlabel('R (m)')
axes.set_ylabel('Z (m)')
if title is not None: axes.set_title(title)
if colorbar: cbar.ax.set_ylabel('$\psi$')
axes.set_aspect('equal')
if show: plt.show()
else:
print(
"ERROR: No poloidal flux found. Please load an equilibrium before plotting.\n"
)
def plotTimeTraces(self, trange=[0, 7]):
"""
This produce a plot of all the collected signal
divided into two groups together with the evolution of
H-5N and of the fueling. This will be combined also
with the equilibrium with the same color code of the LoS
"""
fig = mpl.pylab.figure(figsize=(14, 10))
ax = mpl.pylab.subplot2grid((4, 2), (0, 0), rowspan=2)
self.Eq.set_time(3)
rVessel, zVessel = map_equ.get_gc()
for key in rVessel.iterkeys():
ax.plot(rVessel[key], zVessel[key], 'k', lw=1.5)
ax.contour(self.Eq.R,
self.Eq.Z,
self.Eq.psiN,
np.linspace(0.1, 0.95, num=10),
colors='grey',
linestyles='-')
ax.contour(self.Eq.R,
self.Eq.Z,
self.Eq.psiN, [1],
colors='r',
linewidths=2)
ax.contour(self.Eq.R,
self.Eq.Z,
self.Eq.psiN,
np.linspace(1.01, 1.05, num=4),
colors='grey',
linestyles='--')
# adess facciamo il ciclo sulle LoS salvate e
# costruiamo una legenda a dx
ax.set_color_cycle(palettable.tableau.Tableau_20.mpl_colors)
for name in self.Los.iterkeys():
ax.plot([self.Los[name]['R1'], self.Los[name]['R2']],
[self.Los[name]['Z1'], self.Los[name]['Z2']],
label=name,
lw=2)
ax.legend(loc='upper center',
numpoints=1,
frameon=False,
bbox_to_anchor=(0.5, 1.2),
fontsize=14,
ncol=3)
ax.set_aspect('equal')
ax.set_xlim([0.7, 2.])
ax.set_ylim([-1.5, -0.5])
# now plot the rest including fueling and density
ax1 = mpl.pylab.subplot2grid((4, 2), (0, 1))
ax1.plot(self._Dcn('H-5').time,
self._Dcn('H-5').data / 1e19,
'k',
lw=2,
label=r'# %5i' % self.shot)
ax1.set_ylabel(r'$\overline{n}_e$ H-5 [10$^{19}$]')
ax1.set_xlabel(r't [s]')
ax1.set_xlim(trange)
ax2 = mpl.pylab.subplot2grid((4, 2), (1, 1))
ax2.plot(self._Uvs('D_tot').time,
self._Uvs('D_tot').data / 1e21,
'k',
lw=2,
label=r'# %5i' % self.shot)
ax2.set_ylabel(r'D$_2 [10^{21}$ e/s]')
ax2.set_xlabel(r't [s]')
ax2.set_xlim(trange)
ax3 = mpl.pylab.subplot2grid((4, 2), (2, 1), rowspan=2)
ax3.set_color_cycle(palettable.tableau.Tableau_20.mpl_colors)
for name in self.Los.iterkeys():
ax3.plot(self.time,
self.Los[name]['data'] / 1e20,
label=name,
lw=2)
ax3.set_xlabel('t[s]')
ax3.set_ylabel(r'n$_e [10^{20}$ m$^{-3}$]')
ax3.set_ylim([0, 3])
ax3.set_xlim(trange)
t = 5.33,#s
color = 'b','g'
rho = np.linspace(0,2,101)
R,Z = eqm.rho2rz( rho, t, all_lines=True)
print(len(R))
for r,z,c in zip(R,Z,color):
for rc,zc in zip(r,z):
plt.plot(rc,zc,c)
R,Z = eqm.rhoTheta2rz( np.linspace(0,1,51),np.linspace(-np.pi,np.pi,1000), t)
plt.plot(R[0],Z[0],'r')
Rv,Zv = get_gc()
for key in list(Rv.keys()):
plt.plot(Rv[key],Zv[key])
plt.axis('equal')
plt.show()
eqm._read_pfm()
eqm._read_profiles()
eqm._read_scalars()
rho = np.linspace(0,0.98,100)
q = eqm.getQuantity(rho, 'Qpsi', 2)[0]
ax[2, 1].set_xlim(_xlim)
ax[2, 1].set_ylim([0, 1.2e3])
ax[3, 1].plot(iD2.time,
iD2.data / 1e21,
col,
ls='-',
lw=1.7,
label=r'Shot # %5i' % shot)
ax[3, 1].set_xlabel(r't [s]')
ax[3, 1].set_ylabel(r'D$_{2} [10^{21}]$')
ax[3, 1].set_xlim(_xlim)
# ora plottiamo l'equilibrio in un asse a parte
if i == 0:
rg, zg = map_equ.get_gc()
for key in rg.iterkeys():
axE.plot(rg[key], zg[key], 'k')
axE.contour(Eq.R,
Eq.Z,
Eq.psiN(Eq.R, Eq.Z),
np.linspace(0, 1, 5),
colors=col)
axE.contour(Eq.R,
Eq.Z,
Eq.psiN(Eq.R, Eq.Z),
np.linspace(1, 1.09, 3),
colors=col,
linestyles='--')
axE.set_xlabel('R')
axE.set_ylabel('Z')