def plot_q_of_t(qname='mdot_eh',
diag=None,
path=None,
op=None,
save=False,
display=False,
ymin=None,
ymax=None,
log=False):
"""Plot quantity q, given in the diag dict object by qname,
as a function of time. Options are:
qname -- String, Name of quantity to plot out of diag. Default is 'modt_eh'
diag -- String, diag object attained by using hdf5_to_dict.load_diag.
Default is None, but must be set unless path is not None.
path -- String, path to directory containing diag file. Default is None,
but must be set unless diag is not None.
op -- unary function, operation to apply to q before plotting.
default is the identity.
save -- Save the plot to a file? Plot is saved into
current working directory. Default is False.
ymin -- Minimum y value. Defaults to letting the code choose.
ymax -- Maximum y value. Defaults to letting the code choose.
display -- Whether or not to show the plot. Default is False.
"""
if diag is None:
if path is None:
raise ValueError("Either path or diag must be non-none.")
import hdf5_to_dict as io
diag = io.load_diag(path)
t = diag['t']
q = diag[qname]
if op is not None:
q = op(q)
if log:
plt.semilogy(t, q)
else:
plt.plot(t, q)
plt.xlabel('t (code units)')
plt.ylabel(qname + ' (code units)')
if ymin is not None or ymax is not None:
if ymin is None:
ymin = np.min(q)
if ymax is None:
ymin = np.max(q)
plt.ylim(ymin, ymax)
if save:
for postfix in ['pdf', 'png']:
plt.savefig(qname + '.' + postfix, bbox_inches='tight')
if display:
plt.show()
return
def plot(args):
n = args
print '%08d / ' % (n + 1) + '%08d' % len(files)
diag = io.load_diag(path)
dump = io.load_dump(files[n], geom)
#hdr = dump['hdr']
fig = plt.figure(figsize=(FIGX, FIGY), frameon=False)
ax = fig.gca()
#ax = plt.subplot(1,1,1)
x = geom['x']
y = geom['y']
z = geom['z']
x = bplt.flatten_xz(x, hdr, flip=True)
y = bplt.flatten_xz(y, hdr, flip=True)
z = bplt.flatten_xz(z, hdr)
# rcylindrical
rcyl = np.sqrt(x**2 + y**2)
lrho = bplt.flatten_xz(np.log10(dump['RHO']), hdr)
mesh = ax.pcolormesh(rcyl[0:N1, 0:N2],
z[0:N1, 0:N2],
lrho[0:N1, 0:N2],
cmap='jet',
vmin=-4,
vmax=0,
shading='gouraud')
cax = fig.add_axes([0.135, 0.15, 0.025, 0.7])
cb = fig.colorbar(mesh, cax=cax, ticks=[-4, -2, 0])
cb.set_label(label='Density', fontsize=20)
cax.tick_params(axis='both', which='major', labelsize=20)
lbeta = bplt.flatten_xz(np.log10(dump['beta']), hdr)
mesh = ax.pcolormesh(rcyl[N1:, 0:N2],
z[N1:, 0:N2],
lbeta[N1:, 0:N2],
cmap='inferno',
vmin=-2,
vmax=2,
shading='gouraud')
cax = fig.add_axes([0.865, 0.15, 0.025, 0.7])
cb = fig.colorbar(mesh, cax=cax, ticks=[-2, 0, 2])
cb.set_label(label='Plasma beta', fontsize=20)
cb.ax.yaxis.set_label_position('left')
cax.yaxis.set_ticks_position('left')
cax.tick_params(axis='both', which='major', labelsize=20)
ax.set_xlim([-4. / 3. * SIZE, 4. / 3. * SIZE])
ax.set_ylim([-SIZE, SIZE])
ax.set_xticks([])
ax.set_yticks([])
circle1 = plt.Circle((0, 0),
1. + np.sqrt(1. - hdr['a']**2),
color='k',
zorder=2)
ax.add_artist(circle1)
#ax.axvline(hdr['Risco'], color='k', linestyle='--', linewidth=2)
#ax.axvline(-hdr['Risco'], color='k', linestyle='--', linewidth=2)
bplt.overlay_field(ax,
geom,
dump,
linestyle='-',
linewidth=1,
linecolor='k',
NLEV=10)
plt.savefig(os.path.join(FRAMEDIR, 'frame_%08d.png' % n),
bbox_inches='tight',
pad_inches=0,
dpi=100)
plt.close(fig)
rho = dump['RHO'].mean()
Te_code[n] = (hdr['gam'] - 1.) * uu * hdr['U_unit'] / (
2. * rho * hdr['Ne_unit'] * cgs['KBOL'])
#Te_code[n] = dump['Thetae'][0][0][0]*cgs['ME']*cgs['CL']**2/cgs['KBOL']
#Te_code[n] = (hdr['gam']-1.)*dump['UU'][0,0,0]*hdr['U_unit']/(2.*dump['RHO']*hdr['Ne_unit']*cgs['KBOL'])
R00 = -dump['Rmunu'][:, :, :, 0, 0].mean()
Tr_code[n] = (R00 * hdr['U_unit'] / cgs['AR'])**(1. / 4.)
#Tr2_code[n] = (-dump['Rmunu'][0,0,0,0,0]*hdr['U_unit']/cgs['AR'])**(1./4.)
ne = rho * hdr['Ne_unit']
#ne = dump['RHO'][0,0,0]*hdr['Ne_unit']
Etot[n] = 2. * ne * cgs['KBOL'] * Te_code[n] / (
hdr['gam'] - 1.) + cgs['AR'] * Tr_code[n]**4.
print(ne)
print('gam = %e' % hdr['gam'])
diag = io.load_diag(os.path.join(TMP_DIR, 'dumps/'), hdr=hdr)
print(diag['egas'] + diag['erad'])
# SEMIANALYTIC SOLUTION
from scipy.integrate import odeint
tf = 1.e3
t = np.logspace(-4, np.log10(tf), 1000)
nubins = 100
numin = 1.e13
numax = 1.e18
dlnu = (np.log(numax) - np.log(numin)) / nubins
nu = np.zeros(nubins)
for n in range(nubins):
nu[n] = np.exp(np.log(numin) + (0.5 + n) * dlnu)
gamma_e = hdr['gam']
hdr = io.load_hdr(dumps[0])
geom = io.load_geom(hdr)
# Calculate jmin, jmax
ths = geom['th'][-1, :, 0]
for n in xrange(len(ths)):
if ths[n] > THMIN:
jmin = n
break
for n in xrange(len(ths)):
if ths[n] > THMAX:
jmax = n
break
diag = io.load_diag(path)
dx1 = hdr['dx1']
dx2 = hdr['dx2']
dx3 = hdr['dx3']
N1 = hdr['N1']
N2 = hdr['N2']
N3 = hdr['N3']
r = geom['r'][:, N2 / 2, 0]
ND = len(dumps)
t = np.zeros(ND)
rho_r = np.zeros([ND, N1])
Theta_r = np.zeros([ND, N1])
def plot(args):
n = args
print '%08d / ' % (n + 1) + '%08d' % len(files)
diag = io.load_diag(path)
dump = io.load_dump(files[n], geom)
#hdr = dump['hdr']
fig = plt.figure(figsize=(FIGX, FIGY))
# GET SHELL AVERAGES
Thetae_sadw = np.zeros(hdr['N1'])
sigma = np.zeros(hdr['N1'])
mdot = np.zeros(hdr['N1'])
for i in xrange(hdr['N1']):
vol = 0.
for j in xrange(hdr['N2']):
for k in xrange(hdr['N3']):
Thetae_sadw[i] += dump['Thetae'][i, j, k] * dump['RHO'][
i, j,
k] * geom['gdet'][i,
j] * hdr['dx1'] * hdr['dx2'] * hdr['dx3']
sigma[i] += dump['RHO'][i, j, k] * hdr['dx2'] * geom['gdet'][i,
j]
mdot[i] += dump['RHO'][
i, j, k] * hdr['dx2'] * hdr['dx3'] * geom['gdet'][i, j]
vol += dump['RHO'][i, j, k] * geom['gdet'][
i, j] * hdr['dx1'] * hdr['dx2'] * hdr['dx3']
sigma[i] /= (hdr['N2'] * hdr['N3'])
Thetae_sadw[i] /= vol
ax = plt.subplot(3, 3, 1)
bplt.plot_xz(ax,
geom,
np.log10(dump['RHO']),
dump,
vmin=-4,
vmax=0,
label='RHO',
ticks=[-4, -3, -2, -1, 0])
ax.set_xlim([-SIZE, SIZE])
ax.set_ylim([-SIZE, SIZE])
ax.set_xticks([-SIZE, -SIZE / 2, 0, SIZE / 2, SIZE])
ax.set_yticks([-SIZE, -SIZE / 2, 0, SIZE / 2, SIZE])
ax = plt.subplot(3, 3, 2)
bplt.plot_xz(ax,
geom,
np.log10(dump['Thetae']),
dump,
vmin=-2,
vmax=2,
label='Thetae',
cmap='RdBu_r',
ticks=[-2, -1, 0, 1, 2])
ax.set_xlim([-SIZE, SIZE])
ax.set_ylim([-SIZE, SIZE])
ax.set_xticks([-SIZE, -SIZE / 2, 0, SIZE / 2, SIZE])
ax.set_yticks([-SIZE, -SIZE / 2, 0, SIZE / 2, SIZE])
ax = plt.subplot(3, 3, 3)
ax.plot(geom['r'][:, 0, 0], sigma, color='b', label='Sigma')
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_xlim([1, SIZE])
ax.set_ylim([1.e-2, 1.e2])
ax.set_aspect(np.log(SIZE / 1) / np.log(1.e2 / 1.e-2))
ax.plot(geom['r'][:, 0, 0], Thetae_sadw, color='r', label='SADW Thetae')
ax.legend(loc=2, fontsize=10)
ax.set_xlabel('r/M')
ax = plt.subplot(3, 3, 4)
bplt.plot_xy(ax,
geom,
np.log10(dump['RHO']),
dump,
vmin=-4,
vmax=0,
label='RHO',
ticks=[-4, -3, -2, -1, 0])
ax.set_xlim([-SIZE, SIZE])
ax.set_ylim([-SIZE, SIZE])
ax.set_xticks([-SIZE, -SIZE / 2, 0, SIZE / 2, SIZE])
ax.set_yticks([-SIZE, -SIZE / 2, 0, SIZE / 2, SIZE])
ax = plt.subplot(3, 3, 5)
bplt.plot_xy(ax,
geom,
np.log10(dump['Thetae']),
dump,
vmin=-2,
vmax=2,
label='Thetae',
cmap='RdBu_r',
ticks=[-2, -1, 0, 1, 2])
ax.set_xlim([-SIZE, SIZE])
ax.set_ylim([-SIZE, SIZE])
ax.set_xticks([-SIZE, -SIZE / 2, 0, SIZE / 2, SIZE])
ax.set_yticks([-SIZE, -SIZE / 2, 0, SIZE / 2, SIZE])
ax = plt.subplot(3, 1, 3)
ax.plot(diag['t'], diag['mdot'], color='k')
ax.axvline(dump['t'], color='k', linestyle='--')
ax.set_xlim([0, dump['hdr']['tf']])
ax.set_xlabel('t/M')
ax.set_ylabel('mdot')
ax2 = ax.twinx()
ax2.set_ylabel('phi')
plt.subplots_adjust(hspace=0.25)
#ax.pcolormesh(dump['X1'][:,:,0], dump['X2'][:,:,0], dump['RHO'][:,:,0])
plt.savefig(os.path.join(FRAMEDIR, 'frame_%08d.png' % n),
bbox_inches='tight',
dpi=100)
plt.close(fig)
print(' diag_summary.py [dumpfolder] [fluid/rad/perf]')
sys.exit()
folder = sys.argv[1]
if not os.path.exists(folder):
print('ERROR File ' + folder + ' does not exist!')
sys.exit()
if sys.argv[2] in ['fluid', 'rad', 'perf']:
mode = sys.argv[2]
else:
print('ERROR supported modes are')
print(' fluid rad perf')
sys.exit()
diag = io.load_diag(folder)
if mode == 'perf':
fig = plt.figure(figsize=(14, 12))
vals = [
'TIMER_UPDATE', 'TIMER_FLUXCALC', 'TIMER_FIXUP', 'TIMER_BOUND',
'TIMER_DIAG', 'TIMER_OUT', 'TIMER_MAKE', 'TIMER_PUSH',
'TIMER_INTERACT', 'TIMER_ALL'
]
if diag['hdr']['ELECTRONS']:
vals.append('TIMER_ELECTRON')
for n, val in enumerate(vals):
ax = fig.add_subplot(4, 3, n + 1)
ax.plot(diag['t'], diag[val])
ax.ticklabel_format(style='sci', axis='y', scilimits=(0, 0))
