def cmp_v2_pion(path_to_results):
from pbpb2760 import V2
exp = V2()
cent = ['0-5', '5-10', '10-20', '20-30']
for c in cent:
if c == '0-5':
label0 = r'$ALICE$'
label1 = r'$CLVisc$'
else:
label0, label1 = None, None
pt, vn, yerr0, yerr1 = exp.get_ptdiff('pion', c)
plt.errorbar(pt, vn, yerr=(yerr0, yerr1), label=label0, color='r')
path = os.path.join(path_to_results, c.replace('-', '_'))
vn_clvisc = ebe_mean(path, kind='vn', hadron='pion')
plt.plot(vn_clvisc[:, 0], vn_clvisc[:, 2], label=label1, color='k')
plt.text(2, vn[9] + 0.01, c, fontsize=25)
plt.xlabel(r'$p_T\ [GeV]$')
plt.ylabel(r'$v_2$')
smash_style.set(line_styles=False)
plt.legend(loc='upper left')
plt.tight_layout()
plt.xlim(0, 2.5)
plt.ylim(0.001, 0.3)
plt.title(r'$Pb+Pb\ \sqrt{s_{NN}}=5.02\ TeV$', fontsize=30)
plt.savefig('pbpb5020_pionv2.pdf')
plt.show()
def cmp_dndeta(path_to_results='', cent=['0_6', '6_15', '15_25', '25_35']):
'''compare ebe-mean dndeta with PHOBOS exp data'''
# 1304.0347
for i, c in enumerate(cent):
if i == 0:
label0 = r'$PHOBOS$'
label1 = r'$CLVisc$'
else:
label0, label1 = None, None
dat = dndeta(c)
plt.errorbar(dat[:, 0],
dat[:, 2],
yerr=(-dat[:, 4], dat[:, 3]),
label=label0,
color='r')
path = os.path.join(path_to_results, c)
hydro = ebe_mean(path)
plt.plot(hydro[:, 0], hydro[:, 1], color='k', label=label1)
plt.xlabel(r'$\eta$')
plt.ylabel(r'$dN_{ch}/d\eta$')
smash_style.set(line_styles=False)
plt.legend(loc='best')
plt.tight_layout()
plt.xlim(-8, 8)
plt.ylim(0, 1000)
plt.title(r'$Au+Au\ \sqrt{s_{NN}}=200\ GeV$', fontsize=30)
xcod = [-0.4, -0.5, -0.7, -0.7]
ycod = [720, 550, 400, 260]
text = ['0-6', '6-15', '15-25', '25-35']
for i in range(4):
plt.text(xcod[i], ycod[i], text[i], size=20)
plt.savefig('figs/auau200_dndeta.pdf')
plt.show()
def cmp_dndeta(path_to_results='', cent=['0-5', '5-10', '10-20', '20-30']):
xpos = [-0.5, -1.0, -1, -1]
table = dndeta()
for i, c in enumerate(cent):
if c == '0-5':
label0 = r'$ALICE$'
label1 = r'$CLVisc$'
else:
label0, label1 = None, None
plt.errorbar([table[c]['x']], [table[c]['y']],
yerr=([table[c]['yerr0']], [table[c]['yerr1']]),
label=label0,
color='r')
path = os.path.join(path_to_results, c.replace('-', '_'))
hydro = ebe_mean(path)
plt.plot(hydro[:, 0], hydro[:, 1], color='k', label=label1)
neta = len(hydro[:, 0])
plt.text(xpos[i], hydro[neta / 2, 1] + 100, c, fontsize=25)
plt.xlabel(r'$\eta$')
plt.ylabel(r'$dN_{ch}/d\eta$')
smash_style.set(line_styles=False)
plt.legend(loc='best')
plt.tight_layout()
plt.xlim(-10, 10)
plt.ylim(0, 2500)
plt.title(r'$Pb+Pb\ \sqrt{s_{NN}}=5.02\ TeV$', fontsize=30)
plt.savefig('pbpb5020_dndeta.pdf')
plt.show()
def cmp_ptspec(path_to_results='',
cent=['0-5', '5-10', '10-20'],
hadron='pion'):
from pbpb2760 import dNdPt
exp = dNdPt()
for i, c in enumerate(cent):
if i == 0:
label0 = r'$ALICE$'
label1 = r'$CLVisc$'
else:
label0, label1 = None, None
shift = 5**(-i)
x, y, yerr0, yerr1 = exp.get(hadron, c)
plt.errorbar(x,
y * shift,
yerr=(yerr0 * shift, yerr1 * shift),
label=label0,
fmt='o',
color='r')
path = os.path.join(path_to_results, c.replace('-', '_'))
dndpt = ebe_mean(path, kind='dndpt', hadron=hadron, rap='Y')
plt.semilogy(dndpt[:, 0],
2 * dndpt[:, 1] * shift,
label=label1,
color='k')
ytxt, theta = 1.3, -20
if hadron == 'kaon':
ytxt, theta = 1.8, -12
elif hadron == 'proton':
ytxt, theta = 2.0, -5
plt.text(x[5],
ytxt * y[5] * shift,
r'$%s$' % c,
rotation=theta,
size=25)
plt.text(x[15],
ytxt * y[15] * shift,
r'$\times 5^{%s}$' % (-i),
rotation=theta,
size=25)
plt.xlim(0, 3)
plt.ylim(1.0E-7, 1.0E4)
plt.xlabel(r'$p_T\ [GeV]$')
plt.ylabel(r'$(1/2\pi)d^2 N/dYp_Tdp_T\ [GeV]^{-2}$')
smash_style.set(line_styles=False)
plt.legend(loc='best')
plt.tight_layout()
if hadron == 'pion':
plt.title(r'$Pb+Pb\ \sqrt{s_{NN}}=2.76\ TeV,\ \pi^++\pi^-$',
fontsize=30)
elif hadron == 'kaon':
plt.title(r'$Pb+Pb\ \sqrt{s_{NN}}=2.76\ TeV,\ K^++K^-$', fontsize=30)
elif hadron == 'proton':
plt.title(r'$Pb+Pb\ \sqrt{s_{NN}}=2.76\ TeV,\ p+\bar{p}$', fontsize=30)
plt.savefig('pbpb2760_ptspec_%s.pdf' % hadron)
plt.show()
def curl_free(self):
''' A(r) = \int dr' grad \cdot v(r') / (r-r') dr'
return grad A(r)
'''
N = len(self.x)
x, y = np.meshgrid(self.x, self.y)
dxvx = self.dvx[0]
dyvy = self.dvy[1]
Ar = np.zeros_like(dxvx)
for i in range(N):
for j in range(N):
delta_x = x - (i - N / 2) * self.dx
delta_y = y - (j - N / 2) * self.dx
delta_r = np.sqrt(delta_x * delta_x + delta_y * delta_y)
delta_r[delta_r < 0.001] = 0.001
Ar[i, j] = (dxvx / delta_r + dyvy / delta_r).sum()
dxdy = self.dx**2.0
Ar = -Ar * dxdy / (4 * np.pi)
grad_A = np.gradient(Ar)
dxA, dyA = grad_A[0], grad_A[1]
plt.quiver(self.x, self.y, dxA.T, dyA.T, scale=300)
plt.xlabel(r'$x\ [fm]$')
plt.ylabel(r'$y\ [fm]$')
plt.title(r'$-\nabla\phi(\mathbf{r})$')
smash_style.set()
plt.show()
def quiver(self):
plt.quiver(self.x, self.y, self.vx.T, self.vy.T, scale=20)
plt.xlabel(r'$x\ [fm]$')
plt.ylabel(r'$y\ [fm]$')
plt.title(r'$\mathbf{v}=(v_x, v_y)$')
smash_style.set()
plt.show()
def cmp_dndeta(path_to_results='', cent=['0-5', '5-10', '10-20', '20-30']):
from pbpb2760 import dNdEta
exp = dNdEta()
xpos = [-0.5, -1.0, -1, -1]
for i, c in enumerate(cent):
if c == '0-5':
label0 = r'$ALICE$'
label1 = r'$CLVisc$'
else:
label0, label1 = None, None
plt.errorbar(exp.x,
exp.y[c],
yerr=(exp.yerr[c], exp.yerr[c]),
label=label0,
color='r')
path = os.path.join(path_to_results, c.replace('-', '_'))
dndeta = ebe_mean(path)
plt.plot(dndeta[:, 0], dndeta[:, 1], color='k', label=label1)
neta = len(dndeta[:, 0])
plt.text(xpos[i], dndeta[neta / 2, 1] + 100, c, fontsize=25)
plt.xlabel(r'$\eta$')
plt.ylabel(r'$dN_{ch}/d\eta$')
smash_style.set(line_styles=False)
plt.legend(loc='best')
plt.tight_layout()
plt.xlim(-10, 10)
plt.ylim(0, 2000)
plt.title(r'$Pb+Pb\ \sqrt{s_{NN}}=2.76\ TeV$', fontsize=30)
plt.savefig('pbpb2760_dndeta.pdf')
plt.show()
def plot_auau200_ideal_cent():
'''auau200 polarization as a function of centrality '''
h5_ideal_cent20_25 = h5py.File('vor_int_ideal_cent20_25.hdf5', 'r')
h5_ideal_cent45_50 = h5py.File('vor_int_ideal_cent45_50.hdf5', 'r')
h5_ideal_cent70_75 = h5py.File('vor_int_ideal_cent70_75.hdf5', 'r')
n0, Y, mean_pol_0 = int_Piy(h5_ideal_cent20_25)
n1, Y, mean_pol_1 = int_Piy(h5_ideal_cent45_50)
n2, Y, mean_pol_2 = int_Piy(h5_ideal_cent70_75)
plt.errorbar(Y,
mean_pol_0,
np.sqrt(1.0 / n0) * mean_pol_0,
fmt='rs-',
label=r'auau200 20-25 $\eta/s=0.0$')
plt.errorbar(Y,
mean_pol_1,
np.sqrt(1.0 / n1) * mean_pol_1,
fmt='bo-',
label='auau200 45-50 $\eta/s=0.0$')
plt.errorbar(Y,
mean_pol_2,
np.sqrt(1.0 / n2) * mean_pol_2,
fmt='g^-',
label=r'auau200 70-75 $\eta/s=0.0$')
plt.xlabel(r'$rapidity$')
plt.ylabel(r'$P^y_{int}$')
smash_style.set(line_styles=False)
plt.legend(loc='upper center')
plt.subplots_adjust(left=0.2)
plt.title(r'$Au+Au\ \sqrt{s_{NN}}=200\ GeV,\ \eta/s=0$')
plt.savefig('Pi_auau200.pdf')
#plt.show()
plt.close()
def plot_dNdEta(num_of_events=8):
plot_hydro('cent0_5')
plot_hydro('cent5_10')
plot_hydro('cent10_20')
plot_hydro('cent20_30')
# 1304.0347
dat0 = np.loadtxt('dNdEta_2p76.dat')
plt.errorbar(dat0[:, 0],
dat0[:, 3],
dat0[:, 4],
fmt='ro',
label=r'$ALICE\ Pb+Pb\ \sqrt{s_{NN}}=2.76\ TeV\ 0-5\%$')
plt.xlabel(r'$\eta$')
plt.ylabel(r'$\frac{dN_{ch}}{d\eta}$')
xcod = [-0.7, -0.7, -0.7, -0.7]
ycod = [2000, 1700, 1350, 950]
text = ['0-5%', '5-10%', '10-20%', '20-30%']
for i in range(4):
plt.text(xcod[i], ycod[i], text[i], size=20)
smash_style.set(line_styles=False)
plt.legend(loc='best')
plt.subplots_adjust(left=0.2, bottom=0.2, right=0.95, top=0.95)
plt.xlim(-8, 8)
plt.ylim(0, 2800)
plt.savefig('dNdEta_pbpb5020.pdf')
plt.show()
def plot(self, ntskip=1):
extent = (self.x[0], self.x[-1], self.y[0], self.y[-1])
eos_type = 'EOSI'
if self.hydro_cfg.IEOS == 1:
eos_type = 'EOSL'
for n, Bold in enumerate(self.B):
time = self.hydro_cfg.TAU0+n*self.dt
plt.contourf(Bold[1].T, origin='lower', extent=extent)
plt.xlabel(r'$x\ [fm]$')
plt.ylabel(r'$y\ [fm]$')
plt.title(r'$B^{y}\ [GeV^2]\ @\ t=%s\ [fm]\ %s$'%(time, eos_type))
smash_style.set()
plt.colorbar()
plt.savefig('%s/BY%03d.png'%(self.hydro_dir,n))
plt.close()
#plt.contourf(Bold[0].T)
plt.contourf(Bold[0].T, origin='lower', extent=extent)
#plt.imshow(Bold[0].T, extent=extent, vmin=0, vmax=0.1)
plt.xlabel(r'$x\ [fm]$')
plt.ylabel(r'$y\ [fm]$')
plt.title(r'$B^{x}\ [GeV^2]\ @\ t=%s\ [fm]\ %s$'%(time, eos_type))
smash_style.set()
plt.colorbar()
plt.savefig('%s/BX%03d.png'%(self.hydro_dir, n))
plt.close()
def quiver(self, scale=20, color='k'):
vx = self.vx
vy = self.vy
mask = self.ed < 0.3
vx[mask] = 0.0
vy[mask] = 0.0
plt.quiver(self.x, self.y, vx.T, vy.T, scale=scale, color=color)
smash_style.set()
def test_bjorken(self):
''' initialize with uniform energy density in (tau, x, y, eta) coordinates
to test the Bjorken expansion:
eps/eps0 = (tau/tau0)**(-4.0/3.0)
'''
kernel_src = """
# include "real_type.h"
//# include "eos_table.h"
__kernel void init_ev(global real4 * d_ev1,
// global real * d_pi1,
read_only image2d_t eos_table,
const int size) {
int gid = (int) get_global_id(0);
if ( gid < size ) {
d_ev1[gid] = (real4)(30.0f, 0.0f, 0.0f, 0.0f);
//real S0 = S(30.0f, eos_table);
//d_pi1[10*gid+9] = -4.0/3.0*ETAOS*S0/TAU0;
}
}
"""
cwd, cwf = os.path.split(__file__)
compile_options = ['-I %s' % os.path.join(cwd, '..', 'kernel')]
compile_options.append('-D USE_SINGLE_PRECISION')
compile_options.append('-D ETAOS=%sf' % cfg.ETAOS_YMIN)
compile_options.append('-D TAU0=%sf' % cfg.TAU0)
compile_options.append('-D S0=%sf' % self.visc.ideal.eos.f_S(30.0))
print(compile_options)
prg = cl.Program(self.ctx, kernel_src).build(' '.join(compile_options))
prg.init_ev(self.queue, (self.visc.ideal.size, ), None,
self.visc.ideal.d_ev[1], self.visc.eos_table,
np.int32(self.visc.ideal.size)).wait()
self.visc.evolve(max_loops=2000, save_bulk=False, save_hypersf=False)
history = np.array(self.visc.ideal.history)
tau, edmax = history[:, 0], history[:, 1]
a = tau[0] / tau
T0 = (edmax[0])**0.25
lhs = edmax**0.25 / T0
b = cfg.ETAOS_YMIN / tau[0] / 0.36 * 0.19732 * (1.0 - a**(2.0 / 3.0))
rhs = a**(1.0 / 3.0) * (1 + 2.0 / 3.0 * b)
import matplotlib.pyplot as plt
plt.plot(tau - tau[0], lhs, 'r-', label='CLVisc')
plt.plot(tau - tau[0], rhs, 'b--', label='Bjorken')
plt.text(2., 0.9, r'$\tau_0=0.6\ fm$')
plt.text(2., 0.82, r'$T_0=0.36\ GeV$')
plt.text(2., 0.74, r'$\eta/s=0.08$')
plt.xlabel(r'$\tau - \tau_0\ [fm]$')
plt.ylabel(r'$T/T_0$')
smash_style.set()
plt.legend(loc='best')
#plt.show()
plt.savefig('bjorken_visc.pdf')
def gradient_free(self):
''' nabla \times \vec{A} '''
Ax, Ay = self.int_nabla_v()
dyAx = np.gradient(Ax)[1]
dxAy = np.gradient(Ay)[0]
plt.xlabel(r'$x\ [fm]$')
plt.ylabel(r'$y\ [fm]$')
plt.quiver(self.x, self.y, dyAx.T, dxAy.T, scale=2000)
plt.title(r'$\nabla\times \mathbf{a}(\mathbf{r})$')
smash_style.set()
plt.show()
def cmp_ptspec(path_to_results='',
cent=['0_5', '10_15', '20_30', '30_40'],
hadron='pion'):
for i, c in enumerate(cent):
if c == '0_5':
label0 = r'$PHENIX$'
label1 = r'$CLVisc$'
else:
label0, label1 = None, None
x, y, yerr0, yerr1 = ptspec(hadron, c)
shift = 5**(-i)
plt.errorbar(x,
y * shift,
yerr=(yerr0 * shift, yerr1 * shift),
label=label0,
fmt='ro')
path = os.path.join(path_to_results, c.replace('-', '_'))
dndpt = ebe_mean(path, kind='dndpt', hadron=hadron, rap='Y')
plt.semilogy(dndpt[:, 0], dndpt[:, 1] * shift, 'k-', label=label1)
k, ang = 1.3, 25
if hadron == 'kaon':
k, ang = 2.0, 20
elif hadron == 'proton':
k, ang = 2.5, 15
plt.text(x[8],
shift * y[8] * k,
c.replace('_', '-'),
rotation=-ang,
size=20)
plt.text(x[13],
shift * y[13] * k,
r'$\times\ %s$' % (1 / float(5**i)),
rotation=-ang,
size=20)
plt.xlim(0, 3)
plt.ylim(1.0E-5, 1.0E3)
plt.xlabel(r'$p_T\ [GeV]$')
plt.ylabel(r'$(1/2\pi)d^2 N/dYp_Tdp_T\ [GeV]^{-2}$')
if hadron == 'pion':
plt.title(r'$Au+Au\ \sqrt{s_{NN}}=200\ GeV,\ for\ \pi^+$', fontsize=30)
elif hadron == 'kaon':
plt.title(r'$Au+Au\ \sqrt{s_{NN}}=200\ GeV,\ for\ K^+$', fontsize=30)
elif hadron == 'proton':
plt.title(r'$Au+Au\ \sqrt{s_{NN}}=200\ GeV,\ for\ proton$',
fontsize=30)
smash_style.set(line_styles=False)
plt.legend(loc='best')
plt.tight_layout()
plt.savefig('figs/auau200_ptspec_%s.pdf' % hadron)
plt.show()
def vorticity_vs_pxpy(self, Y=4):
nx, ny = 20, 20
vor = np.zeros((nx, ny))
omg = np.zeros((nx, ny))
for ix, px in enumerate(np.linspace(-3, 3, nx)):
for iy, py in enumerate(np.linspace(-3, 3, ny)):
vor_y, omega_y, rho = self.Pimu_rho(Y, px, py)
vor[ix, iy] = vor_y / rho
omg[ix, iy] = omega_y / rho
print(px, 'finished')
#plt.imshow(vor.T, cmap=plt.get_cmap('bwr'), origin='lower', extent=(-3,3,-3,3), vmin=-0.01, vmax=0.01)
np.savetxt('%s/pol_Y%s.dat' % (self.fpath, Y), vor)
vmax = vor.max()
vmin = vor.min()
if vmax < -vmin:
vmax = -vmin
plt.imshow(vor.T,
cmap=plt.get_cmap('bwr'),
origin='lower',
extent=(-3, 3, -3, 3),
vmin=-vmax,
vmax=vmax)
plt.xlabel(r'$p_x\ [GeV]$')
plt.ylabel(r'$p_y\ [GeV]$')
plt.title(r'$\Pi^{y}\ @\ rapidity=%s$' % Y)
plt.colorbar()
smash_style.set()
plt.savefig('%s/vor_Y%s.png' % (self.fpath, Y))
plt.close()
vmax = omg.max()
vmin = omg.min()
if vmax < -vmin:
vmax = -vmin
plt.imshow(omg.T,
extent=(-3, 3, -3, 3),
cmap=plt.get_cmap('bwr'),
origin='lower',
vmin=-vmax,
vmax=vmax)
plt.xlabel(r'$p_x\ [GeV]$')
plt.ylabel(r'$p_y\ [GeV]$')
plt.title(r'$\omega^{y}\ @\ rapidity=%s$' % Y)
plt.colorbar()
smash_style.set()
plt.savefig('%s/omg_Y%s.png' % (self.fpath, Y))
plt.close()
def plot_pol_corr(h5,
withz=True,
system='auau200',
cent='20_30',
etaos='0p08'):
max0 = plot_range(h5, 0, 1, withz, system, cent, etaos, color='red')
max1 = plot_range(h5, 1, 2, withz, system, cent, etaos, color='blue')
max2 = plot_range(h5, 2, 3, withz, system, cent, etaos, color='green')
max3 = max0
if system == 'pbpb2p76':
max3 = plot_range(h5, 4, 5, withz, system, cent, etaos, color='grey')
plt.gca().yaxis.set_major_formatter(mtick.FormatStrFormatter('%.2e'))
plt.xlabel(r'$|\phi_1 - \phi_2|$')
maxf = max(max0, max1, max2, max3)
plt.ylim(-maxf, maxf)
smash_style.set()
plt.legend(loc='best')
plt.subplots_adjust(left=0.2)
system_title = '$Au+Au\ 200\ GeV$,'
if system == 'pbpb2p76':
system_title = '$Pb+Pb\ 2.76\ TeV$,'
if system == 'auau62p4':
system_title = '$Au+Au\ 62.4\ GeV$,'
fname = ''
if withz:
plt.ylabel(r'$<\Pi_{\eta}(\phi_1) \cdot \Pi_{\eta}(\phi_2)>$')
fname = 'figs/Piz_corr_vs_deltaphi_{system}_{cent}_{etaos}.pdf'.format(
system=system, etaos=etaos, cent=cent.replace('_', '-'))
else:
plt.ylabel(r'$<\Pi_{\perp}(\phi_1) \cdot \Pi_{\perp}(\phi_2)>$')
fname = 'figs/Pixy_corr_vs_deltaphi_{system}_{cent}_{etaos}.pdf'.format(
system=system, etaos=etaos, cent=cent.replace('_', '-'))
plt.title('{system_title} {cent}%, $\eta/s$={etaos}'.format(
system_title=system_title,
etaos=etaos.replace('p', '.'),
cent=cent.replace('_', '-')))
plt.savefig(fname)
plt.close()
def gradient_free(self, scale=20, color='k', flip=False):
''' nabla \times \vec{A} '''
Az = self.__int_nabla_v()
x, y = np.meshgrid(self.x, self.y, indexing='ij')
dyAz = np.gradient(Az)[1]
dxAz = np.gradient(Az)[0]
mask = self.ed < 0.3
dyAz[mask] = 0.0
dxAz[mask] = 0.0
if flip == True:
plt.quiver(x, y, -dyAz, dxAz, scale=scale, color=color)
else:
plt.quiver(x, y, dyAz, -dxAz, scale=scale, color=color)
smash_style.set()
def plot_visc_vs_ideal():
h5_visc = h5py.File('vor_int_visc0p08_cent20_25.hdf5', 'r')
#h5_ideal= h5py.File('vor_int_ideal_cent20_25.hdf5', 'r')
h5_ideal = h5py.File('vor_int_visc0p12_auau62p4_cent20_25.hdf5', 'r')
h5_ideal_cent45_50 = h5py.File('vor_int_visc0p12_auau62p4_cent45_50.hdf5',
'r')
h5_39 = h5py.File('vor_int_visc0p08_auau39_cent20_25.hdf5', 'r')
n0, Y, mean_pol_visc = int_Piy(h5_visc)
n1, Y, mean_pol_ideal = int_Piy(h5_ideal)
n2, Y, mean_pol_ideal_45 = int_Piy(h5_ideal_cent45_50)
n3, Y, mean_pol_3 = int_Piy(h5_39)
plt.errorbar(Y,
mean_pol_ideal_45,
np.sqrt(1.0 / n2) * mean_pol_ideal_45,
fmt='g^',
label=r'auau62.4 45-50 $\eta/s=0.12$')
plt.errorbar(Y,
mean_pol_ideal,
np.sqrt(1.0 / n1) * mean_pol_ideal,
fmt='bo',
label='auau62.4 20-25 $\eta/s=0.12$')
plt.errorbar(Y,
mean_pol_visc,
np.sqrt(1.0 / n0) * mean_pol_visc,
fmt='rs',
label=r'auau200 20-25 $\eta/s=0.08$')
plt.errorbar(Y,
mean_pol_3,
np.sqrt(1.0 / n3) * mean_pol_3,
fmt='cd',
label=r'auau39 20-25 $\eta/s=0.08$')
plt.xlabel(r'$rapidity$')
plt.ylabel(r'$P^y_{int}$')
#smash_style.set(line_styles=False)
smash_style.set()
plt.ylim(0.0002, 0.005)
plt.legend(loc='upper center')
plt.subplots_adjust(left=0.2)
plt.savefig('Pi_visc_cent.pdf')
plt.show()
plt.close()
def plot_vor(vor, Y, fpath='./', kind='visc'):
vmax = vor.max()
vmin = vor.min()
if vmax < -vmin:
vmax = -vmin
plt.imshow(vor.T,
cmap=plt.get_cmap('bwr'),
origin='lower',
extent=(-3, 3, -3, 3),
vmin=-vmax,
vmax=vmax)
plt.xlabel(r'$p_x\ [GeV]$')
plt.ylabel(r'$p_y\ [GeV]$')
plt.title(r'$\Pi^{y}\ @\ rapidity=%s$' % Y)
plt.colorbar()
smash_style.set()
plt.savefig('%s/vor_Y%s_%s.png' % (fpath, Y, kind))
plt.close()
def plot_dNdEta(num_of_events = 8):
plot_hydro('event0_6')
plot_hydro('event6_15')
plot_hydro('event15_25')
plot_hydro('event25_35')
#ebe = np.loadtxt('dNdEta_0_6_ebe.dat')
#plt.plot(ebe[:, 0], ebe[:, 1], 'r--')
# 1304.0347
dat0 = np.loadtxt('dNdEta_0_6.dat')
dat1 = np.loadtxt('dNdEta_6_15.dat')
dat2 = np.loadtxt('dNdEta_15_25.dat')
dat3 = np.loadtxt('dNdEta_25_35.dat')
plt.errorbar(dat0[:,0], dat0[:,2], yerr=[-dat0[:,4], dat0[:, 3]], fmt='ro', label=r'$PHOBOS$')
plt.errorbar(dat1[:,0], dat1[:,2], yerr=[-dat1[:,4], dat1[:, 3]], fmt='bo')
plt.errorbar(dat2[:,0], dat2[:,2], yerr=[-dat2[:,4], dat2[:, 3]], fmt='go')
plt.errorbar(dat3[:,0], dat3[:,2], yerr=[-dat3[:,4], dat3[:, 3]], fmt='mo')
plt.xlabel(r'$\eta$')
plt.ylabel(r'$\frac{dN_{ch}}{d\eta}$')
xcod = [-0.7, -0.7, -0.7, -0.7]
ycod = [720, 550, 400, 260]
text = ['0-6%', '6-15%', '15-25%', '25-35%']
for i in range(4):
plt.text(xcod[i], ycod[i], text[i], size=20)
smash_style.set(line_styles=False)
plt.legend(loc='best', title=r'$Au+Au\ 200\ GeV$')
plt.subplots_adjust(left=0.2, bottom=0.2, right=0.95, top=0.95)
plt.xlim(-8, 8)
plt.ylim(0, 1000)
plt.savefig('dNdEta_AuAu200.pdf')
plt.show()
def plot_dNdEta(num_of_events=8):
tau0p2 = np.loadtxt('../event2/dN_over_2pidYptdpt_mc_charged.dat')
eta = tau0p2[:, 0]
dat = np.loadtxt('dNdPt_2p76.dat')
# 1304.0347
plt.errorbar(dat[:, 0],
dat[:, 3],
dat[:, 4],
fmt='o',
label=r'$ALICE\ 0-5\%$')
plt.semilogy(eta, tau0p2[:, 1], label=r'$CLVisc$')
plt.xlabel(r'$p_T\ [GeV]$')
plt.ylabel(r'$\frac{dN_{ch}}{2\pi dY p_Tdp_T}\ [GeV^{-2}]$')
smash_style.set()
plt.legend(loc='best', title=r'$Pb+Pb\ 2760\ GeV$')
plt.subplots_adjust(left=0.2, bottom=0.2, right=0.95, top=0.95)
#plt.text(-6, 1650, '(b)')
plt.xlim(0, 4)
plt.ylim(1.0E-3, 1.0E4)
plt.show()
def cmp_v2_v3_v4(path_to_results,
hadron='pion',
cent=['0-5'],
save_fig=True,
ini='ampt'):
from pbpb2760 import Vn
v2_exp = Vn(n=2)
v3_exp = Vn(n=3)
v4_exp = Vn(n=4)
v5_exp = Vn(n=5)
label0 = r'$ALICE$'
for c in cent:
pt2, v2, v2_err0, v2_err1 = v2_exp.get_ptdiff(hadron, c)
pt3, v3, v3_err0, v3_err1 = v3_exp.get_ptdiff(hadron, c)
pt4, v4, v4_err0, v4_err1 = v4_exp.get_ptdiff(hadron, c)
pt5, v5, v5_err0, v5_err1 = v5_exp.get_ptdiff(hadron, c)
plt.errorbar(pt2,
v2,
yerr=(v2_err0, v2_err1),
label=label0,
fmt='ko',
ms=10)
plt.errorbar(pt3, v3, yerr=(v3_err0, v3_err1), fmt='rs', ms=10)
plt.errorbar(pt4, v4, yerr=(v4_err0, v4_err1), fmt='b*', ms=10)
plt.errorbar(pt5, v5, yerr=(v5_err0, v5_err1), fmt='md', ms=10)
path = os.path.join(path_to_results, c.replace('-', '_'))
vn_clvisc = ebe_mean(path, kind='vn', hadron=hadron)
colors = ['k', 'r', 'b', 'm']
for n in [2, 3, 4, 5]:
label1 = None
if n == 2: label1 = r'$CLVisc$'
plt.plot(vn_clvisc[:, 0],
vn_clvisc[:, n],
label=label1,
color=colors[n - 2])
plt.text(2.5, v2[10], r"$v_2$", fontsize=40, color='k')
plt.text(2.5, v3[10], r"$v_3$", fontsize=40, color='r')
plt.text(2.5, v4[10], r"$v_4$", fontsize=40, color='b')
plt.text(2.5, v5[10], r"$v_5$", fontsize=40, color='m')
plt.xlabel(r'$p_T\ [GeV]$')
if hadron == 'pion':
plt.ylabel(r'$v_n\ \mathrm{for}\ \pi^+$')
elif hadron == 'kaon':
plt.ylabel(r'$v_n\ \mathrm{for}\ K^+$')
elif hadron == 'proton':
plt.ylabel(r'$v_n\ \mathrm{for}\ proton$')
elif hadron == 'charged':
plt.ylabel(r'$v_n\ \mathrm{for}\ h^{\pm}$')
smash_style.set(line_styles=False)
plt.legend(loc='upper left', title=c + r'$\%$')
plt.tight_layout()
plt.xlim(0, 2.5)
plt.ylim(0.001, 1.3 * vn_clvisc[14, 2])
plt.title(r'$Pb+Pb\ \sqrt{s_{NN}}=2.76\ TeV$', fontsize=30)
plt.savefig('figs/pbpb2760_%s_%s_ini%s_vn.pdf' %
(hadron, c.replace('-', '_'), ini))
plt.show()
def ptspec_identify(path, cent='0_5', data_src=1):
path = os.path.join(path, cent)
pion = ebe_mean(path, kind='dndpt', hadron='pion', rap='Y')
kaon = ebe_mean(path, kind='dndpt', hadron='kaon', rap='Y')
proton = ebe_mean(path, kind='dndpt', hadron='proton', rap='Y')
proton_fix_factor = 0.7
if data_src == 0:
#dat0 = np.loadtxt('dNdPt_2p76.dat', skiprows=10)
dat = np.loadtxt(
'data/dNdYptdpt_Alice/dNdPt_pbpb2760_%s_pion_exp.dat' % cent,
skiprows=10)
dat2 = np.loadtxt(
'data/dNdYptdpt_Alice/dNdPt_pbpb2760_%s_kaon_exp.dat' % cent,
skiprows=10)
dat3 = np.loadtxt(
'data/dNdYptdpt_Alice/dNdPt_pbpb2760_%s_proton_exp.dat' % cent,
skiprows=10)
# 1304.0347
#plt.errorbar(dat0[:,0], dat0[:,3], dat0[:,6], fmt='o', label=r'$ALICE\ charged$')
plt.errorbar(dat[:, 0],
dat[:, 3],
dat[:, 6],
fmt='o',
color='r',
label=r'$ALICE\ \pi^+$')
plt.errorbar(dat2[:, 0],
dat2[:, 3],
dat2[:, 6],
fmt='s',
color='g',
label=r'$ALICE\ K^+$')
plt.errorbar(dat3[:, 0],
dat3[:, 3],
dat3[:, 6],
fmt='d',
color='b',
label=r'$ALICE\ p$')
#plt.semilogy(charged[:, 0], charged[:, 1], label=r'$CLVisc\ \pi^+$')
plt.semilogy(pion[:, 0], pion[:, 1], 'k-', label=r'$CLVisc$')
plt.semilogy(kaon[:, 0], kaon[:, 1], 'k-')
plt.semilogy(proton[:, 0], proton_fix_factor * proton[:, 1], 'k-')
#plt.semilogy(eta, 3.5*proton[:, 1], label=r'$CLVisc$')
elif data_src == 1:
from pbpb2760 import dNdPt
exp = dNdPt()
x_pion, y_pion, yerr0_pion, yerr1_pion = exp.get(
'pion', cent.replace('_', '-'))
x_kaon, y_kaon, yerr0_kaon, yerr1_kaon = exp.get(
'kaon', cent.replace('_', '-'))
x_proton, y_proton, yerr0_proton, yerr1_proton = exp.get(
'proton', cent.replace('_', '-'))
plt.errorbar(x_pion,
y_pion,
yerr=(yerr0_pion, yerr1_pion),
fmt='o',
color='r',
label=r'$ALICE\ \pi^{+}+\pi^{-}$')
plt.errorbar(x_kaon,
y_kaon,
yerr=(yerr0_kaon, yerr1_kaon),
fmt='s',
color='g',
label=r'$ALICE\ K^{+}+K^{-}$')
plt.errorbar(x_proton,
y_proton,
yerr=(yerr0_proton, yerr1_proton),
fmt='d',
color='b',
label=r'$ALICE\ p+\bar{p}$')
#plt.semilogy(charged[:, 0], charged[:, 1], label=r'$CLVisc\ \pi^+$')
plt.semilogy(pion[:, 0], 2 * pion[:, 1], 'k-', label=r'$CLVisc$')
plt.semilogy(kaon[:, 0], 2 * kaon[:, 1], 'k-')
plt.semilogy(proton[:, 0], proton_fix_factor * 2 * proton[:, 1], 'k-')
#plt.semilogy(eta, 3.5*proton[:, 1], label=r'$CLVisc$')
plt.xlabel(r'$p_T\ [GeV]$')
plt.ylabel(r'$\frac{dN}{2\pi dY p_Tdp_T}\ [GeV^{-2}]$')
smash_style.set(line_styles=False)
#plt.legend(loc='best', ncol=2, mode='expand')
plt.title(r'$Pb+Pb\ 2.76\ TeV, centrality\ %s$' % cent.replace('_', '-'))
plt.xlim(0, 4)
plt.ylim(1.0E-2, 1.0E4)
plt.tight_layout()
plt.legend(loc='upper right')
plt.savefig('figs/pbpb2760_ptspec_%s_identify.pdf' % cent)
plt.show()
def evolve(self, nstep=20):
'''time evolution of magnetic field for nstep '''
Bx = np.empty_like(self.B0[0])
By = np.empty_like(Bx)
Bz = np.empty_like(Bx)
Bold = np.array(self.B0)
zeros = np.zeros_like(Bx)
Ex = np.zeros_like(Bx)
Ey = np.zeros_like(Bx)
Ez = np.zeros_like(Bx)
Eold = np.array([Ex, Ey, Ez])
eos_type = 'EOSI'
if self.hydro_cfg.IEOS == 1:
eos_type = 'EOSL'
By_cent_vs_time = []
extent = (self.x[0], self.x[-1], self.y[0], self.y[-1])
sigma = self.sigma
dt = self.dt
for n in range(1, nstep):
# predict step, get B^{n+1'} from B^n and E^n
v0 = self.velocity(n-1)
E0 = Eold
curl_E0 = self.curl(E0)
Bprim = Bold - dt * curl_E0
v1 = self.velocity(n)
#E1 = self.electric_field(E0, v0, Bold, v1, Bprim, dt=self.dt, sigma=self.sigma)
Eprim = (E0 + dt*(self.curl(Bprim) - sigma*self.v_cross_B(v1, Bprim)))/(1+sigma*dt)
B1 = Bold - 0.5*dt*(self.curl(E0) + self.curl(Eprim))
Bold = B1
Eold = (E0 + dt*(self.curl(B1) - sigma*self.v_cross_B(v1, B1)))/(1+sigma*dt)
time = self.hydro_cfg.TAU0+n*dt
divB = self.check_divB(Bold)
plt.contourf(divB.T, origin='lower', extent=extent)
plt.xlabel(r'$x\ [fm]$')
plt.ylabel(r'$y\ [fm]$')
plt.title(r'$div B\ @\ t=%s\ [fm]\ %s$'%(time, eos_type))
smash_style.set()
plt.colorbar()
plt.savefig('%s/divB_%03d.png'%(self.hydro_dir,n))
plt.close()
self.B.append(Bold)
i_cent = self.hydro_cfg.NX//2
j_cent = self.hydro_cfg.NY//2
By_cent = Bold[1, i_cent, j_cent]
By_cent_vs_time.append([time, By_cent])
By_cent_vs_time = np.array(By_cent_vs_time)
fname = '%s/By_cent_vs_time_%s.dat'%(self.hydro_dir, eos_type)
np.savetxt(fname, By_cent_vs_time, header='tau, By(x=0, y=0) GeV^2')
print('evolution finished! start to plot...')
def plot_dNdEta(num_of_events=8):
charged = np.loadtxt(
'/lustre/nyx/hyihp/lpang/new_polarization/pbpb2p76_results/cent0_5/etas0p08/dNdPt_charged_noovers.dat'
)
pion = np.loadtxt(
'/lustre/nyx/hyihp/lpang/new_polarization/pbpb2p76_results/cent0_5/etas0p08/dNdPt_pion_noovers.dat'
)
kaon = np.loadtxt(
'/lustre/nyx/hyihp/lpang/new_polarization/pbpb2p76_results/cent0_5/etas0p08/dNdPt_kaon_noovers.dat'
)
proton = np.loadtxt(
'/lustre/nyx/hyihp/lpang/new_polarization/pbpb2p76_results/cent0_5/etas0p08/dNdPt_proton_noovers.dat'
)
#pion = np.loadtxt('/lustre/nyx/hyihp/lpang/new_polarization/pbpb2p76_results/cent0_5/etas0p08/event5/dN_over_2pidYptdpt_mc_211.dat')
#kaon = np.loadtxt('/lustre/nyx/hyihp/lpang/new_polarization/pbpb2p76_results/cent0_5/etas0p08/event5/dN_over_2pidYptdpt_mc_321.dat')
#proton = np.loadtxt('/lustre/nyx/hyihp/lpang/new_polarization/pbpb2p76_results/cent0_5/etas0p08/event5/dN_over_2pidYptdpt_mc_2212.dat')
eta = pion[:, 0]
dat0 = np.loadtxt('dNdPt_2p76.dat', skiprows=10)
dat = np.loadtxt('dNdPt_Alice/dNdPt_pbpb2760_0_5_pion_exp.dat',
skiprows=10)
dat2 = np.loadtxt('dNdPt_Alice/dNdPt_pbpb2760_0_5_kaon_exp.dat',
skiprows=10)
dat3 = np.loadtxt('dNdPt_Alice/dNdPt_pbpb2760_0_5_proton_exp.dat',
skiprows=10)
# 1304.0347
plt.errorbar(dat0[:, 0],
dat0[:, 3],
dat0[:, 6],
fmt='o',
label=r'$ALICE\ charged$')
plt.errorbar(dat[:, 0],
dat[:, 3],
dat[:, 6],
fmt='o',
label=r'$ALICE\ \pi^+$')
plt.errorbar(dat2[:, 0],
dat2[:, 3],
dat2[:, 6],
fmt='o',
label=r'$ALICE\ K^+$')
plt.errorbar(dat3[:, 0],
dat3[:, 3],
dat3[:, 6],
fmt='o',
label=r'$ALICE\ p$')
plt.semilogy(eta, charged[:, 1], label=r'$CLVisc\ \pi^+$')
plt.semilogy(eta, pion[:, 1], label=r'$CLVisc\ \pi^+$')
plt.semilogy(eta, kaon[:, 1], label=r'$CLVisc\ K^+$')
plt.semilogy(eta, proton[:, 1], label=r'$CLVisc\ p$')
#plt.semilogy(eta, 3.5*proton[:, 1], label=r'$CLVisc$')
plt.xlabel(r'$p_T\ [GeV]$')
plt.ylabel(r'$\frac{dN_{ch}}{2\pi dY p_Tdp_T}\ [GeV^{-2}]$')
smash_style.set()
plt.legend(loc='best', ncol=2, mode='expand')
plt.title(r'$Pb+Pb\ 2.76\ TeV,\ 0-5\%$')
plt.subplots_adjust(left=0.15, bottom=0.15)
#plt.text(-6, 1650, '(b)')
plt.xlim(0, 4)
plt.ylim(1.0E-3, 1.0E5)
plt.savefig('dNdPt_hydro_vs_LHC.pdf')
plt.show()
y,
yerr=yerr,
label=version,
color="darkred",
linestyle='-',
zorder=3)
# store results
save_table(OrderedDict([('x', x), ('y', y), ('yerr', yerr)]),
'multiplicity.txt', version)
if args.comp_prev_version: # Compare to previous version
import comp_to_prev_version as cpv
cpv.plot_previous_results('FOPI_pions', 'multiplicity', '.txt')
smash_style.set(line_styles=False, update_legends=True)
plt.xlabel("$E_{kin}$ [AGeV]")
plt.xlim(0.3, 1.6)
plt.yscale('log')
plt.ylim(2, 100)
plt.ylabel(r"$M(\pi)=3/2\,(N_{\pi^+}+N_{\pi^-})$")
plt.legend(loc="lower right")
### (3) plot ratio
plt.subplot(312)
if args.FOPI_ratio != '':
if 'ced' not in sys.modules:
import comp_to_exp_data as ced
ced.plot_FOPI_data(args.FOPI_ratio, 'ratio')
def test_riemann(self):
''' initialize with step energy density in
(t, x, y, z) coordinates to test the riemann solution,
'''
z = np.linspace(-10, 10, cfg.NZ)
edv = np.zeros((cfg.NX * cfg.NY * cfg.NZ, 4), dtype=np.float32)
from gubser import Riemann
analytical_solution = Riemann(z, pressure_left=1.0)
#cfg.TAU0 = 1.0
self.visc = CLVisc(cfg)
self.ctx = self.visc.ctx
self.queue = self.visc.queue
ed_ini = analytical_solution.energy_density(cfg.TAU0)
vz_ini = analytical_solution.fluid_velocity(cfg.TAU0)
for i in range(cfg.NX):
for j in range(cfg.NY):
for k in range(cfg.NZ):
index = i * cfg.NY * cfg.NZ + j * cfg.NZ + k
edv[index, 0] = ed_ini[k]
edv[index, 3] = vz_ini[k]
self.visc.ideal.load_ini(edv)
self.visc.evolve(max_loops=1001,
plot_bulk=True,
save_hypersf=False,
force_run_to_maxloop=True)
bulk = self.visc.ideal.bulkinfo
import matplotlib.pyplot as plt
import h5py
h5 = h5py.File('riemann_ideal.h5', 'w')
h5.create_dataset('z', data=z)
nstep = 10
tau_list = np.empty(nstep)
cs2 = 1.0 / 3.0
for i in range(nstep):
h5.create_dataset('clvisc/ed/%s' % i, data=bulk.ez[i])
h5.create_dataset('clvisc/pr/%s' % i, data=bulk.ez[i] * cs2)
h5.create_dataset('clvisc/vz/%s' % i, data=bulk.vz[i])
h5.create_dataset('riemann/ed/%s' % i,
data=analytical_solution.energy_density(i))
h5.create_dataset('riemann/pr/%s' % i,
data=analytical_solution.pressure(i))
h5.create_dataset('riemann/vz/%s' % i,
data=analytical_solution.fluid_velocity(i))
tau = cfg.TAU0 + i * cfg.ntskip * cfg.DT
tau_list[i] = tau
h5.create_dataset('tau', data=tau_list)
h5.close()
for i in [0, 2, 4, 8]:
plt.plot(z, bulk.ez[i] / 3.0, '--')
ez = analytical_solution.pressure(i)
plt.plot(z, ez)
plt.text(-i, 0.9, r'$\tau=%s\ [fm]$' % i)
plt.ylim(-0.5, 1.5)
plt.xlabel(r'$z\ [fm]$')
plt.ylabel(r'$\varepsilon$')
smash_style.set(line_styles=False)
plt.savefig('riemann_ed.pdf')
plt.close()
for i in [0, 2, 4, 8]:
plt.plot(z, bulk.vz[i], '--')
vz = analytical_solution.fluid_velocity(i)
plt.plot(z, vz)
plt.text(i, 0.9, r'$\tau=%s\ [fm]$' % i)
plt.ylim(-0.5, 1.5)
plt.xlabel(r'$z\ [fm]$')
plt.ylabel(r'$v_z$')
smash_style.set(line_styles=False)
plt.savefig('riemann_vz.pdf')
plt.close()
def cmp_ideal_vs_visc(
dir_ideal='/lustre/nyx/hyihp/lpang/trento_ebe_hydro/pbpb2p76_results_ampt/etas0p0/',
dir_visc='/lustre/nyx/hyihp/lpang/trento_ebe_hydro/pbpb2p76_results_ampt/etas0p16/',
centrality='0-5',
num_events=200,
subfig_label='A'):
vn_ideal = np.empty((num_events, 20, 7))
vn_visc = np.empty((num_events, 20, 7))
vn_ideal[:] = np.NAN
vn_visc[:] = np.NAN
for eid in range(0, num_events):
try:
path_ideal = os.path.join(dir_ideal, centrality.replace('-', '_'),
'event%d' % eid, 'vn24_vs_eta.txt')
vn_ideal[eid] = np.loadtxt(path_ideal)
path_visc = os.path.join(dir_visc, centrality.replace('-', '_'),
'event%d' % eid, 'vn24_vs_eta.txt')
vn_visc[eid] = np.loadtxt(path_visc)
except BaseException as e:
print("%s when load data for event %s" % (str(e), eid))
# due to statistics, sometimes vn{2} or vn{4} has nan values, skip those events
vn_ideal_mean = np.nanmean(vn_ideal, axis=0)
vn_visc_mean = np.nanmean(vn_visc, axis=0)
pbpb = PbPb2760()
exp_v22 = pbpb.get(centrality, 'v22')
exp_v32 = pbpb.get(centrality, 'v32')
ratio = vn_visc_mean[:, 1].sum() / vn_ideal_mean[:, 1].sum()
plt.plot(vn_visc_mean[:, 0],
vn_visc_mean[:, 1],
'r-',
label=r'$CLVisc\ \eta/s=0.16$')
plt.plot(vn_visc_mean[:, 0], vn_visc_mean[:, 2], 'b-')
plt.plot(vn_ideal_mean[:, 0],
ratio * vn_ideal_mean[:, 1],
'r--',
label=r'$(CLVisc\ \eta/s=0)\times%.2f$' % ratio)
plt.plot(vn_ideal_mean[:, 0], ratio * vn_ideal_mean[:, 2], 'b--')
plt.errorbar(exp_v22[0],
exp_v22[1],
yerr=(exp_v22[4], exp_v22[5]),
elinewidth=20,
ls='',
color='r',
label='ALICE')
plt.errorbar(exp_v32[0],
exp_v32[1],
yerr=(exp_v32[4], exp_v32[5]),
elinewidth=20,
ls='',
color='b')
plt.text(5.5, 1.0 * vn_visc_mean[-1, 1], r'$v_2\{2\}$', color='r')
plt.text(5.5, 1.0 * vn_visc_mean[-1, 2], r'$v_3\{2\}$', color='b')
plt.xlabel(r'$\eta$')
plt.ylabel(r'$v_n\{2\}$')
#plt.title(r'$Pb+Pb\ \sqrt{s_{NN}}=2.76\ TeV,\ 20-30\%$')
plt.xlim(-6.0, 7.0)
plt.ylim(0.0, 1.5 * vn_visc_mean[:, 1].max())
plt.text(0.7,
0.9,
'(' + subfig_label + ') ' + centrality + r'$\%$',
transform=plt.gca().transAxes)
plt.subplots_adjust(left=0.15, bottom=0.15)
smash_style.set(line_styles=False)
plt.tight_layout()
#plt.legend(ncol=2, loc="upper left", bbox_to_anchor=[0, 1])
plt.legend(loc="upper left")
plt.savefig(
'figs/vn2_vs_eta_{cent}.pdf'.format(cent=centrality.replace('-', '_')))
plt.show()
# save mean vn(eta) from hydro calculation to data file
hydro_data_save_path = os.path.join(os.getcwd(),
'figs/hydro_pbpb2760_vn_vs_eta/')
if not os.path.exists(hydro_data_save_path):
os.makedirs(hydro_data_save_path)
df = pd.DataFrame({
"eta": vn_ideal_mean[:, 0],
"v22": vn_ideal_mean[:, 1],
"v32": vn_ideal_mean[:, 2],
"v42": vn_ideal_mean[:, 3]
})
df.to_csv(
os.path.join(hydro_data_save_path,
'etaos0p0_cent%s.csv' % centrality.replace('-', '_')))
df2 = pd.DataFrame({
"eta": vn_visc_mean[:, 0],
"v22": vn_visc_mean[:, 1],
"v32": vn_visc_mean[:, 2],
"v42": vn_visc_mean[:, 3]
})
df2.to_csv(
os.path.join(hydro_data_save_path,
'etaos0p16_cent%s.csv' % centrality.replace('-', '_')))
'b--',
label=r'CLVisc, $\eta/s=0.16$')
#plt.plot(vn_mean[:, 0], vn_mean[:, 3], 'g:', label=r'CLVisc, n=4, $\eta/s=0.08$')
plt.errorbar(exp[:, 0],
exp[:, 1],
yerr=exp[:, 3],
elinewidth=20,
ls='',
color='r',
label=r'ALICE, n=2')
plt.errorbar(exp[:, 0],
exp[:, 7],
yerr=exp[:, 9],
elinewidth=20,
ls='',
color='b',
label=r'ALICE, n=3')
#plt.errorbar(exp[:, 0], exp[:, 10], yerr=exp[:, 11], label=r'ALICE, n=4')
plt.xlabel(r'$\eta$')
plt.ylabel(r'$v_n\{2\}$')
plt.title(r'$Pb+Pb\ \sqrt{s_{NN}}=2.76\ TeV,\ 0-5\%$')
plt.ylim(0.0, 0.04)
plt.subplots_adjust(left=0.15, bottom=0.15)
smash_style.set()
plt.legend(ncol=2, loc="upper left", bbox_to_anchor=[0, 1])
plt.savefig('vn2_vs_eta_0_5.pdf')
plt.show()
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--num_events",
type=int,
default=200,
help="num of events that will use")
parser.add_argument("--input_dir",
help="path to folder containing event*/")
parser.add_argument(
"--etaos",
default='0p16',
help="eta/s string 0p0 or 0p16 in the name of the output figure")
parser.add_argument(
"--centrality",
choices=['0-5', '5-10', '10-20', '20-30', '30-40', '40-50', '50-60'],
help="centrality range")
args = parser.parse_args()
print(args.num_events)
vn = np.empty((args.num_events, 20, 7))
vn[:] = np.NAN
for eid in range(0, args.num_events):
try:
vn_new = np.loadtxt('%s/event%d/vn24_vs_eta.txt' %
(args.input_dir, eid))
vn[eid] = vn_new
except:
print("no data for event %s" % eid)
vn_mean = np.nanmean(vn, axis=0)
pbpb = PbPb2760()
exp_v22 = pbpb.get(args.centrality, 'v22')
exp_v32 = pbpb.get(args.centrality, 'v32')
plt.plot(vn_mean[:, 0], vn_mean[:, 1], 'r-', label='CLVisc')
plt.plot(vn_mean[:, 0], vn_mean[:, 2], 'b-')
#plt.plot(vn_mean[:, 0], vn_mean[:, 3], 'g:', label=r'CLVisc, n=4, $\eta/s=0.08$')
plt.errorbar(exp_v22[0],
exp_v22[1],
yerr=(exp_v22[4], exp_v22[5]),
elinewidth=20,
ls='',
color='r',
label='ALICE')
plt.errorbar(exp_v32[0],
exp_v32[1],
yerr=(exp_v32[4], exp_v32[5]),
elinewidth=20,
ls='',
color='b')
plt.text(5.5, 1.0 * vn_mean[-1, 1], r'$v_2\{2\}$', color='r')
plt.text(5.5, 1.0 * vn_mean[-1, 2], r'$v_3\{2\}$', color='b')
plt.xlabel(r'$\eta$')
plt.ylabel(r'$v_n\{2\}$')
#plt.title(r'$Pb+Pb\ \sqrt{s_{NN}}=2.76\ TeV,\ 20-30\%$')
plt.xlim(-6.0, 7.0)
plt.text(0.4,
0.15,
args.centrality + r'$\%$',
transform=plt.gca().transAxes)
plt.subplots_adjust(left=0.15, bottom=0.15)
smash_style.set()
plt.tight_layout()
#plt.legend(ncol=2, loc="upper left", bbox_to_anchor=[0, 1])
plt.legend(loc="best")
plt.savefig('figs/vn2_vs_eta_{cent}_etaos{etaos}.pdf'.format(
cent=args.centrality.replace('-', '_'), etaos=args.etaos))
plt.show()
# save mean vn(eta) from hydro calculation to data file
hydro_data_save_path = os.path.join(os.getcwd(),
'figs/hydro_pbpb2760_vn_vs_eta/')
if not os.path.exists(hydro_data_save_path):
os.makedirs(hydro_data_save_path)
df = pd.DataFrame({
"eta": vn_mean[:, 0],
"v22": vn_mean[:, 1],
"v32": vn_mean[:, 2],
"v42": vn_mean[:, 3]
})
df.to_csv(
os.path.join(
hydro_data_save_path, 'etaos%s_cent%s' %
(args.etaos, args.centrality.replace('-', '_'))))
