def figure():
fName = './dataUseCase11/Wrz_2Layer_mcml_ISPGauss_R00.15.npz'
c0 = 1.0 # speed of sound in medium
G = 1.0 # efficiency of transforming absorbed energy to acoustic stress
zD = -2.0 # z-location of pointlike detector
Nphi = 1 # number of azimutal mesh points considered for integration
# (in case of on-axis signals Nphi=1 suffices)
# READ VOLUMETRIC ENERGY DENSITY FROM COMPRESSED .npz FILE
(r,z,Wrz) = io.npz.readROI(fName)
# COMPUTE OA SIGNAL AT DETECTOR POSITION
t,p = ps.pressure((r,z,G*Wrz),(0.0,zD),c0,Nphi)
# VISUALIZE USING MATPLOTLIB
plt.rc('font',family='serif',size=14)
plt.figure(figsize=(8.,6.), dpi=80)
plt.plot(c0*t+zD,p, label='$z_D$=%3.2lf cm'%(zD))
plt.xlim(-0.005,0.18)
plt.ylabel('$p$ (Pa)', size=14)
plt.xlabel('$c \\tau$ (cm)', size=14)
plt.axhline(y=0,color='black', ls='--')
plt.legend(loc='upper right', prop={'size':14}, frameon=False)
plt.show()
def figure():
# PARAMETERS FOR OA SIGNAL GENERATION
c0 = 150000.0 # sonic velocity
G = 1.0 # Grueneissen parameter
rD = 0.0 # detector location relative to beam axis
tp = 2.*10**(-8) # gaussian pulse width (s)
# GENERAL FIGURE SETTINGS ---------------------------------------------
plt.rc('font',family='serif',size=12)
plt.figure(figsize=(7.,7.))
# FIG (b): FLAT-TOP ISP
ax12= plt.subplot2grid((2,2), (0,0))
ax12.set_aspect('equal')
r,z,W_ft = io.npz.readROI("./dataUseCase21/Wrz_flatTop.npz")
ax12.contourf(z,r,W_ft,100,lw=0.1)
c2 = ax12.contourf(z,r,W_ft,100)
divider2 = make_axes_locatable(ax12)
cax12 = divider2.append_axes("right", "5%", pad="3%")
c2Bar = plt.colorbar(c2,cax=cax12)
c2Bar.set_label('$W(r,z)$ (J/m$^2$)',fontsize=10)
c2Bar.ax.tick_params(labelsize=10)
ax12.set_ylabel('$r$ (cm)')
ax12.set_xlabel('$z$ (cm)')
ax12.text(0.,1.05,'(a)',transform=ax12.transAxes,fontsize=12)
# FIG (c): DONUT ISP
ax13= plt.subplot2grid((2,2), (0,1))
ax13.set_aspect('equal')
r,z,W_d = io.npz.readROI("./dataUseCase21/Wrz_Donut.npz")
ax13.contour(z,r,W_d,100,lw=0.1)
c3 = ax13.contourf(z,r,W_d,100)
divider3 = make_axes_locatable(ax13)
cax13 = divider3.append_axes("right", "5%", pad="3%")
c3Bar = plt.colorbar(c3,cax=cax13)
c3Bar.set_label('$W(r,z)$ (J/m$^2$)', fontsize=10)
c3Bar.ax.tick_params(labelsize=10)
ax13.set_ylabel('$r$ (cm)')
ax13.set_xlabel('$z$ (cm)')
ax13.text(0.,1.05,'(b)',transform=ax13.transAxes,fontsize=12)
# FIG (d): ON-AXIS OA SIGNALS FOR DONUT ISP
ax21 = plt.subplot2grid((2,2), (1,0), colspan=2)
for zD in [-0.5,-1.0,-2.0,-4.0,-8.0]:
t,p_d = ps.pressure((r,z,G*W_d),(rD,zD),c0,1)
p_d = pp.convolveGauss(p_d,(t,tp))
ax21.plot(c0*t+zD,p_d, label='$z_D$=%3.2lf cm'%(zD))
ax21.set_xlim([-0.005,0.18])
ax21.set_ylabel('$p$ (Pa per J)')
ax21.set_xlabel('$c \\tau$ (cm)')
ax21.axhline(y=0,color='black', ls='--')
ax21.legend(loc='upper right', prop={'size':10}, frameon=False)
ax21.text(0.,1.05,'(c)',transform=ax21.transAxes,fontsize=12)
plt.tight_layout(pad=2.)
#plt.show()
plt.savefig('useCase_mcml_abc.eps',dpi=600,format='eps')
def main():
iPathNpz = sys.argv[1]
(c0, G) = (1.0, 1.0)
(rD, zD) = (0.0, -3.0)
(r, z, Wrz) = io.npz.readROI(iPathNpz)
t, p = ps.pressure((r, z, G * Wrz), (rD, zD), c0, 1)
print "# (c0 tau) (p)"
for i in range(t.size):
print c0 * t[i] + zD, p[i]
def main():
iPathNpz = sys.argv[2]
c0, G = 150000.0, 1.0
rD, zD = 0.0, float(sys.argv[1])
tp = 2. * 10**(-8) # gaussian pulse width (s)
(r, z, Wrz) = io.npz.readROI(iPathNpz)
t, p = ps.pressure((r, z, G * Wrz), (rD, zD), c0, 1)
ptp = pp.convolveGauss(p, (t, tp))
print "# tp = %g s" % (tp)
print "# (c0 tau) (p)"
for i in range(t.size):
print c0 * t[i] + zD, p[i], ptp[i]
def main():
fName = sys.argv[1] # E.g. 'Wrz_SkinOA_1G_ISPGauss_R00.80.npz'
c0 = 1.0 # speed of sound in medium
G = 1.0 # efficiency of transforming absorbed energy to acoustic stress
zD = -2.0 # z-location of pointlike detector
Nphi = 1 # number of azimutal mesh points considered for integration
# (in case of on-axis signals Nphi=1 suffices)
# READ VOLUMETRIC ENERGY DENSITY FROM COMPRESSED .npz FILE
(r, z, Wrz) = io.npz.readROI(fName)
t, p = ps.pressure((r, z, G * Wrz), (0.0, zD), c0, Nphi)
tau = t + zD / c0
print "# (c0 tau) (p(tau))"
for i in range(tau.size):
print c0 * tau[i], p[i]