fluxj_visc_stacked += fluxj_visc_average
fluxj_grav_stacked += fluxj_grav_average
fluxj_adv_stacked /= len(filename_list)
fluxj_visc_stacked /= len(filename_list)
fluxj_grav_stacked /= len(filename_list)
# Plotting...
fig = plt.figure(1, figsize=(7.0, 12.0))
# First map
ax1 = fig.add_axes([0.10, 0.77, 0.77, 0.22])
divider = make_axes_locatable(ax1)
cax = divider.append_axes("right", size="3%", pad=0.08)
fluximage = ImageData()
fluximage.data = fluxj_adv_stacked.T
fluximage.pixelx = grid.R.shape[0]
fluximage.pixely = grid.phi.shape[0]
minval, maxval = -4, 4
extent = [Rmin, Rmax, 0, 2 * np.pi]
cmap = cm.get_cmap('jet')
ax, im = plot_slice(ax1,
fluximage,
cmap=cmap,
vmin=minval,
vmax=maxval,
extent=extent,
scale='linear')
cbar = plt.colorbar(im, cax=cax)
fluxj_grav_stacked += fluxj_grav_average
fluxj_adv_stacked /= len(filename_list)
fluxj_visc_stacked /= len(filename_list)
fluxj_grav_stacked /= len(filename_list)
# Plotting...
fig = plt.figure(1,figsize=(7.0,12.0))
# First map
ax1 = fig.add_axes([0.10,0.77,0.77,0.22])
divider = make_axes_locatable(ax1)
cax = divider.append_axes("right", size="3%", pad=0.08)
fluximage = ImageData()
fluximage.data = fluxj_adv_stacked.T
fluximage.pixelx = grid.R.shape[0]
fluximage.pixely = grid.phi.shape[0]
minval, maxval = -4, 4
extent = [Rmin,Rmax,0,2*np.pi]
cmap = cm.get_cmap('jet')
ax, im = plot_slice(ax1,fluximage,cmap = cmap,
vmin=minval,vmax=maxval,extent=extent,scale='linear')
cbar = plt.colorbar(im,cax=cax)
cbar.set_label(r'$F_{J,{\rm adv}}\,/(\dot{M}_0a_{\rm b}\Omega_{\rm b})$',size=20)
ax1.set_xticklabels([])
ticks = [0,np.pi/2,np.pi,1.5 * np.pi, 2 * np.pi ]
ax1.set_yticks(ticks)
# compute angular momentum flux maps
fluxj_grav = dda.compute_angular_momentum_flux_gravity(snap,grid) / Mdot0
fluxj_visc = dda.compute_angular_momentum_flux_viscosity(snap,grid) / Mdot0
fluxj_adv = dda.compute_angular_momentum_flux_advection(snap,grid) / Mdot0
#######################################################################
# Plotting...
fig = plt.figure(1,figsize=(7.0,12.0))
# First map
ax1 = fig.add_axes([0.10,0.77,0.77,0.22])
divider = make_axes_locatable(ax1)
cax = divider.append_axes("right", size="3%", pad=0.08)
fluximage = ImageData()
fluximage.data = fluxj_adv.T
fluximage.pixelx = grid.X.shape[0]
fluximage.pixely = grid.Y.shape[0]
minval, maxval = None, None
extent = [Rmin,Rmax,0,2*np.pi]
cmap = cm.get_cmap('jet')
ax, im = plot_slice(ax1,fluximage,normalize=6.0e-4,cmap = cmap,
vmin=minval,vmax=maxval,extent=extent,scale='linear')
cbar = plt.colorbar(im,cax=cax)
ax1.set_xticklabels([])
ticks = [0,np.pi/2,np.pi,1.5 * np.pi, 2 * np.pi ]
ax1.set_yticks(ticks)
ticklabels = [r'$0$',r'$\frac{1}{2}\pi$',r'$\pi$',r'$\frac{3}{2}\pi$',r'$\pi$']
outfile = 'averaged_angmomflux_norbits%i-%i_q%.1f_e%.1f_h%.1f_alpha%.1f_eta%.2f.hdf5' % (orbit_range[0],orbit_range[-1],qb,eb,h0,alpha,eta)
print "Saving HDF5 file:",outfile
with h5py.File(outfile, 'w') as hf:
hf.create_dataset("FluxAdvection", data = fluxj_adv_average)
hf.create_dataset("FluxViscosity", data = fluxj_visc_average)
hf.create_dataset("FluxGravity", data = fluxj_grav_average)
# Plotting...
fig = plt.figure(1,figsize=(7.0,12.0))
# First map
ax1 = fig.add_axes([0.10,0.77,0.77,0.22])
divider = make_axes_locatable(ax1)
cax = divider.append_axes("right", size="3%", pad=0.08)
fluximage = ImageData()
fluximage.data = fluxj_adv_average.T
fluximage.pixelx = grid.X.shape[0]
fluximage.pixely = grid.Y.shape[0]
minval, maxval = None, None
extent = [Rmin,Rmax,0,2*np.pi]
cmap = cm.get_cmap('jet')
ax, im = plot_slice(ax1,fluximage,normalize=6.0e-4,cmap = cmap,
vmin=minval,vmax=maxval,extent=extent,scale='linear')
cbar = plt.colorbar(im,cax=cax)
ax1.set_xticklabels([])
ticks = [0,np.pi/2,np.pi,1.5 * np.pi, 2 * np.pi ]
ax1.set_yticks(ticks)
ticklabels = [r'$0$',r'$\frac{1}{2}\pi$',r'$\pi$',r'$\frac{3}{2}\pi$',r'$\pi$']
ms=1.6)
plt.xlim(0.5 * snap.header.boxsize - 5, 0.5 * snap.header.boxsize + 5)
plt.ylim(0.5 * snap.header.boxsize - 5, 0.5 * snap.header.boxsize + 5)
plt.xlabel(r'$x$', size=18)
plt.ylabel(r'$y$', size=18)
plt.axes().set_aspect('equal')
plt.savefig('./accretion_regions.png')
fig.clf()
from disk_data_analysis.plotting import plot_slice, ImageData
import matplotlib.cm as cm
fig = plt.figure(2, figsize=(12.0, 5.5))
fig.subplots_adjust(top=0.98, bottom=0.12, left=0.07, right=0.92)
image = ImageData()
image.read('../data/density_field_close_000')
image.interpolateneg()
image.replaceneg()
#ax = fig.add_subplot(121)
ax = fig.add_axes([-0.17, 0.13, 0.85, 0.85])
minval, maxval = 10**(-4.12), 10**(1.03)
extent = [77.5, 82.5, 77.5, 82.5]
ax, im = plot_slice(ax,
image,
normalize=Sigma_out,
vmin=minval,
vmax=maxval,
extent=extent,
scale='log')
density_average /= len(snap_list)
torquedensity_average /= len(snap_list)
from disk_data_analysis.plotting import plot_slice, ImageData
import matplotlib.cm as cm
# Save image data into HDF5 file
with h5py.File('averaged_density.hdf5', 'w') as hf:
hf.create_dataset("Density", data = density_average)
with h5py.File('averaged_torquedensity.hdf5', 'w') as hf:
hf.create_dataset("TorqueDensity", data = torquedensity_average)
# Create image data structure for the density field...
densityimage = ImageData()
densityimage.data = density_average.T
densityimage.pixelx = grid.X.shape[0]
densityimage.pixely = grid.Y.shape[0]
# ... and plot it
fig = plt.figure()
fig.subplots_adjust(top=0.97,right=0.88,bottom=0.12,left=0.10)
ax = fig.add_subplot(111)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.08)
minval,maxval = 10**(-4.9),10**(0.25)
extent = [74.0,86.0,74.0,86.0]
ax, im = plot_slice(ax,densityimage,normalize=6.0e-4,vmin=minval,vmax=maxval,extent=extent,scale='log')
orbit_range[0], orbit_range[-1], qb, eb, h0, alpha, eta)
print "Saving HDF5 file:", outfile
with h5py.File(outfile, 'w') as hf:
hf.create_dataset("FluxAdvection", data=fluxj_adv_average)
hf.create_dataset("FluxViscosity", data=fluxj_visc_average)
hf.create_dataset("FluxGravity", data=fluxj_grav_average)
# Plotting...
fig = plt.figure(1, figsize=(7.0, 12.0))
# First map
ax1 = fig.add_axes([0.10, 0.77, 0.77, 0.22])
divider = make_axes_locatable(ax1)
cax = divider.append_axes("right", size="3%", pad=0.08)
fluximage = ImageData()
fluximage.data = fluxj_adv_average.T
fluximage.pixelx = grid.X.shape[0]
fluximage.pixely = grid.Y.shape[0]
minval, maxval = None, None
extent = [Rmin, Rmax, 0, 2 * np.pi]
cmap = cm.get_cmap('jet')
ax, im = plot_slice(ax1,
fluximage,
normalize=6.0e-4,
cmap=cmap,
vmin=minval,
vmax=maxval,
extent=extent,
scale='linear')
if __name__ == '__main__':
#binary and disk properties
qb = float(sys.argv[1])
eb = float(sys.argv[2])
h0 = simset.h
alpha = simset.alpha
eta = float(sys.argv[3])
snap_num = int(sys.argv[4])
snap_path = simset.find_simulation(qb, eb, h0, alpha, eta)
fig = plt.figure(2, figsize=(12.0, 5.5))
fig.subplots_adjust(top=0.98, bottom=0.12, left=0.07, right=0.92)
image = ImageData()
image.read(snap_path + 'density_field_%03i' % snap_num)
image.interpolateneg()
image.replaceneg()
ax = fig.add_subplot(111)
minval, maxval = 10**(-4.12), 10**(1.03)
extent = [77.5, 82.5, 77.5, 82.5]
ax, im = plot_slice(ax,
image,
normalize=Sigma_out,
vmin=minval,
vmax=maxval,
extent=extent,
scale='log')
ax.set_xlabel(r'$x/a_{\rm b}$', size=22)
if __name__ == '__main__':
#binary and disk properties
qb=float(sys.argv[1])
eb=float(sys.argv[2])
h0=simset.h
alpha=simset.alpha
eta = float(sys.argv[3])
snap_num = int(sys.argv[4])
snap_path = simset.find_simulation(qb,eb,h0,alpha,eta)
fig = plt.figure(2,figsize = (12.0,5.5))
fig.subplots_adjust(top=0.98,bottom=0.12,left=0.07,right=0.92)
image = ImageData()
image.read(snap_path+'density_field_%03i' % snap_num)
image.interpolateneg()
image.replaceneg()
ax = fig.add_subplot(111)
minval,maxval = 10**(-4.12),10**(1.03)
extent = [77.5,82.5,77.5,82.5]
ax,im = plot_slice(ax,image,normalize=Sigma_out,
vmin=minval,vmax=maxval,extent=extent,scale='log')
ax.set_xlabel(r'$x/a_{\rm b}$',size=22)
ax.set_ylabel(r'$y/a_{\rm b}$',size=22)
ax.set_aspect('equal')
ticklabels = ["%i" % (t - 0.5 * snap.header.boxsize) for t in ax.get_xticks()]
# Divide by the total number of snapshots added
density_average /= len(snap_list)
torquedensity_average /= len(snap_list)
from disk_data_analysis.plotting import plot_slice, ImageData
import matplotlib.cm as cm
# Save image data into HDF5 file
with h5py.File('averaged_density.hdf5', 'w') as hf:
hf.create_dataset("Density", data=density_average)
with h5py.File('averaged_torquedensity.hdf5', 'w') as hf:
hf.create_dataset("TorqueDensity", data=torquedensity_average)
# Create image data structure for the density field...
densityimage = ImageData()
densityimage.data = density_average.T
densityimage.pixelx = grid.X.shape[0]
densityimage.pixely = grid.Y.shape[0]
# ... and plot it
fig = plt.figure()
fig.subplots_adjust(top=0.97, right=0.88, bottom=0.12, left=0.10)
ax = fig.add_subplot(111)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.08)
minval, maxval = 10**(-4.9), 10**(0.25)
extent = [74.0, 86.0, 74.0, 86.0]
ax, im = plot_slice(ax,
densityimage,
plt.plot(snap.gas.POS[str_region,0],snap.gas.POS[str_region,1],'.',color='orange',ms=1.6)
plt.xlim(0.5 * snap.header.boxsize - 5, 0.5 * snap.header.boxsize + 5)
plt.ylim(0.5 * snap.header.boxsize - 5, 0.5 * snap.header.boxsize + 5)
plt.xlabel(r'$x$',size=18)
plt.ylabel(r'$y$',size=18)
plt.axes().set_aspect('equal')
plt.savefig('./accretion_regions.png')
fig.clf()
from disk_data_analysis.plotting import plot_slice, ImageData
import matplotlib.cm as cm
fig = plt.figure(2,figsize = (12.0,5.5))
fig.subplots_adjust(top=0.98,bottom=0.12,left=0.07,right=0.92)
image = ImageData()
image.read('../data/density_field_close_000')
image.interpolateneg()
image.replaceneg()
#ax = fig.add_subplot(121)
ax = fig.add_axes([-0.17,0.13,0.85,0.85])
minval,maxval = 10**(-4.12),10**(1.03)
extent = [77.5,82.5,77.5,82.5]
ax,im = plot_slice(ax,image,normalize=Sigma_out,
vmin=minval,vmax=maxval,extent=extent,scale='log')
ax.set_xlabel(r'$x/a_{\rm b}$',size=22)
ax.set_ylabel(r'$y/a_{\rm b}$',size=22)
ax.set_aspect('equal')