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)
ticklabels = [r'$0$',r'$\frac{1}{2}\pi$',r'$\pi$',r'$\frac{3}{2}\pi$',r'$\pi$']
ax1.set_yticklabels(ticklabels)
[tick.label.set_fontsize(16) for tick in ax1.yaxis.get_major_ticks()]
ax1.set_aspect('auto')
ax1.text(0.45,0.88,"inward angular momentum flux due to",transform = ax1.transAxes)
ax1.text(0.45,0.78," advection",transform = ax1.transAxes,size=16)
# Second map
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')
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()]
ax.set_xticklabels(ticklabels)
ticklabels = ["%i" % (t - 0.5 * snap.header.boxsize) for t in ax.get_yticks()]
ax.set_yticklabels(ticklabels)
cbar = plt.colorbar(im,cax=cax)
cbar.set_label(r'$\Sigma/\Sigma_{\rm out}$',size=20)
figfilename = './density_field_averaged.png'
print "Saving figure",figfilename
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)
ticklabels = [
r'$0$', r'$\frac{1}{2}\pi$', r'$\pi$', r'$\frac{3}{2}\pi$', r'$\pi$'
]
ax1.set_yticklabels(ticklabels)
[tick.label.set_fontsize(16) for tick in ax1.yaxis.get_major_ticks()]
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')
ticklabels = [
"%i" % (t - 0.5 * snap.header.boxsize) for t in ax.get_xticks()
]
ax.set_xticklabels(ticklabels)
ticklabels = [
"%i" % (t - 0.5 * snap.header.boxsize) for t in ax.get_yticks()
]
ax.set_yticklabels(ticklabels)
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()
]
ax.set_xticklabels(ticklabels)
ticklabels = [
"%i" % (t - 0.5 * snap.header.boxsize) for t in ax.get_yticks()
]
ax.set_yticklabels(ticklabels)
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()]
ax.set_xticklabels(ticklabels)
ticklabels = ["%i" % (t - 0.5 * snap.header.boxsize) for t in ax.get_yticks()]
ax.set_yticklabels(ticklabels)
cbar = plt.colorbar(im,cax=cax)
cbar.set_label(r'$\Sigma/\Sigma_{\rm out}$',size=20)
fig.savefig('./density_field.png')
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')
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()
]
ax.set_xticklabels(ticklabels)
ticklabels = [
"%i" % (t - 0.5 * snap.header.boxsize) for t in ax.get_yticks()
]
ax.set_yticklabels(ticklabels)
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')
ticklabels = ["%i" % (t - 0.5 * snap.header.boxsize) for t in ax.get_xticks()]
ax.set_xticklabels(ticklabels)
ticklabels = ["%i" % (t - 0.5 * snap.header.boxsize) for t in ax.get_yticks()]
ax.set_yticklabels(ticklabels)
image = ImageData()
image.read('../data/density_field_000')
image.interpolateneg()
image.replaceneg()