def td(ty,t,E,rho,sigma,wdir):
D = pp.pload(t,w_dir=wdir)
if ty == 'flux':
for k in range(D.rho.shape[2]):
for j in range(D.rho.shape[1]):
for i in range(D.rho.shape[0]):
# if (i-D.rho.shape[0]/2)**2+(j-D.rho.shape[1]/2)**2+(k-D.rho.shape[2]/2)**2 > 30**2:
# D.rho[k,j,i] = rho
if D.rho[k,j,i] > 10:
D.rho[k,j,i] = rho
flux = D.rho*(D.bx1**2+D.bx2**2+D.bx3**2)**1.25*(D.vx1**2+D.vx2**2+D.vx3**2)**0
flux = (flux-np.mean(flux))*1+np.mean(flux)*1
flux = flux.sum(axis=0)
flux = nd.gaussian_filter(flux,sigma=(sigma,sigma),order=0)
fig = plt.figure(figsize=(7,6))
ax = fig.add_subplot(111)
neg = ax.imshow(np.log10(flux).T,origin='lower',extent=[D.x2[0],D.x2[-1],D.x3[0],D.x3[-1]])
levels = np.arange(-5.5, -4, 0.5)
plt.contour(np.log10(flux).T, levels,
origin='lower',
linewidths=2,
extent=[D.x1[0],D.x1[-1],D.x2[0],D.x2[-1]])
cbar = fig.colorbar(neg,ax=ax)
cbar.set_label(r'log(S)')
ax.set_xlabel('l offset (pc)')
ax.set_ylabel('b offset (pc)')
ax.set_title(r't='+str(t)+r'$\ \mathregular{\rho}$='+str(rho)+' E='+str(E))
fig.subplots_adjust(top=0.9,bottom=0.1,left=0.11,right=0.97)
fig.savefig('t'+str(t)+'_density'+str(rho)+'_E'+str(E)+'.eps')
fig.clear()
ax = fig.add_subplot(111)
ax.plot((np.rot90(np.eye(len(flux)))*flux.T).sum(axis=0))
ax.set_xlim(0,256)
fig.savefig('change.eps')
elif ty == 'rho':
# t = 850
fig = plt.figure(figsize=(7,6))
ax = fig.add_subplot(111)
neg = ax.imshow(np.log10(D.rho[:,:,128]).T,origin='lower',extent=[D.x2[0],D.x2[-1],D.x3[0],D.x3[-1]])
cbar = fig.colorbar(neg,ax=ax)
cbar.set_label(r'log($\mathregular{\rho/cm^{-3}}$)')
ax.set_xlabel('x offset (pc)')
ax.set_ylabel('y offset (pc)')
ax.set_title(r't='+str(t)+r'$\ \mathregular{\rho}$='+str(rho)+' E='+str(E))
fig.subplots_adjust(top=0.9,bottom=0.1,left=0.11,right=0.97)
T = pp.Tools()
newdims = 2*(20,)
Xmesh, Ymesh = np.meshgrid(D.x2.T,D.x3.T)
xcong = T.congrid(Xmesh,newdims,method='linear')
ycong = T.congrid(Ymesh,newdims,method='linear')
velxcong = T.congrid(D.bx1[:,:,128].T,newdims,method='linear')
velycong = T.congrid(D.bx2[:,:,128].T,newdims,method='linear')
plt.gca().quiver(xcong, ycong, velxcong, velycong,color='w')
plt.show()
fig.savefig('rho-t='+str(t)+'_density='+str(rho)+'_E='+str(E)+'.eps') # Only to be saved as either .png or .jpg
# close()
else:
print(D.x1.shape)
# arr = np.meshgrid(D.x1,D.x2,D.x3)
# mlab.points3d(arr[0][0:256:8,0:256:8,0:256:8], arr[1][0:256:8,0:256:8,0:256:8], arr[2][0:256:8,0:256:8,0:256:8], D.rho[0:256:8,0:256:8,0:256:8])
vol = mlab.pipeline.volume(mlab.pipeline.scalar_field(np.log10(D.prs*D.rho)))
ctf = ColorTransferFunction()
ctf.add_hsv_point(-8, 0.8, 1, 1)
ctf.add_hsv_point(-6.5, 0.45, 1, 1)
ctf.add_hsv_point(-5.4, 0.15, 1, 1)
vol._volume_property.set_color(ctf)
vol._ctf = ctf
vol.update_ctf = True
otf = PiecewiseFunction()
otf.add_point(-8, 0)
otf.add_point(-5.7, 0.082)
otf.add_point(-5.4, 0.0)
vol._otf = otf
vol._volume_property.set_scalar_opacity(otf)
def td(ty, t, E, rho, sigma, wdir):
D = pp.pload(t, w_dir=wdir)
if ty == 'flux':
for k in range(D.rho.shape[2]):
for j in range(D.rho.shape[1]):
for i in range(D.rho.shape[0]):
# if (i-D.rho.shape[0]/2)**2+(j-D.rho.shape[1]/2)**2+(k-D.rho.shape[2]/2)**2 > 30**2:
# D.rho[k,j,i] = rho
if D.rho[k, j, i] > 10:
D.rho[k, j, i] = rho
flux = D.rho * (D.bx1**2 + D.bx2**2 +
D.bx3**2)**1.25 * (D.vx1**2 + D.vx2**2 + D.vx3**2)**0
flux = (flux - np.mean(flux)) * 1 + np.mean(flux) * 1
flux = flux.sum(axis=0)
flux = nd.gaussian_filter(flux, sigma=(sigma, sigma), order=0)
fig = plt.figure(figsize=(7, 6))
ax = fig.add_subplot(111)
neg = ax.imshow(np.log10(flux).T,
origin='lower',
extent=[D.x2[0], D.x2[-1], D.x3[0], D.x3[-1]])
levels = np.arange(-5.5, -4, 0.5)
plt.contour(np.log10(flux).T,
levels,
origin='lower',
linewidths=2,
extent=[D.x1[0], D.x1[-1], D.x2[0], D.x2[-1]])
cbar = fig.colorbar(neg, ax=ax)
cbar.set_label(r'log(S)')
ax.set_xlabel('l offset (pc)')
ax.set_ylabel('b offset (pc)')
ax.set_title(r't=' + str(t) + r'$\ \mathregular{\rho}$=' + str(rho) +
' E=' + str(E))
fig.subplots_adjust(top=0.9, bottom=0.1, left=0.11, right=0.97)
fig.savefig('t' + str(t) + '_density' + str(rho) + '_E' + str(E) +
'.eps')
fig.clear()
ax = fig.add_subplot(111)
ax.plot((np.rot90(np.eye(len(flux))) * flux.T).sum(axis=0))
ax.set_xlim(0, 256)
fig.savefig('change.eps')
elif ty == 'rho':
# t = 850
fig = plt.figure(figsize=(7, 6))
ax = fig.add_subplot(111)
neg = ax.imshow(np.log10(D.rho[:, :, 128]).T,
origin='lower',
extent=[D.x2[0], D.x2[-1], D.x3[0], D.x3[-1]])
cbar = fig.colorbar(neg, ax=ax)
cbar.set_label(r'log($\mathregular{\rho/cm^{-3}}$)')
ax.set_xlabel('x offset (pc)')
ax.set_ylabel('y offset (pc)')
ax.set_title(r't=' + str(t) + r'$\ \mathregular{\rho}$=' + str(rho) +
' E=' + str(E))
fig.subplots_adjust(top=0.9, bottom=0.1, left=0.11, right=0.97)
T = pp.Tools()
newdims = 2 * (20, )
Xmesh, Ymesh = np.meshgrid(D.x2.T, D.x3.T)
xcong = T.congrid(Xmesh, newdims, method='linear')
ycong = T.congrid(Ymesh, newdims, method='linear')
velxcong = T.congrid(D.bx1[:, :, 128].T, newdims, method='linear')
velycong = T.congrid(D.bx2[:, :, 128].T, newdims, method='linear')
plt.gca().quiver(xcong, ycong, velxcong, velycong, color='w')
plt.show()
fig.savefig('rho-t=' + str(t) + '_density=' + str(rho) + '_E=' +
str(E) + '.eps') # Only to be saved as either .png or .jpg
# close()
else:
print(D.x1.shape)
# arr = np.meshgrid(D.x1,D.x2,D.x3)
# mlab.points3d(arr[0][0:256:8,0:256:8,0:256:8], arr[1][0:256:8,0:256:8,0:256:8], arr[2][0:256:8,0:256:8,0:256:8], D.rho[0:256:8,0:256:8,0:256:8])
vol = mlab.pipeline.volume(
mlab.pipeline.scalar_field(np.log10(D.prs * D.rho)))
ctf = ColorTransferFunction()
ctf.add_hsv_point(-8, 0.8, 1, 1)
ctf.add_hsv_point(-6.5, 0.45, 1, 1)
ctf.add_hsv_point(-5.4, 0.15, 1, 1)
vol._volume_property.set_color(ctf)
vol._ctf = ctf
vol.update_ctf = True
otf = PiecewiseFunction()
otf.add_point(-8, 0)
otf.add_point(-5.7, 0.082)
otf.add_point(-5.4, 0.0)
vol._otf = otf
vol._volume_property.set_scalar_opacity(otf)
