def ldinstable(nparr=None,nmhd=None,w_dir=None,**kwargs):
if nmhd == None : nmhd = 319
if nparr == None : nparr = [198,199,200]
Vref = 1.0e-5*np.sqrt((phc.G*phc.Msun*kwargs.get('Mstar',30.0))/(phc.au*kwargs.get('ul',1.0)))
print '---------------------------------------------'
print 'Mass of Star :',kwargs.get('Mstar',30)
print 'Inner disk radius :',kwargs.get('ul',1.0)
print 'UNIT VELOCITY [km/s] :',Vref
print '----------------------------------------------'
Fc = jana.Force()
Data_List=[plp.pload(nmhd,w_dir=w_dir)]
for j in nparr:
Data_List.append(plp.pload(j,w_dir=w_dir))
Vp_Dict_List =[]
for p in Data_List:
Vp_Dict_List.append(Fc.proj_force(p,p.v1,p.v2,**kwargs))
f1 = plt.figure(num=1)
ax2 = f1.add_subplot(212)
plt.plot( Vp_Dict_List[0]['Qy'], Vref*Vp_Dict_List[0]['para_flvalues'][0,:],'k--')
for q in Vp_Dict_List[1:]:
plt.plot(q['Qy'],Vref*q['para_flvalues'][0,:])
plt.xlabel(r'Distance Along the Field Line : s [AU]',labelpad=6)
plt.ylabel(r'Poloidal Velocity : $V_{\rm pol} [\rm km\, \rm s^{-1}]$',labelpad=10)
def animate_plot(w_dir=None, **kwargs):
if w_dir == None: w_dir=os.getcwd()
cdir = os.getcwd()
files = []
os.chdir(w_dir)
os.system('mkdir j_movie')
D0 = plp.pload(0,w_dir=w_dir)
fig = plt.figure(num=1,figsize=[7,7])
ax = fig.add_subplot(111)
plt.subplots_adjust(left=0.1, bottom=0.15)
frnum = kwargs.get('frames',plp.time_info(w_dir=w_dir)['time'])
for i in [0,10,25,50,150,250,319,325,340,450,638]: # 50 frames
D = plp.pload(i,w_dir=w_dir)
Ra = da.Rad_Average()
xitem = D.x1
yitem = D.x1*D.v3[:,32]
# yitem = Ra.Sigma(D,ul=1.0,urho=1.0e-9,Mstar=10.0,Gammae=5.0/3.0)
ax.cla()
if kwargs.get('pltype','normal') == 'normal':
ax.plot(xitem,yitem,'k-')
if kwargs.get('pltype','normal') == 'logx':
ax.semilogx(xitem,yitem,'k-')
if kwargs.get('pltype','normal') == 'logy':
ax.semilogy(xitem,yitem,'k-')
if kwargs.get('pltype','normal') == 'loglog':
ax.loglog(xitem,yitem,'k-')
ax.minorticks_on()
ax.set_xlabel(kwargs.get('xlabel',r'XLabel'))
ax.set_ylabel(kwargs.get('ylabel',r'YLabel'))
ax.set_title(kwargs.get('title',r'Title'))
#ax.text(90.0,1.3,r'$N_{\rm rot} = %04d$'%i)
axcolor = 'white'
axtime = plt.axes([0.1, 0.01, 0.8, 0.02], axisbg=axcolor)
axtime.cla()
stime = Slider(axtime, 'Nsteps', 0, frnum , valinit=D.time)
#plt.draw()
fname = w_dir+'j_movie/_tmp%03d.png'%i
print 'Saving frame', fname
fig.savefig(fname)
files.append(fname)
print 'Making movie animation.mpg - this make take a while'
os.system("mencoder 'mf://j_movie/_tmp*.png' -mf type=png:fps=10 -ovc lavc -lavcopts vcodec=wmv2 -oac copy -o j_movie/animation.mpg")
os.chdir(cdir)
def compare_images(*args,**kwargs):
I = plp.Image()
fig = plt.figure()
fig.add_subplot(121)
cdir = os.getcwd()+'/'
if kwargs.get('pureimages',False)==True:
var1 = args[1]
var2 = args[2]
Data1 = args[0]
Data2 = args[0]
else:
if kwargs.get('log',False)==True:
var1 = np.log(args[0].__getattribute__(args[2]))
var2 = np.log(args[1].__getattribute__(args[2]))
Data1 = args[0]
Data2 = args[1]
else:
var1 = args[0].__getattribute__(args[2])
var2 = args[1].__getattribute__(args[2])
Data1 = args[0]
Data2 = args[1]
if kwargs.get('scalefact')==None:
scaling = 1.0
else:
scaling = kwargs.get('scalefact')
I.pldisplay(scaling*var1,x1 = Data1.x1,x2=Data1.x2,cbar=(True,'vertical'))
plt.axis([0.0,52.0,0.0,152.0])
if kwargs.get('flines',(False,[3.0,6.0,9.0],[0.01,0.01,0.01]))[0] == True:
I.myfieldlines(args[0],kwargs.get('flines')[1],kwargs.get('flines')[2],stream=True,colors='r',ls='-',lw=2.0)
if kwargs.get('MHDflines',(False,319,cdir))[0] == True:
data1MHD = plp.pload(kwargs.get('MHDflines',(True,319,cdir))[1],w_dir=kwargs.get('MHDflines',(True,319,cdir))[2])
I.myfieldlines(data1MHD,kwargs.get('flines')[1],kwargs.get('flines')[2],stream=True,colors='w',ls='--',lw=2.0)
plt.xlabel(kwargs.get('label1',2*['Xlabel'])[0])
plt.ylabel(kwargs.get('label2',2*['Ylabel'])[0])
plt.title(kwargs.get('title',2*['Title'])[0])
fig.add_subplot(122)
I.pldisplay(scaling*var2,x1 = Data2.x1,x2=Data2.x2,cbar=(True,'vertical'))
plt.axis([0.0,52.0,0.0,152.0])
if kwargs.get('flines',(False,[3.0,6.0,9.0],[0.01,0.01,0.01]))[0] == True:
I.myfieldlines(args[1],kwargs.get('flines')[1],kwargs.get('flines')[2],stream=True,colors='r',ls='-',lw=2.0)
if kwargs.get('MHDflines',(False,319,cdir))[0] == True:
data2MHD = plp.pload(kwargs.get('MHDflines',(True,319,cdir))[1],w_dir=kwargs.get('MHDflines',(True,319,cdir))[2])
I.myfieldlines(data2MHD,kwargs.get('flines')[1],kwargs.get('flines')[2],stream=True,colors='w',ls='--',lw=2.0)
plt.xlabel(kwargs.get('label1',2*['Xlabel'])[1])
plt.ylabel(kwargs.get('label2',2*['Ylabel'])[1])
plt.title(kwargs.get('title',2*['Title'])[1])
def single(ty, t, E, rho, sigma, wdir):
#D = pp.pload(nlinf['nlast'],w_dir=wdir) # Loading the data into a pload object D
D = pp.pload(t / 1000 - 1, w_dir=wdir)
flux = D.rho * (D.bx1**2 + D.bx2**2)**1.25 * (D.vx1**2 + D.vx2**2)**0
flux = (flux - np.mean(flux)) * 1 + np.mean(flux) * 1
flux = nd.gaussian_filter(flux, sigma=(sigma, sigma), order=0)
if ty == 'flux':
fig = plt.figure(figsize=(7, 6))
ax = fig.add_subplot(111)
neg = ax.imshow(np.log10(flux).T,
origin='lower',
extent=[D.x1[0], D.x1[-1], D.x2[0], D.x2[-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') # Only to be saved as either .png or .jpg
else:
fig = plt.figure(figsize=(7, 6))
ax = fig.add_subplot(111)
neg = ax.imshow(np.log10(D.rho).T,
origin='lower',
extent=[D.x1[0], D.x1[-1], D.x2[0], D.x2[-1]])
cbar = fig.colorbar(neg, ax=ax)
cbar.set_label(r'log($\mathregular{\rho/cm^{-3}}$)')
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)
T = pp.Tools()
newdims = 2 * (20, )
Xmesh, Ymesh = np.meshgrid(D.x1.T, D.x2.T)
xcong = T.congrid(Xmesh, newdims, method='linear')
ycong = T.congrid(Ymesh, newdims, method='linear')
velxcong = T.congrid(D.vx1.T, newdims, method='linear')
velycong = T.congrid(D.vx2.T, newdims, method='linear')
plt.gca().quiver(xcong,
ycong,
velxcong,
velycong,
color='black',
scale=0.2)
# plt.show()
fig.savefig('rho-t=' + str(t) + ' density=' + str(rho) + ' E=' +
str(E) + '.eps') # Only to be saved as either .png or .jpg
def loaddata(self):
try:
int(self.enstep.get().strip().split()[0])
except (ValueError, IndexError):
print "Specify the proper value of Nstep"
else:
mynstep=int(self.enstep.get())
self.D = pp.pload(mynstep,w_dir=self.wdir,datatype=self.datatype)
self.LoadedNstep.set("Loaded Nstep = "+self.enstep.get())
self.PresentTime.set("Present Time = "+str(self.D.time_info(self.datatype)['time'])+" [cu]")
return self.D
def loaddata(self):
try:
int(self.enstep.get().strip().split()[0])
except (ValueError, IndexError):
print "Specify the proper value of Nstep"
else:
mynstep = int(self.enstep.get())
self.D = pp.pload(mynstep, w_dir=self.wdir)
self.LoadedNstep.set("Loaded Nstep = " + self.enstep.get())
self.PresentTime.set("Present Time = " +
str(self.D.time_info()['time']) + " [cu]")
return self.D
def stellar_wind(pcdata,width,rho_constant,wdir,number,r):
import pyPLUTO as pp
nlinf = pp.nlast_info(w_dir=wdir)
D = pp.pload(number,w_dir=wdir)
print(D.rho.shape)
for i in range(width):
for j in range(width):
if (i-256)**2+(j-256)**2<r**2:
pcdata[i,j] = D.rho[i,j]
return pcdata
def stellar_wind(pcdata, width, rho_constant, wdir, number, r):
import pyPLUTO as pp
nlinf = pp.nlast_info(w_dir=wdir)
D = pp.pload(number, w_dir=wdir)
print(D.rho.shape)
for i in range(width):
for j in range(width):
if (i - 256)**2 + (j - 256)**2 < r**2:
pcdata[i, j] = D.rho[i, j]
return pcdata
def temp(wdir):
D = pp.pload(30,w_dir=wdir)
I = pp.Image()
flux=D.prs*1.67e-7/D.rho/1000000/1.3806488e-23
# print flux.shape
# flux= (flux-np.mean(flux))*5+np.mean(flux)*5.3
# flux=nd.gaussian_filter(flux,sigma=(4,4),order=0)
I.pldisplay(D, flux,x1=D.x1, \
x2=D.x2,label1='l offset (pc)',label2='b offset (pc)', \
title='Temperature',
cbar=(True,'vertical'))
# savefig('MHD_Blast.png') # Only to be saved as either .png or .jpg
plt.show()
def zetaper(xfl=None,nmhd=None,nrad=None,w_dir=None):
if xfl==None :
print "The time step for MHD run not given - Default xfl=5.0"
xfl=5.0
if nmhd == None :
print "The time step for MHD run not given - Default 319"
nmhd = 319
if nrad == None :
print "The time step for RAD run not given - Default 319"
nrad = 319
if w_dir==None :
print w_dir
dMHD = plp.pload(nmhd,w_dir=w_dir)
dRAD = plp.pload(nrad,w_dir=w_dir)
I = plp.Image()
fldictMHD = I.field_line(dMHD.b1,dMHD.b2,dMHD.x1,dMHD.x2,dMHD.dx1,dMHD.dx2,xfl,0.01)
ZetaMHD = (180.0/np.pi)*np.arctan2(fldictMHD.get('qx'),fldictMHD.get('qy'))
fldictRAD = I.field_line(dRAD.b1,dRAD.b2,dRAD.x1,dRAD.x2,dRAD.dx1,dRAD.dx2,xfl,0.01)
ZetaRAD = (180.0/np.pi)*np.arctan2(fldictRAD.get('qx'),fldictRAD.get('qy'))
nlenmin = np.min([len(ZetaRAD),len(ZetaMHD)])
Diffinper = 100.0*((ZetaRAD[:nlenmin]-ZetaMHD[:nlenmin])/(ZetaMHD[:nlenmin]))
return [dMHD.x2[:nlenmin].T,Diffinper.T]
def plot_jet():
set_pretty()
nlinf = pp.nlast_info()
D = pp.pload(nlinf['nlast'])
I = pp.Image()
I.pldisplay(D, D.rho,x1=D.x1,x2=D.x2,label1='$x$',label2='$y$',
title=r'Density $\rho$ [MHD jet]',cbar=(True,'vertical'),figsize=[7,12])
print (I.pldisplay.__doc__)
# Code to plot field lines. Requires 2 arrays xarr and yarr as
# the starting point of integration i.e. x and y co-ordinate of the field point.
I.myfieldlines(D,linspace(D.x1.min(),D.x1.max(),10),linspace(D.x2.min(),D.x2.min(),10),
colors='w',ls='-',lw=1.5)
savefig('jet_final.png') # Only to be saved as either .png or .jpg
def stellar_wind(wdir, number):
import pyPLUTO as pp
pp.nlast_info(w_dir=wdir)
D = pp.pload(number, w_dir=wdir)
print(D.rho.shape)
rho = D.rho
vx1 = D.vx1
vx2 = D.vx2
rho = np.transpose(rho)
vx1 = np.transpose(vx1)
vx2 = np.transpose(vx2)
return rho, vx1, vx2
def stellar_wind(wdir,number):
import pyPLUTO as pp
pp.nlast_info(w_dir=wdir)
D = pp.pload(number,w_dir=wdir)
print(D.rho.shape)
rho = D.rho
vx1 = D.vx1
vx2 = D.vx2
rho = np.transpose(rho)
vx1 = np.transpose(vx1)
vx2 = np.transpose(vx2)
return rho,vx1,vx2
def single(ty,t,E,rho,sigma,wdir):
#D = pp.pload(nlinf['nlast'],w_dir=wdir) # Loading the data into a pload object D
D = pp.pload(t/1000-1,w_dir=wdir)
flux=D.rho*(D.bx1**2+D.bx2**2)**1.25*(D.vx1**2+D.vx2**2)**0
flux= (flux-np.mean(flux))*1+np.mean(flux)*1
flux=nd.gaussian_filter(flux,sigma=(sigma,sigma),order=0)
if ty == 'flux':
fig = plt.figure(figsize=(7,6))
ax = fig.add_subplot(111)
neg = ax.imshow(np.log10(flux).T,origin='lower',extent=[D.x1[0],D.x1[-1],D.x2[0],D.x2[-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') # Only to be saved as either .png or .jpg
else:
fig = plt.figure(figsize=(7,6))
ax = fig.add_subplot(111)
neg = ax.imshow(np.log10(D.rho).T,origin='lower',extent=[D.x1[0],D.x1[-1],D.x2[0],D.x2[-1]])
cbar = fig.colorbar(neg,ax=ax)
cbar.set_label(r'log($\mathregular{\rho/cm^{-3}}$)')
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)
T = pp.Tools()
newdims = 2*(20,)
Xmesh, Ymesh = np.meshgrid(D.x1.T,D.x2.T)
xcong = T.congrid(Xmesh,newdims,method='linear')
ycong = T.congrid(Ymesh,newdims,method='linear')
velxcong = T.congrid(D.bx1.T,newdims,method='linear')
velycong = T.congrid(D.bx2.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
def multiple(wdir):
#D = pp.pload(nlinf['nlast'],w_dir=wdir) # Loading the data into a pload object D
for i in range(30):
D = pp.pload(i,w_dir=wdir)
#print D.rho.shape[0]
I = pp.Image()
I.pldisplay(D, np.log(D.rho),x1=D.x1, \
x2=D.x2,label1='l offset (pc)',label2='b offset (pc)', \
title=r'$Density (ln(cm^{-3})) - Velocity (km/s)$ '+str(i*1000)+' years',
cbar=(True,'vertical'))
T = pp.Tools()
newdims = 2*(20,)
Xmesh, Ymesh = np.meshgrid(D.x1.T,D.x2.T)
xcong = T.congrid(Xmesh,newdims,method='linear')
ycong = T.congrid(Ymesh,newdims,method='linear')
velxcong = T.congrid(D.vx1.T,newdims,method='linear')
velycong = T.congrid(D.vx2.T,newdims,method='linear')
plt.gca().quiver(xcong, ycong, velxcong, velycong,color='w')
plt.savefig('20_'+str(i)+'.png') # Only to be saved as either .png or .jpg
plt.close()
def analyze(ty,t,E,rho,sigma,wdir):
D = pp.pload(t/1000-1,w_dir=wdir)
flux = D.rho*(D.bx1**2+D.bx2**2)**1.25*(D.vx1**2+D.vx2**2)**0
flux = nd.gaussian_filter(flux,sigma=(sigma,sigma),order=0)
mid = np.rot90(flux)
mid = mid*np.eye(mid.shape[0])
mid = mid.sum(axis=0)
fig = plt.figure(figsize=(7,6))
ax = fig.add_subplot(111)
ax.plot(D.x1*np.sqrt(2),mid)
ax.set_xlabel('offset (pc)')
ax.set_ylabel('S')
ax.set_title(r't='+str(t)+r'$\ \mathregular{\rho}$='+str(rho)+' E='+str(E))
ax.set_xlim(D.x1.min()*np.sqrt(2),D.x1.max()*np.sqrt(2))
plt.ticklabel_format(style='sci', axis='y', scilimits=(0,0))
# ax.yaxis.set_major_formatter(FormatStrFormatter('%.5f'))
fig.subplots_adjust(top=0.9,bottom=0.1,left=0.11,right=0.97)
fig.savefig('flux_t='+str(t)+' density='+str(rho)+' E='+str(E)+'.eps') # Only to be saved as either .png or .jpg
def Quantities(self, *args,**kwargs):
RefVel = np.sqrt(phc.G*phc.Msun/phc.au)*np.sqrt(kwargs.get('Mstar',10.0)/kwargs.get('ul',1.0))
nstep = args[0]+1
wdir = args[1]
Timeyr = np.zeros(nstep)
Mdisk = np.zeros(nstep)
dV = np.zeros(nstep)
IntEdisk = np.zeros(nstep)
Sigdisk = np.zeros(nstep)
Csdisk = np.zeros(nstep)
Omdisk = np.zeros(nstep)
Qdisk = np.zeros(nstep)
for ns in range(nstep):
D = pp.pload(ns,w_dir=wdir)
dV = np.zeros(D.rho.shape)
Sigma = np.zeros(D.rho.shape)
if (D.n3 == 1):
dV = D.x1[:,np.newaxis]*D.x1[:,np.newaxis]*np.sin(D.x2[np.newaxis,:])*D.dx1[:,np.newaxis]*D.dx2[np.newaxis,:]*D.dx3
Sigma = (D.rho*dV)*(D.rho*D.x1[:,np.newaxis]*np.sin(D.x2[np.newaxis,:])*D.dx2[np.newaxis,:])
else:
dV = D.x1[:,np.newaxis,np.newaxis]*D.x1[:,np.newaxis,np.newaxis]*np.sin(D.x2[np.newaxis,:,np.newaxis])*D.dx1[:,np.newaxis,np.newaxis]*D.dx2[np.newaxis,:,np.newaxis]*D.dx3[np.newaxis,np.newaxis,:]
Sigma = (D.rho*dV)*(D.rho*D.x1[:,np.newaxis,np.newaxis]*np.sin(D.x2[np.newaxis,:,np.newaxis])*D.dx2[np.newaxis,:,np.newaxis])
Csound = (D.rho*dV)*(np.sqrt(kwargs.get('Gammae',1.0001)*(D.pr/D.rho)))
IntE = (Csound*Csound)/(kwargs.get('Gammae',1.0001)*((kwargs.get('Gammae',1.0001)-1.0)))
Omega = (D.rho*dV)*(D.v3/D.x1[:,np.newaxis,np.newaxis])
Timeyr[ns] = ns*2.0*np.pi*((kwargs.get('ul',1.0)*phc.au)/RefVel)/phc.yr
Mdisk[ns] = ((D.rho*dV).sum())*((kwargs.get('urho',1.0e-8)*(kwargs.get('ul',1.0)*phc.au)**3)/phc.Msun)
Sigdisk[ns] = (1.0/(D.rho*dV).sum())*(Sigma.sum())*(kwargs.get('urho',1.0e-8)*(kwargs.get('ul',1.0)*phc.au))
Csdisk[ns] = (1.0/(D.rho*dV).sum())*(Csound.sum())*RefVel
IntEdisk[ns] = (1.0/(D.rho*dV).sum())*(IntE.sum())*(RefVel**2)
Omdisk[ns] = (1.0/(D.rho*dV).sum())*(Omega.sum())*(RefVel/(kwargs.get('ul',1.0)*phc.au))
Qdisk[ns] = Csdisk[ns]*Omdisk[ns]/(2.0*np.pi*phc.G*Sigdisk[ns])
return {'Time':Timeyr,'Mdisk':Mdisk,'Sigma':Sigdisk,'Csound':Csdisk*1.0e-5,'Omega':Omdisk,'ToomreQ':Qdisk, 'IntE':IntEdisk}
def stellar_wind(wdir, number):
import pyPLUTO as pp
pp.nlast_info(w_dir=wdir)
D = pp.pload(number, w_dir=wdir)
print(D.rho.shape)
rho = D.rho
bx1 = D.bx1
bx2 = D.bx2
bx3 = D.bx3
vx1 = D.vx1
vx2 = D.vx2
vx3 = D.vx3
rho = np.transpose(rho)
bx1 = np.transpose(bx1)
bx2 = np.transpose(bx2)
bx3 = np.transpose(bx3)
vx1 = np.transpose(vx1)
vx2 = np.transpose(vx2)
vx3 = np.transpose(vx3)
return rho, bx1, bx2, bx3, vx1, vx2, vx3
def stellar_wind(wdir,number):
import pyPLUTO as pp
pp.nlast_info(w_dir=wdir)
D = pp.pload(number,w_dir=wdir)
print(D.rho.shape)
rho = D.rho
bx1 = D.bx1
bx2 = D.bx2
bx3 = D.bx3
vx1 = D.vx1
vx2 = D.vx2
vx3 = D.vx3
rho = np.transpose(rho)
bx1 = np.transpose(bx1)
bx2 = np.transpose(bx2)
bx3 = np.transpose(bx3)
vx1 = np.transpose(vx1)
vx2 = np.transpose(vx2)
vx3 = np.transpose(vx3)
return rho,bx1,bx2,bx3,vx1,vx2,vx3
import os
import sys
from numpy import *
from matplotlib.pyplot import *
import pyPLUTO as pp
wdir = './'
nlinf = pp.nlast_info(w_dir=wdir)
#D = pp.pload(nlinf['nlast'],w_dir=wdir) # Loading the data into a pload object D
D = pp.pload(10,w_dir=wdir)
print D.rho.shape
#f=open('rho.txt','r+')
#temp=f.read()
#temp=
I = pp.Image()
I.pldisplay(D, (D.bx1**2+D.bx2**2)**0.5,x1=D.x1,x2=D.x2,label1='x',label2='y',title=r'Density $\rho$ [MHD_Blast test]',cbar=(True,'vertical'))
#savefig('MHD_Blast.png') # Only to be saved as either .png or .jpg
show()
def loaddata(self):
mynstep=int(self.enstep.get())
self.D = pp.pload(mynstep,w_dir=self.wdir)
return self.D
#Compute how long the simulation takes
start_time = time.time()
start = np.array((100, 75, 20, 14, 5, 2))
amp = np.array((0.005, 0.02, 0.1, 0.1, 0.9, 2.5))
mach = ((0.24, 0.43, 0.76, 0.90, 1.25, 2.1))
no_bins = 200
for i in xrange(0, start.size):
#Load data files
#Declare all parameters and filenames, file location
filedir = '/mnt/lustre/ug4/ugrajs/fiducial_runs/256/amp' + str(
int(amp[i] * 10000)).rjust(5, '0') + '/run1/'
data = pp.pload(start[i], w_dir=filedir)
Rho_mean = np.mean(data.rho)
Prs_mean = np.mean(data.prs)
Rhon = np.log(np.ndarray.flatten(data.rho) / Rho_mean)
Prsn = np.log(np.ndarray.flatten(data.prs) / Prs_mean)
H = np.zeros((no_bins, no_bins))
H, xedges, yedges = np.histogram2d(Rhon,
Prsn,
bins=(no_bins, no_bins),
normed=True)
x_bin_sizes = (xedges[1:] - xedges[:-1]).reshape((1, no_bins))
y_bin_sizes = (yedges[1:] - yedges[:-1]).reshape((no_bins, 1))
pdf = (H * (x_bin_sizes * y_bin_sizes))
X, Y = 0.5 * (xedges[1:] + xedges[:-1]), 0.5 * (yedges[1:] + yedges[:-1])
import os
import sys
from numpy import *
from matplotlib.pyplot import *
import pyPLUTO as pp
#To run this example it is suggested to get data in 2D using pluto_01.ini and set the data in flt datatype instead of dbl.h5
plutodir = os.environ['PLUTO_DIR']
wdir = plutodir + '/Test_Problems/HD/Stellar_Wind/'
nlinf = pp.nlast_info(w_dir=wdir, datatype='float')
D = pp.pload(nlinf['nlast'], w_dir=wdir,
datatype='float') # Loading the data into a pload object D.
I = pp.Image()
I.pldisplay(D,
log10(D.rho),
x1=D.x1,
x2=D.x2,
label1='x',
label2='y',
title=r'Log Density $\rho$ [Stellar Wind]',
cbar=(True, 'vertical'),
figsize=[8, 12])
# Code to plot arrows. --> Spacing between the arrow can be adjusted by modifying the newdims tuple of conrid function.
T = pp.Tools()
newdims = 2 * (20, )
Xmesh, Ymesh = meshgrid(D.x1.T, D.x2.T)
xcong = T.congrid(Xmesh, newdims, method='linear')
import os
import sys
from numpy import *
from matplotlib.pyplot import *
import pyPLUTO as pp
plutodir = os.environ['PLUTO_DIR']
wdir = plutodir + '/Test_Problems/HD/Sod/'
nlinf = pp.nlast_info(w_dir=wdir)
D = pp.pload(nlinf['nlast'],
w_dir=wdir) # Loading the data into a pload object D.
f1 = figure()
ax1 = f1.add_subplot(111)
plot(D.x1, D.rho, 'r', D.x1, D.prs, 'k', D.x1, D.vx1, 'g')
xlabel(r'x')
ylabel(r'$\rho$ [red], P [black], $V_{\rm x}$ [green]')
title(r'Sod shock Tube test')
axis([0.0, 1.0, -0.2, 1.2])
savefig('sod_1.pdf')
show()
k3 = np.zeros((no_files, no_bins))
Ek3 = np.zeros((no_files, no_bins))
for j in xrange(no_files):
filedir = '/mnt/lustre/phy/phyprtek/RAJ_RUNS/fiducial_data/amp' + str(
int(amp[i] * 10000)).rjust(5, '0') + '/run' + str(
int(start[i][:, 0][j])) + '/'
fileno = str(int(start[i][:, 1][j] / time_step)).rjust(4, '0')
filename = filedir + 'Rhoks' + str(fileno) + '.txt'
data = np.loadtxt(filename, usecols=(0, 1, 2))
k2[j] = data[:, 0]
Ek2[j] = data[:, 1]
filename = filedir + 'Prsk' + str(fileno) + '.txt'
data = np.loadtxt(filename, usecols=(0, 1, 2))
k3[j] = data[:, 0]
Ek3[j] = data[:, 1]
all_data = pp.pload(int(start[i][:, 1][j] / time_step + 0.5),
w_dir=filedir)
Ek3_mean = np.average(all_data.prs)
Ek3[j] = Ek3[j] / (Ek3_mean**2.)
#Calculate average energy injection
k2 = k2[0]
ratiok = Ek2 * cs[i]**2. / Ek1
del_ratiok = np.std(ratiok, 0)
ratio_k = np.average(ratiok, 0)
#Calculate standard deviation and mean
del_Ek1 = np.std(Ek1, 0)
Ek1 = np.average(Ek1, 0)
del_Ek2 = np.std(Ek2, 0)
Ek2 = np.average(Ek2, 0)
del_Ek3 = np.std(Ek3, 0)
"""
Created on Fri Aug 10 12:17:34 2018
@author: tedoreve
"""
import numpy as np
import pyPLUTO as pp
import plot_flux as pf
if __name__ == '__main__':
t = 6
sigma = 1.0
vindex = 0.0
i = 1
wdir = './'
nlinf = pp.nlast_info(w_dir=wdir)
#==============================================================================
D = pp.pload(t,w_dir=wdir)
xlabel = 'x (pc)'
ylabel = 'y (pc)'
name = 't'+str(t)+'_xy.eps'
print(D.rho.shape,D.bx1.shape)
b = np.fromfile("a.bin",dtype = 'float64')
b.shape = [128,128,128,10]
for n in range(10):
pf.plot(D,i,np.log(b[:,:,:,n]),xlabel,ylabel,'t'+str(t)+str(n)+'_xy.png',sigma)
start = np.array((10., 51., 10., 42., 10.4, 42., 15.))
no_bins = 200
wdir = ('tabulated_cooling/256/k0-2/', 'tabulated_cooling/256/k12/',
'thermal_heating/256/tabulated_cooling/F5e-1/k0-2/',
'thermal_heating/256/tabulated_cooling/F5e-1/k12/', 'no_turb/2e-1/',
'turb_perturb/DkHC2e-1/', 'turb_perturb/DBh2e-1/F5e-1/')
labels = ('Tl', 'Th', 'Bl', 'Bh', 'QD', 'TDh', 'BDh')
time_step = 0.2
for i in xrange(0, start.size):
#Load data files
#Declare all parameters and filenames, file location
filedir = '/mnt/lustre/ug4/ugrajs/cooling/' + wdir[i]
data = pp.pload(int(start[i] / time_step), w_dir=filedir)
Rho_mean = np.mean(data.rho)
Prs_mean = np.mean(data.prs)
Rhon = np.log(np.ndarray.flatten(data.rho) / Rho_mean)
#print np.average(Rhon)
Prsn = np.log(np.ndarray.flatten(data.prs) / Prs_mean)
H = np.zeros((no_bins, no_bins))
H, xedges, yedges = np.histogram2d(Rhon,
Prsn,
bins=(no_bins, no_bins),
normed=True)
x_bin_sizes = (xedges[1:] - xedges[:-1]).reshape((1, no_bins))
y_bin_sizes = (yedges[1:] - yedges[:-1]).reshape((no_bins, 1))
pdf = (H * (x_bin_sizes * y_bin_sizes))
#Declare all parameters and filenames, file location
k1 = np.zeros((no_files, no_bins))
Ek1 = np.zeros((no_files, no_bins))
filedir = '/mnt/lustre/phy/phyprtek/RAJ_RUNS/cooling_data/' + wdir[i]
for j in xrange(no_files):
fileno = str(int(start[i] / time_step + 0.5 + j)).rjust(4, '0')
filenamex1 = filedir + 'Ekx' + str(fileno) + '.txt'
filenamex2 = filedir + 'Eky' + str(fileno) + '.txt'
filenamex3 = filedir + 'Ekz' + str(fileno) + '.txt'
datax1 = np.loadtxt(filenamex1, usecols=(0, 1, 2))
datax2 = np.loadtxt(filenamex2, usecols=(0, 1, 2))
datax3 = np.loadtxt(filenamex3, usecols=(0, 1, 2))
k1[j] = datax1[:, 0]
Ek1[j] = datax1[:, 1] + datax2[:, 1] + datax3[:, 1]
all_data = pp.pload(int(start[i] / time_step + 0.5 + j), w_dir=filedir)
cs = np.sqrt(np.mean(gamma * all_data.prs / all_data.rho))
Ek1[j] = Ek1[j] / cs**2.
k1 = k1[0]
k2 = np.zeros((no_files, no_bins))
Ek2 = np.zeros((no_files, no_bins))
for j in xrange(no_files):
fileno = str(int(start[i] / time_step + 0.5 + j)).rjust(4, '0')
filename = filedir + 'Rhoks' + str(fileno) + '.txt'
data = np.loadtxt(filename, usecols=(0, 1, 2))
k2[j] = data[:, 0]
Ek2[j] = data[:, 1]
#Calculate average energy injection
k2 = k2[0]
import os
import sys
from numpy import *
from matplotlib.pyplot import *
import pyPLUTO as pp
plutodir = os.environ['PLUTO_DIR']
wdir = plutodir+'/Test_Problems/HD/Rayleigh_Taylor/'
D0 = pp.pload(0,w_dir=wdir)
D1 = pp.pload(1,w_dir=wdir) # Loading the data into a pload object D.
D2 = pp.pload(2,w_dir=wdir)
## SMART WAY##
I = pp.Image()
I.multi_disp(D0.rho,D1.rho,D2.rho,x1=D0.x1,x2=D0.x2,Ncols=3,label1=3*['x'],
label2=3*['y'],title=[r'$\tau=0$',r'$\tau=1$',r'$\tau=2$'],
cbar=(True,'vertical','each'),figsize=[12,7])
##BRUTE FORCE WAY##
## f1 = figure(figsize=[12,7])
## ax1 = f1.add_subplot(131)
## pcolormesh(D0.x1,D0.x2,D0.rho.T)
## colorbar()
## ax1.set_xlabel(r'x')
## ax1.set_ylabel(r'y')
## ax1.axis([-0.5,0.5,0.0,4.0])
## ax1.set_aspect('equal')
## ax1.set_title(r'$\tau$ = 0')
import numpy as np
import math
import matplotlib.pyplot as plt
import os, sys
sys.path.append(
"/Users/prateek/Desktop/codes/Galaxy-Cluster-PLUTO/Tools/pyPLUTO/pyPLUTO/"
) #access linux environment variable
import pyPLUTO as pp
# script to make 2-D snapshots of various quantities
D = pp.pload(0)
x = D.x1
y = D.x2
for i in range(0, 1):
#plt.clf()
D = pp.pload(i)
plt.subplot(111)
plt.contourf(x, y, D.vx1)
#plt.xlim(0.0, 5.0)
#plt.ylim(-5.0, 5.0)
plt.axes().set_aspect('equal')
plt.colorbar()
plt.show()
#fname = 'log10d_%03d.png'%i
#print 'Saving frame', i
#plt.savefig(fname)
import os
import sys
from numpy import *
from matplotlib.pyplot import *
import pyPLUTO as pp
plutodir = os.environ['PLUTO_DIR']
wdir = plutodir+'/Test_Problems/MHD/Jet/'
nlinf = pp.nlast_info(w_dir=wdir)
D = pp.pload(nlinf['nlast'],w_dir=wdir) # Loading the data into a pload object D.
I = pp.Image()
I.pldisplay(D.rho,x1=D.x1,x2=D.x2,label1='x',label2='y',title=r'Density $\rho$ [MHD jet]',cbar=(True,'vertical'),figsize=[7,12])
#Code to plot field lines. Requires 2 arrays xarr and yarr as the starting point of integration i.e. x and y co-ordinate of the field point.
I.myfieldlines(D,linspace(D.x1.min(),D.x1.max(),10),linspace(D.x2.min(),D.x2.min(),10),colors='w',ls='-',lw=1.5)
savefig('jet_final.png') # Only to be saved as either .png or .jpg
show()
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 plot_surface_brightness(timestep,
unit_values,
run_dirs,
filename,
redshift=0.1,
beamsize=5 * u.arcsec,
showbeam=True,
xlim=(-200, 200),
ylim=(-750, 750), #actually z in arcsec
xticks=None,
pixel_size=1.8 * u.arcsec,
beam_x=0.8,
beam_y=0.8,
png=True,
contours=True,
convolve=True,
half_plane=True,
vmin=-3.0,
vmax=2.0,
no_labels=False,
with_hist=False, #set to false
):
from plutokore import radio
from numba import jit
from astropy.convolution import convolve, Gaussian2DKernel
@jit(nopython=True)
def raytrace_surface_brightness(r, theta, x, y, z, raytraced_values, original_values):
phi = 0
rmax = np.max(r)
thetamax = np.max(theta)
x_half_step = (x[1] - x[0]) * 0.5
pi2_recip = (1 / (2 * np.pi))
visited = np.zeros(original_values.shape)
for x_index in range(len(x)):
for z_index in range(len(z)):
visited[:,:] = 0
for y_index in range(len(y)):
# Calculate the coordinates of this point
ri = np.sqrt(x[x_index] **2 + y[y_index] ** 2 + z[z_index] ** 2)
if ri == 0:
continue
if ri > rmax:
continue
thetai = np.arccos(z[z_index] / ri)
if thetai > thetamax:
continue
phii = 0 # Don't care about φi!!
chord_length = np.abs(np.arctan2(y[y_index], x[x_index] + x_half_step) - np.arctan2(y[y_index], x[x_index] - x_half_step))
# Now find index in r and θ arrays corresponding to this point
r_index = np.argmax(r>ri)
theta_index = np.argmax(theta>thetai)
# Only add this if we have not already visited this cell (twice)
if visited[r_index, theta_index] <= 1:
raytraced_values[x_index, z_index] += original_values[r_index, theta_index] * chord_length * pi2_recip
visited[r_index, theta_index] += 1
#return raytraced_values
return
fig, ax = newfig(1, 1.8)
#fig, ax = figsize(10,50)
# calculate beam radius
sigma_beam = (beamsize / 2.355)
# calculate kpc per arcsec
kpc_per_arcsec = cosmo.kpc_proper_per_arcmin(redshift).to(u.kpc / u.arcsec)
# load timestep data file
d = pp.pload(timestep,w_dir = run_dir)
X1, X2 = pk.simulations.sphericaltocartesian(d)
X1 = X1 * (unit_values.length / kpc_per_arcsec).to(u.arcsec).value
X2 = X2 * (unit_values.length / kpc_per_arcsec).to(u.arcsec).value
l = radio.get_luminosity(d, unit_values, redshift, beamsize)
f = radio.get_flux_density(l, redshift).to(u.Jy).value
#sb = radio.get_surface_brightness(f, d, unit_values, redshift, beamsize).to(u.Jy)
xmax = (((xlim[1] * u.arcsec + pixel_size) * kpc_per_arcsec) / unit_values.length).si
xstep = (pixel_size * kpc_per_arcsec / unit_values.length).si
zmax = (((ylim[1] * u.arcsec + pixel_size) * kpc_per_arcsec) / unit_values.length).si
zstep = (pixel_size * kpc_per_arcsec / unit_values.length).si
ymax = max(xmax, zmax)
ystep = min(xstep, zstep)
ystep = 0.5
if half_plane:
x = np.arange(-xmax, xmax, xstep)
z = np.arange(-zmax, zmax, zstep)
else:
x = np.arange(0, xmax, xstep)
z = np.arange(0, zmax, zstep)
y = np.arange(-ymax, ymax, ystep)
raytraced_flux = np.zeros((x.shape[0], z.shape[0]))
# print(f'xlim in arcsec is {xlim[1]}, xlim in code units is {xlim[1] * u.arcsec * kpc_per_arcsec / unit_values.length}')
# print(f'zlim in arcsec is {ylim[1]}, zlim in code units is {ylim[1] * u.arcsec * kpc_per_arcsec / unit_values.length}')
# print(f'xmax is {xmax}, ymax is {ymax}, zmax is {zmax}')
# print(f'x shape is {x.shape}; y shape is {y.shape}; z shape is {z.shape}')
raytrace_surface_brightness(
r=d.x1,
theta=d.x2,
x=x,
y=y,
z=z,
original_values=f,
raytraced_values=raytraced_flux
)
raytraced_flux = raytraced_flux * u.Jy
# beam information
sigma_beam_arcsec = beamsize / 2.355
area_beam_kpc2 = (np.pi * (sigma_beam_arcsec * kpc_per_arcsec)
**2).to(u.kpc**2)
beams_per_cell = (((pixel_size * kpc_per_arcsec) ** 2) / area_beam_kpc2).si
#beams_per_cell = (area_beam_kpc2 / ((pixel_size * kpc_per_arcsec) ** 2)).si
# radio_cell_areas = np.full(raytraced_flux.shape, xstep * zstep) * (unit_values.length ** 2)
# n beams per cell
#n_beams_per_cell = (radio_cell_areas / area_beam_kpc2).si
raytraced_flux /= beams_per_cell
stddev = sigma_beam_arcsec / beamsize
beam_kernel = Gaussian2DKernel(stddev)
if convolve:
flux = convolve(raytraced_flux.to(u.Jy), beam_kernel, boundary='extend') * u.Jy
else:
flux = raytraced_flux.to(u.Jy)
#flux = radio.convolve_surface_brightness(raytraced_flux, unit_values, redshift, beamsize)
#flux = raytraced_flux
#return (x, z, flux) # x_coords, z_coords, surfb = plot_surface_brightness(...)
X1 = x * (unit_values.length / kpc_per_arcsec).to(u.arcsec).value
X2 = z * (unit_values.length / kpc_per_arcsec).to(u.arcsec).value
return (X1, X2, flux) # x_coords, z_coords, surfb = plot_surface_brightness(...)
# plot data keep
ax.set_xlim(xlim)
ax.set_ylim(ylim)
contour_color = 'k'
contour_linewidth = 0.33
#contour_levels = [-3, -1, 1, 2]
contour_levels = [-2, -1, 0, 1, 2] # Contours start at 10 μJy
#with plt.style.context('flux-plot.mplstyle'): keep
im = ax.pcolormesh(
X1,
X2,
np.log10(flux.to(u.mJy).value).T,
shading='flat',
vmin=vmin,
vmax=vmax)
im.set_rasterized(True)
if contours:
ax.contour(X1, X2, np.log10(flux.to(u.mJy).value).T, contour_levels, linewidths=contour_linewidth, colors=contour_color)
im = ax.pcolormesh(
-X1,
X2,
np.log10(flux.to(u.mJy).value).T,
shading='flat',
vmin=vmin,
vmax=vmax)
im.set_rasterized(True)
if contours:
ax.contour(-X1, X2, np.log10(flux.to(u.mJy).value).T, contour_levels, linewidths=contour_linewidth, colors=contour_color)
if not half_plane:
im = ax.pcolormesh(
X1,
-X2,
np.log10(flux.to(u.mJy).value).T,
shading='flat',
vmin=vmin,
vmax=vmax)
im.set_rasterized(True)
if contours:
ax.contour(X1, -X2, np.log10(flux.to(u.mJy).value).T, contour_levels, linewidths=contour_linewidth, colors=contour_color)
im = ax.pcolormesh(
-X1,
-X2,
np.log10(flux.to(u.mJy).value).T,
shading='flat',
vmin=vmin,
vmax=vmax)
im.set_rasterized(True)
if contours:
ax.contour(-X1, -X2, np.log10(flux.to(u.mJy).value).T, contour_levels, linewidths=contour_linewidth, colors=contour_color)
if with_hist:
div = make_axes_locatable(ax) #from mpl_toolkits.axes_grid1 import make_axes_locatable
ax_hist = div.append_axes('right', '30%', pad=0.0)
s = np.sum(flux.to(u.mJy).value, axis=0)
ax_hist.plot(np.concatenate([s, s]), np.concatenate([X2, -X2]))
ax_hist.set_yticklabels([])
if not no_labels:
(ca, div, cax) = create_colorbar(
im, ax, fig, position='right', padding=0.5)
ca.set_label(r'$\log_{10}\mathrm{mJy / beam}$')
circ = plt.Circle(
(xlim[1] * beam_x, ylim[0] * beam_y),
color='w',
fill=True,
radius=sigma_beam.to(u.arcsec).value,
alpha=0.7)
#circ.set_rasterized(True)
if showbeam:
ax.add_artist(circ)
# reset limits
if not no_labels:
ax.set_xlabel('X ($\'\'$)')
ax.set_ylabel('Y ($\'\'$)')
ax.set_aspect('equal')
if xticks is not None:
ax.set_xticks(xticks)
if no_labels:
ax.set_position([0, 0, 1, 1])
ax.set_xticks([])
ax.set_yticks([])
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.axis('off')
ax.set_aspect('equal')
if no_labels:
savefig(filename, fig, png=png, kwargs={
'bbox_inches': 'tight',
'pad_inches': 0}
)
else:
savefig(filename, fig, png=png, dpi = 300)
plt.show()
import os
import sys
from numpy import *
from matplotlib.pyplot import *
import pyPLUTO as pp
#To run this example it is suggested to get data in 2D using pluto_01.ini and set the data in flt datatype instead of dbl.h5
plutodir = os.environ['PLUTO_DIR']
wdir = plutodir+'/Test_Problems/HD/Stellar_Wind/'
nlinf = pp.nlast_info(w_dir=wdir,datatype='float')
D = pp.pload(nlinf['nlast'],w_dir=wdir,datatype='float') # Loading the data into a pload object D.
I = pp.Image()
I.pldisplay(D, log10(D.rho),x1=D.x1,x2=D.x2,label1='x',label2='y',
title=r'Log Density $\rho$ [Stellar Wind]',cbar=(True,'vertical'),figsize=[8,12])
# Code to plot arrows. --> Spacing between the arrow can be adjusted by modifying the newdims tuple of conrid function.
T = pp.Tools()
newdims = 2*(20,)
Xmesh, Ymesh = meshgrid(D.x1.T,D.x2.T)
xcong = T.congrid(Xmesh,newdims,method='linear')
ycong = T.congrid(Ymesh,newdims,method='linear')
velxcong = T.congrid(D.vx1.T,newdims,method='linear')
velycong = T.congrid(D.vx2.T,newdims,method='linear')
gca().quiver(xcong, ycong, velxcong, velycong,color='w')
savefig('stellar_wind_1.png')
show()
import os
import sys
from numpy import *
from matplotlib.pyplot import *
import pyPLUTO as pp
plutodir = os.environ['PLUTO_DIR']
wdir = plutodir + '/Test_Problems/HD/Rayleigh_Taylor/'
D0 = pp.pload(0, w_dir=wdir)
D1 = pp.pload(1, w_dir=wdir) # Loading the data into a pload object D.
D2 = pp.pload(2, w_dir=wdir)
## SMART WAY##
I = pp.Image()
I.multi_disp(D0.rho,
D1.rho,
D2.rho,
x1=D0.x1,
x2=D0.x2,
Ncols=3,
label1=3 * ['x'],
label2=3 * ['y'],
title=[r'$\tau=0$', r'$\tau=1$', r'$\tau=2$'],
cbar=(True, 'vertical', 'each'),
figsize=[12, 7])
##BRUTE FORCE WAY##
## f1 = figure(figsize=[12,7])
## ax1 = f1.add_subplot(131)
## pcolormesh(D0.x1,D0.x2,D0.rho.T)
phi=float(phi) #User input for the Azimuth angle
theta=float(theta) #User input for the polar angle
lengthunit=float(lengthunit) #User input for the length of a cell
m=int(m)
n=int(n)
#Avoid division by zero
if theta==0:
theta=1e-40
if phi==0:
phi=1e-40
#Loading dataframe
start_time=time.time()
D = pp.pload(fr,datatype="vtk")
sqz_m = int(len(D.x1)/m)
sqz_n = int(len(D.x2)/n)
lengthunit = lengthunit*sqz_m
#Determining the dimensions of the loaded data
sx=2*len(D.x1)/sqz_m
sy=2*len(D.x1)/sqz_m
sz=2*len(D.x2)/sqz_n
sr=len(D.x1)/sqz_m
print("Grid size")
print("x1: %f" %(sx))
print("x2: %f" %(sy))
print("x3: %f" %(sz))
dt = 1.0/32
#omega = 1e-3
#dt = omega * 1
nt = int (tmax/dt)
#nt=6
import pyPLUTO as pp
# Animate wave as it's advected by the background shear
for it in range (nt):
# Increment time
t += dt
# Advance radial wave number
kx -= S*dt*ky
# New wave profile
testarr = sin (kx[iy,ix]*x + ky[iy,ix]*y)
D=pp.pload(it)
sim=D.vx1[:,:,0]
f=sim
fft_test = fft_sheet (testarr, t)
sim =D.vx1[:,:,0]
fft_sim = fft_sheet (sim, t)
fft_test_p=log(abs(roll(roll(fft_test,64,axis=0),64,axis=1 ) ))
fft_sim_p=log(abs(roll(roll(fft_sim,64,axis=0),64,axis=1 ) ))
#plt.imshow(f)
# Array of Fourier amplitudes
fk = fft_sheet (f, t)
# Print out wave numbers and amplitude at ix and iy
ampl = abs (fk[iy,ix])*2/(nx*ny)
print 'kx = %.05f, ky = %.05f, ampl = %g' % (kx[iy,ix], ky[iy,ix], ampl)
fig.savefig(name)
#flux_vol = (flux-np.mean(flux))*1+np.mean(flux)*1
#==============================================================================
if __name__ == '__main__':
t = 4
sigma = 1.0
vindex = 0.0
wdir = './'
nlinf = pp.nlast_info(w_dir=wdir)
#==============================================================================
D = pp.pload(t, w_dir=wdir)
v_mean = np.mean(D.vx1**2 + D.vx2**2 + D.vx3**2)
print(np.max(D.rho), np.min(D.rho),
np.max((D.bx1**2 + D.bx2**2 + D.bx3**2)**0.5),
np.min((D.bx1**2 + D.bx2**2 + D.bx3**2)**0.5))
#print(np.sign(D.vx1**2+D.vx2**2+D.vx3**2-v_mean))
for i in range(3):
if i == 0:
xlabel = 'y (pc)'
ylabel = 'z (pc)'
name = 't' + str(t) + '_yz.eps'
# flux_vol = np.log10(D.prs/D.rho)*(D.bx2**2+D.bx3**2)**0.0*1e5**(-(np.sign(D.vx1**2+D.vx2**2+D.vx3**2-v_mean)+1)*(D.vx1**2+D.vx2**2+D.vx3**2)/2)
flux_vol = D.prs / D.rho
def loaddata(self):
mynstep = int(self.enstep.get())
self.D = pp.pload(mynstep, w_dir=self.wdir)
return self.D
start_time = time.time()
#bin_size denotes the division of k space into bins of size 2*pi*bin_size
bin_size = 1
#n is an array that stores the size of the simulation domain
n = np.array([256, 256, 256])
#Declare all parameters and filenames, file location
filedir = "/home/rajsekhar/PLUTO41_old/3D_turb/Tau_c_2/256/"
for filenumber in xrange(3, 4):
#Load data files using pp.pload
D = pp.pload(filenumber, w_dir=filedir)
#Perform fourier transform from real to complex
rho_k = (float(1) / np.prod(n)) * np.fft.rfftn(D.rho, s=n)
rho_k = np.fft.fftshift(rho_k, axes=(0, 1))
print("--- %s seconds ---" % (time.time() - start_time))
#Square and add the absolute values of Vk1, Vk2, Vk3
rhok_sq = np.square(np.absolute(rho_k))
#Write k_sq as a functional 3d array, with value at each element given by (nx/2-i)^2+(ny/2-j)^2+(k)^2
k_sq = np.fromfunction(
import os
import sys
from math import pi
from numpy import *
from matplotlib.pyplot import *
import pyPLUTO as pp
plutodir = os.environ['PLUTO_DIR']
wdir = './'
print(wdir)
nlinf = pp.nlast_info(w_dir=wdir)
print(nlinf)
for timestep in range(1, 2):
D = pp.pload(timestep,
w_dir=wdir) # Loading the data into a pload object D.
x = D.x1
y = D.x2
z = D.x3
vx = D.vx1
vy = D.vx2
vz = D.vx3
rho = D.rho
nx = size(x)
ny = size(y)
nz = size(z)
r = zeros((nx, ny, nz))
for ix in range(nx):
for iy in range(ny):
import pyPLUTO as pp wdir = './' D = pp.pload(1,w_dir=wdir) I = pp.Image() f1 = pp.figure() ax1 = f1.add_subplot(111) I.pldisplay(D.vx2,x1=D.x1,x2=D.x2,cbar=(True,'vertical'),title='Velocity') pp.show() import matplotlib.pyplot as plt plt.imshow(D.vx2) plt.show()
import pylab
import pyPLUTO as pp
import string
import numpy
# Write only every Sk cells
Sk = 8
for Mach100 in range(74, 178):
D = pp.pload(Mach100, datatype='hdf5', level=4)
# Write rho,prs,vx1,vx2,bx1,bx2 to file
Mach = Mach100 / 100.
filename = string.join(('rhoM', '{0:.2f}'.format(Mach), '.dbl'), sep='')
D.rho[0::Sk, 0::Sk].transpose().tofile(filename)
filename = string.join(('prsM', '{0:.2f}'.format(Mach), '.dbl'), sep='')
D.prs[0::Sk, 0::Sk].transpose().tofile(filename)
filename = string.join(('vx1M', '{0:.2f}'.format(Mach), '.dbl'), sep='')
D.vx1[0::Sk, 0::Sk].transpose().tofile(filename)
filename = string.join(('vx2M', '{0:.2f}'.format(Mach), '.dbl'), sep='')
D.vx2[0::Sk, 0::Sk].transpose().tofile(filename)
filename = string.join(('bx1M', '{0:.2f}'.format(Mach), '.dbl'), sep='')
D.bx1[0::Sk, 0::Sk].transpose().tofile(filename)
filename = string.join(('bx2M', '{0:.2f}'.format(Mach), '.dbl'), sep='')
D.bx2[0::Sk, 0::Sk].transpose().tofile(filename)
array input is accessible
'''
cooling = np.loadtxt('cooling.dat')
if plot:
plt.loglog(cooling[:, 0], cooling[:, 1])
plt.loglog(temperature,
np.interp(temperature, cooling[:, 0], cooling[:, 1]), 'o')
return np.interp(temperature, cooling[:, 0], cooling[:, 1])
wdir = './'
nlinf = pp.nlast_info(w_dir=wdir)
#D = pp.pload(nlinf['nlast'],w_dir=wdir) # Loading the data into a pload object D
D = pp.pload(2, w_dir=wdir)
T = (D.prs * un.Ba / (D.rho * un.cm**-3) / con.k_B * 1.67e-6).to('K')
print cf(T.value).shape
#f=open('rho.txt','r+')
#temp=f.read()
#temp=
#imshow(T.value)
I = pp.Image()
I.pldisplay(D,
cf(T.value),
x1=D.x1,
x2=D.x2,
label1='x',
label2='y',
title=r'Density $\rho$ [MHD_Blast test]',
import numpy as np
import math
import matplotlib.pyplot as plt
import os, sys
sys.path.append(os.environ['PLUTO_DIR'] +
"/Tools/pyPLUTO/pyPLUTO") #access linux environment variable
import pyPLUTO as pp
# script to make 2-D snapshots of various quantities
D = pp.pload(
0,
w_dir=
"/home/prateek/Desktop/Public_Codes/Galaxy-Cluster-PLUTO/Bondi_PS_Cluster/"
)
x = np.outer(D.x1, np.sin(D.x2))
z = np.outer(D.x1, np.cos(D.x2))
for i in range(0, 10):
plt.clf()
D = pp.pload(
i,
w_dir=
"/home/prateek/Desktop/Public_Codes/Galaxy-Cluster-PLUTO/Bondi_PS_Cluster/"
)
plt.subplot(111)
plt.contourf(x, z, np.log10(D.rho), np.linspace(-4.0, 0.0, 40))
plt.xlim(0.0, 5.0)
plt.ylim(-5.0, 5.0)
plt.axes().set_aspect('equal')
plt.colorbar()
import pyPLUTO as pp import pylab as pl D=pp.pload(1) print D.bx1.shape pl.imshow(D.bx1[:,:,0]) pl.draw()
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)
density_scaling = scaling_dict['density']
pressure_scaling = scaling_dict['pressure']
speed_scaling = scaling_dict['speed']
# Defining the run directory:
run_dir = '/Users/Katie/Desktop/PLUTO1/KatiesSims/Offset/M25_m14.5_OFF4R_Q38/'
# # The Environment
# In[ ]:
# Choosing the initial time step
tstep = 0
initial = pp.pload(tstep,w_dir = run_dir)
theta_index_first = 0
theta_index_last = -1
r_physical = curObject.x1*length_scaling.value
# loading the density data, and taking a slice along the x-axis
globalVar_density = getattr(initial,'rho').T
globalVar_density = globalVar_density * density_scaling.value
density_slice = np.asarray(np.log10(globalVar_density))
f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
f.set_figheight(5)
f.set_figwidth(10)
import pylab
import pyPLUTO as pp
import string
for N in range(74, 178):
D = pp.pload(N, datatype='hdf5', level=4)
I = pp.Image()
I.pldisplay(D,
D.rho,
x1=D.x1,
x2=D.x2,
cbar=(True, 'vertical'),
polar=[True, True])
figname = string.join(('Plot', str(N), '.png'))
pylab.savefig(figname)
pylab.close()
def cf(temperature,plot=False):
'''
calculate cooling function lambda(T)
temperature(K), return(erg cm^-3 s^-1)
array input is accessible
'''
cooling = np.loadtxt('cooling.dat')
if plot:
plt.loglog(cooling[:,0],cooling[:,1])
plt.loglog(temperature,np.interp(temperature, cooling[:,0],cooling[:,1]),'o')
return np.interp(temperature, cooling[:,0],cooling[:,1])
wdir = './'
nlinf = pp.nlast_info(w_dir=wdir)
#D = pp.pload(nlinf['nlast'],w_dir=wdir) # Loading the data into a pload object D
D = pp.pload(2,w_dir=wdir)
T= (D.prs*un.Ba/(D.rho*un.cm**-3)/con.k_B*1.67e-6).to('K')
print cf(T.value).shape
#f=open('rho.txt','r+')
#temp=f.read()
#temp=
#imshow(T.value)
I = pp.Image()
I.pldisplay(D, cf(T.value),x1=D.x1,x2=D.x2,label1='x',label2='y',title=r'Density $\rho$ [MHD_Blast test]',cbar=(True,'vertical'))
#savefig('MHD_Blast.png') # Only to be saved as either .png or .jpg
plt.show()
import os
import matplotlib.pyplot as plt
import pyPLUTO as pp
import numpy as np
I = pp.Image()
plutodir = os.environ['PLUTO_DIR']
wdir = plutodir+'/Test_Problems/HD/Viscosity/Flow_Past_Cylinder/'
try:
D = pp.pload(10,datatype='hdf5',level=3, w_dir=wdir)
except IndexError:
print 'data.0010.hdf5 not found .. Loading data.0000.hdf5 at level 0'
D = pp.pload(0,datatype='hdf5',w_dir=wdir)
#Plot 2D AMR R-Phi Polar Data on Cartesian X-Y Plane.
I.pldisplay(D, D.rho, x1=D.x1, x2=D.x2,
polar=[True, True], fignum=1,
cbar=[True, 'horizontal'],figsize=[8,8],
label1='X', label2='Y',title=r'Density [$\rho$]')
ax1 = plt.gca()
ax1.axis([-10, 50,-20.0, 20.0])
#Using the OplotBox routine to overplot sublevels.
I.oplotbox(D.AMRLevel,lrange=[0,3],geom=D.geometry)
plt.savefig('amr_flowcyc.png')
