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generate_cylindrical_model_exponnential.py
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Tue, Nov 5, 10:30

generate_cylindrical_model_exponnential.py
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#!/usr/bin/env python
from pNbody import *
from pNbody import ic
from pNbody import libgrid
from pNbody import libutil
from pNbody import libmiyamoto
# model parameters
n = 1e6 # number of points
nf = 8 # particle number mutiplicative number (used to reduce noise)
M = 1.0 # total mass
G = 1.0 # gravitational constant
hR = 3. # radial scale length
hz = 0.3 # horizontal scale length
Rmax = 10*hR # maximal radius
Zmax = 10*hz # maximal z
eps = 0.05 # gravitational softening
AdaptativeSoftenning = False # use an adaptative softenning based on grid cells
ErrTolTheta = 0.5 # gravitational opening criterion
name = 'expd.dat' # output snap file
stats_name = 'stats_expd.dmp' # output stat file
# grid parameters
rmin = 0. # minimal grid radius
rmax = Rmax*1.05 # maximal grid radius
zmin = -Zmax*1.05 # minimal grid z
zmax = Zmax*1.05 # maximal grid z
nr = 64 # number of bins in r
nt = 2 # number of bins in t
nz = 64+1 # number of bins in z
# for an even value of nz, the potential is computed at z=0
# for an odd value of nz, the density is computed at z=0
# define transfert functions
rc = 1.0
g = lambda r:log(r/rc+1)
gm = lambda r:rc*(exp(r)-1)
# set methods used to compute velocity dispersions
mode_sigma_z = {"name":"jeans","param":None}
mode_sigma_r = {"name":"epicyclic_approximation","param":1.}
mode_sigma_p = {"name":"epicyclic_approximation","param":None}
params = [mode_sigma_z,mode_sigma_r,mode_sigma_p]
# create the model
nb = ic.expd(int(n*nf),Hr=hR,Hz=hz,Rmax=rmax,Zmax=zmax,irand=1,name=name,ftype='gadget')
# set all particles to type 2
nb.set_tpe(2)
# open the model
#nb = Nbody(name,ftype='gadget')
# compute velocities
nb,phi,stats = nb.Get_Velocities_From_Cylindrical_Grid(select='disk',disk=('gas','bulge','disk'),eps=eps,nR=nr,nz=nz,nt=nt,Rmax=rmax,zmin=zmin,zmax=zmax,params=params,Phi=None,g=g,gm=gm,ErrTolTheta=ErrTolTheta,AdaptativeSoftenning=AdaptativeSoftenning)
# reduc
if nf>1:
nb = nb.reduc(nf,mass=True)
# write final model
nb.write()
# save output and parameters
stats['n'] = n
stats['M'] = M
stats['G'] = G
stats['hR'] = hR
stats['hz'] = hz
stats['Rmax'] = Rmax
stats['Zmax'] = Zmax
stats['eps'] = eps
stats['nz'] = nz
stats['nr'] = nr
stats['rmin'] = rmin
stats['rmax'] = rmax
stats['zmin'] = zmin
stats['zmax'] = zmax
io.write_dmp(stats_name,stats)
##################
# info
##################
r = stats['R']
z = stats['z']
dr = r[1]-r[0]
dz = z[nz/2+1]-z[nz/2]
print "Delta R :",dr,"=",dr/eps,"eps"
print "Delta z :",dz,"=",dz/eps,"eps"

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