def snap(snapstop):
print("find 3 most massive halos")
# read in pos
x, y, z, m, r = mys.getxyzmr(snapstop, 1)
print('mass ', m)
print('radius ', r)
count = 0
for i in range(len(m)):
print("halo ", i, m[i], r[i])
hassub = False
for j in range(i + 1, len(m)):
# if too low mass halo, continue
if (m[j] < 0.5 * m[i]):
continue
# have massive halo, possibly subhalo
if (dist_mod(x[i], y[i], z[i], x[j], y[j], z[j]) < 10. * r[i]):
hassub = True
print(" lies near ", j, m[j], r[j])
break
if (count >= 3):
break
if (not hassub):
count += 1
print(x[i], y[i], z[i], r[i], m[i])
def snap(snapstop):
print("find 3 most massive halos")
# read in pos
x,y,z,m,r=mys.getxyzmr(snapstop, 1)
print('mass ', m)
print('radius ',r)
count=0
for i in range(len(m)):
print("halo ", i, m[i], r[i])
hassub = False
for j in range(i+1,len(m)):
# if too low mass halo, continue
if(m[j]<0.5*m[i]):
continue
# have massive halo, possibly subhalo
if(dist_mod(x[i],y[i],z[i],x[j],y[j],z[j])<10.*r[i]):
hassub = True
print(" lies near ", j, m[j], r[j])
break
if(count>=3):
break
if(not hassub):
count+=1
print(x[i],y[i],z[i],r[i],m[i])
i = len(sys.argv)
if (i != 3):
print("shrink_sphere.py snap hid")
exit(0)
snap = int(sys.argv[1])
hid = int(sys.argv[2])
eps = 1e-5
frad = 0.90 # shrinked sphere has radius frad*maxr, i.e. frad = 0.90 means 10% smaller
if (not mys.exists_snap(snap)):
print("snapshot " + str(snap) + " missing")
exit(0)
xc, yc, zc, mvir, rvir = mys.getxyzmr(snap, 1)
halo = my.open_file(mys.d(snap) + "dm/dm_" + str(hid) + ".dat", "r")
x = []
y = []
z = []
m = []
for line in halo:
val = line.split()
m.append(float(val[0]))
x.append(float(val[1]))
y.append(float(val[2]))
z.append(float(val[3]))
x = array(x)
y = array(y)
z = array(z)
gsd = "get_sphere_dm"
gss = "get_sphere_stars"
i = len(sys.argv)
if(i!=3):
print("usage: gen_spheres.py snap typ")
exit(1)
facr = 1 # * rvir from AHF for maximal radial distance to include particles
snap = sys.argv[1]
typ = int(sys.argv[2])
sim = mys.d(snap)
# get xc,yc,zc,mvir,rvir
xcl, ycl, zcl, mvirl,rvirl = mys.getxyzmr(snap, typ)
xsl, ysl, zsl, rsl = mys.getxyzrstars(snap, typ)
pdb.set_trace()
#print('select ',xcl[0])
#print(len(xcl))
for i in range(3):
xc=str(xcl[i]); xs=str(xsl[i]);
yc=str(ycl[i]); ys=str(ysl[i]);
zc=str(zcl[i]); zs=str(zsl[i]);
r=str(rvirl[i]*facr); rs=str(rsl[i]*facr*0.9);
sj=str(i+1)
# Dark Matter only
cmd = gsd+" -inp "+sim
cmd +=" -xc "+xc+" -yc "+yc+" -zc "+zc+" -rc "+r
if(i!=3):
print("shrink_sphere.py snap hid")
exit(0)
snap=int(sys.argv[1])
hid=int(sys.argv[2])
eps=1e-5
frad = 0.9 # shrinked sphere has radius frad*maxr, i.e. frad = 0.90 means 10% smaller
#print("missing file")
if(not mys.exists_snap(snap)):
print("snapshot "+str(snap)+" missing")
exit(0)
xc,yc,zc,mvir,rvir=mys.getxyzmr(snap,1)
halo = my.open_file(mys.d(snap)+"stars/stars_"+str(hid)+".dat","r")
x=[];y=[];z=[];m=[]
for line in halo:
val=line.split()
m.append(float(val[0]))
x.append(float(val[1]))
y.append(float(val[2]))
z.append(float(val[3]))
x = array(x); y = array(y); z = array(z); m = array(m)
#print(x)
def converged(xc,yc,zc,xc2,yc2,zc2):
return abs(xc-xc2)/abs(xc) < eps \
and abs(yc-yc2)/abs(yc) < eps \
show= True; run = True; loop=True
# choose: get_particles for all particles
# get_particles_dm for dm particles only
gps = "get_particles"
i = len(sys.argv)
if(i!=2):
print("usage: gen_particles_list.py snap")
exit(1)
facr = 1 # * rvir from AHF for maximal radial distance to include particles
snap = int(sys.argv[1])
d = mys.d(snap)
pdm = d+"particles_dm"
xl,yl,zl,ml,rl = mys.getxyzmr(snap,2)
nhalo= mys.get_nhalo(snap)
os.system("rm "+d+"particles_dm")
os.system("echo '"+str(nhalo)+"' >> "+pdm)
for i in range(len(xl)):
xc=str(xl[i]); yc=str(yl[i]); zc=str(zl[i]); r=str(facr*rl[i])
tmpdat = d+"tmp_"+str(i+1)+".dat"
# all particles (DM/stars mixed)
cmd1 = gps+" -inp "+d
cmd1 += " -xc " + xc + " -yc " + yc + " -zc " + zc + " -r " + r
cmd1 += ">" + tmpdat
cmd2 = "true"
my.threadif(cmd1,cmd2,run,run,show,run)
data2 = np.zeros((ny, nx), 'float')
for i in xrange(nx):
for j in xrange(ny):
if (abs(data[ny - j - 1, i]) < 1E-90):
data2[j, i] = 0
else:
data2[j, i] = log(abs(data[ny - j - 1, i]))
print(np.min(data2), np.max(data2))
pylab.figure()
pylab.xticks([])
pylab.yticks([])
pylab.imshow(data2, aspect=1, cmap='hot')
xc, yc, zc, m, r = mys.getxyzmr(nsnap, 1)
for i in range(len(xc)):
xs = xc[i] * nx
ys = ny - yc[i] * ny
s = r[i] * nx
print(xs, ys, s)
cir = pylab.Circle((xs, ys),
3.0 * s,
facecolor='none',
edgecolor=(0.0, 0, 0.1),
linewidth=1,
alpha=0.5)
pylab.gca().add_patch(cir)
pylab.savefig(fname + ".png")
binvalues = ar.array(IMGSIZE) binvalues.read(f,nx*ny) data = np.array(binvalues,'float') data = np.reshape(data,(ny,nx)) data2=np.zeros((ny,nx),'float') for i in xrange(nx): for j in xrange(ny): if(abs(data[ny-j-1,i])<1E-90): data2[j,i]=0 else: data2[j,i]=log(abs(data[ny-j-1,i])) print(np.min(data2),np.max(data2)) pylab.figure() pylab.xticks([]) pylab.yticks([]) pylab.imshow(data2,aspect=1,cmap='hot') xc,yc,zc,m,r=mys.getxyzmr(nsnap,1) for i in range(len(xc)): xs=xc[i]*nx;ys=ny-yc[i]*ny;s=r[i]*nx print(xs, ys, s) cir = pylab.Circle((xs, ys), 3.0*s, facecolor='none', edgecolor=(0.0,0,0.1), linewidth=1, alpha=0.5) pylab.gca().add_patch(cir) pylab.savefig(fname+".png") print(fname + ".png")
