# sorted dm ids, then all relevant dm physical properties # sorted star ids, then all relevant star physical properties import readsubf import numpy as np import readsnapHDF5 as rs from base_lookup import directory #HERE #import CalcHsml #=============================================================================================== resnap_name = "base102" base = directory(resnap_name)[0] snapnum = 313 #directory(resnap_name)[1] print resnap_name snapname = base + "/snapdir_"+str(snapnum).zfill(3)+ "/snap_"+str(snapnum).zfill(3) #snapname = base + "/snapdir_"+str(snapnum).zfill(3)+"_SAVE"+ "/snap_"+str(snapnum).zfill(3) print "snapname ", snapname #=============================================================================================== cat = readsubf.subfind_catalog(base,snapnum,masstab=True) nsubs = cat.nsubs print str(nsubs) + " subhalos!\n" parttype_list = [0,1,4]
def run_series(base_id="base102", snap_num=313):
import numpy as np
import readsubf
import os
from base_lookup import directory
from run_J_align import run_J_align
from subsnap_write import subsnap_write
import time
import glob
#############
#Control Parameters
base = directory(base_id)[0]
snap_num = 313 #directory(base_id)[1]
save_dir = base_id + "/"
resnap_folder = 'resnaps/'
subsnap_folder = save_dir+'subsnaps/'
# Overwrite past analysis?
overw = 0
# (Analysis restricted to only primary subhalos in given group)
# Analyze subhalos within a given mass range:
M_botlim = 10.
M_toplim = 1000.
# Analyze set # of subhalos?
analyze_N = 3#-1.
# JOB CONTROL:
#Concurrent jobs?
job_limit = 10
#Time between job submission?
wait_time = 1 #seconds
############################################################################################
if not os.path.exists(save_dir):
os.system('mkdir '+save_dir)
if not os.path.exists(subsnap_folder):
os.system('mkdir '+subsnap_folder)
cat = readsubf.subfind_catalog(base,snap_num,masstab=True)
sub_ids = cat.group_firstsub
M_subs = cat.sub_mass[sub_ids]
ind = np.logical_and(M_subs >= M_botlim, M_subs < M_toplim)
M_subs = M_subs[ind]
sub_ids = sub_ids[ind]
#N_subs = np.uint32(M_subs.size)
# Can insert specific subhalos instead:
sub_ids = [469]
if analyze_N > 0.:
sub_ids = sub_ids[0:analyze_N]
print "Will submit "+str(len(sub_ids))+" jobs.\n"
raw_input("Press Enter to continue...")
#################################################################################################
resnap = resnap_folder+base_id+"_"+"S"+str(snap_num).zfill(3)+'.resnap'
#Write intermediate .snap files:
subsnap_write(resnap_name=resnap,base=base,snap_num=snap_num,sub_ids=sub_ids,save_dir=save_dir)
# Job group control:
grp_name = "/bd_decomp"
add_grp = "bgadd -L "+str(job_limit)+" "+grp_name
mod_grp = "bgmod -L "+str(job_limit)+" "+grp_name
os.system(add_grp)
os.system(mod_grp)
################################################
def submit_job(base_id, base, snap_num, sub_id, dat_list, save_dir):
if overw == 0:
subsnap_name = save_dir+str(sub_id).zfill(5)+".dat"
if subsnap_name in dat_list:
print subsnap_name
print "Done with this one!"
else:
print "Could not find ",subsnap_name,"... submitting job."
#run_bd(base_id, base, snap_num, sub_id, grp_name)
run_J_align(base_id, base, snap_num, sub_id, grp_name)
print "Waiting ",str(wait_time), "seconds to submit next job..."
time.sleep(wait_time)
else:
#run_bd(base_id, base, snap_num, sub_id, grp_name)
run_J_align(base_id, base, snap_num, sub_id, grp_name)
print "Waiting ",str(wait_time), "seconds to submit next job..."
time.sleep(wait_time)
################################################
dat_list = glob.glob(save_dir+'*.dat')
n_matches = len(sub_ids)
for i in np.arange(n_matches):
print str(i) +" of "+str(n_matches)
submit_job(base_id=base_id,base=base,snap_num=snap_num,sub_id=sub_ids[i],dat_list=dat_list,save_dir=save_dir)
print "Job submission done!"
def DensityProjection(save_dir, base_id, halo_id, pos, mass, hsml):
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
import pylab
import numpy as np
import os
import SphMap
import readsnapHDF5 as rs
import base_lookup as bl
#map resolution
nx=256
ny=256
#non-zero -> take these values
min_dens=1.0
max_dens=100.0
####################################################################################################
####################################################################################################
base = bl.directory(base_id)[0]
snapnum = bl.directory(base_id)[1]
####################################################################################################
x_width = pos[:,0].max()-pos[:,0].min()
y_width = pos[:,1].max()-pos[:,1].min()
z_width = pos[:,2].max()-pos[:,2].min()
box=np.array([x_width*1.1,y_width*1.1,z_width*1.1], dtype="float32")
center = np.array([0.,0.,0.,])
for i in range(3): center[i] = pos[:,i].min() + box[i]/2.
#Face-on Projection
dim0=0
dim1=1
map_face = SphMap.CalcDensProjection(pos, hsml, mass, mass, nx, ny, box[0], box[1], box[2], center[0], center[1], center[2], dim0, dim1, 1, 1)
#Edge-on Projection
dim0=0
dim1=2
map_edge = SphMap.CalcDensProjection(pos, hsml, mass, mass, nx, ny, box[0], box[1], box[2], center[0], center[1], center[2], dim0, dim1, 1, 1)
if not os.path.exists(save_dir+"projections/"):
os.system('mkdir '+save_dir+"projections/")
#os.path.mkdir(save_dir)
filename_out= save_dir + "projections/"+ str(halo_id) +"_proj.dat"
f = open(filename_out,'wb')
np.array([nx],dtype=np.uint32).tofile(f)
np.array([ny],dtype=np.uint32).tofile(f)
map_face.astype("float32").tofile(f)
map_edge.astype("float32").tofile(f)
f.close()
map = map_face
#Make image
map = map * 10.0**10.0
fig = plt.figure( figsize = (10.,10.) )
pylab.spectral()
ma=map.max()/2.
mi=ma/10000.0
ma=np.log10(ma)
mi=np.log10(mi)
print 'mean density of map = ', map.mean()
map=np.log10(map)
print map.min(), map.max()
print mi,ma
map[map<mi]=mi
map[map>ma]=ma
print 'min/max map=', map.min(), map.max()
#plt.imshow(np.array(map).T,origin='lower', interpolation='nearest',extent=[center[0]-box[0]/2., center[0]+box[0]/2., center[1]-box[1]/2., center[1]+box[1]/2.],vmin=np.log10(min_dens), vmax=np.log10(max_dens))
plt.imshow(np.array(map).T,origin='lower', interpolation='nearest',extent=[center[0]-box[0]/2., center[0]+box[0]/2., center[1]-box[1]/2., center[1]+box[1]/2.])
plt.axis([center[0]-box[0]/2., center[0]+box[0]/2., center[1]-box[1]/2., center[1]+box[1]/2.])
plt.xlabel('kpc/$h$', fontsize=20)
plt.ylabel('kpc/$h$', fontsize=20)
#plt.savefig(imagebase+str(num).zfill(3)+".eps")
plt.savefig(save_dir + "projections/"+ str(halo_id)+"_face.png")
plt.clf()
map = map_edge
#Make image
map = map * 10.0**10.0
fig = plt.figure( figsize = (10.,10.) )
pylab.spectral()
ma=map.max()/2.
mi=ma/10000.0
ma=np.log10(ma)
mi=np.log10(mi)
print 'mean density of map = ', map.mean()
map=np.log10(map)
print map.min(), map.max()
print mi,ma
map[map<mi]=mi
map[map>ma]=ma
print 'min/max map=', map.min(), map.max()
#plt.imshow(np.array(map).T,origin='lower', interpolation='nearest',extent=[center[0]-box[0]/2., center[0]+box[0]/2., center[1]-box[1]/2., center[1]+box[1]/2.],vmin=np.log10(min_dens), vmax=np.log10(max_dens))
plt.imshow(np.array(map).T,origin='lower', interpolation='nearest',extent=[center[0]-box[0]/2., center[0]+box[0]/2., center[1]-box[1]/2., center[1]+box[1]/2.])
plt.axis([center[0]-box[0]/2., center[0]+box[0]/2., center[1]-box[1]/2., center[1]+box[1]/2.])
plt.xlabel('kpc/$h$', fontsize=20)
plt.ylabel('kpc/$h$', fontsize=20)
#plt.savefig(imagebase+str(num).zfill(3)+".eps")
plt.savefig(save_dir + "projections/"+ str(halo_id)+"_edge.png")
plt.clf()
def run_series(base_id="base400", snap_num=314):
import numpy as np
# import readsubf
import readsubfHDF5
import os
from base_lookup import directory
from run_find_disks import run_find_disks
# from run_J_profile import run_J_profile
from subsnap_write import subsnap_write
import time
import glob
#############
# Control Parameters
base = directory(base_id)[0]
snap_num = 311
# snap_num = 314 #directory(base_id)[1]
# snap_num = 189 #for z = 2
# snap_num = 250
base_dir = base_id + "/"
save_dir = base_dir + "snap" + str(snap_num).zfill(3) + "/"
resnap_folder = "resnaps/"
subsnap_folder = save_dir + "subsnaps/"
dat_folder = "data/"
# Overwrite past analysis?
overw = 1
# (Analysis restricted to only primary subhalos in given group)
# Analyze subhalos within a given mass range:
M_botlim = 100.0
M_toplim = 1000.0
# Analyze set # of subhalos?
analyze_N = -1
# JOB CONTROL:
# Concurrent jobs?
job_limit = 10
# Time between job submission?
wait_time = 10 # seconds
############################################################################################
if not os.path.exists(base_dir):
os.system("mkdir " + base_dir)
if not os.path.exists(save_dir):
os.system("mkdir " + save_dir)
if not os.path.exists(subsnap_folder):
os.system("mkdir " + subsnap_folder)
# cat = readsubf.subfind_catalog(base,snap_num,masstab=True)
cat = readsubfHDF5.subfind_catalog(base, snap_num)
sub_ids = cat.group_firstsub
M_subs = cat.sub_mass[sub_ids]
ind = np.logical_and(M_subs >= M_botlim, M_subs < M_toplim)
M_subs = M_subs[ind]
sub_ids = sub_ids[ind]
# N_subs = np.uint32(M_subs.size)
# Can insert specific subhalos instead:
# sub_ids = [1136, 1302, 1536]
# sub_ids = [8037]
# sub_ids = [4987]
# sub_ids = [0]
"""
sub_ids = [4024,\
4987,\
5517,\
5704,\
5791,\
6265,\
6911,\
7542,\
7721,\
7746,\
8005,\
8037]
"""
if analyze_N > 0.0:
sub_ids = sub_ids[0:analyze_N]
print "Will submit " + str(len(sub_ids)) + " jobs.\n"
raw_input("Press Enter to continue...")
#################################################################################################
resnap = resnap_folder + base_id + "_" + "S" + str(snap_num).zfill(3) + ".resnap"
# Write intermediate .snap files:
subsnap_write(resnap_name=resnap, base=base, snap_num=snap_num, sub_ids=sub_ids, save_dir=save_dir)
# Job group control:
grp_name = "/bd_decomp"
add_grp = "bgadd -L " + str(job_limit) + " " + grp_name
mod_grp = "bgmod -L " + str(job_limit) + " " + grp_name
os.system(add_grp)
os.system(mod_grp)
################################################
def submit_job(base_id, base, snap_num, sub_id, dat_list, dat_dir):
if overw == 0:
subsnap_name = dat_dir + str(sub_id).zfill(5) + ".dat"
if subsnap_name in dat_list:
print subsnap_name
print "Done with this one!"
else:
print "Could not find ", subsnap_name, "... submitting job."
# run_bd(base_id, base, snap_num, sub_id, grp_name)
run_find_disks(base_id, base, snap_num, sub_id, grp_name)
# run_J_profile(base_id, base, snap_num, sub_id, grp_name)
print "Waiting ", str(wait_time), "seconds to submit next job..."
time.sleep(wait_time)
else:
# run_bd(base_id, base, snap_num, sub_id, grp_name)
run_find_disks(base_id, base, snap_num, sub_id, grp_name)
# run_J_profile(base_id, base, snap_num, sub_id, grp_name)
print "Waiting ", str(wait_time), "seconds to submit next job..."
time.sleep(wait_time)
################################################
dat_dir = save_dir + dat_folder
dat_list = glob.glob(dat_dir + "*.dat")
n_matches = len(sub_ids)
for i in np.arange(n_matches):
print str(i + 1) + " of " + str(n_matches)
submit_job(base_id=base_id, base=base, snap_num=snap_num, sub_id=sub_ids[i], dat_list=dat_list, dat_dir=dat_dir)
print "Job submission done!"
