def read_snap(gal, snap, snapmin, bh, centerarray, rvirarray, centertype):
if centertype == 'rockstar':
padding = 95 - len(centerarray)
center = centerarray[snap - padding]
rvir = rvirarray[snap - padding]
if centertype == 'gtrace':
center = centerarray[snap - snapmin + 1]
rvir = rvirarray[snap - snapmin + 1]
s = pygad.Snap('/Volumes/Happy/Choi16_Fiducial/Fiducial_' + str(bh) +
'/m0' + str(gal).zfill(3) + '/snap_m0' + str(gal).zfill(3) +
'_sf_x_2x_' + str(snap).zfill(3),
load_double_prec=True)
s.to_physical_units()
pygad.Translation(-center).apply(s)
vel_center = pygad.analysis.mass_weighted_mean(
s.stars[pygad.BallMask('1 kpc',
center=pygad.UnitArr([0.0, 0.0, 0.0], 'kpc'))],
'vel')
s['vel'] -= vel_center
return s
def define_ds(snap,
raw_sim_dir,
raw_sim_name_prefix,
caesar_dir,
name_prefix,
redshiftFile,
verbose=False):
"""
"""
import caesar
import yt
import numpy as np
import os
import pygad as pg
_, zs_table, snaps_table = np.loadtxt(redshiftFile, unpack=True)
infile = caesar_dir + name_prefix + '{:0>3}'.format(int(snap)) + \
'.hdf5'
print("Loading Ceasar file: {}").format(infile)
obj = caesar.load(infile)
obj.galaxies.sort(key=lambda x: x.sfr, reverse=True)
rawSim = raw_sim_dir + raw_sim_name_prefix + \
'{:>03}'.format(int(snap)) + '.hdf5'
ds = yt.load(rawSim, over_refine_factor=1, index_ptype="all")
s = pg.Snap(raw_sim_dir + raw_sim_name_prefix +
'{:0>3}'.format(int(snap)) + '.hdf5')
h = s.cosmology.h() # obj.simulation.hubble_constant
if verbose:
print_ds_info(h, obj, ds)
zred = '{:.3f}'.format(zs_table[snaps_table == snap][0])
return s, h, obj, ds, float(zred)
ba = np.sqrt(eig_vals[0] / eig_vals[2])
ca = np.sqrt(eig_vals[1] / eig_vals[2])
print abs(ba - ba_0) / ba_0, abs(ca - ca_0) / ca_0
if abs(ba - ba_0) / ba_0 <= 1e-3 and abs(ca - ca_0) / ca_0 <= 1e-3:
break
return ba, ca
filename = '../gamma-1.5-bh-6.hdf5'
r_e = 10.722
s = pygad.Snap(filename)
snap = s.stars
com = find_com(snap['pos'][:, :], snap['mass'][:],
max(snap['pos'][:, 0]) * 0.001)
snap['pos'][:, :] = snap['pos'][:, :] - com
snap = vm._orientsnap(snap, axisorientation=1, rangmom=0.5 * 20)
snap.to_physical_units()
print(np.sum(snap['mass'][:]))
mask = snap['pos'][:, 0]**2 + snap['pos'][:, 1]**2 + snap['pos'][:,
2]**2 < r_e**2
coords = snap['pos'][mask, :]
pos = np.array([gp[k] for k in mygals[igal].glist])
if sum(mass) == 0:
rhalf[0][igal] = rhalf[1][igal] = rhalf[2][igal] = 0
continue
for idir0 in range(3):
rhalf[idir0][igal], cent = compute_rfrac(
idir0, mass, pos, frac)
if igal % (len(mygals) / 20) == 0:
print(
'%d logM*= %.3f c= [%.3f,%.3f] rh2d= %.3f %.3f %.3f rh3d= %.3f'
% (igal, np.log10(ms[igal]), cent[0], cent[1],
rhalf[0][igal], rhalf[1][igal], rhalf[2][igal],
mygals[igal].radii['stellar_half_mass']))
else:
pgsnap = pg.Snap(snapfile, physical=True)
print('Computing', plotvar, 'magnitudes for', len(pgsnap.stars),
'stars.')
allL = pg.snapshot.get_luminosities(pgsnap.stars, band=plotvar)
print(allL)
for igal in range(len(mygals)):
mass = np.array([sm[k] for k in mygals[igal].slist])
lum = np.array([allL[k] for k in mygals[igal].slist])
#if igal%(len(mygals)/20)==0: print igal,mags[:3],lum[:3],min(mags),max(mags),min(lum),max(lum)
pos = np.array([sp[k] for k in mygals[igal].slist])
if not conc:
for idir0 in range(3):
rhalf[idir0][igal], cent = compute_rfrac(
idir0, lum, pos)
for idir0 in range(3):
rhalfmass[idir0][igal], cent = compute_rfrac(
#---------------ignore this dotted line for now--------------------------------
#this finds the center for the given snapshot
if centertype == 'rockstar':
padding = 95 - len(centerarray)
center = centerarray[snap - padding]
if centertype == 'gtrace':
center = centerarray[snap - snapmin + 1]
#this is the pygad function to read in the snapshot
s = pygad.Snap('/Volumes/G-RAID/Choi16_Fiducial/Fiducial_MrAGN/m0' +
str(gal).zfill(3) + '/snap_m0' + str(gal).zfill(3) +
'_sf_x_2x_' + str(snap).zfill(3),
load_double_prec=True)
#this converts all of the quantities with factors of the hubble constant and the scale factor to physical units
s.to_physical_units()
#Here we center the positions and velocities on the center that we established above
pygad.Translation(-center).apply(s)
vel_center = pygad.analysis.mass_weighted_mean(
s.stars[pygad.BallMask('1 kpc',
center=pygad.UnitArr([0.0, 0.0, 0.0], 'kpc'))],
'vel')
plt.ylabel('Vrot los (km/s)')
plt.savefig(filename)
plt.clf()
return np.max(vrot)
if __name__ == '__main__':
model = 'm50n512'
wind = 's50j7k'
snap = '151'
results_dir = '/home/sapple/simba_sizes/sigma_rot/'
data_dir = '/home/rad/data/' + model + '/' + wind + '/'
s = pg.Snap(data_dir + 'snap_' + model + '_' + snap + '.hdf5')
sim = caesar.load(data_dir + 'Groups/' + model + '_' + snap + '.hdf5')
h = sim.simulation.hubble_constant
redshift = sim.simulation.redshift
G = sim.simulation.G.in_units('km**3/(Msun*s**2)')
ssfr_min = 0. # look at literature for this range
sm_min = 4.6e9
sfr_min = -1.5
factor = 2. # do profile up to twice half mass radius
NR = 10
Npixels = 100
edge_vec = np.array([0, 1, 0]) # for edge-on projection
import pygad import matplotlib.pyplot as plt # snapname = "snap_m12.5n128_125.hdf5" snapname = "/scratch/shuiyao/data/m25n256/snap_m25n256_125.hdf5" s = pygad.Snap(snapname) print s.loadable_blocks() # snap, halo = pygad.tools.prepare_zoom(s) # R200, M200 = pygad.analysis.virial_info(snap) # fig, ax, cbar = pygad.plotting.image(snap.gas, extent='5 Mpc') # ax.add_artist(plot.Circle([0,0], R200, facecolor='none', edgecolor='w')) # plt.draw() s.cosmology s.redshift s.scale_factor s.parts s['ID'] s.gas['rho'] s.gas['temp'] # Will be derived automatically
bharray = np.loadtxt(path + 'BHSFactivity/bhlist10.txt',
skiprows=1,
dtype='uint',
usecols=(2, ))
swallowarray = np.loadtxt(path + 'blackhole_details/Swallow_shortlist.txt',
dtype='uint',
skiprows=1,
usecols=(1, 2))
# This loops over all of the snapshots
for i in snap:
# This reads in the snapshot, converts it into physical units, and assigns a center to the galaxy
s = pygad.Snap('/Volumes/Happy/Choi16_Fiducial/BH_trace/m0' +
str(gal).zfill(3) + '/snap_m0' + str(gal).zfill(3) +
'_sf_x_2x_' + str(i).zfill(3),
load_double_prec=True)
s.to_physical_units()
center = pygad.analysis.shrinking_sphere(s.stars,
center=[s.boxsize / 2] * 3,
R=s.boxsize)
pygad.Translation(-center).apply(s)
dx = []
dy = []
dz = []
gas_id = s.gas['ID']
gasID = np.array(gas_id)
gasid = [id.astype(np.uint64) for id in gasID]
gasid = np.array(gasid)
# coding: utf-8
# Calculation of y parameter profile, through binning to mapping
# load libraries
import sys
sys.path.append('/home/tazkera/pygad/')
get_ipython().magic(u'pylab inline')
pylab.rcParams['figure.figsize'] = (12, 8)
import pygad as pg
import pygad.binning
# load data
s = pg.Snap('./snap_p50n288gw_108.hdf5', load_double_prec=True)
print s
# create the pressure field
s.gas['pressure'] = s.gas['temp'].in_units_of("K") * pg.physics.kB.in_units_of(
'keV/K') * ((s.gas['ne'] * s.gas['rho'].in_units_of("g/cm**3") * 0.76) /
pg.physics.m_H.in_units_of("g"))
# create the y-parameter field
s.gas['y_param'] = (s.gas['pressure'].in_units_of("cm**-3 keV") *
pg.physics.Th_cs.in_units_of('cm**2')) / (
pg.physics.m_e.in_units_of('g') *
(pg.physics.c.in_units_of('cm/s')**2))
# create a mask to exclude the star formation >0 particles
s.gas['sfr']
sfr_out = s.gas['sfr'] > 0
mask2 = np.array(sfr_out) * 1
