def read_galaxy(self, itype, gn, sgn, centre, load_region_length):
"""
For a given galaxy (defined by its GroupNumber and SubGroupNumber)
extract the Temperature, Density and StarFormationRate of all gas particles
using the read_eagle routine. Conversion factors are still loaded directly
from the hdf5 files.
itype: Particle Type (0 for PartType0 = Baryons)
gn: GroupNumber
sgn: SubGroupNumber
centre: centre of region of particles that we look at
load_region_length: cube length of particle region
"""
data = {}
# Initialize read_eagle module.
eagle_data = EagleSnapshot(DATADIR + SNAPSHOT)
# Put centre into cMpc/h units.
centre *= self.h
# Select region to load, a 'load_region_length' cMpc/h cube centred on 'centre'.
region = np.array([(centre[0] - 0.5 * load_region_length),
(centre[0] + 0.5 * load_region_length),
(centre[1] - 0.5 * load_region_length),
(centre[1] + 0.5 * load_region_length),
(centre[2] - 0.5 * load_region_length),
(centre[2] + 0.5 * load_region_length)])
eagle_data.select_region(*region)
# Load data using read_eagle, load conversion factors manually.
f = h5py.File(DATADIR + SNAPSHOT, 'r')
for att in [
'GroupNumber', 'SubGroupNumber', 'Temperature', 'Density',
'StarFormationRate'
]:
tmp = eagle_data.read_dataset(itype, att)
cgs = f['PartType%i/%s' %
(itype, att)].attrs.get('CGSConversionFactor')
aexp = f['PartType%i/%s' %
(itype, att)].attrs.get('aexp-scale-exponent')
hexp = f['PartType%i/%s' %
(itype, att)].attrs.get('h-scale-exponent')
data[att] = np.multiply(tmp,
cgs * self.a**aexp * self.h**hexp,
dtype='f8')
f.close()
print "Size of data:", len(data)
# Mask to selected GroupNumber and SubGroupNumber.
"""
mask = np.logical_and(data['GroupNumber'] == gn, data['SubGroupNumber'] == sgn)
for att in data.keys():
data[att] = data[att][mask]
"""
return data
def read_galaxy(self,gn, sgn, centre,index,load_region_length):
print indices[index]
indices2 = "%i %s %s " %(indices[index] + 6,indices[index+1],indices[index+2])
fil.write(indices2)
data = {}
eagle_data = EagleSnapshot('/disks/eagle/L0100N1504/REFERENCE/data/snapshot_027_z000p101/snap_027_z000p101.0.hdf5')
centre *= self.h
region = np.array([(centre[0]-0.5*load_region_length), (centre[0]+0.5*load_region_length), (centre[1]-0.5*load_region_length), (centre[1]+0.5*load_region_length), (centre[2]-0.5*load_region_length),(centre[2]+0.5*load_region_length)])
eagle_data.select_region(*region)
f = h5py.File('/disks/eagle/L0100N1504/REFERENCE/data/snapshot_027_z000p101/snap_027_z000p101.0.hdf5', 'r')
constants = f['Constants']
Mpc = constants.attrs['CM_PER_MPC']
totalmass = 0
Rmax = 0
r = 0
Velocity = 0
tmp = eagle_data.read_dataset(0, 'Coordinates')
cgs = f['PartType%i/%s'%(0,'Coordinates')].attrs.get('CGSConversionFactor')
aexp = f['PartType%i/%s'%(0,'Coordinates')].attrs.get('aexp-scale-exponent')
hexp = f['PartType%i/%s'%(0,'Coordinates')].attrs.get('h-scale-exponent')
#print cgs, aexp, hexp
centre2 = np.multiply(centre, cgs / Mpc * self.a**aexp *self.h**hexp)
print self.a, self.h, cgs, aexp, hexp
for j in range(len(myData)):
Velocity += Vectorlength(myData[j][10], myData[j][11], myData[j][12])
myData[j][2] = np.multiply(myData[j][2], cgs / Mpc * self.a**aexp *self.h**hexp *self.h)
myData[j][3] = np.multiply(myData[j][3], cgs / Mpc * self.a**aexp *self.h**hexp *self.h)
myData[j][4] = np.multiply(myData[j][4], cgs / Mpc * self.a**aexp *self.h**hexp *self.h)
for j in range(len(myData)):
totalmass += myData[j][6]
if j < len(myData) - 1:
for k in range(j+1,len(myData)):
#print j,k
Rmax = findRmax(myData[j][2], myData[j][3], myData[j][4], myData[k][2], myData[k][3], myData[k][4],Rmax)
for i in range(len(myData)):
#print i
r = findr(centre2[0],centre2[1],centre2[2],myData[i][2],myData[i][3],myData[i][4])
#print Rmax,r,totalmass,myData[i][6],Velocity
T = Tfric(Rmax,r,totalmass,myData[i][6], Velocity)
print T
string = " %s" %(T)
fil.write(string)
fil.write("\n")
f.close()
def read_particles(self):
"""
Load particles from simulation
"""
data = {}
# Initialize read_eagle module.
eagle_data = EagleSnapshot(self._dataloc)
for centre in self._centres:
# Put centre into cMpc/h units.
centre *= self._h
print centre
# Select region to load, a 'load_region_length' cMpc/h cube centred on 'centre'.
region = np.array([(centre[0] - 0.5 * self._region_length),
(centre[0] + 0.5 * self._region_length),
(centre[1] - 0.5 * self._region_length),
(centre[1] + 0.5 * self._region_length),
(centre[2] - 0.5 * self._region_length),
(centre[2] + 0.5 * self._region_length)])
eagle_data.select_region(*region)
# Load data using read_eagle, load conversion factors manually.
f = h5py.File(self._dataloc, 'r')
for att in self._att:
tmp = eagle_data.read_dataset(self._itype, att)
cgs = f['PartType%i/%s' %
(self._itype, att)].attrs.get('CGSConversionFactor')
aexp = f['PartType%i/%s' %
(self._itype, att)].attrs.get('aexp-scale-exponent')
hexp = f['PartType%i/%s' %
(self._itype, att)].attrs.get('h-scale-exponent')
data[att] = np.multiply(tmp,
cgs * self._a**aexp * self._h**hexp,
dtype='f8')
f.close()
# Mask to selected GroupNumber and SubGroupNumber.
"""
mask = np.logical_and(data['GroupNumber'] == gn, data['SubGroupNumber'] == sgn)
for att in data.keys():
data[att] = data[att][mask]
"""
self._data = data
def read_subvol(path,ivol,nslice,ptypes=None):
file=h5py.File(path,'r')
boxsize=file['Header'].attrs['BoxSize']
hfac=file['Header'].attrs['HubbleParam']
afac=file['Header'].attrs['ExpansionFactor']
lims=get_limits(ivol,nslice,boxsize,buffer=0.1)
if not ptypes:
ptypes={0:['Mass','SubGroupNumber','Temperature','Density','Metallicity'],
4:['Mass','SubGroupNumber','Metallicity']}
snapshot=EagleSnapshot(path)
snapshot.select_region(*lims)
pdata={}
for iptype,ptype in enumerate(ptypes):
pdata[ptype]=pd.DataFrame(data=snapshot.read_dataset(ptype,'ParticleIDs'),columns=['ParticleIDs'])
pdata[ptype].loc[:,[f'Coordinates_{x}' for x in 'xyz']]=snapshot.read_dataset(ptype,'Coordinates')
for field in ptypes[ptype]:
hexp=file[f'PartType{ptype}/{field}'].attrs['h-scale-exponent']
aexp=file[f'PartType{ptype}/{field}'].attrs['aexp-scale-exponent']
cgs=file[f'PartType{ptype}/{field}'].attrs['CGSConversionFactor']
pdata[ptype][field]=snapshot.read_dataset(ptype,field)*(hfac**hexp)*(afac**aexp)*cgs
pdata[ptype].loc[:,'ParticleType']=ptype
snapshot.close()
#for star particles assign a crazy temp, density, entropy
npart_gas=pdata[0].shape[0]
npart_star=pdata[4].shape[0]
for field in ptypes[0]:
if not field in ptypes[4]:
pdata[4][field]=np.ones(npart_star)*10**10
#concat all pdata into one df
pdata=pd.concat([pdata[ptype] for ptype in pdata],ignore_index=True,)
pdata.sort_values(by="ParticleIDs",inplace=True)
pdata.reset_index(inplace=True,drop=True)
#conversions
pdata=convert_pdata(path,pdata)
#generate KDtree
pdata_kdtree=cKDTree(pdata.loc[:,[f'Coordinates_{x}' for x in 'xyz']].values,boxsize=boxsize)
return pdata, pdata_kdtree
def read_galaxy(self, itype, gn, sgn, centre, load_region_length):
p, t = defineprojection(projection[indices[n]])
data = {}
eagle_data = EagleSnapshot(
'/disks/eagle/L0100N1504/REFERENCE/data/snapshot_027_z000p101/snap_027_z000p101.0.hdf5'
)
centre *= self.h
region = np.array([(centre[0] - 0.5 * load_region_length),
(centre[0] + 0.5 * load_region_length),
(centre[1] - 0.5 * load_region_length),
(centre[1] + 0.5 * load_region_length),
(centre[2] - 0.5 * load_region_length),
(centre[2] + 0.5 * load_region_length)])
eagle_data.select_region(*region)
f = h5py.File(
'/disks/eagle/L0100N1504/REFERENCE/data/snapshot_027_z000p101/snap_027_z000p101.0.hdf5',
'r')
constants = f['Constants']
Mpc = constants.attrs['CM_PER_MPC']
SMass = constants.attrs["SOLAR_MASS"]
for att in ['GroupNumber', 'Coordinates', 'Mass']:
if parttype == 1 and att == 'Mass':
data[att] = np.ones(len(data['GroupNumber'])) * SMass * 9.7e6
continue
tmp = eagle_data.read_dataset(itype, att)
cgs = f['PartType%i/%s' %
(itype, att)].attrs.get('CGSConversionFactor')
aexp = f['PartType%i/%s' %
(itype, att)].attrs.get('aexp-scale-exponent')
hexp = f['PartType%i/%s' %
(itype, att)].attrs.get('h-scale-exponent')
data[att] = np.multiply(tmp,
cgs * self.a**aexp * self.h**hexp,
dtype='f8')
if att == 'Coordinates':
centre2 = np.multiply(centre,
cgs / Mpc * self.a**aexp * self.h**hexp)
galx = np.zeros(len(myData))
galy = np.zeros(len(myData))
galz = np.zeros(len(myData))
j = 1
print myData[1][2], cgs, Mpc, self.a, aexp, self.h, hexp
print np.multiply(myData[1][2],
cgs / Mpc * self.a**aexp * self.h**hexp)
for i in range(
1,
len(myData)): #Only if first galaxy in list is centre
galx[j] = np.multiply(
myData[i][2], cgs / Mpc * self.a**aexp * self.h**hexp *
self.h) - centre2[0]
galy[j] = np.multiply(
myData[i][3], cgs / Mpc * self.a**aexp * self.h**hexp *
self.h) - centre2[1]
galz[j] = np.multiply(
myData[i][4], cgs / Mpc * self.a**aexp * self.h**hexp *
self.h) - centre2[2]
j += 1
print galx, galy, galz
x1, y1, x2, y2, x3, y3, l1, l2, l3, l4, l5, l6 = find_galaxy(
projection[indices[n]], galx, galy, galz)
f.close()
mask = (data['GroupNumber'] == gn)
for att in data.keys():
data[att] = data[att][mask]
for i in range(3):
if i == 0:
stringtitle = parttype_string + " for p=0 and t=0" + " at snapshot 27 with redshift z = 0.1"
x = x1
y = y1
xlabel = l1
ylabel = l2
if i == 1:
p = p + 90
stringtitle = parttype_string + " for p=90 and t=0" + " at snapshot 27 with redshift z = 0.1"
x = x2
y = y2
xlabel = l3
ylabel = l4
if i == 2:
stringtitle = parttype_string + " for p=0 and t=90" + " at snapshot 27 with redshift z = 0.1"
t = t + 90
p = p - 90
x = x3
y = y3
xlabel = l5
ylabel = l6
qv_parallel = QuickView(data['Coordinates'] / Mpc,
data['Mass'] / SMass,
r='infinity',
plot=False,
p=p,
t=t,
x=centre2[0],
y=centre2[1],
z=centre2[2],
max_hsml=max_hsml,
extent=[-0.5, 0.5, -0.5, 0.5])
plt.imshow(qv_parallel.get_image(),
extent=qv_parallel.get_extent(),
cmap='viridis',
origin='lower')
plt.colorbar()
color = ['r', 'k', 'm', 'w']
plt.title(stringtitle)
plt.xlabel(xlabel)
print p, t, x, y
plt.ylabel(ylabel)
plt.xlim(-0.21, 0.21)
plt.ylim(-0.21, 0.21)
#plt.clim(5,)
#plt.scatter(x, y, facecolors = 'none', color = 'r')
marker = ['o', 's', '^', 'D']
for j in range(len(x)):
plt.scatter(x[j],
y[j],
marker=marker[j],
facecolors='none',
color='r',
label=myData[j][5])
plt.legend(bbox_to_anchor=(-0.3, -0.15), loc=3, prop={'size': 8})
plt.show()
def read_particles(self):
"""
Load particles from simulation
"""
data = {}
# Initialize read_eagle module.
eagle_data = EagleSnapshot(self._dataloc)
# Select entire cube region
eagle_cube_length = eagle_data.boxsize
print "EAGLE box size:", eagle_cube_length
print ""
region = np.array([3 * [0, eagle_cube_length]]).flatten()
eagle_data.select_region(*region)
# Read all data
f = h5py.File(self._dataloc, 'r')
# Read coordinates as well, need them to extract the particles in the
# volumes of the filaments
if not 'Coordinates' in self._att:
self._att += ['Coordinates']
# * Step 1 *
# Read data from all particles in the simulation at given snapshot
for att in self._att:
tmp = eagle_data.read_dataset(self._itype, att)
cgs = f['PartType%i/%s' %
(self._itype, att)].attrs.get('CGSConversionFactor')
aexp = f['PartType%i/%s' %
(self._itype, att)].attrs.get('aexp-scale-exponent')
hexp = f['PartType%i/%s' %
(self._itype, att)].attrs.get('h-scale-exponent')
if att == 'Coordinates':
data[att] = tmp / self._h # Get co-moving coordinates
else:
data[att] = np.multiply(tmp,
cgs * self._a**aexp * self._h**hexp,
dtype='f8')
f.close()
# * Step 2 *
# Extract particles that are within the filament region
# (= in the volumes of self._volumes)
def in_volume(pos):
# For each volume around the centres, check if the position is contained
# If yes, return True and if it is in no volume return False
for vol in self._volumes:
if (vol[0] < pos[0] < vol[1]) and (vol[2] < pos[1] <
vol[3]) and (vol[4] < pos[2]
< vol[5]):
return True
return False
# Apply `in_volume` to every single particle
mask = np.apply_along_axis(in_volume, axis=1, arr=data['Coordinates'])
# Keep only those particles that are within the filament
for key in data.keys():
data[key] = data[key][mask]
self._data = data
def read_galaxy(self, itype, gn, sgn, centre, velocity, load_region_length, fileloc):
""" For a given galaxy (defined by its GroupNumber and SubGroupNumber)
extract the Temperature, Density and StarFormationRate of all gas particles
using the read_eagle routine. Conversion factors are still loaded directly
from the hdf5 files. """
data = {}
# Initialize read_eagle module.
eagle_data = EagleSnapshot(fileloc+'snap_028_z000p000.0.hdf5')
# Put centre into cMpc/h units.
centre *= self.h
# Select region to load, a 'load_region_length' cMpc/h cube centred on 'centre'.
region = np.array([
(centre[0]-0.5*load_region_length), (centre[0]+0.5*load_region_length),
(centre[1]-0.5*load_region_length), (centre[1]+0.5*load_region_length),
(centre[2]-0.5*load_region_length), (centre[2]+0.5*load_region_length)
])
eagle_data.select_region(*region)
# Load data using read_eagle, load conversion factors manually.
f = h5py.File(fileloc+'snap_028_z000p000.0.hdf5', 'r')
for att in ['GroupNumber', 'SubGroupNumber', 'StarFormationRate', 'Temperature', 'Density', 'Coordinates', 'Velocity','SmoothedElementAbundance/Oxygen','SmoothedElementAbundance/Hydrogen']:
tmp = eagle_data.read_dataset(itype, att)
cgs = f['PartType%i/%s'%(itype, att)].attrs.get('CGSConversionFactor')
aexp = f['PartType%i/%s'%(itype, att)].attrs.get('aexp-scale-exponent')
hexp = f['PartType%i/%s'%(itype, att)].attrs.get('h-scale-exponent')
data[att] = np.multiply(tmp, cgs * self.a**aexp * self.h**hexp, dtype='f8') # This puts all attributes into their proper values (not /h)
f.close()
# Mask to selected GroupNumber and SubGroupNumber.
mask = np.logical_and(data['GroupNumber'] == gn, data['SubGroupNumber'] == sgn)
for att in data.keys():
data[att] = data[att][mask]
# Put centre back into proper units
centre /= self.h
print 'data[\'SmoothedElementAbundance/Oxygen\']:'
print data['SmoothedElementAbundance/Oxygen']
centre_cgs = centre * u.Mpc.to(u.cm)
velocity_cgs = velocity * u.km.to(u.cm)
data['rel_location'] = np.asarray([(partcoords-centre_cgs) for partcoords in data['Coordinates']])
data['radius'] = np.asarray([float(np.sqrt(np.dot(relloc,relloc))) for relloc in data['rel_location']])
data['rel_velocity'] = np.asarray([(partvelocity-velocity_cgs) for partvelocity in data['Velocity']])
data['rel_speed'] = np.asarray([np.sqrt(np.dot(relvel,relvel)) for relvel in data['rel_velocity']])
data['veldot'] = np.asarray([np.dot(relvel,relloc)/np.sqrt(np.dot(relloc,relloc)) for relvel, relloc in zip(data['rel_velocity'],data['rel_location'])])
data['veldot_norm'] = np.asarray([np.dot(relvel,relloc)/(np.sqrt(np.dot(relloc,relloc))*np.sqrt(np.dot(relvel,relvel))) for relvel, relloc in zip(data['rel_velocity'],data['rel_location'])])
print 'radius'
print data['radius']* u.cm.to(u.Mpc)
print 'speed'
print data['rel_speed']* u.cm.to(u.km)
print ''
# following adapted from the make maps perrgn script
atomw_H = 1.00794005e0
uthing = 1.66053892e-24
data['nH'] = (data['SmoothedElementAbundance/Hydrogen'])*(data['Density'])/(atomw_H*uthing)
# DEBUG PLOT
# fig, (ax) = plt.subplots(1,1,figsize = (7,7))
# ax.hist(data['radius']* u.cm.to(u.Mpc))
# plt.show()
"""
for i in range(1):
print data['Velocity'][i] * u.cm.to(u.km)
print velocity_cgs* u.cm.to(u.km)
print (data['Velocity'][i]-velocity_cgs) * u.cm.to(u.km)
print ""
print data['Coordinates'][i] * u.cm.to(u.Mpc)
print centre_cgs * u.cm.to(u.Mpc)
print (data['Coordinates'][i]-centre_cgs) * u.cm.to(u.Mpc)
print ""
print data['rel_location'][i] * u.cm.to(u.Mpc)
print data['rel_velocity'][i] * u.cm.to(u.km)
print ""
print np.dot((data['Velocity'][i]-velocity_cgs),(data['Coordinates'][i]-centre_cgs))
print np.dot(data['rel_velocity'][i],data['rel_location'][i])
print np.sqrt(np.dot(data['rel_velocity'][i],data['rel_velocity'][i]))
print np.sqrt(np.dot(data['rel_location'][i],data['rel_location'][i]))
print ""
print data['veldot'][i] * u.cm.to(u.km)
print np.sqrt(np.dot(data['rel_velocity'][i],data['rel_velocity'][i])) * u.cm.to(u.km)
print data['veldot_norm'][i]
print ""
print len(data['GroupNumber'])
"""
return data