def bgc_to_png(path, axes='xy', resolution=1024, outputdir='output', name_padding=0):
'''converts a single bgc2 file to png. Outputs are named <snapshot #>.png
:path: filepath to bgc2 file,
:axes: axes to take the snapshot across, xy means looking into the page from z,
:resolution: number of bins to use along each axis to put halos in, also res. of output pic,
:outputdir: name of output directory
:name_padding: number of zeroes to pad outputfiles with
'''
if not os.path.isdir(outputdir):
os.makedirs(outputdir)
H = Halos(path, verbose=False)
H.read_data(level=1, strict=True)
coords = []
for c in axes:
if c=='x':
coords.append(np.array([halo.x for halo in H.h]))
elif c=='y':
coords.append(np.array([halo.y for halo in H.h]))
elif c=='z':
coords.append(np.array([halo.z for halo in H.g]))
hist_array, _, _ = np.histogram2d(coords[0], coords[1], bins=resolution,
range=[[0,H.header[0].box_size], [0, H.header[0].box_size]])
#mpimg.imsave(os.path.join(outputdir, str(H.header[0].snapshot)+'.png'), hist_array, cmap=plt.cm.binary)
fig = plt.figure(dpi=100, tight_layout=True, frameon=False, figsize=(resolution/100.,resolution/100.))
fig.figimage(hist_array, cmap=plt.cm.binary)
fig.text(0.8,0.1,'z=%.3f' % H.header[0].redshift, size='medium', backgroundcolor='white', alpha=0.5)
plt.savefig(os.path.join(outputdir, str(H.header[0].snapshot).zfill(name_padding)+'.png'))
plt.close(fig)
def only_fof_halos(self, filepath):
"""identifies which halos in a snapshot have a Friends-Of-Friends
relationship. It does this by checking the parent_id attribute in
halo group data. If ==-1 then FOF halo.
"""
temp_H = Halos(filepath, verbose=False) # read only header for snapshot
temp_H.read_data(level=1)
fof_halos = [halo.id for halo in temp_H.h if halo.parent_id==-1]
return fof_halos
def shared_particles(self, filepath):
"""This function compares the the sharing frequency of unique particles
in distinct halos (Friends-Of-Friends halos). The frequency is:
(total particles - unique particles) / unique particles
"""
halo_filter = self.only_fof_halos(filepath)
H = Halos(filepath, verbose=False)
H.read_data(level=2, sieve=halo_filter, onlyid=True) # read all data w/ filter
s = set([])
unique = 0.
total = 0.
for halo in H.halos:
total += len(halo.particles.id)
s.update(halo.particles.id)
unique = len(s)
redshift = H.header[0].redshift
shared = (total / unique) - 1. # fraction of particles that are shared
return (redshift, shared)
def bgc2_merger_test(f1=r'data\*0000.bgc2', f2=r'data\*0001.bgc2'):
try:
h = Halos(f1, verbose=False)
g = Halos(f2, verbose=False)
h.read_data(level=0)
g.read_data(level=0)
except Exception as e:
print('\t\tFAILURE: Error reading test data files: data\\*0000.bgc2 and data\\*0001.bgc2: ')
print('\t\t' + str(e))
return
try:
h+g
print('\t\tSUCCESS: Compatible files merged.')
except Exception as e:
print('\t\tFAILURE: BGC2 merger failed: ' + str(e))
try:
h+h
except ValueError as e:
print('\t\tSUCCESS: Invalid merger prevented.')
def bgc2_merger_test(f1=r'data\*0000.bgc2', f2=r'data\*0001.bgc2'):
try:
h = Halos(f1, verbose=False)
g = Halos(f2, verbose=False)
h.read_data(level=0)
g.read_data(level=0)
except Exception as e:
print(
'\t\tFAILURE: Error reading test data files: data\\*0000.bgc2 and data\\*0001.bgc2: '
)
print('\t\t' + str(e))
return
try:
h + g
print('\t\tSUCCESS: Compatible files merged.')
except Exception as e:
print('\t\tFAILURE: BGC2 merger failed: ' + str(e))
try:
h + h
except ValueError as e:
print('\t\tSUCCESS: Invalid merger prevented.')
def bgc2_test(path='data\\halos_0.1.bgc2'):
"""
reading data from a sample bgc2 file containing multiple halos
:param path: path to file
:return: a Halos instance containing list of halos (<return_variable>.halos)
"""
print("\n\t->Reading only header data: ")
t = Halos(path, verbose=False)
t.read_data(level=0)
if len(t.header) == 1:
print("\t\tSUCCESS: Header read.")
else:
print("\t\tFAILURE: Header not read.")
print("\n\t->Reading header + halo data: ")
t = Halos(path, verbose=False)
t.read_data(level=1)
if t.header[0].ngroups == len(t.h):
print("\t\tSUCCESS: Halo data read.")
else:
print("\t\tFAILURE: Halo data improperly read.")
sample_ids = np.random.choice(t.h.id, 100, replace=False)
print("\n\t->Reading header + only halo id + only particle id data: ")
t = Halos(path, verbose=False)
t.read_data(level=2, onlyid=True)
if len(t.h.id) == t.header[0].ngroups and len(t.halos[0].particles.id) > 0:
print("\t\tSUCCESS: ID data read.")
else:
print("\t\tFAILURE: ID data improperly read.")
print("\n\t->Reading header + halo + particle data: ")
t = Halos(path, verbose=False)
t.read_data(level=2, sieve=sample_ids)
if len(t.h.id) == len(sample_ids) and len(
t.halos[0].particles.id) == t.h[0].npart:
print("\t\tSUCCESS: Full data read.")
else:
print("\t\tFAILURE: Full data improperly read.")
print("\n\t->Performing calculations. ")
t.filter(100) # filter out halos w/ less than 4 particles
t.center_halos()
t.get_covariance_matrices()
t.get_eigenvectors()
t.convert_bases()
t.get_radii(
) # center_halo(), get_covariance_matrices() and get_eigenvectors() functions must be called before
t.get_half_mass_radii()
print("\t\tSUCCESS: All calculations finished.")
def bgc2_test(path='data\\halos_0.1.bgc2'):
"""
reading data from a sample bgc2 file containing multiple halos
:param path: path to file
:return: a Halos instance containing list of halos (<return_variable>.halos)
"""
print("\n\t->Reading only header data: ")
t = Halos(path, verbose=False)
t.read_data(level=0)
if len(t.header)==1:
print("\t\tSUCCESS: Header read.")
else:
print("\t\tFAILURE: Header not read.")
print("\n\t->Reading header + halo data: ")
t = Halos(path, verbose=False)
t.read_data(level=1)
if t.header[0].ngroups==len(t.h):
print("\t\tSUCCESS: Halo data read.")
else:
print("\t\tFAILURE: Halo data improperly read.")
sample_ids = np.random.choice(t.h.id, 100, replace=False)
print("\n\t->Reading header + only halo id + only particle id data: ")
t = Halos(path, verbose=False)
t.read_data(level=2, onlyid=True)
if len(t.h.id)==t.header[0].ngroups and len(t.halos[0].particles.id)>0:
print("\t\tSUCCESS: ID data read.")
else:
print("\t\tFAILURE: ID data improperly read.")
print("\n\t->Reading header + halo + particle data: ")
t = Halos(path, verbose=False)
t.read_data(level=2, sieve=sample_ids)
if len(t.h.id)==len(sample_ids) and len(t.halos[0].particles.id)==t.h[0].npart:
print("\t\tSUCCESS: Full data read.")
else:
print("\t\tFAILURE: Full data improperly read.")
print("\n\t->Performing calculations. ")
t.filter(100) # filter out halos w/ less than 4 particles
t.center_halos()
t.get_covariance_matrices()
t.get_eigenvectors()
t.convert_bases()
t.get_radii() # center_halo(), get_covariance_matrices() and get_eigenvectors() functions must be called before
t.get_half_mass_radii()
print("\t\tSUCCESS: All calculations finished.")
def worker(self, pool_ids, files, queue, args):
try:
while len(args[1]) < 3:
args[1] = '0' + args[1]
path_2lpt = '/scratch/sissomdj/projects/simulations/rockstar/' + args[0] \
+ '/2lpt/snap' + args[1] + '/halos/*1.bgc2'
path_za = '/scratch/sissomdj/projects/simulations/rockstar/' + args[0] \
+ '/za/snap' + args[1] + '/halos/*1.bgc2'
#path_2lpt = '../data/halos_0.1.bgc2'
#path_2lpt = '../data/halos_0.2.bgc2'
H2 = Halos(path_2lpt, verbose=False)
HZ = Halos(path_za, verbose=False)
bins = np.linspace(1, 5, 26) # 25 bins
bins_mean = [
0.5 * (bins[i] + bins[i + 1]) for i in range(len(bins) - 1)
]
H2.read_data()
H2.filter(100)
H2.center_halos()
H2.get_covariance_matrices()
H2.get_eigenvectors()
H2.convert_bases()
H2.get_radii()
H2.get_half_mass_radii()
ratios2 = [max(h.radii) / h.half_mass_radius for h in H2.halos]
del H2
HZ.read_data()
HZ.filter(100)
HZ.center_halos()
HZ.get_covariance_matrices()
HZ.get_eigenvectors()
HZ.convert_bases()
HZ.get_radii()
HZ.get_half_mass_radii()
ratiosz = [max(h.radii) / h.half_mass_radius for h in HZ.halos]
del HZ
counts2, _ = np.histogram(ratios2, bins)
countsz, _ = np.histogram(ratiosz, bins)
n2 = sum(counts2)
nz = sum(countsz)
fig = plt.figure()
ax1 = fig.add_subplot(111)
_ = ax1.hist(bins_mean,
bins,
label='2LPT',
weights=counts2,
color='r',
histtype='step')
_ = ax1.hist(bins_mean,
bins,
label='ZA',
color='b',
weights=countsz,
histtype='step')
ax1.set_xlabel('Outer to Inner halo radius')
ax1.set_ylabel('Frequency')
fig.suptitle('2LPT vs. ZA')
ax1.set_title('2LPT: $\\mu $= ' + '{:.3f}'.format(np.mean(ratios2)) + \
', N= ' + str(n2) + '\tZA: $\\mu $= ' + '{:.3f}'.format(np.mean(ratiosz)) + \
', N= ' + str(nz))
_ = ax1.legend()
ax1.grid(True)
fig.savefig('results/' + args[0] + '/' + args[1] + '.png')
queue.put((0, None))
return (0, None)
except Exception as e:
queue.put((-1, e))
return -1