def _parse_parameter_file(self):
"""
Get the various simulation parameters & constants.
"""
self.domain_left_edge = np.zeros(3, dtype='float')
self.domain_right_edge = np.zeros(3, dtype='float') + 1.0
self.dimensionality = 3
self.refine_by = 2
self.periodicity = (True, True, True)
self.cosmological_simulation = True
self.parameters = {}
self.unique_identifier = \
int(os.stat(self.parameter_filename)[stat.ST_CTIME])
self.parameters.update(constants)
self.parameters['Time'] = 1.0
# read the amr header
with open(self._file_amr, 'rb') as f:
amr_header_vals = fpu.read_attrs(f, amr_header_struct, '>')
for to_skip in ['tl', 'dtl', 'tlold', 'dtlold', 'iSO']:
fpu.skip(f, endian='>')
(self.ncell) = fpu.read_vector(f, 'i', '>')[0]
# Try to figure out the root grid dimensions
est = int(np.rint(self.ncell**(1.0 / 3.0)))
# Note here: this is the number of *cells* on the root grid.
# This is not the same as the number of Octs.
# domain dimensions is the number of root *cells*
self.domain_dimensions = np.ones(3, dtype='int64') * est
self.root_grid_mask_offset = f.tell()
self.root_nocts = self.domain_dimensions.prod() // 8
self.root_ncells = self.root_nocts * 8
mylog.debug(
"Estimating %i cells on a root grid side," + "%i root octs",
est, self.root_nocts)
self.root_iOctCh = fpu.read_vector(f, 'i', '>')[:self.root_ncells]
self.root_iOctCh = self.root_iOctCh.reshape(self.domain_dimensions,
order='F')
self.root_grid_offset = f.tell()
self.root_nhvar = fpu.skip(f, endian='>')
self.root_nvar = fpu.skip(f, endian='>')
# make sure that the number of root variables is a multiple of
# rootcells
assert self.root_nhvar % self.root_ncells == 0
assert self.root_nvar % self.root_ncells == 0
self.nhydro_variables = ((self.root_nhvar + self.root_nvar) /
self.root_ncells)
self.iOctFree, self.nOct = fpu.read_vector(f, 'i', '>')
self.child_grid_offset = f.tell()
# lextra needs to be loaded as a string, but it's actually
# array values. So pop it off here, and then re-insert.
lextra = amr_header_vals.pop("lextra")
amr_header_vals['lextra'] = np.fromstring(lextra, '>f4')
self.parameters.update(amr_header_vals)
amr_header_vals = None
# estimate the root level
float_center, fl, iocts, nocts, root_level = _read_art_level_info(
f, [0, self.child_grid_offset],
1,
coarse_grid=self.domain_dimensions[0])
del float_center, fl, iocts, nocts
self.root_level = root_level
mylog.info("Using root level of %02i", self.root_level)
# read the particle header
self.particle_types = []
self.particle_types_raw = ()
if not self.skip_particles and self._file_particle_header:
with open(self._file_particle_header, "rb") as fh:
particle_header_vals = fpu.read_attrs(fh,
particle_header_struct,
'>')
fh.seek(seek_extras)
n = particle_header_vals['Nspecies']
wspecies = np.fromfile(fh, dtype='>f', count=10)
lspecies = np.fromfile(fh, dtype='>i', count=10)
# extras needs to be loaded as a string, but it's actually
# array values. So pop it off here, and then re-insert.
extras = particle_header_vals.pop("extras")
particle_header_vals['extras'] = np.fromstring(extras, '>f4')
self.parameters['wspecies'] = wspecies[:n]
self.parameters['lspecies'] = lspecies[:n]
for specie in range(n):
self.particle_types.append("specie%i" % specie)
self.particle_types_raw = tuple(self.particle_types)
ls_nonzero = np.diff(lspecies)[:n - 1]
ls_nonzero = np.append(lspecies[0], ls_nonzero)
self.star_type = len(ls_nonzero)
mylog.info("Discovered %i species of particles", len(ls_nonzero))
mylog.info("Particle populations: " + '%9i ' * len(ls_nonzero),
*ls_nonzero)
self._particle_type_counts = dict(
zip(self.particle_types_raw, ls_nonzero))
for k, v in particle_header_vals.items():
if k in self.parameters.keys():
if not self.parameters[k] == v:
mylog.info("Inconsistent parameter %s %1.1e %1.1e", k,
v, self.parameters[k])
else:
self.parameters[k] = v
self.parameters_particles = particle_header_vals
self.parameters.update(particle_header_vals)
self.parameters['ng'] = self.parameters['Ngridc']
self.parameters['ncell0'] = self.parameters['ng']**3
# setup standard simulation params yt expects to see
self.current_redshift = self.parameters["aexpn"]**-1.0 - 1.0
self.omega_lambda = self.parameters['Oml0']
self.omega_matter = self.parameters['Om0']
self.hubble_constant = self.parameters['hubble']
self.min_level = self.parameters['min_level']
self.max_level = self.parameters['max_level']
if self.limit_level is not None:
self.max_level = min(self.limit_level,
self.parameters['max_level'])
if self.force_max_level is not None:
self.max_level = self.force_max_level
self.hubble_time = 1.0 / (self.hubble_constant * 100 / 3.08568025e19)
self.current_time = self.quan(b2t(self.parameters['t']), 'Gyr')
self.gamma = self.parameters["gamma"]
mylog.info("Max level is %02i", self.max_level)
def _parse_parameter_file(self):
"""
Get the various simulation parameters & constants.
"""
self.domain_left_edge = np.zeros(3, dtype='float')
self.domain_right_edge = np.zeros(3, dtype='float') + 1.0
self.dimensionality = 3
self.refine_by = 2
self.periodicity = (True, True, True)
self.cosmological_simulation = True
self.parameters = {}
self.unique_identifier = \
int(os.stat(self.parameter_filename)[stat.ST_CTIME])
self.parameters.update(constants)
self.parameters['Time'] = 1.0
self.file_count = 1
self.filename_template = self.parameter_filename
# read the particle header
self.particle_types = []
self.particle_types_raw = ()
assert self._file_particle_header
with open(self._file_particle_header, "rb") as fh:
seek = 4
fh.seek(seek)
headerstr = np.fromfile(fh, count=1, dtype=(str, 45))
aexpn = np.fromfile(fh, count=1, dtype='>f4')
aexp0 = np.fromfile(fh, count=1, dtype='>f4')
amplt = np.fromfile(fh, count=1, dtype='>f4')
astep = np.fromfile(fh, count=1, dtype='>f4')
istep = np.fromfile(fh, count=1, dtype='>i4')
partw = np.fromfile(fh, count=1, dtype='>f4')
tintg = np.fromfile(fh, count=1, dtype='>f4')
ekin = np.fromfile(fh, count=1, dtype='>f4')
ekin1 = np.fromfile(fh, count=1, dtype='>f4')
ekin2 = np.fromfile(fh, count=1, dtype='>f4')
au0 = np.fromfile(fh, count=1, dtype='>f4')
aeu0 = np.fromfile(fh, count=1, dtype='>f4')
nrowc = np.fromfile(fh, count=1, dtype='>i4')
ngridc = np.fromfile(fh, count=1, dtype='>i4')
nspecs = np.fromfile(fh, count=1, dtype='>i4')
nseed = np.fromfile(fh, count=1, dtype='>i4')
Om0 = np.fromfile(fh, count=1, dtype='>f4')
Oml0 = np.fromfile(fh, count=1, dtype='>f4')
hubble = np.fromfile(fh, count=1, dtype='>f4')
Wp5 = np.fromfile(fh, count=1, dtype='>f4')
Ocurv = np.fromfile(fh, count=1, dtype='>f4')
wspecies = np.fromfile(fh, count=10, dtype='>f4')
lspecies = np.fromfile(fh, count=10, dtype='>i4')
extras = np.fromfile(fh, count=79, dtype='>f4')
boxsize = np.fromfile(fh, count=1, dtype='>f4')
n = nspecs
particle_header_vals = {}
tmp = np.array([
headerstr, aexpn, aexp0, amplt, astep, istep, partw, tintg, ekin,
ekin1, ekin2, au0, aeu0, nrowc, ngridc, nspecs, nseed, Om0, Oml0,
hubble, Wp5, Ocurv, wspecies, lspecies, extras, boxsize
])
for i in range(len(tmp)):
a1 = dmparticle_header_struct[0][i]
a2 = dmparticle_header_struct[1][i]
if a2 == 1:
particle_header_vals[a1] = tmp[i][0]
else:
particle_header_vals[a1] = tmp[i][:a2]
for specie in range(n):
self.particle_types.append("specie%i" % specie)
self.particle_types_raw = tuple(self.particle_types)
ls_nonzero = np.diff(lspecies)[:n - 1]
ls_nonzero = np.append(lspecies[0], ls_nonzero)
self.star_type = len(ls_nonzero)
mylog.info("Discovered %i species of particles", len(ls_nonzero))
mylog.info("Particle populations: " + '%9i ' * len(ls_nonzero),
*ls_nonzero)
for k, v in particle_header_vals.items():
if k in self.parameters.keys():
if not self.parameters[k] == v:
mylog.info("Inconsistent parameter %s %1.1e %1.1e", k, v,
self.parameters[k])
else:
self.parameters[k] = v
self.parameters_particles = particle_header_vals
self.parameters.update(particle_header_vals)
self.parameters['wspecies'] = wspecies[:n]
self.parameters['lspecies'] = lspecies[:n]
self.parameters['ng'] = self.parameters['Ngridc']
self.parameters['ncell0'] = self.parameters['ng']**3
self.parameters['boxh'] = self.parameters['boxsize']
self.parameters['total_particles'] = ls_nonzero
self.domain_dimensions = np.ones(3, dtype='int64') * 2 # NOT ng
# setup standard simulation params yt expects to see
self.current_redshift = self.parameters["aexpn"]**-1.0 - 1.0
self.omega_lambda = particle_header_vals['Oml0']
self.omega_matter = particle_header_vals['Om0']
self.hubble_constant = particle_header_vals['hubble']
self.min_level = 0
self.max_level = 0
# self.min_level = particle_header_vals['min_level']
# self.max_level = particle_header_vals['max_level']
# if self.limit_level is not None:
# self.max_level = min(
# self.limit_level, particle_header_vals['max_level'])
# if self.force_max_level is not None:
# self.max_level = self.force_max_level
self.hubble_time = 1.0 / (self.hubble_constant * 100 / 3.08568025e19)
self.parameters['t'] = a2b(self.parameters['aexpn'])
self.current_time = self.quan(b2t(self.parameters['t']), 'Gyr')
self.gamma = self.parameters["gamma"]
mylog.info("Max level is %02i", self.max_level)
def _parse_parameter_file(self):
"""
Get the various simulation parameters & constants.
"""
self.domain_left_edge = np.zeros(3, dtype="float")
self.domain_right_edge = np.zeros(3, dtype="float") + 1.0
self.dimensionality = 3
self.refine_by = 2
self.periodicity = (True, True, True)
self.cosmological_simulation = True
self.parameters = {}
self.parameters.update(constants)
self.parameters["Time"] = 1.0
self.file_count = 1
self.filename_template = self.parameter_filename
# read the particle header
self.particle_types = []
self.particle_types_raw = ()
assert self._file_particle_header
with open(self._file_particle_header, "rb") as fh:
seek = 4
fh.seek(seek)
headerstr = fh.read(45).decode("ascii")
aexpn = np.fromfile(fh, count=1, dtype=">f4")
aexp0 = np.fromfile(fh, count=1, dtype=">f4")
amplt = np.fromfile(fh, count=1, dtype=">f4")
astep = np.fromfile(fh, count=1, dtype=">f4")
istep = np.fromfile(fh, count=1, dtype=">i4")
partw = np.fromfile(fh, count=1, dtype=">f4")
tintg = np.fromfile(fh, count=1, dtype=">f4")
ekin = np.fromfile(fh, count=1, dtype=">f4")
ekin1 = np.fromfile(fh, count=1, dtype=">f4")
ekin2 = np.fromfile(fh, count=1, dtype=">f4")
au0 = np.fromfile(fh, count=1, dtype=">f4")
aeu0 = np.fromfile(fh, count=1, dtype=">f4")
nrowc = np.fromfile(fh, count=1, dtype=">i4")
ngridc = np.fromfile(fh, count=1, dtype=">i4")
nspecs = np.fromfile(fh, count=1, dtype=">i4")
nseed = np.fromfile(fh, count=1, dtype=">i4")
Om0 = np.fromfile(fh, count=1, dtype=">f4")
Oml0 = np.fromfile(fh, count=1, dtype=">f4")
hubble = np.fromfile(fh, count=1, dtype=">f4")
Wp5 = np.fromfile(fh, count=1, dtype=">f4")
Ocurv = np.fromfile(fh, count=1, dtype=">f4")
wspecies = np.fromfile(fh, count=10, dtype=">f4")
lspecies = np.fromfile(fh, count=10, dtype=">i4")
extras = np.fromfile(fh, count=79, dtype=">f4")
boxsize = np.fromfile(fh, count=1, dtype=">f4")
n = nspecs[0]
particle_header_vals = {}
tmp = np.array([
headerstr,
aexpn,
aexp0,
amplt,
astep,
istep,
partw,
tintg,
ekin,
ekin1,
ekin2,
au0,
aeu0,
nrowc,
ngridc,
nspecs,
nseed,
Om0,
Oml0,
hubble,
Wp5,
Ocurv,
wspecies,
lspecies,
extras,
boxsize,
])
for i in range(len(tmp)):
a1 = dmparticle_header_struct[0][i]
a2 = dmparticle_header_struct[1][i]
if a2 == 1:
particle_header_vals[a1] = tmp[i][0]
else:
particle_header_vals[a1] = tmp[i][:a2]
for specie in range(n):
self.particle_types.append("specie%i" % specie)
self.particle_types_raw = tuple(self.particle_types)
ls_nonzero = np.diff(lspecies)[:n - 1]
ls_nonzero = np.append(lspecies[0], ls_nonzero)
self.star_type = len(ls_nonzero)
mylog.info("Discovered %i species of particles", len(ls_nonzero))
info_str = "Particle populations: " + "%9i " * len(ls_nonzero)
mylog.info(info_str, *ls_nonzero)
for k, v in particle_header_vals.items():
if k in self.parameters.keys():
if not self.parameters[k] == v:
mylog.info(
"Inconsistent parameter %s %1.1e %1.1e",
k,
v,
self.parameters[k],
)
else:
self.parameters[k] = v
self.parameters_particles = particle_header_vals
self.parameters.update(particle_header_vals)
self.parameters["wspecies"] = wspecies[:n]
self.parameters["lspecies"] = lspecies[:n]
self.parameters["ng"] = self.parameters["Ngridc"]
self.parameters["ncell0"] = self.parameters["ng"]**3
self.parameters["boxh"] = self.parameters["boxsize"]
self.parameters["total_particles"] = ls_nonzero
self.domain_dimensions = np.ones(3, dtype="int64") * 2 # NOT ng
# setup standard simulation params yt expects to see
# Convert to float to please unyt
self.current_redshift = float(self.parameters["aexpn"]**-1.0 - 1.0)
self.omega_lambda = float(particle_header_vals["Oml0"])
self.omega_matter = float(particle_header_vals["Om0"])
self.hubble_constant = float(particle_header_vals["hubble"])
self.min_level = 0
self.max_level = 0
# self.min_level = particle_header_vals['min_level']
# self.max_level = particle_header_vals['max_level']
# if self.limit_level is not None:
# self.max_level = min(
# self.limit_level, particle_header_vals['max_level'])
# if self.force_max_level is not None:
# self.max_level = self.force_max_level
self.hubble_time = 1.0 / (self.hubble_constant * 100 / 3.08568025e19)
self.parameters["t"] = a2b(self.parameters["aexpn"])
self.current_time = self.quan(b2t(self.parameters["t"]), "Gyr")
self.gamma = self.parameters["gamma"]
mylog.info("Max level is %02i", self.max_level)
