def hydro_offset(self):
if self._hydro_offset is not None: return self._hydro_offset
# We now have to open the file and calculate it
f = open(self.hydro_fn, "rb")
fpu.skip(f, 6)
# It goes: level, CPU, 8-variable
min_level = self.ds.min_level
n_levels = self.amr_header['nlevelmax'] - min_level
hydro_offset = np.zeros(n_levels, dtype='int64')
hydro_offset -= 1
level_count = np.zeros(n_levels, dtype='int64')
skipped = []
for level in range(self.amr_header['nlevelmax']):
for cpu in range(self.amr_header['nboundary'] +
self.amr_header['ncpu']):
header = (('file_ilevel', 1, 'I'), ('file_ncache', 1, 'I'))
try:
hvals = fpu.read_attrs(f, header, "=")
except AssertionError:
print "You are running with the wrong number of fields."
print "If you specified these in the load command, check the array length."
print "In this file there are %s hydro fields." % skipped
#print "The last set of field sizes was: %s" % skipped
raise
if hvals['file_ncache'] == 0: continue
assert (hvals['file_ilevel'] == level + 1)
if cpu + 1 == self.domain_id and level >= min_level:
hydro_offset[level - min_level] = f.tell()
level_count[level - min_level] = hvals['file_ncache']
skipped = fpu.skip(f, 8 * self.nvar)
self._hydro_offset = hydro_offset
self._level_count = level_count
return self._hydro_offset
def hydro_offset(self):
if self._hydro_offset is not None: return self._hydro_offset
# We now have to open the file and calculate it
f = open(self.hydro_fn, "rb")
fpu.skip(f, 6)
# It goes: level, CPU, 8-variable
min_level = self.ds.min_level
n_levels = self.amr_header['nlevelmax'] - min_level
hydro_offset = np.zeros(n_levels, dtype='int64')
hydro_offset -= 1
level_count = np.zeros(n_levels, dtype='int64')
skipped = []
for level in range(self.amr_header['nlevelmax']):
for cpu in range(self.amr_header['nboundary'] +
self.amr_header['ncpu']):
header = ( ('file_ilevel', 1, 'I'),
('file_ncache', 1, 'I') )
try:
hvals = fpu.read_attrs(f, header, "=")
except AssertionError:
print("You are running with the wrong number of fields.")
print("If you specified these in the load command, check the array length.")
print("In this file there are %s hydro fields." % skipped)
#print "The last set of field sizes was: %s" % skipped
raise
if hvals['file_ncache'] == 0: continue
assert(hvals['file_ilevel'] == level+1)
if cpu + 1 == self.domain_id and level >= min_level:
hydro_offset[level - min_level] = f.tell()
level_count[level - min_level] = hvals['file_ncache']
skipped = fpu.skip(f, 8 * self.nvar)
self._hydro_offset = hydro_offset
self._level_count = level_count
return self._hydro_offset
def fill(self, content, fields, selector):
# Here we get a copy of the file, which we skip through and read the
# bits we want.
oct_handler = self.oct_handler
all_fields = self.domain.ds.index.fluid_field_list
fields = [f for ft, f in fields]
tr = {}
cell_count = selector.count_oct_cells(self.oct_handler, self.domain_id)
levels, cell_inds, file_inds = self.oct_handler.file_index_octs(
selector, self.domain_id, cell_count)
for field in fields:
tr[field] = np.zeros(cell_count, 'float64')
for level, offset in enumerate(self.domain.hydro_offset):
if offset == -1: continue
content.seek(offset)
nc = self.domain.level_count[level]
temp = {}
for field in all_fields:
temp[field] = np.empty((nc, 8), dtype="float64")
for i in range(8):
for field in all_fields:
if field not in fields:
fpu.skip(content)
else:
temp[field][:, i] = fpu.read_vector(content,
'd') # cell 1
oct_handler.fill_level(level, levels, cell_inds, file_inds, tr,
temp)
return tr
def fill(self, content, fields, selector):
# Here we get a copy of the file, which we skip through and read the
# bits we want.
oct_handler = self.oct_handler
all_fields = self.domain.ds.index.fluid_field_list
fields = [f for ft, f in fields]
tr = {}
cell_count = selector.count_oct_cells(self.oct_handler, self.domain_id)
levels, cell_inds, file_inds = self.oct_handler.file_index_octs(
selector, self.domain_id, cell_count)
for field in fields:
tr[field] = np.zeros(cell_count, 'float64')
for level, offset in enumerate(self.domain.hydro_offset):
if offset == -1: continue
content.seek(offset)
nc = self.domain.level_count[level]
temp = {}
for field in all_fields:
temp[field] = np.empty((nc, 8), dtype="float64")
for i in range(8):
for field in all_fields:
if field not in fields:
fpu.skip(content)
else:
temp[field][:,i] = fpu.read_vector(content, 'd') # cell 1
oct_handler.fill_level(level, levels, cell_inds, file_inds, tr, temp)
return tr
def fill(self, content, fields, selector, file_handler):
# Here we get a copy of the file, which we skip through and read the
# bits we want.
oct_handler = self.oct_handler
all_fields = [f for ft, f in file_handler.field_list]
fields = [f for ft, f in fields]
tr = {}
cell_count = selector.count_oct_cells(self.oct_handler, self.domain_id)
levels, cell_inds, file_inds = self.oct_handler.file_index_octs(
selector, self.domain_id, cell_count)
# Initializing data container
for field in fields:
tr[field] = np.zeros(cell_count, 'float64')
# Loop over levels
for level, offset in enumerate(file_handler.offset):
if offset == -1: continue
content.seek(offset)
nc = file_handler.level_count[level]
tmp = {}
# Initalize temporary data container for io
for field in all_fields:
tmp[field] = np.empty((nc, 8), dtype="float64")
for i in range(8):
# Read the selected fields
for field in all_fields:
if field not in fields:
fpu.skip(content)
else:
tmp[field][:, i] = fpu.read_vector(content,
'd') # i-th cell
oct_handler.fill_level(level, levels, cell_inds, file_inds, tr,
tmp)
return tr
def _read_hydro_header(self):
# If no hydro file is found, return
if not self._is_hydro():
return
if self.nvar > 0: return self.nvar
# Read the number of hydro variables
f = open(self.hydro_fn, "rb")
fpu.skip(f, 1)
self.nvar = fpu.read_vector(f, "i")[0]
def _read_hydro_header(self):
# If no hydro file is found, return
if not self._is_hydro():
return
if self.nvar > 0: return self.nvar
# Read the number of hydro variables
f = open(self.hydro_fn, "rb")
fpu.skip(f, 1)
self.nvar = fpu.read_vector(f, "i")[0]
def read_header(self):
if not self.exists:
self.field_offsets = {}
self.field_types = {}
self.local_particle_count = 0
return
f = open(self.fname, "rb")
f.seek(0, os.SEEK_END)
flen = f.tell()
f.seek(0)
hvals = {}
attrs = self.attrs
hvals.update(fpu.read_attrs(f, attrs))
self.header = hvals
self.local_particle_count = hvals['npart']
if self.has_part_descriptor:
particle_fields = (_read_part_file_descriptor(
self.file_descriptor))
else:
particle_fields = list(self.known_fields)
if self.ds._extra_particle_fields is not None:
particle_fields += self.ds._extra_particle_fields
field_offsets = {}
_pfields = {}
ptype = self.ptype
# Read offsets
for field, vtype in particle_fields:
if f.tell() >= flen: break
field_offsets[ptype, field] = f.tell()
_pfields[ptype, field] = vtype
fpu.skip(f, 1)
iextra = 0
while f.tell() < flen:
iextra += 1
field, vtype = ('particle_extra_field_%i' % iextra, 'd')
particle_fields.append((field, vtype))
field_offsets[ptype, field] = f.tell()
_pfields[ptype, field] = vtype
fpu.skip(f, 1)
if iextra > 0 and not self.ds._warn_extra_fields:
self.ds._warn_extra_fields = True
w = ("Detected %s extra particle fields assuming kind "
"`double`. Consider using the `extra_particle_fields` "
"keyword argument if you have unexpected behavior.")
mylog.warning(w % iextra)
self.field_offsets = field_offsets
self.field_types = _pfields
def read_star_field(file, field=None):
data = {}
with open(file, "rb") as fh:
for dtype, variables in star_struct:
found = (isinstance(variables, tuple)
and field in variables) or field == variables
if found:
data[field] = read_vector(fh, dtype[1], dtype[0])
else:
skip(fh, endian=">")
return data.pop(field)
def offset(self):
'''
Compute the offsets of the fields.
By default, it skips the header (as defined by `cls.attrs`)
and computes the offset at each level.
It should be generic enough for most of the cases, but if the
*structure* of your fluid file is non-canonical, change this.
'''
if getattr(self, '_offset', None) is not None:
return self._offset
nvar = self.parameters['nvar']
ndim = self.domain.ds.dimensionality
twotondim = 2**ndim
with open(self.fname, 'rb') as f:
# Skip headers
nskip = len(self.attrs)
fpu.skip(f, nskip)
# It goes: level, CPU, 8-variable (1 cube)
min_level = self.domain.ds.min_level
n_levels = self.domain.amr_header['nlevelmax'] - min_level
offset = np.zeros(n_levels, dtype='int64')
offset -= 1
level_count = np.zeros(n_levels, dtype='int64')
skipped = []
amr_header = self.domain.amr_header
for level in range(amr_header['nlevelmax']):
for cpu in range(amr_header['nboundary'] + amr_header['ncpu']):
header = (('file_ilevel', 1, 'I'), ('file_ncache', 1, 'I'))
try:
hvals = fpu.read_attrs(f, header, "=")
except AssertionError:
mylog.error(
"You are running with the wrong number of fields. "
"If you specified these in the load command, check the array length. "
"In this file there are %s hydro fields." %
skipped)
raise
if hvals['file_ncache'] == 0: continue
assert (hvals['file_ilevel'] == level + 1)
if cpu + 1 == self.domain_id and level >= min_level:
offset[level - min_level] = f.tell()
level_count[level - min_level] = hvals['file_ncache']
skipped = fpu.skip(f, twotondim * nvar)
self._offset = offset
self._level_count = level_count
return self._offset
def read_header(self):
if not self.exists:
self.field_offsets = {}
self.field_types = {}
self.local_particle_count = 0
return
f = open(self.fname, "rb")
f.seek(0, os.SEEK_END)
flen = f.tell()
f.seek(0)
hvals = {}
# Read the header of the file
attrs = self.attrs
hvals.update(fpu.read_attrs(f, attrs))
self._header = hvals
# This is somehow a trick here: we only want one domain to
# be read, as ramses writes all the sinks in all the
# domains. Here, we set the local_particle_count to 0 except
# for the first domain to be red.
if getattr(self.ds, '_sink_file_flag', False):
self.local_particle_count = 0
else:
self.ds._sink_file_flag = True
self.local_particle_count = hvals['nsink']
# Read the fields + add the sink properties
if self.has_part_descriptor:
fields = (_read_part_file_descriptor(self.file_descriptor))
else:
fields = list(self.known_fields)
for i in range(self.ds.dimensionality * 2 + 1):
for j in range(self.ds.max_level, self.ds.min_level):
fields.append(("particle_prop_%s_%s" % (i, j), "d"))
field_offsets = {}
_pfields = {}
# Fill the fields, offsets and types
self.fields = []
for field, vtype in fields:
self.fields.append(field)
if f.tell() >= flen: break
field_offsets[self.ptype, field] = f.tell()
_pfields[self.ptype, field] = vtype
fpu.skip(f, 1)
self.field_offsets = field_offsets
self.field_types = _pfields
def _read_amr_header(self):
hvals = {}
f = self.amr_file
f.seek(0)
for header in ramses_header(hvals):
hvals.update(fpu.read_attrs(f, header))
# For speedup, skip reading of 'headl' and 'taill'
fpu.skip(f, 2)
hvals['numbl'] = fpu.read_vector(f, 'i')
# That's the header, now we skip a few.
hvals['numbl'] = np.array(hvals['numbl']).reshape(
(hvals['nlevelmax'], hvals['ncpu']))
fpu.skip(f)
if hvals['nboundary'] > 0:
fpu.skip(f, 2)
self.ngridbound = fpu.read_vector(f, 'i').astype("int64")
else:
self.ngridbound = np.zeros(hvals['nlevelmax'], dtype='int64')
free_mem = fpu.read_attrs(f, (('free_mem', 5, 'i'), )) # NOQA
ordering = fpu.read_vector(f, 'c') # NOQA
fpu.skip(f, 4)
# Now we're at the tree itself
# Now we iterate over each level and each CPU.
self.amr_header = hvals
self.amr_offset = f.tell()
self.local_oct_count = hvals['numbl'][self.ds.min_level:,
self.domain_id - 1].sum()
self.total_oct_count = hvals['numbl'][self.ds.min_level:, :].sum(
axis=0)
def _read_particle_header(self):
if not os.path.exists(self.part_fn):
self.local_particle_count = 0
self.particle_field_offsets = {}
return
f = open(self.part_fn, "rb")
f.seek(0, os.SEEK_END)
flen = f.tell()
f.seek(0)
hvals = {}
attrs = ( ('ncpu', 1, 'I'),
('ndim', 1, 'I'),
('npart', 1, 'I') )
hvals.update(fpu.read_attrs(f, attrs))
fpu.read_vector(f, 'I')
attrs = ( ('nstar_tot', 1, 'I'),
('mstar_tot', 1, 'd'),
('mstar_lost', 1, 'd'),
('nsink', 1, 'I') )
hvals.update(fpu.read_attrs(f, attrs))
self.particle_header = hvals
self.local_particle_count = hvals['npart']
particle_fields = [
("particle_position_x", "d"),
("particle_position_y", "d"),
("particle_position_z", "d"),
("particle_velocity_x", "d"),
("particle_velocity_y", "d"),
("particle_velocity_z", "d"),
("particle_mass", "d"),
("particle_identifier", "i"),
("particle_refinement_level", "I")]
if hvals["nstar_tot"] > 0:
particle_fields += [("particle_age", "d"),
("particle_metallicity", "d")]
if self.ds._extra_particle_fields is not None:
particle_fields += self.ds._extra_particle_fields
field_offsets = {}
_pfields = {}
for field, vtype in particle_fields:
if f.tell() >= flen: break
field_offsets["io", field] = f.tell()
_pfields["io", field] = vtype
fpu.skip(f, 1)
self.particle_field_offsets = field_offsets
self.particle_field_types = _pfields
self.particle_types = self.particle_types_raw = ("io",)
def _read_particle_header(self):
if not os.path.exists(self.part_fn):
self.local_particle_count = 0
self.particle_field_offsets = {}
return
f = open(self.part_fn, "rb")
f.seek(0, os.SEEK_END)
flen = f.tell()
f.seek(0)
hvals = {}
attrs = ( ('ncpu', 1, 'I'),
('ndim', 1, 'I'),
('npart', 1, 'I') )
hvals.update(fpu.read_attrs(f, attrs))
fpu.read_vector(f, 'I')
attrs = ( ('nstar_tot', 1, 'I'),
('mstar_tot', 1, 'd'),
('mstar_lost', 1, 'd'),
('nsink', 1, 'I') )
hvals.update(fpu.read_attrs(f, attrs))
self.particle_header = hvals
self.local_particle_count = hvals['npart']
particle_fields = [
("particle_position_x", "d"),
("particle_position_y", "d"),
("particle_position_z", "d"),
("particle_velocity_x", "d"),
("particle_velocity_y", "d"),
("particle_velocity_z", "d"),
("particle_mass", "d"),
("particle_identifier", "I"),
("particle_refinement_level", "I")]
if hvals["nstar_tot"] > 0:
particle_fields += [("particle_age", "d"),
("particle_metallicity", "d")]
field_offsets = {}
_pfields = {}
for field, vtype in particle_fields:
if f.tell() >= flen: break
field_offsets["io", field] = f.tell()
_pfields["io", field] = vtype
fpu.skip(f, 1)
self.particle_field_offsets = field_offsets
self.particle_field_types = _pfields
self.particle_types = self.particle_types_raw = ("io",)
def _read_sink_header(self):
if not self._has_sink:
self.local_sink_count = 0
self.sink_field_offsets = {}
return
f = open(self.sink_fn, "rb")
f.seek(0, os.SEEK_END)
flen = f.tell()
f.seek(0)
hvals = {}
attrs = (('nsink', 1, 'I'), ('nindsink', 1, 'I'))
hvals.update(fpu.read_attrs(f, attrs))
self.sink_header = hvals
self.local_sink_count = hvals['nsink']
sink_fields = [("particle_identifier", "i"), ("particle_mass", "d"),
("particle_position_x", "d"),
("particle_position_y", "d"),
("particle_position_z", "d"),
("particle_velocity_x", "d"),
("particle_velocity_y", "d"),
("particle_velocity_z", "d"), ("particle_age", "d"),
("BH_real_accretion", "d"), ("BH_bondi_accretion", "d"),
("BH_eddington_accretion", "d"), ("BH_esave", "d"),
("gas_spin_x", "d"), ("gas_spin_y", "d"),
("gas_spin_z", "d"), ("BH_spin_x", "d"),
("BH_spin_y", "d"), ("BH_spin_z", "d"),
("BH_spin", "d"), ("BH_efficiency", "d")]
for i in range(self.ds.dimensionality * 2 + 1):
for j in range(self.ds.max_level, self.ds.min_level):
sink_fields.append(("particle_prop_%s_%s" % (i, j), "d"))
field_offsets = {}
_pfields = {}
for field, vtype in sink_fields:
if f.tell() >= flen: break
field_offsets["sink", field] = f.tell()
_pfields["sink", field] = vtype
fpu.skip(f, 1)
self.sink_field_offsets = field_offsets
self.sink_field_types = _pfields
self._add_ptype('sink')
def _read_amr_header(self):
hvals = {}
f = open(self.amr_fn, "rb")
for header in ramses_header(hvals):
hvals.update(fpu.read_attrs(f, header))
# That's the header, now we skip a few.
hvals['numbl'] = np.array(hvals['numbl']).reshape(
(hvals['nlevelmax'], hvals['ncpu']))
fpu.skip(f)
if hvals['nboundary'] > 0:
fpu.skip(f, 2)
self.ngridbound = fpu.read_vector(f, 'i').astype("int64")
else:
self.ngridbound = np.zeros(hvals['nlevelmax'], dtype='int64')
free_mem = fpu.read_attrs(f, (('free_mem', 5, 'i'), ) )
ordering = fpu.read_vector(f, 'c')
fpu.skip(f, 4)
# Now we're at the tree itself
# Now we iterate over each level and each CPU.
self.amr_header = hvals
self.amr_offset = f.tell()
self.local_oct_count = hvals['numbl'][self.ds.min_level:, self.domain_id - 1].sum()
self.total_oct_count = hvals['numbl'][self.ds.min_level:,:].sum(axis=0)
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
# read the amr header
with open(self._file_amr, "rb") as f:
amr_header_vals = fpu.read_attrs(f, amr_header_struct, ">")
n_to_skip = len(("tl", "dtl", "tlold", "dtlold", "iSO"))
fpu.skip(f, n_to_skip, 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))
info_str = "Particle populations: " + "%9i " * len(ls_nonzero)
mylog.info(info_str, *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 _read_amr(self):
"""Open the oct file, read in octs level-by-level.
For each oct, only the position, index, level and domain
are needed - its position in the octree is found automatically.
The most important is finding all the information to feed
oct_handler.add
"""
self.oct_handler = RAMSESOctreeContainer(self.ds.domain_dimensions/2,
self.ds.domain_left_edge, self.ds.domain_right_edge)
root_nodes = self.amr_header['numbl'][self.ds.min_level,:].sum()
self.oct_handler.allocate_domains(self.total_oct_count, root_nodes)
fb = open(self.amr_fn, "rb")
fb.seek(self.amr_offset)
f = BytesIO()
f.write(fb.read())
f.seek(0)
mylog.debug("Reading domain AMR % 4i (%0.3e, %0.3e)",
self.domain_id, self.total_oct_count.sum(), self.ngridbound.sum())
def _ng(c, l):
if c < self.amr_header['ncpu']:
ng = self.amr_header['numbl'][l, c]
else:
ng = self.ngridbound[c - self.amr_header['ncpu'] +
self.amr_header['nboundary']*l]
return ng
min_level = self.ds.min_level
# yt max level is not the same as the RAMSES one.
# yt max level is the maximum number of additional refinement levels
# so for a uni grid run with no refinement, it would be 0.
# So we initially assume that.
max_level = 0
nx, ny, nz = (((i-1.0)/2.0) for i in self.amr_header['nx'])
for level in range(self.amr_header['nlevelmax']):
# Easier if do this 1-indexed
for cpu in range(self.amr_header['nboundary'] + self.amr_header['ncpu']):
#ng is the number of octs on this level on this domain
ng = _ng(cpu, level)
if ng == 0: continue
ind = fpu.read_vector(f, "I").astype("int64")
fpu.skip(f, 2)
pos = np.empty((ng, 3), dtype='float64')
pos[:,0] = fpu.read_vector(f, "d") - nx
pos[:,1] = fpu.read_vector(f, "d") - ny
pos[:,2] = fpu.read_vector(f, "d") - nz
#pos *= self.ds.domain_width
#pos += self.dataset.domain_left_edge
fpu.skip(f, 31)
#parents = fpu.read_vector(f, "I")
#fpu.skip(f, 6)
#children = np.empty((ng, 8), dtype='int64')
#for i in range(8):
# children[:,i] = fpu.read_vector(f, "I")
#cpu_map = np.empty((ng, 8), dtype="int64")
#for i in range(8):
# cpu_map[:,i] = fpu.read_vector(f, "I")
#rmap = np.empty((ng, 8), dtype="int64")
#for i in range(8):
# rmap[:,i] = fpu.read_vector(f, "I")
# We don't want duplicate grids.
# Note that we're adding *grids*, not individual cells.
if level >= min_level:
assert(pos.shape[0] == ng)
n = self.oct_handler.add(cpu + 1, level - min_level, pos,
count_boundary = 1)
self._error_check(cpu, level, pos, n, ng, (nx, ny, nz))
if n > 0: max_level = max(level - min_level, max_level)
self.max_level = max_level
self.oct_handler.finalize()
def _read_amr(self):
"""Open the oct file, read in octs level-by-level.
For each oct, only the position, index, level and domain
are needed - its position in the octree is found automatically.
The most important is finding all the information to feed
oct_handler.add
"""
self.oct_handler = RAMSESOctreeContainer(self.ds.domain_dimensions / 2,
self.ds.domain_left_edge,
self.ds.domain_right_edge)
root_nodes = self.amr_header['numbl'][self.ds.min_level, :].sum()
self.oct_handler.allocate_domains(self.total_oct_count, root_nodes)
mylog.debug("Reading domain AMR % 4i (%0.3e, %0.3e)", self.domain_id,
self.total_oct_count.sum(), self.ngridbound.sum())
f = self.amr_file
f.seek(self.amr_offset)
def _ng(c, l):
if c < self.amr_header['ncpu']:
ng = self.amr_header['numbl'][l, c]
else:
ng = self.ngridbound[c - self.amr_header['ncpu'] +
self.amr_header['nboundary'] * l]
return ng
min_level = self.ds.min_level
# yt max level is not the same as the RAMSES one.
# yt max level is the maximum number of additional refinement levels
# so for a uni grid run with no refinement, it would be 0.
# So we initially assume that.
max_level = 0
nx, ny, nz = (((i - 1.0) / 2.0) for i in self.amr_header['nx'])
for level in range(self.amr_header['nlevelmax']):
# Easier if do this 1-indexed
for cpu in range(self.amr_header['nboundary'] +
self.amr_header['ncpu']):
#ng is the number of octs on this level on this domain
ng = _ng(cpu, level)
if ng == 0: continue
ind = fpu.read_vector(f, "I").astype("int64") # NOQA
fpu.skip(f, 2)
pos = np.empty((ng, 3), dtype='float64')
pos[:, 0] = fpu.read_vector(f, "d") - nx
pos[:, 1] = fpu.read_vector(f, "d") - ny
pos[:, 2] = fpu.read_vector(f, "d") - nz
#pos *= self.ds.domain_width
#pos += self.dataset.domain_left_edge
fpu.skip(f, 31)
#parents = fpu.read_vector(f, "I")
#fpu.skip(f, 6)
#children = np.empty((ng, 8), dtype='int64')
#for i in range(8):
# children[:,i] = fpu.read_vector(f, "I")
#cpu_map = np.empty((ng, 8), dtype="int64")
#for i in range(8):
# cpu_map[:,i] = fpu.read_vector(f, "I")
#rmap = np.empty((ng, 8), dtype="int64")
#for i in range(8):
# rmap[:,i] = fpu.read_vector(f, "I")
# We don't want duplicate grids.
# Note that we're adding *grids*, not individual cells.
if level >= min_level:
assert (pos.shape[0] == ng)
n = self.oct_handler.add(cpu + 1,
level - min_level,
pos,
count_boundary=1)
self._error_check(cpu, level, pos, n, ng, (nx, ny, nz))
if n > 0: max_level = max(level - min_level, max_level)
self.max_level = max_level
self.oct_handler.finalize()
# Close AMR file
f.close()
def _parse_parameter_file(self):
"""
Get the various simulation parameters & constants.
"""
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']:
skipped = 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()
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)
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)
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.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']:
skipped = 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()
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)
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)
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 _read_particle_header(self):
if not os.path.exists(self.part_fn):
self.local_particle_count = 0
self.particle_field_offsets = {}
return
f = open(self.part_fn, "rb")
f.seek(0, os.SEEK_END)
flen = f.tell()
f.seek(0)
hvals = {}
attrs = (('ncpu', 1, 'I'), ('ndim', 1, 'I'), ('npart', 1, 'I'))
hvals.update(fpu.read_attrs(f, attrs))
fpu.read_vector(f, 'I')
attrs = (('nstar_tot', 1, 'I'), ('mstar_tot', 1, 'd'),
('mstar_lost', 1, 'd'), ('nsink', 1, 'I'))
hvals.update(fpu.read_attrs(f, attrs))
self.particle_header = hvals
self.local_particle_count = hvals['npart']
# Try reading particle file descriptor
if self._has_part_descriptor:
particle_fields = (_read_part_file_descriptor(
self._part_file_descriptor))
else:
particle_fields = [("particle_position_x", "d"),
("particle_position_y", "d"),
("particle_position_z", "d"),
("particle_velocity_x", "d"),
("particle_velocity_y", "d"),
("particle_velocity_z", "d"),
("particle_mass", "d"),
("particle_identifier", "i"),
("particle_refinement_level", "I")]
if self.ds._extra_particle_fields is not None:
particle_fields += self.ds._extra_particle_fields
ptype = 'io'
field_offsets = {}
_pfields = {}
# Read offsets
for field, vtype in particle_fields:
if f.tell() >= flen: break
field_offsets[ptype, field] = f.tell()
_pfields[ptype, field] = vtype
fpu.skip(f, 1)
iextra = 0
while f.tell() < flen:
iextra += 1
field, vtype = ('particle_extra_field_%i' % iextra, 'd')
particle_fields.append((field, vtype))
field_offsets[ptype, field] = f.tell()
_pfields[ptype, field] = vtype
fpu.skip(f, 1)
if iextra > 0 and not self.ds._warn_extra_fields:
self.ds._warn_extra_fields = True
w = ("Detected %s extra particle fields assuming kind "
"`double`. Consider using the `extra_particle_fields` "
"keyword argument if you have unexpected behavior.")
mylog.warning(w % iextra)
self.particle_field_offsets = field_offsets
self.particle_field_types = _pfields
# Register the particle type
self._add_ptype(ptype)
