"""
Implements classes and functions for handling RAMSES files.
Note that AMR cells are loaded as particles, although this works surprisingly well for most analysis purposes.
In particular SPH image generation routines automatically switch on an appropriate de-noising scheme to provide
effective interpolation between cells.
"""
import csv
import logging
import os
import re
import warnings
from pathlib import Path
import numpy as np
import pynbody.array.shared
from .. import array, config_parser, family, units
from ..analysis.cosmology import age
from ..extern.cython_fortran_utils import FortranFile
from . import SimSnap, namemapper
logger = logging.getLogger('pynbody.snapshot.ramses')
multiprocess_num = int(config_parser.get('ramses', "parallel-read"))
multiprocess = (multiprocess_num > 1)
if multiprocess:
import atexit
import multiprocessing
remote_exec = pynbody.array.shared.shared_array_remote
remote_map = pynbody.array.shared.remote_map
else:
multiprocessing = None
def remote_exec(fn):
return fn
def remote_map(*args, **kwargs):
return list(map(*args[1:], **kwargs))
_float_type = 'd'
_int_type = 'i'
def _timestep_id(basename):
try:
return re.findall("output_([0-9]*)/*$", str(basename))[0]
except IndexError:
return None
def _cpu_id(i):
return str(i).rjust(5, "0")
@remote_exec
def _cpui_count_particles_with_implicit_families(filename, distinguisher_field, distinguisher_type):
with FortranFile(filename) as f:
f.seek(0, 2)
eof_fpos = f.tell()
f.seek(0, 0)
header = f.read_attrs(ramses_particle_header)
npart_this = header['npart']
f.skip(distinguisher_field)
# Avoid end-of-file issues
if f.tell() == eof_fpos:
data = np.array([])
else:
data = f.read_vector(distinguisher_type)
if len(data) > 0:
my_mask = np.array((data != 0), dtype=np.int8) # -> 0 for dm, 1 for star
else:
my_mask = np.zeros(npart_this, dtype=np.int8)
nstar_this = (data != 0).sum()
return npart_this, nstar_this, my_mask
@remote_exec
def _cpui_count_particles_with_explicit_families(filename, family_field, family_type):
assert np.issubdtype(family_type, np.int8)
counts_array = np.zeros(256, dtype=np.int64)
with FortranFile(filename) as f:
header = f.read_attrs(ramses_particle_header)
npart_this = header['npart']
f.skip(family_field)
my_mask = f.read_vector(family_type)
unique_mask_ids, counts = np.unique(my_mask, return_counts=True)
counts_array[unique_mask_ids] = counts
assert sum(counts) == npart_this
return counts_array, my_mask
@remote_exec
def _cpui_load_particle_block(filename, arrays, offset, first_index, type_, family_mask):
with FortranFile(filename) as f:
_header = f.read_attrs(ramses_particle_header)
f.skip(offset)
data = f.read_vector(type_)
for fam_id, ar in enumerate(arrays):
data_this_family = data[family_mask == fam_id]
ind0 = first_index[fam_id]
ind1 = ind0 + len(data_this_family)
ar[ind0:ind1] = data_this_family
def _cpui_level_iterator(cpu, amr_filename, bisection_order, maxlevel, ndim):
with FortranFile(amr_filename) as f:
header = f.read_attrs(ramses_amr_header)
f.skip(13)
n_per_level = f.read_vector(_int_type).reshape((header['nlevelmax'], header['ncpu']))
f.skip(1)
if header['nboundary'] > 0:
f.skip(2)
n_per_level_boundary = f.read_vector(_int_type).reshape((header['nlevelmax'], header['nboundary']))
f.skip(2)
if bisection_order:
f.skip(5)
else:
f.skip(1)
f.skip(3)
offset = np.array(header['ng'], dtype=_float_type) / 2
offset -= 0.5
coords = np.zeros(3, dtype=_float_type)
for level in range(maxlevel or header['nlevelmax']):
# loop through those CPUs with grid data (includes ghost regions)
for cpuf in 1 + np.where(n_per_level[level, :] != 0)[0]:
if cpuf == cpu:
# this is the data we want
f.skip(3) # grid, next, prev index
# store the coordinates in temporary arrays. We only want
# to copy it if the cell is not refined
coords = [
f.read_vector(_float_type) for ar in range(ndim)]
# stick on zeros if we're in less than 3D
coords += [np.zeros_like(coords[0]) for ar in range(3 - ndim)]
f.skip(1 # father index
+ 2 * ndim # nbor index
# son index,cpumap,refinement map
+ 2 * (2 ** ndim)
)
refine = np.array(
[f.read_vector(_int_type) for i in range(2 ** ndim)])
if (level + 1 == maxlevel or level + 1 == header['nlevelmax']):
refine[:] = 0
coords[0] -= offset[0]
coords[1] -= offset[1]
coords[2] -= offset[2]
# x0-=offset[0]; y0-=offset[1]; z0-=offset[2]
yield coords, refine, cpuf, level
else:
# skip ghost regions from other CPUs
f.skip(3 + ndim + 1 + 2 * ndim + 3 * 2 ** ndim)
if header['nboundary'] > 0:
for boundaryf in np.where(n_per_level_boundary[level, :] != 0)[0]:
f.skip(3 + ndim + 1 + 2 * ndim + 3 * 2 ** ndim)
@remote_exec
def _cpui_count_gas_cells(level_iterator_args):
ncell = 0
for coords, refine, cpu, level in _cpui_level_iterator(*level_iterator_args):
ncell += (refine == 0).sum()
return ncell
@remote_exec
def _cpui_load_gas_pos(pos_array, smooth_array, ndim, boxlen, i0, level_iterator_args):
dims = [pos_array[:, i] for i in range(ndim)]
subgrid_index = np.arange(2 ** ndim)[:, np.newaxis]
subgrid_z = np.floor((subgrid_index) / 4)
subgrid_y = np.floor((subgrid_index - 4 * subgrid_z) / 2)
subgrid_x = np.floor(subgrid_index - 2 * subgrid_y - 4 * subgrid_z)
subgrid_x -= 0.5
subgrid_y -= 0.5
subgrid_z -= 0.5
for (x0, y0, z0), refine, cpu, level in _cpui_level_iterator(*level_iterator_args):
dx = boxlen * 0.5 ** (level + 1)
x0 = boxlen * x0 + dx * subgrid_x
y0 = boxlen * y0 + dx * subgrid_y
z0 = boxlen * z0 + dx * subgrid_z
mark = np.where(refine == 0)
i1 = i0 + len(mark[0])
for q, d in zip(dims, [x0, y0, z0][:ndim]):
q[i0:i1] = d[mark]
smooth_array[i0:i1] = dx
i0 = i1
_gv_load_hydro = 0
_gv_load_gravity = 1
_gv_load_rt = 2
@remote_exec
def _cpui_load_gas_vars(dims, maxlevel, ndim, filename, cpu, lia, i1,
mode=_gv_load_hydro):
logger.info("Loading data from CPU %d", cpu)
nvar = len(dims)
grid_info_iter = _cpui_level_iterator(*lia)
with FortranFile(filename) as f:
exact_nvar = False
if mode is _gv_load_hydro:
header = f.read_attrs(ramses_hydro_header)
nvar_file = header['nvarh']
elif mode is _gv_load_gravity:
header = f.read_attrs(ramses_grav_header)
nvar_file = 4
elif mode is _gv_load_rt:
header = f.read_attrs(ramses_rt_header)
nvar_file = header['nrtvar']
exact_nvar = True
else:
raise ValueError("Unknown RAMSES load mode")
if nvar_file != nvar and exact_nvar:
raise ValueError("Wrong number of variables in RAMSES dump")
elif nvar_file < nvar:
warnings.warn(
"Fewer hydro variables are in this RAMSES dump than are defined in config.ini (expected %d, got %d in file)" % (
nvar, nvar_file), RuntimeWarning)
nvar = nvar_file
dims = dims[:nvar]
elif nvar_file > nvar:
warnings.warn("More hydro variables (%d) are in this RAMSES dump than are defined in config.ini (%d)" % (
nvar_file, nvar), RuntimeWarning)
for level in range(maxlevel or header['nlevelmax']):
for cpuf in range(1, header['ncpu'] + 1):
flevel = f.read_int()
ncache = f.read_int()
assert flevel - 1 == level
if ncache > 0:
if cpuf == cpu:
coords, refine, gi_cpu, gi_level = next(grid_info_iter)
mark = np.where(refine == 0)
assert gi_level == level
assert gi_cpu == cpu
if cpuf == cpu and len(mark[0]) > 0:
for icel in range(2 ** ndim):
i0 = i1
i1 = i0 + (refine[icel] == 0).sum()
for ar in dims:
ar[i0:i1] = f.read_vector(
_float_type)[(refine[icel] == 0)]
f.skip(nvar_file - nvar)
else:
f.skip((2 ** ndim) * nvar_file)
for boundary in range(header['nboundary']):
flevel = f.read_int()
ncache = f.read_int()
if ncache > 0:
f.skip((2 ** ndim) * nvar_file)
ramses_particle_header = (
('ncpu', 1, 'i'),
('ndim', 1, 'i'),
('npart', 1, 'i'),
('randseed', -1, 'i'),
('nstar', 1, 'i'),
('mstar', 1, 'd'),
('mstar_lost', 1, 'd'),
('nsink', 1, 'i')
)
ramses_amr_header = (
('ncpu', 1, 'i'),
('ndim', 1, 'i'),
('ng', 3, 'i'),
('nlevelmax', 1, 'i'),
('ngridmax', 1, 'i'),
('nboundary', 1, 'i'),
('ngrid', 1, 'i'),
('boxlen', 1, 'd')
)
ramses_hydro_header = (
('ncpu', 1, 'i'),
('nvarh', 1, 'i'),
('ndim', 1, 'i'),
('nlevelmax', 1, 'i'),
('nboundary', 1, 'i'),
('gamma', 1, 'd')
)
ramses_grav_header = (
('ncpu', 1, 'i'),
('ndim', 1, 'i'),
('nlevelmax', 1, 'i'),
('nboundary', 1, 'i')
)
ramses_rt_header = (
('ncpu', 1, 'i'),
('nrtvar', 1, 'i'),
('ndim', 1, 'i'),
('nlevelmax', 1, 'i'),
('nboundary', 1, 'i'),
('gamma', 1, 'd')
)
TYPE_MAP = {'i4': 'i',
'i8': 'q',
'f4': 'f',
'f8': 'd'}
particle_blocks = list(map(
str.strip, config_parser.get('ramses', "particle-blocks").split(",")))
particle_format = [TYPE_MAP[str.strip(e)] for e in config_parser.get('ramses', "particle-format").split(",")]
hydro_blocks = [_.strip() for _ in config_parser.get('ramses', "hydro-blocks").split(",")]
grav_blocks = [_.strip() for _ in config_parser.get('ramses', "gravity-blocks").split(",")]
rt_blocks = [_.strip() for _ in config_parser.get('ramses', 'rt-blocks', raw=True).split(",")]
particle_distinguisher = [_.strip() for _ in config_parser.get('ramses', 'particle-distinguisher').split(",")]
positive_typemap = [family.get_family(str.strip(x)) for x in
config_parser.get('ramses', 'type-mapping-positive').split(",")]
negative_typemap = [family.get_family(str.strip(x)) for x in
config_parser.get('ramses', 'type-mapping-negative').split(",")]
def _read_descriptor(fname):
"""Read a RAMSES file descriptor and return a list of the variable names."""
description = []
name_mapping = namemapper.AdaptiveNameMapper('ramses-name-mapping')
with open(fname) as fd:
if fd.readline() != "# version: 1\n":
raise OSError("Wrong file format")
fd.readline() # ivar, variable_name, variable_type
for line in fd.readlines():
i, name, dtype = (_.strip() for _ in line.split(","))
description.append(name_mapping(name, reverse=True))
return description
[docs]
class RamsesSnap(SimSnap):
"""Implements loading of Ramses snapshots.
Note that AMR cells are loaded as particles, although this works surprisingly well for most analysis purposes.
In particular SPH image generation routines automatically switch on an appropriate de-noising scheme to provide
effective interpolation between cells.
"""
reader_pool = None
[docs]
def __init__(self, dirname, cpus=None, maxlevel=None, with_gas=True, force_gas=False, times_are_proper=None,
negative_iords_on_purpose=False):
"""
Initialize a RamsesSnap.
Parameters
----------
dirname : str
Directory containing the RAMSES output
cpus : list of int, optional
A list of the CPU IDs to load. If not set, load all
CPU's data.
maxlevel : int, optional
The maximum refinement level to load. If not set, the
deepest level is loaded.
with_gas : bool, optional
If False, never load any gas cells (particles only).
Default is True
force_gas : bool, optional
If True, load the AMR cells as "gas particles" even if
they don't actually contain gas in the run. Default is
False.
times_are_proper : bool, optional
If True, the times in the output are assumed to be proper
times. If False, they are assumed to be conformal. If
None (default), assume proper for non-cosmological simulations
and conformal for cosmological ones.
"""
global config
super().__init__()
self.__setup_parallel_reading()
if isinstance(dirname, Path):
dirname = str(dirname)
self._timestep_id = _timestep_id(dirname)
self._filename = dirname
if os.path.isdir(dirname):
self._dirname = dirname
else:
self._dirname = os.path.split(dirname)[0]
self._translate_array_name = namemapper.AdaptiveNameMapper('ramses-name-mapping')
self._load_sink_data_to_temporary_store()
self._load_infofile()
self._load_namelistfile()
self._setup_particle_descriptor()
assert self._info['ndim'] <= 3
if self._info['ndim'] < 3:
warnings.warn(
"Snapshots with less than three dimensions are supported only experimentally", RuntimeWarning)
self._ndim = self._info['ndim']
self.ncpu = self._info['ncpu']
if cpus is not None:
self._cpus = cpus
else:
self._cpus = list(range(1, self.ncpu + 1))
self._maxlevel = maxlevel
type_map = self._count_particles()
has_gas = os.path.exists(
self._hydro_filename(1)
) or force_gas
if not with_gas:
has_gas = False
if times_are_proper is not None:
self.times_are_proper = times_are_proper
if negative_iords_on_purpose is not None:
self._negative_iords_on_purpose = negative_iords_on_purpose
ngas = self._count_gas_cells() if has_gas else 0
if ngas > 0:
type_map[family.gas] = ngas
count = 0
for fam in type_map:
self._family_slice[fam] = slice(count, count + type_map[fam])
count += type_map[fam]
self._num_particles = count
self._load_fluid_descriptors()
self._load_rt_infofile()
self._decorate()
self._transfer_sink_data_to_family_array()
def __setup_parallel_reading(self):
if multiprocess:
self._shared_arrays = True
if (RamsesSnap.reader_pool is None):
RamsesSnap.reader_pool = multiprocessing.Pool(multiprocess_num)
atexit.register(self._cleanup_reader_pool)
@classmethod
def _cleanup_reader_pool(cls):
"""Clean up the multiprocessing pool when the process exits"""
if cls.reader_pool is not None:
cls.reader_pool.close()
cls.reader_pool.join()
cls.reader_pool = None
def _load_fluid_descriptors(self):
types = ["hydro", "grav"]
default_blocks = [hydro_blocks, grav_blocks]
descriptors_fnames = [os.path.join(self._filename, f"{ftype}_file_descriptor.txt") for ftype in types]
for desc_type, default_block, descriptor_fname in zip(types, default_blocks, descriptors_fnames):
try:
block = _read_descriptor(descriptor_fname)
except (FileNotFoundError, OSError):
block = default_block
setattr(self, f"_{desc_type}_blocks", block)
def _load_rt_infofile(self):
self._rt_blocks = []
self._rt_blocks_3d = set()
try:
with open(os.path.join(self._filename, f"info_rt_{self._timestep_id}.txt")) as f:
lines = f.readlines()
except OSError:
return
self._load_info_from_specified_file(lines)
for group in range(self._info['nGroups']):
for block in rt_blocks:
self._rt_blocks.append(block % group)
self._rt_unit = self._info['unit_pf'] * units.Unit("cm^-2 s^-1")
for block in self._rt_blocks:
self._rt_blocks_3d.add(self._array_name_1D_to_ND(block) or block)
def _load_info_from_specified_file(self, lines):
for line in lines:
if '=' in line:
name, val = list(map(str.strip, line.split('=')))
try:
if '.' in val:
self._info[name] = float(val)
else:
self._info[name] = int(val)
except ValueError:
self._info[name] = val
def _load_infofile(self):
self._info = {}
info_fname = os.path.join(self._filename, f"info_{self._timestep_id}.txt")
header_fname = os.path.join(self._filename, f"header_{self._timestep_id}.txt")
with open(info_fname) as f:
self._load_info_from_specified_file(f)
try:
# most of this file is unhelpful, but depending on the ramses
# version, there may be information on the particle fields present
with open(header_fname) as f:
for line in f:
if "level" in line:
self._info['particle-blocks'] = line.split()
except OSError:
warnings.warn(
"No header file found -- no particle block information available")
def _load_namelist_from_specified_file(self, f):
for line in f:
line = line.split("!")[0] # remove fortran comments
if '=' in line:
name, val = map(str.strip, line.split('='))
if val == ".true.":
self._namelist[name] = True
elif val == ".false.":
self._namelist[name] = False
else:
try:
if '.' in val:
self._namelist[name] = float(val)
else:
self._namelist[name] = int(val)
except ValueError:
self._namelist[name] = val
def _load_namelistfile(self):
self._namelist = {}
namelist_file = os.path.join(self._filename, "namelist.txt")
if os.path.exists(namelist_file):
with open(namelist_file) as f:
try:
self._load_namelist_from_specified_file(f)
except ValueError:
warnings.warn("Namelist found but unable to read.")
else:
warnings.warn("No namelist file found.")
def _setup_particle_descriptor(self):
try:
self._load_particle_descriptor()
except OSError:
self._guess_particle_descriptor()
self._has_explicit_particle_families = 'family' in self._particle_blocks
def _load_particle_descriptor(self):
with open(os.path.join(self._filename, "part_file_descriptor.txt")) as f:
self._particle_blocks = []
self._particle_types = []
for line in f:
if not line.startswith("#"):
ivar, name, dtype = list(map(str.strip, line.split(",")))
self._particle_blocks.append(self._translate_array_name(name, reverse=True))
self._particle_types.append(dtype)
self._particle_blocks_are_explictly_known = True
def _guess_particle_descriptor(self):
# determine whether we have explicit information about
# what particle blocks are present
self._particle_blocks_are_explictly_known = False
self._particle_types = particle_format
if 'particle-blocks' in self._info:
self._particle_blocks_are_explictly_known = True
self._particle_blocks = self._info['particle-blocks']
self._particle_blocks = ['x', 'y', 'z', 'vx', 'vy', 'vz'] + self._particle_blocks[2:]
else:
self._particle_blocks = particle_blocks
if len(self._particle_types) < len(self._particle_blocks):
warnings.warn("Some fields do not have format configured - assuming they are doubles", RuntimeWarning)
type_ = 'd'
self._particle_types += [type_] * (len(self._particle_blocks) - len(self._particle_types))
def _particle_filename(self, cpu_id):
return os.path.join(self._filename, f"part_{self._timestep_id}.out{_cpu_id(cpu_id)}")
def _amr_filename(self, cpu_id):
return os.path.join(self._filename, f"amr_{self._timestep_id}.out{_cpu_id(cpu_id)}")
def _hydro_filename(self, cpu_id):
return os.path.join(self._filename, f"hydro_{self._timestep_id}.out{_cpu_id(cpu_id)}")
def _grav_filename(self, cpu_id):
return os.path.join(self._filename, f"grav_{self._timestep_id}.out{_cpu_id(cpu_id)}")
def _rt_filename(self, cpu_id):
return os.path.join(self._filename, f"rt_{self._timestep_id}.out{_cpu_id(cpu_id)}")
def _sink_filename(self):
return os.path.join(self._filename, f"sink_{self._timestep_id}.csv")
def _count_particles(self):
if self._has_explicit_particle_families:
return self._count_particles_using_explicit_families()
else:
ndm, nstar = self._count_particles_using_implicit_families()
return {family.dm: ndm, family.star: nstar}
def _has_particle_file(self):
"""Check whether the output has a particle file available"""
if len(self._cpus) > 0:
return os.path.exists(self._particle_filename(self._cpus[0]))
else:
return False
def _count_particles_using_explicit_families(self):
"""Returns an ordered dictionary of family types based on the new explicit RAMSES particle file format"""
if not self._has_particle_file():
return {}
family_block = self._particle_blocks.index('family')
family_dtype = self._particle_types[family_block]
self._particle_family_ids_on_disk = []
self._particle_file_start_indices = []
results = remote_map(self.reader_pool,
_cpui_count_particles_with_explicit_families,
[self._particle_filename(i) for i in self._cpus],
[family_block] * len(self._cpus),
[family_dtype] * len(self._cpus))
aggregate_counts = np.zeros(256, dtype=np.int64)
for counts, family_ids in results:
aggregate_counts += counts
self._particle_family_ids_on_disk.append(family_ids)
# The above family IDs are defined according to ramses' own internal system. We now need
# to map them to pynbody family types
nonzero_families = np.nonzero(aggregate_counts)[0]
# map such that negative integers are correctly represented
ramses_id_to_internal_id = np.zeros(256, dtype=np.uint8)
internal_id_to_family = []
aggregate_counts_remapped = np.zeros(256, dtype=np.int64)
for ramses_family_id in nonzero_families:
if ramses_family_id > 128 or ramses_family_id == 0:
neg_offset = (256 - ramses_family_id) % 256
if neg_offset > len(negative_typemap):
pynbody_family = negative_typemap[-1]
else:
pynbody_family = negative_typemap[neg_offset]
else:
if ramses_family_id > len(positive_typemap):
pynbody_family = positive_typemap[-1]
else:
pynbody_family = positive_typemap[ramses_family_id - 1]
if pynbody_family in internal_id_to_family:
internal_id = internal_id_to_family.index(pynbody_family)
else:
internal_id = len(internal_id_to_family)
internal_id_to_family.append(pynbody_family)
ramses_id_to_internal_id[ramses_family_id] = internal_id
aggregate_counts_remapped[internal_id] += aggregate_counts[ramses_family_id]
# perform the remapping for our stored particle identifiers
for fid in self._particle_family_ids_on_disk:
fid[:] = ramses_id_to_internal_id[fid]
return_d = {}
self._particle_file_start_indices = [[] for x in results]
for internal_family_id in range(256):
if aggregate_counts_remapped[internal_family_id] > 0:
fam = internal_id_to_family[internal_family_id]
count = aggregate_counts_remapped[internal_family_id]
return_d[fam] = count
startpoint = 0
for i, fid in enumerate(self._particle_family_ids_on_disk):
self._particle_file_start_indices[i].append(startpoint)
startpoint += (fid == internal_family_id).sum()
assert startpoint == count
n_sinks = self._count_sink_particles()
if n_sinks > 0:
return_d[self._sink_family] = n_sinks
return return_d
def _load_sink_data_to_temporary_store(self):
if not os.path.exists(self._sink_filename()):
self._after_load_sink_data_failure(warn=False)
return
self._sink_family = family.get_family(config_parser.get('ramses', 'type-sink'))
with open(self._sink_filename()) as sink_file:
reader = csv.reader(sink_file, skipinitialspace=True)
data = list(reader)
if len(data) < 2:
self._after_load_sink_data_failure()
return
column_names = data[0]
dimensions = data[1]
data = np.array(data[2:], dtype=object)
if column_names[0][0] != '#' or dimensions[0][0] != '#':
self._after_load_sink_data_failure()
return
self._fix_fortran_missing_exponent(data)
column_names[0] = column_names[0][1:].strip()
dimensions[0] = dimensions[0][1:].strip()
self._sink_column_names = [self._translate_array_name(c, reverse=True)
for c in column_names]
self._sink_dimensions = dimensions
self._sink_data = data
def _after_load_sink_data_failure(self, warn=True):
if warn:
warnings.warn("Unexpected format in file %s -- sink data has not been loaded" % self._sink_filename())
self._sink_column_names = self._sink_dimensions = self._sink_data = []
self._sink_family = None
@staticmethod
def _fix_fortran_missing_exponent(data_array):
flattened_data = data_array.flat
for i in range(len(flattened_data)):
d = flattened_data[i]
if "-" in d and "E" not in d:
flattened_data[i] = "E-".join(d.split("-"))
def _transfer_sink_data_to_family_array(self):
if len(self._sink_data) == 0:
return
target = self[self._sink_family]
for column, dimension, data in zip(self._sink_column_names,
self._sink_dimensions,
self._sink_data.T):
dtype = np.float64
if column == "id":
dtype = np.int64
target[column] = data.astype(dtype)
unit = dimension.replace("m", "g").replace("l", "cm").replace("t", "yr")
if unit == "1":
target[column].units = "1"
else:
target[column].set_units_like(unit)
def _count_sink_particles(self):
return len(self._sink_data)
def _count_particles_using_implicit_families(self):
"""Returns ndm, nstar where ndm is the number of dark matter particles
and nstar is the number of star particles."""
npart = 0
nstar = 0
dm_i0 = 0
star_i0 = 0
self._particle_family_ids_on_disk = []
self._particle_file_start_indices = []
if not os.path.exists(self._particle_filename(1)):
return 0, 0
if not self._particle_blocks_are_explictly_known:
distinguisher_field = int(particle_distinguisher[0])
distinguisher_type = TYPE_MAP[particle_distinguisher[1]]
else:
# be more cunning about finding the distinguisher field (likely 'age') -
# as it may have moved around in some patches of ramses
distinguisher_name = particle_blocks[int(particle_distinguisher[0])]
try:
distinguisher_field = self._particle_blocks.index(distinguisher_name)
except ValueError:
# couldn't find the named distinguisher field. Fall back to using index.
distinguisher_field = int(particle_distinguisher[0])
if len(self._particle_blocks) > distinguisher_field:
pb_name = "%r" % self._particle_blocks[distinguisher_field]
else:
pb_name = "at offset %d" % distinguisher_field
warnings.warn(
"Using field %s to distinguish stars. If this is wrong, try editing your config.ini, section [ramses], entry particle-distinguisher." % pb_name)
distinguisher_type = self._particle_types[distinguisher_field]
results = remote_map(self.reader_pool,
_cpui_count_particles_with_implicit_families,
[self._particle_filename(i) for i in self._cpus],
[distinguisher_field] * len(self._cpus),
[distinguisher_type] * len(self._cpus))
for npart_this, nstar_this, family_ids in results:
self._particle_file_start_indices.append((dm_i0, star_i0))
dm_i0 += (npart_this - nstar_this)
star_i0 += nstar_this
npart += npart_this
nstar += nstar_this
self._particle_family_ids_on_disk.append(family_ids)
return npart - nstar, nstar
def _count_gas_cells(self):
ncells = remote_map(self.reader_pool, _cpui_count_gas_cells,
[self._cpui_level_iterator_args(xcpu) for xcpu in self._cpus])
self._gas_i0 = np.cumsum([0] + ncells)[:-1]
return np.sum(ncells)
def _cpui_level_iterator_args(self, cpu=None):
if cpu:
return cpu, self._amr_filename(cpu), self._info['ordering type'] == 'bisection', self._maxlevel, self._ndim
else:
return [self._cpui_level_iterator_args(x) for x in self._cpus]
def _level_iterator(self):
"""Walks the AMR grid levels on disk, yielding a tuplet of coordinates and
refinement maps and levels working through the available CPUs and levels."""
for cpu in self._cpus:
yield from _cpui_level_iterator(*self._cpui_level_iterator_args(cpu))
def _load_gas_pos(self):
self.gas['pos'].set_default_units()
smooth = self.gas['smooth']
smooth.set_default_units()
boxlen = self._info['boxlen']
remote_map(self.reader_pool,
_cpui_load_gas_pos,
[self.gas['pos']] * len(self._cpus),
[self.gas['smooth']] * len(self._cpus),
[self._ndim] * len(self._cpus),
[boxlen] * len(self._cpus),
self._gas_i0,
self._cpui_level_iterator_args())
def _load_gas_vars(self, mode=_gv_load_hydro):
dims = []
for i in [self._hydro_blocks, self._grav_blocks, self._rt_blocks][mode]:
if i not in self.gas:
self.gas._create_array(i)
if self._ndim < 3 and i[-1] == 'z':
continue
if self._ndim < 2 and i[-1] == 'y':
continue
dims.append(self.gas[i])
self.gas[i].set_default_units()
if not os.path.exists(self._hydro_filename(1)):
# Case where force_gas = True, make sure rho is non-zero and such that mass=1.
# This does not keep track of units for mass or rho since their value is enforced.
logger.info("No hydro file found, gas likely from force_gas=True => hard setting rho gas")
self.gas['rho'][:] = 1.0
self.gas['rho'] /= (np.array(self.gas['smooth']) ** 3)
self.gas['rho'].set_default_units()
_grid_info_iter = self._level_iterator()
logger.info("Loading %s files", ['hydro', 'grav', 'rt'][mode])
filenamer = [self._hydro_filename, self._grav_filename, self._rt_filename][mode]
remote_map(self.reader_pool,
_cpui_load_gas_vars,
[dims] * len(self._cpus),
[self._maxlevel] * len(self._cpus),
[self._ndim] * len(self._cpus),
[filenamer(i) for i in self._cpus],
self._cpus,
self._cpui_level_iterator_args(),
self._gas_i0,
[mode] * len(self._cpus))
if mode is _gv_load_gravity:
# potential is awkwardly in expected units divided by box size
self.gas['phi'] *= self._info['boxlen']
logger.info("Done")
def _iter_particle_families(self):
for f in self.families():
if f is not family.gas and f is not self._sink_family:
yield f
def _create_array_for_particles(self, name, type_):
for f in self._iter_particle_families():
if name not in list(self[f].keys()):
for f in self._iter_particle_families():
self[f]._create_array(name, dtype=type_)
def _load_particle_block(self, blockname):
offset = self._particle_blocks.index(blockname)
_type = self._particle_types[offset]
self._create_array_for_particles(blockname, _type)
arrays = []
for f in self._iter_particle_families():
arrays.append(self[f][blockname])
try:
remote_map(self.reader_pool,
_cpui_load_particle_block,
[self._particle_filename(i) for i in self._cpus],
[arrays] * len(self._cpus),
[offset] * len(self._cpus),
self._particle_file_start_indices,
[_type] * len(self._cpus),
self._particle_family_ids_on_disk
)
except Exception:
warnings.warn(
"Exception encountered while reading %r; is there an incompatibility in your Ramses configuration?" % blockname)
del self[blockname]
raise
# The potential is awkwardly not in physical units, but in
# physical units divided by the box size. This was different
# in an intermediate version, but then later made consistent
# with the AMR phi output. So, we need to make a correction here
# IF we are dealing with the latest ramses format.
if self._particle_blocks_are_explictly_known and blockname == 'phi':
for f in self._iter_particle_families():
self[f]['phi'] *= self._info['boxlen']
def _load_particle_cpuid(self):
ind0_dm = 0
ind0_star = 0
for i, fam_mask in zip(self._cpus, self._particle_family_ids_on_disk):
mask_dm = fam_mask == 0
mask_st = fam_mask == 1
ind1_dm = ind0_dm + mask_dm.sum()
ind1_star = ind0_star + mask_st.sum()
self.dm['cpu'][ind0_dm:ind1_dm] = i
self.star['cpu'][ind0_star:ind1_star] = i
ind0_dm, ind0_star = ind1_dm, ind1_star
def _load_gas_cpuid(self):
gas_cpu_ar = self.gas['cpu']
i1 = 0
for coords, refine, cpu, level in self._level_iterator():
for cell in range(2 ** self._ndim):
i0 = i1
i1 = i0 + (refine[cell] == 0).sum()
gas_cpu_ar[i0:i1] = cpu
[docs]
def loadable_keys(self, fam=None):
if fam is None:
keys = None
for f0 in self.families():
if keys:
keys.intersection_update(self.loadable_keys(f0))
else:
keys = set(self.loadable_keys(f0))
else:
if fam is family.gas:
keys = ['x', 'y', 'z', 'smooth'] + self._hydro_blocks + self._rt_blocks
elif fam in self._iter_particle_families():
keys = self._particle_blocks
elif fam is self._sink_family:
keys = set(self._sink_column_names)
else:
keys = set()
keys_ND = set()
for key in keys:
keys_ND.add(self._array_name_1D_to_ND(key) or key)
return list(keys_ND)
@property
def is_cosmological(self):
return not self._not_cosmological()
@property
def is_not_cosmological(self):
return self._not_cosmological()
def _not_cosmological(self):
not_cosmological = True
if "cosmo" in self._namelist and self._namelist["cosmo"]:
not_cosmological = False
if not self._namelist:
warnings.warn("Namelist file either not found or unable to read. " +
"Guessing whether run is cosmological from cosmological parameters assuming flat LCDM.")
not_cosmological = (self._info['omega_k'] == self._info['omega_l'] == 0)
return not_cosmological
@property
def has_negative_iords(self):
return self._has_negative_iords()
def _has_negative_iords(self):
return self.dm['iord'].min() < 0
@property
def has_potential_negative_iords_bug(self):
return self._has_potential_negative_iords_bug()
def _has_potential_negative_iords_bug(self):
if self._negative_iords_on_purpose:
return False
if self.is_cosmological and self.has_negative_iords:
return True
return False
def _convert_tform(self):
# Copy the existing t array in weird Ramses format into a hidden raw array
self.star['tform_raw'] = self.star['tform']
self.star['tform_raw'].units = self._file_units_system[1]
if self.is_cosmological:
# Only attempt tform conversion for cosmological runs. The built-in tforms for isolated runs
# are actually meaningful (issue 554)
from ..analysis import ramses_util
# Replace the tform array by its usual meaning using the birth files
ramses_util.get_tform(self, times_are_proper=self.times_are_proper)
@property
def times_are_proper(self) -> bool:
"""Best guess for whether the times in the output (e.g. star formation times) are proper times"""
if hasattr(self, "_is_using_proper_time"):
# Already set, skipping
pass
elif config_parser.has_option("ramses", "proper_time"):
self.times_are_proper = config_parser.getboolean(
"ramses", "proper_time"
)
elif self.is_not_cosmological:
self.times_are_proper = True
else:
# If we detect an RT file, assume proper time
iout = self._timestep_id
rt_file_candidates = (
os.path.join(
self._dirname,
f"rt_{iout}.out{icpu:05d}",
)
for icpu in self._cpus
)
for rt_file in rt_file_candidates:
if not os.path.exists(rt_file):
continue
choice = "proper"
reason = f"one RT file was detected ({rt_file})"
self.times_are_proper = True
break
else:
choice = "conformal"
reason = "no RT file was found"
self.times_are_proper = False
warnings.warn(
f"Assumed times to be in {choice} units because {reason}. "
"If this is incorrect, pass the `times_are_proper` keyword "
"argument when loading the dataset, or set the option `proper_time` "
"in your .pynbodyrc."
)
return self._is_using_proper_time
@times_are_proper.setter
def times_are_proper(self, value):
self._is_using_proper_time = value
def _convert_metal_name(self):
# Name of ramses metallicity has no 's' at the end, contrary to tipsy and gadget
# Correcting this prevents analysis routines relying on 'metals' field from breaking.
self.star['metals'] = self.star['metal']
def _load_array(self, array_name, fam=None):
# Framework always calls with 3D name. Ramses particle blocks are
# stored as 1D slices.
if array_name == 'cpu':
self['cpu'] = np.zeros(len(self), dtype=int)
self._load_particle_cpuid()
self._load_gas_cpuid()
elif fam is not family.gas and fam is not None:
# Deal with tform for stars that require extra conversion
if array_name in ('tform', 'tform_raw'):
if 'tform' in self._particle_blocks: # Only attempt these conversion if tform is actually on disc (Issue #689)
self._load_particle_block('tform')
self._convert_tform()
# Deal with metals for stars that have extra name mapping
elif array_name in ('metals', 'metal'):
if 'metal' in self._particle_blocks:
self._load_particle_block('metal')
self._convert_metal_name()
elif array_name in self._split_arrays:
for array_1D in self._array_name_ND_to_1D(array_name):
self._load_particle_block(array_1D)
elif array_name in self._particle_blocks:
self._load_particle_block(array_name)
else:
raise OSError("No such array on disk")
elif fam is family.gas:
if array_name == 'pos' or array_name == 'smooth':
if 'pos' not in self.gas:
self.gas._create_array('pos', 3)
if 'smooth' not in self.gas:
self.gas._create_array('smooth')
self._load_gas_pos()
elif array_name == 'vel' or array_name in self._hydro_blocks:
self._load_gas_vars()
elif array_name in self._grav_blocks:
self._load_gas_vars(1)
elif array_name in self._rt_blocks_3d:
warnings.warn("Loading RT data from disk. Photon densities are stored in flux units by Ramses and need "
"to be multiplied by the reduced speed of light of the run to obtain a physical number. "
"This is currently left to the user, see issue 542 for more discussion.")
self._load_gas_vars(_gv_load_rt)
for u_block in self._rt_blocks_3d:
self[fam][u_block].units = self._rt_unit
elif array_name == 'mass':
# Very special case. If 'mass' has been created already for particle blocks (or sink blocks), and the
# user requests it across the whole simulation, the SimSnap framework will issue this request rather
# than a request to load across all families (handled below). If we raise an exception, the user
# will see a confusing message about mass being unavailable for gas. So instead we redirect this to
# a derived array, which will result in a full mass array being returned (good) and a warning about
# conjoining derived and non-derived arrays (logical, at least).
self._derive_array('mass', fam)
else:
raise OSError("No such array on disk")
elif fam is None and array_name in ['pos', 'vel']:
# synchronized loading of pos/vel information
if 'pos' not in self:
self._create_array('pos', 3)
if 'vel' not in self:
self._create_array('vel', 3)
if 'smooth' not in self.gas:
self.gas._create_array('smooth')
if len(self.gas) > 0:
self._load_gas_pos()
self._load_gas_vars()
# the below triggers loading ALL particles, not just DM
for name in 'x', 'y', 'z', 'vx', 'vy', 'vz':
self._load_particle_block(name)
elif fam is None and array_name == 'mass':
# mass has to be handled separately because it is present as a particle block
# but not as a gas block -- there it will be derived. It is also present as a sink block,
# which is again handled differently. So somehow we need to bring all of this together in
# a way which doesn't baffle users.
self._create_array('mass')
self._load_particle_block('mass')
self['mass'].set_default_units()
if len(self.gas) > 0:
gasmass = mass(self.gas)
gasmass.convert_units(self['mass'].units)
self.gas['mass'] = gasmass
else:
raise OSError("No such array on disk")
@staticmethod
def _can_load(f):
tsid = _timestep_id(f)
if tsid:
return os.path.isdir(f) and os.path.exists(os.path.join(f, f"info_{tsid}.txt"))
return False
@RamsesSnap.decorator
def _translate_info(sim):
if sim._info['H0'] > 1e-3:
sim.properties['a'] = sim._info['aexp']
sim.properties['omegaM0'] = sim._info['omega_m']
sim.properties['omegaL0'] = sim._info['omega_l']
sim.properties['h'] = sim._info['H0'] / 100.
# N.B. these conversion factors provided by ramses already have the
# correction from comoving to physical units
d_unit = sim._info['unit_d'] * units.Unit("g cm^-3")
t_unit = sim._info['unit_t'] * units.Unit("s")
l_unit = sim._info['unit_l'] * units.Unit("cm")
sim.properties['boxsize'] = sim._info['boxlen'] * l_unit
if sim.is_not_cosmological:
sim.properties['time'] = sim._info['time'] * t_unit
else:
sim.properties["time"] = age(sim, unit="Gyr") * units.Unit("Gyr")
sim._file_units_system = [d_unit, t_unit, l_unit]
[docs]
@RamsesSnap.derived_array
def mass(sim):
"""Gas mass estimated from cell size and density"""
return sim['rho'] * sim['smooth'] ** 3
[docs]
@RamsesSnap.derived_array
def temp(sim):
"""Gas temperature derived from pressure and density """
# Has to be redefined and rederived here from Ramses native variables
# to avoid running into circular dependencies with the traditional derived definition
# Now uses the self-consistent molecular weight field from pynbody (issue 598)
from ..derived import mu
mu_est = array.SimArray(np.ones(len(sim)), units="1")
for i in range(5):
temp = ((sim['p'] / sim['rho']) * (mu_est * units.m_p / units.k)).in_units("K")
mu_est = mu(sim, t0=temp)
return temp
