def get_pbar(title, maxval, parallel=False):
"""
This returns a progressbar of the most appropriate type, given a *title*
and a *maxval*.
"""
maxval = max(maxval, 1)
from yt.config import ytcfg
if (
ytcfg.getboolean("yt", "suppressStreamLogging")
or ytcfg.getboolean("yt", "__withintesting")
or maxval == 1
):
return DummyProgressBar()
elif ytcfg.getboolean("yt", "__parallel"):
# If parallel is True, update progress on root only.
if parallel:
if is_root():
return TqdmProgressBar(title, maxval)
else:
return DummyProgressBar()
else:
return ParallelProgressBar(title, maxval)
pbar = TqdmProgressBar(title, maxval)
return pbar
def get_pbar(title, maxval):
"""
This returns a progressbar of the most appropriate type, given a *title*
and a *maxval*.
"""
from yt.config import ytcfg
if ytcfg.getboolean("yt", "inGui"):
if maxval > ytcfg.getint("reason", "minpbar"): # Arbitrary number
return GUIProgressBar(title, maxval)
else:
return DummyProgressBar()
elif ytcfg.getboolean("yt", "suppressStreamLogging"):
return DummyProgressBar()
elif ytcfg.getboolean("yt", "__parallel"):
return ParallelProgressBar(title, maxval)
elif "SAGE_ROOT" in os.environ:
try:
from sage.server.support import EMBEDDED_MODE
if EMBEDDED_MODE: return DummyProgressBar()
except:
pass
elif "CODENODE" in os.environ:
return DummyProgressBar()
widgets = [
title,
pb.Percentage(), ' ',
pb.Bar(marker=pb.RotatingMarker()), ' ',
pb.ETA(), ' '
]
pbar = pb.ProgressBar(widgets=widgets, maxval=maxval).start()
return pbar
def time_execution(func):
"""
Decorator for seeing how long a given function takes, depending on whether
or not the global 'yt.timefunctions' config parameter is set.
This can be used like so:
.. code-block:: python
@time_execution
def some_longrunning_function(...):
"""
@wraps(func)
def wrapper(*arg, **kw):
t1 = time.time()
res = func(*arg, **kw)
t2 = time.time()
mylog.debug('%s took %0.3f s', func.func_name, (t2 - t1))
return res
from yt.config import ytcfg
if ytcfg.getboolean("yt", "timefunctions") == True:
return wrapper
else:
return func
def __init__(self):
self.counters = defaultdict(lambda: 0.0)
self.counting = defaultdict(lambda: False)
self.starttime = defaultdict(lambda: 0)
self.endtime = defaultdict(lambda: 0)
self._on = ytcfg.getboolean("yt", "timefunctions")
self.exit()
def __init__(self):
self.counters = defaultdict(lambda: 0.0)
self.counting = defaultdict(lambda: False)
self.starttime = defaultdict(lambda: 0)
self.endtime = defaultdict(lambda: 0)
self._on = ytcfg.getboolean("yt", "timefunctions")
self.exit()
def _update_parallel_state(self, new_comm):
from yt.config import ytcfg
ytcfg["yt", "__topcomm_parallel_size"] = str(new_comm.size)
ytcfg["yt", "__topcomm_parallel_rank"] = str(new_comm.rank)
if new_comm.rank > 0 and ytcfg.getboolean("yt", "serialize"):
ytcfg["yt", "onlydeserialize"] = "True"
def enable_parallelism():
global parallel_capable, MPI
try:
from mpi4py import MPI as _MPI
except ImportError:
mylog.info("mpi4py was not found. Disabling parallel computation")
parallel_capable = False
return
MPI = _MPI
exe_name = os.path.basename(sys.executable)
parallel_capable = (MPI.COMM_WORLD.size > 1)
if not parallel_capable: return False
mylog.info("Global parallel computation enabled: %s / %s",
MPI.COMM_WORLD.rank, MPI.COMM_WORLD.size)
communication_system.push(MPI.COMM_WORLD)
ytcfg["yt", "__global_parallel_rank"] = str(MPI.COMM_WORLD.rank)
ytcfg["yt", "__global_parallel_size"] = str(MPI.COMM_WORLD.size)
ytcfg["yt", "__parallel"] = "True"
if exe_name == "embed_enzo" or \
("_parallel" in dir(sys) and sys._parallel == True):
ytcfg["yt", "inline"] = "True"
if MPI.COMM_WORLD.rank > 0:
if ytcfg.getboolean("yt", "LogFile"):
ytcfg["yt", "LogFile"] = "False"
yt.utilities.logger.disable_file_logging()
yt.utilities.logger.uncolorize_logging()
# Even though the uncolorize function already resets the format string,
# we reset it again so that it includes the processor.
f = logging.Formatter("P%03i %s" %
(MPI.COMM_WORLD.rank, yt.utilities.logger.ufstring))
if len(yt.utilities.logger.rootLogger.handlers) > 0:
yt.utilities.logger.rootLogger.handlers[0].setFormatter(f)
if ytcfg.getboolean("yt", "parallel_traceback"):
sys.excepthook = traceback_writer_hook("_%03i" % MPI.COMM_WORLD.rank)
if ytcfg.getint("yt", "LogLevel") < 20:
yt.utilities.logger.ytLogger.warning(
"Log Level is set low -- this could affect parallel performance!")
dtype_names.update(
dict(
float32=MPI.FLOAT,
float64=MPI.DOUBLE,
int32=MPI.INT,
int64=MPI.LONG,
c=MPI.CHAR,
))
op_names.update(dict(sum=MPI.SUM, min=MPI.MIN, max=MPI.MAX))
return True
def _check_deprecated_parameters():
from yt.config import ytcfg
from yt.funcs import issue_deprecation_warning
if ytcfg.getboolean("yt", "loadfieldplugins"):
issue_deprecation_warning(
"Found deprecated parameter 'loadfieldplugins' parameter in yt rcfile."
)
def _initialize_data_storage(self):
if not ytcfg.getboolean("yt", "serialize"):
return
fn = self.ds.storage_filename
if fn is None:
if os.path.isfile(
os.path.join(self.directory,
f"{self.ds.unique_identifier}.yt")):
fn = os.path.join(self.directory,
f"{self.ds.unique_identifier}.yt")
else:
fn = os.path.join(self.directory,
f"{self.dataset.basename}.yt")
dir_to_check = os.path.dirname(fn)
if dir_to_check == "":
dir_to_check = "."
# We have four options:
# Writeable, does not exist : create, open as append
# Writeable, does exist : open as append
# Not writeable, does not exist : do not attempt to open
# Not writeable, does exist : open as read-only
exists = os.path.isfile(fn)
if not exists:
writeable = os.access(dir_to_check, os.W_OK)
else:
writeable = os.access(fn, os.W_OK)
writeable = writeable and not ytcfg.getboolean("yt", "onlydeserialize")
# We now have our conditional stuff
self.comm.barrier()
if not writeable and not exists:
return
if writeable:
try:
if not exists:
self.__create_data_file(fn)
self._data_mode = "a"
except IOError:
self._data_mode = None
return
else:
self._data_mode = "r"
self.__data_filename = fn
self._data_file = h5py.File(fn, self._data_mode)
def _populate_grid_objects(self):
reconstruct = ytcfg.getboolean("yt", "reconstruct_index")
for g, f in izip(self.grids, self.filenames):
g._prepare_grid()
g._setup_dx()
g.set_filename(f[0])
if reconstruct:
if g.Parent is not None: g._guess_properties_from_parent()
del self.filenames # No longer needed.
self.max_level = self.grid_levels.max()
def _populate_grid_objects(self):
reconstruct = ytcfg.getboolean("yt","reconstruct_index")
for g,f in izip(self.grids, self.filenames):
g._prepare_grid()
g._setup_dx()
g.set_filename(f[0])
if reconstruct:
if g.Parent is not None: g._guess_properties_from_parent()
del self.filenames # No longer needed.
self.max_level = self.grid_levels.max()
def only_on_root(func, *args, **kwargs):
"""
This function accepts a *func*, a set of *args* and *kwargs* and then only
on the root processor calls the function. All other processors get "None"
handed back.
"""
from yt.config import ytcfg
if not ytcfg.getboolean("yt", "__parallel"):
return func(*args, **kwargs)
if ytcfg.getint("yt", "__parallel_rank") > 0: return
return func(*args, **kwargs)
def write_out(self, filename_prefix):
if ytcfg.getboolean("yt","__parallel"):
pfn = "%s_%03i_%03i" % (filename_prefix,
ytcfg.getint("yt", "__global_parallel_rank"),
ytcfg.getint("yt", "__global_parallel_size"))
else:
pfn = "%s" % (filename_prefix)
for n, p in sorted(self.profilers.items()):
fn = "%s_%s.cprof" % (pfn, n)
mylog.info("Dumping %s into %s", n, fn)
p.dump_stats(fn)
def write_out(self, filename_prefix):
if ytcfg.getboolean("yt", "__parallel"):
pfn = "%s_%03i_%03i" % (
filename_prefix, ytcfg.getint("yt", "__global_parallel_rank"),
ytcfg.getint("yt", "__global_parallel_size"))
else:
pfn = "%s" % (filename_prefix)
for n, p in sorted(self.profilers.items()):
fn = "%s_%s.cprof" % (pfn, n)
mylog.info("Dumping %s into %s", n, fn)
p.dump_stats(fn)
def is_root():
"""
This function returns True if it is on the root processor of the
topcomm and False otherwise.
"""
from yt.config import ytcfg
cfg_option = "__topcomm_parallel_rank"
if not ytcfg.getboolean("yt","__parallel"):
return True
if ytcfg.getint("yt", cfg_option) > 0:
return False
return True
def is_root():
"""
This function returns True if it is on the root processor of the
topcomm and False otherwise.
"""
from yt.config import ytcfg
cfg_option = "__topcomm_parallel_rank"
if not ytcfg.getboolean("yt", "__parallel"):
return True
if ytcfg.getint("yt", cfg_option) > 0:
return False
return True
def _initialize_data_storage(self):
if not ytcfg.getboolean('yt','serialize'): return
fn = self.ds.storage_filename
if fn is None:
if os.path.isfile(os.path.join(self.directory,
"%s.yt" % self.ds.unique_identifier)):
fn = os.path.join(self.directory,"%s.yt" % self.ds.unique_identifier)
else:
fn = os.path.join(self.directory,
"%s.yt" % self.dataset.basename)
dir_to_check = os.path.dirname(fn)
if dir_to_check == '':
dir_to_check = '.'
# We have four options:
# Writeable, does not exist : create, open as append
# Writeable, does exist : open as append
# Not writeable, does not exist : do not attempt to open
# Not writeable, does exist : open as read-only
exists = os.path.isfile(fn)
if not exists:
writeable = os.access(dir_to_check, os.W_OK)
else:
writeable = os.access(fn, os.W_OK)
writeable = writeable and not ytcfg.getboolean('yt','onlydeserialize')
# We now have our conditional stuff
self.comm.barrier()
if not writeable and not exists: return
if writeable:
try:
if not exists: self.__create_data_file(fn)
self._data_mode = 'a'
except IOError:
self._data_mode = None
return
else:
self._data_mode = 'r'
self.__data_filename = fn
self._data_file = h5py.File(fn, self._data_mode)
def __init__(self,
ts,
num_readers=1,
num_writers=None,
outbase="rockstar_halos",
particle_type="all",
force_res=None,
total_particles=None,
dm_only=False,
particle_mass=None,
min_halo_size=25):
if is_root():
mylog.info(
"The citation for the Rockstar halo finder can be found at")
mylog.info("http://adsabs.harvard.edu/abs/2013ApJ...762..109B")
ParallelAnalysisInterface.__init__(self)
# Decide how we're working.
if ytcfg.getboolean("yt", "inline") == True:
self.runner = InlineRunner()
else:
self.runner = StandardRunner(num_readers, num_writers)
self.num_readers = self.runner.num_readers
self.num_writers = self.runner.num_writers
mylog.info("Rockstar is using %d readers and %d writers",
self.num_readers, self.num_writers)
# Note that Rockstar does not support subvolumes.
# We assume that all of the snapshots in the time series
# use the same domain info as the first snapshots.
if not isinstance(ts, DatasetSeries):
ts = DatasetSeries([ts])
self.ts = ts
self.particle_type = particle_type
self.outbase = outbase
self.min_halo_size = min_halo_size
if force_res is None:
tds = ts[-1] # Cache a reference
self.force_res = tds.index.get_smallest_dx().in_units("Mpc/h")
# We have to delete now to wipe the index
del tds
else:
self.force_res = force_res
self.total_particles = total_particles
self.dm_only = dm_only
self.particle_mass = particle_mass
# Setup pool and workgroups.
self.pool, self.workgroup = self.runner.setup_pool()
p = self._setup_parameters(ts)
params = self.comm.mpi_bcast(p, root=self.pool['readers'].ranks[0])
self.__dict__.update(params)
self.handler = rockstar_interface.RockstarInterface(self.ts)
def ImRoot():
"""
This function accepts a *func*, a set of *args* and *kwargs* and then only
on the root processor calls the function. All other processors get "None"
handed back.
"""
from yt.config import ytcfg
if not ytcfg.getboolean("yt", "__parallel"):
return True
try:
if ytcfg.getint("yt", "__parallel_rank") > 0: return False
except:
if ytcfg.getint("yt", "__topcomm_parallel_rank") > 0: return False
return True
def get_pbar(title, maxval):
"""
This returns a progressbar of the most appropriate type, given a *title*
and a *maxval*.
"""
maxval = max(maxval, 1)
from yt.config import ytcfg
if ytcfg.getboolean("yt", "suppressStreamLogging") or \
"__IPYTHON__" in dir(builtins) or \
ytcfg.getboolean("yt", "__withintesting"):
return DummyProgressBar()
elif ytcfg.getboolean("yt", "__withinreason"):
from yt.gui.reason.extdirect_repl import ExtProgressBar
return ExtProgressBar(title, maxval)
elif ytcfg.getboolean("yt", "__parallel"):
return ParallelProgressBar(title, maxval)
widgets = [ title,
pb.Percentage(), ' ',
pb.Bar(marker=pb.RotatingMarker()),
' ', pb.ETA(), ' ']
pbar = pb.ProgressBar(widgets=widgets,
maxval=maxval).start()
return pbar
def get_pbar(title, maxval):
"""
This returns a progressbar of the most appropriate type, given a *title*
and a *maxval*.
"""
maxval = max(maxval, 1)
from yt.config import ytcfg
if ytcfg.getboolean("yt", "suppressStreamLogging") or \
"__IPYTHON__" in dir(builtins) or \
ytcfg.getboolean("yt", "__withintesting"):
return DummyProgressBar()
elif ytcfg.getboolean("yt", "__withinreason"):
from yt.gui.reason.extdirect_repl import ExtProgressBar
return ExtProgressBar(title, maxval)
elif ytcfg.getboolean("yt", "__parallel"):
return ParallelProgressBar(title, maxval)
widgets = [ title,
pb.Percentage(), ' ',
pb.Bar(marker=pb.RotatingMarker()),
' ', pb.ETA(), ' ']
pbar = pb.ProgressBar(widgets=widgets,
maxval=maxval).start()
return pbar
def only_on_root(func, *args, **kwargs):
"""
This function accepts a *func*, a set of *args* and *kwargs* and then only
on the root processor calls the function. All other processors get "None"
handed back.
"""
from yt.config import ytcfg
if kwargs.pop("global_rootonly", False):
cfg_option = "__global_parallel_rank"
else:
cfg_option = "__topcomm_parallel_rank"
if not ytcfg.getboolean("yt","__parallel"):
return func(*args,**kwargs)
if ytcfg.getint("yt", cfg_option) > 0: return
return func(*args, **kwargs)
def __init__(self, in_memory=False):
"""
This class is designed to be a semi-persistent storage for parameter
files. By identifying each parameter file with a unique hash, objects
can be stored independently of parameter files -- when an object is
loaded, the parameter file is as well, based on the hash. For
storage concerns, only a few hundred will be retained in cache.
"""
if ytcfg.getboolean("yt", "StoreParameterFiles"):
self._read_only = False
self.init_db()
self._records = self.read_db()
else:
self._read_only = True
self._records = {}
def __init__(self, in_memory=False):
"""
Create the dataset database if yt is configured to store them.
Otherwise, use read-only settings.
"""
if self._register == False: return
if ytcfg.getboolean("yt", "StoreParameterFiles"):
self._read_only = False
self.init_db()
self._records = self.read_db()
else:
self._read_only = True
self._records = {}
self._register = False
def __init__(self, in_memory=False):
"""
Create the dataset database if yt is configured to store them.
Otherwise, use read-only settings.
"""
if self._register is False: return
if ytcfg.getboolean("yt", "StoreParameterFiles"):
self._read_only = False
self.init_db()
self._records = self.read_db()
else:
self._read_only = True
self._records = {}
self._register = False
def only_on_root(func, *args, **kwargs):
"""
This function accepts a *func*, a set of *args* and *kwargs* and then only
on the root processor calls the function. All other processors get "None"
handed back.
"""
from yt.config import ytcfg
if kwargs.pop("global_rootonly", False):
cfg_option = "__global_parallel_rank"
else:
cfg_option = "__topcomm_parallel_rank"
if not ytcfg.getboolean("yt", "__parallel"):
return func(*args, **kwargs)
if ytcfg.getint("yt", cfg_option) > 0: return
return func(*args, **kwargs)
def ffalse(func):
# returning a lambda : None causes an error when using pytest. Having
# a function (skip) that returns None does work, but pytest marks the
# test as having passed, which seems bad, since it wasn't actually run.
# Using pytest.skip() means that a change to test_requires_backend was
# needed since None is no longer returned, so we check for the skip
# exception in the xfail case for that test
def skip(*args, **kwargs):
msg = f"`{backend}` backend not found, skipping: `{func.__name__}`"
print(msg)
pytest.skip(msg)
if ytcfg.getboolean("yt", "__withinpytest"):
return skip
else:
return lambda: None
def standard_small_simulation(ds_fn, fields):
if not can_run_ds(ds_fn): return
dso = [None]
tolerance = ytcfg.getint("yt", "answer_testing_tolerance")
bitwise = ytcfg.getboolean("yt", "answer_testing_bitwise")
for field in fields:
if bitwise:
yield GridValuesTest(ds_fn, field)
if 'particle' in field: continue
for dobj_name in dso:
for axis in [0, 1, 2]:
for weight_field in [None, "Density"]:
yield ProjectionValuesTest(
ds_fn, axis, field, weight_field,
dobj_name, decimals=tolerance)
yield FieldValuesTest(
ds_fn, field, dobj_name, decimals=tolerance)
def standard_small_simulation(ds_fn, fields):
if not can_run_ds(ds_fn): return
dso = [None]
tolerance = ytcfg.getint("yt", "answer_testing_tolerance")
bitwise = ytcfg.getboolean("yt", "answer_testing_bitwise")
for field in fields:
if bitwise:
yield GridValuesTest(ds_fn, field)
if 'particle' in field: continue
for dobj_name in dso:
for axis in [0, 1, 2]:
for weight_field in [None, "Density"]:
yield ProjectionValuesTest(
ds_fn, axis, field, weight_field,
dobj_name, decimals=tolerance)
yield FieldValuesTest(
ds_fn, field, dobj_name, decimals=tolerance)
def time_execution(func):
r"""
Decorator for seeing how long a given function takes, depending on whether
or not the global 'yt.timefunctions' config parameter is set.
"""
@wraps(func)
def wrapper(*arg, **kw):
t1 = time.time()
res = func(*arg, **kw)
t2 = time.time()
mylog.debug('%s took %0.3f s', func.__name__, (t2-t1))
return res
from yt.config import ytcfg
if ytcfg.getboolean("yt","timefunctions") == True:
return wrapper
else:
return func
def time_execution(func):
r"""
Decorator for seeing how long a given function takes, depending on whether
or not the global 'yt.timefunctions' config parameter is set.
"""
@wraps(func)
def wrapper(*arg, **kw):
t1 = time.time()
res = func(*arg, **kw)
t2 = time.time()
mylog.debug('%s took %0.3f s', func.__name__, (t2-t1))
return res
from yt.config import ytcfg
if ytcfg.getboolean("yt","timefunctions") is True:
return wrapper
else:
return func
def partition_index_3d(self, ds, padding=0.0, rank_ratio = 1):
LE, RE = np.array(ds.left_edge), np.array(ds.right_edge)
# We need to establish if we're looking at a subvolume, in which case
# we *do* want to pad things.
if (LE == self.ds.domain_left_edge).all() and \
(RE == self.ds.domain_right_edge).all():
subvol = False
else:
subvol = True
if not self._distributed and not subvol:
return False, LE, RE, ds
if not self._distributed and subvol:
return True, LE, RE, \
self.ds.region(self.center, LE-padding, RE+padding)
elif ytcfg.getboolean("yt", "inline"):
# At this point, we want to identify the root grid tile to which
# this processor is assigned.
# The only way I really know how to do this is to get the level-0
# grid that belongs to this processor.
grids = self.ds.index.select_grids(0)
root_grids = [g for g in grids
if g.proc_num == self.comm.rank]
if len(root_grids) != 1: raise RuntimeError
#raise KeyError
LE = root_grids[0].LeftEdge
RE = root_grids[0].RightEdge
return True, LE, RE, self.ds.region(self.center, LE, RE)
cc = MPI.Compute_dims(self.comm.size / rank_ratio, 3)
mi = self.comm.rank % (self.comm.size // rank_ratio)
cx, cy, cz = np.unravel_index(mi, cc)
x = np.mgrid[LE[0]:RE[0]:(cc[0]+1)*1j][cx:cx+2]
y = np.mgrid[LE[1]:RE[1]:(cc[1]+1)*1j][cy:cy+2]
z = np.mgrid[LE[2]:RE[2]:(cc[2]+1)*1j][cz:cz+2]
LE = np.array([x[0], y[0], z[0]], dtype='float64')
RE = np.array([x[1], y[1], z[1]], dtype='float64')
if padding > 0:
return True, \
LE, RE, self.ds.region(self.center,
LE-padding, RE+padding)
return False, LE, RE, self.ds.region(self.center, LE, RE)
def partition_index_3d(self, ds, padding=0.0, rank_ratio = 1):
LE, RE = np.array(ds.left_edge), np.array(ds.right_edge)
# We need to establish if we're looking at a subvolume, in which case
# we *do* want to pad things.
if (LE == self.ds.domain_left_edge).all() and \
(RE == self.ds.domain_right_edge).all():
subvol = False
else:
subvol = True
if not self._distributed and not subvol:
return False, LE, RE, ds
if not self._distributed and subvol:
return True, LE, RE, \
self.ds.region(self.center, LE-padding, RE+padding)
elif ytcfg.getboolean("yt", "inline"):
# At this point, we want to identify the root grid tile to which
# this processor is assigned.
# The only way I really know how to do this is to get the level-0
# grid that belongs to this processor.
grids = self.ds.index.select_grids(0)
root_grids = [g for g in grids
if g.proc_num == self.comm.rank]
if len(root_grids) != 1: raise RuntimeError
#raise KeyError
LE = root_grids[0].LeftEdge
RE = root_grids[0].RightEdge
return True, LE, RE, self.ds.region(self.center, LE, RE)
cc = MPI.Compute_dims(self.comm.size / rank_ratio, 3)
mi = self.comm.rank % (self.comm.size / rank_ratio)
cx, cy, cz = np.unravel_index(mi, cc)
x = np.mgrid[LE[0]:RE[0]:(cc[0]+1)*1j][cx:cx+2]
y = np.mgrid[LE[1]:RE[1]:(cc[1]+1)*1j][cy:cy+2]
z = np.mgrid[LE[2]:RE[2]:(cc[2]+1)*1j][cz:cz+2]
LE = np.array([x[0], y[0], z[0]], dtype='float64')
RE = np.array([x[1], y[1], z[1]], dtype='float64')
if padding > 0:
return True, \
LE, RE, self.ds.region(self.center,
LE-padding, RE+padding)
return False, LE, RE, self.ds.region(self.center, LE, RE)
def can_run_ds(ds_fn, file_check=False):
result_storage = AnswerTestingTest.result_storage
if isinstance(ds_fn, Dataset):
return result_storage is not None
path = ytcfg.get("yt", "test_data_dir")
if not os.path.isdir(path):
return False
if file_check:
return os.path.isfile(os.path.join(
path, ds_fn)) and result_storage is not None
try:
load(ds_fn)
except FileNotFoundError:
if ytcfg.getboolean("yt", "requires_ds_strict"):
if result_storage is not None:
result_storage["tainted"] = True
raise
return False
return result_storage is not None
def can_run_sim(sim_fn, sim_type, file_check=False):
result_storage = AnswerTestingTest.result_storage
if isinstance(sim_fn, SimulationTimeSeries):
return result_storage is not None
path = ytcfg.get("yt", "test_data_dir")
if not os.path.isdir(path):
return False
if file_check:
return os.path.isfile(os.path.join(
path, sim_fn)) and result_storage is not None
try:
simulation(sim_fn, sim_type)
except FileNotFoundError:
if ytcfg.getboolean("yt", "requires_ds_strict"):
if result_storage is not None:
result_storage["tainted"] = True
raise
return False
return result_storage is not None
def __init__(self, ts, num_readers = 1, num_writers = None,
outbase="rockstar_halos", particle_type="all",
force_res=None, total_particles=None, dm_only=False,
particle_mass=None, min_halo_size=25):
if is_root():
mylog.info("The citation for the Rockstar halo finder can be found at")
mylog.info("http://adsabs.harvard.edu/abs/2013ApJ...762..109B")
ParallelAnalysisInterface.__init__(self)
# Decide how we're working.
if ytcfg.getboolean("yt", "inline") == True:
self.runner = InlineRunner()
else:
self.runner = StandardRunner(num_readers, num_writers)
self.num_readers = self.runner.num_readers
self.num_writers = self.runner.num_writers
mylog.info("Rockstar is using %d readers and %d writers",
self.num_readers, self.num_writers)
# Note that Rockstar does not support subvolumes.
# We assume that all of the snapshots in the time series
# use the same domain info as the first snapshots.
if not isinstance(ts, DatasetSeries):
ts = DatasetSeries([ts])
self.ts = ts
self.particle_type = particle_type
self.outbase = outbase
self.min_halo_size = min_halo_size
if force_res is None:
tds = ts[-1] # Cache a reference
self.force_res = tds.index.get_smallest_dx().in_units("Mpc/h")
# We have to delete now to wipe the index
del tds
else:
self.force_res = force_res
self.total_particles = total_particles
self.dm_only = dm_only
self.particle_mass = particle_mass
# Setup pool and workgroups.
self.pool, self.workgroup = self.runner.setup_pool()
p = self._setup_parameters(ts)
params = self.comm.mpi_bcast(p, root = self.pool['readers'].ranks[0])
self.__dict__.update(params)
self.handler = rockstar_interface.RockstarInterface(self.ts)
def can_run_sim(sim_fn, sim_type, file_check=False):
issue_deprecation_warning("This function is no longer used in the " +
"yt project testing framework and is " +
"targeted for deprecation.")
result_storage = AnswerTestingTest.result_storage
if isinstance(sim_fn, SimulationTimeSeries):
return result_storage is not None
path = ytcfg.get("yt", "test_data_dir")
if not os.path.isdir(path):
return False
if file_check:
return os.path.isfile(os.path.join(
path, sim_fn)) and result_storage is not None
try:
load_simulation(sim_fn, sim_type)
except FileNotFoundError:
if ytcfg.getboolean("yt", "__strict_requires"):
if result_storage is not None:
result_storage["tainted"] = True
raise
return False
return result_storage is not None
def test_requires_backend():
backend = matplotlib.get_backend().lower()
other_backends = {"gtkagg", "macosx", "wx", "tkagg"} - {backend}
@requires_backend(other_backends.pop())
def plot_a():
return True
@requires_backend(backend)
def plot_b():
return True
assert_equal(plot_b(), True)
if not ytcfg.getboolean("yt", "__withinpytest"):
assert_equal(plot_a(), None)
else:
# NOTE: This doesn't actually work. pytest.skip() doesn't actually
# raise the exception but rather returns control to the function's
# (test_requires_backend) caller, breaking immediately. As such,
# this assert_rasies never actually happens. See the comment
# in the definition of requires_backend for why pytest.skip is used
np.testing.assert_raises(plot_a(), pytest.skip.Exception)
def __new__(cls, filename=None, *args, **kwargs):
from yt.frontends.stream.data_structures import StreamHandler
if not isinstance(filename, str):
obj = object.__new__(cls)
# The Stream frontend uses a StreamHandler object to pass metadata
# to __init__.
is_stream = (hasattr(filename, 'get_fields')
and hasattr(filename, 'get_particle_type'))
if not is_stream:
obj.__init__(filename, *args, **kwargs)
return obj
apath = os.path.abspath(filename)
#if not os.path.exists(apath): raise IOError(filename)
if ytcfg.getboolean("yt", "skip_dataset_cache"):
obj = object.__new__(cls)
elif apath not in _cached_datasets:
obj = object.__new__(cls)
if obj._skip_cache is False:
_cached_datasets[apath] = obj
else:
obj = _cached_datasets[apath]
return obj
def __new__(cls, filename=None, *args, **kwargs):
from yt.frontends.stream.data_structures import StreamHandler
if not isinstance(filename, str):
obj = object.__new__(cls)
# The Stream frontend uses a StreamHandler object to pass metadata
# to __init__.
is_stream = (hasattr(filename, 'get_fields') and
hasattr(filename, 'get_particle_type'))
if not is_stream:
obj.__init__(filename, *args, **kwargs)
return obj
apath = os.path.abspath(filename)
#if not os.path.exists(apath): raise IOError(filename)
if ytcfg.getboolean("yt","skip_dataset_cache"):
obj = object.__new__(cls)
elif apath not in _cached_datasets:
obj = object.__new__(cls)
if obj._skip_cache is False:
_cached_datasets[apath] = obj
else:
obj = _cached_datasets[apath]
return obj
#
import os
from yt import *
# This next item will handle most of the actual startup procedures, but it will
# also attempt to parse the command line and set up the global state of various
# operations. The variable unparsed_args is not used internally but is
# provided as a convenience for users who wish to parse arguments in scripts.
# See http://lists.spacepope.org/pipermail/yt-dev-spacepope.org/2011-December/
# 001727.html
import yt.startup_tasks as __startup_tasks
unparsed_args = __startup_tasks.unparsed_args
from yt.config import ytcfg, ytcfg_defaults
from yt.utilities.logger import level as __level
if __level >= int(ytcfg_defaults["loglevel"]):
# This won't get displayed.
mylog.debug("Turning off NumPy error reporting")
np.seterr(all = 'ignore')
# We load plugins. Keep in mind, this can be fairly dangerous -
# the primary purpose is to allow people to have a set of functions
# that get used every time that they don't have to *define* every time.
# This way, other command-line tools can be used very simply.
# Unfortunately, for now, I think the easiest and simplest way of doing
# this is also the most dangerous way.
if ytcfg.getboolean("yt","loadfieldplugins"):
enable_plugins()
def _update_parallel_state(self, new_comm):
from yt.config import ytcfg
ytcfg["yt","__topcomm_parallel_size"] = str(new_comm.size)
ytcfg["yt","__topcomm_parallel_rank"] = str(new_comm.rank)
if new_comm.rank > 0 and ytcfg.getboolean("yt","serialize"):
ytcfg["yt","onlydeserialize"] = "True"
def enable_parallelism(suppress_logging=False, communicator=None):
"""
This method is used inside a script to turn on MPI parallelism, via
mpi4py. More information about running yt in parallel can be found
here: http://yt-project.org/docs/3.0/analyzing/parallel_computation.html
Parameters
----------
suppress_logging : bool
If set to True, only rank 0 will log information after the initial
setup of MPI.
communicator : mpi4py.MPI.Comm
The MPI communicator to use. This controls which processes yt can see.
If not specified, will be set to COMM_WORLD.
"""
global parallel_capable, MPI
try:
from mpi4py import MPI as _MPI
except ImportError:
mylog.info("mpi4py was not found. Disabling parallel computation")
parallel_capable = False
return
MPI = _MPI
exe_name = os.path.basename(sys.executable)
# if no communicator specified, set to COMM_WORLD
if communicator is None:
communicator = MPI.COMM_WORLD
parallel_capable = (communicator.size > 1)
if not parallel_capable: return False
mylog.info("Global parallel computation enabled: %s / %s",
communicator.rank, communicator.size)
communication_system.push(communicator)
ytcfg["yt","__global_parallel_rank"] = str(communicator.rank)
ytcfg["yt","__global_parallel_size"] = str(communicator.size)
ytcfg["yt","__parallel"] = "True"
if exe_name == "embed_enzo" or \
("_parallel" in dir(sys) and sys._parallel is True):
ytcfg["yt","inline"] = "True"
if communicator.rank > 0:
if ytcfg.getboolean("yt","LogFile"):
ytcfg["yt","LogFile"] = "False"
yt.utilities.logger.disable_file_logging()
yt.utilities.logger.uncolorize_logging()
# Even though the uncolorize function already resets the format string,
# we reset it again so that it includes the processor.
f = logging.Formatter("P%03i %s" % (communicator.rank,
yt.utilities.logger.ufstring))
if len(yt.utilities.logger.ytLogger.handlers) > 0:
yt.utilities.logger.ytLogger.handlers[0].setFormatter(f)
if ytcfg.getboolean("yt", "parallel_traceback"):
sys.excepthook = traceback_writer_hook("_%03i" % communicator.rank)
else:
sys.excepthook = default_mpi_excepthook
if ytcfg.getint("yt","LogLevel") < 20:
yt.utilities.logger.ytLogger.warning(
"Log Level is set low -- this could affect parallel performance!")
dtype_names.update(dict(
float32 = MPI.FLOAT,
float64 = MPI.DOUBLE,
int32 = MPI.INT,
int64 = MPI.LONG,
c = MPI.CHAR,
))
op_names.update(dict(
sum = MPI.SUM,
min = MPI.MIN,
max = MPI.MAX
))
# Turn off logging on all but the root rank, if specified.
if suppress_logging:
if communicator.rank > 0:
mylog.addFilter(FilterAllMessages())
return True
ravenLogger = logging.getLogger("yt.raven")
lagosLogger = logging.getLogger("yt.lagos")
enkiLogger = logging.getLogger("yt.enki")
deliveratorLogger = logging.getLogger("yt.deliverator")
reasonLogger = logging.getLogger("yt.reason")
# Maybe some day we'll make this more configurable... unfortunately, for now,
# we preserve thread-safety by opening in the current directory.
mb = 10 * 1024 * 1024
bc = 10
loggers = []
file_handlers = []
if ytcfg.getboolean("yt", "logfile") and os.access(".", os.W_OK):
log_file_name = ytcfg.get("yt", "LogFileName")
ytFileHandler = handlers.RotatingFileHandler(log_file_name,
maxBytes=mb,
backupCount=bc)
k = logging.Formatter(fstring)
ytFileHandler.setFormatter(k)
ytLogger.addHandler(ytFileHandler)
loggers.append(ytLogger)
file_handlers.append(ytFileHandler)
def disable_stream_logging():
# We just remove the root logger's handlers
for handler in rootLogger.handlers:
if isinstance(handler, logging.StreamHandler):
elif(levelno >= 20):
color = '\x1b[32m' # green
elif(levelno >= 10):
color = '\x1b[35m' # pink
else:
color = '\x1b[0m' # normal
ln = color + args[0].levelname + '\x1b[0m'
args[0].levelname = ln
return fn(*args)
return new
level = min(max(ytcfg.getint("yt", "loglevel"), 0), 50)
ufstring = "%(name)-3s: [%(levelname)-9s] %(asctime)s %(message)s"
cfstring = "%(name)-3s: [%(levelname)-18s] %(asctime)s %(message)s"
if ytcfg.getboolean("yt", "stdoutStreamLogging"):
stream = sys.stdout
else:
stream = sys.stderr
ytLogger = logging.getLogger("yt")
yt_sh = logging.StreamHandler(stream=stream)
# create formatter and add it to the handlers
formatter = logging.Formatter(ufstring)
yt_sh.setFormatter(formatter)
# add the handler to the logger
ytLogger.addHandler(yt_sh)
ytLogger.setLevel(level)
ytLogger.propagate = False
parser.add_argument("--paste-detailed", action=SetExceptionHandling,
help = "Paste a detailed traceback with local variables to " +
"paste.yt-project.org", nargs = 0)
parser.add_argument("--detailed", action=SetExceptionHandling,
help = "Display detailed traceback.", nargs = 0)
parser.add_argument("--rpdb", action=SetExceptionHandling,
help = "Enable remote pdb interaction (for parallel debugging).", nargs = 0)
parser.add_argument("--parallel", action="store_true", default=False,
dest = "parallel",
help = "Run in MPI-parallel mode (must be launched as an MPI task)")
if not hasattr(sys, 'argv') or sys.argv is None: sys.argv = []
unparsed_args = []
parallel_capable = False
if not ytcfg.getboolean("yt","__command_line"):
opts, unparsed_args = parser.parse_known_args()
# THIS IS NOT SUCH A GOOD IDEA:
#sys.argv = [a for a in unparsed_args]
if opts.parallel:
parallel_capable = turn_on_parallelism()
subparsers = parser.add_subparsers(title="subcommands",
dest='subcommands',
description="Valid subcommands",)
else:
subparsers = parser.add_subparsers(title="subcommands",
dest='subcommands',
description="Valid subcommands",)
def print_help(*args, **kwargs):
parser.print_help()
help_parser = subparsers.add_parser("help", help="Print help message")
def enable_parallelism(suppress_logging=False, communicator=None):
"""
This method is used inside a script to turn on MPI parallelism, via
mpi4py. More information about running yt in parallel can be found
here: https://yt-project.org/docs/3.0/analyzing/parallel_computation.html
Parameters
----------
suppress_logging : bool
If set to True, only rank 0 will log information after the initial
setup of MPI.
communicator : mpi4py.MPI.Comm
The MPI communicator to use. This controls which processes yt can see.
If not specified, will be set to COMM_WORLD.
"""
global parallel_capable, MPI
try:
from mpi4py import MPI as _MPI
except ImportError:
mylog.info("mpi4py was not found. Disabling parallel computation")
parallel_capable = False
return
MPI = _MPI
exe_name = os.path.basename(sys.executable)
# if no communicator specified, set to COMM_WORLD
if communicator is None:
communicator = MPI.COMM_WORLD
parallel_capable = communicator.size > 1
if not parallel_capable:
return False
mylog.info(
"Global parallel computation enabled: %s / %s",
communicator.rank,
communicator.size,
)
communication_system.push(communicator)
ytcfg["yt", "__global_parallel_rank"] = str(communicator.rank)
ytcfg["yt", "__global_parallel_size"] = str(communicator.size)
ytcfg["yt", "__parallel"] = "True"
if exe_name == "embed_enzo" or ("_parallel" in dir(sys) and sys._parallel):
ytcfg["yt", "inline"] = "True"
if communicator.rank > 0:
if ytcfg.getboolean("yt", "LogFile"):
ytcfg["yt", "LogFile"] = "False"
yt.utilities.logger.disable_file_logging()
yt.utilities.logger.uncolorize_logging()
# Even though the uncolorize function already resets the format string,
# we reset it again so that it includes the processor.
f = logging.Formatter("P%03i %s" %
(communicator.rank, yt.utilities.logger.ufstring))
if len(yt.utilities.logger.ytLogger.handlers) > 0:
yt.utilities.logger.ytLogger.handlers[0].setFormatter(f)
if ytcfg.getboolean("yt", "parallel_traceback"):
sys.excepthook = traceback_writer_hook("_%03i" % communicator.rank)
else:
sys.excepthook = default_mpi_excepthook
if ytcfg.getint("yt", "LogLevel") < 20:
yt.utilities.logger.ytLogger.warning(
"Log Level is set low -- this could affect parallel performance!")
dtype_names.update(
dict(
float32=MPI.FLOAT,
float64=MPI.DOUBLE,
int32=MPI.INT,
int64=MPI.LONG,
c=MPI.CHAR,
))
op_names.update(dict(sum=MPI.SUM, min=MPI.MIN, max=MPI.MAX))
# Turn off logging on all but the root rank, if specified.
if suppress_logging:
if communicator.rank > 0:
mylog.addFilter(FilterAllMessages())
return True
nargs=0,
)
parser.add_argument(
"--parallel",
action="store_true",
default=False,
dest="parallel",
help="Run in MPI-parallel mode (must be launched as an MPI task)",
)
if not hasattr(sys, "argv") or sys.argv is None:
sys.argv = []
unparsed_args = []
parallel_capable = False
if not ytcfg.getboolean("yt", "__command_line"):
opts, unparsed_args = parser.parse_known_args()
# THIS IS NOT SUCH A GOOD IDEA:
# sys.argv = [a for a in unparsed_args]
if opts.parallel:
parallel_capable = turn_on_parallelism()
subparsers = parser.add_subparsers(
title="subcommands",
dest="subcommands",
description="Valid subcommands",
)
else:
subparsers = parser.add_subparsers(
title="subcommands",
dest="subcommands",
description="Valid subcommands",