def _check_for_outputs(self, potential_outputs):
r"""
Check a list of files to see if they are valid datasets.
"""
only_on_root(mylog.info, "Checking %d potential outputs.",
len(potential_outputs))
my_outputs = {}
for my_storage, output in parallel_objects(potential_outputs,
storage=my_outputs):
if os.path.exists(output):
try:
ds = load(output)
if ds is not None:
num_steps = ds.num_steps
my_storage.result = {
"filename": output,
"num_steps": num_steps
}
except YTOutputNotIdentified:
mylog.error("Failed to load %s", output)
my_outputs = [
my_output for my_output in my_outputs.values()
if my_output is not None
]
return my_outputs
def _check_for_outputs(self, potential_outputs):
r"""
Check a list of files to see if they are valid datasets.
"""
only_on_root(mylog.info, "Checking %d potential outputs.",
len(potential_outputs))
my_outputs = {}
llevel = mylog.level
# suppress logging as we load every dataset, unless set to debug
if llevel > 10 and llevel < 40:
mylog.setLevel(40)
for my_storage, output in parallel_objects(potential_outputs,
storage=my_outputs):
if os.path.exists(output):
try:
ds = load(output)
if ds is not None:
my_storage.result = {"filename": output,
"time": ds.current_time.in_units("s")}
if ds.cosmological_simulation:
my_storage.result["redshift"] = ds.current_redshift
except YTUnidentifiedDataType:
mylog.error("Failed to load %s", output)
mylog.setLevel(llevel)
my_outputs = [my_output for my_output in my_outputs.values() \
if my_output is not None]
return my_outputs
def get_parameter(self, parameter, type=None):
"""
Gets a parameter not in the parameterDict.
"""
if parameter in self.parameters:
return self.parameters[parameter]
for line in open(self.parameter_filename):
if line.find("#") >= 1: # Keep the commented lines
line = line[:line.find("#")]
line = line.strip().rstrip()
if len(line) < 2:
continue
try:
param, vals = map(string.strip,
map(string.rstrip, line.split("=")))
except ValueError:
mylog.error("ValueError: '%s'", line)
if parameter == param:
if type is None:
t = vals.split()
else:
t = map(type, vals.split())
if len(t) == 1:
self.parameters[param] = t[0]
else:
self.parameters[param] = t
if param.endswith(
"Units") and not param.startswith("Temperature"):
dataType = param[:-5]
self.conversion_factors[dataType] = self.parameters[param]
return self.parameters[parameter]
return ""
def get_parameter(self,parameter,type=None):
"""
Gets a parameter not in the parameterDict.
"""
if parameter in self.parameters:
return self.parameters[parameter]
for line in open(self.parameter_filename):
if line.find("#") >= 1: # Keep the commented lines
line=line[:line.find("#")]
line=line.strip().rstrip()
if len(line) < 2:
continue
try:
param, vals = map(string.strip,map(string.rstrip,
line.split("=")))
except ValueError:
mylog.error("ValueError: '%s'", line)
if parameter == param:
if type is None:
t = vals.split()
else:
t = map(type, vals.split())
if len(t) == 1:
self.parameters[param] = t[0]
else:
self.parameters[param] = t
if param.endswith("Units") and not param.startswith("Temperature"):
dataType = param[:-5]
self.conversion_factors[dataType] = self.parameters[param]
return self.parameters[parameter]
return ""
def default_mpi_excepthook(exception_type, exception_value, tb):
traceback.print_tb(tb)
mylog.error('%s: %s' % (exception_type.__name__, exception_value))
comm = yt.communication_system.communicators[-1]
if comm.size > 1:
mylog.error('Error occured on rank %d.' % comm.rank)
MPI.COMM_WORLD.Abort(1)
def _check_for_outputs(self, potential_outputs):
"""
Check a list of files to see if they are valid datasets.
"""
only_on_root(mylog.info, "Checking %d potential outputs.",
len(potential_outputs))
my_outputs = {}
for my_storage, output in parallel_objects(potential_outputs,
storage=my_outputs):
if self.parameters['DataDumpDir'] in output:
dir_key = self.parameters['DataDumpDir']
output_key = self.parameters['DataDumpName']
else:
dir_key = self.parameters['RedshiftDumpDir']
output_key = self.parameters['RedshiftDumpName']
index = output[output.find(dir_key) + len(dir_key):]
filename = os.path.join(self.parameters['GlobalDir'],
"%s%s" % (dir_key, index),
"%s%s" % (output_key, index))
if os.path.exists(filename):
try:
ds = load(filename)
if ds is not None:
my_storage.result = {'filename': filename,
'time': ds.current_time.in_units("s")}
if ds.cosmological_simulation:
my_storage.result['redshift'] = ds.current_redshift
except YTOutputNotIdentified:
mylog.error('Failed to load %s', filename)
my_outputs = [my_output for my_output in my_outputs.values() \
if my_output is not None]
return my_outputs
def default_mpi_excepthook(exception_type, exception_value, tb):
traceback.print_tb(tb)
mylog.error("%s: %s", exception_type.__name__, exception_value)
comm = yt.communication_system.communicators[-1]
if comm.size > 1:
mylog.error("Error occurred on rank %d.", comm.rank)
MPI.COMM_WORLD.Abort(1)
def _check_for_outputs(self, potential_outputs):
r"""
Check a list of files to see if they are valid datasets.
"""
only_on_root(mylog.info, "Checking %d potential outputs.",
len(potential_outputs))
my_outputs = {}
for my_storage, output in parallel_objects(potential_outputs,
storage=my_outputs):
try:
ds = load(output)
except (FileNotFoundError, YTUnidentifiedDataType):
mylog.error("Failed to load %s", output)
continue
my_storage.result = {
"filename": output,
"time": ds.current_time.in_units("s"),
}
if ds.cosmological_simulation:
my_storage.result["redshift"] = ds.current_redshift
my_outputs = [
my_output for my_output in my_outputs.values()
if my_output is not None
]
return my_outputs
def _check_for_outputs(self, potential_outputs):
r"""
Check a list of files to see if they are valid datasets.
"""
only_on_root(mylog.info, "Checking %d potential outputs.",
len(potential_outputs))
my_outputs = {}
for my_storage, output in parallel_objects(potential_outputs,
storage=my_outputs):
if os.path.exists(output):
try:
ds = load(output)
if ds is not None:
my_storage.result = {
"filename": output,
"time": ds.current_time.in_units("s")
}
if ds.cosmological_simulation:
my_storage.result["redshift"] = ds.current_redshift
except YTOutputNotIdentified:
mylog.error("Failed to load %s", output)
my_outputs = [my_output for my_output in my_outputs.values() \
if my_output is not None]
return my_outputs
def add_field(self, name, function=None, sampling_type=None, **kwargs):
if not isinstance(name, tuple):
if kwargs.setdefault('particle_type', False):
name = ('all', name)
else:
name = ('gas', name)
override = kwargs.get("force_override", False)
# Handle the case where the field has already been added.
if not override and name in self:
mylog.error(
"Field %s already exists. To override use " +
"force_override=True.", name)
if kwargs.setdefault('particle_type', False):
if sampling_type is not None and sampling_type != "particle":
raise RuntimeError(
"Clashing definition of 'sampling_type' and "
"'particle_type'. Note that 'particle_type' is "
"deprecated. Please just use 'sampling_type'.")
else:
sampling_type = "particle"
if sampling_type is None:
warnings.warn("Because 'sampling_type' is not specified, yt will "
"assume a 'cell' sampling_type for the %s field" %
(name, ),
stacklevel=3)
sampling_type = "cell"
return super(LocalFieldInfoContainer,
self).add_field(name, sampling_type, function, **kwargs)
def _read_fluid_file_descriptor(fname):
"""
Read a file descriptor and returns the array of the fields found.
"""
# Mapping
mapping = [
("density", "Density"),
("velocity_x", "x-velocity"),
("velocity_y", "y-velocity"),
("velocity_z", "z-velocity"),
("pressure", "Pressure"),
("metallicity", "Metallicity"),
]
# Add mapping for magnetic fields
mapping += [
(key, key)
for key in (
f"B_{dim}_{side}" for side in ["left", "right"] for dim in ["x", "y", "z"]
)
]
# Convert to dictionary
mapping = {k: v for k, v in mapping}
with open(fname) as f:
line = f.readline()
tmp = VERSION_RE.match(line)
mylog.debug("Reading fluid file descriptor %s.", fname)
if not tmp:
return []
version = int(tmp.group(1))
if version == 1:
# Skip one line (containing the headers)
line = f.readline()
fields = []
for i, line in enumerate(f.readlines()):
tmp = VAR_DESC_RE.match(line)
if not tmp:
raise YTFileNotParseable(fname, i + 1)
# ivar = tmp.group(1)
varname = tmp.group(2)
dtype = tmp.group(3)
if varname in mapping:
varname = mapping[varname]
else:
varname = f"hydro_{varname}"
fields.append((varname, dtype))
else:
mylog.error("Version %s", version)
raise YTParticleOutputFormatNotImplemented()
return fields
def __init__(self,
ds,
data_source=None,
star_mass=None,
star_creation_time=None,
bins=300,
volume=None,
star_filter=None):
self._ds = ds
self._data_source = data_source
self._filter = star_filter
self.ds_provided = self._data_source is not None
self.filter_provided = self._filter is not None
self.bin_count = bins
# Set up for time conversion.
self.cosm = Cosmology(hubble_constant=self._ds.hubble_constant,
omega_matter=self._ds.omega_matter,
omega_lambda=self._ds.omega_lambda)
# Find the time right now.
self.time_now = self._ds.current_time
if not self.ds_provided:
# Check to make sure we have the right set of informations.
if star_mass is None or star_creation_time is None \
or volume is None:
mylog.error("""
If data_source is not provided, all of these parameters
need to be set:
star_mass (array, Msun),
star_creation_time (array, code units),
volume (float, cMpc**3).""")
return None
if isinstance(star_mass, YTArray):
assert star_mass.units.same_dimensions_as(g.units)
elif star_mass is not None:
star_mass = YTArray(star_mass, 'Msun')
self.star_mass = star_mass
if isinstance(star_creation_time, YTArray):
assert star_creation_time.units.same_dimensions_as(s.units)
elif star_creation_time is not None:
star_creation_time = self._ds.arr(star_creation_time,
'code_time')
self.star_creation_time = star_creation_time
if isinstance(volume, YTQuantity):
assert volume.units.same_dimensions_as(
self._ds.quan(1.0, 'Mpccm**3').units)
elif volume is not None:
volume = self._ds.quan(volume, 'Mpccm**3')
self.volume = volume
# Build the distribution.
self.build_dist()
# Attach some convenience arrays.
self.attach_arrays()
def _read_fluid_file_descriptor(fname):
"""
Read a file descriptor and returns the array of the fields found.
"""
# Mapping
mapping = [
('density', 'Density'),
('velocity_x', 'x-velocity'),
('velocity_y', 'y-velocity'),
('velocity_z', 'z-velocity'),
('pressure', 'Pressure'),
('metallicity', 'Metallicity'),
]
# Add mapping for magnetic fields
mapping += [(key, key) for key in
('B_{0}_{1}'.format(dim,side) for side in ['left','right']
for dim in ['x','y','z'])]
# Convert to dictionary
mapping = {k: v for k, v in mapping}
with open(fname, 'r') as f:
line = f.readline()
tmp = VERSION_RE.match(line)
mylog.debug('Reading fluid file descriptor %s.' % fname)
if not tmp:
return []
version = int(tmp.group(1))
if version == 1:
# Skip one line (containing the headers)
line = f.readline()
fields = []
for i, line in enumerate(f.readlines()):
tmp = VAR_DESC_RE.match(line)
if not tmp:
raise YTFileNotParseable(fname, i+1)
# ivar = tmp.group(1)
varname = tmp.group(2)
dtype = tmp.group(3)
if varname in mapping:
varname = mapping[varname]
else:
varname = 'hydro_%s' % varname
fields.append((varname, dtype))
else:
mylog.error('Version %s', version)
raise YTParticleOutputFormatNotImplemented()
return fields
def upload(self):
api_key = ytcfg.get("yt", "hub_api_key")
url = ytcfg.get("yt", "hub_url")
if api_key == '':
raise YTHubRegisterError
metadata, (final_name, chunks) = self._generate_post()
if hasattr(self, "_ds_mrep"):
self._ds_mrep.upload()
for i in metadata:
if isinstance(metadata[i], np.ndarray):
metadata[i] = metadata[i].tolist()
elif hasattr(metadata[i], 'dtype'):
metadata[i] = np.asscalar(metadata[i])
metadata['obj_type'] = self.type
if len(chunks) == 0:
chunk_info = {'chunks': []}
else:
chunk_info = {'final_name': final_name, 'chunks': []}
for cn, cv in chunks:
chunk_info['chunks'].append((cn, cv.size * cv.itemsize))
metadata = json.dumps(metadata)
chunk_info = json.dumps(chunk_info)
datagen, headers = multipart_encode({
'metadata': metadata,
'chunk_info': chunk_info,
'api_key': api_key
})
request = urllib.request.Request(url, datagen, headers)
# Actually do the request, and get the response
try:
rv = urllib.request.urlopen(request).read()
except urllib.error.HTTPError as ex:
if ex.code == 401:
mylog.error("You must create an API key before uploading.")
mylog.error("https://data.yt-project.org/getting_started.html")
return
else:
raise ex
uploader_info = json.loads(rv)
new_url = url + "/handler/%s" % uploader_info['handler_uuid']
for i, (cn, cv) in enumerate(chunks):
remaining = cv.size * cv.itemsize
f = TemporaryFile()
np.save(f, cv)
f.seek(0)
pbar = UploaderBar("%s, % 2i/% 2i" %
(self.type, i + 1, len(chunks)))
datagen, headers = multipart_encode({'chunk_data': f}, cb=pbar)
request = urllib.request.Request(new_url, datagen, headers)
rv = urllib.request.urlopen(request).read()
datagen, headers = multipart_encode({'status': 'FINAL'})
request = urllib.request.Request(new_url, datagen, headers)
rv = json.loads(urllib.request.urlopen(request).read())
mylog.info("Upload succeeded! View here: %s", rv['url'])
return rv
def enable_plugins(pluginfilename=None):
"""Forces a plugin file to be parsed.
A plugin file is a means of creating custom fields, quantities,
data objects, colormaps, and other code classes and objects to be used
in yt scripts without modifying the yt source directly.
If <pluginfilename> is omited, this function will look for a plugin file at
``$HOME/.config/yt/my_plugins.py``, which is the prefered behaviour for a
system-level configuration.
Warning: a script using this function will only be reproducible if your plugin
file is shared with it.
"""
import yt
from yt.config import CONFIG_DIR, ytcfg
from yt.fields.my_plugin_fields import my_plugins_fields
if pluginfilename is not None:
_fn = pluginfilename
if not os.path.isfile(_fn):
raise FileNotFoundError(_fn)
else:
# Determine global plugin location. By decreasing priority order:
# - absolute path
# - CONFIG_DIR
# - obsolete config dir.
my_plugin_name = ytcfg.get("yt", "pluginfilename")
old_config_dir = os.path.join(os.path.expanduser("~"), ".yt")
for base_prefix in ("", CONFIG_DIR, old_config_dir):
if os.path.isfile(os.path.join(base_prefix, my_plugin_name)):
_fn = os.path.join(base_prefix, my_plugin_name)
break
else:
mylog.error("Could not find a global system plugin file.")
return
if _fn.startswith(old_config_dir):
mylog.warning(
"Your plugin file is located in a deprecated directory. "
"Please move it from %s to %s",
os.path.join(old_config_dir, my_plugin_name),
os.path.join(CONFIG_DIR, my_plugin_name),
)
mylog.info("Loading plugins from %s", _fn)
ytdict = yt.__dict__
execdict = ytdict.copy()
execdict["add_field"] = my_plugins_fields.add_field
with open(_fn) as f:
code = compile(f.read(), _fn, "exec")
exec(code, execdict, execdict)
ytnamespace = list(ytdict.keys())
for k in execdict.keys():
if k not in ytnamespace:
if callable(execdict[k]):
setattr(yt, k, execdict[k])
def __init__(self, ds, data_source=None, star_mass=None,
star_creation_time=None, bins=300, volume=None,
star_filter=None):
self._ds = ds
self._data_source = data_source
self._filter = star_filter
self.ds_provided = self._data_source is not None
self.filter_provided = self._filter is not None
self.bin_count = bins
# Set up for time conversion.
self.cosm = Cosmology(
hubble_constant=self._ds.hubble_constant,
omega_matter=self._ds.omega_matter,
omega_lambda=self._ds.omega_lambda)
# Find the time right now.
self.time_now = self._ds.current_time
if not self.ds_provided:
# Check to make sure we have the right set of informations.
if star_mass is None or star_creation_time is None \
or volume is None:
mylog.error("""
If data_source is not provided, all of these parameters
need to be set:
star_mass (array, Msun),
star_creation_time (array, code units),
volume (float, cMpc**3).""")
return None
if isinstance(star_mass, YTArray):
assert star_mass.units.same_dimensions_as(g.units)
elif star_mass is not None:
star_mass = YTArray(star_mass, 'Msun')
self.star_mass = star_mass
if isinstance(star_creation_time, YTArray):
assert star_creation_time.units.same_dimensions_as(s.units)
elif star_creation_time is not None:
star_creation_time = self._ds.arr(star_creation_time,
'code_time')
self.star_creation_time = star_creation_time
if isinstance(volume, YTQuantity):
assert volume.units.same_dimensions_as(
self._ds.quan(1.0, 'Mpccm**3').units
)
elif volume is not None:
volume = self._ds.quan(volume, 'Mpccm**3')
self.volume = volume
# Build the distribution.
self.build_dist()
# Attach some convenience arrays.
self.attach_arrays()
def upload(self):
api_key = ytcfg.get("yt", "hub_api_key")
url = ytcfg.get("yt", "hub_url")
if api_key == '':
raise YTHubRegisterError
metadata, (final_name, chunks) = self._generate_post()
if hasattr(self, "_ds_mrep"):
self._ds_mrep.upload()
for i in metadata:
if isinstance(metadata[i], np.ndarray):
metadata[i] = metadata[i].tolist()
elif hasattr(metadata[i], 'dtype'):
metadata[i] = np.asscalar(metadata[i])
metadata['obj_type'] = self.type
if len(chunks) == 0:
chunk_info = {'chunks': []}
else:
chunk_info = {'final_name': final_name, 'chunks': []}
for cn, cv in chunks:
chunk_info['chunks'].append((cn, cv.size * cv.itemsize))
metadata = json.dumps(metadata)
chunk_info = json.dumps(chunk_info)
datagen, headers = multipart_encode({'metadata': metadata,
'chunk_info': chunk_info,
'api_key': api_key})
request = urllib.request.Request(url, datagen, headers)
# Actually do the request, and get the response
try:
rv = urllib.request.urlopen(request).read()
except urllib.error.HTTPError as ex:
if ex.code == 401:
mylog.error("You must create an API key before uploading.")
mylog.error("https://data.yt-project.org/getting_started.html")
return
else:
raise ex
uploader_info = json.loads(rv)
new_url = url + "/handler/%s" % uploader_info['handler_uuid']
for i, (cn, cv) in enumerate(chunks):
remaining = cv.size * cv.itemsize
f = TemporaryFile()
np.save(f, cv)
f.seek(0)
pbar = UploaderBar("%s, % 2i/% 2i" %
(self.type, i + 1, len(chunks)))
datagen, headers = multipart_encode({'chunk_data': f}, cb=pbar)
request = urllib.request.Request(new_url, datagen, headers)
rv = urllib.request.urlopen(request).read()
datagen, headers = multipart_encode({'status': 'FINAL'})
request = urllib.request.Request(new_url, datagen, headers)
rv = json.loads(urllib.request.urlopen(request).read())
mylog.info("Upload succeeded! View here: %s", rv['url'])
return rv
def _read_fluid_file_descriptor(fname):
"""
Read a file descriptor and returns the array of the fields found.
"""
VERSION_RE = re.compile('# version: *(\d+)')
VAR_DESC_RE = re.compile(r'\s*(\d+),\s*(\w+),\s*(\w+)')
# Mapping
mapping = [
('density', 'Density'),
('velocity_x', 'x-velocity'),
('velocity_y', 'y-velocity'),
('velocity_z', 'z-velocity'),
('pressure', 'Pressure'),
('metallicity', 'Metallicity'),
]
# Convert in dictionary
mapping = {k: v for k, v in mapping}
with open(fname, 'r') as f:
line = f.readline()
tmp = VERSION_RE.match(line)
mylog.info('Reading fluid file descriptor.')
if not tmp:
return []
version = int(tmp.group(1))
if version == 1:
# Skip one line (containing the headers)
line = f.readline()
fields = []
for i, line in enumerate(f.readlines()):
tmp = VAR_DESC_RE.match(line)
if not tmp:
raise YTFileNotParseable(fname, i + 1)
# ivar = tmp.group(1)
varname = tmp.group(2)
dtype = tmp.group(3)
if varname in mapping:
varname = mapping[varname]
else:
varname = 'particle_%s' % varname
fields.append((varname, dtype))
else:
mylog.error('Version %s', version)
raise YTParticleOutputFormatNotImplemented()
return fields
def add_workgroup(self, size=None, ranks=None, name=None):
if size is None:
size = len(self.available_ranks)
if len(self.available_ranks) < size:
mylog.error('Not enough resources available, asked for %d have %d',
size, self.available_ranks)
raise RuntimeError
if ranks is None:
ranks = [self.available_ranks.pop(0) for i in range(size)]
# Default name to the workgroup number.
if name is None:
name = str(len(self.workgroups))
group = self.comm.comm.Get_group().Incl(ranks)
new_comm = self.comm.comm.Create(group)
if self.comm.rank in ranks:
communication_system.communicators.append(Communicator(new_comm))
self.workgroups.append(Workgroup(len(ranks), ranks, new_comm, name))
def add_workgroup(self, size=None, ranks=None, name=None):
if size is None:
size = len(self.available_ranks)
if len(self.available_ranks) < size:
mylog.error('Not enough resources available, asked for %d have %d',
size, self.available_ranks)
raise RuntimeError
if ranks is None:
ranks = [self.available_ranks.pop(0) for i in range(size)]
# Default name to the workgroup number.
if name is None:
name = str(len(self.workgroups))
group = self.comm.comm.Get_group().Incl(ranks)
new_comm = self.comm.comm.Create(group)
if self.comm.rank in ranks:
communication_system.communicators.append(Communicator(new_comm))
self.workgroups.append(Workgroup(len(ranks), ranks, new_comm, name))
def parse_unit_dimension(unit_dimension):
r"""Transforms an openPMD unitDimension into a string.
Parameters
----------
unit_dimension : array_like
integer array of length 7 with one entry for the dimensional component of every
SI unit
[0] length L,
[1] mass M,
[2] time T,
[3] electric current I,
[4] thermodynamic temperature theta,
[5] amount of substance N,
[6] luminous intensity J
References
----------
https://github.com/openPMD/openPMD-standard/blob/latest/STANDARD.md#unit-systems-and-dimensionality
Returns
-------
str
Examples
--------
>>> velocity = [1.0, 0.0, -1.0, 0.0, 0.0, 0.0, 0.0]
>>> print(parse_unit_dimension(velocity))
'm**1*s**-1'
>>> magnetic_field = [0.0, 1.0, -2.0, -1.0, 0.0, 0.0, 0.0]
>>> print(parse_unit_dimension(magnetic_field))
'kg**1*s**-2*A**-1'
"""
if len(unit_dimension) != 7:
mylog.error("SI must have 7 base dimensions!")
unit_dimension = np.asarray(unit_dimension, dtype="int64")
dim = []
si = ["m", "kg", "s", "A", "C", "mol", "cd"]
for i in np.arange(7):
if unit_dimension[i] != 0:
dim.append(f"{si[i]}**{unit_dimension[i]}")
return "*".join(dim)
def _check_for_outputs(self, potential_outputs):
"""
Check a list of files to see if they are valid datasets.
"""
only_on_root(
mylog.info, "Checking %d potential outputs.", len(potential_outputs)
)
my_outputs = {}
llevel = mylog.level
# suppress logging as we load every dataset, unless set to debug
if llevel > 10 and llevel < 40:
mylog.setLevel(40)
for my_storage, output in parallel_objects(
potential_outputs, storage=my_outputs
):
if self.parameters["DataDumpDir"] in output:
dir_key = self.parameters["DataDumpDir"]
output_key = self.parameters["DataDumpName"]
else:
dir_key = self.parameters["RedshiftDumpDir"]
output_key = self.parameters["RedshiftDumpName"]
index = output[output.find(dir_key) + len(dir_key) :]
filename = os.path.join(
self.parameters["GlobalDir"],
f"{dir_key}{index}",
f"{output_key}{index}",
)
try:
ds = load(filename)
except (FileNotFoundError, YTUnidentifiedDataType):
mylog.error("Failed to load %s", filename)
continue
my_storage.result = {
"filename": filename,
"time": ds.current_time.in_units("s"),
}
if ds.cosmological_simulation:
my_storage.result["redshift"] = ds.current_redshift
mylog.setLevel(llevel)
my_outputs = [
my_output for my_output in my_outputs.values() if my_output is not None
]
return my_outputs
def build_dist(self):
"""
Build the data for plotting.
"""
# Pick out the stars.
if self.filter_provided:
ct = self._filter['creation_time']
mass_stars = self._data_source[self._filter, "particle_mass"]
else:
if self.ds_provided:
ct = self._data_source['creation_time']
if ct is None:
errmsg = 'data source must have particle_age!'
mylog.error(errmsg)
raise RuntimeError(errmsg)
mask = ct > 0
if not any(mask):
errmsg = 'all particles have age < 0'
mylog.error(errmsg)
raise RuntimeError(errmsg)
# type = self._data_source['particle_type']
ct_stars = ct[mask]
mass_stars = self._data_source[
'particle_mass'][mask].in_units('Msun')
del mask
else:
ct_stars = self.star_creation_time
mass_stars = self.star_mass
# Find the oldest stars in units of code time.
tmin = ct_stars.min().in_units("s")
# Multiply the end to prevent numerical issues.
self.time_bins = np.linspace(
tmin * 1.01, self._ds.current_time.in_units("s"),
num=self.bin_count + 1)
# Figure out which bins the stars go into.
inds = np.digitize(ct_stars.in_units("s"), self.time_bins) - 1
# Sum up the stars created in each time bin.
self.mass_bins = YTArray(
np.zeros(self.bin_count + 1, dtype='float64'), "Msun"
)
for index in np.unique(inds):
self.mass_bins[index] += (mass_stars[inds == index]).sum()
# We will want the time taken between bins.
self.time_bins_dt = self.time_bins[1:] - self.time_bins[:-1]
def build_dist(self):
"""
Build the data for plotting.
"""
# Pick out the stars.
if self.filter_provided:
ct = self._filter['creation_time']
mass_stars = self._data_source[self._filter, "particle_mass"]
else:
if self.ds_provided:
ct = self._data_source['creation_time']
if ct is None:
errmsg = 'data source must have particle_age!'
mylog.error(errmsg)
raise RuntimeError(errmsg)
mask = ct > 0
if not any(mask):
errmsg = 'all particles have age < 0'
mylog.error(errmsg)
raise RuntimeError(errmsg)
# type = self._data_source['particle_type']
ct_stars = ct[mask]
mass_stars = self._data_source['particle_mass'][mask].in_units(
'Msun')
del mask
else:
ct_stars = self.star_creation_time
mass_stars = self.star_mass
# Find the oldest stars in units of code time.
tmin = ct_stars.min().in_units("s")
# Multiply the end to prevent numerical issues.
self.time_bins = np.linspace(tmin * 1.01,
self._ds.current_time.in_units("s"),
num=self.bin_count + 1)
# Figure out which bins the stars go into.
inds = np.digitize(ct_stars.in_units("s"), self.time_bins) - 1
# Sum up the stars created in each time bin.
self.mass_bins = YTArray(np.zeros(self.bin_count + 1, dtype='float64'),
"Msun")
for index in np.unique(inds):
self.mass_bins[index] += (mass_stars[inds == index]).sum()
# We will want the time taken between bins.
self.time_bins_dt = self.time_bins[1:] - self.time_bins[:-1]
def _check_for_outputs(self, potential_outputs):
"""
Check a list of files to see if they are valid datasets.
"""
only_on_root(mylog.info, "Checking %d potential outputs.",
len(potential_outputs))
my_outputs = {}
llevel = mylog.level
# suppress logging as we load every dataset, unless set to debug
if llevel > 10 and llevel < 40:
mylog.setLevel(40)
for my_storage, output in parallel_objects(potential_outputs,
storage=my_outputs):
if self.parameters['DataDumpDir'] in output:
dir_key = self.parameters['DataDumpDir']
output_key = self.parameters['DataDumpName']
else:
dir_key = self.parameters['RedshiftDumpDir']
output_key = self.parameters['RedshiftDumpName']
index = output[output.find(dir_key) + len(dir_key):]
filename = os.path.join(self.parameters['GlobalDir'],
"%s%s" % (dir_key, index),
"%s%s" % (output_key, index))
if os.path.exists(filename):
try:
ds = load(filename)
if ds is not None:
my_storage.result = {
'filename': filename,
'time': ds.current_time.in_units("s")
}
if ds.cosmological_simulation:
my_storage.result['redshift'] = ds.current_redshift
except YTOutputNotIdentified:
mylog.error('Failed to load %s', filename)
mylog.setLevel(llevel)
my_outputs = [my_output for my_output in my_outputs.values() \
if my_output is not None]
return my_outputs
def _setup_dust_fields(self):
idust = 1
imax = self.__class__.MAXN_DUST_SPECIES
while ("amrvac", "rhod%d" % idust) in self.field_list:
if idust > imax:
mylog.error(
"Only the first %d dust species are currently read by yt. "
"If you read this, please consider issuing a ticket. ",
imax,
)
break
self._setup_velocity_fields(idust)
idust += 1
n_dust_found = idust - 1
us = self.ds.unit_system
if n_dust_found > 0:
def _total_dust_density(field, data):
tot = np.zeros_like(data[("gas", "density")])
for idust in range(1, n_dust_found + 1):
tot += data["dust%d_density" % idust]
return tot
self.add_field(
("gas", "total_dust_density"),
function=_total_dust_density,
dimensions=dimensions.density,
units=us["density"],
sampling_type="cell",
)
def dust_to_gas_ratio(field, data):
return data[("gas", "total_dust_density")] / data[("gas",
"density")]
self.add_field(
("gas", "dust_to_gas_ratio"),
function=dust_to_gas_ratio,
dimensions=dimensions.dimensionless,
sampling_type="cell",
)
def _check_for_outputs(self, potential_outputs):
r"""
Check a list of files to see if they are valid datasets.
"""
only_on_root(mylog.info, "Checking %d potential outputs.",
len(potential_outputs))
my_outputs = {}
for my_storage, output in parallel_objects(potential_outputs,
storage=my_outputs):
try:
ds = load(output)
except (FileNotFoundError, YTUnidentifiedDataType):
mylog.error("Failed to load %s", output)
continue
my_storage.result = {"filename": output, "num_steps": ds.num_steps}
my_outputs = [
my_output for my_output in my_outputs.values()
if my_output is not None
]
return my_outputs
def _check_for_outputs(self, potential_outputs):
r"""
Check a list of files to see if they are valid datasets.
"""
only_on_root(mylog.info, "Checking %d potential outputs.",
len(potential_outputs))
my_outputs = {}
for my_storage, output in parallel_objects(potential_outputs,
storage=my_outputs):
if os.path.exists(output):
try:
ds = load(output)
if ds is not None:
my_storage.result = {"filename": output,
"time": ds.current_time.in_units("s")}
if ds.cosmological_simulation:
my_storage.result["redshift"] = ds.current_redshift
except YTOutputNotIdentified:
mylog.error("Failed to load %s", output)
my_outputs = [my_output for my_output in my_outputs.values() \
if my_output is not None]
return my_outputs
def parallel_objects(objects,
njobs=0,
storage=None,
barrier=True,
dynamic=False):
r"""This function dispatches components of an iterable to different
processors.
The parallel_objects function accepts an iterable, *objects*, and based on
the number of jobs requested and number of available processors, decides
how to dispatch individual objects to processors or sets of processors.
This can implicitly include multi-level parallelism, such that the
processor groups assigned each object can be composed of several or even
hundreds of processors. *storage* is also available, for collation of
results at the end of the iteration loop.
Calls to this function can be nested.
This should not be used to iterate over datasets --
:class:`~yt.data_objects.time_series.DatasetSeries` provides a much nicer
interface for that.
Parameters
----------
objects : Iterable
The list of objects to dispatch to different processors.
njobs : int
How many jobs to spawn. By default, one job will be dispatched for
each available processor.
storage : dict
This is a dictionary, which will be filled with results during the
course of the iteration. The keys will be the dataset
indices and the values will be whatever is assigned to the *result*
attribute on the storage during iteration.
barrier : bool
Should a barier be placed at the end of iteration?
dynamic : bool
This governs whether or not dynamic load balancing will be enabled.
This requires one dedicated processor; if this is enabled with a set of
128 processors available, only 127 will be available to iterate over
objects as one will be load balancing the rest.
Examples
--------
Here is a simple example of iterating over a set of centers and making
slice plots centered at each.
>>> for c in parallel_objects(centers):
... SlicePlot(ds, "x", "Density", center=c).save()
...
Here's an example of calculating the angular momentum vector of a set of
spheres, but with a set of four jobs of multiple processors each. Note
that we also store the results.
>>> storage = {}
>>> for sto, c in parallel_objects(centers, njobs=4, storage=storage):
... sp = ds.sphere(c, (100, "kpc"))
... sto.result = sp.quantities["AngularMomentumVector"]()
...
>>> for sphere_id, L in sorted(storage.items()):
... print(centers[sphere_id], L)
...
"""
if dynamic:
from .task_queue import dynamic_parallel_objects
yield from dynamic_parallel_objects(objects,
njobs=njobs,
storage=storage)
return
if not parallel_capable:
njobs = 1
my_communicator = communication_system.communicators[-1]
my_size = my_communicator.size
if njobs <= 0:
njobs = my_size
if njobs > my_size:
mylog.error(
"You have asked for %s jobs, but you only have %s processors.",
njobs,
my_size,
)
raise RuntimeError
my_rank = my_communicator.rank
all_new_comms = np.array_split(np.arange(my_size), njobs)
for i, comm_set in enumerate(all_new_comms):
if my_rank in comm_set:
my_new_id = i
break
if parallel_capable:
communication_system.push_with_ids(all_new_comms[my_new_id].tolist())
to_share = {}
# If our objects object is slice-aware, like time series data objects are,
# this will prevent intermediate objects from being created.
oiter = itertools.islice(enumerate(objects), my_new_id, None, njobs)
for result_id, obj in oiter:
if storage is not None:
rstore = ResultsStorage()
rstore.result_id = result_id
yield rstore, obj
to_share[rstore.result_id] = rstore.result
else:
yield obj
if parallel_capable:
communication_system.pop()
if storage is not None:
# Now we have to broadcast it
new_storage = my_communicator.par_combine_object(to_share,
datatype="dict",
op="join")
storage.update(new_storage)
if barrier:
my_communicator.barrier()
def calculate_spectrum(self, data_source=None, star_mass=None,
star_creation_time=None,
star_metallicity_fraction=None,
star_metallicity_constant=None,
min_age=YTQuantity(0.0, 'yr')):
r"""For the set of stars, calculate the collective spectrum.
Attached to the output are several useful objects:
Attributes
----------
final_spec: array
The collective spectrum in units of flux binned in wavelength.
wavelength: array
The wavelength for the spectrum bins, in Angstroms.
total_mass: float
Total mass of all the stars.
avg_mass: float
Average mass of all the stars.
avg_metal: float
Average metallicity of all the stars.
Parameters
----------
data_source : AMRRegion object, optional
The region from which stars are extracted for analysis. If this is
not specified, the next three parameters must be supplied.
star_mass : Array or list of floats
An array of star masses in Msun units.
star_creation_time : Array or list of floats
An array of star creation times in code units.
star_metallicity_fraction : Array or list of floats
An array of star metallicity fractions, in code
units (which is not Z/Zsun, rather just Z).
star_metallicity_constant : Float
If desired, override the star
metallicity fraction of all the stars to the given value.
min_age : Float
Removes young stars younger than this number (in years)
from the spectrum. Default: 0 (all stars).
Examples
--------
>>> import yt
>>> from yt.analysis_modules.star_analysis.api import SpectrumBuilder
>>> ds = yt.load("Enzo_64/RD0006/RedshiftOutput0006")
>>> spec = SpectrumBuilder(ds, "bc", model="salpeter")
>>> sp = ds.sphere([0.5, 0.5, 0.5], 0.1)
>>> spec.calculate_spectrum(data_source=sp, min_age=1.e6)
"""
# Initialize values
self.final_spec = np.zeros(self.wavelength.size, dtype='float64')
self._data_source = data_source
if isinstance(star_mass, YTArray):
assert star_mass.units.same_dimensions_as(g.units)
elif star_mass is not None:
star_mass = YTArray(star_mass, 'Msun')
self.star_mass = star_mass
if isinstance(star_creation_time, YTArray):
assert star_creation_time.units.same_dimensions_as(s.units)
elif star_creation_time is not None:
star_creation_time = self._ds.arr(star_creation_time,
'code_time')
self.star_creation_time = star_creation_time
if isinstance(star_metallicity_fraction, YTArray):
assert \
star_metallicity_fraction.units.same_dimensions_as(Zsun.units)
elif star_metallicity_fraction is not None:
star_metallicity_fraction = self._ds.arr(
star_metallicity_fraction, 'code_metallicity'
)
self.star_metallicity_fraction = star_metallicity_fraction
if isinstance(min_age, YTQuantity):
assert min_age.units.same_dimensions_as(s.units)
elif min_age is not None:
min_age = YTQuantity(min_age, 'yr')
self.min_age = min_age
# Check to make sure we have the right set of data.
if data_source is None:
if self.star_mass is None or self.star_creation_time is None or \
(star_metallicity_fraction is None and
star_metallicity_constant is None):
mylog.error(
"""
If data_source is not provided, all of these paramters
need to be set:
star_mass (array, Msun),
star_creation_time (array, code units),
And one of:
star_metallicity_fraction (array, code units).
--OR--
star_metallicity_constant (float, code units).
""")
return None
if star_metallicity_fraction is not None:
self.star_metal = star_metallicity_fraction
else:
self.star_metal = \
self._ds.arr(np.ones_like(self.star_mass) *
star_metallicity_constant, 'Zsun')
else:
# Get the data we need.
if self.filter_provided:
ct = self._filter['creation_time']
# mass_stars = self._data_source[self._filter, "particle_mass"]
if star_metallicity_constant is None:
self.star_metal = self._data_source[
self._filter, "metallicity_fraction"].in_units('Zsun')
else:
self.star_metal = \
self._ds.arr(np.ones_like(
self._data_source[self._filter,
"metallicity_fraction"]) *
star_metallicity_constant, "Zsun")
else:
ct = self._data_source["creation_time"]
if ct is None:
errmsg = 'data source must have particle_age!'
mylog.error(errmsg)
raise RuntimeError(errmsg)
mask = ct > 0
if not any(mask):
errmsg = 'all particles have age < 0'
mylog.error(errmsg)
raise RuntimeError(errmsg)
# type = self._data_source['particle_type']
self.star_creation_time = ct[mask]
self.star_mass = self._data_source[
'particle_mass'][mask].in_units('Msun')
if star_metallicity_constant is not None:
self.star_metal = self._ds.arr(
np.ones_like(self.star_mass) *
star_metallicity_constant, 'Zsun')
else:
self.star_metal = self._data_source[
"metallicity_fraction"][mask].in_units('Zsun')
# Age of star in years.
dt = (self.time_now - self.star_creation_time).in_units('yr')
dt[dt < 0.0] = 0.0
# Remove young stars
sub = dt >= self.min_age
if len(sub) == 0:
return
self.star_metal = self.star_metal[sub]
dt = dt[sub]
self.star_creation_time = self.star_creation_time[sub]
# Figure out which METALS bin the star goes into.
Mindex = np.digitize(self.star_metal.in_units('Zsun'), METALS)
# Replace the indices with strings.
Mname = MtoD[Mindex]
# Figure out which age bin this star goes into.
Aindex = np.digitize(dt, self.age)
# Ratios used for the interpolation.
ratio1 = (dt - self.age[Aindex - 1]) / \
(self.age[Aindex] - self.age[Aindex - 1])
ratio2 = (self.age[Aindex] - dt) / \
(self.age[Aindex] - self.age[Aindex - 1])
# Sort the stars by metallicity and then by age, which should reduce
# memory access time by a little bit in the loop.
indexes = np.arange(self.star_metal.size)
sort = np.asarray([indexes[i]
for i in np.lexsort([indexes, Aindex, Mname])])
Mname = Mname[sort]
Aindex = Aindex[sort]
ratio1 = ratio1[sort]
ratio2 = ratio2[sort]
self.star_mass = self.star_mass[sort]
self.star_creation_time = self.star_creation_time[sort]
self.star_metal = self.star_metal[sort]
# Interpolate the flux for each star, adding to the total by weight.
pbar = get_pbar("Calculating fluxes", len(self.star_mass))
for i, star in enumerate(izip(Mname, Aindex, ratio1, ratio2,
self.star_mass)):
# Pick the right age bin for the right flux array.
flux = self.flux[star[0]][star[1], :]
# Get the one just before the one above.
flux_1 = self.flux[star[0]][star[1] - 1, :]
# interpolate in log(flux), linear in time.
int_flux = star[3] * np.log10(flux_1) + star[2] * np.log10(flux)
# Add this flux to the total, weighted by mass.
self.final_spec += np.power(10., int_flux) * star[4]
pbar.update(i)
pbar.finish()
# Normalize.
self.total_mass = self.star_mass.sum()
self.avg_mass = self.star_mass.mean()
tot_metal = (self.star_metal * self.star_mass).sum()
if tot_metal > 0:
self.avg_metal = math.log10(
(tot_metal / self.total_mass).in_units('Zsun'))
else:
self.avg_metal = -99
def _read_fluid_file_descriptor(fname: Union[str, "os.PathLike[str]"]):
"""
Read a file descriptor and returns the array of the fields found.
"""
# Mapping
mapping_list = [
("density", "Density"),
("velocity_x", "x-velocity"),
("velocity_y", "y-velocity"),
("velocity_z", "z-velocity"),
("pressure", "Pressure"),
("metallicity", "Metallicity"),
# Add mapping for ionized species
# Note: we expect internally that these names use the HII, HeII,
# HeIII, ... convention for historical reasons. So we need to map
# the names read from `hydro_file_descriptor.txt` to this
# convention.
# This will create fields like ("ramses", "HII") which are mapped
# to ("gas", "H_p1_fraction") in fields.py
("H_p1_fraction", "HII"),
("He_p1_fraction", "HeII"),
("He_p2_fraction", "HeIII"),
]
# Add mapping for magnetic fields
mapping_list += [(key, key) for key in (f"B_{dim}_{side}"
for side in ["left", "right"]
for dim in ["x", "y", "z"])]
# Convert to dictionary
mapping = {k: v for k, v in mapping_list}
with open(fname) as f:
line = f.readline()
tmp = VERSION_RE.match(line)
mylog.debug("Reading fluid file descriptor %s.", fname)
if not tmp:
return []
version = int(tmp.group(1))
if version == 1:
# Skip one line (containing the headers)
line = f.readline()
fields = []
for i, line in enumerate(f.readlines()):
tmp = VAR_DESC_RE.match(line)
if not tmp:
raise YTFileNotParseable(fname, i + 1)
# ivar = tmp.group(1)
varname = tmp.group(2)
dtype = tmp.group(3)
if varname in mapping:
varname = mapping[varname]
else:
varname = f"hydro_{varname}"
fields.append((varname, dtype))
else:
mylog.error("Version %s", version)
raise YTParticleOutputFormatNotImplemented()
return fields
def calculate_spectrum(self,
data_source=None,
star_mass=None,
star_creation_time=None,
star_metallicity_fraction=None,
star_metallicity_constant=None,
min_age=YTQuantity(0.0, 'yr')):
r"""For the set of stars, calculate the collective spectrum.
Attached to the output are several useful objects:
Attributes
----------
final_spec: array
The collective spectrum in units of flux binned in wavelength.
wavelength: array
The wavelength for the spectrum bins, in Angstroms.
total_mass: float
Total mass of all the stars.
avg_mass: float
Average mass of all the stars.
avg_metal: float
Average metallicity of all the stars.
Parameters
----------
data_source : AMRRegion object, optional
The region from which stars are extracted for analysis. If this is
not specified, the next three parameters must be supplied.
star_mass : Array or list of floats
An array of star masses in Msun units.
star_creation_time : Array or list of floats
An array of star creation times in code units.
star_metallicity_fraction : Array or list of floats
An array of star metallicity fractions, in code
units (which is not Z/Zsun, rather just Z).
star_metallicity_constant : Float
If desired, override the star
metallicity fraction of all the stars to the given value.
min_age : Float
Removes young stars younger than this number (in years)
from the spectrum. Default: 0 (all stars).
Examples
--------
>>> import yt
>>> from yt.analysis_modules.star_analysis.api import SpectrumBuilder
>>> ds = yt.load("Enzo_64/RD0006/RedshiftOutput0006")
>>> spec = SpectrumBuilder(ds, "bc", model="salpeter")
>>> sp = ds.sphere([0.5, 0.5, 0.5], 0.1)
>>> spec.calculate_spectrum(data_source=sp, min_age=1.e6)
"""
# Initialize values
self.final_spec = np.zeros(self.wavelength.size, dtype='float64')
self._data_source = data_source
if isinstance(star_mass, YTArray):
assert star_mass.units.same_dimensions_as(g.units)
elif star_mass is not None:
star_mass = YTArray(star_mass, 'Msun')
self.star_mass = star_mass
if isinstance(star_creation_time, YTArray):
assert star_creation_time.units.same_dimensions_as(s.units)
elif star_creation_time is not None:
star_creation_time = self._ds.arr(star_creation_time, 'code_time')
self.star_creation_time = star_creation_time
if isinstance(star_metallicity_fraction, YTArray):
assert \
star_metallicity_fraction.units.same_dimensions_as(Zsun.units)
elif star_metallicity_fraction is not None:
star_metallicity_fraction = self._ds.arr(star_metallicity_fraction,
'code_metallicity')
self.star_metallicity_fraction = star_metallicity_fraction
if isinstance(min_age, YTQuantity):
assert min_age.units.same_dimensions_as(s.units)
elif min_age is not None:
min_age = YTQuantity(min_age, 'yr')
self.min_age = min_age
# Check to make sure we have the right set of data.
if data_source is None:
if self.star_mass is None or self.star_creation_time is None or \
(star_metallicity_fraction is None and
star_metallicity_constant is None):
mylog.error("""
If data_source is not provided, all of these paramters
need to be set:
star_mass (array, Msun),
star_creation_time (array, code units),
And one of:
star_metallicity_fraction (array, code units).
--OR--
star_metallicity_constant (float, code units).
""")
return None
if star_metallicity_fraction is not None:
self.star_metal = star_metallicity_fraction
else:
self.star_metal = \
self._ds.arr(np.ones_like(self.star_mass) *
star_metallicity_constant, 'Zsun')
else:
# Get the data we need.
if self.filter_provided:
ct = self._filter['creation_time']
# mass_stars = self._data_source[self._filter, "particle_mass"]
if star_metallicity_constant is None:
self.star_metal = self._data_source[
self._filter, "metallicity_fraction"].in_units('Zsun')
else:
self.star_metal = \
self._ds.arr(np.ones_like(
self._data_source[self._filter,
"metallicity_fraction"]) *
star_metallicity_constant, "Zsun")
else:
ct = self._data_source["creation_time"]
if ct is None:
errmsg = 'data source must have particle_age!'
mylog.error(errmsg)
raise RuntimeError(errmsg)
mask = ct > 0
if not any(mask):
errmsg = 'all particles have age < 0'
mylog.error(errmsg)
raise RuntimeError(errmsg)
# type = self._data_source['particle_type']
self.star_creation_time = ct[mask]
self.star_mass = self._data_source['particle_mass'][
mask].in_units('Msun')
if star_metallicity_constant is not None:
self.star_metal = self._ds.arr(
np.ones_like(self.star_mass) *
star_metallicity_constant, 'Zsun')
else:
self.star_metal = self._data_source[
"metallicity_fraction"][mask].in_units('Zsun')
# Age of star in years.
dt = (self.time_now - self.star_creation_time).in_units('yr')
dt[dt < 0.0] = 0.0
# Remove young stars
sub = dt >= self.min_age
if len(sub) == 0:
return
self.star_metal = self.star_metal[sub]
dt = dt[sub]
self.star_creation_time = self.star_creation_time[sub]
# Figure out which METALS bin the star goes into.
Mindex = np.digitize(self.star_metal.in_units('Zsun'), METALS)
# Replace the indices with strings.
Mname = MtoD[Mindex]
# Figure out which age bin this star goes into.
Aindex = np.digitize(dt, self.age)
# Ratios used for the interpolation.
ratio1 = (dt - self.age[Aindex - 1]) / \
(self.age[Aindex] - self.age[Aindex - 1])
ratio2 = (self.age[Aindex] - dt) / \
(self.age[Aindex] - self.age[Aindex - 1])
# Sort the stars by metallicity and then by age, which should reduce
# memory access time by a little bit in the loop.
indexes = np.arange(self.star_metal.size)
sort = np.asarray(
[indexes[i] for i in np.lexsort([indexes, Aindex, Mname])])
Mname = Mname[sort]
Aindex = Aindex[sort]
ratio1 = ratio1[sort]
ratio2 = ratio2[sort]
self.star_mass = self.star_mass[sort]
self.star_creation_time = self.star_creation_time[sort]
self.star_metal = self.star_metal[sort]
# Interpolate the flux for each star, adding to the total by weight.
pbar = get_pbar("Calculating fluxes", len(self.star_mass))
for i, star in enumerate(
izip(Mname, Aindex, ratio1, ratio2, self.star_mass)):
# Pick the right age bin for the right flux array.
flux = self.flux[star[0]][star[1], :]
# Get the one just before the one above.
flux_1 = self.flux[star[0]][star[1] - 1, :]
# interpolate in log(flux), linear in time.
int_flux = star[3] * np.log10(flux_1) + star[2] * np.log10(flux)
# Add this flux to the total, weighted by mass.
self.final_spec += np.power(10., int_flux) * star[4]
pbar.update(i)
pbar.finish()
# Normalize.
self.total_mass = self.star_mass.sum()
self.avg_mass = self.star_mass.mean()
tot_metal = (self.star_metal * self.star_mass).sum()
if tot_metal > 0:
self.avg_metal = math.log10(
(tot_metal / self.total_mass).in_units('Zsun'))
else:
self.avg_metal = -99
def parallel_objects(objects, njobs = 0, storage = None, barrier = True,
dynamic = False):
r"""This function dispatches components of an iterable to different
processors.
The parallel_objects function accepts an iterable, *objects*, and based on
the number of jobs requested and number of available processors, decides
how to dispatch individual objects to processors or sets of processors.
This can implicitly include multi-level parallelism, such that the
processor groups assigned each object can be composed of several or even
hundreds of processors. *storage* is also available, for collation of
results at the end of the iteration loop.
Calls to this function can be nested.
This should not be used to iterate over datasets --
:class:`~yt.data_objects.time_series.DatasetSeries` provides a much nicer
interface for that.
Parameters
----------
objects : iterable
The list of objects to dispatch to different processors.
njobs : int
How many jobs to spawn. By default, one job will be dispatched for
each available processor.
storage : dict
This is a dictionary, which will be filled with results during the
course of the iteration. The keys will be the dataset
indices and the values will be whatever is assigned to the *result*
attribute on the storage during iteration.
barrier : bool
Should a barier be placed at the end of iteration?
dynamic : bool
This governs whether or not dynamic load balancing will be enabled.
This requires one dedicated processor; if this is enabled with a set of
128 processors available, only 127 will be available to iterate over
objects as one will be load balancing the rest.
Examples
--------
Here is a simple example of iterating over a set of centers and making
slice plots centered at each.
>>> for c in parallel_objects(centers):
... SlicePlot(ds, "x", "Density", center = c).save()
...
Here's an example of calculating the angular momentum vector of a set of
spheres, but with a set of four jobs of multiple processors each. Note
that we also store the results.
>>> storage = {}
>>> for sto, c in parallel_objects(centers, njobs=4, storage=storage):
... sp = ds.sphere(c, (100, "kpc"))
... sto.result = sp.quantities["AngularMomentumVector"]()
...
>>> for sphere_id, L in sorted(storage.items()):
... print centers[sphere_id], L
...
"""
if dynamic:
from .task_queue import dynamic_parallel_objects
for my_obj in dynamic_parallel_objects(objects, njobs=njobs,
storage=storage):
yield my_obj
return
if not parallel_capable:
njobs = 1
my_communicator = communication_system.communicators[-1]
my_size = my_communicator.size
mylog.info("you have %s processors",my_size)
if njobs <= 0:
njobs = my_size
if njobs > my_size:
mylog.error("You have asked for %s jobs, but you only have %s processors.",
njobs, my_size)
raise RuntimeError
my_rank = my_communicator.rank
mylog.info("I am %s processor",my_rank)
all_new_comms = np.array_split(np.arange(my_size), njobs)
for i,comm_set in enumerate(all_new_comms):
if my_rank in comm_set:
my_new_id = i
break
if parallel_capable:
communication_system.push_with_ids(all_new_comms[my_new_id].tolist())
to_share = {}
# If our objects object is slice-aware, like time series data objects are,
# this will prevent intermediate objects from being created.
oiter = itertools.islice(enumerate(objects), my_new_id, None, njobs)
for result_id, obj in oiter:
if storage is not None:
rstore = ResultsStorage()
rstore.result_id = result_id
yield rstore, obj
to_share[rstore.result_id] = rstore.result
else:
yield obj
if parallel_capable:
communication_system.pop()
if storage is not None:
# Now we have to broadcast it
new_storage = my_communicator.par_combine_object(
to_share, datatype = 'dict', op = 'join')
mylog.info("my storage: %s",type(new_storage))
storage.update(new_storage)
if barrier:
my_communicator.barrier()
def enable_parallelism(suppress_logging: bool = False,
communicator=None) -> bool:
"""
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.
Returns
-------
parallel_capable: bool
True if the call was successful. False otherwise.
"""
global parallel_capable, MPI
try:
from mpi4py import MPI as _MPI
except ImportError:
mylog.error("Could not enable parallelism: mpi4py is not installed")
return False
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:
mylog.error(
"Could not enable parallelism: only one mpi process is running. "
"To remedy this, launch the Python interpreter as\n"
" mpirun -n <X> python3 <yourscript>.py # with X > 1 ", )
return False
mylog.info(
"Global parallel computation enabled: %s / %s",
communicator.rank,
communicator.size,
)
communication_system.push(communicator)
ytcfg["yt", "internals", "global_parallel_rank"] = communicator.rank
ytcfg["yt", "internals", "global_parallel_size"] = communicator.size
ytcfg["yt", "internals", "parallel"] = True
if exe_name == "embed_enzo" or ("_parallel" in dir(sys)
and sys._parallel): # type: ignore
ytcfg["yt", "inline"] = True
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.get("yt", "parallel_traceback"):
sys.excepthook = traceback_writer_hook("_%03i" % communicator.rank)
else:
sys.excepthook = default_mpi_excepthook
if ytcfg.get("yt", "log_level") < 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
def load_hexahedral_mesh(
data,
connectivity,
coordinates,
length_unit=None,
bbox=None,
sim_time=0.0,
mass_unit=None,
time_unit=None,
velocity_unit=None,
magnetic_unit=None,
periodicity=(True, True, True),
geometry="cartesian",
unit_system="cgs",
):
r"""Load a hexahedral mesh of data into yt as a
:class:`~yt.frontends.stream.data_structures.StreamHandler`.
This should allow a semistructured grid of data to be loaded directly into
yt and analyzed as would any others. This comes with several caveats:
* Units will be incorrect unless the data has already been converted to
cgs.
* Some functions may behave oddly, and parallelism will be
disappointing or non-existent in most cases.
* Particles may be difficult to integrate.
Particle fields are detected as one-dimensional fields. The number of particles
is set by the "number_of_particles" key in data.
Parameters
----------
data : dict
This is a dict of numpy arrays, where the keys are the field names.
There must only be one. Note that the data in the numpy arrays should
define the cell-averaged value for of the quantity in in the hexahedral
cell.
connectivity : array_like
This should be of size (N,8) where N is the number of zones.
coordinates : array_like
This should be of size (M,3) where M is the number of vertices
indicated in the connectivity matrix.
bbox : array_like (xdim:zdim, LE:RE), optional
Size of computational domain in units of the length unit.
sim_time : float, optional
The simulation time in seconds
mass_unit : string
Unit to use for masses. Defaults to unitless.
time_unit : string
Unit to use for times. Defaults to unitless.
velocity_unit : string
Unit to use for velocities. Defaults to unitless.
magnetic_unit : string
Unit to use for magnetic fields. Defaults to unitless.
periodicity : tuple of booleans
Determines whether the data will be treated as periodic along
each axis
geometry : string or tuple
"cartesian", "cylindrical", "polar", "spherical", "geographic" or
"spectral_cube". Optionally, a tuple can be provided to specify the
axis ordering -- for instance, to specify that the axis ordering should
be z, x, y, this would be: ("cartesian", ("z", "x", "y")). The same
can be done for other coordinates, for instance:
("spherical", ("theta", "phi", "r")).
"""
from yt.frontends.stream.data_structures import (
StreamDictFieldHandler,
StreamHandler,
StreamHexahedralDataset,
)
from yt.frontends.stream.definitions import process_data, set_particle_types
domain_dimensions = np.ones(3, "int32") * 2
nprocs = 1
if bbox is None:
bbox = np.array([[0.0, 1.0], [0.0, 1.0], [0.0, 1.0]], "float64")
domain_left_edge = np.array(bbox[:, 0], "float64")
domain_right_edge = np.array(bbox[:, 1], "float64")
grid_levels = np.zeros(nprocs, dtype="int32").reshape((nprocs, 1))
field_units, data, _ = process_data(data)
sfh = StreamDictFieldHandler()
particle_types = set_particle_types(data)
sfh.update({"connectivity": connectivity, "coordinates": coordinates, 0: data})
# Simple check for axis length correctness
if len(data) > 0:
fn = list(sorted(data))[0]
array_values = data[fn]
if array_values.size != connectivity.shape[0]:
mylog.error(
"Dimensions of array must be one fewer than the coordinate set."
)
raise RuntimeError
grid_left_edges = domain_left_edge
grid_right_edges = domain_right_edge
grid_dimensions = domain_dimensions.reshape(nprocs, 3).astype("int32")
if length_unit is None:
length_unit = "code_length"
if mass_unit is None:
mass_unit = "code_mass"
if time_unit is None:
time_unit = "code_time"
if velocity_unit is None:
velocity_unit = "code_velocity"
if magnetic_unit is None:
magnetic_unit = "code_magnetic"
# I'm not sure we need any of this.
handler = StreamHandler(
grid_left_edges,
grid_right_edges,
grid_dimensions,
grid_levels,
-np.ones(nprocs, dtype="int64"),
np.zeros(nprocs, dtype="int64").reshape(nprocs, 1), # Temporary
np.zeros(nprocs).reshape((nprocs, 1)),
sfh,
field_units,
(length_unit, mass_unit, time_unit, velocity_unit, magnetic_unit),
particle_types=particle_types,
periodicity=periodicity,
)
handler.name = "HexahedralMeshData"
handler.domain_left_edge = domain_left_edge
handler.domain_right_edge = domain_right_edge
handler.refine_by = 2
handler.dimensionality = 3
handler.domain_dimensions = domain_dimensions
handler.simulation_time = sim_time
handler.cosmology_simulation = 0
sds = StreamHexahedralDataset(handler, geometry=geometry, unit_system=unit_system)
return sds