def __init__(self, output_dir,
cparam_filename="inputs",
fparam_filename="probin",
dataset_type='boxlib_native',
storage_filename=None,
units_override=None):
"""
The paramfile is usually called "inputs"
and there may be a fortran inputs file usually called "probin"
plotname here will be a directory name
as per BoxLib, dataset_type will be Native (implemented here), IEEE (not
yet implemented) or ASCII (not yet implemented.)
"""
self.fluid_types += ("boxlib",)
self.output_dir = os.path.abspath(os.path.expanduser(output_dir))
self.cparam_filename = self._localize_check(cparam_filename)
self.fparam_filename = self._localize_check(fparam_filename)
self.storage_filename = storage_filename
Dataset.__init__(self, output_dir, dataset_type,
units_override=units_override)
# These are still used in a few places.
if "HydroMethod" not in self.parameters.keys():
self.parameters["HydroMethod"] = 'boxlib'
self.parameters["Time"] = 1. # default unit is 1...
self.parameters["EOSType"] = -1 # default
self.parameters["gamma"] = self.parameters.get(
"materials.gamma", 1.6667)
def __init__(self, filename, dataset_type='art',
fields=None, storage_filename=None,
skip_particles=False, skip_stars=False,
limit_level=None, spread_age=True,
force_max_level=None, file_particle_header=None,
file_particle_data=None, file_particle_stars=None,
units_override=None, unit_system="cgs"):
self.fluid_types += ("art", )
if fields is None:
fields = fluid_fields
filename = os.path.abspath(filename)
self._fields_in_file = fields
self._file_amr = filename
self._file_particle_header = file_particle_header
self._file_particle_data = file_particle_data
self._file_particle_stars = file_particle_stars
self._find_files(filename)
self.parameter_filename = filename
self.skip_particles = skip_particles
self.skip_stars = skip_stars
self.limit_level = limit_level
self.max_level = limit_level
self.force_max_level = force_max_level
self.spread_age = spread_age
Dataset.__init__(self, filename, dataset_type,
units_override=units_override,
unit_system=unit_system)
self.storage_filename = storage_filename
def __init__(self,
filename,
dataset_type=None,
file_style=None,
parameter_override=None,
conversion_override=None,
storage_filename=None,
units_override=None,
unit_system="cgs"):
"""
This class is a stripped down class that simply reads and parses
*filename* without looking at the index. *dataset_type* gets passed
to the index to pre-determine the style of data-output. However,
it is not strictly necessary. Optionally you may specify a
*parameter_override* dictionary that will override anything in the
parameter file and a *conversion_override* dictionary that consists
of {fieldname : conversion_to_cgs} that will override the #DataCGS.
"""
self.fluid_types += ("enzo", )
if filename.endswith(".hierarchy"): filename = filename[:-10]
if parameter_override is None: parameter_override = {}
self._parameter_override = parameter_override
if conversion_override is None: conversion_override = {}
self._conversion_override = conversion_override
self.storage_filename = storage_filename
Dataset.__init__(self,
filename,
dataset_type,
file_style=file_style,
units_override=units_override,
unit_system=unit_system)
def __init__(self, filename, dataset_type='art',
fields=None, storage_filename=None,
skip_particles=False, skip_stars=False,
limit_level=None, spread_age=True,
force_max_level=None, file_particle_header=None,
file_particle_data=None, file_particle_stars=None,
units_override=None):
self.fluid_types += ("art", )
if fields is None:
fields = fluid_fields
filename = os.path.abspath(filename)
self._fields_in_file = fields
self._file_amr = filename
self._file_particle_header = file_particle_header
self._file_particle_data = file_particle_data
self._file_particle_stars = file_particle_stars
self._find_files(filename)
self.parameter_filename = filename
self.skip_particles = skip_particles
self.skip_stars = skip_stars
self.limit_level = limit_level
self.max_level = limit_level
self.force_max_level = force_max_level
self.spread_age = spread_age
self.domain_left_edge = np.zeros(3, dtype='float')
self.domain_right_edge = np.zeros(3, dtype='float')+1.0
Dataset.__init__(self, filename, dataset_type,
units_override=units_override)
self.storage_filename = storage_filename
def __init__(self,
filename,
dataset_type='athena_pp',
storage_filename=None,
parameters=None,
units_override=None,
unit_system="code"):
self.fluid_types += ("athena_pp", )
if parameters is None:
parameters = {}
self.specified_parameters = parameters
if units_override is None:
units_override = {}
self._handle = HDF5FileHandler(filename)
xrat = self._handle.attrs["RootGridX1"][2]
yrat = self._handle.attrs["RootGridX2"][2]
zrat = self._handle.attrs["RootGridX3"][2]
if xrat != 1.0 or yrat != 1.0 or zrat != 1.0:
self._index_class = AthenaPPLogarithmicIndex
self.logarithmic = True
else:
self._index_class = AthenaPPHierarchy
self.logarithmic = False
Dataset.__init__(self,
filename,
dataset_type,
units_override=units_override,
unit_system=unit_system)
self.filename = filename
if storage_filename is None:
storage_filename = '%s.yt' % filename.split('/')[-1]
self.storage_filename = storage_filename
self.backup_filename = self.filename[:-4] + "_backup.gdf"
def __init__(self,
filename,
dataset_type="openPMD",
storage_filename=None,
units_override=None,
unit_system="mks",
**kwargs):
self._handle = HDF5FileHandler(filename)
self.gridsize = kwargs.pop("open_pmd_virtual_gridsize", 10**9)
self.standard_version = StrictVersion(self._handle.attrs["openPMD"].decode())
self.iteration = kwargs.pop("iteration", None)
self._set_paths(self._handle, path.dirname(filename), self.iteration)
Dataset.__init__(self,
filename,
dataset_type,
units_override=units_override,
unit_system=unit_system)
self.storage_filename = storage_filename
self.fluid_types += ("openPMD",)
try:
particles = tuple(str(c) for c in self._handle[self.base_path + self.particles_path].keys())
if len(particles) > 1:
# Only use on-disk particle names if there is more than one species
self.particle_types = particles
mylog.debug("self.particle_types: {}".format(self.particle_types))
self.particle_types_raw = self.particle_types
self.particle_types = tuple(self.particle_types)
except(KeyError, TypeError, AttributeError):
pass
def __init__(self, filename, dataset_type='flash_hdf5',
storage_filename = None,
particle_filename = None,
units_override = None):
self.fluid_types += ("flash",)
if self._handle is not None: return
self._handle = HDF5FileHandler(filename)
self.particle_filename = particle_filename
if self.particle_filename is None:
self._particle_handle = self._handle
else:
try:
self._particle_handle = HDF5FileHandler(self.particle_filename)
except:
raise IOError(self.particle_filename)
# These should be explicitly obtained from the file, but for now that
# will wait until a reorganization of the source tree and better
# generalization.
self.refine_by = 2
Dataset.__init__(self, filename, dataset_type, units_override=units_override)
self.storage_filename = storage_filename
self.parameters["HydroMethod"] = 'flash' # always PPM DE
self.parameters["Time"] = 1. # default unit is 1...
def __init__(self, filename, dataset_type='skeleton',
storage_filename=None,
units_override=None):
self.fluid_types += ('skeleton',)
Dataset.__init__(self, filename, dataset_type,
units_override=units_override)
self.storage_filename = storage_filename
def __init__(self,
filename,
dataset_type='ramses',
fields=None,
storage_filename=None,
units_override=None,
unit_system="cgs",
extra_particle_fields=None,
cosmological=None,
bbox=None):
# Here we want to initiate a traceback, if the reader is not built.
if isinstance(fields, string_types):
fields = field_aliases[fields]
'''
fields: An array of hydro variable fields in order of position in the hydro_XXXXX.outYYYYY file
If set to None, will try a default set of fields
extra_particle_fields: An array of extra particle variables in order of position in the particle_XXXXX.outYYYYY file.
cosmological: If set to None, automatically detect cosmological simulation. If a boolean, force
its value.
'''
self._fields_in_file = fields
self._extra_particle_fields = extra_particle_fields
self._warn_extra_fields = False
self.force_cosmological = cosmological
self._bbox = bbox
Dataset.__init__(self,
filename,
dataset_type,
units_override=units_override,
unit_system=unit_system)
for FH in get_field_handlers():
if FH.any_exist(self):
self.fluid_types += (FH.ftype, )
self.storage_filename = storage_filename
def __init__(
self,
filename,
dataset_type="flash_hdf5",
storage_filename=None,
particle_filename=None,
units_override=None,
unit_system="cgs",
):
self.fluid_types += ("flash", )
if self._handle is not None:
return
self._handle = HDF5FileHandler(filename)
self.particle_filename = particle_filename
if self.particle_filename is None:
# try to guess the particle filename
try:
self._particle_handle = HDF5FileHandler(
filename.replace("plt_cnt", "part"))
self.particle_filename = filename.replace("plt_cnt", "part")
mylog.info("Particle file found: %s",
self.particle_filename.split("/")[-1])
except OSError:
self._particle_handle = self._handle
else:
# particle_filename is specified by user
self._particle_handle = HDF5FileHandler(self.particle_filename)
# Check if the particle file has the same time
if self._particle_handle != self._handle:
part_time = self._particle_handle.handle.get("real scalars")[0][1]
plot_time = self._handle.handle.get("real scalars")[0][1]
if not np.isclose(part_time, plot_time):
self._particle_handle = self._handle
mylog.warning(
"%s and %s are not at the same time. "
"This particle file will not be used.",
self.particle_filename,
filename,
)
# These should be explicitly obtained from the file, but for now that
# will wait until a reorganization of the source tree and better
# generalization.
self.refine_by = 2
Dataset.__init__(
self,
filename,
dataset_type,
units_override=units_override,
unit_system=unit_system,
)
self.storage_filename = storage_filename
self.parameters["HydroMethod"] = "flash" # always PPM DE
self.parameters["Time"] = 1.0 # default unit is 1...
def __init__(
self, filename, dataset_type="athena", storage_filename=None, parameters=None, units_override=None, nprocs=1
):
self.fluid_types += ("athena",)
self.nprocs = nprocs
if parameters is None:
parameters = {}
self.specified_parameters = parameters.copy()
if units_override is None:
units_override = {}
# This is for backwards-compatibility
already_warned = False
for k, v in self.specified_parameters.items():
if k.endswith("_unit") and k not in units_override:
if not already_warned:
mylog.warning(
"Supplying unit conversions from the parameters dict is deprecated, "
+ "and will be removed in a future release. Use units_override instead."
)
already_warned = True
units_override[k] = self.specified_parameters.pop(k)
Dataset.__init__(self, filename, dataset_type, units_override=units_override)
self.filename = filename
if storage_filename is None:
storage_filename = "%s.yt" % filename.split("/")[-1]
self.storage_filename = storage_filename
self.backup_filename = self.filename[:-4] + "_backup.gdf"
# Unfortunately we now have to mandate that the index gets
# instantiated so that we can make sure we have the correct left
# and right domain edges.
self.index
def __init__(
self,
filename,
dataset_type="athena",
storage_filename=None,
parameters=None,
units_override=None,
nprocs=1,
unit_system="cgs",
default_species_fields=None,
):
self.fluid_types += ("athena", )
self.nprocs = nprocs
if parameters is None:
parameters = {}
self.specified_parameters = parameters.copy()
if units_override is None:
units_override = {}
Dataset.__init__(
self,
filename,
dataset_type,
units_override=units_override,
unit_system=unit_system,
default_species_fields=default_species_fields,
)
self.filename = filename
if storage_filename is None:
storage_filename = f"{filename.split('/')[-1]}.yt"
self.storage_filename = storage_filename
self.backup_filename = self.filename[:-4] + "_backup.gdf"
# Unfortunately we now have to mandate that the index gets
# instantiated so that we can make sure we have the correct left
# and right domain edges.
self.index
def __init__(
self,
filename,
dataset_type="chombo_hdf5",
storage_filename=None,
ini_filename=None,
units_override=None,
unit_system="cgs",
):
self.fluid_types += ("chombo", )
self._handle = HDF5FileHandler(filename)
self.dataset_type = dataset_type
self.geometry = "cartesian"
self.ini_filename = ini_filename
self.fullplotdir = os.path.abspath(filename)
Dataset.__init__(
self,
filename,
self.dataset_type,
units_override=units_override,
unit_system=unit_system,
)
self.storage_filename = storage_filename
self.cosmological_simulation = False
# These are parameters that I very much wish to get rid of.
self.parameters["HydroMethod"] = "chombo"
self.parameters["DualEnergyFormalism"] = 0
self.parameters["EOSType"] = -1 # default
def __init__(
self,
filename,
dataset_type="artio",
storage_filename=None,
max_range=1024,
units_override=None,
unit_system="cgs",
default_species_fields=None,
):
from sys import version
if self._handle is not None:
return
self.max_range = max_range
self.fluid_types += ("artio", )
self._filename = filename
self._fileset_prefix = filename[:-4]
if version < "3":
self._handle = artio_fileset(self._fileset_prefix)
else:
self._handle = artio_fileset(bytes(self._fileset_prefix, "utf-8"))
self.artio_parameters = self._handle.parameters
# Here we want to initiate a traceback, if the reader is not built.
Dataset.__init__(
self,
filename,
dataset_type,
units_override=units_override,
unit_system=unit_system,
default_species_fields=default_species_fields,
)
self.storage_filename = storage_filename
def __init__(self, filename, dataset_type=None,
file_style = None,
parameter_override = None,
conversion_override = None,
storage_filename = None,
units_override=None,
unit_system="cgs"):
"""
This class is a stripped down class that simply reads and parses
*filename* without looking at the index. *dataset_type* gets passed
to the index to pre-determine the style of data-output. However,
it is not strictly necessary. Optionally you may specify a
*parameter_override* dictionary that will override anything in the
parameter file and a *conversion_override* dictionary that consists
of {fieldname : conversion_to_cgs} that will override the #DataCGS.
"""
self.fluid_types += ("enzop",)
if parameter_override is None: parameter_override = {}
self._parameter_override = parameter_override
if conversion_override is None: conversion_override = {}
self._conversion_override = conversion_override
self.storage_filename = storage_filename
Dataset.__init__(self, filename, dataset_type, file_style=file_style,
units_override=units_override, unit_system=unit_system)
warnings.warn(
"The Enzo-P file format is still under development and may " +
"change. If loading fails, simulation data will need to be " +
"re-generated.")
def __init__(
self,
filename,
dataset_type="flash_particle_hdf5",
storage_filename=None,
units_override=None,
index_order=None,
index_filename=None,
unit_system="cgs",
):
self.index_order = validate_index_order(index_order)
self.index_filename = index_filename
if self._handle is not None:
return
self._handle = HDF5FileHandler(filename)
self.refine_by = 2
Dataset.__init__(
self,
filename,
dataset_type,
units_override=units_override,
unit_system=unit_system,
)
self.storage_filename = storage_filename
def __init__(self, filename, dataset_type='grid_data_format',
storage_filename=None, geometry=None,
units_override=None):
self.geometry = geometry
self.fluid_types += ("gdf",)
Dataset.__init__(self, filename, dataset_type, units_override=units_override)
self.storage_filename = storage_filename
self.filename = filename
def __init__(self, parameter_override=None, conversion_override=None):
self.fluid_types += ("enzo", )
if parameter_override is None: parameter_override = {}
self._parameter_override = parameter_override
if conversion_override is None: conversion_override = {}
self._conversion_override = conversion_override
Dataset.__init__(self, "InMemoryParameterFile", self._dataset_type)
def __init__(self, pyne_mesh, dataset_type='moab_hex8_pyne',
storage_filename = None):
self.fluid_types += ("pyne",)
filename = "pyne_mesh_" + str(id(pyne_mesh))
self.pyne_mesh = pyne_mesh
Dataset.__init__(self, str(filename), dataset_type)
self.storage_filename = storage_filename
self.filename = filename
def __init__(self, filename, dataset_type='moab_hex8',
storage_filename = None, units_override=None):
self.fluid_types += ("moab",)
Dataset.__init__(self, filename, dataset_type,
units_override=units_override)
self.storage_filename = storage_filename
self.filename = filename
self._handle = HDF5FileHandler(filename)
def __init__(self, parameter_override=None, conversion_override=None):
self.fluid_types += ("enzo",)
if parameter_override is None: parameter_override = {}
self._parameter_override = parameter_override
if conversion_override is None: conversion_override = {}
self._conversion_override = conversion_override
Dataset.__init__(self, "InMemoryParameterFile", self._dataset_type)
def __init__(self, pyne_mesh, dataset_type='moab_hex8_pyne',
storage_filename = None, units_override=None):
self.fluid_types += ("pyne",)
filename = "pyne_mesh_" + str(id(pyne_mesh))
self.pyne_mesh = pyne_mesh
Dataset.__init__(self, str(filename), dataset_type,
units_override=units_override)
self.storage_filename = storage_filename
self.filename = filename
def __init__(self, filename, dataset_type='ramses',
fields=None, storage_filename=None,
units_override=None, unit_system="cgs",
extra_particle_fields=None, cosmological=None,
bbox=None):
# Here we want to initiate a traceback, if the reader is not built.
if isinstance(fields, string_types):
fields = field_aliases[fields]
'''
fields: An array of hydro variable fields in order of position in the hydro_XXXXX.outYYYYY file
If set to None, will try a default set of fields
extra_particle_fields: An array of extra particle variables in order of position in the particle_XXXXX.outYYYYY file.
cosmological: If set to None, automatically detect cosmological simulation. If a boolean, force
its value.
'''
self._fields_in_file = fields
# By default, extra fields have not triggered a warning
self._warned_extra_fields = defaultdict(lambda: False)
self._extra_particle_fields = extra_particle_fields
self.force_cosmological = cosmological
self._bbox = bbox
# Infer if the output is organized in groups
root_folder, group_folder = os.path.split(os.path.split(filename)[0])
if group_folder == 'group_00001':
# Count the number of groups
# note: we exclude the unlikely event that one of the group is actually a file
# instad of a folder
self.num_groups = len(
[_ for _ in glob(os.path.join(root_folder, 'group_?????'))
if os.path.isdir(_)])
self.root_folder = root_folder
else:
self.root_folder = os.path.split(filename)[0]
self.num_groups = 0
Dataset.__init__(self, filename, dataset_type, units_override=units_override,
unit_system=unit_system)
# Add the particle types
ptypes = []
for PH in get_particle_handlers():
if PH.any_exist(self):
ptypes.append(PH.ptype)
ptypes = tuple(ptypes)
self.particle_types = self.particle_types_raw = ptypes
# Add the fluid types
for FH in get_field_handlers():
FH.purge_detected_fields(self)
if FH.any_exist(self):
self.fluid_types += (FH.ftype, )
self.storage_filename = storage_filename
def __init__(self, filename,
dataset_type='fits',
auxiliary_files=[],
nprocs=None,
storage_filename=None,
nan_mask=None,
suppress_astropy_warnings=True,
parameters=None,
units_override=None,
unit_system="cgs"):
if parameters is None:
parameters = {}
parameters["nprocs"] = nprocs
self.specified_parameters = parameters
if suppress_astropy_warnings:
warnings.filterwarnings('ignore', module="astropy", append=True)
auxiliary_files = ensure_list(auxiliary_files)
self.filenames = [filename] + auxiliary_files
self.num_files = len(self.filenames)
self.fluid_types += ("fits",)
if nan_mask is None:
self.nan_mask = {}
elif isinstance(nan_mask, float):
self.nan_mask = {"all":nan_mask}
elif isinstance(nan_mask, dict):
self.nan_mask = nan_mask
self._handle = FITSFileHandler(self.filenames[0])
if (isinstance(self.filenames[0], _astropy.pyfits.hdu.image._ImageBaseHDU) or
isinstance(self.filenames[0], _astropy.pyfits.HDUList)):
fn = "InMemoryFITSFile_%s" % uuid.uuid4().hex
else:
fn = self.filenames[0]
self._handle._fits_files.append(self._handle)
if self.num_files > 1:
for fits_file in auxiliary_files:
if isinstance(fits_file, _astropy.pyfits.hdu.image._ImageBaseHDU):
f = _astropy.pyfits.HDUList([fits_file])
elif isinstance(fits_file, _astropy.pyfits.HDUList):
f = fits_file
else:
if os.path.exists(fits_file):
fn = fits_file
else:
fn = os.path.join(ytcfg.get("yt","test_data_dir"),fits_file)
f = _astropy.pyfits.open(fn, memmap=True,
do_not_scale_image_data=True,
ignore_blank=True)
self._handle._fits_files.append(f)
self.refine_by = 2
Dataset.__init__(self, fn, dataset_type, units_override=units_override,
unit_system=unit_system)
self.storage_filename = storage_filename
def __init__(self,
filename,
dataset_type='grid_data_format',
storage_filename=None,
geometry='cartesian'):
self.geometry = geometry
self.fluid_types += ("gdf", )
Dataset.__init__(self, filename, dataset_type)
self.storage_filename = storage_filename
self.filename = filename
def __init__(self,
filename,
dataset_type='skeleton',
storage_filename=None,
units_override=None):
self.fluid_types += ('skeleton', )
Dataset.__init__(self,
filename,
dataset_type,
units_override=units_override)
self.storage_filename = storage_filename
def __init__(self, filename, dataset_type='flash_particle_hdf5',
storage_filename = None,
units_override = None,
n_ref = 64, unit_system = "cgs"):
if self._handle is not None: return
self._handle = HDF5FileHandler(filename)
self.n_ref = n_ref
self.refine_by = 2
Dataset.__init__(self, filename, dataset_type, units_override=units_override,
unit_system=unit_system)
self.storage_filename = storage_filename
def __init__(self,
filename,
dataset_type='moab_hex8',
storage_filename=None,
units_override=None):
self.fluid_types += ("moab", )
Dataset.__init__(self,
filename,
dataset_type,
units_override=units_override)
self.storage_filename = storage_filename
self.filename = filename
self._handle = HDF5FileHandler(filename)
def __init__(self, filename, dataset_type='artio',
storage_filename=None, max_range = 1024):
if self._handle is not None:
return
self.max_range = max_range
self.fluid_types += ("artio",)
self._filename = filename
self._fileset_prefix = filename[:-4]
self._handle = artio_fileset(self._fileset_prefix)
self.artio_parameters = self._handle.parameters
# Here we want to initiate a traceback, if the reader is not built.
Dataset.__init__(self, filename, dataset_type)
self.storage_filename = storage_filename
def __init__(self, filename, dataset_type='ramses',
fields = None, storage_filename = None,
units_override=None):
# Here we want to initiate a traceback, if the reader is not built.
if isinstance(fields, str):
fields = field_aliases[fields]
'''
fields: An array of hydro variable fields in order of position in the hydro_XXXXX.outYYYYY file
If set to None, will try a default set of fields
'''
self.fluid_types += ("ramses",)
self._fields_in_file = fields
Dataset.__init__(self, filename, dataset_type, units_override=units_override)
self.storage_filename = storage_filename
def __init__(self,
filename,
dataset_type='grid_data_format',
storage_filename=None,
geometry=None,
units_override=None):
self.geometry = geometry
self.fluid_types += ("gdf", )
Dataset.__init__(self,
filename,
dataset_type,
units_override=units_override)
self.storage_filename = storage_filename
self.filename = filename
def __init__(self,
filename,
dataset_type='enzo-p',
storage_filename=None,
units_override=None):
self.fluid_types += ('enzo-p', )
self.parameter_filename = filename + ".parameters"
Dataset.__init__(self,
filename,
dataset_type,
units_override=units_override)
self.storage_filename = storage_filename
# refinement factor between a grid and its subgrid
self.refine_by = 2
def __init__(self, filename, dataset_type='pluto_hdf5',
storage_filename = None, ini_filename = None):
self._handle = HDF5FileHandler(filename)
self.current_time = self._handle.attrs['time']
self.ini_filename = ini_filename
self.fullplotdir = os.path.abspath(filename)
Dataset.__init__(self,filename,dataset_type)
self.storage_filename = storage_filename
self.cosmological_simulation = False
# These are parameters that I very much wish to get rid of.
self.parameters["HydroMethod"] = 'chombo' # always PPM DE
self.parameters["DualEnergyFormalism"] = 0
self.parameters["EOSType"] = -1 # default
def __init__(self, filename, dataset_type='ramses',
fields = None, storage_filename = None,
units_override=None, unit_system="cgs"):
# Here we want to initiate a traceback, if the reader is not built.
if isinstance(fields, string_types):
fields = field_aliases[fields]
'''
fields: An array of hydro variable fields in order of position in the hydro_XXXXX.outYYYYY file
If set to None, will try a default set of fields
'''
self.fluid_types += ("ramses",)
self._fields_in_file = fields
Dataset.__init__(self, filename, dataset_type, units_override=units_override,
unit_system=unit_system)
self.storage_filename = storage_filename
def __init__(self,
filename,
dataset_type='ramses',
fields=None,
storage_filename=None,
units_override=None,
unit_system="cgs",
extra_particle_fields=None,
cosmological=None,
bbox=None):
# Here we want to initiate a traceback, if the reader is not built.
if isinstance(fields, string_types):
fields = field_aliases[fields]
'''
fields: An array of hydro variable fields in order of position in the hydro_XXXXX.outYYYYY file
If set to None, will try a default set of fields
extra_particle_fields: An array of extra particle variables in order of position in the particle_XXXXX.outYYYYY file.
cosmological: If set to None, automatically detect cosmological simulation. If a boolean, force
its value.
'''
self._fields_in_file = fields
# By default, extra fields have not triggered a warning
self._warned_extra_fields = defaultdict(lambda: False)
self._extra_particle_fields = extra_particle_fields
self.force_cosmological = cosmological
self._bbox = bbox
Dataset.__init__(self,
filename,
dataset_type,
units_override=units_override,
unit_system=unit_system)
# Add the particle types
ptypes = []
for PH in get_particle_handlers():
if PH.any_exist(self):
ptypes.append(PH.ptype)
ptypes = tuple(ptypes)
self.particle_types = self.particle_types_raw = ptypes
# Add the fluid types
for FH in get_field_handlers():
FH.purge_detected_fields(self)
if FH.any_exist(self):
self.fluid_types += (FH.ftype, )
self.storage_filename = storage_filename
def test_schema_validation():
valid_schemas = [
{
"length_unit": 1.0
},
{
"length_unit": [1.0]
},
{
"length_unit": (1.0, )
},
{
"length_unit": int(1.0)
},
{
"length_unit": (1.0, "m")
},
{
"length_unit": [1.0, "m"]
},
{
"length_unit": YTQuantity(1.0, "m")
},
]
for schema in valid_schemas:
uo = Dataset._sanitize_units_override(schema)
for v in uo.values():
q = mock_quan(v) # check that no error (TypeError) is raised
q.to("pc") # check that q is a length
def __init__(self, filename, dataset_type='athena',
storage_filename=None, parameters=None):
self.fluid_types += ("athena",)
if parameters is None:
parameters = {}
self.specified_parameters = parameters
Dataset.__init__(self, filename, dataset_type)
self.filename = filename
if storage_filename is None:
storage_filename = '%s.yt' % filename.split('/')[-1]
self.storage_filename = storage_filename
self.backup_filename = self.filename[:-4] + "_backup.gdf"
# Unfortunately we now have to mandate that the index gets
# instantiated so that we can make sure we have the correct left
# and right domain edges.
self.index
def __init__(self, filename, dataset_type='openPMD',
storage_filename=None,
units_override=None):
# This defines which of the data sets are meshes (fields)
self.fluid_types += ('openPMD',)
# This defines which of the data sets are particles
self.particle_types = ["electrons","ions","all"]
self.particle_types = tuple(self.particle_types)
self.particle_types_raw = self.particle_types
# Opens a HDF5 file and stores its file handle in _handle
# All _handle objects refers to the file
self._handle = HDF5FileHandler(filename)
Dataset.__init__(self, filename, dataset_type,
units_override=units_override)
self.storage_filename = storage_filename
def __init__(self,
pyne_mesh,
dataset_type='moab_hex8_pyne',
storage_filename=None,
units_override=None,
unit_system="cgs"):
self.fluid_types += ("pyne", )
filename = "pyne_mesh_" + str(id(pyne_mesh))
self.pyne_mesh = pyne_mesh
Dataset.__init__(self,
str(filename),
dataset_type,
units_override=units_override,
unit_system=unit_system)
self.storage_filename = storage_filename
self.filename = filename
def __init__(
self,
filename,
dataset_type="gamer",
storage_filename=None,
particle_filename=None,
units_override=None,
unit_system="cgs",
):
if self._handle is not None:
return
self.fluid_types += ("gamer", )
self._handle = HDF5FileHandler(filename)
self.particle_filename = particle_filename
# to catch both the new and old data formats for the grid data
try:
self._group_grid = self._handle["GridData"]
except KeyError:
self._group_grid = self._handle["Data"]
if "Particle" in self._handle:
self._group_particle = self._handle["Particle"]
if self.particle_filename is None:
self._particle_handle = self._handle
else:
try:
self._particle_handle = HDF5FileHandler(self.particle_filename)
except Exception:
raise IOError(self.particle_filename)
# currently GAMER only supports refinement by a factor of 2
self.refine_by = 2
Dataset.__init__(
self,
filename,
dataset_type,
units_override=units_override,
unit_system=unit_system,
)
self.storage_filename = storage_filename
def __init__(
self,
filename,
dataset_type="moab_hex8",
storage_filename=None,
units_override=None,
unit_system="cgs",
):
self.fluid_types += ("moab", )
Dataset.__init__(
self,
filename,
dataset_type,
units_override=units_override,
unit_system=unit_system,
)
self.storage_filename = storage_filename
self._handle = HDF5FileHandler(filename)
def __init__(self, filename, dataset_type='chombo_hdf5',
storage_filename = None, ini_filename = None,
units_override=None):
self.fluid_types += ("chombo",)
self._handle = HDF5FileHandler(filename)
self.dataset_type = dataset_type
self.geometry = "cartesian"
self.ini_filename = ini_filename
self.fullplotdir = os.path.abspath(filename)
Dataset.__init__(self, filename, self.dataset_type,
units_override=units_override)
self.storage_filename = storage_filename
self.cosmological_simulation = False
# These are parameters that I very much wish to get rid of.
self.parameters["HydroMethod"] = 'chombo'
self.parameters["DualEnergyFormalism"] = 0
self.parameters["EOSType"] = -1 # default
def __init__(self, filename, dataset_type='artio',
storage_filename=None, max_range = 1024,
units_override=None):
from sys import version
if self._handle is not None:
return
self.max_range = max_range
self.fluid_types += ("artio",)
self._filename = filename
self._fileset_prefix = filename[:-4]
if version < '3':
self._handle = artio_fileset(self._fileset_prefix)
else:
self._handle = artio_fileset(bytes(self._fileset_prefix,'utf-8'))
self.artio_parameters = self._handle.parameters
# Here we want to initiate a traceback, if the reader is not built.
Dataset.__init__(self, filename, dataset_type,
units_override=units_override)
self.storage_filename = storage_filename
def __init__(self, filename, dataset_type='dm_art',
fields=None, storage_filename=None,
skip_particles=False, skip_stars=False,
limit_level=None, spread_age=True,
force_max_level=None, file_particle_header=None,
file_particle_stars=None):
self.over_refine_factor = 1
self.n_ref = 64
self.particle_types += ("all",)
if fields is None:
fields = particle_fields
filename = os.path.abspath(filename)
self._fields_in_file = fields
self._file_particle = filename
self._file_particle_header = file_particle_header
self._find_files(filename)
self.parameter_filename = filename
self.skip_stars = skip_stars
self.spread_age = spread_age
self.domain_left_edge = np.zeros(3, dtype='float')
self.domain_right_edge = np.zeros(3, dtype='float')+1.0
Dataset.__init__(self, filename, dataset_type)
self.storage_filename = storage_filename
def __init__(self, filename, dataset_type=None,
file_style = None,
parameter_override = None,
conversion_override = None,
storage_filename = None,
units_override=None):
"""
This class is a stripped down class that simply reads and parses
*filename* without looking at the index. *dataset_type* gets passed
to the index to pre-determine the style of data-output. However,
it is not strictly necessary. Optionally you may specify a
*parameter_override* dictionary that will override anything in the
paarmeter file and a *conversion_override* dictionary that consists
of {fieldname : conversion_to_cgs} that will override the #DataCGS.
"""
self.fluid_types += ("enzo",)
if filename.endswith(".hierarchy"): filename = filename[:-10]
if parameter_override is None: parameter_override = {}
self._parameter_override = parameter_override
if conversion_override is None: conversion_override = {}
self._conversion_override = conversion_override
self.storage_filename = storage_filename
Dataset.__init__(self, filename, dataset_type, file_style=file_style,
units_override=units_override)
def __init__(self, filename,
dataset_type='fits',
auxiliary_files=[],
nprocs=None,
storage_filename=None,
nan_mask=None,
spectral_factor=1.0,
z_axis_decomp=False,
suppress_astropy_warnings=True,
parameters=None,
units_override=None):
if parameters is None:
parameters = {}
parameters["nprocs"] = nprocs
self.specified_parameters = parameters
self.z_axis_decomp = z_axis_decomp
self.spectral_factor = spectral_factor
if suppress_astropy_warnings:
warnings.filterwarnings('ignore', module="astropy", append=True)
auxiliary_files = ensure_list(auxiliary_files)
self.filenames = [filename] + auxiliary_files
self.num_files = len(self.filenames)
self.fluid_types += ("fits",)
if nan_mask is None:
self.nan_mask = {}
elif isinstance(nan_mask, float):
self.nan_mask = {"all":nan_mask}
elif isinstance(nan_mask, dict):
self.nan_mask = nan_mask
self._handle = FITSFileHandler(self.filenames[0])
if (isinstance(self.filenames[0], _astropy.pyfits.hdu.image._ImageBaseHDU) or
isinstance(self.filenames[0], _astropy.pyfits.HDUList)):
fn = "InMemoryFITSFile_%s" % uuid.uuid4().hex
else:
fn = self.filenames[0]
self._handle._fits_files.append(self._handle)
if self.num_files > 1:
for fits_file in auxiliary_files:
if isinstance(fits_file, _astropy.pyfits.hdu.image._ImageBaseHDU):
f = _astropy.pyfits.HDUList([fits_file])
elif isinstance(fits_file, _astropy.pyfits.HDUList):
f = fits_file
else:
if os.path.exists(fits_file):
fn = fits_file
else:
fn = os.path.join(ytcfg.get("yt","test_data_dir"),fits_file)
f = _astropy.pyfits.open(fn, memmap=True,
do_not_scale_image_data=True,
ignore_blank=True)
self._handle._fits_files.append(f)
if len(self._handle) > 1 and self._handle[1].name == "EVENTS":
self.events_data = True
self.first_image = 1
self.primary_header = self._handle[self.first_image].header
self.naxis = 2
self.wcs = _astropy.pywcs.WCS(naxis=2)
self.events_info = {}
for k,v in self.primary_header.items():
if k.startswith("TTYP"):
if v.lower() in ["x","y"]:
num = k.strip("TTYPE")
self.events_info[v.lower()] = (self.primary_header["TLMIN"+num],
self.primary_header["TLMAX"+num],
self.primary_header["TCTYP"+num],
self.primary_header["TCRVL"+num],
self.primary_header["TCDLT"+num],
self.primary_header["TCRPX"+num])
elif v.lower() in ["energy","time"]:
num = k.strip("TTYPE")
unit = self.primary_header["TUNIT"+num].lower()
if unit.endswith("ev"): unit = unit.replace("ev","eV")
self.events_info[v.lower()] = unit
self.axis_names = [self.events_info[ax][2] for ax in ["x","y"]]
self.reblock = 1
if "reblock" in self.specified_parameters:
self.reblock = self.specified_parameters["reblock"]
self.wcs.wcs.cdelt = [self.events_info["x"][4]*self.reblock,
self.events_info["y"][4]*self.reblock]
self.wcs.wcs.crpix = [(self.events_info["x"][5]-0.5)/self.reblock+0.5,
(self.events_info["y"][5]-0.5)/self.reblock+0.5]
self.wcs.wcs.ctype = [self.events_info["x"][2],self.events_info["y"][2]]
self.wcs.wcs.cunit = ["deg","deg"]
self.wcs.wcs.crval = [self.events_info["x"][3],self.events_info["y"][3]]
self.dims = [(self.events_info["x"][1]-self.events_info["x"][0])/self.reblock,
(self.events_info["y"][1]-self.events_info["y"][0])/self.reblock]
else:
self.events_data = False
# Sometimes the primary hdu doesn't have an image
if len(self._handle) > 1 and self._handle[0].header["naxis"] == 0:
self.first_image = 1
else:
self.first_image = 0
self.primary_header = self._handle[self.first_image].header
self.naxis = self.primary_header["naxis"]
self.axis_names = [self.primary_header.get("ctype%d" % (i+1),"LINEAR")
for i in range(self.naxis)]
self.dims = [self.primary_header["naxis%d" % (i+1)]
for i in range(self.naxis)]
wcs = _astropy.pywcs.WCS(header=self.primary_header)
if self.naxis == 4:
self.wcs = _astropy.pywcs.WCS(naxis=3)
self.wcs.wcs.crpix = wcs.wcs.crpix[:3]
self.wcs.wcs.cdelt = wcs.wcs.cdelt[:3]
self.wcs.wcs.crval = wcs.wcs.crval[:3]
self.wcs.wcs.cunit = [str(unit) for unit in wcs.wcs.cunit][:3]
self.wcs.wcs.ctype = [type for type in wcs.wcs.ctype][:3]
else:
self.wcs = wcs
self.refine_by = 2
Dataset.__init__(self, fn, dataset_type, units_override=units_override)
self.storage_filename = storage_filename
