import abc
import itertools
import sys
import uuid
from collections import defaultdict
from contextlib import contextmanager
import numpy as np
from more_itertools import always_iterable
from unyt import unyt_array
from unyt.exceptions import UnitConversionError, UnitParseError
import yt.geometry
from yt.data_objects.data_containers import YTDataContainer
from yt.data_objects.derived_quantities import DerivedQuantityCollection
from yt.data_objects.field_data import YTFieldData
from yt.fields.field_exceptions import NeedsGridType
from yt.funcs import fix_axis, is_sequence, iter_fields, validate_width_tuple
from yt.geometry.api import Geometry
from yt.geometry.selection_routines import compose_selector
from yt.units import YTArray
from yt.utilities.exceptions import (
GenerationInProgress,
YTBooleanObjectError,
YTBooleanObjectsWrongDataset,
YTDataSelectorNotImplemented,
YTDimensionalityError,
YTFieldUnitError,
YTFieldUnitParseError,
)
from yt.utilities.lib.marching_cubes import march_cubes_grid, march_cubes_grid_flux
from yt.utilities.logger import ytLogger as mylog
from yt.utilities.parallel_tools.parallel_analysis_interface import (
ParallelAnalysisInterface,
)
if sys.version_info >= (3, 11):
from typing import assert_never
else:
from typing_extensions import assert_never
[docs]
class YTSelectionContainer(YTDataContainer, ParallelAnalysisInterface, abc.ABC):
_locked = False
_sort_by = None
_selector = None
_current_chunk = None
_data_source = None
_dimensionality: int
_max_level = None
_min_level = None
_derived_quantity_chunking = "io"
def __init__(self, ds, field_parameters, data_source=None):
ParallelAnalysisInterface.__init__(self)
super().__init__(ds, field_parameters)
self._data_source = data_source
if data_source is not None:
if data_source.ds != self.ds:
raise RuntimeError(
"Attempted to construct a DataContainer with a data_source "
"from a different Dataset",
ds,
data_source.ds,
)
if data_source._dimensionality < self._dimensionality:
raise RuntimeError(
"Attempted to construct a DataContainer with a data_source "
"of lower dimensionality (%u vs %u)"
% (data_source._dimensionality, self._dimensionality)
)
self.field_parameters.update(data_source.field_parameters)
self.quantities = DerivedQuantityCollection(self)
@property
def selector(self):
if self._selector is not None:
return self._selector
s_module = getattr(self, "_selector_module", yt.geometry.selection_routines)
sclass = getattr(s_module, f"{self._type_name}_selector", None)
if sclass is None:
raise YTDataSelectorNotImplemented(self._type_name)
if self._data_source is not None:
self._selector = compose_selector(
self, self._data_source.selector, sclass(self)
)
else:
self._selector = sclass(self)
return self._selector
[docs]
def chunks(self, fields, chunking_style, **kwargs):
# This is an iterator that will yield the necessary chunks.
self.get_data() # Ensure we have built ourselves
if fields is None:
fields = []
# chunk_ind can be supplied in the keyword arguments. If it's a
# scalar, that'll be the only chunk that gets returned; if it's a list,
# those are the ones that will be.
chunk_ind = kwargs.pop("chunk_ind", None)
if chunk_ind is not None:
chunk_ind = list(always_iterable(chunk_ind))
for ci, chunk in enumerate(self.index._chunk(self, chunking_style, **kwargs)):
if chunk_ind is not None and ci not in chunk_ind:
continue
with self._chunked_read(chunk):
self.get_data(fields)
# NOTE: we yield before releasing the context
yield self
def _identify_dependencies(self, fields_to_get, spatial=False):
inspected = 0
fields_to_get = fields_to_get[:]
for field in itertools.cycle(fields_to_get):
if inspected >= len(fields_to_get):
break
inspected += 1
fi = self.ds._get_field_info(field)
fd = self.ds.field_dependencies.get(
field, None
) or self.ds.field_dependencies.get(field[1], None)
# This is long overdue. Any time we *can't* find a field
# dependency -- for instance, if the derived field has been added
# after dataset instantiation -- let's just try to
# recalculate it.
if fd is None:
try:
fd = fi.get_dependencies(ds=self.ds)
self.ds.field_dependencies[field] = fd
except Exception:
continue
requested = self._determine_fields(list(set(fd.requested)))
deps = [d for d in requested if d not in fields_to_get]
fields_to_get += deps
return sorted(fields_to_get)
[docs]
def get_data(self, fields=None):
if self._current_chunk is None:
self.index._identify_base_chunk(self)
if fields is None:
return
nfields = []
apply_fields = defaultdict(list)
for field in self._determine_fields(fields):
# We need to create the field on the raw particle types
# for particles types (when the field is not directly
# defined for the derived particle type only)
finfo = self.ds.field_info[field]
if (
field[0] in self.ds.filtered_particle_types
and finfo._inherited_particle_filter
):
f = self.ds.known_filters[field[0]]
apply_fields[field[0]].append((f.filtered_type, field[1]))
else:
nfields.append(field)
for filter_type in apply_fields:
f = self.ds.known_filters[filter_type]
with f.apply(self):
self.get_data(apply_fields[filter_type])
fields = nfields
if len(fields) == 0:
return
# Now we collect all our fields
# Here is where we need to perform a validation step, so that if we
# have a field requested that we actually *can't* yet get, we put it
# off until the end. This prevents double-reading fields that will
# need to be used in spatial fields later on.
fields_to_get = []
# This will be pre-populated with spatial fields
fields_to_generate = []
for field in self._determine_fields(fields):
if field in self.field_data:
continue
finfo = self.ds._get_field_info(field)
try:
finfo.check_available(self)
except NeedsGridType:
fields_to_generate.append(field)
continue
fields_to_get.append(field)
if len(fields_to_get) == 0 and len(fields_to_generate) == 0:
return
elif self._locked:
raise GenerationInProgress(fields)
# Track which ones we want in the end
ofields = set(list(self.field_data.keys()) + fields_to_get + fields_to_generate)
# At this point, we want to figure out *all* our dependencies.
fields_to_get = self._identify_dependencies(fields_to_get, self._spatial)
# We now split up into readers for the types of fields
fluids, particles = [], []
finfos = {}
for field_key in fields_to_get:
finfo = self.ds._get_field_info(field_key)
finfos[field_key] = finfo
if finfo.sampling_type == "particle":
particles.append(field_key)
elif field_key not in fluids:
fluids.append(field_key)
# The _read method will figure out which fields it needs to get from
# disk, and return a dict of those fields along with the fields that
# need to be generated.
read_fluids, gen_fluids = self.index._read_fluid_fields(
fluids, self, self._current_chunk
)
for f, v in read_fluids.items():
self.field_data[f] = self.ds.arr(v, units=finfos[f].units)
self.field_data[f].convert_to_units(finfos[f].output_units)
read_particles, gen_particles = self.index._read_particle_fields(
particles, self, self._current_chunk
)
for f, v in read_particles.items():
self.field_data[f] = self.ds.arr(v, units=finfos[f].units)
self.field_data[f].convert_to_units(finfos[f].output_units)
fields_to_generate += gen_fluids + gen_particles
self._generate_fields(fields_to_generate)
for field in list(self.field_data.keys()):
if field not in ofields:
self.field_data.pop(field)
def _generate_fields(self, fields_to_generate):
index = 0
def dimensions_compare_equal(a, b, /) -> bool:
if a == b:
return True
try:
if (a == 1 and b.is_dimensionless) or (a.is_dimensionless and b == 1):
return True
except AttributeError:
return False
return False
with self._field_lock():
# At this point, we assume that any fields that are necessary to
# *generate* a field are in fact already available to us. Note
# that we do not make any assumption about whether or not the
# fields have a spatial requirement. This will be checked inside
# _generate_field, at which point additional dependencies may
# actually be noted.
while any(f not in self.field_data for f in fields_to_generate):
field = fields_to_generate[index % len(fields_to_generate)]
index += 1
if field in self.field_data:
continue
fi = self.ds._get_field_info(field)
try:
fd = self._generate_field(field)
if hasattr(fd, "units"):
fd.units.registry = self.ds.unit_registry
if fd is None:
raise RuntimeError
if fi.units is None:
# first time calling a field with units='auto', so we
# infer the units from the units of the data we get back
# from the field function and use these units for future
# field accesses
units = getattr(fd, "units", "")
if units == "":
sunits = ""
dimensions = 1
else:
sunits = str(
units.get_base_equivalent(self.ds.unit_system.name)
)
dimensions = units.dimensions
if fi.dimensions is None:
mylog.warning(
"Field %s was added without specifying units or dimensions, "
"auto setting units to %r",
fi.name,
sunits or "dimensionless",
)
elif not dimensions_compare_equal(fi.dimensions, dimensions):
raise YTDimensionalityError(fi.dimensions, dimensions)
fi.units = sunits
fi.dimensions = dimensions
self.field_data[field] = self.ds.arr(fd, units)
if fi.output_units is None:
fi.output_units = fi.units
try:
fd.convert_to_units(fi.units)
except AttributeError:
# If the field returns an ndarray, coerce to a
# dimensionless YTArray and verify that field is
# supposed to be unitless
fd = self.ds.arr(fd, "")
if fi.units != "":
raise YTFieldUnitError(fi, fd.units) from None
except UnitConversionError as e:
raise YTFieldUnitError(fi, fd.units) from e
except UnitParseError as e:
raise YTFieldUnitParseError(fi) from e
self.field_data[field] = fd
except GenerationInProgress as gip:
for f in gip.fields:
if f not in fields_to_generate:
fields_to_generate.append(f)
def __or__(self, other):
if not isinstance(other, YTSelectionContainer):
raise YTBooleanObjectError(other)
if self.ds is not other.ds:
raise YTBooleanObjectsWrongDataset()
# Should maybe do something with field parameters here
from yt.data_objects.selection_objects.boolean_operations import (
YTBooleanContainer,
)
return YTBooleanContainer("OR", self, other, ds=self.ds)
def __invert__(self):
# ~obj
asel = yt.geometry.selection_routines.AlwaysSelector(self.ds)
from yt.data_objects.selection_objects.boolean_operations import (
YTBooleanContainer,
)
return YTBooleanContainer("NOT", self, asel, ds=self.ds)
def __xor__(self, other):
if not isinstance(other, YTSelectionContainer):
raise YTBooleanObjectError(other)
if self.ds is not other.ds:
raise YTBooleanObjectsWrongDataset()
from yt.data_objects.selection_objects.boolean_operations import (
YTBooleanContainer,
)
return YTBooleanContainer("XOR", self, other, ds=self.ds)
def __and__(self, other):
if not isinstance(other, YTSelectionContainer):
raise YTBooleanObjectError(other)
if self.ds is not other.ds:
raise YTBooleanObjectsWrongDataset()
from yt.data_objects.selection_objects.boolean_operations import (
YTBooleanContainer,
)
return YTBooleanContainer("AND", self, other, ds=self.ds)
def __add__(self, other):
return self.__or__(other)
def __sub__(self, other):
if not isinstance(other, YTSelectionContainer):
raise YTBooleanObjectError(other)
if self.ds is not other.ds:
raise YTBooleanObjectsWrongDataset()
from yt.data_objects.selection_objects.boolean_operations import (
YTBooleanContainer,
)
return YTBooleanContainer("NEG", self, other, ds=self.ds)
@contextmanager
def _field_lock(self):
self._locked = True
yield
self._locked = False
@contextmanager
def _ds_hold(self, new_ds):
"""
This contextmanager is used to take a data object and preserve its
attributes but allow the dataset that underlies it to be swapped out.
This is typically only used internally, and differences in unit systems
may present interesting possibilities.
"""
old_ds = self.ds
old_index = self._index
self.ds = new_ds
self._index = new_ds.index
old_chunk_info = self._chunk_info
old_chunk = self._current_chunk
old_size = self.size
self._chunk_info = None
self._current_chunk = None
self.size = None
self._index._identify_base_chunk(self)
with self._chunked_read(None):
yield
self._index = old_index
self.ds = old_ds
self._chunk_info = old_chunk_info
self._current_chunk = old_chunk
self.size = old_size
@contextmanager
def _chunked_read(self, chunk):
# There are several items that need to be swapped out
# field_data, size, shape
obj_field_data = []
if hasattr(chunk, "objs"):
for obj in chunk.objs:
obj_field_data.append(obj.field_data)
obj.field_data = YTFieldData()
old_field_data, self.field_data = self.field_data, YTFieldData()
old_chunk, self._current_chunk = self._current_chunk, chunk
old_locked, self._locked = self._locked, False
yield
self.field_data = old_field_data
self._current_chunk = old_chunk
self._locked = old_locked
if hasattr(chunk, "objs"):
for obj in chunk.objs:
obj.field_data = obj_field_data.pop(0)
@contextmanager
def _activate_cache(self):
cache = self._field_cache or {}
old_fields = {}
for field in (f for f in cache if f in self.field_data):
old_fields[field] = self.field_data[field]
self.field_data.update(cache)
yield
for field in cache:
self.field_data.pop(field)
if field in old_fields:
self.field_data[field] = old_fields.pop(field)
self._field_cache = None
def _initialize_cache(self, cache):
# Wipe out what came before
self._field_cache = {}
self._field_cache.update(cache)
@property
def icoords(self):
if self._current_chunk is None:
self.index._identify_base_chunk(self)
return self._current_chunk.icoords
@property
def fcoords(self):
if self._current_chunk is None:
self.index._identify_base_chunk(self)
return self._current_chunk.fcoords
@property
def ires(self):
if self._current_chunk is None:
self.index._identify_base_chunk(self)
return self._current_chunk.ires
@property
def fwidth(self):
if self._current_chunk is None:
self.index._identify_base_chunk(self)
return self._current_chunk.fwidth
@property
def fcoords_vertex(self):
if self._current_chunk is None:
self.index._identify_base_chunk(self)
return self._current_chunk.fcoords_vertex
@property
def max_level(self):
if self._max_level is None:
try:
return self.ds.max_level
except AttributeError:
return None
return self._max_level
@max_level.setter
def max_level(self, value):
if self._selector is not None:
del self._selector
self._selector = None
self._current_chunk = None
self.size = None
self.shape = None
self.field_data.clear()
self._max_level = value
@property
def min_level(self):
if self._min_level is None:
try:
return 0
except AttributeError:
return None
return self._min_level
@min_level.setter
def min_level(self, value):
if self._selector is not None:
del self._selector
self._selector = None
self.field_data.clear()
self.size = None
self.shape = None
self._current_chunk = None
self._min_level = value
[docs]
class YTSelectionContainer0D(YTSelectionContainer):
_spatial = False
_dimensionality = 0
def __init__(self, ds, field_parameters=None, data_source=None):
super().__init__(ds, field_parameters, data_source)
[docs]
class YTSelectionContainer1D(YTSelectionContainer):
_spatial = False
_dimensionality = 1
def __init__(self, ds, field_parameters=None, data_source=None):
super().__init__(ds, field_parameters, data_source)
self._grids = None
self._sortkey = None
self._sorted = {}
[docs]
class YTSelectionContainer2D(YTSelectionContainer):
_key_fields = ["px", "py", "pdx", "pdy"]
_dimensionality = 2
"""
Prepares the YTSelectionContainer2D, normal to *axis*. If *axis* is 4, we are not
aligned with any axis.
"""
_spatial = False
def __init__(self, axis, ds, field_parameters=None, data_source=None):
super().__init__(ds, field_parameters, data_source)
# We need the ds, which will exist by now, for fix_axis.
self.axis = fix_axis(axis, self.ds)
self.set_field_parameter("axis", axis)
def _convert_field_name(self, field):
return field
def _get_pw(self, fields, center, width, origin, plot_type):
from yt.visualization.fixed_resolution import FixedResolutionBuffer as frb
from yt.visualization.plot_window import PWViewerMPL, get_window_parameters
axis = self.axis
skip = self._key_fields
skip += list(set(frb._exclude_fields).difference(set(self._key_fields)))
self.fields = [k for k in self.field_data if k not in skip]
if fields is not None:
self.fields = list(iter_fields(fields)) + self.fields
if len(self.fields) == 0:
raise ValueError("No fields found to plot in get_pw")
(bounds, center, display_center) = get_window_parameters(
axis, center, width, self.ds
)
pw = PWViewerMPL(
self,
bounds,
fields=self.fields,
origin=origin,
frb_generator=frb,
plot_type=plot_type,
geometry=self.ds.geometry,
)
pw._setup_plots()
return pw
[docs]
def to_frb(self, width, resolution, center=None, height=None, periodic=False):
r"""This function returns a FixedResolutionBuffer generated from this
object.
A FixedResolutionBuffer is an object that accepts a variable-resolution
2D object and transforms it into an NxM bitmap that can be plotted,
examined or processed. This is a convenience function to return an FRB
directly from an existing 2D data object.
Parameters
----------
width : width specifier
This can either be a floating point value, in the native domain
units of the simulation, or a tuple of the (value, unit) style.
This will be the width of the FRB.
height : height specifier
This will be the physical height of the FRB, by default it is equal
to width. Note that this will not make any corrections to
resolution for the aspect ratio.
resolution : int or tuple of ints
The number of pixels on a side of the final FRB. If iterable, this
will be the width then the height.
center : array-like of floats, optional
The center of the FRB. If not specified, defaults to the center of
the current object.
periodic : bool
Should the returned Fixed Resolution Buffer be periodic? (default:
False).
Returns
-------
frb : :class:`~yt.visualization.fixed_resolution.FixedResolutionBuffer`
A fixed resolution buffer, which can be queried for fields.
Examples
--------
>>> proj = ds.proj(("gas", "density"), 0)
>>> frb = proj.to_frb((100.0, "kpc"), 1024)
>>> write_image(np.log10(frb[("gas", "density")]), "density_100kpc.png")
"""
if (self.ds.geometry is Geometry.CYLINDRICAL and self.axis == 1) or (
self.ds.geometry is Geometry.POLAR and self.axis == 2
):
if center is not None and center != (0.0, 0.0):
raise NotImplementedError(
"Currently we only support images centered at R=0. "
+ "We plan to generalize this in the near future"
)
from yt.visualization.fixed_resolution import (
CylindricalFixedResolutionBuffer,
)
validate_width_tuple(width)
if is_sequence(resolution):
resolution = max(resolution)
frb = CylindricalFixedResolutionBuffer(self, width, resolution)
return frb
if center is None:
center = self.center
if center is None:
center = (self.ds.domain_right_edge + self.ds.domain_left_edge) / 2.0
elif is_sequence(center) and not isinstance(center, YTArray):
center = self.ds.arr(center, "code_length")
if is_sequence(width):
w, u = width
if isinstance(w, tuple) and isinstance(u, tuple):
height = u
w, u = w
width = self.ds.quan(w, units=u)
elif not isinstance(width, YTArray):
width = self.ds.quan(width, "code_length")
if height is None:
height = width
elif is_sequence(height):
h, u = height
height = self.ds.quan(h, units=u)
elif not isinstance(height, YTArray):
height = self.ds.quan(height, "code_length")
if not is_sequence(resolution):
resolution = (resolution, resolution)
from yt.visualization.fixed_resolution import FixedResolutionBuffer
xax = self.ds.coordinates.x_axis[self.axis]
yax = self.ds.coordinates.y_axis[self.axis]
bounds = (
center[xax] - width * 0.5,
center[xax] + width * 0.5,
center[yax] - height * 0.5,
center[yax] + height * 0.5,
)
frb = FixedResolutionBuffer(self, bounds, resolution, periodic=periodic)
return frb
[docs]
class YTSelectionContainer3D(YTSelectionContainer):
"""
Returns an instance of YTSelectionContainer3D, or prepares one. Usually only
used as a base class. Note that *center* is supplied, but only used
for fields and quantities that require it.
"""
_key_fields = ["x", "y", "z", "dx", "dy", "dz"]
_spatial = False
_num_ghost_zones = 0
_dimensionality = 3
def __init__(self, center, ds, field_parameters=None, data_source=None):
super().__init__(ds, field_parameters, data_source)
self._set_center(center)
self.coords = None
self._grids = None
[docs]
def cut_region(self, field_cuts, field_parameters=None, locals=None):
"""
Return a YTCutRegion, where the a cell is identified as being inside
the cut region based on the value of one or more fields. Note that in
previous versions of yt the name 'grid' was used to represent the data
object used to construct the field cut, as of yt 3.0, this has been
changed to 'obj'.
Parameters
----------
field_cuts : list of strings
A list of conditionals that will be evaluated. In the namespace
available, these conditionals will have access to 'obj' which is a
data object of unknown shape, and they must generate a boolean array.
For instance, conditionals = ["obj[('gas', 'temperature')] < 1e3"]
field_parameters : dictionary
A dictionary of field parameters to be used when applying the field
cuts.
locals : dictionary
A dictionary of local variables to use when defining the cut region.
Examples
--------
To find the total mass of hot gas with temperature greater than 10^6 K
in your volume:
>>> ds = yt.load("RedshiftOutput0005")
>>> ad = ds.all_data()
>>> cr = ad.cut_region(["obj[('gas', 'temperature')] > 1e6"])
>>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))
"""
if locals is None:
locals = {}
cr = self.ds.cut_region(
self, field_cuts, field_parameters=field_parameters, locals=locals
)
return cr
def _build_operator_cut(self, operation, field, value, units=None):
"""
Given an operation (>, >=, etc.), a field and a value,
return the cut_region implementing it.
This is only meant to be used internally.
Examples
--------
>>> ds._build_operator_cut(">", ("gas", "density"), 1e-24)
... # is equivalent to
... ds.cut_region(['obj[("gas", "density")] > 1e-24'])
"""
ftype, fname = self._determine_fields(field)[0]
if units is None:
field_cuts = f'obj["{ftype}", "{fname}"] {operation} {value}'
else:
field_cuts = (
f'obj["{ftype}", "{fname}"].in_units("{units}") {operation} {value}'
)
return self.cut_region(field_cuts)
def _build_function_cut(self, function, field, units=None, **kwargs):
"""
Given a function (np.abs, np.all) and a field,
return the cut_region implementing it.
This is only meant to be used internally.
Examples
--------
>>> ds._build_function_cut("np.isnan", ("gas", "density"), locals={"np": np})
... # is equivalent to
... ds.cut_region(['np.isnan(obj[("gas", "density")])'], locals={"np": np})
"""
ftype, fname = self._determine_fields(field)[0]
if units is None:
field_cuts = f'{function}(obj["{ftype}", "{fname}"])'
else:
field_cuts = f'{function}(obj["{ftype}", "{fname}"].in_units("{units}"))'
return self.cut_region(field_cuts, **kwargs)
[docs]
def exclude_above(self, field, value, units=None):
"""
This function will return a YTCutRegion where all of the regions
whose field is above a given value are masked.
Parameters
----------
field : string
The field in which the conditional will be applied.
value : float
The minimum value that will not be masked in the output
YTCutRegion.
units : string or None
The units of the value threshold. None will use the default units
given in the field.
Returns
-------
cut_region : YTCutRegion
The YTCutRegion with the field above the given value masked.
Examples
--------
To find the total mass of hot gas with temperature colder than 10^6 K
in your volume:
>>> ds = yt.load("RedshiftOutput0005")
>>> ad = ds.all_data()
>>> cr = ad.exclude_above(("gas", "temperature"), 1e6)
>>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))
"""
return self._build_operator_cut("<=", field, value, units)
[docs]
def include_above(self, field, value, units=None):
"""
This function will return a YTCutRegion where only the regions
whose field is above a given value are included.
Parameters
----------
field : string
The field in which the conditional will be applied.
value : float
The minimum value that will not be masked in the output
YTCutRegion.
units : string or None
The units of the value threshold. None will use the default units
given in the field.
Returns
-------
cut_region : YTCutRegion
The YTCutRegion with the field above the given value masked.
Examples
--------
To find the total mass of hot gas with temperature warmer than 10^6 K
in your volume:
>>> ds = yt.load("RedshiftOutput0005")
>>> ad = ds.all_data()
>>> cr = ad.include_above(("gas", "temperature"), 1e6)
>>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))
"""
return self._build_operator_cut(">", field, value, units)
[docs]
def exclude_equal(self, field, value, units=None):
"""
This function will return a YTCutRegion where all of the regions
whose field are equal to given value are masked.
Parameters
----------
field : string
The field in which the conditional will be applied.
value : float
The minimum value that will not be masked in the output
YTCutRegion.
units : string or None
The units of the value threshold. None will use the default units
given in the field.
Returns
-------
cut_region : YTCutRegion
The YTCutRegion with the field equal to the given value masked.
Examples
--------
>>> ds = yt.load("RedshiftOutput0005")
>>> ad = ds.all_data()
>>> cr = ad.exclude_equal(("gas", "temperature"), 1e6)
>>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))
"""
return self._build_operator_cut("!=", field, value, units)
[docs]
def include_equal(self, field, value, units=None):
"""
This function will return a YTCutRegion where only the regions
whose field are equal to given value are included.
Parameters
----------
field : string
The field in which the conditional will be applied.
value : float
The minimum value that will not be masked in the output
YTCutRegion.
units : string or None
The units of the value threshold. None will use the default units
given in the field.
Returns
-------
cut_region : YTCutRegion
The YTCutRegion with the field equal to the given value included.
Examples
--------
>>> ds = yt.load("RedshiftOutput0005")
>>> ad = ds.all_data()
>>> cr = ad.include_equal(("gas", "temperature"), 1e6)
>>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))
"""
return self._build_operator_cut("==", field, value, units)
[docs]
def exclude_inside(self, field, min_value, max_value, units=None):
"""
This function will return a YTCutRegion where all of the regions
whose field are inside the interval from min_value to max_value.
Parameters
----------
field : string
The field in which the conditional will be applied.
min_value : float
The minimum value inside the interval to be excluded.
max_value : float
The maximum value inside the interval to be excluded.
units : string or None
The units of the value threshold. None will use the default units
given in the field.
Returns
-------
cut_region : YTCutRegion
The YTCutRegion with the field inside the given interval excluded.
Examples
--------
>>> ds = yt.load("RedshiftOutput0005")
>>> ad = ds.all_data()
>>> cr = ad.exclude_inside(("gas", "temperature"), 1e5, 1e6)
>>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))
"""
ftype, fname = self._determine_fields(field)[0]
if units is None:
field_cuts = (
f'(obj["{ftype}", "{fname}"] <= {min_value}) | '
f'(obj["{ftype}", "{fname}"] >= {max_value})'
)
else:
field_cuts = (
f'(obj["{ftype}", "{fname}"].in_units("{units}") <= {min_value}) | '
f'(obj["{ftype}", "{fname}"].in_units("{units}") >= {max_value})'
)
cr = self.cut_region(field_cuts)
return cr
[docs]
def include_inside(self, field, min_value, max_value, units=None):
"""
This function will return a YTCutRegion where only the regions
whose field are inside the interval from min_value to max_value are
included.
Parameters
----------
field : string
The field in which the conditional will be applied.
min_value : float
The minimum value inside the interval to be excluded.
max_value : float
The maximum value inside the interval to be excluded.
units : string or None
The units of the value threshold. None will use the default units
given in the field.
Returns
-------
cut_region : YTCutRegion
The YTCutRegion with the field inside the given interval excluded.
Examples
--------
>>> ds = yt.load("RedshiftOutput0005")
>>> ad = ds.all_data()
>>> cr = ad.include_inside(("gas", "temperature"), 1e5, 1e6)
>>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))
"""
ftype, fname = self._determine_fields(field)[0]
if units is None:
field_cuts = (
f'(obj["{ftype}", "{fname}"] > {min_value}) & '
f'(obj["{ftype}", "{fname}"] < {max_value})'
)
else:
field_cuts = (
f'(obj["{ftype}", "{fname}"].in_units("{units}") > {min_value}) & '
f'(obj["{ftype}", "{fname}"].in_units("{units}") < {max_value})'
)
cr = self.cut_region(field_cuts)
return cr
[docs]
def exclude_outside(self, field, min_value, max_value, units=None):
"""
This function will return a YTCutRegion where all of the regions
whose field are outside the interval from min_value to max_value.
Parameters
----------
field : string
The field in which the conditional will be applied.
min_value : float
The minimum value inside the interval to be excluded.
max_value : float
The maximum value inside the interval to be excluded.
units : string or None
The units of the value threshold. None will use the default units
given in the field.
Returns
-------
cut_region : YTCutRegion
The YTCutRegion with the field outside the given interval excluded.
Examples
--------
>>> ds = yt.load("RedshiftOutput0005")
>>> ad = ds.all_data()
>>> cr = ad.exclude_outside(("gas", "temperature"), 1e5, 1e6)
>>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))
"""
cr = self.exclude_below(field, min_value, units)
cr = cr.exclude_above(field, max_value, units)
return cr
[docs]
def include_outside(self, field, min_value, max_value, units=None):
"""
This function will return a YTCutRegion where only the regions
whose field are outside the interval from min_value to max_value are
included.
Parameters
----------
field : string
The field in which the conditional will be applied.
min_value : float
The minimum value inside the interval to be excluded.
max_value : float
The maximum value inside the interval to be excluded.
units : string or None
The units of the value threshold. None will use the default units
given in the field.
Returns
-------
cut_region : YTCutRegion
The YTCutRegion with the field outside the given interval excluded.
Examples
--------
>>> ds = yt.load("RedshiftOutput0005")
>>> ad = ds.all_data()
>>> cr = ad.exclude_outside(("gas", "temperature"), 1e5, 1e6)
>>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))
"""
cr = self.exclude_inside(field, min_value, max_value, units)
return cr
[docs]
def exclude_below(self, field, value, units=None):
"""
This function will return a YTCutRegion where all of the regions
whose field is below a given value are masked.
Parameters
----------
field : string
The field in which the conditional will be applied.
value : float
The minimum value that will not be masked in the output
YTCutRegion.
units : string or None
The units of the value threshold. None will use the default units
given in the field.
Returns
-------
cut_region : YTCutRegion
The YTCutRegion with the field below the given value masked.
Examples
--------
>>> ds = yt.load("RedshiftOutput0005")
>>> ad = ds.all_data()
>>> cr = ad.exclude_below(("gas", "temperature"), 1e6)
>>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))
"""
return self._build_operator_cut(">=", field, value, units)
[docs]
def exclude_nan(self, field, units=None):
"""
This function will return a YTCutRegion where all of the regions
whose field is NaN are masked.
Parameters
----------
field : string
The field in which the conditional will be applied.
units : string or None
The units of the value threshold. None will use the default units
given in the field.
Returns
-------
cut_region : YTCutRegion
The YTCutRegion with the NaN entries of the field masked.
Examples
--------
>>> ds = yt.load("RedshiftOutput0005")
>>> ad = ds.all_data()
>>> cr = ad.exclude_nan(("gas", "temperature"))
>>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))
"""
return self._build_function_cut("~np.isnan", field, units, locals={"np": np})
[docs]
def include_below(self, field, value, units=None):
"""
This function will return a YTCutRegion where only the regions
whose field is below a given value are included.
Parameters
----------
field : string
The field in which the conditional will be applied.
value : float
The minimum value that will not be masked in the output
YTCutRegion.
units : string or None
The units of the value threshold. None will use the default units
given in the field.
Returns
-------
cut_region : YTCutRegion
The YTCutRegion with only regions with the field below the given
value included.
Examples
--------
>>> ds = yt.load("RedshiftOutput0005")
>>> ad = ds.all_data()
>>> cr = ad.include_below(("gas", "temperature"), 1e5, 1e6)
>>> print(cr.quantities.total_quantity(("gas", "cell_mass")).in_units("Msun"))
"""
return self._build_operator_cut("<", field, value, units)
def _extract_isocontours_from_grid(
self, grid, mask, field, value, sample_values=None
):
vc_fields = [field]
if sample_values is not None:
vc_fields.append(sample_values)
vc_data = grid.get_vertex_centered_data(vc_fields, no_ghost=False)
try:
svals = vc_data[sample_values]
except KeyError:
svals = None
my_verts = march_cubes_grid(
value, vc_data[field], mask, grid.LeftEdge, grid.dds, svals
)
return my_verts
[docs]
def calculate_isocontour_flux(
self, field, value, field_x, field_y, field_z, fluxing_field=None
):
r"""This identifies isocontours on a cell-by-cell basis, with no
consideration of global connectedness, and calculates the flux over
those contours.
This function will conduct `marching cubes
<https://en.wikipedia.org/wiki/Marching_cubes>`_ on all the cells in a
given data container (grid-by-grid), and then for each identified
triangular segment of an isocontour in a given cell, calculate the
gradient (i.e., normal) in the isocontoured field, interpolate the local
value of the "fluxing" field, the area of the triangle, and then return:
area * local_flux_value * (n dot v)
Where area, local_value, and the vector v are interpolated at the barycenter
(weighted by the vertex values) of the triangle. Note that this
specifically allows for the field fluxing across the surface to be
*different* from the field being contoured. If the fluxing_field is
not specified, it is assumed to be 1.0 everywhere, and the raw flux
with no local-weighting is returned.
Additionally, the returned flux is defined as flux *into* the surface,
not flux *out of* the surface.
Parameters
----------
field : string
Any field that can be obtained in a data object. This is the field
which will be isocontoured and used as the "local_value" in the
flux equation.
value : float
The value at which the isocontour should be calculated.
field_x : string
The x-component field
field_y : string
The y-component field
field_z : string
The z-component field
fluxing_field : string, optional
The field whose passage over the surface is of interest. If not
specified, assumed to be 1.0 everywhere.
Returns
-------
flux : float
The summed flux. Note that it is not currently scaled; this is
simply the code-unit area times the fields.
Examples
--------
This will create a data object, find a nice value in the center, and
calculate the metal flux over it.
>>> dd = ds.all_data()
>>> rho = dd.quantities["WeightedAverageQuantity"](
... ("gas", "density"), weight=("gas", "cell_mass")
... )
>>> flux = dd.calculate_isocontour_flux(
... ("gas", "density"),
... rho,
... ("gas", "velocity_x"),
... ("gas", "velocity_y"),
... ("gas", "velocity_z"),
... ("gas", "metallicity"),
... )
"""
flux = 0.0
for block, mask in self.blocks:
flux += self._calculate_flux_in_grid(
block, mask, field, value, field_x, field_y, field_z, fluxing_field
)
flux = self.comm.mpi_allreduce(flux, op="sum")
return flux
def _calculate_flux_in_grid(
self, grid, mask, field, value, field_x, field_y, field_z, fluxing_field=None
):
vc_fields = [field, field_x, field_y, field_z]
if fluxing_field is not None:
vc_fields.append(fluxing_field)
vc_data = grid.get_vertex_centered_data(vc_fields)
if fluxing_field is None:
ff = np.ones_like(vc_data[field], dtype="float64")
else:
ff = vc_data[fluxing_field]
return march_cubes_grid_flux(
value,
vc_data[field],
vc_data[field_x],
vc_data[field_y],
vc_data[field_z],
ff,
mask,
grid.LeftEdge,
grid.dds,
)
def _get_bbox(self):
"""
Return the bounding box for this data container.
This generic version will return the bounds of the entire domain.
"""
return self.ds.domain_left_edge, self.ds.domain_right_edge
[docs]
def get_bbox(self) -> tuple[unyt_array, unyt_array]:
"""
Return the bounding box for this data container.
"""
geometry: Geometry = self.ds.geometry
if geometry is Geometry.CARTESIAN:
le, re = self._get_bbox()
le.convert_to_units("code_length")
re.convert_to_units("code_length")
return le, re
elif (
geometry is Geometry.CYLINDRICAL
or geometry is Geometry.POLAR
or geometry is Geometry.SPHERICAL
or geometry is Geometry.GEOGRAPHIC
or geometry is Geometry.INTERNAL_GEOGRAPHIC
or geometry is Geometry.SPECTRAL_CUBE
):
raise NotImplementedError(
f"get_bbox is currently not implemented for {geometry=}!"
)
else:
assert_never(geometry)
[docs]
def volume(self):
"""
Return the volume of the data container.
This is found by adding up the volume of the cells with centers
in the container, rather than using the geometric shape of
the container, so this may vary very slightly
from what might be expected from the geometric volume.
"""
return self.quantities.total_quantity(("index", "cell_volume"))