Source code for yt.data_objects.selection_objects.data_selection_objects

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)
[docs] def extract_isocontours( self, field, value, filename=None, rescale=False, sample_values=None ): r"""This identifies isocontours on a cell-by-cell basis, with no consideration of global connectedness, and returns the vertices of the Triangles in that isocontour. This function simply returns the vertices of all the triangles calculated by the `marching cubes <https://en.wikipedia.org/wiki/Marching_cubes>`_ algorithm; for more complex operations, such as identifying connected sets of cells above a given threshold, see the extract_connected_sets function. This is more useful for calculating, for instance, total isocontour area, or visualizing in an external program (such as `MeshLab <http://www.meshlab.net>`_.) Parameters ---------- field : string Any field that can be obtained in a data object. This is the field which will be isocontoured. value : float The value at which the isocontour should be calculated. filename : string, optional If supplied, this file will be filled with the vertices in .obj format. Suitable for loading into meshlab. rescale : bool, optional If true, the vertices will be rescaled within their min/max. sample_values : string, optional Any field whose value should be extracted at the center of each triangle. Returns ------- verts : array of floats The array of vertices, x,y,z. Taken in threes, these are the triangle vertices. samples : array of floats If `sample_values` is specified, this will be returned and will contain the values of the field specified at the center of each triangle. Examples -------- This will create a data object, find a nice value in the center, and output the vertices to "triangles.obj" after rescaling them. >>> dd = ds.all_data() >>> rho = dd.quantities["WeightedAverageQuantity"]( ... ("gas", "density"), weight=("gas", "cell_mass") ... ) >>> verts = dd.extract_isocontours( ... ("gas", "density"), rho, "triangles.obj", True ... ) """ from yt.data_objects.static_output import ParticleDataset from yt.frontends.stream.data_structures import StreamParticlesDataset verts = [] samples = [] if isinstance(self.ds, (ParticleDataset, StreamParticlesDataset)): raise NotImplementedError for block, mask in self.blocks: my_verts = self._extract_isocontours_from_grid( block, mask, field, value, sample_values ) if sample_values is not None: my_verts, svals = my_verts samples.append(svals) verts.append(my_verts) verts = np.concatenate(verts).transpose() verts = self.comm.par_combine_object(verts, op="cat", datatype="array") verts = verts.transpose() if sample_values is not None: samples = np.concatenate(samples) samples = self.comm.par_combine_object(samples, op="cat", datatype="array") if rescale: mi = np.min(verts, axis=0) ma = np.max(verts, axis=0) verts = (verts - mi) / (ma - mi).max() if filename is not None and self.comm.rank == 0: if hasattr(filename, "write"): f = filename else: f = open(filename, "w") for v1 in verts: f.write(f"v {v1[0]:0.16e} {v1[1]:0.16e} {v1[2]:0.16e}\n") for i in range(len(verts) // 3): f.write(f"f {i * 3 + 1} {i * 3 + 2} {i * 3 + 3}\n") if not hasattr(filename, "write"): f.close() if sample_values is not None: return verts, samples return verts
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, )
[docs] def extract_connected_sets( self, field, num_levels, min_val, max_val, log_space=True, cumulative=True ): """ This function will create a set of contour objects, defined by having connected cell structures, which can then be studied and used to 'paint' their source grids, thus enabling them to be plotted. Note that this function *can* return a connected set object that has no member values. """ if log_space: cons = np.logspace(np.log10(min_val), np.log10(max_val), num_levels + 1) else: cons = np.linspace(min_val, max_val, num_levels + 1) contours = {} for level in range(num_levels): contours[level] = {} if cumulative: mv = max_val else: mv = cons[level + 1] from yt.data_objects.level_sets.api import identify_contours from yt.data_objects.level_sets.clump_handling import add_contour_field nj, cids = identify_contours(self, field, cons[level], mv) unique_contours = set() for sl_list in cids.values(): for _sl, ff in sl_list: unique_contours.update(np.unique(ff)) contour_key = uuid.uuid4().hex # In case we're a cut region already... base_object = getattr(self, "base_object", self) add_contour_field(base_object.ds, contour_key) for cid in sorted(unique_contours): if cid == -1: continue contours[level][cid] = base_object.cut_region( [f"obj['contours_{contour_key}'] == {cid}"], {f"contour_slices_{contour_key}": cids}, ) return cons, contours
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"))