def can_run_sim(sim_fn, sim_type, file_check=False):
if isinstance(sim_fn, SimulationTimeSeries):
return AnswerTestingTest.result_storage is not None
path = ytcfg.get("yt", "test_data_dir")
if not os.path.isdir(path):
return False
if file_check:
return os.path.isfile(os.path.join(path, sim_fn)) and \
AnswerTestingTest.result_storage is not None
try:
simulation(sim_fn, sim_type)
except YTOutputNotIdentified:
return False
return AnswerTestingTest.result_storage is not None
def can_run_sim(sim_fn, sim_type, file_check=False):
r"""
Validates whether or not a given input can be used as a simulation
time-series object.
"""
path = ytcfg.get("yt", "test_data_dir")
if not os.path.isdir(path):
return False
if file_check:
return os.path.isfile(os.path.join(path, sim_fn))
try:
simulation(sim_fn, sim_type)
except YTOutputNotIdentified:
return False
return True
def can_run_sim(sim_fn, sim_type, file_check = False):
if isinstance(sim_fn, SimulationTimeSeries):
return AnswerTestingTest.result_storage is not None
path = ytcfg.get("yt", "test_data_dir")
if not os.path.isdir(path):
return False
with temp_cwd(path):
if file_check:
return os.path.isfile(sim_fn) and \
AnswerTestingTest.result_storage is not None
try:
simulation(sim_fn, sim_type)
except YTOutputNotIdentified:
return False
return AnswerTestingTest.result_storage is not None
def sim_dir_load(sim_fn, path=None, sim_type="Enzo", find_outputs=False):
if path is None and not os.path.exists(sim_fn):
raise IOError
if os.path.exists(sim_fn) or not path:
path = "."
with temp_cwd(path):
return simulation(sim_fn, sim_type, find_outputs=find_outputs)
def sim_dir_load(sim_fn, path = None, sim_type = "Enzo",
find_outputs=False):
if path is None and not os.path.exists(sim_fn):
raise IOError
if os.path.exists(sim_fn) or not path:
path = "."
with temp_cwd(path):
return simulation(sim_fn, sim_type,
find_outputs=find_outputs)
def can_run_sim(sim_fn, sim_type, file_check=False):
result_storage = AnswerTestingTest.result_storage
if isinstance(sim_fn, SimulationTimeSeries):
return result_storage is not None
path = ytcfg.get("yt", "test_data_dir")
if not os.path.isdir(path):
return False
if file_check:
return os.path.isfile(os.path.join(
path, sim_fn)) and result_storage is not None
try:
simulation(sim_fn, sim_type)
except FileNotFoundError:
if ytcfg.getboolean("yt", "requires_ds_strict"):
if result_storage is not None:
result_storage["tainted"] = True
raise
return False
return result_storage is not None
def __init__(self, parameter_filename, simulation_type, find_outputs=False):
self.parameter_filename = parameter_filename
self.simulation_type = simulation_type
self.simulation = simulation(parameter_filename, simulation_type,
find_outputs=find_outputs)
self.cosmology = Cosmology(
hubble_constant=(self.simulation.hubble_constant),
omega_matter=self.simulation.omega_matter,
omega_lambda=self.simulation.omega_lambda)
def __init__(self, parameter_filename, simulation_type, find_outputs=False):
self.parameter_filename = parameter_filename
self.simulation_type = simulation_type
self.simulation = simulation(parameter_filename, simulation_type,
find_outputs=find_outputs)
self.cosmology = Cosmology(
hubble_constant=(self.simulation.hubble_constant),
omega_matter=self.simulation.omega_matter,
omega_lambda=self.simulation.omega_lambda)
def make_compound_ray(parameter_filename, simulation_type,
near_redshift, far_redshift,
lines=None, ftype='gas', fields=None,
solution_filename=None, data_filename=None,
use_minimum_datasets=True, max_box_fraction=1.0,
deltaz_min=0.0, minimum_coherent_box_fraction=0.0,
seed=None, setup_function=None, load_kwargs=None,
line_database=None, ionization_table=None):
"""
Create a yt LightRay object for multiple consecutive datasets (eg IGM).
This is a wrapper function around yt's LightRay interface to reduce some
of the complexity there.
.. note::
The compound ray functionality has only been implemented for the
Enzo and Gadget code. If you would like to help us implement
this functionality for your simulation code, please contact us
about this on the mailing list.
A compound ray is a series of straight lines passing through multiple
consecutive outputs from a single cosmological simulation to approximate
a continuous line of sight to high redshift.
Because a single continuous ray traversing a simulated volume can only
cover a small range in redshift space (e.g. 100 Mpc only covers the
redshift range from z=0 to z=0.023), the compound ray passes rays through
multiple consecutive outputs from the same simulation to approximate the
path of a single line of sight to high redshift. By probing all of the
foreground material out to any given redshift, the compound ray is
appropriate for studies of the intergalactic medium and circumgalactic
medium.
By default, it selects a random starting location and trajectory in
each dataset it traverses, to assure that the same cosmological structures
are not being probed multiple times from the same direction. In doing
this, the ray becomes discontinuous across each dataset.
The compound ray requires the parameter_filename of the simulation run.
This is *not* the dataset filename from a single output, but the parameter
file that was used to run the simulation itself. It is in this parameter
file that the output frequency, simulation volume, and cosmological
parameters are described to assure full redshift coverage can be achieved
for a compound ray. It also requires the simulation_type of the simulation.
Unlike the simple ray, which is specified by its start and end positions
in the dataset volume, the compound ray requires the near_redshift and
far_redshift to determine which datasets to use to get full coverage
in redshift space as the ray propagates from near_redshift to far_redshift.
Like the simple ray produced by :class:`~trident.make_simple_ray`,
each gas cell intersected by the LightRay is sampled for the desired
fields and stored. Several additional fields are created and stored
including ``dl`` to represent the path length in space
for each element in the ray, ``v_los`` to represent the line of
sight velocity along the ray, and ``redshift``, ``redshift_dopp``, and
``redshift_eff`` to represent the cosmological redshift, doppler redshift
and effective redshift (combined doppler and cosmological) for each
element of the ray.
The :lines: keyword can be set to automatically add all fields to the
resulting ray necessary for later use with the SpectrumGenerator class.
If the necessary fields do not exist for your line of choice, they will
be added to your datasets before adding them to the ray.
If using the :lines: keyword with SPH datasets, it is very important
to set the :ftype: keyword appropriately, or you may end up calculating
ion fields by interpolating on data already smoothed to the grid. This is
generally not desired.
**Parameters**
:parameter_filename: string
The simulation parameter file *not* the dataset filename
:simulation_type: string
The simulation type of the parameter file. At present, this
functionality only works with "Enzo" and "Gadget" yt frontends.
:near_redshift, far_redshift: floats
The near and far redshift bounds of the LightRay through the
simulation datasets.
:lines: list of strings, optional
List of strings that determine which fields will be added to the ray
to support line deposition to an absorption line spectrum. List can
include things like "C", "O VI", or "Mg II ####", where #### would be
the integer wavelength value of the desired line. If set to 'all',
includes all possible ions from H to Zn. :lines: can be used
in conjunction with :fields: as they will not override each other.
If using the :lines: keyword with an SPH dataset, it is very important
to set the :ftype: keyword appropriately, or you may end up calculating
ion fields by interpolating on data already smoothed to the grid.
This is generally not desired.
Default: None
:ftype: string, optional
For use with the :lines: keyword. It is the field type of the fields to
be added. It is the first string in the field tuple e.g. "gas" in
("gas", "O_p5_number_density"). For SPH datasets, it is important to
set this to the field type of the gas particles in your dataset
(e.g. 'PartType0'), as it determines the source data for the ion
fields to be added. If you leave it set to "gas", it will calculate
the ion fields based on the hydro fields already smoothed on the grid,
which is usually not desired.
Default: "gas"
:fields: list of strings, optional
The list of which fields to store in the output LightRay.
See :lines: keyword for additional functionality that will add fields
necessary for creating absorption line spectra for certain line
features.
Default: None
:solution_filename: string, optional
Output filename of text file containing trajectory of LightRay
through the dataset.
Default: None
:data_filename: string, optional
Output filename for ray data stored as an HDF5 file. Note that
at present, you *must* save a ray to disk in order for it to be
returned by this function. If set to None, defaults to 'ray.h5'.
Default: None
:use_minimum_datasets: bool, optional
Use the minimum number of datasets to make the ray continuous
through the supplied datasets from the near_redshift to the
far_redshift. If false, the LightRay solution will contain as many
datasets as possible to enable the light ray to traverse the
desired redshift interval.
Default: True
:max_box_fraction: float, optional
The maximum length a light ray segment can be in order to span the
redshift interval from one dataset to another in units of the domain
size. Values larger than 1.0 will result in LightRays crossing the
domain of a given dataset more than once, which is generally undesired.
Zoom-in simulations can use a value equal to the length of the
high-resolution region so as to limit ray segments to that size. If
the high-resolution region is not cubical, the smallest size should b
used.
Default: 1.0 (the size of the box)
:deltaz_min: float, optional
The minimum delta-redshift value between consecutive datasets used
in the LightRay solution.
Default: 0.0
:minimum_coherent_box_fraction: float, optional
When use_minimum_datasets is set to False, this parameter specifies
the fraction of the total box width to be traversed before
rerandomizing the ray location and trajectory.
Default: 0.0
:seed: int, optional
Sets the seed for the random number generator used to determine the
location and trajectory of the LightRay as it traverses the
simulation datasets. For consistent results between LightRays,
use the same seed value.
Default: None
:setup_function: function, optional
A function that will be called on the dataset as it is loaded but
before the LightRay is generated. Very useful for adding derived
fields and other manipulations of the dataset prior to LightRay
creation.
Default: None
:load_kwargs: dict, optional
Dictionary of kwargs to be passed to the yt "load" function prior to
creating the LightRay. Very useful for many frontends like Gadget,
Tipsy, etc. for passing in "bounding_box", "unit_base", etc.
Default: None
:line_database: string, optional
For use with the :lines: keyword. If you want to limit the available
ion fields to be added to those available in a particular subset,
you can use a :class:`~trident.LineDatabase`. This means when you
set :lines:='all', it will only use those ions present in the
corresponding LineDatabase. If :LineDatabase: is set to None,
and :lines:='all', it will add every ion of every element up to Zinc.
Default: None
:ionization_table: string, optional
For use with the :lines: keyword. Path to an appropriately formatted
HDF5 table that can be used to compute the ion fraction as a function
of density, temperature, metallicity, and redshift. When set to None,
it uses the table specified in ~/.trident/config
Default: None
**Example**
Generate a compound ray passing from the redshift 0 to redshift 0.05
through a multi-output enzo simulation.
>>> import trident
>>> fn = 'path/to/simulation/parameter/file'
>>> ray = trident.make_compound_ray(fn, simulation_type='Enzo',
... near_redshift=0.0, far_redshift=0.05, ftype='gas',
... lines=['H', 'O', 'Mg II'])
Generate a compound ray passing from the redshift 0 to redshift 0.05
through a multi-output gadget simulation.
>>> import trident
>>> fn = 'path/to/simulation/parameter/file'
>>> ray = trident.make_compound_ray(fn, simulation_type='Gadget',
... near_redshift=0.0, far_redshift=0.05,
... lines=['H', 'O', 'Mg II'], ftype='PartType0')
"""
if load_kwargs is None:
load_kwargs = {}
if fields is None:
fields = []
if data_filename is None:
data_filename = 'ray.h5'
lr = LightRay(parameter_filename,
simulation_type=simulation_type,
near_redshift=near_redshift,
far_redshift=far_redshift,
use_minimum_datasets=use_minimum_datasets,
max_box_fraction=max_box_fraction,
deltaz_min=deltaz_min,
minimum_coherent_box_fraction=minimum_coherent_box_fraction,
load_kwargs=load_kwargs)
if ionization_table is None:
ionization_table = ion_table_filepath
# We use the final dataset from the simulation in order to test it for
# what fields are present, etc. This all assumes that the fields present
# in this output will be present in ALL outputs. Hopefully this is true,
# because testing each dataset is going to be slow and a pain.
sim = simulation(parameter_filename, simulation_type)
ds = load(sim.all_outputs[-1]['filename'])
# Include some default fields in the ray to assure it's processed correctly.
fields = _add_default_fields(ds, fields)
# If 'lines' kwarg is set, we need to get all the fields required to
# create the desired absorption lines in the grid format, since grid-based
# fields are what are directly probed by the LightRay object.
# We first determine what fields are necessary for the desired lines, and
# inspect the dataset to see if they already exist. If so, we add them
# to the field list for the ray. If not, we have to create them.
# We use the final dataset from the simulation in order to test it for
# the sampling_type of the field, what fields are present, etc. This
# all assumes that the fields present in this output will be present in
# ALL outputs. Hopefully this is true, because testing each dataset
# is going to be slow and a pain.
if lines is not None:
ion_list = _determine_ions_from_lines(line_database, lines)
sampling_type = _check_sampling_types_match(ds, ftype)
fields, fields_to_add_to_ds = _determine_fields_from_ions(ds, ion_list,
fields, ftype, sampling_type)
# actually add the fields we need to add to the dataset
# by adding the fields to the setup_function passed to each dataset
# as it is loaded by make_light_ray()
def setup_ds(ds):
for atom, ion_state in fields_to_add_to_ds:
add_ion_number_density_field(atom, ion_state, ds,
ftype=ftype,
ionization_table=ionization_table,
sampling_type=sampling_type)
if setup_function is not None:
setup_function(ds)
else:
# Define setup_ds in cases when no lines are specified.
def setup_ds(ds):
pass
# To assure there are no fields that are double specified or that collide
# based on being specified as "density" as well as ("gas", "density"),
# we will just assume that all non-tuple fields requested are ftype "gas".
for i in range(len(fields)):
if isinstance(fields[i], str):
fields[i] = ('gas', fields[i])
fields = uniquify(fields)
return lr.make_light_ray(seed=seed,
fields=fields,
setup_function=setup_ds,
solution_filename=solution_filename,
data_filename=data_filename,
redshift=None, njobs=-1)
