"""
LightRay class and member functions.
"""
#-----------------------------------------------------------------------------
# Copyright (c) 2013, yt Development Team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file COPYING.txt, distributed with this software.
#-----------------------------------------------------------------------------
import copy
import h5py
import numpy as np
from yt.analysis_modules.cosmological_observation.cosmology_splice import \
CosmologySplice
from yt.convenience import \
load
from yt.funcs import \
mylog
from yt.units.yt_array import \
YTArray
from yt.utilities.cosmology import \
Cosmology
from yt.utilities.parallel_tools.parallel_analysis_interface import \
parallel_objects, \
parallel_root_only
[docs]class LightRay(CosmologySplice):
"""
LightRay(parameter_filename, simulation_type=None,
near_redshift=None, far_redshift=None,
use_minimum_datasets=True, deltaz_min=0.0,
minimum_coherent_box_fraction=0.0,
time_data=True, redshift_data=True,
find_outputs=False, load_kwargs=None):
Create a LightRay object. A light ray is much like a light cone,
in that it stacks together multiple datasets in order to extend a
redshift interval. Unlike a light cone, which does randomly
oriented projections for each dataset, a light ray consists of
randomly oriented single rays. The purpose of these is to create
synthetic QSO lines of sight.
Light rays can also be made from single datasets.
Once the LightRay object is set up, use LightRay.make_light_ray to
begin making rays. Different randomizations can be created with a
single object by providing different random seeds to make_light_ray.
Parameters
----------
parameter_filename : string
The path to the simulation parameter file or dataset.
simulation_type : optional, string
The simulation type. If None, the first argument is assumed to
refer to a single dataset.
Default: None
near_redshift : optional, float
The near (lowest) redshift for a light ray containing multiple
datasets. Do not use is making a light ray from a single
dataset.
Default: None
far_redshift : optional, float
The far (highest) redshift for a light ray containing multiple
datasets. Do not use is making a light ray from a single
dataset.
Default: None
use_minimum_datasets : optional, bool
If True, the minimum number of datasets is used to connect the
initial and final redshift. If false, the light ray solution
will contain as many entries as possible within the redshift
interval.
Default: True.
deltaz_min : optional, float
Specifies the minimum :math:`\Delta z` between consecutive
datasets in the returned list.
Default: 0.0.
minimum_coherent_box_fraction : optional, float
Used with use_minimum_datasets set to False, this parameter
specifies the fraction of the total box size to be traversed
before rerandomizing the projection axis and center. This
was invented to allow light rays with thin slices to sample
coherent large scale structure, but in practice does not work
so well. Try setting this parameter to 1 and see what happens.
Default: 0.0.
time_data : optional, bool
Whether or not to include time outputs when gathering
datasets for time series.
Default: True.
redshift_data : optional, bool
Whether or not to include redshift outputs when gathering
datasets for time series.
Default: True.
find_outputs : optional, bool
Whether or not to search for datasets in the current
directory.
Default: False.
load_kwargs : optional, dict
Optional dictionary of kwargs to be passed to the "load"
function, appropriate for use of certain frontends. E.g.
Tipsy using "bounding_box"
Gadget using "unit_base", etc.
Default : None
"""
[docs] def __init__(self, parameter_filename, simulation_type=None,
near_redshift=None, far_redshift=None,
use_minimum_datasets=True, deltaz_min=0.0,
minimum_coherent_box_fraction=0.0,
time_data=True, redshift_data=True,
find_outputs=False, load_kwargs=None):
self.near_redshift = near_redshift
self.far_redshift = far_redshift
self.use_minimum_datasets = use_minimum_datasets
self.deltaz_min = deltaz_min
self.minimum_coherent_box_fraction = minimum_coherent_box_fraction
self.parameter_filename = parameter_filename
if load_kwargs is None:
self.load_kwargs = {}
else:
self.load_kwargs = load_kwargs
self.light_ray_solution = []
self._data = {}
# Make a light ray from a single, given dataset.
if simulation_type is None:
ds = load(parameter_filename, **self.load_kwargs)
if ds.cosmological_simulation:
redshift = ds.current_redshift
self.cosmology = Cosmology(
hubble_constant=ds.hubble_constant,
omega_matter=ds.omega_matter,
omega_lambda=ds.omega_lambda,
unit_registry=ds.unit_registry)
else:
redshift = 0.
self.light_ray_solution.append({"filename": parameter_filename,
"redshift": redshift})
# Make a light ray from a simulation time-series.
else:
# Get list of datasets for light ray solution.
CosmologySplice.__init__(self, parameter_filename, simulation_type,
find_outputs=find_outputs)
self.light_ray_solution = \
self.create_cosmology_splice(self.near_redshift, self.far_redshift,
minimal=self.use_minimum_datasets,
deltaz_min=self.deltaz_min,
time_data=time_data,
redshift_data=redshift_data)
def _calculate_light_ray_solution(self, seed=None,
start_position=None, end_position=None,
trajectory=None, filename=None):
"Create list of datasets to be added together to make the light ray."
# Calculate dataset sizes, and get random dataset axes and centers.
np.random.seed(seed)
# If using only one dataset, set start and stop manually.
if start_position is not None:
if len(self.light_ray_solution) > 1:
raise RuntimeError("LightRay Error: cannot specify start_position " + \
"if light ray uses more than one dataset.")
if not ((end_position is None) ^ (trajectory is None)):
raise RuntimeError("LightRay Error: must specify either end_position " + \
"or trajectory, but not both.")
self.light_ray_solution[0]['start'] = np.array(start_position)
if end_position is not None:
self.light_ray_solution[0]['end'] = np.array(end_position)
else:
# assume trajectory given as r, theta, phi
if len(trajectory) != 3:
raise RuntimeError("LightRay Error: trajectory must have length 3.")
r, theta, phi = trajectory
self.light_ray_solution[0]['end'] = self.light_ray_solution[0]['start'] + \
r * np.array([np.cos(phi) * np.sin(theta),
np.sin(phi) * np.sin(theta),
np.cos(theta)])
self.light_ray_solution[0]['traversal_box_fraction'] = \
vector_length(self.light_ray_solution[0]['start'],
self.light_ray_solution[0]['end'])
# the normal way (random start positions and trajectories for each dataset)
else:
# For box coherence, keep track of effective depth travelled.
box_fraction_used = 0.0
for q in range(len(self.light_ray_solution)):
if (q == len(self.light_ray_solution) - 1):
z_next = self.near_redshift
else:
z_next = self.light_ray_solution[q+1]['redshift']
# Calculate fraction of box required for a depth of delta z
self.light_ray_solution[q]['traversal_box_fraction'] = \
self.cosmology.comoving_radial_distance(z_next, \
self.light_ray_solution[q]['redshift']).in_units("Mpccm / h") / \
self.simulation.box_size
# Simple error check to make sure more than 100% of box depth
# is never required.
if (self.light_ray_solution[q]['traversal_box_fraction'] > 1.0):
mylog.error("Warning: box fraction required to go from z = %f to %f is %f" %
(self.light_ray_solution[q]['redshift'], z_next,
self.light_ray_solution[q]['traversal_box_fraction']))
mylog.error("Full box delta z is %f, but it is %f to the next data dump." %
(self.light_ray_solution[q]['dz_max'],
self.light_ray_solution[q]['redshift']-z_next))
# Get dataset axis and center.
# If using box coherence, only get start point and vector if
# enough of the box has been used,
# or if box_fraction_used will be greater than 1 after this slice.
if (q == 0) or (self.minimum_coherent_box_fraction == 0) or \
(box_fraction_used >
self.minimum_coherent_box_fraction) or \
(box_fraction_used +
self.light_ray_solution[q]['traversal_box_fraction'] > 1.0):
# Random start point
self.light_ray_solution[q]['start'] = np.random.random(3)
theta = np.pi * np.random.random()
phi = 2 * np.pi * np.random.random()
box_fraction_used = 0.0
else:
# Use end point of previous segment and same theta and phi.
self.light_ray_solution[q]['start'] = \
self.light_ray_solution[q-1]['end'][:]
self.light_ray_solution[q]['end'] = \
self.light_ray_solution[q]['start'] + \
self.light_ray_solution[q]['traversal_box_fraction'] * \
np.array([np.cos(phi) * np.sin(theta),
np.sin(phi) * np.sin(theta),
np.cos(theta)])
box_fraction_used += \
self.light_ray_solution[q]['traversal_box_fraction']
if filename is not None:
self._write_light_ray_solution(filename,
extra_info={'parameter_filename':self.parameter_filename,
'random_seed':seed,
'far_redshift':self.far_redshift,
'near_redshift':self.near_redshift})
[docs] def make_light_ray(self, seed=None,
start_position=None, end_position=None,
trajectory=None,
fields=None, setup_function=None,
solution_filename=None, data_filename=None,
get_los_velocity=True, redshift=None,
njobs=-1):
"""
make_light_ray(seed=None, start_position=None, end_position=None,
trajectory=None, fields=None, setup_function=None,
solution_filename=None, data_filename=None,
get_los_velocity=True, redshift=None,
njobs=-1)
Create a light ray and get field values for each lixel. A light
ray consists of a list of field values for cells intersected by
the ray and the path length of the ray through those cells.
Light ray data can be written out to an hdf5 file.
Parameters
----------
seed : optional, int
Seed for the random number generator.
Default: None.
start_position : optional, list of floats
Used only if creating a light ray from a single dataset.
The coordinates of the starting position of the ray.
Default: None.
end_position : optional, list of floats
Used only if creating a light ray from a single dataset.
The coordinates of the ending position of the ray.
Default: None.
trajectory : optional, list of floats
Used only if creating a light ray from a single dataset.
The (r, theta, phi) direction of the light ray. Use either
end_position or trajectory, not both.
Default: None.
fields : optional, list
A list of fields for which to get data.
Default: None.
setup_function : optional, callable, accepts a ds
This function will be called on each dataset that is loaded
to create the light ray. For, example, this can be used to
add new derived fields.
Default: None.
solution_filename : optional, string
Path to a text file where the trajectories of each
subray is written out.
Default: None.
data_filename : optional, string
Path to output file for ray data.
Default: None.
get_los_velocity : optional, bool
If True, the line of sight velocity is calculated for
each point in the ray.
Default: True.
redshift : optional, float
Used with light rays made from single datasets to specify a
starting redshift for the ray. If not used, the starting
redshift will be 0 for a non-cosmological dataset and
the dataset redshift for a cosmological dataset.
Default: None.
njobs : optional, int
The number of parallel jobs over which the segments will
be split. Choose -1 for one processor per segment.
Default: -1.
Examples
--------
Make a light ray from multiple datasets:
>>> import yt
>>> from yt.analysis_modules.cosmological_observation.light_ray.api import \
... LightRay
>>> my_ray = LightRay("enzo_tiny_cosmology/32Mpc_32.enzo", "Enzo",
... 0., 0.1, time_data=False)
...
>>> my_ray.make_light_ray(seed=12345,
... solution_filename="solution.txt",
... data_filename="my_ray.h5",
... fields=["temperature", "density"],
... get_los_velocity=True)
Make a light ray from a single dataset:
>>> import yt
>>> from yt.analysis_modules.cosmological_observation.light_ray.api import \
... LightRay
>>> my_ray = LightRay("IsolatedGalaxy/galaxy0030/galaxy0030")
...
>>> my_ray.make_light_ray(start_position=[0., 0., 0.],
... end_position=[1., 1., 1.],
... solution_filename="solution.txt",
... data_filename="my_ray.h5",
... fields=["temperature", "density"],
... get_los_velocity=True)
"""
# Calculate solution.
self._calculate_light_ray_solution(seed=seed,
start_position=start_position,
end_position=end_position,
trajectory=trajectory,
filename=solution_filename)
# Initialize data structures.
self._data = {}
if fields is None: fields = []
data_fields = fields[:]
all_fields = fields[:]
all_fields.extend(['dl', 'dredshift', 'redshift'])
if get_los_velocity:
all_fields.extend(['velocity_x', 'velocity_y',
'velocity_z', 'velocity_los'])
data_fields.extend(['velocity_x', 'velocity_y', 'velocity_z'])
all_ray_storage = {}
for my_storage, my_segment in parallel_objects(self.light_ray_solution,
storage=all_ray_storage,
njobs=njobs):
# Load dataset for segment.
ds = load(my_segment['filename'], **self.load_kwargs)
my_segment['unique_identifier'] = ds.unique_identifier
if redshift is not None:
if ds.cosmological_simulation and redshift != ds.current_redshift:
mylog.warn("Generating light ray with different redshift than " +
"the dataset itself.")
my_segment["redshift"] = redshift
if setup_function is not None:
setup_function(ds)
if start_position is not None:
my_segment["start"] = ds.arr(my_segment["start"], "code_length")
my_segment["end"] = ds.arr(my_segment["end"], "code_length")
else:
my_segment["start"] = ds.domain_width * my_segment["start"] + \
ds.domain_left_edge
my_segment["end"] = ds.domain_width * my_segment["end"] + \
ds.domain_left_edge
if not ds.cosmological_simulation:
next_redshift = my_segment["redshift"]
elif self.near_redshift == self.far_redshift:
next_redshift = my_segment["redshift"] - \
self._deltaz_forward(my_segment["redshift"],
ds.domain_width[0].in_units("Mpccm / h") *
my_segment["traversal_box_fraction"])
elif my_segment.get("next", None) is None:
next_redshift = self.near_redshift
else:
next_redshift = my_segment['next']['redshift']
mylog.info("Getting segment at z = %s: %s to %s." %
(my_segment['redshift'], my_segment['start'],
my_segment['end']))
# Break periodic ray into non-periodic segments.
sub_segments = periodic_ray(my_segment['start'], my_segment['end'],
left=ds.domain_left_edge,
right=ds.domain_right_edge)
# Prepare data structure for subsegment.
sub_data = {}
sub_data['segment_redshift'] = my_segment['redshift']
for field in all_fields:
sub_data[field] = []
# Get data for all subsegments in segment.
for sub_segment in sub_segments:
mylog.info("Getting subsegment: %s to %s." %
(list(sub_segment[0]), list(sub_segment[1])))
sub_ray = ds.ray(sub_segment[0], sub_segment[1])
asort = np.argsort(sub_ray["t"])
sub_data['dl'].extend(sub_ray['dts'][asort] *
vector_length(sub_ray.start_point,
sub_ray.end_point))
for field in data_fields:
sub_data[field].extend(sub_ray[field][asort])
if get_los_velocity:
line_of_sight = sub_segment[1] - sub_segment[0]
line_of_sight /= ((line_of_sight**2).sum())**0.5
sub_vel = ds.arr([sub_ray['velocity_x'],
sub_ray['velocity_y'],
sub_ray['velocity_z']])
sub_data['velocity_los'].extend((np.rollaxis(sub_vel, 1) *
line_of_sight).sum(axis=1)[asort])
del sub_vel
sub_ray.clear_data()
del sub_ray, asort
for key in sub_data:
sub_data[key] = ds.arr(sub_data[key]).in_cgs()
# Get redshift for each lixel. Assume linear relation between l and z.
sub_data['dredshift'] = (my_segment['redshift'] - next_redshift) * \
(sub_data['dl'] / vector_length(my_segment['start'],
my_segment['end']).in_cgs())
sub_data['redshift'] = my_segment['redshift'] - \
sub_data['dredshift'].cumsum() + sub_data['dredshift']
# Remove empty lixels.
sub_dl_nonzero = sub_data['dl'].nonzero()
for field in all_fields:
sub_data[field] = sub_data[field][sub_dl_nonzero]
del sub_dl_nonzero
# Add to storage.
my_storage.result = sub_data
del ds
# Reconstruct ray data from parallel_objects storage.
all_data = [my_data for my_data in all_ray_storage.values()]
# This is now a list of segments where each one is a dictionary
# with all the fields.
all_data.sort(key=lambda a:a['segment_redshift'], reverse=True)
# Flatten the list into a single dictionary containing fields
# for the whole ray.
all_data = _flatten_dict_list(all_data, exceptions=['segment_redshift'])
if data_filename is not None:
self._write_light_ray(data_filename, all_data)
self._data = all_data
return all_data
@parallel_root_only
def _write_light_ray(self, filename, data):
"""
_write_light_ray(filename, data)
Write light ray data to hdf5 file.
"""
mylog.info("Saving light ray data to %s." % filename)
output = h5py.File(filename, 'w')
for field in data.keys():
# if the field is a tuple, only use the second part of the tuple
# in the hdf5 output (i.e. ('gas', 'density') -> 'density')
if isinstance(field, tuple):
fieldname = field[1]
else:
fieldname = field
output.create_dataset(fieldname, data=data[field])
output[fieldname].attrs["units"] = str(data[field].units)
output.close()
@parallel_root_only
def _write_light_ray_solution(self, filename, extra_info=None):
"""
_write_light_ray_solution(filename, extra_info=None)
Write light ray solution to a file.
"""
mylog.info("Writing light ray solution to %s." % filename)
f = open(filename, 'w')
if extra_info is not None:
for par, val in extra_info.items():
f.write("%s = %s\n" % (par, val))
f.write("\nSegment Redshift dl/box Start x y " + \
"z End x y z Dataset\n")
for q, my_segment in enumerate(self.light_ray_solution):
f.write("%04d %.6f %.6f % .10f % .10f % .10f % .10f % .10f % .10f %s\n" % \
(q, my_segment['redshift'], my_segment['traversal_box_fraction'],
my_segment['start'][0], my_segment['start'][1], my_segment['start'][2],
my_segment['end'][0], my_segment['end'][1], my_segment['end'][2],
my_segment['filename']))
f.close()
def _flatten_dict_list(data, exceptions=None):
"""
_flatten_dict_list(data, exceptions=None)
Flatten the list of dicts into one dict.
"""
if exceptions is None: exceptions = []
new_data = {}
for datum in data:
for field in [field for field in datum.keys()
if field not in exceptions]:
if field not in new_data:
new_data[field] = []
new_data[field].extend(datum[field])
for field in new_data:
new_data[field] = YTArray(new_data[field])
return new_data
def vector_length(start, end):
"""
vector_length(start, end)
Calculate vector length.
"""
return np.sqrt(np.power((end - start), 2).sum())
def periodic_distance(coord1, coord2):
"""
periodic_distance(coord1, coord2)
Calculate length of shortest vector between to points in periodic domain.
"""
dif = coord1 - coord2
dim = np.ones(coord1.shape,dtype=int)
def periodic_bind(num):
pos = np.abs(num % dim)
neg = np.abs(num % -dim)
return np.min([pos,neg],axis=0)
dif = periodic_bind(dif)
return np.sqrt((dif * dif).sum(axis=-1))
def periodic_ray(start, end, left=None, right=None):
"""
periodic_ray(start, end, left=None, right=None)
Break up periodic ray into non-periodic segments.
Accepts start and end points of periodic ray as YTArrays.
Accepts optional left and right edges of periodic volume as YTArrays.
Returns a list of lists of coordinates, where each element of the
top-most list is a 2-list of start coords and end coords of the
non-periodic ray:
[[[x0start,y0start,z0start], [x0end, y0end, z0end]],
[[x1start,y1start,z1start], [x1end, y1end, z1end]],
...,]
"""
if left is None:
left = np.zeros(start.shape)
if right is None:
right = np.ones(start.shape)
dim = right - left
vector = end - start
wall = np.zeros(start.shape)
close = np.zeros(start.shape, dtype=object)
left_bound = vector < 0
right_bound = vector > 0
no_bound = vector == 0.0
bound = vector != 0.0
wall[left_bound] = left[left_bound]
close[left_bound] = np.max
wall[right_bound] = right[right_bound]
close[right_bound] = np.min
wall[no_bound] = np.inf
close[no_bound] = np.min
segments = []
this_start = start.copy()
this_end = end.copy()
t = 0.0
tolerance = 1e-6
while t < 1.0 - tolerance:
hit_left = (this_start <= left) & (vector < 0)
if (hit_left).any():
this_start[hit_left] += dim[hit_left]
this_end[hit_left] += dim[hit_left]
hit_right = (this_start >= right) & (vector > 0)
if (hit_right).any():
this_start[hit_right] -= dim[hit_right]
this_end[hit_right] -= dim[hit_right]
nearest = vector.unit_array * \
np.array([close[q]([this_end[q], wall[q]]) \
for q in range(start.size)])
dt = ((nearest - this_start) / vector)[bound].min()
now = this_start + vector * dt
close_enough = np.abs(now - nearest) / np.abs(vector.max()) < 1e-10
now[close_enough] = nearest[close_enough]
segments.append([np.copy(this_start), np.copy(now)])
this_start = now.copy()
t += dt
return segments