Light Cone Generator¶
Light cones are created by stacking multiple datasets together to continuously span a given redshift interval. To make a projection of a field through a light cone, the width of individual slices is adjusted such that each slice has the same angular size. Each slice is randomly shifted and projected along a random axis to ensure that the same structures are not sampled multiple times. A recipe for creating a simple light cone projection can be found in the cookbook under Light Cone Projection.
A light cone projection of the thermal Sunyaev-Zeldovich Y parameter from z = 0 to 0.4 with a 450x450 arcminute field of view using 9 individual slices. The panels shows the contributions from the 9 individual slices with the final light cone image shown in the bottom, right.
Configuring the Light Cone Generator¶
The required arguments to instantiate a
object are the path to the simulation parameter file, the simulation type, the
nearest redshift, and the furthest redshift of the light cone.
from yt.extensions.astro_analysis.cosmological_observation.api import LightCone lc = LightCone("enzo_tiny_cosmology/32Mpc_32.enzo", "Enzo", 0.0, 0.1)
The additional keyword arguments are:
use_minimum_datasets(bool): If True, the minimum number of datasets is used to connect the initial and final redshift. If False, the light cone solution will contain as many entries as possible within the redshift interval. Default: True.
deltaz_min(float): Specifies the minimum Delta-z between consecutive datasets in the returned list. Default: 0.0.
minimum_coherent_box_fraction(float): Used with
use_minimum_datasetsset 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 cones 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(bool): Whether or not to include time outputs when gathering datasets for time series. Default: True.
redshift_data(bool): Whether or not to include redshift outputs when gathering datasets for time series. Default: True.
set_parameters(dict): Dictionary of parameters to attach to ds.parameters. Default: None.
output_dir(string): The directory in which images and data files
will be written. Default: ‘LC’.
output_prefix(string): The prefix of all images and data files. Default: ‘LightCone’.
Creating Light Cone Solutions¶
A light cone solution consists of a list of datasets spanning a redshift
interval with a random orientation for each dataset. A new solution
is calculated with the
The keyword argument are:
seed(int): the seed for the random number generator. Any light cone solution can be reproduced by giving the same random seed. Default: None.
filename(str): if given, a text file detailing the solution will be written out. Default: None.
Making a Light Cone Projection¶
With the light cone solution in place, projections with a given field of view and resolution can be made of any available field:
field = "density" field_of_view = (600.0, "arcmin") resolution = (60.0, "arcsec") lc.project_light_cone( field_of_vew, resolution, field, weight_field=None, save_stack=True, save_slice_images=True, )
The field of view and resolution can be specified either as a tuple of
value and unit string or as a unitful
Additional keyword arguments:
weight_field(str): the weight field of the projection. This has the same meaning as in standard projections. Default: None.
photon_field(bool): if True, the projection data for each slice is decremented by 4 pi R 2 , where R is the luminosity distance between the observer and the slice redshift. Default: False.
save_stack(bool): if True, the unflatted light cone data including each individual slice is written to an hdf5 file. Default: True.
save_final_image(bool): if True, save an image of the final light cone projection. Default: True.
save_slice_images(bool): save images for each individual projection slice. Default: False.
cmap_name(string): color map for images. Default: “algae”.
njobs(int): The number of parallel jobs over which the light cone projection will be split. Choose -1 for one processor per individual projection and 1 to have all processors work together on each projection. Default: 1.
dynamic(bool): If True, use dynamic load balancing to create the projections. Default: False.
yt-3.0, the halo mask and unique light cone functionality no longer exist. These are still available in
yt-2.x. If you would like to use these features in
yt-3.x, help is needed to port them over. Contact the yt-users mailing list if you are interested in doing this.