Datasets¶

The most basic YT object is the Dataset. This is a collection of volumetric data that may be stored on disk, or created in-memory. To load a Dataset from disk, we use load:

julia> ds = YT.load("sloshing_nomag2_hdf5_plt_cnt_0100")
yt : [WARNING  ] 2014-03-31 23:46:27,765 integer runtime parameter checkpointfilenumber overwrites a simulation scalar of the same name
yt : [WARNING  ] 2014-03-31 23:46:27,765 integer runtime parameter plotfilenumber overwrites a simulation scalar of the same name
yt : [INFO     ] 2014-03-31 23:46:27,768 Parameters: current_time              = 7.89058001997e+16
yt : [INFO     ] 2014-03-31 23:46:27,768 Parameters: domain_dimensions         = [16 16 16]
yt : [INFO     ] 2014-03-31 23:46:27,769 Parameters: domain_left_edge          = [ -3.70272000e+24  -3.70272000e+24  -3.70272000e+24]
yt : [INFO     ] 2014-03-31 23:46:27,770 Parameters: domain_right_edge         = [  3.70272000e+24   3.70272000e+24   3.70272000e+24]
yt : [INFO     ] 2014-03-31 23:46:27,770 Parameters: cosmological_simulation   = 0.0
yt : [INFO     ] 2014-03-31 23:46:28,340 Loading field plugins.
yt : [INFO     ] 2014-03-31 23:46:28,340 Loaded angular_momentum (8 new fields)
yt : [INFO     ] 2014-03-31 23:46:28,340 Loaded astro (14 new fields)
yt : [INFO     ] 2014-03-31 23:46:28,340 Loaded cosmology (20 new fields)
yt : [INFO     ] 2014-03-31 23:46:28,341 Loaded fluid (55 new fields)
yt : [INFO     ] 2014-03-31 23:46:28,341 Loaded fluid_vector (87 new fields)
yt : [INFO     ] 2014-03-31 23:46:28,342 Loaded geometric (102 new fields)
yt : [INFO     ] 2014-03-31 23:46:28,342 Loaded local (102 new fields)
yt : [INFO     ] 2014-03-31 23:46:28,342 Loaded magnetic_field (108 new fields)
"sloshing_nomag2_hdf5_plt_cnt_0100"

where you can see that the yt log has been outputted. The Dataset object ds now contains all of the basic metadata about the data stored in the file "sloshing_nomag2_hdf5_plt_cnt_0100". Attached to ds are several useful attributes, as well as a number of methods for creating DataContainers.

Parameters¶

Each simulation Dataset normally has a number of runtime parameters associated with it. This is stored in the parameters dictionary:

julia> collect(keys(ds.parameters))
293-element Array{Any,1}:
 "min_particles_per_blk"
 "zmax"
 "maxcondentr"
 "usemassdiffusivity"
 "saturatedconduction"
 "zmin"
 ⋮
 "flux_correct"
 "nxb"
 "plotfilenumber"
 "log_file"
 "e_modification"
 "order"

julia> ds.parameters["nxb"]
0-dimensional Array{Int32,0}:
 16

Methods¶

print_stats may be used to get a quick synopsis of the structure of the Dataset. In this case, it is a FLASH AMR dataset, so statistics regarding the grids and cells are printed:

julia> YT.print_stats(ds)
level       # grids        # cells       # cells^3
----------------------------------------------
  0          1                4096              15
  1          8               32768              31
  2         64              262144              63
  3        512             2097152             127
  4        256             1048576             101
  5        256             1048576             101
  6        256             1048576             101
----------------------------------------------


t = 7.89058002e+16 = 7.89058002e+16 s = 2.50037393e+09 years

Smallest Cell:

get_smallest_dx returns the length scale of the smallest cell or SPH smoothing length:

julia> YT.get_smallest_dx(ds)
7.231875e21 code_length

Note

These methods apply to Datasets loaded from disk files and to Datasets created from generic in-memory data. For details on how to create the latter, see In-Memory Datasets.