yt.frontends.ytdata.utilities module

Utility functions for ytdata frontend.

yt.frontends.ytdata.utilities.save_as_dataset(ds, filename, data, field_types=None, extra_attrs=None)[source]

Export a set of field arrays to a reloadable yt dataset.

This function can be used to create a yt loadable dataset from a set of arrays. The field arrays can either be associated with a loaded dataset or, if not, a dictionary of dataset attributes can be provided that will be used as metadata for the new dataset. The resulting dataset can be reloaded as a yt dataset.

Parameters:
  • ds (dataset or dict) – The dataset associated with the fields or a dictionary of parameters.
  • filename (str) – The name of the file to be written.
  • data (dict) – A dictionary of field arrays to be saved.
  • field_types (dict, optional) – A dictionary denoting the group name to which each field is to be saved. When the resulting dataset is reloaded, this will be the field type for this field. If not given, “data” will be used.
  • extra_attrs (dict, optional) – A dictionary of additional attributes to be saved.
Returns:

filename – The name of the file that has been created.

Return type:

str

Examples

>>> import numpy as np
>>> import yt
>>> ds = yt.load("enzo_tiny_cosmology/DD0046/DD0046")
>>> sphere = ds.sphere([0.5]*3, (10, "Mpc"))
>>> sphere_density = sphere["density"]
>>> region = ds.box([0.]*3, [0.25]*3)
>>> region_density = region["density"]
>>> data = {}
>>> data["sphere_density"] = sphere_density
>>> data["region_density"] = region_density
>>> yt.save_as_dataset(ds, "density_data.h5", data)
>>> new_ds = yt.load("density_data.h5")
>>> print (new_ds.data["region_density"])
[  7.47650434e-32   7.70370740e-32   9.74692941e-32 ...,   1.22384547e-27
   5.13889063e-28   2.91811974e-28] g/cm**3
>>> print (new_ds.data["sphere_density"])
[  4.46237613e-32   4.86830178e-32   4.46335118e-32 ...,   6.43956165e-30
   3.57339907e-30   2.83150720e-30] g/cm**3
>>> data = {"density": yt.YTArray(1e-24 * np.ones(10), "g/cm**3"),
...         "temperature": yt.YTArray(1000. * np.ones(10), "K")}
>>> ds_data = {"current_time": yt.YTQuantity(10, "Myr")}
>>> yt.save_as_dataset(ds_data, "random_data.h5", data)
>>> new_ds = yt.load("random_data.h5")
>>> print (new_ds.data["temperature"])
[ 1000.  1000.  1000.  1000.  1000.  1000.  1000.  1000.  1000.  1000.] K