Source code for yt.frontends.ytdata.utilities

Utility functions for ytdata frontend.


# Copyright (c) 2015, 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 numpy as np

from yt.funcs import iterable
from yt.units.yt_array import \
from yt.utilities.logger import \
    ytLogger as mylog
from yt.utilities.on_demand_imports import \
    _h5py as h5py

[docs]def save_as_dataset(ds, filename, data, field_types=None, extra_attrs=None): r"""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 : str The name of the file that has been created. 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 =[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 (["region_density"]) [ 7.47650434e-32 7.70370740e-32 9.74692941e-32 ..., 1.22384547e-27 5.13889063e-28 2.91811974e-28] g/cm**3 >>> print (["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 (["temperature"]) [ 1000. 1000. 1000. 1000. 1000. 1000. 1000. 1000. 1000. 1000.] K """"Saving field data to yt dataset: %s." % filename) if extra_attrs is None: extra_attrs = {} base_attrs = ["dimensionality", "domain_left_edge", "domain_right_edge", "current_redshift", "current_time", "domain_dimensions", "periodicity", "cosmological_simulation", "omega_lambda", "omega_matter", "hubble_constant", "length_unit", "mass_unit", "time_unit", "velocity_unit", "magnetic_unit"] fh = h5py.File(filename, "w") if ds is None: ds = {} if hasattr(ds, "parameters") and isinstance(ds.parameters, dict): for attr, val in ds.parameters.items(): _yt_array_hdf5_attr(fh, attr, val) if hasattr(ds, "unit_registry"): _yt_array_hdf5_attr(fh, "unit_registry_json", ds.unit_registry.to_json()) if hasattr(ds, "unit_system"): _yt_array_hdf5_attr(fh, "unit_system_name", for attr in base_attrs: if isinstance(ds, dict): my_val = ds.get(attr, None) else: my_val = getattr(ds, attr, None) if my_val is None: continue _yt_array_hdf5_attr(fh, attr, my_val) for attr in extra_attrs: my_val = extra_attrs[attr] _yt_array_hdf5_attr(fh, attr, my_val) if "data_type" not in extra_attrs: fh.attrs["data_type"] = "yt_array_data" for field in data: if field_types is None: field_type = "data" else: field_type = field_types[field] if field_type not in fh: fh.create_group(field_type) if isinstance(field, tuple): field_name = field[1] else: field_name = field # for python3 if data[field].dtype.kind == 'U': data[field] = data[field].astype('|S') _yt_array_hdf5(fh[field_type], field_name, data[field]) if "num_elements" not in fh[field_type].attrs: fh[field_type].attrs["num_elements"] = data[field].size fh.close()
def _hdf5_yt_array(fh, field, ds=None): r"""Load an hdf5 dataset as a YTArray. Reads in a dataset from an open hdf5 file or group and uses the "units" attribute, if it exists, to apply units. Parameters ---------- fh : an open hdf5 file or hdf5 group The hdf5 file or group in which the dataset exists. field : str The name of the field to be loaded. ds : yt Dataset If not None, the unit_registry of the dataset is used to apply units. Returns ------- A YTArray of the requested field. """ if ds is None: new_arr = YTArray else: new_arr = ds.arr units = "" if "units" in fh[field].attrs: units = fh[field].attrs["units"] if units == "dimensionless": units = "" return new_arr(fh[field].value, units) def _yt_array_hdf5(fh, field, data): r"""Save a YTArray to an open hdf5 file or group. Save a YTArray to an open hdf5 file or group, and save the units to a "units" attribute. Parameters ---------- fh : an open hdf5 file or hdf5 group The hdf5 file or group to which the data will be written. field : str The name of the field to be saved. data : YTArray The data array to be saved. Returns ------- dataset : hdf5 dataset The created hdf5 dataset. """ dataset = fh.create_dataset(str(field), data=data) units = "" if isinstance(data, YTArray): units = str(data.units) dataset.attrs["units"] = units return dataset def _yt_array_hdf5_attr(fh, attr, val): r"""Save a YTArray or YTQuantity as an hdf5 attribute. Save an hdf5 attribute. If it has units, save an additional attribute with the units. Parameters ---------- fh : an open hdf5 file, group, or dataset The hdf5 file, group, or dataset to which the attribute will be written. attr : str The name of the attribute to be saved. val : anything The value to be saved. """ if val is None: val = "None" if hasattr(val, "units"): fh.attrs["%s_units" % attr] = str(val.units) # The following is a crappy workaround for getting # Unicode strings into HDF5 attributes in Python 3 if iterable(val): val = np.array(val) if val.dtype.kind == 'U': val = val.astype('|S') try: fh.attrs[str(attr)] = val # This is raised if no HDF5 equivalent exists. # In that case, save its string representation. except TypeError: fh.attrs[str(attr)] = str(val)