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import numpy as np
from mpi4py import MPI
from .file_base import FileBase
__all__ = ('HDF5File',)
comm = MPI.COMM_WORLD
class HDF5File(FileBase):
"""Class for reading/writing data to HDF5 format
Parameters
----------
h5name : str
Name of hdf5 file to be created.
domain : sequence, optional
An optional spatial mesh or domain to go with the data.
Sequence of either
- 2-tuples, where each 2-tuple contains the (origin, length)
of each dimension, e.g., (0, 2*pi).
- Arrays of coordinates, e.g., np.linspace(0, 2*pi, N). One
array per dimension.
mode : str, optional
``r``, ``w`` or ``a`` for read, write or append. Default is ``a``.
kw : dict, optional
Optional additional keyword arguments used when creating the file
used to store data.
"""
def __init__(self, h5name, domain=None, mode='a', **kw):
FileBase.__init__(self, h5name, domain=domain)
import h5py
self.f = h5py.File(h5name, mode, driver="mpio", comm=comm, **kw)
self.close()
def _check_domain(self, group, field):
if self.domain is None:
self.domain = ((0, 2*np.pi),)*field.dimensions
assert len(self.domain) == field.dimensions
self.f.require_group(group)
if not "shape" in self.f[group].attrs:
self.f[group].attrs.create("shape", field.pencil.shape)
if not "rank" in self.f[group].attrs:
self.f[group].attrs.create("rank", field.rank)
assert field.rank == self.f[group].attrs["rank"]
assert np.all(field.pencil.shape == self.f[group].attrs["shape"])
if isinstance(self.domain[0], np.ndarray):
self.f[group].require_group("mesh")
else:
self.f[group].require_group("domain")
for i in range(field.dimensions):
d = self.domain[i]
if isinstance(d, np.ndarray):
d0 = np.squeeze(d)
self.f[group]["mesh"].require_dataset("x{}".format(i),
shape=d0.shape,
dtype=d0.dtype,
data=d0)
else:
d0 = np.array([d[0], d[1]])
self.f[group]["domain"].require_dataset("x{}".format(i),
shape=d0.shape,
dtype=d0.dtype,
data=d0)
@staticmethod
def backend():
return 'hdf5'
def open(self, mode='r+'):
import h5py
self.f = h5py.File(self.filename, mode, driver="mpio", comm=comm)
def write(self, step, fields, **kw):
"""Write snapshot ``step`` of ``fields`` to HDF5 file
Parameters
----------
step : int
Index of snapshot.
fields : dict
The fields to be dumped to file. (key, value) pairs are group name
and either arrays or 2-tuples, respectively. The arrays are complete
arrays to be stored, whereas 2-tuples are arrays with associated
*global* slices.
as_scalar : boolean, optional
Whether to store rank > 0 arrays as scalars. Default is False.
Example
-------
>>> from mpi4py import MPI
>>> from mpi4py_fft import PFFT, HDF5File, newDistArray
>>> comm = MPI.COMM_WORLD
>>> T = PFFT(comm, (15, 16, 17))
>>> u = newDistArray(T, forward_output=False, val=1)
>>> v = newDistArray(T, forward_output=False, val=2)
>>> f = HDF5File('h5filename.h5', mode='w')
>>> f.write(0, {'u': [u, (u, [slice(None), 4, slice(None)])],
... 'v': [v, (v, [slice(None), 5, 5])]})
>>> f.write(1, {'u': [u, (u, [slice(None), 4, slice(None)])],
... 'v': [v, (v, [slice(None), 5, 5])]})
This stores data within two main groups ``u`` and ``v``. The HDF5 file
will in the end contain groups::
/u/3D/{0, 1}
/u/2D/slice_4_slice/{0, 1}
/v/3D/{0, 1}
/v/1D/slice_5_5/{0, 1}
Note
----
The list of slices used in storing only parts of the arrays are views
of the *global* arrays.
"""
self.open()
FileBase.write(self, step, fields, **kw)
self.close()
def read(self, u, name, **kw):
step = kw.get('step', 0)
self.open()
s = u.local_slice()
dset = "/".join((name, "{}D".format(u.dimensions), str(step)))
u[:] = self.f[dset][s]
self.close()
def _write_slice_step(self, name, step, slices, field, **kw):
rank = field.rank
slices = (slice(None),)*rank + tuple(slices)
slices = list(slices)
ndims = slices[rank:].count(slice(None))
slname = self._get_slice_name(slices[rank:])
s = field.local_slice()
slices, inside = self._get_local_slices(slices, s)
sp = np.nonzero([isinstance(x, slice) for x in slices])[0]
sf = tuple(np.take(s, sp))
sl = tuple(slices)
group = "/".join((name, "{}D".format(ndims), slname))
self.f.require_group(group)
N = field.global_shape
self.f[group].require_dataset(str(step), shape=tuple(np.take(N, sp)), dtype=field.dtype)
if inside == 1:
self.f["/".join((group, str(step)))][sf] = field[sl]
def _write_group(self, name, u, step, **kw):
s = u.local_slice()
group = "/".join((name, "{}D".format(u.dimensions)))
self.f.require_group(group)
self.f[group].require_dataset(str(step), shape=u.global_shape, dtype=u.dtype)
self.f["/".join((group, str(step)))][s] = u
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