# coding: utf-8
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"""This module offers a set of functions to dump a python dictionary indexed
by text strings to following file formats: `HDF5, INI, JSON`
"""
from collections import OrderedDict
import json
import logging
import numpy
import os.path
import sys
import h5py
from .configdict import ConfigDict
from .utils import is_group
from .utils import is_file as is_h5_file_like
from .utils import open as h5open
__authors__ = ["P. Knobel"]
__license__ = "MIT"
__date__ = "17/07/2018"
logger = logging.getLogger(__name__)
string_types = (basestring,) if sys.version_info[0] == 2 else (str,) # noqa
def _prepare_hdf5_dataset(array_like):
"""Cast a python object into a numpy array in a HDF5 friendly format.
:param array_like: Input dataset in a type that can be digested by
``numpy.array()`` (`str`, `list`, `numpy.ndarray`…)
:return: ``numpy.ndarray`` ready to be written as an HDF5 dataset
"""
# simple strings
if isinstance(array_like, string_types):
array_like = numpy.string_(array_like)
# Ensure our data is a numpy.ndarray
if not isinstance(array_like, (numpy.ndarray, numpy.string_)):
array = numpy.array(array_like)
else:
array = array_like
# handle list of strings or numpy array of strings
if not isinstance(array, numpy.string_):
data_kind = array.dtype.kind
# unicode: convert to byte strings
# (http://docs.h5py.org/en/latest/strings.html)
if data_kind.lower() in ["s", "u"]:
array = numpy.asarray(array, dtype=numpy.string_)
return array
class _SafeH5FileWrite(object):
"""Context manager returning a :class:`h5py.File` object.
If this object is initialized with a file path, we open the file
and then we close it on exiting.
If a :class:`h5py.File` instance is provided to :meth:`__init__` rather
than a path, we assume that the user is responsible for closing the
file.
This behavior is well suited for handling h5py file in a recursive
function. The object is created in the initial call if a path is provided,
and it is closed only at the end when all the processing is finished.
"""
def __init__(self, h5file, mode="w"):
"""
:param h5file: HDF5 file path or :class:`h5py.File` instance
:param str mode: Can be ``"r+"`` (read/write, file must exist),
``"w"`` (write, existing file is lost), ``"w-"`` (write, fail if
exists) or ``"a"`` (read/write if exists, create otherwise).
This parameter is ignored if ``h5file`` is a file handle.
"""
self.raw_h5file = h5file
self.mode = mode
def __enter__(self):
if not isinstance(self.raw_h5file, h5py.File):
self.h5file = h5py.File(self.raw_h5file, self.mode)
self.close_when_finished = True
else:
self.h5file = self.raw_h5file
self.close_when_finished = False
return self.h5file
def __exit__(self, exc_type, exc_val, exc_tb):
if self.close_when_finished:
self.h5file.close()
class _SafeH5FileRead(object):
"""Context manager returning a :class:`h5py.File` or a
:class:`silx.io.spech5.SpecH5` or a :class:`silx.io.fabioh5.File` object.
The general behavior is the same as :class:`_SafeH5FileWrite` except
that SPEC files and all formats supported by fabio can also be opened,
but in read-only mode.
"""
def __init__(self, h5file):
"""
:param h5file: HDF5 file path or h5py.File-like object
"""
self.raw_h5file = h5file
def __enter__(self):
if not is_h5_file_like(self.raw_h5file):
self.h5file = h5open(self.raw_h5file)
self.close_when_finished = True
else:
self.h5file = self.raw_h5file
self.close_when_finished = False
return self.h5file
def __exit__(self, exc_type, exc_val, exc_tb):
if self.close_when_finished:
self.h5file.close()
[docs]def dicttoh5(treedict, h5file, h5path='/',
mode="w", overwrite_data=False,
create_dataset_args=None):
"""Write a nested dictionary to a HDF5 file, using keys as member names.
If a dictionary value is a sub-dictionary, a group is created. If it is
any other data type, it is cast into a numpy array and written as a
:mod:`h5py` dataset. Dictionary keys must be strings and cannot contain
the ``/`` character.
If dictionary keys are tuples they are interpreted to set h5 attributes.
The tuples should have the format (dataset_name,attr_name)
.. note::
This function requires `h5py <http://www.h5py.org/>`_ to be installed.
:param treedict: Nested dictionary/tree structure with strings or tuples as
keys and array-like objects as leafs. The ``"/"`` character can be used
to define sub trees. If tuples are used as keys they should have the
format (dataset_name,attr_name) and will add a 5h attribute with the
corresponding value.
:param h5file: HDF5 file name or handle. If a file name is provided, the
function opens the file in the specified mode and closes it again
before completing.
:param h5path: Target path in HDF5 file in which scan groups are created.
Default is root (``"/"``)
:param mode: Can be ``"r+"`` (read/write, file must exist),
``"w"`` (write, existing file is lost), ``"w-"`` (write, fail if
exists) or ``"a"`` (read/write if exists, create otherwise).
This parameter is ignored if ``h5file`` is a file handle.
:param overwrite_data: If ``True``, existing groups and datasets can be
overwritten, if ``False`` they are skipped. This parameter is only
relevant if ``h5file_mode`` is ``"r+"`` or ``"a"``.
:param create_dataset_args: Dictionary of args you want to pass to
``h5f.create_dataset``. This allows you to specify filters and
compression parameters. Don't specify ``name`` and ``data``.
Example::
from silx.io.dictdump import dicttoh5
city_area = {
"Europe": {
"France": {
"Isère": {
"Grenoble": 18.44,
("Grenoble","unit"): "km2"
},
"Nord": {
"Tourcoing": 15.19,
("Tourcoing","unit"): "km2"
},
},
},
}
create_ds_args = {'compression': "gzip",
'shuffle': True,
'fletcher32': True}
dicttoh5(city_area, "cities.h5", h5path="/area",
create_dataset_args=create_ds_args)
"""
if not h5path.endswith("/"):
h5path += "/"
with _SafeH5FileWrite(h5file, mode=mode) as h5f:
if isinstance(treedict, dict) and h5path != "/":
if h5path not in h5f:
h5f.create_group(h5path)
for key in filter(lambda k: not isinstance(k, tuple), treedict):
if isinstance(treedict[key], dict) and len(treedict[key]):
# non-empty group: recurse
dicttoh5(treedict[key], h5f, h5path + key,
overwrite_data=overwrite_data,
create_dataset_args=create_dataset_args)
elif treedict[key] is None or (isinstance(treedict[key], dict) and
not len(treedict[key])):
if (h5path + key) in h5f:
if overwrite_data is True:
del h5f[h5path + key]
else:
logger.warning('key (%s) already exists. '
'Not overwriting.' % (h5path + key))
continue
# Create empty group
h5f.create_group(h5path + key)
else:
ds = _prepare_hdf5_dataset(treedict[key])
# can't apply filters on scalars (datasets with shape == () )
if ds.shape == () or create_dataset_args is None:
if h5path + key in h5f:
if overwrite_data is True:
del h5f[h5path + key]
else:
logger.warning('key (%s) already exists. '
'Not overwriting.' % (h5path + key))
continue
h5f.create_dataset(h5path + key,
data=ds)
else:
if h5path + key in h5f:
if overwrite_data is True:
del h5f[h5path + key]
else:
logger.warning('key (%s) already exists. '
'Not overwriting.' % (h5path + key))
continue
h5f.create_dataset(h5path + key,
data=ds,
**create_dataset_args)
# deal with h5 attributes which have tuples as keys in treedict
for key in filter(lambda k: isinstance(k, tuple), treedict):
if (h5path + key[0]) not in h5f:
# Create empty group if key for attr does not exist
h5f.create_group(h5path + key[0])
logger.warning(
"key (%s) does not exist. attr %s "
"will be written to ." % (h5path + key[0], key[1])
)
if key[1] in h5f[h5path + key[0]].attrs:
if not overwrite_data:
logger.warning(
"attribute %s@%s already exists. Not overwriting."
"" % (h5path + key[0], key[1])
)
continue
# Write attribute
value = treedict[key]
# Makes list/tuple of str being encoded as vlen unicode array
# Workaround for h5py<2.9.0 (e.g. debian 10).
if (isinstance(value, (list, tuple)) and
numpy.asarray(value).dtype.type == numpy.unicode_):
value = numpy.array(value, dtype=h5py.special_dtype(vlen=str))
h5f[h5path + key[0]].attrs[key[1]] = value
[docs]def dicttonx(
treedict,
h5file,
h5path="/",
mode="w",
overwrite_data=False,
create_dataset_args=None,
):
"""
Write a nested dictionary to a HDF5 file, using string keys as member names.
The NeXus convention is used to identify attributes with ``"@"`` character,
therefor the dataset_names should not contain ``"@"``.
:param treedict: Nested dictionary/tree structure with strings as keys
and array-like objects as leafs. The ``"/"`` character can be used
to define sub tree. The ``"@"`` character is used to write attributes.
Detais on all other params can be found in doc of dicttoh5.
Example::
import numpy
from silx.io.dictdump import dicttonx
gauss = {
"entry":{
"title":u"A plot of a gaussian",
"plot": {
"y": numpy.array([0.08, 0.19, 0.39, 0.66, 0.9, 1.,
0.9, 0.66, 0.39, 0.19, 0.08]),
"x": numpy.arange(0,1.1,.1),
"@signal": "y",
"@axes": "x",
"@NX_class":u"NXdata",
"title:u"Gauss Plot",
},
"@NX_class":u"NXentry",
"default":"plot",
}
"@NX_class": u"NXroot",
"@default": "entry",
}
dicttonx(gauss,"test.h5")
"""
def copy_keys_keep_values(original):
# create a new treedict with with modified keys but keep values
copy = dict()
for key, value in original.items():
if "@" in key:
newkey = tuple(key.rsplit("@", 1))
else:
newkey = key
if isinstance(value, dict):
copy[newkey] = copy_keys_keep_values(value)
else:
copy[newkey] = value
return copy
nxtreedict = copy_keys_keep_values(treedict)
dicttoh5(
nxtreedict,
h5file,
h5path=h5path,
mode=mode,
overwrite_data=overwrite_data,
create_dataset_args=create_dataset_args,
)
def _name_contains_string_in_list(name, strlist):
if strlist is None:
return False
for filter_str in strlist:
if filter_str in name:
return True
return False
[docs]def h5todict(h5file, path="/", exclude_names=None, asarray=True):
"""Read a HDF5 file and return a nested dictionary with the complete file
structure and all data.
Example of usage::
from silx.io.dictdump import h5todict
# initialize dict with file header and scan header
header94 = h5todict("oleg.dat",
"/94.1/instrument/specfile")
# add positioners subdict
header94["positioners"] = h5todict("oleg.dat",
"/94.1/instrument/positioners")
# add scan data without mca data
header94["detector data"] = h5todict("oleg.dat",
"/94.1/measurement",
exclude_names="mca_")
.. note:: This function requires `h5py <http://www.h5py.org/>`_ to be
installed.
.. note:: If you write a dictionary to a HDF5 file with
:func:`dicttoh5` and then read it back with :func:`h5todict`, data
types are not preserved. All values are cast to numpy arrays before
being written to file, and they are read back as numpy arrays (or
scalars). In some cases, you may find that a list of heterogeneous
data types is converted to a numpy array of strings.
:param h5file: File name or :class:`h5py.File` object or spech5 file or
fabioh5 file.
:param str path: Name of HDF5 group to use as dictionary root level,
to read only a sub-group in the file
:param List[str] exclude_names: Groups and datasets whose name contains
a string in this list will be ignored. Default is None (ignore nothing)
:param bool asarray: True (default) to read scalar as arrays, False to
read them as scalar
:return: Nested dictionary
"""
with _SafeH5FileRead(h5file) as h5f:
ddict = {}
for key in h5f[path]:
if _name_contains_string_in_list(key, exclude_names):
continue
if is_group(h5f[path + "/" + key]):
ddict[key] = h5todict(h5f,
path + "/" + key,
exclude_names=exclude_names,
asarray=asarray)
else:
# Read HDF5 datset
data = h5f[path + "/" + key][()]
if asarray: # Convert HDF5 dataset to numpy array
data = numpy.array(data, copy=False)
ddict[key] = data
return ddict
[docs]def dicttojson(ddict, jsonfile, indent=None, mode="w"):
"""Serialize ``ddict`` as a JSON formatted stream to ``jsonfile``.
:param ddict: Dictionary (or any object compatible with ``json.dump``).
:param jsonfile: JSON file name or file-like object.
If a file name is provided, the function opens the file in the
specified mode and closes it again.
:param indent: If indent is a non-negative integer, then JSON array
elements and object members will be pretty-printed with that indent
level. An indent level of ``0`` will only insert newlines.
``None`` (the default) selects the most compact representation.
:param mode: File opening mode (``w``, ``a``, ``w+``…)
"""
if not hasattr(jsonfile, "write"):
jsonf = open(jsonfile, mode)
else:
jsonf = jsonfile
json.dump(ddict, jsonf, indent=indent)
if not hasattr(jsonfile, "write"):
jsonf.close()
[docs]def dicttoini(ddict, inifile, mode="w"):
"""Output dict as configuration file (similar to Microsoft Windows INI).
:param dict: Dictionary of configuration parameters
:param inifile: INI file name or file-like object.
If a file name is provided, the function opens the file in the
specified mode and closes it again.
:param mode: File opening mode (``w``, ``a``, ``w+``…)
"""
if not hasattr(inifile, "write"):
inif = open(inifile, mode)
else:
inif = inifile
ConfigDict(initdict=ddict).write(inif)
if not hasattr(inifile, "write"):
inif.close()
[docs]def dump(ddict, ffile, mode="w", fmat=None):
"""Dump dictionary to a file
:param ddict: Dictionary with string keys
:param ffile: File name or file-like object with a ``write`` method
:param str fmat: Output format: ``"json"``, ``"hdf5"`` or ``"ini"``.
When None (the default), it uses the filename extension as the format.
Dumping to a HDF5 file requires `h5py <http://www.h5py.org/>`_ to be
installed.
:param str mode: File opening mode (``w``, ``a``, ``w+``…)
Default is *"w"*, write mode, overwrite if exists.
:raises IOError: if file format is not supported
"""
if fmat is None:
# If file-like object get its name, else use ffile as filename
filename = getattr(ffile, 'name', ffile)
fmat = os.path.splitext(filename)[1][1:] # Strip extension leading '.'
fmat = fmat.lower()
if fmat == "json":
dicttojson(ddict, ffile, indent=2, mode=mode)
elif fmat in ["hdf5", "h5"]:
dicttoh5(ddict, ffile, mode=mode)
elif fmat in ["ini", "cfg"]:
dicttoini(ddict, ffile, mode=mode)
else:
raise IOError("Unknown format " + fmat)
[docs]def load(ffile, fmat=None):
"""Load dictionary from a file
When loading from a JSON or INI file, an OrderedDict is returned to
preserve the values' insertion order.
:param ffile: File name or file-like object with a ``read`` method
:param fmat: Input format: ``json``, ``hdf5`` or ``ini``.
When None (the default), it uses the filename extension as the format.
Loading from a HDF5 file requires `h5py <http://www.h5py.org/>`_ to be
installed.
:return: Dictionary (ordered dictionary for JSON and INI)
:raises IOError: if file format is not supported
"""
must_be_closed = False
if not hasattr(ffile, "read"):
f = open(ffile, "r")
fname = ffile
must_be_closed = True
else:
f = ffile
fname = ffile.name
try:
if fmat is None: # Use file extension as format
fmat = os.path.splitext(fname)[1][1:] # Strip extension leading '.'
fmat = fmat.lower()
if fmat == "json":
return json.load(f, object_pairs_hook=OrderedDict)
if fmat in ["hdf5", "h5"]:
return h5todict(fname)
elif fmat in ["ini", "cfg"]:
return ConfigDict(filelist=[fname])
else:
raise IOError("Unknown format " + fmat)
finally:
if must_be_closed:
f.close()
