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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.abc import Mapping
import json
import logging
import numpy
import os.path
import h5py
try:
from pint import Quantity as PintQuantity
except ImportError:
try:
from pint.quantity import Quantity as PintQuantity
except ImportError:
PintQuantity = None
from .configdict import ConfigDict
from .utils import is_group
from .utils import is_dataset
from .utils import is_link
from .utils import is_softlink
from .utils import is_externallink
from .utils import is_file as is_h5_file_like
from .utils import open as h5open
from .utils import h5py_read_dataset
from .utils import H5pyAttributesReadWrapper
__authors__ = ["P. Knobel"]
__license__ = "MIT"
__date__ = "17/07/2018"
logger = logging.getLogger(__name__)
vlen_utf8 = h5py.special_dtype(vlen=str)
vlen_bytes = h5py.special_dtype(vlen=bytes)
UPDATE_MODE_VALID_EXISTING_VALUES = ("add", "replace", "modify")
def _prepare_hdf5_write_value(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
"""
if PintQuantity is not None and isinstance(array_like, PintQuantity):
return numpy.array(array_like.magnitude)
array = numpy.asarray(array_like)
if numpy.issubdtype(array.dtype, numpy.bytes_):
return numpy.array(array_like, dtype=vlen_bytes)
elif numpy.issubdtype(array.dtype, numpy.str_):
return numpy.array(array_like, dtype=vlen_utf8)
else:
return array
class _SafeH5FileWrite:
"""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:
"""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()
def _normalize_h5_path(h5root, h5path):
"""
:param h5root: File name or h5py-like File, Group or Dataset
:param str h5path: relative to ``h5root``
:returns 2-tuple: (File or file object, h5path)
"""
if is_group(h5root):
group_name = h5root.name
if group_name == "/":
pass
elif h5path:
h5path = group_name + "/" + h5path
else:
h5path = group_name
h5file = h5root.file
elif is_dataset(h5root):
h5path = h5root.name
h5file = h5root.file
else:
h5file = h5root
if not h5path:
h5path = "/"
elif not h5path.endswith("/"):
h5path += "/"
return h5file, h5path
[docs]
def dicttoh5(
treedict,
h5file,
h5path="/",
mode="w",
create_dataset_args=None,
update_mode=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).
Existing HDF5 items can be deleted by providing the dictionary value
``None``, provided that ``update_mode in ["modify", "replace"]``.
.. 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: File name or h5py-like File, Group or Dataset
:param h5path: Target path in the HDF5 file relative to ``h5file``.
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 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``.
:param update_mode: Can be ``add`` (default), ``modify`` or ``replace``.
* ``add``: Extend the existing HDF5 tree when possible. Existing HDF5
items (groups, datasets and attributes) remain untouched.
* ``modify``: Extend the existing HDF5 tree when possible, modify
existing attributes, modify same-sized dataset values and delete
HDF5 items with a ``None`` value in the dict tree.
* ``replace``: Replace the existing HDF5 tree. Items from the root of
the HDF5 tree that are not present in the root of the dict tree
will remain untouched.
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 update_mode is None:
update_mode = "add"
if update_mode not in UPDATE_MODE_VALID_EXISTING_VALUES:
raise ValueError(
(
"Argument 'update_mode' can only have values: {}"
"".format(UPDATE_MODE_VALID_EXISTING_VALUES)
)
)
if not isinstance(treedict, Mapping):
raise TypeError("'treedict' must be a dictionary")
h5file, h5path = _normalize_h5_path(h5file, h5path)
def _iter_treedict(attributes=False):
nonlocal treedict
for key, value in treedict.items():
if isinstance(key, tuple) == attributes:
yield key, value
change_allowed = update_mode in ("replace", "modify")
with _SafeH5FileWrite(h5file, mode=mode) as h5f:
# Create the root of the tree
if h5path in h5f:
if not is_group(h5f[h5path]):
if update_mode == "replace":
del h5f[h5path]
h5f.create_group(h5path)
else:
logger.info(
f'Cannot overwrite {h5f.file.filename}::{h5f[h5path].name} with update_mode="{update_mode}"'
)
return
else:
h5f.create_group(h5path)
# Loop over all groups, links and datasets
for key, value in _iter_treedict(attributes=False):
h5name = h5path + str(key)
exists = h5name in h5f
if value is None:
# Delete HDF5 item
if exists and change_allowed:
del h5f[h5name]
exists = False
elif isinstance(value, Mapping):
# HDF5 group
if exists and update_mode == "replace":
del h5f[h5name]
exists = False
if value:
dicttoh5(
value,
h5f,
h5name,
update_mode=update_mode,
create_dataset_args=create_dataset_args,
)
elif not exists:
h5f.create_group(h5name)
elif is_link(value):
# HDF5 link
if exists and update_mode == "replace":
del h5f[h5name]
exists = False
if not exists:
# Create link from h5py link object
h5f[h5name] = value
else:
# HDF5 dataset
if exists and not change_allowed:
logger.info(
f'Cannot modify dataset {h5f.file.filename}::{h5f[h5name].name} with update_mode="{update_mode}"'
)
continue
data = _prepare_hdf5_write_value(value)
# Edit the existing dataset
attrs_backup = None
if exists:
try:
h5f[h5name][()] = data
continue
except Exception:
# Delete the existing dataset
if update_mode != "replace":
if not is_dataset(h5f[h5name]):
logger.info(
f'Cannot overwrite {h5f.file.filename}::{h5f[h5name].name} with update_mode="{update_mode}"'
)
continue
attrs_backup = dict(h5f[h5name].attrs)
del h5f[h5name]
# Create dataset
# can't apply filters on scalars (datasets with shape == ())
try:
if data.shape == () or create_dataset_args is None:
h5f.create_dataset(h5name, data=data)
else:
h5f.create_dataset(h5name, data=data, **create_dataset_args)
except Exception as e:
if isinstance(data, numpy.ndarray):
dtype = f"numpy.ndarray-{data.dtype}"
else:
dtype = type(data)
raise ValueError(
f"Failed to create dataset '{h5name}' with data ({dtype}) = {data}"
) from e
if attrs_backup:
h5f[h5name].attrs.update(attrs_backup)
# Loop over all attributes
for key, value in _iter_treedict(attributes=True):
if len(key) != 2:
raise ValueError("HDF5 attribute must be described by 2 values")
h5name = h5path + key[0]
attr_name = key[1]
if h5name not in h5f:
# Create an empty group to store the attribute
h5f.create_group(h5name)
h5a = h5f[h5name].attrs
exists = attr_name in h5a
if value is None:
# Delete HDF5 attribute
if exists and change_allowed:
del h5a[attr_name]
exists = False
else:
# Add/modify HDF5 attribute
if exists and not change_allowed:
logger.info(
f'Cannot modify attribute {h5f.file.filename}::{h5f[h5name].name}@{attr_name} with update_mode="{update_mode}"'
)
continue
data = _prepare_hdf5_write_value(value)
h5a[attr_name] = data
def _has_nx_class(treedict, key=""):
return key + "@NX_class" in treedict or (key, "NX_class") in treedict
def _ensure_nx_class(treedict, parents=tuple()):
"""Each group needs an "NX_class" attribute."""
if _has_nx_class(treedict):
return
nparents = len(parents)
if nparents == 0:
treedict[("", "NX_class")] = "NXroot"
elif nparents == 1:
treedict[("", "NX_class")] = "NXentry"
else:
treedict[("", "NX_class")] = "NXcollection"
[docs]
def nexus_to_h5_dict(treedict, parents=tuple(), add_nx_class=True, has_nx_class=False):
"""The following conversions are applied:
* key with "{name}@{attr_name}" notation: key converted to 2-tuple
* key with ">{url}" notation: strip ">" and convert value to
h5py.SoftLink or h5py.ExternalLink
: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.
The ``">"`` prefix is used to define links.
:param parents: Needed to resolve up-links (tuple of HDF5 group names)
:param add_nx_class: Add "NX_class" attribute when missing
:param has_nx_class: The "NX_class" attribute is defined in the parent
:rtype dict:
"""
if not isinstance(treedict, Mapping):
raise TypeError("'treedict' must be a dictionary")
copy = dict()
for key, value in treedict.items():
if "@" in key:
# HDF5 attribute
key = tuple(key.rsplit("@", 1))
elif key.startswith(">"):
# HDF5 link
if isinstance(value, str):
key = key[1:]
first, sep, second = value.partition("::")
if sep:
value = h5py.ExternalLink(first, second)
else:
if ".." in first:
# Up-links not supported: make absolute
parts = []
for p in list(parents) + first.split("/"):
if not p or p == ".":
continue
elif p == "..":
parts.pop(-1)
else:
parts.append(p)
first = "/" + "/".join(parts)
value = h5py.SoftLink(first)
elif is_link(value):
key = key[1:]
if isinstance(value, Mapping):
# HDF5 group
key_has_nx_class = add_nx_class and _has_nx_class(treedict, key)
copy[key] = nexus_to_h5_dict(
value,
parents=parents + (key,),
add_nx_class=add_nx_class,
has_nx_class=key_has_nx_class,
)
elif PintQuantity is not None and isinstance(value, PintQuantity):
copy[key] = value.magnitude
copy[(key, "units")] = f"{value.units:~C}"
else:
# HDF5 dataset or link
copy[key] = value
if add_nx_class and not has_nx_class:
_ensure_nx_class(copy, parents)
return copy
[docs]
def h5_to_nexus_dict(treedict):
"""The following conversions are applied:
* 2-tuple key: converted to string ("@" notation)
* h5py.Softlink value: converted to string (">" key prefix)
* h5py.ExternalLink value: converted to string (">" key prefix)
: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.
:rtype dict:
"""
copy = dict()
for key, value in treedict.items():
if isinstance(key, tuple):
if len(key) != 2:
raise ValueError("HDF5 attribute must be described by 2 values")
key = "%s@%s" % (key[0], key[1])
elif is_softlink(value):
key = ">" + key
value = value.path
elif is_externallink(value):
key = ">" + key
value = value.filename + "::" + value.path
if isinstance(value, Mapping):
copy[key] = h5_to_nexus_dict(value)
else:
copy[key] = value
return copy
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
def _handle_error(mode: str, exception, msg: str, *args) -> None:
"""Handle errors.
:param str mode: 'raise', 'log', 'ignore'
:param type exception: Exception class to use in 'raise' mode
:param str msg: Error message template
:param List[str] args: Arguments for error message template
"""
if mode == "ignore":
return # no-op
elif mode == "log":
logger.error(msg, *args)
elif mode == "raise":
raise exception(msg % args)
else:
raise ValueError("Unsupported error handling: %s" % mode)
[docs]
def h5todict(
h5file,
path="/",
exclude_names=None,
asarray=True,
dereference_links=True,
include_attributes=False,
errors="raise",
):
"""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 h5py-like File, Group or Dataset
:param str path: Target path in the HDF5 file relative to ``h5file``
: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
:param bool dereference_links: True (default) to dereference links, False
to preserve the link itself
:param bool include_attributes: False (default)
:param str errors: Handling of errors (HDF5 access issue, broken link,...):
- 'raise' (default): Raise an exception
- 'log': Log as errors
- 'ignore': Ignore errors
:return: Nested dictionary
"""
h5file, path = _normalize_h5_path(h5file, path)
with _SafeH5FileRead(h5file) as h5f:
ddict = {}
if path not in h5f:
_handle_error(errors, KeyError, 'Path "%s" does not exist in file.', path)
return ddict
try:
root = h5f[path]
except KeyError as e:
if not isinstance(h5f.get(path, getlink=True), h5py.HardLink):
_handle_error(
errors, KeyError, 'Cannot retrieve path "%s" (broken link)', path
)
else:
_handle_error(errors, KeyError, ", ".join(e.args))
return ddict
# Read the attributes of the group
if include_attributes:
attrs = H5pyAttributesReadWrapper(root.attrs)
for aname, avalue in attrs.items():
ddict[("", aname)] = avalue
# Read the children of the group
for key in root:
if _name_contains_string_in_list(key, exclude_names):
continue
h5name = path + "/" + key
# Preserve HDF5 link when requested
if not dereference_links:
lnk = h5f.get(h5name, getlink=True)
if is_link(lnk):
ddict[key] = lnk
continue
try:
h5obj = h5f[h5name]
except KeyError as e:
if not isinstance(h5f.get(h5name, getlink=True), h5py.HardLink):
_handle_error(
errors,
KeyError,
'Cannot retrieve path "%s" (broken link)',
h5name,
)
else:
_handle_error(errors, KeyError, ", ".join(e.args))
continue
if is_group(h5obj):
# Child is an HDF5 group
ddict[key] = h5todict(
h5f,
h5name,
exclude_names=exclude_names,
asarray=asarray,
dereference_links=dereference_links,
include_attributes=include_attributes,
errors=errors,
)
else:
# Child is an HDF5 dataset
try:
data = h5py_read_dataset(h5obj)
except OSError:
_handle_error(
errors, OSError, 'Cannot retrieve dataset "%s"', h5name
)
else:
if asarray: # Convert HDF5 dataset to numpy array
data = numpy.asarray(data)
ddict[key] = data
# Read the attributes of the child
if include_attributes:
attrs = H5pyAttributesReadWrapper(h5obj.attrs)
for aname, avalue in attrs.items():
ddict[(key, aname)] = avalue
return ddict
[docs]
def dicttonx(treedict, h5file, h5path="/", add_nx_class=None, **kw):
"""
Write a nested dictionary to a HDF5 file, using string keys as member names.
The NeXus convention is used to identify attributes with ``"@"`` character,
therefore the dataset_names should not contain ``"@"``.
Similarly, links are identified by keys starting with the ``">"`` character.
The corresponding value can be a soft or external link.
: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.
The ``">"`` prefix is used to define links.
:param add_nx_class: Add "NX_class" attribute when missing. By default it
is ``True`` when ``update_mode`` is ``"add"`` or ``None``.
The named parameters are passed to dicttoh5.
Example::
import numpy
from silx.io.dictdump import dicttonx
gauss = {
"entry":{
"title":u"A plot of a gaussian",
"instrument": {
"@NX_class": u"NXinstrument",
"positioners": {
"@NX_class": u"NXCollection",
"x": numpy.arange(0,1.1,.1)
}
}
"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": "../instrument/positioners/x",
"@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")
"""
h5file, h5path = _normalize_h5_path(h5file, h5path)
parents = tuple(p for p in h5path.split("/") if p)
if add_nx_class is None:
add_nx_class = kw.get("update_mode", None) in (None, "add")
nxtreedict = nexus_to_h5_dict(treedict, parents=parents, add_nx_class=add_nx_class)
dicttoh5(nxtreedict, h5file, h5path=h5path, **kw)
[docs]
def nxtodict(h5file, include_attributes=True, **kw):
"""Read a HDF5 file and return a nested dictionary with the complete file
structure and all data.
As opposed to h5todict, all keys will be strings and no h5py objects are
present in the tree.
The named parameters are passed to h5todict.
"""
nxtreedict = h5todict(h5file, include_attributes=include_attributes, **kw)
return h5_to_nexus_dict(nxtreedict)
[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, the returned dict
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
: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)
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()
