# Copyright (c) 2013-2016, Freja Nordsiek # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are # met: # # 1. Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # 2. Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR # A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT # HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, # SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT # LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, # DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY # THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. """ This is the hdf5storage package, a Python package to read and write python data types to HDF5 (Heirarchal Data Format) files beyond just Numpy types. Version 0.1.14 """ __version__ = "0.1.14" import sys import os import posixpath import copy import inspect import datetime import h5py from . import lowlevel from hdf5storage.lowlevel import Hdf5storageError, CantReadError, \ TypeNotMatlabCompatibleError from . import Marshallers class Options(object): """ Set of options governing how data is read/written to/from disk. There are many ways that data can be transformed as it is read or written from a file, and many attributes can be used to describe the data depending on its format. The option with the most effect is the `matlab_compatible` option. It makes sure that the file is compatible with MATLAB's HDF5 based version 7.3 mat file format. It overrides several options to the values in the following table. ================================== ==================== attribute value ================================== ==================== delete_unused_variables ``True`` structured_numpy_ndarray_as_struct ``True`` make_atleast_2d ``True`` convert_numpy_bytes_to_utf16 ``True`` convert_numpy_str_to_utf16 ``True`` convert_bools_to_uint8 ``True`` reverse_dimension_order ``True`` store_shape_for_empty ``True`` complex_names ``('real', 'imag')`` group_for_references ``'/#refs#'`` compression_algorithm ``'gzip'`` ================================== ==================== In addition to setting these options, a specially formatted block of bytes is put at the front of the file so that MATLAB can recognize its format. Parameters ---------- store_python_metadata : bool, optional See Attributes. matlab_compatible : bool, optional See Attributes. action_for_matlab_incompatible : str, optional See Attributes. Only valid values are 'ignore', 'discard', and 'error'. delete_unused_variables : bool, optional See Attributes. structured_numpy_ndarray_as_struct : bool, optional See Attributes. make_atleast_2d : bool, optional See Attributes. convert_numpy_bytes_to_utf16 : bool, optional See Attributes. convert_numpy_str_to_utf16 : bool, optional See Attributes. convert_bools_to_uint8 : bool, optional See Attributes. reverse_dimension_order : bool, optional See Attributes. store_shape_for_empty : bool, optional See Attributes. complex_names : tuple of two str, optional See Attributes. group_for_references : str, optional See Attributes. oned_as : str, optional See Attributes. compress : bool, optional See Attributes. compress_size_threshold : int, optional See Attributes. compression_algorithm : str, optional See Attributes. gzip_compression_level : int, optional See Attributes. shuffle_filter : bool, optional See Attributes. compressed_fletcher32_filter : bool, optional See Attributes. uncompressed_fletcher32_filter : bool, optional See Attributes. marshaller_collection : MarshallerCollection, optional See Attributes. **keywords : Additional keyword arguments. They are ignored. They are allowed to be given to be more compatible with future versions of this package where more options will be added. Attributes ---------- store_python_metadata : bool matlab_compatible : bool action_for_matlab_incompatible : str delete_unused_variables : bool structured_numpy_ndarray_as_struct : bool make_atleast_2d : bool convert_numpy_bytes_to_utf16 : bool convert_numpy_str_to_utf16 : bool convert_bools_to_uint8 : bool reverse_dimension_order : bool store_shape_for_empty : bool complex_names : tuple of two str group_for_references : str oned_as : {'row', 'column'} compress : bool compress_size_threshold : int compression_algorithm : {'gzip', 'lzf', 'szip'} gzip_compression_level : int shuffle_filter : bool compressed_fletcher32_filter : bool uncompressed_fletcher32_filter : bool scalar_options : dict ``h5py.Group.create_dataset`` options for writing scalars. array_options : dict ``h5py.Group.create_dataset`` options for writing scalars. marshaller_collection : MarshallerCollection Collection of marshallers to disk. """ def __init__(self, store_python_metadata=True, matlab_compatible=True, action_for_matlab_incompatible='error', delete_unused_variables=False, structured_numpy_ndarray_as_struct=False, make_atleast_2d=False, convert_numpy_bytes_to_utf16=False, convert_numpy_str_to_utf16=False, convert_bools_to_uint8=False, reverse_dimension_order=False, store_shape_for_empty=False, complex_names=('r', 'i'), group_for_references="/#refs#", oned_as='row', compress=True, compress_size_threshold=16*1024, compression_algorithm='gzip', gzip_compression_level=7, shuffle_filter=True, compressed_fletcher32_filter=True, uncompressed_fletcher32_filter=False, marshaller_collection=None, **keywords): # Set the defaults. self._store_python_metadata = True self._action_for_matlab_incompatible = 'error' self._delete_unused_variables = False self._structured_numpy_ndarray_as_struct = False self._make_atleast_2d = False self._convert_numpy_bytes_to_utf16 = False self._convert_numpy_str_to_utf16 = False self._convert_bools_to_uint8 = False self._reverse_dimension_order = False self._store_shape_for_empty = False self._complex_names = ('r', 'i') self._group_for_references = "/#refs#" self._oned_as = 'row' self._compress = True self._compress_size_threshold = 16*1024 self._compression_algorithm = 'gzip' self._gzip_compression_level = 7 self._shuffle_filter = True self._compressed_fletcher32_filter = True self._uncompressed_fletcher32_filter = False self._matlab_compatible = True # Apply all the given options using the setters, making sure to # do matlab_compatible last since it will override most of the # other ones. self.store_python_metadata = store_python_metadata self.action_for_matlab_incompatible = \ action_for_matlab_incompatible self.delete_unused_variables = delete_unused_variables self.structured_numpy_ndarray_as_struct = \ structured_numpy_ndarray_as_struct self.make_atleast_2d = make_atleast_2d self.convert_numpy_bytes_to_utf16 = convert_numpy_bytes_to_utf16 self.convert_numpy_str_to_utf16 = convert_numpy_str_to_utf16 self.convert_bools_to_uint8 = convert_bools_to_uint8 self.reverse_dimension_order = reverse_dimension_order self.store_shape_for_empty = store_shape_for_empty self.complex_names = complex_names self.group_for_references = group_for_references self.oned_as = oned_as self.compress = compress self.compress_size_threshold = compress_size_threshold self.compression_algorithm = compression_algorithm self.gzip_compression_level = gzip_compression_level self.shuffle_filter = shuffle_filter self.compressed_fletcher32_filter = compressed_fletcher32_filter self.uncompressed_fletcher32_filter = \ uncompressed_fletcher32_filter self.matlab_compatible = matlab_compatible # Set the h5py options to use for writing scalars and arrays to # blank for now. self.scalar_options = dict() self.array_options = dict() # Use the given marshaller collection if it was # given. Otherwise, use the default. #: Collection of marshallers to disk. #: #: MarshallerCollection #: #: See Also #: -------- #: MarshallerCollection self.marshaller_collection = marshaller_collection if not isinstance(marshaller_collection, MarshallerCollection): self.marshaller_collection = MarshallerCollection() @property def store_python_metadata(self): """ Whether or not to store Python metadata. bool If ``True`` (default), information on the Python type for each object written to disk is put in its attributes so that it can be read back into Python as the same type. """ return self._store_python_metadata @store_python_metadata.setter def store_python_metadata(self, value): # Check that it is a bool, and then set it. This option does not # effect MATLAB compatibility if isinstance(value, bool): self._store_python_metadata = value @property def matlab_compatible(self): """ Whether or not to make the file compatible with MATLAB. bool If ``True`` (default), data is written to file in such a way that it compatible with MATLAB's version 7.3 mat file format which is HDF5 based. Setting it to ``True`` forces other options to hold the specific values in the table below. ================================== ==================== attribute value ================================== ==================== delete_unused_variables ``True`` structured_numpy_ndarray_as_struct ``True`` make_atleast_2d ``True`` convert_numpy_bytes_to_utf16 ``True`` convert_numpy_str_to_utf16 ``True`` convert_bools_to_uint8 ``True`` reverse_dimension_order ``True`` store_shape_for_empty ``True`` complex_names ``('real', 'imag')`` group_for_references ``'/#refs#'`` compression_algorithm ``'gzip'`` ================================== ==================== In addition to setting these options, a specially formatted block of bytes is put at the front of the file so that MATLAB can recognize its format. """ return self._matlab_compatible @matlab_compatible.setter def matlab_compatible(self, value): # If it is a bool, it can be set. If it is set to true, then # several other options need to be set appropriately. if isinstance(value, bool): self._matlab_compatible = value if value: self._delete_unused_variables = True self._structured_numpy_ndarray_as_struct = True self._make_atleast_2d = True self._convert_numpy_bytes_to_utf16 = True self._convert_numpy_str_to_utf16 = True self._convert_bools_to_uint8 = True self._reverse_dimension_order = True self._store_shape_for_empty = True self._complex_names = ('real', 'imag') self._group_for_references = "/#refs#" self._compression_algorithm = 'gzip' @property def action_for_matlab_incompatible(self): """ The action to do when writing non-MATLAB compatible data. {'ignore', 'discard', 'error'} The action to perform when doing MATLAB compatibility but a type being written is not MATLAB compatible. The actions are to write the data anyways ('ignore'), don't write the incompatible data ('discard'), or throw a ``TypeNotMatlabCompatibleError`` exception. The default is 'error'. See Also -------- matlab_compatible hdf5storage.lowlevel.TypeNotMatlabCompatibleError """ return self._action_for_matlab_incompatible @action_for_matlab_incompatible.setter def action_for_matlab_incompatible(self, value): # Check that it is one of the allowed values, and then set # it. This option does not effect MATLAB compatibility. if value in ('ignore', 'discard', 'error'): self._action_for_matlab_incompatible = value @property def delete_unused_variables(self): """ Whether or not to delete file variables not written to. bool If ``True`` (defaults to ``False`` unless MATLAB compatibility is being done), variables in the file below where writing starts that are not written to are deleted. Must be ``True`` if doing MATLAB compatibility. """ return self._delete_unused_variables @delete_unused_variables.setter def delete_unused_variables(self, value): # Check that it is a bool, and then set it. If it is false, we # are not doing MATLAB compatible formatting. if isinstance(value, bool): self._delete_unused_variables = value if not self._delete_unused_variables: self._matlab_compatible = False @property def structured_numpy_ndarray_as_struct(self): """ Whether or not to convert structured ndarrays to structs. bool If ``True`` (defaults to ``False`` unless MATLAB compatibility is being done), all ``numpy.ndarray``s with fields (compound dtypes) are written as HDF5 Groups with the fields as Datasets (correspond to struct arrays in MATLAB). Must be ``True`` if doing MATLAB compatibility. MATLAB cannot handle the compound types made by writing these types. """ return self._structured_numpy_ndarray_as_struct @structured_numpy_ndarray_as_struct.setter def structured_numpy_ndarray_as_struct(self, value): # Check that it is a bool, and then set it. If it is false, we # are not doing MATLAB compatible formatting. if isinstance(value, bool): self._structured_numpy_ndarray_as_struct = value if not self._structured_numpy_ndarray_as_struct: self._matlab_compatible = False @property def make_atleast_2d(self): """ Whether or not to convert scalar types to 2D arrays. bool If ``True`` (defaults to ``False`` unless MATLAB compatibility is being done), all scalar types are converted to 2D arrays when written to file. ``oned_as`` determines whether 1D arrays are turned into row or column vectors. Must be ``True`` if doing MATLAB compatibility. MATLAB can only import 2D and higher dimensional arrays. See Also -------- oned_as """ return self._make_atleast_2d @make_atleast_2d.setter def make_atleast_2d(self, value): # Check that it is a bool, and then set it. If it is false, we # are not doing MATLAB compatible formatting. if isinstance(value, bool): self._make_atleast_2d = value if not self._make_atleast_2d: self._matlab_compatible = False @property def convert_numpy_bytes_to_utf16(self): """ Whether or not to convert numpy.bytes_ to UTF-16. bool If ``True`` (defaults to ``False`` unless MATLAB compatibility is being done), ``numpy.bytes_`` and anything that is converted to them (``bytes``, and ``bytearray``) are converted to UTF-16 before being written to file as ``numpy.uint16``. Must be ``True`` if doing MATLAB compatibility. MATLAB uses UTF-16 for its strings. See Also -------- numpy.bytes_ convert_numpy_str_to_utf16 """ return self._convert_numpy_bytes_to_utf16 @convert_numpy_bytes_to_utf16.setter def convert_numpy_bytes_to_utf16(self, value): # Check that it is a bool, and then set it. If it is false, we # are not doing MATLAB compatible formatting. if isinstance(value, bool): self._convert_numpy_bytes_to_utf16 = value if not self._convert_numpy_bytes_to_utf16: self._matlab_compatible = False @property def convert_numpy_str_to_utf16(self): """ Whether or not to convert numpy.str_ to UTF-16. bool If ``True`` (defaults to ``False`` unless MATLAB compatibility is being done), ``numpy.str_`` and anything that is converted to them (``str``) will be converted to UTF-16 if possible before being written to file as ``numpy.uint16``. If doing so would lead to a loss of data (character can't be translated to UTF-16) or would change the shape of an array of ``numpy.str_`` due to a character being converted into a pair 2-bytes, the conversion will not be made and the string will be stored in UTF-32 form as a ``numpy.uint32``. Must be ``True`` if doing MATLAB compatibility. MATLAB uses UTF-16 for its strings. See Also -------- numpy.bytes_ convert_numpy_str_to_utf16 """ return self._convert_numpy_str_to_utf16 @convert_numpy_str_to_utf16.setter def convert_numpy_str_to_utf16(self, value): # Check that it is a bool, and then set it. If it is false, we # are not doing MATLAB compatible formatting. if isinstance(value, bool): self._convert_numpy_str_to_utf16 = value if not self._convert_numpy_str_to_utf16: self._matlab_compatible = False @property def convert_bools_to_uint8(self): """ Whether or not to convert bools to ``numpy.uint8``. bool If ``True`` (defaults to ``False`` unless MATLAB compatibility is being done), bool types are converted to ``numpy.uint8`` before being written to file. Must be ``True`` if doing MATLAB compatibility. MATLAB doesn't use the enums that ``h5py`` wants to use by default and also uses uint8 intead of int8. """ return self._convert_bools_to_uint8 @convert_bools_to_uint8.setter def convert_bools_to_uint8(self, value): # Check that it is a bool, and then set it. If it is false, we # are not doing MATLAB compatible formatting. if isinstance(value, bool): self._convert_bools_to_uint8 = value if not self._convert_bools_to_uint8: self._matlab_compatible = False @property def reverse_dimension_order(self): """ Whether or not to reverse the order of array dimensions. bool If ``True`` (defaults to ``False`` unless MATLAB compatibility is being done), the dimension order of ``numpy.ndarray`` and ``numpy.matrix`` are reversed. This switches them from C ordering to Fortran ordering. The switch of ordering is essentially a transpose. Must be ``True`` if doing MATLAB compatibility. MATLAB uses Fortran ordering. """ return self._reverse_dimension_order @reverse_dimension_order.setter def reverse_dimension_order(self, value): # Check that it is a bool, and then set it. If it is false, we # are not doing MATLAB compatible formatting. if isinstance(value, bool): self._reverse_dimension_order = value if not self._reverse_dimension_order: self._matlab_compatible = False @property def store_shape_for_empty(self): """ Whether to write the shape if an object has no elements. bool If ``True`` (defaults to ``False`` unless MATLAB compatibility is being done), objects that have no elements (e.g. a 0x0x2 array) will have their shape (an array of the number of elements along each axis) written to disk in place of nothing, which would otherwise be written. Must be ``True`` if doing MATLAB compatibility. For empty arrays, MATLAB requires that the shape array be written in its place along with the attribute 'MATLAB_empty' set to 1 to flag it. """ return self._store_shape_for_empty @store_shape_for_empty.setter def store_shape_for_empty(self, value): # Check that it is a bool, and then set it. If it is false, we # are not doing MATLAB compatible formatting. if isinstance(value, bool): self._store_shape_for_empty = value if not self._store_shape_for_empty: self._matlab_compatible = False @property def complex_names(self): """ Names to use for the real and imaginary fields. tuple of two str ``(r, i)`` where `r` and `i` are two ``str``. When reading and writing complex numbers, the real part gets the name in `r` and the imaginary part gets the name in `i`. ``h5py`` uses ``('r', 'i')`` by default, unless MATLAB compatibility is being done in which case its default is ``('real', 'imag')``. Must be ``('real', 'imag')`` if doing MATLAB compatibility. """ return self._complex_names @complex_names.setter def complex_names(self, value): # Check that it is a tuple of two strings, and then set it. If # it is something other than ('real', 'imag'), then we are not # doing MATLAB compatible formatting. if isinstance(value, tuple) and len(value) == 2 \ and isinstance(value[0], str) \ and isinstance(value[1], str): self._complex_names = value if self._complex_names != ('real', 'imag'): self._matlab_compatible = False @property def group_for_references(self): """ Path for where to put objects pointed at by references. str The absolute POSIX path for the Group to place all data that is pointed to by another piece of data (needed for ``numpy.object_`` and similar types). This path is automatically excluded from its parent group when reading back a ``dict``. Must be ``'/#refs#`` if doing MATLAB compatibility. """ return self._group_for_references @group_for_references.setter def group_for_references(self, value): # Check that it an str and a valid absolute POSIX path, and then # set it. If it is something other than "/#refs#", then we are # not doing MATLAB compatible formatting. if isinstance(value, str): pth = posixpath.normpath(value) if len(pth) > 1 and posixpath.isabs(pth): self._group_for_references = value if self._group_for_references != "/#refs#": self._matlab_compatible = False @property def oned_as(self): """ Vector that 1D arrays become when making everything >= 2D. {'row', 'column'} When the ``make_atleast_2d`` option is set (set implicitly by doing MATLAB compatibility), this option controls whether 1D arrays become row vectors or column vectors. See Also -------- make_atleast_2d """ return self._oned_as @oned_as.setter def oned_as(self, value): # Check that it is one of the valid values before setting it. if value in ('row', 'column'): self._oned_as = value @property def compress(self): """ Whether to compress large python objects (datasets). bool If ``True``, python objects (datasets) larger than ``compress_size_threshold`` will be compressed. See Also -------- compress_size_threshold compression_algorithm shuffle_filter compressed_fletcher32_filter """ return self._compress @compress.setter def compress(self, value): # Check that it is a bool, and then set it. if isinstance(value, bool): self._compress = value @property def compress_size_threshold(self): """ Minimum size of a python object before it is compressed. int Minimum size in bytes a python object must be for it to be compressed if ``compress`` is set. Must be non-negative. See Also -------- compress """ return self._compress_size_threshold @compress_size_threshold.setter def compress_size_threshold(self, value): # Check that it is a non-negative integer, and then set it. if isinstance(value, int) and value >= 0: self._compress_size_threshold = value @property def compression_algorithm(self): """ Algorithm to use for compression. {'gzip', 'lzf', 'szip'} Compression algorithm to use When the ``compress`` option is set and a python object is larger than ``compress_size_threshold``. ``'gzip'`` is the only MATLAB compatible option. ``'gzip'`` is also known as the Deflate algorithm, which is the default compression algorithm of ZIP files and is a common compression algorithm used on tarballs. It is the most compatible option. It has good compression and is reasonably fast. Its compression level is set with the ``gzip_compression_level`` option, which is an integer between 0 and 9 inclusive. ``'lzf'`` is a very fast but low to moderate compression algorithm. It is less commonly used than gzip/Deflate, but doesn't have any patent or license issues. ``'szip'`` is a compression algorithm that has some patents and license restrictions. It is not always available. See Also -------- compress compress_size_threshold h5py.Group.create_dataset http://www.hdfgroup.org/doc_resource/SZIP/Commercial_szip.html """ return self._compression_algorithm @compression_algorithm.setter def compression_algorithm(self, value): # Check that it is one of the valid values before setting it. If # it is something other than 'gzip', then we are not doing # MATLAB compatible formatting. if value in ('gzip', 'lzf', 'szip'): self._compression_algorithm = value if self._compression_algorithm != 'gzip': self._matlab_compatible = False @property def gzip_compression_level(self): """ The compression level to use when doing the gzip algorithm. int Compression level to use when data is being compressed with the ``'gzip'`` algorithm. Must be an integer between 0 and 9 inclusive. Lower values are faster while higher values give better compression. See Also -------- compress compression_algorithm """ return self._gzip_compression_level @gzip_compression_level.setter def gzip_compression_level(self, value): # Check that it is an integer between 0 and 9. if isinstance(value, int) and value >= 0 and value <= 9: self._gzip_compression_level = value @property def shuffle_filter(self): """ Whether to use the shuffle filter on compressed python objects. bool If ``True``, python objects (datasets) that are compressed are run through the shuffle filter, which reversibly rearranges the data to improve compression. See Also -------- compress h5py.Group.create_dataset """ return self._shuffle_filter @shuffle_filter.setter def shuffle_filter(self, value): # Check that it is a bool, and then set it. if isinstance(value, bool): self._shuffle_filter = value @property def compressed_fletcher32_filter(self): """ Whether to use the fletcher32 filter on compressed python objects. bool If ``True``, python objects (datasets) that are compressed are run through the fletcher32 filter, which stores a checksum with each chunk so that data corruption can be more easily detected. See Also -------- compress shuffle_filter uncompressed_flether32_filter h5py.Group.create_dataset """ return self._compressed_fletcher32_filter @compressed_fletcher32_filter.setter def compressed_fletcher32_filter(self, value): # Check that it is a bool, and then set it. if isinstance(value, bool): self._compressed_fletcher32_filter = value @property def uncompressed_fletcher32_filter(self): """ Whether to use the fletcher32 filter on uncompressed non-scalar python objects. bool If ``True``, python objects (datasets) that are **NOT** compressed and are not scalars (when converted to a Numpy type, their shape is not an empty ``tuple``) are run through the fletcher32 filter, which stores a checksum with each chunk so that data corruption can be more easily detected. This forces all uncompressed data to be chuncked regardless of how small and can increase file sizes. See Also -------- compress shuffle_filter compressed_flether32_filter h5py.Group.create_dataset """ return self._uncompressed_fletcher32_filter @uncompressed_fletcher32_filter.setter def uncompressed_fletcher32_filter(self, value): # Check that it is a bool, and then set it. if isinstance(value, bool): self._uncompressed_fletcher32_filter = value class MarshallerCollection(object): """ Represents, maintains, and retreives a set of marshallers. Maintains a list of marshallers used to marshal data types to and from HDF5 files. It includes the builtin marshallers from the ``hdf5storage.Marshallers`` module as well as any user supplied or added marshallers. While the builtin list cannot be changed; user ones can be added or removed. Also has functions to get the marshaller appropriate for ``type`` or type_string for a python data type. User marshallers must provide the same interface as ``hdf5storage.Marshallers.TypeMarshaller``, which is probably most easily done by inheriting from it. Parameters ---------- marshallers : marshaller or list of marshallers, optional The user marshaller/s to add to the collection. Could also be a ``tuple``, ``set``, or ``frozenset`` of marshallers. See Also -------- hdf5storage.Marshallers hdf5storage.Marshallers.TypeMarshaller """ def __init__(self, marshallers=[]): # Two lists of marshallers need to be maintained: one for the # builtin ones in the Marshallers module, and another for user # supplied ones. # Grab all the marshallers in the Marshallers module (they are # the classes) by inspection. self._builtin_marshallers = [m() for key, m in dict( inspect.getmembers(Marshallers, inspect.isclass)).items()] self._user_marshallers = [] # A list of all the marshallers will be needed along with # dictionaries to lookup up the marshaller to use for given # types, type string, or MATLAB class string (they are the # keys). self._marshallers = [] self._types = dict() self._type_strings = dict() self._matlab_classes = dict() # Add any user given marshallers. self.add_marshaller(copy.deepcopy(marshallers)) def _update_marshallers(self): """ Update the full marshaller list and other data structures. Makes a full list of both builtin and user marshallers and rebuilds internal data structures used for looking up which marshaller to use for reading/writing Python objects to/from file. """ # Combine both sets of marshallers. self._marshallers = copy.deepcopy(self._builtin_marshallers) self._marshallers.extend(copy.deepcopy(self._user_marshallers)) # Construct the dictionary to look up the appropriate marshaller # by type. It would normally be a dict comprehension such as # # self._types = {tp: m for m in self._marshallers # for tp in m.types} # # but that is not supported in Python 2.6 so it has to be done # with a for loop. self._types = dict() for m in self._marshallers: for tp in m.types: self._types[tp] = m # The equivalent one to read data types given type strings needs # to be created from it. Basically, we have to make the key be # the python_type_string from it. Same issue as before with # Python 2.6 # # self._type_strings = {type_string: m for key, m in # self._types.items() for type_string in # m.python_type_strings} self._type_strings = dict() for key, m in self._types.items(): for type_string in m.python_type_strings: self._type_strings[type_string] = m # The equivalent one to read data types given MATLAB class # strings needs to be created from it. Basically, we have to # make the key be the matlab_class from it. Same issue as before # with Python 2.6 # # self._matlab_classes = {matlab_class: m for key, m in # self._types.items() for matlab_class in # m.matlab_classes} self._matlab_classes = dict() for key, m in self._types.items(): for matlab_class in m.matlab_classes: self._matlab_classes[matlab_class] = m def add_marshaller(self, marshallers): """ Add a marshaller/s to the user provided list. Adds a marshaller or a list of them to the user provided set of marshallers. Parameters ---------- marshallers : marshaller or list of marshallers The user marshaller/s to add to the user provided collection. Could also be a ``tuple``, ``set``, or ``frozenset`` of marshallers. """ if not isinstance(marshallers, (list, tuple, set, frozenset)): marshallers = [marshallers] for m in marshallers: if m not in self._user_marshallers: self._user_marshallers.append(copy.deepcopy(m)) self._update_marshallers() def remove_marshaller(self, marshallers): """ Removes a marshaller/s from the user provided list. Removes a marshaller or a list of them from the user provided set of marshallers. Parameters ---------- marshallers : marshaller or list of marshallers The user marshaller/s to from the user provided collection. Could also be a ``tuple``, ``set``, or ``frozenset`` of marshallers. """ if not isinstance(marshallers, (list, tuple, set, frozenset)): marshallers = [marshallers] for m in marshallers: if m in self._user_marshallers: self._user_marshallers.remove(m) self._update_marshallers() def clear_marshallers(self): """ Clears the list of user provided marshallers. Removes all user provided marshallers, but not the builtin ones from the ``hdf5storage.Marshallers`` module, from the list of marshallers used. """ self._user_marshallers.clear() self._update_marshallers() def get_marshaller_for_type(self, tp): """ Gets the appropriate marshaller for a type. Retrieves the marshaller, if any, that can be used to read/write a Python object with type 'tp'. Parameters ---------- tp : type Python object ``type``. Returns ------- marshaller The marshaller that can read/write the type to file. ``None`` if no appropriate marshaller is found. See Also -------- hdf5storage.Marshallers.TypeMarshaller.types """ if tp in self._types: return copy.deepcopy(self._types[tp]) else: return None def get_marshaller_for_type_string(self, type_string): """ Gets the appropriate marshaller for a type string. Retrieves the marshaller, if any, that can be used to read/write a Python object with the given type string. Parameters ---------- type_string : str Type string for a Python object. Returns ------- marshaller The marshaller that can read/write the type to file. ``None`` if no appropriate marshaller is found. See Also -------- hdf5storage.Marshallers.TypeMarshaller.python_type_strings """ if type_string in self._type_strings: return copy.deepcopy(self._type_strings[type_string]) else: return None def get_marshaller_for_matlab_class(self, matlab_class): """ Gets the appropriate marshaller for a MATLAB class string. Retrieves the marshaller, if any, that can be used to read/write a Python object associated with the given MATLAB class string. Parameters ---------- matlab_class : str MATLAB class string for a Python object. Returns ------- marshaller The marshaller that can read/write the type to file. ``None`` if no appropriate marshaller is found. See Also -------- hdf5storage.Marshallers.TypeMarshaller.python_type_strings """ if matlab_class in self._matlab_classes: return copy.deepcopy(self._matlab_classes[matlab_class]) else: return None def writes(mdict, filename='data.h5', truncate_existing=False, truncate_invalid_matlab=False, options=None, **keywords): """ Writes data into an HDF5 file (high level). High level function to store one or more Python types (data) to specified pathes in an HDF5 file. The paths are specified as POSIX style paths where the directory name is the Group to put it in and the basename is the name to write it to. There are various options that can be used to influence how the data is written. They can be passed as an already constructed ``Options`` into `options` or as additional keywords that will be used to make one by ``options = Options(**keywords)``. Two very important options are ``store_python_metadata`` and ``matlab_compatible``, which are ``bool``. The first makes it so that enough metadata (HDF5 Attributes) are written that `data` can be read back accurately without it (or its contents if it is a container type) ending up different types, transposed in the case of numpy arrays, etc. The latter makes it so that the appropriate metadata is written, string and bool and complex types are converted properly, and numpy arrays are transposed; which is needed to make sure that MATLAB can import `data` correctly (the HDF5 header is also set so MATLAB will recognize it). Parameters ---------- mdict : dict, dict like The ``dict`` or other dictionary type object of paths and data to write to the file. The paths, the keys, must be POSIX style paths where the directory name is the Group to put it in and the basename is the name to write it to. The values are the data to write. filename : str, optional The name of the HDF5 file to write `data` to. truncate_existing : bool, optional Whether to truncate the file if it already exists before writing to it. truncate_invalid_matlab : bool, optional Whether to truncate a file if matlab_compatibility is being done and the file doesn't have the proper header (userblock in HDF5 terms) setup for MATLAB metadata to be placed. options : Options, optional The options to use when writing. Is mutually exclusive with any additional keyword arguments given (set to ``None`` or don't provide to use them). **keywords : If `options` was not provided or was ``None``, these are used as arguments to make a ``Options``. Raises ------ NotImplementedError If writing `data` is not supported. TypeNotMatlabCompatibleError If writing a type not compatible with MATLAB and `options.action_for_matlab_incompatible` is set to ``'error'``. See Also -------- write : Writes just a single piece of data reads read Options lowlevel.write_data : Low level version """ # Pack the different options into an Options class if an Options was # not given. if not isinstance(options, Options): options = Options(**keywords) # Go through mdict, extract the paths and data, and process the # paths. A list of tulpes for each piece of data to write will be # constructed where he first element is the group name, the second # the target name (name of the Dataset/Group holding the data), and # the third element the data to write. towrite = [] for p, v in mdict.items(): # Remove double slashes and a non-root trailing slash. path = posixpath.normpath(p) # Extract the group name and the target name (will be a dataset if # data can be mapped to it, but will end up being made into a group # otherwise. As HDF5 files use posix path, conventions, posixpath # will do everything. groupname = posixpath.dirname(path) targetname = posixpath.basename(path) # If groupname got turned into blank, then it is just root. if groupname == '': groupname = '/' # If targetname got turned blank, then it is the current directory. if targetname == '': targetname = '.' # Pack into towrite. towrite.append((groupname, targetname, v)) # Open/create the hdf5 file but don't write the data yet since the # userblock still needs to be set. This is all wrapped in a try # block, so that the file can be closed if any errors happen (the # error is re-raised). f = None try: # If the file doesn't already exist or the option is set to # truncate it if it does, just open it truncating whatever is # there. Otherwise, open it for read/write access without # truncating. Now, if we are doing matlab compatibility and it # doesn't have a big enough userblock (for metadata for MATLAB # to be able to tell it is a valid .mat file) and the # truncate_invalid_matlab is set, then it needs to be closed and # re-opened with truncation. Whenever we create the file from # scratch, even if matlab compatibility isn't being done, a # sufficiently sized userblock is going to be allocated # (smallest size is 512) for future use (after all, someone # might want to turn it to a .mat file later and need it and it # is only 512 bytes). if truncate_existing or not os.path.isfile(filename): f = h5py.File(filename, mode='w', userblock_size=512) else: f = h5py.File(filename) if options.matlab_compatible and truncate_invalid_matlab \ and f.userblock_size < 128: f.close() f = h5py.File(filename, mode='w', userblock_size=512) except: raise finally: # If the hdf5 file was opened at all, get the userblock size and # close it since we need to set the userblock. if isinstance(f, h5py.File): userblock_size = f.userblock_size f.close() else: raise IOError('Unable to create or open file.') # If we are doing MATLAB formatting and there is a sufficiently # large userblock, write the new userblock. The same sort of error # handling is used. if options.matlab_compatible and userblock_size >= 128: # Get the time. now = datetime.datetime.now() # Construct the leading string. The MATLAB one looks like # # s = 'MATLAB 7.3 MAT-file, Platform: GLNXA64, Created on: ' \ # + now.strftime('%a %b %d %H:%M:%S %Y') \ # + ' HDF5 schema 1.00 .' # # Platform is going to be changed to CPython version. The # version is just gotten from sys.version_info, which is a class # for Python >= 2.7, but a tuple before that. v = sys.version_info if sys.hexversion >= 0x02070000: v = {'major': v.major, 'minor': v.minor, 'micro': v.micro} else: v = {'major': v[0], 'minor': v[1], 'micro': v[1]} s = 'MATLAB 7.3 MAT-file, Platform: CPython ' \ + '{0}.{1}.{2}'.format(v['major'], v['minor'], v['micro']) \ + ', Created on: ' \ + now.strftime('%a %b %d %H:%M:%S %Y') \ + ' HDF5 schema 1.00 .' # Make the bytearray while padding with spaces up to 128-12 # (the minus 12 is there since the last 12 bytes are special. b = bytearray(s + (128-12-len(s))*' ', encoding='utf-8') # Add 8 nulls (0) and the magic number (or something) that # MATLAB uses. Lengths must be gone to to make sure the argument # to fromhex is unicode because Python 2.6 requires it. b.extend(bytearray.fromhex( b'00000000 00000000 0002494D'.decode())) # Now, write it to the beginning of the file. try: fd = open(filename, 'r+b') fd.write(b) except: raise finally: fd.close() # Open the hdf5 file again and write the data, making the Group if # necessary. This is all wrapped in a try block, so that the file # can be closed if any errors happen (the error is re-raised). f = None try: f = h5py.File(filename) # Go through each element of towrite and write them. for groupname, targetname, data in towrite: # Need to make sure groupname is a valid group in f and grab its # handle to pass on to the low level function. if groupname not in f: grp = f.require_group(groupname) else: grp = f[groupname] # Hand off to the low level function. lowlevel.write_data(f, grp, targetname, data, None, options) except: raise finally: if isinstance(f, h5py.File): f.close() def write(data, path='/', filename='data.h5', truncate_existing=False, truncate_invalid_matlab=False, options=None, **keywords): """ Writes one piece of data into an HDF5 file (high level). A wrapper around ``writes`` to write a single piece of data, `data`, to a single location, `path`. High level function to store a Python type (`data`) to a specified path (`path`) in an HDF5 file. The path is specified as a POSIX style path where the directory name is the Group to put it in and the basename is the name to write it to. There are various options that can be used to influence how the data is written. They can be passed as an already constructed ``Options`` into `options` or as additional keywords that will be used to make one by ``options = Options(**keywords)``. Two very important options are ``store_python_metadata`` and ``matlab_compatible``, which are ``bool``. The first makes it so that enough metadata (HDF5 Attributes) are written that `data` can be read back accurately without it (or its contents if it is a container type) ending up different types, transposed in the case of numpy arrays, etc. The latter makes it so that the appropriate metadata is written, string and bool and complex types are converted properly, and numpy arrays are transposed; which is needed to make sure that MATLAB can import `data` correctly (the HDF5 header is also set so MATLAB will recognize it). Parameters ---------- data : any The data to write. path : str, optional The path to write `data` to. Must be a POSIX style path where the directory name is the Group to put it in and the basename is the name to write it to. filename : str, optional The name of the HDF5 file to write `data` to. truncate_existing : bool, optional Whether to truncate the file if it already exists before writing to it. truncate_invalid_matlab : bool, optional Whether to truncate a file if matlab_compatibility is being done and the file doesn't have the proper header (userblock in HDF5 terms) setup for MATLAB metadata to be placed. options : Options, optional The options to use when writing. Is mutually exclusive with any additional keyword arguments given (set to ``None`` or don't provide to use them). **keywords : If `options` was not provided or was ``None``, these are used as arguments to make a ``Options``. Raises ------ NotImplementedError If writing `data` is not supported. TypeNotMatlabCompatibleError If writing a type not compatible with MATLAB and `options.action_for_matlab_incompatible` is set to ``'error'``. See Also -------- writes : Writes more than one piece of data at once reads read Options lowlevel.write_data : Low level version """ writes(mdict={path: data}, filename=filename, truncate_existing=truncate_existing, truncate_invalid_matlab=truncate_invalid_matlab, options=options, **keywords) def reads(paths, filename='data.h5', options=None, **keywords): """ Reads data from an HDF5 file (high level). High level function to read one or more pieces of data from an HDF5 file located at the paths specified in `paths` into Python types. Each path is specified as a POSIX style path where the data to read is located. There are various options that can be used to influence how the data is read. They can be passed as an already constructed ``Options`` into `options` or as additional keywords that will be used to make one by ``options = Options(**keywords)``. Parameters ---------- paths : iterable of str An iterable of paths to read data from. Each must be a POSIX style path where the directory name is the Group to put it in and the basename is the name to write it to. filename : str, optional The name of the HDF5 file to read data from. options : Options, optional The options to use when reading. Is mutually exclusive with any additional keyword arguments given (set to ``None`` or don't provide to use them). **keywords : If `options` was not provided or was ``None``, these are used as arguments to make a ``Options``. Returns ------- datas : iterable An iterable holding the piece of data for each path in `paths` in the same order. Raises ------ CantReadError If reading the data can't be done. See Also -------- read : Reads just a single piece of data writes write Options lowlevel.read_data : Low level version. """ # Pack the different options into an Options class if an Options was # not given. By default, the matlab_compatible option is set to # False. So, if it wasn't passed in the keywords, this needs to be # added to override the default value (True) for a new Options. if not isinstance(options, Options): kw = copy.deepcopy(keywords) if 'matlab_compatible' not in kw: kw['matlab_compatible'] = False options = Options(**kw) # Process the paths and stuff the group names and target names as # tuples into toread. toread = [] for p in paths: # Remove double slashes and a non-root trailing slash. path = posixpath.normpath(p) # Extract the group name and the target name (will be a dataset if # data can be mapped to it, but will end up being made into a group # otherwise. As HDF5 files use posix path, conventions, posixpath # will do everything. groupname = posixpath.dirname(path) targetname = posixpath.basename(path) # If groupname got turned into blank, then it is just root. if groupname == '': groupname = '/' # If targetname got turned blank, then it is the current directory. if targetname == '': targetname = '.' # Pack them into toread toread.append((groupname, targetname)) # Open the hdf5 file and start reading the data. This is all wrapped # in a try block, so that the file can be closed if any errors # happen (the error is re-raised). try: f = None f = h5py.File(filename, mode='r') # Read the data item by item datas = [] for groupname, targetname in toread: # Check that the containing group is in f and is indeed a # group. If it isn't an error needs to be thrown. if groupname not in f \ or not isinstance(f[groupname], h5py.Group): raise CantReadError('Could not find containing Group ' + groupname + '.') # Hand off everything to the low level reader. datas.append(lowlevel.read_data(f, f[groupname], targetname, options)) except: raise finally: if f is not None: f.close() return datas def read(path='/', filename='data.h5', options=None, **keywords): """ Reads one piece of data from an HDF5 file (high level). A wrapper around ``reads`` to read a single piece of data at the single location `path`. High level function to read data from an HDF5 file located at `path` into Python types. The path is specified as a POSIX style path where the data to read is located. There are various options that can be used to influence how the data is read. They can be passed as an already constructed ``Options`` into `options` or as additional keywords that will be used to make one by ``options = Options(**keywords)``. Parameters ---------- path : str, optional The path to read data from. Must be a POSIX style path where the directory name is the Group to put it in and the basename is the name to write it to. filename : str, optional The name of the HDF5 file to read data from. options : Options, optional The options to use when reading. Is mutually exclusive with any additional keyword arguments given (set to ``None`` or don't provide to use them). **keywords : If `options` was not provided or was ``None``, these are used as arguments to make a ``Options``. Returns ------- data : The piece of data at `path`. Raises ------ CantReadError If reading the data can't be done. See Also -------- reads : Reads more than one piece of data at once writes write Options lowlevel.read_data : Low level version. """ return reads(paths=(path,), filename=filename, options=options, **keywords)[0] def savemat(file_name, mdict, appendmat=True, format='7.3', oned_as='row', store_python_metadata=True, action_for_matlab_incompatible='error', marshaller_collection=None, truncate_existing=False, truncate_invalid_matlab=False, **keywords): """ Save a dictionary of python types to a MATLAB MAT file. Saves the data provided in the dictionary `mdict` to a MATLAB MAT file. `format` determines which kind/vesion of file to use. The '7.3' version, which is HDF5 based, is handled by this package and all types that this package can write are supported. Versions 4 and 5 are not HDF5 based, so everything is dispatched to the SciPy package's ``scipy.io.savemat`` function, which this function is modelled after (arguments not specific to this package have the same names, etc.). Parameters ---------- file_name : str or file-like object Name of the MAT file to store in. The '.mat' extension is added on automatically if not present if `appendmat` is set to ``True``. An open file-like object can be passed if the writing is being dispatched to SciPy (`format` < 7.3). mdict : dict The dictionary of variables and their contents to store in the file. appendmat : bool, optional Whether to append the '.mat' extension to `file_name` if it doesn't already end in it or not. format : {'4', '5', '7.3'}, optional The MATLAB mat file format to use. The '7.3' format is handled by this package while the '4' and '5' formats are dispatched to SciPy. oned_as : {'row', 'column'}, optional Whether 1D arrays should be turned into row or column vectors. store_python_metadata : bool, optional Whether or not to store Python type information. Doing so allows most types to be read back perfectly. Only applicable if not dispatching to SciPy (`format` >= 7.3). action_for_matlab_incompatible: str, optional The action to perform writing data that is not MATLAB compatible. The actions are to write the data anyways ('ignore'), don't write the incompatible data ('discard'), or throw a ``TypeNotMatlabCompatibleError`` exception. marshaller_collection : MarshallerCollection, optional Collection of marshallers to disk to use. Only applicable if not dispatching to SciPy (`format` >= 7.3). truncate_existing : bool, optional Whether to truncate the file if it already exists before writing to it. truncate_invalid_matlab : bool, optional Whether to truncate a file if the file doesn't have the proper header (userblock in HDF5 terms) setup for MATLAB metadata to be placed. **keywords : Additional keywords arguments to be passed onto ``scipy.io.savemat`` if dispatching to SciPy (`format` < 7.3). Raises ------ ImportError If `format` < 7.3 and the ``scipy`` module can't be found. NotImplementedError If writing a variable in `mdict` is not supported. TypeNotMatlabCompatibleError If writing a type not compatible with MATLAB and `action_for_matlab_incompatible` is set to ``'error'``. Notes ----- Writing the same data and then reading it back from disk using the HDF5 based version 7.3 format (the functions in this package) or the older format (SciPy functions) can lead to very different results. Each package supports a different set of data types and converts them to and from the same MATLAB types differently. See Also -------- loadmat : Equivelent function to do reading. scipy.io.savemat : SciPy function this one models after and dispatches to. Options writes : Function used to do the actual writing. """ # If format is a number less than 7.3, the call needs to be # dispatched to the scipy version, if it is available, with all the # relevant and extra keywords options provided. if float(format) < 7.3: import scipy.io scipy.io.savemat(file_name, mdict, appendmat=appendmat, format=format, oned_as=oned_as, **keywords) return # Append .mat if it isn't on the end of the file name and we are # supposed to. if appendmat and not file_name.endswith('.mat'): file_name = file_name + '.mat' # Make the options with matlab compatibility forced. options = Options(store_python_metadata=store_python_metadata, \ matlab_compatible=True, oned_as=oned_as, \ action_for_matlab_incompatible=action_for_matlab_incompatible, \ marshaller_collection=marshaller_collection) # Write the variables in the dictionary to file. writes(mdict=mdict, filename=file_name, truncate_existing=truncate_existing, truncate_invalid_matlab=truncate_invalid_matlab, options=options) def loadmat(file_name, mdict=None, appendmat=True, variable_names=None, marshaller_collection=None, **keywords): """ Loads data to a MATLAB MAT file. Reads data from the specified variables (or all) in a MATLAB MAT file. There are many different formats of MAT files. This package can only handle the HDF5 based ones (the version 7.3 and later). As SciPy's ``scipy.io.loadmat`` function can handle the earlier formats, if this function cannot read the file, it will dispatch it onto the scipy function with all the calling arguments it uses passed on. This function is modelled after the SciPy one (arguments not specific to this package have the same names, etc.). Parameters ---------- file_name : str Name of the MAT file to read from. The '.mat' extension is added on automatically if not present if `appendmat` is set to ``True``. mdict : dict, optional The dictionary to insert read variables into appendmat : bool, optional Whether to append the '.mat' extension to `file_name` if it doesn't already end in it or not. variable_names: None or sequence, optional The variable names to read from the file. ``None`` selects all. marshaller_collection : MarshallerCollection, optional Collection of marshallers from disk to use. Only applicable if not dispatching to SciPy (version 7.3 and newer files). **keywords : Additional keywords arguments to be passed onto ``scipy.io.loadmat`` if dispatching to SciPy if the file is not a version 7.3 or later format. Returns ------- dict Dictionary of all the variables read from the MAT file (name as the key, and content as the value). Raises ------ ImportError If it is not a version 7.3 .mat file and the ``scipy`` module can't be found when dispatching to SciPy. CantReadError If reading the data can't be done. Notes ----- Writing the same data and then reading it back from disk using the HDF5 based version 7.3 format (the functions in this package) or the older format (SciPy functions) can lead to very different results. Each package supports a different set of data types and converts them to and from the same MATLAB types differently. See Also -------- savemat : Equivalent function to do writing. scipy.io.loadmat : SciPy function this one models after and dispatches to. Options reads : Function used to do the actual reading. """ # Will first assume that it is the HDF5 based 7.3 format. If an # OSError occurs, then it wasn't an HDF5 file and the scipy function # can be tried instead. try: # Make the options with the given marshallers. options = Options(marshaller_collection=marshaller_collection) # Append .mat if it isn't on the end of the file name and we are # supposed to. if appendmat and not file_name.endswith('.mat'): filename = file_name + '.mat' else: filename = file_name # Read everything if we were instructed. if variable_names is None: data = dict() with h5py.File(filename, mode='r') as f: for k in f: # Read if not group_for_references. Data that # produces errors when read is dicarded (the OSError # that would happen if this is not an HDF5 file # would already have happened when opening the # file). if f[k].name != options.group_for_references: try: data[k] = lowlevel.read_data(f, f, k, options) except: pass else: # Extract the desired fields all together and then pack them # into a dictionary one by one. values = reads(paths=variable_names, filename=filename, options=options) data = dict() for i, name in enumerate(variable_names): data[name] = values[i] # Read all the variables, stuff them into mdict, and return it. if mdict is None: mdict = dict() for k, v in data.items(): mdict[k] = v return mdict except OSError: import scipy.io return scipy.io.loadmat(file_name, mdict, appendmat=appendmat, variable_names=variable_names, **keywords)