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# -*- coding: UTF-8 -*-
# Copyright (c) 2018, Dirk Gütlin & Thomas Hartmann
# All rights reserved.
#
# This file is part of the pymatreader Project, see:
# https://gitlab.com/obob/pymatreader
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# * 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.
import types
import sys
import numpy
import scipy.io
if sys.version_info <= (2, 7):
chr = unichr # noqa This is needed for python 2 and 3 compatibility
def _import_h5py():
try:
import h5py
except Exception as exc:
raise ImportError('h5py is required to read MATLAB files >= v7.3 '
'(%s)' % (exc,))
return h5py
def _hdf5todict(hdf5_object, variable_names=None, ignore_fields=None):
"""
Recursively converts a hdf5 object to a python dictionary,
converting all types as well.
Parameters
----------
hdf5_object: Union[h5py.Group, h5py.Dataset]
Object to convert. Can be a h5py File, Group or Dataset
variable_names: iterable, optional
Tuple or list of variables to include. If set to none, all
variable are read.
ignore_fields: iterable, optional
Tuple or list of fields to ignore. If set to none, all fields will
be read.
Returns
-------
dict
Python dictionary
"""
h5py = _import_h5py()
if isinstance(hdf5_object, h5py.Group):
return _handle_hdf5_group(hdf5_object, variable_names=variable_names,
ignore_fields=ignore_fields)
elif isinstance(hdf5_object, h5py.Dataset):
return _handle_hdf5_dataset(hdf5_object)
elif isinstance(hdf5_object, (list, types.GeneratorType)):
return [_hdf5todict(item) for item in hdf5_object]
raise TypeError('Unknown type in hdf5 file')
def _handle_hdf5_group(hdf5_object, variable_names=None, ignore_fields=None):
all_keys = set(hdf5_object.keys())
if ignore_fields:
all_keys = all_keys - set(ignore_fields)
if variable_names:
all_keys = all_keys & set(variable_names)
return_dict = dict()
for key in all_keys:
return_dict[key] = _hdf5todict(hdf5_object[key],
variable_names=None,
ignore_fields=ignore_fields)
return return_dict
def _handle_hdf5_dataset(hdf5_object):
if 'MATLAB_empty' in hdf5_object.attrs.keys():
data = numpy.empty((0,))
else:
data = hdf5_object.value
if isinstance(data, numpy.ndarray) and \
data.dtype == numpy.dtype('object'):
data = [hdf5_object.file[cur_data] for cur_data in data.flatten()]
if len(data) == 1 and hdf5_object.attrs['MATLAB_class'] == b'cell':
data = data[0]
data = data.value
return _assign_types(data)
data = _hdf5todict(data)
return _assign_types(data)
def _convert_string_hdf5(values):
if values.size > 1:
assigned_values = u''.join(chr(c) for c in values.flatten())
else:
assigned_values = chr(values)
return assigned_values
def _assign_types(values):
"""private function, which assigns correct types to h5py extracted values
from _browse_dataset()"""
if type(values) == numpy.ndarray:
assigned_values = _handle_ndarray(values)
elif type(values) == numpy.float64:
assigned_values = float(values)
else:
assigned_values = values
return assigned_values
def _handle_ndarray(values):
"""Handle conversion of ndarrays."""
values = numpy.squeeze(values).T
if values.dtype in ("uint8", "uint16", "uint32", "uint64"):
values = _handle_hdf5_strings(values)
if isinstance(values, numpy.ndarray) and \
values.size == 1:
values = values.item()
return values
def _handle_hdf5_strings(values):
if values.ndim in (0, 1):
values = _convert_string_hdf5(values)
elif values.ndim == 2:
values = [_convert_string_hdf5(cur_val)
for cur_val in values]
else:
raise RuntimeError('String arrays with more than 2 dimensions'
'are not supported at the moment.')
return values
def _check_for_scipy_mat_struct(data):
"""
Private function to check all entries of data for occurrences of
scipy.io.matlab.mio5_params.mat_struct and convert them.
Parameters
==========
data: any
data to be checked
Returns
=========
object
checked and converted data
"""
if isinstance(data, dict):
for key in data:
data[key] = _check_for_scipy_mat_struct(data[key])
if isinstance(data, numpy.ndarray):
data = _handle_scipy_ndarray(data)
return data
def _handle_scipy_ndarray(data):
if data.dtype == numpy.dtype('object') and not \
isinstance(data, scipy.io.matlab.mio5.MatlabFunction):
as_list = []
for element in data:
as_list.append(_check_for_scipy_mat_struct(element))
data = as_list
elif isinstance(data.dtype.names, tuple):
data = _todict_from_np_struct(data)
data = _check_for_scipy_mat_struct(data)
if isinstance(data, numpy.ndarray):
data = numpy.array(data)
return data
def _todict_from_np_struct(data):
data_dict = dict()
for cur_field_name in data.dtype.names:
try:
n_items = len(data[cur_field_name])
cur_list = list()
for idx in numpy.arange(n_items):
cur_value = data[cur_field_name].item(idx)
cur_value = _check_for_scipy_mat_struct(cur_value)
cur_list.append(cur_value)
data_dict[cur_field_name] = cur_list
except TypeError:
cur_value = data[cur_field_name].item(0)
cur_value = _check_for_scipy_mat_struct(cur_value)
data_dict[cur_field_name] = cur_value
return data_dict
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