【Python学习笔记】加载MATLAB文件：loadmat()解读
源码解析
def loadmat(file_name, mdict=None, appendmat=True, **kwargs):
variable_names = kwargs.pop('variable_names', None)
MR, file_opened = mat_reader_factory(file_name, appendmat, **kwargs)
matfile_dict = MR.get_variables(variable_names)
if mdict is not None:
mdict.update(matfile_dict)
else:
mdict = matfile_dict
if file_opened:
MR.mat_stream.close()
return mdict1. 参数
file_name: str
.mat文件的文件名（当appendmat = True时，不用加.mat后缀），也可以打开类似文件的对象（file-like object）
mdict: dict, 可选
插入文件变量的字典
appendmat: bool , 可选
若为真则在文件名后添加后缀
byte_order: str / None, 可选
默认为none。表示从mat文件中猜测的字节顺序，可以是“native”，“=”，“little”，“<”，“>”，“BIG”之一。
mat_dtype: bool，可选
若为真，则返回与加载到MATLAB中相同的dtype的数组，而不是保存数组的dtype。
squeeze_me: bool，可选
判断是否压缩单位矩阵的维数
chars_as_string: bool，可选.
将char数组转string数组
matlab_compatible: bool，可选
返回被MATLAB读取的数组（当squeeze_me = false, chars_as_string = false, mat_dtype = true, struct_as_record = true）
struct_as_record: bool，可选
设置flag来判断加载MATLAB以numpy记录数组还是以原形式numpy数组（dtype为对象）。
verify_compressed_data_integrity: bool，可选
MATLAB文件的长度是否已确认。
variable_names: None / sequence
若为None（默认），则读取文件中的所有变量。否则variable_names将是一个string序列，表示需要从mat文件中读取的变量名。此时读取器将跳过不是这个变量名的序列，一定程度上减少读取时间。
2. 返回
mat_dict: dict
以变量名为key，数组为values的字典
3. Notes
MATLAB 7.3及以上版本需要使用HDF5的库，scipy不支持该类版本mat文件的读取。
4. 样例
>>> from os.path import dirname, join as pjoin
>>> import scipy.io as sio
Get the filename for an example .mat file from the tests/data directory.
>>> data_dir = pjoin(dirname(sio.__file__), 'matlab', 'tests', 'data')
>>> mat_fname = pjoin(data_dir, 'testdouble_7.4_GLNX86.mat')
Load the .mat file contents.
>>> mat_contents = sio.loadmat(mat_fname)
The result is a dictionary, one key/value pair for each variable:
>>> sorted(mat_contents.keys())
['__globals__', '__header__', '__version__', 'testdouble']
>>> mat_contents['testdouble']
array([[0. , 0.78539816, 1.57079633, 2.35619449, 3.14159265,
3.92699082, 4.71238898, 5.49778714, 6.28318531]])
By default SciPy reads MATLAB structs as structured NumPy arrays where the
dtype fields are of type `object` and the names correspond to the MATLAB
struct field names. This can be disabled by setting the optional argument
`struct_as_record=False`.
Get the filename for an example .mat file that contains a MATLAB struct
called `teststruct` and load the contents.
>>> matstruct_fname = pjoin(data_dir, 'teststruct_7.4_GLNX86.mat')
>>> matstruct_contents = sio.loadmat(matstruct_fname)
>>> teststruct = matstruct_contents['teststruct']
>>> teststruct.dtype
dtype([('stringfield', 'O'), ('doublefield', 'O'), ('complexfield', 'O')])
The size of the structured array is the size of the MATLAB struct, not the
number of elements in any particular field. The shape defaults to 2-D
unless the optional argument `squeeze_me=True`, in which case all length 1
dimensions are removed.
>>> teststruct.size
1
>>> teststruct.shape
(1, 1)
Get the 'stringfield' of the first element in the MATLAB struct.
>>> teststruct[0, 0]['stringfield']
array(['Rats live on no evil star.'],
dtype='>> teststruct['doublefield'][0, 0]
array([[ 1.41421356, 2.71828183, 3.14159265]])
Load the MATLAB struct, squeezing out length 1 dimensions, and get the item
from the 'complexfield'.
>>> matstruct_squeezed = sio.loadmat(matstruct_fname, squeeze_me=True)
>>> matstruct_squeezed['teststruct'].shape
()
>>> matstruct_squeezed['teststruct']['complexfield'].shape
()
>>> matstruct_squeezed['teststruct']['complexfield'].item()
array([ 1.41421356+1.41421356j, 2.71828183+2.71828183j,
3.14159265+3.14159265j]) 相关文档参见：https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html