mat数据格式是matlab的数据存储的标准格式。 可以在matlab中象M文件一样打开，数据进入workspace。

1、建立mat文件：(1)、打开matlab,点击左上角文件（File），然后点击新建（new），选择变量（Variable），就新建了一个mat文件。点击你新建的文件，即可打开一个编辑器，输入数据即可；(2)、用save直接对数据进行保存。
 
2、读取mat文件：load filename.mat

因为要做以图搜图的后台, 公司山大的实习生写了一个算法, 计算图片的hash值, 但是面太窄了, 自己重新改了一下,他用.mat 文件存储的数据, 但是没有解决覆盖的问题


解决思路：
    在网上找了一圈，好像没有找到解决覆盖的问题
    就是在每次存入数据之前，先把数据提取出来,吧数据和要保存数据整合一下，一起存入mat文件

如果有更好的方法和思路,请各位大佬不吝赐教



mat 文件

.mat文件现在不知道如何追加


.mat文件是以 字典存储的

from scipy.io import savemat, loadmat

savemat  和 loadmat是对.mat文件进行存和读的

savemat()
这是savemat源码
filename  是mat文件路径
mdict  是存储的数据

@docfiller
def savemat(file_name, mdict,
            appendmat=True,
            format='5',
            long_field_names=False,
            do_compression=False,
            oned_as='row'):
    """
    Save a dictionary of names and arrays into a MATLAB-style .mat file.

    This saves the array objects in the given dictionary to a MATLAB-
    style .mat file.

    Parameters
    ----------
    file_name : str or file-like object
        Name of the .mat file (.mat extension not needed if ``appendmat ==
        True``).
        Can also pass open file_like object.
    mdict : dict
        Dictionary from which to save matfile variables.
    appendmat : bool, optional
        True (the default) to append the .mat extension to the end of the
        given filename, if not already present.
    format : {'5', '4'}, string, optional
        '5' (the default) for MATLAB 5 and up (to 7.2),
        '4' for MATLAB 4 .mat files.
    long_field_names : bool, optional
        False (the default) - maximum field name length in a structure is
        31 characters which is the documented maximum length.
        True - maximum field name length in a structure is 63 characters
        which works for MATLAB 7.6+.
    do_compression : bool, optional
        Whether or not to compress matrices on write.  Default is False.
    oned_as : {'row', 'column'}, optional
        If 'column', write 1-D numpy arrays as column vectors.
        If 'row', write 1-D numpy arrays as row vectors.

    See also
    --------
    mio4.MatFile4Writer
    mio5.MatFile5Writer
    """
    file_is_string = isinstance(file_name, string_types)
    if file_is_string:
        if appendmat and file_name[-4:] != ".mat":
            file_name = file_name + ".mat"
        file_stream = open(file_name, 'wb')
    else:
        if not hasattr(file_name, 'write'):
            raise IOError('Writer needs file name or writeable '
                           'file-like object')
        file_stream = file_name
    if format == '4':
        if long_field_names:
            raise ValueError("Long field names are not available for version 4 files")
        MW = MatFile4Writer(file_stream, oned_as)
    elif format == '5':
        MW = MatFile5Writer(file_stream,
                            do_compression=do_compression,
                            unicode_strings=True,
                            long_field_names=long_field_names,
                            oned_as=oned_as)
    else:
        raise ValueError("Format should be '4' or '5'")
    MW.put_variables(mdict)
    if file_is_string:
        file_stream.close()

loadmat()
filename  mat文件路径


@docfiller
def loadmat(file_name, mdict=None, appendmat=True, **kwargs):
    """
    Load MATLAB file.

    Parameters
    ----------
    file_name : str
       Name of the mat file (do not need .mat extension if
       appendmat==True). Can also pass open file-like object.
    mdict : dict, optional
        Dictionary in which to insert matfile variables.
    appendmat : bool, optional
       True to append the .mat extension to the end of the given
       filename, if not already present.
    byte_order : str or None, optional
       None by default, implying byte order guessed from mat
       file. Otherwise can be one of ('native', '=', 'little', '<',
       'BIG', '>').
    mat_dtype : bool, optional
       If True, return arrays in same dtype as would be loaded into
       MATLAB (instead of the dtype with which they are saved).
    squeeze_me : bool, optional
       Whether to squeeze unit matrix dimensions or not.
    chars_as_strings : bool, optional
       Whether to convert char arrays to string arrays.
    matlab_compatible : bool, optional
       Returns matrices as would be loaded by MATLAB (implies
       squeeze_me=False, chars_as_strings=False, mat_dtype=True,
       struct_as_record=True).
    struct_as_record : bool, optional
       Whether to load MATLAB structs as numpy record arrays, or as
       old-style numpy arrays with dtype=object.  Setting this flag to
       False replicates the behavior of scipy version 0.7.x (returning
       numpy object arrays).  The default setting is True, because it
       allows easier round-trip load and save of MATLAB files.
    verify_compressed_data_integrity : bool, optional
        Whether the length of compressed sequences in the MATLAB file
        should be checked, to ensure that they are not longer than we expect.
        It is advisable to enable this (the default) because overlong
        compressed sequences in MATLAB files generally indicate that the
        files have experienced some sort of corruption.
    variable_names : None or sequence
        If None (the default) - read all variables in file. Otherwise
        `variable_names` should be a sequence of strings, giving names of the
        matlab variables to read from the file.  The reader will skip any
        variable with a name not in this sequence, possibly saving some read
        processing.

    Returns
    -------
    mat_dict : dict
       dictionary with variable names as keys, and loaded matrices as
       values.

    Notes
    -----
    v4 (Level 1.0), v6 and v7 to 7.2 matfiles are supported.

    You will need an HDF5 python library to read matlab 7.3 format mat
    files.  Because scipy does not supply one, we do not implement the
    HDF5 / 7.3 interface here.

    """
    variable_names = kwargs.pop('variable_names', None)
    MR = 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 isinstance(file_name, string_types):
        MR.mat_stream.close()
    return mdict


好像是mat文件不能追加数据, 写入数据是覆盖的, 但是我要全局检索存入mat文件里面的数据，
就是每回在存入数据的时候，因为不能追加，所以先把mat文件里面的数据
读取出来, 我读取出来的数据是数组类型的， 因为存入的时候也是数组
类型,  在取出来的数组里面增添相应的数据就行了

数组增添的方式

import numpy as np
np.append(array1, array2, axis=0or1)
array1  是你要添加数据到的数组
array2  要添加数组的数据

axis   0 是行  1 是列































 

【题目】：将两个mat文件(X.mat,Y.mat)合并为一个mat文件

>load('X.mat');%如果文件不在当前文件夹下，则load('文件路径\X.mat');
>load('Y.mat');
>save('XY.mat');

 

目录

一.合并mat文件

二.拼接矩阵或数据

一.合并mat文件

将如下四个mat文件合并，每个文件为1500行101列，合并完为7500行101列



clc
clear all
close all
%%
a=[];
List =dir('C:\Users\Administrator\Desktop\code\lstmdata\pri*.mat');
k =length(List);
for i=1:k
    file_name{i}=List(i).name;
    temp=importdata(file_name{i});
    %temp=temp';
    a=[a;temp];
end
path_pri = ['Sig.mat'];
save(path_pri,'a','-v7.3')


注：次数为按行拼接

结果：在同一文件生成.mat文件，存在变量a中。



二.拼接矩阵或数据

左右合并矩阵，需要对应矩阵行数相同，其中使用空格或者“,”均可。

上下拼接，使用“;”符号实现。