scipy.io.loadmat 嵌套结构（即字典）

Question

使用给定的例程（如何使用 scipy 加载 Matlab .mat 文件），我无法访问更深的嵌套结构以将它们恢复到字典中

为了更详细地展示我遇到的问题，我给出了以下玩具示例：

 load scipy.io as spio
a = {'b':{'c':{'d': 3}}}
# my dictionary: a['b']['c']['d'] = 3
spio.savemat('xy.mat',a)

现在我想将 mat-File 读回 python。我尝试了以下内容：

 vig=spio.loadmat('xy.mat',squeeze_me=True)

如果我现在想访问我得到的字段：

 >> vig['b']
array(((array(3),),), dtype=[('c', '|O8')])
>> vig['b']['c']
array(array((3,), dtype=[('d', '|O8')]), dtype=object)
>> vig['b']['c']['d']
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)

/<ipython console> in <module>()

ValueError: field named d not found.

但是，通过使用选项 struct_as_record=False 可以访问该字段：

 v=spio.loadmat('xy.mat',squeeze_me=True,struct_as_record=False)

现在可以通过以下方式访问它

>> v['b'].c.d
array(3)

原文由 mergen 发布，翻译遵循 CC BY-SA 4.0 许可协议

Answer 1

下面是函数，它重建字典只使用这个 loadmat 而不是 scipy.io 的 loadmat：

 import scipy.io as spio

def loadmat(filename):
    '''
    this function should be called instead of direct spio.loadmat
    as it cures the problem of not properly recovering python dictionaries
    from mat files. It calls the function check keys to cure all entries
    which are still mat-objects
    '''
    data = spio.loadmat(filename, struct_as_record=False, squeeze_me=True)
    return _check_keys(data)

def _check_keys(dict):
    '''
    checks if entries in dictionary are mat-objects. If yes
    todict is called to change them to nested dictionaries
    '''
    for key in dict:
        if isinstance(dict[key], spio.matlab.mio5_params.mat_struct):
            dict[key] = _todict(dict[key])
    return dict

def _todict(matobj):
    '''
    A recursive function which constructs from matobjects nested dictionaries
    '''
    dict = {}
    for strg in matobj._fieldnames:
        elem = matobj.__dict__[strg]
        if isinstance(elem, spio.matlab.mio5_params.mat_struct):
            dict[strg] = _todict(elem)
        else:
            dict[strg] = elem
    return dict

原文由 mergen 发布，翻译遵循 CC BY-SA 3.0 许可协议

Answer 2

只是对 mergen 答案的增强，不幸的是，如果它到达对象的单元格数组，它将停止递归。以下版本将改为列出它们，并在可能的情况下继续递归到元胞数组元素中。

 import scipy.io as spio
import numpy as np

def loadmat(filename):
    '''
    this function should be called instead of direct spio.loadmat
    as it cures the problem of not properly recovering python dictionaries
    from mat files. It calls the function check keys to cure all entries
    which are still mat-objects
    '''
    def _check_keys(d):
        '''
        checks if entries in dictionary are mat-objects. If yes
        todict is called to change them to nested dictionaries
        '''
        for key in d:
            if isinstance(d[key], spio.matlab.mio5_params.mat_struct):
                d[key] = _todict(d[key])
        return d

    def _todict(matobj):
        '''
        A recursive function which constructs from matobjects nested dictionaries
        '''
        d = {}
        for strg in matobj._fieldnames:
            elem = matobj.__dict__[strg]
            if isinstance(elem, spio.matlab.mio5_params.mat_struct):
                d[strg] = _todict(elem)
            elif isinstance(elem, np.ndarray):
                d[strg] = _tolist(elem)
            else:
                d[strg] = elem
        return d

    def _tolist(ndarray):
        '''
        A recursive function which constructs lists from cellarrays
        (which are loaded as numpy ndarrays), recursing into the elements
        if they contain matobjects.
        '''
        elem_list = []
        for sub_elem in ndarray:
            if isinstance(sub_elem, spio.matlab.mio5_params.mat_struct):
                elem_list.append(_todict(sub_elem))
            elif isinstance(sub_elem, np.ndarray):
                elem_list.append(_tolist(sub_elem))
            else:
                elem_list.append(sub_elem)
        return elem_list
    data = spio.loadmat(filename, struct_as_record=False, squeeze_me=True)
    return _check_keys(data)

原文由 jpapon 发布，翻译遵循 CC BY-SA 4.0 许可协议