您需要的是文件中所有数据集的列表。我认为这里需要的是recursive function的概念。这将允许您从一个组中提取所有的“数据集”，但是当其中一个看起来是组本身时，递归地执行相同的操作，直到找到所有数据集为止。例如：/

|- dataset1

|- group1

|- dataset2

|- dataset3

|- dataset4

伪代码中的函数应该如下所示：

^{pr2}$

例如：/dataset1是一个数据集：将路径添加到输出，给出out = ['/dataset1']

/group不是数据集：调用getdatasets('/group',file)/group/dataset2是一个数据集：将路径添加到输出，给出nested_out = ['/group/dataset2']

/group/dataset3是一个数据集：将路径添加到输出，给出nested_out = ['/group/dataset2', '/group/dataset3']

这是我们已经拥有的：out = ['/dataset1', '/group/dataset2', '/group/dataset3']

/dataset4是一个数据集：将路径添加到输出，给出out = ['/dataset1', '/group/dataset2', '/group/dataset3', '/dataset4']

此列表可用于将所有数据复制到另一个文件中。在

要制作一个简单的克隆，可以执行以下操作。在import h5py

import numpy as np

# function to return a list of paths to each dataset

def getdatasets(key,archive):

if key[-1] != '/': key += '/'

out = []

for name in archive[key]:

path = key + name

if isinstance(archive[path], h5py.Dataset):

out += [path]

else:

out += getdatasets(path,archive)

return out

# open HDF5-files

data = h5py.File('old.hdf5','r')

new_data = h5py.File('new.hdf5','w')

# read as much datasets as possible from the old HDF5-file

datasets = getdatasets('/',data)

# get the group-names from the lists of datasets

groups = list(set([i[::-1].split('/',1)[1][::-1] for i in datasets]))

groups = [i for i in groups if len(i)>0]

# sort groups based on depth

idx = np.argsort(np.array([len(i.split('/')) for i in groups]))

groups = [groups[i] for i in idx]

# create all groups that contain dataset that will be copied

for group in groups:

new_data.create_group(group)

# copy datasets

for path in datasets:

# - get group name

group = path[::-1].split('/',1)[1][::-1]

# - minimum group name

if len(group) == 0: group = '/'

# - copy data

data.copy(path, new_data[group])

当然，进一步的定制是可能的，这取决于您想要什么。你描述了一些文件的组合。那样的话你就得new_data = h5py.File('new.hdf5','a')

可能会给这条路加些什么。在