blocks|key|2975493|text|h5py提供datasets和groups的模型。前者基本上是数组，后者可以看作是目录。每一个都是命名的。您应该查看API的文档和示例：|type|unstyled|depth|inlineStyleRanges|offset|length|style|BOLD|entityRanges|data|2975494|http://docs.h5py.org/en/latest/quick.html|2975495|在一个简单的示例中，您预先创建了所有数据，并且只想将其保存到一个hdf5文件中，如下所示：|2975496|In+[1]:+import+numpy+as+np
In+[2]:+import+h5py
In+[3]:+a+=+np.random.random(size=(100,20))
In+[4]:+h5f+=+h5py.File('data.h5',+'w')
In+[5]:+h5f.create_dataset('dataset_1',+data=a)
Out[5]:+<HDF5+dataset+"dataset_1":+shape+(100,+20),+type+"<f8">

In+[6]:+h5f.close()|code-block|syntax|javascript|2975497|然后，您可以使用以下命令将该数据加载回去：‘|2975498|In+[10]:+h5f+=+h5py.File('data.h5','r')
In+[11]:+b+=+h5f['dataset_1'][:]
In+[12]:+h5f.close()

In+[13]:+np.allclose(a,b)
Out[13]:+True|2975499|一定要看看这些文档：|2975500|http://docs.h5py.org|2975501|写入hdf5文件依赖于h5py或pytables+(每种方法都有一个位于hdf5文件规范之上的不同的python+API+)。您还应该看看numpy本身提供的其他简单的二进制格式，如np.save、np.savez等：|CODE|2975502|http://docs.scipy.org/doc/numpy/reference/routines.io.html|2975503|entityMap|0|LINK|mutability|MUTABLE|url|1|http://docs.h5py.org/|2^0|6|8|F|6|0|0|15|0|0|0|0|0|0|0|0|K|1|0|2J|7|2R|8|0|0|1M|2|0^^$0|@$1|2|3|4|5|6|7|1B|8|@$9|1C|A|1D|B|C]|$9|1E|A|1F|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|1G|8|@]|D|@$9|1H|A|1I|1|1J]]|E|$]]|$1|H|3|I|5|6|7|1K|8|@]|D|@]|E|$]]|$1|J|3|K|5|L|7|1L|8|@]|D|@]|E|$M|N]]|$1|O|3|P|5|6|7|1M|8|@]|D|@]|E|$]]|$1|Q|3|R|5|L|7|1N|8|@]|D|@]|E|$M|N]]|$1|S|3|T|5|6|7|1O|8|@]|D|@]|E|$]]|$1|U|3|V|5|6|7|1P|8|@]|D|@$9|1Q|A|1R|1|1S]]|E|$]]|$1|W|3|X|5|6|7|1T|8|@$9|1U|A|1V|B|Y]|$9|1W|A|1X|B|Y]]|D|@]|E|$]]|$1|Z|3|10|5|6|7|1Y|8|@]|D|@$9|1Z|A|20|1|21]]|E|$]]|$1|11|3|-4|5|6|7|22|8|@]|D|@]|E|$]]]|12|$13|$5|14|15|16|E|$17|G]]|18|$5|14|15|16|E|$17|19]]|1A|$5|14|15|16|E|$17|10]]]]

h5py provides a model of datasets and groups. The former is basically arrays and the latter you can think of as directories. Each is named. You should look at the documentation for the API and examples:

<a href="http://docs.h5py.org/en/latest/quick.html">http://docs.h5py.org/en/latest/quick.html</a>

A simple example where you are creating all of the data upfront and just want to save it to an hdf5 file would look something like:

<pre><code>In [1]: import numpy as np
In [2]: import h5py
In [3]: a = np.random.random(size=(100,20))
In [4]: h5f = h5py.File('data.h5', 'w')
In [5]: h5f.create_dataset('dataset_1', data=a)
Out[5]: &lt;HDF5 dataset "dataset_1": shape (100, 20), type "&lt;f8"&gt;

In [6]: h5f.close()
</code></pre>

You can then load that data back in using:
'

<pre><code>In [10]: h5f = h5py.File('data.h5','r')
In [11]: b = h5f['dataset_1'][:]
In [12]: h5f.close()

In [13]: np.allclose(a,b)
Out[13]: True
</code></pre>

Definitely check out the docs:

<a href="http://docs.h5py.org">http://docs.h5py.org</a>

Writing to hdf5 file depends either on h5py or pytables (each has a different python API that sits on top of the hdf5 file specification). You should also take a look at other simple binary formats provided by numpy natively such as <code>np.save</code>, <code>np.savez</code> etc:

<a href="http://docs.scipy.org/doc/numpy/reference/routines.io.html">http://docs.scipy.org/doc/numpy/reference/routines.io.html</a>

blocks|key|2975569|text|处理文件打开/关闭并避免内存泄漏的A+cleaner+way：|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|2975570|准备：|style|BOLD|2975571|import+numpy+as+np
import+h5py

data_to_write+=+np.random.random(size=(100,20))+#+or+some+such|code-block|syntax|javascript|2975572|写入：|2975573|with+h5py.File('name-of-file.h5',+'w')+as+hf:
++++hf.create_dataset("name-of-dataset",++data=data_to_write)|2975574|Read:|2975575|with+h5py.File('name-of-file.h5',+'r')+as+hf:
++++data+=+hf['name-of-dataset'][:]|2975576|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/a/3012921/483647^0|H|D|0|0|0|3|0|0|0|3|0|0|0|5|0|0^^$0|@$1|2|3|4|5|6|7|12|8|@]|9|@$A|13|B|14|1|15]]|C|$]]|$1|D|3|E|5|6|7|16|8|@$A|17|B|18|F|G]]|9|@]|C|$]]|$1|H|3|I|5|J|7|19|8|@]|9|@]|C|$K|L]]|$1|M|3|N|5|6|7|1A|8|@$A|1B|B|1C|F|G]]|9|@]|C|$]]|$1|O|3|P|5|J|7|1D|8|@]|9|@]|C|$K|L]]|$1|Q|3|R|5|6|7|1E|8|@$A|1F|B|1G|F|G]]|9|@]|C|$]]|$1|S|3|T|5|J|7|1H|8|@]|9|@]|C|$K|L]]|$1|U|3|-4|5|6|7|1I|8|@]|9|@]|C|$]]]|V|$W|$5|X|Y|Z|C|$10|11]]]]

<a href="https://stackoverflow.com/a/3012921/483647">A cleaner way</a> to handle file open/close and avoid memory leaks:

Prep: 

<pre><code>import numpy as np
import h5py

data_to_write = np.random.random(size=(100,20)) # or some such
</code></pre>

Write: 

<pre><code>with h5py.File('name-of-file.h5', 'w') as hf:
 hf.create_dataset("name-of-dataset", data=data_to_write)
</code></pre>

Read: 

<pre><code>with h5py.File('name-of-file.h5', 'r') as hf:
 data = hf['name-of-dataset'][:]
</code></pre>

I have a Python code whose output is a <img src="https://i.stack.imgur.com/keJsR.png" alt="enter image description here"> sized matrix, whose entries are all of the type <code>float</code>. If I save it with the extension <code>.dat</code> the file size is of the order of 500 MB. I read that using <code>h5py</code> reduces the file size considerably. So, let's say I have the 2D numpy array named <code>A</code>. How do I save it to an h5py file?
Also, how do I read the same file and put it as a numpy array in a different code, as I need to do manipulations with the array?

Input and output numpy arrays to h5py

Python

我有一个Python代码，它的输出是大小矩阵，其条目均为float类型。如果我用扩展名.dat保存它，那么文件大小大约是500MB。我读到使用h5py可以显著减少文件大小。因此，假设我有一个名为A的2Dnumpy数组。如何将其保存为h5py文件？另外，我如何读取相同的文件并将其作为numpy数组放入不同的代码中，因为我...

问将numpy数组输入和输出到h5py
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