Read HDF5 file into numpy array

Question

I have the following code to read a hdf5 file as a numpy array:

hf = h5py.File('path/to/file', 'r') n1 = hf.get('dataset_name') n2 = np.array(n1) 

and when I print n2 I get this:

Out[15]: array([[<HDF5 object reference>, <HDF5 object reference>,         <HDF5 object reference>, <HDF5 object reference>... 

How can I read the HDF5 object reference to view the data stored in it?

Answer 1

The easiest thing is to use the .value attribute of the HDF5 dataset.

>>> hf = h5py.File('/path/to/file', 'r') >>> data = hf.get('dataset_name').value # `data` is now an ndarray. 

You can also slice the dataset, which produces an actual ndarray with the requested data:

>>> hf['dataset_name'][:10] # produces ndarray as well 

But keep in mind that in many ways the h5py dataset acts like an ndarray. So you can pass the dataset itself unchanged to most, if not all, NumPy functions. So, for example, this works just fine: np.mean(hf.get('dataset_name')).

EDIT:

I misunderstood the question originally. The problem isn't loading the numerical data, it's that the dataset actually contains HDF5 references. This is a strange setup, and it's kind of awkward to read in h5py. You need to dereference each reference in the dataset. I'll show it for just one of them.

First, let's create a file and a temporary dataset:

>>> f = h5py.File('tmp.h5', 'w') >>> ds = f.create_dataset('data', data=np.zeros(10,)) 

Next, create a reference to it and store a few of them in a dataset.

>>> ref_dtype = h5py.special_dtype(ref=h5py.Reference) >>> ref_ds = f.create_dataset('data_refs', data=(ds.ref, ds.ref), dtype=ref_dtype) 

Then you can read one of these back, in a circuitous way, by getting its name ,and then reading from that actual dataset that is referenced.

>>> name = h5py.h5r.get_name(ref_ds[0], f.id) # 2nd argument is the file identifier >>> print(name) b'/data' >>> out = f[name] >>> print(out.shape) (10,) 

It's round-about, but it seems to work. The TL;DR is: get the name of the referenced dataset, and read directly from that.

Note:

The h5py.h5r.dereference function seems pretty unhelpful here, despite the name. It returns the ID of the referenced object. This can be read from directly, but it's very easy to cause a crash in this case (I did it several times in this contrived example here). Getting the name and reading from that is much easier.

Note 2:

As stated in the release notes for h5py 2.1, the use of Dataset.value property is deprecated and should be replaced by using mydataset[...] or mydataset[()] as appropriate.

The property Dataset.value, which dates back to h5py 1.0, is deprecated and will be removed in a later release. This property dumps the entire dataset into a NumPy array. Code using .value should be updated to use NumPy indexing, using mydataset[...] or mydataset[()] as appropriate.

Answer 2

Here is a direct approach to read hdf5 file as a numpy array:

import numpy as np import h5py  hf = h5py.File('path/to/file.h5', 'r') n1 = np.array(hf["dataset_name"][:]) #dataset_name is same as hdf5 object name   print(n1)