User Guide¶

HDF5 standard utilities¶

Before explaining the basics of reading and writing to HDF5 files, it is important to list some HDF5 standard utilities for checking the content of an HDF5 file. These are supplied by the HDF5 project.

h5dump

Dumps the content of the file using the DDL.

h5ls

Lists the content of the file using DDL, but does not show the data.

h5diff

Finds the differences between HDF5 files.

I/O operations using the class bob.io.base.HDF5File¶

Writing operations¶

Let’s take a look at how to write simple scalar data such as integers or floats.

>>> an_integer = 5
>>> a_float = 3.1416
>>> f = bob.io.base.HDF5File('testfile1.hdf5', 'w')
>>> f.set('my_integer', an_integer)
>>> f.set('my_float', a_float)
>>> del f

If after this you use the h5dump utility on the file testfile1.hdf5, you will verify that the file now contains:

HDF5 "testfile1.hdf5" {
GROUP "/" {
  DATASET "my_float" {
     DATATYPE  H5T_IEEE_F64LE
     DATASPACE  SIMPLE { ( 1 ) / ( 1 ) }
     DATA {
     (0): 3.1416
     }
  }
  DATASET "my_integer" {
     DATATYPE  H5T_STD_I32LE
     DATASPACE  SIMPLE { ( 1 ) / ( 1 ) }
     DATA {
     (0): 5
     }
  }
}
}

The dump shows that there are two datasets inside a group named / in the file. HDF5 groups are like file system directories. They create namespaces for the data. In the root group (or directory), you will find the two variables, named as you set them to be. The variable names are the complete path to the location where they live. You could write a new variable in the same file but in a different directory like this:

>>> f = bob.io.base.HDF5File('testfile1.hdf5', 'a')
>>> f.create_group('/test')
>>> f.set('/test/my_float', numpy.float32(6.28))
>>> del f

Line 1 opens the file for reading and writing, but without truncating it. This will allow you to access the file contents. Next, the directory /test is created and a new variable is written inside the subdirectory. As you can verify, for simple scalars, you can also force the storage type. Where normally one would have a 64-bit real value, you can impose that this variable is saved as a 32-bit real value. You can verify the dump correctness with h5dump:

GROUP "/" {
...
 GROUP "test" {
    DATASET "my_float" {
       DATATYPE  H5T_IEEE_F32LE
       DATASPACE  SIMPLE { ( 1 ) / ( 1 ) }
       DATA {
       (0): 6.28
       }
    }
 }
}

Notice the subdirectory test has been created and inside it a floating point number has been stored. Such a float point number has a 32-bit precision as it was defined.

Writing arrays is a little simpler as the numpy.ndarray objects encode all the type information we need to write and read them correctly. Here is an example:

>>> A = numpy.array(range(4), 'int8').reshape(2,2)
>>> f = bob.io.base.HDF5File('testfile1.hdf5', 'a')
>>> f.set('my_array', A)
>>> del f

The result of running h5dump on the file testfile3.hdf5 should be:

...
 DATASET "my_array" {
    DATATYPE  H5T_STD_I8LE
    DATASPACE  SIMPLE { ( 2, 2 ) / ( 2, 2 ) }
    DATA {
    (0,0): 0, 1,
    (1,0): 2, 3
    }
 }
...

You don’t need to limit yourself to single variables, you can also save lists of scalars and arrays using the function bob.io.base.HDF5File.append() instead of bob.io.base.HDF5File.set().

Reading operations¶

Reading data from a file that you just wrote to is just as easy. For this task you should use bob.io.base.HDF5File.read(). The read method will read all the contents of the variable pointed to by the given path. This is the normal way to read a variable you have written with bob.io.base.HDF5File.set(). If you decided to create a list of scalar or arrays, the way to read that up would be using bob.io.base.HDF5File.lread() instead. Here is an example:

>>> f = bob.io.base.HDF5File('testfile1.hdf5') #read only
>>> f.read('my_integer') #reads integer
5
>>> print(f.read('my_array')) # reads the array
[[0 1]
 [2 3]]
>>> del f

Now let’s look at an example where we have used bob.io.base.HDF5File.append() instead of bob.io.base.HDF5File.set() to write data to a file. That is normally the case when you write lists of variables to a dataset.

>>> f = bob.io.base.HDF5File('testfile2.hdf5', 'w')
>>> f.append('arrayset', numpy.array(range(10), 'float64'))
>>> f.append('arrayset', 2*numpy.array(range(10), 'float64'))
>>> f.append('arrayset', 3*numpy.array(range(10), 'float64'))
>>> print(f.lread('arrayset', 0))
[ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9.]
>>> print(f.lread('arrayset', 2))
[  0.   3.   6.   9.  12.  15.  18.  21.  24.  27.]
>>> del f

This is what the h5dump of the file would look like:

HDF5 "testfile4.hdf5" {
GROUP "/" {
   DATASET "arrayset" {
      DATATYPE  H5T_IEEE_F64LE
      DATASPACE  SIMPLE { ( 3, 10 ) / ( H5S_UNLIMITED, 10 ) }
      DATA {
      (0,0): 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
      (1,0): 0, 2, 4, 6, 8, 10, 12, 14, 16, 18,
      (2,0): 0, 3, 6, 9, 12, 15, 18, 21, 24, 27
      }
   }
}
}

Notice that the expansion limits for the first dimension have been correctly set by Bob so you can insert an unlimited number of 1D float vectors. Of course, you can also read the whole contents of the arrayset in a single shot:

>>> f = bob.io.base.HDF5File('testfile2.hdf5')
>>> print(f.read('arrayset'))
[[  0.   1.   2.   3.   4.   5.   6.   7.   8.   9.]
 [  0.   2.   4.   6.   8.  10.  12.  14.  16.  18.]
 [  0.   3.   6.   9.  12.  15.  18.  21.  24.  27.]]

As you can see, the only difference between bob.io.base.HDF5File.read() and bob.io.base.HDF5File.lread() is on how Bob considers the available data (as a single array with N dimensions or as a set of arrays with N-1 dimensions). In the first example, you would have also been able to read the variable my_array as an arrayset using bob.io.base.HDF5File.lread() instead of bob.io.base.HDF5File.read(). In this case, each position readout would return a 1D uint8 array instead of a 2D array.

Array interfaces¶

What we have shown so far is the generic API to read and write data using HDF5. You will use it when you want to import or export data from Bob into other software frameworks, debug your data or just implement your own classes that can serialize and de-serialize from HDF5 file containers. In Bob, most of the time you will be working with numpy.ndarrays. In special situations though, you may be asked to handle bob.io.base.Files. bob.io.base.File objects create a transparent connection between C++ (Blitz++) / Python (NumPy) arrays and file access. You specify the filename from which you want to input data and the bob.io.base.File object decides what is the best codec to be used (from the extension) and how to read the data back into your array.

To create an bob.io.base.File from a file path, just do the following:

>>> a = bob.io.base.File('testfile2.hdf5', 'r')
>>> a.filename
'testfile2.hdf5'

bob.io.base.Files simulate containers for numpy.ndarrays, transparently accessing the file data when requested. Note, however, that when you instantiate an bob.io.base.File it does not load the file contents into memory. It waits until you emit another explicit instruction to do so. We do this with the bob.io.base.File.read() method:

>>> array = a.read()
>>> array
array([[  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.],
       [  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.],
       [  0.,   3.,   6.,   9.,  12.,  15.,  18.,  21.,  24.,  27.]])

Every time you say bob.io.base.File.read(), the file contents will be read from the file and into a new array.

Saving arrays to the bob.io.base.File is as easy, just call the bob.io.base.File.write() method:

>>> f = bob.io.base.File('copy1.hdf5', 'w')
>>> f.write(array)

Numpy ndarray shortcuts¶

To just load an numpy.ndarray in memory, you can use a short cut that lives at bob.io.base.load(). With it, you don’t have to go through the bob.io.base.File container:

>>> t = bob.io.base.load('testfile2.hdf5')
>>> t
array([[  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.],
       [  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.],
       [  0.,   3.,   6.,   9.,  12.,  15.,  18.,  21.,  24.,  27.]])

You can also directly save numpy.ndarrays without going through the bob.io.base.File container:

>>> bob.io.base.save(t, 'copy2.hdf5')

Loading and saving audio files¶

Bob does not yet support audio files (no wav codec). However, it is possible to use the SciPy module scipy.io.wavfile to do the job. For instance, to read a wave file, just use the scipy.io.wavfile.read() function.

>>> import scipy.io.wavfile
>>> filename = '/home/user/sample.wav'
>>> samplerate, data = scipy.io.wavfile.read(filename)
>>> print(type(data))
<... 'numpy.ndarray'>
>>> print(data.shape)
(132474, 2)

In the above example, the stereo audio signal is represented as a 2D NumPy numpy.ndarray. The first dimension corresponds to the time index (132474 frames) and the second dimesnion correpsonds to one of the audio channel (2 channels, stereo). The values in the array correpsond to the wave magnitudes.

To save a NumPy numpy.ndarray into a wave file, the scipy.io.wavfile.write() could be used, which also requires the framerate to be specified.