guide.rst

User Guide

This section gives an overview of the operations for storing and retrieving the basic data structures in |project|, such as `NumPy`_ arrays. |project| uses `HDF5`_ format for storing binary coded data. Using the |project| support for `HDF5`_, it is very simple to import and export data.

`HDF5`_ uses a neat descriptive language for representing the data in the HDF5 files, called Data Description Language (DDL).

To perform the functionalities given in this section, you should have `NumPy`_ and |project| loaded into the `Python`_ environment.

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
     }
  }
}
}

Note

In |project|, when you open a HDF5 file, you can choose one of the following options:

'r' Open the file in reading mode; writing operations will fail (this is the default).

'a' Open the file in reading and writing mode with appending.

'w' Open the file in reading and writing mode, but truncate it.

'x' Read/write/append with exclusive access.

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.

Note

If you need to place lots of variables in a subfolder, it may be better to setup the prefix folder before starting the writing operations on the :py:class:`bob.io.base.HDF5File` object. You can do this using the method :py:meth:`bob.io.base.HDF5File.cd`. Look up its help for more information and usage instructions.

Writing arrays is a little simpler as the :py:class:`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 :py:meth:`bob.io.base.HDF5File.append` instead of :py:meth:`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 :py:meth:`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 :py:meth:`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 :py:meth:`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 :py:meth:`bob.io.base.HDF5File.append` instead of :py:meth:`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 |project| 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 :py:meth:`bob.io.base.HDF5File.read` and :py:meth:`bob.io.base.HDF5File.lread` is on how |project| 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 :py:meth:`bob.io.base.HDF5File.lread` instead of :py:meth:`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 |project| into other software frameworks, debug your data or just implement your own classes that can serialize and de-serialize from HDF5 file containers. In |project|, most of the time you will be working with :py:class:`numpy.ndarray`s. In special situations though, you may be asked to handle :py:class:`bob.io.base.File`s. :py:class:`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 :py:class:`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 :py:class:`bob.io.base.File` from a file path, just do the following:

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

:py:class:`bob.io.base.File`s simulate containers for :py:class:`numpy.ndarray`s, transparently accessing the file data when requested. Note, however, that when you instantiate an :py:class:`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 :py:meth:`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 :py:meth:`bob.io.base.File.read`, the file contents will be read from the file and into a new array.

Saving arrays to the :py:class:`bob.io.base.File` is as easy, just call the :py:meth:`bob.io.base.File.write` method:

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

Numpy ndarray shortcuts

To just load an :py:class:`numpy.ndarray` in memory, you can use a short cut that lives at :py:func:`bob.io.base.load`. With it, you don't have to go through the :py:class:`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 :py:class:`numpy.ndarray`s without going through the :py:class:`bob.io.base.File` container:

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

Note

Under the hood, we still use the :py:class:`bob.io.base.File` API to execute the read and write operations. Have a look at the manual section for :py:mod:`bob.io.base` for more details and other shortcuts available.

Loading and saving audio files

|project| does not yet support audio files (no wav codec). However, it is possible to use the `SciPy`_ module :py:mod:`scipy.io.wavfile` to do the job. For instance, to read a wave file, just use the :py:func:`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 :py:class:`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 :py:class:`numpy.ndarray` into a wave file, the :py:func:`scipy.io.wavfile.write` could be used, which also requires the framerate to be specified.