User Guide

This section gives an overview of the operations for storing and retrieving the basic data structures in Bob, such as NumPy arrays. Bob uses HDF5 format for storing binary coded data. Using the Bob 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 Bob 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 Bob, 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 bob.io.base.HDF5File object. You can do this using the method bob.io.base.HDF5File.cd(). Look up its help for more information and usage instructions.

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)
>>> f.close()

The result of running h5dump on the file testfile1.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'))
>>> numpy.allclose((f.lread('arrayset', 0)),[ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.])
True
>>> numpy.allclose( (f.lread('arrayset', 2)), [  0.,   3.,   6.,   9.,  12.,  15.,  18.,  21.,  24.,  27.])
True
>>> 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')
>>> numpy.allclose((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.]])
True

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.

Pythonic operations on HDF5 files

You can use some Pythonic opertations on bob.io.base.HDF5File:

For example:

>>> f = bob.io.base.HDF5File('testfile3.hdf5', 'w')
>>> array = numpy.arange(5)
>>> f['my_array'] = array # f.set('my_array', array)
>>> f['my_array'] # f.get('my_array')
array([0, 1, 2, 3, 4])
>>> 'my_array' in f # f.has_key('my_array')
True
>>> [key for key in f] # f.keys()
['/my_array']
>>> f.create_group('group1')
>>> f.cd('group1')
>>> f['my_array_in_group'] = array
>>> f.cd('/')
>>> # keys(), values(), and items() just like a dictionary
>>> [key for key in f.keys()]
['/my_array', '/group1/my_array_in_group']
>>> [value for value in f.values()]
[array([0, 1, 2, 3, 4]), array([0, 1, 2, 3, 4])]
>>> [(key, value) for key, value in f.items()]
[('/my_array', array([0, 1, 2, 3, 4])), ('/group1/my_array_in_group', array([0, 1, 2, 3, 4]))]
>>> f.close()
>>> # using a with statement to open and close files
>>> with bob.io.base.HDF5File('testfile3.hdf5', 'a') as f:
...   f['second_array'] = array
>>> f = bob.io.base.HDF5File('testfile3.hdf5', 'r')
>>> 'second_array' in f
True

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()
>>> numpy.allclose(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.]])
True

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')
>>> numpy.allclose(t, [[  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.]])
True

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

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

Note

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