.. vim: set fileencoding=utf-8 :
.. Andre Anjos <andre.dos.anjos@gmail.com>
.. Fri 16 May 11:48:13 2014 CEST

============
 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.

.. testsetup:: *

   import numpy
   import bob.io.base
   import tempfile
   import os

   current_directory = os.path.realpath(os.curdir)
   temp_dir = tempfile.mkdtemp(prefix='bob_doctest_')
   os.chdir(temp_dir)

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.

.. doctest::

   >>> 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:

.. code-block:: none

  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:

.. doctest::

  >>> 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``:

.. code-block:: none

  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:

.. doctest::

  >>> 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:

.. code-block:: none

  ...
   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:

.. doctest::

  >>> 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.

.. doctest::

  >>> 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:

.. code-block:: none

  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:

.. doctest::

  >>> 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.


Pythonic operations on HDF5 files
---------------------------------

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

* Use the ``with`` statement to open (and automatically close) HDF5 files.
* iterate over :py:class:`bob.io.base.HDF5File` objects to get an
  iterable of keys instead of calling :py:meth:`bob.io.base.HDF5File.keys`.
* use the ``in`` keyword instead of calling
  :py:meth:`bob.io.base.HDF5File.has_key`.
* use Python's dictionary syntax instead of :py:meth:`bob.io.base.HDF5File.set`
  and :py:meth:`bob.io.base.HDF5File.get`.

For example:


.. doctest::

  >>> 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 |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:

.. doctest::

  >>> 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:

.. doctest::

  >>> 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:

.. doctest::

  >>> 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:

.. doctest::

  >>> 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:

.. doctest::

  >>> 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.


.. Place here your external references
.. include:: links.rst
.. _ddl: http://www.hdfgroup.org/HDF5/doc/ddl.html