.. _example post processing of saved field data:

Example: post processing of saved field data
--------------------------------------------

Suppose we have saved spatially resolved fields (as, for example, in
:ref:`example 2`), and we would like to read those from the data file to
process the data further.

We can use the :ref:`nmagpp` tool if it provides the required functionality.

Alternatively, we can write a Python script that:

1. reads the data from the ``_dat.h5`` file

2. carries out the required post processing and/or saves the data in
   (another) format.

The program :download:`read_h5.py </example2/read_h5.py>`
demonstrates how to read the saved configuration with id=0 of the ``m_Py``
subfield, and to print this to the screen.

.. include:: read_h5.py
  :literal:

The functions :ref:`get_subfield_from_h5file`,
:ref:`get_subfield_positions_from_h5file` and
:ref:`get_subfield_sites_from_h5file` allow in principle to retrieve all the
field data from the h5 files and stores this in the variables ``m``,
``pos``, and ``site``, respectively.


The program, when run like this::

  $ nsim read_h5.py

in the :ref:`example 2` directory, produces output starting as follows (assuming the ``bar_dat.h5`` file exists)::

  [0] [ 0.  0.  0.] [ 0.70710677  0.          0.70710677]
  [1] [  3.00000000e-09   0.00000000e+00   0.00000000e+00] [ 0.70710677  0.          0.70710677]
  [2] [  6.00000000e-09   0.00000000e+00   0.00000000e+00] [ 0.70710677  0.          0.70710677]
  [3] [  9.00000000e-09   0.00000000e+00   0.00000000e+00] [ 0.70710677  0.          0.70710677]
  [4] [  1.20000000e-08   0.00000000e+00   0.00000000e+00] [ 0.70710677  0.          0.70710677]
  [5] [  1.50000000e-08   0.00000000e+00   0.00000000e+00] [ 0.70710677  0.          0.70710677]
  [6] [  1.80000000e-08   0.00000000e+00   0.00000000e+00] [ 0.70710677  0.          0.70710677]
  [7] [  2.10000000e-08   0.00000000e+00   0.00000000e+00] [ 0.70710677  0.          0.70710677]
  [8] [  2.40000000e-08   0.00000000e+00   0.00000000e+00] [ 0.70710677  0.          0.70710677]
  [9] [  2.70000000e-08   0.00000000e+00   0.00000000e+00] [ 0.70710677  0.          0.70710677]
  [10] [  3.00000000e-08   0.00000000e+00   0.00000000e+00] [ 0.70710677  0.          0.70710677]
  [11] [  3.00000000e-08   3.00000000e-08   1.00000000e-07] [ 0.70710677  0.          0.70710677]
  [12] [  3.00000000e-08   2.70000000e-08   1.00000000e-07] [ 0.70710677  0.          0.70710677]

We can see that the :ref:`site` index is (here) just an integer, the position
(in nanometre) is shown as a triplet of three scalars, and the normalised
magnetisation is also a vector with three components.

The data (in the arrays ``m``, ``site`` and ``position`` in this
example) can be manipulated as explained in the :ref:`numpy` documentation,
because it is of type ``numpy array``. Numpy provides a powerful matrix
processing environment.