.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/comparisons/compare_E.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_comparisons_compare_E.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_comparisons_compare_E.py:


Comparing Calculated and TSUNAMI-IP E for a Set of SDF Files
============================================================

This example demonstrates the use of ``tsunami_ip_utils`` for calculating the similarity index :math:`E` for a set of TSUNAMI-IP 
SDF files and comparing to the TSUNAMI-IP calculated value. This example uses a set of 12 SDF files generated from the highly 
enriched uranium fast metal (HMF) series of critical experiments [Bess2019]_.

.. GENERATED FROM PYTHON SOURCE LINES 9-20

.. code-block:: Python

    from tsunami_ip_utils.comparisons import E_calculation_comparison
    import pandas as pd
    import pickle
    from paths import EXAMPLES

    # Create a list of application and experiment SDF files (same set of applications and experiments)
    applications = [ f"{EXAMPLES}/data/example_sdfs/HMF/HEU-MET-FAST-003-00{i}.sdf" for i in range(1, 3) ]
    experiments = applications
    data = E_calculation_comparison(applications, experiments)
    E_total_comparison = data['total']





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 .. code-block:: none

    Done with calculations
    Creating pandas dataframes




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The output is a pandas dataframe. The application is the first index, and can be accessed via passing the application
number as an index. For example, to access the first application:

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.. code-block:: Python


    print(E_total_comparison[1])





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 .. code-block:: none

    Attribute                    Calculated Manual Uncertainty  ... Relative Difference in Mean Relative Difference in Manual Uncertainty
    Experiment Number                                           ...                                                                      
    1                  1.000E+00+/-1.71E-03           1.71E-03  ...                  3.3307E-15                                2.8798E-01
    2                  9.983E-01+/-1.72E-03           1.72E-03  ...                  2.8934E-04                                2.8323E-01

    [2 rows x 5 columns]




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From the output, you can see that each application has a few attributes that are calculated. The attributes are:

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.. code-block:: Python


    print(E_total_comparison[1].keys())





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    Index(['Calculated', 'Manual Uncertainty', 'TSUNAMI-IP',
           'Relative Difference in Mean',
           'Relative Difference in Manual Uncertainty'],
          dtype='object', name='Attribute')




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To access, for example, the calculated E value, you can pass the appropriate key

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.. code-block:: Python


    print(E_total_comparison[1]['Calculated'])





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    Experiment Number
    1    1.000E+00+/-1.71E-03
    2    9.983E-01+/-1.72E-03
    Name: Calculated, dtype: object




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This gives the calculated E value for application 1 compared to all other experiments. To select a specific application
experiement pair, you can pass the experiment number as the second index. For example, to access the calculated E value
for application 1 compared to experiment 2:

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.. code-block:: Python


    print(E_total_comparison[1]['Calculated'][2])





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 .. code-block:: none

    9.983E-01+/-1.72E-03




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Now the pandas dataframe comparison for each of the :math:`E` types (i.e. :math:`E_{\text{total}}`, :math:`E_{\text{fission}}`, 
:math:`E_{\text{capture}}`, and :math:`E_{\text{scatter}}`) can be written to an excel file. The gold standard data is also 
provided in the ``gold`` directory.

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.. code-block:: Python


    # Save output data as xlsx
    for E_type in data.keys():
        data[E_type].to_excel(f"{E_type}_comparison.xlsx")

    # Test that the calculated E comparison is the same as the gold standard
    for E_type in data.keys():
        dumped_data = pd.read_excel( f"{E_type}_comparison.xlsx"  )
        gold_data   = pd.read_excel( f"{EXAMPLES}/gold/{E_type}_comparison.xlsx" )

        assert dumped_data.equals( gold_data )








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References
----------
.. [Bess2019] J. Bess, "International handbook of evaluated criticality safety benchmark experiments (ICSBEP)," 
  Organization for Economic Co-operation and Development-Nuclear Energy Agency Report NEA/NSC/DOC (95) 3 (2019).


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 47.520 seconds)


.. _sphx_glr_download_auto_examples_comparisons_compare_E.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: compare_E.ipynb <compare_E.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: compare_E.py <compare_E.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: compare_E.zip <compare_E.zip>`


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 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_