.. algorithm::

.. summary::

.. relatedalgorithms::

.. properties::

Description
------------

Given simulated s(q,e) data workspace and a reference OSIRIS s(q,e) workspace,
interpolates the simulated data onto the same (q, e) grid as the reference workspace.
This allows direct comparison between simulated and experimental data upon the
same axes. Currently only supports OSIRIS experimental data.

Usage
-----

**Example 1 - Interpolate a simulated data set onto the axes of an experimental
set**

.. code-block:: python

    #create a simulated S(Q,E) workspace of a sin function
    x_data = np.arange(-2., 2., 0.05)
    q_data = np.arange(0.5, 1.3, 0.1)
    y_data = np.asarray([val*(np.cos(5*x_data)+1) for val in q_data])
    y_data = y_data.flatten()
    x_data = np.tile(x_data, len(q_data))
    sim_ws= CreateWorkspace(DataX=x_data, DataY=y_data, NSpec = len(q_data),
                            VerticalAxisUnit='MomentumTransfer',
                            VerticalAxisValues=q_data)
    #create an empty OSIRIS workspace (this would be your experimental OSIRIS resolution function)
    idf_dir = config['instrumentDefinition.directory']
    osiris = LoadEmptyInstrument(idf_dir + 'OSIRIS_Definition.xml')
    osiris = CropWorkspace(osiris,  StartWorkspaceIndex=970, EndWorkspaceIndex=980)
    osiris = Rebin(osiris, [-0.6, 0.02, 0.6])
    #interpolate the two workspaces
    interpolated_ws = NMoldyn4Interpolation(sim_ws, osiris)
    print('No. of Q-values in simulation = {}'.format(sim_ws.getNumberHistograms()))
    print('No. of Q-values in reference = {}'.format(osiris.getNumberHistograms()))
    print('No. of Q-values in interpolated set = {}'.format(interpolated_ws.getNumberHistograms()))

Output:

.. code-block:: none

    No. of Q-values in simulation = 8
    No. of Q-values in reference = 11
    No. of Q-values in interpolated set = 11

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