===========================================================================
Example4 - Multisetup with Pre Global Estimation Re-scaling (PreGER) method
===========================================================================

In this example, we'll be working with a simulated dataset generated from a finite
element model of a fictitious three-story, L-shaped building. This model was created
using OpenSeesPy, and the corresponding Python script can be found `here <https://github.com/dagghe/pyOMA-test-data/blob/main/test_data/3SL/model.py>`_.

As always, first we import the necessary modules. All the files needed to run this
example are available `here <https://github.com/dagghe/pyOMA-test-data/tree/main/test_data/3SL>`_.

.. code-block:: python

    import numpy as np
    import pandas as pd
    import matplotlib.pyplot as plt

    from pyoma2.algorithms import SSIdat_MS
    from pyoma2.setup import MultiSetup_PreGER
    from pyoma2.support.utils.sample_data import get_sample_data

For the **preGER** merging procedure, we adopt a strategy similar to that used for the single setup class. The first step involves instantiating the ``MultiSetup_PreGER`` class and passing the list of datasets, the lists of reference sensors, and their sampling frequency. Similarly to the single setup class, also for the ``MultiSetup_PreGER`` we have access to a wide set of tools to pre-process the data and get more information regarding its quality (e.g. ``decimate_data()``, ``filter_data()``, ``plot_ch_info()`` methods).

.. code-block:: python

    # import data files
    set1 = np.load(get_sample_data(filename="set1.npy", folder="3SL"), allow_pickle=True)
    set2 = np.load(get_sample_data(filename="set2.npy", folder="3SL"), allow_pickle=True)
    set3 = np.load(get_sample_data(filename="set3.npy", folder="3SL"), allow_pickle=True)

    # list of datasets and reference indices
    data = [set1, set2, set3]
    ref_ind = [[0, 1, 2], [0, 1, 2], [0, 1, 2]]

    # Create multisetup
    msp = MultiSetup_PreGER(fs=100, ref_ind=ref_ind, datasets=data)

    # decimate data
    msp.decimate_data(q=2)

    # Plot TH, PSD and KDE of the (selected) channels of the (selected) datasets
    _, _ = msp.plot_ch_info(data_idx=[1], ch_idx=[2])

.. figure:: /img/Ex4-Fig1.png

Again if we want to be able to plot the mode shapes later, then we need to define the geometry of the structure.

.. code-block:: python

    # Geometry 1
    _geo1 = get_sample_data(filename="Geo1.xlsx", folder="3SL")
    # Geometry 2
    _geo2 = get_sample_data(filename="Geo2.xlsx", folder="3SL")

    # Define geometry1
    msp.def_geo1_by_file(_geo1)
    # Define geometry 2
    msp.def_geo2_by_file(_geo2)

Now we need to instantiate the multi-setup versions of the algorithms we wish to execute, such as SSIdat.

.. code-block:: python

    # Initialise the algorithms
    ssidat = SSIdat_MS(name="SSIdat", br=80, ordmax=80)

    # Add algorithms to the class
    msp.add_algorithms(ssidat)
    msp.run_all()

    # Plot
    _, _ = ssidat.plot_stab(freqlim=(2,18))

.. figure:: /img/Ex4-Fig2.png

After the algorithms have been executed we can exctract the desired poles and plot the mode shapes.

.. code-block:: python

    # get modal parameters
    msp.mpe(
        "SSIdat",
        sel_freq=[2.63, 2.69, 3.43, 8.29, 8.42, 10.62, 14.00, 14.09, 17.57],
        order_in=80)

    # plot mode shapes
    _, _ = msp.plot_mode_geo1(algo_res=ssidat.result, mode_nr=1, view="3D", scaleF=2)
    _ = msp.plot_mode_geo2(algo_res=ssidat.result, mode_nr=6, scaleF=2, notebook=True)

.. figure:: /img/Ex4-Fig3.png
.. figure:: /img/Ex4-Fig4.png

.. code:: python

   ssidat.result.Fn

   >>>   array([ 2.63102473,  2.69617968,  3.42605687,  8.27997956,  8.41882261,
               10.59171709, 13.96998337, 14.03397164, 17.49790384])