=============================================================================
Example3 - Multisetup with Post Separate Estimation Re-scaling (PoSER) 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 SSIcov
    from pyoma2.setup import MultiSetup_PoSER, SingleSetup
    from pyoma2.support.utils.sample_data import get_sample_data

For the **PoSER** approach, after importing the necessary modules and loading the data, the next step is to create a separate instance of the single setup class for each available dataset.

The exact natural frequencies of the system are: 2.63186, 2.69173, 3.43042, 8.29742, 8.42882, 10.6272, 14.0053, 14.093, 17.5741

.. 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)

    # create single setup
    ss1 = SingleSetup(set1, fs=100)
    ss2 = SingleSetup(set2, fs=100)
    ss3 = SingleSetup(set3, fs=100)

    # Detrend and decimate
    ss1.decimate_data(q=2)
    ss2.decimate_data(q=2)
    ss3.decimate_data(q=2)

The process for obtaining the modal properties from each setup remains the same as described in the example for the single setup.

.. code-block:: python

    # Initialise the algorithms for setup 1
    ssicov1 = SSIcov(name="SSIcov_s1", method="cov", br=50, ordmax=80)
    # Add algorithms to the class
    ss1.add_algorithms(ssicov1)
    ss1.run_all()

    # Initialise the algorithms for setup 2
    ssicov2 = SSIcov(name="SSIcov_s2", method="cov", br=50, ordmax=80)
    ss2.add_algorithms(ssicov2)
    ss2.run_all()

    # Initialise the algorithms for setup 3
    ssicov3 = SSIcov(name="SSIcov_s3", method="cov", br=50, ordmax=80)
    ss3.add_algorithms(ssicov3)
    ss3.run_all()

    # Plot stabilisation chart
    _, _ = ssicov1.plot_stab(freqlim=(1,20))
    _, _ = ssicov2.plot_stab(freqlim=(1,20))
    _, _ = ssicov3.plot_stab(freqlim=(1,20))

    # Extract results
    ss1.mpe(
        "SSIcov_s1",
        sel_freq=[2.63, 2.69, 3.43, 8.29, 8.42, 10.62, 14.00, 14.09, 17.57],
        order_in=50)
    ss2.mpe(
        "SSIcov_s2",
        sel_freq=[2.63, 2.69, 3.43, 8.29, 8.42, 10.62, 14.00, 14.09, 17.57],
        order_in=40)
    ss3.mpe(
        "SSIcov_s3",
        sel_freq=[2.63, 2.69, 3.43, 8.29, 8.42, 10.62, 14.00, 14.09, 17.57],
        order_in=40)

.. figure:: /img/Ex3-Fig1.png
.. figure:: /img/Ex3-Fig2.png
.. figure:: /img/Ex3-Fig3.png

After analyzing all datasets, the ``MultiSetup_PoSER`` class can be instantiated by passing the processed single setup and the lists of reference indices. Subsequently, the ``merge_results()`` method is used to combine the results.

.. code-block:: python

    # reference indices
    ref_ind = [[0, 1, 2], [0, 1, 2], [0, 1, 2]]
    # Creating Multi setup
    msp = MultiSetup_PoSER(ref_ind=ref_ind, single_setups=[ss1, ss2, ss3], names=["SSIcov"])

    # Merging results from single setups
    result = msp.merge_results()

    # dictionary of merged results
    res_ssicov = dict(result[SSIcov.__name__])
    result["SSIcov"].Fn

   >>> array([ 2.63245926,  2.69030811,  3.4256547 ,  8.29328508,  8.42526299,
               10.60096486, 13.99307818, 14.09286017, 17.46931459])

Once the class has been instantiated we can define the "global" geometry on it and then plot or animate the mode shapes

.. 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)

.. code-block:: python

    # define results variable
    algoRes = result[SSIcov.__name__]

    # Plot mode 2 (geometry 1)
    _, _ = msp.plot_mode_geo1(
          algo_res=algoRes, mode_nr=2, scaleF=2)

.. code-block:: python

    # Plot mode 1 (geometry 2, pyvista)
    _ = msp.plot_mode_geo2(
          algo_res=algoRes, mode_nr=1, scaleF=3, notebook=True)

.. code-block:: python

    # Plot mode 4 (geometry 2, matplotlib)
    _, _ = msp.plot_mode_geo2_mpl(
          algo_res=algoRes, mode_nr=4, view="xz", scaleF=3)

.. code-block:: python

    # Animate mode 5 (geometry 2, pyvista)
    _ = msp.anim_mode_geo2(
          algo_res=algoRes, mode_nr=5, scaleF=3, notebook=True)

.. figure:: /img/Ex3-Fig4.png
.. figure:: /img/Ex3-Fig5.png
.. figure:: /img/Ex3-Fig6.png

.. image:: /img/Ex3-Fig7.gif

.. code-block:: python

    algoRes.Fn

   >>> array([ 2.63203919,  2.69132343,  3.4254799 ,  8.29357079,  8.42973383,
       10.60678491, 14.00410737, 14.08557463, 17.42890419])