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

.. only:: html

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

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

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

.. _sphx_glr_examples_analysis_db.py:


Cluster Analysis
===============================================

MDANCE provides a pipeline to screen the optimal number of clusters for a given dataset.

The pwd of this script is ``$PATH/MDANCE/examples``.

.. GENERATED FROM PYTHON SOURCE LINES 10-11

To begin with, let's first import the modules we will use:

.. GENERATED FROM PYTHON SOURCE LINES 11-15

.. code-block:: Python


    import matplotlib.pyplot as plt
    import numpy as np








.. GENERATED FROM PYTHON SOURCE LINES 16-18

``scores_csvs`` is the list of screening csv that were outputted from `screen_nani.py`.
The output of this notebook will also be the same directory as the input csvs.

.. GENERATED FROM PYTHON SOURCE LINES 18-21

.. code-block:: Python


    scores_csvs = ['../scripts/nani/outputs/10comp_sim_summary.csv']








.. GENERATED FROM PYTHON SOURCE LINES 22-26

The function below will plot the Davies-Bouldin index and the optimal number of clusters.
  - The optimal number of clusters is determined by the minimum Davies-Bouldin index or the minimum of the second derivative of the Davies-Bouldin index.
  - Potential Errors
      - Please remember to remove the row with ``None,None`` in the screening csv if there is an error.

.. GENERATED FROM PYTHON SOURCE LINES 26-83

.. code-block:: Python


    def plot_scores(scores_csv):
        """Plot the Davies-Bouldin index and the optimal number of clusters.
    
        Parameters
        ----------
        scores_csv : str
            The path to the csv file that contains the screening
    
        Returns
        -------
        file
            A png file that contains the plot of the Davies-Bouldin index and the optimal number of clusters
        """
        base_name = scores_csv.split('\\')[-1].split('.csv')[0]
        n_clus, db = np.loadtxt(scores_csv, unpack=True, delimiter=',', usecols=(0, 3))

        # Plot the Davies-Bouldin index and the optimal number of clusters
        all_indices = np.argsort(db)
        min_db_index = all_indices[0]
        min_db = n_clus[min_db_index]
        all_indices = np.delete(all_indices, 0)
        second_min_index = all_indices[0]
        second_min_db = n_clus[second_min_index]
    
        fig, ax = plt.subplots()
        ax.plot(n_clus, db, color='#005cde', label='DBI', linewidth=2)
        ax.set_xlabel('Cluster Number')
        ax.set_ylabel('Davies-Bouldin Index')
        ax.axvline(x=min_db, color='#de005c', linestyle='--', label=f'Optimal Cluster Number: {int(min_db)}', linewidth=2)
        ax.axvline(x=second_min_db, color='#00ab64', linestyle='--', label=f'Second Optimal Cluster Number: {int(second_min_db)}', linewidth=2)

        # Calculate the second derivative (before + after - 2*current)
        arr = db
        x = n_clus[1:-1]
        result = []
        for start_index, n_clusters in zip(range(1, len(arr) - 1), x):
            temp = arr[start_index + 1] + arr[start_index - 1] - (2 * arr[start_index])
            if arr[start_index] <= arr[start_index - 1] and arr[start_index] <= arr[start_index + 1]:
                result.append((n_clusters, temp))
        result = np.array(result)
        if len(result) == 0:
            print('No maxima found')
        elif len(result) >= 1:
            sorted_indices = np.argsort(result[:, 1])[::-1]
            sorted_result = result[sorted_indices]
            min_x = sorted_result[0][0]
            ax.axvline(x=min_x, color='#de8200', linestyle='--', label=f'Optimal 2nd deriv Cluster Number: {int(min_x)}', linewidth=2)
            if len(sorted_result) >= 2:
                sec_min_x = sorted_result[1][0]
                ax.axvline(x=sec_min_x, color='#6400ab', linestyle='--', label=f'Second Optimal 2nd deriv Cluster Number: {int(sec_min_x)}', linewidth=2)
    
        ax.legend(fontsize=10)
        plt.show()
    
    if __name__ == '__main__':
        for scores_csv in scores_csvs:
            plot_scores(scores_csv)


.. image-sg:: /examples/images/sphx_glr_analysis_db_001.png
   :alt: analysis db
   :srcset: /examples/images/sphx_glr_analysis_db_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 0.079 seconds)


.. _sphx_glr_download_examples_analysis_db.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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