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

.. only:: html

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

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

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

.. _sphx_glr_gallery_network_science_plot_financial_network.py:


Financial network
=================

This example demonstrates the use of ``iplotx`` to visualise financial relationships, mimicking a figure published in this paper: https://www.science.org/doi/10.1126/science.1173644.

.. GENERATED FROM PYTHON SOURCE LINES 7-243



.. image-sg:: /gallery/network_science/images/sphx_glr_plot_financial_network_001.png
   :alt: plot financial network
   :srcset: /gallery/network_science/images/sphx_glr_plot_financial_network_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    from collections import defaultdict
    import igraph as ig
    import numpy as np
    import pandas as pd
    import matplotlib.pyplot as plt
    import iplotx as ipx


    # Circle nodes
    g = ig.Graph(23, directed=True)
    layout = g.layout_circle().coords
    names = [
        "Commerzbank",
        "ING",
        "Prudential fin.",
        "HBOS",
        "Lloyds TSB",
        "Mitsubishi UFJ",
        "Generali",
        "Sumitomo",
        "Royal Bank Scotland",
        "Gen. Electric",
        "Intesa-Sanpaolo",
        "Aberdeen",
        "Mediobanca",
        "Sumitomomitsui",
        "Fidelity Mng.",
        "IFI",
        "Deutsche Bank",
        "Franklin Res.",
        "Merril Lynch",
        "Wellington Mng.",
        "Nomura",
        "Barclays",
        "Unicredito",
    ]

    # Internal nodes
    data_internal = [
        ["Bank Nova Scotia", -0.1, 0.8],
        ["Bear Stearns", -0.4, 0.6],
        ["UBS", -0.7, 0.3],
        ["Friends Provident", -0.4, -0.4],
        ["Cr. Suisse", -0.1, -0.6],
        ["Soc. Generale", -0.3, 0.2],
        ["FMR Corp", -0.15, -0.05],
        ["Bank of America", 0.25, 0.7],
        ["Santander", 0.1, 0.4],
        ["Citigroup", 0.6, -0.3],
        ["BNP Paribas", 0.6, -0.5],
        ["HSBC", 0.35, -0.2],
        ["JP Morgan Chase", 0.22, -0.6],
        ["Morgan Stanley", 0.6, 0.2],
        ["Goldman Sachs", 0.5, 0.5],
        ["Capital Group", 0.3, 0.1],
        ["Key Corp", 0.1, -0.35],
    ]
    layout.extend([x[1:] for x in data_internal])
    names.extend([x[0] for x in data_internal])
    g.add_vertices(len(layout) - g.vcount())


    g.vs["name"] = names

    g.add_edges([
        (0, 1),
        (1, 2),
        (2, 3),
        (22, 0),
        (26, 2),
        (20, 0),
        (20, 1),
        (20, 18),
        (22, 2),
        (22, 3),
        (22, 4),
        (8, 5),
        (8, 9),
        (8, 10),
        (6, 11),
        (11, 6),
        (8, 12),
        (8, 13),
        (8, 23),
        (8, 24),
        (8, 27),
        (8, 31),
        (8, 37),
        (12, 13),
        (12, 14),
        (15, 14),
        (20, 15),
        (16, 17),
        (18, 16),
        (17, 16),
        (18, 19),
        (27, 35),
        (20, 35),
        (18, 35),
        (34, 33),
        (22, 36),
        (38, 21),
        (12, 25),
        (22, 25),
        (28, 38),
        (26, 13),
        (30, 5),
        (4, 30),
        (30, 4),
        (22, 30),
        (37, 4),
        (37, 12),
        (37, 14),
        (37, 15),
        (26, 15),
        (26, 14),
        (26, 24),
        (32, 29),
        (17, 27),
        (27, 17),
        (18, 27),
        (27, 18),
        (19, 18),
        (11, 39),
        (6, 39),
        (8, 35),
        (8, 19),
        (2, 34),
        (2, 32),
        (2, 33),
        (2, 20),
        (1, 34),
        (30, 12),
        (19, 35),
        (26, 20),
        (26, 33),
        # weight 2
        (8, 26),
        # weight 3
        (16, 27),
        (16, 35),
        (16, 18),
        (22, 32),
        (22, 33),
        # weight 4
        (8, 7),
        (27, 16),
        (24, 25),
        (22, 5),
        (22, 37),
        # weight 5
        (23, 6),
        (37, 30),
    ])

    g.es["weight"] = 1
    g.es[-2 -5 -5-1:]["weight"] = 3
    g.es[-2 -5 -5:]["weight"] = 3
    g.es[-2 -5:]["weight"] = 4
    g.es[-2:]["weight"] = 5
    edge_colord = {
        1: "goldenrod",
        2: "darkgoldenrod",
        3: "peru",
        4: "saddlebrown",
        5: "black",
    }
    g.es["color"] = [edge_colord[w] for w in g.es["weight"]]
    g.es["linewidth"] = [0.5 + 0.8 * w for w in g.es["weight"]]

    vertex_colors_inv = {
        "tomato": [
            "Commerzbank",
            "ING",
            "HBOS",
            "Lloyds TSB",
            "Royal Bank Scotland",
            "Deutsche Bank",
            "Barclays",
            "Unicredito",
            "Santander",
            "BNP Paribas",
            "Cr. Suisse",
            "IFI",
            "Friends Provident",
            "Soc. Generale",
            "Mediobanca",
            "BNP Paribas",
            "HSBC",
            "UBS",
            "Aberdeen",
            "Intesa-Sanpaolo",
            "Generali",
        ],
        "limegreen": [
            "Nomura",
            "Sumitomomitsui",
            "Sumitomo",
            "Mitsubishi UFJ",
        ],
    }
    vertex_colors = ["mediumblue" for x in g.vs["name"]]
    for color, names in vertex_colors_inv.items():
        for name in names:
            try:
                idx = g.vs.find(name=name).index
            except ValueError:
                continue

            vertex_colors[idx] = color


    fig, ax = plt.subplots(figsize=(9, 9))
    ipx.network(
        g,
        layout,
        ax=ax,
        edge_color=g.es["color"],
        edge_curved=True,
        edge_tension=0.7,
        vertex_facecolor=vertex_colors,
        vertex_edgecolor="#111",
        vertex_zorder=3,
        vertex_labels=list(g.vs["name"]),
        vertex_label_color="black",
        vertex_label_size=8,
        edge_arrow_marker=")>",
        edge_arrow_width=4,
        edge_arrow_height=7,
        edge_linewidth=g.es["linewidth"],
    )

    fig.tight_layout()

    plt.ion(); plt.show()


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

   **Total running time of the script:** (0 minutes 0.264 seconds)


.. _sphx_glr_download_gallery_network_science_plot_financial_network.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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