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

.. only:: html

    .. meta::
        :keywords: codex

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

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

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

.. _sphx_glr_gallery_specialty_plots_ishikawa_diagram.py:


================
Ishikawa Diagram
================

Ishikawa Diagrams, fishbone diagrams, herringbone diagrams, or cause-and-effect
diagrams are used to identify problems in a system by showing how causes and
effects are linked.
Source: https://en.wikipedia.org/wiki/Ishikawa_diagram

.. GENERATED FROM PYTHON SOURCE LINES 12-204



.. image-sg:: /gallery/specialty_plots/images/sphx_glr_ishikawa_diagram_001.png
   :alt: ishikawa diagram
   :srcset: /gallery/specialty_plots/images/sphx_glr_ishikawa_diagram_001.png, /gallery/specialty_plots/images/sphx_glr_ishikawa_diagram_001_2_00x.png 2.00x
   :class: sphx-glr-single-img





.. code-block:: Python

    import matplotlib.pyplot as plt

    from matplotlib.patches import Polygon, Wedge

    # Create the fishbone diagram
    fig, ax = plt.subplots(figsize=(10, 6), layout='constrained')
    ax.set_xlim(-5, 5)
    ax.set_ylim(-5, 5)
    ax.axis('off')


    def problems(data: str,
                 problem_x: float, problem_y: float,
                 prob_angle_x: float, prob_angle_y: float):
        """
        Draw each problem section of the Ishikawa plot.

        Parameters
        ----------
        data : str
            The category name.
        problem_x, problem_y : float, optional
            The `X` and `Y` positions of the problem arrows (`Y` defaults to zero).
        prob_angle_x, prob_angle_y : float, optional
            The angle of the problem annotations. They are angled towards
            the tail of the plot.

        Returns
        -------
        None.

        """
        ax.annotate(str.upper(data), xy=(problem_x, problem_y),
                    xytext=(prob_angle_x, prob_angle_y),
                    fontsize='10',
                    color='white',
                    weight='bold',
                    xycoords='data',
                    verticalalignment='center',
                    horizontalalignment='center',
                    textcoords='offset fontsize',
                    arrowprops=dict(arrowstyle="->", facecolor='black'),
                    bbox=dict(boxstyle='square',
                              facecolor='tab:blue',
                              pad=0.8))


    def causes(data: list, cause_x: float, cause_y: float,
               cause_xytext=(-9, -0.3), top: bool = True):
        """
        Place each cause to a position relative to the problems
        annotations.

        Parameters
        ----------
        data : indexable object
            The input data. IndexError is
            raised if more than six arguments are passed.
        cause_x, cause_y : float
            The `X` and `Y` position of the cause annotations.
        cause_xytext : tuple, optional
            Adjust to set the distance of the cause text from the problem
            arrow in fontsize units.
        top : bool

        Returns
        -------
        None.

        """
        for index, cause in enumerate(data):
            # First cause annotation is placed in the middle of the problems arrow
            # and each subsequent cause is plotted above or below it in succession.

            # [<x pos>, [<y pos top>, <y pos bottom>]]
            coords = [[0, [0, 0]],
                      [0.23, [0.5, -0.5]],
                      [-0.46, [-1, 1]],
                      [0.69, [1.5, -1.5]],
                      [-0.92, [-2, 2]],
                      [1.15, [2.5, -2.5]]]
            if top:
                cause_y += coords[index][1][0]
            else:
                cause_y += coords[index][1][1]
            cause_x -= coords[index][0]

            ax.annotate(cause, xy=(cause_x, cause_y),
                        horizontalalignment='center',
                        xytext=cause_xytext,
                        fontsize='9',
                        xycoords='data',
                        textcoords='offset fontsize',
                        arrowprops=dict(arrowstyle="->",
                                        facecolor='black'))


    def draw_body(data: dict):
        """
        Place each section in its correct place by changing
        the coordinates on each loop.

        Parameters
        ----------
        data : dict
            The input data (can be list or tuple). ValueError is
            raised if more than six arguments are passed.

        Returns
        -------
        None.

        """
        second_sections = []
        third_sections = []
        # Resize diagram to automatically scale in response to the number
        # of problems in the input data.
        if len(data) == 1 or len(data) == 2:
            spine_length = (-2.1, 2)
            head_pos = (2, 0)
            tail_pos = ((-2.8, 0.8), (-2.8, -0.8), (-2.0, -0.01))
            first_section = [1.6, 0.8]
        elif len(data) == 3 or len(data) == 4:
            spine_length = (-3.1, 3)
            head_pos = (3, 0)
            tail_pos = ((-3.8, 0.8), (-3.8, -0.8), (-3.0, -0.01))
            first_section = [2.6, 1.8]
            second_sections = [-0.4, -1.2]
        else:  # len(data) == 5 or 6
            spine_length = (-4.1, 4)
            head_pos = (4, 0)
            tail_pos = ((-4.8, 0.8), (-4.8, -0.8), (-4.0, -0.01))
            first_section = [3.5, 2.7]
            second_sections = [1, 0.2]
            third_sections = [-1.5, -2.3]

        # Change the coordinates of the annotations on each loop.
        for index, problem in enumerate(data.values()):
            top_row = True
            cause_arrow_y = 1.7
            if index % 2 != 0:  # Plot problems below the spine.
                top_row = False
                y_prob_angle = -16
                cause_arrow_y = -1.7
            else:  # Plot problems above the spine.
                y_prob_angle = 16
            # Plot the 3 sections in pairs along the main spine.
            if index in (0, 1):
                prob_arrow_x = first_section[0]
                cause_arrow_x = first_section[1]
            elif index in (2, 3):
                prob_arrow_x = second_sections[0]
                cause_arrow_x = second_sections[1]
            else:
                prob_arrow_x = third_sections[0]
                cause_arrow_x = third_sections[1]
            if index > 5:
                raise ValueError(f'Maximum number of problems is 6, you have entered '
                                 f'{len(data)}')

            # draw main spine
            ax.plot(spine_length, [0, 0], color='tab:blue', linewidth=2)
            # draw fish head
            ax.text(head_pos[0] + 0.1, head_pos[1] - 0.05, 'PROBLEM', fontsize=10,
                    weight='bold', color='white')
            semicircle = Wedge(head_pos, 1, 270, 90, fc='tab:blue')
            ax.add_patch(semicircle)
            # draw fishtail
            triangle = Polygon(tail_pos, fc='tab:blue')
            ax.add_patch(triangle)
            # Pass each category name to the problems function as a string on each loop.
            problems(list(data.keys())[index], prob_arrow_x, 0, -12, y_prob_angle)
            # Start the cause function with the first annotation being plotted at
            # the cause_arrow_x, cause_arrow_y coordinates.
            causes(problem, cause_arrow_x, cause_arrow_y, top=top_row)


    # Input data
    categories = {
        'Method': ['Time consumption', 'Cost', 'Procedures', 'Inefficient process',
                   'Sampling'],
        'Machine': ['Faulty equipment', 'Compatibility'],
        'Material': ['Poor-quality input', 'Raw materials', 'Supplier',
                     'Shortage'],
        'Measurement': ['Calibration', 'Performance', 'Wrong measurements'],
        'Environment': ['Bad conditions'],
        'People': ['Lack of training', 'Managers', 'Labor shortage',
                   'Procedures', 'Sales strategy']
    }

    draw_body(categories)
    plt.show()


.. _sphx_glr_download_gallery_specialty_plots_ishikawa_diagram.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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