石川图

石川图、鱼骨图、人字形图或因果图用于通过显示因果关系的联系来识别系统中的问题。来源：https://en.wikipedia.org/wiki/Ishikawa_diagram

import math

import matplotlib.pyplot as plt

from matplotlib.patches import Polygon, Wedge

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,
             angle_x: float, angle_y: float):
    """
    Draw each problem section of the Ishikawa plot.

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

    Returns
    -------
    None.

    """
    ax.annotate(str.upper(data), xy=(problem_x, problem_y),
                xytext=(angle_x, 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, default: True
        Determines whether the next cause annotation will be
        plotted above or below the previous one.

    Returns
    -------
    None.

    """
    for index, cause in enumerate(data):
        # [<x pos>, <y pos>]
        coords = [[0.02, 0],
                  [0.23, 0.5],
                  [-0.46, -1],
                  [0.69, 1.5],
                  [-0.92, -2],
                  [1.15, 2.5]]

        # First 'cause' annotation is placed in the middle of the 'problems' arrow
        # and each subsequent cause is plotted above or below it in succession.
        cause_x -= coords[index][0]
        cause_y += coords[index][1] if top else -coords[index][1]

        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 problem section in its correct place by changing
    the coordinates on each loop.

    Parameters
    ----------
    data : dict
        The input data (can be a dict of lists or tuples). ValueError
        is raised if more than six arguments are passed.

    Returns
    -------
    None.

    """
    # Set the length of the spine according to the number of 'problem' categories.
    length = (math.ceil(len(data) / 2)) - 1
    draw_spine(-2 - length, 2 + length)

    # Change the coordinates of the 'problem' annotations after each one is rendered.
    offset = 0
    prob_section = [1.55, 0.8]
    for index, problem in enumerate(data.values()):
        plot_above = index % 2 == 0
        cause_arrow_y = 1.7 if plot_above else -1.7
        y_prob_angle = 16 if plot_above else -16

        # Plot each section in pairs along the main spine.
        prob_arrow_x = prob_section[0] + length + offset
        cause_arrow_x = prob_section[1] + length + offset
        if not plot_above:
            offset -= 2.5
        if index > 5:
            raise ValueError(f'Maximum number of problems is 6, you have entered '
                             f'{len(data)}')

        problems(list(data.keys())[index], prob_arrow_x, 0, -12, y_prob_angle)
        causes(problem, cause_arrow_x, cause_arrow_y, top=plot_above)


def draw_spine(xmin: int, xmax: int):
    """
    Draw main spine, head and tail.

    Parameters
    ----------
    xmin : int
        The default position of the head of the spine's
        x-coordinate.
    xmax : int
        The default position of the tail of the spine's
        x-coordinate.

    Returns
    -------
    None.

    """
    # draw main spine
    ax.plot([xmin - 0.1, xmax], [0, 0], color='tab:blue', linewidth=2)
    # draw fish head
    ax.text(xmax + 0.1, - 0.05, 'PROBLEM', fontsize=10,
            weight='bold', color='white')
    semicircle = Wedge((xmax, 0), 1, 270, 90, fc='tab:blue')
    ax.add_patch(semicircle)
    # draw fish tail
    tail_pos = [[xmin - 0.8, 0.8], [xmin - 0.8, -0.8], [xmin, -0.01]]
    triangle = Polygon(tail_pos, fc='tab:blue')
    ax.add_patch(triangle)


# 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()