gauge chart matplotlib

gauge.py

import math
from dataclasses import dataclass

import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
from scipy.interpolate import interp1d


def gauge_chart(
    risk_value: float,
    colors: list,
    thresholds: list,
    show_image: list = None,
    text: list = None,
    show_value: bool = True,
    text_rotated: bool = True,
    bottom: int = 1,
    height: int = 2,
    icon_size: int = 20,
):

    m = interp1d([min(thresholds), max(thresholds)], [0.0, np.pi])
    x_axis_vals = [m(x) for x in thresholds]
    width = [x - x_axis_vals[i - 1] for i, x in enumerate(x_axis_vals)][1:]

    fig = plt.figure(figsize=(7, 4))

    ax = fig.add_subplot(projection="polar")

    ax.set_thetamin(0)
    ax.set_thetamax(180)

    ax.bar(
        x=x_axis_vals[:-1],
        width=width,
        height=height,
        bottom=bottom,
        linewidth=2,
        edgecolor="white",
        color=colors,
        align="edge",
    )

    ax.set_axis_off()
    ax.set_theta_zero_location("W")  # theta=0 at the top
    ax.set_theta_direction(-1)  # theta increasing clockwise

    color_annotation = "black"
    for col, tre in zip(colors, thresholds):
        if risk_value > tre:
            color_annotation = col

    if show_value:
        ann = plt.annotate(
            str(risk_value),
            xytext=(0, 0),
            xy=(m(risk_value), 1.5),
            arrowprops=dict(
                arrowstyle="wedge, tail_width=0.25",
                # arrowstyle="-|>",
                # arrowstyle="fancy",
                color=color_annotation,
                # shrinkA=0,
                # linewidth=0.8,
                ec="black",
            ),
            bbox=dict(
                boxstyle="circle",
                pad=0.25,
                # facecolor=color_annotation,
                facecolor="none",
                linewidth=0.8,
                # ec="black",
                ec="none",
            ),
            fontsize=20,
            color=color_annotation,
            # color="black",
            ha="center",
            va="center",
        )
        ann.set_zorder(11)
    plt.tight_layout(h_pad=0)
    plt.subplots_adjust(top=1.3, bottom=-0.3, right=1, left=0, hspace=0, wspace=0)

    if text:
        for i, label in enumerate(text):

            text_angle = 0
            if text_rotated:
                text_angle = 180 - math.degrees(x_axis_vals[i] + width[i] / 2)
                if text_angle > 90:
                    text_angle -= 180
            if show_image:
                text_height = height - 0.5
            else:
                text_height = height
            txt = ax.text(
                x_axis_vals[i] + width[i] / 2,
                text_height,
                label,
                fontsize=12,
                color="black",
                ha="center",
                va="center",
                fontweight="bold",
                rotation=text_angle,
            )
            if show_image:
                txt.set_alpha(0)
            else:
                continue

            # Get the bounding box of the text in display coordinates
            bbox = txt.get_window_extent(renderer=fig.canvas.get_renderer())

            # Convert display coordinates to figure coordinates
            bbox_fig = bbox.transformed(fig.transFigure.inverted())

            # Calculate the center point in figure coordinates
            center_x = (bbox_fig.x0 + bbox_fig.x1) / 2
            center_y = (bbox_fig.y0 + bbox_fig.y1) / 2

            # Print the center point in figure coordinates
            print(f"Center point in figure coordinates: ({center_x}, {center_y})")

            # Convert figure coordinates to pixel coordinates
            pixel_x = fig.bbox.x0 + center_x * fig.bbox.width
            pixel_y = fig.bbox.y0 + center_y * fig.bbox.height

            print(f"Figure coordinates: ({fig.bbox.x0}, {fig.bbox.y0})")
            print(f"Figure width: {fig.bbox.width}")
            print(f"Figure height: {fig.bbox.height}")
            print(f"Center point in pixel coordinates: ({pixel_x}, {pixel_y})")
            print("############################################")

            image = Image.open(show_image[i])

            # Place the image at the calculated coordinates
            fig.figimage(
                image.resize([icon_size, icon_size]),
                pixel_x - icon_size / 2,
                pixel_y - icon_size / 2,
                zorder=1,
            )

            txt = ax.text(
                x_axis_vals[i] + width[i] / 2,
                text_height+0.75,
                label,
                fontsize=12,
                color="black",
                ha="center",
                va="center",
                fontweight="bold",
                rotation=text_angle,
            )

    plt.show()


if __name__ == "__main__":

    @dataclass
    class RiskValues:
        risk_name: str
        color: str
        threshold: [float, float]
        icon: str

    model = [
        RiskValues("Risk 1", "tab:green", [0, 25], "icons/play-button.png"),
        RiskValues("Risk 2", "tab:orange", [25, 100], "icons/water-bottle.png"),
        RiskValues("Risk 3", "tab:red", [100, 150], "icons/pause.png"),
        RiskValues("Risk 4", "tab:purple", [150, 200], "icons/no-stopping.png"),
    ]

    colors = [risk.color for risk in model]
    thresholds = []
    for risk in model:
        thresholds.append(risk.threshold[0])
        thresholds.append(risk.threshold[1])
    thresholds = sorted(list(set(thresholds)))
    icons = [risk.icon for risk in model]
    text = [risk.risk_name for risk in model]
    risk_value = 130

    gauge_chart(
        risk_value=risk_value,
        colors=colors,
        thresholds=thresholds,
        text=text,
        # show_value=False,
        text_rotated=True,
        # show_image=icons,
        icon_size=40,
        # bottom=0
    )