Python 通过操作变换来旋转 matplotlib 刻度标签(极坐标)

Rather than using Matplotlib transforms, you can use ax.get_xticklabels(), & then iteratively set the rotation of each label with label.set_rotation(θ) ¹

？其中，Angular θ是从与每个标签相关联的极轴数据中得出的；它实际上等同于极轴图中的x维.

例如，修改您提供的代码，如下所示:

import random

import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np

one_person = {
    "Human": {
        "Collaboration": 4,
        "Growth Mindset": 3,
        "Inclusion": 5,
        "Project and Studio Life": 2,
    },
    "Tectonics": {
        "Office Manual and Procedures": 3,
        "Documentation Standards": 3,
        "Site Stage Services": 2,
        "External and Public Domain Works": 2,
        "Structure": 3,
        "Enclosure": 2,
        "Waterproofing (int. and ext.)": 3,
        "Interiors": 1,
        "Structure and Services": 2,
    },
    "Technology": {
        "Bluebeam": 2,
        "Confluence": 3,
        "Drawing on screens": 0,
        "dRofus": 0,
        "Excel": 2,
        "Grasshopper": 1,
        "InDesign": 2,
        "Outlook": 2,
        "Python": 5,
        "Rhino": 1,
        "Teams": 2,
        "Timesheets and expenses": 3,
    },
    "Regenerative": {
        "REgenerative Design": 3,
        "Materials and Embodied Carbon practice": 1,
        "Materials and Embodied Carbon analysis": 2,
        "Energy": 3,
        "Resilience": 1,
        "Rating Systems": 2,
    },
    "Design": {
        "Predesign - Briefing, Stakeholder Engagement & Establishing Project Values": 2,
        "Predesign - Feasibility Studies And Strategic Organisational Planning": 3,
        "Initiating Design": 2,
        "Conserving Design": 3,
        "Design Communication - Written": 2,
        "Design Communication - Visual": 4,
        "Design Communication - Verbal": 3,
    },
    "Connecting with country": {"Connecting with Country": 2},
}
colours = [
    "b",  # blue.
    "g",  # green.
    "r",  # red.
    "c",  # cyan.
    "m",  # magenta.
    "y",  # yellow.
    "k",  # black.
]


def draw_radar(data, colour_letters, person_name=""):
    """Draw the graph.

    Based substanitally on this SO thread:
    https://stackoverflow.com/questions/60563106/complex-polar-plot-in-matplotlib
    """

    fig, ax = plt.subplots(
        subplot_kw={"projection": "polar"}, figsize=(10, 10)
    )
    num_areas = len(data) - 1
    running_total = 0
    thetas = {}
    for key, value in data.items():
        this_area_num_points = len(value)
        this_area_theta = ((2 * np.pi) / num_areas) / (this_area_num_points)
        thetas[key] = []
        for i in range(len(value)):
            thetas[key].append((i * this_area_theta) + running_total)
        running_total += (2 * np.pi) / num_areas

    labels = []
    for key, value in data.items():
        for area, score in value.items():
            labels.append(f"{score} {key}: {area}")

    for name, theta_list in thetas.items():
        individual_scores = list(data[name].values())
        colour = colour_letters.pop()  # random.choice(colour_letters)
        if len(theta_list) > 1:
            ax.plot(theta_list, individual_scores, c=colour, label=name)
        elif len(theta_list) == 1:
            ax.scatter(theta_list, individual_scores, c=colour, label=name)
    ax.set_yticks(np.arange(-5, 5), [""] * 5 + list(range(5)))
    ax.set_xticks(
        np.concatenate(tuple(list(thetas.values()))), labels,
    )

    plt.gcf().canvas.draw()
    max_label_len = max(list(map(len, labels)))
    t_labels = []
    for label in ax.get_xticklabels():
        x, y = label.get_position()
        text = label.get_text()
        angle = x
        y_adjust = (len(text) / max_label_len) * 0.8
        if text.endswith("Country"):
            x_adjust = 0.05
            angle += 0.05
        else:
            x_adjust = 0
        lab = ax.text(
            x + x_adjust,
            y - y_adjust,
            label.get_text(),
            transform=label.get_transform(),
            ha=label.get_ha(),
            va=label.get_va(),
        )
        if np.cos(angle) < 0:
            angle = angle + np.pi
        angle = np.rad2deg(angle)
        lab.set_rotation(angle)
        t_labels.append(lab)
    ax.set_xticklabels([])
    plt.show()


draw_radar(one_person, colours)

results in:

您可能需要调整以下各项的设置:

1. figsize(在定义fig和ax图形和轴对象时)，并且相应地，
2. y_adjust系数(上面设置为0.8).

一般的逻辑是 for each 极轴数据值找到绘制的theta角，然后将该角值本身用作标签文本的刻度特定旋转.这np.cos个代码判断并适当地将标签倒置.并且每个标签基于其归一化标签文本长度(相对于最大长度标签)向外移动(即，y维上的负向)一定量，导致所有标签或多或少精确地位于圆圈之外(即，文本标签越长，它必须移动的越多).

此外，还有一个标签(以‘Country’结尾)与另一个标签重叠-对于那个标签，我自定义将其向上移动，并略微弯曲其刻度标签旋转的Angular ，以反映它实际上属于/应该指向极图上与其正下方的刻度标签数据具有相同角的数据.