forked from Open-CT/openbrain
66 lines
3.3 KiB
Python
66 lines
3.3 KiB
Python
import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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plt.rcParams['font.sans-serif'] = 'SimSun' # 设置中文字体为宋体，可以根据系统中安装的字体来选择其他字体
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# 时间片段数
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datas = np.array([[10741, 284, 880, 492, 35, 194, 41],
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[10141, 875, 1212, 331, 43, 558, 14],
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[11774, 1016, 1213, 342, 38, 619, 16],
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[9750, 1458, 907, 266, 48, 698, 9]])
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# 频率
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counts = np.array([[4391, 217, 597, 416, 29, 138, 31], [2635, 703, 525, 323, 28, 402, 7], [4433, 1171, 1097, 428, 53, 737, 24], [2277, 582, 760, 177, 40, 401, 15]])
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# 平均持续时长
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times = datas * 10 / counts
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# 平均切换时间
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switch = np.array([[61.14458998261721, 632.2479017581059, 547.6350413944383, 615.7969170851824, 177.65460729746445, 296.090922083119, 383.96258503401356], [51.27205013468541, 636.9488114264699, 524.3238970832911, 621.3669463807968, 345.9795321637427, 451.49136253673464, 278.94736842105266], [45.0405611983205, 396.79175015434987, 482.40695921154395, 508.47649889419984, 434.1753623188406, 503.01220812246856, 215.06280193236717], [59.312898420324544, 597.6178646532704, 396.5266118829706, 1049.9983278733278, 819.579794079794,
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698.9298476374611, 329.31531531531533]])
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# 归一化数据
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datas = (datas - np.min(datas, axis=0)) / \
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(np.max(datas, axis=0) - np.min(datas, axis=0))
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counts = (counts - np.min(counts, axis=0)) / \
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(np.max(counts, axis=0) - np.min(counts, axis=0))
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times = (times - np.min(times, axis=0)) / \
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(np.max(times, axis=0) - np.min(times, axis=0))
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switch = (switch - np.min(switch, axis=0)) / \
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(np.max(switch, axis=0) - np.min(switch, axis=0))
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labels = ['中性', '喜悦', '厌恶蔑视', '愤怒', '恐惧担忧', '惊讶', '悲伤']
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# 设置雷达图的角度
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angles = np.linspace(0, 2*np.pi, len(labels), endpoint=False)
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# 将角度进行闭合
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angles = np.concatenate((angles, [angles[0]]))
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# 创建一个包含四个子图的图表
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fig, axes = plt.subplots(2, 2, subplot_kw=dict(polar=True))
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new_datas = np.c_[datas, datas[:, 0]]
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new_counts = np.c_[counts, counts[:, 0]]
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new_times = np.c_[times, times[:, 0]]
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new_switch = np.c_[switch, switch[:, 0]]
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for i in range(2):
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for j in range(2):
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axes[i, j].plot(angles, new_datas[i*2+j], label='次数', linewidth=2)
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axes[i, j].plot(angles, new_counts[i*2+j], label='频率', linewidth=2)
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axes[i, j].plot(angles, new_times[i*2+j], label='平均持续时长', linewidth=2)
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axes[i, j].plot(angles, new_switch[i*2+j], label='平均切换时长', linewidth=2)
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# 设置雷达图的标签
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for ax in axes.flat:
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ax.set_thetagrids(angles[:-1] * 180/np.pi, labels)
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# 在图表的中心位置添加图例
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fig.legend(['次数', '频率', '平均持续时长', '平均切换时长'],
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loc='center', bbox_to_anchor=(0.5, 0.5))
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# 调整子图之间的间距
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plt.tight_layout()
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# 显示图形
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plt.show()
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