119 lines
3.7 KiB
Python
119 lines
3.7 KiB
Python
from io import BytesIO
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import numpy as np
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import matplotlib.pyplot as plt
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import pandas as pd
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class Radar(object):
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def __init__(self, figure, title, labels, epoch, rect=None):
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if rect is None:
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rect = [0.1, 0.05, 0.75, 0.9]
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self.n = len(title)
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self.angles = np.arange(0, 360, 360.0 / self.n)
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self.labels = labels
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self.axes = [figure.add_axes(rect, projection='polar', label='axes%d' % i) for i in range(self.n)]
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self.ax = self.axes[0]
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self.ax.set_thetagrids(self.angles, labels=title, fontsize=12)
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self.ax.xaxis.set_tick_params(pad=22) # Increase padding to move labels outward
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for ax in self.axes[1:]:
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ax.patch.set_visible(False)
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ax.grid(False)
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ax.xaxis.set_visible(False)
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for ax, angle, label, i in zip(self.axes, self.angles, labels, epoch):
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ax.set_rgrids(i[1:], angle=angle, labels=label)
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ax.spines['polar'].set_visible(False)
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ax.set_ylim(i[0], i[-1])
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def plot(self, values, *args, **kw):
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angle = np.deg2rad(np.r_[self.angles, self.angles[0]])
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# 第一个值保持不变
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adjusted_values = [values[0]]
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# 获取 labels 对应的最后一位的差值
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base_last_value = float(self.labels[0][-1]) # 第一个 labels 列表的最后一位
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# 计算新的 values
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for i in range(1, len(values)):
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current_label_last_value = float(self.labels[i][-1]) # 当前 labels 列表的最后一位
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difference = current_label_last_value - base_last_value
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adjusted_value = values[i] - difference
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adjusted_values.append(adjusted_value)
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# 将第一个值追加到最后，闭合图形
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adjusted_values.append(adjusted_values[0])
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# 绘制雷达图
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self.ax.plot(angle, adjusted_values, *args, **kw)
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def generate_lab(max_values, length=5, step=1):
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"""
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根据最大值向量生成lab（标签）列表。
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"""
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lab = []
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for max_val in max_values:
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labels = [str(round(val, 2)) for val in np.arange(max_val - (length - 1) * step, max_val + step, step)]
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lab.append(labels)
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return lab
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def generate_epo(max_values, length=6, step=1):
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"""
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根据最大值向量生成epo（刻度范围）列表。
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"""
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epo = []
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for max_val in max_values:
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range_values = [round(val, 2) for val in np.arange(max_val - (length - 1) * step, max_val + step, step)]
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epo.append(range_values)
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return epo
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def plot_radar(data, max_values):
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"""
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绘制雷达图，根据数据和最大值生成图表。
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"""
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fig = plt.figure(figsize=(10, 8))
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tit = data.columns.tolist()
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lab = generate_lab(max_values, length=5, step=1)
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epo = generate_epo(max_values, length=6, step=1)
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plt.rcParams['font.sans-serif'] = ['SimHei']
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radar = Radar(fig, tit, lab, epo)
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# 遍历 DataFrame 的每一行
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colors = plt.colormaps.get_cmap('tab20')
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for i, (idx, row) in enumerate(data.iterrows()):
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radar.plot(row.values.tolist(), '-', lw=2, color=colors(i), alpha=0.4, label=idx)
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radar.ax.legend(bbox_to_anchor=(1.2, 1.05))
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# plt.savefig('fig.png', dpi=300)
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# 将图像保存到字节流中
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img_bytes = BytesIO()
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plt.savefig(img_bytes, format='png')
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# 设置字节流的位置到开始
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img_bytes.seek(0)
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# plt.show()
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plt.close()
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return img_bytes
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if __name__ == '__main__':
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# 一个行名为样本名，列名为指标名的pd.dataframe
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data = pd.read_csv('./radartest.csv', index_col=0)
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# 任务设置的每个指标的最大值
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max_values = [10, 15, 15, 10, 15, 5, 10, 10, 5, 5] # 示例最大值向量
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# 使用这个函数
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plot_radar(data, max_values)
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