43 lines
1.3 KiB
Python
43 lines
1.3 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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# 加载数据
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data = np.load("1.npy")
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# 绘制直方图，获取频数
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n, bins, patches = plt.hist(data, bins=32, density=False, alpha=0.7, color='skyblue')
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# 计算数据总数
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total_data = len(data)
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# 将频数转换为频率
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frequencies = n / total_data
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# 计算统计信息
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max_val = np.max(data)
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min_val = np.min(data)
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std_dev = np.std(data)
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# 设置图形属性
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plt.title('Frequency Distribution Histogram')
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plt.xlabel('Value')
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plt.ylabel('Frequency')
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# 重新绘制直方图，使用频率作为高度
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plt.cla() # 清除当前坐标轴上的内容
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plt.bar([(bins[i] + bins[i+1])/2 for i in range(len(bins)-1)], frequencies, width=[bins[i+1]-bins[i] for i in range(len(bins)-1)], alpha=0.7, color='skyblue')
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# 在柱子上注明频率值
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for i in range(len(patches)):
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plt.text(bins[i]+(bins[i+1]-bins[i])/2, frequencies[i], f'{frequencies[i]:.2e}', ha='center', va='bottom', fontsize=6)
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# 在图表一角显示统计信息
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stats_text = f"Max: {max_val:.6f}\nMin: {min_val:.6f}\nStd: {std_dev:.4e}"
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plt.text(0.95, 0.95, stats_text, transform=plt.gca().transAxes,
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ha='right', va='top', bbox=dict(facecolor='white', edgecolor='black', alpha=0.8))
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# 设置 x 轴刻度对齐柱子边界
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plt.xticks(bins, fontsize = 6)
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# 显示图形
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plt.show() |