上传测试报告（含计算均值方差脚本）

Motor.csv

+Second Time dB
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+289.7850400463328 00:04:49.7 48.56111907958984

Result_Cal/MotorLong.csv

+zt,jd
+74.4,73.9
+75.1,74.9
+75.2,74
+75,73.6
+74.1,74.2
+73.8,74
+74.5,73.6
+74.7,74.2
+74.9,74.8
+75.1,73.6
+74.9,74
+73.9,74.6
+74.6,74.3
+74.3,74
+75,73.6
+74.6,74.2
+74.6,73.6
+74.8,74.9
+74.7,74.2
+75.9,73.9
\ No newline at end of file

Result_Cal/_1KHz_High.csv

+zt,jd
+115.2,109.5
+114.6,117.3
+114.7,117.3
+114.4,117.1
+114.5,117.1
+114.1,117.1
+114.6,117.1
+113.6,117.1
+114.6,117.1
+114.6,117.1
+114.5,117.1
+114.5,117.1
+114.6,117.1
+114.6,117.1
+114.7,117.1
+114.7,117
+114.6,117.1
+114.7,117.1
+114.6,117.1
+114.6,116.7
\ No newline at end of file

Result_Cal/_1KHz_Low.csv

+zt,jd
+40.9,46.6
+41.7,43.8
+44.5,62.1
+41.7,43.2
+42.1,68.3
+42,43.6
+42.4,42.2
+43.1,45
+41.7,56.8
+42.5,43.2
+41.7,59.9
+58.3,42.9
+42.4,64
+50.6,43.1
+42.7,65
+45.7,43.6
+42.6,63.9
+42.4,43
+42.3,59.6
+42.8,43.1
+42.5,42.6
+41.2,43.2
+42.6,42.7
+42,43.8
\ No newline at end of file

Result_Cal/deal_data.py

+import numpy as np
+import pandas as pd
+
+# 传感器的数据
+# data_zt = np.array([115.2, 114.6, 114.7, 114.4, 114.5, 114.1, 114.6, 113.6, 114.6, 114.6, 114.5, 114.5, 114.6, 114.6, 114.7, 114.7, 114.6, 114.7, 114.6, 114.6])
+# data_jd = np.array([109.5, 117.3, 117.3, 117.1, 117.1, 117.1, 117.1, 117.1, 117.1, 117.1, 117.1, 117.1, 117.1, 117.1, 117.1, 117, 117.1, 117.1, 117.1, 116.7])
+data_file_Motor_Long = 'MotorLong.csv'
+data_file_1KHz_High = '_1KHz_High.csv'
+data_file_1KHz_Low = '_1KHz_Low.csv'
+
+
+
+data_file = data_file_1KHz_Low
+data = pd.read_csv(data_file)
+data_zt = data['zt'].values
+data_jd = data['jd'].values
+# 计算均值
+mean_zt = np.mean(data_zt)
+mean_jd = np.mean(data_jd)
+
+# 计算方差
+variance_zt = np.var(data_zt)
+variance_jd = np.var(data_jd)
+variance_between = np.var(data_zt - data_jd)  # 两者之间的方差
+
+# 计算百分比差异
+percent_diff = np.abs((mean_zt - mean_jd) / ((mean_zt + mean_jd) / 2)) * 100
+
+# 计算最大值和最小值的偏差
+max_deviation = np.abs(np.max(data_zt) - np.max(data_jd))
+min_deviation = np.abs(np.min(data_zt) - np.min(data_jd))
+
+mean_zt, mean_jd, variance_zt, variance_jd, variance_between, percent_diff, max_deviation, min_deviation
+print(f"mean_zt:\t{mean_zt:.2f}")
+print(f"mean_jd:\t{mean_jd:.2f}")
+print(f"variance_zt:\t{variance_zt:.2f}")
+print(f"variance_jd:\t{variance_jd:.2f}")
+print(f"variance_between:\t{variance_between:.2f}")
+print(f"percent_diff:\t{percent_diff:.2f}")
+print(f"max_deviation:\t{max_deviation:.2f}")
+print(f"min_deviation:\t{min_deviation:.2f}")
+
+# zt传感器均值: 114.55
+# jd传感器均值: 116.71
+# zt传感器方差: 0.0835
+# jd传感器方差: 2.7523
+# 两者之间的方差: 3.3293
+# 百分比差异: 1.87%
+# 最大值偏差: 2.10
+# 最小值偏差: 4.10

data.csv → _1KHz.csv

-Second Time dB
+Second Time dB #Start@11:49:38
 0 00:00:00.0 33.14538955688477
 1.486076213604974 00:00:01.4 33.62919998168945
 2.97213970372286 00:00:02.9 39.49447250366211

noiseLab_dataDeal.py

@@ -3,7 +3,7 @@ import matplotlib.pyplot as plt
 from io import StringIO
 
 
-file_path = 'data.csv'
+file_path = '_1KHz.csv'
 # Read data into a pandas DataFrame
 df = pd.read_csv(file_path, sep=" ", parse_dates=["Time"])