读取数据并画线_根据数据画线-优快云博客

本文链接：https://blog.youkuaiyun.com/W_E_DAY/article/details/125998503

这段代码用于绘制一串指数数据，并计算77为阈值的滑动平均值。数据被处理为从11718到12002的索引，每个索引对应一个浮点值。滑动平均值是通过在每个点上减去前10个值然后除以10来计算的。最后，使用matplotlib库展示指数数据和滑动平均值，同时标出了阈值线。

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import matplotlib.pyplot as plt
x = [11718,11719,11720,11721,11722,11723,11724,11725,11726,11727,11728,11729,11730,11731,11732,11733,11734,11735,11736,11737,11738,11739,11740,11741,11742,11743,11744,11745,11746,11747,11748,11749,11750,11751,11752,11753,11754,11755,11756,11757,11758,11759,11760,11761,11762,11763,11764,11765,11766,11767,11768,11769,11770,11771,11772,11773,11774,11775,11776,11777,11778,11779,11780,11781,11782,11783,11784,11785,11786,11787,11788,11789,11790,11791,11792,11793,11794,11795,11796,11797,11798,11799,11800,11801,11802,11803,11804,11805,11806,11807,11808,11809,11810,11811,11812,11813,11814,11815,11816,11817,11818,11819,11820,11821,11822,11823,11824,11825,11826,11827,11828,11829,11830,11831,11832,11833,11834,11835,11836,11837,11838,11839,11840,11841,11842,11843,11844,11845,11846,11847,11848,11849,11850,11851,11852,11853,11854,11855,11856,11857,11858,11859,11860,11861,11862,11863,11864,11865,11866,11867,11868,11869,11870,11871,11872,11873,11874,11875,11876,11877,11878,11879,11880,11881,11882,11883,11884,11885,11886,11887,11888,11889,11890,11891,11892,11893,11894,11895,11896,11897,11898,11899,11900,11901,11902,11903,11904,11905,11906,11907,11908,11909,11910,11911,11912,11913,11914,11915,11916,11917,11918,11919,11920,11921,11922,11923,11924,11925,11926,11927,11928,11929,11930,11931,11932,11933,11934,11935,11936,11937,11938,11939,11940,11941,11942,11943,11944,11945,11946,11947,11948,11949,11950,11951,11952,11953,11954,11955,11956,11957,11958,11959,11960,11961,11962,11963,11964,11965,11966,11967,11968,11969,11970,11971,11972,11973,11974,11975,11976,11977,11978,11979,11980,11981,11982,11983,11984,11985,11986,11987,11988,11989,11990,11991,11992,11993,11994,11995,11996,11997,11998,11999,12000,12001,12002,12003,12004,12005,12006,12007,12008,12009,12010,12011,12012]
y = [1.19209e-05, 0, 0, 1.19209e-05, 0.0185072, 4.76837e-05, 0.000107288, 0.000798702, 0.00452995, 16.5547, 0.00399351, 4.50826, 5.13576, 25.6722, 19.0842, 27.2999, 34.7599, 8.26077, 0, 0, 1.19209e-05, 5.96046e-05, 0.00810623, 3.74058, 14.7937, 4.70482, 40.3121, 2.70665, 61.1997, 41.8274, 33.7046, 35.6913, 0.737941, 0, 5.96046e-05, 1.19209e-05, 3.57385, 1.19209e-05, 0.0361085, 1.73096, 0.246203, 0.0661194, 0.0291765, 1.07491, 47.583, 7.15256e-05, 0.0557959, 0.142586, 2.38419e-05, 5.96046e-05, 0.240201, 2.96685, 3.86415, 0.0019908, 9.53674e-05, 1.19209e-05, 0.00478029, 4.48851, 0.274134, 4.41325, 34.2379, 51.9485, 65.9217, 62.7658, 0.000357628, 0, 1.01093, 2.38419e-05, 0.00764132, 1.19209e-05, 0, 0.00357628, 1.58097, 9.74982, 51.176, 77.5626, 55.7998, 59.2023, 0.697857, 0.0144601, 3.57628e-05, 0.000202656, 0.000166893, 0.00895262, 0.000596046, 0.352579, 2.68653, 8.55402, 6.90742, 9.53674e-05, 1.19209e-05, 0, 20.3605, 3.57628e-05, 1.19209e-05, 4.76837e-05, 8.34465e-05, 0.00138283, 7.15256e-05, 0.000274181, 0.000154972, 9.53674e-05, 0.0115156, 0.00154972, 0.000739098, 0.375038, 0.0184715, 0.0335813, 0, 0.00113249, 3.57628e-05, 0, 0, 6.87709, 9.53674e-05, 9.26629, 0.0229776, 0.00488758, 7.94951, 23.863, 14.8181, 5.96046e-05, 3.57628e-05, 0.000500679, 0.000298023, 1.19209e-05, 2.99021, 0.000143051, 1.10638, 0.0140667, 0.187272, 30.6437, 30.6095, 77.8461, 70.0823, 64.011, 66.5246, 69.8681, 2.32136, 0, 9.53674e-05, 47.8691, 1.19209e-05, 0, 0, 0, 0.165188, 1.71843, 0.00193119, 0.000846386, 0.0197351, 0.00807047, 0.0046134, 0.0123382, 0.0497222, 0.0372648, 0.117928, 0.0793159, 1.83109, 1.19209e-05, 0.136656, 0.00151396, 1.19209e-05, 0.000584126, 3.04472, 0.0261605, 0.0378847, 0.0538886, 0.0610828, 0.116926, 0.000584126, 0, 0.000834465, 0.000333786, 0.00898838, 0.0673532, 0.555974, 11.0593, 3.69709, 9.86143, 5.86007, 49.3887, 2.38419e-05, 0, 3.57628e-05, 0, 0, 0, 0, 0.000703335, 0.000548363, 3.57628e-05, 1.19209e-05, 0, 0, 1.19209e-05, 0.00619888, 0.0109196, 1.19209e-05, 8.51623, 30.7554, 0, 2.38419e-05, 7.15256e-05, 0.896138, 0.294113, 9.53674e-05, 11.2921, 8.26963, 9.84585, 67.8144, 0.446737, 0, 0, 8.65975, 1.19209e-05, 1.19209e-05, 0.000476837, 0.011754, 8.83971, 26.9, 10.4292, 0.000667572, 2.38419e-05, 0.0748336, 0.000119209, 0.400811, 0.00206232, 0.0219405, 0.667417, 0.0747383, 12.4572, 4.58546, 2.99112, 11.1642, 12.631, 1.19209e-05, 0, 2.38419e-05, 28.2865, 1.19209e-05, 23.9752, 30.4119, 0.000202656, 11.211, 0.269967, 0.00152588, 0.000476837, 0.0079155, 0.00346899, 0.000190735, 0.00365973, 0.222886, 0.460285, 10.1891, 28.9225, 46.1749, 58.2845, 55.2499, 6.46912, 1.19209e-05, 0, 2.38419e-05, 4.76837e-05, 0.0633538, 0.0287831, 0.000524521, 0.0165224, 0.731194, 18.6709, 65.3293, 64.5103, 79.2927, 71.7332, 68.1587, 73.9265, 75.3165, 68.7306, 0.000333786, 0, 0, 0, 0, 0, 0, 1.19209e-05, 0.00407696, 2.38419e-05, 0.000596046, 0.00314713, 0.00478029, 0.00423193, 0.0556529, 3.08769, 0.00371933]

data_buffer = []
data_threshold = []
sum_value = 0

for i in range(len(x)):
    data_threshold.append(77)
    sum_value += y[i]
    if i > 9:
      sum_value -= y[i-10]
      data_buffer.append(sum_value/10)
    else:
      data_buffer.append(y[i])    
     
plt.figure(figsize=(8, 4))

# 在当前绘图对象中画图（x轴,y轴,给所绘制的曲线的名字，画线颜色，画线宽度）
# plt.plot(x, y1, label="$YOLOV3$", color="red", linewidth=2)
# plt.plot(x, y2, label="$LF-YOLO$", color="blue", linewidth=2)
# plt.plot(x, y3, label="$LF-YOLO-SE$", color="green", linewidth=2)
plt.plot(x, y, label="$value$", color="red", linewidth=2)

plt.plot(x, data_buffer, label="$recordAvgVal$", color="blue", linewidth=2)

plt.plot(x, data_threshold, label="$threshold$", color="red", linewidth=2, linestyle = '--')

# X轴的文字
plt.xlabel("index")

# Y轴的文字
plt.ylabel("value")

# 图表的标题
plt.title("value")

# Y轴的范围
#plt.ylim(-1.2, 1.2)

# 显示图示
plt.legend()

# 显示图 plt.savefig(r"C:\Users\YUXIAOYANG\Desktop\LF-YOLO-SE Model Loss .jpg")
plt.show()