1、T₂ 几何均值公式如下：

2、核磁T2几何均值计算代码

CSV 文件文件格式：

1. 每一行是一个样点（样品深度），列为：
2. 第一列是“深度”或其他；
3. 第二列及以后（如 TASPEC0 ~ TASPEC199）是该深度对应代码中的 amplitudes；
4. 对应的 T₂ 值数组 是一个你提供的长度为 200 的列表。

# 项目：核磁T2几何均值计算
# 文件：main.py
# 作者：陈不like
# 日期 ：2025/7/31 15:13
import numpy as np
import pandas as pd# 1. 读取CSV文件
csv_path = 'V12核磁.csv'  # 替换为你的文件路径# 尝试多种编码
encodings = ['utf-8', 'gbk', 'cp936', 'utf-8-sig', 'cp1252']
for enc in encodings:try:df = pd.read_csv(csv_path, encoding=enc)print(f"✅ 成功读取 CSV 文件，使用编码：{enc}")breakexcept UnicodeDecodeError:print(f"❌ 编码失败：{enc}")
else:raise ValueError("无法解析 CSV 文件的编码，请检查文件是否损坏或尝试另存为 UTF-8 编码。")# 2. 提供 T2 值列表（200个）
T2_values = np.array([0.3, 0.314211269, 0.329095739, 0.3446853, 0.361013352, 0.378114879, 0.39602652, 0.414786652, 0.434435468, 0.455015067,0.476569539, 0.499145066, 0.522790016, 0.547555048, 0.573493223, 0.600660112, 0.62911392, 0.658915612, 0.690129036, 0.722821068,0.757061751, 0.792924446, 0.830485989, 0.869826856, 0.911031335, 0.954187708, 0.999388436, 1.046730364, 1.096314921, 1.148248344,1.202641899, 1.259612125, 1.319281083, 1.381776612, 1.447232611, 1.51578932, 1.587593621, 1.662799356, 1.741567655, 1.824067278,1.910474983, 2.000975899, 2.095763924, 2.195042143, 2.29902326, 2.407930057, 2.521995866, 2.641465074, 2.766593647, 2.897649672,3.034913939, 3.178680538, 3.329257489, 3.486967406, 3.652148183, 3.825153722, 4.006354689, 4.196139308, 4.394914195, 4.603105227,4.821158455, 5.04954106, 5.288742354, 5.539274829, 5.801675251, 6.076505818, 6.364355355, 6.665840583, 6.981607437, 7.31233245,7.658724205, 8.021524848, 8.401511683, 8.799498835, 9.216338997, 9.652925251, 10.11019299, 10.58912191, 11.09073812, 11.61611635,12.16638221, 12.74271466, 13.34634849, 13.97857701, 14.64075475, 15.33430045, 16.06070003, 16.82150981, 17.61835984, 18.45295737,19.32709053, 20.24263216, 21.20154382, 22.20587999, 23.25779247, 24.35953498, 25.51346803, 26.72206392, 27.98791208, 29.31372461,30.70234206, 32.15673958, 33.68003321, 35.27548662, 36.9465181, 38.69670784, 40.52980564, 42.44973892, 44.46062117, 46.56676072,48.77266999, 51.08307517, 53.50292631, 56.03740797, 58.69195031, 61.47224069, 64.38423594, 67.43417501, 70.62859243, 73.97433227,77.47856282, 81.14879191, 84.99288306, 89.01907225, 93.23598563, 97.652658, 102.2785521, 107.1235789, 112.1981191, 117.5130447,123.0797432, 128.9101411, 135.0167303, 141.412594, 148.1114356, 155.1276073, 162.4761413, 170.1727821, 178.2340195, 186.6771251,195.5201881, 204.782155, 214.4828696, 224.6431157, 235.2846618, 246.4303075, 258.1039325, 270.3305475, 283.1363483, 296.5487714,310.596553, 325.3097906, 340.7200075, 356.8602202, 373.7650093, 391.4705934, 410.0149069, 429.4376813, 449.7805298, 471.0870374,493.4028534, 516.7757896, 541.2559228, 566.8957019, 593.7500604, 621.8765339, 651.3353837, 682.1897257, 714.5056656, 748.3524406,783.8025677, 820.9319991, 859.8202851, 900.5507441, 943.2106415, 987.8913765, 1034.688678, 1083.70281, 1135.038785, 1188.806592,1245.121427, 1304.103947, 1365.880522, 1430.583509, 1498.351535, 1569.329792, 1643.670354, 1721.532494, 1803.083035, 1888.496697,1977.956481, 2071.654056, 2169.790169, 2272.575078, 2380.229, 2492.982585, 2611.077408, 2734.76649, 2864.314834, 3000
])# ✅ 3. 第一列为深度，后面为200道核磁分布。
# 取每行的第2列（索引1）起的200列作为 amplitudes
amplitudes_array = df.iloc[:, 1:1+200].values  # 索引，从1开始取200列# 4. 计算 T2 几何均值
log_T2 = np.log(T2_values)
weighted_log_T2 = np.sum(amplitudes_array * log_T2, axis=1) / np.sum(amplitudes_array, axis=1)
T2LM= np.exp(weighted_log_T2)# 5. 加入结果列
df['T2LM'] = T2LM# 6. 保存结果
df.to_csv('V12核磁with_T2LM.csv', index=False)
print("✅ 成功计算并保存 T2 几何均值至 'V12核磁with_T2LM.csv'")

核磁T2时间轴

DEPTH	0.3	0.314211269	0.329095739	0.3446853	0.361013352	0.378114879	0.39602652	0.414786652	0.434435468	0.455015067	0.476569539	0.499145066	0.522790016	0.547555048	0.573493223	0.600660112	0.62911392	0.658915612	0.690129036	0.722821068	0.757061751	0.792924446	0.830485989	0.869826856	0.911031335	0.954187708	0.999388436	1.046730364	1.096314921	1.148248344	1.202641899	1.259612125	1.319281083	1.381776612	1.447232611	1.51578932	1.587593621	1.662799356	1.741567655	1.824067278	1.910474983	2.000975899	2.095763924	2.195042143	2.29902326	2.407930057	2.521995866	2.641465074	2.766593647	2.897649672	3.034913939	3.178680538	3.329257489	3.486967406	3.652148183	3.825153722	4.006354689	4.196139308	4.394914195	4.603105227	4.821158455	5.04954106	5.288742354	5.539274829	5.801675251	6.076505818	6.364355355	6.665840583	6.981607437	7.31233245	7.658724205	8.021524848	8.401511683	8.799498835	9.216338997	9.652925251	10.11019299	10.58912191	11.09073812	11.61611635	12.16638221	12.74271466	13.34634849	13.97857701	14.64075475	15.33430045	16.06070003	16.82150981	17.61835984	18.45295737	19.32709053	20.24263216	21.20154382	22.20587999	23.25779247	24.35953498	25.51346803	26.72206392	27.98791208	29.31372461	30.70234206	32.15673958	33.68003321	35.27548662	36.9465181	38.69670784	40.52980564	42.44973892	44.46062117	46.56676072	48.77266999	51.08307517	53.50292631	56.03740797	58.69195031	61.47224069	64.38423594	67.43417501	70.62859243	73.97433227	77.47856282	81.14879191	84.99288306	89.01907225	93.23598563	97.652658	102.2785521	107.1235789	112.1981191	117.5130447	123.0797432	128.9101411	135.0167303	141.412594	148.1114356	155.1276073	162.4761413	170.1727821	178.2340195	186.6771251	195.5201881	204.782155	214.4828696	224.6431157	235.2846618	246.4303075	258.1039325	270.3305475	283.1363483	296.5487714	310.596553	325.3097906	340.7200075	356.8602202	373.7650093	391.4705934	410.0149069	429.4376813	449.7805298	471.0870374	493.4028534	516.7757896	541.2559228	566.8957019	593.7500604	621.8765339	651.3353837	682.1897257	714.5056656	748.3524406	783.8025677	820.9319991	859.8202851	900.5507441	943.2106415	987.8913765	1034.688678	1083.70281	1135.038785	1188.806592	1245.121427	1304.103947	1365.880522	1430.583509	1498.351535	1569.329792	1643.670354	1721.532494	1803.083035	1888.496697	1977.956481	2071.654056	2169.790169	2272.575078	2380.229	2492.982585	2611.077408	2734.76649	2864.314834	3000
DEPTH	0.3	0.347216216	0.401883337	0.46515747	0.538393738	0.623160619	0.721273539	0.834833754	0.966273346	1.118407318	1.294493875	1.498304211	1.734203267	2.007243222	2.323271688	2.68905695	3.112432919	3.602466907	4.169653822	4.826140933	5.585988021	6.465468498	7.483417927	8.66163742	10.02536054	11.60379373	13.4307418	15.54533194	17.99285168	20.8257188	24.10460394	27.89972997	32.29237596	37.37661783	43.26134325	50.07258356	57.95621302	67.08107288	77.64258747	89.8669495	104.0159644	120.3926572	139.3477626	161.2872362	186.680949	216.0727503	250.0921154	289.4676264	335.0425767	387.7930309	448.8487293	519.517283	601.3121788	695.9851928	805.5639078	932.3951375	1079.195187	1249.108029	1445.772634	1673.400907	1936.867893	2241.816184	2594.776763	3000