我有一本改编自书的代码"用R"；(Kelley，2018(：
require(oce)
require(dplyr)
#transform in ctd
df <- mapply(function(dat, nm) {
as.ctd(dat, salinity, temperature, pressure)
},
dat = df, nm = names(df))
df <- mapply(function(dat, nm) {
ctdTrim(dat, method = "downcast") #Smoothing dataset
},
dat = df, nm = names(df))
#Part 1 -> MLD
plotProfile(df$`2018-08-02T162038 SBE0251090`, xtype="temperature",
col.temperature="black")
temperature <- df[["temperature"]]
pressure <- df[["pressure"]]
for (criterion in c(1, 0.125)) { #dP of 0.125kg/m3 and dT of 1
inMLD <- abs(temperature[1]-temperature) < criterion
MLDindex <- which.min(inMLD)
MLDpressure <- pressure[MLDindex]
abline(h=pressure[MLDindex], lwd=2, lty="dashed")
}
MLDpressureMax =pressure[MLDindex] + MLDindex
#Thermocline
plotProfile(df$`2018-08-02T162038 SBE0251090`, xtype="N2",col.N2="black")
mid <- which.max(swN2(df$`2018-08-02T162038 SBE0251090`))
pstar <- pressure[mid] #give thermocline
abline(h=pstar, lwd=2, lty="dashed")
#Compilation
##first station (Remove if not first station)
MLDAll <- data.frame(MLDmin = MLDpressure,MLDmax = MLDpressureMax, Thermocline =pstar)
MLDAll <- MLDAll |> mutate(id = rep("df"))
##following stations (Remove if first station)
#MLDAll2 <- data.frame(MLDmin = MLDpressure,MLDmax = MLDpressureMax, Thermocline =pstar)
#MLDAll2 <- MLDAll2 |> mutate(id = rep("dfn"))
#MLDAll <- rbind(MLDAll,MLDAll2)
该代码的目的是找到MLD和温跃层，并在MDLAll中提取它。它可以与给定的数据集正常工作：
df <- list(`2018-08-02T162038 SBE0251090` = structure(list(ctd_file = structure(c(1L,
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16.3, 16.4, 16.5, 16.6, 16.7, 16.8, 16.9, 17, 17.1, 17.2, 17.3,
17.4, 17.5, 17.6, 17.7, 17.8, 17.9, 18, 18.1, 18.2, 18.3, 18.4,
18.5, 18.6, 18.7, 18.8, 18.9, 19, 19.1, 19.2, 19.3, 19.4, 19.5,
19.6, 19.7, 19.8, 19.9, 20, 20.1, 20.2, 20.3, 20.4, 20.5, 20.6,
20.7, 20.8, 20.9, 21, 21.1, 21.2, 21.3, 21.4, 21.5, 21.6, 21.7,
21.8, 21.9, 22, 22.1, 22.2, 22.3, 22.4, 22.5, 22.6, 22.7, 22.8,
22.9, 23, 23.1, 23.2, 23.3, 23.4, 23.5, 23.6, 23.7, 23.8, 23.9,
24, 24.1, 24.2, 24.3, 24.4, 24.5, 24.6, 24.7, 24.8, 24.9), salinity = c(30.2477,
30.2556, 30.2839, 30.2777, 30.2869, 30.2802, 30.2759, 30.2743,
30.2642, 30.2757, 30.2791, 30.291, 30.3045, 30.39, 30.4223, 30.3371,
30.4686, 30.5722, 30.6134, 30.6391, 30.6429, 30.6473, 30.6566,
30.6723, 30.6845, 30.6947, 30.6926, 30.6933, 30.7016, 30.6995,
30.7043, 30.711, 30.7174, 30.7193, 30.7307, 30.7407, 30.7491,
30.7697, 30.7075, 30.8744, 30.9353, 30.9555, 30.9619, 30.9671,
30.9703, 30.9707, 30.982, 30.9852, 30.9948, 30.995, 30.9949,
30.9992, 31.0017, 31.005, 31.0052, 31.0136, 31.0135, 31.0249,
31.0284, 31.0359, 31.0396, 31.0363, 31.0374, 31.0406, 31.0386,
31.0403, 31.043, 31.0413, 31.0427, 31.0475, 31.0438, 31.041,
31.0421, 31.0423, 31.0425, 31.0433, 31.0411, 31.0342, 31.0419,
31.0461, 31.0466, 31.0469, 31.0486, 31.0468, 31.0388, 31.0424,
31.0419, 31.0514, 31.0546, 31.0574, 31.0581, 31.0643, 31.0639,
31.0636, 31.0647, 31.0701, 31.0748, 31.0712, 31.0776, 31.0776,
31.0724, 31.0706, 31.0734, 31.077, 31.0747, 31.0853, 31.091,
31.1004, 31.113, 31.1218, 31.1291, 31.1296, 31.1314, 31.1293,
31.1316, 31.1344, 31.1332, 31.1366, 31.1369, 31.1384, 31.1396,
31.1401, 31.1407, 31.1409, 31.1419, 31.143, 31.1433, 31.1433,
31.1435, 31.1441, 31.1445, 31.1453, 31.1452, 31.145, 31.1477,
31.15, 31.147, 31.1489, 31.149, 31.1482, 31.1479, 31.1529, 31.1463,
31.1496, 31.1489, 31.1494, 31.1521, 31.1507, 31.1491, 31.1544,
31.1622, 31.158, 31.1588, 31.1589, 31.1515, 31.1511, 31.1532,
31.1497, 31.1574, 31.1523, 31.1471, 31.1504, 31.1632, 31.1503,
31.1528, 31.1576, 31.1616, 31.1553, 31.1534, 31.1566, 31.1592,
31.1572, 31.151, 31.1539, 31.154, 31.1548, 31.1569, 31.1544,
31.1557, 31.154, 31.1595, 31.1546, 31.1561, 31.1548, 31.1641,
31.1583, 31.1517, 31.1509, 31.158, 31.157, 31.152, 31.1611, 31.1635,
31.1566, 31.1568, 31.158, 31.1892, 31.1903, 31.1907, 31.1908,
31.1912, 31.2053, 31.2312, 31.2399, 31.2422, 31.2427, 31.2431,
31.2428, 31.2419, 31.2419, 31.2402, 31.2399, 31.2412, 31.2423,
31.2386, 31.2425, 31.2501, 31.2313, 31.2541, 31.2573, 31.2581,
31.2593, 31.2614, 31.2615, 31.2632, 31.2638, 31.2665, 31.2661,
31.2662), temperature = c(2.6608, 2.6794, 2.6949, 2.6895, 2.6911,
2.6849, 2.6788, 2.6679, 2.6707, 2.6868, 2.69, 2.6927, 2.7276,
2.8024, 2.799, 2.8014, 2.9505, 3.033, 3.048, 3.0486, 3.0485,
3.0484, 3.0456, 3.0366, 3.0254, 3.0215, 3.0201, 3.0237, 3.0274,
3.0262, 3.0396, 3.049, 3.0521, 3.0602, 3.0748, 3.0954, 3.1051,
3.1131, 3.1534, 3.2785, 3.3218, 3.3321, 3.3362, 3.3403, 3.3415,
3.3469, 3.3611, 3.368, 3.3781, 3.3756, 3.3783, 3.3836, 3.3883,
3.3908, 3.3961, 3.3971, 3.4097, 3.4249, 3.438, 3.4515, 3.4541,
3.4482, 3.4523, 3.4542, 3.4548, 3.459, 3.4611, 3.462, 3.4656,
3.4627, 3.453, 3.4533, 3.4538, 3.4538, 3.454, 3.4541, 3.4541,
3.4665, 3.4813, 3.4784, 3.4741, 3.4726, 3.4672, 3.4585, 3.4636,
3.4826, 3.4968, 3.5073, 3.508, 3.5059, 3.5088, 3.5094, 3.506,
3.5083, 3.5119, 3.5183, 3.5183, 3.5201, 3.5231, 3.5178, 3.5132,
3.5154, 3.5203, 3.5237, 3.5271, 3.5415, 3.5531, 3.5627, 3.573,
3.5717, 3.561, 3.5578, 3.5557, 3.5534, 3.5544, 3.5517, 3.5557,
3.5615, 3.5642, 3.5668, 3.5681, 3.568, 3.568, 3.5687, 3.5689,
3.5679, 3.5667, 3.5661, 3.5677, 3.5681, 3.568, 3.565, 3.5619,
3.5611, 3.5629, 3.5512, 3.5466, 3.5465, 3.5401, 3.5353, 3.5335,
3.5297, 3.5293, 3.5337, 3.5342, 3.5354, 3.5322, 3.5251, 3.5261,
3.5227, 3.5015, 3.4722, 3.4552, 3.4397, 3.4292, 3.4322, 3.4319,
3.428, 3.4313, 3.4157, 3.4189, 3.4259, 3.4105, 3.3982, 3.3921,
3.3862, 3.3723, 3.3589, 3.3628, 3.3616, 3.3513, 3.3394, 3.3456,
3.3512, 3.3485, 3.3489, 3.344, 3.3394, 3.3327, 3.3413, 3.3251,
3.3141, 3.3116, 3.3078, 3.2953, 3.2736, 3.2659, 3.2797, 3.2768,
3.2614, 3.2614, 3.2536, 3.2437, 3.2293, 3.2464, 3.251, 3.2259,
3.1659, 3.1472, 3.1448, 3.1571, 3.1691, 3.1665, 3.1558, 3.1514,
3.1467, 3.144, 3.1426, 3.1411, 3.1406, 3.1397, 3.1429, 3.1461,
3.1471, 3.1484, 3.1624, 3.1628, 3.1689, 3.2096, 3.2145, 3.2173,
3.2227, 3.2259, 3.2254, 3.228, 3.2277, 3.2258, 3.2206, 3.2194
), conductivity = c(2.747973, 2.750277, 2.753504, 2.752757, 2.753371,
2.752425, 2.751543, 2.750481, 2.750393, 2.752359, 2.752809, 2.754133,
2.759305, 2.771334, 2.77227, 2.769065, 2.79073, 2.804569, 2.808717,
2.810867, 2.811178, 2.81154, 2.811976, 2.812462, 2.812764, 2.813319,
2.813123, 2.813579, 2.814405, 2.814347, 2.815775, 2.817025, 2.817804,
2.818807, 2.82086, 2.823313, 2.824983, 2.826606, 2.827446, 2.849715,
2.857663, 2.859997, 2.860879, 2.861614, 2.861985, 2.862599, 2.864543,
2.865483, 2.866943, 2.866779, 2.867092, 2.86783, 2.868436, 2.868897,
2.869404, 2.870149, 2.871424, 2.873497, 2.874913, 2.87641, 2.876747,
2.876198, 2.876638, 2.876958, 2.876954, 2.877427, 2.877739, 2.877765,
2.878133, 2.878054, 2.877133, 2.877013, 2.877123, 2.877132, 2.877183,
2.877216, 2.87714, 2.87785, 2.879394, 2.879395, 2.879123, 2.878948,
2.878574, 2.877938, 2.878032, 2.879666, 2.880849, 2.882188, 2.882583,
2.882648, 2.883038, 2.883466, 2.883214, 2.883439, 2.883812, 2.884819,
2.885096, 2.885135, 2.885723, 2.885237, 2.884582, 2.884686, 2.885309,
2.885825, 2.886179, 2.888128, 2.889594, 2.891213, 2.892878, 2.893187,
2.893114, 2.892966, 2.892905, 2.892687, 2.892879, 2.89297, 2.893351,
2.893984, 2.89424, 2.894554, 2.894732, 2.894759, 2.894831, 2.894909,
2.894994, 2.894999, 2.894942, 2.894912, 2.895074, 2.895146, 2.89516,
2.894961, 2.894724, 2.894703, 2.894983, 2.894205, 2.893697, 2.893759,
2.893266, 2.892848, 2.892717, 2.892687, 2.892403, 2.892905, 2.892908,
2.892989, 2.89284, 2.892283, 2.892238, 2.892187, 2.890743, 2.888315,
2.886969, 2.885807, 2.88473, 2.884909, 2.884931, 2.884594, 2.884852,
2.883667, 2.883704, 2.884272, 2.883672, 2.882021, 2.881792, 2.881516,
2.880633, 2.879405, 2.879592, 2.879619, 2.878891, 2.877887, 2.878145,
2.878653, 2.878439, 2.878502, 2.878276, 2.877796, 2.877365, 2.877845,
2.876838, 2.875755, 2.875608, 2.875249, 2.874591, 2.8726, 2.871829,
2.872851, 2.872818, 2.871691, 2.871314, 2.871152, 2.870549, 2.869671,
2.870666, 2.870849, 2.869996, 2.86629, 2.865346, 2.865394, 2.866574,
2.868532, 2.870122, 2.870135, 2.869979, 2.869693, 2.869547, 2.869401,
2.869243, 2.869193, 2.869022, 2.869292, 2.869655, 2.869729, 2.869787,
2.87114, 2.871482, 2.871342, 2.875943, 2.87653, 2.876835, 2.877372,
2.877766, 2.87776, 2.878106, 2.878119, 2.878111, 2.877727, 2.877665
)), row.names = c(NA, 229L), class = "data.frame"))
该代码一次只适用于一个样本(在这种情况下为工作站(。尽管如此，我将不得不在随后的大量电台上应用此代码。考虑到一个由不同站点组成的名为AllStations的大列表，我如何在不同的站点之间循环它？(由于字符数量有限，将通过复制df创建大列表AllStations(
df1 <- df
df2 <- df
df3 <- df
df4 <- df
AllStations <- list(df, df1, df2, df3, df4)
我已经可以用mapply迭代部分代码，但不能迭代全部。。。就像这里的plotProfile()
mapply(function(dat, nm) {
plotProfile(dat, xtype="temperature",
col.temperature="black")
},
dat = AllStations, nm = names(AllStations))
您可以考虑使用并行计算以高效的方式完成所有这些处理。下面是的一个例子
fn_Par <- function(df)
{
require(oce)
require(dplyr)
df <- mapply(function(dat, nm) {
as.ctd(dat, salinity, temperature, pressure)
},
dat = df, nm = names(df))
df <- mapply(function(dat, nm) {
ctdTrim(dat, method = "downcast") #Smoothing dataset
},
dat = df, nm = names(df))
plotProfile(df$`2018-08-02T162038 SBE0251090`, xtype="temperature",
col.temperature="black")
temperature <- df[["temperature"]]
pressure <- df[["pressure"]]
for(criterion in c(1, 0.125))
{
#dP of 0.125kg/m3 and dT of 1
inMLD <- abs(temperature[1] - temperature) < criterion
MLDindex <- which.min(inMLD)
MLDpressure <- pressure[MLDindex]
abline(h=pressure[MLDindex], lwd = 2, lty = "dashed")
}
MLDpressureMax <- pressure[MLDindex] + MLDindex
#Thermocline
plotProfile(df$`2018-08-02T162038 SBE0251090`, xtype = "N2", col.N2 = "black")
mid <- which.max(swN2(df$`2018-08-02T162038 SBE0251090`))
pstar <- pressure[mid] #give thermocline
abline(h = pstar, lwd = 2, lty = "dashed")
MLDAll <- data.frame(MLDmin = MLDpressure,MLDmax = MLDpressureMax, Thermocline = pstar)
MLDAll <- MLDAll |> mutate(id = rep("df"))
return(MLDAll)
}
df1 <- df
df2 <- df
df3 <- df
df4 <- df
AllStations <- list(df, df1, df2, df3, df4)
library(parallel)
library(doParallel)
nb_CPU <- 5
cluster <- parallel::makeCluster(nb_CPU)
doParallel::registerDoParallel(cl = cluster)
list_Res <- parLapply(cluster, X = AllStations, fun = fn_Par)