python 查找指定字符串 ，找出相应的数据行
def get_target_Lines(fr, s): f_handle = open(fr, "r") targets = [] #flag = False for num, line in enumerate(f_handle): if line.startswith(s): tmp = num try: if num > tmp and line.rstrip() != "": targets.append(line.rstrip()) elif num > tmp and line.rstrip() == "": # flag = True break except NameError, e: pass #if flag: # break return targets
tgets = get_target_Lines( "outfile_f" , "Inferred ancestry")
用R 自定义函数
用apply， sprintf ， order等函数
> meanfunction (x, ...) UseMethod("mean") > mean.defaultfunction (x, trim = 0, na.rm = FALSE, ...) { if (!is.numeric(x) && !is.complex(x) && !is.logical(x)) { warning("argument is not numeric or logical: returning NA") return(NA_real_) } if (na.rm) x <- x[!is.na(x)] if (!is.numeric(trim) || length(trim) != 1L) stop("'trim' must be numeric of length one") n <- length(x) if (trim > 0 && n) { if (is.complex(x)) stop("trimmed means are not defined for complex data") if (anyNA(x)) return(NA_real_) if (trim >= 0.5) return(stats::median(x, na.rm = FALSE)) lo <- floor(n * trim) + 1 hi <- n + 1 - lo x <- sort.int(x, partial = unique(c(lo, hi)))[lo:hi] } .Internal(mean(x))} /> mt <- function(v){+ n <- length(v)+ t1 <- v[1:5]+ tp <- sum(v[6:n])+ t2 <- v[6:n]+ if (tp > 35){+ max_index = which(t2==max(t2))[1]+ t2[max_index] = max(t2)- tp + 35+ }+ return(c(t1,t2))+ }> mt(1:10) [1] 1 2 3 4 5 6 7 8 9 5 > a <- 1:10> b <- c(2,5,6,7, 466, 466,466, 34,5,2)> c <- c(2,5,6,7, 466, 89,14, 56,7,2)> data1 <- data.frame(a,b,c)> data1 a b c1 1 2 22 2 5 53 3 6 64 4 7 75 5 466 4666 6 466 897 7 466 148 8 34 569 9 5 710 10 2 2> apply(data1, 2, mt) a b c [1,] 1 2 2 [2,] 2 5 5 [3,] 3 6 6 [4,] 4 7 7 [5,] 5 466 466 [6,] 6 -472 -44 [7,] 7 466 14 [8,] 8 34 56 [9,] 9 5 7[10,] 5 2 2> data1 <- data.frame(rbind(a,b,c) )> data1 X1 X2 X3 X4 X5 X6 X7 X8 X9 X10a 1 2 3 4 5 6 7 8 9 10b 2 5 6 7 466 466 466 34 5 2c 2 5 6 7 466 89 14 56 7 2> apply(data1, 1, mt) a b cX1 1 2 2X2 2 5 5X3 3 6 6X4 4 7 7X5 5 466 466X6 6 -472 -44X7 7 466 14X8 8 34 56X9 9 5 7X10 5 2 2///> data <- read.table("test_plot.txt", skip=1, stringsAsFactors =F)> c("A","B",3)[1] "A" "B" "3"> mt <- function(v){ n <- length(v) t1 <- v[1:5] t2 <- as.numeric(v[6:n]) tp <- sum(t2) if (tp > 1){ max_index = which(t2==max(t2))[1] t2[max_index] = max(t2)- tp + 1 } else if (tp < 1){ min_index = which(t2==min(t2))[1] t2[min_index] = min(t2) + 1 - tp } return(c(t1,t2)) } > > apply(data[1:3,], 1, mt) 1 2 3 V1 "1" "2" "3" V2 "100" "101" "102" V3 "(0)" "(0)" "(0)" V4 "1" "1" "1" V5 ":" ":" ":" "0.663" "0.767" "0.651" "0.185" "0.042" "0.083" "0.039" "0.033" "0.062" "0.113" "0.158" "0.204"> t(apply(data, 1, mt)) V1 V2 V3 V4 V5 [1,] " 1" "100" "(0)" "1" ":" "0.663" "0.185" [2,] " 2" "101" "(0)" "1" ":" "0.767" "0.042" [3,] " 3" "102" "(0)" "1" ":" "0.651" "0.083" [4,] " 4" "103" "(0)" "1" ":" "0.358" "0.078" [5,] " 5" "104" "(0)" "1" ":" "0.062" "0.096" [6,] " 6" "105" "(0)" "1" ":" "0.058" "0.098" > t <- sprintf( "%.7f", 1.44*1.21)> t[1] "1.7424000"> t <- sprintf( "%.3f", 1.44*1.21)> t[1] "1.742"> mt <- function(v){+ n <- length(v)+ t1 <- v[1:5]+ t2 <- as.numeric(v[6:n])+ tp <- sum(t2)+ + if (tp > 1){+ max_index <- which(t2==max(t2))[1]+ t2[max_index] <- sprintf("%.3f", max(t2)-tp+1)+ } else if (tp < 1){+ min_index <- which(t2==min(t2))[1]+ t2[min_index] <- sprintf("%.3f", min(t2) + 1 - tp)+ }+ return(c(t1,t2))+ }> head(data) V1 V2 V3 V4 V5 V6 V7 V8 V91 1 100 (0) 1 : 0.663 0.185 0.038 0.1132 2 101 (0) 1 : 0.767 0.042 0.033 0.1583 3 102 (0) 1 : 0.651 0.083 0.062 0.2044 4 103 (0) 1 : 0.358 0.078 0.185 0.3795 5 104 (0) 1 : 0.062 0.096 0.487 0.3556 6 105 (0) 1 : 0.058 0.098 0.516 0.329> head(data[,6:9]) V6 V7 V8 V91 0.663 0.185 0.038 0.1132 0.767 0.042 0.033 0.1583 0.651 0.083 0.062 0.2044 0.358 0.078 0.185 0.3795 0.062 0.096 0.487 0.3556 0.058 0.098 0.516 0.329 > head(t(apply(data, 1, mt))) V1 V2 V3 V4 V5 [1,] " 1" "100" "(0)" "1" ":" "0.663" "0.185" "0.039" "0.113"[2,] " 2" "101" "(0)" "1" ":" "0.767" "0.042" "0.033" "0.158"[3,] " 3" "102" "(0)" "1" ":" "0.651" "0.083" "0.062" "0.204"[4,] " 4" "103" "(0)" "1" ":" "0.358" "0.078" "0.185" "0.379"[5,] " 5" "104" "(0)" "1" ":" "0.062" "0.096" "0.487" "0.355"[6,] " 6" "105" "(0)" "1" ":" "0.058" "0.098" "0.515" "0.329"> apply(data[,6:9], 1 , sum) [1] 0.999 1.000 1.000 1.000 1.000 1.001 1.000 1.000 1.000 1.000 1.000 1.000 [13] 1.001 1.000 1.000 1.001 0.999 0.999 1.000 1.000 1.000 1.000 1.000 1.000 [25] 1.001 1.000 1.001 1.000 0.999 1.000 1.000 1.000 1.000 1.000 1.001 1.000 [37] 1.000 1.000 0.999 1.000 0.999 1.001 1.000 0.999 1.000 1.000 1.000 1.000 [49] 1.000 0.999 1.000 1.000 1.001 1.000 1.001 1.000 1.000 1.001 1.000 1.000 [61] 1.000 1.000 1.000 1.001 0.999 1.000 0.999 1.001 0.999 0.999 1.000 1.000 [73] 0.999 1.000 1.000 1.000 0.999 1.000 1.000 1.001 1.000 1.000 1.000 1.001 [85] 1.000 1.001 1.000 1.000 1.000 1.001 1.000 1.000 1.000 1.000 1.000 1.001 [97] 1.001 1.000 1.000 1.000 1.000 1.000 1.001 1.001 1.000 1.001 1.000 1.000[109] 1.000 1.000 0.999 0.999 1.001 1.000 1.000 1.000 0.999 1.001 1.000 1.000[121] 1.000 1.000 1.000 1.000 1.000 0.999 1.000 1.000 1.000 1.000 1.000 1.000[133] 1.000 1.001 1.001 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000[145] 1.001 0.999 1.000 0.999 1.000 1.000 0.999 1.000 0.999 1.000 0.999 1.000[157] 0.999 0.999 1.000 1.001 1.000 1.001 1.000 1.000 1.000 0.999 1.000 1.001[169] 1.000 1.000 0.999 1.000 1.000 1.000 0.999> x <- t(apply(data, 1, mt))> head(x) V1 V2 V3 V4 V5 [1,] " 1" "100" "(0)" "1" ":" "0.663" "0.185" "0.039" "0.113"[2,] " 2" "101" "(0)" "1" ":" "0.767" "0.042" "0.033" "0.158"[3,] " 3" "102" "(0)" "1" ":" "0.651" "0.083" "0.062" "0.204"[4,] " 4" "103" "(0)" "1" ":" "0.358" "0.078" "0.185" "0.379"[5,] " 5" "104" "(0)" "1" ":" "0.062" "0.096" "0.487" "0.355"[6,] " 6" "105" "(0)" "1" ":" "0.058" "0.098" "0.515" "0.329"> head(x[,6:9]) [1,] "0.663" "0.185" "0.039" "0.113"[2,] "0.767" "0.042" "0.033" "0.158"[3,] "0.651" "0.083" "0.062" "0.204"[4,] "0.358" "0.078" "0.185" "0.379"[5,] "0.062" "0.096" "0.487" "0.355"[6,] "0.058" "0.098" "0.515" "0.329" > m <- apply(data.frame(x[,6:9]), 1, as.numeric)> apply(m,2,sum) [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [37] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [73] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1[109] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1[145] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1> n <- apply(data.frame(x[,6:9]), 2, as.numeric)> apply(n,1,sum) [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [37] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [73] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1[109] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1[145] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1> /> x <- t(apply(data, 1, mt))> head(x) V1 V2 V3 V4 V5 [1,] " 1" "100" "(0)" "1" ":" "0.663" "0.185" "0.039" "0.113"[2,] " 2" "101" "(0)" "1" ":" "0.767" "0.042" "0.033" "0.158"[3,] " 3" "102" "(0)" "1" ":" "0.651" "0.083" "0.062" "0.204"[4,] " 4" "103" "(0)" "1" ":" "0.358" "0.078" "0.185" "0.379"[5,] " 5" "104" "(0)" "1" ":" "0.062" "0.096" "0.487" "0.355"[6,] " 6" "105" "(0)" "1" ":" "0.058" "0.098" "0.515" "0.329"> x1 <- as.data.frame(x)> head(x1) V1 V2 V3 V4 V5 V6 V7 V8 V91 1 100 (0) 1 : 0.663 0.185 0.039 0.1132 2 101 (0) 1 : 0.767 0.042 0.033 0.1583 3 102 (0) 1 : 0.651 0.083 0.062 0.2044 4 103 (0) 1 : 0.358 0.078 0.185 0.3795 5 104 (0) 1 : 0.062 0.096 0.487 0.3556 6 105 (0) 1 : 0.058 0.098 0.515 0.329> data_p <- x1[order(x1[,6], decreasing=T),]> pdf("graph.pdf", height=5, width=15)> barplot(as.matrix(t(data_p[,6:9])), names.arg=data_p$V2, col=rainbow(4), border= rainbow(4), las=2, cex.names=0.35, cex.axis =0.65)> dev.off()null device 1 > 绘图结果：