Mercurial > repos > ecology > vigiechiro_idvalid
comparison IdValid.R @ 0:8c472c4f1bf5 draft
planemo upload for repository https://github.com/galaxyecology/tools-ecology/tools/vigiechiro commit d2de8e10c11bfa3b04729e59bba58e08d23b56aa
author | ecology |
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date | Wed, 13 Mar 2019 11:18:58 -0400 |
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-1:000000000000 | 0:8c472c4f1bf5 |
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1 #!/usr/bin/env Rscript | |
2 | |
3 suppressMessages(library(data.table)) | |
4 | |
5 ValidHier=function(x,y) #used to write validator id over observer id | |
6 { | |
7 #cat(y) | |
8 if(is.na(y)){x}else{y} | |
9 } | |
10 | |
11 f2p <- function(x) #get date-time data from recording file names | |
12 { | |
13 if (is.data.frame((x)[1])) {pretemps <- vector(length = nrow(x))} | |
14 op <- options(digits.secs = 3) | |
15 pretemps <- paste(substr(x, nchar(x) - 18, nchar(x)-4), ".", substr(x, nchar(x) - 2, nchar(x)), sep = "") | |
16 strptime(pretemps, "%Y%m%d_%H%M%OS",tz="UTC")-7200 | |
17 } | |
18 | |
19 args <- commandArgs(trailingOnly = TRUE) | |
20 | |
21 | |
22 IdCorrect=fread(args[1]) | |
23 RefSeuil=fread(args[2]) | |
24 #IdV=as.data.frame(subset(IdCorrect,select=observateur_taxon:validateur_probabilite)) | |
25 | |
26 #Step 0 :compute id score from 2nd Layer | |
27 test=match("participation",names(IdCorrect)) | |
28 IdCorrect$IdScore=apply(as.data.frame(IdCorrect)[,(test+1):(ncol(IdCorrect)-1)],MARGIN=1,FUN=max) | |
29 #compute true success probabilities according to logistic regression issued from "Referentiel_seuils" | |
30 CorrSp=match(IdCorrect$ProbEsp_C2bs,RefSeuil$Espece) | |
31 PSp=RefSeuil$Pente[CorrSp] | |
32 ISp=RefSeuil$Int[CorrSp] | |
33 suppressWarnings(IdCorrect$IdProb<-mapply(FUN=function(w,x,y) if((!is.na(y))&(y>0)&(y<1000)) {(exp(y*w+x)/(1+exp(y*w+x)))}else{w} ,IdCorrect$IdScore,ISp,PSp)) | |
34 | |
35 #Step 1 :compute id with confidence regarding a hierarchy (validator > observer) | |
36 IdCorrect$IdV=mapply(ValidHier,IdCorrect$observateur_taxon,IdCorrect$validateur_taxon) | |
37 IdCorrect$ConfV=mapply(ValidHier,IdCorrect$observateur_probabilite | |
38 ,IdCorrect$validateur_probabilite) | |
39 | |
40 | |
41 | |
42 #Step 2: Get numerictime data | |
43 suppressWarnings(IdCorrect$Session<-NULL) | |
44 suppressWarnings(IdCorrect$TimeNum<-NULL) | |
45 | |
46 if (substr(IdCorrect$`nom du fichier`[1],2,2)=="i") #for car/walk transects | |
47 { | |
48 FileInfo=as.data.table(tstrsplit(IdCorrect$`nom du fichier`,"-")) | |
49 IdCorrect$Session=as.numeric(substr(FileInfo$V4,5,nchar(FileInfo$V4))) | |
50 TimeSec=as.data.table(tstrsplit(FileInfo$V5,"_")) | |
51 TimeSec=as.data.frame(TimeSec) | |
52 if(sum(TimeSec[,(ncol(TimeSec)-1)]!="00000")==0) #to deal with double Kaleidoscope treatments | |
53 { | |
54 print("NOMS DE FICHIERS NON CONFORMES") | |
55 print("Vous les avez probablement traiter 2 fois par Kaleidoscope") | |
56 stop("Merci de nous signaler cette erreur par mail pour correction") | |
57 }else{ | |
58 IdCorrect$TimeNum=(IdCorrect$Session*800 | |
59 +as.numeric(TimeSec[,(ncol(TimeSec)-1)]) | |
60 +as.numeric(TimeSec[,(ncol(TimeSec))])/1000) | |
61 } | |
62 | |
63 }else{ | |
64 if(substr(IdCorrect$`nom du fichier`[1],2,2)=="a") #for stationary recordings | |
65 { | |
66 DateRec=as.POSIXlt(f2p(IdCorrect$`nom du fichier`)) | |
67 Nuit=format(as.Date(DateRec-43200*(DateRec$hour<12)),format="%d/%m/%Y") | |
68 #Nuit[is.na(Nuit)]=0 | |
69 IdCorrect$Session=Nuit | |
70 IdCorrect$TimeNum=as.numeric(DateRec) | |
71 | |
72 }else{ | |
73 print("NOMS DE FICHIERS NON CONFORMES") | |
74 stop("Ils doivent commencer par Cir (routier/pedestre) ou par Car (points fixes") | |
75 } | |
76 } | |
77 | |
78 | |
79 | |
80 | |
81 #Step 3 :treat sequentially each species identified by Tadarida-C | |
82 IdExtrap=vector() #to store the id extrapolated from validations | |
83 IdC2=IdCorrect[0,] #to store data in the right order | |
84 TypeE=vector() #to store the type of extrapolation made | |
85 for (j in 1:nlevels(as.factor(IdCorrect$ProbEsp_C2bs))) | |
86 { | |
87 IdSp=subset(IdCorrect | |
88 ,IdCorrect$ProbEsp_C2bs==levels(as.factor(IdCorrect$ProbEsp_C2bs))[j]) | |
89 if(sum(is.na(IdSp$IdV))==(nrow(IdSp))) #case 1 : no validation no change | |
90 { | |
91 IdC2=rbind(IdC2,IdSp) | |
92 IdExtrap=c(IdExtrap,rep(IdSp$ProbEsp_C2bs[1],nrow(IdSp))) | |
93 TypeE=c(TypeE,rep(0,nrow(IdSp))) | |
94 }else{ #case 2: some validation | |
95 Vtemp=subset(IdSp,is.na(IdSp$IdV)) | |
96 #case2A: validations are homogeneous | |
97 if(nlevels(as.factor(Vtemp$IdV))==1) | |
98 { | |
99 IdC2=rbind(IdC2,IdSp) | |
100 IdExtrap=c(IdExtrap,rep(Vtemp$IdV[1],nrow(IdSp))) | |
101 TypeE=c(TypeE,rep(2,nrow(IdSp))) | |
102 }else{ | |
103 #case 2B: validations are heterogeneous | |
104 #case 2B1: some validations confirms the species identified by Tadarida and highest confidence are confirmed | |
105 subVT=subset(Vtemp,Vtemp$IdV==levels(as.factor(IdCorrect$ProbEsp_C2bs))[j]) | |
106 subVF=subset(Vtemp,Vtemp$IdV!=levels(as.factor(IdCorrect$ProbEsp_C2bs))[j]) | |
107 if((nrow(subVT)>0)&(max(subVT$IdProb)>max(subVF$IdProb))) | |
108 { | |
109 Vtemp=Vtemp[order(Vtemp$IdProb),] | |
110 test=(Vtemp$IdV!=Vtemp$ProbEsp_C2bs) | |
111 Fr1=max(which(test == TRUE)) #find the error with highest indices | |
112 Thr1=mean(Vtemp$IdProb[(Fr1):(Fr1+1)]) #define first threshold as the median confidence between the first error and the confirmed ID right over it | |
113 #id over this threshold are considered right | |
114 IdHC=subset(IdSp,IdSp$IdProb>Thr1) | |
115 IdC2=rbind(IdC2,IdHC) | |
116 IdExtrap=c(IdExtrap,rep(Vtemp$IdV[nrow(Vtemp)],nrow(IdHC))) | |
117 TypeE=c(TypeE,rep(2,nrow(IdHC))) | |
118 #id under this threshold are attributed to validated id closest in time | |
119 Vtemp=Vtemp[order(Vtemp$TimeNum),] | |
120 cuts <- c(-Inf, Vtemp$TimeNum[-1]-diff(Vtemp$TimeNum)/2, Inf) | |
121 CorrV=findInterval(IdSp$TimeNum, cuts) | |
122 IdE=Vtemp$IdV[CorrV] | |
123 IdEL=subset(IdE,IdSp$IdProb<=Thr1) | |
124 IdLC=subset(IdSp,IdSp$IdProb<=Thr1) | |
125 IdExtrap=c(IdExtrap,IdEL) | |
126 TypeE=c(TypeE,rep(1,length(IdEL))) | |
127 IdC2=rbind(IdC2,IdLC) | |
128 | |
129 | |
130 }else{ | |
131 #case 2B2: all validations concerns errors | |
132 #id are extrapolated on time only | |
133 Vtemp=Vtemp[order(Vtemp$TimeNum),] | |
134 cuts <- c(-Inf, Vtemp$TimeNum[-1]-diff(Vtemp$TimeNum)/2, Inf) | |
135 CorrV=findInterval(IdSp$TimeNum, cuts) | |
136 IdE=Vtemp$IdV[CorrV] | |
137 IdExtrap=c(IdExtrap,IdE) | |
138 TypeE=c(TypeE,rep(1,length(IdE))) | |
139 IdC2=rbind(IdC2,IdSp) | |
140 } | |
141 } | |
142 | |
143 | |
144 } | |
145 | |
146 | |
147 } | |
148 test1=(nrow(IdC2)==length(IdExtrap)) | |
149 test2=(nrow(IdC2)==nrow(IdCorrect)) | |
150 if((test1==F)|(test2==F)) | |
151 { | |
152 (stop("Erreur de traitement !!!")) | |
153 } | |
154 | |
155 IdC2$IdExtrap=IdExtrap | |
156 IdC2$TypeE=TypeE | |
157 | |
158 | |
159 IdC2=IdC2[order(IdC2$IdProb,decreasing=T),] | |
160 IdC2=IdC2[order(IdC2$ConfV,decreasing=T),] | |
161 IdC2=IdC2[order(IdC2$`nom du fichier`),] | |
162 #discard duplicated species within the same files (= false positives corrected by 2nd layer) | |
163 IdC2=unique(IdC2,by=c("nom du fichier","IdExtrap")) | |
164 | |
165 write.table(IdC2,"output.tabular",row.names=F,sep="\t",quote=FALSE,na="NA") |