EPR Spectra Data Analysis PCSL fading study 04/17 paper version

Etapa 1

Espectro do PCSL não irradiado:

fileDSCNI<-"C:/Users/rodri_000/Google Drive/R/PCSL EPR paper/espectros/PCSL_NI_07julho2016_09h33.DSC"
fileDTANI<-"C:/Users/rodri_000/Google Drive/R/PCSL EPR paper/espectros/PCSL_NI_07julho2016_09h33.DTA"
#
dscNI <- readLines(fileDSCNI,encoding="UTF-8")
#
pontos<-grep("XPTS",dscNI,value=T);campos<-strsplit(pontos,'\t')[[1]];XPTS<-as.numeric(campos[2])
  pontos<-grep("XMIN",dscNI,value=T);campos<-strsplit(pontos,'\t')[[1]];XMIN<-as.numeric(campos[2])
  pontos<-grep("XWID",dscNI,value=T);campos<-strsplit(pontos,'\t')[[1]];XWID<-as.numeric(campos[2])
  pontos<-grep("MWFQ",dscNI,value=T);campos<-strsplit(pontos,'    ')[[1]];MF<-as.numeric(campos[2])/1e9#GHz
  pontos<-grep("RCAG",dscNI,value=T);campos<-strsplit(pontos,'    ')[[1]];RG<-as.numeric(campos[2])
  pontos<-grep("SPTP",dscNI,value=T);campos<-strsplit(pontos,'    ')[[1]];CT<-as.numeric(campos[2])*1000#ms
  pontos<-grep("AVGS",dscNI,value=T);campos<-strsplit(pontos,'    ')[[1]];SCAN<-as.numeric(campos[2])
  pontos<-grep("MWPW",dscNI,value=T);campos<-strsplit(pontos,'    ')[[1]];MWPW<-as.numeric(campos[2])*1000#mW
  info<-grep("DATE", dscNI,value=T);campos<-strsplit(info,'    ')[[1]];DATE<-(campos[2])
  info<-grep("TIME", dscNI,value=T);campos<-strsplit(info,'    ')[[1]];TIME<-(campos[2])
  #infos$NOME[[i]]<-nome[1]
  #
  passo<-round(XWID/XPTS,1)
  b<-seq(1:XPTS)
  for(j in 1:XPTS){
    b[j]<-XMIN+(passo*(j-1))
  }
  b<-data.frame(b)
  g<-714.55*MF/b
  s<-readBin(fileDTANI, double(), n = XPTS, size = 8, endian = "big");s<-data.frame(s);
 #Data normalization - Bruker Manual cap.5.6
 sn<-s/(CT*SCAN*20*10^(RG/20))
 #
 dados<-cbind(b,g,s,sn);colnames(dados)[2] <- "g";colnames(dados)[4] <- "sn"
 #
 plot(dados$b,dados$sn,type="n",xlab="Magnetic Field (G)",ylab="EPR Signal Normalized (a.u.)",ylim=c(-20,20))
 lines(dados$b,dados$sn,col="black");grid(NULL,NULL)

Etapa 2

Irradiação em: 21/06/2016

Dose: 23,9 kGy

Leitura dos arquivos do diretório C:/Users/rodri_000/Google Drive/R/PCSL EPR paper:

Geração da planilha com 25 arquivos.

Colunas:
b - Campo Magnético (G)
g - fator g calculado por: \[ gFactor=\frac{714.55*v(GHz)}{B(G)} \] S - Intensidade do sinal de EPR (u.a.)
Sn - Intensidade do sinal de EPR normalizado utilizando a equação da Bruker:
\[ sn=\dfrac{s}{C_{T}(ms)*N_{SCAN}*20*10^{RG(dB)/20}} \]

#

Etapa 3

Acompanhamento do decaimento do sinal e RPE ao longo de aproximadamente 180 dias.

library(baseline)

Attaching package: <U+393C><U+3E31>baseline<U+393C><U+3E32>

The following object is masked from <U+393C><U+3E31>package:stats<U+393C><U+3E32>:

    getCall

library(sqldf)

Carregando pacotes exigidos: gsubfn
Carregando pacotes exigidos: proto
Carregando pacotes exigidos: RSQLite

library(data.table)

data.table 1.9.8
  The fastest way to learn (by data.table authors): https://www.datacamp.com/courses/data-analysis-the-data-table-way
  Documentation: ?data.table, example(data.table) and browseVignettes("data.table")
  Release notes, videos and slides: http://r-datatable.com

library(knitr)
#versão 07 de 08-12-2016
#preparacao de uma tabela
#############################################
#carregando a lista de arquivos DSC do dir
# tratarei o zero em outro script
fileDSC<-list.files(pattern="*.DSC$")
fileDTA<-list.files(pattern="*.DTA$")
#num de arquivos
nn<-length(fileDSC)
#
espectros<-list()#carrego e armazeno todos os espectros: formato espectros[[i]]$(b,g,s,sn)
infos<-list("NOME"=1:nn,"XPTS"=1:nn,"RG"=1:nn,"DATE"=1:nn,"TIME"=1:nn,"MF"=1:nn,"CT"=1:nn,"SCAN"=1:nn,"MWPW"=1:nn,"Hpap"=1:nn,"Hpapsn"=1:nn)
#
for (i in 1:nn)
  {
  dsc <- readLines(fileDSC[i],encoding="UTF-8")
  #
  nome<-(strsplit(fileDSC[i],"\\."))[[1]]
  #
  fileDTA<-paste0(nome[1],".DTA")
  pontos<-grep("XPTS",dsc,value=T);campos<-strsplit(pontos,'\t')[[1]];infos$XPTS[[i]]<-as.numeric(campos[2])
  pontos<-grep("XMIN",dsc,value=T);campos<-strsplit(pontos,'\t')[[1]];XMIN<-as.numeric(campos[2])
  pontos<-grep("XWID",dsc,value=T);campos<-strsplit(pontos,'\t')[[1]];XWID<-as.numeric(campos[2])
  pontos<-grep("MWFQ",dsc,value=T);campos<-strsplit(pontos,'    ')[[1]];infos$MF[[i]]<-as.numeric(campos[2])/1e9#GHz
  pontos<-grep("RCAG",dsc,value=T);campos<-strsplit(pontos,'    ')[[1]];infos$RG[[i]]<-as.numeric(campos[2])
  pontos<-grep("SPTP",dsc,value=T);campos<-strsplit(pontos,'    ')[[1]];infos$CT[[i]]<-as.numeric(campos[2])*1000#ms
  pontos<-grep("AVGS",dsc,value=T);campos<-strsplit(pontos,'    ')[[1]];infos$SCAN[[i]]<-as.numeric(campos[2])
  pontos<-grep("MWPW",dsc,value=T);campos<-strsplit(pontos,'    ')[[1]];infos$MWPW[[i]]<-as.numeric(campos[2])*1000#mW
  info<-grep("DATE", dsc,value=T);campos<-strsplit(info,'    ')[[1]];infos$DATE[[i]]<-(campos[2])
  info<-grep("TIME", dsc,value=T);campos<-strsplit(info,'    ')[[1]];infos$TIME[[i]]<-(campos[2])
  infos$NOME[[i]]<-nome[1]
  #
  passo<-round(XWID/infos$XPTS[[i]],1)
  b<-seq(1:infos$XPTS[[i]])
  for(j in 1:infos$XPTS[[i]]){
    b[j]<-XMIN+(passo*(j-1))
  }
  b<-data.frame(b)
  g<-714.55*infos$MF[[i]]/b
  s<-readBin(fileDTA, double(), n = infos$XPTS[[i]], size = 8, endian = "big");s<-data.frame(s);
  #Data normalization - Bruker Manual cap.5.6
  sn<-s/(infos$CT[[i]]*infos$SCAN[[i]]*20*10^(infos$RG[[i]]/20))
  #
  dados<-cbind(b,g,s,sn);colnames(dados)[2] <- "g";colnames(dados)[4] <- "sn"
  #guardo os espectros
  espectros[[i]]<-dados
  #Hpap sn
  infos$Hpapsn[i]<-max(dados[,4])-min(dados[,4])
  #Hpap s
  infos$Hpap[i]<-max(dados[,3])-min(dados[,3])
 
}

Exemplo do espectro do PCSL irradiado (medida em 2016-06-21 13:58:24):

#
plot(espectros[[21]]$b,espectros[[21]]$sn,type="n",xlab="Magnetic Field (G)",ylab="EPR Signal Normalized (a.u.)",xlim=c(3400,3600))
 lines(espectros[[21]]$b,espectros[[21]]$sn,col="black");grid(NULL,NULL)

#

Arquivos analisados:

# até aqui carreguei os arquivos DSC em infos[[]] e DTA em dados[[]]
# calculei g e sn e estimei os HPaP e HPaPn 
#criando um novo conjunto de dados
tmed<-list("TD"=1:nn,"Arq"=1:nn,"Hpap"=1:nn,"Hpapsn"=1:nn)
for (i in 1:nn)
{
    tmed$TD[i]<-paste(infos$DATE[i],infos$TIME[i])
    tmed$Arq[i]<-infos$NOME[i]
    tmed$Hpap[i]<-infos$Hpap[i]
    tmed$Hpapsn[i]<-infos$Hpapsn[i]
}
tmed$TD=as.POSIXct(tmed$TD,format="%m/%d/%y %H:%M:%S",tz="UTC")
#crio um data.frame
tmed<-data.frame(tmed)
# ordeno os indices pela data 
ind<-order(tmed$TD,decreasing=F)
#tabela
kable(tmed[order(tmed$TD),],caption = "Dados")

	TD	Arq	Hpap	Hpapsn
21	2016-06-21 13:58:24	PCSL25kGy_21junho2016_13h57	14493110.2	10187.59351
22	2016-06-21 14:54:11	PCSL25kGy_21junho2016_14h54	13827080.5	9719.42348
19	2016-06-21 15:33:57	PCSL25kGy_21jun2016_15h33	13776301.4	9683.72945
20	2016-06-21 16:39:45	PCSL25kGy_21jun2016_16h40	13491199.7	9483.32392
23	2016-06-22 08:38:11	PCSL25kGy_22jun2016_08h38	10966520.8	7708.65977
24	2016-06-22 10:48:00	PCSL25kGy_22junho2016_10h38	10438072.2	7337.19919
9	2016-06-22 16:44:00	PCSL25kGy-22jun2016-16h42	9830958.8	6910.44301
10	2016-06-23 09:14:47	PCSL25kGy-23jun2016-09h15	8459219.8	5946.21109
11	2016-06-23 16:13:08	PCSL25kGy-23jun2016-16h13	8023780.0	5640.12888
12	2016-06-24 09:10:52	PCSL25kGy-24jun2016-09h10	6742392.5	4739.40741
13	2016-06-27 09:18:04	PCSL25kGy-27jun2016-09h17	4716107.8	3315.07791
25	2016-06-28 08:55:11	PCSL25kGy_28junho2016_0845	4247508.2	2985.68674
6	2016-06-29 10:06:53	PCSL25kG_29jun2016_10h07	3843868.2	2701.95743
14	2016-06-30 09:33:04	PCSL25kGy-30jun2016-09h33	3405401.4	2393.74745
7	2016-07-01 09:21:03	PCSL25kGy-01jul2016-09h21	3072432.9	2159.69500
8	2016-07-04 09:16:24	PCSL25kGy-04jul2016-09h16	2388973.6	1679.27324
15	2016-07-05 09:23:09	PCSL25kGy_05julho2016_09h13	2237723.9	1572.95578
16	2016-07-07 09:27:40	PCSL25kGy_07julho2016_09h18	1915544.2	1346.48708
17	2016-07-14 15:51:40	PCSL25kGy_14jul2016_15h52	1186932.5	834.32651
4	2016-07-28 13:47:55	PCSL-25kGy-28jul2016-13h48	684015.2	480.81253
1	2016-08-04 16:19:39	PCSL-25kGy-04ago2016-16h20	507293.9	356.59043
3	2016-09-13 10:01:38	PCSL-25kGy-13set2016-10h01	180753.4	127.05637
5	2016-09-30 09:14:40	PCSL-25kGy-30set2016-09h15	141788.2	99.66673
2	2016-10-04 09:53:12	PCSL-25kGy-04out2016-09h53	133777.3	94.03562
18	2016-12-16 14:11:20	PCSL25kGy_16dez2016_14h13	128264.7	50.70109

Observações:
* Hpap: valor pico-a-pico
* Hpapsn: valor pico-a-pico normalizado

#

Resultados para o cálculo do fading:

# calculo os tempos relativos a medida inicial (ind[1])
fading<-list("F"=1:nn,"Hpap"=1:nn,"Hpapsn"=1:nn)
fading$F[1]<-0
fading$Hpap[1]<-tmed$Hpap[ind[1]]
fading$Hpapsn[1]<-tmed$Hpapsn[ind[1]]
#
 for (i in 2:nn)
 {
     #fading$F[i]<-difftime(tmed$TD[ind[i]],tmed$TD[ind[1]],units="hours")
     fading$F[i]<-difftime(tmed$TD[ind[i]],tmed$TD[ind[1]],units="days")
     fading$Hpap[i]<-tmed$Hpap[ind[i]]
     fading$Hpapsn[i]<-tmed$Hpapsn[ind[i]]
 }
df.fading<-data.frame(fading)
#tabela
kable(df.fading,caption = "Decaimento do sinal ao longo dos dias")

F	Hpap	Hpapsn
0.0000000	14493110.2	10187.59351
0.0387384	13827080.5	9719.42348
0.0663542	13776301.4	9683.72945
0.1120486	13491199.7	9483.32392
0.7776273	10966520.8	7708.65977
0.8677778	10438072.2	7337.19919
1.1150000	9830958.8	6910.44301
1.8030440	8459219.8	5946.21109
2.0935648	8023780.0	5640.12888
2.8003241	6742392.5	4739.40741
5.8053241	4716107.8	3315.07791
6.7894329	4247508.2	2985.68674
7.8392245	3843868.2	2701.95743
8.8157407	3405401.4	2393.74745
9.8073958	3072432.9	2159.69500
12.8041667	2388973.6	1679.27324
13.8088542	2237723.9	1572.95578
15.8119907	1915544.2	1346.48708
23.0786574	1186932.5	834.32651
36.9927199	684015.2	480.81253
44.0980903	507293.9	356.59043
83.8355787	180753.4	127.05637
100.8029630	141788.2	99.66673
104.8297222	133777.3	94.03562
178.0089815	128264.7	50.70109

Observação:
* F : intervalo de tempo em dias entre a irradiação e a medida.

#

Curva do decaimento do sinal (sem descontar o sinal NI):

#
plot(fading$F,fading$Hpapsn,xlab="Fading (d)",ylab="EPR Signal Normalized (a.u.)",xlim=c(0,200))
lines(fading$F,fading$Hpapsn,col="red");grid(NULL,NULL)

#

Orlando (abril/2017)