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Originally posted by snowli

U can highlight all data and choose Analysis: Mathematics: Average Multiple Curves...
https://www.originlab.com/doc/Origin-Help/Math-AveCurve

Thanks, Snow

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Originally posted by David_256

OMG, it is so easy... We have been doing it before with multi-step scripts and R...
Thank you indeed - you saved for several people.
D.

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x.min <- c() # vector containing the minimum x values for each data frame
x.max <- c() # vector containing the maximum x values for each data frame
nn <- c() # vector containing number of points for each data frame
for (ii in 3:10) { # finding the limiting x values for the output x vector
  eval(parse(text=paste0("x.min <- c(x.min, df.", ii,"$x[1])")))
  eval(parse(text=paste0("nn <- c(nn, nrow(df.",ii,"))")))
  eval(parse(text=paste0("x.max <- c(x.max, df.", ii,"$x[nrow(df.",ii,")])")))
}
# defining the output x vector
xx <- seq(from = max(x.min), to = min(x.max), length.out = max(nn))
# data frame with averaged data
df.avg <- data.frame(x=xx, y=rep(0, length(xx)))
# averaging
for (ii in 3:10) { eval(parse(text=paste0("df.avg$y <- df.avg$y + approx(df.",ii,"$x,df.",ii,"$y,xout=xx,method=\"linear\")$y"))) }
df.avg$y <-  df.avg$y / 8

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Originally posted by aplotnikov

quote:

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Originally posted by David_256

OMG, it is so easy... We have been doing it before with multi-step scripts and R...
Thank you indeed - you saved for several people.
D.

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Hello!
To be fair - the implementation of curve averaging in R is nearly of the same complexity as in Origin via Labtalk script.
I used Origin sample data (Samples\Spectroscopy\DSC\Data\*.dsc) to create data frames df.3, df.4, ..., df.10 containing two vectors for x and y data in R (you can organize your data even better to avoid substitution in expressions):

x.min <- c() # vector containing the minimum x values for each data frame
x.max <- c() # vector containing the maximum x values for each data frame
nn <- c() # vector containing number of points for each data frame
for (ii in 3:10) { # finding the limiting x values for the output x vector
  eval(parse(text=paste0("x.min <- c(x.min, df.", ii,"$x[1])")))
  eval(parse(text=paste0("nn <- c(nn, nrow(df.",ii,"))")))
  eval(parse(text=paste0("x.max <- c(x.max, df.", ii,"$x[nrow(df.",ii,")])")))
}
# defining the output x vector
xx <- seq(from = max(x.min), to = min(x.max), length.out = max(nn))
# data frame with averaged data
df.avg <- data.frame(x=xx, y=rep(0, length(xx)))
# averaging
for (ii in 3:10) { eval(parse(text=paste0("df.avg$y <- df.avg$y + approx(df.",ii,"$x,df.",ii,"$y,xout=xx,method=\"linear\")$y"))) }
df.avg$y <-  df.avg$y / 8

Voilà. Is it really so complicated? The implementation of the averaging procedure itself does not exceed 3 lines, the rest (initialization, etc.) should take approximately the same code volume in Origin, if you do everything correctly.

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