Hi Yan,
I tried with 10,000 trees each with 1000 tips and it took a bit more
than 1 sec:
R> tr <- rmtree(10000, 1000)
R> system.time(a <- .compressTipLabel(tr))
utilisateur système écoulé
1.124 0.036 1.161
And yes the memory footprint is substantially decreased:
R> print(object.size(tr), unit="Mb")
850.6 Mb
R> print(object.size(a), unit="Mb")
315.7 Mb
What is the size of your problem?
Do you use a recent version of ape? This function was improved one or
two years ago.
Best,
Emmanuel
Le 14/12/2016 à 16:16, Yan Wong a écrit :
Hi,
I’m reading in a large number of newick trees with the same tips, all from a single
file. If I do trees<-read.trees() followed by trees <-
.compressTipLabel(trees), it reduces the memory footprint well, but takes an age to
run. I can’t help thinking this could be sped up during the reading process by
passing an option to read.trees() to specify that the tip labels are the same in each
tree in the multiPhylo object. Has anyone implemented such an option?
Cheers
Yan
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