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With the ATI bug I was hitting earlier fixed, only my btrfs partition continues to show high cpu usage for some operations. Rsync, git pull, git checkout and svn up are typicall operations which trigger the high cpu usage. As an example, this perf report is from using git checkout to change to a new branch; the change needed to checkout 208 files out of about 1600 total files. du(1) reports

Well for a long time now my wine menu entry in gnome has been gone. Most threads I read all say to fine the <deleted> tag in .config/menus/applications.menu but mine doesn't have it.

...;-merge(p,q,all=TRUE) abcd<-merge(ab,cd,all=TRUE) efgh<-merge(ef,gh,all=TRUE) ijkl<-merge(ij,kl,all=TRUE) nopq<-merge(no,pq,all=TRUE) ah<-merge(abcd,efgh,all=TRUE) iq<-merge(ijkl,nopq,all=TRUE) aq<-merge(ah,iq,all=TRUE) all<-merge(aq,r,all=TRUE) write.csv(all,file="all.merged.csv") [[alternative HTML version deleted]]

On Wed, Dec 18, 2019 at 4:56 AM Peter Waller <Peter.Waller at arm.com> wrote: > On 17/12/2019 22:25, James Y Knight wrote: > > I think it would probably make the most sense to land this as a single > > merge-commit (from the 2700-commit rewritten history you've created) > > onto llvm-project master, rather than as 2700 individual toplevel > > commits to

Hello, I am attempting to merge 8 different data sets into a "grand merge" data set; all their variable names are common except for the the gas measured. However, when I did a quick stat summary comparison of merged data with unmerged data, it turned out that R mysteriously duplicated thousands of values in the merged set and I have no idea why. I've not had this problem with merge in the past.... any thoughts? To illustrate: given the following objects (as data frames) with 1 unique and 10 common variab...

Hi, all. Last week I posted some code for extracting subtrees of trees in hclust format. Petra Steiner quickly found an example for which the code breaks, and sent it to me. The problem seems to be that I had not considered the possibility of trees with unlabeled nodes. In the new version of f.make.subtree (below), I steal some code from plot.hclust to assign labels if there are none. That

...],by.x=1,by.y=1,all=T) I noticed that starting file #8, the merge become more and more slower that when it's file #11, the computer was stuck!? Originally I thought something wrong with the later files, but when I change the order of merging, the slow-down still happens at the 8th files to be merged. Can anyone suggest what's going on with merging? Thanks John -------------- next part -------------- An embedded and charset-unspecified text was scrubbed... Name: p1.txt URL: <https://stat.ethz.ch/pipermail/r-help/attachments/20130111/7f91db84/attachment-0022.txt> -------------- next...

Matthias Braun via llvm-dev <llvm-dev at lists.llvm.org> writes: > - Even in the long term I would vote to stay with linear history, I > see little benefits in having "correct" origin information of a commit > that the merging model provides. We often revert entire feature merges when problem arise. It's much easier to do in a merging workflow because you have a

Hello, My problem is that I would like to merge multiple files with a common column but merge accepts only two data.frames to merge. In the real situation, I have 26 different data.frames with a common column. I can of course use merge many times (see below) but what would be more sophisticated solution? For loop? Any ideas? DF1 <- data.frame(var1 = letters[1:5], a = rnorm(5)) DF2 <-

Hi, As part of the migration of LLVM's source code to github, we need to update our developer policy with instructions about how to interact with the new git repository. There are a lot of different topics we will need to discuss, but I would like to start by initiating a discussion about our merge commit policy. Should we: 1. Disallow merge commits and enforce a linear history by

Hello, everyone I have a piece of code that looks like this: mrets <- merge(mrets, BMM.SR=apply(mrets, 1, MyFunc, ret="BMM.AV120", stdev="BMM.SD120")) mrets <- merge(mrets, GM1.SR=apply(mrets, 1, MyFunc, ret="GM1.AV120", stdev="GM1.SD120")) mrets <- merge(mrets, IYC.SR=apply(mrets, 1, MyFunc, ret="IYC.AV120",

Hi everyone, I'm currently working on a new function merging optimization that is more general than the current LLVM function merging optimization that works only on identical functions. I would like to know if the community has any interest in having a more powerful function merging optimization. ---- More Details ---- Up until now, I have been focusing on the quality of the code

Hi All, I am looking into merging 3 data sets I know how to do that by merging data1 with data2 and then merging the result with data 3. I was wondering if it can be done all at once so I tried, M<-merge(data1,data2,data3, by=?ID?) It does not work! Any ideas? -- View this message in context: http://www.nabble.com/merging-3-data-sets-at-once-tp17658873p17658873.html Sent from the R help

Actually I have collected some pointer information in the form [ p -> a,c ]. Now suppose at some node I have information as [p->a,c]. Now i want to find a merge node above this node where this information is actually geting merged. So if I get a merge node above this, I can check in its predecessors if their out has only [p->a] or [p->c] and if not so then I will look for the next merge node above this one, and so on. > ambika wrote: >> Hi, >> >> I have basic block and I want to find a merge node...

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I'd like to be able to cut dendrograms at a height I specify and then plot the resulting subtrees. I wanted to use the dendrogram object for this purpose because there doesn't seem to be a canned way to cut a hclust object and get a list of hclust objects, but there is a function (cut) that does that for dendrograms. The problem I'm having is that when I plot a dendrogram, I

I've downloaded the "data.table" package from CRAN and R-Forge and still can't utilize merge.data.table for faster merges. How do I make this function visible? > install.packages("data.table",repos="http://R-Forge.R-project.org") trying URL 'http://R-Forge.R-project.org/src/contrib/data.table_1.5.1.tar.gz' Content type 'application/x-gzip'

ambika at cse.iitb.ac.in wrote: > Actually I have collected some pointer information in the form [ p -> a,c > ]. Now suppose at some node I have information as [p->a,c]. Now i want to > find a merge node above this node where this information is actually > geting merged. > So if I get a merge node above this, I can check in its predecessors if > their out has only [p->a] or [p->c] and if not so then I will look for the > next merge node above this one, and so on. > Okay. If you're only interested in the intra-procedural merging of points...

Hello, I merged my test commit successfully. (./utils/release/merge.sh --proj llvm --rev 46043 -srcdir ../llvm/) ... D46043 as "Merging r46043" https://reviews.llvm.org/rL330799 But I have issues with other patch - https://reviews.llvm.org/D45418 I ran ./utils/release/merge.sh --proj llvm --rev 45418...

...P1005 0.021088 1487 194073197 P1006 0.021088 1885 194073197 P1007 0.021088 > head(SNP1) Animal Marker x 3213 194073197 P1001 2 1295 194073197 P1002 1 915 194073197 P1004 2 2833 194073197 P1005 0 1487 194073197 P1006 2 1885 194073197 P1007 0 I want these two data frames merged by 'Marker', but when i try > SNP5 <- merge(SNP4, SNP1, by = 'Marker', all = TRUE) Error: cannot allocate vector of size 2.4 Gb In addition: Warning messages: 1: In merge.data.frame(SNP4, SNP1, by = "Marker", all = TRUE) : Reached total allocation of 1535Mb: see...