Error in install packages about libnetcdf.so.15 not found (Ubuntu 22.04.01): a solution

If you install netcdf library using:

sudo apt-get install libapparmor1 libcurl4-openssl-dev netcdf-bin libnetcdf-dev udunits-bin libudunits2-dev

It will install the current version of libnetcdf in  /usr/lib/x86_64-linux-gnu/

It can be seen with :

cd  /usr/lib/x86_64-linux-gnu/

ls -al libnetcdf*

-rw-r--r-- 1 root root    1200 sept. 29  2021 libnetcdf.settings

lrwxrwxrwx 1 root root      15 sept. 29  2021 libnetcdf.so -> libnetcdf.so.19

-rw-r--r-- 1 root root 1346880 sept. 29  2021 libnetcdf.so.19


But when you try to install or update tidync package, it will produce an error:

Erreur dans dyn.load(file, DLLpath = DLLpath, ...) : 

  impossible de charger l'objet partagé '/usr/local/lib/R/site-library/ncdf4/libs/ncdf4.so':

  libnetcdf.so.15: cannot open shared object file: No such file or directory

It search for libnetcdf.so.15 which is an outdated version.

The solution is to symlink libnetcdf.so.15 with the most recent version which is itself a symlink !

sudo ln -s /usr/lib/x86_64-linux-gnu/libnetcdf.so /usr/lib/x86_64-linux-gnu/libnetcdf.so.15

And now you can update tidync


Commentaires

Enregistrer un commentaire

Posts les plus consultés de ce blog

Standard error from Hessian Matrix... what can be done when problem occurs

An Hessian matrix is a square matrix of partial second order derivative. From the square-root of the inverse of the diagonal, it is possible to estimate the standard error of parameters. Let take an example: val=rnorm(100, mean=20, sd=5) # Return -ln L of values in val in Gaussian distribution with mean and sd in par fitnorm<-function(par, val) {   -sum(dnorm(val, par["mean"], par["sd"], log = TRUE)) } # Initial values for search p<-c(mean=20, sd=5) # fit the model result <- optim(par=p, fn=fitnorm, val=val, method="BFGS", hessian=TRUE) # Inverse the hessian matrix to get SE for each parameters mathessian <- result$hessian inversemathessian <- solve(mathessian) res <- sqrt(diag(inversemathessian)) # results data.frame(Mean=result$par, SE=res)   library(MASS) (r<-fitdistr(val, "normal")) It works well. However, the inverse of the matrix cannot be calculated if the second order derivative for some paramete...
Lire la suite

Install treemix in ubuntu 20.04

Informations are available here  https://bitbucket.org/nygcresearch/treemix/downloads/ In terminal, enter for  Ubuntu 20.04: wget https://bitbucket.org/nygcresearch/treemix/downloads/treemix-1.13.tar.gz tar -xvf treemix-1.13.tar.gz sudo apt-get install libgsl-dev sudo apt install libboost-dev sudo apt install libboost-all-dev cd treemix-1.13/ ./configure make sudo make install And it works: $ treemix TreeMix v. 1.13 $Revision: 231 $ Options: -h display this help -i [file name] input file -o [stem] output stem (will be [stem].treeout.gz, [stem].cov.gz, [stem].modelcov.gz) -k [int] number of SNPs per block for estimation of covariance matrix (1) -global Do a round of global rearrangements after adding all populations -tf [file name] Read the tree topology from a file, rather than estimating it -m [int] number of migration edges to add (0) -root [string] comma-delimited list of populations to set on one side of the root (for migration) -g [vertices file name] [edges file na...
Lire la suite

stepAIC from package MASS with AICc

Due to a bug in dropterm() and addterm() in package MASS, it is impossible to use AICc with stepAIC() . Here is a solution. Enter these commands and now you can use: stepAIC(g0, criteria="AICc") or stepAIC(g0, criteria="BIC") For example: datax <- data.frame(y=rnorm(100), x1=rnorm(100),                     x2=rnorm(100), x3=rnorm(100), x4=rnorm(100)) g0 <- glm(y ~ x1+x2+x3+x4, data=datax) gx_AICc <- stepAIC(g0, criteria="AICc") gx_AIC <- stepAIC(g0, criteria="AIC") gx_AIC <- stepAIC(g0, criteria="BIC")
Lire la suite