You type in various characteristics of your project and it produces a .gitignore file. e.g. Java OSX Intellij
https://www.gitignore.io/
[WARNING] This actually ended up causing problems for my Intellij / github working together.
You type in various characteristics of your project and it produces a .gitignore file. e.g. Java OSX Intellij https://www.gitignore.io/
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From http://stackoverflow.com/questions/28761323/how-to-include-external-library-boost-into-clion-c-project-with-cmake
Essentially I modified the default CMakeLists.txt file to be: In the beginning, a compiler was responsible for turning a high-level language (defined as higher level than assembler) into object code (machine instructions), which would then be linked (by a linker) into an executable.
At one point in the evolution of languages, compilers would compile a high-level language into pseudo-code, which would then be interpreted (by an interpreter) to run your program. This eliminated the object code and executables, and allowed these languages to be portable to multiple operating systems and hardware platforms. Pascal (which compiled to P-Code) was one of the first; Java and C# are more recent examples. Eventually the term P-Code was replaced with bytecode, since most of the pseudo-operations are a byte long. A Just-In-Time (JIT) compiler is a feature of the run-time interpreter, that instead of interpreting bytecode every time a method is invoked, will compile the bytecode into the machine code instructions of the running machine, and then invoke this object code instead. Ideally the efficiency of running object code will overcome the inefficiency of recompiling the program every time it runs.
Write your .cpp file, including whatever Boost libraries you need, and including Rcpp:
#include <Rcpp.h> #include <boost/math/common_factor.hpp> // included in BH #include <boost/math/special_functions/bessel.hpp> // ... etc. Between the includes and the other functions, type // [[Rcpp::depends(BH)]] BH is a package that must be installed in R (install.packages(-) and library(-)) (so is Rcpp). Before each function in your .cpp file you need // [[Rcpp::export]] in a line by itself. This allows the function to be accessible from R. This syntax is for an *attribute* in C++. See my file pvm.cpp (in the git .gist below) for an example of everything so far. Once the .cpp file is correctly written, use it from R as follows: > library(BH); library(Rcpp) > sourceCpp('~/.../mypvm.cpp') > mypvm(1,2,3) That's all there is to it! More detailed information at the following links: // - http://dirk.eddelbuettel.com/code/bh.html // - http://gallery.rcpp.org/articles/a-first-boost-example/ // - After "which builds and runs..." at http://stackoverflow.com/questions/16131462/how-to-use-boost-library-in-c-with-rcpp Fast Latex table generator!
http://www.tablesgenerator.com/latex_tables How to actually see the location of a symbolic link, e.g. the command "which java" returns /usr/bin/java or whatever.
Use this instead: readlink -f $(which java) http://www.math.uah.edu/stat/dist/Density.html (and related pages)
Note: The below was turned into a script called "listalljobs" on my search path. So I just run "listalljobs" and it shows what I want. The "Name=mpi" part is because all my scripts submitted to run on the cluster contain that string. So for non-Wang-Schmidler stuff, I'll need to modify the command below.
How to quickly learn what directories my jobs belong to when I'm running lots of jobs. (this removes need for my "runIDinformation.txt" file! Yay.)
8146478 pfConcept4-25prots 8146479 pfConcept4-25prots ... scontrol show job | grep --color -E "UserId=gjl7|WorkDir=/hpchome/sysbio/gjl7|Name=mpi" from http://latex-beamer-class.10966.n7.nabble.com/a-watermark-in-beamer-td679.html
To keep the template changes local to a frame, do it like this { \usebackgroundtemplate{\includegraphics[width=\paperwidth]{Portada_marca2.jpg}} \begin{frame} \titlepage \end{frame} } The outer {} pair are scope delimiters. Hope this helps! Regards Kjell Magne Fauske Tricks for efficiently reading large text files into R
from http://cbio.ensmp.fr/~thocking/reading-large-text-files-into-R.html (the following is copied from that website) There are 4 golden rules, which are explained in detail on the manpage of read.
This is the page I always look for over and over again.
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AboutThis blog is mainly for statistics, R, or Duke-related stuff that is not directly relating to research activity. |