Connecting Python to other programming languages for scientific computing
Details
- An introduction to the Julia language for scientific computing and its connection to Python with IJulia
Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments. It provides a sophisticated compiler, distributed parallel execution (http://docs.julialang.org/en/release-0.2/manual/parallel-computing/), numerical accuracy, and an extensive mathematical function library (http://docs.julialang.org/en/release-0.2/stdlib/). The library, largely written in Julia itself, also integrates mature, best-of-breed C and Fortran libraries for linear algebra (http://docs.julialang.org/en/release-0.2/stdlib/linalg/), random number generation (http://docs.julialang.org/en/release-0.2/stdlib/base/#random-numbers), signal processing (http://docs.julialang.org/en/release-0.2/stdlib/base/#signal-processing), and string processing (http://docs.julialang.org/en/release-0.2/stdlib/base/#strings). In addition, the Julia developer community is contributing a number of external packages (http://pkg.julialang.org/) through Julia’s built-in package manager at a rapid pace.
IJulia (https://github.com/JuliaLang/IJulia.jl), a collaboration between the IPython (http://ipython.org/) and Julia communities, provides a powerful browser-based graphical notebook interface to Julia.
Presented by: Hans Werner Borchers
- A quick introduction R and how to connect to it using pyRServe.
pyRserve (https://pypi.python.org/pypi/pyRserve/) is a library for connecting Python to an R process running under Rserve, a TCP/IP server which allows other programs to use facilities of R from various languages without the need to initialize R or link against R library.
Presented by Ralph Heinkel