i don't think Julia is viewed by the relevant communities as a replacement for Matlab, Octave, et al; likewise, i don't think that was the purpose underlying th

i don't think Julia is viewed by the relevant communities as a replacement for Matlab, Octave, et al; likewise, i don't think that was the purpose underlying the Julia project. "Scientific Python"--ie, python + NumPy + SciPy + Matplotlib (and perhaps including iPython) have been a de facto open source replacement for Matlab for some time now. This is apparent if you look at the similarity in syntax--eg, most significantly) NumPy copied Matlab's beautifully dense array-indexing syntax. Performance-wise, across a reasonably complete set of benchmarks reflecting the workflow of a typical user, Scientific Python and Matlab are interchangeable (https://modelingguru.nasa.gov/docs/DOC-1762 https://modelingguru.nasa.gov/docs/DOC-1762). Instead, what the Julia creators wanted to do, as i understand it, is provide an array-oriented language having true compiled -code performance; this level of performance, Scientific Python user can only achieve through additional steps in their workflow that usually involve re-writing performance-critical portions of the code in another language and compiling them in-line, e.g., cython, weave (C++ inline), numexpr (JIT for a limited set of NumPy functions), direct access to the BLAS (provided via a SciPy module), swig, f2py,....