**How does this compare to Python+Numpy?
**How much faster is Fython, and what are the restrictions on the Python code?
Python+Numpy allows easy processing of vector, but there is a limit to how much
user-defined logic can be used with Numpy.
For example, operating on three different arrays to do something like
if x[i] > 0 :
temp = get_value( y[i] )
if temp > 0:
z[i] = another_operation( z[i] )
may require three different Numpy calls.
With Fython, the if statements and the loop around them is directly compiled to
machine code.
Fython speed is the same as Fortran speed.
In some applications, I get a speed-up of 100 compared to Cython, and a
speed-up of 10 compared to C.
I'm not a Cython nor a C expert, and I know that with some good compiler flags,
one may make the performance of these two languages similar two Fortran.
The advantage of Fython is that everything is tune out-of-box because of its
connection to Fortran.
There is no restriction on the Python code.
Once a Fython program is avalaible, Python can throw any scalar or Numpy array
at it.
**whether Fython is pass by reference (Fortran) or value
**and then there's the 'little' matter of one-based (Fortran) or zero-based
(python) arrays?
Fython is passed by reference, so that any modification made to a variable in
Fython is propagated back to Python.
Fython follows Fortran syntax and is one-based by default.
This doesn't give any interop problem with Python, because what matters is to
know the pointer to the first element of an array, and Fython knows it.
For the programmer convenience, like Fortran, Fython allows the choice between
one-based and zero-based indexing at array declaration.
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