latqcdtools.base.speedify
=============

`compile(func)`


`numbaList(inList)`
 
    Turn a list into List that numba can parse. 
    
`numbaOFF()`
 
    Turn off numba compilation for small functions. 
    
`numbaON()`
 
    Use numba wherever possible. By default it is turned off, since compilation takes some time,
    and hence you will only see a performance boost for particularly long-running functions. Must be
    called at the beginning of your code. 
    
`parallel_function_eval(function, input_array, args=(), nproc=6, parallelizer='pathos.pools')`
 
    Parallelize a function over an input_array. Effectively this can replace a loop over an array and should
    lead to a performance boost.

    Args:
        function (func): to-be-parallelized function 
        input_array (array-like): array over which it should run 
        nproc (int): number of processes 

    Returns:
        array-like: func(input_array)
    
`parallel_reduce(function, input_array, args=(), nproc=6, parallelizer='pathos.pools') -> float`
 
    Parallelize a function over an input_array, then sum over the input_array elements. 

    Args:
        function (func): to-be-parallelized function 
        input_array (array-like): array over which it should run 
        nproc (int): number of processes 

    Returns:
        float
    
`ComputationClass(function, input_array, args, nproc, parallelizer)`