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

```Python
compile(func):
'''
'''
```
```Python
numbaList(inList):
'''
Turn a list into List that numba can parse. 
'''
```
```Python
numbaOFF():
'''
Turn off numba compilation for small functions. 
'''
```
```Python
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. 
'''
```
```Python
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)
'''
```
```Python
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
'''
```
```Python
class ComputationClass(function, input_array, args, nproc, parallelizer):
'''
'''
```