You can simply use multiprocessing.Pool:

from multiprocessing import Pool

def process_image(name):
    sci=fits.open('{}.fits'.format(name))
    <process>

if __name__ == '__main__':
    pool = Pool()                         # Create a multiprocessing Pool
    pool.map(process_image, data_inputs)  # process data_inputs iterable with pool

Joblib does what you want. The basic usage pattern is:

from joblib import Parallel, delayed

def myfun(arg):
     do_stuff
     return result

results = Parallel(n_jobs=-1, verbose=verbosity_level, backend="threading")(
             map(delayed(myfun), arg_instances))

where arg_instances is list of values for which myfun is computed in parallel. The main restriction is that myfun must be a toplevel function. The backend parameter can be either "threading" or "multiprocessing".

You can pass additional common parameters to the parallelized function. The body of myfun can also refer to initialized global variables, the values which will be available to the children.

Args and results can be pretty much anything with the threading backend but results need to be serializable with the multiprocessing backend.

Dask also offers similar functionality. It might be preferable if you are working with out of core data or you are trying to parallelize more complex computations.

The CPython implementation currently has a global interpreter lock (GIL) that prevents threads of the same interpreter from concurrently executing Python code. This means CPython threads are useful for concurrent I/O-bound workloads, but usually not for CPU-bound workloads. The naming calc_stuff() indicates that your workload is CPU-bound, so you want to use multiple processes here (which is often the better solution for CPU-bound workloads anyway, regardless of the GIL).

There are two easy ways of creating a process pool into the Python standard library. The first one is the multiprocessing module, which can be used like this:

pool = multiprocessing.Pool(4)
out1, out2, out3 = zip(*pool.map(calc_stuff, range(0, 10 * offset, offset)))

Note that this won't work in the interactive interpreter due to the way multiprocessing is implemented.

The second way to create a process pool is concurrent.futures.ProcessPoolExecutor:

with concurrent.futures.ProcessPoolExecutor() as pool:
    out1, out2, out3 = zip(*pool.map(calc_stuff, range(0, 10 * offset, offset)))

This uses the multiprocessing module under the hood, so it behaves identically to the first version.

I usually use code of this form:

#!/usr/bin/env python3
import itertools
import multiprocessing

#Generate values for each parameter
a = range(10)
b = range(10)
c = range(10)
d = range(10)

#Generate a list of tuples where each tuple is a combination of parameters.
#The list will contain all possible combinations of parameters.
paramlist = list(itertools.product(a,b,c,d))

#A function which will process a tuple of parameters
def func(params):
  a = params[0]
  b = params[1]
  c = params[2]
  d = params[3]
  return a*b*c*d

#Generate processes equal to the number of cores
pool = multiprocessing.Pool()

#Distribute the parameter sets evenly across the cores
res  = pool.map(func,paramlist)