How to pass **kwargs to another function with changed values for that key?

Question

I have the following function that calculate the propagation of a laser beam in a cavity. It depends on many parameters that are stored in a dict called core_data , which is a basic parameter set.

def propagate(N, core_data, **ddata):
    cd = copy.deepcopy(core_data)  # use initial configuration
    cd.update(ddata)  # update with data I want to change
    cavity = get_new_cavity(cd)  # get new cavity object
    P = []
    for i in range(N):
        cavity.evolve(1)
        P.append(cavity.get_power())
    return P

If I want to change a parameter and see its effect on the laser, I can just call the function like, for instance

P0 = propagate(1000, core_data, L1=1.2, M5=17)

This works very well.

Now, I would write a function that passes this function to a ProcessPoolExecutor , with the values of **ddata being iterated over using the same key. It should work, for instance, like this (simpler example):

propagate_parallel(1000, core_data,
                   L1=np.linspace(1, 2, 2),
                   M5=np.linspace(16, 17, 2))

And should then do this in parallel:

propagate(1000, core_data, L1=1, M5=16)
propagate(1000, core_data, L1=1, M5=17)
propagate(1000, core_data, L1=2, M5=16)
propagate(1000, core_data, L1=2, M5=17)

Something like this works for my case:

xrng = np.linspace(110e-30, 150e-30, Nx)
yrng = np.linspace(6.6e-9, 6.7e-9, Ny)

futures = []
with confu.ProcessPoolExecutor(max_workers=Ncores) as pool:
    for y, x in it.product(yrng, xrng):
        futures.append(pool.submit(propagate, RTs=1000,
                                   core_data=core_data,
                                   gdd_dm=x, dwl_filt=y))

The problem is that this is not flexible and I cannot get this into a nice function, as written above. It should be a function that can be called like this to reproduce the code from above:

propagate_parallel(1000, core_data, gdd_dm=xrng, dwl_filt=yrng)

How would I pass the keys from **ddata with the iterated values of that corresponding key?

FYI, I used:

import numpy as np
import concurrent.futures as confu
import itertools as it

Answer 1

You are looking for iterating over the cartesian product.

Here is a way to iterate over a cartesian.

from itertools import product
import numpy as np

L1=np.linspace(1, 2, 2)
M5=np.linspace(16, 17, 2)
dconf = dict(data=5)
size = L1.size
loop_size = size**2

def propagate(N, data, modifiers):
    data.update(modifiers)
    out = []
    for i in range(N):
        out.append('%s : %s : %s : %s'%(i, *data.values()))
    return out

mod = (dict(L1=i, M5=j) for i, j in product(L1, M5))
m = map(propagate, np.arange(2, 2+loop_size), (dconf,)*loop_size, mod)

for outer in m:
    for inner in outer:
        print(inner)

This you can adapt to your code, and if you really need to go parallell (with all that this means in terms of info split between cores) maybe take a look into Dask.

Hope this is enough to get you going.

edit: your question is quite hard to actually pinpoint. Is your question really how to just achieve the simple "function call"? I suppose one answer is just to make a wrap function, something like...

def propagate(N, data, modifiers):
    ...

def call_propagate(N, data, L1_, M5_):
    mod = ...
    m = map(...
    return m

for outer in call_propagate(1000, dconf, L1, M5)
    for inner in outer:
        print(inner)

Answer 2

I think I was somehow blocked... I kept thinking how to keep a variable name (for instannce L1 ) and pass this as a variable to another function.

@ahead87: Already your first sentence unblocked me and I realized that **data can be handled simply via a dictionary. So, in the end, I simply needed to transform the input dict into a list of dicts for the next function, like so (with some irrelevant parts being snipped):

def propagate_parallel(RTs, cav_data, **ddata):
    keys = list(ddata.keys())
    values = list(ddata.values())
    futures = []
    res = []
    with confu.ProcessPoolExecutor(max_workers=32) as pool:
        for i in it.product(*values):
            futures.append(pool.submit(propagate, RTs=RTs,
                                       cav_data=cav_data,
                                       **dict(zip(keys, list(i)))))
    for fut in futures:
        res.append(fut)
    return res

In the end, I think I finally understand **kwargs , and that it can be handles as a dict. Thank you!