import cellpy
import multiprocessing
import concurrent.futures
import time
import pathlib
import pandas as pd
ASYNC_MODE = "threading"
if ASYNC_MODE == "threading":
PoolExecutor = concurrent.futures.ThreadPoolExecutor
else:
# cellpy.CellpyCell object is not serializable so returning it
# within a process-pool will crash.
# TODO: make it serializable
PoolExecutor = concurrent.futures.ProcessPoolExecutor
[docs]def func(filename):
# time.sleep(1)
print(f"{filename}: {pathlib.Path(filename).is_file()}")
c = cellpy.get(filename)
return c
[docs]def main():
print(" starting ".center(80, "-"))
max_number_processes = multiprocessing.cpu_count()
print(f"{max_number_processes=}")
f1 = r"C:\scripting\cellpy\testdata\batch_project\data\raw\20230221_CLP001_1_02_cc_01.res"
f2 = r"C:\scripting\cellpy\testdata\batch_project\data\raw\20230221_CLP001_1_03_cc_01.res"
f3 = r"C:\scripting\cellpy\testdata\batch_project\data\raw\20230221_CLP001_2_01_cc_01.res"
f4 = r"C:\scripting\cellpy\testdata\batch_project\data\raw\20230221_CLP001_2_04_cc_01.res"
params = [
dict(filename=f1),
dict(filename=f2),
dict(filename=f3),
dict(filename=f4),
]
t0 = time.time()
with PoolExecutor() as executor:
pool = [executor.submit(func, **param) for param in params]
for i in concurrent.futures.as_completed(pool):
c = i.result(timeout=2000)
dt_p = time.time() - t0
t0 = time.time()
for param in params:
c = func(**param)
dt_s = time.time() - t0
print(f"Parallel processing took {dt_p} seconds")
print(f"Sequential processing took {dt_s} seconds")
if __name__ == "__main__":
main()