def profile():
import time
from unpacking_flatten_lists.data import (generate_data,
create_data_increasing_depth,
create_data_decreasing_depth)
all_data = generate_data()
curr_data = all_data[-1][1]
funcs_generated_data = [
create_data_increasing_depth, create_data_decreasing_depth
]
funcs = [
outer_flatten_1, outer_flatten_2, niccolum_flatten, tishka_flatten,
zart_flatten, recursive_flatten_generator, recursive_flatten_iterator,
tishka_flatten_with_stack
]
for func_generated_data in funcs_generated_data:
creating_data = func_generated_data(**curr_data)
for func in funcs:
list(func(creating_data))
time.sleep(0.3)
def common_part(*, data_create_func: Callable, mode: str):
for func in funcs:
func_name = func.__name__
print('\n', func_name, '\n')
for data_example in generate_data():
data = data_create_func(**data_example[1])
data = json.dumps(
data
) # crutch because timeit has s_push: parser stack overflow for list with 100 deep
data_struct_name = data_example[0]
with time_time(data_struct_name) as start_time:
result[func_name][mode][data_struct_name] = mean(
timeit.repeat(RUNNING_TEMPLATE,
setup=SETUP_IMPORT_TEMPLATE.format(
func_name=func_name, data=data),
number=RETRY_NUM))
if time.monotonic() - start_time > TOO_LONG:
break
def get_decrease_list() -> Union[List, Iterator]:
for data in generate_data():
params = data[1]
flatten_result = copy.copy(params['data'])
result = create_data_decreasing_depth(**params)
yield result, list(flatten_result)