在Python中度量实例化+方法执行时间

import time

class MyClass:
    def my_method(self):
        time.sleep(1)  # 模拟耗时操作

start_time = time.time()
obj = MyClass()
obj.my_method()
end_time = time.time()

print(f"Total time: {end_time - start_time} seconds")

import timeit

class MyClass:
    def my_method(self):
        pass  # 实际方法体

setup_code = "from __main__ import MyClass"
stmt = "obj = MyClass(); obj.my_method()"

# 执行1000次取平均值
elapsed_time = timeit.timeit(stmt, setup=setup_code, number=1000)
print(f"Average time per execution: {elapsed_time / 1000} seconds")

import perfplot
import numpy as np

class MyClass:
    def my_method(self, n):
        return sum(np.random.rand(n))

perfplot.show(
    setup=lambda n: MyClass(),
    kernels=[
        lambda obj, n: obj.my_method(n),
    ],
    labels=["my_method"],
    xlabel="n",
    title="my_method performance",
    n_range=[2**k for k in range(10)],
    xlabel="Number of random numbers",
    equality_check=None,
)