有没有一种很好的方法可以在模块之间共享random的种子（在python中）？

我有一个具有不同主文件的项目（用于不同的模拟）。 当我运行一个主文件时，它应该将种子设置为random（和numpy.random），项目中的所有模块都应该使用该种子

我找不到一个好办法。我有一个文件globals.py，其中包含：

import random

myRandom=None


def initSeed(seed):
    global myRandom
    myRandom =random.Random(seed)

然后，我从一个主要的角度：

if __name__ == "__main__":

    seed=10
    globals.initSeed(seed)
...

然后在主调用的模块中，我执行以下操作：

from globals import myRandom

但是myRandom在模块中的值为None（即使我在main中修改了它！）。为什么，以及如何修复它？有更好的方法吗

正如@jDo在评论中提到的，将

globals.py

重命名为

randGlobal.py

为测试添加了模块

testResult.py

---

randGlobal.py testResult.py main.py

---

我会使用一个文件来避免

global

，并稍微分离数据和逻辑

种子处理器.py

# file that stores the shared seed value 
seed_val_file = "seed_val.txt"

def save_seed(val, filename=seed_val_file):
    """ saves val. Called once in simulation1.py """
    with open(filename, "wb") as f:
        f.write(str(val))

def load_seed(filename=seed_val_file):
    """ loads val. Called by all scripts that need the shared seed value """
    with open(filename, "rb") as f:
        # change datatype accordingly (numpy.random.random() returns a float)
        return int(f.read())

import random
import seed_handler

def sim1():
    """ creates a new seed and prints a deterministic "random" number """
    new_seed = int("DEADBEEF",16) # Replace with numpy.random.random() or whatever
    print "New seed:", new_seed
    # do the actual seeding of the pseudo-random number generator
    random.seed(new_seed)
    # the result
    print "Random:  ", random.random()
    # save the seed value so other scripts can use it
    seed_handler.save_seed(new_seed)

if __name__ == "__main__":
    sim1()

import random
import seed_handler

def sim2():
    """ loads the old seed and prints a deterministic "random" number """
    old_seed = seed_handler.load_seed()
    print "Old seed:", old_seed
    # do the actual seeding of the pseudo-random number generator
    random.seed(old_seed)
    # the result
    print "Random:  ", random.random()

if __name__ == "__main__":
    sim2()

模拟1.py

# file that stores the shared seed value 
seed_val_file = "seed_val.txt"

def save_seed(val, filename=seed_val_file):
    """ saves val. Called once in simulation1.py """
    with open(filename, "wb") as f:
        f.write(str(val))

def load_seed(filename=seed_val_file):
    """ loads val. Called by all scripts that need the shared seed value """
    with open(filename, "rb") as f:
        # change datatype accordingly (numpy.random.random() returns a float)
        return int(f.read())

import random
import seed_handler

def sim1():
    """ creates a new seed and prints a deterministic "random" number """
    new_seed = int("DEADBEEF",16) # Replace with numpy.random.random() or whatever
    print "New seed:", new_seed
    # do the actual seeding of the pseudo-random number generator
    random.seed(new_seed)
    # the result
    print "Random:  ", random.random()
    # save the seed value so other scripts can use it
    seed_handler.save_seed(new_seed)

if __name__ == "__main__":
    sim1()

import random
import seed_handler

def sim2():
    """ loads the old seed and prints a deterministic "random" number """
    old_seed = seed_handler.load_seed()
    print "Old seed:", old_seed
    # do the actual seeding of the pseudo-random number generator
    random.seed(old_seed)
    # the result
    print "Random:  ", random.random()

if __name__ == "__main__":
    sim2()

模拟2.py

# file that stores the shared seed value 
seed_val_file = "seed_val.txt"

def save_seed(val, filename=seed_val_file):
    """ saves val. Called once in simulation1.py """
    with open(filename, "wb") as f:
        f.write(str(val))

def load_seed(filename=seed_val_file):
    """ loads val. Called by all scripts that need the shared seed value """
    with open(filename, "rb") as f:
        # change datatype accordingly (numpy.random.random() returns a float)
        return int(f.read())

import random
import seed_handler

def sim1():
    """ creates a new seed and prints a deterministic "random" number """
    new_seed = int("DEADBEEF",16) # Replace with numpy.random.random() or whatever
    print "New seed:", new_seed
    # do the actual seeding of the pseudo-random number generator
    random.seed(new_seed)
    # the result
    print "Random:  ", random.random()
    # save the seed value so other scripts can use it
    seed_handler.save_seed(new_seed)

if __name__ == "__main__":
    sim1()

import random
import seed_handler

def sim2():
    """ loads the old seed and prints a deterministic "random" number """
    old_seed = seed_handler.load_seed()
    print "Old seed:", old_seed
    # do the actual seeding of the pseudo-random number generator
    random.seed(old_seed)
    # the result
    print "Random:  ", random.random()

if __name__ == "__main__":
    sim2()

输出：

user@box:~/$ python simulation1.py 
New seed: 3735928559
Random:   0.0191336454935

user@box:~/$ python simulation2.py 
Old seed: 3735928559
Random:   0.0191336454935

附录

我刚刚在评论中读到这是为了研究。此时，执行simulation1.py会覆盖存储的种子值；这可能不可取。可以添加以下功能之一：

另存为json并加载到字典；那样没什么 将被覆盖，每个种子值都可以有注释、时间戳和 与之关联的用户生成的标签

只需提示用户“是/否”即可覆盖 现有价值

---

如果要使用

global

，则应在所有使用该变量的函数中声明该变量

global

。您可以使用类和子类，但也可以将值写入文件。我想您应该在当前设置中通过

globals.myRandom

访问变量

myRandom

。顺便说一句，

globals

已在Python中定义，因此请将文件名更改为其他文件名（而不是

glob

-也可以）

它应该设置一个随机种子，项目中的所有模块都应该使用该种子。

听起来像是在寻找单例？我只是想知道你的用例场景是什么我的用例是研究：我有模型，我必须以不同的方式运行它们（例如，使用不同的参数，绘制不同的东西，等等）。我为模型和模拟文件创建模块，就像执行概要文件一样。在我看来，这是一个特别好的主意，因为OP提到他们这样做是为了研究目的。他们可能应该以比全局变量更持久的方式存储种子，以防需要复制运行或扩展模型。@Paul谢谢，我认为你是对的。实际上，在这种情况下，覆盖值的整个想法可能是不明智的。必须对随机模块进行反向工程，以找出在给定输出中创建了哪个种子值，这种想法肯定是不吸引人的！