class Input(object):
    def __init__(self, X):
        self.step  = 1.0
        self.noise = 1.0
        self.triuind = (np.arange(23)[:,np.newaxis] <= np.arange(23)[np.newaxis,:]).flatten()
        self.max = 0
        for _ in range(10): self.max = np.maximum(self.max,self.realize(X).max(axis=0))
        X = self.expand(self.realize(X))
        X.append(X)
        self.nbout = X.shape[1]
        self.mean = X.mean(axis=0)
        self.std = (X - self.mean).std()
        self.X = X

    def realize(self,X):
        def _realize_(x):
            inds = np.argsort(-(x**2).sum(axis=0)**.5+np.random.normal(0,self.noise,x[0].shape))
            x = x[inds,:][:,inds]*1
            x = x.flatten()[self.triuind]
            return x
        return np.array([_realize_(z) for z in X])

    def expand(self,X):
        Xexp = []
        for i in range(X.shape[1]):
            for k in np.arange(0,self.max[i]+self.step,self.step):
                Xexp += [np.tanh((X[:, i]-k)/self.step)]

        return np.array(Xexp).T

# top level code of module1.py
inp_x = Input(traindata).X  # I'm assuming traindata is also a global

# later code in the same module
do_stuff(inp_x)

# code in other modules can do:
import module1
do_other_stuff(module1.inp_x)  # a module's global variables are attributes on the module

# functions that use an X value:
def func1(arg1, arg2, X):
    do_stuff(X)

def func2(X):
    do_other_stuff(X)

# main module (or wherever you call func1 and func2 from)
from module1 import func1
from module2 import func2

def main():
    x = Input(traindata).X
    func1("foo", "bar", x)
    func2(x)

inp = Input(traindata)  # save the Input instance, not only X

# later code:
do_stuff(inp.X)  # use the X attribute, as above

# other code
do_other_stuff(inp)  # pass the instance, so you can use other attributes or methods