sklearn GMM classification prediction (component assignment) order

I'm trying to classify cells into populations. When I use:

gmix = mixture.GMM(n_components=3, covariance_type='full') gmix.fit(samples)

The means output, from the code below, changes in order, unless I set: np.radom.seed(0).

print ("gmix.means n", gmix.means_) colors = ['r' if i==0 else ('g' if i==1 else ('b' if i ==2 else 'm'))for i in gmix.predict(samples)]

I would like the classes sorted by the X axis mean (first item of each class) ie:

[[  3.25492404e+02   2.88403293e-02]  
[  3.73942908e+02   3.25283512e-02] 
[  5.92577646e+02   4.40595768e-02]]

So in the code above red would always be 325, green 372 and blue 592. At the moment I don't think there is anything sorting the output.

I tried:

gmix.means_ = np.sort(gmix.means_, axis = 0)

But then the gmix.covars_ and gmix.weights_ also need to be sorted accordingly, which is where I'm stuck!

Many thanks!

Edit 4/5/16:

Thanks for the help and steering me in the right direction. Here is my poorly written but working version:

    sort_indices = gmix.means_.argsort(axis = 0)
    order = sort_indices[:, 0]
    print('norder:', order)
    gmix.means_ = gmix.means_[order,:]    

    gmix.covars_ = gmix.covars_[order, :]
    print ("n sorted gmix.covars n", gmix.covars_) 

    print ("nnori gmix.weights n", gmix.weights_)
    w = np.split(gmix.weights_,3)
    w = np.asarray(w)
    w = np.ravel(w[order,:])
    gmix.weights_ = w