estimator.fit very verbose

I'm running g.fit(cluster_key='clusters', n_states=[4, 12])´on a GPCCA estimator object with slepc and petsc installed and running. On verbosity levelcr.settings.verbosity = 2´, there is a lot of output:
Computing Schur decomposition
Mat Object: 1 MPI processes
  type: seqdense
9.9999999999999978e-01 -1.6027616115423804e-02 -6.9999122819399214e-05 -3.0170264112083060e-02 7.9114474346406118e-04 -2.3379307555267200e-03 3.1099392748332021e-02 -1.2028052641645045e-02 4.5402303093021922e-02 -3.9545106553962851e-02 -1.0459780698478143e-02 -1.0117351207690200e-02 
0.0000000000000000e+00 9.8689949635914975e-01 -1.5019260264085295e-02 -9.6502345112150275e-03 -8.2436478078494781e-03 4.8117357524504597e-03 -2.7577126488919036e-03 -5.9013767212245738e-03 -3.2081374085749730e-02 1.5507024222048180e-02 -8.8740262830442930e-03 -6.2602296077992085e-03 
0.0000000000000000e+00 0.0000000000000000e+00 9.5955555167360695e-01 -4.7106747151722935e-03 -5.4015589471963690e-03 1.0880863078896181e-03 1.3937597585321894e-03 1.8372105147623009e-03 6.3024769849277327e-03 -1.1483429360858510e-02 9.0685877814312213e-03 2.1204960649007783e-02 
0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 8.5826027159383644e-01 -1.9107319001624898e-01 -6.1942578808405584e-02 1.6073785368839228e-03 4.0400245099728699e-02 -4.3648588083049195e-02 -3.8826883387643216e-02 -1.9731749577765637e-02 -5.6535235380355256e-03 
0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 8.1768951387319411e-01 -2.6732079678143389e-01 -9.4044560511276992e-02 -2.2451126376961263e-02 6.8712411049907263e-03 -3.7847968260024852e-02 -3.4914402319057722e-02 8.9826971715635685e-03 
0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 8.0625835778396504e-01 -2.8537594861564752e-01 -1.4587123600279792e-01 5.4949607797179785e-02 -6.6247380186574278e-03 -9.1456542348266209e-02 4.7701112758140235e-02 
0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 7.6274183854589384e-01 -2.4993257801000060e-01 1.4739196906451005e-01 3.7817292284990520e-02 -1.8465391851664605e-02 1.2670038061748071e-01 
0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 7.5161868609936644e-01 1.2509813879744039e-01 2.4901125929345244e-01 -1.0251802980959820e-01 4.3048323583681644e-02 
0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 7.0890111745183082e-01 -1.4762400993423902e-01 -6.9141760014752363e-02 -9.0085554693433711e-02 
0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 6.8877341024868710e-01 -2.7745394332285861e-01 -6.6338596315474965e-02 
0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 6.7707518531451472e-01 2.9197631180195915e-01 
0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 0.0000000000000000e+00 -6.0755250965814536e-02 6.7707518531451472e-01 
When computing macrostates, choose a number of states NOT in `[11]`
Adding `adata.uns['eigendecomposition_fwd']`
       `.schur_vectors`
       `.schur_matrix`
       `.eigendecomposition`
    Finish (0:00:11)
Calculating minChi criterion in interval `[4, 12]`
Computing `4` macrostates
Adding `.macrostates`
       `.macrostates_memberships`
       `.coarse_T`
       `.coarse_initial_distribution
       `.coarse_stationary_distribution`
       `.schur_vectors`
       `.schur_matrix`
       `.eigendecomposition`
    Finish (0:00:00)
Do you have an idea @michalk8 why it's giving me all of these numbers?
theislab / cellrank

estimator.fit very verbose #934
