In [2]:

    

# Load affinitity matrix
aff = np.load('gradient_data/conn_matrices/cosine_affinity.npy')


    

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import sys
sys.path.append("../topography/utils_py/mapalign")
from mapalign import embed

emb, res = embed.compute_diffusion_map(aff, alpha = 0.5)


    

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# Save results
np.save('gradient_data/embedded/embedding_dense_emb.npy', emb)
np.save('gradient_data/embedded/embedding_dense_res.npy', res)


    

In [1]:

    

import numpy as np
res = np.load('gradient_data/embedded/embedding_dense_res.npy').item()
a = [res['vectors'][:,i]/ res['vectors'][:,0] for i in range(302)]
emb = np.array(a)[1:,:].T
len(emb)


    

    
Out[1]:





91282

In [2]:

    

import nibabel as nib
import numpy as np


    

In [3]:

    

res = nib.load('gradient_data/templates/hcp.tmp.lh.dscalar.nii').data
cortL = np.squeeze(np.array(np.where(res != 0)[0], dtype=np.int32))
res = nib.load('gradient_data/templates/hcp.tmp.rh.dscalar.nii').data
cortR = np.squeeze(np.array(np.where(res != 0)[0], dtype=np.int32))
cortLen = len(cortL) + len(cortR)
del res
# save out cortR and cortL in gradient_data/templates/


    

    




pixdim[1,2,3] should be non-zero; setting 0 dims to 1
pixdim[1,2,3] should be non-zero; setting 0 dims to 1

In [4]:

    

emb = np.load('gradient_data/embedded/embedding_dense_emb.npy')


    

In [5]:

    

tmp = nib.nifti2.load('gradient_data/templates/100307_tfMRI_MOTOR_level2_hp200_s2.dscalar.nii')
tmp_cifti = nib.cifti.load('gradient_data/templates/100307_tfMRI_MOTOR_level2_hp200_s2.dscalar.nii')
data = tmp_cifti.data * 0
data[0:10,:len(emb)] = np.reshape(emb.T, [1, 1, 1, 1] + list(emb.T.shape))
tmp2 = nib.nifti2.Nifti2Image(data, tmp.get_affine())
tmp4 = nib.nifti2.create_cifti_image(tmp2, tmp_cifti.header.to_xml(), np.array(3006, dtype=np.int32))
tmp4.to_filename('gradient_data/embedded/ciftis/hcp.embed.dscalar.nii')


    

    




pixdim[1,2,3] should be non-zero; setting 0 dims to 1
pixdim[1,2,3] should be non-zero; setting 0 dims to 1

In [6]:

    

import sys
sys.path.append("../topography/utils_py/mapalign")
from mapalign import embed
    
def embed_macaque(mat_name):
    
    aff = np.load('gradient_data/conn_matrices/macaque_%s_conn.npy' % mat_name)
    print np.shape(aff)
    emb, res = embed.compute_diffusion_map(aff, alpha = 0.5)
    np.save('gradient_data/conn_matrices/macaque_%s_emb.npy' % mat_name, emb)
    np.save('gradient_data/conn_matrices/macaque_%s_res.npy' % mat_name, res)


    

In [7]:

    

embed_macaque('bb47')


    

    




(25, 25)

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