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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import glob\n",
"import random\n",
"import nibabel as nb\n",
"import numpy as np\n",
"from scipy.sparse.linalg import svds, eigs\n",
"import matplotlib.pyplot as plt\n",
"from nilearn import plotting\n",
"from nilearn import image"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"in_list = [nb.load(f) for f in glob.glob('data/cobre/fmri_*.nii.gz')]\n",
"in_mask = nb.load('data/brain_mask.nii.gz')\n",
"go = '3DMIGP'\n",
"dPCA_int = 550\n",
"dPCA_out = 100"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"random.shuffle(in_list)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mask = in_mask.get_fdata().ravel()\n",
"\n",
"def demean(x):\n",
" return x - np.mean(x, axis=0)\n",
"\n",
"for i, f in enumerate(in_list):\n",
" \n",
" print(i, end=' ')\n",
" \n",
" # read data\n",
" grot = f.get_fdata()\n",
" grot = np.reshape(grot, [-1, grot.shape[-1]])\n",
" grot = grot[mask!=0, :].T\n",
" grot = demean(grot)\n",
" \n",
" # var-norm\n",
" [uu, ss, vt] = svds(grot, k=30)\n",
" vt[np.abs(vt) < (2.3 * np.std(vt))] = 0;\n",
" stddevs = np.maximum(np.std(grot - (uu @ np.diag(ss) @ vt), axis=0), 0.001)\n",
" grot = grot/stddevs\n",
" \n",
" if i == 0:\n",
" W = demean(grot)\n",
" else:\n",
" # concat\n",
" W = np.concatenate((W, demean(grot)), axis=0)\n",
" \n",
" # reduce W to dPCA_int eigenvectors\n",
" if W.shape[0]-10 > dPCA_int:\n",
" uu = eigs(W@W.T, dPCA_int)[1]\n",
" uu = np.real(uu)\n",
" W = uu.T @ W\n",
" \n",
" f.uncache()\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"grot = np.zeros([mask.shape[0], dPCA_out])\n",
"grot[mask!=0, :] = W[:dPCA_out, :].T\n",
"grot = np.reshape(grot, in_list[0].shape[:3] + (dPCA_out,))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"nb.Nifti1Image(grot, affine=in_list[0].affine, header=in_list[0].header).to_filename('pyMIGP.nii.gz')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%bash\n",
"\n",
"melodic -i pyMIGP.nii.gz \\\n",
"\t--mask=data/brain_mask.nii.gz \\\n",
"\t-d 20 \\\n",
"\t-v \\\n",
"\t--nobet \\\n",
"\t--disableMigp \\\n",
"\t--varnorm \\\n",
"\t-o pymigp_dim20.ica"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ics = nb.load('pymigp_dim20.ica/melodic_IC.nii.gz')\n",
"\n",
"N = ics.shape[-1]\n",
"\n",
"fig, ax = plt.subplots(int(np.ceil((N/2))),2, figsize=(12, N))\n",
"\n",
"for img, ax0 in zip(image.iter_img(ics), ax.ravel()):\n",
" coord = plotting.find_xyz_cut_coords(img, activation_threshold=1.5)\n",
" plotting.plot_stat_map(img, cut_coords=coord, vmax=5, axes=ax0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 4
}