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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from nilearn import plotting\n",
"from nilearn import image\n",
"import nibabel as nb\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%bash\n",
"\n",
"melodic -i matMIGP.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 matMIGP_dim20.ica"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ics = nb.load('matMIGP_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=2.3)\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
}