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{
"metadata": {
"name": "",
"signature": "sha256:912ea5cca4a50a11a2f9e3756798709e17585ad42b8d2d91f13e48c71fe2ba77"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from ipysurfer.mri import MRI"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mri = MRI.from_mgz(\"/home/domitry/test.mgz\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mri.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAEUAAAD7CAYAAAA8TVksAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0xb1066acc>"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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