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@Gilles86
Created January 29, 2015 00:25
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{
"metadata": {
"name": "",
"signature": "sha256:78951c48c1e033e435c52d4288739ab5e71ab75b0eca4e7be6a4c176e42a8704"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Example for plotting NIFTI files for Felice"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nibabel is a library to load in nifti-files in Python as numpy arrays"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import nibabel as nb"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I was thinking: the number of mm^3 is still hard to grasp. (Wtf if 40,000 mm^3). Therefore, it might be good to redo the graphs, where we put the percentage of the brain that is in limbo on the y-axis!\n",
"\n",
"\n",
"For this we need to know the volume of the _mask_ of the mni_brain"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Size of MNI brain"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mni_brain = nb.load('/usr/share/fsl/data/standard/MNI152_T1_2mm_brain_mask.nii.gz').get_data()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"(mni_brain > 0).sum() / 0.8"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"text": [
"285603.75"
]
}
],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So the whole MNI brain is 285,603 cubic mm! I guess you can now calculate percentages, you're an engineer ;)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### How to plot brains"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is the brain itself, not only the mask."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mni_brain = nb.load('/usr/share/fsl/data/standard/MNI152_T1_2mm_brain.nii.gz').get_data()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mni_brain.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 15,
"text": [
"(91, 109, 91)"
]
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.imshow(mni_brain[25, :, :].T, origin='lower', cmap=plt.cm.gray)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 18,
"text": [
"<matplotlib.image.AxesImage at 0x53bdbd0>"
]
},
{
"metadata": {},
"output_type": "display_data",
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qA1qlqse3y8kw4ZOg51Wr1dBoNLCzs/NWm+mRpE6JfD6P4eFhn7hJklKFYS++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\n",
"text": [
"<matplotlib.figure.Figure at 0x4feae10>"
]
}
],
"prompt_number": 18
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That's equivalent to"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.imshow(mni_brain[[25, slice(None), slice(None)]].T, origin='lower', cmap=plt.cm.gray)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 97,
"text": [
"<matplotlib.image.AxesImage at 0x69584d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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qA1qlqse3y8kw4ZOg51Wr1dBoNLCzs/NWm+mRpE6JfD6P4eFhn7hJklKFYS++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\niampKUxOTmJ4eNjPT8tKWMxaLI/kpcvc8gkt+shxHn9tbQ0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"text": [
"<matplotlib.figure.Figure at 0x7df67d0>"
]
}
],
"prompt_number": 97
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can write a function"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def plot_brain(which_slice, slice_axis='y', data=mni_brain, **kwargs):\n",
" \n",
" # (**kwargs is a dictionary of all other keywords that given to the function\n",
" # we can later push these through to imshow by using same syntax\n",
" \n",
" # We want to set cmap to gray if not explicity passed to the function\n",
" if 'cmap' not in kwargs.keys():\n",
" kwargs['cmap'] = plt.cm.gray\n",
" \n",
" # Interpolation to nearest neighbor gives nicer plot\n",
" if 'interpolation' not in kwargs.keys():\n",
" kwargs['interpolation'] = 'nearest'\n",
" \n",
" index_tuple = [slice(None), slice(None), slice(None)]\n",
" \n",
" slice_axis = {'x':0, 'y':1, 'z':2}[slice_axis]\n",
" \n",
" index_tuple[slice_axis] = which_slice\n",
" \n",
"\n",
" \n",
" plt.imshow(data[index_tuple].T, origin='lower', **kwargs)\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 92
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.figure(figsize=(12,4))\n",
"plt.subplot(131)\n",
"plot_brain(60, 'x')\n",
"plt.axis('off')\n",
"plt.subplot(132)\n",
"plot_brain(60, 'y')\n",
"plt.axis('off')\n",
"plt.subplot(133)\n",
"plot_brain(60, 'z')\n",
"plt.axis('off')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 93,
"text": [
"(-0.5, 90.5, -0.5, 108.5)"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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sche9uqDNI7+4i6PfPwY118S3f5IMy6kf2N58882Sjqt5HvvNw2kdO3ZsMMh7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\nEkuCAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x5b64650>"
]
}
],
"prompt_number": 93
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Overlay "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we want to overlay, we have to make a masked array of the top layer."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"in_limbo = nb.load('/home/public/felice_ciccone/level2_fsl_results/ds2/contrast1/in_limbo/_threshold_2.3/in_limbo.nii.gz').get_data()\n",
"above_threshold = nb.load('/home/public/felice_ciccone/level2_fsl_results/ds2/contrast1/thresholded_z/_threshold_2.3/zstat1_threshold.nii.gz').get_data()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 94
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Mask everything that is zero\n",
"in_limbo = np.ma.masked_equal(in_limbo, 0)\n",
"above_threshold = np.ma.masked_equal(above_threshold, 0)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 95
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here a simple example of how you could make a nice in limbo + above threshold picture\n",
"Think about how you can do this easily for all datasets: you want to write a function. Don't just copy paste everything 13 times!!\n",
"\n",
"What I want for every dataset is a 3x3 subplot, where you have three slices in every direction!"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.figure(figsize=(14,4))\n",
"\n",
"plt.subplot(131)\n",
"# First plot MNI brain\n",
"plot_brain(60, 'x')\n",
"\n",
"# Now plot above_threshold with hot-colormap\n",
"plot_brain(60, 'x', data=above_threshold, cmap=plt.cm.hot, vmin=2.3, vmax=5)\n",
"\n",
"# \n",
"plot_brain(60, 'x', data=in_limbo, cmap=plt.cm.Blues, vmin=0, vmax=.1)\n",
"\n",
"plt.subplot(132)\n",
"plot_brain(60, 'y')\n",
"plot_brain(60, 'y', data=above_threshold, cmap=plt.cm.hot, vmin=2.3, vmax=5)\n",
"plot_brain(60, 'y', data=in_limbo, cmap=plt.cm.Blues, vmin=0, vmax=.1)\n",
"\n",
"plt.subplot(133)\n",
"plot_brain(60, 'z')\n",
"plot_brain(60, 'z', data=above_threshold, cmap=plt.cm.hot, vmin=2.3, vmax=5)\n",
"plot_brain(60, 'z', data=in_limbo, cmap=plt.cm.Blues, vmin=0, vmax=.1)\n",
"\n",
"plt.tight_layout()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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LRNoXUZIUstzr81J9pUyhJR746kkAylmVta/0e2DdKqEryZYpideH1jnnnJPL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\nNg19K1cCuLuqqovko5sAzGmV5wC4sXmsMcaYDYftsTHGGGNMTD8R2/8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/bpK0LaX0aUnf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\nAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x793fe90>"
]
}
],
"prompt_number": 96
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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