We have scanned someone called \u201cSomeone\u201d, and we have a two MRI images of\n their brain, a single EPI volume, and a structural scan. In general we never\n use the person\u2019s name in the image filenames, but we make an\n exception in this case:
\nsomeones_epi.nii.gz.someones_anatomy.nii.gz.someones_epi.nii.gz.someones_anatomy.nii.gz.We can load up the EPI image to get the image data array:
\n>>> import nibabel as nib\n >>> epi_img = nib.load('downloads/someones_epi.nii.gz')\n >>> epi_img_data = epi_img.get_fdata()\n >>> epi_img_data.shape\n (53, 61, 33)\n"
},
{
"source1": "./usr/share/doc/python-nibabel-doc/html/devel/data_pkg_uses.html",
"source2": "./usr/share/doc/python-nibabel-doc/html/devel/data_pkg_uses.html",
"unified_diff": "@@ -297,15 +297,15 @@\n >>> local_usr = dpkg.get_source('local-user')\n >>> local_usr.register(dst)\n >>> local_usr.save()\n but we wanted to be able to avoid using dpkg. To do this, there might be\n a supporting script, in the distribution directory, called register_me.py,\n-of form given in register_me.py.
register_me.py.\n The local discovery sources are ini files, so it would be easy to read and use\n these outside the dpkg system, as long as the locations of the ini files are\n well defined. Here is the code from register_me.py defining these files:
import os\n"
},
{
"source1": "./usr/share/doc/python-nibabel-doc/html/dicom/dicom_orientation.html",
"source2": "./usr/share/doc/python-nibabel-doc/html/dicom/dicom_orientation.html",
"unified_diff": "@@ -363,15 +363,15 @@\n \\[\\begin{Bmatrix}k_{{1}} : \\frac{T^{1}_{{1}} - T^{N}_{{1}}}{1 - N}, & k_{{2}} : \\frac{T^{1}_{{2}} - T^{N}_{{2}}}{1 - N}, & k_{{3}} : \\frac{T^{1}_{{3}} - T^{N}_{{3}}}{1 - N}\\end{Bmatrix}\\]and therefore:
\nSee derivations/spm_dicom_orient.py for the derivations and\n+
See derivations/spm_dicom_orient.py for the derivations and\n some explanations.
We may have the problem (see e.g. Sorting files into volumes) of trying\n to sort a set of slices into anatomical order. For this we want to use\n@@ -418,15 +418,15 @@\n
This is the distance of \u2018ImagePositionPatient\u2019 along the slice direction\n cosine.
\nThe unknown $T^1$ terms pool into a constant, and the operation has the\n neat feature that, because the $n_{123}^2$ terms, by definition, sum to 1,\n the whole can be expressed as $lambda + Delta{s} d$ - i.e. it is\n equal to the slice voxel size ($Delta{s}$) multiplied by $d$,\n plus a constant.
\n-Again, see derivations/spm_dicom_orient.py for the derivations.
Again, see derivations/spm_dicom_orient.py for the derivations.
This formula with the definitions above result in the single and multi\n slice formulae in 3D affine formulae.
\n-See derivations/spm_dicom_orient.py for the derivations and\n+
See derivations/spm_dicom_orient.py for the derivations and\n some explanations.
Just apply scaling and offset from \u2018RescaleSlope\u2019 and \u2018RescaleIntercept\u2019\n for each slice and write volume.
\nimg_data[:, :, k]), the resulting slice will have a first array axis\n going from left to right in terms of spatial position and the second array\n axis going from posterior to anterior. If we display this image with the\n first axis going from left to right on screen and the second from bottom to\n top, it will have the subject\u2019s right towards the right of the screen, and\n anterior towards the top of the screen, as neurologists like it. Here we are\n-showing the middle slice of an image with RAS voxel axes:\n+showing the middle slice of an image with RAS voxel axes:\n >>> import nibabel as nib\n >>> import matplotlib.pyplot as plt\n >>> img = nib.load('downloads/someones_anatomy.nii.gz')\n >>> # The 3x3 part of the affine is diagonal with all +ve values\n >>> img.affine\n array([[ 2.75, 0. , 0. , -78. ],\n [ 0. , 2.75, 0. , -91. ],\n"
},
{
"source1": "./usr/share/doc/python-nibabel-doc/html/notebooks/index.html",
"source2": "./usr/share/doc/python-nibabel-doc/html/notebooks/index.html",
"unified_diff": "@@ -72,15 +72,15 @@\n \n \n \n \n IPython notebooks for Nibabel project\u00b6
\n \n \n \n \n"
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"source1": "./usr/share/doc/python-nibabel-doc/html/_downloads/01c0ddb5a01b1855aa9ab2a257e0e71b/cross_product_error.ipynb",
"source2": "./usr/share/doc/python-nibabel-doc/html/_downloads/c926129d93645beba3b0111671b34176/cross_product_error.ipynb",
"comments": [
"Files identical despite different names"
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"unified_diff": null
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"source1": "./usr/share/doc/python-nibabel-doc/html/_downloads/039dcaa62222d1acc2e10e4c531009f9/ata_error.ipynb.gz",
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"comments": [
"Files identical despite different names"
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"unified_diff": null
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"source1": "./usr/share/doc/python-nibabel-doc/html/_downloads/8e417f4c1773ba9890736796b1bc25d8/someones_epi.nii.gz",
"source2": "./usr/share/doc/python-nibabel-doc/html/_downloads/bebbb638f579dd26e7deaead08854ab4/someones_epi.nii.gz",
"comments": [
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"source1": "./usr/share/doc/python-nibabel-doc/html/_downloads/91f627dc0e05cfed2c760badc0d68b0b/spm_dicom_orient.py",
"source2": "./usr/share/doc/python-nibabel-doc/html/_downloads/600038078c8a32c330fa09f5f4c66d15/spm_dicom_orient.py",
"comments": [
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"source1": "./usr/share/doc/python-nibabel-doc/html/_downloads/e7e029cef54a06cf60644ab5a2646f6e/register_me.py",
"source2": "./usr/share/doc/python-nibabel-doc/html/_downloads/952c6388e9e163bd814e7d6e4ccbfdf7/register_me.py",
"comments": [
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"source1": "./usr/share/doc/python-nibabel-doc/html/_downloads/f698b213dbfaca2fd9126e6691ac06f9/someones_anatomy.nii.gz",
"source2": "./usr/share/doc/python-nibabel-doc/html/_downloads/38974bbb5af6107bf7448530b9559da2/someones_anatomy.nii.gz",
"comments": [
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