From samuel.garcia at cnrs.fr  Mon Oct  2 05:33:00 2017
From: samuel.garcia at cnrs.fr (Samuel Garcia)
Date: Mon, 2 Oct 2017 11:33:00 +0200
Subject: [Neuroimaging] ANN: neo 0.5.2 released.
Message-ID: <a7de7b3f-d02d-65ca-3ea1-7a0633f036e7@cnrs.fr>

Dear list,
I am happy to announce the released of neo 0.5.2 
<http://neo.readthedocs.io/en/latest/releases/0.5.2.html>.

Neo <http://neo.readthedocs.io/en/latest/> is a package for representing 
electrophysiology data in Python, together with support for reading a 
wide range of neurophysiology file formats.

Changes:
 ? * Removed support for Python 2.6
 ? * Pickling AnalogSignal and SpikeTrain now preserves parent objects
 ? * Added NSDFIO, which reads and writes NSDF files
 ? * Fixes and improvements to PlexonIO, NixIO, BlackrockIO, 
NeuralynxIO, IgorIO, ElanIO, MicromedIO, TdtIO and others.


Note: neo will now enter in a new cycle of development for re-factoring 
the io api. The goal will be to have a fast access for long/big files.


Best,

Samuel
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From vinit.k.srivastava at gmail.com  Wed Oct  4 18:16:56 2017
From: vinit.k.srivastava at gmail.com (Vinit Srivastava)
Date: Wed, 4 Oct 2017 18:16:56 -0400
Subject: [Neuroimaging] voxel axes reorientation
Message-ID: <CACUgD2w1j=S6nWEFK=baCUCZ1VqrQYTuSRuFDUEPRF_M8XPhmw@mail.gmail.com>

Hi,

I've been using nib.as_closest_canonical to reorient the voxel axes to RAS+.

Is there an optio to reorient images to a chosen format other than
canonical RAS format?  For example, I'd like to reorient from LPS to LAS.

Thanks,

Vinny
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From christopher.cox-2 at manchester.ac.uk  Thu Oct 12 09:15:37 2017
From: christopher.cox-2 at manchester.ac.uk (Christopher Cox)
Date: Thu, 12 Oct 2017 13:15:37 +0000
Subject: [Neuroimaging] Question about nibabel.processing
Message-ID: <E79184C74CE49644A99336D02E37E2FAE7AE97@MBXP09.ds.man.ac.uk>

Hello,

My first question should really be: how do you review the archive for this mailing list? I imagine this question has been answered, but I do not know where to look.

I am attempting something very simple: I want to resample a volume. The nifti file on disk is 4D, and contains two volumes. Nibabel.processing.resample_to_output() will not work with 4D data. Fortunately for me, I only care about the first volume in this dataset. So I should have no problem.

But... nothing I can think to try works. I apologize it is difficult to read, but I've condensed several of my attempts into an interactive python session, and copied all of my work and the errors I am getting as a post script. I tried to color code, but that might not come through.

My first attempt is to try and pass the sliced data object (as in, data = img.get_data()), but that is a memory map as lacks the metadata the function requires. I tried passing the image (as in, img = nib.load(...)), but it cannot be sliced like the data can. I then tried simply loading only a single volume into memory (as in, vol0 = img.datobj[...,0]), but that doesn't work either (vol0 is a numpy array, and img is still seen as 4D).  I tried updating the shape metadata in img, but that's read only.

Thank you very much for helping me figure out the intended way to use this function.

Best,
Chris

Python 3.6.0 (v3.6.0:41df79263a11, Dec 23 2016, 08:06:12) [MSC v.1900 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import nibabel as nib
>>> nib.__version__
'2.1.0'

>>> import nibabel.processing
>>> img = nib.load('Template_6.nii')
>>> img.shape
(121, 145, 121, 2)

>>> data = img.get_data()
>>> r = nib.processing.resample_to_output(data[...,0], [3,3,3])
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Users\mbmhscc4\AppData\Roaming\Python\Python36\site-packages\nibabel\processing.py", line 242, in resample_to_output
    out_vox_map = vox2out_vox((in_img.shape, in_img.affine), voxel_sizes)
AttributeError: 'memmap' object has no attribute 'affine'

>>> r = nib.processing.resample_to_output(img, [3,3,3])
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Users\mbmhscc4\AppData\Roaming\Python\Python36\site-packages\nibabel\processing.py", line 242, in resample_to_output
    out_vox_map = vox2out_vox((in_img.shape, in_img.affine), voxel_sizes)
  File "C:\Users\mbmhscc4\AppData\Roaming\Python\Python36\site-packages\nibabel\spaces.py", line 76, in vox2out_vox
    raise ValueError('This function can only deal with 3D images')
ValueError: This function can only deal with 3D images

>>> r = nib.processing.resample_to_output(img[...,0], [3,3,3])
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: __getitem__() takes 1 positional argument but 2 were given

>>> vol0 = img.dataobj[...,0]
>>> r = nib.processing.resample_to_output(vol0, [3,3,3])
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Users\mbmhscc4\AppData\Roaming\Python\Python36\site-packages\nibabel\processing.py", line 242, in resample_to_output
    out_vox_map = vox2out_vox((in_img.shape, in_img.affine), voxel_sizes)
AttributeError: 'numpy.ndarray' object has no attribute 'affine'

# This is after the previous attempt, loading only the first volume. img remains 4D
>>> r = nib.processing.resample_to_output(img, [3,3,3])
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Users\mbmhscc4\AppData\Roaming\Python\Python36\site-packages\nibabel\processing.py", line 242, in resample_to_output
    out_vox_map = vox2out_vox((in_img.shape, in_img.affine), voxel_sizes)
  File "C:\Users\mbmhscc4\AppData\Roaming\Python\Python36\site-packages\nibabel\spaces.py", line 76, in vox2out_vox
    raise ValueError('This function can only deal with 3D images')
ValueError: This function can only deal with 3D images


>>> img.shape = img.shape[0:3]
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: can't set attribute


Christopher R. Cox, PhD
Neuroscience and Aphasia Research Unit (NARU)
University of Manchester, UK
christopher.cox-2 at manchester.ac.uk<mailto:christopher.cox-2 at manchester.ac.uk>

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From zoltickb at mail.nih.gov  Thu Oct 12 10:33:32 2017
From: zoltickb at mail.nih.gov (Zoltick, Brad (NIH/NIMH) [E])
Date: Thu, 12 Oct 2017 14:33:32 +0000
Subject: [Neuroimaging] Question about nibabel.processing
In-Reply-To: <E79184C74CE49644A99336D02E37E2FAE7AE97@MBXP09.ds.man.ac.uk>
References: <E79184C74CE49644A99336D02E37E2FAE7AE97@MBXP09.ds.man.ac.uk>
Message-ID: <BN1PR09MB0211ACBE6A36403F53D54A2BEC4B0@BN1PR09MB0211.namprd09.prod.outlook.com>

Hello Chris,


I have used these two functions to convert a 4d nifti dataset to N, 3d volumes.

Perhaps this will help you.  You can easily store the first volume and then resample.

____________________________________________________________

!/usr/bin/env python3

#
# Use nibabel tools to convert a 4D (space+time) single file nifti
# file to a set of single volume pairs of (.img,.hdr) files

import os
import sys
import numpy as np
import nibabel as nib

def nii_4d_to_3d_pair(fname):
    """ convert a nifti1 4D image to N nifti1 3d pair (.img,.hdr) images """
    if not fname.endswith('nii'):
        sys.exit('filename must be a 4D nifti file: {}'.format(fname))

    img4d = nib.load(fname)
    basename = img4d.get_filename()[:-4]
    affine = img4d.affine     # save affine
    header = img4d.header     # save header
    numvols = img4d.shape[3]  # 4th dimension is time (number of volumes)
    data = img4d.get_data()   # 4d numpy array

    for i in range(numvols):
        img3d = nib.Nifti1Pair(data[:,:,:,i], affine, header)
        img3d_name = '{0}_{1:02d}.img'.format(basename, i)
        nib.save(img3d, img3d_name)

def nii_4d_to_3d(fname):
    """ convert a nifti1 4D image to N nifti1 3d single file images """
    if not fname.endswith('nii'):
        sys.exit('filename must be a 4D nifti file: {}'.format(fname))

    img4d = nib.load(fname)
    basename = img4d.get_filename()[:-4]
    affine = img4d.affine     # save affine
    header = img4d.header     # save header
    numvols = img4d.shape[3]  # 4th dimension is time (number of volumes)
    data = img4d.get_data()   # 4d numpy array

    for i in range(numvols):
        img3d = nib.Nifti1Image(data[:,:,:,i], affine, header)
        img3d_name = '{0}_{1:02d}.nii'.format(basename, i)
        nib.save(img3d, img3d_name)

if __name__ == '__main__':
    usage = 'conv_4dto3d.py file.nii'
    if len(sys.argv) < 2:
        sys.exit(usage)
    fname = sys.argv[1]
    nii_4d_to_3d_pair(fname)

__________________________________


Brad J Zoltick
Computer Engineer

NIH/NIMH
Building 10, Room 3C-210
Bethesda, MD 20892-1394

Tel (301)402-3232
Fax (301)480-7795


________________________________
From: Christopher Cox <christopher.cox-2 at manchester.ac.uk>
Sent: Thursday, October 12, 2017 9:15 AM
To: neuroimaging at python.org
Subject: [Neuroimaging] Question about nibabel.processing


Hello,



My first question should really be: how do you review the archive for this mailing list? I imagine this question has been answered, but I do not know where to look.



I am attempting something very simple: I want to resample a volume. The nifti file on disk is 4D, and contains two volumes. Nibabel.processing.resample_to_output() will not work with 4D data. Fortunately for me, I only care about the first volume in this dataset. So I should have no problem.



But... nothing I can think to try works. I apologize it is difficult to read, but I?ve condensed several of my attempts into an interactive python session, and copied all of my work and the errors I am getting as a post script. I tried to color code, but that might not come through.



My first attempt is to try and pass the sliced data object (as in, data = img.get_data()), but that is a memory map as lacks the metadata the function requires. I tried passing the image (as in, img = nib.load(?)), but it cannot be sliced like the data can. I then tried simply loading only a single volume into memory (as in, vol0 = img.datobj[?,0]), but that doesn?t work either (vol0 is a numpy array, and img is still seen as 4D).  I tried updating the shape metadata in img, but that?s read only.



Thank you very much for helping me figure out the intended way to use this function.



Best,

Chris



Python 3.6.0 (v3.6.0:41df79263a11, Dec 23 2016, 08:06:12) [MSC v.1900 64 bit (AMD64)] on win32

Type "help", "copyright", "credits" or "license" for more information.

>>> import nibabel as nib

>>> nib.__version__

'2.1.0'



>>> import nibabel.processing

>>> img = nib.load('Template_6.nii')

>>> img.shape

(121, 145, 121, 2)



>>> data = img.get_data()

>>> r = nib.processing.resample_to_output(data[...,0], [3,3,3])

Traceback (most recent call last):

  File "<stdin>", line 1, in <module>

  File "C:\Users\mbmhscc4\AppData\Roaming\Python\Python36\site-packages\nibabel\processing.py", line 242, in resample_to_output

    out_vox_map = vox2out_vox((in_img.shape, in_img.affine), voxel_sizes)

AttributeError: 'memmap' object has no attribute 'affine'



>>> r = nib.processing.resample_to_output(img, [3,3,3])

Traceback (most recent call last):

  File "<stdin>", line 1, in <module>

  File "C:\Users\mbmhscc4\AppData\Roaming\Python\Python36\site-packages\nibabel\processing.py", line 242, in resample_to_output

    out_vox_map = vox2out_vox((in_img.shape, in_img.affine), voxel_sizes)

  File "C:\Users\mbmhscc4\AppData\Roaming\Python\Python36\site-packages\nibabel\spaces.py", line 76, in vox2out_vox

    raise ValueError('This function can only deal with 3D images')

ValueError: This function can only deal with 3D images



>>> r = nib.processing.resample_to_output(img[...,0], [3,3,3])

Traceback (most recent call last):

  File "<stdin>", line 1, in <module>

TypeError: __getitem__() takes 1 positional argument but 2 were given



>>> vol0 = img.dataobj[...,0]

>>> r = nib.processing.resample_to_output(vol0, [3,3,3])

Traceback (most recent call last):

  File "<stdin>", line 1, in <module>

  File "C:\Users\mbmhscc4\AppData\Roaming\Python\Python36\site-packages\nibabel\processing.py", line 242, in resample_to_output

    out_vox_map = vox2out_vox((in_img.shape, in_img.affine), voxel_sizes)

AttributeError: 'numpy.ndarray' object has no attribute 'affine'



# This is after the previous attempt, loading only the first volume. img remains 4D

>>> r = nib.processing.resample_to_output(img, [3,3,3])

Traceback (most recent call last):

  File "<stdin>", line 1, in <module>

  File "C:\Users\mbmhscc4\AppData\Roaming\Python\Python36\site-packages\nibabel\processing.py", line 242, in resample_to_output

    out_vox_map = vox2out_vox((in_img.shape, in_img.affine), voxel_sizes)

  File "C:\Users\mbmhscc4\AppData\Roaming\Python\Python36\site-packages\nibabel\spaces.py", line 76, in vox2out_vox

    raise ValueError('This function can only deal with 3D images')

ValueError: This function can only deal with 3D images





>>> img.shape = img.shape[0:3]

Traceback (most recent call last):

  File "<stdin>", line 1, in <module>

AttributeError: can't set attribute





Christopher R. Cox, PhD

Neuroscience and Aphasia Research Unit (NARU)

University of Manchester, UK

christopher.cox-2 at manchester.ac.uk<mailto:christopher.cox-2 at manchester.ac.uk>


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From effigies at bu.edu  Thu Oct 12 10:24:37 2017
From: effigies at bu.edu (Christopher Markiewicz)
Date: Thu, 12 Oct 2017 10:24:37 -0400
Subject: [Neuroimaging] Question about nibabel.processing
In-Reply-To: <E79184C74CE49644A99336D02E37E2FAE7AE97@MBXP09.ds.man.ac.uk>
References: <E79184C74CE49644A99336D02E37E2FAE7AE97@MBXP09.ds.man.ac.uk>
Message-ID: <CAHE-diPUQnXpGDASTuBq+Gk9A9Bm2o_AW-0MWEmdjw7iwiogiA@mail.gmail.com>

Hi,

What you need to do here is to get a 3D image from your 4D image. There's a
function four_to_three that will give you a list of 3D images.

>>> imgs = nib.four_to_three(img)
>>> r = nib.processing.resample_to_output(imgs[0], [3, 3, 3])

Chris

On Thu, Oct 12, 2017 at 9:15 AM, Christopher Cox <
christopher.cox-2 at manchester.ac.uk> wrote:

> Hello,
>
>
>
> My first question should really be: how do you review the archive for this
> mailing list? I imagine this question has been answered, but I do not know
> where to look.
>
>
>
> I am attempting something very simple: I want to resample a volume. The
> nifti file on disk is 4D, and contains two volumes.
> Nibabel.processing.resample_to_output() will not work with 4D data.
> Fortunately for me, I only care about the first volume in this dataset. So
> I should have no problem.
>
>
>
> But... nothing I can think to try works. I apologize it is difficult to
> read, but I?ve condensed several of my attempts into an interactive python
> session, and copied all of my work and the errors I am getting as a post
> script. I tried to color code, but that might not come through.
>
>
>
> My first attempt is to try and pass the sliced data object (as in, data =
> img.get_data()), but that is a memory map as lacks the metadata the
> function requires. I tried passing the image (as in, img = nib.load(?)),
> but it cannot be sliced like the data can. I then tried simply loading only
> a single volume into memory (as in, vol0 = img.datobj[?,0]), but that
> doesn?t work either (vol0 is a numpy array, and img is still seen as 4D).
> I tried updating the shape metadata in img, but that?s read only.
>
>
>
> Thank you very much for helping me figure out the intended way to use this
> function.
>
>
>
> Best,
>
> Chris
>
>
>
> Python 3.6.0 (v3.6.0:41df79263a11, Dec 23 2016, 08:06:12) [MSC v.1900 64
> bit (AMD64)] on win32
>
> Type "help", "copyright", "credits" or "license" for more information.
>
> >>> import nibabel as nib
>
> >>> nib.__version__
>
> '2.1.0'
>
>
>
> >>> import nibabel.processing
>
> >>> img = nib.load('Template_6.nii')
>
> >>> img.shape
>
> (121, 145, 121, 2)
>
>
>
> >>> data = img.get_data()
>
> >>> r = nib.processing.resample_to_output(data[...,0], [3,3,3])
>
> Traceback (most recent call last):
>
>   File "<stdin>", line 1, in <module>
>
>   File "C:\Users\mbmhscc4\AppData\Roaming\Python\Python36\site-
> packages\nibabel\processing.py", line 242, in resample_to_output
>
>     out_vox_map = vox2out_vox((in_img.shape, in_img.affine), voxel_sizes)
>
> *AttributeError: 'memmap' object has no attribute 'affine'*
>
>
>
> >>> r = nib.processing.resample_to_output(img, [3,3,3])
>
> Traceback (most recent call last):
>
>   File "<stdin>", line 1, in <module>
>
>   File "C:\Users\mbmhscc4\AppData\Roaming\Python\Python36\site-
> packages\nibabel\processing.py", line 242, in resample_to_output
>
>     out_vox_map = vox2out_vox((in_img.shape, in_img.affine), voxel_sizes)
>
>   File "C:\Users\mbmhscc4\AppData\Roaming\Python\Python36\site-packages\nibabel\spaces.py",
> line 76, in vox2out_vox
>
>     raise ValueError('This function can only deal with 3D images')
>
> *ValueError: This function can only deal with 3D images*
>
>
>
> >>> r = nib.processing.resample_to_output(img[...,0], [3,3,3])
>
> Traceback (most recent call last):
>
>   File "<stdin>", line 1, in <module>
>
> *TypeError: __getitem__() takes 1 positional argument but 2 were given*
>
>
>
> >>> vol0 = img.dataobj[...,0]
>
> >>> r = nib.processing.resample_to_output(vol0, [3,3,3])
>
> Traceback (most recent call last):
>
>   File "<stdin>", line 1, in <module>
>
>   File "C:\Users\mbmhscc4\AppData\Roaming\Python\Python36\site-
> packages\nibabel\processing.py", line 242, in resample_to_output
>
>     out_vox_map = vox2out_vox((in_img.shape, in_img.affine), voxel_sizes)
>
> *AttributeError: 'numpy.ndarray' object has no attribute 'affine'*
>
>
>
> # This is after the previous attempt, loading only the first volume. img
> remains 4D
>
> >>> r = nib.processing.resample_to_output(img, [3,3,3])
>
> Traceback (most recent call last):
>
>   File "<stdin>", line 1, in <module>
>
>   File "C:\Users\mbmhscc4\AppData\Roaming\Python\Python36\site-
> packages\nibabel\processing.py", line 242, in resample_to_output
>
>     out_vox_map = vox2out_vox((in_img.shape, in_img.affine), voxel_sizes)
>
>   File "C:\Users\mbmhscc4\AppData\Roaming\Python\Python36\site-packages\nibabel\spaces.py",
> line 76, in vox2out_vox
>
>     raise ValueError('This function can only deal with 3D images')
>
> *ValueError: This function can only deal with 3D images*
>
>
>
>
>
> >>> img.shape = img.shape[0:3]
>
> Traceback (most recent call last):
>
>   File "<stdin>", line 1, in <module>
>
> *AttributeError: can't set attribute*
>
>
>
>
>
> Christopher R. Cox, PhD
>
> Neuroscience and Aphasia Research Unit (NARU)
>
> University of Manchester, UK
>
> christopher.cox-2 at manchester.ac.uk
>
>
>
> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging
>
>
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From effigies at bu.edu  Thu Oct 12 10:45:45 2017
From: effigies at bu.edu (Christopher Markiewicz)
Date: Thu, 12 Oct 2017 10:45:45 -0400
Subject: [Neuroimaging] voxel axes reorientation
In-Reply-To: <CACUgD2w1j=S6nWEFK=baCUCZ1VqrQYTuSRuFDUEPRF_M8XPhmw@mail.gmail.com>
References: <CACUgD2w1j=S6nWEFK=baCUCZ1VqrQYTuSRuFDUEPRF_M8XPhmw@mail.gmail.com>
Message-ID: <CAHE-diPNcptJHFP093jfy=xOhMKebmC=+V8m26pf1+ugvc4Hig@mail.gmail.com>

The basics are this:

img = nib.load(fname)
orig_ornt = nib.orientations.io_orientation(img.affine)
targ_ornt = nib.orientations.axcodes2ornt('LAS')
ornt_xfm = nib.orientations.ornt_transform(orig_ornt, targ_ornt)

If you're using the latest master, you can simply do:

img_LAS = img.as_reoriented(ornt_xfm)

Otherwise, you'll need to transform the data and the affine:

data = nib.orientations.apply_orientation(img.dataobj, ornt_xfm)
affine = img.affine.dot(nib.orientations.inv_ornt_aff(ornt_xfm, img.shape))
img_LAS = img.__class__(data, affine, img.header)

(You should obviously check my work.)

Chris


On Wed, Oct 4, 2017 at 6:16 PM, Vinit Srivastava <
vinit.k.srivastava at gmail.com> wrote:

> Hi,
>
> I've been using nib.as_closest_canonical to reorient the voxel axes to
> RAS+.
>
> Is there an optio to reorient images to a chosen format other than
> canonical RAS format?  For example, I'd like to reorient from LPS to LAS.
>
> Thanks,
>
> Vinny
>
> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging
>
>
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From effigies at bu.edu  Fri Oct 13 16:30:41 2017
From: effigies at bu.edu (Christopher Markiewicz)
Date: Fri, 13 Oct 2017 16:30:41 -0400
Subject: [Neuroimaging] ANN: Nibabel release 2.2
Message-ID: <CAHE-diNRieK9ebNaNBFuELjCfZH23j0jp_eWZD67uTXdLKO2tw@mail.gmail.com>

Hi all,

Nibabel 2.2 has been released, featuring some very long-awaited CIFTI
support.

Other particularly interesting new features are TCK streamlines and
indexed_gzip support, thanks to Marc-Alexandre C?t? and Paul McCarthy,
respectively.

Thanks to all contributors and reviewers, and of course Matthew Brett for
all of his maintenance work.

Please cite using the Zenodo DOI: https://doi.org/10.5281/zenodo.1011207


The full Changelog follows:

New feature release for the 2.2 series.

Most work on NiBabel so far has been by Matthew Brett (MB), Michael Hanke
(MH)
Ben Cipollini (BC), Marc-Alexandre C?t? (MC), Chris Markiewicz (CM), Stephan
Gerhard (SG) and Eric Larson (EL).

References like "pr/298" refer to github pull request numbers.

New features
------------

* CIFTI support (pr/249) (Satra Ghosh, Michiel Cottaar, BC, CM, Demian
  Wassermann, MB)
* Support for MRtrix TCK streamlines file format (pr/486) (MC, reviewed by
  MB, Arnaud Bore, J-Donald Tournier, Jean-Christophe Houde)
* Added ``get_fdata()`` as default method to retrieve scaled floating point
  data from ``DataobjImage``s (pr/551) (MB, reviewed by CM, Satra Ghosh)

Enhancements
------------

* Support for alternative header field name variants in .PAR files
  (pr/507) (Gregory R. Lee)
* Various enhancements to streamlines API by MC: support for reading TRK
  version 1 (pr/512); concatenation of tractograms using `+`/`+=` operators
  (pr/495); function to concatenate multiple ArraySequence objects (pr/494)
* Support for numpy 1.12 (pr/500, pr/502) (MC, MB)
* Allow dtype specifiers as fileslice input (pr/485) (MB)
* Support "headerless" ArrayProxy specification, enabling memory-efficient
  ArrayProxy reshaping (pr/521) (CM)
* Allow unknown NIfTI intent codes, add FSL codes (pr/528) (Paul McCarthy)
* Improve error handling for ``img.__getitem__`` (pr/533) (Ariel Rokem)
* Delegate reorientation to SpatialImage classes (pr/544) (Mark Hymers, CM,
  reviewed by MB)
* Enable using ``indexed_gzip`` to reduce memory usage when reading from
  gzipped NIfTI and MGH files (pr/552) (Paul McCarthy, reviewed by MB, CM)

Bug fixes
---------

* Miscellaneous MINC reader fixes (pr/493) (Robert D. Vincent, reviewed by
CM,
  MB)
* Fix corner case in ``wrapstruct.get`` (pr/516) (Paul McCarthy, reviewed by
  CM, MB)

Maintenance
-----------

* Fix documentation errors (pr/517, pr/536) (Fernando Perez, Venky Reddy)
* Documentation update (pr/514) (Ivan Gonzalez)
* Update testing to use pre-release builds of dependencies (pr/509) (MB)
* Better warnings when nibabel not on path (pr/503) (MB)

API changes and deprecations
----------------------------

* ``header`` argument to ``ArrayProxy.__init__`` is renamed to ``spec``
* Deprecation of ``header`` property of ``ArrayProxy`` object, for removal
in
  3.0
* ``wrapstruct.get`` now returns entries evaluating ``False``, instead of
``None``
* ``DataobjImage.get_data`` to be deprecated April 2018, scheduled for
removal
  April 2020


Enjoy,

Chris Markiewicz
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From bertrand.thirion at inria.fr  Fri Oct 13 16:44:14 2017
From: bertrand.thirion at inria.fr (bthirion)
Date: Fri, 13 Oct 2017 22:44:14 +0200
Subject: [Neuroimaging] ANN: Nibabel release 2.2
In-Reply-To: <CAHE-diNRieK9ebNaNBFuELjCfZH23j0jp_eWZD67uTXdLKO2tw@mail.gmail.com>
References: <CAHE-diNRieK9ebNaNBFuELjCfZH23j0jp_eWZD67uTXdLKO2tw@mail.gmail.com>
Message-ID: <4848da74-0f8f-39c5-a765-b253e4d089e3@inria.fr>

Great, thx !
Bertrand

On 13/10/2017 22:30, Christopher Markiewicz wrote:
> Hi all,
>
> Nibabel 2.2 has been released, featuring some very long-awaited CIFTI 
> support.
>
> Other particularly interesting new features are TCK streamlines and 
> indexed_gzip support, thanks to Marc-Alexandre C?t? and Paul McCarthy, 
> respectively.
>
> Thanks to all contributors and reviewers, and of course Matthew Brett 
> for all of his maintenance work.
>
> Please cite using the Zenodo DOI: https://doi.org/10.5281/zenodo.1011207
>
>
> The full Changelog follows:
>
> New feature release for the 2.2 series.
>
> Most work on NiBabel so far has been by Matthew Brett (MB), Michael 
> Hanke (MH)
> Ben Cipollini (BC), Marc-Alexandre C?t? (MC), Chris Markiewicz (CM), 
> Stephan
> Gerhard (SG) and Eric Larson (EL).
>
> References like "pr/298" refer to github pull request numbers.
>
> New features
> ------------
>
> * CIFTI support (pr/249) (Satra Ghosh, Michiel Cottaar, BC, CM, Demian
> ? Wassermann, MB)
> * Support for MRtrix TCK streamlines file format (pr/486) (MC, reviewed by
> ? MB, Arnaud Bore, J-Donald Tournier, Jean-Christophe Houde)
> * Added ``get_fdata()`` as default method to retrieve scaled floating 
> point
> ? data from ``DataobjImage``s (pr/551) (MB, reviewed by CM, Satra Ghosh)
>
> Enhancements
> ------------
>
> * Support for alternative header field name variants in .PAR files
> ? (pr/507) (Gregory R. Lee)
> * Various enhancements to streamlines API by MC: support for reading TRK
> ? version 1 (pr/512); concatenation of tractograms using `+`/`+=` 
> operators
> ? (pr/495); function to concatenate multiple ArraySequence objects 
> (pr/494)
> * Support for numpy 1.12 (pr/500, pr/502) (MC, MB)
> * Allow dtype specifiers as fileslice input (pr/485) (MB)
> * Support "headerless" ArrayProxy specification, enabling memory-efficient
> ? ArrayProxy reshaping (pr/521) (CM)
> * Allow unknown NIfTI intent codes, add FSL codes (pr/528) (Paul McCarthy)
> * Improve error handling for ``img.__getitem__`` (pr/533) (Ariel Rokem)
> * Delegate reorientation to SpatialImage classes (pr/544) (Mark 
> Hymers, CM,
> ? reviewed by MB)
> * Enable using ``indexed_gzip`` to reduce memory usage when reading from
> ? gzipped NIfTI and MGH files (pr/552) (Paul McCarthy, reviewed by MB, CM)
>
> Bug fixes
> ---------
>
> * Miscellaneous MINC reader fixes (pr/493) (Robert D. Vincent, 
> reviewed by CM,
> ? MB)
> * Fix corner case in ``wrapstruct.get`` (pr/516) (Paul McCarthy, 
> reviewed by
> ? CM, MB)
>
> Maintenance
> -----------
>
> * Fix documentation errors (pr/517, pr/536) (Fernando Perez, Venky Reddy)
> * Documentation update (pr/514) (Ivan Gonzalez)
> * Update testing to use pre-release builds of dependencies (pr/509) (MB)
> * Better warnings when nibabel not on path (pr/503) (MB)
>
> API changes and deprecations
> ----------------------------
>
> * ``header`` argument to ``ArrayProxy.__init__`` is renamed to ``spec``
> * Deprecation of ``header`` property of ``ArrayProxy`` object, for 
> removal in
> ? 3.0
> * ``wrapstruct.get`` now returns entries evaluating ``False``, instead 
> of ``None``
> * ``DataobjImage.get_data`` to be deprecated April 2018, scheduled for 
> removal
> ? April 2020
>
>
> Enjoy,
>
> Chris Markiewicz
>
>
>
> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging


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From gael.varoquaux at normalesup.org  Fri Oct 13 17:11:45 2017
From: gael.varoquaux at normalesup.org (Gael Varoquaux)
Date: Fri, 13 Oct 2017 23:11:45 +0200
Subject: [Neuroimaging] ANN: Nibabel release 2.2
In-Reply-To: <CAHE-diNRieK9ebNaNBFuELjCfZH23j0jp_eWZD67uTXdLKO2tw@mail.gmail.com>
References: <CAHE-diNRieK9ebNaNBFuELjCfZH23j0jp_eWZD67uTXdLKO2tw@mail.gmail.com>
Message-ID: <20171013211145.GF2693161@phare.normalesup.org>

Very nice!

Ga?l

On Fri, Oct 13, 2017 at 04:30:41PM -0400, Christopher Markiewicz wrote:
> Hi all,

> Nibabel 2.2 has been released, featuring some very long-awaited CIFTI support.

> Other particularly interesting new features are TCK streamlines and
> indexed_gzip support, thanks to Marc-Alexandre C?t? and Paul McCarthy,
> respectively.

> Thanks to all contributors and reviewers, and of course Matthew Brett for all
> of his maintenance work.

> Please cite using the Zenodo DOI:?https://doi.org/10.5281/zenodo.1011207


> The full Changelog follows:

> New feature release for the 2.2 series.

> Most work on NiBabel so far has been by Matthew Brett (MB), Michael Hanke (MH)
> Ben Cipollini (BC), Marc-Alexandre C?t? (MC), Chris Markiewicz (CM), Stephan
> Gerhard (SG) and Eric Larson (EL).

> References like "pr/298" refer to github pull request numbers.

> New features
> ------------

> * CIFTI support (pr/249) (Satra Ghosh, Michiel Cottaar, BC, CM, Demian
> ? Wassermann, MB)
> * Support for MRtrix TCK streamlines file format (pr/486) (MC, reviewed by
> ? MB, Arnaud Bore, J-Donald Tournier, Jean-Christophe Houde)
> * Added ``get_fdata()`` as default method to retrieve scaled floating point
> ? data from ``DataobjImage``s (pr/551) (MB, reviewed by CM, Satra Ghosh)

> Enhancements
> ------------

> * Support for alternative header field name variants in .PAR files
> ? (pr/507) (Gregory R. Lee)
> * Various enhancements to streamlines API by MC: support for reading TRK
> ? version 1 (pr/512); concatenation of tractograms using `+`/`+=` operators
> ? (pr/495); function to concatenate multiple ArraySequence objects (pr/494)
> * Support for numpy 1.12 (pr/500, pr/502) (MC, MB)
> * Allow dtype specifiers as fileslice input (pr/485) (MB)
> * Support "headerless" ArrayProxy specification, enabling memory-efficient
> ? ArrayProxy reshaping (pr/521) (CM)
> * Allow unknown NIfTI intent codes, add FSL codes (pr/528) (Paul McCarthy)
> * Improve error handling for ``img.__getitem__`` (pr/533) (Ariel Rokem)
> * Delegate reorientation to SpatialImage classes (pr/544) (Mark Hymers, CM,
> ? reviewed by MB)
> * Enable using ``indexed_gzip`` to reduce memory usage when reading from
> ? gzipped NIfTI and MGH files (pr/552) (Paul McCarthy, reviewed by MB, CM)

> Bug fixes
> ---------

> * Miscellaneous MINC reader fixes (pr/493) (Robert D. Vincent, reviewed by CM,
> ? MB)
> * Fix corner case in ``wrapstruct.get`` (pr/516) (Paul McCarthy, reviewed by
> ? CM, MB)

> Maintenance
> -----------

> * Fix documentation errors (pr/517, pr/536) (Fernando Perez, Venky Reddy)
> * Documentation update (pr/514) (Ivan Gonzalez)
> * Update testing to use pre-release builds of dependencies (pr/509) (MB)
> * Better warnings when nibabel not on path (pr/503) (MB)

> API changes and deprecations
> ----------------------------

> * ``header`` argument to ``ArrayProxy.__init__`` is renamed to ``spec``
> * Deprecation of ``header`` property of ``ArrayProxy`` object, for removal in
> ? 3.0
> * ``wrapstruct.get`` now returns entries evaluating ``False``, instead of
> ``None``
> * ``DataobjImage.get_data`` to be deprecated April 2018, scheduled for removal
> ? April 2020


> Enjoy,

> Chris Markiewicz


> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging


-- 
    Gael Varoquaux
    Researcher, INRIA Parietal
    NeuroSpin/CEA Saclay , Bat 145, 91191 Gif-sur-Yvette France
    Phone:  ++ 33-1-69-08-79-68
    http://gael-varoquaux.info            http://twitter.com/GaelVaroquaux

From blaise.frederick at gmail.com  Fri Oct 13 17:14:00 2017
From: blaise.frederick at gmail.com (Blaise Frederick)
Date: Fri, 13 Oct 2017 17:14:00 -0400
Subject: [Neuroimaging] ANN: Nibabel release 2.2
In-Reply-To: <20171013211145.GF2693161@phare.normalesup.org>
References: <CAHE-diNRieK9ebNaNBFuELjCfZH23j0jp_eWZD67uTXdLKO2tw@mail.gmail.com>
 <20171013211145.GF2693161@phare.normalesup.org>
Message-ID: <AC8BA24D-BAFF-4459-9726-947E6ED46C85@gmail.com>

Very cool!  Can?t wait to try out the CIFTI stuff.

Blaise

> On Oct 13, 2017, at 5:11 PM, Gael Varoquaux <gael.varoquaux at normalesup.org> wrote:
> 
> Very nice!
> 
> Ga?l
> 
> On Fri, Oct 13, 2017 at 04:30:41PM -0400, Christopher Markiewicz wrote:
>> Hi all,
> 
>> Nibabel 2.2 has been released, featuring some very long-awaited CIFTI support.
> 
>> Other particularly interesting new features are TCK streamlines and
>> indexed_gzip support, thanks to Marc-Alexandre C?t? and Paul McCarthy,
>> respectively.
> 
>> Thanks to all contributors and reviewers, and of course Matthew Brett for all
>> of his maintenance work.
> 
>> Please cite using the Zenodo DOI: https://doi.org/10.5281/zenodo.1011207
> 
> 
>> The full Changelog follows:
> 
>> New feature release for the 2.2 series.
> 
>> Most work on NiBabel so far has been by Matthew Brett (MB), Michael Hanke (MH)
>> Ben Cipollini (BC), Marc-Alexandre C?t? (MC), Chris Markiewicz (CM), Stephan
>> Gerhard (SG) and Eric Larson (EL).
> 
>> References like "pr/298" refer to github pull request numbers.
> 
>> New features
>> ------------
> 
>> * CIFTI support (pr/249) (Satra Ghosh, Michiel Cottaar, BC, CM, Demian
>>   Wassermann, MB)
>> * Support for MRtrix TCK streamlines file format (pr/486) (MC, reviewed by
>>   MB, Arnaud Bore, J-Donald Tournier, Jean-Christophe Houde)
>> * Added ``get_fdata()`` as default method to retrieve scaled floating point
>>   data from ``DataobjImage``s (pr/551) (MB, reviewed by CM, Satra Ghosh)
> 
>> Enhancements
>> ------------
> 
>> * Support for alternative header field name variants in .PAR files
>>   (pr/507) (Gregory R. Lee)
>> * Various enhancements to streamlines API by MC: support for reading TRK
>>   version 1 (pr/512); concatenation of tractograms using `+`/`+=` operators
>>   (pr/495); function to concatenate multiple ArraySequence objects (pr/494)
>> * Support for numpy 1.12 (pr/500, pr/502) (MC, MB)
>> * Allow dtype specifiers as fileslice input (pr/485) (MB)
>> * Support "headerless" ArrayProxy specification, enabling memory-efficient
>>   ArrayProxy reshaping (pr/521) (CM)
>> * Allow unknown NIfTI intent codes, add FSL codes (pr/528) (Paul McCarthy)
>> * Improve error handling for ``img.__getitem__`` (pr/533) (Ariel Rokem)
>> * Delegate reorientation to SpatialImage classes (pr/544) (Mark Hymers, CM,
>>   reviewed by MB)
>> * Enable using ``indexed_gzip`` to reduce memory usage when reading from
>>   gzipped NIfTI and MGH files (pr/552) (Paul McCarthy, reviewed by MB, CM)
> 
>> Bug fixes
>> ---------
> 
>> * Miscellaneous MINC reader fixes (pr/493) (Robert D. Vincent, reviewed by CM,
>>   MB)
>> * Fix corner case in ``wrapstruct.get`` (pr/516) (Paul McCarthy, reviewed by
>>   CM, MB)
> 
>> Maintenance
>> -----------
> 
>> * Fix documentation errors (pr/517, pr/536) (Fernando Perez, Venky Reddy)
>> * Documentation update (pr/514) (Ivan Gonzalez)
>> * Update testing to use pre-release builds of dependencies (pr/509) (MB)
>> * Better warnings when nibabel not on path (pr/503) (MB)
> 
>> API changes and deprecations
>> ----------------------------
> 
>> * ``header`` argument to ``ArrayProxy.__init__`` is renamed to ``spec``
>> * Deprecation of ``header`` property of ``ArrayProxy`` object, for removal in
>>   3.0
>> * ``wrapstruct.get`` now returns entries evaluating ``False``, instead of
>> ``None``
>> * ``DataobjImage.get_data`` to be deprecated April 2018, scheduled for removal
>>   April 2020
> 
> 
>> Enjoy,
> 
>> Chris Markiewicz
> 
> 
>> _______________________________________________
>> Neuroimaging mailing list
>> Neuroimaging at python.org
>> https://mail.python.org/mailman/listinfo/neuroimaging
> 
> 
> -- 
>    Gael Varoquaux
>    Researcher, INRIA Parietal
>    NeuroSpin/CEA Saclay , Bat 145, 91191 Gif-sur-Yvette France
>    Phone:  ++ 33-1-69-08-79-68
>    http://gael-varoquaux.info            http://twitter.com/GaelVaroquaux
> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging


From vinit.k.srivastava at gmail.com  Fri Oct 13 17:58:22 2017
From: vinit.k.srivastava at gmail.com (Vinit Srivastava)
Date: Fri, 13 Oct 2017 17:58:22 -0400
Subject: [Neuroimaging] voxel axes reorientation
In-Reply-To: <CAHE-diPNcptJHFP093jfy=xOhMKebmC=+V8m26pf1+ugvc4Hig@mail.gmail.com>
References: <CACUgD2w1j=S6nWEFK=baCUCZ1VqrQYTuSRuFDUEPRF_M8XPhmw@mail.gmail.com>
 <CAHE-diPNcptJHFP093jfy=xOhMKebmC=+V8m26pf1+ugvc4Hig@mail.gmail.com>
Message-ID: <CACUgD2wWzW=gn3K7yCqiA6U1hkuDesw2FXUCdCBb5MAiwTDWkA@mail.gmail.com>

Thanks Chris!

On Thu, Oct 12, 2017 at 10:45 AM, Christopher Markiewicz <effigies at bu.edu>
wrote:

> The basics are this:
>
> img = nib.load(fname)
> orig_ornt = nib.orientations.io_orientation(img.affine)
> targ_ornt = nib.orientations.axcodes2ornt('LAS')
> ornt_xfm = nib.orientations.ornt_transform(orig_ornt, targ_ornt)
>
> If you're using the latest master, you can simply do:
>
> img_LAS = img.as_reoriented(ornt_xfm)
>
> Otherwise, you'll need to transform the data and the affine:
>
> data = nib.orientations.apply_orientation(img.dataobj, ornt_xfm)
> affine = img.affine.dot(nib.orientations.inv_ornt_aff(ornt_xfm,
> img.shape))
> img_LAS = img.__class__(data, affine, img.header)
>
> (You should obviously check my work.)
>
> Chris
>
>
> On Wed, Oct 4, 2017 at 6:16 PM, Vinit Srivastava <
> vinit.k.srivastava at gmail.com> wrote:
>
>> Hi,
>>
>> I've been using nib.as_closest_canonical to reorient the voxel axes to
>> RAS+.
>>
>> Is there an optio to reorient images to a chosen format other than
>> canonical RAS format?  For example, I'd like to reorient from LPS to LAS.
>>
>> Thanks,
>>
>> Vinny
>>
>> _______________________________________________
>> Neuroimaging mailing list
>> Neuroimaging at python.org
>> https://mail.python.org/mailman/listinfo/neuroimaging
>>
>>
>
> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging
>
>
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From matthew.brett at gmail.com  Fri Oct 13 19:24:56 2017
From: matthew.brett at gmail.com (Matthew Brett)
Date: Sat, 14 Oct 2017 00:24:56 +0100
Subject: [Neuroimaging] ANN: Nibabel release 2.2
In-Reply-To: <CAHE-diNRieK9ebNaNBFuELjCfZH23j0jp_eWZD67uTXdLKO2tw@mail.gmail.com>
References: <CAHE-diNRieK9ebNaNBFuELjCfZH23j0jp_eWZD67uTXdLKO2tw@mail.gmail.com>
Message-ID: <CAH6Pt5qhkYLq8j3V1Lw7zm41RCBqrKM-sEq1k8ajV+u6t7bSwQ@mail.gmail.com>

Hi Chris,

On Fri, Oct 13, 2017 at 9:30 PM, Christopher Markiewicz <effigies at bu.edu> wrote:
> Hi all,
>
> Nibabel 2.2 has been released, featuring some very long-awaited CIFTI
> support.
>
> Other particularly interesting new features are TCK streamlines and
> indexed_gzip support, thanks to Marc-Alexandre C?t? and Paul McCarthy,
> respectively.
>
> Thanks to all contributors and reviewers, and of course Matthew Brett for
> all of his maintenance work.
>
> Please cite using the Zenodo DOI: https://doi.org/10.5281/zenodo.1011207

A tip of my broadest hat, for doing the release, and for all the work
leading up to it.  You've been tireless with your reviews, as well as
contributing a lot of code, and that's really made a big difference in
keeping nibabel moving, and making it a cheerful project to work on,

Thanks much,

Matthew

From pauldmccarthy at gmail.com  Sat Oct 14 13:42:36 2017
From: pauldmccarthy at gmail.com (paul mccarthy)
Date: Sat, 14 Oct 2017 18:42:36 +0100
Subject: [Neuroimaging] ANN: Nibabel release 2.2
In-Reply-To: <CAH6Pt5qhkYLq8j3V1Lw7zm41RCBqrKM-sEq1k8ajV+u6t7bSwQ@mail.gmail.com>
References: <CAHE-diNRieK9ebNaNBFuELjCfZH23j0jp_eWZD67uTXdLKO2tw@mail.gmail.com>
 <CAH6Pt5qhkYLq8j3V1Lw7zm41RCBqrKM-sEq1k8ajV+u6t7bSwQ@mail.gmail.com>
Message-ID: <CAD_xh8Fovuzeb1TzRVg6GTQaFSRE-vOGv6hGTTOz9qeRBRfm7w@mail.gmail.com>

Thanks guys - you've been great to work with, and CIFTI is a big step
forward!

Cheers,

Paul

On 14 October 2017 at 00:24, Matthew Brett <matthew.brett at gmail.com> wrote:

> Hi Chris,
>
> On Fri, Oct 13, 2017 at 9:30 PM, Christopher Markiewicz <effigies at bu.edu>
> wrote:
> > Hi all,
> >
> > Nibabel 2.2 has been released, featuring some very long-awaited CIFTI
> > support.
> >
> > Other particularly interesting new features are TCK streamlines and
> > indexed_gzip support, thanks to Marc-Alexandre C?t? and Paul McCarthy,
> > respectively.
> >
> > Thanks to all contributors and reviewers, and of course Matthew Brett for
> > all of his maintenance work.
> >
> > Please cite using the Zenodo DOI: https://doi.org/10.5281/zenodo.1011207
>
> A tip of my broadest hat, for doing the release, and for all the work
> leading up to it.  You've been tireless with your reviews, as well as
> contributing a lot of code, and that's really made a big difference in
> keeping nibabel moving, and making it a cheerful project to work on,
>
> Thanks much,
>
> Matthew
> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging
>
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From keren.meron at mail.huji.ac.il  Sat Oct 14 12:42:55 2017
From: keren.meron at mail.huji.ac.il (Keren Meron)
Date: Sat, 14 Oct 2017 19:42:55 +0300
Subject: [Neuroimaging] nibabel question
Message-ID: <CAPqp7r-70MrieX8YGsZC01qQGRn+sgeVxw1YjbQS1buvK6Tn0w@mail.gmail.com>

I have a Nifti object generated from a directory of dicom files. It seems
that the Nifti should know how many frames it holds, but all I can find in
the header info is the shape. The problem is, the shape is at times
(num_images, x, y) and at times (x, y, num_images).

The only nibabel functions I found relevant where from the Ecat library. I
am not familiar with ecat format, but I want my method to work for any nii
file. I am working with the nibabel library.

Is there a way to retrieve the number of images in a Nifti file?

<http://www.avg.com/email-signature?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=webmail>
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From effigies at bu.edu  Sat Oct 14 17:05:31 2017
From: effigies at bu.edu (Christopher Markiewicz)
Date: Sat, 14 Oct 2017 17:05:31 -0400
Subject: [Neuroimaging] nibabel question
In-Reply-To: <CAPqp7r-70MrieX8YGsZC01qQGRn+sgeVxw1YjbQS1buvK6Tn0w@mail.gmail.com>
References: <CAPqp7r-70MrieX8YGsZC01qQGRn+sgeVxw1YjbQS1buvK6Tn0w@mail.gmail.com>
Message-ID: <CAHE-diMFUTZDXtig7=VpAawfXbzsaJyfvMJVNUCd1WoLhrV=9w@mail.gmail.com>

I think there's a little bit of a DICOM-NIfTI vocabulary mismatch here. In
my mind, a NIfTI file is an "image", and contains one or more "volumes" of
a given size. The three spatial dimensions may be called x, y, and z, and
the affine matrix encoded by the header translates voxel indices along
these dimensions into coordinates in RAS space, which is millimeters to the
right, anterior and superior of an origin (usually somewhere in the brain).
x, y and z may correspond to phase-, frequency- and slice-encoding
directions. Even if you don't have phase/frequency directions, usually you
have slices.

>From your email, I'm guessing you're using both "frame" and "image" to
refer to what I'd call a slice, or a 2D matrix that, when stacked in
series, produce a volume. So I'm interpreting your question as "How do I
determine the number of slices in a NIfTI image." which really hinges upon
determining the slice direction.

Assuming you've loaded a file:

>>> img = nib.load(fname)

You can look at the shape (as you have):

>>> img.shape
(x, y, z)

This might be enough, as often the slice dimension is different from the
other two, which are the same size. You can also look at the zooms:

>>> img.header.get_zooms()
(1.0, 1.0, 1.0)

Zooms are the width of the voxels in each direction. Again, often the slice
dimension is the odd one out. You can also look at orientation information:

>>> nib.aff2axcodes(img.afffine)  # Will give some sequence of R/L, A/P,
S/I, e.g.
('L', 'A', 'S')

If you know your slice direction was superior/inferior, then the
z-direction is what you want, and you can now go back to `img.shape` and
see how many slices you had.

Finally, there is a chance that the slice dimension is actually encoded in
the NIfTI header:

>>> freq_dim, phase_dim, slice_dim = img.header.get_dim_info()

If slice_dim is not `None`, then you can get the number of slices with

>>> img.shape[slice_dim]

Hopefully the vocabulary mismatch didn't send me off in entirely the wrong
direction, and this was somewhat helpful.

-- 
Chris Markiewicz


On Sat, Oct 14, 2017 at 12:42 PM, Keren Meron <keren.meron at mail.huji.ac.il>
wrote:

> I have a Nifti object generated from a directory of dicom files. It seems
> that the Nifti should know how many frames it holds, but all I can find in
> the header info is the shape. The problem is, the shape is at times
> (num_images, x, y) and at times (x, y, num_images).
>
> The only nibabel functions I found relevant where from the Ecat library. I
> am not familiar with ecat format, but I want my method to work for any nii
> file. I am working with the nibabel library.
>
> Is there a way to retrieve the number of images in a Nifti file?
>
>
> <http://www.avg.com/email-signature?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=webmail> Virus-free.
> www.avg.com
> <http://www.avg.com/email-signature?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=webmail>
> <#m_8563802930650991843_DAB4FAD8-2DD7-40BB-A1B8-4E2AA1F9FDF2>
>
> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging
>
>
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From elef at indiana.edu  Sun Oct 15 22:27:46 2017
From: elef at indiana.edu (Eleftherios Garyfallidis)
Date: Mon, 16 Oct 2017 02:27:46 +0000
Subject: [Neuroimaging] [DIPY] Question about whole tracks *.trk
 registration using SLR
In-Reply-To: <DCF83CCC-DAF6-47CD-96F0-2A8308CEEE0A@mgh.harvard.edu>
References: <63C189EA-8788-4FB9-BDCA-5D3DAC191164@mgh.harvard.edu>
 <CAN6JmWOe=JcSCkKa81B3-tO6R2nyqHQTPAG=h9kRbBvOgRvt5g@mail.gmail.com>
 <DCF83CCC-DAF6-47CD-96F0-2A8308CEEE0A@mgh.harvard.edu>
Message-ID: <CAN6JmWP3GZyDh97bufT9twH9aTqCwAeDA=3zFdTmz_t4OBE_YA@mail.gmail.com>

Hi Rodrigo,

Apologies for the delay. But hey I got a solution for you! I hope you are
excited :)
Look at this gist
https://gist.github.com/Garyfallidis/51e34aab47de99eafa887b2b818384ea

This code shows how to do step by step whole brain SLR and I made it work
with the
specific datasets that you gave me.

The specific datasets don't need exactly a whole brain SLR because the one
brain has much
fewer structures than the other brain but still SLR is robust to incomplete
datasets such as these.

Give it a go and give us feedback! :)

All the best,
Eleftherios

p.s. Notice that to load the streamlines I used a more recent API than what
you originally used. Make sure you have a recent version of dipy and
nibabel.


On Mon, Sep 25, 2017 at 9:50 AM Perea Camargo, Rodrigo Dennis <
RPEREACAMARGO at mgh.harvard.edu> wrote:

> Hi Eleftherios,
> I am included both *.trk files in here:
> http://www.nmr.mgh.harvard.edu/~rdp20/drop/trk_slf/
> IIT2mean.trk was a template TRK downloaded from the tract_querier example (
> http://tract-querier.readthedocs.io/en/latest/ ) and
> ADRC_TMP_wholeBrain.trk.gz was generated using dsi_studio (
> http://dsi-studio.labsolver.org/)
>
> Thanks again,
>
> Rodrigo
>
>
>
> On Sep 21, 2017, at 3:46 PM, Eleftherios Garyfallidis <elef at indiana.edu>
> wrote:
>
> Hi Rodrigo,
>
> Whole brain SLR is a great idea! Thank you for your question.  :)
>
> Have in mind that nibabel.trackvis is the old API. Use
> nibabel.streamlines.load instead.
>
> Can you please share with me (off the list) your trk files so that I can
> correct your
> script?
>
> A wild guess is that you are not loading the streamlines correctly.
>
> Did you use DIPY to create those trks or another software?
>
> Also, another suggestion is to run QuickBundles first to reduce the size
> of your
> datasets. I mean before starting the SLR.
>
> Here is a sample script (in one of my branches).
>
> https://github.com/Garyfallidis/dipy/blob/recobundles/dipy/workflows/align.py
>
> We are working on making something similar available asap in master.
>
> Best,
> Eleftherios
>
>
>
> On Thu, Sep 21, 2017 at 1:36 AM Perea Camargo, Rodrigo Dennis <
> RPEREACAMARGO at mgh.harvard.edu> wrote:
>
> Hi Eleftherios & DIPY community,
>> I am trying to register two whole brain tracts as shown in your recent
>> publication using the streamline-based linear registration. I am diving
>> into Dipy now and I might have some problems loading the files or
>> registering them.
>> Following your example (
>> http://nipy.org/dipy/examples_built/bundle_registration.html#example-bundle-registration),
>> there are 2 issues that may arise when I try it.
>>
>> 1) How to load *.trk files is not shown in your example (it looks like
>> you added to cingulum bundles from your dataset using the dipy.data value?)
>> So I follow this tutorial (
>> http://nipy.org/dipy/examples_built/streamline_formats.html ) to load
>> the streamline and hdr but I am not sure if this is the format SLF() wants
>> it in.
>>
>> 2) So then I try using the srr.optimize( ) function but I get the
>> following problems (check below).
>>
>> I hope you can help me.
>> Rodrigo
>>
>>
>> Here is my Jupyter notebook with the errors I found (any help will be
>> greatly appreciate it):
>>
>>
>>
>>
>>
>>
>> The information in this e-mail is intended only for the person to whom it
>> is
>> addressed. If you believe this e-mail was sent to you in error and the
>> e-mail
>> contains patient information, please contact the Partners Compliance
>> HelpLine at
>> http://www.partners.org/complianceline . If the e-mail was sent to you
>> in error
>> but does not contain patient information, please contact the sender and
>> properly
>> dispose of the e-mail.
>>
> _______________________________________________
>> Neuroimaging mailing list
>> Neuroimaging at python.org
>> https://mail.python.org/mailman/listinfo/neuroimaging
>>
> <PastedGraphic-1.png><PastedGraphic-1.png>
> _______________________________________________
>
>
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging
>
> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging
>
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From RPEREACAMARGO at mgh.harvard.edu  Tue Oct 17 21:14:19 2017
From: RPEREACAMARGO at mgh.harvard.edu (Perea Camargo, Rodrigo Dennis)
Date: Wed, 18 Oct 2017 01:14:19 +0000
Subject: [Neuroimaging] [DIPY] Question about whole tracks *.trk
 registration using SLR
In-Reply-To: <CAN6JmWP3GZyDh97bufT9twH9aTqCwAeDA=3zFdTmz_t4OBE_YA@mail.gmail.com>
References: <63C189EA-8788-4FB9-BDCA-5D3DAC191164@mgh.harvard.edu>
 <CAN6JmWOe=JcSCkKa81B3-tO6R2nyqHQTPAG=h9kRbBvOgRvt5g@mail.gmail.com>
 <DCF83CCC-DAF6-47CD-96F0-2A8308CEEE0A@mgh.harvard.edu>
 <CAN6JmWP3GZyDh97bufT9twH9aTqCwAeDA=3zFdTmz_t4OBE_YA@mail.gmail.com>
Message-ID: <CY4PR04MB048654144A20A0AFB70FA321F34D0@CY4PR04MB0486.namprd04.prod.outlook.com>

Hey Eleftherios,
I tried the code and it works! ?
(Time to digest/adapt some some python code now? ).
Since I just started poking some python code (and for other new users in case needed), I had to install the vtk package (?conda install -c clinicalgraphics vtk?)  within python 3 (anaconda dist).

Also, as suggested I `pip --upgrade nibabel` and ` pip --upgrade dipy`

Thank you!
Rodrigo

From: Neuroimaging [mailto:neuroimaging-bounces+rpereacamargo=mgh.harvard.edu at python.org] On Behalf Of Eleftherios Garyfallidis
Sent: Sunday, October 15, 2017 10:28 PM
To: Neuroimaging analysis in Python <neuroimaging at python.org>
Subject: Re: [Neuroimaging] [DIPY] Question about whole tracks *.trk registration using SLR

Hi Rodrigo,

Apologies for the delay. But hey I got a solution for you! I hope you are excited :)
Look at this gist
https://gist.github.com/Garyfallidis/51e34aab47de99eafa887b2b818384ea

This code shows how to do step by step whole brain SLR and I made it work with the
specific datasets that you gave me.

The specific datasets don't need exactly a whole brain SLR because the one brain has much
fewer structures than the other brain but still SLR is robust to incomplete datasets such as these.

Give it a go and give us feedback! :)

All the best,
Eleftherios

p.s. Notice that to load the streamlines I used a more recent API than what you originally used. Make sure you have a recent version of dipy and nibabel.

On Mon, Sep 25, 2017 at 9:50 AM Perea Camargo, Rodrigo Dennis <RPEREACAMARGO at mgh.harvard.edu<mailto:RPEREACAMARGO at mgh.harvard.edu>> wrote:
Hi Eleftherios,
I am included both *.trk files in here: http://www.nmr.mgh.harvard.edu/~rdp20/drop/trk_slf/
IIT2mean.trk was a template TRK downloaded from the tract_querier example (http://tract-querier.readthedocs.io/en/latest/ ) and ADRC_TMP_wholeBrain.trk.gz was generated using dsi_studio (http://dsi-studio.labsolver.org/)

Thanks again,

Rodrigo



On Sep 21, 2017, at 3:46 PM, Eleftherios Garyfallidis <elef at indiana.edu<mailto:elef at indiana.edu>> wrote:

Hi Rodrigo,

Whole brain SLR is a great idea! Thank you for your question.  :)

Have in mind that nibabel.trackvis is the old API. Use nibabel.streamlines.load instead.

Can you please share with me (off the list) your trk files so that I can correct your
script?

A wild guess is that you are not loading the streamlines correctly.

Did you use DIPY to create those trks or another software?

Also, another suggestion is to run QuickBundles first to reduce the size of your
datasets. I mean before starting the SLR.

Here is a sample script (in one of my branches).
https://github.com/Garyfallidis/dipy/blob/recobundles/dipy/workflows/align.py

We are working on making something similar available asap in master.

Best,
Eleftherios



On Thu, Sep 21, 2017 at 1:36 AM Perea Camargo, Rodrigo Dennis <RPEREACAMARGO at mgh.harvard.edu<mailto:RPEREACAMARGO at mgh.harvard.edu>> wrote:
Hi Eleftherios & DIPY community,
I am trying to register two whole brain tracts as shown in your recent publication using the streamline-based linear registration. I am diving into Dipy now and I might have some problems loading the files or registering them.
Following your example ( http://nipy.org/dipy/examples_built/bundle_registration.html#example-bundle-registration), there are 2 issues that may arise when I try it.

1) How to load *.trk files is not shown in your example (it looks like you added to cingulum bundles from your dataset using the dipy.data value?) So I follow this tutorial (http://nipy.org/dipy/examples_built/streamline_formats.html ) to load the streamline and hdr but I am not sure if this is the format SLF() wants it in.

2) So then I try using the srr.optimize( ) function but I get the following problems (check below).

I hope you can help me.
Rodrigo


Here is my Jupyter notebook with the errors I found (any help will be greatly appreciate it):







The information in this e-mail is intended only for the person to whom it is
addressed. If you believe this e-mail was sent to you in error and the e-mail
contains patient information, please contact the Partners Compliance HelpLine at
http://www.partners.org/complianceline . If the e-mail was sent to you in error
but does not contain patient information, please contact the sender and properly
dispose of the e-mail.
_______________________________________________
Neuroimaging mailing list
Neuroimaging at python.org<mailto:Neuroimaging at python.org>
https://mail.python.org/mailman/listinfo/neuroimaging
<PastedGraphic-1.png><PastedGraphic-1.png>_______________________________________________

Neuroimaging mailing list
Neuroimaging at python.org<mailto:Neuroimaging at python.org>
https://mail.python.org/mailman/listinfo/neuroimaging
_______________________________________________
Neuroimaging mailing list
Neuroimaging at python.org<mailto:Neuroimaging at python.org>
https://mail.python.org/mailman/listinfo/neuroimaging
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From tpengo at umn.edu  Wed Oct 18 11:03:12 2017
From: tpengo at umn.edu (Thomas Pengo)
Date: Wed, 18 Oct 2017 10:03:12 -0500
Subject: [Neuroimaging] [Job] Neuroimaging job at U of Minnesota
Message-ID: <CAA0niGrCAXYRbis6fhAG0dQ7TFNe2zKp_UhkueQtHN3Hao3v5Q@mail.gmail.com>

Dear all

the University of Minnesota Informatics Institute (UMII)
<https://research.umn.edu/units/umii> seeks a skilled, staff scientist to
serve the MR Neuroimaging research community at the University of
Minnesota. The successful candidate is expected to:

   1. carry out consultations with researchers across the University and
   serve in the role of a staff scientist who provides technical assistance
   across multiple research groups
   2. provide technical and analytic support in designing and supporting
   neuroimaging analysis pipelines that will be run on platforms of the
   Minnesota Supercomputing Institute (MSI)
   3. appropriately document work so that researchers are able to prepare
   grants, research papers and reports based on the analysis
   4. simultaneously support different versions of analysis tools that
   might require different libraries and operating system versions
   5. develop new computational environments, such as Docker, for major
   neuroimaging analysis platforms (AFNI, FSL, etc.) in the HPC environment at
   MSI; and
   6. build computational workflows that scale and work across a broad
   range of file system storage.

The successful candidate will join a growing group of imaging analysts in
the University of Minnesota Informatics Institute. The Imaging Informatics
Manager at the University of Minnesota Informatics Institute will serve as
the supervisor. On a daily basis, the analyst will work closely with MR
neuroimaging researchers, and staff at the Minnesota Supercomputing
Institute.

This position is both posted as a Researcher 5
<https://z.umn.edu/umii-neuroimaging-job-5>, Research Scientist (minimum
required qualification: PhD) under Job Opening 319904
<https://z.umn.edu/umii-neuroimaging-job-5> (
https://z.umn.edu/umii-neuroimaging-job-5) and as a Researcher 3
<https://z.umn.edu/umii-neuroimaging-job-3>, Assistant Scientist, (minimum
required qualification: Bachelor's degree) under Job Opening 319899
<https://z.umn.edu/umii-neuroimaging-job-3> (
https://z.umn.edu/umii-neuroimaging-job-3).

-- Qualifications

Required:

? Background in scientific image processing, analysis or multi-dimensional
signal processing

? Have proficiency in at least one programming language (Python, MATLAB,
Java, C++, ?)

? Be familiar with the Linux operating system

? Have excellent interpersonal and organizational skills

? Be able to articulate processes verbally and in writing

? Be highly motivated to learn new technical skills

Additional requirements for the Researcher 5 position (Job Opening 319904):

? Doctoral degree in a science or engineering field

? Experience in MR neuroimaging image analysis (fMRI, DWI, or structural)

? Significant research experience and ability to work independently

? Experience working in a team environment with people with diverse
expertise

? Publications, software, or grant applications demonstrating creativity
and independence

Preferred:

? (for Researcher 3 position) Master?s degree in a science or engineering
field

? (for Researcher 3 position) Experience in MR neuroimaging image analysis
(fMRI, DWI, structural)

? (for both positions) Experience in medical image analysis

? (for both positions) Experience with container technology, such as Docker

? (for both positions) Experience in HPC environments

? (for both positions) Experience with neuroimaging analysis software
packages such as FSL, AFNI, SPM

? (for both positions) Experience working in a team environment with people
with diverse expertise



________________________________________

Dr. Thomas Pengo
Imaging Informatics Manager
University of Minnesota Informatics Institute
University of Minnesota, Twin Cities Campus
Cancer and Cardiovascular Research Building
2231 6th St SE
Minneapolis, MN 55455
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From alexandre.gramfort at inria.fr  Fri Oct 20 03:10:39 2017
From: alexandre.gramfort at inria.fr (Alexandre Gramfort)
Date: Fri, 20 Oct 2017 09:10:39 +0200
Subject: [Neuroimaging] [ANN] MNE-Python 0.15
Message-ID: <CADeotZowh9aOT36ETOrTAcmFfecdm-4OmmaR8pP7_Ve_7bm2nQ@mail.gmail.com>

Hi,

We are very pleased to announce the new 0.15 release of MNE-Python. This
release comes with new features, bug fixes, and many improvements to
usability, visualization, and documentation.

A few highlights

============

   -

   We reinvented our documentation. Our website now unifies tutorials,
   examples and background information into one coherent narrative structure
   while preserving context. Check it out
   <http://martinos.org/mne/stable/documentation.html>!


   -

   Add mne.decoding.cross_val_multiscore()
   <http://mne-tools.github.io/dev/generated/mne.decoding.cross_val_multiscore.html#mne.decoding.cross_val_multiscore>
   to allow scoring of multiple tasks, typically used with the new
   mne.decoding.SlidingEstimator
   <http://mne-tools.github.io/dev/generated/mne.decoding.SlidingEstimator.html#mne.decoding.SlidingEstimator>
   -

   Add mne.decoding.ReceptiveField
   <http://mne-tools.github.io/dev/generated/mne.decoding.ReceptiveField.html#mne.decoding.ReceptiveField>
   module for modeling neural responses to continuous stimulation
   -

   Add mne.decoding.SPoC
   <http://mne-tools.github.io/dev/generated/mne.decoding.SPoC.html#mne.decoding.SPoC>
   to fit and apply spatial filters based on continuous target variables
   -

   mne.io.Raw.plot()
   <http://mne-tools.github.io/dev/generated/mne.io.Raw.html#mne.io.Raw.plot>
   butterfly mode (toggled with ?b? key)
   -

   IO support for EGI MFF format
   -

   mne.fit_dipole()
   <http://mne-tools.github.io/dev/generated/mne.fit_dipole.html#mne.fit_dipole>
   confidence intervals, number of free parameters, and ??
   -

   Add mne.VectorSourceEstimate
   <http://mne-tools.github.io/dev/generated/mne.VectorSourceEstimate.html#mne.VectorSourceEstimate>
   class which enables working with both source power and dipole orientations;
   use option pick_ori='vector' to mne.minimum_norm.apply_inverse()
   -

   New high-frequency somatosensory MEG dataset
   -

   Add unit-noise-gain beamformer and neural activity index (weight
   normalization) to LCMV beamformer with weight_norm parameter
   -

   Add filtering functions mne.Epochs.filter()
   <https://mne-tools.github.io/dev/generated/mne.Epochs.html#mne.Epochs.filter>
   and mne.Evoked.filter()
   <https://mne-tools.github.io/dev/generated/mne.Evoked.html#mne.Evoked.filter>,
   as well as pad argument to mne.io.Raw.filter()
   <https://mne-tools.github.io/dev/generated/mne.io.Raw.html#mne.io.Raw.filter>
   -

   Enable morphing between hemispheres with mne.compute_morph_matrix()
   <https://mne-tools.github.io/dev/generated/mne.compute_morph_matrix.html#mne.compute_morph_matrix>
   -

   Add interactive time cursor and category/amplitude status message in
   window for evoked plot
   -

   We exposed a rank parameter in mne.viz.evoked.plot_evoked_white()
   <http://martinos.org/mne/dev/generated/mne.viz.plot_evoked_white.html#mne.viz.plot_evoked_white>
   that allows for correcting the scaling of the visualization on the spot in
   cases where the rank estimate of the covariance is not accurate (for
   certain SSS?d data)


Notable API changes

================

   -

   ICA channel names have now been reformatted to start from zero, e.g.
   "ICA000", to match indexing schemes in mne.preprocessing.ICA
   <http://mne-tools.github.io/dev/generated/mne.preprocessing.ICA.html#mne.preprocessing.ICA>
   -

   Add skip_by_annotation to mne.io.Raw.filter()
   <https://mne-tools.github.io/dev/generated/mne.io.Raw.html#mne.io.Raw.filter>
   to process data concatenated with e.g. mne.concatenate_raws()
   <https://mne-tools.github.io/dev/generated/mne.concatenate_raws.html#mne.concatenate_raws>
   separately
   -

   Add new filtering mode fir_design='firwin' (default in the next 0.16
   release) that gets improved attenuation using fewer samples compared to
   fir_design='firwin2' (default in 0.15)
   -

   Add mne.beamformer.make_lcmv()
   <http://mne-tools.github.io/dev/generated/mne.beamformer.make_lcmv.html#mne.beamformer.make_lcmv>
   and mne.beamformer.apply_lcmv()
   <http://mne-tools.github.io/dev/generated/mne.beamformer.apply_lcmv.html#mne.beamformer.apply_lcmv>,
   mne.beamformer.apply_lcmv_epochs()
   <http://mne-tools.github.io/dev/generated/mne.beamformer.apply_lcmv_epochs.html#mne.beamformer.apply_lcmv_epochs>,
   and mne.beamformer.apply_lcmv_raw()
   <http://mne-tools.github.io/dev/generated/mne.beamformer.apply_lcmv_raw.html#mne.beamformer.apply_lcmv_raw>
   to enable the separate computation and application of LCMV beamformer
   weights
   -

   mne.set_eeg_reference()
   <http://mne-tools.github.io/dev/generated/mne.set_eeg_reference.html#mne.set_eeg_reference>
   and related methods (e.g. mne.io.Raw.set_eeg_reference()
   <http://mne-tools.github.io/dev/generated/mne.io.Raw.html#mne.io.Raw.set_eeg_reference>)
   have a new argument projection, which if set to False directly applies
   an average reference instead of adding an SSP projector
   -

   mne.find_events()
   <http://mne-tools.github.io/dev/generated/mne.find_events.html#mne.find_events>
   mask_type parameter will change from 'not_and' to 'and' d
   -

   picks parameter in mne.beamformer.lcmv()
   <http://mne-tools.github.io/dev/generated/mne.beamformer.lcmv.html#mne.beamformer.lcmv>,
   mne.beamformer.lcmv_epochs()
   <http://mne-tools.github.io/dev/generated/mne.beamformer.lcmv_epochs.html#mne.beamformer.lcmv_epochs>,
   mne.beamformer.lcmv_raw()
   <http://mne-tools.github.io/dev/generated/mne.beamformer.lcmv_raw.html#mne.beamformer.lcmv_raw>,
   mne.beamformer.tf_lcmv()
   <http://mne-tools.github.io/dev/generated/mne.beamformer.tf_lcmv.html#mne.beamformer.tf_lcmv>
   and mne.beamformer.rap_music()
   <http://mne-tools.github.io/dev/generated/mne.beamformer.rap_music.html#mne.beamformer.rap_music>
   is now deprecated
   -

   The keyword argument frequencies has been deprecated in favor of freqs
   in various time-frequency functions, e.g.
   mne.time_frequency.tfr_array_morlet()
   <http://mne-tools.github.io/dev/generated/mne.time_frequency.tfr_array_morlet.html#mne.time_frequency.tfr_array_morlet>
   -

   Deprecate force_fixed and surf_ori in mne.read_forward_solution()
   <http://mne-tools.github.io/dev/generated/mne.read_forward_solution.html#mne.read_forward_solution>
   -

   The behavior of 'mean_flip' label-flipping in
   mne.extract_label_time_course()
   <https://mne-tools.github.io/dev/generated/mne.extract_label_time_course.html#mne.extract_label_time_course>
   and related functions has been changed such that the flip, instead of
   having arbitrary sign, maximally aligns in the positive direction of the
   normals of the label


For a full list of improvements and API changes, see:

http://martinos.org/mne/stable/whats_new.html#version-0-15

To install the latest release the following command should do the job:

pip install --upgrade --user mne

As usual we welcome your bug reports, feature requests, critiques, and

contributions.

Some links:

- https://github.com/mne-tools/mne-python (code + readme on how to install)

- http://martinos.org/mne/stable/ (full MNE documentation)

Follow us on Twitter: https://twitter.com/mne_news

Regards,

The MNE-Python developers

People who contributed to this release (in alphabetical order):

* akshay0724

* Alejandro Weinstein

* Alexander Rudiuk

* Alexandre Barachant

* Alexandre Gramfort

* Andrew Dykstra

* Britta Westner

* Chris Bailey

* Chris Holdgraf

* Christian Brodbeck

* Christopher Holdgraf

* Clemens Brunner

* Crist?bal Mo?nne-Loccoz

* Daniel McCloy

* Daniel Strohmeier

* Denis A. Engemann

* Emily P. Stephen

* Eric Larson

* Fede Raimondo

* Jaakko Leppakangas

* Jean-Baptiste Schiratti

* Jean-Remi King

* Jesper Duemose Nielsen

* Joan Massich

* Jon Houck

* Jona Sassenhagen

* Jussi Nurminen

* Laetitia Grabot

* Laura Gwilliams

* Luke Bloy

* Luk?? Hejtm?nek

* Mainak Jas

* Marijn van Vliet

* Mathurin Massias

* Matt Boggess

* Mikolaj Magnuski

* Nicolas Barascud

* Nicole Proulx

* Phillip Alday

* Ramonapariciog Apariciogarcia

* Robin Tibor Schirrmeister

* Rodrigo H?bner

* S. M. Gutstein

* Simon Kern

* Teon Brooks

* Yousra Bekhti
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From satra at mit.edu  Mon Oct 23 12:26:43 2017
From: satra at mit.edu (Satrajit Ghosh)
Date: Mon, 23 Oct 2017 12:26:43 -0400
Subject: [Neuroimaging] [ANN] MNE-Python 0.15
In-Reply-To: <CADeotZowh9aOT36ETOrTAcmFfecdm-4OmmaR8pP7_Ve_7bm2nQ@mail.gmail.com>
References: <CADeotZowh9aOT36ETOrTAcmFfecdm-4OmmaR8pP7_Ve_7bm2nQ@mail.gmail.com>
Message-ID: <CA+A4wOmBipJSGtq1zZ9UNqPM9aB2_55Sk_-MmDFdp_+cmM0RiA@mail.gmail.com>

hello

congratulations. the documentation looks really nice!

cheers,

satra

On Fri, Oct 20, 2017 at 3:10 AM, Alexandre Gramfort <
alexandre.gramfort at inria.fr> wrote:

> Hi,
>
> We are very pleased to announce the new 0.15 release of MNE-Python. This
> release comes with new features, bug fixes, and many improvements to
> usability, visualization, and documentation.
>
> A few highlights
>
> ============
>
>    -
>
>    We reinvented our documentation. Our website now unifies tutorials,
>    examples and background information into one coherent narrative structure
>    while preserving context. Check it out
>    <http://martinos.org/mne/stable/documentation.html>!
>
>
>    -
>
>    Add mne.decoding.cross_val_multiscore()
>    <http://mne-tools.github.io/dev/generated/mne.decoding.cross_val_multiscore.html#mne.decoding.cross_val_multiscore>
>    to allow scoring of multiple tasks, typically used with the new
>    mne.decoding.SlidingEstimator
>    <http://mne-tools.github.io/dev/generated/mne.decoding.SlidingEstimator.html#mne.decoding.SlidingEstimator>
>    -
>
>    Add mne.decoding.ReceptiveField
>    <http://mne-tools.github.io/dev/generated/mne.decoding.ReceptiveField.html#mne.decoding.ReceptiveField>
>    module for modeling neural responses to continuous stimulation
>    -
>
>    Add mne.decoding.SPoC
>    <http://mne-tools.github.io/dev/generated/mne.decoding.SPoC.html#mne.decoding.SPoC>
>    to fit and apply spatial filters based on continuous target variables
>    -
>
>    mne.io.Raw.plot()
>    <http://mne-tools.github.io/dev/generated/mne.io.Raw.html#mne.io.Raw.plot>
>    butterfly mode (toggled with ?b? key)
>    -
>
>    IO support for EGI MFF format
>    -
>
>    mne.fit_dipole()
>    <http://mne-tools.github.io/dev/generated/mne.fit_dipole.html#mne.fit_dipole>
>    confidence intervals, number of free parameters, and ??
>    -
>
>    Add mne.VectorSourceEstimate
>    <http://mne-tools.github.io/dev/generated/mne.VectorSourceEstimate.html#mne.VectorSourceEstimate>
>    class which enables working with both source power and dipole orientations;
>    use option pick_ori='vector' to mne.minimum_norm.apply_inverse()
>    -
>
>    New high-frequency somatosensory MEG dataset
>    -
>
>    Add unit-noise-gain beamformer and neural activity index (weight
>    normalization) to LCMV beamformer with weight_norm parameter
>    -
>
>    Add filtering functions mne.Epochs.filter()
>    <https://mne-tools.github.io/dev/generated/mne.Epochs.html#mne.Epochs.filter>
>    and mne.Evoked.filter()
>    <https://mne-tools.github.io/dev/generated/mne.Evoked.html#mne.Evoked.filter>,
>    as well as pad argument to mne.io.Raw.filter()
>    <https://mne-tools.github.io/dev/generated/mne.io.Raw.html#mne.io.Raw.filter>
>    -
>
>    Enable morphing between hemispheres with mne.compute_morph_matrix()
>    <https://mne-tools.github.io/dev/generated/mne.compute_morph_matrix.html#mne.compute_morph_matrix>
>    -
>
>    Add interactive time cursor and category/amplitude status message in
>    window for evoked plot
>    -
>
>    We exposed a rank parameter in mne.viz.evoked.plot_evoked_white()
>    <http://martinos.org/mne/dev/generated/mne.viz.plot_evoked_white.html#mne.viz.plot_evoked_white>
>    that allows for correcting the scaling of the visualization on the spot in
>    cases where the rank estimate of the covariance is not accurate (for
>    certain SSS?d data)
>
>
> Notable API changes
>
> ================
>
>    -
>
>    ICA channel names have now been reformatted to start from zero, e.g.
>    "ICA000", to match indexing schemes in mne.preprocessing.ICA
>    <http://mne-tools.github.io/dev/generated/mne.preprocessing.ICA.html#mne.preprocessing.ICA>
>    -
>
>    Add skip_by_annotation to mne.io.Raw.filter()
>    <https://mne-tools.github.io/dev/generated/mne.io.Raw.html#mne.io.Raw.filter>
>    to process data concatenated with e.g. mne.concatenate_raws()
>    <https://mne-tools.github.io/dev/generated/mne.concatenate_raws.html#mne.concatenate_raws>
>    separately
>    -
>
>    Add new filtering mode fir_design='firwin' (default in the next 0.16
>    release) that gets improved attenuation using fewer samples compared to
>    fir_design='firwin2' (default in 0.15)
>    -
>
>    Add mne.beamformer.make_lcmv()
>    <http://mne-tools.github.io/dev/generated/mne.beamformer.make_lcmv.html#mne.beamformer.make_lcmv>
>    and mne.beamformer.apply_lcmv()
>    <http://mne-tools.github.io/dev/generated/mne.beamformer.apply_lcmv.html#mne.beamformer.apply_lcmv>,
>    mne.beamformer.apply_lcmv_epochs()
>    <http://mne-tools.github.io/dev/generated/mne.beamformer.apply_lcmv_epochs.html#mne.beamformer.apply_lcmv_epochs>,
>    and mne.beamformer.apply_lcmv_raw()
>    <http://mne-tools.github.io/dev/generated/mne.beamformer.apply_lcmv_raw.html#mne.beamformer.apply_lcmv_raw>
>    to enable the separate computation and application of LCMV beamformer
>    weights
>    -
>
>    mne.set_eeg_reference()
>    <http://mne-tools.github.io/dev/generated/mne.set_eeg_reference.html#mne.set_eeg_reference>
>    and related methods (e.g. mne.io.Raw.set_eeg_reference()
>    <http://mne-tools.github.io/dev/generated/mne.io.Raw.html#mne.io.Raw.set_eeg_reference>)
>    have a new argument projection, which if set to False directly applies
>    an average reference instead of adding an SSP projector
>    -
>
>    mne.find_events()
>    <http://mne-tools.github.io/dev/generated/mne.find_events.html#mne.find_events>
>    mask_type parameter will change from 'not_and' to 'and' d
>    -
>
>    picks parameter in mne.beamformer.lcmv()
>    <http://mne-tools.github.io/dev/generated/mne.beamformer.lcmv.html#mne.beamformer.lcmv>,
>    mne.beamformer.lcmv_epochs()
>    <http://mne-tools.github.io/dev/generated/mne.beamformer.lcmv_epochs.html#mne.beamformer.lcmv_epochs>,
>    mne.beamformer.lcmv_raw()
>    <http://mne-tools.github.io/dev/generated/mne.beamformer.lcmv_raw.html#mne.beamformer.lcmv_raw>,
>    mne.beamformer.tf_lcmv()
>    <http://mne-tools.github.io/dev/generated/mne.beamformer.tf_lcmv.html#mne.beamformer.tf_lcmv>
>    and mne.beamformer.rap_music()
>    <http://mne-tools.github.io/dev/generated/mne.beamformer.rap_music.html#mne.beamformer.rap_music>
>    is now deprecated
>    -
>
>    The keyword argument frequencies has been deprecated in favor of freqs
>    in various time-frequency functions, e.g.
>    mne.time_frequency.tfr_array_morlet()
>    <http://mne-tools.github.io/dev/generated/mne.time_frequency.tfr_array_morlet.html#mne.time_frequency.tfr_array_morlet>
>    -
>
>    Deprecate force_fixed and surf_ori in mne.read_forward_solution()
>    <http://mne-tools.github.io/dev/generated/mne.read_forward_solution.html#mne.read_forward_solution>
>    -
>
>    The behavior of 'mean_flip' label-flipping in
>    mne.extract_label_time_course()
>    <https://mne-tools.github.io/dev/generated/mne.extract_label_time_course.html#mne.extract_label_time_course>
>    and related functions has been changed such that the flip, instead of
>    having arbitrary sign, maximally aligns in the positive direction of the
>    normals of the label
>
>
> For a full list of improvements and API changes, see:
>
> http://martinos.org/mne/stable/whats_new.html#version-0-15
>
> To install the latest release the following command should do the job:
>
> pip install --upgrade --user mne
>
> As usual we welcome your bug reports, feature requests, critiques, and
>
> contributions.
>
> Some links:
>
> - https://github.com/mne-tools/mne-python (code + readme on how to
> install)
>
> - http://martinos.org/mne/stable/ (full MNE documentation)
>
> Follow us on Twitter: https://twitter.com/mne_news
>
> Regards,
>
> The MNE-Python developers
>
> People who contributed to this release (in alphabetical order):
>
> * akshay0724
>
> * Alejandro Weinstein
>
> * Alexander Rudiuk
>
> * Alexandre Barachant
>
> * Alexandre Gramfort
>
> * Andrew Dykstra
>
> * Britta Westner
>
> * Chris Bailey
>
> * Chris Holdgraf
>
> * Christian Brodbeck
>
> * Christopher Holdgraf
>
> * Clemens Brunner
>
> * Crist?bal Mo?nne-Loccoz
>
> * Daniel McCloy
>
> * Daniel Strohmeier
>
> * Denis A. Engemann
>
> * Emily P. Stephen
>
> * Eric Larson
>
> * Fede Raimondo
>
> * Jaakko Leppakangas
>
> * Jean-Baptiste Schiratti
>
> * Jean-Remi King
>
> * Jesper Duemose Nielsen
>
> * Joan Massich
>
> * Jon Houck
>
> * Jona Sassenhagen
>
> * Jussi Nurminen
>
> * Laetitia Grabot
>
> * Laura Gwilliams
>
> * Luke Bloy
>
> * Luk?? Hejtm?nek
>
> * Mainak Jas
>
> * Marijn van Vliet
>
> * Mathurin Massias
>
> * Matt Boggess
>
> * Mikolaj Magnuski
>
> * Nicolas Barascud
>
> * Nicole Proulx
>
> * Phillip Alday
>
> * Ramonapariciog Apariciogarcia
>
> * Robin Tibor Schirrmeister
>
> * Rodrigo H?bner
>
> * S. M. Gutstein
>
> * Simon Kern
>
> * Teon Brooks
>
> * Yousra Bekhti
>
>
>
> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging
>
>
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From RPEREACAMARGO at mgh.harvard.edu  Mon Oct 23 19:30:03 2017
From: RPEREACAMARGO at mgh.harvard.edu (Perea Camargo, Rodrigo Dennis)
Date: Mon, 23 Oct 2017 23:30:03 +0000
Subject: [Neuroimaging] [DIPY] Question about whole tracks *.trk
 registration using SLR
In-Reply-To: <CAN6JmWP3GZyDh97bufT9twH9aTqCwAeDA=3zFdTmz_t4OBE_YA@mail.gmail.com>
References: <63C189EA-8788-4FB9-BDCA-5D3DAC191164@mgh.harvard.edu>
 <CAN6JmWOe=JcSCkKa81B3-tO6R2nyqHQTPAG=h9kRbBvOgRvt5g@mail.gmail.com>
 <DCF83CCC-DAF6-47CD-96F0-2A8308CEEE0A@mgh.harvard.edu>
 <CAN6JmWP3GZyDh97bufT9twH9aTqCwAeDA=3zFdTmz_t4OBE_YA@mail.gmail.com>
Message-ID: <C826F6C7-91B7-45CD-B31D-E2C270E70941@mgh.harvard.edu>

Hi Eleftherios and Dipy Community,

I believe my registration is working properly (yay, thanks!). Now I have the following issues.


  1.  After looking at some documentation, I tried to save the moved streamlines using the following code:
```
sname= ?move_trk.trk?
sname = dname + sname1
from nibabel.streamlines import save
from nibabel.streamlines import  Tractogram
save(Tractogram(centroids2, affine_to_rasmm=cur_matrix),cname)

```
I was able to save the moved streamlines but when I load them up in dsi_studio, they do not overlap with each other (as the window.show(ren) shows). I believe the problem might be with the 2nd argument when using save() [ affine_to_rasmm=??].

So my question is, how can I save the streamlines to be in the same space as the ref_sstr? I tried different matrices provided by the initial vox_to_ras in the ref_sstr or the mov_sstr rather than an identity matrix but this doesn?t seem to solve my question :/


  1.  Given that this will work, I am now confused on how could I apply this transformation and extract diffusivity metrics (e.g. FA, AxD, RD, etc?) in the registered space to a template? Should I apply a  matrix transformation to my niftii images?

Thanks in advance,

Rodrigo




From: Neuroimaging <neuroimaging-bounces+rpereacamargo=mgh.harvard.edu at python.org> on behalf of Eleftherios Garyfallidis <elef at indiana.edu>
Reply-To: Neuroimaging analysis in Python <neuroimaging at python.org>
Date: Sunday, October 15, 2017 at 10:29 PM
To: Neuroimaging analysis in Python <neuroimaging at python.org>
Subject: Re: [Neuroimaging] [DIPY] Question about whole tracks *.trk registration using SLR

Hi Rodrigo,

Apologies for the delay. But hey I got a solution for you! I hope you are excited :)
Look at this gist
https://gist.github.com/Garyfallidis/51e34aab47de99eafa887b2b818384ea

This code shows how to do step by step whole brain SLR and I made it work with the
specific datasets that you gave me.

The specific datasets don't need exactly a whole brain SLR because the one brain has much
fewer structures than the other brain but still SLR is robust to incomplete datasets such as these.

Give it a go and give us feedback! :)

All the best,
Eleftherios

p.s. Notice that to load the streamlines I used a more recent API than what you originally used. Make sure you have a recent version of dipy and nibabel.

On Mon, Sep 25, 2017 at 9:50 AM Perea Camargo, Rodrigo Dennis <RPEREACAMARGO at mgh.harvard.edu<mailto:RPEREACAMARGO at mgh.harvard.edu>> wrote:
Hi Eleftherios,
I am included both *.trk files in here: http://www.nmr.mgh.harvard.edu/~rdp20/drop/trk_slf/
IIT2mean.trk was a template TRK downloaded from the tract_querier example (http://tract-querier.readthedocs.io/en/latest/ ) and ADRC_TMP_wholeBrain.trk.gz was generated using dsi_studio (http://dsi-studio.labsolver.org/)

Thanks again,

Rodrigo



On Sep 21, 2017, at 3:46 PM, Eleftherios Garyfallidis <elef at indiana.edu<mailto:elef at indiana.edu>> wrote:

Hi Rodrigo,

Whole brain SLR is a great idea! Thank you for your question.  :)

Have in mind that nibabel.trackvis is the old API. Use nibabel.streamlines.load instead.

Can you please share with me (off the list) your trk files so that I can correct your
script?

A wild guess is that you are not loading the streamlines correctly.

Did you use DIPY to create those trks or another software?

Also, another suggestion is to run QuickBundles first to reduce the size of your
datasets. I mean before starting the SLR.

Here is a sample script (in one of my branches).
https://github.com/Garyfallidis/dipy/blob/recobundles/dipy/workflows/align.py

We are working on making something similar available asap in master.

Best,
Eleftherios



On Thu, Sep 21, 2017 at 1:36 AM Perea Camargo, Rodrigo Dennis <RPEREACAMARGO at mgh.harvard.edu<mailto:RPEREACAMARGO at mgh.harvard.edu>> wrote:
Hi Eleftherios & DIPY community,
I am trying to register two whole brain tracts as shown in your recent publication using the streamline-based linear registration. I am diving into Dipy now and I might have some problems loading the files or registering them.
Following your example ( http://nipy.org/dipy/examples_built/bundle_registration.html#example-bundle-registration), there are 2 issues that may arise when I try it.

1) How to load *.trk files is not shown in your example (it looks like you added to cingulum bundles from your dataset using the dipy.data value?) So I follow this tutorial (http://nipy.org/dipy/examples_built/streamline_formats.html ) to load the streamline and hdr but I am not sure if this is the format SLF() wants it in.

2) So then I try using the srr.optimize( ) function but I get the following problems (check below).

I hope you can help me.
Rodrigo


Here is my Jupyter notebook with the errors I found (any help will be greatly appreciate it):







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http://www.partners.org/complianceline . If the e-mail was sent to you in error
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dispose of the e-mail.
_______________________________________________
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From krzysztof.gorgolewski at gmail.com  Mon Oct 23 20:52:11 2017
From: krzysztof.gorgolewski at gmail.com (Chris Gorgolewski)
Date: Mon, 23 Oct 2017 17:52:11 -0700
Subject: [Neuroimaging] [job] Program Manager for the Brain Imaging Data
 Structure project
Message-ID: <CAAQzouMzdb_t+XeR77_2BcX5Mm31e+CxG18wpWPO0jchCezE9g@mail.gmail.com>

Do you have great people skills and enjoy organizing events? Are you
interested in helping neuroscientists exchange data in a more useful way?
The Brain Imaging Data Structure project is looking for a full time Program
Manager - this might be a perfect job for you!

No neuroimaging experience is required, but some will come handy. This
position might be especially attractive if you appreciate working in an
academic environment, but prefer to avoid the stress of ?publish or perish?
and enjoy a competitive salary.

The position is based in the legendarily sunny San Francisco Bay Area. The
successful candidate will receive relocation support.

More details at
https://stanford.taleo.net/careersection/2/jobdetail.ftl?job=76713&lang=en#.WeUP-AdO9j4.email.
We will start reviewing applications on the 6th of November.

Best regards,
Chris Gorgolewski

PS Apologies for cross posting - please forward this ad if you know someone
who might be interested.
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From vinit.k.srivastava at gmail.com  Tue Oct 24 21:36:51 2017
From: vinit.k.srivastava at gmail.com (Vinit Srivastava)
Date: Tue, 24 Oct 2017 21:36:51 -0400
Subject: [Neuroimaging] Affine 3D registration in Dipy
Message-ID: <CACUgD2ykoo9zSSdC8TfvDFFsJudnyqhV1+iTt2H4u=NmoULB7w@mail.gmail.com>

Hi,

I am attempting to coregister a moving T1 image to a static reference b0
volume by performing affine registration in 3D (I'm following the Dipy
tutorial but using my own data).  Everything goes well until running
'resampled = affine.map.transform(moving)'

The following error message appears:

 File "C:\Users\Vinit\Anaconda2\lib\site-packages\dipy\align\imaffine.py",
line 267, in _apply_transform
    transformed = _transform_method[(dim, interp)](image, shape, comp)

KeyError: (4, 'linear')


Any help would be really appreciated.

Thanks,

Vinny
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From satra at mit.edu  Wed Oct 25 18:24:15 2017
From: satra at mit.edu (Satrajit Ghosh)
Date: Wed, 25 Oct 2017 18:24:15 -0400
Subject: [Neuroimaging] Fwd: Neuroimaing/neuroinformatics Position at Harvard
Message-ID: <CA+A4wO=8-gV2E_z_NLmXDcohuFaYoG1iDCa30Vn6SHY_wO7akQ@mail.gmail.com>

fyi

---------- Forwarded message ----------
From: Bouix, Sylvain,Ph.D. <SYLVAIN at bwh.harvard.edu>
Date: Wed, Oct 25, 2017 at 4:20 PM
Subject: Neuroimaing/neuroinformatics Position at Harvard
To: Satrajit Ghosh <satra at mit.edu>


Dear Satra,



I am Associate Director of the Psychiatry Neuroimaging Laboratory at BWH
where I lead a number of efforts in designing image analysis algorithms and
processing pipelines for various neuroimaging projects including one of the
HCP disease U01. I am in need of a research engineer interested in
designing and maintaining robust pipelines. I was wondering if you?d be
willing to share in your circles.

Here is the job posting:



https://partners.taleo.net/careersection/jobdetail.ftl?
job=3050462&lang=en#.WfDstC-W33w.email



Thank you,

Sylvain





Sylvain Bouix, Ph.D.

Associate Director, Psychiatry Neuroimaging Laboratory,

Assistant Professor, Department of Psychiatry,

Brigham and Women?s Hospital,

Harvard Medical School, Boston, MA.

Tel: 617-525-6233 <(617)%20525-6233>

Fax: 617-525-6170 <(617)%20525-6170>



The information in this e-mail is intended only for the person to whom it is
addressed. If you believe this e-mail was sent to you in error and the
e-mail
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From vinit.k.srivastava at gmail.com  Thu Oct 26 08:05:10 2017
From: vinit.k.srivastava at gmail.com (Vinit Srivastava)
Date: Thu, 26 Oct 2017 08:05:10 -0400
Subject: [Neuroimaging] Reorienting bvecs in Dipy
Message-ID: <CACUgD2wE3JmoENxXK-VTmVJF1EH6-07x0woRq_C37CGTnVv90A@mail.gmail.com>

Hi all,

I used FSL-FLIRT to coregister the diffusion-weighted imaging series to the
anatomical T1 template, which also outputted an affine transformation
matrix (as a .mat file).  I'd like to reorient the bvecs/b-matrix table to
compensate for any rotations and translations performed during the
coregistration process for downstream tractography application.

I understand that Dipy offers such a function to reorient bvecs to
compensate for motion.  However, I do not know how to use the outputted
affine matrix from FSL as an input for the affines argument in the function
reorient_bvecs(gtab, affines).  I get the following error:

'Number of affine transformations must match number of non-zero gradients'

I'm guessing that the argument affines requires 'n' number of affine
matrices?  If so, do I need to modify the FSL outputted affine 4x4 matrix
in some way?

Thanks for your help,

Vinny
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From effigies at bu.edu  Tue Oct 31 21:13:55 2017
From: effigies at bu.edu (Christopher Markiewicz)
Date: Tue, 31 Oct 2017 21:13:55 -0400
Subject: [Neuroimaging] RFC: Changes to nibabel API for .mgh/.mgz files
Message-ID: <CAHE-diOCJToz1ju=pLgghXuRr9aWHNaMR85Q3JeDWDbPHh8bpA@mail.gmail.com>

Hi all,

I'd like to solicit input for the Python interface to .mgh/.mgz files in
nibabel[0].

I've recently had cause to dig into this interface (MGHImage[1]), and found
that the naming of header fields[2] (the low-level interface to the raw
binary data) is inconsistent with all of my experience with how FreeSurfer
refers to these fields in the code (MRI_IMAGE[3]) as well as in the outputs
of many programs, such as mri_info. (In fact, the current names seem to
reflect the intermediate variables used in load/save_mgh.m and the
description of the affine transforms in the FS Coordinates powerpoints[4].)

I'm proposing (https://github.com/nipy/nibabel/pull/569) an API change in
nibabel, with field names[5] that more closely reflect what I deem to be
common FreeSurfer usage (although it does not adhere precisely to the C
structure fields). Given that, it was felt that the Python-using FreeSurfer
community more broadly should have some say in the final API. To put a few
specific questions: Do you depend on the current MGHHeader field names?
Would you be averse to updating the field names? Are there alternatives to
my proposal you would find preferable?

While I would be willing to discuss in more detail on this list, I would
prefer (if reasonably convenient) discussion to remain on the Github pull
request as much as possible, so participants can follow easily.

Thanks,
Chris Markiewicz

[0] http://nipy.org/nibabel/
[1]
https://github.com/nipy/nibabel/blob/master/nibabel/freesurfer/mghformat.py
[2]
https://github.com/nipy/nibabel/blob/2139ce0d24e65a83295bb6b3eaaf005eaeaebb5f/nibabel/freesurfer/mghformat.py#L28-L35
[3]
https://github.com/freesurfer/freesurfer/blob/master/include/mri.h#L157-L252
[4] https://surfer.nmr.mgh.harvard.edu/fswiki/CoordinateSystems
[5]
https://github.com/effigies/nibabel/blob/55c9bf905ec8785617755f900635fc31bae43232/nibabel/freesurfer/mghformat.py#L30-L49
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