From gkiar07 at gmail.com  Tue Dec  1 12:55:30 2015
From: gkiar07 at gmail.com (Greg Kiar)
Date: Tue, 1 Dec 2015 12:55:30 -0500
Subject: [Neuroimaging] Aligning low and high resolution images in voxel
 space
Message-ID: <CAB7SS2vEC0DhuDh9q9QNmvWjjJucqhm+K4O_o-NGH6vjG3mHfg@mail.gmail.com>

Hi,

I'm working with various parcellations of the brain which have been defined
at a 3mm resolution, though my data exists in a 1mm space (the MNI152
space). When opening these images with a nifti viewer it is fine as the
affine transform maps them properly to scale. However, when I'm working
with data in python I would like my labels defined at 3mm to be in the
MNI152 space (that they also overlap in voxel space, as well). I would like
to write a script that "ingests" a low resolution (3mm) atlas into the 1mm
MNI152 space, and nearest-neighbor interpolates values not defined, if you
will. Do you know how I can easily do this?

Thanks so much,
Greg
--
Greg Kiar <gkiar07 at gmail.com>
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From arokem at gmail.com  Tue Dec  1 14:53:53 2015
From: arokem at gmail.com (Ariel Rokem)
Date: Tue, 1 Dec 2015 11:53:53 -0800
Subject: [Neuroimaging] Aligning low and high resolution images in voxel
 space
In-Reply-To: <CAB7SS2vEC0DhuDh9q9QNmvWjjJucqhm+K4O_o-NGH6vjG3mHfg@mail.gmail.com>
References: <CAB7SS2vEC0DhuDh9q9QNmvWjjJucqhm+K4O_o-NGH6vjG3mHfg@mail.gmail.com>
Message-ID: <CA+X4w0w4zUwGjMYAuCfjKf3BtB22zdiHSX-igVwMdTmNNuJi9Q@mail.gmail.com>

Hi Greg,

Using the current development version of dipy, or the
very-very-very-soon-to-be-released version 0.10 of the software, you can do
something like this:

https://gist.github.com/arokem/0aab282a7287245a7e78

Note that this does linear interpolation, but can easily be adjusted to use
nearest neighbor, by setting:

resampled = affine_map.transform(moving.get_data(), interp='nearest')

Cheers,

Ariel

On Tue, Dec 1, 2015 at 9:55 AM, Greg Kiar <gkiar07 at gmail.com> wrote:

> Hi,
>
> I'm working with various parcellations of the brain which have been
> defined at a 3mm resolution, though my data exists in a 1mm space (the
> MNI152 space). When opening these images with a nifti viewer it is fine as
> the affine transform maps them properly to scale. However, when I'm working
> with data in python I would like my labels defined at 3mm to be in the
> MNI152 space (that they also overlap in voxel space, as well). I would like
> to write a script that "ingests" a low resolution (3mm) atlas into the 1mm
> MNI152 space, and nearest-neighbor interpolates values not defined, if you
> will. Do you know how I can easily do this?
>
> Thanks so much,
> Greg
> --
> Greg Kiar <gkiar07 at gmail.com>
>
> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging
>
>
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From matthew.brett at gmail.com  Tue Dec  1 15:01:39 2015
From: matthew.brett at gmail.com (Matthew Brett)
Date: Tue, 1 Dec 2015 12:01:39 -0800
Subject: [Neuroimaging] Aligning low and high resolution images in voxel
 space
In-Reply-To: <CAB7SS2vEC0DhuDh9q9QNmvWjjJucqhm+K4O_o-NGH6vjG3mHfg@mail.gmail.com>
References: <CAB7SS2vEC0DhuDh9q9QNmvWjjJucqhm+K4O_o-NGH6vjG3mHfg@mail.gmail.com>
Message-ID: <CAH6Pt5rb2j=q84D0Txo2EQzqx6UsWr60amx7pUjBYMTQqCYQmA@mail.gmail.com>

Hi,

On Tue, Dec 1, 2015 at 9:55 AM, Greg Kiar <gkiar07 at gmail.com> wrote:
> Hi,
>
> I'm working with various parcellations of the brain which have been defined
> at a 3mm resolution, though my data exists in a 1mm space (the MNI152
> space). When opening these images with a nifti viewer it is fine as the
> affine transform maps them properly to scale. However, when I'm working with
> data in python I would like my labels defined at 3mm to be in the MNI152
> space (that they also overlap in voxel space, as well). I would like to
> write a script that "ingests" a low resolution (3mm) atlas into the 1mm
> MNI152 space, and nearest-neighbor interpolates values not defined, if you
> will. Do you know how I can easily do this?

Maybe something like:

vox2mm3 = img_3mm.affine
vox2mm1 = img_1mm.affine
vox1to3 = np.linalg.inv(vox2mm3).dot(vox2mm1)
mat, vec = nibabel.affines.to_matvec(vox1to3)
out = scipy.ndimage.affine_transform(img_3mm.get_data(), mat, vec, order=0)
out_img = nibabel.Nifti1Image(out, img_1mm.affine, img_1mm.header)
nibabel.save(out_img, 'my_resampled_regions.nii')

Cheers,

Matthew

From bertrand.thirion at inria.fr  Tue Dec  1 15:50:23 2015
From: bertrand.thirion at inria.fr (bthirion)
Date: Tue, 1 Dec 2015 21:50:23 +0100
Subject: [Neuroimaging] Aligning low and high resolution images in voxel
 space
In-Reply-To: <CAB7SS2vEC0DhuDh9q9QNmvWjjJucqhm+K4O_o-NGH6vjG3mHfg@mail.gmail.com>
References: <CAB7SS2vEC0DhuDh9q9QNmvWjjJucqhm+K4O_o-NGH6vjG3mHfg@mail.gmail.com>
Message-ID: <565E080F.7080308@inria.fr>

     Hi Greg,

You might take at look at |nilearn.image.||resample_img
https://nilearn.github.io/modules/generated/nilearn.image.resample_img.html
Best,

Bertrand
|
On 01/12/2015 18:55, Greg Kiar wrote:
> Hi,
>
> I'm working with various parcellations of the brain which have been 
> defined at a 3mm resolution, though my data exists in a 1mm space (the 
> MNI152 space). When opening these images with a nifti viewer it is 
> fine as the affine transform maps them properly to scale. However, 
> when I'm working with data in python I would like my labels defined at 
> 3mm to be in the MNI152 space (that they also overlap in voxel space, 
> as well). I would like to write a script that "ingests" a low 
> resolution (3mm) atlas into the 1mm MNI152 space, and nearest-neighbor 
> interpolates values not defined, if you will. Do you know how I can 
> easily do this?
>
> Thanks so much,
> Greg
> --
> Greg Kiar <gkiar07 at gmail.com <mailto:gkiar07 at gmail.com>>
>
>
> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging

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From matthew.brett at gmail.com  Tue Dec  1 17:05:12 2015
From: matthew.brett at gmail.com (Matthew Brett)
Date: Tue, 1 Dec 2015 14:05:12 -0800
Subject: [Neuroimaging] Aligning low and high resolution images in voxel
 space
In-Reply-To: <565E080F.7080308@inria.fr>
References: <CAB7SS2vEC0DhuDh9q9QNmvWjjJucqhm+K4O_o-NGH6vjG3mHfg@mail.gmail.com>
 <565E080F.7080308@inria.fr>
Message-ID: <CAH6Pt5poKU9B_s=TorvkHa2dnz4fb_-M91yai7ho+Wn0cd0PhA@mail.gmail.com>

On Tue, Dec 1, 2015 at 12:50 PM, bthirion <bertrand.thirion at inria.fr> wrote:
>     Hi Greg,
>
> You might take at look at nilearn.image.resample_img
> https://nilearn.github.io/modules/generated/nilearn.image.resample_img.html

Or indeed :

https://github.com/nipy/nipy/blob/master/nipy/algorithms/resample.py#L19

Or :

https://github.com/nipy/nireg/blob/master/nireg/resample.py#L34

Or :

https://github.com/nipy/nibabel/pull/255
https://github.com/nipy/nibabel/pull/255/files#diff-36210eae1f936c385adb1680895b7db6R114

Not our proudest moment ...

Cheers,

Matthew

From dimitri.papadopoulos at cea.fr  Wed Dec  2 09:45:27 2015
From: dimitri.papadopoulos at cea.fr (Dimitri Papadopoulos Orfanos)
Date: Wed, 2 Dec 2015 15:45:27 +0100
Subject: [Neuroimaging] [nipype] updated external mask handling in SPM
Message-ID: <565F0407.5010705@cea.fr>

Hi,

I have a question on the following commit:
https://github.com/nipy/nipype/commit/fe9d0e07a288afefb34e99f488ef194a443d6089

Could someone explain the rationale behind the addition of this piece of
code in nipype/interfaces/spm/model.py? We came across this code while
trying to reproduce the results obtained with an old version of SPM8 run
manually vs. the latest version of SPM8 run from Nipype.

        if isdefined(self.inputs.mask_image):
            # SPM doesn't handle explicit masking properly, especially
            # when you want to use the entire mask image
            postscript = "load SPM;\n"
            postscript += "SPM.xM.VM = spm_vol('%s');\n" %
list_to_filename(self.inputs.mask_image)
            postscript += "SPM.xM.I = 0;\n"
            postscript += "SPM.xM.T = [];\n"
            postscript += "SPM.xM.TH = ones(size(SPM.xM.TH))*(%s);\n" %
self.inputs.mask_threshold
            postscript += "SPM.xM.xs = struct('Masking', 'explicit
masking only');\n"
            postscript += "save SPM SPM;\n"

We have understood almost all causes for differences in results. The
only cause that remains to be explained is this Nipype hack. Has perhaps
this issue been discussed on the SPM mailing list?

Best,
-- 
Dimitri Papadopoulos
CEA/Saclay
I2BM, NeuroSpin
F-91191 Gif-sur-Yvette cedex, France

From garyfallidis at gmail.com  Tue Dec  1 15:14:45 2015
From: garyfallidis at gmail.com (Eleftherios Garyfallidis)
Date: Tue, 1 Dec 2015 15:14:45 -0500
Subject: [Neuroimaging] [dipy] Installation Problem
In-Reply-To: <CAEwP9aomvLBpX3Uw0qL3xLsH_SeWmLDxFd0JfheSd771sn6tCg@mail.gmail.com>
References: <CAEwP9aomvLBpX3Uw0qL3xLsH_SeWmLDxFd0JfheSd771sn6tCg@mail.gmail.com>
Message-ID: <CAN6JmWNLVWVn4zuf-zF5sJy_z6WW9NE+nSjvH+Uft+FQzeysCQ@mail.gmail.com>

Hi William,

If you install anaconda and then do pip install nibabel and the same for
dipy that should work in windows 7 without a problem.
My feeling is that you may have conflicting installations. Make sure that
you remove all previous versions and try again. If the problem persists
we may have some problem in our setup.py file with windows and python 3.5
which we have missed. If this is the case then we
need to write an issue repost and investigate this further.

Also, you should know that using python 3.5 you will not have VTK working
so no 3D visualization. This is because VTK is still not available
for python 3. If you want to use dipy for visualization of streamlines,
odfs etc. you will need to switch to anaconda for python 2.x and do
conda install vtk.

You can also have a look at these links and see if you can get some ideas
for fixing your problem.
https://groups.google.com/a/continuum.io/forum/#!topic/anaconda/6_reeaIjx5c
http://stackoverflow.com/questions/32366195/python-error-3-the-system-cannot-find-the-path-specified

Let us know how it goes and apologies for the delayed answer.

Cheers,
Eleftherios




On Fri, Nov 27, 2015 at 2:36 AM, William Liu <liuwill14 at gmail.com> wrote:

>
> Hi,
>
> I have been trying to install dipy on my Windows7, tried to download the
> dipy folder from the link then put it into the Python34 folder, then typed
> 'import dipy' on python, but it was no luck. Then I decided to use the
> Anaconda command line:
> pip install dipy
>
> but there was still an error like the one in this picture
>
> ?
> I have also checked that I've had the other libraries like nibabel
> installed. What problems may I have here?  Thank you in advance!
>
>
>
> Best Regards,
> William Liu
> Eletrical & Biomedical Engineering IV
>
>
> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging
>
>
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From gkiar07 at gmail.com  Tue Dec  1 17:03:29 2015
From: gkiar07 at gmail.com (Greg Kiar)
Date: Tue, 1 Dec 2015 17:03:29 -0500
Subject: [Neuroimaging] Aligning low and high resolution images in voxel
 space
In-Reply-To: <565E080F.7080308@inria.fr>
References: <CAB7SS2vEC0DhuDh9q9QNmvWjjJucqhm+K4O_o-NGH6vjG3mHfg@mail.gmail.com>
 <565E080F.7080308@inria.fr>
Message-ID: <CAB7SS2th3Uj6aToPejzcvGheUtfNOGf-bM8vdoordis-rmvt9A@mail.gmail.com>

This is perfect, thank you so much.

Cheers,
Greg

--
Greg Kiar <gkiar07 at gmail.com>

On Tue, Dec 1, 2015 at 3:50 PM, bthirion <bertrand.thirion at inria.fr> wrote:

>     Hi Greg,
>
> You might take at look at nilearn.image.resample_img
> https://nilearn.github.io/modules/generated/nilearn.image.resample_img.html
> Best,
>
> Bertrand
>
> On 01/12/2015 18:55, Greg Kiar wrote:
>
> Hi,
>
> I'm working with various parcellations of the brain which have been
> defined at a 3mm resolution, though my data exists in a 1mm space (the
> MNI152 space). When opening these images with a nifti viewer it is fine as
> the affine transform maps them properly to scale. However, when I'm working
> with data in python I would like my labels defined at 3mm to be in the
> MNI152 space (that they also overlap in voxel space, as well). I would like
> to write a script that "ingests" a low resolution (3mm) atlas into the 1mm
> MNI152 space, and nearest-neighbor interpolates values not defined, if you
> will. Do you know how I can easily do this?
>
> Thanks so much,
> Greg
> --
> Greg Kiar <gkiar07 at gmail.com>
>
>
> _______________________________________________
> Neuroimaging mailing listNeuroimaging at python.orghttps://mail.python.org/mailman/listinfo/neuroimaging
>
>
>
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From satra at mit.edu  Wed Dec  2 17:17:15 2015
From: satra at mit.edu (Satrajit Ghosh)
Date: Wed, 2 Dec 2015 17:17:15 -0500
Subject: [Neuroimaging] [nipype] updated external mask handling in SPM
In-Reply-To: <565F0407.5010705@cea.fr>
References: <565F0407.5010705@cea.fr>
Message-ID: <CA+A4wOnod8seyv9EXBHDzjfSxgPVBKALDNFUJoA_t3OmgMK5Nw@mail.gmail.com>

hi dimitri,

it's been a long while since those lines were written. but i believe this
was written as noted in the comments to support the case where the user
simply wanted spm to use the explicit mask. the other reason for it was
that there were several places that could control spm options (e.g., config
file). we did not want to rely on the config file.

i believe it was more of a perspective on what an explicit mask meant. i
can't remember if this was discussed on the spm list or came from best
practices in our lab at that time.

cheers,

satra

On Wed, Dec 2, 2015 at 9:45 AM, Dimitri Papadopoulos Orfanos <
dimitri.papadopoulos at cea.fr> wrote:

> Hi,
>
> I have a question on the following commit:
>
> https://github.com/nipy/nipype/commit/fe9d0e07a288afefb34e99f488ef194a443d6089
>
> Could someone explain the rationale behind the addition of this piece of
> code in nipype/interfaces/spm/model.py? We came across this code while
> trying to reproduce the results obtained with an old version of SPM8 run
> manually vs. the latest version of SPM8 run from Nipype.
>
>         if isdefined(self.inputs.mask_image):
>             # SPM doesn't handle explicit masking properly, especially
>             # when you want to use the entire mask image
>             postscript = "load SPM;\n"
>             postscript += "SPM.xM.VM = spm_vol('%s');\n" %
> list_to_filename(self.inputs.mask_image)
>             postscript += "SPM.xM.I = 0;\n"
>             postscript += "SPM.xM.T = [];\n"
>             postscript += "SPM.xM.TH = ones(size(SPM.xM.TH))*(%s);\n" %
> self.inputs.mask_threshold
>             postscript += "SPM.xM.xs = struct('Masking', 'explicit
> masking only');\n"
>             postscript += "save SPM SPM;\n"
>
> We have understood almost all causes for differences in results. The
> only cause that remains to be explained is this Nipype hack. Has perhaps
> this issue been discussed on the SPM mailing list?
>
> Best,
> --
> Dimitri Papadopoulos
> CEA/Saclay
> I2BM, NeuroSpin
> F-91191 Gif-sur-Yvette cedex, France
> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging
>
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From dimitri.papadopoulos at cea.fr  Fri Dec  4 10:20:04 2015
From: dimitri.papadopoulos at cea.fr (Dimitri Papadopoulos Orfanos)
Date: Fri, 4 Dec 2015 16:20:04 +0100
Subject: [Neuroimaging] [nipype] updated external mask handling in SPM
In-Reply-To: <CA+A4wOnod8seyv9EXBHDzjfSxgPVBKALDNFUJoA_t3OmgMK5Nw@mail.gmail.com>
References: <565F0407.5010705@cea.fr>
 <CA+A4wOnod8seyv9EXBHDzjfSxgPVBKALDNFUJoA_t3OmgMK5Nw@mail.gmail.com>
Message-ID: <5661AF24.60109@cea.fr>

Hi Satra,

I understand the whole purpose of this piece of code is to get SPM to
use an explicit mask. This is done by manipulating the masking structure
"SPM.xM".

I'm not yet entirely convinced this still needs to be done "behind the
back of SPM" by modifying SPM.mat outside of the SPM code, but here is
one of "John's Gems" that recommends this Nipype hack (it refers to SPM2
though):
  http://blogs.warwick.ac.uk/nichols/entry/spm2_gem_12/

  With fMRI data/models, SPM2 is fully capable of doing explicit
  masking, but the user interface for fMRI doesn't ask for it.
  One way to do this type of masking anyway is to change the
  SPM.mat file *after* you specify your model, but *before*
  clicking 'Estimate'.
  Specifically:
     1. Load the SPM.mat file,
               load SPM
        set the SPM.xM.TH values all to -Inf,
               SPM.xM.TH = -Inf*SPM.xM.TH;
        and, in case that you have an image format not allowing
        NaNs, set SPM.xM.I to 0
               SPM.xM.I = 0;
     2. If using a mask image, set SPM.xM.VM to a vector of
        structures, where each structure element is the output
        of spm_vol. For instance:
               SPM.xM.VM = spm_vol('Maskimage');
     3. Finally, save by
               save SPM SPM


Most importantly, I am puzzled by the fact that we don't find the same
results when running SPM8 via Nipype than when running SPM8 back in
2010-2012 using batches created from the SPM user interface. At this
point I am not sure if this is an issue with Nipype, an issue with our
own scripts created in 2009-2010, or perhaps related to changes in SPM8.
I would like to the bottom of it - time permitting... I don't feel
comfortable with the idea that the default SPM workflow could have been
modified and I'd like to understand the cause of the discrepancy in the
results.

Best,
Dimitri

Le 02/12/2015 23:17, Satrajit Ghosh a ?crit :
> hi dimitri,
> 
> it's been a long while since those lines were written. but i believe
> this was written as noted in the comments to support the case where the
> user simply wanted spm to use the explicit mask. the other reason for it
> was that there were several places that could control spm options (e.g.,
> config file). we did not want to rely on the config file.
> 
> i believe it was more of a perspective on what an explicit mask meant. i
> can't remember if this was discussed on the spm list or came from best
> practices in our lab at that time.
> 
> cheers,
> 
> satra
> 
> On Wed, Dec 2, 2015 at 9:45 AM, Dimitri Papadopoulos Orfanos
> <dimitri.papadopoulos at cea.fr <mailto:dimitri.papadopoulos at cea.fr>> wrote:
> 
>     Hi,
> 
>     I have a question on the following commit:
>     https://github.com/nipy/nipype/commit/fe9d0e07a288afefb34e99f488ef194a443d6089
> 
>     Could someone explain the rationale behind the addition of this piece of
>     code in nipype/interfaces/spm/model.py? We came across this code while
>     trying to reproduce the results obtained with an old version of SPM8 run
>     manually vs. the latest version of SPM8 run from Nipype.
> 
>             if isdefined(self.inputs.mask_image):
>                 # SPM doesn't handle explicit masking properly, especially
>                 # when you want to use the entire mask image
>                 postscript = "load SPM;\n"
>                 postscript += "SPM.xM.VM = spm_vol('%s');\n" %
>     list_to_filename(self.inputs.mask_image)
>                 postscript += "SPM.xM.I = 0;\n"
>                 postscript += "SPM.xM.T = [];\n"
>                 postscript += "SPM.xM.TH <http://SPM.xM.TH> =
>     ones(size(SPM.xM.TH <http://SPM.xM.TH>))*(%s);\n" %
>     self.inputs.mask_threshold
>                 postscript += "SPM.xM.xs = struct('Masking', 'explicit
>     masking only');\n"
>                 postscript += "save SPM SPM;\n"
> 
>     We have understood almost all causes for differences in results. The
>     only cause that remains to be explained is this Nipype hack. Has perhaps
>     this issue been discussed on the SPM mailing list?
> 
>     Best,
>     --
>     Dimitri Papadopoulos
>     CEA/Saclay
>     I2BM, NeuroSpin
>     F-91191 Gif-sur-Yvette cedex, France
>     _______________________________________________
>     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
> https://mail.python.org/mailman/listinfo/neuroimaging

From dimitri.papadopoulos at cea.fr  Fri Dec  4 10:56:11 2015
From: dimitri.papadopoulos at cea.fr (Dimitri Papadopoulos Orfanos)
Date: Fri, 4 Dec 2015 16:56:11 +0100
Subject: [Neuroimaging] [nipype] Interface Dependencies: SPM12
Message-ID: <5661B79B.6010902@cea.fr>

Dear all,

This page:
  http://nipy.org/nipype/users/install.html#interface-dependencies
should probably be changed from:
  SPM5/8
to:
  SPM5 or later

Best,
Dimitri

From vs2426569610 at gmail.com  Mon Dec  7 03:52:25 2015
From: vs2426569610 at gmail.com (=?UTF-8?B?5L2V5bu65b+g?=)
Date: Mon, 7 Dec 2015 16:52:25 +0800
Subject: [Neuroimaging] A problem about use dipy
Message-ID: <CAEgq_KJmnF93ThYDEuC1BrtQg3LwaYJ7HV9oJP=cMCbmPOPF=Q@mail.gmail.com>

arokem?

      Hello?I am VSsmall, My real name is JianzhongHe, from China.

      I have a problem when I was using dipy. I saw an example in dipy
which name is "Introduction to Basic Tracking", and the example is using
"HARDI150.nii.gz", I was thinking if I use "HARDI93.nii.gz" what would it
going to be. But, it have code error. And error is showed in picture.

      I was using dipy 0.10.1 and install by conda.





                   Jianzhong He


    from Zhejiang University of Technology
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From mrbago at gmail.com  Mon Dec  7 14:44:27 2015
From: mrbago at gmail.com (Bago)
Date: Mon, 7 Dec 2015 11:44:27 -0800
Subject: [Neuroimaging] A problem about use dipy
In-Reply-To: <CAEgq_KJmnF93ThYDEuC1BrtQg3LwaYJ7HV9oJP=cMCbmPOPF=Q@mail.gmail.com>
References: <CAEgq_KJmnF93ThYDEuC1BrtQg3LwaYJ7HV9oJP=cMCbmPOPF=Q@mail.gmail.com>
Message-ID: <CAHHh6CZ_fe__Lm43cSA3zJHxhE9N669VvmVLEyoJ9tbuWoFgJg@mail.gmail.com>

Looking at the error makes me think you're using the gradient table for
HARDI150 to fit the HARDI193 data. Make sure your gradient table had the
same number of gradients as the number of diffusion and b0 volumes in your
data set. Let me know if that's helpful.

Bago

On Mon, Dec 7, 2015 at 12:52 AM, ??? <vs2426569610 at gmail.com> wrote:

> arokem?
>
>       Hello?I am VSsmall, My real name is JianzhongHe, from China.
>
>       I have a problem when I was using dipy. I saw an example in dipy
> which name is "Introduction to Basic Tracking", and the example is using
> "HARDI150.nii.gz", I was thinking if I use "HARDI93.nii.gz" what would it
> going to be. But, it have code error. And error is showed in picture.
>
>       I was using dipy 0.10.1 and install by conda.
>
>
>
>
>
>                      Jianzhong He
>
>
>       from Zhejiang University of Technology
>
>
>
>
> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging
>
>
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From gael.varoquaux at normalesup.org  Tue Dec  8 18:55:06 2015
From: gael.varoquaux at normalesup.org (Gael Varoquaux)
Date: Wed, 9 Dec 2015 00:55:06 +0100
Subject: [Neuroimaging] Post-doc position: Learning functional-connectivity
 biomarkers of pathologies
Message-ID: <20151208235506.GY2001665@phare.normalesup.org>

Dear Nipy community,

I have an exciting post-doc position to fill that I beleive can be a much
interest to people of this community.

Ga?l


Post-doc position: Learning functional-connectivity biomarkers of pathologies
==============================================================================

Parietal (https://team.inria.fr/parietal/) is looking to fill a
post-doc position on learning biomarkers from functional
connectivity.

Scientific context
------------------

The challenge is to use resting-state fMRI at the level of a population
to understand how intrinsic functional connectivity captures pathologies
and other cognitive phenotypes. Rest fMRI is a promising tool for
large-scale population analysis of brain function as it is easy to
acquire and accumulate. Scans for thousands of subjects have already been
shared, and more is to come. However, the signature of cognitions in this
modality are weak. Extracting biomarkers is a challenging data processing
and machine learning problem. This challenge is the expertise of my
research group. Medical applications cover a wider range of brain
pathologies, for which diagnosis is challenging, such as autism or
Alzheimer's disease.

This project is a collaboration with the Child Mind Institute
(http://www.childmind.org/), experts on psychiatric disorders and
resting-state fMRI, and coordinators of the major data sharing
initiatives for rest fRMI data (eg ABIDE).

Objectives of the project
--------------------------

The project hinges on processing of very large rest fMRI databases.
Important novelties of the project are:

- Building predictive models to discriminate multiple pathologies in
  large inhomogeneous datasets.

- Using and improving advanced connectomics and brain-parcellation
  techniques in fMRI.

Expected results include the discovery of neurophenotypes for several
brain pathologies, as well as intrinsic brain structures ?eg functional
parcellations or connectomes? that carry signatures of cognition.

Desired profile
-----------------

We are looking for a post-doctoral fellow to hire in spring. The ideal
candidate would have some, but not all, of the following expertise and
interests:

* Experience in advanced processing of fMRI

* General knowledge of brain structure and function

* Good communication skills to write high-impact neuroscience publications

* Good computing skills, in particular with Python. Cluster computing
  experience is desired.

A great research environment
------------------------------

The work environment is dynamic and exiting, using state-of-the-art
machine learning to answer challenging functional neuroimaging question.
The post-doc will be employed by INRIA (http://www.inria.fr), the lead
computing research institute in France. We are a team of computer
scientists specialized in image processing and statistical data analysis,
integrated in one of the top French brain research centers, NeuroSpin
(http://i2bm.cea.fr/dsv/i2bm/Pages/NeuroSpin.aspx), south of Paris. We
work mostly in Python. The team includes core contributors to the
scikit-learn project (http://scikit-learn.org), for machine learning in
Python, and the nilearn project (http://nilearn.github.io), for
statistical learning in NeuroImaging.

In addition, the post-doc will interact closely with researchers from the
Child Mind Institute (http://www.childmind.org), with deep expertise in
brain pathologies and in the details of the fMRI acquisitions. Finally,
he or she will have access to advanced storage and grid computing
facilities at INRIA.

**Contact**: gael.varoquaux at inria.fr, bertrand.thirion at inria.fr

**Application**: Interested candidate should send CV and motivation letter

-- 
    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 dimitri.papadopoulos at cea.fr  Wed Dec  9 06:43:25 2015
From: dimitri.papadopoulos at cea.fr (Dimitri Papadopoulos Orfanos)
Date: Wed, 9 Dec 2015 12:43:25 +0100
Subject: [Neuroimaging] [nipype] updated external mask handling in SPM
In-Reply-To: <5661AF24.60109@cea.fr>
References: <565F0407.5010705@cea.fr>
 <CA+A4wOnod8seyv9EXBHDzjfSxgPVBKALDNFUJoA_t3OmgMK5Nw@mail.gmail.com>
 <5661AF24.60109@cea.fr>
Message-ID: <566813DD.1020805@cea.fr>

Dear all,

I looked into masking in SPM and with some help I came up with the
following findings:


* The SPM user interface only sets "analysis threshold masking", see
file spm_fmri_spm_ui.m around line 375:

%-Masking
%==========================================================================

%-Masking threshold, as proportion of globals
%--------------------------------------------------------------------------
try
    gMT = SPM.xM.gMT;
catch
    gMT = spm_get_defaults('mask.thresh');
end
TH = g.*gSF*gMT;

%-Place masking structure in xM
%--------------------------------------------------------------------------
SPM.xM = struct(...
    'T',   ones(q,1),...
    'TH',  TH,...
    'gMT', gMT,...
    'I',   0,...
    'VM',  {[]},...
    'xs',  struct('Masking','analysis threshold'));


* When specifying the model in batch jobs, SPM itself sets "explicit
masking" in the following way in file /spm_run_fmri_spec.m around line 385:

%-Explicit mask
%--------------------------------------------------------------------------
if ~design_only
    if ~isempty(job.mask{1})
        SPM.xM.VM         = spm_data_hdr_read(job.mask{1});
        SPM.xM.xs.Masking = [SPM.xM.xs.Masking, '+explicit mask'];
    end
end


* The intent of SPM seems to be that the "explicit mask" is a superset
of the final mask, *not* that it will be the exact mask used during
estimation. From spm_spm.m around line 50:

%         xM.VM - struct array of explicit mask image handles
%       - (empty if no explicit masks)
%               - Explicit mask images are >0 for valid voxels to assess.
%               - Mask images can have any orientation, voxel size or data
%                 type. They are interpolated using nearest neighbour
%                 interpolation to the voxel locations of the data Y.
%       - Note that voxels with constant data (i.e. the same value across
%         scans) are also automatically masked out.



* We find different results with Nipype than when running vanilla SPM
batch jobs.


I would therefore suggest removing this Nipype hack by default:
https://github.com/nipy/nipype/commit/fe9d0e07a288afefb34e99f488ef194a443d6089
It could be added as an option, for different "explicit masking" than
initially.
I intend to propose a patch to remove the current hack. Does anyone have
more insight on this Nipype hack that modifies default SPM behavior and
would be against the patch?

Best,
Dimitri

Le 04/12/2015 16:20, Dimitri Papadopoulos Orfanos a ?crit :
> Hi Satra,
> 
> I understand the whole purpose of this piece of code is to get SPM to
> use an explicit mask. This is done by manipulating the masking structure
> "SPM.xM".
> 
> I'm not yet entirely convinced this still needs to be done "behind the
> back of SPM" by modifying SPM.mat outside of the SPM code, but here is
> one of "John's Gems" that recommends this Nipype hack (it refers to SPM2
> though):
>   http://blogs.warwick.ac.uk/nichols/entry/spm2_gem_12/
> 
>   With fMRI data/models, SPM2 is fully capable of doing explicit
>   masking, but the user interface for fMRI doesn't ask for it.
>   One way to do this type of masking anyway is to change the
>   SPM.mat file *after* you specify your model, but *before*
>   clicking 'Estimate'.
>   Specifically:
>      1. Load the SPM.mat file,
>                load SPM
>         set the SPM.xM.TH values all to -Inf,
>                SPM.xM.TH = -Inf*SPM.xM.TH;
>         and, in case that you have an image format not allowing
>         NaNs, set SPM.xM.I to 0
>                SPM.xM.I = 0;
>      2. If using a mask image, set SPM.xM.VM to a vector of
>         structures, where each structure element is the output
>         of spm_vol. For instance:
>                SPM.xM.VM = spm_vol('Maskimage');
>      3. Finally, save by
>                save SPM SPM
> 
> 
> Most importantly, I am puzzled by the fact that we don't find the same
> results when running SPM8 via Nipype than when running SPM8 back in
> 2010-2012 using batches created from the SPM user interface. At this
> point I am not sure if this is an issue with Nipype, an issue with our
> own scripts created in 2009-2010, or perhaps related to changes in SPM8.
> I would like to the bottom of it - time permitting... I don't feel
> comfortable with the idea that the default SPM workflow could have been
> modified and I'd like to understand the cause of the discrepancy in the
> results.
> 
> Best,
> Dimitri
> 
> Le 02/12/2015 23:17, Satrajit Ghosh a ?crit :
>> hi dimitri,
>>
>> it's been a long while since those lines were written. but i believe
>> this was written as noted in the comments to support the case where the
>> user simply wanted spm to use the explicit mask. the other reason for it
>> was that there were several places that could control spm options (e.g.,
>> config file). we did not want to rely on the config file.
>>
>> i believe it was more of a perspective on what an explicit mask meant. i
>> can't remember if this was discussed on the spm list or came from best
>> practices in our lab at that time.
>>
>> cheers,
>>
>> satra
>>
>> On Wed, Dec 2, 2015 at 9:45 AM, Dimitri Papadopoulos Orfanos
>> <dimitri.papadopoulos at cea.fr <mailto:dimitri.papadopoulos at cea.fr>> wrote:
>>
>>     Hi,
>>
>>     I have a question on the following commit:
>>     https://github.com/nipy/nipype/commit/fe9d0e07a288afefb34e99f488ef194a443d6089
>>
>>     Could someone explain the rationale behind the addition of this piece of
>>     code in nipype/interfaces/spm/model.py? We came across this code while
>>     trying to reproduce the results obtained with an old version of SPM8 run
>>     manually vs. the latest version of SPM8 run from Nipype.
>>
>>             if isdefined(self.inputs.mask_image):
>>                 # SPM doesn't handle explicit masking properly, especially
>>                 # when you want to use the entire mask image
>>                 postscript = "load SPM;\n"
>>                 postscript += "SPM.xM.VM = spm_vol('%s');\n" %
>>     list_to_filename(self.inputs.mask_image)
>>                 postscript += "SPM.xM.I = 0;\n"
>>                 postscript += "SPM.xM.T = [];\n"
>>                 postscript += "SPM.xM.TH <http://SPM.xM.TH> =
>>     ones(size(SPM.xM.TH <http://SPM.xM.TH>))*(%s);\n" %
>>     self.inputs.mask_threshold
>>                 postscript += "SPM.xM.xs = struct('Masking', 'explicit
>>     masking only');\n"
>>                 postscript += "save SPM SPM;\n"
>>
>>     We have understood almost all causes for differences in results. The
>>     only cause that remains to be explained is this Nipype hack. Has perhaps
>>     this issue been discussed on the SPM mailing list?
>>
>>     Best,
>>     --
>>     Dimitri Papadopoulos
>>     CEA/Saclay
>>     I2BM, NeuroSpin
>>     F-91191 Gif-sur-Yvette cedex, France
>>     _______________________________________________
>>     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
>> https://mail.python.org/mailman/listinfo/neuroimaging

From satra at mit.edu  Wed Dec  9 07:43:22 2015
From: satra at mit.edu (Satrajit Ghosh)
Date: Wed, 9 Dec 2015 07:43:22 -0500
Subject: [Neuroimaging] [nipype] updated external mask handling in SPM
In-Reply-To: <566813DD.1020805@cea.fr>
References: <565F0407.5010705@cea.fr>
 <CA+A4wOnod8seyv9EXBHDzjfSxgPVBKALDNFUJoA_t3OmgMK5Nw@mail.gmail.com>
 <5661AF24.60109@cea.fr> <566813DD.1020805@cea.fr>
Message-ID: <CA+A4wOkX-dDEP5qR0yS4yc9ez3wD0kyKttAm88FfYaHdE9GX2g@mail.gmail.com>

hi dimitri,

a change that allows both versions would be fine. please submit a PR and we
can iterate on how to convey the differences through the option names and
description.

cheers,

satra

On Wed, Dec 9, 2015 at 6:43 AM, Dimitri Papadopoulos Orfanos <
dimitri.papadopoulos at cea.fr> wrote:

> Dear all,
>
> I looked into masking in SPM and with some help I came up with the
> following findings:
>
>
> * The SPM user interface only sets "analysis threshold masking", see
> file spm_fmri_spm_ui.m around line 375:
>
> %-Masking
> %==========================================================================
>
> %-Masking threshold, as proportion of globals
> %--------------------------------------------------------------------------
> try
>     gMT = SPM.xM.gMT;
> catch
>     gMT = spm_get_defaults('mask.thresh');
> end
> TH = g.*gSF*gMT;
>
> %-Place masking structure in xM
> %--------------------------------------------------------------------------
> SPM.xM = struct(...
>     'T',   ones(q,1),...
>     'TH',  TH,...
>     'gMT', gMT,...
>     'I',   0,...
>     'VM',  {[]},...
>     'xs',  struct('Masking','analysis threshold'));
>
>
> * When specifying the model in batch jobs, SPM itself sets "explicit
> masking" in the following way in file /spm_run_fmri_spec.m around line 385:
>
> %-Explicit mask
> %--------------------------------------------------------------------------
> if ~design_only
>     if ~isempty(job.mask{1})
>         SPM.xM.VM         = spm_data_hdr_read(job.mask{1});
>         SPM.xM.xs.Masking = [SPM.xM.xs.Masking, '+explicit mask'];
>     end
> end
>
>
> * The intent of SPM seems to be that the "explicit mask" is a superset
> of the final mask, *not* that it will be the exact mask used during
> estimation. From spm_spm.m around line 50:
>
> %         xM.VM - struct array of explicit mask image handles
> %       - (empty if no explicit masks)
> %               - Explicit mask images are >0 for valid voxels to assess.
> %               - Mask images can have any orientation, voxel size or data
> %                 type. They are interpolated using nearest neighbour
> %                 interpolation to the voxel locations of the data Y.
> %       - Note that voxels with constant data (i.e. the same value across
> %         scans) are also automatically masked out.
>
>
>
> * We find different results with Nipype than when running vanilla SPM
> batch jobs.
>
>
> I would therefore suggest removing this Nipype hack by default:
>
> https://github.com/nipy/nipype/commit/fe9d0e07a288afefb34e99f488ef194a443d6089
> It could be added as an option, for different "explicit masking" than
> initially.
> I intend to propose a patch to remove the current hack. Does anyone have
> more insight on this Nipype hack that modifies default SPM behavior and
> would be against the patch?
>
> Best,
> Dimitri
>
> Le 04/12/2015 16:20, Dimitri Papadopoulos Orfanos a ?crit :
> > Hi Satra,
> >
> > I understand the whole purpose of this piece of code is to get SPM to
> > use an explicit mask. This is done by manipulating the masking structure
> > "SPM.xM".
> >
> > I'm not yet entirely convinced this still needs to be done "behind the
> > back of SPM" by modifying SPM.mat outside of the SPM code, but here is
> > one of "John's Gems" that recommends this Nipype hack (it refers to SPM2
> > though):
> >   http://blogs.warwick.ac.uk/nichols/entry/spm2_gem_12/
> >
> >   With fMRI data/models, SPM2 is fully capable of doing explicit
> >   masking, but the user interface for fMRI doesn't ask for it.
> >   One way to do this type of masking anyway is to change the
> >   SPM.mat file *after* you specify your model, but *before*
> >   clicking 'Estimate'.
> >   Specifically:
> >      1. Load the SPM.mat file,
> >                load SPM
> >         set the SPM.xM.TH values all to -Inf,
> >                SPM.xM.TH = -Inf*SPM.xM.TH;
> >         and, in case that you have an image format not allowing
> >         NaNs, set SPM.xM.I to 0
> >                SPM.xM.I = 0;
> >      2. If using a mask image, set SPM.xM.VM to a vector of
> >         structures, where each structure element is the output
> >         of spm_vol. For instance:
> >                SPM.xM.VM = spm_vol('Maskimage');
> >      3. Finally, save by
> >                save SPM SPM
> >
> >
> > Most importantly, I am puzzled by the fact that we don't find the same
> > results when running SPM8 via Nipype than when running SPM8 back in
> > 2010-2012 using batches created from the SPM user interface. At this
> > point I am not sure if this is an issue with Nipype, an issue with our
> > own scripts created in 2009-2010, or perhaps related to changes in SPM8.
> > I would like to the bottom of it - time permitting... I don't feel
> > comfortable with the idea that the default SPM workflow could have been
> > modified and I'd like to understand the cause of the discrepancy in the
> > results.
> >
> > Best,
> > Dimitri
> >
> > Le 02/12/2015 23:17, Satrajit Ghosh a ?crit :
> >> hi dimitri,
> >>
> >> it's been a long while since those lines were written. but i believe
> >> this was written as noted in the comments to support the case where the
> >> user simply wanted spm to use the explicit mask. the other reason for it
> >> was that there were several places that could control spm options (e.g.,
> >> config file). we did not want to rely on the config file.
> >>
> >> i believe it was more of a perspective on what an explicit mask meant. i
> >> can't remember if this was discussed on the spm list or came from best
> >> practices in our lab at that time.
> >>
> >> cheers,
> >>
> >> satra
> >>
> >> On Wed, Dec 2, 2015 at 9:45 AM, Dimitri Papadopoulos Orfanos
> >> <dimitri.papadopoulos at cea.fr <mailto:dimitri.papadopoulos at cea.fr>>
> wrote:
> >>
> >>     Hi,
> >>
> >>     I have a question on the following commit:
> >>
> https://github.com/nipy/nipype/commit/fe9d0e07a288afefb34e99f488ef194a443d6089
> >>
> >>     Could someone explain the rationale behind the addition of this
> piece of
> >>     code in nipype/interfaces/spm/model.py? We came across this code
> while
> >>     trying to reproduce the results obtained with an old version of
> SPM8 run
> >>     manually vs. the latest version of SPM8 run from Nipype.
> >>
> >>             if isdefined(self.inputs.mask_image):
> >>                 # SPM doesn't handle explicit masking properly,
> especially
> >>                 # when you want to use the entire mask image
> >>                 postscript = "load SPM;\n"
> >>                 postscript += "SPM.xM.VM = spm_vol('%s');\n" %
> >>     list_to_filename(self.inputs.mask_image)
> >>                 postscript += "SPM.xM.I = 0;\n"
> >>                 postscript += "SPM.xM.T = [];\n"
> >>                 postscript += "SPM.xM.TH <http://SPM.xM.TH> =
> >>     ones(size(SPM.xM.TH <http://SPM.xM.TH>))*(%s);\n" %
> >>     self.inputs.mask_threshold
> >>                 postscript += "SPM.xM.xs = struct('Masking', 'explicit
> >>     masking only');\n"
> >>                 postscript += "save SPM SPM;\n"
> >>
> >>     We have understood almost all causes for differences in results. The
> >>     only cause that remains to be explained is this Nipype hack. Has
> perhaps
> >>     this issue been discussed on the SPM mailing list?
> >>
> >>     Best,
> >>     --
> >>     Dimitri Papadopoulos
> >>     CEA/Saclay
> >>     I2BM, NeuroSpin
> >>     F-91191 Gif-sur-Yvette cedex, France
> >>     _______________________________________________
> >>     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
> >> 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 b02207006 at ntu.edu.tw  Wed Dec  9 11:33:47 2015
From: b02207006 at ntu.edu.tw (=?big5?B?qky4cae7?=)
Date: Wed, 9 Dec 2015 16:33:47 +0000
Subject: [Neuroimaging] Dipy installation problem
Message-ID: <D6B9FA68CFF03C4E93674516EE6B4A9601B07A76C9@MBox2-2.ntu.internal>

Hi, I got a problem while installing Dipy package on my Windows x64 system.
I've solved the problem like "unable to find vcvarsall.bat" by solutions online.
That is, the distutils.cfg has been made.

But now new problem pops up...
Error information like?"TypeError: unoderable types...." and "failed building wheel for dipy."shows
and I can not find usable solution online.
The followings are the full error information. (but some of this limited by the capacity of CMD I think)


ts
  copying dipy\viz\tests\test_fvtk_utils.py -> build\lib.win32-3.5\dipy\viz\test
s
  copying dipy\viz\tests\test_fvtk_widgets.py -> build\lib.win32-3.5\dipy\viz\te
sts
  copying dipy\viz\tests\test_fvtk_window.py -> build\lib.win32-3.5\dipy\viz\tes
ts
  copying dipy\viz\tests\test_regtools.py -> build\lib.win32-3.5\dipy\viz\tests
  copying dipy\viz\tests\__init__.py -> build\lib.win32-3.5\dipy\viz\tests
  creating build\lib.win32-3.5\dipy\testing
  copying dipy\testing\decorators.py -> build\lib.win32-3.5\dipy\testing
  copying dipy\testing\memory.py -> build\lib.win32-3.5\dipy\testing
  copying dipy\testing\spherepoints.py -> build\lib.win32-3.5\dipy\testing
  copying dipy\testing\__init__.py -> build\lib.win32-3.5\dipy\testing
  creating build\lib.win32-3.5\dipy\testing\tests
  copying dipy\testing\tests\test_decorators.py -> build\lib.win32-3.5\dipy\test
ing\tests
  copying dipy\testing\tests\test_memory.py -> build\lib.win32-3.5\dipy\testing\
tests
  copying dipy\testing\tests\__init__.py -> build\lib.win32-3.5\dipy\testing\tes
ts
  creating build\lib.win32-3.5\dipy\boots
  copying dipy\boots\resampling.py -> build\lib.win32-3.5\dipy\boots
  copying dipy\boots\__init__.py -> build\lib.win32-3.5\dipy\boots
  creating build\lib.win32-3.5\dipy\data
  copying dipy\data\fetcher.py -> build\lib.win32-3.5\dipy\data
  copying dipy\data\__init__.py -> build\lib.win32-3.5\dipy\data
  creating build\lib.win32-3.5\dipy\utils
  copying dipy\utils\arrfuncs.py -> build\lib.win32-3.5\dipy\utils
  copying dipy\utils\optpkg.py -> build\lib.win32-3.5\dipy\utils
  copying dipy\utils\six.py -> build\lib.win32-3.5\dipy\utils
  copying dipy\utils\tripwire.py -> build\lib.win32-3.5\dipy\utils
  copying dipy\utils\_importlib.py -> build\lib.win32-3.5\dipy\utils
  copying dipy\utils\__init__.py -> build\lib.win32-3.5\dipy\utils
  creating build\lib.win32-3.5\dipy\utils\tests
  copying dipy\utils\tests\test_arrfuncs.py -> build\lib.win32-3.5\dipy\utils\te
sts
  copying dipy\utils\tests\test_tripwire.py -> build\lib.win32-3.5\dipy\utils\te
sts
  copying dipy\utils\tests\__init__.py -> build\lib.win32-3.5\dipy\utils\tests
  creating build\lib.win32-3.5\dipy\fixes
  copying dipy\fixes\argparse.py -> build\lib.win32-3.5\dipy\fixes
  copying dipy\fixes\__init__.py -> build\lib.win32-3.5\dipy\fixes
  creating build\lib.win32-3.5\dipy\external
  copying dipy\external\fsl.py -> build\lib.win32-3.5\dipy\external
  copying dipy\external\__init__.py -> build\lib.win32-3.5\dipy\external
  creating build\lib.win32-3.5\dipy\external\tests
  copying dipy\external\tests\__init__.py -> build\lib.win32-3.5\dipy\external\t
ests
  creating build\lib.win32-3.5\dipy\segment
  copying dipy\segment\clustering.py -> build\lib.win32-3.5\dipy\segment
  copying dipy\segment\mask.py -> build\lib.win32-3.5\dipy\segment
  copying dipy\segment\metric.py -> build\lib.win32-3.5\dipy\segment
  copying dipy\segment\quickbundles.py -> build\lib.win32-3.5\dipy\segment
  copying dipy\segment\threshold.py -> build\lib.win32-3.5\dipy\segment
  copying dipy\segment\__init__.py -> build\lib.win32-3.5\dipy\segment
  creating build\lib.win32-3.5\dipy\segment\benchmarks
  copying dipy\segment\benchmarks\bench_quickbundles.py -> build\lib.win32-3.5\d
ipy\segment\benchmarks
  copying dipy\segment\benchmarks\__init__.py -> build\lib.win32-3.5\dipy\segmen
t\benchmarks
  creating build\lib.win32-3.5\dipy\segment\tests
  copying dipy\segment\tests\test_clustering.py -> build\lib.win32-3.5\dipy\segm
ent\tests
  copying dipy\segment\tests\test_feature.py -> build\lib.win32-3.5\dipy\segment
\tests
  copying dipy\segment\tests\test_mask.py -> build\lib.win32-3.5\dipy\segment\te
sts
  copying dipy\segment\tests\test_metric.py -> build\lib.win32-3.5\dipy\segment\
tests
  copying dipy\segment\tests\test_qb.py -> build\lib.win32-3.5\dipy\segment\test
s
  copying dipy\segment\tests\test_quickbundles.py -> build\lib.win32-3.5\dipy\se
gment\tests
  copying dipy\segment\tests\__init__.py -> build\lib.win32-3.5\dipy\segment\tes
ts
  creating build\lib.win32-3.5\dipy\sims
  copying dipy\sims\phantom.py -> build\lib.win32-3.5\dipy\sims
  copying dipy\sims\voxel.py -> build\lib.win32-3.5\dipy\sims
  copying dipy\sims\__init__.py -> build\lib.win32-3.5\dipy\sims
  creating build\lib.win32-3.5\dipy\sims\tests
  copying dipy\sims\tests\test_phantom.py -> build\lib.win32-3.5\dipy\sims\tests

  copying dipy\sims\tests\test_voxel.py -> build\lib.win32-3.5\dipy\sims\tests
  copying dipy\sims\tests\__init__.py -> build\lib.win32-3.5\dipy\sims\tests
  creating build\lib.win32-3.5\dipy\denoise
  copying dipy\denoise\nlmeans.py -> build\lib.win32-3.5\dipy\denoise
  copying dipy\denoise\noise_estimate.py -> build\lib.win32-3.5\dipy\denoise
  copying dipy\denoise\__init__.py -> build\lib.win32-3.5\dipy\denoise
  creating build\lib.win32-3.5\dipy\denoise\tests
  copying dipy\denoise\tests\test_denoise.py -> build\lib.win32-3.5\dipy\denoise
\tests
  copying dipy\denoise\tests\test_nlmeans.py -> build\lib.win32-3.5\dipy\denoise
\tests
  copying dipy\denoise\tests\test_noise_estimate.py -> build\lib.win32-3.5\dipy\
denoise\tests
  copying dipy\denoise\tests\__init__.py -> build\lib.win32-3.5\dipy\denoise\tes
ts
  creating build\lib.win32-3.5\dipy\data\files
  copying dipy\data\files\55dir_grad.bval -> build\lib.win32-3.5\dipy\data\files

  copying dipy\data\files\55dir_grad.bvec -> build\lib.win32-3.5\dipy\data\files

  copying dipy\data\files\aniso_vox.nii.gz -> build\lib.win32-3.5\dipy\data\file
s
  copying dipy\data\files\C.npy -> build\lib.win32-3.5\dipy\data\files
  copying dipy\data\files\C1.pkl.gz -> build\lib.win32-3.5\dipy\data\files
  copying dipy\data\files\C3.pkl.gz -> build\lib.win32-3.5\dipy\data\files
  copying dipy\data\files\cb_2.npz -> build\lib.win32-3.5\dipy\data\files
  copying dipy\data\files\circle.npy -> build\lib.win32-3.5\dipy\data\files
  copying dipy\data\files\dipy_colormaps.json -> build\lib.win32-3.5\dipy\data\f
iles
  copying dipy\data\files\dsi4169_b_table.txt -> build\lib.win32-3.5\dipy\data\f
iles
  copying dipy\data\files\dsi515_b_table.txt -> build\lib.win32-3.5\dipy\data\fi
les
  copying dipy\data\files\eg_3voxels.pkl -> build\lib.win32-3.5\dipy\data\files
  copying dipy\data\files\evenly_distributed_sphere_362.npz -> build\lib.win32-3
.5\dipy\data\files
  copying dipy\data\files\evenly_distributed_sphere_642.npz -> build\lib.win32-3
.5\dipy\data\files
  copying dipy\data\files\evenly_distributed_sphere_724.npz -> build\lib.win32-3
.5\dipy\data\files
  copying dipy\data\files\fib0.pkl.gz -> build\lib.win32-3.5\dipy\data\files
  copying dipy\data\files\fib1.pkl.gz -> build\lib.win32-3.5\dipy\data\files
  copying dipy\data\files\fib2.pkl.gz -> build\lib.win32-3.5\dipy\data\files
  copying dipy\data\files\func_coef.nii.gz -> build\lib.win32-3.5\dipy\data\file
s
  copying dipy\data\files\func_discrete.nii.gz -> build\lib.win32-3.5\dipy\data\
files
  copying dipy\data\files\grad_514.txt -> build\lib.win32-3.5\dipy\data\files
  copying dipy\data\files\gtab_3shell.txt -> build\lib.win32-3.5\dipy\data\files

  copying dipy\data\files\gtab_isbi2013_2shell.txt -> build\lib.win32-3.5\dipy\d
ata\files
  copying dipy\data\files\gtab_taiwan_dsi.txt -> build\lib.win32-3.5\dipy\data\f
iles
  copying dipy\data\files\life_matlab_rmse.npy -> build\lib.win32-3.5\dipy\data\
files
  copying dipy\data\files\life_matlab_weights.npy -> build\lib.win32-3.5\dipy\da
ta\files
  copying dipy\data\files\repulsion100.npz -> build\lib.win32-3.5\dipy\data\file
s
  copying dipy\data\files\repulsion724.npz -> build\lib.win32-3.5\dipy\data\file
s
  copying dipy\data\files\S0_10slices.nii.gz -> build\lib.win32-3.5\dipy\data\fi
les
  copying dipy\data\files\ScannerVectors_GQI101.txt -> build\lib.win32-3.5\dipy\
data\files
  copying dipy\data\files\small_101D.bval -> build\lib.win32-3.5\dipy\data\files

  copying dipy\data\files\small_101D.bvec -> build\lib.win32-3.5\dipy\data\files

  copying dipy\data\files\small_101D.nii.gz -> build\lib.win32-3.5\dipy\data\fil
es
  copying dipy\data\files\small_25.bval -> build\lib.win32-3.5\dipy\data\files
  copying dipy\data\files\small_25.bvec -> build\lib.win32-3.5\dipy\data\files
  copying dipy\data\files\small_25.nii.gz -> build\lib.win32-3.5\dipy\data\files

  copying dipy\data\files\small_64D.bvals.npy -> build\lib.win32-3.5\dipy\data\f
iles
  copying dipy\data\files\small_64D.gradients.npy -> build\lib.win32-3.5\dipy\da
ta\files
  copying dipy\data\files\small_64D.nii -> build\lib.win32-3.5\dipy\data\files
  copying dipy\data\files\sphere_grad.txt -> build\lib.win32-3.5\dipy\data\files

  copying dipy\data\files\t1_coronal_slice.npy -> build\lib.win32-3.5\dipy\data\
files
  copying dipy\data\files\test_piesno.nii.gz -> build\lib.win32-3.5\dipy\data\fi
les
  copying dipy\data\files\tracks300.trk -> build\lib.win32-3.5\dipy\data\files
  running build_ext
  Traceback (most recent call last):
    File "<string>", line 1, in <module>
    File "C:\Users\andylin\AppData\Local\Temp\pip-build-8ygf1kth\dipy\setup.py",
 line 241, in <module>
      main(**extra_setuptools_args)
    File "C:\Users\andylin\AppData\Local\Temp\pip-build-8ygf1kth\dipy\setup.py",
 line 234, in main
      **extra_args
    File "C:\Users\andylin\Anaconda3\lib\distutils\core.py", line 148, in setup
      dist.run_commands()
    File "C:\Users\andylin\Anaconda3\lib\distutils\dist.py", line 955, in run_co
mmands
      self.run_command(cmd)
    File "C:\Users\andylin\Anaconda3\lib\distutils\dist.py", line 974, in run_co
mmand
      cmd_obj.run()
    File "C:\Users\andylin\Anaconda3\lib\site-packages\wheel\bdist_wheel.py", li
ne 176, in run
      self.run_command('build')
    File "C:\Users\andylin\Anaconda3\lib\distutils\cmd.py", line 313, in run_com
mand
      self.distribution.run_command(command)
    File "C:\Users\andylin\Anaconda3\lib\distutils\dist.py", line 974, in run_co
mmand
      cmd_obj.run()
    File "C:\Users\andylin\Anaconda3\lib\distutils\command\build.py", line 135,
in run
      self.run_command(cmd_name)
    File "C:\Users\andylin\Anaconda3\lib\distutils\cmd.py", line 313, in run_com
mand
      self.distribution.run_command(command)
    File "C:\Users\andylin\Anaconda3\lib\distutils\dist.py", line 974, in run_co
mmand
      cmd_obj.run()
    File "C:\Users\andylin\Anaconda3\lib\distutils\command\build_ext.py", line 3
07, in run
      force=self.force)
    File "C:\Users\andylin\Anaconda3\lib\distutils\ccompiler.py", line 1031, in
new_compiler
      return klass(None, dry_run, force)
    File "C:\Users\andylin\Anaconda3\lib\distutils\cygwinccompiler.py", line 282
, in __init__
      CygwinCCompiler.__init__ (self, verbose, dry_run, force)
    File "C:\Users\andylin\Anaconda3\lib\distutils\cygwinccompiler.py", line 126
, in __init__
      if self.ld_version >= "2.10.90":
  TypeError: unorderable types: NoneType() >= str()

  ----------------------------------------
  Failed building wheel for dipy
Failed to build dipy
Installing collected packages: dipy
  Running setup.py install for dipy
    Complete output from command C:\Users\andylin\Anaconda3\python.exe -c "impor
t setuptools, tokenize;__file__='C:\\Users\\andylin\\AppData\\Local\\Temp\\pip-b
uild-8ygf1kth\\dipy\\setup.py';exec(compile(getattr(tokenize, 'open', open)(__fi
le__).read().replace('\r\n', '\n'), __file__, 'exec'))" install --record C:\User
s\andylin\AppData\Local\Temp\pip-r_4d6qec-record\install-record.txt --single-ver
sion-externally-managed --compile:
    running install
    running build
    running build_py
    running build_ext
    Traceback (most recent call last):
      File "<string>", line 1, in <module>
      File "C:\Users\andylin\AppData\Local\Temp\pip-build-8ygf1kth\dipy\setup.py
", line 241, in <module>
        main(**extra_setuptools_args)
      File "C:\Users\andylin\AppData\Local\Temp\pip-build-8ygf1kth\dipy\setup.py
", line 234, in main
        **extra_args
      File "C:\Users\andylin\Anaconda3\lib\distutils\core.py", line 148, in setu
p
        dist.run_commands()
      File "C:\Users\andylin\Anaconda3\lib\distutils\dist.py", line 955, in run_
commands
        self.run_command(cmd)
      File "C:\Users\andylin\Anaconda3\lib\distutils\dist.py", line 974, in run_
command
        cmd_obj.run()
      File "C:\Users\andylin\AppData\Local\Temp\pip-build-8ygf1kth\dipy\setup.py
", line 153, in run
        install.install.run(self)
      File "C:\Users\andylin\Anaconda3\lib\site-packages\setuptools-18.5-py3.5.e
gg\setuptools\command\install.py", line 61, in run
      File "C:\Users\andylin\Anaconda3\lib\distutils\command\install.py", line 5
39, in run
        self.run_command('build')
      File "C:\Users\andylin\Anaconda3\lib\distutils\cmd.py", line 313, in run_c
ommand
        self.distribution.run_command(command)
      File "C:\Users\andylin\Anaconda3\lib\distutils\dist.py", line 974, in run_
command
        cmd_obj.run()
      File "C:\Users\andylin\Anaconda3\lib\distutils\command\build.py", line 135
, in run
        self.run_command(cmd_name)
      File "C:\Users\andylin\Anaconda3\lib\distutils\cmd.py", line 313, in run_c
ommand
        self.distribution.run_command(command)
      File "C:\Users\andylin\Anaconda3\lib\distutils\dist.py", line 974, in run_
command
        cmd_obj.run()
      File "C:\Users\andylin\Anaconda3\lib\distutils\command\build_ext.py", line
 307, in run
        force=self.force)
      File "C:\Users\andylin\Anaconda3\lib\distutils\ccompiler.py", line 1031, i
n new_compiler
        return klass(None, dry_run, force)
      File "C:\Users\andylin\Anaconda3\lib\distutils\cygwinccompiler.py", line 2
82, in __init__
        CygwinCCompiler.__init__ (self, verbose, dry_run, force)
      File "C:\Users\andylin\Anaconda3\lib\distutils\cygwinccompiler.py", line 1
26, in __init__
        if self.ld_version >= "2.10.90":
    TypeError: unorderable types: NoneType() >= str()

    ----------------------------------------
Command "C:\Users\andylin\Anaconda3\python.exe -c "import setuptools, tokenize;_
_file__='C:\\Users\\andylin\\AppData\\Local\\Temp\\pip-build-8ygf1kth\\dipy\\set
up.py';exec(compile(getattr(tokenize, 'open', open)(__file__).read().replace('\r
\n', '\n'), __file__, 'exec'))" install --record C:\Users\andylin\AppData\Local\
Temp\pip-r_4d6qec-record\install-record.txt --single-version-externally-managed
--compile" failed with error code 1 in C:\Users\andylin\AppData\Local\Temp\pip-b
uild-8ygf1kth\dipy

C:\Users\andylin\Anaconda3>
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From matthew.brett at gmail.com  Thu Dec 10 01:01:11 2015
From: matthew.brett at gmail.com (Matthew Brett)
Date: Wed, 9 Dec 2015 22:01:11 -0800
Subject: [Neuroimaging] Dipy installation problem
In-Reply-To: <D6B9FA68CFF03C4E93674516EE6B4A9601B07A76C9@MBox2-2.ntu.internal>
References: <D6B9FA68CFF03C4E93674516EE6B4A9601B07A76C9@MBox2-2.ntu.internal>
Message-ID: <CAH6Pt5pmpmAoUuwNNgQbVTM2_d-MLLyY3aFpC+Eg6VdNWe4dvg@mail.gmail.com>

Hi,

Thanks for the report.

On Wed, Dec 9, 2015 at 8:33 AM, ??? <b02207006 at ntu.edu.tw> wrote:
> Hi, I got a problem while installing Dipy package on my Windows x64 system.
> I've solved the problem like "unable to find vcvarsall.bat" by solutions
> online.
> That is, the distutils.cfg has been made.
>
> But now new problem pops up...
> Error information like?"TypeError: unoderable types...." and "failed
> building wheel for dipy."shows
> and I can not find usable solution online.
> The followings are the full error information. (but some of this limited by
> the capacity of CMD I think)
>
>
> ts
>   copying dipy\viz\tests\test_fvtk_utils.py ->
> build\lib.win32-3.5\dipy\viz\test
> s
>   copying dipy\viz\tests\test_fvtk_widgets.py ->
> build\lib.win32-3.5\dipy\viz\te
> sts
>   copying dipy\viz\tests\test_fvtk_window.py ->
> build\lib.win32-3.5\dipy\viz\tes
> ts
>   copying dipy\viz\tests\test_regtools.py ->
> build\lib.win32-3.5\dipy\viz\tests
>   copying dipy\viz\tests\__init__.py -> build\lib.win32-3.5\dipy\viz\tests
>   creating build\lib.win32-3.5\dipy\testing
>   copying dipy\testing\decorators.py -> build\lib.win32-3.5\dipy\testing
>   copying dipy\testing\memory.py -> build\lib.win32-3.5\dipy\testing
>   copying dipy\testing\spherepoints.py -> build\lib.win32-3.5\dipy\testing
>   copying dipy\testing\__init__.py -> build\lib.win32-3.5\dipy\testing
>   creating build\lib.win32-3.5\dipy\testing\tests
>   copying dipy\testing\tests\test_decorators.py ->
> build\lib.win32-3.5\dipy\test
> ing\tests
>   copying dipy\testing\tests\test_memory.py ->
> build\lib.win32-3.5\dipy\testing\
> tests
>   copying dipy\testing\tests\__init__.py ->
> build\lib.win32-3.5\dipy\testing\tes
> ts
>   creating build\lib.win32-3.5\dipy\boots
>   copying dipy\boots\resampling.py -> build\lib.win32-3.5\dipy\boots
>   copying dipy\boots\__init__.py -> build\lib.win32-3.5\dipy\boots
>   creating build\lib.win32-3.5\dipy\data
>   copying dipy\data\fetcher.py -> build\lib.win32-3.5\dipy\data
>   copying dipy\data\__init__.py -> build\lib.win32-3.5\dipy\data
>   creating build\lib.win32-3.5\dipy\utils
>   copying dipy\utils\arrfuncs.py -> build\lib.win32-3.5\dipy\utils
>   copying dipy\utils\optpkg.py -> build\lib.win32-3.5\dipy\utils
>   copying dipy\utils\six.py -> build\lib.win32-3.5\dipy\utils
>   copying dipy\utils\tripwire.py -> build\lib.win32-3.5\dipy\utils
>   copying dipy\utils\_importlib.py -> build\lib.win32-3.5\dipy\utils
>   copying dipy\utils\__init__.py -> build\lib.win32-3.5\dipy\utils
>   creating build\lib.win32-3.5\dipy\utils\tests
>   copying dipy\utils\tests\test_arrfuncs.py ->
> build\lib.win32-3.5\dipy\utils\te
> sts
>   copying dipy\utils\tests\test_tripwire.py ->
> build\lib.win32-3.5\dipy\utils\te
> sts
>   copying dipy\utils\tests\__init__.py ->
> build\lib.win32-3.5\dipy\utils\tests
>   creating build\lib.win32-3.5\dipy\fixes
>   copying dipy\fixes\argparse.py -> build\lib.win32-3.5\dipy\fixes
>   copying dipy\fixes\__init__.py -> build\lib.win32-3.5\dipy\fixes
>   creating build\lib.win32-3.5\dipy\external
>   copying dipy\external\fsl.py -> build\lib.win32-3.5\dipy\external
>   copying dipy\external\__init__.py -> build\lib.win32-3.5\dipy\external
>   creating build\lib.win32-3.5\dipy\external\tests
>   copying dipy\external\tests\__init__.py ->
> build\lib.win32-3.5\dipy\external\t
> ests
>   creating build\lib.win32-3.5\dipy\segment
>   copying dipy\segment\clustering.py -> build\lib.win32-3.5\dipy\segment
>   copying dipy\segment\mask.py -> build\lib.win32-3.5\dipy\segment
>   copying dipy\segment\metric.py -> build\lib.win32-3.5\dipy\segment
>   copying dipy\segment\quickbundles.py -> build\lib.win32-3.5\dipy\segment
>   copying dipy\segment\threshold.py -> build\lib.win32-3.5\dipy\segment
>   copying dipy\segment\__init__.py -> build\lib.win32-3.5\dipy\segment
>   creating build\lib.win32-3.5\dipy\segment\benchmarks
>   copying dipy\segment\benchmarks\bench_quickbundles.py ->
> build\lib.win32-3.5\d
> ipy\segment\benchmarks
>   copying dipy\segment\benchmarks\__init__.py ->
> build\lib.win32-3.5\dipy\segmen
> t\benchmarks
>   creating build\lib.win32-3.5\dipy\segment\tests
>   copying dipy\segment\tests\test_clustering.py ->
> build\lib.win32-3.5\dipy\segm
> ent\tests
>   copying dipy\segment\tests\test_feature.py ->
> build\lib.win32-3.5\dipy\segment
> \tests
>   copying dipy\segment\tests\test_mask.py ->
> build\lib.win32-3.5\dipy\segment\te
> sts
>   copying dipy\segment\tests\test_metric.py ->
> build\lib.win32-3.5\dipy\segment\
> tests
>   copying dipy\segment\tests\test_qb.py ->
> build\lib.win32-3.5\dipy\segment\test
> s
>   copying dipy\segment\tests\test_quickbundles.py ->
> build\lib.win32-3.5\dipy\se
> gment\tests
>   copying dipy\segment\tests\__init__.py ->
> build\lib.win32-3.5\dipy\segment\tes
> ts
>   creating build\lib.win32-3.5\dipy\sims
>   copying dipy\sims\phantom.py -> build\lib.win32-3.5\dipy\sims
>   copying dipy\sims\voxel.py -> build\lib.win32-3.5\dipy\sims
>   copying dipy\sims\__init__.py -> build\lib.win32-3.5\dipy\sims
>   creating build\lib.win32-3.5\dipy\sims\tests
>   copying dipy\sims\tests\test_phantom.py ->
> build\lib.win32-3.5\dipy\sims\tests
>
>   copying dipy\sims\tests\test_voxel.py ->
> build\lib.win32-3.5\dipy\sims\tests
>   copying dipy\sims\tests\__init__.py -> build\lib.win32-3.5\dipy\sims\tests
>   creating build\lib.win32-3.5\dipy\denoise
>   copying dipy\denoise\nlmeans.py -> build\lib.win32-3.5\dipy\denoise
>   copying dipy\denoise\noise_estimate.py -> build\lib.win32-3.5\dipy\denoise
>   copying dipy\denoise\__init__.py -> build\lib.win32-3.5\dipy\denoise
>   creating build\lib.win32-3.5\dipy\denoise\tests
>   copying dipy\denoise\tests\test_denoise.py ->
> build\lib.win32-3.5\dipy\denoise
> \tests
>   copying dipy\denoise\tests\test_nlmeans.py ->
> build\lib.win32-3.5\dipy\denoise
> \tests
>   copying dipy\denoise\tests\test_noise_estimate.py ->
> build\lib.win32-3.5\dipy\
> denoise\tests
>   copying dipy\denoise\tests\__init__.py ->
> build\lib.win32-3.5\dipy\denoise\tes
> ts
>   creating build\lib.win32-3.5\dipy\data\files
>   copying dipy\data\files\55dir_grad.bval ->
> build\lib.win32-3.5\dipy\data\files
>
>   copying dipy\data\files\55dir_grad.bvec ->
> build\lib.win32-3.5\dipy\data\files
>
>   copying dipy\data\files\aniso_vox.nii.gz ->
> build\lib.win32-3.5\dipy\data\file
> s
>   copying dipy\data\files\C.npy -> build\lib.win32-3.5\dipy\data\files
>   copying dipy\data\files\C1.pkl.gz -> build\lib.win32-3.5\dipy\data\files
>   copying dipy\data\files\C3.pkl.gz -> build\lib.win32-3.5\dipy\data\files
>   copying dipy\data\files\cb_2.npz -> build\lib.win32-3.5\dipy\data\files
>   copying dipy\data\files\circle.npy -> build\lib.win32-3.5\dipy\data\files
>   copying dipy\data\files\dipy_colormaps.json ->
> build\lib.win32-3.5\dipy\data\f
> iles
>   copying dipy\data\files\dsi4169_b_table.txt ->
> build\lib.win32-3.5\dipy\data\f
> iles
>   copying dipy\data\files\dsi515_b_table.txt ->
> build\lib.win32-3.5\dipy\data\fi
> les
>   copying dipy\data\files\eg_3voxels.pkl ->
> build\lib.win32-3.5\dipy\data\files
>   copying dipy\data\files\evenly_distributed_sphere_362.npz ->
> build\lib.win32-3
> .5\dipy\data\files
>   copying dipy\data\files\evenly_distributed_sphere_642.npz ->
> build\lib.win32-3
> .5\dipy\data\files
>   copying dipy\data\files\evenly_distributed_sphere_724.npz ->
> build\lib.win32-3
> .5\dipy\data\files
>   copying dipy\data\files\fib0.pkl.gz -> build\lib.win32-3.5\dipy\data\files
>   copying dipy\data\files\fib1.pkl.gz -> build\lib.win32-3.5\dipy\data\files
>   copying dipy\data\files\fib2.pkl.gz -> build\lib.win32-3.5\dipy\data\files
>   copying dipy\data\files\func_coef.nii.gz ->
> build\lib.win32-3.5\dipy\data\file
> s
>   copying dipy\data\files\func_discrete.nii.gz ->
> build\lib.win32-3.5\dipy\data\
> files
>   copying dipy\data\files\grad_514.txt ->
> build\lib.win32-3.5\dipy\data\files
>   copying dipy\data\files\gtab_3shell.txt ->
> build\lib.win32-3.5\dipy\data\files
>
>   copying dipy\data\files\gtab_isbi2013_2shell.txt ->
> build\lib.win32-3.5\dipy\d
> ata\files
>   copying dipy\data\files\gtab_taiwan_dsi.txt ->
> build\lib.win32-3.5\dipy\data\f
> iles
>   copying dipy\data\files\life_matlab_rmse.npy ->
> build\lib.win32-3.5\dipy\data\
> files
>   copying dipy\data\files\life_matlab_weights.npy ->
> build\lib.win32-3.5\dipy\da
> ta\files
>   copying dipy\data\files\repulsion100.npz ->
> build\lib.win32-3.5\dipy\data\file
> s
>   copying dipy\data\files\repulsion724.npz ->
> build\lib.win32-3.5\dipy\data\file
> s
>   copying dipy\data\files\S0_10slices.nii.gz ->
> build\lib.win32-3.5\dipy\data\fi
> les
>   copying dipy\data\files\ScannerVectors_GQI101.txt ->
> build\lib.win32-3.5\dipy\
> data\files
>   copying dipy\data\files\small_101D.bval ->
> build\lib.win32-3.5\dipy\data\files
>
>   copying dipy\data\files\small_101D.bvec ->
> build\lib.win32-3.5\dipy\data\files
>
>   copying dipy\data\files\small_101D.nii.gz ->
> build\lib.win32-3.5\dipy\data\fil
> es
>   copying dipy\data\files\small_25.bval ->
> build\lib.win32-3.5\dipy\data\files
>   copying dipy\data\files\small_25.bvec ->
> build\lib.win32-3.5\dipy\data\files
>   copying dipy\data\files\small_25.nii.gz ->
> build\lib.win32-3.5\dipy\data\files
>
>   copying dipy\data\files\small_64D.bvals.npy ->
> build\lib.win32-3.5\dipy\data\f
> iles
>   copying dipy\data\files\small_64D.gradients.npy ->
> build\lib.win32-3.5\dipy\da
> ta\files
>   copying dipy\data\files\small_64D.nii ->
> build\lib.win32-3.5\dipy\data\files
>   copying dipy\data\files\sphere_grad.txt ->
> build\lib.win32-3.5\dipy\data\files
>
>   copying dipy\data\files\t1_coronal_slice.npy ->
> build\lib.win32-3.5\dipy\data\
> files
>   copying dipy\data\files\test_piesno.nii.gz ->
> build\lib.win32-3.5\dipy\data\fi
> les
>   copying dipy\data\files\tracks300.trk ->
> build\lib.win32-3.5\dipy\data\files
>   running build_ext
>   Traceback (most recent call last):
>     File "<string>", line 1, in <module>
>     File
> "C:\Users\andylin\AppData\Local\Temp\pip-build-8ygf1kth\dipy\setup.py",
>  line 241, in <module>
>       main(**extra_setuptools_args)
>     File
> "C:\Users\andylin\AppData\Local\Temp\pip-build-8ygf1kth\dipy\setup.py",
>  line 234, in main
>       **extra_args
>     File "C:\Users\andylin\Anaconda3\lib\distutils\core.py", line 148, in
> setup
>       dist.run_commands()
>     File "C:\Users\andylin\Anaconda3\lib\distutils\dist.py", line 955, in
> run_co
> mmands
>       self.run_command(cmd)
>     File "C:\Users\andylin\Anaconda3\lib\distutils\dist.py", line 974, in
> run_co
> mmand
>       cmd_obj.run()
>     File
> "C:\Users\andylin\Anaconda3\lib\site-packages\wheel\bdist_wheel.py", li
> ne 176, in run
>       self.run_command('build')
>     File "C:\Users\andylin\Anaconda3\lib\distutils\cmd.py", line 313, in
> run_com
> mand
>       self.distribution.run_command(command)
>     File "C:\Users\andylin\Anaconda3\lib\distutils\dist.py", line 974, in
> run_co
> mmand
>       cmd_obj.run()
>     File "C:\Users\andylin\Anaconda3\lib\distutils\command\build.py", line
> 135,
> in run
>       self.run_command(cmd_name)
>     File "C:\Users\andylin\Anaconda3\lib\distutils\cmd.py", line 313, in
> run_com
> mand
>       self.distribution.run_command(command)
>     File "C:\Users\andylin\Anaconda3\lib\distutils\dist.py", line 974, in
> run_co
> mmand
>       cmd_obj.run()
>     File "C:\Users\andylin\Anaconda3\lib\distutils\command\build_ext.py",
> line 3
> 07, in run
>       force=self.force)
>     File "C:\Users\andylin\Anaconda3\lib\distutils\ccompiler.py", line 1031,
> in
> new_compiler
>       return klass(None, dry_run, force)
>     File "C:\Users\andylin\Anaconda3\lib\distutils\cygwinccompiler.py", line
> 282
> , in __init__
>       CygwinCCompiler.__init__ (self, verbose, dry_run, force)
>     File "C:\Users\andylin\Anaconda3\lib\distutils\cygwinccompiler.py", line
> 126
> , in __init__
>       if self.ld_version >= "2.10.90":
>   TypeError: unorderable types: NoneType() >= str()
>
>   ----------------------------------------
>   Failed building wheel for dipy
> Failed to build dipy
> Installing collected packages: dipy
>   Running setup.py install for dipy
>     Complete output from command C:\Users\andylin\Anaconda3\python.exe -c
> "impor
> t setuptools,
> tokenize;__file__='C:\\Users\\andylin\\AppData\\Local\\Temp\\pip-b
> uild-8ygf1kth\\dipy\\setup.py';exec(compile(getattr(tokenize, 'open',
> open)(__fi
> le__).read().replace('\r\n', '\n'), __file__, 'exec'))" install --record
> C:\User
> s\andylin\AppData\Local\Temp\pip-r_4d6qec-record\install-record.txt
> --single-ver
> sion-externally-managed --compile:
>     running install
>     running build
>     running build_py
>     running build_ext
>     Traceback (most recent call last):
>       File "<string>", line 1, in <module>
>       File
> "C:\Users\andylin\AppData\Local\Temp\pip-build-8ygf1kth\dipy\setup.py
> ", line 241, in <module>
>         main(**extra_setuptools_args)
>       File
> "C:\Users\andylin\AppData\Local\Temp\pip-build-8ygf1kth\dipy\setup.py
> ", line 234, in main
>         **extra_args
>       File "C:\Users\andylin\Anaconda3\lib\distutils\core.py", line 148, in
> setu
> p
>         dist.run_commands()
>       File "C:\Users\andylin\Anaconda3\lib\distutils\dist.py", line 955, in
> run_
> commands
>         self.run_command(cmd)
>       File "C:\Users\andylin\Anaconda3\lib\distutils\dist.py", line 974, in
> run_
> command
>         cmd_obj.run()
>       File
> "C:\Users\andylin\AppData\Local\Temp\pip-build-8ygf1kth\dipy\setup.py
> ", line 153, in run
>         install.install.run(self)
>       File
> "C:\Users\andylin\Anaconda3\lib\site-packages\setuptools-18.5-py3.5.e
> gg\setuptools\command\install.py", line 61, in run
>       File "C:\Users\andylin\Anaconda3\lib\distutils\command\install.py",
> line 5
> 39, in run
>         self.run_command('build')
>       File "C:\Users\andylin\Anaconda3\lib\distutils\cmd.py", line 313, in
> run_c
> ommand
>         self.distribution.run_command(command)
>       File "C:\Users\andylin\Anaconda3\lib\distutils\dist.py", line 974, in
> run_
> command
>         cmd_obj.run()
>       File "C:\Users\andylin\Anaconda3\lib\distutils\command\build.py", line
> 135
> , in run
>         self.run_command(cmd_name)
>       File "C:\Users\andylin\Anaconda3\lib\distutils\cmd.py", line 313, in
> run_c
> ommand
>         self.distribution.run_command(command)
>       File "C:\Users\andylin\Anaconda3\lib\distutils\dist.py", line 974, in
> run_
> command
>         cmd_obj.run()
>       File "C:\Users\andylin\Anaconda3\lib\distutils\command\build_ext.py",
> line
>  307, in run
>         force=self.force)
>       File "C:\Users\andylin\Anaconda3\lib\distutils\ccompiler.py", line
> 1031, i
> n new_compiler
>         return klass(None, dry_run, force)
>       File "C:\Users\andylin\Anaconda3\lib\distutils\cygwinccompiler.py",
> line 2
> 82, in __init__
>         CygwinCCompiler.__init__ (self, verbose, dry_run, force)
>       File "C:\Users\andylin\Anaconda3\lib\distutils\cygwinccompiler.py",
> line 1
> 26, in __init__
>         if self.ld_version >= "2.10.90":
>     TypeError: unorderable types: NoneType() >= str()
>
>     ----------------------------------------
> Command "C:\Users\andylin\Anaconda3\python.exe -c "import setuptools,
> tokenize;_
> _file__='C:\\Users\\andylin\\AppData\\Local\\Temp\\pip-build-8ygf1kth\\dipy\\set
> up.py';exec(compile(getattr(tokenize, 'open',
> open)(__file__).read().replace('\r
> \n', '\n'), __file__, 'exec'))" install --record
> C:\Users\andylin\AppData\Local\
> Temp\pip-r_4d6qec-record\install-record.txt
> --single-version-externally-managed
> --compile" failed with error code 1 in
> C:\Users\andylin\AppData\Local\Temp\pip-b
> uild-8ygf1kth\dipy
>
> C:\Users\andylin\Anaconda3>

I'm afraid I don't personally use Anaconda, so I haven't seen an error
like this.

Have you tried installing any other Python packages that need compilation?

Best,

Matthew

From adam.rybinski at outlook.com  Thu Dec 10 04:35:09 2015
From: adam.rybinski at outlook.com (=?gb2312?B?QWRhbSBSeWJpqL1za2k=?=)
Date: Thu, 10 Dec 2015 09:35:09 +0000
Subject: [Neuroimaging] Dipy installation problem
In-Reply-To: <CAH6Pt5pmpmAoUuwNNgQbVTM2_d-MLLyY3aFpC+Eg6VdNWe4dvg@mail.gmail.com>
References: <D6B9FA68CFF03C4E93674516EE6B4A9601B07A76C9@MBox2-2.ntu.internal>,
 <CAH6Pt5pmpmAoUuwNNgQbVTM2_d-MLLyY3aFpC+Eg6VdNWe4dvg@mail.gmail.com>
Message-ID: <BLU185-W578AC634FE25E6FD5E3AD8E4E90@phx.gbl>

Hi,I remember having similar problem on Windows, it was while back ago, amd now I'm on linux most of the time.Eventually, the way I ran dipy on Windows was using anaconda 2.7, and then installing compiler for Python 2.7 in Windows https://www.microsoft.com/en-us/download/details.aspx?id=44266
I couldn't find microsoft compiler for Python 3.5---this one is somehow bundled with their Visual Studio Python IDE I think.
So If you don't care that much about Python version, try:
conda create --name python2 python=2.7
activate python2
pip install nibabel dipy (and all other dependencies you need)
I hope it helps,
Adam

> From: matthew.brett at gmail.com
> Date: Wed, 9 Dec 2015 22:01:11 -0800
> To: neuroimaging at python.org
> Subject: Re: [Neuroimaging] Dipy installation problem
> 
> Hi,
> 
> Thanks for the report.
> 
> On Wed, Dec 9, 2015 at 8:33 AM, ???x?? <b02207006 at ntu.edu.tw> wrote:
> > Hi, I got a problem while installing Dipy package on my Windows x64 system.
> > I've solved the problem like "unable to find vcvarsall.bat" by solutions
> > online.
> > That is, the distutils.cfg has been made.
> >
> > But now new problem pops up...
> > Error information like??"TypeError: unoderable types...." and "failed
> > building wheel for dipy."shows
> > and I can not find usable solution online.
> > The followings are the full error information. (but some of this limited by
> > the capacity of CMD I think)
> >
> >
> > ts
> >   copying dipy\viz\tests\test_fvtk_utils.py ->
> > build\lib.win32-3.5\dipy\viz\test
> > s
> >   copying dipy\viz\tests\test_fvtk_widgets.py ->
> > build\lib.win32-3.5\dipy\viz\te
> > sts
> >   copying dipy\viz\tests\test_fvtk_window.py ->
> > build\lib.win32-3.5\dipy\viz\tes
> > ts
> >   copying dipy\viz\tests\test_regtools.py ->
> > build\lib.win32-3.5\dipy\viz\tests
> >   copying dipy\viz\tests\__init__.py -> build\lib.win32-3.5\dipy\viz\tests
> >   creating build\lib.win32-3.5\dipy\testing
> >   copying dipy\testing\decorators.py -> build\lib.win32-3.5\dipy\testing
> >   copying dipy\testing\memory.py -> build\lib.win32-3.5\dipy\testing
> >   copying dipy\testing\spherepoints.py -> build\lib.win32-3.5\dipy\testing
> >   copying dipy\testing\__init__.py -> build\lib.win32-3.5\dipy\testing
> >   creating build\lib.win32-3.5\dipy\testing\tests
> >   copying dipy\testing\tests\test_decorators.py ->
> > build\lib.win32-3.5\dipy\test
> > ing\tests
> >   copying dipy\testing\tests\test_memory.py ->
> > build\lib.win32-3.5\dipy\testing\
> > tests
> >   copying dipy\testing\tests\__init__.py ->
> > build\lib.win32-3.5\dipy\testing\tes
> > ts
> >   creating build\lib.win32-3.5\dipy\boots
> >   copying dipy\boots\resampling.py -> build\lib.win32-3.5\dipy\boots
> >   copying dipy\boots\__init__.py -> build\lib.win32-3.5\dipy\boots
> >   creating build\lib.win32-3.5\dipy\data
> >   copying dipy\data\fetcher.py -> build\lib.win32-3.5\dipy\data
> >   copying dipy\data\__init__.py -> build\lib.win32-3.5\dipy\data
> >   creating build\lib.win32-3.5\dipy\utils
> >   copying dipy\utils\arrfuncs.py -> build\lib.win32-3.5\dipy\utils
> >   copying dipy\utils\optpkg.py -> build\lib.win32-3.5\dipy\utils
> >   copying dipy\utils\six.py -> build\lib.win32-3.5\dipy\utils
> >   copying dipy\utils\tripwire.py -> build\lib.win32-3.5\dipy\utils
> >   copying dipy\utils\_importlib.py -> build\lib.win32-3.5\dipy\utils
> >   copying dipy\utils\__init__.py -> build\lib.win32-3.5\dipy\utils
> >   creating build\lib.win32-3.5\dipy\utils\tests
> >   copying dipy\utils\tests\test_arrfuncs.py ->
> > build\lib.win32-3.5\dipy\utils\te
> > sts
> >   copying dipy\utils\tests\test_tripwire.py ->
> > build\lib.win32-3.5\dipy\utils\te
> > sts
> >   copying dipy\utils\tests\__init__.py ->
> > build\lib.win32-3.5\dipy\utils\tests
> >   creating build\lib.win32-3.5\dipy\fixes
> >   copying dipy\fixes\argparse.py -> build\lib.win32-3.5\dipy\fixes
> >   copying dipy\fixes\__init__.py -> build\lib.win32-3.5\dipy\fixes
> >   creating build\lib.win32-3.5\dipy\external
> >   copying dipy\external\fsl.py -> build\lib.win32-3.5\dipy\external
> >   copying dipy\external\__init__.py -> build\lib.win32-3.5\dipy\external
> >   creating build\lib.win32-3.5\dipy\external\tests
> >   copying dipy\external\tests\__init__.py ->
> > build\lib.win32-3.5\dipy\external\t
> > ests
> >   creating build\lib.win32-3.5\dipy\segment
> >   copying dipy\segment\clustering.py -> build\lib.win32-3.5\dipy\segment
> >   copying dipy\segment\mask.py -> build\lib.win32-3.5\dipy\segment
> >   copying dipy\segment\metric.py -> build\lib.win32-3.5\dipy\segment
> >   copying dipy\segment\quickbundles.py -> build\lib.win32-3.5\dipy\segment
> >   copying dipy\segment\threshold.py -> build\lib.win32-3.5\dipy\segment
> >   copying dipy\segment\__init__.py -> build\lib.win32-3.5\dipy\segment
> >   creating build\lib.win32-3.5\dipy\segment\benchmarks
> >   copying dipy\segment\benchmarks\bench_quickbundles.py ->
> > build\lib.win32-3.5\d
> > ipy\segment\benchmarks
> >   copying dipy\segment\benchmarks\__init__.py ->
> > build\lib.win32-3.5\dipy\segmen
> > t\benchmarks
> >   creating build\lib.win32-3.5\dipy\segment\tests
> >   copying dipy\segment\tests\test_clustering.py ->
> > build\lib.win32-3.5\dipy\segm
> > ent\tests
> >   copying dipy\segment\tests\test_feature.py ->
> > build\lib.win32-3.5\dipy\segment
> > \tests
> >   copying dipy\segment\tests\test_mask.py ->
> > build\lib.win32-3.5\dipy\segment\te
> > sts
> >   copying dipy\segment\tests\test_metric.py ->
> > build\lib.win32-3.5\dipy\segment\
> > tests
> >   copying dipy\segment\tests\test_qb.py ->
> > build\lib.win32-3.5\dipy\segment\test
> > s
> >   copying dipy\segment\tests\test_quickbundles.py ->
> > build\lib.win32-3.5\dipy\se
> > gment\tests
> >   copying dipy\segment\tests\__init__.py ->
> > build\lib.win32-3.5\dipy\segment\tes
> > ts
> >   creating build\lib.win32-3.5\dipy\sims
> >   copying dipy\sims\phantom.py -> build\lib.win32-3.5\dipy\sims
> >   copying dipy\sims\voxel.py -> build\lib.win32-3.5\dipy\sims
> >   copying dipy\sims\__init__.py -> build\lib.win32-3.5\dipy\sims
> >   creating build\lib.win32-3.5\dipy\sims\tests
> >   copying dipy\sims\tests\test_phantom.py ->
> > build\lib.win32-3.5\dipy\sims\tests
> >
> >   copying dipy\sims\tests\test_voxel.py ->
> > build\lib.win32-3.5\dipy\sims\tests
> >   copying dipy\sims\tests\__init__.py -> build\lib.win32-3.5\dipy\sims\tests
> >   creating build\lib.win32-3.5\dipy\denoise
> >   copying dipy\denoise\nlmeans.py -> build\lib.win32-3.5\dipy\denoise
> >   copying dipy\denoise\noise_estimate.py -> build\lib.win32-3.5\dipy\denoise
> >   copying dipy\denoise\__init__.py -> build\lib.win32-3.5\dipy\denoise
> >   creating build\lib.win32-3.5\dipy\denoise\tests
> >   copying dipy\denoise\tests\test_denoise.py ->
> > build\lib.win32-3.5\dipy\denoise
> > \tests
> >   copying dipy\denoise\tests\test_nlmeans.py ->
> > build\lib.win32-3.5\dipy\denoise
> > \tests
> >   copying dipy\denoise\tests\test_noise_estimate.py ->
> > build\lib.win32-3.5\dipy\
> > denoise\tests
> >   copying dipy\denoise\tests\__init__.py ->
> > build\lib.win32-3.5\dipy\denoise\tes
> > ts
> >   creating build\lib.win32-3.5\dipy\data\files
> >   copying dipy\data\files\55dir_grad.bval ->
> > build\lib.win32-3.5\dipy\data\files
> >
> >   copying dipy\data\files\55dir_grad.bvec ->
> > build\lib.win32-3.5\dipy\data\files
> >
> >   copying dipy\data\files\aniso_vox.nii.gz ->
> > build\lib.win32-3.5\dipy\data\file
> > s
> >   copying dipy\data\files\C.npy -> build\lib.win32-3.5\dipy\data\files
> >   copying dipy\data\files\C1.pkl.gz -> build\lib.win32-3.5\dipy\data\files
> >   copying dipy\data\files\C3.pkl.gz -> build\lib.win32-3.5\dipy\data\files
> >   copying dipy\data\files\cb_2.npz -> build\lib.win32-3.5\dipy\data\files
> >   copying dipy\data\files\circle.npy -> build\lib.win32-3.5\dipy\data\files
> >   copying dipy\data\files\dipy_colormaps.json ->
> > build\lib.win32-3.5\dipy\data\f
> > iles
> >   copying dipy\data\files\dsi4169_b_table.txt ->
> > build\lib.win32-3.5\dipy\data\f
> > iles
> >   copying dipy\data\files\dsi515_b_table.txt ->
> > build\lib.win32-3.5\dipy\data\fi
> > les
> >   copying dipy\data\files\eg_3voxels.pkl ->
> > build\lib.win32-3.5\dipy\data\files
> >   copying dipy\data\files\evenly_distributed_sphere_362.npz ->
> > build\lib.win32-3
> > .5\dipy\data\files
> >   copying dipy\data\files\evenly_distributed_sphere_642.npz ->
> > build\lib.win32-3
> > .5\dipy\data\files
> >   copying dipy\data\files\evenly_distributed_sphere_724.npz ->
> > build\lib.win32-3
> > .5\dipy\data\files
> >   copying dipy\data\files\fib0.pkl.gz -> build\lib.win32-3.5\dipy\data\files
> >   copying dipy\data\files\fib1.pkl.gz -> build\lib.win32-3.5\dipy\data\files
> >   copying dipy\data\files\fib2.pkl.gz -> build\lib.win32-3.5\dipy\data\files
> >   copying dipy\data\files\func_coef.nii.gz ->
> > build\lib.win32-3.5\dipy\data\file
> > s
> >   copying dipy\data\files\func_discrete.nii.gz ->
> > build\lib.win32-3.5\dipy\data\
> > files
> >   copying dipy\data\files\grad_514.txt ->
> > build\lib.win32-3.5\dipy\data\files
> >   copying dipy\data\files\gtab_3shell.txt ->
> > build\lib.win32-3.5\dipy\data\files
> >
> >   copying dipy\data\files\gtab_isbi2013_2shell.txt ->
> > build\lib.win32-3.5\dipy\d
> > ata\files
> >   copying dipy\data\files\gtab_taiwan_dsi.txt ->
> > build\lib.win32-3.5\dipy\data\f
> > iles
> >   copying dipy\data\files\life_matlab_rmse.npy ->
> > build\lib.win32-3.5\dipy\data\
> > files
> >   copying dipy\data\files\life_matlab_weights.npy ->
> > build\lib.win32-3.5\dipy\da
> > ta\files
> >   copying dipy\data\files\repulsion100.npz ->
> > build\lib.win32-3.5\dipy\data\file
> > s
> >   copying dipy\data\files\repulsion724.npz ->
> > build\lib.win32-3.5\dipy\data\file
> > s
> >   copying dipy\data\files\S0_10slices.nii.gz ->
> > build\lib.win32-3.5\dipy\data\fi
> > les
> >   copying dipy\data\files\ScannerVectors_GQI101.txt ->
> > build\lib.win32-3.5\dipy\
> > data\files
> >   copying dipy\data\files\small_101D.bval ->
> > build\lib.win32-3.5\dipy\data\files
> >
> >   copying dipy\data\files\small_101D.bvec ->
> > build\lib.win32-3.5\dipy\data\files
> >
> >   copying dipy\data\files\small_101D.nii.gz ->
> > build\lib.win32-3.5\dipy\data\fil
> > es
> >   copying dipy\data\files\small_25.bval ->
> > build\lib.win32-3.5\dipy\data\files
> >   copying dipy\data\files\small_25.bvec ->
> > build\lib.win32-3.5\dipy\data\files
> >   copying dipy\data\files\small_25.nii.gz ->
> > build\lib.win32-3.5\dipy\data\files
> >
> >   copying dipy\data\files\small_64D.bvals.npy ->
> > build\lib.win32-3.5\dipy\data\f
> > iles
> >   copying dipy\data\files\small_64D.gradients.npy ->
> > build\lib.win32-3.5\dipy\da
> > ta\files
> >   copying dipy\data\files\small_64D.nii ->
> > build\lib.win32-3.5\dipy\data\files
> >   copying dipy\data\files\sphere_grad.txt ->
> > build\lib.win32-3.5\dipy\data\files
> >
> >   copying dipy\data\files\t1_coronal_slice.npy ->
> > build\lib.win32-3.5\dipy\data\
> > files
> >   copying dipy\data\files\test_piesno.nii.gz ->
> > build\lib.win32-3.5\dipy\data\fi
> > les
> >   copying dipy\data\files\tracks300.trk ->
> > build\lib.win32-3.5\dipy\data\files
> >   running build_ext
> >   Traceback (most recent call last):
> >     File "<string>", line 1, in <module>
> >     File
> > "C:\Users\andylin\AppData\Local\Temp\pip-build-8ygf1kth\dipy\setup.py",
> >  line 241, in <module>
> >       main(**extra_setuptools_args)
> >     File
> > "C:\Users\andylin\AppData\Local\Temp\pip-build-8ygf1kth\dipy\setup.py",
> >  line 234, in main
> >       **extra_args
> >     File "C:\Users\andylin\Anaconda3\lib\distutils\core.py", line 148, in
> > setup
> >       dist.run_commands()
> >     File "C:\Users\andylin\Anaconda3\lib\distutils\dist.py", line 955, in
> > run_co
> > mmands
> >       self.run_command(cmd)
> >     File "C:\Users\andylin\Anaconda3\lib\distutils\dist.py", line 974, in
> > run_co
> > mmand
> >       cmd_obj.run()
> >     File
> > "C:\Users\andylin\Anaconda3\lib\site-packages\wheel\bdist_wheel.py", li
> > ne 176, in run
> >       self.run_command('build')
> >     File "C:\Users\andylin\Anaconda3\lib\distutils\cmd.py", line 313, in
> > run_com
> > mand
> >       self.distribution.run_command(command)
> >     File "C:\Users\andylin\Anaconda3\lib\distutils\dist.py", line 974, in
> > run_co
> > mmand
> >       cmd_obj.run()
> >     File "C:\Users\andylin\Anaconda3\lib\distutils\command\build.py", line
> > 135,
> > in run
> >       self.run_command(cmd_name)
> >     File "C:\Users\andylin\Anaconda3\lib\distutils\cmd.py", line 313, in
> > run_com
> > mand
> >       self.distribution.run_command(command)
> >     File "C:\Users\andylin\Anaconda3\lib\distutils\dist.py", line 974, in
> > run_co
> > mmand
> >       cmd_obj.run()
> >     File "C:\Users\andylin\Anaconda3\lib\distutils\command\build_ext.py",
> > line 3
> > 07, in run
> >       force=self.force)
> >     File "C:\Users\andylin\Anaconda3\lib\distutils\ccompiler.py", line 1031,
> > in
> > new_compiler
> >       return klass(None, dry_run, force)
> >     File "C:\Users\andylin\Anaconda3\lib\distutils\cygwinccompiler.py", line
> > 282
> > , in __init__
> >       CygwinCCompiler.__init__ (self, verbose, dry_run, force)
> >     File "C:\Users\andylin\Anaconda3\lib\distutils\cygwinccompiler.py", line
> > 126
> > , in __init__
> >       if self.ld_version >= "2.10.90":
> >   TypeError: unorderable types: NoneType() >= str()
> >
> >   ----------------------------------------
> >   Failed building wheel for dipy
> > Failed to build dipy
> > Installing collected packages: dipy
> >   Running setup.py install for dipy
> >     Complete output from command C:\Users\andylin\Anaconda3\python.exe -c
> > "impor
> > t setuptools,
> > tokenize;__file__='C:\\Users\\andylin\\AppData\\Local\\Temp\\pip-b
> > uild-8ygf1kth\\dipy\\setup.py';exec(compile(getattr(tokenize, 'open',
> > open)(__fi
> > le__).read().replace('\r\n', '\n'), __file__, 'exec'))" install --record
> > C:\User
> > s\andylin\AppData\Local\Temp\pip-r_4d6qec-record\install-record.txt
> > --single-ver
> > sion-externally-managed --compile:
> >     running install
> >     running build
> >     running build_py
> >     running build_ext
> >     Traceback (most recent call last):
> >       File "<string>", line 1, in <module>
> >       File
> > "C:\Users\andylin\AppData\Local\Temp\pip-build-8ygf1kth\dipy\setup.py
> > ", line 241, in <module>
> >         main(**extra_setuptools_args)
> >       File
> > "C:\Users\andylin\AppData\Local\Temp\pip-build-8ygf1kth\dipy\setup.py
> > ", line 234, in main
> >         **extra_args
> >       File "C:\Users\andylin\Anaconda3\lib\distutils\core.py", line 148, in
> > setu
> > p
> >         dist.run_commands()
> >       File "C:\Users\andylin\Anaconda3\lib\distutils\dist.py", line 955, in
> > run_
> > commands
> >         self.run_command(cmd)
> >       File "C:\Users\andylin\Anaconda3\lib\distutils\dist.py", line 974, in
> > run_
> > command
> >         cmd_obj.run()
> >       File
> > "C:\Users\andylin\AppData\Local\Temp\pip-build-8ygf1kth\dipy\setup.py
> > ", line 153, in run
> >         install.install.run(self)
> >       File
> > "C:\Users\andylin\Anaconda3\lib\site-packages\setuptools-18.5-py3.5.e
> > gg\setuptools\command\install.py", line 61, in run
> >       File "C:\Users\andylin\Anaconda3\lib\distutils\command\install.py",
> > line 5
> > 39, in run
> >         self.run_command('build')
> >       File "C:\Users\andylin\Anaconda3\lib\distutils\cmd.py", line 313, in
> > run_c
> > ommand
> >         self.distribution.run_command(command)
> >       File "C:\Users\andylin\Anaconda3\lib\distutils\dist.py", line 974, in
> > run_
> > command
> >         cmd_obj.run()
> >       File "C:\Users\andylin\Anaconda3\lib\distutils\command\build.py", line
> > 135
> > , in run
> >         self.run_command(cmd_name)
> >       File "C:\Users\andylin\Anaconda3\lib\distutils\cmd.py", line 313, in
> > run_c
> > ommand
> >         self.distribution.run_command(command)
> >       File "C:\Users\andylin\Anaconda3\lib\distutils\dist.py", line 974, in
> > run_
> > command
> >         cmd_obj.run()
> >       File "C:\Users\andylin\Anaconda3\lib\distutils\command\build_ext.py",
> > line
> >  307, in run
> >         force=self.force)
> >       File "C:\Users\andylin\Anaconda3\lib\distutils\ccompiler.py", line
> > 1031, i
> > n new_compiler
> >         return klass(None, dry_run, force)
> >       File "C:\Users\andylin\Anaconda3\lib\distutils\cygwinccompiler.py",
> > line 2
> > 82, in __init__
> >         CygwinCCompiler.__init__ (self, verbose, dry_run, force)
> >       File "C:\Users\andylin\Anaconda3\lib\distutils\cygwinccompiler.py",
> > line 1
> > 26, in __init__
> >         if self.ld_version >= "2.10.90":
> >     TypeError: unorderable types: NoneType() >= str()
> >
> >     ----------------------------------------
> > Command "C:\Users\andylin\Anaconda3\python.exe -c "import setuptools,
> > tokenize;_
> > _file__='C:\\Users\\andylin\\AppData\\Local\\Temp\\pip-build-8ygf1kth\\dipy\\set
> > up.py';exec(compile(getattr(tokenize, 'open',
> > open)(__file__).read().replace('\r
> > \n', '\n'), __file__, 'exec'))" install --record
> > C:\Users\andylin\AppData\Local\
> > Temp\pip-r_4d6qec-record\install-record.txt
> > --single-version-externally-managed
> > --compile" failed with error code 1 in
> > C:\Users\andylin\AppData\Local\Temp\pip-b
> > uild-8ygf1kth\dipy
> >
> > C:\Users\andylin\Anaconda3>
> 
> I'm afraid I don't personally use Anaconda, so I haven't seen an error
> like this.
> 
> Have you tried installing any other Python packages that need compilation?
> 
> Best,
> 
> Matthew
> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging
 		 	   		  
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From effigies at bu.edu  Fri Dec 11 14:43:45 2015
From: effigies at bu.edu (Christopher J Markiewicz)
Date: Fri, 11 Dec 2015 14:43:45 -0500
Subject: [Neuroimaging] Effects of motion outliers on HRF model (in sparse
 acquisition fMRI)
Message-ID: <566B2771.3090104@bu.edu>

Hi all,

I apologize in advance because, as Pythonic as my pipeline is, my issue
here isn't really Python-related. However, the people on this list are
the most likely to have dealt with similar issues (of places I know to
look). If you'd rather I post on NeuroStars, I can, but I'm not sure how
much people are actually using that.

Anyway, my functional data comes from evenly-spaced, sparse acquisitions
(TA=2.25s, TR=3.375s), and I've used artdetect in nipype to tag motion
and intensity outliers. It's a fast, event-related design (one event
every 2 TRs).

In the past, my strategy has been to estimate HRF betas on the full
dataset, and then excluding motion outliers in analysis by removing any
event estimate that had an above-threshold contribution from an outlier
volume. That is, in an NxM design matrix estimating N events from M
scans, if scan j is an outlier, we exclude all events i such that
DM[i,j] > (e.g.) 10% of max(HRF).

Another strategy I'm looking into is to add nuisance regressors for
outlier volumes to the design matrix, and limiting bleed-over into
unrelated events. This is running into problems with "runs" of outliers,
which can leave some events with nothing by which to estimate or only
very small contributions from volumes that are going to be dominated by
other events. I could remove such events, entirely, but for various
reasons (mostly involving maintaining ordering so that off-by-one errors
don't slip into our analysis) I'd like to have some representation of
each event.

The best external-to-our-lab resource I could find was this Gabrieli Lab
protocol
(https://github.com/gablab/mindhive/wiki/Example-of-sparse-fMRI-data-analysis-using-BIPS),
which seems to indicate they've included the full dataset and noted
outliers after estimation.

Does anybody have any experience with outlier exclusion at or before HRF
estimation, or is this the current best practice?

Thanks,
-- 
Christopher J Markiewicz
Ph.D. Candidate, Quantitative Neuroscience Laboratory
Boston University

From satra at mit.edu  Fri Dec 11 15:05:21 2015
From: satra at mit.edu (Satrajit Ghosh)
Date: Fri, 11 Dec 2015 15:05:21 -0500
Subject: [Neuroimaging] Effects of motion outliers on HRF model (in
 sparse acquisition fMRI)
In-Reply-To: <566B2771.3090104@bu.edu>
References: <566B2771.3090104@bu.edu>
Message-ID: <CA+A4wOmSFxON-Fts=SQ48goPtpCdL7__DLSa9f5DeombiitWmg@mail.gmail.com>

hi chris,

this is a standard sparse design and you can use the sparse model in nipype
to get at events and amplitudes that you feed into standard SPM/FSL
designers. the key is to not use a canonical HRF in the modeling stage.

we have a standard openfmri (not BIDS yet) sparse script that can do the
entire preprocessing and estimation on such data and would be happy to
share.

cheers,

satra

On Fri, Dec 11, 2015 at 2:43 PM, Christopher J Markiewicz <effigies at bu.edu>
wrote:

> Hi all,
>
> I apologize in advance because, as Pythonic as my pipeline is, my issue
> here isn't really Python-related. However, the people on this list are
> the most likely to have dealt with similar issues (of places I know to
> look). If you'd rather I post on NeuroStars, I can, but I'm not sure how
> much people are actually using that.
>
> Anyway, my functional data comes from evenly-spaced, sparse acquisitions
> (TA=2.25s, TR=3.375s), and I've used artdetect in nipype to tag motion
> and intensity outliers. It's a fast, event-related design (one event
> every 2 TRs).
>
> In the past, my strategy has been to estimate HRF betas on the full
> dataset, and then excluding motion outliers in analysis by removing any
> event estimate that had an above-threshold contribution from an outlier
> volume. That is, in an NxM design matrix estimating N events from M
> scans, if scan j is an outlier, we exclude all events i such that
> DM[i,j] > (e.g.) 10% of max(HRF).
>
> Another strategy I'm looking into is to add nuisance regressors for
> outlier volumes to the design matrix, and limiting bleed-over into
> unrelated events. This is running into problems with "runs" of outliers,
> which can leave some events with nothing by which to estimate or only
> very small contributions from volumes that are going to be dominated by
> other events. I could remove such events, entirely, but for various
> reasons (mostly involving maintaining ordering so that off-by-one errors
> don't slip into our analysis) I'd like to have some representation of
> each event.
>
> The best external-to-our-lab resource I could find was this Gabrieli Lab
> protocol
> (
> https://github.com/gablab/mindhive/wiki/Example-of-sparse-fMRI-data-analysis-using-BIPS
> ),
> which seems to indicate they've included the full dataset and noted
> outliers after estimation.
>
> Does anybody have any experience with outlier exclusion at or before HRF
> estimation, or is this the current best practice?
>
> Thanks,
> --
> Christopher J Markiewicz
> Ph.D. Candidate, Quantitative Neuroscience Laboratory
> Boston University
> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging
>
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From effigies at bu.edu  Fri Dec 11 15:44:05 2015
From: effigies at bu.edu (Christopher J Markiewicz)
Date: Fri, 11 Dec 2015 15:44:05 -0500
Subject: [Neuroimaging] Effects of motion outliers on HRF model (in
 sparse acquisition fMRI)
In-Reply-To: <CA+A4wOmSFxON-Fts=SQ48goPtpCdL7__DLSa9f5DeombiitWmg@mail.gmail.com>
References: <566B2771.3090104@bu.edu>
 <CA+A4wOmSFxON-Fts=SQ48goPtpCdL7__DLSa9f5DeombiitWmg@mail.gmail.com>
Message-ID: <566B3595.7060909@bu.edu>

On 12/11/2015 03:05 PM, Satrajit Ghosh wrote:
> hi chris,
> 
> this is a standard sparse design and you can use the sparse model in
> nipype to get at events and amplitudes that you feed into standard
> SPM/FSL designers. the key is to not use a canonical HRF in the modeling
> stage.

Thanks Satra. Looking at the SpecifySparseModel code, it looks like
motion outliers are not taken into consideration at estimation time, so
I'm inferring that these pipelines also don't worry about
artifact-induced issues at this stage.

That's fine. I just want to make sure that there isn't a standard (or
emerging consensus) step that we're skipping.

> we have a standard openfmri (not BIDS yet) sparse script that can do the
> entire preprocessing and estimation on such data and would be happy to
> share.

That would be great, at the very least to validate that our approach
hasn't been giving us wildly different results.

<snip>

Thanks,
-- 
Christopher J Markiewicz
Ph.D. Candidate, Quantitative Neuroscience Laboratory
Boston University

From satra at mit.edu  Fri Dec 11 15:50:10 2015
From: satra at mit.edu (Satrajit Ghosh)
Date: Fri, 11 Dec 2015 15:50:10 -0500
Subject: [Neuroimaging] Effects of motion outliers on HRF model (in
 sparse acquisition fMRI)
In-Reply-To: <566B3595.7060909@bu.edu>
References: <566B2771.3090104@bu.edu>
 <CA+A4wOmSFxON-Fts=SQ48goPtpCdL7__DLSa9f5DeombiitWmg@mail.gmail.com>
 <566B3595.7060909@bu.edu>
Message-ID: <CA+A4wOmgRcvJiO2skiFxoecGZF4QOeg8VYX8oScAZ==jCz4MiQ@mail.gmail.com>

hi chris,

the outliers are taken into account at the estimation stage. one column for
each volume discarded, not convolved with a canonical hrf.

cheers,

satra

On Fri, Dec 11, 2015 at 3:44 PM, Christopher J Markiewicz <effigies at bu.edu>
wrote:

> On 12/11/2015 03:05 PM, Satrajit Ghosh wrote:
> > hi chris,
> >
> > this is a standard sparse design and you can use the sparse model in
> > nipype to get at events and amplitudes that you feed into standard
> > SPM/FSL designers. the key is to not use a canonical HRF in the modeling
> > stage.
>
> Thanks Satra. Looking at the SpecifySparseModel code, it looks like
> motion outliers are not taken into consideration at estimation time, so
> I'm inferring that these pipelines also don't worry about
> artifact-induced issues at this stage.
>
> That's fine. I just want to make sure that there isn't a standard (or
> emerging consensus) step that we're skipping.
>
> > we have a standard openfmri (not BIDS yet) sparse script that can do the
> > entire preprocessing and estimation on such data and would be happy to
> > share.
>
> That would be great, at the very least to validate that our approach
> hasn't been giving us wildly different results.
>
> <snip>
>
> Thanks,
> --
> Christopher J Markiewicz
> Ph.D. Candidate, Quantitative Neuroscience Laboratory
> Boston University
> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging
>
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From jetzel at wustl.edu  Sat Dec 12 10:53:21 2015
From: jetzel at wustl.edu (Jo Etzel)
Date: Sat, 12 Dec 2015 09:53:21 -0600
Subject: [Neuroimaging] 1st call for papers: PRNI 2016
Message-ID: <566C42F1.3030302@wustl.edu>

          ******* please accept our apologies for cross-posting *******
------------------------------------------------------------------------------
                           FIRST CALL FOR PAPERS
                                 PRNI 2016
      6th International Workshop on Pattern Recognition in Neuroimaging

                             22-24 June 2016
              Fondazione Bruno Kessler (FBK), Trento, Italy
          www.prni.org - @PRNI2016 - www.facebook.com/PRNI2016/
------------------------------------------------------------------------------

Paper submission deadline:    18 March 2016, 11:59 pm PST
Acceptance notification:      22 April 2016
Camera-ready paper deadline:  7 May 2016
Oral and poster sessions:     22-24 June 2016

Pattern recognition techniques have become an important tool for 
neuroimaging data analysis. These techniques are helping to elucidate 
normal and abnormal brain function, cognition and perception, anatomical 
and functional brain architecture, biomarkers for diagnosis and 
personalized medicine, and as a scientific tool to decipher neural 
mechanisms underlying human cognition. The International Workshop on 
Pattern Recognition in Neuroimaging (PRNI) aims to: (1) foster dialogue 
between developers and users of cutting-edge analysis techniques in 
order to find matches between analysis techniques and neuroscientific 
questions; (2) showcase recent methodological advances in pattern 
recognition algorithms for neuroimaging analysis; and (3) identify 
challenging neuroscientific questions in need of new analysis approaches.


PRNI welcomes submissions on topics including, but not limited to:

* Learning from neuroimaging data
- Algorithms for brain-state decoding or encoding
- Optimization and regularization
- Bayesian analysis of neuroimaging data
- Causal inference and time delay techniques
- Network and connectivity models (the connectome)
- Dynamic and time-varying models
- Dynamical systems and simulations
- Empirical mode decomposition, multiscale decompositions
- Combination of different data modalities
- Efficient algorithms for large-scale data analysis

* Interpretability of models and results
- High-dimensional data visualization
- Multivariate and multiple hypothesis testing
- Summarization and presentation of inference results

* Applications
- Disease diagnosis and prognosis
- Real-time decoding of brain states
- Analysis of resting-state and task-based data
- MEG, EEG, structural MRI, fMRI, diffusion MRI, ECoG, NIRS


Authors should prepare full papers with a maximum length of 4 pages (two 
column IEEE style) for double-blind review. The manuscript submission 
deadline is 18 March 2016. Accepted manuscripts will be assigned either 
to an oral or poster sessions; all accepted manuscripts will be included 
in the workshop proceedings.



-- 
Joset A. Etzel, Ph.D.
Research Analyst
Cognitive Control & Psychopathology Lab
Washington University in St. Louis
http://mvpa.blogspot.com/

From rockelcp at mcmaster.ca  Tue Dec 15 17:55:41 2015
From: rockelcp at mcmaster.ca (C.P. Rockel)
Date: Tue, 15 Dec 2015 17:55:41 -0500
Subject: [Neuroimaging] Mac OSX 10.6.8 problem
Message-ID: <CAGEk_jpH=MtbmuWmrG2a-xZqgafxR=myjmm7LS-PQeCLNQoBRg@mail.gmail.com>

Hi folks,

I am brand new to Python, and I am having difficulties installing Dipy onto
my Mac (OSX 10.6.8).  I already had X-code, and I just downloaded and set
up Anaconda3-2.4.1-MacOSX-x86_64.sh.  I tried "pipping" all of the
dependencies as per the Dipy website, and it said that this packages were
already 'satisfied'.

However, when I then 'pip install dipy', I get errors such as:

  SystemError: Cannot locate working compiler

  ----------------------------------------
  Failed building wheel for dipy
Failed to build dipy

... AND...

Command "/Applications/anaconda3/bin/python3 -c "import setuptools,
tokenize;__file__='/private/var/folders/5p/5pdhhqwBGJCf2rrSB7PRn++++TQ/-Tmp-/pip-build-4zbiedwj/dipy/setup.py';exec(compile(getattr(tokenize,
'open', open)(__file__).read().replace('\r\n', '\n'), __file__, 'exec'))"
install --record
/var/folders/5p/5pdhhqwBGJCf2rrSB7PRn++++TQ/-Tmp-/pip-l7x7k8y9-record/install-record.txt
--single-version-externally-managed --compile" failed with error code 1 in
/private/var/folders/5p/5pdhhqwBGJCf2rrSB7PRn++++TQ/-Tmp-/pip-build-4zbiedwj/dipy

As I'm not familiar with the Python environment, I'm not quite sure where
to start troubleshooting.

Does anyone have any suggestions?

Thanks,

Conrad
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From arokem at gmail.com  Tue Dec 15 18:08:27 2015
From: arokem at gmail.com (Ariel Rokem)
Date: Tue, 15 Dec 2015 15:08:27 -0800
Subject: [Neuroimaging] Mac OSX 10.6.8 problem
In-Reply-To: <CAGEk_jpH=MtbmuWmrG2a-xZqgafxR=myjmm7LS-PQeCLNQoBRg@mail.gmail.com>
References: <CAGEk_jpH=MtbmuWmrG2a-xZqgafxR=myjmm7LS-PQeCLNQoBRg@mail.gmail.com>
Message-ID: <CA+X4w0zM7Wq_hwLU9efh9ocxDxo+GwDDimNOTsLHNNrwkypLJw@mail.gmail.com>

Hi Conrad,

Looks like pip is not managing to find your compiler, though if you have
installed XCode that should be in place.

What do you get when you type "gcc" at the command line?

Assuming that indicates that the shell can find your compiler -- you should
get a message indicating that the compiler couldn't find any input files,
or something to that nature -- does typing (as suggested here:
http://stackoverflow.com/questions/21193936/getting-errors-when-trying-to-install-matplotlib-and-numpy-using-pip
):

export CC=gcc

and then running:

pip install dipy

do the trick for you?

Cheers,

Ariel

On Tue, Dec 15, 2015 at 2:55 PM, C.P. Rockel <rockelcp at mcmaster.ca> wrote:

> Hi folks,
>
> I am brand new to Python, and I am having difficulties installing Dipy
> onto my Mac (OSX 10.6.8).  I already had X-code, and I just downloaded and
> set up Anaconda3-2.4.1-MacOSX-x86_64.sh.  I tried "pipping" all of the
> dependencies as per the Dipy website, and it said that this packages were
> already 'satisfied'.
>
> However, when I then 'pip install dipy', I get errors such as:
>
>   SystemError: Cannot locate working compiler
>
>   ----------------------------------------
>   Failed building wheel for dipy
> Failed to build dipy
>
> ... AND...
>
> Command "/Applications/anaconda3/bin/python3 -c "import setuptools,
> tokenize;__file__='/private/var/folders/5p/5pdhhqwBGJCf2rrSB7PRn++++TQ/-Tmp-/pip-build-4zbiedwj/dipy/setup.py';exec(compile(getattr(tokenize,
> 'open', open)(__file__).read().replace('\r\n', '\n'), __file__, 'exec'))"
> install --record
> /var/folders/5p/5pdhhqwBGJCf2rrSB7PRn++++TQ/-Tmp-/pip-l7x7k8y9-record/install-record.txt
> --single-version-externally-managed --compile" failed with error code 1 in
> /private/var/folders/5p/5pdhhqwBGJCf2rrSB7PRn++++TQ/-Tmp-/pip-build-4zbiedwj/dipy
>
> As I'm not familiar with the Python environment, I'm not quite sure where
> to start troubleshooting.
>
> Does anyone have any suggestions?
>
> Thanks,
>
> Conrad
>
> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging
>
>
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From rockelcp at mcmaster.ca  Tue Dec 15 18:18:29 2015
From: rockelcp at mcmaster.ca (C.P. Rockel)
Date: Tue, 15 Dec 2015 18:18:29 -0500
Subject: [Neuroimaging] Mac OSX 10.6.8 problem
In-Reply-To: <CA+X4w0zM7Wq_hwLU9efh9ocxDxo+GwDDimNOTsLHNNrwkypLJw@mail.gmail.com>
References: <CAGEk_jpH=MtbmuWmrG2a-xZqgafxR=myjmm7LS-PQeCLNQoBRg@mail.gmail.com>
 <CA+X4w0zM7Wq_hwLU9efh9ocxDxo+GwDDimNOTsLHNNrwkypLJw@mail.gmail.com>
Message-ID: <CAGEk_jpuKcdXK8+E6w4BvW-vRSCGYqfKmNtWVDVh=oVdJi9p2w@mail.gmail.com>

Hi Ariel,

when I type 'gcc' I get "command not found".  The rest of the commands did
nothing, and I got a similar error when trying to pip install dipy.

I know I have XCode, because I am looking at its icon on my doc.  Hmmm, any
chance that I don't have the right XCode?  How can I test this?

Thanks,

Conrad

On Tue, Dec 15, 2015 at 6:08 PM, Ariel Rokem <arokem at gmail.com> wrote:

> Hi Conrad,
>
> Looks like pip is not managing to find your compiler, though if you have
> installed XCode that should be in place.
>
> What do you get when you type "gcc" at the command line?
>
> Assuming that indicates that the shell can find your compiler -- you
> should get a message indicating that the compiler couldn't find any input
> files, or something to that nature -- does typing (as suggested here:
> http://stackoverflow.com/questions/21193936/getting-errors-when-trying-to-install-matplotlib-and-numpy-using-pip
> ):
>
> export CC=gcc
>
> and then running:
>
> pip install dipy
>
> do the trick for you?
>
> Cheers,
>
> Ariel
>
> On Tue, Dec 15, 2015 at 2:55 PM, C.P. Rockel <rockelcp at mcmaster.ca> wrote:
>
>> Hi folks,
>>
>> I am brand new to Python, and I am having difficulties installing Dipy
>> onto my Mac (OSX 10.6.8).  I already had X-code, and I just downloaded and
>> set up Anaconda3-2.4.1-MacOSX-x86_64.sh.  I tried "pipping" all of the
>> dependencies as per the Dipy website, and it said that this packages were
>> already 'satisfied'.
>>
>> However, when I then 'pip install dipy', I get errors such as:
>>
>>   SystemError: Cannot locate working compiler
>>
>>   ----------------------------------------
>>   Failed building wheel for dipy
>> Failed to build dipy
>>
>> ... AND...
>>
>> Command "/Applications/anaconda3/bin/python3 -c "import setuptools,
>> tokenize;__file__='/private/var/folders/5p/5pdhhqwBGJCf2rrSB7PRn++++TQ/-Tmp-/pip-build-4zbiedwj/dipy/setup.py';exec(compile(getattr(tokenize,
>> 'open', open)(__file__).read().replace('\r\n', '\n'), __file__, 'exec'))"
>> install --record
>> /var/folders/5p/5pdhhqwBGJCf2rrSB7PRn++++TQ/-Tmp-/pip-l7x7k8y9-record/install-record.txt
>> --single-version-externally-managed --compile" failed with error code 1 in
>> /private/var/folders/5p/5pdhhqwBGJCf2rrSB7PRn++++TQ/-Tmp-/pip-build-4zbiedwj/dipy
>>
>> As I'm not familiar with the Python environment, I'm not quite sure where
>> to start troubleshooting.
>>
>> Does anyone have any suggestions?
>>
>> Thanks,
>>
>> Conrad
>>
>> _______________________________________________
>> 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 arokem at gmail.com  Tue Dec 15 18:23:27 2015
From: arokem at gmail.com (Ariel Rokem)
Date: Tue, 15 Dec 2015 15:23:27 -0800
Subject: [Neuroimaging] Mac OSX 10.6.8 problem
In-Reply-To: <CAGEk_jpuKcdXK8+E6w4BvW-vRSCGYqfKmNtWVDVh=oVdJi9p2w@mail.gmail.com>
References: <CAGEk_jpH=MtbmuWmrG2a-xZqgafxR=myjmm7LS-PQeCLNQoBRg@mail.gmail.com>
 <CA+X4w0zM7Wq_hwLU9efh9ocxDxo+GwDDimNOTsLHNNrwkypLJw@mail.gmail.com>
 <CAGEk_jpuKcdXK8+E6w4BvW-vRSCGYqfKmNtWVDVh=oVdJi9p2w@mail.gmail.com>
Message-ID: <CA+X4w0xV+GovHJr0fo2tfSfnLPbWOoJh5aWr7pXv=410LzX_mg@mail.gmail.com>

Sorry for doing this step-by-step, but did you also install the command
line developer tools?

http://apple.stackexchange.com/questions/48099/gcc-not-found-but-xcode-is-installed

 (occurs to me this needs better explanation in our documentation...)

On Tue, Dec 15, 2015 at 3:18 PM, C.P. Rockel <rockelcp at mcmaster.ca> wrote:

> Hi Ariel,
>
> when I type 'gcc' I get "command not found".  The rest of the commands did
> nothing, and I got a similar error when trying to pip install dipy.
>
> I know I have XCode, because I am looking at its icon on my doc.  Hmmm,
> any chance that I don't have the right XCode?  How can I test this?
>
> Thanks,
>
> Conrad
>
> On Tue, Dec 15, 2015 at 6:08 PM, Ariel Rokem <arokem at gmail.com> wrote:
>
>> Hi Conrad,
>>
>> Looks like pip is not managing to find your compiler, though if you have
>> installed XCode that should be in place.
>>
>> What do you get when you type "gcc" at the command line?
>>
>> Assuming that indicates that the shell can find your compiler -- you
>> should get a message indicating that the compiler couldn't find any input
>> files, or something to that nature -- does typing (as suggested here:
>> http://stackoverflow.com/questions/21193936/getting-errors-when-trying-to-install-matplotlib-and-numpy-using-pip
>> ):
>>
>> export CC=gcc
>>
>> and then running:
>>
>> pip install dipy
>>
>> do the trick for you?
>>
>> Cheers,
>>
>> Ariel
>>
>> On Tue, Dec 15, 2015 at 2:55 PM, C.P. Rockel <rockelcp at mcmaster.ca>
>> wrote:
>>
>>> Hi folks,
>>>
>>> I am brand new to Python, and I am having difficulties installing Dipy
>>> onto my Mac (OSX 10.6.8).  I already had X-code, and I just downloaded and
>>> set up Anaconda3-2.4.1-MacOSX-x86_64.sh.  I tried "pipping" all of the
>>> dependencies as per the Dipy website, and it said that this packages were
>>> already 'satisfied'.
>>>
>>> However, when I then 'pip install dipy', I get errors such as:
>>>
>>>   SystemError: Cannot locate working compiler
>>>
>>>   ----------------------------------------
>>>   Failed building wheel for dipy
>>> Failed to build dipy
>>>
>>> ... AND...
>>>
>>> Command "/Applications/anaconda3/bin/python3 -c "import setuptools,
>>> tokenize;__file__='/private/var/folders/5p/5pdhhqwBGJCf2rrSB7PRn++++TQ/-Tmp-/pip-build-4zbiedwj/dipy/setup.py';exec(compile(getattr(tokenize,
>>> 'open', open)(__file__).read().replace('\r\n', '\n'), __file__, 'exec'))"
>>> install --record
>>> /var/folders/5p/5pdhhqwBGJCf2rrSB7PRn++++TQ/-Tmp-/pip-l7x7k8y9-record/install-record.txt
>>> --single-version-externally-managed --compile" failed with error code 1 in
>>> /private/var/folders/5p/5pdhhqwBGJCf2rrSB7PRn++++TQ/-Tmp-/pip-build-4zbiedwj/dipy
>>>
>>> As I'm not familiar with the Python environment, I'm not quite sure
>>> where to start troubleshooting.
>>>
>>> Does anyone have any suggestions?
>>>
>>> Thanks,
>>>
>>> Conrad
>>>
>>> _______________________________________________
>>> 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
>>
>>
>
> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging
>
>
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From rockelcp at mcmaster.ca  Tue Dec 15 18:35:33 2015
From: rockelcp at mcmaster.ca (C.P. Rockel)
Date: Tue, 15 Dec 2015 18:35:33 -0500
Subject: [Neuroimaging] Mac OSX 10.6.8 problem
In-Reply-To: <CA+X4w0xV+GovHJr0fo2tfSfnLPbWOoJh5aWr7pXv=410LzX_mg@mail.gmail.com>
References: <CAGEk_jpH=MtbmuWmrG2a-xZqgafxR=myjmm7LS-PQeCLNQoBRg@mail.gmail.com>
 <CA+X4w0zM7Wq_hwLU9efh9ocxDxo+GwDDimNOTsLHNNrwkypLJw@mail.gmail.com>
 <CAGEk_jpuKcdXK8+E6w4BvW-vRSCGYqfKmNtWVDVh=oVdJi9p2w@mail.gmail.com>
 <CA+X4w0xV+GovHJr0fo2tfSfnLPbWOoJh5aWr7pXv=410LzX_mg@mail.gmail.com>
Message-ID: <CAGEk_joMOZPXyGCjq+kyee7zGBCMgpu13QMMx=QNSAFo6LAipQ@mail.gmail.com>

I tried to update XCode, but it wouldn't let me since my OS is only
10.6.8.  :(

I'm not in the mood to update my OS at this point in my PhD...

My XCode is version is 3.2.6, and it doesn't have the Downloads option of
which you speak.

Are there any substitutes that you can think of?

Cheers,

Conrad

On Tue, Dec 15, 2015 at 6:23 PM, Ariel Rokem <arokem at gmail.com> wrote:

> Sorry for doing this step-by-step, but did you also install the command
> line developer tools?
>
>
> http://apple.stackexchange.com/questions/48099/gcc-not-found-but-xcode-is-installed
>
>  (occurs to me this needs better explanation in our documentation...)
>
> On Tue, Dec 15, 2015 at 3:18 PM, C.P. Rockel <rockelcp at mcmaster.ca> wrote:
>
>> Hi Ariel,
>>
>> when I type 'gcc' I get "command not found".  The rest of the commands
>> did nothing, and I got a similar error when trying to pip install dipy.
>>
>> I know I have XCode, because I am looking at its icon on my doc.  Hmmm,
>> any chance that I don't have the right XCode?  How can I test this?
>>
>> Thanks,
>>
>> Conrad
>>
>> On Tue, Dec 15, 2015 at 6:08 PM, Ariel Rokem <arokem at gmail.com> wrote:
>>
>>> Hi Conrad,
>>>
>>> Looks like pip is not managing to find your compiler, though if you have
>>> installed XCode that should be in place.
>>>
>>> What do you get when you type "gcc" at the command line?
>>>
>>> Assuming that indicates that the shell can find your compiler -- you
>>> should get a message indicating that the compiler couldn't find any input
>>> files, or something to that nature -- does typing (as suggested here:
>>> http://stackoverflow.com/questions/21193936/getting-errors-when-trying-to-install-matplotlib-and-numpy-using-pip
>>> ):
>>>
>>> export CC=gcc
>>>
>>> and then running:
>>>
>>> pip install dipy
>>>
>>> do the trick for you?
>>>
>>> Cheers,
>>>
>>> Ariel
>>>
>>> On Tue, Dec 15, 2015 at 2:55 PM, C.P. Rockel <rockelcp at mcmaster.ca>
>>> wrote:
>>>
>>>> Hi folks,
>>>>
>>>> I am brand new to Python, and I am having difficulties installing Dipy
>>>> onto my Mac (OSX 10.6.8).  I already had X-code, and I just downloaded and
>>>> set up Anaconda3-2.4.1-MacOSX-x86_64.sh.  I tried "pipping" all of the
>>>> dependencies as per the Dipy website, and it said that this packages were
>>>> already 'satisfied'.
>>>>
>>>> However, when I then 'pip install dipy', I get errors such as:
>>>>
>>>>   SystemError: Cannot locate working compiler
>>>>
>>>>   ----------------------------------------
>>>>   Failed building wheel for dipy
>>>> Failed to build dipy
>>>>
>>>> ... AND...
>>>>
>>>> Command "/Applications/anaconda3/bin/python3 -c "import setuptools,
>>>> tokenize;__file__='/private/var/folders/5p/5pdhhqwBGJCf2rrSB7PRn++++TQ/-Tmp-/pip-build-4zbiedwj/dipy/setup.py';exec(compile(getattr(tokenize,
>>>> 'open', open)(__file__).read().replace('\r\n', '\n'), __file__, 'exec'))"
>>>> install --record
>>>> /var/folders/5p/5pdhhqwBGJCf2rrSB7PRn++++TQ/-Tmp-/pip-l7x7k8y9-record/install-record.txt
>>>> --single-version-externally-managed --compile" failed with error code 1 in
>>>> /private/var/folders/5p/5pdhhqwBGJCf2rrSB7PRn++++TQ/-Tmp-/pip-build-4zbiedwj/dipy
>>>>
>>>> As I'm not familiar with the Python environment, I'm not quite sure
>>>> where to start troubleshooting.
>>>>
>>>> Does anyone have any suggestions?
>>>>
>>>> Thanks,
>>>>
>>>> Conrad
>>>>
>>>> _______________________________________________
>>>> 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
>>>
>>>
>>
>> _______________________________________________
>> 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 arokem at gmail.com  Tue Dec 15 18:41:57 2015
From: arokem at gmail.com (Ariel Rokem)
Date: Tue, 15 Dec 2015 15:41:57 -0800
Subject: [Neuroimaging] Mac OSX 10.6.8 problem
In-Reply-To: <CAGEk_joMOZPXyGCjq+kyee7zGBCMgpu13QMMx=QNSAFo6LAipQ@mail.gmail.com>
References: <CAGEk_jpH=MtbmuWmrG2a-xZqgafxR=myjmm7LS-PQeCLNQoBRg@mail.gmail.com>
 <CA+X4w0zM7Wq_hwLU9efh9ocxDxo+GwDDimNOTsLHNNrwkypLJw@mail.gmail.com>
 <CAGEk_jpuKcdXK8+E6w4BvW-vRSCGYqfKmNtWVDVh=oVdJi9p2w@mail.gmail.com>
 <CA+X4w0xV+GovHJr0fo2tfSfnLPbWOoJh5aWr7pXv=410LzX_mg@mail.gmail.com>
 <CAGEk_joMOZPXyGCjq+kyee7zGBCMgpu13QMMx=QNSAFo6LAipQ@mail.gmail.com>
Message-ID: <CA+X4w0xnNjMoKCS51=GWuuY5T6yjPPQcdjNdk8UmtrYjVJHUVw@mail.gmail.com>

Hmm. Maybe this one is the right for you?

https://github.com/kennethreitz/osx-gcc-installer

(See also:
http://stackoverflow.com/questions/4360110/installing-gcc-to-mac-os-x-leopard-without-installing-xcode
)



On Tue, Dec 15, 2015 at 3:35 PM, C.P. Rockel <rockelcp at mcmaster.ca> wrote:

> I tried to update XCode, but it wouldn't let me since my OS is only
> 10.6.8.  :(
>
> I'm not in the mood to update my OS at this point in my PhD...
>
> My XCode is version is 3.2.6, and it doesn't have the Downloads option of
> which you speak.
>
> Are there any substitutes that you can think of?
>
> Cheers,
>
> Conrad
>
> On Tue, Dec 15, 2015 at 6:23 PM, Ariel Rokem <arokem at gmail.com> wrote:
>
>> Sorry for doing this step-by-step, but did you also install the command
>> line developer tools?
>>
>>
>> http://apple.stackexchange.com/questions/48099/gcc-not-found-but-xcode-is-installed
>>
>>  (occurs to me this needs better explanation in our documentation...)
>>
>> On Tue, Dec 15, 2015 at 3:18 PM, C.P. Rockel <rockelcp at mcmaster.ca>
>> wrote:
>>
>>> Hi Ariel,
>>>
>>> when I type 'gcc' I get "command not found".  The rest of the commands
>>> did nothing, and I got a similar error when trying to pip install dipy.
>>>
>>> I know I have XCode, because I am looking at its icon on my doc.  Hmmm,
>>> any chance that I don't have the right XCode?  How can I test this?
>>>
>>> Thanks,
>>>
>>> Conrad
>>>
>>> On Tue, Dec 15, 2015 at 6:08 PM, Ariel Rokem <arokem at gmail.com> wrote:
>>>
>>>> Hi Conrad,
>>>>
>>>> Looks like pip is not managing to find your compiler, though if you
>>>> have installed XCode that should be in place.
>>>>
>>>> What do you get when you type "gcc" at the command line?
>>>>
>>>> Assuming that indicates that the shell can find your compiler -- you
>>>> should get a message indicating that the compiler couldn't find any input
>>>> files, or something to that nature -- does typing (as suggested here:
>>>> http://stackoverflow.com/questions/21193936/getting-errors-when-trying-to-install-matplotlib-and-numpy-using-pip
>>>> ):
>>>>
>>>> export CC=gcc
>>>>
>>>> and then running:
>>>>
>>>> pip install dipy
>>>>
>>>> do the trick for you?
>>>>
>>>> Cheers,
>>>>
>>>> Ariel
>>>>
>>>> On Tue, Dec 15, 2015 at 2:55 PM, C.P. Rockel <rockelcp at mcmaster.ca>
>>>> wrote:
>>>>
>>>>> Hi folks,
>>>>>
>>>>> I am brand new to Python, and I am having difficulties installing Dipy
>>>>> onto my Mac (OSX 10.6.8).  I already had X-code, and I just downloaded and
>>>>> set up Anaconda3-2.4.1-MacOSX-x86_64.sh.  I tried "pipping" all of
>>>>> the dependencies as per the Dipy website, and it said that this packages
>>>>> were already 'satisfied'.
>>>>>
>>>>> However, when I then 'pip install dipy', I get errors such as:
>>>>>
>>>>>   SystemError: Cannot locate working compiler
>>>>>
>>>>>   ----------------------------------------
>>>>>   Failed building wheel for dipy
>>>>> Failed to build dipy
>>>>>
>>>>> ... AND...
>>>>>
>>>>> Command "/Applications/anaconda3/bin/python3 -c "import setuptools,
>>>>> tokenize;__file__='/private/var/folders/5p/5pdhhqwBGJCf2rrSB7PRn++++TQ/-Tmp-/pip-build-4zbiedwj/dipy/setup.py';exec(compile(getattr(tokenize,
>>>>> 'open', open)(__file__).read().replace('\r\n', '\n'), __file__, 'exec'))"
>>>>> install --record
>>>>> /var/folders/5p/5pdhhqwBGJCf2rrSB7PRn++++TQ/-Tmp-/pip-l7x7k8y9-record/install-record.txt
>>>>> --single-version-externally-managed --compile" failed with error code 1 in
>>>>> /private/var/folders/5p/5pdhhqwBGJCf2rrSB7PRn++++TQ/-Tmp-/pip-build-4zbiedwj/dipy
>>>>>
>>>>> As I'm not familiar with the Python environment, I'm not quite sure
>>>>> where to start troubleshooting.
>>>>>
>>>>> Does anyone have any suggestions?
>>>>>
>>>>> Thanks,
>>>>>
>>>>> Conrad
>>>>>
>>>>> _______________________________________________
>>>>> 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
>>>>
>>>>
>>>
>>> _______________________________________________
>>> 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
>>
>>
>
> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging
>
>
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From rockelcp at mcmaster.ca  Tue Dec 15 18:44:10 2015
From: rockelcp at mcmaster.ca (C.P. Rockel)
Date: Tue, 15 Dec 2015 18:44:10 -0500
Subject: [Neuroimaging] Mac OSX 10.6.8 problem
In-Reply-To: <CA+X4w0xnNjMoKCS51=GWuuY5T6yjPPQcdjNdk8UmtrYjVJHUVw@mail.gmail.com>
References: <CAGEk_jpH=MtbmuWmrG2a-xZqgafxR=myjmm7LS-PQeCLNQoBRg@mail.gmail.com>
 <CA+X4w0zM7Wq_hwLU9efh9ocxDxo+GwDDimNOTsLHNNrwkypLJw@mail.gmail.com>
 <CAGEk_jpuKcdXK8+E6w4BvW-vRSCGYqfKmNtWVDVh=oVdJi9p2w@mail.gmail.com>
 <CA+X4w0xV+GovHJr0fo2tfSfnLPbWOoJh5aWr7pXv=410LzX_mg@mail.gmail.com>
 <CAGEk_joMOZPXyGCjq+kyee7zGBCMgpu13QMMx=QNSAFo6LAipQ@mail.gmail.com>
 <CA+X4w0xnNjMoKCS51=GWuuY5T6yjPPQcdjNdk8UmtrYjVJHUVw@mail.gmail.com>
Message-ID: <CAGEk_jo667S0_zyoafqrh1h8CVjEEOrOQeoKgMpJXV2Q5AvNXA@mail.gmail.com>

Just found that one also!  Currently trying it....

On Tue, Dec 15, 2015 at 6:41 PM, Ariel Rokem <arokem at gmail.com> wrote:

> Hmm. Maybe this one is the right for you?
>
> https://github.com/kennethreitz/osx-gcc-installer
>
> (See also:
> http://stackoverflow.com/questions/4360110/installing-gcc-to-mac-os-x-leopard-without-installing-xcode
> )
>
>
>
> On Tue, Dec 15, 2015 at 3:35 PM, C.P. Rockel <rockelcp at mcmaster.ca> wrote:
>
>> I tried to update XCode, but it wouldn't let me since my OS is only
>> 10.6.8.  :(
>>
>> I'm not in the mood to update my OS at this point in my PhD...
>>
>> My XCode is version is 3.2.6, and it doesn't have the Downloads option of
>> which you speak.
>>
>> Are there any substitutes that you can think of?
>>
>> Cheers,
>>
>> Conrad
>>
>> On Tue, Dec 15, 2015 at 6:23 PM, Ariel Rokem <arokem at gmail.com> wrote:
>>
>>> Sorry for doing this step-by-step, but did you also install the command
>>> line developer tools?
>>>
>>>
>>> http://apple.stackexchange.com/questions/48099/gcc-not-found-but-xcode-is-installed
>>>
>>>  (occurs to me this needs better explanation in our documentation...)
>>>
>>> On Tue, Dec 15, 2015 at 3:18 PM, C.P. Rockel <rockelcp at mcmaster.ca>
>>> wrote:
>>>
>>>> Hi Ariel,
>>>>
>>>> when I type 'gcc' I get "command not found".  The rest of the commands
>>>> did nothing, and I got a similar error when trying to pip install dipy.
>>>>
>>>> I know I have XCode, because I am looking at its icon on my doc.  Hmmm,
>>>> any chance that I don't have the right XCode?  How can I test this?
>>>>
>>>> Thanks,
>>>>
>>>> Conrad
>>>>
>>>> On Tue, Dec 15, 2015 at 6:08 PM, Ariel Rokem <arokem at gmail.com> wrote:
>>>>
>>>>> Hi Conrad,
>>>>>
>>>>> Looks like pip is not managing to find your compiler, though if you
>>>>> have installed XCode that should be in place.
>>>>>
>>>>> What do you get when you type "gcc" at the command line?
>>>>>
>>>>> Assuming that indicates that the shell can find your compiler -- you
>>>>> should get a message indicating that the compiler couldn't find any input
>>>>> files, or something to that nature -- does typing (as suggested here:
>>>>> http://stackoverflow.com/questions/21193936/getting-errors-when-trying-to-install-matplotlib-and-numpy-using-pip
>>>>> ):
>>>>>
>>>>> export CC=gcc
>>>>>
>>>>> and then running:
>>>>>
>>>>> pip install dipy
>>>>>
>>>>> do the trick for you?
>>>>>
>>>>> Cheers,
>>>>>
>>>>> Ariel
>>>>>
>>>>> On Tue, Dec 15, 2015 at 2:55 PM, C.P. Rockel <rockelcp at mcmaster.ca>
>>>>> wrote:
>>>>>
>>>>>> Hi folks,
>>>>>>
>>>>>> I am brand new to Python, and I am having difficulties installing
>>>>>> Dipy onto my Mac (OSX 10.6.8).  I already had X-code, and I just downloaded
>>>>>> and set up Anaconda3-2.4.1-MacOSX-x86_64.sh.  I tried "pipping" all
>>>>>> of the dependencies as per the Dipy website, and it said that this packages
>>>>>> were already 'satisfied'.
>>>>>>
>>>>>> However, when I then 'pip install dipy', I get errors such as:
>>>>>>
>>>>>>   SystemError: Cannot locate working compiler
>>>>>>
>>>>>>   ----------------------------------------
>>>>>>   Failed building wheel for dipy
>>>>>> Failed to build dipy
>>>>>>
>>>>>> ... AND...
>>>>>>
>>>>>> Command "/Applications/anaconda3/bin/python3 -c "import setuptools,
>>>>>> tokenize;__file__='/private/var/folders/5p/5pdhhqwBGJCf2rrSB7PRn++++TQ/-Tmp-/pip-build-4zbiedwj/dipy/setup.py';exec(compile(getattr(tokenize,
>>>>>> 'open', open)(__file__).read().replace('\r\n', '\n'), __file__, 'exec'))"
>>>>>> install --record
>>>>>> /var/folders/5p/5pdhhqwBGJCf2rrSB7PRn++++TQ/-Tmp-/pip-l7x7k8y9-record/install-record.txt
>>>>>> --single-version-externally-managed --compile" failed with error code 1 in
>>>>>> /private/var/folders/5p/5pdhhqwBGJCf2rrSB7PRn++++TQ/-Tmp-/pip-build-4zbiedwj/dipy
>>>>>>
>>>>>> As I'm not familiar with the Python environment, I'm not quite sure
>>>>>> where to start troubleshooting.
>>>>>>
>>>>>> Does anyone have any suggestions?
>>>>>>
>>>>>> Thanks,
>>>>>>
>>>>>> Conrad
>>>>>>
>>>>>> _______________________________________________
>>>>>> 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
>>>>>
>>>>>
>>>>
>>>> _______________________________________________
>>>> 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
>>>
>>>
>>
>> _______________________________________________
>> 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 rockelcp at mcmaster.ca  Tue Dec 15 19:18:11 2015
From: rockelcp at mcmaster.ca (C.P. Rockel)
Date: Tue, 15 Dec 2015 19:18:11 -0500
Subject: [Neuroimaging] Mac OSX 10.6.8 problem
In-Reply-To: <CA+X4w0xnNjMoKCS51=GWuuY5T6yjPPQcdjNdk8UmtrYjVJHUVw@mail.gmail.com>
References: <CAGEk_jpH=MtbmuWmrG2a-xZqgafxR=myjmm7LS-PQeCLNQoBRg@mail.gmail.com>
 <CA+X4w0zM7Wq_hwLU9efh9ocxDxo+GwDDimNOTsLHNNrwkypLJw@mail.gmail.com>
 <CAGEk_jpuKcdXK8+E6w4BvW-vRSCGYqfKmNtWVDVh=oVdJi9p2w@mail.gmail.com>
 <CA+X4w0xV+GovHJr0fo2tfSfnLPbWOoJh5aWr7pXv=410LzX_mg@mail.gmail.com>
 <CAGEk_joMOZPXyGCjq+kyee7zGBCMgpu13QMMx=QNSAFo6LAipQ@mail.gmail.com>
 <CA+X4w0xnNjMoKCS51=GWuuY5T6yjPPQcdjNdk8UmtrYjVJHUVw@mail.gmail.com>
Message-ID: <CAGEk_jqkL5tmktQjEqkGZ4S-j=Sy5bQOVE_qGeL3LtGQpSfTqg@mail.gmail.com>

Ok, that didn't seem to quite work either.  It seems like there is a crazy
amount of errors, which I can send you if that would help.  I uninstalled
XCode beforehand, btw.

Would I need to re-install Anaconda now that the Kenneth Reitz compiler is
in place, perhaps?

Thanks,

C

On Tue, Dec 15, 2015 at 6:41 PM, Ariel Rokem <arokem at gmail.com> wrote:

> Hmm. Maybe this one is the right for you?
>
> https://github.com/kennethreitz/osx-gcc-installer
>
> (See also:
> http://stackoverflow.com/questions/4360110/installing-gcc-to-mac-os-x-leopard-without-installing-xcode
> )
>
>
>
> On Tue, Dec 15, 2015 at 3:35 PM, C.P. Rockel <rockelcp at mcmaster.ca> wrote:
>
>> I tried to update XCode, but it wouldn't let me since my OS is only
>> 10.6.8.  :(
>>
>> I'm not in the mood to update my OS at this point in my PhD...
>>
>> My XCode is version is 3.2.6, and it doesn't have the Downloads option of
>> which you speak.
>>
>> Are there any substitutes that you can think of?
>>
>> Cheers,
>>
>> Conrad
>>
>> On Tue, Dec 15, 2015 at 6:23 PM, Ariel Rokem <arokem at gmail.com> wrote:
>>
>>> Sorry for doing this step-by-step, but did you also install the command
>>> line developer tools?
>>>
>>>
>>> http://apple.stackexchange.com/questions/48099/gcc-not-found-but-xcode-is-installed
>>>
>>>  (occurs to me this needs better explanation in our documentation...)
>>>
>>> On Tue, Dec 15, 2015 at 3:18 PM, C.P. Rockel <rockelcp at mcmaster.ca>
>>> wrote:
>>>
>>>> Hi Ariel,
>>>>
>>>> when I type 'gcc' I get "command not found".  The rest of the commands
>>>> did nothing, and I got a similar error when trying to pip install dipy.
>>>>
>>>> I know I have XCode, because I am looking at its icon on my doc.  Hmmm,
>>>> any chance that I don't have the right XCode?  How can I test this?
>>>>
>>>> Thanks,
>>>>
>>>> Conrad
>>>>
>>>> On Tue, Dec 15, 2015 at 6:08 PM, Ariel Rokem <arokem at gmail.com> wrote:
>>>>
>>>>> Hi Conrad,
>>>>>
>>>>> Looks like pip is not managing to find your compiler, though if you
>>>>> have installed XCode that should be in place.
>>>>>
>>>>> What do you get when you type "gcc" at the command line?
>>>>>
>>>>> Assuming that indicates that the shell can find your compiler -- you
>>>>> should get a message indicating that the compiler couldn't find any input
>>>>> files, or something to that nature -- does typing (as suggested here:
>>>>> http://stackoverflow.com/questions/21193936/getting-errors-when-trying-to-install-matplotlib-and-numpy-using-pip
>>>>> ):
>>>>>
>>>>> export CC=gcc
>>>>>
>>>>> and then running:
>>>>>
>>>>> pip install dipy
>>>>>
>>>>> do the trick for you?
>>>>>
>>>>> Cheers,
>>>>>
>>>>> Ariel
>>>>>
>>>>> On Tue, Dec 15, 2015 at 2:55 PM, C.P. Rockel <rockelcp at mcmaster.ca>
>>>>> wrote:
>>>>>
>>>>>> Hi folks,
>>>>>>
>>>>>> I am brand new to Python, and I am having difficulties installing
>>>>>> Dipy onto my Mac (OSX 10.6.8).  I already had X-code, and I just downloaded
>>>>>> and set up Anaconda3-2.4.1-MacOSX-x86_64.sh.  I tried "pipping" all
>>>>>> of the dependencies as per the Dipy website, and it said that this packages
>>>>>> were already 'satisfied'.
>>>>>>
>>>>>> However, when I then 'pip install dipy', I get errors such as:
>>>>>>
>>>>>>   SystemError: Cannot locate working compiler
>>>>>>
>>>>>>   ----------------------------------------
>>>>>>   Failed building wheel for dipy
>>>>>> Failed to build dipy
>>>>>>
>>>>>> ... AND...
>>>>>>
>>>>>> Command "/Applications/anaconda3/bin/python3 -c "import setuptools,
>>>>>> tokenize;__file__='/private/var/folders/5p/5pdhhqwBGJCf2rrSB7PRn++++TQ/-Tmp-/pip-build-4zbiedwj/dipy/setup.py';exec(compile(getattr(tokenize,
>>>>>> 'open', open)(__file__).read().replace('\r\n', '\n'), __file__, 'exec'))"
>>>>>> install --record
>>>>>> /var/folders/5p/5pdhhqwBGJCf2rrSB7PRn++++TQ/-Tmp-/pip-l7x7k8y9-record/install-record.txt
>>>>>> --single-version-externally-managed --compile" failed with error code 1 in
>>>>>> /private/var/folders/5p/5pdhhqwBGJCf2rrSB7PRn++++TQ/-Tmp-/pip-build-4zbiedwj/dipy
>>>>>>
>>>>>> As I'm not familiar with the Python environment, I'm not quite sure
>>>>>> where to start troubleshooting.
>>>>>>
>>>>>> Does anyone have any suggestions?
>>>>>>
>>>>>> Thanks,
>>>>>>
>>>>>> Conrad
>>>>>>
>>>>>> _______________________________________________
>>>>>> 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
>>>>>
>>>>>
>>>>
>>>> _______________________________________________
>>>> 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
>>>
>>>
>>
>> _______________________________________________
>> 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  Tue Dec 15 19:27:16 2015
From: matthew.brett at gmail.com (Matthew Brett)
Date: Wed, 16 Dec 2015 00:27:16 +0000
Subject: [Neuroimaging] Mac OSX 10.6.8 problem
In-Reply-To: <CAGEk_jqkL5tmktQjEqkGZ4S-j=Sy5bQOVE_qGeL3LtGQpSfTqg@mail.gmail.com>
References: <CAGEk_jpH=MtbmuWmrG2a-xZqgafxR=myjmm7LS-PQeCLNQoBRg@mail.gmail.com>
 <CA+X4w0zM7Wq_hwLU9efh9ocxDxo+GwDDimNOTsLHNNrwkypLJw@mail.gmail.com>
 <CAGEk_jpuKcdXK8+E6w4BvW-vRSCGYqfKmNtWVDVh=oVdJi9p2w@mail.gmail.com>
 <CA+X4w0xV+GovHJr0fo2tfSfnLPbWOoJh5aWr7pXv=410LzX_mg@mail.gmail.com>
 <CAGEk_joMOZPXyGCjq+kyee7zGBCMgpu13QMMx=QNSAFo6LAipQ@mail.gmail.com>
 <CA+X4w0xnNjMoKCS51=GWuuY5T6yjPPQcdjNdk8UmtrYjVJHUVw@mail.gmail.com>
 <CAGEk_jqkL5tmktQjEqkGZ4S-j=Sy5bQOVE_qGeL3LtGQpSfTqg@mail.gmail.com>
Message-ID: <CAH6Pt5ogj-A-C3hs4gdRCr8zn3KAgfH2nALrg8aKFkiq=hrVNg@mail.gmail.com>

Hi,

On Wed, Dec 16, 2015 at 12:18 AM, C.P. Rockel <rockelcp at mcmaster.ca> wrote:
> Ok, that didn't seem to quite work either.  It seems like there is a crazy
> amount of errors, which I can send you if that would help.  I uninstalled
> XCode beforehand, btw.
>
> Would I need to re-install Anaconda now that the Kenneth Reitz compiler is
> in place, perhaps?

Another option is to install Python via Python.org installers or homebrew, then:

pip install scipy dipy

If you use the Python.org installers, you may get away without needing
a compiler - if that isn't true, please let me know what error you
get.

Cheers,

Matthew

From arokem at gmail.com  Wed Dec 16 11:59:47 2015
From: arokem at gmail.com (Ariel Rokem)
Date: Wed, 16 Dec 2015 08:59:47 -0800
Subject: [Neuroimaging] Homebrew Python for Dipy on Mac? (Was: Mac OSX
 10.6.8 problem)
Message-ID: <CA+X4w0xnQn6gFe4p5dk+5=6zt-FS4NQS_bPyBopk-my1t1cr3Q@mail.gmail.com>

Hi,

On Tue, Dec 15, 2015 at 4:27 PM, Matthew Brett <matthew.brett at gmail.com>
wrote:

> Hi,
>
> On Wed, Dec 16, 2015 at 12:18 AM, C.P. Rockel <rockelcp at mcmaster.ca>
> wrote:
> > Ok, that didn't seem to quite work either.  It seems like there is a
> crazy
> > amount of errors, which I can send you if that would help.  I uninstalled
> > XCode beforehand, btw.
> >
> > Would I need to re-install Anaconda now that the Kenneth Reitz compiler
> is
> > in place, perhaps?
>
> Another option is to install Python via Python.org installers or homebrew,
> then:
>

Interesting. Homebrew + pip might actually be a good way to install Dipy +
dependencies on OS X, because it seems that homebrew can also be used to
install VTK:

https://github.com/nipy/dipy/pull/739#issuecomment-152006600

But there are a few wrinkles. For example, tcl-tk needs to be installed
separately, and a flag needs to be passed to the Python installation, for
tkinter to to work (we use tkinter in some of the newer viz stuff). VTK
also needs to be installed separately, through its own incantation.

Here are some instructions that seem to work on my machine:

1. Install homebrew from http://brew.sh/
2. brew install homebrew/dupes/tcl-tk
3. brew install python --with-tcl-tk
4. brew install vtk --with-python
5. sudo pip install scipy dipy

Maybe we can somehow test this out on the Travis Mac machines, or on the
buildbots? If this does generally work well, we can also change the
installation instructions in our docs.

Cheers,

Ariel



> pip install scipy dipy
>
> If you use the Python.org installers, you may get away without needing
> a compiler - if that isn't true, please let me know what error you
> get.
>
> Cheers,
>
> Matthew
> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging
>
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From stjeansam at gmail.com  Thu Dec 17 11:42:27 2015
From: stjeansam at gmail.com (Samuel St-Jean)
Date: Thu, 17 Dec 2015 17:42:27 +0100
Subject: [Neuroimaging] Getting scaling and offset from nifti header in
 nibabel
Message-ID: <CAKADGuoFcAUYUDdsqM8OErvZe4TfT+7N=zKmEYti1xrb3=0Ljw@mail.gmail.com>

 Hello,

I'd like to get the scaling and offset value to compare data from two
volumes acquired in the same scan. Since the floating points values are
wildly different, we need ot put them on the same scaling of intensity.
This can be done from the dicom files directly, but it a painful and error
prone process and difficult to automate.

Turns out mrconvert from mrtrix does not take into account scaling when
your data is in floating point (by design, so even the options to correct
this scaling are disabled), but still stores the correct values. When
opening the nifti files converted from nibabel the headers just show nan
values, while mrtrix can parse them correctly. Here is the header from
mrinfo as an example.

************************************************
Image:               "noise.nii.gz"
************************************************
  Format:            NIfTI-1.1
  Dimensions:        128 x 128 x 78
  Voxel size:        1.79665 x 1.79665 x 1.8
  Dimension labels:  0. left->right (mm)
                     1. posterior->anterior (mm)
                     2. inferior->superior (mm)
  Data type:         signed 16 bit integer (little endian)
  Data layout:       [ -0 -1 +2 ]
  Data scaling:      offset = 0, multiplier = 166.825
  Comments:          SAMUEL 9JUIL (SAMU
                     MRtrix version: 0.3.12-1050-g62ace960
  Transform:               0.9993    -0.00287    -0.03691 -115.6
                       -5.649e-11       0.997    -0.07752 -108.4
                          0.03702     0.07746      0.9963 -60.36
                                0           0           0 1

Same header through nibabel

<class 'nibabel.nifti1.Nifti1Header'> object, endian='<'
sizeof_hdr      : 348
data_type       :
db_name         : SAMUEL 9JUIL (SAMU
extents         : 16384
session_error   : 0
regular         : r
dim_info        : 0
dim             : [  3 128 128  78   1   1   1   1]
intent_p1       : 0.0
intent_p2       : 0.0
intent_p3       : 0.0
intent_code     : none
datatype        : int16
bitpix          : 16
slice_start     : 0
pixdim          : [ 1.          1.79665172  1.79665172  1.79999924
0.          0.          0.
  0.        ]
vox_offset      : 0.0
scl_slope       : nan
scl_inter       : nan
slice_end       : 0
slice_code      : unknown
xyzt_units      : 10
cal_max         : 0.0
cal_min         : 0.0
slice_duration  : 0.0
toffset         : 0.0
glmax           : 0
glmin           : 0
descrip         : MRtrix version: 0.3.12-1050-g62ace960
aux_file        :
qform_code      : scanner
sform_code      : scanner
quatern_b       : -0.0185005571693
quatern_c       : -0.038780156523
quatern_d       : 0.999076247215
qoffset_x       : 111.719642639
qoffset_y       : 119.12978363
qoffset_z       : -34.2374076843
srow_x          : [ -1.79541993e+00   5.15606394e-03 -6.64402023e-02
1.11719643e+02]
srow_y          : [  1.01492648e-10  -1.79124594e+00 -1.39527366e-01
1.19129784e+02]
srow_z          : [ -0.06651677  -0.13917242   1.79335308 -34.23740768]
intent_name     :
magic           : n+1


Those would probably be scl_slope and scl_inter, but the values just got
lost somewhere apparently. The same behavior is seen from the example in
nifti header here :
http://nipy.org/nibabel/nifti_images.html#the-nifti-header

So the questions

1. Did something went wrong, or do both program just use different field?
If so, does nibabel show all fields in the header or only known ones?
2. Is the field scl_slope/scl_inter even related to the dicom scaling (and
offset/multiplier from mrtrix, though this one goes in their mailing list
if nobody knows)? Philips call those SS and SI values, and you simply need
to fix the offset + scaling with these numbers to get sane intensities out
of your scanner, but they are a huge pain to get out.

Thanks,

Samuel
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From matthew.brett at gmail.com  Thu Dec 17 12:57:09 2015
From: matthew.brett at gmail.com (Matthew Brett)
Date: Thu, 17 Dec 2015 17:57:09 +0000
Subject: [Neuroimaging] Getting scaling and offset from nifti header in
 nibabel
In-Reply-To: <CAKADGuoFcAUYUDdsqM8OErvZe4TfT+7N=zKmEYti1xrb3=0Ljw@mail.gmail.com>
References: <CAKADGuoFcAUYUDdsqM8OErvZe4TfT+7N=zKmEYti1xrb3=0Ljw@mail.gmail.com>
Message-ID: <CAH6Pt5q-ahpsqY_LvPe95g+mva6EVJ8ZvhrSUCWp4896QRUw3g@mail.gmail.com>

Hi,

On Thu, Dec 17, 2015 at 4:42 PM, Samuel St-Jean <stjeansam at gmail.com> wrote:
> Hello,
>
> I'd like to get the scaling and offset value to compare data from two
> volumes acquired in the same scan. Since the floating points values are
> wildly different, we need ot put them on the same scaling of intensity. This
> can be done from the dicom files directly, but it a painful and error prone
> process and difficult to automate.
>
> Turns out mrconvert from mrtrix does not take into account scaling when your
> data is in floating point (by design, so even the options to correct this
> scaling are disabled), but still stores the correct values. When opening the
> nifti files converted from nibabel the headers just show nan values, while
> mrtrix can parse them correctly. Here is the header from mrinfo as an
> example.
>
> ************************************************
> Image:               "noise.nii.gz"
> ************************************************
>   Format:            NIfTI-1.1
>   Dimensions:        128 x 128 x 78
>   Voxel size:        1.79665 x 1.79665 x 1.8
>   Dimension labels:  0. left->right (mm)
>                      1. posterior->anterior (mm)
>                      2. inferior->superior (mm)
>   Data type:         signed 16 bit integer (little endian)
>   Data layout:       [ -0 -1 +2 ]
>   Data scaling:      offset = 0, multiplier = 166.825
>   Comments:          SAMUEL 9JUIL (SAMU
>                      MRtrix version: 0.3.12-1050-g62ace960
>   Transform:               0.9993    -0.00287    -0.03691 -115.6
>                        -5.649e-11       0.997    -0.07752 -108.4
>                           0.03702     0.07746      0.9963 -60.36
>                                 0           0           0 1
>
> Same header through nibabel
>
> <class 'nibabel.nifti1.Nifti1Header'> object, endian='<'
> sizeof_hdr      : 348
> data_type       :
> db_name         : SAMUEL 9JUIL (SAMU
> extents         : 16384
> session_error   : 0
> regular         : r
> dim_info        : 0
> dim             : [  3 128 128  78   1   1   1   1]
> intent_p1       : 0.0
> intent_p2       : 0.0
> intent_p3       : 0.0
> intent_code     : none
> datatype        : int16
> bitpix          : 16
> slice_start     : 0
> pixdim          : [ 1.          1.79665172  1.79665172  1.79999924 0.
> 0.          0.
>   0.        ]
> vox_offset      : 0.0
> scl_slope       : nan
> scl_inter       : nan
> slice_end       : 0
> slice_code      : unknown
> xyzt_units      : 10
> cal_max         : 0.0
> cal_min         : 0.0
> slice_duration  : 0.0
> toffset         : 0.0
> glmax           : 0
> glmin           : 0
> descrip         : MRtrix version: 0.3.12-1050-g62ace960
> aux_file        :
> qform_code      : scanner
> sform_code      : scanner
> quatern_b       : -0.0185005571693
> quatern_c       : -0.038780156523
> quatern_d       : 0.999076247215
> qoffset_x       : 111.719642639
> qoffset_y       : 119.12978363
> qoffset_z       : -34.2374076843
> srow_x          : [ -1.79541993e+00   5.15606394e-03 -6.64402023e-02
> 1.11719643e+02]
> srow_y          : [  1.01492648e-10  -1.79124594e+00 -1.39527366e-01
> 1.19129784e+02]
> srow_z          : [ -0.06651677  -0.13917242   1.79335308 -34.23740768]
> intent_name     :
> magic           : n+1
>
>
> Those would probably be scl_slope and scl_inter, but the values just got
> lost somewhere apparently. The same behavior is seen from the example in
> nifti header here :
> http://nipy.org/nibabel/nifti_images.html#the-nifti-header
>
> So the questions
>
> 1. Did something went wrong, or do both program just use different field? If
> so, does nibabel show all fields in the header or only known ones?
> 2. Is the field scl_slope/scl_inter even related to the dicom scaling (and
> offset/multiplier from mrtrix, though this one goes in their mailing list if
> nobody knows)? Philips call those SS and SI values, and you simply need to
> fix the offset + scaling with these numbers to get sane intensities out of
> your scanner, but they are a huge pain to get out.

No - nothing wrong.  The nibabel reader resets the scale and slope to
NaN by default when it reads in the image.

If you want to read the original scale and slope, you can get them
from the image array proxy object, as in:

img.dataobj.slope
img.dataobj.inter

Is that what you needed?

Matthew

From alexandre.gramfort at telecom-paristech.fr  Sat Dec 26 04:14:47 2015
From: alexandre.gramfort at telecom-paristech.fr (Alexandre Gramfort)
Date: Sat, 26 Dec 2015 10:14:47 +0100
Subject: [Neuroimaging] [ANN] MNE-Python 0.11 Release
Message-ID: <CADeotZrELneUerW54ANTXuP2oYuM1QHEx31fdZ1dDkz50oH+1g@mail.gmail.com>

Hi,

We are pleased to announce the new 0.11 release of MNE-python.

A few highlights:

Maxwell filtering (SSS) has been implemented primarily by Mark
Wronkiewicz as part of his Google Summer of Code with the help of Eric
Larson and Samu Taulu. Our implementation includes support for:
- Fine calibration
- Cross-talk correction
- Temporal SSS (tSSS)
- Head position translation
- Internal component regularization
- Compensation for movements using Maxwell filtering on epoched data

Support for raw movement compensation is planned for a future release.

See

http://martinos.org/mne/stable/generated/mne.preprocessing.maxwell_filter.html

New data readers have also been added for Nicolet, EEGLAB (.set), and
CTF data (.ds).

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

http://martinos.org/mne/stable/whats_new.html#version-0-11

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_python

Regards,
The MNE-Python developers

People who contributed to this release with their number of commits:

The committer list for this release is the following (preceded by
number of commits):

   171  Eric Larson
   117  Jaakko Leppakangas
    58  Jona Sassenhagen
    52  Mainak Jas
    46  Alexandre Gramfort
    33  Denis A. Engemann
    28  Teon Brooks
    24  Clemens Brunner
    23  Christian Brodbeck
    15  Mark Wronkiewicz
    10  Jean-Remi King
     5  Marijn van Vliet
     3  Fede Raimondo
     2  Alexander Rudiuk
     2  Emily Stephen
     2  lennyvarghese
     1  Marian Dovgialo

From bcipolli at ucsd.edu  Sat Dec 26 17:31:20 2015
From: bcipolli at ucsd.edu (Ben Cipollini)
Date: Sat, 26 Dec 2015 14:31:20 -0800
Subject: [Neuroimaging] [ANN] MNE-Python 0.11 Release
In-Reply-To: <CADeotZrELneUerW54ANTXuP2oYuM1QHEx31fdZ1dDkz50oH+1g@mail.gmail.com>
References: <CADeotZrELneUerW54ANTXuP2oYuM1QHEx31fdZ1dDkz50oH+1g@mail.gmail.com>
Message-ID: <CANgTvZiES_AVUL5bP0uwf8BV6nHfcifWNgYBVd8HNoA-3SFPwA@mail.gmail.com>

Congratulations!

Also, very cool integration with Zenodo. I haven't seen that before, and
it's just what I've been looking for. On another day, I may ask more about
that :)

Ben

On Sat, Dec 26, 2015 at 1:14 AM, Alexandre Gramfort <
alexandre.gramfort at telecom-paristech.fr> wrote:

> Hi,
>
> We are pleased to announce the new 0.11 release of MNE-python.
>
> A few highlights:
>
> Maxwell filtering (SSS) has been implemented primarily by Mark
> Wronkiewicz as part of his Google Summer of Code with the help of Eric
> Larson and Samu Taulu. Our implementation includes support for:
> - Fine calibration
> - Cross-talk correction
> - Temporal SSS (tSSS)
> - Head position translation
> - Internal component regularization
> - Compensation for movements using Maxwell filtering on epoched data
>
> Support for raw movement compensation is planned for a future release.
>
> See
>
>
> http://martinos.org/mne/stable/generated/mne.preprocessing.maxwell_filter.html
>
> New data readers have also been added for Nicolet, EEGLAB (.set), and
> CTF data (.ds).
>
> For a full list of improvements and API changes, see:
>
> http://martinos.org/mne/stable/whats_new.html#version-0-11
>
> 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_python
>
> Regards,
> The MNE-Python developers
>
> People who contributed to this release with their number of commits:
>
> The committer list for this release is the following (preceded by
> number of commits):
>
>    171  Eric Larson
>    117  Jaakko Leppakangas
>     58  Jona Sassenhagen
>     52  Mainak Jas
>     46  Alexandre Gramfort
>     33  Denis A. Engemann
>     28  Teon Brooks
>     24  Clemens Brunner
>     23  Christian Brodbeck
>     15  Mark Wronkiewicz
>     10  Jean-Remi King
>      5  Marijn van Vliet
>      3  Fede Raimondo
>      2  Alexander Rudiuk
>      2  Emily Stephen
>      2  lennyvarghese
>      1  Marian Dovgialo
> _______________________________________________
> Neuroimaging mailing list
> Neuroimaging at python.org
> https://mail.python.org/mailman/listinfo/neuroimaging
>
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