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    skoudoro closed #1976
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    Add a warning on attempted impo… PEP8 Adds a (currently failing) test. and 3 more (compare)

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Romulus
@romainviard
import dipy.viz import window, actor >>>>Gives an error, I just wondering why ?
File "<ipython-input-61-930dc3d62dde>", line 6
import dipy.viz import window, actor
^
SyntaxError: invalid syntax
Eleftherios Garyfallidis
@Garyfallidis
@portokalh it will give reasonable result. Like an average ODF from the different shells. Make sure the shells are all spherical.
@romainviard from dipy.viz import window, actor
Romulus
@romainviard
@Garyfallidis Sorry I made an mistake when I wrote on gitter..."from dipy.viz import window, actor " didn't work on dipy1.0.0 (python 3) but works on dipy 0.14.0 (python 2.7), Do you confirm that it should work with the last version of dipy ? Thanks you
Eleftherios Garyfallidis
@Garyfallidis
Have you installed fury?
pip install fury
And yes it should work. I do confirm.
AlexBadea
@portokalh
thanks much @Garyfallidis !
Romulus
@romainviard
thanks @Garyfallidis you were right!!
Davis Bennett
@d-v-b
what would need to be done for registration in dipy to support masked images
Davis Bennett
@d-v-b
looks like adding properties to MutualInformationMetric
Romulus
@romainviard
Hi dipy's folks,
I'm using the great function dipy.stats.analysis.afq_profile…
And I 'm wondering two things :
  • Is it better to compute the profile on the full track/bundle (so multiple fibers) or on only one fiber (a cluster obtained using QuickBundles for example)?
  • If I want to do stats on different profiles (for example mean FA on the same tract for various subjects), how would you advice to correct statistics(multiple comparison, and espacially spacial correlation,) with Random Field Theory or another one ? In pratical with python and scipy ?
    Thanks a lot in advance,
nis02002
@nis02002
Anyone know does DIPY can do diffusion compartment imaging study?
Eleftherios Garyfallidis
@Garyfallidis
Hi @romainviard !
nis02002
@nis02002
DCI (diffusion compartment imaing) is more advanced than DTI
Eleftherios Garyfallidis
@Garyfallidis
@romainviard let me introduce you to @BramshQamar that is working on extending our statistical analysis framework and she can explain all the details to you. In short, we have a strategy for you to start with and happy to collaborate in trying new ideas.
nis02002
@nis02002
Or any other tool could implement compartment study will be great, thanks a lot
Eleftherios Garyfallidis
@Garyfallidis
@nis02002 do you have DCI data?
nis02002
@nis02002
i only have diffusion image data
i wonder can I fit the data into diffusion compartment model
nis02002
@nis02002
How can we calcuate the diffusion tensor D using tersor model?
like Apparent diffusion coefficnet?
Ariel Rokem
@arokem
@romainviard : regarding afq_profile, probably better to include multiple streamlines, rather than the centroid of a cluster (if that's what you meant)
albayenes
@albayenes
@nis02002 You can find information to calculate diffusion tensor in this tutorial https://dipy.org/documentation/1.0.0./examples_built/reconst_dti/#example-reconst-dti
There is also a function which is named as apparent_diffusion_coef(q_form, sphere) to calculate ADC in DT model
Tristan MOREAU
@Tristan2Tzara_twitter
Thank you for this great new version of dipy ! Good job :-) I have a basic question on how can we use fury to plot the spherical deconvolution results. I used the Tractoflow pipeline to preprocess my clinical data set. Using scil_compute_fodf.py with spherical harmorics basis set to 'descoteaux07' and order set to 8. I got a file 'fodf.nii.gz' of shape (133, 161, 144, 45). My question is how can I use fury to plot the fodf on coronal plan using the fodf file ? Best regards, sincerely. Tristan :-)
Ariel Rokem
@arokem
@Tristan2Tzara_twitter : check out the odf_slicer in this example: https://dipy.org/documentation/1.0.0./examples_built/reconst_csd/
Davis Bennett
@d-v-b
Can anyone point to literature / examples of dealing with masked data in a multiresolution framework?
(for registration)
the right approach seems to be to mask the data before generating the scale space, but i'm not sure
Eleftherios Garyfallidis
@Garyfallidis
Makes sense @d-v-b first mask the brain then multires. With nonlinear registration you really have to do that. There should be something about that at Arno Klein's registration comparison paper.
Davis Bennett
@d-v-b
cool, thanks
i'm looking at this in the context of nipy/dipy#1969
Davis Bennett
@d-v-b
this might be out of scope w.r.t. masking for image registration, but I notice that a lot of these registration functions / methods take 4 arguments at a minimum -- moving, fixed, moving_grid2world, fixed_grid2world -- and there are a lot of nearly duplicated code blocks which perform some operation on moving, then the same operation on fixed. Is there any appetite to simplifying this with some lightweight OOP? e.g. an object that has a data property, a mask property, and a grid2world property would save a lot of LOC in the registration codebase
this is looking pretty hairy to me:
image.png
Eleftherios Garyfallidis
@Garyfallidis
Yes @skoudoro has a plan to make this more OOP. Let's sync together.
Eleftherios Garyfallidis
@Garyfallidis
I would say more than oop the plan is to simplify some things in the reg API. Not sure yet we can take out these parameters completely at first stage. You may feel this is hairy now but after a while you see that it is quite useful. Also do look into dipy.workflows.align there we have a higher level api. Do you have a suggestion for how to reduce the number of parameters? Be happy to suggest ideas. We are good listeners :)
willi3by
@willi3by
Is there a working implementation of the single shell free water bi tensor model? I found a couple of scripts that are works in progress and have spent two days trying to correct errors so it would be nice to know if there is a working implementation
Davis Bennett
@d-v-b
@Garyfallidis I agree, those parameters are all essential, and it's good if the entry point to registration takes raw numpy arrays. I was thinking more about introducing some data structures (maybe something as simple as a namedtuple that would be used internally by the registration / optimization routines
but I don't have any clear ideas right now
Davis Bennett
@d-v-b
@Garyfallidis i have a question about the scale space code -- it looks like it does not do any downsampling, is this by design?
downsampling images after blurring should greatly reduce memory load + compute time for large images
(for reference, the images I am working with are ~10000 x 10000 pixels, and i have thousands of these images, so performance improvements matter a lot here)
Eleftherios Garyfallidis
@Garyfallidis
I think it does downsampling. This is the factor parameter. Factor of 2 means divides by 2.
Davis Bennett
@d-v-b
yes I found where the downsampling happens -- the scale space does not itself do any downsampling, but the AffineRegistration class uses the scalespace parameters to do the downsampling in the optimization step
Ariel Rokem
@arokem
@willi3by : there is a single-shell free water DTI WIP PR by @mvgolub here: nipy/dipy#1744
willi3by
@willi3by
@arokem @mvgolub thank you for the response. This is actually the file I have been trying to edit but have had trouble editing and troubleshooting. I have made some progress but do not have the expertise to do it efficiently. It looks like the last update was about 4 months ago. Is this still a work in progress?
Ariel Rokem
@arokem
I think so. Maybe @mvgolub or @RafaelNH can say more?
Yaroslav Halchenko
@yarikoptic
AHA -- here is where all the DiPy folks hanging out! Will there be DiPy booth at SfN?
Ariel Rokem
@arokem
@yarikoptic : hello!