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Embedding maps availible? #5

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mwaskom opened this issue Apr 27, 2017 · 4 comments
Open

Embedding maps availible? #5

mwaskom opened this issue Apr 27, 2017 · 4 comments

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@mwaskom
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mwaskom commented Apr 27, 2017

Thanks for making availible the code for your very interesting paper!

I was hoping to get the embedding maps so that I can visualize them myself. I gather that I could create them using the notebooks here, but I would need the HCP connectome data and some processing time. Have you made the embedding maps that you show in the paper availible somewhere?

That is, I am looking for the files that will end up in gradient_data/embed and are shown here: https://github.com/NeuroanatomyAndConnectivity/gradient_analysis/blob/master/03_visualize_embeddings.ipynb. I think I specifically want the gradient_data/embedded/embedding_dense_emb.npy file, which I gather I could display on the 32k_fs_LR mesh?

@margulies
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Thanks for your interest! The gradients are available in cifti format here (please disregard the contrast names when loading in wb_view): https://www.dropbox.com/s/pd9oo5x4xdcn21i/hcp.embed.dscalar.nii?dl=0
I'd also be happy to send along the embedding_dense_emb.npy file, if you'd like to work from that version.
If you have any questions, just let me know. I'll do my best to get the file outputs uploaded to the repo soon.

@Yizhen-Z
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Thank you for sharing the codes and files.
I am looking for the file that can generate the morphological landmarks and equidistant lines in Fig.2. If possible, I would also like to work from the embedding_dense_emb.npy version. Can you also provide the link to that file too?
Thanks again.

@AstonshisL
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Thank you for sharing. Could you please share the file ::"gradient_data/conn_matrices/cosine_affinity.npy"and"mbedding_dense_emb.npy"? I'm curious what the data format looks like.
Looking forward to your reply!

@margulies
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Thanks for your interest in the code. Following is a link to the embedding_dense_emb.npy file: link
I apologize that I don't have the cosine_affinity.npy currently available, but it should be possible to regenerate it using the code in the first ipython notebook in this repo.

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