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There is some overlap with what you made here, but there are two notable differences:
1- my scripts focus on plotting a lot of maps on a single image, on a grid, to summarize the results. For example, plot_correl_maps_overview.ipynb is similar to #7 but can plot more than one result, for example it can plot multiple conditions and contrasts on a 2D grid, with shared colorbar to ease comparison.
2- I made another type of visualization that is not available here, the barplotwithsamples.ipynb notebook, which plots the effect sizes per group and per cluster. It can also automatically detect the clusters center coordinates and atlas regions names, and color them on an atlas and on the bar plots accordingly, which is a lot more readable than a legend. Example: https://github.com/lrq3000/neuro-python-plotting/blob/master/README.md
Note: some notebooks are not yet illustrated in the readme, because I will first publish the figures in my next paper before using them as examples :-)
The text was updated successfully, but these errors were encountered:
Hi @lrq3000!! I'm so sorry for my slow reply! I'm delighted that you found the repo and it would be wonderful to merge your scripts ✨ ✨ ✨
As you've probably noticed, the repo isn't upto date (in fact some of the documentation is coming up to 2 years old 😱) but I'm still very much on board with adding code and developing it. The best way to ask questions is in the (newly created) gitter room: https://gitter.im/WhitakerLab/BrainsForPublication. Ping me there and we can chat about where to put your code!
Hi there,
I just found your repository out of sheer luck. I have made similar visualization scripts for my own needs, maybe this can be merged in this repo?
https://github.com/lrq3000/neuro-python-plotting
There is some overlap with what you made here, but there are two notable differences:
1- my scripts focus on plotting a lot of maps on a single image, on a grid, to summarize the results. For example, plot_correl_maps_overview.ipynb is similar to #7 but can plot more than one result, for example it can plot multiple conditions and contrasts on a 2D grid, with shared colorbar to ease comparison.
2- I made another type of visualization that is not available here, the barplotwithsamples.ipynb notebook, which plots the effect sizes per group and per cluster. It can also automatically detect the clusters center coordinates and atlas regions names, and color them on an atlas and on the bar plots accordingly, which is a lot more readable than a legend. Example: https://github.com/lrq3000/neuro-python-plotting/blob/master/README.md
Note: some notebooks are not yet illustrated in the readme, because I will first publish the figures in my next paper before using them as examples :-)
The text was updated successfully, but these errors were encountered: