AFurryBear/quick-viz

Short-and-sweet cookie cutter visualization scripts or utilities, intended to lower the barrier for looking at all of our results, all of the time.

Quick Viz Tools

Short-and-sweet cookie cutter visualization scripts or utilities, intended to lower the barrier for looking at all of our results, all of the time. Explore the table below of examples, or add yours following the Contribution Instructions, below. An important note is that these are not intended to be perfect or pollished, but to provide quick and easy to use utilities that help streamline our development and testing cycles. Don't be shy to add a new tool, or update one that is here to better suit your needs.

Tool	Purpose	Relevant data	Example
plot_tsv_heatmap.py	Visualize TSV-stored matrices	Connectomes, any numeric matrix
plot_nii_3dheatmap.py	Visualize Nii-stored 3D brain data	Structural Nii, Mean functional Nii, other 3D contrast
plot_nii_overlay.py	Compare placement/alignment of two images	Structural Nii, Mean functional Nii, other 3D contrast
plot_nii_similarity.py	Compare signal distributions of two images	Any two Nii images of the same shape
plot_gii_surface.py	Visualize data on the surface	Surface mesh file, volumetric nii data to be displayed on surface mesh
plot_nii_difference.py	Visualize the difference between two nii images	Any two Nii images of the same shape

Usage Instructions

See a plot type you want to recreate or use in your own work? The path to adoption should be as follows:

1. Clone/Download this repository locally, so you have all of the files.
2. Look for the corresponding resources/requirements/req* file to describe what is needed for the tool to run. If it is a Python tool, you can likely satisfy all of these requirements by running pip install -r {relevant_requirements_file} inside of the Python environment of your choosing.
3. Look at the usage example at the top of the plot type you're interested in, to make sure you know how to provide the arguments.
4. Apply to your own data! Just replace the made-up paths and parameter values shown in the docstring with the real data paths relevant for your work.
5. Hit issues? Open a question here using the issues tab, or patch the bug, as you wish.

Contribution Instructions

Each time you encounter a new type of data that you need to look at, or new way you need to look at old data, ask yourself the question, "is there a possibility that I, or others, may need to do this again?" If the answer is yes, then consider adding it here. When adding a script/tool to this repository, we should try our best to adhere to the following guidelines:

1. Place your script/tool inside the code/ folder. The code should:

   1. Be named according to the convention plot_{file-extension}_{plot-type}.py (or whatever programming language-specific extension is relevant). Note that for compound extensions, join values with a - instead of a ., to avoid confusion (e.g., a function operating on dscalar.nii data should be written as plot_dscalar-nii_{plot_type}).
   2. Accept command-line arguments for input data and where the output should be saved.
   3. Provide brief descriptions of each parameter
   4. Include a usage example where the paths/values are provided to the script.

2. Place any necessary resources inside the resources/ folder. If the resources are truly independently relevant for this new contribution, place them inside a similarly named folder (i.e., the folder name should match the name of the script, without the extension). Examples of resources include:

   1. Installation requirements file (Note: this should always be placed in resources/requirements/req-{script_name}.txt)
   2. Templates or parcellations
   3. Custom CSS

3. Provide an example of the visualization in the examples/ folder, also following the same naving convention.

4. Add a description to this README, following the established format.