[!WARNING] This is a development space and changing rapidly.
We hypothesize the process of science stands to benefit from having the option to suddenly become interactive and shareable - allowing for the poking or plucking, pushing or pulling, drilling in or out, grouping or separating, and sending or receiving of what would otherwise be a static snapshot of the data. The combined use of HoloViz and Bokeh tools could provide the interactivity, shareability, and scalability needed to support research as a collective action rather than a collection of solitary observations.
One of our overall goals is to facilitate the creation of fully open, reproducible, OS-independent, browser-based workflows for biomedical research primarily using sustainable, domain-independent visualization tools. In support of this goal, this repository is the development ground for optimization of HoloViz and Bokeh tools within the realm of neuroscience.
Specific repo objectives:
This repository contains developmental versions of workflows, which can be loosely categorized into two types: generalized and specialized. Generalized workflows aim to be broadly applicable and primarily utilize domain-independent Pandata tools such as Numpy, Pandas, Xarray, SciPy, etc. These generalized workflows serve as the foundational building blocks for specialized workflows. Specialized workflows are designed to cater to specific contexts and have no limitations on the use of domain-specific tools.
Title | Example Modality | Thumbnail | Info & Links | Description |
---|---|---|---|---|
Multi-Channel Timeseries | eeg, ephys | ![]() |
:warning: workflow |
Synchronized examination of stacked time-series with large data handling, scale bar, annotations, minimap, and signal grouping. |
Deep Image Stack | miniscope imaging | ![]() |
:warning: workflow |
Efficient visualization of deep 2D calcium imaging movies with, playback controls, 2D annotation, scale bar, time views, intensity histogram, and summary statistics. |
Waveform | ephys | ![]() |
:warning: workflow |
Oscilloscope-style display of action potential waveform snippets |
Spike Raster | ephys | ![]() |
:warning: workflow |
Efficient visualization of large-scale neuronal spike time data, with a simple API, aggregate views of spike counts, and spike-level metadata management |
Title | Example Modality | Thumbnail | Info & Links | Description |
---|---|---|---|---|
Neuroglancer notebook | electron microscopy, histology | ![]() |
:warning: workflow |
Notebook-based workflow for visualizing 3D volumetric data in a Neuroglancer application |
This work is a collaboration between developers and scientists, and some developer-scientists. While some contributions are visible through the GitHub repo, many other contributions are less visible yet equally important.
Funding:
Before installing the workflow environments, make sure you have Miniconda installed. If not, you can download and install it from the official site.
Clone the Repository: Clone the neuro
repository to your local machine.
git clone https://github.com/holoviz-topics/neuro.git
Navigate to Workflow: Change to the directory of the workflow you're interested in.
cd neuro/workflows/<workflow>
Create Environment: Use conda
to create a new environment from the environment.yml
file.
conda env create -f environment.yml
Activate Environment: After the environment is created, activate it.
conda activate <environment>
If you've already installed a workflow environment and the environment.yml
file has been updated, follow these steps to update the environment:
Update Repository: Pull the latest changes from the repository.
git pull
Navigate to Workflow: Go to the directory of the workflow you're interested in.
cd neuro/workflows/<workflow>
Update Environment: Update the existing Conda environment based on the latest environment.yml
file.
conda env update -f environment.yml --prune
The --prune
option will remove packages from the environment not present in the updated environment.yml
file.
/src/neurodatagen
)./src/hvneuro
for now. However, this should be considered a temporary space until the code can be incorporated into existing libraries, or live in particular workflows./workflows
/example1
readme_example1.md
workflow_example1.ipynb
environment.yml
/dev
date_example-workflow_task.ipynb
readme_<workflow>.md
for any essential workflow-specific info or links.workflow_<workflow>.ipynb
as the latest version of the workflow.environment.yml
with which to create a conda envdev
dir in each workflow is scratch space. There is no expectation that anything here is maintained.