Merge pull request #276 from neural-loop/main

Simplify software entries front matter

Author: neural-loop

.github/workflows/main.yml

@@ -63,6 +63,11 @@ jobs:
 
       - name: Generate OG Images
         run: npm run og-images
+        
+      - name: Fetch GitHub Stars
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        run: npm run fetch-stars
 
       - name: Determine Base URL
         id: base_url

.gitignore

@@ -20,6 +20,7 @@ yarn.lock
 /tmp/ogImageData.json
 /tmp/output_full.txt
 /tmp/
+.env
 
 # Ignore generated OG images and cache files
 content/**/*-og-*.jpg

archetypes/neuromorphic-software.md

@@ -9,10 +9,11 @@ logo: "software-logo.png" # Software logo. Place in this software's folder, or r
 website: "https://official-website.com"
 dependencies: "Key dependencies (e.g., PyTorch, JAX, NumPy, C++17)"
 field_of_application: "e.g., Machine Learning, Neuroscience, Data Processing, Hardware Interface, Robotics"
-source_code: "https://github.com/org/repo"
-stars_widget_url: "https://img.shields.io/github/stars/org/repo.svg?style=social" # Replace org/repo with actual GitHub path
-stars: 0 # This will be auto-updated by a script if implemented, otherwise copy from the badge manually.
-version_widget_url: "https://img.shields.io/pypi/v/package-name.svg" # Or other relevant badge URL (e.g., GitHub release)
+source_code: "https://github.com/org/repo" # This URL is now used to automatically fetch the star count for sorting.
+# To display the version, use one of the following. `pypi_id` is preferred for PyPI packages.
+# Use `version_badge_url_override` for custom badges (e.g., from GitHub releases or other registries).
+pypi_id: "" # e.g., "snntorch". This will auto-generate a PyPI version badge.
+# version_badge_url_override: "" # e.g., "https://img.shields.io/github/v/release/user/repo"
 license: "e.g., MIT, GPL-3.0, AGPL-3.0, Apache-2.0, custom"
 supports_hardware: false # Set to true if it directly supports or deploys to neuromorphic hardware platforms
 supports_NIR: false # Set to true if it supports the Neuromorphic Intermediate Representation
@@ -27,4 +28,4 @@ Detailed information about the software.
 - What problems does it solve?
 - Key features and functionalities.
 - Target users or research areas.
-- How to get started.
+- How to get started.

content/neuromorphic-computing/hardware/brainscales-2-universitat-heidelberg/index.md

@@ -38,6 +38,7 @@ summary: The BrainScaleS-2 is an accelerated spiking neuromorphic system-on-chip
   and non-spiking neural networks using hybrid techniques like surrogate gradients.
 title: BrainScaleS-2 — Heidelberg University
 type: neuromorphic-hardware
+software_tags: ['hxtorch','jaxsnn','pynn-brainscales2']
 ---
 
 The BrainScaleS-2 accelerated neuromorphic system is an integrated circuit architecture for emulating biologically-inspired spiking neural networks. It was developed by researchers at the Heidelberg University and collaborators. Key features of the BrainScaleS-2 system include:
@@ -62,9 +63,9 @@ The BrainScaleS-2 accelerated neuromorphic system is an integrated circuit archi
 - Training of deep spiking neural networks using surrogate and exact gradient techniques
 - Non-spiking neural network execution leveraging synaptic crossbar for analog matrix multiplication
 - Available via three different software frameworks:
-  - [jaxsnn](https://open-neuromorphic.org/neuromorphic-computing/software/snn-frameworks/jaxsnn/), a JAX-based framework for event-based numerical simulation of SNNs
-  - [hxtorch](https://open-neuromorphic.org/neuromorphic-computing/software/snn-frameworks/hxtorch/), a PyTorch-based deep learning Python library for SNNs
-  - [PyNN.brainscales2](https://open-neuromorphic.org/neuromorphic-computing/software/snn-frameworks/pynn-brainscales2), an implementation of the PyNN API
+  - [jaxsnn](/neuromorphic-computing/software/snn-frameworks/jaxsnn/), a JAX-based framework for event-based numerical simulation of SNNs
+  - [hxtorch](/neuromorphic-computing/software/snn-frameworks/hxtorch/), a PyTorch-based deep learning Python library for SNNs
+  - [PyNN.brainscales2](/neuromorphic-computing/software/snn-frameworks/pynn-brainscales2), an implementation of the PyNN API
 
 The accelerated operation and flexible architecture facilitate applications in computational neuroscience research and novel machine learning approaches. The system design serves as a scalable basis for future large-scale neuromorphic computing platforms.
 

content/neuromorphic-computing/software/data-tools/aedat/index.md

@@ -7,9 +7,7 @@ website: https://pypi.org/project/aedat/
 dependencies: Numpy
 field_of_application: Data Processing
 source_code: https://github.com/neuromorphicsystems/aedat
-stars_widget_url: https://img.shields.io/github/stars/neuromorphicsystems/aedat.svg?style=social
-stars: 28
-version_widget_url: https://img.shields.io/pypi/v/aedat.svg
+pypi_id: aedat
 license: MIT
 supports_hardware: False
 supports_NIR: False
@@ -18,5 +16,7 @@ maintainer: Alexandre Marcireau
 draft: false
 ---
 
+
+
 ## Overview
 The project aedat on GitHub is a fast AEDAT 4 python decoder with a Rust implementation, allowing users to efficiently read .aedat data files. It facilitates the processing of event-based data, commonly used in neuromorphic computing and vision systems. Users can easily install the library using pip and apply it to read and process frames using popular Python libraries like Pillow and OpenCV. The repository includes examples and detailed instructions on creating decoder objects, iterating through data packets, and handling different types of events or frames. Licensed under MIT, it is an open-source tool designed for flexibility and speed in handling AEDAT files.

content/neuromorphic-computing/software/data-tools/aestream/index.md

@@ -7,9 +7,7 @@ website: https://aestream.github.io/aestream
 dependencies: Numpy, nanobind, pysdl2-dll 
 field_of_application: Data Processing
 source_code: https://github.com/aestream/aestream
-stars_widget_url: https://img.shields.io/github/stars/aestream/aestream.svg?style=social
-stars: 49
-version_widget_url: https://img.shields.io/pypi/v/aestream.svg
+pypi_id: aestream
 license: MIT
 supports_hardware: False
 supports_NIR: False
@@ -18,6 +16,8 @@ maintainer: Jens E. Pedersen
 draft: false
 ---
 
+
+
 ## Overview
 AEStream is an advanced, flexible tool specifically designed to handle and transmit event-based data efficiently, catering to the unique needs of neuromorphic computing and event-based sensing. It is capable of interfacing with a variety of data sources including different models of event cameras, network streams, and data files, making it highly adaptable for various applications. AEStream supports a range of input and output formats, and can be used in diverse environments: as a command-line tool, through a Python interface, or as a C++ library, allowing users to choose the method that best fits their workflow.
 

content/neuromorphic-computing/software/data-tools/expelliarmus/index.md

@@ -7,9 +7,7 @@ website: https://expelliarmus.readthedocs.io/
 dependencies: Numpy
 field_of_application: Data Processing
 source_code: https://github.com/open-neuromorphic/expelliarmus
-stars_widget_url: https://img.shields.io/github/stars/open-neuromorphic/expelliarmus.svg?style=social
-stars: 25
-version_widget_url: https://img.shields.io/pypi/v/expelliarmus.svg
+pypi_id: expelliarmus
 license: GPL-2.0
 supports_hardware: False
 supports_NIR: False
@@ -18,6 +16,8 @@ maintainer: Fabrizio Ottati
 draft: false
 ---
 
+
+
 ## Overview
 Expelliarmus is a specialized Python library focused on decoding binary data from event-based sensors, specifically designed to work with various binary formats prevalent in event cameras like DAT, EVT2, and EVT3. It converts this binary data into NumPy structured arrays, enabling easier manipulation and analysis within the Python ecosystem. This makes it an essential tool for researchers and developers in neuromorphic computing, robotics, and computer vision who rely on event cameras for capturing visual information in the form of events rather than traditional frames.
 

content/neuromorphic-computing/software/data-tools/tonic/index.md

@@ -7,9 +7,7 @@ website: https://tonic.readthedocs.io/
 dependencies: Numpy
 field_of_application: Data processing
 source_code: https://github.com/neuromorphs/tonic
-stars_widget_url: https://img.shields.io/github/stars/neuromorphs/tonic.svg?style=social
-stars: 167
-version_widget_url: https://img.shields.io/pypi/v/tonic.svg
+pypi_id: tonic
 license: GPL-3.0
 supports_hardware: False
 supports_NIR: False
@@ -20,6 +18,8 @@ maintainer:
 draft: false
 ---
 
+
+
 ## Overview
 Tonic is a specialized Python package designed to facilitate the downloading and manipulation of neuromorphic datasets, particularly focusing on event-based vision and audio data. It is fully compatible with PyTorch Vision/Audio and offers a range of event transformations, making it a flexible tool for working with neuromorphic data. The package includes a variety of publicly available datasets and provides efficient ways to manage and transform these datasets for various applications.
 

content/neuromorphic-computing/software/snn-frameworks/bindsnet/index.md

@@ -7,9 +7,7 @@ website: https://bindsnet-docs.readthedocs.io/
 dependencies: PyTorch
 field_of_application: Machine Learning
 source_code: https://github.com/bindsnet/bindsnet
-stars_widget_url: https://img.shields.io/github/stars/bindsnet/bindsnet.svg?style=social
-stars: 1375
-version_widget_url: https://img.shields.io/pypi/v/bindsnet.svg
+pypi_id: bindsnet
 license: AGPL-3.0
 supports_hardware: False
 supports_NIR: False
@@ -18,6 +16,8 @@ maintainer: Hananel Hazan
 draft: false
 ---
 
+
+
 ## Overview
 **BindsNET** is an open-source computational framework designed to simulate spiking neural networks (SNNs). Built atop the PyTorch deep learning library, it was created in 2018
 by Hazan Hananel and Daniel Saunders. Their work is supported by a Defense Advanced Research Project Agency Grant they acquired. BindsNET provides tools and functionality for

content/neuromorphic-computing/software/snn-frameworks/brian/index.md

@@ -7,9 +7,7 @@ website: https://briansimulator.org/
 dependencies: 
 field_of_application: Neuroscience
 source_code: https://github.com/brian-team/brian2
-stars_widget_url: https://img.shields.io/github/stars/brian-team/brian2.svg?style=social
-stars: 835
-version_widget_url: https://img.shields.io/pypi/v/brian2.svg
+pypi_id: brian2
 license: custom
 supports_hardware: False
 supports_NIR: False
@@ -21,6 +19,8 @@ maintainer:
 draft: false
 ---
 
+
+
 ## Overview
 **Brian2** is an open-source Python library for the simulation of spiking neural networks (SNNs), notable for its user-friendly syntax and flexible approach to the design and
 simulation of neural models. Brian2 has been continually maintained by Romain Brette, Marcel Stimberg, and Dan Goodman since 2012. They heavily encourage and support community