refactor: software pages to use pypi_id and overrides

neural-loop · neural-loop · commit dfb90b654376 · 2025-06-29T05:13:17.000-04:00
Replaced version and stars widget URLs with pypi_id and
version_badge_url_override for better badge management.
diff --git a/content/neuromorphic-computing/software/data-tools/aedat/index.md b/content/neuromorphic-computing/software/data-tools/aedat/index.md
@@ -7,9 +7,8 @@ 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
+pypi_id: aedat
 stars: 28
-version_widget_url: https://img.shields.io/pypi/v/aedat.svg
 license: MIT
 supports_hardware: False
 supports_NIR: False
@@ -18,5 +17,7 @@ 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.
diff --git a/content/neuromorphic-computing/software/data-tools/aestream/index.md b/content/neuromorphic-computing/software/data-tools/aestream/index.md
@@ -7,9 +7,8 @@ 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
+pypi_id: aestream
 stars: 49
-version_widget_url: https://img.shields.io/pypi/v/aestream.svg
 license: MIT
 supports_hardware: False
 supports_NIR: False
@@ -18,6 +17,8 @@ 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.
 
diff --git a/content/neuromorphic-computing/software/data-tools/expelliarmus/index.md b/content/neuromorphic-computing/software/data-tools/expelliarmus/index.md
@@ -7,9 +7,8 @@ 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
+pypi_id: expelliarmus
 stars: 25
-version_widget_url: https://img.shields.io/pypi/v/expelliarmus.svg
 license: GPL-2.0
 supports_hardware: False
 supports_NIR: False
@@ -18,6 +17,8 @@ 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.
 
diff --git a/content/neuromorphic-computing/software/data-tools/tonic/index.md b/content/neuromorphic-computing/software/data-tools/tonic/index.md
@@ -7,9 +7,8 @@ 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
+pypi_id: tonic
 stars: 167
-version_widget_url: https://img.shields.io/pypi/v/tonic.svg
 license: GPL-3.0
 supports_hardware: False
 supports_NIR: False
@@ -20,6 +19,8 @@ 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.
 
diff --git a/content/neuromorphic-computing/software/snn-frameworks/bindsnet/index.md b/content/neuromorphic-computing/software/snn-frameworks/bindsnet/index.md
@@ -7,9 +7,8 @@ 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
+pypi_id: bindsnet
 stars: 1375
-version_widget_url: https://img.shields.io/pypi/v/bindsnet.svg
 license: AGPL-3.0
 supports_hardware: False
 supports_NIR: False
@@ -18,6 +17,8 @@ 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
diff --git a/content/neuromorphic-computing/software/snn-frameworks/brian/index.md b/content/neuromorphic-computing/software/snn-frameworks/brian/index.md
@@ -7,9 +7,8 @@ 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
+pypi_id: brian2
 stars: 835
-version_widget_url: https://img.shields.io/pypi/v/brian2.svg
 license: custom
 supports_hardware: False
 supports_NIR: False
@@ -21,6 +20,8 @@ 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
diff --git a/content/neuromorphic-computing/software/snn-frameworks/carlsim/index.md b/content/neuromorphic-computing/software/snn-frameworks/carlsim/index.md
@@ -2,7 +2,6 @@
 title: CARLsim
 type: neuromorphic-software
 description: GPU-accelerated library for simulating large-scale spiking neural network (SNN) models with high biologically realistic synaptic dynamics.
-stars_widget_url: https://img.shields.io/github/stars/UCI-CARL/CARLsim6.svg?style=social
 stars: 34
 website: https://uci-carl.github.io/CARLsim3/
 field_of_application: Machine Learning, Hardware Interface
@@ -13,6 +12,8 @@ language: C/C++/Python
 maintainer: Jeff Krichmar
 ---
 
+
+
 ## Overview
 
 CARLsim is an efficient, easy-to-use, GPU-accelerated library for simulating large-scale spiking neural network (SNN) models with a high degree of biological detail. CARLsim allows execution of networks of Izhikevich spiking neurons with realistic synaptic dynamics on both generic x86 CPUs and standard off-the-shelf GPUs. The simulator provides a PyNN-like programming interface in C/C++, which allows for details and parameters to be specified at the synapse, neuron, and network level.
diff --git a/content/neuromorphic-computing/software/snn-frameworks/genn/index.md b/content/neuromorphic-computing/software/snn-frameworks/genn/index.md
@@ -7,9 +7,7 @@ website: https://genn-team.github.io/
 dependencies: 
 field_of_application: Neuroscience, Machine learning
 source_code: https://github.com/genn-team/genn
-stars_widget_url: https://img.shields.io/github/stars/genn-team/genn.svg?style=social
 stars: 233
-version_widget_url: https://img.shields.io/github/release/genn-team/genn.svg?label=github%20release
 license: LGPL-2.1
 supports_hardware: False
 supports_NIR: False
@@ -18,6 +16,8 @@ maintainer: James Knight
 draft: false
 ---
 
+
+
 ## Overview
 **GeNN** is a software package to accelerate Spiking Neural Network simulations 
 on hardware including NVIDIA GPUs. GeNN uses code generation with various 'backends' to run simulations. The main backends are currently C++/CUDA for NVIDIA GPUs or C++ for CPU-only mode. GeNN is available on Linux, Windows, MacOS.
diff --git a/content/neuromorphic-computing/software/snn-frameworks/hxtorch/index.md b/content/neuromorphic-computing/software/snn-frameworks/hxtorch/index.md
@@ -6,9 +6,8 @@ website: https://electronicvisions.github.io/documentation-brainscales2/latest/
 dependencies: PyTorch, BrainScaleS-2 OS
 field_of_application: Machine Learning, Neuromorphic Hardware, In-the-loop Training
 source_code: https://github.com/electronicvisions/hxtorch
-stars_widget_url: https://img.shields.io/github/stars/electronicvisions/hxtorch
+version_badge_url_override: "https://img.shields.io/github/v/tag/electronicvisions/releases-ebrains"
 stars: 9
-version_widget_url: https://img.shields.io/github/v/tag/electronicvisions/releases-ebrains
 license: LGPL-2.0-or-later
 supports_hardware: True
 supports_NIR: True
@@ -17,6 +16,8 @@ draft: false
 maintainer: Electronic Visions Group
 ---
 
+
+
 ## Overview
 
 **hxtorch** is a deep learning Python library used for numerical simulation, neuromorphic emulation and training of spiking neural networks (SNNs). Built on top of PyTorch, it integrates the automatic differentiation and modular design of the PyTorch ecosystem with neuromorphic experiment execution, enabling hardware-in-the-loop training workflows on the neuromorphic hardware system [BrainScaleS-2](https://open-neuromorphic.org/neuromorphic-computing/hardware/brainscales-2-universitat-heidelberg/).
diff --git a/content/neuromorphic-computing/software/snn-frameworks/jaxsnn/index.md b/content/neuromorphic-computing/software/snn-frameworks/jaxsnn/index.md
@@ -7,9 +7,7 @@ website: https://electronicvisions.github.io/documentation-brainscales2/latest/
 dependencies: JAX, BrainScaleS-2 OS
 field_of_application: Machine Learning, Neuromorphic Hardware, In-the-loop Training, Event-based Training
 source_code: https://github.com/electronicvisions/jaxsnn
-stars_widget_url: https://img.shields.io/github/stars/electronicvisions/jaxsnn
 stars: 20
-version_widget_url: https://img.shields.io/pypi/v/jaxsnn
 license: LGPL-2.0-or-later
 supports_hardware: True
 supports_NIR: True
@@ -18,6 +16,8 @@ draft: false
 maintainer: Electronic Visions Group
 ---
 
+
+
 ## Overview
 
 **jaxsnn** is a deep learning Python library used for event-based numerical simulation, neuromorphic emulation and training of spiking neural networks (SNNs) with [BrainScaleS-2](https://open-neuromorphic.org/neuromorphic-computing/hardware/brainscales-2-universitat-heidelberg/) neuromorphic hardware in-the-loop. It is maintained by the Electronic Visions group at Heidelberg University.
diff --git a/content/neuromorphic-computing/software/snn-frameworks/lava/index.md b/content/neuromorphic-computing/software/snn-frameworks/lava/index.md
@@ -7,9 +7,8 @@ website: https://lava-nc.org/
 dependencies: 
 field_of_application: Neuro-inspired Applications
 source_code: https://github.com/lava-nc/lava
-stars_widget_url: https://img.shields.io/github/stars/lava-nc/lava.svg?style=social
+pypi_id: lava-nc
 stars: 462
-version_widget_url: https://img.shields.io/pypi/v/lava-nc.svg
 license: custom
 supports_hardware: True
 supports_NIR: True
@@ -18,6 +17,8 @@ maintainer: Intel NC team
 draft: false
 ---
 
+
+
 ## Overview
 **Lava** is an open-source software framework designed for neuromorphic computing, aiming to develop neuro-inspired applications and their mapping to neuromophic hardware. 
 Developed and maintained by the Intel Neuromorphic Computing Team, Lava offers developers and researchers tools and abstractions to develop applications that fully utilize the
diff --git a/content/neuromorphic-computing/software/snn-frameworks/ml_genn/index.md b/content/neuromorphic-computing/software/snn-frameworks/ml_genn/index.md
@@ -7,9 +7,7 @@ website: https://ml-genn.readthedocs.io/en/latest/
 dependencies: GeNN
 field_of_application: Machine learning
 source_code: https://github.com/genn-team/ml_genn
-stars_widget_url: https://img.shields.io/github/stars/genn-team/ml_genn.svg?style=social
 stars: 24
-version_widget_url: https://img.shields.io/github/release/genn-team/ml_genn.svg?label=github%20release
 license: LGPL-2.1
 supports_hardware: False
 supports_NIR: False
@@ -18,6 +16,8 @@ maintainer: James Knight
 draft: false
 ---
 
+
+
 ## Overview
 **mlGeNN** is a library for machine learning with Spiking Neural Networks (SNNs), built on the efficient foundation of the GeNN simulator. 
 
diff --git a/content/neuromorphic-computing/software/snn-frameworks/nengo/index.md b/content/neuromorphic-computing/software/snn-frameworks/nengo/index.md
@@ -7,9 +7,8 @@ website: https://nengo.ai
 dependencies: 
 field_of_application: Machine Learning, Neuroscience
 source_code: https://github.com/nengo/nengo
-stars_widget_url: https://img.shields.io/github/stars/nengo/nengo.svg?style=social
+pypi_id: nengo
 stars: 781
-version_widget_url: https://img.shields.io/pypi/v/nengo.svg
 license: custom
 supports_hardware: True
 supports_NIR: True
@@ -18,6 +17,8 @@ maintainer: Trevor Bekolay
 draft: false
 ---
 
+
+
 ## Overview
 **Nengo**, also known as "Brain Maker", is a versatile open-source Python package designed for building, testing, and applying neural networks, and it supports many backends for 
 spiking neural networks (SNNs). Developed and maintained by Trevor Bekolov, Nengo is accompanied by comprehensive and extensive documentation that covers all of its plugins and
diff --git a/content/neuromorphic-computing/software/snn-frameworks/nest/index.md b/content/neuromorphic-computing/software/snn-frameworks/nest/index.md
@@ -7,9 +7,7 @@ website: https://www.nest-simulator.org/
 dependencies: 
 field_of_application: Neuroscience
 source_code: https://github.com/nest/nest-simulator
-stars_widget_url: https://img.shields.io/github/stars/nest/nest-simulator.svg?style=social
 stars: 502
-version_widget_url: https://img.shields.io/github/release/nest/nest-simulator.svg?label=github%20release
 license: GPL-2.0
 supports_hardware: False
 supports_NIR: False
@@ -18,6 +16,8 @@ maintainer: Jochen Martin Eppler
 draft: false
 ---
 
+
+
 ## Overview
 **NEST** is a prominent open-source simulator for spiking neural network (SNN) models, mainly used in computational neuroscience. The project is developed and maintained by the NEST
 Initiative, which has advanced computational neuroscience by pushing the limits of large-scale simulations of SNNs. They heavily encourage and support community involvement through
diff --git a/content/neuromorphic-computing/software/snn-frameworks/norse/index.md b/content/neuromorphic-computing/software/snn-frameworks/norse/index.md
@@ -7,9 +7,8 @@ website: https://norse.github.io/norse/
 dependencies: PyTorch
 field_of_application: Bio-inspired Neural Networks
 source_code: https://github.com/norse/norse
-stars_widget_url: https://img.shields.io/github/stars/norse/norse.svg?style=social
+pypi_id: norse
 stars: 578
-version_widget_url: https://img.shields.io/pypi/v/norse.svg
 license: LGPL-3.0
 supports_hardware: False
 supports_NIR: True
@@ -20,6 +19,8 @@ maintainer:
 draft: false
 ---
 
+
+
 ## Overview
 **Norse** is a deep learning Python library used for simulating spiking neural networks (SNN)s that leverages PyTorch with bio-inspired neural networks. Norse is maintained 
 and developed by Christian Pehle and Jens Egholm Pedersen, with funding from the EC Horizon 2020 Framework Programme and the DFG, German Research Foundation. Additionally, Norse is
diff --git a/content/neuromorphic-computing/software/snn-frameworks/pynn-brainscales2/index.md b/content/neuromorphic-computing/software/snn-frameworks/pynn-brainscales2/index.md
@@ -6,9 +6,8 @@ website: https://electronicvisions.github.io/documentation-brainscales2/latest/
 dependencies: PyNN, BrainScaleS-2 OS
 field_of_application: Neuromorphic Hardware, Computational Neuroscience, Spiking Neural Networks, Local Plasticity
 source_code: https://github.com/electronicvisions/pynn-brainscales
-stars_widget_url: https://img.shields.io/github/stars/electronicvisions/pynn-brainscales
+version_badge_url_override: "https://img.shields.io/github/v/tag/electronicvisions/releases-ebrains"
 stars: 6
-version_widget_url: https://img.shields.io/github/v/tag/electronicvisions/releases-ebrains
 license: LGPL-2.0-or-later
 supports_hardware: True
 supports_NIR: False
@@ -17,6 +16,8 @@ draft: false
 maintainer: Electronic Visions Group
 ---
 
+
+
 ## Overview
 
 **PyNN.brainscales2** is an implementation of the backend-agnostic PyNN API for [BrainScaleS-2](https://open-neuromorphic.org/neuromorphic-computing/hardware/brainscales-2-universitat-heidelberg/).
diff --git a/content/neuromorphic-computing/software/snn-frameworks/rockpool/index.md b/content/neuromorphic-computing/software/snn-frameworks/rockpool/index.md
@@ -7,9 +7,8 @@ website: https://rockpool.ai
 dependencies: PyTorch, Jax
 field_of_application: Machine Learning
 source_code: https://gitlab.com/synsense/rockpool
-stars_widget_url: https://img.shields.io/github/stars/synsense/rockpool.svg?style=social
+pypi_id: rockpool
 stars: 37
-version_widget_url: https://img.shields.io/pypi/v/rockpool.svg
 license: AGPL-3.0
 supports_hardware: True
 supports_NIR: True
@@ -18,6 +17,8 @@ maintainer: Dylan Muir
 draft: false
 ---
 
+
+
 ## Overview
 **Rockpool** is an open-source Python package focused on dynamic neural network architectures, tailored for event-driven networks and neuromorphic hardware. Managed by SynSense,
 Rockpool facilitates the design, training, and evaluation of recurrent neural networks with either continuous-time or event-driven dynamics. The library is designed for 
diff --git a/content/neuromorphic-computing/software/snn-frameworks/sinabs/index.md b/content/neuromorphic-computing/software/snn-frameworks/sinabs/index.md
@@ -7,9 +7,8 @@ website: https://sinabs.ai
 dependencies: PyTorch
 field_of_application: Vision Models
 source_code: https://github.com/synsense/sinabs
-stars_widget_url: https://img.shields.io/github/stars/synsense/sinabs.svg?style=social
+pypi_id: sinabs
 stars: 54
-version_widget_url: https://img.shields.io/pypi/v/sinabs.svg
 license: AGPL-3.0
 supports_hardware: True
 supports_NIR: True
@@ -18,6 +17,8 @@ maintainer: Sadique Sheik
 draft: false
 ---
 
+
+
 ## Overview
 **Sinabs** (Sinabs Is Not A Brain Simulator) is a deep learning library based on PyTorch, specifically designed for spiking neural networks. It focuses on simplicity, fast training,
 and extendability, demonstrating particularly strong performance with vision models due to its support for weight transfer. The library provides users with an efficient way to convert 
diff --git a/content/neuromorphic-computing/software/snn-frameworks/snntorch/index.md b/content/neuromorphic-computing/software/snn-frameworks/snntorch/index.md
@@ -7,9 +7,8 @@ website: https://snntorch.readthedocs.io
 dependencies: PyTorch
 field_of_application: Machine Learning
 source_code: https://github.com/jeshraghian/snntorch
-stars_widget_url: https://img.shields.io/github/stars/jeshraghian/snntorch.svg?style=social
+pypi_id: snntorch
 stars: 914
-version_widget_url: https://img.shields.io/pypi/v/snntorch.svg
 license: MIT
 supports_hardware: False
 supports_NIR: True
@@ -18,6 +17,8 @@ maintainer: Jason Eshraghian
 draft: false
 ---
 
+
+
 ## Overview
 **snnTorch** is a Python package designed for spiking neural network simulations and extends PyTorch's capabilities to spiking neurons, offering pre-designed models within the 
 PyTorch framework. It aims to bridge the gap between the brain's efficient spike-based information encoding and modern deep learning techniques. snnTorch includes components for 
diff --git a/content/neuromorphic-computing/software/snn-frameworks/spikingjelly/index.md b/content/neuromorphic-computing/software/snn-frameworks/spikingjelly/index.md
@@ -7,9 +7,8 @@ website: https://spikingjelly.readthedocs.io
 dependencies: PyTorch
 field_of_application: Machine Learning
 source_code: https://github.com/fangwei123456/spikingjelly
-stars_widget_url: https://img.shields.io/github/stars/fangwei123456/spikingjelly.svg?style=social
+pypi_id: spikingjelly
 stars: 998
-version_widget_url: https://img.shields.io/pypi/v/spikingjelly.svg
 license: unknown
 supports_hardware: False
 supports_NIR: False
@@ -18,6 +17,8 @@ maintainer: Wei Fang
 draft: false
 ---
 
+
+
 ## Overview
 **SpikingJelly** is an open-source deep learning framework designed for Spiking Neural Networks (SNNs) based on PyTorch. It leverages PyTorch's user-friendly
 interface and computational capabilities, including GPU acceleration. This integration makes SpikingJelly an accessible and powerful tool for both researchers and practitioners
diff --git a/content/neuromorphic-computing/software/snn-frameworks/spyx/index.md b/content/neuromorphic-computing/software/snn-frameworks/spyx/index.md
diff --git a/layouts/partials/components/software-list.html b/layouts/partials/components/software-list.html
diff --git a/layouts/partials/software/at-a-glance.html b/layouts/partials/software/at-a-glance.html
