Merge pull request #60 from OCNS/feat/data-cleanup

Feat/data cleanup

README.md

@@ -25,7 +25,7 @@ python -m pip install -r requirements.txt
 ```
 To run the app, use:
 ```
-panel serve --show src/project_browser.py --static-files assets=./assets
+panel serve --show src/project_browser.py --static-dirs assets=./assets
 ```
 This will open a browser window with the app.
 Firefox/Chrome based browsers are supported.

requirements.txt

@@ -1,2 +1,2 @@
-panel==1.0.4
+panel==1.3.7
 PyYAML==6.0

simtools/ANNarchy.yaml

@@ -1,8 +1,8 @@
 - name: ANNarchy
-- features: frontend, backend
+- features: frontend, simulator
 - operating_system: Linux, MacOS
 - biological_level: Population Model, Single-Compartment (Simple) Model, Single-Compartment (Complex) Model
-- computing_scale: Single Machine, GPU
+- processing_support: Single Machine, GPU
 - interface_language: Python, C++
 - summary: >
     ANNarchy (Artificial Neural Networks architect) is a neural simulator designed for distributed rate-coded or spiking

simtools/Arbor-GUI.yaml

@@ -1,10 +1,7 @@
 - name: Arbor GUI
 - features: frontend
 - operating_system: Linux, MacOS, Windows
-- biological_level: Population Model, Single-Compartment (Simple) Model, Single-Compartment (Complex) Model, Multi-Compartment Model
-- computing_scale: Single Machine, GPU
 - interface_language: GUI
-- model_description_language: NMODL, NeuroML/LEMS, SWC, Neurolucida, ACC
 - summary: >
     Arbor GUI is a comprehensive tool for building single cell models using Arbor.
     It strives to be self-contained, fast, and easy to use.
@@ -29,3 +26,4 @@
     chat: https://gitter.im/arbor-sim/gui
 - relations:
   - name: Arbor
+    description: GUI for

simtools/Arbor-Playground.yaml

@@ -1,10 +1,7 @@
 - name: Arbor Playground
-- features: frontend, backend
+- features: frontend
 - operating_system: Linux, MacOS, Windows
-- biological_level: Population Model, Single-Compartment (Simple) Model, Single-Compartment (Complex) Model, Multi-Compartment Model
-- computing_scale: Single Machine
 - interface_language: GUI, Python
-- model_description_language:
 - summary: Arbor Playground is an Emscripten + Pyodide port of Arbor and is meant to be a simple showcase of neural modelling in Arbor.
 - urls:
     homepage: https://arbor-sim.org/playground
@@ -14,5 +11,3 @@
     issue tracker: https://github.com/arbor-sim/playground/issues
     forum: https://github.com/arbor-sim/arbor/discussions
     chat: https://gitter.im/arbor-sim/community
-- relations:
-  - name: Arbor

simtools/Arbor.yaml

@@ -1,10 +1,9 @@
 - name: Arbor
-- features: frontend, backend
+- features: frontend, simulator
 - operating_system: Linux, MacOS, Windows
 - biological_level: Population Model, Single-Compartment (Simple) Model, Single-Compartment (Complex) Model, Multi-Compartment Model
-- computing_scale: Single Machine, Cluster, Supercomputer, GPU
+- processing_support: Single Machine, Cluster, Supercomputer, GPU
 - interface_language: Python, C++
-- model_description_language: NMODL, NeuroML/LEMS, SWC, Neurolucida, ACC
 - summary: >
     Arbor is a high-performance library for computational neuroscience simulations with multi-compartment, morphologically-detailed cells, from single cell models to very large networks.
     Arbor is written from the ground up with many-cpu and gpu architectures in mind, to help neuroscientists effectively use contemporary and future HPC systems to meet their simulation needs.
@@ -27,6 +26,3 @@
     download: https://pypi.org/project/arbor/
     forum: https://github.com/arbor-sim/arbor/discussions
     chat: https://gitter.im/arbor-sim/community
-- relations:
-      - name: Arbor GUI
-      - name: Arbor Playground

simtools/BMTK.yaml

@@ -1,10 +1,7 @@
 - name: Brain Modelling Toolkit (BMTK)
 - features: frontend
 - operating_system: Linux, MacOS, Windows
-- biological_level: Population Model, Single-Compartment (Simple) Model, Single-Compartment (Complex) Model, Multi-Compartment Model
-- computing_scale: Single Machine, Cluster
 - interface_language: Python
-- model_description_language: SONATA
 - summary: >
     The Brain Modeling Toolkit (BMTK) is a python-based software package for building, simulating and analyzing large-scale neural network models.
     It supports the building and simulation of models of varying levels-of-resolution; from multi-compartment biophysically detailed networks, to point-neuron models, to filter-based models, and even population-level firing rate models.
@@ -24,3 +21,6 @@
     We encourage others to use the BMTK for their own research, and suggestions and contributions to the BMTK are welcome.
 - urls:
       homepage: https://alleninstitute.github.io/bmtk/
+- relations:
+  - name: SONATA
+    description: exports to

simtools/BluePyOpt.yaml

@@ -1,10 +1,7 @@
 - name: BluePyOpt
 - features: tool
 - operating_system: Linux, MacOS, Windows
-- biological_level: Single-Compartment (Simple) Model, Single-Compartment (Complex) Model, Multi-Compartment Model
-- computing_scale: Single Machine
 - interface_language: Python
-- model_description_language: NeuroML/LEMS
 - summary: >
     The Blue Brain Python Optimisation Library (BluePyOpt) is an extensible framework for data-driven model parameter optimisation that wraps and standardises several existing open-source tools.
 
@@ -17,3 +14,6 @@
 
 - urls:
     homepage: https://bluepyopt.readthedocs.io
+- relations:
+    - name: NeuroML
+      description: exports to

simtools/Brain-Scaffold-Builder.yaml

@@ -1,10 +1,7 @@
 - name: Brain Scaffold Builder
-- features: frontend, backend
+- features: frontend
 - operating_system: Linux, MacOS, Windows
-- biological_level: #TODO
-- computing_scale: Single Machine
 - interface_language: Python
-- model_description_language:
 - summary: >
     The Brain Scaffold Builder (BSB) is a black box component framework for multiparadigm neural modelling: we provide structure, architecture and organization, and you provide the use-case specific parts of your model.
     In our framework, your model is described in a code-free configuration of components with parameters.
@@ -18,3 +15,10 @@
 
 - urls:
     homepage: https://bsb.readthedocs.io
+- relations:
+    - name: Neuron
+      description: simulates with
+    - name: NEST
+      description: simulates with
+    - name: Arbor
+      description: simulates with

simtools/Brain-dynamics-toolbox.yaml

@@ -1,10 +1,9 @@
 - name: Brain dynamics toolbox
-- features: frontend, backend
+- features: frontend, simulator
 - operating_system: Linux, MacOS, Windows
 - biological_level: Population Model
-- computing_scale: Single Machine
+- processing_support: Single Machine
 - interface_language: MATLAB
-- model_description_language:
 - summary: >
     The Brain Dynamics Toolbox is open-source Matlab software for simulating bespoke dynamical systems in neuroscience and beyond.
     Users define their system of equations as a custom matlab function.

simtools/Brian.yaml

@@ -1,8 +1,8 @@
 - name: Brian
-- features: frontend, backend
+- features: frontend, simulator
 - operating_system: Linux, MacOS, Windows
 - biological_level: Population Model, Single-Compartment (Simple) Model, Single-Compartment (Complex) Model, Multi-Compartment Model
-- computing_scale: Single Machine, Cluster
+- processing_support: Single Machine, Cluster
 - interface_language: Python
 - summary: >
     Brian is a free, open source simulator for spiking neural networks. It is written in the Python programming language
@@ -20,6 +20,3 @@
     download: https://pypi.org/project/Brian2/
     forum: https://brian.discourse.group
     chat: https://gitter.im/brian-team/brian2
-- relations:
-    - name: Brian2CUDA
-    - name: Brian2GeNN

simtools/Brian2CUDA.yaml

@@ -1,8 +1,8 @@
 - name: Brian2CUDA
-- features: backend
+- features: simulator
 - operating_system: Linux, MacOS
 - biological_level: Population Model, Single-Compartment (Simple) Model, Single-Compartment (Complex) Model, Multi-Compartment Model
-- computing_scale: GPU
+- processing_support: GPU
 - interface_language: Python
 - summary: >
     Brian2CUDA is a Python package for simulating spiking neural networks on graphics processing units (GPUs).
@@ -11,6 +11,7 @@
     then executed on NVIDIA GPUs.
 - relations:
     - name: Brian
+      description: simulates
 - urls:
     documentation: https://brian2cuda.readthedocs.io
     installation: https://brian2cuda.readthedocs.io/en/latest/introduction/install.html

simtools/Brian2GeNN.yaml

@@ -1,15 +1,15 @@
 - name: Brian2GeNN
-- features: backend
+- features: library
 - operating_system: Linux, MacOS, Windows
-- biological_level: Population Model, Single-Compartment (Simple) Model, Single-Compartment (Complex) Model
-- computing_scale: Single Machine, GPU
 - interface_language: Python
 - summary: >
     Brian2GeNN connects Brian 2 to the GeNN simulator, so that users can make use of GeNN GPU acceleration when
     developing their models in Brian, without requiring any technical knowledge about GPUs, C++ or GeNN.
 - relations:
     - name: Brian
+      description: converts from
     - name: GeNN
+      description: exports to
 - urls:
     documentation: https://brian2genn.readthedocs.io
     installation: https://brian2genn.readthedocs.io/en/stable/introduction/index.html#installing-the-brian2genn-interface

simtools/DiPDE.yaml

@@ -1,10 +1,9 @@
 - name: DiPDE
-- features: frontend, backend
+- features: frontend, simulator
 - operating_system: Linux, MacOS, Windows
 - biological_level: Population Model
-- computing_scale: Single Machine
+- processing_support: Single Machine
 - interface_language: Python
-- model_description_language:
 - summary: >
     DiPDE (dipde) is a simulation platform for numerically solving the time evolution of coupled networks of neuronal populations.
     Instead of solving the subthreshold dynamics of individual model leaky-integrate-and-fire (LIF) neurons, dipde models the voltage distribution of a population of neurons with a single population density equation.

simtools/Eden.yaml

@@ -1,14 +1,11 @@
 - name: EDEN
-- features: backend
+- features: simulator
 - operating_system: Linux, MacOS, Windows
 - biological_level: Population Model, Single-Compartment (Simple) Model, Single-Compartment (Complex) Model, Multi-Compartment Model
-- computing_scale: Single Machine, Cluster
+- processing_support: Single Machine, Cluster
 - interface_language: CLI, Python
-- model_description_language: NeuroML/LEMS
 - summary: >
     Extensible Dynamics Engine for Networks (EDEN) is a high-performance NeuroML-based neural simulator.
-- relations:
-    - name: NeuroML
 - urls:
       source: https://gitlab.com/c7859/neurocomputing-lab/Inferior_OliveEMC/eden
       installation: https://gitlab.com/c7859/neurocomputing-lab/Inferior_OliveEMC/eden/-/tree/main#installing

simtools/GeNN.yaml

@@ -1,13 +1,11 @@
 - name: GeNN
-- features: frontend, backend
+- features: frontend, simulator
 - operating_system: Linux, MacOS, Windows
 - biological_level: Population Model, Single-Compartment (Simple) Model, Single-Compartment (Complex) Model
-- computing_scale: Single Machine, GPU
+- processing_support: Single Machine, GPU
 - interface_language: Python, C++
 - summary: >
     GeNN is a GPU enhanced Neuronal Network simulation environment based on NVIDIA CUDA technology.
-- relations:
-    - name: Brian2GeNN
 - urls:
     homepage: https://genn-team.github.io
     documentation: https://genn-team.github.io/genn/documentation/4/html/index.html

simtools/Genesis.yaml

@@ -1,10 +1,9 @@
 - name: GEneral NEural SImulation System (GENESIS)
-- features: frontend, backend
+- features: frontend, simulator
 - operating_system: Linux, MacOS, Windows
 - biological_level: Single-Compartment (Simple) Model, Single-Compartment (Complex) Model, Multi-Compartment Model
-- computing_scale: Single Machine
+- processing_support: Single Machine
 - interface_language: GUI, C
-- model_description_language:
 - summary: >
     GENESIS (the GEneral NEural SImulation System) is a general purpose simululation platform that was developed to support the simulation of neural systems ranging from subcellular components and biochemical reactions to complex models of single neurons, simulations of large networks, and system-level models.
 - urls:

simtools/Geppeto.yaml

@@ -1,10 +1,7 @@
 - name: Geppeto
 - features: frontend
 - operating_system: Linux, MacOS, Windows
-- biological_level: #TODO
-- computing_scale: #TODO
 - interface_language: Python, Javascript
-- model_description_language: #TODO
 - summary: >
     Geppetto is a web-based visualisation and simulation platform to build neuroscience software applications.
     Reuse best practices, best compomnents, best design.

simtools/LFPy.yaml

@@ -1,10 +1,7 @@
 - name: LFPy
 - features: tool
 - operating_system: Linux, MacOS, Windows
-- biological_level: Multi-Compartment Model #TODO
-- computing_scale: Single Machine
 - interface_language: Python
-- model_description_language: NMODL
 - summary: >
     LFPy is a Python module for calculation of extracellular potentials from multicompartment neuron models.
     It relies on the NEURON simulator and uses the Python interface it provides.
@@ -15,3 +12,4 @@
     download: https://pypi.org/project/LFPy/
 - relations:
     - name: Neuron
+      description: simulates with

simtools/MOOSE.yaml

@@ -1,11 +1,9 @@
 - name: MOOSE
-- features: frontend, backend
+- features: frontend, simulator
 - operating_system: Linux, MacOS
 - biological_level: Population Model, Single-Compartment (Simple) Model, Single-Compartment (Complex) Model, Multi-Compartment Model
-- computing_scale: Single Machine, Cluster, Supercomputer, GPU
-- computing_scale: Single Machine
+- processing_support: Single Machine, Cluster, Supercomputer, GPU
 - interface_language: Python, GUI, C++
-- model_description_language: NMODL, NeuroML/LEMS, SBML
 - summary: >
     MOOSE is the Multiscale Object-Oriented Simulation Environment.
     It is designed to simulate neural systems ranging from subcellular components and biochemical reactions to complex models of single neurons, circuits, and large networks.
@@ -21,3 +19,4 @@
     documentation: https://moose.ncbs.res.in/readthedocs/index.html
 - relations:
     - name: NeuroML
+      description: can import, can export to

simtools/MUSIC-(MUlti-SImulation-Coordinator).yaml

@@ -1,10 +1,7 @@
 - name: MUSIC (MUlti-SImulation Coordinator)
-- features: standard
+- features: API
 - operating_system: Linux, MacOS
-- biological_level: #TODO
-- computing_scale: Cluster
 - interface_language: C++
-- model_description_language:
 - summary: >
     MUSIC is an API allowing large scale neuron simulators using MPI internally to exchange data during runtime.
     MUSIC provides mechanisms to transfer massive amounts of event information and continuous values from one parallel application to another.
@@ -13,3 +10,6 @@
 - urls:
     homepage: https://github.com/INCF/MUSIC
     source: https://github.com/INCF/MUSIC
+- relations:
+    - name: NEST
+      description: is used by

simtools/Nest-Desktop.yaml

@@ -1,10 +1,7 @@
 - name: NEST Desktop
 - features: frontend
 - operating_system: Linux, MacOS, Windows
-- biological_level: Population Model, Single-Compartment (Simple) Model
-- computing_scale: Single Machine, Cluster, Supercomputer
 - interface_language: GUI
-- model_description_language: #TODO
 - summary: >
     NEST Desktop is a web-based GUI application for NEST Simulator, an advanced simulation tool for computational neuroscience.
     NEST Desktop enables to construct a neuronal network model graphically and to perform a simulation experiment.
@@ -15,3 +12,4 @@
     source: https://github.com/nest-desktop/nest-desktop
 - relations:
     - name: NEST
+      description: is GUI for

simtools/Nest-GPU.yaml

@@ -1,12 +1,14 @@
 - name: NEST GPU
-- features: frontend, backend
+- features: frontend, simulator
 - operating_system: Linux
 - biological_level: Population Model, Single-Compartment (Simple) Model
-- computing_scale: GPU
+- processing_support: GPU
 - interface_language: Python, C++, C
-- model_description_language: #TODO
 - summary: >
     NEST GPU is a GPU-MPI library for simulation of large-scale networks of spiking neurons.
+
+    With this library it is possible to run relatively fast simulations of large-scale networks of spiking neurons employing GPUs. For instance, on a single NVIDIA GeForce RTX 2080 Ti GPU board it is possible to simulate the activity of 1 million multisynapse AdEx neurons with 1000 synapse per neuron in little more than 70 seconds per second of neural activity using the fifth-order Runge-Kutta method with adaptive stepsize as differential equations solver. The MPI communication is also very efficient. The Python interface is very similar to that of the NEST simulator: the most used commands are practically identical, dictionaries are used to define neurons, connections and synapsis properties in the same way.
+
 - urls:
     homepage: https://nest-gpu.readthedocs.io
     documentation: https://nest-gpu.readthedocs.io
@@ -16,3 +18,4 @@
     issue tracker: https://github.com/nest/nest-gpu/issues
 - relations:
     - name: NEST
+      description: has similar interface to # https://nest-gpu.readthedocs.io/en/latest/guides/differences_nest-gpu_nest.html