Merging back release to main (to v3.2.3)

.readthedocs.yaml

@@ -9,7 +9,7 @@ version: 2
 build:
   os: ubuntu-22.04
   tools:
-    python: "3.11"
+    python: "3.12"
 
 # Build documentation in the docs/ directory with Sphinx
 sphinx:

README.md

@@ -1,6 +1,7 @@
 [![Python Version](https://img.shields.io/badge/python-3.10%20%7C%203.11-blue.svg)](https://www.python.org/downloads)[![License](https://img.shields.io/badge/License-BSD_3--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause)[![Maintenance](https://img.shields.io/badge/Maintained%3F-yes-green.svg)](https://GitHub.com/Naereen/StrapDown.js/graphs/commit-activity)[![Documentation Status](https://readthedocs.org/projects/ngc-learn/badge/?version=latest)](http://ngc-learn.readthedocs.io/en/latest/?badge=latest)[![DOI](https://zenodo.org/badge/483413212.svg)](https://zenodo.org/badge/latestdoi/483413212)
 
-<img src="docs/images/ngc-learn-logo.png" width="300">
+<!-- <img src="docs/images/ngc-learn-logo.png" width="300"> -->
+<img src="https://raw.githubusercontent.com/NACLab/ngc-learn/main/docs/images/ngc-learn-logo.png" width="300">
 
 <b>ngc-learn</b> is a Python library for building, simulating, and analyzing biophysical / neurobiological systems, spiking neuronal networks, predictive coding circuitry, and biomimetic (NeuroAI) agents that learn in a biologically-plausible manner. This simulation toolkit, meant to support computational neuroscience and brain-inspired computing research, is built on top of JAX and is distributed under the 3-Clause BSD license.
 
@@ -26,24 +27,22 @@ chosen as one of the <i>Editors' Highlights for Applied Physics and Mathematics<
 ### Dependencies
 
 ngc-learn requires:
-1) Python (>=3.10)
-2) NumPy (>=1.22.0)
-3) SciPy (>=1.7.0)
+1) Python (>=3.12)
+2i) NumPy (>=2.5.2)
+3) SciPy (>=1.18.1)
 4) ngcsimlib (>=3.1.1), (visit official page <a href="https://github.com/NACLab/ngc-sim-lib">here</a>)
-5) JAX (>=0.4.28) (to enable GPU use, make sure to install one of the CUDA variants)
+5) JAX (>=0.11.1) (to enable GPU use, make sure to install one of the CUDA variants)
 <!--
-5) scikit-learn (>=1.3.1) if using `ngclearn.utils.density`
-6) matplotlib (>=3.4.3) if using `ngclearn.utils.viz`
 6) networkx  (>=2.6.3) (currently optional but required if using `ngclearn.utils.experimental.viz_utils`)
 7) pyviz (>=0.2.0) (currently optional but required if using `ngclearn.utils.experimental.viz_utils`)
 -->
 
 ---
-ngc-learn 3.2.1 and later require Python 3.10 or newer as well as ngcsimlib >=3.1.1. 
+ngc-learn 3.2.3 and later require Python 3.12 or newer as well as ngcsimlib >=3.1.1. 
 ngc-learn's plotting capabilities (routines within `ngclearn.utils.viz`) require
-Matplotlib (>=3.8.0) and imageio (>=2.31.5) and both plotting and density estimation
-tools (routines within ``ngclearn.utils.density``) will require Scikit-learn (>=0.24.2).
-Many of the tutorials will require Matplotlib (>=3.8.0), imageio (>=2.31.5), and Scikit-learn (>=0.24.2).
+Matplotlib (>=3.11.1) and imageio (>=2.37.4) and both plotting and density estimation
+tools (routines within ``ngclearn.utils.density``) will require Scikit-learn (>=1.9.0).
+Many of the tutorials will require Matplotlib (>=3.11.1), imageio (>=2.37.4), and Scikit-learn (>=1.9.0).
 
 <i>Note</i>: If you are working with Cuda 12 and want to use jax/jaxlib versions > 0.4.28, you might need to 
 check that you are working with the right version of Cudnn (e.g., `nvidia-cudnn-cu12==9.10.2.21`) to ensure 
@@ -93,7 +92,7 @@ Python 3.12.13 (main, MONTH  DAY YEAR, TIME) [GCC XX.X.X] on linux
 Type "help", "copyright", "credits" or "license" for more information.
 >>> import ngclearn
 >>> ngclearn.__version__
-'3.2.1'
+'3.2.3'
 ```
 
 <i>Note:</i> For access to the previous Tensorflow-2 version of ngc-learn (of

docs/installation.md

@@ -4,13 +4,14 @@
 
 <i>Setup:</i> <a href="https://github.com/NACLab/ngc-learn">NGC-Learn</a>, in its entirety (including its supporting utility sub-packages), requires that you ensure that you have installed the following base dependencies in your system. Note that this library was developed and tested on Ubuntu 22.04 (with much earlier versions on Ubuntu 18.04/20.04). 
 Specifically, NGC-Learn requires:
-* Python (>=3.10)
+* Python (>=3.12)
 * ngcsimlib (>=3.1.1), (<a href="https://github.com/NACLab/ngc-sim-lib">official page</a>)
-* NumPy (>=1.22.0)
-* SciPy (>=1.7.0)
+* NumPy (>=2.5.2)
+* SciPy (>=1.18.1)
 * JAX (>= 0.11.1; and jaxlib>=0.11.1) <!--(tested for cuda 12)-->
-* Matplotlib (>=3.8.0), (for `ngclearn.utils.viz`)
-* Scikit-learn (>=1.6.1), (for `ngclearn.utils.patch_utils` and `ngclearn.utils.density`)
+* Matplotlib (>=3.11.1), (for `ngclearn.utils.viz`)
+* Scikit-learn (>=1.9.0), (for `ngclearn.utils.patch_utils` and `ngclearn.utils.density`)
+* imageio (>=2.37.4)
 
 Note that the above requirements are taken care of if one installs NGC-Learn through either `pip`. One can either install the CPU version of NGC-Learn (if no JAX is pre-installed or only the CPU version of JAX is currently installed) via: 
 ```console
@@ -51,6 +52,6 @@ Python 3.12.13 (main, MONTH  DAY YEAR, TIME) [GCC XX.X.X] on linux
 Type "help", "copyright", "credits" or "license" for more information.
 >>> import ngclearn
 >>> ngclearn.__version__
-'3.2.2'
+'3.2.3'
 ```
 

docs/requirements.txt

@@ -8,3 +8,6 @@ ngcsimlib>=3.1.1
 imageio>=2.37.4
 pandas>=3.0.5
 typing_extensions>=4.15.0
+sphinx
+myst_parser
+sphinx_rtd_theme

pyproject.toml

@@ -8,7 +8,7 @@ build-backend = "setuptools.build_meta" # using setuptool building engine
 
 [project]
 name = "ngclearn"
-version = "3.2.2"
+version = "3.2.3"
 description = "Simulation software for building and analyzing computational neuroscience models, brain-inspired computing systems, and NeuroAI agents."
 authors = [
   {name = "Alexander Ororbia", email = "ago@cs.rit.edu"},