From dc44ae0cd511b28445b01412594dde8ef555ef81 Mon Sep 17 00:00:00 2001
From: Casper da Costa-Luis <casper.dcl@physics.org>
Date: Fri, 12 Apr 2024 09:52:41 +0100
Subject: [PATCH] CI: test windows & more versions

---
 .github/workflows/build.yml | 10 ++++++----
 Readme.md                   |  4 +---
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 156dd2f..912d3a6 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -49,14 +49,16 @@ jobs:
         conda env remove -n "${{ steps.reqs.outputs.envname }}"
   test:
     defaults: {run: {shell: 'bash -el {0}'}}
-    runs-on: ubuntu-latest
+    runs-on: ${{ matrix.os }}
     strategy:
       matrix:
+        python-version: ['3.10', 3.11]
+        numpy-version: [1.23, 1.26]
+        os: [ubuntu-latest, windows-latest]
         include:
-        - python-version: '3.10' # min supported
-          numpy-version: 1.23
-        - python-version: 3.12 # max supported
+        - python-version: 3.12 # needs numpy>=1.26
           numpy-version: 1.26
+          os: ubuntu-latest
     steps:
     - uses: actions/checkout@v4
       with: {fetch-depth: 0, submodules: recursive}
diff --git a/Readme.md b/Readme.md
index 9b11801..a5a4af3 100644
--- a/Readme.md
+++ b/Readme.md
@@ -6,9 +6,7 @@
 
 Iterative image reconstruction (IIR) methods frequently require regularisation to ensure convergence and make inverse problem well-posed. The CCPi-Regularisation Toolkit (CCPi-RGL) toolkit provides a set of 2D/3D regularisation strategies to guarantee a better performance of IIR methods (higher SNR and resolution). The regularisation modules for scalar and vectorial datasets are based on the [proximal operator](https://en.wikipedia.org/wiki/Proximal_operator) framework and can be used with [proximal splitting algorithms](https://en.wikipedia.org/wiki/Proximal_gradient_method), such as PDHG, Douglas-Rachford, ADMM, FISTA and [others](https://arxiv.org/abs/0912.3522). While the main target for CCPi-RGL is [tomographic image reconstruction](https://github.com/dkazanc/ToMoBAR), the toolkit can be used for image denoising problems. The core modules are written in C-OMP and CUDA languages and wrappers for Matlab and Python are provided. With [CuPy](https://docs.cupy.dev/en/stable/index.html) dependency installed for Python, one can use regularisers directly without the need for explicit compilation. We recommend this option as the simplest to start with if you've got a GPU. This software can also be used by running in parallel across multiple GPU devices on a PC or a cluster compute node.
 
-<div align="center">
-  <img src="demos/images/CCPiRGL_sm.jpg" height="400"><br>
-</div>
+<div align="center"><img src="demos/images/CCPiRGL_sm.jpg" height="400"></div>
 
 ## Prerequisites