Added unit testing to github workflow

.github/test.yml

+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v3
+    - name: Set up Python 3.9
+      uses: actions/setup-python@v4
+      with:
+        python-version: 3.9
+    # - name: Install torch-harmonics
+    #   run: |
+    #     CWD=`pwd`
+    #     echo 'Cloning repos in ${CWD}'
+    #     mkdir git_repos
+    #     cd git_repos
+    #     git clone git@github.com:NVIDIA/torch-harmonics.git
+    #     cd torch-harmonics
+    #     python -m pip install -e .
+    #     cd ../..
+    - name: Install dependencies
+      run: python -m pip install --upgrade pip setuptools wheel
+    - name: Install package
+      run: |
+        python -m pip install -e .
+    - name: Test with pytest
+      run: |
+        pip install pytest pytest-cov
+        pytest ./torch_harmonics/tests.py
\ No newline at end of file

Changelog.md

@@ -2,6 +2,10 @@
 
 ## Versioning
 
+### v0.6.2
+
+* Adding github CI
+
 ### v0.6.1
 
 * Minor bugfixes to export SFNO code

README.md

@@ -32,7 +32,6 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
     <img src="https://raw.githubusercontent.com/NVIDIA/torch-harmonics/main/images/logo/logo.png"  width="568">
     <br>
     <a href="https://pypi.org/project/torch_harmonics/"><img src="https://img.shields.io/pypi/v/torch_harmonics"></a>
-
 </p>
 
 <!--
@@ -43,11 +42,11 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
 <!-- ## What is torch-harmonics? -->
 
-`torch_harmonics` is a differentiable implementation of the Spherical Harmonic transform in PyTorch. It was originally implemented to enable Spherical Fourier Neural Operators (SFNO). It uses quadrature rules to compute the projection onto the associated Legendre polynomials and FFTs for the projection onto the harmonic basis. This algorithm tends to outperform others with better asymptotic scaling for most practical purposes.
+`torch-harmonics` is a differentiable implementation of the Spherical Harmonic transform in PyTorch. It was originally implemented to enable Spherical Fourier Neural Operators (SFNO). It uses quadrature rules to compute the projection onto the associated Legendre polynomials and FFTs for the projection onto the harmonic basis. This algorithm tends to outperform others with better asymptotic scaling for most practical purposes.
 
-`torch_harmonics` uses PyTorch primitives to implement these operations, making it fully differentiable. Moreover, the quadrature can be distributed onto multiple ranks making it spatially distributed.
+`torch-harmonics` uses PyTorch primitives to implement these operations, making it fully differentiable. Moreover, the quadrature can be distributed onto multiple ranks making it spatially distributed.
 
-`torch_harmonics` has been used to implement a variety of differentiable PDE solvers which generated the animations below. Moreover, it has enabled the development of Spherical Fourier Neural Operators (SFNOs) [1].
+`torch-harmonics` has been used to implement a variety of differentiable PDE solvers which generated the animations below. Moreover, it has enabled the development of Spherical Fourier Neural Operators (SFNOs) [1].
 
 
 <table border="0" cellspacing="0" cellpadding="0">
@@ -75,14 +74,15 @@ Build in your environment using the Python package:
 
 ```
 git clone git@github.com:NVIDIA/torch-harmonics.git
-pip install ./torch_harmonics
+cd torch-harmonics
+pip install -e .
 ```
 
 Alternatively, use the Dockerfile to build your custom container after cloning:
 
 ```
 git clone git@github.com:NVIDIA/torch-harmonics.git
-cd torch_harmonics
+cd torch-harmonics
 docker build . -t torch_harmonics
 docker run --gpus all -it --rm --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 torch_harmonics
 ```

setup.py

@@ -87,7 +87,7 @@ config = {
         {'name': "Boris Bonev", 'email': "bbonev@nvidia.com"},
         ],
     'version': VERSION,
-    'install_requires': ['torch','numpy'],
+    'install_requires': ['torch', 'numpy'],
     'extras_require': {
         'sfno':  ['tensorly', 'tensorly-torch'],
     },