Updated python-publish

Generated a new codecov token and updated badge to track the new branch
Example in the documentation fixed

.github/workflows/python-package.yml

@@ -39,7 +39,7 @@ jobs:
     - name: Test with pytest
       run: |
         python -m pytest --cov-config=.coveragerc_cpu --cov=fimpy tests/
-        bash <(curl -s https://codecov.io/bash) -t 9ad55939-f54d-4aad-a7eb-fcd2d22d4ecd
+        bash <(curl -s https://codecov.io/bash) -t ${{ secrets.CODECOV_TOKEN }}
 
     - name: Test installation
       run: |

.github/workflows/python-publish.yml

@@ -28,7 +28,7 @@ jobs:
         python -m pip install --upgrade pip
         pip install build
     - name: Build package
-      run: python -m build
+      run: python -m build -s
     - name: Publish package
       uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29
       with:

README.md

@@ -1,7 +1,7 @@
 # Fast Iterative Method - Numpy/Cupy
 This repository implements the Fast Iterative Method on [tetrahedral domains](https://epubs.siam.org/doi/abs/10.1137/120881956) and [triangulated surfaces](https://epubs.siam.org/doi/abs/10.1137/100788951) purely in python both for CPU (numpy) and GPU (cupy). The main focus is however on the GPU implementation, since it can be better exploited for very large domains.
 
-[![codecov](https://codecov.io/gh/thomgrand/fim-python/branch/main/graph/badge.svg?token=DG05WR5030)](https://codecov.io/gh/thomgrand/fim-python)
+[![codecov](https://codecov.io/gh/thomgrand/fim-python/branch/master/graph/badge.svg?token=DG05WR5030)](https://codecov.io/gh/thomgrand/fim-python)
 [![CI Tests](https://github.com/thomgrand/fim-python/actions/workflows/python-package.yml/badge.svg)](https://github.com/thomgrand/fim-python/actions/workflows/python-package.yml)
 
 # Details

docs/example.inc

@@ -2,27 +2,27 @@
 
     import numpy as np
     import cupy as cp
-    from fimpy import fim_solver
+    from fimpy.solver import FIMPY
     from scipy.spatial import Delaunay
     import matplotlib.pyplot as plt
 
     #Create triangulated points in 2D
     x = np.linspace(-1, 1, num=50)
     X, Y = np.meshgrid(x, x)
-    points = np.stack([X, Y], axis=-1).reshape([-1]).astype(np.float32)
+    points = np.stack([X, Y], axis=-1).reshape([-1, 2]).astype(np.float32)
     elems = Delaunay(points).simplices
     elem_centers = np.mean(points[elems], axis=1)
 
     #The domain will have a small spot where movement will be slow
     velocity_p = (1 / (1 + np.exp(3.5 - 25*np.linalg.norm(points - np.array([[0.33, 0.33]]), axis=-1)**2)))
-    velocity_e = (1 / (1 + np.exp(3.5 - 25*np.linalg.norm(elem_centers - np.array([[0.33, 0.33]]), axis=-1)**2))) 
+    velocity_e = (1 / (1 + np.exp(3.5 - 25*np.linalg.norm(elem_centers - np.array([[0.33, 0.33]]), axis=-1)**2)))
     D = np.eye(2, dtype=np.float32)[np.newaxis] * velocity_e[..., np.newaxis, np.newaxis] #Isotropic propagation
 
     x0 = np.array([np.argmin(np.linalg.norm(points, axis=-1), axis=0)])
     x0_vals = np.array([0.])
 
     #Create a FIM solver, by default the GPU solver will be called with the active list
-    fim = fim_solver(points, elems, D)
+    fim = FIMPY.create_fim_solver(points, elems, D)
     phi = fim.comp_fim(x0, x0_vals)
 
     #Plot the data of all points to the given x0 at the center of the domain

fimpy/__init__.py

@@ -1,6 +1,6 @@
 #TODO: Import all symbols and methods here
 from .solver import FIMPY
 
-__version__ = "1.0.0"
+__version__ = "1.0.1"
 __author__ = "Thomas Grandits"
 #__all__ = ["FIMPY"]

setup.py

@@ -30,7 +30,7 @@
      long_description = readme.read()
 
 setup(name="fim-python",
-    version="1.0.0",    
+    version="1.0.1",    
     description="This repository implements the Fast Iterative Method on tetrahedral domains and triangulated surfaces purely in python both for CPU (numpy) and GPU (cupy).",
     long_description=long_description,
     long_description_content_type="text/markdown",

tests/test_fim_solvers.py

@@ -152,12 +152,3 @@ def test_comp(self, dims, elem_dims, precision, use_active_list, device='cpu'):
     @pytest.mark.parametrize('use_active_list', [True, False])
     def test_comp_gpu(self, dims, elem_dims, precision, use_active_list):
         self.test_comp(dims, elem_dims, precision, use_active_list=use_active_list, device='gpu')
-
-    #def test_init
-    #def test_1D_network
-    #def test_multidim_network
-    #def test_manifold
-    #def test_volumetric_model
-
-#if __name__ == "__main__":
-    #unittest.main()