Fix warnings

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
   - repo: https://github.com/ambv/black
-    rev: 23.1.0
+    rev: 24.1.1
     hooks:
     - id: black
   - repo: https://gitlab.com/pycqa/flake8

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2022-2023 Logan Bishop-Van Horn
+Copyright (c) 2022-2024 Logan Bishop-Van Horn
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

docs/about/license.rst

@@ -6,7 +6,7 @@ License
 
     MIT License
 
-    Copyright (c) 2022-2023 Logan Bishop-Van Horn
+    Copyright (c) 2022-2024 Logan Bishop-Van Horn
 
     Permission is hereby granted, free of charge, to any person obtaining a copy
     of this software and associated documentation files (the "Software"), to deal

docs/installation.rst

@@ -113,6 +113,44 @@ means that essentially all portions of the simulation *except* the sparse linear
 ``cupy`` by setting ``tdgl.SolverOptions.sparse_solver = "cupy"``, however emperically it seems that this is slower than
 performing the sparse linear solve on the CPU.
 
+.. important::
+
+  Using ``cupy`` with an NVIDIA GPU requires the `NVIDIA CUDA Toolkit <https://developer.nvidia.com/cuda-toolkit>`_. You can check whether the CUDA toolkit is
+  installed and on the system path by running 
+
+  .. code-block:: bash
+
+    nvcc --version
+
+  from the command line. The version of ``cupy`` you install must be compatible with the version of the CUDA Toolkit you have installed.
+
+  You can install the CUDA Toolkit either directly from the `NVIDIA website <https://developer.nvidia.com/cuda-toolkit>`_
+  or from the `NVIDIA conda channel <https://anaconda.org/nvidia>`_. To install the CUDA Toolkit using ``conda``, activate the ``conda`` environment
+  for ``tdgl`` and run
+
+  .. code-block:: bash
+
+    conda install cuda -c nvidia
+
+  If you have installed the CUDA Toolkit but ``nvcc --version`` still fails, you may need to update the ``PATH`` and ``LD_LIBRARY_PATH``
+  environment variables to point to your CUDA installation.
+
+  .. code-block:: bash
+
+    # If you installed CUDA Toolkit directly from the NVIDIA website,
+    # resulting in CUDA being installed in /usr/local/cuda:
+    export PATH=/usr/local/cuda/bin${PATH:+:${PATH}}
+    export LD_LIBRARY_PATH=/usr/local/cuda/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
+
+    # If you installed CUDA using conda, activate the appropriate conda environment and run:
+    export PATH=${CONDA_PREFIX}/bin${PATH:+:${PATH}}
+    export LD_LIBRARY_PATH=${CONDA_PREFIX}/lib${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
+
+  The exact path to your CUDA installation may vary depending on operating system and configuration. You may want to add the appropriate
+  ``PATH`` and ``LD_LIBRARY_PATH`` modifications to your ``~/.bashrc`` file.
+
+  For more detailed installation instructions, see the `NVIDIA documentation <https://docs.nvidia.com/cuda/cuda-quick-start-guide/index.html>`_.
+
 Due to overheads related to transferring data between the CPU and GPU, it is expected that ``cupy`` will provide
 a significant speedup only for models with relatively large meshes and/or models that include `screening <notebooks/screening.ipynb>`_.
 Please open a `GitHub issue <https://github.com/loganbvh/py-tdgl/issues>`_ if you have any problems using ``tdgl`` with ``cupy``.

docs/notebooks/logo.ipynb

@@ -233,7 +233,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Constructing Voronoi polygons: 100%|██████████████████████████████████████████████████████████████████████████| 4084/4084 [00:00<00:00, 22134.30it/s]\n"
+      "Constructing Voronoi polygons: 100%|█████| 4084/4084 [00:00<00:00, 21530.56it/s]\n"
      ]
     },
     {
@@ -276,7 +276,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Simulating: 100%|███████████████████████████████████████████████████████████████████████████████████████████████▉| 800/800 [01:55<00:00,  6.94tau/s ]\n"
+      "Simulating: 100%|██████████████████████████▉| 800/800 [01:59<00:00,  6.70tau/s ]\n"
      ]
     }
    ],
@@ -8699,7 +8699,7 @@
     {
      "data": {
       "text/html": [
-       "<table><tr><th>Software</th><th>Version</th></tr><tr><td>tdgl</td><td>0.8.0; git revision 909069a [2023-10-28]</td></tr><tr><td>Numpy</td><td>1.24.3</td></tr><tr><td>SciPy</td><td>1.10.1</td></tr><tr><td>matplotlib</td><td>3.7.1</td></tr><tr><td>cupy</td><td>None</td></tr><tr><td>numba</td><td>0.57.1</td></tr><tr><td>IPython</td><td>8.14.0</td></tr><tr><td>Python</td><td>3.10.11 | packaged by conda-forge | (main, May 10 2023, 19:01:19) [Clang 14.0.6 ]</td></tr><tr><td>OS</td><td>posix [darwin]</td></tr><tr><td>Number of CPUs</td><td>Physical: 10, Logical: 10</td></tr><tr><td>BLAS Info</td><td>OPENBLAS</td></tr><tr><td colspan='2'>Sun Oct 29 16:04:55 2023 PDT</td></tr></table>"
+       "<table><tr><th>Software</th><th>Version</th></tr><tr><td>tdgl</td><td>0.8.1; git revision 268e366 [2024-01-30]</td></tr><tr><td>Numpy</td><td>1.24.3</td></tr><tr><td>SciPy</td><td>1.10.1</td></tr><tr><td>matplotlib</td><td>3.7.1</td></tr><tr><td>cupy</td><td>None</td></tr><tr><td>numba</td><td>0.57.1</td></tr><tr><td>IPython</td><td>8.14.0</td></tr><tr><td>Python</td><td>3.10.11 | packaged by conda-forge | (main, May 10 2023, 19:01:19) [Clang 14.0.6 ]</td></tr><tr><td>OS</td><td>posix [darwin]</td></tr><tr><td>Number of CPUs</td><td>Physical: 10, Logical: 10</td></tr><tr><td>BLAS Info</td><td>OPENBLAS</td></tr><tr><td colspan='2'>Tue Jan 30 14:03:06 2024 PST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"

docs/notebooks/mesh.ipynb

@@ -29,7 +29,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Constructing Voronoi polygons: 100%|████████████████████████████████████████████████████████████████████████████| 525/525 [00:00<00:00, 20666.06it/s]\n"
+      "Constructing Voronoi polygons: 100%|███████| 525/525 [00:00<00:00, 21148.77it/s]\n"
      ]
     }
    ],

docs/notebooks/polygons.ipynb

@@ -469,9 +469,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Constructing Voronoi polygons: 100%|████████████████████████████████████████████████████████████████████████████| 459/459 [00:00<00:00, 16604.01it/s]\n",
-      "Constructing Voronoi polygons: 100%|████████████████████████████████████████████████████████████████████████████| 459/459 [00:00<00:00, 16565.72it/s]\n",
-      "Constructing Voronoi polygons: 100%|████████████████████████████████████████████████████████████████████████████| 459/459 [00:00<00:00, 16859.64it/s]\n"
+      "Constructing Voronoi polygons: 100%|███████| 459/459 [00:00<00:00, 16564.44it/s]\n",
+      "Constructing Voronoi polygons: 100%|███████| 459/459 [00:00<00:00, 16243.28it/s]\n",
+      "Constructing Voronoi polygons: 100%|███████| 459/459 [00:00<00:00, 17103.94it/s]\n"
      ]
     },
     {
@@ -520,7 +520,7 @@
     {
      "data": {
       "text/html": [
-       "<table><tr><th>Software</th><th>Version</th></tr><tr><td>tdgl</td><td>0.8.0; git revision 909069a [2023-10-28]</td></tr><tr><td>Numpy</td><td>1.24.3</td></tr><tr><td>SciPy</td><td>1.10.1</td></tr><tr><td>matplotlib</td><td>3.7.1</td></tr><tr><td>cupy</td><td>None</td></tr><tr><td>numba</td><td>0.57.1</td></tr><tr><td>IPython</td><td>8.14.0</td></tr><tr><td>Python</td><td>3.10.11 | packaged by conda-forge | (main, May 10 2023, 19:01:19) [Clang 14.0.6 ]</td></tr><tr><td>OS</td><td>posix [darwin]</td></tr><tr><td>Number of CPUs</td><td>Physical: 10, Logical: 10</td></tr><tr><td>BLAS Info</td><td>OPENBLAS</td></tr><tr><td colspan='2'>Sun Oct 29 16:04:25 2023 PDT</td></tr></table>"
+       "<table><tr><th>Software</th><th>Version</th></tr><tr><td>tdgl</td><td>0.8.1; git revision 268e366 [2024-01-30]</td></tr><tr><td>Numpy</td><td>1.24.3</td></tr><tr><td>SciPy</td><td>1.10.1</td></tr><tr><td>matplotlib</td><td>3.7.1</td></tr><tr><td>cupy</td><td>None</td></tr><tr><td>numba</td><td>0.57.1</td></tr><tr><td>IPython</td><td>8.14.0</td></tr><tr><td>Python</td><td>3.10.11 | packaged by conda-forge | (main, May 10 2023, 19:01:19) [Clang 14.0.6 ]</td></tr><tr><td>OS</td><td>posix [darwin]</td></tr><tr><td>Number of CPUs</td><td>Physical: 10, Logical: 10</td></tr><tr><td>BLAS Info</td><td>OPENBLAS</td></tr><tr><td colspan='2'>Tue Jan 30 13:57:41 2024 PST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"

docs/notebooks/py-mesh.ipynb

@@ -184,7 +184,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Constructing Voronoi polygons: 100%|██████████████████████████████████████████████████████████████████████████| 4185/4185 [00:00<00:00, 21368.72it/s]\n"
+      "Constructing Voronoi polygons: 100%|█████| 4185/4185 [00:00<00:00, 21925.67it/s]\n"
      ]
     },
     {
@@ -364,7 +364,7 @@
     {
      "data": {
       "text/html": [
-       "<table><tr><th>Software</th><th>Version</th></tr><tr><td>tdgl</td><td>0.8.0; git revision 909069a [2023-10-28]</td></tr><tr><td>Numpy</td><td>1.24.3</td></tr><tr><td>SciPy</td><td>1.10.1</td></tr><tr><td>matplotlib</td><td>3.7.1</td></tr><tr><td>cupy</td><td>None</td></tr><tr><td>numba</td><td>0.57.1</td></tr><tr><td>IPython</td><td>8.14.0</td></tr><tr><td>Python</td><td>3.10.11 | packaged by conda-forge | (main, May 10 2023, 19:01:19) [Clang 14.0.6 ]</td></tr><tr><td>OS</td><td>posix [darwin]</td></tr><tr><td>Number of CPUs</td><td>Physical: 10, Logical: 10</td></tr><tr><td>BLAS Info</td><td>OPENBLAS</td></tr><tr><td colspan='2'>Sun Oct 29 16:04:13 2023 PDT</td></tr></table>"
+       "<table><tr><th>Software</th><th>Version</th></tr><tr><td>tdgl</td><td>0.8.1; git revision 268e366 [2024-01-30]</td></tr><tr><td>Numpy</td><td>1.24.3</td></tr><tr><td>SciPy</td><td>1.10.1</td></tr><tr><td>matplotlib</td><td>3.7.1</td></tr><tr><td>cupy</td><td>None</td></tr><tr><td>numba</td><td>0.57.1</td></tr><tr><td>IPython</td><td>8.14.0</td></tr><tr><td>Python</td><td>3.10.11 | packaged by conda-forge | (main, May 10 2023, 19:01:19) [Clang 14.0.6 ]</td></tr><tr><td>OS</td><td>posix [darwin]</td></tr><tr><td>Number of CPUs</td><td>Physical: 10, Logical: 10</td></tr><tr><td>BLAS Info</td><td>OPENBLAS</td></tr><tr><td colspan='2'>Tue Jan 30 13:57:24 2024 PST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"

tdgl/device/device.py

@@ -151,9 +151,7 @@ def kappa(self) -> float:
     @property
     def Bc2(self) -> pint.Quantity:
         """Upper critical field, :math:`B_{c2}=\\Phi_0/(2\\pi\\xi^2)`."""
-        return (
-            ureg("Phi_0") / (2 * np.pi * self.coherence_length**2)
-        ).to_base_units()
+        return (ureg("Phi_0") / (2 * np.pi * self.coherence_length**2)).to_base_units()
 
     @property
     def A0(self) -> pint.Quantity:

tdgl/finite_volume/operators.py

@@ -6,10 +6,11 @@
 
 try:
     import cupy  # type: ignore
-    from cupyx.scipy.sparse import csc_matrix, csr_matrix  # type: ignore
-    from cupyx.scipy.sparse.linalg import factorized  # type: ignore
 except ImportError:
     cupy = None
+else:
+    from cupyx.scipy.sparse import csc_matrix, csr_matrix  # type: ignore
+    from cupyx.scipy.sparse.linalg import factorized  # type: ignore
 
 from ..solver.options import SparseSolver
 from .mesh import Mesh

tdgl/solution/plot_solution.py

@@ -250,7 +250,7 @@ def plot_currents(
         Jx, Jy = Jgrid
         J = np.sqrt(Jx**2 + Jy**2)
         xy = np.array([xgrid.ravel(), ygrid.ravel()]).T
-        ix = np.where(~solution.device.contains_points(xy)[0])
+        ix = np.where(~solution.device.contains_points(xy))[0]
         ix = np.unravel_index(ix, J.shape)
         Jx[ix] = np.nan
         Jy[ix] = np.nan

tdgl/version.py

@@ -1,4 +1,4 @@
-__version_info__ = (0, 8, 0)
+__version_info__ = (0, 8, 1)
 __version__ = ".".join(map(str, __version_info__))
 
 

tdgl/visualization/common.py

@@ -123,9 +123,12 @@ def auto_grid(
 def non_gui_backend():
     """A contextmanager that temporarily uses a non-GUI backend for matplotlib."""
     with warnings.catch_warnings():
-        warnings.filterwarnings(
-            "ignore", category=UserWarning, message="Matplotlib is currently using agg"
-        )
+        ignore_messages = [
+            "Matplotlib is currently using agg",
+            "FigureCanvasAgg is non-interactive",
+        ]
+        for msg in ignore_messages:
+            warnings.filterwarnings("ignore", category=UserWarning, message=msg)
         try:
             old_backend = mpl.get_backend()
             mpl.use("Agg")