diff --git a/.github/workflows/mpi.yml b/.github/workflows/mpi.yml index 04e042228..8b5b944f9 100644 --- a/.github/workflows/mpi.yml +++ b/.github/workflows/mpi.yml @@ -158,7 +158,11 @@ jobs: sudo apt-get update && sudo apt-get install -y libhdf5-mpi$_ch-dev pkg-config echo NUMBA_THREADING_LAYER=omp >> $GITHUB_ENV - if: startsWith(matrix.platform, 'macos-') - run: brew install hdf5-mpi && echo HDF5_DIR=/opt/homebrew >> $GITHUB_ENV + run: | + brew install hdf5-mpi + echo HDF5_DIR=$(brew --prefix hdf5-mpi) >> $GITHUB_ENV + echo DYLD_FALLBACK_LIBRARY_PATH=$(brew --prefix hdf5-mpi)/lib:$DYLD_FALLBACK_LIBRARY_PATH >> $GITHUB_ENV + - if: matrix.platform == 'macos-14' run: echo DYLD_FALLBACK_LIBRARY_PATH=/opt/homebrew/lib:/usr/local/lib:/usr/lib:$DYLD_FALLBACK_LIBRARY_PATH >> $GITHUB_ENV - run: python -We -c "import PyMPDATA_MPI" diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 7e4184fb6..6da03c4b1 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -81,7 +81,7 @@ jobs: needs: [ precommit, pylint, devops ] strategy: matrix: - python-version: ["3.10", "3.13"] + python-version: ["3.10", "3.13.12"] runs-on: ubuntu-latest env: NUMBA_DISABLE_JIT: 1 @@ -113,7 +113,7 @@ jobs: strategy: matrix: platform: [ubuntu-latest, macos-15-intel, macos-14, windows-latest] - python-version: ["3.10", "3.13"] + python-version: ["3.10", "3.13.12"] exclude: - platform: macos-14 python-version: "3.10" @@ -154,7 +154,7 @@ jobs: strategy: matrix: platform: [ubuntu-latest, macos-15-intel, macos-14, windows-latest] - python-version: ["3.10", "3.13"] + python-version: ["3.10", "3.13.12"] fail-fast: false runs-on: ${{ matrix.platform }} steps: @@ -191,7 +191,7 @@ jobs: NUMBA_OPT: 1 run: python -m pytest --durations=10 -p no:unraisableexception -We tests/devops_tests/test_notebooks.py - - if: ${{ matrix.platform == 'ubuntu-latest' && matrix.python-version == '3.13'}} + - if: ${{ matrix.platform == 'ubuntu-latest' && matrix.python-version == '3.13.12'}} run: | mkdir -p /home/runner/work/_temp/_github_home/figures rm /tmp/pytest-of-runner/pytest-current/test_run_notebooks_examples_Pycurrent @@ -206,7 +206,7 @@ jobs: # with: # limit-access-to-actor: true - - if: ${{ github.ref == 'refs/heads/main' && matrix.platform == 'ubuntu-latest' && matrix.python-version == '3.13'}} + - if: ${{ github.ref == 'refs/heads/main' && matrix.platform == 'ubuntu-latest' && matrix.python-version == '3.13.12'}} uses: eine/tip@master with: token: ${{ secrets.GITHUB_TOKEN }} diff --git a/examples/PyMPDATA_examples/advection_diffusion_1d/demo.ipynb b/examples/PyMPDATA_examples/advection_diffusion_1d/demo.ipynb index cfe1aecdc..d270e96db 100644 --- a/examples/PyMPDATA_examples/advection_diffusion_1d/demo.ipynb +++ b/examples/PyMPDATA_examples/advection_diffusion_1d/demo.ipynb @@ -18,22 +18,30 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-27T13:20:04.085493Z", + "start_time": "2026-04-27T13:20:04.063454Z" + } + }, "source": [ "import sys\n", "if 'google.colab' in sys.modules:\n", " !pip --quiet install open-atmos-jupyter-utils\n", " from open_atmos_jupyter_utils import pip_install_on_colab\n", " pip_install_on_colab('PyMPDATA-examples')" - ] + ], + "outputs": [], + "execution_count": 1 }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-27T13:20:06.182249Z", + "start_time": "2026-04-27T13:20:04.310811Z" + } + }, "source": [ "import os\n", "import numpy as np\n", @@ -44,13 +52,18 @@ "from open_atmos_jupyter_utils import show_plot\n", "from PyMPDATA import Solver, ScalarField, VectorField, Stepper, Options\n", "from PyMPDATA.boundary_conditions import Periodic" - ] + ], + "outputs": [], + "execution_count": 2 }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-27T13:20:06.242757Z", + "start_time": "2026-04-27T13:20:06.204986Z" + } + }, "source": [ "#Eq parameters\n", "mu = 0.05\n", @@ -65,13 +78,18 @@ "#Plot Parameters\n", "r_array = np.arange(1, 7, 2)\n", "c_array = np.linspace(0.05, 0.7, 7)" - ] + ], + "outputs": [], + "execution_count": 3 }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-27T13:24:21.422630Z", + "start_time": "2026-04-27T13:20:06.246144Z" + } + }, "source": [ "results = {}\n", "domains = {}\n", @@ -128,13 +146,49 @@ " error = np.sqrt(np.sum((u(domain, steps) - numerical_results)**2, axis=1)/nx)\n", " max_idx = np.argmax(error)\n", " max_error[key] = error[max_idx]\n" - ] + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "R 1\n", + "\t 0.05\n", + "\t 0.15833333333333333\n", + "\t 0.26666666666666666\n", + "\t 0.37499999999999994\n", + "\t 0.4833333333333333\n", + "\t 0.5916666666666667\n", + "\t 0.7\n", + "R 3\n", + "\t 0.05\n", + "\t 0.15833333333333333\n", + "\t 0.26666666666666666\n", + "\t 0.37499999999999994\n", + "\t 0.4833333333333333\n", + "\t 0.5916666666666667\n", + "\t 0.7\n", + "R 5\n", + "\t 0.05\n", + "\t 0.15833333333333333\n", + "\t 0.26666666666666666\n", + "\t 0.37499999999999994\n", + "\t 0.4833333333333333\n", + "\t 0.5916666666666667\n", + "\t 0.7\n" + ] + } + ], + "execution_count": 4 }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-27T13:24:22.893780Z", + "start_time": "2026-04-27T13:24:21.766531Z" + } + }, "source": [ "theta_array = c_array * np.pi/2\n", "\n", @@ -159,13 +213,49 @@ "\n", "ax.set_title(\"Error log vs R and C\", va='bottom')\n", "show_plot()" - ] + ], + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/svg+xml": "\n\n\n \n \n \n \n 2026-04-27T15:24:22.852050\n image/svg+xml\n \n \n Matplotlib v3.10.8, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "data": { + "text/plain": [ + "HBox(children=(HTML(value=\"./tmpzfqogp4g.pdf
\"), HTML(value…" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "7224b14f5b5f431ca3635ac01000d85f" + } + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 5 }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-27T13:24:23.158104Z", + "start_time": "2026-04-27T13:24:22.975647Z" + } + }, "source": [ "key = (1,.05)\n", "numerical_result = results[key]\n", @@ -198,24 +288,9718 @@ "\n", "animation = FuncAnimation(fig, update, frames=frame_list, interval=interval, blit=True)\n", "fig.show()" - ] + ], + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/svg+xml": "\n\n\n \n \n \n \n 2026-04-27T15:24:23.116839\n image/svg+xml\n \n \n Matplotlib v3.10.8, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 6 }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-27T13:25:42.931731Z", + "start_time": "2026-04-27T13:24:23.186759Z" + } + }, "source": [ "if 'CI' not in os.environ:\n", " display(HTML(animation.to_html5_video()))" - ] + ], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 7 }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-27T13:25:43.581798Z", + "start_time": "2026-04-27T13:25:43.549745Z" + } + }, + "source": [], "outputs": [], - "source": [] + "execution_count": null } ], "metadata": { diff --git a/examples/PyMPDATA_examples/advection_diffusion_2d/advection-diffusion-2d.ipynb b/examples/PyMPDATA_examples/advection_diffusion_2d/advection-diffusion-2d.ipynb index 1a97aea4c..e07fd554d 100644 --- a/examples/PyMPDATA_examples/advection_diffusion_2d/advection-diffusion-2d.ipynb +++ b/examples/PyMPDATA_examples/advection_diffusion_2d/advection-diffusion-2d.ipynb @@ -33,8 +33,8 @@ "id": "e333666d", "metadata": { "ExecuteTime": { - "end_time": "2024-11-17T10:48:24.048516Z", - "start_time": "2024-11-17T10:48:24.040750Z" + "end_time": "2026-04-27T13:07:29.817782Z", + "start_time": "2026-04-27T13:07:29.804353Z" } }, "source": [ @@ -45,7 +45,7 @@ " pip_install_on_colab('PyMPDATA-examples')" ], "outputs": [], - "execution_count": 59 + "execution_count": 1 }, { "cell_type": "code", @@ -53,15 +53,15 @@ "metadata": { "id": "43d2893d-f472-43ac-ad5b-bf342a3783b9", "ExecuteTime": { - "end_time": "2024-11-17T10:48:24.064Z", - "start_time": "2024-11-17T10:48:24.058632Z" + "end_time": "2026-04-27T13:07:30.974310Z", + "start_time": "2026-04-27T13:07:29.873463Z" } }, "source": [ "from open_atmos_jupyter_utils import show_plot" ], "outputs": [], - "execution_count": 60 + "execution_count": 2 }, { "cell_type": "code", @@ -69,8 +69,8 @@ "metadata": { "id": "9aaadc4a5234804a", "ExecuteTime": { - "end_time": "2024-11-17T10:48:24.074911Z", - "start_time": "2024-11-17T10:48:24.072769Z" + "end_time": "2026-04-27T13:07:31.751873Z", + "start_time": "2026-04-27T13:07:31.022222Z" } }, "source": [ @@ -84,7 +84,7 @@ "from PyMPDATA.boundary_conditions import Periodic" ], "outputs": [], - "execution_count": 61 + "execution_count": 3 }, { "cell_type": "code", @@ -92,8 +92,8 @@ "metadata": { "id": "a563a769a256feba", "ExecuteTime": { - "end_time": "2024-11-17T10:48:24.083541Z", - "start_time": "2024-11-17T10:48:24.080757Z" + "end_time": "2026-04-27T13:07:31.764333Z", + "start_time": "2026-04-27T13:07:31.761367Z" } }, "source": [ @@ -117,7 +117,7 @@ "Cy = uy * dt / dy" ], "outputs": [], - "execution_count": 62 + "execution_count": 4 }, { "cell_type": "code", @@ -125,8 +125,8 @@ "metadata": { "id": "9a0f60b51e32ce3e", "ExecuteTime": { - "end_time": "2024-11-17T10:48:24.091078Z", - "start_time": "2024-11-17T10:48:24.089428Z" + "end_time": "2026-04-27T13:07:31.780314Z", + "start_time": "2026-04-27T13:07:31.778015Z" } }, "source": [ @@ -134,7 +134,7 @@ "boundary_conditions = (Periodic(), Periodic())" ], "outputs": [], - "execution_count": 63 + "execution_count": 5 }, { "cell_type": "code", @@ -142,8 +142,8 @@ "metadata": { "id": "cab790be5c425ea5", "ExecuteTime": { - "end_time": "2024-11-17T10:48:24.098801Z", - "start_time": "2024-11-17T10:48:24.096647Z" + "end_time": "2026-04-27T13:07:31.798673Z", + "start_time": "2026-04-27T13:07:31.793242Z" } }, "source": [ @@ -151,7 +151,7 @@ " return np.sin(omega*(x-ux*t+y-uy*t))*np.exp(-2*mu*t*omega**2) + 1" ], "outputs": [], - "execution_count": 64 + "execution_count": 6 }, { "cell_type": "code", @@ -159,8 +159,8 @@ "metadata": { "id": "b454a74473b8f900", "ExecuteTime": { - "end_time": "2024-11-17T10:48:24.108540Z", - "start_time": "2024-11-17T10:48:24.104379Z" + "end_time": "2026-04-27T13:07:31.813795Z", + "start_time": "2026-04-27T13:07:31.809002Z" } }, "source": [ @@ -176,7 +176,7 @@ "advectee = ScalarField(data=z(t=0), halo=opt.n_halo, boundary_conditions=boundary_conditions)" ], "outputs": [], - "execution_count": 65 + "execution_count": 7 }, { "cell_type": "code", @@ -184,8 +184,8 @@ "metadata": { "id": "fb28f958a4920cd", "ExecuteTime": { - "end_time": "2024-11-17T10:48:24.116792Z", - "start_time": "2024-11-17T10:48:24.114299Z" + "end_time": "2026-04-27T13:07:31.832064Z", + "start_time": "2026-04-27T13:07:31.828545Z" } }, "source": [ @@ -199,7 +199,7 @@ ")" ], "outputs": [], - "execution_count": 66 + "execution_count": 8 }, { "cell_type": "code", @@ -207,8 +207,8 @@ "metadata": { "id": "45c8ea60d8490cd4", "ExecuteTime": { - "end_time": "2024-11-17T10:48:24.125339Z", - "start_time": "2024-11-17T10:48:24.122603Z" + "end_time": "2026-04-27T13:07:32.619258Z", + "start_time": "2026-04-27T13:07:31.842318Z" } }, "source": [ @@ -216,7 +216,7 @@ "solver = Solver(stepper=stepper, advector=advector, advectee=advectee)" ], "outputs": [], - "execution_count": 67 + "execution_count": 9 }, { "cell_type": "code", @@ -224,8 +224,8 @@ "metadata": { "id": "bd4722c47297508c", "ExecuteTime": { - "end_time": "2024-11-17T10:48:24.132258Z", - "start_time": "2024-11-17T10:48:24.130678Z" + "end_time": "2026-04-27T13:07:32.626958Z", + "start_time": "2026-04-27T13:07:32.624719Z" } }, "source": [ @@ -233,15 +233,15 @@ "vmax = np.max(solver.advectee.get())" ], "outputs": [], - "execution_count": 68 + "execution_count": 10 }, { "cell_type": "code", "id": "0e66ee78-f623-4e5f-99ef-0e43a2c81da1", "metadata": { "ExecuteTime": { - "end_time": "2024-11-17T10:48:24.145284Z", - "start_time": "2024-11-17T10:48:24.139694Z" + "end_time": "2026-04-27T13:07:32.644342Z", + "start_time": "2026-04-27T13:07:32.640890Z" } }, "source": [ @@ -255,7 +255,7 @@ " axs.set_title(title)" ], "outputs": [], - "execution_count": 69 + "execution_count": 11 }, { "cell_type": "code", @@ -278,8 +278,8 @@ "id": "d7a5cd43651621e6", "outputId": "dcf9d10e-7f93-4491-8c26-a24e322eb4ee", "ExecuteTime": { - "end_time": "2024-11-17T10:48:24.401807Z", - "start_time": "2024-11-17T10:48:24.156844Z" + "end_time": "2026-04-27T13:07:33.160696Z", + "start_time": "2026-04-27T13:07:32.656041Z" } }, "source": [ @@ -295,10 +295,13 @@ "text/plain": [ "
" ], - "image/png": "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" + "image/png": "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" }, "metadata": {}, - "output_type": "display_data" + "output_type": "display_data", + "jetTransient": { + "display_id": null + } }, { "data": { @@ -308,14 +311,17 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "ba8740985b92437e813025222bedab09" + "model_id": "5504bfefaed54d4da0b84cb2be8f5260" } }, "metadata": {}, - "output_type": "display_data" + "output_type": "display_data", + "jetTransient": { + "display_id": null + } } ], - "execution_count": 70 + "execution_count": 12 }, { "cell_type": "code", @@ -333,8 +339,8 @@ "id": "2b279996b00be430", "outputId": "9b04d6f4-e170-42bc-f79b-18114f7c4164", "ExecuteTime": { - "end_time": "2024-11-17T10:48:24.648416Z", - "start_time": "2024-11-17T10:48:24.410361Z" + "end_time": "2026-04-27T13:08:22.589597Z", + "start_time": "2026-04-27T13:07:33.169797Z" } }, "source": [ @@ -356,14 +362,17 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "718e6e7c9a484e17ad0976e18883dcef" + "model_id": "58f9a80f98fd47648a5a6c0d20d2e4dd" } }, "metadata": {}, - "output_type": "display_data" + "output_type": "display_data", + "jetTransient": { + "display_id": null + } } ], - "execution_count": 71 + "execution_count": 13 }, { "cell_type": "code", @@ -386,8 +395,8 @@ "id": "2576fcdbfa20c032", "outputId": "d47a4681-9734-4553-d424-3830b5549d6d", "ExecuteTime": { - "end_time": "2024-11-17T10:48:24.875574Z", - "start_time": "2024-11-17T10:48:24.655381Z" + "end_time": "2026-04-27T13:08:23.205126Z", + "start_time": "2026-04-27T13:08:22.690550Z" } }, "source": [ @@ -403,10 +412,13 @@ "text/plain": [ "
" ], - "image/png": "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" + "image/png": "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" }, "metadata": {}, - "output_type": "display_data" + "output_type": "display_data", + "jetTransient": { + "display_id": null + } }, { "data": { @@ -416,14 +428,17 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "9f20a696185647c0b352673c3822f263" + "model_id": "d0cb71f2850e4401bd8c7cb7dc760eed" } }, "metadata": {}, - "output_type": "display_data" + "output_type": "display_data", + "jetTransient": { + "display_id": null + } } ], - "execution_count": 72 + "execution_count": 14 }, { "cell_type": "code", @@ -431,15 +446,15 @@ "metadata": { "id": "556684f5a2e2483d", "ExecuteTime": { - "end_time": "2024-11-17T10:48:24.892189Z", - "start_time": "2024-11-17T10:48:24.886346Z" + "end_time": "2026-04-27T13:08:23.276351Z", + "start_time": "2026-04-27T13:08:23.267452Z" } }, "source": [ "assert np.allclose(solver.advectee.get(), z(t=nt*dt), atol=0.25)" ], "outputs": [], - "execution_count": 73 + "execution_count": 15 }, { "cell_type": "code", @@ -447,8 +462,8 @@ "metadata": { "id": "cc07e0075e5b9857", "ExecuteTime": { - "end_time": "2024-11-17T10:48:26.052714Z", - "start_time": "2024-11-17T10:48:24.898326Z" + "end_time": "2026-04-27T13:08:24.820679Z", + "start_time": "2026-04-27T13:08:23.315420Z" } }, "source": [ @@ -461,7 +476,7 @@ " plt.close()" ], "outputs": [], - "execution_count": 74 + "execution_count": 16 }, { "cell_type": "code", @@ -469,8 +484,8 @@ "metadata": { "id": "1888a38e39bf2395", "ExecuteTime": { - "end_time": "2024-11-17T10:48:26.063023Z", - "start_time": "2024-11-17T10:48:26.060767Z" + "end_time": "2026-04-27T13:08:24.848200Z", + "start_time": "2026-04-27T13:08:24.840228Z" } }, "source": [ @@ -481,7 +496,7 @@ " writer.append_data(image)" ], "outputs": [], - "execution_count": 75 + "execution_count": 17 }, { "cell_type": "code", @@ -489,15 +504,15 @@ "metadata": { "id": "52bc22d60805baa6", "ExecuteTime": { - "end_time": "2024-11-17T10:48:26.428970Z", - "start_time": "2024-11-17T10:48:26.075064Z" + "end_time": "2026-04-27T13:08:25.768807Z", + "start_time": "2026-04-27T13:08:24.866604Z" } }, "source": [ "merge_images_into_gif(\"animation\", \"advection_diffusion.gif\", duration=0.01)" ], "outputs": [], - "execution_count": 76 + "execution_count": 18 }, { "cell_type": "code", @@ -505,8 +520,8 @@ "metadata": { "id": "5403105d000206da", "ExecuteTime": { - "end_time": "2024-11-17T10:48:26.437388Z", - "start_time": "2024-11-17T10:48:26.435624Z" + "end_time": "2026-04-27T13:08:25.803676Z", + "start_time": "2026-04-27T13:08:25.801235Z" } }, "source": [], diff --git a/examples/PyMPDATA_examples/trixi_comparison/advection_comparison.ipynb b/examples/PyMPDATA_examples/trixi_comparison/advection_comparison.ipynb index 1e95b0d98..be13cf841 100644 --- a/examples/PyMPDATA_examples/trixi_comparison/advection_comparison.ipynb +++ b/examples/PyMPDATA_examples/trixi_comparison/advection_comparison.ipynb @@ -504,14 +504,10 @@ }, "cell_type": "code", "source": [ - "try:\n", - " vtu_filename = [f for f in os.listdir(\"./\") if \"vtu\" in f and \"celldata\" not in f][0]\n", - " mesh = meshio.read(vtu_filename)\n", - " trixi_points = ((mesh.points[:,:2] + 1)*SETUP[\"nx\"]*SETUP[\"polydeg\"]/2).round().astype(np.int16)\n", - " assert trixi_points.shape[0] == SETUP[\"nx\"]**2 * (SETUP[\"polydeg\"] + 1)**2\n", - "except Exception as e:\n", - " e.args += (list(os.walk(os.path.curdir)),)\n", - " raise e" + "vtu_filename = [f for f in os.listdir(\"./\") if \"vtu\" in f and \"celldata\" not in f][0]\n", + "mesh = meshio.read(vtu_filename)\n", + "trixi_points = ((mesh.points[:,:2] + 1)*SETUP[\"nx\"]*SETUP[\"polydeg\"]/2).round().astype(np.int16)\n", + "assert trixi_points.shape[0] == SETUP[\"nx\"]**2 * (SETUP[\"polydeg\"] + 1)**2\n" ], "id": "451911db51e18682", "outputs": [], @@ -526,14 +522,9 @@ }, "cell_type": "code", "source": [ - "try:\n", - " trixi_output = np.zeros_like(pympdata_result_state)\n", - " for i in range(trixi_points.shape[0]):\n", - " trixi_output[trixi_points[i][0], trixi_points[i][1]] = mesh.point_data['scalar'][i][0]\n", - "except Exception as e:\n", - " e.args += (list(mesh.point_data.keys()),)\n", - " e.args += (list(mesh.points.shape),)\n", - " raise e" + "trixi_output = np.zeros_like(pympdata_result_state)\n", + "for i in range(trixi_points.shape[0]):\n", + " trixi_output[trixi_points[i][0], trixi_points[i][1]] = mesh.point_data['scalar'][i][0]\n" ], "id": "58595cff705f196c", "outputs": [],