From ec693938d2aaa970b91f35a8fb411875ae5b5b91 Mon Sep 17 00:00:00 2001 From: George Bisbas Date: Wed, 12 Jul 2023 14:33:22 +0100 Subject: [PATCH 1/4] ci: Try to reinstate --- .github/workflows/jupyter-notebooks.yml | 15 +++++------ .github/workflows/verification.yml | 35 +++++++++++-------------- requirements.txt | 2 +- 3 files changed, 23 insertions(+), 29 deletions(-) diff --git a/.github/workflows/jupyter-notebooks.yml b/.github/workflows/jupyter-notebooks.yml index 0698a8ea..ea77b674 100644 --- a/.github/workflows/jupyter-notebooks.yml +++ b/.github/workflows/jupyter-notebooks.yml @@ -21,7 +21,6 @@ jobs: DEVITO_LANGUAGE: ${{ matrix.language }} DEVITO_BACKEND: "core" PYTHON_VERSION: "3.9" - RUN_CMD: "" strategy: # Prevent all build to stop if a single one fails @@ -61,8 +60,6 @@ jobs: run: | if [ "${{ matrix.compiler }}" = "gcc-9" ]; then brew install gcc - else - sudo xcode-select -s /Applications/Xcode_11.app/Contents/Developer fi - name: Install dependencies run: | @@ -72,21 +69,21 @@ jobs: pip install --user git+https://github.com/devitocodes/devito.git - name: Vibration ODE notebooks (1.1 to 1.8) run: | - $RUN_CMD python -m pytest -W ignore::DeprecationWarning --nbval --cov . --cov-config=.coveragerc --cov-report=xml:vib_coverage.xml $SKIP fdm-devito-notebooks/01_vib/vib_undamped.ipynb + python -m pytest --nbval --cov . --cov-config=.coveragerc --cov-report=xml:vib_coverage.xml fdm-devito-notebooks/01_vib/vib_undamped.ipynb - name: Waves notebooks (2.1 and 2.2) run: | - $RUN_CMD python -m pytest -W ignore::DeprecationWarning --nbval --cov . --cov-config=.coveragerc --cov-report=xml:waves_coverage.xml $SKIP fdm-devito-notebooks/02_wave/wave1D_fd1.ipynb + python -m pytest --nbval --cov . --cov-config=.coveragerc --cov-report=xml:waves_coverage.xml fdm-devito-notebooks/02_wave/wave1D_fd1.ipynb - name: Waves notebooks (2.3 to 2.5) run: | - $RUN_CMD python -m pytest -W ignore::DeprecationWarning --nbval --cov . --cov-config=.coveragerc --cov-report=xml:waves_coverage.xml $SKIP fdm-devito-notebooks/02_wave/wave1D_prog.ipynb + python -m pytest --nbval --cov . --cov-config=.coveragerc --cov-report=xml:waves_coverage.xml fdm-devito-notebooks/02_wave/wave1D_prog.ipynb - name: Diffusion notebooks (3.7) run: | - $RUN_CMD python -m pytest -W ignore::DeprecationWarning --nbval --cov . --cov-config=.coveragerc --cov-report=xml:diffu_coverage.xml $SKIP fdm-devito-notebooks/03_diffu/diffu_rw.ipynb + python -m pytest --nbval --cov . --cov-config=.coveragerc --cov-report=xml:diffu_coverage.xml fdm-devito-notebooks/03_diffu/diffu_rw.ipynb - name: Advection notebook (4) run: | - $RUN_CMD python -m pytest -W ignore::DeprecationWarning --nbval --cov . --cov-config=.coveragerc --cov-report=xml:advec_coverage.xml $SKIP fdm-devito-notebooks/04_advec/advec.ipynb + python -m pytest --nbval --cov . --cov-config=.coveragerc --cov-report=xml:advec_coverage.xml fdm-devito-notebooks/04_advec/advec.ipynb - name: Upload coverage to Codecov - uses: codecov/codecov-action@v1.0.6 + uses: codecov/codecov-action@v3 with: token: ${{ secrets.CODECOV_TOKEN }} name: ${{ matrix.name }} diff --git a/.github/workflows/verification.yml b/.github/workflows/verification.yml index 6662a779..4b5da411 100644 --- a/.github/workflows/verification.yml +++ b/.github/workflows/verification.yml @@ -20,41 +20,40 @@ jobs: DEVITO_ARCH: "${{ matrix.compiler }}" DEVITO_LANGUAGE: ${{ matrix.language }} DEVITO_BACKEND: "core" - PYTHON_VERSION: "3.7" - RUN_CMD: "" + PYTHON_VERSION: "3.9" strategy: # Prevent all build to stop if a single one fails fail-fast: false matrix: - name: [tutos-ubuntu-gcc-py37, - tutos-osx-gcc-py37, - tutos-osx-clang-py37] + name: [tutos-ubuntu-gcc-py39, + tutos-osx-gcc-py39, + tutos-osx-clang-py39] include: - - name: tutos-ubuntu-gcc-py37 - os: ubuntu-16.04 - compiler: gcc-7 + - name: tutos-ubuntu-gcc-py39 + os: ubuntu-latest + compiler: gcc-9 language: "openmp" - - name: tutos-osx-gcc-py37 + - name: tutos-osx-gcc-py39 os: macos-latest compiler: gcc-9 language: "openmp" - - name: tutos-osx-clang-py37 + - name: tutos-osx-clang-py39 os: macos-latest compiler: clang language: "C" steps: - name: Checkout devito_book - uses: actions/checkout@v1 + uses: actions/checkout@v3 - - name: Set up Python 3.7 - uses: actions/setup-python@v1 + - name: Set up Python 3.9 + uses: actions/setup-python@v4 with: - python-version: 3.7 + python-version: 3.9 - name: Install compilers for OSX if: runner.os == 'macOS' @@ -64,8 +63,6 @@ jobs: else sudo xcode-select -s /Applications/Xcode_11.app/Contents/Developer fi - # dask error on osx, skip dask tuto - echo "::set-env name=SKIP::--deselect=examples/seismic/tutorials/04_dask.ipynb" - name: Install dependencies run: | python -m pip install --upgrade pip @@ -75,15 +72,15 @@ jobs: - name: Waves (2.1 to 2.5) run: | cd fdm-devito-notebooks/02_wave/src-wave/wave1D - $RUN_CMD python -m pytest -W ignore::DeprecationWarning -s -v --cov . --cov-config=.coveragerc --cov-report=xml:waves_coverage.xml $SKIP wave1D_u0.py::test_constant + python -m pytest -s -v --cov . --cov-config=.coveragerc --cov-report=xml:waves_coverage.xml wave1D_u0.py::test_constant - name: Diffusion (3.7) run: | cd fdm-devito-notebooks/03_diffu/src-diffu - $RUN_CMD python -m pytest -W ignore::DeprecationWarning -s -v --cov . --cov-config=.coveragerc --cov-report=xml:diffu_coverage.xml $SKIP random_walk.py + python -m pytest -s -v --cov . --cov-config=.coveragerc --cov-report=xml:diffu_coverage.xml random_walk.py - name: Upload coverage to Codecov - uses: codecov/codecov-action@v1.0.6 + uses: codecov/codecov-action@v3 with: token: ${{ secrets.CODECOV_TOKEN }} name: ${{ matrix.name }} diff --git a/requirements.txt b/requirements.txt index a76ad777..a32bb0ec 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,7 +2,7 @@ pytest-runner nbval cached-property py-cpuinfo -devito<4.8.2 +devito jupyter-book matplotlib vtk==9.2.2 From aeba214b49ca0d5c3176fadf1b6604e17b5cc79c Mon Sep 17 00:00:00 2001 From: George Bisbas Date: Thu, 20 Jul 2023 15:15:36 +0300 Subject: [PATCH 2/4] wip --- fdm-devito-notebooks/02_wave/src-wave/wave1D/wave1D_dn.py | 4 ++-- requirements.txt | 7 +++++-- 2 files changed, 7 insertions(+), 4 deletions(-) diff --git a/fdm-devito-notebooks/02_wave/src-wave/wave1D/wave1D_dn.py b/fdm-devito-notebooks/02_wave/src-wave/wave1D/wave1D_dn.py index 850dfe09..4beccff7 100644 --- a/fdm-devito-notebooks/02_wave/src-wave/wave1D/wave1D_dn.py +++ b/fdm-devito-notebooks/02_wave/src-wave/wave1D/wave1D_dn.py @@ -266,7 +266,7 @@ def assert_no_error(u, x, t, n): solver(I, V, f, c, U_0, U_L, L, dt, C, T, user_action=assert_no_error, version='vectorized') - print U_0, U_L + print(U_0, U_L) def test_quadratic(): """ @@ -383,7 +383,7 @@ def guitar(C=1, Nx=50, animate=True, version='scalar', T=2): cpu = viz(I, None, None, c, U_0, U_L, L, dt, C, T, umin=-1.1, umax=1.1, version=version, animate=True) - print 'CPU time: %s version =' % version, cpu + print('CPU time: %s version =' % version, cpu) def moving_end(C=1, Nx=50, reflecting_right_boundary=True, T=2, diff --git a/requirements.txt b/requirements.txt index a32bb0ec..c8c8c5e6 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,11 +1,13 @@ +pytest>=7.2,<8.0 pytest-runner +pytest-cov nbval cached-property py-cpuinfo -devito +devito==4.7 jupyter-book matplotlib -vtk==9.2.2 +vtk mayavi ipyevents ipywidgets @@ -16,3 +18,4 @@ dipy xvfbwrapper scitools3 future +codecov \ No newline at end of file From acdc2bcfb136bf6538a02547ff038fb76a8206ee Mon Sep 17 00:00:00 2001 From: George Bisbas Date: Thu, 20 Jul 2023 15:31:04 +0300 Subject: [PATCH 3/4] wip --- .github/workflows/deploy-jupyter-book.yml | 1 - .github/workflows/jupyter-notebooks.yml | 9 ++++----- .github/workflows/verification.yml | 1 - 3 files changed, 4 insertions(+), 7 deletions(-) diff --git a/.github/workflows/deploy-jupyter-book.yml b/.github/workflows/deploy-jupyter-book.yml index 255b09c0..cc1f54c6 100644 --- a/.github/workflows/deploy-jupyter-book.yml +++ b/.github/workflows/deploy-jupyter-book.yml @@ -23,7 +23,6 @@ jobs: - name: Install dependencies run: | pip install -r requirements.txt - pip install --user git+https://github.com/devitocodes/devito.git # Build the book - name: Build the book diff --git a/.github/workflows/jupyter-notebooks.yml b/.github/workflows/jupyter-notebooks.yml index ea77b674..dc69c2b6 100644 --- a/.github/workflows/jupyter-notebooks.yml +++ b/.github/workflows/jupyter-notebooks.yml @@ -66,16 +66,15 @@ jobs: python -m pip install --upgrade pip pip install -e . pip install matplotlib - pip install --user git+https://github.com/devitocodes/devito.git - name: Vibration ODE notebooks (1.1 to 1.8) run: | - python -m pytest --nbval --cov . --cov-config=.coveragerc --cov-report=xml:vib_coverage.xml fdm-devito-notebooks/01_vib/vib_undamped.ipynb - - name: Waves notebooks (2.1 and 2.2) + python -m py.test --nbval --cov . --cov-config=.coveragerc --cov-report=xml:vib_coverage.xml fdm-devito-notebooks/01_vib/vib_undamped.ipynb + - name: Waves notebooks (2.1 and 2.2) run: | - python -m pytest --nbval --cov . --cov-config=.coveragerc --cov-report=xml:waves_coverage.xml fdm-devito-notebooks/02_wave/wave1D_fd1.ipynb + python -m py.test --nbval --cov . --cov-config=.coveragerc --cov-report=xml:waves_coverage.xml fdm-devito-notebooks/02_wave/wave1D_fd1.ipynb - name: Waves notebooks (2.3 to 2.5) run: | - python -m pytest --nbval --cov . --cov-config=.coveragerc --cov-report=xml:waves_coverage.xml fdm-devito-notebooks/02_wave/wave1D_prog.ipynb + python -m py.test --nbval --cov . --cov-config=.coveragerc --cov-report=xml:waves_coverage.xml fdm-devito-notebooks/02_wave/wave1D_prog.ipynb - name: Diffusion notebooks (3.7) run: | python -m pytest --nbval --cov . --cov-config=.coveragerc --cov-report=xml:diffu_coverage.xml fdm-devito-notebooks/03_diffu/diffu_rw.ipynb diff --git a/.github/workflows/verification.yml b/.github/workflows/verification.yml index 4b5da411..33b7a76e 100644 --- a/.github/workflows/verification.yml +++ b/.github/workflows/verification.yml @@ -68,7 +68,6 @@ jobs: python -m pip install --upgrade pip pip install -e . pip install matplotlib - pip install --user git+https://github.com/devitocodes/devito.git - name: Waves (2.1 to 2.5) run: | cd fdm-devito-notebooks/02_wave/src-wave/wave1D From f6a81125f10eb845b2b78112a751aa93072d5add Mon Sep 17 00:00:00 2001 From: George Bisbas Date: Mon, 24 Jul 2023 16:22:37 +0300 Subject: [PATCH 4/4] upd --- .../01_vib/vib_undamped.ipynb | 351 +++++++----------- 1 file changed, 133 insertions(+), 218 deletions(-) diff --git a/fdm-devito-notebooks/01_vib/vib_undamped.ipynb b/fdm-devito-notebooks/01_vib/vib_undamped.ipynb index 32d462e8..b5be1a52 100644 --- a/fdm-devito-notebooks/01_vib/vib_undamped.ipynb +++ b/fdm-devito-notebooks/01_vib/vib_undamped.ipynb @@ -534,7 +534,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -545,7 +545,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -575,27 +575,41 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Operator `Kernel` run in 0.01 s\n" + "Equation is not affine w.r.t the target, falling back to standardsympy.solve that may be slow\n", + "/tmp/devito-jitcache-uid1000/f79d8171926c667d1a5e2a8fcbd01f488f0e038d.c: In function ‘Kernel’:\n", + "/tmp/devito-jitcache-uid1000/f79d8171926c667d1a5e2a8fcbd01f488f0e038d.c:42:57: error: array subscript is not an integer\n", + " 42 | u[t + 1] = -3.94784176043574e+1F*r0*h_t*h_t + 2.0F*u[-39*r0] - 1.0F*u[-39*r0 - 1];\n", + " | ^\n", + "/tmp/devito-jitcache-uid1000/f79d8171926c667d1a5e2a8fcbd01f488f0e038d.c:42:74: error: array subscript is not an integer\n", + " 42 | u[t + 1] = -3.94784176043574e+1F*r0*h_t*h_t + 2.0F*u[-39*r0] - 1.0F*u[-39*r0 - 1];\n", + " | ^\n", + "FAILED compiler invocation: gcc -O3 -g -fPIC -Wall -std=c99 -march=native -Wno-unused-result -Wno-unused-variable -Wno-unused-but-set-variable -ffast-math -shared -fopenmp /tmp/devito-jitcache-uid1000/f79d8171926c667d1a5e2a8fcbd01f488f0e038d.c -lm -o /tmp/devito-jitcache-uid1000/f79d8171926c667d1a5e2a8fcbd01f488f0e038d.so\n" ] }, { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" + "ename": "CompileError", + "evalue": "module compilation failed", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mCompileError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[5], line 7\u001b[0m\n\u001b[1;32m 5\u001b[0m P \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2\u001b[39m\u001b[38;5;241m*\u001b[39mnp\u001b[38;5;241m.\u001b[39mpi\u001b[38;5;241m/\u001b[39mw \u001b[38;5;66;03m# one period\u001b[39;00m\n\u001b[1;32m 6\u001b[0m T \u001b[38;5;241m=\u001b[39m P\u001b[38;5;241m*\u001b[39mnum_periods\n\u001b[0;32m----> 7\u001b[0m u, t \u001b[38;5;241m=\u001b[39m \u001b[43msolver\u001b[49m\u001b[43m(\u001b[49m\u001b[43mI\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mw\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mT\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 8\u001b[0m visualize(u, t, I, w)\n", + "Cell \u001b[0;32mIn[2], line 18\u001b[0m, in \u001b[0;36msolver\u001b[0;34m(I, w, dt, T)\u001b[0m\n\u001b[1;32m 16\u001b[0m stencil \u001b[38;5;241m=\u001b[39m Eq(u\u001b[38;5;241m.\u001b[39mforward, solve(eqn, u\u001b[38;5;241m.\u001b[39mforward))\n\u001b[1;32m 17\u001b[0m op \u001b[38;5;241m=\u001b[39m Operator(stencil)\n\u001b[0;32m---> 18\u001b[0m \u001b[43mop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mh_t\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt_M\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mNt\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m u\u001b[38;5;241m.\u001b[39mdata, np\u001b[38;5;241m.\u001b[39mlinspace(\u001b[38;5;241m0\u001b[39m, Nt\u001b[38;5;241m*\u001b[39mdt, Nt\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m)\n", + "File \u001b[0;32m~/workspace/environments/devito_book/lib/python3.10/site-packages/devito/operator/operator.py:809\u001b[0m, in \u001b[0;36mOperator.apply\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 807\u001b[0m arg_values \u001b[38;5;241m=\u001b[39m [args[p\u001b[38;5;241m.\u001b[39mname] \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparameters]\n\u001b[1;32m 808\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 809\u001b[0m cfunction \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcfunction\u001b[49m\n\u001b[1;32m 810\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_profiler\u001b[38;5;241m.\u001b[39mtimer_on(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mapply\u001b[39m\u001b[38;5;124m'\u001b[39m, comm\u001b[38;5;241m=\u001b[39margs\u001b[38;5;241m.\u001b[39mcomm):\n\u001b[1;32m 811\u001b[0m cfunction(\u001b[38;5;241m*\u001b[39marg_values)\n", + "File \u001b[0;32m~/workspace/environments/devito_book/lib/python3.10/site-packages/devito/operator/operator.py:691\u001b[0m, in \u001b[0;36mOperator.cfunction\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 689\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"The JIT-compiled C function as a ctypes.FuncPtr object.\"\"\"\u001b[39;00m\n\u001b[1;32m 690\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lib \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 691\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_jit_compile\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 692\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lib \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiler\u001b[38;5;241m.\u001b[39mload(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_soname)\n\u001b[1;32m 693\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lib\u001b[38;5;241m.\u001b[39mname \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_soname\n", + "File \u001b[0;32m~/workspace/environments/devito_book/lib/python3.10/site-packages/devito/operator/operator.py:676\u001b[0m, in \u001b[0;36mOperator._jit_compile\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 674\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lib \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 675\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_profiler\u001b[38;5;241m.\u001b[39mtimer_on(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mjit-compile\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m--> 676\u001b[0m recompiled, src_file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_compiler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjit_compile\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_soname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 677\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mccode\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 679\u001b[0m elapsed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_profiler\u001b[38;5;241m.\u001b[39mpy_timers[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mjit-compile\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 680\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m recompiled:\n", + "File \u001b[0;32m~/workspace/environments/devito_book/lib/python3.10/site-packages/devito/arch/compiler.py:338\u001b[0m, in \u001b[0;36mCompiler.jit_compile\u001b[0;34m(self, soname, code)\u001b[0m\n\u001b[1;32m 336\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m warnings\u001b[38;5;241m.\u001b[39mcatch_warnings():\n\u001b[1;32m 337\u001b[0m warnings\u001b[38;5;241m.\u001b[39msimplefilter(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 338\u001b[0m _, _, _, recompiled \u001b[38;5;241m=\u001b[39m \u001b[43mcompile_from_string\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msrc_file\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 339\u001b[0m \u001b[43m \u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdebug\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdebug\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 340\u001b[0m \u001b[43m \u001b[49m\u001b[43msleep_delay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msleep_delay\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 342\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m recompiled, src_file\n", + "File \u001b[0;32m~/workspace/environments/devito_book/lib/python3.10/site-packages/codepy/jit.py:439\u001b[0m, in \u001b[0;36mcompile_from_string\u001b[0;34m(toolchain, name, source_string, source_name, cache_dir, debug, wait_on_error, debug_recompile, object, source_is_binary, sleep_delay)\u001b[0m\n\u001b[1;32m 437\u001b[0m toolchain\u001b[38;5;241m.\u001b[39mbuild_object(ext_file, source_paths, debug\u001b[38;5;241m=\u001b[39mdebug)\n\u001b[1;32m 438\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 439\u001b[0m \u001b[43mtoolchain\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuild_extension\u001b[49m\u001b[43m(\u001b[49m\u001b[43mext_file\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msource_paths\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdebug\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdebug\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 441\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m info_path \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 442\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpickle\u001b[39;00m\n", + "File \u001b[0;32m~/workspace/environments/devito_book/lib/python3.10/site-packages/codepy/toolchain.py:211\u001b[0m, in \u001b[0;36mGCCLikeToolchain.build_extension\u001b[0;34m(self, ext_file, source_files, debug)\u001b[0m\n\u001b[1;32m 208\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m\n\u001b[1;32m 209\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFAILED compiler invocation: \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(cc_cmdline)),\n\u001b[1;32m 210\u001b[0m file\u001b[38;5;241m=\u001b[39msys\u001b[38;5;241m.\u001b[39mstderr)\n\u001b[0;32m--> 211\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m CompileError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodule compilation failed\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mCompileError\u001b[0m: module compilation failed" + ] } ], "source": [ @@ -673,7 +687,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -788,7 +802,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -849,7 +863,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -877,7 +891,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -927,7 +941,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -1075,7 +1089,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -1088,27 +1102,41 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Operator `Kernel` run in 0.01 s\n" + "Equation is not affine w.r.t the target, falling back to standardsympy.solve that may be slow\n", + "/tmp/devito-jitcache-uid1000/f79d8171926c667d1a5e2a8fcbd01f488f0e038d.c: In function ‘Kernel’:\n", + "/tmp/devito-jitcache-uid1000/f79d8171926c667d1a5e2a8fcbd01f488f0e038d.c:42:57: error: array subscript is not an integer\n", + " 42 | u[t + 1] = -3.94784176043574e+1F*r0*h_t*h_t + 2.0F*u[-39*r0] - 1.0F*u[-39*r0 - 1];\n", + " | ^\n", + "/tmp/devito-jitcache-uid1000/f79d8171926c667d1a5e2a8fcbd01f488f0e038d.c:42:74: error: array subscript is not an integer\n", + " 42 | u[t + 1] = -3.94784176043574e+1F*r0*h_t*h_t + 2.0F*u[-39*r0] - 1.0F*u[-39*r0 - 1];\n", + " | ^\n", + "FAILED compiler invocation: gcc -O3 -g -fPIC -Wall -std=c99 -march=native -Wno-unused-result -Wno-unused-variable -Wno-unused-but-set-variable -ffast-math -shared -fopenmp /tmp/devito-jitcache-uid1000/f79d8171926c667d1a5e2a8fcbd01f488f0e038d.c -lm -o /tmp/devito-jitcache-uid1000/f79d8171926c667d1a5e2a8fcbd01f488f0e038d.so\n" ] }, { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" + "ename": "CompileError", + "evalue": "module compilation failed", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mCompileError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[12], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m dt \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.1\u001b[39m\n\u001b[0;32m----> 2\u001b[0m u_1, t \u001b[38;5;241m=\u001b[39m \u001b[43msolver\u001b[49m\u001b[43m(\u001b[49m\u001b[43mI\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mw\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mT\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m visualize(u_1, t, I, w)\n", + "Cell \u001b[0;32mIn[2], line 18\u001b[0m, in \u001b[0;36msolver\u001b[0;34m(I, w, dt, T)\u001b[0m\n\u001b[1;32m 16\u001b[0m stencil \u001b[38;5;241m=\u001b[39m Eq(u\u001b[38;5;241m.\u001b[39mforward, solve(eqn, u\u001b[38;5;241m.\u001b[39mforward))\n\u001b[1;32m 17\u001b[0m op \u001b[38;5;241m=\u001b[39m Operator(stencil)\n\u001b[0;32m---> 18\u001b[0m \u001b[43mop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mh_t\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt_M\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mNt\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m u\u001b[38;5;241m.\u001b[39mdata, np\u001b[38;5;241m.\u001b[39mlinspace(\u001b[38;5;241m0\u001b[39m, Nt\u001b[38;5;241m*\u001b[39mdt, Nt\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m)\n", + "File \u001b[0;32m~/workspace/environments/devito_book/lib/python3.10/site-packages/devito/operator/operator.py:809\u001b[0m, in \u001b[0;36mOperator.apply\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 807\u001b[0m arg_values \u001b[38;5;241m=\u001b[39m [args[p\u001b[38;5;241m.\u001b[39mname] \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparameters]\n\u001b[1;32m 808\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 809\u001b[0m cfunction \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcfunction\u001b[49m\n\u001b[1;32m 810\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_profiler\u001b[38;5;241m.\u001b[39mtimer_on(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mapply\u001b[39m\u001b[38;5;124m'\u001b[39m, comm\u001b[38;5;241m=\u001b[39margs\u001b[38;5;241m.\u001b[39mcomm):\n\u001b[1;32m 811\u001b[0m cfunction(\u001b[38;5;241m*\u001b[39marg_values)\n", + "File \u001b[0;32m~/workspace/environments/devito_book/lib/python3.10/site-packages/devito/operator/operator.py:691\u001b[0m, in \u001b[0;36mOperator.cfunction\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 689\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"The JIT-compiled C function as a ctypes.FuncPtr object.\"\"\"\u001b[39;00m\n\u001b[1;32m 690\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lib \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 691\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_jit_compile\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 692\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lib \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiler\u001b[38;5;241m.\u001b[39mload(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_soname)\n\u001b[1;32m 693\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lib\u001b[38;5;241m.\u001b[39mname \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_soname\n", + "File \u001b[0;32m~/workspace/environments/devito_book/lib/python3.10/site-packages/devito/operator/operator.py:676\u001b[0m, in \u001b[0;36mOperator._jit_compile\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 674\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lib \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 675\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_profiler\u001b[38;5;241m.\u001b[39mtimer_on(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mjit-compile\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m--> 676\u001b[0m recompiled, src_file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_compiler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjit_compile\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_soname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 677\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mccode\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 679\u001b[0m elapsed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_profiler\u001b[38;5;241m.\u001b[39mpy_timers[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mjit-compile\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 680\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m recompiled:\n", + "File \u001b[0;32m~/workspace/environments/devito_book/lib/python3.10/site-packages/devito/arch/compiler.py:338\u001b[0m, in \u001b[0;36mCompiler.jit_compile\u001b[0;34m(self, soname, code)\u001b[0m\n\u001b[1;32m 336\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m warnings\u001b[38;5;241m.\u001b[39mcatch_warnings():\n\u001b[1;32m 337\u001b[0m warnings\u001b[38;5;241m.\u001b[39msimplefilter(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 338\u001b[0m _, _, _, recompiled \u001b[38;5;241m=\u001b[39m \u001b[43mcompile_from_string\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msrc_file\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 339\u001b[0m \u001b[43m \u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdebug\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdebug\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 340\u001b[0m \u001b[43m \u001b[49m\u001b[43msleep_delay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msleep_delay\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 342\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m recompiled, src_file\n", + "File \u001b[0;32m~/workspace/environments/devito_book/lib/python3.10/site-packages/codepy/jit.py:439\u001b[0m, in \u001b[0;36mcompile_from_string\u001b[0;34m(toolchain, name, source_string, source_name, cache_dir, debug, wait_on_error, debug_recompile, object, source_is_binary, sleep_delay)\u001b[0m\n\u001b[1;32m 437\u001b[0m toolchain\u001b[38;5;241m.\u001b[39mbuild_object(ext_file, source_paths, debug\u001b[38;5;241m=\u001b[39mdebug)\n\u001b[1;32m 438\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 439\u001b[0m \u001b[43mtoolchain\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuild_extension\u001b[49m\u001b[43m(\u001b[49m\u001b[43mext_file\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msource_paths\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdebug\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdebug\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 441\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m info_path \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 442\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpickle\u001b[39;00m\n", + "File \u001b[0;32m~/workspace/environments/devito_book/lib/python3.10/site-packages/codepy/toolchain.py:211\u001b[0m, in \u001b[0;36mGCCLikeToolchain.build_extension\u001b[0;34m(self, ext_file, source_files, debug)\u001b[0m\n\u001b[1;32m 208\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m\n\u001b[1;32m 209\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFAILED compiler invocation: \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(cc_cmdline)),\n\u001b[1;32m 210\u001b[0m file\u001b[38;5;241m=\u001b[39msys\u001b[38;5;241m.\u001b[39mstderr)\n\u001b[0;32m--> 211\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m CompileError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodule compilation failed\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mCompileError\u001b[0m: module compilation failed" + ] } ], "source": [ @@ -1119,29 +1147,9 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Operator `Kernel` run in 0.01 s\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "dt = 0.05\n", "u_2, t = solver(I, w, dt, T)\n", @@ -1171,7 +1179,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -1510,7 +1518,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -1576,7 +1584,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -1610,18 +1618,41 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Operator `Kernel` run in 0.01 s\n", - "Operator `Kernel` run in 0.01 s\n", - "Operator `Kernel` run in 0.01 s\n", - "Operator `Kernel` run in 0.01 s\n", - "Operator `Kernel` run in 0.01 s\n" + "Equation is not affine w.r.t the target, falling back to standardsympy.solve that may be slow\n", + "/tmp/devito-jitcache-uid1000/3495174e117590481cb1546a91e9edf456eb5dd1.c: In function ‘Kernel’:\n", + "/tmp/devito-jitcache-uid1000/3495174e117590481cb1546a91e9edf456eb5dd1.c:42:41: error: array subscript is not an integer\n", + " 42 | u[t + 1] = -1.0F*r0*h_t*h_t + 2.0F*u[-r0] - 1.0F*u[-r0 - 1];\n", + " | ^\n", + "/tmp/devito-jitcache-uid1000/3495174e117590481cb1546a91e9edf456eb5dd1.c:42:55: error: array subscript is not an integer\n", + " 42 | u[t + 1] = -1.0F*r0*h_t*h_t + 2.0F*u[-r0] - 1.0F*u[-r0 - 1];\n", + " | ^\n", + "FAILED compiler invocation: gcc -O3 -g -fPIC -Wall -std=c99 -march=native -Wno-unused-result -Wno-unused-variable -Wno-unused-but-set-variable -ffast-math -shared -fopenmp /tmp/devito-jitcache-uid1000/3495174e117590481cb1546a91e9edf456eb5dd1.c -lm -o /tmp/devito-jitcache-uid1000/3495174e117590481cb1546a91e9edf456eb5dd1.so\n" + ] + }, + { + "ename": "CompileError", + "evalue": "module compilation failed", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mCompileError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[16], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# NBVAL_IGNORE_OUTPUT\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43mdemo_bokeh\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "Cell \u001b[0;32mIn[15], line 14\u001b[0m, in \u001b[0;36mdemo_bokeh\u001b[0;34m()\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m n \u001b[38;5;129;01min\u001b[39;00m num_steps_per_period:\n\u001b[1;32m 13\u001b[0m dt \u001b[38;5;241m=\u001b[39m P\u001b[38;5;241m/\u001b[39mn\n\u001b[0;32m---> 14\u001b[0m u_, t_ \u001b[38;5;241m=\u001b[39m \u001b[43msolver\u001b[49m\u001b[43m(\u001b[49m\u001b[43mI\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mw\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mw\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mT\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mT\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 15\u001b[0m u\u001b[38;5;241m.\u001b[39mappend(u_)\n\u001b[1;32m 16\u001b[0m t\u001b[38;5;241m.\u001b[39mappend(t_)\n", + "Cell \u001b[0;32mIn[2], line 18\u001b[0m, in \u001b[0;36msolver\u001b[0;34m(I, w, dt, T)\u001b[0m\n\u001b[1;32m 16\u001b[0m stencil \u001b[38;5;241m=\u001b[39m Eq(u\u001b[38;5;241m.\u001b[39mforward, solve(eqn, u\u001b[38;5;241m.\u001b[39mforward))\n\u001b[1;32m 17\u001b[0m op \u001b[38;5;241m=\u001b[39m Operator(stencil)\n\u001b[0;32m---> 18\u001b[0m \u001b[43mop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mh_t\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt_M\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mNt\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m u\u001b[38;5;241m.\u001b[39mdata, np\u001b[38;5;241m.\u001b[39mlinspace(\u001b[38;5;241m0\u001b[39m, Nt\u001b[38;5;241m*\u001b[39mdt, Nt\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m)\n", + "File \u001b[0;32m~/workspace/environments/devito_book/lib/python3.10/site-packages/devito/operator/operator.py:809\u001b[0m, in \u001b[0;36mOperator.apply\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 807\u001b[0m arg_values \u001b[38;5;241m=\u001b[39m [args[p\u001b[38;5;241m.\u001b[39mname] \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparameters]\n\u001b[1;32m 808\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 809\u001b[0m cfunction \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcfunction\u001b[49m\n\u001b[1;32m 810\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_profiler\u001b[38;5;241m.\u001b[39mtimer_on(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mapply\u001b[39m\u001b[38;5;124m'\u001b[39m, comm\u001b[38;5;241m=\u001b[39margs\u001b[38;5;241m.\u001b[39mcomm):\n\u001b[1;32m 811\u001b[0m cfunction(\u001b[38;5;241m*\u001b[39marg_values)\n", + "File \u001b[0;32m~/workspace/environments/devito_book/lib/python3.10/site-packages/devito/operator/operator.py:691\u001b[0m, in \u001b[0;36mOperator.cfunction\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 689\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"The JIT-compiled C function as a ctypes.FuncPtr object.\"\"\"\u001b[39;00m\n\u001b[1;32m 690\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lib \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 691\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_jit_compile\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 692\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lib \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiler\u001b[38;5;241m.\u001b[39mload(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_soname)\n\u001b[1;32m 693\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lib\u001b[38;5;241m.\u001b[39mname \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_soname\n", + "File \u001b[0;32m~/workspace/environments/devito_book/lib/python3.10/site-packages/devito/operator/operator.py:676\u001b[0m, in \u001b[0;36mOperator._jit_compile\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 674\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lib \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 675\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_profiler\u001b[38;5;241m.\u001b[39mtimer_on(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mjit-compile\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m--> 676\u001b[0m recompiled, src_file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_compiler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjit_compile\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_soname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 677\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mccode\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 679\u001b[0m elapsed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_profiler\u001b[38;5;241m.\u001b[39mpy_timers[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mjit-compile\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 680\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m recompiled:\n", + "File \u001b[0;32m~/workspace/environments/devito_book/lib/python3.10/site-packages/devito/arch/compiler.py:338\u001b[0m, in \u001b[0;36mCompiler.jit_compile\u001b[0;34m(self, soname, code)\u001b[0m\n\u001b[1;32m 336\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m warnings\u001b[38;5;241m.\u001b[39mcatch_warnings():\n\u001b[1;32m 337\u001b[0m warnings\u001b[38;5;241m.\u001b[39msimplefilter(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 338\u001b[0m _, _, _, recompiled \u001b[38;5;241m=\u001b[39m \u001b[43mcompile_from_string\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msrc_file\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 339\u001b[0m \u001b[43m \u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdebug\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdebug\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 340\u001b[0m \u001b[43m \u001b[49m\u001b[43msleep_delay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msleep_delay\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 342\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m recompiled, src_file\n", + "File \u001b[0;32m~/workspace/environments/devito_book/lib/python3.10/site-packages/codepy/jit.py:439\u001b[0m, in \u001b[0;36mcompile_from_string\u001b[0;34m(toolchain, name, source_string, source_name, cache_dir, debug, wait_on_error, debug_recompile, object, source_is_binary, sleep_delay)\u001b[0m\n\u001b[1;32m 437\u001b[0m toolchain\u001b[38;5;241m.\u001b[39mbuild_object(ext_file, source_paths, debug\u001b[38;5;241m=\u001b[39mdebug)\n\u001b[1;32m 438\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 439\u001b[0m \u001b[43mtoolchain\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuild_extension\u001b[49m\u001b[43m(\u001b[49m\u001b[43mext_file\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msource_paths\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdebug\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdebug\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 441\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m info_path \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 442\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpickle\u001b[39;00m\n", + "File \u001b[0;32m~/workspace/environments/devito_book/lib/python3.10/site-packages/codepy/toolchain.py:211\u001b[0m, in \u001b[0;36mGCCLikeToolchain.build_extension\u001b[0;34m(self, ext_file, source_files, debug)\u001b[0m\n\u001b[1;32m 208\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m\n\u001b[1;32m 209\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFAILED compiler invocation: \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(cc_cmdline)),\n\u001b[1;32m 210\u001b[0m file\u001b[38;5;241m=\u001b[39msys\u001b[38;5;241m.\u001b[39mstderr)\n\u001b[0;32m--> 211\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m CompileError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodule compilation failed\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mCompileError\u001b[0m: module compilation failed" ] } ], @@ -1647,7 +1678,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1685,117 +1716,9 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " | o 0.0\n", - " | o+ 0.1\n", - " | o + 0.1\n", - " | o + 0.2\n", - " | o + 0.2\n", - " | + 0.2\n", - " o + | 0.3\n", - " o + | 0.4\n", - " o + | 0.4\n", - " o + | 0.5\n", - " o | 0.5\n", - " +o | 0.6\n", - " + o | 0.6\n", - " + o | 0.7\n", - " + o | 0.7\n", - " + | 0.8\n", - " | + o 0.8\n", - " | + o 0.9\n", - " | + o 0.9\n", - " | +o 1.0\n", - " | o 1.0\n", - " | o+ 1.1\n", - " | o + 1.1\n", - " | o + 1.2\n", - " | o + 1.2\n", - " | + 1.2\n", - " o + | 1.3\n", - " o + | 1.4\n", - " o + | 1.4\n", - " o+ | 1.5\n", - " o | 1.5\n", - " +o | 1.6\n", - " + o | 1.6\n", - " + o | 1.7\n", - " + o | 1.7\n", - " + | 1.8\n", - " | + o 1.8\n", - " | + o 1.9\n", - " | +o 1.9\n", - " | +o 2.0\n", - " | o 2.0\n", - " | o 2.1\n", - " | o + 2.1\n", - " | o + 2.1\n", - " | o + 2.2\n", - " | + 2.2\n", - " o + | 2.3\n", - " o + | 2.4\n", - " o+ | 2.4\n", - " o+ | 2.5\n", - " o | 2.5\n", - " o | 2.6\n", - " +o | 2.6\n", - " +o | 2.7\n", - " + o | 2.7\n", - " + | 2.8\n", - " | + o 2.8\n", - " | + o 2.9\n", - " | +o 2.9\n", - " | +o 3.0\n", - " | o 3.0\n", - " | o 3.1\n", - " | o+ 3.1\n", - " | o+ 3.2\n", - " | o+ 3.2\n", - " | + 3.2\n", - " o + | 3.3\n", - " o + | 3.4\n", - " o+ | 3.4\n", - " o+ | 3.5\n", - " o | 3.5\n", - " o | 3.6\n", - " +o | 3.6\n", - " +o | 3.7\n", - " +o | 3.7\n", - " +| 3.8\n", - " | + o 3.8\n", - " | +o 3.9\n", - " | +o 3.9\n", - " | +o 4.0\n", - " | o 4.0\n", - " | o 4.0\n", - " | o+ 4.1\n", - " | o+ 4.2\n", - " | o+ 4.2\n", - " |+ 4.2\n", - " o+ | 4.3\n", - " o+ | 4.4\n", - " o | 4.4\n", - " o+ | 4.5\n", - " o | 4.5\n", - " o | 4.5\n", - " +o | 4.6\n", - " o | 4.7\n", - " +o | 4.7\n", - " +| 4.8\n", - " | +o 4.8\n", - " | +o 4.9\n", - " | o 4.9\n", - " | o 5.0\n", - " | o 5.0\n" - ] - } - ], + "outputs": [], "source": [ "visualize_front_ascii(u, t, I, w)" ] @@ -1866,7 +1789,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1901,7 +1824,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1931,7 +1854,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -1989,7 +1912,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -2043,41 +1966,41 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Operator `Kernel` run in 0.01 s\n", - "Operator `Kernel` run in 0.01 s\n", - "Operator `Kernel` run in 0.01 s\n" + "Equation is not affine w.r.t the target, falling back to standardsympy.solve that may be slow\n", + "/tmp/devito-jitcache-uid1000/f79d8171926c667d1a5e2a8fcbd01f488f0e038d.c: In function ‘Kernel’:\n", + "/tmp/devito-jitcache-uid1000/f79d8171926c667d1a5e2a8fcbd01f488f0e038d.c:42:57: error: array subscript is not an integer\n", + " 42 | u[t + 1] = -3.94784176043574e+1F*r0*h_t*h_t + 2.0F*u[-39*r0] - 1.0F*u[-39*r0 - 1];\n", + " | ^\n", + "/tmp/devito-jitcache-uid1000/f79d8171926c667d1a5e2a8fcbd01f488f0e038d.c:42:74: error: array subscript is not an integer\n", + " 42 | u[t + 1] = -3.94784176043574e+1F*r0*h_t*h_t + 2.0F*u[-39*r0] - 1.0F*u[-39*r0 - 1];\n", + " | ^\n", + "FAILED compiler invocation: gcc -O3 -g -fPIC -Wall -std=c99 -march=native -Wno-unused-result -Wno-unused-variable -Wno-unused-but-set-variable -ffast-math -shared -fopenmp /tmp/devito-jitcache-uid1000/f79d8171926c667d1a5e2a8fcbd01f488f0e038d.c -lm -o /tmp/devito-jitcache-uid1000/f79d8171926c667d1a5e2a8fcbd01f488f0e038d.so\n" ] }, { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" + "ename": "CompileError", + "evalue": "module compilation failed", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mCompileError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[19], line 8\u001b[0m\n\u001b[1;32m 5\u001b[0m t_cases \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m dt \u001b[38;5;129;01min\u001b[39;00m dt_values:\n\u001b[1;32m 7\u001b[0m \u001b[38;5;66;03m# Simulate scaled problem for 40 periods\u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m u, t \u001b[38;5;241m=\u001b[39m \u001b[43msolver\u001b[49m\u001b[43m(\u001b[49m\u001b[43mI\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mw\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mpi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mT\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m40\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 9\u001b[0m u_cases\u001b[38;5;241m.\u001b[39mappend(u)\n\u001b[1;32m 10\u001b[0m t_cases\u001b[38;5;241m.\u001b[39mappend(t)\n", + "Cell \u001b[0;32mIn[2], line 18\u001b[0m, in \u001b[0;36msolver\u001b[0;34m(I, w, dt, T)\u001b[0m\n\u001b[1;32m 16\u001b[0m stencil \u001b[38;5;241m=\u001b[39m Eq(u\u001b[38;5;241m.\u001b[39mforward, solve(eqn, u\u001b[38;5;241m.\u001b[39mforward))\n\u001b[1;32m 17\u001b[0m op \u001b[38;5;241m=\u001b[39m Operator(stencil)\n\u001b[0;32m---> 18\u001b[0m \u001b[43mop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mh_t\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt_M\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mNt\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m u\u001b[38;5;241m.\u001b[39mdata, np\u001b[38;5;241m.\u001b[39mlinspace(\u001b[38;5;241m0\u001b[39m, Nt\u001b[38;5;241m*\u001b[39mdt, Nt\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m)\n", + "File \u001b[0;32m~/workspace/environments/devito_book/lib/python3.10/site-packages/devito/operator/operator.py:809\u001b[0m, in \u001b[0;36mOperator.apply\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 807\u001b[0m arg_values \u001b[38;5;241m=\u001b[39m [args[p\u001b[38;5;241m.\u001b[39mname] \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparameters]\n\u001b[1;32m 808\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 809\u001b[0m cfunction \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcfunction\u001b[49m\n\u001b[1;32m 810\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_profiler\u001b[38;5;241m.\u001b[39mtimer_on(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mapply\u001b[39m\u001b[38;5;124m'\u001b[39m, comm\u001b[38;5;241m=\u001b[39margs\u001b[38;5;241m.\u001b[39mcomm):\n\u001b[1;32m 811\u001b[0m cfunction(\u001b[38;5;241m*\u001b[39marg_values)\n", + "File \u001b[0;32m~/workspace/environments/devito_book/lib/python3.10/site-packages/devito/operator/operator.py:691\u001b[0m, in \u001b[0;36mOperator.cfunction\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 689\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"The JIT-compiled C function as a ctypes.FuncPtr object.\"\"\"\u001b[39;00m\n\u001b[1;32m 690\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lib \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 691\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_jit_compile\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 692\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lib \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiler\u001b[38;5;241m.\u001b[39mload(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_soname)\n\u001b[1;32m 693\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lib\u001b[38;5;241m.\u001b[39mname \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_soname\n", + "File \u001b[0;32m~/workspace/environments/devito_book/lib/python3.10/site-packages/devito/operator/operator.py:676\u001b[0m, in \u001b[0;36mOperator._jit_compile\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 674\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lib \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 675\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_profiler\u001b[38;5;241m.\u001b[39mtimer_on(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mjit-compile\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m--> 676\u001b[0m recompiled, src_file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_compiler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjit_compile\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_soname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 677\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mccode\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 679\u001b[0m elapsed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_profiler\u001b[38;5;241m.\u001b[39mpy_timers[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mjit-compile\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 680\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m recompiled:\n", + "File \u001b[0;32m~/workspace/environments/devito_book/lib/python3.10/site-packages/devito/arch/compiler.py:338\u001b[0m, in \u001b[0;36mCompiler.jit_compile\u001b[0;34m(self, soname, code)\u001b[0m\n\u001b[1;32m 336\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m warnings\u001b[38;5;241m.\u001b[39mcatch_warnings():\n\u001b[1;32m 337\u001b[0m warnings\u001b[38;5;241m.\u001b[39msimplefilter(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 338\u001b[0m _, _, _, recompiled \u001b[38;5;241m=\u001b[39m \u001b[43mcompile_from_string\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msrc_file\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 339\u001b[0m \u001b[43m \u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdebug\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdebug\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 340\u001b[0m \u001b[43m \u001b[49m\u001b[43msleep_delay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msleep_delay\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 342\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m recompiled, src_file\n", + "File \u001b[0;32m~/workspace/environments/devito_book/lib/python3.10/site-packages/codepy/jit.py:439\u001b[0m, in \u001b[0;36mcompile_from_string\u001b[0;34m(toolchain, name, source_string, source_name, cache_dir, debug, wait_on_error, debug_recompile, object, source_is_binary, sleep_delay)\u001b[0m\n\u001b[1;32m 437\u001b[0m toolchain\u001b[38;5;241m.\u001b[39mbuild_object(ext_file, source_paths, debug\u001b[38;5;241m=\u001b[39mdebug)\n\u001b[1;32m 438\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 439\u001b[0m \u001b[43mtoolchain\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuild_extension\u001b[49m\u001b[43m(\u001b[49m\u001b[43mext_file\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msource_paths\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdebug\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdebug\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 441\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m info_path \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 442\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpickle\u001b[39;00m\n", + "File \u001b[0;32m~/workspace/environments/devito_book/lib/python3.10/site-packages/codepy/toolchain.py:211\u001b[0m, in \u001b[0;36mGCCLikeToolchain.build_extension\u001b[0;34m(self, ext_file, source_files, debug)\u001b[0m\n\u001b[1;32m 208\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m\n\u001b[1;32m 209\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFAILED compiler invocation: \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(cc_cmdline)),\n\u001b[1;32m 210\u001b[0m file\u001b[38;5;241m=\u001b[39msys\u001b[38;5;241m.\u001b[39mstderr)\n\u001b[0;32m--> 211\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m CompileError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodule compilation failed\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mCompileError\u001b[0m: module compilation failed" + ] } ], "source": [ @@ -2306,17 +2229,9 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "w + dt**2*w**3/24 + O(dt**4)\n" - ] - } - ], + "outputs": [], "source": [ "from sympy import *\n", "dt, w = symbols('dt w')\n", @@ -4875,7 +4790,7 @@ }, { "data": { - "image/png": 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\n", 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", 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\n", 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", 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" ] @@ -9035,7 +8950,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -9049,7 +8964,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.2" + "version": "3.10.6" } }, "nbformat": 4,