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a/graphs/anytree/autograd/bpx/branch-develop/casadi/cmake/env-LD_LIBRARY_PATH-_home_runner_.local_lib/imageio/machine-GitHubRunner/matplotlib/numpy/pybtex/python-3.8/scikit-fem/sympy/tqdm/wget/different_model_options.TimeBuildModelSurfaceForm.time_setup_model.json b/graphs/anytree/autograd/bpx/branch-develop/casadi/cmake/env-LD_LIBRARY_PATH-_home_runner_.local_lib/imageio/machine-GitHubRunner/matplotlib/numpy/pybtex/python-3.8/scikit-fem/sympy/tqdm/wget/different_model_options.TimeBuildModelSurfaceForm.time_setup_model.json index 8c5c0a55f..66e2c17da 100644 --- a/graphs/anytree/autograd/bpx/branch-develop/casadi/cmake/env-LD_LIBRARY_PATH-_home_runner_.local_lib/imageio/machine-GitHubRunner/matplotlib/numpy/pybtex/python-3.8/scikit-fem/sympy/tqdm/wget/different_model_options.TimeBuildModelSurfaceForm.time_setup_model.json +++ 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a/graphs/anytree/autograd/bpx/branch-develop/casadi/cmake/env-LD_LIBRARY_PATH-_home_runner_.local_lib/imageio/machine-GitHubRunner/matplotlib/numpy/pybtex/python-3.8/scikit-fem/sympy/tqdm/wget/different_model_options.TimeSolveParticle.time_solve_model.json +++ b/graphs/anytree/autograd/bpx/branch-develop/casadi/cmake/env-LD_LIBRARY_PATH-_home_runner_.local_lib/imageio/machine-GitHubRunner/matplotlib/numpy/pybtex/python-3.8/scikit-fem/sympy/tqdm/wget/different_model_options.TimeSolveParticle.time_solve_model.json @@ -1 +1 @@ -[[15407, [0.022421766999968895, 0.06839917749994129, 0.014459217499990018, 0.03717780399995263, 0.014763707249926483, 0.03781892249998009, 0.016994448500042836, 0.05462496299998065, 0.5260095945000103, 0.4313426920000438, 0.21882906800004776, 0.1870210135000434, 0.2595449125000755, 0.21129938600006426, 0.2694686834998947, 0.20658092249999527]], [15451, [0.04169994950007094, 0.07685724649991243, 0.017298848750044726, 0.04289030100005675, 0.017630181500010167, 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b/graphs/anytree/autograd/bpx/branch-develop/casadi/cmake/env-LD_LIBRARY_PATH-_home_runner_.local_lib/imageio/machine-GitHubRunner/matplotlib/numpy/pybtex/python-3.8/scikit-fem/sympy/tqdm/wget/different_model_options.TimeSolveSurfaceForm.time_solve_model.json index 7640e2128..cf53fb067 100644 --- a/graphs/anytree/autograd/bpx/branch-develop/casadi/cmake/env-LD_LIBRARY_PATH-_home_runner_.local_lib/imageio/machine-GitHubRunner/matplotlib/numpy/pybtex/python-3.8/scikit-fem/sympy/tqdm/wget/different_model_options.TimeSolveSurfaceForm.time_solve_model.json +++ b/graphs/anytree/autograd/bpx/branch-develop/casadi/cmake/env-LD_LIBRARY_PATH-_home_runner_.local_lib/imageio/machine-GitHubRunner/matplotlib/numpy/pybtex/python-3.8/scikit-fem/sympy/tqdm/wget/different_model_options.TimeSolveSurfaceForm.time_solve_model.json @@ -1 +1 @@ -[[15407, [0.022476321249996545, 0.06872174950007093, 0.05455259649988875, null, 0.07903517349996037, 0.09172162300001219, 0.5291165980000869, 0.43086898149982744, 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ebce2c2f4..f0e6c4ad1 100644 --- a/graphs/anytree/autograd/bpx/branch-develop/casadi/cmake/env-LD_LIBRARY_PATH-_home_runner_.local_lib/imageio/machine-GitHubRunner/matplotlib/numpy/pybtex/python-3.8/scikit-fem/sympy/tqdm/wget/memory_sims.MemDFNSimulationGITT.mem_setup_DFN_simulationGITT.json +++ b/graphs/anytree/autograd/bpx/branch-develop/casadi/cmake/env-LD_LIBRARY_PATH-_home_runner_.local_lib/imageio/machine-GitHubRunner/matplotlib/numpy/pybtex/python-3.8/scikit-fem/sympy/tqdm/wget/memory_sims.MemDFNSimulationGITT.mem_setup_DFN_simulationGITT.json @@ -1 +1 @@ -[[15407, [952.0, 952.0]], [15451, [952.0, 952.0]], [15472, [952.0, 952.0]], [15524, [952.0, 952.0]], [15553, [952.0, 952.0]], [15607, [952.0, 952.0]], [15615, [952.0, 952.0]], [15628, [952.0, 952.0]], [15647, [952.0, 952.0]], [15665, [952.0, 952.0]], [15687, [952.0, 952.0]], [15693, [952.0, 952.0]], [15747, [952.0, 952.0]], [15776, [952.0, 952.0]], [15790, [952.0, 952.0]], [15830, [952.0, 952.0]], [15844, [952.0, 952.0]], 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b/graphs/anytree/autograd/bpx/branch-develop/casadi/cmake/env-LD_LIBRARY_PATH-_home_runner_.local_lib/imageio/machine-GitHubRunner/matplotlib/numpy/pybtex/python-3.8/scikit-fem/sympy/tqdm/wget/memory_unit_benchmarks.MemDiscretiseModel.mem_discretise.json index 0e860c0a9..39b12d380 100644 --- a/graphs/anytree/autograd/bpx/branch-develop/casadi/cmake/env-LD_LIBRARY_PATH-_home_runner_.local_lib/imageio/machine-GitHubRunner/matplotlib/numpy/pybtex/python-3.8/scikit-fem/sympy/tqdm/wget/memory_unit_benchmarks.MemDiscretiseModel.mem_discretise.json +++ b/graphs/anytree/autograd/bpx/branch-develop/casadi/cmake/env-LD_LIBRARY_PATH-_home_runner_.local_lib/imageio/machine-GitHubRunner/matplotlib/numpy/pybtex/python-3.8/scikit-fem/sympy/tqdm/wget/memory_unit_benchmarks.MemDiscretiseModel.mem_discretise.json @@ -1 +1 @@ -[[15407, 192.0], [15451, 192.0], [15472, 192.0], [15524, 192.0], [15553, 192.0], [15607, 192.0], [15615, 192.0], [15628, 192.0], [15647, 192.0], [15665, 192.0], [15687, 192.0], 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a/graphs/anytree/autograd/bpx/branch-develop/casadi/cmake/env-LD_LIBRARY_PATH-_home_runner_.local_lib/imageio/machine-GitHubRunner/matplotlib/numpy/pybtex/python-3.8/scikit-fem/sympy/tqdm/wget/memory_unit_benchmarks.MemParameteriseModel.mem_create_expression.json +++ b/graphs/anytree/autograd/bpx/branch-develop/casadi/cmake/env-LD_LIBRARY_PATH-_home_runner_.local_lib/imageio/machine-GitHubRunner/matplotlib/numpy/pybtex/python-3.8/scikit-fem/sympy/tqdm/wget/memory_unit_benchmarks.MemParameteriseModel.mem_create_expression.json @@ -1 +1 @@ -[[15407, 448.0], [15451, 448.0], [15472, 448.0], [15524, 448.0], [15553, 448.0], [15607, 448.0], [15615, 448.0], [15628, 448.0], [15647, 448.0], [15665, 448.0], [15687, 448.0], [15693, 448.0], [15747, 448.0], [15776, 448.0], [15790, 448.0], [15830, 448.0], [15844, 448.0], [15858, 448.0], [15899, 448.0], [15911, 448.0], [15913, 448.0], [15918, 448.0], [15949, 448.0], [15977, 448.0], [16024, 448.0], [16029, 448.0], [16039, 448.0], [16087, 448.0], 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b/graphs/anytree/autograd/bpx/branch-develop/casadi/cmake/env-LD_LIBRARY_PATH-_home_runner_.local_lib/imageio/machine-GitHubRunner/matplotlib/numpy/pybtex/python-3.8/scikit-fem/sympy/tqdm/wget/memory_unit_benchmarks.MemSolveModel.mem_create_expression.json @@ -1 +1 @@ -[[15407, 448.0], [15451, 448.0], [15472, 448.0], [15524, 448.0], [15553, 448.0], [15607, 448.0], [15615, 448.0], [15628, 448.0], [15647, 448.0], [15665, 448.0], [15687, 448.0], [15693, 448.0], [15747, 448.0], [15776, 448.0], [15790, 448.0], [15830, 448.0], [15844, 448.0], [15858, 448.0], [15899, 448.0], [15911, 448.0], [15913, 448.0], [15918, 448.0], [15949, 448.0], [15977, 448.0], [16024, 448.0], [16029, 448.0], [16039, 448.0], [16087, 448.0], [16093, 448.0], [16105, 448.0], [16112, 448.0]] \ No newline at end of file +[[15407, 448.0], [15451, 448.0], [15472, 448.0], [15524, 448.0], [15553, 448.0], [15607, 448.0], [15615, 448.0], [15628, 448.0], [15647, 448.0], [15665, 448.0], [15687, 448.0], [15693, 448.0], [15747, 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a/graphs/anytree/autograd/bpx/branch-develop/casadi/cmake/env-LD_LIBRARY_PATH-_home_runner_.local_lib/imageio/machine-GitHubRunner/matplotlib/numpy/pybtex/python-3.8/scikit-fem/sympy/tqdm/wget/memory_unit_benchmarks.MemSolveModel.mem_discretise.json +++ b/graphs/anytree/autograd/bpx/branch-develop/casadi/cmake/env-LD_LIBRARY_PATH-_home_runner_.local_lib/imageio/machine-GitHubRunner/matplotlib/numpy/pybtex/python-3.8/scikit-fem/sympy/tqdm/wget/memory_unit_benchmarks.MemSolveModel.mem_discretise.json @@ -1 +1 @@ -[[15407, 192.0], [15451, 192.0], [15472, 192.0], [15524, 192.0], [15553, 192.0], [15607, 192.0], [15615, 192.0], [15628, 192.0], [15647, 192.0], [15665, 192.0], [15687, 192.0], [15693, 192.0], [15747, 192.0], [15776, 192.0], [15790, 192.0], [15830, 192.0], [15844, 192.0], [15858, 192.0], [15899, 192.0], [15911, 192.0], [15913, 192.0], [15918, 192.0], [15949, 192.0], [15977, 192.0], [16024, 192.0], [16029, 192.0], [16039, 192.0], [16087, 192.0], [16093, 192.0], [16105, 192.0], [16112, 192.0]] \ No newline at end of file +[[15407, 192.0], [15451, 192.0], [15472, 192.0], [15524, 192.0], [15553, 192.0], [15607, 192.0], [15615, 192.0], [15628, 192.0], [15647, 192.0], [15665, 192.0], [15687, 192.0], [15693, 192.0], [15747, 192.0], [15776, 192.0], [15790, 192.0], [15830, 192.0], [15844, 192.0], [15858, 192.0], [15899, 192.0], [15911, 192.0], [15913, 192.0], [15918, 192.0], [15949, 192.0], [15977, 192.0], [16024, 192.0], [16029, 192.0], [16039, 192.0], [16087, 192.0], [16093, 192.0], [16105, 192.0], [16109, 192.0]] \ No newline at end of file diff --git a/graphs/anytree/autograd/bpx/branch-develop/casadi/cmake/env-LD_LIBRARY_PATH-_home_runner_.local_lib/imageio/machine-GitHubRunner/matplotlib/numpy/pybtex/python-3.8/scikit-fem/sympy/tqdm/wget/memory_unit_benchmarks.MemSolveModel.mem_parameterise.json b/graphs/anytree/autograd/bpx/branch-develop/casadi/cmake/env-LD_LIBRARY_PATH-_home_runner_.local_lib/imageio/machine-GitHubRunner/matplotlib/numpy/pybtex/python-3.8/scikit-fem/sympy/tqdm/wget/memory_unit_benchmarks.MemSolveModel.mem_parameterise.json index 8f7196b45..7ea11f72d 100644 --- a/graphs/anytree/autograd/bpx/branch-develop/casadi/cmake/env-LD_LIBRARY_PATH-_home_runner_.local_lib/imageio/machine-GitHubRunner/matplotlib/numpy/pybtex/python-3.8/scikit-fem/sympy/tqdm/wget/memory_unit_benchmarks.MemSolveModel.mem_parameterise.json +++ b/graphs/anytree/autograd/bpx/branch-develop/casadi/cmake/env-LD_LIBRARY_PATH-_home_runner_.local_lib/imageio/machine-GitHubRunner/matplotlib/numpy/pybtex/python-3.8/scikit-fem/sympy/tqdm/wget/memory_unit_benchmarks.MemSolveModel.mem_parameterise.json @@ -1 +1 @@ -[[15407, 152.0], [15451, 152.0], [15472, 152.0], [15524, 152.0], [15553, 152.0], [15607, 152.0], [15615, 152.0], [15628, 152.0], [15647, 152.0], [15665, 152.0], [15687, 152.0], [15693, 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a/graphs/summary/different_model_options.TimeSolveLithiumPlating.time_solve_model.json +++ b/graphs/summary/different_model_options.TimeSolveLithiumPlating.time_solve_model.json @@ -1 +1 @@ -[[15407, 0.24466455662788594], [15451, 0.2882436175811133], [15472, 0.25599091043258426], [15524, 0.2526737678556918], [15553, 0.16499244378282282], [15607, 0.3353946102458751], [15615, 0.16594835853219322], [15628, 0.1625003272142744], [15647, 0.16501759545481606], [15665, 0.16464232837245318], [15687, 0.1633326922156079], [15693, 0.1780388963562833], [15747, 0.17531415880733334], [15776, 0.1762148208559707], [15790, 0.17626416247512786], [15830, 0.17652226975036708], [15844, 0.17577610182432618], [15858, 0.17740633972781378], [15899, 0.1773575309048235], [15911, 0.1770678545395191], [15913, 0.17729193880380578], [15918, 0.17822594930180632], [15949, 0.17650988811580864], [15977, 0.17986644944219274], [16024, 0.17803858400662156], [16029, 0.17782492770062977], [16039, 0.1758540579204379], [16087, 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a/graphs/summary/memory_sims.MemDFNSimulationCCCV.mem_setup_DFN_simulationCCCV.json +++ b/graphs/summary/memory_sims.MemDFNSimulationCCCV.mem_setup_DFN_simulationCCCV.json @@ -1 +1 @@ -[[15407, 951.9999999999999], [15451, 951.9999999999999], [15472, 951.9999999999999], [15524, 951.9999999999999], [15553, 951.9999999999999], [15607, 951.9999999999999], [15615, 951.9999999999999], [15628, 951.9999999999999], [15647, 951.9999999999999], [15665, 951.9999999999999], [15687, 951.9999999999999], [15693, 951.9999999999999], [15747, 951.9999999999999], [15776, 951.9999999999999], [15790, 951.9999999999999], [15830, 951.9999999999999], [15844, 951.9999999999999], [15858, 951.9999999999999], [15899, 951.9999999999999], [15911, 951.9999999999999], [15913, 951.9999999999999], [15918, 951.9999999999999], [15949, 951.9999999999999], [15977, 951.9999999999999], [16024, 951.9999999999999], [16029, 951.9999999999999], [16039, 951.9999999999999], [16087, 951.9999999999999], [16093, 951.9999999999999], 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a/graphs/summary/memory_sims.MemDFNSimulationGITT.mem_setup_DFN_simulationGITT.json b/graphs/summary/memory_sims.MemDFNSimulationGITT.mem_setup_DFN_simulationGITT.json index e816ddcf9..8724cef36 100644 --- a/graphs/summary/memory_sims.MemDFNSimulationGITT.mem_setup_DFN_simulationGITT.json +++ b/graphs/summary/memory_sims.MemDFNSimulationGITT.mem_setup_DFN_simulationGITT.json @@ -1 +1 @@ -[[15407, 951.9999999999999], [15451, 951.9999999999999], [15472, 951.9999999999999], [15524, 951.9999999999999], [15553, 951.9999999999999], [15607, 951.9999999999999], [15615, 951.9999999999999], [15628, 951.9999999999999], [15647, 951.9999999999999], [15665, 951.9999999999999], [15687, 951.9999999999999], [15693, 951.9999999999999], [15747, 951.9999999999999], [15776, 951.9999999999999], [15790, 951.9999999999999], [15830, 951.9999999999999], [15844, 951.9999999999999], [15858, 951.9999999999999], [15899, 951.9999999999999], [15911, 951.9999999999999], [15913, 951.9999999999999], [15918, 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a/graphs/summary/memory_unit_benchmarks.MemParameteriseModel.mem_parameterise.json b/graphs/summary/memory_unit_benchmarks.MemParameteriseModel.mem_parameterise.json index 8f7196b45..7ea11f72d 100644 --- a/graphs/summary/memory_unit_benchmarks.MemParameteriseModel.mem_parameterise.json +++ b/graphs/summary/memory_unit_benchmarks.MemParameteriseModel.mem_parameterise.json @@ -1 +1 @@ -[[15407, 152.0], [15451, 152.0], [15472, 152.0], [15524, 152.0], [15553, 152.0], [15607, 152.0], [15615, 152.0], [15628, 152.0], [15647, 152.0], [15665, 152.0], [15687, 152.0], [15693, 152.0], [15747, 152.0], [15776, 152.0], [15790, 152.0], [15830, 152.0], [15844, 152.0], [15858, 152.0], [15899, 152.0], [15911, 152.0], [15913, 152.0], [15918, 152.0], [15949, 152.0], [15977, 152.0], [16024, 152.0], [16029, 152.0], [16039, 152.0], [16087, 152.0], [16093, 152.0], [16105, 152.0], [16112, 152.0]] \ No newline at end of file +[[15407, 152.0], [15451, 152.0], [15472, 152.0], [15524, 152.0], [15553, 152.0], 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b/graphs/summary/memory_unit_benchmarks.MemSolveModel.mem_discretise.json index 0e860c0a9..39b12d380 100644 --- a/graphs/summary/memory_unit_benchmarks.MemSolveModel.mem_discretise.json +++ b/graphs/summary/memory_unit_benchmarks.MemSolveModel.mem_discretise.json @@ -1 +1 @@ -[[15407, 192.0], [15451, 192.0], [15472, 192.0], [15524, 192.0], [15553, 192.0], [15607, 192.0], [15615, 192.0], [15628, 192.0], [15647, 192.0], [15665, 192.0], [15687, 192.0], [15693, 192.0], [15747, 192.0], [15776, 192.0], [15790, 192.0], [15830, 192.0], [15844, 192.0], [15858, 192.0], [15899, 192.0], [15911, 192.0], [15913, 192.0], [15918, 192.0], [15949, 192.0], [15977, 192.0], [16024, 192.0], [16029, 192.0], [16039, 192.0], [16087, 192.0], [16093, 192.0], [16105, 192.0], [16112, 192.0]] \ No newline at end of file +[[15407, 192.0], [15451, 192.0], [15472, 192.0], [15524, 192.0], [15553, 192.0], [15607, 192.0], [15615, 192.0], [15628, 192.0], [15647, 192.0], [15665, 192.0], [15687, 192.0], [15693, 192.0], 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a/graphs/summary/memory_unit_benchmarks.MemSolveModel.mem_solve.json +++ b/graphs/summary/memory_unit_benchmarks.MemSolveModel.mem_solve.json @@ -1 +1 @@ -[[15407, 280.0], [15451, 280.0], [15472, 280.0], [15524, 280.0], [15553, 280.0], [15607, 280.0], [15615, 280.0], [15628, 280.0], [15647, 280.0], [15665, 280.0], [15687, 280.0], [15693, 280.0], [15747, 280.0], [15776, 280.0], [15790, 280.0], [15830, 280.0], [15844, 280.0], [15858, 280.0], [15899, 280.0], [15911, 280.0], [15913, 280.0], [15918, 280.0], [15949, 280.0], [15977, 280.0], [16024, 280.0], [16029, 280.0], [16039, 280.0], [16087, 280.0], [16093, 280.0], [16105, 280.0], [16112, 280.0]] \ No newline at end of file +[[15407, 280.0], [15451, 280.0], [15472, 280.0], [15524, 280.0], [15553, 280.0], [15607, 280.0], [15615, 280.0], [15628, 280.0], [15647, 280.0], [15665, 280.0], [15687, 280.0], [15693, 280.0], [15747, 280.0], [15776, 280.0], [15790, 280.0], [15830, 280.0], [15844, 280.0], [15858, 280.0], [15899, 280.0], [15911, 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a/graphs/summary/unit_benchmarks.TimeDiscretiseModel.time_parameterise.json b/graphs/summary/unit_benchmarks.TimeDiscretiseModel.time_parameterise.json index 3a14af2c3..85ddde934 100644 --- a/graphs/summary/unit_benchmarks.TimeDiscretiseModel.time_parameterise.json +++ b/graphs/summary/unit_benchmarks.TimeDiscretiseModel.time_parameterise.json @@ -1 +1 @@ -[[15407, 0.0003625267857135636], [15451, 0.00047030612000071415], [15472, 0.00041263398077699795], [15524, 0.0004120808600009695], [15553, 0.0003217364852984621], [15607, 0.0006156342647313175], [15615, 0.0003396814354822885], [15628, 0.00034080629999759066], [15647, 0.00033995800000015625], [15665, 0.00036423008333485996], [15687, 0.0003431136451581551], [15693, 0.00034152838333814846], [15747, 0.00033580898386917986], [15776, 0.00033831028332921657], [15790, 0.0003449127096781658], [15830, 0.0003408916833336662], [15844, 0.0003387193064444575], [15858, 0.0003427189166662477], [15899, 0.0003463558103382231], [15911, 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"16024": 1703030170000, "16029": 1703101147000, "16039": 1703168302000, "16087": 1703367274000, "16093": 1703411257000, "16105": 1703538118000, "16112": 1703663277000}, "params": {"machine": ["GitHubRunner"], "python": ["3.8"], "numpy": [""], "casadi": [""], "wget": [""], "cmake": [""], "anytree": [""], "autograd": [""], "scikit-fem": [""], "imageio": [""], "pybtex": [""], "sympy": [""], "bpx": [""], "tqdm": [""], "matplotlib": [""], "env-LD_LIBRARY_PATH": ["/home/runner/.local/lib"], "branch": ["develop"]}, "graph_param_list": [{"machine": "GitHubRunner", "python": "3.8", "numpy": "", "casadi": "", "wget": "", "cmake": "", "anytree": "", "autograd": "", "scikit-fem": "", "imageio": "", "pybtex": "", "sympy": "", "bpx": "", "tqdm": "", "matplotlib": "", "env-LD_LIBRARY_PATH": "/home/runner/.local/lib", "branch": "develop"}], "benchmarks": {"different_model_options.TimeBuildModelLithiumPlating.time_setup_model": {"code": "class TimeBuildModelLithiumPlating:\n def time_setup_model(self, model, params):\n build_model(\"OKane2022\", model, \"lithium plating\", params)\n\n def setup(self, _model, _params):\n set_random_seed()", "min_run_count": 2, "name": "different_model_options.TimeBuildModelLithiumPlating.time_setup_model", "number": 0, "param_names": ["model", "model option"], "params": [["", ""], ["'none'", "'irreversible'", "'reversible'", "'partially reversible'"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "b7715f9cd1f28f249a74a4036dff5452cde2f077c92024efae0ebb5545c3edc0", "warmup_time": -1}, "different_model_options.TimeBuildModelLossActiveMaterial.time_setup_model": {"code": "class TimeBuildModelLossActiveMaterial:\n def time_setup_model(self, model, params):\n build_model(\"Ai2020\", model, \"loss of active material\", params)\n\n def setup(self, _model, _params):\n set_random_seed()", "min_run_count": 2, "name": "different_model_options.TimeBuildModelLossActiveMaterial.time_setup_model", "number": 0, "param_names": ["model", "model option"], "params": [["", ""], ["'none'", "'stress-driven'", "'reaction-driven'", "'stress and reaction-driven'"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "478c22dec000f10359afc36541c1e78f5e93eaaddf53c542d8b86433b7bb0faa", "warmup_time": -1}, "different_model_options.TimeBuildModelParticle.time_setup_model": {"code": "class TimeBuildModelParticle:\n def time_setup_model(self, model, params):\n build_model(\"Marquis2019\", model, \"particle\", params)\n\n def setup(self, _model, _params):\n set_random_seed()", "min_run_count": 2, "name": "different_model_options.TimeBuildModelParticle.time_setup_model", "number": 0, "param_names": ["model", "model option"], "params": [["", ""], ["'Fickian diffusion'", "'uniform profile'", "'quadratic profile'", "'quartic profile'"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "7a2b728d79432d8ed22c8c8680ee758da0ceffe378a6235c09e3614caf624ba6", "warmup_time": -1}, "different_model_options.TimeBuildModelSEI.time_setup_model": {"code": "class TimeBuildModelSEI:\n def time_setup_model(self, model, params):\n build_model(\"Marquis2019\", model, \"SEI\", params)\n\n def setup(self, _model, _params):\n set_random_seed()", "min_run_count": 2, "name": "different_model_options.TimeBuildModelSEI.time_setup_model", "number": 0, "param_names": ["model", "model option"], "params": [["", ""], ["'none'", "'constant'", "'reaction limited'", "'solvent-diffusion limited'", "'electron-migration limited'", "'interstitial-diffusion limited'", "'ec reaction limited'"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "72052d17c62d75ef9abff93a57e2fe2b8776f153d13317ba41c9304374cf3241", "warmup_time": -1}, "different_model_options.TimeBuildModelSurfaceForm.time_setup_model": {"code": "class TimeBuildModelSurfaceForm:\n def time_setup_model(self, model, params):\n build_model(\"Marquis2019\", model, \"surface form\", params)\n\n def setup(self, _model, _params):\n set_random_seed()", "min_run_count": 2, "name": "different_model_options.TimeBuildModelSurfaceForm.time_setup_model", "number": 0, "param_names": ["model", "model option"], "params": [["", ""], ["'false'", "'differential'", "'algebraic'"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "0ae12c41f1ed5675d959bbc049a400985cf3ede740ee73c11075d7aa10f34e49", "warmup_time": -1}, "different_model_options.TimeBuildModelThermal.time_setup_model": {"code": "class TimeBuildModelThermal:\n def time_setup_model(self, model, params):\n build_model(\"Marquis2019\", model, \"thermal\", params)\n\n def setup(self, _model, _params):\n set_random_seed()", "min_run_count": 2, "name": "different_model_options.TimeBuildModelThermal.time_setup_model", "number": 0, "param_names": ["model", "model option"], "params": [["", ""], ["'isothermal'", "'lumped'", "'x-full'"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "a350e87e4e8a8f35aeb9951c78518a60da6177951f7d81d09f3d3088ec055cfd", "warmup_time": -1}, "different_model_options.TimeSolveLithiumPlating.time_solve_model": {"code": "class TimeSolveLithiumPlating:\n def time_solve_model(self, _model, _params, _solver_class):\n self.solver.solve(self.model, t_eval=self.t_eval)\n\n def setup(self, model, params, solver_class):\n set_random_seed()\n SolveModel.solve_setup(\n self, \"OKane2022\", model, \"lithium plating\", params, solver_class\n )", "min_run_count": 2, "name": "different_model_options.TimeSolveLithiumPlating.time_solve_model", "number": 0, "param_names": ["model", "model option", "solver class"], "params": [["", ""], ["'none'", "'irreversible'", "'reversible'", "'partially reversible'"], ["", ""]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "b1d66b1656476391fd4f4a356b350bff995917103286317dffc403f2b8a90fbe", "warmup_time": -1}, "different_model_options.TimeSolveLossActiveMaterial.time_solve_model": {"code": "class TimeSolveLossActiveMaterial:\n def time_solve_model(self, _model, _params, _solver_class):\n self.solver.solve(self.model, t_eval=self.t_eval)\n\n def setup(self, model, params, solver_class):\n set_random_seed()\n SolveModel.solve_setup(\n self, \"Ai2020\", model, \"loss of active material\", params, solver_class\n )", "min_run_count": 2, "name": "different_model_options.TimeSolveLossActiveMaterial.time_solve_model", "number": 0, "param_names": ["model", "model option", "solver class"], "params": [["", ""], ["'none'", "'stress-driven'", "'reaction-driven'", "'stress and reaction-driven'"], ["", ""]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "d5e240fe84c4acc6966e4a3c4fd3b541659eb242ea36508a92c57a0ed958ceb7", "warmup_time": -1}, "different_model_options.TimeSolveParticle.time_solve_model": {"code": "class TimeSolveParticle:\n def time_solve_model(self, _model, _params, _solver_class):\n self.solver.solve(self.model, t_eval=self.t_eval)\n\n def setup(self, model, params, solver_class):\n set_random_seed()\n SolveModel.solve_setup(\n self, \"Marquis2019\", model, \"particle\", params, solver_class\n )", "min_run_count": 2, "name": "different_model_options.TimeSolveParticle.time_solve_model", "number": 0, "param_names": ["model", "model option", "solver class"], "params": [["", ""], ["'Fickian diffusion'", "'uniform profile'", "'quadratic profile'", "'quartic profile'"], ["", ""]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "0fb24c2046b35533f49c825e1f5dbc32c30266f30c6ffc64504048df0f6182f7", "warmup_time": -1}, "different_model_options.TimeSolveSEI.time_solve_model": {"code": "class TimeSolveSEI:\n def time_solve_model(self, _model, _params, _solver_class):\n self.solver.solve(self.model, t_eval=self.t_eval)\n\n def setup(self, model, params, solver_class):\n set_random_seed()\n SolveModel.solve_setup(self, \"Marquis2019\", model, \"SEI\", params, solver_class)", "min_run_count": 2, "name": "different_model_options.TimeSolveSEI.time_solve_model", "number": 0, "param_names": ["model", "model option", "solver class"], "params": [["", ""], ["'none'", "'constant'", "'reaction limited'", "'solvent-diffusion limited'", "'electron-migration limited'", "'interstitial-diffusion limited'", "'ec reaction limited'"], ["", ""]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "53dd91bc8ea837141f3ff3ec6ae6fc8310b0ebfae08e6bbd8a1f692efb7d2f57", "warmup_time": -1}, "different_model_options.TimeSolveSurfaceForm.time_solve_model": {"code": "class TimeSolveSurfaceForm:\n def time_solve_model(self, _model, _params, _solver_class):\n self.solver.solve(self.model, t_eval=self.t_eval)\n\n def setup(self, model, params, solver_class):\n set_random_seed()\n if (model, params, solver_class) == (\n pybamm.lithium_ion.SPM,\n \"differential\",\n pybamm.IDAKLUSolver,\n ):\n raise NotImplementedError\n SolveModel.solve_setup(\n self, \"Marquis2019\", model, \"surface form\", params, solver_class\n )", "min_run_count": 2, "name": "different_model_options.TimeSolveSurfaceForm.time_solve_model", "number": 0, "param_names": ["model", "model option", "solver class"], "params": [["", ""], ["'false'", "'differential'", "'algebraic'"], ["", ""]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "b025abdf6ccd9f47388fd29f0bfc0fc5490bc48eb9c277ba6172b8064339cd15", "warmup_time": -1}, "different_model_options.TimeSolveThermal.time_solve_model": {"code": "class TimeSolveThermal:\n def time_solve_model(self, _model, _params, _solver_class):\n self.solver.solve(self.model, t_eval=self.t_eval)\n\n def setup(self, model, params, solver_class):\n set_random_seed()\n SolveModel.solve_setup(\n self, \"Marquis2019\", model, \"thermal\", params, solver_class\n )", "min_run_count": 2, "name": "different_model_options.TimeSolveThermal.time_solve_model", "number": 0, "param_names": ["model", "model option", "solver class"], "params": [["", ""], ["'isothermal'", "'lumped'", "'x-full'"], ["", ""]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "b8015ca16fbe5a13c5d7b34926d92538382b9575c674f2ef78d923e8e603f5cb", "warmup_time": -1}, "memory_sims.MemDFNSimulationCCCV.mem_setup_DFN_simulationCCCV": {"code": "class MemDFNSimulationCCCV:\n def mem_setup_DFN_simulationCCCV(self, params):\n self.param = pybamm.ParameterValues(params)\n self.model = pybamm.lithium_ion.DFN()\n exp = pybamm.Experiment(\n [\n \"Discharge at C/5 for 10 hours or until 3.3 V\",\n \"Rest for 1 hour\",\n \"Charge at 1 A until 4.1 V\",\n \"Hold at 4.1 V until 10 mA\",\n \"Rest for 1 hour\",\n ]\n )\n self.sim = pybamm.Simulation(\n self.model, parameter_values=self.param, experiment=exp\n )\n return self.sim\n\n def setup(self, _params):\n set_random_seed()", "name": "memory_sims.MemDFNSimulationCCCV.mem_setup_DFN_simulationCCCV", "param_names": ["parameter"], "params": [["'Marquis2019'", "'Chen2020'"]], "type": "memory", "unit": "bytes", "version": "51ef613d1be3b3c97e5cff911f9a71ebfec58bb3741293e2b387b6aec0c55d88"}, "memory_sims.MemDFNSimulationGITT.mem_setup_DFN_simulationGITT": {"code": "class MemDFNSimulationGITT:\n def mem_setup_DFN_simulationGITT(self, params):\n self.param = pybamm.ParameterValues(params)\n self.model = pybamm.lithium_ion.SPM()\n exp = pybamm.Experiment(\n [(\"Discharge at C/20 for 1 hour\", \"Rest for 1 hour\")] * 20\n )\n self.sim = pybamm.Simulation(\n self.model, parameter_values=self.param, experiment=exp\n )\n return self.sim\n\n def setup(self, _params):\n set_random_seed()", "name": "memory_sims.MemDFNSimulationGITT.mem_setup_DFN_simulationGITT", "param_names": ["parameter"], "params": [["'Marquis2019'", "'Chen2020'"]], "type": "memory", "unit": "bytes", "version": "d4b526c693f63c0dca2afa82bc58c24d52b7884ae618e298aff74e44864d84e4"}, "memory_sims.MemSPMSimulationCCCV.mem_setup_SPM_simulationCCCV": {"code": "class MemSPMSimulationCCCV:\n def mem_setup_SPM_simulationCCCV(self, params):\n self.param = pybamm.ParameterValues(params)\n self.model = pybamm.lithium_ion.SPM()\n exp = pybamm.Experiment(\n [\n \"Discharge at C/5 for 10 hours or until 3.3 V\",\n \"Rest for 1 hour\",\n \"Charge at 1 A until 4.1 V\",\n \"Hold at 4.1 V until 10 mA\",\n \"Rest for 1 hour\",\n ]\n )\n self.sim = pybamm.Simulation(\n self.model, parameter_values=self.param, experiment=exp\n )\n return self.sim\n\n def setup(self, _params):\n set_random_seed()", "name": "memory_sims.MemSPMSimulationCCCV.mem_setup_SPM_simulationCCCV", "param_names": ["parameter"], "params": [["'Marquis2019'", "'Chen2020'"]], "type": "memory", "unit": "bytes", "version": "29109fbfa71fad4a6f5d5b2f57bb2c9d364602f218e44f5f537529aae441a717"}, "memory_sims.MemSPMSimulationGITT.mem_setup_SPM_simulationGITT": {"code": "class MemSPMSimulationGITT:\n def mem_setup_SPM_simulationGITT(self, params):\n self.param = pybamm.ParameterValues(params)\n self.model = pybamm.lithium_ion.SPM()\n exp = pybamm.Experiment(\n [(\"Discharge at C/20 for 1 hour\", \"Rest for 1 hour\")] * 20\n )\n self.sim = pybamm.Simulation(\n self.model, parameter_values=self.param, experiment=exp\n )\n return self.sim\n\n def setup(self, _params):\n set_random_seed()", "name": "memory_sims.MemSPMSimulationGITT.mem_setup_SPM_simulationGITT", "param_names": ["parameter"], "params": [["'Marquis2019'", "'Chen2020'"]], "type": "memory", "unit": "bytes", "version": "c00f20a28b027b21fcddb41e38f4b6414026233b5484c1bc7a1ad2810fc0475f"}, "memory_unit_benchmarks.MemCreateExpression.mem_create_expression": {"code": "class MemCreateExpression:\n def mem_create_expression(self):\n self.R = pybamm.Parameter(\"Particle radius [m]\")\n D = pybamm.Parameter(\"Diffusion coefficient [m2.s-1]\")\n j = pybamm.Parameter(\"Interfacial current density [A.m-2]\")\n F = pybamm.Parameter(\"Faraday constant [C.mol-1]\")\n c0 = pybamm.Parameter(\"Initial concentration [mol.m-3]\")\n self.model = pybamm.BaseModel()\n \n c = pybamm.Variable(\"Concentration [mol.m-3]\", domain=\"negative particle\")\n N = -D * pybamm.grad(c)\n dcdt = -pybamm.div(N)\n self.model.rhs = {c: dcdt}\n \n lbc = pybamm.Scalar(0)\n rbc = -j / F / D\n self.model.boundary_conditions = {\n c: {\"left\": (lbc, \"Neumann\"), \"right\": (rbc, \"Neumann\")}\n }\n \n self.model.initial_conditions = {c: c0}\n self.model.variables = {\n \"Concentration [mol.m-3]\": c,\n \"Surface concentration [mol.m-3]\": pybamm.surf(c),\n \"Flux [mol.m-2.s-1]\": N,\n }\n return self.model\n\n def setup(self):\n set_random_seed()", "name": "memory_unit_benchmarks.MemCreateExpression.mem_create_expression", "param_names": [], "params": [], "type": "memory", "unit": "bytes", "version": "3d6830c6c8132d74fbf54e206a2f784c514c86ad079aac662a24cb151775927f"}, "memory_unit_benchmarks.MemDiscretiseModel.mem_create_expression": {"code": "class MemCreateExpression:\n def mem_create_expression(self):\n self.R = pybamm.Parameter(\"Particle radius [m]\")\n D = pybamm.Parameter(\"Diffusion coefficient [m2.s-1]\")\n j = pybamm.Parameter(\"Interfacial current density [A.m-2]\")\n F = pybamm.Parameter(\"Faraday constant [C.mol-1]\")\n c0 = pybamm.Parameter(\"Initial concentration [mol.m-3]\")\n self.model = pybamm.BaseModel()\n \n c = pybamm.Variable(\"Concentration [mol.m-3]\", domain=\"negative particle\")\n N = -D * pybamm.grad(c)\n dcdt = -pybamm.div(N)\n self.model.rhs = {c: dcdt}\n \n lbc = pybamm.Scalar(0)\n rbc = -j / F / D\n self.model.boundary_conditions = {\n c: {\"left\": (lbc, \"Neumann\"), \"right\": (rbc, \"Neumann\")}\n }\n \n self.model.initial_conditions = {c: c0}\n self.model.variables = {\n \"Concentration [mol.m-3]\": c,\n \"Surface concentration [mol.m-3]\": pybamm.surf(c),\n \"Flux [mol.m-2.s-1]\": N,\n }\n return self.model\n\nclass MemDiscretiseModel:\n def setup(self):\n set_random_seed()\n MemCreateExpression.mem_create_expression(self)\n MemParameteriseModel.mem_parameterise(self)", "name": "memory_unit_benchmarks.MemDiscretiseModel.mem_create_expression", "param_names": [], "params": [], "type": "memory", "unit": "bytes", "version": "1e1265e6812795c6b4638a8861b57dc14e9b3e21d54786009ba2f6ea6526eb99"}, "memory_unit_benchmarks.MemDiscretiseModel.mem_discretise": {"code": "class MemDiscretiseModel:\n def mem_discretise(self):\n MemCreateExpression.mem_create_expression(self)\n MemParameteriseModel.mem_parameterise(self)\n submesh_types = {\"negative particle\": pybamm.Uniform1DSubMesh}\n var_pts = {self.r: 20}\n mesh = pybamm.Mesh(self.geometry, submesh_types, var_pts)\n \n spatial_methods = {\"negative particle\": pybamm.FiniteVolume()}\n disc = pybamm.Discretisation(mesh, spatial_methods)\n disc.process_model(self.model)\n return disc\n\n def setup(self):\n set_random_seed()\n MemCreateExpression.mem_create_expression(self)\n MemParameteriseModel.mem_parameterise(self)", "name": "memory_unit_benchmarks.MemDiscretiseModel.mem_discretise", "param_names": [], "params": [], "type": "memory", "unit": "bytes", "version": "495195cd9a6ece265ad424338ffde8465bff6b401aff98f26bf908c480b58724"}, "memory_unit_benchmarks.MemDiscretiseModel.mem_parameterise": {"code": "class MemParameteriseModel:\n def mem_parameterise(self):\n param = pybamm.ParameterValues(\n {\n \"Particle radius [m]\": 10e-6,\n \"Diffusion coefficient [m2.s-1]\": 3.9e-14,\n \"Interfacial current density [A.m-2]\": 1.4,\n \"Faraday constant [C.mol-1]\": 96485,\n \"Initial concentration [mol.m-3]\": 2.5e4,\n }\n )\n \n self.r = pybamm.SpatialVariable(\n \"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\"\n )\n \n self.geometry = {\n \"negative particle\": {self.r: {\"min\": pybamm.Scalar(0), \"max\": self.R}}\n }\n param.process_model(self.model)\n param.process_geometry(self.geometry)\n return param\n\nclass MemDiscretiseModel:\n def setup(self):\n set_random_seed()\n MemCreateExpression.mem_create_expression(self)\n MemParameteriseModel.mem_parameterise(self)", "name": "memory_unit_benchmarks.MemDiscretiseModel.mem_parameterise", "param_names": [], "params": [], "type": "memory", "unit": "bytes", "version": "3193180de8abb381c009afe51426f5d20e7347c3b9761d1663a98425cce0c663"}, "memory_unit_benchmarks.MemParameteriseModel.mem_create_expression": {"code": "class MemCreateExpression:\n def mem_create_expression(self):\n self.R = pybamm.Parameter(\"Particle radius [m]\")\n D = pybamm.Parameter(\"Diffusion coefficient [m2.s-1]\")\n j = pybamm.Parameter(\"Interfacial current density [A.m-2]\")\n F = pybamm.Parameter(\"Faraday constant [C.mol-1]\")\n c0 = pybamm.Parameter(\"Initial concentration [mol.m-3]\")\n self.model = pybamm.BaseModel()\n \n c = pybamm.Variable(\"Concentration [mol.m-3]\", domain=\"negative particle\")\n N = -D * pybamm.grad(c)\n dcdt = -pybamm.div(N)\n self.model.rhs = {c: dcdt}\n \n lbc = pybamm.Scalar(0)\n rbc = -j / F / D\n self.model.boundary_conditions = {\n c: {\"left\": (lbc, \"Neumann\"), \"right\": (rbc, \"Neumann\")}\n }\n \n self.model.initial_conditions = {c: c0}\n self.model.variables = {\n \"Concentration [mol.m-3]\": c,\n \"Surface concentration [mol.m-3]\": pybamm.surf(c),\n \"Flux [mol.m-2.s-1]\": N,\n }\n return self.model\n\nclass MemParameteriseModel:\n def setup(self):\n set_random_seed()\n MemCreateExpression.mem_create_expression(self)", "name": "memory_unit_benchmarks.MemParameteriseModel.mem_create_expression", "param_names": [], "params": [], "type": "memory", "unit": "bytes", "version": "862863fcd24f0545f40184ecdebd166445846c611afed20dcf90f68765c3cc0b"}, "memory_unit_benchmarks.MemParameteriseModel.mem_parameterise": {"code": "class MemParameteriseModel:\n def mem_parameterise(self):\n param = pybamm.ParameterValues(\n {\n \"Particle radius [m]\": 10e-6,\n \"Diffusion coefficient [m2.s-1]\": 3.9e-14,\n \"Interfacial current density [A.m-2]\": 1.4,\n \"Faraday constant [C.mol-1]\": 96485,\n \"Initial concentration [mol.m-3]\": 2.5e4,\n }\n )\n \n self.r = pybamm.SpatialVariable(\n \"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\"\n )\n \n self.geometry = {\n \"negative particle\": {self.r: {\"min\": pybamm.Scalar(0), \"max\": self.R}}\n }\n param.process_model(self.model)\n param.process_geometry(self.geometry)\n return param\n\n def setup(self):\n set_random_seed()\n MemCreateExpression.mem_create_expression(self)", "name": "memory_unit_benchmarks.MemParameteriseModel.mem_parameterise", "param_names": [], "params": [], "type": "memory", "unit": "bytes", "version": "edad12bc03295b4e89366fc5cfb1ed396d7bb91d3f3c81c80c58aa1302e875c5"}, "memory_unit_benchmarks.MemSolveModel.mem_create_expression": {"code": "class MemCreateExpression:\n def mem_create_expression(self):\n self.R = pybamm.Parameter(\"Particle radius [m]\")\n D = pybamm.Parameter(\"Diffusion coefficient [m2.s-1]\")\n j = pybamm.Parameter(\"Interfacial current density [A.m-2]\")\n F = pybamm.Parameter(\"Faraday constant [C.mol-1]\")\n c0 = pybamm.Parameter(\"Initial concentration [mol.m-3]\")\n self.model = pybamm.BaseModel()\n \n c = pybamm.Variable(\"Concentration [mol.m-3]\", domain=\"negative particle\")\n N = -D * pybamm.grad(c)\n dcdt = -pybamm.div(N)\n self.model.rhs = {c: dcdt}\n \n lbc = pybamm.Scalar(0)\n rbc = -j / F / D\n self.model.boundary_conditions = {\n c: {\"left\": (lbc, \"Neumann\"), \"right\": (rbc, \"Neumann\")}\n }\n \n self.model.initial_conditions = {c: c0}\n self.model.variables = {\n \"Concentration [mol.m-3]\": c,\n \"Surface concentration [mol.m-3]\": pybamm.surf(c),\n \"Flux [mol.m-2.s-1]\": N,\n }\n return self.model\n\nclass MemSolveModel:\n def setup(self):\n set_random_seed()\n MemCreateExpression.mem_create_expression(self)\n MemParameteriseModel.mem_parameterise(self)\n MemDiscretiseModel.mem_discretise(self)", "name": "memory_unit_benchmarks.MemSolveModel.mem_create_expression", "param_names": [], "params": [], "type": "memory", "unit": "bytes", "version": "16091c4e58d10d32014c88300ed1dcaf80b55cf5b10e264a66a1ea2b8837581e"}, "memory_unit_benchmarks.MemSolveModel.mem_discretise": {"code": "class MemDiscretiseModel:\n def mem_discretise(self):\n MemCreateExpression.mem_create_expression(self)\n MemParameteriseModel.mem_parameterise(self)\n submesh_types = {\"negative particle\": pybamm.Uniform1DSubMesh}\n var_pts = {self.r: 20}\n mesh = pybamm.Mesh(self.geometry, submesh_types, var_pts)\n \n spatial_methods = {\"negative particle\": pybamm.FiniteVolume()}\n disc = pybamm.Discretisation(mesh, spatial_methods)\n disc.process_model(self.model)\n return disc\n\nclass MemSolveModel:\n def setup(self):\n set_random_seed()\n MemCreateExpression.mem_create_expression(self)\n MemParameteriseModel.mem_parameterise(self)\n MemDiscretiseModel.mem_discretise(self)", "name": "memory_unit_benchmarks.MemSolveModel.mem_discretise", "param_names": [], "params": [], "type": "memory", "unit": "bytes", "version": "34cbd7a52ecdcb0e943a83665c930436c4d296729f856ac0c74a38ebe551e0f9"}, "memory_unit_benchmarks.MemSolveModel.mem_parameterise": {"code": "class MemParameteriseModel:\n def mem_parameterise(self):\n param = pybamm.ParameterValues(\n {\n \"Particle radius [m]\": 10e-6,\n \"Diffusion coefficient [m2.s-1]\": 3.9e-14,\n \"Interfacial current density [A.m-2]\": 1.4,\n \"Faraday constant [C.mol-1]\": 96485,\n \"Initial concentration [mol.m-3]\": 2.5e4,\n }\n )\n \n self.r = pybamm.SpatialVariable(\n \"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\"\n )\n \n self.geometry = {\n \"negative particle\": {self.r: {\"min\": pybamm.Scalar(0), \"max\": self.R}}\n }\n param.process_model(self.model)\n param.process_geometry(self.geometry)\n return param\n\nclass MemSolveModel:\n def setup(self):\n set_random_seed()\n MemCreateExpression.mem_create_expression(self)\n MemParameteriseModel.mem_parameterise(self)\n MemDiscretiseModel.mem_discretise(self)", "name": "memory_unit_benchmarks.MemSolveModel.mem_parameterise", "param_names": [], "params": [], "type": "memory", "unit": "bytes", "version": "e4f639e8ddfb46ec7daa72247577a6c466daa157bef63aa013c20264f9da6a73"}, "memory_unit_benchmarks.MemSolveModel.mem_solve": {"code": "class MemSolveModel:\n def mem_solve(self):\n solver = pybamm.ScipySolver()\n t = np.linspace(0, 3600, 600)\n solver.solve(self.model, t)\n return solver\n\n def setup(self):\n set_random_seed()\n MemCreateExpression.mem_create_expression(self)\n MemParameteriseModel.mem_parameterise(self)\n MemDiscretiseModel.mem_discretise(self)", "name": "memory_unit_benchmarks.MemSolveModel.mem_solve", "param_names": [], "params": [], "type": "memory", "unit": "bytes", "version": "a7b39a512b545d84bd7d5f0a6ed4d6901e6dc414da7e50ed43c6afa3abd62f0a"}, "time_setup_models_and_sims.TimeBuildDFN.time_setup_DFN": {"code": "class TimeBuildDFN:\n def time_setup_DFN(self, params):\n self.param = pybamm.ParameterValues(params)\n self.model = pybamm.lithium_ion.DFN()\n self.param.process_model(self.model)\n compute_discretisation(self.model, self.param).process_model(self.model)\n\n def setup(self, _params):\n set_random_seed()", "min_run_count": 2, "name": "time_setup_models_and_sims.TimeBuildDFN.time_setup_DFN", "number": 0, "param_names": ["parameter"], "params": [["'Marquis2019'", "'ORegan2022'", "'NCA_Kim2011'", "'Prada2013'", "'Ai2020'", "'Ramadass2004'", "'Mohtat2020'", "'Chen2020'", "'OKane2022'", "'Ecker2015'"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "0eee82eaa2ed6557bb7cec497b21c1595f4e2ab7f17c33cf98baa7acc53e6ef7", "warmup_time": -1}, "time_setup_models_and_sims.TimeBuildDFNSimulation.time_setup_DFN_simulation": {"code": "class TimeBuildDFNSimulation:\n def time_setup_DFN_simulation(self, with_experiment, params):\n self.param = pybamm.ParameterValues(params)\n self.model = pybamm.lithium_ion.DFN()\n if with_experiment:\n exp = pybamm.Experiment(\n [\n \"Discharge at 0.1C until 3.105 V\",\n ]\n )\n pybamm.Simulation(self.model, parameter_values=self.param, experiment=exp)\n else:\n pybamm.Simulation(self.model, parameter_values=self.param, C_rate=1)\n\n def setup(self, _with_experiment, _params):\n set_random_seed()", "min_run_count": 2, "name": "time_setup_models_and_sims.TimeBuildDFNSimulation.time_setup_DFN_simulation", "number": 0, "param_names": ["with experiment", "parameter"], "params": [["False", "True"], ["'Marquis2019'", "'ORegan2022'", "'NCA_Kim2011'", "'Prada2013'", "'Ai2020'", "'Ramadass2004'", "'Mohtat2020'", "'Chen2020'", "'OKane2022'", "'Ecker2015'"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "041d84cc1adf4753d7f9652306ad60d0d6364fcc290e4736429f8e7c3c675a12", "warmup_time": -1}, "time_setup_models_and_sims.TimeBuildSPM.time_setup_SPM": {"code": "class TimeBuildSPM:\n def time_setup_SPM(self, params):\n self.param = pybamm.ParameterValues(params)\n self.model = pybamm.lithium_ion.SPM()\n self.param.process_model(self.model)\n compute_discretisation(self.model, self.param).process_model(self.model)\n\n def setup(self, _params):\n set_random_seed()", "min_run_count": 2, "name": "time_setup_models_and_sims.TimeBuildSPM.time_setup_SPM", "number": 0, "param_names": ["parameter"], "params": [["'Marquis2019'", "'ORegan2022'", "'NCA_Kim2011'", "'Prada2013'", "'Ai2020'", "'Ramadass2004'", "'Mohtat2020'", "'Chen2020'", "'OKane2022'", "'Ecker2015'"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "d5778345d68fd67c043a3241f1764d8b6e007490765cb577f15dd6fe38347cad", "warmup_time": -1}, "time_setup_models_and_sims.TimeBuildSPMSimulation.time_setup_SPM_simulation": {"code": "class TimeBuildSPMSimulation:\n def time_setup_SPM_simulation(self, with_experiment, params):\n self.param = pybamm.ParameterValues(params)\n self.model = pybamm.lithium_ion.SPM()\n if with_experiment:\n exp = pybamm.Experiment(\n [\n \"Discharge at 0.1C until 3.105 V\",\n ]\n )\n pybamm.Simulation(self.model, parameter_values=self.param, experiment=exp)\n else:\n pybamm.Simulation(self.model, parameter_values=self.param, C_rate=1)\n\n def setup(self, _with_experiment, _params):\n set_random_seed()", "min_run_count": 2, "name": "time_setup_models_and_sims.TimeBuildSPMSimulation.time_setup_SPM_simulation", "number": 0, "param_names": ["with experiment", "parameter"], "params": [["False", "True"], ["'Marquis2019'", "'ORegan2022'", "'NCA_Kim2011'", "'Prada2013'", "'Ai2020'", "'Ramadass2004'", "'Mohtat2020'", "'Chen2020'", "'OKane2022'", "'Ecker2015'"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "4af04a0ebcb83401d42904cefe3d57346161aa348bdafe191f95ec33fff21cec", "warmup_time": -1}, "time_setup_models_and_sims.TimeBuildSPMe.time_setup_SPMe": {"code": "class TimeBuildSPMe:\n def time_setup_SPMe(self, params):\n self.param = pybamm.ParameterValues(params)\n self.model = pybamm.lithium_ion.SPMe()\n self.param.process_model(self.model)\n compute_discretisation(self.model, self.param).process_model(self.model)\n\n def setup(self, _params):\n set_random_seed()", "min_run_count": 2, "name": "time_setup_models_and_sims.TimeBuildSPMe.time_setup_SPMe", "number": 0, "param_names": ["parameter"], "params": [["'Marquis2019'", "'ORegan2022'", "'NCA_Kim2011'", "'Prada2013'", "'Ai2020'", "'Ramadass2004'", "'Mohtat2020'", "'Chen2020'", "'OKane2022'", "'Ecker2015'"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "970230e78bb53f069b2e13856e844504c8a106f98359e1d7ad09161d6d03c083", "warmup_time": -1}, "time_setup_models_and_sims.TimeBuildSPMeSimulation.time_setup_SPMe_simulation": {"code": "class TimeBuildSPMeSimulation:\n def time_setup_SPMe_simulation(self, with_experiment, params):\n self.param = pybamm.ParameterValues(params)\n self.model = pybamm.lithium_ion.SPMe()\n if with_experiment:\n exp = pybamm.Experiment(\n [\n \"Discharge at 0.1C until 3.105 V\",\n ]\n )\n pybamm.Simulation(self.model, parameter_values=self.param, experiment=exp)\n else:\n pybamm.Simulation(self.model, parameter_values=self.param, C_rate=1)\n\n def setup(self, _with_experiment, _params):\n set_random_seed()", "min_run_count": 2, "name": "time_setup_models_and_sims.TimeBuildSPMeSimulation.time_setup_SPMe_simulation", "number": 0, "param_names": ["with experiment", "parameter"], "params": [["False", "True"], ["'Marquis2019'", "'ORegan2022'", "'NCA_Kim2011'", "'Prada2013'", "'Ai2020'", "'Ramadass2004'", "'Mohtat2020'", "'Chen2020'", "'OKane2022'", "'Ecker2015'"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "6f76c516f3fa71f22c3c769426712e12bc3c63681def5407f510a3d24fe28ebf", "warmup_time": -1}, "time_sims_experiments.TimeSimulation.time_setup": {"code": "class TimeSimulation:\n def time_setup(self, experiment, parameters, model_class, solver_class):\n param = pybamm.ParameterValues(parameters)\n model = model_class()\n solver = solver_class()\n exp = pybamm.Experiment(self.experiment_descriptions[experiment])\n pybamm.Simulation(model, parameter_values=param, experiment=exp, solver=solver)\n\n def setup(self, experiment, parameters, model_class, solver_class):\n set_random_seed()\n if (experiment, parameters, model_class, solver_class) == (\n \"GITT\",\n \"Marquis2019\",\n pybamm.lithium_ion.DFN,\n pybamm.CasadiSolver,\n ):\n raise NotImplementedError\n self.param = pybamm.ParameterValues(parameters)\n self.model = model_class()\n self.solver = solver_class()\n self.exp = pybamm.Experiment(self.experiment_descriptions[experiment])\n self.sim = pybamm.Simulation(\n self.model,\n parameter_values=self.param,\n experiment=self.exp,\n solver=self.solver,\n )", "min_run_count": 2, "name": "time_sims_experiments.TimeSimulation.time_setup", "number": 0, "param_names": ["experiment", "parameter", "model_class", "solver_class"], "params": [["'CCCV'", "'GITT'"], ["'Marquis2019'", "'Chen2020'"], ["", ""], ["", ""]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "f919a29c04da89b51fb66aa41294744b714b2d640df50debeff76b55023af868", "warmup_time": -1}, "time_sims_experiments.TimeSimulation.time_solve": {"code": "class TimeSimulation:\n def time_solve(self, _experiment, _parameters, _model_class, _solver_class):\n self.sim.solve()\n\n def setup(self, experiment, parameters, model_class, solver_class):\n set_random_seed()\n if (experiment, parameters, model_class, solver_class) == (\n \"GITT\",\n \"Marquis2019\",\n pybamm.lithium_ion.DFN,\n pybamm.CasadiSolver,\n ):\n raise NotImplementedError\n self.param = pybamm.ParameterValues(parameters)\n self.model = model_class()\n self.solver = solver_class()\n self.exp = pybamm.Experiment(self.experiment_descriptions[experiment])\n self.sim = pybamm.Simulation(\n self.model,\n parameter_values=self.param,\n experiment=self.exp,\n solver=self.solver,\n )", "min_run_count": 2, "name": "time_sims_experiments.TimeSimulation.time_solve", "number": 0, "param_names": ["experiment", "parameter", "model_class", "solver_class"], "params": [["'CCCV'", "'GITT'"], ["'Marquis2019'", "'Chen2020'"], ["", ""], ["", ""]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "6832512fe2709d359717d531b49d57f9b07241e3af646c02a9865a7e7b4c96ac", "warmup_time": -1}, "time_solve_models.TimeSolveDFN.time_solve_model": {"code": "class TimeSolveDFN:\n def time_solve_model(self, _solve_first, _parameters, _solver_class):\n self.solver.solve(self.model, t_eval=self.t_eval)\n\n def setup(self, solve_first, parameters, solver_class):\n set_random_seed()\n if (parameters, solver_class) == (\n \"ORegan2022\",\n pybamm.CasadiSolver,\n ):\n raise NotImplementedError\n self.solver = solver_class()\n self.model = pybamm.lithium_ion.DFN()\n c_rate = 1\n tmax = 4000 / c_rate\n nb_points = 500\n self.t_eval = np.linspace(0, tmax, nb_points)\n geometry = self.model.default_geometry\n \n # load parameter values and process model and geometry\n param = pybamm.ParameterValues(parameters)\n param.process_model(self.model)\n param.process_geometry(geometry)\n \n # set mesh\n var_pts = {\n \"x_n\": 20,\n \"x_s\": 20,\n \"x_p\": 20,\n \"r_n\": 30,\n \"r_p\": 30,\n \"y\": 10,\n \"z\": 10,\n }\n mesh = pybamm.Mesh(geometry, self.model.default_submesh_types, var_pts)\n \n # discretise model\n disc = pybamm.Discretisation(mesh, self.model.default_spatial_methods)\n disc.process_model(self.model)\n if solve_first:\n solve_model_once(self.model, self.solver, self.t_eval)", "min_run_count": 2, "name": "time_solve_models.TimeSolveDFN.time_solve_model", "number": 0, "param_names": ["solve first", "parameter", "solver_class"], "params": [["False", "True"], ["'Marquis2019'", "'ORegan2022'", "'Prada2013'", "'Ai2020'", "'Ramadass2004'", "'Chen2020'", "'Ecker2015'"], ["", ""]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "f6d7b52e57b2a72e7ebdf55b8998c7f7e4ee88848570e445a0f7bd967d6d14be", "warmup_time": -1}, "time_solve_models.TimeSolveSPM.time_solve_model": {"code": "class TimeSolveSPM:\n def time_solve_model(self, _solve_first, _parameters, _solver_class):\n self.solver.solve(self.model, t_eval=self.t_eval)\n\n def setup(self, solve_first, parameters, solver_class):\n set_random_seed()\n self.solver = solver_class()\n self.model = pybamm.lithium_ion.SPM()\n c_rate = 1\n tmax = 4000 / c_rate\n nb_points = 500\n self.t_eval = np.linspace(0, tmax, nb_points)\n geometry = self.model.default_geometry\n \n # load parameter values and process model and geometry\n param = pybamm.ParameterValues(parameters)\n param.process_model(self.model)\n param.process_geometry(geometry)\n \n # set mesh\n var_pts = {\n \"x_n\": 20,\n \"x_s\": 20,\n \"x_p\": 20,\n \"r_n\": 30,\n \"r_p\": 30,\n \"y\": 10,\n \"z\": 10,\n }\n mesh = pybamm.Mesh(geometry, self.model.default_submesh_types, var_pts)\n \n # discretise model\n disc = pybamm.Discretisation(mesh, self.model.default_spatial_methods)\n disc.process_model(self.model)\n if solve_first:\n solve_model_once(self.model, self.solver, self.t_eval)", "min_run_count": 2, "name": "time_solve_models.TimeSolveSPM.time_solve_model", "number": 0, "param_names": ["solve first", "parameter", "solver_class"], "params": [["False", "True"], ["'Marquis2019'", "'ORegan2022'", "'NCA_Kim2011'", "'Prada2013'", "'Ramadass2004'", "'Chen2020'", "'Ecker2015'"], ["", ""]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "f329b803e6760b144e33d59c3d34fa3d75fa6c26427168501d81c48156c62175", "warmup_time": -1}, "time_solve_models.TimeSolveSPMe.time_solve_model": {"code": "class TimeSolveSPMe:\n def time_solve_model(self, _solve_first, _parameters, _solver_class):\n self.solver.solve(self.model, t_eval=self.t_eval)\n\n def setup(self, solve_first, parameters, solver_class):\n set_random_seed()\n self.solver = solver_class()\n self.model = pybamm.lithium_ion.SPMe()\n c_rate = 1\n tmax = 4000 / c_rate\n nb_points = 500\n self.t_eval = np.linspace(0, tmax, nb_points)\n geometry = self.model.default_geometry\n \n # load parameter values and process model and geometry\n param = pybamm.ParameterValues(parameters)\n param.process_model(self.model)\n param.process_geometry(geometry)\n \n # set mesh\n var_pts = {\n \"x_n\": 20,\n \"x_s\": 20,\n \"x_p\": 20,\n \"r_n\": 30,\n \"r_p\": 30,\n \"y\": 10,\n \"z\": 10,\n }\n mesh = pybamm.Mesh(geometry, self.model.default_submesh_types, var_pts)\n \n # discretise model\n disc = pybamm.Discretisation(mesh, self.model.default_spatial_methods)\n disc.process_model(self.model)\n if solve_first:\n solve_model_once(self.model, self.solver, self.t_eval)", "min_run_count": 2, "name": "time_solve_models.TimeSolveSPMe.time_solve_model", "number": 0, "param_names": ["solve first", "parameter", "solver_class"], "params": [["False", "True"], ["'Marquis2019'", "'ORegan2022'", "'NCA_Kim2011'", "'Prada2013'", "'Ramadass2004'", "'Chen2020'", "'Ecker2015'"], ["", ""]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "26110b628fab649e6153a529860b3104b379d3f288ca020d4321f489c8a38955", "warmup_time": -1}, "unit_benchmarks.TimeCreateExpression.time_create_expression": {"code": "class TimeCreateExpression:\n def time_create_expression(self):\n self.R = pybamm.Parameter(\"Particle radius [m]\")\n D = pybamm.Parameter(\"Diffusion coefficient [m2.s-1]\")\n j = pybamm.Parameter(\"Interfacial current density [A.m-2]\")\n F = pybamm.Parameter(\"Faraday constant [C.mol-1]\")\n c0 = pybamm.Parameter(\"Initial concentration [mol.m-3]\")\n self.model = pybamm.BaseModel()\n \n c = pybamm.Variable(\"Concentration [mol.m-3]\", domain=\"negative particle\")\n N = -D * pybamm.grad(c)\n dcdt = -pybamm.div(N)\n self.model.rhs = {c: dcdt}\n \n lbc = pybamm.Scalar(0)\n rbc = -j / F / D\n self.model.boundary_conditions = {\n c: {\"left\": (lbc, \"Neumann\"), \"right\": (rbc, \"Neumann\")}\n }\n \n self.model.initial_conditions = {c: c0}\n self.model.variables = {\n \"Concentration [mol.m-3]\": c,\n \"Surface concentration [mol.m-3]\": pybamm.surf(c),\n \"Flux [mol.m-2.s-1]\": N,\n }\n\n def setup(self):\n set_random_seed()", "min_run_count": 2, "name": "unit_benchmarks.TimeCreateExpression.time_create_expression", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "79567e8fc10e82bb7efc93e89a5fa48101c5f63b2bcf09f9affa1daeaa660a7a", "warmup_time": -1}, "unit_benchmarks.TimeDiscretiseModel.time_create_expression": {"code": "class TimeCreateExpression:\n def time_create_expression(self):\n self.R = pybamm.Parameter(\"Particle radius [m]\")\n D = pybamm.Parameter(\"Diffusion coefficient [m2.s-1]\")\n j = pybamm.Parameter(\"Interfacial current density [A.m-2]\")\n F = pybamm.Parameter(\"Faraday constant [C.mol-1]\")\n c0 = pybamm.Parameter(\"Initial concentration [mol.m-3]\")\n self.model = pybamm.BaseModel()\n \n c = pybamm.Variable(\"Concentration [mol.m-3]\", domain=\"negative particle\")\n N = -D * pybamm.grad(c)\n dcdt = -pybamm.div(N)\n self.model.rhs = {c: dcdt}\n \n lbc = pybamm.Scalar(0)\n rbc = -j / F / D\n self.model.boundary_conditions = {\n c: {\"left\": (lbc, \"Neumann\"), \"right\": (rbc, \"Neumann\")}\n }\n \n self.model.initial_conditions = {c: c0}\n self.model.variables = {\n \"Concentration [mol.m-3]\": c,\n \"Surface concentration [mol.m-3]\": pybamm.surf(c),\n \"Flux [mol.m-2.s-1]\": N,\n }\n\nclass TimeDiscretiseModel:\n def setup(self):\n set_random_seed()\n TimeCreateExpression.time_create_expression(self)\n TimeParameteriseModel.time_parameterise(self)", "min_run_count": 2, "name": "unit_benchmarks.TimeDiscretiseModel.time_create_expression", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "53f64bb63b703ce09229d62a777fa5f21abf561c70430ff978cd6fce8363f76c", "warmup_time": -1}, "unit_benchmarks.TimeDiscretiseModel.time_discretise": {"code": "class TimeDiscretiseModel:\n def time_discretise(self):\n TimeCreateExpression.time_create_expression(self)\n TimeParameteriseModel.time_parameterise(self)\n submesh_types = {\"negative particle\": pybamm.Uniform1DSubMesh}\n var_pts = {self.r: 20}\n mesh = pybamm.Mesh(self.geometry, submesh_types, var_pts)\n \n spatial_methods = {\"negative particle\": pybamm.FiniteVolume()}\n disc = pybamm.Discretisation(mesh, spatial_methods)\n disc.process_model(self.model)\n\n def setup(self):\n set_random_seed()\n TimeCreateExpression.time_create_expression(self)\n TimeParameteriseModel.time_parameterise(self)", "min_run_count": 2, "name": "unit_benchmarks.TimeDiscretiseModel.time_discretise", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "82d1028bb6552573a282aead5ecf23b4c233624a98d553f417f9b632904cf779", "warmup_time": -1}, "unit_benchmarks.TimeDiscretiseModel.time_parameterise": {"code": "class TimeParameteriseModel:\n def time_parameterise(self):\n param = pybamm.ParameterValues(\n {\n \"Particle radius [m]\": 10e-6,\n \"Diffusion coefficient [m2.s-1]\": 3.9e-14,\n \"Interfacial current density [A.m-2]\": 1.4,\n \"Faraday constant [C.mol-1]\": 96485,\n \"Initial concentration [mol.m-3]\": 2.5e4,\n }\n )\n \n self.r = pybamm.SpatialVariable(\n \"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\"\n )\n \n self.geometry = {\n \"negative particle\": {self.r: {\"min\": pybamm.Scalar(0), \"max\": self.R}}\n }\n param.process_model(self.model)\n param.process_geometry(self.geometry)\n\nclass TimeDiscretiseModel:\n def setup(self):\n set_random_seed()\n TimeCreateExpression.time_create_expression(self)\n TimeParameteriseModel.time_parameterise(self)", "min_run_count": 2, "name": "unit_benchmarks.TimeDiscretiseModel.time_parameterise", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "ef8b306be418ea6cffb6df7d95c34e6e4c058556b81285cfe00a85a746364e11", "warmup_time": -1}, "unit_benchmarks.TimeParameteriseModel.time_create_expression": {"code": "class TimeCreateExpression:\n def time_create_expression(self):\n self.R = pybamm.Parameter(\"Particle radius [m]\")\n D = pybamm.Parameter(\"Diffusion coefficient [m2.s-1]\")\n j = pybamm.Parameter(\"Interfacial current density [A.m-2]\")\n F = pybamm.Parameter(\"Faraday constant [C.mol-1]\")\n c0 = pybamm.Parameter(\"Initial concentration [mol.m-3]\")\n self.model = pybamm.BaseModel()\n \n c = pybamm.Variable(\"Concentration [mol.m-3]\", domain=\"negative particle\")\n N = -D * pybamm.grad(c)\n dcdt = -pybamm.div(N)\n self.model.rhs = {c: dcdt}\n \n lbc = pybamm.Scalar(0)\n rbc = -j / F / D\n self.model.boundary_conditions = {\n c: {\"left\": (lbc, \"Neumann\"), \"right\": (rbc, \"Neumann\")}\n }\n \n self.model.initial_conditions = {c: c0}\n self.model.variables = {\n \"Concentration [mol.m-3]\": c,\n \"Surface concentration [mol.m-3]\": pybamm.surf(c),\n \"Flux [mol.m-2.s-1]\": N,\n }\n\nclass TimeParameteriseModel:\n def setup(self):\n set_random_seed()\n TimeCreateExpression.time_create_expression(self)", "min_run_count": 2, "name": "unit_benchmarks.TimeParameteriseModel.time_create_expression", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "c9c6590d7ea983ffd2ec4a0beb7198254c9872b5d79fafe982b5575dcbf533ea", "warmup_time": -1}, "unit_benchmarks.TimeParameteriseModel.time_parameterise": {"code": "class TimeParameteriseModel:\n def time_parameterise(self):\n param = pybamm.ParameterValues(\n {\n \"Particle radius [m]\": 10e-6,\n \"Diffusion coefficient [m2.s-1]\": 3.9e-14,\n \"Interfacial current density [A.m-2]\": 1.4,\n \"Faraday constant [C.mol-1]\": 96485,\n \"Initial concentration [mol.m-3]\": 2.5e4,\n }\n )\n \n self.r = pybamm.SpatialVariable(\n \"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\"\n )\n \n self.geometry = {\n \"negative particle\": {self.r: {\"min\": pybamm.Scalar(0), \"max\": self.R}}\n }\n param.process_model(self.model)\n param.process_geometry(self.geometry)\n\n def setup(self):\n set_random_seed()\n TimeCreateExpression.time_create_expression(self)", "min_run_count": 2, "name": "unit_benchmarks.TimeParameteriseModel.time_parameterise", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "2db8360d2edcfa78dd7840bfc00d24c0e9649c847253c89fe402671c262b1338", "warmup_time": -1}, "unit_benchmarks.TimeSolveModel.time_create_expression": {"code": "class TimeCreateExpression:\n def time_create_expression(self):\n self.R = pybamm.Parameter(\"Particle radius [m]\")\n D = pybamm.Parameter(\"Diffusion coefficient [m2.s-1]\")\n j = pybamm.Parameter(\"Interfacial current density [A.m-2]\")\n F = pybamm.Parameter(\"Faraday constant [C.mol-1]\")\n c0 = pybamm.Parameter(\"Initial concentration [mol.m-3]\")\n self.model = pybamm.BaseModel()\n \n c = pybamm.Variable(\"Concentration [mol.m-3]\", domain=\"negative particle\")\n N = -D * pybamm.grad(c)\n dcdt = -pybamm.div(N)\n self.model.rhs = {c: dcdt}\n \n lbc = pybamm.Scalar(0)\n rbc = -j / F / D\n self.model.boundary_conditions = {\n c: {\"left\": (lbc, \"Neumann\"), \"right\": (rbc, \"Neumann\")}\n }\n \n self.model.initial_conditions = {c: c0}\n self.model.variables = {\n \"Concentration [mol.m-3]\": c,\n \"Surface concentration [mol.m-3]\": pybamm.surf(c),\n \"Flux [mol.m-2.s-1]\": N,\n }\n\nclass TimeSolveModel:\n def setup(self):\n set_random_seed()\n TimeCreateExpression.time_create_expression(self)\n TimeParameteriseModel.time_parameterise(self)\n TimeDiscretiseModel.time_discretise(self)", "min_run_count": 2, "name": "unit_benchmarks.TimeSolveModel.time_create_expression", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "7d5ffee2c28dcaa231b44408496fce54271121737b80dc5a7498beb92ab2b159", "warmup_time": -1}, "unit_benchmarks.TimeSolveModel.time_discretise": {"code": "class TimeDiscretiseModel:\n def time_discretise(self):\n TimeCreateExpression.time_create_expression(self)\n TimeParameteriseModel.time_parameterise(self)\n submesh_types = {\"negative particle\": pybamm.Uniform1DSubMesh}\n var_pts = {self.r: 20}\n mesh = pybamm.Mesh(self.geometry, submesh_types, var_pts)\n \n spatial_methods = {\"negative particle\": pybamm.FiniteVolume()}\n disc = pybamm.Discretisation(mesh, spatial_methods)\n disc.process_model(self.model)\n\nclass TimeSolveModel:\n def setup(self):\n set_random_seed()\n TimeCreateExpression.time_create_expression(self)\n TimeParameteriseModel.time_parameterise(self)\n TimeDiscretiseModel.time_discretise(self)", "min_run_count": 2, "name": "unit_benchmarks.TimeSolveModel.time_discretise", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "a4e41d181f7a8ac4c6c873e0aafa291f45deb155ad92dc625301917565fe6bdc", "warmup_time": -1}, "unit_benchmarks.TimeSolveModel.time_parameterise": {"code": "class TimeParameteriseModel:\n def time_parameterise(self):\n param = pybamm.ParameterValues(\n {\n \"Particle radius [m]\": 10e-6,\n \"Diffusion coefficient [m2.s-1]\": 3.9e-14,\n \"Interfacial current density [A.m-2]\": 1.4,\n \"Faraday constant [C.mol-1]\": 96485,\n \"Initial concentration [mol.m-3]\": 2.5e4,\n }\n )\n \n self.r = pybamm.SpatialVariable(\n \"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\"\n )\n \n self.geometry = {\n \"negative particle\": {self.r: {\"min\": pybamm.Scalar(0), \"max\": self.R}}\n }\n param.process_model(self.model)\n param.process_geometry(self.geometry)\n\nclass TimeSolveModel:\n def setup(self):\n set_random_seed()\n TimeCreateExpression.time_create_expression(self)\n TimeParameteriseModel.time_parameterise(self)\n TimeDiscretiseModel.time_discretise(self)", "min_run_count": 2, "name": "unit_benchmarks.TimeSolveModel.time_parameterise", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "14eb896be95381d740fa3f69396eb480514a09da23f2525bce6c81124247ca72", "warmup_time": -1}, "unit_benchmarks.TimeSolveModel.time_solve": {"code": "class TimeSolveModel:\n def time_solve(self):\n solver = pybamm.ScipySolver()\n t = np.linspace(0, 3600, 600)\n solver.solve(self.model, t)\n\n def setup(self):\n set_random_seed()\n TimeCreateExpression.time_create_expression(self)\n TimeParameteriseModel.time_parameterise(self)\n TimeDiscretiseModel.time_discretise(self)", "min_run_count": 2, "name": "unit_benchmarks.TimeSolveModel.time_solve", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "3b060ae2823c304802df15803cd5d845699526f2b740e5e252d5890f61967a01", "warmup_time": -1}}, "machines": {"GitHubRunner": {"machine": "GitHubRunner", "version": 1}}, "tags": {"v0.1.0": 3608, "v0.2.0": 4954, "v0.2.1": 5277, "v0.2.2": 5978, "v0.2.3": 6424, "v0.2.4": 6787, "v0.3.0": 7341, "v0.3.0-beta": 7278, "v0.4.0": 8395, "v21.08": 9197, "v21.10": 9483, "v21.11": 9854, "v21.12": 10003, "v21.9": 9369, "v22.1": 10215, "v22.10": 11905, "v22.11": 12283, 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"python": "3.8", "numpy": "", "casadi": "", "wget": "", "cmake": "", "anytree": "", "autograd": "", "scikit-fem": "", "imageio": "", "pybtex": "", "sympy": "", "bpx": "", "tqdm": "", "matplotlib": "", "env-LD_LIBRARY_PATH": "/home/runner/.local/lib", "branch": "develop"}], "benchmarks": {"different_model_options.TimeBuildModelLithiumPlating.time_setup_model": {"code": "class TimeBuildModelLithiumPlating:\n def time_setup_model(self, model, params):\n build_model(\"OKane2022\", model, \"lithium plating\", params)\n\n def setup(self, _model, _params):\n set_random_seed()", "min_run_count": 2, "name": "different_model_options.TimeBuildModelLithiumPlating.time_setup_model", "number": 0, "param_names": ["model", "model option"], "params": [["", ""], ["'none'", "'irreversible'", "'reversible'", "'partially reversible'"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "b7715f9cd1f28f249a74a4036dff5452cde2f077c92024efae0ebb5545c3edc0", "warmup_time": -1}, "different_model_options.TimeBuildModelLossActiveMaterial.time_setup_model": {"code": "class TimeBuildModelLossActiveMaterial:\n def time_setup_model(self, model, params):\n build_model(\"Ai2020\", model, \"loss of active material\", params)\n\n def setup(self, _model, _params):\n set_random_seed()", "min_run_count": 2, "name": "different_model_options.TimeBuildModelLossActiveMaterial.time_setup_model", "number": 0, "param_names": ["model", "model option"], "params": [["", ""], ["'none'", "'stress-driven'", "'reaction-driven'", "'stress and reaction-driven'"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "478c22dec000f10359afc36541c1e78f5e93eaaddf53c542d8b86433b7bb0faa", "warmup_time": -1}, "different_model_options.TimeBuildModelParticle.time_setup_model": {"code": "class TimeBuildModelParticle:\n def time_setup_model(self, model, params):\n build_model(\"Marquis2019\", model, \"particle\", params)\n\n def setup(self, _model, _params):\n set_random_seed()", "min_run_count": 2, "name": "different_model_options.TimeBuildModelParticle.time_setup_model", "number": 0, "param_names": ["model", "model option"], "params": [["", ""], ["'Fickian diffusion'", "'uniform profile'", "'quadratic profile'", "'quartic profile'"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "7a2b728d79432d8ed22c8c8680ee758da0ceffe378a6235c09e3614caf624ba6", "warmup_time": -1}, "different_model_options.TimeBuildModelSEI.time_setup_model": {"code": "class TimeBuildModelSEI:\n def time_setup_model(self, model, params):\n build_model(\"Marquis2019\", model, \"SEI\", params)\n\n def setup(self, _model, _params):\n set_random_seed()", "min_run_count": 2, "name": "different_model_options.TimeBuildModelSEI.time_setup_model", "number": 0, "param_names": ["model", "model option"], "params": [["", ""], ["'none'", "'constant'", "'reaction limited'", "'solvent-diffusion limited'", "'electron-migration limited'", "'interstitial-diffusion limited'", "'ec reaction limited'"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "72052d17c62d75ef9abff93a57e2fe2b8776f153d13317ba41c9304374cf3241", "warmup_time": -1}, "different_model_options.TimeBuildModelSurfaceForm.time_setup_model": {"code": "class TimeBuildModelSurfaceForm:\n def time_setup_model(self, model, params):\n build_model(\"Marquis2019\", model, \"surface form\", params)\n\n def setup(self, _model, _params):\n set_random_seed()", "min_run_count": 2, "name": "different_model_options.TimeBuildModelSurfaceForm.time_setup_model", "number": 0, "param_names": ["model", "model option"], "params": [["", ""], ["'false'", "'differential'", "'algebraic'"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "0ae12c41f1ed5675d959bbc049a400985cf3ede740ee73c11075d7aa10f34e49", "warmup_time": -1}, "different_model_options.TimeBuildModelThermal.time_setup_model": {"code": "class TimeBuildModelThermal:\n def time_setup_model(self, model, params):\n build_model(\"Marquis2019\", model, \"thermal\", params)\n\n def setup(self, _model, _params):\n set_random_seed()", "min_run_count": 2, "name": "different_model_options.TimeBuildModelThermal.time_setup_model", "number": 0, "param_names": ["model", "model option"], "params": [["", ""], ["'isothermal'", "'lumped'", "'x-full'"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "a350e87e4e8a8f35aeb9951c78518a60da6177951f7d81d09f3d3088ec055cfd", "warmup_time": -1}, "different_model_options.TimeSolveLithiumPlating.time_solve_model": {"code": "class TimeSolveLithiumPlating:\n def time_solve_model(self, _model, _params, _solver_class):\n self.solver.solve(self.model, t_eval=self.t_eval)\n\n def setup(self, model, params, solver_class):\n set_random_seed()\n SolveModel.solve_setup(\n self, \"OKane2022\", model, \"lithium plating\", params, solver_class\n )", "min_run_count": 2, "name": "different_model_options.TimeSolveLithiumPlating.time_solve_model", "number": 0, "param_names": ["model", "model option", "solver class"], "params": [["", ""], ["'none'", "'irreversible'", "'reversible'", "'partially reversible'"], ["", ""]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "b1d66b1656476391fd4f4a356b350bff995917103286317dffc403f2b8a90fbe", "warmup_time": -1}, "different_model_options.TimeSolveLossActiveMaterial.time_solve_model": {"code": "class TimeSolveLossActiveMaterial:\n def time_solve_model(self, _model, _params, _solver_class):\n self.solver.solve(self.model, t_eval=self.t_eval)\n\n def setup(self, model, params, solver_class):\n set_random_seed()\n SolveModel.solve_setup(\n self, \"Ai2020\", model, \"loss of active material\", params, solver_class\n )", "min_run_count": 2, "name": "different_model_options.TimeSolveLossActiveMaterial.time_solve_model", "number": 0, "param_names": ["model", "model option", "solver class"], "params": [["", ""], ["'none'", "'stress-driven'", "'reaction-driven'", "'stress and reaction-driven'"], ["", ""]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "d5e240fe84c4acc6966e4a3c4fd3b541659eb242ea36508a92c57a0ed958ceb7", "warmup_time": -1}, "different_model_options.TimeSolveParticle.time_solve_model": {"code": "class TimeSolveParticle:\n def time_solve_model(self, _model, _params, _solver_class):\n self.solver.solve(self.model, t_eval=self.t_eval)\n\n def setup(self, model, params, solver_class):\n set_random_seed()\n SolveModel.solve_setup(\n self, \"Marquis2019\", model, \"particle\", params, solver_class\n )", "min_run_count": 2, "name": "different_model_options.TimeSolveParticle.time_solve_model", "number": 0, "param_names": ["model", "model option", "solver class"], "params": [["", ""], ["'Fickian diffusion'", "'uniform profile'", "'quadratic profile'", "'quartic profile'"], ["", ""]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "0fb24c2046b35533f49c825e1f5dbc32c30266f30c6ffc64504048df0f6182f7", "warmup_time": -1}, "different_model_options.TimeSolveSEI.time_solve_model": {"code": "class TimeSolveSEI:\n def time_solve_model(self, _model, _params, _solver_class):\n self.solver.solve(self.model, t_eval=self.t_eval)\n\n def setup(self, model, params, solver_class):\n set_random_seed()\n SolveModel.solve_setup(self, \"Marquis2019\", model, \"SEI\", params, solver_class)", "min_run_count": 2, "name": "different_model_options.TimeSolveSEI.time_solve_model", "number": 0, "param_names": ["model", "model option", "solver class"], "params": [["", ""], ["'none'", "'constant'", "'reaction limited'", "'solvent-diffusion limited'", "'electron-migration limited'", "'interstitial-diffusion limited'", "'ec reaction limited'"], ["", ""]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "53dd91bc8ea837141f3ff3ec6ae6fc8310b0ebfae08e6bbd8a1f692efb7d2f57", "warmup_time": -1}, "different_model_options.TimeSolveSurfaceForm.time_solve_model": {"code": "class TimeSolveSurfaceForm:\n def time_solve_model(self, _model, _params, _solver_class):\n self.solver.solve(self.model, t_eval=self.t_eval)\n\n def setup(self, model, params, solver_class):\n set_random_seed()\n if (model, params, solver_class) == (\n pybamm.lithium_ion.SPM,\n \"differential\",\n pybamm.IDAKLUSolver,\n ):\n raise NotImplementedError\n SolveModel.solve_setup(\n self, \"Marquis2019\", model, \"surface form\", params, solver_class\n )", "min_run_count": 2, "name": "different_model_options.TimeSolveSurfaceForm.time_solve_model", "number": 0, "param_names": ["model", "model option", "solver class"], "params": [["", ""], ["'false'", "'differential'", "'algebraic'"], ["", ""]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "b025abdf6ccd9f47388fd29f0bfc0fc5490bc48eb9c277ba6172b8064339cd15", "warmup_time": -1}, "different_model_options.TimeSolveThermal.time_solve_model": {"code": "class TimeSolveThermal:\n def time_solve_model(self, _model, _params, _solver_class):\n self.solver.solve(self.model, t_eval=self.t_eval)\n\n def setup(self, model, params, solver_class):\n set_random_seed()\n SolveModel.solve_setup(\n self, \"Marquis2019\", model, \"thermal\", params, solver_class\n )", "min_run_count": 2, "name": "different_model_options.TimeSolveThermal.time_solve_model", "number": 0, "param_names": ["model", "model option", "solver class"], "params": [["", ""], ["'isothermal'", "'lumped'", "'x-full'"], ["", ""]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "b8015ca16fbe5a13c5d7b34926d92538382b9575c674f2ef78d923e8e603f5cb", "warmup_time": -1}, "memory_sims.MemDFNSimulationCCCV.mem_setup_DFN_simulationCCCV": {"code": "class MemDFNSimulationCCCV:\n def mem_setup_DFN_simulationCCCV(self, params):\n self.param = pybamm.ParameterValues(params)\n self.model = pybamm.lithium_ion.DFN()\n exp = pybamm.Experiment(\n [\n \"Discharge at C/5 for 10 hours or until 3.3 V\",\n \"Rest for 1 hour\",\n \"Charge at 1 A until 4.1 V\",\n \"Hold at 4.1 V until 10 mA\",\n \"Rest for 1 hour\",\n ]\n )\n self.sim = pybamm.Simulation(\n self.model, parameter_values=self.param, experiment=exp\n )\n return self.sim\n\n def setup(self, _params):\n set_random_seed()", "name": "memory_sims.MemDFNSimulationCCCV.mem_setup_DFN_simulationCCCV", "param_names": ["parameter"], "params": [["'Marquis2019'", "'Chen2020'"]], "type": "memory", "unit": "bytes", "version": "51ef613d1be3b3c97e5cff911f9a71ebfec58bb3741293e2b387b6aec0c55d88"}, "memory_sims.MemDFNSimulationGITT.mem_setup_DFN_simulationGITT": {"code": "class MemDFNSimulationGITT:\n def mem_setup_DFN_simulationGITT(self, params):\n self.param = pybamm.ParameterValues(params)\n self.model = pybamm.lithium_ion.SPM()\n exp = pybamm.Experiment(\n [(\"Discharge at C/20 for 1 hour\", \"Rest for 1 hour\")] * 20\n )\n self.sim = pybamm.Simulation(\n self.model, parameter_values=self.param, experiment=exp\n )\n return self.sim\n\n def setup(self, _params):\n set_random_seed()", "name": "memory_sims.MemDFNSimulationGITT.mem_setup_DFN_simulationGITT", "param_names": ["parameter"], "params": [["'Marquis2019'", "'Chen2020'"]], "type": "memory", "unit": "bytes", "version": "d4b526c693f63c0dca2afa82bc58c24d52b7884ae618e298aff74e44864d84e4"}, "memory_sims.MemSPMSimulationCCCV.mem_setup_SPM_simulationCCCV": {"code": "class MemSPMSimulationCCCV:\n def mem_setup_SPM_simulationCCCV(self, params):\n self.param = pybamm.ParameterValues(params)\n self.model = pybamm.lithium_ion.SPM()\n exp = pybamm.Experiment(\n [\n \"Discharge at C/5 for 10 hours or until 3.3 V\",\n \"Rest for 1 hour\",\n \"Charge at 1 A until 4.1 V\",\n \"Hold at 4.1 V until 10 mA\",\n \"Rest for 1 hour\",\n ]\n )\n self.sim = pybamm.Simulation(\n self.model, parameter_values=self.param, experiment=exp\n )\n return self.sim\n\n def setup(self, _params):\n set_random_seed()", "name": "memory_sims.MemSPMSimulationCCCV.mem_setup_SPM_simulationCCCV", "param_names": ["parameter"], "params": [["'Marquis2019'", "'Chen2020'"]], "type": "memory", "unit": "bytes", "version": "29109fbfa71fad4a6f5d5b2f57bb2c9d364602f218e44f5f537529aae441a717"}, "memory_sims.MemSPMSimulationGITT.mem_setup_SPM_simulationGITT": {"code": "class MemSPMSimulationGITT:\n def mem_setup_SPM_simulationGITT(self, params):\n self.param = pybamm.ParameterValues(params)\n self.model = pybamm.lithium_ion.SPM()\n exp = pybamm.Experiment(\n [(\"Discharge at C/20 for 1 hour\", \"Rest for 1 hour\")] * 20\n )\n self.sim = pybamm.Simulation(\n self.model, parameter_values=self.param, experiment=exp\n )\n return self.sim\n\n def setup(self, _params):\n set_random_seed()", "name": "memory_sims.MemSPMSimulationGITT.mem_setup_SPM_simulationGITT", "param_names": ["parameter"], "params": [["'Marquis2019'", "'Chen2020'"]], "type": "memory", "unit": "bytes", "version": "c00f20a28b027b21fcddb41e38f4b6414026233b5484c1bc7a1ad2810fc0475f"}, "memory_unit_benchmarks.MemCreateExpression.mem_create_expression": {"code": "class MemCreateExpression:\n def mem_create_expression(self):\n self.R = pybamm.Parameter(\"Particle radius [m]\")\n D = pybamm.Parameter(\"Diffusion coefficient [m2.s-1]\")\n j = pybamm.Parameter(\"Interfacial current density [A.m-2]\")\n F = pybamm.Parameter(\"Faraday constant [C.mol-1]\")\n c0 = pybamm.Parameter(\"Initial concentration [mol.m-3]\")\n self.model = pybamm.BaseModel()\n \n c = pybamm.Variable(\"Concentration [mol.m-3]\", domain=\"negative particle\")\n N = -D * pybamm.grad(c)\n dcdt = -pybamm.div(N)\n self.model.rhs = {c: dcdt}\n \n lbc = pybamm.Scalar(0)\n rbc = -j / F / D\n self.model.boundary_conditions = {\n c: {\"left\": (lbc, \"Neumann\"), \"right\": (rbc, \"Neumann\")}\n }\n \n self.model.initial_conditions = {c: c0}\n self.model.variables = {\n \"Concentration [mol.m-3]\": c,\n \"Surface concentration [mol.m-3]\": pybamm.surf(c),\n \"Flux [mol.m-2.s-1]\": N,\n }\n return self.model\n\n def setup(self):\n set_random_seed()", "name": "memory_unit_benchmarks.MemCreateExpression.mem_create_expression", "param_names": [], "params": [], "type": "memory", "unit": "bytes", "version": "3d6830c6c8132d74fbf54e206a2f784c514c86ad079aac662a24cb151775927f"}, "memory_unit_benchmarks.MemDiscretiseModel.mem_create_expression": {"code": "class MemCreateExpression:\n def mem_create_expression(self):\n self.R = pybamm.Parameter(\"Particle radius [m]\")\n D = pybamm.Parameter(\"Diffusion coefficient [m2.s-1]\")\n j = pybamm.Parameter(\"Interfacial current density [A.m-2]\")\n F = pybamm.Parameter(\"Faraday constant [C.mol-1]\")\n c0 = pybamm.Parameter(\"Initial concentration [mol.m-3]\")\n self.model = pybamm.BaseModel()\n \n c = pybamm.Variable(\"Concentration [mol.m-3]\", domain=\"negative particle\")\n N = -D * pybamm.grad(c)\n dcdt = -pybamm.div(N)\n self.model.rhs = {c: dcdt}\n \n lbc = pybamm.Scalar(0)\n rbc = -j / F / D\n self.model.boundary_conditions = {\n c: {\"left\": (lbc, \"Neumann\"), \"right\": (rbc, \"Neumann\")}\n }\n \n self.model.initial_conditions = {c: c0}\n self.model.variables = {\n \"Concentration [mol.m-3]\": c,\n \"Surface concentration [mol.m-3]\": pybamm.surf(c),\n \"Flux [mol.m-2.s-1]\": N,\n }\n return self.model\n\nclass MemDiscretiseModel:\n def setup(self):\n set_random_seed()\n MemCreateExpression.mem_create_expression(self)\n MemParameteriseModel.mem_parameterise(self)", "name": "memory_unit_benchmarks.MemDiscretiseModel.mem_create_expression", "param_names": [], "params": [], "type": "memory", "unit": "bytes", "version": "1e1265e6812795c6b4638a8861b57dc14e9b3e21d54786009ba2f6ea6526eb99"}, "memory_unit_benchmarks.MemDiscretiseModel.mem_discretise": {"code": "class MemDiscretiseModel:\n def mem_discretise(self):\n MemCreateExpression.mem_create_expression(self)\n MemParameteriseModel.mem_parameterise(self)\n submesh_types = {\"negative particle\": pybamm.Uniform1DSubMesh}\n var_pts = {self.r: 20}\n mesh = pybamm.Mesh(self.geometry, submesh_types, var_pts)\n \n spatial_methods = {\"negative particle\": pybamm.FiniteVolume()}\n disc = pybamm.Discretisation(mesh, spatial_methods)\n disc.process_model(self.model)\n return disc\n\n def setup(self):\n set_random_seed()\n MemCreateExpression.mem_create_expression(self)\n MemParameteriseModel.mem_parameterise(self)", "name": "memory_unit_benchmarks.MemDiscretiseModel.mem_discretise", "param_names": [], "params": [], "type": "memory", "unit": "bytes", "version": "495195cd9a6ece265ad424338ffde8465bff6b401aff98f26bf908c480b58724"}, "memory_unit_benchmarks.MemDiscretiseModel.mem_parameterise": {"code": "class MemParameteriseModel:\n def mem_parameterise(self):\n param = pybamm.ParameterValues(\n {\n \"Particle radius [m]\": 10e-6,\n \"Diffusion coefficient [m2.s-1]\": 3.9e-14,\n \"Interfacial current density [A.m-2]\": 1.4,\n \"Faraday constant [C.mol-1]\": 96485,\n \"Initial concentration [mol.m-3]\": 2.5e4,\n }\n )\n \n self.r = pybamm.SpatialVariable(\n \"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\"\n )\n \n self.geometry = {\n \"negative particle\": {self.r: {\"min\": pybamm.Scalar(0), \"max\": self.R}}\n }\n param.process_model(self.model)\n param.process_geometry(self.geometry)\n return param\n\nclass MemDiscretiseModel:\n def setup(self):\n set_random_seed()\n MemCreateExpression.mem_create_expression(self)\n MemParameteriseModel.mem_parameterise(self)", "name": "memory_unit_benchmarks.MemDiscretiseModel.mem_parameterise", "param_names": [], "params": [], "type": "memory", "unit": "bytes", "version": "3193180de8abb381c009afe51426f5d20e7347c3b9761d1663a98425cce0c663"}, "memory_unit_benchmarks.MemParameteriseModel.mem_create_expression": {"code": "class MemCreateExpression:\n def mem_create_expression(self):\n self.R = pybamm.Parameter(\"Particle radius [m]\")\n D = pybamm.Parameter(\"Diffusion coefficient [m2.s-1]\")\n j = pybamm.Parameter(\"Interfacial current density [A.m-2]\")\n F = pybamm.Parameter(\"Faraday constant [C.mol-1]\")\n c0 = pybamm.Parameter(\"Initial concentration [mol.m-3]\")\n self.model = pybamm.BaseModel()\n \n c = pybamm.Variable(\"Concentration [mol.m-3]\", domain=\"negative particle\")\n N = -D * pybamm.grad(c)\n dcdt = -pybamm.div(N)\n self.model.rhs = {c: dcdt}\n \n lbc = pybamm.Scalar(0)\n rbc = -j / F / D\n self.model.boundary_conditions = {\n c: {\"left\": (lbc, \"Neumann\"), \"right\": (rbc, \"Neumann\")}\n }\n \n self.model.initial_conditions = {c: c0}\n self.model.variables = {\n \"Concentration [mol.m-3]\": c,\n \"Surface concentration [mol.m-3]\": pybamm.surf(c),\n \"Flux [mol.m-2.s-1]\": N,\n }\n return self.model\n\nclass MemParameteriseModel:\n def setup(self):\n set_random_seed()\n MemCreateExpression.mem_create_expression(self)", "name": "memory_unit_benchmarks.MemParameteriseModel.mem_create_expression", "param_names": [], "params": [], "type": "memory", "unit": "bytes", "version": "862863fcd24f0545f40184ecdebd166445846c611afed20dcf90f68765c3cc0b"}, "memory_unit_benchmarks.MemParameteriseModel.mem_parameterise": {"code": "class MemParameteriseModel:\n def mem_parameterise(self):\n param = pybamm.ParameterValues(\n {\n \"Particle radius [m]\": 10e-6,\n \"Diffusion coefficient [m2.s-1]\": 3.9e-14,\n \"Interfacial current density [A.m-2]\": 1.4,\n \"Faraday constant [C.mol-1]\": 96485,\n \"Initial concentration [mol.m-3]\": 2.5e4,\n }\n )\n \n self.r = pybamm.SpatialVariable(\n \"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\"\n )\n \n self.geometry = {\n \"negative particle\": {self.r: {\"min\": pybamm.Scalar(0), \"max\": self.R}}\n }\n param.process_model(self.model)\n param.process_geometry(self.geometry)\n return param\n\n def setup(self):\n set_random_seed()\n MemCreateExpression.mem_create_expression(self)", "name": "memory_unit_benchmarks.MemParameteriseModel.mem_parameterise", "param_names": [], "params": [], "type": "memory", "unit": "bytes", "version": "edad12bc03295b4e89366fc5cfb1ed396d7bb91d3f3c81c80c58aa1302e875c5"}, "memory_unit_benchmarks.MemSolveModel.mem_create_expression": {"code": "class MemCreateExpression:\n def mem_create_expression(self):\n self.R = pybamm.Parameter(\"Particle radius [m]\")\n D = pybamm.Parameter(\"Diffusion coefficient [m2.s-1]\")\n j = pybamm.Parameter(\"Interfacial current density [A.m-2]\")\n F = pybamm.Parameter(\"Faraday constant [C.mol-1]\")\n c0 = pybamm.Parameter(\"Initial concentration [mol.m-3]\")\n self.model = pybamm.BaseModel()\n \n c = pybamm.Variable(\"Concentration [mol.m-3]\", domain=\"negative particle\")\n N = -D * pybamm.grad(c)\n dcdt = -pybamm.div(N)\n self.model.rhs = {c: dcdt}\n \n lbc = pybamm.Scalar(0)\n rbc = -j / F / D\n self.model.boundary_conditions = {\n c: {\"left\": (lbc, \"Neumann\"), \"right\": (rbc, \"Neumann\")}\n }\n \n self.model.initial_conditions = {c: c0}\n self.model.variables = {\n \"Concentration [mol.m-3]\": c,\n \"Surface concentration [mol.m-3]\": pybamm.surf(c),\n \"Flux [mol.m-2.s-1]\": N,\n }\n return self.model\n\nclass MemSolveModel:\n def setup(self):\n set_random_seed()\n MemCreateExpression.mem_create_expression(self)\n MemParameteriseModel.mem_parameterise(self)\n MemDiscretiseModel.mem_discretise(self)", "name": "memory_unit_benchmarks.MemSolveModel.mem_create_expression", "param_names": [], "params": [], "type": "memory", "unit": "bytes", "version": "16091c4e58d10d32014c88300ed1dcaf80b55cf5b10e264a66a1ea2b8837581e"}, "memory_unit_benchmarks.MemSolveModel.mem_discretise": {"code": "class MemDiscretiseModel:\n def mem_discretise(self):\n MemCreateExpression.mem_create_expression(self)\n MemParameteriseModel.mem_parameterise(self)\n submesh_types = {\"negative particle\": pybamm.Uniform1DSubMesh}\n var_pts = {self.r: 20}\n mesh = pybamm.Mesh(self.geometry, submesh_types, var_pts)\n \n spatial_methods = {\"negative particle\": pybamm.FiniteVolume()}\n disc = pybamm.Discretisation(mesh, spatial_methods)\n disc.process_model(self.model)\n return disc\n\nclass MemSolveModel:\n def setup(self):\n set_random_seed()\n MemCreateExpression.mem_create_expression(self)\n MemParameteriseModel.mem_parameterise(self)\n MemDiscretiseModel.mem_discretise(self)", "name": "memory_unit_benchmarks.MemSolveModel.mem_discretise", "param_names": [], "params": [], "type": "memory", "unit": "bytes", "version": "34cbd7a52ecdcb0e943a83665c930436c4d296729f856ac0c74a38ebe551e0f9"}, "memory_unit_benchmarks.MemSolveModel.mem_parameterise": {"code": "class MemParameteriseModel:\n def mem_parameterise(self):\n param = pybamm.ParameterValues(\n {\n \"Particle radius [m]\": 10e-6,\n \"Diffusion coefficient [m2.s-1]\": 3.9e-14,\n \"Interfacial current density [A.m-2]\": 1.4,\n \"Faraday constant [C.mol-1]\": 96485,\n \"Initial concentration [mol.m-3]\": 2.5e4,\n }\n )\n \n self.r = pybamm.SpatialVariable(\n \"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\"\n )\n \n self.geometry = {\n \"negative particle\": {self.r: {\"min\": pybamm.Scalar(0), \"max\": self.R}}\n }\n param.process_model(self.model)\n param.process_geometry(self.geometry)\n return param\n\nclass MemSolveModel:\n def setup(self):\n set_random_seed()\n MemCreateExpression.mem_create_expression(self)\n MemParameteriseModel.mem_parameterise(self)\n MemDiscretiseModel.mem_discretise(self)", "name": "memory_unit_benchmarks.MemSolveModel.mem_parameterise", "param_names": [], "params": [], "type": "memory", "unit": "bytes", "version": "e4f639e8ddfb46ec7daa72247577a6c466daa157bef63aa013c20264f9da6a73"}, "memory_unit_benchmarks.MemSolveModel.mem_solve": {"code": "class MemSolveModel:\n def mem_solve(self):\n solver = pybamm.ScipySolver()\n t = np.linspace(0, 3600, 600)\n solver.solve(self.model, t)\n return solver\n\n def setup(self):\n set_random_seed()\n MemCreateExpression.mem_create_expression(self)\n MemParameteriseModel.mem_parameterise(self)\n MemDiscretiseModel.mem_discretise(self)", "name": "memory_unit_benchmarks.MemSolveModel.mem_solve", "param_names": [], "params": [], "type": "memory", "unit": "bytes", "version": "a7b39a512b545d84bd7d5f0a6ed4d6901e6dc414da7e50ed43c6afa3abd62f0a"}, "time_setup_models_and_sims.TimeBuildDFN.time_setup_DFN": {"code": "class TimeBuildDFN:\n def time_setup_DFN(self, params):\n self.param = pybamm.ParameterValues(params)\n self.model = pybamm.lithium_ion.DFN()\n self.param.process_model(self.model)\n compute_discretisation(self.model, self.param).process_model(self.model)\n\n def setup(self, _params):\n set_random_seed()", "min_run_count": 2, "name": "time_setup_models_and_sims.TimeBuildDFN.time_setup_DFN", "number": 0, "param_names": ["parameter"], "params": [["'Marquis2019'", "'ORegan2022'", "'NCA_Kim2011'", "'Prada2013'", "'Ai2020'", "'Ramadass2004'", "'Mohtat2020'", "'Chen2020'", "'OKane2022'", "'Ecker2015'"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "0eee82eaa2ed6557bb7cec497b21c1595f4e2ab7f17c33cf98baa7acc53e6ef7", "warmup_time": -1}, "time_setup_models_and_sims.TimeBuildDFNSimulation.time_setup_DFN_simulation": {"code": "class TimeBuildDFNSimulation:\n def time_setup_DFN_simulation(self, with_experiment, params):\n self.param = pybamm.ParameterValues(params)\n self.model = pybamm.lithium_ion.DFN()\n if with_experiment:\n exp = pybamm.Experiment(\n [\n \"Discharge at 0.1C until 3.105 V\",\n ]\n )\n pybamm.Simulation(self.model, parameter_values=self.param, experiment=exp)\n else:\n pybamm.Simulation(self.model, parameter_values=self.param, C_rate=1)\n\n def setup(self, _with_experiment, _params):\n set_random_seed()", "min_run_count": 2, "name": "time_setup_models_and_sims.TimeBuildDFNSimulation.time_setup_DFN_simulation", "number": 0, "param_names": ["with experiment", "parameter"], "params": [["False", "True"], ["'Marquis2019'", "'ORegan2022'", "'NCA_Kim2011'", "'Prada2013'", "'Ai2020'", "'Ramadass2004'", "'Mohtat2020'", "'Chen2020'", "'OKane2022'", "'Ecker2015'"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "041d84cc1adf4753d7f9652306ad60d0d6364fcc290e4736429f8e7c3c675a12", "warmup_time": -1}, "time_setup_models_and_sims.TimeBuildSPM.time_setup_SPM": {"code": "class TimeBuildSPM:\n def time_setup_SPM(self, params):\n self.param = pybamm.ParameterValues(params)\n self.model = pybamm.lithium_ion.SPM()\n self.param.process_model(self.model)\n compute_discretisation(self.model, self.param).process_model(self.model)\n\n def setup(self, _params):\n set_random_seed()", "min_run_count": 2, "name": "time_setup_models_and_sims.TimeBuildSPM.time_setup_SPM", "number": 0, "param_names": ["parameter"], "params": [["'Marquis2019'", "'ORegan2022'", "'NCA_Kim2011'", "'Prada2013'", "'Ai2020'", "'Ramadass2004'", "'Mohtat2020'", "'Chen2020'", "'OKane2022'", "'Ecker2015'"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "d5778345d68fd67c043a3241f1764d8b6e007490765cb577f15dd6fe38347cad", "warmup_time": -1}, "time_setup_models_and_sims.TimeBuildSPMSimulation.time_setup_SPM_simulation": {"code": "class TimeBuildSPMSimulation:\n def time_setup_SPM_simulation(self, with_experiment, params):\n self.param = pybamm.ParameterValues(params)\n self.model = pybamm.lithium_ion.SPM()\n if with_experiment:\n exp = pybamm.Experiment(\n [\n \"Discharge at 0.1C until 3.105 V\",\n ]\n )\n pybamm.Simulation(self.model, parameter_values=self.param, experiment=exp)\n else:\n pybamm.Simulation(self.model, parameter_values=self.param, C_rate=1)\n\n def setup(self, _with_experiment, _params):\n set_random_seed()", "min_run_count": 2, "name": "time_setup_models_and_sims.TimeBuildSPMSimulation.time_setup_SPM_simulation", "number": 0, "param_names": ["with experiment", "parameter"], "params": [["False", "True"], ["'Marquis2019'", "'ORegan2022'", "'NCA_Kim2011'", "'Prada2013'", "'Ai2020'", "'Ramadass2004'", "'Mohtat2020'", "'Chen2020'", "'OKane2022'", "'Ecker2015'"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "4af04a0ebcb83401d42904cefe3d57346161aa348bdafe191f95ec33fff21cec", "warmup_time": -1}, "time_setup_models_and_sims.TimeBuildSPMe.time_setup_SPMe": {"code": "class TimeBuildSPMe:\n def time_setup_SPMe(self, params):\n self.param = pybamm.ParameterValues(params)\n self.model = pybamm.lithium_ion.SPMe()\n self.param.process_model(self.model)\n compute_discretisation(self.model, self.param).process_model(self.model)\n\n def setup(self, _params):\n set_random_seed()", "min_run_count": 2, "name": "time_setup_models_and_sims.TimeBuildSPMe.time_setup_SPMe", "number": 0, "param_names": ["parameter"], "params": [["'Marquis2019'", "'ORegan2022'", "'NCA_Kim2011'", "'Prada2013'", "'Ai2020'", "'Ramadass2004'", "'Mohtat2020'", "'Chen2020'", "'OKane2022'", "'Ecker2015'"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "970230e78bb53f069b2e13856e844504c8a106f98359e1d7ad09161d6d03c083", "warmup_time": -1}, "time_setup_models_and_sims.TimeBuildSPMeSimulation.time_setup_SPMe_simulation": {"code": "class TimeBuildSPMeSimulation:\n def time_setup_SPMe_simulation(self, with_experiment, params):\n self.param = pybamm.ParameterValues(params)\n self.model = pybamm.lithium_ion.SPMe()\n if with_experiment:\n exp = pybamm.Experiment(\n [\n \"Discharge at 0.1C until 3.105 V\",\n ]\n )\n pybamm.Simulation(self.model, parameter_values=self.param, experiment=exp)\n else:\n pybamm.Simulation(self.model, parameter_values=self.param, C_rate=1)\n\n def setup(self, _with_experiment, _params):\n set_random_seed()", "min_run_count": 2, "name": "time_setup_models_and_sims.TimeBuildSPMeSimulation.time_setup_SPMe_simulation", "number": 0, "param_names": ["with experiment", "parameter"], "params": [["False", "True"], ["'Marquis2019'", "'ORegan2022'", "'NCA_Kim2011'", "'Prada2013'", "'Ai2020'", "'Ramadass2004'", "'Mohtat2020'", "'Chen2020'", "'OKane2022'", "'Ecker2015'"]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "6f76c516f3fa71f22c3c769426712e12bc3c63681def5407f510a3d24fe28ebf", "warmup_time": -1}, "time_sims_experiments.TimeSimulation.time_setup": {"code": "class TimeSimulation:\n def time_setup(self, experiment, parameters, model_class, solver_class):\n param = pybamm.ParameterValues(parameters)\n model = model_class()\n solver = solver_class()\n exp = pybamm.Experiment(self.experiment_descriptions[experiment])\n pybamm.Simulation(model, parameter_values=param, experiment=exp, solver=solver)\n\n def setup(self, experiment, parameters, model_class, solver_class):\n set_random_seed()\n if (experiment, parameters, model_class, solver_class) == (\n \"GITT\",\n \"Marquis2019\",\n pybamm.lithium_ion.DFN,\n pybamm.CasadiSolver,\n ):\n raise NotImplementedError\n self.param = pybamm.ParameterValues(parameters)\n self.model = model_class()\n self.solver = solver_class()\n self.exp = pybamm.Experiment(self.experiment_descriptions[experiment])\n self.sim = pybamm.Simulation(\n self.model,\n parameter_values=self.param,\n experiment=self.exp,\n solver=self.solver,\n )", "min_run_count": 2, "name": "time_sims_experiments.TimeSimulation.time_setup", "number": 0, "param_names": ["experiment", "parameter", "model_class", "solver_class"], "params": [["'CCCV'", "'GITT'"], ["'Marquis2019'", "'Chen2020'"], ["", ""], ["", ""]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "f919a29c04da89b51fb66aa41294744b714b2d640df50debeff76b55023af868", "warmup_time": -1}, "time_sims_experiments.TimeSimulation.time_solve": {"code": "class TimeSimulation:\n def time_solve(self, _experiment, _parameters, _model_class, _solver_class):\n self.sim.solve()\n\n def setup(self, experiment, parameters, model_class, solver_class):\n set_random_seed()\n if (experiment, parameters, model_class, solver_class) == (\n \"GITT\",\n \"Marquis2019\",\n pybamm.lithium_ion.DFN,\n pybamm.CasadiSolver,\n ):\n raise NotImplementedError\n self.param = pybamm.ParameterValues(parameters)\n self.model = model_class()\n self.solver = solver_class()\n self.exp = pybamm.Experiment(self.experiment_descriptions[experiment])\n self.sim = pybamm.Simulation(\n self.model,\n parameter_values=self.param,\n experiment=self.exp,\n solver=self.solver,\n )", "min_run_count": 2, "name": "time_sims_experiments.TimeSimulation.time_solve", "number": 0, "param_names": ["experiment", "parameter", "model_class", "solver_class"], "params": [["'CCCV'", "'GITT'"], ["'Marquis2019'", "'Chen2020'"], ["", ""], ["", ""]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "6832512fe2709d359717d531b49d57f9b07241e3af646c02a9865a7e7b4c96ac", "warmup_time": -1}, "time_solve_models.TimeSolveDFN.time_solve_model": {"code": "class TimeSolveDFN:\n def time_solve_model(self, _solve_first, _parameters, _solver_class):\n self.solver.solve(self.model, t_eval=self.t_eval)\n\n def setup(self, solve_first, parameters, solver_class):\n set_random_seed()\n if (parameters, solver_class) == (\n \"ORegan2022\",\n pybamm.CasadiSolver,\n ):\n raise NotImplementedError\n self.solver = solver_class()\n self.model = pybamm.lithium_ion.DFN()\n c_rate = 1\n tmax = 4000 / c_rate\n nb_points = 500\n self.t_eval = np.linspace(0, tmax, nb_points)\n geometry = self.model.default_geometry\n \n # load parameter values and process model and geometry\n param = pybamm.ParameterValues(parameters)\n param.process_model(self.model)\n param.process_geometry(geometry)\n \n # set mesh\n var_pts = {\n \"x_n\": 20,\n \"x_s\": 20,\n \"x_p\": 20,\n \"r_n\": 30,\n \"r_p\": 30,\n \"y\": 10,\n \"z\": 10,\n }\n mesh = pybamm.Mesh(geometry, self.model.default_submesh_types, var_pts)\n \n # discretise model\n disc = pybamm.Discretisation(mesh, self.model.default_spatial_methods)\n disc.process_model(self.model)\n if solve_first:\n solve_model_once(self.model, self.solver, self.t_eval)", "min_run_count": 2, "name": "time_solve_models.TimeSolveDFN.time_solve_model", "number": 0, "param_names": ["solve first", "parameter", "solver_class"], "params": [["False", "True"], ["'Marquis2019'", "'ORegan2022'", "'Prada2013'", "'Ai2020'", "'Ramadass2004'", "'Chen2020'", "'Ecker2015'"], ["", ""]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "f6d7b52e57b2a72e7ebdf55b8998c7f7e4ee88848570e445a0f7bd967d6d14be", "warmup_time": -1}, "time_solve_models.TimeSolveSPM.time_solve_model": {"code": "class TimeSolveSPM:\n def time_solve_model(self, _solve_first, _parameters, _solver_class):\n self.solver.solve(self.model, t_eval=self.t_eval)\n\n def setup(self, solve_first, parameters, solver_class):\n set_random_seed()\n self.solver = solver_class()\n self.model = pybamm.lithium_ion.SPM()\n c_rate = 1\n tmax = 4000 / c_rate\n nb_points = 500\n self.t_eval = np.linspace(0, tmax, nb_points)\n geometry = self.model.default_geometry\n \n # load parameter values and process model and geometry\n param = pybamm.ParameterValues(parameters)\n param.process_model(self.model)\n param.process_geometry(geometry)\n \n # set mesh\n var_pts = {\n \"x_n\": 20,\n \"x_s\": 20,\n \"x_p\": 20,\n \"r_n\": 30,\n \"r_p\": 30,\n \"y\": 10,\n \"z\": 10,\n }\n mesh = pybamm.Mesh(geometry, self.model.default_submesh_types, var_pts)\n \n # discretise model\n disc = pybamm.Discretisation(mesh, self.model.default_spatial_methods)\n disc.process_model(self.model)\n if solve_first:\n solve_model_once(self.model, self.solver, self.t_eval)", "min_run_count": 2, "name": "time_solve_models.TimeSolveSPM.time_solve_model", "number": 0, "param_names": ["solve first", "parameter", "solver_class"], "params": [["False", "True"], ["'Marquis2019'", "'ORegan2022'", "'NCA_Kim2011'", "'Prada2013'", "'Ramadass2004'", "'Chen2020'", "'Ecker2015'"], ["", ""]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "f329b803e6760b144e33d59c3d34fa3d75fa6c26427168501d81c48156c62175", "warmup_time": -1}, "time_solve_models.TimeSolveSPMe.time_solve_model": {"code": "class TimeSolveSPMe:\n def time_solve_model(self, _solve_first, _parameters, _solver_class):\n self.solver.solve(self.model, t_eval=self.t_eval)\n\n def setup(self, solve_first, parameters, solver_class):\n set_random_seed()\n self.solver = solver_class()\n self.model = pybamm.lithium_ion.SPMe()\n c_rate = 1\n tmax = 4000 / c_rate\n nb_points = 500\n self.t_eval = np.linspace(0, tmax, nb_points)\n geometry = self.model.default_geometry\n \n # load parameter values and process model and geometry\n param = pybamm.ParameterValues(parameters)\n param.process_model(self.model)\n param.process_geometry(geometry)\n \n # set mesh\n var_pts = {\n \"x_n\": 20,\n \"x_s\": 20,\n \"x_p\": 20,\n \"r_n\": 30,\n \"r_p\": 30,\n \"y\": 10,\n \"z\": 10,\n }\n mesh = pybamm.Mesh(geometry, self.model.default_submesh_types, var_pts)\n \n # discretise model\n disc = pybamm.Discretisation(mesh, self.model.default_spatial_methods)\n disc.process_model(self.model)\n if solve_first:\n solve_model_once(self.model, self.solver, self.t_eval)", "min_run_count": 2, "name": "time_solve_models.TimeSolveSPMe.time_solve_model", "number": 0, "param_names": ["solve first", "parameter", "solver_class"], "params": [["False", "True"], ["'Marquis2019'", "'ORegan2022'", "'NCA_Kim2011'", "'Prada2013'", "'Ramadass2004'", "'Chen2020'", "'Ecker2015'"], ["", ""]], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "26110b628fab649e6153a529860b3104b379d3f288ca020d4321f489c8a38955", "warmup_time": -1}, "unit_benchmarks.TimeCreateExpression.time_create_expression": {"code": "class TimeCreateExpression:\n def time_create_expression(self):\n self.R = pybamm.Parameter(\"Particle radius [m]\")\n D = pybamm.Parameter(\"Diffusion coefficient [m2.s-1]\")\n j = pybamm.Parameter(\"Interfacial current density [A.m-2]\")\n F = pybamm.Parameter(\"Faraday constant [C.mol-1]\")\n c0 = pybamm.Parameter(\"Initial concentration [mol.m-3]\")\n self.model = pybamm.BaseModel()\n \n c = pybamm.Variable(\"Concentration [mol.m-3]\", domain=\"negative particle\")\n N = -D * pybamm.grad(c)\n dcdt = -pybamm.div(N)\n self.model.rhs = {c: dcdt}\n \n lbc = pybamm.Scalar(0)\n rbc = -j / F / D\n self.model.boundary_conditions = {\n c: {\"left\": (lbc, \"Neumann\"), \"right\": (rbc, \"Neumann\")}\n }\n \n self.model.initial_conditions = {c: c0}\n self.model.variables = {\n \"Concentration [mol.m-3]\": c,\n \"Surface concentration [mol.m-3]\": pybamm.surf(c),\n \"Flux [mol.m-2.s-1]\": N,\n }\n\n def setup(self):\n set_random_seed()", "min_run_count": 2, "name": "unit_benchmarks.TimeCreateExpression.time_create_expression", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "79567e8fc10e82bb7efc93e89a5fa48101c5f63b2bcf09f9affa1daeaa660a7a", "warmup_time": -1}, "unit_benchmarks.TimeDiscretiseModel.time_create_expression": {"code": "class TimeCreateExpression:\n def time_create_expression(self):\n self.R = pybamm.Parameter(\"Particle radius [m]\")\n D = pybamm.Parameter(\"Diffusion coefficient [m2.s-1]\")\n j = pybamm.Parameter(\"Interfacial current density [A.m-2]\")\n F = pybamm.Parameter(\"Faraday constant [C.mol-1]\")\n c0 = pybamm.Parameter(\"Initial concentration [mol.m-3]\")\n self.model = pybamm.BaseModel()\n \n c = pybamm.Variable(\"Concentration [mol.m-3]\", domain=\"negative particle\")\n N = -D * pybamm.grad(c)\n dcdt = -pybamm.div(N)\n self.model.rhs = {c: dcdt}\n \n lbc = pybamm.Scalar(0)\n rbc = -j / F / D\n self.model.boundary_conditions = {\n c: {\"left\": (lbc, \"Neumann\"), \"right\": (rbc, \"Neumann\")}\n }\n \n self.model.initial_conditions = {c: c0}\n self.model.variables = {\n \"Concentration [mol.m-3]\": c,\n \"Surface concentration [mol.m-3]\": pybamm.surf(c),\n \"Flux [mol.m-2.s-1]\": N,\n }\n\nclass TimeDiscretiseModel:\n def setup(self):\n set_random_seed()\n TimeCreateExpression.time_create_expression(self)\n TimeParameteriseModel.time_parameterise(self)", "min_run_count": 2, "name": "unit_benchmarks.TimeDiscretiseModel.time_create_expression", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "53f64bb63b703ce09229d62a777fa5f21abf561c70430ff978cd6fce8363f76c", "warmup_time": -1}, "unit_benchmarks.TimeDiscretiseModel.time_discretise": {"code": "class TimeDiscretiseModel:\n def time_discretise(self):\n TimeCreateExpression.time_create_expression(self)\n TimeParameteriseModel.time_parameterise(self)\n submesh_types = {\"negative particle\": pybamm.Uniform1DSubMesh}\n var_pts = {self.r: 20}\n mesh = pybamm.Mesh(self.geometry, submesh_types, var_pts)\n \n spatial_methods = {\"negative particle\": pybamm.FiniteVolume()}\n disc = pybamm.Discretisation(mesh, spatial_methods)\n disc.process_model(self.model)\n\n def setup(self):\n set_random_seed()\n TimeCreateExpression.time_create_expression(self)\n TimeParameteriseModel.time_parameterise(self)", "min_run_count": 2, "name": "unit_benchmarks.TimeDiscretiseModel.time_discretise", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "82d1028bb6552573a282aead5ecf23b4c233624a98d553f417f9b632904cf779", "warmup_time": -1}, "unit_benchmarks.TimeDiscretiseModel.time_parameterise": {"code": "class TimeParameteriseModel:\n def time_parameterise(self):\n param = pybamm.ParameterValues(\n {\n \"Particle radius [m]\": 10e-6,\n \"Diffusion coefficient [m2.s-1]\": 3.9e-14,\n \"Interfacial current density [A.m-2]\": 1.4,\n \"Faraday constant [C.mol-1]\": 96485,\n \"Initial concentration [mol.m-3]\": 2.5e4,\n }\n )\n \n self.r = pybamm.SpatialVariable(\n \"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\"\n )\n \n self.geometry = {\n \"negative particle\": {self.r: {\"min\": pybamm.Scalar(0), \"max\": self.R}}\n }\n param.process_model(self.model)\n param.process_geometry(self.geometry)\n\nclass TimeDiscretiseModel:\n def setup(self):\n set_random_seed()\n TimeCreateExpression.time_create_expression(self)\n TimeParameteriseModel.time_parameterise(self)", "min_run_count": 2, "name": "unit_benchmarks.TimeDiscretiseModel.time_parameterise", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "ef8b306be418ea6cffb6df7d95c34e6e4c058556b81285cfe00a85a746364e11", "warmup_time": -1}, "unit_benchmarks.TimeParameteriseModel.time_create_expression": {"code": "class TimeCreateExpression:\n def time_create_expression(self):\n self.R = pybamm.Parameter(\"Particle radius [m]\")\n D = pybamm.Parameter(\"Diffusion coefficient [m2.s-1]\")\n j = pybamm.Parameter(\"Interfacial current density [A.m-2]\")\n F = pybamm.Parameter(\"Faraday constant [C.mol-1]\")\n c0 = pybamm.Parameter(\"Initial concentration [mol.m-3]\")\n self.model = pybamm.BaseModel()\n \n c = pybamm.Variable(\"Concentration [mol.m-3]\", domain=\"negative particle\")\n N = -D * pybamm.grad(c)\n dcdt = -pybamm.div(N)\n self.model.rhs = {c: dcdt}\n \n lbc = pybamm.Scalar(0)\n rbc = -j / F / D\n self.model.boundary_conditions = {\n c: {\"left\": (lbc, \"Neumann\"), \"right\": (rbc, \"Neumann\")}\n }\n \n self.model.initial_conditions = {c: c0}\n self.model.variables = {\n \"Concentration [mol.m-3]\": c,\n \"Surface concentration [mol.m-3]\": pybamm.surf(c),\n \"Flux [mol.m-2.s-1]\": N,\n }\n\nclass TimeParameteriseModel:\n def setup(self):\n set_random_seed()\n TimeCreateExpression.time_create_expression(self)", "min_run_count": 2, "name": "unit_benchmarks.TimeParameteriseModel.time_create_expression", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "c9c6590d7ea983ffd2ec4a0beb7198254c9872b5d79fafe982b5575dcbf533ea", "warmup_time": -1}, "unit_benchmarks.TimeParameteriseModel.time_parameterise": {"code": "class TimeParameteriseModel:\n def time_parameterise(self):\n param = pybamm.ParameterValues(\n {\n \"Particle radius [m]\": 10e-6,\n \"Diffusion coefficient [m2.s-1]\": 3.9e-14,\n \"Interfacial current density [A.m-2]\": 1.4,\n \"Faraday constant [C.mol-1]\": 96485,\n \"Initial concentration [mol.m-3]\": 2.5e4,\n }\n )\n \n self.r = pybamm.SpatialVariable(\n \"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\"\n )\n \n self.geometry = {\n \"negative particle\": {self.r: {\"min\": pybamm.Scalar(0), \"max\": self.R}}\n }\n param.process_model(self.model)\n param.process_geometry(self.geometry)\n\n def setup(self):\n set_random_seed()\n TimeCreateExpression.time_create_expression(self)", "min_run_count": 2, "name": "unit_benchmarks.TimeParameteriseModel.time_parameterise", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "2db8360d2edcfa78dd7840bfc00d24c0e9649c847253c89fe402671c262b1338", "warmup_time": -1}, "unit_benchmarks.TimeSolveModel.time_create_expression": {"code": "class TimeCreateExpression:\n def time_create_expression(self):\n self.R = pybamm.Parameter(\"Particle radius [m]\")\n D = pybamm.Parameter(\"Diffusion coefficient [m2.s-1]\")\n j = pybamm.Parameter(\"Interfacial current density [A.m-2]\")\n F = pybamm.Parameter(\"Faraday constant [C.mol-1]\")\n c0 = pybamm.Parameter(\"Initial concentration [mol.m-3]\")\n self.model = pybamm.BaseModel()\n \n c = pybamm.Variable(\"Concentration [mol.m-3]\", domain=\"negative particle\")\n N = -D * pybamm.grad(c)\n dcdt = -pybamm.div(N)\n self.model.rhs = {c: dcdt}\n \n lbc = pybamm.Scalar(0)\n rbc = -j / F / D\n self.model.boundary_conditions = {\n c: {\"left\": (lbc, \"Neumann\"), \"right\": (rbc, \"Neumann\")}\n }\n \n self.model.initial_conditions = {c: c0}\n self.model.variables = {\n \"Concentration [mol.m-3]\": c,\n \"Surface concentration [mol.m-3]\": pybamm.surf(c),\n \"Flux [mol.m-2.s-1]\": N,\n }\n\nclass TimeSolveModel:\n def setup(self):\n set_random_seed()\n TimeCreateExpression.time_create_expression(self)\n TimeParameteriseModel.time_parameterise(self)\n TimeDiscretiseModel.time_discretise(self)", "min_run_count": 2, "name": "unit_benchmarks.TimeSolveModel.time_create_expression", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "7d5ffee2c28dcaa231b44408496fce54271121737b80dc5a7498beb92ab2b159", "warmup_time": -1}, "unit_benchmarks.TimeSolveModel.time_discretise": {"code": "class TimeDiscretiseModel:\n def time_discretise(self):\n TimeCreateExpression.time_create_expression(self)\n TimeParameteriseModel.time_parameterise(self)\n submesh_types = {\"negative particle\": pybamm.Uniform1DSubMesh}\n var_pts = {self.r: 20}\n mesh = pybamm.Mesh(self.geometry, submesh_types, var_pts)\n \n spatial_methods = {\"negative particle\": pybamm.FiniteVolume()}\n disc = pybamm.Discretisation(mesh, spatial_methods)\n disc.process_model(self.model)\n\nclass TimeSolveModel:\n def setup(self):\n set_random_seed()\n TimeCreateExpression.time_create_expression(self)\n TimeParameteriseModel.time_parameterise(self)\n TimeDiscretiseModel.time_discretise(self)", "min_run_count": 2, "name": "unit_benchmarks.TimeSolveModel.time_discretise", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "a4e41d181f7a8ac4c6c873e0aafa291f45deb155ad92dc625301917565fe6bdc", "warmup_time": -1}, "unit_benchmarks.TimeSolveModel.time_parameterise": {"code": "class TimeParameteriseModel:\n def time_parameterise(self):\n param = pybamm.ParameterValues(\n {\n \"Particle radius [m]\": 10e-6,\n \"Diffusion coefficient [m2.s-1]\": 3.9e-14,\n \"Interfacial current density [A.m-2]\": 1.4,\n \"Faraday constant [C.mol-1]\": 96485,\n \"Initial concentration [mol.m-3]\": 2.5e4,\n }\n )\n \n self.r = pybamm.SpatialVariable(\n \"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\"\n )\n \n self.geometry = {\n \"negative particle\": {self.r: {\"min\": pybamm.Scalar(0), \"max\": self.R}}\n }\n param.process_model(self.model)\n param.process_geometry(self.geometry)\n\nclass TimeSolveModel:\n def setup(self):\n set_random_seed()\n TimeCreateExpression.time_create_expression(self)\n TimeParameteriseModel.time_parameterise(self)\n TimeDiscretiseModel.time_discretise(self)", "min_run_count": 2, "name": "unit_benchmarks.TimeSolveModel.time_parameterise", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "14eb896be95381d740fa3f69396eb480514a09da23f2525bce6c81124247ca72", "warmup_time": -1}, "unit_benchmarks.TimeSolveModel.time_solve": {"code": "class TimeSolveModel:\n def time_solve(self):\n solver = pybamm.ScipySolver()\n t = np.linspace(0, 3600, 600)\n solver.solve(self.model, t)\n\n def setup(self):\n set_random_seed()\n TimeCreateExpression.time_create_expression(self)\n TimeParameteriseModel.time_parameterise(self)\n TimeDiscretiseModel.time_discretise(self)", "min_run_count": 2, "name": "unit_benchmarks.TimeSolveModel.time_solve", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "3b060ae2823c304802df15803cd5d845699526f2b740e5e252d5890f61967a01", "warmup_time": -1}}, "machines": {"GitHubRunner": {"machine": "GitHubRunner", "version": 1}}, "tags": {"v0.1.0": 3608, "v0.2.0": 4954, "v0.2.1": 5277, "v0.2.2": 5978, "v0.2.3": 6424, "v0.2.4": 6787, "v0.3.0": 7341, "v0.3.0-beta": 7278, "v0.4.0": 8395, "v21.08": 9197, "v21.10": 9483, "v21.11": 9854, "v21.12": 10003, "v21.9": 9369, "v22.1": 10215, "v22.10": 11905, "v22.11": 12283, "v22.11.1": 12414, "v22.12": 12480, "v22.2": 10275, "v22.3": 10402, "v22.4": 10494, "v22.5": 10655, "v22.6": 10796, "v22.7": 11123, "v22.8": 11339, "v22.9": 11581, "v23.1": 12629, "v23.2": 12912, "v23.3": 13248, "v23.4": 13398, "v23.4.1": 13410, "v23.5": 13988, "v23.5rc0": 13809, "v23.9": 15673, "v23.9rc0": 15366, "v23.9rc1": 15618}, "pages": [["", "Grid view", "Display as a agrid"], ["summarylist", "List view", "Display as a list"], ["regressions", "Show regressions", "Display information about recent regressions"]]} \ No newline at end of file diff --git a/info.json b/info.json index 5a16327ce..c2ce76c60 100644 --- a/info.json +++ b/info.json @@ -1,4 +1,4 @@ { "asv-version": "0.6.1", - "timestamp": 1703735934561 + "timestamp": 1703822297636 } \ No newline at end of file diff --git a/regressions.json b/regressions.json index ac1d2e8d1..0fd10e476 100644 --- a/regressions.json +++ b/regressions.json @@ -1 +1 @@ -{"regressions": [["different_model_options.TimeSolveLossActiveMaterial.time_solve_model(, 'stress-driven', )", "graphs/anytree/autograd/bpx/branch-develop/casadi/cmake/env-LD_LIBRARY_PATH-_home_runner_.local_lib/imageio/machine-GitHubRunner/matplotlib/numpy/pybtex/python-3.8/scikit-fem/sympy/tqdm/wget/different_model_options.TimeSolveLossActiveMaterial.time_solve_model.json", {}, 10, 4.718759258500057, 4.167683744999977, [[15977, 16024, 4.167683744999977, 4.718759258500057]]], ["different_model_options.TimeSolveLossActiveMaterial.time_solve_model(, 'stress and reaction-driven', )", "graphs/anytree/autograd/bpx/branch-develop/casadi/cmake/env-LD_LIBRARY_PATH-_home_runner_.local_lib/imageio/machine-GitHubRunner/matplotlib/numpy/pybtex/python-3.8/scikit-fem/sympy/tqdm/wget/different_model_options.TimeSolveLossActiveMaterial.time_solve_model.json", {}, 14, 4.738149467000028, 4.164340683999967, [[15977, 16024, 4.164340683999967, 4.738149467000028]]], ["different_model_options.TimeSolveLithiumPlating.time_solve_model(, 'irreversible', )", "graphs/anytree/autograd/bpx/branch-develop/casadi/cmake/env-LD_LIBRARY_PATH-_home_runner_.local_lib/imageio/machine-GitHubRunner/matplotlib/numpy/pybtex/python-3.8/scikit-fem/sympy/tqdm/wget/different_model_options.TimeSolveLithiumPlating.time_solve_model.json", {}, 2, 0.039012599500040324, 0.022071712249982056, [[null, 15693, 0.022071712249982056, 0.039012599500040324]]], ["different_model_options.TimeSolveLithiumPlating.time_solve_model(, 'irreversible', )", 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