diff --git a/.all-contributorsrc b/.all-contributorsrc index 600933daeb..3a4d544b75 100644 --- a/.all-contributorsrc +++ b/.all-contributorsrc @@ -528,7 +528,8 @@ "avatar_url": "https://avatars.githubusercontent.com/u/74401230?v=4", "profile": "https://github.com/agriyakhetarpal", "contributions": [ - "infra" + "infra", + "code" ] }, { diff --git a/.gitignore b/.gitignore index e0b2d092f2..8e3867aeb5 100644 --- a/.gitignore +++ b/.gitignore @@ -13,6 +13,7 @@ !CMakeLists.txt !pybamm/CITATIONS.txt !pybamm/input/**/*.csv +!tests/unit/test_parameters/*.csv !benchmarks/benchmark_images/*.png # running files diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index da36544414..c66dab806e 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -4,7 +4,7 @@ ci: repos: - repo: https://github.com/pre-commit/mirrors-prettier - rev: v3.0.0-alpha.3 + rev: v3.0.0-alpha.4 hooks: - id: prettier exclude: assets/js/webapp\.js @@ -15,6 +15,6 @@ repos: - id: black - repo: https://github.com/PyCQA/flake8 - rev: 5.0.4 + rev: 6.0.0 hooks: - id: flake8 diff --git a/CHANGELOG.md b/CHANGELOG.md index 662cfbdd10..b8d695b6a1 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,14 +1,45 @@ # [Unreleased](https://github.com/pybamm-team/PyBaMM/) +# [v22.11](https://github.com/pybamm-team/PyBaMM/tree/v22.11) - 2022-11-30 + +## Features + +- Updated parameter sets so that interpolants are created explicitly in the parameter set python file. This does not change functionality but allows finer control, e.g. specifying a "cubic" interpolator instead of the default "linear" ([#2510](https://github.com/pybamm-team/PyBaMM/pull/2510)) +- Equivalent circuit models ([#2478](https://github.com/pybamm-team/PyBaMM/pull/2478)) +- New Idaklu solver options for jacobian type and linear solver, support Sundials v6 ([#2444](https://github.com/pybamm-team/PyBaMM/pull/2444)) +- Added `scale` and `reference` attributes to `Variable` objects, which can be use to make the ODE/DAE solver better conditioned ([#2440](https://github.com/pybamm-team/PyBaMM/pull/2440)) +- SEI reactions can now be asymmetric ([#2425](https://github.com/pybamm-team/PyBaMM/pull/2425)) + +## Bug fixes + +- Switched from `pkg_resources` to `importlib_metadata` for handling entry points ([#2500](https://github.com/pybamm-team/PyBaMM/pull/2500)) +- Fixed some bugs related to processing `FunctionParameter` to `Interpolant` ([#2494](https://github.com/pybamm-team/PyBaMM/pull/2494)) + +## Optimizations + +- `ParameterValues` now avoids trying to process children if a function parameter is an object that doesn't depend on its children ([#2477](https://github.com/pybamm-team/PyBaMM/pull/2477)) +- Implemented memoization via `cache` and `cached_property` from functools ([#2465](https://github.com/pybamm-team/PyBaMM/pull/2465)) +- Added more rules for simplifying expressions, especially around Concatenations. Also, meshes constructed from multiple domains are now cached ([#2443](https://github.com/pybamm-team/PyBaMM/pull/2443)) +- Added more rules for simplifying expressions. Constants in binary operators are now moved to the left by default (e.g. `x*2` returns `2*x`) ([#2424](https://github.com/pybamm-team/PyBaMM/pull/2424)) + +## Breaking changes + +- Interpolants created from parameter data are now "linear" by default (was "cubic") ([#2494](https://github.com/pybamm-team/PyBaMM/pull/2494)) +- Renamed entry point for parameter sets to `pybamm_parameter_sets` ([#2475](https://github.com/pybamm-team/PyBaMM/pull/2475)) +- Removed code for generating `ModelingToolkit` problems ([#2432](https://github.com/pybamm-team/PyBaMM/pull/2432)) +- Removed `FirstOrder` and `Composite` lead-acid models, and some submodels specific to those models ([#2431](https://github.com/pybamm-team/PyBaMM/pull/2431)) + # [v22.10.post1](https://github.com/pybamm-team/PyBaMM/tree/v22.10.post1) - 2022-10-31 +## Breaking changes + - Removed all julia generation code ([#2453](https://github.com/pybamm-team/PyBaMM/pull/2453)). Julia code will be hosted at [PyBaMM.jl](https://github.com/tinosulzer/PyBaMM.jl) from now on. # [v22.10](https://github.com/pybamm-team/PyBaMM/tree/v22.10) - 2022-10-31 ## Features -- Third-party parameter sets can be added by registering entry points to `pybamm_parameter_set` ([#2396](https://github.com/pybamm-team/PyBaMM/pull/2396)) +- Third-party parameter sets can be added by registering entry points to ~~`pybamm_parameter_set`~~`pybamm_parameter_sets` ([#2396](https://github.com/pybamm-team/PyBaMM/pull/2396), changed in [#2475](https://github.com/pybamm-team/PyBaMM/pull/2475)) - Added three-dimensional interpolation ([#2380](https://github.com/pybamm-team/PyBaMM/pull/2380)) ## Bug fixes diff --git a/CITATION.cff b/CITATION.cff index f659d44a5b..e4cce984ae 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -24,6 +24,6 @@ keywords: - "expression tree" - "python" - "symbolic differentiation" -version: "22.10" +version: "22.11" repository-code: "https://github.com/pybamm-team/PyBaMM" title: "Python Battery Mathematical Modelling (PyBaMM)" diff --git a/CMakeBuild.py b/CMakeBuild.py index 8a2ed4716e..5b34bb27df 100644 --- a/CMakeBuild.py +++ b/CMakeBuild.py @@ -82,7 +82,10 @@ def run(self): use_python_casadi = False else: use_python_casadi = True + + build_type = os.getenv("PYBAMM_CPP_BUILD_TYPE", "RELEASE") cmake_args = [ + "-DCMAKE_BUILD_TYPE={}".format(build_type), "-DPYTHON_EXECUTABLE={}".format(sys.executable), "-DUSE_PYTHON_CASADI={}".format("TRUE" if use_python_casadi else "FALSE"), ] diff --git a/CMakeLists.txt b/CMakeLists.txt index fc3bec444d..cc47ba99c0 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -35,14 +35,20 @@ endif() add_subdirectory(${PYBIND11_DIR}) pybind11_add_module(idaklu - pybamm/solvers/c_solvers/idaklu_python.cpp - pybamm/solvers/c_solvers/idaklu_python.hpp + pybamm/solvers/c_solvers/idaklu/casadi_functions.cpp + pybamm/solvers/c_solvers/idaklu/casadi_functions.hpp + pybamm/solvers/c_solvers/idaklu/casadi_solver.cpp + pybamm/solvers/c_solvers/idaklu/casadi_solver.hpp + pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.hpp + pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp + pybamm/solvers/c_solvers/idaklu/common.hpp + pybamm/solvers/c_solvers/idaklu/python.hpp + pybamm/solvers/c_solvers/idaklu/python.cpp + pybamm/solvers/c_solvers/idaklu/solution.cpp + pybamm/solvers/c_solvers/idaklu/solution.hpp + pybamm/solvers/c_solvers/idaklu/options.hpp + pybamm/solvers/c_solvers/idaklu/options.cpp pybamm/solvers/c_solvers/idaklu.cpp - pybamm/solvers/c_solvers/idaklu.hpp - pybamm/solvers/c_solvers/idaklu_casadi.cpp - pybamm/solvers/c_solvers/idaklu_casadi.hpp - pybamm/solvers/c_solvers/solution.cpp - pybamm/solvers/c_solvers/solution.hpp ) if (NOT DEFINED USE_PYTHON_CASADI) diff --git a/FindSUNDIALS.cmake b/FindSUNDIALS.cmake index 643b75bbd4..92a9b785e9 100644 --- a/FindSUNDIALS.cmake +++ b/FindSUNDIALS.cmake @@ -8,6 +8,8 @@ # # * sundials_ida # * sundials_sunlinsolklu +# * sundials_sunlinsoldense +# * sundials_sunlinsollapackdense # * sundials_sunmatrix_sparse # * sundials_nvecserial # @@ -31,6 +33,9 @@ find_path(SUNDIALS_INCLUDE_DIR sundials/sundials_math.h sundials/sundials_types.h sunlinsol/sunlinsol_klu.h + sunlinsol/sunlinsol_dense.h + sunlinsol/sunlinsol_spbcgs.h + sunlinsol/sunlinsol_lapackdense.h sunmatrix/sunmatrix_sparse.h PATH_SUFFIXES include @@ -41,6 +46,9 @@ find_path(SUNDIALS_INCLUDE_DIR set(SUNDIALS_WANT_COMPONENTS sundials_idas sundials_sunlinsolklu + sundials_sunlinsoldense + sundials_sunlinsolspbcgs + sundials_sunlinsollapackdense sundials_sunmatrixsparse sundials_nvecserial ) diff --git a/README.md b/README.md index 4299b067cb..e8fd36dc92 100644 --- a/README.md +++ b/README.md @@ -68,7 +68,9 @@ Additional supporting material can be found Note that the examples on the default `develop` branch are tested on the latest `develop` commit. This may sometimes cause errors when running the examples on the pybamm pip package, which is synced to the `main` branch. You can switch to the `main` branch on github to see the version of the examples that is compatible with the latest pip release. - +## Versioning + +PyBaMM uses [CalVer](https://calver.org/), which means that we make new releases every month with the version number `YY.MM`. There is no difference between releases that increment the year and releases that increment the month; in particular, releases that increment the month may introduce breaking changes. Breaking changes for each release are communicated via the [CHANGELOG](CHANGELOG.md), and come with deprecation warnings or errors that are kept for at least one year (12 releases). If you find a breaking change that is not documented, or think it should be undone, please open an issue on [GitHub](https://github.com/pybamm-team/pybamm). ## 🚀 Installing PyBaMM @@ -214,7 +216,7 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d ndrewwang
ndrewwang
🐛 💻 MichaPhilipp
MichaPhilipp
🐛 Alec Bills
Alec Bills
💻 - Agriya Khetarpal
Agriya Khetarpal
🚇 + Agriya Khetarpal
Agriya Khetarpal
🚇 💻 Alex Wadell
Alex Wadell
💻 ⚠️ 📖 diff --git a/benchmarks/different_model_options.py b/benchmarks/different_model_options.py index 06af900037..b328398177 100644 --- a/benchmarks/different_model_options.py +++ b/benchmarks/different_model_options.py @@ -124,10 +124,12 @@ class TimeBuildModelSEI: "none", "constant", "reaction limited", + "reaction limited (asymmetric)", "solvent-diffusion limited", "electron-migration limited", "interstitial-diffusion limited", "ec reaction limited", + "ec reaction limited (asymmetric)", ], ) @@ -143,10 +145,12 @@ class TimeSolveSEI: "none", "constant", "reaction limited", + "reaction limited (asymmetric)", "solvent-diffusion limited", "electron-migration limited", "interstitial-diffusion limited", "ec reaction limited", + "ec reaction limited (asymmetric)", ], ) diff --git a/build-pure-wheel.sh b/build-pure-wheel.sh deleted file mode 100755 index 68210be2ee..0000000000 --- a/build-pure-wheel.sh +++ /dev/null @@ -1,23 +0,0 @@ -#!/bin/bash - -# This is script is to run inside a manylinux -# docker image, i.e. -# docker run -v `pwd`:/io quay.io/pypa/manylinux2014_x86_64 /io/build-wheel.sh -# -# Builds a pure python PyBaMM wheel, that is without the idaklu module extension. -# In the following, clining the pybind11 is omitted, resulting in the extension module -# compilation to be skipped. -# This pure python wheel is mostly intended to Windows support. - -set -e -x -cd /io - -# Build wheel! -# Using python 3.7 but the resulting wheel is not -# python version dependent -for PYBIN in /opt/python/cp37-cp37m/bin; do - "${PYBIN}/python" setup.py bdist_wheel -d /io/wheelhouse -done - -echo "** --- All good ! --- **" -ls /io/wheelhouse diff --git a/build-wheel.sh b/build-wheel.sh deleted file mode 100755 index 0174b7f06c..0000000000 --- a/build-wheel.sh +++ /dev/null @@ -1,50 +0,0 @@ -#!/bin/bash - -# This is script is to run inside a manylinux -# docker image, i.e. -# docker run -v `pwd`:/io quay.io/pypa/manylinux2014_x86_64 /io/build-wheel.sh - -set -e -x -# Compile wheels -cd /io - -yum -y install openblas-devel - -# Update Cmake -/opt/python/cp37-cp37m/bin/pip install cmake -ln -s /opt/python/cp37-cp37m/bin/cmake /usr/bin/cmake - -# The wget python module is required to download -# SuiteSparse and Sundials. -# https://pypi.org/project/wget/ -/opt/python/cp37-cp37m/bin/pip install wget - -# Clone the pybind11 git repo next to the setup.py -# Required to build the idaklu extension module. -if [ ! -d "pybind11" ] -then - git clone https://github.com/pybind/pybind11.git -fi - - -# Download and build SuiteSparse/Sundials -# in KLU_module_deps/ -/opt/python/cp37-cp37m/bin/python scripts/setup_KLU_module_build.py - -SUITESPARSE_DIR=KLU_module_deps/SuiteSparse-5.6.0 -SUNDIALS_DIR=KLU_module_deps/sundials5 - -# Build wheels! -for PYBIN in /opt/python/cp3[67]-cp3[67]m/bin; do - "${PYBIN}/python" setup.py bdist_wheel\ - --suitesparse-root=${SUITESPARSE_DIR}\ - --sundials-root=${SUNDIALS_DIR} -done - -# And repair them -for whl in dist/*.whl; do - auditwheel repair $whl -w /io/wheelhouse/ -done - -echo "** --- All good ! --- **" -ls /io/wheelhouse diff --git a/docs/conf.py b/docs/conf.py index a849e89092..f39e6d8e8c 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -27,7 +27,7 @@ author = "The PyBaMM Team" # The short X.Y version -version = "22.10" +version = "22.11" # The full version, including alpha/beta/rc tags release = version diff --git a/docs/source/models/equivalent_circuit/index.rst b/docs/source/models/equivalent_circuit/index.rst new file mode 100644 index 0000000000..750c0e4536 --- /dev/null +++ b/docs/source/models/equivalent_circuit/index.rst @@ -0,0 +1,7 @@ +Equivalent Circuit Models +========================= + +.. toctree:: + :maxdepth: 1 + + thevenin diff --git a/docs/source/models/equivalent_circuit/thevenin.rst b/docs/source/models/equivalent_circuit/thevenin.rst new file mode 100644 index 0000000000..cf73b04a0a --- /dev/null +++ b/docs/source/models/equivalent_circuit/thevenin.rst @@ -0,0 +1,5 @@ +Thevenin Model +============== + +.. autoclass:: pybamm.equivalent_circuit.Thevenin + :members: diff --git a/docs/source/models/index.rst b/docs/source/models/index.rst index 173ca722c1..5f944ce4a5 100644 --- a/docs/source/models/index.rst +++ b/docs/source/models/index.rst @@ -14,4 +14,5 @@ to see how these models can be solved, and compared, using PyBaMM. base_models/index lithium_ion/index lead_acid/index + equivalent_circuit/index submodels/index diff --git a/docs/source/models/lead_acid/higher_order.rst b/docs/source/models/lead_acid/higher_order.rst deleted file mode 100644 index 7a0b02264c..0000000000 --- a/docs/source/models/lead_acid/higher_order.rst +++ /dev/null @@ -1,14 +0,0 @@ -Higher-Order Models -=================== - -.. autoclass:: pybamm.lead_acid.BaseHigherOrderModel - :members: - -.. autoclass:: pybamm.lead_acid.FOQS - :members: - -.. autoclass:: pybamm.lead_acid.Composite - :members: - -.. autoclass:: pybamm.lead_acid.CompositeExtended - :members: diff --git a/docs/source/models/lead_acid/index.rst b/docs/source/models/lead_acid/index.rst index 34003e8298..cf7b9b5852 100644 --- a/docs/source/models/lead_acid/index.rst +++ b/docs/source/models/lead_acid/index.rst @@ -6,5 +6,4 @@ Lead Acid Models base_lead_acid_model loqs - higher_order full diff --git a/docs/source/models/submodels/current_collector/composite_potential_pair.rst b/docs/source/models/submodels/current_collector/composite_potential_pair.rst deleted file mode 100644 index 38cfabd366..0000000000 --- a/docs/source/models/submodels/current_collector/composite_potential_pair.rst +++ /dev/null @@ -1,11 +0,0 @@ -Composite Potential Pair models -=============================== - -.. autoclass:: pybamm.current_collector.BaseCompositePotentialPair - :members: - -.. autoclass:: pybamm.current_collector.CompositePotentialPair2plus1D - :members: - -.. autoclass:: pybamm.current_collector.CompositePotentialPair1plus1D - :members: diff --git a/docs/source/models/submodels/current_collector/index.rst b/docs/source/models/submodels/current_collector/index.rst index b638ca80d8..91ca79fea2 100644 --- a/docs/source/models/submodels/current_collector/index.rst +++ b/docs/source/models/submodels/current_collector/index.rst @@ -5,8 +5,6 @@ Current Collector :maxdepth: 1 base_current_collector - composite_potential_pair effective_resistance_current_collector homogeneous_current_collector potential_pair - quite_conductive_potential_pair diff --git a/docs/source/models/submodels/current_collector/quite_conductive_potential_pair.rst b/docs/source/models/submodels/current_collector/quite_conductive_potential_pair.rst deleted file mode 100644 index cdf56df603..0000000000 --- a/docs/source/models/submodels/current_collector/quite_conductive_potential_pair.rst +++ /dev/null @@ -1,11 +0,0 @@ -Quite Conductive Potential Pair models -====================================== - -.. autoclass:: pybamm.current_collector.BaseQuiteConductivePotentialPair - :members: - -.. autoclass:: pybamm.current_collector.QuiteConductivePotentialPair1plus1D - :members: - -.. autoclass:: pybamm.current_collector.QuiteConductivePotentialPair2plus1D - :members: diff --git a/docs/source/models/submodels/electrolyte_diffusion/composite_diffusion.rst b/docs/source/models/submodels/electrolyte_diffusion/composite_diffusion.rst deleted file mode 100644 index 96e8a16d1a..0000000000 --- a/docs/source/models/submodels/electrolyte_diffusion/composite_diffusion.rst +++ /dev/null @@ -1,5 +0,0 @@ -Composite Model -=============== - -.. autoclass:: pybamm.electrolyte_diffusion.Composite - :members: diff --git a/docs/source/models/submodels/electrolyte_diffusion/index.rst b/docs/source/models/submodels/electrolyte_diffusion/index.rst index 3afaa02739..6535f7f5e9 100644 --- a/docs/source/models/submodels/electrolyte_diffusion/index.rst +++ b/docs/source/models/submodels/electrolyte_diffusion/index.rst @@ -6,5 +6,4 @@ Electrolyte Diffusion base_electrolyte_diffusion constant_concentration leading_order_diffusion - composite_diffusion full_diffusion diff --git a/docs/source/models/submodels/equivalent_circuit_elements/index.rst b/docs/source/models/submodels/equivalent_circuit_elements/index.rst new file mode 100644 index 0000000000..c88c0aed71 --- /dev/null +++ b/docs/source/models/submodels/equivalent_circuit_elements/index.rst @@ -0,0 +1,10 @@ +Equivalent Circuit Elements +=========================== + +.. toctree:: + + ocv_element + resistor_element + rc_element + thermal + voltage_model diff --git a/docs/source/models/submodels/equivalent_circuit_elements/ocv_element.rst b/docs/source/models/submodels/equivalent_circuit_elements/ocv_element.rst new file mode 100644 index 0000000000..f34a563edc --- /dev/null +++ b/docs/source/models/submodels/equivalent_circuit_elements/ocv_element.rst @@ -0,0 +1,6 @@ +OCV Element +=========== + +.. autoclass:: pybamm.equivalent_circuit_elements.OCVElement + :members: + diff --git a/docs/source/models/submodels/equivalent_circuit_elements/rc_element.rst b/docs/source/models/submodels/equivalent_circuit_elements/rc_element.rst new file mode 100644 index 0000000000..e783f4b291 --- /dev/null +++ b/docs/source/models/submodels/equivalent_circuit_elements/rc_element.rst @@ -0,0 +1,6 @@ +RC Element +========== + +.. autoclass:: pybamm.equivalent_circuit_elements.RCElement + :members: + diff --git a/docs/source/models/submodels/equivalent_circuit_elements/resistor_element.rst b/docs/source/models/submodels/equivalent_circuit_elements/resistor_element.rst new file mode 100644 index 0000000000..91d6e6d2c5 --- /dev/null +++ b/docs/source/models/submodels/equivalent_circuit_elements/resistor_element.rst @@ -0,0 +1,6 @@ +Resistor Element +================ + +.. autoclass:: pybamm.equivalent_circuit_elements.ResistorElement + :members: + diff --git a/docs/source/models/submodels/equivalent_circuit_elements/thermal.rst b/docs/source/models/submodels/equivalent_circuit_elements/thermal.rst new file mode 100644 index 0000000000..c95ead818f --- /dev/null +++ b/docs/source/models/submodels/equivalent_circuit_elements/thermal.rst @@ -0,0 +1,6 @@ +Thermal SubModel +================ + +.. autoclass:: pybamm.equivalent_circuit_elements.ThermalSubModel + :members: + diff --git a/docs/source/models/submodels/equivalent_circuit_elements/voltage_model.rst b/docs/source/models/submodels/equivalent_circuit_elements/voltage_model.rst new file mode 100644 index 0000000000..bda725dfcb --- /dev/null +++ b/docs/source/models/submodels/equivalent_circuit_elements/voltage_model.rst @@ -0,0 +1,6 @@ +Voltage Model +============= + +.. autoclass:: pybamm.equivalent_circuit_elements.VoltageModel + :members: + diff --git a/docs/source/models/submodels/index.rst b/docs/source/models/submodels/index.rst index 79a8ccb96a..3670073127 100644 --- a/docs/source/models/submodels/index.rst +++ b/docs/source/models/submodels/index.rst @@ -19,3 +19,4 @@ Submodels porosity/index thermal/index transport_efficiency/index + equivalent_circuit_elements/index diff --git a/docs/source/models/submodels/interface/first_order_kinetics.rst b/docs/source/models/submodels/interface/first_order_kinetics.rst deleted file mode 100644 index f68d15c4b9..0000000000 --- a/docs/source/models/submodels/interface/first_order_kinetics.rst +++ /dev/null @@ -1,8 +0,0 @@ -First-order Kinetics -==================== - -.. autoclass:: pybamm.kinetics.FirstOrderKinetics - :members: - -.. autoclass:: pybamm.kinetics.InverseFirstOrderKinetics - :members: diff --git a/docs/source/models/submodels/interface/index.rst b/docs/source/models/submodels/interface/index.rst index d59864fc7c..0267273966 100644 --- a/docs/source/models/submodels/interface/index.rst +++ b/docs/source/models/submodels/interface/index.rst @@ -7,7 +7,6 @@ Interface base_interface kinetics inverse_butler_volmer - first_order_kinetics diffusion_limited sei open_circuit_potential \ No newline at end of file diff --git a/docs/source/models/submodels/oxygen_diffusion/composite_oxygen_diffusion.rst b/docs/source/models/submodels/oxygen_diffusion/composite_oxygen_diffusion.rst deleted file mode 100644 index 0a3c42012a..0000000000 --- a/docs/source/models/submodels/oxygen_diffusion/composite_oxygen_diffusion.rst +++ /dev/null @@ -1,5 +0,0 @@ -Composite Model -=============== - -.. autoclass:: pybamm.oxygen_diffusion.Composite - :members: diff --git a/docs/source/models/submodels/oxygen_diffusion/first_order_oxygen_diffusion.rst b/docs/source/models/submodels/oxygen_diffusion/first_order_oxygen_diffusion.rst deleted file mode 100644 index 9294a77de7..0000000000 --- a/docs/source/models/submodels/oxygen_diffusion/first_order_oxygen_diffusion.rst +++ /dev/null @@ -1,5 +0,0 @@ -First-Order Model -================= - -.. autoclass:: pybamm.oxygen_diffusion.FirstOrder - :members: diff --git a/docs/source/models/submodels/oxygen_diffusion/index.rst b/docs/source/models/submodels/oxygen_diffusion/index.rst index 336259f45c..fccdb4611e 100644 --- a/docs/source/models/submodels/oxygen_diffusion/index.rst +++ b/docs/source/models/submodels/oxygen_diffusion/index.rst @@ -4,8 +4,6 @@ Oxygen Diffusion .. toctree:: base_oxygen_diffusion - composite_oxygen_diffusion - first_order_oxygen_diffusion full_oxygen_diffusion leading_oxygen_diffusion no_oxygen diff --git a/docs/source/parameters/index.rst b/docs/source/parameters/index.rst index 9d7585717d..85bb2eace2 100644 --- a/docs/source/parameters/index.rst +++ b/docs/source/parameters/index.rst @@ -10,3 +10,4 @@ Parameters lithium_ion_parameters lead_acid_parameters parameter_sets + process_parameter_data diff --git a/docs/source/parameters/process_parameter_data.rst b/docs/source/parameters/process_parameter_data.rst new file mode 100644 index 0000000000..d34fa492fa --- /dev/null +++ b/docs/source/parameters/process_parameter_data.rst @@ -0,0 +1,10 @@ +Process Parameter Data +====================== + +.. autofunction:: pybamm.parameters.process_1D_data + +.. autofunction:: pybamm.parameters.process_2D_data + +.. autofunction:: pybamm.parameters.process_2D_data_csv + +.. autofunction:: pybamm.parameters.process_3D_data_csv diff --git a/examples/notebooks/models/lead-acid.ipynb b/examples/notebooks/models/lead-acid.ipynb index d49c570907..2d61e46666 100644 --- a/examples/notebooks/models/lead-acid.ipynb +++ b/examples/notebooks/models/lead-acid.ipynb @@ -135,73 +135,6 @@ "loqs = pybamm.lead_acid.LOQS()" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### \"Composite\" model\n", - "\n", - "#### Electrolyte Concentration\n", - "\n", - "$$\n", - " \\frac{\\partial }{\\partial t}\\left(\\epsilon c\\right) = -\\frac{\\partial N}{\\partial x} + sj, \\\\ \n", - " N = -\\frac{\\epsilon^b D(c)}{\\mathcal{C}_\\text{e}} \\frac{\\partial c}{\\partial x}\\\\\n", - " N\\big|_{x=0}= N\\big|_{x=1}=0, \\\\ \n", - " c\\big|_{t=0} = 1\n", - "$$ \n", - "\n", - "#### Porosity\n", - "\n", - "$$\n", - " \\frac{\\partial \\epsilon}{\\partial t} = -\\beta^\\text{surf}j, \\\\ \n", - " \\epsilon\\big|_{t=0} = \\epsilon^0\n", - "$$ \n", - "\n", - "#### Interfacial current density\n", - "\n", - "$$\n", - "j = \\begin{cases}\n", - " \\mathrm{I}/l_\\text{n}, \\quad &0 < x < l_\\text{n} \\\\\n", - " 0, \\quad &l_\\text{n} < x < 1-l_\\text{p} \\\\\n", - " -\\mathrm{I}/l_\\text{p}, \\quad &1-l_\\text{p} < x < 1 \n", - " \\end{cases} \\\\\n", - "$$\n", - "\n", - "#### Voltage \n", - "\n", - "$$ \n", - "V = U_{\\text{eq}} + \\eta_r + \\eta_c + \\Delta\\Phi_{\\text{Elec}} + \\Delta\\Phi_{\\text{Solid}}\n", - "$$ \n", - "where \n", - "\\begin{align}\n", - " & U_{\\text{eq}} = U_p(\\bar{c}_p) - U_n(\\bar{c}_n), \\\\ \n", - " &\\eta_{r} = -\\sinh^{-1}\\left(\\frac{\\mathcal{I}}{2\\bar{j}_{0n} l_n}\\right)\n", - " -\\sinh^{-1}\\left(\\frac{\\mathcal{I}}{2\\bar{j}_{0p} l_p}\\right), \\\\\n", - " &\\eta_c = \\mathcal{C}_e\\chi\\left(\\log(\\bar{c}_{p}) - \\log(\\bar{c}_{n})\\right), \\\\\n", - " &\\Delta \\Phi_{\\text{Elec}}= -\\frac{\\mathcal{C}_e \\mathcal{I}}{\\kappa(\\bar{c})}\\left(\\frac{l_n}{3\\epsilon_n^b} + \\frac{l_s}{\\epsilon_{s}^b} + \\frac{l_p}{3\\epsilon_p^b} \\right),\n", - " \\label{eqn:SPMe:electrolyte_ohmic_losses} \\\\\n", - " &\\Delta \\Phi_{\\text{Solid}} = -\\frac{\\mathcal{I}}{3}\\left(\\frac{l_p}{\\sigma_p} + \\frac{l_n}{\\sigma_n} \\right), \n", - "\\end{align} \n", - "with \n", - "\\begin{equation} \n", - " \\bar{\\cdot}_{\\text{n}} = \\frac{1}{l_n}\\int_0^{l_n} \\cdot_{\\text{n}} \\, \\text{d}x, \\quad\n", - " \\bar{\\cdot}_{\\text{p}} = \\frac{1}{l_p}\\int_{1-l_p}^{1} \\cdot_{\\text{p}} \\, \\text{d}x,\n", - "\\end{equation}\n", - "\n", - "\n", - "This model is implemented in PyBaMM as `Composite`\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "composite = pybamm.lead_acid.Composite()" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -218,12 +151,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# load models\n", - "models = [loqs, composite, full]\n", + "models = [loqs, full]\n", "\n", "# process parameters\n", "param = models[0].default_parameter_values\n", @@ -241,7 +174,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -265,16 +198,15 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Solved the LOQS model in 125.991 ms\n", - "Solved the Composite model in 1.442 s\n", - "Solved the Full model in 1.688 s\n" + "Solved the LOQS model in 46.545 ms\n", + "Solved the Full model in 833.591 ms\n" ] } ], @@ -306,19 +238,17 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -342,13 +272,13 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e3b80bc0f08a4c26b23ffc501a822ef0", + "model_id": "1bf9e359ac394e759e5df3577b1986b1", "version_major": 2, "version_minor": 0 }, @@ -375,20 +305,28 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": { "scrolled": false }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "At t = 0.0087774 and h = 2.16761e-39, the corrector convergence failed repeatedly or with |h| = hmin.\n", + "At t = 0.00384476 and h = 3.51822e-38, the corrector convergence failed repeatedly or with |h| = hmin.\n" + ] + }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9ff5f46a95004eb4b676868826f34be7", + "model_id": "32a89538e5ee46ad9d3737566e8600f6", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=3563.636363636364, step=35.63636363636364), …" + "interactive(children=(FloatSlider(value=0.0, description='t', max=3415.761176654425, step=34.15761176654425), …" ] }, "metadata": {}, @@ -417,7 +355,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -428,7 +366,7 @@ "[2] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", "[3] Valentin Sulzer, S. Jon Chapman, Colin P. Please, David A. Howey, and Charles W. Monroe. Faster Lead-Acid Battery Simulations from Porous-Electrode Theory: Part I. Physical Model. Journal of The Electrochemical Society, 166(12):A2363–A2371, 2019. doi:10.1149/2.0301910jes.\n", "[4] Valentin Sulzer, S. Jon Chapman, Colin P. Please, David A. Howey, and Charles W. Monroe. Faster Lead-Acid Battery Simulations from Porous-Electrode Theory: Part II. Asymptotic Analysis. Journal of The Electrochemical Society, 166(12):A2372–A2382, 2019. doi:10.1149/2.0441908jes.\n", - "[5] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). ECSarXiv. February, 2020. doi:10.1149/osf.io/67ckj.\n", + "[5] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", "\n" ] } @@ -436,11 +374,18 @@ "source": [ "pybamm.print_citations()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3.9.13 ('conda_jl')", "language": "python", "name": "python3" }, @@ -454,7 +399,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.0" + "version": "3.9.13" + }, + "vscode": { + "interpreter": { + "hash": "612adcc456652826e82b485a1edaef831aa6d5abc680d008e93d513dd8724f14" + } } }, "nbformat": 4, diff --git a/examples/notebooks/solvers/dae-solver.ipynb b/examples/notebooks/solvers/dae-solver.ipynb index b639f2a94c..30fb5b48fa 100644 --- a/examples/notebooks/solvers/dae-solver.ipynb +++ b/examples/notebooks/solvers/dae-solver.ipynb @@ -84,7 +84,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -172,7 +172,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -292,7 +292,7 @@ "\n", "print(f\"y0_guess={model.concatenated_initial_conditions.evaluate().flatten()}\")\n", "dae_solver.set_up(model)\n", - "dae_solver._set_initial_conditions(model, {}, True)\n", + "dae_solver._set_initial_conditions(model, 0, {}, True)\n", "print(f\"y0_fixed={model.y0}\")" ] }, diff --git a/examples/notebooks/spatial_methods/finite-volumes.ipynb b/examples/notebooks/spatial_methods/finite-volumes.ipynb index 607c0d9aa0..136840c8ad 100644 --- a/examples/notebooks/spatial_methods/finite-volumes.ipynb +++ b/examples/notebooks/spatial_methods/finite-volumes.ipynb @@ -55,8 +55,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[33mWARNING: You are using pip version 20.2.4; however, version 21.0.1 is available.\n", - "You should consider upgrading via the '/Users/vsulzer/Documents/Energy_storage/PyBaMM/.tox/dev/bin/python -m pip install --upgrade pip' command.\u001b[0m\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } @@ -182,14 +180,12 @@ }, { "data": { - "image/png": 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"text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -288,8 +284,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "method self.evaluate() not implemented\n", - " for symbol u of type \n" + "method self.evaluate() not implemented for symbol u of type \n" ] } ], @@ -353,14 +348,12 @@ "outputs": [ { "data": { - "image/png": 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", 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", + "image/png": 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zcxNff/217hgA8eWXX+qN6927t3jvvff0jm3atEn4+PjofnZ3dxcbN27UG7N9+3bRoEEDcffuXSGEEMnJyUKSJL3rWZpoVvS4fPmy3mt+9tlnwtHRUcyaNUs4OzuLP/74Qy+mbt26iQkTJogJEyaIbt26iU2bNlX5XKlRo0aJevXqCQBiyJAh5X6v3n33XfHkk0/qHfvtt9+Ei4uLsLOzE+7u7uLbb78td52/+uoroVAodIlmWUxGiExw6ivtP5bLfFep56mEZp679nkzYWwyYtu3aWRsrZyamori4mKEhYXpjrm7u6Nt27Z640JDQ8udu3PnTvTq1Qve3t5wdXXFnDlzkJaWpnv+9OnTCA8P1zun7M937tzR3U6ojnr16uH27dsGn7t+/TouXbqEiRMnwtXVVfdYuHAhzp8/DwBo3Lgx+vfvj82bNwMALly4gMOHD2PMmDG610lKSsKwYcPg7+8PNzc3PPzwwwCg91lNlZmZiaioKLRp0wbu7u5wd3dHQUFBla95/PhxLFiwQO/zPP/880hPT9ddB0PXdPjw4bC3t8eXX34JAFi3bh0eeeQRvds/rVq1qvTRpEkTvdd85pln8OSTTyI6OhqLFy9GmzZ/1xDIzMxEQkICevfujd69eyMhIUF3a6uy50otXboUJ06cwK5du3D+/HlMnz5d7/mvvvpKd4umVNu2bZGcnIwjR47gxRdfxLhx45CSkqI3pl69etBoNCgsLKz0OhNRFTRqYPcbMNRpRoF73WZ2z7S4TRi2XYFVxtbK4t49f0mSDB4v5eLiovfzkSNHMGrUKMyfPx8DBgyAu7s7tm3bhsWLF5v0/h4eHjh58qTesdIdIPfHUHYtSqmcnBzdeoOySndNrFmzBt26ddN7zs7u79LDY8aMwdSpU7FixQps2bIFDz30EDp16gQAuHXrFiIiIhAREYFPP/0UjRs3RlpaGgYMGFDhQl6FQlHu+pWNf/z48bh+/TpiYmLg7+8PR0dHhIeHV7k4WKPRYP78+XjyySfLPVeagHh4eODGjRt6zymVSowdOxbr16/Hk08+iS1btiAmJkZvjKura6Xv3bt3b70dKrdv38bx48dhZ2eHc+fO6Y0tmzy4ubnpjlX2XClvb294e3sjMDAQjRo1Qu/evTFnzhz4+PggIyMDJ06c0FtnVPoZW7VqBUCbPB87dgzLli3Dxx9/rBuTk5MDZ2dn1KtnYfexiczNxUPl1zjqEUDeFe04M622aohtJyP+PbSNgvLSYSjL1LZW9q2V1sotW7aEg4MDjh49Cj8/PwBAXl4ezp07hz59+lR43sGDB+Hv769bsAgAFy9e1BvTrl07HDlyBJGRkbpj9y8oBIDg4GCsXLkSQghdQlSaXKSnp6NBgwYAtAtYy7p79y7Onz+P4OBggzF6eXmhSZMmSE1N1ZvpKGv48OF44YUXsHv3bmzZsgVjx47VPXfmzBlkZWVh0aJFuutTdiFoWY0bN0ZGRobeZyob//79+xEbG4tBgwYBAC5duoSsrCy9MQ4ODlCr9f9V0aVLF/zxxx+6L11DgoODy80IAMCkSZMQFBSE2NhYFBcXl0toDF3j+5X9An/11VehUCjw3XffYdCgQRg8eDD69u2rN2b8+PEVvl5lz92vNLErnc2Ii4tDeHg4PDw8qjyv7AzI77//ji5duhj1vkRUCRln9GuTbScjCjvgsfe1u2YgQT8hqd3Wym5ubhg3bhxmzJiBhg0bwtPTE/PmzYNCoSg3W3K/Vq1aIS0tDdu2bUPXrl3x7bff6m4BlJo6dSrGjRuH0NBQ9OrVC5s3b8apU6fQokUL3ZhHHnkEt27dwqlTpxAUFKR7bT8/P7z99ttYuHAhzp07Z3DG5ciRI7oZhVKRkZFo0qQJoqOjAWh3tkyZMgUqlQoDBw5EYWEhEhMTcePGDd2/xl1cXDBs2DDMmTMHp0+fxujRo3Wv16xZMyiVSqxYsQJRUVH4/fff8c4771R6TR9++GFcv34dH3zwAZ5++mns3r0b3333HVQqld7127RpE0JDQ5GXl4cZM2aU+7Jv3rw5fvjhB/Ts2ROOjo5o0KAB5s6di8cffxx+fn545plnoFAo8Ntvv+HkyZNYuHAhAGDAgAH45JNPysXVrl07dO/eHW+88QYmTJhQ7v0qS3DK+vbbb7Fu3TocPnwYXbp0wcyZMzFu3Dj89ttvugSyOuLj43Ht2jV07doVrq6uSElJweuvv46ePXvqbinFxcVh2LBheue9+eabGDhwIPz8/JCfn49t27Zh79692L17t964/fv3IyIiotrxEdE9Ms7o16paXrtSI2ptAWspA6uSxeJ2tb4IKC8vT4wePVo4OzsLb29vsWTJEhEWFiZmzpwphNAuYF26dGm582bMmCEaNWokXF1dxciRI8XSpUuFu7u73ph3331XeHh4CFdXVzFu3Djx+uuvl1vcOWrUKN17lTpw4IDo0KGDcHJyEr179xY7duwot4D1X//6l3jhhRf0zuvTp48YN26c3rHNmzeLzp07C6VSKRo0aCD+8Y9/iC+++EJvzLfffisAiH/84x/lPueWLVtE8+bNhaOjowgPDxdxcXECgEhKShJCGF5wu3LlSuHn5ydcXFxEZGSkePfdd/UWsJ44cUKEhoYKR0dH0bp1a7Fjx45y1zkuLk60atVK2Nvb6527e/du0aNHD1GvXj2hUqlEWFiYWL16te75nJwcUa9ePXHmzJlyn2Xt2rUCgDh69Gi554yVmZkpvLy89BbSFhcXi7CwMDFixIhqv64QQvz4448iPDxcuLu7CycnJ9G6dWvxxhtv6K5tQUGBcHJyEmfPntU7b8KECcLf318olUrRuHFj8eijj4o9e/bojbl8+bJwcHAQly5dqvD9uYCVyEjqElH0QVuhnivPLlBTGbuAVRLCxGIHMsjLy4O7uztyc3P1/pULaG8ZXLhwAQEBAQ+0IBMateytlW/duoUmTZpg8eLFmDhxYq2/38mTJ9GvXz/8+eefcHMzrmTw9evXERgYiMTERAQEBNRyhJbn9ddfR25urt56CQB49913sW3btnLrdCzFF198gbfeesvgbaiqzJgxA7m5uVi9enWFY2rs95jIyt0tVuP9pR9gzq1FgFS2WNi9WfURG4H2Qw2cXfcq+/6+n23vprmfwk672KfD09o/6yARSUpKwtatW3H+/HmcOHFCt76i7FR4benQoQM++OADvYJmVblw4QJiY2OZiFRg9uzZ8Pf31605KSgowLFjx7BixQpMmTJF5uiqz9XVFe+//361zvX09KzyFhsRGSc6/jTW53TEG3YzIFx99Z9U+ZpVImIKzozIKCkpCZMmTcIff/wBpVKJkJAQLFmyBB06dJA7NKoh48ePx9atWzF8+HBs2bJFbzcR/c2Sf4+J6spPZzLx3IZjAIANz3XFw60byT6jXxVjZ0ZsewGrzIKDg3H8+HG5w6BaVFnDOiIiY13PL8SMnb8CAJ7r2RwPt/XUPmFB23crw9s0REREZkwIgRk7f0VWQRECvd3wxmOBcodU46wmGbGAu01EVAH+/hJV7JNDf2HvH9ehtFdg2ahgODmY162YmmDxyYiDgwMAVFianIjMX+nvb+nvMxFpncnIw3vfnQEAzB7UDm29jdv5aGksfs2InZ0d6tevr+ux4ezsXGnRMCIyH0II3L59G5mZmahfvz4X+BLd526xGlO3JqOoRINH2jZGZLi/3CHVGotPRgBtPw0A5Zp+EZFlqF+/vu73mMhmlal39f5v7vjjWj48XJX44OlOVv0PbatIRiRJgo+PDzw9PSts7EZE5snBwYEzIkQpcdpuvPc1wXteNMRVRSRGPf0SGrs5yhhc7bOKZKSUnZ0d/6NGRESWJSXuXo80/YXc3sjBKmUMJE0IAMsrZGYKi1/ASkREZLE0au2MiIHO8QoJACRg90ztOCvGZISIiEguFw/p3ZopS4IA8q5ox1kxJiNERERyKbhWs+MsFJMRIiIiubh61ew4C8VkhIiISC7+PSBUvgZWjJSSAFUTbRM8K8ZkhIiISC4KOxxsNQNCAJpyGcm9uiKPLTK7brw1jckIERGRTM5dy8ekYz54sXgabjt56j+p8gVGbATaW/e2XsDK6owQERFZirvFary8JQl3izW43XoQnMfNAS4d1lVghX8Pq58RKcVkhIiISAYLv025V+7dEUtGdIbC3h4I6C13WLLgbRoiIqI6tvv3dHx6JA0AsGREJ6sv914VJiNERER16PKN23h9528AgKg+LfGPNo1ljkh+TEaIiIjqSIlag2nbkpF3twSd/erj1Yg2codkFpiMEBER1ZFlP5xD4sUbcHO0x4p/BsPBjl/DABewEhER1R6NWttXpuAafs9zQuxPAoAC7z3ZAX4NneWOzmwwGSEiIqoNKXHajrz3GuEFAdivbIgf/KdjSKfB8sZmZjg/REREVNNS4oDPIst15PWWcvBs2hzt86TDZISIiKgmadTaGREDHWcUuFfkffdM7TgCwGSEiIioZl08VG5GRJ8A8q5oxxGAaiYjsbGxCAgIgJOTE0JCQrB///5Kx2/evBmdOnWCs7MzfHx88NxzzyE7O7taARMREZm1gms1O84GmJyMbN++HdOmTcPs2bORlJSE3r17Y+DAgUhLSzM4/sCBA4iMjMTEiRNx6tQp7NixA8eOHcOkSZMeOHgiIiKz4+pVs+NsgMnJyJIlSzBx4kRMmjQJ7dq1Q0xMDPz8/LBy5UqD448cOYLmzZtjypQpCAgIQK9evfDCCy8gMTGxwvcoLCxEXl6e3oOIiMgi+PdAvtITmvJLRu6RAFUTbSM8AmBiMlJUVITjx48jIiJC73hERAQOHTJ876tHjx64fPky4uPjIYTAtWvXsHPnTgweXPG2pujoaLi7u+sefn5+poRJREQkm0OpNzDj1mgAgNAuV73PvZ8fW2QzHXmNYVIykpWVBbVaDS8v/aklLy8vZGRkGDynR48e2Lx5M0aOHAmlUglvb2/Ur18fK1asqPB9Zs2ahdzcXN3j0qVLpoRJREQki8z8u5iyLRm71WH4tNlCSCof/QEqX2DERqD9UHkCNFPVKnomSfqZnhCi3LFSKSkpmDJlCubOnYsBAwYgPT0dM2bMQFRUFNauXWvwHEdHRzg62nYHQyIisixqjcC0bcnIKihEWy83PDN2MmA/WVeBFa5e2lsznBEpx6RkxMPDA3Z2duVmQTIzM8vNlpSKjo5Gz549MWPGDABAx44d4eLigt69e2PhwoXw8fExeB4REZElWfHjORw6nw1npR0+GtMF9ZT3ko6A3vIGZgFMuk2jVCoREhKChIQEveMJCQno0cPwQpzbt29DodB/Gzs77f9BQlS4uoeIiMhiHPozC8t+OAcAeO+JDmjl6SpzRJbF5N0006dPx3//+1+sW7cOp0+fxiuvvIK0tDRERUUB0K73iIyM1I0fMmQIvvjiC6xcuRKpqak4ePAgpkyZgrCwMPj6+tbcJyEiIpJB6ToRIYBRXf0wPLiJ3CFZHJPXjIwcORLZ2dlYsGAB0tPTERQUhPj4ePj7+wMA0tPT9WqOjB8/Hvn5+fi///s/vPrqq6hfvz769u2L999/v+Y+BRERkQzuXycS6O2Gt4c+JHdIFkkSFnCvJC8vD+7u7sjNzYVKpZI7HCIislUatd6C1KXnPLDsx1Q4K+0Q93Iv3p4pw9jv72rtpiEiIrI5KXHaBnj39Z0ZKRrijCISA5/4FxORB8BkhIiIqCopccBnkSjbidcbOViljIHkGAKAa0Wqi117iYiIKqNRa2dEUH5Vg0ICAAnYPVM7jqqFyQgREVFlLh7SuzVTlgQB5F3RjqNqYTJCRERUmYJrNTuOymEyQkREVBlXwxXGqz2OymEyQkREVBn/HtC4+UJT4QAJUDXR9p2hamEyQkREVAkhKfBf138BAgYSkntNYh9bxAZ4D4DJCBERUSXWHriA9y60wb/Vr6DEuUxzV5UvMGIj0H6oPMFZCdYZISIiqkDiXzlY9N0ZAEDYoPFQdp+jV4EV/j04I1IDmIwQEREZkFVQiJe2nECJRmBIJ19EhvsDkgQE9JY7NKvD2zRERERllKg1+PeWJFzLK0QrT1cserIDJEmSOyyrxWSEiIiojCUJZ3E4NRvOSjuserYLXBx5I6E2MRkhIiK6z+7fMxC79zwAYNFTHdHK003miKwfUz0iIrJtGrVuUeqVEhVe36XtMTOhZwCGdvKVOTjbwGSEiIhsV0qctgnevd4zTQDsRkNs9Z6MKYMGyhubDeFtGiIisk0pccBnkeWa4HlLOZh+8104/PGNTIHZHiYjRERkezRq7YwIRLmnFLhXV3X3TO04qnVMRoiIyPZcPFRuRkSfAPKuaMdRrWMyQkREtqfgWs2OowfCZISIiGyPq1fNjqMHwmSEiIhsjmgWjhv2jaEpv2TkHglQNdH2nqFax2SEiIhszidHLmHm7TEAAIGyZd7v/fzYIjbBqyNMRoiIyKYcvZCDhd+exveaMPzY8T+QVD76A1S+wIiNQPuh8gRog1j0jIiIbEZG7l1M3qztxDu0ky8efXIQICboKrDC1Ut7a4YzInWKyQgREdmEwhI1Xtx8HFkFhQj0dsOip+514pXsgIDecodn03ibhoiIrJ4QAnN3nUJS2k2onOzx8dgQOCv573FzwWSEiIis3qYjF7E98RIUErD8n8Hwb+Qid0h0H6aFRERkXe7rwgtXLxwuaYv5X6cAAGYODMTDbT1lDpDKYjJCRETWo0wXXgAIQCP0w1jU6zwcz/duIWNwVBEmI0REZB1Ku/CWaX7nKbKxShmD4oeCIUnB8sRGleKaESIisnyVdeGVAECCMuFNduE1U0xGiIjI8lXRhVdiF16zxmSEiIgsH7vwWjQmI0REZPnYhdeiMRkhIiLL598DGjdfaCocwC685ozJCBERWbwSIWGFchIgYCAhYRdec8dkhIiILN4H3/+BpVcCMU0zHWoXduG1NKwzQkREFu3LpMtYvS8VADDgmX/BIegtduG1MExGiIjIYh2/eANv7DwJAHjpkZYY3PHerAi78FoU3qYhIiKLdPnGbbywKRFFag0i2nvh1f5t5Q6JqonJCBERWZxbhSWY9EkisgqK0M5HhaUjO0OhkOQOi6qJt2mIiMj83deJV+PiiVf2O+JMRj48XB3x33GhcHHk15kl4/97RERk3sp04lUAeFs0hNJhPCZETkGT+vXkjY8eGJMRIiIyXxV04vVGDlbYLYVUEAyAW3YtHdeMEBGReaqiE68EALtnshOvFWAyQkRE5qmKTrxgJ16rwWSEiIjMEzvx2gwmI0REZJ7YiddmMBkhIiLz5N8DeUpPaMovGbmHnXitBZMRIiIySztOXMWMgtEAAIGyBc3YideaMBkhIiKzc/h8Nt788iS+14Thm3bvQ1KxE681Y50RIiIyK+evFyDq0+MoVgsM7uiDx0cMAvA8O/FaMSYjRERkNrILCvHc+mPIvVOMLs3qY/Ezne71nLFjJ14rxts0RERkFu4Wq/GvTceRlnMbzRo6Y01kKJwcOPthC5iMEBGR7DQagdd2/IrjF29A5WSPdeO7opGro9xhUR3hbRoiIqp793XhhasXlv7RCN/8lg57hYRVY0PQytNV7gipDjEZISKiulWmCy8A/FM0xFlFJPo9OQk9WnrIGBzJoVq3aWJjYxEQEAAnJyeEhIRg//79lY4vLCzE7Nmz4e/vD0dHR7Rs2RLr1q2rVsBERGTBSrvwluk5440crFLG4BnnJJkCIzmZPDOyfft2TJs2DbGxsejZsyc+/vhjDBw4ECkpKWjWrJnBc0aMGIFr165h7dq1aNWqFTIzM1FSUvLAwRMRkQWpoguvgKTtwhs4mNt2bYwkhKiw0K4h3bp1Q5cuXbBy5UrdsXbt2mH48OGIjo4uN3737t0YNWoUUlNT0bBhQ6Peo7CwEIWFhbqf8/Ly4Ofnh9zcXKhUKlPCJSIic3FhP/DJ41WPG/cNt/Faiby8PLi7u1f5/W3SbZqioiIcP34cERERescjIiJw6JDhFs5xcXEIDQ3FBx98gCZNmqBNmzZ47bXXcOfOnQrfJzo6Gu7u7rqHn5+fKWESEZE5YhdeqoBJt2mysrKgVqvh5aXfIdHLywsZGRkGz0lNTcWBAwfg5OSEL7/8EllZWZg8eTJycnIqXDcya9YsTJ8+Xfdz6cwIERFZMHbhpQpUazeNJOk3LBJClDtWSqPRQJIkbN68Ge7u7gCAJUuW4Omnn8ZHH32EevXqlTvH0dERjo7cX05EZFXudeF1LcyEwuBXhqTtOcMuvDbHpNs0Hh4esLOzKzcLkpmZWW62pJSPjw+aNGmiS0QA7RoTIQQuX75cjZCJiMgSrT+cxi68ZJBJyYhSqURISAgSEhL0jickJKBHD8OZbM+ePXH16lUUFBTojp09exYKhQJNmzatRshERGRpvjuZjgXfpOB7TRi+f+hDduElPSbvptm+fTvGjh2LVatWITw8HKtXr8aaNWtw6tQp+Pv7Y9asWbhy5Qo2btwIACgoKEC7du3QvXt3zJ8/H1lZWZg0aRL69OmDNWvWGPWexq7GJSIi85P4Vw5G//cXFJVo8Gz3ZnhnWBAkoWEXXhtg7Pe3yWtGRo4ciezsbCxYsADp6ekICgpCfHw8/P39AQDp6elIS0vTjXd1dUVCQgL+/e9/IzQ0FI0aNcKIESOwcOHCanwsIiKyJH9mFmDiJ4koKtGgXzsvzB8apF1jKLELL/3N5JkROXBmhIjI8mTm38UTHx3ClZt30NmvPrY+3x31lJz9sCW1NjNCRERUTpnGdwXeYXhu/TFcuXkHzRs5Y+24UCYiVCEmI0RE9GAMNL4rtPNA0zvPIsOlFz6ZEIZGrizXQBVjMkJERNVX2viuTL+ZBiVZWOkQg4sPB8K/kYs8sZHFqFbXXiIioqoa30mShIBj72jHEVWCyQgREVXPxUN6t2bKkiCAvCvacUSVYDJCRETVw8Z3VEOYjBARUfWw8R3VECYjRERUPf49UOziA02FAyRA1YSN76hKTEaIiKharuYVYU7hWEDAQELCxndkPCYjRERkshu3ijB27S/YVtAZ8+vNBFx99Qew8R2ZgHVGiIjIJLeLSvDchmM4f/0WfNyd8K+oaVCoXmfjO6o2JiNERGS0YrUGL356AsmXbsK9ngM2TghDk/r1tE+y8R1VE2/TEBGRUTQagRk7fsXPZ6/DyUGBdeO7orWXm9xhkRXgzAgRERl2X/M74eqJ935vgF3JV2GnkLByTAhC/BvIHSFZCSYjRERUXpnmdxKACaIhLikiMeDp5/FIoKe88ZFVYTJCRET6Kmh+540crFIug+QUAqCpLKGRdeKaESIi+ltVze8AYPdMNr+jGsVkhIiI/lZF8zuw+R3VAiYjRET0Nza/IxkwGSEior+x+R3JgMkIERHpXHTthGtoBE35JSP3sPkd1TwmI0REBADIyL2LMesSMbdoLCQJEKXN7nTY/I5qB5MRIiJCzq0iPLv2F1y+cQdnGjyMvKHrIKl89Aex+R3VEtYZISKycfl3izFu3VH8mVkAH3cnfDqxG9wbOgOdh7P5HdUJJiNERDbsbrEaEz9JxMkruWjoosSmid3g19BZ+6TCjs3vqE4wGSEishX39ZqBqxeKmnTHi5uTcPRCDtwc7bFxQhhaebrKHSXZICYjRES2oEyvGQC4Zd8Yyttj4OTQHWvHd0VQE3cZAyRbxmSEiMjaVdBrxr34OlY6xCCl9/8hKKChPLERgbtpiIisW1W9ZiQJQb9Fs9cMyYrJCBGRNaui14zEXjNkBpiMEBFZM/aaIQvAZISIyJqx1wxZACYjRETWzL8HChy92GuGzBqTESIiK7bxl0t4Nf+fAAwtYWWvGTIPTEaIiKzU9mNpmPvVKXyvCUNc20Xa3jL3Y68ZMhOsM0JEZIV2JV3BzC9OAgAm9grAsMGDIIl/sdcMmSUmI0REVubb39Ix/bNkCAE8270Z3hrcDpIkARJ7zZB5YjJCRGTJyvSbSbjVElO3JUMjgBGhTbFgaJA2ESEyY0xGiIgslYF+M0GiIR5FJJw6D0f0kx2hUDARIfPHZISIyBJV0G/GCzlYpYyBJqgL7BTB8sRGZCLupiEisjRV9JsBJNjtmcV+M2QxmIwQEVka9pshK8NkhIjI0rDfDFkZJiNERJaG/WbIyjAZISKyMD/cbol00ZD9ZshqMBkhIrIgP565hhe3/Iq3iyMhSYBA2a277DdDlofJCBGRhfjxzDVEbTqBIrUGdg8NhfrpjZBUPvqD2G+GLBDrjBARWYAfTl/Di59qE5FBHbyxbFQw7O1CgPaPs98MWTwmI0RE5qRMeXf498DulEz8e2sSitUCA4O0iYiD3b2JbQX7zZDlYzJCRGQuDJR3v+Pkja/y/4lidVcM6eSLJSM6/Z2IEFkJ/o0mIjIHpeXdyxQzc7yTgY/sl+LtVucRM7IzExGySvxbTUQktyrKu0sSMC5vFeygqfvYiOoAkxEiIrlVWd4dkFjenawYkxEiIrmxvDvZOCYjRERyY3l3snFMRoiIZCaahSNf6cny7mSzmIwQEclICIHo78/htYLR2p9Z3p1sEJMRIiKZaDQCc776Hav3peJ7TRh+7ryY5d3JJlUrGYmNjUVAQACcnJwQEhKC/fv3G3XewYMHYW9vj86dO1fnbYmIrEaxWoPpnyXj0yNpkCQg+skOeOSJScC034Fx3wBPrdX+Oe0kExGyeiZXYN2+fTumTZuG2NhY9OzZEx9//DEGDhyIlJQUNGvWrMLzcnNzERkZiUcffRTXrnFFOBHZkDIl3u/6dsPL237D/05fg71CwpKRnTG0k692LMu7kw2ShBAVLpkypFu3bujSpQtWrlypO9auXTsMHz4c0dHRFZ43atQotG7dGnZ2dti1axeSk5MrHFtYWIjCwkLdz3l5efDz80Nubi5UKpUp4RIRyctAifdshQfevPss9iq6Y+WzXdA3kLtkyDrl5eXB3d29yu9vk27TFBUV4fjx44iIiNA7HhERgUOHKi7Gs379epw/fx7z5s0z6n2io6Ph7u6ue/j5+ZkSJhGReaigxHsDdRZWOsQg7tEcJiJEMDEZycrKglqthpeX/i+Pl5cXMjIyDJ5z7tw5zJw5E5s3b4a9vXF3hWbNmoXc3Fzd49KlS6aESUQkvypLvEtom/SudhyRjatW115J0t96JoQodwwA1Go1Ro8ejfnz56NNmzZGv76joyMcHR2rExoRkXmossS7AEpLvHONCNk4k5IRDw8P2NnZlZsFyczMLDdbAgD5+flITExEUlISXn75ZQCARqOBEAL29vbYs2cP+vbt+wDhExGZKZZ4JzKaSbdplEolQkJCkJCQoHc8ISEBPXqUrwyoUqlw8uRJJCcn6x5RUVFo27YtkpOT0a1btweLnojIXLHEO5HRTL5NM336dIwdOxahoaEIDw/H6tWrkZaWhqioKADa9R5XrlzBxo0boVAoEBQUpHe+p6cnnJycyh0nIrImh0raoIVoBE9kQ1H+Lja0Jd59WeKdCNVIRkaOHIns7GwsWLAA6enpCAoKQnx8PPz9/QEA6enpSEtLq/FAiYgsRfzJdEzbloxHxFisUsZAQNKuEdFhiXei+5lcZ0QOxu5TJiKS26bDf2Fu3CkIAQwM8sayTpegTJilv5hV1USbiLCyKlk5Y7+/q7WbhoiI9AkhsDThLJb/+CcAYEy3ZlgwLAh2ihDgoSF6FVjh34MzIkT3YTJCRGSqMuXd1X7heCvuNLYe1d6intavNaY+2vrvkgcs8U5UKSYjRESmMFDePdeuMXLujIEkheGdYUF4tru/jAESWR4mI0RExiot716mqmr9kutY6RCD5PDl6MJEhMhkJtUZISKyWUaUd++S8j7LuxNVA5MRIiJjmFLenYhMwmSEiMgYLO9OVGuYjBARGYPl3YlqDZMRIiIjrL3kjauiITQVlomUtMXMWN6dyGRMRoiIKqHWCMz/+hTeiT+L+cWRkCRAoGyzGZZ3J3oQTEaIiCpwt1iNlzafwPqDfwEAggdEAiM2QlL56A9U+QIjNrK8O1E1sc4IERFQrqpqjkcoJm06gRNpN6G0U+DDZzpiWOcmAFoCgY+zvDtRDWIyQkRkoKpqidQIjQvHQuXUA6sjQ9G9RaO/x7O8O1GNYjJCRLatgqqqHppsrFLGIH1Ae/jen4gQUY3jmhEisl1VVFUFJPgens+qqkS1jMkIEdkuVlUlMgtMRojIdrGqKpFZYDJCRDYrz97ItSCsqkpUq5iMEJFN+jOzAMO+VrOqKpEZYDJCRDbnwLksPBF7EBdyCvGR4yRIkgSwqiqRbJiMEJFN2fJLGsatP4r8uyUI8W+A6VNegzRiI8CqqkSyYZ0RIrJOZSqqqv3CEb37LP574AIAYFhnX7z/VEc4OdhpE47AwayqSiQTJiNEZH0MVFTNtfPApTvPAgjDK/3aYMqjre7dnrmHVVWJZMNkhIisSwUVVeuXZGGlQwyOd1uGrv0GyxMbERnENSNEZD2qqKgqSRK6nvmAFVWJzAyTESKyHqyoSmSRmIwQkfVgRVUii8RkhIisRo7UwLiBrKhKZFaYjBCRVTiRdgODdpVoK6pWOIoVVYnMEZMRIrJ4OxIvYdTHR5BRUIL/urwACayoSmRJuLWXiCxDmSJm8O+BIo2EBd+cwqdH0gAAEe29MH3kAEjn25erMwKVrzYRYUVVIrPDZISIzJ+BImZqVx8sVUzAp5kPQZKAaY+2wb/7toJCIbGiKpGFYTJCROatgiJmUkE6Zoh3keH0KoaOisIjgZ7657GiKpHF4JoRIjJflRUxAwAJ+NB1Kx5p06iuIyOiGsRkhIjMVxVFzBQA7AuusogZkYVjMkJE5otFzIhsApMRIjJfxhYnYxEzIovGZISIzFKxWoP3TzfUFjErv2TkHhYxI7IGTEaIyOxk5N7F6DVHsHLfX5hfHAlJAgSLmBFZLW7tJSL5GChk9vOfOXhlezJybhXBzdEew56OgmQXwiJmRFaMyQgRycNAIbN8pSe23hqNHHUY2vuoEDumC5p7uABgETMiayYJISq8G2su8vLy4O7ujtzcXKhUKrnDIaIHVUEhs9K1IVubv4unnn0RTg5MNogsmbHf31wzQkR1q7JCZhIgSRLG3IiFE/MQIpvBZISI6lYVhcwkCCDvCguZEdkQJiNEVLdYyIyIymAyQkR16kSO0riBLGRGZDOYjBBRnSgsUWP+16fw9HeStpBZhSNZyIzI1jAZIaKao1EDF/YDJ3dq/9SoAQB/ZhZg+EeHsP7gX9BAgf0tX4MECWAhMyIC64wQUU0xUDdEqHxxsNUMPH/MF3eK1WjoosR/numIvoGDgRQ/FjIjIgCsM0JENaGCuiECgBDAi8XTUNBiIJaO6AxPldPfAwxUYOWMCJH1MPb7mzMjRPRgKqkbIt07+h+3rXAZPwcK+zL/yVHYAQG96yJKIjJjXDNCRA+mirohCglwK7wGxaXDdRgUEVkSJiNE9GBYN4SIHhCTESJ6IBoXT+MGsm4IEVWAyQgRVVtG7l2M/9FeWzekwqXwrBtCRJVjMkJElaugdsjXv17FgJh92PfnDURrxkOSJAjWDSGiauBuGiKqmIHaIRo3X2xQRWHB+VYAgI5N3TF1xKuQsoJZN4SIqqVaMyOxsbEICAiAk5MTQkJCsH///grHfvHFF+jfvz8aN24MlUqF8PBwfP/999UOmIjqSGntkLI7ZfKvYvzluRhodxRT+rbC5y/2QCtPV23CMe13YNw3wFNrtX9OO8lEhIiqZHIysn37dkybNg2zZ89GUlISevfujYEDByItLc3g+H379qF///6Ij4/H8ePH8cgjj2DIkCFISkp64OCJqJZUUjtEAQASsKz+dkzv1woOdvf9Z6S0bkiHp7V/8tYMERnB5Aqs3bp1Q5cuXbBy5UrdsXbt2mH48OGIjo426jUeeughjBw5EnPnzjVqPCuwEtWxC/uBTx6vety4b1i0jIgqZOz3t0kzI0VFRTh+/DgiIiL0jkdERODQoUNGvYZGo0F+fj4aNmxY4ZjCwkLk5eXpPYioDrF2CBHVIZOSkaysLKjVanh56dcL8PLyQkZGhlGvsXjxYty6dQsjRoyocEx0dDTc3d11Dz8/P1PCJKIHlHzT0biBrB1CRDWgWgtYJUl/+54QotwxQ7Zu3Yq3334b27dvh6dnxYWSZs2ahdzcXN3j0qVL1QmTiEyUd7cYb+z8DU9+C23tkApHsnYIEdUck7b2enh4wM7OrtwsSGZmZrnZkrK2b9+OiRMnYseOHejXr1+lYx0dHeHoaOS/zIjINBV0yt1zKgNzvzqFjLy7kCQFDrScgWdS37x30v1Ly1g7hIhqlknJiFKpREhICBISEvDEE0/ojickJGDYsGEVnrd161ZMmDABW7duxeDBg6sfLRE9GAN1Q9SuPljr9gLeu9AGANC8kTM+eLoTwgIGAylNWTuEiGqdyUXPpk+fjrFjxyI0NBTh4eFYvXo10tLSEBUVBUB7i+XKlSvYuHEjAG0iEhkZiWXLlqF79+66WZV69erB3d29Bj8KEVWqtG5Ime26UkE6JuW/jSS7VxDQexSmPNoaTg73ZjzaDwUCBxucSSEiqikmJyMjR45EdnY2FixYgPT0dAQFBSE+Ph7+/v4AgPT0dL2aIx9//DFKSkrw0ksv4aWXXtIdHzduHDZs2PDgn4CIqlZF3RCNBCyvvx0OEXPKJxqltUOIiGqJyXVG5MA6I0QPiHVDiEgGtVJnhIgsFOuGEJEZYzJCZOVu3i7CmuRbxg1m3RAikgG79hJZAwPbdTVQ4PMTlxH93RncvNUQgx0bwkfKgeGKQJJ2lwzrhhCRDJiMEFk6A9t1i118EOMwER9ltAcAtPFSoaDzQkg/ly4iZ90QIjIfTEaILFkF23XtCtLxKhbiknI6gvo9i+d6Bmi763q5sW4IEZkd7qYhslQaNRATpJ9Y3EcAULv6wn767/ozHhVUYCUiqmnGfn9zZoTIUl08VGEiAmhvvtgXXNWOu3+7LuuGEJGZ4W4aIgt1O/uKcQO5XZeIzBxnRojMUSW3UtQaga1H07D3+3T815jX4nZdIjJzTEaIzI2B3THaRabv44hTT7wddwpnMvKhQCtcr9cIHiIHkoEy79yuS0SWgskIkTmpYHeMyEsHPhuL9UXTcEYTBpWTPab3b4OG7ksh7RwH7QoRbtclIsvEZITIXFTSzE6CgEYA8xw2wbPzE3gloj0auigBBACKjdyuS0QWjckIkbmoYneMQgJ8kY13OucDLsq/n2g/FAgczO26RGSxmIwQmYsHaWbH7bpEZMG4tZfITPx5x8W4gdwdQ0RWhjMjRHWhkq26l3Ju48Pv/8A3v5bggGNDeCMHCoPd7Lg7hoisE5MRotpWwVbd/EfexbIrgdh4+CKK1BpIkgLfN30F46/MvTeIu2OIyDYwGSGqTRVu1b0Kl13P4VLxNBRpwtCzVSPMGtgOQU0GAynNuTuGiGwKG+UR1ZYqGtlpBJBt54FTzxxAn0BvSJKkfy53xxCRhWOjPCK5GbFVt7EmCw87/QlIPmWe5O4YIrId3E1DVEvUeenGDWQjOyKycZwZIaquCm6llKg1+Cr5KvYnXEOMMa/DrbpEZOOYjBBVh4EdMsLNF4fazMCbZwJwMfs2FGiOmU6N4AU2siMiqgxv0xCZqnSHTJn1ICL/KsITX0Hgjb1o6KLEjMfaw/3Jxfc25ZYtHMKtukREpTgzQmSKSprZKQBoJGCx21ZI096ESz1HAC0Beztu1SUiqgSTESJTVLVDBoBr4TUg4+jfu2HYyI6IqFJMRohKVVHbI+dWEY4cTsYgY16r7A4ZbtUlIqoQkxEioMKS7XjsfVzx7Y81+1Kx/dgldFLfxSClEa/HHTJEREZjMkJUYcn2dOCzsXi3+BXEq7sCAG75dMWdu95wunONO2SIiGoIkxGybZUsSJUgoBHAW/YbkevfH1GPtEGvVh6QTn94L3mRwGZ2REQPjlt7ybYZUbLdV8rG5v4a9G7dWNs/pv1QYMRGQFWmhLvKV3ucO2SIiEzCmRGyblUsSs29fgnuxrxO2QWp3CFDRFRjmIyQ9apkUeqp+n2w7sBfuPbbVXxqzG+BoQWp3CFDRFQjmIyQdapiUeryomn4XhMGBdoiS+mBRppsLkglIpIJ14yQ9aliUaoQwDyHTRja0QufT+4Fj6eXsmQ7EZGMmIyQZdGogQv7gZM7tX9q1OXHGLkodXn4HQQ3a8AFqUREMuNtGrIclawBKU0Yrt68g9+OJOMxY17v/kWpXJBKRCQbJiNkGSpYA4K8dIjPInGi2zIsT2+Hfeeuo5t0F49Vp0oqF6QSEcmCt2nI/FWyBgQQEBDwOTIf+89egxAAmoXjtpMXRLk1IKUkQNWEi1KJiMwEZ0bI/BnRKddXysZ7XfLQve9wNPdwAVL+wyqpREQWgjMjJL8qFqUW5aYb9TKj2jlqExGAi1KJiCwIZ0ZIXhUsSlUPWIQjjj2xK+kKsn6/jPUV3XG5X9k1IFyUSkRkEZiMkHwqLEx2FYodkdioK0zWGtecGsET2RWsAqmkMBkXpRIRmT0mI1TzqugHoxuz+w0IA8tMJQAaAbyt3ASPjk9gWHAzNL69FNKOcfdGcA0IEZE1YTJCNcuIWiAajcC5o9+jbd7VCve7KCTAB9l4NzgfCGgIYBggbazgtRdxDQgRkQVjMkI1p4paIH/0+Qjb8jtj9+8ZCCs4iOXG1AJhYTIiIqvHZISMU9WtlyprgQCqvXOwsXAZNFAg37GRce/LwmRERFaPyQhVzYhbL8bWAnmtbTbadBuIXi37Ax+tAfLSYTiBYbdcIiJbwTojVLnSWy9lE428dOCzSNz97Uv8eOYadv6caNTLTQ51Rb/2XnByVGqTGQDslktEZNuYjNgqY7rfVnHrRQOBnM9fxaQNR7HzbIlx73v/bRcWJiMiIvA2jW0y5rYLYPStl8GqC1AF9sedc/+F051rkEy57cJFqURENo/JiK2pZMcLPouEGPEJ/mzUF0cu5KAk+QieM+Illz/uA6ljZyDlw+r1g+GiVCIim8ZkxFqYUGissh0v1z57BQPuane8dFcAzxmx/VZy89b+j9LbLqwFQkREJmAyYg2MvO0iLh6EVMltFwmAN7LR0+EPqJv1QvfmQ1CY9F8ob5tw64W3XYiIyERMRsyZMbMdVRQaO9VrBX62C8fJy7lo/NcPeMeIt133VDM4dO6u/aFJNW698LYLERGZgMlIXTImuShlzGxHJf1dAAEhgIb752FxYeltF1fAiNsuDu737W7hrRciIqplTEYelLEJhrE7WErHGuxmq11k+nuvFTii7Al16j5EVdHfxRfZmNrqOhxb90Fwk67QfLUOinwTC43x1gsREdUiJiNl1fTsRem4Snaw6NXU0Kih+e4NSAa72QpoBNBo/zxEFy7D44pzRs10TO2mAjq01P4w8H3ueCEiIrNSraJnsbGxCAgIgJOTE0JCQrB///5Kx//8888ICQmBk5MTWrRogVWrVlUr2FqXEgfEBAGfPA58PlH7Z0yQ9rihsZVUJtWdY8QOlrxdr+HlzccwZMUBTFiwDIr8KmY7pGxMaXkdvbsEGfe5WGiMiIjMmMkzI9u3b8e0adMQGxuLnj174uOPP8bAgQORkpKCZs2alRt/4cIFDBo0CM8//zw+/fRTHDx4EJMnT0bjxo3x1FNP1ciHqBEmzl5UnmBIuPP1DGzPbo966b9gVKU7WARURdeQdWovTmraY6gi26jZjmndVcBDTwBpC0zv78LbLkREZEYkIYShb7EKdevWDV26dMHKlSt1x9q1a4fhw4cjOjq63Pg33ngDcXFxOH36tO5YVFQUfv31Vxw+fNio98zLy4O7uztyc3OhUqlMCbdSubeLkXe3GIVFxfDf1A32t9INzkgISChQemJZh8+RV6iBz41EvHJlepWvP6roLXjiJpYr/6/KsT8+9B5K2j+FwLu/otnXI6oOftw32tsmuiRKG+nf7n0SznYQEZFMjP3+NmlmpKioCMePH8fMmTP1jkdERODQoUMGzzl8+DAiIiL0jg0YMABr165FcXExHBwcyp1TWFiIwsJCvQ9TG9788iS+PZmO7ooUbFOmVzhOgoBb0TX8fng3jmjaY6gi1ajZi4hmAFzbAn9WPbZvaEcgwBvQNAZ+9jV+toO7XYiIyMKZlIxkZWVBrVbDy8tL77iXlxcyMjIMnpORkWFwfElJCbKysuDj41PunOjoaMyfP9+U0KrFycEOTg4KNLfPN/y9X8Y/2ynRu2lbtLx1GzCiSe2EAd21SUNMtPHJhcJOuwDWlEWmvO1CREQWrFoLWCVJ/2aGEKLcsarGGzpeatasWcjNzdU9Ll26VJ0wq7R4RCeceWcgFkX2N2r8sF5d8NIjrfDYoCe1CUSFy0wlQNXk74Tgsff/Pl52HGA4uTB1kWnpbpcOT2v/ZCJCREQWwqSZEQ8PD9jZ2ZWbBcnMzCw3+1HK29vb4Hh7e3s0atTI4DmOjo5wdHQ0JbQH499D+0VfW7MX1bmVwtkOIiKyESYlI0qlEiEhIUhISMATTzyhO56QkIBhw4YZPCc8PBxff/213rE9e/YgNDTU4HoRWVT31ogpCUZ1kgvW9iAiIhtg8m6a7du3Y+zYsVi1ahXCw8OxevVqrFmzBqdOnYK/vz9mzZqFK1euYOPGjQC0W3uDgoLwwgsv4Pnnn8fhw4cRFRWFrVu3Gr21t7Z205RjsIhZk8oXgppSJI2IiMiG1MpuGgAYOXIksrOzsWDBAqSnpyMoKAjx8fHw9/cHAKSnpyMtLU03PiAgAPHx8XjllVfw0UcfwdfXF8uXLzevGiOlOHtBRERU50yeGZFDnc2MEBERUY0x9vu7WrtpiIiIiGoKkxEiIiKSFZMRIiIikhWTESIiIpIVkxEiIiKSFZMRIiIikhWTESIiIpIVkxEiIiKSFZMRIiIikpXJ5eDlUFokNi8vT+ZIiIiIyFil39tVFXu3iGQkPz8fAODn5ydzJERERGSq/Px8uLu7V/i8RfSm0Wg0uHr1Ktzc3CBJUrVeIy8vD35+frh06RL729QBXu+6x2tet3i96x6ved2qiesthEB+fj58fX2hUFS8MsQiZkYUCgWaNm1aI6+lUqn4l7gO8XrXPV7zusXrXfd4zevWg17vymZESnEBKxEREcmKyQgRERHJymaSEUdHR8ybNw+Ojo5yh2ITeL3rHq953eL1rnu85nWrLq+3RSxgJSIiIutlMzMjREREZJ6YjBAREZGsmIwQERGRrJiMEBERkaysJhmJjY1FQEAAnJycEBISgv3791c6/ueff0ZISAicnJzQokULrFq1qo4itR6mXPMvvvgC/fv3R+PGjaFSqRAeHo7vv/++DqO1fKb+HS918OBB2Nvbo3PnzrUboBUy9ZoXFhZi9uzZ8Pf3h6OjI1q2bIl169bVUbSWz9TrvXnzZnTq1AnOzs7w8fHBc889h+zs7DqK1vLt27cPQ4YMga+vLyRJwq5du6o8p9a+O4UV2LZtm3BwcBBr1qwRKSkpYurUqcLFxUVcvHjR4PjU1FTh7Owspk6dKlJSUsSaNWuEg4OD2LlzZx1HbrlMveZTp04V77//vjh69Kg4e/asmDVrlnBwcBAnTpyo48gtk6nXu9TNmzdFixYtREREhOjUqVPdBGslqnPNhw4dKrp16yYSEhLEhQsXxC+//CIOHjxYh1FbLlOv9/79+4VCoRDLli0TqampYv/+/eKhhx4Sw4cPr+PILVd8fLyYPXu2+PzzzwUA8eWXX1Y6vja/O60iGQkLCxNRUVF6xwIDA8XMmTMNjn/99ddFYGCg3rEXXnhBdO/evdZitDamXnND2rdvL+bPn1/ToVml6l7vkSNHirfeekvMmzePyYiJTL3m3333nXB3dxfZ2dl1EZ7VMfV6f/jhh6JFixZ6x5YvXy6aNm1aazFaM2OSkdr87rT42zRFRUU4fvw4IiIi9I5HRETg0KFDBs85fPhwufEDBgxAYmIiiouLay1Wa1Gda16WRqNBfn4+GjZsWBshWpXqXu/169fj/PnzmDdvXm2HaHWqc83j4uIQGhqKDz74AE2aNEGbNm3w2muv4c6dO3URskWrzvXu0aMHLl++jPj4eAghcO3aNezcuRODBw+ui5BtUm1+d1pEo7zKZGVlQa1Ww8vLS++4l5cXMjIyDJ6TkZFhcHxJSQmysrLg4+NTa/Fag+pc87IWL16MW7duYcSIEbURolWpzvU+d+4cZs6cif3798Pe3uJ/zetcda55amoqDhw4ACcnJ3z55ZfIysrC5MmTkZOTw3UjVajO9e7Rowc2b96MkSNH4u7duygpKcHQoUOxYsWKugjZJtXmd6fFz4yUkiRJ72chRLljVY03dJwqZuo1L7V161a8/fbb2L59Ozw9PWsrPKtj7PVWq9UYPXo05s+fjzZt2tRVeFbJlL/jGo0GkiRh8+bNCAsLw6BBg7BkyRJs2LCBsyNGMuV6p6SkYMqUKZg7dy6OHz+O3bt348KFC4iKiqqLUG1WbX13Wvw/mTw8PGBnZ1cue87MzCyXwZXy9vY2ON7e3h6NGjWqtVitRXWueant27dj4sSJ2LFjB/r161ebYVoNU693fn4+EhMTkZSUhJdffhmA9otSCAF7e3vs2bMHffv2rZPYLVV1/o77+PigSZMmeu3S27VrByEELl++jNatW9dqzJasOtc7OjoaPXv2xIwZMwAAHTt2hIuLC3r37o2FCxdyhrsW1OZ3p8XPjCiVSoSEhCAhIUHveEJCAnr06GHwnPDw8HLj9+zZg9DQUDg4ONRarNaiOtcc0M6IjB8/Hlu2bOF9XROYer1VKhVOnjyJ5ORk3SMqKgpt27ZFcnIyunXrVlehW6zq/B3v2bMnrl69ioKCAt2xs2fPQqFQoGnTprUar6WrzvW+ffs2FAr9rzA7OzsAf/9rnWpWrX53PvASWDNQuiVs7dq1IiUlRUybNk24uLiIv/76SwghxMyZM8XYsWN140u3J73yyisiJSVFrF27llt7TWTqNd+yZYuwt7cXH330kUhPT9c9bt68KddHsCimXu+yuJvGdKZe8/z8fNG0aVPx9NNPi1OnTomff/5ZtG7dWkyaNEmuj2BRTL3e69evF/b29iI2NlacP39eHDhwQISGhoqwsDC5PoLFyc/PF0lJSSIpKUkAEEuWLBFJSUm67dR1+d1pFcmIEEJ89NFHwt/fXyiVStGlSxfx888/654bN26c6NOnj974vXv3iuDgYKFUKkXz5s3FypUr6zhiy2fKNe/Tp48AUO4xbty4ug/cQpn6d/x+TEaqx9Rrfvr0adGvXz9Rr1490bRpUzF9+nRx+/btOo7acpl6vZcvXy7at28v6tWrJ3x8fMSYMWPE5cuX6zhqy/XTTz9V+t/luvzulITgfBYRERHJx+LXjBAREZFlYzJCREREsmIyQkRERLJiMkJERESyYjJCREREsmIyQkRERLJiMkJERESyYjJCREREsmIyQkRERLJiMkJERESyYjJCREREsmIyQkR17vr16/D29sZ7772nO/bLL79AqVRiz549MkZGRHJgozwikkV8fDyGDx+OQ4cOITAwEMHBwRg8eDBiYmLkDo2I6hiTESKSzUsvvYT//e9/6Nq1K3799VccO3YMTk5OcodFRHWMyQgRyebOnTsICgrCpUuXkJiYiI4dO8odEhHJgGtGiEg2qampuHr1KjQaDS5evCh3OEQkE86MEJEsioqKEBYWhs6dOyMwMBBLlizByZMn4eXlJXdoRFTHmIwQkSxmzJiBnTt34tdff4WrqyseeeQRuLm54ZtvvpE7NCKqY7xNQ0R1bu/evYiJicGmTZugUqmgUCiwadMmHDhwACtXrpQ7PCKqY5wZISIiIllxZoSIiIhkxWSEiIiIZMVkhIiIiGTFZISIiIhkxWSEiIiIZMVkhIiIiGTFZISIiIhkxWSEiIiIZMVkhIiIiGTFZISIiIhkxWSEiIiIZPX/TiJq15Qhb4EAAAAASUVORK5CYII=", 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", 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", 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" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -618,8 +607,7 @@ "For a Dirichlet boundary condition $u=a$ on the left-hand boundary, we set the value of the left ghost node to be equal to\n", "$$2*a-u[0],$$\n", "where $u[0]$ is the value of $u$ in the left-most cell in the domain. Similarly, for a Dirichlet condition $u=b$ on the right-hand boundary, we set the right ghost node to be\n", - "$$2*b-u[-1].$$\n", - "Note also that the size of the gradient matrix is now (41,42) instead of (39,40), to account for the presence of boundary conditions in the State Vector." + "$$2*b-u[-1].$$" ] }, { @@ -635,12 +623,12 @@ "text": [ "The gradient object is:\n", "+\n", - "├── @\n", - "│ ├── Sparse Matrix (41, 40)\n", - "│ └── y[0:40]\n", - "└── Column vector of length 41\n", - "The value of u on the left-hand boundary is [-40.]\n", - "The value of u on the right-hand boundary is [80.]\n" + "├── Column vector of length 41\n", + "└── @\n", + " ├── Sparse Matrix (41, 40)\n", + " └── y[0:40]\n", + "The value of u on the left-hand boundary is [1.]\n", + "The value of u on the right-hand boundary is [1.67902865]\n" ] } ], @@ -649,9 +637,10 @@ "grad_u_disc = disc.process_symbol(grad_u)\n", "print(\"The gradient object is:\")\n", "(grad_u_disc.render())\n", - "u_eval = grad_u_disc.children[1].evaluate(y=y)\n", - "print(\"The value of u on the left-hand boundary is {}\".format((u_eval[0] + u_eval[1]) / 2))\n", - "print(\"The value of u on the right-hand boundary is {}\".format((u_eval[-2] + u_eval[-1]) / 2))" + "u_eval = grad_u_disc.evaluate(y=y)\n", + "dx = np.diff(macro_mesh.nodes)[-1]\n", + "print(\"The value of u on the left-hand boundary is {}\".format(y[0] - dx*u_eval[0]/2))\n", + "print(\"The value of u on the right-hand boundary is {}\".format(y[1] + dx*u_eval[-1]/2))" ] }, { @@ -666,7 +655,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 20, "metadata": { "tags": [] }, @@ -677,10 +666,10 @@ "text": [ "The gradient object is:\n", "+\n", - "├── @\n", - "│ ├── Sparse Matrix (41, 40)\n", - "│ └── y[0:40]\n", - "└── Column vector of length 41\n", + "├── Column vector of length 41\n", + "└── @\n", + " ├── Sparse Matrix (41, 40)\n", + " └── y[0:40]\n", "The gradient on the left-hand boundary is [3.]\n", "The gradient of u on the right-hand boundary is [4.]\n" ] @@ -705,7 +694,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 21, "metadata": { "tags": [] }, @@ -716,13 +705,11 @@ "text": [ "The gradient object is:\n", "+\n", - "├── +\n", - "│ ├── @\n", - "│ │ ├── Sparse Matrix (41, 40)\n", - "│ │ └── y[0:40]\n", - "│ └── Column vector of length 41\n", - "└── Column vector of length 41\n", - "The value of u on the left-hand boundary is [0.]\n", + "├── Column vector of length 41\n", + "└── @\n", + " ├── Sparse Matrix (41, 40)\n", + " └── y[0:40]\n", + "The value of u on the left-hand boundary is [0.00036458]\n", "The gradient on the right-hand boundary is [6.]\n" ] } @@ -761,7 +748,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -777,7 +764,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 23, "metadata": { "tags": [] }, @@ -787,10 +774,10 @@ "output_type": "stream", "text": [ "+\n", - "├── @\n", - "│ ├── Sparse Matrix (40, 40)\n", - "│ └── y[0:40]\n", - "└── Column vector of length 40\n" + "├── Column vector of length 40\n", + "└── @\n", + " ├── Sparse Matrix (40, 40)\n", + " └── y[0:40]\n" ] } ], @@ -809,7 +796,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 24, "metadata": { "tags": [] }, @@ -834,7 +821,7 @@ "source": [ "print(\"div(grad) matrix is:\\n\")\n", "print(\"1/dx^2 * \\n{}\".format(\n", - " macro_mesh.d_edges[:,np.newaxis]**2 * div_grad_u_disc.left.left.entries.toarray()\n", + " macro_mesh.d_edges[:,np.newaxis]**2 * div_grad_u_disc.right.left.entries.toarray()\n", "))" ] }, @@ -854,7 +841,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 25, "metadata": { "tags": [] }, @@ -864,7 +851,7 @@ "output_type": "stream", "text": [ "int(u) = [[0.08330729]] is approximately equal to 1/12, 0.08333333333333333\n", - "int(v/r^2) = [[10.57864347]] is approximately equal to 4 * pi * sin(1), 10.574236256325824\n" + "int(v/r^2) = [[11.07985772]] is approximately equal to 4 * pi * sin(1), 10.574236256325824\n" ] } ], @@ -890,7 +877,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 26, "metadata": { "tags": [] }, @@ -909,7 +896,9 @@ "\n", "@\n", "├── Sparse Matrix (1, 10)\n", - "└── y[40:50]\n" + "└── *\n", + " ├── Column vector of length 10\n", + " └── y[40:50]\n" ] } ], @@ -922,7 +911,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -933,7 +922,7 @@ " 1., 1., 1., 1., 1., 1., 1., 1.]])" ] }, - "execution_count": 25, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -942,27 +931,6 @@ "int_u_disc.children[0].evaluate() / macro_mesh.d_edges" ] }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "matrix([[12.56637061, 12.56637061, 12.56637061, 12.56637061, 12.56637061,\n", - " 12.56637061, 12.56637061, 12.56637061, 12.56637061, 12.56637061]])" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "int_v_over_r2_disc.children[0].evaluate() / micro_mesh.d_edges" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -982,7 +950,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -1013,19 +981,17 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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", 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"text/plain": [ - "
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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1122,13 +1086,13 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "981ca5da060e40f59a45c5830b64afaf", + "model_id": "a8807565aaf548f48ddf00a2e1d3f865", "version_major": 2, "version_minor": 0 }, @@ -1178,13 +1142,13 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f83ad987884941ef907dae01b011ef17", + "model_id": "b50896a3c984451282cb4f0efb8389c9", "version_major": 2, "version_minor": 0 }, @@ -1210,13 +1174,13 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2bcaabdc61674c02b24ff1af8cf8c8a4", + "model_id": "a40d1df3cf9745d78ca7b1450ce128b3", "version_major": 2, "version_minor": 0 }, @@ -1264,7 +1228,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -1273,7 +1237,7 @@ "text": [ "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n", "[2] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", - "[3] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). ECSarXiv. February, 2020. doi:10.1149/osf.io/67ckj.\n", + "[3] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", "\n" ] } @@ -1292,7 +1256,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3.9.13 ('conda_jl')", "language": "python", "name": "python3" }, @@ -1306,7 +1270,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.0" + "version": "3.9.13" }, "toc": { "base_numbering": 1, @@ -1320,6 +1284,11 @@ "toc_position": {}, "toc_section_display": true, "toc_window_display": true + }, + "vscode": { + "interpreter": { + "hash": "612adcc456652826e82b485a1edaef831aa6d5abc680d008e93d513dd8724f14" + } } }, "nbformat": 4, diff --git a/examples/scripts/calendar_ageing.py b/examples/scripts/calendar_ageing.py index 5201f0a7ce..b33b4d35c7 100644 --- a/examples/scripts/calendar_ageing.py +++ b/examples/scripts/calendar_ageing.py @@ -7,10 +7,12 @@ pb.lithium_ion.SPM({"SEI": "reaction limited"}), pb.lithium_ion.SPMe({"SEI": "reaction limited"}), pb.lithium_ion.SPM( - {"SEI": "reaction limited", "surface form": "algebraic"}, name="Algebraic SPM" + {"SEI": "reaction limited", "surface form": "algebraic"}, + name="Algebraic SPM", ), pb.lithium_ion.SPMe( - {"SEI": "reaction limited", "surface form": "algebraic"}, name="Algebraic SPMe" + {"SEI": "reaction limited", "surface form": "algebraic"}, + name="Algebraic SPMe", ), pb.lithium_ion.DFN({"SEI": "reaction limited"}), ] diff --git a/examples/scripts/compare_comsol/compare_comsol_DFN.py b/examples/scripts/compare_comsol/compare_comsol_DFN.py index 1d17f7ba33..1b0497eecd 100644 --- a/examples/scripts/compare_comsol/compare_comsol_DFN.py +++ b/examples/scripts/compare_comsol/compare_comsol_DFN.py @@ -78,7 +78,7 @@ def get_interp_fun(variable_name, domain): comsol_x = comsol_variables["x"] # Make sure to use dimensional space - pybamm_x = mesh.combine_submeshes(*domain).nodes * L_x + pybamm_x = mesh[domain].nodes * L_x variable = interp.interp1d(comsol_x, variable, axis=0)(pybamm_x) fun = pybamm.Interpolant( @@ -88,7 +88,7 @@ def get_interp_fun(variable_name, domain): ) fun.domains = {"primary": domain} - fun.mesh = mesh.combine_submeshes(*domain) + fun.mesh = mesh[domain] fun.secondary_mesh = None return fun diff --git a/examples/scripts/compare_lead_acid.py b/examples/scripts/compare_lead_acid.py index 9f73046d35..dedf321298 100644 --- a/examples/scripts/compare_lead_acid.py +++ b/examples/scripts/compare_lead_acid.py @@ -6,12 +6,7 @@ pybamm.set_logging_level("INFO") # load models -models = [ - pybamm.lead_acid.LOQS(), - pybamm.lead_acid.FOQS(), - pybamm.lead_acid.Composite(), - pybamm.lead_acid.Full(), -] +models = [pybamm.lead_acid.LOQS(), pybamm.lead_acid.Full()] # create and run simulations sims = [] diff --git a/examples/scripts/experiment_drive_cycle.py b/examples/scripts/experiment_drive_cycle.py index 2ead334dad..e3e852fa99 100644 --- a/examples/scripts/experiment_drive_cycle.py +++ b/examples/scripts/experiment_drive_cycle.py @@ -30,13 +30,15 @@ def map_drive_cycle(x, min_op_value, max_op_value): experiment = pybamm.Experiment( [ - "Charge at 1 A until 4.0 V", - "Hold at 4.0 V until 50 mA", - "Rest for 30 minutes", - "Run US06_A (A)", - "Rest for 30 minutes", - "Run US06_W (W)", - "Rest for 30 minutes", + ( + "Charge at 1 A until 4.0 V", + "Hold at 4.0 V until 50 mA", + "Rest for 30 minutes", + "Run US06_A (A)", + "Rest for 30 minutes", + "Run US06_W (W)", + "Rest for 30 minutes", + ), ], drive_cycles={ "US06_A": drive_cycle_current, diff --git a/examples/scripts/experimental_protocols/cccv.py b/examples/scripts/experimental_protocols/cccv.py index cdba610154..6851438cf4 100644 --- a/examples/scripts/experimental_protocols/cccv.py +++ b/examples/scripts/experimental_protocols/cccv.py @@ -4,7 +4,7 @@ import pybamm import matplotlib.pyplot as plt -pybamm.set_logging_level("INFO") +pybamm.set_logging_level("NOTICE") experiment = pybamm.Experiment( [ ( diff --git a/examples/scripts/run_ecm.py b/examples/scripts/run_ecm.py new file mode 100644 index 0000000000..c86d5bce02 --- /dev/null +++ b/examples/scripts/run_ecm.py @@ -0,0 +1,44 @@ +import pybamm + +pybamm.set_logging_level("INFO") + +# options = {"number of rc elements": 2} +options = {} +model = pybamm.equivalent_circuit.Thevenin(options=options) + +parameter_values = model.default_parameter_values +# parameter_values.update( +# { +# "R2 [Ohm]": 0.3e-3, +# "C2 [F]": 1000 / 0.3e-3, +# "Element-2 initial overpotential [V]": 0, +# }, +# check_already_exists=False, +# ) + +experiment = pybamm.Experiment( + [ + ( + "Discharge at C/10 for 10 hours or until 3.3 V", + "Rest for 1 hour", + "Charge at 100 A until 4.1 V (1 second period)", + "Hold at 4.1 V until 5 A (1 seconds period)", + "Rest for 1 hour", + ), + ] +) + +sim = pybamm.Simulation(model, experiment=experiment, parameter_values=parameter_values) +sim.solve() +sim.plot( + output_variables=[ + "SoC", + "Open circuit voltage [V]", + "Current [A]", + "Cell temperature [degC]", + "Entropic change [V/K]", + "R0 [Ohm]", + "R1 [Ohm]", + "C1 [F]", + ] +) diff --git a/postBuild b/postBuild deleted file mode 100755 index dee8b9cc12..0000000000 --- a/postBuild +++ /dev/null @@ -1 +0,0 @@ -pip install . diff --git a/pybamm/__init__.py b/pybamm/__init__.py index 7013fb5876..753085663d 100644 --- a/pybamm/__init__.py +++ b/pybamm/__init__.py @@ -111,6 +111,7 @@ ) from .models.full_battery_models import lead_acid from .models.full_battery_models import lithium_ion +from .models.full_battery_models import equivalent_circuit # # Submodel classes @@ -132,6 +133,7 @@ thermal, transport_efficiency, particle_mechanics, + equivalent_circuit_elements, ) from .models.submodels.interface import kinetics from .models.submodels.interface import sei @@ -161,6 +163,7 @@ from .parameters.thermal_parameters import thermal_parameters, ThermalParameters from .parameters.lithium_ion_parameters import LithiumIonParameters from .parameters.lead_acid_parameters import LeadAcidParameters +from .parameters.ecm_parameters import EcmParameters from .parameters.size_distribution_parameters import * from .parameters.parameter_sets import parameter_sets from .parameters_cli import add_parameter, remove_parameter, edit_parameter diff --git a/pybamm/discretisations/discretisation.py b/pybamm/discretisations/discretisation.py index 2068295c2b..f2dff27cd7 100644 --- a/pybamm/discretisations/discretisation.py +++ b/pybamm/discretisations/discretisation.py @@ -60,7 +60,7 @@ def __init__(self, mesh=None, spatial_methods=None): ) ) - self.bcs = {} + self._bcs = {} self.y_slices = {} self._discretised_symbols = {} self.external_variables = {} @@ -187,7 +187,7 @@ def process_model( pybamm.logger.verbose( "Discretise boundary conditions for {}".format(model.name) ) - self.bcs = self.process_boundary_conditions(model) + self._bcs = self.process_boundary_conditions(model) pybamm.logger.verbose( "Set internal boundary conditions for {}".format(model.name) ) @@ -489,7 +489,9 @@ def process_initial_conditions(self, model): """ # Discretise initial conditions - processed_initial_conditions = self.process_dict(model.initial_conditions) + processed_initial_conditions = self.process_dict( + model.initial_conditions, ics=True + ) # Concatenate initial conditions into a single vector # check that all initial conditions are set @@ -728,7 +730,7 @@ def create_mass_matrix(self, model): return mass_matrix, mass_matrix_inv - def process_dict(self, var_eqn_dict): + def process_dict(self, var_eqn_dict, ics=False): """Discretise a dictionary of {variable: equation}, broadcasting if necessary (can be model.rhs, model.algebraic, model.initial_conditions or model.variables). @@ -739,6 +741,9 @@ def process_dict(self, var_eqn_dict): Equations ({variable: equation} dict) to dicretise (can be model.rhs, model.algebraic, model.initial_conditions or model.variables) + ics : bool, optional + Whether the equations are initial conditions. If True, the equations are + scaled by the reference value of the variable, if given Returns ------- @@ -750,14 +755,25 @@ def process_dict(self, var_eqn_dict): for eqn_key, eqn in var_eqn_dict.items(): # Broadcast if the equation evaluates to a number (e.g. Scalar) if np.prod(eqn.shape_for_testing) == 1 and not isinstance(eqn_key, str): - eqn = pybamm.FullBroadcast(eqn, broadcast_domains=eqn_key.domains) + if eqn_key.domain == []: + eqn = eqn * pybamm.Vector([1]) + else: + eqn = pybamm.FullBroadcast(eqn, broadcast_domains=eqn_key.domains) pybamm.logger.debug("Discretise {!r}".format(eqn_key)) - processed_eqn = self.process_symbol(eqn) - new_var_eqn_dict[eqn_key] = processed_eqn + # Calculate scale if the key has a scale + scale = getattr(eqn_key, "scale", 1) + if ics: + reference = getattr(eqn_key, "reference", 0) + else: + reference = 0 + if scale != 1 or reference != 0: + processed_eqn = (processed_eqn - reference) / scale + + new_var_eqn_dict[eqn_key] = processed_eqn return new_var_eqn_dict def process_symbol(self, symbol): @@ -784,14 +800,15 @@ def process_symbol(self, symbol): # Assign mesh as an attribute to the processed variable if symbol.domain != []: - discretised_symbol.mesh = self.mesh.combine_submeshes(*symbol.domain) + discretised_symbol.mesh = self.mesh[symbol.domain] else: discretised_symbol.mesh = None + # Assign secondary mesh if symbol.domains["secondary"] != []: - discretised_symbol.secondary_mesh = self.mesh.combine_submeshes( - *symbol.domains["secondary"] - ) + discretised_symbol.secondary_mesh = self.mesh[ + symbol.domains["secondary"] + ] else: discretised_symbol.secondary_mesh = None return discretised_symbol @@ -897,13 +914,9 @@ def _process_symbol(self, symbol): elif isinstance(symbol, pybamm.Broadcast): # Broadcast new_child to the domain specified by symbol.domain # Different discretisations may broadcast differently - if symbol.domain == []: - out = disc_child * pybamm.Vector([1]) - else: - out = spatial_method.broadcast( - disc_child, symbol.domains, symbol.broadcast_type - ) - return out + return spatial_method.broadcast( + disc_child, symbol.domains, symbol.broadcast_type + ) elif isinstance(symbol, pybamm.DeltaFunction): return spatial_method.delta_function(symbol, disc_child) @@ -935,7 +948,9 @@ def _process_symbol(self, symbol): return symbol._function_new_copy(disc_children) elif isinstance(symbol, pybamm.VariableDot): - return pybamm.StateVectorDot( + # Add symbol's reference and multiply by the symbol's scale + # so that the state vector is of order 1 + return symbol.reference + symbol.scale * pybamm.StateVectorDot( *self.y_slices[symbol.get_variable()], domains=symbol.domains, ) @@ -982,11 +997,29 @@ def _process_symbol(self, symbol): symbol.name ) ) - return pybamm.StateVector(*y_slices, domains=symbol.domains) + # Add symbol's reference and multiply by the symbol's scale + # so that the state vector is of order 1 + return symbol.reference + symbol.scale * pybamm.StateVector( + *y_slices, domains=symbol.domains + ) elif isinstance(symbol, pybamm.SpatialVariable): return spatial_method.spatial_variable(symbol) + elif isinstance(symbol, pybamm.ConcatenationVariable): + # create new children without scale and reference + # the scale and reference will be applied to the concatenation instead + new_children = [] + for child in symbol.children: + child = child.create_copy() + child._scale = 1 + child._reference = 0 + child.set_id() + new_children.append(self.process_symbol(child)) + new_symbol = spatial_method.concatenation(new_children) + # apply scale to the whole concatenation + return symbol.reference + symbol.scale * new_symbol + elif isinstance(symbol, pybamm.Concatenation): new_children = [self.process_symbol(child) for child in symbol.children] new_symbol = spatial_method.concatenation(new_children) diff --git a/pybamm/expression_tree/averages.py b/pybamm/expression_tree/averages.py index 69f9d6275c..629be58a14 100644 --- a/pybamm/expression_tree/averages.py +++ b/pybamm/expression_tree/averages.py @@ -144,36 +144,25 @@ def x_average(symbol): else: # pragma: no cover # It should be impossible to get here raise NotImplementedError - # If symbol is a concatenation of Broadcasts, its average value is the - # thickness-weighted average of the symbols being broadcasted - elif isinstance(symbol, pybamm.Concatenation) and all( - isinstance(child, pybamm.Broadcast) for child in symbol.children + # If symbol is a concatenation, its average value is the + # thickness-weighted average of the average of its children + elif isinstance(symbol, pybamm.Concatenation) and not isinstance( + symbol, pybamm.ConcatenationVariable ): geo = pybamm.geometric_parameters - l_n = geo.n.l - l_s = geo.s.l - l_p = geo.p.l - if symbol.domain == ["negative electrode", "separator", "positive electrode"]: - a, b, c = [orp.orphans[0] for orp in symbol.orphans] - out = (l_n * a + l_s * b + l_p * c) / (l_n + l_s + l_p) - elif symbol.domain == ["separator", "positive electrode"]: - b, c = [orp.orphans[0] for orp in symbol.orphans] - out = (l_s * b + l_p * c) / (l_s + l_p) - # To respect domains we may need to broadcast the child back out - child = symbol.children[0] - # If symbol being returned doesn't have empty domain, return it - if out.domain != []: - return out - # Otherwise we may need to broadcast it - elif child.domains["secondary"] == []: - return out - else: - domain = child.domains["secondary"] - if child.domains["tertiary"] == []: - return pybamm.PrimaryBroadcast(out, domain) - else: - auxiliary_domains = {"secondary": child.domains["tertiary"]} - return pybamm.FullBroadcast(out, domain, auxiliary_domains) + ls = { + ("negative electrode",): geo.n.l, + ("separator",): geo.s.l, + ("positive electrode",): geo.p.l, + ("separator", "positive electrode"): geo.s.l + geo.p.l, + } + out = sum( + ls[tuple(orp.domain)] * x_average(orp) for orp in symbol.orphans + ) / sum(ls[tuple(orp.domain)] for orp in symbol.orphans) + return out + # Average of a sum is sum of averages + elif isinstance(symbol, (pybamm.Addition, pybamm.Subtraction)): + return _sum_of_averages(symbol, x_average) # Otherwise, use Integral to calculate average value else: return XAverage(symbol) @@ -210,6 +199,9 @@ def z_average(symbol): # If symbol is a Broadcast, its average value is its child elif isinstance(symbol, pybamm.Broadcast): return symbol.reduce_one_dimension() + # Average of a sum is sum of averages + elif isinstance(symbol, (pybamm.Addition, pybamm.Subtraction)): + return _sum_of_averages(symbol, z_average) # Otherwise, define a ZAverage else: return ZAverage(symbol) @@ -243,6 +235,9 @@ def yz_average(symbol): # If symbol is a Broadcast, its average value is its child elif isinstance(symbol, pybamm.Broadcast): return symbol.reduce_one_dimension() + # Average of a sum is sum of averages + elif isinstance(symbol, (pybamm.Addition, pybamm.Subtraction)): + return _sum_of_averages(symbol, yz_average) # Otherwise, define a YZAverage else: return YZAverage(symbol) @@ -288,6 +283,9 @@ def r_average(symbol): and has_particle_domain ): return symbol.reduce_one_dimension() + # Average of a sum is sum of averages + elif isinstance(symbol, (pybamm.Addition, pybamm.Subtraction)): + return _sum_of_averages(symbol, r_average) else: return RAverage(symbol) @@ -343,3 +341,10 @@ def size_average(symbol, f_a_dist=None): elif ["positive particle size"] in symbol.domains.values(): f_a_dist = geo.p.prim.f_a_dist(R) return SizeAverage(symbol, f_a_dist) + + +def _sum_of_averages(symbol, average_function): + if isinstance(symbol, pybamm.Addition): + return average_function(symbol.left) + average_function(symbol.right) + elif isinstance(symbol, pybamm.Subtraction): + return average_function(symbol.left) - average_function(symbol.right) diff --git a/pybamm/expression_tree/binary_operators.py b/pybamm/expression_tree/binary_operators.py index e1cdd0b288..d2e557e401 100644 --- a/pybamm/expression_tree/binary_operators.py +++ b/pybamm/expression_tree/binary_operators.py @@ -6,11 +6,12 @@ import numpy as np import sympy from scipy.sparse import csr_matrix, issparse +import functools import pybamm -def preprocess_binary(left, right): +def _preprocess_binary(left, right): if isinstance(left, numbers.Number): left = pybamm.Scalar(left) if isinstance(right, numbers.Number): @@ -54,7 +55,7 @@ class BinaryOperator(pybamm.Symbol): """ def __init__(self, name, left, right): - left, right = preprocess_binary(left, right) + left, right = _preprocess_binary(left, right) domains = self.get_children_domains([left, right]) super().__init__(name, children=[left, right], domains=domains) @@ -267,8 +268,6 @@ def _binary_jac(self, left_jac, right_jac): left, right = self.orphans if left.evaluates_to_constant_number(): return left * right_jac - elif right.evaluates_to_constant_number(): - return right * left_jac else: return right * left_jac + left * right_jac @@ -356,8 +355,6 @@ def _binary_jac(self, left_jac, right_jac): left, right = self.orphans if left.evaluates_to_constant_number(): return -left / right**2 * right_jac - elif right.evaluates_to_constant_number(): - return left_jac / right else: return (right * left_jac - left * right_jac) / right**2 @@ -431,7 +428,7 @@ def _evaluates_on_edges(self, dimension): def inner(left, right): """Return inner product of two symbols.""" - left, right = preprocess_binary(left, right) + left, right = _preprocess_binary(left, right) # simplify multiply by scalar zero, being careful about shape if pybamm.is_scalar_zero(left): return pybamm.zeros_like(right) @@ -664,8 +661,8 @@ def _sympy_operator(self, left, right): return sympy.Max(left, right) -def simplify_elementwise_binary_broadcasts(left, right): - left, right = preprocess_binary(left, right) +def _simplify_elementwise_binary_broadcasts(left, right): + left, right = _preprocess_binary(left, right) def unpack_broadcast_recursive(symbol): if isinstance(symbol, pybamm.Broadcast): @@ -692,7 +689,7 @@ def unpack_broadcast_recursive(symbol): return left, right -def simplified_binary_broadcast_concatenation(left, right, operator): +def _simplified_binary_broadcast_concatenation(left, right, operator): """ Check if there are concatenations or broadcasts that we can commute the operator with @@ -714,16 +711,8 @@ def simplified_binary_broadcast_concatenation(left, right, operator): return left._concatenation_new_copy( [operator(child, right) for child in left.orphans] ) - elif ( - isinstance(right, pybamm.Concatenation) - and not any( - isinstance(child, (pybamm.Variable, pybamm.StateVector)) - for child in right.children - ) - and ( - all(child.is_constant() for child in left.children) - or all(child.is_constant() for child in right.children) - ) + elif isinstance(right, pybamm.Concatenation) and not isinstance( + right, pybamm.ConcatenationVariable ): return left._concatenation_new_copy( [ @@ -741,10 +730,10 @@ def simplified_binary_broadcast_concatenation(left, right, operator): def simplified_power(left, right): - left, right = simplify_elementwise_binary_broadcasts(left, right) + left, right = _simplify_elementwise_binary_broadcasts(left, right) # Check for Concatenations and Broadcasts - out = simplified_binary_broadcast_concatenation(left, right, simplified_power) + out = _simplified_binary_broadcast_concatenation(left, right, simplified_power) if out is not None: return out @@ -790,20 +779,22 @@ def simplified_addition(left, right): (Zero Matrix) + (Zero Scalar) should return (Zero Matrix), not (Zero Scalar). """ - left, right = simplify_elementwise_binary_broadcasts(left, right) + left, right = _simplify_elementwise_binary_broadcasts(left, right) + + # Move constant to always be on the left + if right.is_constant() and not left.is_constant(): + left, right = right, left # Check for Concatenations and Broadcasts - out = simplified_binary_broadcast_concatenation(left, right, simplified_addition) + out = _simplified_binary_broadcast_concatenation(left, right, simplified_addition) if out is not None: return out # anything added by a scalar zero returns the other child - elif pybamm.is_scalar_zero(left): + if pybamm.is_scalar_zero(left): return right - elif pybamm.is_scalar_zero(right): - return left # Check matrices after checking scalars - elif pybamm.is_matrix_zero(left): + if pybamm.is_matrix_zero(left): if right.evaluates_to_number(): return right * pybamm.ones_like(left) # If left object is zero and has size smaller than or equal to right object in @@ -820,20 +811,6 @@ def simplified_addition(left, right): for dim in left.domains.keys() ): return right - elif pybamm.is_matrix_zero(right): - if left.evaluates_to_number(): - return left * pybamm.ones_like(right) - # See comment above - elif all( - left_dim_size >= right_dim_size - for left_dim_size, right_dim_size in zip( - left.shape_for_testing, right.shape_for_testing - ) - ) and all( - left.evaluates_on_edges(dim) == right.evaluates_on_edges(dim) - for dim in left.domains.keys() - ): - return left # Return constant if both sides are constant if left.is_constant() and right.is_constant(): @@ -855,42 +832,29 @@ def simplified_addition(left, right): new_sum.copy_domains(Addition(left, right)) return new_sum - if isinstance(right, Addition) and left.is_constant(): - # Simplify a + (b + c) to (a + b) + c if (a + b) is constant - if right.left.is_constant(): - r_left, r_right = right.orphans - return (left + r_left) + r_right - # Simplify a + (b + c) to (a + c) + b if (a + c) is constant - elif right.right.is_constant(): - r_left, r_right = right.orphans - return (left + r_right) + r_left - elif isinstance(right, Subtraction) and left.is_constant(): - # Simplify a + (b - c) to (a + b) - c if (a + b) is constant - if right.left.is_constant(): + # Turn a + (-b) into a - b + if isinstance(right, pybamm.Negate): + return left - right.orphans[0] + # Turn (-a) + b into b - a + # check for is_constant() to avoid infinite recursion + if isinstance(left, pybamm.Negate) and not left.is_constant(): + return right - left.orphans[0] + + if left.is_constant(): + if isinstance(right, (Addition, Subtraction)) and right.left.is_constant(): + # Simplify a + (b +- c) to (a + b) +- c if (a + b) is constant r_left, r_right = right.orphans - return (left + r_left) - r_right - # Simplify a + (b - c) to (a - c) + b if (a - c) is constant - elif right.right.is_constant(): - r_left, r_right = right.orphans - return (left - r_right) + r_left - if isinstance(left, Addition) and right.is_constant(): - # Simplify (a + b) + c to a + (b + c) if (b + c) is constant - if left.right.is_constant(): - l_left, l_right = left.orphans - return l_left + (l_right + right) - # Simplify (a + b) + c to (a + c) + b if (a + c) is constant - elif left.left.is_constant(): - l_left, l_right = left.orphans - return (l_left + right) + l_right - elif isinstance(left, Subtraction) and right.is_constant(): - # Simplify (a - b) + c to a + (c - b) if (c - b) is constant - if left.right.is_constant(): - l_left, l_right = left.orphans - return l_left + (right - l_right) - # Simplify (a - b) + c to (a + c) - b if (a + c) is constant - elif left.left.is_constant(): - l_left, l_right = left.orphans - return (l_left + right) - l_right + return right._binary_new_copy(left + r_left, r_right) + if isinstance(left, Subtraction): + if right == left.right: + # Simplify (a - b) + b to a + # Make sure shape is preserved + return left.left * pybamm.ones_like(left.right) + if isinstance(right, Subtraction): + if left == right.right: + # Simplify a + (b - a) to b + # Make sure shape is preserved + return right.left * pybamm.ones_like(right.right) return pybamm.simplify_if_constant(Addition(left, right)) @@ -903,18 +867,23 @@ def simplified_subtraction(left, right): (Zero Matrix) - (Zero Scalar) should return (Zero Matrix), not -(Zero Scalar). """ - left, right = simplify_elementwise_binary_broadcasts(left, right) + left, right = _simplify_elementwise_binary_broadcasts(left, right) + + # Move constant to always be on the left + # For a subtraction, this means (var - constant) becomes (-constant + var) + if right.is_constant() and not left.is_constant(): + return -right + left # Check for Concatenations and Broadcasts - out = simplified_binary_broadcast_concatenation(left, right, simplified_subtraction) + out = _simplified_binary_broadcast_concatenation( + left, right, simplified_subtraction + ) if out is not None: return out # anything added by a scalar zero returns the other child if pybamm.is_scalar_zero(left): return -right - if pybamm.is_scalar_zero(right): - return left # Check matrices after checking scalars if pybamm.is_matrix_zero(left): if right.evaluates_to_number(): @@ -930,20 +899,6 @@ def simplified_subtraction(left, right): for dim in left.domains.keys() ): return -right - if pybamm.is_matrix_zero(right): - if left.evaluates_to_number(): - return left * pybamm.ones_like(right) - # See comments in simplified_addition - elif all( - left_dim_size >= right_dim_size - for left_dim_size, right_dim_size in zip( - left.shape_for_testing, right.shape_for_testing - ) - ) and all( - left.evaluates_on_edges(dim) == right.evaluates_on_edges(dim) - for dim in left.domains.keys() - ): - return left # Return constant if both sides are constant if left.is_constant() and right.is_constant(): @@ -953,51 +908,50 @@ def simplified_subtraction(left, right): if left == right: return pybamm.zeros_like(left) - if isinstance(right, Addition) and left.is_constant(): - # Simplify a - (b + c) to (a - b) - c if (a - b) is constant - if right.left.is_constant(): - r_left, r_right = right.orphans - return (left - r_left) - r_right - # Simplify a - (b + c) to (a - c) - b if (a - c) is constant - elif right.right.is_constant(): - r_left, r_right = right.orphans - return (left - r_right) - r_left - elif isinstance(right, Subtraction) and left.is_constant(): - # Simplify a - (b - c) to (a - b) + c if (a - b) is constant - if right.left.is_constant(): - r_left, r_right = right.orphans - return (left - r_left) + r_right - # Simplify a - (b - c) to (a + c) - b if (a + c) is constant - elif right.right.is_constant(): + # Turn a - (-b) into a + b + if isinstance(right, pybamm.Negate): + return left + right.orphans[0] + + if left.is_constant(): + if isinstance(right, (Addition, Subtraction)) and right.left.is_constant(): + # Simplify a - (b +- c) to (a - b) -+ c if (a - b) is constant r_left, r_right = right.orphans - return (left + r_right) - r_left - if isinstance(left, Addition) and right.is_constant(): - # Simplify (a + b) - c to a + (b - c) if (b - c) is constant - if left.right.is_constant(): - l_left, l_right = left.orphans - return l_left + (l_right - right) - # Simplify (a + b) - c to (a - c) + b if (a - c) is constant - elif left.left.is_constant(): - l_left, l_right = left.orphans - return (l_left - right) + l_right - elif isinstance(left, Subtraction) and right.is_constant(): - # Simplify (a - b) - c to a - (c + b) if (c + b) is constant - if left.right.is_constant(): - l_left, l_right = left.orphans - return l_left - (right + l_right) - # Simplify (a - b) - c to (a - c) - b if (a - c) is constant - elif left.left.is_constant(): - l_left, l_right = left.orphans - return (l_left - right) - l_right + return right._binary_new_copy(left - r_left, -r_right) + elif isinstance(left, Addition): + if right == left.right: + # Simplify (b + a) - a to b + return left.left + if right == left.left: + # Simplify (a + b) - a to b + return left.right + elif isinstance(left, Subtraction): + if right == left.left: + # Simplify (b - a) - b to -a + return -left.right + elif isinstance(right, Addition): + if left == right.left: + # Simplify a - (a + b) to -b + return -right.right + if left == right.right: + # Simplify a - (b + a) to -b + return -right.left + elif isinstance(right, Subtraction): + if left == right.left: + # Simplify a - (a - b) to b + return right.right return pybamm.simplify_if_constant(Subtraction(left, right)) def simplified_multiplication(left, right): - left, right = simplify_elementwise_binary_broadcasts(left, right) + left, right = _simplify_elementwise_binary_broadcasts(left, right) + + # Move constant to always be on the left + if right.is_constant() and not left.is_constant(): + left, right = right, left # Check for Concatenations and Broadcasts - out = simplified_binary_broadcast_concatenation( + out = _simplified_binary_broadcast_concatenation( left, right, simplified_multiplication ) if out is not None: @@ -1006,24 +960,18 @@ def simplified_multiplication(left, right): # simplify multiply by scalar zero, being careful about shape if pybamm.is_scalar_zero(left): return pybamm.zeros_like(right) - if pybamm.is_scalar_zero(right): - return pybamm.zeros_like(left) # if one of the children is a zero matrix, we have to be careful about shapes - if pybamm.is_matrix_zero(left) or pybamm.is_matrix_zero(right): + if pybamm.is_matrix_zero(left): return pybamm.zeros_like(Multiplication(left, right)) # anything multiplied by a scalar one returns itself if pybamm.is_scalar_one(left): return right - if pybamm.is_scalar_one(right): - return left # anything multiplied by a scalar negative one returns negative itself if pybamm.is_scalar_minus_one(left): return -right - if pybamm.is_scalar_minus_one(right): - return -left # Return constant if both sides are constant if left.is_constant() and right.is_constant(): @@ -1041,109 +989,80 @@ def simplified_multiplication(left, right): ): if pybamm.is_matrix_one(left): return right - elif pybamm.is_matrix_one(right): - return left # also check for negative one if pybamm.is_matrix_minus_one(left): return -right - elif pybamm.is_matrix_minus_one(right): - return -left except NotImplementedError: pass - # Simplify (B @ c) * a to (a * B) @ c if (a * B) is constant - # This is a common construction that appears from discretisation of spatial - # operators - if ( - isinstance(left, MatrixMultiplication) - and left.left.is_constant() - and right.is_constant() - and not (right.ndim_for_testing == 2 and right.shape_for_testing[1] > 1) - ): - l_left, l_right = left.orphans - new_left = right * l_left - # be careful about domains to avoid weird errors - new_left.clear_domains() - new_mul = new_left @ l_right - # Keep the domain of the old left - new_mul.copy_domains(left) - return new_mul - - elif isinstance(left, Multiplication) and right.is_constant(): - # Simplify (a * b) * c to (a * c) * b if (a * c) is constant - if left.left.is_constant(): - l_left, l_right = left.orphans - return (l_left * right) * l_right - # Simplify (a * b) * c to a * (b * c) if (b * c) is constant - elif left.right.is_constant(): - l_left, l_right = left.orphans - return l_left * (l_right * right) - elif isinstance(left, Division) and right.is_constant(): - # Simplify (a / b) * c to a * (c / b) if (c / b) is constant - if left.right.is_constant(): - l_left, l_right = left.orphans - return l_left * (right / l_right) - - # Simplify a * (B @ c) to (a * B) @ c if (a * B) is constant - if ( - isinstance(right, MatrixMultiplication) - and right.left.is_constant() - and left.is_constant() - and not (left.ndim_for_testing == 2 and left.shape_for_testing[1] > 1) - ): - r_left, r_right = right.orphans - new_left = left * r_left - # be careful about domains to avoid weird errors - new_left.clear_domains() - new_mul = new_left @ r_right - # Keep the domain of the old right - new_mul.copy_domains(right) - return new_mul - - elif isinstance(right, Multiplication) and left.is_constant(): - # Simplify a * (b * c) to (a * b) * c if (a * b) is constant - if right.left.is_constant(): - r_left, r_right = right.orphans - return (left * r_left) * r_right - # Simplify a * (b * c) to (a * c) * b if (a * c) is constant - elif right.right.is_constant(): - r_left, r_right = right.orphans - return (left * r_right) * r_left - elif isinstance(right, Division) and left.is_constant(): - # Simplify a * (b / c) to (a / c) * b if (a / c) is constant - if right.right.is_constant(): - r_left, r_right = right.orphans - return (left / r_right) * r_left - - # Simplify a * (b + c) to (a * b) + (a * c) if (a * b) or (a * c) is constant - # This is a common construction that appears from discretisation of spatial - # operators - # Also do this for cases like a * (b @ c + d) where (a * b) is constant - elif isinstance(right, (Addition, Subtraction)): - mul_classes = (Multiplication, MatrixMultiplication, Division) + if left.is_constant(): + # Simplify a * (B @ c) to (a * B) @ c if (a * B) is constant if ( - right.left.is_constant() - or right.right.is_constant() - or (isinstance(right.left, mul_classes) and right.left.left.is_constant()) - or (isinstance(right.right, mul_classes) and right.right.left.is_constant()) + isinstance(right, MatrixMultiplication) + and right.left.is_constant() + and not (left.ndim_for_testing == 2 and left.shape_for_testing[1] > 1) ): r_left, r_right = right.orphans - if (r_left.domain == right.domain or r_left.domain == []) and ( - r_right.domain == right.domain or r_right.domain == [] + new_left = left * r_left + # be careful about domains to avoid weird errors + new_left.clear_domains() + new_mul = new_left @ r_right + # Keep the domain of the old right + new_mul.copy_domains(right) + return new_mul + + elif isinstance(right, Multiplication): + # Simplify a * (b * c) to (a * b) * c if (a * b) is constant + if right.left.is_constant(): + r_left, r_right = right.orphans + return (left * r_left) * r_right + elif isinstance(right, Division): + # Simplify a * (b / c) to (a * b) / c if (a * c) is constant + if right.left.is_constant(): + r_left, r_right = right.orphans + return (left * r_left) / r_right + + # Simplify a * (b + c) to (a * b) + (a * c) if (a * b) is constant + # This is a common construction that appears from discretisation of spatial + # operators + # Also do this for cases like a * (b @ c + d) where (a * b) is constant + elif isinstance(right, (Addition, Subtraction)): + mul_classes = (Multiplication, MatrixMultiplication) + if ( + right.left.is_constant() + or ( + isinstance(right.left, mul_classes) + and right.left.left.is_constant() + ) + or ( + isinstance(right.right, mul_classes) + and right.right.left.is_constant() + ) ): - if isinstance(right, Addition): - return (left * r_left) + (left * r_right) - elif isinstance(right, Subtraction): - return (left * r_left) - (left * r_right) + r_left, r_right = right.orphans + if (r_left.domain == right.domain or r_left.domain == []) and ( + r_right.domain == right.domain or r_right.domain == [] + ): + if isinstance(right, Addition): + return (left * r_left) + (left * r_right) + elif isinstance(right, Subtraction): + return (left * r_left) - (left * r_right) + + # Cancelling out common terms + if isinstance(left, Division): + # Simplify (a / b) * b to a + if left.right == right: + return left.left + if isinstance(right, Division): + # Simplify a * (b / a) to b + if left == right.right: + return right.left # Negation simplifications if isinstance(left, pybamm.Negate) and isinstance(right, pybamm.Negate): # Double negation cancels out return left.orphans[0] * right.orphans[0] - elif isinstance(left, pybamm.Negate) and right.is_constant(): - # Simplify (-a) * b to a * (-b) if (-b) is constant - return left.orphans[0] * (-right) elif isinstance(right, pybamm.Negate) and left.is_constant(): # Simplify a * (-b) to (-a) * b if (-a) is constant return (-left) * right.orphans[0] @@ -1152,10 +1071,19 @@ def simplified_multiplication(left, right): def simplified_division(left, right): - left, right = simplify_elementwise_binary_broadcasts(left, right) + left, right = _simplify_elementwise_binary_broadcasts(left, right) + + # anything divided by zero raises error + if pybamm.is_scalar_zero(right): + raise ZeroDivisionError + + # Move constant to always be on the left + # For a division, this means (var / constant) becomes (1/constant * var) + if right.is_constant() and not left.is_constant(): + return (1 / right) * left # Check for Concatenations and Broadcasts - out = simplified_binary_broadcast_concatenation(left, right, simplified_division) + out = _simplified_binary_broadcast_concatenation(left, right, simplified_division) if out is not None: return out @@ -1167,120 +1095,53 @@ def simplified_division(left, right): if pybamm.is_matrix_zero(left): return pybamm.zeros_like(Division(left, right)) - # anything divided by zero raises error - if pybamm.is_scalar_zero(right): - raise ZeroDivisionError - - # anything divided by one is itself - if pybamm.is_scalar_one(right): - return left - # a symbol divided by itself is 1s of the same shape if left == right: return pybamm.ones_like(left) - # anything multiplied by a matrix one returns itself if - # - the shapes are the same - # - both left and right evaluate on edges, or both evaluate on nodes, in all - # dimensions - # (and possibly more generally, but not implemented here) - try: - if left.shape_for_testing == right.shape_for_testing and all( - left.evaluates_on_edges(dim) == right.evaluates_on_edges(dim) - for dim in left.domains.keys() - ): - if pybamm.is_matrix_one(right): - return left - # also check for negative one - if pybamm.is_matrix_minus_one(right): - return -left - - except NotImplementedError: - pass - # Return constant if both sides are constant if left.is_constant() and right.is_constant(): return pybamm.simplify_if_constant(Division(left, right)) - # Simplify (B @ c) / a to (B / a) @ c if (B / a) is constant - # This is a common construction that appears from discretisation of averages - elif isinstance(left, MatrixMultiplication) and right.is_constant(): - l_left, l_right = left.orphans - new_left = l_left / right - if new_left.is_constant(): - # be careful about domains to avoid weird errors - new_left.clear_domains() - new_division = new_left @ l_right - # Keep the domain of the old left - new_division.copy_domains(left) - return new_division - - if isinstance(left, Multiplication) and right.is_constant(): - # Simplify (a * b) / c to (a / c) * b if (a / c) is constant - if left.left.is_constant(): - l_left, l_right = left.orphans - return (l_left / right) * l_right - # Simplify (a * b) / c to a * (b / c) if (b / c) is constant - elif left.right.is_constant(): - l_left, l_right = left.orphans - return l_left * (l_right / right) - elif isinstance(left, Division) and right.is_constant(): - # Simplify (a / b) / c to (a / c) / b if (a / c) is constant - if left.left.is_constant(): - l_left, l_right = left.orphans - return (l_left / right) / l_right - # Simplify (a / b) / c to a / (b * c) if (b * c) is constant - elif left.right.is_constant(): - l_left, l_right = left.orphans - return l_left / (l_right * right) - - if isinstance(right, Multiplication) and left.is_constant(): - # Simplify a / (b * c) to (a / b) / c if (a / b) is constant - if right.left.is_constant(): - r_left, r_right = right.orphans - return (left / r_left) / r_right - # Simplify a / (b * c) to (a / c) / b if (a / c) is constant - elif right.right.is_constant(): - r_left, r_right = right.orphans - return (left / r_right) / r_left - elif isinstance(right, Division) and left.is_constant(): - # Simplify a / (b / c) to (a / b) * c if (a / b) is constant - if right.left.is_constant(): - r_left, r_right = right.orphans - return (left / r_left) * r_right - # Simplify a / (b / c) to (a * c) / b if (a * c) is constant - elif right.right.is_constant(): + if left.is_constant(): + if isinstance(right, (Multiplication, Division)) and right.left.is_constant(): r_left, r_right = right.orphans - return (left * r_right) / r_left + # Simplify a / (b */ c) to (a / b) /* c if (a / b) is constant + if isinstance(right, Multiplication): + return (left / r_left) / r_right + elif isinstance(right, Division): + return (left / r_left) * r_right # Cancelling out common terms - if isinstance(left, Multiplication) and isinstance(right, Multiplication): - if left.left == right.left: - _, l_right = left.orphans - _, r_right = right.orphans - return l_right / r_right - if left.right == right.right: - l_left, _ = left.orphans - r_left, _ = right.orphans - return l_left / r_left + if isinstance(left, Multiplication): + if left.left == right: + return left.right + elif left.right == right: + return left.left + elif isinstance(right, Multiplication): + if left.left == right.left: + _, l_right = left.orphans + _, r_right = right.orphans + return l_right / r_right + if left.right == right.right: + l_left, _ = left.orphans + r_left, _ = right.orphans + return l_left / r_left # Negation simplifications - if isinstance(left, pybamm.Negate) and isinstance(right, pybamm.Negate): - # Double negation cancels out - return left.orphans[0] / right.orphans[0] - elif isinstance(left, pybamm.Negate) and right.is_constant(): - # Simplify (-a) / b to a / (-b) if (-b) is constant - return left.orphans[0] / (-right) - - if isinstance(right, pybamm.Negate) and left.is_constant(): - # Simplify a / (-b) to (-a) / b if (-a) is constant - return (-left) / right.orphans[0] + if isinstance(right, pybamm.Negate): + if isinstance(left, pybamm.Negate): + # Double negation cancels out + return left.orphans[0] / right.orphans[0] + elif left.is_constant(): + # Simplify a / (-b) to (-a) / b if (-a) is constant + return (-left) / right.orphans[0] return pybamm.simplify_if_constant(Division(left, right)) def simplified_matrix_multiplication(left, right): - left, right = preprocess_binary(left, right) + left, right = _preprocess_binary(left, right) if pybamm.is_matrix_zero(left) or pybamm.is_matrix_zero(right): return pybamm.zeros_like(MatrixMultiplication(left, right)) @@ -1289,15 +1150,6 @@ def simplified_matrix_multiplication(left, right): if right.left.evaluates_to_constant_number(): r_left, r_right = right.orphans return (left * r_left) @ r_right - # Simplify A @ (b * c) to (A * c) @ b if (A * c) is constant - elif right.right.evaluates_to_constant_number(): - r_left, r_right = right.orphans - return (left * r_right) @ r_left - elif isinstance(right, Division) and left.is_constant(): - # Simplify A @ (b / c) to (A / c) @ b if (A / c) is constant - if right.right.evaluates_to_constant_number(): - r_left, r_right = right.orphans - return (left / r_right) @ r_left # Simplify A @ (B @ c) to (A @ B) @ c if (A @ B) is constant # This is a common construction that appears from discretisation of spatial @@ -1327,12 +1179,12 @@ def simplified_matrix_multiplication(left, right): (right.left.is_constant() or right.right.is_constant()) # these lines should work but don't, possibly because of poorly # conditioned model? - # or ( - # isinstance(right.left, MatrixMultiplication) - # and right.left.left.is_constant() - # and isinstance(right.right, MatrixMultiplication) - # and right.right.left.is_constant() - # ) + or ( + isinstance(right.left, MatrixMultiplication) + and right.left.left.is_constant() + and isinstance(right.right, MatrixMultiplication) + and right.right.left.is_constant() + ) ) and not ( right.left.size_for_testing == 1 or right.right.size_for_testing == 1 ): @@ -1352,10 +1204,16 @@ def minimum(left, right): Returns the smaller of two objects, possibly with a smoothing approximation. Not to be confused with :meth:`pybamm.min`, which returns min function of child. """ + # Check for Concatenations and Broadcasts + left, right = _simplify_elementwise_binary_broadcasts(left, right) + out = _simplified_binary_broadcast_concatenation(left, right, minimum) + if out is not None: + return out + k = pybamm.settings.min_smoothing # Return exact approximation if that is the setting or the outcome is a constant # (i.e. no need for smoothing) - if k == "exact" or (pybamm.is_constant(left) and pybamm.is_constant(right)): + if k == "exact" or (left.is_constant() and right.is_constant()): out = Minimum(left, right) else: out = pybamm.softminus(left, right, k) @@ -1367,16 +1225,61 @@ def maximum(left, right): Returns the larger of two objects, possibly with a smoothing approximation. Not to be confused with :meth:`pybamm.max`, which returns max function of child. """ + # Check for Concatenations and Broadcasts + left, right = _simplify_elementwise_binary_broadcasts(left, right) + out = _simplified_binary_broadcast_concatenation(left, right, maximum) + if out is not None: + return out + k = pybamm.settings.max_smoothing # Return exact approximation if that is the setting or the outcome is a constant # (i.e. no need for smoothing) - if k == "exact" or (pybamm.is_constant(left) and pybamm.is_constant(right)): + if k == "exact" or (left.is_constant() and right.is_constant()): out = Maximum(left, right) else: out = pybamm.softplus(left, right, k) return pybamm.simplify_if_constant(out) +def _heaviside(left, right, equal): + """return a :class:`EqualHeaviside` object, or a smooth approximation.""" + # Check for Concatenations and Broadcasts + left, right = _simplify_elementwise_binary_broadcasts(left, right) + out = _simplified_binary_broadcast_concatenation( + left, right, functools.partial(_heaviside, equal=equal) + ) + if out is not None: + return out + + if ( + left.is_constant() + and isinstance(right, BinaryOperator) + and right.left.is_constant() + ): + if isinstance(right, Addition): + # simplify heaviside(a, b + var) to heaviside(a - b, var) + return _heaviside(left - right.left, right.right, equal=equal) + # elif isinstance(right, Multiplication): + # # simplify heaviside(a, b * var) to heaviside(a/b, var) + # if right.left.evaluate() > 0: + # return _heaviside(left / right.left, right.right, equal=equal) + # else: + # # maintain the sign of each side + # return _heaviside(left / -right.left, -right.right, equal=equal) + + k = pybamm.settings.heaviside_smoothing + # Return exact approximation if that is the setting or the outcome is a constant + # (i.e. no need for smoothing) + if k == "exact" or (left.is_constant() and right.is_constant()): + if equal is True: + out = pybamm.EqualHeaviside(left, right) + else: + out = pybamm.NotEqualHeaviside(left, right) + else: + out = pybamm.sigmoid(left, right, k) + return pybamm.simplify_if_constant(out) + + def softminus(left, right, k): """ Softplus approximation to the minimum function. k is the smoothing parameter, diff --git a/pybamm/expression_tree/broadcasts.py b/pybamm/expression_tree/broadcasts.py index 2c235ecd6d..8c66ee9c97 100644 --- a/pybamm/expression_tree/broadcasts.py +++ b/pybamm/expression_tree/broadcasts.py @@ -87,6 +87,8 @@ def check_and_set_domains(self, child, broadcast_domain): # Can only do primary broadcast from current collector to electrode, # particle-size or particle or from electrode to particle-size or particle. # Note e.g. current collector to particle *is* allowed + if broadcast_domain == []: + raise pybamm.DomainError("Cannot Broadcast an object into empty domain.") if child.domain == []: pass elif child.domain == ["current collector"] and not ( @@ -430,7 +432,10 @@ def __init__( def check_and_set_domains(self, child, broadcast_domains): """See :meth:`Broadcast.check_and_set_domains`""" - + if broadcast_domains["primary"] == []: + raise pybamm.DomainError( + """Cannot do full broadcast to an empty primary domain""" + ) # Variables on the current collector can only be broadcast to 'primary' if child.domain == ["current collector"]: raise pybamm.DomainError( @@ -544,6 +549,10 @@ def full_like(symbols, fill_value): return array_type(entries, domains=sum_symbol.domains) except NotImplementedError: + if sum_symbol.shape_for_testing == (1, 1) or sum_symbol.shape_for_testing == ( + 1, + ): + return pybamm.Scalar(fill_value) if sum_symbol.evaluates_on_edges("primary"): return FullBroadcastToEdges( fill_value, broadcast_domains=sum_symbol.domains diff --git a/pybamm/expression_tree/concatenations.py b/pybamm/expression_tree/concatenations.py index b3abef26f6..1e1d66df7f 100644 --- a/pybamm/expression_tree/concatenations.py +++ b/pybamm/expression_tree/concatenations.py @@ -42,6 +42,7 @@ def __init__(self, *children, name=None, check_domain=True, concat_fun=None): else: domains = {"primary": []} self.concatenation_function = concat_fun + super().__init__(name, children, domains=domains) def __str__(self): @@ -260,7 +261,7 @@ def _get_auxiliary_domain_repeats(self, auxiliary_domains): mesh_pts = 1 for level, dom in auxiliary_domains.items(): if level != "primary" and dom != []: - mesh_pts *= self.full_mesh.combine_submeshes(*dom).npts + mesh_pts *= self.full_mesh[dom].npts return mesh_pts @property @@ -364,9 +365,22 @@ def __init__(self, *children): name = intersect(children[0].name, children[1].name) for child in children[2:]: name = intersect(name, child.name) - name = name.capitalize() - if name == "": + if len(name) == 0: name = None + # name is unchanged if its length is 1 + elif len(name) > 1: + name = name[0].capitalize() + name[1:] + + if len(children) > 0: + if all(child.scale == children[0].scale for child in children): + self._scale = children[0].scale + else: + raise ValueError("Cannot concatenate symbols with different scales") + if all(child.reference == children[0].reference for child in children): + self._reference = children[0].reference + else: + raise ValueError("Cannot concatenate symbols with different references") + super().__init__(*children, name=name) # Overly tight bounds, can edit later if required self.bounds = ( diff --git a/pybamm/expression_tree/interpolant.py b/pybamm/expression_tree/interpolant.py index fdea90b306..eafe887a15 100644 --- a/pybamm/expression_tree/interpolant.py +++ b/pybamm/expression_tree/interpolant.py @@ -68,12 +68,12 @@ def __init__( x1, x2 = x if y.ndim != 2: raise ValueError("y should be two-dimensional if len(x)=2") - if x1.shape[0] != y.shape[1]: + if x1.shape[0] != y.shape[0]: raise ValueError( "len(x1) should equal y=shape[1], " f"but x1.shape={x1.shape} and y.shape={y.shape}" ) - if x2 is not None and x2.shape[0] != y.shape[0]: + if x2 is not None and x2.shape[0] != y.shape[1]: raise ValueError( "len(x2) should equal y=shape[0], " f"but x2.shape={x2.shape} and y.shape={y.shape}" @@ -153,9 +153,18 @@ def __init__( "interpolator should be 'linear' or 'cubic' if x is two-dimensional" ) else: - interpolating_function = interpolate.interp2d( - x1, x2, y, kind=interpolator + if extrapolate: + fill_value = None + else: + fill_value = np.nan + interpolating_function = interpolate.RegularGridInterpolator( + (x1, x2), + y, + method=interpolator, + bounds_error=False, + fill_value=fill_value, ) + elif len(x) == 3: self.dimension = 3 @@ -241,13 +250,7 @@ def _function_evaluate(self, evaluated_children): children_eval_flat.append(child) if self.dimension == 1: return self.function(*children_eval_flat).flatten()[:, np.newaxis] - elif self.dimension == 2: - res = self.function(*children_eval_flat) - if res.ndim > 1: - return np.diagonal(res)[:, np.newaxis] - else: - return res[:, np.newaxis] - elif self.dimension == 3: + elif self.dimension in [2, 3]: # If the children are scalars, we need to add a dimension shapes = [] diff --git a/pybamm/expression_tree/operations/evaluate_python.py b/pybamm/expression_tree/operations/evaluate_python.py index 4c0ab71dc6..b5e526ba96 100644 --- a/pybamm/expression_tree/operations/evaluate_python.py +++ b/pybamm/expression_tree/operations/evaluate_python.py @@ -210,14 +210,9 @@ def find_symbols(symbol, constant_symbols, variable_symbols, output_jax=False): children_vars[0], children_vars[1] ) elif scipy.sparse.issparse(dummy_eval_right): - if output_jax and is_scalar(dummy_eval_left): - symbol_str = "{1}.scalar_multiply({0})".format( - children_vars[0], children_vars[1] - ) - else: - symbol_str = "{1}.multiply({0})".format( - children_vars[0], children_vars[1] - ) + symbol_str = "{1}.multiply({0})".format( + children_vars[0], children_vars[1] + ) else: symbol_str = "{0} * {1}".format(children_vars[0], children_vars[1]) elif isinstance(symbol, pybamm.Division): diff --git a/pybamm/expression_tree/scalar.py b/pybamm/expression_tree/scalar.py index caa8a6e688..912368197b 100644 --- a/pybamm/expression_tree/scalar.py +++ b/pybamm/expression_tree/scalar.py @@ -45,7 +45,7 @@ def set_id(self): """See :meth:`pybamm.Symbol.set_id()`.""" # We must include the value in the hash, since different scalars can be # indistinguishable by class and name alone - self._id = hash((self.__class__, self.name) + tuple(str(self._value))) + self._id = hash((self.__class__, str(self.value))) def _base_evaluate(self, t=None, y=None, y_dot=None, inputs=None): """See :meth:`pybamm.Symbol._base_evaluate()`.""" diff --git a/pybamm/expression_tree/state_vector.py b/pybamm/expression_tree/state_vector.py index 68c6a4fe5d..ad0c456a2a 100644 --- a/pybamm/expression_tree/state_vector.py +++ b/pybamm/expression_tree/state_vector.py @@ -49,7 +49,7 @@ def __init__( raise TypeError("all y_slices must be slice objects") if name is None: if y_slices[0].start is None: - name = base_name + "[:{:d}".format(y_slice.stop) + name = base_name + "[0:{:d}".format(y_slice.stop) else: name = base_name + "[{:d}:{:d}".format( y_slices[0].start, y_slices[0].stop diff --git a/pybamm/expression_tree/symbol.py b/pybamm/expression_tree/symbol.py index cc064934fb..32c226fe56 100644 --- a/pybamm/expression_tree/symbol.py +++ b/pybamm/expression_tree/symbol.py @@ -8,6 +8,7 @@ import sympy from anytree.exporter import DotExporter from scipy.sparse import csr_matrix, issparse +from functools import lru_cache, cached_property import pybamm from pybamm.expression_tree.printing.print_name import prettify_print_name @@ -200,7 +201,12 @@ class Symbol: """ def __init__( - self, name, children=None, domain=None, auxiliary_domains=None, domains=None + self, + name, + children=None, + domain=None, + auxiliary_domains=None, + domains=None, ): super(Symbol, self).__init__() self.name = name @@ -404,6 +410,14 @@ def set_id(self): + tuple([(k, tuple(v)) for k, v in self.domains.items() if v != []]) ) + @property + def scale(self): + return self._scale + + @property + def reference(self): + return self._reference + def __eq__(self, other): try: return self._id == other._id @@ -574,47 +588,19 @@ def __rpow__(self, other): def __lt__(self, other): """return a :class:`NotEqualHeaviside` object, or a smooth approximation.""" - k = pybamm.settings.heaviside_smoothing - # Return exact approximation if that is the setting or the outcome is a constant - # (i.e. no need for smoothing) - if k == "exact" or (is_constant(self) and is_constant(other)): - out = pybamm.NotEqualHeaviside(self, other) - else: - out = pybamm.sigmoid(self, other, k) - return pybamm.simplify_if_constant(out) + return pybamm.expression_tree.binary_operators._heaviside(self, other, False) def __le__(self, other): """return a :class:`EqualHeaviside` object, or a smooth approximation.""" - k = pybamm.settings.heaviside_smoothing - # Return exact approximation if that is the setting or the outcome is a constant - # (i.e. no need for smoothing) - if k == "exact" or (is_constant(self) and is_constant(other)): - out = pybamm.EqualHeaviside(self, other) - else: - out = pybamm.sigmoid(self, other, k) - return pybamm.simplify_if_constant(out) + return pybamm.expression_tree.binary_operators._heaviside(self, other, True) def __gt__(self, other): """return a :class:`NotEqualHeaviside` object, or a smooth approximation.""" - k = pybamm.settings.heaviside_smoothing - # Return exact approximation if that is the setting or the outcome is a constant - # (i.e. no need for smoothing) - if k == "exact" or (is_constant(self) and is_constant(other)): - out = pybamm.NotEqualHeaviside(other, self) - else: - out = pybamm.sigmoid(other, self, k) - return pybamm.simplify_if_constant(out) + return pybamm.expression_tree.binary_operators._heaviside(other, self, False) def __ge__(self, other): """return a :class:`EqualHeaviside` object, or a smooth approximation.""" - k = pybamm.settings.heaviside_smoothing - # Return exact approximation if that is the setting or the outcome is a constant - # (i.e. no need for smoothing) - if k == "exact" or (is_constant(self) and is_constant(other)): - out = pybamm.EqualHeaviside(other, self) - else: - out = pybamm.sigmoid(other, self, k) - return pybamm.simplify_if_constant(out) + return pybamm.expression_tree.binary_operators._heaviside(other, self, True) def __neg__(self): """return a :class:`Negate` object.""" @@ -625,6 +611,9 @@ def __neg__(self): # Move negation inside the broadcast # Apply recursively return self._unary_new_copy(-self.orphans[0]) + elif isinstance(self, pybamm.Subtraction): + # negation flips the subtraction + return self.right - self.left elif isinstance(self, pybamm.Concatenation) and all( child.is_constant() for child in self.children ): @@ -640,7 +629,7 @@ def __abs__(self): elif isinstance(self, pybamm.Broadcast): # Move absolute value inside the broadcast # Apply recursively - abs_self_not_broad = pybamm.simplify_if_constant(abs(self.orphans[0])) + abs_self_not_broad = abs(self.orphans[0]) return self._unary_new_copy(abs_self_not_broad) else: k = pybamm.settings.abs_smoothing @@ -839,6 +828,7 @@ def evaluates_to_number(self): def evaluates_to_constant_number(self): return self.evaluates_to_number() and self.is_constant() + @lru_cache def evaluates_on_edges(self, dimension): """ Returns True if a symbol evaluates on an edge, i.e. symbol contains a gradient @@ -857,12 +847,9 @@ def evaluates_on_edges(self, dimension): Whether the symbol evaluates on edges (in the finite volume discretisation sense) """ - try: - return self._saved_evaluates_on_edges[dimension] - except KeyError: - eval_on_edges = self._evaluates_on_edges(dimension) - self._saved_evaluates_on_edges[dimension] = eval_on_edges - return eval_on_edges + eval_on_edges = self._evaluates_on_edges(dimension) + self._saved_evaluates_on_edges[dimension] = eval_on_edges + return eval_on_edges def _evaluates_on_edges(self, dimension): # Default behaviour: return False @@ -906,48 +893,39 @@ def new_copy(self): obj._print_name = self.print_name return obj - @property + @cached_property def size(self): """ Size of an object, found by evaluating it with appropriate t and y """ - try: - return self._saved_size - except AttributeError: - self._saved_size = np.prod(self.shape) - return self._saved_size + return np.prod(self.shape) - @property + @cached_property def shape(self): """ Shape of an object, found by evaluating it with appropriate t and y. """ + # Default behaviour is to try to evaluate the object directly + # Try with some large y, to avoid having to unpack (slow) try: - return self._saved_shape - except AttributeError: - # Default behaviour is to try to evaluate the object directly - # Try with some large y, to avoid having to unpack (slow) - try: - y = np.nan * np.ones((1000, 1)) - evaluated_self = self.evaluate(0, y, y, inputs="shape test") - # If that fails, fall back to calculating how big y should really be - except ValueError: - unpacker = pybamm.SymbolUnpacker(pybamm.StateVector) - state_vectors_in_node = unpacker.unpack_symbol(self) - min_y_size = max( - max(len(x._evaluation_array) for x in state_vectors_in_node), 1 - ) - # Pick a y that won't cause RuntimeWarnings - y = np.nan * np.ones((min_y_size, 1)) - evaluated_self = self.evaluate(0, y, y, inputs="shape test") - - # Return shape of evaluated object - if isinstance(evaluated_self, numbers.Number): - self._saved_shape = () - else: - self._saved_shape = evaluated_self.shape + y = np.nan * np.ones((1000, 1)) + evaluated_self = self.evaluate(0, y, y, inputs="shape test") + # If that fails, fall back to calculating how big y should really be + except ValueError: + unpacker = pybamm.SymbolUnpacker(pybamm.StateVector) + state_vectors_in_node = unpacker.unpack_symbol(self) + min_y_size = max( + max(len(x._evaluation_array) for x in state_vectors_in_node), 1 + ) + # Pick a y that won't cause RuntimeWarnings + y = np.nan * np.ones((min_y_size, 1)) + evaluated_self = self.evaluate(0, y, y, inputs="shape test") - return self._saved_shape + # Return shape of evaluated object + if isinstance(evaluated_self, numbers.Number): + return () + else: + return evaluated_self.shape @property def size_for_testing(self): diff --git a/pybamm/expression_tree/unary_operators.py b/pybamm/expression_tree/unary_operators.py index c6ab47c116..860fb1dbaa 100644 --- a/pybamm/expression_tree/unary_operators.py +++ b/pybamm/expression_tree/unary_operators.py @@ -965,6 +965,9 @@ def __init__(self, children, initial_condition): def _unary_new_copy(self, child): return self.__class__(child, self.initial_condition) + def is_constant(self): + return False + class BoundaryGradient(BoundaryOperator): """ @@ -1084,7 +1087,10 @@ def grad(symbol): """ # Gradient of a broadcast is zero if isinstance(symbol, pybamm.PrimaryBroadcast): - new_child = pybamm.PrimaryBroadcast(0, symbol.child.domain) + if symbol.child.domain == []: + new_child = pybamm.Scalar(0) + else: + new_child = pybamm.PrimaryBroadcast(0, symbol.child.domain) return pybamm.PrimaryBroadcastToEdges(new_child, symbol.domain) elif isinstance(symbol, pybamm.FullBroadcast): return pybamm.FullBroadcastToEdges(0, broadcast_domains=symbol.domains) @@ -1110,7 +1116,10 @@ def div(symbol): """ # Divergence of a broadcast is zero if isinstance(symbol, pybamm.PrimaryBroadcastToEdges): - new_child = pybamm.PrimaryBroadcast(0, symbol.child.domain) + if symbol.child.domain == []: + new_child = pybamm.Scalar(0) + else: + new_child = pybamm.PrimaryBroadcast(0, symbol.child.domain) return pybamm.PrimaryBroadcast(new_child, symbol.domain) # Divergence commutes with Negate operator if isinstance(symbol, pybamm.Negate): @@ -1245,6 +1254,14 @@ def boundary_value(symbol, side): def sign(symbol): """Returns a :class:`Sign` object.""" + if isinstance(symbol, pybamm.Broadcast): + # Move sign inside the broadcast + # Apply recursively + return symbol._unary_new_copy(sign(symbol.orphans[0])) + elif isinstance(symbol, pybamm.Concatenation) and not isinstance( + symbol, pybamm.ConcatenationVariable + ): + return pybamm.concatenation(*[sign(child) for child in symbol.orphans]) return pybamm.simplify_if_constant(Sign(symbol)) diff --git a/pybamm/expression_tree/variable.py b/pybamm/expression_tree/variable.py index 17efb7f5b8..61f6ed8f55 100644 --- a/pybamm/expression_tree/variable.py +++ b/pybamm/expression_tree/variable.py @@ -18,7 +18,6 @@ class VariableBase(pybamm.Symbol): Parameters ---------- - name : str name of the node domain : iterable of str @@ -39,6 +38,14 @@ class VariableBase(pybamm.Symbol): Physical bounds on the variable print_name : str, optional The name to use for printing. Default is None, in which case self.name is used. + scale : float or :class:`pybamm.Symbol`, optional + The scale of the variable, used for scaling the model when solving. The state + vector representing this variable will be multiplied by this scale. + Default is 1. + reference : float or :class:`pybamm.Symbol`, optional + The reference value of the variable, used for scaling the model when solving. + This value will be added to the state vector representing this variable. + Default is 0. *Extends:* :class:`Symbol` """ @@ -51,9 +58,16 @@ def __init__( domains=None, bounds=None, print_name=None, + scale=1, + reference=0, ): + self._scale = scale + self._reference = reference super().__init__( - name, domain=domain, auxiliary_domains=auxiliary_domains, domains=domains + name, + domain=domain, + auxiliary_domains=auxiliary_domains, + domains=domains, ) if bounds is None: bounds = (-np.inf, np.inf) @@ -66,6 +80,12 @@ def __init__( self.bounds = bounds self.print_name = print_name + def set_id(self): + self._id = hash( + (self.__class__, self.name, self.scale, self.reference) + + tuple([(k, tuple(v)) for k, v in self.domains.items() if v != []]) + ) + def create_copy(self): """See :meth:`pybamm.Symbol.new_copy()`.""" return self.__class__( @@ -73,6 +93,8 @@ def create_copy(self): domains=self.domains, bounds=self.bounds, print_name=self._raw_print_name, + scale=self.scale, + reference=self.reference, ) def _evaluate_for_shape(self): @@ -117,33 +139,26 @@ class Variable(VariableBase): Physical bounds on the variable print_name : str, optional The name to use for printing. Default is None, in which case self.name is used. + scale : float or :class:`pybamm.Symbol`, optional + The scale of the variable, used for scaling the model when solving. The state + vector representing this variable will be multiplied by this scale. + Default is 1. + reference : float or :class:`pybamm.Symbol`, optional + The reference value of the variable, used for scaling the model when solving. + This value will be added to the state vector representing this variable. + Default is 0. *Extends:* :class:`VariableBase` """ - def __init__( - self, - name, - domain=None, - auxiliary_domains=None, - domains=None, - bounds=None, - print_name=None, - ): - super().__init__( - name, - domain=domain, - auxiliary_domains=auxiliary_domains, - domains=domains, - bounds=bounds, - print_name=print_name, - ) - def diff(self, variable): if variable == self: return pybamm.Scalar(1) elif variable == pybamm.t: - return pybamm.VariableDot(self.name + "'", domains=self.domains) + # reference gets differentiated out + return pybamm.VariableDot( + self.name + "'", domains=self.domains, scale=self.scale + ) else: return pybamm.Scalar(0) @@ -180,28 +195,18 @@ class VariableDot(VariableBase): but ignored. print_name : str, optional The name to use for printing. Default is None, in which case self.name is used. + scale : float or :class:`pybamm.Symbol`, optional + The scale of the variable, used for scaling the model when solving. The state + vector representing this variable will be multiplied by this scale. + Default is 1. + reference : float or :class:`pybamm.Symbol`, optional + The reference value of the variable, used for scaling the model when solving. + This value will be added to the state vector representing this variable. + Default is 0. *Extends:* :class:`VariableBase` """ - def __init__( - self, - name, - domain=None, - auxiliary_domains=None, - domains=None, - bounds=None, - print_name=None, - ): - super().__init__( - name, - domain=domain, - auxiliary_domains=auxiliary_domains, - domains=domains, - bounds=bounds, - print_name=print_name, - ) - def get_variable(self): """ return a :class:`.Variable` corresponding to this VariableDot @@ -209,7 +214,7 @@ def get_variable(self): Note: Variable._jac adds a dash to the name of the corresponding VariableDot, so we remove this here """ - return Variable(self.name[:-1], domains=self.domains) + return Variable(self.name[:-1], domains=self.domains, scale=self.scale) def diff(self, variable): if variable == self: @@ -246,13 +251,32 @@ class ExternalVariable(Variable): 'domain' and 'auxiliary_domains', or just 'domains', should be provided (not both). In future, the 'domain' and 'auxiliary_domains' arguments may be deprecated. + scale : float or :class:`pybamm.Symbol`, optional + The scale of the variable, used for scaling the model when solving. The state + vector representing this variable will be multiplied by this scale. + Default is 1. + reference : float or :class:`pybamm.Symbol`, optional + The reference value of the variable, used for scaling the model when solving. + This value will be added to the state vector representing this variable. + Default is 0. *Extends:* :class:`pybamm.Variable` """ - def __init__(self, name, size, domain=None, auxiliary_domains=None, domains=None): + def __init__( + self, + name, + size, + domain=None, + auxiliary_domains=None, + domains=None, + scale=1, + reference=0, + ): self._size = size - super().__init__(name, domain, auxiliary_domains, domains) + super().__init__( + name, domain, auxiliary_domains, domains, scale=scale, reference=reference + ) @property def size(self): diff --git a/pybamm/models/submodels/interface/kinetics/first_order_kinetics/__init__.py b/pybamm/input/parameters/ecm/__init__.py similarity index 100% rename from pybamm/models/submodels/interface/kinetics/first_order_kinetics/__init__.py rename to pybamm/input/parameters/ecm/__init__.py diff --git a/pybamm/input/parameters/ecm/data/ecm_example_c1.csv b/pybamm/input/parameters/ecm/data/ecm_example_c1.csv new file mode 100644 index 0000000000..bc87a76a2c --- /dev/null +++ b/pybamm/input/parameters/ecm/data/ecm_example_c1.csv @@ -0,0 +1,3865 @@ +Temperature [degC],Current [A],SoC,C1 [F] +-20,-400,0.0,8898.358573585827 +-20,-400,0.05,9306.408805841977 +-20,-400,0.1,9704.585692669109 +-20,-400,0.15000000000000002,10085.32357947827 +-20,-400,0.2,10440.312405831746 +-20,-400,0.25,10760.805716894489 +-20,-400,0.30000000000000004,11038.040215325931 +-20,-400,0.35000000000000003,11263.745029855478 +-20,-400,0.4,11430.697772392863 +-20,-400,0.45,11533.266346621709 +-20,-400,0.5,11567.866145661577 +-20,-400,0.55,11533.266346621707 +-20,-400,0.6000000000000001,11430.697772392863 +-20,-400,0.65,11263.745029855478 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+3.749999999999998,20,0.00011985010887166635 +3.749999999999998,40,0.00012841083093392824 +3.7999999999999976,-20,7.613532596253969e-05 +3.7999999999999976,0,9.136239115504763e-05 +3.7999999999999976,20,0.00010658945634755556 +3.7999999999999976,40,0.00011420298894380954 +3.8499999999999974,-20,6.65524586516697e-05 +3.8499999999999974,0,7.986295038200363e-05 +3.8499999999999974,20,9.317344211233757e-05 +3.8499999999999974,40,9.982868797750455e-05 +3.8999999999999972,-20,5.96197388482711e-05 +3.8999999999999972,0,7.154368661792532e-05 +3.8999999999999972,20,8.346763438757953e-05 +3.8999999999999972,40,8.942960827240665e-05 +3.949999999999997,-20,5.809828671071715e-05 +3.949999999999997,0,6.971794405286058e-05 +3.949999999999997,20,8.133760139500401e-05 +3.949999999999997,40,8.714743006607573e-05 +3.999999999999997,-20,6.474922239715908e-05 +3.999999999999997,0,7.76990668765909e-05 +3.999999999999997,20,9.06489113560227e-05 +3.999999999999997,40,9.712383359573862e-05 +4.049999999999997,-20,8.233366606616443e-05 +4.049999999999997,0,9.880039927939732e-05 +4.049999999999997,20,0.0001152671324926302 +4.049999999999997,40,0.00012350049909924665 +4.099999999999996,-20,0.00011361273787588444 +4.099999999999996,0,0.00013633528545106132 +4.099999999999996,20,0.0001590578330262382 +4.099999999999996,40,0.00017041910681382666 +4.149999999999997,-20,0.0001613475579846091 +4.149999999999997,0,0.0001936170695815309 +4.149999999999997,20,0.0002258865811784527 +4.149999999999997,40,0.00024202133697691364 +4.199999999999996,-20,0.00022829924655071165 +4.199999999999996,0,0.000273959095860854 +4.199999999999996,20,0.0003196189451709963 +4.199999999999996,40,0.0003424488698260675 +4.2499999999999964,-20,0.0003172289237324821 +4.2499999999999964,0,0.00038067470847897856 +4.2499999999999964,20,0.00044412049322547494 +4.2499999999999964,40,0.0004758433855987232 +4.299999999999995,-20,0.00043089770968818275 +4.299999999999995,0,0.0005170772516258193 +4.299999999999995,20,0.0006032567935634558 +4.299999999999995,40,0.0006463465645322741 diff --git a/pybamm/input/parameters/ecm/data/ecm_example_ocv.csv b/pybamm/input/parameters/ecm/data/ecm_example_ocv.csv new file mode 100644 index 0000000000..53a6b9e671 --- /dev/null +++ b/pybamm/input/parameters/ecm/data/ecm_example_ocv.csv @@ -0,0 +1,111 @@ +# SoC,OCV [V] +-0.05,2.5554448268104863 +-0.04,2.6965888919665 +-0.03,2.8354243224771927 +-0.019999999999999997,2.9688823518713066 +-0.009999999999999995,3.0922350590723373 +6.938893903907228e-18,3.2 +0.010000000000000009,3.287757008219401 +0.020000000000000004,3.3538087399146304 +0.030000000000000013,3.399652870803843 +0.04000000000000002,3.4291922391832723 +0.05000000000000002,3.447386944382996 +0.06000000000000001,3.4590176950801275 +0.07000000000000002,3.4678233620936925 +0.08000000000000003,3.476084048710994 +0.09000000000000001,3.484703007570683 +0.10000000000000002,3.493689095797286 +0.11000000000000003,3.502728275706306 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+0.9100000000000001,4.056981508878426 +0.9200000000000002,4.06826731640655 +0.9300000000000002,4.079662167637995 +0.9400000000000002,4.091443679530856 +0.9500000000000002,4.104036431774488 +0.9600000000000002,4.11789599455039 +0.9700000000000002,4.13330096876768 +0.9800000000000002,4.150199674158731 +0.9900000000000002,4.168247564719721 +1.0000000000000002,4.187000000000001 +1.0100000000000002,4.206096470888324 +1.0200000000000002,4.225326533329351 +1.0300000000000002,4.244597752168409 +1.0400000000000003,4.263879004150728 diff --git a/pybamm/input/parameters/ecm/data/ecm_example_r0.csv b/pybamm/input/parameters/ecm/data/ecm_example_r0.csv new file mode 100644 index 0000000000..41f35ccad5 --- /dev/null +++ b/pybamm/input/parameters/ecm/data/ecm_example_r0.csv @@ -0,0 +1,3865 @@ +Temperature [degC],Current [A],SoC,R0 [Ohm] +-20,-400,0.0,0.002247605536977195 +-20,-400,0.05,0.0021490566788174253 +-20,-400,0.1,0.0020608813846744738 +-20,-400,0.15000000000000002,0.001983079654548341 +-20,-400,0.2,0.0019156514884390249 +-20,-400,0.25,0.0018585968863465267 +-20,-400,0.30000000000000004,0.0018119158482708465 +-20,-400,0.35000000000000003,0.001775608374211984 +-20,-400,0.4,0.0017496744641699394 +-20,-400,0.45,0.001734114118144713 +-20,-400,0.5,0.0017289273361363038 +-20,-400,0.55,0.0017341141181447133 +-20,-400,0.6000000000000001,0.0017496744641699394 +-20,-400,0.65,0.001775608374211984 +-20,-400,0.7000000000000001,0.0018119158482708465 +-20,-400,0.75,0.0018585968863465267 +-20,-400,0.8,0.0019156514884390249 +-20,-400,0.8500000000000001,0.001983079654548341 +-20,-400,0.9,0.0020608813846744738 +-20,-400,0.9500000000000001,0.0021490566788174258 +-20,-400,1.0,0.002247605536977195 +-20,-350,0.0,0.0022366948304870147 +-20,-350,0.05,0.0021386243648425832 +-20,-350,0.1,0.002050877106108093 +-20,-350,0.15000000000000002,0.0019734530542835425 +-20,-350,0.2,0.001906352209368932 +-20,-350,0.25,0.0018495745713642616 +-20,-350,0.30000000000000004,0.0018031201402695315 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+50,700,0.9500000000000001,0.00042349018345363493 +50,700,1.0,0.00044291008728055136 diff --git a/pybamm/input/parameters/ecm/example_set.py b/pybamm/input/parameters/ecm/example_set.py new file mode 100644 index 0000000000..06a9906811 --- /dev/null +++ b/pybamm/input/parameters/ecm/example_set.py @@ -0,0 +1,103 @@ +import pybamm +import os + +# An example set of parameters for the equivalent circuit model + +path, _ = os.path.split(os.path.abspath(__file__)) + +ocv_data = pybamm.parameters.process_1D_data("ecm_example_ocv.csv", path=path) + +r0_data = pybamm.parameters.process_3D_data_csv("ecm_example_r0.csv", path=path) +r1_data = pybamm.parameters.process_3D_data_csv("ecm_example_r1.csv", path=path) +c1_data = pybamm.parameters.process_3D_data_csv("ecm_example_c1.csv", path=path) + +dUdT_data = pybamm.parameters.process_2D_data_csv("ecm_example_dudt.csv", path=path) + + +def ocv(sto): + name, (x, y) = ocv_data + return pybamm.Interpolant(x, y, sto, name) + + +def r0(T_cell, current, soc): + name, (x, y) = r0_data + return pybamm.Interpolant(x, y, [T_cell, current, soc], name) + + +def r1(T_cell, current, soc): + name, (x, y) = r1_data + return pybamm.Interpolant(x, y, [T_cell, current, soc], name) + + +def c1(T_cell, current, soc): + name, (x, y) = c1_data + return pybamm.Interpolant(x, y, [T_cell, current, soc], name) + + +def dUdT(ocv, T_cell): + name, (x, y) = dUdT_data + return pybamm.Interpolant(x, y, [ocv, T_cell], name) + + +def get_parameter_values(): + """ + Example parameter set for a equivalent circuit model with a + resistor in series with a single RC element. + + This parameter set is for demonstration purposes only and + does not reflect the properties of any particular cell. + Example functional dependancies have been added for each of + the parameters to demonstrate the functionality of + 3D look-up tables models. Where parameters have been taken + from the literature, we do not disclose the source + in order to avoid confusion that these values represent + any particular cell. + + The parameter values have been generated in the following + manner: + + 1. Capacity assumed to be 100Ah + 2. 100A DCIR at T25 S50 assumed to be 1mOhm + 3. DCIR assume to be have the following dependencies: + - quadratic in SoC (increasing to 1.2mOhm + at 0% and 100% SoC) + - Arrhenius in temperature (with Ea=20000) + - linear with the magnitude of the current (with + slope 0.01 mohms per 100 amps) + 4. R0 taken to be 40% of the DCIR + 5. R1 taken to be 60% of the DCIR + 6. C1 is derived from the C1 = tau / R1 where tau=30s + 7. OCV is taken from an undisclosed literature source. + 8. dUdT is taken from an undisclosed literature source. + + """ + + # N.B. actual cell capacity and nominal cell capcity + # can be different hence the two parameters + cell_capacity = 100 + + values = { + "chemistry": "ecm", + "Initial SoC": 0.5, + "Initial cell temperature [degC]": 25, + "Initial jig temperature [degC]": 25, + "Cell capacity [A.h]": cell_capacity, + "Nominal cell capacity [A.h]": cell_capacity, + "Ambient temperature [degC]": 25, + "Current function [A]": 100, + "Upper voltage cut-off [V]": 4.2, + "Lower voltage cut-off [V]": 3.2, + "Cell thermal mass [J/K]": 1000, + "Cell-jig heat transfer coefficient [W/K]": 10, + "Jig thermal mass [J/K]": 500, + "Jig-air heat transfer coefficient [W/K]": 10, + "Open circuit voltage [V]": ocv, + "R0 [Ohm]": r0, + "Element-1 initial overpotential [V]": 0, + "R1 [Ohm]": r1, + "C1 [F]": c1, + "Entropic change [V/K]": dUdT, + "RCR lookup limit [A]": 340, + } + + return values diff --git a/pybamm/input/parameters/lithium_ion/Ai2020.py b/pybamm/input/parameters/lithium_ion/Ai2020.py index 55f178c672..cf05c19108 100644 --- a/pybamm/input/parameters/lithium_ion/Ai2020.py +++ b/pybamm/input/parameters/lithium_ion/Ai2020.py @@ -498,10 +498,22 @@ def electrolyte_conductivity_Ai2020(c_e, T): # Load data in the appropriate format path, _ = os.path.split(os.path.abspath(__file__)) -graphite_ocp_Enertech_Ai2020 = pybamm.parameters.process_1D_data( +graphite_ocp_Enertech_Ai2020_data = pybamm.parameters.process_1D_data( "graphite_ocp_Enertech_Ai2020.csv", path=path ) -lico2_ocp_Ai2020 = pybamm.parameters.process_1D_data("lico2_ocp_Ai2020.csv", path=path) +lico2_ocp_Ai2020_data = pybamm.parameters.process_1D_data( + "lico2_ocp_Ai2020.csv", path=path +) + + +def graphite_ocp_Enertech_Ai2020(sto): + name, (x, y) = graphite_ocp_Enertech_Ai2020_data + return pybamm.Interpolant(x, y, sto, name=name, interpolator="cubic") + + +def lico2_ocp_Ai2020(sto): + name, (x, y) = lico2_ocp_Ai2020_data + return pybamm.Interpolant(x, y, sto, name=name, interpolator="cubic") # Call dict via a function to avoid errors when editing in place diff --git a/pybamm/input/parameters/lithium_ion/Chen2020_composite.py b/pybamm/input/parameters/lithium_ion/Chen2020_composite.py index db123d962a..3ca393d270 100644 --- a/pybamm/input/parameters/lithium_ion/Chen2020_composite.py +++ b/pybamm/input/parameters/lithium_ion/Chen2020_composite.py @@ -308,11 +308,16 @@ def electrolyte_conductivity_Nyman2008(c_e, T): # Load data in the appropriate format path, _ = os.path.split(os.path.abspath(__file__)) -graphite_ocp_Enertech_Ai2020 = pybamm.parameters.process_1D_data( +graphite_ocp_Enertech_Ai2020_data = pybamm.parameters.process_1D_data( "graphite_ocp_Enertech_Ai2020.csv", path=path ) +def graphite_ocp_Enertech_Ai2020(sto): + name, (x, y) = graphite_ocp_Enertech_Ai2020_data + return pybamm.Interpolant(x, y, sto, name=name, interpolator="cubic") + + # Call dict via a function to avoid errors when editing in place def get_parameter_values(): """ diff --git a/pybamm/input/parameters/lithium_ion/Ecker2015.py b/pybamm/input/parameters/lithium_ion/Ecker2015.py index 7299834564..8feee0dc95 100644 --- a/pybamm/input/parameters/lithium_ion/Ecker2015.py +++ b/pybamm/input/parameters/lithium_ion/Ecker2015.py @@ -1,5 +1,4 @@ import pybamm -import os def graphite_diffusivity_Ecker2015(sto, T): @@ -40,7 +39,7 @@ def graphite_diffusivity_Ecker2015(sto, T): return D_ref * arrhenius -def graphite_ocp_Ecker2015_function(sto): +def graphite_ocp_Ecker2015(sto): """ Graphite OCP as a function of stochiometry [1, 2, 3]. @@ -190,7 +189,7 @@ def nco_diffusivity_Ecker2015(sto, T): return D_ref * arrhenius -def nco_ocp_Ecker2015_function(sto): +def nco_ocp_Ecker2015(sto): """ NCO OCP as a function of stochiometry [1, 2, 3]. @@ -383,22 +382,6 @@ def electrolyte_conductivity_Ecker2015(c_e, T): return sigma_e -# Load data in the appropriate format -path, _ = os.path.split(os.path.abspath(__file__)) -measured_graphite_diffusivity_Ecker2015 = pybamm.parameters.process_1D_data( - "measured_graphite_diffusivity_Ecker2015.csv", path=path -) -graphite_ocp_Ecker2015 = pybamm.parameters.process_1D_data( - "graphite_ocp_Ecker2015.csv", path=path -) -measured_nco_diffusivity_Ecker2015 = pybamm.parameters.process_1D_data( - "measured_nco_diffusivity_Ecker2015.csv", path=path -) -nco_ocp_Ecker2015 = pybamm.parameters.process_1D_data( - "nco_ocp_Ecker2015.csv", path=path -) - - # Call dict via a function to avoid errors when editing in place def get_parameter_values(): """ @@ -518,11 +501,8 @@ def get_parameter_values(): # negative electrode "Negative electrode conductivity [S.m-1]": 14.0, "Maximum concentration in negative electrode [mol.m-3]": 31920.0, - "Measured negative electrode diffusivity [m2.s-1]" - "": measured_graphite_diffusivity_Ecker2015, "Negative electrode diffusivity [m2.s-1]": graphite_diffusivity_Ecker2015, - "Measured negative electrode OCP [V]": graphite_ocp_Ecker2015, - "Negative electrode OCP [V]": graphite_ocp_Ecker2015_function, + "Negative electrode OCP [V]": graphite_ocp_Ecker2015, "Negative electrode porosity": 0.329, "Negative electrode active material volume fraction": 0.372403, "Negative particle radius [m]": 1.37e-05, @@ -539,11 +519,8 @@ def get_parameter_values(): # positive electrode "Positive electrode conductivity [S.m-1]": 68.1, "Maximum concentration in positive electrode [mol.m-3]": 48580.0, - "Measured positive electrode diffusivity [m2.s-1]" - "": measured_nco_diffusivity_Ecker2015, "Positive electrode diffusivity [m2.s-1]": nco_diffusivity_Ecker2015, - "Measured positive electrode OCP [V]": nco_ocp_Ecker2015, - "Positive electrode OCP [V]": nco_ocp_Ecker2015_function, + "Positive electrode OCP [V]": nco_ocp_Ecker2015, "Positive electrode porosity": 0.296, "Positive electrode active material volume fraction": 0.40832, "Positive particle radius [m]": 6.5e-06, diff --git a/pybamm/input/parameters/lithium_ion/NCA_Kim2011.py b/pybamm/input/parameters/lithium_ion/NCA_Kim2011.py index eec80ea1c2..56ed2ffa5a 100644 --- a/pybamm/input/parameters/lithium_ion/NCA_Kim2011.py +++ b/pybamm/input/parameters/lithium_ion/NCA_Kim2011.py @@ -267,6 +267,11 @@ def electrolyte_conductivity_Kim2011(c_e, T): ) +def nca_ocp_Kim2011(sto): + name, (x, y) = nca_ocp_Kim2011_data + return pybamm.Interpolant(x, y, sto, name=name, interpolator="linear") + + # Call dict via a function to avoid errors when editing in place def get_parameter_values(): """ @@ -388,7 +393,7 @@ def get_parameter_values(): "Positive electrode conductivity [S.m-1]": 10.0, "Maximum concentration in positive electrode [mol.m-3]": 49000.0, "Positive electrode diffusivity [m2.s-1]": nca_diffusivity_Kim2011, - "Positive electrode OCP [V]": nca_ocp_Kim2011_data, + "Positive electrode OCP [V]": nca_ocp_Kim2011, "Positive electrode porosity": 0.4, "Positive electrode active material volume fraction": 0.41, "Positive particle radius [m]": 1.633e-06, diff --git a/pybamm/input/parameters/lithium_ion/OKane2022.py b/pybamm/input/parameters/lithium_ion/OKane2022.py index 03d5783e1f..0040fb564e 100644 --- a/pybamm/input/parameters/lithium_ion/OKane2022.py +++ b/pybamm/input/parameters/lithium_ion/OKane2022.py @@ -487,11 +487,16 @@ def electrolyte_conductivity_Nyman2008_arrhenius(c_e, T): # Load data in the appropriate format path, _ = os.path.split(os.path.abspath(__file__)) -graphite_LGM50_ocp_Chen2020 = pybamm.parameters.process_1D_data( +graphite_LGM50_ocp_Chen2020_data = pybamm.parameters.process_1D_data( "graphite_LGM50_ocp_Chen2020.csv", path=path ) +def graphite_LGM50_ocp_Chen2020(sto): + name, (x, y) = graphite_LGM50_ocp_Chen2020_data + return pybamm.Interpolant(x, y, sto, name=name, interpolator="cubic") + + # Call dict via a function to avoid errors when editing in place def get_parameter_values(): """ diff --git a/pybamm/input/parameters/lithium_ion/data/lico2_ocp_Ai2020.csv b/pybamm/input/parameters/lithium_ion/data/lico2_ocp_Ai2020.csv index ffd7886e2b..2c81422639 100644 --- a/pybamm/input/parameters/lithium_ion/data/lico2_ocp_Ai2020.csv +++ b/pybamm/input/parameters/lithium_ion/data/lico2_ocp_Ai2020.csv @@ -1,6 +1,42 @@ # OCP for lico2 from Rieger 2016 # stoichiometry , OCP [V] +0.4,4.390781177520233 +0.401,4.387755138269558 +0.402,4.384729099018884 +0.403,4.381703059768209 +0.404,4.378677020517535 +0.405,4.37565098126686 +0.406,4.372624942016186 +0.407,4.369598902765512 +0.408,4.366572863514837 +0.409,4.363546824264163 +0.41,4.360520785013488 +0.411,4.357494745762814 +0.412,4.354468706512139 +0.413,4.351442667261465 +0.414,4.34841662801079 +0.415,4.345390588760116 +0.416,4.342364549509441 +0.417,4.339338510258767 +0.418,4.3363124710080925 +0.419,4.3332864317574185 +0.42,4.330260392506744 +0.421,4.3272343532560695 +0.422,4.3242083140053955 +0.423,4.321182274754721 +0.424,4.318156235504047 +0.425,4.315130196253372 +0.426,4.312104157002698 +0.427,4.309078117752023 +0.428,4.306052078501349 +0.429,4.303026039250674 0.43,4.3 +0.431,4.296973960749326 +0.432,4.293947921498651 +0.433,4.290921882247977 +0.434,4.287895842997302 +0.435,4.284869803746628 +0.436,4.281843764495953 0.436639784,4.279907753 0.437906143,4.276472669 0.439172502,4.273800938 diff --git a/pybamm/meshes/meshes.py b/pybamm/meshes/meshes.py index 1a96137f9b..793ceced44 100644 --- a/pybamm/meshes/meshes.py +++ b/pybamm/meshes/meshes.py @@ -111,12 +111,30 @@ def __init__(self, geometry, submesh_types, var_pts): geometry[domain][spatial_variable][lim] = sym_eval # Create submeshes + self.base_domains = [] for domain in geometry: self[domain] = submesh_types[domain](geometry[domain], submesh_pts[domain]) + self.base_domains.append(domain) # add ghost meshes self.add_ghost_meshes() + def __getitem__(self, domains): + if isinstance(domains, str): + domains = (domains,) + domains = tuple(domains) + try: + return super().__getitem__(domains) + except KeyError: + value = self.combine_submeshes(*domains) + self[domains] = value + return value + + def __setitem__(self, domains, value): + if isinstance(domains, str): + domains = (domains,) + super().__setitem__(domains, value) + def combine_submeshes(self, *submeshnames): """Combine submeshes into a new submesh, using self.submeshclass Raises pybamm.DomainError if submeshes to be combined do not match up (edges are @@ -134,9 +152,6 @@ def combine_submeshes(self, *submeshnames): """ if submeshnames == (): raise ValueError("Submesh domains being combined cannot be empty") - # If there is just a single submesh, we can return it directly - if len(submeshnames) == 1: - return self[submeshnames[0]] # Check that the final edge of each submesh is the same as the first edge of the # next submesh for i in range(len(submeshnames) - 1): @@ -159,7 +174,6 @@ def combine_submeshes(self, *submeshnames): submesh.internal_boundaries = [ self[submeshname].edges[0] for submeshname in submeshnames[1:] ] - return submesh def add_ghost_meshes(self): @@ -172,22 +186,24 @@ def add_ghost_meshes(self): submeshes = [ (domain, submesh) for domain, submesh in self.items() - if not isinstance(submesh, (pybamm.SubMesh0D, pybamm.ScikitSubMesh2D)) + if ( + len(domain) == 1 + and not isinstance(submesh, (pybamm.SubMesh0D, pybamm.ScikitSubMesh2D)) + ) ] for domain, submesh in submeshes: - edges = submesh.edges # left ghost cell: two edges, one node, to the left of existing submesh lgs_edges = np.array([2 * edges[0] - edges[1], edges[0]]) - self[domain + "_left ghost cell"] = pybamm.SubMesh1D( + self[domain[0] + "_left ghost cell"] = pybamm.SubMesh1D( lgs_edges, submesh.coord_sys ) # right ghost cell: two edges, one node, to the right of # existing submesh rgs_edges = np.array([edges[-1], 2 * edges[-1] - edges[-2]]) - self[domain + "_right ghost cell"] = pybamm.SubMesh1D( + self[domain[0] + "_right ghost cell"] = pybamm.SubMesh1D( rgs_edges, submesh.coord_sys ) diff --git a/pybamm/models/base_model.py b/pybamm/models/base_model.py index 76d294cf2d..45e32641bf 100644 --- a/pybamm/models/base_model.py +++ b/pybamm/models/base_model.py @@ -181,9 +181,6 @@ def variables(self, variables): and var.name != name # Exception if the variable is also there under its own name and not (var.name in variables and variables[var.name] == var) - # Exception for the key "Leading-order" - and "leading-order" not in var.name.lower() - and "leading-order" not in name.lower() ): raise ValueError( f"Variable with name '{var.name}' is in variables dictionary with " diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py index a838f7f4e0..ac254ce892 100644 --- a/pybamm/models/full_battery_models/base_battery_model.py +++ b/pybamm/models/full_battery_models/base_battery_model.py @@ -4,6 +4,7 @@ import pybamm import numbers +from functools import cached_property class BatteryModelOptions(pybamm.FuzzyDict): @@ -121,9 +122,10 @@ class BatteryModelOptions(pybamm.FuzzyDict): - "none": :class:`pybamm.sei.NoSEI` (no SEI growth) - "constant": :class:`pybamm.sei.Constant` (constant SEI thickness) - - "reaction limited", "solvent-diffusion limited",\ - "electron-migration limited", "interstitial-diffusion limited", \ - or "ec reaction limited": :class:`pybamm.sei.SEIGrowth` + - "reaction limited", "reaction limited (asymmetric)", \ + "solvent-diffusion limited", "electron-migration limited", \ + "interstitial-diffusion limited", "ec reaction limited" \ + or "ec reaction limited (asymmetric)": :class:`pybamm.sei.SEIGrowth` * "SEI film resistance" : str Set the submodel for additional term in the overpotential due to SEI. The default value is "none" if the "SEI" option is "none", and @@ -255,10 +257,12 @@ def __init__(self, extra_options): "none", "constant", "reaction limited", + "reaction limited (asymmetric)", "solvent-diffusion limited", "electron-migration limited", "interstitial-diffusion limited", "ec reaction limited", + "ec reaction limited (asymmetric)", ], "SEI film resistance": ["none", "distributed", "average"], "SEI on cracks": ["false", "true"], @@ -597,19 +601,14 @@ def phases(self): self._phases[domain] = phases return self._phases - @property + @cached_property def whole_cell_domains(self): - try: - return self._whole_cell_domains - except AttributeError: - if self["working electrode"] == "positive": - wcd = ["separator", "positive electrode"] - elif self["working electrode"] == "negative": - wcd = ["negative electrode", "separator"] - elif self["working electrode"] == "both": - wcd = ["negative electrode", "separator", "positive electrode"] - self._whole_cell_domains = wcd - return wcd + if self["working electrode"] == "positive": + return ["separator", "positive electrode"] + elif self["working electrode"] == "negative": + return ["negative electrode", "separator"] + elif self["working electrode"] == "both": + return ["negative electrode", "separator", "positive electrode"] @property def electrode_types(self): @@ -843,7 +842,7 @@ def options(self, extra_options): raise pybamm.OptionError("Lead-acid models cannot have lithium plating") if ( - isinstance(self, (pybamm.lead_acid.LOQS, pybamm.lead_acid.Composite)) + isinstance(self, pybamm.lead_acid.LOQS) and options["surface form"] == "false" and options["hydrolysis"] == "true" ): diff --git a/pybamm/models/full_battery_models/equivalent_circuit/__init__.py b/pybamm/models/full_battery_models/equivalent_circuit/__init__.py new file mode 100644 index 0000000000..c4bf7d5a56 --- /dev/null +++ b/pybamm/models/full_battery_models/equivalent_circuit/__init__.py @@ -0,0 +1 @@ +from .thevenin import * diff --git a/pybamm/models/full_battery_models/equivalent_circuit/ecm_model_options.py b/pybamm/models/full_battery_models/equivalent_circuit/ecm_model_options.py new file mode 100644 index 0000000000..d9bbe0401a --- /dev/null +++ b/pybamm/models/full_battery_models/equivalent_circuit/ecm_model_options.py @@ -0,0 +1,42 @@ +import types + + +class NaturalNumberOption: + def __init__(self, default_value): + self.value = default_value + + def __contains__(self, value): + is_an_integer = isinstance(value, int) + is_non_negative = value >= 0 + return is_an_integer and is_non_negative + + def __getitem__(self, value): + return self.value + + def __repr__(self): + return "natural numbers (e.g. 0, 1, 2, 3, ...)" + + +class OperatingModes: + def __init__(self, default_mode): + self.default_mode = default_mode + + self.named_modes = [ + "current", + "voltage", + "power", + "differential power", + "explicit power", + "resistance", + "differential resistance", + "explicit resistance", + "CCCV", + ] + + def __contains__(self, value): + named_mode = value in self.named_modes + function = isinstance(value, types.FunctionType) + return named_mode or function + + def __getitem__(self, value): + return self.default_mode diff --git a/pybamm/models/full_battery_models/equivalent_circuit/thevenin.py b/pybamm/models/full_battery_models/equivalent_circuit/thevenin.py new file mode 100644 index 0000000000..078bb5de29 --- /dev/null +++ b/pybamm/models/full_battery_models/equivalent_circuit/thevenin.py @@ -0,0 +1,248 @@ +import pybamm + +from .ecm_model_options import NaturalNumberOption, OperatingModes + + +class Thevenin(pybamm.BaseModel): + """ + The classical Thevenin Equivalent Circuit Model of a battery as + described in, for example, [1]_. + + This equivalent circuit model consists of an OCV element, a resistor + element, and a number of RC elements (by default 1). The model is + coupled to two lumped thermal models, one for the cell and + one for the surrounding jig. Heat generation terms for each element + follow equation (1) of [2]_. + + Parameters + ---------- + name : str, optional + The name of the model. The default is + "Thevenin Equivalent Circuit Model". + options : dict, optional + A dictionary of options to be passed to the model. The default is None. + Possible options are: + + * "number of rc elements" : str + The number of RC elements to be added to the model. The default is 1. + * "calculate discharge energy": str + Whether to calculate the discharge energy, throughput energy and + throughput capacity in addition to discharge capacity. Must be one of + "true" or "false". "false" is the default, since calculating discharge + energy can be computationally expensive for simple models like SPM. + * "operating mode" : str + Sets the operating mode for the model. This determines how the current + is set. Can be: + + - "current" (default) : the current is explicity supplied + - "voltage"/"power"/"resistance" : solve an algebraic equation for \ + current such that voltage/power/resistance is correct + - "differential power"/"differential resistance" : solve a \ + differential equation for the power or resistance + - "CCCV": a special implementation of the common constant-current \ + constant-voltage charging protocol, via an ODE for the current + - callable : if a callable is given as this option, the function \ + defines the residual of an algebraic equation. The applied current \ + will be solved for such that the algebraic constraint is satisfied. + * "external submodels" : list + A list of the submodels that you would like to supply an external + variable for instead of solving in PyBaMM. The entries of the lists + are strings that correspond to the submodel names in the keys + of `self.submodels`. + build : bool, optional + Whether to build the model on instantiation. Default is True. Setting this + option to False allows users to change any number of the submodels before + building the complete model (submodels cannot be changed after the model is + built). + + Examples + -------- + >>> import pybamm + >>> model = pybamm.equivalent_circuit.Thevenin() + >>> model.name + 'Thevenin Equivalent Circuit Model' + + + References + ---------- + .. [1] G Barletta, D Piera, and D Papurello. "Thévenin’s Battery Model + Parameter Estimation Based on Simulink." Energies 15.17 (2022): 6207. + .. [2] N Nieto, L Díaz, J Gastelurrutia, I Alava, F Blanco, JC Ramos, and + A Rivas "Thermal modeling of large format lithium-ion cells." + Journal of The Electrochemical Society, 160(2), (2012) A212. + """ + + def __init__( + self, name="Thevenin Equivalent Circuit Model", options=None, build=True + ): + super().__init__(name) + + self.set_options(options) + self.param = pybamm.EcmParameters() + self.element_counter = 0 + + self.set_submodels(build) + + def set_options(self, extra_options=None): + + possible_options = { + "calculate discharge energy": ["false", "true"], + "operating mode": OperatingModes("current"), + "number of rc elements": NaturalNumberOption(1), + "external submodels": [[]], + } + + default_options = { + name: options[0] for name, options in possible_options.items() + } + + extra_options = extra_options or {} + + options = pybamm.FuzzyDict(default_options) + for name, opt in extra_options.items(): + if name in default_options: + options[name] = opt + else: + raise pybamm.OptionError( + "Option '{}' not recognised. Best matches are {}".format( + name, options.get_best_matches(name) + ) + ) + + for opt, value in options.items(): + if value not in possible_options[opt]: + raise pybamm.OptionError( + "Option '{}' must be one of {}. Got '{}' instead.".format( + opt, possible_options[opt], value + ) + ) + + self.ecm_options = options + + # Hack to deal with submodels requiring electrochemical model + # options + self.options = pybamm.BatteryModelOptions({}) + self.options["calculate discharge energy"] = self.ecm_options[ + "calculate discharge energy" + ] + self.options["operating mode"] = self.ecm_options["operating mode"] + + def set_external_circuit_submodel(self): + """ + Define how the external circuit defines the boundary conditions for the model, + e.g. (not necessarily constant-) current, voltage, etc + """ + + if self.options["operating mode"] == "current": + model = pybamm.external_circuit.ExplicitCurrentControl( + self.param, self.options + ) + elif self.options["operating mode"] == "voltage": + model = pybamm.external_circuit.VoltageFunctionControl( + self.param, self.options + ) + elif self.options["operating mode"] == "power": + model = pybamm.external_circuit.PowerFunctionControl( + self.param, self.options, "algebraic" + ) + elif self.options["operating mode"] == "differential power": + model = pybamm.external_circuit.PowerFunctionControl( + self.param, self.options, "differential without max" + ) + elif self.options["operating mode"] == "resistance": + model = pybamm.external_circuit.ResistanceFunctionControl( + self.param, self.options, "algebraic" + ) + elif self.options["operating mode"] == "differential resistance": + model = pybamm.external_circuit.ResistanceFunctionControl( + self.param, self.options, "differential without max" + ) + elif self.options["operating mode"] == "CCCV": + model = pybamm.external_circuit.CCCVFunctionControl( + self.param, self.options + ) + elif callable(self.options["operating mode"]): + model = pybamm.external_circuit.FunctionControl( + self.param, + self.options["operating mode"], + self.options, + control="differential without max", + ) + self.submodels["external circuit"] = model + + def set_ocv_submodel(self): + self.submodels[ + "Open circuit voltage" + ] = pybamm.equivalent_circuit_elements.OCVElement(self.param, self.ecm_options) + + def set_resistor_submodel(self): + + name = "Element-0 (Resistor)" + self.submodels[name] = pybamm.equivalent_circuit_elements.ResistorElement( + self.param, self.ecm_options + ) + self.element_counter += 1 + + def set_rc_submodels(self): + number_of_rc_elements = self.ecm_options["number of rc elements"] + + for _ in range(number_of_rc_elements): + name = f"Element-{self.element_counter} (RC)" + self.submodels[name] = pybamm.equivalent_circuit_elements.RCElement( + self.param, self.element_counter, self.ecm_options + ) + self.element_counter += 1 + + def set_thermal_submodel(self): + self.submodels["Thermal"] = pybamm.equivalent_circuit_elements.ThermalSubModel( + self.param, self.ecm_options + ) + + def set_voltage_submodel(self): + self.submodels["Voltage"] = pybamm.equivalent_circuit_elements.VoltageModel( + self.param, self.ecm_options + ) + + def set_submodels(self, build): + self.set_external_circuit_submodel() + self.set_ocv_submodel() + self.set_resistor_submodel() + self.set_rc_submodels() + self.set_thermal_submodel() + self.set_voltage_submodel() + + self.summary_variables = [] + + if build: + self.build_model() + + def build_model(self): + + # Build model variables and equations + self._build_model() + + self._built = True + pybamm.logger.info("Finished building {}".format(self.name)) + + @property + def default_parameter_values(self): + return pybamm.ParameterValues("ECM_Example") + + @property + def default_quick_plot_variables(self): + return [ + "Current [A]", + ["Terminal voltage [V]", "Open circuit voltage [V]"], + "SoC", + "Power [W]", + [ + "Cell temperature [degC]", + "Jig temperature [degC]", + "Ambient temperature [degC]", + ], + [ + "Total heat generation [W]", + "Reversible heat generation [W]", + "Irreversible heat generation [W]", + ], + ] diff --git a/pybamm/models/full_battery_models/lead_acid/__init__.py b/pybamm/models/full_battery_models/lead_acid/__init__.py index 3bfef51e4b..5895bddeb4 100644 --- a/pybamm/models/full_battery_models/lead_acid/__init__.py +++ b/pybamm/models/full_battery_models/lead_acid/__init__.py @@ -3,12 +3,5 @@ # from .base_lead_acid_model import BaseModel from .loqs import LOQS -from .higher_order import ( - BaseHigherOrderModel, - FOQS, - Composite, - CompositeAverageCorrection, - CompositeExtended, -) from .full import Full from .basic_full import BasicFull diff --git a/pybamm/models/full_battery_models/lead_acid/basic_full.py b/pybamm/models/full_battery_models/lead_acid/basic_full.py index 5b5d0f232f..2749f6c249 100644 --- a/pybamm/models/full_battery_models/lead_acid/basic_full.py +++ b/pybamm/models/full_battery_models/lead_acid/basic_full.py @@ -181,7 +181,7 @@ def __init__(self, name="Basic full model"): # Current in the electrolyte ###################### i_e = (param.kappa_e(c_e, T) * tor * param.gamma_e / param.C_e) * ( - param.chiT_over_c(c_e, T) * pybamm.grad(c_e) - pybamm.grad(phi_e) + param.chiRT_over_Fc(c_e, T) * pybamm.grad(c_e) - pybamm.grad(phi_e) ) self.algebraic[phi_e] = pybamm.div(i_e) - j self.boundary_conditions[phi_e] = { diff --git a/pybamm/models/full_battery_models/lead_acid/higher_order.py b/pybamm/models/full_battery_models/lead_acid/higher_order.py deleted file mode 100644 index 72cf5cdbdb..0000000000 --- a/pybamm/models/full_battery_models/lead_acid/higher_order.py +++ /dev/null @@ -1,341 +0,0 @@ -# -# Lead-acid higher-order models (FOQS and Composite) -# -import pybamm -from .base_lead_acid_model import BaseModel - - -class BaseHigherOrderModel(BaseModel): - """ - Base model for higher-order models for lead-acid, from [1]_. - Uses leading-order model from :class:`pybamm.lead_acid.LOQS` - - Parameters - ---------- - options : dict, optional - A dictionary of options to be passed to the model. For a detailed list of - options see :class:`~pybamm.BatteryModelOptions`. - name : str, optional - The name of the model. - build : bool, optional - Whether to build the model on instantiation. Default is True. Setting this - option to False allows users to change any number of the submodels before - building the complete model (submodels cannot be changed after the model is - built). - - References - ---------- - .. [1] V Sulzer, SJ Chapman, CP Please, DA Howey, and CW Monroe. Faster lead-acid - battery simulations from porous-electrode theory: Part II. Asymptotic - analysis. Journal of The Electrochemical Society 166.12 (2019), A2372–A2382. - - - **Extends:** :class:`pybamm.lead_acid.BaseModel` - """ - - def __init__(self, options=None, name="Composite model", build=True): - super().__init__(options, name) - - self.set_external_circuit_submodel() - self.set_leading_order_model() - self.set_interface_utilisation_submodel() - # Electrolyte submodel to get first-order concentrations - self.set_electrolyte_diffusion_submodel() - self.set_other_species_diffusion_submodels() - # Average interface submodel to get average first-order potential differences - self.set_open_circuit_potential_submodel() - self.set_average_interfacial_submodel() - # Electrolyte and solid submodels to get full first-order potentials - self.set_negative_electrode_submodel() - self.set_electrolyte_conductivity_submodel() - self.set_positive_electrode_submodel() - # Update interface, porosity and convection with full potentials - self.set_full_interface_submodel() - self.set_full_convection_submodel() - self.set_full_porosity_submodel() - self.set_active_material_submodel() - self.set_transport_efficiency_submodels() - self.set_thermal_submodel() - self.set_current_collector_submodel() - self.set_sei_submodel() - self.set_lithium_plating_submodel() - self.set_total_interface_submodel() - - if build: - self.build_model() - - pybamm.citations.register("Sulzer2019asymptotic") - - def set_current_collector_submodel(self): - cc = pybamm.current_collector - - if self.options["current collector"] in ["uniform"]: - submodel = cc.Uniform(self.param) - elif self.options["current collector"] == "potential pair quite conductive": - if self.options["dimensionality"] == 1: - submodel = cc.QuiteConductivePotentialPair1plus1D(self.param) - elif self.options["dimensionality"] == 2: - submodel = cc.QuiteConductivePotentialPair2plus1D(self.param) - elif self.options["current collector"] == "potential pair": - if self.options["dimensionality"] == 1: - submodel = cc.CompositePotentialPair1plus1D(self.param) - elif self.options["dimensionality"] == 2: - submodel = cc.CompositePotentialPair2plus1D(self.param) - self.submodels["current collector"] = submodel - - def set_leading_order_model(self): - leading_order_model = pybamm.lead_acid.LOQS( - self.options, name="LOQS model (for composite model)" - ) - self.update(leading_order_model) - self.leading_order_reaction_submodels = leading_order_model.reaction_submodels - - # Leading-order variables - leading_order_variables = {} - for variable in self.variables.keys(): - leading_order_variables[ - "Leading-order " + variable.lower() - ] = leading_order_model.variables[variable] - self.variables.update(leading_order_variables) - self.variables[ - "Leading-order electrolyte concentration change" - ] = leading_order_model.rhs[ - leading_order_model.variables["X-averaged electrolyte concentration"] - ] - - def set_average_interfacial_submodel(self): - self.submodels[ - "x-averaged negative interface" - ] = pybamm.kinetics.InverseFirstOrderKinetics( - self.param, - "negative", - self.leading_order_reaction_submodels["negative"], - self.options, - ) - self.submodels[ - "x-averaged positive interface" - ] = pybamm.kinetics.InverseFirstOrderKinetics( - self.param, - "positive", - self.leading_order_reaction_submodels["positive"], - self.options, - ) - - def set_electrolyte_conductivity_submodel(self): - self.submodels[ - "electrolyte conductivity" - ] = pybamm.electrolyte_conductivity.Composite( - self.param, higher_order_terms="first-order" - ) - - def set_negative_electrode_submodel(self): - self.submodels["negative electrode potential"] = pybamm.electrode.ohm.Composite( - self.param, "negative" - ) - - def set_positive_electrode_submodel(self): - self.submodels["positive electrode potential"] = pybamm.electrode.ohm.Composite( - self.param, "positive" - ) - - def set_full_interface_submodel(self): - """ - Set full interface submodel, to get spatially heterogeneous interfacial current - densities - """ - # Main reaction - self.submodels["negative interface"] = pybamm.kinetics.FirstOrderKinetics( - self.param, - "negative", - pybamm.kinetics.SymmetricButlerVolmer( - self.param, "negative", "lead-acid main", self.options - ), - self.options, - ) - self.submodels["positive interface"] = pybamm.kinetics.FirstOrderKinetics( - self.param, - "positive", - pybamm.kinetics.SymmetricButlerVolmer( - self.param, "positive", "lead-acid main", self.options - ), - self.options, - ) - - # Oxygen - if self.options["hydrolysis"] == "true": - self.submodels[ - "positive oxygen interface" - ] = pybamm.kinetics.FirstOrderKinetics( - self.param, - "positive", - pybamm.kinetics.ForwardTafel( - self.param, "positive", "lead-acid oxygen", self.options - ), - self.options, - ) - self.submodels[ - "negative oxygen interface" - ] = pybamm.kinetics.DiffusionLimited( - self.param, - "negative", - "lead-acid oxygen", - self.options, - order="composite", - ) - - def set_full_convection_submodel(self): - """ - Update convection submodel, now that we have the spatially heterogeneous - interfacial current densities - """ - if self.options["convection"] != "none": - self.submodels[ - "through-cell convection" - ] = pybamm.convection.through_cell.Explicit(self.param) - - def set_full_porosity_submodel(self): - """ - Update porosity submodel, now that we have the spatially heterogeneous - interfacial current densities - """ - self.submodels["full porosity"] = pybamm.porosity.ReactionDrivenODE( - self.param, self.options, False - ) - - -class FOQS(BaseHigherOrderModel): - """ - First-order quasi-static model for lead-acid, from [1]_. - Uses leading-order model from :class:`pybamm.lead_acid.LOQS` - - Parameters - ---------- - options : dict, optional - A dictionary of options to be passed to the model. For a detailed list of - options see :class:`~pybamm.BatteryModelOptions`. - name : str, optional - The name of the model. - build : bool, optional - Whether to build the model on instantiation. Default is True. Setting this - option to False allows users to change any number of the submodels before - building the complete model (submodels cannot be changed after the model is - built). - - **Extends:** :class:`pybamm.lead_acid.BaseHigherOrderModel` - """ - - def __init__(self, options=None, name="FOQS model", build=True): - super().__init__(options, name, build=build) - - def set_electrolyte_diffusion_submodel(self): - self.submodels[ - "electrolyte diffusion" - ] = pybamm.electrolyte_diffusion.FirstOrder(self.param) - - def set_other_species_diffusion_submodels(self): - if self.options["hydrolysis"] == "true": - self.submodels["oxygen diffusion"] = pybamm.oxygen_diffusion.FirstOrder( - self.param - ) - - def set_full_porosity_submodel(self): - """ - Update porosity submodel, now that we have the spatially heterogeneous - interfacial current densities - """ - # TODO: fix shape for jacobian - pass - - -class Composite(BaseHigherOrderModel): - """ - Composite model for lead-acid, from [1]_. - Uses leading-order model from :class:`pybamm.lead_acid.LOQS` - - **Extends:** :class:`pybamm.lead_acid.BaseHigherOrderModel` - """ - - def __init__(self, options=None, name="Composite model", build=True): - super().__init__(options, name, build=build) - - def set_electrolyte_diffusion_submodel(self): - self.submodels[ - "electrolyte diffusion" - ] = pybamm.electrolyte_diffusion.Composite(self.param) - - def set_other_species_diffusion_submodels(self): - if self.options["hydrolysis"] == "true": - self.submodels["oxygen diffusion"] = pybamm.oxygen_diffusion.Composite( - self.param - ) - - def set_full_porosity_submodel(self): - """ - Update porosity submodel, now that we have the spatially heterogeneous - interfacial current densities - """ - self.submodels["full porosity"] = pybamm.porosity.ReactionDrivenODE( - self.param, self.options, False - ) - - -class CompositeExtended(Composite): - """ - Extended composite model for lead-acid. - Uses leading-order model from :class:`pybamm.lead_acid.LOQS` - - Parameters - ---------- - options : dict, optional - A dictionary of options to be passed to the model. For a detailed list of - options see :class:`~pybamm.BatteryModelOptions`. - name : str, optional - The name of the model. - build : bool, optional - Whether to build the model on instantiation. Default is True. Setting this - option to False allows users to change any number of the submodels before - building the complete model (submodels cannot be changed after the model is - built). - - - **Extends:** :class:`pybamm.lead_acid.BaseHigherOrderModel` - """ - - def __init__( - self, options=None, name="Extended composite model (distributed)", build=True - ): - super().__init__(options, name, build=build) - - def set_electrolyte_diffusion_submodel(self): - self.submodels[ - "electrolyte diffusion" - ] = pybamm.electrolyte_diffusion.Composite(self.param, extended="distributed") - - def set_other_species_diffusion_submodels(self): - if self.options["hydrolysis"] == "true": - self.submodels["oxygen diffusion"] = pybamm.oxygen_diffusion.Composite( - self.param, extended="distributed" - ) - - -class CompositeAverageCorrection(Composite): - """ - Extended composite model for lead-acid. - Uses leading-order model from :class:`pybamm.lead_acid.LOQS` - - **Extends:** :class:`pybamm.lead_acid.BaseHigherOrderModel` - """ - - def __init__(self, options=None, name="Extended composite model (average)"): - super().__init__(options, name) - - def set_electrolyte_diffusion_submodel(self): - self.submodels[ - "electrolyte diffusion" - ] = pybamm.electrolyte_diffusion.Composite(self.param, extended="average") - - def set_other_species_diffusion_submodels(self): - if self.options["hydrolysis"] == "true": - self.submodels["oxygen diffusion"] = pybamm.oxygen_diffusion.Composite( - self.param, extended="average" - ) diff --git a/pybamm/models/full_battery_models/lead_acid/loqs.py b/pybamm/models/full_battery_models/lead_acid/loqs.py index 339cb53107..c4839a23ea 100644 --- a/pybamm/models/full_battery_models/lead_acid/loqs.py +++ b/pybamm/models/full_battery_models/lead_acid/loqs.py @@ -67,26 +67,20 @@ def set_external_circuit_submodel(self): """ if self.options["operating mode"] == "current": self.submodels[ - "leading order external circuit" - ] = pybamm.external_circuit.LeadingOrderExplicitCurrentControl( - self.param, self.options - ) + "external circuit" + ] = pybamm.external_circuit.ExplicitCurrentControl(self.param, self.options) elif self.options["operating mode"] == "voltage": self.submodels[ - "leading order external circuit" - ] = pybamm.external_circuit.LeadingOrderVoltageFunctionControl( - self.param, self.options - ) + "external circuit" + ] = pybamm.external_circuit.VoltageFunctionControl(self.param, self.options) elif self.options["operating mode"] == "power": self.submodels[ - "leading order external circuit" - ] = pybamm.external_circuit.LeadingOrderPowerFunctionControl( - self.param, self.options - ) + "external circuit" + ] = pybamm.external_circuit.PowerFunctionControl(self.param, self.options) elif callable(self.options["operating mode"]): self.submodels[ - "leading order external circuit" - ] = pybamm.external_circuit.LeadingOrderFunctionControl( + "external circuit" + ] = pybamm.external_circuit.FunctionControl( self.param, self.options["operating mode"], self.options ) @@ -110,14 +104,6 @@ def set_porosity_submodel(self): self.param, self.options, True ) - def set_transport_efficiency_submodels(self): - self.submodels[ - "leading-order electrolyte transport efficiency" - ] = pybamm.transport_efficiency.Bruggeman(self.param, "Electrolyte") - self.submodels[ - "leading-order electrode transport efficiency" - ] = pybamm.transport_efficiency.Bruggeman(self.param, "Electrode") - def set_convection_submodel(self): if self.options["convection"] == "none": diff --git a/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py b/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py index c672594ae4..02db54f03c 100644 --- a/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py +++ b/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py @@ -416,3 +416,11 @@ def set_li_metal_counter_electrode_submodels(self): ] = neg_intercalation_kinetics( self.param, domain, "lithium metal plating", self.options, "primary" ) + + def set_convection_submodel(self): + self.submodels[ + "transverse convection" + ] = pybamm.convection.transverse.NoConvection(self.param, self.options) + self.submodels[ + "through-cell convection" + ] = pybamm.convection.through_cell.NoConvection(self.param, self.options) diff --git a/pybamm/models/full_battery_models/lithium_ion/basic_dfn.py b/pybamm/models/full_battery_models/lithium_ion/basic_dfn.py index 4e39bf74f9..281925076d 100644 --- a/pybamm/models/full_battery_models/lithium_ion/basic_dfn.py +++ b/pybamm/models/full_battery_models/lithium_ion/basic_dfn.py @@ -223,7 +223,7 @@ def __init__(self, name="Doyle-Fuller-Newman model"): # Current in the electrolyte ###################### i_e = (param.kappa_e(c_e, T) * tor * param.gamma_e / param.C_e) * ( - param.chiT_over_c(c_e, T) * pybamm.grad(c_e) - pybamm.grad(phi_e) + param.chiRT_over_Fc(c_e, T) * pybamm.grad(c_e) - pybamm.grad(phi_e) ) self.algebraic[phi_e] = pybamm.div(i_e) - j self.boundary_conditions[phi_e] = { diff --git a/pybamm/models/full_battery_models/lithium_ion/basic_dfn_half_cell.py b/pybamm/models/full_battery_models/lithium_ion/basic_dfn_half_cell.py index 167c9f1eb9..8600dc6130 100644 --- a/pybamm/models/full_battery_models/lithium_ion/basic_dfn_half_cell.py +++ b/pybamm/models/full_battery_models/lithium_ion/basic_dfn_half_cell.py @@ -248,7 +248,7 @@ def __init__(self, options=None, name="Doyle-Fuller-Newman half cell model"): # Current in the electrolyte ###################### i_e = (param.kappa_e(c_e, T) * tor * gamma_e / param.C_e) * ( - param.chiT_over_c(c_e, T) * pybamm.grad(c_e) - pybamm.grad(phi_e) + param.chiRT_over_Fc(c_e, T) * pybamm.grad(c_e) - pybamm.grad(phi_e) ) self.algebraic[phi_e] = pybamm.div(i_e) - j diff --git a/pybamm/models/full_battery_models/lithium_ion/dfn.py b/pybamm/models/full_battery_models/lithium_ion/dfn.py index 856eb3af03..83e62fc15f 100644 --- a/pybamm/models/full_battery_models/lithium_ion/dfn.py +++ b/pybamm/models/full_battery_models/lithium_ion/dfn.py @@ -47,15 +47,6 @@ def __init__(self, options=None, name="Doyle-Fuller-Newman model", build=True): pybamm.citations.register("Doyle1993") - def set_convection_submodel(self): - - self.submodels[ - "transverse convection" - ] = pybamm.convection.transverse.NoConvection(self.param, self.options) - self.submodels[ - "through-cell convection" - ] = pybamm.convection.through_cell.NoConvection(self.param, self.options) - def set_intercalation_kinetics_submodel(self): for domain in ["negative", "positive"]: electrode_type = self.options.electrode_types[domain] diff --git a/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py b/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py index 8b44d8be44..54b70261dc 100644 --- a/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py +++ b/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py @@ -3,6 +3,7 @@ # import pybamm import numpy as np +from functools import lru_cache class ElectrodeSOH(pybamm.BaseModel): @@ -246,28 +247,18 @@ def __init__(self, parameter_values, param=None): - self.param.n.prim.U_dimensional(x, T) ) + @lru_cache def _get_electrode_soh_sims_full(self): - try: - return self._full_sim - except AttributeError: - full_model = ElectrodeSOH(param=self.param) - self._full_sim = pybamm.Simulation( - full_model, parameter_values=self.parameter_values - ) - return self._full_sim + full_model = ElectrodeSOH(param=self.param) + return pybamm.Simulation(full_model, parameter_values=self.parameter_values) + @lru_cache def _get_electrode_soh_sims_split(self): - try: - return self._split_sims - except AttributeError: - x100_model = ElectrodeSOHx100(param=self.param) - x100_sim = pybamm.Simulation( - x100_model, parameter_values=self.parameter_values - ) - x0_model = ElectrodeSOHx0(param=self.param) - x0_sim = pybamm.Simulation(x0_model, parameter_values=self.parameter_values) - self._split_sims = [x100_sim, x0_sim] - return self._split_sims + x100_model = ElectrodeSOHx100(param=self.param) + x100_sim = pybamm.Simulation(x100_model, parameter_values=self.parameter_values) + x0_model = ElectrodeSOHx0(param=self.param) + x0_sim = pybamm.Simulation(x0_model, parameter_values=self.parameter_values) + return [x100_sim, x0_sim] def solve(self, inputs): ics = self._set_up_solve(inputs) @@ -286,19 +277,24 @@ def solve(self, inputs): return sol def _set_up_solve(self, inputs): + # Try with full sim sim = self._get_electrode_soh_sims_full() - x0_min, x100_max, _, _ = self._get_lims(inputs) - - x100_init = x100_max - x0_init = x0_min if sim.solution is not None: - # Update the initial conditions if they are valid - x100_init_sol = sim.solution["x_100"].data[0] - if x0_min < x100_init_sol < x100_max: - x100_init = x100_init_sol - x0_init_sol = sim.solution["x_0"].data[0] - if x0_min < x0_init_sol < x100_max: - x0_init = x0_init_sol + x100_sol = sim.solution["x_100"].data + x0_sol = sim.solution["x_0"].data + return {"x_100": x100_sol, "x_0": x0_sol} + + # Try with split sims + x100_sim, x0_sim = self._get_electrode_soh_sims_split() + if x100_sim.solution is not None and x0_sim.solution is not None: + x100_sol = x100_sim.solution["x_100"].data + x0_sol = x0_sim.solution["x_0"].data + return {"x_100": x100_sol, "x_0": x0_sol} + + # Fall back to initial conditions calculated from limits + x0_min, x100_max, _, _ = self._get_lims(inputs) + x100_init = min(x100_max, 0.8) + x0_init = max(x0_min, 0.2) return {"x_100": np.array(x100_init), "x_0": np.array(x0_init)} def _solve_full(self, inputs, ics): @@ -362,9 +358,11 @@ def _check_esoh_feasible(self, inputs): # Check that the min and max achievable voltages span wider than the desired # voltage range - V_lower_bound = self.OCV_function.evaluate(inputs={"x": x0_min, "y": y0_max}) - V_upper_bound = self.OCV_function.evaluate( - inputs={"x": x100_max, "y": y100_min} + V_lower_bound = float( + self.OCV_function.evaluate(inputs={"x": x0_min, "y": y0_max}) + ) + V_upper_bound = float( + self.OCV_function.evaluate(inputs={"x": x100_max, "y": y100_min}) ) if V_lower_bound > Vmin: raise ( diff --git a/pybamm/models/full_battery_models/lithium_ion/spm.py b/pybamm/models/full_battery_models/lithium_ion/spm.py index 6754817a24..af16e15f53 100644 --- a/pybamm/models/full_battery_models/lithium_ion/spm.py +++ b/pybamm/models/full_battery_models/lithium_ion/spm.py @@ -70,15 +70,6 @@ def __init__(self, options=None, name="Single Particle Model", build=True): ): pybamm.citations.register("BrosaPlanella2022") - def set_convection_submodel(self): - - self.submodels[ - "through-cell convection" - ] = pybamm.convection.through_cell.NoConvection(self.param, self.options) - self.submodels[ - "transverse convection" - ] = pybamm.convection.transverse.NoConvection(self.param, self.options) - def set_intercalation_kinetics_submodel(self): for domain in ["negative", "positive"]: diff --git a/pybamm/models/full_battery_models/lithium_ion/spme.py b/pybamm/models/full_battery_models/lithium_ion/spme.py index 5458ccfe14..e9210ac309 100644 --- a/pybamm/models/full_battery_models/lithium_ion/spme.py +++ b/pybamm/models/full_battery_models/lithium_ion/spme.py @@ -48,27 +48,6 @@ def __init__( # Initialize with the SPM super().__init__(options, name, build) - def set_convection_submodel(self): - - self.submodels[ - "through-cell convection" - ] = pybamm.convection.through_cell.NoConvection(self.param, self.options) - self.submodels[ - "transverse convection" - ] = pybamm.convection.transverse.NoConvection(self.param, self.options) - - def set_transport_efficiency_submodels(self): - self.submodels[ - "electrolyte transport efficiency" - ] = pybamm.transport_efficiency.Bruggeman( - self.param, "Electrolyte", self.options, True - ) - self.submodels[ - "electrode transport efficiency" - ] = pybamm.transport_efficiency.Bruggeman( - self.param, "Electrode", self.options, True - ) - def set_solid_submodel(self): for domain in ["negative", "positive"]: if self.options.electrode_types[domain] == "porous": diff --git a/pybamm/models/submodels/current_collector/__init__.py b/pybamm/models/submodels/current_collector/__init__.py index 40be04244d..b721f14394 100644 --- a/pybamm/models/submodels/current_collector/__init__.py +++ b/pybamm/models/submodels/current_collector/__init__.py @@ -10,13 +10,3 @@ PotentialPair1plus1D, PotentialPair2plus1D, ) -from .composite_potential_pair import ( - BaseCompositePotentialPair, - CompositePotentialPair1plus1D, - CompositePotentialPair2plus1D, -) -from .quite_conductive_potential_pair import ( - BaseQuiteConductivePotentialPair, - QuiteConductivePotentialPair1plus1D, - QuiteConductivePotentialPair2plus1D, -) diff --git a/pybamm/models/submodels/current_collector/composite_potential_pair.py b/pybamm/models/submodels/current_collector/composite_potential_pair.py deleted file mode 100644 index f92ecbbb76..0000000000 --- a/pybamm/models/submodels/current_collector/composite_potential_pair.py +++ /dev/null @@ -1,59 +0,0 @@ -# -# Class for one- and two-dimensional composite potential pair current collector models -# -import pybamm -from .potential_pair import ( - BasePotentialPair, - PotentialPair1plus1D, - PotentialPair2plus1D, -) - - -class BaseCompositePotentialPair(BasePotentialPair): - """ - Composite potential pair model for the current collectors. - This is identical to the BasePotentialPair model, except the name of the fundamental - variables are changed to avoid clashes with leading order. - - Parameters - ---------- - param : parameter class - The parameters to use for this submodel - - - **Extends:** :class:`pybamm.current_collector.BasePotentialPair` - """ - - def __init__(self, param): - super().__init__(param) - - def get_fundamental_variables(self): - - phi_s_cn = pybamm.standard_variables.phi_s_cn_composite - - variables = self._get_standard_negative_potential_variables(phi_s_cn) - - # TODO: grad not implemented for 2D yet - i_cc = pybamm.Scalar(0) - i_boundary_cc = pybamm.standard_variables.i_boundary_cc_composite - - variables.update(self._get_standard_current_variables(i_cc, i_boundary_cc)) - - variables.update( - { - "Composite negative current collector potential": phi_s_cn, - "Composite current collector current density": i_boundary_cc, - } - ) - - return variables - - -class CompositePotentialPair1plus1D(BaseCompositePotentialPair, PotentialPair1plus1D): - def __init__(self, param): - super().__init__(param) - - -class CompositePotentialPair2plus1D(BaseCompositePotentialPair, PotentialPair2plus1D): - def __init__(self, param): - super().__init__(param) diff --git a/pybamm/models/submodels/current_collector/homogeneous_current_collector.py b/pybamm/models/submodels/current_collector/homogeneous_current_collector.py index a8b7267932..6d6b18285f 100644 --- a/pybamm/models/submodels/current_collector/homogeneous_current_collector.py +++ b/pybamm/models/submodels/current_collector/homogeneous_current_collector.py @@ -36,12 +36,4 @@ def get_coupled_variables(self, variables): variables = self._get_standard_current_variables(i_cc, i_boundary_cc) - # Hack to get the leading-order current collector current density - # Note that this should be different from the actual (composite) current - # collector current density for 2+1D models, but not sure how to implement this - # using current structure of lithium-ion models - variables["Leading-order current collector current density"] = variables[ - "Current collector current density" - ] - return variables diff --git a/pybamm/models/submodels/current_collector/potential_pair.py b/pybamm/models/submodels/current_collector/potential_pair.py index ce43c2c806..1bf96449d6 100644 --- a/pybamm/models/submodels/current_collector/potential_pair.py +++ b/pybamm/models/submodels/current_collector/potential_pair.py @@ -42,13 +42,6 @@ def get_fundamental_variables(self): i_boundary_cc = pybamm.standard_variables.i_boundary_cc variables.update(self._get_standard_current_variables(i_cc, i_boundary_cc)) - # Hack to get the leading-order current collector current density - # Note that this should be different from the actual (composite) current - # collector current density for 2+1D models, but not sure how to implement this - # using current structure of lithium-ion models - variables["Leading-order current collector current density"] = variables[ - "Current collector current density" - ] return variables diff --git a/pybamm/models/submodels/current_collector/quite_conductive_potential_pair.py b/pybamm/models/submodels/current_collector/quite_conductive_potential_pair.py deleted file mode 100644 index 4c586119c5..0000000000 --- a/pybamm/models/submodels/current_collector/quite_conductive_potential_pair.py +++ /dev/null @@ -1,100 +0,0 @@ -# -# Class for one- and two-dimensional potential pair "quite conductive" -# current collector models -# -import pybamm -from .potential_pair import ( - BasePotentialPair, - PotentialPair1plus1D, - PotentialPair2plus1D, -) - - -class BaseQuiteConductivePotentialPair(BasePotentialPair): - """A submodel for Ohm's law plus conservation of current in the current collectors, - in the limit of quite conductive electrodes. - - Parameters - ---------- - param : parameter class - The parameters to use for this submodel - - - **Extends:** :class:`pybamm.current_collector.BaseModel` - """ - - def __init__(self, param): - super().__init__(param) - - def get_fundamental_variables(self): - - phi_s_cn = pybamm.standard_variables.phi_s_cn - - variables = self._get_standard_negative_potential_variables(phi_s_cn) - - # TODO: grad not implemented for 2D yet - i_cc = pybamm.Scalar(0) - i_boundary_cc = pybamm.standard_variables.i_boundary_cc - - variables.update(self._get_standard_current_variables(i_cc, i_boundary_cc)) - - # Lagrange multiplier for the composite current (enforce average) - c = pybamm.Variable("Lagrange multiplier") - variables.update({"Lagrange multiplier": c}) - - return variables - - def set_algebraic(self, variables): - - param = self.param - applied_current = variables["Total current density"] - cc_area = self._get_effective_current_collector_area() - z = pybamm.standard_spatial_vars.z - - phi_s_cn = variables["Negative current collector potential"] - phi_s_cp = variables["Positive current collector potential"] - i_boundary_cc = variables["Current collector current density"] - i_boundary_cc_0 = variables["Leading-order current collector current density"] - c = variables["Lagrange multiplier"] - - # Note that the second argument of 'source' must be the same as the argument - # in the laplacian (the variable to which the boundary conditions are applied) - self.algebraic = { - phi_s_cn: (param.n.sigma_cc * param.delta**2 * param.n.l_cc) - * pybamm.laplacian(phi_s_cn) - - pybamm.source(i_boundary_cc_0, phi_s_cn), - i_boundary_cc: (param.p.sigma_cc * param.delta**2 * param.p.l_cc) - * pybamm.laplacian(phi_s_cp) - + pybamm.source(i_boundary_cc_0, phi_s_cp) - + c * pybamm.PrimaryBroadcast(cc_area, "current collector"), - c: pybamm.Integral(i_boundary_cc, z) - applied_current / cc_area, - } - - def set_initial_conditions(self, variables): - - param = self.param - applied_current = param.current_with_time - cc_area = self._get_effective_current_collector_area() - phi_s_cn = variables["Negative current collector potential"] - i_boundary_cc = variables["Current collector current density"] - c = variables["Lagrange multiplier"] - - self.initial_conditions = { - phi_s_cn: pybamm.Scalar(0), - i_boundary_cc: applied_current / cc_area, - c: pybamm.Scalar(0), - } - - -class QuiteConductivePotentialPair1plus1D( - BaseQuiteConductivePotentialPair, PotentialPair1plus1D -): - def __init__(self, param): - super().__init__(param) - - -class QuiteConductivePotentialPair2plus1D( - BaseQuiteConductivePotentialPair, PotentialPair2plus1D -): - def __init__(self, param): - super().__init__(param) diff --git a/pybamm/models/submodels/electrode/ohm/composite_ohm.py b/pybamm/models/submodels/electrode/ohm/composite_ohm.py index 24cace6337..a792903923 100644 --- a/pybamm/models/submodels/electrode/ohm/composite_ohm.py +++ b/pybamm/models/submodels/electrode/ohm/composite_ohm.py @@ -30,7 +30,7 @@ def get_coupled_variables(self, variables): domain = self.domain param = self.param - i_boundary_cc_0 = variables["Leading-order current collector current density"] + i_boundary_cc = variables["Current collector current density"] # import parameters and spatial variables l_n = param.n.l @@ -38,18 +38,16 @@ def get_coupled_variables(self, variables): x_n = pybamm.standard_spatial_vars.x_n x_p = pybamm.standard_spatial_vars.x_p - tor_0 = variables[ - f"Leading-order x-averaged {domain} electrode transport efficiency" - ] + tor = variables[f"X-averaged {domain} electrode transport efficiency"] phi_s_cn = variables["Negative current collector potential"] T = variables[f"X-averaged {domain} electrode temperature"] - sigma_eff_0 = self.domain_param.sigma(T) * tor_0 + sigma_eff = self.domain_param.sigma(T) * tor if self._domain == "negative": - phi_s = phi_s_cn + (i_boundary_cc_0 / sigma_eff_0) * ( + phi_s = phi_s_cn + (i_boundary_cc / sigma_eff) * ( x_n * (x_n - 2 * l_n) / (2 * l_n) ) - i_s = i_boundary_cc_0 * (1 - x_n / l_n) + i_s = i_boundary_cc * (1 - x_n / l_n) elif self.domain == "positive": delta_phi_p_av = variables[ @@ -60,13 +58,13 @@ def get_coupled_variables(self, variables): const = ( delta_phi_p_av + phi_e_p_av - + (i_boundary_cc_0 / sigma_eff_0) * (1 - l_p / 3) + + (i_boundary_cc / sigma_eff) * (1 - l_p / 3) ) - phi_s = const - (i_boundary_cc_0 / sigma_eff_0) * ( + phi_s = const - (i_boundary_cc / sigma_eff) * ( x_p + (x_p - 1) ** 2 / (2 * l_p) ) - i_s = i_boundary_cc_0 * (1 - (1 - x_p) / l_p) + i_s = i_boundary_cc * (1 - (1 - x_p) / l_p) variables.update(self._get_standard_potential_variables(phi_s)) variables.update(self._get_standard_current_variables(i_s)) @@ -80,10 +78,8 @@ def set_boundary_conditions(self, variables): domain, Domain = self.domain_Domain phi_s = variables[f"{Domain} electrode potential"] - tor_0 = variables[ - f"Leading-order x-averaged {domain} electrode transport efficiency" - ] - i_boundary_cc_0 = variables["Leading-order current collector current density"] + tor = variables[f"X-averaged {domain} electrode transport efficiency"] + i_boundary_cc = variables["Current collector current density"] T = variables[f"X-averaged {domain} electrode temperature"] if self.domain == "negative": @@ -92,7 +88,7 @@ def set_boundary_conditions(self, variables): elif self.domain == "positive": lbc = (pybamm.Scalar(0), "Neumann") - sigma_eff_0 = self.param.p.sigma(T) * tor_0 - rbc = (-i_boundary_cc_0 / sigma_eff_0, "Neumann") + sigma_eff = self.param.p.sigma(T) * tor + rbc = (-i_boundary_cc / sigma_eff, "Neumann") self.boundary_conditions[phi_s] = {"left": lbc, "right": rbc} diff --git a/pybamm/models/submodels/electrolyte_conductivity/base_electrolyte_conductivity.py b/pybamm/models/submodels/electrolyte_conductivity/base_electrolyte_conductivity.py index c44eb1d413..c7d35a73ea 100644 --- a/pybamm/models/submodels/electrolyte_conductivity/base_electrolyte_conductivity.py +++ b/pybamm/models/submodels/electrolyte_conductivity/base_electrolyte_conductivity.py @@ -256,7 +256,7 @@ def _get_electrolyte_overpotentials(self, variables): c_e_n = variables["Negative electrolyte concentration"] T_n = variables["Negative electrode temperature"] indef_integral_n = pybamm.IndefiniteIntegral( - param.chiT_over_c(c_e_n, T_n) * pybamm.grad(c_e_n), + param.chiRT_over_Fc(c_e_n, T_n) * pybamm.grad(c_e_n), pybamm.standard_spatial_vars.x_n, ) @@ -270,11 +270,11 @@ def _get_electrolyte_overpotentials(self, variables): # concentration overpotential indef_integral_s = pybamm.IndefiniteIntegral( - param.chiT_over_c(c_e_s, T_s) * pybamm.grad(c_e_s), + param.chiRT_over_Fc(c_e_s, T_s) * pybamm.grad(c_e_s), pybamm.standard_spatial_vars.x_s, ) indef_integral_p = pybamm.IndefiniteIntegral( - param.chiT_over_c(c_e_p, T_p) * pybamm.grad(c_e_p), + param.chiRT_over_Fc(c_e_p, T_p) * pybamm.grad(c_e_p), pybamm.standard_spatial_vars.x_p, ) diff --git a/pybamm/models/submodels/electrolyte_conductivity/composite_conductivity.py b/pybamm/models/submodels/electrolyte_conductivity/composite_conductivity.py index a797773242..df36da19e5 100644 --- a/pybamm/models/submodels/electrolyte_conductivity/composite_conductivity.py +++ b/pybamm/models/submodels/electrolyte_conductivity/composite_conductivity.py @@ -23,47 +23,32 @@ class Composite(BaseElectrolyteConductivity): **Extends:** :class:`pybamm.electrolyte_conductivity.BaseElectrolyteConductivity` """ - def __init__( - self, param, domain=None, options=None, higher_order_terms="composite" - ): + def __init__(self, param, domain=None, options=None): super().__init__(param, domain, options=options) - self.higher_order_terms = higher_order_terms def _higher_order_macinnes_function(self, x): "Function to differentiate between composite and first-order models" - if self.higher_order_terms == "composite": - tol = pybamm.settings.tolerances["macinnes__c_e"] - x = pybamm.maximum(x, tol) - return pybamm.log(x) - elif self.higher_order_terms == "first-order": - return x + tol = pybamm.settings.tolerances["macinnes__c_e"] + x = pybamm.maximum(x, tol) + return pybamm.log(x) def get_coupled_variables(self, variables): - if self.higher_order_terms == "composite": - c_e_av = variables["X-averaged electrolyte concentration"] - elif self.higher_order_terms == "first-order": - c_e_av = variables["Leading-order x-averaged electrolyte concentration"] + c_e_av = variables["X-averaged electrolyte concentration"] - i_boundary_cc_0 = variables["Leading-order current collector current density"] + i_boundary_cc = variables["Current collector current density"] if self.options.electrode_types["negative"] == "porous": c_e_n = variables["Negative electrolyte concentration"] delta_phi_n_av = variables[ "X-averaged negative electrode surface potential difference" ] phi_s_n_av = variables["X-averaged negative electrode potential"] - tor_n_av = variables[ - "Leading-order x-averaged negative electrolyte transport efficiency" - ] + tor_n_av = variables["X-averaged negative electrolyte transport efficiency"] c_e_s = variables["Separator electrolyte concentration"] c_e_p = variables["Positive electrolyte concentration"] - tor_s_av = variables[ - "Leading-order x-averaged separator electrolyte transport efficiency" - ] - tor_p_av = variables[ - "Leading-order x-averaged positive electrolyte transport efficiency" - ] + tor_s_av = variables["X-averaged separator electrolyte transport efficiency"] + tor_p_av = variables["X-averaged positive electrolyte transport efficiency"] T_av = variables["X-averaged cell temperature"] T_av_s = pybamm.PrimaryBroadcast(T_av, "separator") @@ -91,9 +76,9 @@ def get_coupled_variables(self, variables): chi_av_n = pybamm.PrimaryBroadcast(chi_av, "negative electrode") T_av_n = pybamm.PrimaryBroadcast(T_av, "negative electrode") kappa_n_av = param.kappa_e(c_e_av, T_av) * tor_n_av - i_e_n = i_boundary_cc_0 * x_n / l_n - i_e_s = pybamm.PrimaryBroadcast(i_boundary_cc_0, "separator") - i_e_p = i_boundary_cc_0 * (1 - x_p) / l_p + i_e_n = i_boundary_cc * x_n / l_n + i_e_s = pybamm.PrimaryBroadcast(i_boundary_cc, "separator") + i_e_p = i_boundary_cc * (1 - x_p) / l_p i_e = pybamm.concatenation(i_e_n, i_e_s, i_e_p) phi_e_dict = {} @@ -107,7 +92,7 @@ def get_coupled_variables(self, variables): - chi_av * (1 + param.Theta * T_av) * self._higher_order_macinnes_function(c_e_n / c_e_av) - + (i_boundary_cc_0 * param.C_e / param.gamma_e / kappa_s_av) * l_n + + (i_boundary_cc * param.C_e / param.gamma_e / kappa_s_av) * l_n ) else: phi_e_const = ( @@ -121,7 +106,7 @@ def get_coupled_variables(self, variables): ) ) - ( - (i_boundary_cc_0 * param.C_e * l_n / param.gamma_e) + (i_boundary_cc * param.C_e * l_n / param.gamma_e) * (1 / (3 * kappa_n_av) - 1 / kappa_s_av) ) ) @@ -133,10 +118,10 @@ def get_coupled_variables(self, variables): * (1 + param.Theta * T_av_n) * self._higher_order_macinnes_function(c_e_n / c_e_av) ) - - (i_boundary_cc_0 * (param.C_e / param.gamma_e) / kappa_n_av) + - (i_boundary_cc * (param.C_e / param.gamma_e) / kappa_n_av) * (x_n**2 - l_n**2) / (2 * l_n) - - i_boundary_cc_0 * l_n * (param.C_e / param.gamma_e) / kappa_s_av + - i_boundary_cc * l_n * (param.C_e / param.gamma_e) / kappa_s_av ) phi_e_dict["negative electrode"] = phi_e_n @@ -147,7 +132,7 @@ def get_coupled_variables(self, variables): * (1 + param.Theta * T_av_s) * self._higher_order_macinnes_function(c_e_s / c_e_av) ) - - (i_boundary_cc_0 * param.C_e / param.gamma_e / kappa_s_av) * x_s + - (i_boundary_cc * param.C_e / param.gamma_e / kappa_s_av) * x_s ) phi_e_p = ( @@ -157,10 +142,10 @@ def get_coupled_variables(self, variables): * (1 + param.Theta * T_av_p) * self._higher_order_macinnes_function(c_e_p / c_e_av) ) - - (i_boundary_cc_0 * (param.C_e / param.gamma_e) / kappa_p_av) + - (i_boundary_cc * (param.C_e / param.gamma_e) / kappa_p_av) * (x_p * (2 - x_p) + l_p**2 - 1) / (2 * l_p) - - i_boundary_cc_0 * (1 - l_p) * (param.C_e / param.gamma_e) / kappa_s_av + - i_boundary_cc * (1 - l_p) * (param.C_e / param.gamma_e) / kappa_s_av ) phi_e_dict["separator"] = phi_e_s @@ -182,7 +167,7 @@ def get_coupled_variables(self, variables): eta_c_av = chi_av * (1 + param.Theta * T_av) * (macinnes_c_e_p - macinnes_c_e_n) # average electrolyte ohmic losses - delta_phi_e_av = -(param.C_e * i_boundary_cc_0 / param.gamma_e) * ( + delta_phi_e_av = -(param.C_e * i_boundary_cc / param.gamma_e) * ( ohmic_n + param.s.l / (kappa_s_av) + param.p.l / (3 * kappa_p_av) ) diff --git a/pybamm/models/submodels/electrolyte_conductivity/full_conductivity.py b/pybamm/models/submodels/electrolyte_conductivity/full_conductivity.py index 609a7fe79f..74d0569879 100644 --- a/pybamm/models/submodels/electrolyte_conductivity/full_conductivity.py +++ b/pybamm/models/submodels/electrolyte_conductivity/full_conductivity.py @@ -46,7 +46,7 @@ def get_coupled_variables(self, variables): phi_e = variables["Electrolyte potential"] i_e = (param.kappa_e(c_e, T) * tor * param.gamma_e / param.C_e) * ( - param.chiT_over_c(c_e, T) * pybamm.grad(c_e) - pybamm.grad(phi_e) + param.chiRT_over_Fc(c_e, T) * pybamm.grad(c_e) - pybamm.grad(phi_e) ) # Override print_name diff --git a/pybamm/models/submodels/electrolyte_conductivity/integrated_conductivity.py b/pybamm/models/submodels/electrolyte_conductivity/integrated_conductivity.py index 47fa2a7d2e..3958b941e7 100644 --- a/pybamm/models/submodels/electrolyte_conductivity/integrated_conductivity.py +++ b/pybamm/models/submodels/electrolyte_conductivity/integrated_conductivity.py @@ -41,7 +41,7 @@ def _higher_order_macinnes_function(self, x): def get_coupled_variables(self, variables): c_e_av = variables["X-averaged electrolyte concentration"] - i_boundary_cc_0 = variables["Leading-order current collector current density"] + i_boundary_cc = variables["Current collector current density"] c_e_n = variables["Negative electrolyte concentration"] c_e_s = variables["Separator electrolyte concentration"] c_e_p = variables["Positive electrolyte concentration"] @@ -76,14 +76,14 @@ def get_coupled_variables(self, variables): chi_av_p = pybamm.PrimaryBroadcast(chi_av, "positive electrode") # electrolyte current - i_e_n = i_boundary_cc_0 * x_n / l_n - i_e_s = pybamm.PrimaryBroadcast(i_boundary_cc_0, "separator") - i_e_p = i_boundary_cc_0 * (1 - x_p) / l_p + i_e_n = i_boundary_cc * x_n / l_n + i_e_s = pybamm.PrimaryBroadcast(i_boundary_cc, "separator") + i_e_p = i_boundary_cc * (1 - x_p) / l_p i_e = pybamm.concatenation(i_e_n, i_e_s, i_e_p) - i_e_n_edge = i_boundary_cc_0 * x_n_edge / l_n - i_e_s_edge = pybamm.PrimaryBroadcastToEdges(i_boundary_cc_0, "separator") - i_e_p_edge = i_boundary_cc_0 * (1 - x_p_edge) / l_p + i_e_n_edge = i_boundary_cc * x_n_edge / l_n + i_e_s_edge = pybamm.PrimaryBroadcastToEdges(i_boundary_cc, "separator") + i_e_p_edge = i_boundary_cc * (1 - x_p_edge) / l_p # electrolyte potential indef_integral_n = ( diff --git a/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/full_surface_form_conductivity.py b/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/full_surface_form_conductivity.py index 3c6904fa49..9954afb131 100644 --- a/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/full_surface_form_conductivity.py +++ b/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/full_surface_form_conductivity.py @@ -55,7 +55,7 @@ def get_coupled_variables(self, variables): T = variables[f"{Domain} electrode temperature"] i_e = conductivity * ( - param.chiT_over_c(c_e, T) * pybamm.grad(c_e) + param.chiRT_over_Fc(c_e, T) * pybamm.grad(c_e) + pybamm.grad(delta_phi) + i_boundary_cc / sigma_eff ) @@ -77,11 +77,11 @@ def get_coupled_variables(self, variables): tor_s = variables["Separator porosity"] T = variables["Separator temperature"] - chiT_over_c_e_s = param.chiT_over_c(c_e_s, T) + chiRT_over_Fc_e_s = param.chiRT_over_Fc(c_e_s, T) kappa_s_eff = param.kappa_e(c_e_s, T) * tor_s phi_e = phi_e_n_s + pybamm.IndefiniteIntegral( - chiT_over_c_e_s * pybamm.grad(c_e_s) + chiRT_over_Fc_e_s * pybamm.grad(c_e_s) - param.C_e * i_boundary_cc / kappa_s_eff, x_s, ) @@ -159,7 +159,7 @@ def set_boundary_conditions(self, variables): flux_left = -i_boundary_cc * pybamm.BoundaryValue(1 / sigma_eff, "left") flux_right = ( (i_boundary_cc / pybamm.BoundaryValue(conductivity, "right")) - - pybamm.BoundaryValue(param.chiT_over_c(c_e, T), "right") * c_e_flux + - pybamm.BoundaryValue(param.chiRT_over_Fc(c_e, T), "right") * c_e_flux - i_boundary_cc * pybamm.BoundaryValue(1 / sigma_eff, "right") ) @@ -173,7 +173,7 @@ def set_boundary_conditions(self, variables): c_e_flux = pybamm.BoundaryGradient(c_e, "left") flux_left = ( (i_boundary_cc / pybamm.BoundaryValue(conductivity, "left")) - - pybamm.BoundaryValue(param.chiT_over_c(c_e, T), "left") * c_e_flux + - pybamm.BoundaryValue(param.chiRT_over_Fc(c_e, T), "left") * c_e_flux - i_boundary_cc * pybamm.BoundaryValue(1 / sigma_eff, "left") ) flux_right = -i_boundary_cc * pybamm.BoundaryValue(1 / sigma_eff, "right") diff --git a/pybamm/models/submodels/electrolyte_diffusion/__init__.py b/pybamm/models/submodels/electrolyte_diffusion/__init__.py index 36b34611cb..5636b77fea 100644 --- a/pybamm/models/submodels/electrolyte_diffusion/__init__.py +++ b/pybamm/models/submodels/electrolyte_diffusion/__init__.py @@ -1,6 +1,4 @@ from .base_electrolyte_diffusion import BaseElectrolyteDiffusion from .leading_order_diffusion import LeadingOrder -from .first_order_diffusion import FirstOrder -from .composite_diffusion import Composite from .full_diffusion import Full from .constant_concentration import ConstantConcentration diff --git a/pybamm/models/submodels/electrolyte_diffusion/base_electrolyte_diffusion.py b/pybamm/models/submodels/electrolyte_diffusion/base_electrolyte_diffusion.py index 6deb282e8f..0af2ada0e0 100644 --- a/pybamm/models/submodels/electrolyte_diffusion/base_electrolyte_diffusion.py +++ b/pybamm/models/submodels/electrolyte_diffusion/base_electrolyte_diffusion.py @@ -37,16 +37,17 @@ def _get_standard_concentration_variables(self, c_e_dict): electrolyte. """ - c_e_typ = self.param.c_e_typ c_e = pybamm.concatenation(*c_e_dict.values()) # Override print_name c_e.print_name = "c_e" - variables = { - "Electrolyte concentration": c_e, - "X-averaged electrolyte concentration": pybamm.x_average(c_e), - } + variables = self._get_standard_domain_concentration_variables(c_e_dict) + variables.update(self._get_standard_whole_cell_concentration_variables(c_e)) + return variables + def _get_standard_domain_concentration_variables(self, c_e_dict): + c_e_typ = self.param.c_e_typ + variables = {} # Case where an electrode is not included (half-cell) if "negative electrode" not in self.options.whole_cell_domains: c_e_s = c_e_dict["separator"] @@ -75,6 +76,24 @@ def _get_standard_concentration_variables(self, c_e_dict): return variables + def _get_standard_whole_cell_concentration_variables(self, c_e): + c_e_typ = self.param.c_e_typ + + variables = { + "Electrolyte concentration": c_e, + "X-averaged electrolyte concentration": pybamm.x_average(c_e), + } + variables_nondim = variables.copy() + for name, var in variables_nondim.items(): + variables.update( + { + f"{name} [mol.m-3]": c_e_typ * var, + f"{name} [Molar]": c_e_typ * var / 1000, + } + ) + + return variables + def _get_standard_porosity_times_concentration_variables(self, eps_c_e_dict): eps_c_e = pybamm.concatenation(*eps_c_e_dict.values()) variables = {"Porosity times concentration": eps_c_e} diff --git a/pybamm/models/submodels/electrolyte_diffusion/composite_diffusion.py b/pybamm/models/submodels/electrolyte_diffusion/composite_diffusion.py deleted file mode 100644 index be9b4ab9d8..0000000000 --- a/pybamm/models/submodels/electrolyte_diffusion/composite_diffusion.py +++ /dev/null @@ -1,118 +0,0 @@ -# -# Class for composite electrolyte diffusion employing stefan-maxwell -# -import pybamm -import numpy as np -from .base_electrolyte_diffusion import BaseElectrolyteDiffusion - - -class Composite(BaseElectrolyteDiffusion): - """Class for conservation of mass in the electrolyte employing the - Stefan-Maxwell constitutive equations. (Composite refers to composite model by - asymptotic methods) - - Parameters - ---------- - param : parameter class - The parameters to use for this submodel - - extended : bool - Whether to include feedback from the first-order terms - - **Extends:** :class:`pybamm.electrolyte_diffusion.BaseElectrolyteDiffusion` - """ - - def __init__(self, param, extended=False): - super().__init__(param) - self.extended = extended - - def get_fundamental_variables(self): - c_e_dict = {} - for domain in self.options.whole_cell_domains: - Domain = domain.capitalize().split()[0] - c_e_k = pybamm.Variable( - f"{Domain} electrolyte concentration", - domain=domain, - auxiliary_domains={"secondary": "current collector"}, - bounds=(0, np.inf), - ) - c_e_k.print_name = f"c_e_{domain[0]}" - c_e_dict[domain] = c_e_k - - variables = self._get_standard_concentration_variables(c_e_dict) - - return variables - - def get_coupled_variables(self, variables): - - tor_0 = variables["Leading-order electrolyte transport efficiency"] - eps = variables["Leading-order porosity"] - c_e_0_av = variables["Leading-order x-averaged electrolyte concentration"] - c_e = variables["Electrolyte concentration"] - i_e = variables["Electrolyte current density"] - v_box_0 = variables["Leading-order volume-averaged velocity"] - T_0 = variables["Leading-order cell temperature"] - - param = self.param - - N_e_diffusion = -tor_0 * param.D_e(c_e_0_av, T_0) * pybamm.grad(c_e) - N_e_migration = param.C_e * param.t_plus(c_e, T_0) * i_e / param.gamma_e - N_e_convection = param.C_e * c_e_0_av * v_box_0 - - N_e = N_e_diffusion + N_e_migration + N_e_convection - - variables.update(self._get_standard_flux_variables(N_e)) - variables.update(self._get_total_concentration_electrolyte(eps * c_e)) - - return variables - - def set_rhs(self, variables): - """Composite reaction-diffusion with source terms from leading order.""" - - param = self.param - - eps_0 = variables["Leading-order porosity"] - deps_0_dt = variables["Leading-order porosity change"] - c_e = variables["Electrolyte concentration"] - N_e = variables["Electrolyte flux"] - if self.extended is False: - sum_s_j = variables[ - "Leading-order sum of electrolyte reaction source terms" - ] - elif self.extended == "distributed": - sum_s_j = variables["Sum of electrolyte reaction source terms"] - elif self.extended == "average": - sum_s_j_n_av = variables[ - "Sum of x-averaged negative electrode electrolyte reaction source terms" - ] - sum_s_j_p_av = variables[ - "Sum of x-averaged positive electrode electrolyte reaction source terms" - ] - sum_s_j = pybamm.concatenation( - pybamm.PrimaryBroadcast(sum_s_j_n_av, "negative electrode"), - pybamm.FullBroadcast(0, "separator", "current collector"), - pybamm.PrimaryBroadcast(sum_s_j_p_av, "positive electrode"), - ) - source_terms = sum_s_j / self.param.gamma_e - - self.rhs = { - c_e: (1 / eps_0) - * (-pybamm.div(N_e) / param.C_e + source_terms - c_e * deps_0_dt) - } - - def set_initial_conditions(self, variables): - - c_e = variables["Electrolyte concentration"] - - self.initial_conditions = {c_e: self.param.c_e_init} - - def set_boundary_conditions(self, variables): - - c_e = variables["Electrolyte concentration"] - - self.boundary_conditions = { - c_e: { - "left": (pybamm.Scalar(0), "Neumann"), - "right": (pybamm.Scalar(0), "Neumann"), - } - } diff --git a/pybamm/models/submodels/electrolyte_diffusion/first_order_diffusion.py b/pybamm/models/submodels/electrolyte_diffusion/first_order_diffusion.py deleted file mode 100644 index 16d239eb42..0000000000 --- a/pybamm/models/submodels/electrolyte_diffusion/first_order_diffusion.py +++ /dev/null @@ -1,143 +0,0 @@ -# -# Class for electrolyte diffusion employing stefan-maxwell (first-order) -# -import pybamm -from .base_electrolyte_diffusion import BaseElectrolyteDiffusion - - -class FirstOrder(BaseElectrolyteDiffusion): - """Class for conservation of mass in the electrolyte employing the - Stefan-Maxwell constitutive equations. (First-order refers to first-order term in - asymptotic expansion) - - Parameters - ---------- - param : parameter class - The parameters to use for this submodel - - - **Extends:** :class:`pybamm.electrolyte_diffusion.BaseElectrolyteDiffusion` - """ - - def __init__(self, param): - super().__init__(param) - - def get_coupled_variables(self, variables): - param = self.param - l_n = param.n.l - l_s = param.s.l - l_p = param.p.l - x_n = pybamm.standard_spatial_vars.x_n - x_s = pybamm.standard_spatial_vars.x_s - x_p = pybamm.standard_spatial_vars.x_p - - # Unpack - T_0 = variables["Leading-order cell temperature"] - c_e_0 = variables["Leading-order x-averaged electrolyte concentration"] - # v_box_0 = variables["Leading-order volume-averaged velocity"] - dc_e_0_dt = variables["Leading-order electrolyte concentration change"] - eps_n_0 = variables["Leading-order x-averaged negative electrode porosity"] - eps_s_0 = variables["Leading-order x-averaged separator porosity"] - eps_p_0 = variables["Leading-order x-averaged positive electrode porosity"] - tor_n_0 = variables[ - "Leading-order x-averaged negative electrolyte transport efficiency" - ] - tor_s_0 = variables[ - "Leading-order x-averaged separator electrolyte transport efficiency" - ] - tor_p_0 = variables[ - "Leading-order x-averaged positive electrolyte transport efficiency" - ] - deps_n_0_dt = variables[ - "Leading-order x-averaged negative electrode porosity change" - ] - deps_p_0_dt = variables[ - "Leading-order x-averaged positive electrode porosity change" - ] - - # Combined time derivatives - d_epsc_n_0_dt = c_e_0 * deps_n_0_dt + eps_n_0 * dc_e_0_dt - d_epsc_s_0_dt = eps_s_0 * dc_e_0_dt - d_epsc_p_0_dt = c_e_0 * deps_p_0_dt + eps_p_0 * dc_e_0_dt - - # Right-hand sides - sum_a_j_n_0 = variables[ - "Leading-order sum of x-averaged " - "negative electrode volumetric interfacial current densities" - ] - sum_a_j_p_0 = variables[ - "Leading-order sum of x-averaged " - "positive electrode volumetric interfacial current densities" - ] - sum_s_j_n_0 = variables[ - "Leading-order sum of x-averaged " - "negative electrode electrolyte reaction source terms" - ] - sum_s_j_p_0 = variables[ - "Leading-order sum of x-averaged " - "positive electrode electrolyte reaction source terms" - ] - rhs_n = ( - d_epsc_n_0_dt - - (sum_s_j_n_0 - param.t_plus(c_e_0, T_0) * sum_a_j_n_0) / param.gamma_e - ) - rhs_s = d_epsc_s_0_dt - rhs_p = ( - d_epsc_p_0_dt - - (sum_s_j_p_0 - param.t_plus(c_e_0, T_0) * sum_a_j_p_0) / param.gamma_e - ) - - # Diffusivities - D_e_n = tor_n_0 * param.D_e(c_e_0, T_0) - D_e_s = tor_s_0 * param.D_e(c_e_0, T_0) - D_e_p = tor_p_0 * param.D_e(c_e_0, T_0) - - # Fluxes - N_e_n_1 = -rhs_n * x_n - N_e_s_1 = -(rhs_s * (x_s - l_n) + rhs_n * l_n) - N_e_p_1 = -rhs_p * (x_p - 1) - - # Concentrations - c_e_n_1 = (rhs_n / (2 * D_e_n)) * (x_n**2 - l_n**2) - c_e_s_1 = (rhs_s / 2) * ((x_s - l_n) ** 2) + (rhs_n * l_n / D_e_s) * (x_s - l_n) - c_e_p_1 = (rhs_p / (2 * D_e_p)) * ((x_p - 1) ** 2 - l_p**2) + ( - (rhs_s * l_s**2 / (2 * D_e_s)) + (rhs_n * l_n * l_s / D_e_s) - ) - - # Correct for integral - c_e_n_1_av = -rhs_n * l_n**3 / (3 * D_e_n) - c_e_s_1_av = (rhs_s * l_s**3 / 6 + rhs_n * l_n * l_s**2 / 2) / D_e_s - c_e_p_1_av = ( - -rhs_p * l_p**3 / (3 * D_e_p) - + (rhs_s * l_s**2 * l_p / (2 * D_e_s)) - + (rhs_n * l_n * l_s * l_p / D_e_s) - ) - A_e = -(eps_n_0 * c_e_n_1_av + eps_s_0 * c_e_s_1_av + eps_p_0 * c_e_p_1_av) / ( - l_n * eps_n_0 + l_s * eps_s_0 + l_p * eps_p_0 - ) - c_e_dict = {} - for domain, var, var_av in [ - ("negative electrode", c_e_n_1, c_e_n_1_av), - ("separator", c_e_s_1, c_e_s_1_av), - ("positive electrode", c_e_p_1, c_e_p_1_av), - ]: - var += A_e - c_e_dict[domain] = c_e_0 + param.C_e * var - # Update with analytical expressions for first-order x-averages - var_av += A_e - variables.update({f"X-averaged first-order {domain} concentration": var_av}) - - # Update variables - variables.update(self._get_standard_concentration_variables(c_e_dict)) - - N_e = pybamm.concatenation( - param.C_e * N_e_n_1, param.C_e * N_e_s_1, param.C_e * N_e_p_1 - ) - variables.update(self._get_standard_flux_variables(N_e)) - - c_e = variables["Electrolyte concentration"] - eps = variables["Leading-order porosity"] - - variables.update(self._get_total_concentration_electrolyte(eps * c_e)) - - return variables diff --git a/pybamm/models/submodels/electrolyte_diffusion/full_diffusion.py b/pybamm/models/submodels/electrolyte_diffusion/full_diffusion.py index a7cd2bd6aa..4226f75689 100644 --- a/pybamm/models/submodels/electrolyte_diffusion/full_diffusion.py +++ b/pybamm/models/submodels/electrolyte_diffusion/full_diffusion.py @@ -53,10 +53,15 @@ def get_coupled_variables(self, variables): c_e_k = eps_c_e_k / eps_k c_e_dict[domain] = c_e_k - variables.update(self._get_standard_concentration_variables(c_e_dict)) + variables["Electrolyte concentration concatenation"] = pybamm.concatenation( + *c_e_dict.values() + ) + variables.update(self._get_standard_domain_concentration_variables(c_e_dict)) + + c_e = variables["Porosity times concentration"] / variables["Porosity"] + variables.update(self._get_standard_whole_cell_concentration_variables(c_e)) # Whole domain - c_e = variables["Electrolyte concentration"] tor = variables["Electrolyte transport efficiency"] i_e = variables["Electrolyte current density"] v_box = variables["Volume-averaged velocity"] @@ -102,6 +107,7 @@ def set_initial_conditions(self, variables): def set_boundary_conditions(self, variables): param = self.param c_e = variables["Electrolyte concentration"] + c_e_conc = variables["Electrolyte concentration concatenation"] T = variables["Cell temperature"] tor = variables["Electrolyte transport efficiency"] i_boundary_cc = variables["Current collector current density"] @@ -132,6 +138,8 @@ def flux_bc(side): # # right bc at separator/cathode interface # rbc = flux_bc("right") + # add boundary conditions to both forms of the concentration self.boundary_conditions = { c_e: {"left": (lbc, "Neumann"), "right": (rbc, "Neumann")}, + c_e_conc: {"left": (lbc, "Neumann"), "right": (rbc, "Neumann")}, } diff --git a/pybamm/models/submodels/equivalent_circuit_elements/__init__.py b/pybamm/models/submodels/equivalent_circuit_elements/__init__.py new file mode 100644 index 0000000000..a4e76cc5b8 --- /dev/null +++ b/pybamm/models/submodels/equivalent_circuit_elements/__init__.py @@ -0,0 +1,5 @@ +from .ocv_element import OCVElement +from .resistor_element import ResistorElement +from .rc_element import RCElement +from .thermal import ThermalSubModel +from .voltage_model import VoltageModel diff --git a/pybamm/models/submodels/equivalent_circuit_elements/ocv_element.py b/pybamm/models/submodels/equivalent_circuit_elements/ocv_element.py new file mode 100644 index 0000000000..44c9fd4c51 --- /dev/null +++ b/pybamm/models/submodels/equivalent_circuit_elements/ocv_element.py @@ -0,0 +1,62 @@ +import pybamm + + +class OCVElement(pybamm.BaseSubModel): + """ + Open Circuit Voltage (OCV) element for + equivalent circuits. + + Parameters + ---------- + param : parameter class + The parameters to use for this submodel + options : dict, optional + A dictionary of options to be passed to the model. + """ + + def __init__(self, param, options=None): + super().__init__(param) + self.model_options = options + + def get_fundamental_variables(self): + soc = pybamm.Variable("SoC") + ocv = self.param.ocv(soc) + variables = {"SoC": soc, "Open circuit voltage [V]": ocv} + return variables + + def get_coupled_variables(self, variables): + current = variables["Current [A]"] + + ocv = variables["Open circuit voltage [V]"] + T_cell = variables["Cell temperature [degC]"] + + dUdT = self.param.dUdT(ocv, T_cell) + + T_cell_kelvin = variables["Cell temperature [K]"] + Q_rev = -current * T_cell_kelvin * dUdT + + variables.update( + { + "Entropic change [V/K]": dUdT, + "Reversible heat generation [W]": Q_rev, + } + ) + + return variables + + def set_rhs(self, variables): + soc = variables["SoC"] + current = variables["Current [A]"] + cell_capacity = self.param.cell_capacity + self.rhs = {soc: -current / cell_capacity / 3600} + + def set_initial_conditions(self, variables): + soc = variables["SoC"] + self.initial_conditions = {soc: self.param.initial_soc} + + def set_events(self, variables): + soc = variables["SoC"] + self.events = [ + pybamm.Event("Minimum SoC", soc), + pybamm.Event("Maximum SoC", 1 - soc), + ] diff --git a/pybamm/models/submodels/equivalent_circuit_elements/rc_element.py b/pybamm/models/submodels/equivalent_circuit_elements/rc_element.py new file mode 100644 index 0000000000..757488313b --- /dev/null +++ b/pybamm/models/submodels/equivalent_circuit_elements/rc_element.py @@ -0,0 +1,74 @@ +import pybamm + + +class RCElement(pybamm.BaseSubModel): + """ + Parallel Resistor-Capacitor (RC) element for + equivalent circuits. + + Parameters + ---------- + param : parameter class + The parameters to use for this submodel + element_number: int + The number of the element (i.e. whether it + is the first, second, third, etc. element) + options : dict, optional + A dictionary of options to be passed to the model. + """ + + def __init__(self, param, element_number, options=None): + super().__init__(param) + self.element_number = element_number + self.model_options = options + + def get_fundamental_variables(self): + vrc = pybamm.Variable(f"Element-{self.element_number} overpotential [V]") + variables = {f"Element-{self.element_number} overpotential [V]": vrc} + return variables + + def get_coupled_variables(self, variables): + + T_cell = variables["Cell temperature [degC]"] + current = variables["Current [A]"] + soc = variables["SoC"] + + r = self.param.rcr_element( + f"R{self.element_number} [Ohm]", T_cell, current, soc + ) + c = self.param.rcr_element(f"C{self.element_number} [F]", T_cell, current, soc) + tau = r * c + + vrc = variables[f"Element-{self.element_number} overpotential [V]"] + + Q_irr = -current * vrc + + variables.update( + { + f"R{self.element_number} [Ohm]": r, + f"C{self.element_number} [F]": c, + f"tau{self.element_number} [s]": tau, + f"Element-{self.element_number} " + + "irreversible heat generation [W]": Q_irr, + } + ) + + return variables + + def set_rhs(self, variables): + vrc = variables[f"Element-{self.element_number} overpotential [V]"] + current = variables["Current [A]"] + + r = variables[f"R{self.element_number} [Ohm]"] + tau = variables[f"tau{self.element_number} [s]"] + + self.rhs = { + vrc: -vrc / (tau) - current * r / tau, + } + + def set_initial_conditions(self, variables): + vrc = variables[f"Element-{self.element_number} overpotential [V]"] + + self.initial_conditions = { + vrc: self.param.initial_rc_overpotential(self.element_number) + } diff --git a/pybamm/models/submodels/equivalent_circuit_elements/resistor_element.py b/pybamm/models/submodels/equivalent_circuit_elements/resistor_element.py new file mode 100644 index 0000000000..b708d93721 --- /dev/null +++ b/pybamm/models/submodels/equivalent_circuit_elements/resistor_element.py @@ -0,0 +1,39 @@ +import pybamm + + +class ResistorElement(pybamm.BaseSubModel): + """ + Resistor element for equivalent circuits. + + Parameters + ---------- + param : parameter class + The parameters to use for this submodel + options : dict, optional + A dictionary of options to be passed to the model. + """ + + def __init__(self, param, options=None): + super().__init__(param) + self.model_options = options + + def get_coupled_variables(self, variables): + + T_cell = variables["Cell temperature [degC]"] + current = variables["Current [A]"] + soc = variables["SoC"] + + r = self.param.rcr_element("R0 [Ohm]", T_cell, current, soc) + + overpotential = -current * r + Q_irr = current**2 * r + + variables.update( + { + "R0 [Ohm]": r, + "Element-0 overpotential [V]": overpotential, + "Element-0 " + "irreversible heat generation [W]": Q_irr, + } + ) + + return variables diff --git a/pybamm/models/submodels/equivalent_circuit_elements/thermal.py b/pybamm/models/submodels/equivalent_circuit_elements/thermal.py new file mode 100644 index 0000000000..8790f47f7f --- /dev/null +++ b/pybamm/models/submodels/equivalent_circuit_elements/thermal.py @@ -0,0 +1,85 @@ +import pybamm + + +class ThermalSubModel(pybamm.BaseSubModel): + """ + Thermal SubModel for use with equivalent + circuits. + + Parameters + ---------- + param : parameter class + The parameters to use for this submodel + options : dict, optional + A dictionary of options to be passed to the model. + """ + + def __init__(self, param, options=None): + super().__init__(param) + self.model_options = options + + def get_fundamental_variables(self): + T_cell = pybamm.Variable("Cell temperature [degC]") + T_jig = pybamm.Variable("Jig temperature [degC]") + + T_amb = self.param.T_amb(pybamm.t * self.param.timescale) + + Q_cell_cool = -self.param.k_cell_jig * (T_cell - T_jig) + Q_jig_cool = -self.param.k_jig_air * (T_jig - T_amb) + + kelvin = 273.15 + variables = { + "Cell temperature [degC]": T_cell, + "Cell temperature [K]": T_cell + kelvin, + "Jig temperature [degC]": T_jig, + "Jig temperature [K]": T_jig + kelvin, + "Ambient temperature [degC]": T_amb, + "Ambient temperature [K]": T_amb + kelvin, + "Heat transfer from cell to jig [W]": Q_cell_cool, + "Heat transfer from jig to ambient [W]": Q_jig_cool, + } + + return variables + + def get_coupled_variables(self, variables): + + number_of_rc_elements = self.model_options["number of rc elements"] + number_of_elements = number_of_rc_elements + 1 + + Q_irr = pybamm.Scalar(0) + for i in range(number_of_elements): + Q_irr += variables[f"Element-{i} irreversible heat generation [W]"] + + Q_rev = variables["Reversible heat generation [W]"] + + variables.update( + { + "Irreversible heat generation [W]": Q_irr, + "Total heat generation [W]": Q_irr + Q_rev, + } + ) + + return variables + + def set_rhs(self, variables): + T_cell = variables["Cell temperature [degC]"] + T_jig = variables["Jig temperature [degC]"] + + Q_irr = variables["Irreversible heat generation [W]"] + Q_rev = variables["Reversible heat generation [W]"] + + Q_cell_cool = variables["Heat transfer from cell to jig [W]"] + Q_jig_cool = variables["Heat transfer from jig to ambient [W]"] + + self.rhs = { + T_cell: (Q_irr + Q_rev + Q_cell_cool) / self.param.cth_cell, + T_jig: (Q_jig_cool - Q_cell_cool) / self.param.cth_jig, + } + + def set_initial_conditions(self, variables): + T_cell = variables["Cell temperature [degC]"] + T_jig = variables["Jig temperature [degC]"] + self.initial_conditions = { + T_cell: self.param.initial_T_cell, + T_jig: self.param.initial_T_jig, + } diff --git a/pybamm/models/submodels/equivalent_circuit_elements/voltage_model.py b/pybamm/models/submodels/equivalent_circuit_elements/voltage_model.py new file mode 100644 index 0000000000..404d33d2f0 --- /dev/null +++ b/pybamm/models/submodels/equivalent_circuit_elements/voltage_model.py @@ -0,0 +1,90 @@ +import pybamm + + +class VoltageModel(pybamm.BaseSubModel): + """ + Voltage model for use with equivalent + circuits. This model is used to calculate + the voltage and total overpotentials + from the other elements in the circuit. + + Parameters + ---------- + param : parameter class + The parameters to use for this submodel + options : dict, optional + A dictionary of options to be passed to the model. + """ + + def __init__(self, param, options=None): + super().__init__(param) + self.model_options = options + + def get_coupled_variables(self, variables): + + ocv = variables["Open circuit voltage [V]"] + + number_of_rc_elements = self.model_options["number of rc elements"] + number_of_elements = number_of_rc_elements + 1 + + overpotential = pybamm.Scalar(0) + for i in range(number_of_elements): + overpotential += variables[f"Element-{i} overpotential [V]"] + + voltage = ocv + overpotential + + # Power and Resistance + current = variables["Current [A]"] + + def x_not_zero(x): + return ((x > 0) + (x < 0)) * x + (x >= 0) * (x <= 0) + + non_zero_current = x_not_zero(current) + + variables.update( + { + "Terminal voltage [V]": voltage, + "Overpotential [V]": overpotential, + "Battery voltage [V]": voltage, + "Power [W]": voltage * current, + "Resistance [Ohm]": pybamm.sign(current) * voltage / non_zero_current, + } + ) + + return variables + + def set_events(self, variables): + + voltage = variables["Terminal voltage [V]"] + + # Add voltage events + maximum_voltage = pybamm.Event( + "Maximum voltage", + self.param.voltage_high_cut - voltage, + pybamm.EventType.TERMINATION, + ) + self.events.append(maximum_voltage) + + minimum_voltage = pybamm.Event( + "Minimum voltage", + voltage - self.param.voltage_low_cut, + pybamm.EventType.TERMINATION, + ) + self.events.append(minimum_voltage) + + # Cut-off voltage for event switch with casadi 'fast with events' + tol = 0.125 + self.events.append( + pybamm.Event( + "Minimum voltage switch", + voltage - (self.param.voltage_low_cut - tol), + pybamm.EventType.SWITCH, + ) + ) + self.events.append( + pybamm.Event( + "Maximum voltage switch", + voltage - (self.param.voltage_high_cut + tol), + pybamm.EventType.SWITCH, + ) + ) diff --git a/pybamm/models/submodels/external_circuit/__init__.py b/pybamm/models/submodels/external_circuit/__init__.py index e84a8c0e8d..bd71790295 100644 --- a/pybamm/models/submodels/external_circuit/__init__.py +++ b/pybamm/models/submodels/external_circuit/__init__.py @@ -1,9 +1,8 @@ -from .base_external_circuit import BaseModel, LeadingOrderBaseModel +from .base_external_circuit import BaseModel from .explicit_control_external_circuit import ( ExplicitCurrentControl, ExplicitPowerControl, ExplicitResistanceControl, - LeadingOrderExplicitCurrentControl, ) from .function_control_external_circuit import ( FunctionControl, @@ -11,7 +10,4 @@ PowerFunctionControl, ResistanceFunctionControl, CCCVFunctionControl, - LeadingOrderFunctionControl, - LeadingOrderVoltageFunctionControl, - LeadingOrderPowerFunctionControl, ) diff --git a/pybamm/models/submodels/external_circuit/base_external_circuit.py b/pybamm/models/submodels/external_circuit/base_external_circuit.py index b5ad26797b..ad41181e57 100644 --- a/pybamm/models/submodels/external_circuit/base_external_circuit.py +++ b/pybamm/models/submodels/external_circuit/base_external_circuit.py @@ -60,26 +60,3 @@ def set_rhs(self, variables): self.rhs[Q_Wh] = I * V * self.param.timescale / 3600 self.rhs[Qt_Wh] = abs(I * V) * self.param.timescale / 3600 self.rhs[Qt_Ah] = abs(I) * self.param.timescale / 3600 - - -class LeadingOrderBaseModel(BaseModel): - """Model to represent the behaviour of the external circuit, at leading order.""" - - def __init__(self, param, options): - super().__init__(param, options) - - def get_fundamental_variables(self): - Q_Ah = pybamm.Variable("Leading-order discharge capacity [A.h]") - variables = {"Discharge capacity [A.h]": Q_Ah} - if self.options["calculate discharge energy"] == "true": - Q_Wh = pybamm.Variable("Leading-order discharge energy [W.h]") - Qt_Wh = pybamm.Variable("Leading-order throughput energy [W.h]") - Qt_Ah = pybamm.Variable("Leading-order throughput capacity [A.h]") - variables.update( - { - "Discharge energy [W.h]": Q_Wh, - "Throughput energy [W.h]": Qt_Wh, - "Throughput capacity [A.h]": Qt_Ah, - } - ) - return variables diff --git a/pybamm/models/submodels/external_circuit/explicit_control_external_circuit.py b/pybamm/models/submodels/external_circuit/explicit_control_external_circuit.py index d2bb8ee63c..0f4d581a4f 100644 --- a/pybamm/models/submodels/external_circuit/explicit_control_external_circuit.py +++ b/pybamm/models/submodels/external_circuit/explicit_control_external_circuit.py @@ -2,7 +2,7 @@ # External circuit with explicit equations for control # import pybamm -from .base_external_circuit import BaseModel, LeadingOrderBaseModel +from .base_external_circuit import BaseModel class ExplicitCurrentControl(BaseModel): @@ -89,10 +89,3 @@ def get_coupled_variables(self, variables): } return variables - - -class LeadingOrderExplicitCurrentControl(ExplicitCurrentControl, LeadingOrderBaseModel): - """External circuit with current control, for leading order models.""" - - def __init__(self, param, options): - super().__init__(param, options) diff --git a/pybamm/models/submodels/external_circuit/function_control_external_circuit.py b/pybamm/models/submodels/external_circuit/function_control_external_circuit.py index 713ed51ba4..78e3b0b597 100644 --- a/pybamm/models/submodels/external_circuit/function_control_external_circuit.py +++ b/pybamm/models/submodels/external_circuit/function_control_external_circuit.py @@ -2,7 +2,7 @@ # External circuit with an arbitrary function # import pybamm -from .base_external_circuit import BaseModel, LeadingOrderBaseModel +from .base_external_circuit import BaseModel class FunctionControl(BaseModel): @@ -170,43 +170,3 @@ def cccv(self, variables): V = variables["Terminal voltage [V]"] V_CCCV = pybamm.Parameter("Voltage function [V]") return -K_aw * (i_var - i_cell) + K_V * (V - V_CCCV) - - -class LeadingOrderFunctionControl(FunctionControl, LeadingOrderBaseModel): - """External circuit with an arbitrary function, at leading order.""" - - def __init__(self, param, external_circuit_function, options, control="algebraic"): - super().__init__(param, external_circuit_function, options, control=control) - - def _get_current_variable(self): - return pybamm.Variable("Leading-order total current density") - - -class LeadingOrderVoltageFunctionControl(LeadingOrderFunctionControl): - """ - External circuit with voltage control, implemented as an extra algebraic equation, - at leading order. - """ - - def __init__(self, param, options): - super().__init__(param, self.constant_voltage, options, control="algebraic") - - def constant_voltage(self, variables): - V = variables["Terminal voltage [V]"] - return V - pybamm.FunctionParameter( - "Voltage function [V]", {"Time [s]": pybamm.t * self.param.timescale} - ) - - -class LeadingOrderPowerFunctionControl(LeadingOrderFunctionControl): - """External circuit with power control, at leading order.""" - - def __init__(self, param, options): - super().__init__(param, self.constant_power, options, control="algebraic") - - def constant_power(self, variables): - I = variables["Current [A]"] - V = variables["Terminal voltage [V]"] - return I * V - pybamm.FunctionParameter( - "Power function [W]", {"Time [s]": pybamm.t * self.param.timescale} - ) diff --git a/pybamm/models/submodels/interface/base_interface.py b/pybamm/models/submodels/interface/base_interface.py index b7a339efee..dfd0428a29 100644 --- a/pybamm/models/submodels/interface/base_interface.py +++ b/pybamm/models/submodels/interface/base_interface.py @@ -422,25 +422,6 @@ def _get_standard_average_surface_potential_difference_variables( return variables - def _get_standard_surface_potential_difference_variables(self, delta_phi): - domain, Domain = self.domain_Domain - ocp_ref = self.domain_param.U_ref - - # Broadcast if necessary - delta_phi_dim = ocp_ref + delta_phi * self.param.potential_scale - if delta_phi.domain == ["current collector"]: - delta_phi = pybamm.PrimaryBroadcast(delta_phi, f"{domain} electrode") - delta_phi_dim = pybamm.PrimaryBroadcast( - delta_phi_dim, f"{domain} electrode" - ) - - variables = { - f"{Domain} electrode surface potential difference": delta_phi, - f"{Domain} electrode surface potential difference [V]": delta_phi_dim, - } - - return variables - def _get_standard_size_distribution_interfacial_current_variables(self, j): """ Interfacial current density variables that depend on particle size R, diff --git a/pybamm/models/submodels/interface/kinetics/__init__.py b/pybamm/models/submodels/interface/kinetics/__init__.py index 1acd63947b..c8b8552574 100644 --- a/pybamm/models/submodels/interface/kinetics/__init__.py +++ b/pybamm/models/submodels/interface/kinetics/__init__.py @@ -12,5 +12,3 @@ CurrentForInverseButlerVolmer, CurrentForInverseButlerVolmerLithiumMetal, ) -from .first_order_kinetics.first_order_kinetics import FirstOrderKinetics -from .first_order_kinetics.inverse_first_order_kinetics import InverseFirstOrderKinetics diff --git a/pybamm/models/submodels/interface/kinetics/base_kinetics.py b/pybamm/models/submodels/interface/kinetics/base_kinetics.py index bcee5da2ed..03be0d29be 100644 --- a/pybamm/models/submodels/interface/kinetics/base_kinetics.py +++ b/pybamm/models/submodels/interface/kinetics/base_kinetics.py @@ -232,38 +232,3 @@ def set_initial_conditions(self, variables): j_tot_av_init = sgn * current_at_0 / self.domain_param.l self.initial_conditions[j_tot_var] = j_tot_av_init - - def _get_interface_variables_for_first_order(self, variables): - # This is a bit of a hack, but we need to wrap electrolyte concentration with - # the NotConstant class - # to differentiate it from the electrolyte concentration inside the - # surface potential difference when taking j.diff(c_e) later on - domain, Domain = self.domain_Domain - - c_e_0 = pybamm.NotConstant( - variables["Leading-order x-averaged electrolyte concentration"] - ) - c_e = pybamm.PrimaryBroadcast(c_e_0, f"{domain} electrode") - hacked_variables = {**variables, f"{Domain} electrolyte concentration": c_e} - delta_phi = variables[ - f"Leading-order x-averaged {domain} electrode surface potential difference" - ] - j0 = self._get_exchange_current_density(hacked_variables) - ne = self._get_number_of_electrons_in_reaction() - if self.reaction == "lead-acid main": - ocp = self.phase_param.U(c_e_0, self.param.T_init) - elif self.reaction == "lead-acid oxygen": - ocp = self.phase_param.U_Ox - - T = variables["X-averaged cell temperature"] - u = variables[f"X-averaged {domain} electrode interface utilisation"] - - return c_e_0, delta_phi, j0, ne, ocp, T, u - - def _get_j_diffusion_limited_first_order(self, variables): - """ - First-order correction to the interfacial current density due to - diffusion-limited effects. For a general model the correction term is zero, - since the reaction is not diffusion-limited - """ - return pybamm.Scalar(0) diff --git a/pybamm/models/submodels/interface/kinetics/butler_volmer.py b/pybamm/models/submodels/interface/kinetics/butler_volmer.py index 68bdc7461b..a2e130a809 100644 --- a/pybamm/models/submodels/interface/kinetics/butler_volmer.py +++ b/pybamm/models/submodels/interface/kinetics/butler_volmer.py @@ -37,32 +37,6 @@ def _get_kinetics(self, j0, ne, eta_r, T, u): prefactor = ne / (2 * (1 + self.param.Theta * T)) return 2 * u * j0 * pybamm.sinh(prefactor * eta_r) - def _get_dj_dc(self, variables): - """See :meth:`pybamm.interface.kinetics.BaseKinetics._get_dj_dc`""" - ( - c_e, - delta_phi, - j0, - ne, - ocp, - T, - u, - ) = self._get_interface_variables_for_first_order(variables) - eta_r = delta_phi - ocp - prefactor = ne / (2 * (1 + self.param.Theta * T)) - return (2 * u * j0.diff(c_e) * pybamm.sinh(prefactor * eta_r)) - ( - 2 * u * j0 * prefactor * ocp.diff(c_e) * pybamm.cosh(prefactor * eta_r) - ) - - def _get_dj_ddeltaphi(self, variables): - """See :meth:`pybamm.interface.kinetics.BaseKinetics._get_dj_ddeltaphi`""" - _, delta_phi, j0, ne, ocp, T, u = self._get_interface_variables_for_first_order( - variables - ) - eta_r = delta_phi - ocp - prefactor = ne / (2 * (1 + self.param.Theta * T)) - return 2 * u * j0 * prefactor * pybamm.cosh(prefactor * eta_r) - class AsymmetricButlerVolmer(BaseKinetics): """ diff --git a/pybamm/models/submodels/interface/kinetics/diffusion_limited.py b/pybamm/models/submodels/interface/kinetics/diffusion_limited.py index 3523d34a53..e1b22032a8 100644 --- a/pybamm/models/submodels/interface/kinetics/diffusion_limited.py +++ b/pybamm/models/submodels/interface/kinetics/diffusion_limited.py @@ -67,22 +67,6 @@ def get_coupled_variables(self, variables): eta_sei = pybamm.Scalar(0) variables.update(self._get_standard_sei_film_overpotential_variables(eta_sei)) - if self.order == "composite": - # For the composite model, adds the first-order x-averaged interfacial - # current density to the dictionary of variables. - j_0 = variables[ - f"Leading-order {domain} electrode {self.reaction_name}" - "interfacial current density" - ] - j_1_bar = (pybamm.x_average(j) - pybamm.x_average(j_0)) / self.param.C_e - - variables.update( - { - f"First-order x-averaged {domain} electrode" - f" {self.reaction_name}interfacial current density": j_1_bar - } - ) - return variables def _get_diffusion_limited_current_density(self, variables): @@ -107,32 +91,3 @@ def _get_diffusion_limited_current_density(self, variables): j = -N_ox_neg_sep_interface / param.C_e / -param.s_ox_Ox / param.n.l return j - - def _get_dj_dc(self, variables): - return pybamm.Scalar(0) - - def _get_dj_ddeltaphi(self, variables): - return pybamm.Scalar(0) - - def _get_j_diffusion_limited_first_order(self, variables): - """ - First-order correction to the interfacial current density due to - diffusion-limited effects. For a general model the correction term is zero, - since the reaction is not diffusion-limited - """ - domain = self.domain - if self.order == "leading": - j_leading_order = variables[ - f"Leading-order x-averaged {domain} electrode " - f"{self.reaction_name}interfacial current density" - ] - param = self.param - if self.domain == "negative": - N_ox_s_p = variables["Oxygen flux"].orphans[1] - N_ox_neg_sep_interface = pybamm.Index(N_ox_s_p, slice(0, 1)) - - j = -N_ox_neg_sep_interface / param.C_e / -param.s_ox_Ox / param.n.l - - return (j - j_leading_order) / param.C_e - else: - return pybamm.Scalar(0) diff --git a/pybamm/models/submodels/interface/kinetics/first_order_kinetics/first_order_kinetics.py b/pybamm/models/submodels/interface/kinetics/first_order_kinetics/first_order_kinetics.py deleted file mode 100644 index 9b4f962901..0000000000 --- a/pybamm/models/submodels/interface/kinetics/first_order_kinetics/first_order_kinetics.py +++ /dev/null @@ -1,93 +0,0 @@ -# -# First-order Butler-Volmer kinetics -# -import pybamm -from ...base_interface import BaseInterface - - -class FirstOrderKinetics(BaseInterface): - """ - First-order kinetics - - Parameters - ---------- - param : - model parameters - domain : str - The domain to implement the model, either: 'Negative' or 'Positive'. - leading_order_model : :class:`pybamm.interface.kinetics.BaseKinetics` - The leading-order model with respect to which this is first-order - options: dict - A dictionary of options to be passed to the model. See - :class:`pybamm.BaseBatteryModel` - - **Extends:** :class:`pybamm.interface.BaseInterface` - """ - - def __init__(self, param, domain, leading_order_model, options): - super().__init__(param, domain, leading_order_model.reaction, options) - self.leading_order_model = leading_order_model - - def get_coupled_variables(self, variables): - domain, Domain = self.domain_Domain - reaction_name = self.reaction_name - - # Unpack - c_e_0 = variables[f"Leading-order {domain} electrolyte concentration"] - c_e = variables[f"{Domain} electrolyte concentration"] - c_e_1 = (c_e - c_e_0) / self.param.C_e - - dj_dc_0 = self.leading_order_model._get_dj_dc(variables) - dj_ddeltaphi_0 = self.leading_order_model._get_dj_ddeltaphi(variables) - - # Update delta_phi with new phi_e and phi_s - phi_s = variables[f"{Domain} electrode potential"] - phi_e = variables[f"{Domain} electrolyte potential"] - delta_phi = phi_s - phi_e - variables.update( - self._get_standard_average_surface_potential_difference_variables( - pybamm.x_average(delta_phi) - ) - ) - variables.update( - self._get_standard_surface_potential_difference_variables(delta_phi) - ) - - delta_phi_0 = variables[ - f"Leading-order {domain} electrode surface potential difference" - ] - delta_phi_1 = (delta_phi - delta_phi_0) / self.param.C_e - - j_0 = variables[ - f"Leading-order {domain} electrode {reaction_name}" - "interfacial current density" - ] - j_1 = dj_dc_0 * c_e_1 + dj_ddeltaphi_0 * delta_phi_1 - j = j_0 + self.param.C_e * j_1 - # Get exchange-current density - j0 = self._get_exchange_current_density(variables) - # Get open-circuit potential variables and reaction overpotential - ocp = variables[f"{Domain} electrode {reaction_name}open circuit potential"] - eta_r = delta_phi - ocp - - variables.update(self._get_standard_interfacial_current_variables(j)) - variables.update(self._get_standard_exchange_current_variables(j0)) - variables.update(self._get_standard_overpotential_variables(eta_r)) - - # SEI film resistance not implemented in this model - eta_sei = pybamm.Scalar(0) - variables.update(self._get_standard_sei_film_overpotential_variables(eta_sei)) - - # Add first-order averages - j_1_bar = dj_dc_0 * pybamm.x_average(c_e_1) + dj_ddeltaphi_0 * pybamm.x_average( - delta_phi_1 - ) - - variables.update( - { - f"First-order x-averaged {domain} electrode {reaction_name} " - "interfacial current density": j_1_bar - } - ) - - return variables diff --git a/pybamm/models/submodels/interface/kinetics/first_order_kinetics/inverse_first_order_kinetics.py b/pybamm/models/submodels/interface/kinetics/first_order_kinetics/inverse_first_order_kinetics.py deleted file mode 100644 index 61b6089b50..0000000000 --- a/pybamm/models/submodels/interface/kinetics/first_order_kinetics/inverse_first_order_kinetics.py +++ /dev/null @@ -1,86 +0,0 @@ -# -# First-order Butler-Volmer kinetics -# -import pybamm -from ...base_interface import BaseInterface - - -class InverseFirstOrderKinetics(BaseInterface): - """ - Base inverse first-order kinetics. This class needs to consider *all* of the - leading-order submodels simultaneously in order to find the first-order correction - to the potentials - - Parameters - ---------- - param : - model parameters - domain : str - The domain to implement the model, either: 'Negative' or 'Positive'. - leading_order_models : :class:`pybamm.interface.kinetics.BaseKinetics` - The leading-order models with respect to which this is first-order - options: dict - A dictionary of options to be passed to the model. See - :class:`pybamm.BaseBatteryModel` - - **Extends:** :class:`pybamm.interface.BaseInterface` - """ - - def __init__(self, param, domain, leading_order_models, options): - super().__init__(param, domain, "inverse", options) - self.leading_order_models = leading_order_models - - def _get_die1dx(self, variables): - i_boundary_cc = variables["Current collector current density"] - i_boundary_cc_0 = variables["Leading-order current collector current density"] - i_boundary_cc_1 = (i_boundary_cc - i_boundary_cc_0) / self.param.C_e - - sgn = 1 if self.domain == "negative" else -1 - return sgn * i_boundary_cc_1 / self.domain_param.l - - def get_coupled_variables(self, variables): - domain = self.domain - # Unpack - delta_phi_0 = variables[ - f"Leading-order x-averaged {domain} electrode surface potential difference" - ] - c_e_0 = variables["Leading-order x-averaged electrolyte concentration"] - c_e_av = variables[f"X-averaged {domain} electrolyte concentration"] - c_e_1_av = (c_e_av - c_e_0) / self.param.C_e - - # Get first-order current (this is zero in 1D) - die1_dx = self._get_die1dx(variables) - - # Get derivatives of leading-order terms - sum_dj_dc_0 = sum( - submodel._get_dj_dc(variables) for submodel in self.leading_order_models - ) - sum_dj_ddeltaphi_0 = sum( - submodel._get_dj_ddeltaphi(variables) - for submodel in self.leading_order_models - ) - sum_j_diffusion_limited_first_order = sum( - submodel._get_j_diffusion_limited_first_order(variables) - for submodel in self.leading_order_models - ) - - delta_phi_1_av = ( - die1_dx - (sum_dj_dc_0 * c_e_1_av + sum_j_diffusion_limited_first_order) - ) / sum_dj_ddeltaphi_0 - delta_phi = delta_phi_0 + self.param.C_e * delta_phi_1_av - - # Update variables dictionary - variables.update( - self._get_standard_average_surface_potential_difference_variables( - pybamm.x_average(delta_phi) - ) - ) - variables.update( - self._get_standard_surface_potential_difference_variables(delta_phi) - ) - - # SEI film resistance not implemented in this model - eta_sei = pybamm.Scalar(0) - variables.update(self._get_standard_sei_film_overpotential_variables(eta_sei)) - - return variables diff --git a/pybamm/models/submodels/interface/kinetics/no_reaction.py b/pybamm/models/submodels/interface/kinetics/no_reaction.py index 32f18a037f..399e9f06fb 100644 --- a/pybamm/models/submodels/interface/kinetics/no_reaction.py +++ b/pybamm/models/submodels/interface/kinetics/no_reaction.py @@ -45,12 +45,3 @@ def get_coupled_variables(self, variables): self._get_standard_volumetric_current_density_variables(variables) ) return variables - - def _get_dj_dc(self, variables): - return pybamm.Scalar(0) - - def _get_dj_ddeltaphi(self, variables): - return pybamm.Scalar(0) - - def _get_j_diffusion_limited_first_order(self, variables): - return pybamm.Scalar(0) diff --git a/pybamm/models/submodels/interface/kinetics/tafel.py b/pybamm/models/submodels/interface/kinetics/tafel.py index eb9083d068..55deef3509 100644 --- a/pybamm/models/submodels/interface/kinetics/tafel.py +++ b/pybamm/models/submodels/interface/kinetics/tafel.py @@ -38,34 +38,6 @@ def _get_kinetics(self, j0, ne, eta_r, T, u): u * j0 * pybamm.exp((ne * alpha / (2 * (1 + self.param.Theta * T))) * eta_r) ) - def _get_dj_dc(self, variables): - """See :meth:`pybamm.interface.kinetics.BaseKinetics._get_dj_dc`""" - alpha = self.phase_param.alpha_bv - ( - c_e, - delta_phi, - j0, - ne, - ocp, - T, - u, - ) = self._get_interface_variables_for_first_order(variables) - eta_r = delta_phi - ocp - return (2 * u * j0.diff(c_e)) * pybamm.exp( - (ne * alpha / (2 * (1 + self.param.Theta * T))) * eta_r - ) - - def _get_dj_ddeltaphi(self, variables): - """See :meth:`pybamm.interface.kinetics.BaseKinetics._get_dj_ddeltaphi`""" - alpha = self.phase_param.alpha_bv - _, delta_phi, j0, ne, ocp, T, u = self._get_interface_variables_for_first_order( - variables - ) - eta_r = delta_phi - ocp - return (2 * u * j0 * (ne / (2 * (1 + self.param.Theta * T)))) * pybamm.exp( - (ne * alpha / (2 * (1 + self.param.Theta * T))) * eta_r - ) - # backwardtafel not used by any of the models # class BackwardTafel(BaseKinetics): diff --git a/pybamm/models/submodels/interface/sei/base_sei.py b/pybamm/models/submodels/interface/sei/base_sei.py index f3f796a86f..36f4fb7d54 100644 --- a/pybamm/models/submodels/interface/sei/base_sei.py +++ b/pybamm/models/submodels/interface/sei/base_sei.py @@ -198,8 +198,9 @@ def _get_standard_concentration_variables(self, variables): ) v_bar = phase_param.v_bar z_sei = phase_param.z_sei - # Set scales for the "EC Reaction Limited" model - if self.options["SEI"] == "ec reaction limited": + # Set scales for the "EC Reaction Limited" models (both symmetric and + # asymmetric) + if self.options["SEI"].startswith("ec reaction limited"): L_inner_0 = 0 L_outer_0 = 1 L_inner_crack_0 = 0 @@ -275,7 +276,7 @@ def _get_standard_concentration_variables(self, variables): # Calculate change in SEI cracks concentration # Initial state depends on roughness (to avoid division by zero) - roughness_av = pybamm.x_average(roughness) + roughness_av = pybamm.yz_average(pybamm.x_average(roughness)) # choose an initial condition that is as close to zero to get the # physics right, but doesn't cause a division by zero error n_SEI_cr_init = (L_inner_crack_0 + L_outer_crack_0 / v_bar) * ( diff --git a/pybamm/models/submodels/interface/sei/sei_growth.py b/pybamm/models/submodels/interface/sei/sei_growth.py index dcd991919d..9e8b812374 100644 --- a/pybamm/models/submodels/interface/sei/sei_growth.py +++ b/pybamm/models/submodels/interface/sei/sei_growth.py @@ -56,7 +56,7 @@ def get_fundamental_variables(self): L_inner, L_outer = Ls - if self.options["SEI"] == "ec reaction limited": + if self.options["SEI"].startswith("ec reaction limited"): L_inner = 0 * L_inner # Set L_inner to zero, copying domains variables = self._get_standard_thickness_variables(L_inner, L_outer) @@ -100,9 +100,16 @@ def get_coupled_variables(self, variables): # Thermal prefactor for reaction, interstitial and EC models prefactor = 1 / (1 + self.param.Theta * T) - if self.options["SEI"] == "reaction limited": + # Define alpha_SEI depending on whether it is symmetric or asymmetric. This + # applies to "reaction limited" and "EC reaction limited" + if self.options["SEI"].endswith("(asymmetric)"): + alpha_SEI = phase_param.alpha_SEI + else: + alpha_SEI = 0.5 + + if self.options["SEI"].startswith("reaction limited"): C_sei = phase_param.C_sei_reaction - j_sei = -(1 / C_sei) * pybamm.exp(-0.5 * prefactor * eta_SEI) + j_sei = -(1 / C_sei) * pybamm.exp(-alpha_SEI * prefactor * eta_SEI) elif self.options["SEI"] == "electron-migration limited": U_inner = phase_param.U_inner_electron @@ -117,7 +124,7 @@ def get_coupled_variables(self, variables): C_sei = phase_param.C_sei_solvent j_sei = -1 / (C_sei * L_sei_outer) - elif self.options["SEI"] == "ec reaction limited": + elif self.options["SEI"].startswith("ec reaction limited"): C_sei_ec = phase_param.C_sei_ec C_ec = phase_param.C_ec @@ -129,7 +136,7 @@ def get_coupled_variables(self, variables): # so # j_sei = -C_sei_ec * exp() / (1 + L_sei * C_ec * C_sei_ec * exp()) # c_ec = 1 / (1 + L_sei * C_ec * C_sei_ec * exp()) - C_sei_exp = C_sei_ec * pybamm.exp(-0.5 * prefactor * eta_SEI) + C_sei_exp = C_sei_ec * pybamm.exp(-alpha_SEI * prefactor * eta_SEI) j_sei = -C_sei_exp / (1 + L_sei * C_ec * C_sei_exp) c_ec = 1 / (1 + L_sei * C_ec * C_sei_exp) @@ -150,7 +157,7 @@ def get_coupled_variables(self, variables): } ) - if self.options["SEI"] == "ec reaction limited": + if self.options["SEI"].startswith("ec reaction limited"): inner_sei_proportion = 0 else: inner_sei_proportion = phase_param.inner_sei_proportion @@ -224,7 +231,7 @@ def set_rhs(self, variables): Gamma_SEI = self.phase_param.Gamma_SEI - if self.options["SEI"] == "ec reaction limited": + if self.options["SEI"].startswith("ec reaction limited"): self.rhs = {L_outer: -Gamma_SEI * a * j_outer + spreading_outer} else: v_bar = self.phase_param.v_bar @@ -247,7 +254,7 @@ def set_initial_conditions(self, variables): else: L_inner_0 = self.phase_param.L_inner_0 L_outer_0 = self.phase_param.L_outer_0 - if self.options["SEI"] == "ec reaction limited": + if self.options["SEI"].startswith("ec reaction limited"): self.initial_conditions = {L_outer: L_inner_0 + L_outer_0} else: self.initial_conditions = {L_inner: L_inner_0, L_outer: L_outer_0} diff --git a/pybamm/models/submodels/oxygen_diffusion/__init__.py b/pybamm/models/submodels/oxygen_diffusion/__init__.py index dde0318f48..a5161f93f9 100644 --- a/pybamm/models/submodels/oxygen_diffusion/__init__.py +++ b/pybamm/models/submodels/oxygen_diffusion/__init__.py @@ -1,6 +1,4 @@ from .base_oxygen_diffusion import BaseModel from .leading_oxygen_diffusion import LeadingOrder -from .first_order_oxygen_diffusion import FirstOrder -from .composite_oxygen_diffusion import Composite from .full_oxygen_diffusion import Full from .no_oxygen import NoOxygen diff --git a/pybamm/models/submodels/oxygen_diffusion/composite_oxygen_diffusion.py b/pybamm/models/submodels/oxygen_diffusion/composite_oxygen_diffusion.py deleted file mode 100644 index ddd322f1dc..0000000000 --- a/pybamm/models/submodels/oxygen_diffusion/composite_oxygen_diffusion.py +++ /dev/null @@ -1,87 +0,0 @@ -# -# Class for oxygen diffusion -# -import pybamm - -from .full_oxygen_diffusion import Full - - -class Composite(Full): - """Class for conservation of mass of oxygen. (Composite refers to composite - expansion in asymptotic methods) - In this model, extremely fast oxygen kinetics in the negative electrode imposes - zero oxygen concentration there, and so the oxygen variable only lives in the - separator and positive electrode. The boundary condition at the negative electrode/ - separator interface is homogeneous Dirichlet. - - Parameters - ---------- - param : parameter class - The parameters to use for this submodel - - extended : bool - Whether to include feedback from the first-order terms - - **Extends:** :class:`pybamm.oxygen_diffusion.Full` - """ - - def __init__(self, param, extended=False): - super().__init__(param) - self.extended = extended - - def get_coupled_variables(self, variables): - - tor_0_s = variables["Leading-order separator electrolyte transport efficiency"] - tor_0_p = variables["Leading-order positive electrolyte transport efficiency"] - tor_0 = pybamm.concatenation(tor_0_s, tor_0_p) - - c_ox = variables["Separator and positive electrode oxygen concentration"] - - param = self.param - - N_ox_diffusion = -tor_0 * param.curlyD_ox * pybamm.grad(c_ox) - - # Note: no convection because c_ox_0 = 0 (at leading order) - N_ox = N_ox_diffusion - # Flux in the negative electrode is zero - N_ox = pybamm.concatenation( - pybamm.FullBroadcast(0, "negative electrode", "current collector"), N_ox - ) - - variables.update(self._get_standard_flux_variables(N_ox)) - - return variables - - def set_rhs(self, variables): - """Composite reaction-diffusion with source terms from leading order.""" - - param = self.param - - eps_0_s = variables["Leading-order separator porosity"] - eps_0_p = variables["Leading-order positive electrode porosity"] - eps_0 = pybamm.concatenation(eps_0_s, eps_0_p) - - deps_0_dt_s = variables["Leading-order separator porosity change"] - deps_0_dt_p = variables["Leading-order positive electrode porosity change"] - deps_0_dt = pybamm.concatenation(deps_0_dt_s, deps_0_dt_p) - - c_ox = variables["Separator and positive electrode oxygen concentration"] - N_ox = variables["Oxygen flux"].orphans[1] - - if self.extended is False: - j_ox_0 = variables[ - "Leading-order positive electrode oxygen interfacial current density" - ] - pos_reactions = param.s_ox_Ox * j_ox_0 - else: - j_ox_0 = variables["Positive electrode oxygen interfacial current density"] - pos_reactions = param.s_ox_Ox * j_ox_0 - sep_reactions = pybamm.FullBroadcast(0, "separator", "current collector") - source_terms_0 = ( - pybamm.concatenation(sep_reactions, pos_reactions) / param.gamma_e - ) - - self.rhs = { - c_ox: (1 / eps_0) - * (-pybamm.div(N_ox) / param.C_e + source_terms_0 - c_ox * deps_0_dt) - } diff --git a/pybamm/models/submodels/oxygen_diffusion/first_order_oxygen_diffusion.py b/pybamm/models/submodels/oxygen_diffusion/first_order_oxygen_diffusion.py deleted file mode 100644 index 3750f132d4..0000000000 --- a/pybamm/models/submodels/oxygen_diffusion/first_order_oxygen_diffusion.py +++ /dev/null @@ -1,82 +0,0 @@ -# -# Class for oxygen diffusion -# -import pybamm - -from .base_oxygen_diffusion import BaseModel - - -class FirstOrder(BaseModel): - """Class for conservation of mass of oxygen. (First-order refers to first-order - expansion in asymptotic methods) - In this model, extremely fast oxygen kinetics in the negative electrode imposes - zero oxygen concentration there, and so the oxygen variable only lives in the - separator and positive electrode. The boundary condition at the negative electrode/ - separator interface is homogeneous Dirichlet. - - Parameters - ---------- - param : parameter class - The parameters to use for this submodel - - - **Extends:** :class:`pybamm.oxygen_diffusion.BaseModel` - """ - - def __init__(self, param): - super().__init__(param) - - def get_coupled_variables(self, variables): - - param = self.param - l_n = param.n.l - l_s = param.s.l - l_p = param.p.l - x_s = pybamm.standard_spatial_vars.x_s - x_p = pybamm.standard_spatial_vars.x_p - - # Unpack - tor_s_0_av = variables[ - "Leading-order x-averaged separator electrolyte transport efficiency" - ] - tor_p_0_av = variables[ - "Leading-order x-averaged positive electrolyte transport efficiency" - ] - - # Diffusivities - D_ox_s = tor_s_0_av * param.curlyD_ox - D_ox_p = tor_p_0_av * param.curlyD_ox - - # Reactions - j_ox_0 = variables[ - "Leading-order x-averaged positive electrode " - "oxygen interfacial current density" - ] - sj_ox_p = param.s_ox_Ox * j_ox_0 - - # Fluxes - N_ox_n_1 = pybamm.FullBroadcast(0, "negative electrode", "current collector") - N_ox_s_1 = -pybamm.PrimaryBroadcast(sj_ox_p * l_p, "separator") - N_ox_p_1 = sj_ox_p * (x_p - 1) - - # Concentrations - c_ox_n_1 = pybamm.FullBroadcast(0, "negative electrode", "current collector") - c_ox_s_1 = sj_ox_p * l_p / D_ox_s * (x_s - l_n) - c_ox_p_1 = ( - -sj_ox_p / (2 * D_ox_p) * ((x_p - 1) ** 2 - l_p**2) - + sj_ox_p * l_p * l_s / D_ox_s - ) - - # Update variables - variables.update( - self._get_standard_concentration_variables( - param.C_e * c_ox_n_1, param.C_e * c_ox_s_1, param.C_e * c_ox_p_1 - ) - ) - - N_ox = pybamm.concatenation( - param.C_e * N_ox_n_1, param.C_e * N_ox_s_1, param.C_e * N_ox_p_1 - ) - variables.update(self._get_standard_flux_variables(N_ox)) - - return variables diff --git a/pybamm/models/submodels/porosity/base_porosity.py b/pybamm/models/submodels/porosity/base_porosity.py index 80ed95ae60..f06f36ea80 100644 --- a/pybamm/models/submodels/porosity/base_porosity.py +++ b/pybamm/models/submodels/porosity/base_porosity.py @@ -19,7 +19,7 @@ class BaseModel(pybamm.BaseSubModel): def __init__(self, param, options): super().__init__(param, options=options) - def _get_standard_porosity_variables(self, eps_dict, set_leading_order=False): + def _get_standard_porosity_variables(self, eps_dict): eps = pybamm.concatenation(*eps_dict.values()) variables = {"Porosity": eps} @@ -34,17 +34,9 @@ def _get_standard_porosity_variables(self, eps_dict, set_leading_order=False): } ) - if set_leading_order is True: - leading_order_variables = { - "Leading-order " + name.lower(): var for name, var in variables.items() - } - variables.update(leading_order_variables) - return variables - def _get_standard_porosity_change_variables( - self, depsdt_dict, set_leading_order=False - ): + def _get_standard_porosity_change_variables(self, depsdt_dict): deps_dt = pybamm.concatenation(*depsdt_dict.values()) variables = {"Porosity change": deps_dt} @@ -58,12 +50,4 @@ def _get_standard_porosity_change_variables( } ) - if set_leading_order is True: - variables.update( - { - f"Leading-order x-averaged {domain}" - " porosity change": depsdt_k_av, - } - ) - return variables diff --git a/pybamm/models/submodels/porosity/constant_porosity.py b/pybamm/models/submodels/porosity/constant_porosity.py index 934a9baf5b..8e13934666 100644 --- a/pybamm/models/submodels/porosity/constant_porosity.py +++ b/pybamm/models/submodels/porosity/constant_porosity.py @@ -25,14 +25,8 @@ def get_fundamental_variables(self): eps_dict[domain] = self.param.domain_params[domain.split()[0]].epsilon_init depsdt_dict[domain] = pybamm.FullBroadcast(0, domain, "current collector") - variables = self._get_standard_porosity_variables( - eps_dict, set_leading_order=True - ) - variables.update( - self._get_standard_porosity_change_variables( - depsdt_dict, set_leading_order=True - ) - ) + variables = self._get_standard_porosity_variables(eps_dict) + variables.update(self._get_standard_porosity_change_variables(depsdt_dict)) return variables diff --git a/pybamm/models/submodels/transport_efficiency/base_transport_efficiency.py b/pybamm/models/submodels/transport_efficiency/base_transport_efficiency.py index bcc10000ea..27898ece50 100644 --- a/pybamm/models/submodels/transport_efficiency/base_transport_efficiency.py +++ b/pybamm/models/submodels/transport_efficiency/base_transport_efficiency.py @@ -42,12 +42,6 @@ def _get_standard_transport_efficiency_variables(self, tor_dict): } ) - if self.set_leading_order is True: - leading_order_variables = { - "Leading-order " + name.lower(): var for name, var in variables.items() - } - variables.update(leading_order_variables) - # Override print_name tor.print_name = r"\epsilon^{b_e}" diff --git a/pybamm/models/submodels/transport_efficiency/bruggeman_transport_efficiency.py b/pybamm/models/submodels/transport_efficiency/bruggeman_transport_efficiency.py index 876f72d190..89ca38306e 100644 --- a/pybamm/models/submodels/transport_efficiency/bruggeman_transport_efficiency.py +++ b/pybamm/models/submodels/transport_efficiency/bruggeman_transport_efficiency.py @@ -20,9 +20,8 @@ class Bruggeman(BaseModel): **Extends:** :class:`pybamm.transport_efficiency.BaseModel` """ - def __init__(self, param, component, options=None, set_leading_order=False): + def __init__(self, param, component, options=None): super().__init__(param, component, options=options) - self.set_leading_order = set_leading_order def get_coupled_variables(self, variables): if self.component == "Electrolyte": diff --git a/pybamm/parameters/__init__.py b/pybamm/parameters/__init__.py index 2d86081ecb..cf81b82fdd 100644 --- a/pybamm/parameters/__init__.py +++ b/pybamm/parameters/__init__.py @@ -1 +1,6 @@ -from .process_parameter_data import process_1D_data, process_2D_data +from .process_parameter_data import ( + process_1D_data, + process_2D_data, + process_2D_data_csv, + process_3D_data_csv, +) diff --git a/pybamm/parameters/ecm_parameters.py b/pybamm/parameters/ecm_parameters.py new file mode 100644 index 0000000000..e2dc0eb3f4 --- /dev/null +++ b/pybamm/parameters/ecm_parameters.py @@ -0,0 +1,65 @@ +import pybamm + + +class EcmParameters: + def __init__(self): + + self.timescale = pybamm.Scalar(1) + + self.cell_capacity = pybamm.Parameter("Cell capacity [A.h]") + + self._set_current_parameters() + self._set_voltage_parameters() + self._set_thermal_parameters() + self._set_initial_condition_parameters() + self._set_compatibility_parameters() + + def _set_current_parameters(self): + self.dimensional_current_with_time = pybamm.FunctionParameter( + "Current function [A]", {"Time [s]": pybamm.t * self.timescale} + ) + + def _set_voltage_parameters(self): + self.voltage_high_cut = pybamm.Parameter("Upper voltage cut-off [V]") + self.voltage_low_cut = pybamm.Parameter("Lower voltage cut-off [V]") + + def _set_thermal_parameters(self): + self.cth_cell = pybamm.Parameter("Cell thermal mass [J/K]") + self.k_cell_jig = pybamm.Parameter("Cell-jig heat transfer coefficient [W/K]") + + self.cth_jig = pybamm.Parameter("Jig thermal mass [J/K]") + self.k_jig_air = pybamm.Parameter("Jig-air heat transfer coefficient [W/K]") + + def _set_compatibility_parameters(self): + # These are parameters that for compatibility with + # external circuits submodels + self.Q = self.cell_capacity + self.current_with_time = self.dimensional_current_with_time + self.dimensional_current_density_with_time = self.dimensional_current_with_time + self.I_typ = pybamm.Scalar(1) + self.n_electrodes_parallel = pybamm.Scalar(1) + self.A_cc = pybamm.Scalar(1) + self.n_cells = pybamm.Scalar(1) + + def _set_initial_condition_parameters(self): + self.initial_soc = pybamm.Parameter("Initial SoC") + self.initial_T_cell = pybamm.Parameter("Initial cell temperature [degC]") + self.initial_T_jig = pybamm.Parameter("Initial jig temperature [degC]") + + def T_amb(self, t): + return pybamm.FunctionParameter("Ambient temperature [degC]", {"Time [s]": t}) + + def ocv(self, soc): + return pybamm.FunctionParameter("Open circuit voltage [V]", {"SoC": soc}) + + def rcr_element(self, name, T_cell, current, soc): + inputs = {"Cell temperature [degC]": T_cell, "Current [A]": current, "SoC": soc} + + return pybamm.FunctionParameter(name, inputs) + + def initial_rc_overpotential(self, element_number): + return pybamm.Parameter(f"Element-{element_number} initial overpotential [V]") + + def dUdT(self, ocv, T_cell): + inputs = {"Open circuit voltage [V]": ocv, "Cell temperature [degC]": T_cell} + return pybamm.FunctionParameter("Entropic change [V/K]", inputs) diff --git a/pybamm/parameters/lead_acid_parameters.py b/pybamm/parameters/lead_acid_parameters.py index ef2626b6a3..6c9ff95c20 100644 --- a/pybamm/parameters/lead_acid_parameters.py +++ b/pybamm/parameters/lead_acid_parameters.py @@ -422,7 +422,7 @@ def kappa_e(self, c_e, T): kappa_scale = self.F**2 * self.D_e_typ * self.c_e_typ / (self.R * self.T_ref) return self.kappa_e_dimensional(c_e_dimensional, self.T_ref) / kappa_scale - def chiT_over_c(self, c_e, T): + def chiRT_over_Fc(self, c_e, T): """ chi * (1 + Theta * T) / c, as it appears in the electrolyte potential equation diff --git a/pybamm/parameters/lithium_ion_parameters.py b/pybamm/parameters/lithium_ion_parameters.py index 7ffe53186c..c2118edaab 100644 --- a/pybamm/parameters/lithium_ion_parameters.py +++ b/pybamm/parameters/lithium_ion_parameters.py @@ -142,6 +142,27 @@ def _set_dimensional_parameters(self): self.ocv_ref = self.p.U_ref - self.n.U_ref self.ocv_init_dim = self.p.prim.U_init_dim - self.n.prim.U_init_dim + def chi_dimensional(self, c_e, T): + """ + Thermodynamic factor: + (1-2*t_plus) is for Nernst-Planck, + 2*(1-t_plus) for Stefan-Maxwell, + see Bizeray et al (2016) "Resolving a discrepancy ...". + """ + return (2 * (1 - self.t_plus_dimensional(c_e, T))) * ( + self.one_plus_dlnf_dlnc_dimensional(c_e, T) + ) + + def t_plus_dimensional(self, c_e, T): + """Cation transference number (dimensionless)""" + inputs = {"Electrolyte concentration [mol.m-3]": c_e, "Temperature [K]": T} + return pybamm.FunctionParameter("Cation transference number", inputs) + + def one_plus_dlnf_dlnc_dimensional(self, c_e, T): + """Thermodynamic factor (dimensionless)""" + inputs = {"Electrolyte concentration [mol.m-3]": c_e, "Temperature [K]": T} + return pybamm.FunctionParameter("1 + dlnf/dlnc", inputs) + def D_e_dimensional(self, c_e, T): """Dimensional diffusivity in electrolyte""" tol = pybamm.settings.tolerances["D_e__c_e"] @@ -301,9 +322,11 @@ def chi(self, c_e, T): 2*(1-t_plus) for Stefan-Maxwell, see Bizeray et al (2016) "Resolving a discrepancy ...". """ - return (2 * (1 - self.t_plus(c_e, T))) * (self.one_plus_dlnf_dlnc(c_e, T)) + c_e_dimensional = c_e * self.c_e_typ + T_dim = self.Delta_T * T + self.T_ref + return self.chi_dimensional(c_e_dimensional, T_dim) - def chiT_over_c(self, c_e, T): + def chiRT_over_Fc(self, c_e, T): """ chi * (1 + Theta * T) / c, as it appears in the electrolyte potential equation @@ -314,19 +337,9 @@ def chiT_over_c(self, c_e, T): def t_plus(self, c_e, T): """Cation transference number (dimensionless)""" - inputs = { - "Electrolyte concentration [mol.m-3]": c_e * self.c_e_typ, - "Temperature [K]": self.Delta_T * T + self.T_ref, - } - return pybamm.FunctionParameter("Cation transference number", inputs) - - def one_plus_dlnf_dlnc(self, c_e, T): - """Thermodynamic factor (dimensionless)""" - inputs = { - "Electrolyte concentration [mol.m-3]": c_e * self.c_e_typ, - "Temperature [K]": self.Delta_T * T + self.T_ref, - } - return pybamm.FunctionParameter("1 + dlnf/dlnc", inputs) + c_e_dimensional = c_e * self.c_e_typ + T_dim = self.Delta_T * T + self.T_ref + return self.t_plus_dimensional(c_e_dimensional, T_dim) def D_e(self, c_e, T): """Dimensionless electrolyte diffusivity""" @@ -704,6 +717,7 @@ def _set_dimensional_parameters(self): self.E_sei_dimensional = pybamm.Parameter( f"{pref}SEI growth activation energy [J.mol-1]" ) + self.alpha_SEI = pybamm.Parameter(f"{pref}SEI growth transfer coefficient") # EC reaction self.c_ec_0_dim = pybamm.Parameter( @@ -763,18 +777,16 @@ def _set_dimensional_parameters(self): f"{pref}{Domain} electrode active material volume fraction", {"Through-cell distance (x) [m]": x}, ) - self.c_init = ( - pybamm.FunctionParameter( - f"{pref}Initial concentration in {domain} electrode [mol.m-3]", - { - "Radial distance (r) [m]": r, - "Through-cell distance (x) [m]": pybamm.PrimaryBroadcast( - x, f"{domain} {phase_name}particle" - ), - }, - ) - / self.c_max + self.c_init_dimensional = pybamm.FunctionParameter( + f"{pref}Initial concentration in {domain} electrode [mol.m-3]", + { + "Radial distance (r) [m]": r, + "Through-cell distance (x) [m]": pybamm.PrimaryBroadcast( + x, f"{domain} {phase_name}particle" + ), + }, ) + self.c_init = self.c_init_dimensional / self.c_max self.c_init_av = pybamm.xyz_average(pybamm.r_average(self.c_init)) eps_c_init_av = pybamm.xyz_average( self.epsilon_s * pybamm.r_average(self.c_init) diff --git a/pybamm/parameters/parameter_sets.py b/pybamm/parameters/parameter_sets.py index f60f95b412..6144d1fdfc 100644 --- a/pybamm/parameters/parameter_sets.py +++ b/pybamm/parameters/parameter_sets.py @@ -1,5 +1,5 @@ import warnings -import pkg_resources +import importlib_metadata import textwrap from collections.abc import Mapping @@ -34,12 +34,12 @@ class ParameterSets(Mapping): """ def __init__(self): - # Load Parameter Sets registered to `pybamm_parameter_set` - ps = dict() - for entry_point in pkg_resources.iter_entry_points("pybamm_parameter_set"): - ps[entry_point.name] = entry_point.load() - - self.__all_parameter_sets = ps + # Dict of entry points for parameter sets, lazily load entry points as + self.__all_parameter_sets = dict() + for entry_point in importlib_metadata.entry_points( + group="pybamm_parameter_sets" + ): + self.__all_parameter_sets[entry_point.name] = entry_point def __new__(cls): """Ensure only one instance of ParameterSets exists""" @@ -48,7 +48,18 @@ def __new__(cls): return cls.instance def __getitem__(self, key) -> dict: - return self.__all_parameter_sets[key]() + return self.__load_entry_point__(key)() + + def __load_entry_point__(self, key) -> callable: + """Check that ``key`` is a registered ``pybamm_parameter_sets``, + and return the entry point for the parameter set, loading it needed. + """ + if key not in self.__all_parameter_sets: + raise KeyError(f"Unknown parameter set: {key}") + ps = self.__all_parameter_sets[key] + if isinstance(ps, importlib_metadata.EntryPoint): + ps = self.__all_parameter_sets[key] = ps.load() + return ps def __iter__(self): return self.__all_parameter_sets.__iter__() @@ -58,7 +69,7 @@ def __len__(self) -> int: def get_docstring(self, key): """Return the docstring for the ``key`` parameter set""" - return textwrap.dedent(self.__all_parameter_sets[key].__doc__) + return textwrap.dedent(self.__load_entry_point__(key).__doc__) def __getattribute__(self, name): try: @@ -67,16 +78,14 @@ def __getattribute__(self, name): # For backwards compatibility, parameter sets that used to be defined in # this file now return the name as a string, which will load the same # parameter set as before when passed to `ParameterValues` - if name in self.__all_parameter_sets: - out = name - else: - raise error - warnings.warn( - f"Parameter sets should be called directly by their name ({name}), " - f"instead of via pybamm.parameter_sets (pybamm.parameter_sets.{name}).", - DeprecationWarning, - ) - return out + if name in self: + msg = ( + "Parameter sets should be called directly by their name ({0}), " + "instead of via pybamm.parameter_sets (pybamm.parameter_sets.{0})." + ).format(name) + warnings.warn(msg, DeprecationWarning) + return name + raise error #: Singleton Instance of :class:ParameterSets """ diff --git a/pybamm/parameters/parameter_values.py b/pybamm/parameters/parameter_values.py index b6d1151414..4304307289 100644 --- a/pybamm/parameters/parameter_values.py +++ b/pybamm/parameters/parameter_values.py @@ -66,7 +66,7 @@ def __init__(self, values=None, chemistry=None): # Then update with values dictionary or file if values is not None: if isinstance(values, dict): - if "chemistry" in values: + if "negative electrode" in values: warnings.warn( "Creating a parameter set from a dictionary of components has " "been deprecated and will be removed in a future release. " @@ -92,11 +92,6 @@ def __init__(self, values=None, chemistry=None): # Initialise empty _processed_symbols dict (for caching) self._processed_symbols = {} - self.parameter_events = [] - - # Don't touch this parameter unless you know what you are doing - # This is for the conversion to Julia (ModelingToolkit) - self._replace_callable_function_parameters = True # save citations citations = [] @@ -146,9 +141,6 @@ def copy(self): """Returns a copy of the parameter values. Makes sure to copy the internal dictionary.""" new_copy = ParameterValues(self._dict_items.copy()) - new_copy._replace_callable_function_parameters = ( - self._replace_callable_function_parameters - ) return new_copy def search(self, key, print_values=True): @@ -393,7 +385,8 @@ def process_model(self, unprocessed_model, inplace=True): pybamm.logger.verbose( "Processing parameters for {!r} (rhs)".format(variable) ) - new_rhs[variable] = self.process_symbol(equation) + new_variable = self.process_symbol(variable) + new_rhs[new_variable] = self.process_symbol(equation) model.rhs = new_rhs new_algebraic = {} @@ -401,7 +394,8 @@ def process_model(self, unprocessed_model, inplace=True): pybamm.logger.verbose( "Processing parameters for {!r} (algebraic)".format(variable) ) - new_algebraic[variable] = self.process_symbol(equation) + new_variable = self.process_symbol(variable) + new_algebraic[new_variable] = self.process_symbol(equation) model.algebraic = new_algebraic new_initial_conditions = {} @@ -409,7 +403,8 @@ def process_model(self, unprocessed_model, inplace=True): pybamm.logger.verbose( "Processing parameters for {!r} (initial conditions)".format(variable) ) - new_initial_conditions[variable] = self.process_symbol(equation) + new_variable = self.process_symbol(variable) + new_initial_conditions[new_variable] = self.process_symbol(equation) model.initial_conditions = new_initial_conditions model.boundary_conditions = self.process_boundary_conditions(unprocessed_model) @@ -433,7 +428,8 @@ def process_model(self, unprocessed_model, inplace=True): ) ) - for event in self.parameter_events: + interpolant_events = self._get_interpolant_events(model) + for event in interpolant_events: pybamm.logger.verbose( "Processing parameters for event '{}''".format(event.name) ) @@ -475,6 +471,33 @@ def process_model(self, unprocessed_model, inplace=True): return model + def _get_interpolant_events(self, model): + """Add events for functions that have been defined as parameters""" + # Define events to catch extrapolation. In these events the sign is + # important: it should be positive inside of the range and negative + # outside of it + interpolants = model._find_symbols(pybamm.Interpolant) + interpolant_events = [] + for interpolant in interpolants: + xs = interpolant.x + children = interpolant.children + for x, child in zip(xs, children): + interpolant_events.extend( + [ + pybamm.Event( + f"Interpolant '{interpolant.name}' lower bound", + pybamm.min(child - min(x)), + pybamm.EventType.INTERPOLANT_EXTRAPOLATION, + ), + pybamm.Event( + f"Interpolant '{interpolant.name}' upper bound", + pybamm.min(max(x) - child), + pybamm.EventType.INTERPOLANT_EXTRAPOLATION, + ), + ] + ) + return interpolant_events + def process_boundary_conditions(self, model): """ Process boundary conditions for a model @@ -572,7 +595,7 @@ def _process_symbol(self, symbol): # Check not NaN (parameter in csv file but no value given) if np.isnan(value): raise ValueError(f"Parameter '{symbol.name}' not found") - # Scalar inherits name (for updating parameters) + # Scalar inherits name return pybamm.Scalar(value, name=symbol.name) elif isinstance(value, pybamm.Symbol): new_value = self.process_symbol(value) @@ -582,18 +605,29 @@ def _process_symbol(self, symbol): raise TypeError("Cannot process parameter '{}'".format(value)) elif isinstance(symbol, pybamm.FunctionParameter): - new_children = [] - for child in symbol.children: - if symbol.diff_variable is not None and any( - x == symbol.diff_variable for x in child.pre_order() - ): - # Wrap with NotConstant to avoid simplification, - # which would stop symbolic diff from working properly - new_child = pybamm.NotConstant(child) - new_children.append(self.process_symbol(new_child)) - else: - new_children.append(self.process_symbol(child)) function_name = self[symbol.name] + if isinstance( + function_name, + (numbers.Number, pybamm.Interpolant, pybamm.InputParameter), + ) or ( + isinstance(function_name, pybamm.Symbol) + and function_name.size_for_testing == 1 + ): + # no need to process children, they will only be used for shape + new_children = symbol.children + else: + # process children + new_children = [] + for child in symbol.children: + if symbol.diff_variable is not None and any( + x == symbol.diff_variable for x in child.pre_order() + ): + # Wrap with NotConstant to avoid simplification, + # which would stop symbolic diff from working properly + new_child = pybamm.NotConstant(child) + new_children.append(self.process_symbol(new_child)) + else: + new_children.append(self.process_symbol(child)) # Create Function or Interpolant or Scalar object if isinstance(function_name, tuple): @@ -613,31 +647,8 @@ def _process_symbol(self, symbol): input_data[0], input_data[-1], new_children, - interpolator="cubic", name=name, ) - # Define event to catch extrapolation. In these events the sign is - # important: it should be positive inside of the range and negative - # outside of it - for data_index in range(len(data[0])): - self.parameter_events.append( - pybamm.Event( - "Interpolant {} lower bound".format(name), - pybamm.min( - new_children[data_index] - min(data[0][data_index]) - ), - pybamm.EventType.INTERPOLANT_EXTRAPOLATION, - ) - ) - self.parameter_events.append( - pybamm.Event( - "Interpolant {} upper bound".format(name), - pybamm.min( - max(data[0][data_index]) - new_children[data_index] - ), - pybamm.EventType.INTERPOLANT_EXTRAPOLATION, - ) - ) else: # pragma: no cover raise ValueError( @@ -656,30 +667,6 @@ def _process_symbol(self, symbol): elif callable(function_name): # otherwise evaluate the function to create a new PyBaMM object function = function_name(*new_children) - if ( - self._replace_callable_function_parameters is False - and not isinstance( - self.process_symbol(function), (pybamm.Scalar, pybamm.Broadcast) - ) - and symbol.print_name is not None - and symbol.diff_variable is None - ): - # Special trick for printing in Julia ModelingToolkit format - out = pybamm.FunctionParameter( - symbol.print_name, dict(zip(symbol.input_names, new_children)) - ) - - out.arg_names = inspect.getfullargspec(function_name)[0] - out.callable = self.process_symbol( - function_name( - *[ - pybamm.Variable(arg_name, domains=child.domains) - for arg_name, child in zip(out.arg_names, new_children) - ] - ) - ) - - return out elif isinstance( function_name, (pybamm.Interpolant, pybamm.InputParameter) ) or ( @@ -739,6 +726,13 @@ def _process_symbol(self, symbol): new_children = [self.process_symbol(child) for child in symbol.children] return symbol._concatenation_new_copy(new_children) + # Variables: update scale + elif isinstance(symbol, pybamm.Variable): + new_symbol = symbol.create_copy() + new_symbol._scale = self.process_symbol(symbol.scale) + new_symbol._reference = self.process_symbol(symbol.reference) + return new_symbol + else: # Backup option: return the object return symbol diff --git a/pybamm/parameters/process_parameter_data.py b/pybamm/parameters/process_parameter_data.py index dd682ca99b..1373aefea7 100644 --- a/pybamm/parameters/process_parameter_data.py +++ b/pybamm/parameters/process_parameter_data.py @@ -58,3 +58,113 @@ def process_2D_data(name, path=None): data[0] = [np.array(el) for el in data[0]] data[1] = np.array(data[1]) return (name, tuple(data)) + + +def process_2D_data_csv(name, path=None): + """ + Process 2D data from a csv file. Assumes + data is in the form of a three columns + and that all data points lie on a regular + grid. The first column is assumed to + be the 'slowest' changing variable and + the second column the 'fastest' changing + variable, which is the C convention for + indexing multidimensional arrays (as opposed + to the Fortran convention where the 'fastest' + changing variable comes first). + + Parameters + ---------- + name : str + The name to be given to the function + path : str + The path to the file where the three + dimensional data is stored. + + Returns + ------- + formatted_data: tuple + A tuple containing the name of the function + and the data formatted correctly for use + within three-dimensional interpolants. + """ + + filename, name = _process_name(name, path, ".csv") + + df = pd.read_csv(filename) + + x1 = np.array(list(set(df.iloc[:, 0]))) + x2 = np.array(list(set(df.iloc[:, 1]))) + + value = df.iloc[:, 2].to_numpy() + + x1.sort() + x2.sort() + + x = (x1, x2) + + value_data = np.reshape( + value, + (len(x1), len(x2)), + order="C", # use the C convention + ) + + formatted_data = (name, (x, value_data)) + + return formatted_data + + +def process_3D_data_csv(name, path=None): + """ + Process 3D data from a csv file. Assumes + data is in the form of four columns and + that all data points lie on a + regular grid. The first column is assumed to + be the 'slowest' changing variable and + the third column the 'fastest' changing + variable, which is the C convention for + indexing multidimensional arrays (as opposed + to the Fortran convention where the 'fastest' + changing variable comes first). + + Parameters + ---------- + name : str + The name to be given to the function + path : str + The path to the file where the three + dimensional data is stored. + + Returns + ------- + formatted_data: tuple + A tuple containing the name of the function + and the data formatted correctly for use + within three-dimensional interpolants. + """ + + filename, name = _process_name(name, path, ".csv") + + df = pd.read_csv(filename) + + x1 = np.array(list(set(df.iloc[:, 0]))) + x2 = np.array(list(set(df.iloc[:, 1]))) + x3 = np.array(list(set(df.iloc[:, 2]))) + + value = df.iloc[:, 3].to_numpy() + + x1.sort() + x2.sort() + x3.sort() + + x = (x1, x2, x3) + + value_data = np.reshape( + value, + (len(x1), len(x2), len(x3)), + order="C", + ) + + formatted_data = (name, (x, value_data)) + + return formatted_data diff --git a/pybamm/simulation.py b/pybamm/simulation.py index c447ecf14f..9569c4da0a 100644 --- a/pybamm/simulation.py +++ b/pybamm/simulation.py @@ -7,6 +7,7 @@ import copy import warnings import sys +from functools import lru_cache def is_notebook(): @@ -374,7 +375,7 @@ def set_parameters(self): self._model_with_set_params = self._parameter_values.process_model( self._unprocessed_model, inplace=False ) - self._parameter_values.process_geometry(self._geometry) + self._parameter_values.process_geometry(self.geometry) self.model = self._model_with_set_params def set_initial_soc(self, initial_soc): @@ -889,21 +890,19 @@ def step( return self.solution + @lru_cache def get_esoh_solver(self, calc_esoh): if ( calc_esoh is False or isinstance(self.model, pybamm.lead_acid.BaseModel) + or isinstance(self.model, pybamm.equivalent_circuit.Thevenin) or self.model.options["working electrode"] != "both" ): return None - try: - return self._esoh_solver - except AttributeError: - self._esoh_solver = pybamm.lithium_ion.ElectrodeSOHSolver( - self.parameter_values, self.model.param - ) - return self._esoh_solver + return pybamm.lithium_ion.ElectrodeSOHSolver( + self.parameter_values, self.model.param + ) def plot(self, output_variables=None, **kwargs): """ diff --git a/pybamm/solvers/base_solver.py b/pybamm/solvers/base_solver.py index b97b1b1492..0d7ba94978 100644 --- a/pybamm/solvers/base_solver.py +++ b/pybamm/solvers/base_solver.py @@ -47,20 +47,21 @@ def __init__( atol=1e-6, root_method=None, root_tol=1e-6, - extrap_tol=0, + extrap_tol=None, ): self.method = method self.rtol = rtol self.atol = atol self.root_tol = root_tol self.root_method = root_method - self.extrap_tol = extrap_tol + self.extrap_tol = extrap_tol or -1e-10 self.models_set_up = {} # Defaults, can be overwritten by specific solver self.name = "Base solver" self.ode_solver = False self.algebraic_solver = False + self._on_extrapolation = "warn" @property def root_method(self): @@ -450,11 +451,11 @@ def _set_up_events(self, model, t_eval, inputs, vars_for_processing): expr = symbol.right found_t = True # Dimensional - elif symbol.right == (pybamm.t * model.timescale_eval): - expr = symbol.left / symbol.right.right + elif symbol.right == (model.timescale_eval * pybamm.t): + expr = symbol.left / symbol.right.left found_t = True - elif symbol.left == (pybamm.t * model.timescale_eval): - expr = symbol.right / symbol.left.right + elif symbol.left == (model.timescale_eval * pybamm.t): + expr = symbol.right / symbol.left.left found_t = True # Update the events if the heaviside function depended on t @@ -552,7 +553,7 @@ def _set_up_events(self, model, t_eval, inputs, vars_for_processing): discontinuity_events, ) - def _set_initial_conditions(self, model, inputs_dict, update_rhs): + def _set_initial_conditions(self, model, time, inputs_dict, update_rhs): """ Set initial conditions for the model. This is skipped if the solver is an algebraic solver (since this would make the algebraic solver redundant), and if @@ -587,14 +588,14 @@ def _set_initial_conditions(self, model, inputs_dict, update_rhs): elif len(model.algebraic) == 0: if update_rhs is True: # Recalculate initial conditions for the rhs equations - y0 = model.initial_conditions_eval(0, y_zero, inputs) + y0 = model.initial_conditions_eval(time, y_zero, inputs) else: # Don't update model.y0 return else: if update_rhs is True: # Recalculate initial conditions for the rhs equations - y0_from_inputs = model.initial_conditions_eval(0, y_zero, inputs) + y0_from_inputs = model.initial_conditions_eval(time, y_zero, inputs) # Reuse old solution for algebraic equations y0_from_model = model.y0 len_rhs = model.len_rhs @@ -609,7 +610,7 @@ def _set_initial_conditions(self, model, inputs_dict, update_rhs): model.y0 = np.vstack( (y0_from_inputs[:len_rhs], y0_from_model[len_rhs:]) ) - y0 = self.calculate_consistent_state(model, 0, inputs_dict) + y0 = self.calculate_consistent_state(model, time, inputs_dict) # Make y0 a function of inputs if doing symbolic with casadi model.y0 = y0 @@ -645,6 +646,7 @@ def calculate_consistent_state(self, model, time=0, inputs=None): ) pybamm.logger.debug("Found consistent states") + self.check_extrapolation(root_sol, model.events) y0 = root_sol.all_ys[0] return y0 @@ -830,11 +832,13 @@ def solve( "for initial conditions." ) - self._set_initial_conditions(model, ext_and_inputs_list[0], update_rhs=True) - # Non-dimensionalise time t_eval_dimensionless = t_eval / model.timescale_eval + self._set_initial_conditions( + model, t_eval_dimensionless[0], ext_and_inputs_list[0], update_rhs=True + ) + # Check initial conditions don't violate events self._check_events_with_initial_conditions( t_eval_dimensionless, model, ext_and_inputs_list[0] @@ -909,14 +913,7 @@ def solve( for i, solution in enumerate(solutions): # Check if extrapolation occurred - extrapolation = self.check_extrapolation(solution, model.events) - if extrapolation: - warnings.warn( - "While solving {} extrapolation occurred for {}".format( - model.name, extrapolation - ), - pybamm.SolverWarning, - ) + self.check_extrapolation(solution, model.events) # Identify the event that caused termination and update the solution to # include the event time and state solutions[i], termination = self.get_termination_reason( @@ -1174,7 +1171,7 @@ def step( set_up_time = timer.time() # (Re-)calculate consistent initial conditions - self._set_initial_conditions(model, ext_and_inputs, update_rhs=False) + self._set_initial_conditions(model, t, ext_and_inputs, update_rhs=False) # Non-dimensionalise dt dt_dimensionless = dt / model.timescale_eval @@ -1195,14 +1192,7 @@ def step( solution.solve_time = timer.time() # Check if extrapolation occurred - extrapolation = self.check_extrapolation(solution, model.events) - if extrapolation: - warnings.warn( - "While solving {} extrapolation occurred for {}".format( - model.name, extrapolation - ), - pybamm.SolverWarning, - ) + self.check_extrapolation(solution, model.events) # Identify the event that caused termination and update the solution to # include the event time and state @@ -1320,31 +1310,41 @@ def check_extrapolation(self, solution, events): events : dict Dictionary of events """ - extrap_events = {} - - for event in events: - if event.event_type == pybamm.EventType.INTERPOLANT_EXTRAPOLATION: - # First set to False, then loop through and change to True if any - # events extrapolate - extrap_events[event.name] = False - # This might be a little bit slow but is ok for now - for ts, ys, inputs in zip( - solution.all_ts, solution.all_ys, solution.all_inputs - ): - for inner_idx, t in enumerate(ts): - y = ys[:, inner_idx] - if isinstance(y, casadi.DM): - y = y.full() - if ( - event.expression.evaluate(t, y, inputs=inputs) - < self.extrap_tol - ): - extrap_events[event.name] = True - - # Add the event dictionaryto the solution object - solution.extrap_events = extrap_events - - return [k for k, v in extrap_events.items() if v] + extrap_events = [] + + if any( + event.event_type == pybamm.EventType.INTERPOLANT_EXTRAPOLATION + for event in events + ): + last_state = solution.last_state + t = last_state.all_ts[0][0] + y = last_state.all_ys[0][:, 0] + inputs = last_state.all_inputs[0] + + if isinstance(y, casadi.DM): + y = y.full() + for event in events: + if event.event_type == pybamm.EventType.INTERPOLANT_EXTRAPOLATION: + if event.expression.evaluate(t, y, inputs=inputs) < self.extrap_tol: + extrap_events.append(event.name) + + if any(extrap_events): + if self._on_extrapolation == "warn": + name = solution.all_models[-1].name + warnings.warn( + f"While solving {name} extrapolation occurred " + f"for {extrap_events}", + pybamm.SolverWarning, + ) + # Add the event dictionaryto the solution object + solution.extrap_events = extrap_events + elif self._on_extrapolation == "error": + raise pybamm.SolverError( + "Solver failed because the following " + f"interpolation bounds were exceeded: {extrap_events}. " + "You may need to provide additional interpolation points " + "outside these bounds." + ) def _set_up_ext_and_inputs(self, model, external_variables, inputs): """Set up external variables and input parameters""" diff --git a/pybamm/solvers/c_solvers/idaklu.cpp b/pybamm/solvers/c_solvers/idaklu.cpp index 9241487419..ac90172c97 100644 --- a/pybamm/solvers/c_solvers/idaklu.cpp +++ b/pybamm/solvers/c_solvers/idaklu.cpp @@ -1,455 +1,62 @@ -#include "idaklu.hpp" -#include +#include "idaklu/casadi_solver.hpp" +#include "idaklu/common.hpp" +#include "idaklu/python.hpp" -class PybammFunctions -{ -public: - int number_of_states; - int number_of_parameters; - int number_of_events; - - PybammFunctions(const residual_type &res, const jacobian_type &jac, - const sensitivities_type &sens, - const jac_get_type &get_jac_data_in, - const jac_get_type &get_jac_row_vals_in, - const jac_get_type &get_jac_col_ptrs_in, - const event_type &event, - const int n_s, int n_e, const int n_p, - const np_array &inputs) - : number_of_states(n_s), number_of_events(n_e), - number_of_parameters(n_p), - py_res(res), py_jac(jac), - py_sens(sens), - py_event(event), py_get_jac_data(get_jac_data_in), - py_get_jac_row_vals(get_jac_row_vals_in), - py_get_jac_col_ptrs(get_jac_col_ptrs_in), - inputs(inputs) - { - } - - np_array operator()(double t, np_array y, np_array yp) - { - return py_res(t, y, inputs, yp); - } - - np_array res(double t, np_array y, np_array yp) - { - return py_res(t, y, inputs, yp); - } - - void jac(double t, np_array y, double cj) - { - // this function evaluates the jacobian and sets it to be the attribute - // of a python class which can then be called by get_jac_data, - // get_jac_col_ptr, etc - py_jac(t, y, inputs, cj); - } - - void sensitivities( - std::vector& resvalS, - const double t, const np_array& y, const np_array& yp, - const std::vector& yS, const std::vector& ypS) - { - // this function evaluates the sensitivity equations required by IDAS, - // returning them in resvalS, which is preallocated as a numpy array - // of size (np, n), where n is the number of states and np is the number - // of parameters - // - // yS and ypS are also shape (np, n), y and yp are shape (n) - // - // dF/dy * s_i + dF/dyd * sd + dFdp_i for i in range(np) - py_sens(resvalS, t, y, inputs, yp, yS, ypS); - } - - np_array get_jac_data() { return py_get_jac_data(); } - - np_array get_jac_row_vals() { return py_get_jac_row_vals(); } - - np_array get_jac_col_ptrs() { return py_get_jac_col_ptrs(); } - - np_array events(double t, np_array y) { return py_event(t, y, inputs); } +#include +#include +#include +#include -private: - residual_type py_res; - sensitivities_type py_sens; - jacobian_type py_jac; - event_type py_event; - jac_get_type py_get_jac_data; - jac_get_type py_get_jac_row_vals; - jac_get_type py_get_jac_col_ptrs; - const np_array &inputs; -}; +#include -int residual(realtype tres, N_Vector yy, N_Vector yp, N_Vector rr, - void *user_data) +Function generate_function(const std::string &data) { - PybammFunctions *python_functions_ptr = - static_cast(user_data); - PybammFunctions python_functions = *python_functions_ptr; - - realtype *yval, *ypval, *rval; - yval = N_VGetArrayPointer(yy); - ypval = N_VGetArrayPointer(yp); - rval = N_VGetArrayPointer(rr); - - int n = python_functions.number_of_states; - py::array_t y_np = py::array_t(n, yval); - py::array_t yp_np = py::array_t(n, ypval); - - py::array_t r_np; - - r_np = python_functions.res(tres, y_np, yp_np); - - auto r_np_ptr = r_np.unchecked<1>(); - - // just copying data - int i; - for (i = 0; i < n; i++) - { - rval[i] = r_np_ptr[i]; - } - return 0; + return Function::deserialize(data); } -int jacobian(realtype tt, realtype cj, N_Vector yy, N_Vector yp, - N_Vector resvec, SUNMatrix JJ, void *user_data, N_Vector tempv1, - N_Vector tempv2, N_Vector tempv3) -{ - realtype *yval; - yval = N_VGetArrayPointer(yy); - - PybammFunctions *python_functions_ptr = - static_cast(user_data); - PybammFunctions python_functions = *python_functions_ptr; - - int n = python_functions.number_of_states; - py::array_t y_np = py::array_t(n, yval); - - // create pointer to jac data, column pointers, and row values - sunindextype *jac_colptrs = SUNSparseMatrix_IndexPointers(JJ); - sunindextype *jac_rowvals = SUNSparseMatrix_IndexValues(JJ); - realtype *jac_data = SUNSparseMatrix_Data(JJ); +namespace py = pybind11; - py::array_t jac_np_array; +PYBIND11_MAKE_OPAQUE(std::vector); - python_functions.jac(tt, y_np, cj); - - np_array jac_np_data = python_functions.get_jac_data(); - int n_data = jac_np_data.request().size; - auto jac_np_data_ptr = jac_np_data.unchecked<1>(); - - // just copy across data - int i; - for (i = 0; i < n_data; i++) - { - jac_data[i] = jac_np_data_ptr[i]; - } - - np_array jac_np_row_vals = python_functions.get_jac_row_vals(); - int n_row_vals = jac_np_row_vals.request().size; - - auto jac_np_row_vals_ptr = jac_np_row_vals.unchecked<1>(); - // just copy across row vals (this might be unneeded) - for (i = 0; i < n_row_vals; i++) - { - jac_rowvals[i] = jac_np_row_vals_ptr[i]; - } - - np_array jac_np_col_ptrs = python_functions.get_jac_col_ptrs(); - int n_col_ptrs = jac_np_col_ptrs.request().size; - auto jac_np_col_ptrs_ptr = jac_np_col_ptrs.unchecked<1>(); - - // just copy across col ptrs (this might be unneeded) - for (i = 0; i < n_col_ptrs; i++) - { - jac_colptrs[i] = jac_np_col_ptrs_ptr[i]; - } - - return (0); -} - -int events(realtype t, N_Vector yy, N_Vector yp, realtype *events_ptr, - void *user_data) +PYBIND11_MODULE(idaklu, m) { - realtype *yval; - yval = N_VGetArrayPointer(yy); - - PybammFunctions *python_functions_ptr = - static_cast(user_data); - PybammFunctions python_functions = *python_functions_ptr; - - int number_of_events = python_functions.number_of_events; - int number_of_states = python_functions.number_of_states; - py::array_t y_np = py::array_t(number_of_states, yval); - - py::array_t events_np_array; - - events_np_array = python_functions.events(t, y_np); - - auto events_np_data_ptr = events_np_array.unchecked<1>(); - - // just copying data (figure out how to pass pointers later) - int i; - for (i = 0; i < number_of_events; i++) - { - events_ptr[i] = events_np_data_ptr[i]; - } - - return (0); + m.doc() = "sundials solvers"; // optional module docstring + + py::bind_vector>(m, "VectorNdArray"); + + m.def("solve_python", &solve_python, + "The solve function for python evaluators", py::arg("t"), py::arg("y0"), + py::arg("yp0"), py::arg("res"), py::arg("jac"), py::arg("sens"), + py::arg("get_jac_data"), py::arg("get_jac_row_vals"), + py::arg("get_jac_col_ptr"), py::arg("nnz"), py::arg("events"), + py::arg("number_of_events"), py::arg("use_jacobian"), + py::arg("rhs_alg_id"), py::arg("atol"), py::arg("rtol"), + py::arg("inputs"), py::arg("number_of_sensitivity_parameters"), + py::return_value_policy::take_ownership); + + py::class_(m, "CasadiSolver") + .def("solve", &CasadiSolver::solve, "perform a solve", py::arg("t"), + py::arg("y0"), py::arg("yp0"), py::arg("inputs"), + py::return_value_policy::take_ownership); + + m.def("create_casadi_solver", &create_casadi_solver, + "Create a casadi idaklu solver object", py::arg("number_of_states"), + py::arg("number_of_parameters"), py::arg("rhs_alg"), + py::arg("jac_times_cjmass"), py::arg("jac_times_cjmass_colptrs"), + py::arg("jac_times_cjmass_rowvals"), py::arg("jac_times_cjmass_nnz"), + py::arg("jac_action"), py::arg("mass_action"), py::arg("sens"), + py::arg("events"), py::arg("number_of_events"), py::arg("rhs_alg_id"), + py::arg("atol"), py::arg("rtol"), py::arg("inputs"), py::arg("options"), + py::return_value_policy::take_ownership); + + m.def("generate_function", &generate_function, "Generate a casadi function", + py::arg("string"), py::return_value_policy::take_ownership); + + py::class_(m, "Function"); + + py::class_(m, "solution") + .def_readwrite("t", &Solution::t) + .def_readwrite("y", &Solution::y) + .def_readwrite("yS", &Solution::yS) + .def_readwrite("flag", &Solution::flag); } - -int sensitivities(int Ns, realtype t, N_Vector yy, N_Vector yp, - N_Vector resval, N_Vector *yS, N_Vector *ypS, N_Vector *resvalS, - void *user_data, N_Vector tmp1, N_Vector tmp2, N_Vector tmp3) { -// This function computes the sensitivity residual for all sensitivity -// equations. It must compute the vectors -// (∂F/∂y)s i (t)+(∂F/∂ ẏ) ṡ i (t)+(∂F/∂p i ) and store them in resvalS[i]. -// Ns is the number of sensitivities. -// t is the current value of the independent variable. -// yy is the current value of the state vector, y(t). -// yp is the current value of ẏ(t). -// resval contains the current value F of the original DAE residual. -// yS contains the current values of the sensitivities s i . -// ypS contains the current values of the sensitivity derivatives ṡ i . -// resvalS contains the output sensitivity residual vectors. -// Memory allocation for resvalS is handled within idas. -// user data is a pointer to user data. -// tmp1, tmp2, tmp3 are N Vectors of length N which can be used as -// temporary storage. -// -// Return value An IDASensResFn should return 0 if successful, -// a positive value if a recoverable error -// occurred (in which case idas will attempt to correct), -// or a negative value if it failed unrecoverably (in which case the integration is halted and IDA SRES FAIL is returned) -// - PybammFunctions *python_functions_ptr = - static_cast(user_data); - PybammFunctions python_functions = *python_functions_ptr; - - int n = python_functions.number_of_states; - int np = python_functions.number_of_parameters; - - // memory managed by sundials, so pass a destructor that does nothing - auto state_vector_shape = std::vector{n, 1}; - np_array y_np = np_array(state_vector_shape, N_VGetArrayPointer(yy), - py::capsule(&yy, [](void* p) {})); - np_array yp_np = np_array(state_vector_shape, N_VGetArrayPointer(yp), - py::capsule(&yp, [](void* p) {})); - - std::vector yS_np(np); - for (int i = 0; i < np; i++) { - auto capsule = py::capsule(yS + i, [](void* p) {}); - yS_np[i] = np_array(state_vector_shape, N_VGetArrayPointer(yS[i]), capsule); - } - - std::vector ypS_np(np); - for (int i = 0; i < np; i++) { - auto capsule = py::capsule(ypS + i, [](void* p) {}); - ypS_np[i] = np_array(state_vector_shape, N_VGetArrayPointer(ypS[i]), capsule); - } - - std::vector resvalS_np(np); - for (int i = 0; i < np; i++) { - auto capsule = py::capsule(resvalS + i, [](void* p) {}); - resvalS_np[i] = np_array(state_vector_shape, - N_VGetArrayPointer(resvalS[i]), capsule); - } - - realtype *ptr1 = static_cast(resvalS_np[0].request().ptr); - const realtype* resvalSval = N_VGetArrayPointer(resvalS[0]); - - python_functions.sensitivities(resvalS_np, t, y_np, yp_np, yS_np, ypS_np); - - return 0; -} - -/* main program */ -Solution solve_python(np_array t_np, np_array y0_np, np_array yp0_np, - residual_type res, jacobian_type jac, - sensitivities_type sens, - jac_get_type gjd, jac_get_type gjrv, jac_get_type gjcp, - int nnz, event_type event, - int number_of_events, int use_jacobian, np_array rhs_alg_id, - np_array atol_np, double rel_tol, np_array inputs, - int number_of_parameters) -{ - auto t = t_np.unchecked<1>(); - auto y0 = y0_np.unchecked<1>(); - auto yp0 = yp0_np.unchecked<1>(); - auto atol = atol_np.unchecked<1>(); - - int number_of_states = y0_np.request().size; - int number_of_timesteps = t_np.request().size; - void *ida_mem; // pointer to memory - N_Vector yy, yp, avtol; // y, y', and absolute tolerance - N_Vector *yyS, *ypS; // y, y' for sensitivities - realtype rtol, *yval, *ypval, *atval; - std::vector ySval(number_of_parameters); - int retval; - SUNMatrix J; - SUNLinearSolver LS; - - // allocate vectors - yy = N_VNew_Serial(number_of_states); - yp = N_VNew_Serial(number_of_states); - avtol = N_VNew_Serial(number_of_states); - - if (number_of_parameters > 0) { - yyS = N_VCloneVectorArray(number_of_parameters, yy); - ypS = N_VCloneVectorArray(number_of_parameters, yp); - } - - // set initial value - yval = N_VGetArrayPointer(yy); - ypval = N_VGetArrayPointer(yp); - atval = N_VGetArrayPointer(avtol); - int i; - for (i = 0; i < number_of_states; i++) - { - yval[i] = y0[i]; - ypval[i] = yp0[i]; - atval[i] = atol[i]; - } - - for (int is = 0 ; is < number_of_parameters; is++) { - ySval[is] = N_VGetArrayPointer(yyS[is]); - N_VConst(RCONST(0.0), yyS[is]); - N_VConst(RCONST(0.0), ypS[is]); - } - - // allocate memory for solver - ida_mem = IDACreate(); - - // initialise solver - realtype t0 = RCONST(t(0)); - IDAInit(ida_mem, residual, t0, yy, yp); - - // set tolerances - rtol = RCONST(rel_tol); - - IDASVtolerances(ida_mem, rtol, avtol); - - // set events - IDARootInit(ida_mem, number_of_events, events); - - // set pybamm functions by passing pointer to it - PybammFunctions pybamm_functions(res, jac, sens, gjd, gjrv, gjcp, event, - number_of_states, number_of_events, - number_of_parameters, inputs); - void *user_data = &pybamm_functions; - IDASetUserData(ida_mem, user_data); - - // set linear solver - J = SUNSparseMatrix(number_of_states, number_of_states, nnz, CSR_MAT); - - LS = SUNLinSol_KLU(yy, J); - IDASetLinearSolver(ida_mem, LS, J); - - if (use_jacobian == 1) - { - IDASetJacFn(ida_mem, jacobian); - } - - if (number_of_parameters > 0) - { - IDASensInit(ida_mem, number_of_parameters, - IDA_SIMULTANEOUS, sensitivities, yyS, ypS); - IDASensEEtolerances(ida_mem); - } - - int t_i = 1; - realtype tret; - realtype t_next; - realtype t_final = t(number_of_timesteps - 1); - - // set return vectors - std::vector t_return(number_of_timesteps); - std::vector y_return(number_of_timesteps * number_of_states); - std::vector yS_return(number_of_parameters * number_of_timesteps * number_of_states); - - t_return[0] = t(0); - for (int j = 0; j < number_of_states; j++) - { - y_return[j] = yval[j]; - } - for (int j = 0; j < number_of_parameters; j++) { - const int base_index = j * number_of_timesteps * number_of_states; - for (int k = 0; k < number_of_states; k++) { - yS_return[base_index + k] = ySval[j][k]; - } - } - - // calculate consistent initial conditions - N_Vector id; - auto id_np_val = rhs_alg_id.unchecked<1>(); - id = N_VNew_Serial(number_of_states); - realtype *id_val; - id_val = N_VGetArrayPointer(id); - - int ii; - for (ii = 0; ii < number_of_states; ii++) - { - id_val[ii] = id_np_val[ii]; - } - - IDASetId(ida_mem, id); - IDACalcIC(ida_mem, IDA_YA_YDP_INIT, t(1)); - - while (true) - { - t_next = t(t_i); - IDASetStopTime(ida_mem, t_next); - retval = IDASolve(ida_mem, t_final, &tret, yy, yp, IDA_NORMAL); - - if (retval == IDA_TSTOP_RETURN || retval == IDA_SUCCESS || retval == IDA_ROOT_RETURN) - { - if (number_of_parameters > 0) { - IDAGetSens(ida_mem, &tret, yyS); - } - - t_return[t_i] = tret; - for (int j = 0; j < number_of_states; j++) - { - y_return[t_i * number_of_states + j] = yval[j]; - } - for (int j = 0; j < number_of_parameters; j++) { - const int base_index = j * number_of_timesteps * number_of_states - + t_i * number_of_states; - for (int k = 0; k < number_of_states; k++) { - yS_return[base_index + k] = ySval[j][k]; - } - } - t_i += 1; - if (retval == IDA_SUCCESS || retval == IDA_ROOT_RETURN) { - break; - } - - } - } - - /* Free memory */ - if (number_of_parameters > 0) { - IDASensFree(ida_mem); - } - IDAFree(&ida_mem); - SUNLinSolFree(LS); - SUNMatDestroy(J); - N_VDestroy(avtol); - N_VDestroy(yp); - if (number_of_parameters > 0) { - N_VDestroyVectorArray(yyS, number_of_parameters); - N_VDestroyVectorArray(ypS, number_of_parameters); - } - - np_array t_ret = np_array(t_i, &t_return[0]); - np_array y_ret = np_array(t_i * number_of_states, &y_return[0]); - np_array yS_ret = np_array( - std::vector{number_of_parameters, number_of_timesteps, number_of_states}, - &yS_return[0] - ); - - Solution sol(retval, t_ret, y_ret, yS_ret); - - return sol; -} - diff --git a/pybamm/solvers/c_solvers/idaklu/casadi_functions.cpp b/pybamm/solvers/c_solvers/idaklu/casadi_functions.cpp new file mode 100644 index 0000000000..a2de2e7089 --- /dev/null +++ b/pybamm/solvers/c_solvers/idaklu/casadi_functions.cpp @@ -0,0 +1,69 @@ +#include "casadi_functions.hpp" + +CasadiFunction::CasadiFunction(const Function &f) : m_func(f) +{ + size_t sz_arg; + size_t sz_res; + size_t sz_iw; + size_t sz_w; + m_func.sz_work(sz_arg, sz_res, sz_iw, sz_w); + // std::cout << "name = "<< m_func.name() << " arg = " << sz_arg << " res = " + // << sz_res << " iw = " << sz_iw << " w = " << sz_w << std::endl; for (int i + // = 0; i < sz_arg; i++) { + // std::cout << "Sparsity for input " << i << std::endl; + // const Sparsity& sparsity = m_func.sparsity_in(i); + // } + // for (int i = 0; i < sz_res; i++) { + // std::cout << "Sparsity for output " << i << std::endl; + // const Sparsity& sparsity = m_func.sparsity_out(i); + // } + m_arg.resize(sz_arg); + m_res.resize(sz_res); + m_iw.resize(sz_iw); + m_w.resize(sz_w); +} + +// only call this once m_arg and m_res have been set appropriatelly +void CasadiFunction::operator()() +{ + int mem = m_func.checkout(); + m_func(m_arg.data(), m_res.data(), m_iw.data(), m_w.data(), mem); + m_func.release(mem); +} + +CasadiFunctions::CasadiFunctions( + const Function &rhs_alg, const Function &jac_times_cjmass, + const int jac_times_cjmass_nnz, + const np_array_int &jac_times_cjmass_rowvals_arg, + const np_array_int &jac_times_cjmass_colptrs_arg, + const int inputs_length, const Function &jac_action, + const Function &mass_action, const Function &sens, const Function &events, + const int n_s, int n_e, const int n_p, const Options& options) + : number_of_states(n_s), number_of_events(n_e), number_of_parameters(n_p), + number_of_nnz(jac_times_cjmass_nnz), rhs_alg(rhs_alg), + jac_times_cjmass(jac_times_cjmass), jac_action(jac_action), + mass_action(mass_action), sens(sens), events(events), + tmp(number_of_states), + options(options) +{ + + // copy across numpy array values + const int n_row_vals = jac_times_cjmass_rowvals_arg.request().size; + auto p_jac_times_cjmass_rowvals = jac_times_cjmass_rowvals_arg.unchecked<1>(); + jac_times_cjmass_rowvals.resize(n_row_vals); + for (int i = 0; i < n_row_vals; i++) { + jac_times_cjmass_rowvals[i] = p_jac_times_cjmass_rowvals[i]; + } + + const int n_col_ptrs = jac_times_cjmass_colptrs_arg.request().size; + auto p_jac_times_cjmass_colptrs = jac_times_cjmass_colptrs_arg.unchecked<1>(); + jac_times_cjmass_colptrs.resize(n_col_ptrs); + for (int i = 0; i < n_col_ptrs; i++) { + jac_times_cjmass_colptrs[i] = p_jac_times_cjmass_colptrs[i]; + } + + inputs.resize(inputs_length); + +} + +realtype *CasadiFunctions::get_tmp() { return tmp.data(); } diff --git a/pybamm/solvers/c_solvers/idaklu/casadi_functions.hpp b/pybamm/solvers/c_solvers/idaklu/casadi_functions.hpp new file mode 100644 index 0000000000..2e3b6beb8d --- /dev/null +++ b/pybamm/solvers/c_solvers/idaklu/casadi_functions.hpp @@ -0,0 +1,60 @@ +#ifndef PYBAMM_IDAKLU_CASADI_FUNCTIONS_HPP +#define PYBAMM_IDAKLU_CASADI_FUNCTIONS_HPP + +#include "common.hpp" +#include "options.hpp" +#include "solution.hpp" +#include + +using Function = casadi::Function; + +class CasadiFunction +{ +public: + explicit CasadiFunction(const Function &f); + +public: + std::vector m_arg; + std::vector m_res; + void operator()(); + +private: + const Function &m_func; + std::vector m_iw; + std::vector m_w; +}; + +class CasadiFunctions +{ +public: + int number_of_states; + int number_of_parameters; + int number_of_events; + int number_of_nnz; + CasadiFunction rhs_alg; + CasadiFunction sens; + CasadiFunction jac_times_cjmass; + std::vector jac_times_cjmass_rowvals; + std::vector jac_times_cjmass_colptrs; + std::vector inputs; + CasadiFunction jac_action; + CasadiFunction mass_action; + CasadiFunction events; + Options options; + + CasadiFunctions(const Function &rhs_alg, const Function &jac_times_cjmass, + const int jac_times_cjmass_nnz, + const np_array_int &jac_times_cjmass_rowvals, + const np_array_int &jac_times_cjmass_colptrs, + const int inputs_length, const Function &jac_action, + const Function &mass_action, const Function &sens, + const Function &events, const int n_s, int n_e, + const int n_p, const Options& options); + + realtype *get_tmp(); + +private: + std::vector tmp; +}; + +#endif // PYBAMM_IDAKLU_CASADI_FUNCTIONS_HPP diff --git a/pybamm/solvers/c_solvers/idaklu/casadi_solver.cpp b/pybamm/solvers/c_solvers/idaklu/casadi_solver.cpp new file mode 100644 index 0000000000..d1bb76ea68 --- /dev/null +++ b/pybamm/solvers/c_solvers/idaklu/casadi_solver.cpp @@ -0,0 +1,441 @@ +#include "casadi_solver.hpp" +#include "casadi_sundials_functions.hpp" +#include "common.hpp" +#include + +CasadiSolver * +create_casadi_solver(int number_of_states, int number_of_parameters, + const Function &rhs_alg, const Function &jac_times_cjmass, + const np_array_int &jac_times_cjmass_colptrs, + const np_array_int &jac_times_cjmass_rowvals, + const int jac_times_cjmass_nnz, const Function &jac_action, + const Function &mass_action, const Function &sens, + const Function &events, const int number_of_events, + np_array rhs_alg_id, np_array atol_np, double rel_tol, + int inputs_length, py::dict options) +{ + auto options_cpp = Options(options); + auto functions = std::make_unique( + rhs_alg, jac_times_cjmass, jac_times_cjmass_nnz, jac_times_cjmass_rowvals, + jac_times_cjmass_colptrs, inputs_length, jac_action, mass_action, sens, + events, number_of_states, number_of_events, number_of_parameters, + options_cpp); + + return new CasadiSolver(atol_np, rel_tol, rhs_alg_id, number_of_parameters, + number_of_events, jac_times_cjmass_nnz, + std::move(functions), options_cpp); +} + +CasadiSolver::CasadiSolver(np_array atol_np, double rel_tol, + np_array rhs_alg_id, int number_of_parameters, + int number_of_events, int jac_times_cjmass_nnz, + std::unique_ptr functions_arg, + const Options &options) + : number_of_states(atol_np.request().size), + number_of_parameters(number_of_parameters), + number_of_events(number_of_events), + jac_times_cjmass_nnz(jac_times_cjmass_nnz), + functions(std::move(functions_arg)), options(options) +{ + DEBUG("CasadiSolver::CasadiSolver"); + auto atol = atol_np.unchecked<1>(); + + // allocate memory for solver +#if SUNDIALS_VERSION_MAJOR >= 6 + SUNContext_Create(NULL, &sunctx); + ida_mem = IDACreate(sunctx); +#else + ida_mem = IDACreate(); +#endif + + // allocate vectors +#if SUNDIALS_VERSION_MAJOR >= 6 + yy = N_VNew_Serial(number_of_states, sunctx); + yp = N_VNew_Serial(number_of_states, sunctx); + avtol = N_VNew_Serial(number_of_states, sunctx); + id = N_VNew_Serial(number_of_states, sunctx); +#else + yy = N_VNew_Serial(number_of_states); + yp = N_VNew_Serial(number_of_states); + avtol = N_VNew_Serial(number_of_states); + id = N_VNew_Serial(number_of_states); +#endif + + if (number_of_parameters > 0) + { + yyS = N_VCloneVectorArray(number_of_parameters, yy); + ypS = N_VCloneVectorArray(number_of_parameters, yp); + } + + // set initial value + realtype *atval = N_VGetArrayPointer(avtol); + for (int i = 0; i < number_of_states; i++) + { + atval[i] = atol[i]; + } + + for (int is = 0; is < number_of_parameters; is++) + { + N_VConst(RCONST(0.0), yyS[is]); + N_VConst(RCONST(0.0), ypS[is]); + } + + // initialise solver + + IDAInit(ida_mem, residual_casadi, 0, yy, yp); + + // set tolerances + rtol = RCONST(rel_tol); + + IDASVtolerances(ida_mem, rtol, avtol); + + // set events + IDARootInit(ida_mem, number_of_events, events_casadi); + + void *user_data = functions.get(); + IDASetUserData(ida_mem, user_data); + + // set matrix + if (options.jacobian == "sparse") + { + DEBUG("\tsetting sparse matrix"); +#if SUNDIALS_VERSION_MAJOR >= 6 + J = SUNSparseMatrix(number_of_states, number_of_states, + jac_times_cjmass_nnz, CSC_MAT, sunctx); +#else + J = SUNSparseMatrix(number_of_states, number_of_states, + jac_times_cjmass_nnz, CSC_MAT); +#endif + } + else if (options.jacobian == "dense" || options.jacobian == "none") + { + DEBUG("\tsetting dense matrix"); +#if SUNDIALS_VERSION_MAJOR >= 6 + J = SUNDenseMatrix(number_of_states, number_of_states, sunctx); +#else + J = SUNDenseMatrix(number_of_states, number_of_states); +#endif + } + else if (options.jacobian == "matrix-free") + { + DEBUG("\tsetting matrix-free"); + J = NULL; + } + + #if SUNDIALS_VERSION_MAJOR >= 6 + int precon_type = SUN_PREC_NONE; + if (options.preconditioner != "none") { + precon_type = SUN_PREC_LEFT; + } + #else + int precon_type = PREC_NONE; + if (options.preconditioner != "none") { + precon_type = PREC_LEFT; + } + #endif + + // set linear solver + if (options.linear_solver == "SUNLinSol_Dense") + { + DEBUG("\tsetting SUNLinSol_Dense linear solver"); +#if SUNDIALS_VERSION_MAJOR >= 6 + LS = SUNLinSol_Dense(yy, J, sunctx); +#else + LS = SUNLinSol_Dense(yy, J); +#endif + } + else if (options.linear_solver == "SUNLinSol_KLU") + { + DEBUG("\tsetting SUNLinSol_KLU linear solver"); +#if SUNDIALS_VERSION_MAJOR >= 6 + LS = SUNLinSol_KLU(yy, J, sunctx); +#else + LS = SUNLinSol_KLU(yy, J); +#endif + } + else if (options.linear_solver == "SUNLinSol_SPBCGS") + { + DEBUG("\tsetting SUNLinSol_SPBCGS_linear solver"); +#if SUNDIALS_VERSION_MAJOR >= 6 + LS = SUNLinSol_SPBCGS(yy, precon_type, options.linsol_max_iterations, + sunctx); +#else + LS = SUNLinSol_SPBCGS(yy, precon_type, options.linsol_max_iterations); +#endif + } + else if (options.linear_solver == "SUNLinSol_SPFGMR") + { + DEBUG("\tsetting SUNLinSol_SPFGMR_linear solver"); +#if SUNDIALS_VERSION_MAJOR >= 6 + LS = SUNLinSol_SPFGMR(yy, precon_type, options.linsol_max_iterations, + sunctx); +#else + LS = SUNLinSol_SPFGMR(yy, precon_type, options.linsol_max_iterations); +#endif + } + else if (options.linear_solver == "SUNLinSol_SPGMR") + { + DEBUG("\tsetting SUNLinSol_SPGMR solver"); +#if SUNDIALS_VERSION_MAJOR >= 6 + LS = SUNLinSol_SPGMR(yy, precon_type, options.linsol_max_iterations, + sunctx); +#else + LS = SUNLinSol_SPGMR(yy, precon_type, options.linsol_max_iterations); +#endif + } + else if (options.linear_solver == "SUNLinSol_SPTFQMR") + { + DEBUG("\tsetting SUNLinSol_SPGMR solver"); +#if SUNDIALS_VERSION_MAJOR >= 6 + LS = SUNLinSol_SPTFQMR(yy, precon_type, options.linsol_max_iterations, + sunctx); +#else + LS = SUNLinSol_SPTFQMR(yy, precon_type, options.linsol_max_iterations); +#endif + } + + + + IDASetLinearSolver(ida_mem, LS, J); + + if (options.preconditioner != "none") + { + DEBUG("\tsetting IDADDB preconditioner"); + // setup preconditioner + IDABBDPrecInit( + ida_mem, number_of_states, options.precon_half_bandwidth, + options.precon_half_bandwidth, options.precon_half_bandwidth_keep, + options.precon_half_bandwidth_keep, 0.0, residual_casadi_approx, NULL); + } + + if (options.jacobian == "matrix-free") + { + IDASetJacTimes(ida_mem, NULL, jtimes_casadi); + } + else if (options.jacobian != "none") + { + IDASetJacFn(ida_mem, jacobian_casadi); + } + + if (number_of_parameters > 0) + { + IDASensInit(ida_mem, number_of_parameters, IDA_SIMULTANEOUS, + sensitivities_casadi, yyS, ypS); + IDASensEEtolerances(ida_mem); + } + + SUNLinSolInitialize(LS); + + auto id_np_val = rhs_alg_id.unchecked<1>(); + realtype *id_val; + id_val = N_VGetArrayPointer(id); + + int ii; + for (ii = 0; ii < number_of_states; ii++) + { + id_val[ii] = id_np_val[ii]; + } + + IDASetId(ida_mem, id); +} + +CasadiSolver::~CasadiSolver() +{ + + /* Free memory */ + if (number_of_parameters > 0) + { + IDASensFree(ida_mem); + } + SUNLinSolFree(LS); + SUNMatDestroy(J); + N_VDestroy(avtol); + N_VDestroy(yy); + N_VDestroy(yp); + N_VDestroy(id); + if (number_of_parameters > 0) + { + N_VDestroyVectorArray(yyS, number_of_parameters); + N_VDestroyVectorArray(ypS, number_of_parameters); + } + + IDAFree(&ida_mem); +#if SUNDIALS_VERSION_MAJOR >= 6 + SUNContext_Free(&sunctx); +#endif +} + +Solution CasadiSolver::solve(np_array t_np, np_array y0_np, np_array yp0_np, + np_array_dense inputs) +{ + DEBUG("CasadiSolver::solve"); + int number_of_timesteps = t_np.request().size; + + // set inputs + auto p_inputs = inputs.unchecked<2>(); + for (int i = 0; i < functions->inputs.size(); i++) + { + functions->inputs[i] = p_inputs(i, 0); + } + + realtype *yval = N_VGetArrayPointer(yy); + realtype *ypval = N_VGetArrayPointer(yp); + std::vector ySval(number_of_parameters); + for (int is = 0 ; is < number_of_parameters; is++) { + ySval[is] = N_VGetArrayPointer(yyS[is]); + N_VConst(RCONST(0.0), yyS[is]); + N_VConst(RCONST(0.0), ypS[is]); + } + + auto t = t_np.unchecked<1>(); + auto y0 = y0_np.unchecked<1>(); + auto yp0 = yp0_np.unchecked<1>(); + for (int i = 0; i < number_of_states; i++) + { + yval[i] = y0[i]; + ypval[i] = yp0[i]; + } + + realtype t0 = RCONST(t(0)); + IDAReInit(ida_mem, t0, yy, yp); + + int t_i = 1; + realtype tret; + realtype t_next; + realtype t_final = t(number_of_timesteps - 1); + + // set return vectors + realtype *t_return = new realtype[number_of_timesteps]; + realtype *y_return = new realtype[number_of_timesteps * number_of_states]; + realtype *yS_return = new realtype[number_of_parameters * + number_of_timesteps * number_of_states]; + + py::capsule free_t_when_done(t_return, + [](void *f) + { + realtype *vect = + reinterpret_cast(f); + delete[] vect; + }); + py::capsule free_y_when_done(y_return, + [](void *f) + { + realtype *vect = + reinterpret_cast(f); + delete[] vect; + }); + py::capsule free_yS_when_done(yS_return, + [](void *f) + { + realtype *vect = + reinterpret_cast(f); + delete[] vect; + }); + + t_return[0] = t(0); + for (int j = 0; j < number_of_states; j++) + { + y_return[j] = yval[j]; + } + for (int j = 0; j < number_of_parameters; j++) + { + const int base_index = j * number_of_timesteps * number_of_states; + for (int k = 0; k < number_of_states; k++) + { + yS_return[base_index + k] = ySval[j][k]; + } + } + + // calculate consistent initial conditions + DEBUG("IDACalcIC"); + IDACalcIC(ida_mem, IDA_YA_YDP_INIT, t(1)); + + int retval; + while (true) + { + t_next = t(t_i); + IDASetStopTime(ida_mem, t_next); + DEBUG("IDASolve"); + retval = IDASolve(ida_mem, t_final, &tret, yy, yp, IDA_NORMAL); + + if (retval == IDA_TSTOP_RETURN || retval == IDA_SUCCESS || + retval == IDA_ROOT_RETURN) + { + if (number_of_parameters > 0) + { + IDAGetSens(ida_mem, &tret, yyS); + } + + t_return[t_i] = tret; + for (int j = 0; j < number_of_states; j++) + { + y_return[t_i * number_of_states + j] = yval[j]; + } + for (int j = 0; j < number_of_parameters; j++) + { + const int base_index = + j * number_of_timesteps * number_of_states + t_i * number_of_states; + for (int k = 0; k < number_of_states; k++) + { + yS_return[base_index + k] = ySval[j][k]; + } + } + t_i += 1; + if (retval == IDA_SUCCESS || retval == IDA_ROOT_RETURN) + { + break; + } + } + else + { + // failed + break; + } + } + + np_array t_ret = np_array(t_i, &t_return[0], free_t_when_done); + np_array y_ret = + np_array(t_i * number_of_states, &y_return[0], free_y_when_done); + np_array yS_ret = np_array( + std::vector{number_of_parameters, number_of_timesteps, number_of_states}, + &yS_return[0], free_yS_when_done); + + Solution sol(retval, t_ret, y_ret, yS_ret); + + if (options.print_stats) + { + long nsteps, nrevals, nlinsetups, netfails; + int klast, kcur; + realtype hinused, hlast, hcur, tcur; + + IDAGetIntegratorStats(ida_mem, &nsteps, &nrevals, &nlinsetups, &netfails, + &klast, &kcur, &hinused, &hlast, &hcur, &tcur); + + long nniters, nncfails; + IDAGetNonlinSolvStats(ida_mem, &nniters, &nncfails); + + long int ngevalsBBDP = 0; + if (options.using_iterative_solver) + { + IDABBDPrecGetNumGfnEvals(ida_mem, &ngevalsBBDP); + } + + py::print("Solver Stats:"); + py::print("\tNumber of steps =", nsteps); + py::print("\tNumber of calls to residual function =", nrevals); + py::print("\tNumber of calls to residual function in preconditioner =", + ngevalsBBDP); + py::print("\tNumber of linear solver setup calls =", nlinsetups); + py::print("\tNumber of error test failures =", netfails); + py::print("\tMethod order used on last step =", klast); + py::print("\tMethod order used on next step =", kcur); + py::print("\tInitial step size =", hinused); + py::print("\tStep size on last step =", hlast); + py::print("\tStep size on next step =", hcur); + py::print("\tCurrent internal time reached =", tcur); + py::print("\tNumber of nonlinear iterations performed =", nniters); + py::print("\tNumber of nonlinear convergence failures =", nncfails); + } + + return sol; +} diff --git a/pybamm/solvers/c_solvers/idaklu/casadi_solver.hpp b/pybamm/solvers/c_solvers/idaklu/casadi_solver.hpp new file mode 100644 index 0000000000..3eed122e04 --- /dev/null +++ b/pybamm/solvers/c_solvers/idaklu/casadi_solver.hpp @@ -0,0 +1,57 @@ +#ifndef PYBAMM_IDAKLU_CASADI_SOLVER_HPP +#define PYBAMM_IDAKLU_CASADI_SOLVER_HPP + +#include +using Function = casadi::Function; + +#include "casadi_functions.hpp" +#include "common.hpp" +#include "options.hpp" +#include "solution.hpp" + +class CasadiSolver +{ +public: + CasadiSolver(np_array atol_np, double rel_tol, np_array rhs_alg_id, + int number_of_parameters, int number_of_events, + int jac_times_cjmass_nnz, + std::unique_ptr functions, const Options& options); + ~CasadiSolver(); + + void *ida_mem; // pointer to memory + +#if SUNDIALS_VERSION_MAJOR >= 6 + SUNContext sunctx; +#endif + + int number_of_states; + int number_of_parameters; + int number_of_events; + N_Vector yy, yp, avtol; // y, y', and absolute tolerance + N_Vector *yyS, *ypS; // y, y' for sensitivities + N_Vector id; // rhs_alg_id + realtype rtol; + const int jac_times_cjmass_nnz; + + SUNMatrix J; + SUNLinearSolver LS; + + std::unique_ptr functions; + Options options; + + Solution solve(np_array t_np, np_array y0_np, np_array yp0_np, + np_array_dense inputs); +}; + +CasadiSolver * +create_casadi_solver(int number_of_states, int number_of_parameters, + const Function &rhs_alg, const Function &jac_times_cjmass, + const np_array_int &jac_times_cjmass_colptrs, + const np_array_int &jac_times_cjmass_rowvals, + const int jac_times_cjmass_nnz, const Function &jac_action, + const Function &mass_action, const Function &sens, + const Function &event, const int number_of_events, + np_array rhs_alg_id, np_array atol_np, + double rel_tol, int inputs_length, py::dict options); + +#endif // PYBAMM_IDAKLU_CASADI_SOLVER_HPP diff --git a/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp b/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp new file mode 100644 index 0000000000..ce2a892725 --- /dev/null +++ b/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp @@ -0,0 +1,287 @@ +#include "casadi_sundials_functions.hpp" +#include "casadi_functions.hpp" +#include "common.hpp" + +int residual_casadi(realtype tres, N_Vector yy, N_Vector yp, N_Vector rr, + void *user_data) +{ + DEBUG("residual_casadi"); + CasadiFunctions *p_python_functions = + static_cast(user_data); + + p_python_functions->rhs_alg.m_arg[0] = &tres; + p_python_functions->rhs_alg.m_arg[1] = NV_DATA_S(yy); + p_python_functions->rhs_alg.m_arg[2] = p_python_functions->inputs.data(); + p_python_functions->rhs_alg.m_res[0] = NV_DATA_S(rr); + p_python_functions->rhs_alg(); + + realtype *tmp = p_python_functions->get_tmp(); + p_python_functions->mass_action.m_arg[0] = NV_DATA_S(yp); + p_python_functions->mass_action.m_res[0] = tmp; + p_python_functions->mass_action(); + + // AXPY: y <- a*x + y + const int ns = p_python_functions->number_of_states; + casadi::casadi_axpy(ns, -1., tmp, NV_DATA_S(rr)); + + DEBUG_VECTOR(yy); + DEBUG_VECTOR(yp); + DEBUG_VECTOR(rr); + + // now rr has rhs_alg(t, y) - mass_matrix * yp + return 0; +} + +// This Gres function computes G(t, y, yp). It loads the vector gval as a +// function of tt, yy, and yp. +// +// Arguments: +// Nlocal – is the local vector length. +// +// tt – is the value of the independent variable. +// +// yy – is the dependent variable. +// +// yp – is the derivative of the dependent variable. +// +// gval – is the output vector. +// +// user_data – is a pointer to user data, the same as the user_data parameter +// passed to IDASetUserData(). +// +// Return value: +// +// An IDABBDLocalFn function type should return 0 to indicate success, 1 for a +// recoverable error, or -1 for a non-recoverable error. +// +// Notes: +// +// This function must assume that all inter-processor communication of data +// needed to calculate gval has already been done, and this data is accessible +// within user_data. +// +// The case where G is mathematically identical to F is allowed. +int residual_casadi_approx(sunindextype Nlocal, realtype tt, N_Vector yy, + N_Vector yp, N_Vector gval, void *user_data) +{ + DEBUG("residual_casadi_approx"); + + // Just use true residual for now + int result = residual_casadi(tt, yy, yp, gval, user_data); + return result; +} + +// Purpose This function computes the product Jv of the DAE system Jacobian J +// (or an approximation to it) and a given vector v, where J is defined by Eq. +// (2.6). +// J = ∂F/∂y + cj ∂F/∂y˙ +// Arguments tt is the current value of the independent variable. +// yy is the current value of the dependent variable vector, y(t). +// yp is the current value of ˙y(t). +// rr is the current value of the residual vector F(t, y, y˙). +// v is the vector by which the Jacobian must be multiplied to the right. +// Jv is the computed output vector. +// cj is the scalar in the system Jacobian, proportional to the inverse of +// the step +// size (α in Eq. (2.6) ). +// user data is a pointer to user data, the same as the user data parameter +// passed to +// IDASetUserData. +// tmp1 +// tmp2 are pointers to memory allocated for variables of type N Vector +// which can +// be used by IDALsJacTimesVecFn as temporary storage or work space. +int jtimes_casadi(realtype tt, N_Vector yy, N_Vector yp, N_Vector rr, + N_Vector v, N_Vector Jv, realtype cj, void *user_data, + N_Vector tmp1, N_Vector tmp2) +{ + DEBUG("jtimes_casadi"); + CasadiFunctions *p_python_functions = + static_cast(user_data); + + // Jv has ∂F/∂y v + p_python_functions->jac_action.m_arg[0] = &tt; + p_python_functions->jac_action.m_arg[1] = NV_DATA_S(yy); + p_python_functions->jac_action.m_arg[2] = p_python_functions->inputs.data(); + p_python_functions->jac_action.m_arg[3] = NV_DATA_S(v); + p_python_functions->jac_action.m_res[0] = NV_DATA_S(Jv); + p_python_functions->jac_action(); + + // tmp has -∂F/∂y˙ v + realtype *tmp = p_python_functions->get_tmp(); + p_python_functions->mass_action.m_arg[0] = NV_DATA_S(v); + p_python_functions->mass_action.m_res[0] = tmp; + p_python_functions->mass_action(); + + // AXPY: y <- a*x + y + // Jv has ∂F/∂y v + cj ∂F/∂y˙ v + const int ns = p_python_functions->number_of_states; + casadi::casadi_axpy(ns, -cj, tmp, NV_DATA_S(Jv)); + + return 0; +} + +// Arguments tt is the current value of the independent variable t. +// cj is the scalar in the system Jacobian, proportional to the inverse of the +// step +// size (α in Eq. (2.6) ). +// yy is the current value of the dependent variable vector, y(t). +// yp is the current value of ˙y(t). +// rr is the current value of the residual vector F(t, y, y˙). +// Jac is the output (approximate) Jacobian matrix (of type SUNMatrix), J = +// ∂F/∂y + cj ∂F/∂y˙. +// user data is a pointer to user data, the same as the user data parameter +// passed to +// IDASetUserData. +// tmp1 +// tmp2 +// tmp3 are pointers to memory allocated for variables of type N Vector which +// can +// be used by IDALsJacFn function as temporary storage or work space. +int jacobian_casadi(realtype tt, realtype cj, N_Vector yy, N_Vector yp, + N_Vector resvec, SUNMatrix JJ, void *user_data, + N_Vector tempv1, N_Vector tempv2, N_Vector tempv3) +{ + DEBUG("jacobian_casadi"); + + CasadiFunctions *p_python_functions = + static_cast(user_data); + + // create pointer to jac data, column pointers, and row values + sunindextype *jac_colptrs; + sunindextype *jac_rowvals; + realtype *jac_data; + if (p_python_functions->options.using_sparse_matrix) + { + jac_colptrs = SUNSparseMatrix_IndexPointers(JJ); + jac_rowvals = SUNSparseMatrix_IndexValues(JJ); + jac_data = SUNSparseMatrix_Data(JJ); + } + else + { + jac_data = SUNDenseMatrix_Data(JJ); + } + + // args are t, y, cj, put result in jacobian data matrix + p_python_functions->jac_times_cjmass.m_arg[0] = &tt; + p_python_functions->jac_times_cjmass.m_arg[1] = NV_DATA_S(yy); + p_python_functions->jac_times_cjmass.m_arg[2] = + p_python_functions->inputs.data(); + p_python_functions->jac_times_cjmass.m_arg[3] = &cj; + p_python_functions->jac_times_cjmass.m_res[0] = jac_data; + p_python_functions->jac_times_cjmass(); + + if (p_python_functions->options.using_sparse_matrix) + { + // row vals and col ptrs + const int n_row_vals = p_python_functions->jac_times_cjmass_rowvals.size(); + auto p_jac_times_cjmass_rowvals = + p_python_functions->jac_times_cjmass_rowvals.data(); + + // just copy across row vals (do I need to do this every time?) + // (or just in the setup?) + for (int i = 0; i < n_row_vals; i++) + { + jac_rowvals[i] = p_jac_times_cjmass_rowvals[i]; + } + + const int n_col_ptrs = p_python_functions->jac_times_cjmass_colptrs.size(); + auto p_jac_times_cjmass_colptrs = + p_python_functions->jac_times_cjmass_colptrs.data(); + + // just copy across col ptrs (do I need to do this every time?) + for (int i = 0; i < n_col_ptrs; i++) + { + jac_colptrs[i] = p_jac_times_cjmass_colptrs[i]; + } + } + + return (0); +} + +int events_casadi(realtype t, N_Vector yy, N_Vector yp, realtype *events_ptr, + void *user_data) +{ + CasadiFunctions *p_python_functions = + static_cast(user_data); + + // args are t, y, put result in events_ptr + p_python_functions->events.m_arg[0] = &t; + p_python_functions->events.m_arg[1] = NV_DATA_S(yy); + p_python_functions->events.m_arg[2] = p_python_functions->inputs.data(); + p_python_functions->events.m_res[0] = events_ptr; + p_python_functions->events(); + + return (0); +} + +// This function computes the sensitivity residual for all sensitivity +// equations. It must compute the vectors +// (∂F/∂y)s i (t)+(∂F/∂ ẏ) ṡ i (t)+(∂F/∂p i ) and store them in resvalS[i]. +// Ns is the number of sensitivities. +// t is the current value of the independent variable. +// yy is the current value of the state vector, y(t). +// yp is the current value of ẏ(t). +// resval contains the current value F of the original DAE residual. +// yS contains the current values of the sensitivities s i . +// ypS contains the current values of the sensitivity derivatives ṡ i . +// resvalS contains the output sensitivity residual vectors. +// Memory allocation for resvalS is handled within idas. +// user data is a pointer to user data. +// tmp1, tmp2, tmp3 are N Vectors of length N which can be used as +// temporary storage. +// +// Return value An IDASensResFn should return 0 if successful, +// a positive value if a recoverable error +// occurred (in which case idas will attempt to correct), +// or a negative value if it failed unrecoverably (in which case the integration +// is halted and IDA SRES FAIL is returned) +// +int sensitivities_casadi(int Ns, realtype t, N_Vector yy, N_Vector yp, + N_Vector resval, N_Vector *yS, N_Vector *ypS, + N_Vector *resvalS, void *user_data, N_Vector tmp1, + N_Vector tmp2, N_Vector tmp3) +{ + + CasadiFunctions *p_python_functions = + static_cast(user_data); + + const int np = p_python_functions->number_of_parameters; + + // args are t, y put result in rr + p_python_functions->sens.m_arg[0] = &t; + p_python_functions->sens.m_arg[1] = NV_DATA_S(yy); + p_python_functions->sens.m_arg[2] = p_python_functions->inputs.data(); + for (int i = 0; i < np; i++) + { + p_python_functions->sens.m_res[i] = NV_DATA_S(resvalS[i]); + } + // resvalsS now has (∂F/∂p i ) + p_python_functions->sens(); + + for (int i = 0; i < np; i++) + { + // put (∂F/∂y)s i (t) in tmp + realtype *tmp = p_python_functions->get_tmp(); + p_python_functions->jac_action.m_arg[0] = &t; + p_python_functions->jac_action.m_arg[1] = NV_DATA_S(yy); + p_python_functions->jac_action.m_arg[2] = p_python_functions->inputs.data(); + p_python_functions->jac_action.m_arg[3] = NV_DATA_S(yS[i]); + p_python_functions->jac_action.m_res[0] = tmp; + p_python_functions->jac_action(); + + const int ns = p_python_functions->number_of_states; + casadi::casadi_axpy(ns, 1., tmp, NV_DATA_S(resvalS[i])); + + // put -(∂F/∂ ẏ) ṡ i (t) in tmp2 + p_python_functions->mass_action.m_arg[0] = NV_DATA_S(ypS[i]); + p_python_functions->mass_action.m_res[0] = tmp; + p_python_functions->mass_action(); + + // (∂F/∂y)s i (t)+(∂F/∂ ẏ) ṡ i (t)+(∂F/∂p i ) + // AXPY: y <- a*x + y + casadi::casadi_axpy(ns, -1., tmp, NV_DATA_S(resvalS[i])); + } + + return 0; +} diff --git a/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.hpp b/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.hpp new file mode 100644 index 0000000000..a2192030b4 --- /dev/null +++ b/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.hpp @@ -0,0 +1,27 @@ +#ifndef PYBAMM_IDAKLU_CASADI_SUNDIALS_FUNCTIONS_HPP +#define PYBAMM_IDAKLU_CASADI_SUNDIALS_FUNCTIONS_HPP + +#include "common.hpp" + +int residual_casadi(realtype tres, N_Vector yy, N_Vector yp, N_Vector rr, + void *user_data); + +int jtimes_casadi(realtype tt, N_Vector yy, N_Vector yp, N_Vector rr, + N_Vector v, N_Vector Jv, realtype cj, void *user_data, + N_Vector tmp1, N_Vector tmp2); + +int events_casadi(realtype t, N_Vector yy, N_Vector yp, realtype *events_ptr, + void *user_data); + +int sensitivities_casadi(int Ns, realtype t, N_Vector yy, N_Vector yp, + N_Vector resval, N_Vector *yS, N_Vector *ypS, + N_Vector *resvalS, void *user_data, N_Vector tmp1, + N_Vector tmp2, N_Vector tmp3); + +int jacobian_casadi(realtype tt, realtype cj, N_Vector yy, N_Vector yp, + N_Vector resvec, SUNMatrix JJ, void *user_data, + N_Vector tempv1, N_Vector tempv2, N_Vector tempv3); + +int residual_casadi_approx(sunindextype Nlocal, realtype tt, N_Vector yy, + N_Vector yp, N_Vector gval, void *user_data); +#endif // PYBAMM_IDAKLU_CASADI_SUNDIALS_FUNCTIONS_HPP diff --git a/pybamm/solvers/c_solvers/idaklu/common.hpp b/pybamm/solvers/c_solvers/idaklu/common.hpp new file mode 100644 index 0000000000..5bac325fc8 --- /dev/null +++ b/pybamm/solvers/c_solvers/idaklu/common.hpp @@ -0,0 +1,59 @@ +#ifndef PYBAMM_IDAKLU_COMMON_HPP +#define PYBAMM_IDAKLU_COMMON_HPP + +#include /* prototypes for IDAS fcts., consts. */ +#include /* access to IDABBDPRE preconditioner */ + +#include /* access to serial N_Vector */ +#include /* defs. of SUNRabs, SUNRexp, etc. */ +#include /* defs. of SUNRabs, SUNRexp, etc. */ +#include /* defs. of realtype, sunindextype */ + + +#if SUNDIALS_VERSION_MAJOR >= 6 + #include +#endif + +#include /* access to KLU linear solver */ +#include /* access to dense linear solver */ +#include /* access to spbcgs iterative linear solver */ +#include +#include +#include + +#include /* access to sparse SUNMatrix */ +#include /* access to dense SUNMatrix */ + + + +#include +#include + +namespace py = pybind11; +using np_array = py::array_t; +using np_array_dense = py::array_t; +using np_array_int = py::array_t; + +#ifdef NDEBUG +#define DEBUG(x) +#else +#define DEBUG(x) do { std::cerr << __FILE__ << ':' << __LINE__ << ' ' << x << std::endl; } while (0) +#endif + +#ifdef NDEBUG +#define DEBUG_VECTOR(vector) +#else +#define DEBUG_VECTOR(vector) {\ + std::cout << #vector << " = ["; \ + auto array_ptr = N_VGetArrayPointer(vector); \ + auto N = N_VGetLength(vector); \ + for (int i = 0; i < N; i++) { \ + std::cout << array_ptr[i]; \ + if (i < N-1) { \ + std::cout << ", "; \ + } \ + } \ + std::cout << "]" << std::endl; } +#endif + +#endif // PYBAMM_IDAKLU_COMMON_HPP diff --git a/pybamm/solvers/c_solvers/idaklu/options.cpp b/pybamm/solvers/c_solvers/idaklu/options.cpp new file mode 100644 index 0000000000..c3c7cb3583 --- /dev/null +++ b/pybamm/solvers/c_solvers/idaklu/options.cpp @@ -0,0 +1,100 @@ +#include "options.hpp" +#include + + +using namespace std::string_literals; + +Options::Options(py::dict options) + : print_stats(options["print_stats"].cast()), + jacobian(options["jacobian"].cast()), + preconditioner(options["preconditioner"].cast()), + linsol_max_iterations(options["linsol_max_iterations"].cast()), + linear_solver(options["linear_solver"].cast()), + precon_half_bandwidth(options["precon_half_bandwidth"].cast()), + precon_half_bandwidth_keep(options["precon_half_bandwidth_keep"].cast()) +{ + + using_sparse_matrix = true; + if (jacobian == "sparse") + { + } + else if (jacobian == "dense" || jacobian == "none") + { + using_sparse_matrix = false; + } + else if (jacobian == "matrix-free") + { + } + else + { + throw std::domain_error( + "Unknown jacobian type \""s + jacobian + + "\". Should be one of \"sparse\", \"dense\", \"matrix-free\" or \"none\"."s + ); + } + + using_iterative_solver = false; + if (linear_solver == "SUNLinSol_Dense" && (jacobian == "dense" || jacobian == "none")) + { + } + else if (linear_solver == "SUNLinSol_KLU" && jacobian == "sparse") + { + } + else if ((linear_solver == "SUNLinSol_SPBCGS" || + linear_solver == "SUNLinSol_SPFGMR" || + linear_solver == "SUNLinSol_SPGMR" || + linear_solver == "SUNLinSol_SPTFQMR") && + (jacobian == "sparse" || jacobian == "matrix-free")) + { + using_iterative_solver = true; + } + else if (jacobian == "sparse") + { + throw std::domain_error( + "Unknown linear solver or incompatible options: " + "jacobian = \"" + jacobian + "\" linear solver = \"" + linear_solver + + "\". For a sparse jacobian " + "please use the SUNLinSol_KLU linear solver" + ); + } + else if (jacobian == "matrix-free") + { + throw std::domain_error( + "Unknown linear solver or incompatible options. " + "jacobian = \"" + jacobian + "\" linear solver = \"" + linear_solver + + "\". For a matrix-free jacobian " + "please use one of the iterative linear solvers: \"SUNLinSol_SPBCGS\", " + "\"SUNLinSol_SPFGMR\", \"SUNLinSol_SPGMR\", or \"SUNLinSol_SPTFQMR\"." + ); + } + else if (jacobian == "none") + { + throw std::domain_error( + "Unknown linear solver or incompatible options: " + "jacobian = \"" + jacobian + "\" linear solver = \"" + linear_solver + + "\". For no jacobian please use the SUNLinSol_Dense solver" + ); + } + else + { + throw std::domain_error( + "Unknown linear solver or incompatible options. " + "jacobian = \"" + jacobian + "\" linear solver = \"" + linear_solver + "\"" + ); + } + + if (using_iterative_solver) + { + if (preconditioner != "none" && preconditioner != "BBDP") + { + throw std::domain_error( + "Unknown preconditioner \""s + preconditioner + + "\", use one of \"BBDP\" or \"none\""s + ); + } + } + else + { + preconditioner = "none"; + } +} diff --git a/pybamm/solvers/c_solvers/idaklu/options.hpp b/pybamm/solvers/c_solvers/idaklu/options.hpp new file mode 100644 index 0000000000..2fc807e48f --- /dev/null +++ b/pybamm/solvers/c_solvers/idaklu/options.hpp @@ -0,0 +1,20 @@ +#ifndef PYBAMM_OPTIONS_HPP +#define PYBAMM_OPTIONS_HPP + +#include "common.hpp" + +struct Options { + bool print_stats; + bool using_sparse_matrix; + bool using_iterative_solver; + std::string jacobian; + std::string linear_solver; // klu, lapack, spbcg + std::string preconditioner; // spbcg + int linsol_max_iterations; + int precon_half_bandwidth; + int precon_half_bandwidth_keep; + explicit Options(py::dict options); + +}; + +#endif // PYBAMM_OPTIONS_HPP diff --git a/pybamm/solvers/c_solvers/idaklu/python.cpp b/pybamm/solvers/c_solvers/idaklu/python.cpp new file mode 100644 index 0000000000..a1803988d4 --- /dev/null +++ b/pybamm/solvers/c_solvers/idaklu/python.cpp @@ -0,0 +1,478 @@ +#include "common.hpp" +#include "python.hpp" +#include + +class PybammFunctions +{ +public: + int number_of_states; + int number_of_parameters; + int number_of_events; + + PybammFunctions(const residual_type &res, const jacobian_type &jac, + const sensitivities_type &sens, + const jac_get_type &get_jac_data_in, + const jac_get_type &get_jac_row_vals_in, + const jac_get_type &get_jac_col_ptrs_in, + const event_type &event, + const int n_s, int n_e, const int n_p, + const np_array &inputs) + : number_of_states(n_s), number_of_events(n_e), + number_of_parameters(n_p), + py_res(res), py_jac(jac), + py_sens(sens), + py_event(event), py_get_jac_data(get_jac_data_in), + py_get_jac_row_vals(get_jac_row_vals_in), + py_get_jac_col_ptrs(get_jac_col_ptrs_in), + inputs(inputs) + { + } + + np_array operator()(double t, np_array y, np_array yp) + { + return py_res(t, y, inputs, yp); + } + + np_array res(double t, np_array y, np_array yp) + { + return py_res(t, y, inputs, yp); + } + + void jac(double t, np_array y, double cj) + { + // this function evaluates the jacobian and sets it to be the attribute + // of a python class which can then be called by get_jac_data, + // get_jac_col_ptr, etc + py_jac(t, y, inputs, cj); + } + + void sensitivities( + std::vector& resvalS, + const double t, const np_array& y, const np_array& yp, + const std::vector& yS, const std::vector& ypS) + { + // this function evaluates the sensitivity equations required by IDAS, + // returning them in resvalS, which is preallocated as a numpy array + // of size (np, n), where n is the number of states and np is the number + // of parameters + // + // yS and ypS are also shape (np, n), y and yp are shape (n) + // + // dF/dy * s_i + dF/dyd * sd + dFdp_i for i in range(np) + py_sens(resvalS, t, y, inputs, yp, yS, ypS); + } + + np_array get_jac_data() { return py_get_jac_data(); } + + np_array get_jac_row_vals() { return py_get_jac_row_vals(); } + + np_array get_jac_col_ptrs() { return py_get_jac_col_ptrs(); } + + np_array events(double t, np_array y) { return py_event(t, y, inputs); } + +private: + residual_type py_res; + sensitivities_type py_sens; + jacobian_type py_jac; + event_type py_event; + jac_get_type py_get_jac_data; + jac_get_type py_get_jac_row_vals; + jac_get_type py_get_jac_col_ptrs; + const np_array &inputs; +}; + +int residual(realtype tres, N_Vector yy, N_Vector yp, N_Vector rr, + void *user_data) +{ + PybammFunctions *python_functions_ptr = + static_cast(user_data); + PybammFunctions python_functions = *python_functions_ptr; + + realtype *yval, *ypval, *rval; + yval = N_VGetArrayPointer(yy); + ypval = N_VGetArrayPointer(yp); + rval = N_VGetArrayPointer(rr); + + int n = python_functions.number_of_states; + py::array_t y_np = py::array_t(n, yval); + py::array_t yp_np = py::array_t(n, ypval); + + py::array_t r_np; + + r_np = python_functions.res(tres, y_np, yp_np); + + auto r_np_ptr = r_np.unchecked<1>(); + + // just copying data + int i; + for (i = 0; i < n; i++) + { + rval[i] = r_np_ptr[i]; + } + return 0; +} + +int jacobian(realtype tt, realtype cj, N_Vector yy, N_Vector yp, + N_Vector resvec, SUNMatrix JJ, void *user_data, N_Vector tempv1, + N_Vector tempv2, N_Vector tempv3) +{ + realtype *yval; + yval = N_VGetArrayPointer(yy); + + PybammFunctions *python_functions_ptr = + static_cast(user_data); + PybammFunctions python_functions = *python_functions_ptr; + + int n = python_functions.number_of_states; + py::array_t y_np = py::array_t(n, yval); + + // create pointer to jac data, column pointers, and row values + sunindextype *jac_colptrs = SUNSparseMatrix_IndexPointers(JJ); + sunindextype *jac_rowvals = SUNSparseMatrix_IndexValues(JJ); + realtype *jac_data = SUNSparseMatrix_Data(JJ); + + py::array_t jac_np_array; + + python_functions.jac(tt, y_np, cj); + + np_array jac_np_data = python_functions.get_jac_data(); + int n_data = jac_np_data.request().size; + auto jac_np_data_ptr = jac_np_data.unchecked<1>(); + + // just copy across data + int i; + for (i = 0; i < n_data; i++) + { + jac_data[i] = jac_np_data_ptr[i]; + } + + np_array jac_np_row_vals = python_functions.get_jac_row_vals(); + int n_row_vals = jac_np_row_vals.request().size; + + auto jac_np_row_vals_ptr = jac_np_row_vals.unchecked<1>(); + // just copy across row vals (this might be unneeded) + for (i = 0; i < n_row_vals; i++) + { + jac_rowvals[i] = jac_np_row_vals_ptr[i]; + } + + np_array jac_np_col_ptrs = python_functions.get_jac_col_ptrs(); + int n_col_ptrs = jac_np_col_ptrs.request().size; + auto jac_np_col_ptrs_ptr = jac_np_col_ptrs.unchecked<1>(); + + // just copy across col ptrs (this might be unneeded) + for (i = 0; i < n_col_ptrs; i++) + { + jac_colptrs[i] = jac_np_col_ptrs_ptr[i]; + } + + return (0); +} + +int events(realtype t, N_Vector yy, N_Vector yp, realtype *events_ptr, + void *user_data) +{ + realtype *yval; + yval = N_VGetArrayPointer(yy); + + PybammFunctions *python_functions_ptr = + static_cast(user_data); + PybammFunctions python_functions = *python_functions_ptr; + + int number_of_events = python_functions.number_of_events; + int number_of_states = python_functions.number_of_states; + py::array_t y_np = py::array_t(number_of_states, yval); + + py::array_t events_np_array; + + events_np_array = python_functions.events(t, y_np); + + auto events_np_data_ptr = events_np_array.unchecked<1>(); + + // just copying data (figure out how to pass pointers later) + int i; + for (i = 0; i < number_of_events; i++) + { + events_ptr[i] = events_np_data_ptr[i]; + } + + return (0); +} + +int sensitivities(int Ns, realtype t, N_Vector yy, N_Vector yp, + N_Vector resval, N_Vector *yS, N_Vector *ypS, N_Vector *resvalS, + void *user_data, N_Vector tmp1, N_Vector tmp2, N_Vector tmp3) { +// This function computes the sensitivity residual for all sensitivity +// equations. It must compute the vectors +// (∂F/∂y)s i (t)+(∂F/∂ ẏ) ṡ i (t)+(∂F/∂p i ) and store them in resvalS[i]. +// Ns is the number of sensitivities. +// t is the current value of the independent variable. +// yy is the current value of the state vector, y(t). +// yp is the current value of ẏ(t). +// resval contains the current value F of the original DAE residual. +// yS contains the current values of the sensitivities s i . +// ypS contains the current values of the sensitivity derivatives ṡ i . +// resvalS contains the output sensitivity residual vectors. +// Memory allocation for resvalS is handled within idas. +// user data is a pointer to user data. +// tmp1, tmp2, tmp3 are N Vectors of length N which can be used as +// temporary storage. +// +// Return value An IDASensResFn should return 0 if successful, +// a positive value if a recoverable error +// occurred (in which case idas will attempt to correct), +// or a negative value if it failed unrecoverably (in which case the integration is halted and IDA SRES FAIL is returned) +// + PybammFunctions *python_functions_ptr = + static_cast(user_data); + PybammFunctions python_functions = *python_functions_ptr; + + int n = python_functions.number_of_states; + int np = python_functions.number_of_parameters; + + // memory managed by sundials, so pass a destructor that does nothing + auto state_vector_shape = std::vector{n, 1}; + np_array y_np = np_array(state_vector_shape, N_VGetArrayPointer(yy), + py::capsule(&yy, [](void* p) {})); + np_array yp_np = np_array(state_vector_shape, N_VGetArrayPointer(yp), + py::capsule(&yp, [](void* p) {})); + + std::vector yS_np(np); + for (int i = 0; i < np; i++) { + auto capsule = py::capsule(yS + i, [](void* p) {}); + yS_np[i] = np_array(state_vector_shape, N_VGetArrayPointer(yS[i]), capsule); + } + + std::vector ypS_np(np); + for (int i = 0; i < np; i++) { + auto capsule = py::capsule(ypS + i, [](void* p) {}); + ypS_np[i] = np_array(state_vector_shape, N_VGetArrayPointer(ypS[i]), capsule); + } + + std::vector resvalS_np(np); + for (int i = 0; i < np; i++) { + auto capsule = py::capsule(resvalS + i, [](void* p) {}); + resvalS_np[i] = np_array(state_vector_shape, + N_VGetArrayPointer(resvalS[i]), capsule); + } + + realtype *ptr1 = static_cast(resvalS_np[0].request().ptr); + const realtype* resvalSval = N_VGetArrayPointer(resvalS[0]); + + python_functions.sensitivities(resvalS_np, t, y_np, yp_np, yS_np, ypS_np); + + return 0; +} + +/* main program */ +Solution solve_python(np_array t_np, np_array y0_np, np_array yp0_np, + residual_type res, jacobian_type jac, + sensitivities_type sens, + jac_get_type gjd, jac_get_type gjrv, jac_get_type gjcp, + int nnz, event_type event, + int number_of_events, int use_jacobian, np_array rhs_alg_id, + np_array atol_np, double rel_tol, np_array inputs, + int number_of_parameters) +{ + auto t = t_np.unchecked<1>(); + auto y0 = y0_np.unchecked<1>(); + auto yp0 = yp0_np.unchecked<1>(); + auto atol = atol_np.unchecked<1>(); + + int number_of_states = y0_np.request().size; + int number_of_timesteps = t_np.request().size; + void *ida_mem; // pointer to memory + N_Vector yy, yp, avtol; // y, y', and absolute tolerance + N_Vector *yyS, *ypS; // y, y' for sensitivities + N_Vector id; + realtype rtol, *yval, *ypval, *atval; + std::vector ySval(number_of_parameters); + int retval; + SUNMatrix J; + SUNLinearSolver LS; + +#if SUNDIALS_VERSION_MAJOR >= 6 + SUNContext sunctx; + SUNContext_Create(NULL, &sunctx); + + // allocate memory for solver + ida_mem = IDACreate(sunctx); + + // allocate vectors + yy = N_VNew_Serial(number_of_states, sunctx); + yp = N_VNew_Serial(number_of_states, sunctx); + avtol = N_VNew_Serial(number_of_states, sunctx); + id = N_VNew_Serial(number_of_states, sunctx); +#else + // allocate memory for solver + ida_mem = IDACreate(); + + // allocate vectors + yy = N_VNew_Serial(number_of_states); + yp = N_VNew_Serial(number_of_states); + avtol = N_VNew_Serial(number_of_states); + id = N_VNew_Serial(number_of_states); +#endif + + if (number_of_parameters > 0) { + yyS = N_VCloneVectorArray(number_of_parameters, yy); + ypS = N_VCloneVectorArray(number_of_parameters, yp); + } + + // set initial value + yval = N_VGetArrayPointer(yy); + ypval = N_VGetArrayPointer(yp); + atval = N_VGetArrayPointer(avtol); + int i; + for (i = 0; i < number_of_states; i++) + { + yval[i] = y0[i]; + ypval[i] = yp0[i]; + atval[i] = atol[i]; + } + + for (int is = 0 ; is < number_of_parameters; is++) { + ySval[is] = N_VGetArrayPointer(yyS[is]); + N_VConst(RCONST(0.0), yyS[is]); + N_VConst(RCONST(0.0), ypS[is]); + } + + // initialise solver + realtype t0 = RCONST(t(0)); + IDAInit(ida_mem, residual, t0, yy, yp); + + // set tolerances + rtol = RCONST(rel_tol); + + IDASVtolerances(ida_mem, rtol, avtol); + + // set events + IDARootInit(ida_mem, number_of_events, events); + + // set pybamm functions by passing pointer to it + PybammFunctions pybamm_functions(res, jac, sens, gjd, gjrv, gjcp, event, + number_of_states, number_of_events, + number_of_parameters, inputs); + void *user_data = &pybamm_functions; + IDASetUserData(ida_mem, user_data); + + // set linear solver +#if SUNDIALS_VERSION_MAJOR >= 6 + J = SUNSparseMatrix(number_of_states, number_of_states, nnz, CSR_MAT, sunctx); + LS = SUNLinSol_KLU(yy, J, sunctx); +#else + J = SUNSparseMatrix(number_of_states, number_of_states, nnz, CSR_MAT); + LS = SUNLinSol_KLU(yy, J); +#endif + + IDASetLinearSolver(ida_mem, LS, J); + + if (use_jacobian == 1) + { + IDASetJacFn(ida_mem, jacobian); + } + + if (number_of_parameters > 0) + { + IDASensInit(ida_mem, number_of_parameters, + IDA_SIMULTANEOUS, sensitivities, yyS, ypS); + IDASensEEtolerances(ida_mem); + } + + int t_i = 1; + realtype tret; + realtype t_next; + realtype t_final = t(number_of_timesteps - 1); + + // set return vectors + std::vector t_return(number_of_timesteps); + std::vector y_return(number_of_timesteps * number_of_states); + std::vector yS_return(number_of_parameters * number_of_timesteps * number_of_states); + + t_return[0] = t(0); + for (int j = 0; j < number_of_states; j++) + { + y_return[j] = yval[j]; + } + for (int j = 0; j < number_of_parameters; j++) { + const int base_index = j * number_of_timesteps * number_of_states; + for (int k = 0; k < number_of_states; k++) { + yS_return[base_index + k] = ySval[j][k]; + } + } + + // calculate consistent initial conditions + auto id_np_val = rhs_alg_id.unchecked<1>(); + realtype *id_val; + id_val = N_VGetArrayPointer(id); + + int ii; + for (ii = 0; ii < number_of_states; ii++) + { + id_val[ii] = id_np_val[ii]; + } + + IDASetId(ida_mem, id); + IDACalcIC(ida_mem, IDA_YA_YDP_INIT, t(1)); + + while (true) + { + t_next = t(t_i); + IDASetStopTime(ida_mem, t_next); + retval = IDASolve(ida_mem, t_final, &tret, yy, yp, IDA_NORMAL); + + if (retval == IDA_TSTOP_RETURN || retval == IDA_SUCCESS || retval == IDA_ROOT_RETURN) + { + if (number_of_parameters > 0) { + IDAGetSens(ida_mem, &tret, yyS); + } + + t_return[t_i] = tret; + for (int j = 0; j < number_of_states; j++) + { + y_return[t_i * number_of_states + j] = yval[j]; + } + for (int j = 0; j < number_of_parameters; j++) { + const int base_index = j * number_of_timesteps * number_of_states + + t_i * number_of_states; + for (int k = 0; k < number_of_states; k++) { + yS_return[base_index + k] = ySval[j][k]; + } + } + t_i += 1; + if (retval == IDA_SUCCESS || retval == IDA_ROOT_RETURN) { + break; + } + + } + } + + /* Free memory */ + if (number_of_parameters > 0) { + IDASensFree(ida_mem); + } + IDAFree(&ida_mem); + SUNLinSolFree(LS); + SUNMatDestroy(J); + N_VDestroy(avtol); + N_VDestroy(yp); + if (number_of_parameters > 0) { + N_VDestroyVectorArray(yyS, number_of_parameters); + N_VDestroyVectorArray(ypS, number_of_parameters); + } +#if SUNDIALS_VERSION_MAJOR >= 6 + SUNContext_Free(&sunctx); +#endif + + np_array t_ret = np_array(t_i, &t_return[0]); + np_array y_ret = np_array(t_i * number_of_states, &y_return[0]); + np_array yS_ret = np_array( + std::vector{number_of_parameters, number_of_timesteps, number_of_states}, + &yS_return[0] + ); + + Solution sol(retval, t_ret, y_ret, yS_ret); + + return sol; +} + diff --git a/pybamm/solvers/c_solvers/idaklu.hpp b/pybamm/solvers/c_solvers/idaklu/python.hpp similarity index 97% rename from pybamm/solvers/c_solvers/idaklu.hpp rename to pybamm/solvers/c_solvers/idaklu/python.hpp index a39f149252..8c29bbc496 100644 --- a/pybamm/solvers/c_solvers/idaklu.hpp +++ b/pybamm/solvers/c_solvers/idaklu/python.hpp @@ -1,7 +1,7 @@ #ifndef PYBAMM_IDAKLU_HPP #define PYBAMM_IDAKLU_HPP -#include "idaklu_python.hpp" +#include "common.hpp" #include "solution.hpp" #include diff --git a/pybamm/solvers/c_solvers/solution.cpp b/pybamm/solvers/c_solvers/idaklu/solution.cpp similarity index 100% rename from pybamm/solvers/c_solvers/solution.cpp rename to pybamm/solvers/c_solvers/idaklu/solution.cpp diff --git a/pybamm/solvers/c_solvers/solution.hpp b/pybamm/solvers/c_solvers/idaklu/solution.hpp similarity index 62% rename from pybamm/solvers/c_solvers/solution.hpp rename to pybamm/solvers/c_solvers/idaklu/solution.hpp index 08192f3c49..047ae6ef8e 100644 --- a/pybamm/solvers/c_solvers/solution.hpp +++ b/pybamm/solvers/c_solvers/idaklu/solution.hpp @@ -1,7 +1,7 @@ -#ifndef PYBAMM_SOLUTION_HPP -#define PYBAMM_SOLUTION_HPP +#ifndef PYBAMM_IDAKLU_SOLUTION_HPP +#define PYBAMM_IDAKLU_SOLUTION_HPP -#include "idaklu_python.hpp" +#include "common.hpp" class Solution { @@ -17,4 +17,4 @@ class Solution np_array yS; }; -#endif // PYBAMM_SOLUTION_HPP +#endif // PYBAMM_IDAKLU_COMMON_HPP diff --git a/pybamm/solvers/c_solvers/idaklu_casadi.cpp b/pybamm/solvers/c_solvers/idaklu_casadi.cpp deleted file mode 100644 index f611c6ebc3..0000000000 --- a/pybamm/solvers/c_solvers/idaklu_casadi.cpp +++ /dev/null @@ -1,582 +0,0 @@ -#include "idaklu_casadi.hpp" -#include "idaklu_python.hpp" - -#include - -#include -using casadi::casadi_axpy; -using Sparsity = casadi::Sparsity; - -class CasadiFunction { -public: - explicit CasadiFunction(const Function &f):m_func(f) { - size_t sz_arg; - size_t sz_res; - size_t sz_iw; - size_t sz_w; - m_func.sz_work(sz_arg, sz_res, sz_iw, sz_w); - m_arg.resize(sz_arg); - m_res.resize(sz_res); - m_iw.resize(sz_iw); - m_w.resize(sz_w); - } - - // only call this once m_arg and m_res have been set appropriatelly - void operator()() { - int mem = m_func.checkout(); - m_func(m_arg.data(), m_res.data(), m_iw.data(), m_w.data(), mem); - m_func.release(mem); - } - -public: - std::vector m_arg; - std::vector m_res; - -private: - const Function &m_func; - std::vector m_iw; - std::vector m_w; -}; - - -class CasadiFunctions { -public: - int number_of_states; - int number_of_parameters; - int number_of_events; - int number_of_nnz; - CasadiFunction rhs_alg; - CasadiFunction sens; - CasadiFunction jac_times_cjmass; - const np_array_int &jac_times_cjmass_rowvals; - const np_array_int &jac_times_cjmass_colptrs; - const np_array_dense &inputs; - CasadiFunction jac_action; - CasadiFunction mass_action; - CasadiFunction events; - - CasadiFunctions(const Function &rhs_alg, - const Function &jac_times_cjmass, - const int jac_times_cjmass_nnz, - const np_array_int &jac_times_cjmass_rowvals, - const np_array_int &jac_times_cjmass_colptrs, - const np_array_dense &inputs, - const Function &jac_action, - const Function &mass_action, - const Function &sens, - const Function &events, - const int n_s, int n_e, const int n_p) - : number_of_states(n_s), number_of_events(n_e), - number_of_parameters(n_p), - number_of_nnz(jac_times_cjmass_nnz), - rhs_alg(rhs_alg), - jac_times_cjmass(jac_times_cjmass), - jac_times_cjmass_rowvals(jac_times_cjmass_rowvals), - jac_times_cjmass_colptrs(jac_times_cjmass_colptrs), - inputs(inputs), - jac_action(jac_action), - mass_action(mass_action), - sens(sens), - events(events), - tmp(number_of_states) - {} - - realtype *get_tmp() { - return tmp.data(); - } - -private: - std::vector tmp; -}; - -int residual_casadi(realtype tres, N_Vector yy, N_Vector yp, N_Vector rr, - void *user_data) -{ - CasadiFunctions *p_python_functions = - static_cast(user_data); - - py::buffer_info input_buf = p_python_functions->inputs.request(); - p_python_functions->rhs_alg.m_arg[0] = &tres; - p_python_functions->rhs_alg.m_arg[1] = NV_DATA_S(yy); - p_python_functions->rhs_alg.m_arg[2] = static_cast(input_buf.ptr); - p_python_functions->rhs_alg.m_res[0] = NV_DATA_S(rr); - p_python_functions->rhs_alg(); - - realtype *tmp = p_python_functions->get_tmp(); - - // args is yp, put result in tmp - p_python_functions->mass_action.m_arg[0] = NV_DATA_S(yp); - p_python_functions->mass_action.m_res[0] = tmp; - p_python_functions->mass_action(); - - // AXPY: y <- a*x + y - const int ns = p_python_functions->number_of_states; - casadi_axpy(ns, -1., tmp, NV_DATA_S(rr)); - - // now rr has rhs_alg(t, y) - mass_matrix * yp - return 0; -} - -// Purpose This function computes the product Jv of the DAE system Jacobian J -// (or an approximation to it) and a given vector v, where J is defined by Eq. (2.6). -// J = ∂F/∂y + cj ∂F/∂y˙ -// Arguments tt is the current value of the independent variable. -// yy is the current value of the dependent variable vector, y(t). -// yp is the current value of ˙y(t). -// rr is the current value of the residual vector F(t, y, y˙). -// v is the vector by which the Jacobian must be multiplied to the right. -// Jv is the computed output vector. -// cj is the scalar in the system Jacobian, proportional to the inverse of the step -// size (α in Eq. (2.6) ). -// user data is a pointer to user data, the same as the user data parameter passed to -// IDASetUserData. -// tmp1 -// tmp2 are pointers to memory allocated for variables of type N Vector which can -// be used by IDALsJacTimesVecFn as temporary storage or work space. -int jtimes_casadi(realtype tt, N_Vector yy, N_Vector yp, N_Vector rr, - N_Vector v, N_Vector Jv, realtype cj, void *user_data, - N_Vector tmp1, N_Vector tmp2) { - CasadiFunctions *p_python_functions = - static_cast(user_data); - - // rr has ∂F/∂y v - py::buffer_info input_buf = p_python_functions->inputs.request(); - p_python_functions->jac_action.m_arg[0] = &tt; - p_python_functions->jac_action.m_arg[1] = NV_DATA_S(yy); - p_python_functions->jac_action.m_arg[2] = static_cast(input_buf.ptr); - p_python_functions->jac_action.m_arg[3] = NV_DATA_S(v); - p_python_functions->jac_action.m_res[0] = NV_DATA_S(rr); - p_python_functions->jac_action(); - - // tmp has -∂F/∂y˙ v - realtype *tmp = p_python_functions->get_tmp(); - p_python_functions->mass_action.m_arg[0] = NV_DATA_S(v); - p_python_functions->mass_action.m_res[0] = tmp; - p_python_functions->mass_action(); - - // AXPY: y <- a*x + y - // rr has ∂F/∂y v + cj ∂F/∂y˙ v - const int ns = p_python_functions->number_of_states; - casadi_axpy(ns, -cj, tmp, NV_DATA_S(rr)); - - return 0; -} - - -// Arguments tt is the current value of the independent variable t. -// cj is the scalar in the system Jacobian, proportional to the inverse of the step -// size (α in Eq. (2.6) ). -// yy is the current value of the dependent variable vector, y(t). -// yp is the current value of ˙y(t). -// rr is the current value of the residual vector F(t, y, y˙). -// Jac is the output (approximate) Jacobian matrix (of type SUNMatrix), J = -// ∂F/∂y + cj ∂F/∂y˙. -// user data is a pointer to user data, the same as the user data parameter passed to -// IDASetUserData. -// tmp1 -// tmp2 -// tmp3 are pointers to memory allocated for variables of type N Vector which can -// be used by IDALsJacFn function as temporary storage or work space. -int jacobian_casadi(realtype tt, realtype cj, N_Vector yy, N_Vector yp, - N_Vector resvec, SUNMatrix JJ, void *user_data, N_Vector tempv1, - N_Vector tempv2, N_Vector tempv3) { - - CasadiFunctions *p_python_functions = - static_cast(user_data); - - // create pointer to jac data, column pointers, and row values - sunindextype *jac_colptrs = SUNSparseMatrix_IndexPointers(JJ); - sunindextype *jac_rowvals = SUNSparseMatrix_IndexValues(JJ); - realtype *jac_data = SUNSparseMatrix_Data(JJ); - - // args are t, y, cj, put result in jacobian data matrix - py::buffer_info input_buf = p_python_functions->inputs.request(); - p_python_functions->jac_times_cjmass.m_arg[0] = &tt; - p_python_functions->jac_times_cjmass.m_arg[1] = NV_DATA_S(yy); - p_python_functions->jac_times_cjmass.m_arg[2] = static_cast(input_buf.ptr); - p_python_functions->jac_times_cjmass.m_arg[3] = &cj; - p_python_functions->jac_times_cjmass.m_res[0] = jac_data; - p_python_functions->jac_times_cjmass(); - - // row vals and col ptrs - const np_array &jac_times_cjmass_rowvals = p_python_functions->jac_times_cjmass_rowvals; - const int n_row_vals = jac_times_cjmass_rowvals.request().size; - auto p_jac_times_cjmass_rowvals = jac_times_cjmass_rowvals.unchecked<1>(); - - // just copy across row vals (do I need to do this every time?) - // (or just in the setup?) - for (int i = 0; i < n_row_vals; i++) { - jac_rowvals[i] = p_jac_times_cjmass_rowvals[i]; - } - - const np_array &jac_times_cjmass_colptrs = p_python_functions->jac_times_cjmass_colptrs; - const int n_col_ptrs = jac_times_cjmass_colptrs.request().size; - auto p_jac_times_cjmass_colptrs = jac_times_cjmass_colptrs.unchecked<1>(); - - // just copy across col ptrs (do I need to do this every time?) - for (int i = 0; i < n_col_ptrs; i++) { - jac_colptrs[i] = p_jac_times_cjmass_colptrs[i]; - } - - return (0); -} - -int events_casadi(realtype t, N_Vector yy, N_Vector yp, realtype *events_ptr, - void *user_data) -{ - CasadiFunctions *p_python_functions = - static_cast(user_data); - - // args are t, y, put result in events_ptr - py::buffer_info input_buf = p_python_functions->inputs.request(); - p_python_functions->events.m_arg[0] = &t; - p_python_functions->events.m_arg[1] = NV_DATA_S(yy); - p_python_functions->events.m_arg[2] = static_cast(input_buf.ptr); - p_python_functions->events.m_res[0] = events_ptr; - p_python_functions->events(); - - return (0); -} - -// This function computes the sensitivity residual for all sensitivity -// equations. It must compute the vectors -// (∂F/∂y)s i (t)+(∂F/∂ ẏ) ṡ i (t)+(∂F/∂p i ) and store them in resvalS[i]. -// Ns is the number of sensitivities. -// t is the current value of the independent variable. -// yy is the current value of the state vector, y(t). -// yp is the current value of ẏ(t). -// resval contains the current value F of the original DAE residual. -// yS contains the current values of the sensitivities s i . -// ypS contains the current values of the sensitivity derivatives ṡ i . -// resvalS contains the output sensitivity residual vectors. -// Memory allocation for resvalS is handled within idas. -// user data is a pointer to user data. -// tmp1, tmp2, tmp3 are N Vectors of length N which can be used as -// temporary storage. -// -// Return value An IDASensResFn should return 0 if successful, -// a positive value if a recoverable error -// occurred (in which case idas will attempt to correct), -// or a negative value if it failed unrecoverably (in which case the integration is halted and IDA SRES FAIL is returned) -// -int sensitivities_casadi(int Ns, realtype t, N_Vector yy, N_Vector yp, - N_Vector resval, N_Vector *yS, N_Vector *ypS, N_Vector *resvalS, - void *user_data, N_Vector tmp1, N_Vector tmp2, N_Vector tmp3) { - - CasadiFunctions *p_python_functions = - static_cast(user_data); - - const int np = p_python_functions->number_of_parameters; - - // args are t, y put result in rr - py::buffer_info input_buf = p_python_functions->inputs.request(); - p_python_functions->sens.m_arg[0] = &t; - p_python_functions->sens.m_arg[1] = NV_DATA_S(yy); - p_python_functions->sens.m_arg[2] = static_cast(input_buf.ptr); - for (int i = 0; i < np; i++) { - p_python_functions->sens.m_res[i] = NV_DATA_S(resvalS[i]); - } - // resvalsS now has (∂F/∂p i ) - p_python_functions->sens(); - - for (int i = 0; i < np; i++) { - // put (∂F/∂y)s i (t) in tmp - realtype *tmp = p_python_functions->get_tmp(); - p_python_functions->jac_action.m_arg[0] = &t; - p_python_functions->jac_action.m_arg[1] = NV_DATA_S(yy); - p_python_functions->jac_action.m_arg[2] = static_cast(input_buf.ptr); - p_python_functions->jac_action.m_arg[3] = NV_DATA_S(yS[i]); - p_python_functions->jac_action.m_res[0] = tmp; - p_python_functions->jac_action(); - - const int ns = p_python_functions->number_of_states; - casadi_axpy(ns, 1., tmp, NV_DATA_S(resvalS[i])); - - // put -(∂F/∂ ẏ) ṡ i (t) in tmp2 - p_python_functions->mass_action.m_arg[0] = NV_DATA_S(ypS[i]); - p_python_functions->mass_action.m_res[0] = tmp; - p_python_functions->mass_action(); - - // (∂F/∂y)s i (t)+(∂F/∂ ẏ) ṡ i (t)+(∂F/∂p i ) - // AXPY: y <- a*x + y - casadi_axpy(ns, -1., tmp, NV_DATA_S(resvalS[i])); - } - - return 0; -} - - - -/* main program */ - -Solution solve_casadi(np_array t_np, np_array y0_np, np_array yp0_np, - const Function &rhs_alg, - const Function &jac_times_cjmass, - const np_array_int &jac_times_cjmass_colptrs, - const np_array_int &jac_times_cjmass_rowvals, - const int jac_times_cjmass_nnz, - const Function &jac_action, - const Function &mass_action, - const Function &sens, - const Function &events, - const int number_of_events, - int use_jacobian, - np_array rhs_alg_id, - np_array atol_np, double rel_tol, - np_array_dense inputs, - int number_of_parameters) -{ - - auto t = t_np.unchecked<1>(); - auto y0 = y0_np.unchecked<1>(); - auto yp0 = yp0_np.unchecked<1>(); - auto atol = atol_np.unchecked<1>(); - - int number_of_states = y0_np.request().size; - int number_of_timesteps = t_np.request().size; - void *ida_mem; // pointer to memory - N_Vector yy, yp, avtol; // y, y', and absolute tolerance - N_Vector *yyS, *ypS; // y, y' for sensitivities - realtype rtol, *yval, *ypval, *atval; - std::vector ySval(number_of_parameters); - int retval; - SUNMatrix J; - SUNLinearSolver LS; - - // allocate vectors - yy = N_VNew_Serial(number_of_states); - yp = N_VNew_Serial(number_of_states); - avtol = N_VNew_Serial(number_of_states); - - if (number_of_parameters > 0) { - yyS = N_VCloneVectorArray(number_of_parameters, yy); - ypS = N_VCloneVectorArray(number_of_parameters, yp); - } - - // set initial value - yval = N_VGetArrayPointer(yy); - ypval = N_VGetArrayPointer(yp); - atval = N_VGetArrayPointer(avtol); - int i; - for (i = 0; i < number_of_states; i++) - { - yval[i] = y0[i]; - ypval[i] = yp0[i]; - atval[i] = atol[i]; - } - - for (int is = 0 ; is < number_of_parameters; is++) { - ySval[is] = N_VGetArrayPointer(yyS[is]); - N_VConst(RCONST(0.0), yyS[is]); - N_VConst(RCONST(0.0), ypS[is]); - } - - // allocate memory for solver - ida_mem = IDACreate(); - - // initialise solver - realtype t0 = RCONST(t(0)); - IDAInit(ida_mem, residual_casadi, t0, yy, yp); - - // set tolerances - rtol = RCONST(rel_tol); - - IDASVtolerances(ida_mem, rtol, avtol); - - // set events - IDARootInit(ida_mem, number_of_events, events_casadi); - - // set pybamm functions by passing pointer to it - CasadiFunctions pybamm_functions = CasadiFunctions( - rhs_alg, - jac_times_cjmass, - jac_times_cjmass_nnz, - jac_times_cjmass_rowvals, - jac_times_cjmass_colptrs, - inputs, - jac_action, mass_action, - sens, events, - number_of_states, number_of_events, - number_of_parameters); - - void *user_data = &pybamm_functions; - IDASetUserData(ida_mem, user_data); - - // set linear solver - if (use_jacobian == 1) { - J = SUNSparseMatrix(number_of_states, number_of_states, jac_times_cjmass_nnz, CSC_MAT); - LS = SUNLinSol_KLU(yy, J); - } else { - J = SUNDenseMatrix(number_of_states, number_of_states); - LS = SUNLinSol_Dense(yy, J); - } - - IDASetLinearSolver(ida_mem, LS, J); - - if (use_jacobian == 1) { - IDASetJacFn(ida_mem, jacobian_casadi); - } - - - if (number_of_parameters > 0) { - IDASensInit(ida_mem, number_of_parameters, - IDA_SIMULTANEOUS, sensitivities_casadi, yyS, ypS); - IDASensEEtolerances(ida_mem); - } - - SUNLinSolInitialize(LS); - - int t_i = 1; - realtype tret; - realtype t_next; - realtype t_final = t(number_of_timesteps - 1); - - // set return vectors - realtype* t_return = new realtype[number_of_timesteps]; - realtype* y_return = new realtype[number_of_timesteps * number_of_states]; - realtype* yS_return = new realtype[number_of_parameters * number_of_timesteps * number_of_states]; - - py::capsule free_t_when_done(t_return, [](void *f) { - realtype *vect = reinterpret_cast(f); - delete[] vect; - }); - py::capsule free_y_when_done(y_return, [](void *f) { - realtype *vect = reinterpret_cast(f); - delete[] vect; - }); - py::capsule free_yS_when_done(yS_return, [](void *f) { - realtype *vect = reinterpret_cast(f); - delete[] vect; - }); - - t_return[0] = t(0); - for (int j = 0; j < number_of_states; j++) - { - y_return[j] = yval[j]; - } - for (int j = 0; j < number_of_parameters; j++) { - const int base_index = j * number_of_timesteps * number_of_states; - for (int k = 0; k < number_of_states; k++) { - yS_return[base_index + k] = ySval[j][k]; - } - } - - // calculate consistent initial conditions - N_Vector id; - auto id_np_val = rhs_alg_id.unchecked<1>(); - id = N_VNew_Serial(number_of_states); - realtype *id_val; - id_val = N_VGetArrayPointer(id); - - int ii; - for (ii = 0; ii < number_of_states; ii++) - { - id_val[ii] = id_np_val[ii]; - } - - IDASetId(ida_mem, id); - IDACalcIC(ida_mem, IDA_YA_YDP_INIT, t(1)); - - while (true) - { - t_next = t(t_i); - IDASetStopTime(ida_mem, t_next); - retval = IDASolve(ida_mem, t_final, &tret, yy, yp, IDA_NORMAL); - - if (retval == IDA_TSTOP_RETURN || retval == IDA_SUCCESS || retval == IDA_ROOT_RETURN) { - if (number_of_parameters > 0) { - IDAGetSens(ida_mem, &tret, yyS); - } - - t_return[t_i] = tret; - for (int j = 0; j < number_of_states; j++) - { - y_return[t_i * number_of_states + j] = yval[j]; - } - for (int j = 0; j < number_of_parameters; j++) { - const int base_index = j * number_of_timesteps * number_of_states - + t_i * number_of_states; - for (int k = 0; k < number_of_states; k++) { - yS_return[base_index + k] = ySval[j][k]; - } - } - t_i += 1; - if (retval == IDA_SUCCESS || retval == IDA_ROOT_RETURN) { - break; - } - } else { - // failed - break; - } - } - - np_array t_ret = np_array(t_i, &t_return[0], free_t_when_done); - np_array y_ret = np_array(t_i * number_of_states, &y_return[0], free_y_when_done); - np_array yS_ret = np_array( - std::vector{number_of_parameters, number_of_timesteps, number_of_states}, - &yS_return[0], free_yS_when_done - ); - - Solution sol(retval, t_ret, y_ret, yS_ret); - - // TODO config input to choose stuff like this - const bool print_stats = false; - if (print_stats) { - long nsteps, nrevals, nlinsetups, netfails; - int klast, kcur; - realtype hinused, hlast, hcur, tcur; - - IDAGetIntegratorStats(ida_mem, - &nsteps, - &nrevals, - &nlinsetups, - &netfails, - &klast, - &kcur, - &hinused, - &hlast, - &hcur, - &tcur - ); - - long nniters, nncfails; - IDAGetNonlinSolvStats(ida_mem, &nniters, &nncfails); - - std::cout << "Solver Stats: \n" - << " Number of steps = " << nsteps << "\n" - << " Number of calls to residual function = " << nrevals << "\n" - << " Number of linear solver setup calls = " << nlinsetups << "\n" - << " Number of error test failures = " << netfails << "\n" - << " Method order used on last step = " << klast << "\n" - << " Method order used on next step = " << kcur << "\n" - << " Initial step size = " << hinused << "\n" - << " Step size on last step = " << hlast << "\n" - << " Step size on next step = " << hcur << "\n" - << " Current internal time reached = " << tcur << "\n" - << " Number of nonlinear iterations performed = " << nniters << "\n" - << " Number of nonlinear convergence failures = " << nncfails << "\n" - << std::endl; - } - - - - /* Free memory */ - if (number_of_parameters > 0) { - IDASensFree(ida_mem); - } - SUNLinSolFree(LS); - SUNMatDestroy(J); - N_VDestroy(avtol); - N_VDestroy(yy); - N_VDestroy(yp); - N_VDestroy(id); - if (number_of_parameters > 0) { - N_VDestroyVectorArray(yyS, number_of_parameters); - N_VDestroyVectorArray(ypS, number_of_parameters); - } - - IDAFree(&ida_mem); - - return sol; -} - diff --git a/pybamm/solvers/c_solvers/idaklu_casadi.hpp b/pybamm/solvers/c_solvers/idaklu_casadi.hpp deleted file mode 100644 index 6aa655392b..0000000000 --- a/pybamm/solvers/c_solvers/idaklu_casadi.hpp +++ /dev/null @@ -1,32 +0,0 @@ - -#ifndef PYBAMM_IDAKLU_CASADI_HPP -#define PYBAMM_IDAKLU_CASADI_HPP - -#include - -using Function = casadi::Function; - -#include "solution.hpp" - -Solution solve_casadi(np_array t_np, np_array y0_np, np_array yp0_np, - const Function &rhs_alg, - const Function &jac_times_cjmass, - const np_array_int &jac_times_cjmass_colptrs, - const np_array_int &jac_times_cjmass_rowvals, - const int jac_times_cjmass_nnz, - const Function &jac_action, - const Function &mass_action, - const Function &sens, - const Function &event, - const int number_of_events, - int use_jacobian, - np_array rhs_alg_id, - np_array atol_np, - double rel_tol, - np_array_dense inputs, - int number_of_parameters); - - - - -#endif // PYBAMM_IDAKLU_CASADI_HPP diff --git a/pybamm/solvers/c_solvers/idaklu_python.cpp b/pybamm/solvers/c_solvers/idaklu_python.cpp deleted file mode 100644 index 6e8446ace7..0000000000 --- a/pybamm/solvers/c_solvers/idaklu_python.cpp +++ /dev/null @@ -1,68 +0,0 @@ - -#include "idaklu.hpp" - -#include "idaklu_casadi.hpp" -#include "idaklu_python.hpp" - -#include -#include -#include -#include - -#include - -Function generate_function(const std::string& data) { - return Function::deserialize(data); -} - -namespace py = pybind11; - -PYBIND11_MAKE_OPAQUE(std::vector); - -PYBIND11_MODULE(idaklu, m) -{ - m.doc() = "sundials solvers"; // optional module docstring - - py::bind_vector>(m, "VectorNdArray"); - - m.def("solve_python", &solve_python, "The solve function for python evaluators", - py::arg("t"), py::arg("y0"), - py::arg("yp0"), py::arg("res"), py::arg("jac"), py::arg("sens"), - py::arg("get_jac_data"), - py::arg("get_jac_row_vals"), py::arg("get_jac_col_ptr"), py::arg("nnz"), - py::arg("events"), py::arg("number_of_events"), py::arg("use_jacobian"), - py::arg("rhs_alg_id"), py::arg("atol"), py::arg("rtol"), py::arg("inputs"), - py::arg("number_of_sensitivity_parameters"), - py::return_value_policy::take_ownership); - - m.def("solve_casadi", &solve_casadi, "The solve function for casadi evaluators", - py::arg("t"), py::arg("y0"), py::arg("yp0"), - py::arg("rhs_alg"), - py::arg("jac_times_cjmass"), - py::arg("jac_times_cjmass_colptrs"), - py::arg("jac_times_cjmass_rowvals"), - py::arg("jac_times_cjmass_nnz"), - py::arg("jac_action"), - py::arg("mass_action"), - py::arg("sens"), - py::arg("events"), py::arg("number_of_events"), - py::arg("use_jacobian"), - py::arg("rhs_alg_id"), - py::arg("atol"), py::arg("rtol"), py::arg("inputs"), - py::arg("number_of_sensitivity_parameters"), - py::return_value_policy::take_ownership); - - m.def("generate_function", &generate_function, "Generate a casadi function", - py::arg("string"), - py::return_value_policy::take_ownership); - - py::class_(m, "Function"); - - py::class_(m, "solution") - .def_readwrite("t", &Solution::t) - .def_readwrite("y", &Solution::y) - .def_readwrite("yS", &Solution::yS) - .def_readwrite("flag", &Solution::flag); -} - - diff --git a/pybamm/solvers/c_solvers/idaklu_python.hpp b/pybamm/solvers/c_solvers/idaklu_python.hpp deleted file mode 100644 index e7583da371..0000000000 --- a/pybamm/solvers/c_solvers/idaklu_python.hpp +++ /dev/null @@ -1,21 +0,0 @@ -#ifndef PYBAMM_IDAKLU_PYTHON_HPP -#define PYBAMM_IDAKLU_PYTHON_HPP - -#include /* prototypes for IDAS fcts., consts. */ -#include /* access to serial N_Vector */ -#include /* defs. of SUNRabs, SUNRexp, etc. */ -#include /* defs. of realtype, sunindextype */ -#include /* access to KLU linear solver */ -#include /* access to dense linear solver */ -#include /* access to sparse SUNMatrix */ -#include /* access to dense SUNMatrix */ - -#include - -namespace py = pybind11; -using np_array = py::array_t; -using np_array_dense = py::array_t; -using np_array_int = py::array_t; - - -#endif // PYBAMM_IDAKLU_PYTHON_HPP diff --git a/pybamm/solvers/casadi_algebraic_solver.py b/pybamm/solvers/casadi_algebraic_solver.py index b13c08c27f..f726adc5da 100644 --- a/pybamm/solvers/casadi_algebraic_solver.py +++ b/pybamm/solvers/casadi_algebraic_solver.py @@ -87,35 +87,6 @@ def _integrate(self, model, t_eval, inputs_dict=None): alg = model.casadi_algebraic(t_sym, y_sym, inputs) - # Check interpolant extrapolation - if model.interpolant_extrapolation_events_eval: - extrap_event = [ - event(0, y0, inputs) - for event in model.interpolant_extrapolation_events_eval - ] - if extrap_event: - if (np.concatenate(extrap_event) < self.extrap_tol).any(): - extrap_event_names = [] - for event in model.events: - if ( - event.event_type - == pybamm.EventType.INTERPOLANT_EXTRAPOLATION - and ( - event.expression.evaluate( - 0, y0.full(), inputs=inputs_dict - ) - < self.extrap_tol - ) - ): - extrap_event_names.append(event.name[12:]) - - raise pybamm.SolverError( - "CasADi solver failed because the following interpolation " - "bounds were exceeded at the initial conditions: {}. " - "You may need to provide additional interpolation points " - "outside these bounds.".format(extrap_event_names) - ) - # Set constraints vector in the casadi format # Constrain the unknowns. 0 (default): no constraint on ui, 1: ui >= 0.0, # -1: ui <= 0.0, 2: ui > 0.0, -2: ui < 0.0. diff --git a/pybamm/solvers/casadi_solver.py b/pybamm/solvers/casadi_solver.py index 7b4ef000bf..eea853eeda 100644 --- a/pybamm/solvers/casadi_solver.py +++ b/pybamm/solvers/casadi_solver.py @@ -81,7 +81,7 @@ def __init__( root_tol=1e-6, max_step_decrease_count=5, dt_max=None, - extrap_tol=0, + extrap_tol=None, extra_options_setup=None, extra_options_call=None, return_solution_if_failed_early=False, @@ -108,9 +108,10 @@ def __init__( self.extra_options_setup = extra_options_setup or {} self.extra_options_call = extra_options_call or {} - self.extrap_tol = extrap_tol self.return_solution_if_failed_early = return_solution_if_failed_early + self._on_extrapolation = "error" + # Decide whether to perturb algebraic initial conditions, True by default for # "safe" mode, False by default for other modes if perturb_algebraic_initial_conditions is None: @@ -419,7 +420,6 @@ def integer_bisect(): # Return the existing solution if no events have been triggered if event_idx_lower is None: # Flag "final time" for termination - self.check_interpolant_extrapolation(model, coarse_solution) coarse_solution.termination = "final time" return coarse_solution @@ -479,46 +479,11 @@ def integer_bisect(): solution.integration_time = ( coarse_solution.integration_time + dense_step_sol.integration_time ) - self.check_interpolant_extrapolation(model, solution) solution.closest_event_idx = closest_event_idx return solution - def check_interpolant_extrapolation(self, model, solution): - # Check for interpolant extrapolations - if model.interpolant_extrapolation_events_eval: - inputs = casadi.vertcat(*[x for x in solution.all_inputs[-1].values()]) - extrap_event = [ - event(solution.t[-1], solution.y[:, -1], inputs) - for event in model.interpolant_extrapolation_events_eval - ] - - if extrap_event: - if (np.concatenate(extrap_event) < self.extrap_tol).any(): - extrap_event_names = [] - for event in model.events: - if ( - event.event_type - == pybamm.EventType.INTERPOLANT_EXTRAPOLATION - and ( - event.expression.evaluate( - solution.t[-1], - solution.y[:, -1].full(), - inputs=inputs, - ) - < self.extrap_tol - ).any() - ): - extrap_event_names.append(event.name[12:]) - - raise pybamm.SolverError( - "CasADi solver failed because the following " - "interpolation bounds were exceeded: {}. You may need " - "to provide additional interpolation points outside " - "these bounds.".format(extrap_event_names) - ) - def create_integrator(self, model, inputs, t_eval=None, use_event_switch=False): """ Method to create a casadi integrator object. diff --git a/pybamm/solvers/idaklu_solver.py b/pybamm/solvers/idaklu_solver.py index 94316e049a..8a7671f084 100644 --- a/pybamm/solvers/idaklu_solver.py +++ b/pybamm/solvers/idaklu_solver.py @@ -43,6 +43,41 @@ class IDAKLUSolver(pybamm.BaseSolver): The tolerance for the initial-condition solver (default is 1e-6). extrap_tol : float, optional The tolerance to assert whether extrapolation occurs or not (default is 0). + options: dict, optional + Addititional options to pass to the solver, by default: + + .. code-block:: python + + options = { + # print statistics of the solver after every solve + "print_stats": False, + + # jacobian form, can be "none", "dense", "sparse", "matrix-free" + "jacobian": "sparse", + + # name of sundials linear solver to use options are: "SUNLinSol_KLU", + # "SUNLinSol_Dense", "SUNLinSol_SPBCGS", + # "SUNLinSol_SPFGMR", "SUNLinSol_SPGMR", "SUNLinSol_SPTFQMR", + "linear_solver": "SUNLinSol_KLU", + + # preconditioner for iterative solvers, can be "none", "BBDP" + "preconditioner": "BBDP", + + # for iterative linear solvers, max number of iterations + "linsol_max_iterations": 5, + + # for iterative linear solver preconditioner, bandwidth of + # approximate jacobian + "precon_half_bandwidth": 5, + + # for iterative linear solver preconditioner, bandwidth of + # approximate jacobian that is kept + "precon_half_bandwidth_keep": 5 + } + + Note: These options only have an effect if model.convert_to_format == 'casadi' + + """ def __init__( @@ -51,9 +86,29 @@ def __init__( atol=1e-6, root_method="casadi", root_tol=1e-6, - extrap_tol=0, + extrap_tol=None, + options=None, ): + # set default options, + # (only if user does not supply) + default_options = { + "print_stats": False, + "jacobian": "sparse", + "linear_solver": "SUNLinSol_KLU", + "preconditioner": "BBDP", + "linsol_max_iterations": 5, + "precon_half_bandwidth": 5, + "precon_half_bandwidth_keep": 5, + } + if options is None: + options = default_options + else: + for key, value in default_options.items(): + if key not in options: + options[key] = value + self._options = options + if idaklu_spec is None: # pragma: no cover raise ImportError("KLU is not installed") @@ -177,7 +232,10 @@ def inputs_to_dict(inputs): if model.convert_to_format == "jax": mass_matrix = model.mass_matrix.entries.toarray() elif model.convert_to_format == "casadi": - mass_matrix = casadi.DM(model.mass_matrix.entries) + if self._options["jacobian"] == "dense": + mass_matrix = casadi.DM(model.mass_matrix.entries.toarray()) + else: + mass_matrix = casadi.DM(model.mass_matrix.entries) else: mass_matrix = model.mass_matrix.entries @@ -195,7 +253,6 @@ def resfn(t, y, inputs, ydot): if not model.use_jacobian: raise pybamm.SolverError("KLU requires the Jacobian") - use_jac = 1 # need to provide jacobian_rhs_alg - cj * mass_matrix if model.convert_to_format == "casadi": @@ -372,6 +429,14 @@ def sensfn(resvalS, t, y, inputs, yp, yS, ypS): for i, dFdp_i in enumerate(dFdp.values()): resvalS[i][:] = dFdy @ yS[i] - dFdyd @ ypS[i] + dFdp_i + try: + atol = model.atol + except AttributeError: + atol = self.atol + + rtol = self.rtol + atol = self._check_atol_type(atol, y0.size) + if model.convert_to_format == "casadi": rhs_algebraic = idaklu.generate_function(rhs_algebraic.serialize()) jac_times_cjmass = idaklu.generate_function(jac_times_cjmass.serialize()) @@ -381,6 +446,7 @@ def sensfn(resvalS, t, y, inputs, yp, yS, ypS): rootfn = idaklu.generate_function(rootfn.serialize()) mass_action = idaklu.generate_function(mass_action.serialize()) sensfn = idaklu.generate_function(sensfn.serialize()) + self._setup = { "rhs_algebraic": rhs_algebraic, "jac_times_cjmass": jac_times_cjmass, @@ -392,11 +458,32 @@ def sensfn(resvalS, t, y, inputs, yp, yS, ypS): "sensfn": sensfn, "rootfn": rootfn, "num_of_events": num_of_events, - "use_jac": use_jac, "ids": ids, "sensitivity_names": sensitivity_names, "number_of_sensitivity_parameters": number_of_sensitivity_parameters, } + + solver = idaklu.create_casadi_solver( + len(y0), + self._setup["number_of_sensitivity_parameters"], + self._setup["rhs_algebraic"], + self._setup["jac_times_cjmass"], + self._setup["jac_times_cjmass_colptrs"], + self._setup["jac_times_cjmass_rowvals"], + self._setup["jac_times_cjmass_nnz"], + self._setup["jac_rhs_algebraic_action"], + self._setup["mass_action"], + self._setup["sensfn"], + self._setup["rootfn"], + self._setup["num_of_events"], + self._setup["ids"], + atol, + rtol, + len(inputs), + self._options, + ) + + self._setup["solver"] = solver else: self._setup = { "resfn": resfn, @@ -404,7 +491,7 @@ def sensfn(resvalS, t, y, inputs, yp, yS, ypS): "sensfn": sensfn, "rootfn": rootfn, "num_of_events": num_of_events, - "use_jac": use_jac, + "use_jac": 1, "ids": ids, "sensitivity_names": sensitivity_names, "number_of_sensitivity_parameters": number_of_sensitivity_parameters, @@ -452,26 +539,11 @@ def _integrate(self, model, t_eval, inputs_dict=None): timer = pybamm.Timer() if model.convert_to_format == "casadi": - sol = idaklu.solve_casadi( + sol = self._setup["solver"].solve( t_eval, y0, ydot0, - self._setup["rhs_algebraic"], - self._setup["jac_times_cjmass"], - self._setup["jac_times_cjmass_colptrs"], - self._setup["jac_times_cjmass_rowvals"], - self._setup["jac_times_cjmass_nnz"], - self._setup["jac_rhs_algebraic_action"], - self._setup["mass_action"], - self._setup["sensfn"], - self._setup["rootfn"], - self._setup["num_of_events"], - self._setup["use_jac"], - self._setup["ids"], - atol, - rtol, inputs, - self._setup["number_of_sensitivity_parameters"], ) else: sol = idaklu.solve_python( diff --git a/pybamm/solvers/jax_solver.py b/pybamm/solvers/jax_solver.py index 1d117f0f95..b79e371c69 100644 --- a/pybamm/solvers/jax_solver.py +++ b/pybamm/solvers/jax_solver.py @@ -55,7 +55,7 @@ def __init__( root_method=None, rtol=1e-6, atol=1e-6, - extrap_tol=0, + extrap_tol=None, extra_options=None, ): if not pybamm.have_jax(): diff --git a/pybamm/solvers/scikits_dae_solver.py b/pybamm/solvers/scikits_dae_solver.py index 26a4b6bf4a..054ce25300 100644 --- a/pybamm/solvers/scikits_dae_solver.py +++ b/pybamm/solvers/scikits_dae_solver.py @@ -54,7 +54,7 @@ def __init__( atol=1e-6, root_method="casadi", root_tol=1e-6, - extrap_tol=0, + extrap_tol=None, extra_options=None, ): if scikits_odes_spec is None: diff --git a/pybamm/solvers/scikits_ode_solver.py b/pybamm/solvers/scikits_ode_solver.py index 15aa6965de..327d396400 100644 --- a/pybamm/solvers/scikits_ode_solver.py +++ b/pybamm/solvers/scikits_ode_solver.py @@ -48,7 +48,7 @@ def __init__( method="cvode", rtol=1e-6, atol=1e-6, - extrap_tol=0, + extrap_tol=None, extra_options=None, ): if scikits_odes_spec is None: # pragma: no cover diff --git a/pybamm/solvers/scipy_solver.py b/pybamm/solvers/scipy_solver.py index ad7e593c8a..be228e054a 100644 --- a/pybamm/solvers/scipy_solver.py +++ b/pybamm/solvers/scipy_solver.py @@ -32,7 +32,7 @@ def __init__( method="BDF", rtol=1e-6, atol=1e-6, - extrap_tol=0, + extrap_tol=None, extra_options=None, ): super().__init__( diff --git a/pybamm/solvers/solution.py b/pybamm/solvers/solution.py index 6bcade4bde..169e58db7e 100644 --- a/pybamm/solvers/solution.py +++ b/pybamm/solvers/solution.py @@ -9,6 +9,7 @@ import pybamm import pandas as pd from scipy.io import savemat +from functools import cached_property class NumpyEncoder(json.JSONEncoder): @@ -344,15 +345,9 @@ def all_models(self): """Model(s) used for solution""" return self._all_models - @property + @cached_property def all_inputs_casadi(self): - try: - return self._all_inputs_casadi - except AttributeError: - self._all_inputs_casadi = [ - casadi.vertcat(*inp.values()) for inp in self.all_inputs - ] - return self._all_inputs_casadi + return [casadi.vertcat(*inp.values()) for inp in self.all_inputs] @property def t_event(self): @@ -374,63 +369,55 @@ def termination(self, value): """Updates the reason for termination""" self._termination = value - @property + @cached_property def first_state(self): """ A Solution object that only contains the first state. This is faster to evaluate than the full solution when only the first state is needed (e.g. to initialize a model with the solution) """ - try: - return self._first_state - except AttributeError: - new_sol = Solution( - self.all_ts[0][:1], - self.all_ys[0][:, :1], - self.all_models[:1], - self.all_inputs[:1], - None, - None, - "success", - ) - new_sol._all_inputs_casadi = self.all_inputs_casadi[:1] - new_sol._sub_solutions = self.sub_solutions[:1] + new_sol = Solution( + self.all_ts[0][:1], + self.all_ys[0][:, :1], + self.all_models[:1], + self.all_inputs[:1], + None, + None, + "success", + ) + new_sol._all_inputs_casadi = self.all_inputs_casadi[:1] + new_sol._sub_solutions = self.sub_solutions[:1] - new_sol.solve_time = 0 - new_sol.integration_time = 0 - new_sol.set_up_time = 0 + new_sol.solve_time = 0 + new_sol.integration_time = 0 + new_sol.set_up_time = 0 - self._first_state = new_sol - return self._first_state + return new_sol - @property + @cached_property def last_state(self): """ A Solution object that only contains the final state. This is faster to evaluate than the full solution when only the final state is needed (e.g. to initialize a model with the solution) """ - try: - return self._last_state - except AttributeError: - new_sol = Solution( - self.all_ts[-1][-1:], - self.all_ys[-1][:, -1:], - self.all_models[-1:], - self.all_inputs[-1:], - self.t_event, - self.y_event, - self.termination, - ) - new_sol._all_inputs_casadi = self.all_inputs_casadi[-1:] - new_sol._sub_solutions = self.sub_solutions[-1:] + new_sol = Solution( + self.all_ts[-1][-1:], + self.all_ys[-1][:, -1:], + self.all_models[-1:], + self.all_inputs[-1:], + self.t_event, + self.y_event, + self.termination, + ) + new_sol._all_inputs_casadi = self.all_inputs_casadi[-1:] + new_sol._sub_solutions = self.sub_solutions[-1:] - new_sol.solve_time = 0 - new_sol.integration_time = 0 - new_sol.set_up_time = 0 + new_sol.solve_time = 0 + new_sol.integration_time = 0 + new_sol.set_up_time = 0 - self._last_state = new_sol - return self._last_state + return new_sol @property def total_time(self): diff --git a/pybamm/spatial_methods/finite_volume.py b/pybamm/spatial_methods/finite_volume.py index 25c61c71c8..8b069ff626 100644 --- a/pybamm/spatial_methods/finite_volume.py +++ b/pybamm/spatial_methods/finite_volume.py @@ -57,7 +57,7 @@ def spatial_variable(self, symbol): :class:`pybamm.Vector` Contains the discretised spatial variable """ - symbol_mesh = self.mesh.combine_submeshes(*symbol.domain) + symbol_mesh = self.mesh[symbol.domain] repeats = self._get_auxiliary_domain_repeats(symbol.domains) if symbol.evaluates_on_edges("primary"): entries = np.tile(symbol_mesh.edges, repeats) @@ -137,7 +137,7 @@ def gradient_matrix(self, domain, domains): The (sparse) finite volume gradient matrix for the domain """ # Create appropriate submesh by combining submeshes in primary domain - submesh = self.mesh.combine_submeshes(*domain) + submesh = self.mesh[domain] # Create 1D matrix using submesh n = submesh.npts @@ -160,7 +160,7 @@ def divergence(self, symbol, discretised_symbol, boundary_conditions): """Matrix-vector multiplication to implement the divergence operator. See :meth:`pybamm.SpatialMethod.divergence` """ - submesh = self.mesh.combine_submeshes(*symbol.domain) + submesh = self.mesh[symbol.domain] divergence_matrix = self.divergence_matrix(symbol.domains) @@ -195,7 +195,7 @@ def divergence_matrix(self, domains): The (sparse) finite volume divergence matrix for the domain """ # Create appropriate submesh by combining submeshes in domain - submesh = self.mesh.combine_submeshes(*domains["primary"]) + submesh = self.mesh[domains["primary"]] # check coordinate system if submesh.coord_sys in ["cylindrical polar", "spherical polar"]: @@ -276,7 +276,7 @@ def definite_integral_matrix( ) domain = child.domains[integration_dimension] - submesh = self.mesh.combine_submeshes(*domain) + submesh = self.mesh[domain] # check coordinate system if submesh.coord_sys in ["cylindrical polar", "spherical polar"]: @@ -291,7 +291,7 @@ def definite_integral_matrix( if integration_dimension == "primary": # Create appropriate submesh by combining submeshes in domain - submesh = self.mesh.combine_submeshes(*domains["primary"]) + submesh = self.mesh[domains["primary"]] # Create vector of ones for primary domain submesh @@ -306,7 +306,7 @@ def definite_integral_matrix( matrix = kron(eye(second_dim_repeats), d_edges) elif integration_dimension == "secondary": # Create appropriate submesh by combining submeshes in domain - primary_submesh = self.mesh.combine_submeshes(*domains["primary"]) + primary_submesh = self.mesh[domains["primary"]] # Create matrix which integrates in the secondary dimension # Different number of edges depending on whether child evaluates on edges @@ -348,7 +348,7 @@ def indefinite_integral(self, child, discretised_child, direction): # the case where child evaluates on edges # If it becomes necessary to implement this, will need to think about what # the cylindrical/spherical polar indefinite integral should be - submesh = self.mesh.combine_submeshes(*child.domain) + submesh = self.mesh[child.domain] if submesh.coord_sys in ["cylindrical polar", "spherical polar"]: raise NotImplementedError( f"Indefinite integral on a {submesh.coord_sys} domain is not " @@ -438,7 +438,7 @@ def indefinite_integral_matrix_edges(self, domains, direction): """ # Create appropriate submesh by combining submeshes in domain - submesh = self.mesh.combine_submeshes(*domains["primary"]) + submesh = self.mesh[domains["primary"]] n = submesh.npts second_dim_repeats = self._get_auxiliary_domain_repeats(domains) @@ -488,7 +488,7 @@ def indefinite_integral_matrix_nodes(self, domains, direction): """ # Create appropriate submesh by combining submeshes in domain - submesh = self.mesh.combine_submeshes(*domains["primary"]) + submesh = self.mesh[domains["primary"]] n = submesh.npts second_dim_repeats = self._get_auxiliary_domain_repeats(domains) @@ -518,7 +518,7 @@ def delta_function(self, symbol, discretised_symbol): See :meth:`pybamm.SpatialMethod.delta_function` """ # Find the number of submeshes - submesh = self.mesh.combine_submeshes(*symbol.domain) + submesh = self.mesh[symbol.domain] prim_pts = submesh.npts second_dim_repeats = self._get_auxiliary_domain_repeats(symbol.domains) @@ -635,7 +635,7 @@ def add_ghost_nodes(self, symbol, discretised_symbol, bcs): """ # get relevant grid points domain = symbol.domain - submesh = self.mesh.combine_submeshes(*domain) + submesh = self.mesh[domain] # Prepare sizes and empty bcs_vector n = submesh.npts @@ -757,7 +757,7 @@ def add_neumann_values(self, symbol, discretised_gradient, bcs, domain): """ # get relevant grid points - submesh = self.mesh.combine_submeshes(*domain) + submesh = self.mesh[domain] # Prepare sizes and empty bcs_vector n = submesh.npts - 1 @@ -849,7 +849,7 @@ def boundary_value_or_flux(self, symbol, discretised_child, bcs=None): """ # Find the number of submeshes - submesh = self.mesh.combine_submeshes(*discretised_child.domain) + submesh = self.mesh[discretised_child.domain] prim_pts = submesh.npts repeats = self._get_auxiliary_domain_repeats(discretised_child.domains) @@ -1136,7 +1136,7 @@ def concatenation(self, disc_children): See :meth:`pybamm.SpatialMethod.concatenation` """ for idx, child in enumerate(disc_children): - submesh = self.mesh.combine_submeshes(*child.domain) + submesh = self.mesh[child.domain] repeats = self._get_auxiliary_domain_repeats(child.domains) n_nodes = len(submesh.nodes) * repeats n_edges = len(submesh.edges) * repeats @@ -1203,7 +1203,7 @@ def shift(self, discretised_symbol, shift_key, method): def arithmetic_mean(array): """Calculate the arithmetic mean of an array using matrix multiplication""" # Create appropriate submesh by combining submeshes in domain - submesh = self.mesh.combine_submeshes(*array.domain) + submesh = self.mesh[array.domain] # Create 1D matrix using submesh n = submesh.npts @@ -1261,7 +1261,7 @@ def harmonic_mean(array): approximation to the diffusion equation." (2012). """ # Create appropriate submesh by combining submeshes in domain - submesh = self.mesh.combine_submeshes(*array.domain) + submesh = self.mesh[array.domain] # Get second dimension length for use later second_dim_repeats = self._get_auxiliary_domain_repeats( @@ -1398,7 +1398,7 @@ def upwind_or_downwind(self, symbol, discretised_symbol, bcs, direction): direction : str Direction in which to apply the operator (upwind or downwind) """ - submesh = self.mesh.combine_submeshes(*symbol.domain) + submesh = self.mesh[symbol.domain] n = submesh.npts if symbol not in bcs: diff --git a/pybamm/spatial_methods/spatial_method.py b/pybamm/spatial_methods/spatial_method.py index 267bdda3e8..6826414bab 100644 --- a/pybamm/spatial_methods/spatial_method.py +++ b/pybamm/spatial_methods/spatial_method.py @@ -47,7 +47,7 @@ def _get_auxiliary_domain_repeats(self, domains): mesh_pts = 1 for level, dom in domains.items(): if level != "primary" and dom != []: - mesh_pts *= self.mesh.combine_submeshes(*dom).npts + mesh_pts *= self.mesh[dom].npts return mesh_pts @property @@ -70,7 +70,7 @@ def spatial_variable(self, symbol): :class:`pybamm.Vector` Contains the discretised spatial variable """ - symbol_mesh = self.mesh.combine_submeshes(*symbol.domain) + symbol_mesh = self.mesh[symbol.domain] repeats = self._get_auxiliary_domain_repeats(symbol.domains) if symbol.evaluates_on_edges("primary"): entries = np.tile(symbol_mesh.edges, repeats) @@ -99,7 +99,7 @@ def broadcast(self, symbol, domains, broadcast_type): The discretised symbol of the correct size for the spatial method """ domain = domains["primary"] - primary_domain_size = self.mesh.combine_submeshes(*domain).npts + primary_domain_size = self.mesh[domain].npts secondary_domain_size = self._get_auxiliary_domain_repeats( {"secondary": domains["secondary"]} ) @@ -403,8 +403,8 @@ def mass_matrix(self, symbol, boundary_conditions): # to account for Dirichlet boundary conditions. Here, we just have the default # behaviour that the mass matrix is the identity. - # Create appropriate submesh by combining submeshes in domain - submesh = self.mesh.combine_submeshes(*symbol.domain) + # Get submesh + submesh = self.mesh[symbol.domain] # Get number of points in primary dimension n = submesh.npts diff --git a/pybamm/spatial_methods/spectral_volume.py b/pybamm/spatial_methods/spectral_volume.py index dd45fbaf2c..17fe70e040 100644 --- a/pybamm/spatial_methods/spectral_volume.py +++ b/pybamm/spatial_methods/spectral_volume.py @@ -160,8 +160,7 @@ def cv_boundary_reconstruction_matrix(self, domains): :class:`pybamm.Matrix` The (sparse) CV reconstruction matrix for the domain """ - # Create appropriate submesh by combining submeshes in domain - submesh = self.mesh.combine_submeshes(*domains["primary"]) + submesh = self.mesh[domains["primary"]] # Obtain the basic reconstruction matrix. recon_sub_matrix = self.cv_boundary_reconstruction_sub_matrix() @@ -316,8 +315,7 @@ def gradient_matrix(self, domain, domains): :class:`pybamm.Matrix` The (sparse) Spectral Volume gradient matrix for the domain """ - # Create appropriate submesh by combining submeshes in domain - submesh = self.mesh.combine_submeshes(*domain) + submesh = self.mesh[domain] # Obtain the Chebyshev differentiation matrix. # Flip it, since it is defined for the Chebyshev @@ -400,8 +398,7 @@ def penalty_matrix(self, domains): :class:`pybamm.Matrix` The (sparse) Spectral Volume penalty matrix for the domain """ - # Create appropriate submesh by combining submeshes in domain - submesh = self.mesh.combine_submeshes(*domains["primary"]) + submesh = self.mesh[domains["primary"]] # Create 1D matrix using submesh n = submesh.npts @@ -523,7 +520,7 @@ def replace_dirichlet_values(self, symbol, discretised_symbol, bcs): """ # get relevant grid points domain = symbol.domain - submesh = self.mesh.combine_submeshes(*domain) + submesh = self.mesh[domain] # Prepare sizes n = (submesh.npts // self.order) * (self.order + 1) @@ -617,7 +614,7 @@ def replace_neumann_values(self, symbol, discretised_gradient, bcs): """ # get relevant grid points domain = symbol.domain - submesh = self.mesh.combine_submeshes(*domain) + submesh = self.mesh[domain] # Prepare sizes n = submesh.npts + 1 diff --git a/pybamm/version.py b/pybamm/version.py index 943ea9fb60..969639bd2c 100644 --- a/pybamm/version.py +++ b/pybamm/version.py @@ -1 +1 @@ -__version__ = "22.10.post1" +__version__ = "22.11" diff --git a/setup.py b/setup.py index e044444a7b..479fdfabd6 100644 --- a/setup.py +++ b/setup.py @@ -221,7 +221,7 @@ def compile_KLU(): "pybamm_install_odes = pybamm.install_odes:main", "pybamm_install_jax = pybamm.util:install_jax", ], - "pybamm_parameter_set": [ + "pybamm_parameter_sets": [ "Sulzer2019 = pybamm.input.parameters.lead_acid.Sulzer2019:get_parameter_values", # noqa: E501 "Ai2020 = pybamm.input.parameters.lithium_ion.Ai2020:get_parameter_values", # noqa: E501 "Chen2020 = pybamm.input.parameters.lithium_ion.Chen2020:get_parameter_values", # noqa: E501 @@ -235,6 +235,7 @@ def compile_KLU(): "Prada2013 = pybamm.input.parameters.lithium_ion.Prada2013:get_parameter_values", # noqa: E501 "Ramadass2004 = pybamm.input.parameters.lithium_ion.Ramadass2004:get_parameter_values", # noqa: E501 "Xu2019 = pybamm.input.parameters.lithium_ion.Xu2019:get_parameter_values", # noqa: E501 + "ECM_Example = pybamm.input.parameters.ecm.example_set:get_parameter_values", # noqa: E501 ], }, ) diff --git a/tests/integration/test_models/standard_model_tests.py b/tests/integration/test_models/standard_model_tests.py index 9d8040e516..86c9ab909e 100644 --- a/tests/integration/test_models/standard_model_tests.py +++ b/tests/integration/test_models/standard_model_tests.py @@ -123,14 +123,10 @@ def test_sensitivities( output_sens = self.solution[output_name].sensitivities[param_name] # check via finite differencing - h = 1e-6 * param_value + h = 1e-4 * param_value inputs_plus = {param_name: (param_value + 0.5 * h)} inputs_neg = {param_name: (param_value - 0.5 * h)} - sol_plus = self.solver.solve( - self.model, - t_eval, - inputs=inputs_plus, - ) + sol_plus = self.solver.solve(self.model, t_eval, inputs=inputs_plus) output_plus = sol_plus[output_name](t=t_eval) sol_neg = self.solver.solve(self.model, t_eval, inputs=inputs_neg) output_neg = sol_neg[output_name](t=t_eval) diff --git a/tests/integration/test_models/standard_output_tests.py b/tests/integration/test_models/standard_output_tests.py index 641caec961..7add30a0e5 100644 --- a/tests/integration/test_models/standard_output_tests.py +++ b/tests/integration/test_models/standard_output_tests.py @@ -83,11 +83,11 @@ def __init__(self, model, param, disc, solution, operating_condition): self.x_s = disc.mesh["separator"].nodes * L_x self.x_p = disc.mesh["positive electrode"].nodes * L_x whole_cell = ["negative electrode", "separator", "positive electrode"] - self.x = disc.mesh.combine_submeshes(*whole_cell).nodes * L_x + self.x = disc.mesh[whole_cell].nodes * L_x self.x_n_edge = disc.mesh["negative electrode"].edges * L_x self.x_s_edge = disc.mesh["separator"].edges * L_x self.x_p_edge = disc.mesh["positive electrode"].edges * L_x - self.x_edge = disc.mesh.combine_submeshes(*whole_cell).edges * L_x + self.x_edge = disc.mesh[whole_cell].edges * L_x if isinstance(self.model, pybamm.lithium_ion.BaseModel): R_n_typ = model.length_scales["negative particle"].evaluate() @@ -550,7 +550,7 @@ def test_splitting(self): (self.c_e_n(t, x_n), self.c_e_s(t, x_s), self.c_e_p(t, x_p)), axis=0 ) - np.testing.assert_array_equal(self.c_e(t, x), c_e_combined) + np.testing.assert_array_almost_equal(self.c_e(t, x), c_e_combined, decimal=14) def test_all(self): self.test_concentration_limit() diff --git a/tests/integration/test_models/test_full_battery_models/test_equivalent_circuit/__init__.py b/tests/integration/test_models/test_full_battery_models/test_equivalent_circuit/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/tests/integration/test_models/test_full_battery_models/test_equivalent_circuit/test_thevenin.py b/tests/integration/test_models/test_full_battery_models/test_equivalent_circuit/test_thevenin.py new file mode 100644 index 0000000000..fee42ed611 --- /dev/null +++ b/tests/integration/test_models/test_full_battery_models/test_equivalent_circuit/test_thevenin.py @@ -0,0 +1,19 @@ +import pybamm +import unittest +import tests + + +class TestThevenin(unittest.TestCase): + def test_basic_processing(self): + model = pybamm.equivalent_circuit.Thevenin() + modeltest = tests.StandardModelTest(model) + modeltest.test_all() + + +if __name__ == "__main__": + print("Add -v for more debug output") + import sys + + if "-v" in sys.argv: + debug = True + unittest.main() diff --git a/tests/integration/test_models/test_full_battery_models/test_lead_acid/test_asymptotics_convergence.py b/tests/integration/test_models/test_full_battery_models/test_lead_acid/test_asymptotics_convergence.py index 779606dece..14c4cb61be 100644 --- a/tests/integration/test_models/test_full_battery_models/test_lead_acid/test_asymptotics_convergence.py +++ b/tests/integration/test_models/test_full_battery_models/test_lead_acid/test_asymptotics_convergence.py @@ -15,14 +15,12 @@ def test_leading_order_convergence(self): """ # Create models leading_order_model = pybamm.lead_acid.LOQS() - composite_model = pybamm.lead_acid.Composite() full_model = pybamm.lead_acid.Full() # Same parameters, same geometry parameter_values = full_model.default_parameter_values parameter_values["Current function [A]"] = "[input]" parameter_values.process_model(leading_order_model) - parameter_values.process_model(composite_model) parameter_values.process_model(full_model) geometry = full_model.default_geometry parameter_values.process_geometry(geometry) @@ -38,8 +36,6 @@ def test_leading_order_convergence(self): } loqs_disc = pybamm.Discretisation(mesh, spatial_methods) loqs_disc.process_model(leading_order_model) - comp_disc = pybamm.Discretisation(mesh, spatial_methods) - comp_disc.process_model(composite_model) full_disc = pybamm.Discretisation(mesh, spatial_methods) full_disc.process_model(full_model) @@ -53,40 +49,28 @@ def get_max_error(current): solution_loqs = solver.solve( leading_order_model, t_eval, inputs={"Current function [A]": current} ) - solution_comp = solver.solve( - composite_model, t_eval, inputs={"Current function [A]": current} - ) solution_full = solver.solve( full_model, t_eval, inputs={"Current function [A]": current} ) # Post-process variables voltage_loqs = solution_loqs["Terminal voltage"] - voltage_comp = solution_comp["Terminal voltage"] voltage_full = solution_full["Terminal voltage"] # Compare t_loqs = solution_loqs.t - t_comp = solution_comp.t t_full = solution_full.t - t = t_full[: np.min([len(t_loqs), len(t_comp), len(t_full)])] + t = t_full[: np.min([len(t_loqs), len(t_full)])] loqs_error = np.max(np.abs(voltage_loqs(t) - voltage_full(t))) - comp_error = np.max(np.abs(voltage_comp(t) - voltage_full(t))) - return (loqs_error, comp_error) + return loqs_error # Get errors currents = 0.5 / (2 ** np.arange(3)) - errs = np.array([get_max_error(current) for current in currents]) - loqs_errs, comp_errs = [np.array(err) for err in zip(*errs)] - # Get rates: expect linear convergence for loqs, quadratic for composite + loqs_errs = np.array([get_max_error(current) for current in currents]) + # Get rates: expect linear convergence for loqs loqs_rates = np.log2(loqs_errs[:-1] / loqs_errs[1:]) np.testing.assert_array_less(0.99 * np.ones_like(loqs_rates), loqs_rates) - # Composite not converging as expected - comp_rates = np.log2(comp_errs[:-1] / comp_errs[1:]) - np.testing.assert_array_less(0.99 * np.ones_like(comp_rates), comp_rates) - # Check composite more accurate than loqs - np.testing.assert_array_less(comp_errs, loqs_errs) if __name__ == "__main__": diff --git a/tests/integration/test_models/test_full_battery_models/test_lead_acid/test_compare_outputs.py b/tests/integration/test_models/test_full_battery_models/test_lead_acid/test_compare_outputs.py index 9682696ce6..c1008b53e0 100644 --- a/tests/integration/test_models/test_full_battery_models/test_lead_acid/test_compare_outputs.py +++ b/tests/integration/test_models/test_full_battery_models/test_lead_acid/test_compare_outputs.py @@ -13,11 +13,7 @@ def test_compare_averages_asymptotics(self): Check that the average value of certain variables is constant across submodels """ # load models - models = [ - pybamm.lead_acid.LOQS(), - pybamm.lead_acid.Composite(), - pybamm.lead_acid.Full(), - ] + models = [pybamm.lead_acid.LOQS(), pybamm.lead_acid.Full()] # load parameter values (same for all models) param = models[0].default_parameter_values @@ -57,7 +53,6 @@ def test_compare_outputs_surface_form(self): ] model_combos = [ ([pybamm.lead_acid.LOQS(opt) for opt in options]), - ([pybamm.lead_acid.Composite(opt) for opt in options]), ([pybamm.lead_acid.Full(opt) for opt in options]), ] diff --git a/tests/integration/test_models/test_full_battery_models/test_lead_acid/test_composite.py b/tests/integration/test_models/test_full_battery_models/test_lead_acid/test_composite.py deleted file mode 100644 index 4bc6b80896..0000000000 --- a/tests/integration/test_models/test_full_battery_models/test_lead_acid/test_composite.py +++ /dev/null @@ -1,108 +0,0 @@ -# -# Tests for the lead-acid composite model -# -import pybamm -import tests - -import unittest -import numpy as np - - -class TestLeadAcidComposite(unittest.TestCase): - def test_basic_processing(self): - model = pybamm.lead_acid.Composite() - param = model.default_parameter_values - param.update({"Current function [A]": 1}) - modeltest = tests.StandardModelTest(model, parameter_values=param) - modeltest.test_all() - - def test_basic_processing_with_convection(self): - model = pybamm.lead_acid.Composite() - param = model.default_parameter_values - param.update({"Current function [A]": 1}) - modeltest = tests.StandardModelTest(model, parameter_values=param) - modeltest.test_all() - - def test_optimisations(self): - model = pybamm.lead_acid.Composite() - optimtest = tests.OptimisationsTest(model) - - original = optimtest.evaluate_model() - to_python = optimtest.evaluate_model(to_python=True) - np.testing.assert_array_almost_equal(original, to_python) - - def test_set_up(self): - model = pybamm.lead_acid.Composite() - optimtest = tests.OptimisationsTest(model) - optimtest.set_up_model(to_python=True) - optimtest.set_up_model(to_python=False) - - def test_basic_processing_1plus1D(self): - options = {"current collector": "potential pair", "dimensionality": 1} - model = pybamm.lead_acid.Composite(options) - var_pts = {"x_n": 5, "x_s": 5, "x_p": 5, "y": 5, "z": 5} - modeltest = tests.StandardModelTest(model, var_pts=var_pts) - modeltest.test_all(skip_output_tests=True) - - options = { - "current collector": "potential pair", - "dimensionality": 1, - "convection": "full transverse", - } - model = pybamm.lead_acid.Composite(options) - modeltest = tests.StandardModelTest(model, var_pts=var_pts) - modeltest.test_all(skip_output_tests=True) - - -class TestLeadAcidCompositeSurfaceForm(unittest.TestCase): - def test_basic_processing_differential(self): - options = {"surface form": "differential"} - model = pybamm.lead_acid.Composite(options) - param = model.default_parameter_values - param.update({"Current function [A]": 1}) - modeltest = tests.StandardModelTest(model, parameter_values=param) - modeltest.test_all() - - def test_basic_processing_algebraic(self): - options = {"surface form": "algebraic"} - model = pybamm.lead_acid.Composite(options) - param = model.default_parameter_values - param.update({"Current function [A]": 1}) - modeltest = tests.StandardModelTest(model, parameter_values=param) - modeltest.test_all() # solver=pybamm.CasadiSolver()) - - # def test_thermal(self): - # options = {"thermal": "lumped"} - # model = pybamm.lead_acid.Composite(options) - # modeltest = tests.StandardModelTest(model) - # modeltest.test_all() - - # options = {"thermal": "x-full"} - # model = pybamm.lead_acid.Composite(options) - # modeltest = tests.StandardModelTest(model) - # modeltest.test_all() - - -class TestLeadAcidCompositeExtended(unittest.TestCase): - def test_basic_processing(self): - model = pybamm.lead_acid.CompositeExtended() - param = model.default_parameter_values - param.update({"Current function [A]": 1}) - modeltest = tests.StandardModelTest(model, parameter_values=param) - modeltest.test_all() - - def test_basic_processing_averaged(self): - model = pybamm.lead_acid.CompositeAverageCorrection() - param = model.default_parameter_values - param.update({"Current function [A]": 1}) - modeltest = tests.StandardModelTest(model, parameter_values=param) - modeltest.test_all() - - -if __name__ == "__main__": - print("Add -v for more debug output") - import sys - - if "-v" in sys.argv: - debug = True - unittest.main() diff --git a/tests/integration/test_models/test_full_battery_models/test_lead_acid/test_foqs.py b/tests/integration/test_models/test_full_battery_models/test_lead_acid/test_foqs.py deleted file mode 100644 index a92dcfb26a..0000000000 --- a/tests/integration/test_models/test_full_battery_models/test_lead_acid/test_foqs.py +++ /dev/null @@ -1,66 +0,0 @@ -# -# Tests for the lead-acid FOQS model -# -import pybamm -import tests - -import unittest -import numpy as np - - -class TestLeadAcidFOQS(unittest.TestCase): - def test_basic_processing(self): - model = pybamm.lead_acid.FOQS() - param = model.default_parameter_values - param.update({"Current function [A]": 1}) - modeltest = tests.StandardModelTest(model, parameter_values=param) - modeltest.test_all() - - def test_basic_processing_with_convection(self): - options = {"convection": "uniform transverse"} - model = pybamm.lead_acid.FOQS(options) - param = model.default_parameter_values - param.update({"Current function [A]": 1}) - modeltest = tests.StandardModelTest(model, parameter_values=param) - modeltest.test_all() - - def test_optimisations(self): - model = pybamm.lead_acid.FOQS() - optimtest = tests.OptimisationsTest(model) - - original = optimtest.evaluate_model() - to_python = optimtest.evaluate_model(to_python=True) - np.testing.assert_array_almost_equal(original, to_python) - - def test_set_up(self): - model = pybamm.lead_acid.FOQS() - optimtest = tests.OptimisationsTest(model) - optimtest.set_up_model(to_python=True) - optimtest.set_up_model(to_python=False) - - -class TestLeadAcidFOQSSurfaceForm(unittest.TestCase): - def test_basic_processing_differential(self): - options = {"surface form": "differential"} - model = pybamm.lead_acid.FOQS(options) - param = model.default_parameter_values - param.update({"Current function [A]": 1}) - modeltest = tests.StandardModelTest(model, parameter_values=param) - modeltest.test_all() - - def test_basic_processing_algebraic(self): - options = {"surface form": "algebraic"} - model = pybamm.lead_acid.FOQS(options) - param = model.default_parameter_values - param.update({"Current function [A]": 1}) - modeltest = tests.StandardModelTest(model, parameter_values=param) - modeltest.test_all() - - -if __name__ == "__main__": - print("Add -v for more debug output") - import sys - - if "-v" in sys.argv: - debug = True - unittest.main() diff --git a/tests/integration/test_models/test_full_battery_models/test_lead_acid/test_side_reactions/test_composite_side_reactions.py b/tests/integration/test_models/test_full_battery_models/test_lead_acid/test_side_reactions/test_composite_side_reactions.py deleted file mode 100644 index a6b7397fa7..0000000000 --- a/tests/integration/test_models/test_full_battery_models/test_lead_acid/test_side_reactions/test_composite_side_reactions.py +++ /dev/null @@ -1,54 +0,0 @@ -# -# Tests for the lead-acid Full model -# -import pybamm -import tests - -import unittest - - -class TestLeadAcidCompositeSideReactions(unittest.TestCase): - def test_basic_processing_differential(self): - options = {"hydrolysis": "true", "surface form": "differential"} - model = pybamm.lead_acid.Composite(options) - modeltest = tests.StandardModelTest(model) - modeltest.test_all(skip_output_tests=True) - - def test_basic_processing_algebraic(self): - options = {"hydrolysis": "true", "surface form": "algebraic"} - model = pybamm.lead_acid.Composite(options) - modeltest = tests.StandardModelTest(model) - modeltest.test_all(skip_output_tests=True) - - def test_basic_processing_charge(self): - options = {"hydrolysis": "true", "surface form": "differential"} - model = pybamm.lead_acid.Composite(options) - parameter_values = model.default_parameter_values - parameter_values.update( - {"Current function [A]": -1, "Initial State of Charge": 0.5} - ) - modeltest = tests.StandardModelTest(model, parameter_values=parameter_values) - modeltest.test_all(skip_output_tests=True) - - def test_basic_processing_zero_current(self): - options = {"hydrolysis": "true", "surface form": "differential"} - model = pybamm.lead_acid.Composite(options) - parameter_values = model.default_parameter_values - parameter_values.update({"Current function [A]": 0}) - modeltest = tests.StandardModelTest(model, parameter_values=parameter_values) - modeltest.test_all(skip_output_tests=True) - - def test_basic_processing_extended_differential(self): - options = {"hydrolysis": "true", "surface form": "differential"} - model = pybamm.lead_acid.CompositeExtended(options) - modeltest = tests.StandardModelTest(model) - modeltest.test_all(skip_output_tests=True) - - -if __name__ == "__main__": - print("Add -v for more debug output") - import sys - - if "-v" in sys.argv: - debug = True - unittest.main() diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_half_cell_tests.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_half_cell_tests.py index c8055ebcc9..0c203c9fc7 100644 --- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_half_cell_tests.py +++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_half_cell_tests.py @@ -53,6 +53,15 @@ def test_sei_reaction_limited(self): options = {"SEI": "reaction limited"} self.run_basic_processing_test(options) + def test_sei_asymmetric_reaction_limited(self): + options = {"SEI": "reaction limited (asymmetric)"} + parameter_values = pybamm.ParameterValues("Xu2019") + parameter_values.update( + {"SEI growth transfer coefficient": 0.2}, + check_already_exists=False, + ) + self.run_basic_processing_test(options, parameter_values=parameter_values) + def test_sei_solvent_diffusion_limited(self): options = {"SEI": "solvent-diffusion limited"} self.run_basic_processing_test(options) @@ -69,6 +78,15 @@ def test_sei_ec_reaction_limited(self): options = {"SEI": "ec reaction limited"} self.run_basic_processing_test(options) + def test_sei_asymmetric_ec_reaction_limited(self): + options = {"SEI": "ec reaction limited (asymmetric)"} + parameter_values = pybamm.ParameterValues("Xu2019") + parameter_values.update( + {"SEI growth transfer coefficient": 0.2}, + check_already_exists=False, + ) + self.run_basic_processing_test(options, parameter_values=parameter_values) + def test_constant_utilisation(self): options = {"interface utilisation": "constant"} parameter_values = pybamm.ParameterValues("Xu2019") diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py index 6d46766220..33766b4855 100644 --- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py +++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py @@ -169,6 +169,15 @@ def test_sei_reaction_limited(self): options = {"SEI": "reaction limited"} self.run_basic_processing_test(options) + def test_sei_asymmetric_reaction_limited(self): + options = {"SEI": "reaction limited (asymmetric)"} + parameter_values = pybamm.ParameterValues("Marquis2019") + parameter_values.update( + {"SEI growth transfer coefficient": 0.2}, + check_already_exists=False, + ) + self.run_basic_processing_test(options, parameter_values=parameter_values) + def test_sei_solvent_diffusion_limited(self): options = {"SEI": "solvent-diffusion limited"} self.run_basic_processing_test(options) @@ -182,9 +191,24 @@ def test_sei_interstitial_diffusion_limited(self): self.run_basic_processing_test(options) def test_sei_ec_reaction_limited(self): - options = {"SEI": "ec reaction limited", "SEI porosity change": "true"} + options = { + "SEI": "ec reaction limited", + "SEI porosity change": "true", + } self.run_basic_processing_test(options) + def test_sei_asymmetric_ec_reaction_limited(self): + options = { + "SEI": "ec reaction limited (asymmetric)", + "SEI porosity change": "true", + } + parameter_values = pybamm.ParameterValues("Marquis2019") + parameter_values.update( + {"SEI growth transfer coefficient": 0.2}, + check_already_exists=False, + ) + self.run_basic_processing_test(options, parameter_values=parameter_values) + def test_loss_active_material_stress_negative(self): options = {"loss of active material": ("none", "stress-driven")} parameter_values = pybamm.ParameterValues("Ai2020") diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_initial_soc.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_initial_soc.py new file mode 100644 index 0000000000..9cdfb4c8a6 --- /dev/null +++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_initial_soc.py @@ -0,0 +1,33 @@ +# +# Test edge cases for initial SOC +# +import pybamm +import unittest + + +class TestInitialSOC(unittest.TestCase): + def test_interpolant_parameter_sets(self): + model = pybamm.lithium_ion.SPM() + params = [ + "Ai2020", + "Chen2020", + "Ecker2015", + "Marquis2019", + "Mohtat2020", + "OKane2022", + "ORegan2022", + ] + for param in params: + with self.subTest(param=param): + parameter_values = pybamm.ParameterValues(param) + sim = pybamm.Simulation(model=model, parameter_values=parameter_values) + sim.solve([0, 3600], initial_soc=0.2) + + +if __name__ == "__main__": + print("Add -v for more debug output") + import sys + + if "-v" in sys.argv: + debug = True + unittest.main() diff --git a/tests/integration/test_models/test_submodels/test_interface/test_butler_volmer.py b/tests/integration/test_models/test_submodels/test_interface/test_butler_volmer.py index 8ee3e1472a..b2a6966348 100644 --- a/tests/integration/test_models/test_submodels/test_interface/test_butler_volmer.py +++ b/tests/integration/test_models/test_submodels/test_interface/test_butler_volmer.py @@ -223,7 +223,7 @@ def test_discretisation(self): # test concatenated butler-volmer whole_cell = ["negative electrode", "separator", "positive electrode"] - whole_cell_mesh = disc.mesh.combine_submeshes(*whole_cell) + whole_cell_mesh = disc.mesh[whole_cell] self.assertEqual(j.evaluate(None, y).shape, (whole_cell_mesh.npts, 1)) def test_diff_c_e_lead_acid(self): diff --git a/tests/integration/test_models/test_submodels/test_interface/test_lead_acid.py b/tests/integration/test_models/test_submodels/test_interface/test_lead_acid.py index 54bd7c678b..de96380a15 100644 --- a/tests/integration/test_models/test_submodels/test_interface/test_lead_acid.py +++ b/tests/integration/test_models/test_submodels/test_interface/test_lead_acid.py @@ -83,7 +83,7 @@ def test_discretisation_main_reaction(self): # Test whole_cell = ["negative electrode", "separator", "positive electrode"] - submesh = mesh.combine_submeshes(*whole_cell) + submesh = mesh[whole_cell] y = submesh.nodes**2 # should evaluate to vectors with the right shape self.assertEqual(j0_n.evaluate(y=y).shape, (mesh["negative electrode"].npts, 1)) diff --git a/tests/integration/test_models/test_submodels/test_interface/test_lithium_ion.py b/tests/integration/test_models/test_submodels/test_interface/test_lithium_ion.py index fff744c42b..c1970d527e 100644 --- a/tests/integration/test_models/test_submodels/test_interface/test_lithium_ion.py +++ b/tests/integration/test_models/test_submodels/test_interface/test_lithium_ion.py @@ -97,7 +97,7 @@ def test_discretisation_lithium_ion(self): # Test whole_cell = ["negative electrode", "separator", "positive electrode"] - submesh = mesh.combine_submeshes(*whole_cell) + submesh = mesh[whole_cell] y = np.concatenate( [ submesh.nodes**2, diff --git a/tests/integration/test_spatial_methods/test_finite_volume.py b/tests/integration/test_spatial_methods/test_finite_volume.py index 67cf28ceaf..962e61804a 100644 --- a/tests/integration/test_spatial_methods/test_finite_volume.py +++ b/tests/integration/test_spatial_methods/test_finite_volume.py @@ -54,11 +54,11 @@ def get_error(n): disc.set_variable_slices([var]) # Define exact solutions - combined_submesh = mesh.combine_submeshes(*whole_cell) - x = combined_submesh.nodes + submesh = mesh[whole_cell] + x = submesh.nodes y = np.sin(x) ** 2 # var = sin(x)**2 --> dvardx = 2*sin(x)*cos(x) - x_edge = combined_submesh.edges + x_edge = submesh.edges grad_exact = 2 * np.sin(x_edge) * np.cos(x_edge) # Discretise and evaluate @@ -90,8 +90,8 @@ def get_error(n): # create mesh and discretisation mesh = get_mesh_for_testing(n) disc = pybamm.Discretisation(mesh, spatial_methods) - combined_submesh = mesh.combine_submeshes(*whole_cell) - x = combined_submesh.nodes + submesh = mesh[whole_cell] + x = submesh.nodes x_edge = pybamm.standard_spatial_vars.x_edge # Define flux and eqn diff --git a/tests/integration/test_spatial_methods/test_spectral_volume.py b/tests/integration/test_spatial_methods/test_spectral_volume.py index a9e10ea78a..5a3ef6e88a 100644 --- a/tests/integration/test_spatial_methods/test_spectral_volume.py +++ b/tests/integration/test_spatial_methods/test_spectral_volume.py @@ -116,11 +116,11 @@ def get_error(n): disc.set_variable_slices([var]) # Define exact solutions - combined_submesh = mesh.combine_submeshes(*whole_cell) - x = combined_submesh.nodes + submesh = mesh[whole_cell] + x = submesh.nodes y = np.sin(x) ** 2 # var = sin(x)**2 --> dvardx = 2*sin(x)*cos(x) - x_edge = combined_submesh.edges + x_edge = submesh.edges grad_exact = 2 * np.sin(x_edge) * np.cos(x_edge) # Discretise and evaluate @@ -154,8 +154,8 @@ def get_error(n): # create mesh and discretisation mesh = get_mesh_for_testing(n) disc = pybamm.Discretisation(mesh, spatial_methods) - combined_submesh = mesh.combine_submeshes(*whole_cell) - x = combined_submesh.nodes + submesh = mesh[whole_cell] + x = submesh.nodes x_edge = pybamm.standard_spatial_vars.x_edge # Define flux and bcs diff --git a/tests/unit/test_citations.py b/tests/unit/test_citations.py index ebb2a01538..015a20bfde 100644 --- a/tests/unit/test_citations.py +++ b/tests/unit/test_citations.py @@ -132,14 +132,6 @@ def test_sulzer_2019(self): pybamm.lead_acid.LOQS(build=False) self.assertIn("Sulzer2019asymptotic", citations._papers_to_cite) - citations._reset() - pybamm.lead_acid.FOQS(build=False) - self.assertIn("Sulzer2019asymptotic", citations._papers_to_cite) - - citations._reset() - pybamm.lead_acid.Composite(build=False) - self.assertIn("Sulzer2019asymptotic", citations._papers_to_cite) - citations._reset() pybamm.lead_acid.Full(build=False) self.assertIn("Sulzer2019physical", citations._papers_to_cite) diff --git a/tests/unit/test_discretisations/test_discretisation.py b/tests/unit/test_discretisations/test_discretisation.py index 841296614e..01dd02a2b4 100644 --- a/tests/unit/test_discretisations/test_discretisation.py +++ b/tests/unit/test_discretisations/test_discretisation.py @@ -258,9 +258,9 @@ def test_discretise_slicing(self): self.assertEqual(disc.y_slices, {c: [slice(0, 100)]}) - combined_submesh = mesh.combine_submeshes(*whole_cell) + submesh = mesh[whole_cell] - c_true = combined_submesh.nodes**2 + c_true = submesh.nodes**2 y = c_true np.testing.assert_array_equal(y[disc.y_slices[c][0]], c_true) @@ -280,7 +280,7 @@ def test_discretise_slicing(self): np.testing.assert_array_equal( disc.bounds[1], [np.inf] * 100 + [1] * 100 + [np.inf] * 40 ) - d_true = 4 * combined_submesh.nodes + d_true = 4 * submesh.nodes jn_true = mesh["negative electrode"].nodes ** 3 y = np.concatenate([c_true, d_true, jn_true]) np.testing.assert_array_equal(y[disc.y_slices[c][0]], c_true) @@ -310,7 +310,7 @@ def test_discretise_slicing(self): np.testing.assert_array_equal( disc.bounds[1], [np.inf] * 100 + [1] * 100 + [np.inf] * 100 ) - d_true = 4 * combined_submesh.nodes + d_true = 4 * submesh.nodes jn_true = mesh["negative electrode"].nodes ** 3 y = np.concatenate([c_true, d_true, jn_true]) np.testing.assert_array_equal(y[disc.y_slices[c][0]], c_true) @@ -364,15 +364,7 @@ def test_process_symbol_base(self): # binary operator binary = var + scal binary_disc = disc.process_symbol(binary) - self.assertIsInstance(binary_disc, pybamm.Addition) - self.assertIsInstance(binary_disc.children[0], pybamm.StateVector) - self.assertIsInstance(binary_disc.children[1], pybamm.Scalar) - - bin2 = scal + var - bin2_disc = disc.process_symbol(bin2) - self.assertIsInstance(bin2_disc, pybamm.Addition) - self.assertIsInstance(bin2_disc.children[0], pybamm.Scalar) - self.assertIsInstance(bin2_disc.children[1], pybamm.StateVector) + self.assertEqual(binary_disc, 5 + pybamm.StateVector(slice(0, 53))) # non-spatial unary operator un1 = -var @@ -436,27 +428,14 @@ def test_process_complex_expression(self): disc.y_slices = {var1: [slice(53)], var2: [slice(53, 106)]} exp_disc = disc.process_symbol(expression) - self.assertIsInstance(exp_disc, pybamm.Division) - # left side - self.assertIsInstance(exp_disc.left, pybamm.Multiplication) - self.assertIsInstance(exp_disc.left.left, pybamm.Scalar) - self.assertIsInstance(exp_disc.left.right, pybamm.Power) - self.assertIsInstance(exp_disc.left.right.left, pybamm.Scalar) - self.assertIsInstance(exp_disc.left.right.right, pybamm.StateVector) - self.assertEqual( - exp_disc.left.right.right.y_slices[0], - disc.y_slices[var2][0], - ) - # right side - self.assertIsInstance(exp_disc.right, pybamm.Addition) - self.assertIsInstance(exp_disc.right.left, pybamm.Subtraction) - self.assertIsInstance(exp_disc.right.left.left, pybamm.StateVector) self.assertEqual( - exp_disc.right.left.left.y_slices[0], - disc.y_slices[var1][0], + exp_disc, + (5.0 * (3.0 ** pybamm.StateVector(slice(53, 106)))) + / ( + (-4.0 + pybamm.StateVector(slice(0, 53))) + + pybamm.StateVector(slice(53, 106)) + ), ) - self.assertIsInstance(exp_disc.right.left.right, pybamm.Scalar) - self.assertIsInstance(exp_disc.right.right, pybamm.StateVector) def test_discretise_spatial_operator(self): # create discretisation @@ -474,8 +453,8 @@ def test_discretise_spatial_operator(self): self.assertIsInstance(eqn_disc, pybamm.MatrixMultiplication) self.assertIsInstance(eqn_disc.children[0], pybamm.Matrix) - combined_submesh = mesh.combine_submeshes(*whole_cell) - y = combined_submesh.nodes**2 + submesh = mesh[whole_cell] + y = submesh.nodes**2 var_disc = disc.process_symbol(var) # grad and var are identity operators here (for testing purposes) np.testing.assert_array_equal( @@ -491,7 +470,7 @@ def test_discretise_spatial_operator(self): self.assertIsInstance(eqn_disc.children[1], pybamm.MatrixMultiplication) self.assertIsInstance(eqn_disc.children[1].children[0], pybamm.Matrix) - y = combined_submesh.nodes**2 + y = submesh.nodes**2 var_disc = disc.process_symbol(var) # grad and var are identity operators here (for testing purposes) np.testing.assert_array_equal( @@ -532,9 +511,9 @@ def test_process_dict(self): disc = get_discretisation_for_testing() mesh = disc.mesh - combined_submesh = mesh.combine_submeshes(*whole_cell) + submesh = mesh[whole_cell] - y = combined_submesh.nodes[:, np.newaxis] ** 2 + y = submesh.nodes[:, np.newaxis] ** 2 disc.bcs = boundary_conditions disc.set_variable_slices(list(rhs.keys())) @@ -545,7 +524,7 @@ def test_process_dict(self): y0 = disc.process_dict(initial_conditions) np.testing.assert_array_equal( y0[c].evaluate(0, None), - 3 * np.ones_like(combined_submesh.nodes[:, np.newaxis]), + 3 * np.ones_like(submesh.nodes[:, np.newaxis]), ) # vars processed_vars = disc.process_dict(variables) @@ -559,9 +538,9 @@ def test_process_dict(self): rhs = {c: pybamm.div(N), T: pybamm.div(q)} initial_conditions = {c: pybamm.Scalar(3), T: pybamm.Scalar(5)} boundary_conditions = {} - y = np.concatenate( - [combined_submesh.nodes**2, mesh["negative electrode"].nodes ** 4] - )[:, np.newaxis] + y = np.concatenate([submesh.nodes**2, mesh["negative electrode"].nodes ** 4])[ + :, np.newaxis + ] variables = list(rhs.keys()) disc.set_variable_slices(variables) @@ -577,7 +556,7 @@ def test_process_dict(self): y0 = disc.process_dict(initial_conditions) np.testing.assert_array_equal( y0[c].evaluate(0, None), - 3 * np.ones_like(combined_submesh.nodes[:, np.newaxis]), + 3 * np.ones_like(submesh.nodes[:, np.newaxis]), ) np.testing.assert_array_equal( y0[T].evaluate(0, None), @@ -610,12 +589,12 @@ def test_process_model_ode(self): disc = get_discretisation_for_testing() mesh = disc.mesh - combined_submesh = mesh.combine_submeshes(*whole_cell) + submesh = mesh[whole_cell] disc.process_model(model) y0 = model.concatenated_initial_conditions.evaluate() np.testing.assert_array_equal( - y0, 3 * np.ones_like(combined_submesh.nodes[:, np.newaxis]) + y0, 3 * np.ones_like(submesh.nodes[:, np.newaxis]) ) np.testing.assert_array_equal(y0, model.concatenated_rhs.evaluate(None, y0)) @@ -625,15 +604,15 @@ def test_process_model_ode(self): # mass matrix is identity np.testing.assert_array_equal( - np.eye(combined_submesh.nodes.shape[0]), model.mass_matrix.entries.toarray() + np.eye(submesh.nodes.shape[0]), model.mass_matrix.entries.toarray() ) # Create StateVector to differentiate model with respect to - y = pybamm.StateVector(slice(0, combined_submesh.npts)) + y = pybamm.StateVector(slice(0, submesh.npts)) # jacobian is identity jacobian = model.concatenated_rhs.jac(y).evaluate(0, y0) - np.testing.assert_array_equal(np.eye(combined_submesh.npts), jacobian.toarray()) + np.testing.assert_array_equal(np.eye(submesh.npts), jacobian.toarray()) # several equations T = pybamm.Variable("T", domain=["negative electrode"]) @@ -659,7 +638,7 @@ def test_process_model_ode(self): y0_expect = np.empty((0, 1)) for var_id, _ in sorted(disc.y_slices.items(), key=lambda kv: kv[1]): if var_id == c: - vect = 2 * np.ones_like(combined_submesh.nodes[:, np.newaxis]) + vect = 2 * np.ones_like(submesh.nodes[:, np.newaxis]) elif var_id == T: vect = 5 * np.ones_like(mesh["negative electrode"].nodes[:, np.newaxis]) else: @@ -765,36 +744,34 @@ def test_process_model_dae(self): mesh = disc.mesh disc.process_model(model) - combined_submesh = mesh.combine_submeshes(*whole_cell) + submesh = mesh[whole_cell] y0 = model.concatenated_initial_conditions.evaluate() np.testing.assert_array_equal( y0, np.concatenate( [ - 3 * np.ones_like(combined_submesh.nodes), - 6 * np.ones_like(combined_submesh.nodes), + 3 * np.ones_like(submesh.nodes), + 6 * np.ones_like(submesh.nodes), ] )[:, np.newaxis], ) # grad and div are identity operators here np.testing.assert_array_equal( - y0[: combined_submesh.npts], model.concatenated_rhs.evaluate(None, y0) + y0[: submesh.npts], model.concatenated_rhs.evaluate(None, y0) ) np.testing.assert_array_equal( model.concatenated_algebraic.evaluate(None, y0), - np.zeros_like(combined_submesh.nodes[:, np.newaxis]), + np.zeros_like(submesh.nodes[:, np.newaxis]), ) # mass matrix is identity upper left, zeros elsewhere mass = block_diag( ( - np.eye(np.size(combined_submesh.nodes)), - np.zeros( - (np.size(combined_submesh.nodes), np.size(combined_submesh.nodes)) - ), + np.eye(np.size(submesh.nodes)), + np.zeros((np.size(submesh.nodes), np.size(submesh.nodes))), ) ) np.testing.assert_array_equal( @@ -810,17 +787,17 @@ def test_process_model_dae(self): jacobian_actual = np.block( [ [ - np.eye(np.size(combined_submesh.nodes)), + np.eye(np.size(submesh.nodes)), np.zeros( ( - np.size(combined_submesh.nodes), - np.size(combined_submesh.nodes), + np.size(submesh.nodes), + np.size(submesh.nodes), ) ), ], [ - -2 * np.eye(np.size(combined_submesh.nodes)), - np.eye(np.size(combined_submesh.nodes)), + -2 * np.eye(np.size(submesh.nodes)), + np.eye(np.size(submesh.nodes)), ], ] ) @@ -861,12 +838,12 @@ def test_process_model_algebraic(self): mesh = disc.mesh disc.process_model(model) - combined_submesh = mesh.combine_submeshes(*whole_cell) + submesh = mesh[whole_cell] y0 = model.concatenated_initial_conditions.evaluate() np.testing.assert_array_equal( y0, - np.zeros_like(combined_submesh.nodes)[:, np.newaxis], + np.zeros_like(submesh.nodes)[:, np.newaxis], ) # grad and div are identity operators here @@ -876,19 +853,17 @@ def test_process_model_algebraic(self): np.testing.assert_array_equal( model.concatenated_algebraic.evaluate(None, y0), - -np.ones_like(combined_submesh.nodes[:, np.newaxis]), + -np.ones_like(submesh.nodes[:, np.newaxis]), ) # mass matrix is identity upper left, zeros elsewhere - mass = np.zeros( - (np.size(combined_submesh.nodes), np.size(combined_submesh.nodes)) - ) + mass = np.zeros((np.size(submesh.nodes), np.size(submesh.nodes))) np.testing.assert_array_equal(mass, model.mass_matrix.entries.toarray()) # jacobian y = pybamm.StateVector(slice(0, np.size(y0))) jacobian = model.concatenated_algebraic.jac(y).evaluate(0, y0) - np.testing.assert_array_equal(np.eye(combined_submesh.npts), jacobian.toarray()) + np.testing.assert_array_equal(np.eye(submesh.npts), jacobian.toarray()) def test_process_model_concatenation(self): # concatenation of variables as the key @@ -910,14 +885,12 @@ def test_process_model_concatenation(self): disc = get_discretisation_for_testing() mesh = disc.mesh - combined_submesh = mesh.combine_submeshes( - "negative electrode", "separator", "positive electrode" - ) + submesh = mesh[("negative electrode", "separator", "positive electrode")] disc.process_model(model) y0 = model.concatenated_initial_conditions.evaluate() np.testing.assert_array_equal( - y0, 3 * np.ones_like(combined_submesh.nodes[:, np.newaxis]) + y0, 3 * np.ones_like(submesh.nodes[:, np.newaxis]) ) # grad and div are identity operators here @@ -982,28 +955,28 @@ def test_broadcast(self): disc = get_discretisation_for_testing() mesh = disc.mesh - combined_submesh = mesh.combine_submeshes(*whole_cell) + submesh = mesh[whole_cell] # scalar broad = disc.process_symbol(pybamm.FullBroadcast(a, whole_cell, {})) np.testing.assert_array_equal( broad.evaluate(inputs={"a": 7}), - 7 * np.ones_like(combined_submesh.nodes[:, np.newaxis]), + 7 * np.ones_like(submesh.nodes[:, np.newaxis]), ) self.assertEqual(broad.domain, whole_cell) broad_disc = disc.process_symbol(broad) self.assertIsInstance(broad_disc, pybamm.Multiplication) - self.assertIsInstance(broad_disc.children[0], pybamm.InputParameter) - self.assertIsInstance(broad_disc.children[1], pybamm.Vector) + self.assertIsInstance(broad_disc.children[0], pybamm.Vector) + self.assertIsInstance(broad_disc.children[1], pybamm.InputParameter) # process Broadcast variable disc.y_slices = {var: [slice(1)]} broad1 = pybamm.FullBroadcast(var, ["negative electrode"], None) broad1_disc = disc.process_symbol(broad1) self.assertIsInstance(broad1_disc, pybamm.Multiplication) - self.assertIsInstance(broad1_disc.children[0], pybamm.StateVector) - self.assertIsInstance(broad1_disc.children[1], pybamm.Vector) + self.assertIsInstance(broad1_disc.children[0], pybamm.Vector) + self.assertIsInstance(broad1_disc.children[1], pybamm.StateVector) # broadcast to edges broad_to_edges = pybamm.FullBroadcastToEdges(a, ["negative electrode"], None) @@ -1333,7 +1306,7 @@ def test_process_input_variable(self): a = pybamm.InputParameter("a", ["negative electrode", "separator"]) a_disc = disc.process_symbol(a) - n = disc.mesh.combine_submeshes(*a.domain).npts + n = disc.mesh[a.domain].npts self.assertEqual(a_disc._expected_size, n) def test_process_not_constant(self): diff --git a/tests/unit/test_expression_tree/test_averages.py b/tests/unit/test_expression_tree/test_averages.py index 5fbee356d9..fbfa5526d3 100644 --- a/tests/unit/test_expression_tree/test_averages.py +++ b/tests/unit/test_expression_tree/test_averages.py @@ -167,6 +167,16 @@ def test_x_average(self): self.assertEqual(a.domain, ["positive particle"]) self.assertIsInstance(av_a, pybamm.XAverage) + # Addition or Subtraction + a = pybamm.Variable("a", domain="domain") + b = pybamm.Variable("b", domain="domain") + self.assertEqual( + pybamm.x_average(a + b), pybamm.x_average(a) + pybamm.x_average(b) + ) + self.assertEqual( + pybamm.x_average(a - b), pybamm.x_average(a) - pybamm.x_average(b) + ) + def test_size_average(self): # no domain @@ -247,6 +257,16 @@ def test_r_average(self): ): pybamm.r_average(symbol_on_edges) + # Addition or Subtraction + a = pybamm.Variable("a", domain="domain") + b = pybamm.Variable("b", domain="domain") + self.assertEqual( + pybamm.r_average(a + b), pybamm.r_average(a) + pybamm.r_average(b) + ) + self.assertEqual( + pybamm.r_average(a - b), pybamm.r_average(a) - pybamm.r_average(b) + ) + def test_yz_average(self): a = pybamm.Scalar(1) z_average_a = pybamm.z_average(a) @@ -291,6 +311,22 @@ def test_yz_average(self): ): pybamm.z_average(symbol_on_edges) + # Addition or Subtraction + a = pybamm.Variable("a", domain="current collector") + b = pybamm.Variable("b", domain="current collector") + self.assertEqual( + pybamm.yz_average(a + b), pybamm.yz_average(a) + pybamm.yz_average(b) + ) + self.assertEqual( + pybamm.yz_average(a - b), pybamm.yz_average(a) - pybamm.yz_average(b) + ) + self.assertEqual( + pybamm.z_average(a + b), pybamm.z_average(a) + pybamm.z_average(b) + ) + self.assertEqual( + pybamm.z_average(a - b), pybamm.z_average(a) - pybamm.z_average(b) + ) + if __name__ == "__main__": print("Add -v for more debug output") diff --git a/tests/unit/test_expression_tree/test_binary_operators.py b/tests/unit/test_expression_tree/test_binary_operators.py index 58fba662ff..913a943e88 100644 --- a/tests/unit/test_expression_tree/test_binary_operators.py +++ b/tests/unit/test_expression_tree/test_binary_operators.py @@ -284,20 +284,23 @@ def test_source_error(self): pybamm.source(v, w) def test_heaviside(self): - a = pybamm.Scalar(1) b = pybamm.StateVector(slice(0, 1)) - heav = a < b + heav = 1 < b self.assertEqual(heav.evaluate(y=np.array([2])), 1) self.assertEqual(heav.evaluate(y=np.array([1])), 0) self.assertEqual(heav.evaluate(y=np.array([0])), 0) self.assertEqual(str(heav), "1.0 < y[0:1]") - heav = a >= b + heav = 1 >= b self.assertEqual(heav.evaluate(y=np.array([2])), 0) self.assertEqual(heav.evaluate(y=np.array([1])), 1) self.assertEqual(heav.evaluate(y=np.array([0])), 1) self.assertEqual(str(heav), "y[0:1] <= 1.0") + # simplifications + self.assertEqual(1 < b + 2, -1 < b) + self.assertEqual(b + 1 > 2, b > 1) + def test_equality(self): a = pybamm.Scalar(1) b = pybamm.StateVector(slice(0, 1)) @@ -314,13 +317,13 @@ def test_sigmoid(self): self.assertAlmostEqual(sigm.evaluate(y=np.array([2]))[0, 0], 1) self.assertEqual(sigm.evaluate(y=np.array([1])), 0.5) self.assertAlmostEqual(sigm.evaluate(y=np.array([0]))[0, 0], 0) - self.assertEqual(str(sigm), "(1.0 + tanh((10.0 * y[0:1]) - 10.0)) / 2.0") + self.assertEqual(str(sigm), "0.5 + 0.5 * tanh(-10.0 + 10.0 * y[0:1])") sigm = pybamm.sigmoid(b, a, 10) self.assertAlmostEqual(sigm.evaluate(y=np.array([2]))[0, 0], 0) self.assertEqual(sigm.evaluate(y=np.array([1])), 0.5) self.assertAlmostEqual(sigm.evaluate(y=np.array([0]))[0, 0], 1) - self.assertEqual(str(sigm), "(1.0 + tanh(10.0 - (10.0 * y[0:1]))) / 2.0") + self.assertEqual(str(sigm), "0.5 + 0.5 * tanh(10.0 - (10.0 * y[0:1]))") def test_modulo(self): a = pybamm.StateVector(slice(0, 1)) @@ -356,19 +359,19 @@ def test_softminus_softplus(self): self.assertAlmostEqual(minimum.evaluate(y=np.array([2]))[0, 0], 1) self.assertAlmostEqual(minimum.evaluate(y=np.array([0]))[0, 0], 0) self.assertEqual( - str(minimum), "log(1.9287498479639178e-22 + exp(-50.0 * y[0:1])) / -50.0" + str(minimum), "-0.02 * log(1.9287498479639178e-22 + exp(-50.0 * y[0:1]))" ) maximum = pybamm.softplus(a, b, 50) self.assertAlmostEqual(maximum.evaluate(y=np.array([2]))[0, 0], 2) self.assertAlmostEqual(maximum.evaluate(y=np.array([0]))[0, 0], 1) self.assertEqual( - str(maximum)[:15], - "log(5.184705528587072e+21 + exp(50.0 * y[0:1])) / 50.0"[:15], + str(maximum)[:20], + "0.02 * log(5.184705528587072e+21 + exp(50.0 * y[0:1]))"[:20], ) self.assertEqual( - str(maximum)[-33:], - "log(5.184705528587072e+21 + exp(50.0 * y[0:1])) / 50.0"[-33:], + str(maximum)[-20:], + "0.02 * log(5.184705528587072e+21 + exp(50.0 * y[0:1]))"[-20:], ) # Test that smooth min/max are used when the setting is changed @@ -392,6 +395,7 @@ def test_binary_simplifications(self): a = pybamm.Scalar(0) b = pybamm.Scalar(1) c = pybamm.Parameter("c") + d = pybamm.Parameter("d") v = pybamm.Vector(np.zeros((10, 1))) v1 = pybamm.Vector(np.ones((10, 1))) f = pybamm.StateVector(slice(0, 10)) @@ -419,18 +423,15 @@ def test_binary_simplifications(self): self.assertEqual((var**broad2_edge).right, broad2_edge) # addition - self.assertIsInstance((a + b), pybamm.Scalar) - self.assertEqual((a + b).evaluate(), 1) - self.assertIsInstance((b + b), pybamm.Scalar) - self.assertEqual((b + b).evaluate(), 2) - self.assertIsInstance((b + a), pybamm.Scalar) - self.assertEqual((b + a).evaluate(), 1) - self.assertIsInstance((0 + b), pybamm.Scalar) - self.assertEqual((0 + b).evaluate(), 1) - self.assertIsInstance((0 + c), pybamm.Parameter) - self.assertIsInstance((c + 0), pybamm.Parameter) - self.assertIsInstance((c + 1), pybamm.Addition) - self.assertIsInstance((1 + c), pybamm.Addition) + self.assertEqual(a + b, pybamm.Scalar(1)) + self.assertEqual(b + b, pybamm.Scalar(2)) + self.assertEqual(b + a, pybamm.Scalar(1)) + self.assertEqual(0 + b, pybamm.Scalar(1)) + self.assertEqual(0 + c, c) + self.assertEqual(c + 0, c) + # addition with subtraction + self.assertEqual(c + (d - c), d) + self.assertEqual((c - d) + d, c) # addition with broadcast zero self.assertIsInstance((1 + broad0), pybamm.PrimaryBroadcast) np.testing.assert_array_equal((1 + broad0).child.evaluate(), 1) @@ -441,46 +442,43 @@ def test_binary_simplifications(self): # addition with broadcasts self.assertEqual((c + broad2), pybamm.PrimaryBroadcast(c + 2, "domain")) self.assertEqual((broad2 + c), pybamm.PrimaryBroadcast(2 + c, "domain")) + # addition with negate + self.assertEqual(c + -d, c - d) + self.assertEqual(-c + d, d - c) # subtraction - self.assertIsInstance((a - b), pybamm.Scalar) - self.assertEqual((a - b).evaluate(), -1) - self.assertIsInstance((b - b), pybamm.Scalar) - self.assertEqual((b - b).evaluate(), 0) - self.assertIsInstance((b - a), pybamm.Scalar) - self.assertEqual((b - a).evaluate(), 1) + self.assertEqual(a - b, pybamm.Scalar(-1)) + self.assertEqual(b - b, pybamm.Scalar(0)) + self.assertEqual(b - a, pybamm.Scalar(1)) + # subtraction with addition + self.assertEqual(c - (d + c), -d) + self.assertEqual(c - (c - d), d) + self.assertEqual((c + d) - d, c) + self.assertEqual((d + c) - d, c) + self.assertEqual((d - c) - d, -c) # subtraction with broadcasts self.assertEqual((c - broad2), pybamm.PrimaryBroadcast(c - 2, "domain")) self.assertEqual((broad2 - c), pybamm.PrimaryBroadcast(2 - c, "domain")) # subtraction from itself self.assertEqual((c - c), pybamm.Scalar(0)) self.assertEqual((broad2 - broad2), broad0) + # subtraction with negate + self.assertEqual((c - (-d)), c + d) # addition and subtraction with matrix zero - self.assertIsInstance((b + v), pybamm.Array) - np.testing.assert_array_equal((b + v).evaluate(), np.ones((10, 1))) - self.assertIsInstance((v + b), pybamm.Array) - np.testing.assert_array_equal((v + b).evaluate(), np.ones((10, 1))) - self.assertIsInstance((b - v), pybamm.Array) - np.testing.assert_array_equal((b - v).evaluate(), np.ones((10, 1))) - self.assertIsInstance((v - b), pybamm.Array) - np.testing.assert_array_equal((v - b).evaluate(), -np.ones((10, 1))) + self.assertEqual(b + v, pybamm.Vector(np.ones((10, 1)))) + self.assertEqual(v + b, pybamm.Vector(np.ones((10, 1)))) + self.assertEqual(b - v, pybamm.Vector(np.ones((10, 1)))) + self.assertEqual(v - b, pybamm.Vector(-np.ones((10, 1)))) # multiplication - self.assertIsInstance((a * b), pybamm.Scalar) - self.assertEqual((a * b).evaluate(), 0) - self.assertIsInstance((b * a), pybamm.Scalar) - self.assertEqual((b * a).evaluate(), 0) - self.assertIsInstance((b * b), pybamm.Scalar) - self.assertEqual((b * b).evaluate(), 1) - self.assertIsInstance((a * a), pybamm.Scalar) - self.assertEqual((a * a).evaluate(), 0) - self.assertIsInstance((a * c), pybamm.Scalar) - self.assertEqual((a * c).evaluate(), 0) - self.assertIsInstance((c * a), pybamm.Scalar) - self.assertEqual((c * a).evaluate(), 0) - self.assertIsInstance((b * c), pybamm.Parameter) - self.assertIsInstance((2 * c), pybamm.Multiplication) + self.assertEqual(a * b, pybamm.Scalar(0)) + self.assertEqual(b * a, pybamm.Scalar(0)) + self.assertEqual(b * b, pybamm.Scalar(1)) + self.assertEqual(a * a, pybamm.Scalar(0)) + self.assertEqual(a * c, pybamm.Scalar(0)) + self.assertEqual(c * a, pybamm.Scalar(0)) + self.assertEqual(b * c, c) # multiplication with -1 self.assertEqual((c * -1), (-c)) self.assertEqual((-1 * c), (-c)) @@ -488,15 +486,16 @@ def test_binary_simplifications(self): self.assertEqual((-c * -f), (c * f)) self.assertEqual((-c * 4), (c * -4)) self.assertEqual((4 * -c), (-4 * c)) + # multiplication with division + self.assertEqual((c * (d / c)), d) + self.assertEqual((c / d) * d, c) # multiplication with broadcasts self.assertEqual((c * broad2), pybamm.PrimaryBroadcast(c * 2, "domain")) self.assertEqual((broad2 * c), pybamm.PrimaryBroadcast(2 * c, "domain")) # multiplication with matrix zero - self.assertIsInstance((b * v), pybamm.Array) - np.testing.assert_array_equal((b * v).evaluate(), np.zeros((10, 1))) - self.assertIsInstance((v * b), pybamm.Array) - np.testing.assert_array_equal((v * b).evaluate(), np.zeros((10, 1))) + self.assertEqual(b * v, pybamm.Vector(np.zeros((10, 1)))) + self.assertEqual(v * b, pybamm.Vector(np.zeros((10, 1)))) # multiplication with matrix one self.assertEqual((f * v1), f) self.assertEqual((v1 * f), f) @@ -518,8 +517,11 @@ def test_binary_simplifications(self): self.assertEqual((broad2 / broad2), broad1) # division with a negation self.assertEqual((-c / -f), (c / f)) - self.assertEqual((-c / 4), (c / -4)) + self.assertEqual((-c / 4), -0.25 * c) self.assertEqual((4 / -c), (-4 / c)) + # division with multiplication + self.assertEqual((c * d) / c, d) + self.assertEqual((d * c) / c, d) # division with broadcasts self.assertEqual((c / broad2), pybamm.PrimaryBroadcast(c / 2, "domain")) self.assertEqual((broad2 / c), pybamm.PrimaryBroadcast(2 / c, "domain")) @@ -555,7 +557,7 @@ def conc_broad(x, y, z): self.assertEqual((a + b), conc_broad(12, 14, 16)) self.assertIsInstance((a + c), pybamm.Concatenation) - # No simplifications if are Variable or StateVector objects + # No simplifications if all are Variable or StateVector objects v = pybamm.concatenation( pybamm.Variable("x", "negative electrode"), pybamm.Variable("y", "separator"), @@ -568,9 +570,9 @@ def test_advanced_binary_simplifications(self): # MatMul simplifications that often appear when discretising spatial operators A = pybamm.Matrix(np.random.rand(10, 10)) B = pybamm.Matrix(np.random.rand(10, 10)) - # C = pybamm.Matrix(np.random.rand(10, 10)) + C = pybamm.Matrix(np.random.rand(10, 10)) var = pybamm.StateVector(slice(0, 10)) - # var2 = pybamm.StateVector(slice(10, 20)) + var2 = pybamm.StateVector(slice(10, 20)) vec = pybamm.Vector(np.random.rand(10)) # Do A@B first if it is constant @@ -590,10 +592,10 @@ def test_advanced_binary_simplifications(self): self.assertEqual(expr, (((A @ B) @ var) - (A @ vec))) # Distribute the @ operator to a sum if both symbols being summed are matmuls - # expr = A @ (B @ var + C @ var2) - # self.assertEqual(expr, ((A @ B) @ var + (A @ C) @ var2)) - # expr = A @ (B @ var - C @ var2) - # self.assertEqual(expr, ((A @ B) @ var - (A @ C) @ var2)) + expr = A @ (B @ var + C @ var2) + self.assertEqual(expr, ((A @ B) @ var + (A @ C) @ var2)) + expr = A @ (B @ var - C @ var2) + self.assertEqual(expr, ((A @ B) @ var - (A @ C) @ var2)) # Reduce (A@var + B@var) to ((A+B)@var) expr = A @ var + B @ var @@ -605,16 +607,18 @@ def test_advanced_binary_simplifications(self): expr = A @ (var * 5) self.assertEqual(expr, ((A * 5) @ var)) # Do A/e first if it is constant - expr = A @ (var / 5) - self.assertEqual(expr, ((A / 5) @ var)) - # Do (d*A) first if it is constant + expr = A @ (var / 2) + self.assertEqual(expr, ((A / 2) @ var)) + # Do (vec*A) first if it is constant expr = vec * (A @ var) self.assertEqual(expr, ((vec * A) @ var)) expr = (A @ var) * vec self.assertEqual(expr, ((vec * A) @ var)) - # Do (A/d) first if it is constant + # Do (A/vec) first if it is constant expr = (A @ var) / vec - self.assertEqual(expr, ((A / vec) @ var)) + self.assertIsInstance(expr, pybamm.MatrixMultiplication) + np.testing.assert_array_almost_equal(expr.left.evaluate(), (A / vec).evaluate()) + self.assertEqual(expr.children[1], var) # simplify additions and subtractions expr = 7 + (var + 5) @@ -651,44 +655,40 @@ def test_advanced_binary_simplifications(self): self.assertEqual(expr, (-2 - var)) # simplify multiplications and divisions - expr = 7 * (var * 5) - self.assertEqual(expr, (35 * var)) - expr = (var * 5) * 7 - self.assertEqual(expr, (var * 35)) - expr = 7 * (5 * var) - self.assertEqual(expr, (35 * var)) - expr = (5 * var) * 7 - self.assertEqual(expr, (35 * var)) - expr = 7 * (var / 5) - self.assertEqual(expr, ((7 / 5) * var)) - expr = (var / 5) * 7 - self.assertEqual(expr, (var * (7 / 5))) - expr = (var * 5) / 7 - self.assertEqual(expr, (var * (5 / 7))) - expr = (5 * var) / 7 - self.assertEqual(expr, ((5 / 7) * var)) - expr = 5 / (7 * var) - self.assertEqual(expr, ((5 / 7) / var)) - expr = 5 / (var * 7) - self.assertEqual(expr, ((5 / 7) / var)) - expr = (var / 5) / 7 - self.assertEqual(expr, (var / 35)) - expr = (5 / var) / 7 - self.assertEqual(expr, ((5 / 7) / var)) - expr = 5 / (7 / var) - self.assertEqual(expr, ((5 / 7) * var)) - expr = 5 / (var / 7) - self.assertEqual(expr, (35 / var)) + expr = 10 * (var * 5) + self.assertEqual(expr, 50 * var) + expr = (var * 5) * 10 + self.assertEqual(expr, var * 50) + expr = 10 * (5 * var) + self.assertEqual(expr, 50 * var) + expr = (5 * var) * 10 + self.assertEqual(expr, 50 * var) + expr = 10 * (var / 5) + self.assertEqual(expr, (10 / 5) * var) + expr = (var / 5) * 10 + self.assertEqual(expr, var * (10 / 5)) + expr = (var * 5) / 10 + self.assertEqual(expr, var * (5 / 10)) + expr = (5 * var) / 10 + self.assertEqual(expr, (5 / 10) * var) + expr = 5 / (10 * var) + self.assertEqual(expr, (5 / 10) / var) + expr = 5 / (var * 10) + self.assertEqual(expr, (5 / 10) / var) + expr = (5 / var) / 10 + self.assertEqual(expr, (5 / 10) / var) + expr = 5 / (10 / var) + self.assertEqual(expr, (5 / 10) * var) + expr = 5 / (var / 10) + self.assertEqual(expr, 50 / var) # use power rules on multiplications and divisions expr = (var * 5) ** 2 - self.assertEqual(expr, (var**2 * 25)) + self.assertEqual(expr, var**2 * 25) expr = (5 * var) ** 2 - self.assertEqual(expr, (25 * var**2)) - expr = (var / 5) ** 2 - self.assertEqual(expr, (var**2 / 25)) + self.assertEqual(expr, 25 * var**2) expr = (5 / var) ** 2 - self.assertEqual(expr, (25 / var**2)) + self.assertEqual(expr, 25 / var**2) def test_inner_simplifications(self): a1 = pybamm.Scalar(0) diff --git a/tests/unit/test_expression_tree/test_broadcasts.py b/tests/unit/test_expression_tree/test_broadcasts.py index 300deb9710..f9500a6f90 100644 --- a/tests/unit/test_expression_tree/test_broadcasts.py +++ b/tests/unit/test_expression_tree/test_broadcasts.py @@ -52,6 +52,10 @@ def test_primary_broadcast(self): ) a = pybamm.Symbol("a", domain="current collector") + with self.assertRaisesRegex( + pybamm.DomainError, "Cannot Broadcast an object into empty domain" + ): + pybamm.PrimaryBroadcast(a, []) with self.assertRaisesRegex( pybamm.DomainError, "Primary broadcast from current collector" ): @@ -209,6 +213,11 @@ def test_full_broadcast(self): ), ) + with self.assertRaisesRegex( + pybamm.DomainError, "Cannot do full broadcast to an empty primary domain" + ): + pybamm.FullBroadcast(a, []) + def test_full_broadcast_number(self): broad_a = pybamm.FullBroadcast(1, ["negative electrode"], None) self.assertEqual(broad_a.name, "broadcast") diff --git a/tests/unit/test_expression_tree/test_concatenations.py b/tests/unit/test_expression_tree/test_concatenations.py index 2a1b834d6b..26798d7919 100644 --- a/tests/unit/test_expression_tree/test_concatenations.py +++ b/tests/unit/test_expression_tree/test_concatenations.py @@ -97,6 +97,34 @@ def test_concatenation_auxiliary_domains(self): ): pybamm.concatenation(a, b, c) + def test_concatenations_scale(self): + a = pybamm.Variable("a", domain="test a") + b = pybamm.Variable("b", domain="test b") + + conc = pybamm.concatenation(a, b) + self.assertEqual(conc.scale, 1) + self.assertEqual(conc.reference, 0) + + a._scale = 2 + with self.assertRaisesRegex( + ValueError, "Cannot concatenate symbols with different scales" + ): + pybamm.concatenation(a, b) + + b._scale = 2 + conc = pybamm.concatenation(a, b) + self.assertEqual(conc.scale, 2) + + a._reference = 3 + with self.assertRaisesRegex( + ValueError, "Cannot concatenate symbols with different references" + ): + pybamm.concatenation(a, b) + + b._reference = 3 + conc = pybamm.concatenation(a, b) + self.assertEqual(conc.reference, 3) + def test_concatenation_simplify(self): # Primary broadcast var = pybamm.Variable("var", "current collector") diff --git a/tests/unit/test_expression_tree/test_functions.py b/tests/unit/test_expression_tree/test_functions.py index fbc3625654..71af025218 100644 --- a/tests/unit/test_expression_tree/test_functions.py +++ b/tests/unit/test_expression_tree/test_functions.py @@ -263,7 +263,7 @@ def test_log(self): # Base 10 fun = pybamm.log10(a) - self.assertEqual(fun.evaluate(inputs={"a": 3}), np.log10(3)) + self.assertAlmostEqual(fun.evaluate(inputs={"a": 3}), np.log10(3)) h = 0.0000001 self.assertAlmostEqual( fun.diff(a).evaluate(inputs={"a": 3}), diff --git a/tests/unit/test_expression_tree/test_interpolant.py b/tests/unit/test_expression_tree/test_interpolant.py index 64bb3b0590..8c1f9d274f 100644 --- a/tests/unit/test_expression_tree/test_interpolant.py +++ b/tests/unit/test_expression_tree/test_interpolant.py @@ -13,11 +13,11 @@ def test_errors(self): pybamm.Interpolant(np.ones(10), np.ones(11), pybamm.Symbol("a")) with self.assertRaisesRegex(ValueError, "x2"): pybamm.Interpolant( - (np.ones(12), np.ones(11)), np.ones((10, 12)), pybamm.Symbol("a") + (np.ones(10), np.ones(11)), np.ones((10, 12)), pybamm.Symbol("a") ) with self.assertRaisesRegex(ValueError, "x1"): pybamm.Interpolant( - (np.ones(11), np.ones(10)), np.ones((10, 12)), pybamm.Symbol("a") + (np.ones(11), np.ones(12)), np.ones((10, 12)), pybamm.Symbol("a") ) with self.assertRaisesRegex(ValueError, "y should"): pybamm.Interpolant( @@ -35,11 +35,21 @@ def test_errors(self): pybamm.Interpolant( (np.ones(12), np.ones(10)), np.ones((10, 12)), pybamm.Symbol("a") ) - with self.assertWarns(DeprecationWarning): + + with self.assertRaisesRegex( + ValueError, "len\\(x\\) should equal len\\(children\\)" + ): + pybamm.Interpolant( + (np.ones(10), np.ones(12)), np.ones((10, 12)), pybamm.Symbol("a") + ) + + def test_warnings(self): + + with self.assertWarnsRegex(Warning, "cubic spline"): pybamm.Interpolant( - (np.ones(12), np.ones(10)), - np.ones((10, 12)), - (pybamm.Symbol("a"), pybamm.Symbol("b")), + np.linspace(0, 1, 10), + np.ones(10), + pybamm.Symbol("a"), interpolator="cubic spline", ) @@ -82,7 +92,7 @@ def test_interpolation_1_x_2d_y(self): def test_interpolation_2_x_2d_y(self): x = (np.arange(-5.01, 5.01, 0.05), np.arange(-5.01, 5.01, 0.01)) - xx, yy = np.meshgrid(x[0], x[1]) + xx, yy = np.meshgrid(x[0], x[1], indexing="ij") z = np.sin(xx**2 + yy**2) var1 = pybamm.StateVector(slice(0, 1)) var2 = pybamm.StateVector(slice(1, 2)) @@ -97,6 +107,90 @@ def test_interpolation_2_x_2d_y(self): interp.evaluate(y=np.array([0, 0])), 0, decimal=3 ) + def test_interpolation_2_x(self): + def f(x, y): + return 2 * x**3 + 3 * y**2 + + x = np.linspace(1, 4, 11) + y = np.linspace(4, 7, 22) + xg, yg = np.meshgrid(x, y, indexing="ij", sparse=True) + data = f(xg, yg) + + var1 = pybamm.StateVector(slice(0, 1)) + var2 = pybamm.StateVector(slice(1, 2)) + + x_in = (x, y) + interp = pybamm.Interpolant(x_in, data, (var1, var2), interpolator="linear") + + value = interp.evaluate(y=np.array([1, 5])) + np.testing.assert_equal(value, f(1, 5)) + + value = interp.evaluate(y=np.array([x[1], y[1]])) + np.testing.assert_equal(value, f(x[1], y[1])) + + value = interp.evaluate(y=np.array([[1, 1, x[1]], [5, 4, y[1]]])) + np.testing.assert_array_equal( + value, np.array([[f(1, 5)], [f(1, 4)], [f(x[1], y[1])]]) + ) + + # check also works for cubic + interp = pybamm.Interpolant(x_in, data, (var1, var2), interpolator="cubic") + value = interp.evaluate(y=np.array([1, 5])) + np.testing.assert_equal(value, f(1, 5)) + + # Test raising error if data is not 2D + data_3d = np.zeros((11, 22, 33)) + with self.assertRaisesRegex(ValueError, "y should be two-dimensional"): + interp = pybamm.Interpolant( + x_in, data_3d, (var1, var2), interpolator="linear" + ) + + # Test raising error if wrong shapes + with self.assertRaisesRegex(ValueError, "x1.shape"): + interp = pybamm.Interpolant( + x_in, np.zeros((12, 22)), (var1, var2), interpolator="linear" + ) + + with self.assertRaisesRegex(ValueError, "x2.shape"): + interp = pybamm.Interpolant( + x_in, np.zeros((11, 23)), (var1, var2), interpolator="linear" + ) + + # Raise error if not linear + with self.assertRaisesRegex( + ValueError, "interpolator should be 'linear' or 'cubic'" + ): + interp = pybamm.Interpolant(x_in, data, (var1, var2), interpolator="pchip") + + # Check returns nan if extrapolate set to False + interp = pybamm.Interpolant( + x_in, data, (var1, var2), interpolator="linear", extrapolate=False + ) + value = interp.evaluate(y=np.array([0, 0, 0])) + np.testing.assert_equal(value, np.nan) + + # Check testing for shape works (i.e. using nans) + interp = pybamm.Interpolant(x_in, data, (var1, var2), interpolator="cubic") + interp.test_shape() + + # test with inconsistent children shapes + # (this can occur is one child is a scaler and the others + # are variables) + evaluated_children = [np.array([[1]]), 4] + value = interp._function_evaluate(evaluated_children) + + evaluated_children = [np.array([[1]]), np.ones(()) * 4] + value = interp._function_evaluate(evaluated_children) + + # Test evaluation fails with different child shapes + with self.assertRaisesRegex(ValueError, "All children must"): + evaluated_children = [np.array([[1, 1]]), np.array([7])] + value = interp._function_evaluate(evaluated_children) + + # Test runs when all children are scalars + evaluated_children = [1, 4] + value = interp._function_evaluate(evaluated_children) + def test_interpolation_3_x(self): def f(x, y, z): return 2 * x**3 + 3 * y**2 - z diff --git a/tests/unit/test_expression_tree/test_operations/test_convert_to_casadi.py b/tests/unit/test_expression_tree/test_operations/test_convert_to_casadi.py index 3029ce8476..ba91ae8f08 100644 --- a/tests/unit/test_expression_tree/test_operations/test_convert_to_casadi.py +++ b/tests/unit/test_expression_tree/test_operations/test_convert_to_casadi.py @@ -314,16 +314,20 @@ def test_concatenations(self): self.assert_casadi_equal(f(y_eval), casadi.SX(expr.evaluate(y=y_eval))) def test_convert_differentiated_function(self): - a = pybamm.Scalar(0) - b = pybamm.Scalar(1) + a = pybamm.InputParameter("a") + b = pybamm.InputParameter("b") def myfunction(x, y): return x + y**3 f = pybamm.Function(myfunction, a, b).diff(a) - self.assert_casadi_equal(f.to_casadi(), casadi.MX(1), evalf=True) + self.assert_casadi_equal( + f.to_casadi(inputs={"a": 1, "b": 2}), casadi.DM(1), evalf=True + ) f = pybamm.Function(myfunction, a, b).diff(b) - self.assert_casadi_equal(f.to_casadi(), casadi.MX(3), evalf=True) + self.assert_casadi_equal( + f.to_casadi(inputs={"a": 1, "b": 2}), casadi.DM(12), evalf=True + ) def test_convert_input_parameter(self): casadi_t = casadi.MX.sym("t") diff --git a/tests/unit/test_expression_tree/test_operations/test_evaluate_python.py b/tests/unit/test_expression_tree/test_operations/test_evaluate_python.py index e6502d4cb0..813029e821 100644 --- a/tests/unit/test_expression_tree/test_operations/test_evaluate_python.py +++ b/tests/unit/test_expression_tree/test_operations/test_evaluate_python.py @@ -76,7 +76,7 @@ def test_find_symbols(self): # test unary op constant_symbols = OrderedDict() variable_symbols = OrderedDict() - expr = a + (-b) + expr = pybamm.maximum(a, -(b)) pybamm.find_symbols(expr, constant_symbols, variable_symbols) self.assertEqual(len(constant_symbols), 0) @@ -92,7 +92,7 @@ def test_find_symbols(self): self.assertEqual(list(variable_symbols.values())[2], "-{}".format(var_b)) var_child = pybamm.id_to_python_variable(expr.children[1].id) self.assertEqual( - list(variable_symbols.values())[3], "{} + {}".format(var_a, var_child) + list(variable_symbols.values())[3], f"np.maximum({var_a},{var_child})" ) # test function diff --git a/tests/unit/test_expression_tree/test_symbol.py b/tests/unit/test_expression_tree/test_symbol.py index b8a9ea4f82..a68d3de54b 100644 --- a/tests/unit/test_expression_tree/test_symbol.py +++ b/tests/unit/test_expression_tree/test_symbol.py @@ -119,10 +119,9 @@ def test_symbol_methods(self): self.assertIsInstance(-a, pybamm.Negate) self.assertIsInstance(abs(a), pybamm.AbsoluteValue) # special cases - neg_a = -a - self.assertEqual(-neg_a, a) - abs_a = abs(a) - self.assertEqual(abs(abs_a), abs_a) + self.assertEqual(-(-a), a) + self.assertEqual(-(a - b), b - a) + self.assertEqual(abs(abs(a)), abs(a)) # binary - two symbols self.assertIsInstance(a + b, pybamm.Addition) @@ -139,10 +138,10 @@ def test_symbol_methods(self): # binary - symbol and number self.assertIsInstance(a + 2, pybamm.Addition) - self.assertIsInstance(a - 2, pybamm.Subtraction) + self.assertIsInstance(2 - a, pybamm.Subtraction) self.assertIsInstance(a * 2, pybamm.Multiplication) self.assertIsInstance(a @ 2, pybamm.MatrixMultiplication) - self.assertIsInstance(a / 2, pybamm.Division) + self.assertIsInstance(2 / a, pybamm.Division) self.assertIsInstance(a**2, pybamm.Power) # binary - number and symbol @@ -387,7 +386,8 @@ def test_symbol_repr(self): def test_symbol_visualise(self): c = pybamm.Variable("c", "negative electrode") - sym = pybamm.div(c * pybamm.grad(c)) + (c / 2 + c - 1) ** 5 + d = pybamm.Variable("d", "negative electrode") + sym = pybamm.div(c * pybamm.grad(c)) + (c / d + c - d) ** 5 sym.visualise("test_visualize.png") self.assertTrue(os.path.exists("test_visualize.png")) with self.assertRaises(ValueError): diff --git a/tests/unit/test_expression_tree/test_unary_operators.py b/tests/unit/test_expression_tree/test_unary_operators.py index dd587228db..7d4906558f 100644 --- a/tests/unit/test_expression_tree/test_unary_operators.py +++ b/tests/unit/test_expression_tree/test_unary_operators.py @@ -103,6 +103,18 @@ def test_sign(self): np.diag(signb.evaluate().toarray()), [-1, -1, 0, 1, 1] ) + broad = pybamm.PrimaryBroadcast(-4, "test domain") + self.assertEqual(pybamm.sign(broad), pybamm.PrimaryBroadcast(-1, "test domain")) + + conc = pybamm.Concatenation(broad, pybamm.PrimaryBroadcast(2, "another domain")) + self.assertEqual( + pybamm.sign(conc), + pybamm.Concatenation( + pybamm.PrimaryBroadcast(-1, "test domain"), + pybamm.PrimaryBroadcast(1, "another domain"), + ), + ) + def test_floor(self): a = pybamm.Symbol("a") floora = pybamm.Floor(a) @@ -147,10 +159,7 @@ def test_gradient(self): # gradient of broadcast should return broadcasted zero a = pybamm.PrimaryBroadcast(pybamm.Variable("a"), "test domain") grad = pybamm.grad(a) - self.assertIsInstance(grad, pybamm.PrimaryBroadcastToEdges) - self.assertIsInstance(grad.child, pybamm.PrimaryBroadcast) - self.assertIsInstance(grad.child.child, pybamm.Scalar) - self.assertEqual(grad.child.child.value, 0) + self.assertEqual(grad, pybamm.PrimaryBroadcastToEdges(0, "test domain")) # otherwise gradient should work a = pybamm.Symbol("a", domain="test domain") @@ -175,10 +184,17 @@ def test_div(self): # divergence of broadcast should return broadcasted zero a = pybamm.PrimaryBroadcastToEdges(pybamm.Variable("a"), "test domain") div = pybamm.div(a) - self.assertIsInstance(div, pybamm.PrimaryBroadcast) - self.assertIsInstance(div.child, pybamm.PrimaryBroadcast) - self.assertIsInstance(div.child.child, pybamm.Scalar) - self.assertEqual(div.child.child.value, 0) + self.assertEqual(div, pybamm.PrimaryBroadcast(0, "test domain")) + a = pybamm.PrimaryBroadcastToEdges( + pybamm.Variable("a", "some domain"), "test domain" + ) + div = pybamm.div(a) + self.assertEqual( + div, + pybamm.PrimaryBroadcast( + pybamm.PrimaryBroadcast(0, "some domain"), "test domain" + ), + ) # otherwise divergence should work a = pybamm.Symbol("a", domain="test domain") @@ -635,6 +651,14 @@ def test_to_equation(self): sympy.Integral("d", sympy.Symbol("xn")), ) + def test_explicit_time_integral(self): + expr = pybamm.ExplicitTimeIntegral(pybamm.Parameter("param"), pybamm.Scalar(1)) + self.assertEqual(expr.child, pybamm.Parameter("param")) + self.assertEqual(expr.initial_condition, pybamm.Scalar(1)) + self.assertEqual(expr.name, "explicit time integral") + self.assertEqual(expr.new_copy(), expr) + self.assertFalse(expr.is_constant()) + if __name__ == "__main__": print("Add -v for more debug output") diff --git a/tests/unit/test_expression_tree/test_variable.py b/tests/unit/test_expression_tree/test_variable.py index f2a32f2a81..1dbe3b67db 100644 --- a/tests/unit/test_expression_tree/test_variable.py +++ b/tests/unit/test_expression_tree/test_variable.py @@ -17,6 +17,12 @@ def test_variable_init(self): a = pybamm.Variable("a", domain=["test"]) self.assertEqual(a.domain[0], "test") self.assertRaises(TypeError, pybamm.Variable("a", domain="test")) + self.assertEqual(a.scale, 1) + self.assertEqual(a.reference, 0) + + a = pybamm.Variable("a", scale=2, reference=-1) + self.assertEqual(a.scale, 2) + self.assertEqual(a.reference, -1) def test_variable_diff(self): a = pybamm.Variable("a") diff --git a/tests/unit/test_meshes/test_meshes.py b/tests/unit/test_meshes/test_meshes.py index c42497f8f2..b04402128b 100644 --- a/tests/unit/test_meshes/test_meshes.py +++ b/tests/unit/test_meshes/test_meshes.py @@ -81,7 +81,7 @@ def test_mesh_creation(self): self.assertEqual( mesh["positive electrode"].edges[0], mesh["separator"].edges[-1] ) - for domain in mesh: + for domain in mesh.base_domains: if domain != "current collector": self.assertEqual(len(mesh[domain].edges), len(mesh[domain].nodes) + 1) @@ -223,7 +223,7 @@ def test_combine_submeshes(self): mesh = pybamm.Mesh(geometry, submesh_types, var_pts) # create submesh - submesh = mesh.combine_submeshes("negative electrode", "separator") + submesh = mesh[("negative electrode", "separator")] self.assertEqual(submesh.edges[0], 0) self.assertEqual(submesh.edges[-1], mesh["separator"].edges[-1]) np.testing.assert_almost_equal( diff --git a/tests/unit/test_meshes/test_scikit_fem_submesh.py b/tests/unit/test_meshes/test_scikit_fem_submesh.py index 91c2899da8..0e9c5561d4 100644 --- a/tests/unit/test_meshes/test_scikit_fem_submesh.py +++ b/tests/unit/test_meshes/test_scikit_fem_submesh.py @@ -52,7 +52,7 @@ def test_mesh_creation(self): self.assertEqual( mesh["positive electrode"].edges[0], mesh["separator"].edges[-1] ) - for domain in mesh: + for domain in mesh.base_domains: if domain == "current collector": # NOTE: only for degree 1 npts = var_pts["y"] * var_pts["z"] @@ -223,7 +223,7 @@ def test_mesh_creation(self): self.assertEqual( mesh["positive electrode"].edges[0], mesh["separator"].edges[-1] ) - for domain in mesh: + for domain in mesh.base_domains: if domain == "current collector": # NOTE: only for degree 1 npts = var_pts["y"] * var_pts["z"] @@ -303,7 +303,7 @@ def test_mesh_creation(self): self.assertEqual( mesh["positive electrode"].edges[0], mesh["separator"].edges[-1] ) - for domain in mesh: + for domain in mesh.base_domains: if domain == "current collector": # NOTE: only for degree 1 npts = var_pts["y"] * var_pts["z"] @@ -391,7 +391,7 @@ def test_mesh_creation(self): self.assertEqual( mesh["positive electrode"].edges[0], mesh["separator"].edges[-1] ) - for domain in mesh: + for domain in mesh.base_domains: if domain == "current collector": # NOTE: only for degree 1 npts = var_pts["y"] * var_pts["z"] diff --git a/tests/unit/test_models/test_base_model.py b/tests/unit/test_models/test_base_model.py index 7a5f5777c7..161f347a26 100644 --- a/tests/unit/test_models/test_base_model.py +++ b/tests/unit/test_models/test_base_model.py @@ -967,7 +967,7 @@ def get_coupled_variables(self, variables): u = model.variables["u"] v = model.variables["v"] self.assertEqual(model.rhs[u].value, 2) - self.assertIsInstance(model.algebraic[v], pybamm.Subtraction) + self.assertEqual(model.algebraic[v], -1.0 + v) if __name__ == "__main__": diff --git a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py index 2678bca5b0..b2fa082f7e 100644 --- a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py +++ b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py @@ -34,7 +34,7 @@ 'particle phases': '1' (possible: ['1', '2']) 'particle shape': 'spherical' (possible: ['spherical', 'no particles']) 'particle size': 'single' (possible: ['single', 'distribution']) -'SEI': 'none' (possible: ['none', 'constant', 'reaction limited', 'solvent-diffusion limited', 'electron-migration limited', 'interstitial-diffusion limited', 'ec reaction limited']) +'SEI': 'none' (possible: ['none', 'constant', 'reaction limited', 'reaction limited (asymmetric)', 'solvent-diffusion limited', 'electron-migration limited', 'interstitial-diffusion limited', 'ec reaction limited', 'ec reaction limited (asymmetric)']) 'SEI film resistance': 'none' (possible: ['none', 'distributed', 'average']) 'SEI on cracks': 'false' (possible: ['false', 'true']) 'SEI porosity change': 'false' (possible: ['false', 'true']) diff --git a/tests/unit/test_models/test_full_battery_models/test_equivalent_circuit/__init__.py b/tests/unit/test_models/test_full_battery_models/test_equivalent_circuit/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/tests/unit/test_models/test_full_battery_models/test_equivalent_circuit/test_thevenin.py b/tests/unit/test_models/test_full_battery_models/test_equivalent_circuit/test_thevenin.py new file mode 100644 index 0000000000..377bfce07e --- /dev/null +++ b/tests/unit/test_models/test_full_battery_models/test_equivalent_circuit/test_thevenin.py @@ -0,0 +1,120 @@ +# +# Tests for the Thevenin equivalant circuit model +# +import pybamm +import unittest + + +class TestThevenin(unittest.TestCase): + def test_standard_model(self): + model = pybamm.equivalent_circuit.Thevenin() + model.check_well_posedness() + + def test_changing_number_of_rcs(self): + options = {"number of rc elements": 0} + model = pybamm.equivalent_circuit.Thevenin(options=options) + model.check_well_posedness() + + options = {"number of rc elements": 2} + model = pybamm.equivalent_circuit.Thevenin(options=options) + model.check_well_posedness() + + options = {"number of rc elements": 3} + model = pybamm.equivalent_circuit.Thevenin(options=options) + model.check_well_posedness() + + options = {"number of rc elements": 4} + model = pybamm.equivalent_circuit.Thevenin(options=options) + model.check_well_posedness() + + with self.assertRaisesRegex(pybamm.OptionError, "natural numbers"): + options = {"number of rc elements": -1} + model = pybamm.equivalent_circuit.Thevenin(options=options) + model.check_well_posedness() + + def test_calculate_discharge_energy(self): + options = {"calculate discharge energy": "true"} + model = pybamm.equivalent_circuit.Thevenin(options=options) + model.check_well_posedness() + + def test_well_posed_external_circuit_voltage(self): + options = {"operating mode": "voltage"} + model = pybamm.equivalent_circuit.Thevenin(options=options) + model.check_well_posedness() + + def test_well_posed_external_circuit_power(self): + options = {"operating mode": "power"} + model = pybamm.equivalent_circuit.Thevenin(options=options) + model.check_well_posedness() + + def test_well_posed_external_circuit_differential_power(self): + options = {"operating mode": "differential power"} + model = pybamm.equivalent_circuit.Thevenin(options=options) + model.check_well_posedness() + + def test_well_posed_external_circuit_resistance(self): + options = {"operating mode": "resistance"} + model = pybamm.equivalent_circuit.Thevenin(options=options) + model.check_well_posedness() + + def test_well_posed_external_circuit_differential_resistance(self): + options = {"operating mode": "differential resistance"} + model = pybamm.equivalent_circuit.Thevenin(options=options) + model.check_well_posedness() + + def test_well_posed_external_circuit_cccv(self): + options = {"operating mode": "CCCV"} + model = pybamm.equivalent_circuit.Thevenin(options=options) + model.check_well_posedness() + + def test_well_posed_external_circuit_function(self): + def external_circuit_function(variables): + I = variables["Current [A]"] + V = variables["Terminal voltage [V]"] + return ( + V + + I + - pybamm.FunctionParameter( + "Function", {"Time [s]": pybamm.t}, print_name="test_fun" + ) + ) + + options = {"operating mode": external_circuit_function} + + model = pybamm.equivalent_circuit.Thevenin(options=options) + model.check_well_posedness() + + def test_raise_option_error(self): + options = {"not an option": "something"} + with self.assertRaisesRegex( + pybamm.OptionError, "Option 'not an option' not recognised" + ): + pybamm.equivalent_circuit.Thevenin(options=options) + + def test_not_a_valid_option(self): + options = {"operating mode": "not a valid option"} + with self.assertRaisesRegex( + pybamm.OptionError, "Option 'operating mode' must be one of" + ): + pybamm.equivalent_circuit.Thevenin(options=options) + + def test_get_default_parameters(self): + model = pybamm.equivalent_circuit.Thevenin() + values = model.default_parameter_values + self.assertIn("Initial SoC", list(values.keys())) + values.process_model(model) + + def test_get_default_quick_plot_variables(self): + model = pybamm.equivalent_circuit.Thevenin() + variables = model.default_quick_plot_variables + self.assertIn("Current [A]", variables) + + +if __name__ == "__main__": + print("Add -v for more debug output") + import sys + + if "-v" in sys.argv: + debug = True + pybamm.settings.debug_mode = True + unittest.main() diff --git a/tests/unit/test_models/test_full_battery_models/test_lead_acid/test_basic_models.py b/tests/unit/test_models/test_full_battery_models/test_lead_acid/test_basic_models.py index 260df1b254..ac8d9fc974 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lead_acid/test_basic_models.py +++ b/tests/unit/test_models/test_full_battery_models/test_lead_acid/test_basic_models.py @@ -1,5 +1,5 @@ # -# Tests for the basic lithium-ion models +# Tests for the basic lead acid models # import pybamm import unittest diff --git a/tests/unit/test_models/test_full_battery_models/test_lead_acid/test_composite.py b/tests/unit/test_models/test_full_battery_models/test_lead_acid/test_composite.py deleted file mode 100644 index 60050dac07..0000000000 --- a/tests/unit/test_models/test_full_battery_models/test_lead_acid/test_composite.py +++ /dev/null @@ -1,86 +0,0 @@ -# -# Tests for the lead-acid composite models -# -import pybamm -import unittest - - -class TestLeadAcidComposite(unittest.TestCase): - def test_well_posed(self): - model = pybamm.lead_acid.Composite() - model.check_well_posedness() - - def test_well_posed_with_convection(self): - # this test is very slow with debug mode set to true - pybamm.settings.debug_mode = False - options = {"convection": "uniform transverse"} - model = pybamm.lead_acid.Composite(options) - model.check_well_posedness() - - options = {"dimensionality": 1, "convection": "full transverse"} - model = pybamm.lead_acid.Composite(options) - model.check_well_posedness() - pybamm.settings.debug_mode = True - - -class TestLeadAcidCompositeMultiDimensional(unittest.TestCase): - def test_well_posed(self): - model = pybamm.lead_acid.Composite( - {"dimensionality": 1, "current collector": "potential pair"} - ) - self.assertIsInstance( - model.default_solver, (pybamm.ScikitsDaeSolver, pybamm.CasadiSolver) - ) - model.check_well_posedness() - - model = pybamm.lead_acid.Composite( - {"dimensionality": 2, "current collector": "potential pair"} - ) - model.check_well_posedness() - - model = pybamm.lead_acid.Composite( - { - "dimensionality": 1, - "current collector": "potential pair quite conductive", - } - ) - model.check_well_posedness() - - model = pybamm.lead_acid.Composite( - { - "dimensionality": 2, - "current collector": "potential pair quite conductive", - } - ) - model.check_well_posedness() - - -class TestLeadAcidCompositeWithSideReactions(unittest.TestCase): - def test_well_posed_algebraic(self): - options = {"surface form": "algebraic", "hydrolysis": "true"} - model = pybamm.lead_acid.Composite(options) - model.check_well_posedness() - self.assertIsInstance( - model.default_solver, (pybamm.ScikitsDaeSolver, pybamm.CasadiSolver) - ) - - -class TestLeadAcidCompositeExtended(unittest.TestCase): - def test_well_posed_differential_side_reactions(self): - options = {"surface form": "differential", "hydrolysis": "true"} - model = pybamm.lead_acid.CompositeExtended(options) - model.check_well_posedness() - - def test_well_posed_average_correction(self): - model = pybamm.lead_acid.CompositeAverageCorrection() - model.check_well_posedness() - - -if __name__ == "__main__": - print("Add -v for more debug output") - import sys - - if "-v" in sys.argv: - debug = True - pybamm.settings.debug_mode = True - unittest.main() diff --git a/tests/unit/test_models/test_full_battery_models/test_lead_acid/test_foqs.py b/tests/unit/test_models/test_full_battery_models/test_lead_acid/test_foqs.py deleted file mode 100644 index 7dfe12cafe..0000000000 --- a/tests/unit/test_models/test_full_battery_models/test_lead_acid/test_foqs.py +++ /dev/null @@ -1,34 +0,0 @@ -# -# Tests for FOQS lead-acid model -# -import pybamm -import unittest - - -class TestLeadAcidFOQS(unittest.TestCase): - def test_well_posed(self): - # debug mode slows down the FOQS model a fair bit, so turn off - pybamm.settings.debug_mode = False - model = pybamm.lead_acid.FOQS() - pybamm.settings.debug_mode = True - model.check_well_posedness() - - -class TestLeadAcidFOQSWithSideReactions(unittest.TestCase): - def test_well_posed_differential(self): - options = {"surface form": "differential", "hydrolysis": "true"} - # debug mode slows down the FOQS model a fair bit, so turn off - pybamm.settings.debug_mode = False - model = pybamm.lead_acid.FOQS(options) - pybamm.settings.debug_mode = True - model.check_well_posedness() - - -if __name__ == "__main__": - print("Add -v for more debug output") - import sys - - if "-v" in sys.argv: - debug = True - pybamm.settings.debug_mode = True - unittest.main() diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_half_cell_tests.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_half_cell_tests.py index 1635c2ea41..24ccd55d79 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_half_cell_tests.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_half_cell_tests.py @@ -55,6 +55,10 @@ def test_well_posed_reaction_limited_sei(self): options = {"SEI": "reaction limited"} self.check_well_posedness(options) + def test_well_posed_asymmetric_reaction_limited_sei(self): + options = {"SEI": "reaction limited (asymmetric)"} + self.check_well_posedness(options) + def test_well_posed_solvent_diffusion_limited_sei(self): options = {"SEI": "solvent-diffusion limited"} self.check_well_posedness(options) @@ -71,6 +75,10 @@ def test_well_posed_ec_reaction_limited_sei(self): options = {"SEI": "ec reaction limited"} self.check_well_posedness(options) + def test_well_posed_asymmetric_ec_reaction_limited_sei(self): + options = {"SEI": "ec reaction limited (asymmetric)"} + self.check_well_posedness(options) + def test_well_posed_lumped_thermal(self): options = {"thermal": "lumped"} self.check_well_posedness(options) diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py index c3188c2891..7dbe128221 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py @@ -147,8 +147,22 @@ def test_well_posed_sei_reaction_limited(self): options = {"SEI": "reaction limited"} self.check_well_posedness(options) + def test_well_posed_asymmetric_sei_reaction_limited(self): + options = {"SEI": "reaction limited (asymmetric)"} + self.check_well_posedness(options) + def test_well_posed_sei_reaction_limited_average_film_resistance(self): - options = {"SEI": "reaction limited", "SEI film resistance": "average"} + options = { + "SEI": "reaction limited", + "SEI film resistance": "average", + } + self.check_well_posedness(options) + + def test_well_posed_asymmetric_sei_reaction_limited_average_film_resistance(self): + options = { + "SEI": "reaction limited (asymmetric)", + "SEI film resistance": "average", + } self.check_well_posedness(options) def test_well_posed_sei_solvent_diffusion_limited(self): @@ -164,7 +178,17 @@ def test_well_posed_sei_interstitial_diffusion_limited(self): self.check_well_posedness(options) def test_well_posed_sei_ec_reaction_limited(self): - options = {"SEI": "ec reaction limited", "SEI porosity change": "true"} + options = { + "SEI": "ec reaction limited", + "SEI porosity change": "true", + } + self.check_well_posedness(options) + + def test_well_posed_sei_asymmetric_ec_reaction_limited(self): + options = { + "SEI": "ec reaction limited (asymmetric)", + "SEI porosity change": "true", + } self.check_well_posedness(options) def test_well_posed_mechanics_negative_cracking(self): diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py index 0798d095c2..5c824164ea 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py @@ -141,7 +141,10 @@ def test_interstitial_diffusion_limited_not_implemented(self): pybamm.lithium_ion.MPM(options) def test_ec_reaction_limited_not_implemented(self): - options = {"SEI": "ec reaction limited", "SEI porosity change": "true"} + options = { + "SEI": "ec reaction limited", + "SEI porosity change": "true", + } with self.assertRaises(NotImplementedError): pybamm.lithium_ion.MPM(options) diff --git a/tests/unit/test_parameters/data_for_testing_2D.csv b/tests/unit/test_parameters/data_for_testing_2D.csv new file mode 100644 index 0000000000..962c90bb81 --- /dev/null +++ b/tests/unit/test_parameters/data_for_testing_2D.csv @@ -0,0 +1,101 @@ +OCV [V],Temperature [degC],dUdT [V/K] +3.1,-20,-0.0010576663320507884 +3.1,0,-0.001269199598460946 +3.1,20,-0.0014807328648711038 +3.1,40,-0.0015864994980761826 +3.15,-20,-0.0008144336478441427 +3.15,0,-0.0009773203774129712 +3.15,20,-0.0011402071069817998 +3.15,40,-0.001221650471766214 +3.1999999999999997,-20,-0.000607206498346391 +3.1999999999999997,0,-0.0007286477980156691 +3.1999999999999997,20,-0.0008500890976849473 +3.1999999999999997,40,-0.0009108097475195864 +3.2499999999999996,-20,-0.0004332237633991043 +3.2499999999999996,0,-0.0005198685160789251 +3.2499999999999996,20,-0.000606513268758746 +3.2499999999999996,40,-0.0006498356450986564 +3.2999999999999994,-20,-0.00028972432284410377 +3.2999999999999994,0,-0.00034766918741292453 +3.2999999999999994,20,-0.00040561405198174524 +3.2999999999999994,40,-0.00043458648426615565 +3.349999999999999,-20,-0.00017394705652298836 +3.349999999999999,0,-0.00020873646782758603 +3.349999999999999,20,-0.0002435258791321837 +3.349999999999999,40,-0.00026092058478448255 +3.399999999999999,-20,-8.313084427744033e-05 +3.399999999999999,0,-9.97570131329284e-05 +3.399999999999999,20,-0.00011638318198841646 +3.399999999999999,40,-0.0001246962664161605 +3.449999999999999,-20,-1.4514565949169667e-05 +3.449999999999999,0,-1.74174791390036e-05 +3.449999999999999,20,-2.032039232883753e-05 +3.449999999999999,40,-2.17718489237545e-05 +3.4999999999999987,-20,3.4662898620113625e-05 +3.4999999999999987,0,4.1595478344136346e-05 +3.4999999999999987,20,4.852805806815907e-05 +3.4999999999999987,40,5.199434793017044e-05 +3.5499999999999985,-20,6.716266958867179e-05 +3.5499999999999985,0,8.059520350640614e-05 +3.5499999999999985,20,9.40277374241405e-05 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+50,700,0.30000000000000004,0.0002380357802410348 +50,700,0.35000000000000003,0.00023326597930109036 +50,700,0.4,0.00022985897862970152 +50,700,0.45,0.0002278147782268682 +50,700,0.5,0.00022713337809259046 +50,700,0.55,0.00022781477822686827 +50,700,0.6000000000000001,0.00022985897862970152 +50,700,0.65,0.00023326597930109036 +50,700,0.7000000000000001,0.0002380357802410348 +50,700,0.75,0.00024416838144953473 +50,700,0.8,0.0002516637829265902 +50,700,0.8500000000000001,0.00026052198467220125 +50,700,0.9,0.0002707429866863678 +50,700,0.9500000000000001,0.00028232678896908994 +50,700,1.0,0.0002952733915203676 diff --git a/tests/unit/test_parameters/test_ecm_parameters.py b/tests/unit/test_parameters/test_ecm_parameters.py new file mode 100644 index 0000000000..8c1646fc53 --- /dev/null +++ b/tests/unit/test_parameters/test_ecm_parameters.py @@ -0,0 +1,100 @@ +# +# Tests for the equivalent circuit parameters +# +import pybamm +import unittest + + +values = { + "Initial SoC": 0.5, + "Initial cell temperature [degC]": 25, + "Initial jig temperature [degC]": 25, + "Cell capacity [A.h]": 100, + "Nominal cell capacity [A.h]": 100, + "Ambient temperature [degC]": 25, + "Current function [A]": 100, + "Upper voltage cut-off [V]": 4.2, + "Lower voltage cut-off [V]": 3.2, + "Cell thermal mass [J/K]": 1000, + "Cell-jig heat transfer coefficient [W/K]": 10, + "Jig thermal mass [J/K]": 500, + "Jig-air heat transfer coefficient [W/K]": 10, + "R0 [Ohm]": 0.4e-3, + "Element-1 initial overpotential [V]": 0, + "R1 [Ohm]": 0.6e-3, + "C1 [F]": 30 / 0.6e-3, + "Entropic change [V/K]": 0, + "RCR lookup limit [A]": 340, + "Open circuit voltage [V]": 3.4, +} + +parameter_values = pybamm.ParameterValues(values) + + +class TestEcmParameters(unittest.TestCase): + def test_init_parameters(self): + + param = pybamm.EcmParameters() + + simpled_mapped_parameters = [ + (param.cell_capacity, "Cell capacity [A.h]"), + (param.dimensional_current_with_time, "Current function [A]"), + (param.voltage_high_cut, "Upper voltage cut-off [V]"), + (param.voltage_low_cut, "Lower voltage cut-off [V]"), + (param.cth_cell, "Cell thermal mass [J/K]"), + (param.k_cell_jig, "Jig-air heat transfer coefficient [W/K]"), + (param.cth_jig, "Jig thermal mass [J/K]"), + (param.k_jig_air, "Jig-air heat transfer coefficient [W/K]"), + (param.Q, "Cell capacity [A.h]"), + (param.current_with_time, "Current function [A]"), + (param.dimensional_current_density_with_time, "Current function [A]"), + (param.initial_soc, "Initial SoC"), + (param.initial_T_cell, "Initial cell temperature [degC]"), + (param.initial_T_jig, "Initial jig temperature [degC]"), + ] + + for symbol, key in simpled_mapped_parameters: + value = parameter_values.evaluate(symbol) + expected_value = values[key] + self.assertEqual(value, expected_value) + + compatibility_parameters = [ + (param.I_typ, 1), + (param.n_electrodes_parallel, 1), + (param.A_cc, 1), + (param.n_cells, 1), + ] + + for symbol, expected_value in compatibility_parameters: + value = parameter_values.evaluate(symbol) + self.assertEqual(value, expected_value) + + def test_function_parameters(self): + param = pybamm.EcmParameters() + + sym = pybamm.Scalar(1) + + mapped_functions = [ + (param.T_amb(sym), "Ambient temperature [degC]"), + (param.ocv(sym), "Open circuit voltage [V]"), + (param.rcr_element("R0 [Ohm]", sym, sym, sym), "R0 [Ohm]"), + (param.rcr_element("R1 [Ohm]", sym, sym, sym), "R1 [Ohm]"), + (param.rcr_element("C1 [F]", sym, sym, sym), "C1 [F]"), + (param.initial_rc_overpotential(1), "Element-1 initial overpotential [V]"), + (param.dUdT(sym, sym), "Entropic change [V/K]"), + ] + + for symbol, key in mapped_functions: + value = parameter_values.evaluate(symbol) + expected_value = values[key] + self.assertEqual(value, expected_value) + + +if __name__ == "__main__": + print("Add -v for more debug output") + import sys + + if "-v" in sys.argv: + debug = True + pybamm.settings.debug_mode = True + unittest.main() diff --git a/tests/unit/test_parameters/test_lead_acid_parameters.py b/tests/unit/test_parameters/test_lead_acid_parameters.py index 49e9d5d716..bb67d74e21 100644 --- a/tests/unit/test_parameters/test_lead_acid_parameters.py +++ b/tests/unit/test_parameters/test_lead_acid_parameters.py @@ -67,10 +67,8 @@ def test_concatenated_parameters(self): processed_s = disc.process_symbol(parameter_values.process_symbol(s_param)) # test output - combined_submeshes = disc.mesh.combine_submeshes( - "negative electrode", "separator", "positive electrode" - ) - self.assertEqual(processed_s.shape, (combined_submeshes.npts, 1)) + submeshes = disc.mesh[("negative electrode", "separator", "positive electrode")] + self.assertEqual(processed_s.shape, (submeshes.npts, 1)) def test_current_functions(self): # create current functions diff --git a/tests/unit/test_parameters/test_parameter_sets_class.py b/tests/unit/test_parameters/test_parameter_sets_class.py index a31d5976fb..eca1d6b2dd 100644 --- a/tests/unit/test_parameters/test_parameter_sets_class.py +++ b/tests/unit/test_parameters/test_parameter_sets_class.py @@ -22,9 +22,9 @@ def test_name_interface(self): def test_all_registered(self): """Check that all parameter sets have been registered with the - ``pybamm_parameter_set`` entry point""" + ``pybamm_parameter_sets`` entry point""" known_entry_points = set( - ep.name for ep in pkg_resources.iter_entry_points("pybamm_parameter_set") + ep.name for ep in pkg_resources.iter_entry_points("pybamm_parameter_sets") ) self.assertEqual(set(pybamm.parameter_sets.keys()), known_entry_points) self.assertEqual(len(known_entry_points), len(pybamm.parameter_sets)) @@ -32,7 +32,7 @@ def test_all_registered(self): def test_get_docstring(self): """Test that :meth:`pybamm.parameter_sets.get_doctstring` works""" docstring = pybamm.parameter_sets.get_docstring("Marquis2019") - self.assertRegexpMatches(docstring, "Parameters for a Kokam SLPB78205130H cell") + self.assertRegex(docstring, "Parameters for a Kokam SLPB78205130H cell") def test_iter(self): """Test that iterating `pybamm.parameter_sets` iterates over keys""" diff --git a/tests/unit/test_parameters/test_parameter_values.py b/tests/unit/test_parameters/test_parameter_values.py index 3e063dd16c..a89f327ba8 100644 --- a/tests/unit/test_parameters/test_parameter_values.py +++ b/tests/unit/test_parameters/test_parameter_values.py @@ -18,6 +18,7 @@ lico2_ocp_Dualfoil1998, lico2_diffusivity_Dualfoil1998, ) +import casadi class TestParameterValues(unittest.TestCase): @@ -314,10 +315,7 @@ def test_process_input_parameter(self): b = pybamm.Parameter("b") add = a + b processed_add = parameter_values.process_symbol(add) - self.assertIsInstance(processed_add, pybamm.Addition) - self.assertIsInstance(processed_add.children[0], pybamm.InputParameter) - self.assertIsInstance(processed_add.children[1], pybamm.Scalar) - self.assertEqual(processed_add.evaluate(inputs={"a": 4}), 7) + self.assertEqual(processed_add, 3 + pybamm.InputParameter("a")) # process complex input parameter c = pybamm.Parameter("c times 2") @@ -346,7 +344,10 @@ def test_function(var): self.assertEqual(processed_func.evaluate(inputs={"a": 3}), 369) # process constant function - const = pybamm.FunctionParameter("const", {"a": a}) + # this should work even if the parameter in the function is not provided + const = pybamm.FunctionParameter( + "const", {"a": pybamm.Parameter("not provided")} + ) processed_const = parameter_values.process_symbol(const) self.assertIsInstance(processed_const, pybamm.Scalar) self.assertEqual(processed_const.evaluate(), 254) @@ -452,59 +453,33 @@ def D(a, b): processed_func = parameter_values.process_symbol(func) self.assertEqual(processed_func.evaluate(), 3) - def test_function_parameter_replace_callable(self): - # This functionality is used for generating a model in Julia's MTK - def D(a, b): - return a * pybamm.exp(b) - - parameter_values = pybamm.ParameterValues({"a": 3, "Diffusivity": D}) - parameter_values._replace_callable_function_parameters = False - - a = pybamm.Parameter("a") - b = pybamm.Variable("b") - func = pybamm.FunctionParameter("Diffusivity", {"a": a, "b": b}) - func.print_name = "D" - - processed_func = parameter_values.process_symbol(func) - self.assertIsInstance(processed_func, pybamm.FunctionParameter) - self.assertEqual(processed_func.name, "D") - self.assertEqual(processed_func.arg_names, ["a", "b"]) - self.assertIsInstance(processed_func.callable, pybamm.Multiplication) - def test_process_interpolant(self): x = np.linspace(0, 10)[:, np.newaxis] data = np.hstack([x, 2 * x]) - parameter_values = pybamm.ParameterValues( - {"a": 3.01, "Times two": ("times two", data)} - ) + parameter_values = pybamm.ParameterValues({"Times two": ("times two", data)}) - a = pybamm.Parameter("a") + a = pybamm.InputParameter("a") func = pybamm.FunctionParameter("Times two", {"a": a}) processed_func = parameter_values.process_symbol(func) self.assertIsInstance(processed_func, pybamm.Interpolant) - self.assertEqual(processed_func.evaluate(), 6.02) - - # process differentiated function parameter - diff_func = func.diff(a) - processed_diff_func = parameter_values.process_symbol(diff_func) - self.assertEqual(processed_diff_func.evaluate(), 2) + self.assertEqual(processed_func.evaluate(inputs={"a": 3.01}), 6.02) # interpolant defined up front - interp2 = pybamm.Interpolant(data[:, 0], data[:, 1], a) - processed_interp2 = parameter_values.process_symbol(interp2) - self.assertEqual(processed_interp2.evaluate(), 6.02) + interp = pybamm.Interpolant(data[:, 0], data[:, 1], a, interpolator="cubic") + processed_interp = parameter_values.process_symbol(interp) + self.assertEqual(processed_interp.evaluate(inputs={"a": 3.01}), 6.02) - data3 = np.hstack([x, 3 * x]) - interp3 = pybamm.Interpolant(data3[:, 0], data3[:, 1], a) - processed_interp3 = parameter_values.process_symbol(interp3) - self.assertEqual(processed_interp3.evaluate(), 9.03) + # process differentiated function parameter + diff_interp = interp.diff(a) + processed_diff_interp = parameter_values.process_symbol(diff_interp) + self.assertEqual(processed_diff_interp.evaluate(inputs={"a": 3.01}), 2) def test_process_interpolant_2d(self): x_ = [np.linspace(0, 10), np.linspace(0, 20)] - X = list(np.meshgrid(*x_)) + X = list(np.meshgrid(*x_, indexing="ij")) x = np.column_stack([el.reshape(-1, 1) for el in X]) @@ -514,17 +489,17 @@ def test_process_interpolant_2d(self): data = x_, Y - parameter_values = pybamm.ParameterValues( - {"a": 3.01, "b": 4.4, "Times two": ("times two", data)} - ) + parameter_values = pybamm.ParameterValues({"Times two": ("times two", data)}) - a = pybamm.Parameter("a") - b = pybamm.Parameter("b") + a = pybamm.InputParameter("a") + b = pybamm.InputParameter("b") func = pybamm.FunctionParameter("Times two", {"a": a, "b": b}) processed_func = parameter_values.process_symbol(func) self.assertIsInstance(processed_func, pybamm.Interpolant) - self.assertAlmostEqual(processed_func.evaluate()[0][0], 14.82) + self.assertAlmostEqual( + processed_func.evaluate(inputs={"a": 3.01, "b": 4.4})[0][0], 14.82 + ) # process differentiated function parameter # diff_func = func.diff(a) @@ -534,7 +509,9 @@ def test_process_interpolant_2d(self): # interpolant defined up front interp2 = pybamm.Interpolant(data[0], data[1], children=(a, b)) processed_interp2 = parameter_values.process_symbol(interp2) - self.assertEqual(processed_interp2.evaluate(), 14.82) + self.assertEqual( + processed_interp2.evaluate(inputs={"a": 3.01, "b": 4.4}), 14.82 + ) y3 = (3 * x).sum(axis=1) @@ -543,25 +520,29 @@ def test_process_interpolant_2d(self): data3 = x_, Y3 parameter_values = pybamm.ParameterValues( - {"a": 3.01, "b": 4.4, "Times three": ("times three", data3)} + {"Times three": ("times three", data3)} ) - a = pybamm.Parameter("a") - b = pybamm.Parameter("b") + a = pybamm.InputParameter("a") + b = pybamm.InputParameter("b") func = pybamm.FunctionParameter("Times three", {"a": a, "b": b}) processed_func = parameter_values.process_symbol(func) self.assertIsInstance(processed_func, pybamm.Interpolant) # self.assertEqual(processed_func.evaluate().flatten()[0], 22.23) np.testing.assert_almost_equal( - processed_func.evaluate().flatten()[0], 22.23, decimal=4 + processed_func.evaluate(inputs={"a": 3.01, "b": 4.4}).flatten()[0], + 22.23, + decimal=4, ) interp3 = pybamm.Interpolant(data3[0], data3[1], children=(a, b)) processed_interp3 = parameter_values.process_symbol(interp3) # self.assertEqual(processed_interp3.evaluate().flatten()[0], 22.23) np.testing.assert_almost_equal( - processed_interp3.evaluate().flatten()[0], 22.23, decimal=4 + processed_interp3.evaluate(inputs={"a": 3.01, "b": 4.4}).flatten()[0], + 22.23, + decimal=4, ) def test_interpolant_against_function(self): @@ -589,15 +570,6 @@ def test_interpolant_against_function(self): processed_func.evaluate(), processed_interp.evaluate(), decimal=3 ) - # process differentiated function parameter - diff_func = func.diff(a) - diff_interp = interp.diff(a) - processed_diff_func = parameter_values.process_symbol(diff_func) - processed_diff_interp = parameter_values.process_symbol(diff_interp) - np.testing.assert_array_almost_equal( - processed_diff_func.evaluate(), processed_diff_interp.evaluate(), decimal=2 - ) - def test_interpolant_2d_from_json(self): parameter_values = pybamm.ParameterValues( {"function": lico2_diffusivity_Dualfoil1998} @@ -622,6 +594,95 @@ def test_interpolant_2d_from_json(self): processed_func.evaluate(), processed_interp.evaluate(), decimal=4 ) + def test_process_interpolant_3D_from_csv(self): + name = "data_for_testing_3D" + path = os.path.join(pybamm.root_dir(), "tests", "unit", "test_parameters") + + processed = pybamm.parameters.process_3D_data_csv(name, path) + parameter_values = pybamm.ParameterValues({"interpolation": processed}) + + x1 = pybamm.StateVector(slice(0, 1)) + x2 = pybamm.StateVector(slice(1, 2)) + x3 = pybamm.StateVector(slice(2, 3)) + interpolation = pybamm.FunctionParameter( + "interpolation", {"x1": x1, "x2": x2, "x3": x3} + ) + + processed_interpolation = parameter_values.process_symbol(interpolation) + + filename, name = pybamm.parameters.process_parameter_data._process_name( + name, path, ".csv" + ) + raw_df = pd.read_csv(filename) + + # It's also helpful to check the casadi conversion here aswell + # We check elsewhere but this helps catch additional bugs + casadi_y = casadi.MX.sym("y", 3) + interp_casadi = processed_interpolation.to_casadi(y=casadi_y) + casadi_f = casadi.Function("f", [casadi_y], [interp_casadi]) + + # check that passing the input columns give the correct output + for values in raw_df.values: + + y = np.array([values[0], values[1], values[2]]) + f = values[3] + casadi_sol = casadi_f(y) + + np.testing.assert_almost_equal( + processed_interpolation.evaluate(y=y)[0][0], + f, + decimal=10, + ) + + np.testing.assert_almost_equal( + f, + casadi_sol.__float__(), + decimal=10, + ) + + def test_process_interpolant_2D_from_csv(self): + name = "data_for_testing_2D" + path = os.path.join(pybamm.root_dir(), "tests", "unit", "test_parameters") + + processed = pybamm.parameters.process_2D_data_csv(name, path) + parameter_values = pybamm.ParameterValues({"interpolation": processed}) + + x1 = pybamm.StateVector(slice(0, 1)) + x2 = pybamm.StateVector(slice(1, 2)) + interpolation = pybamm.FunctionParameter("interpolation", {"x1": x1, "x2": x2}) + processed_interpolation = parameter_values.process_symbol(interpolation) + + # It's also helpful to check the casadi conversion here aswell + # We check elsewhere but this helps catch additional bugs + casadi_y = casadi.MX.sym("y", 2) + interp_casadi = processed_interpolation.to_casadi(y=casadi_y) + casadi_f = casadi.Function("f", [casadi_y], [interp_casadi]) + + filename, name = pybamm.parameters.process_parameter_data._process_name( + name, path, ".csv" + ) + raw_df = pd.read_csv(filename) + + # check that passing the input columns give the correct output + for values in raw_df.values: + + y = np.array([values[0], values[1]]) + f = values[2] + + casadi_sol = casadi_f(y) + + np.testing.assert_almost_equal( + processed_interpolation.evaluate(y=y)[0][0], + f, + decimal=10, + ) + + np.testing.assert_almost_equal( + f, + casadi_sol.__float__(), + decimal=10, + ) + def test_process_integral_broadcast(self): # Test that the x-average of a broadcast gets processed correctly var = pybamm.Variable("var", domain="negative electrode") @@ -796,23 +857,9 @@ def test_process_complex_expression(self): par2 = pybamm.Parameter("par2") expression = (3 * (par1**var2)) / ((var1 - par2) + var2) - param = pybamm.ParameterValues({"par1": 1, "par2": 2}) + param = pybamm.ParameterValues({"par1": 2, "par2": 4}) exp_param = param.process_symbol(expression) - self.assertIsInstance(exp_param, pybamm.Division) - # left side - self.assertIsInstance(exp_param.left, pybamm.Multiplication) - self.assertIsInstance(exp_param.left.left, pybamm.Scalar) - self.assertIsInstance(exp_param.left.right, pybamm.Power) - self.assertIsInstance(exp_param.left.right.left, pybamm.Scalar) - self.assertEqual(exp_param.left.right.left.value, 1) - self.assertIsInstance(exp_param.left.right.right, pybamm.Variable) - # right side - self.assertIsInstance(exp_param.right, pybamm.Addition) - self.assertIsInstance(exp_param.right.left, pybamm.Subtraction) - self.assertIsInstance(exp_param.right.left.left, pybamm.Variable) - self.assertIsInstance(exp_param.right.left.right, pybamm.Scalar) - self.assertEqual(exp_param.right.left.right.value, 2) - self.assertIsInstance(exp_param.right.right, pybamm.Variable) + self.assertEqual(exp_param, 3.0 * (2.0**var2) / ((-4.0 + var1) + var2)) def test_process_model(self): model = pybamm.BaseModel() diff --git a/tests/unit/test_parameters/test_process_parameter_data.py b/tests/unit/test_parameters/test_process_parameter_data.py index ade04af7d4..310d17002f 100644 --- a/tests/unit/test_parameters/test_process_parameter_data.py +++ b/tests/unit/test_parameters/test_process_parameter_data.py @@ -36,6 +36,29 @@ def test_process_2D_data(self): self.assertIsInstance(processed[1][0][1], np.ndarray) self.assertIsInstance(processed[1][1], np.ndarray) + def test_process_2D_data_csv(self): + name = "data_for_testing_2D" + path = os.path.join(pybamm.root_dir(), "tests", "unit", "test_parameters") + processed = pybamm.parameters.process_2D_data_csv(name, path) + + self.assertEqual(processed[0], name) + self.assertIsInstance(processed[1], tuple) + self.assertIsInstance(processed[1][0][0], np.ndarray) + self.assertIsInstance(processed[1][0][1], np.ndarray) + self.assertIsInstance(processed[1][1], np.ndarray) + + def test_process_3D_data_csv(self): + name = "data_for_testing_3D" + path = os.path.join(pybamm.root_dir(), "tests", "unit", "test_parameters") + processed = pybamm.parameters.process_3D_data_csv(name, path) + + self.assertEqual(processed[0], name) + self.assertIsInstance(processed[1], tuple) + self.assertIsInstance(processed[1][0][0], np.ndarray) + self.assertIsInstance(processed[1][0][1], np.ndarray) + self.assertIsInstance(processed[1][0][2], np.ndarray) + self.assertIsInstance(processed[1][1], np.ndarray) + def test_error(self): with self.assertRaisesRegex(FileNotFoundError, "Could not find file"): pybamm.parameters.process_1D_data("not_a_real_file", "not_a_real_path") diff --git a/tests/unit/test_plotting/test_quick_plot.py b/tests/unit/test_plotting/test_quick_plot.py index ff28a3fc48..a7816a748a 100644 --- a/tests/unit/test_plotting/test_quick_plot.py +++ b/tests/unit/test_plotting/test_quick_plot.py @@ -322,7 +322,7 @@ def test_loqs_spme(self): c_e_var = solution["Electrolyte concentration [mol.m-3]"] # 1D variables should be evaluated on edges L_x = param.evaluate(model.param.L_x) - c_e = c_e_var(t=t, x=mesh.combine_submeshes(*c_e_var.domain).edges * L_x) + c_e = c_e_var(t=t, x=mesh[c_e_var.domain].edges * L_x) for unit, scale in zip(["seconds", "minutes", "hours"], [1, 60, 3600]): quick_plot = pybamm.QuickPlot( diff --git a/tests/unit/test_solvers/test_base_solver.py b/tests/unit/test_solvers/test_base_solver.py index 30a8eece1e..0217b0a4e2 100644 --- a/tests/unit/test_solvers/test_base_solver.py +++ b/tests/unit/test_solvers/test_base_solver.py @@ -121,7 +121,7 @@ def __init__(self): self.convert_to_format = "casadi" self.bounds = (np.array([-np.inf]), np.array([np.inf])) self.len_rhs_and_alg = 1 - self.interpolant_extrapolation_events_eval = [] + self.events = [] def rhs_eval(self, t, y, inputs): return np.array([]) @@ -160,7 +160,7 @@ def __init__(self): self.bounds = (-np.inf * np.ones(4), np.inf * np.ones(4)) self.len_rhs = 1 self.len_rhs_and_alg = 4 - self.interpolant_extrapolation_events_eval = [] + self.events = [] def rhs_eval(self, t, y, inputs): return y[0:1] @@ -209,7 +209,6 @@ def __init__(self): ) self.convert_to_format = "casadi" self.bounds = (np.array([-np.inf]), np.array([np.inf])) - self.interpolant_extrapolation_events_eval = [] def rhs_eval(self, t, y, inputs): return np.array([]) diff --git a/tests/unit/test_solvers/test_casadi_solver.py b/tests/unit/test_solvers/test_casadi_solver.py index 6b9d073ab6..86f671546d 100644 --- a/tests/unit/test_solvers/test_casadi_solver.py +++ b/tests/unit/test_solvers/test_casadi_solver.py @@ -509,7 +509,7 @@ def test_interpolant_extrapolate(self): model = pybamm.lithium_ion.DFN() param = pybamm.ParameterValues("NCA_Kim2011") experiment = pybamm.Experiment( - ["Charge at 1C until 4.6 V"], period="10 seconds" + ["Charge at 1C until 4.2 V"], period="10 seconds" ) param["Upper voltage cut-off [V]"] = 4.8 @@ -528,18 +528,6 @@ def test_interpolant_extrapolate(self): with self.assertRaisesRegex(pybamm.SolverError, "interpolation bounds"): sim.solve() - ci = param["Initial concentration in positive electrode [mol.m-3]"] - param["Initial concentration in positive electrode [mol.m-3]"] = 0.8 * ci - - sim = pybamm.Simulation( - model, - parameter_values=param, - experiment=experiment, - solver=pybamm.CasadiSolver(mode="safe", dt_max=0.05), - ) - with self.assertRaisesRegex(pybamm.SolverError, "interpolation bounds"): - sim.solve() - def test_casadi_safe_no_termination(self): model = pybamm.BaseModel() v = pybamm.Variable("v") diff --git a/tests/unit/test_solvers/test_idaklu_solver.py b/tests/unit/test_solvers/test_idaklu_solver.py index 932dc7876e..9788d0d278 100644 --- a/tests/unit/test_solvers/test_idaklu_solver.py +++ b/tests/unit/test_solvers/test_idaklu_solver.py @@ -1,9 +1,13 @@ # # Tests for the KLU Solver class # -import pybamm -import numpy as np +from contextlib import redirect_stdout +import io import unittest + +import numpy as np + +import pybamm from tests import get_discretisation_for_testing @@ -250,7 +254,7 @@ def test_sensitivities_with_events(self): # this test implements a python version of the ida Roberts # example provided in sundials # see sundials ida examples pdf - for form in ["python", "casadi", "jax"]: + for form in ["casadi", "python", "jax"]: if form == "jax" and not pybamm.have_jax(): continue if form == "casadi": @@ -443,6 +447,81 @@ def test_dae_solver_algebraic_model(self): solution = solver.solve(model, t_eval) np.testing.assert_array_equal(solution.y, -1) + def test_options(self): + model = pybamm.BaseModel() + u = pybamm.Variable("u") + v = pybamm.Variable("v") + model.rhs = {u: -0.1 * u} + model.algebraic = {v: v - u} + model.initial_conditions = {u: 1, v: 1} + disc = pybamm.Discretisation() + disc.process_model(model) + + t_eval = np.linspace(0, 1) + solver = pybamm.IDAKLUSolver() + soln_base = solver.solve(model, t_eval) + + # test print_stats + solver = pybamm.IDAKLUSolver(options={"print_stats": True}) + f = io.StringIO() + with redirect_stdout(f): + solver.solve(model, t_eval) + s = f.getvalue() + self.assertIn("Solver Stats", s) + + solver = pybamm.IDAKLUSolver(options={"print_stats": False}) + f = io.StringIO() + with redirect_stdout(f): + solver.solve(model, t_eval) + s = f.getvalue() + self.assertEqual(len(s), 0) + + # test everything else + for jacobian in ["none", "dense", "sparse", "matrix-free", "garbage"]: + for linear_solver in [ + "SUNLinSol_SPBCGS", + "SUNLinSol_Dense", + "SUNLinSol_KLU", + "SUNLinSol_SPFGMR", + "SUNLinSol_SPGMR", + "SUNLinSol_SPTFQMR", + "garbage", + ]: + for precon in ["none", "BBDP"]: + options = { + "jacobian": jacobian, + "linear_solver": linear_solver, + "preconditioner": precon, + } + solver = pybamm.IDAKLUSolver(options=options) + if ( + jacobian == "none" + and (linear_solver == "SUNLinSol_Dense") + or jacobian == "dense" + and (linear_solver == "SUNLinSol_Dense") + or jacobian == "sparse" + and ( + linear_solver != "SUNLinSol_Dense" + and linear_solver != "garbage" + ) + or jacobian == "matrix-free" + and ( + linear_solver != "SUNLinSol_KLU" + and linear_solver != "SUNLinSol_Dense" + and linear_solver != "garbage" + ) + ): + works = True + else: + works = False + + if works: + soln = solver.solve(model, t_eval) + np.testing.assert_array_almost_equal(soln.y, soln_base.y, 5) + else: + with self.assertRaises(ValueError): + soln = solver.solve(model, t_eval) + if __name__ == "__main__": print("Add -v for more debug output") diff --git a/tests/unit/test_solvers/test_scikits_solvers.py b/tests/unit/test_solvers/test_scikits_solvers.py index 5ad45c2886..9d186a9072 100644 --- a/tests/unit/test_solvers/test_scikits_solvers.py +++ b/tests/unit/test_solvers/test_scikits_solvers.py @@ -73,7 +73,7 @@ def test_dae_integrate_bad_ics(self): t_eval = np.linspace(0, 1, 100) solver.set_up(model) - solver._set_initial_conditions(model, {}, True) + solver._set_initial_conditions(model, 0, {}, True) # check y0 np.testing.assert_array_equal(model.y0.full().flatten(), [0, 0]) # check dae solutions @@ -165,10 +165,8 @@ def test_model_solver_ode_jacobian_python(self): # Add user-supplied Jacobian to model mesh = get_mesh_for_testing() - combined_submesh = mesh.combine_submeshes( - "negative electrode", "separator", "positive electrode" - ) - N = combined_submesh.npts + submesh = mesh[("negative electrode", "separator", "positive electrode")] + N = submesh.npts # Solve testing various linear solvers linsolvers = [ @@ -478,10 +476,8 @@ def test_model_solver_dae_with_jacobian_python(self): # Add user-supplied Jacobian to model mesh = get_mesh_for_testing() - combined_submesh = mesh.combine_submeshes( - "negative electrode", "separator", "positive electrode" - ) - N = combined_submesh.npts + submesh = mesh[("negative electrode", "separator", "positive electrode")] + N = submesh.npts def jacobian(t, y): return np.block( diff --git a/tests/unit/test_solvers/test_scipy_solver.py b/tests/unit/test_solvers/test_scipy_solver.py index 63cd50a40d..6363fea6ce 100644 --- a/tests/unit/test_solvers/test_scipy_solver.py +++ b/tests/unit/test_solvers/test_scipy_solver.py @@ -123,10 +123,8 @@ def test_model_solver_ode_with_jacobian_python(self): disc.process_model(model) # Add user-supplied Jacobian to model - combined_submesh = mesh.combine_submeshes( - "negative electrode", "separator", "positive electrode" - ) - N = combined_submesh.npts + submesh = mesh[("negative electrode", "separator", "positive electrode")] + N = submesh.npts # construct jacobian in order of model.rhs J = [] @@ -503,6 +501,25 @@ def test_model_solver_manually_update_initial_conditions(self): solution.y[0], 2 * np.exp(-solution.t), decimal=5 ) + def test_scale_and_reference(self): + # Create model + model = pybamm.BaseModel() + var1 = pybamm.Variable("var1", scale=2, reference=1) + model.rhs = {var1: -var1} + model.initial_conditions = {var1: 3} + model.variables = {"var1": var1} + solver = pybamm.ScipySolver() + t_eval = np.linspace(0, 5, 100) + solution = solver.solve(model, t_eval) + + # Check that the initial conditions and solution are scaled correctly + np.testing.assert_array_almost_equal( + model.concatenated_initial_conditions.evaluate(), 1 + ) + np.testing.assert_array_almost_equal( + solution.y[0], (solution["var1"].data - 1) / 2, decimal=14 + ) + class TestScipySolverWithSensitivity(unittest.TestCase): def test_solve_sensitivity_scalar_var_scalar_input(self): diff --git a/tests/unit/test_spatial_methods/test_base_spatial_method.py b/tests/unit/test_spatial_methods/test_base_spatial_method.py index 259173092e..46a792f26a 100644 --- a/tests/unit/test_spatial_methods/test_base_spatial_method.py +++ b/tests/unit/test_spatial_methods/test_base_spatial_method.py @@ -107,7 +107,7 @@ def test_discretise_spatial_variable(self): var_disc = spatial_method.spatial_variable(var) self.assertIsInstance(var_disc, pybamm.Vector) np.testing.assert_array_equal( - var_disc.evaluate()[:, 0], mesh.combine_submeshes(*var.domain).nodes + var_disc.evaluate()[:, 0], mesh[var.domain].nodes ) # edges @@ -118,7 +118,7 @@ def test_discretise_spatial_variable(self): var_disc = spatial_method.spatial_variable(var) self.assertIsInstance(var_disc, pybamm.Vector) np.testing.assert_array_equal( - var_disc.evaluate()[:, 0], mesh.combine_submeshes(*var.domain).edges + var_disc.evaluate()[:, 0], mesh[var.domain].edges ) def test_boundary_value_checks(self): diff --git a/tests/unit/test_spatial_methods/test_finite_volume/test_extrapolation.py b/tests/unit/test_spatial_methods/test_finite_volume/test_extrapolation.py index 07462cf3d2..2521755708 100644 --- a/tests/unit/test_spatial_methods/test_finite_volume/test_extrapolation.py +++ b/tests/unit/test_spatial_methods/test_finite_volume/test_extrapolation.py @@ -248,7 +248,7 @@ def test_linear_extrapolate_left_right(self): disc = pybamm.Discretisation(mesh, spatial_methods) whole_cell = ["negative electrode", "separator", "positive electrode"] - macro_submesh = mesh.combine_submeshes(*whole_cell) + macro_submesh = mesh[whole_cell] micro_submesh = mesh["negative particle"] # Macroscale @@ -315,7 +315,7 @@ def test_quadratic_extrapolate_left_right(self): disc = pybamm.Discretisation(mesh, spatial_methods) whole_cell = ["negative electrode", "separator", "positive electrode"] - macro_submesh = mesh.combine_submeshes(*whole_cell) + macro_submesh = mesh[whole_cell] micro_submesh = mesh["negative particle"] # Macroscale diff --git a/tests/unit/test_spatial_methods/test_finite_volume/test_finite_volume.py b/tests/unit/test_spatial_methods/test_finite_volume/test_finite_volume.py index 008e39b26c..cd532ac380 100644 --- a/tests/unit/test_spatial_methods/test_finite_volume/test_finite_volume.py +++ b/tests/unit/test_spatial_methods/test_finite_volume/test_finite_volume.py @@ -64,9 +64,7 @@ def test_concatenation(self): edges = [pybamm.Vector(mesh[dom].edges, domain=dom) for dom in whole_cell] # Concatenation of edges should get averaged to nodes first, using edge_to_node v_disc = fin_vol.concatenation(edges) - np.testing.assert_array_equal( - v_disc.evaluate()[:, 0], mesh.combine_submeshes(*whole_cell).nodes - ) + np.testing.assert_array_equal(v_disc.evaluate()[:, 0], mesh[whole_cell].nodes) # test for bad shape edges = [ @@ -81,12 +79,12 @@ def test_discretise_diffusivity_times_spatial_operator(self): spatial_methods = {"macroscale": pybamm.FiniteVolume()} disc = pybamm.Discretisation(mesh, spatial_methods) whole_cell = ["negative electrode", "separator", "positive electrode"] - combined_submesh = mesh.combine_submeshes(*whole_cell) + submesh = mesh[whole_cell] # Discretise some equations where averaging is needed var = pybamm.Variable("var", domain=whole_cell) disc.set_variable_slices([var]) - y_test = np.ones_like(combined_submesh.nodes[:, np.newaxis]) + y_test = np.ones_like(submesh.nodes[:, np.newaxis]) for eqn in [ var * pybamm.grad(var), var**2 * pybamm.grad(var), @@ -165,9 +163,7 @@ def test_discretise_spatial_variable(self): self.assertIsInstance(x2_disc, pybamm.Vector) np.testing.assert_array_equal( x2_disc.evaluate(), - disc.mesh.combine_submeshes("negative electrode", "separator").nodes[ - :, np.newaxis - ], + disc.mesh[("negative electrode", "separator")].nodes[:, np.newaxis], ) # microscale r = 3 * pybamm.SpatialVariable("r", ["negative particle"]) @@ -194,11 +190,11 @@ def test_mass_matrix_shape(self): mesh = get_mesh_for_testing() spatial_methods = {"macroscale": pybamm.FiniteVolume()} disc = pybamm.Discretisation(mesh, spatial_methods) - combined_submesh = mesh.combine_submeshes(*whole_cell) + submesh = mesh[whole_cell] disc.process_model(model) # Mass matrix - mass = np.eye(combined_submesh.npts) + mass = np.eye(submesh.npts) np.testing.assert_array_equal(mass, model.mass_matrix.entries.toarray()) def test_p2d_mass_matrix_shape(self): @@ -238,15 +234,15 @@ def test_jacobian(self): mesh = get_mesh_for_testing() spatial_methods = {"macroscale": pybamm.FiniteVolume()} disc = pybamm.Discretisation(mesh, spatial_methods) - combined_submesh = mesh.combine_submeshes(*whole_cell) + submesh = mesh[whole_cell] spatial_method = pybamm.FiniteVolume() spatial_method.build(mesh) # Setup variable var = pybamm.Variable("var", domain=whole_cell) disc.set_variable_slices([var]) - y = pybamm.StateVector(slice(0, combined_submesh.npts)) - y_test = np.ones_like(combined_submesh.nodes[:, np.newaxis]) + y = pybamm.StateVector(slice(0, submesh.npts)) + y_test = np.ones_like(submesh.nodes[:, np.newaxis]) # grad eqn = pybamm.grad(var) @@ -422,6 +418,7 @@ def test_upwind_downwind(self): # Remove boundary conditions and check error is raised disc.bcs = {} + disc._discretised_symbols = {} with self.assertRaisesRegex(pybamm.ModelError, "Boundary conditions"): disc.process_symbol(upwind) diff --git a/tests/unit/test_spatial_methods/test_finite_volume/test_ghost_nodes_and_neumann.py b/tests/unit/test_spatial_methods/test_finite_volume/test_ghost_nodes_and_neumann.py index d9f35523f7..0a9d1dd102 100644 --- a/tests/unit/test_spatial_methods/test_finite_volume/test_ghost_nodes_and_neumann.py +++ b/tests/unit/test_spatial_methods/test_finite_volume/test_ghost_nodes_and_neumann.py @@ -30,8 +30,8 @@ def test_add_ghost_nodes(self): sp_meth = pybamm.FiniteVolume() sp_meth.build(mesh) sym_ghost, _ = sp_meth.add_ghost_nodes(var, discretised_symbol, bcs) - combined_submesh = mesh.combine_submeshes(*whole_cell) - y_test = np.linspace(0, 1, combined_submesh.npts) + submesh = mesh[whole_cell] + y_test = np.linspace(0, 1, submesh.npts) np.testing.assert_array_equal( sym_ghost.evaluate(y=y_test)[1:-1], discretised_symbol.evaluate(y=y_test) ) @@ -76,8 +76,8 @@ def test_add_ghost_nodes_concatenation(self): } # Test - combined_submesh = mesh.combine_submeshes(*whole_cell) - y_test = np.ones_like(combined_submesh.nodes[:, np.newaxis]) + submesh = mesh[whole_cell] + y_test = np.ones_like(submesh.nodes[:, np.newaxis]) # both sp_meth = pybamm.FiniteVolume() diff --git a/tests/unit/test_spatial_methods/test_finite_volume/test_grad_div_shapes.py b/tests/unit/test_spatial_methods/test_finite_volume/test_grad_div_shapes.py index a586939a08..ac5ec6f489 100644 --- a/tests/unit/test_spatial_methods/test_finite_volume/test_grad_div_shapes.py +++ b/tests/unit/test_spatial_methods/test_finite_volume/test_grad_div_shapes.py @@ -22,11 +22,11 @@ def test_grad_div_shapes_Dirichlet_bcs(self): mesh = get_mesh_for_testing() spatial_methods = {"macroscale": pybamm.FiniteVolume()} disc = pybamm.Discretisation(mesh, spatial_methods) - combined_submesh = mesh.combine_submeshes(*whole_cell) + submesh = mesh[whole_cell] # Test gradient of constant is zero # grad(1) = 0 - constant_y = np.ones_like(combined_submesh.nodes[:, np.newaxis]) + constant_y = np.ones_like(submesh.nodes[:, np.newaxis]) var = pybamm.Variable("var", domain=whole_cell) grad_eqn = pybamm.grad(var) boundary_conditions = { @@ -40,11 +40,11 @@ def test_grad_div_shapes_Dirichlet_bcs(self): grad_eqn_disc = disc.process_symbol(grad_eqn) np.testing.assert_array_equal( grad_eqn_disc.evaluate(None, constant_y), - np.zeros_like(combined_submesh.edges[:, np.newaxis]), + np.zeros_like(submesh.edges[:, np.newaxis]), ) # Test operations on linear x - linear_y = combined_submesh.nodes + linear_y = submesh.nodes N = pybamm.grad(var) div_eqn = pybamm.div(N) boundary_conditions = { @@ -58,13 +58,13 @@ def test_grad_div_shapes_Dirichlet_bcs(self): grad_eqn_disc = disc.process_symbol(grad_eqn) np.testing.assert_array_almost_equal( grad_eqn_disc.evaluate(None, linear_y), - np.ones_like(combined_submesh.edges[:, np.newaxis]), + np.ones_like(submesh.edges[:, np.newaxis]), ) # div(grad(x)) = 0 div_eqn_disc = disc.process_symbol(div_eqn) np.testing.assert_array_almost_equal( div_eqn_disc.evaluate(None, linear_y), - np.zeros_like(combined_submesh.nodes[:, np.newaxis]), + np.zeros_like(submesh.nodes[:, np.newaxis]), ) def test_cylindrical_grad_div_shapes_Dirichlet_bcs(self): @@ -261,11 +261,11 @@ def test_grad_div_shapes_Neumann_bcs(self): mesh = get_mesh_for_testing() spatial_methods = {"macroscale": pybamm.FiniteVolume()} disc = pybamm.Discretisation(mesh, spatial_methods) - combined_submesh = mesh.combine_submeshes(*whole_cell) + submesh = mesh[whole_cell] # Test gradient of constant is zero # grad(1) = 0 - constant_y = np.ones_like(combined_submesh.nodes[:, np.newaxis]) + constant_y = np.ones_like(submesh.nodes[:, np.newaxis]) var = pybamm.Variable("var", domain=whole_cell) grad_eqn = pybamm.grad(var) boundary_conditions = { @@ -279,11 +279,11 @@ def test_grad_div_shapes_Neumann_bcs(self): grad_eqn_disc = disc.process_symbol(grad_eqn) np.testing.assert_array_equal( grad_eqn_disc.evaluate(None, constant_y), - np.zeros_like(combined_submesh.edges[:, np.newaxis]), + np.zeros_like(submesh.edges[:, np.newaxis]), ) # Test operations on linear x - linear_y = combined_submesh.nodes + linear_y = submesh.nodes N = pybamm.grad(var) div_eqn = pybamm.div(N) boundary_conditions = { @@ -297,13 +297,13 @@ def test_grad_div_shapes_Neumann_bcs(self): grad_eqn_disc = disc.process_symbol(grad_eqn) np.testing.assert_array_almost_equal( grad_eqn_disc.evaluate(None, linear_y), - np.ones_like(combined_submesh.edges[:, np.newaxis]), + np.ones_like(submesh.edges[:, np.newaxis]), ) # div(grad(x)) = 0 div_eqn_disc = disc.process_symbol(div_eqn) np.testing.assert_array_almost_equal( div_eqn_disc.evaluate(None, linear_y), - np.zeros_like(combined_submesh.nodes[:, np.newaxis]), + np.zeros_like(submesh.nodes[:, np.newaxis]), ) def test_grad_div_shapes_Dirichlet_and_Neumann_bcs(self): @@ -316,10 +316,10 @@ def test_grad_div_shapes_Dirichlet_and_Neumann_bcs(self): mesh = get_mesh_for_testing() spatial_methods = {"macroscale": pybamm.FiniteVolume()} disc = pybamm.Discretisation(mesh, spatial_methods) - combined_submesh = mesh.combine_submeshes(*whole_cell) + submesh = mesh[whole_cell] # Test gradient and divergence of a constant - constant_y = np.ones_like(combined_submesh.nodes[:, np.newaxis]) + constant_y = np.ones_like(submesh.nodes[:, np.newaxis]) var = pybamm.Variable("var", domain=whole_cell) grad_eqn = pybamm.grad(var) N = pybamm.grad(var) @@ -336,17 +336,17 @@ def test_grad_div_shapes_Dirichlet_and_Neumann_bcs(self): grad_eqn_disc = disc.process_symbol(grad_eqn) np.testing.assert_array_equal( grad_eqn_disc.evaluate(None, constant_y), - np.zeros_like(combined_submesh.edges[:, np.newaxis]), + np.zeros_like(submesh.edges[:, np.newaxis]), ) # div(grad(1)) = 0 div_eqn_disc = disc.process_symbol(div_eqn) np.testing.assert_array_almost_equal( div_eqn_disc.evaluate(None, constant_y), - np.zeros_like(combined_submesh.nodes[:, np.newaxis]), + np.zeros_like(submesh.nodes[:, np.newaxis]), ) # Test gradient and divergence of linear x - linear_y = combined_submesh.nodes + linear_y = submesh.nodes boundary_conditions = { var: { "left": (pybamm.Scalar(1), "Neumann"), @@ -358,13 +358,13 @@ def test_grad_div_shapes_Dirichlet_and_Neumann_bcs(self): grad_eqn_disc = disc.process_symbol(grad_eqn) np.testing.assert_array_almost_equal( grad_eqn_disc.evaluate(None, linear_y), - np.ones_like(combined_submesh.edges[:, np.newaxis]), + np.ones_like(submesh.edges[:, np.newaxis]), ) # div(grad(x)) = 0 div_eqn_disc = disc.process_symbol(div_eqn) np.testing.assert_array_almost_equal( div_eqn_disc.evaluate(None, linear_y), - np.zeros_like(combined_submesh.nodes[:, np.newaxis]), + np.zeros_like(submesh.nodes[:, np.newaxis]), ) def test_cylindrical_grad_div_shapes_Neumann_bcs(self): @@ -439,13 +439,13 @@ def test_spherical_grad_div_shapes_Neumann_bcs(self): mesh = get_mesh_for_testing() spatial_methods = {"negative particle": pybamm.FiniteVolume()} disc = pybamm.Discretisation(mesh, spatial_methods) - combined_submesh = mesh.combine_submeshes("negative particle") + submesh = mesh["negative particle"] # Test gradient var = pybamm.Variable("var", domain="negative particle") grad_eqn = pybamm.grad(var) # grad(1) = 0 - constant_y = np.ones_like(combined_submesh.nodes[:, np.newaxis]) + constant_y = np.ones_like(submesh.nodes[:, np.newaxis]) boundary_conditions = { var: { "left": (pybamm.Scalar(0), "Neumann"), @@ -457,10 +457,10 @@ def test_spherical_grad_div_shapes_Neumann_bcs(self): grad_eqn_disc = disc.process_symbol(grad_eqn) np.testing.assert_array_equal( grad_eqn_disc.evaluate(None, constant_y), - np.zeros_like(combined_submesh.edges[:, np.newaxis]), + np.zeros_like(submesh.edges[:, np.newaxis]), ) # grad(r) == 1 - linear_y = combined_submesh.nodes + linear_y = submesh.nodes boundary_conditions = { var: { "left": (pybamm.Scalar(1), "Neumann"), @@ -471,12 +471,12 @@ def test_spherical_grad_div_shapes_Neumann_bcs(self): grad_eqn_disc = disc.process_symbol(grad_eqn) np.testing.assert_array_almost_equal( grad_eqn_disc.evaluate(None, linear_y), - np.ones_like(combined_submesh.edges[:, np.newaxis]), + np.ones_like(submesh.edges[:, np.newaxis]), ) # Test divergence of gradient # div(grad(r^2)) = 6 , N_left = 0, N_right = 2 - quadratic_y = combined_submesh.nodes**2 + quadratic_y = submesh.nodes**2 N = pybamm.grad(var) div_eqn = pybamm.div(N) boundary_conditions = { @@ -489,7 +489,7 @@ def test_spherical_grad_div_shapes_Neumann_bcs(self): div_eqn_disc = disc.process_symbol(div_eqn) np.testing.assert_array_almost_equal( div_eqn_disc.evaluate(None, quadratic_y), - 6 * np.ones((combined_submesh.npts, 1)), + 6 * np.ones((submesh.npts, 1)), ) def test_p2d_spherical_grad_div_shapes_Neumann_bcs(self): @@ -548,11 +548,11 @@ def test_grad_div_shapes_mixed_domain(self): mesh = get_mesh_for_testing() spatial_methods = {"macroscale": pybamm.FiniteVolume()} disc = pybamm.Discretisation(mesh, spatial_methods) - combined_submesh = mesh.combine_submeshes("negative electrode", "separator") + submesh = mesh[("negative electrode", "separator")] # Test gradient of constant # grad(1) = 0 - constant_y = np.ones_like(combined_submesh.nodes[:, np.newaxis]) + constant_y = np.ones_like(submesh.nodes[:, np.newaxis]) var = pybamm.Variable("var", domain=["negative electrode", "separator"]) grad_eqn = pybamm.grad(var) boundary_conditions = { @@ -566,17 +566,17 @@ def test_grad_div_shapes_mixed_domain(self): grad_eqn_disc = disc.process_symbol(grad_eqn) np.testing.assert_array_equal( grad_eqn_disc.evaluate(None, constant_y), - np.zeros_like(combined_submesh.edges[:, np.newaxis]), + np.zeros_like(submesh.edges[:, np.newaxis]), ) # Test operations on linear x - linear_y = combined_submesh.nodes + linear_y = submesh.nodes N = pybamm.grad(var) div_eqn = pybamm.div(N) boundary_conditions = { var: { "left": (pybamm.Scalar(0), "Dirichlet"), - "right": (pybamm.Scalar(combined_submesh.edges[-1]), "Dirichlet"), + "right": (pybamm.Scalar(submesh.edges[-1]), "Dirichlet"), } } disc.bcs = boundary_conditions @@ -584,13 +584,13 @@ def test_grad_div_shapes_mixed_domain(self): grad_eqn_disc = disc.process_symbol(grad_eqn) np.testing.assert_array_almost_equal( grad_eqn_disc.evaluate(None, linear_y), - np.ones_like(combined_submesh.edges[:, np.newaxis]), + np.ones_like(submesh.edges[:, np.newaxis]), ) # div(grad(x)) = 0 div_eqn_disc = disc.process_symbol(div_eqn) np.testing.assert_array_almost_equal( div_eqn_disc.evaluate(None, linear_y), - np.zeros_like(combined_submesh.nodes[:, np.newaxis]), + np.zeros_like(submesh.nodes[:, np.newaxis]), ) def test_grad_1plus1d(self): @@ -627,13 +627,11 @@ def test_grad_1plus1d(self): grad_eqn_disc = disc.process_symbol(pybamm.grad(var)) # Evaulate - combined_submesh = mesh.combine_submeshes(*var.domain) - linear_y = np.outer(np.linspace(0, 1, 15), combined_submesh.nodes).reshape( + submesh = mesh[var.domain] + linear_y = np.outer(np.linspace(0, 1, 15), submesh.nodes).reshape(-1, 1) + expected = np.outer(np.linspace(0, 1, 15), np.ones_like(submesh.edges)).reshape( -1, 1 ) - expected = np.outer( - np.linspace(0, 1, 15), np.ones_like(combined_submesh.edges) - ).reshape(-1, 1) np.testing.assert_array_almost_equal( grad_eqn_disc.evaluate(None, linear_y), expected ) diff --git a/tests/unit/test_spatial_methods/test_finite_volume/test_integration.py b/tests/unit/test_spatial_methods/test_finite_volume/test_integration.py index 0885eb58ca..b8b2918682 100644 --- a/tests/unit/test_spatial_methods/test_finite_volume/test_integration.py +++ b/tests/unit/test_spatial_methods/test_finite_volume/test_integration.py @@ -33,15 +33,15 @@ def test_definite_integral(self): integral_eqn = pybamm.Integral(var, x) disc.set_variable_slices([var]) integral_eqn_disc = disc.process_symbol(integral_eqn) - combined_submesh = mesh.combine_submeshes("negative electrode", "separator") + submesh = mesh[("negative electrode", "separator")] - constant_y = np.ones_like(combined_submesh.nodes[:, np.newaxis]) + constant_y = np.ones_like(submesh.nodes[:, np.newaxis]) self.assertEqual(integral_eqn_disc.evaluate(None, constant_y), ln + ls) - linear_y = combined_submesh.nodes + linear_y = submesh.nodes np.testing.assert_array_almost_equal( integral_eqn_disc.evaluate(None, linear_y), (ln + ls) ** 2 / 2 ) - cos_y = np.cos(combined_submesh.nodes[:, np.newaxis]) + cos_y = np.cos(submesh.nodes[:, np.newaxis]) np.testing.assert_array_almost_equal( integral_eqn_disc.evaluate(None, cos_y), np.sin(ln + ls), decimal=4 ) @@ -52,15 +52,15 @@ def test_definite_integral(self): integral_eqn = pybamm.Integral(var, x) disc.set_variable_slices([var]) integral_eqn_disc = disc.process_symbol(integral_eqn) - combined_submesh = mesh.combine_submeshes("separator", "positive electrode") + submesh = mesh[("separator", "positive electrode")] - constant_y = np.ones_like(combined_submesh.nodes[:, np.newaxis]) + constant_y = np.ones_like(submesh.nodes[:, np.newaxis]) self.assertEqual(integral_eqn_disc.evaluate(None, constant_y), ls + lp) - linear_y = combined_submesh.nodes + linear_y = submesh.nodes self.assertAlmostEqual( integral_eqn_disc.evaluate(None, linear_y)[0][0], (1 - (ln) ** 2) / 2 ) - cos_y = np.cos(combined_submesh.nodes[:, np.newaxis]) + cos_y = np.cos(submesh.nodes[:, np.newaxis]) np.testing.assert_array_almost_equal( integral_eqn_disc.evaluate(None, cos_y), np.sin(1) - np.sin(ln), decimal=4 ) @@ -318,7 +318,7 @@ def test_indefinite_integral(self): int_grad_phi = pybamm.IndefiniteIntegral(i, x) disc.set_variable_slices([phi]) # i is not a fundamental variable # Set boundary conditions (required for shape but don't matter) - disc._bcs = { + disc.bcs = { phi: { "left": (pybamm.Scalar(0), "Neumann"), "right": (pybamm.Scalar(0), "Neumann"), @@ -328,22 +328,22 @@ def test_indefinite_integral(self): left_boundary_value = pybamm.BoundaryValue(int_grad_phi, "left") left_boundary_value_disc = disc.process_symbol(left_boundary_value) - combined_submesh = mesh.combine_submeshes("negative electrode", "separator") + submesh = mesh[("negative electrode", "separator")] # constant case - phi_exact = np.ones((combined_submesh.npts, 1)) + phi_exact = np.ones((submesh.npts, 1)) phi_approx = int_grad_phi_disc.evaluate(None, phi_exact) phi_approx += 1 # add constant of integration np.testing.assert_array_equal(phi_exact, phi_approx) self.assertEqual(left_boundary_value_disc.evaluate(y=phi_exact), 0) # linear case - phi_exact = combined_submesh.nodes[:, np.newaxis] + phi_exact = submesh.nodes[:, np.newaxis] phi_approx = int_grad_phi_disc.evaluate(None, phi_exact) np.testing.assert_array_almost_equal(phi_exact, phi_approx) self.assertEqual(left_boundary_value_disc.evaluate(y=phi_exact), 0) # sine case - phi_exact = np.sin(combined_submesh.nodes[:, np.newaxis]) + phi_exact = np.sin(submesh.nodes[:, np.newaxis]) phi_approx = int_grad_phi_disc.evaluate(None, phi_exact) np.testing.assert_array_almost_equal(phi_exact, phi_approx) self.assertEqual(left_boundary_value_disc.evaluate(y=phi_exact), 0) @@ -355,7 +355,7 @@ def test_indefinite_integral(self): x = pybamm.SpatialVariable("x", ["separator", "positive electrode"]) int_grad_phi = pybamm.IndefiniteIntegral(i, x) disc.set_variable_slices([phi]) # i is not a fundamental variable - disc._bcs = { + disc.bcs = { phi: { "left": (pybamm.Scalar(0), "Neumann"), "right": (pybamm.Scalar(0), "Neumann"), @@ -364,17 +364,17 @@ def test_indefinite_integral(self): int_grad_phi_disc = disc.process_symbol(int_grad_phi) left_boundary_value = pybamm.BoundaryValue(int_grad_phi, "left") left_boundary_value_disc = disc.process_symbol(left_boundary_value) - combined_submesh = mesh.combine_submeshes("separator", "positive electrode") + submesh = mesh[("separator", "positive electrode")] # constant case - phi_exact = np.ones((combined_submesh.npts, 1)) + phi_exact = np.ones((submesh.npts, 1)) phi_approx = int_grad_phi_disc.evaluate(None, phi_exact) phi_approx += 1 # add constant of integration np.testing.assert_array_equal(phi_exact, phi_approx) self.assertEqual(left_boundary_value_disc.evaluate(y=phi_exact), 0) # linear case - phi_exact = combined_submesh.nodes[:, np.newaxis] - combined_submesh.edges[0] + phi_exact = submesh.nodes[:, np.newaxis] - submesh.edges[0] phi_approx = int_grad_phi_disc.evaluate(None, phi_exact) np.testing.assert_array_almost_equal(phi_exact, phi_approx) np.testing.assert_array_almost_equal( @@ -382,9 +382,7 @@ def test_indefinite_integral(self): ) # sine case - phi_exact = np.sin( - combined_submesh.nodes[:, np.newaxis] - combined_submesh.edges[0] - ) + phi_exact = np.sin(submesh.nodes[:, np.newaxis] - submesh.edges[0]) phi_approx = int_grad_phi_disc.evaluate(None, phi_exact) np.testing.assert_array_almost_equal(phi_exact, phi_approx) np.testing.assert_array_almost_equal( @@ -417,7 +415,7 @@ def test_indefinite_integral(self): r_n = pybamm.SpatialVariable("r_n", ["negative particle"]) c_integral = pybamm.IndefiniteIntegral(N, r_n) disc.set_variable_slices([c]) # N is not a fundamental variable - disc._bcs = { + disc.bcs = { c: { "left": (pybamm.Scalar(0), "Neumann"), "right": (pybamm.Scalar(0), "Neumann"), @@ -427,17 +425,17 @@ def test_indefinite_integral(self): c_integral_disc = disc.process_symbol(c_integral) left_boundary_value = pybamm.BoundaryValue(c_integral, "left") left_boundary_value_disc = disc.process_symbol(left_boundary_value) - combined_submesh = mesh["negative particle"] + submesh = mesh["negative particle"] # constant case - c_exact = np.ones((combined_submesh.npts, 1)) + c_exact = np.ones((submesh.npts, 1)) c_approx = c_integral_disc.evaluate(None, c_exact) c_approx += 1 # add constant of integration np.testing.assert_array_equal(c_exact, c_approx) self.assertEqual(left_boundary_value_disc.evaluate(y=c_exact), 0) # linear case - c_exact = combined_submesh.nodes[:, np.newaxis] + c_exact = submesh.nodes[:, np.newaxis] c_approx = c_integral_disc.evaluate(None, c_exact) np.testing.assert_array_almost_equal(c_exact, c_approx) np.testing.assert_array_almost_equal( @@ -445,7 +443,7 @@ def test_indefinite_integral(self): ) # sine case - c_exact = np.sin(combined_submesh.nodes[:, np.newaxis]) + c_exact = np.sin(submesh.nodes[:, np.newaxis]) c_approx = c_integral_disc.evaluate(None, c_exact) np.testing.assert_array_almost_equal(c_exact, c_approx, decimal=3) np.testing.assert_array_almost_equal( @@ -466,7 +464,7 @@ def test_backward_indefinite_integral(self): x = pybamm.SpatialVariable("x", ["negative electrode", "separator"]) int_grad_phi = pybamm.BackwardIndefiniteIntegral(i, x) disc.set_variable_slices([phi]) # i is not a fundamental variable - disc._bcs = { + disc.bcs = { phi: { "left": (pybamm.Scalar(0), "Neumann"), "right": (pybamm.Scalar(0), "Neumann"), @@ -475,18 +473,18 @@ def test_backward_indefinite_integral(self): int_grad_phi_disc = disc.process_symbol(int_grad_phi) right_boundary_value = pybamm.BoundaryValue(int_grad_phi, "right") right_boundary_value_disc = disc.process_symbol(right_boundary_value) - combined_submesh = mesh.combine_submeshes("negative electrode", "separator") + submesh = mesh[("negative electrode", "separator")] # Test that the backward_integral(grad(phi)) = -phi # constant case - phi_exact = np.ones((combined_submesh.npts, 1)) + phi_exact = np.ones((submesh.npts, 1)) phi_approx = int_grad_phi_disc.evaluate(None, phi_exact) phi_approx += 1 # add constant of integration np.testing.assert_array_equal(phi_exact, phi_approx) self.assertEqual(right_boundary_value_disc.evaluate(y=phi_exact), 0) # linear case - phi_exact = combined_submesh.nodes - combined_submesh.edges[-1] + phi_exact = submesh.nodes - submesh.edges[-1] phi_approx = int_grad_phi_disc.evaluate(None, phi_exact).flatten() np.testing.assert_array_almost_equal(phi_exact, -phi_approx) np.testing.assert_array_almost_equal( @@ -494,7 +492,7 @@ def test_backward_indefinite_integral(self): ) # sine case - phi_exact = np.sin(combined_submesh.nodes - combined_submesh.edges[-1]) + phi_exact = np.sin(submesh.nodes - submesh.edges[-1]) phi_approx = int_grad_phi_disc.evaluate(None, phi_exact).flatten() np.testing.assert_array_almost_equal(phi_exact, -phi_approx) np.testing.assert_array_almost_equal( @@ -518,8 +516,8 @@ def test_indefinite_integral_of_broadcasted_to_cell_edges(self): i = pybamm.PrimaryBroadcastToEdges(1, phi.domain) x = pybamm.SpatialVariable("x", phi.domain) disc.set_variable_slices([phi]) - combined_submesh = mesh.combine_submeshes("negative electrode", "separator") - x_end = combined_submesh.edges[-1] + submesh = mesh[("negative electrode", "separator")] + x_end = submesh.edges[-1] # take indefinite integral int_phi = pybamm.IndefiniteIntegral(i * phi, x) @@ -528,12 +526,12 @@ def test_indefinite_integral_of_broadcasted_to_cell_edges(self): int_int_phi_disc = disc.process_symbol(int_int_phi) # constant case - phi_exact = np.ones_like(combined_submesh.nodes) + phi_exact = np.ones_like(submesh.nodes) phi_approx = int_int_phi_disc.evaluate(None, phi_exact) np.testing.assert_array_almost_equal(x_end**2 / 2, phi_approx) # linear case - phi_exact = combined_submesh.nodes[:, np.newaxis] + phi_exact = submesh.nodes[:, np.newaxis] phi_approx = int_int_phi_disc.evaluate(None, phi_exact) np.testing.assert_array_almost_equal(x_end**3 / 6, phi_approx, decimal=4) @@ -549,21 +547,21 @@ def test_indefinite_integral_on_nodes(self): disc.set_variable_slices([phi]) int_phi_disc = disc.process_symbol(int_phi) - combined_submesh = mesh.combine_submeshes("negative electrode", "separator") + submesh = mesh[("negative electrode", "separator")] # constant case - phi_exact = np.ones((combined_submesh.npts, 1)) - int_phi_exact = combined_submesh.edges + phi_exact = np.ones((submesh.npts, 1)) + int_phi_exact = submesh.edges int_phi_approx = int_phi_disc.evaluate(None, phi_exact).flatten() np.testing.assert_array_equal(int_phi_exact, int_phi_approx) # linear case - phi_exact = combined_submesh.nodes - int_phi_exact = combined_submesh.edges**2 / 2 + phi_exact = submesh.nodes + int_phi_exact = submesh.edges**2 / 2 int_phi_approx = int_phi_disc.evaluate(None, phi_exact).flatten() np.testing.assert_array_almost_equal(int_phi_exact, int_phi_approx) # cos case - phi_exact = np.cos(combined_submesh.nodes) - int_phi_exact = np.sin(combined_submesh.edges) + phi_exact = np.cos(submesh.nodes) + int_phi_exact = np.sin(submesh.edges) int_phi_approx = int_phi_disc.evaluate(None, phi_exact).flatten() np.testing.assert_array_almost_equal(int_phi_exact, int_phi_approx, decimal=5) @@ -595,21 +593,21 @@ def test_backward_indefinite_integral_on_nodes(self): disc.set_variable_slices([phi]) back_int_phi_disc = disc.process_symbol(back_int_phi) - combined_submesh = mesh.combine_submeshes("negative electrode", "separator") - edges = combined_submesh.edges + submesh = mesh[("negative electrode", "separator")] + edges = submesh.edges # constant case - phi_exact = np.ones((combined_submesh.npts, 1)) + phi_exact = np.ones((submesh.npts, 1)) back_int_phi_exact = edges[-1] - edges back_int_phi_approx = back_int_phi_disc.evaluate(None, phi_exact).flatten() np.testing.assert_array_almost_equal(back_int_phi_exact, back_int_phi_approx) # linear case - phi_exact = combined_submesh.nodes + phi_exact = submesh.nodes back_int_phi_exact = edges[-1] ** 2 / 2 - edges**2 / 2 back_int_phi_approx = back_int_phi_disc.evaluate(None, phi_exact).flatten() np.testing.assert_array_almost_equal(back_int_phi_exact, back_int_phi_approx) # cos case - phi_exact = np.cos(combined_submesh.nodes) + phi_exact = np.cos(submesh.nodes) back_int_phi_exact = np.sin(edges[-1]) - np.sin(edges) back_int_phi_approx = back_int_phi_disc.evaluate(None, phi_exact).flatten() np.testing.assert_array_almost_equal( @@ -637,13 +635,13 @@ def test_forward_plus_backward_integral(self): ) + pybamm.BackwardIndefiniteIntegral(phi, x) int_plus_back_int_phi_disc = disc.process_symbol(int_plus_back_int_phi) - combined_submesh = mesh.combine_submeshes("separator", "positive electrode") + submesh = mesh[("separator", "positive electrode")] # test for phi_exact in [ - np.ones((combined_submesh.npts, 1)), - combined_submesh.nodes, - np.cos(combined_submesh.nodes), + np.ones((submesh.npts, 1)), + submesh.nodes, + np.cos(submesh.nodes), ]: np.testing.assert_array_almost_equal( full_int_phi_disc.evaluate(y=phi_exact).flatten(), diff --git a/tests/unit/test_spatial_methods/test_spectral_volume.py b/tests/unit/test_spatial_methods/test_spectral_volume.py index ee63fc16c7..c2e5f3396d 100644 --- a/tests/unit/test_spatial_methods/test_spectral_volume.py +++ b/tests/unit/test_spatial_methods/test_spectral_volume.py @@ -121,11 +121,11 @@ def test_grad_div_shapes_Dirichlet_bcs(self): mesh = get_mesh_for_testing(1) spatial_methods = {"macroscale": pybamm.SpectralVolume()} disc = pybamm.Discretisation(mesh, spatial_methods) - combined_submesh = mesh.combine_submeshes(*whole_cell) + submesh = mesh[whole_cell] # Test gradient of constant is zero # grad(1) = 0 - constant_y = np.ones_like(combined_submesh.nodes[:, np.newaxis]) + constant_y = np.ones_like(submesh.nodes[:, np.newaxis]) var = pybamm.Variable("var", domain=whole_cell) grad_eqn = pybamm.grad(var) boundary_conditions = { @@ -139,11 +139,11 @@ def test_grad_div_shapes_Dirichlet_bcs(self): grad_eqn_disc = disc.process_symbol(grad_eqn) np.testing.assert_array_almost_equal( grad_eqn_disc.evaluate(None, constant_y), - np.zeros_like(combined_submesh.edges[:, np.newaxis]), + np.zeros_like(submesh.edges[:, np.newaxis]), ) # Test operations on linear x - linear_y = combined_submesh.nodes + linear_y = submesh.nodes N = pybamm.grad(var) div_eqn = pybamm.div(N) boundary_conditions = { @@ -157,13 +157,13 @@ def test_grad_div_shapes_Dirichlet_bcs(self): grad_eqn_disc = disc.process_symbol(grad_eqn) np.testing.assert_array_almost_equal( grad_eqn_disc.evaluate(None, linear_y), - np.ones_like(combined_submesh.edges[:, np.newaxis]), + np.ones_like(submesh.edges[:, np.newaxis]), ) # div(grad(x)) = 0 div_eqn_disc = disc.process_symbol(div_eqn) np.testing.assert_array_almost_equal( div_eqn_disc.evaluate(None, linear_y), - np.zeros_like(combined_submesh.nodes[:, np.newaxis]), + np.zeros_like(submesh.nodes[:, np.newaxis]), ) def test_spherical_grad_div_shapes_Dirichlet_bcs(self): @@ -312,11 +312,11 @@ def test_grad_div_shapes_Neumann_bcs(self): mesh = get_mesh_for_testing() spatial_methods = {"macroscale": pybamm.SpectralVolume()} disc = pybamm.Discretisation(mesh, spatial_methods) - combined_submesh = mesh.combine_submeshes(*whole_cell) + submesh = mesh[whole_cell] # Test gradient of constant is zero # grad(1) = 0 - constant_y = np.ones_like(combined_submesh.nodes[:, np.newaxis]) + constant_y = np.ones_like(submesh.nodes[:, np.newaxis]) var = pybamm.Variable("var", domain=whole_cell) grad_eqn = pybamm.grad(var) boundary_conditions = { @@ -330,11 +330,11 @@ def test_grad_div_shapes_Neumann_bcs(self): grad_eqn_disc = disc.process_symbol(grad_eqn) np.testing.assert_array_almost_equal( grad_eqn_disc.evaluate(None, constant_y), - np.zeros_like(combined_submesh.edges[:, np.newaxis]), + np.zeros_like(submesh.edges[:, np.newaxis]), ) # Test operations on linear x - linear_y = combined_submesh.nodes + linear_y = submesh.nodes N = pybamm.grad(var) div_eqn = pybamm.div(N) boundary_conditions = { @@ -348,13 +348,13 @@ def test_grad_div_shapes_Neumann_bcs(self): grad_eqn_disc = disc.process_symbol(grad_eqn) np.testing.assert_array_almost_equal( grad_eqn_disc.evaluate(None, linear_y), - np.ones_like(combined_submesh.edges[:, np.newaxis]), + np.ones_like(submesh.edges[:, np.newaxis]), ) # div(grad(x)) = 0 div_eqn_disc = disc.process_symbol(div_eqn) np.testing.assert_array_almost_equal( div_eqn_disc.evaluate(None, linear_y), - np.zeros_like(combined_submesh.nodes[:, np.newaxis]), + np.zeros_like(submesh.nodes[:, np.newaxis]), ) def test_grad_div_shapes_Dirichlet_and_Neumann_bcs(self): @@ -367,10 +367,10 @@ def test_grad_div_shapes_Dirichlet_and_Neumann_bcs(self): mesh = get_mesh_for_testing() spatial_methods = {"macroscale": pybamm.SpectralVolume()} disc = pybamm.Discretisation(mesh, spatial_methods) - combined_submesh = mesh.combine_submeshes(*whole_cell) + submesh = mesh[whole_cell] # Test gradient and divergence of a constant - constant_y = np.ones_like(combined_submesh.nodes[:, np.newaxis]) + constant_y = np.ones_like(submesh.nodes[:, np.newaxis]) var = pybamm.Variable("var", domain=whole_cell) grad_eqn = pybamm.grad(var) N = pybamm.grad(var) @@ -387,17 +387,17 @@ def test_grad_div_shapes_Dirichlet_and_Neumann_bcs(self): grad_eqn_disc = disc.process_symbol(grad_eqn) np.testing.assert_array_almost_equal( grad_eqn_disc.evaluate(None, constant_y), - np.zeros_like(combined_submesh.edges[:, np.newaxis]), + np.zeros_like(submesh.edges[:, np.newaxis]), ) # div(grad(1)) = 0 div_eqn_disc = disc.process_symbol(div_eqn) np.testing.assert_array_almost_equal( div_eqn_disc.evaluate(None, constant_y), - np.zeros_like(combined_submesh.nodes[:, np.newaxis]), + np.zeros_like(submesh.nodes[:, np.newaxis]), ) # Test gradient and divergence of linear x - linear_y = combined_submesh.nodes + linear_y = submesh.nodes boundary_conditions = { var: { "left": (pybamm.Scalar(1), "Neumann"), @@ -409,13 +409,13 @@ def test_grad_div_shapes_Dirichlet_and_Neumann_bcs(self): grad_eqn_disc = disc.process_symbol(grad_eqn) np.testing.assert_array_almost_equal( grad_eqn_disc.evaluate(None, linear_y), - np.ones_like(combined_submesh.edges[:, np.newaxis]), + np.ones_like(submesh.edges[:, np.newaxis]), ) # div(grad(x)) = 0 div_eqn_disc = disc.process_symbol(div_eqn) np.testing.assert_array_almost_equal( div_eqn_disc.evaluate(None, linear_y), - np.zeros_like(combined_submesh.nodes[:, np.newaxis]), + np.zeros_like(submesh.nodes[:, np.newaxis]), ) def test_spherical_grad_div_shapes_Neumann_bcs(self): @@ -427,13 +427,13 @@ def test_spherical_grad_div_shapes_Neumann_bcs(self): mesh = get_mesh_for_testing() spatial_methods = {"negative particle": pybamm.SpectralVolume()} disc = pybamm.Discretisation(mesh, spatial_methods) - combined_submesh = mesh.combine_submeshes("negative particle") + submesh = mesh["negative particle"] # Test gradient var = pybamm.Variable("var", domain="negative particle") grad_eqn = pybamm.grad(var) # grad(1) = 0 - constant_y = np.ones_like(combined_submesh.nodes[:, np.newaxis]) + constant_y = np.ones_like(submesh.nodes[:, np.newaxis]) boundary_conditions = { var: { "left": (pybamm.Scalar(0), "Neumann"), @@ -445,10 +445,10 @@ def test_spherical_grad_div_shapes_Neumann_bcs(self): grad_eqn_disc = disc.process_symbol(grad_eqn) np.testing.assert_array_almost_equal( grad_eqn_disc.evaluate(None, constant_y), - np.zeros_like(combined_submesh.edges[:, np.newaxis]), + np.zeros_like(submesh.edges[:, np.newaxis]), ) # grad(r) == 1 - linear_y = combined_submesh.nodes + linear_y = submesh.nodes boundary_conditions = { var: { "left": (pybamm.Scalar(1), "Neumann"), @@ -459,12 +459,12 @@ def test_spherical_grad_div_shapes_Neumann_bcs(self): grad_eqn_disc = disc.process_symbol(grad_eqn) np.testing.assert_array_almost_equal( grad_eqn_disc.evaluate(None, linear_y), - np.ones_like(combined_submesh.edges[:, np.newaxis]), + np.ones_like(submesh.edges[:, np.newaxis]), ) # Test divergence of gradient # div(grad(r^2)) = 6 , N_left = 0, N_right = 2 - quadratic_y = combined_submesh.nodes**2 + quadratic_y = submesh.nodes**2 N = pybamm.grad(var) div_eqn = pybamm.div(N) boundary_conditions = { @@ -477,7 +477,7 @@ def test_spherical_grad_div_shapes_Neumann_bcs(self): div_eqn_disc = disc.process_symbol(div_eqn) np.testing.assert_array_almost_equal( div_eqn_disc.evaluate(None, quadratic_y), - 6 * np.ones((combined_submesh.npts, 1)), + 6 * np.ones((submesh.npts, 1)), ) def test_p2d_spherical_grad_div_shapes_Neumann_bcs(self): @@ -537,11 +537,11 @@ def test_grad_div_shapes_mixed_domain(self): mesh = get_mesh_for_testing() spatial_methods = {"macroscale": pybamm.SpectralVolume()} disc = pybamm.Discretisation(mesh, spatial_methods) - combined_submesh = mesh.combine_submeshes("negative electrode", "separator") + submesh = mesh[("negative electrode", "separator")] # Test gradient of constant # grad(1) = 0 - constant_y = np.ones_like(combined_submesh.nodes[:, np.newaxis]) + constant_y = np.ones_like(submesh.nodes[:, np.newaxis]) var = pybamm.Variable("var", domain=["negative electrode", "separator"]) grad_eqn = pybamm.grad(var) boundary_conditions = { @@ -555,17 +555,17 @@ def test_grad_div_shapes_mixed_domain(self): grad_eqn_disc = disc.process_symbol(grad_eqn) np.testing.assert_array_almost_equal( grad_eqn_disc.evaluate(None, constant_y), - np.zeros_like(combined_submesh.edges[:, np.newaxis]), + np.zeros_like(submesh.edges[:, np.newaxis]), ) # Test operations on linear x - linear_y = combined_submesh.nodes + linear_y = submesh.nodes N = pybamm.grad(var) div_eqn = pybamm.div(N) boundary_conditions = { var: { "left": (pybamm.Scalar(0), "Dirichlet"), - "right": (pybamm.Scalar(combined_submesh.edges[-1]), "Dirichlet"), + "right": (pybamm.Scalar(submesh.edges[-1]), "Dirichlet"), } } disc.bcs = boundary_conditions @@ -573,13 +573,13 @@ def test_grad_div_shapes_mixed_domain(self): grad_eqn_disc = disc.process_symbol(grad_eqn) np.testing.assert_array_almost_equal( grad_eqn_disc.evaluate(None, linear_y), - np.ones_like(combined_submesh.edges[:, np.newaxis]), + np.ones_like(submesh.edges[:, np.newaxis]), ) # div(grad(x)) = 0 div_eqn_disc = disc.process_symbol(div_eqn) np.testing.assert_array_almost_equal( div_eqn_disc.evaluate(None, linear_y), - np.zeros_like(combined_submesh.nodes[:, np.newaxis]), + np.zeros_like(submesh.nodes[:, np.newaxis]), ) def test_grad_1plus1d(self): @@ -616,13 +616,11 @@ def test_grad_1plus1d(self): grad_eqn_disc = disc.process_symbol(pybamm.grad(var)) # Evaulate - combined_submesh = mesh.combine_submeshes(*var.domain) - linear_y = np.outer(np.linspace(0, 1, 15), combined_submesh.nodes).reshape( + submesh = mesh[var.domain] + linear_y = np.outer(np.linspace(0, 1, 15), submesh.nodes).reshape(-1, 1) + expected = np.outer(np.linspace(0, 1, 15), np.ones_like(submesh.edges)).reshape( -1, 1 ) - expected = np.outer( - np.linspace(0, 1, 15), np.ones_like(combined_submesh.edges) - ).reshape(-1, 1) np.testing.assert_array_almost_equal( grad_eqn_disc.evaluate(None, linear_y), expected ) diff --git a/tests/unit/test_spatial_methods/test_zero_dimensional_method.py b/tests/unit/test_spatial_methods/test_zero_dimensional_method.py index 4ba2e71cc6..b853080791 100644 --- a/tests/unit/test_spatial_methods/test_zero_dimensional_method.py +++ b/tests/unit/test_spatial_methods/test_zero_dimensional_method.py @@ -39,7 +39,7 @@ def test_discretise_spatial_variable(self): var_disc = spatial_method.spatial_variable(var) self.assertIsInstance(var_disc, pybamm.Vector) np.testing.assert_array_equal( - var_disc.evaluate()[:, 0], mesh.combine_submeshes(*var.domain).nodes + var_disc.evaluate()[:, 0], mesh[var.domain].nodes ) # edges @@ -50,7 +50,7 @@ def test_discretise_spatial_variable(self): var_disc = spatial_method.spatial_variable(var) self.assertIsInstance(var_disc, pybamm.Vector) np.testing.assert_array_equal( - var_disc.evaluate()[:, 0], mesh.combine_submeshes(*var.domain).edges + var_disc.evaluate()[:, 0], mesh[var.domain].edges ) def test_averages(self): diff --git a/vcpkg.json b/vcpkg.json index 59286c73f0..d812052bb9 100644 --- a/vcpkg.json +++ b/vcpkg.json @@ -1,6 +1,6 @@ { "name": "pybamm", - "version-string": "22.10", + "version-string": "22.11", "dependencies": [ "casadi", {