diff --git a/.all-contributorsrc b/.all-contributorsrc index dcdbc520e2..c026a9df60 100644 --- a/.all-contributorsrc +++ b/.all-contributorsrc @@ -128,7 +128,7 @@ "login": "dalonsoa", "name": "Diego", "avatar_url": "https://avatars1.githubusercontent.com/u/6095790?v=4", - "profile": "https://www.imperial.ac.uk/admin-services/ict/self-service/research-support/rcs/research-software-engineering/", + "profile": "https://github.com/dalonsoa", "contributions": [ "bug", "review", @@ -549,9 +549,47 @@ "name": "iatzak", "avatar_url": "https://avatars.githubusercontent.com/u/112731474?v=4", "profile": "https://github.com/iatzak", + "contributions": [ + "doc", + "bug", + "code" + ] + }, + { + "login": "ayeankit", + "name": "Ankit Kumar", + "avatar_url": "https://avatars.githubusercontent.com/u/72691866?v=4", + "profile": "https://github.com/ayeankit", + "contributions": [ + "code" + ] + }, + { + "login": "dikwickley", + "name": "Aniket Singh Rawat", + "avatar_url": "https://avatars.githubusercontent.com/u/31622972?v=4", + "profile": "https://aniketsinghrawat.vercel.app/", + "contributions": [ + "code" + ] + }, + { + "login": "jeromtom", + "name": "Jerom Palimattom Tom", + "avatar_url": "https://avatars.githubusercontent.com/u/83979298?v=4", + "profile": "https://github.com/jeromtom", "contributions": [ "doc" ] + }, + { + "login": "BradyPlanden", + "name": "Brady Planden", + "avatar_url": "https://avatars.githubusercontent.com/u/55357039?v=4", + "profile": "http://bradyplanden.github.io", + "contributions": [ + "example" + ] } ], "contributorsPerLine": 7, diff --git a/.github/ISSUE_TEMPLATE/new_parameter_set.yml b/.github/ISSUE_TEMPLATE/new_parameter_set.yml index 9df895fd90..7074177736 100644 --- a/.github/ISSUE_TEMPLATE/new_parameter_set.yml +++ b/.github/ISSUE_TEMPLATE/new_parameter_set.yml @@ -6,7 +6,7 @@ body: value: | Third-party parameter sets can be added to PyBaMM by registering an entry point with `pybamm-parameter-sets` as described in our - [documentation](https://pybamm.readthedocs.io/en/latest/source/parameters/parameter_sets.html). + [documentation](https://pybamm.readthedocs.io/en/latest/source/api/parameters/parameter_sets.html). - type: input id: parameter-set-url attributes: diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md index 884ae4c749..e0fc7df49b 100644 --- a/.github/PULL_REQUEST_TEMPLATE.md +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -14,11 +14,11 @@ Please add a line in the relevant section of [CHANGELOG.md](https://github.com/p # Key checklist: -- [ ] No style issues: `$ pre-commit run` -- [ ] All tests pass: `$ python run-tests.py --unit` -- [ ] The documentation builds: `$ cd docs` and then `$ make clean; make html` +- [ ] No style issues: `$ pre-commit run` (see [CONTRIBUTING.md](https://github.com/pybamm-team/PyBaMM/blob/develop/CONTRIBUTING.md#installing-and-using-pre-commit) for how to set this up to run automatically when committing locally, in just two lines of code) +- [ ] All tests pass: `$ python run-tests.py --all` +- [ ] The documentation builds: `$ python run-tests.py --doctest` -You can run all three at once, using `$ python run-tests.py --quick`. +You can run unit and doctests together at once, using `$ python run-tests.py --quick`. ## Further checks: diff --git a/.github/workflows/url_checker.yml b/.github/workflows/url_checker.yml index ed007c6442..88ff52b470 100644 --- a/.github/workflows/url_checker.yml +++ b/.github/workflows/url_checker.yml @@ -13,7 +13,7 @@ jobs: - name: Checkout uses: actions/checkout@v3 - name: URLs-checker - uses: urlstechie/urlchecker-action@0.0.31 + uses: urlstechie/urlchecker-action@master with: # A comma-separated list of file types to cover in the URL checks file_types: .rst,.md,.py,.ipynb diff --git a/.gitignore b/.gitignore index de8fcaf899..6937100b9b 100644 --- a/.gitignore +++ b/.gitignore @@ -6,6 +6,7 @@ *.DS_Store *.mat *.csv +*.hidden # don't ignore important .txt and .csv files !requirements* diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 62ac9bd070..d9378dbac4 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -3,19 +3,13 @@ ci: autofix_commit_msg: "style: pre-commit fixes" repos: - - repo: https://github.com/pre-commit/mirrors-prettier - rev: v3.0.0-alpha.4 - hooks: - - id: prettier - exclude: assets/js/webapp\.js - - repo: https://github.com/psf/black - rev: 22.12.0 + rev: 23.1.0 hooks: - id: black - repo: https://github.com/charliermarsh/ruff-pre-commit - rev: "v0.0.237" + rev: "v0.0.253" hooks: - id: ruff args: [--ignore=E741, --exclude=__init__.py] diff --git a/CHANGELOG.md b/CHANGELOG.md index fa8f7f9e71..4cd81d86b4 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,18 @@ # [Unreleased](https://github.com/pybamm-team/PyBaMM/) +# [v23.2](https://github.com/pybamm-team/PyBaMM/tree/v23.2) - 2023-02-28 + +## Features + +- Added an option for using a banded jacobian and sundials banded solvers for the IDAKLU solve [#2677](https://github.com/pybamm-team/PyBaMM/pull/2677) +- The "particle size" option can now be a tuple to allow different behaviour in each electrode([#2672](https://github.com/pybamm-team/PyBaMM/pull/2672)). +- Added temperature control to experiment class. [#2518](https://github.com/pybamm-team/PyBaMM/pull/2518) + +## Bug fixes + +- Fixed current_sigmoid_ocp to be valid for both electrodes ([#2719](https://github.com/pybamm-team/PyBaMM/pull/2719)). +- Fixed the length scaling for the first dimension of r-R plots ([#2663](https://github.com/pybamm-team/PyBaMM/pull/2663)). + # [v23.1](https://github.com/pybamm-team/PyBaMM/tree/v23.1) - 2023-01-31 ## Features diff --git a/CITATION.cff b/CITATION.cff index 11fbefb93e..4b3a76088c 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -24,6 +24,6 @@ keywords: - "expression tree" - "python" - "symbolic differentiation" -version: "23.1" +version: "23.2" repository-code: "https://github.com/pybamm-team/PyBaMM" title: "Python Battery Mathematical Modelling (PyBaMM)" diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 46bb1c70a9..330d296761 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -37,7 +37,7 @@ We use [GIT](https://en.wikipedia.org/wiki/Git) and [GitHub](https://en.wikipedi 1. Create an [issue](https://guides.github.com/features/issues/) where new proposals can be discussed before any coding is done. 2. Create a [branch](https://help.github.com/articles/creating-and-deleting-branches-within-your-repository/) of this repo (ideally on your own [fork](https://help.github.com/articles/fork-a-repo/)), where all changes will be made 3. Download the source code onto your local system, by [cloning](https://help.github.com/articles/cloning-a-repository/) the repository (or your fork of the repository). -4. [Install](https://pybamm.readthedocs.io/en/latest/install/install-from-source.html) PyBaMM with the developer options. +4. [Install](https://pybamm.readthedocs.io/en/latest/source/user_guide/installation/install-from-source.html) PyBaMM with the developer options. 5. [Test](#testing) if your installation worked, using the test script: `$ python run-tests.py --unit`. You now have everything you need to start making changes! @@ -61,7 +61,7 @@ Finally, if you really, really, _really_ love developing PyBaMM, have a look at ## Coding style guidelines -PyBaMM follows the [PEP8 recommendations](https://www.python.org/dev/peps/pep-0008/) for coding style. These are very common guidelines, and community tools have been developed to check how well projects implement them. We recommend using pre-commit hooks to check your code before committing it. See [installing and using pre-commit](https://github.com/pybamm-team/PyBaMM/blob/develop/CONTRIBUTING.md#installing-and-using-pre-commit) section for more details. +PyBaMM follows the [PEP8 recommendations](https://www.python.org/dev/peps/pep-0008/) for coding style. These are very common guidelines, and community tools have been developed to check how well projects implement them. We recommend using pre-commit hooks to check your code before committing it. See [installing and using pre-commit](#installing-and-using-pre-commit) section for more details. ### Ruff @@ -307,7 +307,7 @@ PyBaMM is documented in several ways. First and foremost, every method and every class should have a [docstring](https://www.python.org/dev/peps/pep-0257/) that describes in plain terms what it does, and what the expected input and output is. -These docstrings can be fairly simple, but can also make use of [reStructuredText](http://docutils.sourceforge.net/docs/user/rst/quickref.html), a markup language designed specifically for writing [technical documentation](https://en.wikipedia.org/wiki/ReStructuredText). For example, you can link to other classes and methods by writing ``:class:`pybamm.Model` `` and ``:meth:`run()` ``. +These docstrings can be fairly simple, but can also make use of [reStructuredText](http://docutils.sourceforge.net/docs/user/rst/quickref.html), a markup language designed specifically for writing [technical documentation](https://en.wikipedia.org/wiki/ReStructuredText). For example, you can link to other classes and methods by writing `` :class:`pybamm.Model` `` and `` :meth:`run()` ``. In addition, we write a (very) small bit of documentation in separate reStructuredText files in the `docs` directory. Most of what these files do is simply import docstrings from the source code. But they also do things like add tables and indexes. If you've added a new class to a module, search the `docs` directory for that module's `.rst` file and add your class (in alphabetical order) to its index. If you've added a whole new module, copy-paste another module's file and add a link to your new file in the appropriate `index.rst` file. diff --git a/README.md b/README.md index cb19f3a180..237492fcf6 100644 --- a/README.md +++ b/README.md @@ -14,9 +14,7 @@ [![black code style](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black) - -[![All Contributors](https://img.shields.io/badge/all_contributors-49-orange.svg)](#-contributors) - +[![All Contributors](https://img.shields.io/badge/all_contributors-53-orange.svg)](#-contributors) @@ -184,67 +182,73 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d - - - - - - - + + + + + + + + + + + + + + + + - - - - - - - + + + + + + + - - - - - - - + + + + + + + - - - - - - - + + + + + + + - - - - - - - + + + + + + + - - - - - - - + + + + + + + - - - - - - - + + + +
Valentin Sulzer
Valentin Sulzer
🐛 💻 📖 💡 🤔 🚧 👀 ⚠️ ✅ 📝		Robert Timms
Robert Timms
🐛 💻 📖 💡 🤔 🚧 👀 ⚠️ ✅		Scott Marquis
Scott Marquis
🐛 💻 📖 💡 🤔 🚧 👀 ⚠️ ✅		Martin Robinson
Martin Robinson
🐛 💻 📖 💡 🤔 👀 ⚠️ ✅		Ferran Brosa Planella
Ferran Brosa Planella
👀 🐛 💻 📖 💡 🤔 🚧 ⚠️ ✅ 📝		Tom Tranter
Tom Tranter
🐛 💻 📖 💡 🤔 👀 ⚠️ ✅		Thibault Lestang
Thibault Lestang
🐛 💻 📖 💡 🤔 👀 ⚠️ 🚇		Valentin Sulzer
Valentin Sulzer
🐛 💻 📖 💡 🤔 🚧 👀 ⚠️ ✅ 📝		Robert Timms
Robert Timms
🐛 💻 📖 💡 🤔 🚧 👀 ⚠️ ✅		Scott Marquis
Scott Marquis
🐛 💻 📖 💡 🤔 🚧 👀 ⚠️ ✅		Martin Robinson
Martin Robinson
🐛 💻 📖 💡 🤔 👀 ⚠️ ✅		Ferran Brosa Planella
Ferran Brosa Planella
👀 🐛 💻 📖 💡 🤔 🚧 ⚠️ ✅ 📝		Tom Tranter
Tom Tranter
🐛 💻 📖 💡 🤔 👀 ⚠️ ✅		Thibault Lestang
Thibault Lestang
🐛 💻 📖 💡 🤔 👀 ⚠️ 🚇
Diego
Diego
🐛 👀 💻 🚇		felipe-salinas
felipe-salinas
💻 ⚠️		suhaklee
suhaklee
💻 ⚠️		viviantran27
viviantran27
💻 ⚠️		gyouhoc
gyouhoc
🐛 💻 ⚠️		Yannick Kuhn
Yannick Kuhn
💻 ⚠️		Jacqueline Edge
Jacqueline Edge
🤔 📋 🔍
Diego
Diego
🐛 👀 💻 🚇		felipe-salinas
felipe-salinas
💻 ⚠️		suhaklee
suhaklee
💻 ⚠️		viviantran27
viviantran27
💻 ⚠️		gyouhoc
gyouhoc
🐛 💻 ⚠️		Yannick Kuhn
Yannick Kuhn
💻 ⚠️		Jacqueline Edge
Jacqueline Edge
🤔 📋 🔍		Fergus Cooper
Fergus Cooper
💻 ⚠️		jonchapman1
jonchapman1
🤔 🔍		Colin Please
Colin Please
🤔 🔍		cwmonroe
cwmonroe
🤔 🔍		Greg
Greg
🤔 🔍		Faraday Institution
Faraday Institution
💵		Alexander Bessman
Alexander Bessman
🐛 💡
Fergus Cooper
Fergus Cooper
💻 ⚠️		jonchapman1
jonchapman1
🤔 🔍		Colin Please
Colin Please
🤔 🔍		cwmonroe
cwmonroe
🤔 🔍		Greg
Greg
🤔 🔍		Faraday Institution
Faraday Institution
💵		Alexander Bessman
Alexander Bessman
🐛 💡		dalbamont
dalbamont
💻		Anand Mohan Yadav
Anand Mohan Yadav
📖		WEILONG AI
WEILONG AI
💻 💡 ⚠️		lonnbornj
lonnbornj
💻 ⚠️ 💡		Priyanshu Agarwal
Priyanshu Agarwal
⚠️ 💻 🐛 👀 🚧 ✅		DrSOKane
DrSOKane
💻 💡 📖 ⚠️ ✅		Saransh Chopra
Saransh Chopra
💻 ⚠️ 📖 ✅ 👀 🚧
dalbamont
dalbamont
💻		Anand Mohan Yadav
Anand Mohan Yadav
📖		WEILONG AI
WEILONG AI
💻 💡 ⚠️		lonnbornj
lonnbornj
💻 ⚠️ 💡		Priyanshu Agarwal
Priyanshu Agarwal
⚠️ 💻 🐛 👀 🚧 ✅		DrSOKane
DrSOKane
💻 💡 📖 ⚠️ ✅		Saransh Chopra
Saransh Chopra
💻 ⚠️ 📖 ✅ 👀 🚧		David Straub
David Straub
🐛 💻		maurosgroi
maurosgroi
🤔		Amarjit Singh Gaba
Amarjit Singh Gaba
💻		KennethNwanoro
KennethNwanoro
💻 ⚠️		Ali Hussain Umar Bhatti
Ali Hussain Umar Bhatti
💻 ⚠️		Leshinka Molel
Leshinka Molel
💻 🤔		tobykirk
tobykirk
🤔 💻 ⚠️ ✅
David Straub
David Straub
🐛 💻		maurosgroi
maurosgroi
🤔		Amarjit Singh Gaba
Amarjit Singh Gaba
💻		KennethNwanoro
KennethNwanoro
💻 ⚠️		Ali Hussain Umar Bhatti
Ali Hussain Umar Bhatti
💻 ⚠️		Leshinka Molel
Leshinka Molel
💻 🤔		tobykirk
tobykirk
🤔 💻 ⚠️ ✅		Chuck Liu
Chuck Liu
🐛 💻		partben
partben
📖		Gavin Wiggins
Gavin Wiggins
🐛 💻		Dion Wilde
Dion Wilde
🐛 💻		Elias Hohl
Elias Hohl
💻		KAschad
KAschad
🐛		Vaibhav-Chopra-GT
Vaibhav-Chopra-GT
💻
Chuck Liu
Chuck Liu
🐛 💻		partben
partben
📖		Gavin Wiggins
Gavin Wiggins
🐛 💻		Dion Wilde
Dion Wilde
🐛 💻		Elias Hohl
Elias Hohl
💻		KAschad
KAschad
🐛		Vaibhav-Chopra-GT
Vaibhav-Chopra-GT
💻		bardsleypt
bardsleypt
🐛 💻		ndrewwang
ndrewwang
🐛 💻		MichaPhilipp
MichaPhilipp
🐛		Alec Bills
Alec Bills
💻		Agriya Khetarpal
Agriya Khetarpal
🚇 💻 📖		Alex Wadell
Alex Wadell
💻 ⚠️ 📖		iatzak
iatzak
📖 🐛 💻
bardsleypt
bardsleypt
🐛 💻		ndrewwang
ndrewwang
🐛 💻		MichaPhilipp
MichaPhilipp
🐛		Alec Bills
Alec Bills
💻		Agriya Khetarpal
Agriya Khetarpal
🚇 💻 📖		Alex Wadell
Alex Wadell
💻 ⚠️ 📖		iatzak
iatzak
📖		Ankit Kumar
Ankit Kumar
💻		Aniket Singh Rawat
Aniket Singh Rawat
💻		Jerom Palimattom Tom
Jerom Palimattom Tom
📖		Brady Planden
Brady Planden
💡
diff --git a/benchmarks/different_model_options.py b/benchmarks/different_model_options.py index a5a662a542..884815794c 100644 --- a/benchmarks/different_model_options.py +++ b/benchmarks/different_model_options.py @@ -83,7 +83,6 @@ class TimeBuildModelLossActiveMaterial: ) def time_setup_model(self, model, params): - build_model("Ai2020", model, "loss of active material", params) diff --git a/benchmarks/work_precision_sets/time_vs_abstols.py b/benchmarks/work_precision_sets/time_vs_abstols.py index 19b8b5985e..6447884083 100644 --- a/benchmarks/work_precision_sets/time_vs_abstols.py +++ b/benchmarks/work_precision_sets/time_vs_abstols.py @@ -35,9 +35,7 @@ itertools.product(solvers.values(), models.values()), itertools.product(solvers, models), ): - for params in parameters: - time_points = [] solver = i[0] @@ -70,13 +68,11 @@ disc.process_model(model) for tol in abstols: - solver.atol = tol solver.solve(model, t_eval=t_eval) time = 0 runs = 20 for k in range(0, runs): - solution = solver.solve(model, t_eval=t_eval) time += solution.solve_time.value time = time / runs diff --git a/benchmarks/work_precision_sets/time_vs_dt_max.py b/benchmarks/work_precision_sets/time_vs_dt_max.py index 438dd885b5..1368dce350 100644 --- a/benchmarks/work_precision_sets/time_vs_dt_max.py +++ b/benchmarks/work_precision_sets/time_vs_dt_max.py @@ -39,9 +39,7 @@ models.values(), models, ): - for params in parameters: - time_points = [] # solver = pybamm.CasadiSolver() @@ -74,14 +72,12 @@ disc.process_model(model) for t in dt_max: - solver = pybamm.CasadiSolver(dt_max=t) solver.solve(model, t_eval=t_eval) time = 0 runs = 20 for k in range(0, runs): - solution = solver.solve(model, t_eval=t_eval) time += solution.solve_time.value time = time / runs diff --git a/benchmarks/work_precision_sets/time_vs_mesh_size.py b/benchmarks/work_precision_sets/time_vs_mesh_size.py index 09407da199..7b4d4145d4 100644 --- a/benchmarks/work_precision_sets/time_vs_mesh_size.py +++ b/benchmarks/work_precision_sets/time_vs_mesh_size.py @@ -29,7 +29,6 @@ itertools.product(solvers.values(), models.values()), itertools.product(solvers, models), ): - for params in parameters: time_points = [] solver = i[0] @@ -42,7 +41,6 @@ i = list(i) for N in npts: - var_pts = { "x_n": N, # negative electrode "x_s": N, # separator @@ -57,7 +55,6 @@ time = 0 runs = 20 for k in range(0, runs): - solution = sim.solve([0, 3500]) time += solution.solve_time.value time = time / runs diff --git a/benchmarks/work_precision_sets/time_vs_no_of_states.py b/benchmarks/work_precision_sets/time_vs_no_of_states.py index 4f800c4ef9..0a88ac8b52 100644 --- a/benchmarks/work_precision_sets/time_vs_no_of_states.py +++ b/benchmarks/work_precision_sets/time_vs_no_of_states.py @@ -28,7 +28,6 @@ itertools.product(solvers.values(), models.values()), itertools.product(solvers, models), ): - for params in parameters: time_points = [] ns = [] @@ -42,7 +41,6 @@ i = list(i) for N in npts: - var_pts = { "x_n": N, # negative electrode "x_s": N, # separator @@ -57,7 +55,6 @@ time = 0 runs = 20 for k in range(0, runs): - solution = sim.solve([0, 3500]) time += solution.solve_time.value time = time / runs diff --git a/benchmarks/work_precision_sets/time_vs_reltols.py b/benchmarks/work_precision_sets/time_vs_reltols.py index 97202d2482..12e41b526f 100644 --- a/benchmarks/work_precision_sets/time_vs_reltols.py +++ b/benchmarks/work_precision_sets/time_vs_reltols.py @@ -41,9 +41,7 @@ itertools.product(solvers.values(), models.values()), itertools.product(solvers, models), ): - for params in parameters: - time_points = [] solver = i[0] @@ -76,13 +74,11 @@ disc.process_model(model) for tol in reltols: - solver.rtol = tol solver.solve(model, t_eval=t_eval) time = 0 runs = 20 for k in range(0, runs): - solution = solver.solve(model, t_eval=t_eval) time += solution.solve_time.value time = time / runs diff --git a/docs/conf.py b/docs/conf.py index 49989b3307..8f49c89f1e 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -26,7 +26,7 @@ author = "The PyBaMM Team" # The short X.Y version -version = "23.1" +version = "23.2" # The full version, including alpha/beta/rc tags release = version @@ -49,6 +49,7 @@ "sphinx.ext.napoleon", "sphinx_design", "sphinx_copybutton", + "myst_parser", ] @@ -99,14 +100,30 @@ # https://pydata-sphinx-theme.readthedocs.io/en/latest/index.html# for more information) # mostly copied from numpy, scipy, pandas html_logo = "source/_static/pybamm_logo.png" +html_favicon = "source/_static/favicon/favicon.png" html_theme_options = { "logo": { "image_light": "pybamm_logo.png", "image_dark": "pybamm_logo.png", }, - "github_url": "https://github.com/pybamm-team/pybamm", - "twitter_url": "https://twitter.com/pybamm_", + "icon_links": [ + { + "name": "GitHub", + "url": "https://github.com/pybamm-team/pybamm", + "icon": "fa-brands fa-square-github", + }, + { + "name": "Twitter", + "url": "https://twitter.com/pybamm_", + "icon": "fa-brands fa-square-twitter", + }, + { + "name": "PyPI", + "url": "https://pypi.org/project/pybamm/", + "icon": "fa-solid fa-box", + }, + ], "collapse_navigation": True, "external_links": [ { diff --git a/docs/index.rst b/docs/index.rst index 3ff5c4d6d4..285793f955 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -13,17 +13,13 @@ PyBaMM documentation :maxdepth: 1 :hidden: - source/user_guide/index + User Guide source/api/index **Version**: |version| -.. **Download documentation**: -.. `PDF Version `_ | -.. `Historical versions of documentation `_ - **Useful links**: -`Project Home Page `_ | +`Project Home Page `_ | `Installation `_ | `Source Repository `_ | `Issue Tracker `_ | diff --git a/docs/requirements.txt b/docs/requirements.txt index e015c3deac..b37de07bb5 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -20,3 +20,4 @@ sphinx>4.0 pydata-sphinx-theme sphinx_design sphinx-copybutton +myst-parser \ No newline at end of file diff --git a/docs/source/_static/favicon/favicon.png b/docs/source/_static/favicon/favicon.png new file mode 100644 index 0000000000..a99e2cc6fd Binary files /dev/null and b/docs/source/_static/favicon/favicon.png differ diff --git a/docs/source/user_guide/fundamentals/index.rst b/docs/source/user_guide/fundamentals/index.md similarity index 50% rename from docs/source/user_guide/fundamentals/index.rst rename to docs/source/user_guide/fundamentals/index.md index e9fabdf34c..4ad4542949 100644 --- a/docs/source/user_guide/fundamentals/index.rst +++ b/docs/source/user_guide/fundamentals/index.md @@ -1,85 +1,82 @@ -Fundamentals -============ +# Fundamentals PyBaMM (Python Battery Mathematical Modelling) is an open-source battery simulation package written in Python. Our mission is to accelerate battery modelling research by -providing open-source tools for multi-institutional, interdisciplinary collaboration. +providing open-source tools for multi-institutional, interdisciplinary collaboration. Broadly, PyBaMM consists of -#. a framework for writing and solving systems of differential equations, -#. a library of battery models and parameters, and -#. specialized tools for simulating battery-specific experiments and visualizing the results. +1. a framework for writing and solving systems of differential equations, +2. a library of battery models and parameters, and +3. specialized tools for simulating battery-specific experiments and visualizing the results. Together, these enable flexible model definitions and fast battery simulations, allowing users to explore the effect of different battery designs and modeling assumptions under a variety of operating scenarios. -.. note:: +> **NOTE**: This user-guide is a work-in-progress, we hope that this brief but incomplete overview will be useful to you. - This user-guide is a work-in-progress, we hope that this brief but incomplete overview will be useful to you. +## Core framework -Core framework -~~~~~~~~~~~~~~ The core of the framework is a custom computer algebra system to define mathematical equations, and a domain specific modeling language to combine these equations into systems of differential equations (usually partial differential equations for variables depending on space and time). -The `expression tree `_ example gives an introduction to the computer algebra system, and the `Getting Started `_ tutorials +The [expression tree](https://github.com/pybamm-team/PyBaMM/blob/develop/examples/notebooks/expression_tree/expression-tree.ipynb) example gives an introduction to the computer algebra system, and the [Getting Started](https://github.com/pybamm-team/PyBaMM/tree/develop/examples/notebooks/Getting%20Started) tutorials walk through creating models of increasing complexity. -Once a model has been defined symbolically, PyBaMM solves it using the Method of Lines. First, the equations are discretised in the spatial dimension, using the finite volume method. Then, the resulting system is solved using third-party numerical solvers. Depending on the form of the model, the system can be ordinary differential equations (ODEs) (if only `model.rhs` is defined), or algebraic equations (if only `model.algebraic` is defined), or differential-algebraic equations (DAEs) (if both `model.rhs` and `model.algebraic` are defined). Jupyter notebooks explaining the solvers can be found `here `_. +Once a model has been defined symbolically, PyBaMM solves it using the Method of Lines. First, the equations are discretised in the spatial dimension, using the finite volume method. Then, the resulting system is solved using third-party numerical solvers. Depending on the form of the model, the system can be ordinary differential equations (ODEs) (if only `model.rhs` is defined), or algebraic equations (if only `model.algebraic` is defined), or differential-algebraic equations (DAEs) (if both `model.rhs` and `model.algebraic` are defined). Jupyter notebooks explaining the solvers can be found [here](https://github.com/pybamm-team/PyBaMM/tree/develop/examples/notebooks/solvers). + +## Model and Parameter Library -Model and Parameter Library -~~~~~~~~~~~~~~~~~~~~~~~~~~~ PyBaMM contains an extensive library of battery models and parameters. The bulk of the library consists of models for lithium-ion, but there are also some other chemistries (lead-acid, lithium metal). -Models are first divided broadly into common named models of varying complexity, such as the single particle model` (SPM) or Doyle-Fuller-Newman model (DFN). +Models are first divided broadly into common named models of varying complexity, such as the single particle model (SPM) or Doyle-Fuller-Newman model (DFN). Most options can be applied to any model, but some are model-specific (an error will be raised if you attempt to set an option is not compatible with a model). -See :ref:`base_battery_model` for a list of options. +See [](base_battery_model) for a list of options. The parameter library is simply a collection of python files each defining a complete set of parameters for a particular battery chemistry, covering all major lithium-ion chemistries (NMC, LFP, NCA, ...). -External parameter sets can be linked using entry points (see :ref:`parameter_sets`). - -Battery-specific tools -~~~~~~~~~~~~~~~~~~~~~~ -One of PyBaMM's unique features is the ``Experiment`` class, which allows users to define synthetic experiments using simple instructions in English +External parameter sets can be linked using entry points (see [](parameter_sets)). -.. code-block:: python +## Battery-specific tools - pybamm.Experiment( - [ - ("Discharge at C/10 for 10 hours or until 3.3 V", - "Rest for 1 hour", - "Charge at 1 A until 4.1 V", - "Hold at 4.1 V until 50 mA", - "Rest for 1 hour") - ] - * 3, - ) +One of PyBaMM's unique features is the `Experiment` class, which allows users to define synthetic experiments using simple instructions in English -The above instruction will conduct a standard discharge / rest / charge / rest cycle three times, with a 10 hour discharge and 1 hour rest at the end of each cycle. +```python +pybamm.Experiment( + [ + ("Discharge at C/10 for 10 hours or until 3.3 V", + "Rest for 1 hour", + "Charge at 1 A until 4.1 V", + "Hold at 4.1 V until 50 mA", + "Rest for 1 hour") + ] + * 3, +) +``` -The ``Simulation`` class handles simulating an ``Experiment``, as well as calculating additional outputs such as capacity as a function of cycle number. For example, the following code will simulate the experiment above and plot the standard output variables: +The above instruction will conduct a standard discharge / rest / charge / rest cycle three times, with a 10 hour discharge and 1 hour rest at the end of each cycle. -.. code-block:: python +The `Simulation` class handles simulating an `Experiment`, as well as calculating additional outputs such as capacity as a function of cycle number. For example, the following code will simulate the experiment above and plot the standard output variables: - import pybamm - import matplotlib.pyplot as plt +```python +import pybamm +import matplotlib.pyplot as plt - # load model and parameter values - model = pybamm.lithium_ion.DFN() - sim = pybamm.Simulation(model, experiment=experiment) - solution = sim.solve() - solution.plot() +# load model and parameter values +model = pybamm.lithium_ion.DFN() +sim = pybamm.Simulation(model, experiment=experiment) +solution = sim.solve() +solution.plot() +``` Finally, PyBaMM provides cusotm visualization tools: -* :ref:`quick_plot`: for easily plotting simulation outputs in a grid, including comparing multiple simulations -* :ref:`plot_voltage_components`: for plotting the component overpotentials that make up a voltage curve +- [](quick_plot): for easily plotting simulation outputs in a grid, including comparing multiple simulations +- [](plot_voltage_components): for plotting the component overpotentials that make up a voltage curve Users are not limited to these tools and can plot the output of a simulation solution by accessing the underlying numpy array for the solution variables as -.. code-block:: python - - solution["variable name"].data +```python +solution["variable name"].data +``` -and using the plotting library of their choice. \ No newline at end of file +and using the plotting library of their choice. diff --git a/docs/source/user_guide/getting_started.md b/docs/source/user_guide/getting_started.md new file mode 100644 index 0000000000..99cb3b022a --- /dev/null +++ b/docs/source/user_guide/getting_started.md @@ -0,0 +1,40 @@ +# Getting Started + +The easiest way to use PyBaMM is to run a 1C constant-current discharge with a model of your choice with all the default settings: + +```python +import pybamm +model = pybamm.lithium_ion.DFN() # Doyle-Fuller-Newman model +sim = pybamm.Simulation(model) +sim.solve([0, 3600]) # solve for 1 hour +sim.plot() +``` + +or simulate an experiment such as a constant-current discharge followed by a constant-current-constant-voltage charge: + +```python +import pybamm +experiment = pybamm.Experiment( + [ + ("Discharge at C/10 for 10 hours or until 3.3 V", + "Rest for 1 hour", + "Charge at 1 A until 4.1 V", + "Hold at 4.1 V until 50 mA", + "Rest for 1 hour") + ] + * 3, +) +model = pybamm.lithium_ion.DFN() +sim = pybamm.Simulation(model, experiment=experiment, solver=pybamm.CasadiSolver()) +sim.solve() +sim.plot() +``` + +However, much greater customisation is available. It is possible to change the physics, parameter values, geometry, submesh type, number of submesh points, methods for spatial discretisation and solver for integration (see DFN [script](https://github.com/pybamm-team/PyBaMM/blob/develop/examples/scripts/DFN.py) or [notebook](https://github.com/pybamm-team/PyBaMM/blob/develop/examples/notebooks/models/DFN.ipynb)). + +For new users we recommend the [Getting Started](https://github.com/pybamm-team/PyBaMM/tree/develop/examples/notebooks/Getting%20Started) guides. These are intended to be very simple step-by-step guides to show the basic functionality of PyBaMM, and can either be downloaded and used locally, or used online through [Google Colab](https://colab.research.google.com/github/pybamm-team/PyBaMM/blob/develop). + +Further details can be found in a number of [detailed examples](https://github.com/pybamm-team/PyBaMM/blob/develop/examples/notebooks/README.md), hosted on +GitHub. In addition, full details of classes and methods can be found in the [](api_docs). +Additional supporting material can be found +[here](https://github.com/pybamm-team/pybamm-supporting-material/). diff --git a/docs/source/user_guide/getting_started.rst b/docs/source/user_guide/getting_started.rst deleted file mode 100644 index 4753ce9a34..0000000000 --- a/docs/source/user_guide/getting_started.rst +++ /dev/null @@ -1,41 +0,0 @@ -Getting Started -=============== - -The easiest way to use PyBaMM is to run a 1C constant-current discharge with a model of your choice with all the default settings: - -.. code-block:: python - - import pybamm - model = pybamm.lithium_ion.DFN() # Doyle-Fuller-Newman model - sim = pybamm.Simulation(model) - sim.solve([0, 3600]) # solve for 1 hour - sim.plot() - -or simulate an experiment such as a constant-current discharge followed by a constant-current-constant-voltage charge: - -.. code-block:: python - - import pybamm - experiment = pybamm.Experiment( - [ - ("Discharge at C/10 for 10 hours or until 3.3 V", - "Rest for 1 hour", - "Charge at 1 A until 4.1 V", - "Hold at 4.1 V until 50 mA", - "Rest for 1 hour") - ] - * 3, - ) - model = pybamm.lithium_ion.DFN() - sim = pybamm.Simulation(model, experiment=experiment, solver=pybamm.CasadiSolver()) - sim.solve() - sim.plot() - -However, much greater customisation is available. It is possible to change the physics, parameter values, geometry, submesh type, number of submesh points, methods for spatial discretisation and solver for integration (see DFN `script `_ or `notebook `_). - -For new users we recommend the `Getting Started `_ guides. These are intended to be very simple step-by-step guides to show the basic functionality of PyBaMM, and can either be downloaded and used locally, or used online through `Google Colab `_. - -Further details can be found in a number of `detailed examples `_, hosted on -GitHub. In addition, full details of classes and methods can be found in the :ref:`api_docs`. -Additional supporting material can be found -`here `_. \ No newline at end of file diff --git a/docs/source/user_guide/index.md b/docs/source/user_guide/index.md new file mode 100644 index 0000000000..ee97bb5875 --- /dev/null +++ b/docs/source/user_guide/index.md @@ -0,0 +1,24 @@ +(user)= + +# PyBaMM user guide + +This guide is an overview and explains the important features; +details are found in [](api_docs). + +```{toctree} +--- +caption: Getting started +maxdepth: 1 +--- + +installation/index +getting_started +``` + +```{toctree} +--- +caption: Fundamentals and usage +maxdepth: 2 +--- +fundamentals/index +``` diff --git a/docs/source/user_guide/index.rst b/docs/source/user_guide/index.rst deleted file mode 100644 index ebf661e9ae..0000000000 --- a/docs/source/user_guide/index.rst +++ /dev/null @@ -1,21 +0,0 @@ -.. _user: - -################# -PyBaMM user guide -################# - -This guide is an overview and explains the important features; -details are found in :ref:`api_docs`. - -.. toctree:: - :caption: Getting started - :maxdepth: 1 - - installation/index - getting_started - -.. toctree:: - :caption: Fundamentals and usage - :maxdepth: 2 - - fundamentals/index diff --git a/docs/source/user_guide/installation/GNU-linux.rst b/docs/source/user_guide/installation/GNU-linux.rst index dca76ce128..f16b92a55b 100644 --- a/docs/source/user_guide/installation/GNU-linux.rst +++ b/docs/source/user_guide/installation/GNU-linux.rst @@ -197,12 +197,6 @@ not being used when I run my Python script. i.e. ``pip install -e .``. This sets the installed location of the source files to your current directory. -**Problem:** When running ``python run-tests.py --quick``, gives error -``FileNotFoundError: [Errno 2] No such file or directory: 'flake8': 'flake8``. - -**Solution:** make sure you have included the ``[dev,docs]`` flags when -you pip installed PyBaMM, i.e. ``pip install -e .[dev,docs]`` - **Problem:** Errors when solving model ``ValueError: Integrator name ida does not exsist``, or ``ValueError: Integrator name cvode does not exsist``. diff --git a/docs/source/user_guide/installation/index.rst b/docs/source/user_guide/installation/index.rst index 3c03521cca..d3ba10fb7e 100644 --- a/docs/source/user_guide/installation/index.rst +++ b/docs/source/user_guide/installation/index.rst @@ -37,8 +37,8 @@ Optional solvers Following GNU/Linux and macOS solvers are optionally available: -* `scikits.odes `_ -based solver, see `Optional - scikits.odes solver `_. -* `jax `_ -based solver, see `Optional - JaxSolver `_. +* `scikits.odes `_ -based solver, see `Optional - scikits.odes solver `_. +* `jax `_ -based solver, see `Optional - JaxSolver `_. Full installation guide ----------------------- diff --git a/docs/source/user_guide/installation/install-from-source.rst b/docs/source/user_guide/installation/install-from-source.rst index 2f73f536b1..569351a47b 100644 --- a/docs/source/user_guide/installation/install-from-source.rst +++ b/docs/source/user_guide/installation/install-from-source.rst @@ -130,7 +130,7 @@ Using Tox (recommended) This creates a virtual environment ``.tox/dev`` (or ``windows-dev``) inside the ``PyBaMM/`` directory. -It comes ready with PyBaMM and some useful development tools like `flake8 `_ and `black `_. +It comes ready with PyBaMM and some useful development tools like `pre-commit `_ and `black `_. You can now activate the environment with @@ -228,7 +228,6 @@ Doctests, examples, style and coverage -------------------------------------- - ``tox -e examples``: Run the example scripts in ``examples/scripts``. -- ``tox -e flake8``: Check for PEP8 compliance. - ``tox -e doctests``: Run doctests. - ``tox -e coverage``: Measure current test coverage. diff --git a/docs/source/user_guide/todo.md b/docs/source/user_guide/todo.md deleted file mode 100644 index dc16b3d010..0000000000 --- a/docs/source/user_guide/todo.md +++ /dev/null @@ -1,14 +0,0 @@ -Fundamentals - -- More details on each section including code examples - -Creating a project using PyBaMM - -- Setup -- Basic use -- Creating your own model - - Your own model - - Making it compatible with experiments - - Extending the existing models (adding submodels) -- Adding your own parameter sets -- Contributing diff --git a/examples/notebooks/Getting Started/Tutorial 4 - Setting parameter values.ipynb b/examples/notebooks/Getting Started/Tutorial 4 - Setting parameter values.ipynb index c40afdac77..129d7992ea 100644 --- a/examples/notebooks/Getting Started/Tutorial 4 - Setting parameter values.ipynb +++ b/examples/notebooks/Getting Started/Tutorial 4 - Setting parameter values.ipynb @@ -42,10 +42,11 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "PyBaMM has a number of in-built parameter sets (check the list [here](https://pybamm.readthedocs.io/en/latest/source/parameters/parameter_sets.html)), which can be selected doing" + "PyBaMM has a number of in-built parameter sets (check the list [here](https://pybamm.readthedocs.io/en/latest/source/api/parameters/parameter_sets.html)), which can be selected doing" ] }, { @@ -554,7 +555,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.9.13 ('conda_jl')", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -585,7 +586,7 @@ }, "vscode": { "interpreter": { - "hash": "612adcc456652826e82b485a1edaef831aa6d5abc680d008e93d513dd8724f14" + "hash": "1a781583db2df3c2e87436f6d22cce842c2e50a5670da93a3bd820b97dc43011" } } }, diff --git a/examples/notebooks/Getting Started/Tutorial 7 - Model options.ipynb b/examples/notebooks/Getting Started/Tutorial 7 - Model options.ipynb index b913a9b5fd..c09598316f 100644 --- a/examples/notebooks/Getting Started/Tutorial 7 - Model options.ipynb +++ b/examples/notebooks/Getting Started/Tutorial 7 - Model options.ipynb @@ -28,10 +28,11 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "In this tutorial, we add a thermal model to the SPMe. From the [documentation](https://pybamm.readthedocs.io/en/latest/source/models/base_models/base_battery_model.html), we see that we have a choice of either a 'x-full' thermal model or a number of different lumped thermal models. For a deeper look at the thermal models see the [thermal models notebook](../models/thermal-models.ipynb). We choose the full thermal model, which solves the spatially-dependent heat equation on our battery geometry, and couples the temperature with the electrochemistry. We set the model options by creating a Python dictionary:" + "In this tutorial, we add a thermal model to the SPMe. From the [documentation](https://pybamm.readthedocs.io/en/latest/source/api/models/base_models/base_battery_model.html), we see that we have a choice of either a 'x-full' thermal model or a number of different lumped thermal models. For a deeper look at the thermal models see the [thermal models notebook](../models/thermal-models.ipynb). We choose the full thermal model, which solves the spatially-dependent heat equation on our battery geometry, and couples the temperature with the electrochemistry. We set the model options by creating a Python dictionary:" ] }, { @@ -108,10 +109,11 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "In this tutorial we have seen how to adjust the model options. To see all of the options currently available in PyBaMM, please take a look at the documentation [here](https://pybamm.readthedocs.io/en/latest/source/models/base_models/base_battery_model.html).\n", + "In this tutorial we have seen how to adjust the model options. To see all of the options currently available in PyBaMM, please take a look at the documentation [here](https://pybamm.readthedocs.io/en/latest/source/api/models/base_models/base_battery_model.html).\n", "\n", "In the [next tutorial](./Tutorial%208%20-%20Solver%20options.ipynb) we show how to change the solver options." ] @@ -149,7 +151,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -163,7 +165,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.0" + "version": "3.11.1" + }, + "vscode": { + "interpreter": { + "hash": "a06befff6f507b2769436dc41c340f64f62afa83086a8cd273928f468e329d0b" + } } }, "nbformat": 4, diff --git a/examples/notebooks/Getting Started/Tutorial 8 - Solver options.ipynb b/examples/notebooks/Getting Started/Tutorial 8 - Solver options.ipynb index accc822653..fabb0decd4 100644 --- a/examples/notebooks/Getting Started/Tutorial 8 - Solver options.ipynb +++ b/examples/notebooks/Getting Started/Tutorial 8 - Solver options.ipynb @@ -32,10 +32,11 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "Here we will change the absolute and relative tolerances, as well as the \"mode\" of the `CasadiSolver`. For a list of all the solver options please consult the [documentation](https://pybamm.readthedocs.io/en/latest/source/solvers/index.html).\n", + "Here we will change the absolute and relative tolerances, as well as the \"mode\" of the `CasadiSolver`. For a list of all the solver options please consult the [documentation](https://pybamm.readthedocs.io/en/latest/source/api/solvers/index.html).\n", "\n", "The `CasadiSolver` can operate in a number of modes, including \"safe\" (default) and \"fast\". Safe mode performs step-and-check integration and supports event handling (e.g. you can integrate until you hit a certain voltage), and is the recommended for simulations of a full charge or discharge. Fast mode performs direct integration, ignoring events, and is recommended when simulating a drive cycle or other simulation where no events should be triggered.\n", "\n", diff --git a/examples/notebooks/README.md b/examples/notebooks/README.md index 8b52c47c74..3f850ed7a5 100644 --- a/examples/notebooks/README.md +++ b/examples/notebooks/README.md @@ -63,8 +63,8 @@ Once you are comfortable with the expression tree structure, a good starting poi #### Lead-acid models -- [Full porous-electrode](https://pybamm.readthedocs.io/en/latest/source/models/lead_acid/full.html) -- [Leading-Order Quasi-Static](https://pybamm.readthedocs.io/en/latest/source/models/lead_acid/loqs.html) +- [Full porous-electrode](https://pybamm.readthedocs.io/en/latest/source/api/models/lead_acid/full.html) +- [Leading-Order Quasi-Static](https://pybamm.readthedocs.io/en/latest/source/api/models/lead_acid/loqs.html) ### Spatial Methods diff --git a/examples/notebooks/callbacks.ipynb b/examples/notebooks/callbacks.ipynb index 66419ba448..8445b09319 100644 --- a/examples/notebooks/callbacks.ipynb +++ b/examples/notebooks/callbacks.ipynb @@ -19,15 +19,16 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "cb2ae3b6", "metadata": {}, "source": [ "Callbacks provide hooks for users to interact with the different parts of the PyBaMM pipeline, for example to log, save, or visualize outputs of intermediate functions. \n", "\n", - "A list of available callbacks can be found in the [API docs](https://pybamm.readthedocs.io/en/latest/source/callbacks.html). Any number of callbacks can be provided as a list, and they will each be called in turn in the order provided.\n", + "A list of available callbacks can be found in the [API docs](https://pybamm.readthedocs.io/en/latest/source/api/callbacks.html). Any number of callbacks can be provided as a list, and they will each be called in turn in the order provided.\n", "\n", - "The base class [`pybamm.callbacks.Callback`](https://pybamm.readthedocs.io/en/latest/source/citations.html#pybamm.callbacks.Callback) documents the available callback methods, at which point in the pipeline they are called, and what arguments are passed to them." + "The base class [`pybamm.callbacks.Callback`](https://pybamm.readthedocs.io/en/latest/source/api/callbacks.html#pybamm.callbacks.Callback) documents the available callback methods, at which point in the pipeline they are called, and what arguments are passed to them." ] }, { diff --git a/examples/notebooks/change-settings.ipynb b/examples/notebooks/change-settings.ipynb index fc619ddf29..efd8d08cfa 100644 --- a/examples/notebooks/change-settings.ipynb +++ b/examples/notebooks/change-settings.ipynb @@ -108,12 +108,13 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Changing the model parameters \n", "\n", - "The parameters are defined using the [`pybamm.ParameterValues`](https://pybamm.readthedocs.io/en/latest/source/parameters/parameter_values.html) class, which takes either a python dictionary or CSV file with the mapping between parameter names and values.\n", + "The parameters are defined using the [`pybamm.ParameterValues`](https://pybamm.readthedocs.io/en/latest/source/api/parameters/parameter_values.html) class, which takes either a python dictionary or CSV file with the mapping between parameter names and values.\n", "\n", "First lets have a look at the default parameters that are included with the SPM model:" ] @@ -385,12 +386,13 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Most of the parameters in this list have numerical values. Some have string values, that point to particular python functions within PyBaMM. These denote parameters that vary over time and/or space, in a manner defined by the given python function. For the moment we will ignore these, and focus on altering one of the numerical parameters.\n", "\n", - "The class [`pybamm.ParameterValues`](https://pybamm.readthedocs.io/en/latest/source/parameters/parameter_values.html) acts like the normal python `dict` data structure, so you can read or write individual parameters accordingly:" + "The class [`pybamm.ParameterValues`](https://pybamm.readthedocs.io/en/latest/source/api/parameters/parameter_values.html) acts like the normal python `dict` data structure, so you can read or write individual parameters accordingly:" ] }, { @@ -473,12 +475,13 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Changing the discretisation \n", "\n", - "The chosen spatial discretisation method to use for each domain is passed into the [`pybamm.Discretisation`](https://pybamm.readthedocs.io/en/latest/source/spatial_methods/discretisation.html) class as one of its arguments. The default spatial methods for the SPM class are given as:\n" + "The chosen spatial discretisation method to use for each domain is passed into the [`pybamm.Discretisation`](https://pybamm.readthedocs.io/en/latest/source/api/spatial_methods/discretisation.html) class as one of its arguments. The default spatial methods for the SPM class are given as:\n" ] }, { @@ -587,12 +590,13 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Changing the solver \n", "\n", - "Which method you use to integrate the discretised model in time can also be changed. PyBaMM has a number of different solvers available, all of which are described in the [documentation](https://pybamm.readthedocs.io/en/latest/source/solvers/).\n" + "Which method you use to integrate the discretised model in time can also be changed. PyBaMM has a number of different solvers available, all of which are described in the [documentation](https://pybamm.readthedocs.io/en/latest/source/api/solvers/index.html).\n" ] }, { @@ -678,7 +682,7 @@ ], "metadata": { "kernelspec": { - "display_name": "env", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -692,11 +696,11 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.13" }, "vscode": { "interpreter": { - "hash": "19e5ebaa8d5a3277b4deed2928f02ad0cad6c3ab0b2beced644d557f155bce64" + "hash": "1a781583db2df3c2e87436f6d22cce842c2e50a5670da93a3bd820b97dc43011" } } }, diff --git a/examples/notebooks/customize-quick-plot.ipynb b/examples/notebooks/customize-quick-plot.ipynb index 583a56d29b..de3db0fded 100644 --- a/examples/notebooks/customize-quick-plot.ipynb +++ b/examples/notebooks/customize-quick-plot.ipynb @@ -161,11 +161,12 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "german-possibility", "metadata": {}, "source": [ - "Some customization of the `QuickPlot` object is possible by passing arguments - see the [docs](https://pybamm.readthedocs.io/en/latest/source/plotting/quick_plot.html) for details\n", + "Some customization of the `QuickPlot` object is possible by passing arguments - see the [docs](https://pybamm.readthedocs.io/en/latest/source/api/plotting/quick_plot.html) for details\n", "\n", "We can also further control the plot by calling `plot.fig` after the figure has been created, and editing the matplotlib objects. For example, here we move the titles to the ylabel, and move the legend." ] @@ -228,7 +229,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -242,7 +243,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.0" + "version": "3.9.13" }, "toc": { "base_numbering": 1, @@ -256,6 +257,11 @@ "toc_position": {}, "toc_section_display": true, "toc_window_display": true + }, + "vscode": { + "interpreter": { + "hash": "1a781583db2df3c2e87436f6d22cce842c2e50a5670da93a3bd820b97dc43011" + } } }, "nbformat": 4, diff --git a/examples/notebooks/expression_tree/expression-tree.ipynb b/examples/notebooks/expression_tree/expression-tree.ipynb index 01d23ef0a9..acd56504e1 100644 --- a/examples/notebooks/expression_tree/expression-tree.ipynb +++ b/examples/notebooks/expression_tree/expression-tree.ipynb @@ -117,6 +117,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -125,12 +126,12 @@ "Proposing, parameter setting and discretising a model in PyBaMM is a pipeline process, consisting of the following steps:\n", "\n", "1. The model is proposed, consisting of equations representing the right-hand-side of an ordinary differential equation (ODE), and/or algebraic equations for a differential algebraic equation (DAE), and also associated boundary condition equations\n", - "2. The parameters present in the model are replaced by actual scalar values from a parameter file, using the [`pybamm.ParamterValues`](https://pybamm.readthedocs.io/en/latest/source/parameters/parameter_values.html) class\n", - "3. The equations in the model are discretised onto a mesh, any spatial gradients are replaced with linear algebra expressions and the variables of the model are replaced with state vector slices. This is done using the [`pybamm.Discretisation`](https://pybamm.readthedocs.io/en/latest/source/spatial_methods/discretisation.html) class.\n", + "2. The parameters present in the model are replaced by actual scalar values from a parameter file, using the [`pybamm.ParamterValues`](https://pybamm.readthedocs.io/en/latest/source/api/parameters/parameter_values.html) class\n", + "3. The equations in the model are discretised onto a mesh, any spatial gradients are replaced with linear algebra expressions and the variables of the model are replaced with state vector slices. This is done using the [`pybamm.Discretisation`](https://pybamm.readthedocs.io/en/latest/source/api/spatial_methods/discretisation.html) class.\n", "\n", "## Stage 1 - Symbolic Expression Trees\n", "\n", - "At each stage, the expression tree consists of certain types of nodes. In the first stage, the model is first proposed using [`pybamm.Parameter`](https://pybamm.readthedocs.io/en/latest/source/expression_tree/parameter.html), [`pybamm.Variable`](https://pybamm.readthedocs.io/en/latest/source/expression_tree/variable.html), and other [unary](https://pybamm.readthedocs.io/en/latest/source/expression_tree/unary_operator.html) and [binary](https://pybamm.readthedocs.io/en/latest/source/expression_tree/binary_operator.html) operators (which also includes spatial operators such as [`pybamm.Gradient`](https://pybamm.readthedocs.io/en/latest/source/expression_tree/unary_operator.html#pybamm.Gradient) and [`pybamm.Divergence`](https://pybamm.readthedocs.io/en/latest/source/expression_tree/unary_operator.html#pybamm.Divergence)). For example, the right hand side of the equation\n", + "At each stage, the expression tree consists of certain types of nodes. In the first stage, the model is first proposed using [`pybamm.Parameter`](https://pybamm.readthedocs.io/en/latest/source/api/expression_tree/parameter.html), [`pybamm.Variable`](https://pybamm.readthedocs.io/en/latest/source/api/expression_tree/variable.html), and other [unary](https://pybamm.readthedocs.io/en/latest/source/api/expression_tree/unary_operator.html) and [binary](https://pybamm.readthedocs.io/en/latest/source/expression_tree/binary_operator.html) operators (which also includes spatial operators such as [`pybamm.Gradient`](https://pybamm.readthedocs.io/en/latest/source/expression_tree/unary_operator.html#pybamm.Gradient) and [`pybamm.Divergence`](https://pybamm.readthedocs.io/en/latest/source/api/expression_tree/unary_operator.html#pybamm.Divergence)). For example, the right hand side of the equation\n", "\n", "$$\\frac{d c}{dt} = D \\nabla \\cdot \\nabla c$$\n", "\n", diff --git a/examples/notebooks/models/SPM.ipynb b/examples/notebooks/models/SPM.ipynb index 53713da6f9..62ff326f5f 100644 --- a/examples/notebooks/models/SPM.ipynb +++ b/examples/notebooks/models/SPM.ipynb @@ -92,10 +92,11 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "The model object is a subtype of [`pybamm.BaseModel`](https://pybamm.readthedocs.io/en/latest/source/models/base_models/base_model.html), and contains all the equations that define this particular model. For example, the `rhs` dict contained in `model` has a dictionary mapping variables such as $c_n$ to the equation representing its rate of change with time (i.e. $\\partial{c_n}/\\partial{t}$). We can see this explicitly by visualising this entry in the `rhs` dict:" + "The model object is a subtype of [`pybamm.BaseModel`](https://pybamm.readthedocs.io/en/latest/source/api/models/base_models/base_model.html), and contains all the equations that define this particular model. For example, the `rhs` dict contained in `model` has a dictionary mapping variables such as $c_n$ to the equation representing its rate of change with time (i.e. $\\partial{c_n}/\\partial{t}$). We can see this explicitly by visualising this entry in the `rhs` dict:" ] }, { @@ -127,10 +128,11 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "We need a geometry in which to define our model equations. In pybamm this is represented by the [`pybamm.Geometry`](https://pybamm.readthedocs.io/en/latest/source/geometry/geometry.html) class. In this case we use the default geometry object defined by the model" + "We need a geometry in which to define our model equations. In pybamm this is represented by the [`pybamm.Geometry`](https://pybamm.readthedocs.io/en/latest/source/api/geometry/index.html) class. In this case we use the default geometry object defined by the model" ] }, { @@ -186,10 +188,11 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "Both the model equations and the geometry include parameters, such as $\\gamma_p$ or $L_p$. We can substitute these symbolic parameters in the model with values by using the [`pybamm.ParameterValues`](https://pybamm.readthedocs.io/en/latest/source/parameters/parameter_values.html) class, which takes either a python dictionary or CSV file with the mapping between parameter names and values. Rather than create our own instance of `pybamm.ParameterValues`, we will use the default parameter set included in the model" + "Both the model equations and the geometry include parameters, such as $\\gamma_p$ or $L_p$. We can substitute these symbolic parameters in the model with values by using the [`pybamm.ParameterValues`](https://pybamm.readthedocs.io/en/latest/source/api/parameters/parameter_values.html) class, which takes either a python dictionary or CSV file with the mapping between parameter names and values. Rather than create our own instance of `pybamm.ParameterValues`, we will use the default parameter set included in the model" ] }, { @@ -219,10 +222,11 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "The next step is to mesh the input geometry. We can do this using the [`pybamm.Mesh`](https://pybamm.readthedocs.io/en/latest/source/meshes/meshes.html) class. This class takes in the geometry of the problem, and also two dictionaries containing the type of mesh to use within each domain of the geometry (i.e. within the positive or negative electrode domains), and the number of mesh points. \n", + "The next step is to mesh the input geometry. We can do this using the [`pybamm.Mesh`](https://pybamm.readthedocs.io/en/latest/source/api/meshes/index.html) class. This class takes in the geometry of the problem, and also two dictionaries containing the type of mesh to use within each domain of the geometry (i.e. within the positive or negative electrode domains), and the number of mesh points. \n", "\n", "The default mesh types and the default number of points to use in each variable for the SPM are:" ] @@ -282,10 +286,11 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "The next step is to discretise the model equations using this mesh. We do this using the [`pybamm.Discretisation`](https://pybamm.readthedocs.io/en/latest/source/spatial_methods/discretisation.html) class, which takes both the mesh we have already created, and a dictionary of spatial methods to use for each geometry domain. For the case of the SPM, we use the following defaults for the spatial discretisation methods:" + "The next step is to discretise the model equations using this mesh. We do this using the [`pybamm.Discretisation`](https://pybamm.readthedocs.io/en/latest/source/api/spatial_methods/discretisation.html) class, which takes both the mesh we have already created, and a dictionary of spatial methods to use for each geometry domain. For the case of the SPM, we use the following defaults for the spatial discretisation methods:" ] }, { diff --git a/examples/notebooks/models/Validating_mechanical_models_Enertech_DFN.ipynb b/examples/notebooks/models/Validating_mechanical_models_Enertech_DFN.ipynb index 2b52254c66..c920d35098 100644 --- a/examples/notebooks/models/Validating_mechanical_models_Enertech_DFN.ipynb +++ b/examples/notebooks/models/Validating_mechanical_models_Enertech_DFN.ipynb @@ -1,11 +1,12 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Test the parameter set of the Enertech cells\n", - "In this notebook, we show how to use pybamm to reproduce the experimental results for the Enertech cells (LCO-G). To see all of the models and submodels available in PyBaMM, please take a look at the documentation [here](https://pybamm.readthedocs.io/en/latest/source/models/index.html)." + "In this notebook, we show how to use pybamm to reproduce the experimental results for the Enertech cells (LCO-G). To see all of the models and submodels available in PyBaMM, please take a look at the documentation [here](https://pybamm.readthedocs.io/en/latest/source/api/models/index.html)." ] }, { @@ -156,7 +157,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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\n", 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", 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" ] @@ -323,7 +324,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -337,7 +338,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.0" + "version": "3.11.1" }, "toc": { "base_numbering": 1, @@ -351,6 +352,11 @@ "toc_position": {}, "toc_section_display": true, "toc_window_display": true + }, + "vscode": { + "interpreter": { + "hash": "a06befff6f507b2769436dc41c340f64f62afa83086a8cd273928f468e329d0b" + } } }, "nbformat": 4, diff --git a/examples/notebooks/models/compare-particle-diffusion-models.ipynb b/examples/notebooks/models/compare-particle-diffusion-models.ipynb index 15b35e87c0..a98244aa5f 100644 --- a/examples/notebooks/models/compare-particle-diffusion-models.ipynb +++ b/examples/notebooks/models/compare-particle-diffusion-models.ipynb @@ -1,11 +1,12 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Compare particle diffusion models\n", - "In this notebook we compare the different models for mass transport within the electrode particles. For a full list of all the particle models, see the [documentation](https://pybamm.readthedocs.io/en/latest/source/models/submodels/particle/index.html).\n", + "In this notebook we compare the different models for mass transport within the electrode particles. For a full list of all the particle models, see the [documentation](https://pybamm.readthedocs.io/en/latest/source/api/models/submodels/particle/index.html).\n", "\n", "With the \"Fickian diffusion\" option a diffusion equation is solved within the particle domain, with the boundary flux prescribed at the surface related to the local current density. Alternatively, one can assume a particular (polynomial) concentration profile within the particle (at present, this can be uniform, quadratic, or quartic). The \"uniform profile\" model assumes that the concentration inside the particle is uniform in space (and therefore equal to the surface concentration through the entire particle - in effect ignoring transport resistance within the particle), and solves an ODE for the average particle concentration. The \"quadratic profile\" model additionally solves an algebraic equation for the surface concentration, taking into account the effect of diffusion within the particle. Finally, the \"quartic profile\" model also solves for the average concentration gradient (the integral of $\\partial c/ \\partial r$) in the particle, giving a higher-order approximation to the concentration profile within the particle.\n", "\n", @@ -137,7 +138,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -188,7 +189,7 @@ "outputs": [ { "data": { - "image/png": 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WrVpx4YUXsm7dOgAaN27MLbfcQvv27Xnttddo3Lgxt912G127dqVjx458+eWXHHnkkey5554MGjSowDj33XdfevfuTfPmzTn11FNZvXp1ged+5ZVXaNWqFS1btuSWW24BoH///owdO5a//e1v3HTTTfm/YXO///47p5xyCp06daJTp058/vnnO/1vXBgmeJIkSVJ5U6kSBAEMHAiRSPQ5CKLvx0hOTg6TJk3iP//5D3fdddcmn9WuXZtnnnmGbt26MW3aNBo0aECfPn0YOnQo33zzDTk5OQwcODD/+7Vq1eLLL7/kzDPPBKBRo0Z8/vnndOvWjT59+vD6668zYcIE7rzzzgJj+e6777jiiiuYOXMm1apV44knntji3N27d+eWW27hk08+Ydq0aXzxxReMGDGCO+64g44dO/LSSy/xwAMPbPX3XnvttVx//fV88cUX/O9//+Oiiy7amX++QjPBkyRJksqbOXPg7LMhPT36Oj0deveGuXNjdsmTTz4ZgA4dOjBv3rxtfve7776jSZMm7LPPPgCcf/75jB49Ov/zM844Y5Pv9+rVC4BWrVpxwAEHULVqVXbbbTcqVqzIn3/+ucX5d999d7p27QrAOeecw9ixY7c49xdffEHPnj3ZbbfdSElJoXfv3pvEsD0fffQRV111FW3btqVXr14sX76clStXFvr4onIPniRJklTe1KsH1arB2rWQlhZ9rlZtp/bhpaSkEIlE8l+vXbt2k88rVqwIQHJyMjk5OUW+DkDlypULPHdSUlL+3xteF3StIAi2+nrzcxdVJBJhwoQJpKWlFcv5CssKniRJklQeLVwIl10GEyZEnxcs2KnT1alTh0WLFrFkyRLWrVvHW2+9VeRzNWvWjHnz5vHDDz8A8OKLL9KjR4+dim9jP//8M+PHjwfg5Zdfzm+WsrH999+fzz77jMWLF5Obm8srr7yyQzEcccQRPPbYY/mvd6R5zM6wgidJkiSVR8OG/fX3gAE7fbrU1FTuuOMO9t9/fxo0aMC+++5b5HOlpaWRmZnJaaedRk5ODp06deKyyy7b6Rg3aNasGQMGDODCCy9kv/324/LLL9/iO/Xq1eO+++7j4IMPJgxDjj32WE444YRCX+PRRx/lyiuvpHXr1uTk5NC9e/cCm74UtyAMw5hfpDh17NgxnDx5cqLD2MKoUaPo2bNnosPQdnifSj7vUengfSodvE+lg/ep5Cst92jmzJk0b9480WEkzIoVK6hatep2vzdv3jyOO+44pk+fHoeodl5B9zUIgilhGBY4m8ElmsUhK4u2116702VtSZIkSdoZJnjFISOD6t98UyzzQyRJkiTFTuPGjUtN9a4oTPB2xkbzQ4IwjMv8EEmSJEnaGhO8nZE3P2RdcjIAYaVKMZ8fIkmSJElbY4K3M/Lmh1SIRFgDxTI/RJIkSZKKygRvZy1cSO7FF9OjQgU+a97cRiuSJEmSEsYEb2cNG8YnN5/Cd1elccofC8h59dVERyRJkiSVKX369OH111/foWNGjBjBjBkz8l/fcccdfPTRR8Ud2lY9+uijNG/enN69e/Pmm29y3333AdCvXz8efPDBmF3XQefFoHJqZZZXWw514ZNPPuGII45IdEiSJEnSdmVlwZlnwtChpXOXUW5uLsl5/TA2N2LECI477jj2228/APrHoOP9tq7/xBNP8NFHH9GwYUMAevXqVezXL4gVvGJw4O4H0jCtIUkdknjVCp4kSZJKiYwMGDs2+lwc7r77bvbZZx8OOuggzjrrrPxKVc+ePZk8eTIAixcvpnHjxkB06Hi3bt1o37497du3Z9y4cQCEYchVV11Fs2bNOOyww1i0aFH+NRo3bswtt9xC+/btee2113j66afp1KkTBx54IKeccgqrV69m3LhxvPnmm9x00020bduWH3/8cZMq4BdffMGBBx5ImzZt2H///VmxYsUmv2PUqFF0796dY489lmbNmnHZZZcRiUQAqFKlCjfeeCNt2rRh/PjxPPTQQ7Rs2ZKWLVvyn//8B4DLLruMOXPmcPTRR/Pwww/z/PPPc9VVV23x7/Xjjz9y1FFH0aFDB7p168asWbN2+h6Y4BWDIAg4pt4xRHaP8Nonr7F+/fpEhyRJkiRtU1YWZGZCJBJ93tlWElOmTGHIkCFMmzaNd955hy+++GK7x9SuXZsPP/yQL7/8kqFDh3LNNdcAMHz4cL777jtmzJjBCy+8kJ/4bVCrVi2+/PJLzjzzTE4++WS++OILxo0bR/PmzXn22Wc58MAD6dWrFw888ADTpk1jzz33zD82OzubM844g0ceeYSvvvqKjz76iEoFjDmbNGkSjz32GDNmzODHH39k2LBhAKxatYoDDjiAr776ikqVKpGZmcnEiROZMGECTz/9NFOnTmXQoEHUr1+fTz/9lOuvv36rv/+SSy7hscceY8qUKTz44INcccUVhfq33hYTvGJyRJ0jSCKJ5U2X8/HHHyc6HEmSJGmbMjKiyR1Abu7OV/HGjBnDSSedRHp6OtWqVSvUksT169dz8cUX06pVK0477bT8PXOjR4/mrLPOIjk5mfr163PIIYdsctwZZ5yR//f06dPp1q0bnTt35qWXXuLbb7/d5jW/++476tWrR6dOnQCoVq0aKSlb7lzbf//9adq0KcnJyZx11lmMHTsWgOTkZE455RQAxo4dy0knnUTlypWpUqUKJ598MmPGjNnu7wZYuXIl48aN47TTTqNt27ZceumlZGVlFerYbTHBKya1Ktbi1q63UmlJJZdpSpIkqUTbUL3Lzo6+zs4unire1qSkpOQvcVy7dm3++w8//DB16tThq6++YvLkyWRvCGg7KleunP93nz59ePzxx5kwYQJ33nnnJuffGUEQFPg6LS1tq/vudkQkEqFGjRpMmzYt/zFz5sydPq8JXjG6+7C7Oa31aQwfPrzQ/8MpSZIkxdvG1bsNdraK1717d0aMGMGaNWtYsWIFI0eOzP+scePGTJkyBWCTbpjLli2jXr16JCUl8eKLL5Kbm5t/rqFDh5Kbm0tWVhaffvrpVq+7YsUK6tWrx/r163nppZfy369ateoWe+sAmjVrRlZWVv4S0hUrVpCTk7PF9yZNmsTcuXOJRCIMHTqUgw46aIvvdOvWjREjRrB69WpWrVrF8OHD6dat2/b+qYBo5bBJkya89tprQHTf4VdffVWoY7fFBK+YHXbiYfxZ708+/PDDRIciSZIkFWj8+L+qdxtkZ8NmW912SPv27TnjjDNo06YNRx99dP4SSIC+ffsycOBA2rVrx+LFi/Pfv+KKKxg8eDBt2rRh1qxZ+ZW5k046ib333pv99tuP8847jy5dumz1uhkZGRxwwAEcfvjh7Lvvvvnvn3nmmTzwwAO0a9eOH3/8Mf/9ChUqMHToUK6++mratGnD4YcfXmDVr1OnTlx11VU0b96cJk2acNJJJxX4m/v06cP+++/PAQccwEUXXUS7du0K/W/20ksv8eyzz9KmTRtatGjBG2+8UehjtyYIw3CnTxJPHTt2DDd04ClJRo0aRc+ePbnu3et4ZPwjnD7/dIZmDk10WNrMhvukkst7VDp4n0oH71Pp4H0q+UrLPZo5cybNmzdPdBj5+vXrR5UqVejbt29crrdixQqqVq1aLOcaNWoUDz74IG+99VaxnG9nFHRfgyCYEoZhx4K+bwWvmF3U4SJIhjfmvsG6desSHY4kSZKkcsRB58WsZe2W7FN5H77f73vef//9uA00lCRJkkqSfv36JTqEIuvZs2epqNoWxApeDFzb7VqoAwPfHJjoUCRJkiSVIyZ4MdC7TW9SIil8OvvTYmvTKkmSJEnbY4IXA9XTqvPqAa+ybvQ63nvvvUSHI0mSJKmcMMErBllZcO21bTcZDHnc4cdRq1Ythrw6ZMsv9+gRuymSkiRJksotE7xikJEB33xTfZPBkKmpqex24W68VvE11qxZs+mXx46F/v3jH6gkSZJUCv3555888cQT+a9/++03Tj311Lhd//fff+eAAw6gXbt2jBkzhmOOOYY///wTgCpVqsQtjsIwwdtJWVmQmQlhGJCZuWlhrnPzzkQaR3j+jeehUiUIAhg4ECKR6HMQRN+XJEmSVKCcnJwtErz69evz+uuvF/t1tubjjz+mVatWTJ06lW7duvHOO+9Qo0aNYr1+cTHB20kZGdF8DSA3l02qeHeccAeEMGDsAJgzB84+G9LTox+mp0Pv3jB3bvyDliRJkmLg7rvvZp999uGggw7irLPO4sEHHwSiYwcmT54MwOLFi2ncuDEA8+bNo1u3brRv35727dszbtw4IDpovFu3bvTq1Yv99tuPW2+9lR9//JG2bdty0003MW/ePFq2bAlAbm4uffv25YADDqB169Y89thjW8TVs2dPrr32Wtq2bUvLli2ZNGkSEB3lcO6559K1a1fOPfdc5s2bxyGHHELr1q059NBD+fnnn5k2bRo333wzb7zxBm3btmXNmjU0btyYxYsXb3GdBx54gE6dOtG6dWvuvPPOYv/3LQzn4O2EDdW77Ozo6+zs6Ot//hPq1oUmuzRh9+zdmVFxBsurVKZatWqwdi2kpUWfq1WLflGSJEkqZj2f77nFe6e3OJ0rOl3B6vWrOealY7b4vE/bPvRp24fFqxdz6qubLoEc1WfUNq83ZcoUhgwZwrRp08jJyaF9+/Z06NBhm8fUrl2bDz/8kLS0NGbPns1ZZ52Vnwh++eWXTJ8+nSZNmjBv3jymT5/OtGnTgGhiuMFTTz3FvHnz+Pzzz6lZsyZ//PFHgddavXo106ZNY/To0Vx44YVMnz4dgBkzZjB27FgqVarE8ccfz/nnn8/555/Pc889xzXXXMOIESPo378/kydP5vHHH9/qb/nggw+YPXs2kyZNIgxDevXqxejRo+nevfs2/w2KmxW8nbBx9W6Dzat4F7S9gLBayAOvPwALF8Jll8GECdFnG61IkiSpjBgzZgwnnXQS6enpVKtWjV69em33mPXr13PxxRfTqlUrTjvtNGbMmJH/2f7770+TJk22e46PPvqISy+9lJSUaO1ql112KfB7Z511FgDdu3dn+fLl+XvoevXqRaW8bVPjx4/n7LPPBuDcc89l7Nix273+Bh988AEffPAB7dq1o3379syaNYvZs2cX+vjiYgVvJ4wf/1f1boPsbMirLANwywm38NAzDzFt5jQYNvKvDwYMiEuMkiRJKp+2VXFLT03f5ue7pu+63YrdjkhJSSGSVxnZeE70ww8/TJ06dfjqq6+IRCKkpaXlf1a5cuViuz5AEAQFvi6u64RhyG233call15aLOcrKit4O2HqVAjD6OPTT0fl/z116l/fSa+Yznl7n8fHb37MypUrExesJEmSFEPdu3dnxIgRrFmzhhUrVjBy5F/FjcaNGzNlyhSATZqjLFu2jHr16pGUlMSLL75Ibm5ugeeuWrUqK1asKPCzww8/nCeffDK/ScrWlmgOHToUgLFjx1K9enWqV6++xXcOPPBAhgyJjjl76aWX6Nat2/Z+dr4jjzyS5557Lv9/5//1119ZtGhRoY8vLiZ4cXD66aezZq813DXkrkSHIkmSJMVE+/btOeOMM2jTpg1HH300nTp1yv+sb9++DBw4kHbt2m3SnOSKK65g8ODBtGnThlmzZm21mlarVi26du1Ky5Ytuemmmzb57KKLLqJRo0Z06dKFNm3a8PLLLxd4jrS0NNq1a8dll13Gs88+W+B3HnvsMTIzM2ndujUvvvgijzzySKF//xFHHMHZZ59Nly5daNWqFaeeeupWk9JYCsIwjPtFd0bHjh3DDRsvS5JRo0bRs2fPAj/Lzc0l7do0KleuzJ/3/xnXuLSpbd0nlQzeo9LB+1Q6eJ9KB+9TyVda7tHMmTNp3rx5osPI169fP6pUqULfvn3jcr0VK1ZQtWrVAj/r2bMnDz74IB07doxLLMWpoPsaBMGUMAwL/DFW8OIgOTmZrpW6six9GWN/LPxGTUmSJEnaETFrshIEQRowGqiYd53XwzC8c7PvPAwcnPcyHagdhmGNWMWUSDcfczOfffIZGSMzeP+69xMdjiRJkhRT/fr1S3QI+UaNGpXoEOImlhW8dcAhYRi2AdoCRwVB0HnjL4RheH0Yhm3DMGwLPAYMi2E8CXVUj6OoNK8Sny75lLU5a7d/AEQH7fXo4TgFSZIkFUpp236lbSvK/YxZghdGbWgbmZr32FaEZwGvxCqeREtKSuLIOkeSszSHGb/O2P4BEB2oN3Ys9O8f2+AkSZJU6qWlpbFkyRKTvDIiDEOWLFmyyeiIwohpk5UgCJKBKcBewIAwDG/Zyvf2ACYADcMw3KI3ahAElwCXANSpU6fDhtalJcnKlSupUqXKNr8zffp0rr76am677TaOOOKIrX6v25FHkrz5gD0gt0IFxrzv8s6dUZj7pMTyHpUO3qfSwftUOnifSr7Sco+CIKBy5cokJycnOpSECMNwi1l3pV1ubi6rVq3aImk/+OCDt9pkJS5dNIMgqAEMB64Ow3B6AZ/fQjS5u3p75yqNXTQ3iEQiNGvWjPVJ6/nHC//gogMuKviLWVnQty+MGAGrV0N6Opx0Ejz4INStW+yxlyelpQtWeeY9Kh28T6WD96l08D6VfN6j0qE83aeEd9EMw/BP4FPgqK185UzK8PLMDZKSkhg8eDA/N/iZi9+7mA9++KDgL9arB9Wqwdq1kJYWfa5WzeROkiRJ0jbFLMELgmC3vModQRBUAg4HZhXwvX2BmsD4WMVSkhx44IH0O6wf/A5nvHIGf679s+AvLlwIl10GEyZEn220IkmSJGk7YjYmAagHDM7bh5cEvBqG4VtBEPQHJodh+Gbe984EhoTlaDfoP279B2+f8jaTak3ivFfO480L3tzyS8M2aig6YED8gpMkSZJUasUswQvD8GugXQHv37HZ636xiqEkysqCM89M4unHRtLl700YmTSSV6a9wlltz0p0aJIkSZJKubjswdNfNkw+ePLJ2rx21WswCd58soAK3o5wXp4kSZIkTPDiKisLMjMhEok+t297DLe2vpUhg4YwZMiQos8scV6eJEmSJEzw4iojI5rcAeTmRl/379+fjgd15Jz3zuGBjx7YsRNWqgRBAAMHRk88cGD0daVKxR+8JEmSpBLPBC9ONlTvNswvz86Ovl6yJJWhLwyFVPj7Z39n9u+zC3/SOXPg7LOjc/Ig+ty7N8ydW/w/QJIkSVKJZ4IXJxtX7zbYUMVr2qQpjx7yKLmRXA599FAiYaTgk2zOeXmSJEmSNmKCFyfjx/9VvdsgOxvGjYv+fcVZV9BjXQ/mp8zniuevKPyJnZcnSZIkKU8s5+BpI1Onbv8772S8Q90b6vL0n09z2y+3sUfDPbZ/kPPyJEmSJOWxgleCpKen886l71BxcEUuOO8CcnNzEx2SJEmSpFLEBK+EOajdQQz4vwF8+tmnXHz3xcV/AWfmSZIkSWWWCV4J1KdPH1pc2YLMnEzufunu4j25M/MkSZKkMssErwQKgoC3//E2FVdV5h8zM7jqoTt2/qTOzJMkSZLKPBO8EmqP2ntwyopZ8Mc+DFh6L1c8vAOdNQvizDxJkiSpzDPBK6GysmDYiw3h+U9hUSsG/vok/e7uRxiGRTuhM/MkSZKkMs8xCSVU/mD07FqkvvIx9Zu/xl2TLmXVn6v497//TRAEO37SDTPzLrkEnnoqmkVKkiRJKjNM8EqgrCzIzPxrMPr6FTVZ9M3F9Okzlwcn3ccX133Bxw99THJy8o6d2Jl5kiRJUpnmEs0SKL96t5Hc3IAKle5ij5578FnNz+h8RWeyN2SAseJIBUmSJKlUMcErgcaP/6t6t0F2NkwaX4GZ/5hJs5RmTK4/mXYXt2P16tWxC8SRCpIkSVKpYoJXAk2dCmG45WPqVKiUWolpt06jTVobZjSdQcuLWrJs2bLiDcCRCpIkSVKpZIJXCqWlpDGp7yQ6V+3Mz9/9zMEHH8yiRYuK7wKOVJAkSZJKJRO8UqpCcgXGXT+Ot+5+i5kz/2T3dp/x6aczi+fkjlSQJEmSSiUTvFIsCAKOOuooupz4DNkXnsshT53NE889UTwn3zBSYcKE6LONViRJkqQSzzEJpVxWFowbcTC0uwcOu4Urv76Gdy55h6EPD6Vy5cpFP7EjFSRJkqRSxwpeKZeRAWEkgPE3kPLiaCpU3I23677NXmfvxYwZM+IThOMUJEmSpBLBBK8U23wges68LiQ9/S3tqx/I8nA5nTp1YvDgwbEPxHEKkiRJUolggleKFTQQPbJqFw6YM4bvBn7H/vvvT59/9qHXpb1iMy/PcQqSJElSiWKCV4ptbSD6+HFJNGzQkA8//JD6F9ZnZO2R7HXmXnz77bfFG4DjFCRJkqQSxQSvFNvWQHSAlJQUvrr5Kzru2pGsDlm0yWjDvx8ZXHzb5RynIEmSJJUoJnhl3K7puzLxmonc1uk2cpvlcsuPNzP6y1/5xz/WFs8FHKcgSZIklRiOSSgHkoIk7jnmHtrvdgSn3/0C4ap6PPvsOg466D369Dlq507uOAVJkiSpxLCCV458ktmT1HefgzAJqv3KBWMv58i/HcnixYvjF4QjFSRJkqSYMcErJzYfqUC1xVA9hw92/4BGlzTiv6/+Nz6BOFJBkiRJihkTvHJii5EKv3Qm9alv2XPpBaxps4Zzx51L9z7dWbhwYWwCcKSCJEmSFHMmeOVEQSMV1q+sRtXRz/HJuZ9Qq2YtPl/2OS1atODll18mDMPiDcCRCpIkSVLMmeCVE9saqXBw04OZ//f5TOg3gb322oveN/bmgPMPYOrUBY5UkCRJkkoREzwBUCm1Ep3adOLzzz+n/VXt+WLPL+h4Xy9Gf7mA/v2LqZrnSAVJkiQppkzwtInk5GTG3zqei5peT2Sfr+CqZgyc+n+8896UnT/5sGHRUQpt2kSfNx6xUBA7bkqSJEk7xARPW6iQXIHU8Q+R+sxX8EtnOOomjv3nA1x55ZUsXbo0foHYcVOSJEnaISZ42sKGkQrrF+wL/30PXnqLpK+fZuDA4TTt3pSHn3mYyCYtOYuZHTclSZKkIjHB0xY2HakQwOxjSaEqp542jbVHr+WGuTfQ9NymTJ46OTYB2HFTkiRJKhITPG2hoJEK2dkw+/vafHPLN7St3paf9vmJTs914qS+J/Hdd8uLd6ucHTclSZKkIjHB0xa2NVJhr1p7MfXmqQw5fgjVa1RnRNURtD7+XsaMCYuv2ybYcVOSJEkqAhM8FckZ7c9g4Z0LubFpBtk/3EEYBgx670P+N3Js8VzAjpuSJEnSDjPBU5FVTKnI6vH/oEJqGlRcTnjmKZz62bEccPkBzPtpXnyDseOmJEmSZIKnotvQbTM7O4B11eCFj+DPFkyqO4mm/27KOXecw8qVK2MbhB03JUmSpHwmeCqyTbttAr8eQOqLn9Mt63nSa6TzUvJLNO3UlOeffz52YxXsuClJkiTlM8FTkRXUbXN9dsCKieez5K4lPNDuAZpWb8oFF1xA05Oa8sZHbxT/Vjk7bkqSJEn5TPBUZNvqtlkxpSJ9e/Vl3LhxDBg8gJ/a/MSJn5xIi0svZPS4NWRkFGMgdtyUJEmSABM8xVhSUhJXnHcFX178JXsmt2Bph0y4al8Gjc/k6+mLiuciO9Bxs8KSJXbblCRJUpllgqe4aLd7O45YMp2Ulz6C1bsROeZKOnQfzD333MPq1avjFsceL7xgt01JkiSVWSZ4iosNHTdzZh8KT0+CpycSWXYNt9/+CLXPqc3tT9xOTk5O7ALI67bZ4M037bYpSZKkMssET3GxScfNMAkWtSIlpSJHnvY26/Zdxz2/38Mul+3CwFcHEoZh8QeQ120zt2LF6Gu7bUqSJKkMMsFTXBTUcTM7GxbO7siSO5ZwRp0zWFl3JVd8ewX1L6nP+6PfL96Om3ndNpOys+22KUmSpDLLBE9xsa2Om9XSqjHksiHM7zufHlV6sKj6Io467Ci6dn2P0WMixddxc+FCfuvVq/DdNot9poMkSZIUWyZ4KjEa1GjAqJtGseifi7jpuoeY+9OB8LcDGfjV/YydOHvnLzBsGLOvu65Q3TaB6LpSG7JIkiSpFDHBU4lTq3otVq68ntTqq2FtdcLDb6Xbq/tz2C2HkbUwK/YB5DVkYeBAG7JIkiSpVDHBU4mzoePm+qV14b/vw/OfwvJmfJz+MQ3uacB1d17H8uXLYxdAXkMW0tOjr23IIkmSpFLCBE8lziYdNwHm9ST1hfF0/+VlGiY15JGMR2jatCm3P3B7/gy9WDRkYe1aG7JIkiSpVDHBU4lTUMfN9dkByyefxc8P/8zkLybT5oA23LPkHmreWJObH7+Zu+7KZexYirUhC5ddZkMWSZIklSomeCpxttVxE6BDhw68++a7XN/6esKaIQ8seYAnVx5LpPZUMjPD4smxhg2LNmKxIYskSZJKERM8lUoVkivw0NkPsbTfUvb5+WZoOAkua8+a9G+54II5sRmWXhAbskiSJKkEMcFTqbZ8SWV+fvl+eGQODHsBlrTkvffq0qhXKzJHZMY+0bMhiyRJkkoQEzyVavkNWdbWgK/PBSApfS2/tPqRC7+8kHqX1OO191+LXQA2ZJEkSVIJYoKnUq2ghiyR1buw3yez6VGlBwvrLeT0safT6JJGvPvZuyxZUqH4e6HsSEMWm7FIkiQphlISHYC0MzY0XtlSQ2AUsxbM4vznzmdS7Ukcc+Ix1K7wOr//HpKRETBgQDEFsXEDlu2ddONmLE88UUwBSJIkSVFW8FSm7Vt3Xyb+fSLzrp3HbZc/yqJFxxCechYDZ/yTNz+YGL9AbMYiSZKkODDBU7mwx2578OefV5NcMQLJ2YQ9/8UJnxxCy8tbMnFqHBI9m7FIkiQpDkzwVC5kZUFmJuSuqwxDh8GTUwjmH8y3db+l85DOHNrnUGbMmBG7AGzGIkmSpDgwwVO5kN9tc4Os9qS+/hbHLxjDvhX3ZeLIibRs2ZLjzj+OqdOjG/uKvR/KjjRjiUkAkiRJKutssqJyoaBum9nZMH/CQcycOpPF1yzmwf97kH+v+DdvD36bjms60nTpSMaOrUtGxvZ7pxTKjjRjARuySJIkaYdZwVO5MHUqhCF8+ukowpD8x4YunLvuuiv33Xsfb1zyBo0qNWLybpN5tW57Ih0f55nnV8e3iGZDFkmSJBWRCZ60keNbH89P/X/iyKx3YemecMzVZO81lO7dP2TmzJnxCcKGLJIkSSoil2hKm8nKgs8GHwVrj4Q9xsD8LsyOrGe/3vVo27kpT13yFJ3adopdADZkkSRJUhFZwZM281dDlgB+6g6RVCpUqETNDgcwrc409n9pf1pf1poJX07IP8aGLJIkSSoJTPCkzRTckCVgj8kfMPLkkTRJb8I39b6hy5AudLqgE1OnTs3vh5KRUUxBDBsWbcTSpk30eeMGLQXZuCGLJEmSyi0TPGkzGxqybP6YOhWOa3Ucc+6aw3unvcde6Xsxc8pM2rc/hkGDFxBJXU5mZpyLaDZkkSRJ0kZM8KQiOHK/I5ndbza/jvmVTp1GEna7F67bgzWdb+fCK6cThmF8ArEhiyRJkjZigifthNWrq/PNNx3h6/Ng3sHQ4x7e3bcz9c7fi/+O+G/sEz0bskiSJGkjJnjSTshvyJLVAYYOgwHTCWadwMIm8zj3hXPp0KEDr7/+OpFo15bY9EKxIYskSZLymOBJO2GLhiy/tyAc9hL7fvgd9x99PytXruS0q06j5oU1ue+Z+7jrrtzibcYCNmSRJElSPhM8aSdsrSHLzHF7cfPFNzNz5kyu7H8lK3ZfwW2/3MaTS08lUudLMjPD+BfQbMgiSZJU5pngSTGUnJzM45c8zoJbFrDnb1dC00/g0g6sOf4UTjhxMn/++Wf8grEhiyRJUplngifFQe6K2vz64uPw8M/w0b2Q1ZFJE/ejQcMOnHrbqcz/ZX7sg7AhiyRJUplngifFQX4zlnXVYeytMObvpKamUaPtpfwv7X80+ncjul3XjW9mfpN/TMIbstiMRZIkqdQxwZPiYItmLMD69UnstupGHu/+OLtV242xNcfS+tnWtLysJaPHj87vhZKwhiw2Y5EkSSp1TPCkONhaM5ZpU5O58uArWZixkNePf52mlZsyo+oMenQ7nSefXEckyI5/QxabsUiSJJVaJnhSCRAEAae0P4Uf7/qRuTfN5cAD3iPCeriiBWuOPI8TLnmDdevWxScYm7FIkiSVWiZ4UglTIXcPvvyyLaTmwg9Hw37DmdThRKpeuSsX3H0Bf/zxR2wDKEozFvfrSZIklQgmeFIJs0lDlncfhYfmk/TJ3eTuUoHnc56nwUENuOaaa5gzZ07+McWeX+1IM5YNQbtfT5IkKeFSEh2ApE1t0ZBlbU0io/9O6xV9Oe/ux/h67tcMGjSIx798nBatW/B/Z/0fI145Ir8hy4ABxRDExs1XtnXCSpWiFb4NBg6MPtLSYM2aYghEkiRJO8IKnlTCbK0hy1dfVuDGo29k8PODmTt3LvsevC/T60znyPePZODPlxGpMTv+DVncrydJklSimOBJpVCDBg2YkTGDCedNoPaiE6BdJlzdjDXt7ufUU6exbNmy+ATi8HRJkqQSxQRPKsUapR3A8hdHwH9+gs/ugLmH8/nn+1C3ZUsOvflQvpn1zRbHJHy/ng1ZJEmSYsYETyrF8huyrKwLo/pBVgdSU9NIP+BgPqn8Ca0zW7P35Xvz6nuvEoZh/jHFOkB9R4anbxyADVkkSZKKnQmeVIpt0ZAFWL8+id1/HMz/jvsf+6btyw+1f+CM8Wew6wW78vDDQ8jMDIlEIDMzzkU0B6hLkiTFnAmeVIptrSHLtKkBJ3c4mZl3zWTm5TM5otoRJK9N5oYb/mDt2nWw73ByWFt8VbzCsCGLJElSzDkmQSrj9q27L+/f+D6//RbSZHiE7DoT4cyTWb9qNwZOvYC2Q7tw0eknEARBbAOxIYskSVLMWcGTyol//SsAkuGXzvDCBzC/C+GBD3LJjJOpcWkNHhj0AKtXr87/fkx6oexIQxabsUiSJO0wEzypnMjfrxcmwZzDYcgb8OiPVP3mKtZVXcfNV99MgwYNOO/m8/j6u6+LvxkL7FhDFpuxSJIk7TATPKmcKHC/3tLGLB/2KKsfWM3oT0Zz+BGH82L2i7QZ3JaB864kssusuA9P73bkkTZjkSRJKiITPEkkJSXRrVs3hg4Zyhvnv8Euvx8JHZ6Bq5qz5rRDOOzCZ1myZElcYpn48ss2Y5EkSSoiEzxJ+YIgoFPdXqz+77vw8Hz46B7YZS7fzqlK/frtOb3P6Qz/dHj+TL0NinO7XHatWjvejMX9epIkSYAJnqTN5A9PX1Ubxt4Gj/xIytyT2HvvF3hjwRucPOpkalxRg6sfvZply5flH1Os+/V2pBnLxgG4X0+SJJVzJniSNrHF8PQwmZzsVFJTezD12akcU/UYVtdYzeNLH6dmv5p0uvZQnnsuUrzD0wvbjMXh6ZIkSZswwZO0ia0NT586FfZrsB9v932bVRmr+Ffrf1EntQ6Ts/5k3bpoRpi96wTuuDN7O1coRg5PlyRJ2oQJnqQdViGlArefdDtfXpdFxbfHA2lQ6zty/9aFp9P2pdMNh/DZlM8KPLZYt8s5PF2SJGkTJniSiiwjA8KcCtEXy/aA/70Eyxsxufqn9HyjJ7WuqMX/Pft/rFmzZpNjErpfz4YskiSpDDPBk1Rkm+zXy0mDb86G50ex93vT6ZnWk+VVltP36r7Ur1+fM68/kyHvfkJmJonZr7eBDVkkSVIZZoInqci2tl/v+/Et+PTvn7L2vrWMencUxxxzDK+uepWzJhzG2pOPgX1HkBNmF18VrzBsyCJJksoBEzxJMZOclEyPHj146aWXePvCLwjG3QL1psGZJ7H+qt0ZOPkx3npr8hZz9SAGKyltyCJJksoBEzxJcTHyhQ6kjr4XHv4ZXh4J8w8kzK7I8cdPZN+2+3L6facz55c5+d8v9r16RWnI4n49SZJUysQswQuCIC0IgklBEHwVBMG3QRDctZXvnR4EwYy877wcq3gkJVb+fr1ICnx/HAwdDtMuYffdzyR371xeW/caew7ckzNfOZM7nn6E5zKLebYeOEBdkiSVeSkxPPc64JAwDFcGQZAKjA2C4N0wDCds+EIQBHsDtwFdwzBcGgRB7RjGIymBpk7d2ie1CMPZvDL2Fe59716m7zqdjN+ug78NhKe/ICenMhkZSQwYUAxBbNyAZVsnrFQpWuHbYODA6CMtDTbqCCpJklTSxKyCF0atzHuZmvfYfKPNxcCAMAyX5h2zKFbxSCq5giDg7G5n883d35DZ4h2SRz4Lcw6H7KqsX5/EwG8yOP/f1/L70t/jE5D79SRJUikVFNTcoNhOHgTJwBRgL6KJ3C2bfT4C+B7oCiQD/cIwfK+A81wCXAJQp06dDkOGDIlZzEW1cuVKqlSpkugwtB3ep5Lv3/9uzIcfNiInJ+///pS0Hq7aF3aZA2tg9z9355S9TuG4dseRnJwMwJIlFejffz/uvHMGu+ySvY2zF97eDz9M/ZEjiaSmkrR+Pb8dfzyzr7++wO9WWLKE/fr3Z8add5K9yy7Fcv2Szv+WSgfvU+ngfSr5vEelQ3m6TwcffPCUMAw7FvRZTBO8/IsEQQ1gOHB1GIbTN3r/LWA9cDrQEBgNtArD8M+tnatjx47h5MmTYxpvUYwaNYqePXsmOgxth/ep5Nt77xX88EPVTd8Mcmlw0LtU6nI9P1T4AVKg5qSaXNPpGs4//3weeKAJTz4Z3VZXLEs5AU4+OdqY5ZJL4Kmnog1XtjZj74or4Mkn4dJL4YkniimAks3/lkoH71Pp4H0q+bxHpUN5uk9BEGw1wYtLF828hO1T4KjNPvoFeDMMw/VhGM4lWs3bOx4xSSqZnn56ypaz9SLJ/DL6OGbfP5usG7O4qPZFtEhqQf/+/Wl6VHsGLj+KSPNXeXbwmuJryFKYAerO1pMkSSVMLLto7pZXuSMIgkrA4cCszb42AuiZ951dgX2AOUjSVtStUZenL3+aMSPHMG/ePPZs3hdqT4fTzmDd1fXY96Zjefh/D5OTk1Pg8cU6+cC9epIkqYSJZQWvHvBpEARfA18AH4Zh+FYQBP2DIOiV9533gSVBEMwgWuG7KQzDJTGMSVIZkpraiF/fvx3+8xMM/ghmncCyRp9xw5ib2KPxHtx6662MnzZ+k2OKdb6es/UkSVIJE8suml+HYdguDMPWYRi2DMOwf977d4Rh+Gbe32EYhjeEYbhfGIatwjAsed1TJJVYGRnRlZGEyTD3UBgxmNRHsujy0wjat2vPAw89wIGvHEiVa6tw+gOn8/mXP5CZSfHO13O2niRJKkHisgdPkmIhf3j6RtavqsqaeccxcuRIfpj7A7126UVuSi6vrX6Ng4Y3Z22vk6DuVHJzw+Kp4hVmrx64X0+SJMWFCZ6kUmvqVLZsyBL+NVS9SYMmvHHLG6x+cDVPtv8fwZQroNE4qLCS7OyAQa99y90vDiQn1/16kiSpbDDBk1TmBUHAtHdPJvXjR+ChX2B+VwAiHZ7gH3OuIO2WNA78x4GMHD9yk+PcrydJkkobEzxJ5UL+cs5IKoR5/0/fJ/dT67OB1Mytyfjk8fT6oBdVr6zKgw8+yNSpC9yvJ0mSSp2URAcgSfGwYdnmpqoAlwGXMX3edO547Q6++OMLbnriJqASnDAZ5h1Mzo8nkJFRfeeHqG+8P29bJ6tUKVrh22DgwOgjLQ3WrNnJICRJUllmBU+SgJaNWzLspmHMf2U+Y8b8QHL1E6DxKDjpfNZfW5eBv5/GNQPuYMXqFVscW+wrKXd0v55LOSVJUh4TPEnazMsv70nymobwyBx49nOY+jfCxqN4bHEGdXvW5eKLL+a9j97Lb85SrHv1YMf367mUU5Ik5THBk6TN/DV+IYD5B8I7j8P//Ubdj1/jhOYnMGTIEI7ufzQVb61Im5u68sxbXxKJhMW3Vw8Kt1/P0QuSJGkzJniStJkCxy/kppI1+lReHvwyCxcu5Jbzb2G3nN34utIE1v+tA1zZgrUd76Fv3z+LJ4jCzNdz9IIkSdqMCZ4k7aD09HTu+9t9TL15ARUfWwgjB8HqXQl3H8dLL1WgXbujOf/f5zPtx2lbHFus2+UcvSBJkjZjgidJRZSRAeHqXWHKpZA5GoYMJzm5Aj8tOZEXVr9Auxfbscv1u9Dn0T7MzZqbf0yx7tdz9IIkSdqICZ4kFdFfe/XyRFLJzU1hj1qX8s4x79Cd7qxMWsngpYNpOrApHc4+jGeeySne2XqFWcoJ7teTJKmcMMGTpCIqcK9eGH3/6P2P5rN+n7H2gbUM7jqY9jntmfnR8axfnwMtX2HNMWdx/I3PsXrt6vgEW5T9ei7nlCSp1DHBk6QYSkpK4rzDzuOtq6cQrrgGSIPKv8Ne7zN5n79RpV81WtzSgsfefIxIJLLJsQnfr+dyTkmSSh0TPEmKg4wMiESC6IuJ18CDC0gaOpzKC7oxI2UG17x9DXvssQc33XQTb419i0gkkrj9ei7nlCSp1DLBk6Q42GK/Xm4FIjNPZK+vPmVB3wU80OUB2rZty8MDHub4944nrW9lBn33DyI1v4//fj3HL0iSVGqZ4ElSHGxrv16dmnXo26cvI0eOZO6cuZyz6zkEK/ci7HYPXN2MNed24IiLMsnKyopPsI5fkCSp1DLBk6QSZPe6u/PvM14k6cVv4KH58P7/AQHffNGCBg060OmkTlzw2AX8tPCn/GNikls5fkGSpFIpJdEBSJI2Fd2vB6xoAONvgPE3kJoaoXX74cyucCST/5jM848/T/1V9Tm71dksGX8nY8dWISMjuvKyWGy8fHNbJ61UKVrh22DgwOgjLQ3WrCmmYCRJUmFZwZOkEmaL/XrA+vVJ5OYewB+v/MGznZ+lbU5bFqQu4MGfHyQzdX8ikZBnnsnlt98iBZ80VnZ0v55LOSVJiikTPEkqYba1Xy8pKYkLj7yQqfdOZVXGKg74/r8Eo28HArLXr2X3u/amyz+78OaEN+MT7I7u13MppyRJMWWCJ0ml1NIlaXw1rDfh9N7RN9JXEVm1DxOSJnDC+ydQ+frKnPzvk5n508z8YxK2X8/RC5IkxYUJniSVUvl79TZYVZsKr73LGb/M4cS0EwkJGb5mOC0Pb8mxxx7LMy89w+13rije2XpQuPELRRm94HJOSZJ2mAmeJJVSBe3Vy86G775swvBbhrP64dW8ccQb3HjyjXz99ddcPPg6Mms1JHLsxTz93tj47tcryugFl3NKkrTDTPAkqZTa1l69DXp16cW/7/s38+bNo2utF+G7XtDqFdaf143d79udE+45gQXxqpAVdvSCyzklSSoyEzxJKgcWLUpmyoiTYPiL8GAWvPEMkdWNeHPyBzRs2JBevXrxr//+izXr/hptUOwrJAuzlBNczilJ0k4wwZOkcmCT/XrZVWHq36jw4nhOT5tH3759GffjOP754z+p8s8qHHD7Abw98e38FZLFul+vMFzOKUlSkZngSVI5sLX9et/PrMN9993H/C/nc/uet1M7pzaTUiZx3HvHMXBtVyI1v+e558L4F8ZczilJUpGY4ElSObC9/XqVKlbiX+f8i6yHsvi6z9c0+u5aSF0LK+qxdu06upz5CK+OejV+AcdyOackSWWYCZ4kaRO7prVi0fD/wFNToss5SWPePi9yxmdnUOXaKvT5Tx/+WPHHJsckbAvcji7ndK+eJKmMM8GTJG1ii/l6QOqr79Lk+xtZn7KewcsGc8q4Uzjw+gOZPHly/jEJ2a8HhV/OCe7VkySVeSZ4kqRNFLRfb/2y3ag+40HWPLCGAZ0GUG9lPSZ/NplOnTrRvENPnpr0LJHk1WRmJqA4VpjlnO7VkySVEyZ4kqRNbGu/XlJSElcccwUvn/Iyiz5ZxIABA/i5yl7kHn8R3FiPNYdcxlk3vE8Yhon+GZty9IIkqZwwwZMkFUmNGjU46aQryJ30FGSOgu9OgLaDGdXsKNKurco9/76HhQsXJjrMKEcvSJLKCRM8SVKRZWRAGEmCn3rA8Bfg/7II3nmUlN96cPstt9OwYUNaXdKK+4fcT05OTmKLYo5ekCSVAyZ4kqQi22K/3toahJOuZp8f32bGjBlccd0VfFvrW2797lbSbk6j03VXMvrLrMQ0Y4nl6AWXc0qSSggTPElSkW1rv17z5s155IFH+OMff3BVg6uoEu7Cr/s9ATfszsDRL3L//YNZsmRJon/CllzOKUkqxUzwJEkxVaNyDR676DHOXreI1EEz4fObCH/uwq23rqROzzo0vLYhtz13GytWrdjkOJdzSpK040zwJEkxl5UFmZmwfsG+8PG9sHwvKla8nC6HH8lv6b9x3/z7qH5Xdfa7cT8eG/4YOTk5iZ2t53JOSVIpZYInSYq5goanh2ESrde9zcp+K7mr+V3swR7MTJ/JNR9dQ/0GHXjqqfVEKi0iMzMsubmQyzklSSWMCZ4kKeYKGp6enQ3jxkF6xXTuOP0O5v57Llk3ZvFA5weonH4fucFKuGYv1ly4H62vO4+n3nqKnJycxPyAbXE5pySpBDHBkyTF3LaasWysbo269D6sLwsWHA1JqfDx3bCyHr83e5lLp1xKxVsqcsRVR/D222+zbt26/OMSuuoxVss5XcopSSoCEzxJUomSv5wzuwpMuhoGf0LqI1m0/OFe6iXVY9wH4zjuuOOo2bYmTa9vyq3P3spt/1yeuP16hbWjyzldyilJKgITPElSiVLQcs71y3YjZfqt/PLALyz5ZgnvvPMOnY7sxLxK87j/l/sZXLsBkVNP4ckvBvHJZ98ShmFigt+ewizndCmnJGknmOBJkkqU7S3nrFixIkcffTSf/eczVt65ks4//Jdg+pnQ4Atye97BoQd/QtOmTTniuiPo92I/fl/6e/65E77qsTDLOe3MKUnaCSmJDkCSpKJa9kc6017vTbi2NxBClYWkpuxKs2ZT+CDyAh/O+ZC7/u8udlm+C13rdCVn5j8YO7YTGRkBAwYkOvqt2NnOnE88Eb9YJUkljhU8SVKpten4hQBW1iUIUthzz+dZcs8S7ml5D+1pz8oKKxmZM5J3VzxBJBIw6Mm19L7vSkZ/NXqryzkdtC5JKo1M8CRJpda2xi/UrFKT2065jSn/msK6B9dx4k9fkzTuVgAiu8zk5XVP0GNED1L7prLnDXty4aMXMunbSfkJX5kdtC5JKtNM8CRJpVZhxy9kZcF7r7Qisnjf6BuL25D67Jccl3wWdanL3IpzyVyayQGnHECjRo044qxzeGpiJpGqP/FcZqTkbm0rynJO9+tJUpnmHjxJUpm36VJOIEwiWNiORlkvM3IA5EZyGTlxJHN3ncv4z8Yz/LdscntdCMDaFfXY87Ym9NizOtcceA3dO3cnfUPFLE9WFpx5Jgwduu3cKiY2LOe85BJ46qloMNvifj1JKtNM8CRJZd62lnICJCclc2KXE6ELnHny9bzZNJec7/4BjcbA7uNZvfvnvLtuHO8e8S4pQQr1TqhHrb1q0b1pd87oegYvPt6FsWMDMjKIf/OWjZdvbuvilSpFK3wbDBwYfaSlwZo1BR+T0MxVklQULtGUJJV5hV3KCdECVxhJhoWt4YsrYdh/qTBwLmdn/crIESO56aabyN0ll2kp03g061G6vt6VQekNiRxzGU89tZ6XX/6En7N+3mosCVshWZT9eg5bl6RSxwqeJEkb2Vq1b8aU+hz3TH2OO+447uEe1qxbw/8+/x93PvMtc7KjSVJOTi69e8+Eqw4luWIydXLr0HKXlhzc7GBO63oaezbYc5PmLXGt9u3Ifr2iVPskSSWCFTxJkjZS2GpfpYqVOLT5Ofw2/F743xB4axCQRoWKl3LYLidRP6zPouRFfBD5gNtm3sZeV+zF7rvvz6An1xDpcj9PfTqS9z6fQmSTzYF/iUmlr7DjF3a02mfjFkkqMazgSZJURFs0bwEIU9hn8TA+zKvOzVswj9c/f535afN5+9tjCKv9DIffSg5w9EcQjAyosbYGPSv05MSWJ9KydUuaNWtGRkbl4q/0FXa/3o5257RxiySVGCZ4kiQV0faatwA0rtuYvqf0JSsLnuoHrAXu/RPqfE1Sgyk06fwsWcEc3nr3LYY/MBwaAecBkbZwfFsGfdWCXTKTufCIE2nSoEmBccSkF0phunPauEWSShwTPEmSiqigJi1bs0m1b111+LkbKQu6cWS76xgwAHJycpg9ezbvTnqX+z/4kkVJC2Hvd4m0e55//Qz/6nwDddbXoeFBDWEfaN+gPYe2OJSjOh5FRkb1xFT75syBvn1hxAhYvTq6lPOkk+DBB7f9D2G1T5JixgRPkqQ42F61LyUlhebNm1OjRnNuv4xopQ+g8iJSGkzjlou+57efvuSj5R8xP3k+UxZP4enPnoZPgGA/qDiGp56qyl4dhnLQgc3osE+HAuMo1gJarBu3WO2TpB1mgidJUhwUttq3xb6+VbVJmncESxcdwXPPRd9at34dH0/9mA+//pAho5ayIHsFrKlJDuu44c3HYf5EgjUB6SvS2fPdPWlXrx2XHnApLVu2JCOjavFW+wo7aN1qnyTFhQmeJEklSGH29VVMrcgx+x9Du92PYdDV/FXtI43UqY9xdKdHmR1OYV7yPL5O/Zqvf/iawdcPBurC8cfCySsZNL0lKY/ncEaPIzhgvwNITk7eIpZCFdBi0bhlR6t9VvokKZ9jEiRJKkF2dCj75l08gwWdaPjbi8y4fwbvnPwO6+9azxdXfcGbb77J/vuPhCCEBpOIHPJPHl1yF12HdaXi+RXp2rUrV1xxBZf/53Je/OBFli5busnMvmIRqzENOzKQ3ZEOkso4K3iSJJVShan2pSSn0HHfjmRVh6+/BiY9G/2g4jJSG07j2L89x5o6C1n982peGvoSy69czqDxg2AMEGkDx7dj0Een0uW/FTn44ObUr1+fIAg2uWahC2jFXe0ryr4+l31KKuNM8CRJKqWK3MUTYF11gvk9qP9Lj/xcKzeSy6hvRvHu1Hd56ePfWcAC2PsdIr/vw7nnVoOqh5N0URK11tdin6r70HmPzhzd/mhefbYHY8emFG8Xz8Ls7duRfX02eZFUTpjgSZJUDhSm2peclMyhbQ5lv9qHMuBy/trbl5RDxYoBV926kmHLnuS3lN/4vOLnfL7gc/7vnf+DsS9A5FwGvfIDf9T7P07qfDBHdz6aqlWqbhFHsVb7dmRfn01eJJUTJniSJJUDO1Xti6QQhrDm11uYM+AWAJauWspbk9/iX8/M5offDiICRJp8wJDcQQz5fBB8ApWWVWL31N05o/4ZdG/fnXbt2pGRUSsxXTwd6SCpnDDBkyRJmyhMta9m5Zocts+5XPI6RDbkQl9dRIUlB9C773+Zvvhzfkj/gdkVZ5PRLwPWAF2rwH7N4JiODPqiDXWfDTn/qONo1KBRgXEUaxdPcKSDpHLBBE+SJG2iyDP7citAVgcqzezApI329S29bilTp07l8kHj+TH7U2g5hEjHJ7njF7jjsSup+0JdOnboyK7td6Vl85ac2OVEmjZqSkZGULzVPkc6SCoHHJMgSZKKpLD7+nbddVdatjycX9+5AwZ/CvcthUd+IGX4KxxT6UwOP+xw5syZw/O/PU/f7/qy17N7kXp1OgN/vYDIfq/wzDO5fPnlb4RhWGAcMZl8UBJGOkhSEVjBkyRJRVL0fX0BLN2TpFV70rjrmQx4IfrujN9mMGLSCEZ9P4rPFy1l9b4jodIysqefRIcOw6nQ5wbqptelzW5tOKT5IZx80Mk0atBok3l9xdbFM54jHd59t+BzW+2TVARW8CRJUswVptq3X/39+PuJf2fwuR8QGfwF/Pt3GP4CkEZKWh9q7FqHX2r8wshgJNfPup49Bu1B1aPr8+ST64iEuTz1xld8PX3RNuNIWLVvRyt9YLVPUpFYwZMkSTFXtGpfANlVAEiKVObU1T/z+L9Dvv7la4ZPHM5nsz/j+zWHsTIC7DaLnIvb0uaVCqT9kcQeqXvQqX4nTut4God0PoQqVarknzsh1b5tVfpmzdr0u3bxlLQTrOBJkqQSZVvVviAIaLN7G/qd2o+X+3zKHxNvByrCivow7AWCaReTVqUq31X/jv+u+y8nXHkC1atXZ6+D9qLlVZ15atSbRCotIjMz3G4Vr9irfbHa1wdW+yTls4InSZJKlCJ18VxbE74+l9RZ53J248d5+L5sPpv5GUvbLuXbL79l2LxhTK81Cc44AYA1S/egyU0N6VOnFQfvfzAHHHAAjRo1IgiCTc5f5rp4SirzTPAkSVKptK1KX4XkChze8nBoCZwAl2XdRZNmK1i3y1RoMAkaTGRtvS959omfGfR/g+BQSGmWQkMa0rFeRw5qfBLPZZ5JJJJEZib8859xXvkYq5l9LuWUyjwTPEmSVCrt6L6+cF1V+Kl79AFUqAAXXJDLRRdN5aHPHuKT3z/hp5SfmJc7j9d/fB0u6gePf8+6des54aJ3eeK+BrRv0X6TKt8GxZ43xaLaBw5kl8oBEzxJklTmba3aN3FiMoMGdeTlji8D0cHsb02cwCnXziA3WAdAJJLKpCZ96fj6bJKfTKZ+pD4d63Tk5A4nc2KPE6lSpUpsmrcUVmGqfS7llMoNm6xIkqQyb+pUCMMtH5tXAZOTknn/xa4kf3UxTLrqr/fff5zG866kbuW6/FrjV4aHwzn36XOpXr06+7U4lEHf3EOk0SiefWElWVkFD2SHGI1pGDYsmlW2aRN93rj6t0FRGrfEJFhJsWYFT5IkaSMFVftyZx9BjcpHMPX5x4mEESb+OJFvW3/Lz61/5qnXdic87BIA1uWm0LBfI/ZOS+XkpidzRs8zaNmyJcnJyUCMxjQUxo4u5QSXc0qllBU8SZKkjWyv2pcUJNFlry5cdNJFXH55f5bNuxju+wNeehvG3URkfT2+q/Y99w66l7Zt21Jj3xrsfuXuHHP72TwzbBaRSEhmZgIKY4Ud01CpEgRBdAlnJBJ9DoLo+5JKPBM8SZKkIsof1bC2Jsw+Bj6+hwovjeWSJauZ9dYsXnzxRboc24Ws6lm8W+EV1l/eHPrWY81xp3LEKW/x0UcfsXbjvXEbKfYVkoVZygku55RKOZdoSpIkFdHWmrdMGp9Gsz2b0WzPZpxzzjn8+luEpvvPIrvO57DHaNh9PN8M78Lhh7cg6aDfqflmTdrXas+pHU7lrMPOomqVqi7nlFQkJniSJElFVNhRDXf/Kwl+3w9+3Q++vBiAChVCDj92IvMb92JW5Vl8mPIhH371IZeOv5RaSxqybPAcIpFUMjND/vnPoGTO4bM7p1TiuERTkiQpxgqu9AX8+usePHLqI6y7fx3f/O0bbtzzRlpXbM3q5fuRk5MLwJrTDqXpTd059e5TeWvMW+Tm5m5xfpdzStrABE+SJCnGCjOmoWXDljx4zoO8d/lXhCPfB9IgKQdWNGRNndn8L+d/HP/J8VS4sQIdL+7Ik08+yY8//ghs2p0zrnZ2OaekYmeCJ0mSVILkN24BiKTA8BdIfew3Tvj1C87d9VwaVGzA3Nlzueyyy9ir415UvKoKg2bfSqThGJ7NXG93Tqmccw+eJElSCVLQcs712QE/fdGRqU+9AEAYhnz//fdkvpfJf74bSXjgg3DQ/axbW50mN7fk2pYHcd5x59G8eXOCIMg/T1YWnHkmDB267SLbDtl4+ea2usHMmQN9+8KIEbB6dXQ550knwYMPFlMgksAKniRJUolSmOWcQRDQrFkzrj39PoLMb+H+JTD0dZhxCmt3m8P9/Z+lRYsW1Dm4Dp1u7sT/Df0/VqxckbilnLDjyzndqycViQmeJElSKZW/nHNddZh5Crz5LKmP/8q5J8/hySefpNp+1ZhcaTJ9Z/WlWkYNBi48i8h+Q3juuUhi8qbCLucE9+pJRWSCJ0mSVEptbTnnN99U5ZJLLuGHJ37g1+t/5bZ9bqPG74fBnh9B5/+wdm02rVu/xnn/Oo/RX44u8NwxKaAVpjune/WknWKCJ0mSVEoVZjln/Rr1ubrnPax95X14cAEMGQGk8fvynry47kV6vNmDSldV4tB/HsrwUcMJwxBIYGfOooxekJTPBE+SJKmMy1/KGSbDyuiet9RwV47PmsixlY8lpVIKn6R8wsmfncwuR+7ChRfezjPPricSCcnMjPM2uKKMXnC/npTPBE+SJKmM29pSzvlT9uetm95ixQMr+PK8Lzlrl7PoWLMjL7zQiPV7/g+u3oc1Xf7OOdd9RiR/dkMc7MhePXC/nrQRxyRIkiSVcRsv2dyadk3a8fLVL5N1KjR9MyR3zSj4szEcdD8fJ91LxWsr0bXa/vz75H/TqX2n2I5fKOzohUqVohW+DQYOjD7S0mDNmmIIRCp9rOBJkiQpX3Q5ZwDzDoYXP4SHfiXp/YdJijTis1WfcUDHA2jRogUX9r+QidMn5h/jfj2pZDDBkyRJUr4tlnOurEtk/HXsN24Wc2+ey6BBg6hVuxaZqzLp/Fpnql6xK09OfI5IyorSsV9PKuNM8CRJkpRvW505G9dvzKWXXsqYUWP44OwPOKTiIayulESk19+gb13W7Ps0F144l5ycnPgFvKP79WzIojLOPXiSJEnaYYe3OZyWtQ+nSdOQdbuNh7bPw++teHdqHWq3ak6rk+vzr9P+Rbe23TY5LmH79TbYuCHLE08UQwBSyWIFT5IkSUWSkQFhJID5B8LIp+CXzqSkVIQ9uzO6wmi6D+/OLlftwjUDrmHpsqX5xyRkv54D1FVOmOBJkiSpSAoav5CTk8wevz7L2DPG0rNCT5ZXXs5jix+j1h21OOvsG3j22VwiEeK/X8+GLConTPAkSZJUJNvar9d13658evunrLlnDfe3uZ8uYRdef6052dnZcPzFrGv6Gv+8c+32L1JcbMiicsIET5IkSTGTmpzKzSfezOu3fU5KykVQdSns+SGR007nmfR96HLT4Uz9bstBfTHphbIjDVlsxqJSygRPkiRJMZc/X29FfXjkRxgyHJbuxYQqH9H+v+3p0bsHn3zyCWEY5n+/2PfqDRsWbcTSpk30eeMGLQUFvKEZi1SKmOBJkiQp5jbZrxcmw6wTYfAnNH1vKvuzP99+9C2HHnooux+9O2fdcznPDV6XmL16BTRj6XnwwTZjUalhgidJkqSY29p+vR/Ht2VixkR++ekXMjMzWb7XcoasH8S6y5tAj/7kVFgc346bBTRjWXjYYTZjUalhgidJkqSES0tLo0+fPsy4bSkpQ0bCgrZw8J2sv3IPBk5+jDFjZscnkAKaseSkp9uMRaWGCZ4kSZJKjHvuTiZpznHw0jswYDrMOJVwSWO6d/+AY08/ltc+fW2LY4q9H8pmzVgqLF1aTCeWYs8ET5IkSSXGJnv1fm8BIwbDj8dTp85JfLz6Y07/7HRqX12bAcMHxK4hy2bNWL7dXqMVO26qBDHBkyRJUomxtb16CxbUZ+azMzm0wqEsrrqYq76+iurXVOeGx+8mMzNMTEOWDey4qRLEBE+SJEmlQpM6Tfjo7x+x8NaFnFrtVFanr+bhz4azdm205JebG8a3IUsBHTcJAjtuKqFM8CRJklSq7FZtN167/jW+u2I5KR++CVSE3WaQfV4XnvroPX79NTc+gRTQcZPeve24qYQywZMkSVKp9H/3p5O0pn70RZUFUCWLnLOPpkm/1jz02kP5e/RipoCOm1SrZsdNJVTMErwgCNKCIJgUBMFXQRB8GwTBXQV8p08QBL8HQTAt73FRrOKRJElS2bJJQ5a5h8Bj38M7j7G+xkJunHEjta+szZgxYzY5JtYdN220okSLZQVvHXBIGIZtgLbAUUEQdC7ge0PDMGyb93gmhvFIkiSpDNmiIUtORcKJV/HHnbPpVbkXa39aS/fu3Tnm2GN45/N3gNh33GTYsG1/346birGYJXhh1Mq8l6l5jxjXySVJklTe1axSkzf6vsHC1xZy//33M+bXMRz7wbHscVVznn3tJztuqkwLYrk2OQiCZGAKsBcwIAzDWzb7vA9wL/A78D1wfRiG8ws4zyXAJQB16tTpMGTIkJjFXFQrV66kSpUqiQ5D2+F9Kvm8R6WD96l08D6VDt6n2Ju/dD73jL6HWVV+AJJgwvUkf34bxx2xkuuum73d44vjHnU78kiS89eT/iW3QgXGvP/+Tp1bUeXpv6WDDz54ShiGHQv6LKYJXv5FgqAGMBy4OgzD6Ru9XwtYGYbhuiAILgXOCMPwkG2dq2PHjuHkyZNjGm9RjBo1ip49eyY6DG2H96nk8x6VDt6n0sH7VDp4n+IjKwuatP2JdQfeAW1fgKy2pDw3iXlzk2jQIHmbxxbLPcrKgr59YcQIWL062nHzpJPgwQdtylJMytN/S0EQbDXBi0sXzTAM/wQ+BY7a7P0lYRiuy3v5DNAhHvFIkiSpfMnIgPDPPWDEYHhmHIy6i5z1ubRuO5T/fvDf2Adgx03FSSy7aO6WV7kjCIJKwOHArM2+U2+jl72AmbGKR5IkSeXXJh03f+kC3/UC0viz6SzO/fxc9r1hX2bMnZH//Zj0QrHjpuIgJYbnrgcMztuHlwS8GobhW0EQ9Acmh2H4JnBNEAS9gBzgD6BPDOORJElSOTV1asHvz198MSc9+g5Tqk6h5ZMtOaXGKbx4/YtkZKTld9s87bRiCmLjDpsDBhTTSaVNxbKL5tdhGLYLw7B1GIYtwzDsn/f+HXnJHWEY3haGYYswDNuEYXhwGIaztn1WSZIkqfjsvuvuTO4/mTeOfYOaOTV5fd3r7HZpI559Nje/2+Yff1RITHCOVFARxGUPniRJklSS9dq/F4sfWEzfJn2JTLqE7Oz1kLqanAqLeeGFPRITlCMVVAQmeJIkSRIQBAE3HP4AkbkZQBoc+nfWX9SKkd9PIisrjuOcK1WCIICBAyESiT4HQfR9aTtM8CRJkqQ8GRkQiQTRF9P6wKraRM44mWY3H8mcX+fEJ4g5c+Dss6OjFCD63Ls3zJ0bn+urVDPBkyRJkvJs0m1zQVt4ehKM/jsrmn7M3g/vzQOvPBD7IBypoJ1ggidJkiTlmToVwnCjR05FPv3n4bx48GAq5lTk5qtv5oILLmDZsmX5xzhSQSWJCZ4kSZK0Hef0PIel9y/lH5f/gxdefIGGf2vIf4b9B/irF0pGRjFecNiw6CiFNm2izxuPWJC2wQRPkiRJKoSKFSuSkZHBO5++w7om67j+6+tpeX1nnnthTf5IBQttSjQTPEmSJGkHHNntSH65/RdaZbfi2xoTWXd+B6g3hdzcYq7iFZbz8rQREzxJkiRpB9WuUZv3r/6alCFvQdpyOKsX2ZG1ZGaG8c+znJenjZjgSZIkSUWQkQFJc46FgV/D0OGQk8badev5x52r4xOA8/JUABM8SZIkqQjyRyqs2QV+3R+AsOsDZIZd+WLWF7EPwHl5KoAJniRJklQEW4xUCOHmvy0nUnsanZ/rzBNvPhHbAJyXpwKY4EmSJEnF5P7z7+eN494gOZLMlZOv5MwHziQMw9hd0Hl52kxKogOQJEmSypJenXvxfZPv2f/+/RkaDGXdJet45bFXSEtLIysLzjwThg4tpkLbxvPxBgwohhOqtLOCJ0mSJBWzxnUa8+u/f+WC4AJGPDOCbt268f3c72MzFF3aiAmeJEmSFAOpKak81+85RowYwbc537LvEy14+r0xiR2K7sy8Ms8ET5IkSYqhE044gSEDhxDk1CLnnEOhzeDEDUV3Zl6ZZ4InSZIkxVinPXqRmjkD5vWAk/qQ3fax+FbxnJlXbpjgSZIkSTGWkQHhml3g5bdg5olwzDWsqz2a/v1j2GFzY87MKzdM8CRJkqQYyx+KnlsRXnsVXhtK5Kdu/O9/WbEdo7CBM/PKDRM8SZIkKcY2GYqem0ru16dy2WVXsChoQKdbO5Gbmxv7IJyZVy44B0+SJEmKs6SkJJ544gm+qvkV4yuOZ79b9uObe7+hQmqF2F3UmXnlghU8SZIkKQGCIGDsv8bSLezG91W/Z+9b9mbNujWJDkulnAmeJEmSlCBJSUmM7jeao1KP4ufqP9P0lqYsW7ks0WGpFDPBkyRJkhLs3b+/y6mVT2XB4gWccsoprFq1ypnkKhITPEmSJKkEeK3vazx32HN8+tGnHHrcodxyx++MHZuggeiAGWbpZIInSZIklRAX9LmAl15+iYl7TuVFjiFS4c/4DkTfWEYGjB0L/fsn4OIqKhM8SZIkqQQ584wzab/6caj3FZxxEjmsjW8Vr1IlCAIYOBAikehzEETfV4lngidJkiSVIFlZMGP4xTAiE5qMYv1RF/NcZiR+Vbw5c+DssyE9Pfo6PR1694a5c+MUgHaGCZ4kSZJUgmRkRAtnfNMbPv4XtPkv6zreF78qXr16UK0arF0LaWnR52rVoG7dOAWgnWGCJ0mSJJUg48dDdnbeizF/h08yCL8+h5EjF8cviIUL4bLLYMKE6LONVkqNlEQHIEmSJOkvU6du/CogJ+dWjj/+eD748AOeGj6QS066JPZBDBv2198DBsT+eio2VvAkSZKkEiwlJYVXX32VXU/flUsnX8rQz4YmOiSVYCZ4kiRJUglXtWpV3rnrHZKzkzn7rbOZNGtSokNSCWWCJ0mSJJUCHfbuwNAThxJJjdD9ye78vOjnRIekEsgET5IkSSolTul6Cve3v591VdfR4e4OZOd3Y5GiTPAkSZKkUuTmU2/mkjqXsHjEYi655BJ++y2kRw8bXSrKBE+SJEkqZZ688kn6XdiPwYMHc/g5Qxg7lvjNydtcVhZmmCWHCZ4kSZJUCt1xxx10vfB4ZvQ4m0irF8jMTFCOlZEBY8dC//4JuLg2Z4InSZIklUJBELBvylCY0xN6XcT6XSfHt4pXqRIEAQwcCJFI9DkIou8rYUzwJEmSpFIoKwteeqESvPY6rNqNnBPO4dkXVsWvijdnDpx9NqSnR1+np0Pv3jB3bpwCUEFM8CRJkqRSKCMjWjhjTS0Y/gLs+h3ret4QvypevXpQrRqsXQtpadHnatWgbt04BaCCmOBJkiRJpdD48ZA/JWHuoTDqDvh1f95998/4BbFwIVx2GUyYEH220UrCpSQ6AEmSJEk7burUTV9nZ99Oly5d+Gn5Lfz229fUr18/9kEMG/bX3wMGxP562i4reJIkSVIZUKFCBV5++WWWN1lOu3vbkZObk+iQlAAmeJIkSVIZ0axZM047+zQW7bqIUx84NdHhKAFM8CRJkqQy5MVrX6TOsjq8seoNXv3s1USHozgzwZMkSZLKkKSkJD697lOSspM4b8R5LFm2JNEhKY5M8CRJkqQypnmj5mR0yGBdjXX0vqt3osNRHJngSZIkSWXQ30//O+etPI/3H36ft956K9HhKE5M8CRJkqQy6ql/PUXr1q0595ZzmT53eqLDURyY4EmSJEllVMWKFRk0eBB/nvAnh/znEH79NUKPHgmcR56VRWIDKPtM8CRJkqQyrEvbLpy6y6n8vsvvdL/xDsaOhYyMBAWTkQFjx0L//gkKoOwzwZMkSZLKuKE3DKXWH/WZs9dDRHaZSWZmnItolSpBEMDAgRCJRJ+DIPq+ipUJniRJklTGJSUlceiqMZBdGU7sQ05ubnyreHPmwNlnQ3p69HV6OvTuDXPnxjGI8sEET5IkSSrjsrLgzZeawruPwuxjWZ+bG98qXr16UK0arF0LaWnR52rVoG7dOAVQfpjgSZIkSWVcRkZ0ZSTTz4LP7oDcCuTmhvGt4i1cCJddBhMmRJ9ttBITKYkOQJIkSVJsjR8P2dkbXoXQfDjZqasYN+7c+AUxbNhffw8YEL/rljNW8CRJkqQybupUCMMNj4Bdj7qU4Jjzef2tHxMdmoqZCZ4kSZJUzgw6ZRBhxZAzHj8j0aGomJngSZIkSeXMKV1PYe/VezMlZQqjvxqd6HBUjEzwJEmSpHLolYtfgRDOfT6O+/AUcyZ4kiRJUjnUYe8OHJF8BD9/8jMTJ05MdDgqJiZ4kiRJUjn1v5v+R52Fdejbty9hGCY6HBUDEzxJkiSpnKpSpQr/7PdPxuaO5a7/3pXocFQMTPAkSZKkcuyCCy4g9cBU7v3yXtauW5vocLSTTPAkSZKkciy9Yjo3tL6B7BrZXPT4RYkORzvJBE+SJEkq5+455x6qLavGKwtfIWtJVqLD0U4wwZMkSZLKuaSkJB466iEilSOc+ciZiQ5HO8EET5IkSRJ/O+JvtFjcgglDJvDrr78mOhwVUcrWPgiC4NFCHL88DMN/FGM8kiRJkhLkrRvfotlTzfjnP//Jc889l9hgsrLgzDNh6FCoWzexsZQi26rgnQBM2c7jlFgHKEmSJCk+GjduzEVXX0TmgkwyR75Fjx6wYEGCgsnIgLFjoX//BAVQOm21ggc8HIbh4G0dHARBzWKOR5IkSVICXXf9dTwx4AmuHP4v1o09jowMGDAgjgFUqgRrNxrXMHBg9JGWBmvWxDGQ0mlbFbzPt3dwGIb/Kb5QJEmSJCXa3g325pCKJ7Bmj4lEGo4hMzPOVbw5c+DssyE9Pfo6PR1694a5c+MYROm1rQTvqSAIZgdBkBEEwX5xi0iSJElSQjXOegFW7QadHyE3N7paMm7q1YNq1aJVvLS06HO1au7DK6StJnhhGLYDjgNygNeDIPgqCIJbgyBoHK/gJEmSJMVXVha8PLgafHUuNHuT7JTf41/FW7gQLrsMJkyIPidsI2Dps609eIRh+B1wF3BXEARtgDOBj4MgWBCGYdd4BChJkiQpfjIyIBIBpl4IacsgZS25a4nvXrxhw/76O64bAEu/bSZ4GwRBkATUBuoAlYFFsQxKkiRJUmKMHw/Z2cDvLeDNZwDIBsaNS2hYKqRtDjoPgqBbEARPAL8AfYExQLMwDE+KR3CSJEmS4mvqVAjD6CMz83moH/Dye68wdWqiI1NhbDXBC4JgPnAvMANoG4bhkWEYZoZhuCxu0UmSJElKmKNOOAougH4f9Et0KCqkbS3RPCgMw5/iFokkSZKkEqVuzbrsvX5vvq/4PQuWLKBuLTtZlnTbWqJ5wfYODoKgX/GFIkmSJKmkuaHnDVAR/v7fvyc6FBXCtip4FwVBsHwbnwdEu2r2K9aIJEmSJJUYlxx5Cdd+fC2v//E6z/FcosPRdmyrgvc0UHUbjyp535EkSZJURiUlJXFYrcNYUWkF46eNT3Q42o6tVvDCMLwrnoFIkiRJKpkeP+9x9tl7H15f/zpd2nZJdDjahm2OSZAkSZKkJg2acMIxJ/DCiy+wbt26RIejbTDBkyRJkrRdR519FItPX0zGkIxEh6JtMMGTJEmStF29j+tNUpUknv3y2USHom3YboIXBME+QRB8HATB9LzXrYMg+EfsQ5MkSZJUUlSqUImOFTqyoPoCvv7h60SHo60oTAXvaeA2YD1AGIZfEx2PIEmSJKkc6XdCP0iG2165LdGhaCsKk+Clh2E4abP3cmIRjCRJkqSS6+gOR1N1eVU+WvIRubm5iQ5HBShMgrc4CII9gRAgCIJTgayYRiVJkiSpRLp0n0vJ/iib0WNGJzoUFaAwCd6VwJPAvkEQ/ApcB1wey6AkSZIklUx3nXsX1eZXI/O5zESHogJsN8ELw3BOGIaHAbsB+4ZheFAYhvNiHpkkSZKkEic9PZ0TzzmRV+a/QtZiF/aVNCnb+0IQBDds9hpgGTAlDMNpsQlLkiRJUknV5fguvFD7BW7/7+08d91ziQ5HGynMEs2OwGVAg7zHpcBRwNNBENwcw9gkSZIklUCXHHEJFVZW4PU5ryc6FG2mMAleQ6B9GIY3hmF4I9ABqA10B/rEMDZJkiRJJVBSUhKH7XoYK2qt4J3x7yQ6HG2kMAlebWDdRq/XA3XCMFyz2fuSJEmSyol7z7gXInDXm3clOhRtZLt78ICXgIlBELyR9/p44OUgCCoDM2IWmSRJkqQSq3Xj1tRbVY+vfvqKdevWUbFixUSHJArXRTOD6L67P/Mel4Vh2D8Mw1VhGPaObXiSJEmSSqpnDnuGda/XpF27ZSxYkMBAsrJoe+21JDaIkqEwSzQJw/AL4BVgOLAoCIJGMY1KkiRJUol35OFHUrny/cycF5CRkcBAMjKo/s030L9/AoMoGbab4AVB0CsIgtnAXOCzvOd3Yx2YJEmSpJJt0aJk1rRcBjfuzrMv/x7/AlqlShAEMHAgQRjCwIHR15UqxTmQkqMwFbwMoDPwfRiGTYDDgAkxjUqSJElSiZeRAcGCjpCyjvW7fxL/Kt6cOXD22ZCeHn2dng69e8PcuXEOpOQoTIK3PgzDJUBSEARJYRh+SnQ2niRJkqRyKisLMjMhd35HyE4n0vBzMjPjvA2uXj2oVg3WriW3QgVYuzb6um7dOAZRshSmi+afQRBUAUYDLwVBsAhYFduwJEmSJJVkGRkQiQCRVPilCzQaQ25u9P0BA+IYyMKFcNllfNmuHZ2mTo1mnuVYYRK8E4A1wPVAb6A64LALSZIkqRwbPx6ys/Ne/NQdevYjO+lPxo2rEd9Ahg0DYNWoUXDRRfG9dglUmCWad4RhGAnDMCcMw8FhGD4K3BLrwCRJkiSVXFOnQhhGH/+9fU94O+SFwSOZOjXRkZVvhUnwDi/gvaOLOxBJkiRJpdNpPU6j0reVmDJuSqJDKfe2muAFQXB5EATfAM2CIPh6o8dc4Ov4hShJkiSpJKtQoQJte7Tlre/eSnQo5d629uC9THTe3b3ArRu9vyIMwz9iGpUkSZKkUiU4IODHyI8s/GMhdXapk+hwyq1tLdFMBpYDVwIrNnoQBMEu2ztxEARpQRBMCoLgqyAIvg2CYKuNWYIgOCUIgjAIAscvSJIkSaXQ0fsdDckw+KPBiQ6lXNtWgjcFmJz3mLLZY3Ihzr0OOCQMwzZAW+CoIAg6b/6lIAiqAtcCE3cockmSJEklxvmHnA/A29+8neBIyretLtEMw7DJzpw4DMMQWJn3MjXvERbw1QzgfuCmnbmeJEmSpMTZfdfdqbS8El+vt11HIgXRPGw7XwqCXkD3vJejwjAs1O7JIAiSiVb89gIGhGF4y2aftwduD8PwlCAIRgF9wzDcojoYBMElwCUAderU6TBkyJDCXD6uVq5cSZUqVRIdhrbD+1TyeY9KB+9T6eB9Kh28TyWf96jwLh5+MT9U/oF3erxDpYqV4nrt8nSfDj744ClhGBa4vW27g86DILgP6AS8lPfWtUEQHBiG4d+3d2wYhrlA2yAIagDDgyBoGYbh9LzzJgEPAX0KcZ6ngKcAOnbsGPbs2XN7h8TdqFGjKIlxaVPep5LPe1Q6eJ9KB+9T6eB9Kvm8R4V325Lb+NsFf6PyUZXp3r379g8oRt6nqMLMwTsGODwMw+fCMHwOOAo4bkcuEobhn8CnecduUBVoCYwKgmAe0Bl400YrkiRJUul00iEnEawMGD16dKJDKbcKk+AB1Njo7+qFOSAIgt3yKncEQVCJ6MD0WRs+D8NwWRiGu4Zh2DgMw8bABKBXQUs0JUmSJJV8NWvWpMHRDXjxxxcTHUq5td0lmkTn4E0NguBTICC6F+/WbR8CQD1gcN4+vCTg1TAM3wqCoD8wOQzDN4satCRJkqSSqUrrKsyKzGL9+vWkpqYmOpxyZ6sJXhAEA4CXwzB8Ja8BSqe8j24Jw3DB9k4chuHXQLsC3r9jK9/vWZiAJUmSJJVc3Rt3Z9aCWYwYO4LTDj4t0eGUO9taovk98GDe/rjrgflhGL5ZmOROkiRJUvl0TrdzAHht0msJjqR82mqCF4bhI2EYdgF6AEuA54IgmBUEwZ1BEOwTtwglSZIklRoHNT+I5DXJjP9tfKJDKZe222QlDMOfwjC8PwzDdsBZwInAzFgHJkmSJKn0CYKARrmNWPjnQgozc1vFa7sJXhAEKUEQHB8EwUvAu8B3wMkxj0ySJElSqXR789tZ/8J6Zs60LhRvW03wgiA4PAiC54BfgIuBt4E9wzA8MwzDN+IVoCRJkqTSpUf3HgCMGTMmwZGUP9uq4N0GjAOah2HYKwzDl8MwXBWnuCRJkiSVUnvuuSdp56Xx8MyHEx1KubPVMQlhGB4Sz0AkSZIklQ1BEFBjtxr8mPNjokMpd7a7B0+SJEmSdlSnOp3IqZnD1O+mJjqUcsUET5IkSVKxO7lDtC/j4E8HJziS8sUET5IkSVKxO+OgMyAHPvnhk0SHUq5sdQ+eJEmSJBVVpQqVaLq4Kb//9HuiQylXrOBJkiRJiolLm1zKgncXsGjRokSHUm6Y4EmSJEmKie7du0NFeOfTdxIdSrlhgidJkiQpJpq3ag43wcCpAxMdSrnhHjxJkiRJMVG9cnWqrqnKjLUzEh1KuWEFT5IkSVLM7FdlP1bWWMnipYsTHUq5YIInSZIkKWaO3u9oSIUXPnoh0aGUCyZ4kiRJkmLm/IPPB2Dk1yMTHEn54B48SZIkSTHTeLfGNP2mKStWrEh0KOWCFTxJkiRJMXVK01P4ZtQ3rF27NtGhlHkmeJIkSZJiqsOBHcjeJ5s3R7+Z6FDKPBM8SZIkSTG1b7t94VR4YYKNVmLNPXiSJEmSYqp1o9akrk5l8urJiQ6lzLOCJ0mSJCmmgiCgcVJjFqUtYv369YkOp0wzwZMkSZIUc9336E5YJeStcW8lOpQyzQRPkiRJUsyd0+0cAC7NmM+CBQkOpgwzwZMkSZIUcz3260GVZx7i94+vJCMj0dGUXSZ4kiRJkmJuwYKANVlXAslkZmIVL0ZM8CRJkiTFXEYGhI0+hxPPJ4e1VvFixARPkiRJUkxlZUFmJkSq/gptX2B95bmJqeJlZUGPHmW6fGiCJ0mSJCmmMjIgEgGW7B19o9ZscnOJfxUvIwPGjoX+/eN84fgxwZMkSZIUU+PHQ3Y28EdegrfLD2Rnw7hxcQqgUiUIAhg4MJppDhwYfV2pUpwCiB8TPEmSJEkxNXUqhCGEq3chaW0S+3R+kjCMvh8Xc+bA2WdDenr0dXo69O4Nc+fGKYD4McGTJEmSFDdV11Vl6Yql8b1ovXpQrRqsXQtpadHnatWgbt34xhEHJniSJEmS4uaM1WcQGR6J/4UXLoTLLoMJE6LPZbTRSkqiA5AkSZJUfuyz9z4sWbKEpUuXUrNmzfhdeNiwv/4eMCB+140zK3iSJEmS4qc+cB58PO3jREdSJpngSZIkSYqb3RvtDk3h8+8/T3QoZZIJniRJkqS46d6yOwDfZn2b4EjKJvfgSZIkSYqbutXrkrQ2iblry96IgpLACp4kSZKkuKqaXZWFOQsTHUaZZIInSZIkKa4aJzdm7e9rCcMw0aGUOSZ4kiRJkuLq/Hrns37oepYsWZLoUMocEzxJkiRJcbX33nsD8MMPPyQ4krLHBE+SJElSXFWoWwGuglenvZroUMocEzxJkiRJcdVun3awK3yd9XWiQylzHJMgSZIkKa52q7YbSWuSmLvOUQnFzQqeJEmSpLirllONRTmLEh1GmWOCJ0mSJCnu6qTWYVXFVY5KKGYmeJIkSZLirtMunQhnhyxc5MDz4mSCJ0mSJCnuzmpxFrwJP/7wY6JDKVNM8CRJkiTF3YZZeDO/n5ngSMoWEzxJkiRJcVe7QW34O7w85+VEh1KmOCZBkiRJUtxVT69Ocm4y81bNS3QoZYoVPEmSJEkJUS2nGotyHZVQnEzwJEmSJCVE3Qp1WZ222lEJxcgET5IkSVJC7FljT8IqIXN+mZPoUMoMEzxJkiRJCXFY08NgFHw3+7tEh1JmmOBJkiRJSojj2x8Po2DBvAWJDqXMMMGTJEmSlBCNGjUipVoKU2dPTXQoZYYJniRJkqSESElJIbw85K3VbyU6lDLDBE+SJElSwlTLqcbvkd8THUaZYYInSZIkKWHqVajHqrRVjkooJiZ4kiRJkhJmr132girw/bzvEx1KmWCCJ0mSJClhWjdoDcCYb8ckOJKywQRPkiRJUsIc0/oYeBOW/bIs0aGUCSZ4kiRJkhJm/333p8L0CiycszDRoZQJJniSJEmSEiY5OZmG7Roy6ZdJiQ6lTEhJdACSJEmSyrc/D/qTBesWJDqMMsEKniRJkqSEql+xPqsrrSYSiSQ6lFLPBE+SJElSQu21y15QGWbNnZXoUEo9EzxJkiRJCdW6YXRUwuhvRyc4ktLPBE+SJElSQh3Y7EAApsyZkuBISj8TPEmSJEkJ1W2/bqQOTSX119REh1LqmeBJkiRJSqj0Cunsk7QPv37/a6JDKfVM8CRJkiQlXK02tZi8dnKiwyj1nIMnSZIkKeH+bPInvzX4jdzcXJKTkxMdTqllBU+SJElSwm0YlfDtj98mOpRSzQRPkiRJUsK1bdQWgDHfjklsIKWcCZ4kSZKkhMsflTDXUQk7wwRPkiRJUsJ1btYZgFmLZiU4ktLNJiuSJEmSEq5yhcrs/cHe1KhVI9GhlGpW8CRJkiSVCK3qtmLO93MSHUapZgVPkiRJUolQsVlFZq+YTU5ODikppipFYQVPkiRJUomwvvZ6Il0jTP9heqJDKbVM8CRJkiSVCG13bwvA6G9HJzaQUswET5IkSVKJ0HXfrgB8Oe/LBEdSepngSZIkSSoR9t97f8BRCTvDBE+SJElSiZBeIZ3U1an8tuK3RIdSapngSZIkSSoxjp93PBU/rJjoMEotEzxJkiRJJca+e+3L3LlzWb9+faJDKZVM8CRJkiSVGJGGEXJPzuWr779KdCilkgmeJEmSpBKjet3q0ALGfDsm0aGUSiZ4kiRJkkqMrs2joxKm/jQ1wZGUTiZ4kiRJkkqMjnt2hBBmLXZUQlGY4EmSJEkqMSqlVqLCmgr8suqXRIdSKpngSZIkSSpRaufUZsXyFYkOo1QywZMkSZJUovRJ78Pql1aTnZ2d6FBKHRM8SZIkSSXK3nvvTSQSYe7cuYkOpdQxwZMkSZJUooR1QvgbvDvt3USHUuqY4EmSJEkqUfZpug/sDvc8E7BgQaKjKV1M8CRJkiSVKO0at4NIwO+5i8nISHQ0pYsJniRJkqQSZeniNFi+O9ScS2YmVvF2gAmeJEmSpBIlIwNYWRcqLyI3F6t4O8AET5IkSVKJkZUFmZnAz91gcTOys7GKtwNM8CRJkiSVGBkZEIkAHzwI7z4GYBVvB5jgSZIkSSoxxo+HzeebZ2fDuHGJiae0McGTJEmSVGJMnQphCJc9fjlcHzBx+iTCMPq+ts8ET5IkSVKJU7NGTagO3/3yXaJDKVVM8CRJkiSVOE12awLAnIVzEhxJ6WKCJ0mS9P/t3XuQZmd9H/jvM9M990trLuoZJDESkpDAAoOQ1wQwSFqw5eAFO7a3fEniZFPFUmuyYSnX2l5vsTbyVsXYSTn2EtukuMWXyJgYm1D4QmzJDgYBEhISEkhC6MKMNKO5aC6tufR097N/9CszKKPLSOp+zjn9+VS91e97+sz7frt+c97p75zzngN0zovPeXGS5MEDDzZO0i8LVvBKKatKKV8opXy5lHJHKeWXTrPO20spt5dSbi2lfKaU8tKFygMAAPTHpedemiTZdWhX4yT9spB78E4kubrW+p1JXpHkmlLKq5+wzh/UWl9Wa31Fkvcm+bcLmAcAAOiJszecnfGvjyeHWifplwUreHXe1Ojh+OhWn7DO4VMern3i9wEAgKWplJILb7ow6x5c1zpKr5RaF65TlVKWJ7k5yUVJ3ldr/dnTrPPTSd6VZEXm9/jdc5p13pbkbUkyOTn5quuuu27BMj9bU1NTWbfOX76uM6fuM6N+MKd+MKd+MKfuM6N23vnOd2Z2bja/+Ru/+bTrLqU5XXXVVTfXWq843fcWtOD9/YuUMpHk40n+Za31K0+yzk8k+b5a60891XNdccUV9aabbnr+Qz5HN9xwQ6688srWMXga5tR9ZtQP5tQP5tQP5tR9ZtTOjnftyJ6Te3L8N48/7bpLaU6llCcteItyFs1a68Ek1ye55ilWuy7JDy5GHgAAoPvWrlyb6ZXTbV784YeTN7wh2b27zes/Swt5Fs2toz13KaWsTvKmJF97wjoXn/LwzUn+u8MzAQCApWnL6i2pq2uOHTu2+C9+7bXJZz6TvOc9i//az8FC7sHbnuT6UsptSb6Y5NO11k+WUt5TSnnLaJ13jC6hcGvmP4f3lIdnAgAAS8e29duSseTrO7++eC+6enVSSvJbv5XMzc1/LWV+eQ+MLdQT11pvS/LK0yx/9yn3/9VCvT4AANBv5551bnI4+drOr+VlF79scV70G99IfuZnkj/5k+To0WTNmuSHfij5tV9bnNd/jhblM3gAAABn6vJzL0++kBw5cGTxXnT79mTDhuT48WTVqvmvGzYk27YtXobnQMEDAAA66XUXvS75VFIPLvLlsvfsSd7+9uTGG+e/9uhEKwt2iCYAAMBzMTk5mSxLdu7eubgv/Md//K3773vf4r72c2QPHgAA0EkrV61MfiH51JFPtY7SGwoeAADQScvKsiyfXp59x/e1jtIbCh4AANBZK2dW5vDs4dYxekPBAwAAOmtd1uWxPNY6Rm8oeAAAQGdtHNuYE+MnWsfoDQUPAADorMtXXZ65G+dy8uTJ1lF6QcEDAAA66w2Tb0g+l+zdu7d1lF5Q8AAAgM7afPbmZGPy4EMPto7SCwoeAADQWQ+OPZj8H8nn7vtc6yi9oOABAACdddG2i5Ik9+29r3GSflDwAACAzrr0vEuTJLsO7mqcpB8UPAAAoLMuOPuCJMnuqd2Nk/SDggcAAHTWyrGVWXZiWfYd29c6Si+MtQ4AAADwVHbctSPry/rWMXrBHjwAAKDTLjt5WU7e40Lnz4SCBwAAdNr67euzc3Zn6xi9oOABAACddvfk3TnwPx3I7Oxs6yidp+ABAACdNrluMlmV7NrjUglPR8EDAAA67ZyN5yRJ7tp5V+Mk3afgAQAAnbZjy44kyd0P3d04SfcpeAAAQKe9aPJFSZL7997fNkgPKHgAAECnffeF3518LFl7eG3rKJ2n4AEAAJ12/rbzM37XeI7vPd46SucpeAAAQKeVUrLxso2589E7W0fpPAUPAADovCNvPJKbVtzUOkbnKXgAAEDnrZ5bnak61TpG5yl4AABA521YtiHHlh9rHaPzFDwAAKDzNq3clJmVM6m1to7SaQoeAADQeVvXbE3WJPsO7GsdpdMUPAAAoPOu2X5N8qHkkT2PtI7SaQoeAADQeS8/9+XJg8m+vfbgPRUFDwAA6LzVZ61OXpbc/s3bW0fpNAUPAADovGXrlyU/nHx+9+dbR+k0BQ8AAOi8S865JEny8OGHGyfptrHWAQAAAJ7OWWvOSmaSvcf2to7SafbgAQAAnVdKyfj0eA5MH2gdpdMUPAAAoBdWz67OkbkjrWN0moIHAAD0wuv3vz7rr1/fOkanKXgAAEAvvHjTi7P/vv2ptbaO0lkKHgAA0AszW2dy7JXHcvjI4dZROkvBAwAAemHf2n3J9yb37LyndZTOUvAAAIBeOG/TeUmSu3fe3ThJdyl4AABAL1yw9YIkyb177m2cpLsUPAAAoBcufsHFSZIH9j/QOEl3KXgAAEAvvOS8lyRJdh3a1ThJdyl4AABAL2zbsC0bP7gxL9zzwtZROkvBAwAAeqGUkhesfUH27dnXOkpnjbUOAAAA8EzNfedcvjz35dYxOkvBAwAAeuPQuYfy6PFHW8foLIdoAgAAvTExPpHp8enWMTpLwQMAAHpjy+otqWtqjh071jpKJyl4AABAb2xbty0ZT+7d6WLnp6PgAQAAvXHeWeclNbl7592to3SSggcAAPTGD1/6w8m1yfjUeOsonaTgAQAAvXHu9nOTuWTPnj2to3SSggcAAPTGurPWJW9O/m7337WO0kkKHgAA0Bsb121Mviu5c+rO1lE6ScEDAAB6Y2zZWJYdX5b9x/e3jtJJCh4AANArq2ZW5dDModYxOknBAwAAemVt1uax8ljrGJ2k4AEAAL1y1vKzcvLkydYxOknBAwAAeuUnVv9EZn5nRsk7DQUPAADolcnJySTJI4880jhJ9yh4AABArxxYdyD58eSOB+9oHaVzFDwAAKBXxtePJ5ckX9311dZROkfBAwAAeuWibRclSe7be1/jJN2j4AEAAL1y6XmXJkl2PrqzcZLuUfAAAIBeOX/r+UmS3Ud2tw3SQWOtAwAAAJyJ1eOrM/7oeI7OHG0dpXMUPAAAoHcuv/HyrF+/vnWMznGIJgAA0DuTk5Oug3ca9uABAAC989CFD+WujXe1jtE5Ch4AANA7dX3NifETmZ2dzfLly1vH6QyHaAIAAL1z9tqzkzXJ7kecSfNUCh4AANA752w8J0ny1W9+tXGSblHwAACA3tmxeUeS5J6H7mmcpFsUPAAAoHde8oKXJN9M9u/f3zpKpyh4AABA71z9kquTDyRrDq1pHaVTFDwAAKB3JiYmsmLFCtfCewIFDwAA6J1SSvIvkr84/heto3SKggcAAPTT+uSRWXvwTqXgAQAAvbRmbk2O1COtY3SKggcAAPTS+mXrc2zZsdYxOkXBAwAAemnTik2ZWTmTWmvrKJ2h4AEAAL10ybpLkvuSAwcOtI7SGQoeAADQS289763Jf0727t3bOkpnKHgAAEAvTU5OJkn27NnTOEl3KHgAAEAv7R3fm/xs8lff+KvWUTpDwQMAAHrphdtemKxOHtj/QOsonaHgAQAAvXTJOZckSR46/FDjJN2h4AEAAL20ac2mZDbZ+5iTrDxOwQMAAHqplJLx6fEcmHaZhMeNtQ4AAADwbJ2779zUgy50/jh78AAAgN567YnXJje3TtEdCh4AANBbk5OT2b1vd2q1Fy9R8AAAgB679axbc/ydx3P06NHWUTpBwQMAAHrr7A1nJ2PJrv27WkfpBAUPAADorfM2nZck+eVfvzS7dzcO0wEKHgAA0FsXbL0gSfLNA3O59trGYTpAwQMAAHpr4/IL5++s3ZsPfShLfi+eggcAAPTWp/7wsuQz70oOXJzZ2Sz5vXgKHgAA0EsPP5x87He3Jf/13yS7X5Hp6Sz5vXgKHgAA0EvXXpvMzZVk1cH5W7Lk9+IpeAAAQC997nPJ9HSS//3C5Or/O8n8489+tm2ulhQ8AACgl265Jak1WVGnsuXcP0yt849vuaV1snYUPAAAoNdWzK3IiXKidYxOUPAAAIBeW5VVObnsZOsYnbBgBa+UsqqU8oVSypdLKXeUUn7pNOu8q5RyZynltlLKX5VSdixUHgAAYJjWLFuTmbGZ1jE6YSH34J1IcnWt9TuTvCLJNaWUVz9hnVuSXFFrfXmSjyV57wLmAQAABuhlcy/Lsi84ODFZwIJX502NHo6PbvUJ61xfaz06enhjknMXKg8AADBMl6+5PCe/cDJzc3OtozRXaq1Pv9azffJSlie5OclFSd5Xa/3Zp1j3/0uyu9b6y6f53tuSvC1JJicnX3XdddctUOJnb2pqKuvWrWsdg6dhTt1nRv1gTv1gTv1gTt1nRt33e3/0e/nARz+QP/3gn2bD+g2t4yy4q6666uZa6xWn+97YQr5wrXU2yStKKRNJPl5KuazW+pUnrldK+cdJrkjyhid5nvcneX+SXHHFFfXKK69csMzP1g033JAu5uLbmVP3mVE/mFM/mFM/mFP3mVH3/fatv528Izn/kvPz8he/vHWcphblQNVa68Ek1ye55onfK6W8MckvJHlLrdW5TQEAgDOyed3mJMmu/bsaJ2lvIc+iuXW05y6llNVJ3pTka09Y55VJfifz5e6RhcoCAAAM19nrz06SPHTgocZJ2lvIQzS3J/nI6HN4y5J8tNb6yVLKe5LcVGv9RJJfTbIuyR+VUpLkwVrrWxYwEwAAMDCTGyeTJHsO7WmcpL0FK3i11tuSvPI0y999yv03LtTrAwAAS8P2TduTJI8cdlCgi0UAAAC99h0v+I7kvyQTxydaR2lOwQMAAHptx+SO5OZkbGpBLxLQCwoeAADQa+Pj41mxY0UeOPRA6yjNKXgAAEDvTf/kdG6sN7aO0ZyCBwAA9N7yk8szNTPVOkZzCh4AANB7YzNjeWz2sdYxmlPwAACA3hufHc/xHG8dozkFDwAA6L2VdWWmy3TrGM0peAAAQO9duu/SrPrMqtYxmlPwAACA3nthXphjdxxLrbV1lKYUPAAAoPfqxprpHdM5duxY6yhNKXgAAEDv7dy4M/nJZPf+3a2jNKXgAQAAvbd+xfokyc59OxsnaUvBAwAAem9i1USSZNf+XW2DNKbgAQAAvfd4wdt90CGaAAAAvbZ57eYkyZ5DexonaUvBAwAAeu/iiYuT3022Tm9tHaUpBQ8AAOi9rRu2Jvcms0dmW0dpSsEDAAB6b+XKlVl22bLcfeju1lGaUvAAAIDeK6Vk7ofm8qWTX2odpSkFDwAA6L1SSpadXJYjJ4+0jtKUggcAAAzC+Mx4Hpt9rHWMphQ8AABgEFbUFTlej7eO0ZSCBwAADMLqrM7xKHgAAAC997ojr8v6v17fOkZTCh4AADAIO9buyNEHj7aO0ZSCBwAADMLRjUczdelUZmZmWkdpRsEDAAAG4cFVDyY/kOx/dH/rKM0oeAAAwCBsWrMpSfLNvd9snKQdBQ8AABiELeu2JEl27d/VOEk7Ch4AADAIW9dvTZLsfnR34yTtKHgAAMAgbJvYliTZfVDBAwAA6LXXnP+a5N8l22e2t47SjIIHAAAMwrbN25JHk6OHl+618BQ8AABgEFavXZ28Jrnt4G2tozSj4AEAAIOwYnxF8qbkzhN3to7SjIIHAAAMwrKyLGW65PD04dZRmlHwAACAwRibGcvUzFTrGM0oeAAAwGCMz43n2Nyx1jGaUfAAAIDBWJVVOZ7jrWM0o+ABAACDcfUjV2fLX25pHaMZBQ8AABiMyQ2TObzPSVYAAAB6b99Z+/Lo5Y+m1to6ShMKHgAAMBj7Vu1LfU3N1NTSPJOmggcAAAzGWWvOSpYlu/buah2lCQUPAAAYjM1rNydJdu7b2ThJGwoeAAAwGFvXb02SPPToQ42TtKHgAQAAgzG5cTJJsufRPY2TtKHgAQAAg3H1BVcn70nOmTundZQmFDwAAGAwNm/anMwlBw8ebB2lCQUPAAAYjPE148mbky8e+GLrKE0oeAAAwGCsXb02+a7k3mP3to7ShIIHAAAMxsqxlclMcmj6UOsoTSh4AADAoCw/uTxTJ6dax2hCwQMAAAZlfHY8j80+1jpGEwoeAAAwKKtmV+XkyZOtYzSh4AEAAIPypp1vytl/c3brGE0oeAAAwKBMTEy4Dh4AAMAQ7NyyM4+89pHWMZpQ8AAAgEGZWjWVuZfM5cSJE62jLDoFDwAAGJSJ1RPJWLJn/57WURadggcAAAzKprWbkiQ79+5snGTxKXgAAMCgbFm7JUny0IGHGidZfAoeAAAwKOdMnJMcTA4cOtA6yqJT8AAAgEF544vemPx6smlmU+soi07BAwAABmViYiJJluS18BQ8AABgUOZWziX/OPnMvs+0jrLoFDwAAGBQNm3clFyU7DzqLJoAAAC9tm7FumQuOXTiUOsoi07BAwAABqWUkmXTy3Lk5JHWURadggcAAAzO2OxYpmamWsdYdGOtAwAAADzfNhzdkBxtnWLx2YMHAAAMzmt3vjabb97cOsaiU/AAAIDBmZiYcB08AACAIbhn2z3ZdfWu1jEWnYIHAAAMzuyq2cxum83c3FzrKItKwQMAAAZn48qNyark0YOPto6yqBQ8AABgcDat2ZQk2bVvaR2mqeABAACDs3nt/Bk0d+7b2TjJ4lLwAACAwblg0wXJfcnhw4dbR1lUCh4AADA4V+64MvlIsmZ6Tesoi0rBAwAABmdiYiJJlty18BQ8AABgcI6NHUv+ZXL9I9e3jrKoFDwAAGBwtkxsSTYne47uaR1lUSl4AADA4GxZvyVJcvDYwbZBFpmCBwAADM7YsrGUkyWHTzqLJgAAQO8tP7k8j518rHWMRTXWOgAAAMBC2Lx/c8anx1vHWFQKHgAAMEiXP3R59u7d2zrGonKIJgAAMEgTExNL7jp49uABAACDdPt5t+f+Tfe3jrGoFDwAAGCQxleMZ6bOpNaaUkrrOIvCIZoAAMAgbVy5MVmVHD16tHWURaPgAQAAgzSxeiJZnuw+sLt1lEWj4AEAAIO0ee3mJMnOvTsbJ1k8Ch4AADBIL9n8kuRLydThqdZRFo2CBwAADNJrzn1N8olk+fHlraMsGgUPAAAYpImJiSTJgUcPtA2yiBQ8AABgkPZnf/Lu5K/3/HXrKItGwQMAAAZp++btybJk/2P7W0dZNAoeAAAwSNsmtiVJDh472DbIIlLwAACAQVozviaZTQ5NH2odZdEoeAAAwCCVUrLs5LIcOXmkdZRFM9Y6AAAAwEKZfHAy68q61jEWjYIHAAAM1kv3vDTHjh1rHWPROEQTAAAYrPFVL8wtd/5ydu9unWRxKHgAAMBg/d22e3PsJ96Ra69tnWRxKHgAAMAgPfxwMnXPm5KHL8+HPpQlsRdPwQMAAAbp2muT5Z/9+eTjv5vZ2bok9uIpeAAAwOA8/HDyoQ8lMzPLkyTT02VJ7MVT8AAAgMG59tpkbu7bl83OZvB78Ras4JVSVpVSvlBK+XIp5Y5Syi+dZp3Xl1K+VEqZKaX8yEJlAQAAlpbPfS6Znv72ZdPTyWc/2ybPYlnI6+CdSHJ1rXWqlDKe5DOllD+rtd54yjoPJvlnSX5mAXMAAABLzC23tE7QxoIVvFprTTI1ejg+utUnrHN/kpRSnrDzFAAAgDNV5nvYAj15KcuT3JzkoiTvq7X+7JOs9+Ekn6y1fuxJvv+2JG9LksnJyVddd911CxP4OZiamsq6detax+BpmFP3mVE/mFM/mFM/mFP3mVE/LKU5XXXVVTfXWq843fcW8hDN1Fpnk7yilDKR5OOllMtqrV95Fs/z/iTvT5IrrriiXnnllc9rzufDDTfckC7m4tuZU/eZUT+YUz+YUz+YU/eZUT+Y07xFOYtmrfVgkuuTXLMYrwcAALAULeRZNLeO9tyllLI6yZuSfG2hXg8AAGCpW8g9eNuTXF9KuS3JF5N8utb6yVLKe0opb0mSUsp3lVJ2JvnRJL9TSrljAfMAAAAM2kKeRfO2JK88zfJ3n3L/i0nOXagMAAAAS8mifAYPAACAhafgAQAADISCBwAAMBAKHgAAwEAoeAAAAAOh4AEAAAyEggcAADAQCh4AAMBAKHgAAAADoeABAAAMhIIHAAAwEAoeAADAQCh4AAAAA6HgAQAADISCBwAAMBAKHgAAwEAoeAAAAAOh4AEAAAyEggcAADAQCh4AAMBAKHgAAAADoeABAAAMhIIHAAAwEAoeAADAQCh4AAAAA1Fqra0znJFSyt4kD7TOcRpbkuxrHYKnZU7dZ0b9YE79YE79YE7dZ0b9sJTmtKPWuvV03+hdweuqUspNtdYrWufgqZlT95lRP5hTP5hTP5hT95lRP5jTPIdoAgAADISCBwAAMBAK3vPn/a0D8IyYU/eZUT+YUz+YUz+YU/eZUT+YU3wGDwAAYDDswQMAABgIBQ8AAGAgFLznQSnlmlLKXaWUr5dSfq51nqWqlHJeKeX6UsqdpZQ7Sin/arT8F0spu0opt45u//CUP/Pzo7ndVUr5vnbpl5ZSyv2llNtH87hptGxTKeXTpZR7Rl/PGi0vpZTfGM3ptlLK5W3TD18p5ZJTtpdbSymHSynvtC21V0r5YCnlkVLKV05ZdsbbTinlp0br31NK+akWP8uQPcmcfrWU8rXRLD5eSpkYLT+/lHLslO3qt0/5M68avVd+fTTL0uDHGawnmdMZv8/5PXDhPMmM/vCU+dxfSrl1tNy29Lhaq9tzuCVZnuTeJC9KsiLJl5O8tHWupXhLsj3J5aP765PcneSlSX4xyc+cZv2Xjua1MskFozkub/1zLIVbkvuTbHnCsvcm+bnR/Z9L8iuj+/8wyZ8lKUleneTzrfMvpdvoPW53kh22pfa3JK9PcnmSr5yy7Iy2nSSbknxj9PWs0f2zWv9sQ7o9yZy+N8nY6P6vnDKn809d7wnP84XR7Mpolt/f+mcb0u1J5nRG73N+D1z8GT3h+/8mybtH921Lo5s9eM/d/5Dk67XWb9Rap5Ncl+StjTMtSbXWh2utXxrdP5Lkq0nOeYo/8tYk19VaT9Ra70vy9czPkzbemuQjo/sfSfKDpyz/j3XejUkmSinbG+Rbqv7HJPfWWh94inVsS4uk1vq3SQ48YfGZbjvfl+TTtdYDtdZHk3w6yTULHn4JOd2caq1/WWudGT28Mcm5T/Uco1ltqLXeWOd/Q/2P+dZseR48yfb0ZJ7sfc7vgQvoqWY02gv3Pyf5T0/1HEtxW1LwnrtzknzzlMc789SlgkVQSjk/ySuTfH606B2jw2I++PjhSzG7lmqSvyyl3FxKedto2WSt9eHR/d1JJkf3zamtH8u3/+NpW+qeM912zKu9/yXzexEed0Ep5ZZSyt+UUr5ntOyczM/mcea0eM7kfc721M73JNlTa73nlGW2pSh4DFApZV2S/5zknbXWw0l+K8mFSV6R5OHM786nrdfVWi9P8v1JfrqU8vpTvzn6HzbXcGmslLIiyVuS/NFokW2p42w73VdK+YUkM0l+f7To4SQvrLW+Msm7kvxBKWVDq3x4n+uRH8+3/wekbWlEwXvudiU575TH546W0UApZTzz5e73a61/nCS11j211tla61yS/5BvHTpmdo3UWneNvj6S5OOZn8mexw+9HH19ZLS6ObXz/Um+VGvdk9iWOuxMtx3zaqSU8s+S/ECSnxyV8YwO+ds/un9z5j/P9eLMz+TUwzjNaRE8i/c521MDpZSxJP8oyR8+vsy29C0K3nP3xSQXl1IuGP1v948l+UTjTEvS6FjsDyT5aq31356y/NTPa/1QksfPxPSJJD9WSllZSrkgycWZ/xAuC6iUsraUsv7x+5k/8cBXMj+Px8/m91NJ/nR0/xNJ/unojICvTnLolMPRWFjf9r+jtqXOOtNt5y+SfG8p5azR4WffO1rGAiqlXJPk/0zyllrr0VOWby2lLB/df1Hmt59vjGZ1uJTy6tG/b/8035otC+RZvM/5PbCNNyb5Wq317w+9tC19y1jrAH1Xa50ppbwj8/84Lk/ywVrrHY1jLVWvTfJPktz++Clzk/xfSX68lPKKzB+2dH+S/zVJaq13lFI+muTOzB8u89O11tlFzrwUTSb5+OgMxWNJ/qDW+uellC8m+Wgp5V8keSDzH5xOkk9l/myAX09yNMk/X/zIS8+ofL8po+1l5L22pbZKKf8pyZVJtpRSdib5f5L865zBtlNrPVBKuTbzv5gmyXtqrc/0RBM8A08yp5/P/BkYPz16/7ux1vr2zJ8l8D2llJNJ5pK8/ZR5/G9JPpxkdeY/s3fq5/Z4jp5kTlee6fuc3wMXzulmVGv9QP77z4cntqW/V0ZHCAAAANBzDtEEAAAYCAUPAABgIBQ8AACAgVDwAAAABkLBAwAAGAgFDwAAYCAUPAAGrZSyuZRy6+i2u5Sya3R/qpTy7xfg9T5cSrmvlPL2p1jne0opd5ZSvvJk6wDAs+E6eAAsGaWUX0wyVWv9tQV8jQ8n+WSt9WNPs975o/UuW6gsACw99uABsCSVUq4spXxydP8XSykfKaX8t1LKA6WUf1RKeW8p5fZSyp+XUsZH672qlPI3pZSbSyl/UUrZ/gxe50dLKV8ppXy5lPK3C/1zAbC0KXgAMO/CJFcneUuS30tyfa31ZUmOJXnzqOT9ZpIfqbW+KskHk/y/z+B5353k+2qt3zl6bgBYMGOtAwBAR/xZrfVkKeX2JMuT/Plo+e1Jzk9ySZLLkny6lJLROg8/g+f9uyQfLqV8NMkfP9+hAeBUCh4AzDuRJLXWuVLKyfqtD6nPZf7fy5LkjlrrPziTJ621vr2U8t1J3pzk5lLKq2qt+5/P4ADwOIdoAsAzc1eSraWUf5AkpZTxUsp3PN0fKqVcWGv9fK313Un2JjlvgXMCsITZgwcAz0CtdbqU8iNJfqOUsjHz/4b+epI7nuaP/mop5eLM7wH8qyRfXtCgACxpLpMAAM8jl0kAoCWHaALA8+tQkmuf7kLnSf5Lkn2LlgqAJcEePAAAgIGwBw8AAGAgFDwAAICBUPAAAAAGQsEDAAAYiP8fiqe3GXg5ujQAAAAASUVORK5CYII=\n", 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", 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" ] @@ -238,7 +239,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] diff --git a/examples/notebooks/models/composite_particle.ipynb b/examples/notebooks/models/composite_particle.ipynb index b4735dc533..f202b04a7b 100644 --- a/examples/notebooks/models/composite_particle.ipynb +++ b/examples/notebooks/models/composite_particle.ipynb @@ -64,8 +64,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2022-08-19 14:55:41.457 - [INFO] base_model._build_model(573): Start building Doyle-Fuller-Newman model\n", - "2022-08-19 14:55:41.519 - [INFO] base_battery_model.build_model(982): Finish building Doyle-Fuller-Newman model\n" + "2023-02-21 09:08:46.318 - [INFO] base_model._build_model(550): Start building Doyle-Fuller-Newman model\n", + "2023-02-21 09:08:46.452 - [INFO] base_battery_model.build_model(970): Finish building Doyle-Fuller-Newman model\n" ] } ], @@ -90,6 +90,14 @@ "})" ] }, + { + "cell_type": "markdown", + "id": "10339d40", + "metadata": {}, + "source": [ + "## Single Cycle Simulations" + ] + }, { "cell_type": "markdown", "id": "cf194af2", @@ -100,7 +108,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "id": "5a0dc425", "metadata": {}, "outputs": [], @@ -124,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "id": "6319cc89", "metadata": {}, "outputs": [ @@ -132,9 +140,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2022-08-19 14:55:41.575 - [INFO] parameter_values.process_model(381): Start setting parameters for Doyle-Fuller-Newman model\n", - "2022-08-19 14:55:41.635 - [INFO] parameter_values.process_model(484): Finish setting parameters for Doyle-Fuller-Newman model\n", - "2022-08-19 14:55:41.636 - [INFO] discretisation.process_model(137): Start discretising Doyle-Fuller-Newman model\n" + "2023-02-21 09:08:46.528 - [INFO] parameter_values.process_model(425): Start setting parameters for Doyle-Fuller-Newman model\n" ] }, { @@ -148,15 +154,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "2022-08-19 14:55:41.965 - [INFO] discretisation.process_model(254): Finish discretising Doyle-Fuller-Newman model\n", - "2022-08-19 14:55:41.966 - [INFO] base_solver.solve(880): Start solving Doyle-Fuller-Newman model with CasADi solver with 'safe' mode\n", - "2022-08-19 14:55:42.012 - [INFO] base_solver.set_up(109): Start solver set-up\n", - "2022-08-19 14:55:42.143 - [INFO] base_solver.set_up(730): Finish solver set-up\n", - "2022-08-19 14:55:50.193 - [INFO] base_solver.solve(1153): Finish solving Doyle-Fuller-Newman model (event: Minimum voltage)\n", - "2022-08-19 14:55:50.194 - [INFO] base_solver.solve(1154): Set-up time: 131.755 ms, Solve time: 7.580 s (of which integration time: 6.564 s), Total time: 7.711 s\n", - "2022-08-19 14:55:50.201 - [INFO] parameter_values.process_model(381): Start setting parameters for Doyle-Fuller-Newman model\n", - "2022-08-19 14:55:50.265 - [INFO] parameter_values.process_model(484): Finish setting parameters for Doyle-Fuller-Newman model\n", - "2022-08-19 14:55:50.267 - [INFO] discretisation.process_model(137): Start discretising Doyle-Fuller-Newman model\n" + "2023-02-21 09:08:46.741 - [INFO] parameter_values.process_model(527): Finish setting parameters for Doyle-Fuller-Newman model\n", + "2023-02-21 09:08:46.743 - [INFO] discretisation.process_model(147): Start discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:08:46.750 - [INFO] discretisation.remove_independent_variables_from_rhs(1120): removing variable Discharge capacity [A.h] from rhs\n", + "2023-02-21 09:08:47.292 - [INFO] discretisation.process_model(259): Finish discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:08:47.293 - [INFO] base_solver.solve(703): Start solving Doyle-Fuller-Newman model with CasADi solver with 'safe' mode\n", + "2023-02-21 09:08:47.297 - [INFO] base_solver.set_up(111): Start solver set-up\n", + "2023-02-21 09:08:47.433 - [INFO] base_solver.set_up(236): Finish solver set-up\n", + "2023-02-21 09:08:55.129 - [INFO] base_solver.solve(937): Finish solving Doyle-Fuller-Newman model (event: Minimum voltage)\n", + "2023-02-21 09:08:55.130 - [INFO] base_solver.solve(938): Set-up time: 136.675 ms, Solve time: 7.685 s (of which integration time: 6.012 s), Total time: 7.821 s\n", + "2023-02-21 09:08:55.137 - [INFO] parameter_values.process_model(425): Start setting parameters for Doyle-Fuller-Newman model\n", + "2023-02-21 09:08:55.250 - [INFO] parameter_values.process_model(527): Finish setting parameters for Doyle-Fuller-Newman model\n", + "2023-02-21 09:08:55.252 - [INFO] discretisation.process_model(147): Start discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:08:55.260 - [INFO] discretisation.remove_independent_variables_from_rhs(1120): removing variable Discharge capacity [A.h] from rhs\n" ] }, { @@ -170,15 +180,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "2022-08-19 14:55:50.674 - [INFO] discretisation.process_model(254): Finish discretising Doyle-Fuller-Newman model\n", - "2022-08-19 14:55:50.674 - [INFO] base_solver.solve(880): Start solving Doyle-Fuller-Newman model with CasADi solver with 'safe' mode\n", - "2022-08-19 14:55:50.677 - [INFO] base_solver.set_up(109): Start solver set-up\n", - "2022-08-19 14:55:50.792 - [INFO] base_solver.set_up(730): Finish solver set-up\n", - "2022-08-19 14:56:01.820 - [INFO] base_solver.solve(1153): Finish solving Doyle-Fuller-Newman model (event: Minimum voltage)\n", - "2022-08-19 14:56:01.823 - [INFO] base_solver.solve(1154): Set-up time: 115.158 ms, Solve time: 10.443 s (of which integration time: 9.421 s), Total time: 10.558 s\n", - "2022-08-19 14:56:01.830 - [INFO] parameter_values.process_model(381): Start setting parameters for Doyle-Fuller-Newman model\n", - "2022-08-19 14:56:01.891 - [INFO] parameter_values.process_model(484): Finish setting parameters for Doyle-Fuller-Newman model\n", - "2022-08-19 14:56:01.892 - [INFO] discretisation.process_model(137): Start discretising Doyle-Fuller-Newman model\n" + "2023-02-21 09:08:55.808 - [INFO] discretisation.process_model(259): Finish discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:08:55.809 - [INFO] base_solver.solve(703): Start solving Doyle-Fuller-Newman model with CasADi solver with 'safe' mode\n", + "2023-02-21 09:08:55.814 - [INFO] base_solver.set_up(111): Start solver set-up\n", + "2023-02-21 09:08:55.947 - [INFO] base_solver.set_up(236): Finish solver set-up\n", + "2023-02-21 09:09:06.233 - [INFO] base_solver.solve(937): Finish solving Doyle-Fuller-Newman model (event: Minimum voltage)\n", + "2023-02-21 09:09:06.234 - [INFO] base_solver.solve(938): Set-up time: 134.145 ms, Solve time: 10.272 s (of which integration time: 8.058 s), Total time: 10.407 s\n", + "2023-02-21 09:09:06.242 - [INFO] parameter_values.process_model(425): Start setting parameters for Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:06.444 - [INFO] parameter_values.process_model(527): Finish setting parameters for Doyle-Fuller-Newman model\n" ] }, { @@ -192,28 +201,37 @@ "name": "stderr", "output_type": "stream", "text": [ - "2022-08-19 14:56:02.232 - [INFO] discretisation.process_model(254): Finish discretising Doyle-Fuller-Newman model\n", - "2022-08-19 14:56:02.233 - [INFO] base_solver.solve(880): Start solving Doyle-Fuller-Newman model with CasADi solver with 'safe' mode\n", - "2022-08-19 14:56:02.235 - [INFO] base_solver.set_up(109): Start solver set-up\n", - "2022-08-19 14:56:02.346 - [INFO] base_solver.set_up(730): Finish solver set-up\n", - "At t = 0.000764821 and h = 1.00062e-23, the corrector convergence failed repeatedly or with |h| = hmin.\n", - "At t = 0.000250494 and h = 2.17606e-17, the corrector convergence failed repeatedly or with |h| = hmin.\n", - "At t = 0.000250495 and h = 1.61173e-21, the corrector convergence failed repeatedly or with |h| = hmin.\n", - "At t = 0.000121908 and h = 1.77817e-17, the corrector convergence failed repeatedly or with |h| = hmin.\n", - "At t = 5.76215e-05 and h = 5.66832e-19, the corrector convergence failed repeatedly or with |h| = hmin.\n", + "2023-02-21 09:09:06.447 - [INFO] discretisation.process_model(147): Start discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:06.453 - [INFO] discretisation.remove_independent_variables_from_rhs(1120): removing variable Discharge capacity [A.h] from rhs\n", + "2023-02-21 09:09:07.047 - [INFO] discretisation.process_model(259): Finish discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:07.048 - [INFO] base_solver.solve(703): Start solving Doyle-Fuller-Newman model with CasADi solver with 'safe' mode\n", + "2023-02-21 09:09:07.052 - [INFO] base_solver.set_up(111): Start solver set-up\n", + "2023-02-21 09:09:07.200 - [INFO] base_solver.set_up(236): Finish solver set-up\n", + "At t = 0.00076482 and h = 3.55373e-22, the corrector convergence failed repeatedly or with |h| = hmin.\n", + "At t = 0.000250492 and h = 1.20507e-16, the corrector convergence failed repeatedly or with |h| = hmin.\n", + "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n", + "At t = 0.000250493 and h = 2.0588e-18, the corrector convergence failed repeatedly or with |h| = hmin.\n", + "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:852: Linear solve failed\n", + "At t = 0.000121913 and h = 6.48118e-25, the corrector convergence failed repeatedly or with |h| = hmin.\n", + "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:852: Linear solve failed\n", + "At t = 5.76225e-05 and h = 3.13053e-22, the corrector convergence failed repeatedly or with |h| = hmin.\n", + "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:852: Linear solve failed\n", "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:852: Linear solve failed\n", - "At t = 2.54772e-05 and h = 5.34124e-24, the corrector convergence failed repeatedly or with |h| = hmin.\n", + "At t = 2.54755e-05 and h = 4.28889e-31, the corrector convergence failed repeatedly or with |h| = hmin.\n", "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:852: Linear solve failed\n", - "At t = 9.4045e-06 and h = 2.17347e-21, the corrector convergence failed repeatedly or with |h| = hmin.\n", - "2022-08-19 14:56:16.397 - [INFO] base_solver.solve(1153): Finish solving Doyle-Fuller-Newman model (event: Minimum voltage)\n", - "2022-08-19 14:56:16.398 - [INFO] base_solver.solve(1154): Set-up time: 111.145 ms, Solve time: 13.414 s (of which integration time: 9.588 s), Total time: 13.525 s\n" + "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:852: Linear solve failed\n", + "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:852: Linear solve failed\n", + "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:852: Linear solve failed\n", + "At t = 9.40462e-06 and h = 4.82262e-22, the corrector convergence failed repeatedly or with |h| = hmin.\n", + "2023-02-21 09:09:21.630 - [INFO] base_solver.solve(937): Finish solving Doyle-Fuller-Newman model (event: Minimum voltage)\n", + "2023-02-21 09:09:21.631 - [INFO] base_solver.solve(938): Set-up time: 148.688 ms, Solve time: 14.417 s (of which integration time: 9.176 s), Total time: 14.566 s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "running time: 34.972963291s\n" + "running time: 35.389444837s\n" ] } ], @@ -248,7 +266,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "id": "ec9bebd1", "metadata": { "scrolled": false @@ -257,18 +275,18 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 7, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" + "
" ] }, "metadata": {}, @@ -296,7 +314,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "id": "9893a85f", "metadata": {}, "outputs": [ @@ -306,15 +324,15 @@ "Text(0.5, 1.0, 'Silicon')" ] }, - "execution_count": 8, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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", 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", 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" + "
" ] }, "metadata": {}, @@ -363,7 +381,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "id": "9eafca0e", "metadata": {}, "outputs": [ @@ -373,15 +391,15 @@ "Text(0.5, 1.0, 'Silicon')" ] }, - "execution_count": 9, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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", 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", 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" + "
" ] }, "metadata": {}, @@ -430,7 +448,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "id": "302e7bb8", "metadata": {}, "outputs": [ @@ -440,15 +458,15 @@ "Text(0.5, 1.0, 'Silicon')" ] }, - "execution_count": 10, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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", 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DJ3JMdc5rBr8JPB07dmThwoVHfX7ChAmVHvPMM8/UZFkBxwgNpX77JOq3/9ts/LOh/zXHO7oQj6eQ4mLIzYac1AKirc/xS9ZeStLTqfNTCLf9tpi4uk6uOud5cnNzyd6bx+q315NfGkmBK4pcTwx5nhic2HATQj7xwF+/pXwO4IHbit7moqIsyMpiBhfzJC9x+uYP+XblX124TfkdO0XEk0EUfxBJBvaQbMJDsimMLGZT02ji490kJkKPvF3E1wkhsn4MYfWTsTdsSGT9+sTGxxMTE+OTyYIi4l12u52tW7f65LX+PqRVHe3bt2fTpk288sorAIwfP778udWrVxMfH8+cOXPweDzk5eXhcDiOeq7qDGmFhoZyyimnsGbNGvr16weA2+1mzZo1jBo1qtJznMgx1TmvGfwm8EjtYBgQHn7wp069CKDFoR+4cABcWMkx/3jk8EfFeDxpFBUZ5OQY5GQbZOcYZGa6SNtfRO5Pf5Cd7qSoQXfWFSfiychgz/ZkWu3eRFJcNpd3vxyHw0FeRj4//Nr8yBdzHfopAbL+2lx2bd48hnIFkwH4jHN5jHuJ42tWxE4lNjaWuLg4ztt9FhFhbsKjnUTGe4isYyWmfjj2+onYGjYkvFmz8l6mqKgov5q/JBJMDMOo8tBSdXnjbunt2rVj2bJlrFu3jv/7v/+rEJzatWvHo48+yqRJk+jXrx+nn376MQNPdYe0xo4dy1133cUpp5xC165def311yksLOSaa/76jffNN9/k448/Lu+EOJFjTmSf/Px8duzYUf54165dpKSkEB8fX6EnqqYo8IjPGQbY7R7sdg8VfwEJgxFlk847lG+98dAP9Dn0Ay4XbN60H8feYrJ2F3BgVx7pe4vJy7awf7+T9JJo9pFAXl4oBfnhlBRG4CQUizWHApdBhMfD77TmU/pzOpCdPYns7Gx27drFVtZRSAT87cKASPKIJJMDZAGZwG4uJZM4I5teoe/SKGITxZGRZIU3YJ+1HdZkC3k9GhAdHU1MTAz1CguJSEwkvF49IhMTiY6OJioqSr1LIkGiQ4cOZGRk0KNHjwpXDAO0bNmSFStWsHLlSiZOnMigQYO44IILvF7DlVdeSWZmJlOmTCEtLY2OHTsye/bsCkNPmZmZ/PHHH1U65kT2+emnnxgyZEj547JpLEOGDOGFF17w+nv9O8MT7NP8OTjO6O1LHQ3DoH79+uzbty/or6TwlWO1uccDhYUGVquH0FCguJgdG4tY9z83UVG5tD+/GIfDQVaGg//e15mcwgjySiPJcUaT64k77mvP5xqu4eBvQ+8yiKt4l86s4Wd6le/Tnp8oxU4s2USTTQTZ2HEQRg6F1nzWxBtERDiJinJyYd4eEsKLSUpwEJVkwRIfjyU+HltSErZ69Qhr3JioqChiYmKIiooqv8zV1/Q5971gbvOcnBxiYmJMeW1v9PAcS2pqKnFxcYSHh7NkyRK+/PJL7rjjjqPO4QkGNpuNjIyMSv/ObTZb8FyWLlIVhgEREYf94xAWRvMzwmh+Bhw+hwjgon6HPyrA5SogJ8fA4bCQnW3B4bDgcBikpTlxfL+TzDQnlsad+MbjgYwMtu9sQfKuPcREFTDkwiHk5uaSm5vLD1+1pIDIygt0Aml/Pfzl0H9fZRwjeB2A1ZzHNSygHd/wBZeU7zuMfxNCJGGWbEJD8giz5RMaWkiYvQR3jJvNnZuQkGAhKSmUFgV5RERGElqnDmF162KvU4fomBj1NomYbNOmTTz++ONYLBbCw8N57rnnzC4p4CjwiBxHSAjEx3uIjy+bIHS4Rof+O6Z8y3WHfqAT8EL59t9+yyc3O5+c/cXkpZeQlVpE1oEiHOlOsopC2R+SSE6OQV6eBSO1mPxSOwVxsNmoQ1hREdsLkzngrEd9SwL169UnLy+P3NxcPmc0qTQ8uCaTGygFCgAHB4flytdlchNFLgk4eJSJjOZNXMDPNONp7iaUP5gX+QrR0dFER0dzmaM18RaD0Egn4dEu7PFgTYjBiIsjpE4dSlq2JCYmhubNm1NcXExcXBxxcXEKTSLV0Lt3b3r37n3E9mDt3akJCjwiPtK+fdlq3iGA/dDP0YRzcGXJxw79QK88g093HcBitKRd+4OLSLrdbl5+MI0D+/bhyPSQk2MhJ99KXmEYuSV2cpzR5LijcLlCAQt5xJJHLEXYyivJpDmzuI2W/EJ+/nPk5+eTmprKUhaznY4VqoojiyTSiSaDH0gH0oEvGEYGrUinMetJtG0mPzyc/IgoimPicCXEsOmUU8onWSbGxhKXlERCQgJ16tQhISFB6y+JSI1T4BGpJaKiPHToUPEWKBaLhQmTj7WwZRFQRFER5OZayM42yM210LDBY+yLvhcjJ4eI35zcvngTEWFF9L9lDXl5eeTk5PDsrW7ys/eTWxpJvjsKAAfxOIgHKi72Nu/Qf5/iAcaWpkBpKT/ktqTb/u9ow69s/rpz+b73MIYkItjLPizsw81+LHYHzlgrhQkJpHTpQp06dahbty5169Shbr161KlTh3r16lX7cmAREQUekSBwcCkBNwfn+B0clvMQgScigibJcH8fgIoTA8/5oWzfHJzOHHJyLGRm/vWTlXXwvyUlMezeuIfMA27qdu7Gtthbce7fz/ebG+PZaMEdbmP8qPFkZWWRlZXFkhV3s9XTocJrUQhRhbnUTd3L77/uAg7+/IN1hLMLF7+wjf2k2e0UJCZSWr8+niZNCOnUiYYNG5b/JCYmqrdIRCqlq7TQVVqBQm3ue8dqc5cLMjMtFBQYNG3619ynpyeGsmOzh7T9BvvTrex32Cl0HnuV7Kd4gAcOrZ+0jZbcxzM0ZyPP89fq7A8BsSEhZCUmUty4MSFt2xLfoQMtWrakWbNmNGzYMCDmFwXz5zyQr9KSI+kqLRGpFUJCoE6dI+/+/MCjJYc9KgEKyMszOHDAwr59IezdG8KePQf/u/d3J3t3GyRf1Jv9MSWUbNvGVylNWLxjEO1sbbjikh/Ys2cPe/bsYUnqh5S6omh1YBstD2yn2XfbaMoXRPAyKezhXJuNJk2a0Lx5c9q3bk3r9u1p27YtrVq1Mu2yfhHxHQUeETFdVJSHqCgXLVr8/Sq4Mqfg4hRCgHZ/hvD4p9lERjbkmmteBsDthjbN61DotLGFtkccHUk2paW/sn37r2zf/gtXrfyVdiwlg93MNwwO1K1LYbt2xHTrRsfOnTn11FOpW7cqN/0VEX+nwCMitUrjxi5Gj86vsM0w4H9rM9mxI4Q//rCyc2cIO7aHsG0z7PgznHxXLHD2oR+4+dBxMxjNQ543Yf9+0vc7+eN/+/ie/6MrHho0aMCpp57KqV260OXUUzn11FOJiory6XsVEe9R4BGRWs8woGFDFw0buujZs6TCcyUlsGOHlc2brWzdamPzZitbfjXYsSuUVmPPIn2/A37+mbm/9+MO91TOtH6I4bqCvXv3sn/vXh776CdSeYo3DIPUli2J7tOH0885hzPPPJPY2Fhz3rCIVJkCj4gEtNBQaNvWSdu2Tg5epn9QSQmEhPSnJOTgPY32/F8E9qkuegzpzNsPb+Lnn39my5K1nPn2czRgDz08azln21ectm0tJa/P5D1K2diyJXX69aNv375069YNm81m0rsUkePRVVroKq1AoTb3vUBrc6cTiosNIiMPvpe1X1gYdl1dnK6Kl7qHU0h3vsbgc1bzGfA1ydHhXNmtG20vv5y+559f5StITlSgtXlV6Cqt4KKrtEREaojVClbrXyGix7luNm3ez08/2fj221C+/dbGdxusZGbb+R+9gd7ARCAfV+4amq/+jJar57GWe9jcuhVJV1/NxVdeScOGDc15QyJSTit0iYgcg93u4ayzSrj11jxmzsxi4y9p/O9/B5g82cEVVxSSmOgCIknjYh5gMmP4iJuAF7Zu5a4nnuC3My/m1bPOYsbzz7Nz506T342YpU+fPkyZMqXS56ZOnUrHjh3JzMz0SS0zZ86ke/futGjRgv79+/PDDz949Zhp06bRsGFDHnnkEW+WfdIUeEREqsAwoFUrJ9ddV8DLL2fx00/7WbXqAP/+dzYXX5BPv94O0q8aTE5MDBHAP/mJOX9+xvTnPuScc87hwgsv5LXnn2fP7t1mvxXxoXbt2rF58+Yjtu/fv5+pU6dy7733kpCQUON1LFmyhIkTJ3L33XezfPlyOnTowLXXXkt6erpXjvnxxx+ZPXs27du3r8m3US0KPCIiJ8EwoF07J2PG5PPGW9lMnhOJ88UXyfv1V76f9T9SLQ3YbTSiUY9GhISE8Ouvv5L6XA7zui/mtVNH8/HkZ3FkZZn9NqSGtW/fnk2bNh2x/emnn6ZJkyaMGDHCJ3W8/vrrDB8+nGuuuYY2bdrw9NNPY7fbmT9//kkfk5+fz6233sozzzxDXFxcDb+TqtMcHhGRmmAY1D+/Fb/8doDffrNyxhmzyMzM5JNly3jjgUv5lc6QBnVf2s/ZL31C87rrOXNMI8656VqsutqrSoyCgqM+57FYDt5M7kT2NQw47Aa1Ffa1WjGcTjwREdWqsV27dvzxxx8UFRWVr+y9ceNGFi1axIIFC6p825OXXnqJqVOnHnOf1atXV5g/VlJSwsaNG7n11lvLt1ksFnr27Ml3331X6TmqcsxDDz3E+eefz7nnnstLL71UpffjCwo8IiI1KCrKwxlnHLy6JyEhgaHXjaC0MISVCzazbnMjDnjqsYTr4cD1hD1VxCmTPyP+gnzuvbcdHTokmlx97VC/deujPlfUty+Zb79d/rjeKadgKSysdN/is88mY9Gi8sd1u3cn5G/zavbu2VOtGjt06IDL5WLbtm106tQJgEcffZRLL72UHj16VHpMamoqkyZNYtq0aUc8N2LECC6//PJjvma9evUqPM7MzMTlcpGUlFRhe506ddi+fXul5zjRY5YsWUJKSgrLli07Zk1mUuAREfEhw4Drx7m4flw0JSXZfL8mk6XTdrLqu8b86WzGN+5LYQWsWOGmbuIWxvbazdB/dyQhMbguQQ80jRo1IiYmhs2bN9OpUyeWLFnCxo0b+eKLL456THJycqVhByA+Pp74+PiaKrdK9uzZwyOPPMK8efP8+r50CjwiIiYJDYWz+oZwVt+WTPLAL+s38Z/paaz8IpGCgg4cyGjHE++3Y/L7pXx86ww6/KMfhB37zvLBaN/WrUd9zmOpOFV1/8aNR9/XMCo8PvD11+V/tlqtOJ3OalZ4UNu2bdm8eTNFRUU8+eSTTJgwoXzIKT8/n3HjxpGamgrAww8/TMuWLRk3bhwff/zxEeeqzpBWQkICISEhR0w2TktLO+qaNidyzM8//0x6ejr9+vUrf97lcrF+/XpmzpzJjh07qjxkVxMUeERE/IBhQKezY3j57IOLrH3yyad8cP837Ei7mGwS6DvtFnJnx+J+/HFWtrmFxDoW6tU72s1Wg0tV5tVUe1+bDc9JLjxYNnH5lVdeAWD8+PHlz61evZr4+HjmzJmDx+MhLy8Ph8Nx1HNVZ0grNDSUU045hTVr1pSHE7fbzZo1axg1alSl5ziRY3r27MmqVasqHHf33XfTsmVLJkyY4BdhBxR4RET80sUXd+Tiizuy8csv+fb2x0g94KGBw0Hhbfcy1Hod2Z46vL8kg65dtfpvbdGuXTuWLVvGunXr+L//+z/sh02QbteuHY8++iiTJk2iX79+nH766ccMPNUd0ho7dix33XUXp5xyCl27duX111+nsLCQa665pnyfN998k48//piFCxee0DFRUVG0a9euwutEREQQHx9/xHYzKfCIiPixU3r1ouO3K5jx8ssUTZ7MNe5k2jt/ZpetJZ07eYCDwzAFBQYREZrn4886dOhARkYGPXr0oH///hWea9myJStWrGDlypVMnDiRQYMGccEFF3i9hiuvvJLMzEymTJlCWloaHTt2ZPbs2RWGtDIzM/njjz+qdExtoHtpoXtpBQq1ue+pzX1rw4YNPHjDDUzKzmZ++3N49qPZhIaGUlIC555bl3PPLeb++3NJTHSbXWqNCOR7aaWmphIXF0d4eDhLlizhyy+/5I477jjqHJ5g4O17aWnhQRGRWuLMM89k6qJFjIqOZtFvXzFp0iQANjz7PX/+aWXOnEh69qzLa69Fovtc1i6bNm3isssu48ILL2T69OmMGzfO7JICjnp4UA9PoFCb+57a3PcMw+C7774rn7D6v8ceo9fjj/O/OoO4LfZNUjZHAtCpUwnPPeegU6eTu7LInwRyD48cST08IiJBrn///gwfPhyAx95+G1fDhvROfYdvCzrx7EM7iYtzk5ISymWX1eGZZ6IpLja5YBE/oMAjIlIL/etf/yImJobPt29n7rhxOJs1I+zPndzx/mWs/nAHl15aiNNp8OKL0VxySR1++03XqEhwU+AREamF4uLiyud5TJ47l/T583HVqYPt119p+/BYXn85jVdfzSQpycXmzTb696/DW29FoJFHCVYKPCIitdSoUaMICwvj119/5YfMTDJnzsQdHk74558T89RT9O9fxKpVafTtW0RRkcFDD8Uxdmw8WVnG8U8uEmAUeEREaqm4uDguu+wyAObMmUPpqafiOHSXasPhALebpCQ3b72VyWOPZWOzefj4Yztz5kSaWLWIORR4RERqsaFDhwKwbNkynE4nRZddRtqHH5L93HNw6D5SFguMHZvP0qXpDB5cwM0355lZsogpFHhERGqx7t27ExcXh8Ph4PvvvwegtGvXv3bweCibuNO5cykvveTAemj+ckkJvPxypK7ikqCgwCMiUotZrVb69u0LwMqVKys8Z9m/n4RRo4h8881Kj3388RgmTYpl9OgETWaWgKfAIyJSy5Xdc+nvd6wO/+QTwj/9lOgnnyRk584jjuvbt5i4ODc33JCPoXnMEuAUeEREarmePXsCB29PkJ2dXb694LrrKD77bCyFhcTdfTe4K95jq0+fYtav38+FF/41ppWdreQjgUmBR0SklktMTKRZs2YA/PDDD389YbHgeP553BERhH39NfaFC484Njr6r7GsXbtCOPfcukydGqUhLi/r06cPU6ZMqfS5qVOn0rFjRzIzM31Sy8yZM+nevTstWrSgf//+FT8zlVi/fj0jR47ktNNOo2HDhixfvtwndXqbAo+ISAA47bTTAPjuu+8qbHc1aULuP/4BQMwTT2BkZR31HMuWhZOeHsLTT8dw111xlJTUXL3Bpl27dmzevPmI7fv372fq1Knce++9JCQk1HgdS5YsYeLEidx9990sX76cDh06cO2115Kenn7UYwoKCujQoQNPPPFEjddXkxR4REQCQLdu3YAjAw9A/pgxlLZtS0hmJjGTJx/1HOPH5/PEEw5CQjy8804Ew4YlkpmpIS5vaN++PZs2bTpi+9NPP02TJk0YMWKET+p4/fXXGT58ONdccw1t2rTh6aefxm63M3/+/KMe07dvX+6//34uueQSn9RYU3RzFRGRAHD66acD8NNPP+HxeDAOn4Vss5H9xBMkXXUVYatXY+Tn44msfPHBG24ooFkzFzffHM/69WEMGJDEnDmZNG7s8sXbqJaCgqqHstBQT/nl+U4nlJQYGIYHu73y81qt4HQaRERUb6yvXbt2/PHHHxQVFREeHg7Axo0bWbRoEQsWLCAkJKRK53vppZeYOnXqMfdZvXo1DRs2LH9cUlLCxo0bufXWW8u3WSwWevbsWWlQDjQKPCIiAaBVq1ZYLBYcDgdpaWnUrVu3wvMlZ59N5ssvU3zBBXgiIo55rt69i3n//XRGjEhg+3YbV16ZxNtvZ9Cxo7Mm30K1tW5dv8rHvPJKJpdfXgTAxx+Hc/PNCZx9djGLFmWU79O9e10yMysGkT179larxg4dOuByudi2bRudOnUC4NFHH+XSSy+lR48elR6TmprKpEmTmDZt2hHPjRgxgssvv/yYr1mvXr0KjzMzM3G5XCQlJVXYXqdOHbZv316Vt1MrKfCIiASA8PBwmjZtyo4dO9i8efMRgQeg6IorTvh87do5WbIknREjEtm0ycagQUnMmJFJz56a2FMdjRo1IiYmhs2bN9OpUyeWLFnCxo0b+eKLL456THJycqVhByA+Pp74+PiaKjcgKfCIiASItm3bsmPHDrZu3UqvXr2OvqPLhX3pUgovvRRCQ4+6W4MGbt57L50xYxJYty6M665L5MUXs7jyyqIaqL76tm7dV+VjQkP/Gpq65JIitm7dh2FUHK76+usD5X+2Wq04nSfXw9W2bVs2b95MUVERTz75JBMmTCgfcsrPz2fcuHGkpqYC8PDDD9OyZUvGjRvHxx9/fMS5qjOklZCQQEhIyBETlNPS0qhTp85JvbfaQIFHRCRAtG7dmuXLl1d6NdDhEkaOJPzzz7FMnEj+jTcec9/YWA+zZ2dwxx3xfPihnVtuSSA1NZubbsr3ZuknpbrzaspYrWC1HnmOw89rs0Fp6cm9TtnE5VdeeQWA8ePHlz+3evVq4uPjmTNnDh6Ph7y8PBwOx1HPVZ0hrdDQUE455RTWrFlDv379AHC73axZs4ZRo0ZV813VHrpKS0QkQLRp0waArVu3HnO/okNX20T/3/9hHLZQ4dGEh8PLL2cxZszBm47++9+xLFxoP85R8nft2rXjxx9/5D//+Q8PP/ww9sNmSLdr147169czadIkvvvuO6Kjo495rvj4eJo3b37MH6v1yD6NsWPHMnfuXBYuXMjWrVt54IEHKCws5Jprrinf58033+Tqq68uf5yfn09KSgopKSkA7Nq1i5SUFPbs2XOyTeJT6uEREQkQZYFny5Ytx9yv4JpriJw+HduWLURPnUrOv/513HNbLDBxYg7167tYsSKcyy8v9ErNwaRDhw5kZGTQo0cP+vfvX+G5li1bsmLFClauXMnEiRMZNGhQ+S1DvOnKK68kMzOTKVOmkJaWRseOHZk9e3aFIa3MzEz++OOP8sc//fQTQ4YMKX88ceJEAIYMGcILL7zg9RpriuHxaD3NtLQ0SktLvXpOwzCoX78++/btQ03sG2pz31Ob+96x2jw3N5d27doBsHnzZqKioo56nrCVK0kcORJPeDj7v/oKd3LyCdfgdFJ+SbfHA6Wlx5wK5DU5OTnExMTU/AtVwmazef3ficOlpqYSFxdHeHg4S5Ys4csvv+SOO+446hyeYGCz2cjIyKj079xms1V53pGGtEREAkR0dDRxcXEA7N69+5j7Fp9/PiWnn45RVETUUa4EOprDR0qeeSaa4cMTyc3VAoUnY9OmTVx22WVceOGFTJ8+nXHjxpldUsBR4BERCSCNGjUCjh94MAxy7r0XgMg5cwipxnyMvXstvPFGJOvWhbF6dViVj5e/9O7dm1WrVvHpp5+ydOlS2rRpQ+PGjYO2d6cmaA6PiEgAadSoESkpKccPPEBJz54Un302RnExRm5ulV+rQQM377yTwTffhJYv4ifirxR4REQCSFkPz59//nlC+2fOmIEnJgaM6g1JnXJKKaec8tfclsxMA4fDQosW/nsrCglOGtISEQkgjRs3Bk5gSOsQT2xstcPO3+XlGYwYkciAAUmkpOj3afEvCjwiIgHkhOfw/I2RnU30s88SchL3VCouNnA6ISMjhKuuSuLrr31w6ZbICVLgEREJIFUd0ioT++CDRL/wAtHHuV3BsSQmHpzT0717Mbm5FoYPT9BkZvEbCjwiIgGkQYMGAGRkZFBcXHzCx+WPHQuA/b33CNm1q9qvHxPjYc6cDM4/v4iiIgujRiWwfHl4tc/3d26322vnEv/m7bW9FHhERAJIXFwcNpsN4IibRB5LadeuFJ17LobLRdR//3tSNdjtMH16JpddVkhJicG4cfG8//7J34oiIiKC3NxchZ4gUVBQQFiY93oINatMRCSAWCwWkpKS2LdvH2lpaRXuln08ebffTvgXXxCxYAG5d95ZpdWX/y40FP773yzuvtvDu+9GcOutcRQWGgwbVlDtc1qtViIjI8nLy6v2OaorNDSUkpISn79usPJ4PMTHx+PxeLzW06PAIyISYOrVq8e+ffs4cOBAlY4rOessis88k7ANG4h69VVyHn30pOqwWuGFFxxERHh4++1I7rknjoICgzFjqn+ndavV6vPbS+gWKr5nGAbJycns27fPa+fUkJaISIApu8dQVQMPhkHe7bcDEPH221gyM0+6FosFnnoqm3HjDvbKPPJILFOnHv0eXyI1xa96eJYvX87SpUtxOBw0bdqU0aNH06pVq6Puv2zZMlasWEF6ejoxMTF0796d4cOHE+qLu9iJiPipunXrAgdvjFxVxb17U3LaaThbtz54V1AvMAx45JEcIiM9/N//RfP00zGEhXkYN676PT0iVeU3gWft2rXMmjWLsWPH0rp1a5YtW8YTTzzBCy+8QGxs7BH7r1mzhrlz5zJ+/HjatGnDvn37+O9//4thGIwcOdKEdyAi4h+q3cMDYBikL15c8Q6hXmAYcM89uUREeJg5M4JLL9WtKMS3/GZI68MPP+T888+nT58+NGrUiLFjxxIaGsrnn39e6f6bN2+mbdu29OzZk7p169KlSxfOOecctm3b5uPKRUT8y0kFHvB62DncLbfksWpVGo0a6dYT4lt+0cPjdDr5/fffGTBgQPk2i8VC586d2bJlS6XHtG3bli+//JJt27bRqlUr9u/fzw8//ECvXr2O+jqlpaWUHtZFaxgGdru9/M/eVHY+b59Xjk5t7ntqc987kTZPPnR1VVpa2kn93Vg3byZy+nRyJk7EExFR7fP83cE5xwfr+uijcFasCGPKlOyazFknRZ9z36uJNveLj1dOTg5ut5u4uLgK2+Pi4ti7d2+lx/Ts2ZOcnBwefvhhAFwuFxdeeCGDBg066ussXryYRYsWlT9u3rw5kydPLv9tqCYkn8RlnVI9anPfU5v73rHavH379sDBxQfr169fvRdwu+Hcc2HbNiLOOgtuvbV65zmGtDS44w7Iz4fzzovgllu8/hJepc+573mzzf0i8FTHL7/8wuLFi7nxxhtp3bo1qampvPnmmyxatIirrrqq0mMGDhxI//79yx+XJce0tDScTqdX6yu7pC41NVWXMfqI2tz31Oa+dyJtbrEcnK2wb98+9u7dW+3fkiPGjCH2wQdxPvMMaVdeWSNDXf/5TxjLloVzxRXZePEKZK/S59z3jtfmVqu1yp0VfhF4YmJisFgsOByOCtsdDscRvT5lFixYwLnnnsv5558PQJMmTSgqKuK1115j0KBB5f/DH85ms5WvQPp3NfUh9uaiSXJi1Oa+pzb3vWO1eWJiIgDFxcXk5eURFVW9y8Dzhwwh6tlnsf75J2EffkjRlVdWu96jufDCIi688OAEZo8HnM6DNyGNjPS/z5M+577nzTb3i0nLVquVFi1akJKSUr7N7XaTkpJCmzZtKj2muLj4iN9aKgs5IiLBxm63Ex5+8P5VmSezlo7dTv7o0QBEvfzywURSg9xuuPvuOK65JhGHQ/NlxLv8JiH079+fVatWsXr1anbv3s306dMpLi6md+/eAEybNo25c+eW79+tWzc+/fRTvvrqKw4cOMDGjRtZsGAB3bp1U/ARkaBmGAbx8fHASQYeIH/kSNx2O6E//0zomjXeKO+odu8OYdWqcH74IZSrr04iI0Pf5eI9fjGkBdCjRw9ycnJYuHAhDoeDZs2a8dBDD5UPaaWnp1fo0Rk8eDCGYTB//nwyMzOJiYmhW7duDBs2zKR3ICLiPxISEti3bx9ZWVkndR5PQgIFw4YR9cYbRL38MpnHuBL2ZDVp4mLRonSGDUvkl19sXHVVIvPnZ1Cvnm4WKifPbwIPQL9+/ejXr1+lzz322GMVHoeEhDBkyBCGDBnig8pERGoXb/XwAOSPG4f9o48oOfvsg+NONdiL3r69k0WL0rnmmiS2bLExaFASCxZkaN0eOWnqLxQRCUAJCQmAdwKPq3Fj9m/YQN5tt9Vo2CnTqpWLxYvTadLEyc6dVgYOTGTHjpAaf10JbAo8IiIByJuBB4AQ3waOJk1cvPtuOi1aONm718rgwUls2eJXgxJSyyjwiIgEoLLAc7JzeCpwuQj/6CMi33jDe+c8hgYN3Lz3Xjrt25eyf38IgwcnkpKi0CPVo8AjIhKAvN7DA4R+/TUJY8cS/eSTGF4877HUqePmnXfS6dKlhMzMEIYMSeK77ypfT03kWBR4REQCUE0EnpKzz6a0Y0cshYVEzprltfMeT3y8h/nzMzjjjGJyciwMG5bI2rWhPnt9CQwKPCIiAajsKi2vDmkZBnmHbngV+cYbUFjovXMfR0yMh7lzM+nZsxiXy0D38ZSqUuAREQlANTKHByjs3x9no0aEZGQQcdjNmH0hIsLDW29l8M476Zx9dolPX1tqPwUeEZEAdPg6PF69/5PVSv7YsQBEvfbawXV5fCg8HE47rbT88aZNVj74INynNUjtpMAjIhKAynp4SktLycvL8+q5C4YOxR0Tg/X33wlbudKr566KffssDB2ayC23xLN8uUKPHJsCj4hIAAoPDyc09ODE3uzsbK+e2xMVRf6IEZSceiqeyEivnrsq6tVz069fEe3aOenevdi0OqR20IIGIiIByDAMYmNjSUtLw+Fw0KhRI6+eP/eee8h98EHMnD1sscBTT2WTl2cQHV2zd3KX2k89PCIiASo2Nhbwfg8PAKGhpoadMoZBhbDz5psRTJkSjTenLUlgUA+PiEiAqtHAc4iRnU3knDkU9u+Pq0mTGnudE/Hbb1b+9a84AAoKDB5+OMcfMpn4CfXwiIgEKF8Enri77iLmiSeInD69xl7jRLVv7+Txxw++11dfjeLBB2N9fRGZ+DEFHhGRABUXFwfUbOApuOEGACLmzcNwOGrsdU7U6NH5TJniwDA8vP12JHffHYfTaXZV4g8UeEREAlRZD4+jBoNIca9elLZvj6WggMjZs2vsdapi2LACpk51EBLi4Z13Irj11nhKS49/nAQ2BR4RkQDliyEtDIO8m24CDt1uosQ/VkAeOLCQV1/NwmbzsHSpnbFjEygqMrsqMZMCj4hIgPJJ4AEKr7wSV3IyIfv3Y3///Rp9raq45JIi3nwzk/BwD59+Gs4NNyRSUKBZzMFKgUdEJED5KvAQGkr+6NEARL36Kv50TXifPsW8/XYGERFuvvwyjGuvTSA3V6EnGCnwiIgEKF9MWi6Tf911uGNjKW3fHiM/v8Zfryp69Chh3rwMYmLcbNgQxtChiWRlKfQEGwUeEZEA5YtJy2U8sbHs/+YbHNOm4YmKqvHXq6rTTy9l4cIM4uNd/PhjKJ9/rntvBRstPCgiEqB8NqR1iJn31ToRnTuX8u67GaxdG8qgQYVmlyM+ph4eEZEAdXjg8fhwXo1161bsCxb47PWqom1bJ6NGFZQ/zsoy2L07xMSKxFfUwyMiEqDK5vC4XC7y8/OJ8sFQU8jvv1O3d288VivFvXrhbtCgxl+zunJzDa67LpH9+0NYsCCdli1dZpckNUg9PCIiAcput2Oz2QDfDWu5WrSg+KyzMJxOIt980yevWV0FBQb5+QZFRVBaqknMgU6BR0QkQBmGUd7L44uJy2XKFyKcPRsjL89nr1tV9eq5effdDBYsyKBdO91/ItAp8IiIBDBfT1wGKL7gAkpbtsSSk0PEvHk+e93qSEx007HjX2Fn3bpQvvkm1MSKpKYo8IiIBDAzAg8WC/njxgEQ+frr1Ja7d6akWLn++gSGDUtgzRqFnkCjwCMiEsB8uRbP4QoGD8aVmIh1zx7Cly3z6WtXV8uWLs44o4TCQgvXX5/IqlVhZpckXqTAIyISwHy52nIFdjv5o0bhSkjAKKwda97Y7R7efDOTiy8upLjYYMyYBJYt0wKFgUKBR0QkgJnVwwOQP3YsBzZsoHDoUJ+/dnWFhcGrr2Zx5ZUFlJYajB8fz6JFdrPLEi/QOjwiIgHMlDk8h/jjLSZOhM0GU6c6sNs9zJ8fye23xxESAgMHml2ZnAz18IiIBDAzA085t5uwTz8lZMcO82qoopAQePbZbMaMOXhZ/YQJMHWqf986Q45NgUdEJID5Q+CJffhhEm+4gaiXXzathuqwWGDixBzuvDMXgKeeiuGpp6Lx4V06xIsUeEREAphpk5YPU3jllQBELFqEJT3dtDqqwzDgvvvyePbZg4+nTYvmn/+Mxe02ty6pOgUeEZEAZuak5TIlZ5xBSdeuGMXFRM6caVodJ+Oee+CZZxwYhoe33opkwYIIs0uSKlLgEREJYP4wpIVhlN9uImLmzFpzmfrfXXddIdOmORgwoIAhQwqOf4D4FQUeEZEAdnjg8Zg4+aTokktwNmlCSFYW9oULTavjZA0YcDD0WA9d4+x0HrwJqfg/BR4RkQBWNofH5XKRn59vXiFWK/ljxwIQ9frr4HKZV8tJMg7lG7cb7r03jmHDEsnOVujxdwo8IiIBzG63Y7PZAJOHtYCCa67BHRuLJyICy4EDptbiDX/+GcInn4Tzww82fvxR997yd1p4UEQkgBmGQUxMDBkZGeTk5NCwYUPTavFERpK2fDmuxo3/6iapxZo2dbFoUTpbt1o577xis8uR41DgEREJcNHR0eWBx2yuJk3MLsGrOnRw0qHDX3eD//PPEFwuaNas9g7ZBSoNaYmIBLiyicv+EHjKGHl5hH36qdlleNX+/RaGDk1k0KAkNm1Sf4K/UeAREQlwMTExgP8EHiMzk3pnnknC6NGE/PGH2eV4jcVy8I7r+/eHMHhwEj/+aDO7JDmMAo+ISICLjo4G/CfweBISDi5E6HYTOX262eV4TZ06bt55J52uXUtwOCxcfXUi69ZpMrO/UOAREQlw/jikVb4Q4fz5GFlZJlfjPfHxHubPz6BHj2Ly8y1cd10iq1aFmV2WoMAjIhLw/G1IC6CkVy9KO3TAUlBA5OzZZpfjVVFRHt5+O4MLLyyiqMhg9OgEliwJN7usoKfAIyIS4Pwx8Bx+u4nIN96A4sC6rDs8HF5/PZMBAwpwOg0mTIhn3jzdf8tMCjwiIgHOLwMPUHjFFbiSkwk5cAD7+++bXY7X2Wzw0ksOrr02H4/H4J574njttUizywpaCjwiIgHOXwMPoaHkjxmDx2LB+vvvZldTI0JCYPLkbMaPzwNg4sRYnnsuGhNvaxa0tFCAiEiA89vAA+SPGEHhZZfhatrU7FJqjGHAP/+ZQ3S0m2eeieH556OJj3czerSJ9zYLQgo8IiIBzp8Djyc6Gtehy+YDmWHAHXfkERXlYf78CAYOLDC7pKCjIS0RkQDnz4HncCE7dxKya5fZZdSoMWPyWbYsjfj4v8a03G4TCwoiCjwiIgHOH9fh+bvI11+nbs+eRD/7rNml1LjQw9YifPPNCG64IYHCQvPqCRYKPCIiAa5speWSkhKKiopMrqZyJd27Y3g82D/4AMvevWaX4xMHDlh44okYVq0K54MP7GaXE/AUeEREAlxUVBSGYQD+28tTesopFJ99NobTSdQbb5hdjk/Uretm9uxMbrstl6uvVhdPTVPgEREJcBaLpVbM4ym/3cTs2Ri5uSZX4xtnnVXCAw/kciiPUlBgkJamf5prglpVRCQI+NsNRCtTfP75lLZqhSU3l4h588wux+eKi+HGG+MZMCCJP/8MMbucgKPAIyISBGpDDw8WC/njxgEcvIt6aanJBflWRoaFHTus7NxpZeDAJLZtU+jxJgUeEZEgUBuu1AIoGDwYV1ISlrw8rFu3ml2OTzVo4Oa999Jp3bqUfftCGDQoiZQULZfnLV4JPAcOHDhi29Yg+6CKiPiz2jCkBUB4OJkzZ7L/m29wduhgdjU+V7++m3ffzaBTpxIyMkIYMiSJb7+1mV1WQPBK4OnZsydz584FwOPx8OyzzzJo0KAqn2f58uVMmDCBa6+9loceeoht27Ydc//8/HymT5/OuHHjGD58OHfccQfff/99td6DiEggqxVDWoeUdu2KJzJ4b7KZmOjmnXcyOOOMYnJyLAwblsiXX4Ye/0A5Jq8Eni+//JLFixdzxRVXcM4557B7926+/vrrKp1j7dq1zJo1i6uuuorJkyfTtGlTnnjiCbKzsyvd3+l0MmnSJNLS0rj77rt54YUXuOmmm0hISPDGWxIRCSi1ZUirAo8H288/m12FKWJiPMydm8l55xVRUGDh+usTWbEizOyyajWvBJ46derQtWtXtm7dyv79++nXrx8RERFVOseHH37I+eefT58+fWjUqBFjx44lNDSUzz//vNL9P/vsM/Ly8rj33ntp164ddevWpUOHDjRr1swL70hEJLDUmiGtMkVF1Ln4Yur06xd0c3nKRER4ePPNTC65pJCSEoMbb0xg8WItUFhdXpkNdfbZZ3Peeefx448/sn//fsaNG8f8+fN56623Tuh4p9PJ77//zoABA8q3WSwWOnfuzJYtWyo95rvvvqN169bMmDGDb7/9lpiYGM455xwGDBiAxVJ5jistLaX0sFn/hmFgt9vL/+xNZefz9nnl6NTmvqc2973qtvnhPTy14u/LbsfVuDG2X34h6rXXyJ4yxbRSzPych4fDq686uPtuD4sWRXDbbXEUFBhcd11gL1RYE23ulcDz4osvctZZZwHQpEkTli9fzhtVWCkzJycHt9tNXFxche1xcXHsPcoS4/v37yctLY2ePXvy4IMPkpqayvTp03G5XAwZMqTSYxYvXsyiRYvKHzdv3pzJkydTp06dE661qpKTk2vs3FI5tbnvqc19r6pt3qRJE+Dg7SXq169fEyV537/+BcuXE/Huu0Q8/zzUq2dqOWZ+zhcsgNtug//+1+C+++KIiopj/HjTyvEZb7a5VwLPWWedRWlpKbt27aK4uLh8W03yeDzExMRw0003YbFYaNGiBZmZmXzwwQdHDTwDBw6kf//+5Y/LkmNaWhpOp9Or9RmGQXJyMqmpqXg8nuMfICdNbe57anPfq26buw/dkvvAgQPs27evpsrzrubNSezWjdDvviP36afJu+8+U8rwl8/5P/8JFks0c+ZE0K5dBvv2efffLX9yvDa3Wq1V7qzwSuCZN28ekyZN4s8//6Rt27b89NNPnH766axdu/aEjo+JicFiseBwOCpsdzgcR/T6lImLi8NqtVYYvmrYsCEOhwOn04nVeuRbs9ls2GyVX95XUx9ij8ejfwh8TG3ue2pz36tqmx8+h6c2/V3ljRtHwk03ETlzJnm33orHbt4cFn/4nD/4YA6jR+dRr56bWvTXWG3ebHOvTFp+6qmn+Oabb2jRogXffPMNGzZsoHHjxid8vNVqpUWLFqSkpJRvc7vdpKSk0KZNm0qPadu2LampqeW/tQDs27eP+Pj4SsOOiEgwq5VXaQFFl1yCs0kTLA4H9gULzC7HL9Sr99e/e+vWhfLgg7G4XCYWVEt4JfCEhYWVX5VVWlrKqaeeyi+//FKlc/Tv359Vq1axevVqdu/ezfTp0ykuLqZ3794ATJs2rXytH4CLLrqIvLw8Zs6cyd69e/n+++9ZvHgxF198sTfekohIQKl1V2mVCQkh79DtJsJXrza3Fj+TnW0wZkwCs2ZF8vrrwbtu0YnySldIcnIyDoeDyy+/nEsuuYTExMQq9fAA9OjRg5ycHBYuXIjD4aBZs2Y89NBD5UNa6enpFWZrJyUl8c9//pO33nqLe++9l4SEBC655JIKV3qJiMhBZT08+fn5Rx3291eF11yDq2lTivv0MbsUvxIb6+HZZx3MmxfBDTfkm12O3zM8Xh6QXL16NTk5OfTr14/Q0NqxMmRaWlqFy9W9wTAM6tevz759+0wf8w0WanPfU5v7XnXbvLS0tHydspSUFOLj42uowsDj759zjwfK+gM8HigqMrDb/a/Oqjhem9tsNnMmLR+ubAhKRET8h81mw263U1hYSE5OTq0NPEZhIUZ2Nm4thVDu8LDzxBMxrFsXyttvZ5CQULtDj7d5JfBs376dqVOn8scff+A6bObUBx984I3Ti4iIF8TGxpYHntoofPlyYu+5h5Kzzybr9dfNLsfvHDhgYf58O1lZB286OnduRoUJzsHOK4Fn4MCB3HrrrVxzzTVHXeVYRETMFRUVBUBeXp7JlVSPs3lzQrKyCF++nJCdO3HpVkIV1Kvn5r33Mhg6NJFNm2wMGpTEggUZNGqkS7jAS1dpRUREMG7cOM4++2y6d+9e/iMiIv6j7Eqt3NxckyupHmfbthT17YvhdhM5fbrZ5filNm2cLF6cTuPGTnbutDJgQBLbtoWYXZZf8ErgefDBB7n//vv59NNP+eKLL8p/RETEf5T18OTn194resouUY+YPx8jM9PkavxT06YuFi9Op1WrUvbtC2HQoCR++aX2XJVXU7zSAp988gmrV69m27Zt5UNahmFw7rnneuP0IiLiBbW9hwegpGdPSjt2xPbLL0S+/TZ5d9xhdkl+qX79g8Nbw4cnkJISypAhScyalcHpp3v3iuTaxCuBZ+XKlWzevLl23IFXRCRIRUYeXJyuts7hAcAwyLv5ZuJvu43IN98k7+abISzM7Kr8UmKim4ULMxg5MoFvvglj2LBE3ngjk169SswuzRReGdI688wz2b59uzdOJSIiNSQQengACi+/HFf9+oSkpRG2bp3Z5fi12FgPc+dmcu65RRQUWBg5MpEVK4IzIHqlhyclJYVOnTrRtm1bwsLC8Hg8GIbBhg0bvHF6ERHxgtp+lVY5mw3H88/jbNAAV6tWZlfj9yIiPMycmcktt8SzfLmdG29M4KWXHAwYUGh2aT7llcCzZMkSb5xGRERqUKD08AAUa45olYSFwauvZnHXXR7ef9+OzRZ8ixJ6JfA0bdrUG6cREZEaFDA9PH9jSUvDXcXbDAQjqxVefNHByJH5QTl5WasEiogEiUDq4QHA4yHuttuo160b1pQUs6upFSwWKoSd3btDeOWVSPzwFmFeVyOBZ9++fRQXF9fEqUVEpJoCrofHMMDjwXC5iHr1VbOrqXUKCw2GDk3k8cdjefnlKLPLqXE1EnhGjBhBu3btuOeee2ri9CIiUg0B18MD5N90EwD2JUuw7NljcjW1i93uYcyYPJo0cTJgQIHZ5dS4Ggk8K1euZMeOHdx44401cXoREamGgOvhAUo7d6a4R4+DvTwzZphdTq0zalQBn32WRoMGf91kNFCHt04q8IwYMQKAM844gzPPPPOIn3bt2nmlSBEROXllgSeQeniAg4sPAhFz5mAE2HvzBbv9r4SzZEk4o0YlUBiAV6yf1FVazzzzDACLFi3ySjEiIlJzyoa0CgoKcLlchIQExk0li/v0obR1a2xbtxIxbx75h+63JVXjcBjcf38cubkWRoxIZObMTKKiAqe756R6eOrXrw/Ali1bSExMpGnTpixbtownnniCwkCMhyIitVhZDw/U7huIHsFiIf/QFIqIhQsDd0ymhsXFeXjrrUyiotysWxfG0KGJZGUFzi2jvDKH55577iEqKor169czZ84cLrjgAsaMGeONU4uIiJeEhYURGhoKBN6wVsHgwWRPnEj6e+8dvHpLqqV79xIWLswgPt7FDz+EctVVSRw4EBgr2Hj1Xbz//vvcfPPNXH311RQUBP6MbxGR2iYQJy4DYLeTf+ONeGJizK6k1uvSpZR3382gXj0XmzbZGDgwid27a//wp1cCT4MGDRgxYgTz5s2jf//+FBcX43K5vHFqERHxokC8NP0IHg+Gfuk+KW3bOnnvvXQaN3ayc6eVgQMT2b69docerwSeRYsWMXDgQFauXEl8fDyZmZlMmTLFG6cWEREvCtgenkNs335L0qWXEnfXXWaXUus1a+bivffSadWqlL17rQwalMQvv3jljlSmqFbgWbt2LSmHLeMdGRnJoEGDaN26NXBwMvNFF13knQpFRMRrAr2HxxMZSejGjYR/9BEhf/5pdjm1XoMGbt59N4OOHUtJTw9hyJAkvv/eZnZZ1VKtwDNhwgS+/vrrI7Zv3749YP8nEhEJBIHew+Ns356ic8/FcLuJfOMNs8sJCElJbt55J53TTy8hO9vC0KGJrF0banZZVVatwLN582Z69+59xPaVK1cybNiwk61JRERqSKAuPni4snV4IubO1UKEXhIb62Hu3AzOOaeY/HwL69aFmV1SlVUr8MTExJCVlXXE9l69erF+/fqTLkpERGpGoPfwABT37k1pmzZY8vKImDfP7HICRmSkh1mzMpgyxcHdd9e+IFmtwNOvX79KJyVbLBZKSkpOuigREakZgT6HBwDDIH/sWAAiZ8wAp9PkggJHeDgMG1ZQvtRRYaHBZ5/Vjt6eagWexx9/nP/9738MHjyYn3/+GYCioiImT57MKaec4tUCRUTEe4KhhwegYOBAXImJWHfvJnzVKrPLCUglJTB2bDzXX5/AvHkRZpdzXNW6vqxx48asX7+e8ePH06VLF8LCwnA6ncTGxrJ06VJv1ygiIl4SFD08AHY7OQ8/jCcmhqILLjC7moBktUKzZk7Cw0Np2dL/e9GqfUF906ZN+eijj9i1axc//vgjNpuN7t27k5CQ4M36RETEi4KlhwegcMgQs0sIaBYLPP54DqNG5dOypf8vNlylwPPII49w5ZVX0q1bt/JtTZo0oUmTJl4vTEREvK8s8ATUzUNPhMsFAXJ3eH9iGFQIO7/8YmX5cjt3353rd7c0q9Icnt27d3PJJZfQqFEjxo8fz8cff6xJyiIitUhExMG5FkETeDweov7v/6h35pmE7NpldjUBLSfHYPjwRJ5/Ppp//jMWt9vsiiqqUuB54403SE1NZd68eURHR3PnnXeSlJTE4MGDmTVrFpmZmTVVp4iIeEFkZCQQRIHHMAj95htCUlMPXrElNSYmxsN99+ViGB7eeiuSu++O86sL5Kp8lZbFYqFXr14888wzbN68ma+//pru3bvz6quv0qBBA84991ymTJnCnj17aqJeERE5CWU9PIWFhSZX4jvlCxHOn4+Rk2NyNYHt2msLmDrVQUiIh3feieCWW+Lxl4Ggk755aPv27bnvvvv46quv2LVrFyNHjuTLL79knhZ7EhHxO0HXwwMUn3eeFiL0oYEDC3nttSxCQz189lkYW7f6xw1HvVpF3bp1GTNmDGPGjPHmaUVExEsOn8Pj8Xgw/G1maU0wDPLHjSPunnuInDGD/DFjDl5TLTWmX78iZs7MxDCgY0f/GNc6qb/xESNG8Pbbb3PGGWdU+j/Nhg0bTub0IiLiZWU9PB6Ph6KiIux2u8kV+UbBwIFEP/UU1j17CP/oI4quuMLskgLeeecVm11CBScVeJ555hkAFi1a5JViRESkZh0ecAoKCoIm8BAeTsHIkUQ//zxRr72mwBOETmoOT/369QHYsmULiYmJNG3alGXLlvHEE08E1YQ4EZHaIiQkpDzkBNM8HoD8kSPJHz4cRyX3gpTAd9KTlgHuueceoqKiWL9+PXPmzOGCCy7QPB4RET8VdGvxHOJOSiL72WdxtmtndiliAq8EnjLvv/8+N998M1dffTUFBQXePLWIiHhJMF6pJeKVaeoNGjRgxIgRfPHFF/z4448UFxfjcvn/fTVERIJRWeAJ1l9MQ7ZtI3rqVNx16pDzr3+ZXY74SLV6eNauXUtKSkr540WLFjFw4EBWrlxJfHw8mZmZTNEYqYiIXyob0grWwGPds4eIRYuImDVLCxEGkWoFngkTJvD111+XP46MjGTQoEFYLBZyc3OpX78+F110kdeKFBER7wn2Ia3ic889uBBhfj4R8+ebXY74SLUCz+bNm+ndu/cR21euXMmwYcNOtiYREalBwTppuZxhHFx8EIh8882Dd1KXgFetwBMTE0NWVtYR23v16sX69etPuigREak5wT6kBVA4eDDuuDisu3YR/umnZpcjPlCtwNOvX79K5+hYLBZK/OUuYSIiUqlgn7QM4LHbyb/uOgAip083uRrxhWoFnscff5z//e9/DB48mJ9//hmAoqIiJk+ezCmnnOLVAkVExLuCfQ5PmfyRI/FYrYStW4f1sAtxJDBV67L0xo0bs379esaPH0+XLl0ICwvD6XQSGxvL0qVLvV2jiIh4UdDP4TnE3aAB+aNG4U5MxNWwodnlSA2r9jo8TZs25aOPPmLXrl38+OOP2Gw2unfvTkJCgjfrExERL9OQ1l9yHnvM7BLER0564cEmTZrQpEkTb9QiIiI+oEnLEoy8emsJERHxf5rD8zcuF+HLlhE/ahQUF5tdjdQQBR4RkSCjOTx/43YT+8gj2FeswL5kidnVSA1R4BERCTKaw/M3Nhv5o0YBEDljBng8JhckNUGBR0QkyGgOz5Hyhw/HHR5OaEoKoYfdOkkChwKPiEiQ0RyeI3kSEigcPBjQQoSBSoFHRCTIKPBULv/GGwEI/+QTQnbtMrka8TYFHhGRIFM2pFVYWIjb7Ta5Gv/hbNOGovPOw3C7D95UVALKSa/DIyIitUtZDw8cDD2HPw52+WPGYMnLo+Sss8wuRbxMgUdEJMiEh4djGAYej4f8/HwFnsMU9+1L8fnnm12G1AANaYmIBBnDMDSP52gMw+wKpIYo8IiIBCEtPnhsRlYWUf/9L6Fr1phdiniJ3w1pLV++nKVLl+JwOGjatCmjR4+mVatWxz3uq6++4sUXX+T000/nvvvu80GlIiK11+ETl+VIUS+/TPR//kNxr15k9upldjniBX7Vw7N27VpmzZrFVVddxeTJk2natClPPPEE2dnZxzzuwIEDvP3227Rv395HlYqI1G5lQ1p5eXkmV+KfCkaOxBMSQtiXX2LdtMnscsQL/CrwfPjhh5x//vn06dOHRo0aMXbsWEJDQ/n888+Peozb7Wbq1KlcffXV1K1b14fViojUXna7HYCioiKTK/FProYNKerXD9BChIHCb4a0nE4nv//+OwMGDCjfZrFY6Ny5M1u2bDnqcYsWLSImJoa+ffvy22+/HfM1SktLKS0tLX9sGEb5//SGlyeqlZ3P2+eVo1Ob+57a3Pe81eaHBx79/VUuf+xY7MuWYX/vPUhPVzv5UE18t/hN4MnJycHtdhMXF1dhe1xcHHv37q30mE2bNvHZZ5/xzDPPnNBrLF68mEWLFpU/bt68OZMnT6ZOnTrVrvt4kpOTa+zcUjm1ue+pzX3vZNs8ISEBAJvNRv369b1RUuC54gro1g3ju+/gtddIfughsysKOt78bvGbwFNVhYWFTJ06lZtuuomYmJgTOmbgwIH079+//HFZckxLS8PpdHq1PsMwSE5OJjU1FY/uvOsTanPfU5v7nrfavOz7LzU1lX379nmrvIBjv+EG4r77Dv7zH1Kvuw6PzWZ2SUHheJ9zq9Va5c4Kvwk8MTExWCwWHA5Hhe0Oh+OIXh+A/fv3k5aWxuTJk8u3lTXK0KFDeeGFF45IhjabDdtRPqw19WXt8Xj0D4GPqc19T23ueyfb5mVDWgUFBfq7O4aC/v2JevpprOedBzk5eA71jIlvePO7xW8Cj9VqpUWLFqSkpHDmmWcCByckp6Sk0O/QxLHDNWjQgClTplTYNn/+fIqKirjhhhtISkrySd0iIrVRWeDRZenHERpK2po11G/WDM++faBwWGv5TeAB6N+/P//5z39o0aIFrVq14qOPPqK4uJjevXsDMG3aNBISEhg+fDihoaE0adKkwvFll1n+fbuIiFSkwFMFYWFmVyBe4FeBp0ePHuTk5LBw4UIcDgfNmjXjoYceKh/SStcseRERr1DgqTrrli3YNmyg4NprzS5FqsGvAg9Av379Kh3CAnjssceOeeyECRNqoCIRkcCjwFNFv/9Ond698YSEUNy7N66GDc2uSKrIrxYeFBER3wgPDwcUeE5YixYUn3MOhstFxFtvmV2NVIMCj4hIENJKy1WXP2YMAJFz5mAoKNY6CjwiIkGo7OahBQUFJldSexRfeCHOJk2wOBwHV1+WWkWBR0QkCGkOTzWEhJB/ww0ARM6YoUvUaxkFHhGRIKTAUz0FQ4fijojAtnkzoWvWmF2OVIECj4hIEFLgqR5PbCyFV1+NKzGRkPR0s8uRKvC7y9JFRKTmKfBUX84995D98MNw6Eo3qR0UeEREgpACT/V54uPNLkGqQUNaIiJB6PDAo5uHVpPbTdjKlRh5eWZXIidAgUdEJAiVBR63201JSYnJ1dROCSNHkjhyJPZ33jG7FDkBCjwiIkGoLPCAhrWqq6hvXwCi3ngD3G6Tq5HjUeAREQlCNpsNm80GaPHB6iocMgR3dDTW338nbPVqs8uR41DgEREJUpq4fHI8UVEUDB0KHFqIUPyaAo+ISJBS4Dl5+aNG4TEMwlevxrptm9nlyDEo8IiIBCkFnpPnatqUogsvBCDyjTdMrkaORYFHRCRIKfB4R/7o0QBYf/1V99fyY1p4UEQkSIUfWim4qKjI5Epqt5KePUn74ANKTzsNDMPscuQoFHhERIKUeni8xDAo7dbN7CrkODSkJSISpBR4vM/IyyNk506zy5BKKPCIiASpiIgIQIHHW8JWrKBet27E3Xef2aVIJRR4RESCVFkPjxYe9A5nx44YhYWEffUV1t9+M7sc+RsFHhGRIKUhLe9yNWxIUb9+gC5R90cKPCIiQUqBx/vyx4wBIOK99zAyM02uRg6nwCMiEqQUeLyv5MwzKenUCaOoiMi5c80uRw6jwCMiEqQUeGqAYZT38kTOnAmlpebWI+UUeEREgpQCT80ovOIKXImJWPbvJ/SHH8wuRw7RwoMiIkFKgaeGhIfjeOklnC1b4mrc2Oxq5BAFHhGRIKXAU3OKe/c2uwT5Gw1piYgEKS086Bu6Wss/KPCIiASpspuHKvDUDKOwkPhRo0g+4wwsaWlmlxP0FHhERIKUhrRqlsduJyQtDaOoiIjZs80uJ+gp8IiIBCkFnpqXf+ONAETOmgUlJSZXE9wUeEREgpQCT80rvPRSXPXqEXLgAPYPPzS7nKCmwCMiEqQUeHwgNJT8668HIHLGDPB4TC4oeCnwiIgEqbLAU1paSqlWBK4xBdddhyc0lNAff8T23XdmlxO0FHhERIJUWeABKCoqMrGSwOZOSqJwwAAAIjV52TRaeFBEJEiFhYVhGAYej4fCwkKio6PNLilg5d10EyWnnkrhVVeZXUrQUuAREQlShmEQERFBfn4+BQUFZpcT0Jzt2uFs187sMoKahrRERIKYJi6bwOMBl8vsKoKOAo+ISBBT4PGt8KVLqXPBBdgXLTK7lKCjwCMiEsQUeHwrZPdubJs2ETV9ui5R9zEFHhGRIKbA41sFQ4fiDg/H9uuvhK5fb3Y5QUWBR0QkiJUFHk1a9g1PfHz5lVqRb7xhcjXBRYFHRCSIlQUercPjO/mjRwMQvnw5IX/+aXI1wUOBR0QkiIWHhwMKPL7kbNuW4l69MNxuImfONLucoKHAIyISxMoCj+bw+FbeoV6eiHnzMDSc6BNaeFBEJIhpSMscxRdcQP7QoRRdfjmeQ6FTapYCj4hIEFMPj0ksFrKfe87sKoKKhrRERIKYengkWCjwiIgEMU1aNpclPZ3op54i7s47zS4l4CnwiIgEMQ1pmcvIySF62jTsixYRsmOH2eUENAUeEZEgpiEtc7latKCob18Mj4fIN980u5yApsAjIhLENKRlvvwbbwQgYsECjNxck6sJXAo8IiJBTPfSMl/xuedS2qoVlrw8IhYuNLucgKXAIyISxNTD4wcMo/x2E5FvvAFut8kFBSYFHhGRIKZJy/6h8KqrcMfEYN25k7DPPjO7nICkhQdFRIKYJi37B09kJPk33oglMxNnq1ZmlxOQFHhERIKYhrT8R+4//mF2CQFNQ1oiIkFMQ1oSLBR4RESCmIa0/IzHg+2bb4i77TYMh8PsagKKAo+ISBA7vIfH4/GYXI0AxD34IBHvvUfE/PlmlxJQ/G4Oz/Lly1m6dCkOh4OmTZsyevRoWh1lAtfKlSv54osv+PPPPwFo0aIFw4YNO+r+IiJSUVkPD0BxcXF5ABKTGAb5Y8YQd889RL75Jvljx0JIiNlVBQS/6uFZu3Yts2bN4qqrrmLy5Mk0bdqUJ554guzs7Er3//XXXznnnHN49NFHmTRpEomJiUyaNInMzEwfVy4iUjsdHnA0rOUfCgYMwBUfj3X3bsJXrDC7nIDhV4Hnww8/5Pzzz6dPnz40atSIsWPHEhoayueff17p/rfffjsXX3wxzZo1o2HDhtx88814PB5+/vlnH1cuIlI72Ww2Qg71IGjisp+w2ym49loAImfMMLmYwOE3Q1pOp5Pff/+dAQMGlG+zWCx07tyZLVu2nNA5iouLcTqdREVFVfp8aWkppaWl5Y8NwyjvzjUMo/rFV6LsfN4+rxyd2tz31Oa+VxNtbrfbycvLo7i4WH+XlTDjc15www1EvfwyYevWYfv1V5wdO/rstf1BTbS53wSenJwc3G43cXFxFbbHxcWxd+/eEzrHnDlzSEhIoHPnzpU+v3jxYhYtWlT+uHnz5kyePJk6depUu+7jSU5OrrFzS+XU5r6nNvc9b7Z5REQEeXl5REdHU79+fa+dN9D49HNevz4MHgwLF1Jn3jwI0p4eb7a53wSek/X+++/z1Vdf8dhjjxEaGlrpPgMHDqR///7lj8uSY1paGk6n06v1GIZBcnIyqampuvLBR9Tmvqc2972aaPOy78xdu3aRlJTklXMGErM+57ZrryVu/XryW7akYN8+n72uPzhem1ut1ip3VvhN4ImJicFiseD427oDDofjiF6fv/vggw94//33efjhh2natOlR97PZbNhstkqfq6kPscfj0T8EPqY29z21ue95s80Pv2O6/h6Pztef85Ju3TiwZs3Bq7SC9O/Fm23uN5OWrVYrLVq0ICUlpXyb2+0mJSWFNm3aHPW4JUuW8O677/LQQw/RsmVLX5QqIhJQtNqynzIMXZLuRX4TeAD69+/PqlWrWL16Nbt372b69OkUFxfTu3dvAKZNm8bcuXPL93///fdZsGAB48ePp27dujgcDhwOhy6tFBGpAq227OdKSrC/9x6hX3xhdiW1mt8MaQH06NGDnJwcFi5ciMPhoFmzZjz00EPlQ1rp6ekVZmx/+umnOJ1Onn/++Qrnueqqq7j66qt9WbqISK2lG4j6t6jXXiPmqaco6daN9HPPNbucWsuvAg9Av3796NevX6XPPfbYYxUe/+c///FBRSIigU1DWv6t4OqriZ4yhdDvvsP244+Unnqq2SXVSn41pCUiIr6nIS3/5q5bl8IrrgC0EOHJUOAREQlyGtLyf/ljxgBgX7oUy/79JldTOynwiIgEucMvSxf/VNqlCyWnn45RWkrk7Nlml1MrKfCIiAQ59fDUDnmjRwMQMWsWFBebXE3to8AjIhLkNGm5dii69FJcyck4W7cmJD3d7HJqHb+7SktERHxLk5ZrCZuNA6tW4TnO3QekcurhEREJchrSqj0UdqpPgUdEJMhpSKv2saSlEf7hh2aXUatoSEtEJMhpSKt2saSmUq9HD3A62d+1K+6GDc0uqVZQD4+ISJBTD0/t4k5OpuS00zBcLiJnzTK7nFpDgUdEJMiph6f2yb/xRgAi5swBBdUTosAjIhLkNGm59im68EKcjRsTkpVFxOLFZpdTKyjwiIgEOQ1p1UIhIeTfcAMAkW+8AR6PufXUAgo8IiJBTkNatVPB0KG47XZsv/1G6Nq1Zpfj9xR4RESCnIa0aidPXByFQ4bgCQvDunWr2eX4PV2WLiIS5DSkVXvl3nknuffcgzsx0exS/J4Cj4hIkCsb0nK5XJSWlmKz2UyuSE6Uu149s0uoNTSkJSIS5Mp6eEC9PLWZNSUFIz/f7DL8lgKPiEiQCwsLwzAMQPN4aqu4u+6i7sUXY1+0yOxS/JYCj4hIkDMMQxOXa7nSzp2BQ5eou90mV+OfFHhEREQTl2u5gquvxh0VhW3bNsK++MLscvySAo+IiGgtnlrOExVFwTXXABA5Y4bJ1fgnBR4REdGQVgDIHz0aj2EQ/tlnhGzfbnY5fkeBR0REynt4NKRVe7maNaP4ggsAiJw509xi/JACj4iIqIcnQOSPHg1A2P/+p8nLf6OFB0VERJOWA0Rxr15kzphB0fnng0V9GodT4BEREU1aDhSGQVG/fmZX4ZcU/0RERENagcjpxJKWZnYVfkOBR0RENKQVYEK/+oq6PXoQd+edZpfiNzSkJSIiGtIKMK5GjQjZuxfrnj1Yt23D2aqV2SWZTj08IiKiHp4A42ralKILLwQg8s03Ta7GPyjwiIiIengCUNkl6vZ33sHIyTG5GvMp8IiIiCYtB6CSnj0pbdMGS34+EQsXml2O6RR4RESE8PBwQkJCcGuxusBhGOW9PJFvvhn0CxEq8IiICOPGjWPXrl0899xzZpciXlQ4eDDu2FisO3cSun692eWYSldpiYgIhmGYXYLUAE9EBNlPPomzSRNKTzvN7HJMpcAjIiISwAoHDDC7BL+gIS0REZFgUVJidgWmUeAREREJcEZhIbEPPEC9M87AyM42uxxTKPCIiIgEOE94OKHffENIejoRCxaYXY4pFHhEREQC3aFL1D0WCyF79phdjSk0aVlERCQIFA4aRHHv3rgaNjS7FFOoh0dERCQIeOz2oA07oMAjIiIiQUCBR0RERAKeAo+IiIgEPAUeERERCXgKPCIiIhLwFHhEREQk4CnwiIiISMBT4BEREZGAp8AjIiIiAU+BR0RERAKeAo+IiIgEPAUeERERCXgKPCIiIhLwFHhEREQk4FnNLsAfWK011ww1eW6pnNrc99Tmvqc29z21ue8drc2r83dheDwez8kWJCIiIuLPNKRVQwoLC7n//vspLCw0u5SgoTb3PbW576nNfU9t7ns10eYKPDXE4/GwY8cO1IHmO2pz31Ob+57a3PfU5r5XE22uwCMiIiIBT4FHREREAp4CTw2x2WxcddVV2Gw2s0sJGmpz31Ob+57a3PfU5r5XE22uq7REREQk4KmHR0RERAKeAo+IiIgEPAUeERERCXgKPCIiIhLwdGOQk7B8+XKWLl2Kw+GgadOmjB49mlatWh11/3Xr1rFgwQLS0tJITk7m2muv5bTTTvNhxbVfVdp89erV/Pe//62wzWazMWfOHF+UWuv9+uuvfPDBB+zYsYOsrCzuuecezjzzzGMe88svvzBr1iz+/PNPEhMTGTx4ML179/ZNwQGgqm3+yy+/MHHixCO2v/baa8TFxdVgpYFj8eLFbNiwgT179hAaGkqbNm247rrraNCgwTGP0/d59VWnzb3xfa7AU01r165l1qxZjB07ltatW7Ns2TKeeOIJXnjhBWJjY4/Yf/Pmzbz44osMHz6c0047jTVr1vDss88yefJkmjRpYsI7qH2q2uYAdrudF1980ceVBobi4mKaNWtG3759mTJlynH3P3DgAE8//TQXXnght912GykpKbzyyivExcVx6qmn1nzBAaCqbV7mhRdeICIiovxxTExMTZQXkH799VcuvvhiWrZsicvlYt68eUyaNInnn3+e8PDwSo/R9/nJqU6bw8l/nyvwVNOHH37I+eefT58+fQAYO3Ys33//PZ9//jkDBgw4Yv+PPvqIU089lSuuuAKAoUOH8vPPP7N8+XLGjRvny9Jrraq2OYBhGPpNt5q6du1K165dT3j/FStWULduXa6//noAGjVqxKZNm1i2bJkCzwmqapuXiY2NJTIysgYqCnz//Oc/KzyeMGECN954I7///jsdOnSo9Bh9n5+c6rQ5nPz3uQJPNTidTn7//fcK/8haLBY6d+7Mli1bKj1my5Yt9O/fv8K2Ll268M0339RkqQGjOm0OUFRUxC233ILH46F58+YMGzaMxo0b+6Di4LN161Y6d+5cYVuXLl2YOXOmOQUFkfvuu4/S0lIaN27MkCFDaNeundkl1VoFBQUAREVFHXUffZ9714m0OZz897kmLVdDTk4Obrf7iKQZFxeHw+Go9BiHw3HEsEtsbOxR95eKqtPmDRo0YPz48dx3333cdtttuN1u/vWvf5GRkVHzBQeho33GCwsLKSkpMamqwBYfH8/YsWP5xz/+wT/+8Q8SExOZOHEiv//+u9ml1Uput5uZM2fStm3bYw5N6fvce060zb3xfa4eHglYbdq0oU2bNhUe33XXXXz66acMHTrUxMpEvKNBgwYVJnq2bduW/fv3s2zZMm677TYTK6udZsyYwZ9//sm///1vs0sJGifa5t74PlcPTzXExMRgsViOSPMOh+Oo44txcXFkZ2dX2Jadna35JSeoOm3+d1arlebNm5Oamur9AuWon3G73U5oaKhJVQWfVq1a6TNeDTNmzOD777/n0UcfJTEx8Zj76vvcO6rS5n9Xne9zBZ5qsFqttGjRgpSUlPJtbreblJSUCgn0cG3atOHnn3+usG3jxo20bt26RmsNFNVp879zu93s2rWL+Pj4miozqLVu3brSz/iJ/v2Id+zcuVOf8SrweDzMmDGDDRs28Mgjj1C3bt3jHqPv85NTnTb/u+p8nyvwVFP//v1ZtWoVq1evZvfu3UyfPp3i4uLyNUemTZvG3Llzy/e/9NJL+emnn1i6dCl79uxh4cKFbN++nX79+pn0Dmqfqrb5okWL+Omnn9i/fz+///47L730EmlpaZx//vkmvYPapaioiJ07d7Jz507g4GXnO3fuJD09HYC5c+cybdq08v0vuugiDhw4wOzZs9mzZw+ffPIJ69at47LLLjOj/Fqpqm2+bNkyvvnmG1JTU9m1axczZ84kJSWFiy++2Izya6UZM2bw5Zdfcscdd2C323E4HDgcjgrzzvR97l3VaXNvfJ9rDk819ejRg5ycHBYuXIjD4aBZs2Y89NBD5V2a6enpGIZRvn/btm25/fbbmT9/PvPmzaN+/frce++9WrOhCqra5nl5ebz66qs4HA4iIyNp0aIFkyZNolGjRia9g9pl+/btFRa1mzVrFgDnnXceEyZMICsrq/wfYoC6devywAMP8NZbb/HRRx+RmJjIzTffrEvSq6Cqbe50Opk1axaZmZmEhYXRtGlTHn74YTp16uTz2murFStWAPDYY49V2H7LLbeU/zKl73Pvqk6be+P73PB4PJ6Trl5ERETEj2lIS0RERAKeAo+IiIgEPAUeERERCXgKPCIiIhLwFHhEREQk4CnwiIiISMBT4BEREZGAp8AjIn7jhhtuYMCAAT5/3ZkzZ2IYBoZhcOedd5Zvb9asGS+88MIxjy07TvdREvFvWmlZRHzi8FVTK/Poo4/y4osvYtZaqDExMWzevJnIyMgqHbdv3z4WLFjAo48+WkOViYg3KPCIiE/s27ev/M8LFizgkUceYfPmzeXboqKiiIqKMqM04GAgS05OrvJxycnJxMbG1kBFIuJNGtISEZ9ITk4u/4mNjS0PGGU/UVFRRwxp9e7dm9tuu40777yT+Ph46tWrx+uvv05+fj6jRo0iOjqaVq1a8fHHH1d4rZSUFC655BKioqKoV68eI0aMqHAPqqooKChg9OjRREdH06RJE1577bWTaQYRMYkCj4j4tbfeeoukpCQ2bNjAbbfdxvjx4xkyZAg9evTg+++/56KLLmLEiBEUFBQA4HA46Nu3L127duXbb79l+fLl7N+/n6uvvrpar//cc89x+umn88MPP3DLLbcwfvz4Cj1TIlI7KPCIiF/r0qUL//rXv2jdujUPPvgg4eHhJCUlMXbsWFq3bs0jjzxCRkYGGzduBGDatGl07dqVJ598knbt2tG1a1feeOMNPv/8c7Zs2VLl17/00ku55ZZbaNWqFffffz9JSUl8/vnn3n6bIlLDNIdHRPzaKaecUv7nkJAQEhMT6dy5c/m2evXqAXDgwAEAfvrpJz7//PNK5wNt376dNm3aVPv1y4bhyl5LRGoPBR4R8Ws2m63CY8MwKmwru/rL7XYDkJeXx+WXX87kyZOPOFf9+vW98vplryUitYcCj4gElNNOO413332XZs2aYbXqK05EDtIcHhEJKBMmTCAzM5Nhw4bxzTffsH37dj755BNGjRqFy+UyuzwRMYkCj4gElAYNGvDVV1/hcrm46KKL6Ny5M3feeSdxcXFYLPrKEwlWhsesZU1FRPzEzJkzufPOO3E4HKYcLyI1T7/uiIgA2dnZREVFcf/991fpuKioKG6++eYaqkpEvEU9PCIS9HJzc9m/fz8AcXFxJCUlnfCx27ZtAw5eMt+8efMaqU9ETp4Cj4iIiAQ8DWmJiIhIwFPgERERkYCnwCMiIiIBT4FHREREAp4Cj4iIiAQ8BR4REREJeAo8IiIiEvAUeERERCTgKfCIiIhIwPt/FQH10MeFPn8AAAAASUVORK5CYII=", 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" + "
" ] }, "metadata": {}, @@ -497,7 +515,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "id": "cfd2994e", "metadata": {}, "outputs": [ @@ -507,15 +525,15 @@ "Text(0.5, 1.0, 'NMC811')" ] }, - "execution_count": 11, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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Qp08fioqKSEpKYs+ePbz33nv06NGDoUOH0r9//1p3kpubi06ns7zW6XScOnWqyrZ6vZ4jR47wl7/8pcr3ExMTSUxMBGD58uUEBQVVeP/ixYuKf5Fd+RfeGn338PCo9Pk3dFqtttHFbC116buXlxcAzZo1a9Sfm92Ou9mM24EDmP70J4KaNbP9/mpRn34r/gvh7e1NdHQ00dHRnDx5ktWrV/Pqq6/y/vvv17qukqkeyv3www906tSp2lNK5TGUu3TpUoX39Xo9GgW1WrVareVbkauxVt/1en2lz7+hCwoKanQxW0td+p6bmwtAQUFBo/7c7Hbci4vxffBBynr3Rt8APq/q+n39nGvVUZwcysrKOHDgALt37+bo0aN06tSJCRMmKFpXp9ORk5NjeZ2Tk1Ptff579+5l4MCBSsMSQtiQXHO4SV5eFMyZ4+gorKLW5JCamsru3bvZv38//v7+DB48mEceeeSmhirt27cnIyODrKwsAgMDSUpKYvbs2ZXaFRUVkZqayqxZs26uF0IIm5BrDjfHLTkZQ/fumH8/HdeY1XjEZ8yYQVFREbfddhtxcXF1nnpBo9EwdepUli1bhslkYtiwYYSGhpLw+xOEMTExABw4cIBevXrh6elZp/0IIaxLRg7Kqa5eJWj8eAqmTeNqXJyjw6m3GpPDfffdR9++fa3yixEREUFERESFZeVJodzQoUMZOnRovfflaGfOnOHhhx/m66+/tizT6/UMGTKEt99+22rV4L755hsWLVqEyWRi0qRJzJw5s1KbOXPmkJiYSFBQkOL5soQoJyMH5Ty++w6VwYB+2DBHh2IV1T7dlp6ezoABA2pNDNdPCS2uaShlQgHuuecetm7dapV9CtcjIwflPL75BpOvL6V9+jg6FKuo9utAXFwcW7ZsqXUD8+fPZ9OmTVYNyprGj9dVuVylUlnuooqOLmHatEJL+wkTipg4sZjcXDWPPlrxwvlHH+VU2taNri8T2qZNG0uZ0I/qWBuiKteXAAUsJUBvTD79+/e3lCsV4mbJyEEhsxmPb79FP2gQOEkirfaI6/V6RRPvuertoLVpCGVChagvmXhPGe2pU2gvXKCgihttGqtqk8O0adMUbaCm6aAbguq+6Vd3r//17QMDTYpGClVpCGVChagvmXhPGUN4ONlffolBwRfAxqLaI+4MF4YdqSGUCRWivkpLS4FrU/iLGqhUlPXs6egorEq+DthIQygTKkR96fV63N3da5yZ2dVpzp/Hd/VqCmbMwNi2raPDsRo54jZSXib0/vvvr1AmtG/fvixdupRffvmlXtu/vkzo0KFDueuuuyqUCc3MzLTsc/To0Zw+fZo+ffrwr3/9q34dEy6lpKREnjuqheeXX+Lz3nvXCvw4ERk52EhDKRO6rpEXHBGOpdfrqx3lims8d+ygrHNnjO3aOToUq5LkYEdSJlQ0NpIcaqbOycH9wAGnukupXLXJYc2aNYrufqnqqVwhhHOQ5FAzz88/R2UyUTxypKNDsbpqk4PS8p9CCOclyaEWRiP6qCgMXbs6OhKrqzY5KJ2OWwjhvEpKSiQ51KDooYcoeughR4dhE4qvORgMBtLT08nPz6+wvHv37lYPqj6qejhM2IZ81s5Pr9fL3UrVUGdmYmreHJz0Nl9FyeH48eO88sorlJWVUVxcjJeXFyUlJeh0Ol5//XVbx3hT1Go1BoNBnui0MYPBIPe+uwC9Xk9AQICjw2iQdJMnYwgL43J8vKNDsQlFf0G3bNnC6NGjufPOO3nooYfYtGkTH330UYN8atLT05OSkhL0en2NF9Q9PDzQ6/V2jKzhqG/fzWYzarVavlG6ADmtVDXtiRO4paZSNGmSo0OxGUXJIT09nZE3XI2PjY1lxowZjB492iaB1ZVKpbIURa+J1BJ2zb6LmyMXpKvm/f77mLVaihvY3z9rUnRewNvbm+LiYgACAgI4f/48BQUFlJSU2DQ4IYRjSXKoQmkpXh9+SElMDKabKJfc2CgaOfTr14/Dhw8zcOBAhg8fzvPPP49Go+G2226zdXxCCAeS5FCZZ2Iimtxcpz6lBAqTw5QpUyw/33XXXXTo0IGSkhJ69eqleEdHjhxh06ZNmEwmRowYQWxsbKU2KSkpbN68GaPRSJMmTXj++ecVb18IYX1yt1JlJTEx5GzZgn7IEEeHYlN1uqWnS5cuN9XeZDIRHx/PggUL0Ol0xMXFERkZSUhIiKVNYWEhGzduZP78+QQFBXHlypW6hCaEsCK5IF0FrRZ9dLSjo7C5apPDsmXLmD9/PgCLFi2q9s4fJd/u09LSCA4OttQbiIqKIjk5uUJy+O677+jXrx9Bv5/D8/f3V94LIYTVGY1GysrKZORwHZ833kBVWEjBU0+BkxfXqjY5DLluyDR8+PB67SQ3Nxed7v9rOet0Ok6dOlWhTUZGBgaDgSVLllBcXMzIkSMrxCCEsK/yQj8ycvhdSQm+a9dS2qeP0ycGqCE5DBw40PJz69atCQ8Pr9QmLS1N0U6UlLQ0Go2cOXOGhQsXUlpayoIFCwgPD69QJxkgMTGRxMREAJYvX24ZadwsrVZb53UbO+m79F2J3NxcAAIDAxv9Z2aN465++200OTlon3qq0Xwe9em3omsOS5cuZcuWLZWWL1u2jE2bNtW6vk6nIyfn/2sx5+TkVKpnoNPpaNKkCZ6ennh6etKlSxfOnTtXKTlER0cTfd35vrrer+/K9/pL36XvSpQXjDIYDI3+M6v3cTebCVq9mrJOncju0QMayedRXb9v/LtalRqfczCZTJhMJsxmM2az2fLaZDKRkZGBRqNRFGD79u3JyMggKysLg8FAUlISkZGRFdpERkZy/PhxjEYjer2etLQ0WrdurWj7QgjrK3+KXq45gHtyMu7HjlE4dapLnFKCWkYOk667j/fee++t8J5arWbs2LGKdqLRaJg6dSrLli3DZDIxbNgwQkNDSUhIACAmJoaQkBB69+7NM888g1qtZvjw4YSFhd1sf4QQVlKeHBriNDn2ZvL1pejuuykeN87RodhNjcnh9ddfx2w2s2TJkgp3JalUKvz8/G7qlyYiIoKIiIgKy2JiYiq8Hj16dIObjkMIV1VUVARcmyHB1Rm6diVvzRpHh2FXNSaHZs2aAVKHWAhXVFhYCICPj4+DI3Esrw8/pDQy0ulqRNdG0QXpgoICtm/fzrlz5yrNpyRPMQvhnCQ5gObCBQKeeYbCyZPJX7rU0eHYlaLksHr1agwGA7fddpucfxTCRZRPtunKp5V8NmwAoPDxxx0cif0pSg4nT55k48aNuLm52ToeIUQDUT5ycNXkoM7Oxue99ygaPx6jC945qWjK7rCwsArPKQghnJ+rn1byXbMGSkspmD7d0aE4hKKRQ/fu3XnxxRcZOnRopZKB9Z1aQwjRMLn6yAG1mqIHHsDYvr2jI3EIxTWkdTodR48erfSeJAchnFNRUREeHh4uezo5f8kSqGLqH1ehKDksXrzY1nEIIRqYwsJClxw1aH75Bc2lS5T27esyT0NXRdE1B4CrV6+ye/dutm/fDlyblEuuQwjhvAoLC13yeoPfsmUETp6M6upVR4fiUIqSQ2pqKk8++SR79uzho48+Aq5NyvXWW2/ZNDghhOMUFRW53MjBfc8evHbsoGD6dMxNmjg6HIdSlBw2b97Mk08+yfz58y2T7XXo0IHTp0/bNDghhOMUFRW51sjBYMB/8WIMYWEUPPaYo6NxOEXXHLKzs+nRo0fFFbVajEajTYISQjieq11z8H7nHdxOnCB340aQmWiVjRxCQkI4cuRIhWVHjx6VWVOFcGIud83B3Z3ikSMpuf12R0fSICgaOUyePJkVK1Zw6623UlpayptvvskPP/zA3LlzbR2fEMJBXC05FN1/P0X33+/oMBoMRcmhY8eOrFy5kj179uDp6UlQUBAvvvhihbrQQgjnUlBQ4BLJwf3779GcO0fxhAkufevqjRQlh+3btzN69GjGjBlTYflnn33GnXfeaZPAhBCOYzabyc/Px9/f39Gh2FZxMQFPPw0mE8V33QVeXo6OqMFQdM3hP//5z00tF0I0biUlJZSWluLn5+foUGyqyT/+gfbMGfKWL5fEcIMaRw7Hjh0DrtWSLv+53MWLF/GSD1MIp5Sfnw/g1MlBe+wYvuvXU3TPPZQOHuzocBqcGpPD+vXrASgtLbX8DNfKhAYEBDB16lTbRieEcIjy5OC0p5UMBprOno0pKIgrCxc6OpoGqcbksHbtWuBaLemZM2fWa0dHjhxh06ZNmEwmRowYQWxsbIX3U1JSePnll2nevDkA/fr1Y/z48fXapxCibq5cuQI48chBqyV/3jzMnp6YAwMdHU2DpOiC9MyZMzEajZw4cYLc3Fx0Oh0dO3a0PC1dG5PJRHx8PAsWLECn0xEXF0dkZCQhISEV2nXp0oV58+bdfC+EEFbl1KeVSkrA0xN9TIyjI2nQFCWH9PR0li9fTmlpKTqdjpycHNzc3Hj22Wcr/YGvSlpaGsHBwbRo0QKAqKgokpOTFa0rhLA/Zz2tpLp8mWZ33MHVJ5+k+N57HR1Og6YoObz11ltER0dz1113ofr9PuDt27cTHx+vaDrv8tFGOZ1Ox6lTpyq1O3nyJHPnzqVp06ZMnjyZ0NDQSm0SExNJTEwEYPny5QQFBSnpQiVarbbO6zZ20nfpe23Kp8Zp27atU3xeWq2WoMBAtI8+iiojA9+oKHycoF+1qc/vu6LkcPbsWRYuXGhJDACjRo3i448/VrQTcxUFM1Q3PGzSrl071q1bh6enJ4cOHWLlypW89tprldaLjo4mOjra8vrSpUuKYrhRUFBQnddt7KTv0vfapKenA2AwGJzi8woKCqJk4UL8Pv+cvKVLKQoLAyfoV22qO+atWrWqdV1FzzkEBgaSmppaYdnPP/9M06ZNFQVYfiqqXE5OTqV1vb298fx9squIiAiMRqNlaCuEsK/8/Hw8PDws/ycbO9WXX9Jk1SqKxo2jaMoUR4fTKCgaOUyaNIkVK1bQp08fSyY6dOgQs2bNUrST9u3bk5GRQVZWFoGBgSQlJTF79uwKbfLy8vD390elUpGWlobJZKKJi8+nLoSjlP9/dBaqtDTKevQgb8UKmSJDIUXJITIykhUrVrBv3z4uX75MaGgo99xzj6KhCYBGo2Hq1KksW7YMk8nEsGHDCA0NJSEhAYCYmBj2799PQkICGo0Gd3d3nnzyyUqnnoQQ9pGTk+NUc6eZZs3i0vjx4KL1sOtCZa7qgkA1zGYzV69epUmTJg3mD3f5udGbJeeepe+u5mb6PmbMGDw8PPjggw9sHJUNmc34P/ccJbffTpNx41zyuNfnmoOikUNhYSH//Oc/2b9/PwaDAa1WS//+/XnooYfw9fW9+YiFEA1aTk5OpQJfjY3PW2/h8/bbGFu1gnHjHB1Oo6PogvS6desoLS1lxYoVvP3226xYsYKysjLWrVtn6/iEEA5w4+3njY17UhJ+S5dSfMcdFNRzdgdXpSg5pKSkMGvWLEJCQvDw8CAkJIQZM2ZUuoNJCNH4lZWVceXKlUabHNTp6TSdNg1Du3bkvfqqXICuI0XJoVWrVmRlZVVYdunSJcUXpIUQjUdubi5w7Rb2xsj73/9GpddzOT4es9zxWGeKrjl0796dZcuWMWjQIMsFjj179jB48GB27txpaTd8+HCbBSqEsI/yZ5Ia68ih4KmnKI6NxXjLLY4OpVFTlBxOnTpFcHAwp06dskx7ERwczMmTJzl58qSlnSQHIRq/xpocPD//nLKuXTG2ayeJwQoUJQcl8ycJIZxDYzyt5HboEE1nzqQkJobLb7zh6HCcgqJrDkII19HYRg7q7GwCH3kEY3DwtXKfwioUjRyEEK4jJycHlUqleO40hzIYaPr446jz8sjetg1zY4i5kZDkIISoICcnh4CAAMXFvBzJZ+NGPPbt4/Jrr2Ho3t3R4TgVSQ5CiAoa07xKhX/+M6YWLSgeO9bRoTgdxclBr9eTmZlJSUlJheWdOnWyelBCCMdpDE9Ha86exaTTYW7SxKUSQ0GBig0bfPnlFw3r1uXZdF+KksOuXbv45z//iVarxd3dvcJ769evt0lgQgjHyMnJITw83NFhVEt19Sq6yZMxtmhBzocfutQT0C+84Me77/pw553F6PXg4WG7fSlKDu+++y5PP/00PXv2tF0kQogGIScnh379+jk6jKqZzQTMmYPm3DnyVq50+sRgNsNnn3kSHm6gc2cDs2YVMHFiERERZTbft6JbWbVaLV27drV1LEIIBzMajeTl5TXY00o+Gzbg9cUX5D/3HKX9+zs6HJvLz1cxb14Ab7/tA0BIiNEuiQEUJoeJEyfy9ttvS9lOIZxcTk4OJpOJ5s2bOzqUStz37cPvxRcpHjWKwscec3Q4NnPypJZFi/wwmcDf38x//3uJF164Yvc4FJ1WatWqFR988AFfffVVpffef/99qwclhHCM8gk2mzVr5uBIKjO0aUNxbCxXXnzRKU8n5eaqWbWqCe+8442Pj5kHHiiiY0cDnToZHBKPouSwZs0aBg8eTFRUVKUL0kII51FeNaxBjRyMRlCrMbVqRd6aNY6OxupKS2HzZh/+8Y8mFBSoeOCBIp555iqBgSaHxqUoORQUFDBx4sR6lQY9cuQImzZtwmQyMWLECGJjY6tsl5aWxvz583nqqafo7wLnFIVoSBriyKHJq6/i9uOP5G7caNvbc+zMbIaEBE9eeMGPM2e0DBtWwqJF+XTs6JiRwo0UXXMYOnQou3fvrvNOTCYT8fHxPPfcc7z66qvs3buX8+fPV9lu69at9O7du877EkLUXXZ2NtBwkoP799/ju3o1Jp3OqRJDaqqWiRN1TJ0aiFZr5p13cnj33dwGkxhA4cghLS2NHTt28N///peAgIAK7z3//POK1g8ODqZFixYAREVFkZycTEhISIV2X375Jf369eP06dMKwxdCWFNWVhY+Pj74+Pg4OhRUeXkEzJyJMSyMK0uXOjocqzp82J2UFDeWLs3jgQeKcHNzdESVKUoOI0aMYMSIEXXeyY1PXOp0OktdiOvbHDhwgMWLF9f4YF1iYiKJiYkALF++nKCgoDrFpNVq67xuYyd9l75XJz8/n+DgYMd/RmYz2lmzUGVlYdi1C13btvXanKOPu9EI69apCQiAyZNNzJwJDz5ooGlTb8DbZvutT78VJYehQ4fWaePlzGZzpWU3Xr/YvHkz999/P2p1zWe6oqOjiY6Otrwuv4B2s8or2rki6bv0vTrnz58nMDDQ4Z+R+uJFmiUlUfDXv1LQti3UMx5HH3ezGf77Xx3Nmxu54448y3Jbh1Rdv5WUeK42OezevZvBgwcDVCgFeiMl1d90Op1ljni4di/1jdMBnz59mtWrVwPXvr0cPnwYtVpN3759a92+EMI6srOzG8TUGaYWLchKTMTs7+/oUOosI0PNihV+/PWv+bRqZWLTplx8fCp/UW6oqk0Oe/futSSHPXv2VLsBJcmhffv2ZGRkkJWVRWBgIElJScyePbtCm7Vr11b4uU+fPpIYhLCz7OxsBgwY4LgASkvxefddCidPbrS1GUpLYeNGX1591RejUcUf/1hCq1Yl+Po2nsQANSSHuLg4y8/1LROq0WiYOnUqy5Ytw2QyMWzYMEJDQ0lISAAgJiamXtsXQtSfXq8nLy/PoXcq+b38Mr7r11PWoQOlv385bUx27/ZgwQI/Tp92IyammCVL8mnTxujosOpE0TUHk6n6hzFqu0ZQLiIigoiIiArLqksKM2bMULRNIYT1OPoBOI/du/Fdv57CyZMbXWI4f17D88/78cUXXrRta+Dtt3MYMULv6LDqRVFymDRpUrXvyfQZQjiH8gfgHHFXjzonh4AnnqAsPJz8ep6psKeSEtiwwZc1a3wBePbZfB59tABPTwcHZgWKksPrr79e4fXly5f55JNPiIyMtElQQgj7K38AzhEjB/9581Dn5ZHz7ruYvbzsvv+6On9ey6uvNuFPfyph8eJ8WrdunKeQqqIoOdx4DrJZs2bMnDmTuLg4RRekhRANnyOfjr76xBOU3H47hm7d7L7vm/Xrrxq++MKTadMK6dDBwK5dWbRt6zxJoZyyCwZVKCoqkim8hXAijjitpCosBMDQvTvF48bZbb/18fHHXqxa1YQLF679+XTGxAA3MSvr9Q+t6fV6fv75ZwYNGmSzwIQQ9pWdnU1AQAAe9prDqLiYoNGjKYmO5up1d0c2RDt3eqDRwJAheh57rIAJE4po1cqxs6bamqLkEBwcXOG1h4cHf/zjH6VsqBBOJDs7267XG/yWLcPt+HHyFy602z5v1vnzGhYv9mPHDi+GDClhyBA9np44fWIABcnBZDJhMpm4++67cWuIs0MJIawiOzvbbqeUPP73P3w3baLg0UfR13N6HlvQ6+GNN3xZvdoXlQqeey6fRx4pcHRYdlXrNQe1Wk1CQgIajcYe8QghHMReIwf1xYsEzJlDWbdu5M+bZ/P93axduzwYMaI5K1b4MXy4nl27spkxowBXq3Om6IL04MGD+d///mfrWIQQDpSVlWWXO5XcTpwAjYbL69Y1qBoNFy5oeOSRptx337UZpLduzeGtty471e2pN+Om6jls374dnU5X4eK0knoOQoiGrbCwkKKiIrskB/3gwVzctw8a0PMMZjNMndqUtDQtzz6bz2OPFTSkvOUQdqnnIIRo2DIyMgBo2bKlzfbhdvAgbqmpFE2e3CASg9kMO3Z4MniwHh8fM8uXXyEoyERoqGuOFG5kl3oOQoiGzdbJQXXlCk1nzAC1muJx4zA3gEpzP/+s5eGHA1my5AqPPFLIrbeWOTqkBkVRcoBrNR327t3L5cuXadq0KQMGDGDYsGGVivYIIRofmyYHs5mAuXPRZGZy6eOPHZoYcnLU7NnjQWxsMV27GvjXv3KIimrcE+TZiqLk8O6775KcnMyoUaMslYU+/fRT0tPTeeCBB2wdoxDCxtLT04HKzzRZg/eWLXh9/jn5zz1H2Q0zM9vLtQnyfFi9ugl6vYoBA/Q0a2Zi8GBJDNVRlBy+/fZbVqxYUaEOdEREBM8++6wkByGcQEZGBjqdDk8rTyeqzsjA//nnKRk+nILHH7fqtpUwGuE///HilVfc+O03d4YPL2HRonyaNXP+h9jqS1Fy8PLywuuGC0heXl54e9uuMLYQwn4yMjJsckrJ1LIluZs2Udq7Nyis/WINZvO1KS9eesmPn392IzLSxKpVOQwYUGq3GBq7apPDxYsXLT+PHDmSv//978TGxhIYGEhOTg7bt29n1KhRdglSCGFbGRkZtG7d2nobLC3FLSWFsltvtfsT0AcPurF8uR/79nnQtq2B9etzeeghX3JyJDHcjGqTw401ngFSUlIqvD527Bi333679aMSQthVRkaG9eqzmM34L1iA97//Tda332K85RbrbFehDz7w5uRJLUuX5nH//UW4u4NK5WvXGJxBtcnB2hXejhw5wqZNmzCZTIwYMYLY2NgK7ycnJ/P++++jUqnQaDRMmTKFzp07WzUGIURlxcXFXL58mVatWllle75r1+KzdStXZ860S2K4cEHD3Ln+zJlzlcjIMubNy2fJkny8vc0237czU3wra32YTCbi4+NZsGABOp2OuLg4IiMjCQkJsbTp0aMHkZGRqFQqzp07x6uvvso//vEPe4QnhEu7cOECgFVOK3l9/DF+L71E0dixXLXhvElmM2RmqmnZ0kTTpiYyMzVkZWmAMgIDJSlYQ7XJYdmyZcyfPx+ARYsWVfs8g5LpM9LS0ggODqZFixYAREVFkZycXCE5XH+XhF6vl+cnhLCT06dPA3BLPb/la48fJ+DJJ9Hfdht5q1aBDf4P6/XwySdevPmmL3q9it27s/D2NvP119m22J1LqzY5DBkyxPJzfUuB5ubmVrgNVqfTcerUqUrtDhw4wHvvvceVK1eIq6b4R2JiIomJiQAsX768zlMMa7VahxRSbwik79L362VmZgIQGRlJQEBA3XcwYADGVatQTZpEkL9/3bdThQsXYPNmNW+9pSEjQ0WPHibmzjURGBiEVsH5D1c97vXpd7Uf68CBAy0/13f6DLO58jCvqpFB37596du3L6mpqbz//vssrKIISHR0NNHR0ZbXly5dqlNM5Q/zuSLpu/T9ekePHiUoKAiDwVCnz8bzq68wtGuHoWNHGD8eysrACp+x0Xht+ux33/UmMdETo1HF0KElvPJKIYMG6VGpIC9P2bZc9bhX128l15eqTQ47d+5UtHMlowqdTkdOTo7ldU5ODk2bNq22fdeuXVm7di35+fn4+fkpikMIUTenT5+u8ykln7fewu/55ymJieHyP/9ptZguXNAwbpyO337TotMZmTatgPvuK3Laes0NUbXJYc+ePYo2oCQ5tG/fnoyMDLKysggMDCQpKanSrbKZmZm0aNEClUrFL7/8gsFgoEmTJopiEELUjcFgICUlhfHjx9/cisXF+C9ahM9771E8ciSXX3ut3rHEx/tgNsPDDxcSHGykd+8y4uLyuf32EpefPtsRqk0OixcvttpONBoNU6dOZdmyZZhMJoYNG0ZoaCgJCQkAxMTEsH//fnbv3o1Go8Hd3Z2nnnpKLkoLYWPHjx+nsLDwpp5xUOfmorvnHtx+/pmrs2Zx9a9/vemnn81mSE3VcuiQO5MnFwGwd6+7JTloNLBhw+Wb2qawrmqTg9lstvxxNpmqn4dErfCXIiIigogbJt2KiYmx/BwbG1vp2QchhG0dPHgQQFlyMJtBpcIUEIAxJIT8+fPRDxumeF85OWr273dn714PEhM9uHBBi0pl5vbbS2jWzMT69ZdlhNCAVJscpkyZwpYtWwCYNGlStRuw9sNyQgj7+eqrrwgLCyM0NLT6RkYjXtu24RMfT+6WLZiCgsjdvLnWbV++rGLPHg/27/dg/353TpxwA8DLy8SgQXqeeqqAESNKLJPgSWJoWKpNDqtWrbL8/Prrr9slGCGE/WRmZvLdd98xc+bMqk/hmkx4fv45TVatwu3UKcq6dEGdmYmpmlsji4pUJCR40rNnKbfcYiQ52Z3HHw/Ex8fEH/5QytixxfTvr6dXrzLc3W3cOVFv1SaH6++NtUddWSGEfa1btw6VSsXEiRMrvacqLiZozJhrk+eFh5O7YQMlo0ZVuLaQkaEmKcmDwEATw4bpKSlRMWNGU+Li8pk5s4CoqFI+/TSbHj3KcHOzZ8+ENSiaPmPNmjXVXhyeOXOmVQMSQtjeb7/9xjvvvMPEiRNp27atZbk6IwNTy5aYvbzQR0VR8NhjFMfGgkbDpUtq9u51JynJg6QkD3755dqfj5Ejixk2TE9goImvv84iPNwAgK+vmYgIKb3ZWClKDjdWh8rLy2P//v0MGjTIJkEJIWzHbDbz17/+FTc3N5588kngWlLwe/llvD7+mOyvvsLQqRP5S5aQmqolYY0niYmeHD587VyQr6+Jfv1Kuf/+QgYMKKVr1/9PAJ07GxzRJWEDipLDhAkTKi0bPnw4H374odUDEkLY1tatW9m9ezcvvvgiIU2b4vPKK/iuW4fKaKTgLw9zvDCMDr+3ffllPxITPejdu4y5c/MZMkRPjx5liqasEI1bnQ9x27Zt+fnnn60ZixDCxn744QcWLlzIoEGDmDxxIs2GD0d77hxFo+7k6vzneON/XVl8lz8HDlykdWsjCxZcYeVKs5TVdEGKksOxY8cqvNbr9ezdu7fCrKpCiIYtJSWFv/zlL4zQ6Xh53TpyC7zZ2nMj77v34fExZka1KeH220vw8THj738tGXToINNVuCpFyWH9+vUVXnt6etKmTRueeOIJmwQlhLCub7/9lg2PPcZmvYYrZSN4anJTvjnaAqMxmK5dy3BzywcgJMTIpElFDo5WNASKksPatWttHYcQwgbSz5/nszmLKdvrRTte517GchU/Wl0s4/HHCxg7tlguIosqKUoONU2fcT2lU2kIIWxr/1eH+O/X/2bbhx/StTSRAwzC36OYUaP0jJt0if79S292OiThYhQlh5qmz7ieTKUhhGMYjVD6y2nOv/J3PvmyF+vK5qNx/zN3T5jAkLAy5vbM5I67ArlyRU4ZCWUUJYepU6eyf/9+xo4daykesW3bNvr168ett95q6xiFEFW4elXFzp3ufLLhNw4cDeUT8wKGsJuLbpkUd2rNX97aSev25bMbmOQpZXFTFCWHzz77jOXLl+Pj4wNcqyJ0yy23EBcXV2FmVSGEbV24oOF/20x8+V4B+861w2jS4gbczhcc6NCc0qfWMmDMGAbKdPeinhQlh6KiIvR6vSU5AJSWllJUJENUIWxNr4fXX/Eg4WMDxy60ACCck0TyKiW9fmXqQ50YNep2vLxHODhS4UwUJYchQ4bwwgsvMGrUKEvJzy+//JIhQ4bYOj4hXFJSkjuZGWpixxZxcN8e/vN6f0K4wHP8HU+fnQSM607/GTNoLc8aCRtRlBweeOABgoODSUpK4vLlywQEBPCnP/2J6OhoW8cnhEu4fFnFvn0ejBxxBY9du9g6rw0HL7XnxZduIyMjnUc8/AkZ3Jdejz9OZN8npUqisDlFyUGtVhMTEyPXF4SworNnNSQkeJKQ4MmB790wmtT85DaYHmUpLCWYb7nC9k79WLhwATExMXh5eTk6ZOFCakwO//znP5k6darl9c6dOxk+fLjl9d///neeeeYZRTs6cuQImzZtwmQyMWLEiEolQffs2cO2bduAa09gP/zwwxWmEhaisSspgQMHPNi1y4Od/9Ny8rQnAM2bXySqyUcsurKF78tSWRMWRuvJkxkzfjxjmzd3cNTCVdWYHHbt2lUhObzzzjsVksPRo0cV7cRkMhEfH8+CBQvQ6XTExcURGRlZYW6m5s2bs2TJEnx9fTl8+DBvvvkmL7744s32R4gGw2y+djHZ0xOO/qRlbKyOYr0Gd1UpA8x7mM52vudT/pV1huBOnUicOpI/3bWCkZ06OTp0IWpODmaz2So7SUtLIzg4mBYtrt1pERUVRXJycoXk0Om6/xDh4eHk5ORYZd9C2IvZfO3aQWCgGYMBBvbXMXZoBo88V8CZH77iUb2aGP5HoHkXiRSR3KEDbcfdw+5Ro2jfvr2jwxeighqTg7UueuXm5qLT6SyvdTodp06dqrb9zp075eE60eAZjZCa6sb+/e58/7073+/T0jkoi229nsB7924ezJ6N93+P0uNfHwDwZx8fPhkwgO6jXuKuIUOYKuV3RQNWY3IwGo0Vpus2mUyVXitR1QikusRz7NgxvvnmG/72t79V+X5iYiKJiYkALF++vEKt65uh1WrrvG5jJ32vW98vXIDvv1eRnKwm+Xszhw6pKCzWANCsWT5D9Nu4O+0z3NP+w7dAqfsLZNwawfN3PM/w4cPp06cPGo3Gep25SXLcXa/v9el3jcnB39+/wnTdvr6+FV77+fkp2kn5sxHlcnJyaNq0aaV2586d44033iAuLo4mTZpUua3o6OgKt9BeunRJUQw3Kp8GxBVJ35X1/fhxLd9+68FjjxWiUsG8KWV8sLsN7pTSiyNMZT+R7OdRdpOdfYE8b2/29OvBqej59L3tNmZ0747bdXNWXL582VbdUkSOu+v1vbp+t2rVqtZ1a0wO1pqqu3379mRkZJCVlUVgYCBJSUnMnj27QptLly7x97//nZkzZyoKXAhrKCuDtDQtKSlupKa6kfoTrJy8nw6533Pg3wG8cGwGualLSM39kU4Hi9mJESM/clRVxrmQEA784Q+8OGAOvXr1omPHjg4dGQhhTXapBKvRaJg6dSrLli3DZDIxbNgwQkNDSUhIACAmJoaPPvqIgoICNm7caFln+fLl9ghPuIj0dDUffKAmJcWPs6fMnEuDc5lelBqv/TdwU5fSw3QU1b7n0JHMvTShE/NZsr0IQ3g4zf8Yzu5evegdMZ/R3bvLcwfCqanM1rolyUHS09PrtJ6rDjPBOfteXKzi3DkN585pCQkx0K1rGRd+ymf8Q6E8OvEwrW89zZHPrrD6P0/gTSEdSKMDaYRzin38xG5+JJQTPOLnQ3Hbtrj17k2Lvn3p0rUrt9xyS4XTQ42VMx53pVy17zY7rSREQ1J+d9CZM9eSwNnTcPakiV/Oe5CV8//f4h92f41/GJ7C3+RJFBs5+doGFrCbQNz5UL0SY5NCrga3oKhtW1Tdu/Nwjz/xt7aPERISIqMBIX4nyUE0GGYz5OaquXpVRdu2RigrI+5JD8Ka5zLw7t9IS/uNWTOnYuZaCbNgMmjPadqTRhangTS2koaP6je+aNGC4lat+NMtH6O+9Xbu6flX2rRpI8/QCKGQJAdhV2YzZGaqOXtWy9nTKs6l6DlzRsXJi0347TcPiovdiHA7wJeaIehKSsjmc/QcYemb8wF4if/SQpOJp98lTM29MYaGYurRg9l9+9KmzUhatWpV4ykgmbBOCGUkOQib+jZRw7ljJYx/pJSzZ8/yzH2h/HSps+V9LWW05gznOAyc5kHSuLUshb0eWq62bcu41i9i7NSJP0ZtJCwsjLCwsGpvcxZCWI8kB1FnZWVw9qyWkyc0nDzlxokTak4fukpGlhd7WkUSeCmTXYUr+Zi7eW5lWwAe5AHuoAme2rN4BVzCK8SMJrwdZVFRtGnThrCwKFq0aIFarXZs54RwcZIcRK0MBlCrr/37dpuef28ycTzNgzN5LTCYy0/hmICzdOAk4znFpXMXOeVloner1fQM3Ur60GcJa9eOdu3aERYWpvgBSiGEY0hyEBUUF8Px426EtSiiRXYqH8fnMOu/9zB9/AucuHoQ8+4unCqaQTcO8SdSaUIqPr7n+CnSl5a9wunUrh23dOpE6w6HCff2dnR3hBB1JMnBhRUWqkhJceNospGfjrnzw1E3zp31wmTWEM8UprKF4XRgGhkc/HALme3dGRhlZGyLxTTp25fQWwfQps19aLXyaySEs5H/1S7CZLp2Wij7goEnJ2Zw8Cc3zua3sNwW6k0mRSThxyGeUB3hiu44r3UeiHu/fkQPDGdWt6/x8fFxcC+EEPYiycHJmM1w4YKGwitGumpPcGXvPv604kF6tdiHqes7nPrxFPpfP6cXhxjDIXw1P+EXnI6+fxsChg6lW7eBtG//ZxkNCOHi5C9AI3bliorjqRpOJF3l8GEDRy405dw5P/R6Twaymz0MpwUwHg1XCn7gq7If6datG2Ojl+HTpw/hfxhNq1bT5N5/IUQlkhwagdJSyMjQEBZmID09nZenFvDdic5klrX8vUVzmpDHVY4CRxnvdoJuAb/wZpfBaHv1YuCAtnTsOZol/v6A684zI4RQTpJDA2I2Q3q6huPJxfyUmMkE3w2oU47ycsoTJOpHUubTgYLCAkazlNtIR6dOpan/rzRrX0RQ35b4jxhOx46DCQyMdXRXhBCNnCQHB7lyRcWJgyUc+iqDlB9KSTvflJ/1HSgrK7/o25bpTMKPswzXbKS1XxL5d91Nh26d6dy+PeFduxIYOMyhfRBCOC9JDnaQe76ELzalE9T5V37LO8Wxd735KG3u7++2w588unKUQPd/4d0ul4Ehlxkclk/K0Pm07deP0dfV3xZCCHuQ5GBF2dlqvv++hO//l8n5PVe507iF2Ksfka/vzbPs5w+MIZnt9Pfpx0PeJbRomU1oLzVtR3QidEAUumYjHd0FIYQAJDnUidEIx3Zf4sC2s/x82Mgv6c04UxzOJXOL31u0pTXnGaby4GRTH7LDjbwS9ixtRo8hPGopOhkJCCEaOEkOtTGbuZqaytuvZFNYcoGz/ns5dfgXUn/9CeiJO3o6kUI/bQKakBz8Bvoz4DYf+vZqTYu2f0OleoEuju6DEELcJEkO1zEYzCQnX+Grr3K4+Fkmvpezea10Fq1MJj7hGAG482vIa3Tv3p07Ap6nfS9PeozpQpu+t6LRjHB0+EIIYTV2Sw5Hjhxh06ZNmEwmRowYQWxsbIX3L1y4wLp16zhz5gz33nsvo0ePtmk8JqOJ5P8e5cDHv3HimCe/5LbjuKEnxbQGwJMColQ7ORwaQnHXrizu+RVd7h6ELvR7m8YlhBANgV2Sg8lkIj4+ngULFqDT6YiLiyMyMpKQkBBLG19fXx566CGSk5NtHk9iYiLxUzXsNt4PgCfFdFL9RLT/x7iN8GfoUF+ih7fGv2kksM/m8QghRENjl+SQlpZGcHAwLVpcu2AbFRVFcnJyheTg7++Pv78/hw4dsnk8wcHBhIXvYJb7FSLuaMpt9/WmSVAoEGrzfQshRGNgl+SQm5tb4Q4dnU7HqVOn6rStxMREEhMTAVi+fDlBQUE3vY2hQ4cSnRKNwWCoUwyNnVarrdPn5gyk79J3V1KfftslOZjN5krL6jrZW3R0NNHR0ZbXdZ0jyJXnF5K+S99djav2vbp+t2rVqtZ17VKoV6fTkZOTY3mdk5ND06ZN7bFrIYQQdWCX5NC+fXsyMjLIysrCYDCQlJREZGSkPXYthBCiDuxyWkmj0TB16lSWLVuGyWRi2LBhhIaGkpCQAEBMTAx5eXnMmzeP4uJiVCoVX3zxBa+88greUodYCCHszm7POURERBAREVFhWUxMjOXngIAANmzYYK9whBBC1MAup5WEEEI0LpIchBBCVCLJQQghRCUqc1UPIQghhHBpLjtymDdvnqNDcBjpu2uSvrue+vTbZZODEEKI6klyEEIIUYnLJofr52dyNdJ31yR9dz316bdckBZCCFGJy44chBBCVE+SgxBCiErsNreSo9RWu9psNrNp0yYOHz6Mh4cH06dP55ZbbnFMsFZWW99TUlJ4+eWXad68OQD9+vVj/PjxDojUutatW8ehQ4fw9/dn1apVld535mNeW9+d9ZhfunSJtWvXkpeXh0qlIjo6mpEjR1Zo46zHXUnf63TczU7MaDSaZ86cac7MzDSXlZWZn3nmGfNvv/1Woc0PP/xgXrZsmdlkMplPnDhhjouLc1C01qWk78eOHTO/9NJLDorQdlJSUsynT582z5kzp8r3nfWYm821991Zj3lubq759OnTZrPZbC4qKjLPnj3bZf6vK+l7XY67U59Wur52tVartdSuvt7BgwcZPHgwKpWKjh07UlhYyOXLlx0UsfUo6buz6tq1K76+vtW+76zHHGrvu7Nq2rSpZRTg5eVF69atyc3NrdDGWY+7kr7XhVMnh6pqV9/4oeXm5laosVpVm8ZISd8BTp48ydy5c3nxxRf57bff7BmiwzjrMVfK2Y95VlYWZ86coUOHDhWWu8Jxr67vcPPH3amvOZgV1K5W0qYxUtKvdu3asW7dOjw9PTl06BArV67ktddes1eIDuOsx1wJZz/mJSUlrFq1iilTplQqFObsx72mvtfluDv1yEFJ7WqdTlehALez1LdW0ndvb288PT2Ba8WYjEYj+fn5do3TEZz1mCvhzMfcYDCwatUqBg0aRL9+/Sq978zHvba+1+W4O3VyUFK7OjIykt27d2M2mzl58iTe3t5O8QujpO95eXmWb1NpaWmYTCaaNGniiHDtylmPuRLOeszNZjMbNmygdevW3HnnnVW2cdbjrqTvdTnuTv+E9KFDh9iyZYuldvXdd99doXa12WwmPj6eH3/8EXd3d6ZPn0779u0dHLV11Nb3HTt2kJCQgEajwd3dnQcffJBOnTo5OOr6+8c//kFqaipXr17F39+fe+65B4PBADj/Ma+t7856zI8fP86iRYsICwuznCqaNGmSZaTgzMddSd/rctydPjkIIYS4eU59WkkIIUTdSHIQQghRiSQHIYQQlUhyEEIIUYkkByGEEJVIchBCCFGJJAchbjBnzhxSUlJsvp+1a9cyadIkZsyYYVm2ZMkSvv766yrbl5WVMXnyZCZNmsS///1vm8cnXJtTz60kRFUmT55s+bm0tBStVotafe170qOPPsorr7xit1jGjBnDvffeq6itm5sb77zzDmvXrrVxVEJIchAu6J133rH8PGPGDB577DF69uzpwIiEaHgkOQhxg+sTxgcffMD58+fRarUcPHiQZs2a8fTTT/P999/z+eef4+bmxrRp0+jVqxcARUVFbNmyhcOHD6NSqRg2bBj33HOPZWSiRHZ2NgsXLuTcuXN07NiR2bNn4+fnZ6vuClElueYgRC1++OEHBg8ezKZNm2jXrh3Lli2zTHY2btw43nzzTUvb119/HY1Gw2uvvcbLL7/Mjz/+WO01hOrs3buXxx9/nI0bN2IwGPj000+t3SUhaiXJQYhadO7cmd69e6PRaOjfvz/5+fnExsai1WoZMGAA2dnZFBYWkpeXx5EjR5gyZQqenp74+/szatQokpKSbmp/Q4cOpVWrVri7u3Pbbbdx9uxZ23RMiBrIaSUhauHv72/52d3dHT8/P8tpInd3d+BaoZXLly9jNBp59NFHLe3NZnOFinxKBAQEWH728PCgpKSkHtELUTeSHISwEp1Oh1arJT4+Ho1G4+hwhKgXOa0khJU0bdqUXr168fbbb1NUVITJZCIzM5PU1FRHhybETZORgxBWNHPmTLZu3cqcOXMoLi6mRYsWjBkzxtFhCXHTpNiPEA6yYcMG9u7dS0BAAGvWrKm1fVlZGY888ghGo5HRo0czYcIEO0QpXJUkByGEEJXINQchhBCVSHIQQghRiSQHIYQQlUhyEEIIUYkkByGEEJVIchBCCFHJ/wHuMWyP2704BQAAAABJRU5ErkJggg==", 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9PIfq/pybzqy9WBgfX23uw5Vl7rXeZiaTibi4OJKSkhzbbDYbSUlJtGjRotxj8vPzy7TVqcOciEjlnJ3kiA/Kz8fvzPI51vNMzeLLvNpclZiYyBtvvEFcXBzx8fGsXLmS/Px8x4R1s2bNIioqiuHDhwPQpUsXVqxYQbNmzRzNVQsWLKBLly5KdkREnKS1q3yb6eBBDDYbttBQbPXrezscr/BqktOjRw8yMzNZuHAhFouF2NhYxo0b52iuSktLK1Vzc8MNN2AwGJg/fz7p6emEhYXRpUsXbrnlFi/dgYhI9aWaHN9mtFiwNmyILToaqvjIKnfxesfj/v37079//3LfmzhxYqnXfn5+DB06tNQq0yIiUjma8di3FXTvzokffoAaPBhGbTw+pHfv3kyfPr3c915//XXatm1Lenq6R2KZM2cO3bt3Jy4ujsTERLZt2+bSY2bNmkWjRo149tlnXRm2SI2i5qoaogZPa6Ikx4e0atWK3bt3l9l+/PhxXn/9dZ544gmioqLcHseSJUuYNGkSjz76KKtXr6ZNmzaMGDGCtLQ0lxyzfft2PvjgA1q3bu3O2xDxeTabDVCSI75LSY4Pad26Nbt27SqzferUqTRt2pRbb73VI3HMnj2b4cOHM2zYMFq0aMHUqVMJCgpi/vz5F3xMdnY2DzzwAC+++OI5pxoQkYrR2lU+rLCQ+l27UvummzCcYz3ImkBPdgUZcnLO+Z7daITAwIrtazDAmWUlzrevPTjY6RhbtWrFoUOHyMvLI/BMPDt27GDRokUsWLDA6b/WXnvtNV5//fXz7rNu3ToaNWrkeF1QUMCOHTt44IEHHNuMRiOXX345W7ZsKfcczhwzbtw4+vbtS8+ePXnttdecuh8RKU3NVb7LdOAAfsnJGDIzsYeGejscr1GSU0ENEhLO+V5enz6k/+9/jtf1O3TAmJtb7r75l17KybOWmajXvTt+5fSTOXb0qNMxtmnThqKiIvbu3Uu7du0AmDBhAgMHDqRHjx7lHpOSksLkyZOZNWtWmfduvfVWrr322vNes/6fhiWmp6dTVFREnTp1Sm2vW7cu+/btK/ccFT1myZIlJCUlsWLFivPGJCIVo+Yq32U6U6tvbdECavAUK0pyfEjjxo0JCwtj9+7dtGvXjiVLlrBjxw6++eabcx4THR1dboIDEBkZSWRkpLvCdcrRo0d59tlnmTdvnqOWSkQujJqrfJf5TP/MwlatvByJd+nJrqDkM1Njl8f+pyz5+I4d5973T3MVnPjuuwsL7E9atmzJ7t27ycvL44UXXuD+++93NCdlZ2dz9913k5KSAsD48eNp3rw5d999N6tWrSpzrso0V0VFReHn51emw3Bqauo51xypyDE7d+4kLS2t1HQDRUVFbN68mTlz5nDgwAH9NSripJIh5JpM1feYziQ51pYtvRyJdynJqSBn+si4a9+KKOl8/NZbbwFw7733Ot5bt24dkZGRfPjhh9jtdrKyssqsHXa2yjRX+fv706FDBzZs2OBISGw2Gxs2bGDkyJHlnqMix1x++eV8+eWXpY579NFHad68OQ899JASHJFKUE2O7zKfaa5STY74lFatWrFixQo2bdrEK6+8QtBZnZxbtWrFhAkTmDx5Mv3796dr167nTXIq21w1evRoHnnkETp06ECnTp2YPXs2ubm5DBs2zLHPe++9x6pVq1i4cGGFjqlVqxat/vTDGhwcTGRkJK1bt9bK7yKVoMkAfVRuLn4HDwJgVZIjvqRNmzacPHmSHj16kJiYWOq95s2bs2bNGr744gsmTZrEkCFDuOqqq1wew/XXX096ejrTp08nNTWVtm3b8sEHH5RqrkpPT+fQmYXjKnqMiLiWmqt8k9FioaBHD4wnTmD704COmsZgry5rr7tYampquX/9Z2ZmEhYWVunzms3mKlurkJKSQkREBIGBgSxZsoT169fz0EMPnbNPTnVxoWV+od/zmsZgMNCgQQOSk5OpoR8fHueuMm/fvj3p6el89dVXtKzhfTf+TM+55/1VmZvNZqf/8FVNTg2ya9cunn/+eYxGI4GBgbz88sveDklEvEhDyMXXKcmpQXr16kWvXr3KbK/OtTgiUnmaDNA3GXJzsZ/VH7MmU0OsiEgNpdFVvqlejx7FE82eYwLWmkRPtohIDaXmKt9jPHECvxMnsBuN2Bo29HY4XqeaHBGRGkrNVb7HnJQEgDUuTk1WKMkREamR7Ha7oyZHzVW+w/zzzwAUnlm/sKZTkiMiUgOVzJEDqsnxJSVJjrVtWy9HUjUoyRERqYFKmqpASY4vKWmuUk1OMSU5IiI10Nk1OWqu8g2GrCzHcg6FqskBNLpKRKRGUnOV7zHk5ZHz97/jl5yMrXZtb4dTJSjJERGpgdRc5XtsdeqQMXWqt8OoUtRcJSJSA51dk6MFOsVX6cn2Ib1792b69Onlvvf666/Ttm1b0tPTPRLLnDlz6N69O3FxcSQmJrJt27bz7r9582Zuv/12OnfuTKNGjVi9erVH4hSpqUqSHJPJhMFg8HI04gqmX36BvDxvh1GlKMnxIa1atWL37t1lth8/fpzXX3+dJ554gqioKLfHsWTJEiZNmsSjjz7K6tWradOmDSNGjCAtLe2cx+Tk5NCmTRumTJni9vhEpHSSIz4gL4+6AwbQoGVLjCkp3o6mylCS40Nat27Nrl27ymyfOnUqTZs25dZbb/VIHLNnz2b48OEMGzaMFi1aMHXqVIKCgpg/f/45j+nTpw9PPvkkAwYM8EiMIjVdSZ8cNVX5BvMvv2CwWrGFh2OrX9/b4VQZSuErKCenYtW5JhNYrcX7+vvbKfkjyWqFggIDBoOds2faPtd5g4PtTsfYqlUrDh06RF5eHoGBgQDs2LGDRYsWsWDBAqc7F7722mu8/vrr591n3bp1NGrUyPG6oKCAHTt28MADDzi2GY1GLr/8crZs2eLU9UXEfVST41vMP/0EQGHHjqDmRwc93RWUkNDA6WPeeiuda68tbh9dtSqQe+6J4tJL81m06KRjn+7d65GeXjb5OHr0mNPXa9OmDUVFRezdu5d2ZyaCmjBhAgMHDqRHjx7lHpOSksLkyZOZNWtWmfduvfVWrr322vNes/6f/mJIT0+nqKiIOnXqlNpet25d9mlFXJEqoyTJ0cgq3+C/fTsAhRdd5NU4qholOT6kcePGhIWFsXv3btq1a8eSJUvYsWMH33zzzTmPiY6OLjfBAYiMjCQyMtJd4YqIF2lxTt9SUpNT0LGjlyOpWpTkVNBvvyVXaD+TyeT48PD3/6PJacCAPH77LRmDoXQz1HffnXBdkEDLli3ZvXs3eXl5vPDCC9x///2O5qTs7GzuvvtuUs50Shs/fjzNmzfn7rvvZtWqVWXOVZnmqqioKPz8/Mp0Mk5NTaVu3boXensi4iJqrvIdhqwsTHv3AqrJ+TM93RVU0T4yZjMUFpbd12QCk6ns9sr0vTmfks7Hb731FgD33nuv471169YRGRnJhx9+iN1uJysrC4vFcs5zVaa5yt/fnw4dOrBhwwb69+8PgM1mY8OGDYwcObKSdyUirlZYWAgoyfEF5h07MNjtWBs1wvanrgI1nZ5uH9OqVStWrFjBpk2beOWVVwg6q5dzq1atmDBhApMnT6Z///507dr1vElOZZurRo8ezSOPPEKHDh3o1KkTs2fPJjc3l2HDhjn2ee+991i1ahULFy4EimuZDhw44Hj/8OHDJCUlERkZWaqmSERco6TGWUlO9VcUE0PG+PGgkXJl6On2MW3atOHkyZP06NGDxMTEUu81b96cNWvW8MUXXzBp0iSGDBnCVVdd5fIYrr/+etLT05k+fTqpqam0bduWDz74oFRzVXp6OocOHXK8/umnnxg6dKjj9aRJkwAYOnQoM2fOdHmMIjVdSU2O2Wz2ciRyoYoaNSL7nnu8HUaVpCTHx3Tr1o2jR4+W+15KSgoRERHcdNNNBAQEsH79erckOQAjR448b/PUY489xmOPPeZ43aNHj3PGLSKup5ocqQn0dNcgu3bt4vnnn8doNBIYGMjLL7/s7ZBExEtUk+MbjOnpBHz5JQXdulEUG+vtcKqcKpHkrF69mmXLlmGxWIiJiWHUqFHEx8eXu+/EiRP55Zdfymzv1KkTTz/9tLtDrdZ69epFr169ymwvb2SViPg21eT4Bv+NG4l8+GEK27Yldc0ab4dT5Xj96d64cSNz585l9OjRJCQksGLFCqZMmcLMmTMJDw8vs//jjz/u+OEEOH36NE888QSXXnqpJ8MWEanWVJPjG/x//BGAgq5dvRxJ1eT1rtjLly+nb9++9O7dm8aNGzN69Gj8/f1Zu3ZtufvXqlWLiIgIx78dO3YQEBDAJZdc4uHIRUSqL9Xk+AYlOefn1afbarWyf/9+Bg0a5NhmNBpp3749e/bsqdA5vvrqK3r06OFYq+nPCgsLHX+xABgMBsewaoPW95Az9CxUXElZqcw8xx1lXpLkmM1mfS/LUS2e89xczElJABR261a1Y60Ad5S5V5OczMxMbDYbERERpbZHRERw7Nhfr920d+9ejhw5UmrCuz9bvHgxixYtcrxu1qwZ06ZNO+fsu7m5uRdcfavqX8+7kDL39/enQQPn1yar6aKjo70dQo3jyjIPCQlx/K/n/9yq9HO+YQMUFkJ0NPUuvthnFuZ0ZZlX63rKr776iqZNm56zkzLA4MGDS80XU5IhpqamlurbU6KgoKBUzY+zzGbzBR0vzrvQMi8oKCA5uWLLdkjxz1B0dDQpKSnY7a6dsVvK544yL1l6paioSM9/OarDcx6yejVhQG7nzljOLNdTnf1VmZtMJqeXB/JqkhMWFobRaCwz667FYilTu/NneXl5fPvtt6Vm0S2P2Ww+51/553pwbTYbRs0cWSOUPANV9UOsKrPb7So3D3NlmZ/dJ0ffx3Orys+5uaQ/TpcuVTbGynBlmXv1N7nJZCIuLo6kM22KUJxgJCUl0aJFi/Meu3nzZqxWK1dccYVLYwoODub06dPYbDaXnleqppycHAICArwdhojHaXRV9ZcxbRrp77xD3oAB3g6lyvJ6c1ViYiJvvPEGcXFxxMfHs3LlSvLz8x3zucyaNYuoqCiGDx9e6rivvvqKbt26ERoa6tJ4TCYTISEhZGVlVep4f39/CgoKXBqTnF9ly9xut2MymZTkSI2k0VXVn61uXfLOLIQs5fP6092jRw8yMzNZuHAhFouF2NhYxo0b52iuSktLK9PT+tixY+zatYtnnnnGLTGZTCbCwsKcPs5gMNCgQQOSk5N9quqwKlOZi1SOanKkJvB6kgPQv39/+p8jG504cWKZbQ0bNnSsXi0iIs5TTU71VuuVV8BuJ/fGGylq2tTb4VRZerpFRGog1eRUY3Y7IXPm4JeWRsFllynJOQ8NIRIRqYFUk1N9mfbuxS8tDXtgIAUXXeTtcKo0JTkiIjWQanKqL/9NmwAo6NwZNHDivJTkiIjUQKrJqb4CziQ5+T16eDmSqk9JjohIDVRSk6Mkp5qx2/+oydHC1H9JSY6ISA109gKdUn347duHX2oq9oAACjp18nY4VZ5SeBGRGkg1OdWT6dAhbKGhFLZrB4GB3g6nytPTLSJSA6kmp3rK79uXlJ9/xpie7u1QqgU1V4mI1EDqeFyN+flhc3I17ppKSY6ISA2k5qpqqLAQtHyNU5TkiIjUQCVJjr+/v5cjkYqqNWsW9S69lOAPP/R2KNWGkhwRkRqooKAAUJJTnQR88w2mI0e8HUa1oiRHRKQGUpJTvRgyM/HfsgWA/J49vRxN9aEkR0SkBtKyDtVLwMaNGIqKsMbFUdSkibfDqTaU5IiI1ECqyaleAr7+GoC8K6/0ciTVi5IcEZEaSElO9RLwzTeAmqqcpSRHRKQGUpJTffgdOIDp4EHsJhMFWpTTKZogQUSkBtIQ8mrEz4+skSMxnj6NvVYtb0dTrVQoyencubNTJzUYDCxdupRGjRpVKigREXGv/Px8QElOdVDUtCmZkyd7O4xqqUJJzvbt23nssceoVYEM0m63M3XqVMcPkIiIVD0aXSU1QYWbq5544gnq1atXoX1ffvnlSgckIiLupz451YMpKQljZiYF3bqBElKnVajj8YEDB6jrxGJgv/zyCzExMZUOSkRE3MdutzuSnICAAC9HI+dT6z//oc7QoYROn+7tUKqlCiU5MTEx/PzzzxU+aZMmTfDz86t0UCIi4j4lTVWg5qoqzWol8KuvAMjv08fLwVRPFR5C3qFDB7p3787s2bM5ffq0O2MSERE3OjvJUXNV1eW/ZQtGiwVbRAQFXbp4O5xqqcJJztdff03btm157LHHaNCgAbfffjvr1693Z2wiIuIGZw8MUZJTdQV+/jkAeX36gEkzvlRGhZOcK664gnfffZfk5GRef/11Dh48yJVXXkmLFi2YNm0aKSkp7oxTRERcpKQ/jp+fn7oWVFV2O4GrVgGQd/XVXg6m+nJ6xuOQkBBGjhzJ119/zZ49exg6dChvvPEGTZs25brrrnNHjCIi4kIaPl71mX79tXiW48BA9ce5ABe0rEN8fDzjxo3jmWeeITQ0lBUrVrgqLhERcZOS5iqNrKq6AtetAyCvVy/NcnwBKt3I98033/Duu+/y8ccfYzQauemmm7jzzjtdGZuIiLiBanKqvqx77yX/ssvAqCUmL4RTSc6xY8eYM2cOc+bMYe/evfTo0YPXXnuNm266iZCQEHfFKCIiLqSJAKsBg4HCjh29HUW1V+EkZ8CAAXzxxRfUqVOH2267jVGjRtGyZUt3xiYiIm6gJEdqigonOWazmUWLFpGYmKje+CIi1VhJn5zAwEAvRyLlqX3TTRQ1bkzmY49h00LXF6TCSc7SpUvdGYeIiHiIOh5XXX4HDhDw7bfY/fzIeOYZb4dT7VWoR9OQIUPIzMys8ElHjBjBiRMnKh2UiIi4j5Kcqivo008ByL/sMuxRUd4NxgdUqCZnyZIlpKamVuiEdrudZcuW8fzzz1d41XIREfGckiRHfXKqGLudoMWLAcgdPNjLwfiGCiU5drudFi1auCWA1atXs2zZMiwWCzExMYwaNYr4+Phz7p+dnc28efP4/vvvycrKom7dutx+++107tzZLfGJiPga1eRUTeakJMz79mEPDCRvwABvh+MTKpTkrF271ukTN6pAZ6mNGzcyd+5cRo8eTUJCAitWrGDKlCnMnDmT8PDwMvtbrVYmT55MWFgYjz76KFFRUaSlpREcHOx0fCIiNVVeXh6gJKeqCfrkE6B4GQd7aKiXo/ENFUpyrrzySrdcfPny5fTt25fevXsDMHr0aLZu3cratWsZNGhQmf2/+uorsrKyeP755zGdWaxMTWIiIs7R6KoqqKiIoDMDfNRU5TpeW9bUarWyf//+UsmM0Wikffv27Nmzp9xjtmzZQkJCAu+88w4//vgjYWFhXHbZZQwaNAjjOWaFLCwsdMzuCWAwGAgKCnJ87Uol53P1eeXcVOaepzL3PFeXeck8OQEBAfo+noPHn/OCAnJuuYWAtWvJ79OnRn5f3FHmXktyMjMzsdlsRERElNoeERHBsWPHyj3m+PHjpKamcvnll/P000+TkpLC22+/TVFREUOHDi33mMWLF7No0SLH62bNmjFt2jTq1q3rsnv5s+joaLedW8qnMvc8lbnnuarMSzocR0VF0aBBA5ec01d59DmfMQOAmv4dcWWZey3JqQy73U5YWBj/+Mc/MBqNxMXFkZ6eztKlS8+Z5AwePJjExETH65IMMTU1FavV6tL4DAYD0dHRpKSkYLfbXXpuKZ/K3PNU5p7n6jJPS0sDimvUk5OTL/h8vkjPuef9VZmbTCanKyi8luSEhYVhNBqxWCyltlssljK1OyUiIiIwmUylmqYaNWqExWLBarU6+umczWw2n3MROnc9uHa7XT8UHqYy9zyVuee5qszPHl2l7+H5eeI599+wAWNGBnlXXw0a1u/SMvfa8qYmk4m4uDiSkpIc22w2G0lJSeccrt6yZUtSUlKw2WyObcnJyURGRpab4IiISFklo6s0T07VEPrKK0TdfTe13n7b26H4nAplBp06dapwR6CtW7dW+OKJiYm88cYbxMXFER8fz8qVK8nPz6dXr14AzJo1i6ioKIYPHw5Av379+Oyzz5gzZw79+/cnJSWFxYsXM0DzCYiIVJhGV1Udfvv3E7B5M3ajkRyNqnK5CiU55Q3ndoUePXqQmZnJwoULsVgsxMbGMm7cOEdzVVpaWqnkqk6dOvzzn//k/fff54knniAqKooBAwa4LT4REV+kyQCrjuAFCwDI79ULmzqBu1yFkpwJEya4LYD+/fvTv3//ct+bOHFimW0tWrRgypQpbotHRMTXKcmpIqxWgv/v/wDIueUWLwfjm7zWJ0dERLxDa1dVDQFr1+J3/DhFtWuTd9VV3g7HJzndW7eoqIhXXnmFhQsXcvjwYcekUiXS09NdFpyIiLheScfjkolRxTuC588HIPeGGzSqyk2crsmZNGkSM2bMYNiwYWRkZPDoo48yZMgQjEZjuc1LIiJSteTm5gJKcrzKasV46hSgpip3cjrJ+fDDD5k9ezaPPfYYJpOJW265hbfffptnn32WzZs3uyNGERFxoZycHAAtbuxNJhMnP/mEE19/jfUc06bIhXM6yUlJSaF9+/YA1KpVi4yMDKB4OPiKFStcG52IiLicanKqDmt8vLdD8GlOJzmNGzd2TAPevHlz1qxZA8APP/ygnvoiItVASU2OkhzvMO3Zg0H9Vz3C6SRn8ODBfPnllwCMGTOG8ePHk5CQwG233caoUaNcHqCIiLhWSU2Omqu8I+Lxx4nu1o2AM5UE4j5Oj66aOnWq4+thw4bRtGlTNm3aREJCAtdee61LgxMREdey2WwaXeVF5h078N+yBbvZTOFFF3k7HJ93wQs+XXrppVx66aWuiEVERNysJMEBJTneEDJnDgC511yDrV497wZTA1QoyVm6dCkDBgzAbDazdOnS8+573XXXuSQwERFxvZKmKlCS42nGkycJWrIEgOw77vBuMDVEhdeuSklJoV69euddJ8pgMFBUVOSq2ERExMVKkpzAwECMRk1670khc+ZgyMujoEMHCrt29XY4NUKFkhybzVbu1yIiUr2UjKzSCuSeZcjNJfi99wDIuuceOGvxaXEfp9P4uXPnOtY9OVtBQQFz5851SVAiIuIeGlnlHeYtWzBmZ2Nt2pS8a67xdjg1htNJzsiRIx0TAJ7t9OnTjBw50iVBiYiIe2giQO8ouPxyjm/ezKnXXgPTBY/5kQpyuqTtdjuGcqrZfv/9d8LDw10SlIiIuIcmAvQeW/362OrX93YYNUqFk5xOnTphMBgwGAz07dsX01mZaFFREQcOHKB///5uCVJERFxDzVUeZrdj2rsXa0KCtyOpkSqc5JSMqtq+fTt/+9vfqFWrluM9f39/YmNjueGGG1weoIiIuI6aqzzLf/166txyC7n9+nHq3XfV4djDKpzkTJgwAYDY2FiGDRumnvkiItWQViD3ILud0FdeAaCocWMlOF7gdJ+c22+/HSgeTXXixIkyQ8qbNm3qmshERMTlVJPjOf7ffkvA999j9/cn6777vB1OjeR0kvPbb78xatQoNm7cWGp7SYdkTQYoIlJ1qeOx55TU4uQMH46tQQMvR1MzOZ3k3HHHHZhMJpYvX06DBg3KHWklIiJVkxbn9Az/jRsJ2LwZu78/p++/39vh1FhOJznbt29ny5YttGrVyh3xiIiIG6m5yjNCZ8wAIOfmm7E1bOjlaGoupycDbNOmDWlpae6IRURE3EzNVe5nTE/H7/Bh7GYzWQ884O1wajSnk5xp06YxduxY1q1bx8mTJ8nMzCz1T0REqi7Nk+N+tqgoTnzzDSc/+ICiRo28HU6N5nRz1VVXXQVA3759S21Xx2MRkapPzVUeEhhIweWXezuKGs/pJGft2rXuiENERDxAzVVuZLUStHw5uYmJWp+qinD6u3DllVe6Iw4REfEATQboPkGLFhH52GMEv/8+Jz/5RJP/VQFO98kBWL9+PX//+9/p0aMHR48eBeB///sfGzZscGlwIiLiWtnZ2QClluYRF8jNJWz6dADy+vdXglNFOJ3kfPzxx/ztb38jKCiIrVu3kp+fD0BGRgYvvPCCywMUERHXOX36NAChoaFejsS31HrnHfySk7E2bEj2mZUBxPucTnImT57MW2+9xezZszGbzY7tl112GVu3bnVpcCIi4lpZWVmAanJcyXj8OLVeew2A008+CVrbscpwOsnZvXs3PXv2LLM9PDwci8XiiphERMQN7Ha7anLcIGzaNIzZ2RR06kTukCHeDkfO4nSSEx0dzd69e8ts37BhA3FxcS4JSkREXC83N9exqLJqclzD/NNPBC9YAEDGpElgrFRXV3ETp0dXjR49moceeoh3330Xg8HAsWPH2LRpE48//jjjx493R4wiIuICJbU4BoNBo6tcxB4YSP4ll1DUsCGFXbp4Oxz5E6eTnKeeegqbzUbfvn3JycmhZ8+eBAQE8PjjjzNmzBh3xCgiIi5wdlOVFld2DWvLlpxctAjDmYVPpWpxOskxGAz885//5IknnmDv3r1kZWXRpk0bVX2KiFRx6nTsQnb7H8PEDQbsmlyxSnI6yRk1ahSvvvoqoaGhtGnTxrE9OzubMWPG8O677zodxOrVq1m2bBkWi4WYmBhGjRpFfHx8ufuuW7eOf//736W2mc1mPvzwQ6evKyJSk6jTseuETp2KIT+f048/jl1JY5XldJLz/vvvM3Xq1DI/JLm5ucydO9fpJGfjxo3MnTuX0aNHk5CQwIoVK5gyZQozZ84kPDy83GOCgoJ49dVXnQ1dRKRGU02Oa5h+/ZVab76JoaiI/J49ye/Tx9shyTlUOMnJzMzEbrc7hiAGnjUPQFFREStXrqRevXpOB7B8+XL69u1L7969geKOzVu3bmXt2rUMGjSo3GMMBgMRERFOX0tEpCZTTY4L2GxEPPUUhqIicgcOVIJTxVU4yYmIiMBgMGAwGGjRokWZ9w0GA5MmTXLq4larlf3795dKZoxGI+3bt2fPnj3nPC4vL4/77rsPu91Os2bNuOWWW2jSpIlT1xYRqWm0pMOFC54/H/8ff8QWElI8ZFyqtAonOWvXrsVut9OnTx8+/vhjoqKiHO/5+/sTExNDw4YNnbp4ZmYmNputTK1MREQEx44dK/eYhg0bcu+99xITE0NOTg5Lly7lmWeeYcaMGdSuXbvM/oWFhRQWFjpeGwwGx+q7rh5dUHI+jVrwHJW556nMPc9VZV7SXKXRVX+tvDI3njxJ2JQpAGQ98QT2Ro1QKbqOOz5bKpzklKw+fuDAAZo0aYLRSxMetWjRolRNUosWLXjkkUf4/PPPufnmm8vsv3jxYhYtWuR43axZM6ZNm0bdunXdFmN0dLTbzi3lU5l7nsrc8y60zO12u+M8DRo0cEVIPq9UmY8dCxYLdOxI2D//SZjJ6W6tUgGu/Gxx+jsUExODxWLhnXfe4ddffwWgbdu2jBo16pwdhc8lLCwMo9FYZjkIi8VS4T43JpOJZs2akZKSUu77gwcPJjEx0fG6JENMTU3FarU6Fe9fMRgMREdHk5KS4vgwEfdSmXueytzzXFXmJZ+TBoOB5ORkV4Xnk/5c5sbjx6m7bBkGo5GTU6ZQmJrq7RB9zl895yaTyekKCqeTnB9//NGxCvnFF18MwIwZM5gyZQpr1qyhc+fOFb+4yURcXBxJSUmOc9lsNpKSkujfv3+FzmGz2Th8+DCdOnUq932z2VxqIdGzuesDuqSDtniOytzzVOaed6FlXtLxuFatWvreVVBJmRfVq8eJr74iYP16Cjp1Kp4nR9zClZ8tTic5jzzyCNdddx2zZ8/GdKaqzmq1ctddd/Hwww/zzTffOHW+xMRE3njjDeLi4oiPj2flypXk5+fTq1cvAGbNmkVUVBTDhw8HYNGiRSQkJBAdHU12djZLly4lNTWVvn37OnsrIiI1ikZXXRhbdDS5Q4d6OwxxQqVqcs5OcKC4Rmbs2LF07drV6QB69OhBZmYmCxcuxGKxEBsby7hx4xzNVWlpaaU6IWVlZfGf//wHi8VCSEgIcXFxTJ48mcaNGzt9bRGRmkTz5DjP/9tvISuL/H79vB2KVILTSU5YWBiHDx+mVatWpbYfOXKk0n8d9O/f/5zNUxMnTiz1+o477uCOO+6o1HVERGqyzMxMQDU5FXb6NOEPP4zp6FFOzZypWpxqyOkhUsOGDePOO+9kwYIFHDlyhCNHjjB//nzuuusubrnlFnfEKCIiLlAyyEOTqVbQk09iOnoUa9Om5F1zjbejkUpwuiZn+vTpGAwGbrvtNsfoJLPZzL333svUqVNdHqCIiLhGRkYGoCSnIvw3bIA33wTA8tJL2IODvRyRVIbTSY6/vz+vvvoq//rXv9i3bx8AzZs3J1gPgIhIlVVYWOjok6Mk5/wMFgsRDz0EQPZtt1Fw+eVejkgqq9IzGQUHBzt+UJTgiIhUbSW1OAaDgbCwMC9HU7WFP/MMfsnJEB/P6Wef9XY4cgGc7pNjtVoZP3484eHhxMbGEhsbS3h4OM8880yp5RNERKTqKOmPEx4ejp+fn3eDqcLM27YRvHgxdj8/+OADNVNVc07X5IwZM4ZPPvmEF198kUsvvRSATZs2MXHiRE6ePMmbZ9owRUSk6jh16hSgpqq/UtipEyffew/zoUOEde8Omhm6WnM6yfnoo4+YP38+AwYMcGzr0KEDTZo04ZZbblGSIyJSBWlkVcXl9+tHgcGAGvWqP6ebqwICAoiNjS2zvVmzZvj7+7siJhERcTElOecX+NlnGFVr43OcTnIeeOABnn/+efLz8x3b8vPzmTJlCg888IBLgxMREddQknNupt27ibz3XupddRV+Bw96OxxxIaebq7Zt28aXX35J48aN6dixIwA//fQTBQUF9O3blyFDhjj2/eSTT1wXqYiIVNrZHY/lLAUFRI4ZgyE/n/zLLqMoJsbbEYkLOZ3kREREcMMNN5Ta1qRJE5cFJCIirqeanPKFTp+O+eefKYqMxPLyy3DWWolS/Tmd5Lz33nvuiENERNxIsx2X5f/dd9T6978ByHjpJWz16nk5InE1p/vkiIhI9aOanNIMmZlEPPQQBrudnGHDyDtrxLD4DiU5IiI1gJKc0mq98QamI0ewNmlCxqRJ3g5H3KTSyzqIiEj1UTIZYGRkpJcjqRqyHn4YY1YWuYMGYQ8N9XY44iZKckREagDV5JRmDwoiY8oUb4chbqbmKhERH2ez2dTxGMBmI2jxYrDZvB2JeEilanJ++OEH1q5dy4kTJ7D96WGZMWOGSwITERHXyMzMxG63AzV7npyQ2bMJf+45ghYvJv399zVcvAZwOsl54YUXeOaZZ2jZsiX169fHcNZDYtADIyJS5ZQ0VYWEhNTY5XdMv/xC2NSpAOT166cEp4ZwOsl59dVXeffdd7njjjvcEI6IiLhaje+Pk5dXPKtxQQF5V19NzogR3o5IPMTpPjlGo5HLLrvMHbGIiIgb1PQkJ+xf/8K8axdFdepgmT5dtTg1iNNJziOPPMIbb7zhjlhERMQNavK6VQFff02tt98GwDJjBrY6dbwckXiS081Vjz/+ONdccw3NmzenTZs2mM3mUu9rUU4RkaolPT0dgKioKC9H4mFWK+FPPglA9u23k9+3r5cDEk9zuibnwQcfZO3atbRo0YLatWsTHh5e6p+IiFQtJUlO7dq1vRyJh5lMpL/zDrkDB5I5fry3o/F5djtMnx7KO++EeDsUB6drct5//30+/vhjrrnmGnfEIyIiLlZja3IAa9u2nJo929th1AgGA6SmGvnPf0K4/vpc6tTx/nxETic5UVFRNG/e3B2xiIiIG5w8eRKoOUmO8dgxjOnpWNu183YoPu/YMSM2m4HGjYsAePrpTK68Mp/atb2f4EAlmqsmTpzIhAkTyMnJcUc8IiLiYjWqJsduJ+Lxx6l7zTUELVjg7Wh8VlERzJ4dQq9e9Rg7Npwzc00SEWFn4MC8KjOAzemanNdee419+/ZRv359YmNjy3Q83rp1q8uCExGRC1eyOGdNSHKCP/iAwK+/xh4YSGGXLt4OxyclJZl44okIduwonlgyK8vI6dMGwsLsXo6sLKeTnEGDBrkhDBERcZea0lzld+gQYc89B0DmU09hjY/3ckS+JSfHwPTpocyeHYLNZiAszMa4cZmMGJGDsYquhOl0kjNhwgR3xCEiIm5gt9trRnOVzUbEo49izMkh/5JLyL7zTm9H5FO+/DKAcePC+f334rTh2mtzmTQpg/r1q0bfm3Op1AKdIiJSPZw+fRqr1Qr4dpIT8s47BGzejC04GMuMGVTZqoVq5sQJI88+G86yZUEANG5s5YUXMujbN9/LkVVMhZKcqKgo9uzZQ506dYiMjDzvQpwlfzGIiIj3lTRVhYSEEBgY6OVo3MPv8GHH4puZzz5LUUyMlyOq/mw2+OijYF54IYyMDCNGo53Ro7N5/PHTBAdXvb4351KhJOeVV14hNDQUgJkzZ7ozHhERcaGa0FRV1LgxmU8/jf+mTeT8/e/eDqfay8uD4cNr8913AQB06FDAiy9m0L59oZcjc16Fkpzbb7+93K9FRKRqqxGzHRuNZN91V3E/nKoydrkaCwyEpk2L2LnTxtixpxk5MhtTNe3c4nTYhw8fPu/7TZs2rXQwIiLiWr5ck2NMS8MWEgJBxf1FlOBU3s6dZurUKaJBg+KOxM8+m8ETT5ymUaMiL0d2YZxOcmJjY8/bJ6eoqHoXiIiILylJciIjI70ciYvZ7UQ89BCmgwc5NWsWhZ06eTuiauvDD4N5+ulwevfOZ86cdAwGiIqyA9X/97nTSc62bdtKvS4sLGTbtm3MmDGDKVOmVCqI1atXs2zZMiwWCzExMYwaNYr4Csxv8O233/Lqq6/StWtXxo4dW6lri4j4Ml+tyQlasoTAdeuwBwRgCwvzdjjVWteuBfj5QXCwnfz84uYqX+F0ktOxY8cy27p27UrDhg156aWXGDJkiFPn27hxI3PnzmX06NEkJCSwYsUKpkyZwsyZM8+7qvmJEyf43//+R+vWrZ29BRGRGsMX++QYTp0i7MycbafHjKFI6yk6JSfHwIYN/vTrVzwMvGVLK19+eYK4uOpfc/NnLptIoGXLlvzwww9OH7d8+XL69u1L7969ady4MaNHj8bf35+1a9ee8xibzcbrr7/OTTfdRL169S4kbBERn+aLsx2HTZmCX1oahQkJZN1/v7fDqVbWr/enb9+63HlnFNu3/7Esky8mOFCJmpzMzMxSr+12O8nJyUycOJGEhASnzmW1Wtm/f3+ppSKMRiPt27dnz5495zxu0aJFhIWF0adPH3799dfzXqOwsJDCwj+GvRkMBoLOdFI7X9+iyig5n6vPK+emMvc8lbnnXUiZn12T4wvfM//NmwmZNw+AjBdfxBAQ4Jbr+Npznplp4Lnnwvjoo2AAGjWyUlBgqFL3544ydzrJiYiIKBOA3W6nSZMmzJ8/36lzZWZmYrPZiIiIKHONY8eOlXvMrl27+Oqrr3jxxRcrdI3FixezaNEix+tmzZoxbdo06tat61SszoiOjnbbuaV8KnPPU5l7XmXKvOQP0xYtWtCgQQNXh+RZ+fnw9NPFX999N3U8sJaiLzzna9bAqFFw9Gjx6/vvh3/9y0RoaB3vBnYOrixzp5OcPzcjGY1G6tatS3x8PCY3D6TPzc3l9ddf5x//+AdhFexoNnjwYBITEx2vSxK01NRUx1TnrmIwGIiOjiYlJQW7vfrMCFmdqcw9T2XueRdS5idOnACKm/mTk5PdEZ7HGE6eJKJxY8zp6aQ+/DB2N96PLzznOTkGnn8+lPffDwGgWTMrL7+cwSWXFJCVBVlZXg7wT/6qzE0mk9MVFE5lJYWFhbz//vuMHz+eZs2aOXWh8oSFhWE0GrFYLKW2WyyWMrU7AMePHyc1NZVp06Y5tpUUxM0338zMmTPLZIBmsxmz2Ux53PXg2u32avtDUV2pzD1PZe55zpZ5YWEhGRkZQHGfnOr+/bJHRZH+/vsYU1KwhYeDB+6nuj7nP/xg5uGHIzl4sPjX/MiRWYwbV7wkQ1W/HVeWuVNJjtls5uOPP2b8+PGuubjJRFxcHElJSVx88cVA8V8bSUlJ9O/fv8z+DRs2ZPr06aW2zZ8/n7y8PO644w7q1KmaVW8iIt5Q8gek0Wg872jVasVgwFbdm93cKD8fZswI5d//roXNZqBBgyJmzDhFz54F3g7NK5weXTVo0CA+/fRTlwWQmJjIl19+ybp16/j99995++23yc/Pp1evXgDMmjWLjz76CAB/f3+aNm1a6l/JonNNmzZ1e3OZiEh1UtLpOCIiAj8/Py9HU3lBCxYQ8eijGLQA9Hnt2mXimmvqMmtWKDabgRtvzOHLL0/U2AQHKtEnJyEhgeeee45vv/2WLl26EBISUur9Bx980Knz9ejRg8zMTBYuXIjFYiE2NpZx48Y5mqvS0tKqVO9vEZHqwheGjxvT0gh/7jmMFguFbdqQfddd3g6pyrJa4bffTERFFfHiixkMGJDn7ZC8zmB3suHrfH1xDAYD+/fvv+CgPCE1NbXU0HJXMBgMNGjQgOTk5GrZhlsdqcw9T2XueZUt8+XLl/OPf/yDiy++mMWLF7sxQveJeOABghcvpqBdO9JWrMBTK0VWl+c8K8tArVp/xLd8eSDduxdQt67Ni1FVzl+Vudlsdm/HY4ADBw44e4iIiHhBdZ/tOGDdOoIXL8ZuNJLx4oseS3CqA7sd3n8/mBdfDOP//i+Ntm2LRwsnJqr25mwXNONxde11LiJSE1Tn5ipDbi7hZ+bEyR45ksJylhSq6davDyAjw8gHH4T89c41VKWSnHfeeYd27doRGBhIYGAg7dq14+2333Z1bCIicgFOnToFVM8VyGu98gqmw4cpatCA01qAGSiuvckvXm4KgwGmTctgyhQLU6ZkeDewKszpur9nn32WGTNmMGbMGC699FIANm3axCOPPMLhw4d57rnnXB6kiIg4r7o2Vxlycwn++GMALC+8gL1WLS9H5H0nTxp56qlw/P3tvPGGBYA6dWzccUeOdwOr4pxOct58801mz57NLbfc4th23XXX0aFDB8aMGaMkR0SkiqiuzVX2oCBOfPEFQcuWkd+vn7fD8bo1awJ44okI0tL8MJnsPPxwFgkJrp2x31c53VxVWFhI165dy2zv0qWLy5dJEBGRyiupyaluSQ6APTKSnNtu83YYXnX6tIFHH41g5MjapKX50bJlIcuXpynBcYLTSc6tt97Km2++WWb7f//7X0aMGOGSoERE5MJVt+YqY3IyQZ9+6pHlGqq6b7/1p2/fuixYEIzBYOfee7NYuTKV9u1dO/WJr6tQc9Wjjz7q+NpgMPD222+zZs0aLrnkEgC+++47Dh8+zG01POsWEakq7HZ7tavJCR8/nqBVqzAnJZH5zDPeDscrcnNh6tQw3n67uB9S06ZWZs600L17zZ21+EJUKMnZtm1bqdddunQBYN++fQDUqVOHOnXq8PPPP7s4PBERqYzs7Gzy8ornTKkONTmBn31G0KpV2E0mcoYM8XY4XrF9u5mHHopg797iRaVHjMjm2WczS032J86pUJKzdu1ad8chIiIulJaWBkBwcDDBwcFejub8DFlZhP/znwBk3XMP1jZtvByRZxUWwquvhvLaa7UoKjJQv34R06db6NMn39uhVXuaPlJExAeVJDl16tTxciR/LfTFF/FLTsYaE8Pphx/2djgeN3nyH81T11+fw5QpGURGqvbGFSqU5AwZMoQ5c+YQFhbGkL+oRvzkk09cEpiIiFReyfDxqt5UZd62jZB33wUgY+pUCAryckSed889WXzxRSBjx2Zy/fValsGVKpTkhIeHO1YCDw8Pd2tAIiJy4apFTY7NRsSTT2Kw28kZMoT8nj29HZFHHD7sx6pVgfzjH9kANGhg4+uvT2hpLjeoUJG+99575X4tIiJVU7VIcoxGMiZNImzqVDInTPB2NB6Rnm6gX7+6nD5tpFkzK/36Ffe7UYLjHipWEREfVF2aqwouvZS0JUu8HYbHREXZGT48h59+MtOqlSb1c7cKJTmdOnVyNFf9la1bt15QQCIicuGqdE2O3Y7xxAls9et7OxK3s9vh00+D6NChgObNiwB46qlM/PzAz8/LwdUAFUpyBg0a5OYwRETElapykhO4dCkRjz3G6XHjyB41ytvhuE3JoporVwbRuXMBn36ahp8f+Pt7O7Kao0JJzoQa0lYqIuIrqmpzlcFiIXzCBIy5uRgyMrwdjtusXh3I2LHhnDxZvKhmnz55Wq3CC9QnR0TEB1XVmpywyZPxS02lMD6erPvu83Y4LpeRYeDZZ8NZtKh4AsZWrQp59dVTtGun/jfeUKEkJyoqij179lCnTh0iIyPP2z+nZK0UERHxjqKiIsdncVVKcvy//ZaQefMAyJg+HQICvByRa339dQCPPhpBSoofRmPxopqPPXba126zWqlQkvPKK68QGhoKwMyZM90Zj4iIXCCLxYLNZgOq0OKcublEjB0LQPZtt1HQrZuXA3Kd7GwDkyeHMXduCACxsVZmzjxFt25aMdzbKpTk3H777eV+LSIiVU9JU1VkZCSmKjIBS+jMmZgOHqQoOprMp5/2djgu8803ATzxRDi//15cziNHZjFu3GmCg9UBpypw+uk/fPjwed9v2rRppYMREZELVxX749gDA7GbTFj+9S/sYWHeDueC2e0wdmw4H31UXHvTuLGV6dMtXHFFgZcjk7M5neTExsaet09OUVHRBQUkIiIXpiomOVmPPELu0KEUNW7s7VBcwmCAgAA7BoOdkSOzeeqp04SEqPamqnE6ydm2bVup14WFhWzbto0ZM2YwZcoUlwUmIiKVUzJ8vEr0x7HbizMCqPYJTnq6kdxcaNSouL/T00+f5vrrc9X3pgpzOsnp2LFjmW1du3alYcOGvPTSS3+5SrmIiLhXamoq4P2aHL+9e4l85BEsU6dibdvWq7FcqG+/9efeeyNp0cLKwoUnMRohJMSuBKeKM7rqRC1btuSHH35w1elERKSSjh8/DkB9by6bUFRE5COP4L91K2HTpnkvDhdp3LiInBwDp04ZOXnSZb86xc2crsnJzMws9dput5OcnMzEiRNJSEhwWWAiIlI5J06cACA6OtprMdR66y38t27FFhqK5V//8loclWW1wurVUNJ4ERNTxIIFJ2nfvlDLMlQjTic5ERERZToe2+12mjRpwvz5810WmIiIVE5KSgrgvZoc065dhE6fDkDGpEnYGjXyShyVtXWrmSefjOCXX+CTT/zp3j0fgC5d1DRV3Tid5Kxdu7bUa6PRSN26dYmPj68y8zGIiNRkXm2uKiwk4qGHMBQUkHf11eTedJPnY6ikjAwDU6eG8b//BWO3G4iKglOnzj2aWKo+p7OSK6+80h1xiIiICxQUFDiWdPBGkhM6Ywb+SUnYIiKwvPiiY2RVVWa3w6efBjFpUhipqX4A3HRTDrNmBWO15mthzWrM6SRn6dKlFd73uuuuc/b0IiJyAUpGVpnNZiIjIz17casV/61bAbD861/Y6tXz7PUrISnJxPjx4Xz/ffECU82bFzJ1agaXXVZI3brBJCd7OUC5IE4nOYMGDcJgMGD/U2r7520Gg0ETA4qIeNjZ/XHON3GrW5hMnPzoIwK++or8q6/27LWddPKkkWnTQvnoo+KmqaAgGw88kMW992adWVCz6tdAyV9zehzcmjVruOiii1i1ahUWiwWLxcKqVavo3Lkzn332GTabDZvNpgRHRMQLvD583M+vSic4hYXwzjshXHFFPT78MAS73cCgQTl8880JHn44SyuG+xina3Iefvhh3nrrLS6//HLHtr/97W8EBwdz99138+uvv7o0QBERqThvJDkhs2djOnCAjPHjISjIY9etjOefD+Odd2oB0LZtIc8/n0H37lpvylc5neTs27ePiIiIMtvDw8M5ePBgpYJYvXo1y5Ytw2KxEBMTw6hRo4iPjy933++++47FixeTkpJCUVER0dHRXHvttfTs2bNS1xYR8SWeHj7uv3EjYc8/j6GoiIJOncgdOtQj13VGURH4Ffcn5rbbslm2LIhHHz3N8OE5ju3im5xOcrp168ajjz7K//73P8cP0fHjx3niiSe4+OKLnQ5g48aNzJ07l9GjR5OQkMCKFSuYMmUKM2fOJDw8vMz+tWrVYsiQITRs2BCTycTWrVv597//TVhYGBdddJHT1xcR8SVHjhwBoLEH1okyHj1K5D33YCgqImfIEHJvvNHt13TGoUN+TJkSRq1admbMsAAQH1/E5s3H1SxVQzjdJ+fdd98lOTmZpk2bEh8fT3x8PE2bNuXo0aO88847TgewfPly+vbtS+/evWncuDGjR4/G39+/zHw8Jdq2bcvFF19M48aNiY6OZuDAgcTExLBr1y6nry0i4msOHz4MQNOmTd17obw8okaPxu/kSQrbtiWjCg4XP3nSyIoVQXz8cRApKX/8ulOCU3M4XZMTHx/Pjh07+Pzzzx2JRevWrbnqqquc7slvtVrZv38/gwYNcmwzGo20b9+ePXv2/OXxdrudpKQkjh07xogRI8rdp7CwkMLCP2apNBgMBJ1pM3b1yIOS83l8REMNpjL3PJW55zlT5mcnOW77HtnthI8di/9PP2GLjOTUu+9CcLDXxyMdPWrkxx/9uf76PAC6dLHyzDOZ9OmTT4MGdpwZMaXn3PPcUeaVmqLYYDDQr18/+vXrd0EXz8zMxGazlenjExERwbFjx855XE5ODv/4xz+wWq0YjUbuvPNOOnToUO6+ixcvZtGiRY7XzZo1Y9q0adStW/eCYj8fb64XU1OpzD1PZe55f1XmWVlZnDx5EoDu3buX2+TvEuPHw8cfg58fxoULqVeJrgqudOQI/Otf8PbbxZVJiYlQ0lr3/PMXdm49557nyjKvcJIzcOBA5s2b5/ihmTp1Kvfcc48jQTl58iRXXHEFv/zyi8uCO5fAwEBeeukl8vLy2LlzJ3PnzqV+/fq0bdu2zL6DBw8mMTHR8bokQ0xNTcVqtbo0LoPBQHR0NCkpKWXmERL3UJl7nsrc8ypa5iWjWyMiIsjJySEnJ8ct8fi3b09kaCiZkyaR27Yt3pox77ffTLz5ZggffxxEYWHxZ/tll+Wzf38mfn4X9vmu59zz/qrMTSaT0xUUFU5yPvvsM/Lz8x2vX3jhBW666SZHkmO1Wtm9e7dTFw8LC8NoNGKxWEptt1gs5Y7gKmE0Gh2ZXmxsLEePHuXTTz8tN8kxm82YzeZyz+OuB9dut+uHwsNU5p6nMve8vyrzs5uq3Pm9yb/8ck58+y222rXx9JoHdjts3uzPW2/V4osvAh3be/TI59FHT3PppQWO/VxzPT3nnubKMq9wkvPnC7oiAJPJRFxcHElJSY6RWTabjaSkJPr371/h89hstlL9bkREaqJDhw4B0KRJE5efO3DlSqwxMVjP/DFpq13b5dc4n/x8WLUqiNmzQ9i+3R8Ag8FO//55/OMfWXTrpt8BUpbXlw1PTEzkjTfeIC4ujvj4eFauXEl+fj69evUCYNasWURFRTF8+HCguI9N8+bNqV+/PoWFhWzbto3169dz1113efEuRES8ryTJiYmJcel5A5ctI/L++7GHhpK6ahVF7h65dZbkZCPvvx/CvHnBpKUVT2oTEGBn6NAc7r47i+bNNbu+nFuFkxyDwVCmx7MrekD36NGDzMxMFi5ciMViITY2lnHjxjmaq9LS0kpdJz8/n7fffpuTJ0/i7+9Po0aNGDNmDD169LjgWEREqrOSEa8tWrRw2TmD33uP8PHjMdjt5F51FUWNGrns3BWxa5eZ118PBSA6uogRI7K57bYc6tSxeTQOqZ6caq664447CDgzwUBeXh733HMPISEhAKX66zirf//+52yemjhxYqnXN998MzfffHOlryUi4ovsdruj43Hr1q1dcUJCp04ldNYsALJHjCDjX//CnVMEHzjgx/vvhxAZaeOhh7IAuPLKfG64IYf+/fO4+uo8ztHFUqRcFU5ybr/99lKv//73v5fZ57bbbrvwiERExGnHjh3DYrHg5+d3zmVxKsqQkUHEI48Q9NlnAGSOHUvWgw+6ZbI/mw2MZ+bp++03E7Nn16Ju3SLuvz8Lk6n4vddes7j8ulIzVDjJee+999wZh4iIXIAtW7YA0KZNGwIDA/9i7/Or9cYbBH32GXZ/fyzTppF7002uCNHh99/9WLEikBUrgrjkknzGjTsNQO/e+QwdmsPAgblVbfJkqaa83vFYREQu3I8//ghA165dL/hcWY88gum338h6+GEKO3a84PPZ7cW1NJ9/HsiKFYH89JO/472TJ408/fRpDAYwm2HmTMsFX0+khJIcEREf8PXXXwNwySWXOH2sae9eQt57j4znngM/P+xBQZy6wNp7i8XA+vUBfP11AOvWBZKc/EdfHqPRTvfuBSQm5tK/f55qbcRtlOSIiFRz+/fvZ+/evfj5+XHFFVdU+DjD6dPUeu01as2ejaGwEGuzZmRfwHQcyclG5s0LZt26QLZtM2Oz/ZG9BATYueSSfAYOzKN//zyNjhKPUJIjIlLN/d///R8AV155ZcXWq7LZCFq4kLCpU/FLTQUgr08fcgcMcOq6yclGcnMNxMUVz1Vz+rSRl18Oc7yfkFDIlVfm06tXPpdcUkBQkGYOFs9SkiMiUo0VFRU5FiG+qQIdhP1/+IGwZ5/Ff8cOAKzNmpExcSL5ffs6NXrq7bdDmDAhnOuuy+XNN08BkJBgZfjwbDp1Kk5uGjXSRH3iXUpyRESqsZUrV3Ls2DEiIiK4+uqrz7+z3U7oCy/gv2MHttBQTj/8MNmjRoG//zkPOXjQj7VrA/jqq0DuuCObvn2L50Tr0KEQg8FOVtYfiZHBAC+9lOGS+xJxBSU5IiLVlM1m45VXXgHgzjvvLH/oeF4eBpsNe3AwGAxkTppE8P/+x+knn8RWp055u/PddwF8+WUAa9cGsn//H78mGjUqciQ5nTsXsGNHClFRaoKSqktJjohINbVixQp2795NWFgYd955Z+k37XYC16whbOJE8q65hsxnngGgsEMHMl56qdSup08b+OKLQFauDGTt2gByc42O90wmO926FdC7dz79+uWdtR0lOFLlKckREamG8vPzmTp1KgB33XVXqQ7HxtRUwp9+mqBVqwAIXLGCzMcfh7NqeiwWA6tXF0/It2FDAAUFfzQ7RUcX0bt3Hr1753PFFfmEhSmZkepJSY6ISDX03//+l4MHD1KvXj3uvvvu4o12O4FLlxL+z3/id+oUdpOJrHvuKV6S4U9NWfPnB/P8838kRvHxhQwcmMfAgXm0a1eouWvEJyjJERGpZn7//XdeffVVAMaPH09oaCjGkyeLa29WrACgsE0bTr3yCtZ27di1y8RHHwVzySUFDBxY3OQ0aFAun3wSzMCBuQwcmEeLFlav3Y+IuyjJERGpRoqKinjooYfIzc2le/fuDB48GABDbi4B69YV1948+CCnx4xxjJpatiyId96pxZ49+Y4kJzraxpo1qV67DxFPUJIjIlKNvPnmm2zevJmQkBBe/de/MJxpVypq3BjLzJkcCGjBnO87cdnmAnr2LB4JNWJENvv2mRg2LMeboYt4nJIcEZFqYs2aNY7Oxh8NGULXYcM49dpr5F3ek6+/DuD9hX/niy8CsNsN/PxzniPJadjQxltvnfJm6CJeoSRHRKQa+Pbbb7nvvvuob7eztEkTuv3vf6QTyezxFv5TWI9Dh/74OL/iinz+/nfV2ogoyRERqeK+/PJL7hs9mrvy8phsMrH7SD1GGp5jvnE4eXuL+92EhdkYOjSH227LJj5eyymIgJIcEZEqy2q18sorr7B15kzWEEkSI7jKOprv6Q52oAjatCnkjjuyGTw4l+BgzWcjcjYlOSIiVdAPP/zA008/za+//ko//PkbB8ikeF4bf387iYm53HZbNl27ak4bkXNRkiMiUoUkrdvMJ0+s54djTfiVX4mIiOD6yZMJm32CX/MDuH5IAcOG5VCnjs3boYpUeUpyRES8zWbj8PvvUzhrFu1TCriGFKyYua7/Yaa8dBe1a9dm1J0NOHUqGbtdTVIiFaUkR0TECwoLYf2S08yffoSc3618ZX/G8d4N/h8QdlFT7n7mEaKizECZVRlEpAKU5IiIeEhREXz3nT8LFsCKFYHk5jYEWgKwg6Yca26m9lNPMWPg37wbqIiPUJIjIuJGdjts2WJm2Uc2li7150RupOM9P44z2Pgxbbvuxz5zIR1imngxUhHfoyRHRMTF7Hb4+WcTSz4JYNkiI0dOhjreCyGdbD6hefMfeeihjlx77UD8z6wxJSKupSRHRMSF7HYYPKAWP+wMc2yrxWkG8SltWADdLLR/9ik6d37Wi1GK1AxKckRELkBampEvvghg2LBcDAb45eck4nYdZwdDSWQ5A/mI9MB1FNx8Pf0ffJ769et7O2SRGkNJjohIJRUUwOU96nA624Ql5Qu+2PAKmzZt4i4a8BEPsCEmkpwHH+SGwS8TEBDg7XBFahwlOSIiFVBQAOvWBfD99wE889hxglauJHzuXBKzH2Ev8Xz30iw2sQk/Pz9ODOyK35138ljXrhg0HbGI1yjJERE5B5sNfvzRn08+CWLZsiAsFiMAo9/vS6ec7wF4hzv4gkL+HRrKmDvGcNttt9GwYUNvhi0iZyjJERH5k927TXzySRCffhrE77//8TEZTTI3M5+onBQOAW8D3ybEcu299zLruusICgryWswiUpaSHBER4OhRP5YuDeSTT4L55RezY3twsJUmTX7k2LGXePv0p+Rh4z6jEePfBjDyrru4p3t3NUmJVFFKckSkRluwIIj//S+Ebdv+mKvGTAEDWMUtfMiDOcvYvTsPgLvr1+eWv/+dSTffrCYpkWpASY6I1CiHD/vRsGERpjOffr/+WMC2bZEYsHEF67mFeQzl/zCQzjzADPTq1YvbbruNvn37YjLpY1OkuqgSP62rV69m2bJlWCwWYmJiGDVqFPHx8eXu+8UXX/DNN99w5MgRAOLi4rjlllvOub+ISIlbb43iq68CmTcvjZ49CwC4zvwhHdnHED6hHimsAv4BbGnQgGtvvJFFt9xCTEyMV+MWkcrxepKzceNG5s6dy+jRo0lISGDFihVMmTKFmTNnEh4eXmb/X375hcsuu4yWLVtiNptZsmQJkydPZsaMGURFRXnhDkSkqikogM2b/dmwIYCnnjqN0Qh+v/9Ok8zjGOnAT29uZNu2b1mxYgUpP//MIuB5YFVQEN0SExk2dCgzL70Uo9Ho7VsRkQvg9SRn+fLl9O3bl969ewMwevRotm7dytq1axk0aFCZ/R988MFSr++55x6+++47du7cyZVXXumJkEWkCjp50sjatQF8/nkgX38dwOnTxQnK4JPvcsWO/+L/yy88T2NeJJ8936RyxTfFx/n5+THx8su54YYbeHTAAIKDg714FyLiSl5NcqxWK/v37y+VzBiNRtq3b8+ePXsqdI78/HysViu1atUq9/3CwkIKCwsdrw0Gg2OYp6tHRJScTyMtPEdl7nlVpczz8+GHH/z5+usAvvkmgJ07zaXer29MJdG2hOj5/8WfXygCDvA7S4Elfn707tmTxMRE/va3v1X5WuCqUuY1icrc89xR5l5NcjIzM7HZbERERJTaHhERwbFjxyp0jg8//JCoqCjat29f7vuLFy9m0aJFjtfNmjVj2rRp1K1bt9Jx/5Xo6Gi3nVvKpzL3PG+U+S+/wGefweefw7p1kJtb+v02bQpo2nQnp09/xLiNr9KTIlYD04Av/f3pdPXV3Hjjjfzz+uuJjIz0ePwXSs+556nMPc+VZe715qoL8emnn/Ltt98yceJE/P39y91n8ODBJCYmOl6XZIipqalYrVaXxmMwGIiOjiYlJQW73e7Sc0v5VOae56kyt9vh6FEjjRvbHNtGjKjN9u1//KxHG1LoZ/+Mq/mcq/iCbr8cZ/Uvxe/dDZijo7ni6qu56qqrmHDZZY6mqLy8PJKTk90Wu6vpOfc8lbnn/VWZm0wmpysovJrkhIWFYTQasVgspbZbLJYytTt/tnTpUj799FPGjx9/3pEPZrMZs9lc7nvuenDtdrt+KDxMZe557izzzEwDvXrVIzXVyC8/JxMaVrz9Gv/V1Meffqzhaj6nnT2J08BXwHNAjsFA1y5d6Nu3L1dddRWtW7cuVfVd3Z8RPeeepzL3PFeWuVeTHJPJRFxcHElJSVx88cUA2Gw2kpKS6N+//zmPW7JkCZ988gn//Oc/ad68uafCFREXys+HnTvNbNniz5Yt/gQH25n5yin8Dh4kevNmgrNGYLJFsnzacg6E7OD777+n5datLKeILcCnwH1AWvPmXNqzJ1dccQVjLrmk3FGZIlIzeb25KjExkTfeeIO4uDji4+NZuXIl+fn59OrVC4BZs2YRFRXF8OHDgeImqoULF/Lggw9Sr149Ry1QYGAggYGBXroLEfkrR48a2bLFn61bi5OapCQzBQV/1LKE+WUTtaYDgRnpAKzmPzTlMBPn5PPGmX2SgM316tHxiiu44oormHn55TRo0MDzNyMi1YLXk5wePXqQmZnJwoULsVgsxMbGMm7cOEdzVVpaWqnq5s8//xyr1cqMGTNKnefGG2/kpptu8mToInIOp08b2LLFn507zezYYWbrVn9SUvzK7BcamktM2M+MOLqIy4s2YM6wkA98D3zNb3wNpDRrxi2XXMLFF1/MxRdfTExMjEa8iEiFGOw1tLExNTW11NByVzAYDDRo0IDk5GS14XqIytzz/lzmaWlGfvrJTKNGRbRqVdyZf/06P24eUb/UcX5Y6cAOLmUT1oCt/Dd/HbAff+A94DvgBz8/rG3b0ulMUtOtWzfq1Knj4TusevSce57K3PP+qszNZnP16ngsItVHbi7s3Wtm924zd955ZqPdzoznjby/qDYPXL+Hx2b6s3fvXpJ376QFA+nMVrqwha78SDd+wEoO24BF+cUfaPHxCXTs2JEDF13E1R078lCbNmp2FhGXUZIjIqUUFcGhQ37s2mVm1y4Tv/5qZvduEwcOmLDZipuJOn32PO0Ozif4wAGuzB3Gd4zFunIeLVZOddSQfs+jnAS2Af8GUhs3JrRjbzp26kSvjh0Z0749oaGhXrtPEfF9SnJEarDCQvjkkyD27zexb5+JfXv9OHjQREFh+Ws2RXKSDuzEuHI1kRRPSHMrc7iUOawvhLconhqidevWvNmmDa1bt6Z969YMbdVKyyWIiMcpyRGpIX76yczcucFE1y/iibFZABQVFfDPsbXJtQaU2jeQXNrwC7Hs5BN2UjyuaSevkEw4sA6YGxpKVkwMxtataRIfT4sWLfi+bVsaNmyojsEiUiUoyRGp5ux2SEszFtfEOP75sX+PkX/etJ3r635N3o4dnPiuNvP3vkor489s/+kBDhw4wJEjR7jW9m8aUkRLdtOS3TRiN7kcZj82DgYEcKB1a5rFxREXN4KcuDii4+IYc+mlZGdnq0OmiFRpSnJEqoncXDhw4I9EZv9+E/v3wL79ZjKzy5/V+/fpK6jN8wD0I5qJRNDGtpMR69ZRMrYwzP9B6tapQ0pMDCfbtqV22/40i4ujXVwcPaOiuO1P5zQYDISFhZGdne2+mxURcQElOSJVTHZ28RwzFouB667Lc2wf2DOEPcfKn83XgA07B4HdwG5uZzd/ZzcR7OQzYJ/RSHodG9amK9nZsiUvdJhGXHw8zZo1o169empeEhGfpCRHxAtOnzaUal66qE02A+OSMO3bx75vM7jlf2MJM2ayd/d09h86yL59+whLfp5ILnc0K7VgDy3ZTTi7sbKX2/2s1IqJoVmzZkQ0aMDWhF40bXE3cXFxXNewIUZj+Z2JRUR8lZIcETfKyjKwZ4+JPXtM7N5tZs8vsGd/IMeOlf7RG8n/cQfFk890I4DWJNLCtoeFM9/iCLkA3MpN3Es+qeHhZDVsSG5CAnvat6dxy0HExcXxfePG51yMVkSkJlKSI+JiH3wQzOqlRvbs8uPoyZDz7JlCLLu5mt305UtOUdzYtId8/h7UlawGDYjqNJAGLVvSvHlzmjVrRkxMjCbLExGpICU5IpVUUAD33xPGrzsNLF96lNSsZHbv3s3GGZGsPT7UsV80ybTlZ8e/f/MLP/EzgYF5tGrYkNr16rGzbVtS2r1C8/h4usbFcdWZtdtERKTylOSInENhIfz2m4mffzbz889mft1mo75fKm92f438H36g1m+/sT1tO8doxK3d72Jr0XoA+nMZb/AlEfxMET9z3JxFZoMG2Fq2JK1zZ8Z2uI/4+Hgaqp+MiIhbKckRoXiumSNH/Ni61Z8tP5rZshl+3RtcZubfaLKp+91rjtev8SAGLMwp2squ4GBatGhBnbj6nEyIIKzdP2jVsiV9NDmeiIhXKMmRGuvQIT+WLwtk6wYrW38K4kRmecsOZADbiWY7z7OdULbzPnAkPJzc+HjMXerQpNsAnm47mX83aaKaGRGRKkRJjtQIv//ux/ff+9O6VQGt2xRRWFjIxjk/8sJ/r3PsY6KQTmzjUjbRlc1s5Dtm+x2hZcsWtG/dmpRWrYi46Bq6tm7N1ZGRXrwbERGpCCU54nOysw389JOZi7vkEnjgN/y3buWV1zox/0hfbop+h4NN/8uOHTsIyQvhBt6iG5uozWZy2Epy7RCsHTpwrFcvrut2G2NbtSIgIOCvLyoiIlWOkhyp1ux2+O03WLUqiB9/NLP1RxO/7jJjsxvZbOpGd+uPAPTjDg4QQv2UH1iY8j0AAeEBRDb/F2mXXUbDbvdwSadOREVFefN2RETEhZTkSLWSlWVg+3YzW340s/WbQrbuDCY9ByCi1H6NOYLFGkUW8D1w3PA+XRt/ia17d2b0mEGXLl2Ii4tTHxoRER+mJEeqPLsdnhkbxA8b4Ncj4djspRMTf/IoYAuwGdjE3WwmNDSTpR07suGKp+nUtSs3duhAcHB5HYtFRMRXKcmRKuX4cSPz5gWTcTSHpybn8uuvv7J9+3a+X5DIL0UdAGjKIS5lE53ZRDCbOWXcycou7ejYuTOdO/ejU6cnaahh2yIiNZ6SHPGK06cNJCWZ2bHDTPNaKQyM+IbsDRvI3JjCS3s/w0wgHy6sTbY1C4DR/Mj9QBGbSa1dQM5FF1F05ZUkXPwcffr04a60NOx2u3dvSkREqhQlOeJ2mZlnEprtfuxMCmDHTjP79/+xkOQQvuA27qYO0BS4i9m0YidvW+0cj4ykU6dOhLVrQli3blx00XOlOgcbDAYtSikiIuVSkiMulZlpYOdOMzu3QtLGfHb8HMS+k+WPWArlMH3ZwpV8yVZgh58fJxo1Iu6i5dTq25d3u31O06ZN1ewkIiKVoiRHKi0jozih6dixkNBQOzk5OUwbeZg5m/uU2bcph+jCFn5jC0lswWjcTue4UFo1b05Bz56kdf2M3i1bqlZGRERcRkmOVMipUwYOHzbRKfp3TDt3krdxI4lznmJ/fgNGtHiQzUUr2b9/P93sg4ilGV3YQku2EMxWikw7yGsajK1jRy679FLatn+YFi1aEBgY6O3bEhERH6YkR8pITzewc6c/O3aY2b7dwE/f20hOD6cWp8mgC0aKO/hewhXYsZG75yj72AdARu31DG80CEPnzjTp3p227a4hNjZW89GIiIjHKcmp4dLTjezYaiBpXSZbNhaQdKg2x/Kiy923Hic4Rn0ySeEng4F2dZ4gvmVz/C+5hCEXfUDbtm2pV6+eh+9ARESkfEpyapCiIvDzg8zMTNYt+oVJz19KSkFJQlO/1L71+I0TbAG2UCdiPw9HHcLcsSnLLnucFhddxKXNm3Olv7/H70FERKSilOT4MEN2Nlnffsv//mvl7R8H0K7W5+wJm8ihQ4cIIZRsMgFIYA8d2EI9thIR9htR8acJ7NGGeldcQevWt1G7dm0v34mIiIjzlOT4gOxsAzt3mlj/dTa/fPo7u5Ib84rfnQzK+4oGQDy3ksxd1D3VnEOnDgEQ0SiM+w1DadLSRmTPjjS+4gqaNR+DyaRHQkREfIN+o1UzRUWw6ycr384/xA8brOw8EcOR3BjAD6gLxAJwpLAdRr7iKBAc9A3/rH07jS8xE3njfNq1a0dkZKT3bkJERMQDlORUA5+9d5SvP0kjaU8Uv2a1IYdQiucGPtsRDIYtDAz+lQ51DhLS3cDqAe/R/LLL6BESQg9vBC4iIuJFSnKqkKIi2Ln+FD8u3YOhbRLbt29n27ZthBz4H0kMcOxXi9NcxHfE+m+lUaPfCbilBT16JtCiRUcCAi724h2IiIhUHUpyvOi0pYgDn/1A4Kb5BPz4I+bfi7im8BBGWhHEbWRTvDjl31hMjHE/TWvvoXmnPNoNbUNC754EBrXx8h2IiIhUXUpyPOj33wv55JMTrFtXSMr2MH7Pb0U/AlnJ/zn2ac8OanGCxp36EdM3jk6dOtGxY8czfWj6ei94ERGRasbrSc7q1atZtmwZFouFmJgYRo0aRXx8fLn7HjlyhAULFnDgwAFSU1O5/fbbueaaazwcccXY7bDjs72sm7ufnT+F8UtGew7ZE4CYUvv9TkM21golrUUChh49eOfa4zRs2waD4XXvBC4iIuIjvJrkbNy4kblz5zJ69GgSEhJYsWIFU6ZMYebMmYSHh5fZPz8/n/r163PppZfy/vvveyHi89v3w0GmT/mMbTtr83NuV07QE+h51h42/Px+pWHD/fRoe4ohF+fTZcSVBNXadWZMlIiIiLiKV5Oc5cuX07dvX3r37g3A6NGj2bp1K2vXrmXQoEFl9o+Pj3fU8nz00UeeDPUvrVy5kndGf8bms5qezBTQ1rCFFpE7aX1RFpff1YbWPdtjMHTxYqQiIiI1g9eSHKvVyv79+0slM0ajkfbt27Nnzx6XXaewsJDCwkLHa4PBQFBQkONrV+nWrRsP8iBd/L6hXaNfuehKP/rc04W6zWJB9TRuU/I9dOX3Us5PZe55KnPPU5l7njvK3GtJTmZmJjabjYiIiFLbIyIiOHbsmMuus3jxYhYtWuR43axZM6ZNm0bdunVddg2ABg0asO/YPho0aEDpJirxhOjo8hcVFfdRmXueytzzVOae58oy93rHY3cbPHgwiYmJjtclGWJqaipWq9Wl1yo5d0pKCna73aXnlvIZDAaio6NV5h6kMvc8lbnnqcw976/K3GQyOV1B4bUkJywsDKPRiMViKbXdYrGUqd25EGazGbPZXO577npw7Xa7fig8TGXueSpzz1OZe57K3PNcWeZGl5ylEkwmE3FxcSQlJTm22Ww2kpKSaNGihbfCEhERER/h1eaqxMRE3njjDeLi4oiPj2flypXk5+fTq1cvAGbNmkVUVBTDhw8Hijsr//77746v09PTOXjwIIGBgWo3FRERkVK8muT06NGDzMxMFi5ciMViITY2lnHjxjmaq9LS0kr1sk5PT2fs2LGO18uWLWPZsmW0adOGiRMnejh6ERERqcoM9hra2JiamlpqaLkrGAwGGjRoQHJystpwPURl7nkqc89TmXueytzz/qrMzWaz0x2PvdYnR0RERMSdlOSIiIiIT1KSIyIiIj5JSY6IiIj4JCU5IiIi4pOU5IiIiIhPUpIjIiIiPklJjoiIiPgkn1+F/FxMJvfdujvPLeVTmXueytzzVOaepzL3vHOVeWW+FzV2xmMRERHxbWqucqHc3FyefPJJcnNzvR1KjaEy9zyVueepzD1PZe557ihzJTkuZLfbOXDggNY58SCVueepzD1PZe55KnPPc0eZK8kRERERn6QkR0RERHySkhwXMpvN3HjjjZjNZm+HUmOozD1PZe55KnPPU5l7njvKXKOrRERExCepJkdERER8kpIcERER8UlKckRERMQnKckRERERn6RFOZy0evVqli1bhsViISYmhlGjRhEfH3/O/Tdt2sSCBQtITU0lOjqaESNG0LlzZw9GXP05U+br1q3j3//+d6ltZrOZDz/80BOhVnu//PILS5cu5cCBA5w6dYrHH3+ciy+++LzH/Pzzz8ydO5cjR45Qu3ZtbrjhBnr16uWZgH2As2X+888/M2nSpDLb//vf/xIREeHGSH3H4sWL+f777zl69Cj+/v60aNGCv//97zRs2PC8x+nzvPIqU+au+DxXkuOEjRs3MnfuXEaPHk1CQgIrVqxgypQpzJw5k/Dw8DL77969m1dffZXhw4fTuXNnNmzYwEsvvcS0adNo2rSpF+6g+nG2zAGCgoJ49dVXPRypb8jPzyc2NpY+ffowffr0v9z/xIkTTJ06lauvvpoxY8aQlJTEW2+9RUREBBdddJH7A/YBzpZ5iZkzZxIcHOx4HRYW5o7wfNIvv/zC3/72N5o3b05RURHz5s1j8uTJzJgxg8DAwHKP0ef5halMmcOFf54ryXHC8uXL6du3L7179wZg9OjRbN26lbVr1zJo0KAy+69cuZKLLrqI6667DoCbb76ZnTt3snr1au6++25Phl5tOVvmAAaDQX/RVlKnTp3o1KlThfdfs2YN9erV47bbbgOgcePG7Nq1ixUrVijJqSBny7xEeHg4ISEhbojI9/3zn/8s9fr+++/nrrvuYv/+/bRp06bcY/R5fmEqU+Zw4Z/nSnIqyGq1sn///lK/WI1GI+3bt2fPnj3lHrNnzx4SExNLbevYsSM//PCDO0P1GZUpc4C8vDzuu+8+7HY7zZo145ZbbqFJkyYeiLjm+e2332jfvn2pbR07dmTOnDneCagGGTt2LIWFhTRp0oShQ4fSqlUrb4dUbeXk5ABQq1atc+6jz3PXqkiZw4V/nqvjcQVlZmZis9nKZJQRERFYLJZyj7FYLGWaVMLDw8+5v5RWmTJv2LAh9957L2PHjmXMmDHYbDaeeeYZTp486f6Aa6BzPeO5ubkUFBR4KSrfFhkZyejRo3nsscd47LHHqF27NpMmTWL//v3eDq1astlszJkzh5YtW5632Umf565T0TJ3xee5anLEp7Ro0YIWLVqUev3II4/w+eefc/PNN3sxMhHXaNiwYanOmi1btuT48eOsWLGCMWPGeDGy6umdd97hyJEjPPfcc94OpcaoaJm74vNcNTkVFBYWhtFoLJO1WyyWc7YXRkREkJGRUWpbRkaG+otUUGXK/M9MJhPNmjUjJSXF9QHKOZ/xoKAg/P39vRRVzRMfH69nvBLeeecdtm7dyoQJE6hdu/Z599XnuWs4U+Z/VpnPcyU5FWQymYiLiyMpKcmxzWazkZSUVCrTPFuLFi3YuXNnqW07duwgISHBrbH6isqU+Z/ZbDYOHz5MZGSku8Ks0RISEsp9xiv6/RHXOHjwoJ5xJ9jtdt555x2+//57nn32WerVq/eXx+jz/MJUpsz/rDKf50pynJCYmMiXX37JunXr+P3333n77bfJz893zAkya9YsPvroI8f+AwcO5KeffmLZsmUcPXqUhQsXsm/fPvr37++lO6h+nC3zRYsW8dNPP3H8+HH279/Pa6+9RmpqKn379vXSHVQveXl5HDx4kIMHDwLFQ8QPHjxIWloaAB999BGzZs1y7N+vXz9OnDjBBx98wNGjR/nss8/YtGkT11xzjTfCr5acLfMVK1bwww8/kJKSwuHDh5kzZw5JSUn87W9/80b41dI777zD+vXreeihhwgKCsJisWCxWEr1I9PnuWtVpsxd8XmuPjlO6NGjB5mZmSxcuBCLxUJsbCzjxo1zVFempaVhMBgc+7ds2ZIHH3yQ+fPnM2/ePBo0aMATTzyhORWc4GyZZ2Vl8Z///AeLxUJISAhxcXFMnjyZxo0be+kOqpd9+/aVmmhu7ty5AFx55ZXcf//9nDp1yvHLF6BevXo89dRTvP/++6xcuZLatWtzzz33aPi4E5wtc6vVyty5c0lPTycgIICYmBjGjx9Pu3btPB57dbVmzRoAJk6cWGr7fffd5/gDSp/nrlWZMnfF57nBbrfbLzh6ERERkSpGzVUiIiLik5TkiIiIiE9SkiMiIiI+SUmOiIiI+CQlOSIiIuKTlOSIiIiIT1KSIyIiIj5JSY6IeNUdd9zBoEGDPH7dOXPmYDAYMBgMPPzww47tsbGxzJw587zHlhyndYtEqjbNeCwibnP27KXlmTBhAq+++irempM0LCyM3bt3ExIS4tRxycnJLFiwgAkTJrgpMhFxBSU5IuI2ycnJjq8XLFjAs88+y+7dux3batWqRa1atbwRGlCchEVHRzt9XHR0NOHh4W6ISERcSc1VIuI20dHRjn/h4eGOpKLkX61atco0V/Xq1YsxY8bw8MMPExkZSf369Zk9ezbZ2dmMHDmS0NBQ4uPjWbVqValrJSUlMWDAAGrVqkX9+vW59dZbS6355IycnBxGjRpFaGgoTZs25b///e+FFIOIeImSHBGpct5//33q1KnD999/z5gxY7j33nsZOnQoPXr0YOvWrfTr149bb72VnJwcACwWC3369KFTp078+OOPrF69muPHj3PTTTdV6vovv/wyXbt2Zdu2bdx3333ce++9pWqgRKR6UJIjIlVOx44deeaZZ0hISODpp58mMDCQOnXqMHr0aBISEnj22Wc5efIkO3bsAGDWrFl06tSJF154gVatWtGpUyfeffdd1q5dy549e5y+/sCBA7nvvvuIj4/nySefpE6dOqxdu9bVtykibqY+OSJS5XTo0MHxtZ+fH7Vr16Z9+/aObfXr1wfgxIkTAPz000+sXbu23P49+/bto0WLFpW+fkkTW8m1RKT6UJIjIlWO2Wwu9dpgMJTaVjJqy2azAZCVlcW1117LtGnTypyrQYMGLrl+ybVEpPpQkiMi1V7nzp35+OOPiY2NxWTSx5qIFFOfHBGp9u6//37S09O55ZZb+OGHH9i3bx+fffYZI0eOpKioyNvhiYiXKMkRkWqvYcOGfPvttxQVFdGvXz/at2/Pww8/TEREBEajPuZEaiqD3VtTjYqIeNGcOXN4+OGHsVgsXjleRNxPf+KISI2VkZFBrVq1ePLJJ506rlatWtxzzz1uikpEXEU1OSJSI50+fZrjx48DEBERQZ06dSp87N69e4Hi4e3NmjVzS3wicuGU5IiIiIhPUnOViIiI+CQlOSIiIuKTlOSIiIiIT1KSIyIiIj5JSY6IiIj4JCU5IiIi4pOU5IiIiIhPUpIjIiIiPklJjoiIiPik/wewed9RlqEe2AAAAABJRU5ErkJggg==", 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", + "image/png": 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", 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" ] }, "metadata": {}, @@ -574,6 +592,306 @@ "plt.title('NMC811')" ] }, + { + "cell_type": "markdown", + "id": "dbc0edb2", + "metadata": {}, + "source": [ + "## Multi-Cycle Simulations\n", + "For multi-cycling, an experiment definition for static C/2 discharge and charge cycling is presented." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "4cb719b9", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-02-21 09:09:26.957 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Discharge at C/2 until 3.0 V', using temperature from parameter values.\n", + "2023-02-21 09:09:26.959 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", + "2023-02-21 09:09:26.961 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Charge at C/2 until 4.2 V', using temperature from parameter values.\n", + "2023-02-21 09:09:26.963 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", + "2023-02-21 09:09:26.964 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Discharge at C/2 until 3.0 V', using temperature from parameter values.\n", + "2023-02-21 09:09:26.965 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", + "2023-02-21 09:09:26.966 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Charge at C/2 until 4.2 V', using temperature from parameter values.\n", + "2023-02-21 09:09:26.967 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n" + ] + } + ], + "source": [ + "experiment = pybamm.Experiment(\n", + " [\n", + " (\n", + " \"Discharge at C/2 until 3.0 V\",\n", + " \"Rest for 1 hour\",\n", + " \"Charge at C/2 until 4.2 V\",\n", + " \"Rest for 1 hour\",\n", + " ),\n", + " ]\n", + " * 2\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "40467b70", + "metadata": {}, + "source": [ + "The solution is reintroduced, with `calc_esoh=False` passed into the solve function. Currently, composite electrode state of health predictions are not included in this model. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "dac3f3bb", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-02-21 09:09:27.094 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Discharge at C/2 until 3.0 V', using temperature from parameter values.\n", + "2023-02-21 09:09:27.096 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", + "2023-02-21 09:09:27.097 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Charge at C/2 until 4.2 V', using temperature from parameter values.\n", + "2023-02-21 09:09:27.098 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", + "2023-02-21 09:09:27.099 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Discharge at C/2 until 3.0 V', using temperature from parameter values.\n", + "2023-02-21 09:09:27.099 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", + "2023-02-21 09:09:27.100 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Charge at C/2 until 4.2 V', using temperature from parameter values.\n", + "2023-02-21 09:09:27.101 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", + "2023-02-21 09:09:27.115 - [INFO] callbacks.on_experiment_start(166): Start running experiment\n", + "2023-02-21 09:09:27.118 - [INFO] parameter_values.process_model(425): Start setting parameters for Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:27.244 - [INFO] parameter_values.process_model(527): Finish setting parameters for Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:27.246 - [INFO] parameter_values.process_model(425): Start setting parameters for Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:27.362 - [INFO] parameter_values.process_model(527): Finish setting parameters for Doyle-Fuller-Newman model\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.001\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-02-21 09:09:27.364 - [INFO] parameter_values.process_model(425): Start setting parameters for Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:27.482 - [INFO] parameter_values.process_model(527): Finish setting parameters for Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:27.486 - [INFO] discretisation.process_model(147): Start discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:27.492 - [INFO] discretisation.remove_independent_variables_from_rhs(1120): removing variable Discharge capacity [A.h] from rhs\n", + "2023-02-21 09:09:28.059 - [INFO] discretisation.process_model(259): Finish discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:28.060 - [INFO] discretisation.process_model(147): Start discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:28.066 - [INFO] discretisation.remove_independent_variables_from_rhs(1120): removing variable Discharge capacity [A.h] from rhs\n", + "2023-02-21 09:09:28.601 - [INFO] discretisation.process_model(259): Finish discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:28.602 - [INFO] discretisation.process_model(147): Start discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:28.610 - [INFO] discretisation.remove_independent_variables_from_rhs(1120): removing variable Discharge capacity [A.h] from rhs\n", + "2023-02-21 09:09:29.139 - [INFO] discretisation.process_model(259): Finish discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:29.140 - [NOTICE] callbacks.on_cycle_start(174): Cycle 1/2 (20.167 us elapsed) --------------------\n", + "2023-02-21 09:09:29.141 - [NOTICE] callbacks.on_step_start(182): Cycle 1/2, step 1/4: Discharge at C/2 until 3.0 V\n", + "2023-02-21 09:09:29.145 - [INFO] base_solver.set_up(111): Start solver set-up\n", + "2023-02-21 09:09:29.290 - [INFO] base_solver.set_up(236): Finish solver set-up\n", + "2023-02-21 09:09:30.657 - [NOTICE] callbacks.on_step_start(182): Cycle 1/2, step 2/4: Rest for 1 hour\n", + "2023-02-21 09:09:30.661 - [INFO] base_solver.set_up(111): Start solver set-up\n", + "2023-02-21 09:09:30.800 - [INFO] base_solver.set_up(236): Finish solver set-up\n", + "2023-02-21 09:09:31.643 - [NOTICE] callbacks.on_step_start(182): Cycle 1/2, step 3/4: Charge at C/2 until 4.2 V\n", + "2023-02-21 09:09:31.647 - [INFO] base_solver.set_up(111): Start solver set-up\n", + "2023-02-21 09:09:31.796 - [INFO] base_solver.set_up(236): Finish solver set-up\n", + "2023-02-21 09:09:33.027 - [NOTICE] callbacks.on_step_start(182): Cycle 1/2, step 4/4: Rest for 1 hour\n", + "2023-02-21 09:09:33.910 - [NOTICE] callbacks.on_cycle_start(174): Cycle 2/2 (4.771 s elapsed) --------------------\n", + "2023-02-21 09:09:33.911 - [NOTICE] callbacks.on_step_start(182): Cycle 2/2, step 1/4: Discharge at C/2 until 3.0 V\n", + "2023-02-21 09:09:35.119 - [NOTICE] callbacks.on_step_start(182): Cycle 2/2, step 2/4: Rest for 1 hour\n", + "2023-02-21 09:09:35.867 - [NOTICE] callbacks.on_step_start(182): Cycle 2/2, step 3/4: Charge at C/2 until 4.2 V\n", + "2023-02-21 09:09:37.032 - [NOTICE] callbacks.on_step_start(182): Cycle 2/2, step 4/4: Rest for 1 hour\n", + "2023-02-21 09:09:37.783 - [NOTICE] callbacks.on_experiment_end(222): Finish experiment simulation, took 8.643 s\n", + "2023-02-21 09:09:37.784 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Discharge at C/2 until 3.0 V', using temperature from parameter values.\n", + "2023-02-21 09:09:37.785 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", + "2023-02-21 09:09:37.785 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Charge at C/2 until 4.2 V', using temperature from parameter values.\n", + "2023-02-21 09:09:37.786 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", + "2023-02-21 09:09:37.788 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Discharge at C/2 until 3.0 V', using temperature from parameter values.\n", + "2023-02-21 09:09:37.789 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", + "2023-02-21 09:09:37.790 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Charge at C/2 until 4.2 V', using temperature from parameter values.\n", + "2023-02-21 09:09:37.791 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", + "2023-02-21 09:09:37.792 - [INFO] callbacks.on_experiment_start(166): Start running experiment\n", + "2023-02-21 09:09:37.794 - [INFO] parameter_values.process_model(425): Start setting parameters for Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:37.912 - [INFO] parameter_values.process_model(527): Finish setting parameters for Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:37.914 - [INFO] parameter_values.process_model(425): Start setting parameters for Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:38.035 - [INFO] parameter_values.process_model(527): Finish setting parameters for Doyle-Fuller-Newman model\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.04\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-02-21 09:09:38.037 - [INFO] parameter_values.process_model(425): Start setting parameters for Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:38.341 - [INFO] parameter_values.process_model(527): Finish setting parameters for Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:38.344 - [INFO] discretisation.process_model(147): Start discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:38.349 - [INFO] discretisation.remove_independent_variables_from_rhs(1120): removing variable Discharge capacity [A.h] from rhs\n", + "2023-02-21 09:09:38.914 - [INFO] discretisation.process_model(259): Finish discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:38.915 - [INFO] discretisation.process_model(147): Start discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:38.922 - [INFO] discretisation.remove_independent_variables_from_rhs(1120): removing variable Discharge capacity [A.h] from rhs\n", + "2023-02-21 09:09:39.441 - [INFO] discretisation.process_model(259): Finish discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:39.442 - [INFO] discretisation.process_model(147): Start discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:39.448 - [INFO] discretisation.remove_independent_variables_from_rhs(1120): removing variable Discharge capacity [A.h] from rhs\n", + "2023-02-21 09:09:39.986 - [INFO] discretisation.process_model(259): Finish discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:39.987 - [NOTICE] callbacks.on_cycle_start(174): Cycle 1/2 (16.366 us elapsed) --------------------\n", + "2023-02-21 09:09:39.988 - [NOTICE] callbacks.on_step_start(182): Cycle 1/2, step 1/4: Discharge at C/2 until 3.0 V\n", + "2023-02-21 09:09:39.993 - [INFO] base_solver.set_up(111): Start solver set-up\n", + "2023-02-21 09:09:40.141 - [INFO] base_solver.set_up(236): Finish solver set-up\n", + "2023-02-21 09:09:41.853 - [NOTICE] callbacks.on_step_start(182): Cycle 1/2, step 2/4: Rest for 1 hour\n", + "2023-02-21 09:09:41.858 - [INFO] base_solver.set_up(111): Start solver set-up\n", + "2023-02-21 09:09:41.990 - [INFO] base_solver.set_up(236): Finish solver set-up\n", + "2023-02-21 09:09:42.685 - [NOTICE] callbacks.on_step_start(182): Cycle 1/2, step 3/4: Charge at C/2 until 4.2 V\n", + "2023-02-21 09:09:42.690 - [INFO] base_solver.set_up(111): Start solver set-up\n", + "2023-02-21 09:09:42.834 - [INFO] base_solver.set_up(236): Finish solver set-up\n", + "2023-02-21 09:09:44.378 - [NOTICE] callbacks.on_step_start(182): Cycle 1/2, step 4/4: Rest for 1 hour\n", + "2023-02-21 09:09:45.649 - [NOTICE] callbacks.on_cycle_start(174): Cycle 2/2 (5.662 s elapsed) --------------------\n", + "2023-02-21 09:09:45.650 - [NOTICE] callbacks.on_step_start(182): Cycle 2/2, step 1/4: Discharge at C/2 until 3.0 V\n", + "2023-02-21 09:09:47.096 - [NOTICE] callbacks.on_step_start(182): Cycle 2/2, step 2/4: Rest for 1 hour\n", + "2023-02-21 09:09:47.757 - [NOTICE] callbacks.on_step_start(182): Cycle 2/2, step 3/4: Charge at C/2 until 4.2 V\n", + "2023-02-21 09:09:49.184 - [NOTICE] callbacks.on_step_start(182): Cycle 2/2, step 4/4: Rest for 1 hour\n", + "2023-02-21 09:09:49.955 - [NOTICE] callbacks.on_experiment_end(222): Finish experiment simulation, took 9.968 s\n", + "2023-02-21 09:09:49.956 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Discharge at C/2 until 3.0 V', using temperature from parameter values.\n", + "2023-02-21 09:09:49.957 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", + "2023-02-21 09:09:49.958 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Charge at C/2 until 4.2 V', using temperature from parameter values.\n", + "2023-02-21 09:09:49.959 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", + "2023-02-21 09:09:49.960 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Discharge at C/2 until 3.0 V', using temperature from parameter values.\n", + "2023-02-21 09:09:49.961 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", + "2023-02-21 09:09:49.962 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Charge at C/2 until 4.2 V', using temperature from parameter values.\n", + "2023-02-21 09:09:49.963 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", + "2023-02-21 09:09:49.964 - [INFO] callbacks.on_experiment_start(166): Start running experiment\n", + "2023-02-21 09:09:49.966 - [INFO] parameter_values.process_model(425): Start setting parameters for Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:50.085 - [INFO] parameter_values.process_model(527): Finish setting parameters for Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:50.088 - [INFO] parameter_values.process_model(425): Start setting parameters for Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:50.206 - [INFO] parameter_values.process_model(527): Finish setting parameters for Doyle-Fuller-Newman model\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-02-21 09:09:50.209 - [INFO] parameter_values.process_model(425): Start setting parameters for Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:50.322 - [INFO] parameter_values.process_model(527): Finish setting parameters for Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:50.326 - [INFO] discretisation.process_model(147): Start discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:50.332 - [INFO] discretisation.remove_independent_variables_from_rhs(1120): removing variable Discharge capacity [A.h] from rhs\n", + "2023-02-21 09:09:50.868 - [INFO] discretisation.process_model(259): Finish discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:50.869 - [INFO] discretisation.process_model(147): Start discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:50.877 - [INFO] discretisation.remove_independent_variables_from_rhs(1120): removing variable Discharge capacity [A.h] from rhs\n", + "2023-02-21 09:09:51.557 - [INFO] discretisation.process_model(259): Finish discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:51.557 - [INFO] discretisation.process_model(147): Start discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:51.563 - [INFO] discretisation.remove_independent_variables_from_rhs(1120): removing variable Discharge capacity [A.h] from rhs\n", + "2023-02-21 09:09:52.101 - [INFO] discretisation.process_model(259): Finish discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:52.101 - [NOTICE] callbacks.on_cycle_start(174): Cycle 1/2 (16.504 us elapsed) --------------------\n", + "2023-02-21 09:09:52.102 - [NOTICE] callbacks.on_step_start(182): Cycle 1/2, step 1/4: Discharge at C/2 until 3.0 V\n", + "2023-02-21 09:09:52.108 - [INFO] base_solver.set_up(111): Start solver set-up\n", + "2023-02-21 09:09:52.250 - [INFO] base_solver.set_up(236): Finish solver set-up\n", + "2023-02-21 09:09:54.101 - [NOTICE] callbacks.on_step_start(182): Cycle 1/2, step 2/4: Rest for 1 hour\n", + "2023-02-21 09:09:54.107 - [INFO] base_solver.set_up(111): Start solver set-up\n", + "2023-02-21 09:09:54.236 - [INFO] base_solver.set_up(236): Finish solver set-up\n", + "2023-02-21 09:09:54.910 - [NOTICE] callbacks.on_step_start(182): Cycle 1/2, step 3/4: Charge at C/2 until 4.2 V\n", + "2023-02-21 09:09:54.914 - [INFO] base_solver.set_up(111): Start solver set-up\n", + "2023-02-21 09:09:55.056 - [INFO] base_solver.set_up(236): Finish solver set-up\n", + "2023-02-21 09:09:56.719 - [NOTICE] callbacks.on_step_start(182): Cycle 1/2, step 4/4: Rest for 1 hour\n", + "2023-02-21 09:09:57.598 - [NOTICE] callbacks.on_cycle_start(174): Cycle 2/2 (5.496 s elapsed) --------------------\n", + "2023-02-21 09:09:57.598 - [NOTICE] callbacks.on_step_start(182): Cycle 2/2, step 1/4: Discharge at C/2 until 3.0 V\n", + "2023-02-21 09:09:59.232 - [NOTICE] callbacks.on_step_start(182): Cycle 2/2, step 2/4: Rest for 1 hour\n", + "2023-02-21 09:09:59.886 - [NOTICE] callbacks.on_step_start(182): Cycle 2/2, step 3/4: Charge at C/2 until 4.2 V\n", + "2023-02-21 09:10:01.516 - [NOTICE] callbacks.on_step_start(182): Cycle 2/2, step 4/4: Rest for 1 hour\n", + "2023-02-21 09:10:02.299 - [NOTICE] callbacks.on_experiment_end(222): Finish experiment simulation, took 10.198 s\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "running time: 76.058786603s\n" + ] + } + ], + "source": [ + "solution=[]\n", + "for v in v_si:\n", + " param.update({\n", + " \"Primary: Negative electrode active material volume fraction\": (1-v) * total_am_volume_fraction, #primary\n", + " \"Secondary: Negative electrode active material volume fraction\": v * total_am_volume_fraction,\n", + " })\n", + " print(v)\n", + " sim = pybamm.Simulation(\n", + " model,\n", + " experiment=experiment,\n", + " parameter_values=param,\n", + " solver=pybamm.CasadiSolver(dt_max = 5)\n", + " )\n", + " solution.append(sim.solve(calc_esoh=False))\n", + "stop = timeit.default_timer()\n", + "print(\"running time: \" + str(stop - start) + \"s\")" + ] + }, + { + "cell_type": "markdown", + "id": "977b4c09", + "metadata": {}, + "source": [ + "## Cycling Results\n", + "The previously displayed single discharge results can be extended to the cycling solution. As an example, terminal voltage is displayed below." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "15b6f3ee", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ltype=['k-','r--','b-.','g:','m-','c--','y-.'];\n", + "for i in range(0,len(v_si)):\n", + " t_i = solution[i][\"Time [s]\"].entries / 3600\n", + " V_i = solution[i][\"Terminal voltage [V]\"].entries\n", + " plt.plot(t_i, V_i,ltype[i],label=\"$V_\\mathrm{si}=$\"+str(v_si[i]))\n", + "plt.xlabel('Time [h]')\n", + "plt.ylabel('Terminal voltage [V]')\n", + "plt.legend()" + ] + }, { "cell_type": "markdown", "id": "e9a2ba08", @@ -586,7 +904,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "id": "5e9e5819", "metadata": {}, "outputs": [ @@ -609,19 +927,11 @@ "source": [ "pybamm.print_citations()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9cff9f95", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3.9.13 ('conda_jl')", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -635,7 +945,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.8.10" }, "toc": { "base_numbering": 1, @@ -652,7 +962,7 @@ }, "vscode": { "interpreter": { - "hash": "612adcc456652826e82b485a1edaef831aa6d5abc680d008e93d513dd8724f14" + "hash": "c9bd1bb48411923570c08daa2bc8068dae5eb4b582894c14e668f51575eba180" } } }, diff --git a/examples/notebooks/models/submodel_cracking_DFN_or_SPM.ipynb b/examples/notebooks/models/submodel_cracking_DFN_or_SPM.ipynb index 0b05a112f7..1610474e59 100644 --- a/examples/notebooks/models/submodel_cracking_DFN_or_SPM.ipynb +++ b/examples/notebooks/models/submodel_cracking_DFN_or_SPM.ipynb @@ -1,11 +1,12 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Using crack submodels in PyBaMM\n", - "In this notebook we show how to use the crack submodel with battery DFN or SPM models. To see all of the models and submodels available in PyBaMM, please take a look at the documentation [here](https://pybamm.readthedocs.io/en/latest/source/models/index.html)." + "In this notebook we show how to use the crack submodel with battery DFN or SPM models. To see all of the models and submodels available in PyBaMM, please take a look at the documentation [here](https://pybamm.readthedocs.io/en/latest/source/api/models/index.html)." ] }, { diff --git a/examples/notebooks/models/unsteady-heat-equation.ipynb b/examples/notebooks/models/unsteady-heat-equation.ipynb index 71dc35930f..20555a8fec 100644 --- a/examples/notebooks/models/unsteady-heat-equation.ipynb +++ b/examples/notebooks/models/unsteady-heat-equation.ipynb @@ -255,10 +255,11 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "Now that the model has been discretised we are ready to solve. We must first choose a solver to use. For this model we choose the Scipy ODE solver, but other solvers are available in PyBaMM (see [here](https://pybamm.readthedocs.io/en/latest/source/solvers/index.html)). To solve the model, we use the method `solver.solve` which takes in a model and an array of times at which we would like the solution to be returned. Ths solution is then stored in the `solution` object. The times and states can be accessed with `solver.t` and `solver.y`." + "Now that the model has been discretised we are ready to solve. We must first choose a solver to use. For this model we choose the Scipy ODE solver, but other solvers are available in PyBaMM (see [here](https://pybamm.readthedocs.io/en/latest/source/api/solvers/index.html)). To solve the model, we use the method `solver.solve` which takes in a model and an array of times at which we would like the solution to be returned. Ths solution is then stored in the `solution` object. The times and states can be accessed with `solver.t` and `solver.y`." ] }, { @@ -374,7 +375,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] diff --git a/examples/notebooks/models/using-model-options_thermal-example.ipynb b/examples/notebooks/models/using-model-options_thermal-example.ipynb index e50d655427..9c3e552fad 100644 --- a/examples/notebooks/models/using-model-options_thermal-example.ipynb +++ b/examples/notebooks/models/using-model-options_thermal-example.ipynb @@ -1,11 +1,12 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Using model options in PyBaMM\n", - "In this notebook we show how to pass options to models. This allows users to do things such as include extra physics (e.g. thermal effects) or change the macroscopic dimension of the problem (e.g. change from a 1D model to a 2+1D pouch cell model). To see all of the options currently available in PyBaMM, please take a look at the documentation [here](https://pybamm.readthedocs.io/en/latest/source/models/base_models/base_battery_model.html). For more information on combining submodels explicitly to create your own custom model, please see the [Using Submodels notebook](./using-submodels.ipynb)." + "In this notebook we show how to pass options to models. This allows users to do things such as include extra physics (e.g. thermal effects) or change the macroscopic dimension of the problem (e.g. change from a 1D model to a 2+1D pouch cell model). To see all of the options currently available in PyBaMM, please take a look at the documentation [here](https://pybamm.readthedocs.io/en/latest/source/api/models/base_models/base_battery_model.html). For more information on combining submodels explicitly to create your own custom model, please see the [Using Submodels notebook](./using-submodels.ipynb)." ] }, { diff --git a/examples/notebooks/models/using-submodels.ipynb b/examples/notebooks/models/using-submodels.ipynb index 423245c74f..7ba1fa5ca8 100644 --- a/examples/notebooks/models/using-submodels.ipynb +++ b/examples/notebooks/models/using-submodels.ipynb @@ -1,11 +1,12 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Using submodels in PyBaMM\n", - "In this notebook we show how to modify existing models by swapping out submodels, and how to build your own model from scratch using existing submodels. To see all of the models and submodels available in PyBaMM, please take a look at the documentation [here](https://pybamm.readthedocs.io/en/latest/source/models/index.html)." + "In this notebook we show how to modify existing models by swapping out submodels, and how to build your own model from scratch using existing submodels. To see all of the models and submodels available in PyBaMM, please take a look at the documentation [here](https://pybamm.readthedocs.io/en/latest/source/api/models/index.html)." ] }, { @@ -68,38 +69,38 @@ "name": "stdout", "output_type": "stream", "text": [ - "external circuit \n", - "porosity \n", - "negative interface utilisation \n", - "positive interface utilisation \n", - "negative particle mechanics \n", - "positive particle mechanics \n", - "negative primary active material \n", - "positive primary active material \n", - "electrolyte transport efficiency \n", - "electrode transport efficiency \n", - "through-cell convection \n", - "transverse convection \n", - "negative primary open circuit potential \n", - "positive primary open circuit potential \n", - "negative interface \n", - "negative interface current \n", - "positive interface \n", - "positive interface current \n", - "negative primary particle \n", - "positive primary particle \n", - "negative electrode potential \n", - "positive electrode potential \n", - "leading-order electrolyte conductivity \n", - "negative surface potential difference \n", - "positive surface potential difference \n", - "electrolyte diffusion \n", - "thermal \n", - "current collector \n", - "sei \n", - "sei on cracks \n", - "lithium plating \n", - "total interface \n" + "external circuit \n", + "porosity \n", + "Negative interface utilisation \n", + "Positive interface utilisation \n", + "negative particle mechanics \n", + "positive particle mechanics \n", + "negative primary active material \n", + "positive primary active material \n", + "electrolyte transport efficiency \n", + "electrode transport efficiency \n", + "transverse convection \n", + "through-cell convection \n", + "negative primary open circuit potential \n", + "positive primary open circuit potential \n", + "negative interface \n", + "negative interface current \n", + "positive interface \n", + "positive interface current \n", + "negative primary particle \n", + "positive primary particle \n", + "negative electrode potential \n", + "positive electrode potential \n", + "electrolyte diffusion \n", + "leading-order electrolyte conductivity \n", + "negative surface potential difference \n", + "positive surface potential difference \n", + "thermal \n", + "current collector \n", + "primary sei \n", + "primary sei on cracks \n", + "lithium plating \n", + "total interface \n" ] } ], @@ -138,7 +139,7 @@ "outputs": [], "source": [ "model.submodels[\"negative primary particle\"] = pybamm.particle.XAveragedPolynomialProfile(\n", - " model.param, \"Negative\", options={**model.options, \"particle\": \"uniform profile\"}\n", + " model.param, \"negative\", options={**model.options, \"particle\": \"uniform profile\"}\n", ")" ] }, @@ -165,38 +166,38 @@ "name": "stdout", "output_type": "stream", "text": [ - "external circuit \n", - "porosity \n", - "negative interface utilisation \n", - "positive interface utilisation \n", - "negative particle mechanics \n", - "positive particle mechanics \n", - "negative primary active material \n", - "positive primary active material \n", - "electrolyte transport efficiency \n", - "electrode transport efficiency \n", - "through-cell convection \n", - "transverse convection \n", - "negative primary open circuit potential \n", - "positive primary open circuit potential \n", - "negative interface \n", - "negative interface current \n", - "positive interface \n", - "positive interface current \n", - "negative primary particle \n", - "positive primary particle \n", - "negative electrode potential \n", - "positive electrode potential \n", - "leading-order electrolyte conductivity \n", - "negative surface potential difference \n", - "positive surface potential difference \n", - "electrolyte diffusion \n", - "thermal \n", - "current collector \n", - "sei \n", - "sei on cracks \n", - "lithium plating \n", - "total interface \n" + "external circuit \n", + "porosity \n", + "Negative interface utilisation \n", + "Positive interface utilisation \n", + "negative particle mechanics \n", + "positive particle mechanics \n", + "negative primary active material \n", + "positive primary active material \n", + "electrolyte transport efficiency \n", + "electrode transport efficiency \n", + "transverse convection \n", + "through-cell convection \n", + "negative primary open circuit potential \n", + "positive primary open circuit potential \n", + "negative interface \n", + "negative interface current \n", + "positive interface \n", + "positive interface current \n", + "negative primary particle \n", + "positive primary particle \n", + "negative electrode potential \n", + "positive electrode potential \n", + "electrolyte diffusion \n", + "leading-order electrolyte conductivity \n", + "negative surface potential difference \n", + "positive surface potential difference \n", + "thermal \n", + "current collector \n", + "primary sei \n", + "primary sei on cracks \n", + "lithium plating \n", + "total interface \n" ] } ], @@ -263,9 +264,9 @@ { "data": { "text/plain": [ - "{Variable(-0x3424fcee14f4853a, Discharge capacity [A.h], children=[], domains={}): Division(-0x42460e77ad8fefc6, /, children=['Current function [A] * 96485.33212 * Maximum concentration in negative electrode [mol.m-3] * (Negative electrode thickness [m] + Separator thickness [m] + Positive electrode thickness [m]) / absolute(Typical current [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]))', '3600.0'], domains={}),\n", - " Variable(-0x51cb6922fc1868b9, Average negative particle concentration, children=[], domains={'primary': ['current collector']}): MatrixMultiplication(-0x70e9ee195fdfc364, @, children=['mass(Average negative particle concentration)', '-3.0 * (Current function [A] / Typical current [A]) * sign(Typical current [A]) / ((3.0 * x-average(Negative electrode active material volume fraction) / x-average(Negative particle radius [m]) / (3.0 * yz-average(x-average(Negative electrode active material volume fraction)) / yz-average(x-average(Negative particle radius [m])))) * Negative electrode thickness [m] / (Negative electrode thickness [m] + Separator thickness [m] + Positive electrode thickness [m])) / ((3.0 * yz-average(x-average(Negative electrode active material volume fraction)) / yz-average(x-average(Negative particle radius [m]))) * yz-average(x-average(Negative particle radius [m])))'], domains={'primary': ['current collector']}),\n", - " Variable(0x2e9c664dae24e44e, X-averaged positive particle concentration, children=[], domains={'primary': ['positive particle'], 'secondary': ['current collector']}): Multiplication(0x7408b351bd6f5fd3, *, children=['1.0 / ((yz-average(x-average(Positive particle radius [m])) ** 2.0) / Positive electrode diffusivity [m2.s-1] / (96485.33212 * Maximum concentration in negative electrode [mol.m-3] * (Negative electrode thickness [m] + Separator thickness [m] + Positive electrode thickness [m]) / absolute(Typical current [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]))))', 'div((Positive electrode diffusivity [m2.s-1] / Positive electrode diffusivity [m2.s-1]) * grad(X-averaged positive particle concentration))'], domains={'primary': ['positive particle'], 'secondary': ['current collector']})}" + "{Variable(-0x5af83e49ccfa2efe, Discharge capacity [A.h], children=[], domains={}): Multiplication(-0x1e880e8d2f9f2856, *, children=['0.0002777777777777778', 'Current function [A] * 96485.33212 * Maximum concentration in negative electrode [mol.m-3] * (Negative electrode thickness [m] + Separator thickness [m] + Positive electrode thickness [m]) / absolute(Typical current [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]))'], domains={}),\n", + " Variable(-0x4da9784eecd104f8, Average negative particle concentration, children=[], domains={'primary': ['current collector']}): MatrixMultiplication(0x692f00645886dc73, @, children=['mass(Average negative particle concentration)', '-3.0 * (Current function [A] / Typical current [A]) * sign(Typical current [A]) / (Negative electrode thickness [m] / (Negative electrode thickness [m] + Separator thickness [m] + Positive electrode thickness [m])) / (3.0 * x-average(Negative electrode active material volume fraction) / x-average(Negative particle radius [m]) / (3.0 * yz-average(x-average(Negative electrode active material volume fraction)) / yz-average(x-average(Negative particle radius [m])))) / (3.0 * yz-average(x-average(Negative electrode active material volume fraction)))'], domains={'primary': ['current collector']}),\n", + " Variable(-0xec406a730de74c7, X-averaged positive particle concentration, children=[], domains={'primary': ['positive particle'], 'secondary': ['current collector']}): Multiplication(-0x42712776d646461e, *, children=['1.0 / ((yz-average(x-average(Positive particle radius [m])) ** 2.0) / Positive electrode diffusivity [m2.s-1] / (96485.33212 * Maximum concentration in negative electrode [mol.m-3] * (Negative electrode thickness [m] + Separator thickness [m] + Positive electrode thickness [m]) / absolute(Typical current [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]))))', 'div((Positive electrode diffusivity [m2.s-1] / Positive electrode diffusivity [m2.s-1]) * grad(X-averaged positive particle concentration))'], domains={'primary': ['positive particle'], 'secondary': ['current collector']})}" ] }, "execution_count": 9, @@ -292,7 +293,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "63e0af39646e4cdaa8994a215ed1770e", + "model_id": "d025c955fe0b47fbbad5c554c38b74bb", "version_major": 2, "version_minor": 0 }, @@ -306,7 +307,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 10, @@ -374,10 +375,10 @@ "model.submodels[\"thermal\"] = pybamm.thermal.isothermal.Isothermal(model.param)\n", "model.submodels[\"porosity\"] = pybamm.porosity.Constant(model.param, model.options)\n", "model.submodels[\"negative active material\"] = pybamm.active_material.Constant(\n", - " model.param, \"Negative\", model.options\n", + " model.param, \"negative\", model.options\n", ")\n", "model.submodels[\"positive active material\"] = pybamm.active_material.Constant(\n", - " model.param, \"Positive\", model.options\n", + " model.param, \"positive\", model.options\n", ")" ] }, @@ -395,10 +396,10 @@ "outputs": [], "source": [ "model.submodels[\"negative electrode potentials\"] = pybamm.electrode.ohm.LeadingOrder(\n", - " model.param, \"Negative\"\n", + " model.param, \"negative\"\n", ")\n", "model.submodels[\"positive electrode potentials\"] = pybamm.electrode.ohm.LeadingOrder(\n", - " model.param, \"Positive\"\n", + " model.param, \"positive\"\n", ")" ] }, @@ -416,8 +417,8 @@ "outputs": [], "source": [ "options = {**model.options, \"particle\": \"uniform profile\"}\n", - "model.submodels[\"negative primary particle\"] = pybamm.particle.XAveragedPolynomialProfile(model.param, \"Negative\", options)\n", - "model.submodels[\"positive primary particle\"] = pybamm.particle.XAveragedPolynomialProfile(model.param, \"Positive\", options)" + "model.submodels[\"negative primary particle\"] = pybamm.particle.XAveragedPolynomialProfile(model.param, \"negative\", options)\n", + "model.submodels[\"positive primary particle\"] = pybamm.particle.XAveragedPolynomialProfile(model.param, \"positive\", options)" ] }, { @@ -436,38 +437,38 @@ "model.submodels[\n", " \"negative open circuit potential\"\n", "] = pybamm.open_circuit_potential.SingleOpenCircuitPotential(\n", - " model.param, \"Negative\", \"lithium-ion main\", options=model.options\n", + " model.param, \"negative\", \"lithium-ion main\", options=model.options\n", ")\n", "model.submodels[\n", " \"positive open circuit potential\"\n", "] = pybamm.open_circuit_potential.SingleOpenCircuitPotential(\n", - " model.param, \"Positive\", \"lithium-ion main\", options=model.options\n", + " model.param, \"positive\", \"lithium-ion main\", options=model.options\n", ")\n", "model.submodels[\n", " \"negative interface\"\n", "] = pybamm.kinetics.InverseButlerVolmer(\n", - " model.param, \"Negative\", \"lithium-ion main\", options=model.options\n", + " model.param, \"negative\", \"lithium-ion main\", options=model.options\n", ")\n", "model.submodels[\n", " \"positive interface\"\n", "] = pybamm.kinetics.InverseButlerVolmer(\n", - " model.param, \"Positive\", \"lithium-ion main\", options=model.options\n", + " model.param, \"positive\", \"lithium-ion main\", options=model.options\n", ")\n", "model.submodels[\n", " \"negative interface current\"\n", "] = pybamm.kinetics.CurrentForInverseButlerVolmer(\n", - " model.param, \"Negative\", \"lithium-ion main\"\n", + " model.param, \"negative\", \"lithium-ion main\"\n", ")\n", "model.submodels[\n", " \"positive interface current\"\n", "] = pybamm.kinetics.CurrentForInverseButlerVolmer(\n", - " model.param, \"Positive\", \"lithium-ion main\"\n", + " model.param, \"positive\", \"lithium-ion main\"\n", ")\n", "model.submodels[\"negative interface utilisation\"] = pybamm.interface_utilisation.Full(\n", - " model.param, \"Negative\", model.options\n", + " model.param, \"negative\", model.options\n", ")\n", "model.submodels[\"positive interface utilisation\"] = pybamm.interface_utilisation.Full(\n", - " model.param, \"Positive\", model.options\n", + " model.param, \"positive\", model.options\n", ")" ] }, @@ -486,10 +487,10 @@ "source": [ "model.submodels[\n", " \"Negative particle mechanics\"\n", - "] = pybamm.particle_mechanics.NoMechanics(model.param, \"Negative\", model.options)\n", + "] = pybamm.particle_mechanics.NoMechanics(model.param, \"negative\", model.options)\n", "model.submodels[\n", " \"Positive particle mechanics\"\n", - "] = pybamm.particle_mechanics.NoMechanics(model.param, \"Positive\", model.options)\n", + "] = pybamm.particle_mechanics.NoMechanics(model.param, \"positive\", model.options)\n", "model.submodels[\"sei\"] = pybamm.sei.NoSEI(model.param, model.options)\n", "model.submodels[\"sei on cracks\"] = pybamm.sei.NoSEI(model.param, model.options, cracks=True)\n", "model.submodels[\"lithium plating\"] = pybamm.lithium_plating.NoPlating(model.param)" @@ -547,7 +548,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ec99a3d4ae49416d88a8c9d0d9abcebf", + "model_id": "27214eb6120641528ebc937964c9ca6d", "version_major": 2, "version_minor": 0 }, @@ -561,7 +562,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 20, diff --git a/examples/notebooks/parameterization/parameter-values.ipynb b/examples/notebooks/parameterization/parameter-values.ipynb index 553673b320..e83db7ebdd 100644 --- a/examples/notebooks/parameterization/parameter-values.ipynb +++ b/examples/notebooks/parameterization/parameter-values.ipynb @@ -1,12 +1,13 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Parameter Values\n", "\n", - "In this notebook, we explain how parameter values are set for a model. Information on creating new parameter sets is provided in our [online documentation](https://pybamm.readthedocs.io/en/latest/source/parameters/parameter_sets.html#adding-parameter-sets)" + "In this notebook, we explain how parameter values are set for a model. Information on creating new parameter sets is provided in our [online documentation](https://pybamm.readthedocs.io/en/latest/source/api/parameters/parameter_sets.html#adding-parameter-sets)" ] }, { diff --git a/examples/notebooks/parameterization/parameterization.ipynb b/examples/notebooks/parameterization/parameterization.ipynb index 704ea34aec..feae534e79 100644 --- a/examples/notebooks/parameterization/parameterization.ipynb +++ b/examples/notebooks/parameterization/parameterization.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -10,7 +11,7 @@ "\n", "For other notebooks about parameterization, see:\n", "\n", - "- The API documentation of [Parameters](https://pybamm.readthedocs.io/en/latest/source/parameters/index.html)\n", + "- The API documentation of [Parameters](https://pybamm.readthedocs.io/en/latest/source/api/parameters/index.html)\n", "- [Setting parameter values](https://github.com/pybamm-team/PyBaMM/blob/develop/examples/notebooks/Getting%20Started/Tutorial%204%20-%20Setting%20parameter%20values.ipynb) can be found at `pybamm/examples/notebooks/Getting Started/Tutorial 4 - Setting parameter values.ipynb`. This explains the basics of how to set the parameters of a model (in less detail than here).\n", "- [parameter-values.ipynb](https://github.com/pybamm-team/PyBaMM/blob/develop/examples/notebooks/parameterization/parameter-values.ipynb) can be found at `pybamm/examples/notebooks/parameterization/parameter-values.ipynb`. This explains the basics of the `ParameterValues` class.\n" ] diff --git a/examples/notebooks/simulation-class.ipynb b/examples/notebooks/simulation-class.ipynb index f9ba35ae1e..7506e7a426 100644 --- a/examples/notebooks/simulation-class.ipynb +++ b/examples/notebooks/simulation-class.ipynb @@ -1,11 +1,12 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# A step-by-step look at the Simulation class\n", - "The simplest way to solve a model is to use the `Simulation` class. This automatically processes the model (setting of parameters, setting up the mesh and discretisation, etc.) for you, and provides built-in functionality for solving and plotting. Changing things such as parameters in handled by passing options to the `Simulation`, as shown in the [Getting Started](./Getting%20Started/) guides, [example notebooks](./) and [documentation](https://pybamm.readthedocs.io/en/latest/source/simulation.html?highlight=simulation).\n", + "The simplest way to solve a model is to use the `Simulation` class. This automatically processes the model (setting of parameters, setting up the mesh and discretisation, etc.) for you, and provides built-in functionality for solving and plotting. Changing things such as parameters in handled by passing options to the `Simulation`, as shown in the [Getting Started](./Getting%20Started/) guides, [example notebooks](./) and [documentation](https://pybamm.readthedocs.io/en/latest/source/api/simulation.html).\n", "\n", "In this notebook we show how to solve a model using a `Simulation` and compare this to manually handling the different stages of the process, such as setting parameters, ourselves step-by-step." ] @@ -140,11 +141,12 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Processing the model step-by-step\n", - "One way of gaining more control over the simulation processing is by passing options, as outlined in the [documentation](https://pybamm.readthedocs.io/en/latest/source/simulation.html?highlight=simulation). However, you can also process the model step-by-step yourself. A detailed example of this can be found in the [SPM notebook](./models/SPM.ipynb), but here we outline the basic steps.\n", + "One way of gaining more control over the simulation processing is by passing options, as outlined in the [documentation](https://pybamm.readthedocs.io/en/latest/source/api/simulation.html). However, you can also process the model step-by-step yourself. A detailed example of this can be found in the [SPM notebook](./models/SPM.ipynb), but here we outline the basic steps.\n", "\n", "First we pick a model" ] @@ -357,7 +359,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -371,7 +373,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.0" + "version": "3.11.1" + }, + "vscode": { + "interpreter": { + "hash": "a06befff6f507b2769436dc41c340f64f62afa83086a8cd273928f468e329d0b" + } } }, "nbformat": 4, diff --git a/examples/notebooks/solvers/speed-up-solver.ipynb b/examples/notebooks/solvers/speed-up-solver.ipynb index 6fc6967e99..081c6a7b36 100644 --- a/examples/notebooks/solvers/speed-up-solver.ipynb +++ b/examples/notebooks/solvers/speed-up-solver.ipynb @@ -44,12 +44,13 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Since it is very easy to switch which solver is used for the model, we recommend you try different solvers for your particular use case. In general, the `CasadiSolver` is the fastest.\n", "\n", - "Once you have found a good solver, you can further improve performance by trying out different values for the `method`, `rtol`, and `atol` arguments. Further options are sometimes available, but are solver specific. See [solver API docs](https://pybamm.readthedocs.io/en/latest/source/solvers/index.html) for details." + "Once you have found a good solver, you can further improve performance by trying out different values for the `method`, `rtol`, and `atol` arguments. Further options are sometimes available, but are solver specific. See [solver API docs](https://pybamm.readthedocs.io/en/latest/source/api/solvers/index.html) for details." ] }, { @@ -1019,7 +1020,7 @@ ], "metadata": { "kernelspec": { - "display_name": "env", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -1033,7 +1034,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.13" }, "toc": { "base_numbering": 1, @@ -1050,7 +1051,7 @@ }, "vscode": { "interpreter": { - "hash": "19e5ebaa8d5a3277b4deed2928f02ad0cad6c3ab0b2beced644d557f155bce64" + "hash": "1a781583db2df3c2e87436f6d22cce842c2e50a5670da93a3bd820b97dc43011" } } }, diff --git a/examples/notebooks/spatial_methods/finite-volumes.ipynb b/examples/notebooks/spatial_methods/finite-volumes.ipynb index 136840c8ad..93e5f89b37 100644 --- a/examples/notebooks/spatial_methods/finite-volumes.ipynb +++ b/examples/notebooks/spatial_methods/finite-volumes.ipynb @@ -1206,10 +1206,11 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "Since this notebook is only an introduction to the discretisation, we have not covered everything. More advanced concepts, such as the ones below, can be explored by looking into the [API docs](https://pybamm.readthedocs.io/en/latest/source/spatial_methods/finite_volume.html).\n", + "Since this notebook is only an introduction to the discretisation, we have not covered everything. More advanced concepts, such as the ones below, can be explored by looking into the [API docs](https://pybamm.readthedocs.io/en/latest/source/api/solvers/index.html).\n", "\n", "- Gradient and divergence of microscale variables in the P2D model\n", "- Indefinite integral\n", diff --git a/examples/scripts/compare_interface_utilisation.py b/examples/scripts/compare_interface_utilisation.py index 7c80d8343a..bb2f00a4b3 100644 --- a/examples/scripts/compare_interface_utilisation.py +++ b/examples/scripts/compare_interface_utilisation.py @@ -36,7 +36,6 @@ t_eval = np.linspace(0, 3600, 100) for model in models: - sim = pybamm.Simulation(model, parameter_values=param) solution = sim.solve(t_eval) solutions.append(solution) diff --git a/examples/scripts/run_ecm.py b/examples/scripts/run_ecm.py index c86d5bce02..0ca4e10484 100644 --- a/examples/scripts/run_ecm.py +++ b/examples/scripts/run_ecm.py @@ -2,43 +2,22 @@ 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, -# ) +model = pybamm.equivalent_circuit.Thevenin() 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", + "Discharge at C/10 for 10 hours or until 3.3 V at 15oC", + "Rest for 30 minutes at 15oC", + "Rest for 2 hours at 35oC", + "Charge at 100 A until 4.1 V at 35oC (1 second period)", + "Hold at 4.1 V until 5 A at 35oC (1 seconds period)", + "Rest for 30 minutes at 35oC", + "Rest for 1 hour at 25oC", ), ] ) -sim = pybamm.Simulation(model, experiment=experiment, parameter_values=parameter_values) +sim = pybamm.Simulation(model, experiment=experiment) 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]", - ] -) +sim.plot() diff --git a/pybamm/discretisations/discretisation.py b/pybamm/discretisations/discretisation.py index e792e1808f..4eea6c9168 100644 --- a/pybamm/discretisations/discretisation.py +++ b/pybamm/discretisations/discretisation.py @@ -9,7 +9,6 @@ def has_bc_of_form(symbol, side, bcs, form): - if symbol in bcs: if bcs[symbol][side][1] == form: return True @@ -346,7 +345,6 @@ def set_internal_boundary_conditions(self, model): """ def boundary_gradient(left_symbol, right_symbol): - pybamm.logger.debug( "Calculate boundary gradient ({} and {})".format( left_symbol, right_symbol @@ -1001,7 +999,6 @@ def check_model(self, model): self.check_variables(model) def check_initial_conditions(self, model): - # Check initial conditions are a numpy array # Individual for var, eqn in model.initial_conditions.items(): diff --git a/pybamm/experiments/experiment.py b/pybamm/experiments/experiment.py index c907e767d6..67144db028 100644 --- a/pybamm/experiments/experiment.py +++ b/pybamm/experiments/experiment.py @@ -3,25 +3,28 @@ # import numpy as np +import re + examples = """ - Discharge at 1C for 0.5 hours, - Discharge at C/20 for 0.5 hours, - Charge at 0.5 C for 45 minutes, - Discharge at 1 A for 90 seconds, - Charge at 200mA for 45 minutes (1 minute period), - Discharge at 1 W for 0.5 hours, - Charge at 200 mW for 45 minutes, - Rest for 10 minutes (5 minute period), - Hold at 1 V for 20 seconds, - Charge at 1 C until 4.1V, - Hold at 4.1 V until 50 mA, - Hold at 3V until C/50, - Run US06 (A), - Run US06 (A) for 20 seconds, - Run US06 (V) for 45 minutes, - Run US06 (W) for 2 hours, + "Discharge at 1C for 0.5 hours at 27oC", + "Discharge at C/20 for 0.5 hours at 29oC", + "Charge at 0.5 C for 45 minutes at -5oC", + "Discharge at 1 A for 0.5 hours at -5.1oC", + "Charge at 200 mA for 45 minutes at 10.2oC (1 minute period)", + "Discharge at 1W for 0.5 hours at -10.4oC", + "Charge at 200mW for 45 minutes", + "Rest for 10 minutes (5 minute period)", + "Hold at 1V for 20 seconds", + "Charge at 1 C until 4.1V", + "Hold at 4.1 V until 50mA", + "Hold at 3V until C/50", + "Discharge at C/3 for 2 hours or until 2.5 V at 26oC", + "Run US06 (A) at -5oC", + "Run US06 (V) for 5 minutes", + "Run US06 (W) for 0.5 hours", + """ @@ -31,12 +34,18 @@ class Experiment: list of operating conditions should be passed in. Each operating condition should be of the form "Do this for this long" or "Do this until this happens". For example, "Charge at 1 C for 1 hour", or "Charge at 1 C until 4.2 V", or "Charge at 1 C for 1 - hour or until 4.2 V". The instructions can be of the form "(Dis)charge at x A/C/W", - "Rest", or "Hold at x V". The running time should be a time in seconds, minutes or + hour or until 4.2 V at 25oC". The instructions can be of the form + "(Dis)charge at x A/C/W", "Rest", or "Hold at x V until y A at z oC". The running + time should be a time in seconds, minutes or hours, e.g. "10 seconds", "3 minutes" or "1 hour". The stopping conditions should be a circuit state, e.g. "1 A", "C/50" or "3 V". The parameter drive_cycles is mandatory to run drive cycle. For example, "Run x", then x must be the key - of drive_cycles dictionary. + of drive_cycles dictionary. The temperature should be provided after the stopping + condition but before the period, e.g. "1 A at 25 oC (1 second period)". It is + not essential to provide a temperature and a global temperature can be set either + from within the paramter values of passing a temperature to this experiment class. + If the temperature is not specified in a line, then the global temperature is used, + even if another temperature has been set in an earlier line. Parameters ---------- @@ -45,6 +54,10 @@ class Experiment: period : string, optional Period (1/frequency) at which to record outputs. Default is 1 minute. Can be overwritten by individual operating conditions. + temperature: float, optional + The ambient air temperature in degrees Celsius at which to run the experiment. + Default is None whereby the ambient temperature is taken from the parameter set. + This value is overwritten if the temperature is specified in a step. termination : list, optional List of conditions under which to terminate the experiment. Default is None. drive_cycles : dict @@ -60,6 +73,7 @@ def __init__( self, operating_conditions, period="1 minute", + temperature=None, termination=None, drive_cycles={}, cccv_handling="two-step", @@ -71,12 +85,15 @@ def __init__( self.args = ( operating_conditions, period, + temperature, termination, drive_cycles, cccv_handling, ) self.period = self.convert_time_to_seconds(period.split()) + self.temperature = temperature + operating_conditions_cycles = [] for cycle in operating_conditions: # Check types and convert strings to 1-tuples @@ -163,16 +180,26 @@ def read_string(self, cond, drive_cycles): cond_CC, cond_CV = cond.split(" then ") op_CC = self.read_string(cond_CC, drive_cycles) op_CV = self.read_string(cond_CV, drive_cycles) + + if op_CC["temperature"] != op_CV["temperature"]: + raise ValueError( + "The temperature for the CC and CV steps must be the same." + f"Got {op_CC['temperature']} and {op_CV['temperature']}" + f"from {op_CC['string']} and {op_CV['string']}" + ) + tag_CC = op_CC["tags"] or [] tag_CV = op_CV["tags"] or [] tags = list(np.unique(tag_CC + tag_CV)) if len(tags) == 0: tags = None + outputs = { "type": "CCCV", "Voltage input [V]": op_CV["Voltage input [V]"], "time": op_CV["time"], "period": op_CV["period"], + "temperature": op_CC["temperature"], "dc_data": None, "string": cond, "events": op_CV["events"], @@ -198,6 +225,11 @@ def read_string(self, cond, drive_cycles): period = self.convert_time_to_seconds(time.split()) else: period = self.period + + # Temperature part of the condition is removed here + unprocessed_cond = cond + temperature, cond = self._read_and_drop_temperature(cond) + # Read instructions if "Run" in cond: cond_list = cond.split() @@ -234,13 +266,17 @@ def read_string(self, cond, drive_cycles): cond_list = cond.split() idx_for = cond_list.index("for") idx_until = cond_list.index("or") + electric = self.convert_electric(cond_list[:idx_for]) + time = self.convert_time_to_seconds(cond_list[idx_for + 1 : idx_until]) events = self.convert_electric(cond_list[idx_until + 2 :]) + elif "for" in cond: # e.g. for 3 hours cond_list = cond.split() idx = cond_list.index("for") + electric = self.convert_electric(cond_list[:idx]) time = self.convert_time_to_seconds(cond_list[idx + 1 :]) events = None @@ -264,8 +300,9 @@ def read_string(self, cond, drive_cycles): **electric, "time": time, "period": period, + "temperature": temperature, "dc_data": dc_data, - "string": cond, + "string": unprocessed_cond, "events": events, "tags": tags, } @@ -329,6 +366,9 @@ def convert_electric(self, electric): raise ValueError( "Instruction must be 'discharge', 'charge', 'rest', 'hold' or " f"'Run'. For example: {examples}" + "" + "The following instruction does not comply: " + f"{instruction}" ) elif len(electric) == 2: # e.g. 3 A, 4.1 V @@ -377,6 +417,26 @@ def convert_electric(self, electric): ) ) + def _read_and_drop_temperature(self, cond): + matches = re.findall(r"at\s-*\d+\.*\d*\s*oC", cond) + + if len(matches) == 0: + if "oC" in cond: + raise ValueError(f"Temperature not written correctly on step: '{cond}'") + temperature = self.temperature + reduced_cond = cond + + elif len(matches) == 1: + match = matches[0] + numerical_part = re.findall(r"-*\d+\.*\d*", match)[0] + temperature = float(numerical_part) + reduced_cond = cond.replace(match, "") + + else: + raise ValueError(f"More than one temperature found on step: '{cond}'") + + return temperature, reduced_cond + def convert_time_to_seconds(self, time_and_units): """Convert a time in seconds, minutes or hours to a time in seconds""" time, units = time_and_units @@ -442,7 +502,7 @@ def is_cccv(self, step, next_step): # e.g. step="Charge at 2.0 A until 4.2V" # next_step="Hold at 4.2V until C/50" if ( - step.startswith("Charge") + (step.startswith("Charge") or step.startswith("Discharge")) and "until" in step and "V" in step and "Hold at " in next_step diff --git a/pybamm/expression_tree/interpolant.py b/pybamm/expression_tree/interpolant.py index eafe887a15..3fd4c53755 100644 --- a/pybamm/expression_tree/interpolant.py +++ b/pybamm/expression_tree/interpolant.py @@ -251,7 +251,6 @@ def _function_evaluate(self, evaluated_children): if self.dimension == 1: return self.function(*children_eval_flat).flatten()[:, np.newaxis] elif self.dimension in [2, 3]: - # If the children are scalars, we need to add a dimension shapes = [] for child in evaluated_children: @@ -273,7 +272,6 @@ def _function_evaluate(self, evaluated_children): shape = shapes.pop() new_evaluated_children = [] for child in evaluated_children: - if hasattr(child, "shape") and child.shape == shape: new_evaluated_children.append(child.flatten()) else: diff --git a/pybamm/expression_tree/operations/evaluate_python.py b/pybamm/expression_tree/operations/evaluate_python.py index b5e526ba96..d8c61f6bab 100644 --- a/pybamm/expression_tree/operations/evaluate_python.py +++ b/pybamm/expression_tree/operations/evaluate_python.py @@ -40,7 +40,7 @@ class JaxCooMatrix: def __init__(self, row, col, data, shape): if not pybamm.have_jax(): # pragma: no cover raise ModuleNotFoundError( - "Jax or jaxlib is not installed, please see https://pybamm.readthedocs.io/en/latest/install/GNU-linux.html#optional-jaxsolver" # noqa: E501 + "Jax or jaxlib is not installed, please see https://pybamm.readthedocs.io/en/latest/source/user_guide/installation/GNU-linux.html#optional-jaxsolver" # noqa: E501 ) self.row = jax.numpy.array(row) @@ -304,7 +304,6 @@ def find_symbols(symbol, constant_symbols, variable_symbols, output_jax=False): symbol_str = "{}({})".format(funct_var, children_str) elif isinstance(symbol, pybamm.Concatenation): - # no need to concatenate if there is only a single child if isinstance(symbol, pybamm.NumpyConcatenation): if len(children_vars) == 1: @@ -537,7 +536,7 @@ class EvaluatorJax: def __init__(self, symbol): if not pybamm.have_jax(): # pragma: no cover raise ModuleNotFoundError( - "Jax or jaxlib is not installed, please see https://pybamm.readthedocs.io/en/latest/install/GNU-linux.html#optional-jaxsolver" # noqa: E501 + "Jax or jaxlib is not installed, please see https://pybamm.readthedocs.io/en/latest/source/user_guide/installation/GNU-linux.html#optional-jaxsolver" # noqa: E501 ) constants, python_str = pybamm.to_python(symbol, debug=False, output_jax=True) diff --git a/pybamm/geometry/battery_geometry.py b/pybamm/geometry/battery_geometry.py index 75121b1b6c..59b825da4d 100644 --- a/pybamm/geometry/battery_geometry.py +++ b/pybamm/geometry/battery_geometry.py @@ -44,36 +44,56 @@ def battery_geometry( "separator": {"x_s": {"min": l_n, "max": l_n_l_s}}, "positive electrode": {"x_p": {"min": l_n_l_s, "max": 1}}, } + # Add particle domains if include_particles is True: zero_one = {"min": 0, "max": 1} - geometry.update( - { - "negative particle": {"r_n": zero_one}, - "positive particle": {"r_p": zero_one}, - } - ) for domain in ["negative", "positive"]: - phases = int(getattr(options, domain)["particle phases"]) - if phases >= 2: + if options.electrode_types[domain] == "porous": geometry.update( { - f"{domain} primary particle": {"r_n_prim": zero_one}, - f"{domain} secondary particle": {"r_n_sec": zero_one}, + f"{domain} particle": {f"r_{domain[0]}": zero_one}, } ) + phases = int(getattr(options, domain)["particle phases"]) + if phases >= 2: + geometry.update( + { + f"{domain} primary particle": { + f"r_{domain[0]}_prim": zero_one + }, + f"{domain} secondary particle": { + f"r_{domain[0]}_sec": zero_one + }, + } + ) + # Add particle size domains - if options is not None and options["particle size"] == "distribution": + if ( + options is not None + and options.negative["particle size"] == "distribution" + and options.electrode_types["negative"] == "porous" + ): R_min_n = geo.n.prim.R_min - R_min_p = geo.p.prim.R_min R_max_n = geo.n.prim.R_max - R_max_p = geo.p.prim.R_max geometry.update( { "negative particle size": {"R_n": {"min": R_min_n, "max": R_max_n}}, + } + ) + if ( + options is not None + and options.positive["particle size"] == "distribution" + and options.electrode_types["positive"] == "porous" + ): + R_min_p = geo.p.prim.R_min + R_max_p = geo.p.prim.R_max + geometry.update( + { "positive particle size": {"R_p": {"min": R_min_p, "max": R_max_p}}, } ) + # Add current collector domains current_collector_dimension = options["dimensionality"] if form_factor == "pouch": diff --git a/pybamm/input/parameters/ecm/example_set.py b/pybamm/input/parameters/ecm/example_set.py index 06a9906811..52ca0609d0 100644 --- a/pybamm/input/parameters/ecm/example_set.py +++ b/pybamm/input/parameters/ecm/example_set.py @@ -79,11 +79,10 @@ def get_parameter_values(): values = { "chemistry": "ecm", "Initial SoC": 0.5, - "Initial cell temperature [degC]": 25, - "Initial jig temperature [degC]": 25, + "Initial temperature [K]": 25 + 273.15, "Cell capacity [A.h]": cell_capacity, "Nominal cell capacity [A.h]": cell_capacity, - "Ambient temperature [degC]": 25, + "Ambient temperature [K]": 25 + 273.15, "Current function [A]": 100, "Upper voltage cut-off [V]": 4.2, "Lower voltage cut-off [V]": 3.2, diff --git a/pybamm/meshes/one_dimensional_submeshes.py b/pybamm/meshes/one_dimensional_submeshes.py index 4ac32c1e88..f1ef7fd4d2 100644 --- a/pybamm/meshes/one_dimensional_submeshes.py +++ b/pybamm/meshes/one_dimensional_submeshes.py @@ -92,7 +92,6 @@ class Uniform1DSubMesh(SubMesh1D): """ def __init__(self, lims, npts): - spatial_var, spatial_lims, tabs = self.read_lims(lims) npts = npts[spatial_var.name] @@ -112,14 +111,16 @@ class Exponential1DSubMesh(SubMesh1D): If side is "left", the gridpoints are given by .. math:: - x_{k} = (b-a) + \\frac{\\exp{\\alpha k / N} - 1}{\\exp{\\alpha} - 1} + a, + x_{k} = (b-a) + + \\frac{\mathrm{e}^{\\alpha k / N} - 1}{\mathrm{e}^{\\alpha} - 1} + a, for k = 1, ..., N, where N is the number of nodes. Is side is "right", the gridpoints are given by .. math:: - x_{k} = (b-a) + \\frac{\\exp{-\\alpha k / N} - 1}{\\exp{-\\alpha} - 1} + a, + x_{k} = (b-a) + + \\frac{\mathrm{e}^{-\\alpha k / N} - 1}{\mathrm{e}^{-\\alpha} - 1} + a, for k = 1, ..., N. @@ -127,7 +128,8 @@ class Exponential1DSubMesh(SubMesh1D): gridpoints .. math:: - x_{k} = (b/2-a) + \\frac{\\exp{\\alpha k / N} - 1}{\\exp{\\alpha} - 1} + a, + x_{k} = (b/2-a) + + \\frac{\mathrm{e}^{\\alpha k / N} - 1}{\mathrm{e}^{\\alpha} - 1} + a, for k = 1, ..., N. The grid spacing is then reflected to contruct the grid on the full interval [a,b]. @@ -156,7 +158,6 @@ class Exponential1DSubMesh(SubMesh1D): """ def __init__(self, lims, npts, side="symmetric", stretch=None): - spatial_var, spatial_lims, tabs = self.read_lims(lims) a = spatial_lims["min"] b = spatial_lims["max"] @@ -236,7 +237,6 @@ class Chebyshev1DSubMesh(SubMesh1D): """ def __init__(self, lims, npts, tabs=None): - spatial_var, spatial_lims, tabs = self.read_lims(lims) npts = npts[spatial_var.name] @@ -275,7 +275,6 @@ class UserSupplied1DSubMesh(SubMesh1D): """ def __init__(self, lims, npts, edges=None): - # raise error if no edges passed if edges is None: raise pybamm.GeometryError("User mesh requires parameter 'edges'") @@ -340,7 +339,6 @@ class SpectralVolume1DSubMesh(SubMesh1D): """ def __init__(self, lims, npts, edges=None, order=2): - spatial_var, spatial_lims, tabs = self.read_lims(lims) npts = npts[spatial_var.name] diff --git a/pybamm/meshes/scikit_fem_submeshes.py b/pybamm/meshes/scikit_fem_submeshes.py index 32a49d9cb9..4fa64517b6 100644 --- a/pybamm/meshes/scikit_fem_submeshes.py +++ b/pybamm/meshes/scikit_fem_submeshes.py @@ -218,7 +218,6 @@ class ScikitExponential2DSubMesh(ScikitSubMesh2D): """ def __init__(self, lims, npts, side="top", stretch=2.3): - # check side is top if side != "top": raise pybamm.GeometryError( @@ -333,7 +332,6 @@ class UserSupplied2DSubMesh(ScikitSubMesh2D): """ def __init__(self, lims, npts, y_edges=None, z_edges=None): - # raise error if no edges passed if y_edges is None: raise pybamm.GeometryError("User mesh requires parameter 'y_edges'") @@ -346,7 +344,6 @@ def __init__(self, lims, npts, y_edges=None, z_edges=None): # check and store edges edges = {"y": y_edges, "z": z_edges} for var in spatial_vars: - # check that npts equals number of user-supplied edges if npts[var.name] != len(edges[var.name]): raise pybamm.GeometryError( diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py index e2a30f21a4..e47380f0bd 100644 --- a/pybamm/models/full_battery_models/base_battery_model.py +++ b/pybamm/models/full_battery_models/base_battery_model.py @@ -73,11 +73,6 @@ class BatteryModelOptions(pybamm.FuzzyDict): "stress-driven", "reaction-driven", or "stress and reaction-driven". A 2-tuple can be provided for different behaviour in negative and positive electrodes. - * "particle phases": str - Number of phases present in the electrode. A 2-tuple can be provided for - different behaviour in negative and positive electrodes. - For example, set to ("2", "1") for a negative electrode with 2 phases, - e.g. graphite and silicon. * "operating mode" : str Sets the operating mode for the model. This determines how the current is set. Can be: @@ -97,7 +92,19 @@ class BatteryModelOptions(pybamm.FuzzyDict): * "particle" : str Sets the submodel to use to describe behaviour within the particle. Can be "Fickian diffusion" (default), "uniform profile", - "quadratic profile", or "quartic profile". + "quadratic profile", or "quartic profile". A 2-tuple can be provided + for different behaviour in negative and positive electrodes. + * "particle mechanics" : str + Sets the model to account for mechanical effects such as particle + swelling and cracking. Can be "none" (default), "swelling only", + or "swelling and cracking". + A 2-tuple can be provided for different behaviour in negative and + positive electrodes. + * "particle phases": str + Number of phases present in the electrode. A 2-tuple can be provided for + different behaviour in negative and positive electrodes. + For example, set to ("2", "1") for a negative electrode with 2 phases, + e.g. graphite and silicon. * "particle shape" : str Sets the model shape of the electrode particles. This is used to calculate the surface area to volume ratio. Can be "spherical" @@ -106,12 +113,6 @@ class BatteryModelOptions(pybamm.FuzzyDict): Sets the model to include a single active particle size or a distribution of sizes at any macroscale location. Can be "single" (default) or "distribution". Option applies to both electrodes. - * "particle mechanics" : str - Sets the model to account for mechanical effects such as particle - swelling and cracking. Can be "none" (default), "swelling only", - or "swelling and cracking". - A 2-tuple can be provided for different behaviour in negative and - positive electrodes. * "SEI" : str Set the SEI submodel to be used. Options are: @@ -528,7 +529,7 @@ def __init__(self, extra_options): ): raise pybamm.OptionError( "If there are multiple particle phases: 'surface form' cannot be " - "'false', 'particle size' must be 'false', 'particle' must be " + "'false', 'particle size' must be 'single', 'particle' must be " "'Fickian diffusion'. Also the following must " "be 'none': 'particle mechanics', " "'loss of active material', 'lithium plating'" @@ -554,9 +555,10 @@ def __init__(self, extra_options): "interface utilisation", "loss of active material", "open circuit potential", - "particle mechanics", "particle", + "particle mechanics", "particle phases", + "particle size", "stress-induced diffusion", ] and isinstance(value, tuple) @@ -845,12 +847,14 @@ def options(self, extra_options): raise pybamm.OptionError( "x-average side reactions cannot be 'false' for SPM models" ) - if isinstance(self, pybamm.lithium_ion.SPM) and not isinstance( - self, pybamm.lithium_ion.MPM - ): - if options["particle size"] == "distribution": + if isinstance(self, pybamm.lithium_ion.SPM): + if ( + "distribution" in options["particle size"] + and options["surface form"] == "false" + ): raise pybamm.OptionError( - "'particle size' should be 'single' for SPM and SPMe models" + "surface form must be 'algebraic' or 'differential' if " + " 'particle size' contains a 'distribution'" ) if isinstance(self, pybamm.lead_acid.BaseModel): if options["thermal"] != "isothermal" and options["dimensionality"] != 0: @@ -948,7 +952,6 @@ def build_model_equations(self): self.check_no_repeated_keys() def build_model(self): - # Build model variables and equations self._build_model() @@ -1071,7 +1074,6 @@ def set_transport_efficiency_submodels(self): ] = pybamm.transport_efficiency.Bruggeman(self.param, "Electrode", self.options) def set_thermal_submodel(self): - if self.options["thermal"] == "isothermal": thermal_submodel = pybamm.thermal.isothermal.Isothermal elif self.options["thermal"] == "lumped": @@ -1090,7 +1092,6 @@ def set_thermal_submodel(self): self.submodels["thermal"] = thermal_submodel(self.param, self.options) def set_current_collector_submodel(self): - if self.options["current collector"] in ["uniform"]: submodel = pybamm.current_collector.Uniform(self.param) elif self.options["current collector"] == "potential pair": diff --git a/pybamm/models/full_battery_models/equivalent_circuit/thevenin.py b/pybamm/models/full_battery_models/equivalent_circuit/thevenin.py index 6de80aafac..493b96f912 100644 --- a/pybamm/models/full_battery_models/equivalent_circuit/thevenin.py +++ b/pybamm/models/full_battery_models/equivalent_circuit/thevenin.py @@ -79,7 +79,6 @@ def __init__( self.set_submodels(build) def set_options(self, extra_options=None): - possible_options = { "calculate discharge energy": ["false", "true"], "operating mode": OperatingModes("current"), @@ -162,7 +161,6 @@ def set_ocv_submodel(self): ] = pybamm.equivalent_circuit_elements.OCVElement(self.param, self.options) def set_resistor_submodel(self): - name = "Element-0 (Resistor)" self.submodels[name] = pybamm.equivalent_circuit_elements.ResistorElement( self.param, self.options @@ -203,7 +201,6 @@ def set_submodels(self, build): self.build_model() def build_model(self): - # Build model variables and equations self._build_model() diff --git a/pybamm/models/full_battery_models/lead_acid/base_lead_acid_model.py b/pybamm/models/full_battery_models/lead_acid/base_lead_acid_model.py index 7a43825c19..ccbfb32cb9 100644 --- a/pybamm/models/full_battery_models/lead_acid/base_lead_acid_model.py +++ b/pybamm/models/full_battery_models/lead_acid/base_lead_acid_model.py @@ -115,11 +115,9 @@ def set_active_material_submodel(self): ) def set_sei_submodel(self): - self.submodels["sei"] = pybamm.sei.NoSEI(self.param, self.options) def set_lithium_plating_submodel(self): - self.submodels["lithium plating"] = pybamm.lithium_plating.NoPlating(self.param) def set_total_interface_submodel(self): diff --git a/pybamm/models/full_battery_models/lead_acid/full.py b/pybamm/models/full_battery_models/lead_acid/full.py index 9709eb3778..229f51e367 100644 --- a/pybamm/models/full_battery_models/lead_acid/full.py +++ b/pybamm/models/full_battery_models/lead_acid/full.py @@ -92,7 +92,6 @@ def set_solid_submodel(self): self.submodels["positive electrode potential"] = submod_p def set_electrolyte_submodel(self): - surf_form = pybamm.electrolyte_conductivity.surface_potential_form self.submodels["electrolyte diffusion"] = pybamm.electrolyte_diffusion.Full( diff --git a/pybamm/models/full_battery_models/lead_acid/loqs.py b/pybamm/models/full_battery_models/lead_acid/loqs.py index 97ab4cc15d..79f9da1992 100644 --- a/pybamm/models/full_battery_models/lead_acid/loqs.py +++ b/pybamm/models/full_battery_models/lead_acid/loqs.py @@ -73,7 +73,6 @@ def set_external_circuit_submodel(self): ) def set_current_collector_submodel(self): - if self.options["current collector"] in [ "uniform", "potential pair quite conductive", @@ -87,13 +86,11 @@ def set_current_collector_submodel(self): self.submodels["leading-order current collector"] = submodel def set_porosity_submodel(self): - self.submodels["leading-order porosity"] = pybamm.porosity.ReactionDrivenODE( self.param, self.options, True ) def set_convection_submodel(self): - if self.options["convection"] == "none": self.submodels[ "leading-order transverse convection" @@ -115,7 +112,6 @@ def set_convection_submodel(self): ] = pybamm.convection.through_cell.Explicit(self.param) def set_intercalation_kinetics_submodel(self): - if self.options["surface form"] == "false": self.submodels[ "leading-order negative interface" @@ -165,7 +161,6 @@ def set_intercalation_kinetics_submodel(self): } def set_electrode_submodels(self): - self.submodels[ "leading-order negative electrode potential" ] = pybamm.electrode.ohm.LeadingOrder(self.param, "negative") @@ -174,7 +169,6 @@ def set_electrode_submodels(self): ] = pybamm.electrode.ohm.LeadingOrder(self.param, "positive") def set_electrolyte_submodel(self): - surf_form = pybamm.electrolyte_conductivity.surface_potential_form if self.options["surface form"] == "false": diff --git a/pybamm/models/full_battery_models/lithium_ion/mpm.py b/pybamm/models/full_battery_models/lithium_ion/mpm.py index 0012ec56e6..40eff1eb23 100644 --- a/pybamm/models/full_battery_models/lithium_ion/mpm.py +++ b/pybamm/models/full_battery_models/lithium_ion/mpm.py @@ -54,5 +54,7 @@ def __init__(self, options=None, name="Many-Particle Model", build=True): @property def default_parameter_values(self): default_params = super().default_parameter_values - default_params = pybamm.get_size_distribution_parameters(default_params) + default_params = pybamm.get_size_distribution_parameters( + default_params, electrode=self.options["working electrode"] + ) return default_params diff --git a/pybamm/models/full_battery_models/lithium_ion/newman_tobias.py b/pybamm/models/full_battery_models/lithium_ion/newman_tobias.py index c0d0384db6..46c3e5acb0 100644 --- a/pybamm/models/full_battery_models/lithium_ion/newman_tobias.py +++ b/pybamm/models/full_battery_models/lithium_ion/newman_tobias.py @@ -33,7 +33,6 @@ class NewmanTobias(DFN): """ def __init__(self, options=None, name="Newman-Tobias model", build=True): - # Set default option "uniform profile" for particle submodel. Other # default options are those given in `pybamm.BatteryModelOptions` defined in # `base_battery_model.py`. diff --git a/pybamm/models/full_battery_models/lithium_ion/spm.py b/pybamm/models/full_battery_models/lithium_ion/spm.py index cd994be210..48e8492071 100644 --- a/pybamm/models/full_battery_models/lithium_ion/spm.py +++ b/pybamm/models/full_battery_models/lithium_ion/spm.py @@ -60,7 +60,6 @@ def __init__(self, options=None, name="Single Particle Model", build=True): pybamm.citations.register("BrosaPlanella2022") def set_intercalation_kinetics_submodel(self): - for domain in ["negative", "positive"]: electrode_type = self.options.electrode_types[domain] if electrode_type == "planar": @@ -128,7 +127,6 @@ def set_electrolyte_concentration_submodel(self): ] = pybamm.electrolyte_diffusion.ConstantConcentration(self.param, self.options) def set_electrolyte_potential_submodel(self): - surf_form = pybamm.electrolyte_conductivity.surface_potential_form if self.options["electrolyte conductivity"] not in ["default", "leading order"]: diff --git a/pybamm/models/submodels/active_material/base_active_material.py b/pybamm/models/submodels/active_material/base_active_material.py index 90bf89cf04..254eb327e7 100644 --- a/pybamm/models/submodels/active_material/base_active_material.py +++ b/pybamm/models/submodels/active_material/base_active_material.py @@ -83,10 +83,11 @@ def _get_standard_active_material_variables(self, eps_solid): # R_n, R_p. For a size distribution, calculate the area-weighted # mean using the distribution instead. Then the surface area is # calculated the same way - if self.options["particle size"] == "single": + domain_options = getattr(self.options, domain) + if domain_options["particle size"] == "single": R = self.phase_param.R R_dim = self.phase_param.R_dimensional - elif self.options["particle size"] == "distribution": + elif domain_options["particle size"] == "distribution": if self.domain == "negative": R_ = pybamm.standard_spatial_vars.R_n elif self.domain == "positive": diff --git a/pybamm/models/submodels/convection/through_cell/explicit_convection.py b/pybamm/models/submodels/convection/through_cell/explicit_convection.py index 4ca5e3df4e..0a810bdcd0 100644 --- a/pybamm/models/submodels/convection/through_cell/explicit_convection.py +++ b/pybamm/models/submodels/convection/through_cell/explicit_convection.py @@ -21,7 +21,6 @@ def __init__(self, param): super().__init__(param) def get_coupled_variables(self, variables): - # Set up param = self.param p_s = variables["X-averaged separator pressure"] diff --git a/pybamm/models/submodels/convection/through_cell/full_convection.py b/pybamm/models/submodels/convection/through_cell/full_convection.py index 6066258977..14b6cc7799 100644 --- a/pybamm/models/submodels/convection/through_cell/full_convection.py +++ b/pybamm/models/submodels/convection/through_cell/full_convection.py @@ -46,7 +46,6 @@ def get_fundamental_variables(self): return variables def get_coupled_variables(self, variables): - # Set up param = self.param l_n = param.n.l diff --git a/pybamm/models/submodels/convection/through_cell/no_convection.py b/pybamm/models/submodels/convection/through_cell/no_convection.py index f4b80840e2..454d9e767b 100644 --- a/pybamm/models/submodels/convection/through_cell/no_convection.py +++ b/pybamm/models/submodels/convection/through_cell/no_convection.py @@ -38,7 +38,6 @@ def get_fundamental_variables(self): return variables def get_coupled_variables(self, variables): - # Simple formula for velocity in the separator v_box_s = pybamm.FullBroadcast(0, "separator", "current collector") div_v_box_s = pybamm.FullBroadcast(0, "separator", "current collector") diff --git a/pybamm/models/submodels/convection/transverse/full_convection.py b/pybamm/models/submodels/convection/transverse/full_convection.py index 577b23664f..44afbc1414 100644 --- a/pybamm/models/submodels/convection/transverse/full_convection.py +++ b/pybamm/models/submodels/convection/transverse/full_convection.py @@ -21,7 +21,6 @@ def __init__(self, param): super().__init__(param) def get_fundamental_variables(self): - p_s = pybamm.Variable( "X-averaged separator pressure", domain="current collector" ) diff --git a/pybamm/models/submodels/convection/transverse/no_convection.py b/pybamm/models/submodels/convection/transverse/no_convection.py index 9f27b316ba..d16bbf2f8e 100644 --- a/pybamm/models/submodels/convection/transverse/no_convection.py +++ b/pybamm/models/submodels/convection/transverse/no_convection.py @@ -23,7 +23,6 @@ def __init__(self, param, options=None): super().__init__(param, options=options) def get_fundamental_variables(self): - p_s = pybamm.PrimaryBroadcast(0, "current collector") variables = self._get_standard_separator_pressure_variables(p_s) diff --git a/pybamm/models/submodels/convection/transverse/uniform_convection.py b/pybamm/models/submodels/convection/transverse/uniform_convection.py index 79bb0982d9..57301393ed 100644 --- a/pybamm/models/submodels/convection/transverse/uniform_convection.py +++ b/pybamm/models/submodels/convection/transverse/uniform_convection.py @@ -22,14 +22,12 @@ def __init__(self, param): super().__init__(param) def get_fundamental_variables(self): - p_s = pybamm.PrimaryBroadcast(0, "current collector") variables = self._get_standard_separator_pressure_variables(p_s) return variables def get_coupled_variables(self, variables): - # Set up param = self.param z = pybamm.standard_spatial_vars.z diff --git a/pybamm/models/submodels/current_collector/homogeneous_current_collector.py b/pybamm/models/submodels/current_collector/homogeneous_current_collector.py index 6d6b18285f..5d8c61af46 100644 --- a/pybamm/models/submodels/current_collector/homogeneous_current_collector.py +++ b/pybamm/models/submodels/current_collector/homogeneous_current_collector.py @@ -27,7 +27,6 @@ def get_fundamental_variables(self): return variables def get_coupled_variables(self, variables): - # TODO: grad not implemented for 2D yet i_cc = pybamm.Scalar(0) i_boundary_cc = pybamm.PrimaryBroadcast( diff --git a/pybamm/models/submodels/current_collector/potential_pair.py b/pybamm/models/submodels/current_collector/potential_pair.py index 1bf96449d6..bc0aeab444 100644 --- a/pybamm/models/submodels/current_collector/potential_pair.py +++ b/pybamm/models/submodels/current_collector/potential_pair.py @@ -32,7 +32,6 @@ def __init__(self, param): pybamm.citations.register("Timms2021") def get_fundamental_variables(self): - phi_s_cn = pybamm.standard_variables.phi_s_cn variables = self._get_standard_negative_potential_variables(phi_s_cn) @@ -46,7 +45,6 @@ def get_fundamental_variables(self): return variables def set_algebraic(self, variables): - param = self.param phi_s_cn = variables["Negative current collector potential"] @@ -63,7 +61,6 @@ def set_algebraic(self, variables): } def set_initial_conditions(self, variables): - applied_current = self.param.current_with_time phi_s_cn = variables["Negative current collector potential"] i_boundary_cc = variables["Current collector current density"] @@ -81,7 +78,6 @@ def __init__(self, param): super().__init__(param) def set_boundary_conditions(self, variables): - phi_s_cn = variables["Negative current collector potential"] phi_s_cp = variables["Positive current collector potential"] @@ -121,7 +117,6 @@ def __init__(self, param): super().__init__(param) def set_boundary_conditions(self, variables): - phi_s_cn = variables["Negative current collector potential"] phi_s_cp = variables["Positive current collector potential"] diff --git a/pybamm/models/submodels/electrode/ohm/full_ohm.py b/pybamm/models/submodels/electrode/ohm/full_ohm.py index c09c1987cf..893c185896 100644 --- a/pybamm/models/submodels/electrode/ohm/full_ohm.py +++ b/pybamm/models/submodels/electrode/ohm/full_ohm.py @@ -24,7 +24,6 @@ def __init__(self, param, domain, options=None): super().__init__(param, domain, options=options) def get_fundamental_variables(self): - if self.domain == "negative": phi_s = pybamm.standard_variables.phi_s_n elif self.domain == "positive": diff --git a/pybamm/models/submodels/electrode/ohm/surface_form_ohm.py b/pybamm/models/submodels/electrode/ohm/surface_form_ohm.py index de3510959b..92f5356c0d 100644 --- a/pybamm/models/submodels/electrode/ohm/surface_form_ohm.py +++ b/pybamm/models/submodels/electrode/ohm/surface_form_ohm.py @@ -44,7 +44,6 @@ def get_coupled_variables(self, variables): phi_s = phi_s_cn - pybamm.IndefiniteIntegral(i_s / conductivity, x_n) elif self.domain == "positive": - phi_e_s = variables["Separator electrolyte potential"] delta_phi_p = variables["Positive electrode surface potential difference"] diff --git a/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/composite_surface_form_conductivity.py b/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/composite_surface_form_conductivity.py index 15bacfa8e8..04c3520e55 100644 --- a/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/composite_surface_form_conductivity.py +++ b/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/composite_surface_form_conductivity.py @@ -28,7 +28,6 @@ def __init__(self, param, domain, options=None): super().__init__(param, domain, options) def get_fundamental_variables(self): - if self.domain == "negative": delta_phi_av = pybamm.standard_variables.delta_phi_n_av elif self.domain == "separator": diff --git a/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/leading_surface_form_conductivity.py b/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/leading_surface_form_conductivity.py index aead3015bc..7fe51308da 100644 --- a/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/leading_surface_form_conductivity.py +++ b/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/leading_surface_form_conductivity.py @@ -28,7 +28,6 @@ def __init__(self, param, domain, options=None): super().__init__(param, domain, options) def get_fundamental_variables(self): - if self.domain == "negative": delta_phi_av = pybamm.standard_variables.delta_phi_n_av elif self.domain == "positive": diff --git a/pybamm/models/submodels/electrolyte_diffusion/full_diffusion.py b/pybamm/models/submodels/electrolyte_diffusion/full_diffusion.py index bd93e2f848..9cb4f8e990 100644 --- a/pybamm/models/submodels/electrolyte_diffusion/full_diffusion.py +++ b/pybamm/models/submodels/electrolyte_diffusion/full_diffusion.py @@ -44,7 +44,6 @@ def get_fundamental_variables(self): return variables def get_coupled_variables(self, variables): - c_e_dict = {} for domain in self.options.whole_cell_domains: Domain = domain.capitalize() @@ -87,7 +86,6 @@ def get_coupled_variables(self, variables): return variables def set_rhs(self, variables): - param = self.param eps_c_e = variables["Porosity times concentration"] @@ -108,7 +106,6 @@ def set_rhs(self, variables): } def set_initial_conditions(self, variables): - eps_c_e = variables["Porosity times concentration"] self.initial_conditions = { diff --git a/pybamm/models/submodels/electrolyte_diffusion/leading_order_diffusion.py b/pybamm/models/submodels/electrolyte_diffusion/leading_order_diffusion.py index d52c8d4810..febe637179 100644 --- a/pybamm/models/submodels/electrolyte_diffusion/leading_order_diffusion.py +++ b/pybamm/models/submodels/electrolyte_diffusion/leading_order_diffusion.py @@ -55,7 +55,6 @@ def get_coupled_variables(self, variables): return variables def set_rhs(self, variables): - param = self.param c_e_av = variables["X-averaged electrolyte concentration"] diff --git a/pybamm/models/submodels/equivalent_circuit_elements/rc_element.py b/pybamm/models/submodels/equivalent_circuit_elements/rc_element.py index 757488313b..557bc48a66 100644 --- a/pybamm/models/submodels/equivalent_circuit_elements/rc_element.py +++ b/pybamm/models/submodels/equivalent_circuit_elements/rc_element.py @@ -28,7 +28,6 @@ def get_fundamental_variables(self): return variables def get_coupled_variables(self, variables): - T_cell = variables["Cell temperature [degC]"] current = variables["Current [A]"] soc = variables["SoC"] diff --git a/pybamm/models/submodels/equivalent_circuit_elements/resistor_element.py b/pybamm/models/submodels/equivalent_circuit_elements/resistor_element.py index b708d93721..22f6cf6568 100644 --- a/pybamm/models/submodels/equivalent_circuit_elements/resistor_element.py +++ b/pybamm/models/submodels/equivalent_circuit_elements/resistor_element.py @@ -18,7 +18,6 @@ def __init__(self, param, options=None): self.model_options = options def get_coupled_variables(self, variables): - T_cell = variables["Cell temperature [degC]"] current = variables["Current [A]"] soc = variables["SoC"] diff --git a/pybamm/models/submodels/equivalent_circuit_elements/thermal.py b/pybamm/models/submodels/equivalent_circuit_elements/thermal.py index 8790f47f7f..ab7520c192 100644 --- a/pybamm/models/submodels/equivalent_circuit_elements/thermal.py +++ b/pybamm/models/submodels/equivalent_circuit_elements/thermal.py @@ -42,7 +42,6 @@ def get_fundamental_variables(self): 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 diff --git a/pybamm/models/submodels/equivalent_circuit_elements/voltage_model.py b/pybamm/models/submodels/equivalent_circuit_elements/voltage_model.py index 404d33d2f0..4eff08177a 100644 --- a/pybamm/models/submodels/equivalent_circuit_elements/voltage_model.py +++ b/pybamm/models/submodels/equivalent_circuit_elements/voltage_model.py @@ -21,7 +21,6 @@ def __init__(self, param, options=None): 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"] @@ -54,7 +53,6 @@ def x_not_zero(x): return variables def set_events(self, variables): - voltage = variables["Terminal voltage [V]"] # Add voltage events diff --git a/pybamm/models/submodels/interface/base_interface.py b/pybamm/models/submodels/interface/base_interface.py index dfd0428a29..2da5c98a91 100644 --- a/pybamm/models/submodels/interface/base_interface.py +++ b/pybamm/models/submodels/interface/base_interface.py @@ -73,7 +73,8 @@ def _get_exchange_current_density(self, variables): if self.reaction == "lithium-ion main": # For "particle-size distribution" submodels, take distribution version # of c_s_surf that depends on particle size. - if self.options["particle size"] == "distribution": + domain_options = getattr(self.options, domain) + if domain_options["particle size"] == "distribution": c_s_surf = variables[ f"{Domain} {phase_name}particle surface concentration distribution" ] diff --git a/pybamm/models/submodels/interface/kinetics/base_kinetics.py b/pybamm/models/submodels/interface/kinetics/base_kinetics.py index 03be0d29be..367bbefa85 100644 --- a/pybamm/models/submodels/interface/kinetics/base_kinetics.py +++ b/pybamm/models/submodels/interface/kinetics/base_kinetics.py @@ -72,16 +72,20 @@ def get_coupled_variables(self, variables): delta_phi = delta_phi.orphans[0] # For "particle-size distribution" models, delta_phi must then be # broadcast to "particle size" domain + domain_options = getattr(self.options, domain) if ( self.reaction == "lithium-ion main" - and self.options["particle size"] == "distribution" + and domain_options["particle size"] == "distribution" ): delta_phi = pybamm.PrimaryBroadcast(delta_phi, [f"{domain} particle size"]) # Get exchange-current density j0 = self._get_exchange_current_density(variables) # Get open-circuit potential variables and reaction overpotential - if self.options["particle size"] == "distribution": + if ( + domain_options["particle size"] == "distribution" + and self.options.electrode_types[domain] == "porous" + ): ocp = variables[ f"{Domain} electrode {reaction_name}open circuit potential distribution" ] diff --git a/pybamm/models/submodels/interface/open_circuit_potential/current_sigmoid_ocp.py b/pybamm/models/submodels/interface/open_circuit_potential/current_sigmoid_ocp.py index 1b8ba76a07..22d4334ee1 100644 --- a/pybamm/models/submodels/interface/open_circuit_potential/current_sigmoid_ocp.py +++ b/pybamm/models/submodels/interface/open_circuit_potential/current_sigmoid_ocp.py @@ -7,18 +7,37 @@ class CurrentSigmoidOpenCircuitPotential(BaseOpenCircuitPotential): def get_coupled_variables(self, variables): + domain, Domain = self.domain_Domain current = variables["Total current density"] k = 100 - m_lith = pybamm.sigmoid(current, 0, k) # for lithation (current < 0) - m_delith = 1 - m_lith # for delithiation (current > 0) - Domain = self.domain.capitalize() + if Domain == "Positive": + lithiation_current = current + elif Domain == "Negative": + lithiation_current = -current + + m_lith = pybamm.sigmoid(0, lithiation_current, k) # lithiation_current > 0 + m_delith = 1 - m_lith # lithiation_current < 0 + phase_name = self.phase_name if self.reaction == "lithium-ion main": T = variables[f"{Domain} electrode temperature"] - # Particle size distribution is not yet implemented - if self.options["particle size"] != "distribution": + # For "particle-size distribution" models, take distribution version + # of c_s_surf that depends on particle size. + domain_options = getattr(self.options, domain) + if domain_options["particle size"] == "distribution": + c_s_surf = variables[ + f"{Domain} {phase_name}particle surface concentration distribution" + ] + # If variable was broadcast, take only the orphan + if isinstance(c_s_surf, pybamm.Broadcast) and isinstance( + T, pybamm.Broadcast + ): + c_s_surf = c_s_surf.orphans[0] + T = T.orphans[0] + T = pybamm.PrimaryBroadcast(T, [f"{domain} particle size"]) + else: c_s_surf = variables[ f"{Domain} {phase_name}particle surface concentration" ] diff --git a/pybamm/models/submodels/interface/open_circuit_potential/single_ocp.py b/pybamm/models/submodels/interface/open_circuit_potential/single_ocp.py index fb3eb2edde..6a1752e9b5 100644 --- a/pybamm/models/submodels/interface/open_circuit_potential/single_ocp.py +++ b/pybamm/models/submodels/interface/open_circuit_potential/single_ocp.py @@ -11,11 +11,11 @@ def get_coupled_variables(self, variables): phase_name = self.phase_name if self.reaction == "lithium-ion main": - T = variables[f"{Domain} electrode temperature"] # For "particle-size distribution" models, take distribution version # of c_s_surf that depends on particle size. - if self.options["particle size"] == "distribution": + domain_options = getattr(self.options, domain) + if domain_options["particle size"] == "distribution": c_s_surf = variables[ f"{Domain} {phase_name}particle surface concentration distribution" ] diff --git a/pybamm/models/submodels/oxygen_diffusion/full_oxygen_diffusion.py b/pybamm/models/submodels/oxygen_diffusion/full_oxygen_diffusion.py index 4c7bee0b42..aa14b0b277 100644 --- a/pybamm/models/submodels/oxygen_diffusion/full_oxygen_diffusion.py +++ b/pybamm/models/submodels/oxygen_diffusion/full_oxygen_diffusion.py @@ -49,7 +49,6 @@ def get_fundamental_variables(self): return variables def get_coupled_variables(self, variables): - tor_s = variables["Separator electrolyte transport efficiency"] tor_p = variables["Positive electrolyte transport efficiency"] tor = pybamm.concatenation(tor_s, tor_p) @@ -73,7 +72,6 @@ def get_coupled_variables(self, variables): return variables def set_rhs(self, variables): - param = self.param eps_s = variables["Separator porosity"] @@ -99,7 +97,6 @@ def set_rhs(self, variables): } def set_boundary_conditions(self, variables): - c_ox = variables["Separator and positive electrode oxygen concentration"] self.boundary_conditions = { @@ -110,7 +107,6 @@ def set_boundary_conditions(self, variables): } def set_initial_conditions(self, variables): - c_ox = variables["Separator and positive electrode oxygen concentration"] self.initial_conditions = {c_ox: self.param.c_ox_init} diff --git a/pybamm/models/submodels/oxygen_diffusion/leading_oxygen_diffusion.py b/pybamm/models/submodels/oxygen_diffusion/leading_oxygen_diffusion.py index 059bee6321..6c6593d3f0 100644 --- a/pybamm/models/submodels/oxygen_diffusion/leading_oxygen_diffusion.py +++ b/pybamm/models/submodels/oxygen_diffusion/leading_oxygen_diffusion.py @@ -33,7 +33,6 @@ def get_fundamental_variables(self): return self._get_standard_concentration_variables(c_ox_n, c_ox_s, c_ox_p) def get_coupled_variables(self, variables): - N_ox = pybamm.FullBroadcast( 0, ["negative electrode", "separator", "positive electrode"], @@ -45,7 +44,6 @@ def get_coupled_variables(self, variables): return variables def set_rhs(self, variables): - param = self.param c_ox_av = variables["X-averaged oxygen concentration"] diff --git a/pybamm/models/submodels/particle/base_particle.py b/pybamm/models/submodels/particle/base_particle.py index 52b0ee9d07..dc4893103f 100644 --- a/pybamm/models/submodels/particle/base_particle.py +++ b/pybamm/models/submodels/particle/base_particle.py @@ -26,7 +26,8 @@ class BaseParticle(pybamm.BaseSubModel): def __init__(self, param, domain, options, phase="primary"): super().__init__(param, domain, options=options, phase=phase) # Read from options to see if we have a particle size distribution - self.size_distribution = self.options["particle size"] == "distribution" + domain_options = getattr(self.options, domain) + self.size_distribution = domain_options["particle size"] == "distribution" def _get_effective_diffusivity(self, c, T): param = self.param diff --git a/pybamm/models/submodels/porosity/reaction_driven_porosity_ode.py b/pybamm/models/submodels/porosity/reaction_driven_porosity_ode.py index cfff704f4c..8d4c684954 100644 --- a/pybamm/models/submodels/porosity/reaction_driven_porosity_ode.py +++ b/pybamm/models/submodels/porosity/reaction_driven_porosity_ode.py @@ -48,7 +48,6 @@ def get_fundamental_variables(self): return variables def get_coupled_variables(self, variables): - depsdt_dict = {} for domain in self.options.whole_cell_domains: domain_param = self.param.domain_params[domain.split()[0]] diff --git a/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_1D_current_collectors.py b/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_1D_current_collectors.py index 1623e854a1..886185026b 100644 --- a/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_1D_current_collectors.py +++ b/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_1D_current_collectors.py @@ -36,7 +36,6 @@ def __init__(self, param, options=None): pybamm.citations.register("Timms2021") def get_fundamental_variables(self): - T_x_av = pybamm.Variable( "X-averaged cell temperature", domain="current collector" ) diff --git a/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_2D_current_collectors.py b/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_2D_current_collectors.py index f1b8a5c286..7c6c318263 100644 --- a/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_2D_current_collectors.py +++ b/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_2D_current_collectors.py @@ -36,7 +36,6 @@ def __init__(self, param, options=None): pybamm.citations.register("Timms2021") def get_fundamental_variables(self): - T_x_av = pybamm.Variable( "X-averaged cell temperature", domain="current collector" ) diff --git a/pybamm/parameters/ecm_parameters.py b/pybamm/parameters/ecm_parameters.py index e2dc0eb3f4..919b40d0ce 100644 --- a/pybamm/parameters/ecm_parameters.py +++ b/pybamm/parameters/ecm_parameters.py @@ -3,7 +3,6 @@ class EcmParameters: def __init__(self): - self.timescale = pybamm.Scalar(1) self.cell_capacity = pybamm.Parameter("Cell capacity [A.h]") @@ -43,11 +42,14 @@ def _set_compatibility_parameters(self): 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]") + self.initial_T_cell = pybamm.Parameter("Initial temperature [K]") - 273.15 + self.initial_T_jig = pybamm.Parameter("Initial temperature [K]") - 273.15 def T_amb(self, t): - return pybamm.FunctionParameter("Ambient temperature [degC]", {"Time [s]": t}) + ambient_temperature_K = pybamm.FunctionParameter( + "Ambient temperature [K]", {"Time [s]": t} + ) + return ambient_temperature_K - 273.15 def ocv(self, soc): return pybamm.FunctionParameter("Open circuit voltage [V]", {"SoC": soc}) diff --git a/pybamm/parameters/electrical_parameters.py b/pybamm/parameters/electrical_parameters.py index a2bf2f22d4..c6c6eb7fbf 100644 --- a/pybamm/parameters/electrical_parameters.py +++ b/pybamm/parameters/electrical_parameters.py @@ -16,7 +16,6 @@ class ElectricalParameters(BaseParameters): """ def __init__(self): - # Get geometric parameters self.geo = pybamm.geometric_parameters diff --git a/pybamm/parameters/lead_acid_parameters.py b/pybamm/parameters/lead_acid_parameters.py index eb79e5851e..9393d2157a 100644 --- a/pybamm/parameters/lead_acid_parameters.py +++ b/pybamm/parameters/lead_acid_parameters.py @@ -20,7 +20,6 @@ class LeadAcidParameters(BaseParameters): """ def __init__(self): - # Get geometric, electrical and thermal parameters self.geo = pybamm.geometric_parameters self.elec = pybamm.electrical_parameters diff --git a/pybamm/parameters/parameter_values.py b/pybamm/parameters/parameter_values.py index d8c5752061..b3c63ffd91 100644 --- a/pybamm/parameters/parameter_values.py +++ b/pybamm/parameters/parameter_values.py @@ -818,7 +818,6 @@ def _ipython_key_completions_(self): return list(self._dict_items.keys()) def export_csv(self, filename): - # process functions and data to output # like they appear in inputs csv files parameter_output = {} diff --git a/pybamm/parameters/size_distribution_parameters.py b/pybamm/parameters/size_distribution_parameters.py index 7b378cc37f..d100d52d50 100644 --- a/pybamm/parameters/size_distribution_parameters.py +++ b/pybamm/parameters/size_distribution_parameters.py @@ -1,8 +1,7 @@ -""" -Adding particle-size distribution parameter values to a parameter set -""" - - +# +# Helper function for adding particle-size distribution parameter values +# to a parameter set +# import pybamm import numpy as np @@ -17,6 +16,7 @@ def get_size_distribution_parameters( R_min_p=None, R_max_n=None, R_max_p=None, + electrode="both", ): """ A convenience method to add standard area-weighted particle-size distribution @@ -54,50 +54,70 @@ def get_size_distribution_parameters( R_max_p : float (optional) Maximum radius in positive electrode, scaled by the mean radius R_p_av. Default is 5 standard deviations above the mean. + electrode : str (optional) + Which electrode to add parameters for. If "both" (default), size distribution + parameters are added for both electrodes. Otherwise can be "negative" or + "positive" to indicate a half-cell model, in which case size distribution + parameters are only added for a single electrode. """ - - # Radii from given parameter set - R_n_typ = param["Negative particle radius [m]"] - R_p_typ = param["Positive particle radius [m]"] - - # Set the mean particle radii for each electrode - R_n_av = R_n_av or R_n_typ - R_p_av = R_p_av or R_p_typ - - # Minimum radii - R_min_n = R_min_n or np.max([0, 1 - sd_n * 5]) - R_min_p = R_min_p or np.max([0, 1 - sd_p * 5]) - - # Max radii - R_max_n = R_max_n or (1 + sd_n * 5) - R_max_p = R_max_p or (1 + sd_p * 5) - - # Area-weighted particle-size distributions - def f_a_dist_n_dim(R): - return lognormal(R, R_n_av, sd_n * R_n_av) - - def f_a_dist_p_dim(R): - return lognormal(R, R_p_av, sd_p * R_p_av) - - param.update( - { - "Negative area-weighted mean particle radius [m]": R_n_av, - "Positive area-weighted mean particle radius [m]": R_p_av, - "Negative area-weighted particle-size " - + "standard deviation [m]": sd_n * R_n_av, - "Positive area-weighted particle-size " - + "standard deviation [m]": sd_p * R_p_av, - "Negative minimum particle radius [m]": R_min_n * R_n_av, - "Positive minimum particle radius [m]": R_min_p * R_p_av, - "Negative maximum particle radius [m]": R_max_n * R_n_av, - "Positive maximum particle radius [m]": R_max_p * R_p_av, - "Negative area-weighted " - + "particle-size distribution [m-1]": f_a_dist_n_dim, - "Positive area-weighted " - + "particle-size distribution [m-1]": f_a_dist_p_dim, - }, - check_already_exists=False, - ) + if electrode in ["both", "negative"]: + # Radii from given parameter set + R_n_typ = param["Negative particle radius [m]"] + + # Set the mean particle radii + R_n_av = R_n_av or R_n_typ + + # Minimum radii + R_min_n = R_min_n or np.max([0, 1 - sd_n * 5]) + + # Max radii + R_max_n = R_max_n or (1 + sd_n * 5) + + # Area-weighted particle-size distribution + def f_a_dist_n_dim(R): + return lognormal(R, R_n_av, sd_n * R_n_av) + + param.update( + { + "Negative area-weighted mean particle radius [m]": R_n_av, + "Negative area-weighted particle-size " + + "standard deviation [m]": sd_n * R_n_av, + "Negative minimum particle radius [m]": R_min_n * R_n_av, + "Negative maximum particle radius [m]": R_max_n * R_n_av, + "Negative area-weighted " + + "particle-size distribution [m-1]": f_a_dist_n_dim, + }, + check_already_exists=False, + ) + if electrode in ["both", "positive"]: + # Radii from given parameter set + R_p_typ = param["Positive particle radius [m]"] + + # Set the mean particle radii + R_p_av = R_p_av or R_p_typ + + # Minimum radii + R_min_p = R_min_p or np.max([0, 1 - sd_p * 5]) + + # Max radii + R_max_p = R_max_p or (1 + sd_p * 5) + + # Area-weighted particle-size distribution + def f_a_dist_p_dim(R): + return lognormal(R, R_p_av, sd_p * R_p_av) + + param.update( + { + "Positive area-weighted mean particle radius [m]": R_p_av, + "Positive area-weighted particle-size " + + "standard deviation [m]": sd_p * R_p_av, + "Positive minimum particle radius [m]": R_min_p * R_p_av, + "Positive maximum particle radius [m]": R_max_p * R_p_av, + "Positive area-weighted " + + "particle-size distribution [m-1]": f_a_dist_p_dim, + }, + check_already_exists=False, + ) return param diff --git a/pybamm/parameters/thermal_parameters.py b/pybamm/parameters/thermal_parameters.py index 71bcd075f1..b10d648b25 100644 --- a/pybamm/parameters/thermal_parameters.py +++ b/pybamm/parameters/thermal_parameters.py @@ -17,7 +17,6 @@ class ThermalParameters(BaseParameters): """ def __init__(self): - # Get geometric parameters self.geo = pybamm.geometric_parameters diff --git a/pybamm/simulation.py b/pybamm/simulation.py index b41b48581b..e2647fa1cb 100644 --- a/pybamm/simulation.py +++ b/pybamm/simulation.py @@ -143,6 +143,7 @@ def set_up_and_parameterise_experiment(self): # Update experiment using parameters such as timescale and capacity timescale = self._parameter_values.evaluate(model.timescale) capacity = self._parameter_values["Nominal cell capacity [A.h]"] + for op_conds in experiment.operating_conditions: op_type = op_conds["type"] if op_conds["dc_data"] is not None: @@ -211,7 +212,7 @@ def set_up_and_parameterise_model_for_experiment(self): """ self.op_type_to_model = {} self.op_string_to_model = {} - for op in self.experiment.operating_conditions: + for op_number, op in enumerate(self.experiment.operating_conditions): # Create model for this operating condition type (current/voltage/power) # if it has not already been seen before if op["type"] not in self.op_type_to_model: @@ -258,7 +259,12 @@ def set_up_and_parameterise_model_for_experiment(self): self.update_new_model_events(new_model, op) # Update parameter values new_parameter_values = self.parameter_values.copy() - experiment_parameter_values = self.get_experiment_parameter_values(op) + self._original_temperature = new_parameter_values[ + "Ambient temperature [K]" + ] + experiment_parameter_values = self.get_experiment_parameter_values( + op, op_number + ) new_parameter_values.update( experiment_parameter_values, check_already_exists=False ) @@ -342,7 +348,7 @@ def update_new_model_events(self, new_model, op): event.name, event.expression + 1, event.event_type ) - def get_experiment_parameter_values(self, op): + def get_experiment_parameter_values(self, op, op_number): experiment_parameter_values = {} if op["type"] == "current": experiment_parameter_values.update( @@ -360,6 +366,26 @@ def get_experiment_parameter_values(self, op): experiment_parameter_values.update( {"Power function [W]": op["Power input [W]"]} ) + + if op["temperature"] is not None: + ambient_temperature = op["temperature"] + 273.15 + experiment_parameter_values.update( + {"Ambient temperature [K]": ambient_temperature} + ) + + # If at the first operation, then the intial temperature + # should be the ambient temperature. + if op_number == 0: + experiment_parameter_values.update( + { + "Initial temperature [K]": ambient_temperature, + } + ) + else: + experiment_parameter_values.update( + {"Ambient temperature [K]": self._original_temperature} + ) + return experiment_parameter_values def set_parameters(self): diff --git a/pybamm/solvers/algebraic_solver.py b/pybamm/solvers/algebraic_solver.py index 2a15108df6..c550a15339 100644 --- a/pybamm/solvers/algebraic_solver.py +++ b/pybamm/solvers/algebraic_solver.py @@ -142,7 +142,6 @@ def jac_fn(y_alg): # Methods which use least-squares are specified as either "lsq", # which uses the default method, or with "lsq__methodname" if self.method.startswith("lsq"): - if self.method == "lsq": method = "trf" else: diff --git a/pybamm/solvers/base_solver.py b/pybamm/solvers/base_solver.py index 25071562b5..b106f834a3 100644 --- a/pybamm/solvers/base_solver.py +++ b/pybamm/solvers/base_solver.py @@ -415,7 +415,6 @@ def _set_up_model_sensitivities_inplace( model.mass_matrix is not None and model.mass_matrix.shape[0] == model.len_rhs_and_alg ): - if model.mass_matrix_inv is not None: model.mass_matrix_inv = pybamm.Matrix( block_diag( diff --git a/pybamm/solvers/c_solvers/idaklu.cpp b/pybamm/solvers/c_solvers/idaklu.cpp index ac90172c97..132e8883f4 100644 --- a/pybamm/solvers/c_solvers/idaklu.cpp +++ b/pybamm/solvers/c_solvers/idaklu.cpp @@ -44,6 +44,7 @@ PYBIND11_MODULE(idaklu, m) 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_bandwidth_lower"), py::arg("jac_bandwidth_upper"), 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"), diff --git a/pybamm/solvers/c_solvers/idaklu/casadi_functions.cpp b/pybamm/solvers/c_solvers/idaklu/casadi_functions.cpp index a2de2e7089..e56b0902b2 100644 --- a/pybamm/solvers/c_solvers/idaklu/casadi_functions.cpp +++ b/pybamm/solvers/c_solvers/idaklu/casadi_functions.cpp @@ -34,16 +34,20 @@ void CasadiFunction::operator()() CasadiFunctions::CasadiFunctions( const Function &rhs_alg, const Function &jac_times_cjmass, const int jac_times_cjmass_nnz, + const int jac_bandwidth_lower, const int jac_bandwidth_upper, 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), + number_of_nnz(jac_times_cjmass_nnz), + jac_bandwidth_lower(jac_bandwidth_lower), jac_bandwidth_upper(jac_bandwidth_upper), + 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), + tmp_state_vector(number_of_states), + tmp_sparse_jacobian_data(jac_times_cjmass_nnz), options(options) { @@ -66,4 +70,5 @@ CasadiFunctions::CasadiFunctions( } -realtype *CasadiFunctions::get_tmp() { return tmp.data(); } +realtype *CasadiFunctions::get_tmp_state_vector() { return tmp_state_vector.data(); } +realtype *CasadiFunctions::get_tmp_sparse_jacobian_data() { return tmp_sparse_jacobian_data.data(); } diff --git a/pybamm/solvers/c_solvers/idaklu/casadi_functions.hpp b/pybamm/solvers/c_solvers/idaklu/casadi_functions.hpp index 2e3b6beb8d..03264a8478 100644 --- a/pybamm/solvers/c_solvers/idaklu/casadi_functions.hpp +++ b/pybamm/solvers/c_solvers/idaklu/casadi_functions.hpp @@ -31,6 +31,8 @@ class CasadiFunctions int number_of_parameters; int number_of_events; int number_of_nnz; + int jac_bandwidth_lower; + int jac_bandwidth_upper; CasadiFunction rhs_alg; CasadiFunction sens; CasadiFunction jac_times_cjmass; @@ -44,6 +46,7 @@ class CasadiFunctions CasadiFunctions(const Function &rhs_alg, const Function &jac_times_cjmass, const int jac_times_cjmass_nnz, + const int jac_bandwidth_lower, const int jac_bandwidth_upper, const np_array_int &jac_times_cjmass_rowvals, const np_array_int &jac_times_cjmass_colptrs, const int inputs_length, const Function &jac_action, @@ -51,10 +54,12 @@ class CasadiFunctions const Function &events, const int n_s, int n_e, const int n_p, const Options& options); - realtype *get_tmp(); + realtype *get_tmp_state_vector(); + realtype *get_tmp_sparse_jacobian_data(); private: - std::vector tmp; + std::vector tmp_state_vector; + std::vector tmp_sparse_jacobian_data; }; #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 index d1bb76ea68..d10d0bdbf6 100644 --- a/pybamm/solvers/c_solvers/idaklu/casadi_solver.cpp +++ b/pybamm/solvers/c_solvers/idaklu/casadi_solver.cpp @@ -8,7 +8,9 @@ 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 int jac_times_cjmass_nnz, + const int jac_bandwidth_lower, const int jac_bandwidth_upper, + 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, @@ -16,19 +18,21 @@ create_casadi_solver(int number_of_states, int number_of_parameters, { auto options_cpp = Options(options); auto functions = std::make_unique( - rhs_alg, jac_times_cjmass, jac_times_cjmass_nnz, jac_times_cjmass_rowvals, + rhs_alg, jac_times_cjmass, jac_times_cjmass_nnz, jac_bandwidth_lower, jac_bandwidth_upper, 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, + number_of_events, jac_times_cjmass_nnz, + jac_bandwidth_lower, jac_bandwidth_upper, 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, + int jac_bandwidth_lower, int jac_bandwidth_upper, std::unique_ptr functions_arg, const Options &options) : number_of_states(atol_np.request().size), @@ -107,7 +111,14 @@ CasadiSolver::CasadiSolver(np_array atol_np, double rel_tol, jac_times_cjmass_nnz, CSC_MAT); #endif } - else if (options.jacobian == "dense" || options.jacobian == "none") + else if (options.jacobian == "banded") { + DEBUG("\tsetting banded matrix"); + #if SUNDIALS_VERSION_MAJOR >= 6 + J = SUNBandMatrix(number_of_states, jac_bandwidth_upper, jac_bandwidth_lower, sunctx); + #else + J = SUNBandMatrix(number_of_states, jac_bandwidth_upper, jac_bandwidth_lower); + #endif + } else if (options.jacobian == "dense" || options.jacobian == "none") { DEBUG("\tsetting dense matrix"); #if SUNDIALS_VERSION_MAJOR >= 6 @@ -151,6 +162,15 @@ CasadiSolver::CasadiSolver(np_array atol_np, double rel_tol, LS = SUNLinSol_KLU(yy, J, sunctx); #else LS = SUNLinSol_KLU(yy, J); +#endif + } + else if (options.linear_solver == "SUNLinSol_Band") + { + DEBUG("\tsetting SUNLinSol_Band linear solver"); +#if SUNDIALS_VERSION_MAJOR >= 6 + LS = SUNLinSol_Band(yy, J, sunctx); +#else + LS = SUNLinSol_Band(yy, J); #endif } else if (options.linear_solver == "SUNLinSol_SPBCGS") diff --git a/pybamm/solvers/c_solvers/idaklu/casadi_solver.hpp b/pybamm/solvers/c_solvers/idaklu/casadi_solver.hpp index 3eed122e04..09c4434d5b 100644 --- a/pybamm/solvers/c_solvers/idaklu/casadi_solver.hpp +++ b/pybamm/solvers/c_solvers/idaklu/casadi_solver.hpp @@ -14,7 +14,7 @@ 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, + int jac_times_cjmass_nnz, int jac_bandwidth_lower, int jac_bandwidth_upper, std::unique_ptr functions, const Options& options); ~CasadiSolver(); @@ -48,7 +48,9 @@ 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 int jac_times_cjmass_nnz, + const int jac_bandwidth_lower, const int jac_bandwidth_upper, + 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, diff --git a/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp b/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp index ce2a892725..031ef67d20 100644 --- a/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp +++ b/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp @@ -15,7 +15,7 @@ int residual_casadi(realtype tres, N_Vector yy, N_Vector yp, N_Vector rr, p_python_functions->rhs_alg.m_res[0] = NV_DATA_S(rr); p_python_functions->rhs_alg(); - realtype *tmp = p_python_functions->get_tmp(); + realtype *tmp = p_python_functions->get_tmp_state_vector(); 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(); @@ -108,7 +108,7 @@ int jtimes_casadi(realtype tt, N_Vector yy, N_Vector yp, N_Vector rr, p_python_functions->jac_action(); // tmp has -∂F/∂y˙ v - realtype *tmp = p_python_functions->get_tmp(); + realtype *tmp = p_python_functions->get_tmp_state_vector(); 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(); @@ -148,15 +148,14 @@ int jacobian_casadi(realtype tt, realtype cj, N_Vector yy, N_Vector yp, 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 if (p_python_functions->options.using_banded_matrix) { + jac_data = p_python_functions->get_tmp_sparse_jacobian_data(); + } else { jac_data = SUNDenseMatrix_Data(JJ); @@ -169,10 +168,31 @@ int jacobian_casadi(realtype tt, realtype cj, N_Vector yy, N_Vector yp, 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) + + if (p_python_functions->options.using_banded_matrix) { + // copy data from temporary matrix to the banded matrix + auto jac_colptrs = p_python_functions->jac_times_cjmass_colptrs.data(); + auto jac_rowvals = p_python_functions->jac_times_cjmass_rowvals.data(); + int ncols = p_python_functions->number_of_states; + for (int col_ij = 0; col_ij < ncols; col_ij++) { + realtype *banded_col = SM_COLUMN_B(JJ, col_ij); + for (auto data_i = jac_colptrs[col_ij]; data_i < jac_colptrs[col_ij+1]; data_i++) { + auto row_ij = jac_rowvals[data_i]; + const realtype value_ij = jac_data[data_i]; + DEBUG("(" << row_ij << ", " << col_ij << ") = " << value_ij); + SM_COLUMN_ELEMENT_B(banded_col, row_ij, col_ij) = value_ij; + } + } + } + else if (p_python_functions->options.using_sparse_matrix) + { + + sunindextype *jac_colptrs = SUNSparseMatrix_IndexPointers(JJ); + sunindextype *jac_rowvals = SUNSparseMatrix_IndexValues(JJ); // row vals and col ptrs const int n_row_vals = p_python_functions->jac_times_cjmass_rowvals.size(); auto p_jac_times_cjmass_rowvals = @@ -262,7 +282,7 @@ int sensitivities_casadi(int Ns, realtype t, N_Vector yy, N_Vector yp, for (int i = 0; i < np; i++) { // put (∂F/∂y)s i (t) in tmp - realtype *tmp = p_python_functions->get_tmp(); + realtype *tmp = p_python_functions->get_tmp_state_vector(); 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(); diff --git a/pybamm/solvers/c_solvers/idaklu/common.hpp b/pybamm/solvers/c_solvers/idaklu/common.hpp index 5bac325fc8..b1947654ea 100644 --- a/pybamm/solvers/c_solvers/idaklu/common.hpp +++ b/pybamm/solvers/c_solvers/idaklu/common.hpp @@ -16,6 +16,7 @@ #include /* access to KLU linear solver */ #include /* access to dense linear solver */ +#include /* access to dense linear solver */ #include /* access to spbcgs iterative linear solver */ #include #include diff --git a/pybamm/solvers/c_solvers/idaklu/options.cpp b/pybamm/solvers/c_solvers/idaklu/options.cpp index c3c7cb3583..f5d1d8c79e 100644 --- a/pybamm/solvers/c_solvers/idaklu/options.cpp +++ b/pybamm/solvers/c_solvers/idaklu/options.cpp @@ -1,4 +1,5 @@ #include "options.hpp" +#include #include @@ -15,9 +16,14 @@ Options::Options(py::dict options) { using_sparse_matrix = true; + using_banded_matrix = false; if (jacobian == "sparse") { } + else if (jacobian == "banded") { + using_banded_matrix = true; + using_sparse_matrix = false; + } else if (jacobian == "dense" || jacobian == "none") { using_sparse_matrix = false; @@ -29,7 +35,7 @@ Options::Options(py::dict options) { throw std::domain_error( "Unknown jacobian type \""s + jacobian + - "\". Should be one of \"sparse\", \"dense\", \"matrix-free\" or \"none\"."s + "\". Should be one of \"sparse\", \"banded\", \"dense\", \"matrix-free\" or \"none\"."s ); } @@ -40,6 +46,17 @@ Options::Options(py::dict options) else if (linear_solver == "SUNLinSol_KLU" && jacobian == "sparse") { } + else if (linear_solver == "SUNLinSol_Band" && jacobian == "banded") + { + } + else if (jacobian == "banded") { + throw std::domain_error( + "Unknown linear solver or incompatible options: " + "jacobian = \"" + jacobian + "\" linear solver = \"" + linear_solver + + "\". For a banded jacobian " + "please use the SUNLinSol_Band linear solver" + ); + } else if ((linear_solver == "SUNLinSol_SPBCGS" || linear_solver == "SUNLinSol_SPFGMR" || linear_solver == "SUNLinSol_SPGMR" || diff --git a/pybamm/solvers/c_solvers/idaklu/options.hpp b/pybamm/solvers/c_solvers/idaklu/options.hpp index 2fc807e48f..ecaffe8307 100644 --- a/pybamm/solvers/c_solvers/idaklu/options.hpp +++ b/pybamm/solvers/c_solvers/idaklu/options.hpp @@ -6,6 +6,7 @@ struct Options { bool print_stats; bool using_sparse_matrix; + bool using_banded_matrix; bool using_iterative_solver; std::string jacobian; std::string linear_solver; // klu, lapack, spbcg diff --git a/pybamm/solvers/casadi_solver.py b/pybamm/solvers/casadi_solver.py index 8c9248389d..75c9fc1c87 100644 --- a/pybamm/solvers/casadi_solver.py +++ b/pybamm/solvers/casadi_solver.py @@ -322,7 +322,6 @@ def _solve_for_event(self, coarse_solution): inputs = casadi.vertcat(*[x for x in inputs_dict.values()]) def find_t_event(sol, typ): - # Check most recent y to see if any events have been crossed if model.terminate_events_eval: y_last = sol.all_ys[-1][:, -1] diff --git a/pybamm/solvers/idaklu_solver.py b/pybamm/solvers/idaklu_solver.py index 1c2822d968..f29d7faf29 100644 --- a/pybamm/solvers/idaklu_solver.py +++ b/pybamm/solvers/idaklu_solver.py @@ -52,11 +52,12 @@ class IDAKLUSolver(pybamm.BaseSolver): # print statistics of the solver after every solve "print_stats": False, - # jacobian form, can be "none", "dense", "sparse", "matrix-free" + # jacobian form, can be "none", "dense", + # "banded", "sparse", "matrix-free" "jacobian": "sparse", # name of sundials linear solver to use options are: "SUNLinSol_KLU", - # "SUNLinSol_Dense", "SUNLinSol_SPBCGS", + # "SUNLinSol_Dense", "SUNLinSol_Band", "SUNLinSol_SPBCGS", # "SUNLinSol_SPFGMR", "SUNLinSol_SPGMR", "SUNLinSol_SPTFQMR", "linear_solver": "SUNLinSol_KLU", @@ -89,7 +90,6 @@ def __init__( extrap_tol=None, options=None, ): - # set default options, # (only if user does not supply) default_options = { @@ -275,7 +275,10 @@ def resfn(t, y, inputs, ydot): - cj_casadi * mass_matrix ], ) + jac_times_cjmass_sparsity = jac_times_cjmass.sparsity_out(0) + jac_bw_lower = jac_times_cjmass_sparsity.bw_lower() + jac_bw_upper = jac_times_cjmass_sparsity.bw_upper() jac_times_cjmass_nnz = jac_times_cjmass_sparsity.nnz() jac_times_cjmass_colptrs = np.array( jac_times_cjmass_sparsity.colind(), dtype=np.int64 @@ -448,6 +451,8 @@ def sensfn(resvalS, t, y, inputs, yp, yS, ypS): sensfn = idaklu.generate_function(sensfn.serialize()) self._setup = { + "jac_bandwidth_upper": jac_bw_upper, + "jac_bandwidth_lower": jac_bw_lower, "rhs_algebraic": rhs_algebraic, "jac_times_cjmass": jac_times_cjmass, "jac_times_cjmass_colptrs": jac_times_cjmass_colptrs, @@ -471,6 +476,8 @@ def sensfn(resvalS, t, y, inputs, yp, yS, ypS): self._setup["jac_times_cjmass_colptrs"], self._setup["jac_times_cjmass_rowvals"], self._setup["jac_times_cjmass_nnz"], + jac_bw_lower, + jac_bw_upper, self._setup["jac_rhs_algebraic_action"], self._setup["mass_action"], self._setup["sensfn"], diff --git a/pybamm/solvers/jax_bdf_solver.py b/pybamm/solvers/jax_bdf_solver.py index 674470b538..7449180938 100644 --- a/pybamm/solvers/jax_bdf_solver.py +++ b/pybamm/solvers/jax_bdf_solver.py @@ -1039,7 +1039,7 @@ def jax_bdf_integrate(func, y0, t_eval, *args, rtol=1e-6, atol=1e-6, mass=None): """ if not pybamm.have_jax(): raise ModuleNotFoundError( - "Jax or jaxlib is not installed, please see https://pybamm.readthedocs.io/en/latest/install/GNU-linux.html#optional-jaxsolver" # noqa: E501 + "Jax or jaxlib is not installed, please see https://pybamm.readthedocs.io/en/latest/source/user_guide/installation/GNU-linux.html#optional-jaxsolver" # noqa: E501 ) def _check_arg(arg): diff --git a/pybamm/solvers/jax_solver.py b/pybamm/solvers/jax_solver.py index 35097fea95..748e1cb39e 100644 --- a/pybamm/solvers/jax_solver.py +++ b/pybamm/solvers/jax_solver.py @@ -60,7 +60,7 @@ def __init__( ): if not pybamm.have_jax(): raise ModuleNotFoundError( - "Jax or jaxlib is not installed, please see https://pybamm.readthedocs.io/en/latest/install/GNU-linux.html#optional-jaxsolver" # noqa: E501 + "Jax or jaxlib is not installed, please see https://pybamm.readthedocs.io/en/latest/source/user_guide/installation/GNU-linux.html#optional-jaxsolver" # noqa: E501 ) # note: bdf solver itself calculates consistent initial conditions so can set diff --git a/pybamm/solvers/processed_variable.py b/pybamm/solvers/processed_variable.py index 9043098d82..8905a13d6e 100644 --- a/pybamm/solvers/processed_variable.py +++ b/pybamm/solvers/processed_variable.py @@ -362,14 +362,9 @@ def initialise_2D(self): # assign attributes for reference self.entries = entries self.dimensions = 2 - if self.first_dimension == "r" and self.second_dimension == "R": - # for an r-R variable, must leave r nondimensional as it was scaled using - # R - first_length_scale = 1 - else: - first_length_scale = self.get_spatial_scale( - self.first_dimension, self.domain[0] - ) + first_length_scale = self.get_spatial_scale( + self.first_dimension, self.domain[0] + ) first_dim_pts_for_interp = first_dim_pts * first_length_scale second_length_scale = self.get_spatial_scale( diff --git a/pybamm/solvers/solution.py b/pybamm/solvers/solution.py index 090673cfa9..bf2e310096 100644 --- a/pybamm/solvers/solution.py +++ b/pybamm/solvers/solution.py @@ -465,7 +465,7 @@ def update(self, variables): # Iterate through all models, some may be in the list several times and # therefore only get set up once vars_casadi = [] - for (i, (model, ys, inputs, var_pybamm)) in enumerate( + for i, (model, ys, inputs, var_pybamm) in enumerate( zip(self.all_models, self.all_ys, self.all_inputs, vars_pybamm) ): if isinstance(var_pybamm, pybamm.ExplicitTimeIntegral): diff --git a/pybamm/spatial_methods/finite_volume.py b/pybamm/spatial_methods/finite_volume.py index b8f5bf6a80..90692d8939 100644 --- a/pybamm/spatial_methods/finite_volume.py +++ b/pybamm/spatial_methods/finite_volume.py @@ -849,14 +849,12 @@ def boundary_value_or_flux(self, symbol, discretised_child, bcs=None): # Derivation of extrapolation formula can be found at: # https://github.com/Scottmar93/extrapolation-coefficents/tree/master if isinstance(symbol, pybamm.BoundaryValue): - if use_bcs and pybamm.has_bc_of_form(child, symbol.side, bcs, "Dirichlet"): # just use the value from the bc: f(x*) sub_matrix = csr_matrix((1, prim_pts)) additive = bcs[child][symbol.side][0] elif symbol.side == "left": - if extrap_order == "linear": # to find value at x* use formula: # f(x*) = f_1 - (dx0 / dx1) (f_2 - f_1) @@ -876,7 +874,6 @@ def boundary_value_or_flux(self, symbol, discretised_child, bcs=None): additive = pybamm.Scalar(0) elif extrap_order == "quadratic": - if use_bcs and pybamm.has_bc_of_form( child, symbol.side, bcs, "Neumann" ): @@ -903,9 +900,7 @@ def boundary_value_or_flux(self, symbol, discretised_child, bcs=None): raise NotImplementedError elif symbol.side == "right": - if extrap_order == "linear": - if use_bcs and pybamm.has_bc_of_form( child, symbol.side, bcs, "Neumann" ): @@ -928,7 +923,6 @@ def boundary_value_or_flux(self, symbol, discretised_child, bcs=None): ) additive = pybamm.Scalar(0) elif extrap_order == "quadratic": - if use_bcs and pybamm.has_bc_of_form( child, symbol.side, bcs, "Neumann" ): @@ -963,14 +957,12 @@ def boundary_value_or_flux(self, symbol, discretised_child, bcs=None): raise NotImplementedError elif isinstance(symbol, pybamm.BoundaryGradient): - if use_bcs and pybamm.has_bc_of_form(child, symbol.side, bcs, "Neumann"): # just use the value from the bc: f'(x*) sub_matrix = csr_matrix((1, prim_pts)) additive = bcs[child][symbol.side][0] elif symbol.side == "left": - if extrap_order == "linear": # f'(x*) = (f_2 - f_1) / dx1 sub_matrix = (1 / dx1) * csr_matrix( @@ -979,7 +971,6 @@ def boundary_value_or_flux(self, symbol, discretised_child, bcs=None): additive = pybamm.Scalar(0) elif extrap_order == "quadratic": - a = -(2 * dx0 + 2 * dx1 + dx2) / (dx1**2 + dx1 * dx2) b = (2 * dx0 + dx1 + dx2) / (dx1 * dx2) c = -(2 * dx0 + dx1) / (dx1 * dx2 + dx2**2) @@ -992,7 +983,6 @@ def boundary_value_or_flux(self, symbol, discretised_child, bcs=None): raise NotImplementedError elif symbol.side == "right": - if extrap_order == "linear": # use formula: # f'(x*) = (f_N - f_Nm1) / dxNm1 diff --git a/pybamm/spatial_methods/spatial_method.py b/pybamm/spatial_methods/spatial_method.py index 166c00fc3b..a7e5dfc164 100644 --- a/pybamm/spatial_methods/spatial_method.py +++ b/pybamm/spatial_methods/spatial_method.py @@ -21,7 +21,6 @@ class SpatialMethod: """ def __init__(self, options=None): - self.options = {"extrapolation": {"order": "linear", "use bcs": False}} # update double-layered dict diff --git a/pybamm/version.py b/pybamm/version.py index e153821edb..029e0b2224 100644 --- a/pybamm/version.py +++ b/pybamm/version.py @@ -1 +1 @@ -__version__ = "23.1" +__version__ = "23.2" diff --git a/run-tests.py b/run-tests.py index 9c1094a56b..a564bf16fa 100755 --- a/run-tests.py +++ b/run-tests.py @@ -58,30 +58,6 @@ def run_code_tests(executable=False, folder: str = "unit", interpreter="python") sys.exit(ret) -def run_flake8(): - """ - Runs flake8 in a subprocess, exits if it doesn't finish. - """ - print("Running flake8 ... ") - sys.stdout.flush() - p = subprocess.Popen(["flake8"], stderr=subprocess.PIPE) - try: - ret = p.wait() - except KeyboardInterrupt: - try: - p.terminate() - except OSError: - pass - p.wait() - print("") - sys.exit(1) - if ret == 0: - print("ok") - else: - print("FAILED") - sys.exit(ret) - - def run_doc_tests(): """ Checks if the documentation can be built, runs any doctests (currently not @@ -315,24 +291,27 @@ def export_notebook(ipath, opath): description="Run unit tests for PyBaMM.", epilog="To run individual unit tests, use e.g. '$ tests/unit/test_timer.py'", ) + # Unit tests + parser.add_argument( + "--integration", + action="store_true", + help="Run integration tests using the python interpreter.", + ) parser.add_argument( "--unit", action="store_true", help="Run unit tests using the `python` interpreter.", ) parser.add_argument( - "--nosub", + "--all", action="store_true", - help="Run unit tests without starting a subprocess.", + help="Run all tests (unit and integration) using the `python` interpreter.", ) - # Daily tests vs unit tests parser.add_argument( - "--folder", - nargs=1, - default=["unit"], - choices=["unit", "integration", "all"], - help="Which folder to run the tests from.", + "--nosub", + action="store_true", + help="Run unit tests without starting a subprocess.", ) # Notebook tests parser.add_argument( @@ -346,9 +325,11 @@ def export_notebook(ipath, opath): metavar=("in", "out"), help="Export a Jupyter notebook to a Python file for manual testing.", ) - # Doctests + # Flake8 (deprecated) parser.add_argument( - "--flake8", action="store_true", help="Run flake8 to check for style issues" + "--flake8", + action="store_true", + help="Run flake8 to check for style issues (deprecated, use pre-commit)", ) # Doctests parser.add_argument( @@ -360,7 +341,7 @@ def export_notebook(ipath, opath): parser.add_argument( "--quick", action="store_true", - help="Run quick checks (unit tests, flake8, docs)", + help="Run quick checks (code tests, docs)", ) # Non-standard Python interpreter name for subprocesses parser.add_argument( @@ -377,19 +358,23 @@ def export_notebook(ipath, opath): # Run tests has_run = False # Unit vs integration - folder = args.folder[0] interpreter = args.interpreter # Unit tests + if args.integration: + has_run = True + run_code_tests(True, "integration", interpreter) if args.unit: has_run = True - run_code_tests(True, folder, interpreter) + run_code_tests(True, "unit", interpreter) + if args.all: + has_run = True + run_code_tests(True, "all", interpreter) if args.nosub: has_run = True - run_code_tests(folder=folder, interpreter=interpreter) + run_code_tests(folder="unit", interpreter=interpreter) # Flake8 if args.flake8: - has_run = True - run_flake8() + raise NotImplementedError("flake8 is no longer used. Use pre-commit instead.") # Doctests if args.doctest: has_run = True @@ -404,8 +389,7 @@ def export_notebook(ipath, opath): # Combined test sets if args.quick: has_run = True - run_flake8() - run_code_tests(folder, interpreter=interpreter) + run_code_tests("all", interpreter=interpreter) run_doc_tests() # Help if not has_run: diff --git a/setup.py b/setup.py index df64abe44b..f5aa1638a7 100644 --- a/setup.py +++ b/setup.py @@ -213,9 +213,10 @@ def compile_KLU(): "pydata-sphinx-theme", "sphinx_design", "sphinx-copybutton", + "myst-parser", ], # For doc generation "dev": [ - "flake8>=3", # For code style checking + "pre-commit", # For code style checking "black", # For code style auto-formatting ], }, diff --git a/tests/integration/test_models/standard_model_tests.py b/tests/integration/test_models/standard_model_tests.py index 86c9ab909e..8f587fc400 100644 --- a/tests/integration/test_models/standard_model_tests.py +++ b/tests/integration/test_models/standard_model_tests.py @@ -98,7 +98,6 @@ def test_outputs(self): def test_sensitivities( self, param_name, param_value, output_name="Terminal voltage [V]" ): - self.parameter_values.update({param_name: param_value}) Crate = abs( self.parameter_values["Current function [A]"] @@ -181,7 +180,6 @@ def __init__(self, model, parameter_values=None, disc=None): def evaluate_model(self, to_python=False, to_jax=False): result = np.empty((0, 1)) for eqn in [self.model.concatenated_rhs, self.model.concatenated_algebraic]: - y = self.model.concatenated_initial_conditions.evaluate(t=0) if to_python: evaluator = pybamm.EvaluatorPython(eqn) diff --git a/tests/integration/test_models/standard_output_tests.py b/tests/integration/test_models/standard_output_tests.py index 4f0be09b14..8920dd6d11 100644 --- a/tests/integration/test_models/standard_output_tests.py +++ b/tests/integration/test_models/standard_output_tests.py @@ -644,7 +644,6 @@ def test_average_potential_differences(self): ) def test_gradient_splitting(self): - t, x_n, x_s, x_p, x = self.t, self.x_n, self.x_s, self.x_p, self.x grad_phi_e_combined = np.concatenate( ( diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py index 0a722f3af5..a0bd97186b 100644 --- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py +++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py @@ -59,6 +59,14 @@ def test_basic_processing(self): ) modeltest.test_all() + def test_basic_processing_tuple(self): + options = {"particle size": ("single", "distribution")} + model = pybamm.lithium_ion.DFN(options) + modeltest = tests.StandardModelTest( + model, parameter_values=self.params, var_pts=self.var_pts + ) + modeltest.test_all() + def test_uniform_profile(self): options = {"particle size": "distribution", "particle": "uniform profile"} model = pybamm.lithium_ion.DFN(options) diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py index aa8674acca..6679d9bf9b 100644 --- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py +++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py @@ -48,6 +48,23 @@ def test_differential_surface_form(self): modeltest = tests.StandardModelTest(model) modeltest.test_all() + def test_current_sigmoid_ocp(self): + options = {"open circuit potential": ("current sigmoid", "single")} + model = pybamm.lithium_ion.MPM(options) + parameter_values = pybamm.ParameterValues("Chen2020") + parameter_values = pybamm.get_size_distribution_parameters(parameter_values) + parameter_values.update( + { + "Negative electrode lithiation OCP [V]" + "": parameter_values["Negative electrode OCP [V]"], + "Negative electrode delithiation OCP [V]" + "": parameter_values["Negative electrode OCP [V]"], + }, + check_already_exists=False, + ) + modeltest = tests.StandardModelTest(model, parameter_values=parameter_values) + modeltest.test_all(skip_output_tests=True) + def test_voltage_control(self): options = {"operating mode": "voltage"} model = pybamm.lithium_ion.MPM(options) 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 b2a6966348..edb45c6ba2 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 @@ -227,7 +227,6 @@ def test_discretisation(self): self.assertEqual(j.evaluate(None, y).shape, (whole_cell_mesh.npts, 1)) def test_diff_c_e_lead_acid(self): - # With intercalation param = pybamm.LeadAcidParameters() model_n = pybamm.kinetics.SymmetricButlerVolmer( @@ -298,7 +297,6 @@ def j_p(c_e): ) def test_diff_delta_phi_e_lead_acid(self): - # With intercalation param = pybamm.LeadAcidParameters() model_n = pybamm.kinetics.SymmetricButlerVolmer( 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 c1970d527e..c08408b539 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 @@ -28,7 +28,7 @@ def setUp(self): "Negative electrode temperature": 0, "Positive electrode temperature": 0, } - self.options = {"particle size": "single"} + self.options = pybamm.BatteryModelOptions({"particle size": "single"}) def tearDown(self): del self.variables diff --git a/tests/integration/test_spatial_methods/test_spectral_volume.py b/tests/integration/test_spatial_methods/test_spectral_volume.py index 5a3ef6e88a..727f9aa948 100644 --- a/tests/integration/test_spatial_methods/test_spectral_volume.py +++ b/tests/integration/test_spatial_methods/test_spectral_volume.py @@ -324,7 +324,6 @@ def get_error(m): if __name__ == "__main__": - print("Add -v for more debug output") import sys diff --git a/tests/unit/test_experiments/test_experiment.py b/tests/unit/test_experiments/test_experiment.py index af0d5ea23c..eca5621267 100644 --- a/tests/unit/test_experiments/test_experiment.py +++ b/tests/unit/test_experiments/test_experiment.py @@ -21,162 +21,178 @@ def test_read_strings(self): experiment = pybamm.Experiment( [ - "Discharge at 1C for 0.5 hours [tag1]", - "Discharge at C/20 for 0.5 hours [tag2,tag3]", - "Charge at 0.5 C for 45 minutes", - "Discharge at 1 A for 0.5 hours", - "Charge at 200 mA for 45 minutes (1 minute period)", - "Discharge at 1W for 0.5 hours", + "Discharge at 1C for 0.5 hours at 27oC [tag1]", + "Discharge at C/20 for 0.5 hours at 29oC [tag2,tag3]", + "Charge at 0.5 C for 45 minutes at -5oC", + "Discharge at 1 A for 0.5 hours at -5.1oC", + "Charge at 200 mA for 45 minutes at 10.2oC (1 minute period)", + "Discharge at 1W for 0.5 hours at -10.4oC", "Charge at 200mW for 45 minutes", "Rest for 10 minutes (5 minute period) [tag1,tag3]", "Hold at 1V for 20 seconds", "Charge at 1 C until 4.1V", "Hold at 4.1 V until 50mA", "Hold at 3V until C/50", - "Discharge at C/3 for 2 hours or until 2.5 V", - "Run US06 (A)", + "Discharge at C/3 for 2 hours or until 2.5 V at 26oC", + "Run US06 (A) at -5oC", "Run US06 (V) for 5 minutes", "Run US06 (W) for 0.5 hours", ], + temperature=43, drive_cycles={"US06": drive_cycle}, period="20 seconds", ) + expected_result = [ + { + "C-rate input [-]": 1.0, + "type": "C-rate", + "time": 1800.0, + "period": 20.0, + "temperature": 27.0, + "dc_data": None, + "string": "Discharge at 1C for 0.5 hours at 27oC", + "events": None, + "tags": ["tag1"], + }, + { + "C-rate input [-]": 0.05, + "type": "C-rate", + "time": 1800.0, + "period": 20.0, + "temperature": 29.0, + "dc_data": None, + "string": "Discharge at C/20 for 0.5 hours at 29oC", + "events": None, + "tags": ["tag2", "tag3"], + }, + { + "C-rate input [-]": -0.5, + "type": "C-rate", + "time": 2700.0, + "period": 20.0, + "temperature": -5.0, + "dc_data": None, + "string": "Charge at 0.5 C for 45 minutes at -5oC", + "events": None, + "tags": None, + }, + { + "Current input [A]": 1.0, + "type": "current", + "time": 1800.0, + "period": 20.0, + "temperature": -5.1, + "dc_data": None, + "string": "Discharge at 1 A for 0.5 hours at -5.1oC", + "events": None, + "tags": None, + }, + { + "Current input [A]": -0.2, + "type": "current", + "time": 2700.0, + "period": 60.0, + "temperature": 10.2, + "dc_data": None, + "string": "Charge at 200 mA for 45 minutes at 10.2oC", + "events": None, + "tags": None, + }, + { + "Power input [W]": 1.0, + "type": "power", + "time": 1800.0, + "period": 20.0, + "temperature": -10.4, + "dc_data": None, + "string": "Discharge at 1W for 0.5 hours at -10.4oC", + "events": None, + "tags": None, + }, + { + "Power input [W]": -0.2, + "type": "power", + "time": 2700.0, + "period": 20.0, + "temperature": 43, + "dc_data": None, + "string": "Charge at 200mW for 45 minutes", + "events": None, + "tags": None, + }, + { + "Current input [A]": 0, + "type": "current", + "time": 600.0, + "period": 300.0, + "temperature": 43, + "dc_data": None, + "string": "Rest for 10 minutes", + "events": None, + "tags": ["tag1", "tag3"], + }, + { + "Voltage input [V]": 1, + "type": "voltage", + "time": 20.0, + "period": 20.0, + "temperature": 43, + "dc_data": None, + "string": "Hold at 1V for 20 seconds", + "events": None, + "tags": None, + }, + { + "C-rate input [-]": -1, + "type": "C-rate", + "time": None, + "period": 20.0, + "temperature": 43, + "dc_data": None, + "string": "Charge at 1 C until 4.1V", + "events": {"Voltage input [V]": 4.1, "type": "voltage"}, + "tags": None, + }, + { + "Voltage input [V]": 4.1, + "type": "voltage", + "time": None, + "period": 20.0, + "temperature": 43, + "dc_data": None, + "string": "Hold at 4.1 V until 50mA", + "events": {"Current input [A]": 0.05, "type": "current"}, + "tags": None, + }, + { + "Voltage input [V]": 3, + "type": "voltage", + "time": None, + "period": 20.0, + "temperature": 43, + "dc_data": None, + "string": "Hold at 3V until C/50", + "events": {"C-rate input [-]": 0.02, "type": "C-rate"}, + "tags": None, + }, + { + "C-rate input [-]": 1 / 3, + "type": "C-rate", + "time": 7200.0, + "period": 20.0, + "temperature": 26, + "dc_data": None, + "string": "Discharge at C/3 for 2 hours or until 2.5 V at 26oC", + "events": {"Voltage input [V]": 2.5, "type": "voltage"}, + "tags": None, + }, + ] + + for expected, actual in zip(expected_result, experiment.operating_conditions): + for k in expected.keys(): + # useful form for debugging + self.assertEqual([k, expected[k]], [k, actual[k]]) - self.assertEqual( - experiment.operating_conditions[:-3], - [ - { - "C-rate input [-]": 1, - "type": "C-rate", - "time": 1800.0, - "period": 20.0, - "dc_data": None, - "string": "Discharge at 1C for 0.5 hours", - "events": None, - "tags": ["tag1"], - }, - { - "C-rate input [-]": 0.05, - "type": "C-rate", - "time": 1800.0, - "period": 20.0, - "dc_data": None, - "string": "Discharge at C/20 for 0.5 hours", - "events": None, - "tags": ["tag2", "tag3"], - }, - { - "C-rate input [-]": -0.5, - "type": "C-rate", - "time": 2700.0, - "period": 20.0, - "dc_data": None, - "string": "Charge at 0.5 C for 45 minutes", - "events": None, - "tags": None, - }, - { - "Current input [A]": 1, - "type": "current", - "time": 1800.0, - "period": 20.0, - "dc_data": None, - "string": "Discharge at 1 A for 0.5 hours", - "events": None, - "tags": None, - }, - { - "Current input [A]": -0.2, - "type": "current", - "time": 2700.0, - "period": 60.0, - "dc_data": None, - "string": "Charge at 200 mA for 45 minutes", - "events": None, - "tags": None, - }, - { - "Power input [W]": 1, - "type": "power", - "time": 1800.0, - "period": 20.0, - "dc_data": None, - "string": "Discharge at 1W for 0.5 hours", - "events": None, - "tags": None, - }, - { - "Power input [W]": -0.2, - "type": "power", - "time": 2700.0, - "period": 20.0, - "dc_data": None, - "string": "Charge at 200mW for 45 minutes", - "events": None, - "tags": None, - }, - { - "Current input [A]": 0, - "type": "current", - "time": 600.0, - "period": 300.0, - "dc_data": None, - "string": "Rest for 10 minutes", - "events": None, - "tags": ["tag1", "tag3"], - }, - { - "Voltage input [V]": 1, - "type": "voltage", - "time": 20.0, - "period": 20.0, - "dc_data": None, - "string": "Hold at 1V for 20 seconds", - "events": None, - "tags": None, - }, - { - "C-rate input [-]": -1, - "type": "C-rate", - "time": None, - "period": 20.0, - "dc_data": None, - "string": "Charge at 1 C until 4.1V", - "events": {"Voltage input [V]": 4.1, "type": "voltage"}, - "tags": None, - }, - { - "Voltage input [V]": 4.1, - "type": "voltage", - "time": None, - "period": 20.0, - "dc_data": None, - "string": "Hold at 4.1 V until 50mA", - "events": {"Current input [A]": 0.05, "type": "current"}, - "tags": None, - }, - { - "Voltage input [V]": 3, - "type": "voltage", - "time": None, - "period": 20.0, - "dc_data": None, - "string": "Hold at 3V until C/50", - "events": {"C-rate input [-]": 0.02, "type": "C-rate"}, - "tags": None, - }, - { - "C-rate input [-]": 1 / 3, - "type": "C-rate", - "time": 7200.0, - "period": 20.0, - "dc_data": None, - "string": "Discharge at C/3 for 2 hours or until 2.5 V", - "events": {"Voltage input [V]": 2.5, "type": "voltage"}, - "tags": None, - }, - ], - ) # Calculation for operating conditions of drive cycle time_0 = drive_cycle[:, 0][-1] period_0 = np.min(np.diff(drive_cycle[:, 0])) @@ -193,6 +209,7 @@ def test_read_strings(self): self.assertEqual(experiment.operating_conditions[-3]["type"], "current") self.assertEqual(experiment.operating_conditions[-3]["time"], time_0) self.assertEqual(experiment.operating_conditions[-3]["period"], period_0) + self.assertEqual(experiment.operating_conditions[-3]["temperature"], -5) self.assertEqual(experiment.operating_conditions[-3]["tags"], None) np.testing.assert_array_equal( experiment.operating_conditions[-2]["dc_data"], drive_cycle_1 @@ -214,50 +231,56 @@ def test_read_strings_cccv_combined(self): experiment = pybamm.Experiment( [ ( - "Discharge at C/20 for 0.5 hours", - "Charge at 0.5 C until 1V", - "Hold at 1V until C/50", + "Discharge at C/20 for 0.5 hours at 34 oC", + "Charge at 0.5 C until 1V at 32 oC", + "Hold at 1V until C/50 at 32 oC", "Discharge at C/20 for 0.5 hours", ), ], cccv_handling="ode", ) - self.assertEqual( - experiment.operating_conditions, - [ - { - "C-rate input [-]": 0.05, - "type": "C-rate", - "time": 1800.0, - "period": 60.0, - "dc_data": None, - "string": "Discharge at C/20 for 0.5 hours", - "events": None, - "tags": None, - }, - { - "type": "CCCV", - "C-rate input [-]": -0.5, - "Voltage input [V]": 1, - "time": None, - "period": 60.0, - "dc_data": None, - "string": "Charge at 0.5 C until 1V then hold at 1V until C/50", - "events": {"C-rate input [-]": 0.02, "type": "C-rate"}, - "tags": None, - }, - { - "C-rate input [-]": 0.05, - "type": "C-rate", - "time": 1800.0, - "period": 60.0, - "dc_data": None, - "string": "Discharge at C/20 for 0.5 hours", - "events": None, - "tags": None, - }, - ], - ) + + expected_result = [ + { + "C-rate input [-]": 0.05, + "type": "C-rate", + "time": 1800.0, + "period": 60.0, + "temperature": 34.0, + "dc_data": None, + "string": "Discharge at C/20 for 0.5 hours at 34 oC", + "events": None, + "tags": None, + }, + { + "type": "CCCV", + "C-rate input [-]": -0.5, + "Voltage input [V]": 1, + "time": None, + "period": 60.0, + "temperature": 32.0, + "dc_data": None, + "string": "Charge at 0.5 C until 1V at 32 oC " + "then hold at 1V until C/50 at 32 oC", + "events": {"C-rate input [-]": 0.02, "type": "C-rate"}, + "tags": None, + }, + { + "C-rate input [-]": 0.05, + "type": "C-rate", + "time": 1800.0, + "period": 60.0, + "temperature": None, + "dc_data": None, + "string": "Discharge at C/20 for 0.5 hours", + "events": None, + "tags": None, + }, + ] + + for expected, actual in zip(expected_result, experiment.operating_conditions): + for k in expected.keys(): + self.assertEqual([k, expected[k]], [k, actual[k]]) # Cases that don't quite match shouldn't do CCCV setup experiment = pybamm.Experiment( @@ -275,6 +298,7 @@ def test_read_strings_cccv_combined(self): "type": "C-rate", "time": None, "period": 60.0, + "temperature": None, "dc_data": None, "string": "Charge at 0.5 C until 2V", "events": {"Voltage input [V]": 2, "type": "voltage"}, @@ -285,6 +309,7 @@ def test_read_strings_cccv_combined(self): "type": "voltage", "time": None, "period": 60.0, + "temperature": None, "dc_data": None, "string": "Hold at 1V until C/50", "events": {"C-rate input [-]": 0.02, "type": "C-rate"}, @@ -307,6 +332,7 @@ def test_read_strings_cccv_combined(self): "type": "C-rate", "time": 120.0, "period": 60.0, + "temperature": None, "dc_data": None, "string": "Charge at 0.5 C for 2 minutes", "events": None, @@ -317,6 +343,7 @@ def test_read_strings_cccv_combined(self): "type": "voltage", "time": None, "period": 60.0, + "temperature": None, "dc_data": None, "string": "Hold at 1V until C/50", "events": {"C-rate input [-]": 0.02, "type": "C-rate"}, @@ -341,6 +368,7 @@ def test_cycle_unpacking(self): "type": "C-rate", "time": 1800.0, "period": 60.0, + "temperature": None, "dc_data": None, "string": "Discharge at C/20 for 0.5 hours", "events": None, @@ -351,6 +379,7 @@ def test_cycle_unpacking(self): "type": "C-rate", "time": 2700.0, "period": 60.0, + "temperature": None, "dc_data": None, "string": "Charge at C/5 for 45 minutes", "events": None, @@ -361,6 +390,7 @@ def test_cycle_unpacking(self): "type": "C-rate", "time": 1800.0, "period": 60.0, + "temperature": None, "dc_data": None, "string": "Discharge at C/20 for 0.5 hours", "events": None, @@ -371,6 +401,7 @@ def test_cycle_unpacking(self): "type": "C-rate", "time": 2700.0, "period": 60.0, + "temperature": None, "dc_data": None, "string": "Charge at C/5 for 45 minutes", "events": None, @@ -408,7 +439,7 @@ def test_bad_strings(self): TypeError, "Operating conditions should be strings or tuples of strings" ): pybamm.Experiment([(1, 2, 3)]) - with self.assertRaisesRegex(ValueError, "Operating conditions must contain"): + with self.assertRaisesRegex(ValueError, "Operating conditions must"): pybamm.Experiment(["Discharge at 1 A at 2 hours"]) with self.assertRaisesRegex(ValueError, "Instruction must be"): pybamm.Experiment(["Run at 1 A for 2 hours"]) @@ -418,12 +449,35 @@ def test_bad_strings(self): pybamm.Experiment(["Run at at 1 A for 2 hours"]) with self.assertRaisesRegex(ValueError, "Instruction must be"): pybamm.Experiment(["Play at 1 A for 2 hours"]) + with self.assertRaisesRegex(ValueError, "Operating conditions must"): + pybamm.Experiment(["Do at 1 A"]) + with self.assertRaisesRegex(ValueError, "Instruction must be"): + pybamm.Experiment(["Run US06 at 1 A"]) with self.assertRaisesRegex(ValueError, "Instruction"): pybamm.Experiment(["Cell Charge at 1 A for 2 hours"]) with self.assertRaisesRegex(ValueError, "units must be"): pybamm.Experiment(["Discharge at 1 B for 2 hours"]) with self.assertRaisesRegex(ValueError, "time units must be"): pybamm.Experiment(["Discharge at 1 A for 2 years"]) + with self.assertRaisesRegex(ValueError, "More than one temperature found"): + pybamm.Experiment(["Discharge at 1 A for 2 hours at 25oC at 30oC"]) + with self.assertRaisesRegex( + ValueError, "The temperature for the CC and CV steps" + ): + pybamm.Experiment( + [ + ( + "Discharge at 1A until 3.2V at 24oC", + "Hold at 3.2V until C/50 at 27oC", + ) + ], + cccv_handling="ode", + ) + + with self.assertRaisesRegex( + ValueError, "Temperature not written correctly on step" + ): + pybamm.Experiment(["Discharge at 1 A for 2 hours 25oC"]) def test_termination(self): experiment = pybamm.Experiment(["Discharge at 1 C for 20 seconds"]) diff --git a/tests/unit/test_experiments/test_simulation_with_experiment.py b/tests/unit/test_experiments/test_simulation_with_experiment.py index 4f38100fba..39ac926a6a 100644 --- a/tests/unit/test_experiments/test_simulation_with_experiment.py +++ b/tests/unit/test_experiments/test_simulation_with_experiment.py @@ -55,12 +55,13 @@ def test_run_experiment(self): experiment = pybamm.Experiment( [ ( - "Discharge at C/20 for 1 hour", - "Charge at 1 A until 4.1 V", - "Hold at 4.1 V until C/2", + "Discharge at C/20 for 1 hour at 30.5oC", + "Charge at 1 A until 4.1 V at 24oC", + "Hold at 4.1 V until C/2 at 24oC", "Discharge at 2 W for 1 hour", ) - ] + ], + temperature=-14, ) model = pybamm.lithium_ion.SPM() sim = pybamm.Simulation(model, experiment=experiment) @@ -79,6 +80,22 @@ def test_run_experiment(self): sol.cycles[0].steps[3]["Power [W]"].data, 2, decimal=5 ) + np.testing.assert_array_equal( + sol.cycles[0].steps[0]["Ambient temperature [C]"].data[0], 30.5 + ) + + np.testing.assert_array_equal( + sol.cycles[0].steps[1]["Ambient temperature [C]"].data[0], 24 + ) + + np.testing.assert_array_equal( + sol.cycles[0].steps[2]["Ambient temperature [C]"].data[0], 24 + ) + + np.testing.assert_array_equal( + sol.cycles[0].steps[3]["Ambient temperature [C]"].data[0], -14 + ) + for i, step in enumerate(sol.cycles[0].steps[:-1]): len_rhs = sol.all_models[0].concatenated_rhs.size y_left = step.all_ys[-1][:len_rhs, -1] @@ -114,8 +131,8 @@ def test_run_experiment_multiple_times(self): experiment = pybamm.Experiment( [ ( - "Discharge at C/20 for 1 hour", - "Charge at C/20 until 4.1 V", + "Discharge at C/20 for 1 hour at 24oC", + "Charge at C/20 until 4.1 V at 26oC", ) ] * 3 @@ -134,9 +151,9 @@ def test_run_experiment_cccv_ode(self): experiment_2step = pybamm.Experiment( [ ( - "Discharge at C/20 for 1 hour", - "Charge at 1 A until 4.1 V", - "Hold at 4.1 V until C/2", + "Discharge at C/20 for 1 hour at 20oC", + "Charge at 1 A until 4.1 V at 24oC", + "Hold at 4.1 V until C/2 at 24oC", "Discharge at 2 W for 1 hour", ), ], @@ -144,9 +161,9 @@ def test_run_experiment_cccv_ode(self): experiment_ode = pybamm.Experiment( [ ( - "Discharge at C/20 for 1 hour", - "Charge at 1 A until 4.1 V", - "Hold at 4.1 V until C/2", + "Discharge at C/20 for 1 hour at 20oC", + "Charge at 1 A until 4.1 V at 24oC", + "Hold at 4.1 V until C/2 at 24oC", "Discharge at 2 W for 1 hour", ), ], @@ -169,6 +186,12 @@ def test_run_experiment_cccv_ode(self): solutions[1]["Current [A]"].data, decimal=0, ) + + np.testing.assert_array_equal( + solutions[0]["Ambient temperature [C]"].data, + solutions[1]["Ambient temperature [C]"].data, + ) + self.assertEqual(solutions[1].termination, "final time") @unittest.skipIf(not pybamm.have_idaklu(), "idaklu solver is not installed") @@ -209,7 +232,7 @@ def test_run_experiment_drive_cycle(self): experiment = pybamm.Experiment( [ ( - "Run drive_cycle (A)", + "Run drive_cycle (A) at 35oC", "Run drive_cycle (V)", "Run drive_cycle (W)", ) @@ -219,7 +242,7 @@ def test_run_experiment_drive_cycle(self): model = pybamm.lithium_ion.SPM() sim = pybamm.Simulation(model, experiment=experiment) sim.build_for_experiment() - self.assertIn(("Run drive_cycle (A)"), sim.op_string_to_model) + self.assertIn(("Run drive_cycle (A) at 35oC"), sim.op_string_to_model) self.assertIn(("Run drive_cycle (V)"), sim.op_string_to_model) self.assertIn(("Run drive_cycle (W)"), sim.op_string_to_model) diff --git a/tests/unit/test_expression_tree/test_averages.py b/tests/unit/test_expression_tree/test_averages.py index fbfa5526d3..1351ee3c05 100644 --- a/tests/unit/test_expression_tree/test_averages.py +++ b/tests/unit/test_expression_tree/test_averages.py @@ -178,7 +178,6 @@ def test_x_average(self): ) def test_size_average(self): - # no domain a = pybamm.Scalar(1) average_a = pybamm.size_average(a) diff --git a/tests/unit/test_expression_tree/test_d_dt.py b/tests/unit/test_expression_tree/test_d_dt.py index 9171452406..57ee480b5b 100644 --- a/tests/unit/test_expression_tree/test_d_dt.py +++ b/tests/unit/test_expression_tree/test_d_dt.py @@ -25,7 +25,6 @@ def test_time_derivative(self): self.assertEqual(a.evaluate(t=1), 2) def test_time_derivative_of_variable(self): - a = (pybamm.Variable("a")).diff(pybamm.t) self.assertIsInstance(a, pybamm.VariableDot) self.assertEqual(a.name, "a'") @@ -41,7 +40,6 @@ def test_time_derivative_of_variable(self): a = (pybamm.Variable("a")).diff(pybamm.t).diff(pybamm.t) def test_time_derivative_of_state_vector(self): - sv = pybamm.StateVector(slice(0, 10)) y_dot = np.linspace(0, 2, 19) diff --git a/tests/unit/test_expression_tree/test_interpolant.py b/tests/unit/test_expression_tree/test_interpolant.py index 8c1f9d274f..e22cda85aa 100644 --- a/tests/unit/test_expression_tree/test_interpolant.py +++ b/tests/unit/test_expression_tree/test_interpolant.py @@ -44,7 +44,6 @@ def test_errors(self): ) def test_warnings(self): - with self.assertWarnsRegex(Warning, "cubic spline"): pybamm.Interpolant( np.linspace(0, 1, 10), diff --git a/tests/unit/test_expression_tree/test_parameter.py b/tests/unit/test_expression_tree/test_parameter.py index 191d02929c..2e3a7d8a5d 100644 --- a/tests/unit/test_expression_tree/test_parameter.py +++ b/tests/unit/test_expression_tree/test_parameter.py @@ -69,7 +69,6 @@ def test_get_children_domains(self): pybamm.FunctionParameter("a", {"var": var, "var 2": var_2}) def test_set_input_names(self): - var = pybamm.Variable("var") func = pybamm.FunctionParameter("a", {"var": var}) diff --git a/tests/unit/test_geometry/test_battery_geometry.py b/tests/unit/test_geometry/test_battery_geometry.py index 9190e6a0c4..9b0266b1b8 100644 --- a/tests/unit/test_geometry/test_battery_geometry.py +++ b/tests/unit/test_geometry/test_battery_geometry.py @@ -59,10 +59,10 @@ def test_geometry(self): self.assertEqual(geometry["negative primary particle"]["r_n_prim"]["max"], 1) self.assertEqual(geometry["negative secondary particle"]["r_n_sec"]["min"], 0) self.assertEqual(geometry["negative secondary particle"]["r_n_sec"]["max"], 1) - self.assertEqual(geometry["positive primary particle"]["r_n_prim"]["min"], 0) - self.assertEqual(geometry["positive primary particle"]["r_n_prim"]["max"], 1) - self.assertEqual(geometry["positive secondary particle"]["r_n_sec"]["min"], 0) - self.assertEqual(geometry["positive secondary particle"]["r_n_sec"]["max"], 1) + self.assertEqual(geometry["positive primary particle"]["r_p_prim"]["min"], 0) + self.assertEqual(geometry["positive primary particle"]["r_p_prim"]["max"], 1) + self.assertEqual(geometry["positive secondary particle"]["r_p_sec"]["min"], 0) + self.assertEqual(geometry["positive secondary particle"]["r_p_sec"]["max"], 1) def test_geometry_error(self): with self.assertRaisesRegex(pybamm.GeometryError, "Invalid current"): diff --git a/tests/unit/test_meshes/test_one_dimensional_submesh.py b/tests/unit/test_meshes/test_one_dimensional_submesh.py index 7356da95f6..2d72caabd0 100644 --- a/tests/unit/test_meshes/test_one_dimensional_submesh.py +++ b/tests/unit/test_meshes/test_one_dimensional_submesh.py @@ -326,7 +326,7 @@ def test_mesh_creation_no_parameters(self): ) # check Chebyshev subdivision locations - for (a, b) in zip( + for a, b in zip( mesh["negative particle"].edges.tolist(), [0, 0.075, 0.225, 0.3, 0.475, 0.825, 1], ): @@ -343,7 +343,7 @@ def test_mesh_creation_no_parameters(self): # create mesh mesh = pybamm.Mesh(geometry, submesh_types, var_pts) - for (a, b) in zip( + for a, b in zip( mesh["negative particle"].edges.tolist(), [0.0, 0.125, 0.375, 0.5, 0.625, 0.875, 1.0], ): diff --git a/tests/unit/test_meshes/test_scikit_fem_submesh.py b/tests/unit/test_meshes/test_scikit_fem_submesh.py index 63f57b437e..217a059432 100644 --- a/tests/unit/test_meshes/test_scikit_fem_submesh.py +++ b/tests/unit/test_meshes/test_scikit_fem_submesh.py @@ -232,7 +232,6 @@ def test_mesh_creation(self): self.assertEqual(len(mesh[domain].edges), len(mesh[domain].nodes) + 1) def test_init_failure(self): - # only one lim lims = {"x_n": {"min": pybamm.Scalar(0), "max": pybamm.Scalar(1)}} with self.assertRaises(pybamm.GeometryError): @@ -312,7 +311,6 @@ def test_mesh_creation(self): self.assertEqual(len(mesh[domain].edges), len(mesh[domain].nodes) + 1) def test_init_failure(self): - # only one lim lims = {"x_n": {"min": pybamm.Scalar(0), "max": pybamm.Scalar(1)}} with self.assertRaises(pybamm.GeometryError): 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 a48c6c5def..6afcb2458c 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 @@ -101,7 +101,6 @@ def test_timescale_lengthscale_errors(self): model.length_scales = {} def test_default_geometry(self): - model = pybamm.BaseBatteryModel({"dimensionality": 0}) self.assertEqual( model.default_geometry["current collector"]["z"]["position"], 1 diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py index c4620d687d..20e5e6e920 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py @@ -23,6 +23,17 @@ def test_well_posed_size_distribution_uniform_profile(self): options = {"particle size": "distribution", "particle": "uniform profile"} self.check_well_posedness(options) + def test_well_posed_size_distribution_tuple(self): + options = {"particle size": ("single", "distribution")} + self.check_well_posedness(options) + + def test_well_posed_current_sigmoid_ocp_with_psd(self): + options = { + "open circuit potential": "current sigmoid", + "particle size": "distribution", + } + self.check_well_posedness(options) + def test_well_posed_external_circuit_explicit_power(self): options = {"operating mode": "explicit power"} self.check_well_posedness(options) diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py index a0e390603d..033b515c6a 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py @@ -65,7 +65,6 @@ def test_known_solution_cell_capacity(self): self.assertAlmostEqual(sol["Q"].data[0], Q, places=5) def test_error(self): - param = pybamm.LithiumIonParameters() parameter_values = pybamm.ParameterValues("Ai2020") @@ -212,7 +211,6 @@ def test_initial_soc_cell_capacity(self): self.assertAlmostEqual(V, 4.2) def test_error(self): - parameter_values = pybamm.ParameterValues("Chen2020") with self.assertRaisesRegex( 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 4e78727e4e..cc5b343590 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 @@ -60,6 +60,11 @@ def test_differential_surface_form(self): model = pybamm.lithium_ion.MPM(options) model.check_well_posedness() + def test_current_sigmoid(self): + options = {"open circuit potential": "current sigmoid"} + model = pybamm.lithium_ion.MPM(options) + model.check_well_posedness() + def test_necessary_options(self): options = {"particle size": "single"} with self.assertRaises(pybamm.OptionError): diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm_half_cell.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm_half_cell.py new file mode 100644 index 0000000000..21efc176c7 --- /dev/null +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm_half_cell.py @@ -0,0 +1,80 @@ +# +# Tests for the lithium-ion MPM model +# +import pybamm +import unittest + + +class TestMPM(unittest.TestCase): + def test_well_posed(self): + options = {"thermal": "isothermal", "working electrode": "positive"} + model = pybamm.lithium_ion.MPM(options) + model.check_well_posedness() + + # Test build after init + model = pybamm.lithium_ion.MPM({"working electrode": "positive"}, build=False) + model.build_model() + model.check_well_posedness() + + def test_default_parameter_values(self): + # check default parameters are added correctly + model = pybamm.lithium_ion.MPM({"working electrode": "positive"}) + self.assertEqual( + model.default_parameter_values[ + "Positive area-weighted mean particle radius [m]" + ], + 5.3e-06, + ) + + def test_lumped_thermal_model_1D(self): + options = {"thermal": "lumped", "working electrode": "positive"} + model = pybamm.lithium_ion.MPM(options) + model.check_well_posedness() + + def test_particle_uniform(self): + options = {"particle": "uniform profile", "working electrode": "positive"} + model = pybamm.lithium_ion.MPM(options) + model.check_well_posedness() + + def test_differential_surface_form(self): + options = { + "surface form": "differential", + "working electrode": "positive", + } + model = pybamm.lithium_ion.MPM(options) + model.check_well_posedness() + + +class TestMPMExternalCircuits(unittest.TestCase): + def test_well_posed_voltage(self): + options = {"operating mode": "voltage", "working electrode": "positive"} + model = pybamm.lithium_ion.MPM(options) + model.check_well_posedness() + + def test_well_posed_power(self): + options = {"operating mode": "power", "working electrode": "positive"} + model = pybamm.lithium_ion.MPM(options) + model.check_well_posedness() + + def test_well_posed_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}) + + options = { + "operating mode": external_circuit_function, + "working electrode": "positive", + } + model = pybamm.lithium_ion.MPM(options) + 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/test_spm.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_spm.py index ee5dffe25c..65e79747bd 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_spm.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_spm.py @@ -40,9 +40,13 @@ def test_x_average_options(self): pybamm.lithium_ion.SPM(options) def test_distribution_options(self): - with self.assertRaisesRegex(pybamm.OptionError, "particle size"): + with self.assertRaisesRegex(pybamm.OptionError, "surface form"): pybamm.lithium_ion.SPM({"particle size": "distribution"}) + def test_particle_size_distribution(self): + options = {"surface form": "algebraic", "particle size": "distribution"} + self.check_well_posedness(options) + def test_new_model(self): model = pybamm.lithium_ion.SPM({"thermal": "x-full"}) new_model = model.new_copy() diff --git a/tests/unit/test_parameters/test_ecm_parameters.py b/tests/unit/test_parameters/test_ecm_parameters.py index 8c1646fc53..53e79cd16d 100644 --- a/tests/unit/test_parameters/test_ecm_parameters.py +++ b/tests/unit/test_parameters/test_ecm_parameters.py @@ -7,11 +7,10 @@ values = { "Initial SoC": 0.5, - "Initial cell temperature [degC]": 25, - "Initial jig temperature [degC]": 25, + "Initial temperature [K]": 25 + 273.15, "Cell capacity [A.h]": 100, "Nominal cell capacity [A.h]": 100, - "Ambient temperature [degC]": 25, + "Ambient temperature [K]": 25 + 273.15, "Current function [A]": 100, "Upper voltage cut-off [V]": 4.2, "Lower voltage cut-off [V]": 3.2, @@ -33,7 +32,6 @@ class TestEcmParameters(unittest.TestCase): def test_init_parameters(self): - param = pybamm.EcmParameters() simpled_mapped_parameters = [ @@ -49,8 +47,6 @@ def test_init_parameters(self): (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: @@ -58,6 +54,12 @@ def test_init_parameters(self): expected_value = values[key] self.assertEqual(value, expected_value) + value = parameter_values.evaluate(param.initial_T_cell) + self.assertEqual(value, values["Initial temperature [K]"] - 273.15) + + value = parameter_values.evaluate(param.initial_T_jig) + self.assertEqual(value, values["Initial temperature [K]"] - 273.15) + compatibility_parameters = [ (param.I_typ, 1), (param.n_electrodes_parallel, 1), @@ -75,7 +77,6 @@ def test_function_parameters(self): 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]"), @@ -89,6 +90,9 @@ def test_function_parameters(self): expected_value = values[key] self.assertEqual(value, expected_value) + value = parameter_values.evaluate(param.T_amb(sym)) + self.assertEqual(value, values["Ambient temperature [K]"] - 273.15) + if __name__ == "__main__": print("Add -v for more debug output") diff --git a/tests/unit/test_parameters/test_parameter_values.py b/tests/unit/test_parameters/test_parameter_values.py index 703a34d529..1b401219d1 100644 --- a/tests/unit/test_parameters/test_parameter_values.py +++ b/tests/unit/test_parameters/test_parameter_values.py @@ -494,7 +494,6 @@ def test_process_interpolant(self): 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_, indexing="ij")) @@ -641,7 +640,6 @@ def test_process_interpolant_3D_from_csv(self): # 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) @@ -683,7 +681,6 @@ def test_process_interpolant_2D_from_csv(self): # 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] diff --git a/tests/unit/test_parameters/test_size_distribution_parameters.py b/tests/unit/test_parameters/test_size_distribution_parameters.py index 26c3443594..b5e62379ee 100644 --- a/tests/unit/test_parameters/test_size_distribution_parameters.py +++ b/tests/unit/test_parameters/test_size_distribution_parameters.py @@ -13,8 +13,15 @@ def test_parameter_values(self): values = pybamm.lithium_ion.BaseModel().default_parameter_values param = pybamm.LithiumIonParameters() - # add distribution parameter values - values = pybamm.get_size_distribution_parameters(values) + # add distribution parameter values for negative electrode + values = pybamm.get_size_distribution_parameters(values, electrode="negative") + + # check positive parameters aren't there yet + with self.assertRaises(KeyError): + values["Positive maximum particle radius [m]"] + + # now add distribution parameter values for positive electrode + values = pybamm.get_size_distribution_parameters(values, electrode="positive") # check dimensionless parameters diff --git a/tests/unit/test_simulation.py b/tests/unit/test_simulation.py index dcb71e51a4..e5fad0f290 100644 --- a/tests/unit/test_simulation.py +++ b/tests/unit/test_simulation.py @@ -20,7 +20,6 @@ def test_simple_model(self): np.testing.assert_array_almost_equal(sol.y.full()[0], np.exp(-sol.t), decimal=5) def test_basic_ops(self): - model = pybamm.lithium_ion.SPM() sim = pybamm.Simulation(model) @@ -62,7 +61,6 @@ def test_basic_ops(self): self.assertTrue(val.has_symbol_of_classes(pybamm.Matrix)) def test_solve(self): - sim = pybamm.Simulation(pybamm.lithium_ion.SPM()) sim.solve([0, 600]) self.assertFalse(sim._solution is None) @@ -88,7 +86,6 @@ def test_solve(self): sim.solve(starting_solution=sol) def test_solve_non_battery_model(self): - model = pybamm.BaseModel() v = pybamm.Variable("v") model.rhs = {v: -v} @@ -105,7 +102,6 @@ def test_solve_non_battery_model(self): ) def test_solve_already_partially_processed_model(self): - model = pybamm.lithium_ion.SPM() # Process model manually @@ -126,7 +122,6 @@ def test_solve_already_partially_processed_model(self): sim.solve([0, 600]) def test_reuse_commands(self): - sim = pybamm.Simulation(pybamm.lithium_ion.SPM()) sim.set_parameters() @@ -150,7 +145,6 @@ def test_set_crate(self): self.assertEqual(sim.C_rate, 2) def test_step(self): - dt = 0.001 model = pybamm.lithium_ion.SPM() sim = pybamm.Simulation(model) diff --git a/tests/unit/test_solvers/test_idaklu_solver.py b/tests/unit/test_solvers/test_idaklu_solver.py index 9788d0d278..c834c5ca62 100644 --- a/tests/unit/test_solvers/test_idaklu_solver.py +++ b/tests/unit/test_solvers/test_idaklu_solver.py @@ -447,6 +447,30 @@ def test_dae_solver_algebraic_model(self): solution = solver.solve(model, t_eval) np.testing.assert_array_equal(solution.y, -1) + def test_banded(self): + model = pybamm.lithium_ion.SPM() + model.convert_to_format = "casadi" + param = model.default_parameter_values + param.process_model(model) + geometry = model.default_geometry + param.process_geometry(geometry) + mesh = pybamm.Mesh(geometry, model.default_submesh_types, model.default_var_pts) + disc = pybamm.Discretisation(mesh, model.default_spatial_methods) + disc.process_model(model) + + t_eval = np.linspace(0, 3600, 100) + solver = pybamm.IDAKLUSolver() + soln = solver.solve(model, t_eval) + + options = { + "jacobian": "banded", + "linear_solver": "SUNLinSol_Band", + } + solver_banded = pybamm.IDAKLUSolver(options=options) + soln_banded = solver_banded.solve(model, t_eval) + + np.testing.assert_array_almost_equal(soln.y, soln_banded.y, 5) + def test_options(self): model = pybamm.BaseModel() u = pybamm.Variable("u") diff --git a/tests/unit/test_solvers/test_scikits_solvers.py b/tests/unit/test_solvers/test_scikits_solvers.py index 193cc511f4..4a44f73f25 100644 --- a/tests/unit/test_solvers/test_scikits_solvers.py +++ b/tests/unit/test_solvers/test_scikits_solvers.py @@ -843,7 +843,6 @@ def nonsmooth_rate(t): model3.events = [pybamm.Event("var1 = 1.5", pybamm.min(1.5 - var1))] for model in [model1, model2, model3]: - disc = get_discretisation_for_testing() disc.process_model(model) diff --git a/tests/unit/test_solvers/test_scipy_solver.py b/tests/unit/test_solvers/test_scipy_solver.py index 4c8ec6ce3d..6012d06cc6 100644 --- a/tests/unit/test_solvers/test_scipy_solver.py +++ b/tests/unit/test_solvers/test_scipy_solver.py @@ -10,7 +10,6 @@ class TestScipySolver(unittest.TestCase): def test_model_solver_python_and_jax(self): - if pybamm.have_jax(): formats = ["python", "jax"] else: 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 2521755708..5dbf302cbd 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 @@ -12,7 +12,6 @@ def errors(pts, function, method_options, bcs=None): - domain = "test" x = pybamm.SpatialVariable("x", domain=domain) geometry = {domain: {x: {"min": pybamm.Scalar(0), "max": pybamm.Scalar(1)}}} @@ -47,7 +46,6 @@ def errors(pts, function, method_options, bcs=None): def get_errors(function, method_options, pts, bcs=None): - l_errors = np.zeros(pts.shape) r_errors = np.zeros(pts.shape) @@ -59,7 +57,6 @@ def get_errors(function, method_options, pts, bcs=None): class TestExtrapolation(unittest.TestCase): def test_convergence_without_bcs(self): - # all tests are performed on x in [0, 1] linear = {"extrapolation": {"order": "linear"}} quad = {"extrapolation": {"order": "quadratic"}} 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 b8b2918682..737014b61f 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 @@ -297,7 +297,6 @@ def test_definite_integral_vector(self): self.assertEqual(vec_disc.shape[1], 1) def test_indefinite_integral(self): - # create discretisation mesh = get_mesh_for_testing() spatial_methods = { @@ -451,7 +450,6 @@ def test_indefinite_integral(self): ) def test_backward_indefinite_integral(self): - # create discretisation mesh = get_mesh_for_testing() spatial_methods = {"macroscale": pybamm.FiniteVolume()} diff --git a/tests/unit/test_spatial_methods/test_spectral_volume.py b/tests/unit/test_spatial_methods/test_spectral_volume.py index 5ce7e78bad..0ba2426a36 100644 --- a/tests/unit/test_spatial_methods/test_spectral_volume.py +++ b/tests/unit/test_spatial_methods/test_spectral_volume.py @@ -532,7 +532,6 @@ def test_p2d_spherical_grad_div_shapes_Neumann_bcs(self): np.testing.assert_array_almost_equal(div_eval, 6 * np.ones([sec_pts, prim_pts])) def test_grad_div_shapes_mixed_domain(self): - # Create discretisation mesh = get_mesh_for_testing() spatial_methods = {"macroscale": pybamm.SpectralVolume()} diff --git a/tox.ini b/tox.ini index c741a7854a..555a7f073b 100644 --- a/tox.ini +++ b/tox.ini @@ -12,17 +12,18 @@ setenv = deps = dev-!windows-!mac: cmake dev: black - dev,doctests: sphinx>=1.5 - dev,doctests: pydata-sphinx-theme - dev,doctests: sphinx_design - dev,doctests: sphinx-copybutton + doctests: sphinx>=1.5 + doctests: pydata-sphinx-theme + doctests: sphinx_design + doctests: sphinx-copybutton + doctests: myst-parser !windows-!mac: scikits.odes commands = tests-!windows-!mac: sh -c "pybamm_install_jax" # install jax, jaxlib for ubuntu - tests: python run-tests.py --unit --folder all + tests: python run-tests.py --all unit: python run-tests.py --unit - integration: python run-tests.py --unit --folder integration + integration: python run-tests.py --integration examples: python run-tests.py --examples dev-!windows-!mac: sh -c "echo export LD_LIBRARY_PATH={env:LD_LIBRARY_PATH} >> {envbindir}/activate" doctests: python run-tests.py --doctest @@ -61,6 +62,9 @@ deps = sphinx>=1.5 pydata-sphinx-theme sphinx-autobuild + sphinx_design + sphinx-copybutton + myst-parser changedir = docs commands = sphinx-autobuild --open-browser -qT . {envtmpdir}/html diff --git a/vcpkg.json b/vcpkg.json index cda6b86cf2..8e393e9ad0 100644 --- a/vcpkg.json +++ b/vcpkg.json @@ -1,6 +1,6 @@ { "name": "pybamm", - "version-string": "23.1", + "version-string": "23.2", "dependencies": [ "casadi", {