From 7d4c8cfdb7edce7343408a8cc98066ac2ec4e230 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Wed, 30 Aug 2023 23:02:43 +0530
Subject: [PATCH 001/199] #3049 initial draft: metadata, dependencies, extras,
 entry points

---
 pyproject.toml | 172 +++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 172 insertions(+)
 create mode 100644 pyproject.toml

diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000000..3e1ad42e76
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,172 @@
+# From the pip documentation:
+
+# Fallback Behaviour
+# If a project does not have a pyproject.toml file containing a build-system section,
+# it will be assumed to have the following backend settings:
+
+# [build-system]
+# requires = ["setuptools>=40.8.0", "wheel"]
+# build-backend = "setuptools.build_meta:__legacy__"
+
+# TODO: add appropriate build-system section
+[build-system]
+# TODO: specify minimum version of setuptools otherwise scikits.odes, NumPy, and others
+# will fail to install
+requires = ["setuptools", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "pybamm"
+# TODO: try picking up version from the package itself
+# dynamic = ["version", "readme"]
+# [tool.setuptools.dynamic]
+# version = {attr = "my_package.VERSION"}
+version = "23.5"
+# Unsure: specify BSD-3-Clause?
+# license = {text = "BSD-3-Clause"}
+license = { file = "LICENCE.txt" }
+
+# TODO: add appropriate long description
+description = "Python Battery Mathematical Modelling"
+
+# TODO: correctly specify all authors and maintainers
+# Note: these are currently missing when running `pip show pybamm`, so we should add
+# them in some form
+authors = [{name = "The PyBaMM Team"}]
+maintainers = [{name = "The PyBaMM Team", email = "pybamm@pybamm.org"}]
+requires-python = ">=3.8, <3.12"
+readme = "README.md"
+
+classifiers = [
+    "Development Status :: 5 - Production/Stable",
+    "Intended Audience :: Developers",
+    "Intended Audience :: Science/Research",
+    "License :: OSI Approved :: BSD License",
+    "Programming Language :: Python",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3 :: Only",
+    "Programming Language :: Python :: 3.8",
+    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Topic :: Scientific/Engineering",
+]
+
+dependencies = [
+    "numpy>=1.16",
+    "scipy>=1.3",
+    "casadi>=3.6.0",
+    "xarray",
+]
+
+[project.optional-dependencies]
+# For the generation of documentation
+docs = [
+    "sphinx>=6",
+    "sphinx_rtd_theme>=0.5",
+    "pydata-sphinx-theme",
+    "sphinx_design",
+    "sphinx-copybutton",
+    "myst-parser",
+    "sphinx-inline-tabs",
+    "sphinxcontrib-bibtex",
+    "sphinx-autobuild",
+    "sphinx-last-updated-by-git",
+    "nbsphinx",
+    "ipykernel",
+    "ipywidgets",
+    "sphinx-gallery",
+    "sphinx-hoverxref",
+    "sphinx-docsearch",
+]
+# For example notebooks
+examples = [
+    "jupyter",
+]
+# Plotting functionality
+plot = [
+    "imageio>=2.9.0",
+    # Note: Matplotlib is loaded for debug plots, but to ensure pybamm runs
+    # on systems without an attached display, it should never be imported
+    # outside of plot() methods.
+    "matplotlib>=2.0",
+]
+# For the Citations class
+cite = [
+    "pybtex>=0.24.0",
+]
+# To generate LaTeX strings
+latexify = [
+    "sympy>=1.8",
+]
+# Battery Parameter eXchange format
+bpx = [
+    "bpx",
+]
+# Low-overhead progress bars
+tqdm = [
+    "tqdm",
+]
+# Dependencies intended for use by developers
+dev = [
+    # For code style checking
+    "pre-commit",
+    # For code style auto-formatting
+    "ruff",
+    # For running testing sessions
+    "nox",
+]
+# Reading CSV files
+pandas =  [
+    "pandas>=0.24",
+]
+# For the Jax solver
+jax = [
+    "jax==0.4.8",
+    "jaxlib==0.4.7",
+]
+# For the scikits.odes solver
+odes = [
+    "scikits.odes"
+]
+# Contains all optional dependencies, except for odes, jax, and dev dependencies
+all = [
+    "anytree>=2.4.3",
+    "autograd>=1.2",
+    "pandas>=0.24",
+    "scikit-fem>=0.2.0",
+    "imageio>=2.9.0",
+    "matplotlib>=2.0",
+    "pybtex>=0.24.0",
+    "sympy>=1.8",
+    "bpx",
+    "tqdm",
+    "jupyter",
+]
+
+# Equivalent to the console scripts in the entry_points section of the setup()
+# function in setup.py
+[project.scripts]
+pybamm_edit_parameter = "pybamm.parameters_cli:edit_parameter"
+pybamm_add_parameter = "pybamm.parameters_cli:add_parameter"
+pybamm_rm_parameter = "pybamm.parameters_cli:remove_parameter"
+pybamm_install_odes = "pybamm.install_odes:main"
+pybamm_install_jax = "pybamm.util:install_jax"
+
+# Equivalent to the "pybamm_parameter_sets" entry_points section of the setup()
+# function in setup.py
+[project.entry-points."pybamm_parameter_sets"]
+Sulzer2019 = "pybamm.input.parameters.lead_acid.Sulzer2019:get_parameter_values"
+Ai2020 = "pybamm.input.parameters.lithium_ion.Ai2020:get_parameter_values"
+Chen2020 = "pybamm.input.parameters.lithium_ion.Chen2020:get_parameter_values"
+Chen2020_composite = "pybamm.input.parameters.lithium_ion.Chen2020_composite:get_parameter_values"
+Ecker2015 = "pybamm.input.parameters.lithium_ion.Ecker2015:get_parameter_values"
+Marquis2019 = "pybamm.input.parameters.lithium_ion.Marquis2019:get_parameter_values"
+Mohtat2020 = "pybamm.input.parameters.lithium_ion.Mohtat2020:get_parameter_values"
+NCA_Kim2011 = "pybamm.input.parameters.lithium_ion.NCA_Kim2011:get_parameter_values"
+OKane2022 = "pybamm.input.parameters.lithium_ion.OKane2022:get_parameter_values"
+ORegan2022 = "pybamm.input.parameters.lithium_ion.ORegan2022:get_parameter_values"
+Prada2013 = "pybamm.input.parameters.lithium_ion.Prada2013:get_parameter_values"
+Ramadass2004 = "pybamm.input.parameters.lithium_ion.Ramadass2004:get_parameter_values"
+Xu2019 = "pybamm.input.parameters.lithium_ion.Xu2019:get_parameter_values"
+ECM_Example = "pybamm.input.parameters.ecm.example_set:get_parameter_values"

From aca9a6a553022f7bbcf47960b1899da40d0c1b9a Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Wed, 30 Aug 2023 23:05:36 +0530
Subject: [PATCH 002/199] #3049 Fix LICENSE spelling

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 3e1ad42e76..5ff07e93e2 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -24,7 +24,7 @@ name = "pybamm"
 version = "23.5"
 # Unsure: specify BSD-3-Clause?
 # license = {text = "BSD-3-Clause"}
-license = { file = "LICENCE.txt" }
+license = { file = "LICENSE.txt" }
 
 # TODO: add appropriate long description
 description = "Python Battery Mathematical Modelling"

From 3af50925654220757fc7fdfcf906784ea30cf938 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Wed, 30 Aug 2023 23:22:10 +0530
Subject: [PATCH 003/199] #3049 Temporarily build wheels on pull requests

---
 .github/workflows/publish_pypi.yml | 79 ++++++++++++++++--------------
 1 file changed, 41 insertions(+), 38 deletions(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 6d89da1387..ba693ec88e 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -1,13 +1,16 @@
-name: Build and publish package to PyPI
-
+# name: Build and publish package to PyPI
+name: Test building wheels on Windows, GNU/Linux and macOS
+# Temporarily disable publishing to PyPI and enable
+# building wheels on pull requests
 on:
-  push:
-    branches: main
+  # push:
+  #   branches: main
+  pull_request:
   workflow_dispatch:
     inputs:
-      target:
-        description: 'Deployment target. Can be "pypi" or "testpypi"'
-        default: "pypi"
+      # target:
+      #   description: 'Deployment target. Can be "pypi" or "testpypi"'
+      #   default: "pypi"
       debug_enabled:
           type: boolean
           description: 'Run the build with tmate debugging enabled (https://github.com/marketplace/actions/debugging-with-tmate)'
@@ -153,34 +156,34 @@ jobs:
           path: ./dist/*.tar.gz
           if-no-files-found: error
 
-  publish_pypi:
-    name: Upload package to PyPI
-    needs: [build_wheels, build_windows_wheels, build_sdist]
-    runs-on: ubuntu-latest
-    steps:
-      - name: Download all artifacts
-        uses: actions/download-artifact@v3
-
-      - name: Move all package files to files/
-        run: |
-          mkdir files
-          mv windows_wheels/* wheels/* sdist/* files/
-
-      - name: Publish on PyPI
-        if: |
-          github.event.inputs.target == 'pypi' ||
-          (github.event_name == 'push' && github.ref == 'refs/heads/main')
-        uses: pypa/gh-action-pypi-publish@release/v1
-        with:
-          user: __token__
-          password: ${{ secrets.PYPI_TOKEN }}
-          packages_dir: files/
-
-      - name: Publish on TestPyPI
-        if: github.event.inputs.target == 'testpypi'
-        uses: pypa/gh-action-pypi-publish@release/v1
-        with:
-          user: __token__
-          password: ${{ secrets.TESTPYPI_TOKEN }}
-          packages_dir: files/
-          repository_url: https://test.pypi.org/legacy/
+  # publish_pypi:
+  #   name: Upload package to PyPI
+  #   needs: [build_wheels, build_windows_wheels, build_sdist]
+  #   runs-on: ubuntu-latest
+  #   steps:
+  #     - name: Download all artifacts
+  #       uses: actions/download-artifact@v3
+
+  #     - name: Move all package files to files/
+  #       run: |
+  #         mkdir files
+  #         mv windows_wheels/* wheels/* sdist/* files/
+
+  #     - name: Publish on PyPI
+  #       if: |
+  #         github.event.inputs.target == 'pypi' ||
+  #         (github.event_name == 'push' && github.ref == 'refs/heads/main')
+  #       uses: pypa/gh-action-pypi-publish@release/v1
+  #       with:
+  #         user: __token__
+  #         password: ${{ secrets.PYPI_TOKEN }}
+  #         packages_dir: files/
+
+  #     - name: Publish on TestPyPI
+  #       if: github.event.inputs.target == 'testpypi'
+  #       uses: pypa/gh-action-pypi-publish@release/v1
+  #       with:
+  #         user: __token__
+  #         password: ${{ secrets.TESTPYPI_TOKEN }}
+  #         packages_dir: files/
+  #         repository_url: https://test.pypi.org/legacy/

From 8768ed7f3d8814aa778f259030703bba0c8ba93c Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Wed, 30 Aug 2023 23:37:11 +0530
Subject: [PATCH 004/199] #3049 Add CMakeBuild steps to `setup.py` instead of
 importing it

---
 setup.py | 295 ++++++++++++++++++++++++++++++++-----------------------
 1 file changed, 173 insertions(+), 122 deletions(-)

diff --git a/setup.py b/setup.py
index dfdd455a16..1917b62728 100644
--- a/setup.py
+++ b/setup.py
@@ -1,4 +1,5 @@
 import os
+import sys
 import glob
 import logging
 import subprocess
@@ -13,12 +14,173 @@
     from distutils.core import setup, find_packages
     from distutils.command.install import install
 
-import CMakeBuild
+# import CMakeBuild
+
+# ---------- cmakebuild was integrated into setup.py directly --------------------------
+
+try:
+    from setuptools.command.build_ext import build_ext
+except ImportError:
+    from distutils.command.build_ext import build_ext
 
 default_lib_dir = (
     "" if system() == "Windows" else os.path.join(os.getenv("HOME"), ".local")
 )
 
+
+def set_vcpkg_environment_variables():
+    if not os.getenv("VCPKG_ROOT_DIR"):
+        raise EnvironmentError("Environment variable 'VCPKG_ROOT_DIR' is undefined.")
+    if not os.getenv("VCPKG_DEFAULT_TRIPLET"):
+        raise EnvironmentError(
+            "Environment variable 'VCPKG_DEFAULT_TRIPLET' is undefined."
+        )
+    if not os.getenv("VCPKG_FEATURE_FLAGS"):
+        raise EnvironmentError(
+            "Environment variable 'VCPKG_FEATURE_FLAGS' is undefined."
+        )
+    return (
+        os.getenv("VCPKG_ROOT_DIR"),
+        os.getenv("VCPKG_DEFAULT_TRIPLET"),
+        os.getenv("VCPKG_FEATURE_FLAGS"),
+    )
+
+
+class CMakeBuild(build_ext):
+    user_options = build_ext.user_options + [
+        ("suitesparse-root=", None, "suitesparse source location"),
+        ("sundials-root=", None, "sundials source location"),
+    ]
+
+    def initialize_options(self):
+        build_ext.initialize_options(self)
+        self.suitesparse_root = None
+        self.sundials_root = None
+
+    def finalize_options(self):
+        build_ext.finalize_options(self)
+        # Determine the calling command to get the
+        # undefined options from.
+        # If build_ext was called directly then this
+        # doesn't matter.
+        try:
+            self.get_finalized_command("install", create=0)
+            calling_cmd = "install"
+        except AttributeError:
+            calling_cmd = "bdist_wheel"
+        self.set_undefined_options(
+            calling_cmd,
+            ("suitesparse_root", "suitesparse_root"),
+            ("sundials_root", "sundials_root"),
+        )
+        if not self.suitesparse_root:
+            self.suitesparse_root = os.path.join(default_lib_dir)
+        if not self.sundials_root:
+            self.sundials_root = os.path.join(default_lib_dir)
+
+    def get_build_directory(self):
+        # distutils outputs object files in directory self.build_temp
+        # (typically build/temp.*). This is our CMake build directory.
+        # On Windows, distutils is too smart and appends "Release" or
+        # "Debug" to self.build_temp. So in this case we want the
+        # build directory to be the parent directory.
+        if system() == "Windows":
+            return Path(self.build_temp).parents[0]
+        return self.build_temp
+
+    def run(self):
+        if not self.extensions:
+            return
+
+        if system() == "Windows":
+            use_python_casadi = False
+        else:
+            use_python_casadi = True
+
+        build_type = os.getenv("PYBAMM_CPP_BUILD_TYPE", "RELEASE")
+        cmake_args = [
+            "-DCMAKE_BUILD_TYPE={}".format(build_type),
+            "-DPYTHON_EXECUTABLE={}".format(sys.executable),
+            "-DUSE_PYTHON_CASADI={}".format("TRUE" if use_python_casadi else "FALSE"),
+        ]
+        if self.suitesparse_root:
+            cmake_args.append(
+                "-DSuiteSparse_ROOT={}".format(os.path.abspath(self.suitesparse_root))
+            )
+        if self.sundials_root:
+            cmake_args.append(
+                "-DSUNDIALS_ROOT={}".format(os.path.abspath(self.sundials_root))
+            )
+
+        build_dir = self.get_build_directory()
+        if not os.path.exists(build_dir):
+            os.makedirs(build_dir)
+
+        # The CMakeError.log file is generated by cmake is the configure step
+        # encounters error. In the following the existence of this file is used
+        # to determine whether or not the cmake configure step went smoothly.
+        # So must make sure this file does not remain from a previous failed build.
+        if os.path.isfile(os.path.join(build_dir, "CMakeError.log")):
+            os.remove(os.path.join(build_dir, "CMakeError.log"))
+
+        build_env = os.environ
+        if os.getenv("PYBAMM_USE_VCPKG"):
+            (
+                vcpkg_root_dir,
+                vcpkg_default_triplet,
+                vcpkg_feature_flags,
+            ) = set_vcpkg_environment_variables()
+            build_env["vcpkg_root_dir"] = vcpkg_root_dir
+            build_env["vcpkg_default_triplet"] = vcpkg_default_triplet
+            build_env["vcpkg_feature_flags"] = vcpkg_feature_flags
+
+        cmake_list_dir = os.path.abspath(os.path.dirname(__file__))
+        print("-" * 10, "Running CMake for idaklu solver", "-" * 40)
+        subprocess.run(
+            ["cmake", cmake_list_dir] + cmake_args, cwd=build_dir, env=build_env
+        )
+
+        if os.path.isfile(os.path.join(build_dir, "CMakeError.log")):
+            msg = (
+                "cmake configuration steps encountered errors, and the idaklu module"
+                " could not be built. Make sure dependencies are correctly "
+                "installed. See "
+                "https://github.com/pybamm-team/PyBaMM/tree/develop"
+                "INSTALL-LINUX-MAC.md"
+            )
+            raise RuntimeError(msg)
+        else:
+            print("-" * 10, "Building idaklu module", "-" * 40)
+            subprocess.run(
+                ["cmake", "--build", ".", "--config", "Release"],
+                cwd=build_dir,
+                env=build_env,
+            )
+
+            # Move from build temp to final position
+            for ext in self.extensions:
+                self.move_output(ext)
+
+    def move_output(self, ext):
+        # Copy built module to dist/ directory
+        build_temp = Path(self.build_temp).resolve()
+        # Get destination location
+        # self.get_ext_fullpath(ext.name) -->
+        # build/lib.linux-x86_64-3.5/idaklu.cpython-37m-x86_64-linux-gnu.so
+        # using resolve() with python < 3.6 will result in a FileNotFoundError
+        # since the location does not yet exists.
+        dest_path = Path(self.get_ext_fullpath(ext.name)).resolve()
+        source_path = build_temp / os.path.basename(self.get_ext_filename(ext.name))
+        dest_directory = dest_path.parents[0]
+        dest_directory.mkdir(parents=True, exist_ok=True)
+        self.copy_file(source_path, dest_path)
+
+# ---------- end of cmakebuild steps ---------------------------------------------------
+
+# default_lib_dir = (
+#     "" if system() == "Windows" else os.path.join(os.getenv("HOME"), ".local")
+# )
+
 log_format = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
 logger = logging.getLogger("PyBaMM setup")
 
@@ -123,6 +285,7 @@ def compile_KLU():
 
 # Build the list of package data files to be included in the PyBaMM package.
 # These are mainly the parameter files located in the input/parameters/ subdirectories.
+# TODO: might be possible to include in pyproject.toml with data configuration values
 pybamm_data = []
 for file_ext in ["*.csv", "*.py", "*.md", "*.txt"]:
     # Get all the files ending in file_ext in pybamm/input dir.
@@ -162,144 +325,32 @@ def compile_KLU():
 ext_modules = [idaklu_ext] if compile_KLU() else []
 
 # Defines __version__
+# TODO: might not be needed anymore, because we define it in pyproject.toml
+# and can therefore access it with importlib.metadata.version("pybamm") (python 3.8+)
+# The version.py file can then be imported with attr: pybamm.__version__ dynamically
 root = os.path.abspath(os.path.dirname(__file__))
 with open(os.path.join(root, "pybamm", "version.py")) as f:
     exec(f.read())
 
 # Load text for description and license
+# TODO: might not be needed anymore, because we define the description and license
+# in pyproject.toml
+# TODO: add long description there and remove it from setup()
 with open("README.md", encoding="utf-8") as f:
     readme = f.read()
 
+# Project metadata was moved to pyproject.toml (which is read by pip).
+# However, custom build commands and setuptools extension modules are still defined here
 setup(
-    name="pybamm",
-    version=__version__,  # noqa: F821
-    description="Python Battery Mathematical Modelling.",
     long_description=readme,
     long_description_content_type="text/markdown",
     url="https://github.com/pybamm-team/PyBaMM",
     packages=find_packages(include=("pybamm", "pybamm.*")),
     ext_modules=ext_modules,
     cmdclass={
-        "build_ext": CMakeBuild.CMakeBuild,
+        "build_ext": CMakeBuild,
         "bdist_wheel": bdist_wheel,
         "install": CustomInstall,
     },
     package_data={"pybamm": pybamm_data},
-    # Python version
-    python_requires=">=3.8,<3.12",
-    classifiers=[
-        "Development Status :: 5 - Production/Stable",
-        "Intended Audience :: Developers",
-        "Intended Audience :: Science/Research",
-        "License :: OSI Approved :: BSD License",
-        "Programming Language :: Python",
-        "Programming Language :: Python :: 3",
-        "Programming Language :: Python :: 3 :: Only",
-        "Programming Language :: Python :: 3.8",
-        "Programming Language :: Python :: 3.9",
-        "Programming Language :: Python :: 3.10",
-        "Programming Language :: Python :: 3.11",
-        "Topic :: Scientific/Engineering",
-    ],
-    # List of dependencies
-    install_requires=[
-        "numpy>=1.16",
-        "scipy>=1.3",
-        "casadi>=3.6.0",
-        "xarray",
-    ],
-    extras_require={
-        "docs": [
-            "sphinx>=6",
-            "sphinx_rtd_theme>=0.5",
-            "pydata-sphinx-theme",
-            "sphinx_design",
-            "sphinx-copybutton",
-            "myst-parser",
-            "sphinx-inline-tabs",
-            "sphinxcontrib-bibtex",
-            "sphinx-autobuild",
-            "sphinx-last-updated-by-git",
-            "nbsphinx",
-            "ipykernel",
-            "ipywidgets",
-            "sphinx-gallery",
-            "sphinx-hoverxref",
-            "sphinx-docsearch",
-        ],  # For doc generation
-        "examples": [
-            "jupyter",  # For example notebooks
-        ],
-        "plot": [
-            "imageio>=2.9.0",
-            # Note: Matplotlib is loaded for debug plots, but to ensure pybamm runs
-            # on systems without an attached display, it should never be imported
-            # outside of plot() methods.
-            # Should not be imported
-            "matplotlib>=2.0",
-        ],
-        "cite": [
-            "pybtex>=0.24.0",
-        ],
-        "latexify": [
-            "sympy>=1.8",
-        ],
-        "bpx": [
-            "bpx",
-        ],
-        "tqdm": [
-            "tqdm",
-        ],
-        "dev": [
-            "pre-commit",  # For code style checking
-            "ruff", # For code style auto-formatting
-            "nox", # For running testing sessions
-        ],
-        "pandas": [
-            "pandas>=0.24",
-        ],
-        "jax": [
-            "jax==0.4.8",
-            "jaxlib==0.4.7",
-        ],
-        "odes": ["scikits.odes"],
-        "all": [
-            "anytree>=2.4.3",
-            "autograd>=1.2",
-            "pandas>=0.24",
-            "scikit-fem>=0.2.0",
-            "imageio>=2.9.0",
-            "pybtex>=0.24.0",
-            "sympy>=1.8",
-            "bpx",
-            "tqdm",
-            "matplotlib>=2.0",
-            "jupyter",
-        ],
-    },
-    entry_points={
-        "console_scripts": [
-            "pybamm_edit_parameter = pybamm.parameters_cli:edit_parameter",
-            "pybamm_add_parameter = pybamm.parameters_cli:add_parameter",
-            "pybamm_rm_parameter = pybamm.parameters_cli:remove_parameter",
-            "pybamm_install_odes = pybamm.install_odes:main",
-            "pybamm_install_jax = pybamm.util:install_jax",
-        ],
-        "pybamm_parameter_sets": [
-            "Sulzer2019 = pybamm.input.parameters.lead_acid.Sulzer2019:get_parameter_values",  # noqa: E501
-            "Ai2020 = pybamm.input.parameters.lithium_ion.Ai2020:get_parameter_values",  # noqa: E501
-            "Chen2020 = pybamm.input.parameters.lithium_ion.Chen2020:get_parameter_values",  # noqa: E501
-            "Chen2020_composite = pybamm.input.parameters.lithium_ion.Chen2020_composite:get_parameter_values",  # noqa: E501
-            "Ecker2015 = pybamm.input.parameters.lithium_ion.Ecker2015:get_parameter_values",  # noqa: E501
-            "Marquis2019 = pybamm.input.parameters.lithium_ion.Marquis2019:get_parameter_values",  # noqa: E501
-            "Mohtat2020 = pybamm.input.parameters.lithium_ion.Mohtat2020:get_parameter_values",  # noqa: E501
-            "NCA_Kim2011 = pybamm.input.parameters.lithium_ion.NCA_Kim2011:get_parameter_values",  # noqa: E501
-            "OKane2022 = pybamm.input.parameters.lithium_ion.OKane2022:get_parameter_values",  # noqa: E501
-            "ORegan2022 = pybamm.input.parameters.lithium_ion.ORegan2022:get_parameter_values",  # noqa: E501
-            "Prada2013 = pybamm.input.parameters.lithium_ion.Prada2013:get_parameter_values",  # noqa: E501
-            "Ramadass2004 = pybamm.input.parameters.lithium_ion.Ramadass2004:get_parameter_values",  # noqa: E501
-            "Xu2019 = pybamm.input.parameters.lithium_ion.Xu2019:get_parameter_values",  # noqa: E501
-            "ECM_Example = pybamm.input.parameters.ecm.example_set:get_parameter_values",  # noqa: E501
-        ],
-    },
 )

From 66e930264daefd8c1817feb3003cb853aee3c8ad Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 31 Aug 2023 00:19:53 +0530
Subject: [PATCH 005/199] #3049 Temporarily install `casadi` before installing
 editable

---
 .github/workflows/test_on_push.yml | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 2fd4c92b2e..ee633bd5dc 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -90,6 +90,8 @@ jobs:
       - name: Install PyBaMM dependencies
         run: |
           pip install --upgrade pip wheel setuptools nox
+          # For some reason casadi needs to be installed first
+          pip install casadi
           pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
@@ -150,6 +152,8 @@ jobs:
       - name: Install PyBaMM dependencies
         run: |
           pip install --upgrade pip wheel setuptools nox
+          # For some reason casadi needs to be installed first
+          pip install casadi
           pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
@@ -233,6 +237,8 @@ jobs:
       - name: Install PyBaMM dependencies
         run: |
           pip install --upgrade pip wheel setuptools nox
+          # For some reason casadi needs to be installed first
+          pip install casadi
           pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
@@ -293,6 +299,8 @@ jobs:
       - name: Install PyBaMM dependencies
         run: |
           pip install --upgrade pip wheel setuptools nox
+          # For some reason casadi needs to be installed first
+          pip install casadi
           pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
@@ -354,6 +362,8 @@ jobs:
       - name: Install PyBaMM dependencies
         run: |
           pip install --upgrade pip wheel setuptools nox
+          # For some reason casadi needs to be installed first
+          pip install casadi
           pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux

From 8b6a184ad261a51f4d27f4f9a1ea4690e365a65e Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 31 Aug 2023 00:20:48 +0530
Subject: [PATCH 006/199] #3049 Better error message if `casadi` path is not
 found

---
 CMakeLists.txt | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index c3c5141d4f..889e1c1584 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -63,8 +63,9 @@ execute_process(
 
 if (CASADI_DIR)
   file(TO_CMAKE_PATH ${CASADI_DIR} CASADI_DIR)
+  message("Found python casadi path: ${CASADI_DIR}")
 endif()
-message("Found python casadi path: ${CASADI_DIR}")
+message("Could not find python casadi path")
 
 if(${USE_PYTHON_CASADI})
   message("Trying to link against python casadi package")

From 3bec0ba944676009671fc92fa9cc25f769bdbf8d Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 31 Aug 2023 01:12:58 +0530
Subject: [PATCH 007/199] #3049 Rename wheel build workflow name

---
 .github/workflows/publish_pypi.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index ba693ec88e..fbdcf6fcc3 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -1,5 +1,5 @@
 # name: Build and publish package to PyPI
-name: Test building wheels on Windows, GNU/Linux and macOS
+name: Test building wheels
 # Temporarily disable publishing to PyPI and enable
 # building wheels on pull requests
 on:

From bfafc753db11160f010b59998dfa8429a79ceaf6 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 31 Aug 2023 01:15:37 +0530
Subject: [PATCH 008/199] #3049 Temporarily use `--no-build-isolation` in CI

---
 .github/workflows/test_on_push.yml | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index ee633bd5dc..67bac67be7 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -92,7 +92,7 @@ jobs:
           pip install --upgrade pip wheel setuptools nox
           # For some reason casadi needs to be installed first
           pip install casadi
-          pip install -e .[all,docs]
+          pip install -e .[all,docs] --no-build-isolation
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -154,7 +154,7 @@ jobs:
           pip install --upgrade pip wheel setuptools nox
           # For some reason casadi needs to be installed first
           pip install casadi
-          pip install -e .[all,docs]
+          pip install -e .[all,docs] --no-build-isolation
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -239,7 +239,7 @@ jobs:
           pip install --upgrade pip wheel setuptools nox
           # For some reason casadi needs to be installed first
           pip install casadi
-          pip install -e .[all,docs]
+          pip install -e .[all,docs] --no-build-isolation
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -301,7 +301,7 @@ jobs:
           pip install --upgrade pip wheel setuptools nox
           # For some reason casadi needs to be installed first
           pip install casadi
-          pip install -e .[all,docs]
+          pip install -e .[all,docs] --no-build-isolation
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -364,7 +364,7 @@ jobs:
           pip install --upgrade pip wheel setuptools nox
           # For some reason casadi needs to be installed first
           pip install casadi
-          pip install -e .[all,docs]
+          pip install -e .[all,docs] --no-build-isolation
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3

From f4d148aea20a3aefcb75618091c18ba26f0fdf1c Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 31 Aug 2023 01:37:37 +0530
Subject: [PATCH 009/199] #3049 add `cmake` to build-system requirements

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 5ff07e93e2..46d7117164 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -12,7 +12,7 @@
 [build-system]
 # TODO: specify minimum version of setuptools otherwise scikits.odes, NumPy, and others
 # will fail to install
-requires = ["setuptools", "wheel"]
+requires = ["setuptools", "wheel", "cmake"]
 build-backend = "setuptools.build_meta"
 
 [project]

From 60caf0bb0505e0b1ea8df7b0849e387b63838c1a Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 31 Aug 2023 01:50:49 +0530
Subject: [PATCH 010/199] Revert "#3049 add `cmake` to build-system
 requirements"

This reverts commit f4d148aea20a3aefcb75618091c18ba26f0fdf1c.
---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 46d7117164..5ff07e93e2 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -12,7 +12,7 @@
 [build-system]
 # TODO: specify minimum version of setuptools otherwise scikits.odes, NumPy, and others
 # will fail to install
-requires = ["setuptools", "wheel", "cmake"]
+requires = ["setuptools", "wheel"]
 build-backend = "setuptools.build_meta"
 
 [project]

From d8d61949bdd0c08e5871d31d929ed0d0b3b1c584 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 31 Aug 2023 19:20:26 +0530
Subject: [PATCH 011/199] #3049 Clarify author emails

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 5ff07e93e2..d49d887191 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -32,7 +32,7 @@ description = "Python Battery Mathematical Modelling"
 # TODO: correctly specify all authors and maintainers
 # Note: these are currently missing when running `pip show pybamm`, so we should add
 # them in some form
-authors = [{name = "The PyBaMM Team"}]
+authors = [{name = "The PyBaMM Team", email = "pybamm@pybamm.org"}]
 maintainers = [{name = "The PyBaMM Team", email = "pybamm@pybamm.org"}]
 requires-python = ">=3.8, <3.12"
 readme = "README.md"

From bdd191e72252fd1431a2c9976ad272606fe406d3 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 31 Aug 2023 19:21:24 +0530
Subject: [PATCH 012/199] #3049 clarify idaklu attributes (`setuptools` API)

---
 setup.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 1917b62728..5072e17fad 100644
--- a/setup.py
+++ b/setup.py
@@ -310,8 +310,8 @@ def compile_KLU():
 pybamm_data.append("../CMakeBuild.py")
 
 idaklu_ext = Extension(
-    "pybamm.solvers.idaklu",
-    [
+    name="pybamm.solvers.idaklu",
+    sources=[
         "pybamm/solvers/c_solvers/idaklu.cpp"
         "pybamm/solvers/c_solvers/idaklu.hpp"
         "pybamm/solvers/c_solvers/idaklu_casadi.cpp"

From 9f0f250cda2a5fabecfc17dc1f29e60ea863130f Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 31 Aug 2023 21:14:57 +0530
Subject: [PATCH 013/199] #3049 Specify `casadi` as a build-time dependency to
 overcome venv isolated build error

---
 pyproject.toml | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index d49d887191..5bc365e9ad 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -12,7 +12,7 @@
 [build-system]
 # TODO: specify minimum version of setuptools otherwise scikits.odes, NumPy, and others
 # will fail to install
-requires = ["setuptools", "wheel"]
+requires = ["setuptools", "wheel", "casadi>=3.6.0"]
 build-backend = "setuptools.build_meta"
 
 [project]
@@ -170,3 +170,6 @@ Prada2013 = "pybamm.input.parameters.lithium_ion.Prada2013:get_parameter_values"
 Ramadass2004 = "pybamm.input.parameters.lithium_ion.Ramadass2004:get_parameter_values"
 Xu2019 = "pybamm.input.parameters.lithium_ion.Xu2019:get_parameter_values"
 ECM_Example = "pybamm.input.parameters.ecm.example_set:get_parameter_values"
+
+# [tool.setuptools.packages.find]
+# include = ["pybamm", "pybamm.*"]

From 1e480eec59f5d34b7dbe540ef1a4aaf971429165 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 31 Aug 2023 21:15:49 +0530
Subject: [PATCH 014/199] Revert "#3049 Temporarily use `--no-build-isolation`
 in CI"

This reverts commit bfafc753db11160f010b59998dfa8429a79ceaf6.
---
 .github/workflows/test_on_push.yml | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 67bac67be7..ee633bd5dc 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -92,7 +92,7 @@ jobs:
           pip install --upgrade pip wheel setuptools nox
           # For some reason casadi needs to be installed first
           pip install casadi
-          pip install -e .[all,docs] --no-build-isolation
+          pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -154,7 +154,7 @@ jobs:
           pip install --upgrade pip wheel setuptools nox
           # For some reason casadi needs to be installed first
           pip install casadi
-          pip install -e .[all,docs] --no-build-isolation
+          pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -239,7 +239,7 @@ jobs:
           pip install --upgrade pip wheel setuptools nox
           # For some reason casadi needs to be installed first
           pip install casadi
-          pip install -e .[all,docs] --no-build-isolation
+          pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -301,7 +301,7 @@ jobs:
           pip install --upgrade pip wheel setuptools nox
           # For some reason casadi needs to be installed first
           pip install casadi
-          pip install -e .[all,docs] --no-build-isolation
+          pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -364,7 +364,7 @@ jobs:
           pip install --upgrade pip wheel setuptools nox
           # For some reason casadi needs to be installed first
           pip install casadi
-          pip install -e .[all,docs] --no-build-isolation
+          pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3

From c2e2734e787a0a156b80eba54fa5d7cec33a0459 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 31 Aug 2023 21:15:59 +0530
Subject: [PATCH 015/199] #3049 Remove `casadi` installation prior to editable

This reverts commit 66e930264daefd8c1817feb3003cb853aee3c8ad.
---
 .github/workflows/test_on_push.yml | 10 ----------
 1 file changed, 10 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index ee633bd5dc..2fd4c92b2e 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -90,8 +90,6 @@ jobs:
       - name: Install PyBaMM dependencies
         run: |
           pip install --upgrade pip wheel setuptools nox
-          # For some reason casadi needs to be installed first
-          pip install casadi
           pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
@@ -152,8 +150,6 @@ jobs:
       - name: Install PyBaMM dependencies
         run: |
           pip install --upgrade pip wheel setuptools nox
-          # For some reason casadi needs to be installed first
-          pip install casadi
           pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
@@ -237,8 +233,6 @@ jobs:
       - name: Install PyBaMM dependencies
         run: |
           pip install --upgrade pip wheel setuptools nox
-          # For some reason casadi needs to be installed first
-          pip install casadi
           pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
@@ -299,8 +293,6 @@ jobs:
       - name: Install PyBaMM dependencies
         run: |
           pip install --upgrade pip wheel setuptools nox
-          # For some reason casadi needs to be installed first
-          pip install casadi
           pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
@@ -362,8 +354,6 @@ jobs:
       - name: Install PyBaMM dependencies
         run: |
           pip install --upgrade pip wheel setuptools nox
-          # For some reason casadi needs to be installed first
-          pip install casadi
           pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux

From 2cd36af55a91517e46622cd0beb1aedffdae6533 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 31 Aug 2023 22:35:27 +0530
Subject: [PATCH 016/199] #3049 specify `cmake`, fix `casadi` build-time
 requirements

---
 pyproject.toml | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 5bc365e9ad..1bef595bbe 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -12,7 +12,12 @@
 [build-system]
 # TODO: specify minimum version of setuptools otherwise scikits.odes, NumPy, and others
 # will fail to install
-requires = ["setuptools", "wheel", "casadi>=3.6.0"]
+requires = [
+    "setuptools",
+    "wheel",
+    "casadi>=3.6.0; platform_system!='Windows'",
+    "cmake; platform_system=='Linux'",
+    ]
 build-backend = "setuptools.build_meta"
 
 [project]

From 0cdfd5a81b75c95916e8bb6206c093de4825225d Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Fri, 1 Sep 2023 00:22:36 +0530
Subject: [PATCH 017/199] #3049 Fix doctests, trigger example notebook tests

---
 docs/source/user_guide/installation/windows-wsl.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/source/user_guide/installation/windows-wsl.rst b/docs/source/user_guide/installation/windows-wsl.rst
index d08545edc0..6453c92211 100644
--- a/docs/source/user_guide/installation/windows-wsl.rst
+++ b/docs/source/user_guide/installation/windows-wsl.rst
@@ -22,13 +22,13 @@ Get PyBaMM's Source Code
 
      sudo apt install git-core
 
-3. Clone the PyBaMM repository::
+3. Clone the PyBaMM repository:
 
 .. code:: bash
 
      git clone https://github.com/pybamm-team/PyBaMM.git
 
-4. Enter the PyBaMM Directory by running::
+4. Enter the PyBaMM Directory by running:
 
 .. code:: bash
 

From fc222ca897a7545b3321476497ea4789beae3ede Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Fri, 1 Sep 2023 00:48:59 +0530
Subject: [PATCH 018/199] #3049 Remove non-colour `nox` output in the CI

---
 noxfile.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/noxfile.py b/noxfile.py
index d1f119cdf1..4bc91d7b44 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -16,9 +16,7 @@
     "SUNDIALS_INST": f"{homedir}/.local",
     "LD_LIBRARY_PATH": f"{homedir}/.local/lib:",
 }
-# Do not stdout ANSI colours on GitHub Actions
 if os.getenv("CI") == "true":
-    os.environ["NO_COLOR"] = "1"
     # The setup-python action installs and caches dependencies by default, so we skip
     # installing them again in nox environments. The dev and docs sessions will still
     # require a virtual environment, but we don't run them in the CI

From 5cfc07adcbccd083e25ca5529e432ef5ce95b156 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Fri, 1 Sep 2023 00:58:05 +0530
Subject: [PATCH 019/199] #3049 Force colour output on GitHub Actions

---
 .github/workflows/test_on_push.yml | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 2fd4c92b2e..3fe00ccc0f 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -4,6 +4,9 @@ on:
   workflow_dispatch:
   pull_request:
 
+env:
+  FORCE_COLOR: 3
+
 concurrency:
   # github.workflow: name of the workflow, so that we don't cancel other workflows
   # github.event.pull_request.number || github.ref: pull request number or branch name if not a pull request

From 82197eb1addccbd54a710e6ae154e467a85e7849 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Fri, 1 Sep 2023 01:09:09 +0530
Subject: [PATCH 020/199] #3049 Remove inessential editable install job in
 favour of `nox`

---
 .github/workflows/test_on_push.yml | 15 +++++----------
 1 file changed, 5 insertions(+), 10 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 3fe00ccc0f..ad3ab3c6b0 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -90,10 +90,9 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install PyBaMM dependencies
+      - name: Install standard Python dependencies
         run: |
           pip install --upgrade pip wheel setuptools nox
-          pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -150,10 +149,9 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install PyBaMM dependencies
+      - name: Install standard Python dependencies
         run: |
           pip install --upgrade pip wheel setuptools nox
-          pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -233,10 +231,9 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install PyBaMM dependencies
+      - name: Install standard Python dependencies
         run: |
           pip install --upgrade pip wheel setuptools nox
-          pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -293,10 +290,9 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install PyBaMM dependencies
+      - name: Install standard Python dependencies
         run: |
           pip install --upgrade pip wheel setuptools nox
-          pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -354,10 +350,9 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install PyBaMM dependencies
+      - name: Install standard Python dependencies
         run: |
           pip install --upgrade pip wheel setuptools nox
-          pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3

From 13ed52dd52851ff58278cde1b1fb7aefd27d44ef Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Fri, 1 Sep 2023 20:23:05 +0530
Subject: [PATCH 021/199] #3049 Remove improper CMake message, add for macOS

---
 .github/workflows/test_on_push.yml | 4 ++--
 CMakeLists.txt                     | 1 -
 pyproject.toml                     | 2 +-
 3 files changed, 3 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index ad3ab3c6b0..81b0908135 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -76,7 +76,7 @@ jobs:
         run: |
           brew analytics off
           brew update
-          brew install graphviz openblas
+          brew install graphviz openblas cmake
 
       - name: Install Windows system dependencies
         if: matrix.os == 'windows-latest'
@@ -217,7 +217,7 @@ jobs:
         run: |
           brew analytics off
           brew update
-          brew install graphviz openblas
+          brew install graphviz openblas cmake
 
       - name: Install Windows system dependencies
         if: matrix.os == 'windows-latest'
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 889e1c1584..a58ef66933 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -65,7 +65,6 @@ if (CASADI_DIR)
   file(TO_CMAKE_PATH ${CASADI_DIR} CASADI_DIR)
   message("Found python casadi path: ${CASADI_DIR}")
 endif()
-message("Could not find python casadi path")
 
 if(${USE_PYTHON_CASADI})
   message("Trying to link against python casadi package")
diff --git a/pyproject.toml b/pyproject.toml
index 1bef595bbe..0c22507716 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -16,7 +16,7 @@ requires = [
     "setuptools",
     "wheel",
     "casadi>=3.6.0; platform_system!='Windows'",
-    "cmake; platform_system=='Linux'",
+    "cmake; platform_system!='Windows'",
     ]
 build-backend = "setuptools.build_meta"
 

From 24fbb8f7419cb6ed6540bec27882e04443b25b13 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 2 Sep 2023 13:39:40 +0530
Subject: [PATCH 022/199] #3049 Fix installation link

---
 setup.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 5072e17fad..fc657e7791 100644
--- a/setup.py
+++ b/setup.py
@@ -145,8 +145,7 @@ def run(self):
                 "cmake configuration steps encountered errors, and the idaklu module"
                 " could not be built. Make sure dependencies are correctly "
                 "installed. See "
-                "https://github.com/pybamm-team/PyBaMM/tree/develop"
-                "INSTALL-LINUX-MAC.md"
+                "https://docs.pybamm.org/en/latest/source/user_guide/installation/install-from-source.html" # noqa: E501
             )
             raise RuntimeError(msg)
         else:

From 2561a6e770d721b2b311665ed6f67efdfddb3c27 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 2 Sep 2023 13:46:00 +0530
Subject: [PATCH 023/199] #3049 Remove casadi rpath fix because its shared
 object cannot be found

---
 .github/workflows/publish_pypi.yml | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index fbdcf6fcc3..864bec5cb1 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -99,7 +99,7 @@ jobs:
           brew update
           brew reinstall gcc
           brew install libomp
-          python -m pip install cmake wget
+          python -m pip install wget
           python scripts/install_KLU_Sundials.py
 
       - name: Build wheels on Linux and MacOS
@@ -113,8 +113,7 @@ jobs:
           CIBW_BEFORE_BUILD_LINUX: "python -m pip install cmake casadi numpy"
           CIBW_BEFORE_BUILD_MACOS: >
             python -m pip
-            install cmake casadi numpy &&
-            python scripts/fix_casadi_rpath_mac.py &&
+            install casadi numpy &&
             scripts/fix_suitesparse_rpath_mac.sh
           # got error "re.error: multiple repeat at position 104" on python 3.7 when --require-archs added, so remove
           # it for mac

From 921010ec8cc494ff0c736cb9b5444c240edd2c15 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 2 Sep 2023 21:18:27 +0530
Subject: [PATCH 024/199] #3049 Remove `cmake` from macOS in CI

---
 .github/workflows/test_on_push.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 81b0908135..ad3ab3c6b0 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -76,7 +76,7 @@ jobs:
         run: |
           brew analytics off
           brew update
-          brew install graphviz openblas cmake
+          brew install graphviz openblas
 
       - name: Install Windows system dependencies
         if: matrix.os == 'windows-latest'
@@ -217,7 +217,7 @@ jobs:
         run: |
           brew analytics off
           brew update
-          brew install graphviz openblas cmake
+          brew install graphviz openblas
 
       - name: Install Windows system dependencies
         if: matrix.os == 'windows-latest'

From 1ee48c139a9448716c8645ebc92c866feeac6641 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sun, 3 Sep 2023 13:43:16 +0530
Subject: [PATCH 025/199] Revert "#3049 Remove casadi rpath fix because its
 shared object cannot be found"

This reverts commit 2561a6e770d721b2b311665ed6f67efdfddb3c27.
---
 .github/workflows/publish_pypi.yml | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 864bec5cb1..fbdcf6fcc3 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -99,7 +99,7 @@ jobs:
           brew update
           brew reinstall gcc
           brew install libomp
-          python -m pip install wget
+          python -m pip install cmake wget
           python scripts/install_KLU_Sundials.py
 
       - name: Build wheels on Linux and MacOS
@@ -113,7 +113,8 @@ jobs:
           CIBW_BEFORE_BUILD_LINUX: "python -m pip install cmake casadi numpy"
           CIBW_BEFORE_BUILD_MACOS: >
             python -m pip
-            install casadi numpy &&
+            install cmake casadi numpy &&
+            python scripts/fix_casadi_rpath_mac.py &&
             scripts/fix_suitesparse_rpath_mac.sh
           # got error "re.error: multiple repeat at position 104" on python 3.7 when --require-archs added, so remove
           # it for mac

From e4ea1995bdcf338dfd5c09b5c3132fc1d93b887d Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sun, 3 Sep 2023 22:47:52 +0530
Subject: [PATCH 026/199] #3049 Fix macOS universal ABI and platform wheels
 creation bug

---
 .github/workflows/test_on_push.yml | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index ad3ab3c6b0..4913a6f5ca 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -108,8 +108,8 @@ jobs:
             ${{ env.HOME }}/.local/examples/
           key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
-      - name: Install SuiteSparse and SUNDIALS on GNU/Linux
-        if: matrix.os == 'ubuntu-latest'
+      - name: Install SuiteSparse and SUNDIALS on GNU/Linux and macOS
+        if: matrix.os != 'windows-latest'
         run: nox -s pybamm-requires
 
       - name: Run unit tests for ${{ matrix.os }} with Python ${{ matrix.python-version }}
@@ -249,8 +249,8 @@ jobs:
             ${{ env.HOME }}/.local/examples/
           key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
-      - name: Install SuiteSparse and SUNDIALS on GNU/Linux
-        if: matrix.os == 'ubuntu-latest'
+      - name: Install SuiteSparse and SUNDIALS on GNU/Linux and macOS
+        if: matrix.os != 'windows-latest'
         run: nox -s pybamm-requires
 
       - name: Run integration tests for ${{ matrix.os }} with Python ${{ matrix.python-version }}

From a91885a56a4c8eb58b0541cc5571dc5bb3be01cb Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sun, 3 Sep 2023 23:00:31 +0530
Subject: [PATCH 027/199] #3049 Add a Fortran compiler via Homebrew

---
 .github/workflows/test_on_push.yml | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 4913a6f5ca..2040b25bfd 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -76,7 +76,7 @@ jobs:
         run: |
           brew analytics off
           brew update
-          brew install graphviz openblas
+          brew install graphviz openblas gcc gfortran libomp
 
       - name: Install Windows system dependencies
         if: matrix.os == 'windows-latest'
@@ -94,9 +94,9 @@ jobs:
         run: |
           pip install --upgrade pip wheel setuptools nox
 
-      - name: Cache pybamm-requires nox environment for GNU/Linux
+      - name: Cache pybamm-requires nox environment for GNU/Linux and macOS
         uses: actions/cache@v3
-        if: matrix.os == 'ubuntu-latest'
+        if: matrix.os != 'windows-latest'
         with:
           path: |
             # Repository files
@@ -217,7 +217,7 @@ jobs:
         run: |
           brew analytics off
           brew update
-          brew install graphviz openblas
+          brew install graphviz openblas gcc gfortran libomp
 
       - name: Install Windows system dependencies
         if: matrix.os == 'windows-latest'
@@ -235,9 +235,9 @@ jobs:
         run: |
           pip install --upgrade pip wheel setuptools nox
 
-      - name: Cache pybamm-requires nox environment for GNU/Linux
+      - name: Cache pybamm-requires nox environment for GNU/Linux and macOS
         uses: actions/cache@v3
-        if: matrix.os == 'ubuntu-latest'
+        if: matrix.os != 'windows-latest'
         with:
           path: |
             # Repository files

From ab246c658fe1e207581ac87cb76cf6547629734f Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sun, 3 Sep 2023 23:41:22 +0530
Subject: [PATCH 028/199] #3049 Install optional solvers for macOS `nox`
 sessions

---
 .github/workflows/test_on_push.yml               |  2 +-
 .../installation/install-from-source.rst         | 16 ++++++++--------
 noxfile.py                                       | 11 ++++++-----
 3 files changed, 15 insertions(+), 14 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 2040b25bfd..ddaeeb7edf 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -247,7 +247,7 @@ jobs:
             ${{ env.HOME }}/.local/lib/
             ${{ env.HOME }}/.local/include/
             ${{ env.HOME }}/.local/examples/
-          key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux and macOS
         if: matrix.os != 'windows-latest'
diff --git a/docs/source/user_guide/installation/install-from-source.rst b/docs/source/user_guide/installation/install-from-source.rst
index 787778fa01..cd846a6ec2 100644
--- a/docs/source/user_guide/installation/install-from-source.rst
+++ b/docs/source/user_guide/installation/install-from-source.rst
@@ -164,10 +164,10 @@ guidelines <https://github.com/pybamm-team/PyBaMM/blob/develop/CONTRIBUTING.md>`
 Running the tests
 -----------------
 
-Using Nox (recommended)
+Using ``Nox`` (recommended)
 ~~~~~~~~~~~~~~~~~~~~~~~
 
-You can use Nox to run the unit tests and example notebooks in isolated virtual environments.
+You can use ``Nox`` to run the unit tests and example notebooks in isolated virtual environments.
 
 The default command
 
@@ -175,7 +175,7 @@ The default command
 
 	nox
 
-will run pre-commit, install ``Linux`` dependencies, and run the unit tests.
+will run pre-commit, install ``Linux`` and ``macOS`` dependencies, and run the unit tests.
 This can take several minutes.
 
 To just run the unit tests, use
@@ -245,7 +245,7 @@ Doctests, examples, and coverage
 - ``nox -s coverage``: Measure current test coverage and generate a coverage report.
 - ``nox -s quick``: Run integration tests, unit tests, and doctests sequentially.
 
-Extra tips while using Nox
+Extra tips while using ``Nox``
 --------------------------
 Here are some additional useful commands you can run with ``Nox``:
 
@@ -278,11 +278,11 @@ sure each command was successful.
 One possibility is that you have not set your ``LD_LIBRARY_PATH`` to
 point to the sundials library, type ``echo $LD_LIBRARY_PATH`` and make
 sure one of the directories printed out corresponds to where the
-sundials libraries are located.
+SUNDIALS libraries are located.
 
 Another common reason is that you forget to install a BLAS library such
-as OpenBLAS before installing sundials. Check the cmake output when you
-configured Sundials, it might say:
+as OpenBLAS before installing SUNDIALS. Check the cmake output when you
+configured SUNDIALS, it might say:
 
 ::
 
@@ -291,5 +291,5 @@ configured Sundials, it might say:
 
 If this is the case, on a Debian or Ubuntu system you can install
 OpenBLAS using ``sudo apt-get install libopenblas-dev`` (or
-``brew install openblas`` for Mac OS) and then re-install sundials using
+``brew install openblas`` for Mac OS) and then re-install SUNDIALS using
 the instructions above.
diff --git a/noxfile.py b/noxfile.py
index 4bc91d7b44..f9b97aa909 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -5,7 +5,7 @@
 
 # Options to modify nox behaviour
 nox.options.reuse_existing_virtualenvs = True
-if sys.platform == "linux":
+if sys.platform != "win32":
     nox.options.sessions = ["pre-commit", "pybamm-requires", "unit"]
 else:
     nox.options.sessions = ["pre-commit", "unit"]
@@ -77,8 +77,9 @@ def run_integration(session):
     """Run the integration tests."""
     set_environment_variables(PYBAMM_ENV, session=session)
     session.run_always("pip", "install", "-e", ".[all]")
-    if sys.platform == "linux":
+    if sys.platform != "win32":
         session.run_always("pip", "install", "-e", ".[odes]")
+        session.run_always("pip", "install", "-e", ".[jax]")
     session.run("python", "run-tests.py", "--integration")
 
 
@@ -94,7 +95,7 @@ def run_unit(session):
     """Run the unit tests."""
     set_environment_variables(PYBAMM_ENV, session=session)
     session.run_always("pip", "install", "-e", ".[all]")
-    if sys.platform == "linux":
+    if sys.platform != "win32":
         session.run_always("pip", "install", "-e", ".[odes]")
         session.run_always("pip", "install", "-e", ".[jax]")
     session.run("python", "run-tests.py", "--unit")
@@ -123,7 +124,7 @@ def set_dev(session):
     envbindir = session.bin
     session.install("-e", ".[all]")
     session.install("cmake")
-    if sys.platform == "linux" or sys.platform == "darwin":
+    if sys.platform != "win32":
         session.run(
             "echo",
             "export",
@@ -139,7 +140,7 @@ def run_tests(session):
     """Run the unit tests and integration tests sequentially."""
     set_environment_variables(PYBAMM_ENV, session=session)
     session.run_always("pip", "install", "-e", ".[all]")
-    if sys.platform == "linux" or sys.platform == "darwin":
+    if sys.platform != "win32":
         session.run_always("pip", "install", "-e", ".[odes]")
         session.run_always("pip", "install", "-e", ".[jax]")
     session.run("python", "run-tests.py", "--all")

From 60ac7bf1e2236fb49c1ed1861312fe196dcae6f8 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sun, 3 Sep 2023 23:55:35 +0530
Subject: [PATCH 029/199] #3049 Add remaining `pybamm-requires` caches

---
 .github/workflows/test_on_push.yml | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index ddaeeb7edf..34bd87c148 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -106,7 +106,7 @@ jobs:
             ${{ env.HOME }}/.local/lib/
             ${{ env.HOME }}/.local/include/
             ${{ env.HOME }}/.local/examples/
-          key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux and macOS
         if: matrix.os != 'windows-latest'
@@ -164,7 +164,7 @@ jobs:
             ${{ env.HOME }}/.local/lib/
             ${{ env.HOME }}/.local/include/
             ${{ env.HOME }}/.local/examples/
-          key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         run: nox -s pybamm-requires
@@ -305,7 +305,7 @@ jobs:
             ${{ env.HOME }}/.local/lib/
             ${{ env.HOME }}/.local/include/
             ${{ env.HOME }}/.local/examples/
-          key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         run: nox -s pybamm-requires
@@ -365,7 +365,7 @@ jobs:
             ${{ env.HOME }}/.local/lib/
             ${{ env.HOME }}/.local/include/
             ${{ env.HOME }}/.local/examples/
-          key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         run: nox -s pybamm-requires

From 95d72e5dd3e401ac47af22830fe73bcb192ffbb4 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 4 Sep 2023 00:16:57 +0530
Subject: [PATCH 030/199] #3049 Fix failing doctests

---
 .../user_guide/installation/install-from-source.rst  | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/docs/source/user_guide/installation/install-from-source.rst b/docs/source/user_guide/installation/install-from-source.rst
index cd846a6ec2..2a43b15096 100644
--- a/docs/source/user_guide/installation/install-from-source.rst
+++ b/docs/source/user_guide/installation/install-from-source.rst
@@ -105,8 +105,8 @@ Installing PyBaMM
 
 You should now have everything ready to build and install PyBaMM successfully.
 
-Using Nox (recommended)
-~~~~~~~~~~~~~~~~~~~~~~~
+Using ``Nox`` (recommended)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. code:: bash
 
@@ -164,7 +164,7 @@ guidelines <https://github.com/pybamm-team/PyBaMM/blob/develop/CONTRIBUTING.md>`
 Running the tests
 -----------------
 
-Using ``Nox`` (recommended)
+Using Nox (recommended)
 ~~~~~~~~~~~~~~~~~~~~~~~
 
 You can use ``Nox`` to run the unit tests and example notebooks in isolated virtual environments.
@@ -246,7 +246,7 @@ Doctests, examples, and coverage
 - ``nox -s quick``: Run integration tests, unit tests, and doctests sequentially.
 
 Extra tips while using ``Nox``
---------------------------
+------------------------------
 Here are some additional useful commands you can run with ``Nox``:
 
 - ``--verbose or -v``: Enables verbose mode, providing more detailed output during the execution of Nox sessions.
@@ -258,9 +258,9 @@ Here are some additional useful commands you can run with ``Nox``:
 - ``--report output.json``: Generates a JSON report of the Nox session execution and saves it to the specified file, in this case, "output.json".
 
 Troubleshooting
-===============
+---------------
 
-**Problem:** I’ve made edits to source files in PyBaMM, but these are
+**Problem:** I have made edits to source files in PyBaMM, but these are
 not being used when I run my Python script.
 
 **Solution:** Make sure you have installed PyBaMM using the ``-e`` flag,

From 5dae55fd7fbd933b5c848cd5a5638e4c85dc618c Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 4 Sep 2023 02:21:02 +0530
Subject: [PATCH 031/199] #3049 Speed up solvers installation without extras

Remove dependence on `setuptools` and `wheel`, and use `pipx` which GitHub Actions already comes with.
---
 .github/workflows/test_on_push.yml | 42 ++++++++----------------------
 noxfile.py                         | 26 ++++++++++++------
 2 files changed, 29 insertions(+), 39 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 34bd87c148..f2be240d39 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -90,10 +90,6 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install standard Python dependencies
-        run: |
-          pip install --upgrade pip wheel setuptools nox
-
       - name: Cache pybamm-requires nox environment for GNU/Linux and macOS
         uses: actions/cache@v3
         if: matrix.os != 'windows-latest'
@@ -110,10 +106,10 @@ jobs:
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux and macOS
         if: matrix.os != 'windows-latest'
-        run: nox -s pybamm-requires
+        run: pipx run nox -s pybamm-requires
 
       - name: Run unit tests for ${{ matrix.os }} with Python ${{ matrix.python-version }}
-        run: nox -s unit
+        run: pipx run nox -s unit
 
   # Runs only on Ubuntu with Python 3.11
   check_coverage:
@@ -149,10 +145,6 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install standard Python dependencies
-        run: |
-          pip install --upgrade pip wheel setuptools nox
-
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
         with:
@@ -167,10 +159,10 @@ jobs:
           key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
-        run: nox -s pybamm-requires
+        run: pipx run nox -s pybamm-requires
 
       - name: Run unit tests for Ubuntu with Python 3.11 and generate coverage report
-        run: nox -s coverage
+        run: pipx run nox -s coverage
 
       - name: Upload coverage report
         uses: codecov/codecov-action@v3.1.4
@@ -231,10 +223,6 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install standard Python dependencies
-        run: |
-          pip install --upgrade pip wheel setuptools nox
-
       - name: Cache pybamm-requires nox environment for GNU/Linux and macOS
         uses: actions/cache@v3
         if: matrix.os != 'windows-latest'
@@ -251,10 +239,10 @@ jobs:
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux and macOS
         if: matrix.os != 'windows-latest'
-        run: nox -s pybamm-requires
+        run: pipx run nox -s pybamm-requires
 
       - name: Run integration tests for ${{ matrix.os }} with Python ${{ matrix.python-version }}
-        run: nox -s integration
+        run: pipx run nox -s integration
 
   # Runs only on Ubuntu with Python 3.11
   run_doctests_and_example_tests:
@@ -290,10 +278,6 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install standard Python dependencies
-        run: |
-          pip install --upgrade pip wheel setuptools nox
-
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
         with:
@@ -308,13 +292,13 @@ jobs:
           key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
-        run: nox -s pybamm-requires
+        run: pipx run nox -s pybamm-requires
 
       - name: Install docs dependencies and run doctests for GNU/Linux with Python 3.11
-        run: nox -s doctests
+        run: pipx run nox -s doctests
 
       - name: Install dev dependencies and run example tests for GNU/Linux with Python 3.11
-        run: nox -s examples
+        run: pipx run nox -s examples
 
   # Runs only on Ubuntu with Python 3.11
   run_scripts_tests:
@@ -350,10 +334,6 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install standard Python dependencies
-        run: |
-          pip install --upgrade pip wheel setuptools nox
-
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
         with:
@@ -368,7 +348,7 @@ jobs:
           key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
-        run: nox -s pybamm-requires
+        run: pipx run nox -s pybamm-requires
 
       - name: Install dev dependencies and run example scripts tests for GNU/Linux with Python 3.11
-        run: nox -s scripts
+        run: pipx run nox -s scripts
diff --git a/noxfile.py b/noxfile.py
index f9b97aa909..58a66b7c76 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -16,6 +16,12 @@
     "SUNDIALS_INST": f"{homedir}/.local",
     "LD_LIBRARY_PATH": f"{homedir}/.local/lib:",
 }
+# Versions compatible with the current version of PyBaMM. Installed directly in the
+# sessions to skip redundant installation of dependencies and building wheels both in
+# the CI and locally
+JAX_VERSION = "0.4.8"
+JAXLIB_VERSION = "0.4.7"
+
 if os.getenv("CI") == "true":
     # The setup-python action installs and caches dependencies by default, so we skip
     # installing them again in nox environments. The dev and docs sessions will still
@@ -65,8 +71,9 @@ def run_coverage(session):
     session.run_always("pip", "install", "coverage")
     session.run_always("pip", "install", "-e", ".[all]")
     if sys.platform != "win32":
-        session.run_always("pip", "install", "-e", ".[odes]")
-        session.run_always("pip", "install", "-e", ".[jax]")
+        session.run_always("pip", "install", "scikits.odes")
+        session.run_always("pip", "install", f"jax=={JAX_VERSION}")
+        session.run_always("pip", "install", f"jaxlib=={JAXLIB_VERSION}")
     session.run("coverage", "run", "--rcfile=.coveragerc", "run-tests.py", "--nosub")
     session.run("coverage", "combine")
     session.run("coverage", "xml")
@@ -78,8 +85,9 @@ def run_integration(session):
     set_environment_variables(PYBAMM_ENV, session=session)
     session.run_always("pip", "install", "-e", ".[all]")
     if sys.platform != "win32":
-        session.run_always("pip", "install", "-e", ".[odes]")
-        session.run_always("pip", "install", "-e", ".[jax]")
+        session.run_always("pip", "install", "scikits.odes")
+        session.run_always("pip", "install", f"jax=={JAX_VERSION}")
+        session.run_always("pip", "install", f"jaxlib=={JAXLIB_VERSION}")
     session.run("python", "run-tests.py", "--integration")
 
 
@@ -96,8 +104,9 @@ def run_unit(session):
     set_environment_variables(PYBAMM_ENV, session=session)
     session.run_always("pip", "install", "-e", ".[all]")
     if sys.platform != "win32":
-        session.run_always("pip", "install", "-e", ".[odes]")
-        session.run_always("pip", "install", "-e", ".[jax]")
+        session.run_always("pip", "install", "scikits.odes")
+        session.run_always("pip", "install", f"jax=={JAX_VERSION}")
+        session.run_always("pip", "install", f"jaxlib=={JAXLIB_VERSION}")
     session.run("python", "run-tests.py", "--unit")
 
 
@@ -141,8 +150,9 @@ def run_tests(session):
     set_environment_variables(PYBAMM_ENV, session=session)
     session.run_always("pip", "install", "-e", ".[all]")
     if sys.platform != "win32":
-        session.run_always("pip", "install", "-e", ".[odes]")
-        session.run_always("pip", "install", "-e", ".[jax]")
+        session.run_always("pip", "install", "scikits.odes")
+        session.run_always("pip", "install", f"jax=={JAX_VERSION}")
+        session.run_always("pip", "install", f"jaxlib=={JAXLIB_VERSION}")
     session.run("python", "run-tests.py", "--all")
 
 

From 4c53cc53f35f3279717850048c790cf437276e32 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 4 Sep 2023 02:21:47 +0530
Subject: [PATCH 032/199] #3049 Cleanup scheduled tests workflow

---
 .github/workflows/run_periodic_tests.yml | 42 +++++++++---------------
 1 file changed, 15 insertions(+), 27 deletions(-)

diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml
index f70a748800..fbe664abb0 100644
--- a/.github/workflows/run_periodic_tests.yml
+++ b/.github/workflows/run_periodic_tests.yml
@@ -12,24 +12,19 @@ on:
   schedule:
     - cron: "0 3 * * *"
 
-jobs:
-  pre_job:
-    runs-on: ubuntu-latest
-    # Map a step output to a job output
-    outputs:
-      should_skip: ${{ steps.skip_check.outputs.should_skip }}
-    steps:
-      - id: skip_check
-        uses: fkirc/skip-duplicate-actions@master
-        with:
-          # All of these options are optional, so you can remove them if you are happy with the defaults
-          concurrent_skipping: "never"
-          cancel_others: "true"
-          paths_ignore: '["**/README.md"]'
+env:
+  FORCE_COLOR: 3
 
+concurrency:
+  # github.workflow: name of the workflow, so that we don't cancel other workflows
+  # github.event.pull_request.number || github.ref: pull request number or branch name if not a pull request
+  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
+  # Cancel in-progress runs when a new workflow with the same group name is triggered
+  # This avoids workflow runs on both pushes and PRs
+  cancel-in-progress: true
+
+jobs:
   style:
-    needs: pre_job
-    if: ${{ needs.pre_job.outputs.should_skip != 'true' }}
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v3
@@ -66,19 +61,12 @@ jobs:
           sudo apt install gfortran gcc libopenblas-dev graphviz pandoc
           sudo apt install texlive-full
 
-      # Added fixes to homebrew installs:
-      # rm -f /usr/local/bin/2to3
-      # (see https://github.com/actions/virtual-environments/issues/2322)
       - name: Install MacOS system dependencies
         if: matrix.os == 'macos-latest'
         run: |
-          rm -f /usr/local/bin/2to3*
-          rm -f /usr/local/bin/idle3*
-          rm -f /usr/local/bin/pydoc3*
-          rm -f /usr/local/bin/python3*
+          brew analytics off
           brew update
-          brew install graphviz
-          brew install openblas
+          brew install graphviz openblas gcc gfortran libomp
 
       - name: Install Windows system dependencies
         if: matrix.os == 'windows-latest'
@@ -88,8 +76,8 @@ jobs:
         run: |
           python -m pip install --upgrade pip wheel setuptools nox
 
-      - name: Install SuiteSparse and SUNDIALS on GNU/Linux
-        if: matrix.os == 'ubuntu-latest'
+      - name: Install SuiteSparse and SUNDIALS on GNU/Linux and macOS
+        if: matrix.os != 'windows-latest'
         run: nox -s pybamm-requires
 
       - name: Run unit tests for GNU/Linux with Python 3.8, 3.9, and 3.10, and for macOS and Windows with all Python versions

From 0c611d915ea380e28ad958d4613320cecd10247c Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 4 Sep 2023 02:22:32 +0530
Subject: [PATCH 033/199] #3049 Remove dependence on deprecated `pkg_resources`

---
 pybamm/util.py                                          | 8 ++++----
 tests/unit/test_parameters/test_parameter_sets_class.py | 4 ++--
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/pybamm/util.py b/pybamm/util.py
index 5f84f37e0a..772ab8b78b 100644
--- a/pybamm/util.py
+++ b/pybamm/util.py
@@ -6,6 +6,7 @@
 #
 import argparse
 import importlib.util
+import importlib.metadata
 import numbers
 import os
 import pathlib
@@ -18,11 +19,10 @@
 from warnings import warn
 
 import numpy as np
-import pkg_resources
 
 import pybamm
 
-# versions of jax and jaxlib compatible with PyBaMM
+# versions of jax and jaxlib compatible with PyBaMM, also in noxfile.py
 JAX_VERSION = "0.4.8"
 JAXLIB_VERSION = "0.4.7"
 
@@ -272,8 +272,8 @@ def have_jax():
 def is_jax_compatible():
     """Check if the available version of jax and jaxlib are compatible with PyBaMM"""
     return (
-        pkg_resources.get_distribution("jax").version == JAX_VERSION
-        and pkg_resources.get_distribution("jaxlib").version == JAXLIB_VERSION
+        importlib.metadata.version("jax") == JAX_VERSION
+        and importlib.metadata.version("jaxlib") == JAXLIB_VERSION
     )
 
 
diff --git a/tests/unit/test_parameters/test_parameter_sets_class.py b/tests/unit/test_parameters/test_parameter_sets_class.py
index f548fd7955..309b18bbf2 100644
--- a/tests/unit/test_parameters/test_parameter_sets_class.py
+++ b/tests/unit/test_parameters/test_parameter_sets_class.py
@@ -1,10 +1,10 @@
 #
 # Tests for the ParameterSets class
 #
+import importlib.metadata
 from tests import TestCase
 
 import pybamm
-import pkg_resources
 import unittest
 
 
@@ -25,7 +25,7 @@ def test_all_registered(self):
         """Check that all parameter sets have been registered with the
         ``pybamm_parameter_sets`` entry point"""
         known_entry_points = set(
-            ep.name for ep in pkg_resources.iter_entry_points("pybamm_parameter_sets")
+            ep.name for ep in importlib.metadata.entry_points()["pybamm_parameter_sets"]
         )
         self.assertEqual(set(pybamm.parameter_sets.keys()), known_entry_points)
         self.assertEqual(len(known_entry_points), len(pybamm.parameter_sets))

From 82082f278674885b299be3b181ef9061e8695a46 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 4 Sep 2023 02:27:08 +0530
Subject: [PATCH 034/199] #3049 Improvements to scheduled test workflow

---
 .github/workflows/run_periodic_tests.yml | 16 ++++++----------
 1 file changed, 6 insertions(+), 10 deletions(-)

diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml
index fbe664abb0..9420049a7b 100644
--- a/.github/workflows/run_periodic_tests.yml
+++ b/.github/workflows/run_periodic_tests.yml
@@ -72,36 +72,32 @@ jobs:
         if: matrix.os == 'windows-latest'
         run: choco install graphviz --version=2.38.0.20190211
 
-      - name: Install standard python dependencies
-        run: |
-          python -m pip install --upgrade pip wheel setuptools nox
-
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux and macOS
         if: matrix.os != 'windows-latest'
-        run: nox -s pybamm-requires
+        run: pipx run nox -s pybamm-requires
 
       - name: Run unit tests for GNU/Linux with Python 3.8, 3.9, and 3.10, and for macOS and Windows with all Python versions
         if: (matrix.os == 'ubuntu-latest' && matrix.python-version != 3.11) || (matrix.os != 'ubuntu-latest')
-        run: nox -s unit
+        run: pipx run nox -s unit
 
       - name: Run unit tests for GNU/Linux with Python 3.11 and generate coverage report
         if: matrix.os == 'ubuntu-latest' && matrix.python-version == 3.11
-        run: nox -s coverage
+        run: pipx run nox -s coverage
 
       - name: Upload coverage report
         if: matrix.os == 'ubuntu-latest' && matrix.python-version == 3.11
         uses: codecov/codecov-action@v3.1.4
 
       - name: Run integration tests
-        run: nox -s integration
+        run: pipx run nox -s integration
 
       - name: Install docs dependencies and run doctests
         if: matrix.os == 'ubuntu-latest'
-        run: nox -s doctests
+        run: pipx run nox -s doctests
 
       - name: Install dev dependencies and run example tests
         if: matrix.os == 'ubuntu-latest'
-        run: nox -s examples
+        run: pipx run nox -s examples
 
   #M-series Mac Mini
   build-apple-mseries:

From ee4080f1c5785caf4962239afc2c4393bdbf5d67 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 4 Sep 2023 03:23:45 +0530
Subject: [PATCH 035/199] #3049 Remove separate CMakeBuild file

---
 CMakeBuild.py | 162 --------------------------------------------------
 1 file changed, 162 deletions(-)
 delete mode 100644 CMakeBuild.py

diff --git a/CMakeBuild.py b/CMakeBuild.py
deleted file mode 100644
index 5b34bb27df..0000000000
--- a/CMakeBuild.py
+++ /dev/null
@@ -1,162 +0,0 @@
-import os
-import sys
-import subprocess
-from pathlib import Path
-from platform import system
-
-try:
-    from setuptools.command.build_ext import build_ext
-except ImportError:
-    from distutils.command.build_ext import build_ext
-
-default_lib_dir = (
-    "" if system() == "Windows" else os.path.join(os.getenv("HOME"), ".local")
-)
-
-
-def set_vcpkg_environment_variables():
-    if not os.getenv("VCPKG_ROOT_DIR"):
-        raise EnvironmentError("Environment variable 'VCPKG_ROOT_DIR' is undefined.")
-    if not os.getenv("VCPKG_DEFAULT_TRIPLET"):
-        raise EnvironmentError(
-            "Environment variable 'VCPKG_DEFAULT_TRIPLET' is undefined."
-        )
-    if not os.getenv("VCPKG_FEATURE_FLAGS"):
-        raise EnvironmentError(
-            "Environment variable 'VCPKG_FEATURE_FLAGS' is undefined."
-        )
-    return (
-        os.getenv("VCPKG_ROOT_DIR"),
-        os.getenv("VCPKG_DEFAULT_TRIPLET"),
-        os.getenv("VCPKG_FEATURE_FLAGS"),
-    )
-
-
-class CMakeBuild(build_ext):
-    user_options = build_ext.user_options + [
-        ("suitesparse-root=", None, "suitesparse source location"),
-        ("sundials-root=", None, "sundials source location"),
-    ]
-
-    def initialize_options(self):
-        build_ext.initialize_options(self)
-        self.suitesparse_root = None
-        self.sundials_root = None
-
-    def finalize_options(self):
-        build_ext.finalize_options(self)
-        # Determine the calling command to get the
-        # undefined options from.
-        # If build_ext was called directly then this
-        # doesn't matter.
-        try:
-            self.get_finalized_command("install", create=0)
-            calling_cmd = "install"
-        except AttributeError:
-            calling_cmd = "bdist_wheel"
-        self.set_undefined_options(
-            calling_cmd,
-            ("suitesparse_root", "suitesparse_root"),
-            ("sundials_root", "sundials_root"),
-        )
-        if not self.suitesparse_root:
-            self.suitesparse_root = os.path.join(default_lib_dir)
-        if not self.sundials_root:
-            self.sundials_root = os.path.join(default_lib_dir)
-
-    def get_build_directory(self):
-        # distutils outputs object files in directory self.build_temp
-        # (typically build/temp.*). This is our CMake build directory.
-        # On Windows, distutils is too smart and appends "Release" or
-        # "Debug" to self.build_temp. So in this case we want the
-        # build directory to be the parent directory.
-        if system() == "Windows":
-            return Path(self.build_temp).parents[0]
-        return self.build_temp
-
-    def run(self):
-        if not self.extensions:
-            return
-
-        if system() == "Windows":
-            use_python_casadi = False
-        else:
-            use_python_casadi = True
-
-        build_type = os.getenv("PYBAMM_CPP_BUILD_TYPE", "RELEASE")
-        cmake_args = [
-            "-DCMAKE_BUILD_TYPE={}".format(build_type),
-            "-DPYTHON_EXECUTABLE={}".format(sys.executable),
-            "-DUSE_PYTHON_CASADI={}".format("TRUE" if use_python_casadi else "FALSE"),
-        ]
-        if self.suitesparse_root:
-            cmake_args.append(
-                "-DSuiteSparse_ROOT={}".format(os.path.abspath(self.suitesparse_root))
-            )
-        if self.sundials_root:
-            cmake_args.append(
-                "-DSUNDIALS_ROOT={}".format(os.path.abspath(self.sundials_root))
-            )
-
-        build_dir = self.get_build_directory()
-        if not os.path.exists(build_dir):
-            os.makedirs(build_dir)
-
-        # The CMakeError.log file is generated by cmake is the configure step
-        # encounters error. In the following the existence of this file is used
-        # to determine whether or not the cmake configure step went smoothly.
-        # So must make sure this file does not remain from a previous failed build.
-        if os.path.isfile(os.path.join(build_dir, "CMakeError.log")):
-            os.remove(os.path.join(build_dir, "CMakeError.log"))
-
-        build_env = os.environ
-        if os.getenv("PYBAMM_USE_VCPKG"):
-            (
-                vcpkg_root_dir,
-                vcpkg_default_triplet,
-                vcpkg_feature_flags,
-            ) = set_vcpkg_environment_variables()
-            build_env["vcpkg_root_dir"] = vcpkg_root_dir
-            build_env["vcpkg_default_triplet"] = vcpkg_default_triplet
-            build_env["vcpkg_feature_flags"] = vcpkg_feature_flags
-
-        cmake_list_dir = os.path.abspath(os.path.dirname(__file__))
-        print("-" * 10, "Running CMake for idaklu solver", "-" * 40)
-        subprocess.run(
-            ["cmake", cmake_list_dir] + cmake_args, cwd=build_dir, env=build_env
-        )
-
-        if os.path.isfile(os.path.join(build_dir, "CMakeError.log")):
-            msg = (
-                "cmake configuration steps encountered errors, and the idaklu module"
-                " could not be built. Make sure dependencies are correctly "
-                "installed. See "
-                "https://github.com/pybamm-team/PyBaMM/tree/develop"
-                "INSTALL-LINUX-MAC.md"
-            )
-            raise RuntimeError(msg)
-        else:
-            print("-" * 10, "Building idaklu module", "-" * 40)
-            subprocess.run(
-                ["cmake", "--build", ".", "--config", "Release"],
-                cwd=build_dir,
-                env=build_env,
-            )
-
-            # Move from build temp to final position
-            for ext in self.extensions:
-                self.move_output(ext)
-
-    def move_output(self, ext):
-        # Copy built module to dist/ directory
-        build_temp = Path(self.build_temp).resolve()
-        # Get destination location
-        # self.get_ext_fullpath(ext.name) -->
-        # build/lib.linux-x86_64-3.5/idaklu.cpython-37m-x86_64-linux-gnu.so
-        # using resolve() with python < 3.6 will result in a FileNotFoundError
-        # since the location does not yet exists.
-        dest_path = Path(self.get_ext_fullpath(ext.name)).resolve()
-        source_path = build_temp / os.path.basename(self.get_ext_filename(ext.name))
-        dest_directory = dest_path.parents[0]
-        dest_directory.mkdir(parents=True, exist_ok=True)
-        self.copy_file(source_path, dest_path)

From f2a8724db36da504cc1dea4f1f50a253738481b1 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 4 Sep 2023 03:24:59 +0530
Subject: [PATCH 036/199] #3049 Add configuration for package data files

---
 pyproject.toml | 17 +++++++++++++++--
 setup.py       | 35 -----------------------------------
 2 files changed, 15 insertions(+), 37 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 0c22507716..05c70d6cbb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -176,5 +176,18 @@ Ramadass2004 = "pybamm.input.parameters.lithium_ion.Ramadass2004:get_parameter_v
 Xu2019 = "pybamm.input.parameters.lithium_ion.Xu2019:get_parameter_values"
 ECM_Example = "pybamm.input.parameters.ecm.example_set:get_parameter_values"
 
-# [tool.setuptools.packages.find]
-# include = ["pybamm", "pybamm.*"]
+[tool.setuptools]
+include-package-data = true
+
+# List of files to include as package data. These are mainly the parameter CSV files in
+# the input/parameters/ subdirectories. Other files such as the  CITATIONS file, relevant
+# README.md files, and specific .txt files inside the pybamm/ directory are also included.
+[tool.setuptools.package-data]
+pybamm = [
+    "*.txt",
+    "*.md",
+    "*.csv",
+    "*.py",
+    "pybamm/CITATIONS.bib",
+    "pybamm/plotting/mplstyle",
+]
diff --git a/setup.py b/setup.py
index fc657e7791..5da1d2b16c 100644
--- a/setup.py
+++ b/setup.py
@@ -1,6 +1,5 @@
 import os
 import sys
-import glob
 import logging
 import subprocess
 from pathlib import Path
@@ -14,8 +13,6 @@
     from distutils.core import setup, find_packages
     from distutils.command.install import install
 
-# import CMakeBuild
-
 # ---------- cmakebuild was integrated into setup.py directly --------------------------
 
 try:
@@ -176,10 +173,6 @@ def move_output(self, ext):
 
 # ---------- end of cmakebuild steps ---------------------------------------------------
 
-# default_lib_dir = (
-#     "" if system() == "Windows" else os.path.join(os.getenv("HOME"), ".local")
-# )
-
 log_format = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
 logger = logging.getLogger("PyBaMM setup")
 
@@ -281,33 +274,6 @@ def compile_KLU():
 
     return CMakeFound and PyBind11Found
 
-
-# Build the list of package data files to be included in the PyBaMM package.
-# These are mainly the parameter files located in the input/parameters/ subdirectories.
-# TODO: might be possible to include in pyproject.toml with data configuration values
-pybamm_data = []
-for file_ext in ["*.csv", "*.py", "*.md", "*.txt"]:
-    # Get all the files ending in file_ext in pybamm/input dir.
-    # list_of_files = [
-    #    'pybamm/input/drive_cycles/car_current.csv',
-    #    'pybamm/input/drive_cycles/US06.csv',
-    # ...
-    list_of_files = glob.glob("pybamm/input/**/" + file_ext, recursive=True)
-
-    # Add these files to pybamm_data.
-    # The path must be relative to the package dir (pybamm/), so
-    # must process the content of list_of_files to take out the top
-    # pybamm/ dir, i.e.:
-    # ['input/drive_cycles/car_current.csv',
-    #  'input/drive_cycles/US06.csv',
-    # ...
-    pybamm_data.extend(
-        [os.path.join(*Path(filename).parts[1:]) for filename in list_of_files]
-    )
-pybamm_data.append("./CITATIONS.bib")
-pybamm_data.append("./plotting/pybamm.mplstyle")
-pybamm_data.append("../CMakeBuild.py")
-
 idaklu_ext = Extension(
     name="pybamm.solvers.idaklu",
     sources=[
@@ -351,5 +317,4 @@ def compile_KLU():
         "bdist_wheel": bdist_wheel,
         "install": CustomInstall,
     },
-    package_data={"pybamm": pybamm_data},
 )

From 4a3a03c513fc1d1a645771469091ac1f17a12dbf Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 4 Sep 2023 06:34:55 +0530
Subject: [PATCH 037/199] #3049 Check links in `toml`, `yaml`, and `json` files

---
 .github/workflows/lychee_url_checker.yml | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/.github/workflows/lychee_url_checker.yml b/.github/workflows/lychee_url_checker.yml
index a6735d2806..20eff22b8c 100644
--- a/.github/workflows/lychee_url_checker.yml
+++ b/.github/workflows/lychee_url_checker.yml
@@ -49,6 +49,10 @@ jobs:
             './**/*.md'
             './**/*.py'
             './**/*.ipynb'
+            './**/*.yml'
+            './**/*.yaml'
+            './**/*.json'
+            './**/*.toml'
           # fail the action on broken links
           fail: true
         env:

From 7c64841cae0f99e694e102fb9678b9f74cb6a7bd Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 4 Sep 2023 06:40:44 +0530
Subject: [PATCH 038/199] #3049 clean up some project configuration options

---
 pyproject.toml | 49 ++++++++++++++-----------------------------------
 setup.py       | 30 +++++++-----------------------
 2 files changed, 21 insertions(+), 58 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 05c70d6cbb..76a601b46f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,17 +1,4 @@
-# From the pip documentation:
-
-# Fallback Behaviour
-# If a project does not have a pyproject.toml file containing a build-system section,
-# it will be assumed to have the following backend settings:
-
-# [build-system]
-# requires = ["setuptools>=40.8.0", "wheel"]
-# build-backend = "setuptools.build_meta:__legacy__"
-
-# TODO: add appropriate build-system section
 [build-system]
-# TODO: specify minimum version of setuptools otherwise scikits.odes, NumPy, and others
-# will fail to install
 requires = [
     "setuptools",
     "wheel",
@@ -22,26 +9,13 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "pybamm"
-# TODO: try picking up version from the package itself
-# dynamic = ["version", "readme"]
-# [tool.setuptools.dynamic]
-# version = {attr = "my_package.VERSION"}
 version = "23.5"
-# Unsure: specify BSD-3-Clause?
-# license = {text = "BSD-3-Clause"}
 license = { file = "LICENSE.txt" }
-
-# TODO: add appropriate long description
 description = "Python Battery Mathematical Modelling"
-
-# TODO: correctly specify all authors and maintainers
-# Note: these are currently missing when running `pip show pybamm`, so we should add
-# them in some form
-authors = [{name = "The PyBaMM Team", email = "pybamm@pybamm.org"}]
+authors = [{name = "The PyBaMM Team"}, {email = "pybamm@pybamm.org"}]
 maintainers = [{name = "The PyBaMM Team", email = "pybamm@pybamm.org"}]
 requires-python = ">=3.8, <3.12"
-readme = "README.md"
-
+readme = {file = "README.md", content-type = "text/markdown"}
 classifiers = [
     "Development Status :: 5 - Production/Stable",
     "Intended Audience :: Developers",
@@ -56,7 +30,6 @@ classifiers = [
     "Programming Language :: Python :: 3.11",
     "Topic :: Scientific/Engineering",
 ]
-
 dependencies = [
     "numpy>=1.16",
     "scipy>=1.3",
@@ -64,6 +37,13 @@ dependencies = [
     "xarray",
 ]
 
+[project.urls]
+Homepage = "https://pybamm.org"
+Documentation = "https://docs.pybamm.org"
+Repository = "https://github.com/pybamm-team/PyBaMM"
+Releases = "https://github.com/pybamm-team/PyBaMM/releases"
+Changelog = "https://github.com/pybamm-team/PyBaMM/blob/develop/CHANGELOG.md"
+
 [project.optional-dependencies]
 # For the generation of documentation
 docs = [
@@ -91,7 +71,7 @@ examples = [
 # Plotting functionality
 plot = [
     "imageio>=2.9.0",
-    # Note: Matplotlib is loaded for debug plots, but to ensure pybamm runs
+    # Note: matplotlib is loaded for debug plots, but to ensure pybamm runs
     # on systems without an attached display, it should never be imported
     # outside of plot() methods.
     "matplotlib>=2.0",
@@ -149,8 +129,6 @@ all = [
     "jupyter",
 ]
 
-# Equivalent to the console scripts in the entry_points section of the setup()
-# function in setup.py
 [project.scripts]
 pybamm_edit_parameter = "pybamm.parameters_cli:edit_parameter"
 pybamm_add_parameter = "pybamm.parameters_cli:add_parameter"
@@ -158,8 +136,6 @@ pybamm_rm_parameter = "pybamm.parameters_cli:remove_parameter"
 pybamm_install_odes = "pybamm.install_odes:main"
 pybamm_install_jax = "pybamm.util:install_jax"
 
-# Equivalent to the "pybamm_parameter_sets" entry_points section of the setup()
-# function in setup.py
 [project.entry-points."pybamm_parameter_sets"]
 Sulzer2019 = "pybamm.input.parameters.lead_acid.Sulzer2019:get_parameter_values"
 Ai2020 = "pybamm.input.parameters.lithium_ion.Ai2020:get_parameter_values"
@@ -180,7 +156,7 @@ ECM_Example = "pybamm.input.parameters.ecm.example_set:get_parameter_values"
 include-package-data = true
 
 # List of files to include as package data. These are mainly the parameter CSV files in
-# the input/parameters/ subdirectories. Other files such as the  CITATIONS file, relevant
+# the input/parameters/ subdirectories. Other files such as the CITATIONS file, relevant
 # README.md files, and specific .txt files inside the pybamm/ directory are also included.
 [tool.setuptools.package-data]
 pybamm = [
@@ -191,3 +167,6 @@ pybamm = [
     "pybamm/CITATIONS.bib",
     "pybamm/plotting/mplstyle",
 ]
+
+[tool.setuptools.packages.find]
+include = ["pybamm", "pybamm.*"]
diff --git a/setup.py b/setup.py
index 5da1d2b16c..2af56c6ef6 100644
--- a/setup.py
+++ b/setup.py
@@ -7,10 +7,10 @@
 import wheel.bdist_wheel as orig
 
 try:
-    from setuptools import setup, find_packages, Extension
+    from setuptools import setup, Extension
     from setuptools.command.install import install
 except ImportError:
-    from distutils.core import setup, find_packages
+    from distutils.core import setup
     from distutils.command.install import install
 
 # ---------- cmakebuild was integrated into setup.py directly --------------------------
@@ -173,6 +173,8 @@ def move_output(self, ext):
 
 # ---------- end of cmakebuild steps ---------------------------------------------------
 
+# ---------- configure setup logger ----------------------------------------------------
+
 log_format = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
 logger = logging.getLogger("PyBaMM setup")
 
@@ -213,6 +215,7 @@ def finalize_options(self):
     def run(self):
         install.run(self)
 
+# ---------- custom wheel build (non-Windows) ------------------------------------------
 
 class bdist_wheel(orig.bdist_wheel):
     """A custom install command to add 2 build options"""
@@ -289,28 +292,9 @@ def compile_KLU():
 )
 ext_modules = [idaklu_ext] if compile_KLU() else []
 
-# Defines __version__
-# TODO: might not be needed anymore, because we define it in pyproject.toml
-# and can therefore access it with importlib.metadata.version("pybamm") (python 3.8+)
-# The version.py file can then be imported with attr: pybamm.__version__ dynamically
-root = os.path.abspath(os.path.dirname(__file__))
-with open(os.path.join(root, "pybamm", "version.py")) as f:
-    exec(f.read())
-
-# Load text for description and license
-# TODO: might not be needed anymore, because we define the description and license
-# in pyproject.toml
-# TODO: add long description there and remove it from setup()
-with open("README.md", encoding="utf-8") as f:
-    readme = f.read()
-
-# Project metadata was moved to pyproject.toml (which is read by pip).
-# However, custom build commands and setuptools extension modules are still defined here
+# Project metadata was moved to pyproject.toml (which is read by pip). However, custom
+# build commands and setuptools extension modules are still defined here.
 setup(
-    long_description=readme,
-    long_description_content_type="text/markdown",
-    url="https://github.com/pybamm-team/PyBaMM",
-    packages=find_packages(include=("pybamm", "pybamm.*")),
     ext_modules=ext_modules,
     cmdclass={
         "build_ext": CMakeBuild,

From 2e332b5348cc58558d737183f9924a86b0037342 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 4 Sep 2023 06:41:37 +0530
Subject: [PATCH 039/199] #3049, #3249, #2881 Update version in
 `pyproject.toml`

---
 scripts/update_version.py | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/scripts/update_version.py b/scripts/update_version.py
index 4a5f60d8d8..8912035889 100644
--- a/scripts/update_version.py
+++ b/scripts/update_version.py
@@ -32,6 +32,16 @@ def update_version():
         file.seek(0)
         file.write(replace_version)
 
+    # pyproject.toml
+    with open(os.path.join(pybamm.root_dir(), "pyproject.toml"), "r+") as file:
+        output = file.read()
+        replace_version = re.sub(
+            '(?<=version = ")(.+)(?=")', release_version, output
+        )
+        file.truncate(0)
+        file.seek(0)
+        file.write(replace_version)
+
     # CITATION.cff
     with open(os.path.join(pybamm.root_dir(), "CITATION.cff"), "r+") as file:
         output = file.read()

From b51bea7796bf1caac313aeb511d4c66e4d7ffbd2 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Wed, 6 Sep 2023 21:29:53 +0530
Subject: [PATCH 040/199] Clean up PyPI publishing workflow jobs

---
 .github/workflows/publish_pypi.yml | 32 +++++++++---------------------
 1 file changed, 9 insertions(+), 23 deletions(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index fbdcf6fcc3..1f5d39877e 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -27,9 +27,6 @@ jobs:
         with:
           python-version: 3.8
 
-      - name: Install cibuildwheel
-        run: python -m pip install cibuildwheel==2.12.3
-
       - name: Clone pybind11 repo (no history)
         run: git clone --depth 1 --branch v2.10.4 https://github.com/pybind/pybind11.git
 
@@ -56,8 +53,7 @@ jobs:
         if: ${{ github.event_name == 'workflow_dispatch' && inputs.debug_enabled }}
 
       - name: Build 64 bits wheels on Windows
-        run: |
-          python -m cibuildwheel --output-dir wheelhouse
+        run: pipx run cibuildwheel --output-dir wheelhouse
         env:
           CIBW_ENVIRONMENT: 'PYBAMM_USE_VCPKG=ON VCPKG_ROOT_DIR=C:\vcpkg VCPKG_DEFAULT_TRIPLET=x64-windows-static-md VCPKG_FEATURE_FLAGS=manifests,registries CMAKE_GENERATOR="Visual Studio 17 2022" CMAKE_GENERATOR_PLATFORM=x64'
           CIBW_ARCHS: "AMD64"
@@ -82,28 +78,19 @@ jobs:
         with:
           python-version: 3.8
 
-      - name: Install cibuildwheel
-        run: python -m pip install cibuildwheel==2.12.3
-
       - name: Clone pybind11 repo (no history)
-        run: git clone --depth 1 --branch v2.10.4 https://github.com/pybind/pybind11.git
+        run: git clone --depth 1 --branch v2.11.1 https://github.com/pybind/pybind11.git
 
       - name: Install SUNDIALS on macOS
         if: matrix.os == 'macos-latest'
         run: |
-          # https://github.com/actions/virtual-environments/issues/1280
-          rm -f /usr/local/bin/2to3*
-          rm -f /usr/local/bin/idle3*
-          rm -f /usr/local/bin/pydoc3*
-          rm -f /usr/local/bin/python3*
           brew update
-          brew reinstall gcc
-          brew install libomp
+          brew install gcc gfortran libomp graphviz openblas
           python -m pip install cmake wget
           python scripts/install_KLU_Sundials.py
 
-      - name: Build wheels on Linux and MacOS
-        run: python -m cibuildwheel --output-dir wheelhouse
+      - name: Build wheels on Linux and macOS
+        run: pipx run cibuildwheel --output-dir wheelhouse
         env:
           # TODO: openblas no longer available on centos 7 i686 image, use blas instead for now
           CIBW_BEFORE_ALL_LINUX: >
@@ -114,8 +101,7 @@ jobs:
           CIBW_BEFORE_BUILD_MACOS: >
             python -m pip
             install cmake casadi numpy &&
-            python scripts/fix_casadi_rpath_mac.py &&
-            scripts/fix_suitesparse_rpath_mac.sh
+            python scripts/fix_casadi_rpath_mac.py && python scripts/fix_suitesparse_rpath_mac.sh
           # got error "re.error: multiple repeat at position 104" on python 3.7 when --require-archs added, so remove
           # it for mac
           CIBW_REPAIR_WHEEL_COMMAND_MACOS: >
@@ -172,7 +158,7 @@ jobs:
   #     - name: Publish on PyPI
   #       if: |
   #         github.event.inputs.target == 'pypi' ||
-  #         (github.event_name == 'push' && github.ref == 'refs/heads/main')
+  #         (github.event-name == 'push' && github.ref == 'refs/heads/main')
   #       uses: pypa/gh-action-pypi-publish@release/v1
   #       with:
   #         user: __token__
@@ -185,5 +171,5 @@ jobs:
   #       with:
   #         user: __token__
   #         password: ${{ secrets.TESTPYPI_TOKEN }}
-  #         packages_dir: files/
-  #         repository_url: https://test.pypi.org/legacy/
+  #         packages-dir: files/
+  #         repository-url: https://test.pypi.org/legacy/

From 378eed11ea3c0335a72ca97c7aac15e16a8a4b46 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 7 Sep 2023 04:56:21 +0530
Subject: [PATCH 041/199] Add rpath config for `casadi` directory

Co-Authored-By: Martin Robinson <martinjrobins@gmail.com>
---
 CMakeLists.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index a58ef66933..2605c89b5c 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -81,6 +81,7 @@ find_package(SUNDIALS REQUIRED)
 message("sundials ${SUNDIALS_INCLUDE_DIR} ${SUNDIALS_LIBRARIES}")
 target_include_directories(idaklu PRIVATE ${SUNDIALS_INCLUDE_DIR})
 target_link_libraries(idaklu PRIVATE ${SUNDIALS_LIBRARIES} casadi)
+set_property(TARGET idaklu APPEND PROPERTY INSTALL_RPATH "${CASADI_DIR}")
 
 # link suitesparse
 # if using vcpkg, use config mode to

From 324c31677a3816532c2a595e153ca3a2aa1a901e Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 7 Sep 2023 05:07:45 +0530
Subject: [PATCH 042/199] #3049 Add gcc reinstall step again otherwise Fortran
 compiler is not found

---
 .github/workflows/publish_pypi.yml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 1f5d39877e..15af4ec945 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -85,7 +85,8 @@ jobs:
         if: matrix.os == 'macos-latest'
         run: |
           brew update
-          brew install gcc gfortran libomp graphviz openblas
+          brew install gfortran libomp graphviz openblas
+          brew reinstall gcc
           python -m pip install cmake wget
           python scripts/install_KLU_Sundials.py
 

From 5976ec35832ab1871076c77a8b686aaffe1c7242 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 7 Sep 2023 05:30:31 +0530
Subject: [PATCH 043/199] #3049 Fix cibuildwheel job

---
 .github/workflows/publish_pypi.yml | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 15af4ec945..be07bcad05 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -86,7 +86,6 @@ jobs:
         run: |
           brew update
           brew install gfortran libomp graphviz openblas
-          brew reinstall gcc
           python -m pip install cmake wget
           python scripts/install_KLU_Sundials.py
 
@@ -102,7 +101,7 @@ jobs:
           CIBW_BEFORE_BUILD_MACOS: >
             python -m pip
             install cmake casadi numpy &&
-            python scripts/fix_casadi_rpath_mac.py && python scripts/fix_suitesparse_rpath_mac.sh
+            python scripts/fix_casadi_rpath_mac.py && scripts/fix_suitesparse_rpath_mac.sh
           # got error "re.error: multiple repeat at position 104" on python 3.7 when --require-archs added, so remove
           # it for mac
           CIBW_REPAIR_WHEEL_COMMAND_MACOS: >

From 8d88f5b71a549018ec78a4f4a9c353fc31dd4090 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 7 Sep 2023 05:42:56 +0530
Subject: [PATCH 044/199] #3049 Revert "Add rpath config for `casadi`
 directory"

This reverts commit 378eed11ea3c0335a72ca97c7aac15e16a8a4b46.
---
 CMakeLists.txt | 1 -
 1 file changed, 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 2605c89b5c..a58ef66933 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -81,7 +81,6 @@ find_package(SUNDIALS REQUIRED)
 message("sundials ${SUNDIALS_INCLUDE_DIR} ${SUNDIALS_LIBRARIES}")
 target_include_directories(idaklu PRIVATE ${SUNDIALS_INCLUDE_DIR})
 target_link_libraries(idaklu PRIVATE ${SUNDIALS_LIBRARIES} casadi)
-set_property(TARGET idaklu APPEND PROPERTY INSTALL_RPATH "${CASADI_DIR}")
 
 # link suitesparse
 # if using vcpkg, use config mode to

From 91efeab3f7467aaa350e05cb77e3340bf08b5f49 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 7 Sep 2023 17:48:19 +0530
Subject: [PATCH 045/199] #3049 Fix `gcc`/`gfortran` installation (Homebrew)

---
 .github/workflows/publish_pypi.yml | 4 +++-
 .github/workflows/test_on_push.yml | 4 ++++
 2 files changed, 7 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index be07bcad05..99bf0aa10a 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -81,11 +81,13 @@ jobs:
       - name: Clone pybind11 repo (no history)
         run: git clone --depth 1 --branch v2.11.1 https://github.com/pybind/pybind11.git
 
+      # sometimes gfortran cannot be found, so reinstall gcc just to be sure
       - name: Install SUNDIALS on macOS
         if: matrix.os == 'macos-latest'
         run: |
           brew update
-          brew install gfortran libomp graphviz openblas
+          brew install gcc gfortran libomp graphviz openblas
+          brew reinstall gcc
           python -m pip install cmake wget
           python scripts/install_KLU_Sundials.py
 
diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index f2be240d39..6ad30f7668 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -73,10 +73,12 @@ jobs:
           HOMEBREW_NO_COLOR: 1
           # Speed up CI
           NONINTERACTIVE: 1
+        # sometimes gfortran cannot be found, so reinstall gcc just to be sure
         run: |
           brew analytics off
           brew update
           brew install graphviz openblas gcc gfortran libomp
+          brew reinstall gcc
 
       - name: Install Windows system dependencies
         if: matrix.os == 'windows-latest'
@@ -206,10 +208,12 @@ jobs:
           HOMEBREW_NO_COLOR: 1
           # Speed up CI
           NONINTERACTIVE: 1
+        # sometimes gfortran cannot be found, so reinstall gcc just to be sure
         run: |
           brew analytics off
           brew update
           brew install graphviz openblas gcc gfortran libomp
+          brew reinstall gcc
 
       - name: Install Windows system dependencies
         if: matrix.os == 'windows-latest'

From c3708ff1ad23dbd804cf5d903191044fd8e81775 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sun, 10 Sep 2023 22:51:59 +0530
Subject: [PATCH 046/199] #3312 Update CMake, Python versions and add `nox`

Co-Authored-By: Arjun <arjxn.py@gmail.com>
---
 scripts/Dockerfile | 41 +++++++++++++++++++++++------------------
 1 file changed, 23 insertions(+), 18 deletions(-)

diff --git a/scripts/Dockerfile b/scripts/Dockerfile
index 3cfbeaa11c..afa287fa48 100644
--- a/scripts/Dockerfile
+++ b/scripts/Dockerfile
@@ -4,7 +4,7 @@ WORKDIR /
 
 # Install the necessary dependencies
 RUN apt-get update && apt-get -y upgrade
-RUN apt-get install -y libopenblas-dev gcc gfortran graphviz git make g++ build-essential cmake
+RUN apt-get install -y libopenblas-dev gcc gfortran graphviz git make g++ build-essential cmake pandoc texlive-latex-extra dvipng
 RUN rm -rf /var/lib/apt/lists/*
 
 RUN useradd -m -s /bin/bash pybamm
@@ -21,45 +21,50 @@ ENV CMAKE_C_COMPILER=/usr/bin/gcc
 ENV CMAKE_CXX_COMPILER=/usr/bin/g++
 ENV CMAKE_MAKE_PROGRAM=/usr/bin/make
 ENV SUNDIALS_INST=/home/pybamm/.local
-ENV LD_LIBRARY_PATH=/home/pybamm/.local/lib:
+ENV LD_LIBRARY_PATH=/home/pybamm/.local/lib
+
+RUN conda create -n pybamm python=3.11
+RUN conda init --all
+SHELL ["conda", "run", "-n", "pybamm", "/bin/bash", "-c"]
+RUN conda install -y pip
 
 ARG IDAKLU
 ARG ODES
 ARG JAX
 ARG ALL
 
-RUN conda create -n pybamm python=3.9
-RUN conda init --all
-SHELL ["conda", "run", "-n", "pybamm", "/bin/bash", "-c"]
-RUN conda install -y pip
-
 RUN if [ "$IDAKLU" = "true" ]; then \
-    pip install --upgrade --user pip setuptools wheel wget && \
-    pip install cmake==3.22 && \
+    pip install --upgrade --user pip setuptools wheel wget nox && \
+    pip install cmake && \
     python scripts/install_KLU_Sundials.py && \
     git clone https://github.com/pybind/pybind11.git && \
-    pip install --user -e ".[all,dev]"; \
+    pip install --user -e ".[all,dev,docs]"; \
     fi
 
 RUN if [ "$ODES" = "true" ]; then \
-    pip install cmake==3.22 && \
-    pip install --upgrade --user pip wget && \
+    pip install --upgrade --user pip setuptools wheel wget nox && \
+    pip install cmake && \
     python scripts/install_KLU_Sundials.py && \
-    pip install --user -e ".[all,odes,dev]"; \
+    git clone https://github.com/pybind/pybind11.git && \
+    pip install --user -e ".[all,dev,docs,odes]"; \
     fi
 
 RUN if [ "$JAX" = "true" ]; then \
-    pip install --user -e ".[jax,all,dev]";\
+    pip install --upgrade --user pip setuptools wheel wget nox && \
+    pip install cmake && \
+    python scripts/install_KLU_Sundials.py && \
+    git clone https://github.com/pybind/pybind11.git && \
+    pip install --user -e ".[all,dev,docs,jax]"; \
     fi
 
 RUN if [ "$ALL" = "true" ]; then \
-    pip install cmake==3.22 && \
-    pip install --upgrade --user pip setuptools wheel wget && \
+    pip install --upgrade --user pip setuptools wheel wget nox && \
+    pip install cmake && \
     python scripts/install_KLU_Sundials.py && \
     git clone https://github.com/pybind/pybind11.git && \
-    pip install --user -e ".[all,dev,jax,odes]"; \
+    pip install --user -e ".[all,dev,docs,jax,odes]"; \
     fi
 
-RUN pip install --user -e ".[all,dev]"
+RUN pip install --user -e ".[all,dev,docs]"
 
 ENTRYPOINT ["/bin/bash"]

From a8661a20314ef944ebcd9dd9418f8dae0a034a97 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sun, 10 Sep 2023 22:53:37 +0530
Subject: [PATCH 047/199] #3312 Minor cleanups for Docker installation docs

Co-Authored-By: Arjun <arjxn.py@gmail.com>
---
 .../user_guide/installation/install-from-docker.rst  | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/docs/source/user_guide/installation/install-from-docker.rst b/docs/source/user_guide/installation/install-from-docker.rst
index f25a57d713..34e33e9ec2 100644
--- a/docs/source/user_guide/installation/install-from-docker.rst
+++ b/docs/source/user_guide/installation/install-from-docker.rst
@@ -3,12 +3,13 @@ Install from source (Docker)
 
 .. contents::
 
-This page describes the build and installation of PyBaMM from the source code, available on GitHub. Note that this is **not the recommended approach for most users** and should be reserved to people wanting to participate in the development of PyBaMM, or people who really need to use bleeding-edge feature(s) not yet available in the latest released version. If you do not fall in the two previous categories, you would be better off installing PyBaMM using pip or conda.
+This page describes the build and installation of PyBaMM using a Dockerfile, available on GitHub. Note that this is **not the recommended approach for most users** and should be reserved to people wanting to participate in the development of PyBaMM, or people who really need to use bleeding-edge feature(s) not yet available in the latest released version. If you do not fall in the two previous categories, you would be better off installing PyBaMM using ``pip`` or ``conda``.
 
 Prerequisites
 -------------
+
 Before you begin, make sure you have Docker installed on your system. You can download and install Docker from the official `Docker website <https://www.docker.com/get-started/>`_.
-Ensure Docker installation by running :
+Ensure Docker installation by running:
 
 .. code:: bash
 
@@ -16,6 +17,7 @@ Ensure Docker installation by running :
 
 Pulling the Docker image
 ------------------------
+
 Use the following command to pull the PyBaMM Docker image from Docker Hub:
 
 .. tab:: No optional solver
@@ -135,8 +137,8 @@ If you want to build the PyBaMM Docker image locally from the PyBaMM source code
 
       conda activate pybamm
 
-Building Docker images with optional args
------------------------------------------
+Building Docker images with optional arguments
+----------------------------------------------
 
 When building the PyBaMM Docker images locally, you have the option to include specific solvers by using optional arguments. These solvers include:
 
@@ -189,7 +191,7 @@ If you want to exit the Docker container's shell, you can simply type:
 
       exit
 
-Using Visual Studio Code Inside a Running Docker Container
+Using Visual Studio Code inside a running Docker container
 ----------------------------------------------------------
 
 You can easily use Visual Studio Code inside a running Docker container by attaching it directly. This provides a seamless development environment within the container. Here's how:

From c3eaba5b2f2df8f95fbf8e5ee94564d969e7fe53 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 11 Sep 2023 00:10:56 +0530
Subject: [PATCH 048/199] #3312 Install build-time requirements in a single RUN
 command

---
 scripts/Dockerfile | 10 ++--------
 1 file changed, 2 insertions(+), 8 deletions(-)

diff --git a/scripts/Dockerfile b/scripts/Dockerfile
index afa287fa48..ffbd320f59 100644
--- a/scripts/Dockerfile
+++ b/scripts/Dockerfile
@@ -33,33 +33,27 @@ ARG ODES
 ARG JAX
 ARG ALL
 
+RUN pip install --upgrade --user pip setuptools wheel wget nox cmake
+
 RUN if [ "$IDAKLU" = "true" ]; then \
-    pip install --upgrade --user pip setuptools wheel wget nox && \
-    pip install cmake && \
     python scripts/install_KLU_Sundials.py && \
     git clone https://github.com/pybind/pybind11.git && \
     pip install --user -e ".[all,dev,docs]"; \
     fi
 
 RUN if [ "$ODES" = "true" ]; then \
-    pip install --upgrade --user pip setuptools wheel wget nox && \
-    pip install cmake && \
     python scripts/install_KLU_Sundials.py && \
     git clone https://github.com/pybind/pybind11.git && \
     pip install --user -e ".[all,dev,docs,odes]"; \
     fi
 
 RUN if [ "$JAX" = "true" ]; then \
-    pip install --upgrade --user pip setuptools wheel wget nox && \
-    pip install cmake && \
     python scripts/install_KLU_Sundials.py && \
     git clone https://github.com/pybind/pybind11.git && \
     pip install --user -e ".[all,dev,docs,jax]"; \
     fi
 
 RUN if [ "$ALL" = "true" ]; then \
-    pip install --upgrade --user pip setuptools wheel wget nox && \
-    pip install cmake && \
     python scripts/install_KLU_Sundials.py && \
     git clone https://github.com/pybind/pybind11.git && \
     pip install --user -e ".[all,dev,docs,jax,odes]"; \

From fd0c1a5f3766d2cdfe88e467f24b084b19587edf Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 12 Sep 2023 02:24:42 +0530
Subject: [PATCH 049/199] #3049 Copy `libcasadi.3.7.dylib` to `LD_LIBRARY_PATH`

---
 scripts/fix_casadi_rpath_mac.py | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/scripts/fix_casadi_rpath_mac.py b/scripts/fix_casadi_rpath_mac.py
index 9b0a181391..82c21a34a8 100644
--- a/scripts/fix_casadi_rpath_mac.py
+++ b/scripts/fix_casadi_rpath_mac.py
@@ -14,6 +14,7 @@
 libcpp_name = "libc++.1.0.dylib"
 libcppabi_name = "libc++abi.dylib"
 libcasadi_name = "libcasadi.dylib"
+libcasadi_37_name = "libcasadi.3.7.dylib"
 install_name_tool_args = [
     "-change",
     os.path.join("@rpath", libcpp_name),
@@ -34,3 +35,13 @@
 print(" ".join(["install_name_tool"] + install_name_tool_args))
 subprocess.run(["install_name_tool"] + install_name_tool_args)
 subprocess.run(["otool"] + ["-L", os.path.join(casadi_dir, libcpp_name)])
+
+# Copy libcasadi.3.7.dylib to LD_LIBRARY_PATH ($HOME/.local/lib)
+# This is needed for the casadi python bindings to work
+
+subprocess.run(
+    ["cp",
+    os.path.join(casadi_dir, libcasadi_37_name),
+    os.path.join(os.getenv("HOME"),".local/lib")
+    ]
+)

From e7dd32a00aa8ef829625edacf2f60276562078c2 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 12 Sep 2023 02:35:15 +0530
Subject: [PATCH 050/199] #3049 Add `libc++.1.0.dylib` as well

---
 scripts/fix_casadi_rpath_mac.py | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/scripts/fix_casadi_rpath_mac.py b/scripts/fix_casadi_rpath_mac.py
index 82c21a34a8..9779c88ab3 100644
--- a/scripts/fix_casadi_rpath_mac.py
+++ b/scripts/fix_casadi_rpath_mac.py
@@ -36,7 +36,7 @@
 subprocess.run(["install_name_tool"] + install_name_tool_args)
 subprocess.run(["otool"] + ["-L", os.path.join(casadi_dir, libcpp_name)])
 
-# Copy libcasadi.3.7.dylib to LD_LIBRARY_PATH ($HOME/.local/lib)
+# Copy libcasadi.3.7.dylib and libc++.1.0.dylib to LD_LIBRARY_PATH ($HOME/.local/lib)
 # This is needed for the casadi python bindings to work
 
 subprocess.run(
@@ -45,3 +45,10 @@
     os.path.join(os.getenv("HOME"),".local/lib")
     ]
 )
+
+subprocess.run(
+    ["cp",
+    os.path.join(casadi_dir, libcpp_name),
+    os.path.join(os.getenv("HOME"),".local/lib")
+    ]
+)

From 8388ac6e640d5959ff1c559ba0af26235355a6a2 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 12 Sep 2023 02:40:32 +0530
Subject: [PATCH 051/199] #3049 Refactor casadi lib rpaths

---
 scripts/fix_casadi_rpath_mac.py | 24 +++++++++++++++++++-----
 1 file changed, 19 insertions(+), 5 deletions(-)

diff --git a/scripts/fix_casadi_rpath_mac.py b/scripts/fix_casadi_rpath_mac.py
index 9779c88ab3..fb275e339a 100644
--- a/scripts/fix_casadi_rpath_mac.py
+++ b/scripts/fix_casadi_rpath_mac.py
@@ -1,6 +1,6 @@
 """
-Removes the rpath from libcasadi.dylib in the casadi python install
-and uses a fixed path
+Removes the rpath from libcasadi.dylib and libcasadi.3.7.dylib in the casadi python
+install and uses a fixed path
 
 Used when building the wheels for macos
 """
@@ -15,16 +15,28 @@
 libcppabi_name = "libc++abi.dylib"
 libcasadi_name = "libcasadi.dylib"
 libcasadi_37_name = "libcasadi.3.7.dylib"
-install_name_tool_args = [
+install_name_tool_args_for_libcasadi_name = [
     "-change",
     os.path.join("@rpath", libcpp_name),
     os.path.join(casadi_dir, libcpp_name),
     os.path.join(casadi_dir, libcasadi_name),
 ]
+install_name_tool_args_for_libcasadi_37_name = [
+    "-change",
+    os.path.join("@rpath", libcpp_name),
+    os.path.join(casadi_dir, libcpp_name),
+    os.path.join(casadi_dir, libcasadi_37_name),
+]
 subprocess.run(["otool"] + ["-L", os.path.join(casadi_dir, libcasadi_name)])
-print(" ".join(["install_name_tool"] + install_name_tool_args))
-subprocess.run(["install_name_tool"] + install_name_tool_args)
+
+print(" ".join(["install_name_tool"] + install_name_tool_args_for_libcasadi_name))
+subprocess.run(["install_name_tool"] + install_name_tool_args_for_libcasadi_name)
+
+print(" ".join(["install_name_tool"] + install_name_tool_args_for_libcasadi_37_name))
+subprocess.run(["install_name_tool"] + install_name_tool_args_for_libcasadi_37_name)
+
 subprocess.run(["otool"] + ["-L", os.path.join(casadi_dir, libcasadi_name)])
+
 install_name_tool_args = [
     "-change",
     os.path.join("@rpath", libcppabi_name),
@@ -32,8 +44,10 @@
     os.path.join(casadi_dir, libcpp_name),
 ]
 subprocess.run(["otool"] + ["-L", os.path.join(casadi_dir, libcpp_name)])
+
 print(" ".join(["install_name_tool"] + install_name_tool_args))
 subprocess.run(["install_name_tool"] + install_name_tool_args)
+
 subprocess.run(["otool"] + ["-L", os.path.join(casadi_dir, libcpp_name)])
 
 # Copy libcasadi.3.7.dylib and libc++.1.0.dylib to LD_LIBRARY_PATH ($HOME/.local/lib)

From cbba6b9e72dcfc380ee8d258b30ee70d9425504a Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 12 Sep 2023 03:23:43 +0530
Subject: [PATCH 052/199] Update link checker with job summary

---
 .github/workflows/lychee_url_checker.yml | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/lychee_url_checker.yml b/.github/workflows/lychee_url_checker.yml
index 20eff22b8c..dcf2b7e8ee 100644
--- a/.github/workflows/lychee_url_checker.yml
+++ b/.github/workflows/lychee_url_checker.yml
@@ -45,16 +45,17 @@ jobs:
             --accept 200,429
             --exclude-path ./CHANGELOG.md
             --exclude-path ./scripts/update_version.py
+            --exclude-path asv.conf.json
             './**/*.rst'
             './**/*.md'
             './**/*.py'
             './**/*.ipynb'
-            './**/*.yml'
-            './**/*.yaml'
             './**/*.json'
             './**/*.toml'
           # fail the action on broken links
           fail: true
+          jobSummary: true
+          format: markdown
         env:
           # to be used in case rate limits are surpassed
           GITHUB_TOKEN: ${{secrets.GITHUB_TOKEN}}

From 9432e612f0e8273ddcc332cd4b0b63d9dc047f2a Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 12 Sep 2023 08:11:06 +0530
Subject: [PATCH 053/199] #3049 Run rpath fixes on macOS and Linux

---
 ...sadi_rpath_mac.py => fix_casadi_rpath_macos_linux.py} | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)
 rename scripts/{fix_casadi_rpath_mac.py => fix_casadi_rpath_macos_linux.py} (90%)

diff --git a/scripts/fix_casadi_rpath_mac.py b/scripts/fix_casadi_rpath_macos_linux.py
similarity index 90%
rename from scripts/fix_casadi_rpath_mac.py
rename to scripts/fix_casadi_rpath_macos_linux.py
index fb275e339a..8b4618f2d9 100644
--- a/scripts/fix_casadi_rpath_mac.py
+++ b/scripts/fix_casadi_rpath_macos_linux.py
@@ -2,7 +2,7 @@
 Removes the rpath from libcasadi.dylib and libcasadi.3.7.dylib in the casadi python
 install and uses a fixed path
 
-Used when building the wheels for macos
+Used when building the wheels for macOS and GNU/Linux
 """
 import casadi
 import os
@@ -15,18 +15,21 @@
 libcppabi_name = "libc++abi.dylib"
 libcasadi_name = "libcasadi.dylib"
 libcasadi_37_name = "libcasadi.3.7.dylib"
+
 install_name_tool_args_for_libcasadi_name = [
     "-change",
     os.path.join("@rpath", libcpp_name),
     os.path.join(casadi_dir, libcpp_name),
     os.path.join(casadi_dir, libcasadi_name),
 ]
+
 install_name_tool_args_for_libcasadi_37_name = [
     "-change",
     os.path.join("@rpath", libcpp_name),
     os.path.join(casadi_dir, libcpp_name),
     os.path.join(casadi_dir, libcasadi_37_name),
 ]
+
 subprocess.run(["otool"] + ["-L", os.path.join(casadi_dir, libcasadi_name)])
 
 print(" ".join(["install_name_tool"] + install_name_tool_args_for_libcasadi_name))
@@ -50,8 +53,8 @@
 
 subprocess.run(["otool"] + ["-L", os.path.join(casadi_dir, libcpp_name)])
 
-# Copy libcasadi.3.7.dylib and libc++.1.0.dylib to LD_LIBRARY_PATH ($HOME/.local/lib)
-# This is needed for the casadi python bindings to work
+# Copy libcasadi.3.7.dylib and libc++.1.0.dylib to $HOME/.local/lib
+# This is needed for the casadi python bindings to work while repairing the wheel
 
 subprocess.run(
     ["cp",

From c099385819f80cdc77e1e0116b30d3d245b4b8b2 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 12 Sep 2023 08:11:30 +0530
Subject: [PATCH 054/199] #3049 Add repair script to cibuildwheel

---
 .github/workflows/publish_pypi.yml | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 99bf0aa10a..2742ec6f56 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -82,7 +82,7 @@ jobs:
         run: git clone --depth 1 --branch v2.11.1 https://github.com/pybind/pybind11.git
 
       # sometimes gfortran cannot be found, so reinstall gcc just to be sure
-      - name: Install SUNDIALS on macOS
+      - name: Install SuiteSparse and SUNDIALS on macOS
         if: matrix.os == 'macos-latest'
         run: |
           brew update
@@ -91,19 +91,20 @@ jobs:
           python -m pip install cmake wget
           python scripts/install_KLU_Sundials.py
 
-      - name: Build wheels on Linux and macOS
+      - name: Build wheels on ${{ matrix.os }}
         run: pipx run cibuildwheel --output-dir wheelhouse
         env:
           # TODO: openblas no longer available on centos 7 i686 image, use blas instead for now
           CIBW_BEFORE_ALL_LINUX: >
             yum -y install blas-devel lapack-devel &&
             bash build_manylinux_wheels/install_sundials.sh 5.8.1 6.5.0
-
-          CIBW_BEFORE_BUILD_LINUX: "python -m pip install cmake casadi numpy"
+          CIBW_BEFORE_BUILD_LINUX: >
+            python -m pip install cmake casadi numpy &&
+            python scripts/fix_casadi_rpath_macos_linux.py
           CIBW_BEFORE_BUILD_MACOS: >
             python -m pip
             install cmake casadi numpy &&
-            python scripts/fix_casadi_rpath_mac.py && scripts/fix_suitesparse_rpath_mac.sh
+            python scripts/fix_casadi_rpath_macos_linux.py && scripts/fix_suitesparse_rpath_mac.sh
           # got error "re.error: multiple repeat at position 104" on python 3.7 when --require-archs added, so remove
           # it for mac
           CIBW_REPAIR_WHEEL_COMMAND_MACOS: >

From 3502303bf0649f791df98a59efb07cf55b9b8c3a Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 12 Sep 2023 16:36:41 +0530
Subject: [PATCH 055/199] #3049 Fix casadi rpath on macOS

---
 .github/workflows/publish_pypi.yml                           | 5 ++---
 ...x_casadi_rpath_macos_linux.py => fix_casadi_rpath_mac.py} | 4 ++--
 2 files changed, 4 insertions(+), 5 deletions(-)
 rename scripts/{fix_casadi_rpath_macos_linux.py => fix_casadi_rpath_mac.py} (94%)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 2742ec6f56..16314250b6 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -99,12 +99,11 @@ jobs:
             yum -y install blas-devel lapack-devel &&
             bash build_manylinux_wheels/install_sundials.sh 5.8.1 6.5.0
           CIBW_BEFORE_BUILD_LINUX: >
-            python -m pip install cmake casadi numpy &&
-            python scripts/fix_casadi_rpath_macos_linux.py
+            python -m pip install cmake casadi numpy
           CIBW_BEFORE_BUILD_MACOS: >
             python -m pip
             install cmake casadi numpy &&
-            python scripts/fix_casadi_rpath_macos_linux.py && scripts/fix_suitesparse_rpath_mac.sh
+            python scripts/fix_casadi_rpath_mac.py && scripts/fix_suitesparse_rpath_mac.sh
           # got error "re.error: multiple repeat at position 104" on python 3.7 when --require-archs added, so remove
           # it for mac
           CIBW_REPAIR_WHEEL_COMMAND_MACOS: >
diff --git a/scripts/fix_casadi_rpath_macos_linux.py b/scripts/fix_casadi_rpath_mac.py
similarity index 94%
rename from scripts/fix_casadi_rpath_macos_linux.py
rename to scripts/fix_casadi_rpath_mac.py
index 8b4618f2d9..23c8a32d59 100644
--- a/scripts/fix_casadi_rpath_macos_linux.py
+++ b/scripts/fix_casadi_rpath_mac.py
@@ -2,7 +2,7 @@
 Removes the rpath from libcasadi.dylib and libcasadi.3.7.dylib in the casadi python
 install and uses a fixed path
 
-Used when building the wheels for macOS and GNU/Linux
+Used when building the wheels for macOS
 """
 import casadi
 import os
@@ -53,7 +53,7 @@
 
 subprocess.run(["otool"] + ["-L", os.path.join(casadi_dir, libcpp_name)])
 
-# Copy libcasadi.3.7.dylib and libc++.1.0.dylib to $HOME/.local/lib
+# Copy libcasadi.3.7.dylib and libc++.1.0.dylib to LD_LIBRARY_PATH
 # This is needed for the casadi python bindings to work while repairing the wheel
 
 subprocess.run(

From a0592f84babbd2cf6eac22ea18c09bac8ae8edcf Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 12 Sep 2023 23:16:58 +0530
Subject: [PATCH 056/199] Remove `nox` because it is already installed

Co-authored-by: Saransh Chopra <saransh0701@gmail.com>
---
 scripts/Dockerfile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/Dockerfile b/scripts/Dockerfile
index ffbd320f59..b6c0a02f67 100644
--- a/scripts/Dockerfile
+++ b/scripts/Dockerfile
@@ -33,7 +33,7 @@ ARG ODES
 ARG JAX
 ARG ALL
 
-RUN pip install --upgrade --user pip setuptools wheel wget nox cmake
+RUN pip install --upgrade --user pip setuptools wheel wget cmake
 
 RUN if [ "$IDAKLU" = "true" ]; then \
     python scripts/install_KLU_Sundials.py && \

From 51db35f0910620570a5da8ca7de2cb52c5bd1a6b Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Wed, 13 Sep 2023 00:10:15 +0530
Subject: [PATCH 057/199] #3049 try to move casadi shared objects to local path

---
 .github/workflows/publish_pypi.yml | 3 ++-
 scripts/fix_casadi_rpath_linux.sh  | 7 +++++++
 2 files changed, 9 insertions(+), 1 deletion(-)
 create mode 100644 scripts/fix_casadi_rpath_linux.sh

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 16314250b6..08025195df 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -99,7 +99,8 @@ jobs:
             yum -y install blas-devel lapack-devel &&
             bash build_manylinux_wheels/install_sundials.sh 5.8.1 6.5.0
           CIBW_BEFORE_BUILD_LINUX: >
-            python -m pip install cmake casadi numpy
+            python -m pip install cmake casadi numpy &&
+            scripts/fix_casadi_rpath_linux.sh
           CIBW_BEFORE_BUILD_MACOS: >
             python -m pip
             install cmake casadi numpy &&
diff --git a/scripts/fix_casadi_rpath_linux.sh b/scripts/fix_casadi_rpath_linux.sh
new file mode 100644
index 0000000000..188bd68781
--- /dev/null
+++ b/scripts/fix_casadi_rpath_linux.sh
@@ -0,0 +1,7 @@
+#!/usr/bin/env bash
+
+LD_LIBRARY_PATH=${HOME}/.local/lib
+CASADI_PATH=$(python -c "import casadi; print(casadi.__path__[0])")
+
+cp ${CASADI_PATH}/libcasadi.so.3.7 ${LD_LIBRARY_PATH}
+cp ${CASADI_PATH}/libcasadi.so ${LD_LIBRARY_PATH}

From 78bff9a6f817552106af1e644130d6fa95d45768 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Wed, 13 Sep 2023 00:14:21 +0530
Subject: [PATCH 058/199] #3049 fix copying script for Linux

---
 scripts/fix_casadi_rpath_linux.sh | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/scripts/fix_casadi_rpath_linux.sh b/scripts/fix_casadi_rpath_linux.sh
index 188bd68781..5c7e7801b7 100644
--- a/scripts/fix_casadi_rpath_linux.sh
+++ b/scripts/fix_casadi_rpath_linux.sh
@@ -3,5 +3,5 @@
 LD_LIBRARY_PATH=${HOME}/.local/lib
 CASADI_PATH=$(python -c "import casadi; print(casadi.__path__[0])")
 
-cp ${CASADI_PATH}/libcasadi.so.3.7 ${LD_LIBRARY_PATH}
-cp ${CASADI_PATH}/libcasadi.so ${LD_LIBRARY_PATH}
+sudo cp ${CASADI_PATH}/libcasadi.so.3.7 ${LD_LIBRARY_PATH}
+sudo cp ${CASADI_PATH}/libcasadi.so ${LD_LIBRARY_PATH}

From cc07fc4782c1517266f7bf626e68794900183784 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 28 Sep 2023 19:12:09 +0530
Subject: [PATCH 059/199] #3049 fix LD_LIBRARY_PATH for `nox`

---
 noxfile.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/noxfile.py b/noxfile.py
index 58a66b7c76..d9412db495 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -14,7 +14,7 @@
 homedir = os.getenv("HOME")
 PYBAMM_ENV = {
     "SUNDIALS_INST": f"{homedir}/.local",
-    "LD_LIBRARY_PATH": f"{homedir}/.local/lib:",
+    "LD_LIBRARY_PATH": f"{homedir}/.local/lib",
 }
 # Versions compatible with the current version of PyBaMM. Installed directly in the
 # sessions to skip redundant installation of dependencies and building wheels both in

From 1e164aeddddcb59aff815ff04f2e11d2c0a3c77e Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 28 Sep 2023 19:13:01 +0530
Subject: [PATCH 060/199] #3049 add custom wheel repair command for Linux

---
 .github/workflows/publish_pypi.yml | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 08025195df..ba797f2b58 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -101,6 +101,9 @@ jobs:
           CIBW_BEFORE_BUILD_LINUX: >
             python -m pip install cmake casadi numpy &&
             scripts/fix_casadi_rpath_linux.sh
+          # override; point to casadi install path so that it can be found by the repair command
+          CIBW_REPAIR_WHEEL_COMMAND_LINUX: >
+            LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:$(python -c 'import casadi; print(casadi.__path__[0])')" auditwheel repair -w {dest_dir} {wheel}
           CIBW_BEFORE_BUILD_MACOS: >
             python -m pip
             install cmake casadi numpy &&

From f30d37288525ba4b08ea8866c7ad87686cf6b036 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 28 Sep 2023 19:29:17 +0530
Subject: [PATCH 061/199] #3049 remove Linux rpath fix script

---
 .github/workflows/publish_pypi.yml | 3 +--
 scripts/fix_casadi_rpath_linux.sh  | 7 -------
 2 files changed, 1 insertion(+), 9 deletions(-)
 delete mode 100644 scripts/fix_casadi_rpath_linux.sh

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index f9df08cb33..5a012f64a8 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -99,8 +99,7 @@ jobs:
             yum -y install blas-devel lapack-devel &&
             bash build_manylinux_wheels/install_sundials.sh 5.8.1 6.5.0
           CIBW_BEFORE_BUILD_LINUX: >
-            python -m pip install cmake casadi numpy &&
-            scripts/fix_casadi_rpath_linux.sh
+            python -m pip install cmake casadi numpy
           # override; point to casadi install path so that it can be found by the repair command
           CIBW_REPAIR_WHEEL_COMMAND_LINUX: >
             LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:$(python -c 'import casadi; print(casadi.__path__[0])')" auditwheel repair -w {dest_dir} {wheel}
diff --git a/scripts/fix_casadi_rpath_linux.sh b/scripts/fix_casadi_rpath_linux.sh
deleted file mode 100644
index 5c7e7801b7..0000000000
--- a/scripts/fix_casadi_rpath_linux.sh
+++ /dev/null
@@ -1,7 +0,0 @@
-#!/usr/bin/env bash
-
-LD_LIBRARY_PATH=${HOME}/.local/lib
-CASADI_PATH=$(python -c "import casadi; print(casadi.__path__[0])")
-
-sudo cp ${CASADI_PATH}/libcasadi.so.3.7 ${LD_LIBRARY_PATH}
-sudo cp ${CASADI_PATH}/libcasadi.so ${LD_LIBRARY_PATH}

From c069e44f28549fdb0fbd7f9630fc6733d14d3419 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 28 Sep 2023 20:13:39 +0530
Subject: [PATCH 062/199] #3049 Add MSMR parameters entry point

---
 pyproject.toml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pyproject.toml b/pyproject.toml
index 7842ce39a4..c3c6583adb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -155,6 +155,7 @@ Prada2013 = "pybamm.input.parameters.lithium_ion.Prada2013:get_parameter_values"
 Ramadass2004 = "pybamm.input.parameters.lithium_ion.Ramadass2004:get_parameter_values"
 Xu2019 = "pybamm.input.parameters.lithium_ion.Xu2019:get_parameter_values"
 ECM_Example = "pybamm.input.parameters.ecm.example_set:get_parameter_values"
+MSMR_Example = "pybamm.input.parameters.lithium_ion.MSMR_example_set:get_parameter_values"
 
 [tool.setuptools]
 include-package-data = true

From 4bf4537730d487b2524e473fda43c99c8786008b Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 28 Sep 2023 21:12:28 +0530
Subject: [PATCH 063/199] Set up MANIFEST.in

---
 MANIFEST.in    | 6 ++++++
 pyproject.toml | 3 ++-
 2 files changed, 8 insertions(+), 1 deletion(-)
 create mode 100644 MANIFEST.in

diff --git a/MANIFEST.in b/MANIFEST.in
new file mode 100644
index 0000000000..24ae488d04
--- /dev/null
+++ b/MANIFEST.in
@@ -0,0 +1,6 @@
+graft pybamm
+prune tests
+
+exclude CHANGELOG.md CODE-OF-CONDUCT.md CONTRIBUTING.md GOVERNANCE.md CMakeLists.txt
+
+global-exclude __pycache__ *.py[cod] .venv
diff --git a/pyproject.toml b/pyproject.toml
index c3c6583adb..a69a7926ca 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -163,6 +163,7 @@ include-package-data = true
 # List of files to include as package data. These are mainly the parameter CSV files in
 # the input/parameters/ subdirectories. Other files such as the CITATIONS file, relevant
 # README.md files, and specific .txt files inside the pybamm/ directory are also included.
+# These are specified to be included in the SDist through MANIFEST.in.
 [tool.setuptools.package-data]
 pybamm = [
     "*.txt",
@@ -174,4 +175,4 @@ pybamm = [
 ]
 
 [tool.setuptools.packages.find]
-include = ["pybamm", "pybamm.*"]
+include = ["pybamm"]

From 1c202741835ff57282693b3de973207b82a867db Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 28 Sep 2023 22:30:15 +0530
Subject: [PATCH 064/199] #3049 some installation cleanups (`nox`)

---
 noxfile.py | 58 ++++++++++++++++++++++++------------------------------
 setup.py   | 16 +++++++++------
 2 files changed, 36 insertions(+), 38 deletions(-)

diff --git a/noxfile.py b/noxfile.py
index 904770d952..c6d300d0b0 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -22,12 +22,6 @@
 JAX_VERSION = "0.4.8"
 JAXLIB_VERSION = "0.4.7"
 
-if os.getenv("CI") == "true":
-    # The setup-python action installs and caches dependencies by default, so we skip
-    # installing them again in nox environments. The dev and docs sessions will still
-    # require a virtual environment, but we don't run them in the CI
-    nox.options.default_venv_backend = "none"
-
 
 def set_environment_variables(env_dict, session):
     """
@@ -50,7 +44,7 @@ def run_pybamm_requires(session):
     """Download, compile, and install the build-time requirements for Linux and macOS: the SuiteSparse and SUNDIALS libraries."""  # noqa: E501
     set_environment_variables(PYBAMM_ENV, session=session)
     if sys.platform != "win32":
-        session.run_always("pip", "install", "wget", "cmake")
+        session.install("wget", "cmake" , silent=False)
         session.run("python", "scripts/install_KLU_Sundials.py")
         if not os.path.exists("./pybind11"):
             session.run(
@@ -61,19 +55,19 @@ def run_pybamm_requires(session):
                 external=True,
             )
     else:
-        session.error("nox -s pybamm-requires is only available on Linux & MacOS.")
+        session.error("nox -s pybamm-requires is only available on Linux & macOS.")
 
 
 @nox.session(name="coverage")
 def run_coverage(session):
     """Run the coverage tests and generate an XML report."""
     set_environment_variables(PYBAMM_ENV, session=session)
-    session.run_always("pip", "install", "coverage")
-    session.run_always("pip", "install", "-e", ".[all]")
+    session.install("coverage", silent=False)
+    session.install("-e", ".[all]", silent=False)
     if sys.platform != "win32":
-        session.run_always("pip", "install", "scikits.odes")
-        session.run_always("pip", "install", f"jax=={JAX_VERSION}")
-        session.run_always("pip", "install", f"jaxlib=={JAXLIB_VERSION}")
+        session.install("scikits.odes", silent=False)
+        session.install(f"jax=={JAX_VERSION}", silent=False)
+        session.install(f"jaxlib=={JAXLIB_VERSION}", silent=False)
     session.run("coverage", "run", "--rcfile=.coveragerc", "run-tests.py", "--nosub")
     session.run("coverage", "combine")
     session.run("coverage", "xml")
@@ -83,18 +77,18 @@ def run_coverage(session):
 def run_integration(session):
     """Run the integration tests."""
     set_environment_variables(PYBAMM_ENV, session=session)
-    session.run_always("pip", "install", "-e", ".[all]")
+    session.install("-e", ".[all]", silent=False)
     if sys.platform != "win32":
-        session.run_always("pip", "install", "scikits.odes")
-        session.run_always("pip", "install", f"jax=={JAX_VERSION}")
-        session.run_always("pip", "install", f"jaxlib=={JAXLIB_VERSION}")
+        session.install("scikits.odes", silent=False)
+        session.install(f"jax=={JAX_VERSION}", silent=False)
+        session.install(f"jaxlib=={JAXLIB_VERSION}", silent=False)
     session.run("python", "run-tests.py", "--integration")
 
 
 @nox.session(name="doctests")
 def run_doctests(session):
     """Run the doctests and generate the output(s) in the docs/build/ directory."""
-    session.run_always("pip", "install", "-e", ".[all,docs]")
+    session.install("-e", ".[all,docs]", silent=False)
     session.run("python", "run-tests.py", "--doctest")
 
 
@@ -102,11 +96,11 @@ def run_doctests(session):
 def run_unit(session):
     """Run the unit tests."""
     set_environment_variables(PYBAMM_ENV, session=session)
-    session.run_always("pip", "install", "-e", ".[all]")
+    session.install("-e", ".[all]", silent=False)
     if sys.platform != "win32":
-        session.run_always("pip", "install", "scikits.odes")
-        session.run_always("pip", "install", f"jax=={JAX_VERSION}")
-        session.run_always("pip", "install", f"jaxlib=={JAXLIB_VERSION}")
+        session.install("scikits.odes", silent=False)
+        session.install(f"jax=={JAX_VERSION}", silent=False)
+        session.install(f"jaxlib=={JAXLIB_VERSION}", silent=False)
     session.run("python", "run-tests.py", "--unit")
 
 
@@ -115,7 +109,7 @@ def run_examples(session):
     """Run the examples tests for Jupyter notebooks."""
     set_environment_variables(PYBAMM_ENV, session=session)
     notebooks_to_test = session.posargs if session.posargs else []
-    session.run_always("pip", "install", "-e", ".[all,dev]")
+    session.install("-e", ".[all,dev]", silent=False)
     session.run("pytest", "--nbmake", *notebooks_to_test, external=True)
 
 
@@ -123,7 +117,7 @@ def run_examples(session):
 def run_scripts(session):
     """Run the scripts tests for Python scripts."""
     set_environment_variables(PYBAMM_ENV, session=session)
-    session.run_always("pip", "install", "-e", ".[all]")
+    session.install("-e", ".[all]", silent=False)
     session.run("python", "run-tests.py", "--scripts")
 
 
@@ -132,8 +126,8 @@ def set_dev(session):
     """Install PyBaMM in editable mode."""
     set_environment_variables(PYBAMM_ENV, session=session)
     envbindir = session.bin
-    session.install("-e", ".[all]")
-    session.install("cmake")
+    session.install("-e", ".[all]", silent=False)
+    session.install("cmake", silent=False)
     if sys.platform != "win32":
         session.run(
             "echo",
@@ -149,11 +143,11 @@ def set_dev(session):
 def run_tests(session):
     """Run the unit tests and integration tests sequentially."""
     set_environment_variables(PYBAMM_ENV, session=session)
-    session.run_always("pip", "install", "-e", ".[all]")
+    session.install("-e", ".[all]", silent=False)
     if sys.platform != "win32":
-        session.run_always("pip", "install", "scikits.odes")
-        session.run_always("pip", "install", f"jax=={JAX_VERSION}")
-        session.run_always("pip", "install", f"jaxlib=={JAXLIB_VERSION}")
+        session.install("scikits.odes", silent=False)
+        session.install(f"jax=={JAX_VERSION}", silent=False)
+        session.install(f"jaxlib=={JAXLIB_VERSION}", silent=False)
     session.run("python", "run-tests.py", "--all")
 
 
@@ -161,7 +155,7 @@ def run_tests(session):
 def build_docs(session):
     """Build the documentation and load it in a browser tab, rebuilding on changes."""
     envbindir = session.bin
-    session.install("-e", ".[all,docs]")
+    session.install("-e", ".[all,docs]", silent=False)
     session.chdir("docs")
     session.run(
         "sphinx-autobuild",
@@ -177,7 +171,7 @@ def build_docs(session):
 @nox.session(name="pre-commit")
 def lint(session):
     """Check all files against the defined pre-commit hooks."""
-    session.install("pre-commit")
+    session.install("pre-commit", silent=False)
     session.run("pre-commit", "run", "--all-files")
 
 
diff --git a/setup.py b/setup.py
index 2af56c6ef6..018bf9eee0 100644
--- a/setup.py
+++ b/setup.py
@@ -9,16 +9,15 @@
 try:
     from setuptools import setup, Extension
     from setuptools.command.install import install
+    from setuptools.command.build_ext import build_ext
 except ImportError:
     from distutils.core import setup
     from distutils.command.install import install
+    from distutils.command.build_ext import build_ext
 
-# ---------- cmakebuild was integrated into setup.py directly --------------------------
 
-try:
-    from setuptools.command.build_ext import build_ext
-except ImportError:
-    from distutils.command.build_ext import build_ext
+# ---------- CMake steps for IDAKLU target (non-Windows) -------------------------------
+
 
 default_lib_dir = (
     "" if system() == "Windows" else os.path.join(os.getenv("HOME"), ".local")
@@ -171,10 +170,13 @@ def move_output(self, ext):
         dest_directory.mkdir(parents=True, exist_ok=True)
         self.copy_file(source_path, dest_path)
 
-# ---------- end of cmakebuild steps ---------------------------------------------------
+
+# ---------- end of CMake steps --------------------------------------------------------
+
 
 # ---------- configure setup logger ----------------------------------------------------
 
+
 log_format = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
 logger = logging.getLogger("PyBaMM setup")
 
@@ -215,8 +217,10 @@ def finalize_options(self):
     def run(self):
         install.run(self)
 
+
 # ---------- custom wheel build (non-Windows) ------------------------------------------
 
+
 class bdist_wheel(orig.bdist_wheel):
     """A custom install command to add 2 build options"""
 

From ddac82e448fb3c31106c08621be3c9bed50aa37d Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 28 Sep 2023 22:40:38 +0530
Subject: [PATCH 065/199] #3049 #3121 sync jax, jaxlib version requirements

---
 noxfile.py     | 7 ++++---
 pyproject.toml | 7 ++++---
 2 files changed, 8 insertions(+), 6 deletions(-)

diff --git a/noxfile.py b/noxfile.py
index c6d300d0b0..dafa114221 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -18,9 +18,10 @@
 }
 # Versions compatible with the current version of PyBaMM. Installed directly in the
 # sessions to skip redundant installation of dependencies and building wheels both in
-# the CI and locally
-JAX_VERSION = "0.4.8"
-JAXLIB_VERSION = "0.4.7"
+# the CI and locally. These should be updated when the version of PyBaMM is updated and
+# must be kept in sync with the constants defined in pybamm/util.py.
+JAX_VERSION = "0.4"
+JAXLIB_VERSION = "0.4"
 
 
 def set_environment_variables(env_dict, session):
diff --git a/pyproject.toml b/pyproject.toml
index a69a7926ca..5f225cb5f5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -109,10 +109,11 @@ dev = [
 pandas =  [
     "pandas>=0.24",
 ]
-# For the Jax solver
+# For the Jax solver. Note: these should be kept in sync with the versions defined
+# in noxfile.py and pybamm/util.py.
 jax = [
-    "jax==0.4.8",
-    "jaxlib==0.4.7",
+    "jax>=0.4,<=0.5",
+    "jaxlib>=0.4,<=0.5",
 ]
 # For the scikits.odes solver
 odes = [

From 8016c712f417a816760f02a9b51734e825beb0b7 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 28 Sep 2023 22:51:18 +0530
Subject: [PATCH 066/199] #3049 Improve docs about project installation
 infrastructure

---
 CONTRIBUTING.md | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 577dbd67c6..8e8fdd36e5 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -385,21 +385,22 @@ wherever code is called that uses that citation (for example, in functions or in
 
 ## Infrastructure
 
-### Setuptools
+### Installation
 
-Installation of PyBaMM _and dependencies_ is handled via [setuptools](http://setuptools.readthedocs.io/)
+Installation of PyBaMM and its dependencies is handled via [pip](https://pip.pypa.io/en/stable/) and [setuptools](http://setuptools.readthedocs.io/). It uses `CMake` to compile C extensions using [`pybind11`](https://pybind11.readthedocs.io/en/stable/) and [`casadi`](https://web.casadi.org/) (non-Windows). The installation process is described in detail in the [source installation](https://docs.pybamm.org/en/latest/source/user_guide/installation/install-from-source.html) page and is configured through the `CMakeLists.txt` file.
 
 Configuration files:
 
 ```
 setup.py
+pyproject.toml
 ```
 
-Note that this file must be kept in sync with the version number in [pybamm/**init**.py](pybamm/__init__.py).
+Note that this file must be kept in sync with the version number in [`pybamm/__init__.py`](pybamm/__init__.py).
 
-### Continuous Integration using GitHub actions
+### Continuous Integration using GitHub Actions
 
-Each change pushed to the PyBaMM GitHub repository will trigger the test and benchmark suites to be run, using [GitHub actions](https://github.com/features/actions).
+Each change pushed to the PyBaMM GitHub repository will trigger the test and benchmark suites to be run, using [GitHub Actions](https://github.com/features/actions).
 
 Tests are run for different operating systems, and for all Python versions officially supported by PyBaMM. If you opened a Pull Request, feedback is directly available on the corresponding page. If all tests pass, a green tick will be displayed next to the corresponding test run. If one or more test(s) fail, a red cross will be displayed instead.
 

From 9307ee48939464b6a2844b8fe4c75fdedef9c4c0 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 28 Sep 2023 23:00:51 +0530
Subject: [PATCH 067/199] #3049 Update cache hashes to include `nox` changes

---
 .github/workflows/test_on_push.yml | 10 +++++-----
 noxfile.py                         |  3 ++-
 2 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index d6a2c73f5c..5fb2e3ab15 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -104,7 +104,7 @@ jobs:
             ${{ env.HOME }}/.local/lib/
             ${{ env.HOME }}/.local/include/
             ${{ env.HOME }}/.local/examples/
-          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py', '**/noxfile.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux and macOS
         if: matrix.os != 'windows-latest'
@@ -158,7 +158,7 @@ jobs:
             ${{ env.HOME }}/.local/lib/
             ${{ env.HOME }}/.local/include/
             ${{ env.HOME }}/.local/examples/
-          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py', '**/noxfile.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         run: pipx run nox -s pybamm-requires
@@ -239,7 +239,7 @@ jobs:
             ${{ env.HOME }}/.local/lib/
             ${{ env.HOME }}/.local/include/
             ${{ env.HOME }}/.local/examples/
-          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py', '**/noxfile.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux and macOS
         if: matrix.os != 'windows-latest'
@@ -293,7 +293,7 @@ jobs:
             ${{ env.HOME }}/.local/lib/
             ${{ env.HOME }}/.local/include/
             ${{ env.HOME }}/.local/examples/
-          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py', '**/noxfile.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         run: pipx run nox -s pybamm-requires
@@ -349,7 +349,7 @@ jobs:
             ${{ env.HOME }}/.local/lib/
             ${{ env.HOME }}/.local/include/
             ${{ env.HOME }}/.local/examples/
-          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py', '**/noxfile.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         run: pipx run nox -s pybamm-requires
diff --git a/noxfile.py b/noxfile.py
index dafa114221..c6175cd183 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -18,7 +18,8 @@
 }
 # Versions compatible with the current version of PyBaMM. Installed directly in the
 # sessions to skip redundant installation of dependencies and building wheels both in
-# the CI and locally. These should be updated when the version of PyBaMM is updated and
+# the CI and locally
+# Note: These should be updated when the version of PyBaMM is updated and
 # must be kept in sync with the constants defined in pybamm/util.py.
 JAX_VERSION = "0.4"
 JAXLIB_VERSION = "0.4"

From 3720f04649c30cbb6beb54914bbf7cd50b15f7d6 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 28 Sep 2023 23:16:44 +0530
Subject: [PATCH 068/199] #3049 temporarily skip i686 Linux builds

See https://github.com/numpy/numpy/issues/24703 for more information
---
 .github/workflows/publish_pypi.yml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 5a012f64a8..b0c5f5faae 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -94,6 +94,8 @@ jobs:
       - name: Build wheels on ${{ matrix.os }}
         run: pipx run cibuildwheel --output-dir wheelhouse
         env:
+          # NumPy requires BLAS now which is no longer available on manylinux2014 i686, so skip it
+          CIBW_ARCHS_LINUX: x86_64
           # TODO: openblas no longer available on centos 7 i686 image, use blas instead for now
           CIBW_BEFORE_ALL_LINUX: >
             yum -y install blas-devel lapack-devel &&

From aa86d7268d713805ae90ef80117345fc8ebebf3a Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Fri, 29 Sep 2023 00:10:32 +0530
Subject: [PATCH 069/199] #3049 Fix UNKNOWN name error on SDist

See https://github.com/pypa/setuptools/issues/3269 for more details
---
 .github/workflows/publish_pypi.yml | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index b0c5f5faae..6f82c6ca2e 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -124,22 +124,22 @@ jobs:
           if-no-files-found: error
 
   build_sdist:
-    name: Build sdist
+    name: Build SDist
     runs-on: ubuntu-latest
 
     steps:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v4
         with:
-          python-version: 3.8
+          python-version: 3.11
 
       - name: Install dependencies
-        run: pip install wheel
+        run: pip install --upgrade pip setuptools wheel
 
-      - name: Build sdist
-        run: python setup.py sdist --formats=gztar
+      - name: Build SDist
+        run: pipx run build --sdist
 
-      - name: Upload sdist
+      - name: Upload SDist
         uses: actions/upload-artifact@v3
         with:
           name: sdist

From 588496f198ef3ebc1214028ffebffa20d2166bb1 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 2 Oct 2023 02:49:23 +0530
Subject: [PATCH 070/199] #3049 keep solvers extras in sync, don't install
 yanked versions

---
 noxfile.py     | 35 ++++++++++++-----------------------
 pybamm/util.py |  5 ++---
 pyproject.toml |  3 +--
 3 files changed, 15 insertions(+), 28 deletions(-)

diff --git a/noxfile.py b/noxfile.py
index c6175cd183..c59538d94e 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -16,13 +16,6 @@
     "SUNDIALS_INST": f"{homedir}/.local",
     "LD_LIBRARY_PATH": f"{homedir}/.local/lib",
 }
-# Versions compatible with the current version of PyBaMM. Installed directly in the
-# sessions to skip redundant installation of dependencies and building wheels both in
-# the CI and locally
-# Note: These should be updated when the version of PyBaMM is updated and
-# must be kept in sync with the constants defined in pybamm/util.py.
-JAX_VERSION = "0.4"
-JAXLIB_VERSION = "0.4"
 
 
 def set_environment_variables(env_dict, session):
@@ -65,11 +58,10 @@ def run_coverage(session):
     """Run the coverage tests and generate an XML report."""
     set_environment_variables(PYBAMM_ENV, session=session)
     session.install("coverage", silent=False)
-    session.install("-e", ".[all]", silent=False)
     if sys.platform != "win32":
-        session.install("scikits.odes", silent=False)
-        session.install(f"jax=={JAX_VERSION}", silent=False)
-        session.install(f"jaxlib=={JAXLIB_VERSION}", silent=False)
+        session.install("-e", ".[all,odes,jax]", silent=False)
+    else:
+        session.install("-e", ".[all]", silent=False)
     session.run("coverage", "run", "--rcfile=.coveragerc", "run-tests.py", "--nosub")
     session.run("coverage", "combine")
     session.run("coverage", "xml")
@@ -79,11 +71,10 @@ def run_coverage(session):
 def run_integration(session):
     """Run the integration tests."""
     set_environment_variables(PYBAMM_ENV, session=session)
-    session.install("-e", ".[all]", silent=False)
     if sys.platform != "win32":
-        session.install("scikits.odes", silent=False)
-        session.install(f"jax=={JAX_VERSION}", silent=False)
-        session.install(f"jaxlib=={JAXLIB_VERSION}", silent=False)
+        session.install("-e", ".[all,odes,jax]", silent=False)
+    else:
+        session.install("-e", ".[all]", silent=False)
     session.run("python", "run-tests.py", "--integration")
 
 
@@ -98,11 +89,10 @@ def run_doctests(session):
 def run_unit(session):
     """Run the unit tests."""
     set_environment_variables(PYBAMM_ENV, session=session)
-    session.install("-e", ".[all]", silent=False)
     if sys.platform != "win32":
-        session.install("scikits.odes", silent=False)
-        session.install(f"jax=={JAX_VERSION}", silent=False)
-        session.install(f"jaxlib=={JAXLIB_VERSION}", silent=False)
+        session.install("-e", ".[all,odes,jax]", silent=False)
+    else:
+        session.install("-e", ".[all]", silent=False)
     session.run("python", "run-tests.py", "--unit")
 
 
@@ -145,11 +135,10 @@ def set_dev(session):
 def run_tests(session):
     """Run the unit tests and integration tests sequentially."""
     set_environment_variables(PYBAMM_ENV, session=session)
-    session.install("-e", ".[all]", silent=False)
     if sys.platform != "win32":
-        session.install("scikits.odes", silent=False)
-        session.install(f"jax=={JAX_VERSION}", silent=False)
-        session.install(f"jaxlib=={JAXLIB_VERSION}", silent=False)
+            session.install("-e", ".[all,odes,jax]", silent=False)
+    else:
+        session.install("-e", ".[all]", silent=False)
     session.run("python", "run-tests.py", "--all")
 
 
diff --git a/pybamm/util.py b/pybamm/util.py
index 4799ee0285..f31715e551 100644
--- a/pybamm/util.py
+++ b/pybamm/util.py
@@ -21,9 +21,8 @@
 import numpy as np
 import pybamm
 
-# versions of jax and jaxlib compatible with PyBaMM. These are also defined in
-# noxfile.py and in the extras dependencies in pyproject.toml, and therefore must be
-# kept in sync.
+# Versions of jax and jaxlib compatible with PyBaMM. Note: these are also defined in
+# in the extras dependencies in pyproject.toml, and therefore must be kept in sync.
 JAX_VERSION = "0.4"
 JAXLIB_VERSION = "0.4"
 
diff --git a/pyproject.toml b/pyproject.toml
index 5f225cb5f5..912f4576c9 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -109,8 +109,7 @@ dev = [
 pandas =  [
     "pandas>=0.24",
 ]
-# For the Jax solver. Note: these should be kept in sync with the versions defined
-# in noxfile.py and pybamm/util.py.
+# For the Jax solver. Note: these must be kept in sync with the versions defined in pybamm/util.py.
 jax = [
     "jax>=0.4,<=0.5",
     "jaxlib>=0.4,<=0.5",

From 690b8145e4c683241e92c0ab3275f6e9066369d2 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 2 Oct 2023 04:13:16 +0530
Subject: [PATCH 071/199] #3049 Fix up authors name and email in project

---
 pyproject.toml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 912f4576c9..352f35725e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -3,6 +3,7 @@ requires = [
     "setuptools",
     "wheel",
     "casadi>=3.6.0; platform_system!='Windows'",
+    # use CMake bundled from MSVC on Windows
     "cmake; platform_system!='Windows'",
     ]
 build-backend = "setuptools.build_meta"
@@ -12,7 +13,7 @@ name = "pybamm"
 version = "23.5"
 license = { file = "LICENSE.txt" }
 description = "Python Battery Mathematical Modelling"
-authors = [{name = "The PyBaMM Team"}, {email = "pybamm@pybamm.org"}]
+authors = [{name = "The PyBaMM Team", email = "pybamm@pybamm.org"}]
 maintainers = [{name = "The PyBaMM Team", email = "pybamm@pybamm.org"}]
 requires-python = ">=3.8, <3.12"
 readme = {file = "README.md", content-type = "text/markdown"}

From b18d6661129ea433b4473588c5546c6b394fb810 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 2 Oct 2023 17:51:20 +0530
Subject: [PATCH 072/199] Cleanup extras list, resolve conflicts

---
 pyproject.toml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 352f35725e..685656432e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -95,9 +95,9 @@ tqdm = [
 ]
 # Dependencies intended for use by developers
 dev = [
-    # For code style checking
+    # For working with pre-commit hooks
     "pre-commit",
-    # For code style auto-formatting
+    # For code style checks: linting and auto-formatting
     "ruff",
     # For running testing sessions
     "nox",

From a91c87b9b4e44835dd2046cdc92fcd0106e9d844 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 2 Oct 2023 18:16:46 +0530
Subject: [PATCH 073/199] #3049 add `pipx` for doctests job

---
 .github/workflows/test_on_push.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index caf019d08e..6821016e45 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -284,10 +284,10 @@ jobs:
           cache-dependency-path: setup.py
 
       - name: Install docs dependencies and run doctests for GNU/Linux with Python 3.11
-        run: nox -s doctests
+        run: pipx run nox -s doctests
 
       - name: Check if the documentation can be built for GNU/Linux with Python 3.11
-        run: nox -s docs
+        run: pipx run nox -s docs
 
   # Runs only on Ubuntu with Python 3.11
   run_example_tests:

From 5cda9bc041dd43ca5c02b5957b6e04e730a885c5 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 3 Oct 2023 23:05:59 +0530
Subject: [PATCH 074/199] Install all,dev,docs only if no build-args given

Co-Authored-By: Saransh Chopra <saransh0701@gmail.com>
---
 scripts/Dockerfile | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/scripts/Dockerfile b/scripts/Dockerfile
index b6c0a02f67..7015feef5a 100644
--- a/scripts/Dockerfile
+++ b/scripts/Dockerfile
@@ -59,6 +59,11 @@ RUN if [ "$ALL" = "true" ]; then \
     pip install --user -e ".[all,dev,docs,jax,odes]"; \
     fi
 
-RUN pip install --user -e ".[all,dev,docs]"
+RUN if [ -z "$IDAKLU" ] \
+    && [ -z "$ODES" ] \
+    && [ -z "$JAX" ] \
+    && [ -z "$ALL" ]; then \
+    pip install --user -e ".[all,dev,docs]"; \
+    fi
 
 ENTRYPOINT ["/bin/bash"]

From 14c3c61719e0ffc8a44e90a763e173baa8df8285 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 5 Oct 2023 15:28:15 +0530
Subject: [PATCH 075/199] #3049 try to bump `pybind11`, `vcpkg` versions
 (Windows)

---
 .github/workflows/publish_pypi.yml | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 6f82c6ca2e..c38092906e 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -28,14 +28,13 @@ jobs:
           python-version: 3.8
 
       - name: Clone pybind11 repo (no history)
-        run: git clone --depth 1 --branch v2.10.4 https://github.com/pybind/pybind11.git
+        run: git clone --depth 1 --branch v2.11.1 https://github.com/pybind/pybind11.git
 
-      # remove when a new vcpkg version is released
-      - name: Install the latest commit of vcpkg on windows
+      - name: Install vcpkg on windows
         run: |
           cd C:\
           rm -r -fo 'C:\vcpkg'
-          git clone https://github.com/microsoft/vcpkg
+          git clone https://github.com/microsoft/vcpkg --branch 2023.08.09
           cd vcpkg
           .\bootstrap-vcpkg.bat
 

From 30d76cd1994e566120054a355eaa97cde53e2be5 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 5 Oct 2023 15:31:43 +0530
Subject: [PATCH 076/199] #3049 add OKane2022 parameter entry points

---
 pyproject.toml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/pyproject.toml b/pyproject.toml
index 685656432e..a416dfd2a2 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -147,10 +147,12 @@ Ai2020 = "pybamm.input.parameters.lithium_ion.Ai2020:get_parameter_values"
 Chen2020 = "pybamm.input.parameters.lithium_ion.Chen2020:get_parameter_values"
 Chen2020_composite = "pybamm.input.parameters.lithium_ion.Chen2020_composite:get_parameter_values"
 Ecker2015 = "pybamm.input.parameters.lithium_ion.Ecker2015:get_parameter_values"
+Ecker2015_graphite_halfcell = "pybamm.input.parameters.lithium_ion.Ecker2015_graphite_halfcell:get_parameter_values"
 Marquis2019 = "pybamm.input.parameters.lithium_ion.Marquis2019:get_parameter_values"
 Mohtat2020 = "pybamm.input.parameters.lithium_ion.Mohtat2020:get_parameter_values"
 NCA_Kim2011 = "pybamm.input.parameters.lithium_ion.NCA_Kim2011:get_parameter_values"
 OKane2022 = "pybamm.input.parameters.lithium_ion.OKane2022:get_parameter_values"
+OKane2022_graphite_SiOx_halfcell = "pybamm.input.parameters.lithium_ion.OKane2022_graphite_SiOx_halfcell:get_parameter_values"
 ORegan2022 = "pybamm.input.parameters.lithium_ion.ORegan2022:get_parameter_values"
 Prada2013 = "pybamm.input.parameters.lithium_ion.Prada2013:get_parameter_values"
 Ramadass2004 = "pybamm.input.parameters.lithium_ion.Ramadass2004:get_parameter_values"

From 5c18e229b5f9a111dcbf46c0bd9c9fece2bfe069 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 7 Oct 2023 03:27:38 +0530
Subject: [PATCH 077/199] #3049 correctly specify inclusion of packages

---
 pyproject.toml | 2 +-
 setup.py       | 2 ++
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index a416dfd2a2..c91d275789 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -178,4 +178,4 @@ pybamm = [
 ]
 
 [tool.setuptools.packages.find]
-include = ["pybamm"]
+include = ["pybamm", "pybamm.*"]
diff --git a/setup.py b/setup.py
index 018bf9eee0..a0180cb3e8 100644
--- a/setup.py
+++ b/setup.py
@@ -299,6 +299,8 @@ def compile_KLU():
 # Project metadata was moved to pyproject.toml (which is read by pip). However, custom
 # build commands and setuptools extension modules are still defined here.
 setup(
+    # silence "Package would be ignored" warnings
+    include_package_data=True,
     ext_modules=ext_modules,
     cmdclass={
         "build_ext": CMakeBuild,

From c016a64a4c41138acd962ab1c36bacbcc69ec2e3 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 7 Oct 2023 17:04:30 +0530
Subject: [PATCH 078/199] #3049 Remove unneeded files (used for old manylinux)

Not needed anymore after we have been using `cibuildwheel`, which uses the Dockerfile from PyPA
---
 build_manylinux_wheels/Dockerfile    | 18 -----------------
 build_manylinux_wheels/action.yml    | 17 ----------------
 build_manylinux_wheels/entrypoint.sh | 30 ----------------------------
 3 files changed, 65 deletions(-)
 delete mode 100644 build_manylinux_wheels/Dockerfile
 delete mode 100644 build_manylinux_wheels/action.yml
 delete mode 100644 build_manylinux_wheels/entrypoint.sh

diff --git a/build_manylinux_wheels/Dockerfile b/build_manylinux_wheels/Dockerfile
deleted file mode 100644
index a6c2dcc41c..0000000000
--- a/build_manylinux_wheels/Dockerfile
+++ /dev/null
@@ -1,18 +0,0 @@
-FROM quay.io/pypa/manylinux2014_x86_64:2020-11-11-bc8ce45
-
-ENV PLAT manylinux2014_x86_64
-
-RUN yum -y update
-RUN yum -y remove cmake
-RUN yum -y install wget openblas-devel
-RUN /opt/python/cp37-cp37m/bin/pip install --upgrade pip cmake
-RUN ln -s /opt/python/cp37-cp37m/bin/cmake /usr/bin/cmake
-
-COPY install_sundials.sh /install_sundials.sh
-RUN chmod +x /install_sundials.sh
-COPY entrypoint.sh /entrypoint.sh
-RUN chmod +x /entrypoint.sh
-
-RUN ./install_sundials.sh
-
-ENTRYPOINT ["/entrypoint.sh"]
diff --git a/build_manylinux_wheels/action.yml b/build_manylinux_wheels/action.yml
deleted file mode 100644
index 7264606b30..0000000000
--- a/build_manylinux_wheels/action.yml
+++ /dev/null
@@ -1,17 +0,0 @@
-# action.yml
-# Based on  RalfG/python-wheels-manylinux-build/action.yml by Ralf Gabriels
-
-name: "Python wheels manylinux build"
-author: "Thibault Lestang"
-description: "Build manylinux wheels for PyBaMM"
-inputs:
-  python-versions:
-    description: "Python versions to target, space-separated"
-    required: true
-    default: "cp36-cp36m cp37-cp37m"
-
-runs:
-  using: "docker"
-  image: "Dockerfile"
-  args:
-    - ${{ inputs.python-versions }}
diff --git a/build_manylinux_wheels/entrypoint.sh b/build_manylinux_wheels/entrypoint.sh
deleted file mode 100644
index 203e5471d3..0000000000
--- a/build_manylinux_wheels/entrypoint.sh
+++ /dev/null
@@ -1,30 +0,0 @@
-#!/bin/bash
-set -e -x
-
-# GitHub runners add "-e LD_LIBRARY_PATH" option to "docker run",
-# overriding default value of LD_LIBRARY_PATH in manylinux image. This
-# causes libcrypt.so.2 to be missing (it lives in /usr/local/lib)
-export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH
-
-# CLI arguments
-PY_VERSIONS=$1
-
-git clone https://github.com/pybind/pybind11.git /github/workspace/pybind11
-# Compile wheels
-arrPY_VERSIONS=(${PY_VERSIONS// / })
-for PY_VER in "${arrPY_VERSIONS[@]}"; do
-    # Update pip
-    /opt/python/"${PY_VER}"/bin/pip install --upgrade --no-cache-dir pip
-
-    # Build wheels
-    /opt/python/"${PY_VER}"/bin/pip wheel /github/workspace/ -w /github/workspace/wheelhouse/ --no-deps || { echo "Building wheels failed."; exit 1; }
-done
-ls -l /github/workspace/wheelhouse/
-
-# Bundle external shared libraries into the wheels
-for whl in /github/workspace/wheelhouse/*-linux*.whl; do
-    auditwheel repair "$whl" --plat "${PLAT}" -w /github/workspace/dist/ || { echo "Repairing wheels failed."; auditwheel show "$whl"; exit 1; }
-done
-
-echo "Succesfully built wheels:"
-ls -l /github/workspace/dist/

From b70e0a64b44acf0e3596efe9dd1cd27d48f1d7c5 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 7 Oct 2023 17:06:01 +0530
Subject: [PATCH 079/199] #3049 move SUNDIALS installation to `scripts/`

---
 .github/workflows/publish_pypi.yml                      | 2 +-
 {build_manylinux_wheels => scripts}/install_sundials.sh | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)
 rename {build_manylinux_wheels => scripts}/install_sundials.sh (93%)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index c38092906e..671cf4a0b0 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -98,7 +98,7 @@ jobs:
           # TODO: openblas no longer available on centos 7 i686 image, use blas instead for now
           CIBW_BEFORE_ALL_LINUX: >
             yum -y install blas-devel lapack-devel &&
-            bash build_manylinux_wheels/install_sundials.sh 5.8.1 6.5.0
+            bash scripts/install_sundials.sh 5.8.1 6.5.0
           CIBW_BEFORE_BUILD_LINUX: >
             python -m pip install cmake casadi numpy
           # override; point to casadi install path so that it can be found by the repair command
diff --git a/build_manylinux_wheels/install_sundials.sh b/scripts/install_sundials.sh
similarity index 93%
rename from build_manylinux_wheels/install_sundials.sh
rename to scripts/install_sundials.sh
index 709d9c13c7..56435066b4 100644
--- a/build_manylinux_wheels/install_sundials.sh
+++ b/scripts/install_sundials.sh
@@ -1,10 +1,10 @@
 #!/bin/bash
 
 # This script installs both SuiteSparse
-# (https://people.engr.tamu.edu/davis/suitesparse.html) and Sundials
+# (https://people.engr.tamu.edu/davis/suitesparse.html) and SUNDIALS
 # (https://computing.llnl.gov/projects/sundials) from source. For each
 # two library:
-# - Archive downloaded and source code extrated in current working
+# - Archive downloaded and source code extracted in current working
 #   directory.
 # - Library is built and installed.
 #

From 5583d01844a8f7e4eac6ce7e8be6ea81c61233aa Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 7 Oct 2023 17:08:36 +0530
Subject: [PATCH 080/199] #3049 delete older SUNDIALS CMake files

these were used for finding KLU earlier but are for now outdated versions of SUNDIALS (<6). We currently use a custom vcpkg registry which comes with modified portfiles for this purpose.
---
 scripts/replace-cmake/README.md               |    1 -
 .../sundials-3.1.1/CMakeLists.txt             | 1597 -----------------
 .../sundials-4.1.0/CMakeLists.txt             | 1151 ------------
 .../sundials-5.0.0/CMakeLists.txt             | 1151 ------------
 4 files changed, 3900 deletions(-)
 delete mode 100644 scripts/replace-cmake/README.md
 delete mode 100644 scripts/replace-cmake/sundials-3.1.1/CMakeLists.txt
 delete mode 100644 scripts/replace-cmake/sundials-4.1.0/CMakeLists.txt
 delete mode 100644 scripts/replace-cmake/sundials-5.0.0/CMakeLists.txt

diff --git a/scripts/replace-cmake/README.md b/scripts/replace-cmake/README.md
deleted file mode 100644
index e578a96abb..0000000000
--- a/scripts/replace-cmake/README.md
+++ /dev/null
@@ -1 +0,0 @@
-A modified sundials cmake file which finds the KLU solvers correctly
diff --git a/scripts/replace-cmake/sundials-3.1.1/CMakeLists.txt b/scripts/replace-cmake/sundials-3.1.1/CMakeLists.txt
deleted file mode 100644
index 81f4267c22..0000000000
--- a/scripts/replace-cmake/sundials-3.1.1/CMakeLists.txt
+++ /dev/null
@@ -1,1597 +0,0 @@
-# ---------------------------------------------------------------
-# Programmer:  Radu Serban @ LLNL
-# ---------------------------------------------------------------
-# LLNS Copyright Start
-# Copyright (c) 2014, Lawrence Livermore National Security
-# This work was performed under the auspices of the U.S. Department
-# of Energy by Lawrence Livermore National Laboratory in part under
-# Contract W-7405-Eng-48 and in part under Contract DE-AC52-07NA27344.
-# Produced at the Lawrence Livermore National Laboratory.
-# All rights reserved.
-# For details, see the LICENSE file.
-# LLNS Copyright End
-# ---------------------------------------------------------------
-# Top level CMakeLists.txt for SUNDIALS (for cmake build system)
-# ---------------------------------------------------------------
-
-# ---------------------------------------------------------------
-# Initial commands
-# ---------------------------------------------------------------
-
-# Require a fairly recent cmake version
-CMAKE_MINIMUM_REQUIRED(VERSION 2.8.1)
-
-# Set CMake policy to allow examples to build
-if(COMMAND cmake_policy)
-  cmake_policy(SET CMP0003 NEW)
-endif(COMMAND cmake_policy)
-
-# Project SUNDIALS (initially only C supported)
-# sets PROJECT_SOURCE_DIR and PROJECT_BINARY_DIR variables
-PROJECT(sundials C)
-
-# Set some variables with info on the SUNDIALS project
-SET(PACKAGE_BUGREPORT "woodward6@llnl.gov")
-SET(PACKAGE_NAME "SUNDIALS")
-SET(PACKAGE_STRING "SUNDIALS 3.1.1")
-SET(PACKAGE_TARNAME "sundials")
-
-# set SUNDIALS version numbers
-# (use "" for the version label if none is needed)
-SET(PACKAGE_VERSION_MAJOR "3")
-SET(PACKAGE_VERSION_MINOR "1")
-SET(PACKAGE_VERSION_PATCH "1")
-SET(PACKAGE_VERSION_LABEL "")
-
-IF(PACKAGE_VERSION_LABEL)
-  SET(PACKAGE_VERSION "${PACKAGE_VERSION_MAJOR}.${PACKAGE_VERSION_MINOR}.${PACKAGE_VERSION_PATCH}-${PACKAGE_VERSION_LABEL}")
-ELSE()
-  SET(PACKAGE_VERSION "${PACKAGE_VERSION_MAJOR}.${PACKAGE_VERSION_MINOR}.${PACKAGE_VERSION_PATCH}")
-ENDIF()
-
-#
-SET_PROPERTY(GLOBAL PROPERTY USE_FOLDERS ON)
-
-# Prohibit in-source build
-IF("${CMAKE_SOURCE_DIR}" STREQUAL "${CMAKE_BINARY_DIR}")
-  MESSAGE(FATAL_ERROR "In-source build prohibited.")
-ENDIF("${CMAKE_SOURCE_DIR}" STREQUAL "${CMAKE_BINARY_DIR}")
-
-# Hide some cache variables
-MARK_AS_ADVANCED(EXECUTABLE_OUTPUT_PATH LIBRARY_OUTPUT_PATH)
-
-# Always show the C compiler and flags
-MARK_AS_ADVANCED(CLEAR
-  CMAKE_C_COMPILER
-  CMAKE_C_FLAGS)
-
-# Specify the VERSION and SOVERSION for shared libraries
-
-SET(arkodelib_VERSION "2.1.1")
-SET(arkodelib_SOVERSION "2")
-
-SET(cvodelib_VERSION "3.1.1")
-SET(cvodelib_SOVERSION "3")
-
-SET(cvodeslib_VERSION "3.1.1")
-SET(cvodeslib_SOVERSION "3")
-
-SET(idalib_VERSION "3.1.1")
-SET(idalib_SOVERSION "3")
-
-SET(idaslib_VERSION "2.1.0")
-SET(idaslib_SOVERSION "2")
-
-SET(kinsollib_VERSION "3.1.1")
-SET(kinsollib_SOVERSION "3")
-
-SET(cpodeslib_VERSION "0.0.0")
-SET(cpodeslib_SOVERSION "0")
-
-SET(nveclib_VERSION "3.1.1")
-SET(nveclib_SOVERSION "3")
-
-SET(sunmatrixlib_VERSION "1.1.1")
-SET(sunmatrixlib_SOVERSION "1")
-
-SET(sunlinsollib_VERSION "1.1.1")
-SET(sunlinsollib_SOVERSION "1")
-
-# Specify the location of additional CMAKE modules
-SET(CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/config)
-
-# ---------------------------------------------------------------
-# MACRO definitions
-# ---------------------------------------------------------------
-INCLUDE(SundialsCMakeMacros)
-
-# ---------------------------------------------------------------
-# Check for deprecated SUNDIALS CMake options/variables
-# ---------------------------------------------------------------
-INCLUDE(SundialsDeprecated)
-
-# ---------------------------------------------------------------
-# Which modules to build?
-# ---------------------------------------------------------------
-
-# For each SUNDIALS solver available (i.e. for which we have the
-# sources), give the user the option of enabling/disabling it.
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/arkode")
-  OPTION(BUILD_ARKODE "Build the ARKODE library" ON)
-ELSE()
-  SET(BUILD_ARKODE OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/cvode")
-  OPTION(BUILD_CVODE "Build the CVODE library" ON)
-ELSE()
-  SET(BUILD_CVODE OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/cvodes")
-  OPTION(BUILD_CVODES "Build the CVODES library" ON)
-ELSE()
-  SET(BUILD_CVODES OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/ida")
-  OPTION(BUILD_IDA "Build the IDA library" ON)
-ELSE()
-  SET(BUILD_IDA OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/idas")
-  OPTION(BUILD_IDAS "Build the IDAS library" ON)
-ELSE()
-  SET(BUILD_IDAS OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/kinsol")
-  OPTION(BUILD_KINSOL "Build the KINSOL library" ON)
-ELSE()
-  SET(BUILD_KINSOL OFF)
-ENDIF()
-
-# CPODES is always OFF for now.  (commented out for Release); ToDo: better way to do this?
-#IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/cpodes")
-#  OPTION(BUILD_CPODES  "Build the CPODES library"  OFF)
-#ELSE()
-#  SET(BUILD_CPODES OFF)
-#ENDIF()
-
-# ---------------------------------------------------------------
-# xSDK specific options
-# ---------------------------------------------------------------
-INCLUDE(SundialsXSDK)
-
-# ---------------------------------------------------------------
-# Build specific C flags
-# ---------------------------------------------------------------
-
-# Hide all build type specific flags
-MARK_AS_ADVANCED(FORCE
-  CMAKE_C_FLAGS_DEBUG
-  CMAKE_C_FLAGS_MINSIZEREL
-  CMAKE_C_FLAGS_RELEASE
-  CMAKE_C_FLAGS_RELWITHDEBINFO)
-
-# Only show flags for the current build type it is set
-# NOTE: Build specific flags are appended those in CMAKE_C_FLAGS
-IF(CMAKE_BUILD_TYPE)
-  IF(CMAKE_BUILD_TYPE MATCHES "Debug")
-    MESSAGE("Appending C debug flags")
-    MARK_AS_ADVANCED(CLEAR CMAKE_C_FLAGS_DEBUG)
-  ELSEIF(CMAKE_BUILD_TYPE MATCHES "MinSizeRel")
-    MESSAGE("Appending C min size release flags")
-    MARK_AS_ADVANCED(CLEAR CMAKE_C_FLAGS_MINSIZEREL)
-  ELSEIF(CMAKE_BUILD_TYPE MATCHES "Release")
-    MESSAGE("Appending C release flags")
-    MARK_AS_ADVANCED(CLEAR CMAKE_C_FLAGS_RELEASE)
-  ELSEIF(CMAKE_BUILD_TYPE MATCHES "RelWithDebInfo")
-    MESSAGE("Appending C release with debug info flags")
-    MARK_AS_ADVANCED(CLEAR CMAKE_C_FLAGS_RELWITHDEBINFO)
-  ENDIF()
-ENDIF()
-
-# ---------------------------------------------------------------
-# Option to specify precision (realtype)
-# ---------------------------------------------------------------
-
-SET(DOCSTR "single, double, or extended")
-SHOW_VARIABLE(SUNDIALS_PRECISION STRING "${DOCSTR}" "double")
-
-# prepare substitution variable PRECISION_LEVEL for sundials_config.h
-STRING(TOUPPER ${SUNDIALS_PRECISION} SUNDIALS_PRECISION)
-SET(PRECISION_LEVEL "#define SUNDIALS_${SUNDIALS_PRECISION}_PRECISION 1")
-
-# prepare substitution variable FPRECISION_LEVEL for sundials_fconfig.h
-IF(SUNDIALS_PRECISION MATCHES "SINGLE")
-  SET(FPRECISION_LEVEL "4")
-ENDIF(SUNDIALS_PRECISION MATCHES "SINGLE")
-IF(SUNDIALS_PRECISION MATCHES "DOUBLE")
-  SET(FPRECISION_LEVEL "8")
-ENDIF(SUNDIALS_PRECISION MATCHES "DOUBLE")
-IF(SUNDIALS_PRECISION MATCHES "EXTENDED")
-  SET(FPRECISION_LEVEL "16")
-ENDIF(SUNDIALS_PRECISION MATCHES "EXTENDED")
-
-# ---------------------------------------------------------------
-# Option to specify index type
-# ---------------------------------------------------------------
-
-SET(DOCSTR "Signed 64-bit (int64_t) or signed 32-bit (int32_t) integer")
-SHOW_VARIABLE(SUNDIALS_INDEX_TYPE STRING "${DOCSTR}" "int64_t")
-
-# prepare substitution variable INDEX_TYPE for sundials_config.h
-STRING(TOUPPER ${SUNDIALS_INDEX_TYPE} SUNDIALS_INDEX_TYPE)
-SET(INDEX_TYPE "#define SUNDIALS_${SUNDIALS_INDEX_TYPE} 1")
-
-# prepare substitution variable FINDEX_TYPE for sundials_fconfig.h
-IF(SUNDIALS_INDEX_TYPE MATCHES "INT32_T")
-  SET(FINDEX_TYPE "4")
-ENDIF(SUNDIALS_INDEX_TYPE MATCHES "INT32_T")
-IF(SUNDIALS_INDEX_TYPE MATCHES "INT64_T")
-  SET(FINDEX_TYPE "8")
-ENDIF(SUNDIALS_INDEX_TYPE MATCHES "INT64_T")
-
-# ---------------------------------------------------------------
-# Enable Fortran interface?
-# ---------------------------------------------------------------
-
-# Fortran interface is disabled by default
-SET(DOCSTR "Enable Fortran-C support")
-SHOW_VARIABLE(FCMIX_ENABLE BOOL "${DOCSTR}" OFF)
-
-# Check that at least one solver with a Fortran interface is built
-IF(NOT BUILD_ARKODE AND NOT BUILD_CVODE AND NOT BUILD_IDA AND NOT BUILD_KINSOL)
-  IF(FCMIX_ENABLE)
-    PRINT_WARNING("Enabled packages do not support Fortran" "Disabling FCMIX")
-    FORCE_VARIABLE(FCMIX_ENABLE BOOL "${DOCSTR}" OFF)
-  ENDIF()
-  HIDE_VARIABLE(FCMIX_ENABLE)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Options to build static and/or shared libraries
-# ---------------------------------------------------------------
-
-OPTION(BUILD_STATIC_LIBS "Build static libraries" ON)
-OPTION(BUILD_SHARED_LIBS "Build shared libraries" ON)
-
-# Make sure we build at least one type of libraries
-IF(NOT BUILD_STATIC_LIBS AND NOT BUILD_SHARED_LIBS)
-  PRINT_WARNING("Both static and shared library generation were disabled"
-                "Building static libraries was re-enabled")
-  FORCE_VARIABLE(BUILD_STATIC_LIBS BOOL "Build static libraries" ON)
-ENDIF(NOT BUILD_STATIC_LIBS AND NOT BUILD_SHARED_LIBS)
-
-# ---------------------------------------------------------------
-# Option to use the generic math libraries (UNIX only)
-# ---------------------------------------------------------------
-
-IF(UNIX)
-  OPTION(USE_GENERIC_MATH "Use generic (std-c) math libraries" ON)
-  IF(USE_GENERIC_MATH)
-    # executables will be linked against -lm
-    SET(EXTRA_LINK_LIBS -lm)
-    # prepare substitution variable for sundials_config.h
-    SET(SUNDIALS_USE_GENERIC_MATH TRUE)
-  ENDIF(USE_GENERIC_MATH)
-ENDIF(UNIX)
-
-## clock-monotonic, see if we need to link with rt
-include(CheckSymbolExists)
-set(CMAKE_REQUIRED_LIBRARIES_SAVE ${CMAKE_REQUIRED_LIBRARIES})
-set(CMAKE_REQUIRED_LIBRARIES rt)
-CHECK_SYMBOL_EXISTS(_POSIX_TIMERS "unistd.h;time.h" SUNDIALS_POSIX_TIMERS)
-set(CMAKE_REQUIRED_LIBRARIES ${CMAKE_REQUIRED_LIBRARIES_SAVE})
-if(SUNDIALS_POSIX_TIMERS)
-  find_library(SUNDIALS_RT_LIBRARY NAMES rt)
-  mark_as_advanced(SUNDIALS_RT_LIBRARY)
-  if(SUNDIALS_RT_LIBRARY)
-    # sundials_config.h symbol
-    SET(SUNDIALS_HAVE_POSIX_TIMERS TRUE)
-    set(EXTRA_LINK_LIBS ${EXTRA_LINK_LIBS} ${SUNDIALS_RT_LIBRARY})
-  endif()
-endif()
-
-
-# ===============================================================
-# Options for Parallelism
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Enable MPI support?
-# ---------------------------------------------------------------
-OPTION(MPI_ENABLE "Enable MPI support" OFF)
-
-# ---------------------------------------------------------------
-# Enable OpenMP support?
-# ---------------------------------------------------------------
-OPTION(OPENMP_ENABLE "Enable OpenMP support" OFF)
-
-# ---------------------------------------------------------------
-# Enable Pthread support?
-# ---------------------------------------------------------------
-OPTION(PTHREAD_ENABLE "Enable Pthreads support" OFF)
-
-# -------------------------------------------------------------
-# Enable CUDA support?
-# -------------------------------------------------------------
-OPTION(CUDA_ENABLE "Enable CUDA support" OFF)
-
-# -------------------------------------------------------------
-# Enable RAJA support?
-# -------------------------------------------------------------
-OPTION(RAJA_ENABLE "Enable RAJA support" OFF)
-
-
-# ===============================================================
-# Options for external packages
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Enable BLAS support?
-# ---------------------------------------------------------------
-OPTION(BLAS_ENABLE "Enable BLAS support" OFF)
-
-# ---------------------------------------------------------------
-# Enable LAPACK/BLAS support?
-# ---------------------------------------------------------------
-OPTION(LAPACK_ENABLE "Enable Lapack support" OFF)
-
-# LAPACK does not support extended precision
-IF(LAPACK_ENABLE AND SUNDIALS_PRECISION MATCHES "EXTENDED")
-  PRINT_WARNING("LAPACK is not compatible with ${SUNDIALS_PRECISION} precision"
-                "Disabling LAPACK")
-  FORCE_VARIABLE(LAPACK_ENABLE BOOL "LAPACK is disabled" OFF)
-ENDIF()
-
-# LAPACK does not support 64-bit integer index types
-IF(LAPACK_ENABLE AND SUNDIALS_INDEX_TYPE MATCHES "INT64_T")
-  PRINT_WARNING("LAPACK is not compatible with ${SUNDIALS_INDEX_TYPE} integers"
-                "Disabling LAPACK")
-  SET(LAPACK_ENABLE OFF CACHE BOOL "LAPACK is disabled" FORCE)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Enable SuperLU_MT support?
-# ---------------------------------------------------------------
-OPTION(SUPERLUMT_ENABLE "Enable SUPERLUMT support" OFF)
-
-# SuperLU_MT does not support extended precision
-IF(SUPERLUMT_ENABLE AND SUNDIALS_PRECISION MATCHES "EXTENDED")
-  PRINT_WARNING("SuperLU_MT is not compatible with ${SUNDIALS_PRECISION} precision"
-                "Disabling SuperLU_MT")
-  FORCE_VARIABLE(SUPERLUMT_ENABLE BOOL "SuperLU_MT is disabled" OFF)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Enable KLU support?
-# ---------------------------------------------------------------
-OPTION(KLU_ENABLE "Enable KLU support" OFF)
-
-# KLU does not support single or extended precision
-IF(KLU_ENABLE AND
-  (SUNDIALS_PRECISION MATCHES "SINGLE" OR SUNDIALS_PRECISION MATCHES "EXTENDED"))
-  PRINT_WARNING("KLU is not compatible with ${SUNDIALS_PRECISION} precision"
-                "Disabling KLU")
-  FORCE_VARIABLE(KLU_ENABLE BOOL "KLU is disabled" OFF)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Enable hypre Vector support?
-# ---------------------------------------------------------------
-OPTION(HYPRE_ENABLE "Enable hypre support" OFF)
-
-# Using hypre requres building with MPI enabled
-IF(HYPRE_ENABLE AND NOT MPI_ENABLE)
-  PRINT_WARNING("MPI not enabled - Disabling hypre"
-                "Set MPI_ENABLE to ON to use parhyp")
-  FORCE_VARIABLE(HYPRE_ENABLE BOOL "Enable hypre support" OFF)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Enable PETSc support?
-# ---------------------------------------------------------------
-OPTION(PETSC_ENABLE "Enable PETSc support" OFF)
-
-# Using PETSc requires building with MPI enabled
-IF(PETSC_ENABLE AND NOT MPI_ENABLE)
-  PRINT_WARNING("MPI not enabled - Disabling PETSc"
-                "Set MPI_ENABLE to ON to use PETSc")
-  FORCE_VARIABLE(PETSC_ENABLE BOOL "Enable PETSc support" OFF)
-ENDIF()
-
-
-# ===============================================================
-# Options for examples
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Enable examples?
-# ---------------------------------------------------------------
-
-# Enable C examples (on by default)
-OPTION(EXAMPLES_ENABLE_C "Build SUNDIALS C examples" ON)
-
-# F77 examples (on by default) are an option only if the Fortran
-# interface is enabled
-SET(DOCSTR "Build SUNDIALS Fortran examples")
-IF(FCMIX_ENABLE)
-  OPTION(EXAMPLES_ENABLE_F77 "${DOCSTR}" ON)
-  # Fortran examples do not support single or extended precision
-  IF(SUNDIALS_PRECISION MATCHES "EXTENDED" OR SUNDIALS_PRECISION MATCHES "SINGLE")
-    PRINT_WARNING("F77 examples are not compatible with ${SUNDIALS_PRECISION} precision"
-                  "EXAMPLES_ENABLE_F77")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_F77 BOOL "Fortran examples are disabled" OFF)
-  ENDIF()
-ELSE()
-  # set back to OFF (in case was ON)
-  IF(EXAMPLES_ENABLE_F77)
-    PRINT_WARNING("EXAMPLES_ENABLE_F77 is ON but FCMIX is OFF"
-                  "Disabling EXAMPLES_ENABLE_F77")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_F77 BOOL "${DOCSTR}" OFF)
-  ENDIF()
-  HIDE_VARIABLE(EXAMPLES_ENABLE_F77)
-ENDIF()
-
-# C++ examples (off by default) are an option only if ARKode is enabled
-SET(DOCSTR "Build ARKode C++ examples")
-IF(BUILD_ARKODE)
-  SHOW_VARIABLE(EXAMPLES_ENABLE_CXX BOOL "${DOCSTR}" OFF)
-ELSE()
-  # set back to OFF (in case was ON)
-  IF(EXAMPLES_ENABLE_CXX)
-    PRINT_WARNING("EXAMPLES_ENABLE_CXX is ON but BUILD_ARKODE is OFF"
-                  "Disabling EXAMPLES_ENABLE_CXX")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_CXX BOOL "${DOCSTR}" OFF)
-  ENDIF()
-  HIDE_VARIABLE(EXAMPLES_ENABLE_CXX)
-ENDIF()
-
-# F90 examples (off by default) are an option only if ARKode is
-# built and the Fortran interface is enabled
-SET(DOCSTR "Build ARKode F90 examples")
-IF(FCMIX_ENABLE AND BUILD_ARKODE)
-  SHOW_VARIABLE(EXAMPLES_ENABLE_F90 BOOL "${DOCSTR}" OFF)
-  # Fortran90 examples do not support single or extended precision
-  # NOTE: This check can be removed after Fortran configure file is integrated into examples
-  IF(SUNDIALS_PRECISION MATCHES "EXTENDED" OR SUNDIALS_PRECISION MATCHES "SINGLE")
-    PRINT_WARNING("F90 examples are not compatible with ${SUNDIALS_PRECISION} precision"
-                  "EXAMPLES_ENABLE_F90")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_F90 BOOL "Fortran90 examples are disabled" OFF)
-  ENDIF()
-ELSE()
-  # set back to OFF (in case was ON)
-  IF(EXAMPLES_ENABLE_F90)
-    PRINT_WARNING("EXAMPLES_ENABLE_F90 is ON but FCMIX or BUILD_ARKODE is OFF"
-                  "Disabling EXAMPLES_ENABLE_F90")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_F90 BOOL "${DOCSTR}" OFF)
-  ENDIF()
-  HIDE_VARIABLE(EXAMPLES_ENABLE_F90)
-ENDIF()
-
-# CUDA examples (off by default)
-SET(DOCSTR "Build SUNDIALS CUDA examples")
-IF(CUDA_ENABLE)
-  SHOW_VARIABLE(EXAMPLES_ENABLE_CUDA BOOL "${DOCSTR}" OFF)
-ELSE()
-  IF(EXAMPLES_ENABLE_CUDA)
-    PRINT_WARNING("EXAMPLES_ENABLE_CUDA is ON but CUDA_ENABLE is OFF"
-                  "Disabling EXAMPLES_ENABLE_CUDA")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_CUDA BOOL "${DOCSTR}" OFF)
-  ENDIF()
-ENDIF()
-
-# RAJA examples (off by default)
-SET(DOCSTR "Build SUNDIALS RAJA examples")
-IF(RAJA_ENABLE)
-  SHOW_VARIABLE(EXAMPLES_ENABLE_RAJA BOOL "${DOCSTR}" OFF)
-ELSE()
-  IF(EXAMPLES_ENABLE_RAJA)
-    PRINT_WARNING("EXAMPLES_ENABLE_RAJA is ON but RAJA_ENABLE is OFF"
-                  "Disabling EXAMPLES_ENABLE_RAJA")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_RAJA BOOL "${DOCSTR}" OFF)
-  ENDIF()
-ENDIF()
-
-# If any of the above examples are enabled set EXAMPLES_ENABLED to TRUE
-IF(EXAMPLES_ENABLE_C OR
-    EXAMPLES_ENABLE_F77 OR
-    EXAMPLES_ENABLE_CXX OR
-    EXAMPLES_ENABLE_F90 OR
-    EXAMPLES_ENABLE_CUDA OR
-    EXAMPLES_ENABLE_RAJA)
-  SET(EXAMPLES_ENABLED TRUE)
-ELSE()
-  SET(EXAMPLES_ENABLED FALSE)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Install examples?
-# ---------------------------------------------------------------
-
-IF(EXAMPLES_ENABLED)
-
-  # If examples are enabled, set different options
-
-  # The examples will be linked with the library corresponding to the build type.
-  # Whenever building shared libraries, use them to link the examples.
-  IF(BUILD_SHARED_LIBS)
-    SET(LINK_LIBRARY_TYPE "shared")
-  ELSE(BUILD_SHARED_LIBS)
-    SET(LINK_LIBRARY_TYPE "static")
-  ENDIF(BUILD_SHARED_LIBS)
-
-  # Enable installing examples by default
-  SHOW_VARIABLE(EXAMPLES_INSTALL BOOL "Install example files" ON)
-
-  # If examples are to be exported, check where we should install them.
-  IF(EXAMPLES_INSTALL)
-
-    SHOW_VARIABLE(EXAMPLES_INSTALL_PATH PATH
-      "Output directory for installing example files" "${CMAKE_INSTALL_PREFIX}/examples")
-
-    IF(NOT EXAMPLES_INSTALL_PATH)
-      PRINT_WARNING("The example installation path is empty"
-                    "Example installation path was reset to its default value")
-      SET(EXAMPLES_INSTALL_PATH "${CMAKE_INSTALL_PREFIX}/examples" CACHE STRING
-        "Output directory for installing example files" FORCE)
-    ENDIF(NOT EXAMPLES_INSTALL_PATH)
-
-    # create test_install target and directory for running smoke tests after
-    # installation
-    ADD_CUSTOM_TARGET(test_install)
-
-    SET(TEST_INSTALL_DIR ${PROJECT_BINARY_DIR}/Testing_Install)
-
-    IF(NOT EXISTS ${TEST_INSTALL_DIR})
-      FILE(MAKE_DIRECTORY ${TEST_INSTALL_DIR})
-    ENDIF()
-
-
-  ELSE(EXAMPLES_INSTALL)
-
-    HIDE_VARIABLE(EXAMPLES_INSTALL_PATH)
-
-  ENDIF(EXAMPLES_INSTALL)
-
-ELSE(EXAMPLES_ENABLED)
-
-  # If examples are disabled, hide all options related to
-  # building and installing the SUNDIALS examples
-
-  HIDE_VARIABLE(EXAMPLES_INSTALL)
-  HIDE_VARIABLE(EXAMPLES_INSTALL_PATH)
-
-ENDIF(EXAMPLES_ENABLED)
-
-# ---------------------------------------------------------------
-# Include development examples in regression tests?
-# ---------------------------------------------------------------
-OPTION(SUNDIALS_DEVTESTS "Include development tests in make test" OFF)
-MARK_AS_ADVANCED(FORCE SUNDIALS_DEVTESTS)
-
-# ===============================================================
-# Add any other necessary compiler flags & definitions
-# ===============================================================
-
-IF(APPLE)
-  SET(CMAKE_SHARED_LIBRARY_CREATE_C_FLAGS "${CMAKE_SHARED_LIBRARY_CREATE_C_FLAGS} -undefined dynamic_lookup")
-ENDIF(APPLE)
-
-# ---------------------------------------------------------------
-# A Fortran compiler is needed if:
-# (a) FCMIX is enabled
-# (b) BLAS is enabled (for the name-mangling scheme)
-# (c) LAPACK is enabled (for the name-mangling scheme)
-# ---------------------------------------------------------------
-
-IF(FCMIX_ENABLE OR BLAS_ENABLE OR LAPACK_ENABLE)
-  INCLUDE(SundialsFortran)
-  IF(NOT F77_FOUND AND FCMIX_ENABLE)
-    PRINT_WARNING("Fortran compiler not functional"
-                  "FCMIX support will not be provided")
-  ENDIF()
-ENDIF()
-
-# ---------------------------------------------------------------
-# A Fortran90 compiler is needed if:
-# (a) F90 ARKODE examples are enabled
-# ---------------------------------------------------------------
-
-IF(EXAMPLES_ENABLE_F90)
-  INCLUDE(SundialsFortran90)
-  IF(NOT F90_FOUND)
-    PRINT_WARNING("Fortran90 compiler not functional"
-                  "F90 support will not be provided")
-  ENDIF()
-ENDIF()
-
-# ---------------------------------------------------------------
-# A C++ compiler is needed if:
-# (a) C++ ARKODE examples are enabled
-# (b) CUDA is enabled
-# (c) RAJA is enabled
-# ---------------------------------------------------------------
-
-IF(EXAMPLES_ENABLE_CXX OR CUDA_ENABLE OR RAJA_ENABLE)
-  INCLUDE(SundialsCXX)
-  IF(NOT CXX_FOUND)
-    PRINT_WARNING("C++ compiler not functional"
-                  "C++ support will not be provided")
-  ENDIF()
-ENDIF()
-
-# ---------------------------------------------------------------
-# Check if we need an alternate way of specifying the Fortran
-# name-mangling scheme if we were unable to infer it using a
-# compiler.
-# Ask the user to specify the case and number of appended underscores
-# corresponding to the Fortran name-mangling scheme of symbol names
-# that do not themselves contain underscores (recall that this is all
-# we really need for the interfaces to LAPACK).
-# Note: the default scheme is lower case - one underscore
-# ---------------------------------------------------------------
-
-IF(BLAS_ENABLE OR LAPACK_ENABLE AND NOT F77SCHEME_FOUND)
-  # Specify the case for the Fortran name-mangling scheme
-  SHOW_VARIABLE(SUNDIALS_F77_FUNC_CASE STRING
-    "case of Fortran function names (lower/upper)"
-    "lower")
-  # Specify the number of appended underscores for the Fortran name-mangling scheme
-  SHOW_VARIABLE(SUNDIALS_F77_FUNC_UNDERSCORES STRING
-    "number of underscores appended to Fortran function names"
-    "one")
-  # Based on the given case and number of underscores,
-  # set the C preprocessor macro definition
-  IF(${SUNDIALS_F77_FUNC_CASE} MATCHES "lower")
-    IF(${SUNDIALS_F77_FUNC_UNDERSCORES} MATCHES "none")
-      SET(CMAKE_Fortran_SCHEME_NO_UNDERSCORES "mysub")
-    ENDIF(${SUNDIALS_F77_FUNC_UNDERSCORES} MATCHES "none")
-    IF(${SUNDIALS_F77_FUNC_UNDERSCORES} MATCHES "one")
-      SET(CMAKE_Fortran_SCHEME_NO_UNDERSCORES "mysub_")
-    ENDIF(${SUNDIALS_F77_FUNC_UNDERSCORES} MATCHES "one")
-    IF(${SUNDIALS_F77_FUNC_UNDERSCORES} MATCHES "two")
-      SET(CMAKE_Fortran_SCHEME_NO_UNDERSCORES "mysub__")
-    ENDIF(${SUNDIALS_F77_FUNC_UNDERSCORES} MATCHES "two")
-  ELSE(${SUNDIALS_F77_FUNC_CASE} MATCHES "lower")
-    IF(${SUNDIALS_F77_FUNC_UNDERSCORES} MATCHES "none")
-      SET(CMAKE_Fortran_SCHEME_NO_UNDERSCORES "MYSUB")
-    ENDIF(${SUNDIALS_F77_FUNC_UNDERSCORES} MATCHES "none")
-    IF(${SUNDIALS_F77_FUNC_UNDERSCORES} MATCHES "one")
-      SET(CMAKE_Fortran_SCHEME_NO_UNDERSCORES "MYSUB_")
-    ENDIF(${SUNDIALS_F77_FUNC_UNDERSCORES} MATCHES "one")
-    IF(${SUNDIALS_F77_FUNC_UNDERSCORES} MATCHES "two")
-      SET(CMAKE_Fortran_SCHEME_NO_UNDERSCORES "MYSUB__")
-    ENDIF(${SUNDIALS_F77_FUNC_UNDERSCORES} MATCHES "two")
-  ENDIF(${SUNDIALS_F77_FUNC_CASE} MATCHES "lower")
-  # Since the SUNDIALS codes never use symbol names containing
-  # underscores, set a default scheme (probably wrong) for symbols
-  # with underscores.
-  SET(CMAKE_Fortran_SCHEME_WITH_UNDERSCORES "my_sub_")
-  # We now "have" a scheme.
-  SET(F77SCHEME_FOUND TRUE)
-ENDIF(BLAS_ENABLE OR LAPACK_ENABLE AND NOT F77SCHEME_FOUND)
-
-# ---------------------------------------------------------------
-# If we have a name-mangling scheme (either automatically
-# inferred or provided by the user), set the SUNDIALS
-# compiler preprocessor macro definitions.
-# ---------------------------------------------------------------
-
-SET(F77_MANGLE_MACRO1 "")
-SET(F77_MANGLE_MACRO2 "")
-
-IF(F77SCHEME_FOUND)
-  # Symbols WITHOUT underscores
-  IF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "mysub")
-    SET(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) name")
-  ENDIF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "mysub")
-  IF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "mysub_")
-    SET(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) name ## _")
-  ENDIF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "mysub_")
-  IF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "mysub__")
-    SET(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) name ## __")
-  ENDIF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "mysub__")
-  IF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "MYSUB")
-    SET(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) NAME")
-  ENDIF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "MYSUB")
-  IF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "MYSUB_")
-    SET(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) NAME ## _")
-  ENDIF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "MYSUB_")
-  IF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "MYSUB__")
-    SET(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) NAME ## __")
-  ENDIF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "MYSUB__")
-  # Symbols with underscores
-  IF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "my_sub")
-    SET(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) name")
-  ENDIF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "my_sub")
-  IF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "my_sub_")
-    SET(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) name ## _")
-  ENDIF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "my_sub_")
-  IF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "my_sub__")
-    SET(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) name ## __")
-  ENDIF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "my_sub__")
-  IF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "MY_SUB")
-    SET(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) NAME")
-  ENDIF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "MY_SUB")
-  IF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "MY_SUB_")
-    SET(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) NAME ## _")
-  ENDIF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "MY_SUB_")
-  IF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "MY_SUB__")
-    SET(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) NAME ## __")
-  ENDIF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "MY_SUB__")
-ENDIF(F77SCHEME_FOUND)
-
-# ---------------------------------------------------------------
-# Decide how to compile MPI codes.
-# ---------------------------------------------------------------
-
-IF(MPI_ENABLE)
-  # show command to run MPI codes (defaults to mpirun)
-  SHOW_VARIABLE(MPI_RUN_COMMAND STRING "MPI run command" "mpirun")
-
-  INCLUDE(SundialsMPIC)
-  IF(MPIC_FOUND)
-    IF(CXX_FOUND AND EXAMPLES_ENABLE_CXX)
-      INCLUDE(SundialsMPICXX)
-    ENDIF()
-    IF(F77_FOUND AND EXAMPLES_ENABLE_F77)
-      INCLUDE(SundialsMPIF)
-    ENDIF()
-    IF(F90_FOUND AND EXAMPLES_ENABLE_F90)
-      INCLUDE(SundialsMPIF90)
-    ENDIF()
-  ELSE()
-    PRINT_WARNING("MPI not functional"
-                  "Parallel support will not be provided")
-  ENDIF()
-
-  IF(MPIC_MPI2)
-    SET(F77_MPI_COMM_F2C "#define SUNDIALS_MPI_COMM_F2C 1")
-  ELSE()
-    SET(F77_MPI_COMM_F2C "#define SUNDIALS_MPI_COMM_F2C 0")
-  ENDIF()
-
-ELSE()
-
-  HIDE_VARIABLE(MPI_INCLUDE_PATH)
-  HIDE_VARIABLE(MPI_LIBRARIES)
-  HIDE_VARIABLE(MPI_EXTRA_LIBRARIES)
-  HIDE_VARIABLE(MPI_MPICC)
-  HIDE_VARIABLE(MPI_MPICXX)
-  HIDE_VARIABLE(MPI_MPIF77)
-  HIDE_VARIABLE(MPI_MPIF90)
-
-ENDIF(MPI_ENABLE)
-
-# ---------------------------------------------------------------
-# If using MPI with C++, disable C++ extensions (for known wrappers)
-# ---------------------------------------------------------------
-
-# IF(MPICXX_FOUND)
-#   set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DMPICH_SKIP_MPICXX -DOMPI_SKIP_MPICXX -DLAM_BUILDING")
-# ENDIF(MPICXX_FOUND)
-
-# -------------------------------------------------------------
-# Find OpenMP
-# -------------------------------------------------------------
-
-IF(OPENMP_ENABLE)
-  FIND_PACKAGE(OpenMP)
-  IF(NOT OPENMP_FOUND)
-    message(STATUS "Disabling OpenMP support, could not determine compiler flags")
-  ENDIF(NOT OPENMP_FOUND)
-ENDIF(OPENMP_ENABLE)
-
-# -------------------------------------------------------------
-# Find PThreads
-# -------------------------------------------------------------
-
-IF(PTHREAD_ENABLE)
-  FIND_PACKAGE(Threads)
-  IF(CMAKE_USE_PTHREADS_INIT)
-    message(STATUS "Using Pthreads")
-    SET(PTHREADS_FOUND TRUE)
-    # SGS
-  ELSE()
-    message(STATUS "Disabling Pthreads support, could not determine compiler flags")
-  endif()
-ENDIF(PTHREAD_ENABLE)
-
-# -------------------------------------------------------------
-# Find CUDA
-# -------------------------------------------------------------
-
-# disable CUDA if a working C++ compiler is not found
-IF(CUDA_ENABLE AND (NOT CXX_FOUND))
-  PRINT_WARNING("C++ compiler required for CUDA support" "Disabling CUDA")
-  FORCE_VARIABLE(CUDA_ENABLE BOOL "CUDA disabled" OFF)
-ENDIF()
-
-if(CUDA_ENABLE)
-  find_package(CUDA)
-
-  if (CUDA_FOUND)
-    #message("CUDA found!")
-    set(CUDA_NVCC_FLAGS "-lineinfo")
-  else()
-    message(STATUS "Disabling CUDA support, could not find CUDA.")
-  endif()
-endif(CUDA_ENABLE)
-
-# -------------------------------------------------------------
-# Find RAJA
-# -------------------------------------------------------------
-
-# disable RAJA if CUDA is not enabled/working
-IF(RAJA_ENABLE AND (NOT CUDA_FOUND))
-  PRINT_WARNING("CUDA is required for RAJA support" "Please enable CUDA and RAJA")
-  FORCE_VARIABLE(RAJA_ENABLE BOOL "RAJA disabled" OFF)
-ENDIF()
-
-# Check if C++11 compiler is available
-IF(RAJA_ENABLE)
-  include(CheckCXXCompilerFlag)
-  CHECK_CXX_COMPILER_FLAG("-std=c++11" COMPILER_SUPPORTS_CXX11)
-
-  IF(COMPILER_SUPPORTS_CXX11)
-    set(CMAKE_CXX_STANDARD 11)
-  ELSE()
-    PRINT_WARNING("C++11 compliant compiler required for RAJA support" "Disabling RAJA")
-    FORCE_VARIABLE(RAJA_ENABLE BOOL "RAJA disabled" OFF)
-  ENDIF()
-ENDIF()
-
-if(RAJA_ENABLE)
-  # Look for CMake configuration file in RAJA installation
-  find_package(RAJA CONFIGS)
-  if (RAJA_FOUND)
-    #message("RAJA found!")
-    include_directories(${RAJA_INCLUDE_DIR})
-    set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} ${RAJA_NVCC_FLAGS})
-  else()
-    PRINT_WARNING("RAJA configuration not found" "Please set RAJA_DIR to provide path to RAJA CMake configuration file.")
-  endif()
-endif(RAJA_ENABLE)
-
-# ===============================================================
-# Find (and test) external packages
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Find (and test) the BLAS libraries
-# ---------------------------------------------------------------
-
-# If BLAS is needed, first try to find the appropriate
-# libraries and linker flags needed to link against them.
-
-IF(BLAS_ENABLE)
-
-  # find BLAS
-  INCLUDE(SundialsBlas)
-
-  # show after include so FindBlas can locate BLAS_LIBRARIES if necessary
-  SHOW_VARIABLE(BLAS_LIBRARIES STRING "Blas libraries" "${BLAS_LIBRARIES}")
-
-  IF(BLAS_LIBRARIES AND NOT BLAS_FOUND)
-    PRINT_WARNING("BLAS not functional"
-                  "BLAS support will not be provided")
-  ELSE()
-    #set sundials_config.h symbol via sundials_config.in
-    SET(SUNDIALS_BLAS TRUE)
-  ENDIF()
-
-ELSE()
-
-  IF(NOT LAPACK_ENABLE)
-    HIDE_VARIABLE(SUNDIALS_F77_FUNC_CASE)
-    HIDE_VARIABLE(SUNDIALS_F77_FUNC_UNDERSCORES)
-  ENDIF()
-  HIDE_VARIABLE(BLAS_LIBRARIES)
-
-ENDIF()
-
-# ---------------------------------------------------------------
-# Find (and test) the Lapack libraries
-# ---------------------------------------------------------------
-
-# If LAPACK is needed, first try to find the appropriate
-# libraries and linker flags needed to link against them.
-
-IF(LAPACK_ENABLE)
-
-  # find LAPACK and BLAS Libraries
-  INCLUDE(SundialsLapack)
-
-  # show after include so FindLapack can locate LAPCK_LIBRARIES if necessary
-  SHOW_VARIABLE(LAPACK_LIBRARIES STRING "Lapack and Blas libraries" "${LAPACK_LIBRARIES}")
-
-  IF(LAPACK_LIBRARIES AND NOT LAPACK_FOUND)
-    PRINT_WARNING("LAPACK not functional"
-                  "Blas/Lapack support will not be provided")
-  ELSE()
-    #set sundials_config.h symbol via sundials_config.in
-    SET(SUNDIALS_BLAS_LAPACK TRUE)
-  ENDIF()
-
-ELSE()
-
-  IF(NOT BLAS_ENABLE)
-    HIDE_VARIABLE(SUNDIALS_F77_FUNC_CASE)
-    HIDE_VARIABLE(SUNDIALS_F77_FUNC_UNDERSCORES)
-  ENDIF()
-  HIDE_VARIABLE(LAPACK_LIBRARIES)
-
-ENDIF()
-
-# ---------------------------------------------------------------
-# Find (and test) the SUPERLUMT libraries
-# ---------------------------------------------------------------
-
-# >>>>>>> NOTE: Need to add check for SuperLU_MT integer type
-
-# If SUPERLUMT is needed, first try to find the appropriate
-# libraries to link against them.
-
-IF(SUPERLUMT_ENABLE)
-
-  # Show SuperLU_MT options and set default thread type (Pthreads)
-  SHOW_VARIABLE(SUPERLUMT_THREAD_TYPE STRING "SUPERLUMT threading type: OpenMP or Pthread" "Pthread")
-  SHOW_VARIABLE(SUPERLUMT_INCLUDE_DIR PATH "SUPERLUMT include directory" "${SUPERLUMT_INCLUDE_DIR}")
-  SHOW_VARIABLE(SUPERLUMT_LIBRARY_DIR PATH "SUPERLUMT library directory" "${SUPERLUMT_LIBRARY_DIR}")
-
-  INCLUDE(SundialsSuperLUMT)
-
-  IF(SUPERLUMT_FOUND)
-    # sundials_config.h symbols
-    SET(SUNDIALS_SUPERLUMT TRUE)
-    SET(SUNDIALS_SUPERLUMT_THREAD_TYPE ${SUPERLUMT_THREAD_TYPE})
-    INCLUDE_DIRECTORIES(${SUPERLUMT_INCLUDE_DIR})
-  ENDIF()
-
-  IF(SUPERLUMT_LIBRARIES AND NOT SUPERLUMT_FOUND)
-    PRINT_WARNING("SUPERLUMT not functional - support will not be provided"
-                  "Double check spelling specified libraries (search is case sensitive)")
-  ENDIF(SUPERLUMT_LIBRARIES AND NOT SUPERLUMT_FOUND)
-
-ELSE()
-
-  HIDE_VARIABLE(SUPERLUMT_THREAD_TYPE)
-  HIDE_VARIABLE(SUPERLUMT_LIBRARY_DIR)
-  HIDE_VARIABLE(SUPERLUMT_INCLUDE_DIR)
-  SET (SUPERLUMT_DISABLED TRUE CACHE INTERNAL "GUI - return when first set")
-
-ENDIF()
-
-# ---------------------------------------------------------------
-# Find (and test) the KLU libraries
-# ---------------------------------------------------------------
-
-# If KLU is requested, first try to find the appropriate libraries to
-# link against them.
-
-IF(KLU_ENABLE)
-
-  SHOW_VARIABLE(KLU_INCLUDE_DIR PATH "KLU include directory"
-    "${KLU_INCLUDE_DIR}")
-  SHOW_VARIABLE(KLU_LIBRARY_DIR PATH
-    "Klu library directory" "${KLU_LIBRARY_DIR}")
-
-  set(KLU_FOUND TRUE)
-  get_filename_component(PYBAMM_DIR ${PROJECT_SOURCE_DIR} DIRECTORY)
-  set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} ${PYBAMM_DIR}) # use FindSuiteSparse.cmake that is in PyBaMM root
-  set(SuiteSparse_ROOT ${PYBAMM_DIR}/SuiteSparse-5.6.0)
-  find_package(SuiteSparse OPTIONAL_COMPONENTS KLU AMD COLAMD BTF)
-  include_directories(${SuiteSparse_INCLUDE_DIRS})
-  set(KLU_LIBRARIES ${SuiteSparse_LIBRARIES})
-
-  IF(KLU_LIBRARIES AND NOT KLU_FOUND)
-    PRINT_WARNING("KLU not functional - support will not be provided"
-                  "Double check spelling of include path and specified libraries (search is case sensitive)")
-  ENDIF(KLU_LIBRARIES AND NOT KLU_FOUND)
-
-ELSE()
-
-  HIDE_VARIABLE(KLU_LIBRARY_DIR)
-  HIDE_VARIABLE(KLU_INCLUDE_DIR)
-  SET (KLU_DISABLED TRUE CACHE INTERNAL "GUI - return when first set")
-
-ENDIF(KLU_ENABLE)
-
-# ---------------------------------------------------------------
-# Find (and test) the hypre libraries
-# ---------------------------------------------------------------
-
-# >>>>>>> NOTE: Need to add check for hypre precision and integer type
-
-IF(HYPRE_ENABLE)
-  SHOW_VARIABLE(HYPRE_INCLUDE_DIR PATH "HYPRE include directory"
-    "${HYPRE_INCLUDE_DIR}")
-  SHOW_VARIABLE(HYPRE_LIBRARY_DIR PATH
-    "HYPRE library directory" "${HYPRE_LIBRARY_DIR}")
-
-  INCLUDE(SundialsHypre)
-
-  IF(HYPRE_FOUND)
-    # sundials_config.h symbol
-    SET(SUNDIALS_HYPRE TRUE)
-    INCLUDE_DIRECTORIES(${HYPRE_INCLUDE_DIR})
-  ENDIF(HYPRE_FOUND)
-
-  IF(HYPRE_LIBRARIES AND NOT HYPRE_FOUND)
-    PRINT_WARNING("HYPRE not functional - support will not be provided"
-                  "Found hypre library, test code does not work")
-  ENDIF(HYPRE_LIBRARIES AND NOT HYPRE_FOUND)
-
-ELSE()
-
-  HIDE_VARIABLE(HYPRE_INCLUDE_DIR)
-  HIDE_VARIABLE(HYPRE_LIBRARY_DIR)
-  SET (HYPRE_DISABLED TRUE CACHE INTERNAL "GUI - return when first set")
-
-ENDIF()
-
-# ---------------------------------------------------------------
-# Find (and test) the PETSc libraries
-# ---------------------------------------------------------------
-
-# >>>>>>> NOTE: Need to add check for PETSc precision and integer type
-
-IF(PETSC_ENABLE)
-  SHOW_VARIABLE(PETSC_INCLUDE_DIR PATH "PETSc include directory"
-    "${PETSC_INCLUDE_DIR}")
-  SHOW_VARIABLE(PETSC_LIBRARY_DIR PATH
-    "PETSc library directory" "${PETSC_LIBRARY_DIR}")
-
-  INCLUDE(SundialsPETSc)
-
-  IF(PETSC_FOUND)
-    # sundials_config.h symbol
-    SET(SUNDIALS_PETSC TRUE)
-    INCLUDE_DIRECTORIES(${PETSC_INCLUDE_DIR})
-  ENDIF(PETSC_FOUND)
-
-  IF(PETSC_LIBRARIES AND NOT PETSC_FOUND)
-    PRINT_WARNING("PETSC not functional - support will not be provided"
-                  "Double check spelling specified libraries (search is case sensitive)")
-  ENDIF(PETSC_LIBRARIES AND NOT PETSC_FOUND)
-
-ELSE()
-
-  HIDE_VARIABLE(PETSC_LIBRARY_DIR)
-  HIDE_VARIABLE(PETSC_INCLUDE_DIR)
-  SET (PETSC_DISABLED TRUE CACHE INTERNAL "GUI - return when first set")
-
-ENDIF()
-
-
-# ===============================================================
-# Add source and configuration files
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Configure the header file sundials_config.h
-# ---------------------------------------------------------------
-
-# All required substitution variables should be available at this point.
-# Generate the header file and place it in the binary dir.
-CONFIGURE_FILE(
-  ${PROJECT_SOURCE_DIR}/include/sundials/sundials_config.in
-  ${PROJECT_BINARY_DIR}/include/sundials/sundials_config.h
-  )
-CONFIGURE_FILE(
-  ${PROJECT_SOURCE_DIR}/include/sundials/sundials_fconfig.in
-  ${PROJECT_BINARY_DIR}/include/sundials/sundials_fconfig.h
-  )
-
-# Add the include directory in the source tree and the one in
-# the binary tree (for the header file sundials_config.h)
-INCLUDE_DIRECTORIES(${PROJECT_SOURCE_DIR}/include ${PROJECT_BINARY_DIR}/include)
-
-# ---------------------------------------------------------------
-# Add selected modules to the build system
-# ---------------------------------------------------------------
-
-# Shared components
-
-ADD_SUBDIRECTORY(src/sundials)
-ADD_SUBDIRECTORY(src/nvec_ser)
-ADD_SUBDIRECTORY(src/sunmat_dense)
-ADD_SUBDIRECTORY(src/sunmat_band)
-ADD_SUBDIRECTORY(src/sunmat_sparse)
-ADD_SUBDIRECTORY(src/sunlinsol_band)
-ADD_SUBDIRECTORY(src/sunlinsol_dense)
-IF(KLU_FOUND)
-  ADD_SUBDIRECTORY(src/sunlinsol_klu)
-ENDIF(KLU_FOUND)
-IF(SUPERLUMT_FOUND)
-  ADD_SUBDIRECTORY(src/sunlinsol_superlumt)
-ENDIF(SUPERLUMT_FOUND)
-IF(LAPACK_FOUND)
-  ADD_SUBDIRECTORY(src/sunlinsol_lapackband)
-  ADD_SUBDIRECTORY(src/sunlinsol_lapackdense)
-ENDIF(LAPACK_FOUND)
-ADD_SUBDIRECTORY(src/sunlinsol_spgmr)
-ADD_SUBDIRECTORY(src/sunlinsol_spfgmr)
-ADD_SUBDIRECTORY(src/sunlinsol_spbcgs)
-ADD_SUBDIRECTORY(src/sunlinsol_sptfqmr)
-ADD_SUBDIRECTORY(src/sunlinsol_pcg)
-IF(MPIC_FOUND)
-  ADD_SUBDIRECTORY(src/nvec_par)
-ENDIF(MPIC_FOUND)
-
-IF(HYPRE_FOUND)
-  ADD_SUBDIRECTORY(src/nvec_parhyp)
-ENDIF(HYPRE_FOUND)
-
-IF(OPENMP_FOUND)
-  ADD_SUBDIRECTORY(src/nvec_openmp)
-ENDIF(OPENMP_FOUND)
-
-IF(PTHREADS_FOUND)
-  ADD_SUBDIRECTORY(src/nvec_pthreads)
-ENDIF(PTHREADS_FOUND)
-
-IF(PETSC_FOUND)
-  ADD_SUBDIRECTORY(src/nvec_petsc)
-ENDIF(PETSC_FOUND)
-
-IF(CUDA_FOUND)
-  ADD_SUBDIRECTORY(src/nvec_cuda)
-ENDIF(CUDA_FOUND)
-
-IF(RAJA_FOUND)
-  ADD_SUBDIRECTORY(src/nvec_raja)
-ENDIF(RAJA_FOUND)
-
-# ARKODE library
-
-IF(BUILD_ARKODE)
-  ADD_SUBDIRECTORY(src/arkode)
-  IF(FCMIX_ENABLE AND F77_FOUND)
-    ADD_SUBDIRECTORY(src/arkode/fcmix)
-  ENDIF(FCMIX_ENABLE AND F77_FOUND)
-ENDIF(BUILD_ARKODE)
-
-# CVODE library
-
-IF(BUILD_CVODE)
-  ADD_SUBDIRECTORY(src/cvode)
-  IF(FCMIX_ENABLE AND F77_FOUND)
-    ADD_SUBDIRECTORY(src/cvode/fcmix)
-  ENDIF(FCMIX_ENABLE AND F77_FOUND)
-ENDIF(BUILD_CVODE)
-
-# CVODES library
-
-IF(BUILD_CVODES)
-  ADD_SUBDIRECTORY(src/cvodes)
-ENDIF(BUILD_CVODES)
-
-# IDA library
-
-IF(BUILD_IDA)
-  ADD_SUBDIRECTORY(src/ida)
-  IF(FCMIX_ENABLE AND F77_FOUND)
-    ADD_SUBDIRECTORY(src/ida/fcmix)
-  ENDIF(FCMIX_ENABLE AND F77_FOUND)
-ENDIF(BUILD_IDA)
-
-# IDAS library
-
-IF(BUILD_IDAS)
-  ADD_SUBDIRECTORY(src/idas)
-ENDIF(BUILD_IDAS)
-
-# KINSOL library
-
-IF(BUILD_KINSOL)
-  ADD_SUBDIRECTORY(src/kinsol)
-  IF(FCMIX_ENABLE AND F77_FOUND)
-    ADD_SUBDIRECTORY(src/kinsol/fcmix)
-  ENDIF(FCMIX_ENABLE AND F77_FOUND)
-ENDIF(BUILD_KINSOL)
-
-# CPODES library
-
-IF(BUILD_CPODES)
-  ADD_SUBDIRECTORY(src/cpodes)
-ENDIF(BUILD_CPODES)
-
-# ---------------------------------------------------------------
-# Include the subdirectories corresponding to various examples
-# ---------------------------------------------------------------
-
-# If building and installing the examples is enabled, include
-# the subdirectories for those examples that will be built.
-# Also, if we will generate exported example Makefiles, set
-# variables needed in generating them from templates.
-
-# For now, TestRunner is not being distributed.
-# So:
-#  - Don't show TESTRUNNER variable
-#  - Don't enable testing if TestRunner if not found.
-#  - There will be no 'make test' target
-
-INCLUDE(SundialsAddTest)
-HIDE_VARIABLE(TESTRUNNER)
-
-IF(EXAMPLES_ENABLED)
-
-  # enable regression testing with 'make test'
-  IF(TESTRUNNER)
-    ENABLE_TESTING()
-  ENDIF()
-
-  # set variables used in generating CMake and Makefiles for examples
-  IF(EXAMPLES_INSTALL)
-
-    SET(SHELL "sh")
-    SET(prefix "${CMAKE_INSTALL_PREFIX}")
-    SET(exec_prefix "${CMAKE_INSTALL_PREFIX}")
-    SET(includedir "${prefix}/include")
-    SET(libdir "${exec_prefix}/lib")
-    SET(CPP "${CMAKE_C_COMPILER}")
-    SET(CPPFLAGS "${CMAKE_C_FLAGS_RELEASE}")
-    SET(CC "${CMAKE_C_COMPILER}")
-    SET(CFLAGS "${CMAKE_C_FLAGS_RELEASE}")
-    SET(LDFLAGS "${CMAKE_EXE_LINKER_FLAGS_RELEASE}")
-    LIST2STRING(EXTRA_LINK_LIBS LIBS)
-
-    IF(CXX_FOUND)
-      SET(CXX "${CMAKE_CXX_COMPILER}")
-      SET(CXX_LNKR "${CMAKE_CXX_COMPILER}")
-      SET(CXXFLAGS "${CMAKE_CXX_FLAGS_RELEASE}")
-      SET(CXX_LDFLAGS "${CMAKE_CXX_FLAGS_RELEASE}")
-      LIST2STRING(EXTRA_LINK_LIBS CXX_LIBS)
-    ENDIF(CXX_FOUND)
-
-    IF(F77_FOUND)
-      SET(F77 "${CMAKE_Fortran_COMPILER}")
-      SET(F77_LNKR "${CMAKE_Fortran_COMPILER}")
-      SET(FFLAGS "${CMAKE_Fortran_FLAGS_RELEASE}")
-      SET(F77_LDFLAGS "${CMAKE_Fortran_FLAGS_RELEASE}")
-      LIST2STRING(EXTRA_LINK_LIBS F77_LIBS)
-    ENDIF(F77_FOUND)
-
-    IF(F90_FOUND)
-      SET(F90 "${CMAKE_Fortran_COMPILER}")
-      SET(F90_LNKR "${CMAKE_Fortran_COMPILER}")
-      SET(F90FLAGS "${CMAKE_Fortran_FLAGS_RELEASE}")
-      SET(F90_LDFLAGS "${CMAKE_Fortran_FLAGS_RELEASE}")
-      LIST2STRING(EXTRA_LINK_LIBS F90_LIBS)
-    ENDIF(F90_FOUND)
-
-    IF(SUPERLUMT_FOUND)
-      LIST2STRING(SUPERLUMT_LIBRARIES SUPERLUMT_LIBS)
-      SET(SUPERLUMT_LIBS "${SUPERLUMT_LINKER_FLAGS} ${SUPERLUMT_LIBS}")
-    ENDIF(SUPERLUMT_FOUND)
-
-    IF(KLU_FOUND)
-      LIST2STRING(KLU_LIBRARIES KLU_LIBS)
-      SET(KLU_LIBS "${KLU_LINKER_FLAGS} ${KLU_LIBS}")
-    ENDIF(KLU_FOUND)
-
-    IF(BLAS_FOUND)
-      LIST2STRING(BLAS_LIBRARIES BLAS_LIBS)
-    ENDIF(BLAS_FOUND)
-
-    IF(LAPACK_FOUND)
-      LIST2STRING(LAPACK_LIBRARIES LAPACK_LIBS)
-    ENDIF(LAPACK_FOUND)
-
-    IF(MPIC_FOUND)
-      IF(MPI_MPICC)
-        SET(MPICC "${MPI_MPICC}")
-        SET(MPI_INC_DIR ".")
-        SET(MPI_LIB_DIR ".")
-        SET(MPI_LIBS "")
-        SET(MPI_FLAGS "")
-      ELSE(MPI_MPICC)
-        SET(MPICC "${CMAKE_C_COMPILER}")
-        SET(MPI_INC_DIR "${MPI_INCLUDE_PATH}")
-        SET(MPI_LIB_DIR ".")
-        LIST2STRING(MPI_LIBRARIES MPI_LIBS)
-      ENDIF(MPI_MPICC)
-      SET(HYPRE_INC_DIR "${HYPRE_INCLUDE_DIR}")
-      SET(HYPRE_LIB_DIR "${HYPRE_LIBRARY_DIR}")
-      SET(HYPRE_LIBS "${HYPRE_LIBRARIES}")
-    ENDIF(MPIC_FOUND)
-
-    IF(MPICXX_FOUND)
-      IF(MPI_MPICXX)
-        SET(MPICXX "${MPI_MPICXX}")
-      ELSE(MPI_MPICXX)
-        SET(MPICXX "${CMAKE_CXX_COMPILER}")
-        LIST2STRING(MPI_LIBRARIES MPI_LIBS)
-      ENDIF(MPI_MPICXX)
-    ENDIF(MPICXX_FOUND)
-
-    IF(MPIF_FOUND)
-      IF(MPI_MPIF77)
-        SET(MPIF77 "${MPI_MPIF77}")
-        SET(MPIF77_LNKR "${MPI_MPIF77}")
-      ELSE(MPI_MPIF77)
-        SET(MPIF77 "${CMAKE_Fortran_COMPILER}")
-        SET(MPIF77_LNKR "${CMAKE_Fortran_COMPILER}")
-        SET(MPI_INC_DIR "${MPI_INCLUDE_PATH}")
-        SET(MPI_LIB_DIR ".")
-        LIST2STRING(MPI_LIBRARIES MPI_LIBS)
-      ENDIF(MPI_MPIF77)
-    ENDIF(MPIF_FOUND)
-
-    IF(MPIF90_FOUND)
-      IF(MPI_MPIF90)
-        SET(MPIF90 "${MPI_MPIF90}")
-        SET(MPIF90_LNKR "${MPI_MPIF90}")
-      ELSE(MPI_MPIF90)
-        SET(MPIF90 "${CMAKE_Fortran_COMPILER}")
-        SET(MPIF90_LNKR "${CMAKE_Fortran_COMPILER}")
-        LIST2STRING(MPI_LIBRARIES MPI_LIBS)
-      ENDIF(MPI_MPIF90)
-    ENDIF(MPIF90_FOUND)
-
-  ENDIF(EXAMPLES_INSTALL)
-
-  # add ARKode examples
-  IF(BUILD_ARKODE)
-    # C examples
-    IF(EXAMPLES_ENABLE_C)
-      ADD_SUBDIRECTORY(examples/arkode/C_serial)
-      IF(OPENMP_FOUND)
-        ADD_SUBDIRECTORY(examples/arkode/C_openmp)
-      ENDIF()
-      IF(MPIC_FOUND)
-        ADD_SUBDIRECTORY(examples/arkode/C_parallel)
-      ENDIF()
-      IF(HYPRE_ENABLE AND HYPRE_FOUND)
-        ADD_SUBDIRECTORY(examples/arkode/C_parhyp)
-      ENDIF()
-    ENDIF()
-    # C++ examples
-    IF(EXAMPLES_ENABLE_CXX)
-      IF(CXX_FOUND)
-        ADD_SUBDIRECTORY(examples/arkode/CXX_serial)
-      ENDIF()
-      IF(MPICXX_FOUND)
-        ADD_SUBDIRECTORY(examples/arkode/CXX_parallel)
-      ENDIF()
-    ENDIF()
-    # F77 examples
-    IF(EXAMPLES_ENABLE_F77)
-      IF(F77_FOUND)
-        ADD_SUBDIRECTORY(examples/arkode/F77_serial)
-      ENDIF()
-      IF(MPIF_FOUND)
-        ADD_SUBDIRECTORY(examples/arkode/F77_parallel)
-      ENDIF()
-    ENDIF()
-    # F90 examples
-    IF(EXAMPLES_ENABLE_F90)
-      IF(F90_FOUND)
-        ADD_SUBDIRECTORY(examples/arkode/F90_serial)
-      ENDIF()
-      IF(MPIF90_FOUND)
-        ADD_SUBDIRECTORY(examples/arkode/F90_parallel)
-      ENDIF()
-    ENDIF()
-  ENDIF(BUILD_ARKODE)
-
-  # add CVODE examples
-  IF(BUILD_CVODE)
-    # C examples
-    IF(EXAMPLES_ENABLE_C)
-      ADD_SUBDIRECTORY(examples/cvode/serial)
-      IF(OPENMP_FOUND)
-        ADD_SUBDIRECTORY(examples/cvode/C_openmp)
-      ENDIF()
-      IF(MPIC_FOUND)
-        ADD_SUBDIRECTORY(examples/cvode/parallel)
-      ENDIF()
-      IF(HYPRE_ENABLE AND HYPRE_FOUND)
-        ADD_SUBDIRECTORY(examples/cvode/parhyp)
-      ENDIF()
-    ENDIF()
-    # Fortran examples
-    IF(EXAMPLES_ENABLE_F77)
-      IF(F77_FOUND)
-        ADD_SUBDIRECTORY(examples/cvode/fcmix_serial)
-      ENDIF()
-      IF(MPIF_FOUND)
-        ADD_SUBDIRECTORY(examples/cvode/fcmix_parallel)
-      ENDIF()
-    ENDIF()
-    # cuda examples
-    IF(EXAMPLES_ENABLE_CUDA)
-      IF(CUDA_ENABLE AND CUDA_FOUND)
-        ADD_SUBDIRECTORY(examples/cvode/cuda)
-      ENDIF()
-    ENDIF(EXAMPLES_ENABLE_CUDA)
-    # raja examples
-    IF(EXAMPLES_ENABLE_RAJA)
-      IF(RAJA_ENABLE AND RAJA_FOUND)
-        ADD_SUBDIRECTORY(examples/cvode/raja)
-      ENDIF()
-    ENDIF(EXAMPLES_ENABLE_RAJA)
-  ENDIF(BUILD_CVODE)
-
-  # add CVODES Examples
-  IF(BUILD_CVODES)
-    # C examples
-    IF(EXAMPLES_ENABLE_C)
-      ADD_SUBDIRECTORY(examples/cvodes/serial)
-      IF(MPIC_FOUND)
-        ADD_SUBDIRECTORY(examples/cvodes/parallel)
-      ENDIF()
-      IF(OPENMP_FOUND)
-        ADD_SUBDIRECTORY(examples/cvodes/C_openmp)
-      ENDIF()
-    ENDIF()
-  ENDIF(BUILD_CVODES)
-
-  # add IDA examples
-  IF(BUILD_IDA)
-    # C examples
-    IF(EXAMPLES_ENABLE_C)
-      ADD_SUBDIRECTORY(examples/ida/serial)
-      IF(OPENMP_FOUND)
-        ADD_SUBDIRECTORY(examples/ida/C_openmp)
-      ENDIF()
-      IF(MPIC_FOUND)
-        ADD_SUBDIRECTORY(examples/ida/parallel)
-      ENDIF()
-      IF(PETSC_FOUND)
-        ADD_SUBDIRECTORY(examples/ida/petsc)
-      ENDIF()
-    ENDIF()
-    # Fortran examples
-    IF(EXAMPLES_ENABLE_F77)
-      IF(F77_FOUND)
-        ADD_SUBDIRECTORY(examples/ida/fcmix_serial)
-      ENDIF()
-      IF(OPENMP_FOUND)
-        ADD_SUBDIRECTORY(examples/ida/fcmix_openmp)
-      ENDIF()
-      IF(PTHREADS_FOUND)
-	ADD_SUBDIRECTORY(examples/ida/fcmix_pthreads)
-      ENDIF()
-      IF(MPIF_FOUND)
-        ADD_SUBDIRECTORY(examples/ida/fcmix_parallel)
-      ENDIF()
-    ENDIF()
-  ENDIF(BUILD_IDA)
-
-  # add IDAS examples
-  IF(BUILD_IDAS)
-    # C examples
-    IF(EXAMPLES_ENABLE_C)
-      ADD_SUBDIRECTORY(examples/idas/serial)
-      IF(OPENMP_FOUND)
-        ADD_SUBDIRECTORY(examples/idas/C_openmp)
-      ENDIF()
-      IF(MPIC_FOUND)
-        ADD_SUBDIRECTORY(examples/idas/parallel)
-      ENDIF()
-    ENDIF()
-  ENDIF(BUILD_IDAS)
-
-  # add KINSOL examples
-  IF(BUILD_KINSOL)
-    # C examples
-    IF(EXAMPLES_ENABLE_C)
-      ADD_SUBDIRECTORY(examples/kinsol/serial)
-      IF(OPENMP_FOUND)
-        # the only example here need special handling from testrunner (not yet implemented)
-        ADD_SUBDIRECTORY(examples/kinsol/C_openmp)
-      ENDIF()
-      IF(MPIC_FOUND)
-        ADD_SUBDIRECTORY(examples/kinsol/parallel)
-      ENDIF()
-    ENDIF()
-    # Fortran examples
-    IF(EXAMPLES_ENABLE_F77)
-      IF(F77_FOUND)
-        ADD_SUBDIRECTORY(examples/kinsol/fcmix_serial)
-      ENDIF()
-      IF(MPIF_FOUND)
-        ADD_SUBDIRECTORY(examples/kinsol/fcmix_parallel)
-      ENDIF()
-    ENDIF()
-  ENDIF(BUILD_KINSOL)
-
-  # add CPODES examples
-  IF(BUILD_CPODES)
-    IF(EXAMPLES_ENABLE_C)
-      ADD_SUBDIRECTORY(examples/cpodes/serial)
-      IF(MPIC_FOUND)
-        ADD_SUBDIRECTORY(examples/cpodes/parallel)
-      ENDIF()
-    ENDIF()
-  ENDIF(BUILD_CPODES)
-
-  # Always add the nvector serial examples
-  ADD_SUBDIRECTORY(examples/nvector/serial)
-
-  # # Always add the serial sunmatrix dense/band/sparse examples
-  ADD_SUBDIRECTORY(examples/sunmatrix/dense)
-  ADD_SUBDIRECTORY(examples/sunmatrix/band)
-  ADD_SUBDIRECTORY(examples/sunmatrix/sparse)
-
-  # # Always add the serial sunlinearsolver dense/band/spils examples
-  ADD_SUBDIRECTORY(examples/sunlinsol/band)
-  ADD_SUBDIRECTORY(examples/sunlinsol/dense)
-  IF(KLU_FOUND)
-    ADD_SUBDIRECTORY(examples/sunlinsol/klu)
-  ENDIF(KLU_FOUND)
-  IF(SUPERLUMT_FOUND)
-    ADD_SUBDIRECTORY(examples/sunlinsol/superlumt)
-  ENDIF(SUPERLUMT_FOUND)
-  IF(LAPACK_FOUND)
-    ADD_SUBDIRECTORY(examples/sunlinsol/lapackband)
-    ADD_SUBDIRECTORY(examples/sunlinsol/lapackdense)
-  ENDIF(LAPACK_FOUND)
-  ADD_SUBDIRECTORY(examples/sunlinsol/spgmr/serial)
-  ADD_SUBDIRECTORY(examples/sunlinsol/spfgmr/serial)
-  ADD_SUBDIRECTORY(examples/sunlinsol/spbcgs/serial)
-  ADD_SUBDIRECTORY(examples/sunlinsol/sptfqmr/serial)
-  ADD_SUBDIRECTORY(examples/sunlinsol/pcg/serial)
-
-  IF(MPIC_FOUND)
-      ADD_SUBDIRECTORY(examples/nvector/parallel)
-      ADD_SUBDIRECTORY(examples/sunlinsol/spgmr/parallel)
-      ADD_SUBDIRECTORY(examples/sunlinsol/spfgmr/parallel)
-      ADD_SUBDIRECTORY(examples/sunlinsol/spbcgs/parallel)
-      ADD_SUBDIRECTORY(examples/sunlinsol/sptfqmr/parallel)
-      #ADD_SUBDIRECTORY(examples/sunlinsol/pcg/parallel)
-  ENDIF(MPIC_FOUND)
-
-  IF(HYPRE_FOUND)
-      ADD_SUBDIRECTORY(examples/nvector/parhyp)
-  ENDIF()
-
-  IF(PTHREADS_FOUND)
-      ADD_SUBDIRECTORY(examples/nvector/pthreads)
-  ENDIF()
-
-  IF(OPENMP_FOUND)
-      ADD_SUBDIRECTORY(examples/nvector/C_openmp)
-  ENDIF()
-
-  IF(PETSC_FOUND)
-      ADD_SUBDIRECTORY(examples/nvector/petsc)
-  ENDIF()
-
-  IF(CUDA_FOUND)
-      ADD_SUBDIRECTORY(examples/nvector/cuda)
-  ENDIF(CUDA_FOUND)
-
-  IF(RAJA_FOUND)
-      ADD_SUBDIRECTORY(examples/nvector/raja)
-  ENDIF(RAJA_FOUND)
-
-ENDIF(EXAMPLES_ENABLED)
-
-# ---------------------------------------------------------------
-# Install configuration header files and license file
-# ---------------------------------------------------------------
-
-# install configured header file
-INSTALL(
-  FILES ${PROJECT_BINARY_DIR}/include/sundials/sundials_config.h
-  DESTINATION include/sundials
-  )
-
-# install configured header file for Fortran 90
-INSTALL(
-  FILES ${PROJECT_BINARY_DIR}/include/sundials/sundials_fconfig.h
-  DESTINATION include/sundials
-  )
-
-# install license file
-INSTALL(
-  FILES ${PROJECT_SOURCE_DIR}/LICENSE
-  DESTINATION .)
diff --git a/scripts/replace-cmake/sundials-4.1.0/CMakeLists.txt b/scripts/replace-cmake/sundials-4.1.0/CMakeLists.txt
deleted file mode 100644
index fc8acbddc9..0000000000
--- a/scripts/replace-cmake/sundials-4.1.0/CMakeLists.txt
+++ /dev/null
@@ -1,1151 +0,0 @@
-# ---------------------------------------------------------------
-# Programmer: Radu Serban, David J. Gardner, Cody J. Balos,
-#             and Slaven Peles @ LLNL
-# ---------------------------------------------------------------
-# SUNDIALS Copyright Start
-# Copyright (c) 2002-2019, Lawrence Livermore National Security
-# and Southern Methodist University.
-# All rights reserved.
-#
-# See the top-level LICENSE and NOTICE files for details.
-#
-# SPDX-License-Identifier: BSD-3-Clause
-# SUNDIALS Copyright End
-# ---------------------------------------------------------------
-# Top level CMakeLists.txt for SUNDIALS (for cmake build system)
-# ---------------------------------------------------------------
-
-# ---------------------------------------------------------------
-# Initial commands
-# ---------------------------------------------------------------
-
-# Require a fairly recent cmake version
-cmake_minimum_required(VERSION 3.1.3)
-
-# Libraries linked via full path no longer produce linker search paths
-# Allows examples to build
-if(COMMAND cmake_policy)
-  cmake_policy(SET CMP0003 NEW)
-endif(COMMAND cmake_policy)
-
-# MACOSX_RPATH is enabled by default
-# Fixes dynamic loading on OSX
-if(POLICY CMP0042)
-  cmake_policy(SET CMP0042 NEW) # Added in CMake 3.0
-else()
-  if(APPLE)
-    set(CMAKE_MACOSX_RPATH 1)
-  endif()
-endif()
-
-# Project SUNDIALS (initially only C supported)
-# sets PROJECT_SOURCE_DIR and PROJECT_BINARY_DIR variables
-PROJECT(sundials C)
-
-# Set some variables with info on the SUNDIALS project
-SET(PACKAGE_BUGREPORT "woodward6@llnl.gov")
-SET(PACKAGE_NAME "SUNDIALS")
-SET(PACKAGE_STRING "SUNDIALS 4.1.0")
-SET(PACKAGE_TARNAME "sundials")
-
-# set SUNDIALS version numbers
-# (use "" for the version label if none is needed)
-SET(PACKAGE_VERSION_MAJOR "4")
-SET(PACKAGE_VERSION_MINOR "1")
-SET(PACKAGE_VERSION_PATCH "0")
-SET(PACKAGE_VERSION_LABEL "")
-
-IF(PACKAGE_VERSION_LABEL)
-  SET(PACKAGE_VERSION "${PACKAGE_VERSION_MAJOR}.${PACKAGE_VERSION_MINOR}.${PACKAGE_VERSION_PATCH}-${PACKAGE_VERSION_LABEL}")
-ELSE()
-  SET(PACKAGE_VERSION "${PACKAGE_VERSION_MAJOR}.${PACKAGE_VERSION_MINOR}.${PACKAGE_VERSION_PATCH}")
-ENDIF()
-
-SET_PROPERTY(GLOBAL PROPERTY USE_FOLDERS ON)
-
-# Prohibit in-source build
-IF("${CMAKE_SOURCE_DIR}" STREQUAL "${CMAKE_BINARY_DIR}")
-  MESSAGE(FATAL_ERROR "In-source build prohibited.")
-ENDIF("${CMAKE_SOURCE_DIR}" STREQUAL "${CMAKE_BINARY_DIR}")
-
-# Hide some cache variables
-MARK_AS_ADVANCED(EXECUTABLE_OUTPUT_PATH LIBRARY_OUTPUT_PATH)
-
-# Always show the C compiler and flags
-MARK_AS_ADVANCED(CLEAR
-  CMAKE_C_COMPILER
-  CMAKE_C_FLAGS)
-
-# Specify the VERSION and SOVERSION for shared libraries
-
-SET(arkodelib_VERSION "3.1.0")
-SET(arkodelib_SOVERSION "3")
-
-SET(cvodelib_VERSION "4.1.0")
-SET(cvodelib_SOVERSION "4")
-
-SET(cvodeslib_VERSION "4.1.0")
-SET(cvodeslib_SOVERSION "4")
-
-SET(idalib_VERSION "4.1.0")
-SET(idalib_SOVERSION "4")
-
-SET(idaslib_VERSION "3.1.0")
-SET(idaslib_SOVERSION "3")
-
-SET(kinsollib_VERSION "4.1.0")
-SET(kinsollib_SOVERSION "4")
-
-SET(cpodeslib_VERSION "0.0.0")
-SET(cpodeslib_SOVERSION "0")
-
-SET(nveclib_VERSION "4.1.0")
-SET(nveclib_SOVERSION "4")
-
-SET(sunmatrixlib_VERSION "2.1.0")
-SET(sunmatrixlib_SOVERSION "2")
-
-SET(sunlinsollib_VERSION "2.1.0")
-SET(sunlinsollib_SOVERSION "2")
-
-SET(sunnonlinsollib_VERSION "1.1.0")
-SET(sunnonlinsollib_SOVERSION "1")
-
-# Specify the location of additional CMAKE modules
-SET(CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/config)
-
-# Get correct build paths automatically, but expose CMAKE_INSTALL_LIBDIR
-# as a regular cache variable so that a user can more easily see what
-# the library dir was set to be by GNUInstallDirs.
-INCLUDE(GNUInstallDirs)
-MARK_AS_ADVANCED(CLEAR CMAKE_INSTALL_LIBDIR)
-
-# ---------------------------------------------------------------
-# Which modules to build?
-# ---------------------------------------------------------------
-
-# For each SUNDIALS solver available (i.e. for which we have the
-# sources), give the user the option of enabling/disabling it.
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/arkode")
-  OPTION(BUILD_ARKODE "Build the ARKODE library" ON)
-ELSE()
-  SET(BUILD_ARKODE OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/cvode")
-  OPTION(BUILD_CVODE "Build the CVODE library" ON)
-ELSE()
-  SET(BUILD_CVODE OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/cvodes")
-  OPTION(BUILD_CVODES "Build the CVODES library" ON)
-ELSE()
-  SET(BUILD_CVODES OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/ida")
-  OPTION(BUILD_IDA "Build the IDA library" ON)
-ELSE()
-  SET(BUILD_IDA OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/idas")
-  OPTION(BUILD_IDAS "Build the IDAS library" ON)
-ELSE()
-  SET(BUILD_IDAS OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/kinsol")
-  OPTION(BUILD_KINSOL "Build the KINSOL library" ON)
-ELSE()
-  SET(BUILD_KINSOL OFF)
-ENDIF()
-
-# CPODES is always OFF for now.  (commented out for Release); ToDo: better way to do this?
-#IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/cpodes")
-#  OPTION(BUILD_CPODES  "Build the CPODES library"  OFF)
-#ELSE()
-#  SET(BUILD_CPODES OFF)
-#ENDIF()
-
-# ---------------------------------------------------------------
-# MACRO definitions
-# ---------------------------------------------------------------
-INCLUDE(CMakeParseArguments) # can be removed when CMake 3.5+ is required
-INCLUDE(SundialsCMakeMacros)
-INCLUDE(SundialsAddF2003InterfaceLibrary)
-INCLUDE(SundialsAddTest)
-INCLUDE(SundialsAddTestInstall)
-
-# ---------------------------------------------------------------
-# Check for deprecated SUNDIALS CMake options/variables
-# ---------------------------------------------------------------
-INCLUDE(SundialsDeprecated)
-
-# ---------------------------------------------------------------
-# xSDK specific options
-# ---------------------------------------------------------------
-INCLUDE(SundialsXSDK)
-
-# ---------------------------------------------------------------
-# Build specific C flags
-# ---------------------------------------------------------------
-
-# Hide all build type specific flags
-MARK_AS_ADVANCED(FORCE
-  CMAKE_C_FLAGS_DEBUG
-  CMAKE_C_FLAGS_MINSIZEREL
-  CMAKE_C_FLAGS_RELEASE
-  CMAKE_C_FLAGS_RELWITHDEBINFO)
-
-# Only show flags for the current build type if it is set
-# NOTE: Build specific flags are appended those in CMAKE_C_FLAGS
-IF(CMAKE_BUILD_TYPE)
-  IF(CMAKE_BUILD_TYPE MATCHES "Debug")
-    MESSAGE("Appending C debug flags")
-    MARK_AS_ADVANCED(CLEAR CMAKE_C_FLAGS_DEBUG)
-  ELSEIF(CMAKE_BUILD_TYPE MATCHES "MinSizeRel")
-    MESSAGE("Appending C min size release flags")
-    MARK_AS_ADVANCED(CLEAR CMAKE_C_FLAGS_MINSIZEREL)
-  ELSEIF(CMAKE_BUILD_TYPE MATCHES "Release")
-    MESSAGE("Appending C release flags")
-    MARK_AS_ADVANCED(CLEAR CMAKE_C_FLAGS_RELEASE)
-  ELSEIF(CMAKE_BUILD_TYPE MATCHES "RelWithDebInfo")
-    MESSAGE("Appending C release with debug info flags")
-    MARK_AS_ADVANCED(CLEAR CMAKE_C_FLAGS_RELWITHDEBINFO)
-  ENDIF()
-ENDIF()
-
-# ---------------------------------------------------------------
-# Option to specify precision (realtype)
-# ---------------------------------------------------------------
-
-SET(DOCSTR "single, double, or extended")
-SHOW_VARIABLE(SUNDIALS_PRECISION STRING "${DOCSTR}" "double")
-
-# prepare substitution variable PRECISION_LEVEL for sundials_config.h
-STRING(TOUPPER ${SUNDIALS_PRECISION} SUNDIALS_PRECISION)
-SET(PRECISION_LEVEL "#define SUNDIALS_${SUNDIALS_PRECISION}_PRECISION 1")
-
-# prepare substitution variable FPRECISION_LEVEL for sundials_fconfig.h
-IF(SUNDIALS_PRECISION MATCHES "SINGLE")
-  SET(FPRECISION_LEVEL "4")
-ENDIF(SUNDIALS_PRECISION MATCHES "SINGLE")
-IF(SUNDIALS_PRECISION MATCHES "DOUBLE")
-  SET(FPRECISION_LEVEL "8")
-ENDIF(SUNDIALS_PRECISION MATCHES "DOUBLE")
-IF(SUNDIALS_PRECISION MATCHES "EXTENDED")
-  SET(FPRECISION_LEVEL "16")
-ENDIF(SUNDIALS_PRECISION MATCHES "EXTENDED")
-
-# ---------------------------------------------------------------
-# Option to specify index type
-# ---------------------------------------------------------------
-
-SET(DOCSTR "Signed 64-bit (64) or signed 32-bit (32) integer")
-SHOW_VARIABLE(SUNDIALS_INDEX_SIZE STRING "${DOCSTR}" "64")
-SET(DOCSTR "Integer type to use for indices in SUNDIALS")
-SHOW_VARIABLE(SUNDIALS_INDEX_TYPE STRING "${DOCSTR}" "")
-MARK_AS_ADVANCED(SUNDIALS_INDEX_TYPE)
-include(SundialsIndexSize)
-
-# ---------------------------------------------------------------
-# Enable Fortran interface?
-# ---------------------------------------------------------------
-
-# Fortran interface is disabled by default
-SET(DOCSTR "Enable Fortran 77 interfaces")
-OPTION(F77_INTERFACE_ENABLE "${DOCSTR}" OFF)
-
-# Check that at least one solver with a Fortran 77 interface is built
-IF(NOT BUILD_ARKODE AND NOT BUILD_CVODE AND NOT BUILD_IDA AND NOT BUILD_KINSOL)
-  IF(F77_INTERFACE_ENABLE)
-    PRINT_WARNING("Enabled packages do not support Fortran 77 interface" "Disabling F77 interface")
-    FORCE_VARIABLE(F77_INTERFACE_ENABLE BOOL "${DOCSTR}" OFF)
-  ENDIF()
-  HIDE_VARIABLE(F77_INTERFACE_ENABLE)
-ENDIF()
-
-# Fortran 2003 interface is disabled by default
-SET(DOCSTR "Enable Fortran 2003 interfaces")
-OPTION(F2003_INTERFACE_ENABLE "${DOCSTR}" OFF)
-
-# Check that at least one solver with a Fortran 2003 interface is built
-IF(NOT BUILD_CVODE)
-  IF(F2003_INTERFACE_ENABLE)
-    PRINT_WARNING("Enabled packages do not support Fortran 2003 interface" "Disabling F2003 interface")
-    FORCE_VARIABLE(F2003_INTERFACE_ENABLE BOOL "${DOCSTR}" OFF)
-  ENDIF()
-  HIDE_VARIABLE(F2003_INTERFACE_ENABLE)
-ENDIF()
-
-IF(F2003_INTERFACE_ENABLE)
-  # F2003 interface only supports double precision
-  IF(NOT (SUNDIALS_PRECISION MATCHES "DOUBLE"))
-    PRINT_WARNING("F2003 interface is not compatible with ${SUNDIALS_PRECISION} precision"
-                  "Disabling F2003 interface")
-    FORCE_VARIABLE(F2003_INTERFACE_ENABLE BOOL "${DOCSTR}" OFF)
-  ENDIF()
-
-  # F2003 interface only supports 64-bit indices
-  IF(NOT (SUNDIALS_INDEX_SIZE MATCHES "64"))
-    PRINT_WARNING("F2003 interface is not compatible with ${SUNDIALS_INDEX_SIZE}-bit indicies"
-                  "Disabling F2003 interface")
-    FORCE_VARIABLE(F2003_INTERFACE_ENABLE BOOL "${DOCSTR}" OFF)
-  ENDIF()
-
-  # Put all F2003 modules into one build directory
-  SET(CMAKE_Fortran_MODULE_DIRECTORY "${CMAKE_BINARY_DIR}/fortran")
-
-  # Allow a user to set where the Fortran modules will be installed
-  SET(DOCSTR "Directory where Fortran module files are installed")
-  SHOW_VARIABLE(Fortran_INSTALL_MODDIR DIRECTORY "${DOCSTR}" "fortran")
-ENDIF()
-
-# ---------------------------------------------------------------
-# Options to build static and/or shared libraries
-# ---------------------------------------------------------------
-
-OPTION(BUILD_STATIC_LIBS "Build static libraries" ON)
-OPTION(BUILD_SHARED_LIBS "Build shared libraries" ON)
-
-# Make sure we build at least one type of libraries
-IF(NOT BUILD_STATIC_LIBS AND NOT BUILD_SHARED_LIBS)
-  PRINT_WARNING("Both static and shared library generation were disabled"
-                "Building static libraries was re-enabled")
-  FORCE_VARIABLE(BUILD_STATIC_LIBS BOOL "Build static libraries" ON)
-ENDIF(NOT BUILD_STATIC_LIBS AND NOT BUILD_SHARED_LIBS)
-
-# ---------------------------------------------------------------
-# Option to use the generic math libraries (UNIX only)
-# ---------------------------------------------------------------
-
-IF(UNIX)
-  OPTION(USE_GENERIC_MATH "Use generic (std-c) math libraries" ON)
-  IF(USE_GENERIC_MATH)
-    # executables will be linked against -lm
-    SET(EXTRA_LINK_LIBS -lm)
-    # prepare substitution variable for sundials_config.h
-    SET(SUNDIALS_USE_GENERIC_MATH TRUE)
-  ENDIF(USE_GENERIC_MATH)
-ENDIF(UNIX)
-
-# ---------------------------------------------------------------
-# Check for POSIX timers
-# ---------------------------------------------------------------
-INCLUDE(SundialsPOSIXTimers)
-
-# ===============================================================
-# Options for Parallelism
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Enable MPI support?
-# ---------------------------------------------------------------
-OPTION(MPI_ENABLE "Enable MPI support" OFF)
-
-# ---------------------------------------------------------------
-# Enable OpenMP support?
-# ---------------------------------------------------------------
-OPTION(OPENMP_ENABLE "Enable OpenMP support" OFF)
-
-# provide OPENMP_DEVICE_ENABLE option
-OPTION(OPENMP_DEVICE_ENABLE "Enable OpenMP device offloading support" OFF)
-
-# Advanced option to skip OpenMP device offloading support check.
-# This is needed for a specific compiler that doesn't correctly
-# report its OpenMP spec date (with CMake >= 3.9).
-OPTION(SKIP_OPENMP_DEVICE_CHECK "Skip the OpenMP device offloading support check" OFF)
-MARK_AS_ADVANCED(FORCE SKIP_OPENMP_DEVICE_CHECK)
-
-# ---------------------------------------------------------------
-# Enable Pthread support?
-# ---------------------------------------------------------------
-OPTION(PTHREAD_ENABLE "Enable Pthreads support" OFF)
-
-# -------------------------------------------------------------
-# Enable CUDA support?
-# -------------------------------------------------------------
-OPTION(CUDA_ENABLE "Enable CUDA support" OFF)
-
-# -------------------------------------------------------------
-# Enable RAJA support?
-# -------------------------------------------------------------
-OPTION(RAJA_ENABLE "Enable RAJA support" OFF)
-
-
-# ===============================================================
-# Options for external packages
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Enable BLAS support?
-# ---------------------------------------------------------------
-OPTION(BLAS_ENABLE "Enable BLAS support" OFF)
-
-# ---------------------------------------------------------------
-# Enable LAPACK/BLAS support?
-# ---------------------------------------------------------------
-OPTION(LAPACK_ENABLE "Enable Lapack support" OFF)
-
-# LAPACK does not support extended precision
-IF(LAPACK_ENABLE AND SUNDIALS_PRECISION MATCHES "EXTENDED")
-  PRINT_WARNING("LAPACK is not compatible with ${SUNDIALS_PRECISION} precision"
-                "Disabling LAPACK")
-  FORCE_VARIABLE(LAPACK_ENABLE BOOL "LAPACK is disabled" OFF)
-ENDIF()
-
-# LAPACK does not support 64-bit integer index types
-IF(LAPACK_ENABLE AND SUNDIALS_INDEX_SIZE MATCHES "64")
-  PRINT_WARNING("LAPACK is not compatible with ${SUNDIALS_INDEX_SIZE} integers"
-                "Disabling LAPACK")
-  SET(LAPACK_ENABLE OFF CACHE BOOL "LAPACK is disabled" FORCE)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Enable SuperLU_MT support?
-# ---------------------------------------------------------------
-OPTION(SUPERLUMT_ENABLE "Enable SUPERLUMT support" OFF)
-
-# SuperLU_MT does not support extended precision
-IF(SUPERLUMT_ENABLE AND SUNDIALS_PRECISION MATCHES "EXTENDED")
-  PRINT_WARNING("SuperLU_MT is not compatible with ${SUNDIALS_PRECISION} precision"
-                "Disabling SuperLU_MT")
-  FORCE_VARIABLE(SUPERLUMT_ENABLE BOOL "SuperLU_MT is disabled" OFF)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Enable KLU support?
-# ---------------------------------------------------------------
-OPTION(KLU_ENABLE "Enable KLU support" OFF)
-
-# KLU does not support single or extended precision
-IF(KLU_ENABLE AND
-  (SUNDIALS_PRECISION MATCHES "SINGLE" OR SUNDIALS_PRECISION MATCHES "EXTENDED"))
-  PRINT_WARNING("KLU is not compatible with ${SUNDIALS_PRECISION} precision"
-                "Disabling KLU")
-  FORCE_VARIABLE(KLU_ENABLE BOOL "KLU is disabled" OFF)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Enable hypre Vector support?
-# ---------------------------------------------------------------
-OPTION(HYPRE_ENABLE "Enable hypre support" OFF)
-
-# Using hypre requres building with MPI enabled
-IF(HYPRE_ENABLE AND NOT MPI_ENABLE)
-  PRINT_WARNING("MPI not enabled - Disabling hypre"
-                "Set MPI_ENABLE to ON to use parhyp")
-  FORCE_VARIABLE(HYPRE_ENABLE BOOL "Enable hypre support" OFF)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Enable PETSc support?
-# ---------------------------------------------------------------
-OPTION(PETSC_ENABLE "Enable PETSc support" OFF)
-
-# Using PETSc requires building with MPI enabled
-IF(PETSC_ENABLE AND NOT MPI_ENABLE)
-  PRINT_WARNING("MPI not enabled - Disabling PETSc"
-                "Set MPI_ENABLE to ON to use PETSc")
-  FORCE_VARIABLE(PETSC_ENABLE BOOL "Enable PETSc support" OFF)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Enable Trilinos support?
-# ---------------------------------------------------------------
-OPTION(Trilinos_ENABLE "Enable Trilinos support" OFF)
-
-
-# ===============================================================
-# Options for examples
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Enable examples?
-# ---------------------------------------------------------------
-
-# Enable C examples (on by default)
-OPTION(EXAMPLES_ENABLE_C "Build SUNDIALS C examples" ON)
-
-# C++ examples (off by default, unless Trilinos is enabled)
-SET(DOCSTR "Build C++ examples")
-OPTION(EXAMPLES_ENABLE_CXX "${DOCSTR}" ${Trilinos_ENABLE})
-
-# F77 examples (on by default) are an option only if the Fortran
-# interface is enabled
-SET(DOCSTR "Build SUNDIALS Fortran examples")
-IF(F77_INTERFACE_ENABLE)
-  SHOW_VARIABLE(EXAMPLES_ENABLE_F77 BOOL "${DOCSTR}" ON)
-  # Fortran 77 examples do not support single or extended precision
-  IF(EXAMPLES_ENABLE_F77 AND (SUNDIALS_PRECISION MATCHES "EXTENDED" OR SUNDIALS_PRECISION MATCHES "SINGLE"))
-    PRINT_WARNING("F77 examples are not compatible with ${SUNDIALS_PRECISION} precision"
-                  "EXAMPLES_ENABLE_F77")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_F77 BOOL "${DOCSTR}" OFF)
-  ENDIF()
-ELSE()
-  # set back to OFF (in case was ON)
-  IF(EXAMPLES_ENABLE_F77)
-    PRINT_WARNING("EXAMPLES_ENABLE_F77 is ON but F77_INTERFACE_ENABLE is OFF"
-                  "Disabling EXAMPLES_ENABLE_F77")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_F77 BOOL "${DOCSTR}" OFF)
-  ENDIF()
-  HIDE_VARIABLE(EXAMPLES_ENABLE_F77)
-ENDIF()
-
-# F90 examples (on by default) are an option only if a Fortran interface is enabled.
-SET(DOCSTR "Build SUNDIALS F90 examples")
-IF(F77_INTERFACE_ENABLE OR F2003_INTERFACE_ENABLE)
-  SHOW_VARIABLE(EXAMPLES_ENABLE_F90 BOOL "${DOCSTR}" ON)
-  # Fortran 90 examples do not support extended precision
-  IF(EXAMPLES_ENABLE_F90 AND (SUNDIALS_PRECISION MATCHES "EXTENDED"))
-    PRINT_WARNING("F90 examples are not compatible with ${SUNDIALS_PRECISION} precision"
-                  "Disabling EXAMPLES_ENABLE_F90")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_F90 BOOL "${DOCSTR}" OFF)
-  ENDIF()
-ELSE()
-  # set back to OFF (in case was ON)
-  IF(EXAMPLES_ENABLE_F90)
-    PRINT_WARNING("EXAMPLES_ENABLE_F90 is ON but both F77 and F2003 interfaces are OFF"
-                  "Disabling EXAMPLES_ENABLE_F90")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_F90 BOOL "${DOCSTR}" OFF)
-  ENDIF()
-  HIDE_VARIABLE(EXAMPLES_ENABLE_F90)
-ENDIF()
-
-# CUDA examples (off by default)
-SET(DOCSTR "Build SUNDIALS CUDA examples")
-IF(CUDA_ENABLE)
-  OPTION(EXAMPLES_ENABLE_CUDA "${DOCSTR}" OFF)
-ELSE()
-  IF(EXAMPLES_ENABLE_CUDA)
-    PRINT_WARNING("EXAMPLES_ENABLE_CUDA is ON but CUDA_ENABLE is OFF"
-                  "Disabling EXAMPLES_ENABLE_CUDA")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_CUDA BOOL "${DOCSTR}" OFF)
-  ENDIF()
-ENDIF()
-
-# If any of the above examples are enabled set EXAMPLES_ENABLED to TRUE
-IF(EXAMPLES_ENABLE_C OR
-    EXAMPLES_ENABLE_F77 OR
-    EXAMPLES_ENABLE_CXX OR
-    EXAMPLES_ENABLE_F90 OR
-    EXAMPLES_ENABLE_CUDA)
-  SET(EXAMPLES_ENABLED TRUE)
-ELSE()
-  SET(EXAMPLES_ENABLED FALSE)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Install examples?
-# ---------------------------------------------------------------
-
-# Enable installing examples by default
-SET(DOCSTR "Install SUNDIALS examples")
-IF(EXAMPLES_ENABLED)
-  OPTION(EXAMPLES_INSTALL "${DOCSTR}" ON)
-ELSE()
-  FORCE_VARIABLE(EXAMPLES_INSTALL BOOL "${DOCSTR}" OFF)
-  HIDE_VARIABLE(EXAMPLES_INSTALL)
-ENDIF()
-
-# If examples are to be exported, check where we should install them.
-IF(EXAMPLES_INSTALL)
-
-  SHOW_VARIABLE(EXAMPLES_INSTALL_PATH PATH
-    "Output directory for installing example files"
-    "${CMAKE_INSTALL_PREFIX}/examples")
-
-  IF(NOT EXAMPLES_INSTALL_PATH)
-    PRINT_WARNING("The example installation path is empty"
-      "Example installation path was reset to its default value")
-    SET(EXAMPLES_INSTALL_PATH "${CMAKE_INSTALL_PREFIX}/examples" CACHE STRING
-      "Output directory for installing example files" FORCE)
-  ENDIF()
-
-ELSE()
-
-  HIDE_VARIABLE(EXAMPLES_INSTALL_PATH)
-
-ENDIF()
-
-
-# ==============================================================================
-# Advanced (hidden) options
-# ==============================================================================
-
-# ------------------------------------------------------------------------------
-# Manually specify the Fortran name-mangling scheme
-#
-# The build system tries to infer the Fortran name-mangling scheme using a
-# Fortran compiler and defaults to using lower case and one underscore if the
-# scheme can not be determined. If a working Fortran compiler is not available
-# or the user needs to override the inferred or default scheme, the following
-# options specify the case and number of appended underscores corresponding to
-# the Fortran name-mangling scheme of symbol names that do not themselves
-# contain underscores. This is all we really need for the FCMIX and LAPACK
-# interfaces. A working Fortran compiler is only necessary for building Fortran
-# example programs.
-# ------------------------------------------------------------------------------
-
-# The case to use in the name-mangling scheme
-show_variable(SUNDIALS_F77_FUNC_CASE STRING
-  "case of Fortran function names (lower/upper)"
-  "")
-
-# The number of underscores of appended in the name-mangling scheme
-show_variable(SUNDIALS_F77_FUNC_UNDERSCORES STRING
-  "number of underscores appended to Fortran function names (none/one/two)"
-  "")
-
-# Hide the name-mangling varibales as advanced options
-mark_as_advanced(FORCE SUNDIALS_F77_FUNC_CASE)
-mark_as_advanced(FORCE SUNDIALS_F77_FUNC_UNDERSCORES)
-
-# If used, both case and underscores must be set
-if((NOT SUNDIALS_F77_FUNC_CASE) AND SUNDIALS_F77_FUNC_UNDERSCORES)
-  message(FATAL_ERROR
-    "If SUNDIALS_F77_FUNC_UNDERSCORES is set, SUNDIALS_F77_FUNC_CASE must also be set.")
-endif()
-
-if(SUNDIALS_F77_FUNC_CASE AND (NOT SUNDIALS_F77_FUNC_UNDERSCORES))
-  message(FATAL_ERROR
-    "If SUNDIALS_F77_FUNC_CASE is set, SUNDIALS_F77_FUNC_UNDERSCORES must also be set.")
-endif()
-
-# ------------------------------------------------------------------------------
-# Include development examples in regression tests?
-#
-# NOTE: Development examples are currently used for internal testing and may
-# produce erroneous failures when run on different systems as the pass/fail
-# status is determined by comparing the output against a saved output file.
-# ------------------------------------------------------------------------------
-OPTION(SUNDIALS_DEVTESTS "Include development tests in make test" OFF)
-MARK_AS_ADVANCED(FORCE SUNDIALS_DEVTESTS)
-
-# ===============================================================
-# Add any platform specifc settings
-# ===============================================================
-
-IF(APPLE)
-  SET(CMAKE_SHARED_LIBRARY_CREATE_C_FLAGS "${CMAKE_SHARED_LIBRARY_CREATE_C_FLAGS} -undefined dynamic_lookup")
-ENDIF(APPLE)
-
-# ===============================================================
-# Fortran and C++ settings
-# ===============================================================
-
-# ---------------------------------------------------------------
-# A Fortran compiler is needed to:
-# (a) Determine the name-mangling scheme if FCMIX, BLAS, or
-#     LAPACK are enabled
-# (b) Compile example programs if F77 or F90 examples are enabled
-# ---------------------------------------------------------------
-
-# Do we need a Fortran name-mangling scheme?
-if(F77_INTERFACE_ENABLE OR BLAS_ENABLE OR LAPACK_ENABLE)
-  set(NEED_FORTRAN_NAME_MANGLING TRUE)
-endif()
-
-# Did the user provide a name-mangling scheme?
-if(SUNDIALS_F77_FUNC_CASE AND SUNDIALS_F77_FUNC_UNDERSCORES)
-
-  STRING(TOUPPER ${SUNDIALS_F77_FUNC_CASE} SUNDIALS_F77_FUNC_CASE)
-  STRING(TOUPPER ${SUNDIALS_F77_FUNC_UNDERSCORES} SUNDIALS_F77_FUNC_UNDERSCORES)
-
-  # Based on the given case and number of underscores, set the C preprocessor
-  # macro definitions. Since SUNDIALS never uses symbols names containing
-  # underscores we set the name-mangling schemes to be the same. In general,
-  # names of symbols with and without underscore may be mangled differently
-  # (e.g. g77 mangles mysub to mysub_ and my_sub to my_sub__)
-  if(SUNDIALS_F77_FUNC_CASE MATCHES "LOWER")
-    if(SUNDIALS_F77_FUNC_UNDERSCORES MATCHES "NONE")
-      set(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) name")
-      set(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) name")
-    elseif(SUNDIALS_F77_FUNC_UNDERSCORES MATCHES "ONE")
-      set(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) name ## _")
-      SET(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) name ## _")
-    elseif(SUNDIALS_F77_FUNC_UNDERSCORES MATCHES "TWO")
-      set(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) name ## __")
-      set(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) name ## __")
-    else()
-      message(FATAL_ERROR "Invalid SUNDIALS_F77_FUNC_UNDERSCORES option.")
-    endif()
-  elseif(SUNDIALS_F77_FUNC_CASE MATCHES "UPPER")
-    if(SUNDIALS_F77_FUNC_UNDERSCORES MATCHES "NONE")
-      set(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) NAME")
-      set(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) NAME")
-    elseif(SUNDIALS_F77_FUNC_UNDERSCORES MATCHES "ONE")
-      set(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) NAME ## _")
-      set(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) NAME ## _")
-    elseif(SUNDIALS_F77_FUNC_UNDERSCORES MATCHES "TWO")
-      set(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) NAME ## __")
-      set(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) NAME ## __")
-    else()
-      message(FATAL_ERROR "Invalid SUNDIALS_F77_FUNC_UNDERSCORES option.")
-    endif()
-  else()
-    message(FATAL_ERROR "Invalid SUNDIALS_F77_FUNC_CASE option.")
-  endif()
-
-  # name-mangling scheme has been manually set
-  set(NEED_FORTRAN_NAME_MANGLING FALSE)
-
-endif()
-
-# Do we need a Fortran compiler?
-if(F2003_INTERFACE_ENABLE OR EXAMPLES_ENABLE_F77 OR EXAMPLES_ENABLE_F90 OR NEED_FORTRAN_NAME_MANGLING)
-  include(SundialsFortran)
-endif()
-
-# Ensure that F90 compiler is found if F90 examples are enabled
-if (EXAMPLES_ENABLE_F90 AND (NOT F90_FOUND))
-  PRINT_WARNING("Compiler with F90 support not found" "Disabling F90 Examples")
-  SET(DOCSTR "Build F90 examples")
-  FORCE_VARIABLE(EXAMPLES_ENABLE_F90 "${DOCSTR}" OFF)
-endif()
-
-# Ensure that F90 compiler found if F2003 interface is enabled
-if (F2003_INTERFACE_ENABLE AND (NOT F90_FOUND))
-  PRINT_WARNING("Compiler with F90 support not found" "Disabling F2003 Interface")
-  SET(DOCSTR "Enable Fortran 2003 interfaces")
-  FORCE_VARIABLE(F2003_INTERFACE_ENABLE BOOL "${DOCSTR}" OFF)
-endif()
-
-# F2003 interface requires ISO_C_BINDING
-IF(F2003_INTERFACE_ENABLE AND (NOT Fortran_COMPILER_SUPPORTS_ISOCBINDING))
-  PRINT_WARNING("Fortran compiler does not provide ISO_C_BINDING support"
-                "Disabling F2003 interface")
-  SET(DOCSTR "Enable Fortran 2003 interfaces")
-  FORCE_VARIABLE(F2003_INTERFACE_ENABLE BOOL "${DOCSTR}" OFF)
-ENDIF()
-
-
-# ---------------------------------------------------------------
-# A C++ compiler is needed if:
-# (a) C++ examples are enabled
-# (b) CUDA is enabled
-# (c) RAJA is enabled
-# (d) Trilinos is enabled
-# ---------------------------------------------------------------
-
-if(EXAMPLES_ENABLE_CXX OR CUDA_ENABLE OR RAJA_ENABLE OR Trilinos_ENABLE)
-  include(SundialsCXX)
-endif()
-
-# ---------------------------------------------------------------
-# Setup CUDA. Since CUDA is its own language we do this
-# separate from the TPLs.
-# ---------------------------------------------------------------
-
-if(CUDA_ENABLE)
-  find_package(CUDA)
-  if (CUDA_FOUND)
-    set(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} -lineinfo")
-  else()
-    message(STATUS "Disabling CUDA support, could not find CUDA.")
-    set(CUDA_ENABLE OFF)
-  endif()
-endif(CUDA_ENABLE)
-
-# ---------------------------------------------------------------
-# Now that all languages are setup, we can configure them more.
-# ---------------------------------------------------------------
-
-# C++11 is needed if:
-#   (a) CUDA is enabled
-# C++11 should not be enabled if
-#   (a) RAJA is enabled (they provide a std flag)
-if (CXX_FOUND AND CUDA_ENABLE AND CUDA_FOUND AND (NOT RAJA_ENABLE))
-  USE_CXX_STD(11)
-endif()
-
-# ---------------------------------------------------------------
-# Decide how to compile MPI codes. We must check for MPI if
-# MPI is enabled or if Trilinos is enabled because the Trilinos
-# examples may need MPI without us turning on the MPI SUNDIALS
-# components.
-# ---------------------------------------------------------------
-
-if(MPI_ENABLE OR Trilinos_ENABLE)
-  include(SundialsMPI)
-endif()
-
-if(MPI_ENABLE)
-  if(NOT MPI_C_FOUND)
-    print_warning("MPI not functional" "Parallel support will not be provided")
-  else()
-    set(IS_MPI_ENABLED "#ifndef SUNDIALS_MPI_ENABLED\n#define SUNDIALS_MPI_ENABLED 1\n#endif")
-  endif()
-endif()
-
-# always define FMPI_COMM_F2C in sundials_fconfig.h file
-if(MPIC_MPI2)
-  set(F77_MPI_COMM_F2C "#define SUNDIALS_MPI_COMM_F2C 1")
-  set(FMPI_COMM_F2C ".true.")
-else()
-  set(F77_MPI_COMM_F2C "#define SUNDIALS_MPI_COMM_F2C 0")
-  set(FMPI_COMM_F2C ".false.")
-endif()
-
-# -------------------------------------------------------------
-# Find OpenMP
-# -------------------------------------------------------------
-
-if(OPENMP_ENABLE OR OPENMP_DEVICE_ENABLE)
-
-  include(SundialsOpenMP)
-
-  # turn off OPENMP_ENABLE and OPENMP_DEVICE_ENABLE if OpenMP is not found
-  if(NOT OPENMP_FOUND)
-    print_warning("Could not determine OpenMP compiler flags" "Disabling OpenMP support")
-    force_variable(OPENMP_ENABLE BOOL "Enable OpenMP support" OFF)
-    force_variable(OPENMP_DEVICE_ENABLE BOOL "Enable OpenMP device offloading support" OFF)
-  endif()
-
-  # turn off OPENMP_DEVICE_ENABLE if offloading is not supported
-  if(OPENMP_DEVICE_ENABLE AND (NOT OPENMP_SUPPORTS_DEVICE_OFFLOADING))
-    print_warning("OpenMP found does not support device offloading"
-                  "Disabling OpenMP device offloading support")
-    force_variable(OPENMP_DEVICE_ENABLE BOOL "Enable OpenMP device offloading support" OFF)
-  endif()
-
-endif()
-
-# -------------------------------------------------------------
-# Find PThreads
-# -------------------------------------------------------------
-
-IF(PTHREAD_ENABLE)
-  FIND_PACKAGE(Threads)
-  IF(CMAKE_USE_PTHREADS_INIT)
-    message(STATUS "Using Pthreads")
-    SET(PTHREADS_FOUND TRUE)
-    # SGS
-  ELSE()
-    message(STATUS "Disabling Pthreads support, could not determine compiler flags")
-  endif()
-ENDIF(PTHREAD_ENABLE)
-
-# -------------------------------------------------------------
-# Find RAJA
-# -------------------------------------------------------------
-
-# disable RAJA if CUDA is not enabled/working
-if(RAJA_ENABLE AND (NOT CUDA_FOUND))
-  PRINT_WARNING("CUDA is required for RAJA support" "Please enable CUDA and RAJA")
-  FORCE_VARIABLE(RAJA_ENABLE BOOL "RAJA disabled" OFF)
-endif()
-
-if(RAJA_ENABLE)
-  # Look for CMake configuration file in RAJA installation
-  find_package(RAJA)
-  if (RAJA_FOUND)
-    include_directories(${RAJA_INCLUDE_DIR})
-    set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} ${RAJA_NVCC_FLAGS})
-  else()
-    PRINT_WARNING("RAJA configuration not found"
-                  "Please set RAJA_DIR to provide path to RAJA CMake configuration file.")
-  endif()
-endif(RAJA_ENABLE)
-
-# ===============================================================
-# Find (and test) external packages
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Find (and test) the BLAS libraries
-# ---------------------------------------------------------------
-
-# If BLAS is needed, first try to find the appropriate
-# libraries and linker flags needed to link against them.
-
-IF(BLAS_ENABLE)
-
-  # find BLAS
-  INCLUDE(SundialsBlas)
-
-  # show after include so FindBlas can locate BLAS_LIBRARIES if necessary
-  SHOW_VARIABLE(BLAS_LIBRARIES STRING "Blas libraries" "${BLAS_LIBRARIES}")
-
-  IF(BLAS_LIBRARIES AND NOT BLAS_FOUND)
-    PRINT_WARNING("BLAS not functional"
-                  "BLAS support will not be provided")
-  ELSE()
-    #set sundials_config.h symbol via sundials_config.in
-    SET(SUNDIALS_BLAS TRUE)
-  ENDIF()
-
-ELSE()
-
-  HIDE_VARIABLE(BLAS_LIBRARIES)
-
-ENDIF()
-
-# ---------------------------------------------------------------
-# Find (and test) the Lapack libraries
-# ---------------------------------------------------------------
-
-# If LAPACK is needed, first try to find the appropriate
-# libraries and linker flags needed to link against them.
-
-IF(LAPACK_ENABLE)
-
-  # find LAPACK and BLAS Libraries
-  INCLUDE(SundialsLapack)
-
-  # show after include so FindLapack can locate LAPCK_LIBRARIES if necessary
-  SHOW_VARIABLE(LAPACK_LIBRARIES STRING "Lapack and Blas libraries" "${LAPACK_LIBRARIES}")
-
-  IF(LAPACK_LIBRARIES AND NOT LAPACK_FOUND)
-    PRINT_WARNING("LAPACK not functional"
-                  "Blas/Lapack support will not be provided")
-  ELSE()
-    #set sundials_config.h symbol via sundials_config.in
-    SET(SUNDIALS_BLAS_LAPACK TRUE)
-  ENDIF()
-
-ELSE()
-
-  HIDE_VARIABLE(LAPACK_LIBRARIES)
-
-ENDIF()
-
-# ---------------------------------------------------------------
-# Find (and test) the SUPERLUMT libraries
-# ---------------------------------------------------------------
-
-# >>>>>>> NOTE: Need to add check for SuperLU_MT integer type
-
-# If SUPERLUMT is needed, first try to find the appropriate
-# libraries to link against them.
-
-IF(SUPERLUMT_ENABLE)
-
-  # Show SuperLU_MT options and set default thread type (Pthreads)
-  SHOW_VARIABLE(SUPERLUMT_THREAD_TYPE STRING "SUPERLUMT threading type: OpenMP or Pthread" "Pthread")
-  SHOW_VARIABLE(SUPERLUMT_INCLUDE_DIR PATH "SUPERLUMT include directory" "${SUPERLUMT_INCLUDE_DIR}")
-  SHOW_VARIABLE(SUPERLUMT_LIBRARY_DIR PATH "SUPERLUMT library directory" "${SUPERLUMT_LIBRARY_DIR}")
-
-  INCLUDE(SundialsSuperLUMT)
-
-  IF(SUPERLUMT_FOUND)
-    # sundials_config.h symbols
-    SET(SUNDIALS_SUPERLUMT TRUE)
-    SET(SUNDIALS_SUPERLUMT_THREAD_TYPE ${SUPERLUMT_THREAD_TYPE})
-    INCLUDE_DIRECTORIES(${SUPERLUMT_INCLUDE_DIR})
-  ENDIF()
-
-  IF(SUPERLUMT_LIBRARIES AND NOT SUPERLUMT_FOUND)
-    PRINT_WARNING("SUPERLUMT not functional - support will not be provided"
-                  "Double check spelling specified libraries (search is case sensitive)")
-  ENDIF(SUPERLUMT_LIBRARIES AND NOT SUPERLUMT_FOUND)
-
-ELSE()
-
-  HIDE_VARIABLE(SUPERLUMT_THREAD_TYPE)
-  HIDE_VARIABLE(SUPERLUMT_LIBRARY_DIR)
-  HIDE_VARIABLE(SUPERLUMT_INCLUDE_DIR)
-  SET (SUPERLUMT_DISABLED TRUE CACHE INTERNAL "GUI - return when first set")
-
-ENDIF()
-
-# ---------------------------------------------------------------
-# Find (and test) the KLU libraries
-# ---------------------------------------------------------------
-
-# If KLU is requested, first try to find the appropriate libraries to
-# link against them.
-
-IF(KLU_ENABLE)
-
-  SHOW_VARIABLE(KLU_INCLUDE_DIR PATH "KLU include directory"
-    "${KLU_INCLUDE_DIR}")
-  SHOW_VARIABLE(KLU_LIBRARY_DIR PATH
-    "Klu library directory" "${KLU_LIBRARY_DIR}")
-
-  set(KLU_FOUND TRUE)
-  get_filename_component(PYBAMM_DIR ${PROJECT_SOURCE_DIR} DIRECTORY)
-  set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} ${PYBAMM_DIR}) # use FindSuiteSparse.cmake that is in PyBaMM root
-  set(SuiteSparse_ROOT ${PYBAMM_DIR}/SuiteSparse-5.6.0)
-  find_package(SuiteSparse OPTIONAL_COMPONENTS KLU AMD COLAMD BTF)
-  include_directories(${SuiteSparse_INCLUDE_DIRS})
-  set(KLU_LIBRARIES ${SuiteSparse_LIBRARIES})
-
-
-  IF(KLU_LIBRARIES AND NOT KLU_FOUND)
-    PRINT_WARNING("KLU not functional - support will not be provided"
-                  "Double check spelling of include path and specified libraries (search is case sensitive)")
-  ENDIF(KLU_LIBRARIES AND NOT KLU_FOUND)
-
-ELSE()
-
-  HIDE_VARIABLE(KLU_LIBRARY_DIR)
-  HIDE_VARIABLE(KLU_INCLUDE_DIR)
-  SET (KLU_DISABLED TRUE CACHE INTERNAL "GUI - return when first set")
-
-ENDIF(KLU_ENABLE)
-
-# ---------------------------------------------------------------
-# Find (and test) the hypre libraries
-# ---------------------------------------------------------------
-
-# >>>>>>> NOTE: Need to add check for hypre precision and integer type
-
-IF(HYPRE_ENABLE)
-  SHOW_VARIABLE(HYPRE_INCLUDE_DIR PATH "HYPRE include directory"
-    "${HYPRE_INCLUDE_DIR}")
-  SHOW_VARIABLE(HYPRE_LIBRARY_DIR PATH
-    "HYPRE library directory" "${HYPRE_LIBRARY_DIR}")
-
-  INCLUDE(SundialsHypre)
-
-  IF(HYPRE_FOUND)
-    # sundials_config.h symbol
-    SET(SUNDIALS_HYPRE TRUE)
-    INCLUDE_DIRECTORIES(${HYPRE_INCLUDE_DIR})
-  ENDIF(HYPRE_FOUND)
-
-  IF(HYPRE_LIBRARIES AND NOT HYPRE_FOUND)
-    PRINT_WARNING("HYPRE not functional - support will not be provided"
-                  "Found hypre library, test code does not work")
-  ENDIF(HYPRE_LIBRARIES AND NOT HYPRE_FOUND)
-
-ELSE()
-
-  HIDE_VARIABLE(HYPRE_INCLUDE_DIR)
-  HIDE_VARIABLE(HYPRE_LIBRARY_DIR)
-  SET (HYPRE_DISABLED TRUE CACHE INTERNAL "GUI - return when first set")
-
-ENDIF()
-
-# ---------------------------------------------------------------
-# Find (and test) the PETSc libraries
-# ---------------------------------------------------------------
-
-# >>>>>>> NOTE: Need to add check for PETSc precision and integer type
-
-IF(PETSC_ENABLE)
-  SHOW_VARIABLE(PETSC_INCLUDE_DIR PATH "PETSc include directory"
-    "${PETSC_INCLUDE_DIR}")
-  SHOW_VARIABLE(PETSC_LIBRARY_DIR PATH
-    "PETSc library directory" "${PETSC_LIBRARY_DIR}")
-
-  INCLUDE(SundialsPETSc)
-
-  IF(PETSC_FOUND)
-    # sundials_config.h symbol
-    SET(SUNDIALS_PETSC TRUE)
-    INCLUDE_DIRECTORIES(${PETSC_INCLUDE_DIR})
-  ENDIF(PETSC_FOUND)
-
-  IF(PETSC_LIBRARIES AND NOT PETSC_FOUND)
-    PRINT_WARNING("PETSC not functional - support will not be provided"
-                  "Double check spelling specified libraries (search is case sensitive)")
-  ENDIF(PETSC_LIBRARIES AND NOT PETSC_FOUND)
-
-ELSE()
-
-  HIDE_VARIABLE(PETSC_LIBRARY_DIR)
-  HIDE_VARIABLE(PETSC_INCLUDE_DIR)
-  SET (PETSC_DISABLED TRUE CACHE INTERNAL "GUI - return when first set")
-
-ENDIF()
-
-# -------------------------------------------------------------
-# Find Trilinos
-# -------------------------------------------------------------
-
-if(Trilinos_ENABLE)
-  include(SundialsTrilinos)
-  if(NOT Trilinos_FUNCTIONAL)
-    PRINT_WARNING("Trilinos not functional" "Verify the path to Trilinos and check the Trilinos installation")
-  endif()
-endif(Trilinos_ENABLE)
-
-
-# ===============================================================
-# At this point all the configuration options are set.
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Configure the header file sundials_config.h
-# ---------------------------------------------------------------
-
-# All required substitution variables should be available at this point.
-# Generate the header file and place it in the binary dir.
-CONFIGURE_FILE(
-  ${PROJECT_SOURCE_DIR}/include/sundials/sundials_config.in
-  ${PROJECT_BINARY_DIR}/include/sundials/sundials_config.h
-  )
-CONFIGURE_FILE(
-  ${PROJECT_SOURCE_DIR}/include/sundials/sundials_fconfig.in
-  ${PROJECT_BINARY_DIR}/include/sundials/sundials_fconfig.h
-  )
-
-# Add the include directory in the source tree and the one in
-# the binary tree (for the header file sundials_config.h)
-INCLUDE_DIRECTORIES(${PROJECT_SOURCE_DIR}/include ${PROJECT_BINARY_DIR}/include)
-
-# ---------------------------------------------------------------
-# Enable testing and add source and example files to the build.
-# ---------------------------------------------------------------
-
-# Enable testing
-IF(EXAMPLES_ENABLED)
-  INCLUDE(SundialsTesting)
-ENDIF()
-
-# Add selected packages and modules to the build
-ADD_SUBDIRECTORY(src)
-
-# Add selected examples to the build
-IF(EXAMPLES_ENABLED)
-  ADD_SUBDIRECTORY(examples)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Install configuration header files and license file
-# ---------------------------------------------------------------
-
-# install configured header file
-INSTALL(
-  FILES ${PROJECT_BINARY_DIR}/include/sundials/sundials_config.h
-  DESTINATION include/sundials
-  )
-
-# install configured header file for Fortran 90
-INSTALL(
-  FILES ${PROJECT_BINARY_DIR}/include/sundials/sundials_fconfig.h
-  DESTINATION include/sundials
-  )
-
-# install shared Fortran 2003 modules
-IF(F2003_INTERFACE_ENABLE)
-  # While the .mod files get generated for static and shared
-  # libraries, they are identical. So only install one set
-  # of the .mod files.
-  IF(BUILD_STATIC_LIBS)
-    INSTALL(
-      DIRECTORY ${CMAKE_Fortran_MODULE_DIRECTORY}_STATIC/
-      DESTINATION ${Fortran_INSTALL_MODDIR}
-      )
-  ELSE()
-    INSTALL(
-      DIRECTORY ${CMAKE_Fortran_MODULE_DIRECTORY}_SHARED/
-      DESTINATION ${Fortran_INSTALL_MODDIR}
-      )
-  ENDIF()
-ENDIF()
-
-# install license and notice files
-INSTALL(
-  FILES ${PROJECT_SOURCE_DIR}/LICENSE
-  DESTINATION include/sundials
-  )
-INSTALL(
-  FILES ${PROJECT_SOURCE_DIR}/NOTICE
-  DESTINATION include/sundials
-  )
diff --git a/scripts/replace-cmake/sundials-5.0.0/CMakeLists.txt b/scripts/replace-cmake/sundials-5.0.0/CMakeLists.txt
deleted file mode 100644
index fc8acbddc9..0000000000
--- a/scripts/replace-cmake/sundials-5.0.0/CMakeLists.txt
+++ /dev/null
@@ -1,1151 +0,0 @@
-# ---------------------------------------------------------------
-# Programmer: Radu Serban, David J. Gardner, Cody J. Balos,
-#             and Slaven Peles @ LLNL
-# ---------------------------------------------------------------
-# SUNDIALS Copyright Start
-# Copyright (c) 2002-2019, Lawrence Livermore National Security
-# and Southern Methodist University.
-# All rights reserved.
-#
-# See the top-level LICENSE and NOTICE files for details.
-#
-# SPDX-License-Identifier: BSD-3-Clause
-# SUNDIALS Copyright End
-# ---------------------------------------------------------------
-# Top level CMakeLists.txt for SUNDIALS (for cmake build system)
-# ---------------------------------------------------------------
-
-# ---------------------------------------------------------------
-# Initial commands
-# ---------------------------------------------------------------
-
-# Require a fairly recent cmake version
-cmake_minimum_required(VERSION 3.1.3)
-
-# Libraries linked via full path no longer produce linker search paths
-# Allows examples to build
-if(COMMAND cmake_policy)
-  cmake_policy(SET CMP0003 NEW)
-endif(COMMAND cmake_policy)
-
-# MACOSX_RPATH is enabled by default
-# Fixes dynamic loading on OSX
-if(POLICY CMP0042)
-  cmake_policy(SET CMP0042 NEW) # Added in CMake 3.0
-else()
-  if(APPLE)
-    set(CMAKE_MACOSX_RPATH 1)
-  endif()
-endif()
-
-# Project SUNDIALS (initially only C supported)
-# sets PROJECT_SOURCE_DIR and PROJECT_BINARY_DIR variables
-PROJECT(sundials C)
-
-# Set some variables with info on the SUNDIALS project
-SET(PACKAGE_BUGREPORT "woodward6@llnl.gov")
-SET(PACKAGE_NAME "SUNDIALS")
-SET(PACKAGE_STRING "SUNDIALS 4.1.0")
-SET(PACKAGE_TARNAME "sundials")
-
-# set SUNDIALS version numbers
-# (use "" for the version label if none is needed)
-SET(PACKAGE_VERSION_MAJOR "4")
-SET(PACKAGE_VERSION_MINOR "1")
-SET(PACKAGE_VERSION_PATCH "0")
-SET(PACKAGE_VERSION_LABEL "")
-
-IF(PACKAGE_VERSION_LABEL)
-  SET(PACKAGE_VERSION "${PACKAGE_VERSION_MAJOR}.${PACKAGE_VERSION_MINOR}.${PACKAGE_VERSION_PATCH}-${PACKAGE_VERSION_LABEL}")
-ELSE()
-  SET(PACKAGE_VERSION "${PACKAGE_VERSION_MAJOR}.${PACKAGE_VERSION_MINOR}.${PACKAGE_VERSION_PATCH}")
-ENDIF()
-
-SET_PROPERTY(GLOBAL PROPERTY USE_FOLDERS ON)
-
-# Prohibit in-source build
-IF("${CMAKE_SOURCE_DIR}" STREQUAL "${CMAKE_BINARY_DIR}")
-  MESSAGE(FATAL_ERROR "In-source build prohibited.")
-ENDIF("${CMAKE_SOURCE_DIR}" STREQUAL "${CMAKE_BINARY_DIR}")
-
-# Hide some cache variables
-MARK_AS_ADVANCED(EXECUTABLE_OUTPUT_PATH LIBRARY_OUTPUT_PATH)
-
-# Always show the C compiler and flags
-MARK_AS_ADVANCED(CLEAR
-  CMAKE_C_COMPILER
-  CMAKE_C_FLAGS)
-
-# Specify the VERSION and SOVERSION for shared libraries
-
-SET(arkodelib_VERSION "3.1.0")
-SET(arkodelib_SOVERSION "3")
-
-SET(cvodelib_VERSION "4.1.0")
-SET(cvodelib_SOVERSION "4")
-
-SET(cvodeslib_VERSION "4.1.0")
-SET(cvodeslib_SOVERSION "4")
-
-SET(idalib_VERSION "4.1.0")
-SET(idalib_SOVERSION "4")
-
-SET(idaslib_VERSION "3.1.0")
-SET(idaslib_SOVERSION "3")
-
-SET(kinsollib_VERSION "4.1.0")
-SET(kinsollib_SOVERSION "4")
-
-SET(cpodeslib_VERSION "0.0.0")
-SET(cpodeslib_SOVERSION "0")
-
-SET(nveclib_VERSION "4.1.0")
-SET(nveclib_SOVERSION "4")
-
-SET(sunmatrixlib_VERSION "2.1.0")
-SET(sunmatrixlib_SOVERSION "2")
-
-SET(sunlinsollib_VERSION "2.1.0")
-SET(sunlinsollib_SOVERSION "2")
-
-SET(sunnonlinsollib_VERSION "1.1.0")
-SET(sunnonlinsollib_SOVERSION "1")
-
-# Specify the location of additional CMAKE modules
-SET(CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/config)
-
-# Get correct build paths automatically, but expose CMAKE_INSTALL_LIBDIR
-# as a regular cache variable so that a user can more easily see what
-# the library dir was set to be by GNUInstallDirs.
-INCLUDE(GNUInstallDirs)
-MARK_AS_ADVANCED(CLEAR CMAKE_INSTALL_LIBDIR)
-
-# ---------------------------------------------------------------
-# Which modules to build?
-# ---------------------------------------------------------------
-
-# For each SUNDIALS solver available (i.e. for which we have the
-# sources), give the user the option of enabling/disabling it.
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/arkode")
-  OPTION(BUILD_ARKODE "Build the ARKODE library" ON)
-ELSE()
-  SET(BUILD_ARKODE OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/cvode")
-  OPTION(BUILD_CVODE "Build the CVODE library" ON)
-ELSE()
-  SET(BUILD_CVODE OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/cvodes")
-  OPTION(BUILD_CVODES "Build the CVODES library" ON)
-ELSE()
-  SET(BUILD_CVODES OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/ida")
-  OPTION(BUILD_IDA "Build the IDA library" ON)
-ELSE()
-  SET(BUILD_IDA OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/idas")
-  OPTION(BUILD_IDAS "Build the IDAS library" ON)
-ELSE()
-  SET(BUILD_IDAS OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/kinsol")
-  OPTION(BUILD_KINSOL "Build the KINSOL library" ON)
-ELSE()
-  SET(BUILD_KINSOL OFF)
-ENDIF()
-
-# CPODES is always OFF for now.  (commented out for Release); ToDo: better way to do this?
-#IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/cpodes")
-#  OPTION(BUILD_CPODES  "Build the CPODES library"  OFF)
-#ELSE()
-#  SET(BUILD_CPODES OFF)
-#ENDIF()
-
-# ---------------------------------------------------------------
-# MACRO definitions
-# ---------------------------------------------------------------
-INCLUDE(CMakeParseArguments) # can be removed when CMake 3.5+ is required
-INCLUDE(SundialsCMakeMacros)
-INCLUDE(SundialsAddF2003InterfaceLibrary)
-INCLUDE(SundialsAddTest)
-INCLUDE(SundialsAddTestInstall)
-
-# ---------------------------------------------------------------
-# Check for deprecated SUNDIALS CMake options/variables
-# ---------------------------------------------------------------
-INCLUDE(SundialsDeprecated)
-
-# ---------------------------------------------------------------
-# xSDK specific options
-# ---------------------------------------------------------------
-INCLUDE(SundialsXSDK)
-
-# ---------------------------------------------------------------
-# Build specific C flags
-# ---------------------------------------------------------------
-
-# Hide all build type specific flags
-MARK_AS_ADVANCED(FORCE
-  CMAKE_C_FLAGS_DEBUG
-  CMAKE_C_FLAGS_MINSIZEREL
-  CMAKE_C_FLAGS_RELEASE
-  CMAKE_C_FLAGS_RELWITHDEBINFO)
-
-# Only show flags for the current build type if it is set
-# NOTE: Build specific flags are appended those in CMAKE_C_FLAGS
-IF(CMAKE_BUILD_TYPE)
-  IF(CMAKE_BUILD_TYPE MATCHES "Debug")
-    MESSAGE("Appending C debug flags")
-    MARK_AS_ADVANCED(CLEAR CMAKE_C_FLAGS_DEBUG)
-  ELSEIF(CMAKE_BUILD_TYPE MATCHES "MinSizeRel")
-    MESSAGE("Appending C min size release flags")
-    MARK_AS_ADVANCED(CLEAR CMAKE_C_FLAGS_MINSIZEREL)
-  ELSEIF(CMAKE_BUILD_TYPE MATCHES "Release")
-    MESSAGE("Appending C release flags")
-    MARK_AS_ADVANCED(CLEAR CMAKE_C_FLAGS_RELEASE)
-  ELSEIF(CMAKE_BUILD_TYPE MATCHES "RelWithDebInfo")
-    MESSAGE("Appending C release with debug info flags")
-    MARK_AS_ADVANCED(CLEAR CMAKE_C_FLAGS_RELWITHDEBINFO)
-  ENDIF()
-ENDIF()
-
-# ---------------------------------------------------------------
-# Option to specify precision (realtype)
-# ---------------------------------------------------------------
-
-SET(DOCSTR "single, double, or extended")
-SHOW_VARIABLE(SUNDIALS_PRECISION STRING "${DOCSTR}" "double")
-
-# prepare substitution variable PRECISION_LEVEL for sundials_config.h
-STRING(TOUPPER ${SUNDIALS_PRECISION} SUNDIALS_PRECISION)
-SET(PRECISION_LEVEL "#define SUNDIALS_${SUNDIALS_PRECISION}_PRECISION 1")
-
-# prepare substitution variable FPRECISION_LEVEL for sundials_fconfig.h
-IF(SUNDIALS_PRECISION MATCHES "SINGLE")
-  SET(FPRECISION_LEVEL "4")
-ENDIF(SUNDIALS_PRECISION MATCHES "SINGLE")
-IF(SUNDIALS_PRECISION MATCHES "DOUBLE")
-  SET(FPRECISION_LEVEL "8")
-ENDIF(SUNDIALS_PRECISION MATCHES "DOUBLE")
-IF(SUNDIALS_PRECISION MATCHES "EXTENDED")
-  SET(FPRECISION_LEVEL "16")
-ENDIF(SUNDIALS_PRECISION MATCHES "EXTENDED")
-
-# ---------------------------------------------------------------
-# Option to specify index type
-# ---------------------------------------------------------------
-
-SET(DOCSTR "Signed 64-bit (64) or signed 32-bit (32) integer")
-SHOW_VARIABLE(SUNDIALS_INDEX_SIZE STRING "${DOCSTR}" "64")
-SET(DOCSTR "Integer type to use for indices in SUNDIALS")
-SHOW_VARIABLE(SUNDIALS_INDEX_TYPE STRING "${DOCSTR}" "")
-MARK_AS_ADVANCED(SUNDIALS_INDEX_TYPE)
-include(SundialsIndexSize)
-
-# ---------------------------------------------------------------
-# Enable Fortran interface?
-# ---------------------------------------------------------------
-
-# Fortran interface is disabled by default
-SET(DOCSTR "Enable Fortran 77 interfaces")
-OPTION(F77_INTERFACE_ENABLE "${DOCSTR}" OFF)
-
-# Check that at least one solver with a Fortran 77 interface is built
-IF(NOT BUILD_ARKODE AND NOT BUILD_CVODE AND NOT BUILD_IDA AND NOT BUILD_KINSOL)
-  IF(F77_INTERFACE_ENABLE)
-    PRINT_WARNING("Enabled packages do not support Fortran 77 interface" "Disabling F77 interface")
-    FORCE_VARIABLE(F77_INTERFACE_ENABLE BOOL "${DOCSTR}" OFF)
-  ENDIF()
-  HIDE_VARIABLE(F77_INTERFACE_ENABLE)
-ENDIF()
-
-# Fortran 2003 interface is disabled by default
-SET(DOCSTR "Enable Fortran 2003 interfaces")
-OPTION(F2003_INTERFACE_ENABLE "${DOCSTR}" OFF)
-
-# Check that at least one solver with a Fortran 2003 interface is built
-IF(NOT BUILD_CVODE)
-  IF(F2003_INTERFACE_ENABLE)
-    PRINT_WARNING("Enabled packages do not support Fortran 2003 interface" "Disabling F2003 interface")
-    FORCE_VARIABLE(F2003_INTERFACE_ENABLE BOOL "${DOCSTR}" OFF)
-  ENDIF()
-  HIDE_VARIABLE(F2003_INTERFACE_ENABLE)
-ENDIF()
-
-IF(F2003_INTERFACE_ENABLE)
-  # F2003 interface only supports double precision
-  IF(NOT (SUNDIALS_PRECISION MATCHES "DOUBLE"))
-    PRINT_WARNING("F2003 interface is not compatible with ${SUNDIALS_PRECISION} precision"
-                  "Disabling F2003 interface")
-    FORCE_VARIABLE(F2003_INTERFACE_ENABLE BOOL "${DOCSTR}" OFF)
-  ENDIF()
-
-  # F2003 interface only supports 64-bit indices
-  IF(NOT (SUNDIALS_INDEX_SIZE MATCHES "64"))
-    PRINT_WARNING("F2003 interface is not compatible with ${SUNDIALS_INDEX_SIZE}-bit indicies"
-                  "Disabling F2003 interface")
-    FORCE_VARIABLE(F2003_INTERFACE_ENABLE BOOL "${DOCSTR}" OFF)
-  ENDIF()
-
-  # Put all F2003 modules into one build directory
-  SET(CMAKE_Fortran_MODULE_DIRECTORY "${CMAKE_BINARY_DIR}/fortran")
-
-  # Allow a user to set where the Fortran modules will be installed
-  SET(DOCSTR "Directory where Fortran module files are installed")
-  SHOW_VARIABLE(Fortran_INSTALL_MODDIR DIRECTORY "${DOCSTR}" "fortran")
-ENDIF()
-
-# ---------------------------------------------------------------
-# Options to build static and/or shared libraries
-# ---------------------------------------------------------------
-
-OPTION(BUILD_STATIC_LIBS "Build static libraries" ON)
-OPTION(BUILD_SHARED_LIBS "Build shared libraries" ON)
-
-# Make sure we build at least one type of libraries
-IF(NOT BUILD_STATIC_LIBS AND NOT BUILD_SHARED_LIBS)
-  PRINT_WARNING("Both static and shared library generation were disabled"
-                "Building static libraries was re-enabled")
-  FORCE_VARIABLE(BUILD_STATIC_LIBS BOOL "Build static libraries" ON)
-ENDIF(NOT BUILD_STATIC_LIBS AND NOT BUILD_SHARED_LIBS)
-
-# ---------------------------------------------------------------
-# Option to use the generic math libraries (UNIX only)
-# ---------------------------------------------------------------
-
-IF(UNIX)
-  OPTION(USE_GENERIC_MATH "Use generic (std-c) math libraries" ON)
-  IF(USE_GENERIC_MATH)
-    # executables will be linked against -lm
-    SET(EXTRA_LINK_LIBS -lm)
-    # prepare substitution variable for sundials_config.h
-    SET(SUNDIALS_USE_GENERIC_MATH TRUE)
-  ENDIF(USE_GENERIC_MATH)
-ENDIF(UNIX)
-
-# ---------------------------------------------------------------
-# Check for POSIX timers
-# ---------------------------------------------------------------
-INCLUDE(SundialsPOSIXTimers)
-
-# ===============================================================
-# Options for Parallelism
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Enable MPI support?
-# ---------------------------------------------------------------
-OPTION(MPI_ENABLE "Enable MPI support" OFF)
-
-# ---------------------------------------------------------------
-# Enable OpenMP support?
-# ---------------------------------------------------------------
-OPTION(OPENMP_ENABLE "Enable OpenMP support" OFF)
-
-# provide OPENMP_DEVICE_ENABLE option
-OPTION(OPENMP_DEVICE_ENABLE "Enable OpenMP device offloading support" OFF)
-
-# Advanced option to skip OpenMP device offloading support check.
-# This is needed for a specific compiler that doesn't correctly
-# report its OpenMP spec date (with CMake >= 3.9).
-OPTION(SKIP_OPENMP_DEVICE_CHECK "Skip the OpenMP device offloading support check" OFF)
-MARK_AS_ADVANCED(FORCE SKIP_OPENMP_DEVICE_CHECK)
-
-# ---------------------------------------------------------------
-# Enable Pthread support?
-# ---------------------------------------------------------------
-OPTION(PTHREAD_ENABLE "Enable Pthreads support" OFF)
-
-# -------------------------------------------------------------
-# Enable CUDA support?
-# -------------------------------------------------------------
-OPTION(CUDA_ENABLE "Enable CUDA support" OFF)
-
-# -------------------------------------------------------------
-# Enable RAJA support?
-# -------------------------------------------------------------
-OPTION(RAJA_ENABLE "Enable RAJA support" OFF)
-
-
-# ===============================================================
-# Options for external packages
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Enable BLAS support?
-# ---------------------------------------------------------------
-OPTION(BLAS_ENABLE "Enable BLAS support" OFF)
-
-# ---------------------------------------------------------------
-# Enable LAPACK/BLAS support?
-# ---------------------------------------------------------------
-OPTION(LAPACK_ENABLE "Enable Lapack support" OFF)
-
-# LAPACK does not support extended precision
-IF(LAPACK_ENABLE AND SUNDIALS_PRECISION MATCHES "EXTENDED")
-  PRINT_WARNING("LAPACK is not compatible with ${SUNDIALS_PRECISION} precision"
-                "Disabling LAPACK")
-  FORCE_VARIABLE(LAPACK_ENABLE BOOL "LAPACK is disabled" OFF)
-ENDIF()
-
-# LAPACK does not support 64-bit integer index types
-IF(LAPACK_ENABLE AND SUNDIALS_INDEX_SIZE MATCHES "64")
-  PRINT_WARNING("LAPACK is not compatible with ${SUNDIALS_INDEX_SIZE} integers"
-                "Disabling LAPACK")
-  SET(LAPACK_ENABLE OFF CACHE BOOL "LAPACK is disabled" FORCE)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Enable SuperLU_MT support?
-# ---------------------------------------------------------------
-OPTION(SUPERLUMT_ENABLE "Enable SUPERLUMT support" OFF)
-
-# SuperLU_MT does not support extended precision
-IF(SUPERLUMT_ENABLE AND SUNDIALS_PRECISION MATCHES "EXTENDED")
-  PRINT_WARNING("SuperLU_MT is not compatible with ${SUNDIALS_PRECISION} precision"
-                "Disabling SuperLU_MT")
-  FORCE_VARIABLE(SUPERLUMT_ENABLE BOOL "SuperLU_MT is disabled" OFF)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Enable KLU support?
-# ---------------------------------------------------------------
-OPTION(KLU_ENABLE "Enable KLU support" OFF)
-
-# KLU does not support single or extended precision
-IF(KLU_ENABLE AND
-  (SUNDIALS_PRECISION MATCHES "SINGLE" OR SUNDIALS_PRECISION MATCHES "EXTENDED"))
-  PRINT_WARNING("KLU is not compatible with ${SUNDIALS_PRECISION} precision"
-                "Disabling KLU")
-  FORCE_VARIABLE(KLU_ENABLE BOOL "KLU is disabled" OFF)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Enable hypre Vector support?
-# ---------------------------------------------------------------
-OPTION(HYPRE_ENABLE "Enable hypre support" OFF)
-
-# Using hypre requres building with MPI enabled
-IF(HYPRE_ENABLE AND NOT MPI_ENABLE)
-  PRINT_WARNING("MPI not enabled - Disabling hypre"
-                "Set MPI_ENABLE to ON to use parhyp")
-  FORCE_VARIABLE(HYPRE_ENABLE BOOL "Enable hypre support" OFF)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Enable PETSc support?
-# ---------------------------------------------------------------
-OPTION(PETSC_ENABLE "Enable PETSc support" OFF)
-
-# Using PETSc requires building with MPI enabled
-IF(PETSC_ENABLE AND NOT MPI_ENABLE)
-  PRINT_WARNING("MPI not enabled - Disabling PETSc"
-                "Set MPI_ENABLE to ON to use PETSc")
-  FORCE_VARIABLE(PETSC_ENABLE BOOL "Enable PETSc support" OFF)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Enable Trilinos support?
-# ---------------------------------------------------------------
-OPTION(Trilinos_ENABLE "Enable Trilinos support" OFF)
-
-
-# ===============================================================
-# Options for examples
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Enable examples?
-# ---------------------------------------------------------------
-
-# Enable C examples (on by default)
-OPTION(EXAMPLES_ENABLE_C "Build SUNDIALS C examples" ON)
-
-# C++ examples (off by default, unless Trilinos is enabled)
-SET(DOCSTR "Build C++ examples")
-OPTION(EXAMPLES_ENABLE_CXX "${DOCSTR}" ${Trilinos_ENABLE})
-
-# F77 examples (on by default) are an option only if the Fortran
-# interface is enabled
-SET(DOCSTR "Build SUNDIALS Fortran examples")
-IF(F77_INTERFACE_ENABLE)
-  SHOW_VARIABLE(EXAMPLES_ENABLE_F77 BOOL "${DOCSTR}" ON)
-  # Fortran 77 examples do not support single or extended precision
-  IF(EXAMPLES_ENABLE_F77 AND (SUNDIALS_PRECISION MATCHES "EXTENDED" OR SUNDIALS_PRECISION MATCHES "SINGLE"))
-    PRINT_WARNING("F77 examples are not compatible with ${SUNDIALS_PRECISION} precision"
-                  "EXAMPLES_ENABLE_F77")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_F77 BOOL "${DOCSTR}" OFF)
-  ENDIF()
-ELSE()
-  # set back to OFF (in case was ON)
-  IF(EXAMPLES_ENABLE_F77)
-    PRINT_WARNING("EXAMPLES_ENABLE_F77 is ON but F77_INTERFACE_ENABLE is OFF"
-                  "Disabling EXAMPLES_ENABLE_F77")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_F77 BOOL "${DOCSTR}" OFF)
-  ENDIF()
-  HIDE_VARIABLE(EXAMPLES_ENABLE_F77)
-ENDIF()
-
-# F90 examples (on by default) are an option only if a Fortran interface is enabled.
-SET(DOCSTR "Build SUNDIALS F90 examples")
-IF(F77_INTERFACE_ENABLE OR F2003_INTERFACE_ENABLE)
-  SHOW_VARIABLE(EXAMPLES_ENABLE_F90 BOOL "${DOCSTR}" ON)
-  # Fortran 90 examples do not support extended precision
-  IF(EXAMPLES_ENABLE_F90 AND (SUNDIALS_PRECISION MATCHES "EXTENDED"))
-    PRINT_WARNING("F90 examples are not compatible with ${SUNDIALS_PRECISION} precision"
-                  "Disabling EXAMPLES_ENABLE_F90")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_F90 BOOL "${DOCSTR}" OFF)
-  ENDIF()
-ELSE()
-  # set back to OFF (in case was ON)
-  IF(EXAMPLES_ENABLE_F90)
-    PRINT_WARNING("EXAMPLES_ENABLE_F90 is ON but both F77 and F2003 interfaces are OFF"
-                  "Disabling EXAMPLES_ENABLE_F90")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_F90 BOOL "${DOCSTR}" OFF)
-  ENDIF()
-  HIDE_VARIABLE(EXAMPLES_ENABLE_F90)
-ENDIF()
-
-# CUDA examples (off by default)
-SET(DOCSTR "Build SUNDIALS CUDA examples")
-IF(CUDA_ENABLE)
-  OPTION(EXAMPLES_ENABLE_CUDA "${DOCSTR}" OFF)
-ELSE()
-  IF(EXAMPLES_ENABLE_CUDA)
-    PRINT_WARNING("EXAMPLES_ENABLE_CUDA is ON but CUDA_ENABLE is OFF"
-                  "Disabling EXAMPLES_ENABLE_CUDA")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_CUDA BOOL "${DOCSTR}" OFF)
-  ENDIF()
-ENDIF()
-
-# If any of the above examples are enabled set EXAMPLES_ENABLED to TRUE
-IF(EXAMPLES_ENABLE_C OR
-    EXAMPLES_ENABLE_F77 OR
-    EXAMPLES_ENABLE_CXX OR
-    EXAMPLES_ENABLE_F90 OR
-    EXAMPLES_ENABLE_CUDA)
-  SET(EXAMPLES_ENABLED TRUE)
-ELSE()
-  SET(EXAMPLES_ENABLED FALSE)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Install examples?
-# ---------------------------------------------------------------
-
-# Enable installing examples by default
-SET(DOCSTR "Install SUNDIALS examples")
-IF(EXAMPLES_ENABLED)
-  OPTION(EXAMPLES_INSTALL "${DOCSTR}" ON)
-ELSE()
-  FORCE_VARIABLE(EXAMPLES_INSTALL BOOL "${DOCSTR}" OFF)
-  HIDE_VARIABLE(EXAMPLES_INSTALL)
-ENDIF()
-
-# If examples are to be exported, check where we should install them.
-IF(EXAMPLES_INSTALL)
-
-  SHOW_VARIABLE(EXAMPLES_INSTALL_PATH PATH
-    "Output directory for installing example files"
-    "${CMAKE_INSTALL_PREFIX}/examples")
-
-  IF(NOT EXAMPLES_INSTALL_PATH)
-    PRINT_WARNING("The example installation path is empty"
-      "Example installation path was reset to its default value")
-    SET(EXAMPLES_INSTALL_PATH "${CMAKE_INSTALL_PREFIX}/examples" CACHE STRING
-      "Output directory for installing example files" FORCE)
-  ENDIF()
-
-ELSE()
-
-  HIDE_VARIABLE(EXAMPLES_INSTALL_PATH)
-
-ENDIF()
-
-
-# ==============================================================================
-# Advanced (hidden) options
-# ==============================================================================
-
-# ------------------------------------------------------------------------------
-# Manually specify the Fortran name-mangling scheme
-#
-# The build system tries to infer the Fortran name-mangling scheme using a
-# Fortran compiler and defaults to using lower case and one underscore if the
-# scheme can not be determined. If a working Fortran compiler is not available
-# or the user needs to override the inferred or default scheme, the following
-# options specify the case and number of appended underscores corresponding to
-# the Fortran name-mangling scheme of symbol names that do not themselves
-# contain underscores. This is all we really need for the FCMIX and LAPACK
-# interfaces. A working Fortran compiler is only necessary for building Fortran
-# example programs.
-# ------------------------------------------------------------------------------
-
-# The case to use in the name-mangling scheme
-show_variable(SUNDIALS_F77_FUNC_CASE STRING
-  "case of Fortran function names (lower/upper)"
-  "")
-
-# The number of underscores of appended in the name-mangling scheme
-show_variable(SUNDIALS_F77_FUNC_UNDERSCORES STRING
-  "number of underscores appended to Fortran function names (none/one/two)"
-  "")
-
-# Hide the name-mangling varibales as advanced options
-mark_as_advanced(FORCE SUNDIALS_F77_FUNC_CASE)
-mark_as_advanced(FORCE SUNDIALS_F77_FUNC_UNDERSCORES)
-
-# If used, both case and underscores must be set
-if((NOT SUNDIALS_F77_FUNC_CASE) AND SUNDIALS_F77_FUNC_UNDERSCORES)
-  message(FATAL_ERROR
-    "If SUNDIALS_F77_FUNC_UNDERSCORES is set, SUNDIALS_F77_FUNC_CASE must also be set.")
-endif()
-
-if(SUNDIALS_F77_FUNC_CASE AND (NOT SUNDIALS_F77_FUNC_UNDERSCORES))
-  message(FATAL_ERROR
-    "If SUNDIALS_F77_FUNC_CASE is set, SUNDIALS_F77_FUNC_UNDERSCORES must also be set.")
-endif()
-
-# ------------------------------------------------------------------------------
-# Include development examples in regression tests?
-#
-# NOTE: Development examples are currently used for internal testing and may
-# produce erroneous failures when run on different systems as the pass/fail
-# status is determined by comparing the output against a saved output file.
-# ------------------------------------------------------------------------------
-OPTION(SUNDIALS_DEVTESTS "Include development tests in make test" OFF)
-MARK_AS_ADVANCED(FORCE SUNDIALS_DEVTESTS)
-
-# ===============================================================
-# Add any platform specifc settings
-# ===============================================================
-
-IF(APPLE)
-  SET(CMAKE_SHARED_LIBRARY_CREATE_C_FLAGS "${CMAKE_SHARED_LIBRARY_CREATE_C_FLAGS} -undefined dynamic_lookup")
-ENDIF(APPLE)
-
-# ===============================================================
-# Fortran and C++ settings
-# ===============================================================
-
-# ---------------------------------------------------------------
-# A Fortran compiler is needed to:
-# (a) Determine the name-mangling scheme if FCMIX, BLAS, or
-#     LAPACK are enabled
-# (b) Compile example programs if F77 or F90 examples are enabled
-# ---------------------------------------------------------------
-
-# Do we need a Fortran name-mangling scheme?
-if(F77_INTERFACE_ENABLE OR BLAS_ENABLE OR LAPACK_ENABLE)
-  set(NEED_FORTRAN_NAME_MANGLING TRUE)
-endif()
-
-# Did the user provide a name-mangling scheme?
-if(SUNDIALS_F77_FUNC_CASE AND SUNDIALS_F77_FUNC_UNDERSCORES)
-
-  STRING(TOUPPER ${SUNDIALS_F77_FUNC_CASE} SUNDIALS_F77_FUNC_CASE)
-  STRING(TOUPPER ${SUNDIALS_F77_FUNC_UNDERSCORES} SUNDIALS_F77_FUNC_UNDERSCORES)
-
-  # Based on the given case and number of underscores, set the C preprocessor
-  # macro definitions. Since SUNDIALS never uses symbols names containing
-  # underscores we set the name-mangling schemes to be the same. In general,
-  # names of symbols with and without underscore may be mangled differently
-  # (e.g. g77 mangles mysub to mysub_ and my_sub to my_sub__)
-  if(SUNDIALS_F77_FUNC_CASE MATCHES "LOWER")
-    if(SUNDIALS_F77_FUNC_UNDERSCORES MATCHES "NONE")
-      set(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) name")
-      set(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) name")
-    elseif(SUNDIALS_F77_FUNC_UNDERSCORES MATCHES "ONE")
-      set(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) name ## _")
-      SET(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) name ## _")
-    elseif(SUNDIALS_F77_FUNC_UNDERSCORES MATCHES "TWO")
-      set(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) name ## __")
-      set(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) name ## __")
-    else()
-      message(FATAL_ERROR "Invalid SUNDIALS_F77_FUNC_UNDERSCORES option.")
-    endif()
-  elseif(SUNDIALS_F77_FUNC_CASE MATCHES "UPPER")
-    if(SUNDIALS_F77_FUNC_UNDERSCORES MATCHES "NONE")
-      set(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) NAME")
-      set(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) NAME")
-    elseif(SUNDIALS_F77_FUNC_UNDERSCORES MATCHES "ONE")
-      set(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) NAME ## _")
-      set(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) NAME ## _")
-    elseif(SUNDIALS_F77_FUNC_UNDERSCORES MATCHES "TWO")
-      set(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) NAME ## __")
-      set(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) NAME ## __")
-    else()
-      message(FATAL_ERROR "Invalid SUNDIALS_F77_FUNC_UNDERSCORES option.")
-    endif()
-  else()
-    message(FATAL_ERROR "Invalid SUNDIALS_F77_FUNC_CASE option.")
-  endif()
-
-  # name-mangling scheme has been manually set
-  set(NEED_FORTRAN_NAME_MANGLING FALSE)
-
-endif()
-
-# Do we need a Fortran compiler?
-if(F2003_INTERFACE_ENABLE OR EXAMPLES_ENABLE_F77 OR EXAMPLES_ENABLE_F90 OR NEED_FORTRAN_NAME_MANGLING)
-  include(SundialsFortran)
-endif()
-
-# Ensure that F90 compiler is found if F90 examples are enabled
-if (EXAMPLES_ENABLE_F90 AND (NOT F90_FOUND))
-  PRINT_WARNING("Compiler with F90 support not found" "Disabling F90 Examples")
-  SET(DOCSTR "Build F90 examples")
-  FORCE_VARIABLE(EXAMPLES_ENABLE_F90 "${DOCSTR}" OFF)
-endif()
-
-# Ensure that F90 compiler found if F2003 interface is enabled
-if (F2003_INTERFACE_ENABLE AND (NOT F90_FOUND))
-  PRINT_WARNING("Compiler with F90 support not found" "Disabling F2003 Interface")
-  SET(DOCSTR "Enable Fortran 2003 interfaces")
-  FORCE_VARIABLE(F2003_INTERFACE_ENABLE BOOL "${DOCSTR}" OFF)
-endif()
-
-# F2003 interface requires ISO_C_BINDING
-IF(F2003_INTERFACE_ENABLE AND (NOT Fortran_COMPILER_SUPPORTS_ISOCBINDING))
-  PRINT_WARNING("Fortran compiler does not provide ISO_C_BINDING support"
-                "Disabling F2003 interface")
-  SET(DOCSTR "Enable Fortran 2003 interfaces")
-  FORCE_VARIABLE(F2003_INTERFACE_ENABLE BOOL "${DOCSTR}" OFF)
-ENDIF()
-
-
-# ---------------------------------------------------------------
-# A C++ compiler is needed if:
-# (a) C++ examples are enabled
-# (b) CUDA is enabled
-# (c) RAJA is enabled
-# (d) Trilinos is enabled
-# ---------------------------------------------------------------
-
-if(EXAMPLES_ENABLE_CXX OR CUDA_ENABLE OR RAJA_ENABLE OR Trilinos_ENABLE)
-  include(SundialsCXX)
-endif()
-
-# ---------------------------------------------------------------
-# Setup CUDA. Since CUDA is its own language we do this
-# separate from the TPLs.
-# ---------------------------------------------------------------
-
-if(CUDA_ENABLE)
-  find_package(CUDA)
-  if (CUDA_FOUND)
-    set(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} -lineinfo")
-  else()
-    message(STATUS "Disabling CUDA support, could not find CUDA.")
-    set(CUDA_ENABLE OFF)
-  endif()
-endif(CUDA_ENABLE)
-
-# ---------------------------------------------------------------
-# Now that all languages are setup, we can configure them more.
-# ---------------------------------------------------------------
-
-# C++11 is needed if:
-#   (a) CUDA is enabled
-# C++11 should not be enabled if
-#   (a) RAJA is enabled (they provide a std flag)
-if (CXX_FOUND AND CUDA_ENABLE AND CUDA_FOUND AND (NOT RAJA_ENABLE))
-  USE_CXX_STD(11)
-endif()
-
-# ---------------------------------------------------------------
-# Decide how to compile MPI codes. We must check for MPI if
-# MPI is enabled or if Trilinos is enabled because the Trilinos
-# examples may need MPI without us turning on the MPI SUNDIALS
-# components.
-# ---------------------------------------------------------------
-
-if(MPI_ENABLE OR Trilinos_ENABLE)
-  include(SundialsMPI)
-endif()
-
-if(MPI_ENABLE)
-  if(NOT MPI_C_FOUND)
-    print_warning("MPI not functional" "Parallel support will not be provided")
-  else()
-    set(IS_MPI_ENABLED "#ifndef SUNDIALS_MPI_ENABLED\n#define SUNDIALS_MPI_ENABLED 1\n#endif")
-  endif()
-endif()
-
-# always define FMPI_COMM_F2C in sundials_fconfig.h file
-if(MPIC_MPI2)
-  set(F77_MPI_COMM_F2C "#define SUNDIALS_MPI_COMM_F2C 1")
-  set(FMPI_COMM_F2C ".true.")
-else()
-  set(F77_MPI_COMM_F2C "#define SUNDIALS_MPI_COMM_F2C 0")
-  set(FMPI_COMM_F2C ".false.")
-endif()
-
-# -------------------------------------------------------------
-# Find OpenMP
-# -------------------------------------------------------------
-
-if(OPENMP_ENABLE OR OPENMP_DEVICE_ENABLE)
-
-  include(SundialsOpenMP)
-
-  # turn off OPENMP_ENABLE and OPENMP_DEVICE_ENABLE if OpenMP is not found
-  if(NOT OPENMP_FOUND)
-    print_warning("Could not determine OpenMP compiler flags" "Disabling OpenMP support")
-    force_variable(OPENMP_ENABLE BOOL "Enable OpenMP support" OFF)
-    force_variable(OPENMP_DEVICE_ENABLE BOOL "Enable OpenMP device offloading support" OFF)
-  endif()
-
-  # turn off OPENMP_DEVICE_ENABLE if offloading is not supported
-  if(OPENMP_DEVICE_ENABLE AND (NOT OPENMP_SUPPORTS_DEVICE_OFFLOADING))
-    print_warning("OpenMP found does not support device offloading"
-                  "Disabling OpenMP device offloading support")
-    force_variable(OPENMP_DEVICE_ENABLE BOOL "Enable OpenMP device offloading support" OFF)
-  endif()
-
-endif()
-
-# -------------------------------------------------------------
-# Find PThreads
-# -------------------------------------------------------------
-
-IF(PTHREAD_ENABLE)
-  FIND_PACKAGE(Threads)
-  IF(CMAKE_USE_PTHREADS_INIT)
-    message(STATUS "Using Pthreads")
-    SET(PTHREADS_FOUND TRUE)
-    # SGS
-  ELSE()
-    message(STATUS "Disabling Pthreads support, could not determine compiler flags")
-  endif()
-ENDIF(PTHREAD_ENABLE)
-
-# -------------------------------------------------------------
-# Find RAJA
-# -------------------------------------------------------------
-
-# disable RAJA if CUDA is not enabled/working
-if(RAJA_ENABLE AND (NOT CUDA_FOUND))
-  PRINT_WARNING("CUDA is required for RAJA support" "Please enable CUDA and RAJA")
-  FORCE_VARIABLE(RAJA_ENABLE BOOL "RAJA disabled" OFF)
-endif()
-
-if(RAJA_ENABLE)
-  # Look for CMake configuration file in RAJA installation
-  find_package(RAJA)
-  if (RAJA_FOUND)
-    include_directories(${RAJA_INCLUDE_DIR})
-    set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} ${RAJA_NVCC_FLAGS})
-  else()
-    PRINT_WARNING("RAJA configuration not found"
-                  "Please set RAJA_DIR to provide path to RAJA CMake configuration file.")
-  endif()
-endif(RAJA_ENABLE)
-
-# ===============================================================
-# Find (and test) external packages
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Find (and test) the BLAS libraries
-# ---------------------------------------------------------------
-
-# If BLAS is needed, first try to find the appropriate
-# libraries and linker flags needed to link against them.
-
-IF(BLAS_ENABLE)
-
-  # find BLAS
-  INCLUDE(SundialsBlas)
-
-  # show after include so FindBlas can locate BLAS_LIBRARIES if necessary
-  SHOW_VARIABLE(BLAS_LIBRARIES STRING "Blas libraries" "${BLAS_LIBRARIES}")
-
-  IF(BLAS_LIBRARIES AND NOT BLAS_FOUND)
-    PRINT_WARNING("BLAS not functional"
-                  "BLAS support will not be provided")
-  ELSE()
-    #set sundials_config.h symbol via sundials_config.in
-    SET(SUNDIALS_BLAS TRUE)
-  ENDIF()
-
-ELSE()
-
-  HIDE_VARIABLE(BLAS_LIBRARIES)
-
-ENDIF()
-
-# ---------------------------------------------------------------
-# Find (and test) the Lapack libraries
-# ---------------------------------------------------------------
-
-# If LAPACK is needed, first try to find the appropriate
-# libraries and linker flags needed to link against them.
-
-IF(LAPACK_ENABLE)
-
-  # find LAPACK and BLAS Libraries
-  INCLUDE(SundialsLapack)
-
-  # show after include so FindLapack can locate LAPCK_LIBRARIES if necessary
-  SHOW_VARIABLE(LAPACK_LIBRARIES STRING "Lapack and Blas libraries" "${LAPACK_LIBRARIES}")
-
-  IF(LAPACK_LIBRARIES AND NOT LAPACK_FOUND)
-    PRINT_WARNING("LAPACK not functional"
-                  "Blas/Lapack support will not be provided")
-  ELSE()
-    #set sundials_config.h symbol via sundials_config.in
-    SET(SUNDIALS_BLAS_LAPACK TRUE)
-  ENDIF()
-
-ELSE()
-
-  HIDE_VARIABLE(LAPACK_LIBRARIES)
-
-ENDIF()
-
-# ---------------------------------------------------------------
-# Find (and test) the SUPERLUMT libraries
-# ---------------------------------------------------------------
-
-# >>>>>>> NOTE: Need to add check for SuperLU_MT integer type
-
-# If SUPERLUMT is needed, first try to find the appropriate
-# libraries to link against them.
-
-IF(SUPERLUMT_ENABLE)
-
-  # Show SuperLU_MT options and set default thread type (Pthreads)
-  SHOW_VARIABLE(SUPERLUMT_THREAD_TYPE STRING "SUPERLUMT threading type: OpenMP or Pthread" "Pthread")
-  SHOW_VARIABLE(SUPERLUMT_INCLUDE_DIR PATH "SUPERLUMT include directory" "${SUPERLUMT_INCLUDE_DIR}")
-  SHOW_VARIABLE(SUPERLUMT_LIBRARY_DIR PATH "SUPERLUMT library directory" "${SUPERLUMT_LIBRARY_DIR}")
-
-  INCLUDE(SundialsSuperLUMT)
-
-  IF(SUPERLUMT_FOUND)
-    # sundials_config.h symbols
-    SET(SUNDIALS_SUPERLUMT TRUE)
-    SET(SUNDIALS_SUPERLUMT_THREAD_TYPE ${SUPERLUMT_THREAD_TYPE})
-    INCLUDE_DIRECTORIES(${SUPERLUMT_INCLUDE_DIR})
-  ENDIF()
-
-  IF(SUPERLUMT_LIBRARIES AND NOT SUPERLUMT_FOUND)
-    PRINT_WARNING("SUPERLUMT not functional - support will not be provided"
-                  "Double check spelling specified libraries (search is case sensitive)")
-  ENDIF(SUPERLUMT_LIBRARIES AND NOT SUPERLUMT_FOUND)
-
-ELSE()
-
-  HIDE_VARIABLE(SUPERLUMT_THREAD_TYPE)
-  HIDE_VARIABLE(SUPERLUMT_LIBRARY_DIR)
-  HIDE_VARIABLE(SUPERLUMT_INCLUDE_DIR)
-  SET (SUPERLUMT_DISABLED TRUE CACHE INTERNAL "GUI - return when first set")
-
-ENDIF()
-
-# ---------------------------------------------------------------
-# Find (and test) the KLU libraries
-# ---------------------------------------------------------------
-
-# If KLU is requested, first try to find the appropriate libraries to
-# link against them.
-
-IF(KLU_ENABLE)
-
-  SHOW_VARIABLE(KLU_INCLUDE_DIR PATH "KLU include directory"
-    "${KLU_INCLUDE_DIR}")
-  SHOW_VARIABLE(KLU_LIBRARY_DIR PATH
-    "Klu library directory" "${KLU_LIBRARY_DIR}")
-
-  set(KLU_FOUND TRUE)
-  get_filename_component(PYBAMM_DIR ${PROJECT_SOURCE_DIR} DIRECTORY)
-  set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} ${PYBAMM_DIR}) # use FindSuiteSparse.cmake that is in PyBaMM root
-  set(SuiteSparse_ROOT ${PYBAMM_DIR}/SuiteSparse-5.6.0)
-  find_package(SuiteSparse OPTIONAL_COMPONENTS KLU AMD COLAMD BTF)
-  include_directories(${SuiteSparse_INCLUDE_DIRS})
-  set(KLU_LIBRARIES ${SuiteSparse_LIBRARIES})
-
-
-  IF(KLU_LIBRARIES AND NOT KLU_FOUND)
-    PRINT_WARNING("KLU not functional - support will not be provided"
-                  "Double check spelling of include path and specified libraries (search is case sensitive)")
-  ENDIF(KLU_LIBRARIES AND NOT KLU_FOUND)
-
-ELSE()
-
-  HIDE_VARIABLE(KLU_LIBRARY_DIR)
-  HIDE_VARIABLE(KLU_INCLUDE_DIR)
-  SET (KLU_DISABLED TRUE CACHE INTERNAL "GUI - return when first set")
-
-ENDIF(KLU_ENABLE)
-
-# ---------------------------------------------------------------
-# Find (and test) the hypre libraries
-# ---------------------------------------------------------------
-
-# >>>>>>> NOTE: Need to add check for hypre precision and integer type
-
-IF(HYPRE_ENABLE)
-  SHOW_VARIABLE(HYPRE_INCLUDE_DIR PATH "HYPRE include directory"
-    "${HYPRE_INCLUDE_DIR}")
-  SHOW_VARIABLE(HYPRE_LIBRARY_DIR PATH
-    "HYPRE library directory" "${HYPRE_LIBRARY_DIR}")
-
-  INCLUDE(SundialsHypre)
-
-  IF(HYPRE_FOUND)
-    # sundials_config.h symbol
-    SET(SUNDIALS_HYPRE TRUE)
-    INCLUDE_DIRECTORIES(${HYPRE_INCLUDE_DIR})
-  ENDIF(HYPRE_FOUND)
-
-  IF(HYPRE_LIBRARIES AND NOT HYPRE_FOUND)
-    PRINT_WARNING("HYPRE not functional - support will not be provided"
-                  "Found hypre library, test code does not work")
-  ENDIF(HYPRE_LIBRARIES AND NOT HYPRE_FOUND)
-
-ELSE()
-
-  HIDE_VARIABLE(HYPRE_INCLUDE_DIR)
-  HIDE_VARIABLE(HYPRE_LIBRARY_DIR)
-  SET (HYPRE_DISABLED TRUE CACHE INTERNAL "GUI - return when first set")
-
-ENDIF()
-
-# ---------------------------------------------------------------
-# Find (and test) the PETSc libraries
-# ---------------------------------------------------------------
-
-# >>>>>>> NOTE: Need to add check for PETSc precision and integer type
-
-IF(PETSC_ENABLE)
-  SHOW_VARIABLE(PETSC_INCLUDE_DIR PATH "PETSc include directory"
-    "${PETSC_INCLUDE_DIR}")
-  SHOW_VARIABLE(PETSC_LIBRARY_DIR PATH
-    "PETSc library directory" "${PETSC_LIBRARY_DIR}")
-
-  INCLUDE(SundialsPETSc)
-
-  IF(PETSC_FOUND)
-    # sundials_config.h symbol
-    SET(SUNDIALS_PETSC TRUE)
-    INCLUDE_DIRECTORIES(${PETSC_INCLUDE_DIR})
-  ENDIF(PETSC_FOUND)
-
-  IF(PETSC_LIBRARIES AND NOT PETSC_FOUND)
-    PRINT_WARNING("PETSC not functional - support will not be provided"
-                  "Double check spelling specified libraries (search is case sensitive)")
-  ENDIF(PETSC_LIBRARIES AND NOT PETSC_FOUND)
-
-ELSE()
-
-  HIDE_VARIABLE(PETSC_LIBRARY_DIR)
-  HIDE_VARIABLE(PETSC_INCLUDE_DIR)
-  SET (PETSC_DISABLED TRUE CACHE INTERNAL "GUI - return when first set")
-
-ENDIF()
-
-# -------------------------------------------------------------
-# Find Trilinos
-# -------------------------------------------------------------
-
-if(Trilinos_ENABLE)
-  include(SundialsTrilinos)
-  if(NOT Trilinos_FUNCTIONAL)
-    PRINT_WARNING("Trilinos not functional" "Verify the path to Trilinos and check the Trilinos installation")
-  endif()
-endif(Trilinos_ENABLE)
-
-
-# ===============================================================
-# At this point all the configuration options are set.
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Configure the header file sundials_config.h
-# ---------------------------------------------------------------
-
-# All required substitution variables should be available at this point.
-# Generate the header file and place it in the binary dir.
-CONFIGURE_FILE(
-  ${PROJECT_SOURCE_DIR}/include/sundials/sundials_config.in
-  ${PROJECT_BINARY_DIR}/include/sundials/sundials_config.h
-  )
-CONFIGURE_FILE(
-  ${PROJECT_SOURCE_DIR}/include/sundials/sundials_fconfig.in
-  ${PROJECT_BINARY_DIR}/include/sundials/sundials_fconfig.h
-  )
-
-# Add the include directory in the source tree and the one in
-# the binary tree (for the header file sundials_config.h)
-INCLUDE_DIRECTORIES(${PROJECT_SOURCE_DIR}/include ${PROJECT_BINARY_DIR}/include)
-
-# ---------------------------------------------------------------
-# Enable testing and add source and example files to the build.
-# ---------------------------------------------------------------
-
-# Enable testing
-IF(EXAMPLES_ENABLED)
-  INCLUDE(SundialsTesting)
-ENDIF()
-
-# Add selected packages and modules to the build
-ADD_SUBDIRECTORY(src)
-
-# Add selected examples to the build
-IF(EXAMPLES_ENABLED)
-  ADD_SUBDIRECTORY(examples)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Install configuration header files and license file
-# ---------------------------------------------------------------
-
-# install configured header file
-INSTALL(
-  FILES ${PROJECT_BINARY_DIR}/include/sundials/sundials_config.h
-  DESTINATION include/sundials
-  )
-
-# install configured header file for Fortran 90
-INSTALL(
-  FILES ${PROJECT_BINARY_DIR}/include/sundials/sundials_fconfig.h
-  DESTINATION include/sundials
-  )
-
-# install shared Fortran 2003 modules
-IF(F2003_INTERFACE_ENABLE)
-  # While the .mod files get generated for static and shared
-  # libraries, they are identical. So only install one set
-  # of the .mod files.
-  IF(BUILD_STATIC_LIBS)
-    INSTALL(
-      DIRECTORY ${CMAKE_Fortran_MODULE_DIRECTORY}_STATIC/
-      DESTINATION ${Fortran_INSTALL_MODDIR}
-      )
-  ELSE()
-    INSTALL(
-      DIRECTORY ${CMAKE_Fortran_MODULE_DIRECTORY}_SHARED/
-      DESTINATION ${Fortran_INSTALL_MODDIR}
-      )
-  ENDIF()
-ENDIF()
-
-# install license and notice files
-INSTALL(
-  FILES ${PROJECT_SOURCE_DIR}/LICENSE
-  DESTINATION include/sundials
-  )
-INSTALL(
-  FILES ${PROJECT_SOURCE_DIR}/NOTICE
-  DESTINATION include/sundials
-  )

From ba2365bdc29eb85bb09bc6bb07492a891267cdc0 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sun, 8 Oct 2023 18:34:09 +0530
Subject: [PATCH 081/199] Don't bundle IDAKLU with Jax solver, remove pybind11

Co-Authored-By: Saransh Chopra <saransh0701@gmail.com>
---
 scripts/Dockerfile | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/scripts/Dockerfile b/scripts/Dockerfile
index 7015feef5a..c3d12bb7fe 100644
--- a/scripts/Dockerfile
+++ b/scripts/Dockerfile
@@ -37,24 +37,25 @@ RUN pip install --upgrade --user pip setuptools wheel wget cmake
 
 RUN if [ "$IDAKLU" = "true" ]; then \
     python scripts/install_KLU_Sundials.py && \
+    rm -rf pybind11 && \
     git clone https://github.com/pybind/pybind11.git && \
     pip install --user -e ".[all,dev,docs]"; \
     fi
 
 RUN if [ "$ODES" = "true" ]; then \
     python scripts/install_KLU_Sundials.py && \
+    rm -rf pybind11 && \
     git clone https://github.com/pybind/pybind11.git && \
     pip install --user -e ".[all,dev,docs,odes]"; \
     fi
 
 RUN if [ "$JAX" = "true" ]; then \
-    python scripts/install_KLU_Sundials.py && \
-    git clone https://github.com/pybind/pybind11.git && \
     pip install --user -e ".[all,dev,docs,jax]"; \
     fi
 
 RUN if [ "$ALL" = "true" ]; then \
     python scripts/install_KLU_Sundials.py && \
+    rm -rf pybind11 && \
     git clone https://github.com/pybind/pybind11.git && \
     pip install --user -e ".[all,dev,docs,jax,odes]"; \
     fi

From ce8e56b52e4598c5183ee2492b5744d16d9299a1 Mon Sep 17 00:00:00 2001
From: Robert Timms <robertwtimms@gmail.com>
Date: Wed, 11 Oct 2023 11:42:00 +0100
Subject: [PATCH 082/199] simplify expression of cooling terms in x-lumped
 thermal models

---
 .../notebooks/models/pouch-cell-model.ipynb   | 12 ++--
 pybamm/models/submodels/thermal/lumped.py     |  5 +-
 .../pouch_cell_1D_current_collectors.py       | 64 ++++++++-----------
 .../pouch_cell_2D_current_collectors.py       | 47 +++++++-------
 4 files changed, 60 insertions(+), 68 deletions(-)

diff --git a/docs/source/examples/notebooks/models/pouch-cell-model.ipynb b/docs/source/examples/notebooks/models/pouch-cell-model.ipynb
index 8e84374fbe..a9431211af 100644
--- a/docs/source/examples/notebooks/models/pouch-cell-model.ipynb
+++ b/docs/source/examples/notebooks/models/pouch-cell-model.ipynb
@@ -49,7 +49,11 @@
                               "name": "stdout",
                               "output_type": "stream",
                               "text": [
-                                    "zsh:1: no matches found: pybamm[plot,cite]\n",
+                                    "\u001b[33mWARNING: pybamm 23.5 does not provide the extra 'cite'\u001b[0m\u001b[33m\n",
+                                    "\u001b[0m\u001b[33mWARNING: pybamm 23.5 does not provide the extra 'plot'\u001b[0m\u001b[33m\n",
+                                    "\u001b[0m\n",
+                                    "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n",
+                                    "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
                                     "Note: you may need to restart the kernel to use updated packages.\n"
                               ]
                         }
@@ -82,7 +86,7 @@
                               "name": "stderr",
                               "output_type": "stream",
                               "text": [
-                                    "/Users/robertwtimms/Documents/PyBaMM/pybamm/models/full_battery_models/base_battery_model.py:835: OptionWarning: The 'lumped' thermal option with 'dimensionality' 0 now uses the parameters 'Cell cooling surface area [m2]', 'Cell volume [m3]' and 'Total heat transfer coefficient [W.m-2.K-1]' to compute the cell cooling term, regardless of the value of the the 'cell geometry' option. Please update your parameters accordingly.\n",
+                                    "/Users/robertwtimms/Documents/PyBaMM/pybamm/models/full_battery_models/base_battery_model.py:910: OptionWarning: The 'lumped' thermal option with 'dimensionality' 0 now uses the parameters 'Cell cooling surface area [m2]', 'Cell volume [m3]' and 'Total heat transfer coefficient [W.m-2.K-1]' to compute the cell cooling term, regardless of the value of the the 'cell geometry' option. Please update your parameters accordingly.\n",
                                     "  options = BatteryModelOptions(extra_options)\n"
                               ]
                         }
@@ -619,7 +623,7 @@
                   "outputs": [
                         {
                               "data": {
-                                    "image/png": 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",
+                                    "image/png": 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",
                                     "text/plain": [
                                           "<Figure size 1300x700 with 5 Axes>"
                                     ]
@@ -683,7 +687,7 @@
                   "outputs": [
                         {
                               "data": {
-                                    "image/png": 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",
                                     "text/plain": [
                                           "<Figure size 1300x700 with 5 Axes>"
                                     ]
diff --git a/pybamm/models/submodels/thermal/lumped.py b/pybamm/models/submodels/thermal/lumped.py
index 62c147755b..0f396a3f77 100644
--- a/pybamm/models/submodels/thermal/lumped.py
+++ b/pybamm/models/submodels/thermal/lumped.py
@@ -56,10 +56,9 @@ def set_rhs(self, variables):
         # Newton cooling, accounting for surface area to volume ratio
         cell_surface_area = self.param.A_cooling
         cell_volume = self.param.V_cell
-        total_cooling_coefficient = (
-            -self.param.h_total * cell_surface_area / cell_volume
+        Q_cool_vol_av = (
+            -self.param.h_total * (T_vol_av - T_amb) * cell_surface_area / cell_volume
         )
-        Q_cool_vol_av = total_cooling_coefficient * (T_vol_av - T_amb)
 
         self.rhs = {
             T_vol_av: (Q_vol_av + Q_cool_vol_av) / self.param.rho_c_p_eff(T_vol_av)
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 a6555170fc..2611dbafdc 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
@@ -58,33 +58,29 @@ def set_rhs(self, variables):
         y = pybamm.standard_spatial_vars.y
         z = pybamm.standard_spatial_vars.z
 
-        # Account for surface area to volume ratio of pouch cell in surface and side
-        # cooling terms
-        cell_volume = self.param.L * self.param.L_y * self.param.L_z
-
+        # Calculate cooling, accounting for surface area to volume ratio of pouch cell
+        edge_area = self.param.L_z * self.param.L
         yz_surface_area = self.param.L_y * self.param.L_z
-        yz_surface_cooling_coefficient = (
+        cell_volume = self.param.L * self.param.L_y * self.param.L_z
+        Q_yz_surface = (
             -(self.param.n.h_cc(y, z) + self.param.p.h_cc(y, z))
+            * (T_av - T_amb)
             * yz_surface_area
             / cell_volume
         )
-
-        side_edge_area = self.param.L_z * self.param.L
-        side_edge_cooling_coefficient = (
+        Q_edge = (
             -(self.param.h_edge(0, z) + self.param.h_edge(self.param.L_y, z))
-            * side_edge_area
+            * (T_av - T_amb)
+            * edge_area
             / cell_volume
         )
-
-        total_cooling_coefficient = (
-            yz_surface_cooling_coefficient + side_edge_cooling_coefficient
-        )
+        Q_cool_total = Q_yz_surface + Q_edge
 
         self.rhs = {
             T_av: (
                 pybamm.div(self.param.lambda_eff(T_av) * pybamm.grad(T_av))
                 + Q_av
-                + total_cooling_coefficient * (T_av - T_amb)
+                + Q_cool_total
             )
             / self.param.rho_c_p_eff(T_av)
         }
@@ -94,7 +90,7 @@ def set_boundary_conditions(self, variables):
         T_amb = variables["Ambient temperature [K]"]
         T_av = variables["X-averaged cell temperature [K]"]
 
-        # find tab locations (top vs bottom)
+        # Find tab locations (top vs bottom)
         L_y = param.L_y
         L_z = param.L_z
         neg_tab_z = param.n.centre_z_tab
@@ -104,11 +100,10 @@ def set_boundary_conditions(self, variables):
         pos_tab_top_bool = pybamm.Equality(pos_tab_z, L_z)
         pos_tab_bottom_bool = pybamm.Equality(pos_tab_z, 0)
 
-        # calculate tab vs non-tab area on top and bottom
+        # Calculate tab vs non-tab area on top and bottom
         neg_tab_area = param.n.L_tab * param.n.L_cc
         pos_tab_area = param.p.L_tab * param.p.L_cc
         total_area = param.L * param.L_y
-
         non_tab_top_area = (
             total_area
             - neg_tab_area * neg_tab_top_bool
@@ -120,18 +115,22 @@ def set_boundary_conditions(self, variables):
             - pos_tab_area * pos_tab_bottom_bool
         )
 
-        # calculate effective cooling coefficients
+        # Calculate heat fluxes weighted by area
         # Note: can't do y-average of h_edge here since y isn't meshed. Evaluate at
         # midpoint.
-        top_cooling_coefficient = (
-            param.n.h_tab * neg_tab_area * neg_tab_top_bool
-            + param.p.h_tab * pos_tab_area * pos_tab_top_bool
-            + param.h_edge(L_y / 2, L_z) * non_tab_top_area
+        q_tab_n = -param.n.h_tab * (T_av - T_amb)
+        q_tab_p = -param.p.h_tab * (T_av - T_amb)
+        q_edge_top = -param.h_edge(L_y / 2, L_z) * (T_av - T_amb)
+        q_edge_bottom = -param.h_edge(L_y / 2, 0) * (T_av - T_amb)
+        q_top = (
+            q_tab_n * neg_tab_area * neg_tab_top_bool
+            + q_tab_p * pos_tab_area * pos_tab_top_bool
+            + q_edge_top * non_tab_top_area
         ) / total_area
-        bottom_cooling_coefficient = (
-            param.n.h_tab * neg_tab_area * neg_tab_bottom_bool
-            + param.p.h_tab * pos_tab_area * pos_tab_bottom_bool
-            + param.h_edge(L_y / 2, 0) * non_tab_bottom_area
+        q_bottom = (
+            q_tab_n * neg_tab_area * neg_tab_bottom_bool
+            + q_tab_p * pos_tab_area * pos_tab_bottom_bool
+            + q_edge_bottom * non_tab_bottom_area
         ) / total_area
 
         # just use left and right for clarity
@@ -141,21 +140,14 @@ def set_boundary_conditions(self, variables):
         self.boundary_conditions = {
             T_av: {
                 "left": (
-                    pybamm.boundary_value(
-                        bottom_cooling_coefficient * (T_av - T_amb),
-                        "left",
-                    )
-                    / pybamm.boundary_value(lambda_eff, "left"),
+                    pybamm.boundary_value(-q_bottom / lambda_eff, "left"),
                     "Neumann",
                 ),
                 "right": (
-                    pybamm.boundary_value(
-                        -top_cooling_coefficient * (T_av - T_amb), "right"
-                    )
-                    / pybamm.boundary_value(lambda_eff, "right"),
+                    pybamm.boundary_value(q_top / lambda_eff, "right"),
                     "Neumann",
                 ),
-            }
+            },
         }
 
     def set_initial_conditions(self, variables):
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 eb8e1b7e49..a5c7c42b17 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
@@ -58,20 +58,22 @@ def set_rhs(self, variables):
         y = pybamm.standard_spatial_vars.y
         z = pybamm.standard_spatial_vars.z
 
+        # Calculate cooling
+        Q_yz_surface_W_per_m2 = -(self.param.n.h_cc(y, z) + self.param.p.h_cc(y, z)) * (
+            T_av - T_amb
+        )
+        Q_edge_W_per_m2 = -self.param.h_edge(y, z) * (T_av - T_amb)
+
         # Account for surface area to volume ratio of pouch cell in surface cooling
         # term
-        cell_volume = self.param.L * self.param.L_y * self.param.L_z
-
         yz_surface_area = self.param.L_y * self.param.L_z
-        yz_surface_cooling_coefficient = (
-            -(self.param.n.h_cc(y, z) + self.param.p.h_cc(y, z))
-            * yz_surface_area
-            / cell_volume
+        cell_volume = self.param.L * self.param.L_y * self.param.L_z
+        Q_yz_surface = pybamm.source(
+            Q_yz_surface_W_per_m2 * yz_surface_area / cell_volume, T_av
         )
-
         # Edge cooling appears as a boundary term, so no need to account for surface
         # area to volume ratio
-        edge_cooling_coefficient = -self.param.h_edge(y, z)
+        Q_edge = pybamm.source(Q_edge_W_per_m2, T_av, boundary=True)
 
         # Governing equations contain:
         #   - source term for y-z surface cooling
@@ -88,10 +90,8 @@ def set_rhs(self, variables):
             T_av: (
                 self.param.lambda_eff(T_av) * pybamm.laplacian(T_av)
                 + pybamm.source(Q_av, T_av)
-                + pybamm.source(yz_surface_cooling_coefficient * (T_av - T_amb), T_av)
-                + pybamm.source(
-                    edge_cooling_coefficient * (T_av - T_amb), T_av, boundary=True
-                )
+                + Q_yz_surface
+                + Q_edge
             )
             / self.param.rho_c_p_eff(T_av)
         }
@@ -102,24 +102,21 @@ def set_boundary_conditions(self, variables):
         y = pybamm.standard_spatial_vars.y
         z = pybamm.standard_spatial_vars.z
 
+        # Calculate heat fluxes
+        q_tab_n = -self.param.n.h_tab * (T_av - T_amb)
+        q_tab_p = -self.param.p.h_tab * (T_av - T_amb)
+        q_edge = -self.param.h_edge(y, z) * (T_av - T_amb)
+
         # Subtract the edge cooling from the tab portion so as to not double count
         # Note: tab cooling is also only applied on the current collector hence
-        # the (l_cn / l) and (l_cp / l) prefactors. We also still have edge cooling
+        # the (l_cn / l) and (l_cp / l) prefactors. We still have edge cooling
         # in the region: x in (0, 1)
-        h_tab_n_corrected = (self.param.n.L_cc / self.param.L) * (
-            self.param.n.h_tab - self.param.h_edge(y, z)
-        )
-        h_tab_p_corrected = (self.param.p.L_cc / self.param.L) * (
-            self.param.p.h_tab - self.param.h_edge(y, z)
-        )
-
-        negative_tab_bc = pybamm.boundary_value(
-            -h_tab_n_corrected * (T_av - T_amb) / self.param.n.lambda_cc(T_av),
+        negative_tab_bc = (self.param.n.L_cc / self.param.L) * pybamm.boundary_value(
+            (q_tab_n - q_edge) / self.param.n.lambda_cc(T_av),
             "negative tab",
         )
-        positive_tab_bc = pybamm.boundary_value(
-            -h_tab_p_corrected * (T_av - T_amb) / self.param.p.lambda_cc(T_av),
-            "positive tab",
+        positive_tab_bc = (self.param.p.L_cc / self.param.L) * pybamm.boundary_value(
+            (q_tab_p - q_edge) / self.param.p.lambda_cc(T_av), "positive tab"
         )
 
         self.boundary_conditions = {

From 45b85a7652aef8aac71c153eb0e9a2c027907754 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 17 Oct 2023 16:35:40 +0530
Subject: [PATCH 083/199] #3049 add `pyproject.toml` to release workflows

---
 .github/release_workflow.md | 5 ++++-
 scripts/update_version.py   | 2 +-
 2 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/.github/release_workflow.md b/.github/release_workflow.md
index 1af23fca25..280a1c160f 100644
--- a/.github/release_workflow.md
+++ b/.github/release_workflow.md
@@ -9,6 +9,7 @@ This file contains the workflow required to make a `PyBaMM` release on GitHub an
    - `pybamm/version.py`
    - `docs/conf.py`
    - `CITATION.cff`
+   - `pyproject.toml`
    - `vcpkg.json`
    - `docs/_static/versions.json`
    - `CHANGELOG.md`
@@ -32,6 +33,7 @@ If a new release candidate is required after the release of `rc0` -
    - `pybamm/version.py`
    - `docs/conf.py`
    - `CITATION.cff`
+   - `pyproject.toml`
    - `vcpkg.json`
    - `docs/_static/versions.json`
    - `CHANGELOG.md`
@@ -53,6 +55,7 @@ Once satisfied with the release candidates -
    - `pybamm/version.py`
    - `docs/conf.py`
    - `CITATION.cff`
+   - `pyproject.toml`
    - `vcpkg.json`
    - `docs/_static/versions.json`
    - `CHANGELOG.md`
@@ -70,7 +73,7 @@ Once satisfied with the release candidates -
 Some other essential things to check throughout the release process -
 
 - If updating our custom vcpkg registory entries [pybamm-team/sundials-vcpkg-registry](https://github.com/pybamm-team/sundials-vcpkg-registry) or [pybamm-team/casadi-vcpkg-registry](https://github.com/pybamm-team/casadi-vcpkg-registry) (used to build Windows wheels), make sure to update the baseline of the registories in vcpkg-configuration.json to the latest commit id.
-- Update jax and jaxlib to the latest version in `pybamm.util` and `setup.py`, fixing any bugs that arise
+- Update jax and jaxlib to the latest version in `pybamm.util` and `pyproject.toml`, fixing any bugs that arise
 - Make sure the URLs in `docs/_static/versions.json` are valid
 - As the release workflow is initiated by the `release` event, it's important to note that the default `GITHUB_REF` used by `actions/checkout` during the checkout process will correspond to the tag created during the release process. Consequently, the workflows will consistently build PyBaMM based on the commit associated with this tag. Should new commits be introduced to the `vYY.MM` branch, such as those addressing build issues, it becomes necessary to manually update this tag to point to the most recent commit -
   ```
diff --git a/scripts/update_version.py b/scripts/update_version.py
index 003edee274..8a2d832e59 100644
--- a/scripts/update_version.py
+++ b/scripts/update_version.py
@@ -48,7 +48,7 @@ def update_version():
         file.seek(0)
         file.write(replace_version)
 
-    # docs/source/_static/versions.json for readthedocs build
+    # docs/_static/versions.json for readthedocs build
     if "rc" not in release_version:
         with open(
             os.path.join(pybamm.root_dir(), "docs", "_static", "versions.json"),

From 37c991d5907c22e7436c3d0299b463e3bb3b3ee0 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 17 Oct 2023 16:36:16 +0530
Subject: [PATCH 084/199] #3049 remove `setup.py` as CI cache dependency path

---
 .github/workflows/test_on_push.yml | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 6821016e45..88ada069b4 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -90,7 +90,6 @@ jobs:
         with:
           python-version: ${{ matrix.python-version }}
           cache: 'pip'
-          cache-dependency-path: setup.py
 
       - name: Cache pybamm-requires nox environment for GNU/Linux and macOS
         uses: actions/cache@v3
@@ -145,7 +144,6 @@ jobs:
         with:
           python-version: 3.11
           cache: 'pip'
-          cache-dependency-path: setup.py
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -225,7 +223,6 @@ jobs:
         with:
           python-version: ${{ matrix.python-version }}
           cache: 'pip'
-          cache-dependency-path: setup.py
 
       - name: Cache pybamm-requires nox environment for GNU/Linux and macOS
         uses: actions/cache@v3
@@ -281,7 +278,6 @@ jobs:
         with:
           python-version: 3.11
           cache: 'pip'
-          cache-dependency-path: setup.py
 
       - name: Install docs dependencies and run doctests for GNU/Linux with Python 3.11
         run: pipx run nox -s doctests
@@ -321,7 +317,6 @@ jobs:
         with:
           python-version: 3.11
           cache: 'pip'
-          cache-dependency-path: setup.py
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -374,7 +369,6 @@ jobs:
         with:
           python-version: 3.11
           cache: 'pip'
-          cache-dependency-path: setup.py
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3

From 925d39005b81918bc0984f7b572232278da043b0 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 17 Oct 2023 16:36:36 +0530
Subject: [PATCH 085/199] #3049 add note about new file to manage deps

---
 docs/source/user_guide/installation/index.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/user_guide/installation/index.rst b/docs/source/user_guide/installation/index.rst
index 6338323e79..93e54c51fe 100644
--- a/docs/source/user_guide/installation/index.rst
+++ b/docs/source/user_guide/installation/index.rst
@@ -76,7 +76,7 @@ Optional Dependencies
 PyBaMM has a number of optional dependencies for different functionalities.
 If the optional dependency is not installed, PyBaMM will raise an ImportError when the method requiring that dependency is called.
 
-If using pip, optional PyBaMM dependencies can be installed or managed in a file (e.g. requirements.txt or setup.py)
+If you are using ``pip``, optional PyBaMM dependencies can be installed or managed in a file (e.g. requirements.txt, setup.py, or pyproject.toml)
 as optional extras (e.g.,``pybamm[dev,plot]``). All optional dependencies can be installed with ``pybamm[all]``,
 and specific sets of dependencies are listed in the sections below.
 

From 1d16f5d6ee3feecb16abb191d1f621b312c22932 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 17 Oct 2023 16:45:39 +0530
Subject: [PATCH 086/199] #3049 clarify usage of `cmake` and `casadi`

In the build-time requirements for Windows, we don't use cmake and casadi from pip, but from other sources. This is because we use Visual Studio to compile.
---
 pyproject.toml |  4 ++--
 setup.py       | 23 +++++++++++++----------
 2 files changed, 15 insertions(+), 12 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index c91d275789..002c29d76b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -2,8 +2,8 @@
 requires = [
     "setuptools",
     "wheel",
+    # On Windows, use the CasADi vcpkg registry and CMake bundled from MSVC
     "casadi>=3.6.0; platform_system!='Windows'",
-    # use CMake bundled from MSVC on Windows
     "cmake; platform_system!='Windows'",
     ]
 build-backend = "setuptools.build_meta"
@@ -72,7 +72,7 @@ examples = [
 # Plotting functionality
 plot = [
     "imageio>=2.9.0",
-    # Note: matplotlib is loaded for debug plots, but to ensure pybamm runs
+    # Note: matplotlib is loaded for debug plots, but to ensure PyBaMM runs
     # on systems without an attached display, it should never be imported
     # outside of plot() methods.
     "matplotlib>=2.0",
diff --git a/setup.py b/setup.py
index a0180cb3e8..9cfc4df4ff 100644
--- a/setup.py
+++ b/setup.py
@@ -16,13 +16,11 @@
     from distutils.command.build_ext import build_ext
 
 
-# ---------- CMake steps for IDAKLU target (non-Windows) -------------------------------
-
-
 default_lib_dir = (
     "" if system() == "Windows" else os.path.join(os.getenv("HOME"), ".local")
 )
 
+# ---------- set environment variables for vcpkg on Windows ----------------------------
 
 def set_vcpkg_environment_variables():
     if not os.getenv("VCPKG_ROOT_DIR"):
@@ -41,6 +39,7 @@ def set_vcpkg_environment_variables():
         os.getenv("VCPKG_FEATURE_FLAGS"),
     )
 
+# ---------- CMakeBuild class (custom build_ext for IDAKLU target) ---------------------
 
 class CMakeBuild(build_ext):
     user_options = build_ext.user_options + [
@@ -119,6 +118,8 @@ def run(self):
         if os.path.isfile(os.path.join(build_dir, "CMakeError.log")):
             os.remove(os.path.join(build_dir, "CMakeError.log"))
 
+# ---------- configuration for vcpkg on Windows ----------------------------------------
+
         build_env = os.environ
         if os.getenv("PYBAMM_USE_VCPKG"):
             (
@@ -130,26 +131,29 @@ def run(self):
             build_env["vcpkg_default_triplet"] = vcpkg_default_triplet
             build_env["vcpkg_feature_flags"] = vcpkg_feature_flags
 
+# ---------- Run CMake and build IDAKLU module -----------------------------------------
+
         cmake_list_dir = os.path.abspath(os.path.dirname(__file__))
-        print("-" * 10, "Running CMake for idaklu solver", "-" * 40)
+        print("-" * 10, "Running CMake for IDAKLU solver", "-" * 40)
         subprocess.run(
             ["cmake", cmake_list_dir] + cmake_args, cwd=build_dir, env=build_env
-        )
+        , check=True)
 
         if os.path.isfile(os.path.join(build_dir, "CMakeError.log")):
             msg = (
-                "cmake configuration steps encountered errors, and the idaklu module"
+                "cmake configuration steps encountered errors, and the IDAKLU module"
                 " could not be built. Make sure dependencies are correctly "
                 "installed. See "
                 "https://docs.pybamm.org/en/latest/source/user_guide/installation/install-from-source.html" # noqa: E501
             )
             raise RuntimeError(msg)
         else:
-            print("-" * 10, "Building idaklu module", "-" * 40)
+            print("-" * 10, "Building IDAKLU module", "-" * 40)
             subprocess.run(
                 ["cmake", "--build", ".", "--config", "Release"],
                 cwd=build_dir,
                 env=build_env,
+                check=True,
             )
 
             # Move from build temp to final position
@@ -218,7 +222,7 @@ def run(self):
         install.run(self)
 
 
-# ---------- custom wheel build (non-Windows) ------------------------------------------
+# ---------- Custom class for building wheels ------------------------------------------
 
 
 class bdist_wheel(orig.bdist_wheel):
@@ -250,8 +254,7 @@ def compile_KLU():
     # Return True if:
     # - Not running on Windows AND
     # - CMake is found AND
-    # - The pybind11 and casadi-headers directories are found
-    #   in the PyBaMM project directory
+    # - The pybind11/ directory is found in the PyBaMM project directory
     CMakeFound = True
     PyBind11Found = True
     windows = (not system()) or system() == "Windows"

From 15c4a8b086acc70f838c162797bad9f9d1ae4e0d Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 17 Oct 2023 16:51:48 +0530
Subject: [PATCH 087/199] #3049 update version to 23.9rc0

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 002c29d76b..5f26d260de 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -10,7 +10,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "pybamm"
-version = "23.5"
+version = "23.9rc0"
 license = { file = "LICENSE.txt" }
 description = "Python Battery Mathematical Modelling"
 authors = [{name = "The PyBaMM Team", email = "pybamm@pybamm.org"}]

From 39ee77b465bffbeedc1de5bf576e201bb4f3aa44 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 17 Oct 2023 20:00:59 +0530
Subject: [PATCH 088/199] #3049 make cmake a bit verbose about sundials and
 suitesparse

---
 CMakeLists.txt | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index bea0b0e5a4..182fd489f3 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -87,7 +87,7 @@ endif()
 set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} ${PROJECT_SOURCE_DIR})
 # Sundials
 find_package(SUNDIALS REQUIRED)
-message("sundials ${SUNDIALS_INCLUDE_DIR} ${SUNDIALS_LIBRARIES}")
+message("SUNDIALS found in ${SUNDIALS_INCLUDE_DIR}: ${SUNDIALS_LIBRARIES}")
 target_include_directories(idaklu PRIVATE ${SUNDIALS_INCLUDE_DIR})
 target_link_libraries(idaklu PRIVATE ${SUNDIALS_LIBRARIES} casadi)
 
@@ -98,6 +98,7 @@ if(DEFINED VCPKG_ROOT_DIR)
   find_package(SuiteSparse CONFIG REQUIRED)
 else()
   find_package(SuiteSparse REQUIRED)
+  message("SuiteSparse found in ${SuiteSparse_INCLUDE_DIRS}: ${SuiteSparse_LIBRARIES}")
 endif()
 include_directories(${SuiteSparse_INCLUDE_DIRS})
 target_link_libraries(idaklu PRIVATE ${SuiteSparse_LIBRARIES})

From 761ad1b5b6da0c8f643edc47bc55537cc6aaf33f Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 19 Oct 2023 06:39:03 +0530
Subject: [PATCH 089/199] Do not perform user installation for CMake

---
 scripts/Dockerfile | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/scripts/Dockerfile b/scripts/Dockerfile
index c3d12bb7fe..429ed64eed 100644
--- a/scripts/Dockerfile
+++ b/scripts/Dockerfile
@@ -33,7 +33,8 @@ ARG ODES
 ARG JAX
 ARG ALL
 
-RUN pip install --upgrade --user pip setuptools wheel wget cmake
+RUN pip install --upgrade --user pip setuptools wheel wget
+RUN pip install cmake
 
 RUN if [ "$IDAKLU" = "true" ]; then \
     python scripts/install_KLU_Sundials.py && \

From c976eeae87d4e389e6bcb9c884b76398cd45cd8b Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 19 Oct 2023 06:51:11 +0530
Subject: [PATCH 090/199] The `scikits.odes` solver does not need `pybind11`

---
 scripts/Dockerfile | 2 --
 1 file changed, 2 deletions(-)

diff --git a/scripts/Dockerfile b/scripts/Dockerfile
index 429ed64eed..8def7ced9e 100644
--- a/scripts/Dockerfile
+++ b/scripts/Dockerfile
@@ -45,8 +45,6 @@ RUN if [ "$IDAKLU" = "true" ]; then \
 
 RUN if [ "$ODES" = "true" ]; then \
     python scripts/install_KLU_Sundials.py && \
-    rm -rf pybind11 && \
-    git clone https://github.com/pybind/pybind11.git && \
     pip install --user -e ".[all,dev,docs,odes]"; \
     fi
 

From 62ed4c4e607f6f95c98b4193705ab2b0cfc6b2f9 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 21 Oct 2023 15:59:32 +0530
Subject: [PATCH 091/199] #3049 add suggestions from code review

Co-Authored-By: Saransh Chopra <saransh0701@gmail.com>
---
 .github/workflows/publish_pypi.yml | 105 ++++++++++++++---------------
 1 file changed, 50 insertions(+), 55 deletions(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 29c352cf6c..8c07858825 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -1,7 +1,4 @@
-# name: Build and publish package to PyPI
-name: Test building wheels
-# Temporarily disable publishing to PyPI and enable
-# building wheels on pull requests
+name: Build and publish package to PyPI
 on:
   release:
     types: [published]
@@ -33,15 +30,15 @@ jobs:
       - name: Clone pybind11 repo (no history)
         run: git clone --depth 1 --branch v2.11.1 https://github.com/pybind/pybind11.git
 
-      - name: Install vcpkg on windows
+      - name: Install vcpkg on Windows
         run: |
           cd C:\
           rm -r -fo 'C:\vcpkg'
-          git clone https://github.com/microsoft/vcpkg --branch 2023.08.09
+          git clone https://github.com/microsoft/vcpkg
           cd vcpkg
           .\bootstrap-vcpkg.bat
 
-      - name: Cache packages installed through vcpkg on windows
+      - name: Cache packages installed through vcpkg on Windows
         uses: actions/cache@v3
         env:
           cache-name: vckpg_binary_cache
@@ -54,13 +51,13 @@ jobs:
         uses: mxschmitt/action-tmate@v3
         if: ${{ github.event_name == 'workflow_dispatch' && inputs.debug_enabled }}
 
-      - name: Build 64 bits wheels on Windows
+      - name: Build 64-bit wheels on Windows
         run: pipx run cibuildwheel --output-dir wheelhouse
         env:
           CIBW_ENVIRONMENT: 'PYBAMM_USE_VCPKG=ON VCPKG_ROOT_DIR=C:\vcpkg VCPKG_DEFAULT_TRIPLET=x64-windows-static-md VCPKG_FEATURE_FLAGS=manifests,registries CMAKE_GENERATOR="Visual Studio 17 2022" CMAKE_GENERATOR_PLATFORM=x64'
           CIBW_ARCHS: "AMD64"
 
-      - name: Upload windows wheels
+      - name: Upload Windows wheels
         uses: actions/upload-artifact@v3
         with:
           name: windows_wheels
@@ -109,8 +106,6 @@ jobs:
             python -m pip
             install cmake casadi numpy &&
             python scripts/fix_casadi_rpath_mac.py && scripts/fix_suitesparse_rpath_mac.sh
-          # got error "re.error: multiple repeat at position 104" on python 3.7 when --require-archs added, so remove
-          # it for mac
           CIBW_REPAIR_WHEEL_COMMAND_MACOS: >
             delocate-listdeps {wheel} &&
             delocate-wheel -v -w {dest_dir} {wheel}
@@ -146,47 +141,47 @@ jobs:
           path: ./dist/*.tar.gz
           if-no-files-found: error
 
-  # publish_pypi:
-  #   if: github.event_name != 'schedule'
-  #   name: Upload package to PyPI
-  #   needs: [build_wheels, build_windows_wheels, build_sdist]
-  #   runs-on: ubuntu-latest
-  #   steps:
-  #     - name: Download all artifacts
-  #       uses: actions/download-artifact@v3
-
-  #     - name: Move all package files to files/
-  #       run: |
-  #         mkdir files
-  #         mv windows_wheels/* wheels/* sdist/* files/
-
-  #     - name: Publish on PyPI
-  #       if: github.event.inputs.target == 'pypi' || github.event_name == 'release'
-  #       uses: pypa/gh-action-pypi-publish@release/v1
-  #       with:
-  #         user: __token__
-  #         password: ${{ secrets.PYPI_TOKEN }}
-  #         packages-dir: files/
-
-  #     - name: Publish on TestPyPI
-  #       if: github.event.inputs.target == 'testpypi'
-  #       uses: pypa/gh-action-pypi-publish@release/v1
-  #       with:
-  #         user: __token__
-  #         password: ${{ secrets.TESTPYPI_TOKEN }}
-  #         packages-dir: files/
-  #         repository-url: https://test.pypi.org/legacy/
-
-  # open_failure_issue:
-  #   needs: [build_windows_wheels, build_wheels, build_sdist]
-  #   name: Open an issue if build fails
-  #   if: ${{ always() && contains(needs.*.result, 'failure') }}
-  #   runs-on: ubuntu-latest
-  #   steps:
-  #   - uses: actions/checkout@v4
-  #   - uses: JasonEtco/create-an-issue@v2
-  #     env:
-  #       GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-  #       LOGS: ${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }}
-  #     with:
-  #       filename: .github/wheel_failure.md
+  publish_pypi:
+    if: github.event_name != 'schedule'
+    name: Upload package to PyPI
+    needs: [build_wheels, build_windows_wheels, build_sdist]
+    runs-on: ubuntu-latest
+    steps:
+      - name: Download all artifacts
+        uses: actions/download-artifact@v3
+
+      - name: Move all package files to files/
+        run: |
+          mkdir files
+          mv windows_wheels/* wheels/* sdist/* files/
+
+      - name: Publish on PyPI
+        if: github.event.inputs.target == 'pypi' || github.event_name == 'release'
+        uses: pypa/gh-action-pypi-publish@release/v1
+        with:
+          user: __token__
+          password: ${{ secrets.PYPI_TOKEN }}
+          packages-dir: files/
+
+      - name: Publish on TestPyPI
+        if: github.event.inputs.target == 'testpypi'
+        uses: pypa/gh-action-pypi-publish@release/v1
+        with:
+          user: __token__
+          password: ${{ secrets.TESTPYPI_TOKEN }}
+          packages-dir: files/
+          repository-url: https://test.pypi.org/legacy/
+
+  open_failure_issue:
+    needs: [build_windows_wheels, build_wheels, build_sdist]
+    name: Open an issue if build fails
+    if: ${{ always() && contains(needs.*.result, 'failure') }}
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v4
+    - uses: JasonEtco/create-an-issue@v2
+      env:
+        GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        LOGS: ${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }}
+      with:
+        filename: .github/wheel_failure.md

From db91a889efd01ff8ce904298b088b1172cc988ab Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 21 Oct 2023 16:07:08 +0530
Subject: [PATCH 092/199] #3049 clarify extension language

---
 CONTRIBUTING.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 7e9a0e94af..9f583e324d 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -387,7 +387,7 @@ wherever code is called that uses that citation (for example, in functions or in
 
 ### Installation
 
-Installation of PyBaMM and its dependencies is handled via [pip](https://pip.pypa.io/en/stable/) and [setuptools](http://setuptools.readthedocs.io/). It uses `CMake` to compile C extensions using [`pybind11`](https://pybind11.readthedocs.io/en/stable/) and [`casadi`](https://web.casadi.org/) (non-Windows). The installation process is described in detail in the [source installation](https://docs.pybamm.org/en/latest/source/user_guide/installation/install-from-source.html) page and is configured through the `CMakeLists.txt` file.
+Installation of PyBaMM and its dependencies is handled via [pip](https://pip.pypa.io/en/stable/) and [setuptools](http://setuptools.readthedocs.io/). It uses `CMake` to compile C++ extensions using [`pybind11`](https://pybind11.readthedocs.io/en/stable/) and [`casadi`](https://web.casadi.org/) (non-Windows). The installation process is described in detail in the [source installation](https://docs.pybamm.org/en/latest/source/user_guide/installation/install-from-source.html) page and is configured through the `CMakeLists.txt` file.
 
 Configuration files:
 

From 076860934c089d8fec07232593be7576c26223bf Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 21 Oct 2023 16:07:35 +0530
Subject: [PATCH 093/199] #3049 include citation file in source distribution

Co-Authored-By: Saransh Chopra <saransh0701@gmail.com>
---
 MANIFEST.in | 1 +
 1 file changed, 1 insertion(+)

diff --git a/MANIFEST.in b/MANIFEST.in
index 24ae488d04..bfc9d0e718 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -1,4 +1,5 @@
 graft pybamm
+include CITATION.cff
 prune tests
 
 exclude CHANGELOG.md CODE-OF-CONDUCT.md CONTRIBUTING.md GOVERNANCE.md CMakeLists.txt

From f56e3664c3b7fcf9f3cf8434c2eaf1e6bf216875 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 21 Oct 2023 16:08:32 +0530
Subject: [PATCH 094/199] #3049 remove note about requirements.txt

Co-Authored-By: Saransh Chopra <saransh0701@gmail.com>
---
 docs/source/user_guide/installation/index.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/user_guide/installation/index.rst b/docs/source/user_guide/installation/index.rst
index 93e54c51fe..0e9b02de01 100644
--- a/docs/source/user_guide/installation/index.rst
+++ b/docs/source/user_guide/installation/index.rst
@@ -76,7 +76,7 @@ Optional Dependencies
 PyBaMM has a number of optional dependencies for different functionalities.
 If the optional dependency is not installed, PyBaMM will raise an ImportError when the method requiring that dependency is called.
 
-If you are using ``pip``, optional PyBaMM dependencies can be installed or managed in a file (e.g. requirements.txt, setup.py, or pyproject.toml)
+If you are using ``pip``, optional PyBaMM dependencies can be installed or managed in a file (e.g., setup.py, or pyproject.toml)
 as optional extras (e.g.,``pybamm[dev,plot]``). All optional dependencies can be installed with ``pybamm[all]``,
 and specific sets of dependencies are listed in the sections below.
 

From e311029ab5631bdae38b67e4011a80ed58fe8d0e Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 21 Oct 2023 16:13:21 +0530
Subject: [PATCH 095/199] #3049 fix docs indentation

Co-Authored-By: Saransh Chopra <saransh0701@gmail.com>
---
 docs/source/user_guide/installation/install-from-source.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/user_guide/installation/install-from-source.rst b/docs/source/user_guide/installation/install-from-source.rst
index d4de957b16..003c7f143a 100644
--- a/docs/source/user_guide/installation/install-from-source.rst
+++ b/docs/source/user_guide/installation/install-from-source.rst
@@ -106,7 +106,7 @@ Installing PyBaMM
 You should now have everything ready to build and install PyBaMM successfully.
 
 Using ``Nox`` (recommended)
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. code:: bash
 

From 052a1637eb910d2ce87323cc1d56ebfd0cfade62 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 21 Oct 2023 16:24:11 +0530
Subject: [PATCH 096/199] #3049 specify lower bounds for `setuptools`

Co-Authored-By: Saransh Chopra <saransh0701@gmail.com>
---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 5f26d260de..32912383f7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [build-system]
 requires = [
-    "setuptools",
+    "setuptools>=64",
     "wheel",
     # On Windows, use the CasADi vcpkg registry and CMake bundled from MSVC
     "casadi>=3.6.0; platform_system!='Windows'",

From 5e587c0df28cd4c609f5b6c3fb3ead545bcdd415 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 21 Oct 2023 16:37:19 +0530
Subject: [PATCH 097/199] #3049 cleanup and clarify user installation page

---
 docs/source/user_guide/installation/GNU-linux.rst | 2 +-
 docs/source/user_guide/installation/windows.rst   | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/source/user_guide/installation/GNU-linux.rst b/docs/source/user_guide/installation/GNU-linux.rst
index e66c3c2291..ca95bbe1b5 100644
--- a/docs/source/user_guide/installation/GNU-linux.rst
+++ b/docs/source/user_guide/installation/GNU-linux.rst
@@ -6,7 +6,7 @@ GNU-Linux & MacOS
 Prerequisites
 -------------
 
-To use and/or contribute to PyBaMM, you must have Python 3.8, 3.9, 3.10, or 3.11 installed.
+To use PyBaMM, you must have Python 3.8, 3.9, 3.10, or 3.11 installed.
 
 .. tab:: Debian-based distributions (Debian, Ubuntu, Linux Mint)
 
diff --git a/docs/source/user_guide/installation/windows.rst b/docs/source/user_guide/installation/windows.rst
index 6ff48293bd..5b104e91bd 100644
--- a/docs/source/user_guide/installation/windows.rst
+++ b/docs/source/user_guide/installation/windows.rst
@@ -6,7 +6,7 @@ Windows
 Prerequisites
 -------------
 
-To use and/or contribute to PyBaMM, you must have Python 3.8, 3.9, 3.10, or 3.11 installed.
+To use PyBaMM, you must have Python 3.8, 3.9, 3.10, or 3.11 installed.
 
 To install Python 3 download the installation files from `Python’s
 website <https://www.python.org/downloads/windows/>`__. Make sure to
@@ -27,7 +27,7 @@ install PyBaMM. You can find a reminder of how to navigate the terminal
 We recommend to install PyBaMM within a virtual environment, in order
 not to alter any distribution python files.
 
-To install virtualenv type:
+To install ``virtualenv``, type:
 
 .. code:: bash
 

From 3b00b181245827b1420b0fa4162b2cd401990635 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 21 Oct 2023 22:57:33 +0530
Subject: [PATCH 098/199] #3049 fix whitespace in `pybamm-requires` session

Co-Authored-By: Eric G. Kratz <kratman@users.noreply.github.com>
---
 noxfile.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/noxfile.py b/noxfile.py
index 4af892565f..45787b6f4e 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -41,7 +41,7 @@ def run_pybamm_requires(session):
     """Download, compile, and install the build-time requirements for Linux and macOS: the SuiteSparse and SUNDIALS libraries."""  # noqa: E501
     set_environment_variables(PYBAMM_ENV, session=session)
     if sys.platform != "win32":
-        session.install("wget", "cmake" , silent=False)
+        session.install("wget", "cmake", silent=False)
         session.run("python", "scripts/install_KLU_Sundials.py")
         if not os.path.exists("./pybind11"):
             session.run(

From b49b6f331625587665e45f013459dee8c336d5be Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 21 Oct 2023 23:00:28 +0530
Subject: [PATCH 099/199] #3049 code review suggestions from Eric

do not install gcc or gfortran if they are already installed, just reinstall them

Co-Authored-By: Eric G. Kratz <kratman@users.noreply.github.com>
---
 .github/workflows/publish_pypi.yml | 2 +-
 .github/workflows/test_on_push.yml | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 8c07858825..fda75d4489 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -85,7 +85,7 @@ jobs:
         if: matrix.os == 'macos-latest'
         run: |
           brew update
-          brew install gcc gfortran libomp graphviz openblas
+          brew install graphviz openblas libomp
           brew reinstall gcc
           python -m pip install cmake wget
           python scripts/install_KLU_Sundials.py
diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 88ada069b4..5aac923da2 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -77,7 +77,7 @@ jobs:
         run: |
           brew analytics off
           brew update
-          brew install graphviz openblas gcc gfortran libomp
+          brew install graphviz openblas libomp
           brew reinstall gcc
 
       - name: Install Windows system dependencies
@@ -210,7 +210,7 @@ jobs:
         run: |
           brew analytics off
           brew update
-          brew install graphviz openblas gcc gfortran libomp
+          brew install graphviz openblas libomp
           brew reinstall gcc
 
       - name: Install Windows system dependencies

From 4c237c12fb973827eb167f1390e285f3b3229e0a Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Thu, 26 Oct 2023 12:12:50 +0530
Subject: [PATCH 100/199] prevent `pybtex` default installation

---
 pybamm/__init__.py | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/pybamm/__init__.py b/pybamm/__init__.py
index 9aa1ca79a0..a8ffbcf83b 100644
--- a/pybamm/__init__.py
+++ b/pybamm/__init__.py
@@ -52,7 +52,13 @@
 )
 from .logger import logger, set_logging_level, get_new_logger
 from .settings import settings
-from .citations import Citations, citations, print_citations
+try:
+    import pybtex
+
+    if pybtex is not None:
+        from .citations import Citations, citations, print_citations
+except ImportError:
+    pass
 
 #
 # Classes for the Expression Tree

From 6d30b3adea028d33ab3a377fe1fc870cf3f53abc Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Thu, 26 Oct 2023 20:42:17 +0530
Subject: [PATCH 101/199] resolve `anytree` default installation

---
 pybamm/expression_tree/symbol.py | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/pybamm/expression_tree/symbol.py b/pybamm/expression_tree/symbol.py
index 5d28884ed5..88c4d02ab8 100644
--- a/pybamm/expression_tree/symbol.py
+++ b/pybamm/expression_tree/symbol.py
@@ -3,10 +3,14 @@
 #
 import numbers
 
-import anytree
+
+try:
+    import anytree
+    from anytree.exporter import DotExporter
+except ImportError:
+    pass
 import numpy as np
 import sympy
-from anytree.exporter import DotExporter
 from scipy.sparse import csr_matrix, issparse
 from functools import lru_cache, cached_property
 

From e3b3b35aa48fe60d9c08454574e2df8aa150b590 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Thu, 26 Oct 2023 21:02:00 +0530
Subject: [PATCH 102/199] resolve `autograd` default imports

---
 pybamm/expression_tree/functions.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/pybamm/expression_tree/functions.py b/pybamm/expression_tree/functions.py
index 80c2848ad9..788af40d50 100644
--- a/pybamm/expression_tree/functions.py
+++ b/pybamm/expression_tree/functions.py
@@ -3,7 +3,10 @@
 #
 import numbers
 
-import autograd
+try:
+    import autograd
+except ImportError:
+    pass
 import numpy as np
 import sympy
 from scipy import special

From 4dd231799f25f45238b58458a386c882fa64fc5e Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Thu, 26 Oct 2023 21:06:51 +0530
Subject: [PATCH 103/199] resolve `skfem` default imports

---
 pybamm/meshes/scikit_fem_submeshes.py           | 5 ++++-
 pybamm/spatial_methods/scikit_finite_element.py | 5 ++++-
 2 files changed, 8 insertions(+), 2 deletions(-)

diff --git a/pybamm/meshes/scikit_fem_submeshes.py b/pybamm/meshes/scikit_fem_submeshes.py
index f25dce80b1..c067c43a8a 100644
--- a/pybamm/meshes/scikit_fem_submeshes.py
+++ b/pybamm/meshes/scikit_fem_submeshes.py
@@ -4,7 +4,10 @@
 import pybamm
 from .meshes import SubMesh
 
-import skfem
+try:
+    import skfem
+except ImportError:
+    pass
 import numpy as np
 
 
diff --git a/pybamm/spatial_methods/scikit_finite_element.py b/pybamm/spatial_methods/scikit_finite_element.py
index 0f0a42bbcb..7556645028 100644
--- a/pybamm/spatial_methods/scikit_finite_element.py
+++ b/pybamm/spatial_methods/scikit_finite_element.py
@@ -6,7 +6,10 @@
 from scipy.sparse import csr_matrix, csc_matrix
 from scipy.sparse.linalg import inv
 import numpy as np
-import skfem
+try:
+    import skfem
+except ImportError:
+    pass
 
 
 class ScikitFiniteElement(pybamm.SpatialMethod):

From 9e24562b7bf00a7e1149b7910f3f2e2f6b3a0107 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Thu, 26 Oct 2023 21:20:40 +0530
Subject: [PATCH 104/199] resolve `tqdm` default imports

---
 pybamm/simulation.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/pybamm/simulation.py b/pybamm/simulation.py
index 380105d215..dfca7e0583 100644
--- a/pybamm/simulation.py
+++ b/pybamm/simulation.py
@@ -8,7 +8,10 @@
 import sys
 from functools import lru_cache
 from datetime import timedelta
-import tqdm
+try:
+    import tqdm
+except ImportError:
+    pass
 
 
 def is_notebook():

From 50315e7f838ecbf2b3cf73e2d02239124aecb286 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sat, 28 Oct 2023 21:17:12 +0530
Subject: [PATCH 105/199] Raise import error for `anytree` requiring functions

---
 pybamm/expression_tree/symbol.py | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/pybamm/expression_tree/symbol.py b/pybamm/expression_tree/symbol.py
index 88c4d02ab8..6904854050 100644
--- a/pybamm/expression_tree/symbol.py
+++ b/pybamm/expression_tree/symbol.py
@@ -8,7 +8,9 @@
     import anytree
     from anytree.exporter import DotExporter
 except ImportError:
-    pass
+    _has_anytree = False
+else:
+    _has_anytree = True
 import numpy as np
 import sympy
 from scipy.sparse import csr_matrix, issparse
@@ -446,6 +448,8 @@ def render(self):  # pragma: no cover
         """
         Print out a visual representation of the tree (this node and its children)
         """
+        if not _has_anytree:
+            raise ImportError("Module 'anytree' is required to do this")
         for pre, _, node in anytree.RenderTree(self):
             if isinstance(node, pybamm.Scalar) and node.name != str(node.value):
                 print("{}{} = {}".format(pre, node.name, node.value))
@@ -463,6 +467,8 @@ def visualise(self, filename):
         filename : str
             filename to output, must end in ".png"
         """
+        if not _has_anytree:
+            raise ImportError("Module 'anytree' is required to do this")
 
         # check that filename ends in .png.
         if filename[-4:] != ".png":
@@ -483,6 +489,8 @@ def relabel_tree(self, symbol, counter):
         Finds all children of a symbol and assigns them a new id so that they can be
         visualised properly using the graphviz output
         """
+        if not _has_anytree:
+            raise ImportError("Module 'anytree' is required to do this")
         name = symbol.name
         if name == "div":
             name = "&nabla;&sdot;"
@@ -526,6 +534,8 @@ def pre_order(self):
         a
         b
         """
+        if not _has_anytree:
+            raise ImportError("Module 'anytree' is required to do this")
         return anytree.PreOrderIter(self)
 
     def __str__(self):

From e09fcea3888ce5571812f1a5a54b44d2176554db Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Mon, 30 Oct 2023 05:04:02 +0530
Subject: [PATCH 106/199] Make simple function to check optional dependency

---
 pybamm/__init__.py | 1 +
 pybamm/util.py     | 9 +++++++++
 2 files changed, 10 insertions(+)

diff --git a/pybamm/__init__.py b/pybamm/__init__.py
index a8ffbcf83b..8f92c71e18 100644
--- a/pybamm/__init__.py
+++ b/pybamm/__init__.py
@@ -47,6 +47,7 @@
     get_parameters_filepath,
     have_jax,
     install_jax,
+    have_optional_dependency,
     is_jax_compatible,
     get_git_commit_info,
 )
diff --git a/pybamm/util.py b/pybamm/util.py
index 562352bfac..c98ee6beda 100644
--- a/pybamm/util.py
+++ b/pybamm/util.py
@@ -345,3 +345,12 @@ def install_jax(arguments=None):  # pragma: no cover
             f"jaxlib>={JAXLIB_VERSION}",
         ]
     )
+
+
+def have_optional_dependency(module):
+    try:
+        importlib.import_module(module)
+        _has_module = True
+    except ImportError:
+        _has_module = False
+    return _has_module

From a07b34251586319c4d98cd6a9d0c9ac4b49bab44 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Mon, 30 Oct 2023 07:57:22 +0530
Subject: [PATCH 107/199] Make decorater function

---
 pybamm/__init__.py  |  9 +--------
 pybamm/citations.py |  1 +
 pybamm/util.py      | 29 ++++++++++++++++++++++-------
 3 files changed, 24 insertions(+), 15 deletions(-)

diff --git a/pybamm/__init__.py b/pybamm/__init__.py
index 8f92c71e18..07d8a1c0ea 100644
--- a/pybamm/__init__.py
+++ b/pybamm/__init__.py
@@ -53,14 +53,7 @@
 )
 from .logger import logger, set_logging_level, get_new_logger
 from .settings import settings
-try:
-    import pybtex
-
-    if pybtex is not None:
-        from .citations import Citations, citations, print_citations
-except ImportError:
-    pass
-
+from .citations import Citations, citations, print_citations
 #
 # Classes for the Expression Tree
 #
diff --git a/pybamm/citations.py b/pybamm/citations.py
index da619062e0..87f8271dde 100644
--- a/pybamm/citations.py
+++ b/pybamm/citations.py
@@ -177,6 +177,7 @@ def _tag_citations(self):
             for key, entry in self._citation_tags.items():
                 print(f"{key} was cited due to the use of {entry}")
 
+    @pybamm.util.have_optional_dependency("pybtex")
     def print(self, filename=None, output_format="text", verbose=False):
         """Print all citations that were used for running simulations. The verbose
         option is provided to print tags for citations in the output such that it can
diff --git a/pybamm/util.py b/pybamm/util.py
index c98ee6beda..0b68173504 100644
--- a/pybamm/util.py
+++ b/pybamm/util.py
@@ -347,10 +347,25 @@ def install_jax(arguments=None):  # pragma: no cover
     )
 
 
-def have_optional_dependency(module):
-    try:
-        importlib.import_module(module)
-        _has_module = True
-    except ImportError:
-        _has_module = False
-    return _has_module
+def have_optional_dependency(module_name, attribute=None):
+    def decorator(func):
+        def wrapper(*args, **kwargs):
+            try:
+                module = importlib.import_module(module_name)
+                if attribute:
+                    if hasattr(module, attribute):
+                        imported_attribute = getattr(module, attribute)
+                        print(f"The {module_name}.{attribute} is available.")
+                        kwargs[attribute] = imported_attribute
+                    else:
+                        print(f"The {module_name}.{attribute} is not available.")
+                else:
+                    print(f"The {module_name} module is available.")
+                return func(*args, **kwargs)
+            except ImportError:
+                if attribute:
+                    print(f"The {module_name}.{attribute} is not available.")
+                else:
+                    print(f"The {module_name} module is not available.")
+        return wrapper
+    return decorator

From 9d9db2bf2dc089f17adabf7014ea1d63109c6883 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Mon, 30 Oct 2023 16:31:25 +0530
Subject: [PATCH 108/199] Make normal reusable function for optional deps

---
 pybamm/util.py | 40 +++++++++++++++++++---------------------
 1 file changed, 19 insertions(+), 21 deletions(-)

diff --git a/pybamm/util.py b/pybamm/util.py
index 0b68173504..f481480635 100644
--- a/pybamm/util.py
+++ b/pybamm/util.py
@@ -348,24 +348,22 @@ def install_jax(arguments=None):  # pragma: no cover
 
 
 def have_optional_dependency(module_name, attribute=None):
-    def decorator(func):
-        def wrapper(*args, **kwargs):
-            try:
-                module = importlib.import_module(module_name)
-                if attribute:
-                    if hasattr(module, attribute):
-                        imported_attribute = getattr(module, attribute)
-                        print(f"The {module_name}.{attribute} is available.")
-                        kwargs[attribute] = imported_attribute
-                    else:
-                        print(f"The {module_name}.{attribute} is not available.")
-                else:
-                    print(f"The {module_name} module is available.")
-                return func(*args, **kwargs)
-            except ImportError:
-                if attribute:
-                    print(f"The {module_name}.{attribute} is not available.")
-                else:
-                    print(f"The {module_name} module is not available.")
-        return wrapper
-    return decorator
+    try:
+        module = importlib.import_module(module_name)
+        if attribute:
+            if hasattr(module, attribute):
+                imported_attribute = getattr(module, attribute)
+                print(f"The {module_name}.{attribute} is available.")
+                return imported_attribute
+            else:
+                print(f"The {module_name}.{attribute} is not available.")
+                return None
+        else:
+            print(f"The {module_name} module is available.")
+            return module
+    except ImportError:
+        if attribute:
+            print(f"The {module_name}.{attribute} is not available.")
+        else:
+            print(f"The {module_name} module is not available.")
+        return None

From 34311ee63326d936bffa473acebdfc1462e6eb14 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Mon, 30 Oct 2023 16:32:25 +0530
Subject: [PATCH 109/199] Update `citations.py` for `pybtex` as optional
 dependency

---
 pybamm/citations.py | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/pybamm/citations.py b/pybamm/citations.py
index 87f8271dde..fa3c4651b7 100644
--- a/pybamm/citations.py
+++ b/pybamm/citations.py
@@ -6,10 +6,10 @@
 import pybamm
 import os
 import warnings
-import pybtex
+# import pybtex
 from sys import _getframe
-from pybtex.database import parse_file, parse_string, Entry
-from pybtex.scanner import PybtexError
+# from pybtex.database import parse_file, parse_string, Entry
+# from pybtex.scanner import PybtexError
 
 
 class Citations:
@@ -76,6 +76,7 @@ def read_citations(self):
         """Reads the citations in `pybamm.CITATIONS.bib`. Other works can be cited
         by passing a BibTeX citation to :meth:`register`.
         """
+        parse_file = pybamm.util.have_optional_dependency("pybtex.database","parse_file")
         citations_file = os.path.join(pybamm.root_dir(), "pybamm", "CITATIONS.bib")
         bib_data = parse_file(citations_file, bib_format="bibtex")
         for key, entry in bib_data.entries.items():
@@ -86,6 +87,7 @@ def _add_citation(self, key, entry):
         previous entry is overwritten
         """
 
+        Entry = pybamm.util.have_optional_dependency("pybtex.database","Entry")
         # Check input types are correct
         if not isinstance(key, str) or not isinstance(entry, Entry):
             raise TypeError()
@@ -151,6 +153,8 @@ def _parse_citation(self, key):
         key: str
             A BibTeX formatted citation
         """
+        PybtexError = pybamm.util.have_optional_dependency("pybtex.scanner","PybtexError")
+        parse_string = pybamm.util.have_optional_dependency("pybtex.database","parse_string")
         try:
             # Parse string as a bibtex citation, and check that a citation was found
             bib_data = parse_string(key, bib_format="bibtex")
@@ -177,7 +181,6 @@ def _tag_citations(self):
             for key, entry in self._citation_tags.items():
                 print(f"{key} was cited due to the use of {entry}")
 
-    @pybamm.util.have_optional_dependency("pybtex")
     def print(self, filename=None, output_format="text", verbose=False):
         """Print all citations that were used for running simulations. The verbose
         option is provided to print tags for citations in the output such that it can
@@ -218,6 +221,7 @@ def print(self, filename=None, output_format="text", verbose=False):
         """
         # Parse citations that were not known keys at registration, but do not
         # fail if they cannot be parsed
+        pybtex = pybamm.util.have_optional_dependency("pybtex")
         try:
             for key in self._unknown_citations:
                 self._parse_citation(key)

From 12180658beaf910c513756269b0f3c6df9a16941 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Mon, 30 Oct 2023 16:42:31 +0530
Subject: [PATCH 110/199] Execute silently, raise ImportError & import function
 correctly

---
 pybamm/citations.py | 14 ++++++--------
 pybamm/util.py      | 10 +++-------
 2 files changed, 9 insertions(+), 15 deletions(-)

diff --git a/pybamm/citations.py b/pybamm/citations.py
index fa3c4651b7..7d0959d89c 100644
--- a/pybamm/citations.py
+++ b/pybamm/citations.py
@@ -6,10 +6,8 @@
 import pybamm
 import os
 import warnings
-# import pybtex
 from sys import _getframe
-# from pybtex.database import parse_file, parse_string, Entry
-# from pybtex.scanner import PybtexError
+from pybamm.util import have_optional_dependency
 
 
 class Citations:
@@ -76,7 +74,7 @@ def read_citations(self):
         """Reads the citations in `pybamm.CITATIONS.bib`. Other works can be cited
         by passing a BibTeX citation to :meth:`register`.
         """
-        parse_file = pybamm.util.have_optional_dependency("pybtex.database","parse_file")
+        parse_file = have_optional_dependency("pybtex.database","parse_file")
         citations_file = os.path.join(pybamm.root_dir(), "pybamm", "CITATIONS.bib")
         bib_data = parse_file(citations_file, bib_format="bibtex")
         for key, entry in bib_data.entries.items():
@@ -87,7 +85,7 @@ def _add_citation(self, key, entry):
         previous entry is overwritten
         """
 
-        Entry = pybamm.util.have_optional_dependency("pybtex.database","Entry")
+        Entry = have_optional_dependency("pybtex.database","Entry")
         # Check input types are correct
         if not isinstance(key, str) or not isinstance(entry, Entry):
             raise TypeError()
@@ -153,8 +151,8 @@ def _parse_citation(self, key):
         key: str
             A BibTeX formatted citation
         """
-        PybtexError = pybamm.util.have_optional_dependency("pybtex.scanner","PybtexError")
-        parse_string = pybamm.util.have_optional_dependency("pybtex.database","parse_string")
+        PybtexError = have_optional_dependency("pybtex.scanner","PybtexError")
+        parse_string = have_optional_dependency("pybtex.database","parse_string")
         try:
             # Parse string as a bibtex citation, and check that a citation was found
             bib_data = parse_string(key, bib_format="bibtex")
@@ -221,7 +219,7 @@ def print(self, filename=None, output_format="text", verbose=False):
         """
         # Parse citations that were not known keys at registration, but do not
         # fail if they cannot be parsed
-        pybtex = pybamm.util.have_optional_dependency("pybtex")
+        pybtex = have_optional_dependency("pybtex")
         try:
             for key in self._unknown_citations:
                 self._parse_citation(key)
diff --git a/pybamm/util.py b/pybamm/util.py
index f481480635..a2625e5405 100644
--- a/pybamm/util.py
+++ b/pybamm/util.py
@@ -353,17 +353,13 @@ def have_optional_dependency(module_name, attribute=None):
         if attribute:
             if hasattr(module, attribute):
                 imported_attribute = getattr(module, attribute)
-                print(f"The {module_name}.{attribute} is available.")
                 return imported_attribute
             else:
-                print(f"The {module_name}.{attribute} is not available.")
-                return None
+                raise ImportError(f"{module_name}.{attribute} is not available.")
         else:
-            print(f"The {module_name} module is available.")
             return module
     except ImportError:
         if attribute:
-            print(f"The {module_name}.{attribute} is not available.")
+            raise ImportError(f"{module_name}.{attribute} is not available.")
         else:
-            print(f"The {module_name} module is not available.")
-        return None
+            raise ImportError(f"{module_name} module is not available.")

From 90ac2ee2f812c34dfaec49434c128c7f789937c6 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Mon, 30 Oct 2023 16:56:59 +0530
Subject: [PATCH 111/199] Update `Symbol` for `anytree` as optional dependency

---
 pybamm/expression_tree/symbol.py | 21 +++++----------------
 1 file changed, 5 insertions(+), 16 deletions(-)

diff --git a/pybamm/expression_tree/symbol.py b/pybamm/expression_tree/symbol.py
index 6904854050..8ad717f7ff 100644
--- a/pybamm/expression_tree/symbol.py
+++ b/pybamm/expression_tree/symbol.py
@@ -3,20 +3,13 @@
 #
 import numbers
 
-
-try:
-    import anytree
-    from anytree.exporter import DotExporter
-except ImportError:
-    _has_anytree = False
-else:
-    _has_anytree = True
 import numpy as np
 import sympy
 from scipy.sparse import csr_matrix, issparse
 from functools import lru_cache, cached_property
 
 import pybamm
+from pybamm.util import have_optional_dependency
 from pybamm.expression_tree.printing.print_name import prettify_print_name
 
 DOMAIN_LEVELS = ["primary", "secondary", "tertiary", "quaternary"]
@@ -448,8 +441,7 @@ def render(self):  # pragma: no cover
         """
         Print out a visual representation of the tree (this node and its children)
         """
-        if not _has_anytree:
-            raise ImportError("Module 'anytree' is required to do this")
+        anytree = have_optional_dependency("anytree")
         for pre, _, node in anytree.RenderTree(self):
             if isinstance(node, pybamm.Scalar) and node.name != str(node.value):
                 print("{}{} = {}".format(pre, node.name, node.value))
@@ -467,9 +459,8 @@ def visualise(self, filename):
         filename : str
             filename to output, must end in ".png"
         """
-        if not _has_anytree:
-            raise ImportError("Module 'anytree' is required to do this")
 
+        DotExporter = have_optional_dependency("anytree.exporter","DotExporter")
         # check that filename ends in .png.
         if filename[-4:] != ".png":
             raise ValueError("filename should end in .png")
@@ -489,8 +480,7 @@ def relabel_tree(self, symbol, counter):
         Finds all children of a symbol and assigns them a new id so that they can be
         visualised properly using the graphviz output
         """
-        if not _has_anytree:
-            raise ImportError("Module 'anytree' is required to do this")
+        anytree = have_optional_dependency("anytree")
         name = symbol.name
         if name == "div":
             name = "&nabla;&sdot;"
@@ -534,8 +524,7 @@ def pre_order(self):
         a
         b
         """
-        if not _has_anytree:
-            raise ImportError("Module 'anytree' is required to do this")
+        anytree = have_optional_dependency("anytree")
         return anytree.PreOrderIter(self)
 
     def __str__(self):

From aca3a86b66344fafb64203be83e355d354cb0325 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Mon, 30 Oct 2023 19:19:49 +0000
Subject: [PATCH 112/199] Bump awalsh128/cache-apt-pkgs-action from 1.3.0 to
 1.3.1

Bumps [awalsh128/cache-apt-pkgs-action](https://github.com/awalsh128/cache-apt-pkgs-action) from 1.3.0 to 1.3.1.
- [Release notes](https://github.com/awalsh128/cache-apt-pkgs-action/releases)
- [Commits](https://github.com/awalsh128/cache-apt-pkgs-action/compare/v1.3.0...v1.3.1)

---
updated-dependencies:
- dependency-name: awalsh128/cache-apt-pkgs-action
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
---
 .github/workflows/test_on_push.yml | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index cb22fb87f7..db71c32586 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -50,7 +50,7 @@ jobs:
 
       # Install and cache apt packages
       - name: Install Linux system dependencies
-        uses: awalsh128/cache-apt-pkgs-action@v1.3.0
+        uses: awalsh128/cache-apt-pkgs-action@v1.3.1
         if: matrix.os == 'ubuntu-latest'
         with:
           packages: gfortran gcc graphviz pandoc
@@ -130,7 +130,7 @@ jobs:
 
       # Install and cache apt packages
       - name: Install Linux system dependencies
-        uses: awalsh128/cache-apt-pkgs-action@v1.3.0
+        uses: awalsh128/cache-apt-pkgs-action@v1.3.1
         with:
           packages: gfortran gcc graphviz pandoc
           execute_install_scripts: true
@@ -193,7 +193,7 @@ jobs:
 
       # Install and cache apt packages
       - name: Install Linux system dependencies
-        uses: awalsh128/cache-apt-pkgs-action@v1.3.0
+        uses: awalsh128/cache-apt-pkgs-action@v1.3.1
         if: matrix.os == 'ubuntu-latest'
         with:
           packages: gfortran gcc graphviz pandoc
@@ -274,7 +274,7 @@ jobs:
 
       # Install and cache apt packages
       - name: Install Linux system dependencies
-        uses: awalsh128/cache-apt-pkgs-action@v1.3.0
+        uses: awalsh128/cache-apt-pkgs-action@v1.3.1
         with:
           packages: gfortran gcc graphviz pandoc
           execute_install_scripts: true
@@ -319,7 +319,7 @@ jobs:
 
       # Install and cache apt packages
       - name: Install Linux system dependencies
-        uses: awalsh128/cache-apt-pkgs-action@v1.3.0
+        uses: awalsh128/cache-apt-pkgs-action@v1.3.1
         with:
           packages: gfortran gcc graphviz pandoc
           execute_install_scripts: true
@@ -377,7 +377,7 @@ jobs:
 
       # Install and cache apt packages
       - name: Install Linux system dependencies
-        uses: awalsh128/cache-apt-pkgs-action@v1.3.0
+        uses: awalsh128/cache-apt-pkgs-action@v1.3.1
         with:
           packages: gfortran gcc graphviz
           execute_install_scripts: true

From 3e686173bc57e434209a08346214c382a2519b41 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Wed, 1 Nov 2023 13:38:54 +0530
Subject: [PATCH 113/199] Update `simulation` for `tqdm` as optional dependency

---
 pybamm/simulation.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/pybamm/simulation.py b/pybamm/simulation.py
index dfca7e0583..0b1a6b2525 100644
--- a/pybamm/simulation.py
+++ b/pybamm/simulation.py
@@ -8,10 +8,7 @@
 import sys
 from functools import lru_cache
 from datetime import timedelta
-try:
-    import tqdm
-except ImportError:
-    pass
+from pybamm.util import have_optional_dependency
 
 
 def is_notebook():
@@ -535,6 +532,7 @@ def solve(
             Additional key-word arguments passed to `solver.solve`.
             See :meth:`pybamm.BaseSolver.solve`.
         """
+        tqdm = have_optional_dependency("tqdm")
         # Setup
         if solver is None:
             solver = self._solver

From 5551dac392adfb0111b5b208a46d22c6bec747f2 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Wed, 1 Nov 2023 13:47:08 +0530
Subject: [PATCH 114/199] Update `Function` class for `autograd` as optional
 dependency

---
 pybamm/expression_tree/functions.py | 7 ++-----
 1 file changed, 2 insertions(+), 5 deletions(-)

diff --git a/pybamm/expression_tree/functions.py b/pybamm/expression_tree/functions.py
index 788af40d50..ebfb313199 100644
--- a/pybamm/expression_tree/functions.py
+++ b/pybamm/expression_tree/functions.py
@@ -3,16 +3,12 @@
 #
 import numbers
 
-try:
-    import autograd
-except ImportError:
-    pass
 import numpy as np
 import sympy
 from scipy import special
 
 import pybamm
-
+from pybamm.util import have_optional_dependency
 
 class Function(pybamm.Symbol):
     """
@@ -99,6 +95,7 @@ def _function_diff(self, children, idx):
         Derivative with respect to child number 'idx'.
         See :meth:`pybamm.Symbol._diff()`.
         """
+        autograd = have_optional_dependency("autograd")
         # Store differentiated function, needed in case we want to convert to CasADi
         if self.derivative == "autograd":
             return Function(

From 64d9037299a1b48c0e2801919699a3465df935d4 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Wed, 1 Nov 2023 14:03:03 +0530
Subject: [PATCH 115/199] Resolve `scikit-fem` based methods

---
 pybamm/meshes/scikit_fem_submeshes.py           |  8 +++-----
 pybamm/spatial_methods/scikit_finite_element.py | 13 +++++++++----
 2 files changed, 12 insertions(+), 9 deletions(-)

diff --git a/pybamm/meshes/scikit_fem_submeshes.py b/pybamm/meshes/scikit_fem_submeshes.py
index c067c43a8a..23c024dbbb 100644
--- a/pybamm/meshes/scikit_fem_submeshes.py
+++ b/pybamm/meshes/scikit_fem_submeshes.py
@@ -3,13 +3,10 @@
 #
 import pybamm
 from .meshes import SubMesh
-
-try:
-    import skfem
-except ImportError:
-    pass
 import numpy as np
 
+from pybamm.util import have_optional_dependency
+
 
 class ScikitSubMesh2D(SubMesh):
     """
@@ -30,6 +27,7 @@ class ScikitSubMesh2D(SubMesh):
     """
 
     def __init__(self, edges, coord_sys, tabs):
+        skfem = have_optional_dependency("skfem")
         self.edges = edges
         self.nodes = dict.fromkeys(["y", "z"])
         for var in self.nodes.keys():
diff --git a/pybamm/spatial_methods/scikit_finite_element.py b/pybamm/spatial_methods/scikit_finite_element.py
index 7556645028..2d51e16c32 100644
--- a/pybamm/spatial_methods/scikit_finite_element.py
+++ b/pybamm/spatial_methods/scikit_finite_element.py
@@ -6,10 +6,8 @@
 from scipy.sparse import csr_matrix, csc_matrix
 from scipy.sparse.linalg import inv
 import numpy as np
-try:
-    import skfem
-except ImportError:
-    pass
+
+from pybamm.util import have_optional_dependency
 
 
 class ScikitFiniteElement(pybamm.SpatialMethod):
@@ -90,6 +88,7 @@ def gradient(self, symbol, discretised_symbol, boundary_conditions):
             to the y-component of the gradient and the second column corresponds
             to the z component of the gradient.
         """
+        skfem = have_optional_dependency("skfem")
         domain = symbol.domain[0]
         mesh = self.mesh[domain]
 
@@ -145,6 +144,7 @@ def gradient_matrix(self, symbol, boundary_conditions):
         :class:`pybamm.Matrix`
             The (sparse) finite element gradient matrix for the domain
         """
+        skfem = have_optional_dependency("skfem")
         # get primary domain mesh
         domain = symbol.domain[0]
         mesh = self.mesh[domain]
@@ -190,6 +190,7 @@ def laplacian(self, symbol, discretised_symbol, boundary_conditions):
             Contains the result of acting the discretised gradient on
             the child discretised_symbol
         """
+        skfem = have_optional_dependency("skfem")
         domain = symbol.domain[0]
         mesh = self.mesh[domain]
 
@@ -261,6 +262,7 @@ def stiffness_matrix(self, symbol, boundary_conditions):
         :class:`pybamm.Matrix`
             The (sparse) finite element stiffness matrix for the domain
         """
+        skfem = have_optional_dependency("skfem")
         # get primary domain mesh
         domain = symbol.domain[0]
         mesh = self.mesh[domain]
@@ -323,6 +325,7 @@ def definite_integral_matrix(self, child, vector_type="row"):
         :class:`pybamm.Matrix`
             The finite element integral vector for the domain
         """
+        skfem = have_optional_dependency("skfem")
         # get primary domain mesh
         domain = child.domain[0]
         mesh = self.mesh[domain]
@@ -384,6 +387,7 @@ def boundary_integral_vector(self, domain, region):
         :class:`pybamm.Matrix`
             The finite element integral vector for the domain
         """
+        skfem = have_optional_dependency("skfem")
         # get primary domain mesh
         mesh = self.mesh[domain[0]]
 
@@ -501,6 +505,7 @@ def assemble_mass_form(self, symbol, boundary_conditions, region="interior"):
         :class:`pybamm.Matrix`
             The (sparse) mass matrix for the spatial method.
         """
+        skfem = have_optional_dependency("skfem")
         # get primary domain mesh
         domain = symbol.domain[0]
         mesh = self.mesh[domain]

From 9ee911bd3cb3a16b869e17a62c0dcc26c16aca11 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Wed, 1 Nov 2023 15:26:41 +0530
Subject: [PATCH 116/199] Resolve `sympy` based methods

---
 pybamm/expression_tree/array.py                          | 3 ++-
 pybamm/expression_tree/binary_operators.py               | 6 +++++-
 pybamm/expression_tree/concatenations.py                 | 3 ++-
 pybamm/expression_tree/functions.py                      | 5 ++++-
 pybamm/expression_tree/independent_variable.py           | 5 +++--
 pybamm/expression_tree/operations/latexify.py            | 6 ++++--
 pybamm/expression_tree/parameter.py                      | 4 +++-
 pybamm/expression_tree/printing/sympy_overrides.py       | 4 +++-
 pybamm/expression_tree/scalar.py                         | 4 ++--
 pybamm/expression_tree/symbol.py                         | 2 +-
 pybamm/expression_tree/unary_operators.py                | 9 ++++++---
 pybamm/expression_tree/variable.py                       | 3 ++-
 tests/unit/test_expression_tree/test_binary_operators.py | 3 ++-
 tests/unit/test_expression_tree/test_concatenations.py   | 3 ++-
 tests/unit/test_expression_tree/test_functions.py        | 3 ++-
 .../test_expression_tree/test_independent_variable.py    | 3 ++-
 tests/unit/test_expression_tree/test_parameter.py        | 5 +++--
 .../test_printing/test_sympy_overrides.py                | 4 ++--
 tests/unit/test_expression_tree/test_symbol.py           | 3 ++-
 tests/unit/test_expression_tree/test_unary_operators.py  | 9 ++++++---
 tests/unit/test_expression_tree/test_variable.py         | 3 ++-
 21 files changed, 60 insertions(+), 30 deletions(-)

diff --git a/pybamm/expression_tree/array.py b/pybamm/expression_tree/array.py
index a9141041b3..2736886d95 100644
--- a/pybamm/expression_tree/array.py
+++ b/pybamm/expression_tree/array.py
@@ -2,10 +2,10 @@
 # NumpyArray class
 #
 import numpy as np
-import sympy
 from scipy.sparse import csr_matrix, issparse
 
 import pybamm
+from pybamm.util import have_optional_dependency
 
 
 class Array(pybamm.Symbol):
@@ -125,6 +125,7 @@ def is_constant(self):
 
     def to_equation(self):
         """Returns the value returned by the node when evaluated."""
+        sympy = have_optional_dependency("sympy")
         entries_list = self.entries.tolist()
         return sympy.Array(entries_list)
 
diff --git a/pybamm/expression_tree/binary_operators.py b/pybamm/expression_tree/binary_operators.py
index 749384e9bc..9fc6d2642e 100644
--- a/pybamm/expression_tree/binary_operators.py
+++ b/pybamm/expression_tree/binary_operators.py
@@ -4,11 +4,11 @@
 import numbers
 
 import numpy as np
-import sympy
 from scipy.sparse import csr_matrix, issparse
 import functools
 
 import pybamm
+from pybamm.util import have_optional_dependency
 
 
 def _preprocess_binary(left, right):
@@ -147,6 +147,7 @@ def _sympy_operator(self, left, right):
 
     def to_equation(self):
         """Convert the node and its subtree into a SymPy equation."""
+        sympy = have_optional_dependency("sympy")
         if self.print_name is not None:
             return sympy.Symbol(self.print_name)
         else:
@@ -323,6 +324,7 @@ def _binary_evaluate(self, left, right):
 
     def _sympy_operator(self, left, right):
         """Override :meth:`pybamm.BinaryOperator._sympy_operator`"""
+        sympy = have_optional_dependency("sympy")
         left = sympy.Matrix(left)
         right = sympy.Matrix(right)
         return left * right
@@ -626,6 +628,7 @@ def _binary_new_copy(self, left, right):
 
     def _sympy_operator(self, left, right):
         """Override :meth:`pybamm.BinaryOperator._sympy_operator`"""
+        sympy = have_optional_dependency("sympy")
         return sympy.Min(left, right)
 
 
@@ -662,6 +665,7 @@ def _binary_new_copy(self, left, right):
 
     def _sympy_operator(self, left, right):
         """Override :meth:`pybamm.BinaryOperator._sympy_operator`"""
+        sympy = have_optional_dependency("sympy")
         return sympy.Max(left, right)
 
 
diff --git a/pybamm/expression_tree/concatenations.py b/pybamm/expression_tree/concatenations.py
index 2185a0fad6..1c82aff122 100644
--- a/pybamm/expression_tree/concatenations.py
+++ b/pybamm/expression_tree/concatenations.py
@@ -5,10 +5,10 @@
 from collections import defaultdict
 
 import numpy as np
-import sympy
 from scipy.sparse import issparse, vstack
 
 import pybamm
+from pybamm.util import have_optional_dependency
 
 
 class Concatenation(pybamm.Symbol):
@@ -135,6 +135,7 @@ def is_constant(self):
 
     def _sympy_operator(self, *children):
         """Apply appropriate SymPy operators."""
+        sympy = have_optional_dependency("sympy")
         self.concat_latex = tuple(map(sympy.latex, children))
 
         if self.print_name is not None:
diff --git a/pybamm/expression_tree/functions.py b/pybamm/expression_tree/functions.py
index ebfb313199..0c7e98b508 100644
--- a/pybamm/expression_tree/functions.py
+++ b/pybamm/expression_tree/functions.py
@@ -4,7 +4,6 @@
 import numbers
 
 import numpy as np
-import sympy
 from scipy import special
 
 import pybamm
@@ -202,6 +201,7 @@ def _sympy_operator(self, child):
 
     def to_equation(self):
         """Convert the node and its subtree into a SymPy equation."""
+        sympy = have_optional_dependency("sympy")
         if self.print_name is not None:
             return sympy.Symbol(self.print_name)
         else:
@@ -250,6 +250,7 @@ def _function_new_copy(self, children):
 
     def _sympy_operator(self, child):
         """Apply appropriate SymPy operators."""
+        sympy = have_optional_dependency("sympy")
         class_name = self.__class__.__name__.lower()
         sympy_function = getattr(sympy, class_name)
         return sympy_function(child)
@@ -267,6 +268,7 @@ def _function_diff(self, children, idx):
 
     def _sympy_operator(self, child):
         """Override :meth:`pybamm.Function._sympy_operator`"""
+        sympy = have_optional_dependency("sympy")
         return sympy.asinh(child)
 
 
@@ -287,6 +289,7 @@ def _function_diff(self, children, idx):
 
     def _sympy_operator(self, child):
         """Override :meth:`pybamm.Function._sympy_operator`"""
+        sympy = have_optional_dependency("sympy")
         return sympy.atan(child)
 
 
diff --git a/pybamm/expression_tree/independent_variable.py b/pybamm/expression_tree/independent_variable.py
index efeb73f8bc..4c139c30a8 100644
--- a/pybamm/expression_tree/independent_variable.py
+++ b/pybamm/expression_tree/independent_variable.py
@@ -1,9 +1,8 @@
 #
 # IndependentVariable class
 #
-import sympy
-
 import pybamm
+from pybamm.utili import have_optional_dependency
 
 KNOWN_COORD_SYS = ["cartesian", "cylindrical polar", "spherical polar"]
 
@@ -44,6 +43,7 @@ def _jac(self, variable):
 
     def to_equation(self):
         """Convert the node and its subtree into a SymPy equation."""
+        sympy = have_optional_dependency("sympy")
         if self.print_name is not None:
             return sympy.Symbol(self.print_name)
         else:
@@ -77,6 +77,7 @@ def _evaluate_for_shape(self):
 
     def to_equation(self):
         """Convert the node and its subtree into a SymPy equation."""
+        sympy = have_optional_dependency("sympy")
         return sympy.Symbol("t")
 
 
diff --git a/pybamm/expression_tree/operations/latexify.py b/pybamm/expression_tree/operations/latexify.py
index 67e0199656..9f2949069e 100644
--- a/pybamm/expression_tree/operations/latexify.py
+++ b/pybamm/expression_tree/operations/latexify.py
@@ -5,10 +5,9 @@
 import re
 import warnings
 
-import sympy
-
 import pybamm
 from pybamm.expression_tree.printing.sympy_overrides import custom_print_func
+from pybamm.util import have_optional_dependency
 
 
 def get_rng_min_max_name(rng, min_or_max):
@@ -88,6 +87,7 @@ def _get_bcs_displays(self, var):
         Returns a list of boundary condition equations with ranges in front of
         the equations.
         """
+        sympy = have_optional_dependency("sympy")
         bcs_eqn_list = []
         bcs = self.model.boundary_conditions.get(var, None)
 
@@ -118,6 +118,7 @@ def _get_bcs_displays(self, var):
 
     def _get_param_var(self, node):
         """Returns a list of parameters and a list of variables."""
+        sympy = have_optional_dependency("sympy")
         param_list = []
         var_list = []
         dfs_nodes = [node]
@@ -160,6 +161,7 @@ def _get_param_var(self, node):
         return param_list, var_list
 
     def latexify(self, output_variables=None):
+        sympy = have_optional_dependency("sympy")
         # Voltage is the default output variable if it exists
         if output_variables is None:
             if "Voltage [V]" in self.model.variables:
diff --git a/pybamm/expression_tree/parameter.py b/pybamm/expression_tree/parameter.py
index 10addae464..eebe77ad2f 100644
--- a/pybamm/expression_tree/parameter.py
+++ b/pybamm/expression_tree/parameter.py
@@ -5,9 +5,9 @@
 import sys
 
 import numpy as np
-import sympy
 
 import pybamm
+from pybamm.util import have_optional_dependency
 
 
 class Parameter(pybamm.Symbol):
@@ -44,6 +44,7 @@ def is_constant(self):
 
     def to_equation(self):
         """Convert the node and its subtree into a SymPy equation."""
+        sympy = have_optional_dependency("sympy")
         if self.print_name is not None:
             return sympy.Symbol(self.print_name)
         else:
@@ -217,6 +218,7 @@ def _evaluate_for_shape(self):
 
     def to_equation(self):
         """Convert the node and its subtree into a SymPy equation."""
+        sympy = have_optional_dependency("sympy")
         if self.print_name is not None:
             return sympy.Symbol(self.print_name)
         else:
diff --git a/pybamm/expression_tree/printing/sympy_overrides.py b/pybamm/expression_tree/printing/sympy_overrides.py
index a96aa19729..59f9567c5d 100644
--- a/pybamm/expression_tree/printing/sympy_overrides.py
+++ b/pybamm/expression_tree/printing/sympy_overrides.py
@@ -3,9 +3,10 @@
 #
 import re
 
-from sympy.printing.latex import LatexPrinter
+from pybamm.util import have_optional_dependency
 
 
+LatexPrinter = have_optional_dependency("sympy.printing.latex","LatexPrinter")
 class CustomPrint(LatexPrinter):
     """Override SymPy methods to match PyBaMM's requirements"""
 
@@ -21,4 +22,5 @@ def _print_Derivative(self, expr):
 
 
 def custom_print_func(expr, **settings):
+    have_optional_dependency("sympy.printing.latex","LatexPrinter")
     return CustomPrint().doprint(expr)
diff --git a/pybamm/expression_tree/scalar.py b/pybamm/expression_tree/scalar.py
index 3149bf7bee..0209c02a8e 100644
--- a/pybamm/expression_tree/scalar.py
+++ b/pybamm/expression_tree/scalar.py
@@ -2,10 +2,9 @@
 # Scalar class
 #
 import numpy as np
-import sympy
 
 import pybamm
-
+from pybamm.util import have_optional_dependency
 
 class Scalar(pybamm.Symbol):
     """
@@ -70,6 +69,7 @@ def is_constant(self):
 
     def to_equation(self):
         """Returns the value returned by the node when evaluated."""
+        sympy = have_optional_dependency("sympy")
         if self.print_name is not None:
             return sympy.Symbol(self.print_name)
         else:
diff --git a/pybamm/expression_tree/symbol.py b/pybamm/expression_tree/symbol.py
index 8ad717f7ff..85c392e590 100644
--- a/pybamm/expression_tree/symbol.py
+++ b/pybamm/expression_tree/symbol.py
@@ -4,7 +4,6 @@
 import numbers
 
 import numpy as np
-import sympy
 from scipy.sparse import csr_matrix, issparse
 from functools import lru_cache, cached_property
 
@@ -987,4 +986,5 @@ def print_name(self, name):
         self._print_name = prettify_print_name(name)
 
     def to_equation(self):
+        sympy = have_optional_dependency("sympy")
         return sympy.Symbol(str(self.name))
diff --git a/pybamm/expression_tree/unary_operators.py b/pybamm/expression_tree/unary_operators.py
index 7f9c45775c..e555f48455 100644
--- a/pybamm/expression_tree/unary_operators.py
+++ b/pybamm/expression_tree/unary_operators.py
@@ -4,11 +4,9 @@
 import numbers
 
 import numpy as np
-import sympy
 from scipy.sparse import csr_matrix, issparse
-from sympy.vector.operators import Divergence as sympy_Divergence
-from sympy.vector.operators import Gradient as sympy_Gradient
 import pybamm
+from pybamm.util import have_optional_dependency
 
 
 class UnaryOperator(pybamm.Symbol):
@@ -83,6 +81,7 @@ def _sympy_operator(self, child):
 
     def to_equation(self):
         """Convert the node and its subtree into a SymPy equation."""
+        sympy = have_optional_dependency("sympy")
         if self.print_name is not None:
             return sympy.Symbol(self.print_name)
         else:
@@ -368,6 +367,7 @@ def _unary_new_copy(self, child):
 
     def _sympy_operator(self, child):
         """Override :meth:`pybamm.UnaryOperator._sympy_operator`"""
+        sympy_Gradient = have_optional_dependency("sympy.vector.operators","Gradient")
         return sympy_Gradient(child)
 
 
@@ -403,6 +403,7 @@ def _unary_new_copy(self, child):
 
     def _sympy_operator(self, child):
         """Override :meth:`pybamm.UnaryOperator._sympy_operator`"""
+        sympy_Divergence = have_optional_dependency("sympy.vector.operators","Divergence")
         return sympy_Divergence(child)
 
 
@@ -579,6 +580,7 @@ def _evaluates_on_edges(self, dimension):
 
     def _sympy_operator(self, child):
         """Override :meth:`pybamm.UnaryOperator._sympy_operator`"""
+        sympy = have_optional_dependency("sympy")
         return sympy.Integral(child, sympy.Symbol("xn"))
 
 
@@ -889,6 +891,7 @@ def _unary_new_copy(self, child):
 
     def _sympy_operator(self, child):
         """Override :meth:`pybamm.UnaryOperator._sympy_operator`"""
+        sympy = have_optional_dependency("sympy")
         if (
             self.child.domain[0] in ["negative particle", "positive particle"]
             and self.side == "right"
diff --git a/pybamm/expression_tree/variable.py b/pybamm/expression_tree/variable.py
index f9f7d94efc..0d1e1fd424 100644
--- a/pybamm/expression_tree/variable.py
+++ b/pybamm/expression_tree/variable.py
@@ -3,9 +3,9 @@
 #
 
 import numpy as np
-import sympy
 import numbers
 import pybamm
+from pybamm.util import have_optional_dependency
 
 
 class VariableBase(pybamm.Symbol):
@@ -124,6 +124,7 @@ def _evaluate_for_shape(self):
 
     def to_equation(self):
         """Convert the node and its subtree into a SymPy equation."""
+        sympy = have_optional_dependency("sympy")
         if self.print_name is not None:
             return sympy.Symbol(self.print_name)
         else:
diff --git a/tests/unit/test_expression_tree/test_binary_operators.py b/tests/unit/test_expression_tree/test_binary_operators.py
index 6acd7c41b0..225f8e93c9 100644
--- a/tests/unit/test_expression_tree/test_binary_operators.py
+++ b/tests/unit/test_expression_tree/test_binary_operators.py
@@ -5,10 +5,10 @@
 import unittest
 
 import numpy as np
-import sympy
 from scipy.sparse import coo_matrix
 
 import pybamm
+from pybamm.util import have_optional_dependency
 
 
 class TestBinaryOperators(TestCase):
@@ -746,6 +746,7 @@ def test_inner_simplifications(self):
         self.assertEqual(pybamm.inner(a3, a3).evaluate(), 9)
 
     def test_to_equation(self):
+        sympy = have_optional_dependency("sympy")
         # Test print_name
         pybamm.Addition.print_name = "test"
         self.assertEqual(pybamm.Addition(1, 2).to_equation(), sympy.Symbol("test"))
diff --git a/tests/unit/test_expression_tree/test_concatenations.py b/tests/unit/test_expression_tree/test_concatenations.py
index df5add0f98..4b07b09fea 100644
--- a/tests/unit/test_expression_tree/test_concatenations.py
+++ b/tests/unit/test_expression_tree/test_concatenations.py
@@ -5,9 +5,9 @@
 from tests import TestCase
 
 import numpy as np
-import sympy
 
 import pybamm
+from pybamm.util import have_optional_dependency
 from tests import get_discretisation_for_testing, get_mesh_for_testing
 
 
@@ -370,6 +370,7 @@ def test_numpy_concatenation(self):
         )
 
     def test_to_equation(self):
+        sympy = have_optional_dependency("sympy")
         a = pybamm.Symbol("a", domain="test a")
         b = pybamm.Symbol("b", domain="test b")
         func_symbol = sympy.Symbol(r"\begin{cases}a\\b\end{cases}")
diff --git a/tests/unit/test_expression_tree/test_functions.py b/tests/unit/test_expression_tree/test_functions.py
index ac5410d9e1..6d22571a01 100644
--- a/tests/unit/test_expression_tree/test_functions.py
+++ b/tests/unit/test_expression_tree/test_functions.py
@@ -5,10 +5,10 @@
 import unittest
 
 import numpy as np
-import sympy
 from scipy import special
 
 import pybamm
+from pybamm.util import have_optional_dependency
 
 
 def test_function(arg):
@@ -120,6 +120,7 @@ def test_function_unnamed(self):
         self.assertEqual(fun.name, "function (cos)")
 
     def test_to_equation(self):
+        sympy = have_optional_dependency("sympy")
         a = pybamm.Symbol("a", domain="test")
 
         # Test print_name
diff --git a/tests/unit/test_expression_tree/test_independent_variable.py b/tests/unit/test_expression_tree/test_independent_variable.py
index 95141f0f03..b748a6fbe9 100644
--- a/tests/unit/test_expression_tree/test_independent_variable.py
+++ b/tests/unit/test_expression_tree/test_independent_variable.py
@@ -4,9 +4,9 @@
 from tests import TestCase
 import unittest
 
-import sympy
 
 import pybamm
+from pybamm.util import have_optional_dependency
 
 
 class TestIndependentVariable(TestCase):
@@ -64,6 +64,7 @@ def test_spatial_variable_edge(self):
         self.assertTrue(x.evaluates_on_edges("primary"))
 
     def test_to_equation(self):
+        sympy = have_optional_dependency("sympy")
         # Test print_name
         func = pybamm.IndependentVariable("a")
         func.print_name = "test"
diff --git a/tests/unit/test_expression_tree/test_parameter.py b/tests/unit/test_expression_tree/test_parameter.py
index f67ee2dd62..d9a756b45d 100644
--- a/tests/unit/test_expression_tree/test_parameter.py
+++ b/tests/unit/test_expression_tree/test_parameter.py
@@ -5,9 +5,8 @@
 import numbers
 import unittest
 
-import sympy
-
 import pybamm
+from pybamm.util import have_optional_dependency
 
 
 class TestParameter(TestCase):
@@ -21,6 +20,7 @@ def test_evaluate_for_shape(self):
         self.assertIsInstance(a.evaluate_for_shape(), numbers.Number)
 
     def test_to_equation(self):
+        sympy = have_optional_dependency("sympy")
         func = pybamm.Parameter("test_string")
         func1 = pybamm.Parameter("test_name")
 
@@ -98,6 +98,7 @@ def _myfun(x):
         self.assertEqual(_myfun(x).print_name, None)
 
     def test_function_parameter_to_equation(self):
+        sympy = have_optional_dependency("sympy")
         func = pybamm.FunctionParameter("test", {"x": pybamm.Scalar(1)})
         func1 = pybamm.FunctionParameter("func", {"var": pybamm.Variable("var")})
 
diff --git a/tests/unit/test_expression_tree/test_printing/test_sympy_overrides.py b/tests/unit/test_expression_tree/test_printing/test_sympy_overrides.py
index b5ae229ae5..de3ff08c43 100644
--- a/tests/unit/test_expression_tree/test_printing/test_sympy_overrides.py
+++ b/tests/unit/test_expression_tree/test_printing/test_sympy_overrides.py
@@ -4,14 +4,14 @@
 from tests import TestCase
 import unittest
 
-import sympy
-
 import pybamm
 from pybamm.expression_tree.printing.sympy_overrides import custom_print_func
+from pybamm.util import have_optional_dependency
 
 
 class TestCustomPrint(TestCase):
     def test_print_Derivative(self):
+        sympy = have_optional_dependency("sympy")
         # Test force_partial
         der1 = sympy.Derivative("y", "x")
         der1.force_partial = True
diff --git a/tests/unit/test_expression_tree/test_symbol.py b/tests/unit/test_expression_tree/test_symbol.py
index 3f91633fbe..3eb7adae47 100644
--- a/tests/unit/test_expression_tree/test_symbol.py
+++ b/tests/unit/test_expression_tree/test_symbol.py
@@ -8,10 +8,10 @@
 
 import numpy as np
 from scipy.sparse import csr_matrix, coo_matrix
-import sympy
 
 import pybamm
 from pybamm.expression_tree.binary_operators import _Heaviside
+from pybamm.util import have_optional_dependency
 
 
 class TestSymbol(TestCase):
@@ -484,6 +484,7 @@ def test_test_shape(self):
             (y1 + y2).test_shape()
 
     def test_to_equation(self):
+        sympy = have_optional_dependency("sympy")
         self.assertEqual(pybamm.Symbol("test").to_equation(), sympy.Symbol("test"))
 
     def test_numpy_array_ufunc(self):
diff --git a/tests/unit/test_expression_tree/test_unary_operators.py b/tests/unit/test_expression_tree/test_unary_operators.py
index b0513c974b..d8bf30d79f 100644
--- a/tests/unit/test_expression_tree/test_unary_operators.py
+++ b/tests/unit/test_expression_tree/test_unary_operators.py
@@ -5,12 +5,10 @@
 from tests import TestCase
 
 import numpy as np
-import sympy
 from scipy.sparse import diags
-from sympy.vector.operators import Divergence as sympy_Divergence
-from sympy.vector.operators import Gradient as sympy_Gradient
 
 import pybamm
+from pybamm.util import have_optional_dependency
 
 
 class TestUnaryOperators(TestCase):
@@ -613,6 +611,11 @@ def test_not_constant(self):
         self.assertFalse((2 * a).is_constant())
 
     def test_to_equation(self):
+
+        sympy = have_optional_dependency("sympy")
+        sympy_Divergence = have_optional_dependency("sympy.vector.operators","Divergence")
+        sympy_Gradient = have_optional_dependency("sympy.vector.operators","Gradient")
+
         a = pybamm.Symbol("a", domain="negative particle")
         b = pybamm.Symbol("b", domain="current collector")
         c = pybamm.Symbol("c", domain="test")
diff --git a/tests/unit/test_expression_tree/test_variable.py b/tests/unit/test_expression_tree/test_variable.py
index be791903e2..583008f882 100644
--- a/tests/unit/test_expression_tree/test_variable.py
+++ b/tests/unit/test_expression_tree/test_variable.py
@@ -5,9 +5,9 @@
 import unittest
 
 import numpy as np
-import sympy
 
 import pybamm
+from pybamm.util import have_optional_dependency
 
 
 class TestVariable(TestCase):
@@ -55,6 +55,7 @@ def test_variable_bounds(self):
             pybamm.Variable("var", bounds=(1, 1))
 
     def test_to_equation(self):
+        sympy = have_optional_dependency("sympy")
         # Test print_name
         func = pybamm.Variable("test_string")
         func.print_name = "test"

From efe887747422cf379b706af19e2dfd1116c5a5b7 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Wed, 1 Nov 2023 15:43:29 +0530
Subject: [PATCH 117/199] Fix Typo

---
 pybamm/expression_tree/independent_variable.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pybamm/expression_tree/independent_variable.py b/pybamm/expression_tree/independent_variable.py
index 4c139c30a8..2f30da9a5e 100644
--- a/pybamm/expression_tree/independent_variable.py
+++ b/pybamm/expression_tree/independent_variable.py
@@ -2,7 +2,7 @@
 # IndependentVariable class
 #
 import pybamm
-from pybamm.utili import have_optional_dependency
+from pybamm.util import have_optional_dependency
 
 KNOWN_COORD_SYS = ["cartesian", "cylindrical polar", "spherical polar"]
 

From 911105521377a8fa8e014a31f5c3e5a2a4b1fe7e Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Wed, 1 Nov 2023 15:55:42 +0530
Subject: [PATCH 118/199] Return more helpful message

---
 pybamm/util.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/pybamm/util.py b/pybamm/util.py
index a2625e5405..9af22a8ab3 100644
--- a/pybamm/util.py
+++ b/pybamm/util.py
@@ -355,11 +355,11 @@ def have_optional_dependency(module_name, attribute=None):
                 imported_attribute = getattr(module, attribute)
                 return imported_attribute
             else:
-                raise ImportError(f"{module_name}.{attribute} is not available.")
+                raise ImportError(f"Optional dependency {module_name}.{attribute} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details.")
         else:
             return module
     except ImportError:
         if attribute:
-            raise ImportError(f"{module_name}.{attribute} is not available.")
+            raise ImportError(f"Optional dependency {module_name}.{attribute} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details.")
         else:
-            raise ImportError(f"{module_name} module is not available.")
+            raise ImportError(f"Optional dependency {module_name} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details.")

From c65a2a29e785f71bfeb4905f45761a7c394000a9 Mon Sep 17 00:00:00 2001
From: Arjun <arjunverma.oc@gmail.com>
Date: Fri, 3 Nov 2023 14:12:08 +0530
Subject: [PATCH 119/199] Abstraction to only show module name if not available

Co-authored-by: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
---
 pybamm/util.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pybamm/util.py b/pybamm/util.py
index 9af22a8ab3..8656f00701 100644
--- a/pybamm/util.py
+++ b/pybamm/util.py
@@ -355,11 +355,11 @@ def have_optional_dependency(module_name, attribute=None):
                 imported_attribute = getattr(module, attribute)
                 return imported_attribute
             else:
-                raise ImportError(f"Optional dependency {module_name}.{attribute} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details.")
+                raise ImportError(f"Optional dependency {module_name} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details.")
         else:
             return module
     except ImportError:
         if attribute:
-            raise ImportError(f"Optional dependency {module_name}.{attribute} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details.")
+            raise ImportError(f"Optional dependency {module_name} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details.")
         else:
             raise ImportError(f"Optional dependency {module_name} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details.")

From 4d32e32c7ac0a195d532f266de9dfaab26e11df7 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Fri, 3 Nov 2023 23:48:03 +0530
Subject: [PATCH 120/199] Update docs for have_optional_deps

---
 CONTRIBUTING.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index bec0fee02a..8eceda7972 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -100,9 +100,9 @@ On the other hand... We _do_ want to compare several tools, to generate document
 
 Only 'core pybamm' is installed by default. The others have to be specified explicitly when running the installation command.
 
-### Matplotlib
+### Managing Optional Dependencies and Their Imports
 
-We use Matplotlib in PyBaMM, but with two caveats:
+PyBaMM utilizes optional dependencies to allow users to choose which additional libraries they want to use. Managing these optional dependencies and their imports is essential to provide flexibility to PyBaMM users.
 
 First, Matplotlib should only be used in plotting methods, and these should _never_ be called by other PyBaMM methods. So users who don't like Matplotlib will not be forced to use it in any way. Use in notebooks is OK and encouraged.
 

From fd0916322fd390fee7502d9022d8e5536c59124a Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Mon, 6 Nov 2023 15:50:06 +0530
Subject: [PATCH 121/199] Update for `have_optional_dependency`

---
 CONTRIBUTING.md | 25 ++++++++++++++-----------
 1 file changed, 14 insertions(+), 11 deletions(-)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 8eceda7972..648996a024 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -54,7 +54,7 @@ You now have everything you need to start making changes!
 10. [Test your code!](#testing)
 11. PyBaMM has online documentation at http://docs.pybamm.org/. To make sure any new methods or classes you added show up there, please read the [documentation](#documentation) section.
 12. If you added a major new feature, perhaps it should be showcased in an [example notebook](#example-notebooks).
-13. When you feel your code is finished, or at least warrants serious discussion, run the [pre-commit checks](#pre-commit-checks) and then create a [pull request](https://help.github.com/articles/about-pull-requests/) (PR) on [PyBaMM's GitHub page](https://github.com/pybamm-team/PyBaMM).
+13. When you feel your code is finished, or at least warrants serious discussion, run the [pre-commit checks](#pre-commit-checks) and then create a [pull request](https://help.github.com/articles/about-pull-requests/) (PR) on [PyBaMM&#39;s GitHub page](https://github.com/pybamm-team/PyBaMM).
 14. Once a PR has been created, it will be reviewed by any member of the community. Changes might be suggested which you can make by simply adding new commits to the branch. When everything's finished, someone with the right GitHub permissions will merge your changes into PyBaMM main repository.
 
 Finally, if you really, really, _really_ love developing PyBaMM, have a look at the current [project infrastructure](#infrastructure).
@@ -104,17 +104,25 @@ Only 'core pybamm' is installed by default. The others have to be specified expl
 
 PyBaMM utilizes optional dependencies to allow users to choose which additional libraries they want to use. Managing these optional dependencies and their imports is essential to provide flexibility to PyBaMM users.
 
-First, Matplotlib should only be used in plotting methods, and these should _never_ be called by other PyBaMM methods. So users who don't like Matplotlib will not be forced to use it in any way. Use in notebooks is OK and encouraged.
+PyBaMM provides a utility function `have_optional_dependency`, to check for the availability of optional dependencies within methods. This function can be used to conditionally import optional dependencies only if they are available. Here's how to use it:
 
-Second, Matplotlib should never be imported at the module level, but always inside methods. For example:
+Optional Dependencies should never be imported at the module level, but always inside methods. For example:
 
 ```
-def plot_great_things(self, x, y, z):
-    import matplotlib.pyplot as pl
+def use_pybtex(x,y,z):
+    pybtex = have_optional_dependency("pybtex")
     ...
 ```
 
-This allows people to (1) use PyBaMM without ever importing Matplotlib and (2) configure Matplotlib's back-end in their scripts, which _must_ be done before e.g. `pyplot` is first imported.
+While importing a specific attribute instead of whole module:
+
+```
+def use_parse_file(x,y,z):
+    parse_file = have_optional_dependency("pybtex.database","parse_file")
+    ...
+```
+
+This allows people to (1) use PyBaMM without importing Optional dependency by default and (2) configure module dependent functionality in their scripts, which _must_ be done before e.g. `print_citations` method is first imported.
 
 ## Testing
 
@@ -266,7 +274,6 @@ This also means that, if you can't fix the bug yourself, it will be much easier
       ```
 
       This will start the debugger at the point where the `ValueError` was raised, and allow you to investigate further. Sometimes, it is more informative to put the try-except block further up the call stack than exactly where the error is raised.
-
    2. Warnings. If functions are raising warnings instead of errors, it can be hard to pinpoint where this is coming from. Here, you can use the `warnings` module to convert warnings to errors:
 
       ```python
@@ -276,7 +283,6 @@ This also means that, if you can't fix the bug yourself, it will be much easier
       ```
 
       Then you can use a try-except block, as in a., but with, for example, `RuntimeWarning` instead of `ValueError`.
-
    3. Stepping through the expression tree. Most calls in PyBaMM are operations on [expression trees](https://github.com/pybamm-team/PyBaMM/blob/develop/docs/source/examples/notebooks/expression_tree/expression-tree.ipynb). To view an expression tree in ipython, you can use the `render` command:
 
       ```python
@@ -284,11 +290,8 @@ This also means that, if you can't fix the bug yourself, it will be much easier
       ```
 
       You can then step through the expression tree, using the `children` attribute, to pinpoint exactly where a bug is coming from. For example, if `expression_tree.jac(y)` is failing, you can check `expression_tree.children[0].jac(y)`, then `expression_tree.children[0].children[0].jac(y)`, etc.
-
 3. To isolate whether a bug is in a model, its Jacobian or its simplified version, you can set the `use_jacobian` and/or `use_simplify` attributes of the model to `False` (they are both `True` by default for most models).
-
 4. If a model isn't giving the answer you expect, you can try comparing it to other models. For example, you can investigate parameter limits in which two models should give the same answer by setting some parameters to be small or zero. The `StandardOutputComparison` class can be used to compare some standard outputs from battery models.
-
 5. To get more information about what is going on under the hood, and hence understand what is causing the bug, you can set the [logging](https://realpython.com/python-logging/) level to `DEBUG` by adding the following line to your test or script:
 
    ```python3

From 926f8d74b6a40c98a3c9a40a40af5ffb4fad154b Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Mon, 6 Nov 2023 16:00:02 +0530
Subject: [PATCH 122/199] Add comments to `have_optional_dependency`

---
 pybamm/util.py | 11 +++++++++--
 1 file changed, 9 insertions(+), 2 deletions(-)

diff --git a/pybamm/util.py b/pybamm/util.py
index 8656f00701..78a5cff27d 100644
--- a/pybamm/util.py
+++ b/pybamm/util.py
@@ -346,19 +346,26 @@ def install_jax(arguments=None):  # pragma: no cover
         ]
     )
 
-
+# https://docs.pybamm.org/en/latest/source/user_guide/contributing.html#managing-optional-dependencies-and-their-imports
 def have_optional_dependency(module_name, attribute=None):
     try:
+        # Attempt to import the specified module
         module = importlib.import_module(module_name)
+
         if attribute:
+            # If an attribute is specified, check if it's available
             if hasattr(module, attribute):
                 imported_attribute = getattr(module, attribute)
-                return imported_attribute
+                return imported_attribute  # Return the imported attribute
             else:
+                # Raise an ImportError if the attribute is not available
                 raise ImportError(f"Optional dependency {module_name} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details.")
         else:
+            # Return the entire module if no attribute is specified
             return module
+
     except ImportError:
+        # Raise an ImportError if the module or attribute is not available
         if attribute:
             raise ImportError(f"Optional dependency {module_name} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details.")
         else:

From 29e70898778934b58549cd3dd0c9902a7e52be43 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 6 Nov 2023 19:21:44 +0000
Subject: [PATCH 123/199] chore: update pre-commit hooks
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

updates:
- [github.com/astral-sh/ruff-pre-commit: v0.1.3 → v0.1.4](https://github.com/astral-sh/ruff-pre-commit/compare/v0.1.3...v0.1.4)
---
 .pre-commit-config.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 5d7c85492f..fa0de6f56c 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -4,7 +4,7 @@ ci:
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: "v0.1.3"
+    rev: "v0.1.4"
     hooks:
       - id: ruff
         args: [--fix, --show-fixes]

From 0f739bf202ab752ae7e6aea5e56341b0062f7cf3 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 6 Nov 2023 19:21:58 +0000
Subject: [PATCH 124/199] style: pre-commit fixes

---
 docs/source/examples/notebooks/models/lithium-plating.ipynb | 1 -
 1 file changed, 1 deletion(-)

diff --git a/docs/source/examples/notebooks/models/lithium-plating.ipynb b/docs/source/examples/notebooks/models/lithium-plating.ipynb
index a7329b0b70..57049a0ea7 100644
--- a/docs/source/examples/notebooks/models/lithium-plating.ipynb
+++ b/docs/source/examples/notebooks/models/lithium-plating.ipynb
@@ -29,7 +29,6 @@
     "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import os\n",
-    "import matplotlib.pyplot as plt\n",
     "os.chdir(pybamm.__path__[0]+'/..')"
    ]
   },

From f25b957f5b9cc2ed61be7884106cc9ff80fa5d46 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 7 Nov 2023 21:43:37 +0530
Subject: [PATCH 125/199] Fix `gfortran` installation

for #3475
---
 .github/workflows/run_periodic_tests.yml | 11 +++--------
 .github/workflows/test_on_push.yml       |  4 ++++
 2 files changed, 7 insertions(+), 8 deletions(-)

diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml
index f6e51bc11b..2fdf19f56d 100644
--- a/.github/workflows/run_periodic_tests.yml
+++ b/.github/workflows/run_periodic_tests.yml
@@ -66,19 +66,14 @@ jobs:
           sudo apt install gfortran gcc libopenblas-dev graphviz pandoc
           sudo apt install texlive-full
 
-      # Added fixes to homebrew installs:
-      # rm -f /usr/local/bin/2to3
-      # (see https://github.com/actions/virtual-environments/issues/2322)
+      # sometimes gfortran cannot be found, so reinstall gcc just to be sure
       - name: Install MacOS system dependencies
         if: matrix.os == 'macos-latest'
-        run: |
-          rm -f /usr/local/bin/2to3*
-          rm -f /usr/local/bin/idle3*
-          rm -f /usr/local/bin/pydoc3*
-          rm -f /usr/local/bin/python3*
+        run:
           brew update
           brew install graphviz
           brew install openblas
+          brew reinstall gcc
 
       - name: Install Windows system dependencies
         if: matrix.os == 'windows-latest'
diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index cb22fb87f7..1ccdb48213 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -64,6 +64,7 @@ jobs:
           sudo dot -c
           sudo apt-get install libopenblas-dev texlive-latex-extra dvipng
 
+      # sometimes gfortran cannot be found, so reinstall gcc just to be sure
       - name: Install macOS system dependencies
         if: matrix.os == 'macos-latest'
         env:
@@ -77,6 +78,7 @@ jobs:
           brew analytics off
           brew update
           brew install graphviz openblas
+          brew reinstall gcc
 
       - name: Install Windows system dependencies
         if: matrix.os == 'windows-latest'
@@ -207,6 +209,7 @@ jobs:
           sudo dot -c
           sudo apt-get install libopenblas-dev texlive-latex-extra dvipng
 
+      # sometimes gfortran cannot be found, so reinstall gcc just to be sure
       - name: Install macOS system dependencies
         if: matrix.os == 'macos-latest'
         env:
@@ -220,6 +223,7 @@ jobs:
           brew analytics off
           brew update
           brew install graphviz openblas
+          brew reinstall gcc
 
       - name: Install Windows system dependencies
         if: matrix.os == 'windows-latest'

From 65a81e37175849fae9cba876799802e6c62fe556 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 7 Nov 2023 22:45:33 +0530
Subject: [PATCH 126/199] Re-install OpenBLAS `scipy` meson linkage errors

---
 .github/workflows/run_periodic_tests.yml | 2 +-
 .github/workflows/test_on_push.yml       | 3 ++-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml
index 2fdf19f56d..2322adf993 100644
--- a/.github/workflows/run_periodic_tests.yml
+++ b/.github/workflows/run_periodic_tests.yml
@@ -72,7 +72,7 @@ jobs:
         run:
           brew update
           brew install graphviz
-          brew install openblas
+          brew reinstall openblas
           brew reinstall gcc
 
       - name: Install Windows system dependencies
diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 1ccdb48213..df114224b3 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -222,8 +222,9 @@ jobs:
         run: |
           brew analytics off
           brew update
-          brew install graphviz openblas
+          brew install graphviz
           brew reinstall gcc
+          brew reinstall openblas
 
       - name: Install Windows system dependencies
         if: matrix.os == 'windows-latest'

From ec963d1ae3dbaf673e6f512156f6cddc36f4e52b Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Wed, 8 Nov 2023 23:19:19 +0530
Subject: [PATCH 127/199] Add `test_have_optional_dependency`

---
 tests/unit/test_util.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/tests/unit/test_util.py b/tests/unit/test_util.py
index c5060e65a6..8f706d8149 100644
--- a/tests/unit/test_util.py
+++ b/tests/unit/test_util.py
@@ -88,6 +88,10 @@ def test_git_commit_info(self):
         self.assertIsInstance(git_commit_info, str)
         self.assertEqual(git_commit_info[:2], "v2")
 
+    def test_have_optional_dependency(self):
+        with self.assertRaisesRegex(ImportError,"Optional dependency pybtex is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details."):
+            pybamm.print_citations()
+
 
 class TestSearch(TestCase):
     def test_url_gets_to_stdout(self):

From 3aa79ca2d44f2f7298504a5a20e96d72558e27cc Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 9 Nov 2023 18:28:27 +0530
Subject: [PATCH 128/199] Temporarily remove lower bounds: `numpy`, `scipy`

---
 setup.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/setup.py b/setup.py
index f6fd37f75c..70bb69a56e 100644
--- a/setup.py
+++ b/setup.py
@@ -203,9 +203,9 @@ def compile_KLU():
     ],
     # List of dependencies
     install_requires=[
-        "numpy>=1.16",
-        "scipy>=1.3",
-        "casadi>=3.6.0",
+        "numpy",
+        "scipy",
+        "casadi",
         "xarray",
     ],
     extras_require={

From dd8a6f21d573f397767b97b662cd26c591d06e0d Mon Sep 17 00:00:00 2001
From: Arjun <arjunverma.oc@gmail.com>
Date: Thu, 9 Nov 2023 18:52:12 +0530
Subject: [PATCH 129/199] Apply suggestions from code review

Co-authored-by: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
---
 CONTRIBUTING.md                                         | 8 ++++----
 pybamm/expression_tree/unary_operators.py               | 4 ++--
 pybamm/util.py                                          | 4 ++--
 tests/unit/test_expression_tree/test_unary_operators.py | 4 ++--
 4 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 648996a024..78fbb0fdec 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -106,7 +106,7 @@ PyBaMM utilizes optional dependencies to allow users to choose which additional
 
 PyBaMM provides a utility function `have_optional_dependency`, to check for the availability of optional dependencies within methods. This function can be used to conditionally import optional dependencies only if they are available. Here's how to use it:
 
-Optional Dependencies should never be imported at the module level, but always inside methods. For example:
+Optional dependencies should never be imported at the module level, but always inside methods. For example:
 
 ```
 def use_pybtex(x,y,z):
@@ -114,15 +114,15 @@ def use_pybtex(x,y,z):
     ...
 ```
 
-While importing a specific attribute instead of whole module:
+While importing a specific module instead of an entire package/library:
 
-```
+```python
 def use_parse_file(x,y,z):
     parse_file = have_optional_dependency("pybtex.database","parse_file")
     ...
 ```
 
-This allows people to (1) use PyBaMM without importing Optional dependency by default and (2) configure module dependent functionality in their scripts, which _must_ be done before e.g. `print_citations` method is first imported.
+This allows people to (1) use PyBaMM without importing optional dependencies by default and (2) configure module-dependent functionalities in their scripts, which _must_ be done before e.g. `print_citations` method is first imported.
 
 ## Testing
 
diff --git a/pybamm/expression_tree/unary_operators.py b/pybamm/expression_tree/unary_operators.py
index e555f48455..81c3dc28c2 100644
--- a/pybamm/expression_tree/unary_operators.py
+++ b/pybamm/expression_tree/unary_operators.py
@@ -367,7 +367,7 @@ def _unary_new_copy(self, child):
 
     def _sympy_operator(self, child):
         """Override :meth:`pybamm.UnaryOperator._sympy_operator`"""
-        sympy_Gradient = have_optional_dependency("sympy.vector.operators","Gradient")
+        sympy_Gradient = have_optional_dependency("sympy.vector.operators", "Gradient")
         return sympy_Gradient(child)
 
 
@@ -403,7 +403,7 @@ def _unary_new_copy(self, child):
 
     def _sympy_operator(self, child):
         """Override :meth:`pybamm.UnaryOperator._sympy_operator`"""
-        sympy_Divergence = have_optional_dependency("sympy.vector.operators","Divergence")
+        sympy_Divergence = have_optional_dependency("sympy.vector.operators", "Divergence")
         return sympy_Divergence(child)
 
 
diff --git a/pybamm/util.py b/pybamm/util.py
index 78a5cff27d..b6825f7eda 100644
--- a/pybamm/util.py
+++ b/pybamm/util.py
@@ -359,12 +359,12 @@ def have_optional_dependency(module_name, attribute=None):
                 return imported_attribute  # Return the imported attribute
             else:
                 # Raise an ImportError if the attribute is not available
-                raise ImportError(f"Optional dependency {module_name} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details.")
+                raise ModuleNotFoundError(f"Optional dependency {module_name} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details.")
         else:
             # Return the entire module if no attribute is specified
             return module
 
-    except ImportError:
+    except ModuleNotFoundError:
         # Raise an ImportError if the module or attribute is not available
         if attribute:
             raise ImportError(f"Optional dependency {module_name} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details.")
diff --git a/tests/unit/test_expression_tree/test_unary_operators.py b/tests/unit/test_expression_tree/test_unary_operators.py
index d8bf30d79f..fc845cb574 100644
--- a/tests/unit/test_expression_tree/test_unary_operators.py
+++ b/tests/unit/test_expression_tree/test_unary_operators.py
@@ -613,8 +613,8 @@ def test_not_constant(self):
     def test_to_equation(self):
 
         sympy = have_optional_dependency("sympy")
-        sympy_Divergence = have_optional_dependency("sympy.vector.operators","Divergence")
-        sympy_Gradient = have_optional_dependency("sympy.vector.operators","Gradient")
+        sympy_Divergence = have_optional_dependency("sympy.vector.operators", "Divergence")
+        sympy_Gradient = have_optional_dependency("sympy.vector.operators", "Gradient")
 
         a = pybamm.Symbol("a", domain="negative particle")
         b = pybamm.Symbol("b", domain="current collector")

From aa2327edd7dfd25298e7b4076902bca74814b880 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Thu, 9 Nov 2023 13:22:22 +0000
Subject: [PATCH 130/199] style: pre-commit fixes

---
 CONTRIBUTING.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 78fbb0fdec..de0d626940 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -117,8 +117,8 @@ def use_pybtex(x,y,z):
 While importing a specific module instead of an entire package/library:
 
 ```python
-def use_parse_file(x,y,z):
-    parse_file = have_optional_dependency("pybtex.database","parse_file")
+def use_parse_file(x, y, z):
+    parse_file = have_optional_dependency("pybtex.database", "parse_file")
     ...
 ```
 

From 8782a7af8f713e7802f07d3e68b2562469036075 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 9 Nov 2023 18:55:52 +0530
Subject: [PATCH 131/199] Exercise minimum version bounds: `numpy`, `scipy`,
 and `casadi`

---
 setup.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/setup.py b/setup.py
index 70bb69a56e..87dd89ec6b 100644
--- a/setup.py
+++ b/setup.py
@@ -203,9 +203,9 @@ def compile_KLU():
     ],
     # List of dependencies
     install_requires=[
-        "numpy",
-        "scipy",
-        "casadi",
+        "numpy>=1.24.4",
+        "scipy>=1.10.1",
+        "casadi>=3.6.3",
         "xarray",
     ],
     extras_require={

From dc1f6eddd4e8e838628ef496ce3d4d9a2a30ac79 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 9 Nov 2023 18:56:10 +0530
Subject: [PATCH 132/199] Remove redundant PyBaMM dependencies caching step

---
 .github/workflows/test_on_push.yml | 20 +++++++-------------
 1 file changed, 7 insertions(+), 13 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index df114224b3..2def84a60c 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -92,10 +92,9 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install PyBaMM dependencies
+      - name: Install Python dependencies
         run: |
           pip install --upgrade pip wheel setuptools
-          pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -152,10 +151,9 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install PyBaMM dependencies
+      - name: Install Python dependencies
         run: |
-          pip install --upgrade pip wheel setuptools nox
-          pip install -e .[all,docs]
+          pip install --upgrade pip wheel setuptools
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -238,10 +236,9 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install PyBaMM dependencies
+      - name: Install Python dependencies
         run: |
           pip install --upgrade pip wheel setuptools
-          pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -299,10 +296,9 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install PyBaMM dependencies
+      - name: Install Python dependencies
         run: |
           pip install --upgrade pip wheel setuptools
-          pip install -e .[all,docs]
 
       - name: Install docs dependencies and run doctests for GNU/Linux with Python 3.11
         run: pipx run nox -s doctests
@@ -344,10 +340,9 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install PyBaMM dependencies
+      - name: Install Python dependencies
         run: |
           pip install --upgrade pip wheel setuptools
-          pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -402,10 +397,9 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install PyBaMM dependencies
+      - name: Install Python dependencies
         run: |
           pip install --upgrade pip wheel setuptools
-          pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3

From 5a6f03cf8a308e6598b428f8631c647db11e1711 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 9 Nov 2023 19:32:12 +0530
Subject: [PATCH 133/199] Add a lower bound for `sympy`

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 87dd89ec6b..0335c89f77 100644
--- a/setup.py
+++ b/setup.py
@@ -241,7 +241,7 @@ def compile_KLU():
             "pybtex>=0.24.0",
         ],
         "latexify": [
-            "sympy>=1.8",
+            "sympy>=1.12",
         ],
         "bpx": [
             "bpx",

From 844fcec79a23248b3aa74adde3f9f61484da997b Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 9 Nov 2023 20:32:31 +0530
Subject: [PATCH 134/199] Clean up `brew` changes and re-trigger build

---
 .github/workflows/test_on_push.yml | 19 +++++++------------
 1 file changed, 7 insertions(+), 12 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 2def84a60c..b660f0a7c9 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -64,7 +64,6 @@ jobs:
           sudo dot -c
           sudo apt-get install libopenblas-dev texlive-latex-extra dvipng
 
-      # sometimes gfortran cannot be found, so reinstall gcc just to be sure
       - name: Install macOS system dependencies
         if: matrix.os == 'macos-latest'
         env:
@@ -78,7 +77,6 @@ jobs:
           brew analytics off
           brew update
           brew install graphviz openblas
-          brew reinstall gcc
 
       - name: Install Windows system dependencies
         if: matrix.os == 'windows-latest'
@@ -92,7 +90,7 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install Python dependencies
+      - name: Install standard Python dependencies
         run: |
           pip install --upgrade pip wheel setuptools
 
@@ -151,7 +149,7 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install Python dependencies
+      - name: Install standard Python dependencies
         run: |
           pip install --upgrade pip wheel setuptools
 
@@ -207,7 +205,6 @@ jobs:
           sudo dot -c
           sudo apt-get install libopenblas-dev texlive-latex-extra dvipng
 
-      # sometimes gfortran cannot be found, so reinstall gcc just to be sure
       - name: Install macOS system dependencies
         if: matrix.os == 'macos-latest'
         env:
@@ -220,9 +217,7 @@ jobs:
         run: |
           brew analytics off
           brew update
-          brew install graphviz
-          brew reinstall gcc
-          brew reinstall openblas
+          brew install graphviz openblas
 
       - name: Install Windows system dependencies
         if: matrix.os == 'windows-latest'
@@ -236,7 +231,7 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install Python dependencies
+      - name: Install standard Python dependencies
         run: |
           pip install --upgrade pip wheel setuptools
 
@@ -296,7 +291,7 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install Python dependencies
+      - name: Install standard Python dependencies
         run: |
           pip install --upgrade pip wheel setuptools
 
@@ -340,7 +335,7 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install Python dependencies
+      - name: Install standard Python dependencies
         run: |
           pip install --upgrade pip wheel setuptools
 
@@ -397,7 +392,7 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install Python dependencies
+      - name: Install standard Python dependencies
         run: |
           pip install --upgrade pip wheel setuptools
 

From c7aa1172dc8f3b3d9b7846fb0eabce5cc7f299ee Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 9 Nov 2023 20:39:08 +0530
Subject: [PATCH 135/199] Update `sympy>=1.12` for the `[all]` extra as well

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 0335c89f77..8bc6437945 100644
--- a/setup.py
+++ b/setup.py
@@ -276,7 +276,7 @@ def compile_KLU():
             "scikit-fem>=0.2.0",
             "imageio>=2.9.0",
             "pybtex>=0.24.0",
-            "sympy>=1.8",
+            "sympy>=1.12",
             "bpx",
             "tqdm",
             "matplotlib>=2.0",

From c28c7fbfe0f8a3405b2251cc84aa6b159a2891bb Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Thu, 9 Nov 2023 21:24:34 +0530
Subject: [PATCH 136/199] Raise simple ModuleNotFoundError even if attribute
 not found

---
 pybamm/util.py | 9 +++------
 1 file changed, 3 insertions(+), 6 deletions(-)

diff --git a/pybamm/util.py b/pybamm/util.py
index b6825f7eda..dee77b8841 100644
--- a/pybamm/util.py
+++ b/pybamm/util.py
@@ -358,15 +358,12 @@ def have_optional_dependency(module_name, attribute=None):
                 imported_attribute = getattr(module, attribute)
                 return imported_attribute  # Return the imported attribute
             else:
-                # Raise an ImportError if the attribute is not available
+                # Raise an ModuleNotFoundError if the attribute is not available
                 raise ModuleNotFoundError(f"Optional dependency {module_name} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details.")
         else:
             # Return the entire module if no attribute is specified
             return module
 
     except ModuleNotFoundError:
-        # Raise an ImportError if the module or attribute is not available
-        if attribute:
-            raise ImportError(f"Optional dependency {module_name} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details.")
-        else:
-            raise ImportError(f"Optional dependency {module_name} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details.")
+        # Raise an ModuleNotFoundError if the module or attribute is not available
+        raise ModuleNotFoundError(f"Optional dependency {module_name} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details.")

From fd9ae61636bc92cc31e3efab7765f601218281d7 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Thu, 9 Nov 2023 21:44:01 +0530
Subject: [PATCH 137/199] Set pybtex to None to avoid import

---
 tests/unit/test_util.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tests/unit/test_util.py b/tests/unit/test_util.py
index 8f706d8149..bfcac5fa5f 100644
--- a/tests/unit/test_util.py
+++ b/tests/unit/test_util.py
@@ -89,7 +89,8 @@ def test_git_commit_info(self):
         self.assertEqual(git_commit_info[:2], "v2")
 
     def test_have_optional_dependency(self):
-        with self.assertRaisesRegex(ImportError,"Optional dependency pybtex is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details."):
+        with self.assertRaisesRegex(ModuleNotFoundError,"Optional dependency pybtex.database is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details."):
+            sys.modules['pybtex'] = None
             pybamm.print_citations()
 
 

From 7cb2ef69250dba93955ca46468c17ee35ad581ee Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Thu, 9 Nov 2023 22:20:28 +0530
Subject: [PATCH 138/199] Add more testcases for optional dependencies

---
 tests/unit/test_util.py | 17 +++++++++++++++--
 1 file changed, 15 insertions(+), 2 deletions(-)

diff --git a/tests/unit/test_util.py b/tests/unit/test_util.py
index bfcac5fa5f..8edf4ad6ec 100644
--- a/tests/unit/test_util.py
+++ b/tests/unit/test_util.py
@@ -11,7 +11,8 @@
 from unittest.mock import patch
 from io import StringIO
 
-
+def test_function(arg):
+    return arg + arg
 class TestUtil(TestCase):
     """
     Test the functionality in util.py
@@ -89,9 +90,21 @@ def test_git_commit_info(self):
         self.assertEqual(git_commit_info[:2], "v2")
 
     def test_have_optional_dependency(self):
-        with self.assertRaisesRegex(ModuleNotFoundError,"Optional dependency pybtex.database is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details."):
+        with self.assertRaisesRegex(ModuleNotFoundError,"Optional dependency pybtex is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details."):
             sys.modules['pybtex'] = None
             pybamm.print_citations()
+        with self.assertRaisesRegex(ModuleNotFoundError,"Optional dependency tqdm is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details."):
+            sys.modules['tqdm'] = None
+            model = pybamm.BaseModel()
+            v = pybamm.Variable("v")
+            model.rhs = {v: -v}
+            model.initial_conditions = {v: 1}
+            sim = pybamm.Simulation(model)
+            sim.solve([0, 1])
+        with self.assertRaisesRegex(ModuleNotFoundError,"Optional dependency autograd is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details."):
+            sys.modules['autograd'] = None
+            a = pybamm.StateVector(slice(0, 1))
+            pybamm.Function(test_function, a)
 
 
 class TestSearch(TestCase):

From b681bbc69e15171429ee5ea48a1282ba83fdc30c Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Thu, 9 Nov 2023 23:54:54 +0530
Subject: [PATCH 139/199] Add test for case if dependency is available

---
 tests/unit/test_util.py | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/tests/unit/test_util.py b/tests/unit/test_util.py
index 8edf4ad6ec..7b6864f443 100644
--- a/tests/unit/test_util.py
+++ b/tests/unit/test_util.py
@@ -101,10 +101,9 @@ def test_have_optional_dependency(self):
             model.initial_conditions = {v: 1}
             sim = pybamm.Simulation(model)
             sim.solve([0, 1])
-        with self.assertRaisesRegex(ModuleNotFoundError,"Optional dependency autograd is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details."):
-            sys.modules['autograd'] = None
-            a = pybamm.StateVector(slice(0, 1))
-            pybamm.Function(test_function, a)
+
+        sys.modules['pybtex'] = pybamm.util.have_optional_dependency("pybtex")
+        pybamm.print_citations()
 
 
 class TestSearch(TestCase):

From f2e37cf0439ec06a994b13c957894c2f194d1dc9 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Fri, 10 Nov 2023 00:33:43 +0530
Subject: [PATCH 140/199] Reset pybtex to run dependent function

---
 tests/unit/test_util.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/tests/unit/test_util.py b/tests/unit/test_util.py
index 7b6864f443..3ac78986bb 100644
--- a/tests/unit/test_util.py
+++ b/tests/unit/test_util.py
@@ -91,6 +91,7 @@ def test_git_commit_info(self):
 
     def test_have_optional_dependency(self):
         with self.assertRaisesRegex(ModuleNotFoundError,"Optional dependency pybtex is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details."):
+            pybtex = sys.modules['pybtex']
             sys.modules['pybtex'] = None
             pybamm.print_citations()
         with self.assertRaisesRegex(ModuleNotFoundError,"Optional dependency tqdm is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details."):
@@ -102,7 +103,8 @@ def test_have_optional_dependency(self):
             sim = pybamm.Simulation(model)
             sim.solve([0, 1])
 
-        sys.modules['pybtex'] = pybamm.util.have_optional_dependency("pybtex")
+        sys.modules['pybtex'] = pybtex
+        pybamm.util.have_optional_dependency("pybtex")
         pybamm.print_citations()
 
 

From 8d6db99511e3bdbe6f20bacac6714c85fc4a0b11 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Fri, 10 Nov 2023 01:46:00 +0530
Subject: [PATCH 141/199] Add test for full coverage

---
 tests/unit/test_util.py | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/tests/unit/test_util.py b/tests/unit/test_util.py
index 3ac78986bb..a9f70bbcc7 100644
--- a/tests/unit/test_util.py
+++ b/tests/unit/test_util.py
@@ -10,6 +10,7 @@
 import unittest
 from unittest.mock import patch
 from io import StringIO
+from tempfile import TemporaryDirectory
 
 def test_function(arg):
     return arg + arg
@@ -102,6 +103,15 @@ def test_have_optional_dependency(self):
             model.initial_conditions = {v: 1}
             sim = pybamm.Simulation(model)
             sim.solve([0, 1])
+        with self.assertRaisesRegex(ModuleNotFoundError,"Optional dependency anytree is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details."):
+            with TemporaryDirectory() as dir_name:
+                sys.modules['anytree'] = None
+                test_stub = os.path.join(dir_name, "test_visualize")
+                test_name = f"{test_stub}.png"
+                c = pybamm.Variable("c", "negative electrode")
+                d = pybamm.Variable("d", "negative electrode")
+                sym = pybamm.div(c * pybamm.grad(c)) + (c / d + c - d) ** 5
+                sym.visualise(test_name)
 
         sys.modules['pybtex'] = pybtex
         pybamm.util.have_optional_dependency("pybtex")

From 700ab5af6b0805dc503ded489bbacf23a38cd2d7 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Fri, 10 Nov 2023 02:07:17 +0530
Subject: [PATCH 142/199] Declare `anytree` onn top to pass
 `test_is_constant_and_can_evaluate`

---
 tests/unit/test_util.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/tests/unit/test_util.py b/tests/unit/test_util.py
index a9f70bbcc7..b9dd428a4b 100644
--- a/tests/unit/test_util.py
+++ b/tests/unit/test_util.py
@@ -14,6 +14,8 @@
 
 def test_function(arg):
     return arg + arg
+
+anytree = sys.modules['anytree']
 class TestUtil(TestCase):
     """
     Test the functionality in util.py
@@ -31,6 +33,7 @@ def test_rmse(self):
             pybamm.rmse(np.ones(5), np.zeros(3))
 
     def test_is_constant_and_can_evaluate(self):
+        sys.modules['anytree'] = anytree
         symbol = pybamm.PrimaryBroadcast(0, "negative electrode")
         self.assertEqual(False, pybamm.is_constant_and_can_evaluate(symbol))
         symbol = pybamm.StateVector(slice(0, 1))

From c83711b11f44dfdce2666e7b518de0a773ec32b2 Mon Sep 17 00:00:00 2001
From: Valentin Sulzer <valentinsulzer@hotmail.com>
Date: Thu, 9 Nov 2023 21:18:06 -0500
Subject: [PATCH 143/199] #3506 vectorize theoretical energy

---
 pybamm/expression_tree/binary_operators.py    |  4 +-
 pybamm/expression_tree/broadcasts.py          |  6 +-
 .../lithium_ion/electrode_soh.py              | 55 +++++++++----------
 pybamm/parameters/lithium_ion_parameters.py   |  1 +
 pybamm/parameters/thermal_parameters.py       |  6 ++
 .../test_lithium_ion/test_electrode_soh.py    |  4 +-
 6 files changed, 40 insertions(+), 36 deletions(-)

diff --git a/pybamm/expression_tree/binary_operators.py b/pybamm/expression_tree/binary_operators.py
index 749384e9bc..bde9a17271 100644
--- a/pybamm/expression_tree/binary_operators.py
+++ b/pybamm/expression_tree/binary_operators.py
@@ -14,12 +14,12 @@
 def _preprocess_binary(left, right):
     if isinstance(left, numbers.Number):
         left = pybamm.Scalar(left)
-    if isinstance(right, numbers.Number):
-        right = pybamm.Scalar(right)
     elif isinstance(left, np.ndarray):
         if left.ndim > 1:
             raise ValueError("left must be a 1D array")
         left = pybamm.Vector(left)
+    if isinstance(right, numbers.Number):
+        right = pybamm.Scalar(right)
     elif isinstance(right, np.ndarray):
         if right.ndim > 1:
             raise ValueError("right must be a 1D array")
diff --git a/pybamm/expression_tree/broadcasts.py b/pybamm/expression_tree/broadcasts.py
index 32cf2c002b..d30762ad70 100644
--- a/pybamm/expression_tree/broadcasts.py
+++ b/pybamm/expression_tree/broadcasts.py
@@ -546,8 +546,10 @@ def full_like(symbols, fill_value):
         return array_type(entries, domains=sum_symbol.domains)
 
     except NotImplementedError:
-        if sum_symbol.shape_for_testing == (1, 1) or sum_symbol.shape_for_testing == (
-            1,
+        if (
+            sum_symbol.shape_for_testing == (1, 1)
+            or sum_symbol.shape_for_testing == (1,)
+            or sum_symbol.domain == []
         ):
             return pybamm.Scalar(fill_value)
         if sum_symbol.evaluates_on_edges("primary"):
diff --git a/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py b/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py
index c6a445f316..d975de859c 100644
--- a/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py
+++ b/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py
@@ -410,10 +410,7 @@ def solve(self, inputs):
         # Calculate theoretical energy
         # TODO: energy calc for MSMR
         if self.options["open-circuit potential"] != "MSMR":
-            energy = pybamm.lithium_ion.electrode_soh.theoretical_energy_integral(
-                self.parameter_values,
-                sol_dict,
-            )
+            energy = self.theoretical_energy_integral(sol_dict)
             sol_dict.update({"Maximum theoretical energy [W.h]": energy})
         return sol_dict
 
@@ -829,6 +826,27 @@ def get_min_max_ocps(self):
         sol = self.solve(inputs)
         return [sol["Un(x_0)"], sol["Un(x_100)"], sol["Up(y_100)"], sol["Up(y_0)"]]
 
+    def theoretical_energy_integral(self, inputs, points=1000):
+        x_0 = inputs["x_0"]
+        y_0 = inputs["y_0"]
+        x_100 = inputs["x_100"]
+        y_100 = inputs["y_100"]
+        Q_p = inputs["Q_p"]
+        x_vals = np.linspace(x_100, x_0, num=points)
+        y_vals = np.linspace(y_100, y_0, num=points)
+        # Calculate OCV at each stoichiometry
+        param = self.param
+        T = param.T_amb_av(0)
+        Vs = self.parameter_values.evaluate(
+            param.p.prim.U(y_vals, T) - param.n.prim.U(x_vals, T)
+        ).flatten()
+        # Calculate dQ
+        Q = Q_p * (y_0 - y_100)
+        dQ = Q / (points - 1)
+        # Integrate and convert to W-h
+        E = np.trapz(Vs, dx=dQ)
+        return E
+
 
 def get_initial_stoichiometries(
     initial_value,
@@ -972,7 +990,7 @@ def get_min_max_ocps(
     return esoh_solver.get_min_max_ocps()
 
 
-def theoretical_energy_integral(parameter_values, inputs, points=100):
+def theoretical_energy_integral(parameter_values, param, inputs, points=100):
     """
     Calculate maximum energy possible from a cell given OCV, initial soc, and final soc
     given voltage limits, open-circuit potentials, etc defined by parameter_values
@@ -991,30 +1009,8 @@ def theoretical_energy_integral(parameter_values, inputs, points=100):
     E
         The total energy of the cell in Wh
     """
-    x_0 = inputs["x_0"]
-    y_0 = inputs["y_0"]
-    x_100 = inputs["x_100"]
-    y_100 = inputs["y_100"]
-    Q_p = inputs["Q_p"]
-    x_vals = np.linspace(x_100, x_0, num=points)
-    y_vals = np.linspace(y_100, y_0, num=points)
-    # Calculate OCV at each stoichiometry
-    param = pybamm.LithiumIonParameters()
-    y = pybamm.standard_spatial_vars.y
-    z = pybamm.standard_spatial_vars.z
-    T = pybamm.yz_average(param.T_amb(y, z, 0))
-    Vs = np.empty(x_vals.shape)
-    for i in range(x_vals.size):
-        Vs[i] = (
-            parameter_values.evaluate(param.p.prim.U(y_vals[i], T)).item()
-            - parameter_values.evaluate(param.n.prim.U(x_vals[i], T)).item()
-        )
-    # Calculate dQ
-    Q = Q_p * (y_0 - y_100)
-    dQ = Q / (points - 1)
-    # Integrate and convert to W-h
-    E = np.trapz(Vs, dx=dQ)
-    return E
+    esoh_solver = ElectrodeSOHSolver(parameter_values, param)
+    return esoh_solver.theoretical_energy_integral(inputs, points=points)
 
 
 def calculate_theoretical_energy(
@@ -1045,6 +1041,7 @@ def calculate_theoretical_energy(
     Q_p = parameter_values.evaluate(pybamm.LithiumIonParameters().p.prim.Q_init)
     E = theoretical_energy_integral(
         parameter_values,
+        pybamm.LithiumIonParameters(),
         {"x_100": x_100, "x_0": x_0, "y_100": y_100, "y_0": y_0, "Q_p": Q_p},
         points=points,
     )
diff --git a/pybamm/parameters/lithium_ion_parameters.py b/pybamm/parameters/lithium_ion_parameters.py
index 726e876aa0..c459a4ef1e 100644
--- a/pybamm/parameters/lithium_ion_parameters.py
+++ b/pybamm/parameters/lithium_ion_parameters.py
@@ -50,6 +50,7 @@ def _set_parameters(self):
         self.T_ref = self.therm.T_ref
         self.T_init = self.therm.T_init
         self.T_amb = self.therm.T_amb
+        self.T_amb_av = self.therm.T_amb_av
         self.h_edge = self.therm.h_edge
         self.h_total = self.therm.h_total
         self.rho_c_p_eff = self.therm.rho_c_p_eff
diff --git a/pybamm/parameters/thermal_parameters.py b/pybamm/parameters/thermal_parameters.py
index ea1dd12065..8e92ff8d34 100644
--- a/pybamm/parameters/thermal_parameters.py
+++ b/pybamm/parameters/thermal_parameters.py
@@ -51,6 +51,12 @@ def T_amb(self, y, z, t):
             },
         )
 
+    def T_amb_av(self, t):
+        """YZ-averaged ambient temperature [K]"""
+        y = pybamm.standard_spatial_vars.y
+        z = pybamm.standard_spatial_vars.z
+        return pybamm.yz_average(self.T_amb(y, z, t))
+
     def h_edge(self, y, z):
         """Cell edge heat transfer coefficient [W.m-2.K-1]"""
         inputs = {
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 628017d5d8..e5e79a6ae4 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
@@ -40,9 +40,7 @@ def test_known_solution(self):
                     k: sol_split[k].data[0]
                     for k in ["x_0", "y_0", "x_100", "y_100", "Q_p"]
                 }
-                energy = pybamm.lithium_ion.electrode_soh.theoretical_energy_integral(
-                    parameter_values, inputs
-                )
+                energy = esoh_solver.theoretical_energy_integral(inputs)
                 self.assertAlmostEqual(sol[key], energy, places=5)
 
         # should still work with old inputs

From bfbe41e36d32b9afc7d8643fd078f9fe70333bff Mon Sep 17 00:00:00 2001
From: Arjun <arjunverma.oc@gmail.com>
Date: Fri, 10 Nov 2023 18:20:35 +0530
Subject: [PATCH 144/199] Apply suggestions from code review

Co-authored-by: Saransh Chopra <saransh0701@gmail.com>
---
 pybamm/citations.py                                | 8 ++++----
 pybamm/expression_tree/printing/sympy_overrides.py | 4 ++--
 pybamm/expression_tree/symbol.py                   | 2 +-
 3 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/pybamm/citations.py b/pybamm/citations.py
index 7d0959d89c..b72262989b 100644
--- a/pybamm/citations.py
+++ b/pybamm/citations.py
@@ -74,7 +74,7 @@ def read_citations(self):
         """Reads the citations in `pybamm.CITATIONS.bib`. Other works can be cited
         by passing a BibTeX citation to :meth:`register`.
         """
-        parse_file = have_optional_dependency("pybtex.database","parse_file")
+        parse_file = have_optional_dependency("pybtex.database", "parse_file")
         citations_file = os.path.join(pybamm.root_dir(), "pybamm", "CITATIONS.bib")
         bib_data = parse_file(citations_file, bib_format="bibtex")
         for key, entry in bib_data.entries.items():
@@ -85,7 +85,7 @@ def _add_citation(self, key, entry):
         previous entry is overwritten
         """
 
-        Entry = have_optional_dependency("pybtex.database","Entry")
+        Entry = have_optional_dependency("pybtex.database", "Entry")
         # Check input types are correct
         if not isinstance(key, str) or not isinstance(entry, Entry):
             raise TypeError()
@@ -151,8 +151,8 @@ def _parse_citation(self, key):
         key: str
             A BibTeX formatted citation
         """
-        PybtexError = have_optional_dependency("pybtex.scanner","PybtexError")
-        parse_string = have_optional_dependency("pybtex.database","parse_string")
+        PybtexError = have_optional_dependency("pybtex.scanner", "PybtexError")
+        parse_string = have_optional_dependency("pybtex.database", "parse_string")
         try:
             # Parse string as a bibtex citation, and check that a citation was found
             bib_data = parse_string(key, bib_format="bibtex")
diff --git a/pybamm/expression_tree/printing/sympy_overrides.py b/pybamm/expression_tree/printing/sympy_overrides.py
index 59f9567c5d..64743f557d 100644
--- a/pybamm/expression_tree/printing/sympy_overrides.py
+++ b/pybamm/expression_tree/printing/sympy_overrides.py
@@ -6,7 +6,7 @@
 from pybamm.util import have_optional_dependency
 
 
-LatexPrinter = have_optional_dependency("sympy.printing.latex","LatexPrinter")
+LatexPrinter = have_optional_dependency("sympy.printing.latex", "LatexPrinter")
 class CustomPrint(LatexPrinter):
     """Override SymPy methods to match PyBaMM's requirements"""
 
@@ -22,5 +22,5 @@ def _print_Derivative(self, expr):
 
 
 def custom_print_func(expr, **settings):
-    have_optional_dependency("sympy.printing.latex","LatexPrinter")
+    have_optional_dependency("sympy.printing.latex", "LatexPrinter")
     return CustomPrint().doprint(expr)
diff --git a/pybamm/expression_tree/symbol.py b/pybamm/expression_tree/symbol.py
index 85c392e590..8f1608e7ba 100644
--- a/pybamm/expression_tree/symbol.py
+++ b/pybamm/expression_tree/symbol.py
@@ -459,7 +459,7 @@ def visualise(self, filename):
             filename to output, must end in ".png"
         """
 
-        DotExporter = have_optional_dependency("anytree.exporter","DotExporter")
+        DotExporter = have_optional_dependency("anytree.exporter", "DotExporter")
         # check that filename ends in .png.
         if filename[-4:] != ".png":
             raise ValueError("filename should end in .png")

From 2f1d3ceea469a4de03c9143706cd9f2e23bdd14d Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Fri, 10 Nov 2023 23:40:05 +0530
Subject: [PATCH 145/199] Shorten assert string

---
 tests/unit/test_util.py | 10 ++++------
 1 file changed, 4 insertions(+), 6 deletions(-)

diff --git a/tests/unit/test_util.py b/tests/unit/test_util.py
index b9dd428a4b..ea087ad4c4 100644
--- a/tests/unit/test_util.py
+++ b/tests/unit/test_util.py
@@ -12,10 +12,8 @@
 from io import StringIO
 from tempfile import TemporaryDirectory
 
-def test_function(arg):
-    return arg + arg
-
 anytree = sys.modules['anytree']
+
 class TestUtil(TestCase):
     """
     Test the functionality in util.py
@@ -94,11 +92,11 @@ def test_git_commit_info(self):
         self.assertEqual(git_commit_info[:2], "v2")
 
     def test_have_optional_dependency(self):
-        with self.assertRaisesRegex(ModuleNotFoundError,"Optional dependency pybtex is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details."):
+        with self.assertRaisesRegex(ModuleNotFoundError,"Optional dependency pybtex is not available."):
             pybtex = sys.modules['pybtex']
             sys.modules['pybtex'] = None
             pybamm.print_citations()
-        with self.assertRaisesRegex(ModuleNotFoundError,"Optional dependency tqdm is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details."):
+        with self.assertRaisesRegex(ModuleNotFoundError,"Optional dependency tqdm is not available."):
             sys.modules['tqdm'] = None
             model = pybamm.BaseModel()
             v = pybamm.Variable("v")
@@ -106,7 +104,7 @@ def test_have_optional_dependency(self):
             model.initial_conditions = {v: 1}
             sim = pybamm.Simulation(model)
             sim.solve([0, 1])
-        with self.assertRaisesRegex(ModuleNotFoundError,"Optional dependency anytree is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details."):
+        with self.assertRaisesRegex(ModuleNotFoundError,"Optional dependency anytree is not available."):
             with TemporaryDirectory() as dir_name:
                 sys.modules['anytree'] = None
                 test_stub = os.path.join(dir_name, "test_visualize")

From fe6b9105f0ed0abad3e7284cb94edaea46056ed9 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sat, 11 Nov 2023 00:19:27 +0530
Subject: [PATCH 146/199] Improve readibility & add case to fix coverage

---
 pybamm/util.py          | 5 +++--
 tests/unit/test_util.py | 3 +++
 2 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/pybamm/util.py b/pybamm/util.py
index dee77b8841..6c91948394 100644
--- a/pybamm/util.py
+++ b/pybamm/util.py
@@ -348,6 +348,7 @@ def install_jax(arguments=None):  # pragma: no cover
 
 # https://docs.pybamm.org/en/latest/source/user_guide/contributing.html#managing-optional-dependencies-and-their-imports
 def have_optional_dependency(module_name, attribute=None):
+    err_msg = f"Optional dependency {module_name} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details."
     try:
         # Attempt to import the specified module
         module = importlib.import_module(module_name)
@@ -359,11 +360,11 @@ def have_optional_dependency(module_name, attribute=None):
                 return imported_attribute  # Return the imported attribute
             else:
                 # Raise an ModuleNotFoundError if the attribute is not available
-                raise ModuleNotFoundError(f"Optional dependency {module_name} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details.")
+                raise ModuleNotFoundError(err_msg)
         else:
             # Return the entire module if no attribute is specified
             return module
 
     except ModuleNotFoundError:
         # Raise an ModuleNotFoundError if the module or attribute is not available
-        raise ModuleNotFoundError(f"Optional dependency {module_name} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details.")
+        raise ModuleNotFoundError(err_msg)
diff --git a/tests/unit/test_util.py b/tests/unit/test_util.py
index ea087ad4c4..b2ef72fcbc 100644
--- a/tests/unit/test_util.py
+++ b/tests/unit/test_util.py
@@ -118,6 +118,9 @@ def test_have_optional_dependency(self):
         pybamm.util.have_optional_dependency("pybtex")
         pybamm.print_citations()
 
+        with self.assertRaisesRegex(ModuleNotFoundError,"Optional dependency flask is not available."):
+            pybamm.util.have_optional_dependency("flask","Flask")
+
 
 class TestSearch(TestCase):
     def test_url_gets_to_stdout(self):

From 6239653a57fff5a4f33c35b8919ab8f4814de6a7 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sat, 11 Nov 2023 00:58:15 +0530
Subject: [PATCH 147/199] Modify CONTRIBUTING.md for optional dependency tests

---
 CONTRIBUTING.md | 23 +++++++++++++++++++++++
 pybamm/util.py  |  2 +-
 2 files changed, 24 insertions(+), 1 deletion(-)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index de0d626940..9a7e3d779d 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -124,6 +124,29 @@ def use_parse_file(x, y, z):
 
 This allows people to (1) use PyBaMM without importing optional dependencies by default and (2) configure module-dependent functionalities in their scripts, which _must_ be done before e.g. `print_citations` method is first imported.
 
+**Writing Tests for Optional Dependencies**
+
+Whenever a new optional dependency is added for optional functionality, it is recommended to write a corresponding unit test in _test_util.py_. This ensures that an error is raised upon the absence of said dependency. Here's an example:
+
+```python
+from tests import TestCase
+import pybamm
+
+
+class TestUtil(TestCase):
+    def test_optional_dependency(self):
+        # Test that an error is raised when pybtex is not available
+        with self.assertRaisesRegex(
+            ModuleNotFoundError, "Optional dependency pybtex is not available"
+        ):
+            sys.modules["pybtex"] = None
+            pybamm.function_using_pybtex(x, y, z)
+
+        # Test that the function works when pybtex is available
+        sys.modules["pybtex"] = pybamm.util.have_optional_dependency("pybtex")
+        pybamm.function_using_pybtex(x, y, z)
+```
+
 ## Testing
 
 All code requires testing. We use the [unittest](https://docs.python.org/3.3/library/unittest.html) package for our tests. (These tests typically just check that the code runs without error, and so, are more _debugging_ than _testing_ in a strict sense. Nevertheless, they are very useful to have!)
diff --git a/pybamm/util.py b/pybamm/util.py
index 6c91948394..90cb290c6e 100644
--- a/pybamm/util.py
+++ b/pybamm/util.py
@@ -360,7 +360,7 @@ def have_optional_dependency(module_name, attribute=None):
                 return imported_attribute  # Return the imported attribute
             else:
                 # Raise an ModuleNotFoundError if the attribute is not available
-                raise ModuleNotFoundError(err_msg)
+                raise ModuleNotFoundError(err_msg)      # pragma: no cover
         else:
             # Return the entire module if no attribute is specified
             return module

From 9f7121b984c4ba176aa9c6b0e3b88313c7d75232 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 11 Nov 2023 17:13:14 +0530
Subject: [PATCH 148/199] #3442 Update release workflow instructions to add
 details about `conda-forge`

---
 .github/release_workflow.md | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/.github/release_workflow.md b/.github/release_workflow.md
index 7afa24a6d6..8334a1d5dc 100644
--- a/.github/release_workflow.md
+++ b/.github/release_workflow.md
@@ -1,6 +1,6 @@
 # Release workflow
 
-This file contains the workflow required to make a `PyBaMM` release on GitHub and PyPI by the maintainers.
+This file contains the workflow required to make a `PyBaMM` release on GitHub, PyPI, and conda-forge by the maintainers.
 
 ## rc0 releases (automated)
 
@@ -77,3 +77,5 @@ Some other essential things to check throughout the release process -
   git tag -f <tag_name> <commit_hash>
   git push -f <pybamm-team/PyBaMM_remote_name> <tag_name>  # can only be carried out by the maintainers
   ```
+- If changes are made to the API, console scripts, entry points, new optional dependencies are added, support for major Python versions is dropped or added, or core project information and metadata are modified at the time of the release, make sure to update the `meta.yaml` file in the `recipe/` folder of the [conda-forge/pybamm-feedstock](https://github.com/conda-forge/pybamm-feedstock) repository accordingly by following the instructions in the [conda-forge documentation](https://conda-forge.org/docs/maintainer/updating_pkgs.html#updating-the-feedstock-repository) and re-rendering the recipe
+- The conda-forge release workflow will automatically be triggered following a stable PyPI release, and the aforementioned updates can be carried out either in a personal fork of the feedstock repository, or directly in the main repository by pushing changes to the automated PR created by the conda-forge-bot.

From 6f5823fed4825ab44da90a79f65a94246ffec0bb Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sat, 11 Nov 2023 18:55:49 +0530
Subject: [PATCH 149/199] Prevent inheriting LatexPrinter instead use a
 function

---
 .../printing/sympy_overrides.py               | 25 +++++++++++--------
 1 file changed, 14 insertions(+), 11 deletions(-)

diff --git a/pybamm/expression_tree/printing/sympy_overrides.py b/pybamm/expression_tree/printing/sympy_overrides.py
index 64743f557d..3e89542d10 100644
--- a/pybamm/expression_tree/printing/sympy_overrides.py
+++ b/pybamm/expression_tree/printing/sympy_overrides.py
@@ -6,21 +6,24 @@
 from pybamm.util import have_optional_dependency
 
 
-LatexPrinter = have_optional_dependency("sympy.printing.latex", "LatexPrinter")
-class CustomPrint(LatexPrinter):
+def custom_latex_printer(expr, **settings):
+    latex = have_optional_dependency("sympy","latex")
+    Derivative = have_optional_dependency("sympy","Derivative")
+    if isinstance(expr, Derivative) and getattr(expr, "force_partial", False):
+        latex_str = latex(expr, **settings)
+        var1, var2 = re.findall(r"^\\frac{(\w+)}{(\w+) .+", latex_str)[0]
+        latex_str = latex_str.replace(var1, "\partial").replace(var2, "\partial")
+        return latex_str
+    else:
+        return latex(expr, **settings)
+
+class CustomPrint:
     """Override SymPy methods to match PyBaMM's requirements"""
 
     def _print_Derivative(self, expr):
         """Override :meth:`sympy.printing.latex.LatexPrinter._print_Derivative`"""
-        eqn = super()._print_Derivative(expr)
-
-        if getattr(expr, "force_partial", False) and "partial" not in eqn:
-            var1, var2 = re.findall(r"^\\frac{(\w+)}{(\w+) .+", eqn)[0]
-            eqn = eqn.replace(var1, "\partial").replace(var2, "\partial")
-
-        return eqn
-
+        return custom_latex_printer(expr)
 
 def custom_print_func(expr, **settings):
     have_optional_dependency("sympy.printing.latex", "LatexPrinter")
-    return CustomPrint().doprint(expr)
+    return CustomPrint()._print_Derivative(expr)

From c093d44614e2dfdb3f24f96357169a9cfb3d6ca3 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sat, 11 Nov 2023 18:59:32 +0530
Subject: [PATCH 150/199] Remove redundant testcase

---
 tests/unit/test_util.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/tests/unit/test_util.py b/tests/unit/test_util.py
index b2ef72fcbc..ea087ad4c4 100644
--- a/tests/unit/test_util.py
+++ b/tests/unit/test_util.py
@@ -118,9 +118,6 @@ def test_have_optional_dependency(self):
         pybamm.util.have_optional_dependency("pybtex")
         pybamm.print_citations()
 
-        with self.assertRaisesRegex(ModuleNotFoundError,"Optional dependency flask is not available."):
-            pybamm.util.have_optional_dependency("flask","Flask")
-
 
 class TestSearch(TestCase):
     def test_url_gets_to_stdout(self):

From 2324af9254e4e567264c073f3d0da4e19fc4f230 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sun, 12 Nov 2023 01:26:33 +0530
Subject: [PATCH 151/199] Add a sentence about manual PRs in the feedstock

Co-Authored-By: Saransh Chopra <saransh0701@gmail.com>
---
 .github/release_workflow.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/release_workflow.md b/.github/release_workflow.md
index 8334a1d5dc..ed94962b92 100644
--- a/.github/release_workflow.md
+++ b/.github/release_workflow.md
@@ -78,4 +78,4 @@ Some other essential things to check throughout the release process -
   git push -f <pybamm-team/PyBaMM_remote_name> <tag_name>  # can only be carried out by the maintainers
   ```
 - If changes are made to the API, console scripts, entry points, new optional dependencies are added, support for major Python versions is dropped or added, or core project information and metadata are modified at the time of the release, make sure to update the `meta.yaml` file in the `recipe/` folder of the [conda-forge/pybamm-feedstock](https://github.com/conda-forge/pybamm-feedstock) repository accordingly by following the instructions in the [conda-forge documentation](https://conda-forge.org/docs/maintainer/updating_pkgs.html#updating-the-feedstock-repository) and re-rendering the recipe
-- The conda-forge release workflow will automatically be triggered following a stable PyPI release, and the aforementioned updates can be carried out either in a personal fork of the feedstock repository, or directly in the main repository by pushing changes to the automated PR created by the conda-forge-bot.
+- The conda-forge release workflow will automatically be triggered following a stable PyPI release, and the aforementioned updates should be carried out directly in the main repository by pushing changes to the automated PR created by the conda-forge-bot. A manual PR can also be created if the updates are not included in the automated PR for some reason. This manual PR **must** bump the build number in `meta.yaml` and **must** be from a personal fork of the repository.

From a5d25736d79c0571eccada2ddee7a330fbb7b2dc Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Mon, 13 Nov 2023 17:22:05 +0530
Subject: [PATCH 152/199] Add `anytree` to required & install `[plot,cite]` in
 `examples` session

---
 noxfile.py | 2 +-
 setup.py   | 1 +
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/noxfile.py b/noxfile.py
index 430ad59659..83f4c3d717 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -101,7 +101,7 @@ def run_unit(session):
 def run_examples(session):
     """Run the examples tests for Jupyter notebooks."""
     set_environment_variables(PYBAMM_ENV, session=session)
-    session.install("-e", ".[all,dev]", silent=False)
+    session.install("-e", ".[plot,cite,dev]", silent=False)
     notebooks_to_test = session.posargs if session.posargs else []
     session.run("pytest", "--nbmake", *notebooks_to_test, external=True)
 
diff --git a/setup.py b/setup.py
index f6fd37f75c..fca5b83de8 100644
--- a/setup.py
+++ b/setup.py
@@ -207,6 +207,7 @@ def compile_KLU():
         "scipy>=1.3",
         "casadi>=3.6.0",
         "xarray",
+        "anytree>=2.4.3",
     ],
     extras_require={
         "docs": [

From a3952ddfef3361d4411a045228abbda98ef8a840 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Mon, 13 Nov 2023 20:32:26 +0530
Subject: [PATCH 153/199] Set iterator based upon `tqdm`

---
 noxfile.py           |  2 +-
 pybamm/simulation.py | 18 +++++++++++++-----
 2 files changed, 14 insertions(+), 6 deletions(-)

diff --git a/noxfile.py b/noxfile.py
index 83f4c3d717..0d5d6e0d20 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -101,7 +101,7 @@ def run_unit(session):
 def run_examples(session):
     """Run the examples tests for Jupyter notebooks."""
     set_environment_variables(PYBAMM_ENV, session=session)
-    session.install("-e", ".[plot,cite,dev]", silent=False)
+    session.install("-e", ".[plot,cite,examples,dev]", silent=False)
     notebooks_to_test = session.posargs if session.posargs else []
     session.run("pytest", "--nbmake", *notebooks_to_test, external=True)
 
diff --git a/pybamm/simulation.py b/pybamm/simulation.py
index 0b1a6b2525..49b46f1dac 100644
--- a/pybamm/simulation.py
+++ b/pybamm/simulation.py
@@ -532,7 +532,10 @@ def solve(
             Additional key-word arguments passed to `solver.solve`.
             See :meth:`pybamm.BaseSolver.solve`.
         """
-        tqdm = have_optional_dependency("tqdm")
+        try:
+            tqdm = have_optional_dependency("tqdm")
+        except ModuleNotFoundError:
+            tqdm = False
         # Setup
         if solver is None:
             solver = self._solver
@@ -727,13 +730,18 @@ def solve(
                         # Update _solution
                         self._solution = current_solution
 
-            for cycle_num, cycle_length in enumerate(
-                # tqdm is the progress bar.
-                tqdm.tqdm(
+            if tqdm:
+                iterator = tqdm.tqdm(
                     self.experiment.cycle_lengths,
                     disable=(not showprogress),
                     desc="Cycling",
-                ),
+                )
+            else:
+                iterator = self.experiment.cycle_lengths
+
+            for cycle_num, cycle_length in enumerate(
+                # tqdm is the progress bar.
+                iterator,
                 start=1,
             ):
                 logs["cycle number"] = (

From ae22805107b98aae6867c0cf91c4d8fcd6ba8ba6 Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Mon, 13 Nov 2023 20:47:10 +0530
Subject: [PATCH 154/199] Clean up tqdm mess

---
 noxfile.py              |  2 +-
 pybamm/simulation.py    | 16 ++++++----------
 tests/unit/test_util.py |  6 +++---
 3 files changed, 10 insertions(+), 14 deletions(-)

diff --git a/noxfile.py b/noxfile.py
index 0d5d6e0d20..430ad59659 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -101,7 +101,7 @@ def run_unit(session):
 def run_examples(session):
     """Run the examples tests for Jupyter notebooks."""
     set_environment_variables(PYBAMM_ENV, session=session)
-    session.install("-e", ".[plot,cite,examples,dev]", silent=False)
+    session.install("-e", ".[all,dev]", silent=False)
     notebooks_to_test = session.posargs if session.posargs else []
     session.run("pytest", "--nbmake", *notebooks_to_test, external=True)
 
diff --git a/pybamm/simulation.py b/pybamm/simulation.py
index 49b46f1dac..42bda08e31 100644
--- a/pybamm/simulation.py
+++ b/pybamm/simulation.py
@@ -532,10 +532,6 @@ def solve(
             Additional key-word arguments passed to `solver.solve`.
             See :meth:`pybamm.BaseSolver.solve`.
         """
-        try:
-            tqdm = have_optional_dependency("tqdm")
-        except ModuleNotFoundError:
-            tqdm = False
         # Setup
         if solver is None:
             solver = self._solver
@@ -730,18 +726,18 @@ def solve(
                         # Update _solution
                         self._solution = current_solution
 
-            if tqdm:
-                iterator = tqdm.tqdm(
+            # check if a user has tqdm installed
+            if showprogress:
+                tqdm = have_optional_dependency("tqdm")
+                cycle_lengths = tqdm.tqdm(
                     self.experiment.cycle_lengths,
-                    disable=(not showprogress),
                     desc="Cycling",
                 )
             else:
-                iterator = self.experiment.cycle_lengths
+                cycle_lengths = self.experiment.cycle_lengths
 
             for cycle_num, cycle_length in enumerate(
-                # tqdm is the progress bar.
-                iterator,
+                cycle_lengths,
                 start=1,
             ):
                 logs["cycle number"] = (
diff --git a/tests/unit/test_util.py b/tests/unit/test_util.py
index ea087ad4c4..5079842003 100644
--- a/tests/unit/test_util.py
+++ b/tests/unit/test_util.py
@@ -92,11 +92,11 @@ def test_git_commit_info(self):
         self.assertEqual(git_commit_info[:2], "v2")
 
     def test_have_optional_dependency(self):
-        with self.assertRaisesRegex(ModuleNotFoundError,"Optional dependency pybtex is not available."):
+        with self.assertRaisesRegex(ModuleNotFoundError, "Optional dependency pybtex is not available."):
             pybtex = sys.modules['pybtex']
             sys.modules['pybtex'] = None
             pybamm.print_citations()
-        with self.assertRaisesRegex(ModuleNotFoundError,"Optional dependency tqdm is not available."):
+        with self.assertRaisesRegex(ModuleNotFoundError, "Optional dependency tqdm is not available."):
             sys.modules['tqdm'] = None
             model = pybamm.BaseModel()
             v = pybamm.Variable("v")
@@ -104,7 +104,7 @@ def test_have_optional_dependency(self):
             model.initial_conditions = {v: 1}
             sim = pybamm.Simulation(model)
             sim.solve([0, 1])
-        with self.assertRaisesRegex(ModuleNotFoundError,"Optional dependency anytree is not available."):
+        with self.assertRaisesRegex(ModuleNotFoundError, "Optional dependency anytree is not available."):
             with TemporaryDirectory() as dir_name:
                 sys.modules['anytree'] = None
                 test_stub = os.path.join(dir_name, "test_visualize")

From b5f74ad7b76fef4adb6c6496f0456dba8f182d24 Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Mon, 13 Nov 2023 21:03:17 +0530
Subject: [PATCH 155/199] Fix matplotlib errors

---
 pybamm/plotting/plot.py                    |  3 ++-
 pybamm/plotting/plot2D.py                  |  3 ++-
 pybamm/plotting/plot_summary_variables.py  |  3 ++-
 pybamm/plotting/plot_voltage_components.py |  4 +++-
 pybamm/plotting/quick_plot.py              | 18 ++++++++++--------
 5 files changed, 19 insertions(+), 12 deletions(-)

diff --git a/pybamm/plotting/plot.py b/pybamm/plotting/plot.py
index 19aa9dc5e0..88c8dfe442 100644
--- a/pybamm/plotting/plot.py
+++ b/pybamm/plotting/plot.py
@@ -3,6 +3,7 @@
 #
 import pybamm
 from .quick_plot import ax_min, ax_max
+from pybamm.util import have_optional_dependency
 
 
 def plot(x, y, ax=None, testing=False, **kwargs):
@@ -25,7 +26,7 @@ def plot(x, y, ax=None, testing=False, **kwargs):
         Keyword arguments, passed to plt.plot
 
     """
-    import matplotlib.pyplot as plt
+    plt = have_optional_dependency("matplotlib.pyplot")
 
     if not isinstance(x, pybamm.Array):
         raise TypeError("x must be 'pybamm.Array'")
diff --git a/pybamm/plotting/plot2D.py b/pybamm/plotting/plot2D.py
index 80bb5d0ee2..d4f6d31e3a 100644
--- a/pybamm/plotting/plot2D.py
+++ b/pybamm/plotting/plot2D.py
@@ -3,6 +3,7 @@
 #
 import pybamm
 from .quick_plot import ax_min, ax_max
+from pybamm.util import have_optional_dependency
 
 
 def plot2D(x, y, z, ax=None, testing=False, **kwargs):
@@ -25,7 +26,7 @@ def plot2D(x, y, z, ax=None, testing=False, **kwargs):
         Whether to actually make the plot (turned off for unit tests)
 
     """
-    import matplotlib.pyplot as plt
+    plt = have_optional_dependency("matplotlib.pyplot")
 
     if not isinstance(x, pybamm.Array):
         raise TypeError("x must be 'pybamm.Array'")
diff --git a/pybamm/plotting/plot_summary_variables.py b/pybamm/plotting/plot_summary_variables.py
index 6fe71518db..e50f38fddf 100644
--- a/pybamm/plotting/plot_summary_variables.py
+++ b/pybamm/plotting/plot_summary_variables.py
@@ -3,6 +3,7 @@
 #
 import numpy as np
 import pybamm
+from pybamm.util import have_optional_dependency
 
 
 def plot_summary_variables(
@@ -25,7 +26,7 @@ def plot_summary_variables(
         Keyword arguments, passed to plt.subplots.
 
     """
-    import matplotlib.pyplot as plt
+    plt = have_optional_dependency("matplotlib.pyplot")
 
     if isinstance(solutions, pybamm.Solution):
         solutions = [solutions]
diff --git a/pybamm/plotting/plot_voltage_components.py b/pybamm/plotting/plot_voltage_components.py
index ad0e9a8b71..a681094bea 100644
--- a/pybamm/plotting/plot_voltage_components.py
+++ b/pybamm/plotting/plot_voltage_components.py
@@ -3,6 +3,8 @@
 #
 import numpy as np
 
+from pybamm.util import have_optional_dependency
+
 
 def plot_voltage_components(
     solution,
@@ -32,7 +34,7 @@ def plot_voltage_components(
         Keyword arguments, passed to ax.fill_between
 
     """
-    import matplotlib.pyplot as plt
+    plt = have_optional_dependency("matplotlib.pyplot")
 
     # Set a default value for alpha, the opacity
     kwargs_fill = {"alpha": 0.6, **kwargs_fill}
diff --git a/pybamm/plotting/quick_plot.py b/pybamm/plotting/quick_plot.py
index 5e9c9ef941..00a07d16a1 100644
--- a/pybamm/plotting/quick_plot.py
+++ b/pybamm/plotting/quick_plot.py
@@ -5,6 +5,7 @@
 import numpy as np
 import pybamm
 from collections import defaultdict
+from pybamm.util import have_optional_dependency
 
 
 class LoopList(list):
@@ -46,7 +47,7 @@ def split_long_string(title, max_words=None):
 
 def close_plots():
     """Close all open figures"""
-    import matplotlib.pyplot as plt
+    plt = have_optional_dependency("matplotlib", "pyplot")
 
     plt.close("all")
 
@@ -469,9 +470,10 @@ def plot(self, t, dynamic=False):
             Dimensional time (in 'time_units') at which to plot.
         """
 
-        import matplotlib.pyplot as plt
-        import matplotlib.gridspec as gridspec
-        from matplotlib import cm, colors
+        plt = have_optional_dependency("matplotlib.pyplot")
+        gridspec = have_optional_dependency("matplotlib.gridspec")
+        cm = have_optional_dependency("matplotlib", "cm")
+        colors = have_optional_dependency("matplotlib", "colors")
 
         t_in_seconds = t * self.time_scaling_factor
         self.fig = plt.figure(figsize=self.figsize)
@@ -668,8 +670,8 @@ def dynamic_plot(self, testing=False, step=None):
                 continuous_update=False,
             )
         else:
-            import matplotlib.pyplot as plt
-            from matplotlib.widgets import Slider
+            plt = have_optional_dependency("matplotlib.pyplot")
+            Slider = have_optional_dependency("matplotlib.widgets", "Slider")
 
             # create an initial plot at time self.min_t
             self.plot(self.min_t, dynamic=True)
@@ -773,8 +775,8 @@ def create_gif(self, number_of_images=80, duration=0.1, output_filename="plot.gi
             Name of the generated GIF file.
 
         """
-        import imageio.v2 as imageio
-        import matplotlib.pyplot as plt
+        imageio = have_optional_dependency("imageio.v2")
+        plt = have_optional_dependency("matplotlib.pyplot")
 
         # time stamps at which the images/plots will be created
         time_array = np.linspace(self.min_t, self.max_t, num=number_of_images)

From 78792bcb3aa73840e2db9378792a67a5ae91739f Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Mon, 13 Nov 2023 21:13:14 +0530
Subject: [PATCH 156/199] Apply suggestions from code review

---
 CONTRIBUTING.md                                    | 4 ++--
 pybamm/expression_tree/printing/sympy_overrides.py | 4 ++--
 pybamm/plotting/quick_plot.py                      | 2 +-
 3 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 9a7e3d779d..0a5b17bcb0 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -54,7 +54,7 @@ You now have everything you need to start making changes!
 10. [Test your code!](#testing)
 11. PyBaMM has online documentation at http://docs.pybamm.org/. To make sure any new methods or classes you added show up there, please read the [documentation](#documentation) section.
 12. If you added a major new feature, perhaps it should be showcased in an [example notebook](#example-notebooks).
-13. When you feel your code is finished, or at least warrants serious discussion, run the [pre-commit checks](#pre-commit-checks) and then create a [pull request](https://help.github.com/articles/about-pull-requests/) (PR) on [PyBaMM&#39;s GitHub page](https://github.com/pybamm-team/PyBaMM).
+13. When you feel your code is finished, or at least warrants serious discussion, run the [pre-commit checks](#pre-commit-checks) and then create a [pull request](https://help.github.com/articles/about-pull-requests/) (PR) on [PyBaMM's GitHub page](https://github.com/pybamm-team/PyBaMM).
 14. Once a PR has been created, it will be reviewed by any member of the community. Changes might be suggested which you can make by simply adding new commits to the branch. When everything's finished, someone with the right GitHub permissions will merge your changes into PyBaMM main repository.
 
 Finally, if you really, really, _really_ love developing PyBaMM, have a look at the current [project infrastructure](#infrastructure).
@@ -126,7 +126,7 @@ This allows people to (1) use PyBaMM without importing optional dependencies by
 
 **Writing Tests for Optional Dependencies**
 
-Whenever a new optional dependency is added for optional functionality, it is recommended to write a corresponding unit test in _test_util.py_. This ensures that an error is raised upon the absence of said dependency. Here's an example:
+Whenever a new optional dependency is added for optional functionality, it is recommended to write a corresponding unit test in `test_util.py`. This ensures that an error is raised upon the absence of said dependency. Here's an example:
 
 ```python
 from tests import TestCase
diff --git a/pybamm/expression_tree/printing/sympy_overrides.py b/pybamm/expression_tree/printing/sympy_overrides.py
index 3e89542d10..ec70de22b2 100644
--- a/pybamm/expression_tree/printing/sympy_overrides.py
+++ b/pybamm/expression_tree/printing/sympy_overrides.py
@@ -7,8 +7,8 @@
 
 
 def custom_latex_printer(expr, **settings):
-    latex = have_optional_dependency("sympy","latex")
-    Derivative = have_optional_dependency("sympy","Derivative")
+    latex = have_optional_dependency("sympy", "latex")
+    Derivative = have_optional_dependency("sympy", "Derivative")
     if isinstance(expr, Derivative) and getattr(expr, "force_partial", False):
         latex_str = latex(expr, **settings)
         var1, var2 = re.findall(r"^\\frac{(\w+)}{(\w+) .+", latex_str)[0]
diff --git a/pybamm/plotting/quick_plot.py b/pybamm/plotting/quick_plot.py
index 00a07d16a1..ff657ee375 100644
--- a/pybamm/plotting/quick_plot.py
+++ b/pybamm/plotting/quick_plot.py
@@ -47,7 +47,7 @@ def split_long_string(title, max_words=None):
 
 def close_plots():
     """Close all open figures"""
-    plt = have_optional_dependency("matplotlib", "pyplot")
+    plt = have_optional_dependency("matplotlib.pyplot")
 
     plt.close("all")
 

From a8ac4c784d4b9299d1c1f8beb7e656b1be7b1cc1 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Mon, 13 Nov 2023 21:34:56 +0530
Subject: [PATCH 157/199] Remove test for tqdm as ModuleNotFoundError no longer
 being raised for `Simulation.solve()`

---
 tests/unit/test_util.py | 8 --------
 1 file changed, 8 deletions(-)

diff --git a/tests/unit/test_util.py b/tests/unit/test_util.py
index 5079842003..730e4cc08d 100644
--- a/tests/unit/test_util.py
+++ b/tests/unit/test_util.py
@@ -96,14 +96,6 @@ def test_have_optional_dependency(self):
             pybtex = sys.modules['pybtex']
             sys.modules['pybtex'] = None
             pybamm.print_citations()
-        with self.assertRaisesRegex(ModuleNotFoundError, "Optional dependency tqdm is not available."):
-            sys.modules['tqdm'] = None
-            model = pybamm.BaseModel()
-            v = pybamm.Variable("v")
-            model.rhs = {v: -v}
-            model.initial_conditions = {v: 1}
-            sim = pybamm.Simulation(model)
-            sim.solve([0, 1])
         with self.assertRaisesRegex(ModuleNotFoundError, "Optional dependency anytree is not available."):
             with TemporaryDirectory() as dir_name:
                 sys.modules['anytree'] = None

From 36ec186d828919d7835bdc76275a0d8a82ac9ea9 Mon Sep 17 00:00:00 2001
From: Valentin Sulzer <valentinsulzer@hotmail.com>
Date: Mon, 13 Nov 2023 11:52:29 -0500
Subject: [PATCH 158/199] #3506 changelog

---
 CHANGELOG.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index b02df8ed4c..c1cf4b91ed 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -2,6 +2,7 @@
 
 ## Bug fixes
 
+- Fixed bug in calculation of theoretical energy that made it very slow ([#3506](https://github.com/pybamm-team/PyBaMM/pull/3506))
 - Fixed a bug where the JaxSolver would fails when using GPU support with no input parameters ([#3423](https://github.com/pybamm-team/PyBaMM/pull/3423))
 
 # [v23.9rc0](https://github.com/pybamm-team/PyBaMM/tree/v23.9rc0) - 2023-10-31

From bd2d009c74d29a326857277129f2c713f8a1020e Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Mon, 13 Nov 2023 22:55:28 +0530
Subject: [PATCH 159/199] Fix sympy overrides

---
 .../printing/sympy_overrides.py               | 28 +++++--------
 pybamm/models/base_model.py                   | 39 ++++++++++++++++---
 2 files changed, 44 insertions(+), 23 deletions(-)

diff --git a/pybamm/expression_tree/printing/sympy_overrides.py b/pybamm/expression_tree/printing/sympy_overrides.py
index 3e89542d10..d127534a0e 100644
--- a/pybamm/expression_tree/printing/sympy_overrides.py
+++ b/pybamm/expression_tree/printing/sympy_overrides.py
@@ -3,27 +3,19 @@
 #
 import re
 
-from pybamm.util import have_optional_dependency
+from sympy.printing.latex import LatexPrinter
 
 
-def custom_latex_printer(expr, **settings):
-    latex = have_optional_dependency("sympy","latex")
-    Derivative = have_optional_dependency("sympy","Derivative")
-    if isinstance(expr, Derivative) and getattr(expr, "force_partial", False):
-        latex_str = latex(expr, **settings)
-        var1, var2 = re.findall(r"^\\frac{(\w+)}{(\w+) .+", latex_str)[0]
-        latex_str = latex_str.replace(var1, "\partial").replace(var2, "\partial")
-        return latex_str
-    else:
-        return latex(expr, **settings)
+class CustomPrint(LatexPrinter):
+        """Override SymPy methods to match PyBaMM's requirements"""
+        def _print_Derivative(self, expr):
+                """Override :meth:`sympy.printing.latex.LatexPrinter._print_Derivative`"""
+                eqn = super()._print_Derivative(expr)
+                if getattr(expr, "force_partial", False) and "partial" not in eqn:
+                        var1, var2 = re.findall(r"^\\frac{(\w+)}{(\w+) .+", eqn)[0]
+                        eqn = eqn.replace(var1, "\partial").replace(var2, "\partial")
 
-class CustomPrint:
-    """Override SymPy methods to match PyBaMM's requirements"""
-
-    def _print_Derivative(self, expr):
-        """Override :meth:`sympy.printing.latex.LatexPrinter._print_Derivative`"""
-        return custom_latex_printer(expr)
+                return eqn
 
 def custom_print_func(expr, **settings):
-    have_optional_dependency("sympy.printing.latex", "LatexPrinter")
     return CustomPrint()._print_Derivative(expr)
diff --git a/pybamm/models/base_model.py b/pybamm/models/base_model.py
index 41192dbe1f..08890757b7 100644
--- a/pybamm/models/base_model.py
+++ b/pybamm/models/base_model.py
@@ -9,7 +9,7 @@
 import numpy as np
 
 import pybamm
-from pybamm.expression_tree.operations.latexify import Latexify
+from pybamm.util import have_optional_dependency
 
 
 class BaseModel:
@@ -1055,14 +1055,43 @@ def generate(
         C.generate()
 
     def latexify(self, filename=None, newline=True, output_variables=None):
-        # For docstring, see pybamm.expression_tree.operations.latexify.Latexify
+        """
+        Converts all model equations in latex.
+
+        Parameters
+        ----------
+        filename: str (optional)
+            Accepted file formats - any image format, pdf and tex
+            Default is None, When None returns all model equations in latex
+            If not None, returns all model equations in given file format.
+
+        newline: bool (optional)
+            Default is True, If True, returns every equation in a new line.
+            If False, returns the list of all the equations.
+
+        Load model
+        >>> model = pybamm.lithium_ion.SPM()
+
+        This will returns all model equations in png
+        >>> model.latexify("equations.png")
+
+        This will return all the model equations in latex
+        >>> model.latexify()
+
+        This will return the list of all the model equations
+        >>> model.latexify(newline=False)
+
+        This will return first five model equations
+        >>> model.latexify(newline=False)[1:5]
+        """
+        sympy = have_optional_dependency("sympy")
+        if sympy:
+            from pybamm.expression_tree.operations.latexify import Latexify
+
         return Latexify(self, filename, newline).latexify(
             output_variables=output_variables
         )
 
-    # Set :meth:`latexify` docstring from :class:`Latexify`
-    latexify.__doc__ = Latexify.__doc__
-
     def process_parameters_and_discretise(self, symbol, parameter_values, disc):
         """
         Process parameters and discretise a symbol using supplied parameter values

From 9d342c0f34ef4b7a579a011526d5441c0069bfc9 Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Mon, 13 Nov 2023 23:00:40 +0530
Subject: [PATCH 160/199] fix tabs

---
 .../expression_tree/printing/sympy_overrides.py  | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/pybamm/expression_tree/printing/sympy_overrides.py b/pybamm/expression_tree/printing/sympy_overrides.py
index d127534a0e..e189e536d7 100644
--- a/pybamm/expression_tree/printing/sympy_overrides.py
+++ b/pybamm/expression_tree/printing/sympy_overrides.py
@@ -7,15 +7,15 @@
 
 
 class CustomPrint(LatexPrinter):
-        """Override SymPy methods to match PyBaMM's requirements"""
-        def _print_Derivative(self, expr):
-                """Override :meth:`sympy.printing.latex.LatexPrinter._print_Derivative`"""
-                eqn = super()._print_Derivative(expr)
-                if getattr(expr, "force_partial", False) and "partial" not in eqn:
-                        var1, var2 = re.findall(r"^\\frac{(\w+)}{(\w+) .+", eqn)[0]
-                        eqn = eqn.replace(var1, "\partial").replace(var2, "\partial")
+    """Override SymPy methods to match PyBaMM's requirements"""
+    def _print_Derivative(self, expr):
+        """Override :meth:`sympy.printing.latex.LatexPrinter._print_Derivative`"""
+        eqn = super()._print_Derivative(expr)
+        if getattr(expr, "force_partial", False) and "partial" not in eqn:
+                var1, var2 = re.findall(r"^\\frac{(\w+)}{(\w+) .+", eqn)[0]
+                eqn = eqn.replace(var1, "\partial").replace(var2, "\partial")
 
-                return eqn
+        return eqn
 
 def custom_print_func(expr, **settings):
     return CustomPrint()._print_Derivative(expr)

From 2e30131d7ef35c19f538065fc894dc4bf32236f8 Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Mon, 13 Nov 2023 23:05:22 +0530
Subject: [PATCH 161/199] Fix CustomPrinter

---
 pybamm/expression_tree/printing/sympy_overrides.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/pybamm/expression_tree/printing/sympy_overrides.py b/pybamm/expression_tree/printing/sympy_overrides.py
index e189e536d7..678d4f5a37 100644
--- a/pybamm/expression_tree/printing/sympy_overrides.py
+++ b/pybamm/expression_tree/printing/sympy_overrides.py
@@ -12,10 +12,10 @@ def _print_Derivative(self, expr):
         """Override :meth:`sympy.printing.latex.LatexPrinter._print_Derivative`"""
         eqn = super()._print_Derivative(expr)
         if getattr(expr, "force_partial", False) and "partial" not in eqn:
-                var1, var2 = re.findall(r"^\\frac{(\w+)}{(\w+) .+", eqn)[0]
-                eqn = eqn.replace(var1, "\partial").replace(var2, "\partial")
+            var1, var2 = re.findall(r"^\\frac{(\w+)}{(\w+) .+", eqn)[0]
+            eqn = eqn.replace(var1, "\partial").replace(var2, "\partial")
 
         return eqn
 
 def custom_print_func(expr, **settings):
-    return CustomPrint()._print_Derivative(expr)
+    return CustomPrint().doprint(expr)

From 47113627907f9f2baf7936b82a202ab1fd1bbaf3 Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Mon, 13 Nov 2023 23:30:31 +0530
Subject: [PATCH 162/199] Fix test

---
 pybamm/expression_tree/printing/sympy_overrides.py            | 1 +
 .../test_expression_tree/test_operations/test_latexify.py     | 4 ----
 2 files changed, 1 insertion(+), 4 deletions(-)

diff --git a/pybamm/expression_tree/printing/sympy_overrides.py b/pybamm/expression_tree/printing/sympy_overrides.py
index 678d4f5a37..1898822ea8 100644
--- a/pybamm/expression_tree/printing/sympy_overrides.py
+++ b/pybamm/expression_tree/printing/sympy_overrides.py
@@ -17,5 +17,6 @@ def _print_Derivative(self, expr):
 
         return eqn
 
+
 def custom_print_func(expr, **settings):
     return CustomPrint().doprint(expr)
diff --git a/tests/unit/test_expression_tree/test_operations/test_latexify.py b/tests/unit/test_expression_tree/test_operations/test_latexify.py
index be7cc21115..7e0703534e 100644
--- a/tests/unit/test_expression_tree/test_operations/test_latexify.py
+++ b/tests/unit/test_expression_tree/test_operations/test_latexify.py
@@ -8,7 +8,6 @@
 import uuid
 
 import pybamm
-from pybamm.expression_tree.operations.latexify import Latexify
 
 
 class TestLatexify(TestCase):
@@ -19,9 +18,6 @@ def test_latexify(self):
         model_spme = pybamm.lithium_ion.SPMe()
         func_spme = str(model_spme.latexify())
 
-        # Test docstring
-        self.assertEqual(pybamm.BaseModel.latexify.__doc__, Latexify.__doc__)
-
         # Test model name
         self.assertIn("Single Particle Model with electrolyte Equations", func_spme)
 

From 9970d9736569056393f608305a64485baba1203e Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Tue, 14 Nov 2023 00:08:04 +0530
Subject: [PATCH 163/199] Fix failing notebook tests

---
 .../notebooks/models/lithium-plating.ipynb    | 72 ++++++-------------
 1 file changed, 23 insertions(+), 49 deletions(-)

diff --git a/docs/source/examples/notebooks/models/lithium-plating.ipynb b/docs/source/examples/notebooks/models/lithium-plating.ipynb
index 57049a0ea7..d5fa0e6123 100644
--- a/docs/source/examples/notebooks/models/lithium-plating.ipynb
+++ b/docs/source/examples/notebooks/models/lithium-plating.ipynb
@@ -13,18 +13,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.1.2\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
@@ -70,17 +59,7 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The linesearch algorithm failed with too small a step.\n",
-      "The linesearch algorithm failed with too small a step.\n",
-      "The linesearch algorithm failed with too small a step.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# specify experiments\n",
     "pybamm.citations.register(\"Ren2018\")\n",
@@ -159,14 +138,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 936x648 with 4 Axes>"
+       "<Figure size 1300x900 with 4 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -187,6 +164,7 @@
     "\n",
     "\n",
     "def plot(sims):\n",
+    "    import matplotlib.pyplot as plt\n",
     "    fig, axs = plt.subplots(2, 2, figsize=(13,9))\n",
     "    for (C_rate,sim), color in zip(sims.items(),colors):\n",
     "        # Isolate final equilibration phase\n",
@@ -260,11 +238,11 @@
     {
      "data": {
       "text/plain": [
-       "(<Figure size 936x648 with 4 Axes>,\n",
-       " array([[<AxesSubplot:xlabel='Time [minutes]', ylabel='Voltage [V]'>,\n",
-       "         <AxesSubplot:xlabel='Time [minutes]', ylabel='Volumetric interfacial current density [A.m-3]'>],\n",
-       "        [<AxesSubplot:xlabel='Time [minutes]', ylabel='Plated lithium capacity [Ah]'>,\n",
-       "         <AxesSubplot:xlabel='Time [minutes]', ylabel='Intercalated lithium capacity [Ah]'>]],\n",
+       "(<Figure size 1300x900 with 4 Axes>,\n",
+       " array([[<Axes: xlabel='Time [minutes]', ylabel='Voltage [V]'>,\n",
+       "         <Axes: xlabel='Time [minutes]', ylabel='Volumetric interfacial current density [A.m-3]'>],\n",
+       "        [<Axes: xlabel='Time [minutes]', ylabel='Plated lithium capacity [Ah]'>,\n",
+       "         <Axes: xlabel='Time [minutes]', ylabel='Intercalated lithium capacity [Ah]'>]],\n",
        "       dtype=object))"
       ]
      },
@@ -274,14 +252,12 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 936x648 with 4 Axes>"
+       "<Figure size 1300x900 with 4 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -313,11 +289,11 @@
     {
      "data": {
       "text/plain": [
-       "(<Figure size 936x648 with 4 Axes>,\n",
-       " array([[<AxesSubplot:xlabel='Time [minutes]', ylabel='Voltage [V]'>,\n",
-       "         <AxesSubplot:xlabel='Time [minutes]', ylabel='Volumetric interfacial current density [A.m-3]'>],\n",
-       "        [<AxesSubplot:xlabel='Time [minutes]', ylabel='Plated lithium capacity [Ah]'>,\n",
-       "         <AxesSubplot:xlabel='Time [minutes]', ylabel='Intercalated lithium capacity [Ah]'>]],\n",
+       "(<Figure size 1300x900 with 4 Axes>,\n",
+       " array([[<Axes: xlabel='Time [minutes]', ylabel='Voltage [V]'>,\n",
+       "         <Axes: xlabel='Time [minutes]', ylabel='Volumetric interfacial current density [A.m-3]'>],\n",
+       "        [<Axes: xlabel='Time [minutes]', ylabel='Plated lithium capacity [Ah]'>,\n",
+       "         <Axes: xlabel='Time [minutes]', ylabel='Intercalated lithium capacity [Ah]'>]],\n",
        "       dtype=object))"
       ]
      },
@@ -327,14 +303,12 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 936x648 with 4 Axes>"
+       "<Figure size 1300x900 with 4 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -398,7 +372,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.11.5"
   },
   "toc": {
    "base_numbering": 1,

From 05c74e41e54a88bea8d0cfaaf24ee93b3f3457b4 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Tue, 14 Nov 2023 01:07:30 +0530
Subject: [PATCH 164/199] Add `anytree` to required deps in docs

---
 docs/source/user_guide/installation/index.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/source/user_guide/installation/index.rst b/docs/source/user_guide/installation/index.rst
index 6338323e79..2cf61093be 100644
--- a/docs/source/user_guide/installation/index.rst
+++ b/docs/source/user_guide/installation/index.rst
@@ -66,6 +66,7 @@ Package                                                          Minimum support
 `SciPy <https://docs.scipy.org/doc/scipy/>`__                    2.8.2
 `CasADi <https://web.casadi.org/docs/>`__                        3.6.0
 `Xarray <https://docs.xarray.dev/en/stable/>`__                  2023.04.0
+`Anytree <https://anytree.readthedocs.io/en/stable/>`__          2.4.3
 ================================================================ ==========================
 
 .. _install.optional_dependencies:

From b9b3cb2bd8d7df83f141809209419107ccb3d5a7 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 13 Nov 2023 19:42:56 +0000
Subject: [PATCH 165/199] chore: update pre-commit hooks
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

updates:
- [github.com/astral-sh/ruff-pre-commit: v0.1.4 → v0.1.5](https://github.com/astral-sh/ruff-pre-commit/compare/v0.1.4...v0.1.5)
---
 .pre-commit-config.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index fa0de6f56c..5871b334bf 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -4,7 +4,7 @@ ci:
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: "v0.1.4"
+    rev: "v0.1.5"
     hooks:
       - id: ruff
         args: [--fix, --show-fixes]

From 4d118abc78115b9f27ffd970d64d52ca697294a9 Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Tue, 14 Nov 2023 14:42:02 +0530
Subject: [PATCH 166/199] Fix CHANGELOG

---
 CHANGELOG.md | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index b02df8ed4c..afbc5073b0 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -2,7 +2,7 @@
 
 ## Bug fixes
 
-- Fixed a bug where the JaxSolver would fails when using GPU support with no input parameters ([#3423](https://github.com/pybamm-team/PyBaMM/pull/3423))
+- Fixed bug in calculation of theoretical energy that made it very slow ([#3506](https://github.com/pybamm-team/PyBaMM/pull/3506))
 
 # [v23.9rc0](https://github.com/pybamm-team/PyBaMM/tree/v23.9rc0) - 2023-10-31
 
@@ -23,6 +23,7 @@
 
 ## Bug fixes
 
+- Fixed a bug where the JaxSolver would fail when using GPU support with no input parameters ([#3423](https://github.com/pybamm-team/PyBaMM/pull/3423))
 - Fixed a bug where empty lists passed to QuickPlot resulted in an IndexError and did not return a meaningful error message ([#3359](https://github.com/pybamm-team/PyBaMM/pull/3359))
 - Fixed a bug where there was a missing thermal conductivity in the thermal pouch cell models ([#3330](https://github.com/pybamm-team/PyBaMM/pull/3330))
 - Fixed a bug that caused incorrect results of “{Domain} electrode thickness change [m]” due to the absence of dimension for the variable `electrode_thickness_change`([#3329](https://github.com/pybamm-team/PyBaMM/pull/3329)).
@@ -61,7 +62,7 @@
 - Added option to use an empirical hysteresis model for the diffusivity and exchange-current density ([#3194](https://github.com/pybamm-team/PyBaMM/pull/3194))
 - Double-layer capacity can now be provided as a function of temperature ([#3174](https://github.com/pybamm-team/PyBaMM/pull/3174))
 - `pybamm_install_jax` is deprecated. It is now replaced with `pip install pybamm[jax]` ([#3163](https://github.com/pybamm-team/PyBaMM/pull/3163))
-- PyBaMM now has optional dependencies that can be installed with the pattern `pip install pybamm[option]` e.g. `pybamm[plot]` ([#3044](https://github.com/pybamm-team/PyBaMM/pull/3044))
+- PyBaMM now has optional dependencies that can be installed with the pattern `pip install pybamm[option]` e.g. `pybamm[plot]` ([#3044](https://github.com/pybamm-team/PyBaMM/pull/3044), [#3475](https://github.com/pybamm-team/PyBaMM/pull/3475))
 
 # [v23.5](https://github.com/pybamm-team/PyBaMM/tree/v23.5) - 2023-06-18
 

From 72f8ff9991413d84921ae8409d223b91debd67f2 Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Wed, 15 Nov 2023 02:20:42 +0530
Subject: [PATCH 167/199] Bump to v23.9rc1 manually

---
 CHANGELOG.md      | 8 ++++++--
 CITATION.cff      | 2 +-
 pybamm/version.py | 2 +-
 vcpkg.json        | 2 +-
 4 files changed, 9 insertions(+), 5 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index afbc5073b0..82b3824272 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -4,6 +4,12 @@
 
 - Fixed bug in calculation of theoretical energy that made it very slow ([#3506](https://github.com/pybamm-team/PyBaMM/pull/3506))
 
+# [v23.9rc1](https://github.com/pybamm-team/PyBaMM/tree/v23.9rc0) - 2023-11-15
+
+- Fixed a bug where the JaxSolver would fails when using GPU support with no input parameters ([#3423](https://github.com/pybamm-team/PyBaMM/pull/3423))
+- Make pybamm importable with minimal dependencies ([#3044](https://github.com/pybamm-team/PyBaMM/pull/3044), [#3475](https://github.com/pybamm-team/PyBaMM/pull/3475))
+- Fixed a bug where supplying an initial soc did not work with half cell models ([#3456](https://github.com/pybamm-team/PyBaMM/pull/3456))
+
 # [v23.9rc0](https://github.com/pybamm-team/PyBaMM/tree/v23.9rc0) - 2023-10-31
 
 ## Features
@@ -23,7 +29,6 @@
 
 ## Bug fixes
 
-- Fixed a bug where the JaxSolver would fail when using GPU support with no input parameters ([#3423](https://github.com/pybamm-team/PyBaMM/pull/3423))
 - Fixed a bug where empty lists passed to QuickPlot resulted in an IndexError and did not return a meaningful error message ([#3359](https://github.com/pybamm-team/PyBaMM/pull/3359))
 - Fixed a bug where there was a missing thermal conductivity in the thermal pouch cell models ([#3330](https://github.com/pybamm-team/PyBaMM/pull/3330))
 - Fixed a bug that caused incorrect results of “{Domain} electrode thickness change [m]” due to the absence of dimension for the variable `electrode_thickness_change`([#3329](https://github.com/pybamm-team/PyBaMM/pull/3329)).
@@ -42,7 +47,6 @@
 - Error generated when invalid parameter values are passed ([#3132](https://github.com/pybamm-team/PyBaMM/pull/3132))
 - Parameters in `Prada2013` have been updated to better match those given in the paper, which is a 2.3 Ah cell, instead of the mix-and-match with the 1.1 Ah cell from Lain2019 ([#3096](https://github.com/pybamm-team/PyBaMM/pull/3096))
 - The `OneDimensionalX` thermal model has been updated to account for edge/tab cooling and account for the current collector volumetric heat capacity. It now gives the correct behaviour compared with a lumped model with the correct total heat transfer coefficient and surface area for cooling. ([#3042](https://github.com/pybamm-team/PyBaMM/pull/3042))
-- Fixed a bug where supplying an initial soc did not work with half cell models ([#3456](https://github.com/pybamm-team/PyBaMM/pull/3456))
 
 ## Optimizations
 
diff --git a/CITATION.cff b/CITATION.cff
index 5a9e1e2ddc..b7f68164fc 100644
--- a/CITATION.cff
+++ b/CITATION.cff
@@ -24,6 +24,6 @@ keywords:
   - "expression tree"
   - "python"
   - "symbolic differentiation"
-version: "23.9rc0"
+version: "23.9rc1"
 repository-code: "https://github.com/pybamm-team/PyBaMM"
 title: "Python Battery Mathematical Modelling (PyBaMM)"
diff --git a/pybamm/version.py b/pybamm/version.py
index c8d63f83e1..e5cfaa0882 100644
--- a/pybamm/version.py
+++ b/pybamm/version.py
@@ -1 +1 @@
-__version__ = "23.9rc0"
+__version__ = "23.9rc1"
diff --git a/vcpkg.json b/vcpkg.json
index 6877dfa094..de71a5a87d 100644
--- a/vcpkg.json
+++ b/vcpkg.json
@@ -1,6 +1,6 @@
 {
   "name": "pybamm",
-  "version-string": "23.9rc0",
+  "version-string": "23.9rc1",
   "dependencies": [
     "casadi",
     {

From 88c5d8daa51f0d92b961a05e88b7ce546f5336cf Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Wed, 15 Nov 2023 13:48:29 +0530
Subject: [PATCH 168/199] Update CHANGELOG.md

---
 CHANGELOG.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 82b3824272..d615b3d714 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -4,7 +4,7 @@
 
 - Fixed bug in calculation of theoretical energy that made it very slow ([#3506](https://github.com/pybamm-team/PyBaMM/pull/3506))
 
-# [v23.9rc1](https://github.com/pybamm-team/PyBaMM/tree/v23.9rc0) - 2023-11-15
+# [v23.9rc1](https://github.com/pybamm-team/PyBaMM/tree/v23.9rc1) - 2023-11-15
 
 - Fixed a bug where the JaxSolver would fails when using GPU support with no input parameters ([#3423](https://github.com/pybamm-team/PyBaMM/pull/3423))
 - Make pybamm importable with minimal dependencies ([#3044](https://github.com/pybamm-team/PyBaMM/pull/3044), [#3475](https://github.com/pybamm-team/PyBaMM/pull/3475))

From 9e69391d9ea2476f2345c9425bb1f44113af5ecd Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 16 Nov 2023 01:07:45 +0530
Subject: [PATCH 169/199] Remove reinstall of OpenBLAS for scheduled tests

---
 .github/workflows/run_periodic_tests.yml | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml
index 2322adf993..06bd358f71 100644
--- a/.github/workflows/run_periodic_tests.yml
+++ b/.github/workflows/run_periodic_tests.yml
@@ -66,13 +66,11 @@ jobs:
           sudo apt install gfortran gcc libopenblas-dev graphviz pandoc
           sudo apt install texlive-full
 
-      # sometimes gfortran cannot be found, so reinstall gcc just to be sure
-      - name: Install MacOS system dependencies
+      - name: Install macOS system dependencies
         if: matrix.os == 'macos-latest'
         run:
           brew update
-          brew install graphviz
-          brew reinstall openblas
+          brew install graphviz openblas
           brew reinstall gcc
 
       - name: Install Windows system dependencies

From f87b6bf5c8306581cdc4ed1e2f4fda3d61f077e4 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 16 Nov 2023 01:09:54 +0530
Subject: [PATCH 170/199] Temporarily remove `pip` wheel caches and test

---
 .github/workflows/test_on_push.yml | 12 ------------
 1 file changed, 12 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index b660f0a7c9..8a97bdffc8 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -87,8 +87,6 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python-version }}
-          cache: 'pip'
-          cache-dependency-path: setup.py
 
       - name: Install standard Python dependencies
         run: |
@@ -146,8 +144,6 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: 3.11
-          cache: 'pip'
-          cache-dependency-path: setup.py
 
       - name: Install standard Python dependencies
         run: |
@@ -228,8 +224,6 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python-version }}
-          cache: 'pip'
-          cache-dependency-path: setup.py
 
       - name: Install standard Python dependencies
         run: |
@@ -288,8 +282,6 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: 3.11
-          cache: 'pip'
-          cache-dependency-path: setup.py
 
       - name: Install standard Python dependencies
         run: |
@@ -332,8 +324,6 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: 3.11
-          cache: 'pip'
-          cache-dependency-path: setup.py
 
       - name: Install standard Python dependencies
         run: |
@@ -389,8 +379,6 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: 3.11
-          cache: 'pip'
-          cache-dependency-path: setup.py
 
       - name: Install standard Python dependencies
         run: |

From 778d2a4b756b83afce0d5709c6956678e8f48263 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 16 Nov 2023 01:21:58 +0530
Subject: [PATCH 171/199] Don't run "brew update" in CI

---
 .github/workflows/run_periodic_tests.yml | 1 -
 .github/workflows/test_on_push.yml       | 2 --
 2 files changed, 3 deletions(-)

diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml
index 06bd358f71..8e3a8b2644 100644
--- a/.github/workflows/run_periodic_tests.yml
+++ b/.github/workflows/run_periodic_tests.yml
@@ -69,7 +69,6 @@ jobs:
       - name: Install macOS system dependencies
         if: matrix.os == 'macos-latest'
         run:
-          brew update
           brew install graphviz openblas
           brew reinstall gcc
 
diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 8a97bdffc8..e948337b3b 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -75,7 +75,6 @@ jobs:
           NONINTERACTIVE: 1
         run: |
           brew analytics off
-          brew update
           brew install graphviz openblas
 
       - name: Install Windows system dependencies
@@ -212,7 +211,6 @@ jobs:
           NONINTERACTIVE: 1
         run: |
           brew analytics off
-          brew update
           brew install graphviz openblas
 
       - name: Install Windows system dependencies

From 9098bf0f7290087724ce7c4197be4f62103ceb8c Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 16 Nov 2023 01:45:28 +0530
Subject: [PATCH 172/199] Even lower bounds for NumPy and SciPy

---
 setup.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 8bc6437945..bf69298c08 100644
--- a/setup.py
+++ b/setup.py
@@ -203,8 +203,8 @@ def compile_KLU():
     ],
     # List of dependencies
     install_requires=[
-        "numpy>=1.24.4",
-        "scipy>=1.10.1",
+        "numpy>=1.18.5",
+        "scipy>=1.9.3",
         "casadi>=3.6.3",
         "xarray",
     ],

From b0dc5f9803ad4febc944a190b54b8fb86d8b64b5 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 16 Nov 2023 02:16:01 +0530
Subject: [PATCH 173/199] Exercise tighter lower bounds for dependencies

---
 setup.py | 26 +++++++++++++-------------
 1 file changed, 13 insertions(+), 13 deletions(-)

diff --git a/setup.py b/setup.py
index 7dfd713a34..1ec40d9637 100644
--- a/setup.py
+++ b/setup.py
@@ -203,11 +203,11 @@ def compile_KLU():
     ],
     # List of dependencies
     install_requires=[
-        "numpy>=1.18.5",
-        "scipy>=1.9.3",
+        "numpy>=1.24.4",
+        "scipy>=1.10.1",
         "casadi>=3.6.3",
-        "xarray",
-        "anytree>=2.4.3",
+        "xarray>=2023.1.0",
+        "anytree>=2.12.0",
     ],
     extras_require={
         "docs": [
@@ -231,12 +231,12 @@ def compile_KLU():
             "jupyter",  # For example notebooks
         ],
         "plot": [
-            "imageio>=2.9.0",
+            "imageio>=2.32.0",
             # Note: Matplotlib is loaded for debug plots, but to ensure pybamm runs
             # on systems without an attached display, it should never be imported
             # outside of plot() methods.
             # Should not be imported
-            "matplotlib>=2.0",
+            "matplotlib>=3.7.3",
         ],
         "cite": [
             "pybtex>=0.24.0",
@@ -263,7 +263,7 @@ def compile_KLU():
             "nbmake",
         ],
         "pandas": [
-            "pandas>=0.24",
+            "pandas>=2.0.3",
         ],
         "jax": [
             "jax==0.4.8",
@@ -271,16 +271,16 @@ def compile_KLU():
         ],
         "odes": ["scikits.odes"],
         "all": [
-            "anytree>=2.4.3",
-            "autograd>=1.2",
-            "pandas>=0.24",
-            "scikit-fem>=0.2.0",
-            "imageio>=2.9.0",
+            "anytree>=2.12.0",
+            "autograd>=1.6.2",
+            "pandas>=2.0.3",
+            "scikit-fem>=8.1.0",
+            "imageio>=2.32.0",
             "pybtex>=0.24.0",
             "sympy>=1.12",
             "bpx",
             "tqdm",
-            "matplotlib>=2.0",
+            "matplotlib>=3.7.3",
             "jupyter",
         ],
     },

From 2b8e225e6813ee44f12111a8f92889eeade56392 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 16 Nov 2023 02:23:19 +0530
Subject: [PATCH 174/199] Add recursive optional dependencies

---
 setup.py | 10 +---------
 1 file changed, 1 insertion(+), 9 deletions(-)

diff --git a/setup.py b/setup.py
index 1ec40d9637..9ff587f5ea 100644
--- a/setup.py
+++ b/setup.py
@@ -271,17 +271,9 @@ def compile_KLU():
         ],
         "odes": ["scikits.odes"],
         "all": [
-            "anytree>=2.12.0",
             "autograd>=1.6.2",
-            "pandas>=2.0.3",
             "scikit-fem>=8.1.0",
-            "imageio>=2.32.0",
-            "pybtex>=0.24.0",
-            "sympy>=1.12",
-            "bpx",
-            "tqdm",
-            "matplotlib>=3.7.3",
-            "jupyter",
+            "pybamm[examples,plot,cite,latexify,bpx,tqdm,pandas]"
         ],
     },
     entry_points={

From 66037ab035beb3070d82b8ff37ced1ccb61e743f Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 16 Nov 2023 02:54:56 +0530
Subject: [PATCH 175/199] Remove bounds for `xarray` and `pandas`

---
 setup.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 9ff587f5ea..26396805c8 100644
--- a/setup.py
+++ b/setup.py
@@ -206,7 +206,7 @@ def compile_KLU():
         "numpy>=1.24.4",
         "scipy>=1.10.1",
         "casadi>=3.6.3",
-        "xarray>=2023.1.0",
+        "xarray",
         "anytree>=2.12.0",
     ],
     extras_require={
@@ -263,7 +263,7 @@ def compile_KLU():
             "nbmake",
         ],
         "pandas": [
-            "pandas>=2.0.3",
+            "pandas",
         ],
         "jax": [
             "jax==0.4.8",

From f7266365d5b183955421a67c54d537aced97008c Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 16 Nov 2023 16:16:24 +0530
Subject: [PATCH 176/199] Use `python -m nox` instead of `pipx run nox`

---
 .github/workflows/run_periodic_tests.yml | 18 ++++++------
 .github/workflows/test_on_push.yml       | 36 ++++++++++++------------
 2 files changed, 27 insertions(+), 27 deletions(-)

diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml
index 8e3a8b2644..06c0f0fb68 100644
--- a/.github/workflows/run_periodic_tests.yml
+++ b/.github/workflows/run_periodic_tests.yml
@@ -78,42 +78,42 @@ jobs:
 
       - name: Install standard Python dependencies
         run: |
-          python -m pip install --upgrade pip wheel setuptools
+          python -m pip install --upgrade pip wheel setuptools nox
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         if: matrix.os == 'ubuntu-latest'
-        run: pipx run nox -s pybamm-requires
+        run: python -m nox -s pybamm-requires
 
       - name: Run unit tests for GNU/Linux with Python 3.8, 3.9, and 3.10, and for macOS and Windows with all Python versions
         if: (matrix.os == 'ubuntu-latest' && matrix.python-version != 3.11) || (matrix.os != 'ubuntu-latest')
-        run: pipx run nox -s unit
+        run: python -m nox -s unit
 
       - name: Run unit tests for GNU/Linux with Python 3.11 and generate coverage report
         if: matrix.os == 'ubuntu-latest' && matrix.python-version == 3.11
-        run: pipx run nox -s coverage
+        run: python -m nox -s coverage
 
       - name: Upload coverage report
         if: matrix.os == 'ubuntu-latest' && matrix.python-version == 3.11
         uses: codecov/codecov-action@v3.1.4
 
       - name: Run integration tests
-        run: pipx run nox -s integration
+        run: python -m nox -s integration
 
       - name: Install docs dependencies and run doctests
         if: matrix.os == 'ubuntu-latest'
-        run: pipx run nox -s doctests
+        run: python -m nox -s doctests
 
       - name: Check if the documentation can be built
         if: matrix.os == 'ubuntu-latest'
-        run: pipx run nox -s docs
+        run: python -m nox -s docs
 
       - name: Install dev dependencies and run example tests
         if: matrix.os == 'ubuntu-latest'
-        run: pipx run nox -s examples
+        run: python -m nox -s examples
 
       - name: Run example scripts tests
         if: matrix.os == 'ubuntu-latest'
-        run: pipx run nox -s scripts
+        run: python -m nox -s scripts
 
   #M-series Mac Mini
   build-apple-mseries:
diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 38735a15f5..09a98b1131 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -89,7 +89,7 @@ jobs:
 
       - name: Install standard Python dependencies
         run: |
-          pip install --upgrade pip wheel setuptools
+          pip install --upgrade pip wheel setuptools nox
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -107,10 +107,10 @@ jobs:
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         if: matrix.os == 'ubuntu-latest'
-        run: pipx run nox -s pybamm-requires
+        run: python -m nox -s pybamm-requires
 
       - name: Run unit tests for ${{ matrix.os }} with Python ${{ matrix.python-version }}
-        run: pipx run nox -s unit
+        run: python -m nox -s unit
 
   # Runs only on Ubuntu with Python 3.11
   check_coverage:
@@ -146,7 +146,7 @@ jobs:
 
       - name: Install standard Python dependencies
         run: |
-          pip install --upgrade pip wheel setuptools
+          pip install --upgrade pip wheel setuptools nox
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -162,10 +162,10 @@ jobs:
           key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
-        run: pipx run nox -s pybamm-requires
+        run: python -m nox -s pybamm-requires
 
       - name: Run unit tests for Ubuntu with Python 3.11 and generate coverage report
-        run: pipx run nox -s coverage
+        run: python -m nox -s coverage
 
       - name: Upload coverage report
         uses: codecov/codecov-action@v3.1.4
@@ -225,7 +225,7 @@ jobs:
 
       - name: Install standard Python dependencies
         run: |
-          pip install --upgrade pip wheel setuptools
+          pip install --upgrade pip wheel setuptools nox
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -243,10 +243,10 @@ jobs:
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         if: matrix.os == 'ubuntu-latest'
-        run: pipx run nox -s pybamm-requires
+        run: python -m nox -s pybamm-requires
 
       - name: Run integration tests for ${{ matrix.os }} with Python ${{ matrix.python-version }}
-        run: pipx run nox -s integration
+        run: python -m nox -s integration
 
 # Runs only on Ubuntu with Python 3.11. Skips IDAKLU module compilation
 # for speedups, which is already tested in other jobs.
@@ -283,13 +283,13 @@ jobs:
 
       - name: Install standard Python dependencies
         run: |
-          pip install --upgrade pip wheel setuptools
+          pip install --upgrade pip wheel setuptools nox
 
       - name: Install docs dependencies and run doctests for GNU/Linux with Python 3.11
-        run: pipx run nox -s doctests
+        run: python -m nox -s doctests
 
       - name: Check if the documentation can be built for GNU/Linux with Python 3.11
-        run: pipx run nox -s docs
+        run: python -m nox -s docs
 
   # Runs only on Ubuntu with Python 3.11
   run_example_tests:
@@ -325,7 +325,7 @@ jobs:
 
       - name: Install standard Python dependencies
         run: |
-          pip install --upgrade pip wheel setuptools
+          pip install --upgrade pip wheel setuptools nox
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -341,10 +341,10 @@ jobs:
           key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
-        run: pipx run nox -s pybamm-requires
+        run: python -m nox -s pybamm-requires
 
       - name: Install dev dependencies and run example tests for GNU/Linux with Python 3.11
-        run: pipx run nox -s examples
+        run: python -m nox -s examples
 
   # Runs only on Ubuntu with Python 3.11
   run_scripts_tests:
@@ -380,7 +380,7 @@ jobs:
 
       - name: Install standard Python dependencies
         run: |
-          pip install --upgrade pip wheel setuptools
+          pip install --upgrade pip wheel setuptools nox
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -396,7 +396,7 @@ jobs:
           key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
-        run: pipx run nox -s pybamm-requires
+        run: python -m nox -s pybamm-requires
 
       - name: Install dev dependencies and run example scripts tests for GNU/Linux with Python 3.11
-        run: pipx run nox -s scripts
+        run: python -m nox -s scripts

From 363c6f11d0cf01b503c92441e8e6dfc7eeb029e5 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 16 Nov 2023 16:24:28 +0530
Subject: [PATCH 177/199] Temporarily remove cache, let Linux tests run

---
 .github/workflows/test_on_push.yml | 124 ++++++++++++++---------------
 1 file changed, 62 insertions(+), 62 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 09a98b1131..eb366a024a 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -91,19 +91,19 @@ jobs:
         run: |
           pip install --upgrade pip wheel setuptools nox
 
-      - name: Cache pybamm-requires nox environment for GNU/Linux
-        uses: actions/cache@v3
-        if: matrix.os == 'ubuntu-latest'
-        with:
-          path: |
-            # Repository files
-            ${{ github.workspace }}/pybind11/
-            ${{ github.workspace }}/install_KLU_Sundials/
-            # Headers and dynamic library files for SuiteSparse and SUNDIALS
-            ${{ env.HOME }}/.local/lib/
-            ${{ env.HOME }}/.local/include/
-            ${{ env.HOME }}/.local/examples/
-          key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+      # - name: Cache pybamm-requires nox environment for GNU/Linux
+      #   uses: actions/cache@v3
+      #   if: matrix.os == 'ubuntu-latest'
+      #   with:
+      #     path: |
+      #       # Repository files
+      #       ${{ github.workspace }}/pybind11/
+      #       ${{ github.workspace }}/install_KLU_Sundials/
+      #       # Headers and dynamic library files for SuiteSparse and SUNDIALS
+      #       ${{ env.HOME }}/.local/lib/
+      #       ${{ env.HOME }}/.local/include/
+      #       ${{ env.HOME }}/.local/examples/
+      #     key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         if: matrix.os == 'ubuntu-latest'
@@ -148,18 +148,18 @@ jobs:
         run: |
           pip install --upgrade pip wheel setuptools nox
 
-      - name: Cache pybamm-requires nox environment for GNU/Linux
-        uses: actions/cache@v3
-        with:
-          path: |
-            # Repository files
-            ${{ github.workspace }}/pybind11/
-            ${{ github.workspace }}/install_KLU_Sundials/
-            # Headers and dynamic library files for SuiteSparse and SUNDIALS
-            ${{ env.HOME }}/.local/lib/
-            ${{ env.HOME }}/.local/include/
-            ${{ env.HOME }}/.local/examples/
-          key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+      # - name: Cache pybamm-requires nox environment for GNU/Linux
+      #   uses: actions/cache@v3
+      #   with:
+      #     path: |
+      #       # Repository files
+      #       ${{ github.workspace }}/pybind11/
+      #       ${{ github.workspace }}/install_KLU_Sundials/
+      #       # Headers and dynamic library files for SuiteSparse and SUNDIALS
+      #       ${{ env.HOME }}/.local/lib/
+      #       ${{ env.HOME }}/.local/include/
+      #       ${{ env.HOME }}/.local/examples/
+      #     key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         run: python -m nox -s pybamm-requires
@@ -227,19 +227,19 @@ jobs:
         run: |
           pip install --upgrade pip wheel setuptools nox
 
-      - name: Cache pybamm-requires nox environment for GNU/Linux
-        uses: actions/cache@v3
-        if: matrix.os == 'ubuntu-latest'
-        with:
-          path: |
-            # Repository files
-            ${{ github.workspace }}/pybind11/
-            ${{ github.workspace }}/install_KLU_Sundials/
-            # Headers and dynamic library files for SuiteSparse and SUNDIALS
-            ${{ env.HOME }}/.local/lib/
-            ${{ env.HOME }}/.local/include/
-            ${{ env.HOME }}/.local/examples/
-          key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+      # - name: Cache pybamm-requires nox environment for GNU/Linux
+      #   uses: actions/cache@v3
+      #   if: matrix.os == 'ubuntu-latest'
+      #   with:
+      #     path: |
+      #       # Repository files
+      #       ${{ github.workspace }}/pybind11/
+      #       ${{ github.workspace }}/install_KLU_Sundials/
+      #       # Headers and dynamic library files for SuiteSparse and SUNDIALS
+      #       ${{ env.HOME }}/.local/lib/
+      #       ${{ env.HOME }}/.local/include/
+      #       ${{ env.HOME }}/.local/examples/
+      #     key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         if: matrix.os == 'ubuntu-latest'
@@ -327,18 +327,18 @@ jobs:
         run: |
           pip install --upgrade pip wheel setuptools nox
 
-      - name: Cache pybamm-requires nox environment for GNU/Linux
-        uses: actions/cache@v3
-        with:
-          path: |
-            # Repository files
-            ${{ github.workspace }}/pybind11/
-            ${{ github.workspace }}/install_KLU_Sundials/
-            # Headers and dynamic library files for SuiteSparse and SUNDIALS
-            ${{ env.HOME }}/.local/lib/
-            ${{ env.HOME }}/.local/include/
-            ${{ env.HOME }}/.local/examples/
-          key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+      # - name: Cache pybamm-requires nox environment for GNU/Linux
+      #   uses: actions/cache@v3
+      #   with:
+      #     path: |
+      #       # Repository files
+      #       ${{ github.workspace }}/pybind11/
+      #       ${{ github.workspace }}/install_KLU_Sundials/
+      #       # Headers and dynamic library files for SuiteSparse and SUNDIALS
+      #       ${{ env.HOME }}/.local/lib/
+      #       ${{ env.HOME }}/.local/include/
+      #       ${{ env.HOME }}/.local/examples/
+      #     key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         run: python -m nox -s pybamm-requires
@@ -382,18 +382,18 @@ jobs:
         run: |
           pip install --upgrade pip wheel setuptools nox
 
-      - name: Cache pybamm-requires nox environment for GNU/Linux
-        uses: actions/cache@v3
-        with:
-          path: |
-            # Repository files
-            ${{ github.workspace }}/pybind11/
-            ${{ github.workspace }}/install_KLU_Sundials/
-            # Headers and dynamic library files for SuiteSparse and SUNDIALS
-            ${{ env.HOME }}/.local/lib/
-            ${{ env.HOME }}/.local/include/
-            ${{ env.HOME }}/.local/examples/
-          key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+      # - name: Cache pybamm-requires nox environment for GNU/Linux
+      #   uses: actions/cache@v3
+      #   with:
+      #     path: |
+      #       # Repository files
+      #       ${{ github.workspace }}/pybind11/
+      #       ${{ github.workspace }}/install_KLU_Sundials/
+      #       # Headers and dynamic library files for SuiteSparse and SUNDIALS
+      #       ${{ env.HOME }}/.local/lib/
+      #       ${{ env.HOME }}/.local/include/
+      #       ${{ env.HOME }}/.local/examples/
+      #     key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         run: python -m nox -s pybamm-requires

From 7c15368aa80d3ed3aa08ae3e0c41682363e06dd3 Mon Sep 17 00:00:00 2001
From: Ferran Brosa Planella <Ferran.Brosa-Planella@warwick.ac.uk>
Date: Thu, 16 Nov 2023 11:07:58 +0000
Subject: [PATCH 178/199] Fix bug with identical steps with different end times
 (#3516)

* fix bug with identical steps with different end times

* add copy method for steps

* undo testing changes

* fix failing tests

* update CHANGELOG

* remove copy method as it is unused

* remove raw termination as unused
---
 CHANGELOG.md                                   |  1 +
 pybamm/experiment/experiment.py                |  6 +++---
 pybamm/simulation.py                           | 13 +++++++++----
 tests/unit/test_experiments/test_experiment.py | 12 ++++++++++--
 4 files changed, 23 insertions(+), 9 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index d615b3d714..483ca91a5e 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -2,6 +2,7 @@
 
 ## Bug fixes
 
+- Fixed bug that made identical Experiment steps with different end times crash ([#3516](https://github.com/pybamm-team/PyBaMM/pull/3516))
 - Fixed bug in calculation of theoretical energy that made it very slow ([#3506](https://github.com/pybamm-team/PyBaMM/pull/3506))
 
 # [v23.9rc1](https://github.com/pybamm-team/PyBaMM/tree/v23.9rc1) - 2023-11-15
diff --git a/pybamm/experiment/experiment.py b/pybamm/experiment/experiment.py
index d1c45015b6..9b02e3a20f 100644
--- a/pybamm/experiment/experiment.py
+++ b/pybamm/experiment/experiment.py
@@ -78,7 +78,7 @@ def __init__(
         self.operating_conditions_cycles = operating_conditions_cycles
         self.cycle_lengths = [len(cycle) for cycle in operating_conditions_cycles]
 
-        operating_conditions_steps_unprocessed = self._set_next_start_time(
+        self.operating_conditions_steps_unprocessed = self._set_next_start_time(
             [cond for cycle in operating_conditions_cycles for cond in cycle]
         )
 
@@ -89,7 +89,7 @@ def __init__(
         self.temperature = _convert_temperature_to_kelvin(temperature)
 
         processed_steps = {}
-        for step in operating_conditions_steps_unprocessed:
+        for step in self.operating_conditions_steps_unprocessed:
             if repr(step) in processed_steps:
                 continue
             elif isinstance(step, str):
@@ -106,7 +106,7 @@ def __init__(
 
         self.operating_conditions_steps = [
             processed_steps[repr(step)]
-            for step in operating_conditions_steps_unprocessed
+            for step in self.operating_conditions_steps_unprocessed
         ]
 
         # Save the processed unique steps and the processed operating conditions
diff --git a/pybamm/simulation.py b/pybamm/simulation.py
index 42bda08e31..f9aebb1c54 100644
--- a/pybamm/simulation.py
+++ b/pybamm/simulation.py
@@ -774,14 +774,19 @@ def solve(
                     # human-intuitive
                     op_conds = self.experiment.operating_conditions_steps[idx]
 
+                    # Hacky patch to allow correct processing of end_time and next_starting time
+                    # For efficiency purposes, op_conds treats identical steps as the same object
+                    # regardless of the initial time. Should be refactored as part of #3176
+                    op_conds_unproc = self.experiment.operating_conditions_steps_unprocessed[idx]
+
                     start_time = current_solution.t[-1]
 
                     # If step has an end time, dt must take that into account
-                    if op_conds.end_time:
+                    if getattr(op_conds_unproc, "end_time", None):
                         dt = min(
                             op_conds.duration,
                             (
-                                op_conds.end_time
+                                op_conds_unproc.end_time
                                 - (
                                     initial_start_time
                                     + timedelta(seconds=float(start_time))
@@ -834,9 +839,9 @@ def solve(
                     step_termination = step_solution.termination
 
                     # Add a padding rest step if necessary
-                    if op_conds.next_start_time is not None:
+                    if getattr(op_conds_unproc, "next_start_time", None) is not None:
                         rest_time = (
-                            op_conds.next_start_time
+                            op_conds_unproc.next_start_time
                             - (
                                 initial_start_time
                                 + timedelta(seconds=float(step_solution.t[-1]))
diff --git a/tests/unit/test_experiments/test_experiment.py b/tests/unit/test_experiments/test_experiment.py
index 23548be433..ec1a1cbeae 100644
--- a/tests/unit/test_experiments/test_experiment.py
+++ b/tests/unit/test_experiments/test_experiment.py
@@ -183,41 +183,49 @@ def test_no_initial_start_time(self):
             )
 
     def test_set_next_start_time(self):
-        # Defined dummy experiment to access _set_next_start_time
-        experiment = pybamm.Experiment(["Rest for 1 hour"])
         raw_op = [
             pybamm.step._Step(
                 "current", 1, duration=3600, start_time=datetime(2023, 1, 1, 8, 0)
             ),
+            pybamm.step._Step("voltage", 2.5, duration=3600, start_time=None),
             pybamm.step._Step(
                 "current", 1, duration=3600, start_time=datetime(2023, 1, 1, 12, 0)
             ),
             pybamm.step._Step("current", 1, duration=3600, start_time=None),
+            pybamm.step._Step("voltage", 2.5, duration=3600, start_time=None),
             pybamm.step._Step(
                 "current", 1, duration=3600, start_time=datetime(2023, 1, 1, 15, 0)
             ),
         ]
+        experiment = pybamm.Experiment(raw_op)
         processed_op = experiment._set_next_start_time(raw_op)
 
         expected_next = [
+            None,
             datetime(2023, 1, 1, 12, 0),
             None,
+            None,
             datetime(2023, 1, 1, 15, 0),
             None,
         ]
 
         expected_end = [
             datetime(2023, 1, 1, 12, 0),
+            datetime(2023, 1, 1, 12, 0),
+            datetime(2023, 1, 1, 15, 0),
             datetime(2023, 1, 1, 15, 0),
             datetime(2023, 1, 1, 15, 0),
             None,
         ]
 
+        # Test method directly
         for next, end, op in zip(expected_next, expected_end, processed_op):
             # useful form for debugging
             self.assertEqual(op.next_start_time, next)
             self.assertEqual(op.end_time, end)
 
+        # TODO: once #3176 is completed, the test should pass for
+        # operating_conditions_steps (or equivalent) as well
 
 if __name__ == "__main__":
     print("Add -v for more debug output")

From b25e3ee21533605bb76d01428b5599be2e373d20 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 16 Nov 2023 17:26:09 +0530
Subject: [PATCH 179/199] Undo cache changes

This reverts commit 363c6f11d0cf01b503c92441e8e6dfc7eeb029e5.
---
 .github/workflows/test_on_push.yml | 124 ++++++++++++++---------------
 1 file changed, 62 insertions(+), 62 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index eb366a024a..09a98b1131 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -91,19 +91,19 @@ jobs:
         run: |
           pip install --upgrade pip wheel setuptools nox
 
-      # - name: Cache pybamm-requires nox environment for GNU/Linux
-      #   uses: actions/cache@v3
-      #   if: matrix.os == 'ubuntu-latest'
-      #   with:
-      #     path: |
-      #       # Repository files
-      #       ${{ github.workspace }}/pybind11/
-      #       ${{ github.workspace }}/install_KLU_Sundials/
-      #       # Headers and dynamic library files for SuiteSparse and SUNDIALS
-      #       ${{ env.HOME }}/.local/lib/
-      #       ${{ env.HOME }}/.local/include/
-      #       ${{ env.HOME }}/.local/examples/
-      #     key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+      - name: Cache pybamm-requires nox environment for GNU/Linux
+        uses: actions/cache@v3
+        if: matrix.os == 'ubuntu-latest'
+        with:
+          path: |
+            # Repository files
+            ${{ github.workspace }}/pybind11/
+            ${{ github.workspace }}/install_KLU_Sundials/
+            # Headers and dynamic library files for SuiteSparse and SUNDIALS
+            ${{ env.HOME }}/.local/lib/
+            ${{ env.HOME }}/.local/include/
+            ${{ env.HOME }}/.local/examples/
+          key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         if: matrix.os == 'ubuntu-latest'
@@ -148,18 +148,18 @@ jobs:
         run: |
           pip install --upgrade pip wheel setuptools nox
 
-      # - name: Cache pybamm-requires nox environment for GNU/Linux
-      #   uses: actions/cache@v3
-      #   with:
-      #     path: |
-      #       # Repository files
-      #       ${{ github.workspace }}/pybind11/
-      #       ${{ github.workspace }}/install_KLU_Sundials/
-      #       # Headers and dynamic library files for SuiteSparse and SUNDIALS
-      #       ${{ env.HOME }}/.local/lib/
-      #       ${{ env.HOME }}/.local/include/
-      #       ${{ env.HOME }}/.local/examples/
-      #     key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+      - name: Cache pybamm-requires nox environment for GNU/Linux
+        uses: actions/cache@v3
+        with:
+          path: |
+            # Repository files
+            ${{ github.workspace }}/pybind11/
+            ${{ github.workspace }}/install_KLU_Sundials/
+            # Headers and dynamic library files for SuiteSparse and SUNDIALS
+            ${{ env.HOME }}/.local/lib/
+            ${{ env.HOME }}/.local/include/
+            ${{ env.HOME }}/.local/examples/
+          key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         run: python -m nox -s pybamm-requires
@@ -227,19 +227,19 @@ jobs:
         run: |
           pip install --upgrade pip wheel setuptools nox
 
-      # - name: Cache pybamm-requires nox environment for GNU/Linux
-      #   uses: actions/cache@v3
-      #   if: matrix.os == 'ubuntu-latest'
-      #   with:
-      #     path: |
-      #       # Repository files
-      #       ${{ github.workspace }}/pybind11/
-      #       ${{ github.workspace }}/install_KLU_Sundials/
-      #       # Headers and dynamic library files for SuiteSparse and SUNDIALS
-      #       ${{ env.HOME }}/.local/lib/
-      #       ${{ env.HOME }}/.local/include/
-      #       ${{ env.HOME }}/.local/examples/
-      #     key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+      - name: Cache pybamm-requires nox environment for GNU/Linux
+        uses: actions/cache@v3
+        if: matrix.os == 'ubuntu-latest'
+        with:
+          path: |
+            # Repository files
+            ${{ github.workspace }}/pybind11/
+            ${{ github.workspace }}/install_KLU_Sundials/
+            # Headers and dynamic library files for SuiteSparse and SUNDIALS
+            ${{ env.HOME }}/.local/lib/
+            ${{ env.HOME }}/.local/include/
+            ${{ env.HOME }}/.local/examples/
+          key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         if: matrix.os == 'ubuntu-latest'
@@ -327,18 +327,18 @@ jobs:
         run: |
           pip install --upgrade pip wheel setuptools nox
 
-      # - name: Cache pybamm-requires nox environment for GNU/Linux
-      #   uses: actions/cache@v3
-      #   with:
-      #     path: |
-      #       # Repository files
-      #       ${{ github.workspace }}/pybind11/
-      #       ${{ github.workspace }}/install_KLU_Sundials/
-      #       # Headers and dynamic library files for SuiteSparse and SUNDIALS
-      #       ${{ env.HOME }}/.local/lib/
-      #       ${{ env.HOME }}/.local/include/
-      #       ${{ env.HOME }}/.local/examples/
-      #     key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+      - name: Cache pybamm-requires nox environment for GNU/Linux
+        uses: actions/cache@v3
+        with:
+          path: |
+            # Repository files
+            ${{ github.workspace }}/pybind11/
+            ${{ github.workspace }}/install_KLU_Sundials/
+            # Headers and dynamic library files for SuiteSparse and SUNDIALS
+            ${{ env.HOME }}/.local/lib/
+            ${{ env.HOME }}/.local/include/
+            ${{ env.HOME }}/.local/examples/
+          key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         run: python -m nox -s pybamm-requires
@@ -382,18 +382,18 @@ jobs:
         run: |
           pip install --upgrade pip wheel setuptools nox
 
-      # - name: Cache pybamm-requires nox environment for GNU/Linux
-      #   uses: actions/cache@v3
-      #   with:
-      #     path: |
-      #       # Repository files
-      #       ${{ github.workspace }}/pybind11/
-      #       ${{ github.workspace }}/install_KLU_Sundials/
-      #       # Headers and dynamic library files for SuiteSparse and SUNDIALS
-      #       ${{ env.HOME }}/.local/lib/
-      #       ${{ env.HOME }}/.local/include/
-      #       ${{ env.HOME }}/.local/examples/
-      #     key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+      - name: Cache pybamm-requires nox environment for GNU/Linux
+        uses: actions/cache@v3
+        with:
+          path: |
+            # Repository files
+            ${{ github.workspace }}/pybind11/
+            ${{ github.workspace }}/install_KLU_Sundials/
+            # Headers and dynamic library files for SuiteSparse and SUNDIALS
+            ${{ env.HOME }}/.local/lib/
+            ${{ env.HOME }}/.local/include/
+            ${{ env.HOME }}/.local/examples/
+          key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         run: python -m nox -s pybamm-requires

From 72773c43b60b27646db40469a4bca41eebc31a2b Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 16 Nov 2023 17:26:44 +0530
Subject: [PATCH 180/199] Undo `pip` cache changes

This reverts commit f87b6bf5c8306581cdc4ed1e2f4fda3d61f077e4.
---
 .github/workflows/test_on_push.yml | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 09a98b1131..f007b38e33 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -86,6 +86,8 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python-version }}
+          cache: 'pip'
+          cache-dependency-path: setup.py
 
       - name: Install standard Python dependencies
         run: |
@@ -143,6 +145,8 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: 3.11
+          cache: 'pip'
+          cache-dependency-path: setup.py
 
       - name: Install standard Python dependencies
         run: |
@@ -222,6 +226,8 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python-version }}
+          cache: 'pip'
+          cache-dependency-path: setup.py
 
       - name: Install standard Python dependencies
         run: |
@@ -280,6 +286,8 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: 3.11
+          cache: 'pip'
+          cache-dependency-path: setup.py
 
       - name: Install standard Python dependencies
         run: |
@@ -322,6 +330,8 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: 3.11
+          cache: 'pip'
+          cache-dependency-path: setup.py
 
       - name: Install standard Python dependencies
         run: |
@@ -377,6 +387,8 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: 3.11
+          cache: 'pip'
+          cache-dependency-path: setup.py
 
       - name: Install standard Python dependencies
         run: |

From e63346983e4fbdf396c5f09b7ab0ac9cf1abe61a Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 16 Nov 2023 17:32:00 +0530
Subject: [PATCH 181/199] Re-introduce `xarray` and `pandas` lower bounds

---
 setup.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 26396805c8..f6dd93960f 100644
--- a/setup.py
+++ b/setup.py
@@ -206,7 +206,7 @@ def compile_KLU():
         "numpy>=1.24.4",
         "scipy>=1.10.1",
         "casadi>=3.6.3",
-        "xarray",
+        "xarray>=23.1.0",
         "anytree>=2.12.0",
     ],
     extras_require={
@@ -263,7 +263,7 @@ def compile_KLU():
             "nbmake",
         ],
         "pandas": [
-            "pandas",
+            "pandas>=2.0.3",
         ],
         "jax": [
             "jax==0.4.8",

From 0755c35b4a00975793b50aca5b76834604067ef5 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Fri, 17 Nov 2023 14:10:12 +0530
Subject: [PATCH 182/199] Bump versions according to SPEC 0000

Co-Authored-By: Saransh Chopra <saransh0701@gmail.com>
---
 setup.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/setup.py b/setup.py
index f6dd93960f..886378f44f 100644
--- a/setup.py
+++ b/setup.py
@@ -203,10 +203,10 @@ def compile_KLU():
     ],
     # List of dependencies
     install_requires=[
-        "numpy>=1.24.4",
-        "scipy>=1.10.1",
+        "numpy>=1.23.5",
+        "scipy>=1.9.3",
         "casadi>=3.6.3",
-        "xarray>=23.1.0",
+        "xarray>=2022.6.0",
         "anytree>=2.12.0",
     ],
     extras_require={
@@ -236,7 +236,7 @@ def compile_KLU():
             # on systems without an attached display, it should never be imported
             # outside of plot() methods.
             # Should not be imported
-            "matplotlib>=3.7.3",
+            "matplotlib>=3.6.0",
         ],
         "cite": [
             "pybtex>=0.24.0",
@@ -263,7 +263,7 @@ def compile_KLU():
             "nbmake",
         ],
         "pandas": [
-            "pandas>=2.0.3",
+            "pandas>=1.5.0",
         ],
         "jax": [
             "jax==0.4.8",

From 553c5621e3bb9be7e9d2b29ce5e5076babee4318 Mon Sep 17 00:00:00 2001
From: Valentin Sulzer <valentinsulzer@hotmail.com>
Date: Fri, 17 Nov 2023 09:33:05 -0500
Subject: [PATCH 183/199] #3532 fix bug

---
 pybamm/models/full_battery_models/base_battery_model.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py
index 971bd1a880..ee3e0b5c6f 100644
--- a/pybamm/models/full_battery_models/base_battery_model.py
+++ b/pybamm/models/full_battery_models/base_battery_model.py
@@ -603,7 +603,7 @@ def __init__(self, extra_options):
                     "current collectors in a half-cell configuration"
                 )
 
-        if options["particle phases"] != "1":
+        if options["particle phases"] not in ["1", ("1", "1")]:
             if not (
                 options["surface form"] != "false"
                 and options["particle size"] == "single"

From 0834d795a9e599617056e6e2da52b10f7e08530d Mon Sep 17 00:00:00 2001
From: Robert Timms <robertwtimms@gmail.com>
Date: Fri, 17 Nov 2023 14:36:51 +0000
Subject: [PATCH 184/199] fix hysteresis option bug

---
 .../submodels/interface/base_interface.py     |  5 +++--
 .../submodels/particle/base_particle.py       |  5 +++--
 .../base_lithium_ion_tests.py                 | 19 +++++++++++++++++++
 3 files changed, 25 insertions(+), 4 deletions(-)

diff --git a/pybamm/models/submodels/interface/base_interface.py b/pybamm/models/submodels/interface/base_interface.py
index 190130064f..b7e160ee2f 100644
--- a/pybamm/models/submodels/interface/base_interface.py
+++ b/pybamm/models/submodels/interface/base_interface.py
@@ -110,9 +110,10 @@ def _get_exchange_current_density(self, variables):
                     c_e = c_e.orphans[0]
                     T = T.orphans[0]
             # Get main reaction exchange-current density (may have empirical hysteresis)
-            if domain_options["exchange-current density"] == "single":
+            j0_option = getattr(domain_options, self.phase)["exchange-current density"]
+            if j0_option == "single":
                 j0 = phase_param.j0(c_e, c_s_surf, T)
-            elif domain_options["exchange-current density"] == "current sigmoid":
+            elif j0_option == "current sigmoid":
                 current = variables["Total current density [A.m-2]"]
                 k = 100
                 if Domain == "Positive":
diff --git a/pybamm/models/submodels/particle/base_particle.py b/pybamm/models/submodels/particle/base_particle.py
index ad751c3911..dd5a94afc6 100644
--- a/pybamm/models/submodels/particle/base_particle.py
+++ b/pybamm/models/submodels/particle/base_particle.py
@@ -35,9 +35,10 @@ def _get_effective_diffusivity(self, c, T, current):
         domain_options = getattr(self.options, domain)
 
         # Get diffusivity (may have empirical hysteresis)
-        if domain_options["diffusivity"] == "single":
+        diffusivity_option = getattr(domain_options, self.phase)["diffusivity"]
+        if diffusivity_option == "single":
             D = phase_param.D(c, T)
-        elif domain_options["diffusivity"] == "current sigmoid":
+        elif diffusivity_option == "current sigmoid":
             k = 100
             if Domain == "Positive":
                 lithiation_current = current
diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py
index 6815698588..48832c4726 100644
--- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py
+++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py
@@ -389,3 +389,22 @@ def test_well_posed_current_sigmoid_diffusivity(self):
     def test_well_posed_psd(self):
         options = {"particle size": "distribution", "surface form": "algebraic"}
         self.check_well_posedness(options)
+
+    def test_well_posed_composite_kinetic_hysteresis(self):
+        options = {
+            "particle phases": ("2", "1"),
+            "exchange current density": (
+                ("current sigmoid", "single"),
+                "current sigmoid",
+            ),
+            "open-circuit potential": (("current sigmoid", "single"), "single"),
+        }
+        self.check_well_posedness(options)
+
+    def test_well_posed_composite_diffusion_hysteresis(self):
+        options = {
+            "particle phases": ("2", "1"),
+            "diffusivity": (("current sigmoid", "current sigmoid"), "current sigmoid"),
+            "open-circuit potential": (("current sigmoid", "single"), "single"),
+        }
+        self.check_well_posedness(options)

From 797eb9c158a6164da779d89741eb69ccaf6617bd Mon Sep 17 00:00:00 2001
From: Robert Timms <robertwtimms@gmail.com>
Date: Fri, 17 Nov 2023 15:09:26 +0000
Subject: [PATCH 185/199] fix tests

---
 .../test_lithium_ion/base_lithium_ion_tests.py              | 2 +-
 .../test_lithium_ion/test_newman_tobias.py                  | 6 ++++++
 2 files changed, 7 insertions(+), 1 deletion(-)

diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py
index 48832c4726..f4e3c3cceb 100644
--- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py
+++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py
@@ -393,7 +393,7 @@ def test_well_posed_psd(self):
     def test_well_posed_composite_kinetic_hysteresis(self):
         options = {
             "particle phases": ("2", "1"),
-            "exchange current density": (
+            "exchange-current density": (
                 ("current sigmoid", "single"),
                 "current sigmoid",
             ),
diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_newman_tobias.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_newman_tobias.py
index be7d2499c6..4d65804156 100644
--- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_newman_tobias.py
+++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_newman_tobias.py
@@ -22,6 +22,12 @@ def test_well_posed_particle_phases(self):
     def test_well_posed_particle_phases_sei(self):
         pass  # skip this test
 
+    def test_well_posed_composite_kinetic_hysteresis(self):
+        pass  # skip this test
+
+    def test_well_posed_composite_diffusion_hysteresis(self):
+        pass  # skip this test
+
 
 if __name__ == "__main__":
     print("Add -v for more debug output")

From 74e924a7eef9ff3d8376567ad4c14f6d1f6c5d48 Mon Sep 17 00:00:00 2001
From: Valentin Sulzer <valentinsulzer@hotmail.com>
Date: Fri, 17 Nov 2023 10:15:52 -0500
Subject: [PATCH 186/199] #3532 fix example notebook

---
 .../examples/notebooks/models/lithium-plating.ipynb       | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/docs/source/examples/notebooks/models/lithium-plating.ipynb b/docs/source/examples/notebooks/models/lithium-plating.ipynb
index 57049a0ea7..c2e8b198c6 100644
--- a/docs/source/examples/notebooks/models/lithium-plating.ipynb
+++ b/docs/source/examples/notebooks/models/lithium-plating.ipynb
@@ -159,7 +159,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
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",
       "text/plain": [
        "<Figure size 936x648 with 4 Axes>"
       ]
@@ -171,6 +171,8 @@
     }
    ],
    "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
     "colors = [\"tab:purple\", \"tab:cyan\", \"tab:red\", \"tab:green\", \"tab:blue\"]\n",
     "linestyles = [\"dashed\", \"dotted\", \"solid\"]\n",
     "\n",
@@ -274,7 +276,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 936x648 with 4 Axes>"
       ]
@@ -327,7 +329,7 @@
     },
     {
      "data": {
-      "image/png": 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62lV47KWXXuLiiy/Gy8vrX/s5HA7efvvt4z7v2LFjGTt27HHv31AcDgdGo/Gwy/WxcOFCfHx86Nu3b0OH16SkB/QQ/D00zppAVq5d7u5QhBDitDdw4EACAwMPaHvyySeZNm0aNpsNAJvNxuTJk90RnqiHQdGDeGvkWwxrMYzAK64g4vHHUWZz3UijzzPzGbliC0sLSzEYFD4BVgDW/55Bzs5id4YuhGggmZmZBAcHY7G4vlwKDg4mMjKSqVOnsmfPHoYMGcKQIUMA8PHx4Y477qBLly789ddfDB48mBUrVtStu+222+jQoQPDhg1j31RRgwcP5pZbbiEpKYmOHTuybNkyAN5//31uvPFGwPWF5s0330zfvn2Jj49n5kzX1MhOp5Prr7+edu3aMWLECEaPHl23bn/btm1j+PDhdOnShW7durF9+3YWLlzImDFj6ra58cYbef/99wGIjY3lnnvuoVu3bnz55Zf/Wv7ll1/o06cP3bp1Y+LEiZSWltbt99BDD9GtWzc6derEpk2bSEtL44033uDFF18kKSmJ33//vaH/EzUZ6QE9hEh/b9L2eLExbR2jcP83JkIIcTJ4YGs660orGvSYHX08eax19DHtU1xcTElJCfHx8Q0ay9EopY6WCSkgU2vdpiniOZUopegd0RuArLIsCioLaOUIIuPmWwi9607GJnWlxOGgh5933T7VlXZWzEkjpl0gQy+1uSt0IZqtWc+vol2fCNr3jcDhcPLtSykk9o+kba9waqodfP/KGjoOiqJ1chhVFXZ+fH0tnYdGk9A1lIrSan56cx1JI1oQ1zmYsqKqo45aGDlyJI8++iht2rRh+PDhTJo0iUGDBnHzzTfzwgsvsGDBAoKDgwEoKyujV69ePP/8v2eeKisrIzk5mRdffJFHH32URx55hFdffRWA8vJyUlJSWLRoEVdccQXr1q371/6ZmZn88ccfbNq0ibFjx3Luuefy9ddfk5aWxoYNG8jJyaF9+/ZcccUV/9r3oosu4t5772X8+PFUVlbidDrZvXv3v7bbX1BQEKtWrQLg3nvvrVvOzc3lnHPOYd68eXh7ezNlyhReeOEFHnzwQcCVoK9atYrXX3+d5557jrfffptrr70WHx8f7rzzziOe82QnPaCH0KFFNGAgNT/b3aEIIYQ4eWzXWtuO8OMLlLk7yJOZ1pq7fruLuxfdjTYb0XY7zrIyPI0GrooOwaAUhTV29lRW42E1cc6d3Rl8UVt3hy2EaAA+Pj6sXLmS6dOnExISwqRJk+p6Cg9mNBqZMGHCIdcZDAYmTZoEwMUXX8wff/xRt+6CCy4AXCNniouLKSws/Nf+48aNw2AwkJiYSHa267P+H3/8wcSJEzEYDISHh9f1xO6vpKSEjIwMxo8fD4DVaj3kkOGD7Yv14OUlS5awYcMG+vXrR1JSEh988AE7d+6s2+6cc84BoHv37qSlpR31PKcS6QE9hD5t43hryWqyZC5QIYSoc6w9lY3FZrPh4+NDampqU/eCHvrT0LFvc9pSSvFQn4dQSuHhH0TsF5+jDP98F6615oI1qTjRzOneBt9A11Dcqgo7a+btInl0LAajfHcuREMYf0e3utdGo+GAZbOH8YBli6fpgGVPH48Dluv7zLbRaGTw4MEMHjyYTp068cEHH3DZZZf9azur1Vrv5yP3Vdc++PWhloG6IcBAgxQcNZlMOJ3OuuXKg/IHb2/vQy5rrRkxYgSffvrpIY+7L06j0Yjdbj/hOE8m8lf8EFqF+gOQr+XtEUKIk9F9993HDTfcQHGxa1RsaWlpo1fB1VqnHtymlAo82jbiQK0CWpHgnwDA6r0paK0pnjOHjNtvB6eT/8ZH8EirKAz7fXDctT6PlXN2krVDngcV4lS1efNmtm7dWreckpJCy5au6ZZ8fX0pKSmp13GcTmfd85mffPIJ/fv3r1v3+eefA64eTT8/P/z8/Op1zH79+vHVV1/hdDrJzs5m4cKF/9rG19eX6OhovvnmGwCqqqooLy+nZcuWbNiwgaqqKgoLC5k/f369ztm7d28WL17Mtm3bANfQ4i1bthxxn2N5n05mkmEdQt1coOro3epCCCEa1wUXXECfPn3YvHkz0dHRvPPOO1x33XUMGTKEHj160LFjRwYMGIDB0Li3NKVUP6XURqXUeqVUL6XUXGC5Umq3UqpPo568GVqZvZLJP03mm23fYM/LpyYrG2d5OQMCfent7wPA1rJKtNa0Tg7jwkd6EdnK371BCyGOW2lpKZMnTyYxMZHOnTuzYcMGHn74YQCuvvpqRo0adcihrwfz9vZm2bJldOzYkV9//bXumUlw9Zx27dqVa6+9lnfeqf9UxxMmTCA6OprExEQuvvhiunXrdsjkdcaMGUydOpXOnTvTt29fsrKyiImJ4bzzzqNjx46cd955dO3atV7nDAkJ4f333+eCCy6gc+fO9OnTh02bNh1xn7POOotZs2ad8kWIVHOZ6zI5OVnvq47VENrc9xkmzy2sf+CBQ3bfCyHE6WDjxo20b9/e3WE0mENdj1JqpdY6+Wj7KqWWAVcCPsB3wDit9R9KqW7AK1rrfo0Rs7s1xP21uLKGF37Zwp1ntMXH4nr6R2vNN9u+YUz8GEwGE9jtKLO5bp+VRWWMXb2VF9u14LzwfzqaM7YUUFlWQ0LX0BOKSYjTTXP5e+7j41NXLXZ/gwcP5rnnniM5+ah/zg+ptLQUHx8f8vLy6NmzJ4sXLyY8PPxEwz1tHMv9VXpAD8PLWEEVAezNyjz6xkIIIU4HZq3131rrv4C9Wus/ALTWqwBP94Z2ctuwp5jPlu9iRVp+XZtSivGtx2M2mql0VJJekYWzspI9995HRUoKXW1e3BsXwZnB//RCaK1Z/sMOVvyYhtPZPL5AF0KcHMaMGUNSUhIDBgzggQcekOSzEUkRosPwt2iKqgNYsWYZoyPGuTscIYQQ7rf/l7b3HbTOoykDOdX0jg/i97uHEuJ76EIldy+6m60FW/l60AeUr16FtXMnPJOSuKllGAB2p2ZPVTUtPC2M+k8nUGAwyOgkIU5Hh+r9BA753OaxONH9Rf1JD+hhRNl80DU2Nu/a4O5QhBBCnBweUMpVHEBr/c2+RqVUAtC4FZCagX3J59LUPL5amX7Aums6X8O9Pe/FKziM+NmzCbzwwgPW371lN2ev3kaJ3YHVx4zV24zTqVnxYxoVJdVNdg1CCCFOnCSgh5HYMgowkponc4EKIYQArfW3Wuvy/duUUuFa6+1a62fcFdep5q3fd/DW76nUOP6ZtqBjcEcGxwwGIMuej9aayg0byHzkEbTTyVXRIdwVG46v6Z9pGQqzylkxJ42tK+Q+LYQQpxJJQA+jb1vX3HJZVTIXqBBCiMP60d0BnGpemNSFz6/pg/kQ83luzt/M2d+czaxtsyhfuYrShb9hz8kh0ceTCyODAEivrMahNYGR3lzwYE86D4lp6ksQQghxAiQBPYz4UBsABTIXqBBCiMOTBxGPkc1qxs/TjMOpmbZwO0XlNXXrWge05tIOlzIweiABF19E/LezMe9XCCSnqoaRKzYzJdVVINAvxDVdWnFuBat/2dW0FyKEEOK4SHZ1GBF+noCmWOYCFUIIt8rKyuL8888nISGB7t27M3r06LrJus8880zS09O56KKLaNu2LR07duSKK66gpqbmKEdtMG811Ymamy3ZJbwwdzPfrt1T12ZQBm7qehPBnsEAFJlr0FqTO/0typcvJ9Ri5oYWYVwQEXTAsTb+mcnKn9IoLahq0msQQtRfXl4eSUlJJCUlER4eTlRUVN1ydfWBz3K/9NJLlJeXH+ZI/xg8eDANOQ1jQ/nmm2/YsEHqyByOJKCH4WEy4KHKKDX40VzmShVCiFON1prx48czePBgtm/fzsqVK3nqqafIzs6moqKCvLw8oqOjueiii9i0aRN///03FRUVvP32240al1IqQCnVGViilOpWOxeoOAbtI2z8dOtALund8pDrn1r2FJfOuZTSor0UffMNxXN+AuCGFqHEebkKGu2scCWcPcbEcd5/e+ATcOgqu0II9wsKCiIlJYWUlBSuvfZabrvttrplD48DC4nXNwFtDA6H44jL9SEJ6JE1egKqlDIqpVYrpb4/xLqBSqlVSim7Uurc/dqHKKVS9vupVEqNa+xYD+aaC9SfnD0ZTX1qIYQQwIIFCzCbzVx77bV1bV26dGHAgAEsXLiQwYMHAzB69GiUUiil6NmzJ+np6Yc54olTSj0GrAWmAs/X/jzXaCdsxhJCfADYnV/O7JQD77WjYkcxrtU4fPxCaPnxR4Q9cP8B6z/IyGXQsk1sLK3AYFDYgl1TsW78M5Pdm/IRQpz85s+fT9euXenUqRNXXHEFVVVVTJ06lT179jBkyBCGDBkCwHXXXUdycjIdOnTgoYceOupxly9fTt++fenSpQs9e/akpKSE999/nxtvvLFumzFjxtRNveLj48Mdd9xBly5d+Ouvv/61/NFHH9GzZ0+SkpK45ppr6pJSHx8f/ve//9GlSxd69+5NdnY2f/75J99++y133XUXSUlJbN++veHfuFNcU8wDeguwEbAdYt0u4DLgzv0btdYLgCQApVQgsA34pTGDPJS6uUDXLuf/oqKb+vRCCHFSyXrySao2bmrQY1ratyP8v/897Pp169bRvXv3Q66bM2cO48aNO6CtpqaGGTNm8PLLLzdkmAc7D0jQWsv8Hw1k6vytzNuYzZB2odisZgC6hXWjW5irY7nc24gvYM/NJe+ddwm9/TbGhPizt9pOay9r3XEcdidr5u/CP9SLmHaB7rgUIU4Jv3+xhdzdh55P83gFx/gw4Lw29d6+srKSyy67jPnz59OmTRsuvfRSpk2bxq233soLL7zAggULCA52Dcd/4oknCAwMxOFwMGzYMNauXUvnzp0Pedzq6momTZrE559/To8ePSguLsbT0/OIsZSVldGrVy+ef/75fy1v3LiRKVOmsHjxYsxmM9dffz0ff/wxl156KWVlZfTu3ZsnnniCu+++m7feeov777+fsWPHMmbMGM4999wjnvd01ag9oEqpaOD/gEOOhdJap2mt1wLOQ62vdS4w5+DS900hys8bXeMnc4EKIcRJaPHixfTv3/+Atuuvv56BAwcyYMCAxjz1OsC/MU9wunl4bAe+vr5fXfK5v+yybCZ8O4GPNn5E2dKlFHz2GZWbtxDkYeLOuHBMBkWx3UFetR2jycDYW7oy4soObrgKIcSxcDgcxMXF0aaNK2mdPHkyixYtOuS2X3zxBd26daNr166sX7/+iMNbN2/eTEREBD169ADAZrNhMh25z81oNDJhwoRDLs+fP5+VK1fSo0cPkpKSmD9/PqmpqQB4eHgwZswYALp3705aWlr9Lv4019g9oC8BdwO+J3CM84EXDrVCKXU1cDVAixYtTuAUh9ahZTSL9xSwI1/mGBNCiCP1VDaWDh06MHPmzH+1p6amEhMTc8BzQ4888gh79+7lzTffbOywngJWK6XWAXVVb7TWYxv7xM2Vt8VEnMX1keT7tXvolxBMgLfrv22IVwhDY4aSHJaMX2J7vJJ7YA4LrdvXqTWTUrZjNSq+TmqFl821X02Vg7++3kbPs+Kx+vw7sRXidHYsPZXutmPHDp577jmWL19OQEAAl112GZWVxz5Noslkwun8p89r/2NYrVaMRuMhl7XWTJ48maeeeupfxzSbzSjlKoZuNBqx2+3HHNfpqNF6QJVSY4AcrfXKEzhGBNAJ+PlQ67XW07XWyVrr5JCQkOM9zWH1kblAhRDCrYYOHUpVVRXTp0+va1u7di0zZsxg1KhRdW1vv/02P//8M59++ikGQ6OXN/gAmAI8zT/PgD7f2Cc9HewprOD2L9bwxqJ/npkyKAP39bqP9kHtAagJdD03WvLrrxR9/wMGpbi5ZSi3tgyv+yAIkJ9ZxsYlWezZWtik1yCEqB+j0UhaWhrbtm0DYMaMGQwaNAgAX19fSkpKACguLsbb2xs/Pz+ys7OZM2fOEY/btm1bMjMzWb58OQAlJSXY7XZiY2NJSUnB6XSye/duli1bVq84hw0bxsyZM8nJyQEgPz+fnTt3HnGf/eMX/9aYPaD9gLFKqdGAFbAppT7SWl98DMc4D5iltW6yevr7iwt2ddwWaONRthRCCNEYlFLMmjWLW2+9lSlTpmC1WomNjcXpdDJt2rS67a699lpatmxJnz59ADjnnHN48MEHGyuscq311MY6+Oks0t+Tz67uTacov0Ou/3zT57yz7h0+OvMjKmbMQFdVYxt9JmeG+Ndts7mskjZeFsJibVzyWJ+6HlEhxMnFarXy3nvvMXHiROx2Oz169KgrOHf11VczatQoIiMjWbBgAV27dqVdu3bExMTQr1+/Ix7Xw8ODzz//nJtuuomKigo8PT2ZN28e/fr1Iy4ujsTERNq3b0+3bvUrXp6YmMjjjz/OyJEjcTqdmM1mXnvtNVq2PHQFb4Dzzz+f//znP0ydOpWZM2eSkJBQ/zfmNKCaYooRpdRg4E6t9ZjDrH8f+F5rPfOg9iXAfbVFiY4oOTlZN/Q8QFV2B23v/4lQz/kse+iQo4CFEKJZ27hxI+3bt3d3GAeoqqqiX79+xzX326GuRym1UmudXN9jKKVewDX09lsOHIK76pgDOgU0xv21Psqq7CzaspczO0XUtW3K38THGz/m/t73YyqrQplMGLz+ma97Y2kFI1ds4aFWkVwV/c/IqKzUIjYtyWLQ+W1QBoUQp6OT8e+5aD6O5f7aFFVwDw7kUWCF1vpbpVQPYBYQAJyllHpEa92hdrtYIAb4ralj3MdiMmJWpZTVzgW6/9AeIYQQ7mGxWNw98XjX2t+992vTwFA3xNJsvfnbdl5fuJ1fI/1oEeRKMtsFtuOxfo8BUOXtGp6rqqvJefllgi6/nHZBQfwvPoJzwwIOOFbmtiJ2b8ynorRGekSFEMLNmiQB1VovBBbWvn5wv/blwCHnN9FapwFRjR/dkXkbKyjRAWTu3k1kIxQ6EkIIcWrRWg9xdwyngxuGtqJ/65C65HN/NY4arvr5KhL8E7g3+EIKPvkUS3wC/hPO4doWrgJFDq1ZX1pBZ18vkkbE0GFgJB7WJv/eXQghxEEavVLDqc7fAs4af1b+7dZv24UQQpzElFL1e5hI1JvFZKRnnGsuz1W7CtiRW1a3zmw00zeqL30i+2Bt04aEn37Cf8I5B+z/QloWZ63ays6KKpRSeFhNaK358+ttrPl1d5NeixAni6Z49E6cfo7135UkoEfhmgvUn827ZS5QIYQQh3WduwNorqrsDm78eBUPf7v+gPbrulzHGbFnAFAd6O3adutWsp99Fq01V0aH8HSbaFp6Wur20RoKs8spyi6XD+LitGO1WsnLy5N/+6JBaa3Jy8vDarXWex8Zi3IUHWOjWbwnn7QCmQtUCCHEYV3v7gCaK4vJyPRLk4nwO/SHm5ScFG789UZeHPwicQtTKP72OwIvvZTAsDAuiAgCIK2iCofWJHhZOePqjhgMCqUU2qmlKJE4bURHR5Oens7evXvdHYpoZqxWK9HRh3yq8pAkAT2K3q3jePPPfHJkLlAhhBD7Ua7KdEOBC4ExQJh7I2q+OtZOy+J0ar5bu4ezOkdiqE0c4/3j6RvZl5a2lgRdlYz/hAmYAgPr9nVqzZXrdmBE8XNyG4xG1+CvsqIqfnhtLb3HxdMiMajpL0qIJmY2m4mLi3N3GELIENyjiQ1xzQWaL3OBCiGEW2RlZXH++eeTkJBA9+7dGT16NFu2bAHgzDPPJD09vW7bm2++GR8fn0aNRynVWyk1FdgJzAYWAe0a9aQCgF835XDLZyn8vD6rrs3mYeOZgc8Q6uUqPlTq5fpok/fuexT98AMGpXi5XQteSWx5QDV7o8mAycOAwSgfhYQQoinJX92j2Dfkp9jw7yp8QgghGpfWmvHjxzN48GC2b9/OypUreeqpp8jOzqaiooK8vLy6YT8rVqygoKCg0WJRSj2plNoKPAGsxTUdy16t9Qda68Y7sagzrH0oH17Rk1Edww+5/vkVz3PxnIspLsun9NdfKV3omsmto68Xbb1d9/PZOQUU2x1Yvc2Mv6Mb0W1dU7bUVDma5iKEEOI0JwnoUVjN++YCtclD20II0cQWLFiA2Wzm2muvrWvr0qULAwYMYOHChQwePBgAh8PBXXfdxTPPPNOY4VwFZAPTgBla6zxc8382GKXUu0qpHKXUuv3aApVSc5VSW2t/B9S2K6XUVKXUNqXU2v0r8SqlJtduv1UpNXm/9u5Kqb9r95mqTrEJrpVSDGwTglKKPYUV/LQu64D1w1sOZ3TcaHy9Aoh58w0in37qgPW7Kqq4ccMuXt+VU3c8gB1rc5lx/5/k7SltmgsRQojTmDwDWg/exkpKdAB7du0iqmVLd4cjhBBuMWXZFDblb2rQY7YLbMc9Pe857Pp169bRvXv3Q66bM2cO48aNA+DVV19l7NixRERENGh8B4kARgAXAC8ppRYAnkopk9ba3kDneB94Ffhwv7Z7gfla66eVUvfWLt8DnAm0rv3phSsx7qWUCgQeApJxJcgrlVLf1vbSTgP+AywFfgRGAXMaKPYm9dwvm/l1Uw59WwVhs5oBSApNIik0CYB8QyW+mDEWlbLnnnsJveN2WrRuzVdJCXS1eR9wrKBIb6LaBuATUP8qjkIIIY6P9IDWg2su0ACZC1QIIU4iixcvpn///uzZs4cvv/ySm266qVHPp7V2aK1/0lpPBhKAb4DFQIZS6pMGOsciIP+g5rOBD2pffwCM26/9Q+2yBPBXSkUAZwBztdb5tUnnXGBU7Tqb1nqJdg3p+XC/Y51yHj27I59f3acu+dxfhb2CS+ZcwqN/PYqjpISqrVup3u16Vrinvw9mg6LM7uC5HVnUODW2YE/OuKojFk8TDoeT0gIpPCiEEI1FekDrIcrfm7QMD7bsWgNMcHc4QgjhFkfqqWwsHTp0YObMmf9qT01NJSYmBg8PD1avXs22bdto1aoVAOXl5bRq1Ypt27Y1Wlxa6yrgK+ArpZSNxk3kwrTWmbWvs/in2m4UsHu/7dJr247Unn6I9n9RSl0NXA3QokWLEwy/cfhYTLQNdxUK/G7NHhIjbSSEuApQeZo8uSTxEjoGdcQjJJr4OT9i8PAAQDudKIOB+fklvLgzi34BPvTx/6dw1aLPtrBrXR7nP9gLi6d8TBJCiIYmPaD10DkuBjCSlrv7qNsKIYRoOEOHDqWqqorp06fXta1du5YZM2YwatQoAP7v//6PrKws0tLSSEtLw8vLq1GST6XUmEO1a62LtdYfHmmbhlLbc9noBQm01tO11sla6+SQkJDGPt0JKa2y8+j3G3h9wfYD2i9odwGdQjoBsLV0h2vbxYtJO28S9oICxob680fP9gcknwCdBkXT7YyWknwKIUQjkb+u9dC7dSzT/shlt73Q3aEIIcRpRSnFrFmzuPXWW5kyZQpWq5XY2FicTifTpk1r6nCeVUplAEcq3PMk8H0DnzdbKRWhtc6sHUabU9ueAcTst110bVsGMPig9oW17dGH2P6U5mMx8dnVvYny9zzk+pXZK7n8p8t5ov8TDDWHoYxGcLgq3sZ5WQBYWljK7JxCHm8dRXC0D8HRrqS0IKsMg9GAX8ihjy2EEOLYSQJaDzGBrmIFGV5WqsvL8fCSKVmEEKKpREZG8sUXX9QtV1VV0a9fP2JjYw+5fWlpo1UyzQZeOMo2WxvhvN8Ck4Gna3/P3q/9RqXUZ7iKEBXVJqk/A0/uq5YLjATu01rnK6WKlVK9cRUhuhR4pRHibXL7ht5W1jh47ufN3DS0NX5etYWJQpK4I/kOhrccjmeCJ16ffYpSCu10omtqMFgsLCks47f8EgrtDgLNro9G2qn5afo6TB5Gzr2nO6dYwWAhhDhpSQJaD9EBXhiUgyJDJHN/+o7/O2eSu0MSQojTlsViYcWKpi8Kp7Ue3NjnUEp9iqv3MlgplY6rmu3TwBdKqSuBncB5tZv/CIwGtgHlwOW1ceYrpR4Dltdu96jWel9ho+txVdr1xFX99pSsgHs4GzKLmbFkJ8mxAYzq6KqIbDQYmdzBNRNNtaOa5VnL6RfVj6zHHqN6Rxotpr/JzS1DuTI6GB+TsW7KNWVQDL88EaPRIMmnEEI0IElA68HDZKBVsAfbSuJYsuFnSUCFEEI0Cq31BYdZNewQ22rghsMc513g3UO0rwA6nkiMJ7NuLQJYdPcQwmyHnk7l7b/fZvra6Xw77lv8unbF6O8PZjNKqbrk86Fte7BrzROtowiJ8a3bd8Mfe4huF4AtWIbjCiHEiZAiRPU0qlMszspIdlTvdHcoQgghhDiMfcnn3+lF3PHFGmoczrp1l3e8nJeHvEwLWwv8xo4l9JZbUEpRk52Ds6oKAKXAeFCHZ0VpNX/O2kbKfClGKIQQJ0oS0HrqHR8MGNjmHYC9UuYHE0IIIU5m6/YUsSQ1j9zSqro2T5Mng2IGAbAxbyO/p/+Os7KSnRdfTOZ//4dSiocTInm0VRRKKfJr7Di1xtPHg3PvSabfua3cdTlCCNFsSAJaT11bBKBwkm+IY+G8n90djhBCCDdRSq1USt2wX5EfcRK6oGcLfrltIBF+nmitcToPnL3mpVUvMWX5FBweRoKvvYbAy1zPiSqlUEpRZncwdtVW/rfVVSjYP9QLo9FAdaWdH15fy95dJU1+TUII0RxIAlpPnh5GYgON2Mvj+H3tD+4ORwghhPtMAiKB5Uqpz5RSZyipUnNS8ra4Sl28OG8rd3y5Bsd+SegzA5/hzRFvYjaY8Z8wAc9OrjlDy5YsQVdX420ycl54IGeH+h9wzMqyGvIzyyjJk9FQQghxPCQBPQZndIzFWRFNWuX2o28shBCiQWRlZXH++eeTkJBA9+7dGT16NFu2bAHgzDPPJD09nfnz59OtWzeSkpLo378/27Zta7R4tNbbtNb/A9oAn+Aq9rNTKfWIUiqw0U4sjpuHUWE2qgMmcPWz+BHlEwXA9LXTWbh7IVWpqey64kry3nkHgJtbhtHb3zXFy9LCUmqcGluQJxc+2Iv4riEAVFfam/JShBDilCcJ6DHo0yoEMLLNy4azutrd4QghRLOntWb8+PEMHjyY7du3s3LlSp566imys7OpqKggLy+P6OhorrvuOj7++GNSUlK48MILefzxxxs1LqVUZ+B54FngK2AiUAz82qgnFsflxqGtmTKhMwaDoriy5oCe0CpHFQt2LWDh7oVY4uOJevklAi+77ID9d5RXcU7KNl7amQWA0ez6+JSVWsSM+/8iY3NBU12KEEKc8mQalmPQvaXrOdC9xjj+WDifgSPPdHdIQgjRrC1YsACz2cy1115b19alSxcA5syZw+DBgwHXc3vFxcUAFBUVERkZ2WgxKaVWAoXAO8C9Wut9VW6WKqX6NdqJxQlRSlFZ42DSm0tIivHnqXNcQ24tRgtvn/E2VqOreq7P8GEYlAFnVRW5r71O0NVXE+fjzbTEWIYG+h5wTL8QT6LaBBAQ4d3k1yOEEKcqSUCPgY/FRLSfgT3l8fy28jtJQIUQp5VHvlvPhj3FDXrMxEgbD53V4bDr161bR/fu3Q+5bs6cOYwbNw6At99+m9GjR+Pp6YnNZmPJkiUNGudBJmqtU/dvUErFaa13aK3PacwTixNjNRsZ0zmCTlF+B7R7m10JZFFVEdfPu57LOl5G3yw/8t57D8+kLvgOHcrY2mdBa5yap1IzuaFFKEG+Hoy62jWtqtaa3RvzaZEY1KTXJIQQpxoZgnuMRnRsiaMihtSKze4ORQghTmuLFy+mf//+ALz44ov8+OOPpKenc/nll3P77bc35qln1rNNnIRuGNKKgW1cz2+u3lVwwDyhBmXA0+SJl8kL7149afXTHHyHDgVcCSbA+tIK3s3Yy8L8A7+M2bw0i++mriF9U34TXYkQQpyapAf0GPVNCOHdxTvZ7umN027HYJK3UAhxejhST2Vj6dChAzNn/ju3S01NJSYmBg8PD/bu3cuaNWvo1asXAJMmTWLUqFENHotSqh3QAfBTSu3f02kDrA1+QtGoMgormDR9CVf2j+OeUe0A8PXw5a2Rb7GvqHG2TRMNVG7YQPaTTxH14gskhYTwZ6/2RFo9AHBqjUEp2vQMx2BURLWV2XmEEOJIpAf0GPWICwQ0WeZ4lv2+yN3hCCFEszZ06FCqqqqYPn16XdvatWuZMWNGXZIZEBBAUVFRXWXcuXPn0r59+8YIpy0wBvAHztrvpxvwn8Y4oWg8Uf6ePHtuZ64ZGH9A+77kc0PeBsZ+M5ZZW2fhKC7Gnp+Ptrsq3u5LPreUVTJk+WbWl1ZgMCja9AhHKUVpQRWLPtuCvcbRtBclhBCnAOm+O0Z+nmYifDU55bHMXz6b3kOGujskIYRotpRSzJo1i1tvvZUpU6ZgtVqJjY3F6XQybdo0AEwmE2+99RYTJkzAYDAQEBDAu+++2+CxaK1nA7OVUn201n81+AlEkzs7yTUNi8OpeX3BNi7tG4ufpxmANgFtuLLTlQxtMRTv1n7EfzsbZTKhtcaenY05PByDAk+DAW/jgd/np2/OZ/OSTDoOiiJQChQJIcQBJAE9DsMTWzBjmZ3Umu/dHYoQQjR7kZGRfPHFF3XLVVVV9OvXj9jY2Lq28ePHM378+EaNQyl1t9b6GeBCpdQFB6/XWt/cqAGIRrN+TxFTf91KmJ+V85JjADAZTNyQdAMADqeDb3d8x9iEsRR99Al7X36Z2Jlf0ioujjndW9f1mq4pKaeLrxftekfQIjEIL5urp7S60o6HVT5yCSEESAJ6XPq1DmXG0nR2WK1opxNlkJHMQgjRVCwWCytWrHDHqTfW/nbLyUXj6Rztzy+3DSIu2NVbqbWuSyoBfkv/jQf/fJAAawD9Ro7AXpCPR8uWwD9Ddn/YW8iV69L4pHM8Q4NsdcnntpU5LPp8C+Nu6yq9oUIIgTwDelx6xAYCkGGJZfWfMgpLCCFOB1rr72p/f7DvB5gBzKp9LU5h+5LP3fnlnP3aYjZnldStG9piKO+d8R6DYwZjDg8n9JZbUAYD9oICir7/AYARQTYebx3FoIPmCg2K8iamfQC2YKlTJYQQIAnocQnysRDs5cBeEc/cv752dzhCCNGo9k0/caprqOtQSn2ilLIppbyBdcAGpdRdDXJw4XZl1XaqapwY1IHtyeHJAGSUZnDVz1eRWZpJ3ttvk3n//dRk5+BhMHBVdAhGpSiqsfPA1nTK7A4Cwr0ZcXkHTGYj9moHW5ZnueGqhBDi5CEJ6HEamhiDozyWbcVr3R2KEEI0GqvVSl5e3imfhGqtycvLw2ptkF6oRK11MTAOmAPEAZc0xIGF+7ULtzHnlgG0DnP1ZG7LKT1gfU55Duml6ZTbywm99VZafvgB5rBQ4J8vOf4qLGPGnjw2llUesO+6RRnMfXcDuekHHlMIIU4n8gzocerfOowvVuxhh9X8r2dFhBCiuYiOjiY9PZ29e/e6O5QTZrVaiY6ObohDmZVSZlwJ6Kta6xql1KmdoYsDGGq7P+duyObqGSt4//KeDGoTAkDX0K58N/47zAZXtdyC+GA8gZIFC8h/732ip77MqBB/lvZOJMxSu02NnQCzic5DYwiO9iE42gcA7dSog7tahRCimZME9Dj1jnM9B5pubcmGFSvp0CPZzREJIUTDM5vNxMXFuTuMk82bQBqwBliklGoJFLs1ItEoBrQO5o4RbegTH3RA+77k8+e0n7n393t594x3SaiqQtvtKA9X8aF9yefSwlIuWpvKux3jGBjoS3Q71+eHvbtLmP/+RkZd3RH/MK8mvCohhHAvGYJ7nEJtVgKsDmoq4vlp0ZfuDkcIIUQT0VpP1VpHaa1Ha5edwBB3xyUantVs5MahrfEwGSirsjPlp01UVDvq1veJ7MPkxMl0DO6IbdQoWn40A4OXF7q6msoNGwBo7W3l7FB/kmwHJpkOuxOjSWG2Gpv0moQQwt0kAT0Bg9pF4iyPY2ueVMIVQojThVLKopS6UCn1X6XUg0qpB4H/ujsu0bj+3J7H27+nkrK7sK7N5mHj1u63YjaYKa8p57mVz1NWU8be118n7fwLqMnMJNBs4vl2LbCZjDi05pWd2ZTZHYTH+XHuvcl4+1nQWrNjbe4p/6y1EELUhySgJ2Bgm3C005M1wRZyd+52dzhCCCGaxmzgbMAOlO33I5qxEYlhLLhzMH0SXMNxS6vsB6xfkb2CTzd9yvrc9QRddhnhDz2EOSLigG2WFZXxVGomc/NcI7b31Y/YkZLLj6+vJe3vvCa4EiGEcK9GT0CVUkal1Gql1PeHWDdQKbVKKWVXSp170LoWSqlflFIblVIblFKxjR3rsepV+0xIvkrg489fdXM0Qgghmki01nqS1voZrfXz+37cHZRofNEBrmG0a3YX0n/Kryzellu3bmD0QOacM4eeET0x+vtjOGs4AFXbt7P7+huw5+fTx9+HBT3bMS4sAHAVJwKISwrmjP90JLaT63OF0yk9oUKI5qspekBvATYeZt0u4DLgk0Os+xB4VmvdHugJ5DRKdCcgyt+TVqHeOIo6sa5okbvDEUII0TT+VEp1cncQwn2iAjwZ2DqE9hG2A9rDvMMAWJe7jlEzR/F7+u9UpaZStWkTuroagLberqmAMiqr6b90E+9l5KKUolX3UJRSVJRU89ljy0j7OxchhGiOGjUBVUpFA/8HvH2o9VrrNK31WsB50H6JgElrPbd2u1KtdXljxnq8xneNxl4ZS0q4iZ3r17s7HCGEEI2vP7BSKbVZKbVWKfW3UkomhT6NBPtYmHpBVwK9PXA6NW8tSqWksqZufYxvDENbDHUVJxoxgvif5mAOD0drTcWaNQAEmU2MC/VnYIDPAcd22DVevma8/SxNek1CCNFUGrsH9CXgbg5KMOuhDVColPq6dvjus0qpf5WJU0pdrZRaoZRa4a456sZ2iQSgqLoLn8163S0xCCGEaFJnAq2BkcBZwJja3+I0tDajiKd/2sSPf2fWtflZ/Hi8/+MEWANwaidT/36djNIMSn7+hbRJ51O6eDFWo4En2kST4OXqEX1lZzYpxeX4BFg4+7auhLTwBWDz0izKiqrccm1CCNEYGi0BVUqNAXK01iuPY3cTMAC4E+gBxOMaqnsArfV0rXWy1jo5JCTkRMI9bjGBXnSJ9sNRlMTGqqVSwU4IIZq52mlXYoChta/LkaJ+p62kGH9+vHkA5yXHAFBYXn3A+t0lu/li8xcsSl+E79AhhD/0IN59+gDUfWYoqrHzfkYus7ILgH+KE1WUVPPbJ5tZ+dPOprocIYRodI15w+wHjFVKpQGfAUOVUh/Vc990IEVrnaq1tgPfAN0aJcoGML5rFI7qCFLC/Vj/1xJ3hyOEEKIRKaUeAu4B7qttMgP1vb+JZqhtuC9KKXJLqzjjpUW88dv2unUtbS2ZPW4257c9H+XhQflZA9EKHMXFpJ07kdLf/8DPbGJej7b8N8FVNXdnRRW51XY8fT04995kep8dD7gSUkfNsQ4qE0KIk0ujJaBa6/u01tFa61jgfOBXrfXF9dx9OeCvlNrXrTkU2NAIYTaI/+sciUJTXp7E1z+/4e5whBBCNK7xwFhqp17RWu8BfN0akTgp2KxmzuocyaA2B47KCvEKQSlFcXUxl/x4CU8ufRJnaSnKZMLo63oGNMBswmIwoLXmug07OS9lG06tCYzwxsNqQjs1c978m+9eSZHRVkKIU5qpqU+olHoUWKG1/lYp1QOYBQQAZymlHtFad9BaO5RSdwLzlWscykrgraaOtb5CfC30bRXMkrQkNhtfQDudKIOMxhJCiGaqWmutlVIaQCnl7e6AxMnBw2Tg/jGJdctv/Lad1qE+DGvvqo7ra/bl+qTr6RjcEXNgJC0+/QRD7eeFou9/wKtbV8yRkTzTJpqCGgcGpdBa4wSMBkXS8BY4HbpuiK4QQpyKmiRL0lov1FqPqX39oNb629rXy2t7Sb211kFa6w777TNXa91Za91Ja32Z1rr6cMc/GYxLisJhD2RNaDhLfvnZ3eEIIYRoPF8opd7ENVLnP8A8muBLUqVUWm3F3RSl1IratkCl1Fyl1Nba3wG17UopNVUpta22Um+3/Y4zuXb7rUqpyY0d9+mq2u7k+7V7+GV9dl2bUopz25xLu8B2ALy25jWmLJtCTXER2Y89xt7XXcUMO/p6MSDQ1an+eVY+Y1dtJa/aTnxSCK26hwKQunovv7y9jupKexNfmRBCnBjppmsgZ3QMx2SAqtKufP/He+4ORwghRCPRWj8HzAS+AtoCD2qtX2mi0w/RWidprZNrl+8F5mutWwPza5fhn0q9rYGrgWngSliBh4BeuObYfmhf0ioalofJwFfX9eXhsa7v1nfnl7M1u6Ruvdaa8ppyymrKMNv8iJ35JWF33QWAPTcXR1ERAF5GI6EeZgLMB04GUJJfSUl+JUazfJQTQpxa5K9WA7FZzQxvH4ajqDNbPLbgrD6pO2yFEEKcgNpROndpre/cN2e1m5wNfFD7+gNg3H7tH2qXJbh6ayOAM4C5Wut8rXUBMBcY1cQxnzYsJiOeHq7E8ZHvNnDh20uprHEArt7Qe3rew8N9HwYgN8DIN9nz0Fqz53//I+38C9B2O2ND/XmvUxwGpSixO7h5404yKqvpMiyG8Xd2x2g0YK92sOSb7VRXSG+oEOLkJwloAxqbFIXT6cv6oFh++Wamu8MRQgjRgJRSJUqp4sP9NEEIGvhFKbVSKXV1bVuY1nrfBJRZQFjt6yhg9377pte2Ha79ACfDPNvNzZPndOTlSUlYa3syi8prADAo10exTzd9yrMrniW3IpeQG28i5OabUCZXqQ7tcCWta0vKmbO3iKyq2n0NrmdB0zcXsOqXXeTsbIp/hkIIcWIkAW1AQ9uFYjUrqou7Mn/1p+4ORwghRAPSWvtqrW3Ay7iGukYB0bimZHmpCULor7Xuhmt47Q1KqYEHxadxJakn7GSYZ7u5CfW10rdVMABz/s5k4LML2Jj5T8J4W/fb+GT0J4R4heDZqSObuwajtab0999JHXs21bt30y/Al5V9O9Ddz1X36pvsAjIqq4ntFMzFj/Umul0gALs35FNVm+AKIcTJRhLQBmQ1GxndKRJncUc2+6ZTU17u7pCEEEI0vLFa69e11iVa62Kt9TRcQ14bldY6o/Z3Dq4K8j2B7NqhtdT+zqndPAOI2W/36Nq2w7WLJtQ23JfRnSJoFeqagkVrjUEZiPd3zfe5PGs5V/x8Bd+nfo8ymzFHRWIKc3Vu20yuHtQSu4N7tqTzfFqWqz3IE4CqCjtzpv/N4pnbmvqyhBCiXiQBbWBnJ0Xh1Fa2+Lblm0/fd3c4QgghGl6ZUuoipZRRKWVQSl1E7ZygjUUp5a2U8t33GhgJrAO+BfZVsp0MzK59/S1waW013N5AUe1Q3Z+BkUqpgNriQyNr20QTig/x4alzOmE2GqiscTDxjb+Yu+Gfarndw7rzeL/HGRU7Cu/evfF6+UmU2Yyurmb3dddTtmQJviYjvyS34b/xkQBkVFazsbQCi6eJcbd1pedZcQBUlFRTkl/plusUQohDkQS0gfVLCMLP04i9uAt/bv7S3eEIIYRoeBcC5wHZtT8Ta9saUxjwh1JqDbAM+EFr/RPwNDBCKbUVGF67DPAjkApswzVFzPUAWut84DFgee3Po7Vtwk0Ky2twaI3nflVuDcrA2a3Oxmw0U+Oo4aqfr+J/f/yPmpy9VO/cibO8AoCWnhaCPVzPiT6Vmsm41dsotTsIbWnDJ8AKwJ9fb+PzJ5bJdC1CiJOGcj0ycupLTk7WK1ascHcYADw4ex0z/tpOVOSjvNdnOq17dHd3SEIIIQ5DKbVyv2lNxEFOpvtrc6W1RilXQaEP/0qjvNrB1QPiMRgUTu1k1tZZhHuH0y+qH/aqSuxGsJqsFM76Bl1Tjf+551LgcLKmuJwhQTYAVheXk+TrSUl+Jdk7immd7BrCW5hdjn+Yl9uuVQhx+jjc/VV6QBvB2C6RaEzk6o588tXTR99BCCGEEKetfcknwKqdBSzfkc++JoMyMKHNBPpF9QNg5o5vOOfbc8ityKVk3jyK58wBpQg0m+qSz2WFpZy5cgtfZhdgC/KsSz6z04r5+OElbF6a1bQXKIQQ+5EEtBF0axFAi0AvnHl9WOW/ieKs7KPvJIQQQojT3kvnd+W1i7qhlCK3tIqHZq+joOyfucUT/BPoHdGbIGsQ0a++QugLz6KUwlFURPZTT2EvKKCrzZtn20ZzVog/AKnlVRTbHQRGetP77HjiOruq8ZbkV2KvdrjjMoUQpzFJQBuBwaC4vF8s1dUxbPWP4aO3pBdUCCGaC6VUXH3ahDhe++YKXZKax+crdpNf/k8C2iO8Bw/2eRClFCU1JZw173xmbZ1F2bJlFHz6GfasLMwGxSWRwXgaDWituXZDGhNTtmEyG+g+KhYPTxNaa+a+s55ZL6ymuTyOJYQ4NUgC2kgmJsfgYzHiyO3PsqoFOKqq3B2SEEKIhvHVIdpmNnkUotkb0zmSxfcMJSHENV3LK/O3HlAt1+600zO8J+0C22EbMYKYuXMwt20DQN4771Iybx5KKZ5tG8P98ZGunlKtWZDnmn+059h4uo9qiVIKrTVZqUVNf5FCiNOOJKCNxMdi4vweLagq7cSKFt58+8Hb7g5JCCHECVBKtVNKTQD8lFLn7PdzGWB1c3iimQrysQBQZXfw/dpMFm/LrVsXaA3kyQFP0j6oPQBv7P6UC3+8kKqqcoq+/57S3xYB0MXXiwGBvgD8sLeIC9am8mt+CdFtA4hPCgFgx5pcvnpmJWl/5yKEEI1JEtBGNLlvLAZloKawLwu2fSRDXIQQ4tTWFhgD+ANn7ffTDfiP+8ISpwOLycj3N/fn7lFtAdicVcIdX6xhb8k/I6w6BXeiX2Q/LBYv4r78AsdNriliq3bsIP3W26jJzmZ0sB9vdmjJkNqEdGF+MRtLK2jRIZDBF7WlRWIgAJnbCinaW97EVymEOB1IAtqIYgK9OKNDOPaC3iyJrSRl3lx3hySEEOI4aa1na60vB8ZorS/f7+dmrfWf7o5PNH9mowGv2nk/16YX8vvWvXgYXR/ltNaMjB3Jzd1uBiCjIouzfj6Xzzd9TtWWrZSvWIEymTAZFGODbBhqh90+uHUP925Jx2Q20mFAFIba50Z/+3Qzv7y93m3XKoRovkzuDqC5u7J/HHPWZVFU3Z2Zv7xE1xEj3R2SEEKIE7NNKfVfIJb97qNa6yvcFpE47UxMjmFsUiQWk6tg0VUfrKBztD+3DG8NuIbnXt/legbHDMbWLozCHq3ZZSwiniAybr0VU3gE4f/7L7O7tSKvxg5Amd3BPVvSubllGGfdnERFiav4kb3GweIvt9FleAz+oTKHqBDixBw2AVVKTa3H/sVa6/sbMJ5mp3vLADpH+bExeyDLwp8le/t2whIS3B2WEEKI4zcb+B2YB8gcFsJt9iWf1XYnwT4WbJ6uj3Vaa0orDfyn8z8jw19f/yaLdi9i3rlzMce0wBTsmoolwGzCJycbvKJYX1rB3LxirogKxtvPG4uvBwB7d5WyaUkmCd1D8Q/1Qmt9wNylQghxLNThnktUSu0EHjzK/vdqrds3eFTHITk5Wa9YscLdYRzS7JQMbvksBc+Y97gi08Zdj7/r7pCEEELUUkqt1FonH8P2KVrrpEYM6aRyMt9fxaEt2JzDNR+u5NOre9G9peuZzryKPDbmb6R/VH8AXln9CgOiBtB6RzW7LruMmDem4TNoEGUOB95GV2L7xPY9LC8q48ukVjgq7Fi8TCilWPHjDjK3FzH6us4YTfI0lxDi0A53fz3SENwXtdYfHOWgAScc2WngzI4RPOG7kcK9A1hpfoeq4hIsNl93hyWEEOL4fK+UGq21/tHdgQhxKK1DfbiifxydovwB13yiPhZTXfJZWFnIV1u+wsvkRcdW4wi86QYsPWo/Iy5fTnFJCb7DhhHraaHS6cRsUJi9zfxRUEI3mzcWLzNefpa65DMrtYiQGF+MZklGhRBHd6S/FIuPtrPW+qWGC6X58jAZmNw3lurKVqwND+WLt15wd0hCCCGO3y24ktBKpVSxUqpEKVXs7qCE2Cc6wIt7z2yHR22C+OzPm7nzyzV11fj9rf78NOEnLmx/IaagILadncRZcyaQWpRKwSefkvPcc6A1F0UG8WhCJAC51XYuWJPKlB2ZdBoczbBLXQPgKstqmP3iahZ/vc09FyuEOOUcKQGdrpTaqpR6TCmV2GQRNVMX9myBxWTAntefP/Z+g6Oiwt0hCSGEOA5aa1+ttUFrbdVa22qXbe6OS4jDee/yHky9oCtKKartTsa/vpiFmwrwNHkCYDVZaRPQhhifGKJeeJ6C529nR+lOtMPBjnHjyf/oY4LMRr5MSuCqaNe8oRtLK7hkbSqZysHo6zrTeXA0AIU55fw0fZ1M4SKEOKzDJqBa66645juzAzOVUmuUUvcqpWKbKrjmJMDbgwndo6kp6cafCR7MnPacu0MSQghxHJTLxUqpB2qXY5RSPd0dlxCHY7OaaRPmevQnr6wKi8mA1ex6zrOovAaLvRVTh07FbDSjTCZe2vkhd/12F47SUqyJiZjCQlFK0cOk8Pt1Hrq6mt2V1Wwqq8TPbCImMZACXyN7q2soyCpnz9YCTB6u45fkV1JZVuO2axdCnHyOOFhfa71Za/2I1joRuBTwA+YrpY46PFf82xX9YtHaiL2gL3P3foW9tNTdIQkhhDh2rwN9gAtrl0uB19wXjhD1F+HnyWdX92Fw21AAPl+xizGv/MGO3LK6baYOncrj/R7H5OdHyJOPMrn8dWZtnUXJL7+QcfsdVG7axMhgP5Z0b0Wg2VVO5KFtGYxasYXYTkFc9nQ/vP0sAPz51TY+e2wZ2nnoopdCiNNPvZ4WV0oZgFAgDPAGchozqOaqVagvIxLDqC4cxNJWZj577Wl3hySEEOLY9dJa3wBUAmitCwAP94YkxPGZlNyCVy7oSlywNwBP/biRJ79Lp11gOwBKqktI8E8g2DMYv3Fn4/fOq3xn2URJdQl5r7zKjgnnomtqeCAhkqfbRKOUQhkU41dv5c3dOXQb1ZL+E1ujDK5pW36ctpa1C3a77XqFEO53xARUKTVAKfU6kA7ciWves7Za6/FNEVxzdPuINjicZuy5g5hX8h3VxVK3QgghTjE1SikjoAGUUiGA070hCXF8/LzMnNUlsm7ZYjLg5WGsm+dzzpoSrmzzIAOiB6AMBpaGFvPYksfIKsvC0qY1Nb06U0ENrb2tdJ31BSXz5lHh1IR7mPE1GQmJ8SU6KZjnd2SRVlKB1rBvBkCnw0nKvF2UFlS549KFEG5y2ARUKbUbeArYACRprc/QWr+ntS5qsuiaofYRNsZ0jqC6cADL4zz55JXH3B2SEEKIYzMVmAWEKqWeAP4AnnRvSEI0jNtHtuWxcR0BKK2y89j3G/huzR4AtNZ0sg3n67Ff0zqgNX5nncWswRZGfjWSquoKir76mrK/luBlNDCtQyxnbVqLs6yMlJJynkvLYkd1Df93fWei+0ewpaySnJ0lLJ65jZydri/jq8prKM6VIo1CNHdH6gHtr7Xur7V+VWstQ24b0G0j2qAx4cgZwrzqn6nKL3B3SEIIIeqh9pGUHcDduL6kzQTGaa2/dGtgQjQCH4uJJfcN48r+cQCsTS9i+IuL2J7hA4Dd4WRk7BnclHQTFg9P4n/8gef65jFl2RSqUneQft31FH71Fb39fUhJbk0f5QDgi6x8Bi7bRE2kJ5c83oeAtn44tWbLsmxm3P8XhTmuCrr2akfd1DFCiObjSAno5UfbWSn1cMOFcvpICPHhnG7RVBf3ZXWMLzNefdjdIQkhhKgHrbUTeE1rvUlr/Vrtl7Qb3R2XEI3F38uDIB9XQaEWgV48fFYiveKCAPjh70yufTuXfmFnAaAMBkJ8wwm0BuIR25IWH37AC9Hr+D39d7xWLGNH//5UpKRwTlgAr7WJIsbqgS3Yk6d2ZdNv6UZadApi0IVt8QtxTQ/z1zfb+eThpTilgJEQzYrpCOuuOsrE2go4H3i4QSM6TdwyrDXfrM7AmT2M+XoWF+3NxTMk2N1hCSGEOLr5SqkJwNdaumfEaSTA24PL+sXVLYfbrAxoHUKknythnLZwO6m7h3HXRd1RBkV159as/nE9nUq70yuhD97XXcUrlT9xbqWNwXMXsf2zz4n7aibDg2y08vTAL8gTv4FRXLBmOy09LfynlT9WbzOG2gJGP01fh5efBwMntXHL9QshGsaRekDfAnyP8ONTu404DjGBXlzQswWVJT1YGxnIB6884O6QhBBC1M81wJdAlVKqWClVcpQvbIVolnrFB/HcxC51CaLRAB4mY93ya/MyGe77AhPaTMCjRQvyJw3h861fklOeg0dMDAX9Evlwx5f08HEy9v3p7L7uerTWdPDxpJWXhYRuoSSPjmXosk28k74XH38L3n6ugtNaa75+biV/L0x32/ULIY7PYXtAtdaPNGUgp6Mbh7biixW7cWQPZ57HF1ycmYlPRIS7wxJCCHEYtc+AjtJay3zYQhzk6oEJByznl1VT7TBhMrg+br4118nNrT4nOSwWU4SJnTGFPP/Xw5wZdybmmBjW2YpYuPEj7mwzkZzrbiYrLhafu+8h0ceTEJOR/ue1pqDGTvc/1/NQbARevh4YPQxorampcjDz6RX0PjuB+K4hdc+O7qvmK4Q4edRrHlDROMJsVi7t05LK0q5sCA3h/Vf+6+6QhBBCHEHtM6CvujsOIU4FL0xK4tGzXRV1axxOCiuqqbEbMBlMVNudfDg3nP91mEmYdxiBl01m3YAYXk95HZPBhCUhgV+jCpmxfjpT28XQ/sJzyX1zOqUOJz38vImuLmPUNZ2o7OBHh8XrWLK3BL9QL5yeBkrsDvIySvnwv3+Sua0QAHuNA3uNw43vhhBiH0lA3ezaQQl4eZhwZp/BL37Lyf77b3eHJIQQ4sjmK6UmKOlaEaLezEYDH1/Vm6sGxANQUF6NzWoi2MsfgJ155bz3fXv+2/FjTAYTfnfcydoW/izavQiqq7GNPIO3g9fx9YZpvBrph+eokez94AOsRgOjfD2J37mRMycnsNJP0eb3v9lZXUN4vB95XooVRWVsTdnLW7cuIj+zDICS/Epy00ukwJEQbiAJqJsF+Vi4sn8clWUd2eYfydvv3CElx4UQ4uQmz4AKcYLCbFZmXNmL4YlhdW1ndgynfXgoAH9tz+Ozn7twS+JUDFYr1VffxGbiyC8vQxkMhN13L9d7f81Xfz/HE5RRctWVLF/4GfGWKh7ygphfvmDo2BC+ryzn7NVb8Qv3ImlEC5YZa3hzdw4b/9rD508sx17t6hXdvSmfdYsy0JKQCtHojpqAKqXaKKXmK6XW1S53Vkrd3/ihnT6uGhCPzWpCZ57Nj633sHr2V+4OSQghxGForX211gattYfW2la7bHN3XEKcymKDvXl6QmdahbrmGG0V6sMDYxLpGBUAwNwN2SxY3oYbutyO0c+PlT1GUZl/Ie0DkvBIaEXwtGnckP8Gv277kAuLcsh96WVe+XsavSwZfFWaRfW915Pc05P5BSV8umkHbdpaOPOajjyxO5ur1u1g28oclv+wg9TKKnKqalgyezs/v72uLr7ivAqqymvc8t4I0dzUpwf0LeA+oAZAa70W1/QrooH4eZq5ZXgbKipjyXd2YMbvU3BWVro7LCGEEIeglBp4qB93xyVEcxIT6MWV/ePwsbgKGF3QswXf3NCPkNo5SfcUVlBUGMo5bcZg9PHmrYpQKrc/wrhW47GNHMmuT77m3c3byCzeQgcfKyUmO8MXnc9Qj3V8smMtaeOGs8NzOd6U0m19Ch2LFzDp3m7csWk3V/+9HZPZgNnDyMtp2XyamceCGZv49uUUSuwOtNas+XU3m5dm1cXrcDjd8j4JcSo60jyg+3hprZcd9KiLvZHiOW1d2qclny3bxc6sc/i1/VN8+9qTjLvjUXeHJYQQ4t/u2u+1FegJrASGuiccIZo/Tw8jSTH+dcuT+8YyuW9s3XKfhGD8PD1oG9gKgG83lhNcfiUT2vTF0t7Ce3u90BmbifCOIHBQAj/g4IVfp/Hq/91Gp93bWTPvCz6JW84F7W6g9duzYfWf+L7zFD/vqmbYlq2Mt1RjGDaSocs309/Xk75Lc7GFePJjGCT5erFn6gYiWvnR44I2+BgN/L0wncBIH6LbunpwnQ4nBqM8+SYE1C8BzVVKJQAaQCl1LpBZ3xMopYzACiBDaz3moHUDgZeAzsD5WuuZ+61zAPsq8uzSWo+t7zlPRWajgUfGduDCt0sx5Q/my/JZjMy4Fq+oSHeHJoQQYj9a67P2X1ZKxeC6lwkh3GRI21CGtA2tW35+YhK5pVVYjK4e0zahQYT59iAptB2EwvfzsmmtWpEclozfncOY7pvE7s1/c28vX8IH9OcpfxvffP0kMyc9ju2DBfxelcr3MT9wQdt7GPzM01SST/65l/F6WjmP/L2aRIPGq/142v/xN3d6KXy+zKT10Hie1yWMC/Fn86Or6TQ8Gu/BEbS0evD3NzuI6xxMVNsAtNYU5VTgHWDB7GF011soRJOpTwJ6AzAdaKeUygB2ABcfwzluATYCh3o+ZhdwGXDnIdZVaK2TjuE8p7y+rYIZ3Smcn9YNJSVhBe9MvYObpnzq7rCEEEIcWTrQ3t1BCCH+4elhJCbQq2752kEHzlE67aJkKmoc+Flcz5x2bxnNcK842gQkwBlt+HWlokdIF+L94rG+/DLPPvkzXtvTeG14DOrMUVy6OZctcz7nr6teoOTdV3mphT+/r5nNHYNfZfhtd7C+nQe/hHXg19yzGf/qC8R4W9kZPIJblxfzRtoGKn6roDpwBJcUZXO3t4XUl7fR6fw2pCRYGebtzcZ3N9NxVAssrW0EOBU7l+XQokMg/qFeOBxOKktr8PQxS6+qOCUdNQHVWqcCw5VS3oBBa11S34MrpaKB/wOeAG4/xLHTareTgfO1/vd/ify6MQfnnrP5NvwDxi39i5hefdwdlhBCiFpKqVeoHRWEq5ZCErDKbQEdI6XUKOBlwAi8rbV+2s0hCdHkIv09D1i+Z1S7A5YX3DmEarsTT5MHADcNTSQuuAcB1gD0+HFUv7yIi1p3JtAzkID3P+Db//1IN2M3bomPo+jGGxjzaxX+q34n5eYupH1t5GKvVhSvepd3J75K1AO38NjoIWTuvoeEbk8SfuVFLBrZlVXlgXyfOoHebzzD3ugwvsrvwdQliXzx20+sSzPhe9UQpqd68lT6bjb+UEbCjb351dfBRO3B5q920e7cePYEmOhgN5C+JIf4fuEY/S1YKhwUZJQSHueHh6cJR40Th8OJ2WLkdJxNqsbhun6nBo3GbrdTU1OJ1azQDjuF5VXUmD2wGj2hopqK8mIq7aX4WxVOp4OsMjtOmx++HgFQWEppST5ljgICrBqlNXtKNM6gELxMwZCVR1HpXioNRYT6asxodhWa0MGhWMwRGDIyKSzNwWEuJNTmxApsL/KEkDCsHi0w7thBQWUuWAsI83XiqWB7sR+OwGCs1ng8tm0l356D2bOIYF8HntpAamkA9sBgPD3bYNm8jr2OXDx9CgnytuOpjWyvCMEREILFoxVeG9eQY8jHx7eAAK8arA4zLSJ7ccaIEY363+CoCahS6vaDlgGKgJVa65Sj7P4ScDfgexyxWZVSK3A9b/q01vqbQ8R2NXA1QIsWLY7jFCefKH9Prh/SihfmOkkPbsX0T+/m0R6/oQzyDZcQQpwkVuz32g58qrVe7K5gjkXtYzGvASNw9dwuV0p9q7Xe4N7IhDi5eHmY8PL4Z/mK/nF1r5VS/HTroLplp9b8fOtAbFYzBmXAc8hQbjOk0iehL0opAh9/Gv93/mBix7aMDg1g9yefs2LaKkb4xPFW53jWX3czn2z0pd3e5Wwb1ZGUz8KYYh5K5Na5TD1jGDV3/8j00ZPx3fYmHTrdhv2JR/jgnLF4bZ5HTtClnHn3zXwybiReGy0sNw7h/ddf5K3E1nh7JfBLTXs++G4Wsyw++Ezqya+Vobzw5xLmZToIvmYAy6osXLUujZXbamhxZQ822Z0M2V3I6o2V+A+JZldNDa3KIW1HNl0HBVOtqihJKyAlvRJbWAA+eFKdU8DmojQGxpZipJINOQa22MOwWgPxqrZSlZXNNkcWPUJWA9XsKophp26NyRyAtcKEzs8l01JGl6Cv0TjJKO1OtrMDRoMf5kowlJdS5FVB6+DpaAWZRWdQVN0Bpbww1WgM9mqqraVERLyEVpCbewEVFe1QmFBOjQaURz7+sc/hVFCS/h8c5Qf2iBsse/COnwpA2Y4bcFbGHLDe6LkDr9g3Xeu3346zOvTA9d6b8GrxPgClW+9D26tw/Yl1MfluwjPaNaqxZPPD4CwACmrXmjH7pWGNdM2CUbLxSaAC2Fy73oY5cC3WsO/RTjOlmx8DnMA2QAFheASvw2KYi9PuQ9nWfZOVbMNVpiAaS+haPMyLcFYHUbZ9XxmDHbgGqNqwGFfjYVmKozKSbktMjZ6AqqPNOamU+gRIBr6rbRoDrAVigS+11s8cZr8xwGit9fVKqcHAnQc/A7rftu8D3x/0DGiU1jpDKRUP/AoM01pvP1ycycnJesWKFYdbfUqprHEw/IXfyClJxy/mOaZ63USfS/7j7rCEEKJZUkqt1FonH8P23kCl1tpRu2wELFrr8saKsaEopfoAD2utz6hdvg9Aa/3U4fZpiPtr0gMvUGSPPaFjCNGcaFypwz/LBsCJql2HNoJyotBoFGgTKPt+y2bAAUqDNgBGUDW4EhND7fr9Bxga/lmvja7t4cAoVA0oJziNHLKPSlW7zuc07bf//uurXIdzmjjkRBuqunb9vvMflIOofdPcGGuv6eD1+2qgGmrX70+Dcuy3/qCeXQWw33rUgYdXmn/er8N1+hxpvebAgTEnsv5wvdJHWn/AxZzgeriru4Ebzh13mDjq73D31/o8AxoNdNNal9Ye6CHgB2Agrqp/h0xAgX7AWKXUaFzpt00p9ZHWul7Pj2qtM2p/pyqlFgJdgcMmoM2J1WzkwTGJXD2jgpLSvry/41WSc8djDg52d2hCCCFgPjAcKK1d9gR+Afq6LaL6iwJ277ecDvQ6eKOGHmGUYK0ip2rTCR9HiFOJK6Uw4NRGnBgx4MCkanBioNQRhBMjunadAwNWUy4exgLs2oOCyvagza5ks5bFkobRIxu705fq0iTAdEDeYPTahsGSi7M6AEdZK5SyY3KC0WmnymjC6LUdk7kIR1Uw9uoYjIZSrHaNwa4oNftgtWzBQjnVNWGUO6KxWPbgXe3EafekwBCKj3UtXo4aKmoiKNEt8Pb8G1sVVNYEUGBsia/XanwrocIZQYGhJb4+S/EvM1BWE0q+KQY/7xT8yhQljggKjJH4+azErwyK7ZEUGsPw91qDrRwK7dEUm4Lx90rBVgYFzhaUGP0J8PobW5mTPGcspSYbAZ7rsJU5ydVxlJm8CLBswq/czl4dT7nZgr/HVvzK7eToeCpMJgI8UrGV2clR8VSaFQGmXfhW1JBDPFUmjb8pHd+KGrJJoNqjBn9DFr6V1WSTQI1HNf6GbHwrq8kigRpLJQFqLz6V1WSSgMNSjr/Kx6eyikwScFrK8FMFrmXVCqelGH9djHdVJZmqFdpShJ8uwbuykkxDK7AU4qdL8apbzsdPl+NVWcUeQwIGSx5+ugJrZTVZhniUJRc/XYm1soYsYywGj1xsugpLpZ1sY0uMHnvxdVZjqbKTbWyByZKLr6MajyoH2cYYTB578XXWYK5ykGOKweyRg4/DjrlKU2H0olVUv0b9f6M+CWgoULXfcg0QprWuUEpVHWYftNb34Zo/lP16QOuVfCqlAoByrXWVUioYVzJ7uES3WRqRGMbA1sEs3jaKP1qn8P5zt/Cfpz92d1hCCCHAuu9LWQCtdalSyutIO5xqtNbTcRUgJDk5+chDperhq//dd8IxCXGy0FqzalcBe0uqyC2tJr/M9dM1xp+hHWzsLsrh4je2UFzpxO74Z7/WYesIC1pMXpWDjO03AU78PC34FOVR4VFKVfBynH4pOO3emHNL8TBprAH/x3kz3+PvuCpKLbuwVRTi5fSmzLqdwoBWpCQM5cavPiQ9pJxyuwGDwZv+3p7k7kojLbIV62NDaJ25E+VhIabDUIIiA+hv8GXPknyKfI3stSp8PIz4eJho2/H/CPG3Yi13UJpbgdliwuRhwGwxYvIwYvEcjzLs33vmmqCixuGkssaBj2U0SimKK2soLKshJtC1nFVUQWpeHkkxw/DUJrbt3sua4lyGtR+ArcZEysadrKgpZFzS9QSUKv5K2cpfhjLG97iSwHwHS5dv4g+vaib0vpjgrCoW/7WBv/ydnDPwfCLTSlj453qWh9YwbvAEWmzJY+6SLayM0Jw9dBwJ67KYszyVNVFGRg8dS+LqNL5ZncHGKAMjh4+h61+b+Wx9LtsiNMOG/R99Fq3h/W0lpIXaGTh0NEPmL+ONXaXsCamk99DRjP7xN17KKWNvUAldh4xm4je/MKW4goKgAjoOGs0lX87m0aoqSgPzaTXw/7j248+5T1VTGZ5L3IDx3PLe+9zh6cAekENM/0ncPX0aN/oloAOzCO93EQ9MfZ7/hLXBGJhFSN/LeeC5x7i6ZVvMQRn497mGh5+8n/+0bos1eA++va7m4Ufu4+qObfEOycSzx3949IG7uaZrG3xDMzF3/w+P3Hcn1/Zsi2/YHgxJV/Pg3bdyQ98r8IvIxNH5Kv53523cPPBK/COzcLSdzD3/vY9VI8ZzRp9/fS/ZoOozBPcBYDwwu7bpLOBb4Hlgutb6oqOeZL8huEqpR4EVWutvlVI9gFlAAFAJZGmtOyil+gJvUjeOgJe01u8c6RzNaQjuPttySjnjxUV4eKUQYfuE6e2fpdXIM90dlhBCNCvHMQR3MXCT1npV7XJ34FWt9UlfMc5dQ3CFONnVOJyUVtoJ8HY9+DljyU525ZWRVVxFdlElOSWVdG5h5dJBZnLKc7jlXQc19n+GUloMNRj8/8IU9iNaQ1XmBDCWM6LtRFp+/BGpAZlsjc8huDoXn3Lw9InB2aYXs4LGcPeMt8n3riDfZqba7MfAkGjsSzzZHd6Wn7r7EJ6Xh8JKXGI47VoH0dvgwe5vd+L0MVHqYyLI00Swlwfx7YPwC/GkptpBRXE1Fm8zHhbjQUmjEE3ncPfXoyagtTv34J+hRYu11ifdnai53iCf+WkTry/cjmfMO4xK3cVzj/yG0cfb3WEJIUSzcRwJaA/gM2AProdpwoFJWuuVjRRig1FKmYAtwDAgA1gOXKi1Xn+4fZrr/VWcXuwOJwXlNYT4uuYFfeePHaxNLySjoIKMwgqyiyvpFOPJbWcZ2FO6h2e+slJcbiLcZiVCOaiqTGerbRkegUtcxyuLw8ti4Nzu99L38ZfZW7yG5W2MWJxe+Jv8ifSNp7rLOdwdHEDvdWtxKgMOX3+8osI4t1UsuVM2Um2Cda08CTYaCfUwkdQ5lB49IvB0alJX7cXLZsHTZsbTxwOrjxmjSQpSilPLCSWgtQcIxfUsJwBa610NF96Ja643yMoaB6Nf/p3dBTlY46dwf0ZvJj34mrvDEkKIZuNYE9DafcxA29rFzVrrmiNtfzKprc3wEq4qIO9qrZ840vbN9f4qmh+tdd20InP+zuTP7Xmk5ZWxK7+cjIIKogKsvHVVNDuKdvDst6VkFUJsoI0EDzM1RVv41boAs20dAKZqDyKDIji30z3Ev/Ql9tXzWd3OF2X0x887gh4BbanpeT4TPQrxLczFpM1YvWx0aBHAqGAbhc+tp6bSQX5LK5GeFqL8LMQlBtOqu6t6an5mGd7+Fjysp+dUKOL0cNwJqFJqLK7htpFADtAC2KS17tAYgR6v5nyDXLmzgHOn/YnVtoxYj694b8B0wnudCrUuhBDi5Hc8CejppDnfX8WpK6uokpTdhWzOKmFLTgnbc0rJKq5k1f0jqHJWcuvny/htcwlRAR60CwnEV+czO/9jTP6uKXu1hhDPIG5LvovWX6yj5Iv3WdXeCw9TMFafaAbFJ2I8+3LGZWSQXVEKRgshTgOdov3o5++Dz4dp7N1VQlWohSiblaBgTyJb+ZPYPxKAytIaLF4mGf4qTmsnUgX3MaA3ME9r3VUpNQSoVzEh0TC6twzgiv5xvPMHpLVIYernd/B4198weHgcfWchhBBCiFNUWZWdjZnF/J1RxIY9xdz/f4n4eZn5dNkuXp6/FaUgJsCL+BAv7NZ1jPnyVdIr03A6DZjiHYxMvJj/bIkm46nH8exnIDIfgo1hdAhrS9z9j3BpZgmrOviiH+1LqbcvcV4Wevp74/drIWnPpzDKZsDXqYgKMBIWa2PIKNd8oMVX+2D1MeNhPfRHaauPuSnfJiFOKfVJQGu01nlKKYNSyqC1XqCUeqmxAxMHunNkW35Zl0VWxvn8kPgMA199lFG3P+7usIQQQgghGkRljatkrNVsZPG2XB6cvY7U3DL2DdYL8DbRISGHErWJ1ZWphLXJoFeLFjwbdx05L77IHS0W47urml450DLHTs8bHmJry+HcX7ABfeXNrA2NZFdYBNrDg5+9w/n+o3Siqyrwx0BIkYOQ8gKufX4gZg8j6cqX9n0jCIryxjfIE8NBPZm2YM+mfnuEaDbqk4AWKqV8gEXAx0qpHKCsccMSB/P0MPLseV04f/oSqveeyZt8Q9+NF2Jrn+ju0IQQ4rSglOp2pPX7quIKIY5Oa016QQWrdhWwelchq3YVsGFPMS9MSmJsl0h8rQaCbU7atzQwLrEbHcO9uWneRTz/dyoGrWhRaGJwUHsGxA+mQhko2rKV6+x92RwTyx+9ornirGGE+Abw44YMVhn9iIzrQ7uMSh7tn0CvGH92Lc3GYXcyJjaYkBa+BMf4EBTpg9HsKvQT3TbAze+QEM1XfZ4B9QYqcE2HchHgB3yktc5v/PDq73R5RuX+WX/z0dKdeLWcxgVbSrn/mXkoswzzEEKI41XfZ0CVUguOsFprrYc2YFgnjdPl/ioal9aatLxyHE5Nq1AfdueXM+AZ1/9SnmYjnaP9aBHqwOq3jl1Vf7E+bz1Vjio8TZ78MW4+OwYPY51/CSYnxNUE4NshieCJE1nSuRuXrduBo/bjbEuLB939vbmwxsKaaRtcjQqCIr0Ji7XR9YyW+Ic2q2l7hThpncgzoA9qre/BNSfnB7UHmwLc07Ahivq4d3R75q3PJjfjAr5u/xy9X3mUEbc/5u6whBCi2dNaD3F3DEKcSrKKKlm0ZS+Lt+eyJDWP7OIqxnaJZOoFXQm1Gbl2uC8Vxs3c3ud8bB7evDLvUd7Z9TWti70Ysa2aTl5tGPbA61RavMg773zy/IP5JTKWxZ6+PNk2hvNtNrw27GVMkZGgtHL8U8sZeVYCSX1bUFFSjXVsPOHxNkJjbYd9VlMI0fTq83/jCP6dbJ55iDbRBHwsJp6d1IVL3qmiMn8ULxtm0XXpaIJ7nfTznwshRLOhlOoIJHLg9GQfui8iIdyvssZB6t4yEiNtAFz67lK2ZJcS7GOhT0IQnWOsVHv8zU3z32Vp1lIq7BUYlIGzE7sT/OCb9Fm6iP5O8IsLx5CcTPCA/mAJJvGPddQkD8XToOjm5cndITZ6eHny/r2LcTo0PbxMRLTyJ3JMBC07BgHg6etB8uhYN74bQojDOWwCqpS6DrgeiFdKrd1vlS+wuLEDE4c3oHUI53WL5otV/dneYhNTvryZpxN/xejr6+7QhBCi2VNKPQQMxpWA/ojrS9k/AElAxWlnb0kV8zdmM3dDNou352IyGFj94AjMRgMPjGnHnoqtdIkKpl1QOzbkrmfSD48R4fBl8A4Tnf6Gs977Gf+AcPZMOJfCAUP4Pr4dcxwGutu8+bRLAmWFVVyrvPHcUYpldQGRLR2Mv6MNAMaL2hEY6U1IC99/FQkSQpy8jtQD+gkwB3gKuHe/9pKT7fnP09HD4zqwZHseGekX82P7Z+n6zE1c+Nj77g5LCCFOB+cCXYDVWuvLlVJhwEdujkmIJqO1RinFB3+m8fB369Eaovw9mZQcQ+9Wfvy661cWZSxg4e6FFFcXM7FsIrdVDcR4//285LQTUVSMd3IyPmcPxtfsw31b0vnAHIIzKoRQbWJMiI0RQTZ++2Qz6xZl4A/4BlmJ7RtJy05BdXG07xvhrrdACHECjpSAGoFi4IaDVyilAiUJdS8vDxPTJndn7Ct/YE8/n2mh79Dz2y9oNfY8d4cmhBDNXYXW2qmUsiulbEAOEOPuoIRoTDvzyvhuzR6+W5PJg2cl0q9VMMmxAdw6rA3DE0NIjPBDKcVZs84irTgNX+VJz/wAhredxNDkqzCmZeDTsyfRfQfwU5uO/FDt5OukVhhNRrr4VHFVQAAJuyoxrCxg4t1tsXqb2dqmCp9AC7GdggmM9EYp6eUUojk4UgK6EthXIvfg/+M1EN8oEYl66xDpx32j2vH4HNhb3Y8nlj/Bm8n98YiMdHdoQgjRnK1QSvkDb+G6V5YCf7k1IiEaQWWNg4+W7OS7NXtYk14EQI/YAIwGRY2jhr2O1WR4/Mi9Szcy66yvqfj9Dy7Y4I95hZl2m0uw2IyE3hlJqdPMx2Zfvj3/ajaWVaLyyujl501abjklS3KoWZlD2N4Kyg2K6PYBVJbWYPU20zo5zM3vgBCiMRw2AdVaxzVlIOL4XDkwnl/XZfPX7jEsjdvB6y9cyS3P/IAyGNwdmhBCNEta6+trX76hlPoJsGmt1x5pHyFOFZU1DtLyymgXbsNkUExbuJ0Ifyv3ndmOMV0iqdAZfLzpVe5Y+gvF1cX4mX0ZGTeKCkcl2Y89TvfSUnxHnAk3j8SenIy/rzcbSyuYsiOLXn7ePBgVxjAvb9pG2yjMKeeTX3YR3S6A7qNaEt8lBKuPTC0nRHN31HlAAZRSY4GBtYsLtdbfN2pUx+F0nqcsr7SKoVMWUKb34hfzAq+q8+lztRQpFkKI+jiGeUDbaa03KaW6HWq91npVw0fnfqfz/fV0obXm74wivlixm9kpe7BZzSy6ewhGg6KgrBqHoRiFIsgziMUZi7l1wa30r4mlz58FdNhQRuKC31AeHpRu387vnja+yCtlXl4xZ4f682piS6qr7KxYnU3usr2kb8wnLimEM6/pBEBlmau3UwjR/Bz3PKBKqaeBHsDHtU23KKX6aq3/28AxiuMU5GPhlQu7cekHyynNPZunTDN4f/lAAnvI1CxCCNGAbgeuBp4/xDoNDG3acIQ4cfM3ZvPsz5vZlFWCxWRgdKcIJiZHo7WDRel/MmvrLBbuXsglHS7hes9RRL32CdMXV2CtXIe1S2f8b7wSrTUvpWXxVmY5+TUlhHiYuCI6mPPDA1n2XSop83dTU+nAJ9BC9zNjadsrvO78knwKcfqpzzygo4EkrbUTQCn1AbAakAT0JDKwfSiTk6L5IAW2RG3hkZk38nz8L5iCgo66rxBCiKPTWl9d+3uIu2MR4kRsyynFZjURarNiMCg8TAaeGN+Rs7pEYrOa+WjDRzwyawZ7yvYQ4OHPBbHnMK7VOJxb8qhenULUxZfhPW48v/sFMyLYhkEpqp2avv4+TAzxJ2ZnJW3jwjAaDazyMBKfFEK7PhFEtfZHyXQpQpz26pOAAvgD+6re+jVOKOJE3T+xE4u35bJ9z3nMTZjK9CmTue6p2Sij0d2hCSFEs6GUugH4WGtdWLscAFygtX7drYEJcQQOp2bBphw++CuN37fmcs2geO47sz2D24QwpG0o6SXp2Kyu3sh1eesIN/hzRUYkiV+uIuQcRfjAeHRyHN4//cx7uSV8nJlHzu4dfNo5niFBNq6z+fP36gw2vbeObWU1eFnNxHYOptsZLd185UKIk019EtCngNVKqQW4quEO5MB5QcVJwmw08N51vTnjud+o3n05b7V6iY6vPsTAWx53d2hCCNGc/Edr/dq+Ba11gVLqP4AkoOKk9PnyXUxbuJ20vHLCbVbuHNmG83u2QGvN7xm/8+GGD1mauZSZZ80kamMuV72bSc1fa1FeXviNHY//eRMpqLFz/9YMZucU4NAwLMjG5Mggelqs/PTm36Sm7AWliO8STIdBUUS3DXD3ZQshTlKHTUCVUq8Bn2itP1VKLcT1HCjAPVrrrKYIThy7mCBvXpmYxFVfrKY863we8f2QGfP7EjlstLtDE0KI5sKolFK6toqfUsoIeLg5JiEOUFnjwGp2jYD6c3seNk8zr17YlTM6hKOUk1/SfuGdde+wpWALoZ6h3NLtFsK9wyn56SN06k5C77oT28SJ7DFbsHpaMDk1G0oruCIqhCsig/AtshMU7IN2asqKquh6Rks6DYrGJ8Di5isXQpzsDlsFVyl1C3A+EAF8AXyqtV7dhLEdE6nSd6Apn65l2prdWIJ/oXfpQt688nusMTJPuhBCHKy+VXD32/5ZoCXwZm3TNcBurfUdjRGfu8n99dSSW1rFe4t3MOOvnXx+TR/aR9gor7bjaTailOv5y+LqYkbOHEmYZyiTCtvS9YNlxL48Fc8uXXAUFuKwevJ1QSmv7cqh0O5gee9ErEYD1dV2Nv+ZRcq8XVSV25n8VD/MFiNa67pjCyHEPsdcBVdr/TLwslKqJa5E9F2llCfwKa5kdEujRStO2N3nd2J1Wj5LckewLDqD51+5hPse+xmDRb6ZFEKIE3QPrqTzutrlucDb7gtHCCgoq+bNRal88GcalXYHZ3YMx8PkmhPcYLAzY8MnLMlcwmvDXsPHYea1qol4v/Y1Ou97vHr1AqOJaqeTL8sdvLxxO7sqq+ngY+XhhEi03cmahRms+mUn5UXVhMf70W9Ca4xm1/El+RRCHIt6zQNat7FSXYF3gc5a65Oqso18Q/tvxSXVjHjqV3J0OV6xL/NkXnvO/u8b7g5LCCFOKsfaA3q6kfvrya/a7qT/lF/ZW1rF2V0iuWlYaxJCfKhx1jBr6yzeXPsmOeU59AzvyfMDnyPvnIuo3rED7759CL7hBry6dwfgj4ISzk3ZTpKvF7fHhjEiyIZSiqzUIr56ZiVRbf3pMTqOyDb+knQKIY7qcPfXoyagSikTcCauXtBhwEJcPaCzGyHO4yY3yENbuyGXcz9citO8l+Dwqbxlu4JOl9zo7rCEEOKkcRxDcFvjKtCXCFj3tWut4xshPLeT++vJqbzazvdrM5nYPRqlFLNTMmgXbqNtuC8AO4p2cP2860kvTScpuAtXlHZl8MTbUAYDRd99jzkyAnPXbnySmUex3cFNLcPQWrOsqIxunlb+XphBVYWdPuMSAMhNLyE42tedlyyEOMUc8xBcpdQI4AJc84AuAz4DrtZalzValKLBdU4M5sEe8dy/XJOffz53VrzJ+7+1JWLQCHeHJoQQp6r3gIeAF4EhwOWAwa0RidOGw6mZuXI3z/2yhb0lVbQK9aFbiwDOTopCa012WTZh3mFE+0TTyr8Vt6jhJLzwI/aMlZS37I93717Yxvwf3+0t4qllG9lRUc2gAF9ubBGKdmp81xXz8fdrKCusIrZzMNqpUQYlyacQosEcaRqW+4BPgDu01gVNFI9oBBed047V2/L4qqADaR5ncseiO3kr6ku8W7Vxd2hCCHEq8tRaz6+thLsTeFgptRJ40N2Biebtz+25PP79RjZkFtOthT/TLupGtxau6U425m3kmeXPsKtkF9+N+w69ah23T8uict08jO3aEfH2o3j37sXfJeXctTmdlJJy2nlbmdEpjuFBNnLSSpj/wQYKssoJi7Mx4opEotrIVCpCiIZ3pCJEQ5syENF4lFI8cWNPdj2xiOUFg1gZVsAD717ClDvnYA4MdHd4QghxqqlSShmArUqpG4EMwMfNMYlmrsru4PbP12A0KF65oCtjOkeglCK3IpdXVr/CrK2z8LP4cWPSjZi1gZ3/+x/a4SDi6afwO+sstMHVSW82KPJr7LzcrgXnhgeAw1XB1upjxmA0cOY1nYhLCpZnPIUQjeaYihCdzOQZlaPL3lXCpNcWk6bseEZ/yLV7irn54W9R/8/efcfHUZwNHP/N9SKdepdc5N6bbLrp3WB6C6GGGhIIIQkJqfASCIQWAoTeS4BQTO8d3HvvsorV++n6zvvHnWTZ2OAi6VSeb7Kf3Z3ZvXsGyZp7bndnbDJ9nRCi/9qLZ0CnAquAZOAWwAPcqbWe3TURxpf0r/HT7A/x1DebueLQIdgsJlZXNDEozd0+v+eWpi2c9fZZBMIBzh16JmcudDDwZ1djcjgIrF+PNT+fBrOFf2zcSlM4wkNjBgEQ0ZrW+gDfvbaeoD/CjGsmAMh0KkKITrWr/lWeWelHsgYkct/J40jBjL/sPB4p0Lxx1zX0lS8hhBCiKymlno1tHqi1btFal2qtL9Zan95Xk08RH1pr3l22laPu/oK7P17L7I21AIzM9uCwmqnwVgBQkFjAT0b+hGcSf8Epv30P/wOP4f32OwDMhUN4qqaZA2ev4rmttaTbLES0JhyMsPDdzbzw19lsXFJDxsBEDCP6OUCSTyFEd/ihZ0BFHzThoDxu3tTIb1ZuxldyCbfk/Zv8Z+5i6oU3xDs0IYTo6aYopXKBS5RSzwDbfVrXWtfFJyzRl2ypbeXPs5bz+ZpqRud4+M/5U5gUe86zurWaf8z7B1+WfsmbM98kpayJk+6eg2/+AqzjxlHw0IM4x41jtdfHVSuKWeX1c3ByAv83PI+RbifVJc28959lNNf6GTI5gwNPG4on3RnnFgsh+htJQPuhE84ZSemdzdzZrGmsuJTr0x7gyXcKGHri2fEOTQgherL/AJ8AhcACtk9AdaxciH1y/cuLWbW1iT/NGM2FBwzEYjYRMSK8vPZl/rXwXwQjQS4bfxlpzjS23vJbgus3kH3LzSSffjrErmCmW62YleLxsYM4IT2JthudPOlOkjKcHHHBKPJHyABDQoj4kGdA+ylvY4Db/v4dz5lasNg3U5j4OE/tfye5Bx0V79CEEKJb7cUzoA9pra/qyph6Eulfu96SkgYGpbtJclpZV9lMgsNCTlL0ymQwEuTi9y9mac1S9s/Zn+utxzNs/KFY0tIIlpRgTkxEJSXxwtY63q1u4NnxhZiVQmuNEdEs+nALm5bWcNpvJmM2y5NXQojuI8+Aiu24k+xcfdlEjg04CPsHsdF7Pld99Rvqli2Md2hCCNFjKaXMROf+FGKfBcIR7nh/Nac++A3/+mQdAMOyEslJcraPz2Az25iaPZW/T/kzf/0sHa66idpHH4vWFRSw0ergtEXruWFNCX5D0xiOAFCxsYmX/z6PObM2kphqJxyIxKeRQgixA0lA+7Hcocn8/KQRHBS2E24dyYrw2Vzzxs9o2bQ+3qEJIUSPpLWOAGuUUgO64/2UUn9VSpUppRbHlhM61P1eKbVeKbVGKXVsh/LjYmXrlVI3digfrJSaEyv/r1JKhkCPo6WlDZx0/9c8+PkGzpiSz7VHDWuvW1O3hnPeOYflNcsBuKRmNCOu/BdNs94i7coryPjVdQQNg7s3V3DkvDWs9vq5e2QB/5s4hERD8fkLa3jtzgUEfWFOvHo8x10+DrvLGq+mCiHEduQZ0H5u3GH5XLa1Fd+yLSxsGcfcpBDXP30e9189C3tmdrzDE0KInigFWKGUmgt42wq11id30fvdo7X+Z8cCpdRo4BxgDJALfKyUGh6rfgA4GigF5imlZmmtVwL/iL3WS0qp/wCXAg91UcziB7y+qJQbXllKeoKNJy+ayuEjMwEIGSEeX/Y4Dy95mCR7Ei2hFmoff4KqO+/EMXo0Ax59BMeoUQBEIgavVtRzfEYS/zcsjwxbNME0WRSVmxqZcGQB004ajM0hH/WEED2L/FXq55RSHHL2MBqrWvHV1bKqcTKfJQe46d+nc/sN72HxeOIdohBC9DR/incAwEzgJa11ANiklFoPTIvVrddabwRQSr0EzFRKrQKOAM6LHfM08FckAe1WbfNs7jc4jbOnFvC7Y0eSFLsyua5+HTd9fROr6lZxwuAT+N2kX5OamEn4lGFgMpH60/MJm8w8WlLNT3LTcJlNvDdlGElWC0F/mG//t54pxw/E7rJyxu+KMFvkJjchRM8kf50EZrOJ4y4fy/nKzaAEB6GGA3gz4SBu++dpGD5fvMMTQogeRWv9BbAZsMa25wFd+QD9NUqppUqpJ5RSbUOX5gElHY4pjZXtqjwNaNBah3co3yml1OVKqflKqfnV1dWd1Y5+S2vNc7OLueLZBRiGJjfZyd9PHdeefAJ8suUTKlsruevA2/nV526arrweHYlgSUsj7eKLWBcIM2PhWv60voy3qhoASLJaKF1Tz0u3zGXxx1soWVUPIMmnEKJHk79QAgC7y8qMayZwfr2VrBQnobpDedYzln/+41SMQCDe4QkhRI+hlLoMeBV4OFaUB7yxD6/3sVJq+U6WmUSvUA4BJgJbgbv2KfjdpLV+RGtdpLUuysjI6I637LPqvUGueHYBf3xjOf6wQWto22BA9f56llUvA+CycZfx0vDbGPLLB6h/4UWc48ZBJIKhNY+UVHHM/DWU+IM8MmYQZ+ekEgpG+Oq/a3nznkWYTIpTb5jC0CmZ8WqmEELsti6/BTc2YuB8oExrPWOHuunAvcB44Byt9as71HuAlcAbWutrujrW/i4pw8UJV44jcO8iHh3spKHmaB5LN6FvO5Xf/OENTDYZr0IIIYCfE73ddQ6A1nqdUmqvP/lrrXdr/iul1KPA27HdMqCgQ3V+rIxdlNcCyUopS+wqaMfjRRf5bkMtv/rvYmq9Af544iguOWgwJlN0rs45W+fw+69+j8Vk4e2TZ9H0xFM0/PsBLGlpDHjyCdwHHADAn9aV8lhpDUenebhrRAGZ9uhV06/+u5ZV32xl3OH5HHDKEKx2c9zaKYQQe6I7roBeC6zaRd0W4CLghV3U3wJ82QUxiV3IHZbCET8ZxUUbNZ5sF8GaI3nMNZa7bjsVHQzGOzwhhOgJAlrr9j+ISikL0CWTaiulcjrsngosj23PAs5RStmVUoOBYcBcorcDD4uNeGsjOlDRLB2d0+Mz4IzY+RcCb3ZFzCIqGDa44ZUluGxmXr/6IH52SCEmkyJkhLhv4X1c9uFlJNgSuP+I+zEHIzS89jqeY46mcNabuA84gEhsGpaf5Wdw98gCnhk3mAxb9HlPgP1OKmTmdROZfvZwST6FEL1Kl14BVUrlAycCtwLX71ivtd4cO87YyblTgCzgfWC3JwgX+27UgTk0VLaiP97Mk6PcNJUeyiMpVvjHGdxw4/9QVhnKXQjRr32hlPoD4FRKHQ1cDbzVRe91h1JqItEEdzNwBYDWeoVS6mWidwmFgZ/HpohBKXUN8AFgBp7QWq+IvdbvgJeUUv8HLAIe76KY+7U6bxCPw4LNYuLJi6eSl+zEbY9+3GoKNnHVx1extHoppw87nV/ajyPZPQiT3c6g/76EOTmZoNb8bV0ZZYEgj40ZxCCnnUFOO61NQT59ZhWRsMHJv5yIO9mOO9ke59YKIcSe6+oroPcCvwW+l2D+EKWUiehzLjf8yHEySEIX2X9mIeOKcrhwdYTEQYmE6g/kEctk7vrHmehQKN7hCSFEPN0IVAPLiCaE72qtb+qKN9Ja/1RrPU5rPV5rfbLWemuHulu11kO01iO01u91KH9Xaz08Vndrh/KNWutpWuuhWuszYyPoik60cEs9J9z3Ff/8cC0Aw7MS25NPgERrIoVJhdx5yB1cszSHyp9eSu3DjwBgSUlhky/ISQvW8UhpNVk2K+HYdfWSVXX89//mUrq6nsKJGaC6vWlCCNFpuiwBVUrNAKq01gv24vSriXbopT90kAyS0HWUSXH4BSMZPiKNC1aFSRiaSKhxKv8xFXHXnWdLEiqE6M9+obV+NJbEnaG1flQpdW28gxLxo7Xm2dnFnP3wd1gtipMmbLtz2tAGTy5/kpLmEpRS/GX09Yy+7XWq770Xz/HHk3rJJQC8VlnP0fPXsMUf5Kmxg/n78HzMWvPdGxuY9a/F2F0WzrixiHGH5aOUZKBCiN6rK2/BPQg4WSl1AuAAPEqp57TW5+/GuQcAhyilrgYSAJtSqkVrfWMXxit2YDabOPaysfjvXcyFa7w8OcpD69pJ/CfRSssdp/GnX7+C2eGId5hCCNHdLgTu26Hsop2UiX7AF4xw0xvLeG1hGYePyODesye1T6/SFGzipq9v4vOSz2kNt3KJ5TBKr7mGSF0d2X/9K8lnn4VSisZQmD+tK2NsgpMHRw8kzxEd9C/oj7B2bgWjD8zh4LPkWU8hRN/QZQmo1vr3wO8BlFKHATfsZvKJ1vonbdtKqYuAIkk+48PmsDDjmvG8dudCLt4Q4vFRyfhXjeVZl4v6u07hzl++jC3RE+8whRCiyymlzgXOAwYrpWZ1qEoE6uITlYi34jov7y2r4FdHDecXRwxtH+V2Td0afvX5r9jaspUbp93IeSPPI7hpE+bkZAoefADH6NFUB0OkWS0kWS28OXkogxx2LCZF9ZZmUnPdONxWzr5pGg63jL0ghOg7un0eUKXUzUqpk2PbU5VSpcCZwMNKqRU/fLaIB2eCjZN+OYH0sOLSYoVzYhqR1oG8FTiLy/91Nv46ef5WCNEvfEt0fILVsXXb8mvg2DjGJeKgrMEHwMhsD1/89jCuPWpYe/K5sHIh5797PoFwgMePeoQZ6xIBsBcWMvj113CMHs13DS0cMW8N9xVXAjDU5cCsYMknJbx6+3wWflAMIMmnEKLP6ZYEVGv9edscoFrrP2utZ8W252mt87XWbq11mtZ6zE7OfUrmAI0/T5qTk34xkeT6MFduViTsnwvhVL7wXcC5D11CY3lxvEMUQogupbUujvVnB2itv+iwLIzNrSn6idcWlnL4nZ8za0k5AJmJ2z+OMjJ1JDOGzOD5g/9D2u/up/y3v8M3f357/cMlVZyxeD0es5kTMpIBCPrDfPj4Cr5+ZR0DxqYx/vD8bmuPEEJ0p26/Aip6r/T8BE68ejyOMh9XrjVIPGQgZuws8l7EGU/dQPWmXU33KoQQfYdS6jSl1DqlVKNSqkkp1ayUaop3XKLrGYbmjvdXc/3LS5gyMIXpw9Lb6/xhP/ctvA9vyIvL6uK3njNoPv8KfEuXknvHP3BNnUprxOCqlcX8ZX05x6Yl8X7RcEa4HTRUtfLqPxawYUEV+59SyAlXjsPukiufQoi+SRJQsUdyhyVzwtXjsWxp5aoVYTyHD8Fm1axr+ikzX/oHm5Z8Fe8QhRCiq90BnKy1TtJae7TWiVpreRi+j2sNhrnq+QU8+PkGzp02gGcunUayKzpYUHVrNRe/fzGPL3uc78q/o+mjjyg+7yegYeDzz5N08skArG7x8X5NI38ozOHxsYNItEQHFQoHI4QCYU66diJTjhuEMskot0KIvksSULHHCkalctwVY9GbWrhqSYjkI0didwcobzyTk995k+8+eDreIQohRFeq1FrLLR/9zDfra/l4VRV/OWk0fz91LFZz9CPUytqVnPPOOWxo3MA9h9/DUQOPwmSz4Rg7lsGvvoJz7BiqAtGpyyYnuZm9/2h+OTALgLK19QCk5ydy/s0HUDAyNT6NE0KIbiQJqNgrg8alc+xlYwmvb+bKRQHSjhyDPSNIc9PhXDCnhuefuTneIQohRFeZr5T6r1Lq3NjtuKcppU6Ld1Cia/hDEQCOHp3FJ9cfysUHDW6fh/Pb8m+56P2LMCkTzxz9JPuXJwCQcOihDHzuWSxpabxRWc9+s1fyXnUDANl2K5GwwRcvrOGNuxexeVkNAGaLfCQTQvQP8tdO7LXCiRkcfekY/KsbuWJBgLxDRmEaaiLUOow/rh/An+77OUYkEu8whRCis3mAVuAY4KTYMiOuEYkuMW9zHdPv+Ix5m6Oz7AxKd29XP9AzkP2y9+O5Qx/F9ft72HLJJQQ2bgJAA3ds2sqVK4sZn+hialI0OfW1BJl132JWfFXO5OMGMnBMWre2SQgh4q3L5gEV/cPQKZlEwqP5+KmVXKEULx0wlLlJW3EtCvBs5RGsu+NqnvrlnTjc8niUEKJv0FpfHO8YRNd7f/lWfvnSYvKTnWR7to1yq7Xmg+IPOGbgMeQl5HH3yBspueQqAps2kXPLzdgLB+OLGPxy1Rbeqm7gnOxU/jEiH7vJRN1WL+88sARvQ5CjLh7NiP2y49hCIYSID0lAxT4bsV82RsTg02dXc15Yk3J4Pu+6nGTOXs3sxhkcdffNvHrRZWQXjIh3qEIIsdeUUr/VWt+hlLqf6AWu7WitfxmHsEQXeOa7zfxl1gomFiTz+IVTSXVHBxsKGSFu/u5m3lj/BhwKh7UOYMsVV6D9AQY8+gjuAw4A4KPaJt6ubuDPQ3K5qiCj/ZbdunIv4aDBqb+eTNZg+WJWCNE/SQIqOsWoA3MxmU188tRKjg8apJ6YyXN2C0PmLKa0+XAOe/wd/j19GUcdcUa8QxVCiL3VNvDQ/B88SvRqn66u5M9vruCoUVncf+4knLboSLWtoVau/+J6vin7hqsmXMWxA4+l7smnUBYrA194AvuwYYQNjcWkODkzmWGuEYxKcALQVOPDk+5k6JRMBoxJxeaQj19CiP5Laf29L3F7paKiIj1/vnwmiLcNC6v48PEVpOYlsPmMXO4prWLCmjWsKXaizF5+OmATf7v8D+3fBgshRLwppRZorYviHUdP1d/6V8PQvDy/hDOm5GOJjXRb66vl6k+uZnXdav60/584NfsYzB4PWmuMpibMSUksaW7lihWb+c/oQUz0uIDo7boL3tvMvHc2c+oNk8kenBTPpgkhRLfaVf8qgxCJTjVkcibHXzmO+q1eBrxUyv8NzGHZyJEU7peAgzDPbBrPCX//C00tDfEOVQghhAAgFDG45e2VlNa3YjIpzpk2oD35BNjctJnS5lL+dfi/OHKxwYZjjiWwYQNKKcxJSXxS28Spi9YT1hp37LxIxOCz51YzZ9YmhhZlkpGfGK/mCSFEjyIJqOh0g8alM+OaCTTV+nE8tYlHB+dTkpaB+cgxFDq3sKp5Pw6481nmLe8/36gLIYTomfyhCFc/v5DHv97El2trtqtrDbUCMCVrCu+f9j5j3llFxZ//gmP8OKw5OQC8UF7LBcs2MsRp593JwxnmdhD0h3nngaWs+mYrRScM4qiLRmO2ykcuIYQASUBFF8kfkcLMayfiawlR8/AaXhiYj9vpZMOhB3FMQSneYD5nv7ieW59/PN6hCiGE6Kdag2F+9vR8PlpZyS0zx3DefgPa69bVr+Ok10/irQ1voQ0D7z/vp/re+/CcfBIFDzyAyeXiw5pGrl9TwvSURF6fNJRMuxWA1d9VULq6nsN/OpL9Ti6Ux06EEKIDSUBFl8kuTOKUX00iEjZY8q/lPJmaxSSPm1mj9+OsaRbsqoVHl2Vz2P/dQ0VdbbzDFUKI3aKUelopldxhP0Up9UQcQxJ7ockf4oLH5/LthhruOnMCPz1gUHvd8prlXPzBxWg0o9NGU//SS9Q/+yypF15I7u23o6zRRPOIVA+3DM3jmXGFJFjMtI2rMe6wPM68sYjRB+XGo2lCCNGjSQIqulTGgERO/20RDreVL+9fxu14ODcnlWeSCzjopGlMdC1ic8sQDr7rA578+KN4hyuEELtjvNa6oW1Ha10PTIpfOGJvaA0RrXngvMmcPiW/vXxexTwu/eBSEqwJPH380wxJHkLyGWeQe+cdZN74Owyl+MfGrVQFQlhMissKMrCaFLXlLbxy23waKltRSpExQJ75FEKInZEEVHS5pAwnp/92CukFCXzy6Aou2gp/GZLLO60GdceewXUjy1AqwN8+DnLKXY/Q5AvEO2QhhPghJqVUStuOUioVmdas12hoDeIPRUhyWvnflQdy/Lic9rqyljKu+vgqst3ZPHnkY9geeolwfT0mm42kk04iqDVXrNjMPcWVvFPT2H5excZGXv/nQrwNASJhIx7NEkKIXkMSUNEtnAk2Zv5qEoPHp/P1f9czYV4TL40vpCYU4v7Cg7j5pNEMss9mcXUO0259ldfnL413yEIIsSt3Ad8ppW5RSv0f8C1wR5xjEruh3hvk3Efn8MsXFwFgMm3/bGZeQh5/2O8PPHH4w4R+ezN1TzyB95tvAfBGIly4dBNvVzfy1yG5XJyXDkDxilrevHcRdreV034zhbS8hO5tlBBC9DKSgIpuY7WZOe6KcYydnseij7YQeKOE9ycOY6Tbwa9a7Rx69qVcnP4dIcL86tUSTr7neSqbfPEOWwghtqO1fgY4DagEKoDTtNbPxjcq8WPqvUHOe2wOG6tbOH//gdvVfVP2DStqVgBwSt5xtFz7e7xffU32LTeTNONEGkNhzlm8kS/rm7l7ZAFXDsgEoHR1He8+sJTkLBen/2YKSRnObm+XEEL0NpKAim5lMimmnzuc/U8pZN28SuY9uJznCgdwaV46D1c2sOCIi3n28BQK7J+xtCqBA29/l/s/nodh6HiHLoTo55RSntg6lWji+UJsqYiViR6qLfncUN3CoxcUMX14RnvdV6Vf8YtPf8E9C+4h3NzMlp9dRuvcueT+43ZSzjwTgJAGv2Hw8JhBnJeT1n5u1uAkxh6WxynXT8blsXV7u4QQojdSbSO29XZFRUV6/nyZV7I3Wb+gik+eXonDbeX4K8fxjSPC9atLcJtN3D8onfkv3MYjrUMI+YeQ7/HyyEXHMDo3Od5hCyH6GKXUAq110W4c97bWeoZSahPQsfNUgNZaF3ZZkHHUF/rX8x+bw9zNdTy2Q/L5ZemXXPfZdQxNHsqjxzyKqyHAlksvIeOaa/AcdxwNoTAuswmbyUREa8yx6VQ2LKqiYFQqNoc8+iuEELuyq/5VElARV9Ulzbz70FJ8zSGOvGAUxugkLlu+mbWtfi7Pz+D88iVc++mrrA4fi464mDnRw99OPoBkl3zTLIToHLubgMaOVUCB1npLF4fVY/SF/nVleRM1LYFdJp8PH/IvkhMzUGYzOhxGWSxUB0OctXgDoxOcPDB62y27Sz4t4euX1zHl+IHsP3NIPJojhBC9wq76V7kFV8RVRkEiZ944lcyBiXz4+ArqPyrj/cnDuCQvnUdKq7nCWchdV9/BTUmf40qYy5uLm5h26zv858vVhCIy0qAQonvp6Le278Q7DvHjWgJh/jtvC1prRud6tks+Ad5c/yZDk4fyyCH/ouma37D1pj8CoCwWqgIhTl+0gc2+AGdnb7u7euEHxXz98joKJ2Uw9cTB3doeIYToKyQBFXHn8tiYed0kRh+cy4L3i/ns0RX8pSCb58YXUh0Mc/zqMtR5N/PxkUdwjOVRIvZibn93Awfd/jYfr6ygr1zFF0L0GguVUlPjHYTYNV8wwqVPzeMPry9nfVXLdnVtfcbth9zOI9P/TeO1N9K6cCHugw4CoCIQ4rTF6ynxB3lufCHTUxPRWjPnrY189/oGhk3N4tifjcFskY9QQgixN+Svp+gRzBYTh/1kBNPPGU7x8lpe/vs8JngVn00bwcHJidy0rozfuAZy840v8XR2C4Xup6gO1PGzZxZw+n8+ZXlZ44+/iRBCdI79iE7DskEptVQptUwpJXNH9RDBsMFVzy9g7uY67j5rAsOyEtvrllQv4aL3L6LWV4s5FKHpVzdGBxy6/TaSTpqB1pqLlm1iayDEixMKOSgleq7fG2LV1+WMOjCHoy4ejcksH5+EEGJvyTOgosfZur6BDx5bgb8lxCFnD2PUQTk8VV7LzRvKsZtM/G1oLjPDXp555DoeSEinufFotOHiyNGp/P64sQzNTPzxNxFCiJg9eQY0dvzAnZVrrYs7L6qeozf1r+GIwS9fWsS7yyq47bRxnDttQHvd6rrVXPLBJSTbk3n6uKcJ/O4Wmj/+mJy//53kU09pP25BoxcDmJrkbr9aqpTC2xjAlWhD7TB3qBBCiJ2TQYhEr+JrDvLRkyspWVnH8P2yOPTcEZQYYX69uoTZjV4OT03kjuH5mL56nzs//SfvpRYRrD8EtI2TJ+bw66NHMSDNFe9mCCF6gb1IQJ/VWv/0x8r6it7Uv87ZWMu5j87mDyeM4meHbBuUeGPDRi56/yLsFjtPH/c0uQm5eOfOJVRSSvLpp1ETDPNRbSPndphiRWvNnFkbCfojHHLWMJSSxFMIIfaEDEIkehVnoo2TrpnAficPZt3cSl69fT7JDWFemzSUvw/LY06jl8PmreGjMQfxz798yEtJWRxivhNL6lfMWlLCYf/8lD+8tpTyBl+8myKE6HvGdNxRSpmBKXGKRXSwX2EaH1w3fbvks6SphJ99+DNMysSjRz1C0vISANzTppF8+mnUhcKctXg9f1hbSpk/CESTz7lvbWLBe8VEgpHtJ90RQgixTyQBFT2WMimKThjMyddOxN8a5tXb57Pq63Iuzkvn86kjKPK4uXFtKWesKiXxp9fxxLWzeCjUzBj3PzAnzebFuZs55B+fcsMri783CIUQQuwppdTvlVLNwHilVJNSqjm2XwW8uY+vfaZSaoVSylBKFe1Q93ul1Hql1Bql1LEdyo+Lla1XSt3YoXywUmpOrPy/SilbrNwe218fqx+0LzH3JPd/so5PV1cCbPfMJ4DVbCUvIY9Hjn4E+33PsOWii/AtjT6y2xAKc/biDWzwBXh6XCF5Dlt78jn/3c2MPiiHw34yUm67FUKITiQJqOjx8kemcvZNU8kuTOLz59fw7kPLSA8pXppQyN0jC1jp9XH43DXc0RTigBvv55XTn+CvdcsYmH4HpuRv+N/CzRx99xdc+ex8lpY2xLs5QoheSmt9m9Y6EbhTa+3RWifGljSt9e/38eWXA6cBX3YsVEqNBs4hetX1OOBBpZQ5dtX1AeB4YDRwbuxYgH8A92ithwL1wKWx8kuB+lj5PbHjer1nZxdz10dr+Whl1XblLcEWDG2Q7c7mmeOfIfnJt2l48SXSfnYpjnHjaA5HOHfJRtZ4/TwxdjDTU6OJ6/x3NzP/3c2MkuRTCCG6hCSgoldwJ9k5+ZcTOfjMYZSsrOOlW+aweVkt5+Wk8c1+ozg1K5l/bali+pzVfJqew7m3v84bU2/hd+VzGZRxG9a0T/lo1RZO/vc3/OSx2Xy5tlqmbxFC7K2blFLnK6X+BKCUKlBKTduXF9Rar9Jar9lJ1UzgJa11QGu9CVgPTIst67XWG7XWQeAlYKaKPqh4BPBq7PyngVM6vNbTse1XgSNVL3+w8b1lW/nzm8s5alQmt8zcdmd0a6iVKz6+gj998ycAah99jNpHHyX57LPJ+PWvUUrxRV0zK1p8PDp2EEemedrPTS9IZMwhuRwuyacQQnQJSUBFr6FMiglHFnDm74twJdl598GlfPb8apIMxb9GDeTNSUNJspi5dPlmfrJsE3VTD+TSez/if2P/xg3FCxmYfiu2zHeZu3kLFzwxl6Pv+YLnZhfTGgzHu2lCiN7lAeAA4LzYfkusrCvkASUd9ktjZbsqTwMatNbhHcq3e61YfWPs+O9RSl2ulJqvlJpfXV3dSU3pXN9tqOXalxYzqSCZ+8+djCU2NUrICPHrL37N8prlHFFwBL4VK6i++248M2aQ/ec/tQ8mNCMzmW/2H8Wx6UkANFa3AjB4fLpc+RRCiC4kCajoddLyEjjzd0VMOmYAK78u5+Vb57F1fQP7JSfwYdEI/m9YHvMbvRw2dzW3bNiK+fCjuOxfn/DK8L/w6/XLKEi7FUfOy5Q2beCPbyxn/79/wm3vrqK0vjXeTRNC9A77aa1/DvgBtNb1gO3HTlJKfayUWr6TZWZXB7yntNaPaK2LtNZFGRkZ8Q5npz5bU8WANBdPXDQVp80MgKEN/vzNn/m67Gv+tP+fOHLgkTjHjCH/wQfJve3vRJSJa1YW83V9MwAFjuiPbcVXZTz/lzmUrqmPW3uEEKK/kARU9Epmq4kDTxvKKb+ahBHRvPbPhXzx4hqMQISf5WfwzX6jOCUrmYdKqth/9ioeLash8YQZXP6vT3l50J/5/aotDLPfg3PgQ4StS3nkqw1Mv+MzLntmPp+triJiyO25QohdCsWewdQASqkMwPixk7TWR2mtx+5k+aEBjMqAgg77+bGyXZXXAslKKcsO5du9Vqw+KXZ8r/T740fyv6sOJNm1Lfe/b+F9vL3xba6ZeA3H1+TiW7IEgMQjDkdbLPxqzRZeraxnrdfffs6aORV8/sIaBoxJJWdIUre3Qwgh+htJQEWvljc8hXP+PI3xR+Sz/MsyXrx5DpuX1ZBpt/KvUQP5qGg44xKd/Hl9OYfMWc2sumbSTjmNCx78hOeL7uG2JZrJwadwDbkdR8qXfL2+lIufmsch//iUez5aK9O4CCF25l/A60CmUupW4Gvg7130XrOAc2Ij2A4GhgFzgXnAsNiItzaiAxXN0tGH2z8DzoidfyHbRuidFdsnVv+p7mUPw9d5g/z08Tmsr2pBKUWS07pd/fT86fxs3M/4qekgSq/5BZV/vw2tNVpr/rSujFcq6vnt4GwuyY9e1d2wsIpPnlpJ3vAUjrtsLGaLfCwSQoiupnpZ37NLvWmibNE1KjY28tlzq6kr9zKsKJODzxqOyxMdUv/zumZu3lDOKq+fyR4XNxXmcFBKIlprWmfPZt4L9/GyYxnfjrTibx2F23sktQ05mBQcOjyDs4oKOGJUJnaLOd7NFEJ0sl1NlP0j54wEjgQU8InWetU+xnAqcD+QATQAi7XWx8bqbgIuAcLAdVrr92LlJwD3AmbgCa31rbHyQqKDEqUCi4DztdYBpZQDeBaYBNQB52itN/5YbD2lf/WHIpz36GxWlDfx3M/2Y+qg1Pa6spYy8hKij7oGi4vZfO55mBwOBr74ItasTO7YtJW7N1dyRX4Gfx2ai1KKuq1e/vt/c8kc6OGkX07A5rDs6q2FEELshV31r5KAij4lEjZY+EEx89/djM1h4YDThjDqgByUSRHRmpcr6rhjUwVbAyEOSk7gN4Oz2T85AQDfsuVsfPEx3qj6hA8naGocqdibphNsmUaLz4zHYeHE8bmcOimPooEpmGSACiH6hL1MQFOI3s7anrVorRd2dmw9QU/oXw1Dc82LC3lveQUPnjeZ48fltNd9V/4dV39yNbcedCvHeKax+dzzMJqbGfjCC9gLB2NozbWrt2BRirtHFLQPQqS1ZsVX5QwrysTusu7qrYUQQuwlSUBFv1JX7uXz51ezdUMjmQMTmX7OCLIGR4fZ90cMnttay33FlVQHw0xPSeA3g3OYmuQGIFxdTc1/X+KD2c/x3vAWlg+wgHcYKaFjqazJJRiG/BQnp0zM45RJuQzNTPyhUIQQPdyeJqBKqVuAi4ANxJ4DBbTW+oguCC/uekL/ett7q3j4i43cdMIoLpte2F6+pm4NF75/IbkJuTx93NP47n+EuueeZ+BTT+KcMIGwobGYFIbWaMCsFNVbmjFbTKTmuuPXICGE6AckARX9jtaatXMr+fa19bQ2Bhl5YA4HnDIElyc6YEVrxOCZshru31JFbSjM4amJXD8ouz0RNYJBmt9/n8WvP857rvV8OdZEk8OG0zsNV+gISqpcGBqGZSZwwrgcThiXw/CshPZv14UQvcNeJKBrgHGx+Tf7vHj3r4FwhJ8+PpfhWQncMnNs+9/YSm8l5717Hmh4/sTnyXZno8NhAuvX4xg5ko9rm/jb+jJemDCkfbTb+govr/1zIYmpDs78fZH8vRZCiC4UtwQ0NlLgfKBMaz1jh7rpRJ9fGU/0WZRXY+UDiQ7wYAKswP1a6//80PvEu4MUPVfQH2b+O5tZ8mkJFquJqTMGM+7wfMyxOeO8kQhPltbwYEkVdaEI05LcXDMgk6PSPJhiH078q1ZR/cp/+XTlW3wyzM+SQhPhSCLZkWPQvslsqjCjgSEZbk4cl8OxY7MZneORDzdC9AJ7kYD+D7hKa13VhWH1GD2hfw2EI5iV2jbXZyTEue+cS2lLKU8d+xRpL3xM8llnYs3KAmBho5fTF29gmMvOa5OGkmAx01zn57U7FxAJG5x2wxSSs1zxbJIQQvR58UxArweKAM9OEtBBgAe4gejofW0JqC0WW0AplQAsBw7UWpfv6n16Qgcperb6Ci9fv7yOLSvrSM5ysf/MQgonZbQnid5IhBe31vHQlirKAiGGuxxcPSCD07JSsJmiH3oMn4+m9z9gw6wXeJ/lfDXWRFm6QoU9FKgZhFrGsX6rwtCQm+TgiFGZHDUqi/0L03BYZQAjIXqivUhAi4iOLLscCLSVa61P7oLw4i5e/evK8ibu+nANd501YbupVtq8svYVct25DHt1PrUP/YesP/ye1AsuYH2rn5MXriPRbObtKcPIsFnxtQR5/Z8L8TYEOOX6yWQMkEcnhBCiq8UlAVVK5QNPA7cC1++YgHY47ing7bYEdIe6NKKj+O0vCajYV1prNi+r5bvXN1C/1UvWYA8HnjaU3GHJ7ceEDM2sqnoe2FLFSq+fHLuVS/PSOS83jVTrtlESAxs20DBrFku+fp0vMmr5ZoyJWg9YI8kMtp5M2DuatWUmfCEDl83MIcPSOWJkJtOHZ5CT5IxD64UQO7MXCegK4GFgGR3m/9Raf9EF4cVdPPrXrY0+Tn3gW5SC168+iOwkBxD9G17aUkpBYnQK1Ib//Y+tN/2RpDNOJ+eWW6gMhpmxcC3+iOatycMY7LID8NXLa1nxZTkn/XICecNTurUtQgjRX8UrAX0VuA1IBG7YkwRUKVUAvAMMBX6jtX5gJ+ddDlwOMGDAgCnFxcWd3gbRNxkRg9WzK5g7ayPexiCDxqdzwClDthuUQmvNZ3XNPLClim8aWnCYFKdmpXBpXjpjE7fduqUNA9/ChdTPmsW8Je/y1SAfc0eaaXBpLIaDoY4TsAWmsL7cSWVT9JGxYZkJHDIsg+nD09lvcBpOm1wdFSJe9iIBnae1ntqVMfUk3Z2AegNhzvzPd2ypa+WVKw9gVI6nve6RpY/w6NJHeWnGS2StqKDkiitx77cfBf95CGW1UhMMc9XKzfxxSC4TOvydDoci1JS0kF2Y1G3tEEKI/q7bE1Cl1AzgBK311Uqpw9jDBLRDXS7wBnCS1rpyV+8nV0DF3ggFIyz9tISF7xcTCkQYsV82U04YRHLm9s8GrWrx8WRZDa9U1OMzDKYlubkkL50TM5KxdpiOxQgGafniCxree4+Faz9j9sAgc0eaqfZoTNrEcPehpOiDqK3LYlmpj2DYwGYxMXVQCgcUpnHAkDTG5SVjk8nQheg2e5GA3k301ttZbH8LrkzDso8MQ3Plcwv4eFUlj180lcNHZLbXfbD5A2744gZmFM7g1oNuZct5P8FobWXgC88TdrlQgM1kQmuNUgptaBa8v5mxh+bjcMs0K0II0d3ikYDeBvyU6MTZDqLPer6mtT5/J8c+xS4S0Fj9E8C7u6oHSUDFvvG1BFnwXjHLvyzDCBsM3y+bouMHfW+QioZQmJe21vFkWQ3F/iAZNgtnZqVyXm4qQ12O7Y41/H6833xD4wfvs2zZp8wu8LNguJnijOi/uRxHPkOcJ6BbR7KpwsaaihYAnFYzRYNSOGBIGvsXpjE2N0kSUiG60F4koJ/tpFimYekEFY1+Tn/oWy49eDCXHDy4vXx5zXIuev8iRqeN5rFjHsNmthFpasLw+TBnZnLVymKawhGeG1+IWSm01nz9yjqWflrK4eePZPTBud0SvxBCiG3iOg3Lnl4BjT07Wqu19sUm+54DnK61Xrar95AEVHQGb2OARR9tYcUXZUTCBsOnZVN0wvcTUUNrPq1r5vnyWj6qbSSsYVqSm/NyUjkpMxm3eftbao1gEO+339Ly6WdsnvcJ85LqWDDMxPLBJoJmjV3ZGJu2P5nqIHzNBawpN1hbGU1I7RYTE/KTmTIohamDUpg8IGWnA3IIIfbOniag/U1396/N/hAJdkv7AHHVrdWc9fZZ2M12nj3sMfTTr5D+86sx2aPPd96yoZwHtlTxx8IcrhkYGwX3w2K+e20DE44o4KAzh8qI5EIIEQc9JgFVSt0MzNdaz1JKTSU63UoK4AcqtNZjlFJHA3cRneBbAf/WWj/yQ+8hCajoTK1NQRZ9WMzyWCI6ZEomk44eQOZAz/eOrQqEeLmijhe31rHBFyDBbOLkzGROy0rhgOQEzDt88NFa41+xkpbPP6f2i09Y6F3NkkLFsiEWSlKj45kk25KYmDadVIrwteSypjzMivImwkb03+vQzAQm5CczsSCJ8fnJjMxJxG6R50iF2Bt7cQX0zzsr11rf3HlR9Rzd0b8uKK7jrSVbuenEUVjN29/xETbC3LfwPk4ePAPHH+6h5euvGfj0U7iKiniqrIYb15ZyYW4atw/PRynFmjkVfPzkSoYWZXLMJWNQJkk+hRAiHuKagHYHSUBFV2htCrL4oy0s/6qMkD9C3ohkJh41gIFj0r73oUZrzdxGLy9srePt6ga8EYNsm5WZWcmcnpXCuATnTr+FD1VW4f3mG7zffEPJ4q9ZktLE0kGKZUOt1DsjAKTb05iQOY0MNZVw6wA2VymWlTVS0xId1MhmNjEq18OE/CTG5iYxJs/DsMxEuXVXiN2wFwnorzvsOoAZwCqt9SWdHlwP0NX9a2l9KzP//Q2JDgtv/vxgklzR5zUjRoSmYBMpjuiotZW33U7d00+T/Zc/k3LuuXxY08hFyzZxZJqHJ8cOxmJSRMIGL948h4QUOyddMxGzVf4GCiFEvEgCKsQ+CPjCrPyqnCWfluBtCJCS42biUQWMmJa90w84rRGDj2obea2ynk9rmwlpzVCXnZmZyczISGak27HTZFQbBv5Vq/B+8y0tX3/FhuJFrMyJsHKAiVWFVmqdYQCSrB7GZUxgsHsytvAwGhuTWVneyvKyRrzBaNJqM5sYnp0QTUhzPYzM8TAiOxGPQwbjEKKjfb0FVyllBz7QWh/WeVH1HF3Zv7YEwpzx0LeUNfh44+cHMSQjob3urvl38f7m93l5xsvwxodU/PWvpPz0p2Tf9AcAlje3csemCh4aM3C7xx68jQEsNjN2p+V77yeEEKL7SAIqRCeIhA3WL6hi0YdbqC1rwZloZdRBuYydnkdiqmOn59SHwrxT3cj/KuuY3eBFA4VOOydmJHFiRjITEnd+ZRSiAxn5Fi+hde5cvHPnULx5CStzIqwuUKwbbKfEEwJAoRiaPJRx6ePJto1DBQdQ2+hiVXkLK8obqW8Ntb9mXrKTkdmJjMxJZES2h2GZCRRmuOUWXtFvdUICmgLM01oP7cSweoyu6l8jhubyZ+bz+dpqnrp4KocMy2ive33d6/z52z9z9oiz+f3YX7Hh2GNxjBlNwYMP4kWRsMPfq6YaH8u/KGP/UwoxmeWqpxBC9ASSgArRibTWlK6qZ+nnpRQvqwFg0Ph0xh6aR8HI1F0+c1QVCPFeTSPvVjfydUMzEQ15divHZyRxdFoS+ye7sZt2/eGpPSGdPx/f4sXUrFrMGo+XtXmKdYOsbMhRtFiiV0ltJhsjU0cyKnU0uY7RWMIDaGpOZG2ll9UVTWys9rY/U2pSMCjNzdDMBIZlJTAsM5HCDDeFGQkk2OUqgujb9uIW3GVExygAMAMZwM1a6393RXzx1lX966qtTZz24Lf84YSR/PSAQe3lCysXcumHlzI1ayoPHvUgFpOFYHEx5rQ0mu0OTlq4jhkZyfyuMAcAf0uI/925AF9zkLP+MBVPurPTYxVCCLHnJAEVoos01fpY8VU5q74px9ccIinTyZiD8xi+XxbuJPsuz6sPhfmgppF3qhv5qr4Zv6Fxm00cmpLIUekejkr1kGn/4dtltWEQ3LCB1kWL8C1aTOuSxZTUb2ZDNmzIUWwa5GBjegSfOXpbrkVZGJI8hBGpIxiaNBK3HkIkkEF5vcG6yhbWVTWzubaViLHt70Jmop3CDDeD0xMYkuFmUJqbgWkuClJdOKxy1VT0fnuRgA7ssBsGKrXW4c6PrGfoyv51a6OPnKRtCWN5SznnvnMuibZEnjnwQfj4K1LOOw+lFEHD4NwlG5nb6OW/E4ZwYEoC4VCEWfctpnJzEzOvnUTusOQuiVMIIcSekwRUiC4WCRlsWFTF8i/K2LqhEWVSDBybxqgDchg4Lg3zDwwI1Box+Lq+mY9rm/i4tonyQPSW2fEJTqanJnJoSiJTk9w4duPWskhLC/7lK/AvX4Zv2XJaly1li38rxVmKzZmK4gF2Nmcq6m3B9nPSHGkMSxnG0OShDPYMw6UHEgqkUl4fYVONl43VLWys8dLQ4VZepSDH42BAmotBaW4KUl3kpzgpSHVRkOIiPcEmUx+IXmF3E1ClVOoP1Wut6zovqp6js/vX7zbUsqG6hfP3H/i9ugZ/A3/97q/8ctxVmH91K77Fixn85pvYBg/iV6tLeKmijn+PGsAZ2aloQ/PhEytYP7+KY342hmFFWZ0WoxBCiH0nCagQ3ai+wsvq77ayenYFrY1BHAlWRkzLZsT+2aQXJPxgYqa1ZpXXz0c1TXxW18T8Ji9hDU6TYr+kBKanJjI9JYFRCc7vTfGyK+G6OvyrVhFYtQr/ylX4V62iqnozmzOgNB1KcqyU5Nko8YQImCLt52U4MyhMKmRQ0iAKkwpJtw3EFM6iudXBllofxXVeimtbKa71to/I28ZpNZOf4iQvxUluspO82JKbHC3LSrRjkWe1RA+wBwnoJrZND7YjrbUu7PTgeoDO7F831Xg55YFvyEi08/YvDm6/i8LQBhEjgtVsRWvN1j/9icZX/0funXeQdNJJ3F9cya0bt3L9oCx+Ozh6621teQuv3j6fqScOZvKx309mhRBCxJckoELEgREx2LKyjtXfbWXTkhqMiCYl28XQoiyGFWWSku3+0dfwhiN829DCl/XNfFHXwtpWPwBJFjPTktzsn5zAAcluxiW4sO7BfHeG14t/7VoC69YRWLuOwLp1+NauYatuoCRDUZYG5dk2tmZHE9NW87Y7DO1mOwWJBQxIHMBAz0AGeAaQ4cjHHMnA53NRVu+npN5HSV0r5Y0+yhv81Hm3T1BNCjIS7WQnOcn22MlJcpKd5CDb4yAz0U6mx0GWx77dhPRCdIV9HYSor+us/rWxNcSpD35DfWuQN35+EAPTtv39e2jxQ3y39Tv+c9R/8L/wCpW33U7alVeQed11AMyqauCzuibuHlGw3d+DphofiWk7H1VcCCFEfEkCKkSc+VtCbFhUxbp5lZStawAN6QUJDCvKYuiUzN0eOGNrIMg39S3MbvDyXUMLG3wBAFxmE0UeF0VJboo8bqZ4XCRZ92wAIa01kZqaaFK6YSOBjRsIbtiIf+MGan21lKUrtqbA1jQTlblOKlIVFc4AIWW0v4ZFmclNyCM/MZ/8hHxyE3LJTcgl1ZaFKZJOq89OeWOArY1+Khp9sXV0aQ58/zE6l80cTUgTHWQk2r+/JNhJS7CR5rbLvKdir+xNAqqUOhmYHtv9XGv9dudH1jN0Rv8aihhc9ORc5m6q4/mf7c+0wdvuZv6o+COu//x6Zg6ZyZ+H/JyNxxxLwmGHknffffg1OHe4U2Lzshpa6gOMnZ63TzEJIYToWpKACtGDeBsCrF9Qxbr5lVRuagKiyejgCRkUTswgLc+929/oVwVCzG6MJqNzGlpY7fXTlg4OdzkoSnJR5HEz0eNiuMuBZQ+uknYUaWwksGEjweJigsWbCW4uJlhcjK94EzVWP1XJispkqEwxUZPtoDLNTIU7RLM5tN3rWE1Wctw55LhzyHJnkeXKItudTbY7G48lExVJwuuzUtUcoLLJT1VzgIomP9XNAWqaA1Q3B3aaqAJ4HBbSE+2ku6NJaYrbRprbRmpsSXPbSXFbSXHZSHHZcNpkECWxV4MQ3Q5MBZ6PFZ1LdBqWP3RFfPHWGf3rZ2uquPjJedx5xnjOLCpoL19dt5oL3ruA4SnDeeLYJ7CZbbTOm4djzBhKlZlTFq3nr0PzODkzGYDqLc28dtdCUrJcnP7bKT/4bL0QQoj4kgRUiB6qsdrHxkXVbFpSzdaNjaDBk+5g8MQMCiekk1WYhHkPnpVsCUdY1NTK/CYv8xq9LGxqpSEcfa7TaTIxLtHJxEQXExKdTPS4GOy0Y9qH29e01oSrqgmVlhDcUkKopIRgyba1t6mW6iSoTlLRdbKZ2iwHtclmatwR6iwBDLX93yG72U6GM4NMVyaZrkwyXBlkODNId6aT4cogwZyKMpLw+a3UeoPUtgSpaQlQ2xKgxhukpjlAnTdInTdIfWsQYxd/5hxWEykuG8kuGymuaGKa5LKS7LSS7LKS7IzuJzmteBzW9m23zSy3/PUhe5GALgUmaq2N2L4ZWKS1Ht9VMcZTZ/WvqyuaGJntad+v8dVw7jvnorXm+UMexr1uKwmHHAxAUzjCjAXrqAyGeHvyMIa5HTTX+Xn19vmYLIozflf0g6OMCyGEiL9d9a8ywZ8QcZaU4WTSMQOYdMwAvI0BNi+tYePiGpZ9XsqSj0uwOS0UjEphwJg0Bo5Jw538wx+6EixmDklN5JDURAAMrdnoC7C4qZUlza0sbvLxbHkNj8SyMpfZxBi3kzGJTsYmOBmT4GSk2/G92952RSmFNSsTa1YmrilTvldv+HyEtm4lVFZOqKyMUHlsvbaC8Nat+KuraHCEqfVATaKiLhHqkwI0plVTl1zPMtcqam1B/B0GR2pjNVlJc6aR6kglzZFGWnoaQ/NT2c+RRqozlVR7Kh5bHhY8GGEXzT5NfWuQ+tZQdO2NbXuDNPhCrK5ootEXoqE11D5H6s5YTAqP00qiw4LHYcXjtJBoj60d0fJEh5VEu4VEh4UEh4WEtm27FbfdjNtmwbSXV6NFj5AMtI16mxTHOHqNjsknQHOwmURbIrfs91f8v/s/6hctYujHH6HT0rl8+WY2+vy8NGEIw9wOgr4w7zywhHAwwmnXTZHkUwghejFJQIXoQdxJdsYckseYQ/II+sKUrK6jeHktW5bXsmFhNRC9VXfA6DTyR6aQMyQJy4/cRmpSiqEuB0NdDs7Ijj53FTY0a1v9LG5uZUWzjxUtPl6tqOOpSPTmXRNQ6LIzwu1gpNvBSHc0KR3stO/xLbwmpxN7YSH2wp0PEKojEcK1tYQrKghtrSBcWUm4uopQZSXh4mrClZWEKivwhgM0JECDGxoSFPUJUJ8IzSl1NCY1Ue4qZpUjQoMlSLjDM6nb/fe1ukm2J0cXRzLJnmQyMlIYak9qL/fYPXhsHmwkog0XwZCFZl+ERl+ofWnyR9fN/jBNvhBN/jDVzS00+cI0+UO0Br+fLO80HpsZtz2anLrtlvbE1N22bzPjalvbzLhs0WOctmiZ0xY93hXbdtksmCWp7Q63AYuUUp8RHRF3OnBjfEPqPdruvBqcNJhXT3qVqlv+j/rZs8m57TbM6en8bm0pn9c3c/eIAg5OiX6RVryilvqKVmZcM4G03IR4hi+EEGIfyS24QvQCWmtqy1ooXl5L8fJaKjY2oQ2N2WIie4iH/BGp5I9MIXNgIqa9nNrE0JoSf5DlLT6WN/tY4/Wz2utnky9A218Jm1IUuuwMczkY6rIz3B1dD3E5cHXxlCqG10u4pia6VFcTro6ta2uI1NYRrq0lUltLqLaGFhWkyUVsUdu2Ey20JNloSTDT7IRmu6bJGqLVtPNnSgFMykSCNQGPzYPH7iHRlhjdtkW3E22JJFgTttt2WRLAcKC0k3DYii8Izf4QLYEw3kCElkCIlkCEFn8YbyBMSzBMa6zOG4yVBSL4gmG8u5nMtrFZTDit0YTVaY0mpm1rhzW23WHfYTVF1xZTe5ndEi1vW0eP21Zmt0TL+kqyuwfTsDwAvKC1/kYplUP0OVCAuVrrii4NMo46u399esXTbGzcyB/3/yPNL71C5c23kHrpJWT95jdorbljUwUhrfnjkNztzmuq8e32YG1CCCHiT54BFaIPCfrDlK9roHRNPWVr6qkpaQHAajeTXeghZ2gyuUOTyRrs+dErpD+mNWKwvjWajK5u8bOu1c/6Vj/FviAdrzPm2q0MdtopdNmja6edwS47Ax02HN0436fWGsPrJVJXR6SujnBdPZH6OsJ1dUTq6onU1xNpaIguse1ASxMtdk2LE1oc0OJU2217Eyz4Eqx4XSa8ToXXpvFaDbzm8Hbzpu6Kw+zAbXW3Ly6rK7pt2bbtsrpwWaLbTosTl9WF0+LEYXZi1g7QdrRhJxKxEIlY8IcMWoMRfKFo4uoLRvCFItGyYDhWt63cF4rgDxn4Y2WtwTD+sEEwvPOrxbvDYlLYLSbs1mhSareYsFm2Jam29n0TNosZm7njvql9f7vt2Nravlbt5daOdWYTLruZ9IR9vxVzDxLQa4FzgBzgZeBFrfWifQ6gh+vM/vXL0i/5xae/4MgBR3Jr7hVsPvV0Eg45hPwH/k1YmdqnktJao5Ri6WelpOe7yR2W0invL4QQovtIAipEH+ZrCVK2poGytfVsXd9AbbkXNJjMisyBieQMSSa7MImswZ4ffYZ0d/kjBpt8Ada1Bljf6mdja4BNvuhSF9o+Kcu2WRnotDHAaWOgw85Ap418h40Ch41sm3WvR+btLDoSwWhuJtLYuG1piK2bGjGamok0NWE0NxFpbCLS3IzR2EikpYVgawutVgOvA1rt4HUo/Lbodtvisyv8Lgt+lwWfw4QvdozPqvGZDXzmyE6fcf0hDrMDl9WFw+zAaXFGF6sTh9mBw+KIJa/RbbvZjtPixG6247BsK3OYHVjNNkzaDoYNra2grRiGGR2xYBhmQhFFIBxNXANhg0DbOlbmDxkEIxECIYNgxCAQMgiEo8cEw9vKgpHo/o51+9IFTRmYwv+uOnDvXyBmLwYhGkg0ET0HcAIvEk1G1+5zMD1QZ/WvGxo2cP6755OfmM/Txz2N0+Kk8fU3SDzmGFZoxc+Wb+bRsYOYkOgCYP2CKj54dDnD98vi6IvH7PP7CyGE6F6SgArRj/i9ISo2NrJ1fQPl6xqpKm7CiET/rSek2Mka7CFrUDQhTS9IwObo3MfBG0JhNvoCbGoNsMUfpNgXpNgfYIsvSHkgRMe/OmYFOXYr+XYbBU4beXYbuXYrOXYreQ4bOXYryZaeO+qsNgyM1tZoAtvcjNHcjOH1YrS0EGlpwWiJbhveFiJeb7TO68Xwtm7b9vkIt3oJhHz4bESTUxsErBCwKfzWaFnbOmiNlgXsJoIOMwG7maBdEbCZCFggaNUEzBq/2SBgNgiqCMZe/udTKOxmG3ZzNGm1mq3YzXbsZjs2sy26mGzt5TZTtMxqsrbXt23vuLYoCxZlQ2EFbYkuWNCGBbQZtBmtzRiGCa1NaMNExDBhGIqwASkuG0eOytrnn+HezAPa4dxJwBPAeK11n5zXpzP61wZ/A+e9ex6toVaePeghMv1W7EOHAlDuD3LCgnWYFbw3ZTiZdisVmxp54+5FZBQkMPO6Sft8J4cQQojuJ6PgCtGPONxWBo1LZ9C4dADCoQg1JS1UbmqiclMjlZub2gc1QkFKlouMAYlkDEgkc2Ai6QWJ+5SUJlstTLZamOxxf68uYBiU+IOU+oOU+kOU+oPt+9/Wt1ARDBHZ4Xsxp8lEtt1Ctt1Kts1KVixBzYptZ9osZNmsuM2mbk9UlcmEOSEBc0IC1pycfXotbRhony+a0La2YrRv+zBavdvqfH4Mvy+67/Nj+FrRTT4MfwDtj5X5/dF6f/TYYDhAIBwgYNEELRC0El1bIGhRBC0Qiu2HLB3qrIqQ2UfQ6iNkUYRsZsJWRchqImRVtFgVYXP0nJAZwiZNyKwJmTQhpQmZDCKq87/oVCgmpY7nyFHPdfpr/+h7K2UBjid6BfRI4HPgr90eSC+yoXEDLcEW7jvkLiI33EJxSQlDP/oQn9XGBcs20RKJ8NbkYWTarTRW+3j3waW4k2yccNV4ST6FEKKPkQRUiH7AYjWTXZhEdmESEJ0EvrUpSNXmJqq2NFO9pZmytQ2snVvZfo4n3UF6fiJpeW7S8hNIy0sgKd2J2sfbZe0mU/uovDsT0ZqqYIit/hBlgRBbA0HKAiEqAyEqAiEWNbdSURPCv5NpUpwmE1l2C5k2Kxk2C+lWCxk2K+nt2xbSbBbSrBaSLOZ9mv+0KyiTCeV2Y3J/P3HvDFprdCgUS0wD6GAA7fdjBILogB8dCGwrDwQwAgF0IIgOBqIJbTCEDgbRgQA6FMRoCWwr23EJBaN1oRDhUIBwJETQCBHUIcJGmLAZwqZo4ho2xxaT2rYdW0JmiLTXbyuPmBRZKdVwUpf8p9oppdTRwLnACcBc4CXgcq21t/ui6J2mZE3hvdPeo+EPf6Vp0SLy7r0Xw+7giuWbWNni47nxhYxKiA4wtOKrMgxDM+OaCTgTbXGOXAghRGeTBFSIfsrlsTFofDqDxqe3l7U2BakqbqKmpJnaMi81pS1sWlLd/pyexWYiNcdNSo67w9pFYpqz0+a0NCtFjt1Gjt3G5F0co7WmMRxhayBEdTBMVTBEZWxdFYhur/MG+C7U8r3nUbe9D6Rao8lo2zrFaibVaiHVaibFaiHFaiHVYibZaiHZaibJYsbcw5LWPaGUQtlsYLNhjuPMlVprCEWT0/YlGESHw9v227aDQXQojA5Hy2ivC2NO6faBaX4PvAD8Wmtd391v3tu1PvoUTW+/TcZ11+E57lh8EQOLgtuH53NE2rY5Qg84ZQhjDsklKcMVx2iFEEJ0FUlAhRDtXB7bdrfuAoSCEeq3RpPR2rIW6itaKVtTz5rZ22adMFtNJGc6Sc50kZTlIiXLRXKWi+RMF44Ea6fHqZSKJYUWRv3IsSFDUxcKUx2MJqu1oehSF4pQ22F/lddHXShMQyjCD40Lm2Qxk2wxkxRLSNsWj8VMssWCx2rGYzaR2KE8MbZOMJt63FXXeFBKgc0WTYZ7Ea31EV312kqpM4nexjsKmKa1nh8rHwSsAtbEDp2ttb4yVjcFeIroQEjvAtdqrbVSKhX4LzAI2AycpbWuV9H70+8jegW3FbhIa72wq9rUUcvX31Bz/79JmjmTtCsuJ2AYOM0mnhw7GKUU2tDMfnMjYw7JxZPulORTCCH6MElAhRA/yGozkznQQ+ZAz3blAV+Y+gov9Vu91G1tpaGyldpyL5uW1GB0uD3W7rLEPlA68WRE10npThLTHSQk2/d63tLdjt+kyLJHnxXdHUbs6mp9KEJ9KExdKExjOEJDOLrfEIpuN4QiNIbDVARCNIYjNIUjO70teEcJseS04zrBbMZtMZFoju1bzLjNJtxtdW3bsXJX22Lq/mdeRZdZDpwGPLyTug1a64k7KX8IuAyYQzQBPQ54D7gR+ERrfbtS6sbY/u+IPrc6LLbsFzt/v85txs65pk0l49fXk3rhhTxVXstz5TW8PGEoabbox5DZb25g4QdbcCfbGH94QXeEJIQQIk4kARVC7BW700L24CSyB29/L2ckYtBc46ehKpqUNlb7aKr2Ub2lmY2LqrdLTpVJkZBix5PmIDHNQWKak4QUO4kpDhJS7SSkOLDau3cAEpNS7bffwp5NWeOPGDSFIzRFoglpdDFoDkdoDEdoiURoCRs0RyI0h7dtVwbDtIQjeCPR/R0HYdoVBThjyanL1CExNZtwmrbfdu6w3bZ2mBQuswmHKbrtbN+O7jvMpl5923FvobVeBez2FwpKqRzAo7WeHdt/BjiFaAI6EzgsdujTRAdJ+l2s/BkdHf5+tlIqWSmVo7Xe2mkN2QWTzUb6ZZfxTnUDf1hbytFpHpIs0X/by78sY+EHWxg7PY9xh+V3dShCCCHiTBJQIUSnMptN0dtvs1wwbvs6I2LQXBegqdpHc52fplofzbV+mmv9lKyqx9tYATskX3aXhYQUB+5kOwnJNtzJ9u2XJDvOBOs+D47UGRxmEw6ziUz2/rZjrTV+Q9MSidAaMfDGlrYEta28ra41YtBqRLd9bfsRg7pQEF9E0xox8BnRuuBeTrtlVao9GbWbVHuCajcp7KZtZXaTwrbdtgmbUtu2TbFtte3YtmNsbftq27E2pbDG3sMaO6afJsODlVKLgCbgj1rrr4A8oLTDMaWxMoCsDkllBdA2V00eULKTc76XgCqlLgcuBxgwYECnNOK7hhauXlnMZI+L/4wZhMWk2Ly0hi9fXMPAcWkccvYwuaIvhBD9gCSgQohuYzKborfgZjh3Wh8JG3gbArTU+2mui65b6gOxxU91STO+5uD3klRlUrgSrbiS7LiSbLg80cWZ2GEd27a7LD0iWd0VpRROc/RKZGcLGxq/EU1ItyWmsbKI0WGt8RkG/ti234jWBWLbAUMTMAz8sXPrQwZBIxQt19vqg4YmYOgdf1z7xATYTAqrUkxNcvPChCGd+OpdSyn1MZC9k6qbtNZv7uK0rcAArXVt7JnPN5RSY3b3PWPPhO7xj0Br/QjwCETnAd3T83e0qsXHhcs2UuCw8ez4QlxmE1prFn5YTHpBIsdcOqbLb8cXQgjRM0gCKoToMcwWE550J570nSeoEL3Ft7UxiLchEF0ag7Q2BmhtCuKNlVcXRxPVnV3wUyaFw23BmWjD4bbiTLTiTLDhSLDicFtxuC3Y3dYO+1bszp6dtO4ui0mRYDKTQPfd1qy1JqR1ezIaMAxCOrodjCWpwfZ6o/3YoNaEdihrrzM0IW2QZ+91gxgdtRfnBIBAbHuBUmoDMBwoAzrer5ofKwOobLu1NnarblWsvIy2eZi+f06X8ljMTEp088+RBaRaox89lFKc+PMJGBFjn+YdFkII0bvIX3whRK9iNptITHWQmLrzeUTbaEPjbw3R2hTE1xzC1xSMbrcE8bWE8LeE8DUHqSv34mtuwN8a+t6V1XYq+syr3WXB7oolqS4rNpdlW7nTgs1lweaIbccWu9OC1W7uEwns3lCq7bZaSIh3ML2QUioDqNNaR5RShUQHENqota5TSjUppfYnOgjRBcD9sdNmARcCt8fWb3Yov0Yp9RLRwYcau+P5T4A8h43/Toxera4ta2HBe5s5/IJR2J3yMUQIIfob+csvhOiTlEnhTLDhTNi9q2SGoQm2hvF7Q9GlJdS+HWgNx5YQfm903VwXIOCLbhvhH7lDUYHNbsYWS0atDgs2hxmbw4LVYcZmN2N1mKN1dsu2bVt0bbG31W0rN1mUPC/XhyilTiWaQGYA7yilFmutjwWmAzcrpUKAAVypta6LnXY126ZheS+2QDTxfFkpdSlQDJwVK3+X6BQs64lOw3JxV7drRw2Vrbx532JMJoWvOYg1bdd3OwghhOibJAEVQgjAZFLR2273Yt7ScChCoDVM0BfetvaFCfkjBHzR/aA/ut9x3doUJOSPEApECAbCP57IdqBMCovNhNUWS1BtJiw2M5a2tXVbmdlmwmI1YbHG6q0mzNttR+uia9N2a7MlujaZJOHtSlrr14HXd1L+P+B/uzhnPjB2J+W1wJE7KdfAz/c52L3UXOfnzXsXoQ3NzOsn4ZHkUwgh+iVJQIUQYh9ZrGYsSWbcSXs2bcuOImGDUCCWkPrDhAMGoWB0PxwrDwUihIIRwsFItD60rS4Sih7f2hgkHGw7ziAcMogEIzt9JnZ3KcW2ZNRiwmJpS1BVtNxiwtRx22zCbFWYzdHjzWa13dpkbqtT3y/bbl9haitrW0zRfatj3/+bi+7hbQzw5j2LCPojnPKrSaRku+MdkhBCiDiRBFQIIXqItuTN4d77aVx2RWuNEdGEQ0Y0eQ0aREIG4VAktjY6rCNEwrq9LBKORLfDmki4rczosB0tNyIGIX+ISERjxOqNiN5uHQlrtNE54+JmFyZx+m+ndMpria7lbwmhteakX0wgY0BivMMRQggRR5KACiFEP6CUar9aGe+BX7QRTYYjke0TVKN9f9u2ETGiCW37YrRvd0WiLrpGWl4C5/1tf8wy1YoQQvR7koAKIYToVsqkMJsUZqskI/2JJJ9CCCEgOqe3EEIIIYQQQgjR5SQBFUIIIYQQQgjRLSQBFUIIIYQQQgjRLSQBFUIIIYQQQgjRLSQBFUIIIYQQQgjRLbo8AVVKmZVSi5RSb++kbrpSaqFSKqyUOqND+USl1HdKqRVKqaVKqbO7Ok4hhBBCCCGEEF2rO66AXgus2kXdFuAi4IUdyluBC7TWY4DjgHuVUsldFaAQQgghhBBCiK7XpQmoUiofOBF4bGf1WuvNWuulgLFD+Vqt9brYdjlQBWR0ZaxCCCGEEEIIIbpWV18BvRf4LTskmHtCKTUNsAEbdlJ3uVJqvlJqfnV19V4HKYQQQgghhBCi63VZAqqUmgFUaa0X7MNr5ADPAhdrrb+XxGqtH9FaF2mtizIy5AKpEEIIIYQQQvRkli587YOAk5VSJwAOwKOUek5rff7unKyU8gDvADdprWf/2PELFiyoUUoV71PE26QDNZ30Wr1Bf2sv9L8297f2Qv9rc39rL3Remwd2wmv0WZ3Yv8rvaN/X39oL/a/N/a290P/a3Jnt3Wn/qrTWnfT6u6aUOgy4QWs9Yxf1TwFva61fje3bgPeAt7TW93Z5gN+PZ77Wuqi73zde+lt7of+1ub+1F/pfm/tbe6F/trk3648/r/7W5v7WXuh/be5v7YX+1+buaG+3zwOqlLpZKXVybHuqUqoUOBN4WCm1InbYWcB04CKl1OLYMrG7YxVCCCGEEEII0Xm68hbcdlrrz4HPY9t/7lA+D8jfyfHPAc91R2xCCCGEEEIIIbpHt18B7SUeiXcA3ay/tRf6X5v7W3uh/7W5v7UX+mebe7P++PPqb23ub+2F/tfm/tZe6H9t7vL2dsszoEIIIYQQQgghhFwBFUIIIYQQQgjRLSQBFUIIIYQQQgjRLSQB7UApdZxSao1Sar1S6sZ4x9MVlFJPKKWqlFLLO5SlKqU+Ukqti61T4hljZ1JKFSilPlNKrVRKrVBKXRsr78ttdiil5iqllsTa/LdY+WCl1JzY7/d/Y9Md9RlKKbNSapFS6u3Yfl9v72al1LLYKOHzY2V9+fc6WSn1qlJqtVJqlVLqgL7c3r6mr/ev/a1vhf7Xv0rfKn1rX/udbhOP/lUS0BillBl4ADgeGA2cq5QaHd+ousRTwHE7lN0IfKK1HgZ8EtvvK8LAr7XWo4H9gZ/Hfq59uc0B4Ait9QRgInCcUmp/4B/APVrroUA9cGn8QuwS1wKrOuz39fYCHK61nthhvq6+/Ht9H/C+1nokMIHoz7ovt7fP6Cf961P0r74V+l//Kn1rVF9vL/SvvhXi0L9KArrNNGC91nqj1joIvATMjHNMnU5r/SVQt0PxTODp2PbTwCndGVNX0lpv1VovjG03E/1HlUffbrPWWrfEdq2xRQNHAK/GyvtUm5VS+cCJwGOxfUUfbu8P6JO/10qpJKJzQz8OoLUOaq0b6KPt7YP6fP/a3/pW6H/9q/St0rfGtvtUm+PVv0oCuk0eUNJhvzRW1h9kaa23xrYrgKx4BtNVlFKDgEnAHPp4m2O3zCwGqoCPgA1Ag9Y6HDukr/1+3wv8FjBi+2n07fZC9IPPh0qpBUqpy2NlffX3ejBQDTwZuxXsMaWUm77b3r6mv/av/eb3s7/0r9K3St9KH/udJk79qySgYjs6Oi9Pn5ubRymVAPwPuE5r3dSxri+2WWsd0VpPBPKJXn0YGd+Iuo5SagZQpbVeEO9YutnBWuvJRG9r/LlSanrHyj72e20BJgMPaa0nAV52uB2oj7VX9DF9+fezP/Wv0rf2C/2pb4U49a+SgG5TBhR02M+PlfUHlUqpHIDYuirO8XQqpZSVaOf4vNb6tVhxn25zm9htFJ8BBwDJSilLrKov/X4fBJyslNpM9Na+I4g+z9BX2wuA1rostq4CXif6Yaiv/l6XAqVa6zmx/VeJdph9tb19TX/tX/v872d/7V+lb+2T7QX6Xd8KcepfJQHdZh4wLDa6lw04B5gV55i6yyzgwtj2hcCbcYylU8WeV3gcWKW1vrtDVV9uc4ZSKjm27QSOJvpszmfAGbHD+kybtda/11rna60HEf13+6nW+if00fYCKKXcSqnEtm3gGGA5ffT3WmtdAZQopUbEio4EVtJH29sH9df+tU//fva3/lX6VulbY4f1qTbHq39V0auqAkApdQLR+93NwBNa61vjG1HnU0q9CBwGpAOVwF+AN4CXgQFAMXCW1nrHwRR6JaXUwcBXwDK2PcPwB6LPqfTVNo8n+sC4meiXTC9rrW9WShUS/RYzFVgEnK+1DsQv0s6nlDoMuEFrPaMvtzfWttdjuxbgBa31rUqpNPru7/VEogNh2ICNwMXEfr/pg+3ta/p6/9rf+lbof/2r9K3St9LHfqfbxKN/lQRUCCGEEEIIIUS3kFtwhRBCCCGEEEJ0C0lAhRBCCCGEEEJ0C0lAhRBCCCGEEEJ0C0lAhRBCCCGEEEJ0C0lAhRBCCCGEEEJ0C0lAhRBCCCGEEEJ0C0lAhehESqk0pdTi2FKhlCqLbbcopR7sgvd7Sim1SSl15R6e927bhNp78Z4TY3P67c25zth/j6BSKn1vXkMIIUT/I/3rj54r/avoNSzxDkCIvkRrXQtMBFBK/RVo0Vr/s4vf9jda61f35ASt9V51cDETgSLg3T09UWvtAyYqpTbvw/sLIYToZ6R//dH3lf5V9BpyBVSIbqCUOkwp9XZs+69KqaeVUl8ppYqVUqcppe5QSi1TSr2vlLLGjpuilPpCKbVAKfWBUipnN97nKaXUQ0qp2UqpjbH3fUIptUop9VSH4zYrpdKVUoNidY8qpVYopT5USjljx3yulCqKbafHzrEBNwNnx75pPVsp5Y69x1yl1CKl1MzYOWNiZYuVUkuVUsM6/T+sEEKIfk36V+lfRe8jCagQ8TEEOAI4GXgO+ExrPQ7wASfGOsn7gTO01lOAJ4Bbd/O1U4ADgF8Bs4B7gDHAOKXUxJ0cPwx4QGs9BmgATt/VC2utg8Cfgf9qrSdqrf8L3AR8qrWeBhwO3KmUcgNXAvdprScS/Ua3dDfjF0IIIfaW9K9C9HByC64Q8fGe1jqklFoGmIH3Y+XLgEHACGAs8JFSitgxW3fztd/SWuvYa1dqrZcBKKVWxF578Q7Hb9Jat5UtiB2zJ44BTlZK3RDbdwADgO+Am5RS+cBrWut1e/i6QgghxJ6S/lWIHk4SUCHiIwCgtTaUUiGttY6VG0T/XSpghdb6gL197dhrBTqUt732ro4HiADO2HaYbXdJOH7g/RRwutZ6zQ7lq5RSc4ATgXeVUldorT/djfiFEEKIvSX9qxA9nNyCK0TPtAbIUEodAKCUsiqlxnRzDJuBKbHtMzqUNwOJHfY/AH6hYl8lK6UmxdaFwEat9b+AN4HxXR2wEEII8SOkfxUiziQBFaIHij0LcgbwD6XUEqK39RzYzWH8E7hKKbUI6Dik+2fA6LZBEoBbACuwNHYb0i2x484CliulFhO93emZbotcCCGE2AnpX4WIP7XtzgQhRG8TG3nv7T0dJj7eVHSY+CKtdU28YxFCCCF2JP2rEF1HroAK0bs1AreoPZwoO15UbKJsot/oGnEORwghhNgV6V+F6CJyBVQIIYQQQgghRLeQK6BCCCGEEEIIIbqFJKBCCCGEEEIIIbqFJKBCCCGEEEIIIbqFJKBCCCGEEEIIIbqFJKBCCCGEEEIIIbqFJKBCCCGEEEIIIbqFJKBCCCGEEEIIIbqFJKBCCCGEEEIIIbqFJd4BdJb09HQ9aNCgeIchhBCil1mwYEGN1joj3nH0VNK/CiGE2Bu76l/7TAI6aNAg5s+fH+8whBBC9DJKqeJ4x9CTSf8qhBBib+yqf5VbcIUQQgghhBBCdAtJQIUQQgghhBBCdAtJQIUQQgghhBBCdAtJQIUQQgghhBBCdAtJQIUQQgghhBBCdAtJQIUQQgghhBBCdAtJQIUQQgghhBBCdAtJQIUQQgghhBBCdAtJQIUQQgghhBBCdAtLvAPoaSo2NpI5yIPJpOIdihBCCCGE6KO01kQMTURrtIaIoTG0xjDA0NFyQ2vQYOhomQYMQwOx/Wg1OlanNURLaK/bEx0//Sq1fWnbvgKUUrE1KFR7ncn0/fK2bZOKnmdqqzOBKfY6JrXtWJNSsSV6vOh7JAHtoHpLM/+7cwFTjhvI/jOHxDscIYQQQgixDwxD4w9HaA1G8AUj+ELb1oGwgT8UwR+KEAgZ+MMRgmGDQGwJxpZAOEIoYhCKaIKRaFl03yAU1oQMg3BEE4oYhI1oUhmKGEQM3b4f7rBvxBJPY0+zw35qW1K6LYFtS1I71plNapf10bptx7YnuabtE95o2bbX61jX9vrmHc7bdlzsHNMO76EUZtO21zZ3XKto0m6O7auO7xsrM5tof9+21/+h8o6vuS2+bf8dvhfvDuVmk8LjsGAxd92NspKAdpBekMCoA3NY8F4xGQMSGTIpM94hCSGEEEL0O4ahafaHafSF2pcmf4hmf4hmf5hmf5iWQJhmf4iWQBhvIII3EC1rDUa3vcEw/pCx1zHYLCbsZhM2S3SxmjuszQqL2YTVrEiwWrCYtu1bTCYsZoXFpDCbTLF1dOm43fGDf8ckwdQhieiYULVdKST6/+2uGna8Ctl2dTK6Ha3bHbrD9VKt28ra9rev08Suvu5wBZYOV2q3P27b1VtN9Iqujl3Bbb+yq7cd23bF12jfbzt2274Ru2oMHa4e621Xltte0zA6vl7Hq82xY2PHb/c6BtEvDdpi6fDFgda0X502Ylew269a7/BeHd870ou+fHjv2kMYlePpsteXBLQDpRSHnjOCunIvHz+1iuQsF2m5CfEOSwghhBCiV/OHItR6g9Q0B6hpiS613iD13iB13hD1rcHo4g1S3xpNNvWPfEh328wkOqwkOCy47RbcNjMFbhdumxlXbN9ps+CymXHZzDis0bXTGt12WE3YLTtuxxJOs0lu/xRdpi35jnRITNsS6o63ZhtGLNndLnnVRIydJbXbXqMtMe6YUEc6JsyxpHrb+3RYG5osj6NL2y8J6A7MVhPHXT6OV26bx3sPLeOMG4twuK3xDksIIYQQoseJGJqalgBlDT62NvipbPJT2eynqilAZZOfiiY/1U0BmgPhnZ7vsJpIddlIcdtIddsoSHGR4rKS5LTicW6/TnJaSXRYSLRHk06zjNcheinVdnswCqs53tF0P0lAdyIhxc5xl4/ljXsW8dETKznx5+NlUCIhhBBC9DuGoalo8rOlrpUtda2U1LVSWu+jrMFHeYOPikY/4R3uJ7SZTWR67GR5HIzK9jB9mJ2MRDtpbhvpCXbSEqLr9AQ7Tls//PQtRD8nCegu5AxN5pCzh/PFC2uY+9ZGGZRICCGEEH2S1prq5gAbqr1srGlhY7WXjdUtFNdGk81gZNtzlGaTItvjIC/ZSdHAFHKTneQkO8lLdpDtcZKd5CDFZZXbV4UQuyQJ6A8Yc0gu1cVNMiiREEIIIXq9tkRzTWUzayqaWVvZzJrKFjZUtdDS4RZZh9XE4PQERmQncvSYLAakutqX3GQn1i4cHVMI0fdJAvoDlFJMP2cEtW2DEmW6SMuTQYmEEEII0bNFDM2mmhaWlzWxvKyRFeVNrK5oor411H5MeoKN4VmJnD45j8KMBAoz3BRmJJDjccijR0KILiMJ6I8wW00cf8U4Xr5tHu88uJQzfleEy2OLd1hCCCGEEED0ymZpvY+FW+pZtKWBZWWNrCxvwheKANHpREbleDh2TDYjshMZkZ3I8KxE0hPscY5cCNEfSQK6G9zJdk68ejyv/3Mh7/1nKTN/NQlLfxyySgghhBBx5w9FWFrayILi+vaks6YlAIDTamZsnoezpxYwNi+JsXkehmQkyG2zQogeo0sTUKXUccB9gBl4TGt9+w7104F7gfHAOVrrV2PlE4GHAA8QAW7VWv+3K2P9MZkDPRx50Wg+eHQ5nz6zmqMvGS0P2AshhBCiy/lDERYW1zN7Ux1zNtayqKSBYDg6MNDgdDfTh6czaUAKkwckMyIrEYskm0KIHqzLElCllBl4ADgaKAXmKaVmaa1XdjhsC3ARcMMOp7cCF2it1ymlcoEFSqkPtNYNXRXv7hg6JZPG6kJmv7GR5CwX02YMjmc4QgghhOiDDEOzvLyRL9dW8+W6GhZvaSAYMTApGJ3r4YL9B7JfYRpTBqaQ6pbHgoQQvUtXXgGdBqzXWm8EUEq9BMwE2hNQrfXmWJ3R8USt9doO2+VKqSogA2jownh3y+RjB9JQ0cq8tzeRnOVk+NTseIckhBBCiF6uqtnPl2tr+HJtNV+vr6HOGwRgTK6Hiw4axP6FqRQNSsXjsMY5UiGE2DddmYDmASUd9kuB/fb0RZRS0wAbsGEndZcDlwMMGDBg76Lc83g47Ccjaazx8enTq/GkOckuTOqW9xZCCCFE36C1ZkN1Cx+sqOSjlZUsLmkAoiPTHjo8g+nD0zl4aAYZiTJQkBCib+nRgxAppXKAZ4ELtdbGjvVa60eARwCKiop0d8Vltpo4/spxvPqPBbz7UHRkXE+6s7veXgghhBC9kGFoFpXU8+GKSj5cWcmmGi8A4/OT+PXRwzl8ZCajczwyBYoQok/rygS0DCjosJ8fK9stSikP8A5wk9Z6difHts+cCTZm/Hw8/7tjAW/dv4TTfjMZZ4I8hyGEEKJrKaWSgceAsYAGLtFaf7fDMYcRHeTPCtRorQ/d3XNF59Jas7ysibeWlvP2knLKG/1YzYr9C9O45ODBHD0qi+wkR7zDFEKIbtOVCeg8YJhSajDRxPMc4LzdOVEpZQNeB55pGxm3J0rJdnPCVeOZdd9i3nlgKTOvm4TVLtOzCCGE6FL3Ae9rrc+I9ZeujpWxJPNB4Dit9RalVObunis6z7rKZmYtKeetJeVsrm3FalZMH5bBb44bwZGjsuRZTiFEv9VlCajWOqyUugb4gOg0LE9orVcopW4G5mutZymlphJNNFOAk5RSf9NajwHOAqYDaUqpi2IveZHWenFXxbu3coclc8zPxvD+w8v44NHlHH/VOMwy/LkQQoguoJRKIto/XgSgtQ4CwR0OOw94TWu9JXZM1R6cK/ZBoy/EW0vKeWV+CUtKGzEpOHBIOlcdNoRjx2ST7JI7pYQQokufAdVavwu8u0PZnztszyN6a+6O5z0HPNeVsXWmwokZHHreCD5/fg2fP7uaIy4cJXOECiGE6AqDgWrgSaXUBGABcK3W2tvhmOGAVSn1OZAI3Ke1fmY3zxV7yDA0szfW8vL8Et5bXkEgbDAyO5E/zRjNSRNyyEyU22uFEKKjHj0IUW8y5pA8vI1B5r29CVeSnQNOHRLvkIQQQvQ9FmAy8Aut9Ryl1H3AjcCfdjhmCnAk4AS+U0rN3s1zgfiMMt/b1HuD/Hd+Cc/PKaakzkeiw8JZRQWcVVTA2DyPfBEthBC7IAloJ5p64iC8jQEWflCMK8nGhCMKfvwkIYQQYveVAqVa6zmx/VeJJpE7HlMbu7LpVUp9CUwAvtqNc4H4jTLfG6wob+Tpbzfz5uJyAmGD/QancsMxIzh2TDYOq4wDIYQQP0YS0E6klOLQc0fgawry9SvrcCZaGT41O95hCSGE6CO01hVKqRKl1Ait9RqiVzlX7nDYm8C/lVIWovNo7wfcs5vnip0IRwzeW17B099uZn5xPU6rmdOn5HPhAYMYkZ0Y7/CEEKJXkQS0k5lMimMuHcNb9y/h4ydXYbGaKZyYEe+whBBC9B2/AJ6PjWK7EbhYKXUlgNb6P1rrVUqp94GlgAE8prVevqtzuz/83sMXjPDKghIe+XIjpfU+Bqa5+OOJozizqIAkp4xiK4QQe0Np3TfurCkqKtLz58+Pdxjtgv4ws+5bTHVJMydcNZ6BY9LiHZIQQoidUEot0FoXxTuOnqqn9a/dobE1xDPfbeapbzdT6w0yeUAyVx02lCNHZmIyybOdQgixO3bVv8oV0C5ic1g46RcTeOOeRbz3n2XMuGYC+SNS4h2WEEIIIXahqtnPo19u5IU5W/AGIxw+IoOrDhvK1EEpMqiQEEJ0EklAu5DdZeXkayfyxt2LeOfBpZz8iwnkDE2Od1hCCCGE6KDOG+ThLzfw9LebCUU0J43P4YpDhzAqxxPv0IQQos+RBLSLORNsnHztRF6/ayFv/3sJM381icyB0qEJIYQQ8dboC/H4Vxt5/OtNtIYinDIxj2uPHMagdHe8QxNCiD5LEtBu4E6yM/O6Sbx+10Jm3beYU66fTHp+QrzDEkIIIfql1mCYJ7/ZzMNfbKDJH+bEcTlcd9QwhmXJiLZCCNHVTPEOoL9ITHVwyq8mYbWbeeOehVRvaY53SEIIIUS/YhiaV+aXcNidn3PnB2uYNjiVd355MA/8ZLIkn0II0U0kAe1GnnQnp1w/GZvdwhv3LKJiU2O8QxJCCCH6he821HLSv7/mN68uJTfZyf+uOoDHLpzKmNykeIcmhBD9iiSg3Swpw8kpv56EI8HKrHsXU76uId4hCSGEEH3Wphovlz8zn3MfnU1Da4j7zpnI61cfyJSBqfEOTQgh+iVJQOPAk+bk1Osnk5Bi5637F1O6ui7eIQkhhBB9SmswzG3vreKYe77gm/U1/ObYEXzy60OZOTFPplQRQog4kgQ0ThJS7Jxy/WQ86U7efmApxStq4x2SEEII0Sd8vLKSo+/+koe/2MjMiXl89pvD+PnhQ3FYzfEOTQgh+j1JQOPI5bFxyvWTSMl28e5DS9m4uDreIQkhhBC9VlmDj8ufmc/PnpmP227mlSsP4J9nTiAz0RHv0IQQQsRIAhpnzgQbM6+bREZBIu8/vIyVX5fHOyQhhBCiVwlFDB75cgNH3fUFX66r5sbjR/LOLw9h6iB5zlMIIXoamQe0B3C4rcy8bhLvP7Kcz55bjbcxQNEJg+QZFSGEEOJHrKlo5tevLGZ5WRNHjcrkLyeNoSDVFe+whBBC7IIkoD2E1W7mhKvH8fmzq5n71iZaG4Mccs5wTCZJQoUQQogdhSMGD3+5kXs/XovHYeWhn0zm+HE58Q5LCCHEj5AEtAcxm00cceEoXEl2Fn5QTGtzkKMvGY1FBk0QQggh2q2rbObXryxhaWkjJ47P4eaTx5CWYI93WEIIIXaDJKA9jFKKA04dgivJxtevrOOtfy3hhKvGYXdZ4x2aEEIIEVfhiMGjX23ino/WkuCw8MB5kzlxvFz1FEKI3kQS0B5qwhEFuDw2Pn5yJf+7YwEn/nw8SRnyTIsQQoj+qbS+leteWsz84nqOG5PN/506lnS56imEEL2OjILbgw0ryuLkX06ktTnIq7cvoGxtfbxDEkIIIbrde8u2csJ9X7G6opl7zp7AQ+dPluRTCCF6KUlAe7i8ESmc8bsinIlWZt23mJXfyDQtQggh+gdfMMIfXl/GVc8vZHC6m3d+eTCnTsqXUeKFEKIXkwS0F0jOdHH6b6eQNyKFz55dzdevrsMwdLzDEkIIIbrMmopmZj7wNS/M2cIVhxbyypUHMjDNHe+whBBC7KMuTUCVUscppdYopdYrpW7cSf10pdRCpVRYKXXGDnXvK6UalFJvd2WMvYXdZWXGz8cz7vB8lnxcwrsPLiXoC8c7LCGEEKJTaa15ce4WTv7319R5QzxzyTR+f/wobBb5zlwIIfqCLvtrrpQyAw8AxwOjgXOVUqN3OGwLcBHwwk5e4k7gp10VX29kMpuYfvZwDj1vBFtW1vHqP+ZTt9Ub77CEEEKITuEPRfjd/5by+9eWMW1wKu9dewjTh2fEOywhhBCdqCtHwZ0GrNdabwRQSr0EzARWth2gtd4cqzN2PFlr/YlS6rAujK/XGjs9j+QsFx8+tpxXbp/PEeePZNjUrHiHJYQQ4kcopZbuxmHVWusjuzyYHqaswcdVzy1gaWkjvzhiKNcdNRyzSZ71FEKIvqYrE9A8oKTDfimwX2e+gVLqcuBygAEDBnTmS/d4+SNSOOsP0/jg0eV8+PgKtm5s5KDTh2KWW5SEEKInMwMn/EC9AmZ1Uyw9xjfra/jFi4sIhQ0e+ekUjhmTHe+QhNgprTVhI0wgEiAQCRAyQgQjwZ2uw0aYsBHetq3D7WVhI0xER7ZbG9ogoiNEjEj7dsf1jotGb9vWGoNt2xq9bY0m+v/o+CFtdW3b7W1j98YXUbH/QXT++vZtFNH/f7/+e+u27R33UZiUCZOKfp7d2XbbMe1rte0ckzJhYvsyhcKszN8r2+6cnZS1ndNx3fE9TKbYeofX+cHzYvU7O2fHczvu9zW9eh5QrfUjwCMARUVF/W5UnoQUO6f8ehLfvbaBJZ+UULW5ieMuH0tCiiPeoQkhhNi5K7TWxT90gFLq6u4KJt601jz85UbueH81QzISePinUyjMSIh3WKKXM7SBN+SlJdgSXYdaaA214g178Ya8tIZaaQ234gv72pfWUHQ/EAngD/vxR/wEwoHoOpZsBiNBApFAl8a+Y0JiUZadJjHtCU6HpKljAgfsNLlr873EsUP5D2lLamH7RLbj/k4T3Vi5oY3t6toS6bZjDGIJdMdt9HYJd8fzOu63JeB9TcefccfE1GTaPpnd422T+fuvGVvfMPUG8hLyuqxNXZmAlgEFHfbzY2WiE5nNJg4+cxjZhUl8+swq/nvrPI65ZAwFo1PjHZoQQogdaK2/7oxj+gJ/KMINryzh7aVbOXFcDnecMR63vVd/Ly46WSASoN5f3740BBpoDDbSGIguTcEmmgJN0XWwiZZQS3vSuTtX8xQKp8W5bbE6cZqd2C120qxpOCwO7GY7drMdh8WBzWTDZrZhN9uxmaPbbWVWkxWr2YrNZMNqtmI1WbGYLFhMlvZtq9pWZjaZo9squm1W5j57tau7aa2J6Eh7khvRkfYkNaIjALu8otx2bMd1+zEYGMa2RLfj8oPndThmV+fsav+Hynd83V1dOd/l6xgRQjq002NDkVCX/oy68i/9PGCYUmow0cTzHOC8Lny/fm3olEzS8ty89/ByZt2/mElHDWC/kwsxW+WWXCGE6GmUUgcBfwUGEu2LFaC11oXxjKu7VDX7ueyZBSwtbeDG40dyxfRC+eDdT4SMEDWtNVS2VlLtq6bWV0uNr4Zaf2ztq6XOX0edvw5f2LfL13FZXCTZk0iyJ+GxeRiQOIBEWyKJtkQSbAkkWBNItCXisrpIsCbgtrpxWVzRtdWFy+LCbrbL710fpJTCouTLrJ6sy346WuuwUuoa4AOiz7w8obVeoZS6GZivtZ6llJoKvA6kACcppf6mtR4DoJT6ChgJJCilSoFLtdYfdFW8fUFKtpszf1/EN6+uZ9FHWyhZXcfRl4whNUfmTRNCiB7mceBXwAIgEudYutXqiiYufWo+dd4g/zl/CsfK8559hqENan21lHvL2dqyla3erZS3lFPhraCytZKq1irq/HXfuzqpUKQ6UklzppHuTGeQZxApjhRSHCkk25Oj2/bodpI9CY/dg9VkjVMrhRD7SnW8d7s3Kyoq0vPnz493GD3GpiXVfPrsasKBCAedOYwxh+TKt3xCCLETSqkFWuuibn7POVrrTh2Yr6t0Zv/62ZoqfvHCItx2M49fOJWxeUmd8rqi+wQjQUqbS9nSvIWS5pL2pbS5lLKWMkLG9rfuJdoSyXHnkOnKJMuVRaYrc7sl3ZlOsj0Zi0muWAnR1+yqf5V/7X3U4AkZnDPIwydPr+KLF9ZQvLyWI346EmeiLd6hCSFEv6WUmhzb/EwpdSfwGtA+qonWemFcAusGT32ziZvfXsmoHA+PXziV7CQZMK8naww0srFxI5saN7GpcVP7dllL2XYDvbitbgoSCxiWMozDCw4nJyGHXHdu+zrBJoNKCSG2JwloH+ZOsnPSNRNY+lkp376+nhdvnsP0c0YwdEpmvEMTQoj+6q4d9jt+M6yBI7oxlm6hteZvb63kqW83c9SoLO47Z6IMNtSDBCNBNjZuZF39OtbVr2Ntw1rW1a2jylfVfozdbGegZyCj00ZzYuGJDPQMpCCxgILEAlLsKXKHlRBij0gP0Mcpk2LCkQXkj0zhk6dX8cGjy1k3P4Pp5wzHnWSPd3hCCNGvaK0P31WdUiqrO2PpLkopkl1WLjtkMDcePwqzSZKVeAlGgqyrX8eK2hWsrF3JytqVrGtYR9gIA2A1WRmSPIT9cvZjeMpwCpMLKUwqJMedg9lkjnP0Qoi+QhLQfiItL4EzfjeFxR+XMPetTby4Zg4HnzWMEftlyzeXQggRJ0qpZOB0oqPEjwJy4xpQF7n2yGHS13QzrTVbvVtZUr2ExVWLWVK9hDX1a9qTTY/Nw+i00Vww+gJGpo5keMpwBngGyOA+QoguJwloP2Iym5h87EAGT0jns2dX88lTq1g3r5LDfjKSxFR5FkcIIbqDUsoJzCSadE4CEoFTgC938/xk4DFgLNHbdi/RWn+3wzGHAfcCVqBGa31ohzozMB8o01rP2Je27C5JPrueoQ3W1q9lXsU8FlUtYknVkvbbaJ0WJ2PTx3LB6AsYnTaa0WmjyU/Il5+LECIuJAHth1Ky3Zz668ks+6KU797YyAt/m8PUEwYx4cgCzBaZN1QIIbqKUuoF4BDgQ+B+4FNgvdb68z14mfuA97XWZyilbIBrh/dIBh4EjtNab1FK7fjg/7XAKsCzV40QPYLWmvUN65lbMZd5FfOYXzmfxkAjALnuXIqyi5iQMYGJmRMZnjJcRpkVQvQY8teon1ImxfjDCxg0Lp2vXl7Hd69vYNW3W5l+9nAKRqfGOzwhhOirRgP1RBPAVVrriFJqt+dDU0olAdOBiwC01kEguMNh5wGvaa23xI6p6nB+PnAicCtw/d43Q8RDvb+e78q/4+uyr/mm/Bvq/HUA5CXkcXjB4UzLnsbU7Klku2VuVSFEz7XLBFQptTtZiKG1bui8cER386Q7OfHq8WxeVsNXL69j1r8WM2RSBgedOUxuyxVCiE6mtZ6olBoJnAt8rJSqARKVUlla68rdeInBQDXwpFJqArAAuFZr7e1wzHDAqpT6nOjtvfdprZ+J1d0L/DZWLno4Qxssr1nO12Vf83XZ1yyvWY5Gk2xP5sDcA9k/Z3+m5UwjLyEv3qEKIcRu+6EroOWx5YceEDADAzo1ojhb3NTK+EQnpn72XMSgcenkj0xh8UdbWPBeMcUraply3CAmHlWAxSYj3wkhRGfRWq8G/gL8RSk1hWgyOk8pVaq1PvBHTrcAk4FfaK3nKKX+n737DqyjuBYw/s3ert4lW8Vy7zY2xvTeCSXhQWihEwKEGkhCEtJIQkvDCQm919BCD71XYxtcwL1Lli1ZktWlW/a8P/ZKvpKbbEu6Kuf33mZ3Z2f3nmsZr87OzswM4Hrg1x3q7AkcDgSAz4wxn+MkpuUiMjvaR3SbjDEXAxcDFBX1q9t8rxeKhPhyw5e8u+Zd3lvzHuVN5RgME7Mncukel3LA4AMYlzlOR6VVSvVZ20tAF4rIlO2dbIz5qovjiat5dY0cN3sJVw3J5efDBsU7nB7n9riYdtxQRu2dxyfPLOOLl1bwzUelTD9hKKP3GYSlQ+crpVSXEpHZwGxjzE9x+obuSAlQIiJfRPefxUlAO9apjLaKNhhjPgQm4ySuJxpjjgP8QIox5jER+cFW4roHuAdg2rRpnX5FWO2alkgLH5V8xNtr3ubDtR9SF6oj4A6w/+D9OazoMA7MP5A0f1q8w1RKqS6xvQR0306c35k6fcbEpACnDcrg76s3MDLRz8m56fEOKS5SMgMce8lESpdU8+lzy3j3kUV8/fZa9jt5BEXjM3TUPKWU2kXGmIujyV07IiJER8HdVp1ovfXGmLXGmNEishinlfPbDtVeBO4wxrgBL7A38HcReQb4RfQzDgGu21ryqXpG2A4zc/1MXlvxGu+seYf6UD2pvlQOKzqMw4oOY7/B++F3a1cYpVT/s80EVESaW7ejQ7bnxtYXkTWxdfoDYwy3jipgZWML1yxaQ7Hfy9TUxHiHFTf5o9I55fppLJ9TwWcvLOeVO+aSPzqN/U4eQc4QHTxRKaV2wfXRfp/bYnBGqd1qAhp1BfB4dATcFcD5xphLAETkLhFZaIx5HZgH2MB9IrKga8JXu0NEWLBxAa+ufJXXV75OZXMlSZ4kDi86nOOGHsf0QdN1tFqlVL9nnIeu26lgzBU4fVU24NzIwHlYO6mbY9sp06ZNk1mzZnXJtSqDYY6bvYRG2+Z/e46iwO/tkuv2ZZGwzTcflfLlq6torg9RPCmL6ccPJbtIx7FQSvVtxpjZIjKthz7rwU5UqxGRq7s7ls7qyvvrQFXVXMXLy1/mv0v/y/Ka5XgtLwcXHsxxQ4/jwIID8bl88Q5RKaW63Lbur51JQJcBe4tIZXcF1xW6+ga5uKGZ42cvoSjg5aUpI0l0a2d/gJamMPPeXcvcd9bS0himeGImex0/VFtElVJ9Vk8moH2RJqC7JmJH+Lzsc55b+hzvrX2PsB1mUvYkTh5xMkcVH0WyVx/gKqX6t23dXzvznsdaoKbrQ+rdRif6uXt8MT+Yt4IfL1zNAxOGDriRcbfGF3Cz13eGMumwQua/t5av317LMzfPYsjETPY6bii5QzURVUopNXBVN1fz3NLneHrx05Q1lJHmS+OMMWdw8oiTGZE+It7hKaVU3G1vHtDWCapXAO8bY14FWlqPi8jfujm2uDssM4UbR+Zzw9JSbl5Rxq+GD453SL2GL+Bm2nFDmXRoIfPeL+Hrt9fw7K2zyB+Vxh5HFDFkQiZGR81VSik1QCyuWswTi57g1RWv0hJpYe+8vbl22rUcWngoXpd25VFKqVbbawFtfTdkTXTxRheAATMk+4X5WSxpaOafa8oZluDjjEGZ8Q6pV/EG3Ew7tphJhxbwzUfrmPfuWl799zzS8xKYfHgho/fJw+3R15eVUiqWMcYlIpF4x6F2T8SO8N7a93h84ePM2jALv8vPicNP5IwxZzAyfWS8w1NKqV5pe6Pg/n5r5cYYP3BCt0XUyxhj+NPIAlY3Bblu8VpyvB4Oz9TXTDvy+t1MObKISYcVsHx2OV+/vZb3H1/MFy+tYMLBBYw/cDCJqTrIglJKRS01xjwHPCgiHadRUb1cKBLi5RUv8+CCB1lVu4rBiYO5ds9r+d7I75HqS413eEop1at1aqzv6DQsRwNnAEcCHwPPdGNcvYrHMtw/oZjvfrWMH36ziuf3GMEeKQnxDqtXcrksRk3PY+Reuaxbsomv3l7Dl6+sZPZrqxg6OYvxB+VTMDpdX89VSg10k4HTgfuMMRbwAPCUiNTGNyy1PY2hRp5d8iwPf/sw5Y3ljM0Yy18O/gtHFB2By9K3fZRSqjO2OwquMeZg4EzgOGAmsD8wTEQaeya8zuuJUfo2tIT4zpwlNEeEV/ccyZCAtuh1xqYNjXzz8ToWfVpGc0OI1JwA4w/MZ+y+g/AneeIdnlJqgIv3KLjRe+0TQBrwLPAHEVkWr3g60lFwoS5Yx2MLH+OJhU+wqWUT03Kn8cOJP2TfwftidIBCpZTaqp2ehsUYU4LT9/NO4AURqTPGrBSRod0b6q7pqRvk0oZmTpyzlHSPm5emjiTLqxNGd1Y4FGH5nAq++aiUsmU1WG7D0ElZjN47j6IJmbhcVrxDVEoNQPFIQKNvFn0HOB8oBh4FHgcOBG4SkVE9Gc/2DOQEtDHUyBOLnuDBBQ9SG6zlkIJDuHDiheyRs0e8Q1NKqV5vV6ZheRb4LnAaEDHGvMgAGnxoW0Ym+nlk0jBO/XoZ58xfwbN7jCBBE6dOcXtcjN47j9F751FZWs+3n6xj6ZcbWD6nAn+Sh5F75TJ67zxyhiTrE2WlVH+3FHgP+LOIfBpT/qwx5qA4xaSiWiItPLP4Ge6dfy9VzVUcVHAQl+9xOWMzx8Y7NKWU6vN29AquAQ7B6ft5HJAKXAi8JiL1PRFgZ/X0E9r/VWziwgWrOCIzhQcmDMWtfRp3SSRis/bbKhZ/vp6VczcSCduk5yUwYs8chk/NIWNwoiajSqluFacW0ANE5OMOZfuLyCc9GUdnDKQW0LAd5sVlL3LXvLtY37Ce6XnTuWLKFdriqZRSu2CnX8HdygU8bB6I6GgRyeraEHdPPG6QD5Vu5PolJZyel8HfxhRiaaK0W1oaQyybXc6SmRtYt2wTCKTlJjB8ajYj9swhMz9Jk1GlVJeLUwI6R0Sm7qisNxgoCegnpZ/wl1l/YdmmZUzKmsQVU69gn0H7xDsspZTqs3blFdx2RCQEvAK8YowJdPJDjwFmAC7gPhG5pcPxg4DbgUnA6SLybMyxc4Ebort/FJGHOxtrTzkvP4uKYIi/rtpAosvijyPzNUHaDb4ED+MPzGf8gfk01LSw8usKls2pYM7rq5n9v9WkZgconpxF8YRMBo1Iw+XWV5+VUn2LMWZfYD8g2xjzk5hDKTj3StXDVtSs4C9f/oWPSj+iMLmQ2w+5ncOKDtP7uVJKdZNtJqDGmHtE5OKtHRORph3ViQ6w8C+caVtKgC+NMS91mO9sDXAecF2HczOA3wLTcPqdzo6eW93ZL9ZTrivOoz5ic/faCpLcLn4xbFC8Q+oXElN9TDi4gAkHF9BUF2TF1xWs+KqC+e+XMPfttXj9LgrHZTBkQhZDJmSSkOKNd8hKKdUZXiAJ5/6bHFNeC5wSl4gGqE3Nm7hz7p38Z/F/CLgDXLvntZw59ky8Lr2fKKVUd9peC+h3jTHN2zlugEO3c3w6sExEVgAYY54CTgLaElARWRU9Znc492jgLRGpih5/CzgGeHI7nxcXxhh+N3wwTRGbGas3kGBZXFWcG++w+pVAsretZTTYHKZkUTWr529k9YJKls+pAANZBUkUjMmgYEw6g0ek4fFpQ4JSqvcRkQ+AD4wxD4nI6njHMxDZYvPskmeZMWcG9aF6Th11KpftcRkZ/ox4h6aUUgPC9hLQn3bi/I+2cywfWBuzXwLs3ZmgtnFufifP7XHGGG4ZVUBDxObmlWUkui0uKsiOd1j9ktfvZtge2QzbIxsRYePaelYv2EjJomrmvbeWr99ag+Uy5A1LbUtGc4am4PFqQqqUij9jzO0icjVwhzFmi0EYROTEno9q4FhctZgbP7uReRvnMT1vOtdPv56R6SPjHZZSSg0o20xAe2Ofy46MMRcDFwMUFRXFNRbLGGaMKaIxYnPD0lISXBZnDsqMa0z9nTGG7KJksouSmXbcUELBCGXLNlGyqJqSRdXMfGUlCFiWIasomUEjUhk0PJVBw9P0lV2lVLw8Gl3/Ja5RDDCNoUb+/fW/eWzhY6T6UrnpgJs4ftjx2s9TKaXioNODEO2CUqAwZr8gWtbZcw/pcO77HSuJyD3APeCM0rcrQXYlt2W4a/wQzp23kmsXrcVvWZycmx7vsAYMj9dF0bhMisY5iX9zQ4j1K2ooW15D2bJNLHi/lLlvOw3ryZl+coakkFucQk6xk8R6/d35n4NSSoGIzI5uzgKaRMSGtnETfHELrB97b8173DTzJtY3rOeUUadw9dSrSfWlxjsspZQasLrzN+4vgZHGmKE4CeXpwJmdPPcN4CZjTGv2dhTwi64Psev5LIsHJg7lrHnLufzb1dginJKn/UriwZ/ooXhiFsUTnRmDIiGbirV1lC2rYcOqWspX17J8TrlT2UB6XiLZRUlk5SeTWZBIVkGytpQqpbrLO8ARQOuc2gHgTZwRclUXqG6u5o+f/5E3V7/JiLQRPHrsozqfp1JK9QI7TECNMRNFZP7OXlhEwsaYy3GSSRfwgIh8Y4y5EZglIi8ZY/YC/gukAycYY34vIuNFpMoY8wecJBbgxtYBifqCBJfFY5OGce68lVyxcA0hEc7Q13HjzuWxyBuWSt6wzU++m+qC0WS0jvLVtZQu3sSSLza0HQ+keMkqSCJjcCIZgxJJz0skPS8Bf6InHl9BKdV/+EWkNflEROqNMQnxDKg/eWf1O9z4+Y3UBmu5csqVnDfhPDyW/rutlFK9QWdaQP9tjPEBDwGPi0hNZy8uIq8Br3Uo+03M9pc4r9du7dwHgAc6+1m9TaLLxSOThnH+/JVcs2gtYRHOHpwV77BUB4Fkb7tWUoCm+iCVJfVsLKl31qX1LPhgE5HQ5sGaE1K8pA9KIC03kdTsAGk5AVKzE0jJ9uP26IBHSqkdajDGTBWROQDGmD2BpjjH1OfVtNRw88ybeXXFq4zNGMu9R93LqPRR8Q5LKaVUjB0moCJyoDFmJHABznycM4EHReStbo+uj0twWTw8cSgXLFjJTxeXELKFC3R03F4vkOSNTumy+dVp2xbqKpuoLmukan0D1WUNVK9vZNmsDbQ0hjefbCAp3UdqVoDkTD/JGX6SMwOkZPpJzvSTmO7D5bLi8K2UUr3M1cAzxph1ONOa5QGnxTWiPu7Dkg/53ae/o7q5mssmX8ZFky7SVk/VLUQEwmHslhakpQUJBpGWFuxgEGkJIsEWJBTavARb10EkHEbCzj7hsLMfCiORMEQiSDjibIcjSCS6HbHBjh6zIxCxnbUtYHfYFrttGxEn1ug2IggCQts+Iq1fylnRut/JPwwTu2nAxCwQ3d7Ksdhyy2pfZqzNZZZx9k1rvfbHjWUguh+73XZe67bl2qKOcVnR7c3Ht1rXZWEsCyxXzHGDcbnARK9jtcYQU8/lcmJ0ucByOdezXO3rx17D5YqeH3Osrb6rfZ2Yuq37WFafGVitU31ARWSpMeYGnEET/gFMMc43/KWIPN+dAfZ1TtZLqAAAdo5JREFUfpfFgxOH8sMFq/jl0lIiAj8s1CS0r7EsQ2p2AqnZCRRPat+S3dwQoqa8iZqKRjaVN1FT3khdZTNrF1bTUNPS/h9x47SeJqX5SEzzkZTuJyndR2Kql4QUHwmpXhJSvPgTPc4/VEqpfklEvjTGjAFGR4sWi0gonjH1Vc3hZm778jaeWfIMI9JGcMfhdzAuc1y8w1K9hN3Sgl1fj11XR6S+Abu+ztlvaMBubIxZNzrrpibspiakqQm7uXnzdksL0tzctsbuOIX9bnK5nITD7ca0bm+xtpzkp23tchIOK5oktSZqLqstsTNtSZuBLZJAnASpNYs021hvi0j77bYENyaJbZfoRhPijuW2HS2XtqS5LZHueNy2QaL7W0u0W+tGIpvLI5EtyzvW7eqfZ7xYW0lSt5KsbrGOJrmtf7fy//JnvMXF3RZmZ/qATgLOB74DvAWcICJzjDGDgc8ATUB3wGdZ3DehmEu/Xc2vl5XSbNtcXpTTZ55SqO3zJ3rwD/WQOzRli2ORkE1ddTN1ldGlupmG6hYaNrVQU9FE6ZJNBJvCW5xnLENCsodAipdAspdAkodAkhd/kodAsrPtS3TjS/DgT3TjS/Tg9vSdJ19KKcBJPscBfmCqMQYReSTOMfUpy6qX8dMPf8qyTcs4f/z5XD7lcrwuHTyuP7Kbm4lUVhKu3kRkU3Sprt68XVNDpLYGu6aWSK2z2LW1TktjJ1gJCZjEBKxAAlYggOX3YwIBPKmpGL8Pyx9w1j5/dN+P8fqcba8X4/NhPF6M11ksnxfj8YDHg+m4uN2b1263k3Ra+nZUvG0tWZXW1uNIpH1ZawLbuo4mts5xcVqsI06yvLUyaU16bdsp28oxiXRs7Y60r9tuHVN3O2vn+vbm77HVdQTc3fv2SGdaQP8J3IfT2tnWP0VE1kVbRVUneC2Lu8YVc8XC1fxpRRkbQ2F+O3wwliYM/ZrLY5GWk0BazrbHFgk2h2msCdJY27q00FgTpKE2SFNtkKb6EDXljTTVhQi1RLb9WW4LX4IbX4Ibb8CNL+CsvQlufH43Hr8Lb3Tt8cVse519j8+F2+fC47Ww9DVh1YuJLdi28xTd5embf1eNMb/FmW5sHM5YCccCHwOagHaCiPDMkme47cvbSPQkcvcRd7Nfvg4g3NdIJEKkqopQeTnh8nLC5RXOuqKCcFUlkY2VhKuqiGzciN3YuM3rWKmpuFJSnCU1BfegQbiSk3GlpmAlJWMlJ+FKSsJKTsZKTMJKSsSVmIiVmOgknoGAJoDKeYjvdlIj/e28e3UmAf2viDwaW2CMuUpEZnQsV9vnsQz/HjeEDI+bu9dWUBkM8/cxRXj0VcsBzet34/W7Scvd8QCY4VCE5voQTXUhWhpDNDeEo+sQLQ1hmhtDBJvCBJvCNDeGqa1spiW6HzuI0o5YboPH68LlsXB7LNxeV9va5bFwuZ1yl9ty9j0WLpfB5baw3BYud3Tb5WxbLoPlsqLrmG3LWUx027TuW86NYGv7xrTf3vxqkdNybGhfRvQto819UGK+aGf+03PeGmrrNyMx5RLzmpFA9HWfzfUl+kfe+sqRRI9v3u9wzG6tE7vd/hjRbVsEYuvEJGW2LVtex26/7bzF5NSx7fbH2+3b0vZZdtt1oteIfm7rZ7eeY8fWabeOOSaCHWndBjtib/78SPvjdmws9uZXvgYNT+Xkn+7Z6b/XvcwpwGTgKxE53xiTCzwW55j6hNpgLb/79He8tfot9hu8H3864E9kBXSQv97IbmggWFJKqLSEUFkZ4bIyQuvKCK1f7+yXlzstTh24MjJwZ2biyswkMHEirswM3JlZuDLScWdk4EpLw5We7qxTUpxWRKVUn9GZ/2LPAW7vUHYeMKOrgxkILGP408h8crxublm5nqpQmHsnFJPo0pFT1Y65PS6S0l0kpft3+txIxCbUHCHUEiHYFHbWzWHCLTahoFMejlmHg7azDtnOEi0LNoWJhG0i0fJIyHb2wzZ2uH2CoPouY6IJfWvS3/owwEQfCMTutz5AMGC5Ws9p/wDBsgzGvflBQ7uHC9H61hYPImLOtbY8NznDF+8/pt3RJCK2MSZsjEkByoHCeAfV282rmMdPP/gp5Y3lXLPnNZw3/jwsoy1X8RSuria4chXBVasIrllNaG0JwZK1hNaWEKlqP4Oe8XhwDxqEJy+PxOnTcefl4c7NwZOTgzs7G3dODu7MTIxXX6NWqj/bZgJqjDkDOBMYaox5KeZQMtBn5uTsjYwxXF2cR5bXw88Wr+XUr5fz2KRhZHj0CZ7qPi6XhSvR6vY5TG1bsCM2kbAQCdnYEWffWQu2HbstSHQdu93Wcmd32JfNLXyIRMcMiGl1jG1hdA45q9bR/WJz43Z5cuvONppE2w3oZ9qXY7Y83tpK21re2kLbmtRFm2SNFR0DIpqAtZ5nWdHt2Bbf1lZdy3mQFdsqHHteW3mHOrFJpDM2hWmXYHbc1/7E3W6WMSYNuBeYDdTjjKugtqL1ldubZ95MbkIuDx/7MJOyJ8U7rAFDbJtQaSktS5fRsnQpwRUrnIRz1SoiNTGz87lceAYPxltYgP/ww/EUFuItLMCTn49n0CBcmZn6qqtSarstoJ8CZUAW8NeY8jpgXncGNVD8YHAmGR4Xl367mpPmLOWpycPJ9+tTP9W3Oa/Vupz+64F4R6NU7yQil0U37zLGvA6kiIjeW7eiJdLCTV/cxPNLn2f//P259cBbSfWlxjusfitSU0PzwoU0L1xEy+LFtCxbRsvy5UjT5mlq3bm5eIcOJfnYY/ANHYq3uBhvcTGe/Hx9HVYptUPb/FdCRFYDq4F9ey6cgee47DSenOTm3PkrOG72Eh6eOIw9UnbcF1AppVTfZow5GTgApwn+Y/Th7hbK6su45v1r+KbyGy6edDGXTb4Ml6VdVrpKuLqa5nnzaPrmG1oWLqT524WESkvbjruys/CPHEn690/FO2IEvujiSk6OY9RKqb5ue6/gfiwiBxhj6thiJkNERLacc6KPi9TUUHbDr8m+5hp8w4b22Oful57ES1NHcvb8FXzvq6X8c+wQjs9J67HPV0op1bOMMf8GRgBPRot+ZIw5QkR+HMewepUvyr7gpx/8lKAdZMahMzis6LB4h9SnSTBI8+LFNH09l6Z582iaN5fQ6jVtx71DhhCYPIm000/DP3Yc/rFjcGdmxjFipVR/tb0W0AOi6wHzmCtSV0fj7Nms/dGPKH7qyR79h3dsUoD/7TmK8+av5KJvVvHLpkFcoXOFKqVUf3UYMFaiHZSNMQ8D38Q3pN5BRHhs4WP8ZdZfKE4p5vZDb2doas89FO4v7KYmmubOpfHLWTTOmkXT3LlIczMA7uxsAntMJu2UUwhMnox/3HhcSYlxjlgpNVDs8EV9Y8w+wDciUhfdTwbGicgX3R1cT/MWFFB4579Zfc65rL3sMoY8/DCWf+dHG91V2V4Pz+0xgmsWreGmFWUsbWzmL6ML8WmHfaWU6m+WAUU4XV3AGQF3WfzC6R1CdohbvriFp5c8zeFFh/OnA/5EokcTo86wg0Ga5nxFw6ef0jhzJk3ffAOhEFgW/jFjSD/t+wSmTCUweRLuvDx9wK2UipvO9BS/E5gas9+wlbJ+IzB5MoP/8mdKr7yKdT/9Gfm3/x3Tg1Ok+F0W/x43hBEJfv68aj1rmoLcP2EoWV7t1K+UUv1IMrDQGDMTp5vLdJyRcV8CEJET4xlcPNQF67jug+v4dN2nXDDhAq6aepVOsbIdIkJw+XIaPvmE+k8/pXHml85AQW43gfHjyTzvXBKmTSMwdar22VRK9SqdyWqMyObJC6LzlvXrbCjlyCMJX/9zNtx8C+W3/ZncX1zfo59vjOHaoXkMT/Bx9aI1HDN7MfeNH6qDEymlVP/xm109MTp9y33ABJzk9QIR+axDnUNw5vD2ABtF5GBjTCHwCJAbPe8eEekVc3qX1pfy47d/zOra1fx+v99z8siT4x1Sr2QHgzR+8QV1775L/XvvE16/HgBvcTFpJ59M4v77kTB9Oq6kpDhHqpRS29aZRHKFMeZKnFZPgMuAFd0XUu+Qfs45BNeWUPXww3gKCsg4+wc9HsN3c9MZEvBx0YKVnDhnKTePKuCswToggFJK9XUi8sFunD4DeF1ETjHGeIF2TyejCeq/gWNEZI0xJid6KAxcKyJzot1pZhtj3hKRb3cjlt02t2IuV757JSE7xF1H3sXeg/aOZzi9Tri6mvr3P6D+3Xep/+QTpLERk5BA0v77kXjZpSTtvz+e/Px4h6mUUp3WmQT0EuAfwA04T0zfAS7uzqB6A2MMub+4nlBZGRtuvhlP/mCSD+v5EfimpCTw5rTRXPbtaq5dvJZZtQ3cNLKAgEtfS1JKqb4qOr7CP4GxgBdwAQ07GmHeGJMKHAScByAiQSDYodqZwPMisiZapzy6LsOZ3xsRqTPGLATygbgloG+uepNffvxLsgPZ/OuIfzEsdVi8QulVwtXV1L31FnWvv07D51+AbePOzSX1pBNJPvRQEvbeG8vni3eYSim1S3aYgEZvXKf3QCy9jnG5yP/zbaw+9zxKf3ItQx55mMCkST0eR6bXzROTh/Hnleu5ffUGvqlr4t4JxQwJ6M1HKaX6qDtw7q3PANOAc4BRnThvKFABPGiMmQzMBq4SkYaYOqMAjzHmfZy+pjNE5JHYixhjioEpwFYHFDTGXEz0YXNRUVGnv9TOeGrRU9z0xU1Mzp7MPw77B+n+9G75nL4iUlND3dtvU/u/12n47DOIRPAMKSLz4h+SctRR+MaO1YGDlFL9gonp3rn1Csb4gQuB8UDbkLAickH3hrZzpk2bJrNmzeqWa4c3bmTV6Wdg19Ux5PHH8I0Y0S2f0xlvbqzh8oWrsTDcMW4IR2T2u+lYlVKqRxljZovItB7+zFkiMs0YM09EJkXLvhKRKTs4bxrwObC/iHxhjJkB1IrIr2Pq3IGT1B4OBIDPgO+IyJLo8STgA+BPIvL8jmLt6vuriHDn3Du5c+6dHFJwCH8++M/43T034nxvIqEQ9R9/TM1/X6DuvfcgFMJTWEjKMceQctyx+MaM0aRTKdVnbev+2plXcB8FFgFHAzcCZwELuza83s2dlUXRA/ez6qyzWHPBhQx54gm8BfHpb3FUVipv7DmaCxes5AfzVnBxQTa/HDYIv76Sq5RSfUljtP/m18aY23Beje3MP+QlQEnMVGjPAh1HyisBKqOtog3GmA+BycASY4wHeA54vDPJZ1eL2BFunnkz/1n8H7474rv8dt/f4rb69biGW9W8eAk1//0vNa+8QmTjRlwZGWSceQYpJ5yIf/w4TTqVUv1aZ252I6JPVhtE5GHgO8CAGyHAW1RE0X33Yzc3s+bCCwhXVMQtlqEJPl7dcxQX5GdxT0kFx81ewuKG5rjFo5RSaqedjXMPvhxnerNC4P92dJKIrAfWGmNGR4sOZ8s+nC8CBxhj3MaYBJx79kLjZDX3AwtF5G9d8zU6LxgJ8rMPf8Z/Fv+HCyZcwI373Tigkk+7sZHqp59m5cn/x8qTTqLq8cdJmDKFgn//m5EfvE/uL35BYMJ4TT6VUv1eZ/7lD0XXm4wxE4D1QM526vdb/tGjKLz7LtZccCFrLvohQx59BFdKfF6BDbgsbhpVwKEZyVy9aC1Hz1rM70bkc+7gTL15KaVU77cRCIpIM/B7Y4wL6GzH/iuAx6MtqCuA840xlwCIyF0istAY8zowD7CB+0RkgTHmAJzEd74x5uvotX4pIq913dfauoZQA1e9dxVflH3BddOu49zx53b3R/YaLcuXU/3kU9S88AJ2fT2+UaPI/dWvSDn+O7jTB3a/V6XUwNSZPqAX4byuMwl4EEgCfi0id3d/eJ3XnX1AO6r/+BPWXnopgQkTKLr/PqyE+M7PWd4S4qpFa3ivqo6js1L42+giMr0D56myUkrtjjj1Af0cOEJE6qP7ScCbIrJfT8bRGbt7fxURLnzzQuZsmMMf9v8DJww/oQuj650kHKbu7XeofvJJGr/4AuPxkHzMMaSfcTqBKVP0QbFSakDY1v11hwloX9GTCShA7etvUPqTn5C4//4U/usOjNfbY5+9NbYI95VU8MflZaR6XNw6qoDjstPiGpNSSvUFcUpAvxaRPXZU1ht0xf31s3WfEbJDHFRwUBdF1TvZDQ1seu55qh5+mFBpKZ78fNJOP420//s/3BkZ8Q5PKaV61C4PQmSMyQR+B+yPMw/oR8AfRKSyq4PsS1KOOZpI3e9Y/+vfUHrtteT/7W8Yjydu8VjGcHFhDvunJ3P1wjVcsGAVJ+akcdPIArK0NVQppXqbBmPMVBGZA2CM2RNoinNM3WbfwfvGO4RuFa6ooOqxx6l+6insmhoCU6eS+4vrSTr0UIzLFe/wlFKqV+lMZvIU8CGbB0c4C/gPcER3BdVXpJ96KtLUxIabbqb0J9eS/7e/xjUJBRifFOC1PUfxrzUb+NuqDXxcXcdNIws4KSdNX/lRSqne42rgGWPMOsAAecBpcY1I7bTg6tVU3ncfNS+8iITDJB95JBnnn0fClO3OpqOUUgNaZxLQQSLyh5j9PxpjOnWTNMYcA8wAXDiDINzS4bgPeATYE6gEThORVdGBFe7GmcfMxplk+/3OfGZPyzjnHBBhw8239Jok1GMZri7O45jsVK5ZuJZLvl3NC+XV3DqqkFxffGNTSikFIvKlMWYM0Dqa7WIRCW3vHNV7BFevZuNdd1Pz0ksYt5vUU/6PzPPOwztkSLxDU0qpXq8zCeibxpjTgaej+6cAb+zopOiIfv8CjsSZk+xLY8xLIhI7XPyFQLWIjIh+xq04T4B/CCAiE40xOcD/jDF7iYjd2S/WkzLOdUbz23DzLZReex35f/1L3JNQgDGJAV6eOpJ7Siq4bWUZB85cyM+HDuLcwVm4LW0NVUqpeIomnAviHYfqvI6JZ8YPziLjwgvx5AzIyQGUUmqXdGYe0B8CTwDB6PIU8CNjTJ0xpnY7500HlonIChFpPe+kDnVOAh6Obj8LHB6dp2wc8C6AiJQDm3BaQ3utjHPPJfcX11P35puUXnsdEuodD7LdluGyohze3WsMU5IT+dXSUo6ZvYQvaxriHZpSSinVJ4RKS1n3i1+y/LjvUPvaa2T84CyGv/Umub/4hSafSim1k3aYgIpIsohYIuKOLla0LFlEtjcJZj6wNma/JFq21ToiEgZqgExgLnBidBLtoTiv6BZ2/ABjzMXGmFnGmFkVFRU7+irdLuPcc8m5/udOEnrdT3tNEgowLMHHU5OHcd/4YqpCYU6Ys5SrF66hIth7YlRKKaV6k0hNDRtu+zPLjzlWE0+llOoinRoe1RiTDowE/K1lIvJhdwUFPACMBWYBq4FPgUjHSiJyD3APOMPEd2M8nZZ53nkAlN9yKyWRMPl//SuWr7Nzi3cvYwzH56RxaEYyf1+9gbvWlvO/jTX8bGge5wzOwqOv5SqlVI8xxkwCiom5F4vI83ELSLWxW1qofvwJNt59N3ZtLanf/S7ZV16BZ9CgeIemlFJ9XmemYbkIuAooAL4G9gE+Aw7bwamltG+1LIiWba1OiTHGDaQCleJMTnpNTAyfAkt2FGtvkXneeRi3hw1//CNrf3QJBXfcgSspMd5htUl0u7hh+GC+n5fBr5aW8KulpTxQspFfDR/EsVmpOlquUkp1M2PMA8Ak4BucwfbAmepME9A4Etum9tXXqPj73wmtW0figQeSc921+EeP3vHJSimlOqUzLaBXAXsBn4vIodFR+27qxHlfAiOjr9CWAqcDZ3ao8xJwLk5CewrwroiIMSYBMCLSYIw5Egh3GLyo18v4wVm4UpJZ94tfsub88ym8527c6enxDqudUYl+np48nLcqa/nD8nVcsGAVe6Uk8psRg9krtfckzEop1Q/tIyLj4h2E2qzpm2/Y8Ic/0vT11/jGjaXoj38gcb/94h2WUkr1O50ZhKhZRJrBmTZFRBaxedj4bYr26bwcZ8TchcDTIvKNMeZGY8yJ0Wr3A5nGmGXAT4Dro+U5wBxjzELg58DZO/OleovUE0+k4J//pGXxYlb/4GxC69fHO6QtGGM4KiuV9/Yaw19GF7K6uYUT5izlwgUrWd7YHO/wlFKqv/rMGKMJaC8Qqalh/Y03surU7xNcs4ZBN93E0Gef1eRTKaW6iXHedt1OBWP+C5yPM2n2YUA14BGR47o9up0wbdo0mTVrVrzD2KqGmTMpufQyXKmpFD1wP97i4niHtE0NkQh3r63gX2vKabZtTsnN4OohuQxN6B39WJVSqqsZY2aLSI+OtG6MORjnLaD1QAtgABGRST0ZR2f05vvr7hDbpub55yn/69+I1NSQfuaZZF95Ba6U7Y2vqJRSqrO2dX/dYQLa4SIH4/TTfD06tUqv0dtvkE0LvmHtD38IlkXRfffiHzs23iFtV0UwxB2ry3l43UZCIvxfbjrXDMnTRFQp1e/EKQFtffNnPpv7gCIiq3syjs7o7ffXXdG8cCFlv/sdzXPnEdhzT/J+fQP+MWPiHZZSSvUr27q/7vAVXGPMPsaYZAAR+QB4H5jS5RH2c4EJ4xny+GMYr5fVZ/2A+g+7cxDh3Zft9fD7kfnM3GccF+Vn81L5Jg6YuZArFq5mRWNLvMNTSqm+rkJEXhKRlSKyunWJd1D9nd3SQvnfb2flKacSKill8K23MOSxRzX5VEqpHtSZPqB3AvUx+/XRMrWTfMOGUfzUU3iKh7D2kkupfvLJeIe0Qzm+mES0IJtXyjdxwBcLuXDBSmbXNMQ7PKWU6qu+MsY8YYw5wxhzcusS76D6s8Y5X7HyeydTeffdpJ5wAsNffYXUk07Skd+VUqqHdWYUXCMx7+mKiB2dMkXtAk9uDsWPPkrptdex/vc3Ely9hpyfXodxueId2nbl+Dz8fkQ+Py7M4b6SCh5eV8mrFTVMT03k0sJsjspKxaU3caWU6qwATt/Po2LKdBqWbmA3NFB++wyqH3sM96A8Cu+9l6QDD4h3WGqAERGCIgRtocUWQmITtJ39ULQ8HLMOiRCKbscuESFm29lvW+OU2eK81x+J1rEBEbDZfMyO/mpvR8slpk7rdlvs0aX1e8SWd0brb4etD3vMFuWb9w3GWZvW/WhZzL5lTHQd3cfZaN02xmlhs4xpa2mzjHMsdm1i6lgm9roxZR2u54r5fJdpvZ6z3Xquq8NnuNqut/nzXSb2c5x9V2t80XJXtNxE67s61I+9Tl/TmURyhTHmSja3el4GrOi+kPo/KzGRgn/dwYZbbqXqoYcIlqwl/7bbsBIS4h3aDuX4PPxy+GCuGpLLk+uruHttBecvWMWwgI+LC7M5NTedRHfvTqaVUireROT8eMcwEDR8/gVlv/oVoXXrnEGGrrmmV83LrXqPkC3URSLUhyM0RGwaIjb1EWe7PmzTaNs0RmwaI5Ho2qbJtmmKCM227SwRocm2abFtmm2hxbZpia6b7c6PubK7WpOn2IQlNhEy0aTG2krSRszx1oRvc1l0m80Jz45Sn7bENbq1OZHtkNgibUlva7lTR9rt22xOnAUnkRZoS7Jb69vR7daEu7/bVsIamwxvL4F1sTmhdhnDv8YO6dZxXzqTgF4C/AO4Aedn/Q5wcbdFNEAYl4u8X/0Sb2EhG265hdXnnEvhnf/GnZ0d79A6JdHt4qKCbM4bnMWrGzdx55oKrl9Swh+Xr+OUvAzOHZzJ2KRAvMNUSqleyRjzIFtpQBCRC+IQTr9jB4NUzJhB1QMP4i0qYshjj5Kw557xDkt1s5AtVIfCVIbCVIXCVIcibApH2BQKUxOOUBOOUB2KUBvdro8423XhCE07kSAGLEPAZRGwLBJcFn7LWQIuQ7rHg99l4bcMPsvCF7s2Fl7LbF6i+x5j2tYey+A2zrbbMniNwWWcMpfBKTcGyxjchnbbmxPMvtci1t0kmqhGOiSmEm0hjsjmxNWOJsPtyyXacuwcj8jm5DcSe6xtO1qnw7Vaj9nRFmtb2pe3nhNbvrlVe3OLd+vnbq9+RITIdj6v/XU3X9sW5wFGd9phAioi5cDp3RvGwJVxztl4CgoovfZaVp5yKgX/mEFg8uR4h9VpbstwUk46J2anMae2kYfWbeTJskoeKt3I9NREzh2cyXey0/C7OtPdWCmlBoxXYrb9wPeAdXGKpV9pWbaM0p/+jJaFC0k7/TRyf/azPvGGkdq6FttmQ0uIDcEw61tCVARDVATDbAyFN28HnYSzLrLtti6PMaS6XaR5XKS6XaR7XBQFvKS4XCS7LVLcLpLdLpJcFomu1rVFkttFostJNBMsi4DL6pOvPA50Jua13R2326rutlPTsPRmfX2Y+OZFiyi5/ArCGzaQ++sbSP/+9+Md0i6rCoV5uqyKR9ZVsqKphQyPi+/mpPP9vAwmJwf0yZxSqleJxzQsW4nBAj4Wkf3iGcfW9JX7q4hQ/fgTlP/5z1iJiQz64x9JPuzQeIeltqPFtilrCVHSHKS0OURpS5DS5iDrWkKsbwmxIRiiKhTZ4jwDZHjcZHudJcvjJtPrJsMTu7hI97hJc7tIc7tIcFn6+4dSPWxb91cdTKiX8I8Zw9Bnn3EGJ/rNb2mev4DcX9+A5fXGO7SdluFxc0lRDhcXZvNxdT2PlVXyeFklD5RuZGSCj+/nZXBybjr5/r733ZRSqpuMBHLiHURfFd64kXW/+CUNH31E4sEHMfiPf+wzXVr6MxFhYyjMysYW1jQHWd0UZHVzC2uagqxuDlLWEtrinGyvm8E+D0MCXqanJpLn85Dr85DnddY50URTBz5Uqu/SBLQXcaWlUXjP3VTM+AeV99xD85LFFMyYgScvL96h7RLLGA7KSOagjGRqQmFerqjhmfVV/GlFGTetKOOA9CROyknnmKxUsrz6V1EpNXAYY+pw+oCa6Ho98PO4BtVHNcycSem112LX1jlvEJ15prZ09bCQLSxvamZpQwvLG5tZ1tjC8sYWVjS1UBNu34I52OehyO/loPRkCv1eCvweCvxe8n1eBvk82mVHqQFgh6/gGmPSgHOAYmISVhG5sjsD21l95RWhzqp9803Krv8FJhAg/+9/I3H69HiH1GVWNbXw7PpqnttQxcqmIC4D+6UlcXx2Gsdlp5Lt9cQ7RKXUANIbXsHtzXrr/VVsm8p776Nixgy8RUXkz5iBf/SoeIfVr9kirG4K8m1DE4sbmlnU0MzihmaWNzYTjvl1crDPw7CAj+EJzjI04KM44KPQ79UEU6kBZFv3184koJ8CnwPziRnJWEQe7uogd0dvvUHujpZlyyi5/AqCa9aQdemlZF16Ccbdf1oKRYRvG5p5uXwTL5dvYnlTCxawd1oiR2emcmRWCsMT/PEOUynVz/VkAmqMGSMii4wxU7d2XETm9EQcO6M33l8jmzax7ufXU//BB6Qcdyx5N/5Bp1fpYiFbWNrYzPy6JhbUNzK/rolv6pvaDfQzxO9ldKKfMYl+Rif6GZXoZ1jAp9OxKaWA3UtA54jIVm+UvUlvvEF2hUh9Axv+cCM1L75EYNqe5P/5z3gGDYp3WF1ORFjU0MzLFZt4taKGxQ3NAAwNeDkyM5UjMlPYJy0Rr6VPTpVSXauHE9B7RORiY8x7WzksInJYT8SxM3rb/bVp/nxKr7qaUEUFudf/XF+57QIiwrqWELNrG5lT28BXtY3Mq2tsm5okYBnGJQWYkBRgUnIC45ICjEr0kejSRFMptW27k4BeA9TjDBnf0louIlVdHeTu6G03yK5W8+KLrP/9jeDxMOiPfyDlyCPjHVK3WtPUwtuVtbxdWcsnm+ppsYVEl8X+aUkcmJ7MgRlJjE7w6y8dSqndpq/gbl9vur9WP/Uf1v/pT7izsyi4/XYCkybFO6Q+KSLCwvomPq9p4LNN9cyqaWBDMAyAzzJMSAowNSWBPZITmJicwPAEnw76o5TaabszCm4Q+DPwKzZPmi3AsK4LT+1I6kknEZg8mdJrr6P0iitpOON0cn/+cyx//3xFtSjg44KCbC4oyKYhEuGT6nrerqzlo+o63qysBSDH6+aA9GQOSE9iv7Qkhvi9mpAqpfoMY8x+bDm+wiNxC6gXk1CI9TfdxKYnnyLxwAMZfNutuNPT4x1WnxERYW5dI59W1/N5TQMza+qpDTuv0hb6vRyQnszUlASmpiQyPsmvbxsppbpVZxLQa4ERIrKxu4NR2+ctLqb4ySco//vtVD34II1ffsngm28mMHFivEPrVokuF0dlpXJUVioAa5uDfFRdx0dVdXxYVcfzG6oBJyGdnprI3qlJTE9LZHxiALelCalSqvcxxjwKDAe+BlqHCRVAE9AOwtXVlF51NY0zZ5Jx4QXk/OQnGH31c4dWN7XwQVUdH1TX8XF1fdtotCMTfJyUk84+qYnsnZZEgU6JppTqYZ1JQJcBjd0diOoc4/WS+/OfkbjffpTdcAOrTj+DzAsvJOvyH/fJOUN3RaHfy5mDMjlzUGZb39GZNQ3MrGngi5p6XqmoASDRZTEpOcAeyQnsEX2VqEhbSZVSvcM0YJzsqB/MANe8ZAkll/2YcHk5g2+9hdSTTop3SL1Wc8Tmk031vLmxhver6ljdHAScEWmPy07l4PRk9k9P0pHmlVJx15kEtAH4OjpgQmwf0F41DUtXCEVCzJgzg/MmnEdWICve4WxX0oEHMOzll9hwy61U3nMPde++MyBaQzsyxjA2KcDYpADn5js/s9LmIF9GE9Kvahu5v2QjwejveBkeF5OTE6KDKPgZnxRgaED7tiiletwCIA8oi3cgvVXdu++y7rqfYiUmMuTRRwhMnhzvkHqdimCItytreWtjLe9X19EYsQlYFgekJ/HDwmwOTk9mRIJPH7wqpXqVziSgL0SXfm9V7SqeXvI0M9fP5MFjHiTR07uHdHelpDD4pj+RcvRRlP3mt05r6EUXkfXjywZMa+jW5Pu95Pu9fDfX6R8UtG0WNjTzdW0jX9c18nVtIx9Vb2ibsyxgWYxJdJLRMUl+RiX4GZnoI8/r0Zu2UqpLGWNexnnVNhn41hgzk/YPd0+MV2y9hYhQdf/9lP/1b/jHj6fgX3fgyc2Nd1i9xrrmIK9UbOLl8hpm1TYgwCCfh1Ny0zk6K5X905J0rk2lVK+2w1Fw+4quGqXvw5IPufLdK5meN51/Hf4vPK6+8apKpLaWDbfcSs3zz+MdPpy83/yGxL2nxzusXqvFtlnS0Mw39U18W9+6bqI6HGmrk+yyGJnoZ2SCnxHRibSHJvgoDnh16Hml+pEenobl4O0dF5EPeiKOndGTo+BKJMKGP/2J6ieeJOW4Yxl00039drC9nVHWEuSV8hpertjEzJoGAMYn+TkuK42jslKYkBTQB6ZKqV5nd6ZhWcnm0W/biEivGgW3K2+QLyx7gV9/8mu+M+w73HTATVim7zxJrP/wQ9b//kZCpaWknHgCuT/7Ge6s3v06cW8hIlQEwyxpbGZJQzNLG1tY0tDMksZmKqLD07fK83ooDngpDvgo8HspbF0CXgZ5PTr4kVJ9SDymYTHG3CoiP99R2TbOTQPuAybg3J8vEJHPOtQ5BLgd8AAbReTgaPkxwAzABdwnIrfs6PN6KgG1m5oove6n1L/zDpkXXUj2T36CGcCjsdaGI7xcvoln1lfxeTTpHJfo58ScNE7ISWN4gibmSqnebXemYYk9yQ+cCmR0VWC90XdHfJeNTRuZMWcG2YFsrp12bbxD6rSkgw5i2Csvs/Huu6m8/wHq33uf7KuvIv3003XUwB0wxpDj85Dj83BAenK7Y3XhCKuaWljR1MKqxiArmlpY2dTCB9V1rG8JtXtC4zJOgjrY5yXP52Gwz8Mgn4dBfg+DvM71s71ubUVVamA7EuiYbB67lbKtmQG8LiKnGGO8QELswWiC+m/gGBFZY4zJiZa7gH9FP7sE+NIY85KIfLtb36QLhKuqWHvppTTPm0/ur28g46yz4h1SXERE+LCqjqfXV/G/jTU028KIBB8/Lc7jxJw0RiZq0qmU6vt2mICKSGWHotuNMbOB33RPSL3DhRMupLyxnIe+eYisQBbnjj833iF1mhUIkHP11aSeeBLr/3AjG/7wR2qe/y95v/2NTtq9i5LdLiZGJ+TuqMW2WdccYm1zkLXNQdY0ByltDrK+JcS39U28XVlDk73lmwaJLoscr5tcr4dMr5tMT3TxusmK2U5zu0jzuAlYRl+xUqqPM8ZcClwGDDPGzIs5lAx80onzU4GDgPMARCSIM193rDOB50VkTbROebR8OrBMRFZEr/UUcBIQ1wQ0uHo1ay6+mPD6DRT88x8kH3FEPMOJi1VNLTy2rpJn11ezPhgize3itLwMTsvLYEpKgv7br5TqV3aYgBpjpsbsWjgtop1pOe3TjDH8fK+fs7FpI3+Z9ReyA9kcN+y4eIe1U3zDhlL0wAPU/e9/bLj5FlZ9/zRSTjiBnKuvwpOfH+/w+g2fZTE0wekfujUiQk04wrqWEBtaQpQHw5QHQ1QEw2wIhigPhljS0ExlKEx1KLLl++5tn2PaktE0t4tkt4sUt4tkl0VqzH6SyyLJ7SLRZZHcuu9ykeCySHBZOuKvUvH1BPA/4Gbg+pjyOhGp6sT5Q4EK4EFjzGRgNnCViDTE1BkFeIwx7+MktjNE5BEgH1gbU68E2HtrH2KMuRi4GKCoqKgTYe2apnnzWPujS0CEooceJGHKlG77rN4mbAtvV9by8LqNvFdVh8vAYRkp/CEvn6OyUvAN4NePlVL9W2cSyb/GbIeBVcD3O3PxHfU1Mcb4cCbd3hOoBE4TkVXGGA9O/5ap0RgfEZGbO/OZXcllubj5wJupbq7mV5/8ilRfKvvn79/TYewWYwwpxx1H4kEHUXnvfVQ99BB1b7xBxrnnkHnxxbiSk3d8EbVbjDFO0uhxMy4psN26ERGqQmEqQ2Eqg2GqQhFqwhGqQ2E2hSNsaltHKG8JsbShmbqIUyfSyfHEfJYh0WURsJyENBDdDljOtt8y+F0WPsvCZxn80XXrvtcYPNF9rzF4LWdxG4Mnurit6Dpa12UMbgPuaJnbGCwDLpxjLgPWAEuMRQQbsAVshIhsLovEHmvbFiLRtbTWEYggiDj7kZhzIrHXitazhXblkS32N9dtO7aVOpEO14u0fX7MdrQ8vLVzd3COU7Zl3dZrhaN190hO4InJw+Py89sNEr3P/bjjAWNMRieSUDfOvfEKEfnCGDMDJ5H9dYc6ewKHAwHgM2PM5zsZ5D3APeD0Ad2Zczur4fPPWXvZj3FnZFB47z34hg7tjo/pdda3hHiirJLH1lWyriVEntfDdcV5nDU4g0G+gTuCvVJq4OjMK7iH7sqFO9nX5EKgWkRGGGNOB24FTsPpZ+oTkYnGmAScoeqfFJFVuxLL7vC5fMw4bAYXvH4BV793Nf8+4t/slbdXT4ex21xJSeRcczXpp59Gxe0zqLz3PjY9+xxZP/4x6ad9H+PpG6P99ncuY8j2epyJwndiFiARodG2qQvb1Eci1EfXDRGbunCE+ohNY+xi2zRGjzdHhGbbpi4SoTwYotkWmmybFtumxRZabLttypru5oompZYBQ2tiCla0DJxtY5zXMSxjaE1b29bRc7dWvi2tY7G1fk0BJLoXe0yi+xI9asvmuiKb67QmjU79zXXtaNJm7+KfT29jQdvDBSv6IMFtDFb0Z+c8XDBtP9e27dh1TF2fZbY4x4p5cNG63VpnaKBP/rL+BHA8TsulsPmvKNH9HQ3wVwKUiMgX0f1nad+S2lqnMtoq2mCM+RCYHC0vjKlXAJTuypfYXXXvvkfp1VfjHTKEwvvvw5OTE48wetSCukbuWlvBC+XVhAUOTk/mjyPzOSozVQeuU0oNKNtMQI0xPxCRx4wxP9nacRH52w6u3Zm+JicBv4tuPwvcYZyODgIkGmPcOE9vg0Dtjr9O90jxpnDPUfdw/uvnc/k7l3P3kXezR84e8Qpnt3gGDWLwrbeQfs7ZlN96Gxv++EeqH32UrMsuJeX443Wgoj7KGEOiyxUd2KjrHyaEbaFFnIQ0FE1KQyIEbSEYXYdsp4UqKELYFkLiLK0tV87itGSFbGFbLXF2TLnEtg6yufWvNRGMxCSJbYkgm5PGVrGJ5fZ+zYs95iSyreVOQttaZqJlbUlxNBFuXVymff3W4y5jsKLlVluivbl+63ErmtC1Jtku0z7xbq3nMk5Za6Lm2so5reWuaHnsvsuYtrqx19i833qdDslk2/fQX5p3logcH13vUnOfiKw3xqw1xowWkcU4rZwd+3C+iHM/dQNenNds/w4sAkYaY4biJJ6n4/QX7VE1r7zKup//HP+4cRTeczfu9PSeDqHH2CK8W1XHXWvK+XhTPYkui/Pzszg/P5th2+i2oZRS/d32WkBb21929R3NzvQ1aasjImFjTA2QiZOMngSU4Yzud83WXkvqqT4qABn+DO476j7Oe/08Lnv7Mu47+j7GZY7r1s/sToHx4yl6+CHq33+fittnsO7n17PxzrvI+vFlpBx3nCaiqh23ZXDjIlH/Wii1WzqMq7AFEZnTictcATweHQF3BXC+MeaS6Pl3ichCY8zrwDycBvf7RGRB9PMvB97A6RrzgIh8s+vfZudV/+dp1v/udyRMm0bBnXfiStqJVz36kKBt8+z6au5cW87SxhYG+TzcMGwQZw/OJNXT74fRUEqp7drhPKC7fGFjTsEZAv6i6P7ZwN4icnlMnQXROiXR/eU4SeponFECzwPSgY+AY1tbU7emp+YpK6sv47zXz6Mx3MgDRz/AyPSR3f6Z3U1sm7q332bjHf+iZckSvMOGkXXZZaQce4wmokqpfq8n5wE1xry3ncMiIof1RBw7o6vur5X3P0D5n/9M0sEHkz/jdix//5tSpMW2ebKsin+u3kBpS4gJSQEuKczmxJw0vDqokFJqgNnleUCNMdnAD4Hi2PoicsEOTi1lx31NWuuURF8VSsUZjOhMnDnOQkC5MeYTnNF3t5mA9pRBSYPaWkIvevMiHjrmIYam9u2BE4xlkXLUUSQfcQR1b77Fxn/9i3XXXcfGf/+bzAvOJ+XEE7G8fbKvlVJK9Sq7Oq5CXyYibPznP9n47ztJOe5YBt9yC6af3VOaIzaPlVXyrzXllLWE2Cslkb+MLuSQjGR9VV0ppTrozOO4F3ESw7eBV2OWHfmSaF+T6GtCpwMvdajzEtA6weYpwLviNMmuAQ4DMMYkAvvg9F3pFQpTCrn36HsBuOjNi1hbu3YHZ/QNxrJIOeZohr74Avl//xvG56Pshl+z7PDD2Xj3PURqauIdolJKqT7Ibmwi7dRTGPznP/er5LM5YnPP2nL2/vxbblhayhC/l2cmD+elqSM4NDNFk0+llNqKHb6Ca4z5WkT22KWLG3MccDub+5r8yRhzIzBLRF4yxviBR4EpQBVwuoisMMYkAQ8C43DG6HhQRP68vc/qqVdwYy2pXsIFb1yAz+XjvqPu6/MtoR2JCI2ffUbl/Q/Q8MknmIQE0k75PzLPPVfnEVVK9Rs9+QpuX9QV99fW3zX6S0IWEeHZ9dXctrKM0pYQ+6cl8ZPiXPZP16nNlFKq1bbur51JQP8IfCoir3VXcF0hHgkowOKqxVz81sUYDPcedW+/6BO6Nc2LF1P1wAPUvPoa2DZJhxxC+hlnkLj/fhjt16KU6sM0Ad2+eN1feyMR4e3KWv60ooxFDc1MTg7w6+GDOUATT6WU2sJOJ6DGmDo2z1qQCLQAoei+iEhK94W78+J5g1xRs4IfvvFDgnaQu4+8u0+PjrsjobIyqp98ik3PPUekshJPURHpp32f1JNP7tdD6Sul+q8eHoSoK0bB7VGagDpm1zTwh+Xr+LymgaEBL78YNpgTslP7TauuUkp1tV1uAe0r4n2DXFu7lovevIi6YB13Hnknk7Mnxy2WniDBILVvvUX1k0/SNGs2xusl+ZijSTv5ZBKmT9dWUaVUnxGnUXD9OIPrzcV5sDsJp3vKvj0Rx86I9/013spagvxheRnPb6gm2+vm2uI8zhqUicfSxFMppbZnW/fXHWYJxph3OlM20BWmFPLQMQ+R7k/n4jcvZtb6/n2zNl4vqd/5DsWPPcbQl14k7ZT/o/7d91hz3vksO/wIyv9+Oy0rVsY7TKWU6lVE5NDoSLhlwFQRmSYie+KMhdBxpHgVRy22zT9Xb2D/LxbxasUmrhmSy+d7j+W8/CxNPpVSajdsMwE1xviNMZlAljEm3RiTEV2KAR2BZisGJQ3iwWMeJC8xj0vfvpRPSj+Jd0g9wj9qFHm/+Q0jP/6IwX/9C74RI6i8915WHHccK087jaonniBcWRnvMJVSqjcZLSLzW3dEZAEwNo7xqBhvbazhkJmL+NOKMg5OT+bD6WP4+bBBJLp1bmyllNpd2+sDehVwNTAYWBdzqBa4V0Tu6PbodkJvekWoqrmKH731I5ZVL+P3+/+eE4efGO+QelyovJzal1+h5oUXaFm6FCyLhL32IuWYo0k+8kjcWVnxDlEppYD4DEJkjHkSaAAeixadBSSJyBk9GUdn9Kb7a3db3dTCL5eU8k5VLSMTfPxhZD6HZPSqIS+UUqrP2J1RcK8QkX92W2RdpLfdIOuD9Vz9/tV8UfYFV029igsnXDggByoQEVqWLKXujdepff0NgitWOMnotGkkH30UyYcdhmfQoHiHqZQawOKUgPqBS4GDokUfAneKSHNPxtEZve3+2h3CtnBPSQV/XlmGyxiuK87jgoIsvDqegVJK7bJdGQX3MBF51xhz8taOi8jzXRzjbumNN8hQJMQNn9zAaytf4/TRp3P99OtxWQP39R0RoWXpUupef4PaN94guHw5AL7Ro0k6+GCSDjmEwORJGNfA/TNSSvW8eE3DYowJAEUisrinP3tn9Mb7a1eaX9fItYvWMq++iaOzUrh5ZAGD/d54h6WUUn3etu6v7u2cczDwLnDCVo4J0KsS0N7I4/Jw84E3k5uQy4PfPEhFUwW3HHgLfrc/3qHFhTEG/6hR+EeNIvvKK2hZsYL6996n/oMPqLz/firvuQdXWhqJBx1I4n77kbjvvnhyc+MdtlJKdTljzInAnwEvMNQYswdwo4gMvD4bcdIYsfnrqvXctbacDI+be8cXc7xOq6KUUt1Op2HpIY99+xi3fXkbe+TswT8P+yepvtR4h9SrRGprafj4Y+o/+ID6Dz8iUl0NgHfYMBL33ZfEffchYfp0XCnaF0cp1bXi9ArubOAw4H0RmRItmy8iE3syjs7o7ffXXfHZpnquXriG1c1BzhyUwW+GDybNs71n8koppXbWTreAGmN+sr0LisjfuiKwgeIH435AVkIWv/zol5z12ln847B/MCx1WLzD6jVcKSmkHHccKccdh9g2LYsX0/DpZzR8/jmbnn+e6scfB8vCN2Y0CVOmkrDnVAJ77qktpEqpviokIjUdWtv6xxPhXqw5YnPryjLuWlvBkICXZ/cYzgHpyfEOSymlBpTtPe7Tf5G72DHFx5ATyOGa96/hB6/+gNsOvo0D8g+Id1i9jrEs/GPH4h87lswLL0CCQZrmzqXh8y9onDN7c0IKePLzCUydSmDSJAITJ+AbOxbL54vzN1BKqR36xhhzJuAyxowErgQ+jXNM/do39U38+NvVLGpo5pzBmfx2+GCdVkUppeJAX8GNg3X167jy3StZumkpP9nzJ5wz7hztc7ITJBSiedFimubMpnHOVzTOmU2kYqNz0O3GN2okgQkT8U+cgH/sOHwjR2hSqpTapji9gpsA/Ao4Klr0BvAHEWnpyTg6oy/dX7cmIsK/15Rz28r1pHtc/G1MEUdkancOpZTqbrs8DUtf0ddukI2hRn718a94e83bnDT8JH6z72/wunTUvV0hIoQ3bKBp/nya5y+gecF8mhZ8g11b61RwufAWF+MfPRrf6NH4x4zGN2IE7kGDMDrEvlIDXpwS0FNF5JkdlfUGfe3+Gmt1UwtXLFzDzJoGjs9O5dZRhWR6ta+nUkr1BE1AeyFbbO6aexd3zr2TydmTuf3Q28kKZMU7rH5BbJvQmjU0L1pMy5LFznrxYkKlpW11TCCAb+hQvMOH4xs+HO/wYfiKi/EUFmL5B+ZIxUoNRHFKQOeIyNQdlfUGffH+CvBqxSauWbQGEbh5VAH/l5uubxsppVQP2pVpWFQ3s4zFZXtcxvC04dzw8Q2c+vKp3HbQbeyVt1e8Q+vzjGXhLS7GW1wMxxzdVh6pq6NlyRJali0nuGI5LctX0DhrFrUvv9zufHdeHt4hQ/AWFeEdUoSnoABPfj6ewYNxZWToLzFKqV1ijDkWOA7IN8b8I+ZQChCOT1T9S4tt8/tl63igdCN7JCdw9/ghDAloNwyllOotdBTcXuDo4qMpTinm2g+u5aI3L+KKKVdwwYQLsIy+HtrVXMnJJOy5Jwl77tmu3G5ooGXFSoKrVxNcs5rQ6jUE16yh7t13iVRWtqtr/H48gwc7y6BBuHNz8eTl4s51Fk9uLlZKiiapSqmtWQfMAk4EZseU1wHXxCWifmRVUwsXf7OKeXVNXFyQzQ3DB+HVrhZKKdWrdGYU3NHAXsBL0f0TgJndGdRANDpjNP85/j/8/tPfM2PODGZvmM1NB9xEuj893qENCFZiIoGJEwhMnLDFsUh9PaHS0uiyzlmvW0eopITmhQu3SFABjM+HOysLd1YWrujanZWFKzMDd0YGrvQMXOlpznZaGsatLyMoNRCIyFxgrjHmCREJxTue/uSl8k1cu2gNljE8NGEox2TrfNtKKdUb7bAPqDHmQ+A7IlIX3U8GXhWRg3ogvk7rq31UOhIRnl78NLd+eSsZ/gz+cvBf2CNnj3iHpbbDDgYJl1cQLt9AeMMGQus3EK6oILyxgsjGjYQ3VhLeuJFIVdU2r2GlpuJKScGVmhpdUqJlqbiSk7CSkrGSk3AlJzvbSYm4EhOxoovxeHrwGyvVv8SpD+hI4GZgHNDW6VxEet0E0b39/hqyhd8vL+W+ko1MTUng7vHFFPp1UD+llIq33ekDmgsEY/aD0TLVDYwxnDbmNCZmT+Ta96/l/NfP54qpV3DuuHNxWTpfWW9keb14C/LxFuRvt56EQoSrq4lUbyJSXU2kusrZr6p29mtridTWYG+qIVRaSqSmhkhtLUQiO4zBeDxOMpqQgEkIYAUSsAIBTMDvbPv9zrbPj/H7nH2fH8vvw3h9GK8X4/NivF4sX3Tf68V4PFsubje4PRiPG+N2Y1z697IriW1DJIKIOOuIDbKVMjt2HQHb3mJNJLL5ejHnSCS8uV7Ha4UjiO2Uix2B2P1IpK1Ou3XrNdutI9HPjbS/bru1DeEwEokpiz0vum5XFg4j0dgDkyYx5KEH4/0j21UPAr8F/g4cCpwP6LuiO2ljMMzF36zi0031/LAgi18PH6yv3CqlVC/XmQT0EWCmMea/0f3vAg93W0QKgHGZ4/jPCf/hd5/+jr/P/jsflnzInw74E/lJ209yVO9lPB48OTl4cnI6fY6IIE1NROrqsetqidTVYdfXO0tDA3ZjY/t1QyN2U1N0acTeWEmoqQS7qQlpbsZuaUGam53kpMu+mIkmpU4yalwuaF273c5UNy7XFmssgzEWWFb7bWPAgME42zFlzscZYnacZdt/gO3XSNufKxItj1mEbZSLOH9mIojYYEeP2fbm/bbj0XI70lbeVqc1iYxNMm27LUns0p9LT2j9+UbX7X72W6wtjMvdtm47z+XCeNxYlgvcLufYVs4z7mhZTD1PYUG8/wR2R0BE3jHGGBFZDfzOGDMb+E28A+sr5tU1cv78lWwMhfnn2CJOzcuId0hKKaU6YYcJqIj8yRjzP+DAaNH5IvJV94alAFK8Kfz14L/y0vKXuHnmzfzfS//H9dOv56ThJ+kANwOEMQaTkICVkAC5nU9ct0dEIBRqS0YlGMRuCSLBFiQYRFpanP1wCAmFnLrBIBKK7ofDSCiMhMNOnXA42oplQyTstHS1toKFwx1a6mLWW0ngJJrEtUv+ImHnuBM8EpNUCtvoQiBszlFjk9WO6w7JrjFW+/LWZNfliu7HJsnGSaLaEmgD0fONy3K2XZZTJ7bcikm+W69rRRMtywWW1eF81+brtJ4bW98VTfysba1dbZ9rXNbmxK5jueVykrwt9mOu445JKrWVaXe1GGMsYKkx5nKgFEiKc0x9xnPrq7h28VoyPG5enDKSPVIS4h2SUkqpTursyCcJQK2IPGiMyTbGDBWRld0ZmHIYYzhpxElMy5vGrz7+Fb/+5Ne8v/Z9frPvb8jw69NetfOMMeD14vJ6ITl5xycopbrDVTj31iuBPwCHAefGNaI+IGwLf1yxjrvWVrBPaiL3Tigm26t94JVSqi/Z4SNsY8xvgZ8Dv4gWeYDHujMotaX8pHzuP+p+rt3zWj4s+ZCTXzyZ99a8F++wlFJK7QIR+VJE6kWkRETOF5GTReTzeMfVm9WFI5w9fwV3ra3ggvwsntljhCafSinVB3WmBfR7wBRgDoCIrIuOhKt6mMtycd6E89gvfz9+8dEvuPK9KzlqyFFcP/16shOy4x2eUkqpHTDGvAzbenccROTEHgynzyhtDnL2vBUsbmzmL6ML+cHgzHiHpJRSahd1JgENiogYYwTAGJPYzTGpHRiVPoqnvvMUD37zIHfPvZvP1n3GT6b9hJNHnoxltF+WUkr1Yn+JdwB9zfy6Rs6et5KGSIQnJg3n4Ax9Bq6UUn1ZZxLQp40xdwNpxpgfAhcA93Xm4saYY4AZgAu4T0Ru6XDchzPK7p5AJXCaiKwyxpwF/DSm6iRgqoh83ZnPHQg8Lg8XT7qYo4YcxY2f38jvP/s9Ly9/md/u91uGpfa6aeSUUkoBIvJBvGPoS97aWMOPvl1NutvFS1NHMjYpEO+QlOoxti3YYRs7ItgRIRLZvG1HbGxbEFs2l9mCtK7tzWuxnWu1jgAvEi1v3Rba9oF2x2gb9y96XNg8AGDMuxxtAwTuhC0G1IwdPDBmkPu2em1lZvMg+Ma0r9c6jmBruTFt+8SUd6zrDAjoDFtojAGrfV0TPd56DcvqcD0rpq7VoSw6UOFWy6wO5wyQQUZNZ/7CGGOOBI7C+avxhoi81YlzXMAS4EigBPgSOENEvo2pcxkwSUQuMcacDnxPRE7rcJ2JwAsiMnx7n9fbJ8ruTiLCC8te4C+z/kJTuIkLJ17IBRMuIODWG7VSSu3ItibK7ubPHAncDIwD/K3lItLrniDG6/76QEkFNywtZUJygEcnDiPXp/09VXzZthBqiRBqDhNsjhBqiRBuiRAKRreDEcJBe/N2yCYcsokEI4SCNpHW/bCzHQk7SzhkY4db952kMxJxkkQ1wLQlse0TU2tbCavVoc4WCa6JDt4fPWbFJtDRY23nbT6+7/eGk5zh33G8O/o627i/7rAF1Bhzq4j8HHhrK2XbMx1YJiIrouc8BZwEfBtT5yTgd9HtZ4E7onOixf4Xdwbw1I7iHMiMMXxv5Pc4sOBAbvvyNu6aexcvLHuBn+z5E44pPmbAPE1RSqk+5EHgt8DfgUOB8+nEwIADgYhw4/J13Lm2gqMyU7hz/BASXa54h6X6gUjEpqUhTHN9iObGEC2NYVoaQ7Q0RNdNYYJNYYJNkc3bzc461OwklDvD5bZwey1cHgu3x8LtdeH2RPe9Fr5ENy635SweC5fL4HJbWG4Llzu67TJYLmftitluK7cMpnXfak04NicbVofkojWRIZqwQPsWuq21ErbVgQ5zckfrt37hnfl1s2MjakxLa+u+OAXtpvJuna+7rYW2tVzat9o6h5zW39brir35nNb91m3bjjkv5lpiR9t77fatxbYtW1y33bYdc370mnaHMrE7ntO+NTr2uC0CtjjTkLeVtz/Xtls/J+Z8u/2+HRHEttt9j46fb9tCOBjZiR/mzuvMK7hH4oyCG+vYrZR1lA+sjdkvAfbeVh0RCRtjaoBMYGNMndNwEtUtGGMuBi4GKCoq2kE4/V9WIIvbDrqN00afxq0zb+VnH/6MJxc9yc+n/5zxmePjHZ5SSqnNAiLyTvSh62rgd8aY2cBvdnSiMSYNpyvMBJzf0S4Qkc9ijh8CvAi0Tpf2vIjcGD12DXBR9Lz5OHN7N3fVl9pdYVu4dvFa/rO+ivPzs/jjyHxc+hBVbYOI0NIYpqGmhabaII11QZpqQzTWBmmqc/ab60PO0uAknNvj9bvwBtx4A258ATcJKV7ScgJ4Am68fjdevwuPL7r4XXh8bmfb68Lts5y114Xb6ySbrQmeUqq9bSagxphLgcuAYcaYeTGHkoFPujuwaAx7A40ismBrx0XkHuAecF4R6omY+oI9c/fkye88yYvLX2TGnBmc8coZfHfEd7ly6pVkBbLiHZ5SSiloMcZYwFJjzOVAKZDUyXNnAK+LyCnGGC/OfKIdfSQix8cWGGPyceYdHSciTcaYp4HTgYd29Ut0paaIzSXfruKNjbX8tDiPnxTn6hs8A5jYQmNtkLrqZuqrWqiraqa+upmGTUEaa1poqGmhYVOQSHjLVknLMgSSPQRSvASSPKRkBfAnefAneghE175EN74ED74EN/4ED96AC8ulLyEo1RO21wL6BPA/nD4q18eU14lIVSeuXQoUxuwXRMu2VqfEGOMGUnEGI2p1OvBkJz5LdeCyXJw88mSOHHIk98y7h8cWPsbrq17n7HFnc+74c0nxpsQ7RKWUGsiuwkkcrwT+gPMa7rk7OskYkwocBJwHICJBILgTn+sGAsaYUPTz1+1U1N2kNhzh3Pkr+HxTAzeNzOeCAp1arL8TEZobQtRWNFO7sYnayiZqK5qo2dhMXWUT9dUt2JH2bQtun4ukNB+JqV7yhqWSmOojMc1HQqqXhGQvgRQvCSlefAG3M2CMUqpX6tQgRADGmBzaD5SwZgf13TiDEB2Ok2h+CZwpIt/E1PkxMDFmEKKTReT70WMWzuu5B7b2I92egTwIUWesrl3NHV/dweurXifFm8IFEy7gzLFn6kBFSqkBLx6DEO0qY8weOG/+fAtMBmYDV4lIQ0ydQ4DncLq+rAOua733GmOuAv4ENAFvishZ2/ic2C4ue65evbp7vhBQEQxx5twVLGxo4p9jh/C93PRu+yzV88KhCJs2NLFpQ+PmpdxZd3wlNpDiJTXLT3JmgOQMP8kZPpLS/SRFt70Bt7aKK9WHbOv+usME1BhzAvA3YDBQDgwBForIDjsVGmOOA27HmYblARH5kzHmRmCWiLxkjPEDjwJTgCrg9JhBiw4BbhGRfTrzBTUB7ZxFVYv451f/5MOSD8kKZHHxpIs5ZeQpeFw6uqBSamCK0yi4bwGnisim6H468JSIHL2D86YBnwP7i8gXxpgZQK2I/DqmTgpgi0h99D48Q0RGRj/jOZyxFTYBzwDPishj2/vM7ry/rmlq4fS5KyhrCXLfhKEcnqlv5/RVkYjNpg2NVK1r2LyUNVBT3kjsr5pJ6T7SchNIy0kgNSdAanaAlCxn8fh0sCml+pPdSUDnAocBb4vIFGPMocAPROTC7gl112gCunO+Kv+KGXNmMHvDbAYlDuK88edx8siT8bt3f8hlpZTqS+KUgH4lIlN2VLaV8/KAz0WkOLp/IHC9iHxnO+esAqbhvOZ7TOv92xhzDrCPiFy2vc/srvvrysYW/u/rZTREbB6bNIy9UhO7/DNU9wi1RKgsradiTR0b19ZRsbaeqnUNbf0xjYHUnAQyBic6y6DEtqRTk0ylBo5dnoYFCIlIpTHGMsZYIvKeMeb2rg8x/uqaQ/z8uXlcd9RohmV3diyIvmlKzhQePPpBPl33KXfNvYubZ97M3fPu5gdjf8BpY07TPqJKKdW9bGNMUWt3FmPMENpN6751IrLeGLPWGDNaRBbjdHOJnd6sNUndICJijJmOM71LJbAG2McYk4DzCu7hQFye3C5rbOaUr5YTFJvn9hjOhOStjaOkegM7YlNV1sCGlbVsWFnL+pW1VK9vaPvb6kt0k12YzMRDC8gqSCIz30k23R5NNJVSW9eZBHSTMSYJ+BB43BhTDjTs4Jw+aWN9kC9WVHHmvV/w9I/2pSizf98QjTHsn78/+w3ej9kbZnPfgvv4x1f/4IEFD3Da6NP4wbgf6Ki5SinVPX4JfGyM+QBn9rwDifa57IQrcO7HXmAFcL4x5hIAEbkLOAW41BgTxkk0T4/Or/2FMeZZYA4QBr4iOpJ8T1rc0MypXy8jIvDcHiMYm6RjEfQmLU1h1i+vYd3STaxfUUP56lrCQadl05/oIXdoCiOmZpNdlExWYTJJ6T7tl6mU2imdeQU3EWjGuUGehTNS7eMiUrndE3tYV70itLCsljPu/Zwkn5v//Ghf8tMG1o1xYeVC7l9wP2+uehOP5eGYocdw1tizGJc5Lt6hKaVUt+jpV3Cjg+ydArwLtI5z8LmIbNz2WfHTla/gLqxv4pSvl2MZeHaPEYxO1G4f8dZUH2Td0k1tS2VJPSLOVCZZRcnkDU0hN7qkZAU02VRKddou9wHtK7ryBjm/pIYz7/2czCQv//nRvuSmDLwb5KqaVTy28DFeWv4STeEmpuRM4cwxZ3L4kMPxWDpgkVKq/4hTH9BZfWXk3a66v35T38SpXy/DayyenTKcEQkD797aG4RDEcqW11CysIo131axcW09AG6PRe6wVAaPTGPwiFRyh6Xi8eprtEqpXbfTCagxpg7nDf/WR12tFQ0gItKrOgl29SAJs1dXc/b9XzA4LcBTF+9DVpKvy67dl9QGa3lx2Ys8sfAJSupLyAnkcOroU/neiO+Rm5gb7/CUUmq3xSkBvQXYCPyHmG4tnZxnu0d1xf11Xl0jp329nIDL4rk9RjA0YWDeU+Nl04ZGVs3fyJpvq1i3dBORkI3lMuQNS6VwbDr5ozPIGZKMy23FO1SlVD+iLaC74PMVlZz34EyKMxN56uJ9SEvwdun1+xJbbD4u/ZjHFz7Op+s+xTIW+w3ej5NHnswhBYfoNC5KqT4rTgnoyq0Ui4gM68k4OmN3768iwolzlrGuJcjzU0YwJKDJZ3ezIzZly2tYNW8jq+ZXsmlDIwDpeQkUjsugcGwGg0em4fV3ZigQpZTaNbvSAuoHLgFGAPNw5vEMb7VyL9Bdw8R/tLSCCx+exejcZB67aG9SA5poraldwwvLXuDF5S9S3lhOui+d44cfz3dHfJdR6aPiHZ5SSu2UeCSgfUlX3F83tIQIilDoH7gPcrtbOBRh7bdVLJtTzur5lbQ0hrFchvzR6RRPzKJ4YiYpWQNrXAulVHztSgL6HyAEfAQcC6wWkau6Ncrd0J3zgL67aAM/enQ2Y/JSeOSC6aQn6g0UIGJH+HTdp/x32X95b+17hO0wI9NHcmzxsRwz9BgKkwvjHaJSSu1QnFpAE4CfAEUicrExZiQwWkRe6ck4OkPn2e69YpPOlXM3EmqO4EtwM3RSFsWTsigcl6GtnEqpuNmVBHS+iEyMbruBmSIytXvD3HXdfYN8d9EGLnlsDsOyEnn0wr3JTtZXiGJVNVfxv5X/4/WVr/N1xdcATMyayDHFx3B08dHaX1Qp1WvFKQH9DzAbOEdEJkQT0k9FZI+ejKMzNAHtXWxbKF1czeLP17NibkVb0jlsj2xG7JlD/ph0XC7ty6mUir9dSUDnxCacHfd7m564QX6ybCMXPTyLQWl+nrhoH/JSdQS/rVlXv443Vr3B/1b+j4VVCwGYlDWJQ4sO5dDCQxmWOkyHcVdK9RrxHAXXGPOViEyJls0Vkck9GUdnaALaO1Svb2DRZ+tZMnM99dUteANuhk1xks4CTTqVUr3QriSgETaPzGeAANDIABkFd1u+XFXF+Q9+SUail8cv2pvCjIRu/8y+bGXNSt5a/RbvrXmPBZULAChKLuLQwkM5tOhQJmdPxm3p60FKqfiJUwL6KXA48ImITDXGDAeeFJHpPRlHZ2gCGj8tTWGWfLGeRZ+VUb66DmMZisZlMHqfPIZOysKt06QopXoxHQW3C81du4lzHphJgtfF4xftzbDspB753L5uQ8MGPij5gHfXvsvMspmE7BBJniSm501n//z92XfwvtpvVCnV4+KUgB4F/AoYB7wJ7A+cLyLv9WQcnaEJaM+rWFPHgg9KWPLlBsJBm8z8JMbsm8fIvXJJTNUuQEqpvkET0C727bpafnD/F1jG8NhF0xmT16sahHu9+mA9n5V9xqfrPuXT0k9Z17AOgMLkQvYbvB/T8qYxLXcaWYGsOEeqlOrv4jUKrjEmE9gH582iz0VkY0/H0BmagPaMUDDC0i838M2HpZSvrsPtsRg5PZcJB+WTM0R/x1BK9T2agHaDZeV1nHXfFzQGI9xz9jT2HZ7Zo5/fX4gIa+rW8EnpJ3y27jNmrp9JY9iZs6w4pZg9c/dkz9w9mZY7jUFJg+IcrVKqv4lTC+g7InL4jsp6A01Au1d9dQvz31/LNx+to6UxTPqgRCYcNJjRe+fhS9Cp35RSfZcmoN2kdFMT5z4wkzWVjfz1+5M5YfLgHo+hvwnbYRZVLWLW+lnM3jCb2eWzqQvWAZATyGFi9kQmZk1kUvYkxmeOJ8Gj/XCVUruuJxPQ6BzbCcB7wCE4rZ8AKcDrIjKmJ+LYGZqAdo+KtXV8/fYaln1ZjogwbEo2kw4tYNCINB2oTynVL2zr/qqjv+ym/LQAz16yLxc/MpsrnvyKDbXNXHTgsHiH1ae5LTcTsiYwIWsC5004D1tsllYvZfaG2czbOI/5FfN5Z807AFjGYnjacMZljGNs5ljGZIxhdPpokrzaL1cp1Sv9CLgaGIwzDUtrplEL3BGnmFQPERFWL6jk67fXUrq4GrfPxYRD8pl8WCEpWYF4h6eUUj1CW0C7SHMowk+e/prX5q/ngv2HcsN3xmJZ+gSzu1Q3VzN/43xnqZjPwqqFVDVXtR0vTC5kTMYYRqaPZETaCIanDqcwpRCPpa8zKaXai9MruFeIyD978jN3Vbzvr/2B2MKKuRXMem0VG9fWk5jmY9JhBYw/YLC+ZquU6re0BbSb+T0u7jhjKn9I+ZYHPlnJhtpm/vr9yfg9OkR6d0j3p3NQwUEcVHAQ4DxVrmiqYFHVorZlYeVC3l79NoLzkMVtuSlOKWZE2giKU4sZkjKE4pRiilKKSPHqAA9KqZ4jIv80xuwHFBNzLxaRR+IWlOpyti0sn1POrNdWUbWugdScAIedM5ZRe+fqvJ1KqQFLE9AuZFmG354wnvy0AH98dSFlNU3cdfae5CT74x1av2eMISchh5yEnLakFKAp3MTKmpUs37ScZZuWsWLTChZsXMCbq9/EFrutXoY/gyEpQyhIKiA/OZ/8JGcpSCogJyEHl6UPEpRSXccY8ygwHPgaiESLBdAEtB+wbWHplxuY/b9VVK9vJD0vgSMvGMeIPXOwNPFUSg1wmoB2g4sOHMbgtADXPj2Xk+74hHvOnsbEgtR4hzUgBdwBxmWOY1zmuHblwUiQkroSVtWuYk3tGlbVrmJ17Wq+3PAlr6x4pa3VFJyW09yEXHITcslLzCMvMa9tOzchl8xAJpmBTH29Vym1M6YB46S/9INRQLSP5/xKPnthOVXrGsjMT+ToH05g+JRsjHbLUUopQBPQbnPcxEEMyUzg4kdmc8pdn3LbKZM4aY/8eIelorwuL8PShjEsbcsBo0KREGUNZZTUl1BaX0ppXSllDWVsaNzA3Iq5vLn6TcJ2uN05BkO6P52sQBbZgWwyA5lk+DPI8GeQ7k9vt53qTSXRk6ijHCo1sC0A8oCyeAeiukbZsk189sJyypbVkJoT4KiLxjNiao4mnkop1YEmoN1o/OBUXrx8fy57bA5XPfU1i9bXcd1Ro3HpzahX87g8FKUUUZRStNXjtthUNVexoWED5Y3lVDRVsLFpo7NudNYralZQ2VRJ0A5u9Rpu4ybFl0KaL41UXyqp3lSSvclbLEmeJJI8SSR6E0l0J5LkTSLBk0CiO1FfC1YDkogQkQgRiSAi+N19totDFvCtMWYm0NJaKCInxi8ktSsqS+v5/MUVrJq3kYQULwefOZqx+w/SPp5KKbUNmoB2s6wkH49dtDe/e/kb7nx/OYvX13H76XuQ4tfXNfsqy1hkBbLICmQxnvHbrCciNIYbqWqucpamKja1bKKmpYaaYM3m7ZYayhrKWLppKbXBWuqD9e1eAd4Wr+UlwZNAwB1ot/jdfvwuv7OObvtcPnxuHz6XD6/lxevyOmUuHx7Lg8flwevy4rE8eC0vHpcHj+XBbblxW+52227jrF3GpUnwThIRbLGxsZ212G0JVet+7HbsfmvCFbtv2+2Pdzw/Ym/9WNgOb/Ocjsda922xCUu47brbu2br8dbtrZ0be3xnzontuz0lZwqPHNtnu0z+Lt4BqN3TVB/ki5dW8u1HpXh8LvY+aRiTDyvE49N/F5VSans0Ae0BXrfFTd+byNhBKfz+pW846Y5PuOPMKYwfrP1C+zNjDImeRBI9iRQmF3b6PFtsGkIN1AXrqAvW0RBqaLfUh+ppDDXSFG6iMeys27ZDTVQ3V9MSaaEp3ERzuJmWSAvN4WbCEt7xh+/sd8Tgsly4jRvLWLiMC8ty1i7jwjLW1hcsjDHOgsEyFobN+yY6NWLrvvP/pt3ndhSbtLdty+ZtEaHt/6Ld7lq3O5bbYrc71pqAtR6LPd663TGhbC1rTRxb6/U1rT+z1p/p1n7Gbsvdvo7Vvm7r3w+vy4vL7dpmPZdxbXGt1ut3rO+yXOQl5sX7j2eXicgH8Y5B7Ro7YrPgw3XMfHkFweYIEw4pYPp3huJP0gfLSinVGd2agBpjjgFmAC7gPhG5pcNxH86If3sClcBpIrIqemwScDeQAtjAXiLS3J3xdrez9xnC6NxkrnhyDt/796f89oRxnDm9SPsCqnYsY7W9gtuVInaEoB0kGAnSEmmhJdJCMBIkZIcIRoJt2637YTtMyA4RtsOEJUwo4my3tkSF7FBby1RrecdWt9YyEXFa/ezNrX+xSVnHZA6iiaPQLjFsK4/Z3lZi2vrfVWsC27q9RaJr2pdbxmr7ObSWW5a11UQ5NqluOx69hsu4nG2stvKtJeWt523tWGxZa5JmsbncZbm2qNtubW1Z7jbuduXtjsUkf7HfQXUdY0wdbPVJhAFERHROqF6sZFEVHz29lKp1DRSMSeeA748kc3BSvMNSSqk+pdsSUGOMC/gXcCRQAnxpjHlJRL6NqXYhUC0iI4wxpwO3AqcZY9zAY8DZIjLXGJMJhLor1p40fWgGr115INc8PZdf/XcBX6yo4qaTJ5Lk08Zo1b1clouA5bymq5SKDxHp2idLqkfUVTXzyTNLWf5VBcmZfo790USG7pGlD2iUUmoXdGfWMx1YJiIrAIwxTwEnAbEJ6Els7gfzLHCHcf41PwqYJyJzAUSkshvj7HGZST4eOm8v7vxgOX99czHzS2v415lTGTdYH3wrpZRSvYVtCws+KOHzF1YgtrD3iUPZ44gi3F7t56mUUruqO4doywfWxuyXRMu2WkdEwkANkAmMAsQY84YxZo4x5mdb+wBjzMXGmFnGmFkVFRVd/gW6k2UZfnzoCJ784T40tIT53r8/4dHPV7d71VAppZRS8bGxpJ7nbpvNR/9ZyqARqZzx272ZdtxQTT6VUmo39dYxwt3AAcBZ0fX3jDGHd6wkIveIyDQRmZadnd3TMXaJvYdl8tpVB7L3sEx+/cICzn3wS9bX9OmurkoppVSfFQ5G+Oy/y3nmpi+pq2ziyAvHcfzlk0nJ0u4LSinVFbozAS0FYof+LIiWbbVOtN9nKs5gRCXAhyKyUUQagdeAqd0Ya1xlJfl4+Py9+MNJ45m5spKjb/+Ql+aui3dYSiml1IBSuqSaJ/8wkzlvrGb0Pnmc+bt9GLVXnvb1VEqpLtSdCeiXwEhjzFBjjBc4HXipQ52XgHOj26cA74rzDuobwERjTEI0MT2Y9n1H+x1jDGfvW8z/rjqIYdmJXPnkV1z+xByqG4LxDk0ppZTq18KhCB8/u5QX/v4VACddM4XDzhmLP1GnVlFKqa7WbYMQiUjYGHM5TjLpAh4QkW+MMTcCs0TkJeB+4FFjzDKgCidJRUSqjTF/w0liBXhNRF7trlh7k6FZiTzzo325+8MV3P72Er5YWcUtJ0/k8LG58Q5NKaWU6nfKV9fy9oPfUr2+kQkH57PfySPw+LSfp1JKdRfTXwa9mTZtmsyaNSveYXSpb9bV8JP/zGXxhjq+M3EQvz1hHDkp/niHpZRS/YoxZraITIt3HL1Vf7y/AkQiNrP/t5pZr60iIcXLYeeMoWhcZrzDUkqpfmNb91edfLIXGz84lZevOIB7PlzOP95dxodLKvjZsWM4a3oRlqX9UZRSSqldUb2+gbcf/Jby1XWMmp7LgaeN0tdtlVKqh2gC2st53RaXHzaS4ycN5lcvzOfXLyzg+Tkl3HzyRMbk6byhSiml1M5Y/HkZ7z+5BJfbcPQPJzBiz5x4h6SUUgNKb52GRXVQnJXIYxfuzd++P5nVlY0c/4+Pufm1hdQ1h+IdmlJKqR5kjEkzxjxrjFlkjFlojNm3w/FDjDE1xpivo8tvOntufxZsDvPOQ9/y9kMLySlK5vQbpmvyqZRScaAtoH2IMYaTpxZw6Ogcbv7fQu7+cAXPzSnhuqNGc+q0Qlz6Wq5SSg0EM4DXReSU6CjzCVup85GIHL+L5/Y7G0vqeOPeb9hU3si07xSz13HFWC59Bq+UUvGg//r2QemJXm47ZTIvXb4/xZmJXP/8fI7/58d8trwy3qEppZTqRsaYVOAgnFHkEZGgiGzq7nP7KhFhwQclPHvLbILNYU66egp7nzBMk0+llIoj/Re4D5tUkMYzl+zLHWdOobYpxBn3fs6PHp3Fqo0N8Q5NKaVU9xgKVAAPGmO+MsbcZ4xJ3Eq9fY0xc40x/zPGjN/JczHGXGyMmWWMmVVRUdE936SbhVoivPXAt3zw5BLyR6Vx2q+mUzA6Pd5hKaXUgKcJaB9njOH4SYN559qDue6oUXy0dCNH/O0DfvH8fMpqmuIdnlJKqa7lBqYCd4rIFKABuL5DnTnAEBGZDPwTeGEnzgVARO4RkWkiMi07O7vrv0U3q6lo5LnbZrN01gb2PmkYx18+mYQUb7zDUkophSag/Ybf4+Lyw0by/nWHcObeRTw7ey0H//l9fv/yN1TUtcQ7PKWUUl2jBCgRkS+i+8/iJJVtRKRWROqj268BHmNMVmfO7Q9Wf1PJMzfPor66meMvn8y0Y4sxOkaCUkr1GpqA9jM5KX5uPGkC7113CN/bI59HPlvNQbe9x62vL2JTYzDe4SmllNoNIrIeWGuMGR0tOhz4NraOMSbPGGOi29Nx7vWVnTm3LxMRZv1vFa/cMZekdD+n/mIvhozPjHdYSimlOtBRcPupgvQEbj1lEj86eBgz3lnKXR8s55FPV3HWPkO4YP+h5KX64x2iUkqpXXMF8Hh0FNsVwPnGmEsAROQu4BTgUmNMGGgCThcR2da5PR59Nwg2h3nn4YWs+KqCkXvlcugPxuDxueIdllJKqa0wm+9Jfdu0adNk1qxZ8Q6j11q8vo5/v7+Ml+euw2UZTp5SwMUHD2N4dlK8Q1NKqbgyxswWkWnxjqO36u3317qqZl7911yqyhrZ7+ThTD68kGgDsFJKqTja1v1VW0AHiNF5ycw4fQrXHTWaez9awX++XMvTs9dy9Lg8fnTwMKYU6ciASiml+pYNq2p59d/ziAQjHH/5JIrG6Su3SinV22kCOsAUZiRw40kTuPLwkTz86Soe/nQVr3+znskFqZy9bzHHTxqE36OvLSmllOrdln9VztsPfEsgxct3r55CxuCtziijlFKql9FBiAaorCQf1x41mk9/cTg3njSe+pYw1z0zl31vfodb/reIkurGeIeolFJKbUFEmPPGal6/ewGZBUmc8vNpmnwqpVQfoi2gA1ySz805+xZz9j5D+HR5JY98top7PlzOPR8u57AxOZw6rZDDxuTgcemzCqWUUvEVidh88MRiFn5SxohpORx+zljcXn1rRyml+hJNQBUAxhj2H5HF/iOyKN3UxBNfrObpWSW8vbCczEQv352Sz6nTChiTlxLvUJVSSg1AweYwr9+zgLXfVjHtuGKmHz9U5/dUSqk+SBNQtYX8tAA/PXoM1xwxig+WVPDMrBIe+WwV93+8kon5qfzf1HyOmzSInGSdykUppVT3a6oL8sodc6lYW8+hZ49h3P6D4x2SUkqpXaQJqNomt8vi8LG5HD42l6qGIC9+Xcozs0r43cvfcuMr37L30EyOnzyIY8bnkZnki3e4Siml+qHayiZe/sdc6qqaOfaSiQydlBXvkJRSSu0GnQdU7bQlG+p4Ze46XplXxoqNDbgsw77DMvnOpEEcPjZHW0aVUn2KzgO6ffG8v1aW1vPyP74mHLI57rJJDB6RFpc4lFJK7TydB1R1mVG5yfzkqNFcc+QoFpbV8ep8Jxn9xfPzAZhcmMYRY3I4YlwuY/KSdUJwpZRSO23dsk289u95uD0W37t2Kpn5SfEOSSmlVBfQBFTtMmMM4wanMG5wCtcdNZqFZXW8s3ADby8q569vLeGvby0hPy3A4WNzOHBkNvsMyyDZ74l32EoppXq5VfM28vq9C0jO8HPClZNJyQzEOySllFJdRBNQ1SVik9ErDh9JeW0z7y4q5+2F5Tw9ay2PfLYal2WYUpjG/iOyOHBkFpML03R6F6WUUu0sn1POm/d9Q1ZhEsdfPplAsjfeISmllOpCmoCqbpGT4uf06UWcPr2IlnCE2aur+WTZRj5eupF/vLuUGe8sJcnnZuqQdPYaks5eQzPYozANv0fnc1NKqYFqycz1vP3QQnKLUzj+isn4AvprilJK9Tf6L7vqdj63i/2GZ7Hf8Cx+ejRsagzy2fJKPlm+kS9XVvPXt5YA4HVZTCxIZVpxOlMK09mjMI28VB3QSCmlBoJvP1nHe48tIn9kGsddNgmvX39FUUqp/qhb/3U3xhwDzABcwH0ickuH4z7gEWBPoBI4TURWGWOKgYXA4mjVz0Xkku6MVfWctAQvx04cxLETBwFOQjprVTVfrqriy1VVPPDxSkKRFQDkpviYXJDG5MI0JhekMX5wCumJ+jqWUkr1Jws+LOWDJxZTOC6DYy+ZiMerb8MopVR/1W0JqDHGBfwLOBIoAb40xrwkIt/GVLsQqBaREcaY04FbgdOix5aLyB7dFZ/qPdISvBwxLpcjxuUC0ByK8G1ZLXPXbmLu2k3MK6nhzW83tNUflOpn7KAUxg5KZtygVMYOSmZIZiIuS0fbVUqpvmbuO2v5+JmlFE/K4ugfjsetXTGUUqpf684W0OnAMhFZAWCMeQo4CYhNQE8Cfhfdfha4w+icHQOe3+NialE6U4vS28pqGkPMK93EwrJavl1Xy8KyOj5YUkHEduax9bothmUlMjI3mRHZSYzMTWJEThJDMhPwufWXGaWU6o3mvLmaz55fzvAp2Rx54Xhcbh2YTiml+rvuTEDzgbUx+yXA3tuqIyJhY0wNkBk9NtQY8xVQC9wgIh91Y6yql0tN8HDgyGwOHJndVtYcirCsvJ5vy2pZVl7P0g11fL22mpfnrmurYwwMTg1QnJVAcWais2QlUpgRoCA9gSSf9jFSSql4+PrtNXz2/HJGTsvhiPPHYemo6EopNSD01t++y4AiEak0xuwJvGCMGS8itbGVjDEXAxcDFBUVxSFMFU9+j4sJ+alMyE9tV94YDLOiooFl5fWs3NjAqsoGVlU28ur8MjY1htrVTUvwUJAeoCAtgYL0AIPSAgxK9ZOX6mdQqp/sJB9u/aVIKaW61Pz3S/jk2WUMn5qtyadSSg0w3ZmAlgKFMfsF0bKt1SkxxriBVKBSRARoARCR2caY5cAoYFbsySJyD3APwLRp06Q7voTqexK87q0mpuAMeLRyYwMl1U2UbmqipLqRkuomllXU8/6ScppDdrv6loHsZB+5KU4ymp0csyT5yEzykZHoISPRR1rAg6X9UJVSaru++aiUD59awtDJWRx54XhNPpVSaoDpzgT0S2CkMWYoTqJ5OnBmhzovAecCnwGnAO+KiBhjsoEqEYkYY4YBI4EV3RirGiDSErxMKfIyJaZ/aSsRoaYpRFlNM+trmqPrJspqmimva6Gsppl5pTVU1rdgb+Vxh2Wc62ckeslI8JKa4CE14CEt4CEtwUNqgpcUv5uUgIcUv5tkv4fk6DrR60K7Pyul+ruFn5bx/uOLGTIhk6MvmoBLk0+llBpwui0BjfbpvBx4A2calgdE5BtjzI3ALBF5CbgfeNQYswyowklSAQ4CbjTGhAAbuEREqrorVqUAjDGkJXhJS/AydlDKNutFbKGqIUh5XTNVDcF2S2VDkKr6IJuagqytamRBU4iaphCNwch2P9sykOh1k+hzk+BzkeRzR/ddBLxuEjwuAl4XCdHF74ldLPzuzds+twuv28LnttqtvW4Lj2VpK20fJiJEbCEigm3jrEWw7fblYdtuOx6xnToRe/N22N7ynEj0OmF7y3Na60W2dywi7a8Rs922FiEc2fJa4cjmuCIdl46fFVM+YXAq/zprarx/LKqTFn+xnncfXUjh2HSO+dEEXB5NPpVSaiDq1j6gIvIa8FqHst/EbDcDp27lvOeA57ozNqV2lcsyba/hdlZLOEJNY4ja5hC1zWHqmsPUNYeoaw5T2xSiviVMfUuYhpYwDS2Rtu3STSGagmEagxGaghEaQ5G2kX93ldsyeFwWHpfB67ZwWxYuy+BxGdwuq+2422VwWwaXZdrquCyDZQwuy/lzMMbgMia6DS7jHLcsJ6G3DBii62gLr2Wcuq1psIk5Ztr+Zztky00RQcTZd9bOftuxmHJbiB5zEq/WurbEbjv17OhF7dZET5zrtR6L2LH1o+WtCVpbXSFix5Y757QmUiKtSRYx2zF1WhNM2fydeju3ZbCs6N8fY3C5Nv89af075N5WmbW53Ove/PfOHVPHMs7+sOykeH9V1UnLZpfzzkPfkj8qjWMvnaRTrSil1ADWWwchUqpf8bld5KS4yEnx79Z1RIRgxKY5aNMcjtAcitAcsmkJO+vmUIRg2KYlbBOMxGyHbYIRm1BYCEYihCLSVhaO2IQjQsgWIrZNKCJOWUxrU3Mo0m6/rQUsJjmy7fbJWeu6NQG0bWfdmtBFNzcnimw7wRLa56Wxbyub6JHWpNYY05bItu3HbFvRg05S7JzfetyKJjmWcRLl1mu0Jkgm5piJrp1jm6/tjiZNrfuu6Hkdk3bLbP48l3ESNpdF9PM3J/pWTEJmdUjYYuu7okmfK5qcxV6vNalrTQq3fk1wWVa0Hu2Svo5J49auE/sZSsUSERZ/Xkbe8FSOu3QSHq8mn0opNZBpAqpUH2KMwed24XO7SMUT73CUUmqHjDEcffEE7Ijg9euvHUopNdDpnUAppZRS3crtcaHPzJRSSgHoCABKKaWUUkoppXrE/7d3r6GWlXUcx78/xhkcLLIckWgytYZEy44XwqkQnUgsQyNFiwKJoAsRFll0gTLFF5V0xYQyG6ObYpkm5oWcyhelOTk16iSWjqSok5WVIZrNvxf7Ocxp8DJn2metOWt9P3DYaz177bWe39nP4X+evdde2wmoJEmSJKkTTkAlSZIkSZ1wAipJkiRJ6oQTUEmSJElSJ5yASpIkSZI64QRUkiRJktQJJ6CSJEmSpE44AZUkSZIkdcIJqCRJkiSpE6mqvvswFUn+DNwzpd2tAB6a0r4Wg7HlhfFlHlteGF/mseWF6WV+UVXtPYX9DNIU66tjdPjGlhfGl3lseWF8maeZ90nr62AmoNOU5OaqOqLvfnRlbHlhfJnHlhfGl3lseWGcmRezMT5fY8s8trwwvsxjywvjy9xFXk/BlSRJkiR1wgmoJEmSJKkTTkCf3Nf67kDHxpYXxpd5bHlhfJnHlhfGmXkxG+PzNbbMY8sL48s8trwwvswLntfPgEqSJEmSOuE7oJIkSZKkTjgBlSRJkiR1wgnoHEmOS3JHkj8k+Wjf/VkISS5MsiXJrXPanpfkuiR3ttvn9tnHaUrywiTrktye5LYkp7f2IWfePclNSX7bMn+6te+f5MY2vi9Osqzvvk5TkiVJbklyZVsfet7NSTYm2ZDk5tY25HG9Z5JLk/w+yaYkq4ecd2iGXl/HVlthfPXV2mptHdqYntVHfXUC2iRZApwHvB44CHhrkoP67dWCWAsct13bR4GfVtUq4KdtfSieAD5UVQcBRwLva8/rkDM/BqypqlcAM8BxSY4EPgN8oapeAvwNeGd/XVwQpwOb5qwPPS/AMVU1M+f7uoY8rr8EXF1VBwKvYPJcDznvYIykvq5lXLUVxldfra0TQ88L46qt0EN9dQK6zSuBP1TVXVX1OPB94MSe+zR1VfUL4K/bNZ8IXNSWLwLe1GWfFlJV3V9Vv2nL/2TyR/UChp25quqRtrq0/RSwBri0tQ8qc5KVwPHABW09DDjv0xjkuE7yHOAo4BsAVfV4VT3MQPMO0ODr69hqK4yvvlpbra1teVCZ+6qvTkC3eQHwpznr97a2Mdinqu5vyw8A+/TZmYWSZD/gUOBGBp65nTKzAdgCXAf8EXi4qp5omwxtfH8R+Aiwta3vxbDzwuQfn2uTrE/yrtY21HG9P/Bn4JvtVLALkuzBcPMOzVjr62jG51jqq7XV2srAxjQ91VcnoPofNflensF9N0+SZwE/AD5QVf+Ye98QM1fVf6pqBljJ5N2HA/vt0cJJ8kZgS1Wt77svHXtNVR3G5LTG9yU5au6dAxvXuwGHAedX1aHAv9judKCB5dXADHl8jqm+WltHYUy1FXqqr05At7kPeOGc9ZWtbQweTPJ8gHa7pef+TFWSpUyK43eq6oetedCZZ7XTKNYBq4E9k+zW7hrS+H41cEKSzUxO7VvD5PMMQ80LQFXd1263AJcx+WdoqOP6XuDeqrqxrV/KpGAONe/QjLW+Dn58jrW+WlsHmRcYXW2FnuqrE9Btfg2salf3Wga8Bbii5z515QrgtLZ8GnB5j32ZqvZ5hW8Am6rq83PuGnLmvZPs2ZaXA69j8tmcdcDJbbPBZK6qj1XVyqraj8nf7fVV9TYGmhcgyR5Jnj27DBwL3MpAx3VVPQD8KclLW9NrgdsZaN4BGmt9HfT4HFt9tbZaW9tmg8rcV33N5F1VASR5A5Pz3ZcAF1bVOf32aPqSfA84GlgBPAh8CvgRcAmwL3APcEpVbX8xhUUpyWuAG4CNbPsMw8eZfE5lqJkPYfKB8SVMXmS6pKrOSnIAk1cxnwfcAry9qh7rr6fTl+Ro4IyqeuOQ87Zsl7XV3YDvVtU5SfZiuON6hsmFMJYBdwHvoI1vBph3aIZeX8dWW2F89dXaam1lYGN6Vh/11QmoJEmSJKkTnoIrSZIkSeqEE1BJkiRJUiecgEqSJEmSOuEEVJIkSZLUCSegkiRJkqROOAGVJEmSJHXCCag0RUn2SrKh/TyQ5L62/EiSry7A8dYmuTvJe+b5uKtmv1B7J445077Tb2ceu7z9Ph5PsmJn9iFJGh/r6zM+1vqqRWO3vjsgDUlV/QWYAUhyJvBIVZ27wIf9cFVdOp8HVNVOFbhmBjgCuGq+D6yqR4GZJJv/j+NLkkbG+vqMx7W+atHwHVCpA0mOTnJlWz4zyUVJbkhyT5I3J/lsko1Jrk6ytG13eJKfJ1mf5Jokz9+B46xNcn6SXyW5qx33wiSbkqyds93mJCuS7Nfu+3qS25Jcm2R52+ZnSY5oyyvaY5YBZwGntldaT02yRzvGTUluSXJie8zBrW1Dkt8lWTX1X6wkadSsr9ZXLT5OQKV+vBhYA5wAfBtYV1UvBx4Fjm9F8ivAyVV1OHAhcM4O7vu5wGrgg8AVwBeAg4GXJ5l5ku1XAedV1cHAw8BJT7Xjqnoc+CRwcVXNVNXFwCeA66vqlcAxwOeS7AG8B/hSVc0weUX33h3svyRJO8v6Ku3iPAVX6sdPqurfSTYCS4CrW/tGYD/gpcDLgOuS0La5fwf3/eOqqrbvB6tqI0CS29q+N2y3/d1VNdu2vm0zH8cCJyQ5o63vDuwL/BL4RJKVwA+r6s557leSpPmyvkq7OCegUj8eA6iqrUn+XVXV2rcy+bsMcFtVrd7Zfbd9PTanfXbfT7U9wH+A5W35CbadJbH70xwvwElVdcd27ZuS3AgcD1yV5N1Vdf0O9F+SpJ1lfZV2cZ6CK+2a7gD2TrIaIMnSJAd33IfNwOFt+eQ57f8Enj1n/Rrg/WkvJSc5tN0eANxVVV8GLgcOWegOS5L0DKyvUs+cgEq7oPZZkJOBzyT5LZPTel7VcTfOBd6b5BZg7iXd1wEHzV4kATgbWAr8rp2GdHbb7hTg1iQbmJzu9K3Oei5J0pOwvkr9y7YzEyQtNu3Ke1fO9zLxfcvkMvFHVNVDffdFkqTtWV+lheM7oNLi9nfg7Mzzi7L7kvZF2Uxe0d3ac3ckSXoq1ldpgfgOqCRJkiSpE74DKkmSJEnqhBNQSZIkSVInnIBKkiRJkjrhBFSSJEmS1In/AkC1A3VXzKRGAAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
        "<Figure size 936x648 with 4 Axes>"
       ]

From 74ced7e92af78839266d3dfe7176faad87287dc5 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 20 Nov 2023 13:53:22 +0000
Subject: [PATCH 187/199] style: pre-commit fixes

---
 docs/source/examples/notebooks/models/lithium-plating.ipynb | 1 -
 1 file changed, 1 deletion(-)

diff --git a/docs/source/examples/notebooks/models/lithium-plating.ipynb b/docs/source/examples/notebooks/models/lithium-plating.ipynb
index 63ba992818..8969f237ef 100644
--- a/docs/source/examples/notebooks/models/lithium-plating.ipynb
+++ b/docs/source/examples/notebooks/models/lithium-plating.ipynb
@@ -148,7 +148,6 @@
     }
    ],
    "source": [
-    "import matplotlib.pyplot as plt\n",
     "\n",
     "colors = [\"tab:purple\", \"tab:cyan\", \"tab:red\", \"tab:green\", \"tab:blue\"]\n",
     "linestyles = [\"dashed\", \"dotted\", \"solid\"]\n",

From c3af572136b996a6c1dc5cae520309a82721219d Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 20 Nov 2023 20:22:09 +0530
Subject: [PATCH 188/199] Lint changes, pre-install wheel plus prerequisites

---
 noxfile.py | 58 ++++++++++++++++++++++++++++++++----------------------
 1 file changed, 35 insertions(+), 23 deletions(-)

diff --git a/noxfile.py b/noxfile.py
index 430ad59659..a908ce4aca 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -17,7 +17,7 @@
     "SUNDIALS_INST": f"{homedir}/.local",
     "LD_LIBRARY_PATH": f"{homedir}/.local/lib:",
 }
-VENV_DIR = Path('./venv').resolve()
+VENV_DIR = Path("./venv").resolve()
 
 
 def set_environment_variables(env_dict, session):
@@ -121,13 +121,25 @@ def set_dev(session):
     session.install("virtualenv", "cmake")
     session.run("virtualenv", os.fsdecode(VENV_DIR), silent=True)
     python = os.fsdecode(VENV_DIR.joinpath("bin/python"))
+    session.run(
+        python,
+        "-m",
+        "pip",
+        "install",
+        "--upgrade",
+        "pip",
+        "setuptools",
+        "wheel",
+        external=True,
+    )
     if sys.platform == "linux":
-        session.run(python,
-                    "-m",
-                    "pip",
-                    "install",
-                    ".[all,dev,jax,odes]",
-                    external=True,
+        session.run(
+            python,
+            "-m",
+            "pip",
+            "install",
+            ".[all,dev,jax,odes]",
+            external=True,
         )
     else:
         session.run(python, "-m", "pip", "install", "-e", ".[all,dev]", external=True)
@@ -153,26 +165,26 @@ def build_docs(session):
     # Local development
     if session.interactive:
         session.run(
-        "sphinx-autobuild",
-        "-j",
-        "auto",
-        "--open-browser",
-        "-qT",
-        ".",
-        f"{envbindir}/../tmp/html",
+            "sphinx-autobuild",
+            "-j",
+            "auto",
+            "--open-browser",
+            "-qT",
+            ".",
+            f"{envbindir}/../tmp/html",
         )
     # Runs in CI only, treating warnings as errors
     else:
         session.run(
-        "sphinx-build",
-        "-j",
-        "auto",
-        "-b",
-        "html",
-        "-W",
-        "--keep-going",
-        ".",
-        f"{envbindir}/../tmp/html",
+            "sphinx-build",
+            "-j",
+            "auto",
+            "-b",
+            "html",
+            "-W",
+            "--keep-going",
+            ".",
+            f"{envbindir}/../tmp/html",
         )
 
 

From 09c4942a7627e186762478d9161e84f38d0b71e0 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 20 Nov 2023 21:08:17 +0530
Subject: [PATCH 189/199] Bump new minimum versions of dependencies

---
 pyproject.toml | 31 ++++++++++++-------------------
 1 file changed, 12 insertions(+), 19 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 32912383f7..675a7de335 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -32,10 +32,11 @@ classifiers = [
     "Topic :: Scientific/Engineering",
 ]
 dependencies = [
-    "numpy>=1.16",
-    "scipy>=1.3",
-    "casadi>=3.6.0",
-    "xarray",
+    "numpy>=1.23.5",
+    "scipy>=1.9.3",
+    "casadi>=3.6.3",
+    "xarray>=2022.6.0",
+    "anytree>=2.12.0",
 ]
 
 [project.urls]
@@ -71,11 +72,11 @@ examples = [
 ]
 # Plotting functionality
 plot = [
-    "imageio>=2.9.0",
+    "imageio>=2.32.0",
     # Note: matplotlib is loaded for debug plots, but to ensure PyBaMM runs
     # on systems without an attached display, it should never be imported
     # outside of plot() methods.
-    "matplotlib>=2.0",
+    "matplotlib>=3.6.0",
 ]
 # For the Citations class
 cite = [
@@ -83,7 +84,7 @@ cite = [
 ]
 # To generate LaTeX strings
 latexify = [
-    "sympy>=1.8",
+    "sympy>=1.12",
 ]
 # Battery Parameter eXchange format
 bpx = [
@@ -108,7 +109,7 @@ dev = [
 ]
 # Reading CSV files
 pandas =  [
-    "pandas>=0.24",
+    "pandas>=1.5.0",
 ]
 # For the Jax solver. Note: these must be kept in sync with the versions defined in pybamm/util.py.
 jax = [
@@ -121,17 +122,9 @@ odes = [
 ]
 # Contains all optional dependencies, except for odes, jax, and dev dependencies
 all = [
-    "anytree>=2.4.3",
-    "autograd>=1.2",
-    "pandas>=0.24",
-    "scikit-fem>=0.2.0",
-    "imageio>=2.9.0",
-    "matplotlib>=2.0",
-    "pybtex>=0.24.0",
-    "sympy>=1.8",
-    "bpx",
-    "tqdm",
-    "jupyter",
+    "autograd>=1.6.2",
+    "scikit-fem>=8.1.0",
+    "pybamm[examples,plot,cite,latexify,bpx,tqdm,pandas]",
 ]
 
 [project.scripts]

From e51d73fc93814aa3396037aa3610447aee56e23e Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Mon, 20 Nov 2023 17:37:01 +0530
Subject: [PATCH 190/199] Bump version to v23.9

---
 CHANGELOG.md               | 11 ++++-------
 CITATION.cff               |  2 +-
 docs/_static/versions.json |  5 +++++
 pybamm/version.py          |  2 +-
 vcpkg.json                 |  2 +-
 5 files changed, 12 insertions(+), 10 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 483ca91a5e..4403405f90 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -5,13 +5,7 @@
 - Fixed bug that made identical Experiment steps with different end times crash ([#3516](https://github.com/pybamm-team/PyBaMM/pull/3516))
 - Fixed bug in calculation of theoretical energy that made it very slow ([#3506](https://github.com/pybamm-team/PyBaMM/pull/3506))
 
-# [v23.9rc1](https://github.com/pybamm-team/PyBaMM/tree/v23.9rc1) - 2023-11-15
-
-- Fixed a bug where the JaxSolver would fails when using GPU support with no input parameters ([#3423](https://github.com/pybamm-team/PyBaMM/pull/3423))
-- Make pybamm importable with minimal dependencies ([#3044](https://github.com/pybamm-team/PyBaMM/pull/3044), [#3475](https://github.com/pybamm-team/PyBaMM/pull/3475))
-- Fixed a bug where supplying an initial soc did not work with half cell models ([#3456](https://github.com/pybamm-team/PyBaMM/pull/3456))
-
-# [v23.9rc0](https://github.com/pybamm-team/PyBaMM/tree/v23.9rc0) - 2023-10-31
+# [v23.9](https://github.com/pybamm-team/PyBaMM/tree/v23.9) - 2023-10-31
 
 ## Features
 
@@ -30,6 +24,9 @@
 
 ## Bug fixes
 
+- Fixed a bug where the JaxSolver would fails when using GPU support with no input parameters ([#3423](https://github.com/pybamm-team/PyBaMM/pull/3423))
+- Make pybamm importable with minimal dependencies ([#3044](https://github.com/pybamm-team/PyBaMM/pull/3044), [#3475](https://github.com/pybamm-team/PyBaMM/pull/3475))
+- Fixed a bug where supplying an initial soc did not work with half cell models ([#3456](https://github.com/pybamm-team/PyBaMM/pull/3456))
 - Fixed a bug where empty lists passed to QuickPlot resulted in an IndexError and did not return a meaningful error message ([#3359](https://github.com/pybamm-team/PyBaMM/pull/3359))
 - Fixed a bug where there was a missing thermal conductivity in the thermal pouch cell models ([#3330](https://github.com/pybamm-team/PyBaMM/pull/3330))
 - Fixed a bug that caused incorrect results of “{Domain} electrode thickness change [m]” due to the absence of dimension for the variable `electrode_thickness_change`([#3329](https://github.com/pybamm-team/PyBaMM/pull/3329)).
diff --git a/CITATION.cff b/CITATION.cff
index b7f68164fc..44f1c5d407 100644
--- a/CITATION.cff
+++ b/CITATION.cff
@@ -24,6 +24,6 @@ keywords:
   - "expression tree"
   - "python"
   - "symbolic differentiation"
-version: "23.9rc1"
+version: "23.9"
 repository-code: "https://github.com/pybamm-team/PyBaMM"
 title: "Python Battery Mathematical Modelling (PyBaMM)"
diff --git a/docs/_static/versions.json b/docs/_static/versions.json
index 5c9bba7c17..675ecbcf88 100644
--- a/docs/_static/versions.json
+++ b/docs/_static/versions.json
@@ -9,6 +9,11 @@
         "version": "stable",
         "url": "https://docs.pybamm.org/en/stable/"
     },
+    {
+        "name": "v23.9",
+        "version": "23.9",
+        "url": "https://docs.pybamm.org/en/v23.9_a/"
+    },
     {
         "name": "v23.5",
         "version": "23.5",
diff --git a/pybamm/version.py b/pybamm/version.py
index e5cfaa0882..970be77f66 100644
--- a/pybamm/version.py
+++ b/pybamm/version.py
@@ -1 +1 @@
-__version__ = "23.9rc1"
+__version__ = "23.9"
diff --git a/vcpkg.json b/vcpkg.json
index de71a5a87d..f62c18ddd2 100644
--- a/vcpkg.json
+++ b/vcpkg.json
@@ -1,6 +1,6 @@
 {
   "name": "pybamm",
-  "version-string": "23.9rc1",
+  "version-string": "23.9",
   "dependencies": [
     "casadi",
     {

From 9e94bdd45adc0f5477438a83eb495050ed7beac8 Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Mon, 20 Nov 2023 21:37:23 +0530
Subject: [PATCH 191/199] Fix schedule tests

---
 .github/workflows/run_periodic_tests.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml
index 06c0f0fb68..c58d5ca215 100644
--- a/.github/workflows/run_periodic_tests.yml
+++ b/.github/workflows/run_periodic_tests.yml
@@ -68,7 +68,7 @@ jobs:
 
       - name: Install macOS system dependencies
         if: matrix.os == 'macos-latest'
-        run:
+        run: |
           brew install graphviz openblas
           brew reinstall gcc
 

From cfc550b14d46621e01999a6209fc76200648921e Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 20 Nov 2023 21:38:47 +0530
Subject: [PATCH 192/199] A sentence about package data and extra files

---
 CONTRIBUTING.md | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 6944cce074..b9800dcd61 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -413,16 +413,17 @@ wherever code is called that uses that citation (for example, in functions or in
 
 ### Installation
 
-Installation of PyBaMM and its dependencies is handled via [pip](https://pip.pypa.io/en/stable/) and [setuptools](http://setuptools.readthedocs.io/). It uses `CMake` to compile C++ extensions using [`pybind11`](https://pybind11.readthedocs.io/en/stable/) and [`casadi`](https://web.casadi.org/) (non-Windows). The installation process is described in detail in the [source installation](https://docs.pybamm.org/en/latest/source/user_guide/installation/install-from-source.html) page and is configured through the `CMakeLists.txt` file.
+Installation of PyBaMM and its dependencies is handled via [pip](https://pip.pypa.io/en/stable/) and [setuptools](http://setuptools.readthedocs.io/). It uses `CMake` to compile C++ extensions using [`pybind11`](https://pybind11.readthedocs.io/en/stable/) and [`casadi`](https://web.casadi.org/). The installation process is described in detail in the [source installation](https://docs.pybamm.org/en/latest/source/user_guide/installation/install-from-source.html) page and is configured through the `CMakeLists.txt` file.
 
 Configuration files:
 
 ```
 setup.py
 pyproject.toml
+MANIFEST.in
 ```
 
-Note that this file must be kept in sync with the version number in [`pybamm/__init__.py`](https://github.com/pybamm-team/PyBaMM/blob/develop/pybamm/__init__.py).
+Note: `MANIFEST.in` is used to include and exclude non-Python files and auxiliary package data for PyBaMM when distributing it. If a file is not included in `MANIFEST.in`, it will not be included in the source distribution (SDist) and subsequently not be included in the binary distribution (wheel).
 
 ### Continuous Integration using GitHub Actions
 

From b04dcb5fec54042641a4c0761d6f5b1fdfef777e Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Mon, 20 Nov 2023 22:22:12 +0530
Subject: [PATCH 193/199] Fix governance link

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index c7ca111873..474b528bb6 100644
--- a/README.md
+++ b/README.md
@@ -34,7 +34,7 @@ explore the effect of different battery designs and modeling assumptions under a
 
 [//]: # "numfocus-fiscal-sponsor-attribution"
 
-PyBaMM uses an [open governance model](./GOVERNANCE.md)
+PyBaMM uses an [open governance model](https://pybamm.org/governance/)
 and is fiscally sponsored by [NumFOCUS](https://numfocus.org/). Consider making
 a [tax-deductible donation](https://numfocus.org/donate-for-pybamm) to help the project
 pay for developer time, professional services, travel, workshops, and a variety of other needs.

From 7850587187a9719d8ccbb856f4073b8cf2e7bf82 Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Mon, 20 Nov 2023 22:23:28 +0530
Subject: [PATCH 194/199] Add Martin to GOVERNANCE.md

---
 GOVERNANCE.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/GOVERNANCE.md b/GOVERNANCE.md
index f11b785106..aa97669187 100644
--- a/GOVERNANCE.md
+++ b/GOVERNANCE.md
@@ -23,6 +23,7 @@ handled on a case-by-case basis.
 - Scott Marquis
 - [Gregory Offer](https://www.imperial.ac.uk/people/gregory.offer)
 - [Valentin Sulzer](https://sites.google.com/view/valentinsulzer)
+- [Martin Robinson](https://www.sabsr3.ox.ac.uk/people/dr-martin-robinson)
 
 ## Advisory Committee
 

From d5dae10a0f95beef7c8cf02268df13027e3b389e Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 20 Nov 2023 19:17:48 +0000
Subject: [PATCH 195/199] chore: update pre-commit hooks
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

updates:
- [github.com/astral-sh/ruff-pre-commit: v0.1.5 → v0.1.6](https://github.com/astral-sh/ruff-pre-commit/compare/v0.1.5...v0.1.6)
---
 .pre-commit-config.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 5871b334bf..ed837e6fdb 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -4,7 +4,7 @@ ci:
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: "v0.1.5"
+    rev: "v0.1.6"
     hooks:
       - id: ruff
         args: [--fix, --show-fixes]

From 0aa469b6f94cc69d57d9e47fd78187edf714233d Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 21 Nov 2023 02:00:52 +0530
Subject: [PATCH 196/199] Install `nox` with `pip` instead of `pipx`

---
 .github/workflows/run_periodic_tests.yml |  5 ++++-
 .github/workflows/test_on_push.yml       | 26 ++++++++++++++++++++----
 2 files changed, 26 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml
index f182ffaf99..4545dc26df 100644
--- a/.github/workflows/run_periodic_tests.yml
+++ b/.github/workflows/run_periodic_tests.yml
@@ -72,9 +72,12 @@ jobs:
         if: matrix.os == 'windows-latest'
         run: choco install graphviz --version=2.38.0.20190211
 
+      - name: Install nox
+        run: python -m pip install nox
+
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux and macOS
         if: matrix.os != 'windows-latest'
-        run: pipx run nox -s pybamm-requires
+        run: python -m nox -s pybamm-requires
 
       - name: Run unit tests for GNU/Linux with Python 3.8, 3.9, and 3.10, and for macOS and Windows with all Python versions
         if: (matrix.os == 'ubuntu-latest' && matrix.python-version != 3.11) || (matrix.os != 'ubuntu-latest')
diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index a0e3ffe4b2..2f7f94c9bc 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -90,6 +90,9 @@ jobs:
           python-version: ${{ matrix.python-version }}
           cache: 'pip'
 
+      - name: Install nox
+        run: python -m pip install nox
+
       - name: Cache pybamm-requires nox environment for GNU/Linux and macOS
         uses: actions/cache@v3
         if: matrix.os != 'windows-latest'
@@ -106,7 +109,7 @@ jobs:
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux and macOS
         if: matrix.os != 'windows-latest'
-        run: pipx run nox -s pybamm-requires
+        run: python -m nox -s pybamm-requires
 
       - name: Run unit tests for ${{ matrix.os }} with Python ${{ matrix.python-version }}
         run: python -m nox -s unit
@@ -144,6 +147,9 @@ jobs:
           python-version: 3.11
           cache: 'pip'
 
+      - name: Install nox
+        run: python -m pip install nox
+
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
         with:
@@ -222,6 +228,9 @@ jobs:
           python-version: ${{ matrix.python-version }}
           cache: 'pip'
 
+      - name: Install nox
+        run: python -m pip install nox
+
       - name: Cache pybamm-requires nox environment for GNU/Linux and macOS
         uses: actions/cache@v3
         if: matrix.os != 'windows-latest'
@@ -238,7 +247,7 @@ jobs:
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux and macOS
         if: matrix.os != 'windows-latest'
-        run: pipx run nox -s pybamm-requires
+        run: python -m nox -s pybamm-requires
 
       - name: Run integration tests for ${{ matrix.os }} with Python ${{ matrix.python-version }}
         run: python -m nox -s integration
@@ -277,6 +286,9 @@ jobs:
           python-version: 3.11
           cache: 'pip'
 
+      - name: Install nox
+        run: python -m pip install nox
+
       - name: Install docs dependencies and run doctests for GNU/Linux with Python 3.11
         run: python -m nox -s doctests
 
@@ -316,6 +328,9 @@ jobs:
           python-version: 3.11
           cache: 'pip'
 
+      - name: Install nox
+        run: python -m pip install nox
+
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
         with:
@@ -333,7 +348,7 @@ jobs:
         run: python -m nox -s pybamm-requires
 
       - name: Run example notebooks tests for GNU/Linux with Python 3.11
-        run: pipx run nox -s examples
+        run: python -m nox -s examples
 
   # Runs only on Ubuntu with Python 3.11
   run_scripts_tests:
@@ -368,6 +383,9 @@ jobs:
           python-version: 3.11
           cache: 'pip'
 
+      - name: Install nox
+        run: python -m pip install nox
+
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
         with:
@@ -385,4 +403,4 @@ jobs:
         run: python -m nox -s pybamm-requires
 
       - name: Run example scripts tests for GNU/Linux with Python 3.11
-        run: pipx run nox -s scripts
+        run: python -m nox -s scripts

From 8e91218d3a7ac3f59267dfcda602efd06c8b3d2c Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 21 Nov 2023 02:16:45 +0530
Subject: [PATCH 197/199] Remove `brew update` from wheels and disable wheels
 on PRs

---
 .github/workflows/publish_pypi.yml | 2 --
 1 file changed, 2 deletions(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index fda75d4489..3073c95f09 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -2,7 +2,6 @@ name: Build and publish package to PyPI
 on:
   release:
     types: [published]
-  pull_request:
   schedule:
     # Run at 10 am UTC on day-of-month 1 and 15.
     - cron: "0 10 1,15 * *"
@@ -84,7 +83,6 @@ jobs:
       - name: Install SuiteSparse and SUNDIALS on macOS
         if: matrix.os == 'macos-latest'
         run: |
-          brew update
           brew install graphviz openblas libomp
           brew reinstall gcc
           python -m pip install cmake wget

From f1fd05f58b6f2e2918151429988cebb46059f5b0 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 21 Nov 2023 04:34:29 +0530
Subject: [PATCH 198/199] Update version to 23.9

Co-authored-by: Eric G. Kratz <kratman@users.noreply.github.com>
---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 675a7de335..4569c7c6c3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -10,7 +10,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "pybamm"
-version = "23.9rc0"
+version = "23.9"
 license = { file = "LICENSE.txt" }
 description = "Python Battery Mathematical Modelling"
 authors = [{name = "The PyBaMM Team", email = "pybamm@pybamm.org"}]

From 2ee8da23ee24e9e946a60b0dd9e64070b1244552 Mon Sep 17 00:00:00 2001
From: Simon O'Kane <42972513+DrSOKane@users.noreply.github.com>
Date: Wed, 22 Nov 2023 15:48:10 +0000
Subject: [PATCH 199/199] Issue 3339 dead lithium (#3485)

* fixed tests

* Added graphite half-cell parameter files

* Revert "Added graphite half-cell parameter files"

This reverts commit 78001e81eecc38919364190940e095e0e51fab76.

* Revert "fixed tests"

This reverts commit cf53ff1d9e74eda7e68bc65b5dea5c18f7fcf872.

* ruff

* changelog

* coverage

* Fixed minor error in example notebook

* Removed duplicate entry from changelog
---
 CHANGELOG.md                                  |  1 +
 .../notebooks/models/lithium-plating.ipynb    |  4 ++--
 .../interface/lithium_plating/plating.py      | 22 +++++++++++++------
 3 files changed, 18 insertions(+), 9 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 4403405f90..2eea9ed7c0 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -4,6 +4,7 @@
 
 - Fixed bug that made identical Experiment steps with different end times crash ([#3516](https://github.com/pybamm-team/PyBaMM/pull/3516))
 - Fixed bug in calculation of theoretical energy that made it very slow ([#3506](https://github.com/pybamm-team/PyBaMM/pull/3506))
+- The irreversible plating model now increments `f"{Domain} dead lithium concentration [mol.m-3]"`, not `f"{Domain} lithium plating concentration [mol.m-3]"` as it did previously. ([#3485](https://github.com/pybamm-team/PyBaMM/pull/3485))
 
 # [v23.9](https://github.com/pybamm-team/PyBaMM/tree/v23.9) - 2023-10-31
 
diff --git a/docs/source/examples/notebooks/models/lithium-plating.ipynb b/docs/source/examples/notebooks/models/lithium-plating.ipynb
index 8969f237ef..1e14513620 100644
--- a/docs/source/examples/notebooks/models/lithium-plating.ipynb
+++ b/docs/source/examples/notebooks/models/lithium-plating.ipynb
@@ -194,8 +194,8 @@
     "    axs[0,0].set_ylabel(\"Voltage [V]\")\n",
     "    axs[0,1].set_ylabel(\"Volumetric interfacial current density [A.m-3]\")\n",
     "    axs[0,1].legend(('Deintercalation current','Stripping current','Total current'))\n",
-    "    axs[1,0].set_ylabel(\"Plated lithium capacity [Ah]\")\n",
-    "    axs[1,1].set_ylabel(\"Intercalated lithium capacity [Ah]\")\n",
+    "    axs[1,0].set_ylabel(\"Plated lithium capacity [A.h]\")\n",
+    "    axs[1,1].set_ylabel(\"Intercalated lithium capacity [A.h]\")\n",
     "\n",
     "    for ax in axs.flat:\n",
     "        ax.set_xlabel(\"Time [minutes]\")\n",
diff --git a/pybamm/models/submodels/interface/lithium_plating/plating.py b/pybamm/models/submodels/interface/lithium_plating/plating.py
index a1828dcaa2..9f4de08d2f 100644
--- a/pybamm/models/submodels/interface/lithium_plating/plating.py
+++ b/pybamm/models/submodels/interface/lithium_plating/plating.py
@@ -115,18 +115,26 @@ def set_rhs(self, variables):
             ]
             L_sei = variables[f"{Domain} total SEI thickness [m]"]
 
-        # In the partially reversible plating model, coupling term turns reversible
-        # lithium into dead lithium. In other plating models, it is zero.
         lithium_plating_option = getattr(self.options, domain)["lithium plating"]
-        if lithium_plating_option == "partially reversible":
+        if lithium_plating_option == "reversible":
+            # In the reversible plating model, there is no dead lithium
+            dc_plated_Li = -a_j_stripping / self.param.F
+            dc_dead_Li = pybamm.Scalar(0)
+        elif lithium_plating_option == "irreversible":
+            # In the irreversible plating model, all plated lithium is dead lithium
+            dc_plated_Li = pybamm.Scalar(0)
+            dc_dead_Li = -a_j_stripping / self.param.F
+        elif lithium_plating_option == "partially reversible":
+            # In the partially reversible plating model, the coupling term turns
+            # reversible lithium into dead lithium over time.
             dead_lithium_decay_rate = self.param.dead_lithium_decay_rate(L_sei)
             coupling_term = dead_lithium_decay_rate * c_plated_Li
-        else:
-            coupling_term = pybamm.Scalar(0)
+            dc_plated_Li = -a_j_stripping / self.param.F - coupling_term
+            dc_dead_Li = coupling_term
 
         self.rhs = {
-            c_plated_Li: -a_j_stripping / self.param.F - coupling_term,
-            c_dead_Li: coupling_term,
+            c_plated_Li: dc_plated_Li,
+            c_dead_Li: dc_dead_Li,
         }
 
     def set_initial_conditions(self, variables):