From 148ea1870320cc293a1a8a89e94c1e16f405a36f Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Fri, 6 Oct 2023 21:57:05 +0530
Subject: [PATCH 001/109] Test `pybamm_install_odes` on macOS on CI

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

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index b740da2e1b..8e315c6950 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -299,6 +299,10 @@ jobs:
           pip install --upgrade pip wheel setuptools nox
           pip install -e .[all,docs]
 
+      - name: Test pybamm_install_odes on MacOS (for only this PR)
+        if: matrix.os == 'macos-latest'
+        run: pybamm_install_odes
+
       - name: Install docs dependencies and run doctests for GNU/Linux with Python 3.11
         run: nox -s doctests
 

From d9743ec3c453a57d6e2f20278f9997e27854f6e8 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sat, 7 Oct 2023 19:01:45 +0530
Subject: [PATCH 002/109] Add parallel job

---
 .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 8e315c6950..235ae91202 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -155,6 +155,10 @@ jobs:
           pip install --upgrade pip wheel setuptools nox
           pip install -e .[all,docs]
 
+      - name: Test pybamm_install_odes on MacOS (for only this PR)
+        if: matrix.os == 'macos-latest'
+        run: pybamm_install_odes
+
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
         with:
@@ -299,10 +303,6 @@ jobs:
           pip install --upgrade pip wheel setuptools nox
           pip install -e .[all,docs]
 
-      - name: Test pybamm_install_odes on MacOS (for only this PR)
-        if: matrix.os == 'macos-latest'
-        run: pybamm_install_odes
-
       - name: Install docs dependencies and run doctests for GNU/Linux with Python 3.11
         run: nox -s doctests
 

From 7c7420a41ebf17f17057f39736f627a0e9d38ccc Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sat, 7 Oct 2023 19:14:28 +0530
Subject: [PATCH 003/109] Test before unit tests in mac

---
 .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 235ae91202..3589a52e78 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -95,6 +95,10 @@ jobs:
           pip install --upgrade pip wheel setuptools nox
           pip install -e .[all,docs]
 
+      - name: Test pybamm_install_odes on MacOS (for only this PR)
+        if: matrix.os == 'macos-latest'
+        run: pybamm_install_odes
+
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
         if: matrix.os == 'ubuntu-latest'
@@ -155,10 +159,6 @@ jobs:
           pip install --upgrade pip wheel setuptools nox
           pip install -e .[all,docs]
 
-      - name: Test pybamm_install_odes on MacOS (for only this PR)
-        if: matrix.os == 'macos-latest'
-        run: pybamm_install_odes
-
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
         with:

From 906683b2f21589122c36d1e75cbafd961e8e37c7 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sat, 7 Oct 2023 19:27:51 +0530
Subject: [PATCH 004/109] Install `wget` before odes

---
 .github/workflows/test_on_push.yml | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 3589a52e78..aff1cfbe49 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -97,7 +97,9 @@ jobs:
 
       - name: Test pybamm_install_odes on MacOS (for only this PR)
         if: matrix.os == 'macos-latest'
-        run: pybamm_install_odes
+        run: |
+          pip install wget
+          pybamm_install_odes
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3

From dda612a03501f69dd5189b8d660c370cd72ae1fd Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Fri, 13 Oct 2023 18:39:41 +0530
Subject: [PATCH 005/109] Add parallel job to test `install_odes`

---
 .github/workflows/test_on_push.yml | 50 ++++++++++++++++++++++++++----
 1 file changed, 44 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index aff1cfbe49..e8c82f5200 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -95,12 +95,6 @@ jobs:
           pip install --upgrade pip wheel setuptools nox
           pip install -e .[all,docs]
 
-      - name: Test pybamm_install_odes on MacOS (for only this PR)
-        if: matrix.os == 'macos-latest'
-        run: |
-          pip install wget
-          pybamm_install_odes
-
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
         if: matrix.os == 'ubuntu-latest'
@@ -183,6 +177,50 @@ jobs:
       - name: Upload coverage report
         uses: codecov/codecov-action@v3.1.4
 
+  test_install_odes:
+    needs: style
+    runs-on: macos-latest
+    strategy:
+      fail-fast: false
+    name: Test pybamm_install_odes on MacOS
+
+    steps:
+      - name: Check out PyBaMM repository
+        uses: actions/checkout@v4
+
+      - name: Install macOS system dependencies
+        if: matrix.os == 'macos-latest'
+        env:
+          # Homebrew environment variables
+          HOMEBREW_NO_INSTALL_CLEANUP: 1
+          HOMEBREW_NO_AUTO_UPDATE: 1
+          HOMEBREW_NO_COLOR: 1
+          # Speed up CI
+          NONINTERACTIVE: 1
+        run: |
+          brew analytics off
+          brew update
+          brew install graphviz openblas
+
+      - name: Set up Python ${{ matrix.python-version }}
+        id: setup-python
+        uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python-version }}
+          cache: 'pip'
+          cache-dependency-path: setup.py
+
+      - name: Install PyBaMM dependencies
+        run: |
+          pip install --upgrade pip wheel setuptools nox
+          pip install -e .[all,docs]
+
+      - name: Test pybamm_install_odes on MacOS
+        if: matrix.os == 'macos-latest'
+        run: |
+          pip install wget
+          pybamm_install_odes
+
   run_integration_tests:
     needs: style
     runs-on: ${{ matrix.os }}

From af4399657e26150d53bc39187c3bb391e63e39c3 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sun, 15 Oct 2023 01:42:58 +0530
Subject: [PATCH 006/109] Install `pathlib` as required

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

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index e8c82f5200..e12722aff2 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -218,7 +218,7 @@ jobs:
       - name: Test pybamm_install_odes on MacOS
         if: matrix.os == 'macos-latest'
         run: |
-          pip install wget
+          pip install wget pathlib
           pybamm_install_odes
 
   run_integration_tests:

From 7018c19a00b563b0eaa55987d67dc536b0a11185 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sun, 15 Oct 2023 02:19:20 +0530
Subject: [PATCH 007/109] Remove condition

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

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index e12722aff2..3811dfddfd 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -189,7 +189,6 @@ jobs:
         uses: actions/checkout@v4
 
       - name: Install macOS system dependencies
-        if: matrix.os == 'macos-latest'
         env:
           # Homebrew environment variables
           HOMEBREW_NO_INSTALL_CLEANUP: 1
@@ -218,7 +217,7 @@ jobs:
       - name: Test pybamm_install_odes on MacOS
         if: matrix.os == 'macos-latest'
         run: |
-          pip install wget pathlib
+          pip install wget
           pybamm_install_odes
 
   run_integration_tests:

From 7f0bea9e0d5832659fd8072b5d0bb523c54d3ed5 Mon Sep 17 00:00:00 2001
From: Arjun <arjunverma.oc@gmail.com>
Date: Sun, 15 Oct 2023 16:07:42 +0530
Subject: [PATCH 008/109] Apply suggestions from code review

Co-authored-by: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
---
 .github/workflows/test_on_push.yml | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 3811dfddfd..dca8e3c9b1 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -181,6 +181,9 @@ jobs:
     needs: style
     runs-on: macos-latest
     strategy:
+        matrix:
+            os: [ubuntu-latest, macos-latest]
+            python-version: ["3.8", "3.9", "3.10", "3.11"]
       fail-fast: false
     name: Test pybamm_install_odes on MacOS
 
@@ -199,7 +202,7 @@ jobs:
         run: |
           brew analytics off
           brew update
-          brew install graphviz openblas
+          brew install openblas
 
       - name: Set up Python ${{ matrix.python-version }}
         id: setup-python
@@ -212,12 +215,12 @@ jobs:
       - name: Install PyBaMM dependencies
         run: |
           pip install --upgrade pip wheel setuptools nox
-          pip install -e .[all,docs]
+          pip install -e .[all]
 
       - name: Test pybamm_install_odes on MacOS
         if: matrix.os == 'macos-latest'
         run: |
-          pip install wget
+          pip install wget cmake
           pybamm_install_odes
 
   run_integration_tests:

From 7ee2a9a522d0ddf8652b04857267fdbd67501e78 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sun, 15 Oct 2023 17:57:13 +0530
Subject: [PATCH 009/109] Correctly indent key

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

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index dca8e3c9b1..65afcffe6c 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -184,7 +184,7 @@ jobs:
         matrix:
             os: [ubuntu-latest, macos-latest]
             python-version: ["3.8", "3.9", "3.10", "3.11"]
-      fail-fast: false
+        fail-fast: false
     name: Test pybamm_install_odes on MacOS
 
     steps:

From 9728d171fc8c7e1de8e632b97b5726f8499cfee6 Mon Sep 17 00:00:00 2001
From: Arjun <arjunverma.oc@gmail.com>
Date: Sun, 15 Oct 2023 18:51:09 +0530
Subject: [PATCH 010/109] Apply suggestions from code review

Co-authored-by: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
---
 .github/workflows/test_on_push.yml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 65afcffe6c..ef6391d4ad 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -185,7 +185,7 @@ jobs:
             os: [ubuntu-latest, macos-latest]
             python-version: ["3.8", "3.9", "3.10", "3.11"]
         fail-fast: false
-    name: Test pybamm_install_odes on MacOS
+    name: Test pybamm_install_odes on ${{ matrix.os }}
 
     steps:
       - name: Check out PyBaMM repository
@@ -203,6 +203,7 @@ jobs:
           brew analytics off
           brew update
           brew install openblas
+          brew reinstall gcc gfortran
 
       - name: Set up Python ${{ matrix.python-version }}
         id: setup-python
@@ -217,8 +218,7 @@ jobs:
           pip install --upgrade pip wheel setuptools nox
           pip install -e .[all]
 
-      - name: Test pybamm_install_odes on MacOS
-        if: matrix.os == 'macos-latest'
+      - name: Test pybamm_install_odes on ${{ matrix.os }}
         run: |
           pip install wget cmake
           pybamm_install_odes

From 2713ce5b946d96cf962734e95d75ee77d9a1a6a3 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sat, 21 Oct 2023 01:46:28 +0530
Subject: [PATCH 011/109] Add `.zshrc` for macOS

---
 pybamm/install_odes.py | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/pybamm/install_odes.py b/pybamm/install_odes.py
index 4bf310a0f2..27ff19f356 100644
--- a/pybamm/install_odes.py
+++ b/pybamm/install_odes.py
@@ -89,13 +89,19 @@ def update_LD_LIBRARY_PATH(install_dir):
     if venv_path:
         script_path = os.path.join(venv_path, "bin/activate")
     else:
-        script_path = os.path.join(os.environ.get("HOME"), ".bashrc")
+        if sys.platform == "linux":
+            script_path = os.path.join(os.environ.get("HOME"), ".bashrc")
+        if sys.platform == "darwin":
+            script_path = os.path.join(os.environ.get("HOME"), ".zshrc")
 
     if os.getenv("LD_LIBRARY_PATH") and "{}/lib".format(install_dir) in os.getenv(
         "LD_LIBRARY_PATH"
     ):
         print("{}/lib was found in LD_LIBRARY_PATH.".format(install_dir))
-        print("--> Not updating venv activate or .bashrc scripts")
+        if sys.platform == "linux":
+            print("--> Not updating venv activate or .bashrc scripts")
+        if sys.platform == "darwin":
+            print("--> Not updating venv activate or .zshrc scripts")
     else:
         with open(script_path, "a+") as fh:
             # Just check that export statement is not already there.

From 8db4f7ced9a90e37ad3910bd88d43d70857a354c Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sat, 21 Oct 2023 02:05:59 +0530
Subject: [PATCH 012/109] Install required modules before initializing

---
 pybamm/install_odes.py | 23 +++++++++++------------
 1 file changed, 11 insertions(+), 12 deletions(-)

diff --git a/pybamm/install_odes.py b/pybamm/install_odes.py
index 27ff19f356..fe5eae1314 100644
--- a/pybamm/install_odes.py
+++ b/pybamm/install_odes.py
@@ -8,23 +8,22 @@
 
 from pybamm.util import root_dir
 
-try:
-    # wget module is required to download SUNDIALS or SuiteSparse.
-    import wget
+def install_required_module(module):
+    try:
+        __import__(module)
+    except ModuleNotFoundError:
+        print(f"{module} module not found. Installing {module}...")
+        subprocess.run(["pip", "install", module], check=True)
+
+required_modules = ["wget", "cmake"]
 
-    NO_WGET = False
-except ModuleNotFoundError:
-    NO_WGET = True
+for module in required_modules:
+    install_required_module(module)
 
+import wget # noqa: E402
 
 def download_extract_library(url, directory):
     # Download and extract archive at url
-    if NO_WGET:
-        error_msg = (
-            "Could not find wget module."
-            " Please install wget module (pip install wget)."
-        )
-        raise ModuleNotFoundError(error_msg)
     archive = wget.download(url, out=directory)
     tar = tarfile.open(archive)
     tar.extractall(directory)

From 45de35620ced05e73e450be3b0421e004171625e Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sat, 21 Oct 2023 02:21:09 +0530
Subject: [PATCH 013/109] Using f-strings instead of `format()`

---
 pybamm/install_odes.py | 45 ++++++++++++++----------------------------
 1 file changed, 15 insertions(+), 30 deletions(-)

diff --git a/pybamm/install_odes.py b/pybamm/install_odes.py
index fe5eae1314..528be140aa 100644
--- a/pybamm/install_odes.py
+++ b/pybamm/install_odes.py
@@ -20,7 +20,7 @@ def install_required_module(module):
 for module in required_modules:
     install_required_module(module)
 
-import wget # noqa: E402
+import wget  # noqa: E402
 
 def download_extract_library(url, directory):
     # Download and extract archive at url
@@ -28,7 +28,6 @@ def download_extract_library(url, directory):
     tar = tarfile.open(archive)
     tar.extractall(directory)
 
-
 def install_sundials(download_dir, install_dir):
     # Download the SUNDIALS library and compile it.
     logger = logging.getLogger("scikits.odes setup")
@@ -40,10 +39,7 @@ def install_sundials(download_dir, install_dir):
         raise RuntimeError("CMake must be installed to build SUNDIALS.")
 
     url = (
-        "https://github.com/LLNL/"
-        + "sundials/releases/download/v{}/sundials-{}.tar.gz".format(
-            sundials_version, sundials_version
-        )
+        f"https://github.com/LLNL/sundials/releases/download/v{sundials_version}/sundials-{sundials_version}.tar.gz"
     )
     logger.info("Downloading sundials")
     download_extract_library(url, download_dir)
@@ -53,7 +49,7 @@ def install_sundials(download_dir, install_dir):
         "-DSUNDIALS_INDEX_SIZE=32",
         "-DBUILD_ARKODE:BOOL=OFF",
         "-DEXAMPLES_ENABLE:BOOL=OFF",
-        "-DCMAKE_INSTALL_PREFIX=" + install_dir,
+        f"-DCMAKE_INSTALL_PREFIX={install_dir}",
     ]
 
     # SUNDIALS are built within directory 'build_sundials' in the PyBaMM root
@@ -65,7 +61,7 @@ def install_sundials(download_dir, install_dir):
 
     print("-" * 10, "Running CMake prepare", "-" * 40)
     subprocess.run(
-        ["cmake", "../sundials-{}".format(sundials_version)] + cmake_args,
+        ["cmake", f"../sundials-{sundials_version}"] + cmake_args,
         cwd=build_directory,
         check=True,
     )
@@ -74,15 +70,12 @@ def install_sundials(download_dir, install_dir):
     make_cmd = ["make", "install"]
     subprocess.run(make_cmd, cwd=build_directory, check=True)
 
-
 def update_LD_LIBRARY_PATH(install_dir):
-    # Look for current python virtual env and add export statement
-    # for LD_LIBRARY_PATH in activate script.  If no virtual env found,
-    # then the current user's .bashrc file is modified instead.
+    # Look for the current python virtual env and add an export statement
+    # for LD_LIBRARY_PATH in the activate script. If no virtual env is found,
+    # the current user's .bashrc file is modified instead.
 
-    export_statement = "export LD_LIBRARY_PATH={}/lib:$LD_LIBRARY_PATH".format(
-        install_dir
-    )
+    export_statement = f"export LD_LIBRARY_PATH={install_dir}/lib:$LD_LIBRARY_PATH"
 
     venv_path = os.environ.get("VIRTUAL_ENV")
     if venv_path:
@@ -93,10 +86,8 @@ def update_LD_LIBRARY_PATH(install_dir):
         if sys.platform == "darwin":
             script_path = os.path.join(os.environ.get("HOME"), ".zshrc")
 
-    if os.getenv("LD_LIBRARY_PATH") and "{}/lib".format(install_dir) in os.getenv(
-        "LD_LIBRARY_PATH"
-    ):
-        print("{}/lib was found in LD_LIBRARY_PATH.".format(install_dir))
+    if os.getenv("LD_LIBRARY_PATH") and f"{install_dir}/lib" in os.getenv("LD_LIBRARY_PATH"):  # noqa: E501
+        print(f"{install_dir}/lib was found in LD_LIBRARY_PATH.")
         if sys.platform == "linux":
             print("--> Not updating venv activate or .bashrc scripts")
         if sys.platform == "darwin":
@@ -106,14 +97,9 @@ def update_LD_LIBRARY_PATH(install_dir):
             # Just check that export statement is not already there.
             if export_statement not in fh.read():
                 fh.write(export_statement)
-                print(
-                    "Adding {}/lib to LD_LIBRARY_PATH"
-                    " in {}".format(install_dir, script_path)
-                )
-
+                print(f"Adding {install_dir}/lib to LD_LIBRARY_PATH in {script_path}")
 
 def main(arguments=None):
-
     log_format = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
     logger = logging.getLogger("scikits.odes setup")
 
@@ -145,24 +131,24 @@ def main(arguments=None):
         else os.path.join(pybamm_dir, args.install_dir)
     )
 
-    # Check is sundials is already installed
+    # Check if sundials is already installed
     SUNDIALS_LIB_DIRS = [join(os.getenv("HOME"), ".local"), "/usr/local", "/usr"]
 
     if args.sundials_libs:
         SUNDIALS_LIB_DIRS.insert(0, args.sundials_libs)
     for DIR in SUNDIALS_LIB_DIRS:
-        logger.info("Looking for sundials at {}".format(DIR))
+        logger.info(f"Looking for sundials at {DIR}")
         SUNDIALS_FOUND = isfile(join(DIR, "lib", "libsundials_ida.so")) or isfile(
             join(DIR, "lib", "libsundials_ida.dylib")
         )
         if SUNDIALS_FOUND:
             SUNDIALS_LIB_DIR = DIR
-            logger.info("Found sundials at {}".format(SUNDIALS_LIB_DIR))
+            logger.info(f"Found sundials at {SUNDIALS_LIB_DIR}")
             break
 
     if not SUNDIALS_FOUND:
         logger.info("Could not find sundials libraries.")
-        logger.info("Installing sundials in {}".format(install_dir))
+        logger.info(f"Installing sundials in {install_dir}")
         download_dir = os.path.join(pybamm_dir, "sundials")
         if not os.path.exists(download_dir):
             os.makedirs(download_dir)
@@ -178,6 +164,5 @@ def main(arguments=None):
     env = os.environ.copy()
     subprocess.run(["pip", "install", "scikits.odes"], env=env, check=True)
 
-
 if __name__ == "__main__":
     main(sys.argv[1:])

From 4a8f13ca5e1d6cf5c361a275beec37748b869ff1 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sat, 21 Oct 2023 02:23:53 +0530
Subject: [PATCH 014/109] gitignore `scikits_odes_setup.log`

---
 .gitignore | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/.gitignore b/.gitignore
index 3e01fcac83..374e52cb45 100644
--- a/.gitignore
+++ b/.gitignore
@@ -107,6 +107,9 @@ KLU_module_deps
 # setup
 setup.log
 
+# odes setup
+scikits_odes_setup.log
+
 # test
 test.c
 test.json

From edcccf5029cb92ac97032da49ae6bb72009b3b96 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sat, 21 Oct 2023 03:05:03 +0530
Subject: [PATCH 015/109] Update doc in solver section for `install_odes`

---
 docs/source/user_guide/installation/GNU-linux.rst | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/docs/source/user_guide/installation/GNU-linux.rst b/docs/source/user_guide/installation/GNU-linux.rst
index e66c3c2291..5abb373404 100644
--- a/docs/source/user_guide/installation/GNU-linux.rst
+++ b/docs/source/user_guide/installation/GNU-linux.rst
@@ -125,9 +125,11 @@ Currently, only GNU/Linux and macOS are supported.
 
    .. code:: bash
 
-	  pip install scikits.odes
+      brew install openblas
+      pybamm_install_odes
 
-   Assuming that SUNDIALS was installed as described :ref:`above<user-install-label>`.
+   The ``pybamm_install_odes`` command is installed with PyBaMM. It automatically downloads and installs the SUNDIALS library on your
+   system (under ``~/.local``), before installing ``scikits.odes``. (Alternatively, one can install SUNDIALS without this script and run ``pip install pybamm[odes]`` to install ``pybamm`` with ``scikits.odes``.)
 
 Optional - JaxSolver
 ~~~~~~~~~~~~~~~~~~~~

From 965555004aa5bf30ba574cfd128ffa220bd715d8 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sat, 21 Oct 2023 03:12:23 +0530
Subject: [PATCH 016/109] Exit early on windows

---
 pybamm/install_odes.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/pybamm/install_odes.py b/pybamm/install_odes.py
index 528be140aa..639af99473 100644
--- a/pybamm/install_odes.py
+++ b/pybamm/install_odes.py
@@ -8,6 +8,12 @@
 
 from pybamm.util import root_dir
 
+def check_platform():
+    if sys.platform == "win32":
+        raise Exception("pybamm_install_odes is not supported on Windows.")
+
+check_platform()
+
 def install_required_module(module):
     try:
         __import__(module)

From 8b33770fd014172b5b97db4b7786cd15651c1d2b Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sat, 21 Oct 2023 03:17:26 +0530
Subject: [PATCH 017/109] Changelog

---
 CHANGELOG.md | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 008cad125f..00a6e974d7 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,5 +1,9 @@
 # [Unreleased](https://github.com/pybamm-team/PyBaMM/)
 
+## Features
+
+- Extend `pybamm_install_odes` to include support for macOS systems ([#3417](https://github.com/pybamm-team/PyBaMM/pull/3417))
+
 # [v23.9rc0](https://github.com/pybamm-team/PyBaMM/tree/v23.9rc0) - 2023-10-31
 
 ## Features

From ac803c5009db5866ab6a2eb681cb7ef9af13d764 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sat, 21 Oct 2023 03:20:13 +0530
Subject: [PATCH 018/109] Remove cache before installation

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

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 3bd78cbcd2..ead0bb4b3d 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -220,6 +220,7 @@ jobs:
 
       - name: Test pybamm_install_odes on ${{ matrix.os }}
         run: |
+          pip cache purge
           pip install wget cmake
           pybamm_install_odes
 

From 0ed80bbbcff95b5bfb69f7e711aa596c442f5621 Mon Sep 17 00:00:00 2001
From: Arjun <arjunverma.oc@gmail.com>
Date: Mon, 23 Oct 2023 23:22:42 +0530
Subject: [PATCH 019/109] Applied suggestions

Co-authored-by: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
---
 .github/workflows/test_on_push.yml | 2 --
 CHANGELOG.md                       | 2 +-
 2 files changed, 1 insertion(+), 3 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index ead0bb4b3d..68bdc187b4 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -210,8 +210,6 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python-version }}
-          cache: 'pip'
-          cache-dependency-path: setup.py
 
       - name: Install PyBaMM dependencies
         run: |
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 00a6e974d7..b6148896df 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -2,7 +2,7 @@
 
 ## Features
 
-- Extend `pybamm_install_odes` to include support for macOS systems ([#3417](https://github.com/pybamm-team/PyBaMM/pull/3417))
+- The `pybamm_install_odes` command now includes support for macOS systems and can be used to set up SUNDIALS and install the `scikits.odes` solver on macOS ([#3417](https://github.com/pybamm-team/PyBaMM/pull/3417))
 
 # [v23.9rc0](https://github.com/pybamm-team/PyBaMM/tree/v23.9rc0) - 2023-10-31
 

From 2698a875d99cda32736724e8bb4f93d988fc8bf9 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Mon, 23 Oct 2023 23:59:40 +0530
Subject: [PATCH 020/109] Move `test_install_odes` to scheduled

---
 .github/workflows/run_periodic_tests.yml | 45 ++++++++++++++++++++++++
 .github/workflows/test_on_push.yml       | 45 ------------------------
 2 files changed, 45 insertions(+), 45 deletions(-)

diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml
index f6e51bc11b..197c8e5872 100644
--- a/.github/workflows/run_periodic_tests.yml
+++ b/.github/workflows/run_periodic_tests.yml
@@ -164,3 +164,48 @@ jobs:
           eval "$(pyenv init -)"
           pyenv activate pybamm-${{ matrix.python-version }}
           pyenv uninstall -f $( python --version )
+
+  test_install_odes:
+    needs: style
+    runs-on: macos-latest
+    strategy:
+        matrix:
+            os: [ubuntu-latest, macos-latest]
+            python-version: ["3.8", "3.9", "3.10", "3.11"]
+        fail-fast: false
+    name: Test pybamm_install_odes on ${{ matrix.os }}
+
+    steps:
+      - name: Check out PyBaMM repository
+        uses: actions/checkout@v4
+
+      - name: Install macOS system dependencies
+        env:
+          # Homebrew environment variables
+          HOMEBREW_NO_INSTALL_CLEANUP: 1
+          HOMEBREW_NO_AUTO_UPDATE: 1
+          HOMEBREW_NO_COLOR: 1
+          # Speed up CI
+          NONINTERACTIVE: 1
+        run: |
+          brew analytics off
+          brew update
+          brew install openblas
+          brew reinstall gcc gfortran
+
+      - name: Set up Python ${{ matrix.python-version }}
+        id: setup-python
+        uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install PyBaMM dependencies
+        run: |
+          pip install --upgrade pip wheel setuptools nox
+          pip install -e .[all]
+
+      - name: Test pybamm_install_odes on ${{ matrix.os }}
+        run: |
+          pip cache purge
+          pip install wget cmake
+          pybamm_install_odes
diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 68bdc187b4..cb22fb87f7 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -177,51 +177,6 @@ jobs:
       - name: Upload coverage report
         uses: codecov/codecov-action@v3.1.4
 
-  test_install_odes:
-    needs: style
-    runs-on: macos-latest
-    strategy:
-        matrix:
-            os: [ubuntu-latest, macos-latest]
-            python-version: ["3.8", "3.9", "3.10", "3.11"]
-        fail-fast: false
-    name: Test pybamm_install_odes on ${{ matrix.os }}
-
-    steps:
-      - name: Check out PyBaMM repository
-        uses: actions/checkout@v4
-
-      - name: Install macOS system dependencies
-        env:
-          # Homebrew environment variables
-          HOMEBREW_NO_INSTALL_CLEANUP: 1
-          HOMEBREW_NO_AUTO_UPDATE: 1
-          HOMEBREW_NO_COLOR: 1
-          # Speed up CI
-          NONINTERACTIVE: 1
-        run: |
-          brew analytics off
-          brew update
-          brew install openblas
-          brew reinstall gcc gfortran
-
-      - name: Set up Python ${{ matrix.python-version }}
-        id: setup-python
-        uses: actions/setup-python@v4
-        with:
-          python-version: ${{ matrix.python-version }}
-
-      - name: Install PyBaMM dependencies
-        run: |
-          pip install --upgrade pip wheel setuptools nox
-          pip install -e .[all]
-
-      - name: Test pybamm_install_odes on ${{ matrix.os }}
-        run: |
-          pip cache purge
-          pip install wget cmake
-          pybamm_install_odes
-
   run_integration_tests:
     needs: style
     runs-on: ${{ matrix.os }}

From c062b17b2a54f4a3a2e1440c1e575cde9eb544f3 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Tue, 24 Oct 2023 11:14:16 +0530
Subject: [PATCH 021/109] Check platform without function

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

diff --git a/pybamm/install_odes.py b/pybamm/install_odes.py
index 639af99473..90df811219 100644
--- a/pybamm/install_odes.py
+++ b/pybamm/install_odes.py
@@ -8,11 +8,8 @@
 
 from pybamm.util import root_dir
 
-def check_platform():
-    if sys.platform == "win32":
-        raise Exception("pybamm_install_odes is not supported on Windows.")
-
-check_platform()
+if sys.platform == "win32":
+    raise Exception("pybamm_install_odes is not supported on Windows.")
 
 def install_required_module(module):
     try:

From e3c62b59a3ee3a7eda7537d89c0920bf4836794d Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Tue, 24 Oct 2023 11:30:50 +0530
Subject: [PATCH 022/109] Import module with importlib

---
 pybamm/install_odes.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/pybamm/install_odes.py b/pybamm/install_odes.py
index 90df811219..20c84c0fbd 100644
--- a/pybamm/install_odes.py
+++ b/pybamm/install_odes.py
@@ -5,6 +5,7 @@
 import sys
 import logging
 import subprocess
+from importlib import import_module
 
 from pybamm.util import root_dir
 
@@ -13,7 +14,7 @@
 
 def install_required_module(module):
     try:
-        __import__(module)
+        import_module(module)
     except ModuleNotFoundError:
         print(f"{module} module not found. Installing {module}...")
         subprocess.run(["pip", "install", module], check=True)

From 79539f46e625c13d3b4c473a028038fc33c18de3 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Tue, 24 Oct 2023 12:10:47 +0530
Subject: [PATCH 023/109] Define sundials version on top

---
 pybamm/install_odes.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/pybamm/install_odes.py b/pybamm/install_odes.py
index 20c84c0fbd..a3050a8b27 100644
--- a/pybamm/install_odes.py
+++ b/pybamm/install_odes.py
@@ -12,6 +12,8 @@
 if sys.platform == "win32":
     raise Exception("pybamm_install_odes is not supported on Windows.")
 
+SUNDIALS_VERSION = "6.5.0"
+
 def install_required_module(module):
     try:
         import_module(module)
@@ -35,7 +37,6 @@ def download_extract_library(url, directory):
 def install_sundials(download_dir, install_dir):
     # Download the SUNDIALS library and compile it.
     logger = logging.getLogger("scikits.odes setup")
-    sundials_version = "6.5.0"
 
     try:
         subprocess.run(["cmake", "--version"])
@@ -43,7 +44,7 @@ def install_sundials(download_dir, install_dir):
         raise RuntimeError("CMake must be installed to build SUNDIALS.")
 
     url = (
-        f"https://github.com/LLNL/sundials/releases/download/v{sundials_version}/sundials-{sundials_version}.tar.gz"
+        f"https://github.com/LLNL/sundials/releases/download/v{SUNDIALS_VERSION}/sundials-{SUNDIALS_VERSION}.tar.gz"
     )
     logger.info("Downloading sundials")
     download_extract_library(url, download_dir)
@@ -65,7 +66,7 @@ def install_sundials(download_dir, install_dir):
 
     print("-" * 10, "Running CMake prepare", "-" * 40)
     subprocess.run(
-        ["cmake", f"../sundials-{sundials_version}"] + cmake_args,
+        ["cmake", f"../sundials-{SUNDIALS_VERSION}"] + cmake_args,
         cwd=build_directory,
         check=True,
     )

From 6292cbfba65562ffe37e355d900b064fbb073806 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Tue, 24 Oct 2023 14:06:13 +0530
Subject: [PATCH 024/109] Detect terminal with `os.environ`

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

diff --git a/pybamm/install_odes.py b/pybamm/install_odes.py
index a3050a8b27..8cec6fc68b 100644
--- a/pybamm/install_odes.py
+++ b/pybamm/install_odes.py
@@ -86,16 +86,16 @@ def update_LD_LIBRARY_PATH(install_dir):
     if venv_path:
         script_path = os.path.join(venv_path, "bin/activate")
     else:
-        if sys.platform == "linux":
+        if 'BASH' in os.environ:
             script_path = os.path.join(os.environ.get("HOME"), ".bashrc")
-        if sys.platform == "darwin":
+        if 'ZSH' in os.environ:
             script_path = os.path.join(os.environ.get("HOME"), ".zshrc")
 
     if os.getenv("LD_LIBRARY_PATH") and f"{install_dir}/lib" in os.getenv("LD_LIBRARY_PATH"):  # noqa: E501
         print(f"{install_dir}/lib was found in LD_LIBRARY_PATH.")
-        if sys.platform == "linux":
+        if 'BASH' in os.environ:
             print("--> Not updating venv activate or .bashrc scripts")
-        if sys.platform == "darwin":
+        if 'ZSH' in os.environ:
             print("--> Not updating venv activate or .zshrc scripts")
     else:
         with open(script_path, "a+") as fh:

From 71e624589d7ac83fd568d68c13e621bcdb5f3080 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 27 Nov 2023 14:19:05 +0530
Subject: [PATCH 025/109] #3558 Add CasADi to RPATH when linking `idaklu`
 target

---
 CMakeLists.txt | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 182fd489f3..61abf440d4 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -72,18 +72,24 @@ execute_process(
 
 if (CASADI_DIR)
   file(TO_CMAKE_PATH ${CASADI_DIR} CASADI_DIR)
-  message("Found python casadi path: ${CASADI_DIR}")
+  message("Found Python casadi path: ${CASADI_DIR}")
 endif()
 
 if(${USE_PYTHON_CASADI})
-  message("Trying to link against python casadi package")
+  message("Trying to link against Python casadi package")
   find_package(casadi CONFIG PATHS ${CASADI_DIR} REQUIRED)
 else()
-  message("Trying to link against any casadi package apart from the python one")
+  message("Trying to link against any casadi package apart from the Python one")
   set(CMAKE_IGNORE_PATH "${CASADI_DIR}/cmake")
   find_package(casadi CONFIG REQUIRED)
 endif()
 
+set_target_properties(
+  idaklu PROPERTIES
+  INSTALL_RPATH "${CASADI_DIR}"
+  INSTALL_RPATH_USE_LINK_PATH TRUE
+)
+
 set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} ${PROJECT_SOURCE_DIR})
 # Sundials
 find_package(SUNDIALS REQUIRED)

From 1d08d0fd2c56dc96ee545b8a416dd133d6c947db Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 27 Nov 2023 14:19:26 +0530
Subject: [PATCH 026/109] #3558 Import `casadi` using `importlib` instead

---
 CMakeLists.txt | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 61abf440d4..cd10b0cf9d 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -64,9 +64,11 @@ if (NOT DEFINED USE_PYTHON_CASADI)
   set(USE_PYTHON_CASADI TRUE)
 endif()
 
+# Use importlib to find the casadi path without importing it. This is useful
+# to find the path for the build-time dependency, not the run-time dependency.
 execute_process(
     COMMAND "${PYTHON_EXECUTABLE}" -c
-    "import casadi as _; print(_.__path__[0])"
+    "import importlib.util; print(importlib.util.find_spec('casadi').submodule_search_locations[0])"
     OUTPUT_VARIABLE CASADI_DIR
     OUTPUT_STRIP_TRAILING_WHITESPACE)
 

From dfc0901f4653a33745ada73d52dca8c82b3b57d6 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 27 Nov 2023 14:22:31 +0530
Subject: [PATCH 027/109] #3558 add minimal test command and remove
 LD_LIBRARY_PATH override

---
 .github/workflows/publish_pypi.yml | 29 ++++++++++++++++++-----------
 1 file changed, 18 insertions(+), 11 deletions(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 3073c95f09..d0cc3ceb81 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -16,6 +16,13 @@ on:
         required: false
         default: false
 
+# Set options available for all jobs that use cibuildwheel
+env:
+  # Increase pip debugging output, equivalent to `pip -vv`
+  CIBW_BUILD_VERBOSITY: 2
+  # Disable build isolation to allow pre-installing build-time dependencies
+  # CIBW_BUILD_FRONTEND: "pip; args: --no-build-isolation"
+
 jobs:
   build_windows_wheels:
     name: Build wheels on windows-latest
@@ -55,6 +62,9 @@ jobs:
         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"
+          CIBW_BEFORE_BUILD: >
+            python -m pip install --upgrade setuptools wheel
+          CIBW_TEST_COMMAND: "python -c 'import pybamm; print(pybamm.have_idaklu())' | grep 'True'"
 
       - name: Upload Windows wheels
         uses: actions/upload-artifact@v3
@@ -63,7 +73,7 @@ jobs:
           path: ./wheelhouse/*.whl
           if-no-files-found: error
 
-  build_wheels:
+  build_macos_and_linux_wheels:
     name: Build wheels on ${{ matrix.os }}
     runs-on: ${{ matrix.os }}
     strategy:
@@ -96,17 +106,14 @@ jobs:
             yum -y install openblas-devel lapack-devel &&
             bash scripts/install_sundials.sh 6.0.3 6.5.0
           CIBW_BEFORE_BUILD_LINUX: >
-            python -m pip install cmake casadi numpy
-          # override; point to casadi install path so that it can be found by the repair command
+            python -m pip install cmake casadi setuptools wheel
           CIBW_REPAIR_WHEEL_COMMAND_LINUX: >
-            LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:$(python -c 'import casadi; print(casadi.__path__[0])')" auditwheel repair -w {dest_dir} {wheel}
+            auditwheel repair -w {dest_dir} {wheel}
           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 -m pip install --upgrade cmake casadi setuptools wheel && scripts/fix_suitesparse_rpath_mac.sh
           CIBW_REPAIR_WHEEL_COMMAND_MACOS: >
-            delocate-listdeps {wheel} &&
-            delocate-wheel -v -w {dest_dir} {wheel}
+            delocate-listdeps {wheel} && delocate-wheel -v -w {dest_dir} {wheel}
+          CIBW_TEST_COMMAND: "python -c 'import pybamm; print(pybamm.have_idaklu())' | grep 'True'"
           CIBW_SKIP: "pp* *musllinux*"
 
       - name: Upload wheels
@@ -142,7 +149,7 @@ jobs:
   publish_pypi:
     if: github.event_name != 'schedule'
     name: Upload package to PyPI
-    needs: [build_wheels, build_windows_wheels, build_sdist]
+    needs: [build_macos_and_linux_wheels, build_windows_wheels, build_sdist]
     runs-on: ubuntu-latest
     steps:
       - name: Download all artifacts
@@ -171,7 +178,7 @@ jobs:
           repository-url: https://test.pypi.org/legacy/
 
   open_failure_issue:
-    needs: [build_windows_wheels, build_wheels, build_sdist]
+    needs: [build_windows_wheels, build_macos_and_linux_wheels, build_sdist]
     name: Open an issue if build fails
     if: ${{ always() && contains(needs.*.result, 'failure') }}
     runs-on: ubuntu-latest

From d0be7ba47c06023985a2569e9239ee54527838c0 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 27 Nov 2023 14:22:58 +0530
Subject: [PATCH 028/109] #3558 remove script for RPATH adjustment

---
 scripts/fix_casadi_rpath_mac.py | 71 ---------------------------------
 1 file changed, 71 deletions(-)
 delete mode 100644 scripts/fix_casadi_rpath_mac.py

diff --git a/scripts/fix_casadi_rpath_mac.py b/scripts/fix_casadi_rpath_mac.py
deleted file mode 100644
index 23c8a32d59..0000000000
--- a/scripts/fix_casadi_rpath_mac.py
+++ /dev/null
@@ -1,71 +0,0 @@
-"""
-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
-"""
-import casadi
-import os
-import subprocess
-
-casadi_dir = casadi.__path__[0]
-print("Removing rpath references in python casadi install at", casadi_dir)
-
-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_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))
-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),
-    os.path.join(casadi_dir, libcppabi_name),
-    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
-# This is needed for the casadi python bindings to work while repairing the wheel
-
-subprocess.run(
-    ["cp",
-    os.path.join(casadi_dir, libcasadi_37_name),
-    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 b9edb5ca35f3e8b804a34a09212413449595b45e Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 27 Nov 2023 14:28:03 +0530
Subject: [PATCH 029/109] #3558 enable comment to disable build isolation

---
 .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 d0cc3ceb81..969d79317f 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -21,7 +21,7 @@ env:
   # Increase pip debugging output, equivalent to `pip -vv`
   CIBW_BUILD_VERBOSITY: 2
   # Disable build isolation to allow pre-installing build-time dependencies
-  # CIBW_BUILD_FRONTEND: "pip; args: --no-build-isolation"
+  CIBW_BUILD_FRONTEND: "pip; args: --no-build-isolation"
 
 jobs:
   build_windows_wheels:

From 8b2cb45c20275f8927cf2d66a6a14bf473e7e46d Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 27 Nov 2023 23:27:46 +0530
Subject: [PATCH 030/109] #3558 cleanup jobs, skip PyPI deployment on forks

---
 .github/workflows/publish_pypi.yml | 51 +++++++++++++++++-------------
 1 file changed, 29 insertions(+), 22 deletions(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 969d79317f..6b34a69907 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -20,12 +20,16 @@ on:
 env:
   # Increase pip debugging output, equivalent to `pip -vv`
   CIBW_BUILD_VERBOSITY: 2
-  # Disable build isolation to allow pre-installing build-time dependencies
+  # Disable build isolation to allow pre-installing build-time dependencies.
+  # Note: CIBW_BEFORE_BUILD must be present in all jobs using cibuildwheel.
   CIBW_BUILD_FRONTEND: "pip; args: --no-build-isolation"
+  # Skip PyPy and MUSL builds in any and all jobs
+  CIBW_SKIP: "pp* *musllinux*"
+  FORCE_COLOR: 3
 
 jobs:
   build_windows_wheels:
-    name: Build wheels on windows-latest
+    name: Wheels (windows-latest)
     runs-on: windows-latest
     steps:
       - uses: actions/checkout@v4
@@ -62,9 +66,7 @@ jobs:
         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"
-          CIBW_BEFORE_BUILD: >
-            python -m pip install --upgrade setuptools wheel
-          CIBW_TEST_COMMAND: "python -c 'import pybamm; print(pybamm.have_idaklu())' | grep 'True'"
+          CIBW_BEFORE_BUILD: python -m pip install setuptools wheel
 
       - name: Upload Windows wheels
         uses: actions/upload-artifact@v3
@@ -74,7 +76,7 @@ jobs:
           if-no-files-found: error
 
   build_macos_and_linux_wheels:
-    name: Build wheels on ${{ matrix.os }}
+    name: Wheels ${{ matrix.os }}
     runs-on: ${{ matrix.os }}
     strategy:
       fail-fast: false
@@ -82,7 +84,10 @@ jobs:
         os: [ubuntu-latest, macos-latest]
     steps:
       - uses: actions/checkout@v4
+        name: Check out PyBaMM repository
+
       - uses: actions/setup-python@v4
+        name: Set up Python
         with:
           python-version: 3.8
 
@@ -98,28 +103,32 @@ jobs:
           python -m pip install cmake wget
           python scripts/install_KLU_Sundials.py
 
-      - name: Build wheels on ${{ matrix.os }}
+      - name: Build wheels on Linux
         run: pipx run cibuildwheel --output-dir wheelhouse
+        if: matrix.os == 'ubuntu-latest'
         env:
           CIBW_ARCHS_LINUX: x86_64
           CIBW_BEFORE_ALL_LINUX: >
             yum -y install openblas-devel lapack-devel &&
             bash scripts/install_sundials.sh 6.0.3 6.5.0
-          CIBW_BEFORE_BUILD_LINUX: >
-            python -m pip install cmake casadi setuptools wheel
-          CIBW_REPAIR_WHEEL_COMMAND_LINUX: >
-            auditwheel repair -w {dest_dir} {wheel}
+          CIBW_BEFORE_BUILD_LINUX: python -m pip install cmake casadi setuptools wheel
+          CIBW_REPAIR_WHEEL_COMMAND_LINUX: auditwheel repair -w {dest_dir} {wheel}
+          CIBW_TEST_COMMAND: python -c "import pybamm; print(pybamm.have_idaklu())" | grep True
+
+      - name: Build wheels on macOS
+        if: matrix.os == 'macos-latest'
+        run: pipx run cibuildwheel --output-dir wheelhouse
+        env:
           CIBW_BEFORE_BUILD_MACOS: >
-            python -m pip install --upgrade cmake casadi setuptools wheel && scripts/fix_suitesparse_rpath_mac.sh
-          CIBW_REPAIR_WHEEL_COMMAND_MACOS: >
-            delocate-listdeps {wheel} && delocate-wheel -v -w {dest_dir} {wheel}
-          CIBW_TEST_COMMAND: "python -c 'import pybamm; print(pybamm.have_idaklu())' | grep 'True'"
-          CIBW_SKIP: "pp* *musllinux*"
+            python -m pip install --upgrade cmake casadi setuptools wheel &&
+            scripts/fix_suitesparse_rpath_mac.sh
+          CIBW_REPAIR_WHEEL_COMMAND_MACOS: delocate-listdeps {wheel} && delocate-wheel -v -w {dest_dir} {wheel}
+          CIBW_TEST_COMMAND: python -c "import pybamm; print(pybamm.have_idaklu())" | grep True
 
       - name: Upload wheels
         uses: actions/upload-artifact@v3
         with:
-          name: wheels
+          name: macos_linux_wheels
           path: ./wheelhouse/*.whl
           if-no-files-found: error
 
@@ -133,9 +142,6 @@ jobs:
         with:
           python-version: 3.11
 
-      - name: Install dependencies
-        run: pip install --upgrade pip setuptools wheel
-
       - name: Build SDist
         run: pipx run build --sdist
 
@@ -147,7 +153,8 @@ jobs:
           if-no-files-found: error
 
   publish_pypi:
-    if: github.event_name != 'schedule'
+    # This job is only of value to PyBaMM and would always be skipped in forks
+    if: github.event_name != 'schedule' && github.repository == 'pybamm-team/PyBaMM'
     name: Upload package to PyPI
     needs: [build_macos_and_linux_wheels, build_windows_wheels, build_sdist]
     runs-on: ubuntu-latest
@@ -158,7 +165,7 @@ jobs:
       - name: Move all package files to files/
         run: |
           mkdir files
-          mv windows_wheels/* wheels/* sdist/* files/
+          mv windows_wheels/* macos_linux_wheels/* sdist/* files/
 
       - name: Publish on PyPI
         if: github.event.inputs.target == 'pypi' || github.event_name == 'release'

From 4a0bbd3521485fbadf210f72b0502adfc66afa3c Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 27 Nov 2023 23:29:23 +0530
Subject: [PATCH 031/109] #3558 cover Windows wheel job with tests

---
 .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 6b34a69907..75e3ebc94b 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -66,7 +66,8 @@ jobs:
         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"
-          CIBW_BEFORE_BUILD: python -m pip install setuptools wheel
+          CIBW_BEFORE_BUILD: python -m pip install setuptools wheel # skip CasADi and CMake
+          CIBW_TEST_COMMAND: python -c "import pybamm; pybamm.IDAKLUSolver()"
 
       - name: Upload Windows wheels
         uses: actions/upload-artifact@v3
@@ -76,7 +77,7 @@ jobs:
           if-no-files-found: error
 
   build_macos_and_linux_wheels:
-    name: Wheels ${{ matrix.os }}
+    name: Wheels (${{ matrix.os }})
     runs-on: ${{ matrix.os }}
     strategy:
       fail-fast: false

From d150be3dc647f37399cf1c037afae36d37db7a7e Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 30 Nov 2023 15:32:07 +0530
Subject: [PATCH 032/109] Use `next(iter())` to evaluate `casadi` search paths

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

diff --git a/CMakeLists.txt b/CMakeLists.txt
index cd10b0cf9d..17c85a81bf 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -68,7 +68,7 @@ endif()
 # to find the path for the build-time dependency, not the run-time dependency.
 execute_process(
     COMMAND "${PYTHON_EXECUTABLE}" -c
-    "import importlib.util; print(importlib.util.find_spec('casadi').submodule_search_locations[0])"
+    "import importlib.util; print(next(iter(importlib.util.find_spec('casadi').submodule_search_locations)))"
     OUTPUT_VARIABLE CASADI_DIR
     OUTPUT_STRIP_TRAILING_WHITESPACE)
 

From 5df9f8a624880de6d63a190beb346db893ee5fb8 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Fri, 1 Dec 2023 09:00:22 +0530
Subject: [PATCH 033/109] #3558 try to initialise IDAKLU solver instead of just
 importing it

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

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 75e3ebc94b..a1db0e9a39 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -114,7 +114,7 @@ jobs:
             bash scripts/install_sundials.sh 6.0.3 6.5.0
           CIBW_BEFORE_BUILD_LINUX: python -m pip install cmake casadi setuptools wheel
           CIBW_REPAIR_WHEEL_COMMAND_LINUX: auditwheel repair -w {dest_dir} {wheel}
-          CIBW_TEST_COMMAND: python -c "import pybamm; print(pybamm.have_idaklu())" | grep True
+          CIBW_TEST_COMMAND: python -c "import pybamm; pybamm.IDAKLUSolver()"
 
       - name: Build wheels on macOS
         if: matrix.os == 'macos-latest'
@@ -124,7 +124,7 @@ jobs:
             python -m pip install --upgrade cmake casadi setuptools wheel &&
             scripts/fix_suitesparse_rpath_mac.sh
           CIBW_REPAIR_WHEEL_COMMAND_MACOS: delocate-listdeps {wheel} && delocate-wheel -v -w {dest_dir} {wheel}
-          CIBW_TEST_COMMAND: python -c "import pybamm; print(pybamm.have_idaklu())" | grep True
+          CIBW_TEST_COMMAND: python -c "import pybamm; pybamm.IDAKLUSolver()"
 
       - name: Upload wheels
         uses: actions/upload-artifact@v3

From 52697f2ea5acd1ed46ea3adf63d17320b3d80824 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Fri, 1 Dec 2023 20:30:18 +0530
Subject: [PATCH 034/109] #3558 #3100 keep equal `casadi` dependency versions

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

diff --git a/pyproject.toml b/pyproject.toml
index f02286ad18..19c8800a63 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -3,7 +3,7 @@ requires = [
     "setuptools>=64",
     "wheel",
     # On Windows, use the CasADi vcpkg registry and CMake bundled from MSVC
-    "casadi>=3.6.0; platform_system!='Windows'",
+    "casadi>=3.6.3; platform_system!='Windows'",
     "cmake; platform_system!='Windows'",
     ]
 build-backend = "setuptools.build_meta"

From 8608682903c4bba0c6abf4911912f3a91ca1d879 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Fri, 1 Dec 2023 20:31:26 +0530
Subject: [PATCH 035/109] #3558 #3100 Don't use a default path to search for
 alternative `casadi` installations

Co-Authored-By: jsbrittain <98161205+jsbrittain@users.noreply.github.com>
---
 CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 17c85a81bf..e9b3675e59 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -79,7 +79,7 @@ endif()
 
 if(${USE_PYTHON_CASADI})
   message("Trying to link against Python casadi package")
-  find_package(casadi CONFIG PATHS ${CASADI_DIR} REQUIRED)
+  find_package(casadi CONFIG PATHS ${CASADI_DIR} REQUIRED NO_DEFAULT_PATH)
 else()
   message("Trying to link against any casadi package apart from the Python one")
   set(CMAKE_IGNORE_PATH "${CASADI_DIR}/cmake")

From d900a81c8345b9e97803c969fc6107bebd17e2e5 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 14 Dec 2023 19:08:23 +0530
Subject: [PATCH 036/109] #3558 build SuiteSparse with INSTALL_RPATH

---
 scripts/install_KLU_Sundials.py | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/scripts/install_KLU_Sundials.py b/scripts/install_KLU_Sundials.py
index 8f41f5969a..5a09421c2b 100755
--- a/scripts/install_KLU_Sundials.py
+++ b/scripts/install_KLU_Sundials.py
@@ -70,20 +70,24 @@ def download_extract_library(url, download_dir):
 # - BTF
 suitesparse_dir = "SuiteSparse-{}".format(suitesparse_version)
 suitesparse_src = os.path.join(download_dir, suitesparse_dir)
+# Build with INSTALL_RPATH set to install_dir and set
+# INSTALL_RPATH_USE_LINK_PATH to TRUE to use RPATH when linking
 print("-" * 10, "Building SuiteSparse_config", "-" * 40)
 make_cmd = [
     "make",
     "library",
-    'CMAKE_OPTIONS="-DCMAKE_INSTALL_PREFIX={}"'.format(install_dir),
 ]
 install_cmd = [
     "make",
     "install",
 ]
 print("-" * 10, "Building SuiteSparse", "-" * 40)
+# # Set CMAKE_OPTIONS as environment variables to pass to GNU Make
+env = os.environ.copy()
+env["CMAKE_OPTIONS"] = f"-DCMAKE_INSTALL_PREFIX={install_dir} -DCMAKE_INSTALL_RPATH={install_dir} -DCMAKE_INSTALL_RPATH_USE_LINK_PATH=TRUE"
 for libdir in ["SuiteSparse_config", "AMD", "COLAMD", "BTF", "KLU"]:
     build_dir = os.path.join(suitesparse_src, libdir)
-    subprocess.run(make_cmd, cwd=build_dir, check=True)
+    subprocess.run(make_cmd, cwd=build_dir, env=env, shell=True, check=True)
     subprocess.run(install_cmd, cwd=build_dir, check=True)
 
 # 2 --- Download SUNDIALS
@@ -140,10 +144,7 @@ def download_extract_library(url, download_dir):
         "-DLDFLAGS=" + LDFLAGS,
         "-DCPPFLAGS=" + CPPFLAGS,
         "-DOpenMP_C_FLAGS=" + OpenMP_C_FLAGS,
-        "-DOpenMP_CXX_FLAGS=" + OpenMP_CXX_FLAGS,
         "-DOpenMP_C_LIB_NAMES=" + OpenMP_C_LIB_NAMES,
-        "-DOpenMP_CXX_LIB_NAMES=" + OpenMP_CXX_LIB_NAMES,
-        "-DOpenMP_libomp_LIBRARY=" + OpenMP_libomp_LIBRARY,
         "-DOpenMP_omp_LIBRARY=" + OpenMP_omp_LIBRARY,
     ]
 

From 6a9743d8a38aefb93c60cbcb47b8febed5133e43 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 14 Dec 2023 19:09:05 +0530
Subject: [PATCH 037/109] #3558 Remove some unused CMake arguments

CMake showed a warning about these arguments not being used during the compilation of the project.
---
 scripts/install_KLU_Sundials.py | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/scripts/install_KLU_Sundials.py b/scripts/install_KLU_Sundials.py
index 5a09421c2b..5f6f2ff110 100755
--- a/scripts/install_KLU_Sundials.py
+++ b/scripts/install_KLU_Sundials.py
@@ -108,13 +108,15 @@ def download_extract_library(url, download_dir):
 cmake_args = [
     "-DENABLE_LAPACK=ON",
     "-DSUNDIALS_INDEX_SIZE=32",
-    "-DEXAMPLES_ENABLE:BOOL=OFF",
+    "-DEXAMPLES_ENABLE_C=OFF",
+    "-DEXAMPLES_ENABLE_CXX=OFF",
+    "-DEXAMPLES_INSTALL=OFF",
     "-DENABLE_KLU=ON",
     "-DENABLE_OPENMP=ON",
     "-DKLU_INCLUDE_DIR={}".format(KLU_INCLUDE_DIR),
     "-DKLU_LIBRARY_DIR={}".format(KLU_LIBRARY_DIR),
     "-DCMAKE_INSTALL_PREFIX=" + install_dir,
-    # on mac use fixed paths rather than rpath
+    # on macOS use fixed paths rather than rpath
     "-DCMAKE_INSTALL_NAME_DIR=" + KLU_LIBRARY_DIR,
 ]
 
@@ -125,9 +127,7 @@ def download_extract_library(url, download_dir):
         LDFLAGS = "-L/opt/homebrew/opt/libomp/lib"
         CPPFLAGS = "-I/opt/homebrew/opt/libomp/include"
         OpenMP_C_FLAGS = "-Xpreprocessor -fopenmp -I/opt/homebrew/opt/libomp/include"
-        OpenMP_CXX_FLAGS = "-Xpreprocessor -fopenmp -I/opt/homebrew/opt/libomp/include"
         OpenMP_C_LIB_NAMES = "omp"
-        OpenMP_CXX_LIB_NAMES = "omp"
         OpenMP_libomp_LIBRARY = "/opt/homebrew/opt/libomp/lib/libomp.dylib"
         OpenMP_omp_LIBRARY = "/opt/homebrew/opt/libomp/lib/libomp.dylib"
     elif platform.processor() == "i386":
@@ -137,7 +137,6 @@ def download_extract_library(url, download_dir):
         OpenMP_CXX_FLAGS = "-Xpreprocessor -fopenmp -I/usr/local/opt/libomp/include"
         OpenMP_C_LIB_NAMES = "omp"
         OpenMP_CXX_LIB_NAMES = "omp"
-        OpenMP_libomp_LIBRARY = "/usr/local/opt/libomp/lib/libomp.dylib"
         OpenMP_omp_LIBRARY = "/usr/local/opt/libomp/lib/libomp.dylib"
 
     cmake_args += [

From 29941cec3fd33093e809782a0e07526683f809a0 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 14 Dec 2023 19:09:23 +0530
Subject: [PATCH 038/109] #3558 Remove SuiteSparse macOS RPATH fixer script

---
 scripts/fix_suitesparse_rpath_mac.sh | 17 -----------------
 1 file changed, 17 deletions(-)
 delete mode 100755 scripts/fix_suitesparse_rpath_mac.sh

diff --git a/scripts/fix_suitesparse_rpath_mac.sh b/scripts/fix_suitesparse_rpath_mac.sh
deleted file mode 100755
index 987d936ef5..0000000000
--- a/scripts/fix_suitesparse_rpath_mac.sh
+++ /dev/null
@@ -1,17 +0,0 @@
-#!/usr/bin/env bash
-
-LIBDIR=${HOME}/.local/lib
-
-otool -L ${LIBDIR}/libklu.2.dylib
-
-install_name_tool -change @rpath/libsuitesparseconfig.6.dylib ${LIBDIR}/libsuitesparseconfig.6.dylib ${LIBDIR}/libklu.2.dylib
-
-install_name_tool -change @rpath/libamd.3.dylib ${LIBDIR}/libamd.3.dylib ${LIBDIR}/libklu.2.dylib
-install_name_tool -change @rpath/libcolamd.3.dylib ${LIBDIR}/libcolamd.3.dylib ${LIBDIR}/libklu.2.dylib
-install_name_tool -change @rpath/libbtf.2.dylib ${LIBDIR}/libbtf.2.dylib ${LIBDIR}/libklu.2.dylib
-
-install_name_tool -change @rpath/libsuitesparseconfig.6.dylib ${LIBDIR}/libsuitesparseconfig.6.dylib ${LIBDIR}/libcolamd.3.dylib
-install_name_tool -change @rpath/libsuitesparseconfig.6.dylib ${LIBDIR}/libsuitesparseconfig.6.dylib ${LIBDIR}/libbtf.2.dylib
-install_name_tool -change @rpath/libsuitesparseconfig.6.dylib ${LIBDIR}/libsuitesparseconfig.6.dylib ${LIBDIR}/libcolamd.3.dylib
-
-otool -L ${LIBDIR}/libklu.2.dylib

From 8e59c1b6f92e560fab4c653b33d6513cc9d0e735 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 14 Dec 2023 19:10:22 +0530
Subject: [PATCH 039/109] #3361 #3558 Improve caching and remove `examples/`

---
 .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 2f7f94c9bc..0ac4acf80b 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -104,8 +104,7 @@ jobs:
             # Headers and dynamic library files for SuiteSparse and SUNDIALS
             ${{ 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', '**/noxfile.py') }}
+          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py', '**/noxfile.py', '**/test_on_push.yml') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux and macOS
         if: matrix.os != 'windows-latest'
@@ -160,8 +159,7 @@ jobs:
             # Headers and dynamic library files for SuiteSparse and SUNDIALS
             ${{ 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', '**/noxfile.py') }}
+          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py', '**/noxfile.py', '**/test_on_push.yml') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         run: python -m nox -s pybamm-requires
@@ -242,8 +240,7 @@ jobs:
             # Headers and dynamic library files for SuiteSparse and SUNDIALS
             ${{ 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', '**/noxfile.py') }}
+          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py', '**/noxfile.py', '**/test_on_push.yml') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux and macOS
         if: matrix.os != 'windows-latest'
@@ -341,8 +338,7 @@ jobs:
             # Headers and dynamic library files for SuiteSparse and SUNDIALS
             ${{ 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', '**/noxfile.py') }}
+          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py', '**/noxfile.py', '**/test_on_push.yml') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         run: python -m nox -s pybamm-requires
@@ -396,8 +392,7 @@ jobs:
             # Headers and dynamic library files for SuiteSparse and SUNDIALS
             ${{ 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', '**/noxfile.py') }}
+          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py', '**/noxfile.py', '**/test_on_push.yml') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         run: python -m nox -s pybamm-requires

From 7f1f74f7697d220e7649cf69da02c13db6ef8886 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 14 Dec 2023 19:19:22 +0530
Subject: [PATCH 040/109] #3558 Remove `scripts/fix_suitesparse_rpath_mac.sh`

---
 .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 45569b0dd9..534b8a1905 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -121,8 +121,7 @@ jobs:
         run: pipx run cibuildwheel --output-dir wheelhouse
         env:
           CIBW_BEFORE_BUILD_MACOS: >
-            python -m pip install --upgrade cmake casadi setuptools wheel &&
-            scripts/fix_suitesparse_rpath_mac.sh
+            python -m pip install --upgrade cmake casadi setuptools wheel
           CIBW_REPAIR_WHEEL_COMMAND_MACOS: delocate-listdeps {wheel} && delocate-wheel -v -w {dest_dir} {wheel}
           CIBW_TEST_COMMAND: python -c "import pybamm; pybamm.IDAKLUSolver()"
 

From ade8e7d973b9053bcf66e603e2c3085609f30d9c Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 14 Dec 2023 23:07:50 +0530
Subject: [PATCH 041/109] #3558 Set BUILD AND INSTALL RPATHs correctly

SuiteSparse dynamic libraries were being repeated in the list of paths without this configuration. Setting build rpaths for AMD, COLAMD, BTF, and KLU ensures that they do not reference the SuiteSparse config in the build folder but the one that is installed into the install prefix.
---
 scripts/install_KLU_Sundials.py | 14 ++++++++++++--
 1 file changed, 12 insertions(+), 2 deletions(-)

diff --git a/scripts/install_KLU_Sundials.py b/scripts/install_KLU_Sundials.py
index 5f6f2ff110..8793eb09da 100755
--- a/scripts/install_KLU_Sundials.py
+++ b/scripts/install_KLU_Sundials.py
@@ -82,11 +82,21 @@ def download_extract_library(url, download_dir):
     "install",
 ]
 print("-" * 10, "Building SuiteSparse", "-" * 40)
-# # Set CMAKE_OPTIONS as environment variables to pass to GNU Make
+# Set CMAKE_OPTIONS as environment variables to pass to the GNU Make command
 env = os.environ.copy()
-env["CMAKE_OPTIONS"] = f"-DCMAKE_INSTALL_PREFIX={install_dir} -DCMAKE_INSTALL_RPATH={install_dir} -DCMAKE_INSTALL_RPATH_USE_LINK_PATH=TRUE"
 for libdir in ["SuiteSparse_config", "AMD", "COLAMD", "BTF", "KLU"]:
     build_dir = os.path.join(suitesparse_src, libdir)
+    # We want to ensure that libsuitesparseconfig.dylib is not repeated in
+    # multiple paths at the time of wheel repair. Therefore, it should not be
+    # built with an RPATH since it is copied to the install prefix.
+    if libdir == "SuiteSparse_config":
+        env["CMAKE_OPTIONS"] = f"-DCMAKE_INSTALL_PREFIX={install_dir}"
+    else:
+    # For AMD, COLAMD, BTF and KLU; do not set a BUILD RPATH but use an
+    # INSTALL RPATH in order to ensure that the dynamic libraries are found
+    # at runtime just once. Otherwise delocate complains about multiple
+    # references to the SuiteSparse_config dynamic libaries.
+        env["CMAKE_OPTIONS"] = f"-DCMAKE_INSTALL_PREFIX={install_dir} -DCMAKE_INSTALL_RPATH={install_dir}/lib -DCMAKE_INSTALL_RPATH_USE_LINK_PATH=FALSE -DCMAKE_BUILD_WITH_INSTALL_RPATH=FALSE"
     subprocess.run(make_cmd, cwd=build_dir, env=env, shell=True, check=True)
     subprocess.run(install_cmd, cwd=build_dir, check=True)
 

From 60c6e02896fcf239268545f2ce646e1761d0b590 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Mon, 18 Dec 2023 02:58:30 +0530
Subject: [PATCH 042/109] Prevent separate function to install dependencies

---
 pybamm/install_odes.py | 26 ++++++++++++--------------
 1 file changed, 12 insertions(+), 14 deletions(-)

diff --git a/pybamm/install_odes.py b/pybamm/install_odes.py
index e01be3a7f3..f7b50150ca 100644
--- a/pybamm/install_odes.py
+++ b/pybamm/install_odes.py
@@ -5,7 +5,6 @@
 import sys
 import logging
 import subprocess
-from importlib import import_module
 
 from pybamm.util import root_dir
 
@@ -14,22 +13,21 @@
 
 SUNDIALS_VERSION = "6.5.0"
 
-def install_required_module(module):
-    try:
-        import_module(module)
-    except ModuleNotFoundError:
-        print(f"{module} module not found. Installing {module}...")
-        subprocess.run(["pip", "install", module], check=True)
-
-required_modules = ["wget", "cmake"]
-
-for module in required_modules:
-    install_required_module(module)
-
-import wget  # noqa: E402
+try:
+    # wget module is required to download SUNDIALS or SuiteSparse.
+    import wget
+    NO_WGET = False
+except ModuleNotFoundError:
+    NO_WGET = True
 
 def download_extract_library(url, directory):
     # Download and extract archive at url
+    if NO_WGET:
+        error_msg = (
+            "Could not find wget module."
+            " Please install wget module (pip install wget)."
+        )
+        raise ModuleNotFoundError(error_msg)
     archive = wget.download(url, out=directory)
     tar = tarfile.open(archive)
     tar.extractall(directory)

From f365ea557b1dc2b676c53d7e1f30ac66f0fd6ee3 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 19 Dec 2023 16:39:13 +0530
Subject: [PATCH 043/109] #3100 bump `vcpkg` baseline for `casadi` `3.6.4`

---
 vcpkg-configuration.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vcpkg-configuration.json b/vcpkg-configuration.json
index 8ab4e738fc..f33d9205b0 100644
--- a/vcpkg-configuration.json
+++ b/vcpkg-configuration.json
@@ -13,7 +13,7 @@
     {
       "kind": "git",
       "repository": "https://github.com/pybamm-team/casadi-vcpkg-registry.git",
-      "baseline": "70f49f3c22fee4874fb8a36ef1a559f2c185ef1f",
+      "baseline": "baa26c2e629ea18fbb1aefa7d27c6612c4068fa7",
       "packages": ["casadi"]
     }
   ]

From 65a9d6edde2c3661f054d7f813e1333b8c555e12 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 19 Dec 2023 16:45:24 +0530
Subject: [PATCH 044/109] #3100 #3193 Add note for keeping `casadi` version in
 sync

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

diff --git a/pyproject.toml b/pyproject.toml
index e95017eb75..bd912ba23a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,6 +4,9 @@ requires = [
     "wheel",
     # On Windows, use the CasADi vcpkg registry and CMake bundled from MSVC
     "casadi>=3.6.3; platform_system!='Windows'",
+    # Note: the version of CasADi as a build-time dependency should be matched
+    # cross platforms, so updates to its minimum version here should be accompanied
+    # by a version bump in https://github.com/pybamm-team/casadi-vcpkg-registry.
     "cmake; platform_system!='Windows'",
     ]
 build-backend = "setuptools.build_meta"

From c8266ed8551b08b96094cd5806df414122598142 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Wed, 20 Dec 2023 21:05:20 +0530
Subject: [PATCH 045/109] Check for shell files directly

---
 .all-contributorsrc                           |  10 +
 .github/workflows/periodic_benchmarks.yml     |   8 +-
 .github/workflows/publish_pypi.yml            |  10 +-
 .../workflows/run_benchmarks_over_history.yml |   8 +-
 .github/workflows/run_periodic_tests.yml      |   8 +-
 .github/workflows/test_on_push.yml            |  46 +-
 .gitignore                                    |   1 +
 .pre-commit-config.yaml                       |   2 +-
 .readthedocs.yaml                             |   2 +-
 CHANGELOG.md                                  |   4 +
 README.md                                     |   3 +-
 .../parameterization/parameterization.ipynb   | 664 ++++++------------
 .../user_guide/installation/GNU-linux.rst     |  14 +-
 .../installation/install-from-source.rst      |   2 +-
 .../user_guide/installation/windows.rst       |   2 +-
 noxfile.py                                    |  62 +-
 pybamm/input/parameters/lithium_ion/Ai2020.py |   2 +-
 pybamm/install_odes.py                        |  17 +-
 pybamm/models/base_model.py                   |  66 +-
 pybamm/solvers/base_solver.py                 |   7 +-
 pyproject.toml                                |   3 +-
 setup.py                                      |  15 +-
 22 files changed, 391 insertions(+), 565 deletions(-)

diff --git a/.all-contributorsrc b/.all-contributorsrc
index 7cc68678e0..1cc25d48f8 100644
--- a/.all-contributorsrc
+++ b/.all-contributorsrc
@@ -773,6 +773,16 @@
       "contributions": [
         "infra"
       ]
+    },
+    {
+      "login": "XuboGU",
+      "name": "XuboGU",
+      "avatar_url": "https://avatars.githubusercontent.com/u/53944452?v=4",
+      "profile": "https://github.com/XuboGU",
+      "contributions": [
+        "code",
+        "bug"
+      ]
     }
   ],
   "contributorsPerLine": 7,
diff --git a/.github/workflows/periodic_benchmarks.yml b/.github/workflows/periodic_benchmarks.yml
index 9bd105ae92..c778c934bf 100644
--- a/.github/workflows/periodic_benchmarks.yml
+++ b/.github/workflows/periodic_benchmarks.yml
@@ -48,9 +48,9 @@ jobs:
           LD_LIBRARY_PATH: $HOME/.local/lib
 
       - name: Upload results as artifact
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
-          name: asv_new_results
+          name: asv_periodic_results
           path: results
 
   publish-results:
@@ -73,9 +73,9 @@ jobs:
           token: ${{ secrets.BENCH_PAT }}
 
       - name: Download results artifact
-        uses: actions/download-artifact@v3
+        uses: actions/download-artifact@v4
         with:
-          name: asv_new_results
+          name: asv_periodic_results
           path: new_results
 
       - name: Copy new results and push to pybamm-bench repo
diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index b003152802..90b67e9f87 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -57,7 +57,7 @@ jobs:
           CIBW_ARCHS: "AMD64"
 
       - name: Upload Windows wheels
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
           name: windows_wheels
           path: ./wheelhouse/*.whl
@@ -110,7 +110,7 @@ jobs:
           CIBW_SKIP: "pp* *musllinux*"
 
       - name: Upload wheels
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
           name: wheels
           path: ./wheelhouse/*.whl
@@ -124,7 +124,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v5
         with:
-          python-version: 3.11
+          python-version: 3.12
 
       - name: Install dependencies
         run: pip install --upgrade pip setuptools wheel
@@ -133,7 +133,7 @@ jobs:
         run: pipx run build --sdist
 
       - name: Upload SDist
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
           name: sdist
           path: ./dist/*.tar.gz
@@ -146,7 +146,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Download all artifacts
-        uses: actions/download-artifact@v3
+        uses: actions/download-artifact@v4
 
       - name: Move all package files to files/
         run: |
diff --git a/.github/workflows/run_benchmarks_over_history.yml b/.github/workflows/run_benchmarks_over_history.yml
index cb16f65847..4f7302a4a5 100644
--- a/.github/workflows/run_benchmarks_over_history.yml
+++ b/.github/workflows/run_benchmarks_over_history.yml
@@ -42,9 +42,9 @@ jobs:
           asv run -m "GitHubRunner" -s ${{ github.event.inputs.ncommits }} \
           ${{ github.event.inputs.commit_start }}..${{ github.event.inputs.commit_end }}
       - name: Upload results as artifact
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
-          name: asv_new_results
+          name: asv_over_history_results
           path: results
 
   publish-results:
@@ -65,9 +65,9 @@ jobs:
           repository: pybamm-team/pybamm-bench
           token: ${{ secrets.BENCH_PAT }}
       - name: Download results artifact
-        uses: actions/download-artifact@v3
+        uses: actions/download-artifact@v4
         with:
-          name: asv_new_results
+          name: asv_over_history_results
           path: new_results
       - name: Copy new results and push to pybamm-bench repo
         env:
diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml
index 3f041de65d..1c402d312e 100644
--- a/.github/workflows/run_periodic_tests.yml
+++ b/.github/workflows/run_periodic_tests.yml
@@ -31,7 +31,7 @@ jobs:
       - name: Setup python
         uses: actions/setup-python@v5
         with:
-          python-version: 3.11
+          python-version: 3.12
 
       - name: Check style
         run: |
@@ -46,7 +46,7 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-latest, macos-latest, windows-latest]
-        python-version: ["3.8", "3.9", "3.10", "3.11"]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
 
     steps:
       - uses: actions/checkout@v4
@@ -80,7 +80,7 @@ jobs:
         if: matrix.os != 'windows-latest'
         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
+      - name: Run unit tests for GNU/Linux with Python 3.8, 3.9, 3.10, and 3.12; and for macOS and Windows with all Python versions
         if: (matrix.os == 'ubuntu-latest' && matrix.python-version != 3.11) || (matrix.os != 'ubuntu-latest')
         run: python -m nox -s unit
 
@@ -121,7 +121,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11"]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
 
     steps:
       - uses: actions/checkout@v4
diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 77d28d8f88..53942acd31 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -23,7 +23,7 @@ jobs:
       - name: Setup Python
         uses: actions/setup-python@v5
         with:
-          python-version: 3.11
+          python-version: 3.12
 
       - name: Check style
         run: |
@@ -38,8 +38,9 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-latest, macos-latest, windows-latest]
-        python-version: ["3.8", "3.9", "3.10", "3.11"]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
         # We check coverage on Ubuntu with Python 3.11, so we skip unit tests for it here
+        # TODO: check coverage with Python 3.12 when [odes] supports it
         exclude:
           - os: ubuntu-latest
             python-version: "3.11"
@@ -116,6 +117,7 @@ jobs:
         run: python -m nox -s unit
 
   # Runs only on Ubuntu with Python 3.11
+  # TODO: check coverage with Python 3.12 when [odes] supports it
   check_coverage:
     needs: style
     runs-on: ubuntu-latest
@@ -180,7 +182,7 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-latest, macos-latest, windows-latest]
-        python-version: ["3.8", "3.9", "3.10", "3.11"]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
     name: Integration tests (${{ matrix.os }} / Python ${{ matrix.python-version }})
 
     steps:
@@ -253,14 +255,14 @@ jobs:
       - name: Run integration tests for ${{ matrix.os }} with Python ${{ matrix.python-version }}
         run: python -m nox -s integration
 
-# Runs only on Ubuntu with Python 3.11. Skips IDAKLU module compilation
+# Runs only on Ubuntu with Python 3.12. Skips IDAKLU module compilation
 # for speedups, which is already tested in other jobs.
   run_doctests:
     needs: style
     runs-on: ubuntu-latest
     strategy:
       fail-fast: false
-    name: Doctests (ubuntu-latest / Python 3.11)
+    name: Doctests (ubuntu-latest / Python 3.12)
 
     steps:
       - name: Check out PyBaMM repository
@@ -270,39 +272,39 @@ jobs:
       - name: Install Linux system dependencies
         uses: awalsh128/cache-apt-pkgs-action@v1.3.1
         with:
-          packages: gfortran gcc graphviz pandoc
+          packages: graphviz pandoc
           execute_install_scripts: true
 
       # dot -c is for registering graphviz fonts and plugins
-      - name: Install OpenBLAS and TeXLive for Linux
+      - name: Install TeXLive for Linux
         run: |
           sudo apt-get update
           sudo dot -c
-          sudo apt-get install libopenblas-dev texlive-latex-extra dvipng
+          sudo apt-get install texlive-latex-extra dvipng
 
-      - name: Set up Python 3.11
+      - name: Set up Python 3.12
         id: setup-python
         uses: actions/setup-python@v5
         with:
-          python-version: 3.11
+          python-version: 3.12
           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
+      - name: Install docs dependencies and run doctests for GNU/Linux with Python 3.12
         run: python -m nox -s doctests
 
-      - name: Check if the documentation can be built for GNU/Linux with Python 3.11
+      - name: Check if the documentation can be built for GNU/Linux with Python 3.12
         run: python -m nox -s docs
 
-  # Runs only on Ubuntu with Python 3.11
+  # Runs only on Ubuntu with Python 3.12
   run_example_tests:
     needs: style
     runs-on: ubuntu-latest
     strategy:
       fail-fast: false
-    name: Example notebooks (ubuntu-latest / Python 3.11)
+    name: Example notebooks (ubuntu-latest / Python 3.12)
 
     steps:
       - name: Check out PyBaMM repository
@@ -322,11 +324,11 @@ jobs:
           sudo dot -c
           sudo apt-get install libopenblas-dev texlive-latex-extra dvipng
 
-      - name: Set up Python 3.11
+      - name: Set up Python 3.12
         id: setup-python
         uses: actions/setup-python@v5
         with:
-          python-version: 3.11
+          python-version: 3.12
           cache: 'pip'
 
       - name: Install nox
@@ -348,16 +350,16 @@ jobs:
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         run: python -m nox -s pybamm-requires
 
-      - name: Run example notebooks tests for GNU/Linux with Python 3.11
+      - name: Run example notebooks tests for GNU/Linux with Python 3.12
         run: python -m nox -s examples
 
-  # Runs only on Ubuntu with Python 3.11
+  # Runs only on Ubuntu with Python 3.12
   run_scripts_tests:
     needs: style
     runs-on: ubuntu-latest
     strategy:
       fail-fast: false
-    name: Example scripts (ubuntu-latest / Python 3.11)
+    name: Example scripts (ubuntu-latest / Python 3.12)
 
     steps:
       - name: Check out PyBaMM repository
@@ -377,11 +379,11 @@ jobs:
           sudo dot -c
           sudo apt-get install libopenblas-dev texlive-latex-extra dvipng
 
-      - name: Set up Python 3.11
+      - name: Set up Python 3.12
         id: setup-python
         uses: actions/setup-python@v5
         with:
-          python-version: 3.11
+          python-version: 3.12
           cache: 'pip'
 
       - name: Install nox
@@ -403,5 +405,5 @@ jobs:
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         run: python -m nox -s pybamm-requires
 
-      - name: Run example scripts tests for GNU/Linux with Python 3.11
+      - name: Run example scripts tests for GNU/Linux with Python 3.12
         run: python -m nox -s scripts
diff --git a/.gitignore b/.gitignore
index 3dfafa2a8f..46c7e02b9f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -113,6 +113,7 @@ scikits_odes_setup.log
 # test
 test.c
 test.json
+.pytest_cache/
 
 # tox
 .tox/
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 41b19d7073..9b3a8f9d4b 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: "v0.1.7"
+    rev: "v0.1.8"
     hooks:
       - id: ruff
         args: [--fix, --show-fixes]
diff --git a/.readthedocs.yaml b/.readthedocs.yaml
index f907ac23d5..fb84bce9cb 100644
--- a/.readthedocs.yaml
+++ b/.readthedocs.yaml
@@ -24,7 +24,7 @@ build:
     - "graphviz"
   os: ubuntu-22.04
   tools:
-    python: "3.11"
+    python: "3.12"
     # You can also specify other tool versions:
     # nodejs: "19"
     # rust: "1.64"
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 2151b72324..a6b3f53e16 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -3,12 +3,16 @@
 ## Features
 
 - The `pybamm_install_odes` command now includes support for macOS systems and can be used to set up SUNDIALS and install the `scikits.odes` solver on macOS ([#3417](https://github.com/pybamm-team/PyBaMM/pull/3417))
+- Added support for Python 3.12 ([#3531](https://github.com/pybamm-team/PyBaMM/pull/3531))
 - Added method to get QuickPlot axes by variable ([#3596](https://github.com/pybamm-team/PyBaMM/pull/3596))
 - Added custom experiment terminations ([#3596](https://github.com/pybamm-team/PyBaMM/pull/3596))
 - Mechanical parameters are now a function of stoichiometry and temperature ([#3576](https://github.com/pybamm-team/PyBaMM/pull/3576))
 - Added a new unary operator, `EvaluateAt`, that evaluates a spatial variable at a given position ([#3573](https://github.com/pybamm-team/PyBaMM/pull/3573))
 - Added a method, `insert_reference_electrode`, to `pybamm.lithium_ion.BaseModel` that insert a reference electrode to measure the electrolyte potential at a given position in space and adds new variables that mimic a 3E cell setup. ([#3573](https://github.com/pybamm-team/PyBaMM/pull/3573))
 - Serialisation added so models can be written to/read from JSON ([#3397](https://github.com/pybamm-team/PyBaMM/pull/3397))
+- Added a `get_parameter_info` method for models and modified "print_parameter_info" functionality to extract all parameters and their type in a tabular and readable format ([#3584](https://github.com/pybamm-team/PyBaMM/pull/3584))
+- Mechanical parameters are now a function of stoichiometry and temperature ([#3576](https://github.com/pybamm-team/PyBaMM/pull/3576))
+
 
 ## Bug fixes
 
diff --git a/README.md b/README.md
index 8bad257378..d5050cfe55 100644
--- a/README.md
+++ b/README.md
@@ -14,7 +14,7 @@
 [![code style](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
 
 <!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section -->
-[![All Contributors](https://img.shields.io/badge/all_contributors-71-orange.svg)](#-contributors)
+[![All Contributors](https://img.shields.io/badge/all_contributors-72-orange.svg)](#-contributors)
 <!-- ALL-CONTRIBUTORS-BADGE:END -->
 
 </div>
@@ -277,6 +277,7 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
     </tr>
     <tr>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/prady0t"><img src="https://avatars.githubusercontent.com/u/99216956?v=4?s=100" width="100px;" alt="Pradyot Ranjan"/><br /><sub><b>Pradyot Ranjan</b></sub></a><br /><a href="#infra-prady0t" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/XuboGU"><img src="https://avatars.githubusercontent.com/u/53944452?v=4?s=100" width="100px;" alt="XuboGU"/><br /><sub><b>XuboGU</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/commits?author=XuboGU" title="Code">💻</a> <a href="https://github.com/pybamm-team/PyBaMM/issues?q=author%3AXuboGU" title="Bug reports">🐛</a></td>
     </tr>
   </tbody>
 </table>
diff --git a/docs/source/examples/notebooks/parameterization/parameterization.ipynb b/docs/source/examples/notebooks/parameterization/parameterization.ipynb
index 3ec04e9654..50be5e8ed9 100644
--- a/docs/source/examples/notebooks/parameterization/parameterization.ipynb
+++ b/docs/source/examples/notebooks/parameterization/parameterization.ipynb
@@ -29,13 +29,18 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:18.822760400Z",
+     "start_time": "2023-12-10T12:14:16.732217100Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "zsh:1: no matches found: pybamm[plot,cite]\n",
+      "/bin/bash: warning: setlocale: LC_ALL: cannot change locale (en_US.UTF-8)\r\n",
       "Note: you may need to restart the kernel to use updated packages.\n"
      ]
     }
@@ -60,7 +65,12 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:18.832156400Z",
+     "start_time": "2023-12-10T12:14:18.822760400Z"
+    }
+   },
    "outputs": [],
    "source": [
     "c = pybamm.Variable(\"Concentration [mol.m-3]\", domain=\"negative particle\")\n",
@@ -83,7 +93,12 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:18.841423200Z",
+     "start_time": "2023-12-10T12:14:18.827008900Z"
+    }
+   },
    "outputs": [],
    "source": [
     "model = pybamm.BaseModel()\n",
@@ -119,7 +134,12 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:18.843095800Z",
+     "start_time": "2023-12-10T12:14:18.841423200Z"
+    }
+   },
    "outputs": [],
    "source": [
     "r = pybamm.SpatialVariable(\"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\")\n",
@@ -145,16 +165,22 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:18.852037800Z",
+     "start_time": "2023-12-10T12:14:18.845139Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Initial concentration [mol.m-3] (Parameter)\n",
-      "Interfacial current density [A.m-2] (InputParameter)\n",
-      "Diffusion coefficient [m2.s-1] (FunctionParameter with input(s) 'Concentration [mol.m-3]')\n",
-      "\n",
+      "| Parameter                           | Type of parameter                                          |\n",
+      "| =================================== | ========================================================== |\n",
+      "| Initial concentration [mol.m-3]     | Parameter                                                  |\n",
+      "| Interfacial current density [A.m-2] | InputParameter                                             |\n",
+      "| Diffusion coefficient [m2.s-1]      | FunctionParameter with inputs(s) 'Concentration [mol.m-3]' |\n",
       "Particle radius [m] (Parameter)\n"
      ]
     }
@@ -185,7 +211,12 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:18.854076300Z",
+     "start_time": "2023-12-10T12:14:18.849343800Z"
+    }
+   },
    "outputs": [],
    "source": [
     "def D_fun(c):\n",
@@ -210,19 +241,16 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:18.889781200Z",
+     "start_time": "2023-12-10T12:14:18.853120600Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "{'Boltzmann constant [J.K-1]': 1.380649e-23,\n",
-       " 'Diffusion coefficient [m2.s-1]': <function D_fun at 0x140e14b80>,\n",
-       " 'Electron charge [C]': 1.602176634e-19,\n",
-       " 'Faraday constant [C.mol-1]': 96485.33212,\n",
-       " 'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n",
-       " 'Initial concentration [mol.m-3]': 2.5,\n",
-       " 'Particle radius [m]': 2}"
-      ]
+      "text/plain": "{'Boltzmann constant [J.K-1]': 1.380649e-23,\n 'Diffusion coefficient [m2.s-1]': <function D_fun at 0x7f48221f0220>,\n 'Electron charge [C]': 1.602176634e-19,\n 'Faraday constant [C.mol-1]': 96485.33212,\n 'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n 'Initial concentration [mol.m-3]': 2.5,\n 'Particle radius [m]': 2}"
      },
      "execution_count": 7,
      "metadata": {},
@@ -248,19 +276,16 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:18.890819200Z",
+     "start_time": "2023-12-10T12:14:18.859679800Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "{'Boltzmann constant [J.K-1]': 1.380649e-23,\n",
-       " 'Diffusion coefficient [m2.s-1]': <function D_fun at 0x140e14b80>,\n",
-       " 'Electron charge [C]': 1.602176634e-19,\n",
-       " 'Faraday constant [C.mol-1]': 96485.33212,\n",
-       " 'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n",
-       " 'Initial concentration [mol.m-3]': 1.5,\n",
-       " 'Particle radius [m]': 2}"
-      ]
+      "text/plain": "{'Boltzmann constant [J.K-1]': 1.380649e-23,\n 'Diffusion coefficient [m2.s-1]': <function D_fun at 0x7f48221f0220>,\n 'Electron charge [C]': 1.602176634e-19,\n 'Faraday constant [C.mol-1]': 96485.33212,\n 'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n 'Initial concentration [mol.m-3]': 1.5,\n 'Particle radius [m]': 2}"
      },
      "execution_count": 8,
      "metadata": {},
@@ -294,16 +319,16 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "scrolled": true
+    "scrolled": true,
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:18.891821400Z",
+     "start_time": "2023-12-10T12:14:18.864911Z"
+    }
    },
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "[Parameter(-0x6a2dafa7592b0120, Initial concentration [mol.m-3], children=[], domains={}),\n",
-       " InputParameter(0x217db8be7d80d00, Interfacial current density [A.m-2], children=[], domains={}),\n",
-       " FunctionParameter(-0x1834ea6ea33ab3ac, Diffusion coefficient [m2.s-1], children=['Concentration [mol.m-3]'], domains={'primary': ['negative particle']})]"
-      ]
+      "text/plain": "[Parameter(-0x60748912cbf94f86, Initial concentration [mol.m-3], children=[], domains={}),\n InputParameter(0x650425db234f99f4, Interfacial current density [A.m-2], children=[], domains={}),\n FunctionParameter(-0x302b1e5afcbfd4d9, Diffusion coefficient [m2.s-1], children=['Concentration [mol.m-3]'], domains={'primary': ['negative particle']})]"
      },
      "execution_count": 9,
      "metadata": {},
@@ -326,7 +351,12 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:18.891821400Z",
+     "start_time": "2023-12-10T12:14:18.868969800Z"
+    }
+   },
    "outputs": [],
    "source": [
     "param.process_model(model)\n",
@@ -344,7 +374,12 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:18.951625100Z",
+     "start_time": "2023-12-10T12:14:18.875173500Z"
+    }
+   },
    "outputs": [],
    "source": [
     "submesh_types = {\"negative particle\": pybamm.Uniform1DSubMesh}\n",
@@ -367,14 +402,17 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:19.168402100Z",
+     "start_time": "2023-12-10T12:14:18.890819200Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 1300x400 with 2 Axes>"
-      ]
+      "text/plain": "<Figure size 1300x400 with 2 Axes>",
+      "image/png": "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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -424,7 +462,12 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:19.267027300Z",
+     "start_time": "2023-12-10T12:14:19.197131800Z"
+    }
+   },
    "outputs": [],
    "source": [
     "spm = pybamm.lithium_ion.SPM()"
@@ -437,59 +480,65 @@
    "source": [
     "## Finding the parameters in a model\n",
     "\n",
-    "We can print the `parameters` of a model by using the `get_parameters_info` function."
+    "We can print the `parameters` of a model by using the `print_parameter_info` function."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:19.268048600Z",
+     "start_time": "2023-12-10T12:14:19.202421100Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Negative electrode Bruggeman coefficient (electrolyte) (Parameter)\n",
-      "Positive electrode Bruggeman coefficient (electrode) (Parameter)\n",
-      "Lower voltage cut-off [V] (Parameter)\n",
-      "Faraday constant [C.mol-1] (Parameter)\n",
-      "Ideal gas constant [J.K-1.mol-1] (Parameter)\n",
-      "Electrode width [m] (Parameter)\n",
-      "Positive electrode thickness [m] (Parameter)\n",
-      "Separator Bruggeman coefficient (electrolyte) (Parameter)\n",
-      "Positive electrode Bruggeman coefficient (electrolyte) (Parameter)\n",
-      "Upper voltage cut-off [V] (Parameter)\n",
-      "Number of electrodes connected in parallel to make a cell (Parameter)\n",
-      "Maximum concentration in negative electrode [mol.m-3] (Parameter)\n",
-      "Nominal cell capacity [A.h] (Parameter)\n",
-      "Reference temperature [K] (Parameter)\n",
-      "Maximum concentration in positive electrode [mol.m-3] (Parameter)\n",
-      "Separator thickness [m] (Parameter)\n",
-      "Initial concentration in electrolyte [mol.m-3] (Parameter)\n",
-      "Negative electrode Bruggeman coefficient (electrode) (Parameter)\n",
-      "Electrode height [m] (Parameter)\n",
-      "Number of cells connected in series to make a battery (Parameter)\n",
-      "Negative electrode thickness [m] (Parameter)\n",
-      "Ambient temperature [K] (FunctionParameter with input(s) 'Distance across electrode width [m]', 'Distance across electrode height [m]', 'Time [s]')\n",
-      "Positive electrode OCP entropic change [V.K-1] (FunctionParameter with input(s) 'Positive particle stoichiometry', 'Maximum positive particle surface concentration [mol.m-3]')\n",
-      "Positive electrode active material volume fraction (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n",
-      "Negative electrode OCP [V] (FunctionParameter with input(s) 'Negative particle stoichiometry')\n",
-      "Negative electrode OCP entropic change [V.K-1] (FunctionParameter with input(s) 'Negative particle stoichiometry', 'Maximum negative particle surface concentration [mol.m-3]')\n",
-      "Negative particle radius [m] (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n",
-      "Initial concentration in positive electrode [mol.m-3] (FunctionParameter with input(s) 'Radial distance (r) [m]', 'Through-cell distance (x) [m]')\n",
-      "Positive particle radius [m] (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n",
-      "Negative electrode porosity (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n",
-      "Negative electrode exchange-current density [A.m-2] (FunctionParameter with input(s) 'Electrolyte concentration [mol.m-3]', 'Negative particle surface concentration [mol.m-3]', 'Maximum negative particle surface concentration [mol.m-3]', 'Temperature [K]')\n",
-      "Positive electrode OCP [V] (FunctionParameter with input(s) 'Positive particle stoichiometry')\n",
-      "Positive electrode diffusivity [m2.s-1] (FunctionParameter with input(s) 'Positive particle stoichiometry', 'Temperature [K]')\n",
-      "Positive electrode porosity (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n",
-      "Initial concentration in negative electrode [mol.m-3] (FunctionParameter with input(s) 'Radial distance (r) [m]', 'Through-cell distance (x) [m]')\n",
-      "Negative electrode diffusivity [m2.s-1] (FunctionParameter with input(s) 'Negative particle stoichiometry', 'Temperature [K]')\n",
-      "Negative electrode active material volume fraction (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n",
-      "Separator porosity (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n",
-      "Current function [A] (FunctionParameter with input(s) 'Time[s]')\n",
-      "Positive electrode exchange-current density [A.m-2] (FunctionParameter with input(s) 'Electrolyte concentration [mol.m-3]', 'Positive particle surface concentration [mol.m-3]', 'Maximum positive particle surface concentration [mol.m-3]', 'Temperature [K]')\n",
-      "\n"
+      "| Parameter                                                 | Type of parameter                                                                                                                                                                                           |\n",
+      "| ========================================================= | =========================================================================================================================================================================================================== |\n",
+      "| Positive electrode Bruggeman coefficient (electrolyte)    | Parameter                                                                                                                                                                                                   |\n",
+      "| Electrode width [m]                                       | Parameter                                                                                                                                                                                                   |\n",
+      "| Positive electrode thickness [m]                          | Parameter                                                                                                                                                                                                   |\n",
+      "| Negative electrode Bruggeman coefficient (electrolyte)    | Parameter                                                                                                                                                                                                   |\n",
+      "| Negative electrode Bruggeman coefficient (electrode)      | Parameter                                                                                                                                                                                                   |\n",
+      "| Initial concentration in electrolyte [mol.m-3]            | Parameter                                                                                                                                                                                                   |\n",
+      "| Number of cells connected in series to make a battery     | Parameter                                                                                                                                                                                                   |\n",
+      "| Lower voltage cut-off [V]                                 | Parameter                                                                                                                                                                                                   |\n",
+      "| Ideal gas constant [J.K-1.mol-1]                          | Parameter                                                                                                                                                                                                   |\n",
+      "| Separator Bruggeman coefficient (electrolyte)             | Parameter                                                                                                                                                                                                   |\n",
+      "| Upper voltage cut-off [V]                                 | Parameter                                                                                                                                                                                                   |\n",
+      "| Positive electrode Bruggeman coefficient (electrode)      | Parameter                                                                                                                                                                                                   |\n",
+      "| Separator thickness [m]                                   | Parameter                                                                                                                                                                                                   |\n",
+      "| Maximum concentration in negative electrode [mol.m-3]     | Parameter                                                                                                                                                                                                   |\n",
+      "| Faraday constant [C.mol-1]                                | Parameter                                                                                                                                                                                                   |\n",
+      "| Reference temperature [K]                                 | Parameter                                                                                                                                                                                                   |\n",
+      "| Electrode height [m]                                      | Parameter                                                                                                                                                                                                   |\n",
+      "| Nominal cell capacity [A.h]                               | Parameter                                                                                                                                                                                                   |\n",
+      "| Maximum concentration in positive electrode [mol.m-3]     | Parameter                                                                                                                                                                                                   |\n",
+      "| Number of electrodes connected in parallel to make a cell | Parameter                                                                                                                                                                                                   |\n",
+      "| Negative electrode thickness [m]                          | Parameter                                                                                                                                                                                                   |\n",
+      "| Separator porosity                                        | FunctionParameter with inputs(s) 'Through-cell distance (x) [m]'                                                                                                                                            |\n",
+      "| Negative electrode active material volume fraction        | FunctionParameter with inputs(s) 'Through-cell distance (x) [m]'                                                                                                                                            |\n",
+      "| Negative electrode OCP [V]                                | FunctionParameter with inputs(s) 'Negative particle stoichiometry'                                                                                                                                          |\n",
+      "| Positive electrode porosity                               | FunctionParameter with inputs(s) 'Through-cell distance (x) [m]'                                                                                                                                            |\n",
+      "| Negative particle radius [m]                              | FunctionParameter with inputs(s) 'Through-cell distance (x) [m]'                                                                                                                                            |\n",
+      "| Positive electrode exchange-current density [A.m-2]       | FunctionParameter with inputs(s) 'Electrolyte concentration [mol.m-3]', 'Positive particle surface concentration [mol.m-3]', 'Maximum positive particle surface concentration [mol.m-3]', 'Temperature [K]' |\n",
+      "| Positive particle radius [m]                              | FunctionParameter with inputs(s) 'Through-cell distance (x) [m]'                                                                                                                                            |\n",
+      "| Positive electrode OCP [V]                                | FunctionParameter with inputs(s) 'Positive particle stoichiometry'                                                                                                                                          |\n",
+      "| Negative electrode exchange-current density [A.m-2]       | FunctionParameter with inputs(s) 'Electrolyte concentration [mol.m-3]', 'Negative particle surface concentration [mol.m-3]', 'Maximum negative particle surface concentration [mol.m-3]', 'Temperature [K]' |\n",
+      "| Negative electrode OCP entropic change [V.K-1]            | FunctionParameter with inputs(s) 'Negative particle stoichiometry', 'Maximum negative particle surface concentration [mol.m-3]'                                                                             |\n",
+      "| Current function [A]                                      | FunctionParameter with inputs(s) 'Time[s]'                                                                                                                                                                  |\n",
+      "| Initial concentration in positive electrode [mol.m-3]     | FunctionParameter with inputs(s) 'Radial distance (r) [m]', 'Through-cell distance (x) [m]'                                                                                                                 |\n",
+      "| Initial concentration in negative electrode [mol.m-3]     | FunctionParameter with inputs(s) 'Radial distance (r) [m]', 'Through-cell distance (x) [m]'                                                                                                                 |\n",
+      "| Positive electrode OCP entropic change [V.K-1]            | FunctionParameter with inputs(s) 'Positive particle stoichiometry', 'Maximum positive particle surface concentration [mol.m-3]'                                                                             |\n",
+      "| Positive electrode diffusivity [m2.s-1]                   | FunctionParameter with inputs(s) 'Positive particle stoichiometry', 'Temperature [K]'                                                                                                                       |\n",
+      "| Negative electrode porosity                               | FunctionParameter with inputs(s) 'Through-cell distance (x) [m]'                                                                                                                                            |\n",
+      "| Positive electrode active material volume fraction        | FunctionParameter with inputs(s) 'Through-cell distance (x) [m]'                                                                                                                                            |\n",
+      "| Negative electrode diffusivity [m2.s-1]                   | FunctionParameter with inputs(s) 'Negative particle stoichiometry', 'Temperature [K]'                                                                                                                       |\n",
+      "| Ambient temperature [K]                                   | FunctionParameter with inputs(s) 'Distance across electrode width [m]', 'Distance across electrode height [m]', 'Time [s]'                                                                                  |\n"
      ]
     }
    ],
@@ -517,53 +566,16 @@
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {
-    "scrolled": true
+    "scrolled": true,
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:19.401195400Z",
+     "start_time": "2023-12-10T12:14:19.232194200Z"
+    }
    },
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "{'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n",
-       " 'Faraday constant [C.mol-1]': 96485.33212,\n",
-       " 'Negative electrode thickness [m]': 0.0001,\n",
-       " 'Separator thickness [m]': 2.5e-05,\n",
-       " 'Positive electrode thickness [m]': 0.0001,\n",
-       " 'Electrode height [m]': 0.137,\n",
-       " 'Electrode width [m]': 0.207,\n",
-       " 'Nominal cell capacity [A.h]': 0.680616,\n",
-       " 'Current function [A]': 0.680616,\n",
-       " 'Maximum concentration in negative electrode [mol.m-3]': 24983.2619938437,\n",
-       " 'Negative electrode diffusivity [m2.s-1]': <function pybamm.input.parameters.lithium_ion.Marquis2019.graphite_mcmb2528_diffusivity_Dualfoil1998(sto, T)>,\n",
-       " 'Negative electrode OCP [V]': <function pybamm.input.parameters.lithium_ion.Marquis2019.graphite_mcmb2528_ocp_Dualfoil1998(sto)>,\n",
-       " 'Negative electrode porosity': 0.3,\n",
-       " 'Negative electrode active material volume fraction': 0.6,\n",
-       " 'Negative particle radius [m]': 1e-05,\n",
-       " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
-       " 'Negative electrode Bruggeman coefficient (electrode)': 1.5,\n",
-       " 'Negative electrode exchange-current density [A.m-2]': <function pybamm.input.parameters.lithium_ion.Marquis2019.graphite_electrolyte_exchange_current_density_Dualfoil1998(c_e, c_s_surf, c_s_max, T)>,\n",
-       " 'Negative electrode OCP entropic change [V.K-1]': <function pybamm.input.parameters.lithium_ion.Marquis2019.graphite_entropic_change_Moura2016(sto, c_s_max)>,\n",
-       " 'Maximum concentration in positive electrode [mol.m-3]': 51217.9257309275,\n",
-       " 'Positive electrode diffusivity [m2.s-1]': <function pybamm.input.parameters.lithium_ion.Marquis2019.lico2_diffusivity_Dualfoil1998(sto, T)>,\n",
-       " 'Positive electrode OCP [V]': <function pybamm.input.parameters.lithium_ion.Marquis2019.lico2_ocp_Dualfoil1998(sto)>,\n",
-       " 'Positive electrode porosity': 0.3,\n",
-       " 'Positive electrode active material volume fraction': 0.5,\n",
-       " 'Positive particle radius [m]': 1e-05,\n",
-       " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
-       " 'Positive electrode Bruggeman coefficient (electrode)': 1.5,\n",
-       " 'Positive electrode exchange-current density [A.m-2]': <function pybamm.input.parameters.lithium_ion.Marquis2019.lico2_electrolyte_exchange_current_density_Dualfoil1998(c_e, c_s_surf, c_s_max, T)>,\n",
-       " 'Positive electrode OCP entropic change [V.K-1]': <function pybamm.input.parameters.lithium_ion.Marquis2019.lico2_entropic_change_Moura2016(sto, c_s_max)>,\n",
-       " 'Separator porosity': 1.0,\n",
-       " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n",
-       " 'Initial concentration in electrolyte [mol.m-3]': 1000.0,\n",
-       " 'Reference temperature [K]': 298.15,\n",
-       " 'Ambient temperature [K]': 298.15,\n",
-       " 'Number of electrodes connected in parallel to make a cell': 1.0,\n",
-       " 'Number of cells connected in series to make a battery': 1.0,\n",
-       " 'Lower voltage cut-off [V]': 3.105,\n",
-       " 'Upper voltage cut-off [V]': 4.1,\n",
-       " 'Initial concentration in negative electrode [mol.m-3]': 19986.609595075,\n",
-       " 'Initial concentration in positive electrode [mol.m-3]': 30730.7554385565}"
-      ]
+      "text/plain": "{'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n 'Faraday constant [C.mol-1]': 96485.33212,\n 'Negative electrode thickness [m]': 0.0001,\n 'Separator thickness [m]': 2.5e-05,\n 'Positive electrode thickness [m]': 0.0001,\n 'Electrode height [m]': 0.137,\n 'Electrode width [m]': 0.207,\n 'Nominal cell capacity [A.h]': 0.680616,\n 'Current function [A]': 0.680616,\n 'Maximum concentration in negative electrode [mol.m-3]': 24983.2619938437,\n 'Negative electrode diffusivity [m2.s-1]': <function pybamm.input.parameters.lithium_ion.Marquis2019.graphite_mcmb2528_diffusivity_Dualfoil1998(sto, T)>,\n 'Negative electrode OCP [V]': <function pybamm.input.parameters.lithium_ion.Marquis2019.graphite_mcmb2528_ocp_Dualfoil1998(sto)>,\n 'Negative electrode porosity': 0.3,\n 'Negative electrode active material volume fraction': 0.6,\n 'Negative particle radius [m]': 1e-05,\n 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n 'Negative electrode Bruggeman coefficient (electrode)': 1.5,\n 'Negative electrode exchange-current density [A.m-2]': <function pybamm.input.parameters.lithium_ion.Marquis2019.graphite_electrolyte_exchange_current_density_Dualfoil1998(c_e, c_s_surf, c_s_max, T)>,\n 'Negative electrode OCP entropic change [V.K-1]': <function pybamm.input.parameters.lithium_ion.Marquis2019.graphite_entropic_change_Moura2016(sto, c_s_max)>,\n 'Maximum concentration in positive electrode [mol.m-3]': 51217.9257309275,\n 'Positive electrode diffusivity [m2.s-1]': <function pybamm.input.parameters.lithium_ion.Marquis2019.lico2_diffusivity_Dualfoil1998(sto, T)>,\n 'Positive electrode OCP [V]': <function pybamm.input.parameters.lithium_ion.Marquis2019.lico2_ocp_Dualfoil1998(sto)>,\n 'Positive electrode porosity': 0.3,\n 'Positive electrode active material volume fraction': 0.5,\n 'Positive particle radius [m]': 1e-05,\n 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n 'Positive electrode Bruggeman coefficient (electrode)': 1.5,\n 'Positive electrode exchange-current density [A.m-2]': <function pybamm.input.parameters.lithium_ion.Marquis2019.lico2_electrolyte_exchange_current_density_Dualfoil1998(c_e, c_s_surf, c_s_max, T)>,\n 'Positive electrode OCP entropic change [V.K-1]': <function pybamm.input.parameters.lithium_ion.Marquis2019.lico2_entropic_change_Moura2016(sto, c_s_max)>,\n 'Separator porosity': 1.0,\n 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n 'Initial concentration in electrolyte [mol.m-3]': 1000.0,\n 'Reference temperature [K]': 298.15,\n 'Ambient temperature [K]': 298.15,\n 'Number of electrodes connected in parallel to make a cell': 1.0,\n 'Number of cells connected in series to make a battery': 1.0,\n 'Lower voltage cut-off [V]': 3.105,\n 'Upper voltage cut-off [V]': 4.1,\n 'Initial concentration in negative electrode [mol.m-3]': 19986.609595075,\n 'Initial concentration in positive electrode [mol.m-3]': 30730.7554385565}"
      },
      "execution_count": 15,
      "metadata": {},
@@ -571,7 +583,7 @@
     }
    ],
    "source": [
-    "{k: v for k,v in spm.default_parameter_values.items() if k in spm._parameter_info}"
+    "{k: v for k,v in spm.default_parameter_values.items() if k in spm.get_parameter_info()}"
    ]
   },
   {
@@ -585,251 +597,16 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:19.460184100Z",
+     "start_time": "2023-12-10T12:14:19.418960800Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "{'Ambient temperature [K]': 298.15,\n",
-       " 'Boltzmann constant [J.K-1]': 1.380649e-23,\n",
-       " 'Current function [A]': 5.0,\n",
-       " 'Electrode height [m]': 0.065,\n",
-       " 'Electrode width [m]': 1.58,\n",
-       " 'Electron charge [C]': 1.602176634e-19,\n",
-       " 'Faraday constant [C.mol-1]': 96485.33212,\n",
-       " 'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n",
-       " 'Initial concentration in electrolyte [mol.m-3]': 1000,\n",
-       " 'Initial concentration in negative electrode [mol.m-3]': 29866.0,\n",
-       " 'Initial concentration in positive electrode [mol.m-3]': 17038.0,\n",
-       " 'Initial temperature [K]': 298.15,\n",
-       " 'Lower voltage cut-off [V]': 2.5,\n",
-       " 'Maximum concentration in negative electrode [mol.m-3]': 33133.0,\n",
-       " 'Maximum concentration in positive electrode [mol.m-3]': 63104.0,\n",
-       " 'Negative electrode Bruggeman coefficient (electrode)': 1.5,\n",
-       " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
-       " 'Negative electrode OCP [V]': ('graphite_LGM50_ocp_Chen2020',\n",
-       "                                ([array([0.        , 0.03129623, 0.03499902, 0.0387018 , 0.04240458,\n",
-       "       0.04610736, 0.04981015, 0.05351292, 0.05721568, 0.06091845,\n",
-       "       0.06462122, 0.06832399, 0.07202675, 0.07572951, 0.07943227,\n",
-       "       0.08313503, 0.08683779, 0.09054054, 0.09424331, 0.09794607,\n",
-       "       0.10164883, 0.10535158, 0.10905434, 0.1127571 , 0.11645985,\n",
-       "       0.12016261, 0.12386536, 0.12756811, 0.13127086, 0.13497362,\n",
-       "       0.13867638, 0.14237913, 0.14608189, 0.14978465, 0.15348741,\n",
-       "       0.15719018, 0.16089294, 0.1645957 , 0.16829847, 0.17200122,\n",
-       "       0.17570399, 0.17940674, 0.1831095 , 0.18681229, 0.19051504,\n",
-       "       0.1942178 , 0.19792056, 0.20162334, 0.2053261 , 0.20902886,\n",
-       "       0.21273164, 0.2164344 , 0.22013716, 0.22383993, 0.2275427 ,\n",
-       "       0.23124547, 0.23494825, 0.23865101, 0.24235377, 0.24605653,\n",
-       "       0.2497593 , 0.25346208, 0.25716486, 0.26086762, 0.26457039,\n",
-       "       0.26827314, 0.2719759 , 0.27567867, 0.27938144, 0.28308421,\n",
-       "       0.28678698, 0.29048974, 0.29419251, 0.29789529, 0.30159806,\n",
-       "       0.30530083, 0.30900361, 0.31270637, 0.31640913, 0.32011189,\n",
-       "       0.32381466, 0.32751744, 0.33122021, 0.33492297, 0.33862575,\n",
-       "       0.34232853, 0.34603131, 0.34973408, 0.35343685, 0.35713963,\n",
-       "       0.36084241, 0.36454517, 0.36824795, 0.37195071, 0.37565348,\n",
-       "       0.37935626, 0.38305904, 0.38676182, 0.3904646 , 0.39416737,\n",
-       "       0.39787015, 0.40157291, 0.40527567, 0.40897844, 0.41268121,\n",
-       "       0.41638398, 0.42008676, 0.42378953, 0.4274923 , 0.43119506,\n",
-       "       0.43489784, 0.43860061, 0.44230338, 0.44600615, 0.44970893,\n",
-       "       0.45341168, 0.45711444, 0.46081719, 0.46451994, 0.46822269,\n",
-       "       0.47192545, 0.47562821, 0.47933098, 0.48303375, 0.48673651,\n",
-       "       0.49043926, 0.49414203, 0.49784482, 0.50154759, 0.50525036,\n",
-       "       0.50895311, 0.51265586, 0.51635861, 0.52006139, 0.52376415,\n",
-       "       0.52746692, 0.53116969, 0.53487245, 0.53857521, 0.54227797,\n",
-       "       0.54598074, 0.5496835 , 0.55338627, 0.55708902, 0.56079178,\n",
-       "       0.56449454, 0.5681973 , 0.57190006, 0.57560282, 0.57930558,\n",
-       "       0.58300835, 0.58671112, 0.59041389, 0.59411664, 0.59781941,\n",
-       "       0.60152218, 0.60522496, 0.60892772, 0.61263048, 0.61633325,\n",
-       "       0.62003603, 0.6237388 , 0.62744156, 0.63114433, 0.63484711,\n",
-       "       0.63854988, 0.64225265, 0.64595543, 0.64965823, 0.653361  ,\n",
-       "       0.65706377, 0.66076656, 0.66446934, 0.66817212, 0.67187489,\n",
-       "       0.67557767, 0.67928044, 0.68298322, 0.686686  , 0.69038878,\n",
-       "       0.69409156, 0.69779433, 0.70149709, 0.70519988, 0.70890264,\n",
-       "       0.7126054 , 0.71630818, 0.72001095, 0.72371371, 0.72741648,\n",
-       "       0.73111925, 0.73482204, 0.7385248 , 0.74222757, 0.74593034,\n",
-       "       0.74963312, 0.75333589, 0.75703868, 0.76074146, 0.76444422,\n",
-       "       0.76814698, 0.77184976, 0.77555253, 0.77925531, 0.78295807,\n",
-       "       0.78666085, 0.79036364, 0.79406641, 0.79776918, 0.80147197,\n",
-       "       0.80517474, 0.80887751, 0.81258028, 0.81628304, 0.81998581,\n",
-       "       0.82368858, 0.82739136, 0.83109411, 0.83479688, 0.83849965,\n",
-       "       0.84220242, 0.84590519, 0.84960797, 0.85331075, 0.85701353,\n",
-       "       0.86071631, 0.86441907, 0.86812186, 0.87182464, 0.87552742,\n",
-       "       0.87923019, 0.88293296, 0.88663573, 0.89033849, 0.89404126,\n",
-       "       0.89774404, 0.9014468 , 1.        ])],\n",
-       "                                 array([1.81772748, 1.0828807 , 0.99593794, 0.90023398, 0.79649431,\n",
-       "       0.73354429, 0.66664314, 0.64137149, 0.59813869, 0.5670836 ,\n",
-       "       0.54746181, 0.53068399, 0.51304734, 0.49394092, 0.47926274,\n",
-       "       0.46065259, 0.45992726, 0.43801501, 0.42438665, 0.41150269,\n",
-       "       0.40033659, 0.38957134, 0.37756538, 0.36292541, 0.34357086,\n",
-       "       0.3406314 , 0.32299468, 0.31379458, 0.30795386, 0.29207319,\n",
-       "       0.28697687, 0.27405477, 0.2670497 , 0.25857493, 0.25265783,\n",
-       "       0.24826777, 0.2414345 , 0.23362778, 0.22956218, 0.22370236,\n",
-       "       0.22181271, 0.22089651, 0.2194268 , 0.21830064, 0.21845333,\n",
-       "       0.21753715, 0.21719357, 0.21635373, 0.21667822, 0.21738444,\n",
-       "       0.21469313, 0.21541846, 0.21465495, 0.2135479 , 0.21392964,\n",
-       "       0.21074206, 0.20873788, 0.20465319, 0.20205732, 0.19774358,\n",
-       "       0.19444147, 0.19190285, 0.18850531, 0.18581399, 0.18327537,\n",
-       "       0.18157659, 0.17814088, 0.17529686, 0.1719375 , 0.16934161,\n",
-       "       0.16756649, 0.16609676, 0.16414985, 0.16260378, 0.16224113,\n",
-       "       0.160027  , 0.15827096, 0.1588054 , 0.15552238, 0.15580869,\n",
-       "       0.15220118, 0.1511132 , 0.14987253, 0.14874637, 0.14678037,\n",
-       "       0.14620776, 0.14555879, 0.14389819, 0.14359279, 0.14242846,\n",
-       "       0.14038612, 0.13882096, 0.13954628, 0.13946992, 0.13780934,\n",
-       "       0.13973714, 0.13698858, 0.13523254, 0.13441178, 0.1352898 ,\n",
-       "       0.13507985, 0.13647321, 0.13601512, 0.13435452, 0.1334765 ,\n",
-       "       0.1348317 , 0.13275118, 0.13286571, 0.13263667, 0.13456447,\n",
-       "       0.13471718, 0.13395369, 0.13448814, 0.1334765 , 0.13298023,\n",
-       "       0.13259849, 0.13338107, 0.13309476, 0.13275118, 0.13443087,\n",
-       "       0.13315202, 0.132713  , 0.1330184 , 0.13278936, 0.13225491,\n",
-       "       0.13317111, 0.13263667, 0.13187316, 0.13265574, 0.13250305,\n",
-       "       0.13324745, 0.13204496, 0.13242669, 0.13233127, 0.13198769,\n",
-       "       0.13254122, 0.13145325, 0.13298023, 0.13168229, 0.1313578 ,\n",
-       "       0.13235036, 0.13120511, 0.13089971, 0.13109058, 0.13082336,\n",
-       "       0.13011713, 0.129869  , 0.12992626, 0.12942998, 0.12796026,\n",
-       "       0.12862831, 0.12656689, 0.12734947, 0.12509716, 0.12110791,\n",
-       "       0.11839751, 0.11244226, 0.11307214, 0.1092165 , 0.10683058,\n",
-       "       0.10433014, 0.10530359, 0.10056993, 0.09950104, 0.09854668,\n",
-       "       0.09921473, 0.09541635, 0.09980643, 0.0986612 , 0.09560722,\n",
-       "       0.09755413, 0.09612258, 0.09430929, 0.09661885, 0.09366032,\n",
-       "       0.09522548, 0.09535909, 0.09316404, 0.09450016, 0.0930877 ,\n",
-       "       0.09343126, 0.0932404 , 0.09350762, 0.09339309, 0.09291591,\n",
-       "       0.09303043, 0.0926296 , 0.0932404 , 0.09261052, 0.09249599,\n",
-       "       0.09240055, 0.09253416, 0.09209515, 0.09234329, 0.09366032,\n",
-       "       0.09333583, 0.09322131, 0.09264868, 0.09253416, 0.09243873,\n",
-       "       0.09230512, 0.09310678, 0.09165615, 0.09159888, 0.09207606,\n",
-       "       0.09175158, 0.09177067, 0.09236237, 0.09241964, 0.09320222,\n",
-       "       0.09199972, 0.09167523, 0.09322131, 0.09190428, 0.09167523,\n",
-       "       0.09285865, 0.09180884, 0.09150345, 0.09186611, 0.0920188 ,\n",
-       "       0.09320222, 0.09131257, 0.09117896, 0.09133166, 0.09089265,\n",
-       "       0.09058725, 0.09051091, 0.09033912, 0.09041547, 0.0911217 ,\n",
-       "       0.0894611 , 0.08999555, 0.08921297, 0.08881213, 0.08797229,\n",
-       "       0.08709427, 0.08503284, 0.07601531]))),\n",
-       " 'Negative electrode OCP entropic change [V.K-1]': 0.0,\n",
-       " 'Negative electrode active material volume fraction': 0.75,\n",
-       " 'Negative electrode diffusivity [m2.s-1]': 3.3e-14,\n",
-       " 'Negative electrode electrons in reaction': 1.0,\n",
-       " 'Negative electrode exchange-current density [A.m-2]': <function graphite_LGM50_electrolyte_exchange_current_density_Chen2020 at 0x10403e4c0>,\n",
-       " 'Negative electrode porosity': 0.25,\n",
-       " 'Negative electrode thickness [m]': 8.52e-05,\n",
-       " 'Negative particle radius [m]': 5.86e-06,\n",
-       " 'Nominal cell capacity [A.h]': 5.0,\n",
-       " 'Number of cells connected in series to make a battery': 1.0,\n",
-       " 'Number of electrodes connected in parallel to make a cell': 1.0,\n",
-       " 'Positive electrode Bruggeman coefficient (electrode)': 1.5,\n",
-       " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
-       " 'Positive electrode OCP [V]': ('nmc_LGM50_ocp_Chen2020',\n",
-       "                                ([array([0.24879728, 0.26614516, 0.26886763, 0.27159011, 0.27431258,\n",
-       "       0.27703505, 0.27975753, 0.28248   , 0.28520247, 0.28792495,\n",
-       "       0.29064743, 0.29336992, 0.29609239, 0.29881487, 0.30153735,\n",
-       "       0.30425983, 0.30698231, 0.30970478, 0.31242725, 0.31514973,\n",
-       "       0.3178722 , 0.32059466, 0.32331714, 0.32603962, 0.32876209,\n",
-       "       0.33148456, 0.33420703, 0.3369295 , 0.33965197, 0.34237446,\n",
-       "       0.34509694, 0.34781941, 0.3505419 , 0.35326438, 0.35598685,\n",
-       "       0.35870932, 0.3614318 , 0.36415428, 0.36687674, 0.36959921,\n",
-       "       0.37232169, 0.37504418, 0.37776665, 0.38048913, 0.38321161,\n",
-       "       0.38593408, 0.38865655, 0.39137903, 0.39410151, 0.39682398,\n",
-       "       0.39954645, 0.40226892, 0.4049914 , 0.40771387, 0.41043634,\n",
-       "       0.41315882, 0.41588129, 0.41860377, 0.42132624, 0.42404872,\n",
-       "       0.4267712 , 0.42949368, 0.43221616, 0.43493864, 0.43766111,\n",
-       "       0.44038359, 0.44310607, 0.44582856, 0.44855103, 0.45127351,\n",
-       "       0.453996  , 0.45671848, 0.45944095, 0.46216343, 0.46488592,\n",
-       "       0.46760838, 0.47033085, 0.47305333, 0.47577581, 0.47849828,\n",
-       "       0.48122074, 0.48394321, 0.48666569, 0.48938816, 0.49211064,\n",
-       "       0.4948331 , 0.49755557, 0.50027804, 0.50300052, 0.50572298,\n",
-       "       0.50844545, 0.51116792, 0.51389038, 0.51661284, 0.51933531,\n",
-       "       0.52205777, 0.52478024, 0.52750271, 0.53022518, 0.53294765,\n",
-       "       0.53567012, 0.53839258, 0.54111506, 0.54383753, 0.54656   ,\n",
-       "       0.54928247, 0.55200494, 0.5547274 , 0.55744986, 0.56017233,\n",
-       "       0.5628948 , 0.56561729, 0.56833976, 0.57106222, 0.57378469,\n",
-       "       0.57650716, 0.57922963, 0.5819521 , 0.58467456, 0.58739702,\n",
-       "       0.59011948, 0.59284194, 0.5955644 , 0.59828687, 0.60100935,\n",
-       "       0.60373182, 0.60645429, 0.60917677, 0.61189925, 0.61462172,\n",
-       "       0.61734419, 0.62006666, 0.62278914, 0.62551162, 0.62823408,\n",
-       "       0.63095656, 0.63367903, 0.6364015 , 0.63912397, 0.64184645,\n",
-       "       0.64456893, 0.6472914 , 0.65001389, 0.65273637, 0.65545884,\n",
-       "       0.65818131, 0.66090379, 0.66362625, 0.66634874, 0.66907121,\n",
-       "       0.67179369, 0.67451616, 0.67723865, 0.67996113, 0.68268361,\n",
-       "       0.68540608, 0.68812855, 0.69085103, 0.6935735 , 0.69629597,\n",
-       "       0.69901843, 0.7017409 , 0.70446338, 0.70718585, 0.70990833,\n",
-       "       0.71263081, 0.71535328, 0.71807574, 0.72079822, 0.72352069,\n",
-       "       0.72624317, 0.72896564, 0.7316881 , 0.73441057, 0.73713303,\n",
-       "       0.73985551, 0.74257799, 0.74530047, 0.74802293, 0.7507454 ,\n",
-       "       0.75346787, 0.75619034, 0.75891281, 0.76163529, 0.76435776,\n",
-       "       0.76708024, 0.7698027 , 0.77252517, 0.77524765, 0.77797012,\n",
-       "       0.78069258, 0.78341506, 0.78613753, 0.78885999, 0.79158246,\n",
-       "       0.79430494, 0.79702741, 0.79974987, 0.80247234, 0.8051948 ,\n",
-       "       0.80791727, 0.81063974, 0.81336221, 0.81608468, 0.81880714,\n",
-       "       0.82152961, 0.82425208, 0.82697453, 0.829697  , 0.83241946,\n",
-       "       0.83514192, 0.83786439, 0.84058684, 0.84330931, 0.84603177,\n",
-       "       0.84875424, 0.8514767 , 0.85419916, 0.85692162, 0.85964409,\n",
-       "       0.86236656, 0.86508902, 0.86781149, 0.87053395, 0.87325642,\n",
-       "       0.87597888, 0.87870135, 0.88142383, 0.8841463 , 0.88686877,\n",
-       "       0.88959124, 0.89231371, 0.8950362 , 0.89775868, 0.90048116,\n",
-       "       0.90320364, 0.90592613, 1.        ])],\n",
-       "                                 array([4.4       , 4.2935653 , 4.2768621 , 4.2647018 , 4.2540312 ,\n",
-       "       4.2449446 , 4.2364879 , 4.2302647 , 4.2225528 , 4.2182574 ,\n",
-       "       4.213294  , 4.2090373 , 4.2051239 , 4.2012677 , 4.1981564 ,\n",
-       "       4.1955218 , 4.1931167 , 4.1889744 , 4.1881533 , 4.1865883 ,\n",
-       "       4.1850228 , 4.1832285 , 4.1808805 , 4.1805749 , 4.1789522 ,\n",
-       "       4.1768146 , 4.1768146 , 4.1752872 , 4.173111  , 4.1726718 ,\n",
-       "       4.1710877 , 4.1702285 , 4.168797  , 4.1669831 , 4.1655135 ,\n",
-       "       4.1634517 , 4.1598248 , 4.1571712 , 4.154079  , 4.1504135 ,\n",
-       "       4.1466532 , 4.1423388 , 4.1382346 , 4.1338248 , 4.1305799 ,\n",
-       "       4.1272392 , 4.1228104 , 4.1186109 , 4.114182  , 4.1096005 ,\n",
-       "       4.1046948 , 4.1004758 , 4.0956464 , 4.0909696 , 4.0864644 ,\n",
-       "       4.0818448 , 4.077683  , 4.0733309 , 4.0690737 , 4.0647216 ,\n",
-       "       4.0608654 , 4.0564747 , 4.0527525 , 4.0492401 , 4.0450211 ,\n",
-       "       4.041986  , 4.0384736 , 4.035171  , 4.0320406 , 4.0289288 ,\n",
-       "       4.02597   , 4.0227437 , 4.0199757 , 4.0175133 , 4.0149746 ,\n",
-       "       4.0122066 , 4.009954  , 4.0075679 , 4.0050669 , 4.0023184 ,\n",
-       "       3.9995501 , 3.9969349 , 3.9926589 , 3.9889555 , 3.9834003 ,\n",
-       "       3.9783037 , 3.9755929 , 3.9707632 , 3.9681098 , 3.9635665 ,\n",
-       "       3.9594433 , 3.9556634 , 3.9521511 , 3.9479132 , 3.9438281 ,\n",
-       "       3.9400866 , 3.9362304 , 3.9314201 , 3.9283848 , 3.9242232 ,\n",
-       "       3.9192028 , 3.9166257 , 3.9117961 , 3.90815   , 3.9038739 ,\n",
-       "       3.8995597 , 3.8959136 , 3.8909314 , 3.8872662 , 3.8831048 ,\n",
-       "       3.8793442 , 3.8747628 , 3.8702576 , 3.8666878 , 3.8623927 ,\n",
-       "       3.8581741 , 3.854146  , 3.8499846 , 3.8450022 , 3.8422534 ,\n",
-       "       3.8380919 , 3.8341596 , 3.8309333 , 3.8272109 , 3.823164  ,\n",
-       "       3.8192315 , 3.8159864 , 3.8123021 , 3.8090379 , 3.8071671 ,\n",
-       "       3.8040555 , 3.8013639 , 3.7970879 , 3.7953317 , 3.7920673 ,\n",
-       "       3.788383  , 3.7855389 , 3.7838206 , 3.78111   , 3.7794874 ,\n",
-       "       3.7769294 , 3.773608  , 3.7695992 , 3.7690265 , 3.7662776 ,\n",
-       "       3.7642922 , 3.7626889 , 3.7603791 , 3.7575538 , 3.7552056 ,\n",
-       "       3.7533159 , 3.7507198 , 3.7487535 , 3.7471499 , 3.7442865 ,\n",
-       "       3.7423012 , 3.7400677 , 3.7385788 , 3.7345319 , 3.7339211 ,\n",
-       "       3.7301605 , 3.7301033 , 3.7278316 , 3.7251589 , 3.723861  ,\n",
-       "       3.7215703 , 3.7191267 , 3.7172751 , 3.7157097 , 3.7130945 ,\n",
-       "       3.7099447 , 3.7071004 , 3.7045615 , 3.703588  , 3.70208   ,\n",
-       "       3.7002664 , 3.6972122 , 3.6952841 , 3.6929362 , 3.6898055 ,\n",
-       "       3.6890991 , 3.686522  , 3.6849759 , 3.6821697 , 3.6808143 ,\n",
-       "       3.6786573 , 3.6761947 , 3.674763  , 3.6712887 , 3.6697233 ,\n",
-       "       3.6678908 , 3.6652565 , 3.6630611 , 3.660274  , 3.6583652 ,\n",
-       "       3.6554828 , 3.6522949 , 3.6499848 , 3.6470451 , 3.6405547 ,\n",
-       "       3.6383405 , 3.635076  , 3.633549  , 3.6322317 , 3.6306856 ,\n",
-       "       3.6283948 , 3.6268487 , 3.6243098 , 3.6223626 , 3.6193655 ,\n",
-       "       3.6177621 , 3.6158531 , 3.6128371 , 3.6118062 , 3.6094582 ,\n",
-       "       3.6072438 , 3.6049912 , 3.6030822 , 3.6012688 , 3.5995889 ,\n",
-       "       3.5976417 , 3.5951984 , 3.593843  , 3.5916286 , 3.5894907 ,\n",
-       "       3.587429  , 3.5852909 , 3.5834775 , 3.5817785 , 3.5801177 ,\n",
-       "       3.5778842 , 3.5763381 , 3.5737801 , 3.5721002 , 3.5702102 ,\n",
-       "       3.5684922 , 3.5672133 , 3.52302167]))),\n",
-       " 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n",
-       " 'Positive electrode active material volume fraction': 0.665,\n",
-       " 'Positive electrode diffusivity [m2.s-1]': 4e-15,\n",
-       " 'Positive electrode electrons in reaction': 1.0,\n",
-       " 'Positive electrode exchange-current density [A.m-2]': <function nmc_LGM50_electrolyte_exchange_current_density_Chen2020 at 0x1411e7700>,\n",
-       " 'Positive electrode porosity': 0.335,\n",
-       " 'Positive electrode thickness [m]': 7.56e-05,\n",
-       " 'Positive particle radius [m]': 5.22e-06,\n",
-       " 'Reference temperature [K]': 298.15,\n",
-       " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n",
-       " 'Separator porosity': 0.47,\n",
-       " 'Separator thickness [m]': 1.2e-05,\n",
-       " 'Typical current [A]': 5.0,\n",
-       " 'Typical electrolyte concentration [mol.m-3]': 1000.0,\n",
-       " 'Upper voltage cut-off [V]': 4.4}"
-      ]
+      "text/plain": "{'Ambient temperature [K]': 298.15,\n 'Boltzmann constant [J.K-1]': 1.380649e-23,\n 'Current function [A]': 5.0,\n 'Electrode height [m]': 0.065,\n 'Electrode width [m]': 1.58,\n 'Electron charge [C]': 1.602176634e-19,\n 'Faraday constant [C.mol-1]': 96485.33212,\n 'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n 'Initial concentration in electrolyte [mol.m-3]': 1000,\n 'Initial concentration in negative electrode [mol.m-3]': 29866.0,\n 'Initial concentration in positive electrode [mol.m-3]': 17038.0,\n 'Initial temperature [K]': 298.15,\n 'Lower voltage cut-off [V]': 2.5,\n 'Maximum concentration in negative electrode [mol.m-3]': 33133.0,\n 'Maximum concentration in positive electrode [mol.m-3]': 63104.0,\n 'Negative electrode Bruggeman coefficient (electrode)': 1.5,\n 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n 'Negative electrode OCP [V]': ('graphite_LGM50_ocp_Chen2020',\n                                ([array([0.        , 0.03129623, 0.03499902, 0.0387018 , 0.04240458,\n       0.04610736, 0.04981015, 0.05351292, 0.05721568, 0.06091845,\n       0.06462122, 0.06832399, 0.07202675, 0.07572951, 0.07943227,\n       0.08313503, 0.08683779, 0.09054054, 0.09424331, 0.09794607,\n       0.10164883, 0.10535158, 0.10905434, 0.1127571 , 0.11645985,\n       0.12016261, 0.12386536, 0.12756811, 0.13127086, 0.13497362,\n       0.13867638, 0.14237913, 0.14608189, 0.14978465, 0.15348741,\n       0.15719018, 0.16089294, 0.1645957 , 0.16829847, 0.17200122,\n       0.17570399, 0.17940674, 0.1831095 , 0.18681229, 0.19051504,\n       0.1942178 , 0.19792056, 0.20162334, 0.2053261 , 0.20902886,\n       0.21273164, 0.2164344 , 0.22013716, 0.22383993, 0.2275427 ,\n       0.23124547, 0.23494825, 0.23865101, 0.24235377, 0.24605653,\n       0.2497593 , 0.25346208, 0.25716486, 0.26086762, 0.26457039,\n       0.26827314, 0.2719759 , 0.27567867, 0.27938144, 0.28308421,\n       0.28678698, 0.29048974, 0.29419251, 0.29789529, 0.30159806,\n       0.30530083, 0.30900361, 0.31270637, 0.31640913, 0.32011189,\n       0.32381466, 0.32751744, 0.33122021, 0.33492297, 0.33862575,\n       0.34232853, 0.34603131, 0.34973408, 0.35343685, 0.35713963,\n       0.36084241, 0.36454517, 0.36824795, 0.37195071, 0.37565348,\n       0.37935626, 0.38305904, 0.38676182, 0.3904646 , 0.39416737,\n       0.39787015, 0.40157291, 0.40527567, 0.40897844, 0.41268121,\n       0.41638398, 0.42008676, 0.42378953, 0.4274923 , 0.43119506,\n       0.43489784, 0.43860061, 0.44230338, 0.44600615, 0.44970893,\n       0.45341168, 0.45711444, 0.46081719, 0.46451994, 0.46822269,\n       0.47192545, 0.47562821, 0.47933098, 0.48303375, 0.48673651,\n       0.49043926, 0.49414203, 0.49784482, 0.50154759, 0.50525036,\n       0.50895311, 0.51265586, 0.51635861, 0.52006139, 0.52376415,\n       0.52746692, 0.53116969, 0.53487245, 0.53857521, 0.54227797,\n       0.54598074, 0.5496835 , 0.55338627, 0.55708902, 0.56079178,\n       0.56449454, 0.5681973 , 0.57190006, 0.57560282, 0.57930558,\n       0.58300835, 0.58671112, 0.59041389, 0.59411664, 0.59781941,\n       0.60152218, 0.60522496, 0.60892772, 0.61263048, 0.61633325,\n       0.62003603, 0.6237388 , 0.62744156, 0.63114433, 0.63484711,\n       0.63854988, 0.64225265, 0.64595543, 0.64965823, 0.653361  ,\n       0.65706377, 0.66076656, 0.66446934, 0.66817212, 0.67187489,\n       0.67557767, 0.67928044, 0.68298322, 0.686686  , 0.69038878,\n       0.69409156, 0.69779433, 0.70149709, 0.70519988, 0.70890264,\n       0.7126054 , 0.71630818, 0.72001095, 0.72371371, 0.72741648,\n       0.73111925, 0.73482204, 0.7385248 , 0.74222757, 0.74593034,\n       0.74963312, 0.75333589, 0.75703868, 0.76074146, 0.76444422,\n       0.76814698, 0.77184976, 0.77555253, 0.77925531, 0.78295807,\n       0.78666085, 0.79036364, 0.79406641, 0.79776918, 0.80147197,\n       0.80517474, 0.80887751, 0.81258028, 0.81628304, 0.81998581,\n       0.82368858, 0.82739136, 0.83109411, 0.83479688, 0.83849965,\n       0.84220242, 0.84590519, 0.84960797, 0.85331075, 0.85701353,\n       0.86071631, 0.86441907, 0.86812186, 0.87182464, 0.87552742,\n       0.87923019, 0.88293296, 0.88663573, 0.89033849, 0.89404126,\n       0.89774404, 0.9014468 , 1.        ])],\n                                 array([1.81772748, 1.0828807 , 0.99593794, 0.90023398, 0.79649431,\n       0.73354429, 0.66664314, 0.64137149, 0.59813869, 0.5670836 ,\n       0.54746181, 0.53068399, 0.51304734, 0.49394092, 0.47926274,\n       0.46065259, 0.45992726, 0.43801501, 0.42438665, 0.41150269,\n       0.40033659, 0.38957134, 0.37756538, 0.36292541, 0.34357086,\n       0.3406314 , 0.32299468, 0.31379458, 0.30795386, 0.29207319,\n       0.28697687, 0.27405477, 0.2670497 , 0.25857493, 0.25265783,\n       0.24826777, 0.2414345 , 0.23362778, 0.22956218, 0.22370236,\n       0.22181271, 0.22089651, 0.2194268 , 0.21830064, 0.21845333,\n       0.21753715, 0.21719357, 0.21635373, 0.21667822, 0.21738444,\n       0.21469313, 0.21541846, 0.21465495, 0.2135479 , 0.21392964,\n       0.21074206, 0.20873788, 0.20465319, 0.20205732, 0.19774358,\n       0.19444147, 0.19190285, 0.18850531, 0.18581399, 0.18327537,\n       0.18157659, 0.17814088, 0.17529686, 0.1719375 , 0.16934161,\n       0.16756649, 0.16609676, 0.16414985, 0.16260378, 0.16224113,\n       0.160027  , 0.15827096, 0.1588054 , 0.15552238, 0.15580869,\n       0.15220118, 0.1511132 , 0.14987253, 0.14874637, 0.14678037,\n       0.14620776, 0.14555879, 0.14389819, 0.14359279, 0.14242846,\n       0.14038612, 0.13882096, 0.13954628, 0.13946992, 0.13780934,\n       0.13973714, 0.13698858, 0.13523254, 0.13441178, 0.1352898 ,\n       0.13507985, 0.13647321, 0.13601512, 0.13435452, 0.1334765 ,\n       0.1348317 , 0.13275118, 0.13286571, 0.13263667, 0.13456447,\n       0.13471718, 0.13395369, 0.13448814, 0.1334765 , 0.13298023,\n       0.13259849, 0.13338107, 0.13309476, 0.13275118, 0.13443087,\n       0.13315202, 0.132713  , 0.1330184 , 0.13278936, 0.13225491,\n       0.13317111, 0.13263667, 0.13187316, 0.13265574, 0.13250305,\n       0.13324745, 0.13204496, 0.13242669, 0.13233127, 0.13198769,\n       0.13254122, 0.13145325, 0.13298023, 0.13168229, 0.1313578 ,\n       0.13235036, 0.13120511, 0.13089971, 0.13109058, 0.13082336,\n       0.13011713, 0.129869  , 0.12992626, 0.12942998, 0.12796026,\n       0.12862831, 0.12656689, 0.12734947, 0.12509716, 0.12110791,\n       0.11839751, 0.11244226, 0.11307214, 0.1092165 , 0.10683058,\n       0.10433014, 0.10530359, 0.10056993, 0.09950104, 0.09854668,\n       0.09921473, 0.09541635, 0.09980643, 0.0986612 , 0.09560722,\n       0.09755413, 0.09612258, 0.09430929, 0.09661885, 0.09366032,\n       0.09522548, 0.09535909, 0.09316404, 0.09450016, 0.0930877 ,\n       0.09343126, 0.0932404 , 0.09350762, 0.09339309, 0.09291591,\n       0.09303043, 0.0926296 , 0.0932404 , 0.09261052, 0.09249599,\n       0.09240055, 0.09253416, 0.09209515, 0.09234329, 0.09366032,\n       0.09333583, 0.09322131, 0.09264868, 0.09253416, 0.09243873,\n       0.09230512, 0.09310678, 0.09165615, 0.09159888, 0.09207606,\n       0.09175158, 0.09177067, 0.09236237, 0.09241964, 0.09320222,\n       0.09199972, 0.09167523, 0.09322131, 0.09190428, 0.09167523,\n       0.09285865, 0.09180884, 0.09150345, 0.09186611, 0.0920188 ,\n       0.09320222, 0.09131257, 0.09117896, 0.09133166, 0.09089265,\n       0.09058725, 0.09051091, 0.09033912, 0.09041547, 0.0911217 ,\n       0.0894611 , 0.08999555, 0.08921297, 0.08881213, 0.08797229,\n       0.08709427, 0.08503284, 0.07601531]))),\n 'Negative electrode OCP entropic change [V.K-1]': 0.0,\n 'Negative electrode active material volume fraction': 0.75,\n 'Negative electrode diffusivity [m2.s-1]': 3.3e-14,\n 'Negative electrode electrons in reaction': 1.0,\n 'Negative electrode exchange-current density [A.m-2]': <function graphite_LGM50_electrolyte_exchange_current_density_Chen2020 at 0x7f481dd1f380>,\n 'Negative electrode porosity': 0.25,\n 'Negative electrode thickness [m]': 8.52e-05,\n 'Negative particle radius [m]': 5.86e-06,\n 'Nominal cell capacity [A.h]': 5.0,\n 'Number of cells connected in series to make a battery': 1.0,\n 'Number of electrodes connected in parallel to make a cell': 1.0,\n 'Positive electrode Bruggeman coefficient (electrode)': 1.5,\n 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n 'Positive electrode OCP [V]': ('nmc_LGM50_ocp_Chen2020',\n                                ([array([0.24879728, 0.26614516, 0.26886763, 0.27159011, 0.27431258,\n       0.27703505, 0.27975753, 0.28248   , 0.28520247, 0.28792495,\n       0.29064743, 0.29336992, 0.29609239, 0.29881487, 0.30153735,\n       0.30425983, 0.30698231, 0.30970478, 0.31242725, 0.31514973,\n       0.3178722 , 0.32059466, 0.32331714, 0.32603962, 0.32876209,\n       0.33148456, 0.33420703, 0.3369295 , 0.33965197, 0.34237446,\n       0.34509694, 0.34781941, 0.3505419 , 0.35326438, 0.35598685,\n       0.35870932, 0.3614318 , 0.36415428, 0.36687674, 0.36959921,\n       0.37232169, 0.37504418, 0.37776665, 0.38048913, 0.38321161,\n       0.38593408, 0.38865655, 0.39137903, 0.39410151, 0.39682398,\n       0.39954645, 0.40226892, 0.4049914 , 0.40771387, 0.41043634,\n       0.41315882, 0.41588129, 0.41860377, 0.42132624, 0.42404872,\n       0.4267712 , 0.42949368, 0.43221616, 0.43493864, 0.43766111,\n       0.44038359, 0.44310607, 0.44582856, 0.44855103, 0.45127351,\n       0.453996  , 0.45671848, 0.45944095, 0.46216343, 0.46488592,\n       0.46760838, 0.47033085, 0.47305333, 0.47577581, 0.47849828,\n       0.48122074, 0.48394321, 0.48666569, 0.48938816, 0.49211064,\n       0.4948331 , 0.49755557, 0.50027804, 0.50300052, 0.50572298,\n       0.50844545, 0.51116792, 0.51389038, 0.51661284, 0.51933531,\n       0.52205777, 0.52478024, 0.52750271, 0.53022518, 0.53294765,\n       0.53567012, 0.53839258, 0.54111506, 0.54383753, 0.54656   ,\n       0.54928247, 0.55200494, 0.5547274 , 0.55744986, 0.56017233,\n       0.5628948 , 0.56561729, 0.56833976, 0.57106222, 0.57378469,\n       0.57650716, 0.57922963, 0.5819521 , 0.58467456, 0.58739702,\n       0.59011948, 0.59284194, 0.5955644 , 0.59828687, 0.60100935,\n       0.60373182, 0.60645429, 0.60917677, 0.61189925, 0.61462172,\n       0.61734419, 0.62006666, 0.62278914, 0.62551162, 0.62823408,\n       0.63095656, 0.63367903, 0.6364015 , 0.63912397, 0.64184645,\n       0.64456893, 0.6472914 , 0.65001389, 0.65273637, 0.65545884,\n       0.65818131, 0.66090379, 0.66362625, 0.66634874, 0.66907121,\n       0.67179369, 0.67451616, 0.67723865, 0.67996113, 0.68268361,\n       0.68540608, 0.68812855, 0.69085103, 0.6935735 , 0.69629597,\n       0.69901843, 0.7017409 , 0.70446338, 0.70718585, 0.70990833,\n       0.71263081, 0.71535328, 0.71807574, 0.72079822, 0.72352069,\n       0.72624317, 0.72896564, 0.7316881 , 0.73441057, 0.73713303,\n       0.73985551, 0.74257799, 0.74530047, 0.74802293, 0.7507454 ,\n       0.75346787, 0.75619034, 0.75891281, 0.76163529, 0.76435776,\n       0.76708024, 0.7698027 , 0.77252517, 0.77524765, 0.77797012,\n       0.78069258, 0.78341506, 0.78613753, 0.78885999, 0.79158246,\n       0.79430494, 0.79702741, 0.79974987, 0.80247234, 0.8051948 ,\n       0.80791727, 0.81063974, 0.81336221, 0.81608468, 0.81880714,\n       0.82152961, 0.82425208, 0.82697453, 0.829697  , 0.83241946,\n       0.83514192, 0.83786439, 0.84058684, 0.84330931, 0.84603177,\n       0.84875424, 0.8514767 , 0.85419916, 0.85692162, 0.85964409,\n       0.86236656, 0.86508902, 0.86781149, 0.87053395, 0.87325642,\n       0.87597888, 0.87870135, 0.88142383, 0.8841463 , 0.88686877,\n       0.88959124, 0.89231371, 0.8950362 , 0.89775868, 0.90048116,\n       0.90320364, 0.90592613, 1.        ])],\n                                 array([4.4       , 4.2935653 , 4.2768621 , 4.2647018 , 4.2540312 ,\n       4.2449446 , 4.2364879 , 4.2302647 , 4.2225528 , 4.2182574 ,\n       4.213294  , 4.2090373 , 4.2051239 , 4.2012677 , 4.1981564 ,\n       4.1955218 , 4.1931167 , 4.1889744 , 4.1881533 , 4.1865883 ,\n       4.1850228 , 4.1832285 , 4.1808805 , 4.1805749 , 4.1789522 ,\n       4.1768146 , 4.1768146 , 4.1752872 , 4.173111  , 4.1726718 ,\n       4.1710877 , 4.1702285 , 4.168797  , 4.1669831 , 4.1655135 ,\n       4.1634517 , 4.1598248 , 4.1571712 , 4.154079  , 4.1504135 ,\n       4.1466532 , 4.1423388 , 4.1382346 , 4.1338248 , 4.1305799 ,\n       4.1272392 , 4.1228104 , 4.1186109 , 4.114182  , 4.1096005 ,\n       4.1046948 , 4.1004758 , 4.0956464 , 4.0909696 , 4.0864644 ,\n       4.0818448 , 4.077683  , 4.0733309 , 4.0690737 , 4.0647216 ,\n       4.0608654 , 4.0564747 , 4.0527525 , 4.0492401 , 4.0450211 ,\n       4.041986  , 4.0384736 , 4.035171  , 4.0320406 , 4.0289288 ,\n       4.02597   , 4.0227437 , 4.0199757 , 4.0175133 , 4.0149746 ,\n       4.0122066 , 4.009954  , 4.0075679 , 4.0050669 , 4.0023184 ,\n       3.9995501 , 3.9969349 , 3.9926589 , 3.9889555 , 3.9834003 ,\n       3.9783037 , 3.9755929 , 3.9707632 , 3.9681098 , 3.9635665 ,\n       3.9594433 , 3.9556634 , 3.9521511 , 3.9479132 , 3.9438281 ,\n       3.9400866 , 3.9362304 , 3.9314201 , 3.9283848 , 3.9242232 ,\n       3.9192028 , 3.9166257 , 3.9117961 , 3.90815   , 3.9038739 ,\n       3.8995597 , 3.8959136 , 3.8909314 , 3.8872662 , 3.8831048 ,\n       3.8793442 , 3.8747628 , 3.8702576 , 3.8666878 , 3.8623927 ,\n       3.8581741 , 3.854146  , 3.8499846 , 3.8450022 , 3.8422534 ,\n       3.8380919 , 3.8341596 , 3.8309333 , 3.8272109 , 3.823164  ,\n       3.8192315 , 3.8159864 , 3.8123021 , 3.8090379 , 3.8071671 ,\n       3.8040555 , 3.8013639 , 3.7970879 , 3.7953317 , 3.7920673 ,\n       3.788383  , 3.7855389 , 3.7838206 , 3.78111   , 3.7794874 ,\n       3.7769294 , 3.773608  , 3.7695992 , 3.7690265 , 3.7662776 ,\n       3.7642922 , 3.7626889 , 3.7603791 , 3.7575538 , 3.7552056 ,\n       3.7533159 , 3.7507198 , 3.7487535 , 3.7471499 , 3.7442865 ,\n       3.7423012 , 3.7400677 , 3.7385788 , 3.7345319 , 3.7339211 ,\n       3.7301605 , 3.7301033 , 3.7278316 , 3.7251589 , 3.723861  ,\n       3.7215703 , 3.7191267 , 3.7172751 , 3.7157097 , 3.7130945 ,\n       3.7099447 , 3.7071004 , 3.7045615 , 3.703588  , 3.70208   ,\n       3.7002664 , 3.6972122 , 3.6952841 , 3.6929362 , 3.6898055 ,\n       3.6890991 , 3.686522  , 3.6849759 , 3.6821697 , 3.6808143 ,\n       3.6786573 , 3.6761947 , 3.674763  , 3.6712887 , 3.6697233 ,\n       3.6678908 , 3.6652565 , 3.6630611 , 3.660274  , 3.6583652 ,\n       3.6554828 , 3.6522949 , 3.6499848 , 3.6470451 , 3.6405547 ,\n       3.6383405 , 3.635076  , 3.633549  , 3.6322317 , 3.6306856 ,\n       3.6283948 , 3.6268487 , 3.6243098 , 3.6223626 , 3.6193655 ,\n       3.6177621 , 3.6158531 , 3.6128371 , 3.6118062 , 3.6094582 ,\n       3.6072438 , 3.6049912 , 3.6030822 , 3.6012688 , 3.5995889 ,\n       3.5976417 , 3.5951984 , 3.593843  , 3.5916286 , 3.5894907 ,\n       3.587429  , 3.5852909 , 3.5834775 , 3.5817785 , 3.5801177 ,\n       3.5778842 , 3.5763381 , 3.5737801 , 3.5721002 , 3.5702102 ,\n       3.5684922 , 3.5672133 , 3.52302167]))),\n 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n 'Positive electrode active material volume fraction': 0.665,\n 'Positive electrode diffusivity [m2.s-1]': 4e-15,\n 'Positive electrode electrons in reaction': 1.0,\n 'Positive electrode exchange-current density [A.m-2]': <function nmc_LGM50_electrolyte_exchange_current_density_Chen2020 at 0x7f481dd1f240>,\n 'Positive electrode porosity': 0.335,\n 'Positive electrode thickness [m]': 7.56e-05,\n 'Positive particle radius [m]': 5.22e-06,\n 'Reference temperature [K]': 298.15,\n 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n 'Separator porosity': 0.47,\n 'Separator thickness [m]': 1.2e-05,\n 'Typical current [A]': 5.0,\n 'Typical electrolyte concentration [mol.m-3]': 1000.0,\n 'Upper voltage cut-off [V]': 4.4}"
      },
      "execution_count": 16,
      "metadata": {},
@@ -1405,52 +1182,16 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:19.608102800Z",
+     "start_time": "2023-12-10T12:14:19.450757200Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "{'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n",
-       " 'Faraday constant [C.mol-1]': 96485.33212,\n",
-       " 'Negative electrode thickness [m]': 8.52e-05,\n",
-       " 'Separator thickness [m]': 1.2e-05,\n",
-       " 'Positive electrode thickness [m]': 7.56e-05,\n",
-       " 'Electrode height [m]': 0.065,\n",
-       " 'Electrode width [m]': 1.58,\n",
-       " 'Nominal cell capacity [A.h]': 5.0,\n",
-       " 'Current function [A]': 5.0,\n",
-       " 'Maximum concentration in negative electrode [mol.m-3]': 33133.0,\n",
-       " 'Negative electrode diffusivity [m2.s-1]': 3.3e-14,\n",
-       " 'Negative electrode OCP [V]': <function pybamm.input.parameters.lithium_ion.Chen2020.graphite_LGM50_ocp_Chen2020(sto)>,\n",
-       " 'Negative electrode porosity': 0.25,\n",
-       " 'Negative electrode active material volume fraction': 0.75,\n",
-       " 'Negative particle radius [m]': 5.86e-06,\n",
-       " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
-       " 'Negative electrode Bruggeman coefficient (electrode)': 0,\n",
-       " 'Negative electrode exchange-current density [A.m-2]': <function pybamm.input.parameters.lithium_ion.Chen2020.graphite_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, c_s_max, T)>,\n",
-       " 'Negative electrode OCP entropic change [V.K-1]': 0.0,\n",
-       " 'Maximum concentration in positive electrode [mol.m-3]': 63104.0,\n",
-       " 'Positive electrode diffusivity [m2.s-1]': 4e-15,\n",
-       " 'Positive electrode OCP [V]': <function pybamm.input.parameters.lithium_ion.Chen2020.nmc_LGM50_ocp_Chen2020(sto)>,\n",
-       " 'Positive electrode porosity': 0.335,\n",
-       " 'Positive electrode active material volume fraction': 0.665,\n",
-       " 'Positive particle radius [m]': 5.22e-06,\n",
-       " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
-       " 'Positive electrode Bruggeman coefficient (electrode)': 0,\n",
-       " 'Positive electrode exchange-current density [A.m-2]': <function pybamm.input.parameters.lithium_ion.Chen2020.nmc_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, c_s_max, T)>,\n",
-       " 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n",
-       " 'Separator porosity': 0.47,\n",
-       " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n",
-       " 'Initial concentration in electrolyte [mol.m-3]': 1000.0,\n",
-       " 'Reference temperature [K]': 298.15,\n",
-       " 'Ambient temperature [K]': 298.15,\n",
-       " 'Number of electrodes connected in parallel to make a cell': 1.0,\n",
-       " 'Number of cells connected in series to make a battery': 1.0,\n",
-       " 'Lower voltage cut-off [V]': 2.5,\n",
-       " 'Upper voltage cut-off [V]': 4.2,\n",
-       " 'Initial concentration in negative electrode [mol.m-3]': 29866.0,\n",
-       " 'Initial concentration in positive electrode [mol.m-3]': 17038.0}"
-      ]
+      "text/plain": "{'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n 'Faraday constant [C.mol-1]': 96485.33212,\n 'Negative electrode thickness [m]': 8.52e-05,\n 'Separator thickness [m]': 1.2e-05,\n 'Positive electrode thickness [m]': 7.56e-05,\n 'Electrode height [m]': 0.065,\n 'Electrode width [m]': 1.58,\n 'Nominal cell capacity [A.h]': 5.0,\n 'Current function [A]': 5.0,\n 'Maximum concentration in negative electrode [mol.m-3]': 33133.0,\n 'Negative electrode diffusivity [m2.s-1]': 3.3e-14,\n 'Negative electrode OCP [V]': <function pybamm.input.parameters.lithium_ion.Chen2020.graphite_LGM50_ocp_Chen2020(sto)>,\n 'Negative electrode porosity': 0.25,\n 'Negative electrode active material volume fraction': 0.75,\n 'Negative particle radius [m]': 5.86e-06,\n 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n 'Negative electrode Bruggeman coefficient (electrode)': 0,\n 'Negative electrode exchange-current density [A.m-2]': <function pybamm.input.parameters.lithium_ion.Chen2020.graphite_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, c_s_max, T)>,\n 'Negative electrode OCP entropic change [V.K-1]': 0.0,\n 'Maximum concentration in positive electrode [mol.m-3]': 63104.0,\n 'Positive electrode diffusivity [m2.s-1]': 4e-15,\n 'Positive electrode OCP [V]': <function pybamm.input.parameters.lithium_ion.Chen2020.nmc_LGM50_ocp_Chen2020(sto)>,\n 'Positive electrode porosity': 0.335,\n 'Positive electrode active material volume fraction': 0.665,\n 'Positive particle radius [m]': 5.22e-06,\n 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n 'Positive electrode Bruggeman coefficient (electrode)': 0,\n 'Positive electrode exchange-current density [A.m-2]': <function pybamm.input.parameters.lithium_ion.Chen2020.nmc_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, c_s_max, T)>,\n 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n 'Separator porosity': 0.47,\n 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n 'Initial concentration in electrolyte [mol.m-3]': 1000.0,\n 'Reference temperature [K]': 298.15,\n 'Ambient temperature [K]': 298.15,\n 'Number of electrodes connected in parallel to make a cell': 1.0,\n 'Number of cells connected in series to make a battery': 1.0,\n 'Lower voltage cut-off [V]': 2.5,\n 'Upper voltage cut-off [V]': 4.2,\n 'Initial concentration in negative electrode [mol.m-3]': 29866.0,\n 'Initial concentration in positive electrode [mol.m-3]': 17038.0}"
      },
      "execution_count": 17,
      "metadata": {},
@@ -1459,7 +1200,7 @@
    ],
    "source": [
     "param_same = pybamm.ParameterValues(\"Chen2020\")\n",
-    "{k: v for k,v in param_same.items() if k in spm._parameter_info}"
+    "{k: v for k,v in param_same.items() if k in spm.get_parameter_info()}"
    ]
   },
   {
@@ -1489,7 +1230,12 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:19.611194400Z",
+     "start_time": "2023-12-10T12:14:19.609138100Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1500,9 +1246,7 @@
     },
     {
      "data": {
-      "text/plain": [
-       "4.0"
-      ]
+      "text/plain": "4.0"
      },
      "execution_count": 18,
      "metadata": {},
@@ -1528,13 +1272,16 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:19.641429500Z",
+     "start_time": "2023-12-10T12:14:19.616345800Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<function __main__.curren_func(time)>"
-      ]
+      "text/plain": "<function __main__.curren_func(time)>"
      },
      "execution_count": 19,
      "metadata": {},
@@ -1572,23 +1319,24 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:19.700673700Z",
+     "start_time": "2023-12-10T12:14:19.627406900Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": "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"
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
+      "text/plain": "<Axes: >"
      },
      "execution_count": 20,
      "metadata": {},
@@ -1616,23 +1364,24 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:19.785875100Z",
+     "start_time": "2023-12-10T12:14:19.699175500Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": "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"
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
+      "text/plain": "<Axes: >"
      },
      "execution_count": 21,
      "metadata": {},
@@ -1661,27 +1410,28 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:21.137222900Z",
+     "start_time": "2023-12-10T12:14:19.775429Z"
+    }
+   },
    "outputs": [
     {
      "data": {
+      "text/plain": "interactive(children=(FloatSlider(value=0.0, description='t', max=3599.0, step=35.99), Output()), _dom_classes…",
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "eea07489478640aab13bd2aab1fe5020",
        "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "interactive(children=(FloatSlider(value=0.0, description='t', max=3599.0, step=35.99), Output()), _dom_classes…"
-      ]
+       "version_minor": 0,
+       "model_id": "e3e2a10c3de140de8cc785ae5421b534"
+      }
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "text/plain": [
-       "<pybamm.plotting.quick_plot.QuickPlot at 0x1412c2e50>"
-      ]
+      "text/plain": "<pybamm.plotting.quick_plot.QuickPlot at 0x7f481ffd4990>"
      },
      "execution_count": 22,
      "metadata": {},
@@ -1707,7 +1457,12 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:21.184199300Z",
+     "start_time": "2023-12-10T12:14:21.136110400Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1718,8 +1473,7 @@
       "[3] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n",
       "[4] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n",
       "[5] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n",
-      "[6] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n",
-      "\n"
+      "[6] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n"
      ]
     }
    ],
diff --git a/docs/source/user_guide/installation/GNU-linux.rst b/docs/source/user_guide/installation/GNU-linux.rst
index db3f6c3bb1..3bfd3b6de6 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 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, 3.11, or 3.12 installed.
 
 .. tab:: Debian-based distributions (Debian, Ubuntu, Linux Mint)
 
@@ -50,7 +50,7 @@ User install
 
 We recommend to install PyBaMM within a virtual environment, in order
 not to alter any distribution Python files.
-First, make sure you are using Python 3.8, 3.9, 3.10, or 3.11.
+First, make sure you are using Python 3.8, 3.9, 3.10, 3.11, or 3.12.
 To create a virtual environment ``env`` within your current directory type:
 
 .. code:: bash
@@ -105,7 +105,15 @@ Optional - scikits.odes solver
 Users can install `scikits.odes <https://github.com/bmcage/odes>`__ in
 order to use the wrapped SUNDIALS ODE and DAE
 `solvers <https://docs.pybamm.org/en/latest/source/api/solvers/scikits_solvers.html>`__.
-Currently, only GNU/Linux and macOS are supported.
+
+.. note::
+
+   Currently, only GNU/Linux and macOS are supported.
+
+.. note::
+
+   The ``scikits.odes`` solver is not supported on Python 3.12 yet, please refer to https://github.com/bmcage/odes/issues/162.
+   There is support for Python 3.8, 3.9, 3.10, and 3.11.
 
 .. tab:: GNU/Linux
 
diff --git a/docs/source/user_guide/installation/install-from-source.rst b/docs/source/user_guide/installation/install-from-source.rst
index 003c7f143a..26b6b5cf20 100644
--- a/docs/source/user_guide/installation/install-from-source.rst
+++ b/docs/source/user_guide/installation/install-from-source.rst
@@ -25,7 +25,7 @@ or download the source archive on the repository's homepage.
 
 To install PyBaMM, you will need:
 
-- Python 3 (PyBaMM supports versions 3.8, 3.9, 3.10, and 3.11)
+- Python 3 (PyBaMM supports versions 3.8, 3.9, 3.10, 3.11, and 3.12)
 - The Python headers file for your current Python version.
 - A BLAS library (for instance `openblas <https://www.openblas.net/>`_).
 - A C compiler (ex: ``gcc``).
diff --git a/docs/source/user_guide/installation/windows.rst b/docs/source/user_guide/installation/windows.rst
index 5ad77b6f7f..6e815b33c8 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 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, 3.11, or 3.12 installed.
 
 To install Python 3 download the installation files from `Python’s
 website <https://www.python.org/downloads/windows/>`__. Make sure to
diff --git a/noxfile.py b/noxfile.py
index 297fc5b3d7..4805bff83c 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -22,7 +22,7 @@
 
 def set_environment_variables(env_dict, session):
     """
-    Sets environment variables for a nox session object.
+    Sets environment variables for a nox Session object.
 
     Parameters
     -----------
@@ -61,7 +61,10 @@ def run_coverage(session):
     set_environment_variables(PYBAMM_ENV, session=session)
     session.install("coverage", silent=False)
     if sys.platform != "win32":
-        session.install("-e", ".[all,jax,odes]", silent=False)
+        if sys.version_info > (3, 12):
+            session.install("-e", ".[all,jax]", silent=False)
+        else:
+            session.install("-e", ".[all,jax,odes]", silent=False)
     else:
         if sys.version_info < (3, 9):
             session.install("-e", ".[all]", silent=False)
@@ -77,7 +80,10 @@ def run_integration(session):
     """Run the integration tests."""
     set_environment_variables(PYBAMM_ENV, session=session)
     if sys.platform != "win32":
-        session.install("-e", ".[all,jax,odes]", silent=False)
+        if sys.version_info > (3, 12):
+            session.install("-e", ".[all,jax]", silent=False)
+        else:
+            session.install("-e", ".[all,jax,odes]", silent=False)
     else:
         if sys.version_info < (3, 9):
             session.install("-e", ".[all]", silent=False)
@@ -98,7 +104,10 @@ def run_unit(session):
     """Run the unit tests."""
     set_environment_variables(PYBAMM_ENV, session=session)
     if sys.platform != "win32":
-        session.install("-e", ".[all,jax,odes]", silent=False)
+        if sys.version_info > (3, 12):
+            session.install("-e", ".[all,jax]", silent=False)
+        else:
+            session.install("-e", ".[all,jax,odes]", silent=False)
     else:
         if sys.version_info < (3, 9):
             session.install("-e", ".[all]", silent=False)
@@ -131,27 +140,27 @@ 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",
-            "-e",
-            ".[all,dev,jax,odes]",
-            external=True,
-        )
+        if sys.version_info > (3, 12):
+            session.run(
+                python,
+                "-m",
+                "pip",
+                "install",
+                "-e",
+                ".[all,dev,jax]",
+                external=True,
+            )
+        else:
+            session.run(
+                python,
+                "-m",
+                "pip",
+                "install",
+                "-e",
+                ".[all,dev,jax,odes]",
+                external=True,
+            )
     else:
         if sys.version_info < (3, 9):
             session.run(
@@ -159,6 +168,7 @@ def set_dev(session):
                 "-m",
                 "pip",
                 "install",
+                "-e",
                 ".[all,dev]",
                 external=True,
             )
@@ -168,6 +178,7 @@ def set_dev(session):
                 "-m",
                 "pip",
                 "install",
+                "-e",
                 ".[all,dev,jax]",
                 external=True,
             )
@@ -178,6 +189,9 @@ def run_tests(session):
     """Run the unit tests and integration tests sequentially."""
     set_environment_variables(PYBAMM_ENV, session=session)
     if sys.platform != "win32":
+        if sys.version_info > (3, 12):
+            session.install("-e", ".[all,jax]", silent=False)
+        else:
             session.install("-e", ".[all,jax,odes]", silent=False)
     else:
         if sys.version_info < (3, 9):
diff --git a/pybamm/input/parameters/lithium_ion/Ai2020.py b/pybamm/input/parameters/lithium_ion/Ai2020.py
index abae3087ea..31b9ab228d 100644
--- a/pybamm/input/parameters/lithium_ion/Ai2020.py
+++ b/pybamm/input/parameters/lithium_ion/Ai2020.py
@@ -451,7 +451,7 @@ def electrolyte_diffusivity_Ai2020(c_e, T):
         Solid diffusivity
     """
 
-    D_c_e = 10 ** (-8.43 - 54 / (T - 229 - 5e-3 * c_e) - 0.22e-3 * c_e)
+    D_c_e = 10 ** (-4.43 - 54 / (T - 229 - 5e-3 * c_e) - 0.22e-3 * c_e)
 
     return D_c_e
 
diff --git a/pybamm/install_odes.py b/pybamm/install_odes.py
index f7b50150ca..798482c94f 100644
--- a/pybamm/install_odes.py
+++ b/pybamm/install_odes.py
@@ -80,20 +80,29 @@ def update_LD_LIBRARY_PATH(install_dir):
 
     export_statement = f"export LD_LIBRARY_PATH={install_dir}/lib:$LD_LIBRARY_PATH"
 
+    home_dir = os.environ.get("HOME")
+    bashrc_path = os.path.join(home_dir, ".bashrc")
+    zshrc_path = os.path.join(home_dir, ".zshrc")
     venv_path = os.environ.get("VIRTUAL_ENV")
+
     if venv_path:
         script_path = os.path.join(venv_path, "bin/activate")
     else:
-        if 'BASH' in os.environ:
+        if os.path.exists(bashrc_path):
             script_path = os.path.join(os.environ.get("HOME"), ".bashrc")
-        if 'ZSH' in os.environ:
+        elif os.path.exists(zshrc_path):
             script_path = os.path.join(os.environ.get("HOME"), ".zshrc")
+        elif os.path.exists(bashrc_path) and os.path.exists(zshrc_path):
+            print("Both .bashrc and .zshrc found in the home directory. Setting .bashrc as path")
+            script_path = os.path.join(os.environ.get("HOME"), ".bashrc")
+        else:
+            print("Neither .bashrc nor .zshrc found in the home directory.")
 
     if os.getenv("LD_LIBRARY_PATH") and f"{install_dir}/lib" in os.getenv("LD_LIBRARY_PATH"):
         print(f"{install_dir}/lib was found in LD_LIBRARY_PATH.")
-        if 'BASH' in os.environ:
+        if os.path.exists(bashrc_path):
             print("--> Not updating venv activate or .bashrc scripts")
-        if 'ZSH' in os.environ:
+        if os.path.exists(zshrc_path):
             print("--> Not updating venv activate or .zshrc scripts")
     else:
         with open(script_path, "a+") as fh:
diff --git a/pybamm/models/base_model.py b/pybamm/models/base_model.py
index 8e4c80a625..3da6b53618 100644
--- a/pybamm/models/base_model.py
+++ b/pybamm/models/base_model.py
@@ -421,31 +421,63 @@ def input_parameters(self):
             self._input_parameters = self._find_symbols(pybamm.InputParameter)
         return self._input_parameters
 
-    def print_parameter_info(self):
-        """Returns parameters used in the model"""
-        self._parameter_info = ""
+    def get_parameter_info(self):
+        """
+        Extracts the parameter information and returns it as a dictionary.
+        To get a list of all parameter-like objects without extra information,
+        use :py:attr:`model.parameters`.
+        """
+        parameter_info = {}
         parameters = self._find_symbols(pybamm.Parameter)
         for param in parameters:
-            self._parameter_info += f"{param.name} (Parameter)\n"
+            parameter_info[param.name] = (param, "Parameter")
+
         input_parameters = self._find_symbols(pybamm.InputParameter)
         for input_param in input_parameters:
-            if input_param.domain == []:
-                self._parameter_info += f"{input_param.name} (InputParameter)\n"
+            if not input_param.domain:
+                parameter_info[input_param.name] = (input_param, "InputParameter")
             else:
-                self._parameter_info += (
-                    f"{input_param.name} (InputParameter in {input_param.domain})\n"
-                )
+                parameter_info[input_param.name] = (input_param, f"InputParameter in {input_param.domain}")
+
         function_parameters = self._find_symbols(pybamm.FunctionParameter)
         for func_param in function_parameters:
-            # don't double count function parameters
-            if func_param.name not in self._parameter_info:
-                input_names = "'" + "', '".join(func_param.input_names) + "'"
-                self._parameter_info += (
-                    f"{func_param.name} (FunctionParameter "
-                    f"with input(s) {input_names})\n"
-                )
+            if func_param.name not in parameter_info:
+                input_names =  "', '".join(func_param.input_names)
+                parameter_info[func_param.name] = (func_param, f"FunctionParameter with inputs(s) '{input_names}'")
 
-        print(self._parameter_info)
+        return parameter_info
+
+    def print_parameter_info(self):
+        """Print parameter information in a formatted table from a dictionary of parameters"""
+        info = self.get_parameter_info()
+        max_param_name_length = 0
+        max_param_type_length = 0
+
+        for param, param_type in info.values():
+            param_name_length = len(getattr(param, 'name', str(param)))
+            param_type_length = len(param_type)
+            max_param_name_length = max(max_param_name_length, param_name_length)
+            max_param_type_length = max(max_param_type_length, param_type_length)
+
+        header_format = f"| {{:<{max_param_name_length}}} | {{:<{max_param_type_length}}} |"
+        row_format = f"| {{:<{max_param_name_length}}} | {{:<{max_param_type_length}}} |"
+
+        table = [header_format.format("Parameter", "Type of parameter"),
+                 header_format.format("=" * max_param_name_length, "=" * max_param_type_length)]
+
+        for param, param_type in info.values():
+            param_name = getattr(param, 'name', str(param))
+            param_name_lines = [param_name[i:i + max_param_name_length] for i in range(0, len(param_name), max_param_name_length)]
+            param_type_lines = [param_type[i:i + max_param_type_length] for i in range(0, len(param_type), max_param_type_length)]
+            max_lines = max(len(param_name_lines), len(param_type_lines))
+
+            for i in range(max_lines):
+                param_line = param_name_lines[i] if i < len(param_name_lines) else ""
+                type_line = param_type_lines[i] if i < len(param_type_lines) else ""
+                table.append(row_format.format(param_line, type_line))
+
+        for line in table:
+            print(line)
 
     def _find_symbols(self, typ):
         """Find all the instances of `typ` in the model"""
diff --git a/pybamm/solvers/base_solver.py b/pybamm/solvers/base_solver.py
index 36f101b1d0..76cf3e9367 100644
--- a/pybamm/solvers/base_solver.py
+++ b/pybamm/solvers/base_solver.py
@@ -698,7 +698,6 @@ def solve(
         model,
         t_eval=None,
         inputs=None,
-        initial_conditions=None,
         nproc=None,
         calculate_sensitivities=False,
     ):
@@ -717,14 +716,10 @@ def solve(
         inputs : dict or list, optional
             A dictionary or list of dictionaries describing any input parameters to
             pass to the model when solving
-        initial_conditions : :class:`pybamm.Symbol`, optional
-            Initial conditions to use when solving the model. If None (default),
-            `model.concatenated_initial_conditions` is used. Otherwise, must be a symbol
-            of size `len(model.rhs) + len(model.algebraic)`.
         nproc : int, optional
             Number of processes to use when solving for more than one set of input
             parameters. Defaults to value returned by "os.cpu_count()".
-        calculate_sensitivites : list of str or bool
+        calculate_sensitivities : list of str or bool
             If true, solver calculates sensitivities of all input parameters.
             If only a subset of sensitivities are required, can also pass a
             list of input parameter names
diff --git a/pyproject.toml b/pyproject.toml
index 69fb9bfc1e..e95017eb75 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -15,7 +15,7 @@ license = { file = "LICENSE.txt" }
 description = "Python Battery Mathematical Modelling"
 authors = [{name = "The PyBaMM Team", email = "pybamm@pybamm.org"}]
 maintainers = [{name = "The PyBaMM Team", email = "pybamm@pybamm.org"}]
-requires-python = ">=3.8, <3.12"
+requires-python = ">=3.8, <3.13"
 readme = {file = "README.md", content-type = "text/markdown"}
 classifiers = [
     "Development Status :: 5 - Production/Stable",
@@ -29,6 +29,7 @@ classifiers = [
     "Programming Language :: Python :: 3.9",
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
     "Topic :: Scientific/Engineering",
 ]
 dependencies = [
diff --git a/setup.py b/setup.py
index 6b62aacc99..2c89603b74 100644
--- a/setup.py
+++ b/setup.py
@@ -6,14 +6,9 @@
 from platform import system
 import wheel.bdist_wheel as orig
 
-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
+from setuptools import setup, Extension
+from setuptools.command.install import install
+from setuptools.command.build_ext import build_ext
 
 
 default_lib_dir = (
@@ -71,9 +66,9 @@ def finalize_options(self):
             self.sundials_root = os.path.join(default_lib_dir)
 
     def get_build_directory(self):
-        # distutils outputs object files in directory self.build_temp
+        # setuptools 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
+        # On Windows, setuptools 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":

From a04fce6b994c62fe2aeff8e1d8ce85ee6df38b48 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 21 Dec 2023 18:18:11 +0530
Subject: [PATCH 046/109] #3646 fix parallel level, set environment variable

---
 scripts/install_KLU_Sundials.py | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/scripts/install_KLU_Sundials.py b/scripts/install_KLU_Sundials.py
index 34148920e6..0bfa02cefa 100755
--- a/scripts/install_KLU_Sundials.py
+++ b/scripts/install_KLU_Sundials.py
@@ -37,6 +37,9 @@ def download_extract_library(url, download_dir):
 except OSError:
     raise RuntimeError("CMake must be installed.")
 
+# Build in parallel wherever possible
+os.environ["CMAKE_BUILD_PARALLEL_LEVEL"] = str(cpu_count())
+
 # Create download directory in PyBaMM dir
 pybamm_dir = os.path.split(os.path.abspath(os.path.dirname(__file__)))[0]
 download_dir = os.path.join(pybamm_dir, "install_KLU_Sundials")
@@ -78,6 +81,7 @@ def download_extract_library(url, download_dir):
 ]
 install_cmd = [
     "make",
+    f"-j{cpu_count()}",
     "install",
 ]
 print("-" * 10, "Building SuiteSparse", "-" * 40)
@@ -89,13 +93,13 @@ def download_extract_library(url, download_dir):
     # multiple paths at the time of wheel repair. Therefore, it should not be
     # built with an RPATH since it is copied to the install prefix.
     if libdir == "SuiteSparse_config":
-        env["CMAKE_OPTIONS"] = f"-DCMAKE_INSTALL_PREFIX={install_dir} -DCMAKE_BUILD_PARALLEL_LEVEL={cpu_count()}"
+        env["CMAKE_OPTIONS"] = f"-DCMAKE_INSTALL_PREFIX={install_dir}"
     else:
     # For AMD, COLAMD, BTF and KLU; do not set a BUILD RPATH but use an
     # INSTALL RPATH in order to ensure that the dynamic libraries are found
     # at runtime just once. Otherwise, delocate complains about multiple
     # references to the SuiteSparse_config dynamic library (auditwheel does not).
-        env["CMAKE_OPTIONS"] = f"-DCMAKE_INSTALL_PREFIX={install_dir} -DCMAKE_INSTALL_RPATH={install_dir}/lib -DCMAKE_INSTALL_RPATH_USE_LINK_PATH=FALSE -DCMAKE_BUILD_WITH_INSTALL_RPATH=FALSE -DCMAKE_BUILD_PARALLEL_LEVEL={cpu_count()}"
+        env["CMAKE_OPTIONS"] = f"-DCMAKE_INSTALL_PREFIX={install_dir} -DCMAKE_INSTALL_RPATH={install_dir}/lib -DCMAKE_INSTALL_RPATH_USE_LINK_PATH=FALSE -DCMAKE_BUILD_WITH_INSTALL_RPATH=FALSE"
     subprocess.run(make_cmd, cwd=build_dir, env=env, shell=True, check=True)
     subprocess.run(install_cmd, cwd=build_dir, check=True)
 
@@ -168,5 +172,5 @@ def download_extract_library(url, download_dir):
 subprocess.run(["cmake", sundials_src, *cmake_args], cwd=build_dir, check=True)
 
 print("-" * 10, "Building the sundials", "-" * 40)
-make_cmd = ["make", "install"]
+make_cmd = ["make", f"-j{cpu_count()}", "install"]
 subprocess.run(make_cmd, cwd=build_dir, check=True)

From 3dc8c8c8a55ecc5a8c10ab34ca94342fdfb714cb Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 21 Dec 2023 18:19:11 +0530
Subject: [PATCH 047/109] #3646 set parallel variable for `build_ext` (IDAKLU)

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

diff --git a/setup.py b/setup.py
index 2c89603b74..d0b96c7951 100644
--- a/setup.py
+++ b/setup.py
@@ -2,6 +2,7 @@
 import sys
 import logging
 import subprocess
+from multiprocessing import cpu_count
 from pathlib import Path
 from platform import system
 import wheel.bdist_wheel as orig
@@ -79,6 +80,9 @@ def run(self):
         if not self.extensions:
             return
 
+        # Build in parallel wherever possible
+        os.environ["CMAKE_BUILD_PARALLEL_LEVEL"] = str(cpu_count())
+
         if system() == "Windows":
             use_python_casadi = False
         else:

From 139e34dfed33de1fb859d8572507d48ec76e14fb Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Fri, 22 Dec 2023 14:30:33 +0530
Subject: [PATCH 048/109] #3646 set parallel jobs for `pybamm_install_odes`

---
 pybamm/install_odes.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/pybamm/install_odes.py b/pybamm/install_odes.py
index a51c9eea76..fa2d3af289 100644
--- a/pybamm/install_odes.py
+++ b/pybamm/install_odes.py
@@ -5,6 +5,7 @@
 import sys
 import logging
 import subprocess
+from multiprocessing import cpu_count
 
 from pybamm.util import root_dir
 
@@ -16,6 +17,8 @@
 except ModuleNotFoundError:
     NO_WGET = True
 
+# Build in parallel wherever possible
+os.environ["CMAKE_BUILD_PARALLEL_LEVEL"] = str(cpu_count())
 
 def download_extract_library(url, directory):
     # Download and extract archive at url

From 971ef8a277a1f5f025a8c2b81235f7d21b0ed85d Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Fri, 22 Dec 2023 14:31:04 +0530
Subject: [PATCH 049/109] #3646 set parallel jobs for `install_sundials.sh`

for Linux wheel builds
---
 scripts/install_sundials.sh | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/scripts/install_sundials.sh b/scripts/install_sundials.sh
index 0fdd4cdc6a..020166a188 100644
--- a/scripts/install_sundials.sh
+++ b/scripts/install_sundials.sh
@@ -43,6 +43,10 @@ download $SUNDIALS_ROOT_ADDR $SUNDIALS_ARCHIVE_NAME
 extract $SUITESPARSE_ARCHIVE_NAME
 extract $SUNDIALS_ARCHIVE_NAME
 
+# Build in parallel wherever possible
+export MAKEFLAGS="-j$(nproc)"
+export CMAKE_BUILD_PARALLEL_LEVEL=$(nproc)
+
 ### Compile and install SUITESPARSE ###
 # SuiteSparse is required to compile SUNDIALS's
 # KLU solver.

From 7e0cc70c0f9d2d54f16d7eb2c8a4102e56d3c5ae Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Fri, 22 Dec 2023 18:53:19 +0530
Subject: [PATCH 050/109] Add note to avoid installation failure

---
 .../user_guide/installation/GNU-linux.rst     | 20 +++++++++++--------
 1 file changed, 12 insertions(+), 8 deletions(-)

diff --git a/docs/source/user_guide/installation/GNU-linux.rst b/docs/source/user_guide/installation/GNU-linux.rst
index 3bfd3b6de6..4af1e58144 100644
--- a/docs/source/user_guide/installation/GNU-linux.rst
+++ b/docs/source/user_guide/installation/GNU-linux.rst
@@ -102,9 +102,7 @@ For an introduction to virtual environments, see
 Optional - scikits.odes solver
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Users can install `scikits.odes <https://github.com/bmcage/odes>`__ in
-order to use the wrapped SUNDIALS ODE and DAE
-`solvers <https://docs.pybamm.org/en/latest/source/api/solvers/scikits_solvers.html>`__.
+Users can install `scikits.odes <https://github.com/bmcage/odes>`__ to utilize the wrapped SUNDIALS ODE and DAE `solvers <https://docs.pybamm.org/en/latest/source/api/solvers/scikits_solvers.html>`__ in PyBaMM.
 
 .. note::
 
@@ -112,7 +110,7 @@ order to use the wrapped SUNDIALS ODE and DAE
 
 .. note::
 
-   The ``scikits.odes`` solver is not supported on Python 3.12 yet, please refer to https://github.com/bmcage/odes/issues/162.
+   The ``scikits.odes`` solver is not supported on Python 3.12 yet. Please refer to https://github.com/bmcage/odes/issues/162.
    There is support for Python 3.8, 3.9, 3.10, and 3.11.
 
 .. tab:: GNU/Linux
@@ -121,10 +119,10 @@ order to use the wrapped SUNDIALS ODE and DAE
 
    .. code:: bash
 
-	   apt install libopenblas-dev
-	   pybamm_install_odes
+      apt install libopenblas-dev
+      pybamm_install_odes
 
-   The ``pybamm_install_odes`` command is installed with PyBaMM. It automatically downloads and installs the SUNDIALS library on your
+   The ``pybamm_install_odes`` command, installed with PyBaMM, automatically downloads and installs the SUNDIALS library on your
    system (under ``~/.local``), before installing ``scikits.odes``. (Alternatively, one can install SUNDIALS without this script and run ``pip install pybamm[odes]`` to install ``pybamm`` with ``scikits.odes``.)
 
 .. tab:: macOS
@@ -136,9 +134,15 @@ order to use the wrapped SUNDIALS ODE and DAE
       brew install openblas
       pybamm_install_odes
 
-   The ``pybamm_install_odes`` command is installed with PyBaMM. It automatically downloads and installs the SUNDIALS library on your
+   The ``pybamm_install_odes`` command, installed with PyBaMM, automatically downloads and installs the SUNDIALS library on your
    system (under ``~/.local``), before installing ``scikits.odes``. (Alternatively, one can install SUNDIALS without this script and run ``pip install pybamm[odes]`` to install ``pybamm`` with ``scikits.odes``.)
 
+   To avoid installation failures when using ``pip install pybamm[odes]``, make sure to set the ``SUNDIALS_INST`` environment variable. If you have installed SUNDIALS using Homebrew, set the variable to the appropriate location. For example:
+
+   .. code:: bash
+
+      export SUNDIALS_INST=$(brew --prefix sundials)
+
 Optional - JaxSolver
 ~~~~~~~~~~~~~~~~~~~~
 

From 15e059769d839d4f90bab2c4ef37884529e8c61b Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sat, 23 Dec 2023 16:13:30 +0530
Subject: [PATCH 051/109] Add note for path validation

---
 .../user_guide/installation/GNU-linux.rst      | 18 +++++++++++++++---
 1 file changed, 15 insertions(+), 3 deletions(-)

diff --git a/docs/source/user_guide/installation/GNU-linux.rst b/docs/source/user_guide/installation/GNU-linux.rst
index 4af1e58144..479cbeeecf 100644
--- a/docs/source/user_guide/installation/GNU-linux.rst
+++ b/docs/source/user_guide/installation/GNU-linux.rst
@@ -137,11 +137,23 @@ Users can install `scikits.odes <https://github.com/bmcage/odes>`__ to utilize t
    The ``pybamm_install_odes`` command, installed with PyBaMM, automatically downloads and installs the SUNDIALS library on your
    system (under ``~/.local``), before installing ``scikits.odes``. (Alternatively, one can install SUNDIALS without this script and run ``pip install pybamm[odes]`` to install ``pybamm`` with ``scikits.odes``.)
 
-   To avoid installation failures when using ``pip install pybamm[odes]``, make sure to set the ``SUNDIALS_INST`` environment variable. If you have installed SUNDIALS using Homebrew, set the variable to the appropriate location. For example:
+To avoid installation failures when using ``pip install pybamm[odes]``, make sure to set the ``SUNDIALS_INST`` environment variable. If you have installed SUNDIALS using Homebrew, set the variable to the appropriate location. For example:
 
-   .. code:: bash
+.. code:: bash
+
+   export SUNDIALS_INST=$(brew --prefix sundials)
+
+Ensure that the path matches the installation location on your system. You can verify the installation location by running:
+
+.. code:: bash
+
+   brew info sundials
+
+Look for the installation path, and use that path to set the ``SUNDIALS_INST`` variable.
+
+Note: The location where Homebrew installs SUNDIALS might vary based on the system architecture (ARM or Intel). Adjust the path in the ``export SUNDIALS_INST`` command accordingly.
 
-      export SUNDIALS_INST=$(brew --prefix sundials)
+To avoid manual setup of path the ``pybamm_install_odes`` is recommended for a smoother installation process, as it takes care of automatically downloading and installing the SUNDIALS library on your system.
 
 Optional - JaxSolver
 ~~~~~~~~~~~~~~~~~~~~

From 62210175832968d55b82dac608d90e03a95ce359 Mon Sep 17 00:00:00 2001
From: Arjun <arjunverma.oc@gmail.com>
Date: Sat, 23 Dec 2023 18:44:14 +0530
Subject: [PATCH 052/109] Apply suggestions from code review

Co-authored-by: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
---
 .github/workflows/run_periodic_tests.yml          | 1 -
 docs/source/user_guide/installation/GNU-linux.rst | 5 ++---
 2 files changed, 2 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml
index 1c402d312e..f247176e40 100644
--- a/.github/workflows/run_periodic_tests.yml
+++ b/.github/workflows/run_periodic_tests.yml
@@ -178,7 +178,6 @@ jobs:
           NONINTERACTIVE: 1
         run: |
           brew analytics off
-          brew update
           brew install openblas
           brew reinstall gcc gfortran
 
diff --git a/docs/source/user_guide/installation/GNU-linux.rst b/docs/source/user_guide/installation/GNU-linux.rst
index 479cbeeecf..ee1d5b3f8a 100644
--- a/docs/source/user_guide/installation/GNU-linux.rst
+++ b/docs/source/user_guide/installation/GNU-linux.rst
@@ -102,7 +102,7 @@ For an introduction to virtual environments, see
 Optional - scikits.odes solver
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Users can install `scikits.odes <https://github.com/bmcage/odes>`__ to utilize the wrapped SUNDIALS ODE and DAE `solvers <https://docs.pybamm.org/en/latest/source/api/solvers/scikits_solvers.html>`__ in PyBaMM.
+Users can install `scikits.odes <https://github.com/bmcage/odes>`__ to utilize its interfaced SUNDIALS ODE and DAE `solvers <https://docs.pybamm.org/en/latest/source/api/solvers/scikits_solvers.html>`__ wrapped in PyBaMM.
 
 .. note::
 
@@ -122,7 +122,6 @@ Users can install `scikits.odes <https://github.com/bmcage/odes>`__ to utilize t
       apt install libopenblas-dev
       pybamm_install_odes
 
-   The ``pybamm_install_odes`` command, installed with PyBaMM, automatically downloads and installs the SUNDIALS library on your
    system (under ``~/.local``), before installing ``scikits.odes``. (Alternatively, one can install SUNDIALS without this script and run ``pip install pybamm[odes]`` to install ``pybamm`` with ``scikits.odes``.)
 
 .. tab:: macOS
@@ -131,7 +130,7 @@ Users can install `scikits.odes <https://github.com/bmcage/odes>`__ to utilize t
 
    .. code:: bash
 
-      brew install openblas
+      brew install openblas gcc gfortran
       pybamm_install_odes
 
    The ``pybamm_install_odes`` command, installed with PyBaMM, automatically downloads and installs the SUNDIALS library on your

From b8eaaabcfb783ae0de8185ffcea79118eb9f011a Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sat, 23 Dec 2023 19:07:28 +0530
Subject: [PATCH 053/109] Rename file & suggested fixes

---
 .../installation/{GNU-linux.rst => gnu-linux-mac.rst}         | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)
 rename docs/source/user_guide/installation/{GNU-linux.rst => gnu-linux-mac.rst} (92%)

diff --git a/docs/source/user_guide/installation/GNU-linux.rst b/docs/source/user_guide/installation/gnu-linux-mac.rst
similarity index 92%
rename from docs/source/user_guide/installation/GNU-linux.rst
rename to docs/source/user_guide/installation/gnu-linux-mac.rst
index ee1d5b3f8a..c8e26369b8 100644
--- a/docs/source/user_guide/installation/GNU-linux.rst
+++ b/docs/source/user_guide/installation/gnu-linux-mac.rst
@@ -133,8 +133,8 @@ Users can install `scikits.odes <https://github.com/bmcage/odes>`__ to utilize i
       brew install openblas gcc gfortran
       pybamm_install_odes
 
-   The ``pybamm_install_odes`` command, installed with PyBaMM, automatically downloads and installs the SUNDIALS library on your
-   system (under ``~/.local``), before installing ``scikits.odes``. (Alternatively, one can install SUNDIALS without this script and run ``pip install pybamm[odes]`` to install ``pybamm`` with ``scikits.odes``.)
+The ``pybamm_install_odes`` command, installed with PyBaMM, automatically downloads and installs the SUNDIALS library on your
+system (under ``~/.local``), before installing `scikits.odes <https://scikits-odes.readthedocs.io/en/stable/installation.html>`__ . (Alternatively, one can install SUNDIALS without this script and run ``pip install pybamm[odes]`` to install ``pybamm`` with `scikits.odes <https://scikits-odes.readthedocs.io/en/stable/installation.html>`__)
 
 To avoid installation failures when using ``pip install pybamm[odes]``, make sure to set the ``SUNDIALS_INST`` environment variable. If you have installed SUNDIALS using Homebrew, set the variable to the appropriate location. For example:
 

From 4e1dbec54fdab4f42da5b9a2fd648d47d30cbde6 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sat, 23 Dec 2023 19:11:11 +0530
Subject: [PATCH 054/109] Set `CMAKE_BUILD_PARALLEL_LEVEL`

---
 pybamm/install_odes.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/pybamm/install_odes.py b/pybamm/install_odes.py
index 798482c94f..2e33cf0994 100644
--- a/pybamm/install_odes.py
+++ b/pybamm/install_odes.py
@@ -5,6 +5,7 @@
 import sys
 import logging
 import subprocess
+from multiprocessing import cpu_count
 
 from pybamm.util import root_dir
 
@@ -13,6 +14,9 @@
 
 SUNDIALS_VERSION = "6.5.0"
 
+# Build in parallel wherever possible
+os.environ["CMAKE_BUILD_PARALLEL_LEVEL"] = str(cpu_count())
+
 try:
     # wget module is required to download SUNDIALS or SuiteSparse.
     import wget

From e770c9250914b3098e58504ec0882cac9dda547d Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sat, 23 Dec 2023 19:23:26 +0530
Subject: [PATCH 055/109] Fix broken doctree due to rename

---
 README.md                                             |  6 +++---
 docs/source/user_guide/installation/gnu-linux-mac.rst |  2 +-
 docs/source/user_guide/installation/index.rst         | 10 +++++-----
 docs/source/user_guide/installation/windows-wsl.rst   |  2 +-
 pybamm/expression_tree/operations/evaluate_python.py  |  4 ++--
 pybamm/solvers/jax_bdf_solver.py                      |  2 +-
 pybamm/solvers/jax_solver.py                          |  2 +-
 7 files changed, 14 insertions(+), 14 deletions(-)

diff --git a/README.md b/README.md
index d5050cfe55..e176d4f54c 100644
--- a/README.md
+++ b/README.md
@@ -104,7 +104,7 @@ PyBaMM makes releases every four months and we use [CalVer](https://calver.org/)
 
 PyBaMM is available on GNU/Linux, MacOS and Windows.
 We strongly recommend to install PyBaMM within a python virtual environment, in order not to alter any distribution python files.
-For instructions on how to create a virtual environment for PyBaMM, see [the documentation](https://docs.pybamm.org/en/latest/source/user_guide/installation/GNU-linux.html#user-install).
+For instructions on how to create a virtual environment for PyBaMM, see [the documentation](https://docs.pybamm.org/en/latest/source/user_guide/installation/gnu-linux-mac.html#user-install).
 
 ### Using pip
 
@@ -130,8 +130,8 @@ conda install -c conda-forge pybamm
 
 Following GNU/Linux and macOS solvers are optionally available:
 
-- [scikits.odes](https://scikits-odes.readthedocs.io/en/latest/)-based solver, see [the documentation](https://docs.pybamm.org/en/latest/source/user_guide/installation/GNU-linux.html#optional-scikits-odes-solver).
-- [jax](https://jax.readthedocs.io/en/latest/notebooks/quickstart.html)-based solver, see [the documentation](https://docs.pybamm.org/en/latest/source/user_guide/installation/GNU-linux.html#optional-jaxsolver).
+- [scikits.odes](https://scikits-odes.readthedocs.io/en/latest/)-based solver, see [the documentation](https://docs.pybamm.org/en/latest/source/user_guide/installation/gnu-linux-mac.html#optional-scikits-odes-solver).
+- [jax](https://jax.readthedocs.io/en/latest/notebooks/quickstart.html)-based solver, see [the documentation](https://docs.pybamm.org/en/latest/source/user_guide/installation/gnu-linux-mac.html#optional-jaxsolver).
 
 ## 📖 Citing PyBaMM
 
diff --git a/docs/source/user_guide/installation/gnu-linux-mac.rst b/docs/source/user_guide/installation/gnu-linux-mac.rst
index c8e26369b8..0e765a37a3 100644
--- a/docs/source/user_guide/installation/gnu-linux-mac.rst
+++ b/docs/source/user_guide/installation/gnu-linux-mac.rst
@@ -1,4 +1,4 @@
-GNU-Linux & MacOS
+gnu-linux-mac & MacOS
 =================
 
 .. contents::
diff --git a/docs/source/user_guide/installation/index.rst b/docs/source/user_guide/installation/index.rst
index 65cbad33fb..5f1b5eaab8 100644
--- a/docs/source/user_guide/installation/index.rst
+++ b/docs/source/user_guide/installation/index.rst
@@ -47,8 +47,8 @@ Optional solvers
 
 Following GNU/Linux and macOS solvers are optionally available:
 
-*  `scikits.odes <https://scikits-odes.readthedocs.io/en/latest/>`_ -based solver, see `Optional - scikits.odes solver <https://docs.pybamm.org/en/latest/source/user_guide/installation/GNU-linux.html#optional-scikits-odes-solver>`_.
-*  `jax <https://jax.readthedocs.io/en/latest/notebooks/quickstart.html>`_ -based solver, see `Optional - JaxSolver <https://docs.pybamm.org/en/latest/source/user_guide/installation/GNU-linux.html#optional-jaxsolver>`_.
+*  `scikits.odes <https://scikits-odes.readthedocs.io/en/latest/>`_ -based solver, see `Optional - scikits.odes solver <https://docs.pybamm.org/en/latest/source/user_guide/installation/gnu-linux-mac.html#optional-scikits-odes-solver>`_.
+*  `jax <https://jax.readthedocs.io/en/latest/notebooks/quickstart.html>`_ -based solver, see `Optional - JaxSolver <https://docs.pybamm.org/en/latest/source/user_guide/installation/gnu-linux-mac.html#optional-jaxsolver>`_.
 
 Dependencies
 ------------
@@ -236,12 +236,12 @@ Installable with ``pip install "pybamm[odes]"``
 ================================================================================================================================ ================== ================== =============================
 Dependency                                                                                                                       Minimum Version    pip extra          Notes
 ================================================================================================================================ ================== ================== =============================
-`scikits.odes <https://docs.pybamm.org/en/latest/source/user_guide/installation/GNU-linux.html#optional-scikits-odes-solver>`__  \-                 odes               For scikits ODE & DAE solvers
+`scikits.odes <https://docs.pybamm.org/en/latest/source/user_guide/installation/gnu-linux-mac.html#optional-scikits-odes-solver>`__  \-                 odes               For scikits ODE & DAE solvers
 ================================================================================================================================ ================== ================== =============================
 
 .. note::
 
-   Before running ``pip install "pybamm[odes]"``, make sure to install ``scikits.odes`` build-time requirements as described `here <https://docs.pybamm.org/en/latest/source/user_guide/installation/GNU-linux.html#optional-scikits-odes-solver>`_ .
+   Before running ``pip install "pybamm[odes]"``, make sure to install ``scikits.odes`` build-time requirements as described `here <https://docs.pybamm.org/en/latest/source/user_guide/installation/gnu-linux-mac.html#optional-scikits-odes-solver>`_ .
 
 Full installation guide
 -----------------------
@@ -251,7 +251,7 @@ Installing a specific version? Installing from source? Check the advanced instal
 .. toctree::
    :maxdepth: 1
 
-   GNU-linux
+   gnu-linux-mac
    windows
    windows-wsl
    install-from-source
diff --git a/docs/source/user_guide/installation/windows-wsl.rst b/docs/source/user_guide/installation/windows-wsl.rst
index 6453c92211..6692789176 100644
--- a/docs/source/user_guide/installation/windows-wsl.rst
+++ b/docs/source/user_guide/installation/windows-wsl.rst
@@ -37,7 +37,7 @@ Get PyBaMM's Source Code
 5. Follow the Installation Steps
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Follow the `installation instructions for PyBaMM on Linux <GNU-linux.html>`__.
+Follow the `installation instructions for PyBaMM on Linux <gnu-linux-mac.html>`__.
 
 Using Visual Studio Code with the WSL
 ---------------------------------------
diff --git a/pybamm/expression_tree/operations/evaluate_python.py b/pybamm/expression_tree/operations/evaluate_python.py
index f65ecc7159..bd6dbd0165 100644
--- a/pybamm/expression_tree/operations/evaluate_python.py
+++ b/pybamm/expression_tree/operations/evaluate_python.py
@@ -42,7 +42,7 @@ class JaxCooMatrix:
     def __init__(self, row, col, data, shape):
         if not pybamm.have_jax():  # pragma: no cover
             raise ModuleNotFoundError(
-                "Jax or jaxlib is not installed, please see https://docs.pybamm.org/en/latest/source/user_guide/installation/GNU-linux.html#optional-jaxsolver"
+                "Jax or jaxlib is not installed, please see https://docs.pybamm.org/en/latest/source/user_guide/installation/gnu-linux-mac.html#optional-jaxsolver"
             )
 
         self.row = jax.numpy.array(row)
@@ -515,7 +515,7 @@ class EvaluatorJax:
     def __init__(self, symbol):
         if not pybamm.have_jax():  # pragma: no cover
             raise ModuleNotFoundError(
-                "Jax or jaxlib is not installed, please see https://docs.pybamm.org/en/latest/source/user_guide/installation/GNU-linux.html#optional-jaxsolver"
+                "Jax or jaxlib is not installed, please see https://docs.pybamm.org/en/latest/source/user_guide/installation/gnu-linux-mac.html#optional-jaxsolver"
             )
 
         constants, python_str = pybamm.to_python(symbol, debug=False, output_jax=True)
diff --git a/pybamm/solvers/jax_bdf_solver.py b/pybamm/solvers/jax_bdf_solver.py
index 8f5b8ed817..9fb2b64f39 100644
--- a/pybamm/solvers/jax_bdf_solver.py
+++ b/pybamm/solvers/jax_bdf_solver.py
@@ -1005,7 +1005,7 @@ def jax_bdf_integrate(func, y0, t_eval, *args, rtol=1e-6, atol=1e-6, mass=None):
     """
     if not pybamm.have_jax():
         raise ModuleNotFoundError(
-            "Jax or jaxlib is not installed, please see https://docs.pybamm.org/en/latest/source/user_guide/installation/GNU-linux.html#optional-jaxsolver"
+            "Jax or jaxlib is not installed, please see https://docs.pybamm.org/en/latest/source/user_guide/installation/gnu-linux-mac.html#optional-jaxsolver"
         )
 
     def _check_arg(arg):
diff --git a/pybamm/solvers/jax_solver.py b/pybamm/solvers/jax_solver.py
index 5e98c5bf07..6c89bed4dd 100644
--- a/pybamm/solvers/jax_solver.py
+++ b/pybamm/solvers/jax_solver.py
@@ -61,7 +61,7 @@ def __init__(
     ):
         if not pybamm.have_jax():
             raise ModuleNotFoundError(
-                "Jax or jaxlib is not installed, please see https://docs.pybamm.org/en/latest/source/user_guide/installation/GNU-linux.html#optional-jaxsolver"
+                "Jax or jaxlib is not installed, please see https://docs.pybamm.org/en/latest/source/user_guide/installation/gnu-linux-mac.html#optional-jaxsolver"
             )
 
         # note: bdf solver itself calculates consistent initial conditions so can set

From 34d3e6bd6bfddc741bfaa2af6b756e1bdabfd540 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sun, 24 Dec 2023 00:22:41 +0530
Subject: [PATCH 056/109] Fix title underline

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

diff --git a/docs/source/user_guide/installation/gnu-linux-mac.rst b/docs/source/user_guide/installation/gnu-linux-mac.rst
index 0e765a37a3..ddd58e963e 100644
--- a/docs/source/user_guide/installation/gnu-linux-mac.rst
+++ b/docs/source/user_guide/installation/gnu-linux-mac.rst
@@ -1,5 +1,5 @@
 gnu-linux-mac & MacOS
-=================
+=====================
 
 .. contents::
 

From e766cca98c05d2617517670625ee1408095bc4df Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sun, 24 Dec 2023 00:39:10 +0530
Subject: [PATCH 057/109] Fix table malformation

---
 docs/source/user_guide/installation/index.rst | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/docs/source/user_guide/installation/index.rst b/docs/source/user_guide/installation/index.rst
index 5f1b5eaab8..f0c12d46fc 100644
--- a/docs/source/user_guide/installation/index.rst
+++ b/docs/source/user_guide/installation/index.rst
@@ -233,11 +233,11 @@ odes dependencies
 
 Installable with ``pip install "pybamm[odes]"``
 
-================================================================================================================================ ================== ================== =============================
-Dependency                                                                                                                       Minimum Version    pip extra          Notes
-================================================================================================================================ ================== ================== =============================
-`scikits.odes <https://docs.pybamm.org/en/latest/source/user_guide/installation/gnu-linux-mac.html#optional-scikits-odes-solver>`__  \-                 odes               For scikits ODE & DAE solvers
-================================================================================================================================ ================== ================== =============================
+======================================================================================================================================= ================== ================== =============================
+Dependency                                                                                                                              Minimum Version    pip extra          Notes
+======================================================================================================================================= ================== ================== =============================
+`scikits.odes <https://docs.pybamm.org/en/latest/source/user_guide/installation/gnu-linux-mac.html#optional-scikits-odes-solver>`__     \-                 odes               For scikits ODE & DAE solvers
+======================================================================================================================================= ================== ================== =============================
 
 .. note::
 

From 96d63deb8b4716f95c524313edffeaa46c9fcaa5 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sun, 24 Dec 2023 02:07:51 +0530
Subject: [PATCH 058/109] Add non-fixable link to `.lycheeignore`

---
 .lycheeignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.lycheeignore b/.lycheeignore
index 399827d27c..fd332a54ff 100644
--- a/.lycheeignore
+++ b/.lycheeignore
@@ -1,4 +1,5 @@
 # a list of links/files to be ignored by lychee link checker (see workflow file)
+https://github.com/LLNL/sundials/releases/download/v%7BSUNDIALS_VERSION%7D/sundials-%7BSUNDIALS_VERSION%7D.tar.gz
 
 # Errors in docs/source/user_guide/getting_started.md
 file:///home/runner/work/PyBaMM/PyBaMM/docs/source/user_guide/api_docs

From 430c86fd92f7b0ae6dfe85ab2837758f845f202c Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Sun, 24 Dec 2023 15:25:33 +0000
Subject: [PATCH 059/109] style: pre-commit fixes

---
 pybamm/install_odes.py | 18 +++++++++++++-----
 1 file changed, 13 insertions(+), 5 deletions(-)

diff --git a/pybamm/install_odes.py b/pybamm/install_odes.py
index 44773fa5c6..b1c1a069b1 100644
--- a/pybamm/install_odes.py
+++ b/pybamm/install_odes.py
@@ -20,10 +20,12 @@
 try:
     # wget module is required to download SUNDIALS or SuiteSparse.
     import wget
+
     NO_WGET = False
 except ModuleNotFoundError:
     NO_WGET = True
 
+
 def download_extract_library(url, directory):
     # Download and extract archive at url
     if NO_WGET:
@@ -36,6 +38,7 @@ def download_extract_library(url, directory):
     tar = tarfile.open(archive)
     tar.extractall(directory)
 
+
 def install_sundials(download_dir, install_dir):
     # Download the SUNDIALS library and compile it.
     logger = logging.getLogger("scikits.odes setup")
@@ -45,9 +48,7 @@ def install_sundials(download_dir, install_dir):
     except OSError:
         raise RuntimeError("CMake must be installed to build SUNDIALS.")
 
-    url = (
-        f"https://github.com/LLNL/sundials/releases/download/v{SUNDIALS_VERSION}/sundials-{SUNDIALS_VERSION}.tar.gz"
-    )
+    url = f"https://github.com/LLNL/sundials/releases/download/v{SUNDIALS_VERSION}/sundials-{SUNDIALS_VERSION}.tar.gz"
     logger.info("Downloading sundials")
     download_extract_library(url, download_dir)
 
@@ -77,6 +78,7 @@ def install_sundials(download_dir, install_dir):
     make_cmd = ["make", "install"]
     subprocess.run(make_cmd, cwd=build_directory, check=True)
 
+
 def update_LD_LIBRARY_PATH(install_dir):
     # Look for the current python virtual env and add an export statement
     # for LD_LIBRARY_PATH in the activate script. If no virtual env is found,
@@ -97,12 +99,16 @@ def update_LD_LIBRARY_PATH(install_dir):
         elif os.path.exists(zshrc_path):
             script_path = os.path.join(os.environ.get("HOME"), ".zshrc")
         elif os.path.exists(bashrc_path) and os.path.exists(zshrc_path):
-            print("Both .bashrc and .zshrc found in the home directory. Setting .bashrc as path")
+            print(
+                "Both .bashrc and .zshrc found in the home directory. Setting .bashrc as path"
+            )
             script_path = os.path.join(os.environ.get("HOME"), ".bashrc")
         else:
             print("Neither .bashrc nor .zshrc found in the home directory.")
 
-    if os.getenv("LD_LIBRARY_PATH") and f"{install_dir}/lib" in os.getenv("LD_LIBRARY_PATH"):
+    if os.getenv("LD_LIBRARY_PATH") and f"{install_dir}/lib" in os.getenv(
+        "LD_LIBRARY_PATH"
+    ):
         print(f"{install_dir}/lib was found in LD_LIBRARY_PATH.")
         if os.path.exists(bashrc_path):
             print("--> Not updating venv activate or .bashrc scripts")
@@ -117,6 +123,7 @@ def update_LD_LIBRARY_PATH(install_dir):
                     f"Adding {install_dir}/lib to LD_LIBRARY_PATH" f" in {script_path}"
                 )
 
+
 def main(arguments=None):
     log_format = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
     logger = logging.getLogger("scikits.odes setup")
@@ -182,5 +189,6 @@ def main(arguments=None):
     env = os.environ.copy()
     subprocess.run(["pip", "install", "scikits.odes"], env=env, check=True)
 
+
 if __name__ == "__main__":
     main(sys.argv[1:])

From 6aa6685b7b9d705169b9fdb8993235ad9d294e96 Mon Sep 17 00:00:00 2001
From: Arjun <arjunverma.oc@gmail.com>
Date: Sun, 24 Dec 2023 20:56:41 +0530
Subject: [PATCH 060/109] Apply suggestions from code review

Co-authored-by: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
---
 .github/workflows/run_periodic_tests.yml          | 15 +++++++++------
 .../user_guide/installation/gnu-linux-mac.rst     |  4 ++--
 2 files changed, 11 insertions(+), 8 deletions(-)

diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml
index f247176e40..1178b2ec96 100644
--- a/.github/workflows/run_periodic_tests.yml
+++ b/.github/workflows/run_periodic_tests.yml
@@ -155,8 +155,7 @@ jobs:
           pyenv uninstall -f $( python --version )
 
   test_install_odes:
-    needs: style
-    runs-on: macos-latest
+    runs-on: ${{ matrix.os }}
     strategy:
         matrix:
             os: [ubuntu-latest, macos-latest]
@@ -168,7 +167,13 @@ jobs:
       - name: Check out PyBaMM repository
         uses: actions/checkout@v4
 
+      - name: Install Linux system dependencies
+        if: matrix.os == 'ubuntu-latest'
+        run: |
+          sudo apt-get update
+          sudo apt-get install gfortran gcc libopenblas-dev
       - name: Install macOS system dependencies
+        if: matrix.os == 'macos-latest'
         env:
           # Homebrew environment variables
           HOMEBREW_NO_INSTALL_CLEANUP: 1
@@ -187,10 +192,8 @@ jobs:
         with:
           python-version: ${{ matrix.python-version }}
 
-      - name: Install PyBaMM dependencies
-        run: |
-          pip install --upgrade pip wheel setuptools nox
-          pip install -e .[all]
+      - name: Install PyBaMM
+        run: pip install -e .
 
       - name: Test pybamm_install_odes on ${{ matrix.os }}
         run: |
diff --git a/docs/source/user_guide/installation/gnu-linux-mac.rst b/docs/source/user_guide/installation/gnu-linux-mac.rst
index ddd58e963e..3e93587cde 100644
--- a/docs/source/user_guide/installation/gnu-linux-mac.rst
+++ b/docs/source/user_guide/installation/gnu-linux-mac.rst
@@ -1,5 +1,5 @@
-gnu-linux-mac & MacOS
-=====================
+GNU/Linux & macOS
+=================
 
 .. contents::
 

From 0218ac4c60fca54878688be70c74bd1fe834406e Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Wed, 3 Jan 2024 15:35:28 +0000
Subject: [PATCH 061/109] style: pre-commit fixes

---
 pybamm/install_odes.py          |  1 +
 scripts/install_KLU_Sundials.py | 12 +++++++-----
 2 files changed, 8 insertions(+), 5 deletions(-)

diff --git a/pybamm/install_odes.py b/pybamm/install_odes.py
index d1c38a61af..caf36f226e 100644
--- a/pybamm/install_odes.py
+++ b/pybamm/install_odes.py
@@ -20,6 +20,7 @@
 # Build in parallel wherever possible
 os.environ["CMAKE_BUILD_PARALLEL_LEVEL"] = str(cpu_count())
 
+
 def download_extract_library(url, directory):
     # Download and extract archive at url
     if NO_WGET:
diff --git a/scripts/install_KLU_Sundials.py b/scripts/install_KLU_Sundials.py
index 0bfa02cefa..2aa8394ac4 100755
--- a/scripts/install_KLU_Sundials.py
+++ b/scripts/install_KLU_Sundials.py
@@ -95,11 +95,13 @@ def download_extract_library(url, download_dir):
     if libdir == "SuiteSparse_config":
         env["CMAKE_OPTIONS"] = f"-DCMAKE_INSTALL_PREFIX={install_dir}"
     else:
-    # For AMD, COLAMD, BTF and KLU; do not set a BUILD RPATH but use an
-    # INSTALL RPATH in order to ensure that the dynamic libraries are found
-    # at runtime just once. Otherwise, delocate complains about multiple
-    # references to the SuiteSparse_config dynamic library (auditwheel does not).
-        env["CMAKE_OPTIONS"] = f"-DCMAKE_INSTALL_PREFIX={install_dir} -DCMAKE_INSTALL_RPATH={install_dir}/lib -DCMAKE_INSTALL_RPATH_USE_LINK_PATH=FALSE -DCMAKE_BUILD_WITH_INSTALL_RPATH=FALSE"
+        # For AMD, COLAMD, BTF and KLU; do not set a BUILD RPATH but use an
+        # INSTALL RPATH in order to ensure that the dynamic libraries are found
+        # at runtime just once. Otherwise, delocate complains about multiple
+        # references to the SuiteSparse_config dynamic library (auditwheel does not).
+        env[
+            "CMAKE_OPTIONS"
+        ] = f"-DCMAKE_INSTALL_PREFIX={install_dir} -DCMAKE_INSTALL_RPATH={install_dir}/lib -DCMAKE_INSTALL_RPATH_USE_LINK_PATH=FALSE -DCMAKE_BUILD_WITH_INSTALL_RPATH=FALSE"
     subprocess.run(make_cmd, cwd=build_dir, env=env, shell=True, check=True)
     subprocess.run(install_cmd, cwd=build_dir, check=True)
 

From 3bf9084113b1e9191c322dca1b6445a7666219af Mon Sep 17 00:00:00 2001
From: Simon O'Kane <42972513+DrSOKane@users.noreply.github.com>
Date: Fri, 5 Jan 2024 18:42:02 +0000
Subject: [PATCH 062/109] Degradation example update (#3691)

* 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.

* Restored original experiment protocol to coupled degradation example notebook

* changelog

* changelog
---
 CHANGELOG.md                                    |  1 +
 .../notebooks/models/coupled-degradation.ipynb  | 17 ++++++++---------
 2 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index ef2f5c2bab..599e1fc696 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -15,6 +15,7 @@
 
 ## Bug fixes
 
+- Reverted a change to the coupled degradation example notebook that caused it to be unstable for large numbers of cycles ([#3691](https://github.com/pybamm-team/PyBaMM/pull/3691))
 - Fixed a bug where simulations using the CasADi-based solvers would fail randomly with the half-cell model ([#3494](https://github.com/pybamm-team/PyBaMM/pull/3494))
 - 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))
diff --git a/docs/source/examples/notebooks/models/coupled-degradation.ipynb b/docs/source/examples/notebooks/models/coupled-degradation.ipynb
index 1551a79a64..8c083d986a 100644
--- a/docs/source/examples/notebooks/models/coupled-degradation.ipynb
+++ b/docs/source/examples/notebooks/models/coupled-degradation.ipynb
@@ -105,22 +105,21 @@
     "cycle_number = 10\n",
     "exp = pybamm.Experiment(\n",
     "    [\n",
-    "        \"Hold at 4.2 V until C/100\",\n",
-    "        \"Rest for 4 hours\",\n",
-    "        \"Discharge at 0.1C until 2.5 V\",  # initial capacity check\n",
-    "        \"Charge at 0.3C until 4.2 V\",\n",
-    "        \"Hold at 4.2 V until C/100\",\n",
+    "        \"Hold at 4.2 V until C/100 (5 minute period)\",\n",
+    "        \"Rest for 4 hours (5 minute period)\",\n",
+    "        \"Discharge at 0.1C until 2.5 V (5 minute period)\",  # initial capacity check\n",
+    "        \"Charge at 0.3C until 4.2 V (5 minute period)\",\n",
+    "        \"Hold at 4.2 V until C/100 (5 minute period)\",\n",
     "    ]\n",
     "    + [\n",
     "        (\n",
     "            \"Discharge at 1C until 2.5 V\",  # ageing cycles\n",
-    "            \"Charge at 0.3C until 4.2 V\",\n",
-    "            \"Hold at 4.2 V until C/100\",\n",
+    "            \"Charge at 0.3C until 4.2 V (5 minute period)\",\n",
+    "            \"Hold at 4.2 V until C/100 (5 minute period)\",\n",
     "        )\n",
     "    ]\n",
     "    * cycle_number\n",
-    "    + [\"Discharge at 0.1C until 2.5 V\"],  # final capacity check\n",
-    "    period=\"5 minutes\",\n",
+    "    + [\"Discharge at 0.1C until 2.5 V (5 minute period)\"],  # final capacity check\n",
     ")\n",
     "sim = pybamm.Simulation(model, parameter_values=param, experiment=exp, var_pts=var_pts)\n",
     "sol = sim.solve()"

From 738cd5797cc94bd675219f013323bc5544894957 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 6 Jan 2024 03:56:30 +0530
Subject: [PATCH 063/109] Use `python -m pip` invocation instead

Co-authored-by: Saransh Chopra <saransh0701@gmail.com>
---
 .github/workflows/run_periodic_tests.yml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml
index 1178b2ec96..446ad9a9fb 100644
--- a/.github/workflows/run_periodic_tests.yml
+++ b/.github/workflows/run_periodic_tests.yml
@@ -193,10 +193,10 @@ jobs:
           python-version: ${{ matrix.python-version }}
 
       - name: Install PyBaMM
-        run: pip install -e .
+        run: python -m pip install -e .
 
       - name: Test pybamm_install_odes on ${{ matrix.os }}
         run: |
-          pip cache purge
-          pip install wget cmake
+          python -m pip cache purge
+          python -m pip install wget cmake
           pybamm_install_odes

From 9017c21bdc69c7d36461f7235fef6951c227f86e Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 6 Jan 2024 15:38:55 +0530
Subject: [PATCH 064/109] #3646 set CMake parallelism for Windows wheels

---
 .github/workflows/publish_pypi.yml | 19 ++++++++++++++++++-
 1 file changed, 18 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 10b318b9ed..556ffd1a1f 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -37,6 +37,16 @@ jobs:
         with:
           python-version: 3.8
 
+      - name: Get number of cores on Windows
+        id: get_num_cores
+        shell: python
+        run: |
+          from os import environ, sched_getaffinity
+          num_cpus = len(sched_getaffinity(0))
+          output_file = environ['GITHUB_OUTPUT']
+          with open(output_file, "a", encoding="utf-8") as output_stream:
+              output_stream.write(f"count={num_cpus}\n")
+
       - name: Clone pybind11 repo (no history)
         run: git clone --depth 1 --branch v2.11.1 https://github.com/pybind/pybind11.git
 
@@ -64,7 +74,14 @@ jobs:
       - 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_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'
+            CMAKE_BUILD_PARALLEL_LEVEL: ${{ steps.get_num_cores.outputs.num_cpus }}
           CIBW_ARCHS: "AMD64"
           CIBW_BEFORE_BUILD: python -m pip install setuptools wheel # skip CasADi and CMake
           CIBW_TEST_COMMAND: python -c "import pybamm; pybamm.IDAKLUSolver()"

From 632bcecc40a8e22044354818fbb303a255b36542 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 6 Jan 2024 15:47:39 +0530
Subject: [PATCH 065/109] #3646 Use `os.cpu_count` rather than processor
 affinity

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

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 556ffd1a1f..8a8126b0e4 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -41,8 +41,8 @@ jobs:
         id: get_num_cores
         shell: python
         run: |
-          from os import environ, sched_getaffinity
-          num_cpus = len(sched_getaffinity(0))
+          from os import environ, cpu_count
+          num_cpus = cpu_count()
           output_file = environ['GITHUB_OUTPUT']
           with open(output_file, "a", encoding="utf-8") as output_stream:
               output_stream.write(f"count={num_cpus}\n")
@@ -80,9 +80,9 @@ jobs:
             VCPKG_DEFAULT_TRIPLET=x64-windows-static-md
             VCPKG_FEATURE_FLAGS=manifests,registries
             CMAKE_GENERATOR="Visual Studio 17 2022"
-            CMAKE_GENERATOR_PLATFORM=x64'
-            CMAKE_BUILD_PARALLEL_LEVEL: ${{ steps.get_num_cores.outputs.num_cpus }}
-          CIBW_ARCHS: "AMD64"
+            CMAKE_GENERATOR_PLATFORM=x64
+            CMAKE_BUILD_PARALLEL_LEVEL=${{ steps.get_num_cores.outputs.count }}
+          CIBW_ARCHS: AMD64
           CIBW_BEFORE_BUILD: python -m pip install setuptools wheel # skip CasADi and CMake
           CIBW_TEST_COMMAND: python -c "import pybamm; pybamm.IDAKLUSolver()"
 

From c602d7cfbfe7b94f24b93f26cc10ebb58843a22c Mon Sep 17 00:00:00 2001
From: Saransh-cpp <Saransh-cpp@users.noreply.github.com>
Date: Mon, 1 Jan 2024 10:10:07 +0000
Subject: [PATCH 066/109] Bump to v24.1rc0

---
 CHANGELOG.md             | 2 ++
 CITATION.cff             | 2 +-
 pybamm/version.py        | 2 +-
 pyproject.toml           | 4 ++--
 vcpkg-configuration.json | 2 +-
 vcpkg.json               | 2 +-
 6 files changed, 8 insertions(+), 6 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index eda34bcdd1..17adc3a31f 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,5 +1,7 @@
 # [Unreleased](https://github.com/pybamm-team/PyBaMM/)
 
+# [v24.1rc0](https://github.com/pybamm-team/PyBaMM/tree/v24.1rc0) - 2024-01-31
+
 ## Features
 
 - The `pybamm_install_odes` command now includes support for macOS systems and can be used to set up SUNDIALS and install the `scikits.odes` solver on macOS ([#3417](https://github.com/pybamm-team/PyBaMM/pull/3417))
diff --git a/CITATION.cff b/CITATION.cff
index 44f1c5d407..494f226a89 100644
--- a/CITATION.cff
+++ b/CITATION.cff
@@ -24,6 +24,6 @@ keywords:
   - "expression tree"
   - "python"
   - "symbolic differentiation"
-version: "23.9"
+version: "24.1rc0"
 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 970be77f66..b2305df5cb 100644
--- a/pybamm/version.py
+++ b/pybamm/version.py
@@ -1 +1 @@
-__version__ = "23.9"
+__version__ = "24.1rc0"
diff --git a/pyproject.toml b/pyproject.toml
index d01e4f8fc3..6e01e80812 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -13,7 +13,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "pybamm"
-version = "23.9"
+version = "24.1rc0"
 license = { file = "LICENSE.txt" }
 description = "Python Battery Mathematical Modelling"
 authors = [{name = "The PyBaMM Team", email = "pybamm@pybamm.org"}]
@@ -230,7 +230,7 @@ ignore = [
 
 # NOTE: currently used only for notebook tests with the nbmake plugin.
 [tool.pytest.ini_options]
-minversion = "6"
+minversion = "24.1rc0"
 # Use pytest-xdist to run tests in parallel by default, exit with
 # error if not installed
 required_plugins = [
diff --git a/vcpkg-configuration.json b/vcpkg-configuration.json
index f33d9205b0..d97bc3c617 100644
--- a/vcpkg-configuration.json
+++ b/vcpkg-configuration.json
@@ -7,7 +7,7 @@
     {
       "kind": "git",
       "repository": "https://github.com/pybamm-team/sundials-vcpkg-registry.git",
-      "baseline": "af9f5e4bc730bf2361c47f809dcfb733e7951faa",
+      "baseline": "13d432fcf5da8591bb6cb2d46be9d6acf39cd02b",
       "packages": ["sundials"]
     },
     {
diff --git a/vcpkg.json b/vcpkg.json
index f62c18ddd2..911703e7cf 100644
--- a/vcpkg.json
+++ b/vcpkg.json
@@ -1,6 +1,6 @@
 {
   "name": "pybamm",
-  "version-string": "23.9",
+  "version-string": "24.1rc0",
   "dependencies": [
     "casadi",
     {

From 82f04dcf8890011990da7ce21e23f4bd1a6c1244 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 1 Jan 2024 16:09:12 +0530
Subject: [PATCH 067/109] Fix up `pytest` minversion

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

diff --git a/pyproject.toml b/pyproject.toml
index 6e01e80812..a39a37ecc4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -230,7 +230,7 @@ ignore = [
 
 # NOTE: currently used only for notebook tests with the nbmake plugin.
 [tool.pytest.ini_options]
-minversion = "24.1rc0"
+minversion = "6"
 # Use pytest-xdist to run tests in parallel by default, exit with
 # error if not installed
 required_plugins = [

From 0182ab1c6fc69bdb6d43b2fe38aef874e5117e5e Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Tue, 2 Jan 2024 00:03:42 +0530
Subject: [PATCH 068/109] Fix regex for version in pyproject.toml

---
 scripts/update_version.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/scripts/update_version.py b/scripts/update_version.py
index 30d2240e9c..1d2d64ce41 100644
--- a/scripts/update_version.py
+++ b/scripts/update_version.py
@@ -32,7 +32,9 @@ def update_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)
+        replace_version = re.sub(
+            r'(?<=\bversion = ")(.+)(?=")', release_version, output
+        )
         file.truncate(0)
         file.seek(0)
         file.write(replace_version)

From 09632a291f3e24f64f1227cb284af0fa6710eeec Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Tue, 2 Jan 2024 00:06:50 +0530
Subject: [PATCH 069/109] Fix release issue tag

---
 .github/release_reminder.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/release_reminder.md b/.github/release_reminder.md
index 94066e80c8..4b7b361b28 100644
--- a/.github/release_reminder.md
+++ b/.github/release_reminder.md
@@ -1,6 +1,6 @@
 ---
 title: Create {{ date | date('YY.MM') }} (final or rc0) release
-labels: priority:high
+labels: priority: high
 ---
 Quarterly reminder to create a -
 

From d978f6f1e6520a90d79e14ff2ebbc0d073fa5701 Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Tue, 2 Jan 2024 00:07:12 +0530
Subject: [PATCH 070/109] Use quotes

---
 .github/release_reminder.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/release_reminder.md b/.github/release_reminder.md
index 4b7b361b28..09c524fbec 100644
--- a/.github/release_reminder.md
+++ b/.github/release_reminder.md
@@ -1,6 +1,6 @@
 ---
 title: Create {{ date | date('YY.MM') }} (final or rc0) release
-labels: priority: high
+labels: "priority: high"
 ---
 Quarterly reminder to create a -
 

From 17a4494cc2b70882195a5bee0dda1c88d57210f7 Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Tue, 2 Jan 2024 00:07:40 +0530
Subject: [PATCH 071/109] Update wheel_failure.md

---
 .github/wheel_failure.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/wheel_failure.md b/.github/wheel_failure.md
index 107b4dd6d6..d2a8a74ce9 100644
--- a/.github/wheel_failure.md
+++ b/.github/wheel_failure.md
@@ -1,6 +1,6 @@
 ---
 title: Fortnightly build for wheels failed
-labels: priority:high, bug
+labels: "priority: high", bug
 ---
 
 The build is failing with the following logs - {{ env.LOGS }}

From 0580f06e57df074235912d317331dd5b2db88b5a Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Tue, 2 Jan 2024 00:07:54 +0530
Subject: [PATCH 072/109] Fix YAML

---
 .github/wheel_failure.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/wheel_failure.md b/.github/wheel_failure.md
index d2a8a74ce9..2bbe659358 100644
--- a/.github/wheel_failure.md
+++ b/.github/wheel_failure.md
@@ -1,6 +1,6 @@
 ---
 title: Fortnightly build for wheels failed
-labels: "priority: high", bug
+labels: "priority: high, bug"
 ---
 
 The build is failing with the following logs - {{ env.LOGS }}

From 23a87972170a02c6e15af8bfa2a5bc898468aed1 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Mon, 8 Jan 2024 19:27:08 +0000
Subject: [PATCH 073/109] Bump the actions group with 2 updates

Bumps the actions group with 2 updates: [actions/setup-python](https://github.com/actions/setup-python) and [lycheeverse/lychee-action](https://github.com/lycheeverse/lychee-action).


Updates `actions/setup-python` from 4 to 5
- [Release notes](https://github.com/actions/setup-python/releases)
- [Commits](https://github.com/actions/setup-python/compare/v4...v5)

Updates `lycheeverse/lychee-action` from 1.8.0 to 1.9.0
- [Release notes](https://github.com/lycheeverse/lychee-action/releases)
- [Commits](https://github.com/lycheeverse/lychee-action/compare/v1.8.0...v1.9.0)

---
updated-dependencies:
- dependency-name: actions/setup-python
  dependency-type: direct:production
  update-type: version-update:semver-major
  dependency-group: actions
- dependency-name: lycheeverse/lychee-action
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: actions
...

Signed-off-by: dependabot[bot] <support@github.com>
---
 .github/workflows/lychee_url_checker.yml | 2 +-
 .github/workflows/run_periodic_tests.yml | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/lychee_url_checker.yml b/.github/workflows/lychee_url_checker.yml
index 93dde63845..1ce20decd9 100644
--- a/.github/workflows/lychee_url_checker.yml
+++ b/.github/workflows/lychee_url_checker.yml
@@ -28,7 +28,7 @@ jobs:
 
       # use stable version for now to avoid breaking changes
       - name: Lychee URL checker
-        uses: lycheeverse/lychee-action@v1.8.0
+        uses: lycheeverse/lychee-action@v1.9.0
         with:
           # arguments with file types to check
           args: >-
diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml
index 446ad9a9fb..cc09203ef3 100644
--- a/.github/workflows/run_periodic_tests.yml
+++ b/.github/workflows/run_periodic_tests.yml
@@ -188,7 +188,7 @@ jobs:
 
       - name: Set up Python ${{ matrix.python-version }}
         id: setup-python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
 

From e1c63b27897c3fe2f446ca9de2ca55bb216be2c2 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 8 Jan 2024 19:29:56 +0000
Subject: [PATCH 074/109] 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: v0.1.9 → v0.1.11](https://github.com/astral-sh/ruff-pre-commit/compare/v0.1.9...v0.1.11)
---
 .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 3998ad1076..20fce209d2 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: "v0.1.9"
+    rev: "v0.1.11"
     hooks:
       - id: ruff
         args: [--fix, --show-fixes]

From 62d09f4ae3cb151ca4b80379866ceadbb6274c3d Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Wed, 10 Jan 2024 18:35:20 +0530
Subject: [PATCH 075/109] Add `cmake` and `wget` as Pythonic prerequisites

---
 docs/source/user_guide/installation/gnu-linux-mac.rst | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/docs/source/user_guide/installation/gnu-linux-mac.rst b/docs/source/user_guide/installation/gnu-linux-mac.rst
index 3e93587cde..dd6a677dee 100644
--- a/docs/source/user_guide/installation/gnu-linux-mac.rst
+++ b/docs/source/user_guide/installation/gnu-linux-mac.rst
@@ -119,7 +119,8 @@ Users can install `scikits.odes <https://github.com/bmcage/odes>`__ to utilize i
 
    .. code:: bash
 
-      apt install libopenblas-dev
+      apt-get install libopenblas-dev
+      pip install wget cmake
       pybamm_install_odes
 
    system (under ``~/.local``), before installing ``scikits.odes``. (Alternatively, one can install SUNDIALS without this script and run ``pip install pybamm[odes]`` to install ``pybamm`` with ``scikits.odes``.)
@@ -131,6 +132,7 @@ Users can install `scikits.odes <https://github.com/bmcage/odes>`__ to utilize i
    .. code:: bash
 
       brew install openblas gcc gfortran
+      pip install wget cmake
       pybamm_install_odes
 
 The ``pybamm_install_odes`` command, installed with PyBaMM, automatically downloads and installs the SUNDIALS library on your

From 3f29ea435fcd61fe7cf2943e774532c068166b5b Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Wed, 10 Jan 2024 18:39:11 +0530
Subject: [PATCH 076/109] Use `sys.executable` to invoke `pip`, make it verbose

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

diff --git a/pybamm/install_odes.py b/pybamm/install_odes.py
index 128d3ca396..d04d04ab8a 100644
--- a/pybamm/install_odes.py
+++ b/pybamm/install_odes.py
@@ -190,8 +190,8 @@ def main(arguments=None):
     # see https://scikits-odes.readthedocs.io/en/latest/installation.html#id1
     os.environ["SUNDIALS_INST"] = SUNDIALS_LIB_DIR
     env = os.environ.copy()
-    subprocess.run(["pip", "install", "scikits.odes"], env=env, check=True)
-
+    logger.info("Installing scikits.odes via pip")
+    subprocess.run([f"{sys.executable}", "-m", "pip", "install", "scikits.odes", "--verbose"], env=env, check=True)
 
 if __name__ == "__main__":
     main(sys.argv[1:])

From c68c8a9e449fc4cd1705f25576d4ba76bddeb5fe Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Wed, 10 Jan 2024 13:21:07 +0000
Subject: [PATCH 077/109] style: pre-commit fixes

---
 pybamm/install_odes.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/pybamm/install_odes.py b/pybamm/install_odes.py
index d04d04ab8a..3809d763f2 100644
--- a/pybamm/install_odes.py
+++ b/pybamm/install_odes.py
@@ -191,7 +191,12 @@ def main(arguments=None):
     os.environ["SUNDIALS_INST"] = SUNDIALS_LIB_DIR
     env = os.environ.copy()
     logger.info("Installing scikits.odes via pip")
-    subprocess.run([f"{sys.executable}", "-m", "pip", "install", "scikits.odes", "--verbose"], env=env, check=True)
+    subprocess.run(
+        [f"{sys.executable}", "-m", "pip", "install", "scikits.odes", "--verbose"],
+        env=env,
+        check=True,
+    )
+
 
 if __name__ == "__main__":
     main(sys.argv[1:])

From 62195f2ccdc9ff5f60ffbd6b2ed9c7e2da415812 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Wed, 10 Jan 2024 18:52:20 +0530
Subject: [PATCH 078/109] Add changelog entry for `pybamm_install_odes`

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

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 17adc3a31f..965a2aa7b4 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -4,7 +4,7 @@
 
 ## Features
 
-- The `pybamm_install_odes` command now includes support for macOS systems and can be used to set up SUNDIALS and install the `scikits.odes` solver on macOS ([#3417](https://github.com/pybamm-team/PyBaMM/pull/3417))
+- The `pybamm_install_odes` command now includes support for macOS systems and can be used to set up SUNDIALS and install the `scikits.odes` solver on macOS ([#3417](https://github.com/pybamm-team/PyBaMM/pull/3417), [#3706](https://github.com/pybamm-team/PyBaMM/3706]))
 - Added support for Python 3.12 ([#3531](https://github.com/pybamm-team/PyBaMM/pull/3531))
 - Added method to get QuickPlot axes by variable ([#3596](https://github.com/pybamm-team/PyBaMM/pull/3596))
 - Added custom experiment terminations ([#3596](https://github.com/pybamm-team/PyBaMM/pull/3596))

From 64681dd7ee9ca6e9c6c18a9e704e5d984a90466c Mon Sep 17 00:00:00 2001
From: "Eric G. Kratz" <kratman@users.noreply.github.com>
Date: Wed, 10 Jan 2024 09:49:37 -0500
Subject: [PATCH 079/109] Run pre-commit on all files (#3705)

* Run pre-commit on all files

* Apply suggestions from code review
---
 .github/workflows/run_periodic_tests.yml | 3 +--
 .github/workflows/test_on_push.yml       | 3 +--
 2 files changed, 2 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml
index cc09203ef3..0fdf5d1ecc 100644
--- a/.github/workflows/run_periodic_tests.yml
+++ b/.github/workflows/run_periodic_tests.yml
@@ -36,8 +36,7 @@ jobs:
       - name: Check style
         run: |
           python -m pip install pre-commit
-          git add .
-          pre-commit run ruff
+          pre-commit run -a
 
   build:
     needs: style
diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 7297f48fad..d1f448c110 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -28,8 +28,7 @@ jobs:
       - name: Check style
         run: |
           python -m pip install pre-commit
-          git add .
-          pre-commit run ruff
+          pre-commit run -a
 
   run_unit_tests:
     needs: style

From fdbd8865c6be13ffb7cfef1524817df4ad897684 Mon Sep 17 00:00:00 2001
From: AlessioBugetti <alessiobugetti98@gmail.com>
Date: Fri, 12 Jan 2024 11:06:48 +0100
Subject: [PATCH 080/109] Add PEP 723 support for SuiteSparse and SUNDIALS
 installation script

Introduce a TOML-style comment block at the top of the `install_KLU_Sundials.py` script. This block contains essential metadata, including the required Python version and a list of dependencies. This addition aligns the script with the PEP 723 guidelines, enhancing readability and portability for script runners and developers.

The metadata includes:
- The Python version requirement (<=3.9)
- Dependencies required for the script (wget, cmake)
- Additional information like the repository and documentation links

This enhancement facilitates easier script sharing and collaboration, providing a standardized way to specify and access script dependencies and supported Python versions. It also lays the groundwork for potential future tooling that could automate environment setup and dependency installation.

Refer to PEP 723 for more details on this format.

Resolves: #3647
---
 scripts/install_KLU_Sundials.py | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/scripts/install_KLU_Sundials.py b/scripts/install_KLU_Sundials.py
index 2aa8394ac4..edcf90160c 100755
--- a/scripts/install_KLU_Sundials.py
+++ b/scripts/install_KLU_Sundials.py
@@ -1,3 +1,15 @@
+# /// pyproject
+# [run]
+# requires-python = "<=3.9"
+# dependencies = [
+#   "wget",
+#   "cmake",
+# ]
+#
+# [additional-info]
+# repository = "https://github.com/pybamm-team/PyBaMM"
+# documentation = "https://docs.pybamm.org"
+# ///
 import os
 import subprocess
 import tarfile

From 0bd42a59e4d65ffdd393cd576ce758ce094d7499 Mon Sep 17 00:00:00 2001
From: AlessioBugetti <alessiobugetti98@gmail.com>
Date: Fri, 12 Jan 2024 12:45:02 +0100
Subject: [PATCH 081/109] Update Python version compatibility in
 install_KLU_Sundials.py

---
 scripts/install_KLU_Sundials.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/install_KLU_Sundials.py b/scripts/install_KLU_Sundials.py
index edcf90160c..ef24b92c64 100755
--- a/scripts/install_KLU_Sundials.py
+++ b/scripts/install_KLU_Sundials.py
@@ -1,6 +1,6 @@
 # /// pyproject
 # [run]
-# requires-python = "<=3.9"
+# requires-python = "">=3.8, <=3.12""
 # dependencies = [
 #   "wget",
 #   "cmake",

From 79f93161354fc1b0d9a61a23fce112c1ce47e048 Mon Sep 17 00:00:00 2001
From: AlessioBugetti <alessiobugetti98@gmail.com>
Date: Fri, 12 Jan 2024 12:56:44 +0100
Subject: [PATCH 082/109] Adjust Python version range in requirements to >=3.8,
 <3.13 in install_KLU_Sundials.py

---
 scripts/install_KLU_Sundials.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/install_KLU_Sundials.py b/scripts/install_KLU_Sundials.py
index ef24b92c64..5aaccd0e15 100755
--- a/scripts/install_KLU_Sundials.py
+++ b/scripts/install_KLU_Sundials.py
@@ -1,6 +1,6 @@
 # /// pyproject
 # [run]
-# requires-python = "">=3.8, <=3.12""
+# requires-python = "">=3.8, <3.13""
 # dependencies = [
 #   "wget",
 #   "cmake",

From f6af07e306a76d62862453c7c1663df1ac949feb Mon Sep 17 00:00:00 2001
From: "allcontributors[bot]"
 <46447321+allcontributors[bot]@users.noreply.github.com>
Date: Fri, 12 Jan 2024 13:04:06 +0000
Subject: [PATCH 083/109] docs: update README.md [skip ci]

---
 README.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 2aa9220e70..02be55daf8 100644
--- a/README.md
+++ b/README.md
@@ -14,7 +14,7 @@
 [![code style](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
 
 <!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section -->
-[![All Contributors](https://img.shields.io/badge/all_contributors-73-orange.svg)](#-contributors)
+[![All Contributors](https://img.shields.io/badge/all_contributors-74-orange.svg)](#-contributors)
 <!-- ALL-CONTRIBUTORS-BADGE:END -->
 
 </div>
@@ -279,6 +279,7 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/prady0t"><img src="https://avatars.githubusercontent.com/u/99216956?v=4?s=100" width="100px;" alt="Pradyot Ranjan"/><br /><sub><b>Pradyot Ranjan</b></sub></a><br /><a href="#infra-prady0t" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/XuboGU"><img src="https://avatars.githubusercontent.com/u/53944452?v=4?s=100" width="100px;" alt="XuboGU"/><br /><sub><b>XuboGU</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/commits?author=XuboGU" title="Code">💻</a> <a href="https://github.com/pybamm-team/PyBaMM/issues?q=author%3AXuboGU" title="Bug reports">🐛</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/cringeyburger"><img src="https://avatars.githubusercontent.com/u/121183876?v=4?s=100" width="100px;" alt="Ankit Meda"/><br /><sub><b>Ankit Meda</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/commits?author=cringeyburger" title="Code">💻</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/AlessioBugetti"><img src="https://avatars.githubusercontent.com/u/38499721?v=4?s=100" width="100px;" alt="Alessio Bugetti"/><br /><sub><b>Alessio Bugetti</b></sub></a><br /><a href="#infra-AlessioBugetti" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a></td>
     </tr>
   </tbody>
 </table>

From 8162d1b5be5025de3b255d08126c7aee8eaf80fb Mon Sep 17 00:00:00 2001
From: "allcontributors[bot]"
 <46447321+allcontributors[bot]@users.noreply.github.com>
Date: Fri, 12 Jan 2024 13:04:07 +0000
Subject: [PATCH 084/109] docs: update .all-contributorsrc [skip ci]

---
 .all-contributorsrc | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/.all-contributorsrc b/.all-contributorsrc
index 5b003ed874..7d05f65d0f 100644
--- a/.all-contributorsrc
+++ b/.all-contributorsrc
@@ -793,6 +793,15 @@
       "contributions": [
         "code"
       ]
+    },
+    {
+      "login": "AlessioBugetti",
+      "name": "Alessio Bugetti",
+      "avatar_url": "https://avatars.githubusercontent.com/u/38499721?v=4",
+      "profile": "https://github.com/AlessioBugetti",
+      "contributions": [
+        "infra"
+      ]
     }
   ],
   "contributorsPerLine": 7,

From 025ac6f85404383cbc3b3eb059c5c5d1d30d606b Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Fri, 12 Jan 2024 23:36:34 +0530
Subject: [PATCH 085/109] Fix docs about Jax solver compatibility with Python
 versions (#3702)

* Ensure correct Python versions for Jax solver compatibility

* Simplify array of Python versions

Co-authored-by: Eric G. Kratz <kratman@users.noreply.github.com>

* Use different conjunction

Co-authored-by: Eric G. Kratz <kratman@users.noreply.github.com>

---------

Co-authored-by: Eric G. Kratz <kratman@users.noreply.github.com>
---
 docs/source/user_guide/installation/gnu-linux-mac.rst | 2 +-
 docs/source/user_guide/installation/windows.rst       | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/source/user_guide/installation/gnu-linux-mac.rst b/docs/source/user_guide/installation/gnu-linux-mac.rst
index 3e93587cde..c73f549299 100644
--- a/docs/source/user_guide/installation/gnu-linux-mac.rst
+++ b/docs/source/user_guide/installation/gnu-linux-mac.rst
@@ -161,7 +161,7 @@ Users can install ``jax`` and ``jaxlib`` to use the Jax solver.
 
 .. note::
 
-   The Jax solver is not supported on Python 3.8. It is supported on Python 3.9, 3.10, and 3.11.
+   The Jax solver is only supported for Python versions 3.9 through 3.12.
 
 .. code:: bash
 
diff --git a/docs/source/user_guide/installation/windows.rst b/docs/source/user_guide/installation/windows.rst
index 6e815b33c8..d99d1f2eb2 100644
--- a/docs/source/user_guide/installation/windows.rst
+++ b/docs/source/user_guide/installation/windows.rst
@@ -73,7 +73,7 @@ Users can install ``jax`` and ``jaxlib`` to use the Jax solver.
 
 .. note::
 
-   The Jax solver is not supported on Python 3.8. It is supported on Python 3.9, 3.10, and 3.11.
+   The Jax solver is only supported for Python versions 3.9 through 3.12.
 
 .. code:: bash
 

From 515177959fe308236d23f01a0df101c7de21c617 Mon Sep 17 00:00:00 2001
From: AlessioBugetti <alessiobugetti98@gmail.com>
Date: Fri, 12 Jan 2024 20:14:22 +0100
Subject: [PATCH 086/109] Replace wget with urllib for downloading and
 parallelize with concurrent.futures for efficiency

This update replaces the previous wget-based approach with urllib from the Python standard library. Additionally, it introduces parallelization using concurrent.futures, specifically employing ThreadPoolExecutor to download SuiteSparse and SUNDIALS tarballs simultaneously. This parallelization significantly reduces the download time.

Resolves: #3651
---
 scripts/install_KLU_Sundials.py | 63 ++++++++++++++++++---------------
 1 file changed, 35 insertions(+), 28 deletions(-)

diff --git a/scripts/install_KLU_Sundials.py b/scripts/install_KLU_Sundials.py
index 5aaccd0e15..b4f57cf205 100755
--- a/scripts/install_KLU_Sundials.py
+++ b/scripts/install_KLU_Sundials.py
@@ -2,7 +2,6 @@
 # [run]
 # requires-python = "">=3.8, <3.13""
 # dependencies = [
-#   "wget",
 #   "cmake",
 # ]
 #
@@ -15,28 +14,31 @@
 import tarfile
 import argparse
 import platform
+import concurrent.futures
+import urllib.request
 from multiprocessing import cpu_count
 
-try:
-    # wget module is required to download SUNDIALS or SuiteSparse.
-    import wget
-
-    NO_WGET = False
-except ModuleNotFoundError:
-    NO_WGET = True
-
 
 def download_extract_library(url, download_dir):
     # Download and extract archive at url
-    if NO_WGET:
-        error_msg = (
-            "Could not find wget module."
-            " Please install wget module (pip install wget)."
-        )
-        raise ModuleNotFoundError(error_msg)
-    archive = wget.download(url, out=download_dir)
-    tar = tarfile.open(archive)
-    tar.extractall(download_dir)
+    file_name = url.split("/")[-1]
+    file_path = os.path.join(download_dir, file_name)
+    with urllib.request.urlopen(url) as response:
+        os.makedirs(download_dir, exist_ok=True)
+        with open(file_path, "wb") as out_file:
+            out_file.write(response.read())
+    with tarfile.open(file_path) as tar:
+        tar.extractall(download_dir)
+
+
+def parallel_download(urls, download_dir):
+    # Use 2 processes for parallel downloading
+    with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executor:
+        futures = [
+            executor.submit(download_extract_library, url, download_dir) for url in urls
+        ]
+        for future in concurrent.futures.as_completed(futures):
+            future.result()
 
 
 # First check requirements: make and cmake
@@ -71,13 +73,25 @@ def download_extract_library(url, download_dir):
     else os.path.join(pybamm_dir, args.install_dir)
 )
 
-# 1 --- Download SuiteSparse
+# Parallel download
+
+# 1 --- SuiteSparse
 suitesparse_version = "6.0.3"
 suitesparse_url = (
     "https://github.com/DrTimothyAldenDavis/"
     + f"SuiteSparse/archive/v{suitesparse_version}.tar.gz"
 )
-download_extract_library(suitesparse_url, download_dir)
+
+# 2 --- SUNDIALS
+sundials_version = "6.5.0"
+sundials_url = (
+    "https://github.com/LLNL/sundials/"
+    + f"releases/download/v{sundials_version}/sundials-{sundials_version}.tar.gz"
+)
+
+parallel_download([suitesparse_url, sundials_url], download_dir)
+
+# 1 --- Install SuiteSparse
 
 # The SuiteSparse KLU module has 4 dependencies:
 # - suitesparseconfig
@@ -117,14 +131,7 @@ def download_extract_library(url, download_dir):
     subprocess.run(make_cmd, cwd=build_dir, env=env, shell=True, check=True)
     subprocess.run(install_cmd, cwd=build_dir, check=True)
 
-# 2 --- Download SUNDIALS
-sundials_version = "6.5.0"
-sundials_url = (
-    "https://github.com/LLNL/sundials/"
-    + f"releases/download/v{sundials_version}/sundials-{sundials_version}.tar.gz"
-)
-
-download_extract_library(sundials_url, download_dir)
+# 2 --- Install SUNDIALS
 
 # Set install dir for SuiteSparse libs
 # Ex: if install_dir -> "/usr/local/" then

From 440d6f2c5f38c548235b176f7fed09643947e9d4 Mon Sep 17 00:00:00 2001
From: AlessioBugetti <alessiobugetti98@gmail.com>
Date: Fri, 12 Jan 2024 20:37:45 +0100
Subject: [PATCH 087/109] Secure URL validation in download_extract_library
 function

---
 scripts/install_KLU_Sundials.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/scripts/install_KLU_Sundials.py b/scripts/install_KLU_Sundials.py
index b4f57cf205..6a218093c0 100755
--- a/scripts/install_KLU_Sundials.py
+++ b/scripts/install_KLU_Sundials.py
@@ -16,11 +16,15 @@
 import platform
 import concurrent.futures
 import urllib.request
+from urllib.parse import urlparse
 from multiprocessing import cpu_count
 
 
 def download_extract_library(url, download_dir):
     # Download and extract archive at url
+    parsed_url = urlparse(url)
+    if parsed_url.scheme not in ["http", "https"]:
+        raise ValueError(f"Invalid URL scheme: {parsed_url.scheme}. Only HTTP and HTTPS are allowed.")
     file_name = url.split("/")[-1]
     file_path = os.path.join(download_dir, file_name)
     with urllib.request.urlopen(url) as response:

From 1243cb39ba64edf1f1c32dc6e9d82f04a40ad1de Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Fri, 12 Jan 2024 19:40:21 +0000
Subject: [PATCH 088/109] style: pre-commit fixes

---
 scripts/install_KLU_Sundials.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/scripts/install_KLU_Sundials.py b/scripts/install_KLU_Sundials.py
index 6a218093c0..bbc915721b 100755
--- a/scripts/install_KLU_Sundials.py
+++ b/scripts/install_KLU_Sundials.py
@@ -24,7 +24,9 @@ def download_extract_library(url, download_dir):
     # Download and extract archive at url
     parsed_url = urlparse(url)
     if parsed_url.scheme not in ["http", "https"]:
-        raise ValueError(f"Invalid URL scheme: {parsed_url.scheme}. Only HTTP and HTTPS are allowed.")
+        raise ValueError(
+            f"Invalid URL scheme: {parsed_url.scheme}. Only HTTP and HTTPS are allowed."
+        )
     file_name = url.split("/")[-1]
     file_path = os.path.join(download_dir, file_name)
     with urllib.request.urlopen(url) as response:

From 87e39ab1771c561fa1276039f491f29624d025c9 Mon Sep 17 00:00:00 2001
From: AlessioBugetti <alessiobugetti98@gmail.com>
Date: Fri, 12 Jan 2024 23:23:49 +0100
Subject: [PATCH 089/109] Add SHA-256 checksum validation for .tar.gz files

Implement SHA-256 checksum verification for .tar.gz archives in the install_KLU_Sundials/ directory. This enhancement ensures that files previously downloaded are not re-downloaded.

Files present in the directory are now checked against their expected checksums. If a file's checksum matches the expected value, the download step is skipped, and the script proceeds directly to extraction. This change significantly improves the efficiency of the installation process, particularly in scenarios where the script is re-run multiple times.

This commit includes:
- A new function, `calculate_sha256`, to compute the checksum.
- Modifications to `download_extract_library` to incorporate checksum validation.
---
 scripts/install_KLU_Sundials.py | 44 +++++++++++++++++++++++++++++----
 1 file changed, 39 insertions(+), 5 deletions(-)

diff --git a/scripts/install_KLU_Sundials.py b/scripts/install_KLU_Sundials.py
index bbc915721b..3c0178a7ec 100755
--- a/scripts/install_KLU_Sundials.py
+++ b/scripts/install_KLU_Sundials.py
@@ -14,21 +14,45 @@
 import tarfile
 import argparse
 import platform
+import hashlib
 import concurrent.futures
 import urllib.request
 from urllib.parse import urlparse
 from multiprocessing import cpu_count
 
 
-def download_extract_library(url, download_dir):
+def calculate_sha256(file_path):
+    sha256_hash = hashlib.sha256()
+    with open(file_path, "rb") as f:
+        # Read and update hash in chunks of 4K
+        for byte_block in iter(lambda: f.read(4096), b""):
+            sha256_hash.update(byte_block)
+    return sha256_hash.hexdigest()
+
+
+def download_extract_library(url, expected_checksum, download_dir):
+    file_name = url.split("/")[-1]
+    file_path = os.path.join(download_dir, file_name)
+
+    # Check if file already exists and validate checksum
+    if os.path.exists(file_path):
+        print(f"Validating checksum for {file_name}...")
+        actual_checksum = calculate_sha256(file_path)
+        if actual_checksum == expected_checksum:
+            print(f"Checksum valid. Skipping download for {file_name}.")
+            # Extract the archive as the checksum is valid
+            with tarfile.open(file_path) as tar:
+                tar.extractall(download_dir)
+            return
+        else:
+            print(f"Checksum invalid. Redownloading {file_name}.")
+
     # Download and extract archive at url
     parsed_url = urlparse(url)
     if parsed_url.scheme not in ["http", "https"]:
         raise ValueError(
             f"Invalid URL scheme: {parsed_url.scheme}. Only HTTP and HTTPS are allowed."
         )
-    file_name = url.split("/")[-1]
-    file_path = os.path.join(download_dir, file_name)
     with urllib.request.urlopen(url) as response:
         os.makedirs(download_dir, exist_ok=True)
         with open(file_path, "wb") as out_file:
@@ -41,7 +65,10 @@ def parallel_download(urls, download_dir):
     # Use 2 processes for parallel downloading
     with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executor:
         futures = [
-            executor.submit(download_extract_library, url, download_dir) for url in urls
+            executor.submit(
+                download_extract_library, url, expected_checksum, download_dir
+            )
+            for (url, expected_checksum) in urls
         ]
         for future in concurrent.futures.as_completed(futures):
             future.result()
@@ -87,6 +114,9 @@ def parallel_download(urls, download_dir):
     "https://github.com/DrTimothyAldenDavis/"
     + f"SuiteSparse/archive/v{suitesparse_version}.tar.gz"
 )
+suitesparse_checksum = (
+    "7111b505c1207f6f4bd0be9740d0b2897e1146b845d73787df07901b4f5c1fb7"
+)
 
 # 2 --- SUNDIALS
 sundials_version = "6.5.0"
@@ -94,8 +124,12 @@ def parallel_download(urls, download_dir):
     "https://github.com/LLNL/sundials/"
     + f"releases/download/v{sundials_version}/sundials-{sundials_version}.tar.gz"
 )
+sundials_checksum = "4e0b998dff292a2617e179609b539b511eb80836f5faacf800e688a886288502"
 
-parallel_download([suitesparse_url, sundials_url], download_dir)
+parallel_download(
+    [(suitesparse_url, suitesparse_checksum), (sundials_url, sundials_checksum)],
+    download_dir,
+)
 
 # 1 --- Install SuiteSparse
 

From 4622d7709d725eb595b2e5d7f0fff9debded42bd Mon Sep 17 00:00:00 2001
From: AlessioBugetti <alessiobugetti98@gmail.com>
Date: Sat, 13 Jan 2024 11:53:46 +0100
Subject: [PATCH 090/109] Add check for the SUNDIALS and SuiteSparse .so or
 .dylib files inside install_KLU_Sundials.py

---
 scripts/install_KLU_Sundials.py | 270 +++++++++++++++++++-------------
 1 file changed, 161 insertions(+), 109 deletions(-)

diff --git a/scripts/install_KLU_Sundials.py b/scripts/install_KLU_Sundials.py
index 3c0178a7ec..66bce42d98 100755
--- a/scripts/install_KLU_Sundials.py
+++ b/scripts/install_KLU_Sundials.py
@@ -17,10 +17,151 @@
 import hashlib
 import concurrent.futures
 import urllib.request
+from os.path import join, isfile
 from urllib.parse import urlparse
 from multiprocessing import cpu_count
 
 
+def install_suitesparse(suitesparse_version, download_dir):
+    # The SuiteSparse KLU module has 4 dependencies:
+    # - suitesparseconfig
+    # - AMD
+    # - COLAMD
+    # - BTF
+    suitesparse_dir = f"SuiteSparse-{suitesparse_version}"
+    suitesparse_src = os.path.join(download_dir, suitesparse_dir)
+    print("-" * 10, "Building SuiteSparse_config", "-" * 40)
+    make_cmd = [
+        "make",
+        "library",
+    ]
+    install_cmd = [
+        "make",
+        f"-j{cpu_count()}",
+        "install",
+    ]
+    print("-" * 10, "Building SuiteSparse", "-" * 40)
+    # Set CMAKE_OPTIONS as environment variables to pass to the GNU Make command
+    env = os.environ.copy()
+    for libdir in ["SuiteSparse_config", "AMD", "COLAMD", "BTF", "KLU"]:
+        build_dir = os.path.join(suitesparse_src, libdir)
+        # We want to ensure that libsuitesparseconfig.dylib is not repeated in
+        # multiple paths at the time of wheel repair. Therefore, it should not be
+        # built with an RPATH since it is copied to the install prefix.
+        if libdir == "SuiteSparse_config":
+            env["CMAKE_OPTIONS"] = f"-DCMAKE_INSTALL_PREFIX={install_dir}"
+        else:
+            # For AMD, COLAMD, BTF and KLU; do not set a BUILD RPATH but use an
+            # INSTALL RPATH in order to ensure that the dynamic libraries are found
+            # at runtime just once. Otherwise, delocate complains about multiple
+            # references to the SuiteSparse_config dynamic library (auditwheel does not).
+            env[
+                "CMAKE_OPTIONS"
+            ] = f"-DCMAKE_INSTALL_PREFIX={install_dir} -DCMAKE_INSTALL_RPATH={install_dir}/lib -DCMAKE_INSTALL_RPATH_USE_LINK_PATH=FALSE -DCMAKE_BUILD_WITH_INSTALL_RPATH=FALSE"
+        subprocess.run(make_cmd, cwd=build_dir, env=env, shell=True, check=True)
+        subprocess.run(install_cmd, cwd=build_dir, check=True)
+
+
+def install_sundials(sundials_version, download_dir, install_dir):
+    # Set install dir for SuiteSparse libs
+    # Ex: if install_dir -> "/usr/local/" then
+    # KLU_INCLUDE_DIR -> "/usr/local/include"
+    # KLU_LIBRARY_DIR -> "/usr/local/lib"
+    KLU_INCLUDE_DIR = os.path.join(install_dir, "include")
+    KLU_LIBRARY_DIR = os.path.join(install_dir, "lib")
+    cmake_args = [
+        "-DENABLE_LAPACK=ON",
+        "-DSUNDIALS_INDEX_SIZE=32",
+        "-DEXAMPLES_ENABLE_C=OFF",
+        "-DEXAMPLES_ENABLE_CXX=OFF",
+        "-DEXAMPLES_INSTALL=OFF",
+        "-DENABLE_KLU=ON",
+        "-DENABLE_OPENMP=ON",
+        f"-DKLU_INCLUDE_DIR={KLU_INCLUDE_DIR}",
+        f"-DKLU_LIBRARY_DIR={KLU_LIBRARY_DIR}",
+        "-DCMAKE_INSTALL_PREFIX=" + install_dir,
+        # on macOS use fixed paths rather than rpath
+        "-DCMAKE_INSTALL_NAME_DIR=" + KLU_LIBRARY_DIR,
+    ]
+
+    # try to find OpenMP on mac
+    if platform.system() == "Darwin":
+        # flags to find OpenMP on mac
+        if platform.processor() == "arm":
+            LDFLAGS = "-L/opt/homebrew/opt/libomp/lib"
+            CPPFLAGS = "-I/opt/homebrew/opt/libomp/include"
+            OpenMP_C_FLAGS = (
+                "-Xpreprocessor -fopenmp -I/opt/homebrew/opt/libomp/include"
+            )
+            OpenMP_C_LIB_NAMES = "omp"
+            OpenMP_libomp_LIBRARY = "/opt/homebrew/opt/libomp/lib/libomp.dylib"
+            OpenMP_omp_LIBRARY = "/opt/homebrew/opt/libomp/lib/libomp.dylib"
+        elif platform.processor() == "i386":
+            LDFLAGS = "-L/usr/local/opt/libomp/lib"
+            CPPFLAGS = "-I/usr/local/opt/libomp/include"
+            OpenMP_C_FLAGS = "-Xpreprocessor -fopenmp -I/usr/local/opt/libomp/include"
+            OpenMP_CXX_FLAGS = "-Xpreprocessor -fopenmp -I/usr/local/opt/libomp/include"
+            OpenMP_C_LIB_NAMES = "omp"
+            OpenMP_CXX_LIB_NAMES = "omp"
+            OpenMP_omp_LIBRARY = "/usr/local/opt/libomp/lib/libomp.dylib"
+
+        cmake_args += [
+            "-DLDFLAGS=" + LDFLAGS,
+            "-DCPPFLAGS=" + CPPFLAGS,
+            "-DOpenMP_C_FLAGS=" + OpenMP_C_FLAGS,
+            "-DOpenMP_C_LIB_NAMES=" + OpenMP_C_LIB_NAMES,
+            "-DOpenMP_omp_LIBRARY=" + OpenMP_omp_LIBRARY,
+        ]
+
+        # SUNDIALS are built within download_dir 'build_sundials' in the PyBaMM root
+        # download_dir
+        build_dir = os.path.abspath(os.path.join(download_dir, "build_sundials"))
+        if not os.path.exists(build_dir):
+            print("\n-" * 10, "Creating build dir", "-" * 40)
+            os.makedirs(build_dir)
+
+        sundials_src = f"../sundials-{sundials_version}"
+        print("-" * 10, "Running CMake prepare", "-" * 40)
+        subprocess.run(["cmake", sundials_src, *cmake_args], cwd=build_dir, check=True)
+
+        print("-" * 10, "Building the sundials", "-" * 40)
+        make_cmd = ["make", f"-j{cpu_count()}", "install"]
+        subprocess.run(make_cmd, cwd=build_dir, check=True)
+
+
+def check_libraries_installed(install_dir):
+    # Define the directories to check for SUNDIALS and SuiteSparse libraries
+    lib_dirs = [install_dir, join(os.getenv("HOME"), ".local"), "/usr/local", "/usr"]
+
+    sundials_lib_found = False
+    # Check for SUNDIALS libraries in each directory
+    for lib_dir in lib_dirs:
+        if isfile(join(lib_dir, "lib", "libsundials_ida.so")) or isfile(
+            join(lib_dir, "lib", "libsundials_ida.dylib")
+        ):
+            sundials_lib_found = True
+            print(f"SUNDIALS library found at {lib_dir}")
+            break
+
+    if not sundials_lib_found:
+        print("SUNDIALS library not found. Proceeding with installation.")
+
+    suitesparse_lib_found = False
+    # Check for SuiteSparse libraries in each directory
+    for lib_dir in lib_dirs:
+        if isfile(join(lib_dir, "lib", "libklu.so")) or isfile(
+            join(lib_dir, "lib", "libklu.dylib")
+        ):
+            suitesparse_lib_found = True
+            print(f"SuiteSparse library found at {lib_dir}")
+            break
+
+    if not suitesparse_lib_found:
+        print("SuiteSparse library not found. Proceeding with installation.")
+
+    return sundials_lib_found, suitesparse_lib_found
+
+
 def calculate_sha256(file_path):
     sha256_hash = hashlib.sha256()
     with open(file_path, "rb") as f:
@@ -63,7 +204,7 @@ def download_extract_library(url, expected_checksum, download_dir):
 
 def parallel_download(urls, download_dir):
     # Use 2 processes for parallel downloading
-    with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executor:
+    with concurrent.futures.ThreadPoolExecutor(max_workers=len(urls)) as executor:
         futures = [
             executor.submit(
                 download_extract_library, url, expected_checksum, download_dir
@@ -126,112 +267,23 @@ def parallel_download(urls, download_dir):
 )
 sundials_checksum = "4e0b998dff292a2617e179609b539b511eb80836f5faacf800e688a886288502"
 
-parallel_download(
-    [(suitesparse_url, suitesparse_checksum), (sundials_url, sundials_checksum)],
-    download_dir,
-)
-
-# 1 --- Install SuiteSparse
-
-# The SuiteSparse KLU module has 4 dependencies:
-# - suitesparseconfig
-# - AMD
-# - COLAMD
-# - BTF
-suitesparse_dir = f"SuiteSparse-{suitesparse_version}"
-suitesparse_src = os.path.join(download_dir, suitesparse_dir)
-print("-" * 10, "Building SuiteSparse_config", "-" * 40)
-make_cmd = [
-    "make",
-    "library",
-]
-install_cmd = [
-    "make",
-    f"-j{cpu_count()}",
-    "install",
-]
-print("-" * 10, "Building SuiteSparse", "-" * 40)
-# Set CMAKE_OPTIONS as environment variables to pass to the GNU Make command
-env = os.environ.copy()
-for libdir in ["SuiteSparse_config", "AMD", "COLAMD", "BTF", "KLU"]:
-    build_dir = os.path.join(suitesparse_src, libdir)
-    # We want to ensure that libsuitesparseconfig.dylib is not repeated in
-    # multiple paths at the time of wheel repair. Therefore, it should not be
-    # built with an RPATH since it is copied to the install prefix.
-    if libdir == "SuiteSparse_config":
-        env["CMAKE_OPTIONS"] = f"-DCMAKE_INSTALL_PREFIX={install_dir}"
-    else:
-        # For AMD, COLAMD, BTF and KLU; do not set a BUILD RPATH but use an
-        # INSTALL RPATH in order to ensure that the dynamic libraries are found
-        # at runtime just once. Otherwise, delocate complains about multiple
-        # references to the SuiteSparse_config dynamic library (auditwheel does not).
-        env[
-            "CMAKE_OPTIONS"
-        ] = f"-DCMAKE_INSTALL_PREFIX={install_dir} -DCMAKE_INSTALL_RPATH={install_dir}/lib -DCMAKE_INSTALL_RPATH_USE_LINK_PATH=FALSE -DCMAKE_BUILD_WITH_INSTALL_RPATH=FALSE"
-    subprocess.run(make_cmd, cwd=build_dir, env=env, shell=True, check=True)
-    subprocess.run(install_cmd, cwd=build_dir, check=True)
-
-# 2 --- Install SUNDIALS
-
-# Set install dir for SuiteSparse libs
-# Ex: if install_dir -> "/usr/local/" then
-# KLU_INCLUDE_DIR -> "/usr/local/include"
-# KLU_LIBRARY_DIR -> "/usr/local/lib"
-KLU_INCLUDE_DIR = os.path.join(install_dir, "include")
-KLU_LIBRARY_DIR = os.path.join(install_dir, "lib")
-cmake_args = [
-    "-DENABLE_LAPACK=ON",
-    "-DSUNDIALS_INDEX_SIZE=32",
-    "-DEXAMPLES_ENABLE_C=OFF",
-    "-DEXAMPLES_ENABLE_CXX=OFF",
-    "-DEXAMPLES_INSTALL=OFF",
-    "-DENABLE_KLU=ON",
-    "-DENABLE_OPENMP=ON",
-    f"-DKLU_INCLUDE_DIR={KLU_INCLUDE_DIR}",
-    f"-DKLU_LIBRARY_DIR={KLU_LIBRARY_DIR}",
-    "-DCMAKE_INSTALL_PREFIX=" + install_dir,
-    # on macOS use fixed paths rather than rpath
-    "-DCMAKE_INSTALL_NAME_DIR=" + KLU_LIBRARY_DIR,
-]
-
-# try to find OpenMP on mac
-if platform.system() == "Darwin":
-    # flags to find OpenMP on mac
-    if platform.processor() == "arm":
-        LDFLAGS = "-L/opt/homebrew/opt/libomp/lib"
-        CPPFLAGS = "-I/opt/homebrew/opt/libomp/include"
-        OpenMP_C_FLAGS = "-Xpreprocessor -fopenmp -I/opt/homebrew/opt/libomp/include"
-        OpenMP_C_LIB_NAMES = "omp"
-        OpenMP_libomp_LIBRARY = "/opt/homebrew/opt/libomp/lib/libomp.dylib"
-        OpenMP_omp_LIBRARY = "/opt/homebrew/opt/libomp/lib/libomp.dylib"
-    elif platform.processor() == "i386":
-        LDFLAGS = "-L/usr/local/opt/libomp/lib"
-        CPPFLAGS = "-I/usr/local/opt/libomp/include"
-        OpenMP_C_FLAGS = "-Xpreprocessor -fopenmp -I/usr/local/opt/libomp/include"
-        OpenMP_CXX_FLAGS = "-Xpreprocessor -fopenmp -I/usr/local/opt/libomp/include"
-        OpenMP_C_LIB_NAMES = "omp"
-        OpenMP_CXX_LIB_NAMES = "omp"
-        OpenMP_omp_LIBRARY = "/usr/local/opt/libomp/lib/libomp.dylib"
-
-    cmake_args += [
-        "-DLDFLAGS=" + LDFLAGS,
-        "-DCPPFLAGS=" + CPPFLAGS,
-        "-DOpenMP_C_FLAGS=" + OpenMP_C_FLAGS,
-        "-DOpenMP_C_LIB_NAMES=" + OpenMP_C_LIB_NAMES,
-        "-DOpenMP_omp_LIBRARY=" + OpenMP_omp_LIBRARY,
-    ]
-
-# SUNDIALS are built within download_dir 'build_sundials' in the PyBaMM root
-# download_dir
-build_dir = os.path.abspath(os.path.join(download_dir, "build_sundials"))
-if not os.path.exists(build_dir):
-    print("\n-" * 10, "Creating build dir", "-" * 40)
-    os.makedirs(build_dir)
-
-sundials_src = f"../sundials-{sundials_version}"
-print("-" * 10, "Running CMake prepare", "-" * 40)
-subprocess.run(["cmake", sundials_src, *cmake_args], cwd=build_dir, check=True)
+# Check whether the libraries are installed
+sundials_found, suitesparse_found = check_libraries_installed(install_dir)
 
-print("-" * 10, "Building the sundials", "-" * 40)
-make_cmd = ["make", f"-j{cpu_count()}", "install"]
-subprocess.run(make_cmd, cwd=build_dir, check=True)
+# Determine which libraries to download based on whether they were found
+if not sundials_found and not suitesparse_found:
+    # Both SUNDIALS and SuiteSparse are missing, download and install both
+    parallel_download(
+        [(suitesparse_url, suitesparse_checksum), (sundials_url, sundials_checksum)],
+        download_dir,
+    )
+    install_suitesparse(suitesparse_version, download_dir)
+    install_sundials(sundials_version, download_dir, install_dir)
+elif not sundials_found and suitesparse_found:
+    # Only SUNDIALS is missing, download and install it
+    parallel_download([(sundials_url, sundials_checksum)], download_dir)
+    install_sundials(sundials_version, download_dir, install_dir)
+elif sundials_found and not suitesparse_found:
+    # Only SuiteSparse is missing, download and install it
+    parallel_download([(suitesparse_url, suitesparse_checksum)], download_dir)
+    install_suitesparse(suitesparse_version, download_dir)

From bdb1bf03807323dead2be33ac68ed48f8db22b27 Mon Sep 17 00:00:00 2001
From: AlessioBugetti <alessiobugetti98@gmail.com>
Date: Sat, 13 Jan 2024 12:50:08 +0100
Subject: [PATCH 091/109] Refactor install_KLU_Sundials.py script for improved
 readability and maintenance

---
 scripts/install_KLU_Sundials.py | 50 +++++++++++++--------------------
 1 file changed, 19 insertions(+), 31 deletions(-)

diff --git a/scripts/install_KLU_Sundials.py b/scripts/install_KLU_Sundials.py
index 66bce42d98..c25bb59a5d 100755
--- a/scripts/install_KLU_Sundials.py
+++ b/scripts/install_KLU_Sundials.py
@@ -22,13 +22,21 @@
 from multiprocessing import cpu_count
 
 
-def install_suitesparse(suitesparse_version, download_dir):
+SUITESPARSE_VERSION = "6.0.3"
+SUNDIALS_VERSION = "6.5.0"
+SUITESPARSE_URL = f"https://github.com/DrTimothyAldenDavis/SuiteSparse/archive/v{SUITESPARSE_VERSION}.tar.gz"
+SUNDIALS_URL = f"https://github.com/LLNL/sundials/releases/download/v{SUNDIALS_VERSION}/sundials-{SUNDIALS_VERSION}.tar.gz"
+SUITESPARSE_CHECKSUM = "7111b505c1207f6f4bd0be9740d0b2897e1146b845d73787df07901b4f5c1fb7"
+SUNDIALS_CHECKSUM = "4e0b998dff292a2617e179609b539b511eb80836f5faacf800e688a886288502"
+
+
+def install_suitesparse(download_dir):
     # The SuiteSparse KLU module has 4 dependencies:
     # - suitesparseconfig
     # - AMD
     # - COLAMD
     # - BTF
-    suitesparse_dir = f"SuiteSparse-{suitesparse_version}"
+    suitesparse_dir = f"SuiteSparse-{SUITESPARSE_VERSION}"
     suitesparse_src = os.path.join(download_dir, suitesparse_dir)
     print("-" * 10, "Building SuiteSparse_config", "-" * 40)
     make_cmd = [
@@ -62,7 +70,7 @@ def install_suitesparse(suitesparse_version, download_dir):
         subprocess.run(install_cmd, cwd=build_dir, check=True)
 
 
-def install_sundials(sundials_version, download_dir, install_dir):
+def install_sundials(download_dir, install_dir):
     # Set install dir for SuiteSparse libs
     # Ex: if install_dir -> "/usr/local/" then
     # KLU_INCLUDE_DIR -> "/usr/local/include"
@@ -120,7 +128,7 @@ def install_sundials(sundials_version, download_dir, install_dir):
             print("\n-" * 10, "Creating build dir", "-" * 40)
             os.makedirs(build_dir)
 
-        sundials_src = f"../sundials-{sundials_version}"
+        sundials_src = f"../sundials-{SUNDIALS_VERSION}"
         print("-" * 10, "Running CMake prepare", "-" * 40)
         subprocess.run(["cmake", sundials_src, *cmake_args], cwd=build_dir, check=True)
 
@@ -247,26 +255,6 @@ def parallel_download(urls, download_dir):
     else os.path.join(pybamm_dir, args.install_dir)
 )
 
-# Parallel download
-
-# 1 --- SuiteSparse
-suitesparse_version = "6.0.3"
-suitesparse_url = (
-    "https://github.com/DrTimothyAldenDavis/"
-    + f"SuiteSparse/archive/v{suitesparse_version}.tar.gz"
-)
-suitesparse_checksum = (
-    "7111b505c1207f6f4bd0be9740d0b2897e1146b845d73787df07901b4f5c1fb7"
-)
-
-# 2 --- SUNDIALS
-sundials_version = "6.5.0"
-sundials_url = (
-    "https://github.com/LLNL/sundials/"
-    + f"releases/download/v{sundials_version}/sundials-{sundials_version}.tar.gz"
-)
-sundials_checksum = "4e0b998dff292a2617e179609b539b511eb80836f5faacf800e688a886288502"
-
 # Check whether the libraries are installed
 sundials_found, suitesparse_found = check_libraries_installed(install_dir)
 
@@ -274,16 +262,16 @@ def parallel_download(urls, download_dir):
 if not sundials_found and not suitesparse_found:
     # Both SUNDIALS and SuiteSparse are missing, download and install both
     parallel_download(
-        [(suitesparse_url, suitesparse_checksum), (sundials_url, sundials_checksum)],
+        [(SUITESPARSE_URL, SUITESPARSE_CHECKSUM), (SUNDIALS_URL, SUNDIALS_CHECKSUM)],
         download_dir,
     )
-    install_suitesparse(suitesparse_version, download_dir)
-    install_sundials(sundials_version, download_dir, install_dir)
+    install_suitesparse(download_dir)
+    install_sundials(download_dir, install_dir)
 elif not sundials_found and suitesparse_found:
     # Only SUNDIALS is missing, download and install it
-    parallel_download([(sundials_url, sundials_checksum)], download_dir)
-    install_sundials(sundials_version, download_dir, install_dir)
+    parallel_download([(SUNDIALS_URL, SUNDIALS_CHECKSUM)], download_dir)
+    install_sundials(download_dir, install_dir)
 elif sundials_found and not suitesparse_found:
     # Only SuiteSparse is missing, download and install it
-    parallel_download([(suitesparse_url, suitesparse_checksum)], download_dir)
-    install_suitesparse(suitesparse_version, download_dir)
+    parallel_download([(SUITESPARSE_URL, SUITESPARSE_CHECKSUM)], download_dir)
+    install_suitesparse(download_dir)

From f2290d136f020ecdcfb60f4d607d0f8f4b9b6851 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Sat, 13 Jan 2024 11:50:28 +0000
Subject: [PATCH 092/109] style: pre-commit fixes

---
 scripts/install_KLU_Sundials.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/scripts/install_KLU_Sundials.py b/scripts/install_KLU_Sundials.py
index c25bb59a5d..6cbd03aedc 100755
--- a/scripts/install_KLU_Sundials.py
+++ b/scripts/install_KLU_Sundials.py
@@ -26,7 +26,9 @@
 SUNDIALS_VERSION = "6.5.0"
 SUITESPARSE_URL = f"https://github.com/DrTimothyAldenDavis/SuiteSparse/archive/v{SUITESPARSE_VERSION}.tar.gz"
 SUNDIALS_URL = f"https://github.com/LLNL/sundials/releases/download/v{SUNDIALS_VERSION}/sundials-{SUNDIALS_VERSION}.tar.gz"
-SUITESPARSE_CHECKSUM = "7111b505c1207f6f4bd0be9740d0b2897e1146b845d73787df07901b4f5c1fb7"
+SUITESPARSE_CHECKSUM = (
+    "7111b505c1207f6f4bd0be9740d0b2897e1146b845d73787df07901b4f5c1fb7"
+)
 SUNDIALS_CHECKSUM = "4e0b998dff292a2617e179609b539b511eb80836f5faacf800e688a886288502"
 
 

From cc01574eb016f25b8983563b8c9bb71edd2b2142 Mon Sep 17 00:00:00 2001
From: AlessioBugetti <alessiobugetti98@gmail.com>
Date: Sat, 13 Jan 2024 13:05:56 +0100
Subject: [PATCH 093/109] Define default install dir as top-level constants

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

diff --git a/scripts/install_KLU_Sundials.py b/scripts/install_KLU_Sundials.py
index 6cbd03aedc..281091ac6f 100755
--- a/scripts/install_KLU_Sundials.py
+++ b/scripts/install_KLU_Sundials.py
@@ -30,6 +30,7 @@
     "7111b505c1207f6f4bd0be9740d0b2897e1146b845d73787df07901b4f5c1fb7"
 )
 SUNDIALS_CHECKSUM = "4e0b998dff292a2617e179609b539b511eb80836f5faacf800e688a886288502"
+DEFAULT_INSTALL_DIR = os.path.join(os.getenv("HOME"), ".local")
 
 
 def install_suitesparse(download_dir):
@@ -245,11 +246,10 @@ def parallel_download(urls, download_dir):
     os.makedirs(download_dir)
 
 # Get installation location
-default_install_dir = os.path.join(os.getenv("HOME"), ".local")
 parser = argparse.ArgumentParser(
     description="Download, compile and install Sundials and SuiteSparse."
 )
-parser.add_argument("--install-dir", type=str, default=default_install_dir)
+parser.add_argument("--install-dir", type=str, default=DEFAULT_INSTALL_DIR)
 args = parser.parse_args()
 install_dir = (
     args.install_dir

From 4b8cf03626d8450f7b9f1de83547049ad99b70cc Mon Sep 17 00:00:00 2001
From: AlessioBugetti <alessiobugetti98@gmail.com>
Date: Sat, 13 Jan 2024 14:41:00 +0100
Subject: [PATCH 094/109] Extend list of SUNDIALS and SuiteSparse library files

---
 scripts/install_KLU_Sundials.py | 66 ++++++++++++++++++++++++---------
 1 file changed, 49 insertions(+), 17 deletions(-)

diff --git a/scripts/install_KLU_Sundials.py b/scripts/install_KLU_Sundials.py
index 281091ac6f..d28b624acc 100755
--- a/scripts/install_KLU_Sundials.py
+++ b/scripts/install_KLU_Sundials.py
@@ -144,30 +144,62 @@ def check_libraries_installed(install_dir):
     # Define the directories to check for SUNDIALS and SuiteSparse libraries
     lib_dirs = [install_dir, join(os.getenv("HOME"), ".local"), "/usr/local", "/usr"]
 
-    sundials_lib_found = False
+    sundials_files = [
+        "libsundials_idas",
+        "libsundials_sunlinsolklu",
+        "libsundials_sunlinsoldense",
+        "libsundials_sunlinsolspbcgs",
+        "libsundials_sunlinsollapackdense",
+        "libsundials_sunmatrixsparse",
+        "libsundials_nvecserial",
+        "libsundials_nvecopenmp",
+    ]
+    if platform.system() == "linux":
+        sundials_files = [file + ".so" for file in sundials_files]
+    elif platform.system() == "Darwin":
+        sundials_files = [file + ".dylib" for file in sundials_files]
+    sundials_lib_found = True
     # Check for SUNDIALS libraries in each directory
-    for lib_dir in lib_dirs:
-        if isfile(join(lib_dir, "lib", "libsundials_ida.so")) or isfile(
-            join(lib_dir, "lib", "libsundials_ida.dylib")
-        ):
-            sundials_lib_found = True
-            print(f"SUNDIALS library found at {lib_dir}")
+    for lib_file in sundials_files:
+        file_found = False
+        for lib_dir in lib_dirs:
+            if isfile(join(lib_dir, "lib", lib_file)):
+                file_found = True
+                break
+        if not file_found:
+            sundials_lib_found = False
             break
-
-    if not sundials_lib_found:
+    if sundials_lib_found:
+        print("SUNDIALS library found.")
+    else:
         print("SUNDIALS library not found. Proceeding with installation.")
 
+    suitesparse_files = [
+        "libsuitesparseconfig",
+        "libklu",
+        "libamd",
+        "libcolamd",
+        "libbtf",
+    ]
+    if platform.system() == "linux":
+        suitesparse_files = [file + ".so" for file in suitesparse_files]
+    elif platform.system() == "Darwin":
+        suitesparse_files = [file + ".dylib" for file in suitesparse_files]
+
     suitesparse_lib_found = False
     # Check for SuiteSparse libraries in each directory
-    for lib_dir in lib_dirs:
-        if isfile(join(lib_dir, "lib", "libklu.so")) or isfile(
-            join(lib_dir, "lib", "libklu.dylib")
-        ):
-            suitesparse_lib_found = True
-            print(f"SuiteSparse library found at {lib_dir}")
+    for lib_file in suitesparse_files:
+        file_found = False
+        for lib_dir in lib_dirs:
+            if isfile(join(lib_dir, "lib", lib_file)):
+                file_found = True
+                break
+        if not file_found:
+            suitesparse_lib_found = False
             break
-
-    if not suitesparse_lib_found:
+    if suitesparse_lib_found:
+        print("SuiteSparse library found.")
+    else:
         print("SuiteSparse library not found. Proceeding with installation.")
 
     return sundials_lib_found, suitesparse_lib_found

From 557a401343f1d925e0dfd5f307324822447ad631 Mon Sep 17 00:00:00 2001
From: AlessioBugetti <alessiobugetti98@gmail.com>
Date: Sat, 13 Jan 2024 20:33:07 +0100
Subject: [PATCH 095/109] Fix parallel download

---
 scripts/install_KLU_Sundials.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/install_KLU_Sundials.py b/scripts/install_KLU_Sundials.py
index d28b624acc..079b905975 100755
--- a/scripts/install_KLU_Sundials.py
+++ b/scripts/install_KLU_Sundials.py
@@ -254,7 +254,7 @@ def parallel_download(urls, download_dir):
             )
             for (url, expected_checksum) in urls
         ]
-        for future in concurrent.futures.as_completed(futures):
+        for future in futures:
             future.result()
 
 

From 50a903a87ad0134ac112d0bd4db33c7c9e29daba Mon Sep 17 00:00:00 2001
From: AlessioBugetti <alessiobugetti98@gmail.com>
Date: Sat, 13 Jan 2024 23:25:03 +0100
Subject: [PATCH 096/109] Fix style job failures

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

diff --git a/scripts/install_KLU_Sundials.py b/scripts/install_KLU_Sundials.py
index 079b905975..feaff731a8 100755
--- a/scripts/install_KLU_Sundials.py
+++ b/scripts/install_KLU_Sundials.py
@@ -105,15 +105,15 @@ def install_sundials(download_dir, install_dir):
                 "-Xpreprocessor -fopenmp -I/opt/homebrew/opt/libomp/include"
             )
             OpenMP_C_LIB_NAMES = "omp"
-            OpenMP_libomp_LIBRARY = "/opt/homebrew/opt/libomp/lib/libomp.dylib"
+            # OpenMP_libomp_LIBRARY = "/opt/homebrew/opt/libomp/lib/libomp.dylib"
             OpenMP_omp_LIBRARY = "/opt/homebrew/opt/libomp/lib/libomp.dylib"
         elif platform.processor() == "i386":
             LDFLAGS = "-L/usr/local/opt/libomp/lib"
             CPPFLAGS = "-I/usr/local/opt/libomp/include"
             OpenMP_C_FLAGS = "-Xpreprocessor -fopenmp -I/usr/local/opt/libomp/include"
-            OpenMP_CXX_FLAGS = "-Xpreprocessor -fopenmp -I/usr/local/opt/libomp/include"
+            # OpenMP_CXX_FLAGS = "-Xpreprocessor -fopenmp -I/usr/local/opt/libomp/include"
             OpenMP_C_LIB_NAMES = "omp"
-            OpenMP_CXX_LIB_NAMES = "omp"
+            # OpenMP_CXX_LIB_NAMES = "omp"
             OpenMP_omp_LIBRARY = "/usr/local/opt/libomp/lib/libomp.dylib"
 
         cmake_args += [

From 1c00a62c0ffebd2f7f0a0ceab1f7955906c79197 Mon Sep 17 00:00:00 2001
From: AlessioBugetti <alessiobugetti98@gmail.com>
Date: Sun, 14 Jan 2024 10:53:28 +0100
Subject: [PATCH 097/109] Fix indentation in install_sundials method of
 install_KLU_Sundials.py

---
 scripts/install_KLU_Sundials.py | 28 ++++++++++++++--------------
 1 file changed, 14 insertions(+), 14 deletions(-)

diff --git a/scripts/install_KLU_Sundials.py b/scripts/install_KLU_Sundials.py
index feaff731a8..8e8d3566db 100755
--- a/scripts/install_KLU_Sundials.py
+++ b/scripts/install_KLU_Sundials.py
@@ -15,10 +15,10 @@
 import argparse
 import platform
 import hashlib
-import concurrent.futures
 import urllib.request
 from os.path import join, isfile
 from urllib.parse import urlparse
+from concurrent.futures import ThreadPoolExecutor
 from multiprocessing import cpu_count
 
 
@@ -124,20 +124,20 @@ def install_sundials(download_dir, install_dir):
             "-DOpenMP_omp_LIBRARY=" + OpenMP_omp_LIBRARY,
         ]
 
-        # SUNDIALS are built within download_dir 'build_sundials' in the PyBaMM root
-        # download_dir
-        build_dir = os.path.abspath(os.path.join(download_dir, "build_sundials"))
-        if not os.path.exists(build_dir):
-            print("\n-" * 10, "Creating build dir", "-" * 40)
-            os.makedirs(build_dir)
+    # SUNDIALS are built within download_dir 'build_sundials' in the PyBaMM root
+    # download_dir
+    build_dir = os.path.abspath(os.path.join(download_dir, "build_sundials"))
+    if not os.path.exists(build_dir):
+        print("\n-" * 10, "Creating build dir", "-" * 40)
+        os.makedirs(build_dir)
 
-        sundials_src = f"../sundials-{SUNDIALS_VERSION}"
-        print("-" * 10, "Running CMake prepare", "-" * 40)
-        subprocess.run(["cmake", sundials_src, *cmake_args], cwd=build_dir, check=True)
+    sundials_src = f"../sundials-{SUNDIALS_VERSION}"
+    print("-" * 10, "Running CMake prepare", "-" * 40)
+    subprocess.run(["cmake", sundials_src, *cmake_args], cwd=build_dir, check=True)
 
-        print("-" * 10, "Building the sundials", "-" * 40)
-        make_cmd = ["make", f"-j{cpu_count()}", "install"]
-        subprocess.run(make_cmd, cwd=build_dir, check=True)
+    print("-" * 10, "Building the sundials", "-" * 40)
+    make_cmd = ["make", f"-j{cpu_count()}", "install"]
+    subprocess.run(make_cmd, cwd=build_dir, check=True)
 
 
 def check_libraries_installed(install_dir):
@@ -247,7 +247,7 @@ def download_extract_library(url, expected_checksum, download_dir):
 
 def parallel_download(urls, download_dir):
     # Use 2 processes for parallel downloading
-    with concurrent.futures.ThreadPoolExecutor(max_workers=len(urls)) as executor:
+    with ThreadPoolExecutor(max_workers=len(urls)) as executor:
         futures = [
             executor.submit(
                 download_extract_library, url, expected_checksum, download_dir

From 00f3269a344cbec391b5e4b488a2df869d6e0189 Mon Sep 17 00:00:00 2001
From: AlessioBugetti <alessiobugetti98@gmail.com>
Date: Mon, 15 Jan 2024 00:51:40 +0100
Subject: [PATCH 098/109] Refactored install_KLU_Sundials.py, Updated
 noxfile.py and Docs

This commit introduces several changes:

- Added the `--force` command-line argument to enable users to force the installation of SuiteSparse and SUNDIALS, even if they are already found in the specified installation directory.

- Resolved an issue where the `--install-dir` argument was not correctly respected. The script now accurately checks for existing libraries in the specified installation directory and creates the folder if it doesn't exist.

- Improved the logic for detecting the presence of SUNDIALS and SuiteSparse libraries.

- Cleaned up the script by removing unused variables.

- Added more descriptive print statements for each step and for individual file checks, providing better feedback and clarity on the script's execution process.

- Corrected the platform check from `linux` to `Linux`.

- Eliminated the unnecessary wget installation from `noxfile.py`, as it's no longer required by the updated script.

- Incorporated details about the `--force` and `--install-dir` flags in the "Installation from source" section of the user guide, providing clear instructions and examples for their usage.
---
 .../installation/install-from-source.rst      | 19 ++++
 noxfile.py                                    |  1 -
 scripts/install_KLU_Sundials.py               | 88 ++++++++++---------
 3 files changed, 66 insertions(+), 42 deletions(-)

diff --git a/docs/source/user_guide/installation/install-from-source.rst b/docs/source/user_guide/installation/install-from-source.rst
index 26b6b5cf20..8f2e479ff5 100644
--- a/docs/source/user_guide/installation/install-from-source.rst
+++ b/docs/source/user_guide/installation/install-from-source.rst
@@ -98,6 +98,25 @@ PyBaMM ships with a Python script that automates points 2. and 3. You can run it
 
 	  python scripts/install_KLU_Sundials.py
 
+This script supports optional arguments for custom installations:
+
+- ``--install-dir``: Specify a custom installation directory for SUNDIALS and SuiteSparse.
+  By default, they are installed in your local directory (usually ``~/.local``).
+
+  Example:
+
+  .. code:: bash
+
+      python scripts/install_KLU_Sundials.py --install-dir ./custom_install_dir
+
+- ``--force``: Force the installation of SUNDIALS and SuiteSparse, even if they are already found in the specified directory.
+
+  Example:
+
+  .. code:: bash
+
+      python scripts/install_KLU_Sundials.py --force
+
 .. _pybamm-install:
 
 Installing PyBaMM
diff --git a/noxfile.py b/noxfile.py
index a670b48e17..faac981690 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -41,7 +41,6 @@ def run_pybamm_requires(session):
     """Download, compile, and install the build-time requirements for Linux and macOS: the SuiteSparse and SUNDIALS libraries."""
     set_environment_variables(PYBAMM_ENV, session=session)
     if sys.platform != "win32":
-        session.install("wget", "cmake", silent=False)
         session.run("python", "scripts/install_KLU_Sundials.py")
         if not os.path.exists("./pybind11"):
             session.run(
diff --git a/scripts/install_KLU_Sundials.py b/scripts/install_KLU_Sundials.py
index 8e8d3566db..db969066ed 100755
--- a/scripts/install_KLU_Sundials.py
+++ b/scripts/install_KLU_Sundials.py
@@ -99,26 +99,17 @@ def install_sundials(download_dir, install_dir):
     if platform.system() == "Darwin":
         # flags to find OpenMP on mac
         if platform.processor() == "arm":
-            LDFLAGS = "-L/opt/homebrew/opt/libomp/lib"
-            CPPFLAGS = "-I/opt/homebrew/opt/libomp/include"
             OpenMP_C_FLAGS = (
                 "-Xpreprocessor -fopenmp -I/opt/homebrew/opt/libomp/include"
             )
             OpenMP_C_LIB_NAMES = "omp"
-            # OpenMP_libomp_LIBRARY = "/opt/homebrew/opt/libomp/lib/libomp.dylib"
             OpenMP_omp_LIBRARY = "/opt/homebrew/opt/libomp/lib/libomp.dylib"
         elif platform.processor() == "i386":
-            LDFLAGS = "-L/usr/local/opt/libomp/lib"
-            CPPFLAGS = "-I/usr/local/opt/libomp/include"
             OpenMP_C_FLAGS = "-Xpreprocessor -fopenmp -I/usr/local/opt/libomp/include"
-            # OpenMP_CXX_FLAGS = "-Xpreprocessor -fopenmp -I/usr/local/opt/libomp/include"
             OpenMP_C_LIB_NAMES = "omp"
-            # OpenMP_CXX_LIB_NAMES = "omp"
             OpenMP_omp_LIBRARY = "/usr/local/opt/libomp/lib/libomp.dylib"
 
         cmake_args += [
-            "-DLDFLAGS=" + LDFLAGS,
-            "-DCPPFLAGS=" + CPPFLAGS,
             "-DOpenMP_C_FLAGS=" + OpenMP_C_FLAGS,
             "-DOpenMP_C_LIB_NAMES=" + OpenMP_C_LIB_NAMES,
             "-DOpenMP_omp_LIBRARY=" + OpenMP_omp_LIBRARY,
@@ -135,14 +126,14 @@ def install_sundials(download_dir, install_dir):
     print("-" * 10, "Running CMake prepare", "-" * 40)
     subprocess.run(["cmake", sundials_src, *cmake_args], cwd=build_dir, check=True)
 
-    print("-" * 10, "Building the sundials", "-" * 40)
+    print("-" * 10, "Building SUNDIALS", "-" * 40)
     make_cmd = ["make", f"-j{cpu_count()}", "install"]
     subprocess.run(make_cmd, cwd=build_dir, check=True)
 
 
 def check_libraries_installed(install_dir):
     # Define the directories to check for SUNDIALS and SuiteSparse libraries
-    lib_dirs = [install_dir, join(os.getenv("HOME"), ".local"), "/usr/local", "/usr"]
+    lib_dirs = [install_dir]
 
     sundials_files = [
         "libsundials_idas",
@@ -154,7 +145,7 @@ def check_libraries_installed(install_dir):
         "libsundials_nvecserial",
         "libsundials_nvecopenmp",
     ]
-    if platform.system() == "linux":
+    if platform.system() == "Linux":
         sundials_files = [file + ".so" for file in sundials_files]
     elif platform.system() == "Darwin":
         sundials_files = [file + ".dylib" for file in sundials_files]
@@ -164,15 +155,15 @@ def check_libraries_installed(install_dir):
         file_found = False
         for lib_dir in lib_dirs:
             if isfile(join(lib_dir, "lib", lib_file)):
+                print(f"{lib_file} found.")
                 file_found = True
                 break
         if not file_found:
+            print(
+                f"{lib_file} not found. Proceeding with SUNDIALS library installation."
+            )
             sundials_lib_found = False
             break
-    if sundials_lib_found:
-        print("SUNDIALS library found.")
-    else:
-        print("SUNDIALS library not found. Proceeding with installation.")
 
     suitesparse_files = [
         "libsuitesparseconfig",
@@ -181,26 +172,26 @@ def check_libraries_installed(install_dir):
         "libcolamd",
         "libbtf",
     ]
-    if platform.system() == "linux":
+    if platform.system() == "Linux":
         suitesparse_files = [file + ".so" for file in suitesparse_files]
     elif platform.system() == "Darwin":
         suitesparse_files = [file + ".dylib" for file in suitesparse_files]
 
-    suitesparse_lib_found = False
+    suitesparse_lib_found = True
     # Check for SuiteSparse libraries in each directory
     for lib_file in suitesparse_files:
         file_found = False
         for lib_dir in lib_dirs:
             if isfile(join(lib_dir, "lib", lib_file)):
+                print(f"{lib_file} found.")
                 file_found = True
                 break
         if not file_found:
+            print(
+                f"{lib_file} not found. Proceeding with SuiteSparse library installation."
+            )
             suitesparse_lib_found = False
             break
-    if suitesparse_lib_found:
-        print("SuiteSparse library found.")
-    else:
-        print("SuiteSparse library not found. Proceeding with installation.")
 
     return sundials_lib_found, suitesparse_lib_found
 
@@ -281,6 +272,11 @@ def parallel_download(urls, download_dir):
 parser = argparse.ArgumentParser(
     description="Download, compile and install Sundials and SuiteSparse."
 )
+parser.add_argument(
+    "--force",
+    action="store_true",
+    help="Force installation even if libraries are already found. This will overwrite the pre-existing files.",
+)
 parser.add_argument("--install-dir", type=str, default=DEFAULT_INSTALL_DIR)
 args = parser.parse_args()
 install_dir = (
@@ -289,23 +285,33 @@ def parallel_download(urls, download_dir):
     else os.path.join(pybamm_dir, args.install_dir)
 )
 
-# Check whether the libraries are installed
-sundials_found, suitesparse_found = check_libraries_installed(install_dir)
-
-# Determine which libraries to download based on whether they were found
-if not sundials_found and not suitesparse_found:
-    # Both SUNDIALS and SuiteSparse are missing, download and install both
-    parallel_download(
-        [(SUITESPARSE_URL, SUITESPARSE_CHECKSUM), (SUNDIALS_URL, SUNDIALS_CHECKSUM)],
-        download_dir,
+if args.force:
+    print(
+        "The '--force' option is activated: installation will be forced, ignoring any existing libraries."
     )
-    install_suitesparse(download_dir)
-    install_sundials(download_dir, install_dir)
-elif not sundials_found and suitesparse_found:
-    # Only SUNDIALS is missing, download and install it
-    parallel_download([(SUNDIALS_URL, SUNDIALS_CHECKSUM)], download_dir)
-    install_sundials(download_dir, install_dir)
-elif sundials_found and not suitesparse_found:
-    # Only SuiteSparse is missing, download and install it
-    parallel_download([(SUITESPARSE_URL, SUITESPARSE_CHECKSUM)], download_dir)
-    install_suitesparse(download_dir)
+    sundials_found, suitesparse_found = False, False
+else:
+    # Check whether the libraries are installed
+    sundials_found, suitesparse_found = check_libraries_installed(install_dir)
+
+if __name__ == "__main__":
+    # Determine which libraries to download based on whether they were found
+    if not sundials_found and not suitesparse_found:
+        # Both SUNDIALS and SuiteSparse are missing, download and install both
+        parallel_download(
+            [
+                (SUITESPARSE_URL, SUITESPARSE_CHECKSUM),
+                (SUNDIALS_URL, SUNDIALS_CHECKSUM),
+            ],
+            download_dir,
+        )
+        install_suitesparse(download_dir)
+        install_sundials(download_dir, install_dir)
+    elif not sundials_found and suitesparse_found:
+        # Only SUNDIALS is missing, download and install it
+        parallel_download([(SUNDIALS_URL, SUNDIALS_CHECKSUM)], download_dir)
+        install_sundials(download_dir, install_dir)
+    elif sundials_found and not suitesparse_found:
+        # Only SuiteSparse is missing, download and install it
+        parallel_download([(SUITESPARSE_URL, SUITESPARSE_CHECKSUM)], download_dir)
+        install_suitesparse(download_dir)

From a7fed0e2f73c53f72f2cdb58837671632dc34486 Mon Sep 17 00:00:00 2001
From: AlessioBugetti <alessiobugetti98@gmail.com>
Date: Mon, 15 Jan 2024 09:20:38 +0100
Subject: [PATCH 099/109] Enhance install_KLU_Sundials.py and noxfile.py

- In `noxfile.py`, retained `cmake` in the installation command ensuring compatibility for local development environments where `cmake` might not be pre-installed.
- Implemented handling for `--install-dir` and `--force` arguments in `nox` sessions. This allows users to specify custom installation directories and force installation if required. The command usage is now documented in the docstring for clarity.
- In `scripts/install_KLU_Sundials.py`:
  - Modified print statements to include the directory where libraries are found, making the output more informative.
  - Enhanced checksum validation messages to display both actual and expected checksums for better clarity and troubleshooting.
  - Addressed an issue with `--force` installation. The script now removes directories (`build_sundials`, `SuiteSparse-{SUITESPARSE_VERSION}`, `sundials-{SUNDIALS_VERSION}`) if `--force` is used. This ensures a clean state for forced re-installations, preventing CMake cache errors and conflicts with previous configurations.
---
 .../installation/install-from-source.rst      | 22 +++++++++++++++++--
 noxfile.py                                    |  5 +++--
 scripts/install_KLU_Sundials.py               | 14 ++++++++++--
 3 files changed, 35 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 8f2e479ff5..e5d793e043 100644
--- a/docs/source/user_guide/installation/install-from-source.rst
+++ b/docs/source/user_guide/installation/install-from-source.rst
@@ -81,6 +81,24 @@ If you are running windows, you can simply skip this section and jump to :ref:`p
 This will download, compile and install the SuiteSparse and SUNDIALS libraries.
 Both libraries are installed in ``~/.local``.
 
+For users requiring more control over the installation process, the ``pybamm-requires`` session supports additional command-line arguments:
+
+- ``--install-dir``: Specify a custom installation directory for SUNDIALS and SuiteSparse.
+
+  Example:
+
+  .. code:: bash
+
+      nox -s pybamm-requires -- --install-dir [custom_directory_path]
+
+- ``--force``: Force the installation of SUNDIALS and SuiteSparse, even if they are already found in the specified directory.
+
+  Example:
+
+  .. code:: bash
+
+      nox -s pybamm-requires -- --force
+
 Manual install of build time requirements
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -101,13 +119,13 @@ PyBaMM ships with a Python script that automates points 2. and 3. You can run it
 This script supports optional arguments for custom installations:
 
 - ``--install-dir``: Specify a custom installation directory for SUNDIALS and SuiteSparse.
-  By default, they are installed in your local directory (usually ``~/.local``).
+  By default, they are installed in ``~/.local``.
 
   Example:
 
   .. code:: bash
 
-      python scripts/install_KLU_Sundials.py --install-dir ./custom_install_dir
+      python scripts/install_KLU_Sundials.py --install-dir [custom_directory_path]
 
 - ``--force``: Force the installation of SUNDIALS and SuiteSparse, even if they are already found in the specified directory.
 
diff --git a/noxfile.py b/noxfile.py
index faac981690..fb1216be62 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -38,10 +38,11 @@ def set_environment_variables(env_dict, session):
 
 @nox.session(name="pybamm-requires")
 def run_pybamm_requires(session):
-    """Download, compile, and install the build-time requirements for Linux and macOS: the SuiteSparse and SUNDIALS libraries."""
+    """Download, compile, and install the build-time requirements for Linux and macOS. Supports --install-dir for custom installation paths and --force to force installation."""
     set_environment_variables(PYBAMM_ENV, session=session)
     if sys.platform != "win32":
-        session.run("python", "scripts/install_KLU_Sundials.py")
+        session.install("cmake", silent=False)
+        session.run("python", "scripts/install_KLU_Sundials.py", *session.posargs)
         if not os.path.exists("./pybind11"):
             session.run(
                 "git",
diff --git a/scripts/install_KLU_Sundials.py b/scripts/install_KLU_Sundials.py
index db969066ed..4e66544b43 100755
--- a/scripts/install_KLU_Sundials.py
+++ b/scripts/install_KLU_Sundials.py
@@ -15,6 +15,7 @@
 import argparse
 import platform
 import hashlib
+import shutil
 import urllib.request
 from os.path import join, isfile
 from urllib.parse import urlparse
@@ -33,6 +34,11 @@
 DEFAULT_INSTALL_DIR = os.path.join(os.getenv("HOME"), ".local")
 
 
+def safe_remove_dir(path):
+    if os.path.exists(path):
+        shutil.rmtree(path)
+
+
 def install_suitesparse(download_dir):
     # The SuiteSparse KLU module has 4 dependencies:
     # - suitesparseconfig
@@ -155,7 +161,7 @@ def check_libraries_installed(install_dir):
         file_found = False
         for lib_dir in lib_dirs:
             if isfile(join(lib_dir, "lib", lib_file)):
-                print(f"{lib_file} found.")
+                print(f"{lib_file} found in {lib_dir}.")
                 file_found = True
                 break
         if not file_found:
@@ -183,7 +189,7 @@ def check_libraries_installed(install_dir):
         file_found = False
         for lib_dir in lib_dirs:
             if isfile(join(lib_dir, "lib", lib_file)):
-                print(f"{lib_file} found.")
+                print(f"{lib_file} found in {lib_dir}.")
                 file_found = True
                 break
         if not file_found:
@@ -213,6 +219,7 @@ def download_extract_library(url, expected_checksum, download_dir):
     if os.path.exists(file_path):
         print(f"Validating checksum for {file_name}...")
         actual_checksum = calculate_sha256(file_path)
+        print(f"Found {actual_checksum} against {expected_checksum}")
         if actual_checksum == expected_checksum:
             print(f"Checksum valid. Skipping download for {file_name}.")
             # Extract the archive as the checksum is valid
@@ -289,6 +296,9 @@ def parallel_download(urls, download_dir):
     print(
         "The '--force' option is activated: installation will be forced, ignoring any existing libraries."
     )
+    safe_remove_dir(os.path.join(download_dir, "build_sundials"))
+    safe_remove_dir(os.path.join(download_dir, f"SuiteSparse-{SUITESPARSE_VERSION}"))
+    safe_remove_dir(os.path.join(download_dir, f"sundials-{SUNDIALS_VERSION}"))
     sundials_found, suitesparse_found = False, False
 else:
     # Check whether the libraries are installed

From a2481fa48acc3afcafa0533db0ddbf59c8cec075 Mon Sep 17 00:00:00 2001
From: Robert Timms <43040151+rtimms@users.noreply.github.com>
Date: Mon, 15 Jan 2024 16:14:25 +0000
Subject: [PATCH 100/109] #3690 fix issue with skipped steps (#3708)

* #3690 fix issue with skipped steps

* #3690 changelog

* #3690 add test
---
 CHANGELOG.md                                  |  3 +++
 pybamm/simulation.py                          | 15 ++++++++++++++-
 .../test_simulation_with_experiment.py        | 19 +++++++++++++++++++
 3 files changed, 36 insertions(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 965a2aa7b4..00ad65f9a0 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,5 +1,8 @@
 # [Unreleased](https://github.com/pybamm-team/PyBaMM/)
 
+## Bug Fixes
+
+- Fixed a bug where if the first step(s) in a cycle are skipped then the cycle solution started from the model's initial conditions instead of from the last state of the previous cycle ([#3708](https://github.com/pybamm-team/PyBaMM/pull/3708))
 # [v24.1rc0](https://github.com/pybamm-team/PyBaMM/tree/v24.1rc0) - 2024-01-31
 
 ## Features
diff --git a/pybamm/simulation.py b/pybamm/simulation.py
index c95ab3039c..8a6150cc4e 100644
--- a/pybamm/simulation.py
+++ b/pybamm/simulation.py
@@ -839,7 +839,20 @@ def solve(
 
                     steps.append(step_solution)
 
-                    cycle_solution = cycle_solution + step_solution
+                    # If there haven't been any successful steps yet in this cycle, then
+                    # carry the solution over from the previous cycle (but
+                    # `step_solution` should still be an EmptySolution so that in the
+                    # list of returned step solutions we can see which steps were
+                    # skipped)
+                    if (
+                        cycle_solution is None
+                        and isinstance(step_solution, pybamm.EmptySolution)
+                        and not isinstance(current_solution, pybamm.EmptySolution)
+                    ):
+                        cycle_solution = current_solution.last_state
+                    else:
+                        cycle_solution = cycle_solution + step_solution
+
                     current_solution = cycle_solution
 
                     callbacks.on_step_end(logs)
diff --git a/tests/unit/test_experiments/test_simulation_with_experiment.py b/tests/unit/test_experiments/test_simulation_with_experiment.py
index cc04177ba2..36475081c3 100644
--- a/tests/unit/test_experiments/test_simulation_with_experiment.py
+++ b/tests/unit/test_experiments/test_simulation_with_experiment.py
@@ -519,6 +519,25 @@ def test_run_experiment_skip_steps(self):
                 decimal=5,
             )
 
+    def test_skipped_step_continuous(self):
+        model = pybamm.lithium_ion.SPM({"SEI": "solvent-diffusion limited"})
+        experiment = pybamm.Experiment(
+            [
+                ("Rest for 24 hours (1 hour period)",),
+                (
+                    "Charge at C/3 until 4.1 V",
+                    "Hold at 4.1V until C/20",
+                    "Discharge at C/3 until 2.5 V",
+                ),
+            ]
+        )
+        sim = pybamm.Simulation(model, experiment=experiment)
+        sim.solve(initial_soc=1)
+        np.testing.assert_array_almost_equal(
+            sim.solution.cycles[0].last_state.y.full(),
+            sim.solution.cycles[1].steps[-1].first_state.y.full(),
+        )
+
     def test_all_empty_solution_errors(self):
         model = pybamm.lithium_ion.SPM()
         parameter_values = pybamm.ParameterValues("Chen2020")

From 6d2227eefe8193842f1d29103c44fa5cc20114e9 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 15 Jan 2024 14:54:33 -0500
Subject: [PATCH 101/109] chore: update pre-commit hooks (#3728)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

updates:
- [github.com/astral-sh/ruff-pre-commit: v0.1.11 → v0.1.13](https://github.com/astral-sh/ruff-pre-commit/compare/v0.1.11...v0.1.13)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
---
 .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 20fce209d2..3ecec61480 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: "v0.1.11"
+    rev: "v0.1.13"
     hooks:
       - id: ruff
         args: [--fix, --show-fixes]

From 34bc51b3e8877de26f85d94ef0cd16326e48f462 Mon Sep 17 00:00:00 2001
From: AlessioBugetti <alessiobugetti98@gmail.com>
Date: Mon, 15 Jan 2024 22:29:48 +0100
Subject: [PATCH 102/109] Add else statement to if-elif chain for completeness
 and clarity.

---
 scripts/install_KLU_Sundials.py | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/scripts/install_KLU_Sundials.py b/scripts/install_KLU_Sundials.py
index 4e66544b43..785071335b 100755
--- a/scripts/install_KLU_Sundials.py
+++ b/scripts/install_KLU_Sundials.py
@@ -114,6 +114,8 @@ def install_sundials(download_dir, install_dir):
             OpenMP_C_FLAGS = "-Xpreprocessor -fopenmp -I/usr/local/opt/libomp/include"
             OpenMP_C_LIB_NAMES = "omp"
             OpenMP_omp_LIBRARY = "/usr/local/opt/libomp/lib/libomp.dylib"
+        else:
+            raise NotImplementedError("Unsupported processor architecture.")
 
         cmake_args += [
             "-DOpenMP_C_FLAGS=" + OpenMP_C_FLAGS,
@@ -182,6 +184,10 @@ def check_libraries_installed(install_dir):
         suitesparse_files = [file + ".so" for file in suitesparse_files]
     elif platform.system() == "Darwin":
         suitesparse_files = [file + ".dylib" for file in suitesparse_files]
+    else:
+        raise NotImplementedError(
+            f"Unsupported operating system: {platform.system()}. This script currently supports only Linux and macOS."
+        )
 
     suitesparse_lib_found = True
     # Check for SuiteSparse libraries in each directory
@@ -325,3 +331,6 @@ def parallel_download(urls, download_dir):
         # Only SuiteSparse is missing, download and install it
         parallel_download([(SUITESPARSE_URL, SUITESPARSE_CHECKSUM)], download_dir)
         install_suitesparse(download_dir)
+    else:
+        # Both libraries are found and no force installation is requested
+        print("Both SUNDIALS and SuiteSparse libraries are already installed.")

From 3e57733aeee3032e8d6248182c7dae6d1e2a28cf Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Mon, 15 Jan 2024 16:31:47 -0500
Subject: [PATCH 103/109] Bump the actions group with 1 update (#3729)

Bumps the actions group with 1 update: [lycheeverse/lychee-action](https://github.com/lycheeverse/lychee-action).


Updates `lycheeverse/lychee-action` from 1.9.0 to 1.9.1
- [Release notes](https://github.com/lycheeverse/lychee-action/releases)
- [Commits](https://github.com/lycheeverse/lychee-action/compare/v1.9.0...v1.9.1)

---
updated-dependencies:
- dependency-name: lycheeverse/lychee-action
  dependency-type: direct:production
  update-type: version-update:semver-patch
  dependency-group: actions
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 .github/workflows/lychee_url_checker.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/lychee_url_checker.yml b/.github/workflows/lychee_url_checker.yml
index 1ce20decd9..f94ecb7876 100644
--- a/.github/workflows/lychee_url_checker.yml
+++ b/.github/workflows/lychee_url_checker.yml
@@ -28,7 +28,7 @@ jobs:
 
       # use stable version for now to avoid breaking changes
       - name: Lychee URL checker
-        uses: lycheeverse/lychee-action@v1.9.0
+        uses: lycheeverse/lychee-action@v1.9.1
         with:
           # arguments with file types to check
           args: >-

From 63a4ed3e7be0fc9039af2b6f0001c0ffdf70bb5e Mon Sep 17 00:00:00 2001
From: AlessioBugetti <alessiobugetti98@gmail.com>
Date: Mon, 15 Jan 2024 22:50:22 +0100
Subject: [PATCH 104/109] Remove redundant checks for SUNDIALS and SuiteSparse

---
 scripts/install_KLU_Sundials.py | 18 ++++++++----------
 1 file changed, 8 insertions(+), 10 deletions(-)

diff --git a/scripts/install_KLU_Sundials.py b/scripts/install_KLU_Sundials.py
index 785071335b..92dd6cce7d 100755
--- a/scripts/install_KLU_Sundials.py
+++ b/scripts/install_KLU_Sundials.py
@@ -323,14 +323,12 @@ def parallel_download(urls, download_dir):
         )
         install_suitesparse(download_dir)
         install_sundials(download_dir, install_dir)
-    elif not sundials_found and suitesparse_found:
-        # Only SUNDIALS is missing, download and install it
-        parallel_download([(SUNDIALS_URL, SUNDIALS_CHECKSUM)], download_dir)
-        install_sundials(download_dir, install_dir)
-    elif sundials_found and not suitesparse_found:
-        # Only SuiteSparse is missing, download and install it
-        parallel_download([(SUITESPARSE_URL, SUITESPARSE_CHECKSUM)], download_dir)
-        install_suitesparse(download_dir)
     else:
-        # Both libraries are found and no force installation is requested
-        print("Both SUNDIALS and SuiteSparse libraries are already installed.")
+        if not sundials_found:
+            # Only SUNDIALS is missing, download and install it
+            parallel_download([(SUNDIALS_URL, SUNDIALS_CHECKSUM)], download_dir)
+            install_sundials(download_dir, install_dir)
+        if not suitesparse_found:
+            # Only SuiteSparse is missing, download and install it
+            parallel_download([(SUITESPARSE_URL, SUITESPARSE_CHECKSUM)], download_dir)
+            install_suitesparse(download_dir)

From 59eb517eadc72619b2b3631c95ba47374d974561 Mon Sep 17 00:00:00 2001
From: AlessioBugetti <alessiobugetti98@gmail.com>
Date: Tue, 16 Jan 2024 09:44:34 +0100
Subject: [PATCH 105/109] Add more helpful message

---
 scripts/install_KLU_Sundials.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/scripts/install_KLU_Sundials.py b/scripts/install_KLU_Sundials.py
index 92dd6cce7d..546d7a313c 100755
--- a/scripts/install_KLU_Sundials.py
+++ b/scripts/install_KLU_Sundials.py
@@ -115,7 +115,9 @@ def install_sundials(download_dir, install_dir):
             OpenMP_C_LIB_NAMES = "omp"
             OpenMP_omp_LIBRARY = "/usr/local/opt/libomp/lib/libomp.dylib"
         else:
-            raise NotImplementedError("Unsupported processor architecture.")
+            raise NotImplementedError(
+                f"Unsupported processor architecture: {platform.processor()}. Only 'arm' and 'i386' architectures are supported."
+            )
 
         cmake_args += [
             "-DOpenMP_C_FLAGS=" + OpenMP_C_FLAGS,

From c2634b4fafcb39c7bf2f605fc2d8f36b085cc661 Mon Sep 17 00:00:00 2001
From: "allcontributors[bot]"
 <46447321+allcontributors[bot]@users.noreply.github.com>
Date: Tue, 16 Jan 2024 11:28:38 -0500
Subject: [PATCH 106/109] docs: add AlessioBugetti as a contributor for code,
 doc, and test (#3730)

* docs: update README.md [skip ci]

* docs: update .all-contributorsrc [skip ci]

---------

Co-authored-by: allcontributors[bot] <46447321+allcontributors[bot]@users.noreply.github.com>
---
 .all-contributorsrc | 5 ++++-
 README.md           | 2 +-
 2 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/.all-contributorsrc b/.all-contributorsrc
index 7d05f65d0f..95595e033b 100644
--- a/.all-contributorsrc
+++ b/.all-contributorsrc
@@ -800,7 +800,10 @@
       "avatar_url": "https://avatars.githubusercontent.com/u/38499721?v=4",
       "profile": "https://github.com/AlessioBugetti",
       "contributions": [
-        "infra"
+        "infra",
+        "code",
+        "doc",
+        "test"
       ]
     }
   ],
diff --git a/README.md b/README.md
index 02be55daf8..86c35d9c2f 100644
--- a/README.md
+++ b/README.md
@@ -279,7 +279,7 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/prady0t"><img src="https://avatars.githubusercontent.com/u/99216956?v=4?s=100" width="100px;" alt="Pradyot Ranjan"/><br /><sub><b>Pradyot Ranjan</b></sub></a><br /><a href="#infra-prady0t" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/XuboGU"><img src="https://avatars.githubusercontent.com/u/53944452?v=4?s=100" width="100px;" alt="XuboGU"/><br /><sub><b>XuboGU</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/commits?author=XuboGU" title="Code">💻</a> <a href="https://github.com/pybamm-team/PyBaMM/issues?q=author%3AXuboGU" title="Bug reports">🐛</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/cringeyburger"><img src="https://avatars.githubusercontent.com/u/121183876?v=4?s=100" width="100px;" alt="Ankit Meda"/><br /><sub><b>Ankit Meda</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/commits?author=cringeyburger" title="Code">💻</a></td>
-      <td align="center" valign="top" width="14.28%"><a href="https://github.com/AlessioBugetti"><img src="https://avatars.githubusercontent.com/u/38499721?v=4?s=100" width="100px;" alt="Alessio Bugetti"/><br /><sub><b>Alessio Bugetti</b></sub></a><br /><a href="#infra-AlessioBugetti" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/AlessioBugetti"><img src="https://avatars.githubusercontent.com/u/38499721?v=4?s=100" width="100px;" alt="Alessio Bugetti"/><br /><sub><b>Alessio Bugetti</b></sub></a><br /><a href="#infra-AlessioBugetti" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a> <a href="https://github.com/pybamm-team/PyBaMM/commits?author=AlessioBugetti" title="Code">💻</a> <a href="https://github.com/pybamm-team/PyBaMM/commits?author=AlessioBugetti" title="Documentation">📖</a> <a href="https://github.com/pybamm-team/PyBaMM/commits?author=AlessioBugetti" title="Tests">⚠️</a></td>
     </tr>
   </tbody>
 </table>

From 5900ada32b4a9b54daa93887f921acff5a12211d Mon Sep 17 00:00:00 2001
From: Robert Timms <43040151+rtimms@users.noreply.github.com>
Date: Tue, 16 Jan 2024 17:00:50 +0000
Subject: [PATCH 107/109] #3611 use actual cell volume for average total
 heating (#3707)

* #3611 use actual cell volume for average total heating

* #3611 changelog

* #3611 account for number of electrode pairs

* #3611 update variable names
---
 CHANGELOG.md                                  |   7 +-
 .../notebooks/models/jelly-roll-model.ipynb   |  19 +-
 .../notebooks/models/pouch-cell-model.ipynb   |  31 +-
 .../notebooks/models/thermal-models.ipynb     | 950 +++++++++---------
 examples/scripts/thermal_lithium_ion.py       |  34 +-
 .../full_battery_models/base_battery_model.py |  19 -
 .../models/submodels/thermal/base_thermal.py  |  52 +-
 pybamm/parameters/geometric_parameters.py     |  15 +-
 8 files changed, 606 insertions(+), 521 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 00ad65f9a0..0692d152ca 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,8 +1,13 @@
 # [Unreleased](https://github.com/pybamm-team/PyBaMM/)
 
-## Bug Fixes
+## Bug fixes
 
 - Fixed a bug where if the first step(s) in a cycle are skipped then the cycle solution started from the model's initial conditions instead of from the last state of the previous cycle ([#3708](https://github.com/pybamm-team/PyBaMM/pull/3708))
+- Fixed a bug where the lumped thermal model conflates cell volume with electrode volume  ([#3707](https://github.com/pybamm-team/PyBaMM/pull/3707))
+
+## Breaking changes
+- The parameters `GeometricParameters.A_cooling` and `GeometricParameters.V_cell` are now automatically computed from the electrode heights, widths and thicknesses if the "cell geometry" option is "pouch" and from the parameters "Cell cooling surface area [m2]" and "Cell volume [m3]", respectively, otherwise. When using the lumped thermal model we recommend using the "arbitrary" cell geometry and specifying the parameters "Cell cooling surface area [m2]", "Cell volume [m3]" and "Total heat transfer coefficient [W.m-2.K-1]" directly. ([#3707](https://github.com/pybamm-team/PyBaMM/pull/3707))
+
 # [v24.1rc0](https://github.com/pybamm-team/PyBaMM/tree/v24.1rc0) - 2024-01-31
 
 ## Features
diff --git a/docs/source/examples/notebooks/models/jelly-roll-model.ipynb b/docs/source/examples/notebooks/models/jelly-roll-model.ipynb
index 43e65fbe7d..86dd684b64 100644
--- a/docs/source/examples/notebooks/models/jelly-roll-model.ipynb
+++ b/docs/source/examples/notebooks/models/jelly-roll-model.ipynb
@@ -46,10 +46,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\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;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n",
+      "\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;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.2\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"
      ]
@@ -154,7 +152,7 @@
     {
      "data": {
       "text/plain": [
-       "<pybamm.models.base_model.BaseModel at 0x7f98ac190a90>"
+       "<pybamm.models.base_model.BaseModel at 0x280b0ed90>"
       ]
      },
      "execution_count": 4,
@@ -356,7 +354,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArcAAAINCAYAAAAkzFdkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACCNklEQVR4nOzdeZxN9R/H8de9s5oZM/Z97LuRsWfLWqJkKQkhqX6KEpUlIiraFFmSvZQQkiJikGzZ92WsWWfsM2Zf7vn9cdyJbDNjZu6d8X4+HvfR3HPP8rkn6e3rc75fi2EYBiIiIiIiWYDV0QWIiIiIiKQVhVsRERERyTIUbkVEREQky1C4FREREZEsQ+FWRERERLIMhVsRERERyTIUbkVEREQky1C4FREREZEsw9XRBTiazWbj7NmzZM+eHYvF4uhyREREROQ/DMPg2rVrFCpUCKv17mOzD3y4PXv2LP7+/o4uQ0RERETu4dSpUxQpUuSu+zzw4TZ79uyAebN8fX0dXI2IiIiI/Fd4eDj+/v5Jue1uHvhwa29F8PX1VbgVERERcWLJaSHVA2UiIiIikmUo3IqIiIhIlqFwKyIiIiJZxgPfc5schmGQkJBAYmKio0txGm5ubri4uDi6DBEREZGbKNzeQ1xcHOfOnSMqKsrRpTgVi8VCkSJF8PHxcXQpIiIiIkkUbu/CZrNx/PhxXFxcKFSoEO7u7lroAXMk+8KFC5w+fZoyZcpoBFdERESchsLtXcTFxWGz2fD398fLy8vR5TiVvHnzcuLECeLj4xVuRURExGnogbJkuNcybw8ijWCLiIiIM1JqExEREZEsQ+FWRERERLIMhVsRERERyTIUbh8gFSpUYOrUqY4uQ0RERCTdaLaEB0R0dDSHDx+mSpUqd9wnMDCQhISEW7b/8ccfFCpUKD3LExEREUkTCrcPiL1792IYBgEBAXfcZ+fOnRlXkIiIiEg6UFtCFrdz506aNGlC/fr1sdlsFC1alDFjxji6LBEREZF0oZHbFDIMcNRKvF5ekJLpZY8ePUrDhg155513yJ07NzabjZo1a9K3b18aNWpEYGBgutUqIiIi4ghONXK7du1aWrVqRaFChbBYLCxatOiex6xZs4Zq1arh4eFB6dKlmTlzZrrWGBUFPj6OeaU0VPfs2ZN27doxZMgQTp48Sb169ejfvz++vr789ddf6XODREREJMtb8M47/Dl2rKPLuC2nCreRkZFUqVKFCRMmJGv/48eP88QTT9C4cWN27tzJm2++yUsvvcTy5cvTuVLnFxISwqpVq+jZsyeJiYns2bOHwMBArFYrLi4uuLu7O7pEERERyYTCTp6k5+jRNHrzTZa8/76jy7mFU7UltGjRghYtWiR7/0mTJlGiRAlGjx4NmFNdrVu3ji+//JLmzZunS41eXhARkS6nTta1k2vTpk3YbDYCAwM5dOgQ0dHRBAYGcuLECa5cuULdunXTr1ARERHJsj7u0IGLhkF5d3eaDxzo6HJu4VThNqU2btxIs2bNbtrWvHlz3nzzzTseExsbS2xsbNL78PDwFF3TYgFv7xQd4hBxcXEAxMTEsGPHDooVK0auXLn49NNPCQgIoHLlyg6uUERERDKbk5s2MWbTJgA+efttXD09HVzRrTJ1uA0JCSF//vw3bcufPz/h4eFER0eTLVu2W44ZNWoUw4cPz6gSHaZOnTq4uroyYsQIIiIiKFmyJOPHj2fcuHGsXbvW0eWJiIhIJjSkc2digEf8/Gj1wQeOLue2MnW4TY1BgwbRr1+/pPfh4eH4+/s7sKL04e/vz/Tp0xkwYADnzp3D1dWVqKgoli1bRvXq1R1dnoiIiGQysz74gFnHjgHw+dixWKxO9ehWkkwdbgsUKEBoaOhN20JDQ/H19b3tqC2Ah4cHHh4eGVGew3Xp0oUuXbqQK1cuZs6cyVNPPeXokkRERCQTMmw23hwxAoDauXNTs1s3B1d0Z84ZuZOpTp06BAUF3bRtxYoV1KlTx0EVOZ/Tp09z5cqVu65MJiIiInI3CUuWYCQkANBn6FAHV3N3ThVuIyIi2LlzZ9IysMePH2fnzp2cPHkSMFsKunbtmrR/z549OXbsGP379+fgwYNMnDiRefPm0bdvX0eU75T27NmDt7c3JUqUcHQpIiIikhklJOAycCCR19/WcfK/CXaqcLt161aqVq1K1apVAejXrx9Vq1Zl6PU/IZw7dy4p6AKUKFGCJUuWsGLFCqpUqcLo0aOZOnVquk0Dlhm1aNGCiIgILClZ2kxERETEbuZMQvbvJw5wcXGhSJEijq7orpyq57ZRo0YYhnHHz2+3+lijRo3YsWNHOlYlIiIi8oCKjIShQzlx/W2RIkVwdXWq+HgLpxq5FREREREnMno0nDvH8bx5ATJFm6PCrYiIiIjcKiQEPv0UgBNNmgBQvHhxBxaUPAq3IiIiInKrYcPMtoTatTl+fXlWjdyKiIiISOazfz9MnWr+PHo0J/75B9DIrYiIiIhkRv37g80GbdtCvXocP34c0MitiIiIiGQ2q1fDkiXg6goff0xiYmLSVKzFihVzcHH3pnArIiIiIqbERHj7bfPnnj2hbFlOnz5NQkIC7u7uFC5c2LH1JYPCrYiIiIiYZsyA7dvB1xeuL6J17NgxwOy3dXFxcWR1yaJwKyIiIiJw9SoMGmT+PHw4XJ/b1h5uM0O/LSjcym3MnDnztqvBiYiISBY2bBhcvAgVK0KvXkmb7eG2ZMmSjqosRRRuHyAVKlRgqn1aDxERERG7vXthwgTz57Fjwc0t6SOFW3FK0dHRHD58mCpVqji6FBEREXEmhgF9+pgPk7VrB82a3fRxZgu3ro4uQDLG3r17MQyDgICA234eFxdHrVq1ALh8+TIAY8aMAWDz5s24u7tnSJ0iIiKSwRYsgFWrwNMTRo++5WOFW3EqO3fupF+/fqxfvx6bzUbRokUZPHgwb7755k37ubu7s3PnToCkftsXXnghQ2sVERGRDBYVBW+9Zf48YAD8ZwWy8PBwLl68CGSeB8oUbrOwo0eP0rBhQ9555x1y586NzWajZs2a9O3bl0aNGhEYGOjoEkVERMSRPvkETp6EokXNVcn+w74yWe7cufHz88vo6lJF4TalDMP8U44jeHmBxZLs3Xv27Em7du0YMmQItWvXpkOHDrz55puMGjWKv/76S+FWRETkQXbkiBluwWxH8PK6ZZejR48CmaclARRuUy4qCnx8HHPtiAjw9k7WriEhIaxatYoNGzaQmJjInj17GDVqFFarFRcXl7v20KodQUREJIszDHj1VYiNhUcfhaefvu1uR44cAaB06dIZWd190WwJWdSmTZuw2WwEBgZy6NAhoqOjCQwM5MSJE1y5coW6des6ukQRERFxlNmzYeVK8yGyr7++498M20duM1O41chtSnl5mSOojrp2MsXFxQEQExPDjh07KFasGLly5eLTTz8lICCAypUrp1eVIiIi4swuX4a+fc2f33sPSpW6466ZceRW4TalLJZktwY4Up06dXB1dWXEiBFERERQsmRJxo8fz7hx41i7dq2jyxMRERFHGTgQLlwwVyJ7++277mofuS11lwDsbBRusyh/f3+mT5/OgAEDOHfuHK6urkRFRbFs2TKqV6/u6PJERETEEdatgylTzJ+/+Qbu8gxObGwsJ0+eBDLXyK16brOwLl26cPbsWXLmzMmCBQvYtGkTDRo0cHRZIiIi4ghxcfC//5k/v/QS1K9/192PHz+OYRj4+PiQL1++DCgwbSjcZnGnT5/mypUrd1yZTERERB4Qn38O+/dD3rz/TgF2Fzc+TGZJwVSkjqZwm8Xt2bMHb2/vTLOqiIiIiKSDw4fhgw/Mn7/8EnLluuch9ofJMlO/LajnNstr0aIFEY6a3UFEREQcz2aDF1+EmBho1gw6dUrWYYcPHwYyV78taORWREREJGsbP958kMzHx3yYLJktBsHBwQCUK1cuPatLcwq3IiIiIlnVkSPm1F8An30GxYsn+1B7uC1Tpkw6FJZ+FG5FREREsiKbDXr0gOhoaNIEXnkl2YfGxMQkTQNWtmzZ9KowXSjcioiIiGRFEyfC2rXm4lNTp4I1+bHv6NGjGIaBn58fefPmTcci057CrYiIiEhWc+wYDBhg/vzpp5DCWZPsLQlly5bNVNOAgcKtiIiISNZib0eIioJGjaBnzxSfIrP224LCrYiIiEjW8vXXsGaN2Y4wbVqK2hHs7NOAZbZ+W1C4FREREck6jh//tx3hk0+gZMlUnebQoUOARm5FRERExFESE6FrV4iMhIYN4dVXU30qe7itUKFCWlWXYRRuRURERLKCjz82F2vInh1mzEhVOwLApUuXuHDhAqC2BBERERFxgP1z57Ju6FDzzfjxKZ4d4Ub2UVt/f3+8vb3TorwMpXArIiIikont27yZwOeeo6HNxrrGjaFLl/s638GDBwEoX758WpSX4RRuHyAVKlRg6tSpji5DRERE0tCEF14gHrABy6pWhfucl1bhVjKF6OhoDh8+TJUqVRxdioiIiKSRXwcP5usDB5LeT5wxg7CwsPs6p8KtZAp79+7FMAwCAgIcXYqIiIikgbULFvDCyJEA9KlWjfLly3PlyhUmTpx4X+dVuBWntnPnTpo0aUL9+vWx2WwULVqUMWPGOLosERERuQ+2uDjaPvcclwF/Nzc+Wb2aQYMGATBmzBiio6NTdd7Y2FiOHTsGQLly5dKq3Azl6ugCMhvDMIiKinLItb28vFK0vvPRo0dp2LAh77zzDrlz58Zms1GzZk369u1Lo0aNCAwMTL9iRUREJN1EDh1KeEICAGO++AIPX186duzI0KFD+eeff5g5cyavpmKe2+DgYBITE/Hz86NQoUJpXXaG0MhtCkVFReHj4+OQV0pDdc+ePWnXrh1Dhgzh5MmT1KtXj/79++Pr68tff/2VTndIRERE0tWqVRz95BMSAG8PD1pfD7Fubm689dZbAHz11VcYhpHiU+/fvx+AihUrpmhAzZko3GZRISEhrFq1ip49e5KYmMiePXsIDAzEarXi4uKCu7u7o0sUERGRlDp/Hjp3Zv31t/UaNsTFxSXp4xdeeIHs2bNz8OBBVq5cmeLT79u3DzDDbWaltoQU8vLyIiIiwmHXTq5NmzZhs9kIDAzk0KFDREdHExgYyIkTJ7hy5Qp169ZNx0pFREQkzdls5vK6ISGs9/WF8HDq1at30y7Zs2fnhRdeYNy4cYwbN45HH300RZewj9xWqlQpzcrOaAq3KWSxWDLFah1xcXEAxMTEsGPHDooVK0auXLn49NNPCQgIoHLlyg6uUERERFLk889h+XLw9GS9t/dtwy1Ar169GDduHEuWLOHMmTMULlw42Ze4sS0hs1JbQhZVp04dXF1dGTFiBGvXrqVkyZKMHz+ecePGMXPmTEeXJyIiIimxaRMMHgzAqfff5+S5c7i4uFC7du1bdi1XrlzSLEnffvttsi8RFxfH4cOHgcwdbjVym0X5+/szffp0BgwYwLlz53B1dSUqKoply5ZRvXp1R5cnIiIiyXXxIjz7LCQkwHPP8ef1WQyqV6+Oj4/PbQ/p0aMH69atY/r06QwaNChZD4cdPnyYhIQEsmfPTpEiRdL0K2QkjdxmYV26dOHs2bPkzJmTBQsWsGnTJho0aODoskRERCS5EhOhUyc4dQrKlIFvvuHPtWsBeOSRR+54WPv27fHx8eHo0aNs2LAhWZfau3cvkLlnSgCF2yzv9OnTXLlyRSuTiYiIZEbDh8OKFeDlBQsXgq8va6+H24YNG97xMG9vb9q1awfADz/8kKxL2cNtZn8uR+E2i9uzZw/e3t6UKFHC0aWIiIhISixZAh98YP48eTIEBHDu3DmCg4OxWCy3fZjsRp07dwZg3rx5xMfH3/Nye/bsARRuxcm1aNGCiIiITP3XCyIiIg+c48fh+efNn3v1gutB9c8//wTgoYceImfOnHc9RZMmTcifPz+XLl3ijz/+uOcl7SO3mf1vexVuRURERJxJdDQ8/TRcvQq1a8MXXyR9tHr1agAaN258z9O4urrSvn17AObPn3/XfSMjIzl27BigkVsRERERSSuGYY7U7tgBefLATz/BDauK2sNtkyZNknW6Z555BoBffvnlrq0J+/fvxzAM8uXLR968ee/jCziewq2IiIiIs5gwAWbMAKsVfvwR/P2TPjp9+jSHDx/GarXedaaEG9WvX598+fJx5cqVpGB8O1ml3xYUbpPFMAxHl+B0dE9ERETS2Jo18Oab5s+ffgrNmt30sT2cVq9eHT8/v2Sd0sXFhbZt2wKwcOHCO+63e/duQOE2y3NzcwMgKirKwZU4H/vyvi4uLg6uREREJAs4cQLatzfntX3+eejX75ZdgoKCgOT1296odevWAPz22293HJzatWsXAFWqVEnRuZ2RVii7CxcXF3LkyMH58+cB8PLy0qwDgM1m48KFC3h5eeHqql9CIiIi9yUyEtq0MVciq17dnPbrP3nDMAxWrlwJQLP/jOjeS+PGjfH29ubMmTPs2LGDatWq3XJuhdsHSIECBQCSAq6YrFYrRYsWVdgXERG5H4YBPXrArl2QLx/8/DNky3bLbocOHeLMmTN4eHhQv379FF3C09OTRx99lEWLFvHbb7/dEm5PnTrFlStXcHV1pWLFivf1dZyBwu09WCwWChYsSL58+ZI1AfKDwt3dHatVXS0iIiL35ZNPYO5ccHWF+fNveoDsRitWrADMB8Sy3Sb83suTTz7JokWL+PXXXxk6dOhNn9lHbStUqICHh0eKz+1sFG6TycXFRf2lIiIiknYWL4Z33zV/HjcOGjS44672loRHH300VZdq2bIlANu2bePixYvkyZMn6bOs1JIAeqBMREREJOPt2gWdOpltCT17mq87iIuLY9WqVUDqw23BggWpXLkyhmEkjQLb7dy5E1C4FREREZHUCAmBVq3MB8maNoWvvrrr7hs2bCAiIoJ8+fIRGBiY6ss2b94cgOXLl9+0fceOHQD3dW5nonArIiIiklGio82ZEU6dgrJlzRXIrk89eifLli0DzHB6P8+72MPtH3/8kTQl2JUrV5KW3f3vg2aZlcKtiIiISEYwDHjxRfj7b8iZE377zfznPdjD7eOPP35fl7c/jHbu3DkOHDgA/NuSULx4cXLlypWs8xgGhIbChg3mz85GD5SJiIiIZIQRI2DOHHNmhIULoUyZex5y5swZdu3ahcViSXW/rZ2npyf169dnxYoVrFq1iooVK7J9+3bg1lHbuDj45x84etR8HTv27z+PHTM7KgDOnYPrs6Y6DYVbERERkfT2/ffw/vvmz19/DY0aJeuwpUuXAlCrVi3y5s1732U0adIkKdz27t07KdwWK1adGTNg/XrzFRwMNtudz2OxmLOWXbyocCsiIiLyYFm1ymxHAHj7bXjppWQfumTJEgCeeOKJNCnFvnRvUNAqunQZy7x5iwH48stbZ0rw8oKSJaFUqVv/WawYOOuUuAq3IiIiIull715o2xbi4+HZZ81FG5IpJiYmadquJ5988r5LuXwZdu+uisXiQnh4GN9//2bSZ66uVh5+GOrVM181apgjsplxIVKFWxEREZH0cOYMtGgB4eFQvz58+y2kYLaDNWvWEBUVRaFChVI9TVd8vLmi7/TpEBQECQnuQB4gFFfXUgQGtiAgICfjxjXDxydVl3A6CrciIiIiaSz8zBm8W7bE5fRpKFcOfvkFPD1TdI7Fi82WgSeffBJLCodQz5yByZNhyhTzoS+7hx6CypWn06pVXjp0qJmic2YWCrciIiIiaSghOppSJUqQLT6eZblyUXHpUkjmNFt2hmEkhdvWrVsn+7hNm2D0aHO0NjHR3JY/P7z8MnTpYk6tCy1TVEtm43Tz3E6YMIHixYvj6elJ7dq12bx58133HzNmDOXKlSNbtmz4+/vTt29fYmJiMqhaERERkX8ZNhsdK1bkYnw8p4AqYWG8O3Uq0dHRKTrPtm3bOHPmDN7e3jRp0uTu1zRg5UpzsbM6dWD+fDPYNmhgzjx28iR88IE92GZ9ThVu586dS79+/Rg2bBjbt2+nSpUqNG/enPPnz992/9mzZzNw4ECGDRvGgQMHmDZtGnPnzuXdd9/N4MpFREREYFTz5sw/cQKAKsWLk5CYyKhRo6hevTq7du1K9nl++eUXwFy4wfMO7QyGYXY71K4Njz5qTsrg6govvAC7d8PatdChA7i73++3ylycKtx+8cUXvPzyy3Tv3p2KFSsyadIkvLy8mD59+m3337BhA/Xq1aNTp04UL16cxx57jI4dO95ztFdEREQkrbWtVo3BK1cC8FX79uw4doxFixZRsGBBDhw4QN26dVmwYEGyzmXfr02bNrf9fOVKM9S2aQNbtkC2bPD66+ZCCzNmQOXKafGNMienCbdxcXFs27aNZs2aJW2zWq00a9aMjRs33vaYunXrsm3btqQwe+zYMZYuXUrLlnfuJYmNjSU8PPyml4iIiMj9ODd9Or/s2AFAu7JleX3ePCwWC61bt2b37t089thjREVF0b59e77++uu7nuvAgQMcOHAANze3W6YA+/tvs/3g0UfNUOvtDYMGwYkT8NVXULRoen3DzMNpwu3FixdJTEwkf/78N23Pnz8/ISEhtz2mU6dOjBgxgvr16+Pm5kapUqVo1KjRXdsSRo0ahZ+fX9LL398/Tb+HiIiIPGD+/JN1//sfBuDt6sqPu3ff9HGePHlYsmQJr732GoZh8NprrzF+/Pg7nm7hwoUANG3alBw5cgBw4IA5Svvww2b7gbs7vPGGOVI7ciTky5dO3y0Tcppwmxpr1qxh5MiRTJw4ke3bt7Nw4UKWLFnCBx98cMdjBg0aRFhYWNLr1KlTGVixiIiIZCk7dsBTTzEtIQGAPm+/jfttlu5ydXVl/PjxDBw4EIDXX3+d77///rannD9/PgBPP/00ly+bIbZyZbO/1mqF7t3N5XHHjjVnQpCbOc1UYHny5MHFxYXQ0NCbtoeGhlLgDosWv/fee3Tp0oWXri9jV7lyZSIjI3nllVcYPHgw1ttMlOzh4YGHs64XJyIiIpnHkSPw+OP8Ex7OH9c3vXiXpXUtFgsjR44kJiaGMWPG8OKLL1K0aFEeeeSRpH2Cg4PZuXMnLi4uXLrUhjJlzJXFAFq3hlGjoEKFdPxOWYDTjNy6u7tTvXp1goKCkrbZbDaCgoKoU6fObY+Jioq6JcC6uLgA5vxwIiIiIuni3Dlo3hzOn2dKvnwYmG0EpUqVuuthFouF0aNH8/TTTxMfH88zzzzDmTNnkj6fN28eANmyNWPgwDxcvgwBAeYDZIsWKdgmh9OEW4B+/foxZcoUvv32Ww4cOMCrr75KZGQk3bt3B6Br164MGjQoaf9WrVrx9ddfM2fOHI4fP86KFSt47733aNWqVVLIFREREUlTV6/C44/DsWPElSzJ1OsDaq+++mqyDrdarXz33XdUqVKFCxcu0KFDBxISEti69SQjRnwGQETEM+TODRMnmp0PTZum15fJepymLQGgQ4cOXLhwgaFDhxISEkJgYCDLli1Lesjs5MmTN43UDhkyBIvFwpAhQzhz5gx58+alVatWfPTRR476CiIiIpKVRUfDU0+ZE8kWKMCiN98k9I03KFCgAE899VSyT+Pl5cX8+fOpXr0669evp3LlJzh4cC1gLkT15JOF+e47yJkznb5HFmYxHvC/vw8PD8fPz4+wsDB8fX0dXY6IiIg4q4QEePppWLwY/Pzgzz9p+MYbrF27liFDhtz1gfY7GTJkCh999ErSey+vGrz44tOMGzcwLSvP9FKS15xq5FZERETEKSUmQrduZrD19IRff2W3xcLatWtxdXWlZ8+eKTpddDQMGwaff/4S8AFwmsaN+/DHH1/g6mpJl6/woFC4FREREbkbmw1eeQVmzzbXt/3pJ2jQgHEvvwxAu3btKFy4cLJPt24dvPgiHD4MYOGJJ1bSt280TZtWSZ/6HzAKtyIiIiJ3YhjmRLPTp5uTzP74Izz5JBcvXkyap/b1119P1qkiIuDdd2H8ePO0hQrBpEnQqlXZ9PwGDxyFWxEREZHbMQzo3x8mTACLBb79Fp55BoApU6YQExND9erVqVev3j1PtXIlvPyyuUwuQI8e8PnncH0BMklDCrciIiIit2M2xZo/T54Mzz8PQFxcXNLyuX369MFiuXOPbFgYvP02TJ1qvi9WDKZMgUcfTdfKH2hONc+tiIiIiFMYNQrssx989RXcsPLYjz/+yNmzZylUqBAdOnS44yl+/x0qVfo32PbuDXv3KtimN43cioiIiNxozBizORbgk0/ghp5awzD4/Ppobp8+fXB3d7/l8KgoeOcdcwEGgDJlYNo0aNAgvQsXULgVERER+dc330DfvubP779v9tze4Pfff2fv3r34+Pjwyiuv3HL4tm3QuTMcOmS+f/NNGDkSsmVL37LlX2pLEBEREQHzgTH7fLUDBsDQobfs8vHHHwPQs2dPctzwNFhiotnJ8PDDZrAtVAhWrIAvv1SwzWgauRURERGZO9ecfBbMqb9GjTJnSLjB+vXr+euvv3B3d6evfXQXcwaELl3M+WvBnFDhm28gV64Mql1uopFbERERebAtWmTOhGCzmfN1jRlzS7AFkpbX7datG4UKFcIw4Lvv4KGHzGCbPbs5+DtvnoKtI2nkVkRERB5cCxbAc89BQoIZcCdNum2w3bx5M8uXL8fFxYWBAwdy+bLZwfDTT+bn9erBrFlQokQG1y+30MitiIiIPJh++gk6dDCDbefOMGOGuQrZbYwYMQKAzp07c+xYSSpXNg93dYWPPoI//1SwdRYauRUREZEHz5w55khtYiJ07Wour+victtdN2/ezJIlS3BxccHDY0jSPLVly8IPP0CNGhlYt9yTRm5FRETkwfLDD+ZIbWIivPDCXYMtwLBhwwDImfN5pkwpA8D//gfbtyvYOiON3IqIiMiD47vvzEBrGOaqY998c8dWBIC//vqLZcuWAa5cvPgeOXKYK449/XRGFSwppXArIiIiD4YZM6BHDzPY/u9/5hJidwm2589H0Lx5++vvXqRu3VLMng3FimVMuZI6CrciIiKSpUVERDD86ae5/Mcf+AAhpUpx7Z9/sLVsiaenJ7ly5cLf35/y5ctTo0YNSpcuzcKFe3juuadISAgFoH37VsyebT5AJs5N/4pEREQkS5vRowef//HHvxuOHjVfd5AtWw6io68BiVgsuWnXrivz5j2Z/oVKmlC4FRERkSzJMAxmdOnCG/PmAVDC15dn/vc/ChcujK+vLy4uLkRHR3Px4kX++ecfdu7cw7Zt24mOvgqAn9/DbNz4KxUq5HHgt5CUUrgVERGRLOnJgACW7t8PQM8qVZiwbRvWO8yKsHcvtG8PNlsMFsunFC++kv37V+Lp6Z6RJUsa0FRgIiIikuX8/eabScH2iZIlmbh9+x2D7cyZUKsWHDwIhQt7snbtUI4dW6tgm0kp3IqIiEjWMmoUn4wdC0BxPz8WBwdjuc2sCFFR0L27+YqOhubNYccOqF8/owuWtKRwKyIiIlmDYcDAgfzx7rv8DLhYLPy2bt1tR2wPHDBHa2fONGcD+/BDWLoU8ubN8KoljannVkRERDI/mw169yb266/pfX1T7zfeoFJAwC27fv899OwJkZFQoAD8+CM0apSh1Uo6UrgVERGRzC0hAV58EWbN4jPgMFCgQAGGDx9+027R0fDGG+YKYwBNm5or8ebPn+EVSzpSW4KIiIhkXrGx8OyzMGsWR6xWPnRzA2D06NH4+fkl7RYcDA8/bAZbiwXefx+WL1ewzYoUbkVERCRzioyEp56Cn3/GcHPjtcqViY2Pp1mzZnTs2DFpt/nzoXp12L0b8uWDFStg2DC4w+QJkskp3IqIiEjmExZmTm/wxx/g5cX3ffuyYtcuPDw8mDhxIhaLhfh4eOstc/7aiAho2BB27jTbESTrUrgVERGRzOXCBWjSBNavBz8/zs+bR99p0wAYNmwYZcqU4dw5M8R+8YV5yIABsHIlFCzowLolQ+iBMhEREck8zp6FZs3Mubzy5oU//uCNjz/m0qVLVKlShbfffpu//jLbcENCwNcXvv0W2rRxdOGSUTRyKyIiIpnD0aPmCgsHDkCRIrB2LQuPHWPu3Lm4uLgwbdp0xo93o3FjM9gGBMCWLQq2DxqN3IqIiIjz27EDHn8czp+HkiUhKIiLPj68+uqrALz5Zn8+/bQa8+aZu3fqBJMng7e3A2sWh1C4FREREee2ejW0bg3XrkFgIPz+O0b+/PRs357z589TunQlfvttGIcOgasrfPkl9OplTvklDx6FWxEREXFeCxaYw7BxceYyYosWgZ8fP3z/PQsWLMDFxZUzZ74jOtqDwoXhp5+gTh1HFy2OpJ5bERERcU7ffGPO4xUXB+3awe+/g58f//zzD7169QIgMXEo0dHVaNwYtm9XsBWFWxEREXE2hgEjRkDPnubPr7wC8+aBpyeJiYl06NCF8PBw4GFgEAMGmNPd5svn6MLFGagtQURERJxHYiL06QMTJpjv33sPhg9PaqB9/PGu/P33X4APPj6zmDXLVbMhyE0UbkVERMQ5xMZC167mKK3FAl99Bb17A5CQkEjdui+xZctsAPLnH8LataUpW9aRBYszUrgVERERx7t2zeyrXbkS3Nxg1izo0AGAXbuO0KjRs1y9ugOA7NnLsn//m+TK5ciCxVmp51ZEREQcy76c7sqV4O1NwuLF/FmgAP3796d06UoEBpa5Hmw96dr1ey5e3EuuXB6OrlqclEZuRURExHFOnIDmzTGCg/nLz4/ZTZqwoEsXLl68+J8d3XjvvcmMGNHZEVVKJqJwKyIiIo6xfTsXWrRg+vnzTHF15WhYGPz8MwCenrmIiXkCaEHt2jX4/vt8lC7t59h6JVNQuBUREZEMN+WNN1g0cSJBiYnEAiQk4OPjQ8uWz7BvX0f27WsMuDFgAHz4obnymEhy6JeKiIiIZKhBLVvy8e+/J72vUbUqr73+OoULP0u3bt6EhICvL3z7LZrmS1JMD5SJiIhIhoiJjuajxo2Tgq2/lxdrVqxg87btXLnSnZYtzWAbEABbtijYSupo5FZERETSXWJMDFULFuRgWBgAfWrV4osNG4iMcqFDB/jpJ3O/Tp1g8mTw9nZgsZKpaeRWRERE0te1a3zx0ENJwfa1Ro0Y8/ffHAp2oVYtM9i6ucH48fD99wq2cn80cisiIiLp59w5NjdsyJDDhwHo1LAhE1avZt48ePFFiIyEwoXNgFunjoNrlSxBI7ciIiKSPvbvJ7RmTdodPkwc0LphQ6YvX0XfvubiY5GR0LgxbN+uYCtpRyO3IiIikvbWriX2qadoFxbGGaB8qVJ8OnkxzZpZWbfO3EXTfEl60C8nERERSVvff4/RvTu9EhLYAPj5+jJk+FIeecSX0FBN8yXpS+FWRERE0oZhwPDhMHw4Y4BpgNVqpf2zc+nWrSyJieY0XwsXQpkyDq5VsiyFWxEREbl/sbHQowf88ANLgbctFjAMAgI+Z+rUxwHo3Bm++UazIUj6UrgVERGR+3PpErRtC3/9xW4XF55zc8MWE4OfXw92734TNzf48kt47TWwWBxdrGR1CrciIiKSesHB8MQTcOQIZ7Nn50lPT65duIDV2oiwsIkULmzRNF+SoTQVmIiIiKTO2rVmaj1yhGtFi/JE4SKcunABKIfNtpDGjd01zZdkOIVbERERSblZs6BZM7h8mfiaNWntX4qdBw8AeYGlDBiQkz/+gHz5HF2oPGjUliAiIiLJZxjw/vswYoT59plnaHMtG6uXzwK88Pb+je+/L6lpvsRhFG5FREQkeWJizBkRZs8GwDZgIBUWbif48HzAStGic1mxohZlyzq2THmwKdyKiIjIvYWEmDMibNoErq5c+mQs5UfN4+LFPwGoUuVD1q9/UtN8icMp3IqIiMjd7dwJTz0Fp05BzpzMeWkwXQaMJCHhDGChQoXmbN8+EKue5BEnoF+GIiIicmeLFkG9enDqFEeKF6d+0Vp0/OxtEhLOYLHkZvz4VezbtxSrVRPYinPQyK2IiIjcyjDg44/h3Xc5AQwvWJBv/zmFYZwAwMXFj7lzf+Lppxs5sEiRWyncioiIyM1iYuDllwn9/ns+BL6xWok/d+76hy147rn+TJ9ej2zZ3BxZpchtKdyKiIjIv0JDiWjVitFbtvAZEAlgswHN8PL6gB9+eFjTfIlTU7gVERERABK3b+fbZs0YfOUKIUlbawGjCAhowoIFaJovcXp6oExEREQY1aULFWrUoMf1YOvnVRSYB2yiU6cmbNqkYCuZg0ZuRUREHmCGzUbX6tX5fudOALK7uJAj5zBOXRyAq6s7X34JvXqBRZMhSCahcCsiIvKAOnP4MK81asTis2cBKOOdkxDbDk5dLEahQjB/PtSp4+AiRVJIbQkiIiIPoODVqylbrhyLz57FBXihzLMcjrzMtehiNGoE27cr2ErmpHArIiLygIlctozujz1GlGFgARoUGsjMw3MB6N8fVqyA/PkdW6NIajlduJ0wYQLFixfH09OT2rVrs3nz5rvuf/XqVXr16kXBggXx8PCgbNmyLF26NIOqFRERyUQMg+ixY2ndsiUbEhLwsVgp5jOVNWdH4esLCxfCJ5+Aq5oWJRNzql++c+fOpV+/fkyaNInatWszZswYmjdvzqFDh8iXL98t+8fFxfHoo4+SL18+5s+fT+HChfnnn3/IkSNHxhcvIiLizOLiiHn1VdpNn04Q4GV1Jd72ByciGlOlitlfW7q0o4sUuX8WwzAMRxdhV7t2bWrWrMn48eMBsNls+Pv78/rrrzNw4MBb9p80aRKfffYZBw8exM0tdaukhIeH4+fnR1hYGL6+vvdVv4iIiFMKDSW2bVvabdzIUsDd4kacsQJoSI8eMG4cZMvm6CJF7iwlec1p2hLi4uLYtm0bzZo1S9pmtVpp1qwZGzduvO0xixcvpk6dOvTq1Yv8+fMTEBDAyJEjSUxMzKiyRUREnNu2bcRWr87T14OtCx7EGcvx9GzI9OkwdaqCrWQtTtOWcPHiRRITE8n/nw72/Pnzc/Dgwdsec+zYMVatWkXnzp1ZunQpR44c4bXXXiM+Pp5hw4bd9pjY2FhiY2OT3oeHh6fdlxAREXEms2cT++KLPB0byxLAggeJLKV06cbMnw9Vqji6QJG05zQjt6lhs9nIly8fkydPpnr16nTo0IHBgwczadKkOx4zatQo/Pz8kl7+/v4ZWLGIiEgGSEiA/v2J6dyZtteDLXhisIR27ZqwdauCrWRdThNu8+TJg4uLC6GhoTdtDw0NpUCBArc9pmDBgpQtWxYXF5ekbRUqVCAkJIS4uLjbHjNo0CDCwsKSXqdOnUq7LyEiIuJoFy/C448T9dlnPAX8DkA2rNYlfPFFU+bPBz8/x5Yokp6cJty6u7tTvXp1goKCkrbZbDaCgoKoc4dZpOvVq8eRI0ew2WxJ24KDgylYsCDu7u63PcbDwwNfX9+bXiIiIlnCtm1QvToRQUG0tFhZAYA3uXMvZe3aJvTtq2V0JetzmnAL0K9fP6ZMmcK3337LgQMHePXVV4mMjKR79+4AdO3alUGDBiXt/+qrr3L58mX69OlDcHAwS5YsYeTIkfTq1ctRX0FERMQxZs6EevW4cPIkjVw8+NOwAdmpXn05+/c3ol49RxcokjGc5oEygA4dOnDhwgWGDh1KSEgIgYGBLFu2LOkhs5MnT2K1/pvH/f39Wb58OX379uWhhx6icOHC9OnThwEDBjjqK4iIiGSsuDjo2xcmTmQ3UBsXYhJjgRy8+OJyJk+uxQ3deyJZnlPNc+sImudWREQyrbNnoX172LCBZUAb3IklDrDwwQe/M2RIc0dXKJImUpLXnGrkVkRERJJp3Tpo356wkBBaWFzYaCQCcbi7F2fatG94/vnHHF2hiEMo3IqIiGQmhgETJ2L06cO8xERet7hywUgAoFixluzevQBfX08HFyniOE71QJmIiIjcRXQ0dO/O3t69aZyYyHNwPdjm5vHH+3D8+G8KtvLA08itiIhIZnD8OOFt2/L+rl18BZgLzXuSL98gli9/h8BAraErAhq5FRERcXrGb78xu3Jlyu3axZfYg207nn32IMePD1WwFbmBwq2IiIizSkzk4P/+R9NWregcGUkI4EoJPDyW8d13C5g7txheXo4uUsS5qC1BRETECZ3ZtYsxrVox9tQp4gE3XEhkKGUqDGDBAg8qVHB0hSLOSSO3IiIiTmb6oEEUDwzk8+vBthBViecwL7w4lK1bFWxF7kYjtyIiIk4iMSGBrzt0oN/ChSQALkA+PuBKtsF8O8lC166OrlDE+SncioiIOIGwU6eoWqECxyMjAShNLs6ynpwVy7PyJ6hY0cEFimQSaksQERFxsMjNm3mqXLmkYFuROhzhPM++UJ7NmxVsRVJC4VZERMSBLn39NU3r1GFtdDTugD+vcTzbBmbMcGHGDPD2dnSFIpmL2hJEREQcISaGf3r0oPns2RwCsuOGlUX4VGjJ7z9BpUqOLlAkc1K4FRERyWgnTrC7ZUseP3CAc4AvvlxjPV26BjBxokZrRe6H2hJEREQy0q+/srpyZRpcD7Y+FCPOcx/TZwTw7bcKtiL3S+FWREQkI8TFwVtvMeepp2geEUE44EEtCpbZyZYtRXjhBUcXKJI1KNyKiIiktxMnMBo04LMvvqAjEA9YaMvTnf5k+/YcBAQ4ukCRrEPhVkREJD0tWkRiYCBvbN5M/+ubXF37MH3GT/zwgyc+Pg6tTiTL0QNlIiIi6SEuDvr3J3zsWJ4HfgXAQqFCo1m9ui9lyzq2PJGsSuFWREQkrR0/Dh06sH3LFhoCEQB40KLF9/z88zN4eDi2PJGsTG0JIiIiaWnhQqhalZ+3bKEB9mBrYfDg+SxdqmArkt40cisiIpIWYmPh7bcJHT+e5sCu65vd3YsyY8Z0OnVq6sjqRB4YCrciIiL36+hRjGefZfb27fQBLl3fXKnSc2ze/C1eXu6OrE7kgaK2BBERkfvx00/sq1KFxtu38zxmsLVa8/Piix+yd++PCrYiGUwjtyIiIqkRGUlE796MmDmTL4EEwIInZcq+R1DQWxQpouZaEUfQyK2IiEgKGTt3Mr9sWSrMnMlnmMHWamnF4CEHOHDgXQVbEQfSyK2IiEhyGQZHhg6l90cfsdwwAPCkIDkKTObXX5+kRg0H1yciCrciIiLJcW7vXsa2acOXR48SB7hixY1+PNl+BFOnZsPX19EViggo3IqIiNzThDfeoM+4cSRef+9PeS54/MKEr8vywgtgsTiyOhG5kcKtiIjIHUSHh/NB8+Z8smkTNsAFKMAIclQewvK5FipUcHSFIvJfCrciIiK3sWfJEhq0bk1YojleW5oCnGId7d8sxahR4Onp4AJF5LbuK9zu3r2bv/76C3d3d+rWrUulSpXSqi4RERGHOfjZZ7QaMICw6w+NVeBZLuWby6Jv4fHHHVyciNxVqsPt2LFj6du3L76+vri4uHDlyhUqV67Mt99+S2BgYBqWKCIikkGuXWNlu3Y8s3IlYUAu3LAwheIturF6BuTP7+gCReReUjTP7fTp09m+fTuxsbF89NFHfPzxx1y5coVLly5x7NgxWrRoQYMGDdiwYUN61SsiIpI+tm5lYqlSPH492PrjT7TbCYZ91Y0lSxRsRTILi2Fc/zuXZKhYsSKHDx8GwGaz0a5dO+rVq0fVqlUJDAzEz8+PCRMm8MMPP2SagBseHo6fnx9hYWH4ah4XEZEHj81Gwmef0efdd5loswGQl8fJU2ERc+d6ULmyg+sTkRTltRSN3O7fv59r166xYcMG3NzcsFqtzJkzh5YtW5IrVy5KlizJzz//zLZt21iyZAknTpy4n+8hIiKSvk6f5mqjRrQcOJCJNhsWwJOhPNtrKdu2KdiKZEYp7rn19PSkZs2a1KtXjypVqjB37lxsNhsHDx5k586drF27llWrVtG1a1euXLmCj48P4eHh6VG7iIhI6s2bR/BLL/HUtWscAlxxJ5vPj8z+sR1PPuno4kQktVL9QNno0aNp1KgRx44do2fPnlSpUgV/f3+2b99OoUKFOH36NKdPn2bv3r1pWa+IiMj9CQuD3r0J+v572gNXAFcKUOPh31m4MJCCBR1doIjcj1SH28DAQLZt20bPnj15+OGHsbfuurq6Mn36dACKFClCkSJF0qZSERGR+7V2LXTpwtcnT/I6kAhYqMXgYb8wdGgBrClq1hMRZ3Rf89yWKlWKFStWEBoayqZNm4iLi6NOnToKtCIi4lzi4mDYMCI//pgGwI7rm319O7N8+VQeflgrMohkFWmyQln+/Plp3bp1WpxKREQkbR04AJ07c3DHDpoBZ65vrlZtKGvXvo+3t8WBxYlIWtNfwIiISNZkGDBhAvGBgXTcsYPK2IOtG+3aDWTbtuEKtiJZUJqM3IqIiDiVkBDo3p0/ly3jJeDI9c1+fnVYteonqlUr7MjqRCQdKdyKiEjWsmgRJ158kXeuXGF+0kYPAgOfZNOm2Xh4uDuwOBFJb2pLEBGRrCEsjGudOzO4bVvKJwVbK3nzvsrGjafZsWO+gq3IA0AjtyIikukl/vEHM597jiFXrhByfZuVRrz0yleMH18ZNzeHliciGUjhVkREMq+oKFZ17ky/RYvYdX2TJ4XJUWA8Cxa0pm5dPTAm8qBRW4KIiGRKP334Ic3y5KHp9WCbDXfcGUnH7sc4fLiNgq3IA0ojtyIikqnER0TwTNWqLD5izoHgChSkNZG5pjJ9eh407brIg00jtyIikmnM/fRTKuXOnRRsc5INT9bw0BOL2L9fwVZENHIrIiKZQUICwx57jBGrVwPgCxSnB0e8pvLFF/DKK2BRF4KIoHArIiJOLvHAAd5t2pRPz50DwBd3XFiFR8167PgeypZ1cIEi4lTUliAiIs4pMZGrH31Eq4CApGBbnkcJJ4LXh9Zj/XoFWxG5lUZuRUTE+Rw6xKGOHXlqxw6CAQ+seDMGo9zr/P0d1Krl6AJFxFlp5FZERJxHYiKMHs3SypWpdT3YepOTWDbT9c3X2bFDwVZE7k4jtyIi4hwOHcLo3p3PNm5kIGAA7tQkl/+v/PZdfho1cnB9IpIpaORWREQc6/pobXSVKnTduJEBmMEWXuL5F9exb5+CrYgkn0ZuRUTEcQ4dgu7dObtxI22ALQC44Os7htmze/HEE5rfS0RSRiO3IiKS8a6P1hIYyPyNG6mG5XqwzUWTJn9w/HhvBVsRSRWN3IqISMbavx969MC2aRM9gJkAGLi4VGTMmMX07l3KoeWJSOamcCsiIhkjLg4+/hg+/JAN8fH8Dyt7sQHg7p6HLVuCeOihAg4uUkQyO7UliIhI+tuyBapX58KwYXSIj6ceXA+2XjzxxEAiIs4p2IpImtDIrYiIpJ+oKBg6lMtffMFow2AsEHn9IxcXX+bOXcLTT9d3ZIUiksUo3IqISPpYvZrL3bsz5p9/GAuEX99stVbn+ef7MHVqJ9zcXBxZoYhkQQq3IiKStq5e5cLrr/Pl998zHriW9MFDlC8/nF9+aU3ZspoJQUTSh3puRUQkzZydMYO3ihSh+PffMwoz2FoJwN19Pl98sYO9e9so2IpIutLIrYiI3LeNS5bQuUMHTkVGknB9mw9lieBT6tZvxYwZVkqXdmiJIvKA0MitiIikWmx0NJO6dKHZk09y/HqwLUZh3PiZxGwH+eqr1vz5p4KtiGQcjdyKiEiqbJ43j8c6dSIsMREAP1zwZTD/MJyGDWHaNCil9RhEJINp5FZERFImJoZd//sfbTt0SAq2NS2NuEYEl7yGM348rFqlYCsijqGRWxERSb41a1jQqRNdz50jCshlccfFmMYW43kaNTJHa0uWdHSRIvIg08itiIjc26VL2Lp3Z3jjxjxzPdgWsFThsnGOKO/nmTABgoIUbEXE8TRyKyIid2YY8MMPRPXtywsXL/LT9c3uvEaIMZbGjV2ZNg1KlHBolSIiSZxy5HbChAkUL14cT09PateuzebNm5N13Jw5c7BYLLRp0yZ9CxQReRAcPQrNm3OmSxceuR5sLbgC03D3mcDXX7uycqWCrYg4F6cLt3PnzqVfv34MGzaM7du3U6VKFZo3b8758+fvetyJEyd4++23adCgQQZVKiKSRcXGwgcfQEAA21asoBawDYA8GKyiadMX2bMHevYEq9P9X0REHnRO99vSF198wcsvv0z37t2pWLEikyZNwsvLi+nTp9/xmMTERDp37szw4cMpqYYvEZHUW7kSKleGoUNZGBNDfYuVswBUxMfnb775pgErVkDx4o4tU0TkTpwq3MbFxbFt2zaaNWuWtM1qtdKsWTM2btx4x+NGjBhBvnz56NGjxz2vERsbS3h4+E0vEZEH3tmz8Nxz8OijGIcPM9LLh6eBGMMGPM7jj2/gwIGSvPIKWLR6rog4MacKtxcvXiQxMZH8+fPftD1//vyEhITc9ph169Yxbdo0pkyZkqxrjBo1Cj8/v6SXv7//fdctIpJpJSTA2LFQvjzMnUs4UMYzN4OjIgDw9OzNDz/8ytKlfhQp4thSRUSSw6nCbUpdu3aNLl26MGXKFPLkyZOsYwYNGkRYWFjS69SpU+lcpYiIk9q0CWrWhDffhGvX+KNoBfJY/DgacwmA6tW/4NSpcXTq5KrRWhHJNJxqKrA8efLg4uJCaGjoTdtDQ0MpUKDALfsfPXqUEydO0KpVq6RtNpsNAFdXVw4dOkSp/yyR4+HhgYeHRzpULyKSSVy+DIMGwZQpYBjs9PGhtS0HJ08euL6DGx06vMOcOX0dWqaISGo41citu7s71atXJygoKGmbzWYjKCiIOnXq3LJ/+fLl2bNnDzt37kx6PfXUUzRu3JidO3eq5UBE5EY2G8ycCeXKkTh5MosNg0a5ClM1IoKTUacBKF36OU6cCGHOnI8cW6uISCo51cgtQL9+/ejWrRs1atSgVq1ajBkzhsjISLp37w5A165dKVy4MKNGjcLT05OAgICbjs+RIwfALdtFRB5o27dD796c2LiRGcB0V1dOJyTA5TOABVfXEnTv3oNJkwZi1fxeIpKJOV247dChAxcuXGDo0KGEhIQQGBjIsmXLkh4yO3nypH7jFRFJrkuXuPbOOyyYMYPvgNX27QkJQG4slhf43/9e5csvS+Hp6bgyRUTSisUwDMPRRThSeHg4fn5+hIWF4evr6+hyRETSRNS1a/Rv1Yqj69fzZ0IC0UmfWIAmQA+qVGnHzJkeBAY6qkoRkeRJSV5zupFbERFJPZvNxrbp02n72muciY9P2l4whz8XI18hPr4Lnp7FeP99eOstcNX/BUQki9FvayIiWcTJrVtp1LgxxyPMOWotQK0ipbjm/S37D9UFLDzyiDlJQtmyDi1VRCTdqHlVRCSzi4/nwvDhtKhdOynYtvYvxptdd7H13BH2H6qHn5+FSZNg9WoFWxHJ2jRyKyKSma1YQXDPnrQ4doxjgKfFwqtt3+Ln7Z/xy3fmLs8+C2PGQMGCjixURCRjKNyKiGRGhw/DW2+x/tdfeQq4DBTLlYeAh//ky4UVAfD3h4kT4cknHVqpiEiGUluCiEhmcvWq+SRYpUos/PVXmmIG21LFa3A1cR9LllbEaoW+fWH/fgVbEXnwaORWRCQzSEyEqVNhyBC4eJHxwBuAAeTO3ZqjJ2YDXlStaj4wVr26Y8sVEXEUjdyKiDi7VaugalXo2RPj4kXezZ2b1zGDrdX6KpcuLcDLy4vPP4fNmxVsReTBppFbERFndeQIvP02/PILAAk5cvC/8uWZvmnT9R0+wGYbzOOPW5g4EUqUcFypIiLOQuFWRMTZhIXBhx/C2LEQHw8uLkS/8gpPHz7O7yuXYf6l22Ty5evB2LHQoQNYLI4uWkTEOagtQUTEWSQkwKRJUKYMfP65GWybN+fKX+uovmbf9WDrAfxMjx49OHAAnntOwVZE5EYauRURcTTDgN9+g/794eBBc1u5cvDFFyyOz8fTjzQlISEc8MXf/1dmzXqEhg0dWrGIiNPSyK2IiCNt2wZNmsBTT5nBNndu+OorIjZs47Exu2jdps71YGulR4+VBAcr2IqI3I3CrYiII/zzDzz/PNSoAWvWgIcHMW+/TdD06Ty+ZBu+efKwYsW7QAJubvmYPPk3pk6tiaenowsXEXFuaksQEclIV6/CqFEwdiyXYmPZAmysXJm/fHzYMG4csZ9/ftPuL744iSlTXsFqVWOtiEhyKNyKiGSAowcPMvKFF/DYuZPQ2Fh2AMftH+7Zc8OeBYFmVK6ck+nTe1GjRtkMr1VEJDNTuBURSUdXL1/mjw8+4LWvvuKSzXbL5/7+pYmOrsPFi/WAhlStWo5vvrFQs2bG1yoikhUo3IqIpAfDYPmoUbQdMoRowwDM33BrlCjBM6++SrkK1VmypCqTJ+fEZoPs2eGjj+C118DFxbGli4hkZgq3IiJpbfNmTvbty8sbNhANWIC+derw/vz5+BQsxIIF0LMnnDlj7t6hA3zxBRQq5MiiRUSyBoVbEZG0cvAgDBnCsQULaAKcAvJ4erLk55+p9fjjHDsGHZ6A3383dy9VCiZMgObNHVm0iEjWoqnARETu1+nT8NJLUKkSwQsW8AjwD1C2ZEm2BwdTpfHjfPQRVKpkBlt3d3jvPfM5MgVbEZG0pZFbEZHUunzZnNZr3DiIjeUQ0NjDg3OxsVSsWJGgoCAOHCjAo4/CoUPmIU2awMSJ5gJkIiKS9hRuRURSKiICY+xYLJ99BmFhAByqXp1GJ04QcukSAQEB/PhjEP3752PWLPOQfPngyy+hY0ewaMpaEZF0o3ArIpJcUVHmsOsnn8DFi+a2hx7icO/eNB427HqwrUzHjkHUr5+XsDAzyL76qjkTQo4cDq1eROSBoHArInIvMTEwebLZghASAoCldGl4/32O1a5N40aNOHfuHCVLVsZmW8XgwXkAqF7dzMK1ajmyeBGRB4vCrYjIncTFwfTp5rDr6dPmtuLFzafBunbl5NmzNHnkEc6cOYOfXwWOHVsJ5CFnThg5El5+WXPWiohkNIVbEZH/MOLjscyaBR98ACdOmBuLFIEhQ6B7d3B3JyQkhKZNm/LPP/9gtZYhLCwIyMdLL5kDvHnyOPIbiIg8uBRuRUSuMxIS4McfYfhwOHrU3FigALz7rjkM6+kJwOXLl6lX71GOHTsCFMdmC6J69YJMmAC1azuufhERUbgVEYHERPjpJxg+HMvBg+a2PHlg4EDzaTAvr6Rdt249QYMGtYmJOQ8UxNc3iE8+8VcLgoiIk1C4FZEHVmJsLMbs2bh+8gkcOoQFIGdO6N8fevcGH5+kfePibHTrNo05c14HYgEX2rX7g2++KakWBBERJ6JwKyIPnISoKPq2asXU1aspZxhUAXw8PMhWvTqutWrB1askvP8+UVFRhIeHs3v3Cfbu3YFhRAFgsXjRv/8XfPxxgGO/iIiI3ELhVkQeGHHXrvFMvXrs2r+fk4mJAOy6/iI2FjZsMF93ZOGZZ75g1qxeeHq6ZUDFIiKSUgq3IpL1RUfDlCm0HzCAX2NiAPAFnqpWjZodOxJjs3Ht2jViYmJITEzEZoO9e11Zty4bsbHZgZxUqLCdYcNa0aHD4w79KiIicncKtyKSdUVGwqRJ8NlnzAgNZfH1za0rV+b7oCB88ua95ZC//oLXX4ddu8z31arBhAnw8MM9Mq5uERFJNaujCxARSXOXL5tz1BYvDm+/zbLQUF6+/tGAt99m0e7dtwTb06ehUyd45BEz2ObMCV9/DZs3w8MPZ/g3EBGRVNLIrYhkHadOwZdfmkvlRkYCsLNIEdpfuEBibCxdunRh1Kef3nRITAx88YW5CFlUFFgs8Mor8OGHWohBRCQz0sitiGR+Bw6YK4eVLGmG28hIqFKFMxMm8ITNRkRsLE2aNGHq1KlYLBYADAMWL4ZKlWDwYDPY1qsHW7eanQwKtiIimZNGbkUk89q4ET75BH755d9tjRrBwIFE1K1Lq4YNOXv2LBUrVmTBggW4u7sDcPAgvPkmLF9uHlKoEHz2GXTsaI7ciohI5qVwKyKZi2HA77+boXbtWnObxQJt28KAAVCrFjabjS5PP82OHTvIly8fS5YsIUeOHISHw4gRMHYsJCSAuzu89Za5uu4N6zWIiEgmpnArIplDXBzMm2cOse7ebW5zc4MuXeCdd6B8+aRd33vvPRYtWoS7uzuLFi2iaNHizJxprqYbGmru06qV2WtbunTGfxUREUk/Crci4twuX4ZvvoHx4+HsWXObjw/873/Qty8ULnzT7nPmzGHkyJEATJs2DReXOtSpY856AFC2LIwZAy1aZOB3EBGRDKNwKyLOKTjY7B+YOdN82gugYEHo3RtefdWcq+s/duzYwYsvvghAr179WbXqebp0MT/z8YGhQ6FPH7MdQUREsiaFWxFxCoZhmP20f/6J5csv4bffzPcAgYHQrx906HDHZHrhwgXatGlDdHQ05cu34LvvRnLtmvlZ167w8cdmNhYRkaxN4VZEHC4+MpJRXbvy8c8/08Uw+ALwBrMxtm9fcwaEu0xjkJCQQMeOHTl58iRubqU5eHA24EL16jBuHNSpkzHfQ0REHE/hVkQc5tyuXbR4/HHOhIZy8foo7WRgtpsbHVq3pvNrr/HII4/gco/5uV544Q2CgoIAb+LjF5E3bw5GjTKnvrVqNm8RkQeKftsXkYxlGOb8tM8/T4/AQHaFhHDRMMgNNC9ThhJFixIRH8+0+fNp0qQJhQoVokePHixYsIBLly7ddKpz5yIoX749P/zwNQAWy3TefLMSwcHQo4eCrYjIg8hiGPamtgdTeHg4fn5+hIWF4evr6+hyRLKu6GiYM8ec9WD7dlYDTQEDeK1ZM75YuBCP7NkxDIO//vqLWbNmsWDBAq5cuXLTaSpVqkTNmrUIDk5g48alGIYZeLNlK83WrYepWDHDv5mIiKSzlOQ1hVuFW5H0dfw4fP01TJtmTusFXHR3p4qrK2ejoujRowdTp0697aHx8fH8+eef/Pbbb/zxxx8cOHDgln1cXUvw9NNd+PbbwXh4aBoEEZGsSOE2BRRuRdKBzQYrV5qjtDfOelCsGMarr9JmzRoWL1tG+fLl2bp1K97e3vc85T//QJ8+5/nll43AOmAi/v6V2bNnDX5+nun5bURExMFSktfUkSYiaefCBRg9GipUgObN4ddfzWD72GOweDEcPcrkHDlYvGwZ7u7u/Pjjj/cMthER8N575gJkv/ySD4ulNS+//BmnT4dx8uQmBVsREbmJZksQkftjs8Hq1TB5Mvz8M8THm9t9fc3pCl57zVwWDAgODqZv374AfPzxxwQGBt71tN9/D4MG/bswWcOG5upi5mH67UtERG6l/zuISOqEhJirh02ZAseO/bu9Rg14+WXo1MlcFuy6hIQEunTpQnR0NM2aNaNPnz53PPXGjfDmm/8umVuiBHz+ObRte9fpbkVERBRuRSQFEhNhxQpzlPbXXyEhwdzu6wvPP2+G2juMxn788cds3ryZHDlyMGPGDKy3mafr1CkYOBBmzzbf+/jA4MFm0PVU94GIiCSDwq2I3Nvp0zB9ujnjwcmT/26vUwdeeQXat4e79M7u3LmT4cOHAzBu3DiKFCly0+dRUfDpp+YrOtocne3eHT76CAoUSJdvJCIiWZTCrYjcVvi5c/Rq3573bDbKbtr074wHOXNC167w0ksQEHDP88TFxfHCCy+QkJBAu3bt6Ny5c9JnhmGO0g4caOZngPr1zb7a6tXT4UuJiEiWp3ArIklsCQkMe/FFQv7+m++Cg4nDnHRrJ+DXsKHZdvD00ynqERg5ciS7du0id+7cfP3111iuN81u3gx9+sCmTeZ+xYrBZ5/BM8+or1ZERFJP4VZE4MgRmDWLF0eP5tvIyKTNFuAEULdMGX6ZOpXSpUun6LS7d+/mo48+AmD8+PHky5ePM2fMGRBmzTL38fY23/frB9mypc3XERGRB5fmuRV5UIWFmTMd1K8PZcoQPWIEQdeDbaUcOfhj9Gg2b95MoUKF2H/4MDVr1mTp0qXJPn1CQgI9evQgISGBNm3a8OSTHXj/fXNWMHuw7dYNgoPNh8YUbEVEJC1o5FbkQRIdDUuXwpw55sphMTHmdquVL0qW5PSRIxQqWJBNwcH4XJ/Ga+vWrbRt25a///6bJ554goEDBzJixAjc3NzueqmvvvqKrVu34ufnR8OGEylXzpI0X23dumZfbc2a6fhdRUTkgaTld7X8rmR18fHm9F1z5sCiRXDt2r+fVawI3bpxpkkTyjZsSFRUFD/88AOdOnW66RSxsbH069ePiRMnAlCrVi1mzZpF2euLM/zXiRMnqFSpElFRUfj7T+HUqZcAKF7cnBFBfbUiIpISWn5X5EGXmGiuGvbKK+ZcWk88YfYCXLsG/v7wzjuwbRvs3Qv9+zNw7FiioqKoV68eHTt2vOV0Hh4eTJgwgXnz5pEjRw42b95MlSpVGD16NAn2uW6vMwyD5557kaioKKAhp071wNfXDLUHDpizhinYiohIetHIrUZuJaswDPj7b/jxR5g3z1xBzC5/fnj2WXjuOXj4YbhhAYXNmzdTu3ZtALZs2UKNGjXueplTp07RvXt3goKCAKhatSrjx4+nbt26nDgRTtOmXTl27BfAFat1D6++Wp5hwyBv3jT/xiIi8oBISV5TuFW4lcwsMRHWr4eff4aFC29eYCFnTnParueeg4YNwfXWFnvDMGjQoAHr16+nW7duzJw5M1mXNQyD6dOn884773DlyhUAihSpzunTx4HLAPj4BLB58x4qVLjfLykiIg86hdsUULiVTCcuDlatMsPsokVw4cK/n/n4QOvW0LEjPPoouLvf9VQLFy7k6aefJlu2bBw+fJjChQunqJTQ0PN07vwuQUHTAfO3Eje30jz3XBe++eYdsmkKBBERSQMpyWuaLUHEyRmGQfTFi8wYNIiPZs9mnGHwtH2WAzBHaFu1gnbt4LHHkj2nVnx8PAMHDgTgrbfeSnGw3bYN+vXLx9q1U4EuQEsCAx9l48af8PS8+0wKIiIi6UXhVsRZnT3Lj++/zyvTpxObmEj89c19gLYFCmBt29YMtA0bwj2m5bqdKVOmcPjwYfLmzcs777yT7ONOnzbnpf3uO/O9pye89VZD3nnnGtmzg9Wq51RFRMRxFG5FnIXNBlu2wJIl5mv7dmYCEdc/LuTiwgXD4IzNxpdvvcVbb7+d6ktFREQwYsQIAIYOHZqslpyICHN53M8+M6fLBXj+eRg50pyAQZOviIiIM9D/jUQcKSwMfvoJXngBChY0ZzL44APYvp2rwPrro6BfDRzI6bg4xn/9NQCD3n2XHTt2pPqyY8aMITQ0lFKlSvHKK6/cdd/ERJg+3VxZbMQIM9jWrw+bN5uzi5nBVkRExDnogTI9UCYZyTBg3z5Yvtwcnf3rL7hxnlhfX2jeHJ58ko+Dgxn00UcEBASwe/duLBYLhmHQrl07Fi1aRNmyZdm6dSvZs2dPUQmXLl2iZMmShIeHM3v27NvOa2u3ahX06we7dpnvS5Y056tt105z1YqISMbRA2UizuTMGVi58t/XjfPPApQvby6y8MQT5pComxtxcXF8Vbw4AG+//TaW60nSYrEwdepUtmzZQnBwMD179uT7779P+jw5Pv30U8LDw6lSpQodOnS47T6HDpnrPPz6q/nezw+GDoVevcDDI8V3QEREJMMo3IqktfBw+PNPc8nblSvNZblulC0bPPIItGxpBtpSpW45xY8//si5c+coVKjQLSOruXPnZu7cuTRs2JDZs2dTr149XnvttWSVFhISwrhx4wD48MMPb3n46/x5GD4cvvnGbEdwcYHXXjODbZ48KbgHIiIiDqJwK3IfDMPAEhtrPggWFGSG2U2bzGRoZ7VCjRrQrJk592ydOncd/jQMg9GjRwPQp08f3G8zV229evX45JNPePvtt3nzzTcJDAykbt2696x31KhRREdHU6dOHZ544omk7VFR8OWX8Mkn5gq9YM4u9umn5sCyiIhIpmE4ofHjxxvFihUzPDw8jFq1ahl///33HfedPHmyUb9+fSNHjhxGjhw5jKZNm951//8KCwszACMsLCwtSpcsLi4uzgjescP487PPjPYVKhg+VqvxtaurYZjdtP++Spc2jJ49DWPBAsO4fDlF11ixYoUBGN7e3saVK1fuuJ/NZjPat29vAEaBAgWM06dP3/W8p06dMtzd3Q3AWLlypWEYhpGQYBgzZhhG4cL/ll69umGsXp2ikkVERNJVSvKa043czp07l379+jFp0iRq167NmDFjaN68OYcOHSJfvny37L9mzRo6duxI3bp18fT05JNPPuGxxx5j3759KZ6UXuS2LlyAdevgr7947bvvmHrp0k0fj7PZ6JkvHzRqZI7MNmsG1/tlU2PMmDEAdO/enRw5ctxxP4vFwvTp0zl48CB79uzhqaeeYu3atXh7e992/5EjRxIXF0fDhg1p0qQJf/xh9tXu3m1+XqyYOa3Xc8+Zg80iIiKZkdPNllC7dm1q1qzJ+PHjAbDZbPj7+/P6668nraZ0N4mJieTMmZPx48fTtWvXe+6v2RLkJjab+TTV33/Dxo3mbAY39MzWAzYAXhYL9fLnZ0VICC5WK0eOHKF4iRL3ffng4GDKlSuHxWLh0KFDlClT5p7HHD9+nFq1anHx4kXatGnD/PnzcXFxuWmfU6dOUapUKeLj45k6dTXz5jXijz/Mz3LkMBdl6N3bXJBBRETE2aQkrznV+ExcXBzbtm2jWbNmSdusVivNmjVj48aNyTpHVFQU8fHx5MqVK73KlKzk7FlYtAgGDYKmTc2kV7EidO8Okyf/G2wrVeLaiy+y43r/6+pNm/jj3DmaNWtGos3GF19+mSbl2P9Q17Jly2QFW4ASJUqwaNEiPDw8WLRoEX369OG/f2b9+OOPiY+Pp0CBRrz8shls3dygb184cgTeflvBVkREsganCrcXL14kMTGR/Pnz37Q9f/78hPx3+qQ7GDBgAIUKFbopIN8oNjaW8PDwm17ygAgPh9Wrzaem2rWDIkWgcGFo2xY+/tic1PXaNfDyggYNzMS3aBFcvAh797KoUSOi4+IoW7YsNWvWBMxfbwBTp07l/Pnz91leODNmzADMB8lSol69enz33XdYLBYmTJjAhx9+mPTZjh1H+PrrbwAICRmGYcCzz5q5/YsvIHfu+ypbRETEqThdz+39+Pjjj5kzZw5r1qzB8w7DUKNGjWL48OEZXJlkKMOAkyfNlQd27vz3n8eO3bqv1QoBAVCrFtSubf6zYkVwvfU/jR9++AGAzp07J80r27RpU2rVqsXmzZv58ssvGTVqVKrL/vbbb4mIiKB8+fJ3/MPZ3Tz77LOEhobyxhtvMHToULJl82HXrpx8/31vIBEoRL16DRk92vyqIiIiWZFT9dzGxcXh5eXF/PnzadOmTdL2bt26cfXqVX755Zc7Hvv555/z4YcfsnLlSmrUqHHH/WJjY4mNjU16Hx4ejr+/v3puM6mThw6x7LvvcD17lh+Dgthy9iwfu7nRMybm9gcULfpviK1dG6pVgzs8gHWj0NBQChUqhM1m4/Dhw5QuXTrps8WLF9O6dWuyZ8/OiRMnUtUSY7PZqFChAsHBwUyYMCHZ89bezrBh7zNixH//AOdK+/YDmDv3Q60sJiIimU6mXaHM3d2d6tWrExQUlBRubTYbQUFB9O7d+47Hffrpp3z00UcsX778rsEWwMPDAw8tsZT5XL5s/j36f14vnzjBH//Z9cfERHq6upojsIGBUKXKv/9M5d/Bz5s3D5vNRq1atW4KtgCtWrWiSpUq7Nq1i7Fjx6bqbwaCgoIIDg4me/bsdOnSJVU1gvkc3KpVw4DNwO+AlVatPuTbb18nRw5vBVsREcnynCrcAvTr149u3bpRo0YNatWqxZgxY4iMjKR79+4AdO3alcKFCyf99e8nn3zC0KFDmT17NsWLF0/qzfXx8cHHx8dh30NSITYWTpww2wcOH745yN6hn9U+KVdxDw/KFSjA8n/+4USBAhjHj2NJwyekZs+eDUCnTp1u+cxisfDee+/xzDPPMHbsWN58801y5syZovNPnDgRMH99Z8+ePcX1HTsG774Lc+cCWHB3X4y/f2t+/PFzataskOLziYiIZFZO1ZZgN378eD777DNCQkIIDAzkq6++ovb1JsFGjRpRvHhxZs6cCUDx4sX5559/bjnHsGHDeP/99+95LU0FloEMw3w469gx83X06L8/HzsGp0+b+9xJkSJQoULSK75MGXK3acO1iAi2bNlChQoVyJs3L9HR0Wzfvp2qVaumSdnHjx+nZMmSWK1WTp8+TcGCBW/Zx2azERgYyJ49exgyZAgffPBBss9/+vRpihUrhs1mY9++fVSsWDHZx16+DB9+COPHQ3w8WCzQrRt88IF5u0RERLKCTNuWYNe7d+87tiGsWbPmpvcnTpxI/4Lk3gwDrl5ly7JlrFm5Er+4OFbv3MmOU6f4qEQJnk5MNEdl7Wu73om3N5QqBSVLmuu+2sNs+fLwnxHNjWvXci0igrx581KtWjWsVistW7ZkwYIF/PTTT2kWbufNmweYf7C6XbAFc8q6999/n6effpoxY8bQp08f8uTJk6zzT5kyBZvNRsOGDZMdbGNiYMIEM9hevWpue/RR+Owzs/tCRETkQeWU4VachGFAZKTZEnDhwr8v+/vQUDhzxhxxPXMGoqJ4G1j7n9PM37mTp2/cULjwvwHW/rK/z5uX5DaGLlu2DIDmzZtjvb6kVvv27VmwYAHz5s3jo48+SprV4H7MmTMHgOeee+6u+7Vt25aqVauyY8cOPvnkEz777LN7njs+Pp4pU6YA8Oqrr95zf5vNbD14913zzwoAlSubobZ583seLiIikuU5ZVtCRsrSbQmGwYUzZ4i4cAG3+HhOHT7Mtq1baVmxIiW9vMwhv6tXISzs35+vXjX/rtseYO8068AdBLq4sCsxkVLZspE9WzZ2Xr5Mk/LlCfryS3NJ2uLF02y1gGrVqrFjxw5mzZrF888/D0BERAT58uUjOjqarVu3Ur169fu6xqFDhyhfvjyurq6EhISQ+x4PpC1dupQnnngCT09Pjhw5cs8loBctWkTbtm3Jly8fp06dwv36IhG38+ef5tS7W7ea7wsVMkduu3aF/yxIJiIikqVk+raErGzDpEns37CBxIQEdp86xekrV3i3cWNqFy4MCQkpe8XHQ3Q0REX9+4qMvOl9a5uN/67tdgr4JCVFe3pCvnzmqGrevP/+nC+fOQp7fTEEo1AhQkqWhNBQvlu5EsMwqF+/PnsuXcJo3jxNRlHtQkND2bFjBwCPPfZY0nYfHx+efPJJfvrpJ+bMmXPf4fann34CoFmzZvcMtgAtWrSgQYMG/PXXXwwfPpzJkyffdf9vvjEXV+jevfsdg+2BAzBwICxebL738THf9+1rrjchIiIi/9LIbQaP3L4WEMDX+/bdtG0IkPzHj1KmErAfcyk6VyAOeC5nTn6sVs1catb+8vP79+ecOW8Os97eyWoVOHbsGKVKlcLNzY3w8HCsVis5cuQgOjo6xQ9K3cv3339Ply5dqFq1Ktu3b7/pswULFvDMM89QtGhRjh8/ntSykBoPPfQQe/bsYcaMGbzwwgvJOmb9+vXUr18fq9XK3r17qVDh9rMVnDhxgpIlS2IYBkeOHKFUqVI3fX72LLz/PkybZrYjuLjAK6/AsGHwn0X8REREsjSN3Dqxhx56iFYXL2K1Wtl29Sqno6MJL18e6tY1V8Wyv1xcbn5/p5eX180vb++b3huNGkFwML8uWcKhQ4fo168f0Y88Yi4rm8Y2bjTHiKtVq5a0QlydOnVYtWoVa9asSdNwu3z5csDst/2vli1bkj17dk6ePMnGjRupV69eqq5x4MAB9uzZg5ubG61bt072cfXq1aNNmzYsWrSIAQMGsNg+5Pof06dPxzAMmjVrdlOwDQ+HTz81l8aNjja3tW5trhBcvnyqvoqIiMiDw3jAhYWFGYARFhaW4dceNmyYARg9evRIl/NHR0cbLi4uBmCcOXPGWLNmjQEYRYsWTZfr9erVywCMvn37Jm0bMWKEARjt27dPs+skJiYa+fPnNwBj1apVt92na9euBmC89tprqb7O8OHDDcBo2bJlio89cOBA0r2/XY3x8fFG4cKFDcCYO3euYRiGERtrGGPHGkaePIZhPs1nGHXqGMa6dan+CiIiIllCSvJa6v++Vu5b+evDcAcPHkyX8+/fv5/ExERy585NwYIFCQwMBODkyZNcunTp7gengn3ktk6dOknbGjVqBMCff/6JkUYdMHv27CE0NBQvLy/q1q172306duwImD2zCQkJqbrO/PnzAXMGhpQqX7580uwHffv2JTEx8abPly9fzpkzZ8idOzetWrXmxx/NUdk+fcypgMuVg4ULYf16SOXAs4iIyANJ4daB7OH20KFD6XL+Xbt2AVClShUsFgt+fn6ULFkSgJ07d6bptSIjI5Oud2O4rVWrFtmyZeP8+fPs378/Ta61YsUKABo2bHjHpZSbNm1K3rx5uXDhQtL+KXHo0CH27NmDq6srTz31VKrqfP/998mRIwe7du1i6tSpN302bdo0ABo27EKDBh506gTHj0OBAvDNN7B3L7Rtm+xZ0UREROQ6hVsHKlOmDAAXL17k4sWLaX7+3bt3A2afr519YYP/PoR1v7Zt20ZiYiKFCxemyA1LY3l4eCT1vK5evTpNrmUPqzfOkvBfbm5udOjQAYAffvghxddYsGABYIbkXLlypaJKyJ07NyNGjABg8ODBXL58GYDz58+zePGvACxc2INt28z1KT74AI4cMR8ac1U3vIiISKoo3DqQt7c3RYsWBdJn9NY+knpjuK1WrRqQ9uF206ZNADz88MO3fNa4cWMgbcJtTEwMa9eay0Q8+uijd923S5cuAPz8889ERESk6Dr2cPv000/fY8+7e/XVVwkICODSpUsMHjyYLVvOUalSaxITE4DSuLkF8Prr5krEQ4aYzwOKiIhI6incOlh69d0ahnHbkdv0Cre367e1s4fbNWvWYLPZ7us6GzZsICYmhoIFC95z9oWaNWtSpkwZoqKi+Pnnn5N9jRMnTrB9+3asVitt2rS5r3pdXV0ZP348AJMmTaJWrRJcvGj+QSBfvqIcOABffWXOuCYiIiL3T+HWwezh9sCBA2l63pCQEC5duoTVar0pBNrDbXBwMOHh4WlyLcMw7jpyW6NGDXx8fLh8+XJS4E6tlStXAuao7b0WhbBYLEkrl3333XfJvoY9CD/yyCPkvc/UGR0NmzY1xGq1z3Ubi49PLd599xuCgxfyn6ltRURE5D4p3DpYeo3c2kNk2bJlyZYtW9L2fPnyJfXEptVDZSdPniQkJARXV9ek8HwjNzc3GjZsCEBQUNB9Xcsebps2bZqs/e2tCUFBQZw+fTpZxyxcuBCAdu3apaJCU2IizJgBZcuaq4nZbHOwWLLRvv1AwsI28dFHr+Dn55fq84uIiMjtKdw6mH31qrQOt3v27AGgcuXKt3xmX5I2rVoT/v77b8CcleHGIH2jJk2aALBq1apUX+fKlSts3boVSH64LVGiBI888giGYfD999/fc//Q0FDWr18PkKqWBMOAJUsgMBBefBFOnwZ/f/j224eIjY1g3rxRWK2aAkFERCS9KNw6mH3k9vjx48TExKTZeW/Xb2uX1n239nBbu3btO+5jD7dr164lPj4+VddZvXo1hmFQoUIFChcunOzjunXrBsCMGTPuOdfu4sWLMQyDGjVq4O/vn6L6Nm+Gxo3hySfNqbxy5oTPPoPgYOjaFdzc9J+biIhIetP/bR0sf/785MiRA5vNRnBwcJqd927h1j5yu23btjS5VnLC7UMPPUTu3LmJiIhg8+bNqbqOvaUhuaO2du3bt8fLy4vg4OCkB9/uxN5v27Zt22Sf/8gRePZZqF0b/vwTPDzgnXfMGRDefhuur0QsIiIiGUDh1sEsFkua993Gx8cnPaB2t7aEgwcPpniKrNtdyx6S7xZurVZrUii1982mlL2lIaXhNnv27EmrjE2fPv2O+4WHhycF6OS0JISGQu/eUKEC/PSTueBCt27mSO2nn5ojtyIiIpKxFG6dgL3vNq1mTAgODiYuLg4fHx+KFSt2y+cFChSgUKFC2Gy2+36obM+ePcTExJAjR46kRSnupFmzZkDqwu3Zs2c5ePAgFosl6eG0lHjxxRcBmDt3LteuXbvtPsuXLycuLo4yZcok/Tu5nYgIGD4cSpeGCRMgIQFatICdO2HmTLg+dbGIiIg4gMKtE0jrkVv7w2QBAQFYrbf/V1yjRg3g/lsT7C0JNWvWvOO17OzhdtOmTXcMmHdiXwCiWrVq5EzFkGiDBg0oXbo0ERERzJs377b7/PLLLwC0bt36ttOMxcfD11+bofb9982QW7MmrFoFS5fCbTpAREREJIMp3DqBtB65vdtMCXb21gT77AOpZe+fvVtLgl2JEiUoVaoUCQkJ/Pnnnym6jr0lwf5gWkpZLBZeeuklAKZOnXrL5/Hx8SxZsgQww+2NDAMWLICAAHjtNbMdoVQpmDsX/v7bfIhMREREnIPCrROwh9tDhw6RmJh43+dLSbi935Fbe7itVatWsva3j96uWLEiRdexh9vG95Eku3XrhqurK5s2bUq6R3br1q3j6tWr5MmT56ZV1v76C+rWhWeeMXtp8+aFceNg/37zIbJ7rCMhIiIiGUzh1gmUKFECDw8PYmJiOHHixH2fLyXh9uDBgyluEbALDw9PGm2uWbNmso559NFHgZSF2xMnTnDixAlcXFyoX79+ygu9rkCBAjz11FMAfPPNNzd9tnjxYgCefPJJXFxc2LcPnnoKHnkENm0CLy947z1zZoTevcHdPdVliIiISDpSuHUCLi4ulC1bFrj/1oRr164lBeS7hdsCBQpQpEgRDMNgx44dqbrWtm3bMAyDokWLUqBAgWQd06RJE6xWKwcOHODUqVPJOmbNmjWAGaCzZ8+eqlrtevbsCcCsWbOIjIwEzOWDf/31VwAefrgV3bub/bO//gouLvC//5mhdsQI8PW9r8uLiIhIOlO4dRIVK1YE7j/c7tu3D4CCBQuSO3fuu+5rf6gstX23KW1JAMiZM2fS/skdvbU/THY/LQl2TZs2pWTJkoSHh/Pjjz8C5uj10aNHcXFx5403HmPmTLDZoF072LcPJk2CggXv+9IiIiKSARRunURaPVS2d+9ewJwp4V7srQRbtmxJ1bXsx6Uk3MK/rQl//PHHPfc1DCNp5DYtwq3Vak0avZ04cSIXLkTRuvVbACQmViMuzofGjc0HxRYsgHLl7vuSIiIikoEUbp2EPdzu37//vs5jD7d3a0mwu9+RW3u4TW6/rV3z5s0Bc+T2Xg/QnThxgpMnT+Lm5kbdunVTVed/de/eHQ8PD3bs2EH+/MU5fPh3ALy9XVm2DIKCIIV5XURERJyEwq2TuLEtwTCMVJ8nJSO39nB75MgRrly5kqLrhIaGcvLkSSwWS9LDaclVu3Zt/Pz8uHz58j2D9Y39tt7e3im6zu3YbLBiRR5sNnNxC8O4gItLEZo27cn+/T/SvLlmQBAREcnMFG6dRJkyZXBxcSE8PJyzZ8+m+jwpCbe5cuWiZMmSQMqnBLOP2pYvXz7FD3m5uromTQm2fPnyu+5rD7eNGjVK0TX+yzBg+XKoUQM6dYL4+KGAlUqVnuDKlSOsXPk1RYsWua9riIiIiOMp3DoJDw8PSpUqBaS+7/bChQuEhoYC/44E30tq+25T25Jg9/jjjwPw+++/33W/tAi3mzdD06bw+OOwYwdkzw4ffNCZkJBw9u79jezZPVJ9bhEREXEuCrdOxB5IU9t3a58poWTJksn+K3z7w2D2mQ+Sy95OcL/hdvPmzVy+fPm2+9j7bV1dXVPVb3vokLn4Qu3asHq1OTdt375w7BgMGQL5899/m4OIiIg4F4VbJ1KpUiUg9eE2JS0JdvZwm5KRW8Mw7jvcFilShICAAGw22x1nTbAv0VujRo0U9dueOQOvvAKVKpkzHlgs0K2bucLYF19AnjypKllEREQyAYVbJ3K/I7f2cGsPyclRtWpVrFYrZ86cSXav7+nTpzl//jyurq489NBDqaoVoEWLFgAsXbr0tp+vXbsWgIYNGybrfFeuwIABULo0TJkCiYnmKmO7d8PMmVCsWKpLFRERkUxC4daJ2MPtvn37UjVjQmpGbr29vZP2T+7orX3UNiAggGzZsqWwyn+1bNkSgGXLlmGz2W753B5uH3nkkbueJyoKPvkESpaETz+FmBioXx/WrYNffoEU3A4RERHJ5BRunUi5cuWwWCxcvnyZCxcupOhYwzCSem5TMnIL/7Ym/P3338na3x5u7VOJpVa9evXInj07Fy5cuGW2hrNnz3LkyBGsViv16tW77fEJCeYIbZkyMHAgXL1qBtlff4W1a+EOh4mIiEgWpnDrRLJly5Y0NZc9qCbXuXPnuHr1Ki4uLpRL4bJaKX2oLK3CrZubG4899hgAS5Ysuemzv/76C4DAwED8/Pxu+swwYP58s6f2lVfg7Fmz5eC772DnTnjySc1VKyIi8qBSuHUyqe27tbcklC5dGk9PzxQde+NDZbdrD7jRjQ+T3W+4BXjiiScA+O23327abm9JaNCgwU3bV60yZz9o3958QCxPHhgzxpwZoUsXcHG575JEREQkE1O4dTL2loKUjtymtiXBfoyXlxfh4eEcPHjwrvueOHGCy5cv4+bmlqLe3jux991u27aNc+fOJW23j9zaw+2OHdC8uTlf7ZYt4O0NQ4fC0aPQpw94aKpaERERQeHW6Tgi3Lq6uiaNwt6rNcHeG1u5cmU80iBR5s+fP2k6MfusCVeuXEkaiS5cuD4dO0K1avDHH+DmBr17m6F2+HDw9b3vEkRERCQLUbh1MqmdMeF+wi0k/6Eye7itXr16qq5zO08++SQAv/76KwDr16/HMAxy5ChLgwb5mTPH3K9TJzh4EMaNg/z50+zyIiIikoUo3DqZ8uXLY7FYuHTpEufPn0/WMYZhJPXopjbc1q5dG4BNmzbddT97uE2Lflu7Vq1aAbBixQqCg0Pp2XMkAFevViIhAVq0MNsSfvjBnO5LRERE5E4Ubp2Ml5dX0owJyX2o7MyZM4SHh+Pq6krZsmVTdd2HH34YgD179hAZGXnbfQzDSJeR28DAQAoVKkxUVBTlypXmzJmNAPj5RbNmDSxdCoGBaXY5ERERycIUbp1QSvtu7fuVKVMGd3f3VF2zSJEiFC5cmMTExFvmnLX7559/0vRhMoD4eJg82UJoqH153Qjc3ErTpk1/DhyYQTIXJxMREREBFG6dkr3v1v5Q1b3YR3jtx6WWffT2Tq0J27dvB8yVye73YTKbDWbPhgoVoGdPSEzsDbhRu3ZHwsMP8PPPn1CwYIH7uoaIiIg8eBRunZB9VDSlI7ep7be1S264vZ+WBMMwVxALDITOnc1ZD/Llg6++ep2IiCg2bZqNp6drqs8vIiIiDzalCCd0Y7g1DAPLPZbbSuuR240bN972uvZwW61atVSdf80aePdd2Gi21OLnB/37wxtvgI8P6JejiIiI3C+N3DqhcuXKYbVauXLlyk0LG9xOWsyUYFetWjVcXV0JCQnh5MmTt1zH3oub0nC7dau5AEPjxmawzZYNBg6EY8fMsGsGWxEREZH7p3DrhDw9PSlTpgxw79aEs2fPEhYWhouLS9IxqeXl5UWVKlUAc/T2v9c5f/48Li4uPPTQQ8k634ED8MwzULOmuQCDqyu89prZijBqFOTKdV/lioiIiNxC4dZJ2Udh7/VQ2YEDBwAoXbp0mqwYVrduXeDWcGtvSahYsSLZsmW76zn++Qe6d4eAAFiwACwW6NIFDh2CCROgYMH7LlNERETkthRunZS97/Ze4Tat+m3t6tSpA9w53FatWvWOx4aGmv2zZcrAzJnmjAht2sCePfDdd1qAQURERNKfnuBxUskduU2vcLtjxw6io6OTRml37NgB3L7f9upV+OwzGDMGoqLMbU2bwsiRcH1VXxEREZEMoZFbJ3XjjAk2m+2O+6V1uC1WrBgFChQgISGBrVu3Jm23h9sbR26jouDjj6FECTPIRkWZYXblSvOlYCsiIiIZTeHWSdlXG4uMjOSff/65435pHW4tFktS3+2GDRsAuHTpUtLsCYGBgcTFwcSJUKoUDBpkjtxWqgQ//wybNpmjtiIiIiKOoHDrpNzc3Chfvjxw59aECxcucOnSJSwWC+XKlUuza9vD7fr164F/R21LlSrFL7/4Ur489OoFISHmqO1338GuXWZ/7T2m5BURERFJVwq3Tqxy5coA7Nmz57af20dtixcvfs8ZDFKiXr16gDlyaxhG0sNkFy5UpWtXOH4cChQwZz44eNCcCcHFJc0uLyIiIpJqeqDMid1rxoS0bkmwq1atGh4eHly6dIl33pnJmDGfAhAe7kvOnDBgAPTuDd7eaXpZERERkfumcOvE7OH2TiO39jluK1SokKbXdXd3p0iR8hw9uovRo19M2l6jhjcrVkCOHGl6OREREZE0o7YEJ2ZvSzh48CBxcXG3fG4Pt2k5crt5s7lU7tGj9hkarFSp8jrr1h3kr78+VbAVERERp6Zw68SKFi1K9uzZSUhIIDg4+JbP03LkdtcuaN0aatc2l8q1Wt8nT57qTJkyn507v6JevXJ4enre93VERERE0pPCrROzWCx3fKgsPDycM2fOAPcXbg8cgGefhcBAWLwYrFZ44QU4cqQdFy5s5aWX2qb63CIiIiIZTeHWydnD7e7du2/afvDgQQAKFiyIn59fis979Ch07QoBAfDTT+YUXh07wv79MGOGOcWXiIiISGajB8qc3J1GblPbknDyJHz4IUyfDomJ5ra2bWH4cLh+KREREZFMS+HWyaVVuD13zlwid/JksD+b1qIFjBgBNWqkXb0iIiIijqRw6+Ts4fbkyZOEhYUltSAkN9xeuACffgrjx0NMjLmtcWP44AO4vlaDiIiISJahnlsnlzNnTooUKQLcvJiDvefWvkTvf129Cu+9ByVLwuefm8G2Th0ICoJVqxRsRUREJGtSuM0EHnroIeDfh8ri4uI4evQocOvIbXi42VNbooT5z4gIqFYNli6F9euhSZOMrV1EREQkIyncZgL/DbdHjhwhMTGR7NmzU7BgQcAMtSNHmqH2vffMkduAAPj5Z9i61eyvtVgc9Q1EREREMoZ6bjOB/4Zbe79t+fLluXbNwvjxMHo0XL5s7l+uHAwbBh06mPPWioiIiDwoFG4zAXu43bNnDzabLanfNiGhAiVK3Bxqhw41Q62Li6OqFREREXEchdtMoGzZsri7u3Pt2jW2bTvE11//DMCOHf6AQq2IiIiIncJtJuDm5ka5chXZs2cntWrVA64AkD37MSZNUqgVERERsVO4dXLXrplz1O7de+H6litYrXmpWbMxP/wwklKlHFqeiIiIiFNRuHVS167BhAnmHLWXLgG0BGZSu3ZbVq36Di8vDwdXKCIiIuJ89Cy9k7l2DT7+2JzSa9AgM9iWLQuzZn1DZOQ1Nm2aq2ArIiIicgdOGW4nTJhA8eLF8fT0pHbt2mzevPmu+//000+UL18eT09PKleuzNKlSzOo0rRzu1BbpgzMmgX79sHzz1sUakVERETuwenC7dy5c+nXrx/Dhg1j+/btVKlShebNm3P+/Pnb7r9hwwY6duxIjx492LFjB23atKFNmzY3LVXrzO4Wavfvh+efB1c1j4iIiIgki8UwDMPRRdyodu3a1KxZk/HjxwNgs9nw9/fn9ddfZ+DAgbfs36FDByIjI/ntt9+Stj388MMEBgYyadKke14vPDwcPz8/wsLC8PX1Tbsvcg8REWZP7Wef2XtqzVD73nvQsaMCrYiIiIhdSvKaU43cxsXFsW3bNpo1a5a0zWq10qxZMzZu3HjbYzZu3HjT/gDNmze/4/6xsbGEh4ff9MpIERHwySdQvDgMHPjvSO1335kjtV26KNiKiIiIpJZThduLFy+SmJhI/vz5b9qeP39+QkJCbntMSEhIivYfNWoUfn5+SS9/f/+0KT6Z2rT5N9SWLq1QKyIiIpKWnCrcZoRBgwYRFhaW9Dp16lSGXr9XLzPUfvstHDigUCsiIiKSlpwqVuXJkwcXFxdCQ0Nv2h4aGkqBAgVue0yBAgVStL+HhwceHo6bdaB1a2jVSoFWREREJD041citu7s71atXJygoKGmbzWYjKCiIOnXq3PaYOnXq3LQ/wIoVK+64v6NZrQq2IiIiIunF6WJWv3796NatGzVq1KBWrVqMGTOGyMhIunfvDkDXrl0pXLgwo0aNAqBPnz40bNiQ0aNH88QTTzBnzhy2bt3K5MmTHfk1RERERMQBnC7cdujQgQsXLjB06FBCQkIIDAxk2bJlSQ+NnTx5Eqv13wHnunXrMnv2bIYMGcK7775LmTJlWLRoEQEBAY76CiIiIiLiIE43z21Gc9Q8tyIiIiKSPJl2nlsRERERkfuhcCsiIiIiWYbCrYiIiIhkGQq3IiIiIpJlKNyKiIiISJahcCsiIiIiWYbCrYiIiIhkGQq3IiIiIpJlKNyKiIiISJahcCsiIiIiWYbCrYiIiIhkGQq3IiIiIpJlKNyKiIiISJahcCsiIiIiWYarowtwNMMwAAgPD3dwJSIiIiJyO/acZs9td/PAh9tr164B4O/v7+BKRERERORurl27hp+f3133sRjJicBZmM1m4+zZs2TPnh2LxeLochwqPDwcf39/Tp06ha+vr6PLcWq6V8mne5V8ulfJo/uUfLpXyad7lXyOuFeGYXDt2jUKFSqE1Xr3rtoHfuTWarVSpEgRR5fhVHx9ffUfdjLpXiWf7lXy6V4lj+5T8uleJZ/uVfJl9L2614itnR4oExEREZEsQ+FWRERERLIMhVtJ4uHhwbBhw/Dw8HB0KU5P9yr5dK+ST/cqeXSfkk/3Kvl0r5LP2e/VA/9AmYiIiIhkHRq5FREREZEsQ+FWRERERLIMhVsRERERyTIUbkVEREQky1C4fcBMmDCB4sWL4+npSe3atdm8efMd9124cCE1atQgR44ceHt7ExgYyKxZszKwWsdKyb260Zw5c7BYLLRp0yZ9C3QiKblXM2fOxGKx3PTy9PTMwGodJ6W/pq5evUqvXr0oWLAgHh4elC1blqVLl2ZQtY6VknvVqFGjW35NWSwWnnjiiQys2HFS+utqzJgxlCtXjmzZsuHv70/fvn2JiYnJoGodKyX3Kj4+nhEjRlCqVCk8PT2pUqUKy5Yty8BqHWPt2rW0atWKQoUKYbFYWLRo0T2PWbNmDdWqVcPDw4PSpUszc+bMdK/zrgx5YMyZM8dwd3c3pk+fbuzbt894+eWXjRw5chihoaG33X/16tXGwoULjf379xtHjhwxxowZY7i4uBjLli3L4MozXkrvld3x48eNwoULGw0aNDBat26dMcU6WErv1YwZMwxfX1/j3LlzSa+QkJAMrjrjpfQ+xcbGGjVq1DBatmxprFu3zjh+/LixZs0aY+fOnRlcecZL6b26dOnSTb+e9u7da7i4uBgzZszI2MIdIKX36ocffjA8PDyMH374wTh+/LixfPlyo2DBgkbfvn0zuPKMl9J71b9/f6NQoULGkiVLjKNHjxoTJ040PD09je3bt2dw5Rlr6dKlxuDBg42FCxcagPHzzz/fdf9jx44ZXl5eRr9+/Yz9+/cb48aNc3hWULh9gNSqVcvo1atX0vvExESjUKFCxqhRo5J9jqpVqxpDhgxJj/KcSmruVUJCglG3bl1j6tSpRrdu3R6YcJvSezVjxgzDz88vg6pzHim9T19//bVRsmRJIy4uLqNKdBr3+3vVl19+aWTPnt2IiIhIrxKdRkrvVa9evYwmTZrctK1fv35GvXr10rVOZ5DSe1WwYEFj/PjxN21r166d0blz53St05kkJ9z279/fqFSp0k3bOnToYDRv3jwdK7s7tSU8IOLi4ti2bRvNmjVL2ma1WmnWrBkbN2685/GGYRAUFMShQ4d45JFH0rNUh0vtvRoxYgT58uWjR48eGVGmU0jtvYqIiKBYsWL4+/vTunVr9u3blxHlOkxq7tPixYupU6cOvXr1In/+/AQEBDBy5EgSExMzqmyHuN/fqwCmTZvGc889h7e3d3qV6RRSc6/q1q3Ltm3bkv46/tixYyxdupSWLVtmSM2Okpp7FRsbe0vLVLZs2Vi3bl261prZbNy48ab7CtC8efNk//eaHlwddmXJUBcvXiQxMZH8+fPftD1//vwcPHjwjseFhYVRuHBhYmNjcXFxYeLEiTz66KPpXa5DpeZerVu3jmnTprFz584MqNB5pOZelStXjunTp/PQQw8RFhbG559/Tt26ddm3bx9FihTJiLIzXGru07Fjx1i1ahWdO3dm6dKlHDlyhNdee434+HiGDRuWEWU7RGp/r7LbvHkze/fuZdq0aelVotNIzb3q1KkTFy9epH79+hiGQUJCAj179uTdd9/NiJIdJjX3qnnz5nzxxRc88sgjlCpViqCgIBYuXJjl/4CZUiEhIbe9r+Hh4URHR5MtW7YMr0kjt3JX2bNnZ+fOnWzZsoWPPvqIfv36sWbNGkeX5VSuXbtGly5dmDJlCnny5HF0OU6vTp06dO3alcDAQBo2bMjChQvJmzcv33zzjaNLcyo2m418+fIxefJkqlevTocOHRg8eDCTJk1ydGlObdq0aVSuXJlatWo5uhSntGbNGkaOHMnEiRPZvn07CxcuZMmSJXzwwQeOLs3pjB07ljJlylC+fHnc3d3p3bs33bt3x2pVdHJ2Grl9QOTJkwcXFxdCQ0Nv2h4aGkqBAgXueJzVaqV06dIABAYGcuDAAUaNGkWjRo3Ss1yHSum9Onr0KCdOnKBVq1ZJ22w2GwCurq4cOnSIUqVKpW/RDpLaX1c3cnNzo2rVqhw5ciQ9SnQKqblPBQsWxM3NDRcXl6RtFSpUICQkhLi4ONzd3dO1Zke5n19TkZGRzJkzhxEjRqRniU4jNffqvffeo0uXLrz00ksAVK5cmcjISF555RUGDx6cZYNbau5V3rx5WbRoETExMVy6dIlChQoxcOBASpYsmRElZxoFChS47X319fV1yKgtaOT2geHu7k716tUJCgpK2maz2QgKCqJOnTrJPo/NZiM2NjY9SnQaKb1X5cuXZ8+ePezcuTPp9dRTT9G4cWN27tyJv79/RpafodLi11ViYiJ79uyhYMGC6VWmw6XmPtWrV48jR44k/UEJIDg4mIIFC2bZYAv392vqp59+IjY2lueffz69y3QKqblX/2/njkHh/+M4jn9Ed9+SIvEbRO4oGRQLKToZDWxYrtsMMljUlQHFLWSRyWJzkUUWZyCDbtHdd3AYTiizoiyuXr/J1f9nuv/f7/vp//F81A3Xfa9en3ffq9fdfb+f9/f3LwX28wuUpL8X1rL/cl55nmdaWlpMqVQyh4eHZmJi4m/H/V8ZHBz8x1yNMeb09LSibvHtrN3KhsCl02mFw2Ht7u6qUChoZmZG9fX15W2Y4vG4kslk+fhUKqVMJqNisahCoaCNjQ3V1NRoZ2fH1hICU+ms/vSTdkuodFYrKys6OTlRsVjU1dWVpqen5Xmerq+vbS0hEJXO6enpSXV1dZqbm9Pd3Z2Oj4/V3Nys1dVVW0sIzL/9/A0NDWlqairouFZVOqulpSXV1dVpb29P9/f3ymQy6ujo0OTkpK0lBKbSWWWzWR0eHqpYLOri4kKjo6OKRCJ6eXmxtIJgvL29KZfLKZfLyRijzc1N5XI5PT4+SpKSyaTi8Xj5+M+twBYWFnRzc6Pt7W22AkOwtra21NbWplAopP7+fmWz2fJrsVhMiUSi/HxxcVGdnZ3yPE8NDQ0aHBxUOp22kNqOSmb1p59UbqXKZjU/P18+9tevXxobG3N+38hPlZ5Tl5eXGhgYUDgcVjQa1dramkqlUsCp7ah0Vre3tzLGKJPJBJzUvkpm9fHxoeXlZXV0dMjzPLW2tmp2dtb5wvapklmdn5+ru7tb4XBYjY2Nisfjen5+tpA6WGdnZzLGfHl8ziaRSCgWi315T29vr0KhkKLRqPU9pqskh/+HAAAAwI/CNbcAAABwBuUWAAAAzqDcAgAAwBmUWwAAADiDcgsAAABnUG4BAADgDMotAAAAnEG5BQAAgDMotwAAAHAG5RYAHFcqlWxHAIDAUG4BwCEPDw+mqqrK7O/vm+HhYRMOh83R0ZHtWAAQmBrbAQAA38f3fWOMMevr6yaVSplIJGKampospwKA4FBuAcAh+Xze1NbWmoODA9Pe3m47DgAEjssSAMAhvu+b8fFxii2AH4tyCwAOyefzZmRkxHYMALCGcgsAjnh9fTUPDw+mr6/PdhQAsIZyCwCO8H3fVFdXm56eHttRAMAayi0AOML3fdPV1WU8z7MdBQCsqZIk2yEAAACA78AvtwAAAHAG5RYAAADOoNwCAADAGZRbAAAAOINyCwAAAGdQbgEAAOAMyi0AAACcQbkFAACAMyi3AAAAcAblFgAAAM6g3AIAAMAZlFsAAAA44ze/0jmRc0EPewAAAABJRU5ErkJggg==",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -421,7 +419,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "venv",
    "language": "python",
    "name": "python3"
   },
@@ -435,7 +433,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.0"
+   "version": "3.11.6"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "9ff3d0c7e37de5f5aa47f4f719e4c84fc6cba7b39c571a05173422444e82fa58"
+   }
   }
  },
  "nbformat": 4,
diff --git a/docs/source/examples/notebooks/models/pouch-cell-model.ipynb b/docs/source/examples/notebooks/models/pouch-cell-model.ipynb
index 69cfbfec40..a11046d967 100644
--- a/docs/source/examples/notebooks/models/pouch-cell-model.ipynb
+++ b/docs/source/examples/notebooks/models/pouch-cell-model.ipynb
@@ -49,10 +49,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\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",
+      "\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;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.2\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"
      ]
@@ -81,16 +79,7 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/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"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "cc_model = pybamm.current_collector.EffectiveResistance({\"dimensionality\": 1})\n",
     "dfn_av = pybamm.lithium_ion.DFN({\"thermal\": \"lumped\"}, name=\"Average DFN\")\n",
@@ -579,7 +568,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1300x700 with 5 Axes>"
       ]
@@ -623,7 +612,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAABHoAAAKSCAYAAACtCLygAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOydeXwURdrHf9UzmZwkgXAkgQSi3IiCAREQFI0cIorigaILyiuuAoJ44InHLqJ4IV7oqqCryOoqqKyiiHKoERFE7ku5FAJqOHJOZqbr/aOnu6v6mCSQg4Tny2eY6bqe56mu7kw983QV45xzEARBEARBEARBEARBEHUepbYVIAiCIAiCIAiCIAiCIKoGcvQQBEEQBEEQBEEQBEHUE8jRQxAEQRAEQRAEQRAEUU8gRw9BEARBEARBEARBEEQ9gRw9BEEQBEEQBEEQBEEQ9QRy9BAEQRAEQRAEQRAEQdQTyNFDEARBEARBEARBEARRTyBHD0EQBEEQBEEQBEEQRD2BHD0EQRAEQRAEQRAEQRD1hBPa0fPXX3+hadOm2LVrV4XK33PPPRg/fnz1KkUQBEEQBFFPEb97LV26FIwxHD582LX8okWL0KVLF6iqWnNKEgRBEAQRkRPa0TN16lRceumlaNWqVYXK33nnnXjzzTfx66+/Vq9iBEEQBEEQ9ZDKfvcaOHAgoqKi8M4771SvYgRBEARBVBhvbSvgRnFxMV5//XV8/vnnFa7TuHFjDBgwAC+//DKefPLJatSOIAiCIAiifnEs370AYNSoUZg5cyauv/76atLMmVAohEAgUKMyCYIgCOJY8fl8UJSaibU5YR09n376KaKjo3H22WcD0P6YjxkzBl999RXy8vKQmZmJW2+9FRMmTJDqDRkyBPfffz85egiiEsyZMwetWrXCeeedV9uqVCsni50EQRDHgvW7l863336Le++9F9u2bUOXLl3w2muv4bTTTjPyhwwZgnHjxuGXX37BqaeeWu16cs6Rl5cX8ZEygiAIgjjRUBQFWVlZ8Pl81S7rhHX0rFixAtnZ2caxqqpo0aIF3n//faSkpOC7777DmDFjkJaWhquuusood9ZZZ+G3337Drl27Khx2TBAnK3PnzoXH4wGgfXF+/vnn0bFjR1xwwQW1rFnVcrLYSRAEcTxYv3vp3HXXXXjuueeQmpqK++67D0OGDMG2bdsQFRUFAMjMzESzZs2wYsWKGnH06E6epk2bIi4uDoyxapdJEARBEMeDqqrYt28f9u/fj8zMzGr/23XCOnp2796N9PR04zgqKgqPPPKIcZyVlYXc3Fy89957kqNHr7N7925y9BAnPdnZ2cjMzMT8+fMd86+++mq88MILmD17NmJjY3HrrbfWS+fH8dg5atQovPnmmwCATp06YcOGDcekw4wZM3D77bcbx3/88QcaN258TG0RBEFUB9bvXjoPPfQQLrzwQgDAm2++iRYtWmD+/Pm271+7d++udh1DoZDh5ElJSal2eQRBEARRVTRp0gT79u1DMBg0fiypLk7YxZhLSkoQExMjpb344ovIzs5GkyZNkJCQgFdffRV79uyRysTGxgLQnjMniJMZzjm2bNmCjh07Riyne5MZY0bUy4nKDTfcgJiYGIRCIdcygwYNQlxcHH777Tcp/XjsbNy4Mf7973/j8ccfBwBccskliIuLQ0FBgWudESNGwOfz4a+//gKgLVj673//G5dddlmlZBMEQdQUTt+9AKBnz57G50aNGqFdu3bYvHmzVCY2NrZGvnvpa/LExcVVuyyCIAiCqEr0R7YizWWqihPW0dO4cWMcOnTIOJ43bx7uvPNOjB49Gl988QXWrl2LG264AWVlZVK9/Px8AJq3jCBOZnbt2oXi4uKIjp7//Oc/aNq0KSZOnIipU6fizz//xJIlS2pQy8rRoUMH+P1+7Ny50zF/xYoVWLRoEW677Ta0aNHCSD9eO+Pj43Hdddfh4osvBqA5cUpKSlwjpYqLi/HRRx9h4MCBxi/O7du3x3XXXYfTTz+9wnIJgiBqEut3r8qQn59fo9+96HEtgiAIoq5Rk3+7TlhHT9euXbFp0ybj+Ntvv0WvXr1w6623omvXrmjdujV++eUXW70NGzYgKioKnTp1qkl1CeKEQ79+Ijl6rr32WgwfPhyAduO57bbbTuhHt3RbtmzZ4ph/7733olGjRrjnnnuk9Kq285JLLkGDBg0wd+5cx/yPPvoIRUVFGDFixDHLIAiCqGms3710vv/+e+PzoUOHsG3bNnTo0MFIKy0txS+//IKuXbvWiJ4EQRAEQUTmhHX0DBgwABs3bjR+WWrTpg1+/PFHfP7559i2bRsefPBBrFq1ylZvxYoV6NOnj/EIF0GcbMyfPx/Z2dnGI0J9+vTBiBEjcOTIEdc6o0aNqhM7UemOHusjAwDwv//9z9gZJjk52bF+VdkZGxuLyy+/HEuWLMHBgwdt+XPnzkWDBg1wySWXHLcsgiCImsL63Uvn0UcfxZIlS7BhwwaMGjUKjRs3xtChQ43877//HtHR0dIjXic6oVAIS5cuxbvvvoulS5fWSBg9oC0kPX78eJxyyimIjo5GRkYGhgwZIkWZfvfdd7jooovQsGFDxMTEoHPnznjmmWdsOjLGwBiTHHEA4Pf7kZKSAsYYli5daqQvW7YM559/Pho1aoS4uDi0adMGI0eOlKLjQ6EQnn32WXTu3BkxMTFo2LAhBg0ahG+//VaSMWfOHNe/tUT9Yvny5RgyZAjS09PBGMOCBQtqRcaoUaOMMR8VFYVmzZrhwgsvxBtvvAFVVatcJ+LEoKLnvVWrVkY5/SVG9+v51vvlxIkTbXODo0eP4v7770f79u0RExOD1NRU5OTk4MMPPwTn3Ci3Y8cO3HDDDWjRogWio6ORlZWFa665Bj/++GP1dEYlOWEdPZ07d8aZZ56J9957DwBw88034/LLL8fVV1+NHj164K+//sKtt95qqzdv3jzcdNNNNa0uQZwQPPnkk7j88svRrl07tG/fHi1btsR1112HuXPn4pZbbqlt9Y6bVq1aITY21hbRwznHAw88gIyMDIwbN65GdBkxYgSCwaBxj9LJz8/H559/jssuu4wczgRB1Cms3710Hn/8cUyYMAHZ2dnIy8vDJ598Im0N++6772LEiBF1Zt2cDz/8EK1bt0a/fv1w7bXXol+/fmjdujU+/PDDapW7a9cuZGdn46uvvsKTTz6J9evXY9GiRejXrx/Gjh0LQPux5txzz0WLFi3w9ddfY8uWLZgwYQL++c9/Yvjw4dIkAwAyMjIwe/ZsKW3+/PlISEiQ0jZt2oSBAweiW7duWL58OdavX4/nn38ePp/PcCBxzjF8+HA8+uijmDBhAjZv3oylS5ciIyMD5513XrVM8IkTn6KiIpxxxhl48cUXK133vPPOw5w5c6pMxsCBA7F//37s2rULn332Gfr164cJEybg4osvRjAYrLR+RN2gouf90Ucfxf79+43XTz/9JLUTExODyZMnR5R1+PBh9OrVC2+99RbuvfderFmzBsuXL8fVV1+Nu+++2/jh/Mcff0R2dja2bduGV155BZs2bcL8+fPRvn173HHHHVXfCccCP4FZuHAh79ChAw+FQhUq/+mnn/IOHTrwQCBQzZoRxInHDz/8wBlj/M477+Scc962bVt+zTXXcM45v/DCC7nX6+VFRUW1qWKV0LVrV96zZ08p7d133+UA+OzZs6tc3siRI3nLli1t6cFgkKelpdl0mTVrFgfAP//8c8f2HnroIQ6A//HHH1WuK0EQxPFS2e9ef/zxB2/UqBH/9ddfq1kzjZKSEr5p0yZeUlJyTPU/+OADzhjjQ4YM4bm5ubygoIDn5ubyIUOGcMYY/+CDD6pYY5NBgwbx5s2b88LCQlveoUOHeGFhIU9JSeGXX365Lf/jjz/mAPi8efOMNAD8gQce4ImJiby4uNhIv/DCC/mDDz7IAfCvv/6ac875s88+y1u1ahVRv3nz5nEA/OOPP7blXX755TwlJcXQffbs2TwpKakiZhP1CAB8/vz5FS5/7rnnVvq7mZuMkSNH8ksvvdSWvmTJEg6A/+tf/6qUHKJuUNHz3rJlS/7ss8+6ttOyZUt+2223cZ/Px//3v/8Z6RMmTODnnnuucXzLLbfw+Ph4/vvvv9vaKCgo4IFAgKuqyjt16sSzs7Md/1YeOnTIVY/j/RtWGU7YiB4AGDx4MMaMGYPff/+9QuWLioowe/ZseL0n7K7xBFFtPPHEE2jSpAkeffRRlJSUYMeOHTjjjDMAAL1790YwGHR8zKimUFUVpaWlFXpxyy+WIh07dsTWrVuN42AwiClTpqBz587429/+VhOmAAA8Hg+GDx+O3Nxc7Nq1y0ifO3cumjVrdkKvdUQQBOFGZb977dq1Cy+99BKysrKqWbPjJxQK4Y477sDFF1+MBQsW4Oyzz0ZCQgLOPvtsLFiwABdffDHuvPPOanmMKz8/H4sWLcLYsWMRHx9vy09OTsYXX3yBv/76C3feeactf8iQIWjbti3effddKT07OxutWrXCBx98AADYs2cPli9fjuuvv14ql5qaiv3792P58uWuOs6dOxdt27bFkCFDbHl33HEH/vrrLyxevLhC9hLlwzlHUVFRjb8ifceqi5x//vk444wzqj0ir77iNC7KyspQVFQEv9/vWFZ8ZCoQCKCoqAilpaUVKltVHMt5z8rKwt///nfce++9jo/7qaqKefPmYcSIEUhPT7flJyQkwOv1Yu3atdi4cSPuuOMOKIrdnXKiPNZ6Qjt6AO25uYyMjAqVveKKK9CjR49q1oggTjyCwSAWLVqEQYMGITY2Fhs2bICqqsYOT0VFRQCAhg0b1pqOy5cvR2xsbIVeoiPHSocOHZCfn284rWbPno3t27dj2rRpjjfb6kRfbFlflPm3337DihUrMHz48BN+q3qCIAg3KvPdq1u3brj66qurWaOqYcWKFdi1axfuu+8+298LRVFw7733YufOnVixYkWVy96xYwc452jfvr1rmW3btgGAtNC1SPv27Y0yIjfeeCPeeOMNANraORdddJFtB7Qrr7wS11xzDc4991ykpaXhsssuwwsvvICjR49K8t1k6+lO8oljo7i4GAkJCTX+Ki4urm3Tq5z27dtLP7oRFUcfF3/++aeR9uSTTyIhIcG2HELTpk2RkJCAPXv2GGkvvvgiEhISMHr0aKlsq1atkJCQIK2rWZHH+CqD9bxPnjxZGuszZ8601XnggQewc+dOvPPOO7a8P//8E4cOHYp4nwaA7du3G/JPZCj0hSDqATt27EBRURE6d+4MAFi3bh0AGBE9a9euRcuWLZGUlFRrOrZv3962joAbaWlprnnigsxJSUn4xz/+gb59+2Lw4MFVomdlyM7ORvv27fHuu+/ivvvuw7vvvgvOOe22RRAEcQKyf/9+AMBpp53mmK+n6+WqkspEUVQ24uK6667DPffcg19//RVz5sxxnNx4PB7Mnj0b//znP/HVV19h5cqVeOyxx/DEE0/ghx9+MP7u1rdoD6Jmeeyxx/DYY48ZxyUlJfj+++8lh8GmTZuQmZlZpXI55zW6bTVxYmA973fddRdGjRplHDdu3NhWp0mTJrjzzjsxZcoU248UFb3/1ZX7JDl6CKIeoO+QooeD//zzz2jcuDHS09Px559/YtmyZfj73/9emyoiNTVVuvkeK+IW62vWrMHevXvx/vvvH3e7x8qIESPw4IMPYt26dZg7dy7atGmD7t2715o+BEEQhDO6M2PDhg04++yzbfkbNmyQylUlbdq0AWPMtpmASNu2bQFoP2T06tXLlr9582bjb6BISkoKLr74YowePRqlpaUYNGgQCgoKHGU0b94c119/Pa6//nr84x//QNu2bTFr1iw88sgjaNu2reOulrpsUUfi+ImLi0NhYWGtyK0u/v73v+Oqq64yjkeMGIFhw4bh8ssvN9KcHok5XjZv3lwnHh89EdHHoDgu7rrrLkycONG2HIoeTS9uNjJ27FjcdNNNtkh2PdJGLFsV8wAR63lv3LgxWrduXW69SZMm4aWXXsJLL70kpTdp0gTJyckR79OAeR/csmULunbtegya1wwn/KNbBEGUT/PmzQEAubm5ALSIHj2a5/bbb4eiKJg4cWJtqVeltG7dGj6fD6tWrcK0adNw+eWX1+ojm3r0zpQpU7B27VqK5iEIgjhB6dOnD1q1aoXHHnvMtj6DqqqYNm0asrKy0KdPnyqX3ahRIwwYMAAvvvii8Ti1yOHDh9G/f380atQITz/9tC3/448/xvbt23HNNdc4tn/jjTdi6dKl+Nvf/lbhR4cbNmyItLQ0Q5/hw4dj+/bt+OSTT2xln376aaSkpODCCy+sUNtE+TDGEB8fX+Ov6ox8adSoEVq3bm28YmNj0bRpUymtqtdS/eqrr7B+/XoMGzasSts9WXAaFz6fD/Hx8YiOjnYsKz76GhUVhfj4eMTExFSobFVxPOc9ISEBDz74IKZOnSo5xRVFwfDhw/HOO+9g3759tnqFhYUIBoPo0qULOnbsiKefftpxrZ/Dhw9XWqfqgBw9BFEPyMzMxHnnnYd///vfuPvuu/Hzzz/D7/djyJAhePfdd/Haa68hKysLqqritttuQ+PGjZGcnIzu3btLz+Q6sXPnTgwePBgpKSlIS0uTQsIZY3j++eeRmZmJ1NRUPPnkk9VtKjweD9q2bYs5c+bg0KFDUohwbZCVlYVevXrho48+AgBy9BAEQZygeDwePP3001i4cCGGDh2K3NxcFBQUIDc3F0OHDsXChQvx1FNPVdsaay+++CJCoRDOOussfPDBB9i+fTs2b96MmTNnomfPnoiPj8crr7yCjz76CGPGjMG6deuwa9cuvP766xg1ahSuuOIKKVpCZODAgfjjjz/w6KOPOua/8soruOWWW/DFF1/gl19+wcaNGzF58mRs3LjRWHx5+PDhuOyyyzBy5Ei8/vrr2LVrF9atW4ebb74ZH3/8MV577TVpIelQKIS1a9dKL7eIIKLuUlhYaJxfQPteuHbtWmmdlpqS4ff7kZeXh99//x1r1qzBY489hksvvRQXX3xxjW7IQdQs1XHex4wZg6SkJGOdTZ2pU6ciIyMDPXr0wFtvvYVNmzZh+/bteOONN9C1a1cUFhaCMYbZs2dj27Zt6NOnDz799FP8+uuvWLduHaZOnYpLL720Ksw+fqp9Xy+CIGqE/fv384svvpjHxMRwANzn8/HevXvzJUuWGGU+++wznp2dzY8cOcKDwSBfvXo1LygocG0zEAjwDh068IceeoiXlJTwI0eO8B9//NHIB8D79+/Pjxw5wjdv3sxTU1P5l19+Wa12cs75VVddxQHwMWPGVLsst+3VRV588UUOgJ911lnltkfbqxMEQRw7VbE17QcffMBbtWrFARivrKysat1aXWffvn187NixvGXLltzn8/HmzZvzSy65xNgGnXPOly9fzgcMGMATExO5z+fjnTp14k899RQPBoNSW4iw1fWhQ4ek7dXXrFnDr7vuOp6VlcWjo6N5SkoK79u3r20r9UAgwJ988kneqVMn7vP5eGJiIh8wYAD/5ptvpHKzZ8+W+k9/nXrqqcfdR8SJxddff+14rkeOHFlu3Ypur14RGSNHjjTSvV4vb9KkCc/JyeFvvPGG4xbXRP2goue9IturW/Pnzp3LAUjbq3PO+eHDh/k999zD27Rpw30+H2/WrBnPycnh8+fP56qqGuW2bt3K//a3v/H09HTu8/l4y5Yt+TXXXMPXrFnjqkdNbq/OOK8jqwkRBFEhFi5ciCFDhuCnn35Cly5dpLwlS5bg1ltvxb///W9079693PDdb7/9FsOHD8fu3bsdd7RijOHrr7/GeeedB0Bbyf7gwYN49dVXq8qcWmfUqFH46quvsGbNGni93mPeMrG0tBSFhYWYPn06nnzySfzxxx+Oi8QRBEEQ7pSWlmLnzp3IysqyPSpQGUKhEFasWIH9+/cjLS0Nffr0od0SCYIgiGqlqv6GVQRajJkg6hlbtmwBYwzt2rWz5V1wwQX4+9//jjFjxiAvLw/XXXcdpk2b5vrM7G+//YaWLVtG3LZc3II3IyMDP//88/EbcYKxd+9eNGnSBJ06dTIW66wss2bNwu23317FmhEEQRDHgsfjMX6kIAiCIIj6Bjl6CKKesWXLFmRmZkqr3IvcfvvtuP3227F3715cdNFFOO2001xXwc/IyMDu3bsjblu5d+9enHrqqcbn6titpDa5++67cd111wHQFm87VoYNGyZt6VubW90TBEEQBEEQBFF/IUcPQdQztmzZgvbt2zvm/fjjj+Cco2vXrmjQoAGioqKkUHXd4TNnzhwAwFlnnYUGDRrgH//4B+6++26UlZVh+/btyM7ONuo88cQTOPPMM7F//3688cYbeOutt6rNttqgY8eOjtvZVpaMjAwp+okgCIIgCIIgCKI6oF23CKKe8c0332DRokWOeUeOHMGNN96I5ORktGvXDr1798a1115r5P/222/o3bu3cez1erFw4UJ89913SEtLQ7t27Ywt3HX0qKC+ffvitttuQ05OTvUYRhAEQRAEQRAEQZQLLcZMEAQAIBgM4vTTT8fPP//sumaPFcYY9u7dixYtWlSzdgRBEARRswtZEgRBEERVQosxEwRR43i9XmzatKm21SAIgiCIcqHfKQmCIIi6Rk3+7aJHtwiCIAiCIIg6gR5xWlxcXMuaEARBEETlKCsrAwBpjdTqgiJ6CII4ZugXVYIgCKIm8Xg8SE5OxsGDBwEAcXFxrrtCEgRBEMSJgqqq+OOPPxAXFwevt/rdMOToIQiCIAiCIOoMqampAGA4ewiCIAiiLqAoCjIzM2vkBwpajJkgCIIgCIKoc4RCIQQCgdpWgyAIgiAqhM/ng6LUzOo55OghCIIgCIIgCIIgCIKoJ9BizARBEARBEARBEARBEPUEcvQQBEEQBEEQBEEQBEHUE8jRQxAEQRAEQRAEQRAEUU8gRw9BEARBEARBEARBEEQ9gRw9BEEQBEEQBEEQBEEQ9QRy9BAEQRAEQRAEQRAEQdQTyNFDEARBEARBEARBEARRTyBHD0EQBEEQBEEQBEEQRD2BHD21xPLlyzFkyBCkp6eDMYYFCxZUSbtLly7FmWeeiejoaLRu3Rpz5sxxLfv444+DMYaJEydWiezymDZtGrp3744GDRqgadOmGDp0KLZu3Volbb///vto3749YmJi0LlzZ3z66aeuZf/+97+DMYYZM2ZUiexIvPzyyzj99NORmJiIxMRE9OzZE5999tlxt3ui2mulKsfYiWzzww8/DMaY9Grfvv1xt3si2/z777/juuuuQ0pKCmJjY9G5c2f8+OOPx93uiXoPa9Wqle0cM8YwduzY42r3RD7HBEEQBEEQRN2EHD21RFFREc444wy8+OKLVdbmzp07MXjwYPTr1w9r167FxIkT8X//93/4/PPPbWVXrVqFV155BaeffnqVyS+PZcuWYezYsfj++++xePFiBAIB9O/fH0VFRcfV7nfffYdrrrkGo0ePxk8//YShQ4di6NCh2LBhg63s/Pnz8f333yM9Pf24ZFaUFi1a4PHHH8fq1avx448/4vzzz8ell16KjRs3HnObJ7K9IlU5xuqCzZ06dcL+/fuN1zfffHNc7Z3INh86dAi9e/dGVFQUPvvsM2zatAlPP/00GjZseFztnsj3sFWrVknnd/HixQCAK6+88pjbPJHPMUEQBEEQBFGH4UStA4DPnz9fSistLeV33HEHT09P53Fxcfyss87iX3/9dcR27r77bt6pUycp7eqrr+YDBgyQ0goKCnibNm344sWL+bnnnssnTJhQBVZUnoMHD3IAfNmyZUbaoUOH+OjRo3njxo15gwYNeL9+/fjatWsjtnPVVVfxwYMHS2k9evTgN998s5T222+/8ebNm/MNGzbwli1b8meffbbKbKkMDRs25K+99hrnvP7aG2mM1UebH3roIX7GGWe45tc3mydPnszPOeeciGXq+z1swoQJ/NRTT+WqqnLO6985JgiCIAiCIOouFNFzgjJu3Djk5uZi3rx5WLduHa688koMHDgQ27dvd62Tm5uLnJwcKW3AgAHIzc2V0saOHYvBgwfbytY0R44cAQA0atTISLvyyitx8OBBfPbZZ1i9ejXOPPNMXHDBBcjPz3dtpyJ2q6qK66+/HnfddRc6depUxZZUjFAohHnz5qGoqAg9e/YEUH/tjTTG6qvN27dvR3p6Ok455RSMGDECe/bsMfLqm80ff/wxunXrhiuvvBJNmzZF165d8a9//UsqU5/vYWVlZXj77bdx4403gjEGoP6dY4IgCIIgCKLu4q1tBQg7e/bswezZs7Fnzx4jTP/OO+/EokWLMHv2bDz22GOO9fLy8tCsWTMprVmzZjh69ChKSkoQGxuLefPmYc2aNVi1alW12xEJVVUxceJE9O7dG6eddhoA4JtvvsEPP/yAgwcPIjo6GgDw1FNPYcGCBfjvf/+LMWPGOLblZndeXp5x/MQTT8Dr9eK2226rJovcWb9+PXr27InS0lIkJCRg/vz56NixY721N9IYq6829+jRA3PmzEG7du2wf/9+PPLII+jTpw82bNiAn3/+ud7Z/Ouvv+Lll1/GpEmTcN9992HVqlW47bbb4PP5MHLkyHp/D1uwYAEOHz6MUaNGAai/45ogCIIgCIKom5Cj5wRk/fr1CIVCaNu2rZTu9/uRkpICAEhISDDSr7vuOsyaNavcdvfu3YsJEyZg8eLFiImJqVqlK8nYsWOxYcMGaR2Tn3/+GYWFhYaNOiUlJfjll1+wZ88edOzY0Ui/7777cN9995Ura/Xq1XjuueewZs0a49f3mqRdu3ZYu3Ytjhw5gv/+978YOXIkli1bVi/tLW+M1UebAWDQoEHG59NPPx09evRAy5Yt8d5776G0tLTe2ayqKrp162Y4bLp27YoNGzZg1qxZGDlyZL2/h73++usYNGiQ4cSqr+OaIAiCIAiCqJuQo+cEpLCwEB6PB6tXr4bH45Hy9MnR2rVrjbTExEQAQGpqKg4cOCCVP3DgABITExEbG4vVq1fj4MGDOPPMM438UCiE5cuX44UXXoDf77fJqw7GjRuHhQsXYvny5WjRooWRXlhYiLS0NCxdutRWJzk5GcnJyZLd+iNfbnanpqYCAFasWIGDBw8iMzPTyA+FQrjjjjswY8YM7Nq1q+qMc8Dn86F169YAgOzsbKxatQrPPfccTjnllHpnb3ljbOrUqfXOZieSk5PRtm1b7NixA8nJyfXO5rS0NMlxAQAdOnTABx98AKB+38N2796NL7/8Eh9++KGRVl/vXQRBEARBEETdhBw9JyBdu3ZFKBTCwYMH0adPH8cyuuNApGfPnrateRcvXmysB3PBBRdg/fr1Uv4NN9yA9u3bY/LkydU+QeKcY/z48Zg/fz6WLl2KrKwsKf/MM89EXl4evF4vWrVq5diGm91LliyRtlgW7b7++usd18G4/vrrccMNNxyfUceAqqrw+/310t7yxtj+/fvrnc1OFBYW4pdffsH111+PDh061Dube/fuja1bt0pp27ZtQ8uWLQHU33sYAMyePRtNmzbF4MGDjbT6eC0TBEEQBEEQdZjaXg36ZKWgoID/9NNP/KeffuIA+DPPPMN/+uknvnv3bs455yNGjOCtWrXiH3zwAf/111/5ypUr+WOPPcYXLlzo2uavv/7K4+Li+F133cU3b97MX3zxRe7xePiiRYtc69TkjjW33HILT0pK4kuXLuX79+83XsXFxZxzzlVV5eeccw4/44wz+Oeff8537tzJv/32W37ffffxVatWubb77bffcq/Xy5966im+efNm/tBDD/GoqCi+fv161zo1tXPNPffcw5ctW8Z37tzJ161bx++55x7OGONffPFFvbTXCXGM1Veb77jjDr506VLDnpycHN64cWN+8ODBemnzDz/8wL1eL586dSrfvn07f+edd3hcXBx/++23jTL18R4WCoV4ZmYmnzx5spReH88xQRAEQRAEUXchR08t8fXXX3MAttfIkSM555yXlZXxKVOm8FatWvGoqCielpbGL7vsMr5u3bpy2+3SpQv3+Xz8lFNO4bNnz45YviYnSU72ApB0PHr0KB8/fjxPT0/nUVFRPCMjg48YMYLv2bMnYtvvvfceb9u2Lff5fLxTp078f//7X8TyNTVZuvHGG3nLli25z+fjTZo04RdccAH/4osvjPz6Zq8T1jFWH22++uqreVpaGvf5fLx58+b86quv5jt27DDy66PNn3zyCT/ttNN4dHQ0b9++PX/11Vel/Pp4D/v88885AL5161ZbXn08xwRBEARBEETdhHHOea2EEhEEQRAEQRDEMRIKhRAIBGpbDYIgCIKoED6fD4qi1IgsWqOHIAiCIAiCqDNwzpGXl4fDhw/XtioEQRAEUWEURUFWVhZ8Pl+1y6KIHoIgCIIgCKLOsH//fhw+fBhNmzZFXFwcGGO1rRJBEARBRERVVezbtw9RUVHIzMys9r9dFNFDEARBEARB1AlCoZDh5ElJSaltdQiCIAiiwjRp0gT79u1DMBhEVFRUtcqqmQfECIIgCIIgCOI40dfkiYuLq2VNCIIgCKJy6I9shUKhapdFjh6CIAiCIAiiTkGPaxEEQRB1jZr820WOHoIgCIIgCIIgCIIgiHoCOXrqOH6/Hw8//DD8fn9tq1JjnGw2n2z2AmTzyQLZTBDEycK0adPQvXt3NGjQAE2bNsXQoUOxdetWqUxpaSnGjh2LlJQUJCQkYNiwYThw4IBUZs+ePRg8eDDi4uLQtGlT3HXXXQgGgzVpClGP+f3333HdddchJSUFsbGx6Ny5M3788Ucjn3OOKVOmIC0tDbGxscjJycH27dulNvLz8zFixAgkJiYiOTkZo0ePRmFhYU2bQtQzli9fjiFDhiA9PR2MMSxYsMBWpqrG57p169CnTx/ExMQgIyMD06dPr07Tqg1y9NRx/H4/HnnkkZNq0nCy2Xyy2QuQzScLZDNBECcLy5Ytw9ixY/H9999j8eLFCAQC6N+/P4qKiowyt99+Oz755BO8//77WLZsGfbt24fLL7/cyA+FQhg8eDDKysrw3Xff4c0338ScOXMwZcqU2jCJqGccOnQIvXv3RlRUFD777DNs2rQJTz/9NBo2bGiUmT59OmbOnIlZs2Zh5cqViI+Px4ABA1BaWmqUGTFiBDZu3IjFixdj4cKFWL58OcaMGVMbJhH1iKKiIpxxxhl48cUXXctUxfg8evQo+vfvj5YtW2L16tV48skn8fDDD+PVV1+tVvuqBU7UaY4cOcIB8CNHjtS2KjXGyWbzyWYv52TzyQLZTBBEZSkpKeGbNm3iJSUlta3KcXHw4EEOgC9btoxzzvnhw4d5VFQUf//9940ymzdv5gB4bm4u55zzTz/9lCuKwvPy8owyL7/8Mk9MTOR+v99Rjt/v52PHjuWpqak8OjqaZ2Zm8scee6waLSPqKpMnT+bnnHOOa76qqjw1NZU/+eSTRtrhw4d5dHQ0f/fddznnnG/atIkD4KtWrTLKfPbZZ5wxxn///XfXdh966CGekZHBfT4fT0tL4+PHj68iq4j6CAA+f/58Ka2qxudLL73EGzZsKN1TJ0+ezNu1a+eqT35+Pr/22mt548aNeUxMDG/dujV/4403HMvW5N8w2l6dIAiCIAiCqLNwzlFcXFzjcuPi4o55Yc0jR44AABo1agQAWL16NQKBAHJycowy7du3R2ZmJnJzc3H22WcjNzcXnTt3RrNmzYwyAwYMwC233IKNGzeia9euNjkzZ87Exx9/jPfeew+ZmZnYu3cv9u7de0w6E8cG5xzBkrJake2N9VV4jH788ccYMGAArrzySixbtgzNmzfHrbfeiptuugkAsHPnTuTl5UljNCkpCT169EBubi6GDx+O3NxcJCcno1u3bkaZnJwcKIqClStX4rLLLrPJ/eCDD/Dss89i3rx56NSpE/Ly8vDzzz8fp+VEReGcA6Gav38CADzHfg+1UlXjMzc3F3379jV2xwK0++wTTzyBQ4cOSRFuOg8++CA2bdqEzz77DI0bN8aOHTtQUlJSJXYdD+ToOU5KS0tRVlY7N29ACy8T308GTjabTzZ7AbL5ZIFsPvHw+XyIiYmpbTUIolIUFxcjISG5xuUWFh5GfHx8peupqoqJEyeid+/eOO200wAAeXl58Pl8SE5Olso2a9YMeXl5RhnRyaPn63lO7NmzB23atME555wDxhhatmxZaX2J4yNYUoZXuk6oFdk3//QcouKiK1T2119/xcsvv4xJkybhvvvuw6pVq3DbbbfB5/Nh5MiRxhhzGoPiGG3atKmU7/V60ahRo4hjNDU1FTk5OYiKikJmZibOOuusyppKHCuhYqjvNS2/XDWgXHUQ8Fb+HupEVY3PvLw8ZGVl2drQ85wcPXv27EHXrl0NB1KrVq2O36AqgBw9x0FpaSmSYhuiDKXlF65mMjIyaluFGudks/lksxcgm08WyOYTh9TUVOzcuZOcPQRRjYwdOxYbNmzAN998U+2yRo0ahQsvvBDt2rXDwIEDcfHFF6N///7VLpeoe6iqim7duuGxxx4DAHTt2hUbNmzArFmzMHLkyGqTe+WVV2LGjBk45ZRTMHDgQFx00UUYMmQIvF6aphJ1g1tuuQXDhg3DmjVr0L9/fwwdOhS9evWqbbXI0XM8lJWVoQylOAcXwcs0bzlTGMAU8zMA6CFpCjM+M0Ux88TP4XcWbgPWNhzLW2RIeYo9DcyxPDfSYGnDLC+VEeWLeYI+jmlC+5yZn21tKfZ2reU5TJN0W7hgpq08E2VEyFPc2zBgsiz39i39YMlzLu/QLlzSmKUfymvDRaZjmsUmHdc0S1uR9AHjDmkO7YvlYUEqz+1tCXXtesjlmVsbsJYztWAR2mDGu5Msbqoolgu/K7Y2uGueAm6qK+TZyonlrWlCeUXQzS1PYRwKrGmqUNeUped5mD1Nv/14oOuqGm2a5YU04TMAeJhqyPIYddVwm+JnQaa1jfCxh6mGbkZbUI3boSlbFerIOnrE9oW2PJb+8Ah6Mase4IJuXEiD3H+6XgzwhM+WmcagGGnyu5anWNIUKGA4WqCiZfYulJWVkaOHqFPExcWhsPBwrcitLOPGjTMWAG3RooWRnpqairKyMhw+fFiK6jlw4ABSU1ONMj/88IPUnr4rl17GyplnnomdO3fis88+w5dffomrrroKOTk5+O9//1tp3Yljwxvrw80/PVdrsitKWloaOnbsKKV16NABH3zwAQBzjB04cABpaWlGmQMHDqBLly5GmYMHD0ptBINB5Ofnu47RjIwMbN26FV9++SUWL16MW2+9FU8++SSWLVuGqKioCutPHCOeOC2yppZkVxVVNT5TU1Ntux2Wd58dNGgQdu/ejU8//RSLFy/GBRdcgLFjx+Kpp56qEtuOFXL0VAFeRMHLtBsRY8x09IjOFi3BdPRIaRbHjVJZRw+T6sp5Do4el/LH7uhxz6t2R484Ia8hR0+5DhknR0xVOXrcykMuX22OHqc0HJtNx+ToscisVUcPq7ijx17OydHj7swp19FjXM5V5+gRHTmVdfQ45Tk7emSHhnIMjh6bc4aJDhO7o8fWRjmOHo9RjoXrMeOzqaN+zASnCxfSrA4hCDrq7Tul2R09nko6esw8JuQ5OXpoE06i7sIYO6ZHqGoSzjnGjx+P+fPnY+nSpbZHA7KzsxEVFYUlS5Zg2LBhAICtW7diz5496NmzJwCgZ8+emDp1Kg4ePGg8frB48WIkJibaJugiiYmJuPrqq3H11VfjiiuuwMCBA5Gfn2+sD0RUL4yxCj8+VZv07t0bW7duldK2bdtmPO6XlZWF1NRULFmyxJg4Hz16FCtXrsQtt9wCQBujhw8fxurVq5GdnQ0A+Oqrr6CqKnr06OEqOzY2FkOGDMGQIUMwduxYtG/fHuvXr8eZZ55ZDZYSIoyxKnt8qjapqvHZs2dP3H///QgEAoajcfHixWjXrp3jY1s6TZo0wciRIzFy5Ej06dMHd911Fzl6CIIgCIIgCKI+M3bsWMydOxcfffQRGjRoYKwHkZSUhNjYWCQlJWH06NGYNGkSGjVqhMTERIwfPx49e/bE2WefDQDo378/OnbsiOuvvx7Tp09HXl4eHnjgAYwdOxbR0c6OhGeeeQZpaWno2rUrFEXB+++/j9TUVNtaQARx++23o1evXnjsscdw1VVX4YcffsCrr75qbCvNGMPEiRPxz3/+E23atEFWVhYefPBBpKenY+jQoQC0CKCBAwfipptuwqxZsxAIBDBu3DgMHz4c6enpjnLnzJmDUCiEHj16IC4uDm+//TZiY2NpPSlCorCwEDt27DCOd+7cibVr16JRo0bIzMyssvF57bXX4pFHHsHo0aMxefJkbNiwAc899xyeffZZV92mTJmC7OxsdOrUCX6/HwsXLkSHDh2qtT8qAjl6CIIgCIIgCKIaefnllwEA5513npQ+e/ZsjBo1CgDw7LPPQlEUDBs2DH6/HwMGDMBLL71klPV4PFi4cCFuueUW9OzZE/Hx8Rg5ciQeffRRV7kNGjTA9OnTsX37dng8HnTv3h2ffvopFIWi+AiZ7t27Y/78+bj33nvx6KOPIisrCzNmzMCIESOMMnfffTeKioowZswYHD58GOeccw4WLVokPe77zjvvYNy4cbjggguM8Txz5kxXucnJyXj88ccxadIkhEIhdO7cGZ988glSUlKq1V6ibvHjjz+iX79+xvGkSZMAACNHjsScOXMAVM34TEpKwhdffIGxY8ciOzsbjRs3xpQpUzBmzBhX3Xw+H+69917s2rULsbGx6NOnD+bNm1fFPVB5GOfctuwFUTGOHj2KpKQknIdL4VUirdEjPIZVkTV66NEtenTLqd1I5SGXp0e3ZD3o0S16dMvQ45gf3arYGj2eiGv0qFL78mNaFVujpzof3TpaEELDtr/iyJEjSExMBEGciJSWlmLnzp3IysqitaQIgiCIOkVN/g0jdz5BEARBEARBEARBEEQ9gRw9BEEQBEEQBEEQBEEQ9QRy9BAEQRAEQRAEQRAEQdQTyNFDEARBEARBEARBEARRTyBHD0EQBEEQBEEQBEEQRD2BHD0EQRAEQRAEQRAEQRD1BHL0EARBEARBEARBEARB1BPI0UMQBEEQBEEQBEEQBFFPIEcPQRAEQRAEQRAEQRBEPYEcPQRBEARBEARBEARBEPUEcvQQBEEQBEEQRA3x+OOPgzGGiRMnSumlpaUYO3YsUlJSkJCQgGHDhuHAgQNSmT179mDw4MGIi4tD06ZNcddddyEYDNag9kR9JRQK4cEHH0RWVhZiY2Nx6qmn4h//+Ac450YZzjmmTJmCtLQ0xMbGIicnB9u3b5fayc/Px4gRI5CYmIjk5GSMHj0ahYWFNW0OQZz0kKOHIAiCIAiCIGqAVatW4ZVXXsHpp59uy7v99tvxySef4P3338eyZcuwb98+XH755UZ+KBTC4MGDUVZWhu+++w5vvvkm5syZgylTptSkCUQ95YknnsDLL7+MF154AZs3b8YTTzyB6dOn4/nnnzfKTJ8+HTNnzsSsWbOwcuVKxMfHY8CAASgtLTXKjBgxAhs3bsTixYuxcOFCLF++HGPGjKkNkwjipIYcPQRBEARBEARRzRQWFmLEiBH417/+hYYNG0p5R44cweuvv45nnnkG559/PrKzszF79mx89913+P777wEAX3zxBTZt2oS3334bXbp0waBBg/CPf/wDL774IsrKyhxllpWVYdy4cUhLS0NMTAxatmyJadOmVbutRN3ju+++w6WXXorBgwejVatWuOKKK9C/f3/88MMPALRonhkzZuCBBx7ApZdeitNPPx1vvfUW9u3bhwULFgAANm/ejEWLFuG1115Djx49cM455+D555/HvHnzsG/fPke5nHM8/PDDyMzMRHR0NNLT03HbbbfVlNkEUW8hRw9BEARBEARRZ+Gco6TIX+Mv8ZGWijB27FgMHjwYOTk5trzVq1cjEAhIee3bt0dmZiZyc3MBALm5uejcuTOaNWtmlBkwYACOHj2KjRs3OsqcOXMmPv74Y7z33nvYunUr3nnnHbRq1apSehPHB+ccamlJrbwqM0Z79eqFJUuWYNu2bQCAn3/+Gd988w0GDRoEANi5cyfy8vKkMZqUlIQePXpIYzQ5ORndunUzyuTk5EBRFKxcudJR7gcffIBnn30Wr7zyCrZv344FCxagc+fOle5ngiBkvLWtAEEQBEEQBEEcK6XFZbi46cQal7vw4AzExkdXqOy8efOwZs0arFq1yjE/Ly8PPp8PycnJUnqzZs2Ql5dnlBGdPHq+nufEnj170KZNG5xzzjlgjKFly5YV0peoOri/FLuutTv3aoJWc78Ei4mtUNl77rkHR48eRfv27eHxeBAKhTB16lSMGDECgDnGnMagOEabNm0q5Xu9XjRq1CjiGE1NTUVOTg6ioqKQmZmJs846q1J2EgRhhxw9VUAQAYBrwVGMM+iBUtpnAAi/c2Z8Zlwx88TPAKAyMGZJY+K7YkkTPtvKK/Y0MPMzN/O4kQZLG2Z5qYwoX8xTYS8v6Giay2B2kUNbCmxtWMsbv1MwAIouE+7lzVMQOU9xb8OAybLc27f0gyXPubxDu3BJY5Z+KK8NF5mOaRabdFzTLG1F0geMO6Q5tC+WhwWpPLe3JdS16yGXZ25twFrO1IJFaIMZ706yuKmiWC78zm1tcNc8Dm6oqwp5iqWcfqyA29OE8oqgm1uewjgUWNNUoa4pS8/zMHta+JKFB7quqtGmWV5IEz4DgIephiyPUVcNtyl+FmRa2wgfe5hq6Ga0BdUIezVlq0IdWUeP2L7QlsfSHx5BL2bVA1zQjQtpkPtP14sBnvDZMtMYFCNNftfyYEnT2j1aoIIgiKpn7969mDBhAhYvXoyYmJgalT1q1ChceOGFaNeuHQYOHIiLL74Y/fv3r1EdiLrBe++9h3feeQdz585Fp06dsHbtWkycOBHp6ekYOXJktcm98sorMWPGDJxyyikYOHAgLrroIgwZMgReL01TCeJ4oCvoOPD5fEhNTcU3eZ+aM+1QrapEEARBEMdMamoqfD5fbatBEJUiJs6HhQdn1IrcirB69WocPHgQZ555ppEWCoWwfPlyvPDCC/D7/UhNTUVZWRkOHz4sRfUcOHAAqampALTrU18vRczX85w488wzsXPnTnz22Wf48ssvcdVVVyEnJwf//e9/K2MqcRyw6Bi0mvtlrcmuKHfddRfuueceDB8+HADQuXNn7N69G9OmTcPIkSONMXbgwAGkpaUZ9Q4cOIAuXboA0MbhwYMHpXaDwSDy8/Ndx2hGRga2bt2KL7/8EosXL8att96KJ598EsuWLUNUVFRlzCUIQoAcPcdBTEwMdu7c6boAHkEQBEHUJXw+X41HHBDE8cIYq/AjVLXBBRdcgPXr10tpN9xwA9q3b4/JkyfD4/EgOzsbUVFRWLJkCYYNGwYA2Lp1K/bs2YOePXsCAHr27ImpU6fi4MGDxuMxixcvRmJiIjp27OgqPzExEVdffTWuvvpqXHHFFRg4cCDy8/PRqFGjarKYEGGMVfjxqdqkuLgYiiIv3+rxeKCqWrRnVlYWUlNTsWTJEsOxc/ToUaxcuRK33HILAG2MHj58GKtXr0Z2djYA4KuvvoKqqujRo4er7NjYWAwZMgRDhgzB2LFj0b59e6xfv15yjhIEUTnI0XOcxMTE0JdigiAIgiAIwpEGDRrgtNNOk9Li4+ORkpJipCclJWH06NGYNGkSGjVqhMTERIwfPx49e/bE2WefDQDo378/OnbsiOuvvx7Tp09HXl4eHnjgAYwdOxbR0c6OrmeeeQZpaWno2rUrFEXB+++/j9TUVNtaQAQxZMgQTJ06FZmZmejUqRN++uknPPPMM7jxxhsBaA6riRMn4p///CfatGmDrKwsPPjgg0hPT8fQoUMBAB06dMDAgQNx0003YdasWQgEAhg3bhyGDx+O9PR0R7lz5sxBKBRCjx49EBcXh7fffhuxsbG0nhRBHCfk6CEIgiAIgiCIWubZZ5+FoigYNmwY/H4/BgwYgJdeesnI93g8WLhwIW655Rb07NkT8fHxGDlyJB599FHXNhs0aIDp06dj+/bt8Hg86N69Oz799FNb5AZBPP/883jwwQdx66234uDBg0hPT8fNN9+MKVOmGGXuvvtuFBUVYcyYMTh8+DDOOeccLFq0SPrR+5133sG4ceNwwQUXGON55syZrnKTk5Px+OOPY9KkSQiFQujcuTM++eQTpKSkVKu9BFHfYbyye0MSBEEQBEEQRC1QWlqKnTt3IisriyKqCYIgiDpFTf4NI3c+QRAEQRAEQRAEQRBEPYEcPQRBEARBEARBEARBEPUEcvQQBEEQBEEQBEEQBEHUE8jRQxAEQRAEQRAEQRAEUU8gRw9BEARBEARBEARBEEQ9gRw9BEEQBEEQRJ2CNo0lCIIg6ho1+beLHD0EQRAEQRBEnSAqKgoAUFxcXMuaEARBEETlKCsrAwB4PJ5ql+WtdgkEQRAEQRAEUQV4PB4kJyfj4MGDAIC4uDgwxmpZK4IgCIKIjKqq+OOPPxAXFwevt/rdMOToIQiCIAiCIOoMqampAGA4ewiCIAiiLqAoCjIzM2vkBwrG6SFngiAIgiAIoo4RCoUQCARqWw2CIAiCqBA+nw+KUjOr55CjhyAIgiAIgiAIgiAIop5AizETBEEQBEEQBEEQBEHUE8jRQxAEQRAEQRAEQRAEUU8gRw9BEARBEARBEARBEEQ9gRw9BEEQBEEQBEEQBEEQ9QRy9BAEQRAEQRAEQRAEQdQTyNFDEARBEARBEARBEARRTyBHD0EQBEEQBEEQBEEQRD2BHD0EQRAEQRAEQRAEQRD1BHL0EARBEARBEARBEARB1BNOSEfP8uXLMWTIEKSnp4MxhgULFhh5gUAAkydPRufOnREfH4/09HT87W9/w759+6Q28vPzMWLECCQmJiI5ORmjR49GYWGhVGbdunXo06cPYmJikJGRgenTp9eEeQRBEARBEARBEARBENXCCenoKSoqwhlnnIEXX3zRlldcXIw1a9bgwQcfxJo1a/Dhhx9i69atuOSSS6RyI0aMwMaNG7F48WIsXLgQy5cvx5gxY4z8o0ePon///mjZsiVWr16NJ598Eg8//DBeffXVarePIAiCIAiCIAiCIAiiOmCcc17bSkSCMYb58+dj6NChrmVWrVqFs846C7t370ZmZiY2b96Mjh07YtWqVejWrRsAYNGiRbjooovw22+/IT09HS+//DLuv/9+5OXlwefzAQDuueceLFiwAFu2bKkJ0wiCIAiCIAiCIAiCIKqUEzKip7IcOXIEjDEkJycDAHJzc5GcnGw4eQAgJycHiqJg5cqVRpm+ffsaTh4AGDBgALZu3YpDhw7VqP4EQRAEQRAEQRAEQRBVgbe2FTheSktLMXnyZFxzzTVITEwEAOTl5aFp06ZSOa/Xi0aNGiEvL88ok5WVJZVp1qyZkdewYUObLL/fD7/fbxyrqor8/HykpKSAMValdhEEQRBEdcM5R0FBAdLT06Eo9eK3H6Keo6oq9u3bhwYNGtB3L4IgCKJOUZPfu+q0oycQCOCqq64C5xwvv/xytcubNm0aHnnkkWqXQxAEQRA1yd69e9GiRYvaVoMgymXfvn3IyMiobTUIgiAI4pipie9dddbRozt5du/eja+++sqI5gGA1NRUHDx4UCofDAaRn5+P1NRUo8yBAwekMvqxXsbKvffei0mTJhnHR44cQWZmJvbu3SvJJwiCIIi6wNGjR5GRkYEGDRrUtioEUSH0sUrfvQiCIIi6Rk1+76qTjh7dybN9+3Z8/fXXSElJkfJ79uyJw4cPY/Xq1cjOzgYAfPXVV1BVFT169DDK3H///QgEAoiKigIALF68GO3atXN8bAsAoqOjER0dbUtPTEykLxsEQRBEnYUegSHqCvpYpe9eBEEQRF2lJr53nZAP5BcWFmLt2rVYu3YtAGDnzp1Yu3Yt9uzZg0AggCuuuAI//vgj3nnnHYRCIeTl5SEvLw9lZWUAgA4dOmDgwIG46aab8MMPP+Dbb7/FuHHjMHz4cKSnpwMArr32Wvh8PowePRobN27Ef/7zHzz33HNSxA5BEARBEARRPykpLMDS+0fjm4kDsfT+0SgpLKhtlQiCIAiiSjght1dfunQp+vXrZ0sfOXIkHn74Ydsiyjpff/01zjvvPABAfn4+xo0bh08++QSKomDYsGGYOXMmEhISjPLr1q3D2LFjsWrVKjRu3Bjjx4/H5MmTK6zn0aNHkZSUhCNHjtCvSgRBEESdg/6OEXWNqhqzyyddgu7tV8CXUGaklRX6sGpLH/R95uOqUNWgpLAAK6dNhLfodwTjm6PHvTMQm0CPSxIEQZxs1OT3rhPS0VNXoC/IBEEQRF2G/o4RdY2qGLPLJ12CXtlLkL+3ETaXXYY2w27C9g/+hQ6++WiUkY/vVl9QZc6emnQoEQRBECc2Nfm9q06u0XOisXvLPxEfHwXOtS3TwAGVq+FjCMdaAg+/jGfzmPacHgPAFAYwQGFMS1OgpTMFigJA5QghCMZ8UAFwVQXAoKoqODR5WhqHyllYpgqucugePcY4oDBo/2DKYQBDWA60Y0VRwHkIQTAw5oXKObiq28jBodmkckBVOcAAzlVwNfwOLU8xbFEAcDAFUBTdfgUK0/qDMQWMcYRCAYQUHxi8Whth+6CG+xLQZIBD5VofcFUN68IBxsAZC/ejdg6YwrT+DdusKAwcHB5FAQMQ5CGEEAsW7kdV1ezjnCGkqmCcQwWggoOr4fOKEMAZQioHYx6Aca3PAK1/w3ZzCOeUwej/QDAEVYkDY1p/qqqmPwOg6n3IOTgYOFehqoBmNRBSGRgUMEXLZwoD4xxQwicTHJ5wPzAwMA8DwFCmcjAWG+5DDs5VhLjWt1pfayNF1ceyqgJM6/eAqmh2wOxLMLNPGWNAuN85AKYoYApQGgAUJUbrV64CYTtCuj3ha0IfXzwsX0vzaCJYePSwsGyuXRdcv26gjVcwDjAPSgMcXiVaGDuqJjYUPofhi1PvZxYe0yo4oBoXHsC0C0Ix7BQvEC1d5YCieOAPcEQpPnBVG/tc5eF2tfsBVABM62PjfgBADYXvB9y8H4ABgKJdpYrQvwAUpmjXPecIBYFo5g2fT9XsT6NvEb7vqOHrFoZcpobHpnQeFUO+bisL5zNFgRoKwRtQ4PF6jPMWPrHaPSl8rGqDVZOl93F4zClMAWeadZJMaHlgDEw7sYDCEAwEEc098ChKePzoYwaAGgpfH2EdwvcDcBUq51A0pUxbGAO4fj9iYdu0fubQrl9whlAwhFjmCeusX4faOOLhe542bsL3nPD9Amr4vIvnKnxPYtCHlfFJG2KcQVEUBEr9iGFarxh/QxCWrYbv5Vy/L4Wv1bB9ajBg3l8QvgY5g7RzJ9d00C4fBQqAZpnyGncEUd8pKSxA9/YrkL+3EZLHb0Df2HgwxYv0TjNRVvI48p9rh+7tvkFJYcFxR91IDqU82aHUK3sJlk+6pEqdPVwNAX98C16SBxabCjTpDaZ4qqx9giAIou5Ajp4qoODPuVCLtdm84WwxXuHJgSVP5SpKS7WJpjmp0tpj4IbTRZ8ZKOF3Bo6Y2LADSW9Pb5+bx7qDiYcnepwDQZWjtEzVpjZMa0Cfq+vymf5ZTGOANxqGA4TDSQ6MiZeuj8qBQJCjNMCN1aD0uTMc5ADmwlRM4fBEM8lGs0+ZYb/ucIKQ7w8A/iAzJlYQ5BkydVnhY6YA8DDA67XJRFie6XCx9D/nKCrzIqgyzeHCdMeOMW+XOlafiEEBEOWFqnjAuaaVGu5PcLE/mSBPn2wyHC31QOWmQQza5NnsX2aTzxQgFBUNlXnMcyfZKp9D8/xqxhwpUTRdjUYZxEOu97lwchkDyqJiwaGE7TDHpGSr5iWUrxeVo7BY0fKMNctEI+W+1a4QzfaANwacK+H2uClPOMGqIEvvBFXlKCkWZsfm4ISQCG6Ty6B6YrT+EfrSNEYXInzW7wchgJdoDgvJJvHcSf0KQD/fiDbbNE6kaaORyeU0HlDBSkPh+wEAbuljyWZZF6/qC6dwmy0A128vklzOARYIgZWpQteaFwnnppPCcHgJukWFvNpHONvFhLEkdDKYPwSUhaTbgHGRCjYyvUrYM+tRGaJCHtORZTl3XLhQWPheq/c7K/FD82KG2+bGJ3D9LHNzPGlOLQaPn0MJcbN96Pc26YYTVkO6+SJYVBAeSCycznSDDMnMMCHsSAMPOwcJ4uRh5bSJ6NOpDJvzLkP8Uzega5vPoYYYggEvQgEPYpMBXwM/jrzeBn8GGkJlMVA98WC+RChxyYhu2BTxjdMR0ygNLK4RWGxDsKgEIKoB4I0HvA0ATwxKiwpNh9KErTg3Ng4AqsWhBAB870cIrb4HrHiPdgyAx2XCk/04WMalx90+QRAEUbcgR08V0K61D4kNKreudTi24JgI8pDxmUcoZyUcq1BuHaf8AI+ssVubqkM9t7JiekgF/BHKWNuQ6nIGFR5beqQ6ABBQgVJEhfOYQ1nm2l6Z6oUKxdE2a5+Ln/0hL0p5tCVP/7U/Ul2G0lCUoadTGbMtSOVKQz741SihvDkFdpInHieEfNAn4pLfxEE/0YaioA8B1bzdGHNxUSaHzR4VgC/kg4joqzHOCbfrXRyIQTCkOOinOxDlcyza5Al5AS6fb8k+Yd4tJvlLo8FVS7tOWBrWnD0eu0525eUsFWClHoBb7j8R6oidq4SYkcas+WCWY7OepwSVlskAMBVQQoJTxSbT5TioIqrEni+145QGgIVUeAIubYvlrMdlQUSVhGxlmSRPcP4IdRVPKOywsRjleMFwU7ZSBk9J+PEOyYtukQUgHNKIsJcJ3Bsb9hTDfBccQTY9wo6qIHO60xJE/cVb9DsAoM2wm/D7a38HACgeDp8nAMSYN4ukJgVIgsPizEXh127t0Ol7FVcBT0iBJ0FFPC9Cwb9aIxiKQlkoDgFvCrzJLVHETkVKg1VY/cwodL97FhCdYkaMVhK+9yOEll+Lgt+TUbStDQJHYhCVVIr4tn+gQdG18PSdS84egiCIkwxy9NQS+m+tVdGOE85tM/NX53LLusiKUNgpSwEQKqeMW16k6XKk/jN+9RdaqMi8V6sru3R0x4PeJhOcPaYOzFUfLrThrCuDIpwVsx1Tf7G22J7Cwo/nIVJf6blmKYWrUJhqccrIn8z+Eif7ChSmQuWKGQmll+OiJF2eXaYx12W6Q0frU1NDa7+H8xw7V7Zaj3kx5sSca48pOvaJVl53rIR9RlKkhdWpI0qyOgtMZxO3RP64iLcWcXA6lduGeOAm0jowRdmWPN0Ga7SJk1yOCGZGkmnTXTiOdHFa29CDVkR5zPB5SDrYpk3i8HSSBZjnUg/LEy4jW9dwbgswY+LN0jp4bXbKdyApekpy8FiuTik0Mqwgd3HHixeqJNapIwiifhOMbw4A2P7Bv1CaMRrvvrsEPlaCGFaC6KgytGj2B9r3/gW/rW2OksJY+KKDiPJpL29UEJ6okPFSvCqYNwQlSgXzqlC82jXIFMCjaJ9jGvgR00B0qO4B8BOQoR1lt14E9cNWUEMMZaU+lJbEoCQQjwBrBJbQHA1adkLSqWdAaXgKWGwaENMETBF+OFFDOPr5GCiHE/HzF51wpNuF6Hbn5fhxzodI+mIxsgeuh7roZiSOvpge4yIIgjiJoMWYjwN9MaU/t2bVWkSPG85zpvKlug2GYCUiesQJsmqRWZHBxqFF9AQcyrtNS8S0oBDRI+ZFmtcBWkRPGbzgwtRQrCs6bKx1/aoHKjzO/QBAjAYSHSz+kAcB+Gx53FLHWg8ASkNaFJGbTlxwW4ltlgajEIBX94UYZd31NPOLglGAQ1m9vP6omWw3Q3HQhyA8RqCBOUaY5Exysr84aEb0SPlcPi/S+eEMxQEfQlxxbVeasYtlOFAW9CJStJSTM4iDwe/3godlRsTqVOBAKOiBq8fGZeBzlYH5FdiiawC4XrB6OyEzoscpMgZ6jlVuCPD4UWmZDAALAR4nz285FycLqvD64XgyGHc51usGVHiC7m07tQEASlkQXj8XLxLJFtkJI39WSsWIHm7TW4qeCtdlAHhpGTylQS3ReI5TLyvIEqN9wnm8uBiwypQeY+OCPFNuMOTH0rL/0mLMRJ3heBeyLCksAOZmoiA/AckTtsIXfqQKAMpKinH4uXZo0LAIGLHb9kgV5xyFRwpxcM8B/Lk3D4d/O4jC/QdRkn8EgcNHoBYVwBsohI8XoXWTnTgzZxM2Lm+NwsJ4xPgCiI8vRXyCH7HxpYhJKkFMSjHUMgWKr+LfCTkHykqj4C+JQYk/DvBGoVmT37BvfRpSrr4XMekdweIzgLh0BPxlWH9DP3QZsg6B3gsQ0/LCSveXE2qgDGWr/w1+eBdYciv4sq+HEuUrvyJBEMRJDi3GfBJQVRE9ViK3acajuDskKtAut+e5tcEqUMZJlrW8+NkpYMAqkwGuDqZIc0rts+rQpjyxN+1iUr5VD60197grzRZVakvOd44ggi1NliyncUljhBfJdp5j6+PDGi0T1kYPcBAmtpy79Y2gDVc1B4hNT3MCym12hfuW6Wv6mHJER43sfgtHOImLBNvQH5XR9JG/XsvRFLYACD3Tmh7uFGkdICdc85ixXo1N5YgD1qGCfcA412Ww22l1Qri0y93KuVycUkCPHoDiNABdZNr6Vex/vT3hGDDTmGCnm44cchvg4buAtM6RoI9RUb4iDTsNmZbJm1TfLGvGWnL5pQ8uyX6X822ENHEXWy0dIziJCOJkIjahAZZv6aMtkvxcO2z2D0Xry0Zjx/zX0SF6gbnrlsO6OYwxNEhugAbJDXDq6a0jyikpLEDZ3EyktspH8oRcyaFUfPQoCl/sCMUXxPurb0Pozz8QFzyAZN8RJMUVITGhGAnxfsQmlCI63g9vbACemAA8MUEwBkTHBhAdG0Ci8GhZeuf9wKbbwDdpl3coqKD4aBxST48FAPz27j04ZWQ0WMP2QHQTc0OQSlKyZCqUXc8iKjb8PG0+ENg4GWqr2xF7wf3H1CZBEARR9ZCjp5aorq/WkR1IdidP+XXMMkYrzDnPyRlknQNWBN1JY3d7OOvpNLfUHwlyUCniUySGQyNcytEJ4FDP6ZE4ey2Ls8D4pO/SZbXRHlFi7RV3Z43pBBGjdbQ3bccmcaIuR69Ye94e5SPNm3W/h0sXaAspmzspGbZy85EtY91Ya3SO4OAR1u0N1+M2vblw3sydjUTdhHPIrHmS0rJtsI8zs0VmOj9EH5DbYBXzxML6gtT27pcHhpDPFGY+XmSVWd7FIrQpfd+X5OgXMpP0sM0PnAei2f1cEB3pQnS5OJ2cNcztKSUnP0hFbrjh9pmhBzMHG2CseyO5I/U+stzvTEeWw83S6vySyun9rJ9XVfZcOS60bbWDCc4bF6+WkcVtKhLEyUDfZz42tj0/J+ENYMMbSG0DlBVEV9nW6uU6lDIP4bvVF+Bvrz7i2gbnHPt/+R1bvvsZB37eipK9exBTmockzyEkxRUiKaEEzdLzkdLmDxQfSIDiVeGNCcATG4DHq6JBo0I0aFQIAMjK3AL+9SBwAMEyDwqPxqPQn4JQwqlofEY/JLTuBZbcDsyX5KpPyZKpiMp7DIcONsKPy3vgt9+boEXzP9Ct7zo0jHkMJUtAzh6CIIgTBHp06zg4nke3uEPUSEVxe3SrvPbCm/Iax5V5eKysnF+m3WRzqCjnqQnHPP3RrfLq6PlSXc4QgubMcMq3tqnnawtAex3KWFfusabbF2O2y7Q7Szi0R4S0x8WsTh3rY1EWBwiAUjVKWy8njBg5xG31zHZLgh4EYD6CJZW1Oej0xZK1siXCAtAAM9d8dZAp2l4U8CHAHR6nM+adou7mZxUMJUF5gWxzlzBnp5cecVMY8EHlCqzn2njsStJdeASOA2XhxZgleZIwh/OpAv5AFBwfaSpnEHLOoAb1evKuck7ljSQV2qNb8FTsQhHfVUAJMimfRVoriJsvpVRzUFZEpugvYiFhMWaxzfJkhlREWdcMdrqgIPtmwAElqEIRb0KqpZ6op5geCMBX6jDAAWHgcqOuZKc/BCUk3DNV7n4uRdmlfnhKA/oAt8sU30WnDefgxSWAqprp1ke/4KQDRzBQiqWBD+jRLaLOUJVh7yWFBVg5bSK8Rb8jGN8cPe6dUSU7YInoDiVfQpmRVlYQjVVbz6kSh9J/xz+EIafPxB9lLfFbixnYu3ozCrZtRVLpLjSLP4gzem1DdLwf/kNx8CX44YkLRFxKrqwkCoUFCSgMNAGS26JptwsR26oHeGwmyv6dhaK/4vHc6/3R/m/not91Ofj67S+x5a1lmDD6C8SnFCF6VB49xkUQBOFCTT66RY6e4+BEXKMnsiPFedetigyAgMXR4zQXA2B7CkCFCqu2FZEXFBw9bvXcnCqao8d5wcFItgdVoAxRkuNBfvzLKcJGOw6oHoQsa/RY64nHhoMo5IGf+xzzxM9OOviFNXqsTiVhA2uLDgwlQW/Y0WM6TJwdWXrfmvoXhbyGI8M851YHE4PVQVQU9CHEzZ3Q9HmsrpOTnRyaI6NEdVijh5vlRLsNWzhQFPDJzi/rLlqq3VcAaPPjslCU4eiR5tOGfXbbVQB+vxfS8r9uF4olj6sRdt1yasuQD23XLV2m0wVura+/h0xHD3MqZ6tn9oenFJWWycIyvcEI5dx0DaqIKrPkhwe56GCxtRF29HhCcO178bMippcFEeXnZhl50IbrhRdetfSB4g9BCQqJTg4bvZ6YXxqAx19mpumP5nEjQbgpiY4bDl5Yog0k25pC3Cwr6a+lB4N+cvQQdYqa/JJcVVSnQ6mkqBh5d5yNjD6/AGkD4el8N5DcETi8CaH104H9i7B3xako/dvb2PTFtyjYsB4NQ7vRrMFfaJxSgKSGRYhNKkZUgh/eWKcbtMzRgwnIL8uEN7UzUs8aCF/mOQh6UvDhpUNxxd+WoyTjGST0ublKbCMIgqhv0Bo9JwFuj4EcK5Vp61hku82jrD8SO8mqbLt6uqvMCqDvReXmDHJqSysjrpUhP/qk95v4SBiH+ZCU/qu+PWDA+oiRsD4OF48gSGfC/4DpPND10h7xMB+Fsm+NLsvmpn3caqW+FpC1tixTkyI8pmSLOrJaoZ0BfQWiSD5l0069n8I9y8yzyCGfD3OdIP1Y7gEO67pAHLZFlAWZpo2WJ6zCT9OY0Uvc8ckZ7hQpIh67ORoA41ko8VdWwY/gXk8FHJWpCIKhRlcaWQ6PgwnvnNsfkSpPlP6uPwVmtOnUL07v1nLhQaOfI92ZJN1zGKTNqSI5e/QnmaRE65i1F7KMHvHAUtdhvR+935mRrwq2OlzR3GqEcGjcoIwL1O70cYSe3SKI6iY2oQHOm/p69bQdH4fvj/ZDTG4IiZ2/RkzeIiOvtCAaBetb4fuj/XBNr9PRodfptvqhUAibvl2HjQuXwb9jAxpjL5olHULjlAI0aFiE2MQSeBv44fFpP9slNi1EIjYB2ASs+Q/UNUCwJAp9z9ccV/u/eR2ndhkAltDymNcBEuFqCPjjW/CSPLDYVKBJb9o9jCAIogKQo6eWqEonD1Bx5015zo5I7VvrsPB/0g/HFWhf1NXtK4BtwubSrrujSK6tt8ctaWIbWhl93Ri7Q8Bp1y1n54bofpAdFPKcU1vcmBnOBybUcZKv1eRygiCdSbJ1B5HNjcE0K8U1cuQeMZuXnS+AsY6MLlWIQLHKFN1D4PpCzpby3GITLP3M5TQupMlL7DBpDR+uajba1unRy7j4RnQXmHW+L9uH8MLJljFmPnkl1bMdOwrmsI5ZaRA5HQOaL44x5xtAJAeK/lmwzVbZmqh7aTjsa+aUI1MckzYHUaQbgjig7ReU4eyUrwVZB+m+ZL04dVPF9oxGrP3qfEKt17pZQh93QqreuNO5BNMGkW6rKnivXBcWF4UzuYxkt+gVE20nJw9B1Aeumfsc3r12Ajp9shYpzQ/BExNEqNSLv35viI2JXXDN3Odc63o8HnTu2xWd+3a15QXKAljzxffY8cU3yPjrE/Qe/DN2/ZQBn8KRkFyEmOQSRCWWwhcbQNMW+QCAU1puBP+kE8r8XhzOT0Yhy0JK9iVI6nIxWGJrMFbxCHi+9yOEVt8DVrxHOwbA4zLhyX4cLOPSynUSQRDESQY5emqJijpmKkJl2inP2VERGbayESpHcuQ4/F4tNek8Ea+QWGjxKY7TV9e6mkyzhPXRKdEBZJ8vMkebRHlyFI32SeV6zAuTpMu7fCmCTNMdxJm5lotbdI24o5cunYd33QKsTg3R8SPLNDRiTHLQ6OsAy3NQff0pZkgxo2vknndyNBmWhJ1DokXMLAauMqkvRT30fcWkKB/ro1uCTFkf0eFmFjHm20DYMWOZ+Lus/VIhmB5TJehjHVBObekRPRWVZ3Wq2G4AYWeE05Z1ohjRD1SOTCbI1H1FpjPFQY6TTKcLiwtFdIcPLG2LNrrJFP0uoi/E5lyxO0pssrg5imWjIQ8g0XkkneOQy/l0MMKoo8vRPVaCPABGlJD1BAIAVAfPG0EQdZFr5j6HkqJi/O+eJ1H2ex58aakY/J+7cHp8XPmVXYjyRaHHxX3Q4+I+eP3uRHQv3IyGzQNIHr8V/pIAvnt/MfYuXIxmgc3o2+8n+BLKECiMhi+pFFHRQTRJ+xNN8Cfw+yrw3x9EMKDgcH4SjgZbIrFzfzQ+axhYcnswxT4d4Xs/Qmj5tSj4PRlF29ogcCQGUUmliG/7BxoUXQtP37nk7CEIgogArdFzHNTWGj2B41ijJ2RzNkQqL8pUbWliObdRpBquhsrJVB3W6BHz3RwqgH0xZqf6TulBFfALixRbyzsvdqyhr9HjrqvimKev0SM6OwSXhq3vxMiWspDXaJcbL1FHc5Yrtlkc9CLAo2zppmPGap/pICkJhdeg4YLTA8yin11mYdCHoMNizABzWOxY0EMF/GqUKYM76eYQAcT1NXrkRYqNdi2OAmksccsaPTa5LHws2qm9G2v0uDplHNLDdqohr20+LinuksxKPHBdANrpXf+sVnKNHh0V8JSySstkYZmVWqNHJ+C+GDML62TLC+cba/RY2xV0tPo6GAcQCCKqVChk6wfTS2Tdml3xB831ftycO1YbOIAScY0es33pxghrO9rFyItLw4s+c0ueIEhMC3/W1uj5b51a74Q4uamLa/TUB0pL/Mgd1R99LvkRZcWt4DnzAXjbnI/g9q8QWvNP+OJ2YcXH3eC57D7s/HQxGhVtRMuUA2iWdhgJjQvgSy6B4rH/MQsFFRw91ABH/M0R26YfUvtcDSR1RMGbraAcVrB6UWcc6XYhuo26HD/O+RBJPy5G9sD1UJM4Ekf/To9xEQRRp6A1ek4Cjjei51jqcrhH2Di1KeroNFeU6rk0zLjoRrDPsyLNaZ1cYeXbzaBaJv9iPbsTwyQIhBeONnvKXtbZ2ROCYmtPzHfeY40hJEWGAGbUD4O4qK/TEy2ig0VPtT5UYl3zR3NyyD0vr6jj7LA02hWdH0yUqM8bxb3dzHV/OFdhXcTZlG1GD4WbkJ5e4WItUbxlLmufV6uavtzpXIqxWDJ6RI+kp8UTYl1bCWCyI6cSzhqtuiWSx6meq/OIu+dbVBROtfmS6rHIkUThdJWHz1o5MplFpusjX+XJdGpfMNsaNSS1WV4b3HJvNMaVoJBDlA2zCYTQEBPqSKE+MB6xkhwxershSS+b0uIjWEY9y0VhS9PbU2WZ0iLPBEEQkYmJjcaa6EHwfQKcef46eLf+H7BVm0ioajRWftINa6IH4Y7hA9B3+ACjHuccP3+1Cuve/QSJ+WuR2Wg/0lIPoUGTAkQnl8ATpaJhkyNoiCNAySbwL14EV4GEOODwn8loNepUZFzQC0rTFrj08btQVjoe62/ohy5D1sG/9yvEtLyw9jqFIAjiBIYcPbXE8X61rozDRqwTIaggYhvM8i6Ws87RrPlueWJ7TvNRNxvLm7tFcqKJ80xrOS3Pbqmoi+bEkB0HAKCAh6OInO0QV8oRI4MUxqQtrXUnhNPTJtZ5MLf0ku5W4YLTSdOBSS2K68s4uTvc+lWrywHOBN+Cnis7pVjY2Wb0AVOgP15m9TcY9vBwChNs5cLc1U1HxsIORW23MX0uzKDJ1Ktp41/oCQZ57VuhdevuV/pTN6bOzGjPKKMHkTkNXg55bWtbJ9sdCTacLojyZJbn7HByJlhPjvWEqfoYKl+maJbRh5GcQ042MkDauo/Zs402HHwXut/FdrFLioU/q25lmE1HbqyJEx6ZkmfawVDjPHDhRmRxyDAFQEjQSXAScaZdDKpQXoregeWdCyfAYrx4btVIf00IgiBM7njrHjz9N+Db6a3RteNvaJBYjIKjcfhpUwt4erXBHW/dY6vDGEOXC85ClwvOktK3rtqEVXM+RMy+1WiZ/DvSUg8hqUkBohsVGws/J2ceRjLeAr5+C8EgQ/6fDXFUOQ3+Nq0BrMPa2a/h7IfJ0UMQBOEEOXpqiUjOiIpwLHX1OVh5sq3zt/KcNeXpUlmHTaR2nRxCTsdONnLLZ+d5r2a9NVpDd8BAShVtsEuUZXDBKWCmamv02NwMgMO26WJt63nkUilukcQl/bktikj/3x6VZB8vzDY3tfaXGO1i7pClQuVOj6+ZUUlMzjA+cocM0/GieR64oIPZJ+auW5K1Dmv1mDaGLWJ2Z4/ugDJat3gUuO6tchu4rheKPvHmwmehnqmWPcxN99yWd7E45TneDByuVrfHoiooUzx9uo8x4rIwFb3ZcAdtLd0X8SbjtA6R00VlccJJbl6xfHh4Mw7Yt08LZ+h5HDC2aZNsUmV5Vo+ZbTet8Elk3MzT9RLvAvrCSEZUj95GxIFJEARh44637kFpiR9vPzwHf+08iJQOTXHbf0YhJja6Uu20694R7bp3lNJ2b9mJ7177EK3z5+HMnE34Y3sTxMX7EZNSBE90CI1T89EYy3FKU618x8ZfYfvT5yMt5ybEd7wYLKpqtqwnCIKoD5Cjp5Y43q/WkX6DdWvb+sN4Zes6/Xiv51V0PlUZLBtju+pltcnNiWOt5+Qo0uM9ZJvkB8HCQQ3GZ+2dw+2xJx3FKGk6QBQGYdctvS1mlNP1EHVxssnJySWulyO6fxhjULi5Do5ZgtnasK7M5BQgIPd3eLersDwj2IRp8TVidTMQRZsAG9NO0aFiNC73kYwoEzD7zlzMWepHxk2/Svjd7IfwItwOziCrTrI95mLVNkSnio4l+kPyD0VyoFidQHpEj92H6IzYjlN4n9NiW8xyoMuriEzBLsY0P4PNyeN0cTrh0rW2NiwXhqNTyTzhZtvWGwfTKzOpDgdgeh/DI044gVw/dnIGiU4avV1xjXJFMR1BTGhQb8SILBLa5xY5Ul9YB7m1E5nzmCUIgohATGw0/u+Jm6u83Zbts9DyqTvw3/FHcXrRDoSaNUTpRZ/i81feg2fL12jTZDfSM/9EYsYhKB6O+EbFOAUrgY0rEVoPHPkrEfn+1mhy7igkdR0G5kuusOySwgKsnDYR3qLfEYxvjh73zkBsAjmOCIKou5Cjp5YoL6omEsdTz81Zo+M2d3OTWZH6btMI69y1PJm2OYxQvyLynORb2zMjepxaY7Y6Zl153ytn3TR3kOg8UbkHYiSKcx9ID1456mbW1f+XJctzP6eIHga5hbAjymgrLDFcjDOxrl2mHtUjRvRwi0NHdpMJFlkcOyycqM995XNmzm5Vw2FmrkWkrUfk3meQ2pO9KNb1h6Q5eliOavU0qKJ2clYk3HZId0Qs5OisKUeug9PI6d5gKyB8dnXWON0QHHweFbpGrRe1xWPsFhUkrQtUnrBIdlh33RJssJVnlrNn3c480o1L9PKpwgmVdBOcOvr1YdgmyLKOR4XJY1KSF65A+zEQBHGCMfjxydh3x3xk9NkG7BiHKybeDSTfCBzehLKfp4EdWIy8VRn4oygeLTL+RIPUo/DGlyG5yVEkYw3wyxqEdtyGgvwE/FmchYY9rkXK2deCxTR2lLd80iXo3n4F+nQqM9LK5n6I5Vv6oO8zH9eU2QRBEFVK5baKqiGWL1+OIUOGID09HYwxLFiwQMrnnGPKlClIS0tDbGwscnJysH37dqlMfn4+RowYgcTERCQnJ2P06NEoLCyUyqxbtw59+vRBTEwMMjIyMH369Oo2zbThOOoyl1dlZLs5FdwcGW6yIkX66Pnc5eUm02Ee6qiD875aVg3c+9q5H5nFTllrJrxM15DoZtEXgtYjZOTWNL2Z+WKQJIvynXqXO/aivQWxJSb1FoPCnCw2W7I6OLj4zzJHRHixYxZ+wVj8mEFfI0eyijEw4yU6cbilSQ7OdOnMWFSZW/RmegXo7bGwZEVb/0iQo5nNjRcDB2PaS5Osai/OIZko9IXYVQyAh2nrLClMkypF2DChYDkXrDnXd/NeuHx2ugjKuznox9ySZBMt9AATX4D1qbaK3pBsfpdIN0KxLQ7NVqF/9civ8l4VlmG7uRiDBvrYBRPvABbHrySYR2ifWWxnglxFkGVVVK8rCOIqzMe93O7MTK6vhF9MOCYIgjiBiI2Pw/dH++GP3Fbwb/8a6uLzob6fCnXx+Qj8shx/5LbC8gMD0OXV1Ui+Zwe+9r+M/35wCdYu6Yi/tjdFoCAajAGJKYU4JWM9Gu67F+qHLXHkxSbY8Xg28j57DGrxfgCak6dX9hIU5Cfgmx2jceCM7/HNjtEoyE9Ar+wlWD7pklruDYIgiGPjhIzoKSoqwhlnnIEbb7wRl19+uS1/+vTpmDlzJt58801kZWXhwQcfxIABA7Bp0ybExMQAAEaMGIH9+/dj8eLFCAQCuOGGGzBmzBjMnTsXgLa1Wf/+/ZGTk4NZs2Zh/fr1uPHGG5GcnIwxY8ZUu4361/Vj5XgdRU7tWL/uOzlfypPvVK4y0wi9vJvzJ9IP5CbMduSkr7Ut7bd002Fjd7IwqbwsTf/ftheTUIcZravhY32dFzk/Es4zfr1d0ya55xnM6BqVc7jJdHbymXYzZs4pI8lk+rHxiJgWOeAsU94tzLo4NQOXHQtcGB/WyIkwKjhUKaLHei7FtiwjlElvRlkm5IsbXekOMIADqrDNa6STacljYuNOA9ZtALtdKE51rAPeyVlj8+A417duJ16uTBeR5d4IRV2FiB5nx1Q4r7I3RyeddWeNGG0j+G4cnWLSILaMS+t50huy3vBCXBhcLu/SANXHi9NCSmIdLr1JXjCnKDSCIIha5pq5z+Hdayeg0ydrkdL8EDwxQYRKvfjr94bYmNgF18x9DgDgjYrCwHHDgXHDAQChUAgr3v4f9s//L05J2IqWGX8gKe0IfEmlSGhYjISGW4BDU8EXTEXBkVj0PMOPor/ikDR6Mc5t0h4AkN5pJspKHkf+c+3Qvd03KCksoMe4CIKoczDOT+y4bcYY5s+fj6FDhwIAOOdIT0/HHXfcgTvvvBMAcOTIETRr1gxz5szB8OHDsXnzZnTs2BGrVq1Ct27dAACLFi3CRRddhN9++w3p6el4+eWXcf/99yMvLw8+nw8AcM8992DBggXYsmVLhXQ7evQokpKS8OfWLCQ2qFxwlHqMK9dwcARtX+or5vhRETKkVtRxoxMQZEZymliPOecIurQZSQdVBQIR6rg7gRiCHAjBY8svT4eACpTB5yoH4fad5m5B1YugESBndcDI9cV0f9CDMvhsNrg5lUSZpaEocEGm1XkifuZCWmnAizJEWdpkrraJehQHfaZ9FoeMqDM32tLSigJeBC0y7fNga3vafyWhaNs8FVJ9Ib6Km8clgSiEuEeqZ60v5elOKRUoC0XZHUBG+zpMaoNzoLTMC3BP+ReURSFVBdSQB2bfll9H/8hKFEAY75HKa8LMdyUoOAPddHaor5QyMO5wz4sgkwFACPCGHMqXJzOgIspvb9OpDhOPOaAEVHicFvKyYHNelQXhK1XlNGsZLoxywSmklAWhhCwnCmZ5eeALI80fgKckYJaVHh1TTZniBaRHoJWUmE4bo5pQRtyhy6jPEeQBLPW/jyNHjiAxMdHeMQRxgqF/96Ixe3JQUlSM/93zJMr258GXlorBj9+F2Pi4CtfnnGPlgq/x63/eRSvfRrTMPIiGaYfhSy6xLVFWWhCNA/ktEHvm35B6/s1Y/th9OKfNG1ixcTjOm/p6FVtGEMTJSE3+DTshI3oisXPnTuTl5SEnJ8dIS0pKQo8ePZCbm4vhw4cjNzcXycnJhpMHAHJycqAoClauXInLLrsMubm56Nu3r+HkAYABAwbgiSeewKFDh9CwYUObbL/fD7/fnG0cPXoUgOZ8se6cVD7cdU5UHmqla4hSnQVGiuZxqWLLc5ubusUIWH/Ml34Qd2nTyT1mLcMsEt3acnYWcVh3vDLL2Z0vVp25Qw3rkX6sPSjEw+dTdHI495rVGSLrzi1p+rG50qse1WOOV+Zgo2yb6Kyx2cPEh9ZkmfoCywjLVGFG11jlmTbJjhPJNiaWF9/lyAnjUTDOpIgeUY4k22kbdT2Yy+k6YYZZMCOUBH3cPKlWLPnakzqCfpEiXUSVQ2GFreUregGH6+nBIVrZcmLwuOAUqaRMp7WAI+qn5zn0h6OW3CE9kjluOqsQwtfkPCmih0Fy8MhCuUP/MEtZbnaKyoGQsOuWzTEjtCFGAxkZukzxIhKcQkD43HK5P9VjXS6fIAii+omNj8MVzz90zPUZYzj7svNx9mXnG2lrl/yADW+8id4tFiPzjN9Rmh+L6OQSxDTwo2WDX4C/HkJw3sPokJAMAEgIbjteMwiCIGqcE3KNnkjk5eUBAJo1ayalN2vWzMjLy8tD06ZNpXyv14tGjRpJZZzaEGVYmTZtGpKSkoxXRkbGcVjivrBvdWFO0M2XroP1ZYVZXtY8OKTraW4yKiLbKte6BIrTy8npVtF+1trQXDDmu74ihxp+yWv0aC4E62NXeoSM+W5dv0cBgwf6SjbW/tPblXtGli0vMizK0FeqkdsVV+2R1yISY3HsZ8OUabVaXJ9HW6NHsZxIrWUPtPWI5DVxTJug9294HRgl/NL3z+LcXKeHcQaoukxAXCcI4XIMCK/RA6NNxlSAqWBMk8PAoYSPrXbb4hz1Obcx97auvxSWL27zVZlXuHcr7DA2h4DpdXG6UMTT4HQRCz4B0x8g9AWXTqb2URF8QZWU6egTsTpDnLqAa0NL6i8mvPRjxZJeGSePqLMCgKvhtWzCieF3zi1r9IjeK72oo5MnbJw4/pnQzwzmrluwyJVusoIzR0+U2hd1EduxltcPI3USQRBE/aPLBWfhundexG5vHwDANzsvwX+W3IzlC7ORtzENgYJoKB6OlLRDAICuZ6xBwazG2DatBw798DZ4qCxS8wRBECcEdS6ipza59957MWnSJOP46NGjx+HskWMzKuPwEX+MrQymI8QU6vQV363tSOmR8o51GuEy1y63nLzVtp7mXt7MFx0z9rJO0TSmXm77bkWWzxm3bYrj5Hpx08Gcr3Phs5kmz/rCbgSFSUt6iM4im35W7Zk51wTMtXNku/WJpHAWFAY15DYSwv1udToY71yai3JACLZgQim9tfB5YAxclW2SHg9z8AIYa+iGm7X4q0yNwxNzab6thGVX5OJ0KMMYs83fXS8s6WQzezmnAewkW2iH2WQyuw4M2mNYThdjBWQaaxuL5Zjls5OODGBi4AmXi4uDhlnKuOni2L/CMQOTo10c27ddvNpHbnH96p1rkxdWQtQlUkSP3hgDjEe5rJ3FhTaMQWxVXpdNEARx8tLj3hkom/shujZZjOR/boUvVnskbMXcT5H34Wu46Pxl8CX4wRQgLrEEpyZuAHbcjMDmW3Bgf1OUpQ9E1rD7ocSn17IlBEEQduqcoyc1NRUAcODAAaSlpRnpBw4cQJcuXYwyBw8elOoFg0Hk5+cb9VNTU3HgwAGpjH6sl7ESHR2N6OjoKrFDm9wei7vm2Jw8kkxLAxVtjwGOk1inuZmOAu3pEjcq4yAKzzErRIT5W4Sytn10hPmXOXGzPgKkBQCwCHaazharE4VxBkUaCbpE3XXkju7s4IJOdpvkEAsGACo3ZJqOHP2hLtlJpsA2jRQcU0I/cHeZ0GUy1ZiDWiOgFMFppGsDwIieEJ1AopGG3Y7OBT16yFJJ6A3d2WOcSwZoUUGOVYQ0QX8u9gEvP0LCaXBzJj1mVu5Fae0Idx9aZGeR1U+hN2VzIOiHZn+5mlmeJ/pYnFLWPrM64sJpzH57s28qZV5i7nrp51Jh9nTbueO2vmDijc9607TZKSoteMKMftQHv+VOawgNGw4G+6LMkQRzOHcEQRDEyUFsQgMs39IHvbKXIP+5dtjsH4rWl42Guv0znNttFaKTSrHi+3Oxr7gjsoLfofUp+5DU4hC8sUGkZ+YBmAP+0Rwc+rMBDpR0RMaw+xB36vlgzP2BiZLCAqycNhHeot8RjG+OHvfOoIWeCYKoFuqcoycrKwupqalYsmSJ4dg5evQoVq5ciVtuuQUA0LNnTxw+fBirV69GdnY2AOCrr76Cqqro0aOHUeb+++9HIBBAVJS2SOzixYvRrl07x/V5IuH0I3X58OOKzDFbqQxOv+pWrD1dV+uExuoEkNJ4eKmLSulotiX9yG3RIxLWOaHTsSzLqqH5B1qW726JaWfknbPk4AUGNexYkPOZmR+hJd0xUNGxZ9igMIihOFaZen+JfY5wGe4wXphQyPHRHLDwzFd7rEtf50Z26DjUMdo3nSf28x9ed0ia+OuRWYopzNK20Rf6nNqit+jgcgt8MHTRHQxMezxNBSKfDKsRxmSeyWVgLQN7vv64mFN+eV5RQSQTjyVVmH0gqDAiniotE5Z7iKNM2GQakUAWmUwsq9/eKuBXEds26lgGveJ0ZQnjRTKIC4lc87cwSXlzgBlipQGm/z3gZnm9L/VHt4xIH+biPLKcUOeLUXYqiRcvQRDESUjfZz7G8kmXoHv7FTgn4Q1gwxtIbQOUFUTju9UXoN/zHxtlQ6EQPp/5NkJfvIeOLX5Faqs/EZNSjMTGBUjESuCHS1G6LAp5B5rD23E4mg8aDyU62aivy+nTyXz0q2zuh1i+pQ/6PvMxCIIgqpITctetwsJC7NixAwDQtWtXPPPMM+jXrx8aNWqEzMxMPPHEE3j88cel7dXXrVsnba8+aNAgHDhwALNmzTK2V+/WrZuxvfqRI0fQrl079O/fH5MnT8aGDRtw44034tlnn63w9ur6qtl/bG0VYdct5+5VUYF4HpcCgYgxMpGa48ZCzg5rjEYkIC4B7VDB7XdhDm7+sO1Qx42gCmm3Lrcf/eVjbcIS5AwqFDg86RFR56AKYzcquzzTnSfPZbW0gOpFyMHJIzuI7PXLgh74rbtRCRMvDnsUkO6IKlWjjJ2iVMtkTdZbbrckvOuWmxPLKlNL02QWh3ejMh+bEuafEWQWBaIQ4B4pTbbLSX8FnAPFapQ2T+ayDFEHcPs5Lgz4oHK3hbUBqIrDuNXaLQt5NTvdHnETbObCh9KyKMBpNyp7I1Ia5yy86xZcd8p2OuYqwPyWXbfsA8a5HRVQQky+EERHntXDJZRRSgAmLvFWjkzjCggBivXRL7eLW0wLWnbdsjp8HG5Aut9FCapQgi46Wn04YnogAF8pj9CH3AhvM+zT7fWHtF239JNZzg3MuOJKyuApDULe2l3QwRpqxs00XlyiPWomXShCG8Zb+LOq1Q2GSrE08AHtYETUGWjXLaI6OJZIm22rNmH1jGfRNu4nZJ2ShwbpR6BECbvUqgz5B5Lxl6c7CvMOo2vnH5C/txE2l12GNsNuwvYP/oUOvvlolJGP71ZfQM4egjgJqMm/YSeko2fp0qXo16+fLX3kyJGYM2cOOOd46KGH8Oqrr+Lw4cM455xz8NJLL6Ft27ZG2fz8fIwbNw6ffPIJFEXBsGHDMHPmTCQkJBhl1q1bh7Fjx2LVqlVo3Lgxxo8fj8mTJ1dYT/1EHdza8hi2V4/Q7eWckaDg6KnMyePhTd0d67j8+GvKtGsccd5kzCV4xB/53WSGhO3VxTLl77rFEOQMIcs6407zLGua6ehRXMozW12dgOoRZLrtZmV3gPhDCsq4dUt3ezmnvNKQF1zQ1Vmug6MnGGU6tLiTHKeFpbW8olCU4QCwOogcH+kKR9cUB30IwmPOTfV8bpcntquqDCWq1REGZ92NY61Pist8hgPMdEYJ55A7O4k4B8qCXlgf0ZN0E+fNup0c8Pu94LqjJ9IFZTlRnANq0OtexcGRoaUzwK+YziVeTnmxTCjs6IElysR2oQsOHw5ABTx+wIh8q6BMhrCjJ2SJIXGUaakbUuH1O6Q7XNDWiB4WUOEJymmuzigu6FYWRJRfOEHckm8MYLszSCkNO3rsA8UmT8or9cPjDwgOHOGCEQeL6MwJ5/HiYvMilBb+4pa6kPKCoVIsLSNHD1F3IEcPcSJSWlyKhf98AUm7PkX7VnvROPMv+BL9Uhk1qGDnnjZI7nsLmvS+HswTg7KSYhx+rh0aNCwCRuymx7gIop5z0jt66grH4+hx3F+ngmeiKiJ6dHEVPftlVheLZf7iPr/jkrZOjhYnxIieitQRJ/AhzhC0PH7lNI+0thsqN6LH6uwxPwdUrxRVI++ybUb0iHI5tIieMltEj1nWKkuUWaJGGU4FbtNNbkdy9AS8CFjstOomyzJ1KQ75ID5uJS5obJUj1i0O+BAwtk2yy7Gfn/CuRhwoCQn9w4XyovPGYT5dGPBJ/SPlW9bhMXfQghHRYzi0xLm2WN/SJlcBf5nmKLTh6qjR7WJQg4Kj0OnCcqgvRfSUdy1b21QBJSg7cZhbFI9oAweU0nBETwVkipEyNkdPBZw8+sUZ5XcpZ3k3z2RYVz2iR0e11BP1FNP1iB5HeVx6N2TqOvhDUELCPVO1O4McZZf64SkVHD0Wp4xRQdJByzMiesAFeRZBNh04ggGK6CHqFuToIeoCqxYux+63X0aHlE1o1WEfYhoVS/mhgIJ9vzeHt8v/4dcff0Xv1m9ixcbhOG/q67WkMUEQNUFN/g2rc2v01Bds38ErgdNSFhWTad822mmpjfJkApAWzpV0sTRmbZtZPrvJFndq1stFmCdDs04rHUkmF9LEdpmltL6+htyWlmp/ssZcZIRLJUWZzPF/hZmTax6uZ7bvtN6PUNYybxOlc0sNc/FjBsYYpLV4xIWFy3msSlvMW+itcPiE7vAR+1F0/HDGjEd9hLmprX2rnfI+3tqbMaFmWl1zmREWnsvquijG1te6nWLzTHQaSfZaPjPTLuu5lBbMVoxE58Yi5XEutQ2L2dIgFeoxBZC2dBcvlEgXtuWCMvvQ4jGT5Ivn3WJTBJn6fcG8SgTcLk6nY/H0WW8IVluEdJvTzHYzM9OYpKDtxAM8PLrEhZGZRY7eBBdPOHM8l7a1gfR1pQCYq61z84IHIjuNjPYtJ0hc6Ft3+NBvPARBENVG94v7ovvFfQEAK8ZfiF49v8PKxafhlJYH0TAjH97YIDJa7QUOP4Qmmdrj143xE7gaBFNoekYQxPFTuTAUosowJ1aVhwuvyspkzHluVRmZ4rzJmIe4KORkol5Uhb1dPV11+Oxmk/nZ6myw6+4kH1I+D//THzoztdT/SRED0Fwa3Cabh9dhsrYqtsQR4uYxwiWYYD0zdJCtYdDmkeZ0joU1sTouzJa11lVwzrWFnPV3qIJbRjXkOsfqMG0MMUGmfRsiQyoTrRbkahNms4w1nsiId2NcskzawN4YgFpVrurtqwDXzp8uU7cTgt0cVtv1vnTGGONCm+JLVbm9ktvFZi3KmDGupPElD0HbRcPD66w4XiiODVryBHWMuT8PZ3LLS5Ctd2dFZeqLKVt9XeVenE55+hBiQhKD5SzCcKhIPg8nudZ2Dd+K9ebGw75EDs7Mq9a1Wxm39IWlrfAY5MYjWtq4NS9/QxGzdf3cWG/kkhKis5A722ooy+HeEEEQBFEVhBIzAQCBrB5oOuUXrFBfw6JP+mL/xjQES7zw+rTY9w6dtsI/OwXb/3kG/sj9N3jEnRQJgiAiQ46eWkL87n4sHEs1cVJ0LPLsDg5hAufSppuDxtqm9aVY3p3mqk7SxAAD25zG5QXpMwv/U4xXJOm6DGtgA6BfXKYDSGvP/AcweBiEY1Gm9uLGZ9PVors7xLVILPEggraKIUuzSosYYGFvjSlL721RNoRW9celuDn3hRwh5iQT0GWw8Fhh8ktw4+gtWB+j0vNV3XbdByQ1wcAU7aXv8KU5pRgYU7QXFMFup1EluuDCtstzfNNWxkwZ+j99H2/HC0USY4dzacxycWA5DVTpQmHlXyjc8m61TX8x2BOlArr9qJRMUTwTDdVtgvC5/ItUqqP7K/Rsaz9KgStubQmfdR8K0w/KOaHGOLGYZZQzEi0OGpud4bFrXJLMPMdGJzoMHslwwWDRSWTIlW4a0Lb9IwiCIKqTHvfOQFmhDx188xH0lyLnpisx+N3P0GLqDiwtfRGFB+MRCigI+T2Iig3ilFN2oNHOv6PktRRs/Wc35P/0EWilDYIgKgs5emqJ4/16fSy3+/KnLJHlufy4bv5QXUmZTj80WyN5nH7YL0/PSHa5RRGZLzGiR5zyO2sg2uLmCjJtU23thrgpC0a+HlEjR/NY3SB6FIOWJ0s3z5MpU/ukgnPVjOgRZFojiETXiyFfn/uGUxTh2OwdvT2hL7kqROCYLzOaRpweh6N3LI4HI51pL2PchdvlnIOrHEY0D/RoG9Nm027386n3pm6v/hLt5FLfhdvTI3pcL5QICBE9Uue7XSjSBcPt8lzlWD7bLpSwodZoHuMVFuNkYwSZom+D22RWwE63G5D+0c3xI8py09PSrhEkZo3mgfUY9nECcwwZDUr9zIXmhGvBiN4JmWliWScPl6EzN29AoveTKdqzoRDkQKgDANC3QCPqIy+++CJatWqFmJgY9OjRAz/88EPE8u+//z7at2+PmJgYdO7cGZ9++qmUzznHlClTkJaWhtjYWOTk5GD79u1Smfz8fIwYMQKJiYlITk7G6NGjUVhYaGvnqaeeQtu2bREdHY3mzZtj6tSpVWM0QZyAxCY0wKotfdAoIx+Hn2uHZY+Mx+/r1mLZI+PRteBexDcpwvdrz8OS/BlY+ulZ+HN7E6hlCqLjy9D6lM1I2nwtil9NwZZ/9sSRLV+S04cgiApBD4HWEtItmlkTqlemMTGqhExxriQeo5y2Is0FIzmY9B+13VQsT3W3fKc25R/YnV1E1jpWe1jYPWOFCZ/MdXLMuh7GEOSK0K/mWjpi+7IbJDyvEwroi2yLj5Bx6JErZtsAwJgizYyZ0APWdWoFF4+9Ty3ryugy9Ra5YTXAxLVHbO1Y22fG3FaPWtb7xRqdYcq0nDUelq07LcJijHHMpaIW+c4Lpctj3zyfRllFKOB4ocChE0WF5EWmDSJ5EvXIDzd5Tu+iLm6GOsnS00Lh6KxKyNTHq3XcSvUiyRSdXhaZDJCebuSWD0ys43LxM+5wzKyGMYut3DhfUnQdBxjngldL6AQwuzBJNw9gLF/P7TpbQ+iMfhNlhD8Yg0mXKd7Fw2PGci8g6g//+c9/MGnSJMyaNQs9evTAjBkzMGDAAGzduhVNmza1lf/uu+9wzTXXYNq0abj44osxd+5cDB06FGvWrMFpp50GAJg+fTpmzpyJN998E1lZWXjwwQcxYMAAbNq0CTExMQCAESNGYP/+/Vi8eDECgQBuuOEGjBkzBnPnzjVkTZgwAV988QWeeuopdO7cGfn5+cjPz6+ZjiGIWqLvMx9j+aRL0L39CpyT8Aaw4Q2ktgHKCqK1rdWf1bdWvxGhUAj/e2wWkrbPQ8d2u5GccQgxDfxo02AdsOZSFH4di735HdHy2icQn9XTUd6xbBdPEET9gnbdOg7q2q5bWjSHu6hI4vVdtypbj3N5e3WnuaZTe+L26uXJktPM7dXd9HLZdEfYXl1rRyxjOjSYVEd/t+66Jefbd7TS2ykNKgjA51hHm/hb2zSPS0NeqNLuYmaek8NGzy8JRCEo7LolO1h0uVa7NYpDURB3uRL7yTmgRcsvCvgQ4l5JJ7F9x23Pw3aIu25J9TmgclNfQ2Z4jlsciILKPeFEB8cZt59LQGszEPLa1h7iRr7F4aTbYN11y1omgqNH33XL2t+mUs51y911y0kHoU0lyKQ0KTLM7ULh5ey6ZfFFSMcqtK3Oj+HijCqz5LnIsckMqPCEHNq0yGTWvLIAfOL26tZ2I3gLldIQFNsW50IZcVcsLsgu9cPjD4X7WRgwQlnj3RKhw4tLtAGhVzMcPTx8cXJBf24M5KBaRrtu1UN69OiB7t2744UXXgAAqKqKjIwMjB8/Hvfcc4+t/NVXX42ioiIsXLjQSDv77LPRpUsXzJo1C5xzpKen44477sCdd94JADhy5AiaNWuGOXPmYPjw4di8eTM6duyIVatWoVu3bgCARYsW4aKLLsJvv/2G9PR0bN68Gaeffjo2bNiAdu3aHZNttOsWUZeprAMmGAjgk4efRdN9C9Ch7R40aHEYise8/xfmx+O3o6fj1BueQUza6QBgOJR8CWVGubJCH1Zt6YO+z3xsk0EQRM1Rk3/D6NGtWsJxnlMTP6yGAxzEJzEizD8l1Risq7iYaW6q6227PYYlPdUgpFmX/3AqZ9VbdjC447T+j4Lwj95cCb/kjmJcAQtvya1yJr14OJ07pusvBeAegHugci9U7oHKPWDwgFteKjzgxvo8+vo0SvjFEAq/nJw4MD4zcOlMeaCvx8OEdUfMf+Z6M27nkOtHQoL5OJQZu6OvBcTgCb+H18jR18cR1uhhxrFgR7jb9eVDzDVdzPOh7RRmrvtjrD8EYT0ecQ0dFt7hTJSr2O3TbbQvMG0W0FcAUqQrQl8fSCjvNoCdLhZjVyRmV8jpYpEGrks+IMuK4Hgyq4S9WFpYivZSnD5XTKawVrZppptzSOgC+8UplLXIZJY+tTwd6Ixbe9xaRh+EgrDwi0Mxrn0A8g0OHLbFiIxDLtjFLC/Foh8T3hzGF3jYuWNxRBkOIW72q7UDGAOUSHdvoq5SVlaG1atXIycnx0hTFAU5OTnIzc11rJObmyuVB4ABAwYY5Xfu3Im8vDypTFJSEnr06GGUyc3NRXJysuHkAYCcnBwoioKVK1cCAD755BOccsopWLhwIbKystCqVSv83//9X8SIHr/fj6NHj0ovgqirxCY0wHlTX8c5MxbhvKmvlxtl442KwmVT70bv2d8h/vZf8PHau7BqyWko+C0ZXGVIaFSE9q1yEfV1T+Q/l4qdj7RBr+wlKMhPwDc7RuPAGd/jmx2jUZCfgF7ZS7B80iU1ZClBELUNOXpqCenX44p6W6oKYd5WkTlouIq0bo51Z6xIqlfWYWNNd+siq65MqOc4l7a8nNbrgbDminVh3kjOKbdtus2+EVszezDkKs1p9RqrbOsixvpLd5nYt2tiMHf5kf+pjufBOueWJp7G/JQJA0rY3QuquS4Rt+yAZXw2J/+GbcL4BGCu6SJkMCb2uCnTXB9IlwtjCRQ17JhCeHMjcTML2T4Gx+eLTD8TONN7zDyXXJ9wOw1aq6fT2rD4LJTTABMdEeUN4orcU0SnilUX60uVDIfNQ1yOTEMMB4Qn6dxxskvME162hZ1h8ZuI9awyxHrmZSeXge4s4cZnxjkYD6+jxQQXa9jnZyw6zi39aDjCwsdWjxTnMCNyRMUgyZfTmSmUw3LqxE4QxphVnusgIeoqf/75J0KhEJo1ayalN2vWDHl5eY518vLyIpbX38srY30szOv1olGjRkaZX3/9Fbt378b777+Pt956C3PmzMHq1atxxRVXuNozbdo0JCUlGa+MjIzyuoAg6iXRMdEY9vRDOPv1lYi6eSs+XHkbflraAYX7E8FVIKlJATLb7NP8+NFBpMT9jqYZSTh3ykwkT9iK/L2N0L3dNygpLKhtUwiCqAHI0VNLcOO/Gparzw8ivJzQJ/zWaB67I8CO21y0PJxkOb3c5oOIcGzV23w5/ZPdLqJDxr7tt1Nr2idFal+zTBFKQpJone+bskV3jh3rjlJyL/JwRI+smRnRI/YPYLgwzH/WibEwBzain6AYkTxmRA+TXmY0D6TZOgeHyrQX19OF9rkY1SPpK8bviLtuQYjmCc91FQ6mmIs6i+fWcI5x2dEmja+wg0Hcc0uP8RG2bJIvCuupkNC3e7f3Laxp1natG6ZFulCsjiOnC9G6+LBurO1VQZmimLBvw+ZDc7o43frQ8rL5nXT/hX7MHNp3kmONjAKEc6kPHO2d69E8UMwrX3dO6cE1Tv0oGcyN9qTBqSiCY0a0n5k6GQrq14cqyBNEGR0iyHWKIHJbO4sgqgFVVeH3+/HWW2+hT58+OO+88/D666/j66+/xtatWx3r3HvvvThy5Ijx2rt3bw1rTRAnHvFJCbjq+cfQ7dUfwW7Yig9XjMHe9ekAtFt/w2ZH0SF9EfBxB+x7oiX2zH8MW/yXwNfAj5XTJtau8gRB1Ajk6KkljO/ukSZj1SXY+l3fZW4q4jZtsU5hnOpXZM5ZnkynNN2B5CSvIjjZZI/nMV0AsktGkyS6etxaFd1DqvRJFZxgYinZpSP2EXPpRS58kltQ7cdctsxqrbh+lOycYlKAgD7RBuNGlIs1qsewl4f1cIzmsVjHAcYZWDjdnGdbd+nS9dTsUjmHqu+ype/6FZZjbGpkcRRxLkZFiZ4LbutlJnc0AG6X6RbR4zbn19vWbawIYhsVjHxxzIsoz82jor2cImkcZVoui0h+F1c79XYstlr9TqJPxjguz86I58kYoNpL3ymOC1c9D49a603U6uWy3jT1ItaIHlVw2kj1wpUMncLnQnLWMKFzwx2jCCfA8IiJF4Tw2BdRb2jcuDE8Hg8OHDggpR84cACpqamOdVJTUyOW19/LK3Pw4EEpPxgMIj8/3yiTlpYGr9eLtm3bGmU6dOgAANizZ4+jbtHR0UhMTJReBEGYJDZKxFWvPIvfSk4FAHy0aBg2f9caJX/GgylAs4w/kYVncVaLtwEACYEttakuQRA1BDl6agluPYgwCawW+Zb5RXkqVMZJwy35kea6bvNFvW4kPdxXl+ARjpzbMtuUVnYR7HPqGdPxwIzZqP2l/8/A4BE+66vK2Eu59yt3OVPM+N8ej6SE8/TIE8Wmn+i6ktfp0WWKET3czNCcMeGJv8JhOgOgr9Nj6UlLNI+4fbphVdhppEf06A4ZSE6ZcKt6Bejr8IhrBMlyjOgh/fEvCFu1h51FZkiGy/zcclIUxoyXFtFjOf2APc3h5JrrybiMVutFZyjg3F4kWcaxfThbDty9Kq5bpJcj0+Z3iXS/E9visEUQVfgpsorKsEUnGYMG4ti1xvGJvhWbUMdzL3aG2DbCET16mkMHGp5W3RmkP4cYPjeOhjG5vsLkKCKK6Kl3+Hw+ZGdnY8mSJUaaqqpYsmQJevZ03qGnZ8+eUnkAWLx4sVE+KysLqampUpmjR49i5cqVRpmePXvi8OHDWL16tVHmq6++gqqq6NGjBwCgd+/eCAaD+OWXX4wy27ZtAwC0bNnyeMwmiJOeYHxzAEDjNsk47YWfUTB4DRYsugx7fm6BYKkXUbFBAEDXLmvxx9PNsXnWLVADxbWpMkEQ1QjtunUcHPeuW8fY88e66xaHilAlZIpF9V233PLd0lTLrlvlldePVRUoc0i3vtvbYAhxhqDFhxlJV/29LASUCTtgAeK6O8xSRz4us+y6ZdXJqq8+SfSHPJZdt8R2re3J7ZTqO2DBuT+sbej6lQS8CCLKYgszPtvbMmWUBO0y7fK0fJWbOhcFvAhxq0ynz4C4QxY37BTzRQeMOWnmgLGIsgqguEzfdctp9zJI9aR+4kBZ0AvrOZfqq2Y74uZK/tIoaFtku1S0KRA+5EAo6JHsiVTemOdzmLtuWcu5tSG8KwHTRailOUz6rfVUwFMKOO4uFkEmA8xdt5zKRJIZUhFVYmYzMc9Sh1n0MXbdcpMp+GQkZ00gAJ9ftekiybYPJK1fy0JQQpa6NkeaqHO4fkkZPP7wfoPGc7bWdyeZKnhRiXMdbi2rXyxaiFQwRLtu1Uf+85//YOTIkXjllVdw1llnYcaMGXjvvfewZcsWNGvWDH/729/QvHlzTJs2DYC2vfq5556Lxx9/HIMHD8a8efPw2GOPSdurP/HEE3j88cel7dXXrVsnba8+aNAgHDhwALNmzTK2V+/WrZuxvbqqqujevTsSEhIwY8YMqKqKsWPHIjExEV988UWFbKNdtwjCmZLCAmBuJgryE5A8YSt8sXFG3pJZb+OsstsRk1wC5uGGjz/o92D3niw0uuQxNO46uJY0J4iTh5r8G+at1tYJV8JzYEtCzckUf3SuCNbpH4ez+tZgAQZ3MeXNZSPJd2qXO3yK1K7eht62HjBgczxIn/SJMbekcogBclb9mOA0YEKKB0BQaNds0+lXdm5pV27Neg6YYAsXdNAjeuyBHUyoJ8uxzWuB8KNVIqYsLmhhSGPMZpW2ARGX6zChb3k43XISWbiY3lfcksugRdtwi5VSH0mDmIcf4zILOfofpIEdPod6VIk+BCpzLVvm5a6CnQa8NeJFT3ORYTvhjhdQBOWFgcUZMx0qlZBp2wSqvL4S6wt9G1FVi8xy41XE8mJ/OkXW6GPO6HN9BOo3VssJFesyMV9sUyjrsUTYcC4cW502usNGqG+0y6Q3U1fh5OsLOCvl9hBRB7n66qvxxx9/YMqUKcjLy0OXLl2waNEiYzHlPXv2QFHMv1m9evXC3Llz8cADD+C+++5DmzZtsGDBAsPJAwB33303ioqKMGbMGBw+fBjnnHMOFi1aZDh5AOCdd97BuHHjcMEFF0BRFAwbNgwzZ8408hVFwSeffILx48ejb9++iI+Px6BBg/D000/XQK8QRP0mNqEBlm/pg17ZS5D/XDts9g9F68tGY8f819E5egFiM4rxzQ/98GdJBk5PWI7MDvsQFV+GU9vsADZfhUPLG+C34LnoOHYWPDENa9scgiCOE4roOQ6OJ6JHjTSbLIfjiehRgQpHEonFnCJ6rOWcmq1sRI+eJkb0iGWc2rJO8p0iepxkWefWwRBQCh/sThcYabITw8wvUz1QXaKI3KJJVDAhokds35QHy7HY1/5wRE9ku+yRSCUBLwKIsrTJHO2yOlJK9CgirtvgJFfrB3NOy1AQjuhxipIxdbRE54RfpaEoSYahmzHjNusgrK/KgSIhosepL7Q27JNcPaLHet4kO7ksU59vGxE95V1jlnzOATXolZMjnVg9KQSwUg/AlPIHuPUzd4joAWCL6rG2GxIieiohU4/oUYIOzpfy2qloRA+32gKwoBo5ikj0yZgXJxAKwVccjFwOML1XQrriD0FR7dFAthMstqlyoMQPjz8olFUNuyyD3OJU4uDFJebaO1K+IFgVPoefFwyG/BTRQ9QpKKKHICKzfNIl6N5+BXwJZmx8WUE0Vm09B32f+dhIW/3x1zj036no0n4rkjMPgSna3wc1qGDv7haI7jUZ6eeP1DbRIAiiSqCInpOJGozqMeYLlh+MK4JTRI+Yx4V3a71IIioiXpRdXsBEeMpTaTliVI+4xK8Y46IfaxEoslPEOl22z+uY7ZP+wE1QKMml2B97v0b6U2t1EGlH1mNztyh7+069ZMYF6c4M0SbrDkqyc4QZOnnAwuupmJNPbvSa3rfh/5kZWGE8/sXFVhGOBBJkho9VoZQW0aOV4JKccCscYCwcTVTunt+6zk52WiKETCEVRttVuyJn2dTFKKqfhIpE1oioDsVEp4D0UWiAab4gx4WOrTcGS57j/aY8h1a4T7limsqtss2L09HB5IjbcAe0izPItWgX/cap62F11rBwPJ10MXGH9qVBG5bH5fb1NXq4nsfMwWW079BB4nZ4UkSSKF7Ik84TfYEnCIKoT/R95mOUFBZgxbSJ8Bb9jmB8c/S4dwb6JjSQymVf0g+4pB8CZWX4+O6H0Ta4EKee9ht8iX60PHUPcGAsCl++EzsPdUeHcf9CVFILR3klhQVYaZEVa5FFEETNQxE9x0GVRPToVOIsHHdETzkynZLLi+hxU/9YInoAIKQC/grKkdO0iJ4QlHLLWvMDIaAU0a7tRoqeCagehCoV0aOl+UMK/Igx0u3OGqf1fRCu6wXXo2ci2KXroKeXBqPg5z5LeTOiR6yvWvQpDnm1GbfNRottgiOHAygKRiGgWmWaNqpClJBoI4cc0SPJ0p1A4UTRAcUBFPqjoXK5fyQHnct6NCqAspDXMd967Yh3Tw6gtCQajuvcuDZkthMKOvjdyzmxXAVYiQe2dfUj1dOPVUAJmRFRzFaeWY7Nep5iVFqmEdETcoqKcdFRJyhH9Oj5ks5OaQBYSIUn4NK+IMemU1kQvpKQrY5RTnSKWW5OSlkIStDhzud4wXCzzdIyeErCv8Bad98SZQEIeweNMrxEj+jR65p59rpmuaDqx9Ky/1J0BFFnoIgegqg+tn2/DjteuBdnttmIlKy/oHi1v2U8xLBvbzME2t2MUy6/E4xp3wEco4cKfVi1pY8UPUQQhAZF9JwEiD/81qRU7vBLc0VUMH6Idihs/ZFaRAEiuqXcZOs/XlvlRHIaRWo3ko3Sj9tSTIjVsWNdI0d2yDjFDlgdN9a5rRZbo7tT9F189FLW2CG5LcZgibARnTZOOuqFGBSI27frUTuaNPF8WiOomKWHAMhPiEgyBV05g8JUYw5q7VsmeBrMGCKY259bZRjHLs4IAAoTN4o35Zi9K8owVWZcjiBy9pwxSRfjWnbbz9vahi2ST4/KcLfHdqKtnVFRxNMjOqqsjgwpE5IDywg0ORbcLs5INlu7VY+ugZzGxLRwGVcXfKSbhG6/wmzp+uky07gtKMbWN1aPoFWwpLR0IZgVuHhlAsYOXHp4FWMwVgl3xMlg7pBOEARBnKy0Pft0tD37fwiFQvj0H8+ged48tOu0BzEpxWjeKg/wP4KSfz2OX/efhsJCH3pl5yJ/byNszrsMbYbdhO0f/AsdfPPRK3sJlk+6hJw9BFGL1Mnt1UOhEB588EFkZWUhNjYWp556Kv7xj39ADE7inGPKlClIS0tDbGwscnJysH37dqmd/Px8jBgxAomJiUhOTsbo0aNRWFhYIzbYvtdXYtZ0DFUMqQyQdgyuaHvG3JDJL7GyU5pqab+yL1NzZ+ePs5X2Ntza1fLM7b/1Lcitey1zh3cutWx1CYly5dKmVCWsqyyP2bSVZRvHXJQBoX2n2CNTrvUEMqEVJ9ni/xzm/NJpTOi2c2svMEV7QdF+BTK2WHcYNJLNMMasvjM005tj0LZGZ9CCjBjCW24z8LAsa9vm+VOMl66LaJUi2uiinlFFF6MwbV3byl+YmrMAwoN2kQasmA/mvJiuPiTL83C63QOcjBeGuuPTPhWRGS4nbc/udJFa9GAu/cAAfcd38wk2DnEneEe/ilWeYarQsGJTBrau0MamJRG6v8VpsDDzH9ffFSPd6FxRcWNLdIRvE4JOoiOMW2SK+dayhj3HOmAJgiCI+ozH48GQh+/CmbNW48igNVi46CLs35QGtUxBdIIfHdqsRrcuuQiWenGw0RXo+8DTSO/UGedOmYnkCVuRv7cRurf7RtsJjCCIWqFOOnqeeOIJvPzyy3jhhRewefNmPPHEE5g+fTqef/55o8z06dMxc+ZMzJo1CytXrkR8fDwGDBiA0tJSo8yIESOwceNGLF68GAsXLsTy5csxZsyYGrFB+mpdyR9Vj/13WN0NIDdQkfbEMuE1PI0nC/SXUxpzqh9+qS7pYp6seeQf4cWy1jmntX3ZLj3yQ39XoT1cZ9bQHi5ShWPdmSK6e7iUb8qwb++tyVLDepryRF2sPcSMNN1Op52zTJmy5YLFnIOHX5DkmTLlNPvqMbZW9XMPaZopyFTBjZdFB+40EoSHxlhYB86hGvX0dWeZNs7CTWgTfA7GVa1frQNSPJ9WPay9yOTq5mdmH/cAoHJtTaHKXpgcxuReujbdLgZpYHN5f3cxP9L83ZJvPMkjtcHsdbhZvkIynbqVGafULCOWdbDVdnq4kM1kVfVjacxanUfW9i1qg4cfr7V2jFCeM9jHDg83r/ttw9e2+RiWcJUz8x0s/K43Ij22xc1HssTFlq2dYdgm6CPZbXVIMbN9olpYsWIFAODbb7+tZU0IgiCOnfTWGbj0rffR4p87sKzwefzw1ekoPRQLxoCo2CA6JLwK/+wm2PjQ2Sjer23rvtk/FL4GfqycNrG21SeIk5Y6+ejWd999h0svvRSDBw8GALRq1QrvvvsufvjhBwDal+8ZM2bggQcewKWXXgoAeOutt9CsWTMsWLAAw4cPx+bNm7Fo0SKsWrUK3bp1AwA8//zzuOiii/DUU08hPT29Wm2QvlpbJx7lUMnizqWFRhi35TrINCvokypreenX8/DkQXePVFAr49ic6jvO71zb0etZ15Zxk2n9xZ8JThnZWWRzXQgtAFZnjtwmN/43UYRABj2yx9525CAJ+4NdslZ6KUvoBBMWY+ZyDSdb9RRDD64fm1rrj5GZMi21mQIWttlmD3fqW10/cxbvFEVi6BBu2IzYAcxQG1GgOI7D7bpM9A27LH1gRR8tXJHLV+jiFB02kmfCJsC8IKyTd/HxIv2UlHehWPQTd/A2Zeg6MSFNS5bOhSjT6aZgkcWt9cU2rDrqTiHRewvzs3UxcMCexo3/HHDQj4n/GXK5oSOT+toYeEZlruvFAdvDj4INhrnhPjYeKxQ9U/qCzHpB43lNsUMtnS/pJHaa5UZvuS0QVc9nn30Gr9eL//3vf+jdu3dtq0MQBHHc5IwbBWAUVozvj149v8Xe9elIb3MQUbEBtG+3HvzLbOzalY7Gbf4PAOAt+r1W9SWIk5k6GdHTq1cvLFmyBNu2bQMA/Pzzz/jmm28waNAgAMDOnTuRl5eHnJwco05SUhJ69OiB3NxcAEBubi6Sk5MNJw8A5OTkQFEUrFy5stptcPxuXUGvDQcc51QVlWr8oMth/yXfBTHmQlOC2yIi9EgN7Vdr+9zNqjtH5KieSPNGKzxCGXGaU758eynmmma2bi8T2R4ONfzSP1ujedx0EA21O0h0beRjLv8Tomr0tmX95eglvQWjZSFkR4rusvS3lMI5uKpKY4TpY8jFVm0eLc6atXoqFwIb9OYMdbUDxoVICi7bpqczroYrWkNmIA8iLtupRxRJ8jkHV7l7cER5k+nw5Fy8NkV1Ja+n9CrnImYOLwedjKYA+zm1XkAQutR6Y3CQFUGss95c/iydQmtR5tAllr62db37RWnYpvWFw+A2qojXk9107UN47Eo3W3PQcC5f88a1L54AcWgaulp7VOgMsdMgXhiWDjn2XwuICvLII48gGAzi/PPPRygUwqOPPlrbKhEEQVQZocQMAMDu2EFYEz8X3315JooPJoB5ODJO/R3t8QiA/2fv2wO1Ksq9f7PevTcgV0EBUfBKooigiIjXPiWp6GLaOXqOpRlpX2GJaCWaeiwV9aippVF+mdXJNCs9ZmkRBt4QETSv4K0UL4CK3Pd+b2u+P9aamWeembXed29gv8CeH679rjVr5rnMmrV85/c+6xmg9w6rIeOOLSITEBCwadgmI3ouuOACrF27FiNGjEChUEC1WsUVV1yBU089FQCwfPlyAMCgQYOsdoMGDdLnli9fjoEDB1rnm5qa0L9/f12Ho1gsolg0a0GtXbu2wz5I/af9yCIyaut0J6K1fvzP0hkTkoHLsHIl1ZDj+UEdAia3j6WzDjt9mur9wTqZ/9grSknrLK+vKAkVs2PoHx6AIEF9NRE1yd/I0w8k8oSVaUogzesi4R8TdpmJpkmia0y72JoZG5KIlyXl0ppJizTcgo4r6qdUJULovtV9Ik09bjtNxqwmt2qOG7F6amItgSSRspZnInqEkuUYmb2SmuovmjLF0DGmro52i2BfdE8whVdR2hlWP/iiLTwykkUvhF3PFwrnA79mmTeKNB2RHgpuX45O3R3Sc9rnH78ZPH1hvfrlE0ns0txI3s2Zltu5tIVrn6TxfmkhDaBRpI9jn7D9EqSeuTGhB5FA0slxTNqkAmNfR8J0sLpW+lMXGgNSXwK2HC699FLceuut+P73v49+/frhK1/5SqNNCggICNhsGD/jBpTu+AP2a7kH/T5zFVpOnoz331qJeRd9DYcOX4j+e34AIYADDngOG3+6M159bzxGTv8Vmnru1GjTAwK6DLbJiJ7f/va3+PWvf4077rgDixcvxi9+8Qtce+21+MUvfrFF9c6cORN9+/bV29ChQzssK39ilQ/fj9H16hSeuUtHdGoZPJKHTR58LlI5NJ6i1pbxO3bmRJ0eZ2WBcfXY0TW+aB57hmhH99D6/tpIJScRPTGEpdNnHY+4kUoaiZgwvgpHX5z6Fau8QyxPjkitoFfElFFPElJDJ19Or4g7XyQ20t5kY0V4/WNUk7DTJwtIK+BBh6HoQJ3EP6Q5eqxoMxLhQ8t4tJQv+oTrpFE9SlbMJ+B5Nxc/J8xLg3qc0xsk4yZJkv5Ku5wSI5ZArtM2RyKVp5Pc8M3YLbltVKZHp3reOWZk3STcX1ZXSvijebhsqtMhbVzZPHKLXfiUUHTj75QM7TbvB3VBpe2Eui/0xVRLpscwuZe0gvQ6pMSpC/VAoBeWyKD3i7aRDpiAzY1KpYLzzz8f1Wr4NTsgIGD7Qo9evbFwyVHoP3QVVt+4L+Zd9g0UV72D3nsNAZqS/9Gs+ld/VIsFdO9TxAF7P4z47r2w5NIx+HDJEw22PiCga2CbJHq+9a1v4YILLsApp5yCUaNG4Ytf/CLOPfdczJw5EwAwePBgAMCKFSusditWrNDnBg8ejJUrV1rnK5UKVq1apetwzJgxA2vWrNHbsmXLOuwD/e7eEXSk2abo9M0XE+JIzfr96zzxMmd+286Nm+/TmfxAbk/CfLbb62qpja/AZTa+6pUqsykC2o7rpBoSCxLywv0HTWuYjXohifPcT9eaJJomQpInR68ypaNsVE9E1uauLAZo2kemfSxtfQkij68izQUr7M3pW9j7EqSf7TxIiYh0Jh8JiHTTq3shSlf2Slb6UscmRxEfFf4X6KwJvLqSwvZPQOlG9pYHFgUnabfTjS8Ip1ZhqqUr6+Y0fJpeodtetirdIrVv+t65gbJ0SmvXfpNO+Qmyn3fjex4C2mSoe5/xGtR3nyxSbg9Pe4y6Rvpd5s8D64JSpl2YIuh7I0r6tQBTNwLsHszwxXFYkjrCNVkRSgFbDF/72tcAAF/96lcbbElAQEDA5sfR19+Hxxcdh9791+PI4bdh8PNH4Mjht6H3jhvw+KLjsPOFy/B4+WY88/AIlNZ2Q6GliuH7voLeTx2Ht67cE6/e+0PnB+KAgIDNh22S6Nm4cSOiyDa9UCggTsPc99xzTwwePBhz5szR59euXYsFCxZgwoQJAIAJEyZg9erVWLRoka7z0EMPIY5jjB8/3qu3W7du6NOnj7V1FLXmfbXQkceiT2c9c1ClL+PHdc9sKlunO7Vu3wbU9l39Ru2b19TWa+fMoFN+wSSol20EK5Okvt1fXG6MKrOGnjMRL4YOUV4IWIotG2wfk78mnsdEuSQRBWrFLxPJQ1NZCyYTEGR+KfRy53YtE4Kio5dkTCJwzGZHTCm5abkiHtg1ECLZIJH6kchVeXJ0NI/21az2Zfy2ddr9KOzrSifkyitporL0NVMRPXmDl3en1pFxF1LmwhfdE6s+zdCVBy9xosiJdIvJPlnKysqZU0sn4zZyiaH2PAQk2c0ifqiuvOtA/HIieqhyxlI54wRmDGnBFlckiQ9Sj1uddEpWyfOUXFdFGPk6Tl1/gYQo0gNWvbpIbee+S3gzWgdsVnz44YeNNiEgICBgi+Do6+8D/vNNPPLCKZj/5FF45IVTgFPfSMoBfPSsL2LsrEV4d+zDmPvXCVj3Th+ICNhlj5XYc+MFWH3TYDxz/RmIq6UGexIQsP1hm8zR8+lPfxpXXHEFhg0bhpEjR+Lpp5/G9ddfjy9/+csAkl9ip02bhssvvxzDhw/HnnvuiYsvvhhDhgzBCSecAADYb7/98PGPfxxnnnkmZs2ahXK5jLPPPhunnHLKFl9xC2DzIsELtqxOPW9oh046V6LHACCFyGTkfaU2bZCtr1ZgQpb5eTK5/dSvRKe96pbR5YtY4mXCuZTCU5fqLECgQvhWvRoW2PUiMugcjfpldBtvFCLA+CUEmRkL6y9PtSLJWS1bL7tmKC1jh63XxM6QFbA4yCxd6dNjNDZyFE+k6wmqk5AIaSclr5kRX4l635xXkhLnWpP5uSGD2PXQS2rDXAImI3PQSnPlHMMcMoac9+Xo4e0zdfrOkUGlPiXbl8LwB9wurpNcL92kPTcuHdwZOnk+HOf+4PZzfdJD1kiw8ZoaoduQ68W4EimRJBkXgFkRS30I6Fw6VL6uVgCgXvORrs2+ztNkqLphJOuEVAhfWk2mZBDPXh2w2TF8+HBcfvnl+OpXv5pGAwYEBARsP+jRqzc+esXPcuvsNe4A7HX737Bh9Tr86fypOGTXRzFwn5Xos/N6jMJvUfz5PXhl2Wh85JxfoXv/YV4ZcbmE0qJfQa7+F0S/PdAy9ouImlu2hEsBAdsFhNwGY+bWrVuHiy++GPfccw9WrlyJIUOG4D/+4z9wySWXoKUlueGllLj00kvx05/+FKtXr8aRRx6JW265BR/5yEe0nFWrVuHss8/GH//4R0RRhJNOOgk33XQTevXqVZcda9euRd++fbFy6e7o07t9wVEqpoAU1I0yOva+f6xXefLrzDOhRCgEjvz5q0Ql1yY/KjFQzpKZpw9AVQrE6azbV4/rVHUqVaCEJqsdJVm4PPoaUSkuIE6XSedyEwLF/nKvzrVVC6igWct25QurPrWhWG2CJDppW58+ZW9ruSnVyfvSLEvuI3oAYEO1CUBkJq2kHk0pYtondTaUW1CVTY6dhthhZE86+YwBtFWb/f0qVSJpu+9UgMPGcjNiWUgLXQLNJZgSxFKgXG2ykzeTOnEqxB5bAjIGiqVm6EBJn9EZN1AMAVmOrH6w4Fk8CUjm9aIUAcpPn0O8HbElKhNSjl1T782Z+h61peRd3o0IwkEoHTEQVTKCSOyBY5dXYjSX2DkqN09nOUah6pGpoMlEdr5URktRDWrptBOeMrUfFWNEVdKBnFmMiVxJbG4rolCqELuoDmk+6blUjtzYaidvlqROzAggHbYEVOIi5pZ/jzVr1mxSlGpANmbOnImZM2dir732wg9/+EMcddRRjTZpm4b67hXGbEDAto37L70C+6z9DfY8YBmaeiT/74srEd54fSh6Tr4Wu4z7pK7bOucKRP/6AZp6tOqySmsPxHucix7HXdTptgcEdBSd+f+wbZLo2VqwKURP7J211dd2U4geZ3GcunV6ohyIiKxzVSm91tZS6yN6+Pwua64YS4EKCp6z+XrLVaAI+5cBm+gxLzVxn8sW0eMSCJKsOEUn8+VqZOnkxAtvQ8+XGNGjYGykdpj2bZUmlGUzs1Hoeu5825BLrdUmSGkm+DZJJJxjVWdDpQVVWci4doIsJMT8lEBbpdk65xJ1hnjS41sqoididQmJRwgba64ugVLF/YXImqtrIsomZtqKTQDTaTmdMQBlDFQrTXb9Wkjn6qJNJXXx6PPJ04wVEFWEIRpy9FioAlFRQHA/a+gWadsmH/ObdTMrlGM0F/3nte18CfoUUTlGwUeUMRsFlQEA5Qpa2pgwTurE5O4kVaNiBRFNfGyJccu1D20lFNrSJ1/MDNQkj7TlqFfAWtsM0UOJJF1XMvuTg0qliLnl34VJ8xbGu+++iwsvvBC/+tWv8G//9m+49tprseuuuzbarG0SgegJCNi+sPAPD6DywPdw4JhX0H1HQ+S8t2wAVg3+MvbctQnNK2biwzf746mHD8Rbb++M3XZ9D4cc/Sx2HLYK5cEXBrInYJtBZ/4/bJvM0bM9QH+xl3AmJltcr4TJswp7y4LM2GqtmpX3GhV9kYBvvnJuS56tNs2R74+9eJC96pZ3dR24/WWTHElt2lbJTWoajdXUAp8mn+/8WmX3C89/E+sytcqP/S92dFF95q0kYZ2wEiKngyqRZvLkxLBz5NAVv2h6J+1bOjYjNVdlJ1SOHr0qmDQ6Vb+aHD2J/FiqNCipTjbx5y+ceUNO0rfApDAa6OiRWTl6AHOT+CCTjtRjNuuG5INVwn/zwXPsA309y3p3iWx85a04jaDKuuGJTufZIpPxUvNNIZ9f9FyOTgCwEiorQ3g/ZPWXdVOnhXz1LSkhZJxsQtpdp99QVI6SjfexR65+V5ESNOpYbTRkSaby1DLq+pp6OoHn6/HdCAFbDLvssgt+/vOfY8GCBXjrrbew77774vLLL0exyBnMgICAgK6FcSd+AhNunY/1H38as//6f7D6zR0hJbDz0A+wb/N/o/ndq9D64Q64+Zf/B6sOPxmT//dKrDr8ZNx02/FYvaw/8Pp1iMshx09AAEcgehoEqf90sl4yP8iZK3nhI4UUCeBfwao2IaNk1KOPz0vz2rmpk13ChOtJboZkxSj3n+klnnDZjmWxLbbX8KKrPSW9ViA6werYfhrdZt/vvZ1KmK6klSxS7l0xSvtt9w+lTaxVtsigMbljk8ktXeFL76crfakNeh8JeUP8i4XZEgVKl0giadLN5BwC8SDxNRKpP+n8ViWNTjYJEXHCyFzPRKdNk+nxk+6odb3UCmnQK3o5Q8A3LNglS4kvNYrybkguJ+vGy9LHbwqqTx04S4qnG8ynMG5n6tRDQolXXISPdMnz0XezUj1EB3kjSevPRJ4eQDE2ZuDADKIkki6yr1lKwEnnIrKLqVky3+BUkVl8LAnzqQ1VYzVVynO/6A6hBdSPCCZhc0BnYezYsXjkkUfws5/9DD/72c+w33774Z577mm0WQEBAQENx6C9h+Ljt9+P3ue+hj8/cQbeen4I4oqAKEjs0H8jZnzrfzG69CP0ij7EqRefhkte+DHmzD0ALT2L2PjEzxttfkDAVodA9DQI6kfWmpONLaHYmvSSOUSOCVnEUL0kUT1mbapOOm910yR75nFMVvLbNo/oMRSAIW+ULN+y37aFdA0vlSVJ7ct01S13pS+b0qH2+5cFp33BJcTuseT2uR4reREYJaRm6YZvSYgaAUiyHLeKsDEEUWqHL5pHkzapdxIQUkCkERFm+Wl3BEjtfYxYSsR6ha101a9Uj17USFqGQxLdNnPBF7T3jVGJmK7qpWb5HblZUgIqF8oYd9DWr4+TJpl6yMOBXmxF5vlk17o5ZTsfb1QW99sMNTsykT/Paj2YqK3OPlOoo3nIXS/TUes8RMnIyeoTAVikGl11C55xBFJfXRNN2AimI+0YvSyeBE2ijnR1Ok0SBXQ6Tj75ZCxZsgRTpkzB6aefjo997GONNikgICBgq0BLt274zA9/hN2vfAWLFx0EAChvaEZT9wr22+8F9Hh4PF6+ZCTWvLoY4rAkj8+S2X9ppMkBAVsltslVt7YHSGenc5W3Vy2foElPGT1HP/Nk5tVpr04jTzrn6tGhojRcmohSH4LIlFpn0sLlTQXbK4DmdRHWK1HUPzqvVYhzvDHUBO+jhDyJIXQdGqNk6iv9IlenTlAs7Xa2DckZtdKXBJLoHSl8C2DpFdt0Th9B+lvNaz1XXqjzyhIh9NxZ01MsWsGKg5ImmMFEYUBPiH1cBR+0EZEv1cSbN6rjRqGvsGXCvmDQQ66eG8V3U2ZlJXcPGEGUXEfBq2XpVOd8z516b056e0nro+aDJpfs4cQX7RPFGrF+l55+Et78N9LVoQr0kuiCXC8JRIS0EdxRENJGJsZycoc7xgeWIqRouxDR06kolUpYsmQJnn/+eb316NEDDz30UKNNCwgICNjq8H5xFwDAq/JsfDDnERx84EvYYecN2HvEvyAXTcRR3QcAAFasDFPagACODt0V9913X7vbfOxjH0OPHj06om67hJmQdq7OzLmHB7KO/Zr62gE+/6hXv2uPiQ7hc17fj+pqbmfy9JiZnbRq8pbUVpsBkKwuf9Eq8VMihkpQzMgOps1HwlCbpPM3q58kkldOJLMd+iw9ptSRIrQkksmikefqEyA5YwGTn8er02hJ5re2X4ISP2mhHid64pvaopMkq2gbQ8O5/UWa0uvLyCgfv0KNiamwmPV61sWgEHY/ZzKgdAhm5bCpVyeVZ90onruWy5OMPKnn5szhPHLtpJ1PSBiHZKIi2/tc9dksYZMknLfxPUgseYzZ8l0n2u+K+KlSnVmfaWMRETlZrF3GeIy1k53/P6IuiMsuu0yTOq+99hoqlQr69u2LAw44AAceeCA++clP4sADD2y0mQEBAQFbHZb3OQal9bMxZONvsP9Pl2LNytX424Wn47BRi9Fr8FoMHPY+pAT2H7QYbz3+J+x2+ORGmxwQsNWgQ6tuRVH73vgSQuCVV17BXnvt1V5VWzU266pb7bgKm23VrXbozFte3a8r/cxYdYvL4nKrZNWt2sSOPY+qSiBGAYBwghby5CTLqzdbJIQ7fbKnqup8OW5Cla30lUfKKDklveqWIOdcPb6+SpYdV+POv5S8vVpXst9WbkIJZtUttUoXlcHnpWp/Y8XYyiNfuC5KoGwgS7pbbax5NO+j5E9rpZvTTh0oHXRuro5by82opsuOe6e90i6TKi9PDJSq9jjQTaxOYfZKoK3UBL3Ued4AZ47GEogryZjNrcuOJWBW3aqX8NE3ChBVU4rJR0Rk6Y/bseqWQpyOhCpQqOYTNd725XR5dd6nnj4W7HykllfPkw9ikzpXqqClLbbbOPola5scR6Uyoiqrq/c5AURGWrGMQmvZ1LPIIkk2mHMqIo6uuqX1kTrWK2GGPKrIEuYWw6pbWxIHHHAARo0ahQMPPFB/Dhs2rNFmbbMIq24FBHQdtLUWMf9Lx+OozzyF0sY9UDj4u2gafizW/+N+yMUXoddO60x0dgy888/BiI+4Cnsc+2+NNTwgIAOd+f+wDse5LV++HAMHDqyrbu/evTuqZvtHJ/6Yql6qoYuz5ME3T63X3Hrk0x/zabQE15VH9Pjt4REp/vo8QMJ8Sk9LNwLFlZvVO3ZEj6obQ6Q5eoxO33ybEiYys4zLh6d+Eo1j8gRlk0l29ArpT8VDSABCWkQYzewj9ASV6JRGp20jjeixiY6Et7F7RM9xhWmlJ/nkjRUa0UP1uGSRPV7o62X8Wlj+E491II/FpHKjMyCgc71IpdjXhperwRJnGFvrRhEZ+3x8e8gVIT3n6tApsnTWIn3qjejxPdtqPYx8NseAXsmKnbN002gcCxH0q1W0vu8C00FbJTlzLDKH7JsQML8T3gggafch9Y1HoQVsdjz//PONNiEgICBgm0T3Ht2wuNsn0PJH4OBjn0XT0q8AS4FeAErdu2HBHw/Bu6074NjDn0Xfoaux697LId/9Et664tsoHvRf2PuTpzfahYCAhqFDRM/pp5/ertewvvCFL4RfXbJQ72RnM0DPF4Q5rldnPcQN1ZMnOmN+5uiy3ijJscudp7kaas27lY6IyDC6DVHjs8XIcl8N8p2jugoAKqRVkkPHtZATYj74LBTOMfR6WGo8mD70kUWafknq07kjhPOajE2CmaieAoSbe8fpXUO0KFIyTgeslWYktcNMl01USkxqRcKs5UX1aFpJH5hXvmxf3AJzXe36kUDy9kw9gzZDrtULtW4SEB3809cmf+DSrvAogs1+ifQaZfmZpVPavInPDu+x4vIiQzBJqruGj5mrfOWRXxGAikwvLKwbxcnRk+aG0oPSImV4XW4zJXJg5+hRStWF0TZIWwYUaUOYKAm4+aKoE9Tn9gzWgICAgICAzsV5v7wA150GPHbNPjho/7fQu89GrFu7A55+cTcUDh+O8+68AMW2Ih745hQctuc87Lj7Kuyy50pg9dfxzsxLsHbfCzDixK812o2AgE5Hh17dCkiwrb26JdNXt+ohePjpYiaFki8j79UtLo/uV2OAv6lRq41uK0X66lbtOSQtr1SBNrRk1FWkgT8KphwXUGXJmLNIAUqiFKsFFGFeTTKvUdl6aVt1XCSvF+X1Ca/TVmlGUXI/BYyPXJ+xZ2O1Gc4rSx6/1PLkCusqzajE2X2rE1SnJ2hgTFu12aqtdRKyiM9dJQTWF7shTl8v8l0LH7mDlCwqVZqc62bZTQkHamtrN3jX9M67cVLyoFptqm/AUp0xINoKCQuS1S5Ld5y+uiUNl2BAfOCDIgYKbWifzlSHiIGomkO+ZB1XYjS3uvJ8NgpaBkBUYhQqsOEhe5w+KFXQ0lp12lj1PDl8ACAqVhFVPS+Ocr381a+2EgqtJSZbDW5O8EiSaweQG1vT1bRIW3jaUiJKSlTiIuaWwqtbAdsOwqtbAQFdE22tRfzPf92OD/65EgP2HIgv/NeX0L1HN6tOuVTCg9O+hvFDZqP/nh/o3zLeW9Yfq4aeg/3/8/wGWB4QYLBNvLql0NraCikldthhBwDAG2+8gXvuuQf7778/jj/++E02cHsF/eG3M7VKzy/N9ZhQz2++XE4SPQJkparwlflktBdZbbL0WD9u1+gN+yUs/h5KFjHgRtWoc4ZeUcd2jJKdGhmeGr594dS3jRI6csn21rRW5b43RLj/dNJu6zcEUyQFIiGtOSZto/tdJGSMXmBJkTmeOa7yTsnUUUdplUiYBePdWCvTM5KSUsIEWXiakDLh1pGqMzoYIUHDVOq5UWhncJW0M7LMIRfaugd8DyYVapXKk8gJBOE62XEuyZN1TsKs9JVR1bl2qX9RPQ8E2kafk3ZEj69ZGmHD+0I4oW4ZzxVnsPHniLSuk03iII3+YRUsW1QED39qCGS/ZxgQEBAQELD1oXuPbvjK1V/NrdPc0oJP3/IzVKtVPDD9GzhkwJ+x017vYeehq7AzLsXK//4Blu/0VRx4xiWdZHVAQOPQvjAUDz772c/il7/8JQBg9erVGD9+PK677jp89rOfxY9//ONNNnB7hTUZ8XMEmehAE61VIJ2gEQH1yKNT96z6vjL+m7avPS+zaYJ88Drk923nvI+W8etMSgQ5K9mnu++jTLKPjDxoPVSjz0puA0137FIrUvcF3/QSy4K2Ufuu7/QvhOTNk4k/MZVfg6ROZPpLJMue0w1MH/VZjVkhknm3EICI1DhOJrYySnmItLIUEdTa49SXZIsgyQaPDZGw/fNtwikT2aRCLUhjqVcnhy4X2UxGrfk7IWAsVdkHyYeEn+QRGTpZXcn98t2Y7Fhdfy6TDAFNIOkymWGSx0ZBz6XnI+43u/bmsqtCU11KTwM1vmV6r8skobVQg5d2rtVnZOBHwrze5VwDYW4CRcqpCB9I6Ne7aOZtfY0DAgICAgK2HxQKBXzqxluw80Wv4S/PfBXLlw6CjIEBu67GyG5X44PrhuCZWRfA92JL6/p1mHvRFDw67eOYe9EUtK5f1wAPAgI2HZtM9CxevBhHHXUUAOB3v/sdBg0ahDfeeAO//OUvcdNNN22ygdsrrK/WbmhFLqxJewe06h/sya/5teTZPyhLvanVXLLK6Lwti3jw6a/Xv6x5pT/TjSuf607okWShdUqVCGc9a1UvaZlQCrS+scU/kTILmUutT6VJ5vKVTFVmPKV9S8ul0+tUNblWlj6jk9NEUksWfql0HunzW6Y+ymSzbJBZfavmoakN0iyZniRbTifUEhCpiGSCLyFknPar70rHEIgh2Nh1+knYzXQVNYkm5TLt1w7ltZXQE3fr3qzn5lAdweVJ5M/f2Xnlg6Tn6StozA7ne1GeTt6ttF/BPjN8dS4PfXYJf/eYMcvs8hBMzmWT6eu1vGOUPWqXjx3J+DE1ls3gMf+E+dTMlBLCV8hShE0Mt/Np9JDWKR0fLaZMGankB3Q6Xn75ZVQq/J3CgICAgIDNiUKhgMnXXo9dL3sdf3nxHLz94i6QVYF+u6zBqD4/xOobd8Him87RhM/D0z8D3DEMR428ExMOfQRHjbwTuGNYUh4QsI1hk4mejRs36lW1/vrXv+LEE09EFEU47LDD8MYbb2yygdsrrK/Web/ce9DO6n6t9MfmOmRFaZ0I6S/YbNORGenG527cbkHleXzK8pFPSWrVywgQ8NqQnFclEVQkSEKx2JZKpjnxk56nOn3ZXgTUC0pC6zM6/TZwb6TVv7Tcto71akYkDTx+2rSZISI4jWZHeChiiFwFEUGICFH6aY0fb9+muXVMmA6LAkoDWXQ4R7Il1SMT0aN12P0KRJBq7PpGESUuTLd5B1AiJjGIR8vVDbpyU9Yghe982hH85qlLJxGp/LB0sNFFdDh+WgOEgdkjWb/m3vTCuOncyKmJQrqnaHVNnoEWuD5ZRSId9bR/qT26nrqPzHmLrPNeTGH/kwKQUfIJpVzppsewH1baH9b5tZY284ztgM7Hfvvth9dff73RZgQEBAR0GUy+8koMu/xV/O31b2PZ80MQVwX6DFyH0Tv9P6z94WC8cMGBOHzsHKxb1QuPvjoFK0Y/gUdfnYJ1q3rh8LFzAtkTsM1hk4meffbZB/feey+WLVuGv/zlLzovz8qVK0OSvBw43639M3YvfD98t0er/m7v+cE6Cyo2wiRzlnrzRfPYGl27JZPHfcry0ZnjZNRTJVkyqX7bBreWyCijZIehKAzlk2dn8lt+nJaZSJ78aB7+S77wzP3MzM20kPY/GZtrldEjdgSToW74pJtecjPd9HgvY8g4tsdIhg10rSwhZLKp82k0D29qi4kh0ggiV4cp1xE93r4lH44OaZmciJGQsbRV1QNVLw1zofemNUBpOhVLh3TssQZ8rk67SIvhtzG/gZAxdHLgpR6y2jJ9yiafjnryXgv1J4sIY35q/8E7gthj3U/0GUD0RZKQZfag1c9Mcs/rcSjJuI3h9IffS19npLJ8Y9vD/wR0LsI6GAEBAQGNwccvvQR7XPkK5r3zX/jXP3ZDXInQa6f1GHHga4grEf5VPQZHffcHGDJyFI655Cb0O2cpVi3rj3H7Phpe4wrYprDJRM8ll1yC888/H3vssQfGjx+PCRMmAEiiew466KBNNnB7hZqjtWtSmML3Y3h9OqUziatXBg0siADrV+esaJ5EZ7bdVJ7/d29783VVtv02qZW1ubrNP0Pd5EtRNARgR91wu+gZoSN4ABW5Y0eWREw+p3SEvoj2VM5+bcxsRocQEcuPEzG9ae4az0hTr00phUKYRLSS6QXpFxXRY0WDEf/s/oy0F1KKNFlyWi+N5uHRNSYlT3rAInqMn7Sc95LrBwA61FO9wtat+jKyy2veWNaNImHRefwGybhJhPKB1ldoh37tluo+q49TwoL45ARJ1aNKsvs3q5/qKKdmcViXlerkDyNPQ6sfVAdbncGfE7DGv7VAlr4xycUS9n5yP/KLnZ6PIrsflNNZ7Lyqp1lXdWPwJwLsTqk1TgICAgICArZDTPzO+dj76qV4dPU1WPnqzgCAQnOMsXveg42zBmHh909BXK2gpccOeKl4Alp6F7Fg5rTGGh0Q0A5s8qpbn//853HkkUfi3XffxejRo3X5cccdh8997nObKn67RToH6RA62o7PnToSeEBlqV8ks4gdVS9PVl7QQeY8poZtPpk8KbQv6EH9rk6Ps6+TOUMpAlqaFVxBI0hiNCHJJcN7Suoy1+9Ei1pIx6yglU2yKR3JZDTWfkpdStf+ojojq0+s11SkyMxJw0cGjXywvXD9s3pWUBotnT9LRQL5nDV9KxEDMmJXFDryQaYzcjuttX0ljE7bO5lO4KmfMQ+eaM+NKtzV0aRndW4uU8apFZzE4GyVr73wFMcpwcHbcL0ZJItPnyDnvJxC9qDNPKdf/8oielR9Yeu3bkiPbHtMSVaQyPBFYigOxi5glaR0dCpSSGjlMbFN6nrmUzFtVAA3Rhi7JZfBDBCANywqICAgICCgi+D/fPNreHTa/2Ig3sNrTw/FHiPfQY9+rTi43x+x/se74OXiv2OfE74CvHAbmja83WhzAwLqxiYTPQAwePBgDB482Co79NBDN4fo7Rb6O3sHWJsswqNenR2JGPfSEOmk354DS6de3nHWFMM3T9J6weY+3nqUQvCTCX6dyV93HskXXqf0TrbFLplhVpWSQBq74ralpI1N8BDZ0swJpW4lHB9lGnsglX+CHFvLhHMbXCor6VmRzjFlOqc0BInRTfsw7Vmh9lNJWjEnqOw61jHMPFZFsyTjmuQRUgma0zw82jo1L868gSTrT1PPXo5eaL8tUsiXP0UbzYQ6NwanxmCUUnDbVRSTT2etMuKoMBfO31jbn/atQG2fWLGqTl/zs2XD9o+7xc9JUkxJIKY883nH9UrWlSpyS7f3GenXa90Rup+FMZb4p+1XUWcqQipWRiFhGhN23e8HkBA29LzTry7RZBFKAQEBAQEBXRSVnrsCAN7tfTxWNR+J3k9ejuFj/oWe/TfiINyODQ/fDQww9QICtgV06NWtZ599FnHs+7nZjxdeeGGzry7x9ttv4wtf+AIGDBiAHj16YNSoUXjqqaf0eSklLrnkEuyyyy7o0aMHJk6ciFdeecWSsWrVKpx66qno06cP+vXrhylTpmD9+vWb1c4sCGenfjgT0XbqdBZfaYdO30Zz9WTppMc2bdC+DajPd+8P6nXptXNm2Ctv2VZIXQ6rNa0vmT4jN0m8UrXKqM5Y61O+OHl6cq4jJ4BinQco1itXJa8L0RXGVG6emMm0p7DC2hPOq1tIZVm+6le+pN5oTiPv1ZFqru1eA7VykySJZWScbJBqBbOY5I9K/HYT/NhQE3Rr/AhYA0pFJzm5ldTKSLUGrW8Akxw9DpQcmq+HlmXl6OEyfM4Ksq926BLdMftM5UjuWw2dlhrWn46f9T4EJNk1piWiU17ESuJsD2O/LGuI0HHCHZeWKArnGlr+SqKXPDf1VtXjWdeVqRDplW6uv5CEbUzrRcTReh6cAQEBAQEBXRDjZ9yA0voW7NdyDw4+8VMYecOzeDK+Gf/8x1DIqkDPARsAAMN3+BtefeCOBlsbEFAfOkT0HHTQQfjggw/qrj9hwgS8+eabHVHlxYcffogjjjgCzc3NeOCBB/Diiy/iuuuuw4477qjrXHPNNbjpppswa9YsLFiwAD179sSkSZPQ1tam65x66ql44YUXMHv2bNx///14+OGHcdZZZ202O/NgfefuANmzKTrpvKHe7/58XmbNXQSPdnF1ZpWJDmx5soHs7vTZ76Y/oXtuvh4uxX5diufZEblyAYFCaoWtTeXJsekdhxqRtl+UcOI6qYbImgzSHlC5eSJnKktJIzPnlSnZQvVTOYkvEc074ttIv0p6dQSVnfZFSkCoV3dMzpwkR46I0nw56p9IcxIhQkTyEklHJywfnXuDFKi+jXhuJV+OHj5wM28U6Z62mAvY+XrsZfA6dqN4HwASoMt9R+RTJxeGu7BTlk7CV+jiLELK5wPPTeSpS4kdSzzlaVBbBi1LhpVHsR6zxFXmC02RY7VVD159w5J7IFJyC8QQZm/WE1t1gGWMNJ2gZbm2u8uZBWxPuPnmm7HHHnuge/fuGD9+PJ588snc+nfffTdGjBiB7t27Y9SoUfjzn/9snd/cP6C9+uqr6N27N/r167dJfgYEBARsKnr06o2FS45C/6GrsPrGfTHvsm9gz0NG4e0dPoY1y/vo7xSD93wfe3xwJl67dF+88/QjjTY7ICAXQnZg6YcoinDWWWdhhx12qKv+LbfcghdffBF77bVXuw304YILLsBjjz2GRx7x32BSSgwZMgTnnXcezj//fADAmjVrMGjQINx+++045ZRT8NJLL2H//ffHwoULccghhwAAHnzwQXzyk5/EW2+9hSFDhtS0Y+3atejbty9WLt0dfXq3jzNTcRukoG6U0ziQ9iKJ6yCqJD+fjZKZ5nvk5s1fJfJiubLiwioxUM6SmacPQFUKxOkM0VcvK19PpQqU0GS1s3+3z37FqBQXECNi5009HquhzrVVCyijhemC1sVfW6LcbGu1mfhiy4+ZD9SPjeUmVNDs2OLTmZy3derJOusPnTvH0SuwsdyMqmxy/FTnHT9TWXGqE06bZCeW9nXRE3wAG8vNiGU6cSa2mfa8b40f5WqTfiWN14kZeQHlQwwUS83Q18g/GFzIpM/jcuT0vTHK31bGgChFgPLT5xBvR2yJykafUEQE1emxFRKI2hL6MPdGtOSm+zFQqPh9sS4Ot78So7nk2q/lkmPBz1Viv059QUk7eq5URkuRKGMPHsHz35D9qBgjqpIOpP+bVZFfqpyQVGgtolCqELuoDml/WquBSciNrWmiKDjy9epb9s0DSKASFzG3/HusWbMmrJTZiYiiCEuWLMFHPvKRLSL/rrvuwmmnnYZZs2Zh/PjxuOGGG3D33Xdj6dKlGDhwoFP/8ccfx9FHH42ZM2fiU5/6FO644w5cffXVWLx4MQ444AAAwNVXX42ZM2fiF7/4Bfbcc09cfPHFeO655/Diiy+ie/fuAIBPfOITePfdd/GTn/wE5XIZZ5xxBsaNG4c77rB/AS+Xyzj88MOx88474/HHH8fq1avr9k199wpjNiAgYHPj4emfwbgRj6ClV0mXldZ1w8KlR2KdGIbxO/8vdhy2CgAQVwReXrIPdvv679B3t30aZXLANobO/H9Yh4iej370o3ZS1jpwxx13YJdddmmvKi/2339/TJo0CW+99RbmzZuHXXfdFV//+tdx5plnAgBef/117L333nj66acxZswY3e6YY47BmDFjcOONN+K2227Deeedhw8//FCfr1Qq6N69O+6+++66EklvCtETe2dt9bXtKNETp0SPpapunTKzah7xUpXSsbYelT6ih8+nsuaKsRSooOA5m6+7XAWKKeni1jfEgx3hkrbVRI+fHFBkCSdSytUIRbRkEkhmfi+scxJAsVqAWR2Lz5M5AWJkbqw0oSJtoofrtH2mOpuAdCUsu73RyfVJABsrLajKQgbfIcjcl/kigbZKs3XOJeoiTZbQ+W1C9NjLUFl9RAgbq/8lUKo2gyeptefVdt8otBWbAMmeBS7z5UDGQLXSZNevBZnYJNoigI533sk5BEdUETbRkKHHQhWIigKC+1lDt0jbNvmY36ybWaEco7noP69t50vQp4gqMQpVjw5mo6AyAKBcQUsbE8YfQjG5e0jVQrEKQV9vdgaeXa7bF0sotKVPvpgZqMgduu69Jnwk5MY2Q/TErE4GOQQAlUoRc8u/C5PmTsaMGTNw/vnnY8CAAVtE/vjx4zFu3Dj86Ec/AgDEcYyhQ4fiG9/4Bi644AKn/sknn4wNGzbg/vvv12WHHXYYxowZg1mzZm32H9C+853v4J133sFxxx2HadOmBaInICBgq0Hr+nVYMHMamja8jUrPXTF+xg3o0as3AKBcKmHOOV/E4fvORc+BSbRitVjACy8fiJEX/REtvXZspOkB2wA68/9hHUrGPHfu3M1sRvvw+uuv48c//jGmT5+OCy+8EAsXLsQ3v/lNtLS04PTTT8fy5csBAIMGDbLaDRo0SJ9bvny586tWU1MT+vfvr+twFItFFItmtrF27doO+yBgvn93JgTgJkMlaC+Zk0fyKH15ZXnts8prZYeSUFN6N6JH9btvDhynf90oGtXSlApwosNegcmUG63qDM3dU02P7PmjBH1lxFAfvDcFqa9KsuywJ3h8dTGu05wRTkmiiGpRdkol3tKYzC9jSOmLeCIRJUyJQEp0iYwWitzRrz1xn9JrKe0+UcmkKehrP75RK9NizZHQybbai0XHbhQp0jd60jHL1RNixkFGpE/NGxMgrxBRPUx5Rvhb5qpbnNhh54RI3c2zrd6HQkYd4RuwNUgecnMyQoYSK+acXulMsLapb8kl5a9JqTZqnMSur2qpNT6+6DF/iCk9kpyjibO0QcQXQhB1+v+EAgAAM2fO3GKyS6USFi1ahBkzZuiyKIowceJEzJ8/39tm/vz5mD59ulU2adIk3HvvvQCAf/7zn1i+fDkmTpyoz/ft2xfjx4/H/Pnzccopp2D+/Pno16+fJnkAYOLEiYiiCAsWLNA/oD300EO4++678cwzz+APf/hDTX8253evgICAgFro0as3PnrFz7znmlta8PEf34V1q1Zj3ndOxmEHPYVu/dpw4KinUfrNXnjyzaMw9pLfodDc4m0fENCZ6FCOnkYjjmMcfPDBuPLKK3HQQQfhrLPOwplnnolZs2ZtUb0zZ85E37599TZ06NAOy2oEyQOw7/meLQ+CbarMzXNjtlrzOSWjHn3CU57Vji81rvT5iB+1JTcDz5ej/pleon+VJnutK7PZWXvoUdJrBVIurFp2QmAJYaU3NqSFr0doNE+anyb9FIisHDbI0WkiYIhOxULQhL1SJKt3pblzVJ4fQT9JfhyhcuqINJGzygeT6omFhBTJJ9SEWiY6JdGpCDmRTqojmPxGkSDXUZgUKMkmISIJIcz1o9dS+cnvC33PSmjPBLmWIitXDh+4HOkEXI+irJvSJzfrxvPpB5NnMXGkkCYHpvl6YD6FcTtTp1TDRIkXGQRR3k3J7ffpITq06TD6c5GlBzBkCc0lle7LNA+Vdc3SRNk66XjWhdTnUnnW4CQ5eizbiC1In6yKuJFKKYPuEFpg+wERAe2Mzg3Y+vH++++jWq3m/tjFsXz58po/jqmyvDq1fkD74IMP8KUvfQm333573b9kbs7vXgEBAQGbA73798Oxt/4Fq49biCcfHoPyxma09Cxh7H5zsPGnQ/DkVWd6F6kJCOhMbJNEzy677IL999/fKttvv/10wme11PuKFSusOitWrNDnBg8ejJUrV1rnK5UKVq1a5SwVrzBjxgysWbNGb8uWLeuwD0L9yZvUbAnw7/lqq6E+ixgi85tM4iiPkMlDnk4faUPnrd55m2ef+8FXUqJra5kUx3aCZjsHj20hXcMLRLbqtSqp5a7zZYgcM6e3B4tgOl0CKtETg61EZVnm1wlLC+vRlIxJPmNIkRAnyaZW8iLLREmz2pfZ1MQ8na2rnqXkEYSZ21LSQbAF6IVeUyz1Me1rmbzFEus5sTCbNNcw+TRJpBP5nluTkDZS2NdSrS7mvUGyBq1iKhTpZTrdRfagrf9GoTKy9CliQW2M1NMsTt6Nn+rUq18pngjw+8bBZQm/DmtIULNB9NajA3D7iq/SplaLk3GyIYaQ0vgUUU6IMVH6U51T/UAHp0Sy6lZqiLZPkjIJE/XDyRp6fyqbSLn2k+rLIIkCArYQzjzzTPznf/4njj766LrbbM7vXgEBAQGbE0P23QsTZj2Gfw3/M55/4iOoFgvYYcdWjB12Bz78wRAs/slljTYxoAtjmyR6jjjiCCxdutQqe/nll7H77rsDAPbcc08MHjwYc+bM0efXrl2LBQsWYMKECQCSlcBWr16NRYsW6ToPPfQQ4jjG+PHjvXq7deuGPn36WFtHIelOHnuxJeCbv9RQ7+Oi9FyCbbxePS5lzWuzdPqIKbNvyAul3+cbl2PiT3g0TyLFjuqRVpl67YZvPE4nskoj69jVSb0xC5bbxA7XF2mNhoAi0TxkrSjbRv6iiSR9x3vQEDHJZ6T3DWljjwohhLXqlYrmocun654VZhNQy7JnEA5Q54z3EekHHjCBKI3miSgxpa5hury8Wk0M7qYHjiYXzLUUeqbPBqdv0OrOTkkqtcw2VcxBul+390XUZOnkMnw3CCcXeFSPYk7UJc7yjXAb6vKBiHZ84uCyJLwPGCrfMp24UrcOtjK5JxQMOppH0IieVHkMwskwNsoMGOY7lQ0gIqSNtoUYaa2cpYiaGBY5RH3WHSCNTI9PAdsXdtppJxQKhdwfuzgGDx5c88cxVZZXp9YPaA899BCuvfZaNDU1oampCVOmTMGaNWvQ1NSE2267zWvb5vzuFRAQELAlMOKYwzH6pqfxdPf/h9efGYa4KtB38FqM7n0N3r1yd7x0r//51rp+HeZeNAWPTvs45l40Ba3r13Wy5QHbM7ZJoufcc8/FE088gSuvvBKvvvoq7rjjDvz0pz/F1KlTAQBCCEybNg2XX3457rvvPjz33HM47bTTMGTIEJxwwgkAkgigj3/84zjzzDPx5JNP4rHHHsPZZ5+NU045pa4VtzYVgu7ksRebWaeaN3hXus4xwTsXzNg2xT6li+tsX+CCTVzkdauaQyodKjaDR7lkzaSl9ekSQYYw8UXRxBZ5Y5915/Ui9cyduXOZvFdiq07s2GF0+vrHIoUE9ISfR/RIGm0j7CuWTIB90TzQfah7NiWRhHR1Wq94KZt1WRKtFMu0F7Iih8irX1InlibEVDrx5WNGH0vT84BMI5XSiB6VVbqdN0pyD7p0m+di2CRNvTdG3o0pDOdgb4oEoKyKsLgLjVp6pXHB8SkPhKOgEUROJE+NzZHp0+E8eCQhUMwmINNIHpJ+3Nd3vn6hOZXoowTpuKlSncQOUDvSRiqaJ/JE9PCLrn1TpJTtU0BjEccxfvnLX+Kss87CV7/6VfzqV79CtdqxRRcAoKWlBWPHjrV+7IrjGHPmzNE/dnFMmDDBqg8As2fP1vU31w9o8+fPxzPPPKO3733ve+jduzeeeeaZuhbBCAgICNiaMf4//h3Dr3kJj6y4DO8sGQwpgYF7vI/h67+Bf176ESx7wjxDH57+GeCOYThq5J2YcOgjOGrkncAdw5LygIDNgA4lY240xo0bh3vuuQczZszA9773Pey555644YYbcOqpp+o63/72t7FhwwacddZZWL16NY488kg8+OCDeglQAPj1r3+Ns88+G8cddxyiKMJJJ52Em266qVN8cL5ad8J3bWfe1Q6dfApRa35WK2EyV0/3BfusBUqxGGmJlXku8vmWmur7V86ya/tTHhveVID7xL2h+XDoX3vPnaNLtudrRY9oH5jYkyydvv6ykhxLU1fnlnVsNiV0H8LthSR/rImPUoK1RkXQMCjux2pneQlEgi9rb5NZwhrIUi8PbxM5rr3KALUceUGQ+XhEGtZzo1g8Qo0G/CZR+mrdnL52rFzwek5D0CEFKYT9WlSWTmEf1xXN49PJ+paOj9w2viruzemXQckTIlPfAuQhqke2ZI66tz0ha+DWLShyjShTyZsBmLw85LxuT+WaewnUNuumTeVG9T5pA7YUpkyZglKphFNOOQVCCNx1112YM2cObr/99g7LnD59Ok4//XQccsghOPTQQ3HDDTdgw4YNOOOMMwAAp512GnbddVedFPqcc87BMcccg+uuuw6TJ0/GnXfeiaeeego//elPAcD6AW348OF6efWsH9BmzZqFcrns/IC23377WXY+9dRTiKJIL+EeEBAQsD3g2PPPA3Ae/nLheTi492/Rf/dVGLbv24hf/ixevG8vrGodhMMPeRyrlvXHS8s/h+EnnYlXfn8r9mu5B4ePnYOHp38GR19/X6PdCNjG0aHl1QMSbJbl1TvQ+x1dXl1HkNSpk1YrOsRGffAtr56lQx1LJKsEl3LqZLVPdArE6Qw4ry1FDKBaBdqglh3nxIc59q3KVUqXV/frEh6bk+w5xWoBJb2ku/DqA9FJdbdVm8Ay1bC6Rg6Vv7HchJLXT9dn7m+rWl4dNmSW7Wnd9eVmVGWTRwfvT2Hmr+nWVm0GRZwKsPwjc1yZ6txQakYsC6kf7jUz7eyJbiyBUqUJvvFj/LJ1qtWsi23N0Eudt4PUkDEQV5oc27IbQL8qJIrp8urtvVHU8urgRA+b+NMBluotFAGky9rXpVOm3EkMRJU6o2zofjVGc6sp8hJT0uix9FZiFPiS7h47LZnpA6GlteIQY45uStykn1Gpiqgau6SaO/iITAlsLKJQrBi59GagA47+/4Mvr07JJl4/Ju2QkEaVahFzy78PS1U3EPvvvz9efPHFmmXtxY9+9CP893//N5YvX44xY8bgpptu0pE1H/3oR7HHHntYZNLdd9+N7373u/jXv/6F4cOH45prrsEnP/lJfV5KiUsvvRQ//elP9Q9ot9xyCz7ykY/oOqtWrcLZZ5+NP/7xj9YPaL169fLaePvtt4fl1QMCArZrVCoV/G3a6Ziw9xz0GpS8miUl0LamO6qfewZ9dzEJ5kutG7H6xn3Re8cNwKlv6GXdA7YfdOb/w9pN9DzyyCM46qij8Nhjj+GII47YUnZtE9gsRA9FnVeio0RPnBI9lro6dZbaQfTQOU2cQfTUImCqMVDO0FdzTiiBGGqCX58+AKhUgRKaWZtaxESyleMmVNUEH/DodX81lxAoViOU0GKRM5yg8RFGEkBrtYURLD4bk09z3QVay00oMz/r1llpsf23lPt1SghsLDehQgIIzStbrj56HEugtdLNvX6kQMuRts7WcjOqeUu6W3NlY0tC9DQ7BBAnkzhkDLSVmgBZcM7VIm3iGIgrBce2TBlkLi/aUqLHVz9PbwWIqiSmK68+9TcGoiIgpOeZlyVDpvKrQBN/INRzc5ZjNJVgoohqEEqWreUYhaqnPtPDCSKUKmgpsiXQnbbpiNKES1IUlSqIKtYgtRXS9jHR3VZGoa1sETFJNbbPbZExZGtrSuRIT/+k7S2WNCV64hLmln4XJs0NxMknn4wLLrgABx10EADgmWeewdVXX43f/OY3DbZs60QgegICArZFbFy7HvO/9e84/OD5aOmd/Jxd3tCMZ18/HAdfdg+aWroBAOZd9g0cOfw2PPLCKZnLvAdsu+jM/4e1O0fPAw88gPnz5+NPf/rTlrCna6LWhGwzgbyIoCdeAv4ty8R6tnbbsgk6/QsauV749Pn8TeTR1bQMOZCk643YeQFDhhhL7CTN9jm+RHpM9PgIF9sWuvoXMq01epJMNqZOnJbyfuOki19n8h/xUyUzFiBJlFX2HNVaRZKZnDg2iUKWq07Px+kmkbwjZSVOFhKIkk+p3uGCZCt/qdw5AqB6JdURIZYRpCRZeGXWHWAPGJrEWW+RBGTUvpsl3Uhcja3Lp59WlelB1o0BZOv1vJlkfBSwc/QQSEIKUX21dMIvruZxWsbSPtk5emRy3srdowiULPl5Dx0FlcsG5tO6+2WyWRmgfcK1IWyTgvU1mD4Y2Zaw5F5M6sS2Ot968yp3lLKD2hOCehuOJUuWYNy4cRgxYgRGjBiBsWPH4sUXX8S4ceNw6KGHNtq8gICAgIDNgB369MJxP/kznnr+YAAJydPcs4yxo+Zh4093w8IbLgAA7PO5KQCApg1vN8zWgO0D7crRc9lll6FSqeDYY4/FN7/5TXzve9/DJZdcsqVs266hv6N3slZfAFc9ZmRMfXPl+OZ0Pl28jB7Xo5cjq42sY9+N3+GyVZ4XTiT5CQJ/XXNOwLCtZp4odU1qj4QgxBF0mc9KEqdhSZTpWaoz+bR1qvW8HJ3S1in1H1cnlV9ASgRpGaauIqJUU0ntU/psEwxxoFqmE346105y9Egzv7UsIr1LX0VLeCV3BPCJNq+jyRNb2yYh78ZQxyLd8ZEZlNDxQdVhooXkJapueh1SLstarIkO6lpdkOdX1gNCppxIXlV+7VL/MlPQ+HRZBJqElVTZ1yx9kHNCyYmz8z7spXUN/CSRrw5xUihyUtr1dXPaabxzYlddQENw330hD0NAQEBAV0G1zx4AnsCjSz+DAcXnMfLgV9Gz/0YcjB9i2ffvwj/XH4LBo4FKz10bbWrANo52RfRceumlGD58OL7//e9j+PDhgeTZBKRzY3urEx1oorUKj8565NWjyycni/zhwQm+YIV6dXLYZIm/rue3dVKu/pqzkn3aGrIInuwjWx6ldmBpt0u4N3xxdaNB6l7g8UOAEMJaobxdOgX0Cm3acwFAR/MYnTQAAyIy1jpLrIsMnSIlmuCuEBdRvUgCabRPAjJZS117Za9WppbGTjZlD+8Jy3frtEQSZQS7IyLR8by2Hq6mvptSdDyZLiFkrDHkO5CkIuE/OqSW++W7MWGXOcFF1G5pPq0yxZFkEUtEj6Dn0vORUkztMkPYrFTIly0E0lf7WAM1tmQ6vmSkN6tTmCxLYZQOfhH5B4y1slzqPItIst7R09c4oJG4+eab0bdvX+y+++7Yfffd0adPH/z4xz/WxwEBAQEB2w/Gz7gBpfUtOHCnv2PkNY/i6eYf462XdoGUwJC9V+LwUX9GpVjAgV+/tNGmBmzjaPerW5VKBeeff/4mLf0ZwL5a01l4HZBodxNXKxNQS54+Z62LbTbJNlVG5yuyjg2ezzxkBTmIGhKydSuKwn39KLuVoiRofVVu/nILVCSLf1H1Wr1kS7VbitQiU9O6DvzatVen9Oikk0umM5lsp30qkw3WWOEvr7F+FKZuzJdml0K/pqPmsOp1Gko12cvdxxCI9Ss32g7ikSDRR+5GJ9KkVSx1rtt2QSKdyDOBvpvC7Xg4Sun5rDm8tM9Jplb7mSHXS55k6WR1hYDrD5Xh8VVm9Ie+HLwJKXNeF/P0iXPZZJpHzZsPJ/2QYGMn+bDIMiTjm5It+p8wn3aCJEkMIoNdvx7HOkk43pvzlt+c2SJkUEBDMXv2bPTr108f77jjjvjrX//aOIMCAgICArYYevTqjYVLjkL/oauw+sZ90fbaE2j6t99hwWMHo1osQERAU7cqut8/BvMvO937NkZAQD1oN9Hzta99DQDw1a9+dbMb05Vg3bLWL+i10Y6q2VqZzlrydAAFicagIRa8jM/duEq1RRnl9frp6zpDEbgvVOXZAN0iWWhdtU7oGNtSyTRTnTbJwo9MqUCUzrd51IlqxVu6OumZPH2EwrCvF2jsiokwMvK4TqnlWdaKrP8JpVE5IoIQEaL0E3S8IGJ9R/ZTUoWOOzt9jLRMTKqnEQ9ptI4b95Sck2rsekefGu/slGfTUUmR8OegqQdSwhNiZZtsGUYMjEQ2kZH33YA+DpQfqrnqW98zA3D9VPucqOH2qDp5NzyTa10HJpNG7uhTrEwTVx6/sx4ySV+QgUZstLtf3UfmvEUQKhl0sFh3fBLhA6meN2D6iA3pkHb7k3iaOwAZm+fr74CGII5jrFu3Th+vXbsW5XK5gRYFBAQEBGxJHH39fXh80XHo3X89jhx+GwY/fwQOO3IxqqUI/3p2N1Q2NqNb7yIOHf47vPffQ/HC70NS5oD2o105egI2HwTYfKgdZG3Hed1Eq54WyPplmdWMSOM67FHzCN/8L69tVv162hsaxLesefZ+opPOgmx50ilTGXsMJWQ05yzNbekpwKzBZs/QFQVB21C5kUgTwYL2l9Ce0ytkiCiJSFYhHI5XkBrQPimdai8CkqTHgImo0RJsPVZfyZi8zmL3BQcdpUJI2xdJRjARYP/YEZtPGbn3GhJSxc3FkzHjZUEdqr11mkQatesG1SyoAIRMJv3UHy7Lka0iPjx18m4uwHLXIkOsYBDhlScpuVKHv4If0H7K81ES2zx69Btl0i4TRI4mrlQZHxDcDt2d6oJ67NENkgKHLxFIk3MDTlQQXLHqDkvkxaZdpp1ZTz5J/PPY7yMBO/4/lIDNhHPOOQdHHnkkTj75ZADAXXfdhXPPPbfBVgUEBAQEbEkcff19aF2/Do/MnIamDW+j0nNXjJ9xA/bu1Rsv/m0uCn/5OvY+8E0M2O1D9G/9Jl668AcY/NV7sePu+zTa9IBtBO1eXp3j8MMPx4MPPtgll7jc5OXVO9jzFbZgef1kTZz5akktGWVWI/bU0fMGMqRioK7F4Ln+Srq8ei2djm4AsRTWUuc+Hb59tby6mg3FVj3htFOkioRAOY4Qo5CjQ7D95LhUFSiimz5Hl0HPKlNETLFaSFcB43BJJknqtVWaUSIcbyy5bX4/AWBDpQkqvIT2jT0nVTKEtnVjpQUVsuy47lvp2mstNR8LtMbNHpvoPJjUJ0t+byi1IKb9Llm/SE8ZkjehytUmZwl1M8YM+WHpl0BbsQmgy47nTaYpsSCBaqUG7+4jOySAtgKskJ+Ync/SG6fLq0tGUniJF2G1KxTh15lDLgmZ1PMur55lp745YzSXmDz+QLAIKlKvEqNQ8ejwEFtWYFqpguYiKYil3VeUgWKyorYqoioxMOPB4CwX31ZCoY04qkmftJK1zLpth9y4kawSRs+TjvScq1TbMLf0+7BUdYPx/PPP4+9//zsA4Nhjj8XIkSMbbNHWi7C8ekBAQFfBQ1f+Fw6M/h92HPYhAKDS2oRnXj0SY793D5qaWxpsXUBH0Jn/D9vkiJ4nnngCbW1tjqFr167FFVdcgauvvnpTVWyXcKMM6kdH2/FfnbPmV/XoTCI6pHPOVy9PVs68MFNW1nwt77jGHI/U4xRHlo009sVIMMEKNmFi6zGaYjQhySXjvI+hy1y/09//hXlFJZHlJyaS/URHQoYYCkvqUhqPRHVGVp9Yr6lIUYM4lORYgnPKtG/5fqz2BKWLUj951IulKiZFSUSP5JWs9mZ1Mau/pd3vNpEl0//sPottZrB9N6oQzr2Zy6aqwzi9crScd2ZWex7Rk+rkKbh9N5CO6Kl1w1NiRLinHZvcGzLznLXYlM8ETvyoHU5KERlWJBUlVYgM6Rl4DknkPGwZwaJ8SD+FrsOIHcdOItQJSVMRPcJeFl635fWlR0ZAo3DAAQfggAMOaLQZAQEBAQFbEY698L9QKV+EueeciAkHPo5ufdpwyKi5WHfzULzaMhWHfD0sjBSQjXbn6FH4/Oc/j6uuugpCCKxcudI5v2HDBlx77bWbZNz2DP6dvT1wXhNop872EDxcJ9Xty9Xj1dkOubU2oLbv6jUI6ifvs2wdJmcLz93i5npR5a5U21aafUdpiNJ/Ms0GRP+lOWQgyEZzBKWyyRxOUTW8n6hO/Umum7ASfwiYRCBqVSreW5IQH9LKzWP3r9uHfKxk5uUhlpskwaYv9EJIIiWeBJL8OOmm8vMIsqKWEJHx1VrCifYYTZRr5vpqIk56Hiq3j/VP5crJGrQOk0OPDWWkT/kSWPGySHWERydHDgOq89oIeuDbTN/XpZP5RPuUDFwDX5/5+lCwJuQaWeJ9ZBGXQeTSVd2s8Zp7MW0/jQphTqg2fMk6JV4pR5Rc0wIxJkqFCC3MhuW4NCxcRGz3PZ878j+CgICAgICAgE5FU3Mzjrvlj1h11KN4fsG+iMsReg9cj4P6XY1/XTocbz31SKNNDNhK0WGiZ9iwYbj//vshpcTo0aMxcOBAfOxjH8P555+P//mf/8Ett9yCXXbZZXPaul1BODv1o6Pfz635RDvV87mZmUhJPbnwvQXok8/nu+3ZADjzNp+tvvmnL1jA3exVsOyVt2wraEYaXqbqm+m7+atW9pKIUbU00YXJzUpUlO6xvMi4jm6fyXQNsVQ6XSHNWWFMLe0Dyy+qSVh7hniRpL5a8Ur7Q1ZjUxtfqczxQFIOw74GauUmqZkgCRknG2Ss+9esBBcj5qvFeUaPmqDrsUMGknXlpURs9arUr/ZkDlrfANaKBfHP7Qrr8jjHHr0cXp0gDwVSSJfojtlxKsdLoOTotNRkkUPtfQhIskuuj0X8pGY7Ngp47beGCDzjxVn+y89TWd1h+SuJD/S5qfRU0+tKy1Ih6pND1YmQMFScuQK13YcO/E8ooC488kjy5fuxxx5rV7t3330XxWJxS5gUEBAQELCNYreR+2H0jYuxsHwdlr8yEAAwdN93MPD5T2LRt49EaePaBlsYsLWhw69uXX/99QCAlpYWPPbYY3jnnXfw9NNP45lnnsE999yDOI5xzTXXbDZDtzc4E4Gs7+BbQKeeN7RDJ58r6eN0lp+V6qmWiqwpRj2BCfnTFuHUod3M57dS6/StPOWdggNOmcjuJ8eypKwAgQroyldGrs9Pyw7p+qU8EaQ+kMS3qDqRiFCFSkZMbRIZfUv7UpqZs5RWG0VVKP22JSqSwTdR9VFKaf3YbmO9ASOoTkJgpjN/AbOymCKNlHzfK2e0D6XnhPEJ4By5VEW1Bm0WWF86AwieYz25z1BaU6epY/ySLnNhTpq+5c8sPvDZpxIhAfsVJ4osVtjcnN5+4YuD8WsX+W9G65gT74KTKuoVJ81qktEiYUXXaXkChIEihA2XZaEAiCr0A9rq54yO0wSSAMhrmVqvEqLZUadxwBbCAw88gKamJvzpT3/CEUccUXe7L37xi3jttddw0kknhcjogICAgAALh3/lLABn4a/nno4Jez2AHQZswJgxT6P19r2waPW/YcKFP260iQFbCTY5R8+GDRvQ3JwkS/3sZz+7yQZ1FXQSt5Or0/eDehZkxn49OvNsyZKVd64WNCGRoZ3LpXNSe60q3l8u2WNr4Bl+VKmdy4XuVYkWe09Jt8kjW59LZiWl0tEpYVYNozl6bJuVdN9KVS4Z5OSV0RNLO64pKU3y5fA+4ro5GZDMk0ktQfpBCqLT9J+KxkgietTk1tdXvt5MbLY4ATaMEp2xK6ueHD2ZJAdjVPjQ5bKtcj55r8MOnw5emJHsGDAcRM2bVNj19FtQvsGVlURako2XgXAmSgeT643oYXKse0t1py9siRxyPcYYpVgZxBRYtx+7drJq2jnkORkblKnUg5QwTITfMbIk06uO89LcB3QUl112GSqVCo499lh885vfxPe+9z1cckl9uRT+9re/AQCWLFmyJU0MCAgICNiGcfwPfoE1K9/Hwss+i4PGPo8e/VpxaL9f4t0r/4Q1o6/BiMmn6Lqt69dhAVvdq0ev3g20PqAzsMmrbnVlbPKqWxx1XolyXetY+XTGnmXS69XpJzGUiKxzVSkda+tR6Vt1i7bN0imRJOuteFbdqqW7XAWKsDPYcyJCvdTE9ZdjtQIWp37Up1mpitYpVyNLpy2XRtgI53yp2gS6mpaCsZFGypj91koTyrLZe859Qc22o7WS6KSTbGMrTZts27yBrbrFSRL11hO4nzJZJYyei1lbtQqWRSxJYGO5GTFdAYv7Rua+1lxbAqWKWX2Nlmu/yD7tqyJfdYs6m3OjyBioVgoAkV0T6VxdtKmkLkynfRFde2IgqgjDD+TosVAFoqKA4H7Suh7dIm3bVKlDBz8ux2jmb7JwYoc+3Ej7qBKjUM3RQfgY+6auoKWNFXKyKSajjlSNilVEMVt1yxp4drn2oa2EQlvZ1FM3ga4rSXtyXkrIjUUgTp+2sa8O153sVypFzC3/LqxgtAVw6623Ys2aNejXrx++8pWvZNabPXs2JkyYgF69euGWW27BM888g/POOw/77rtvJ1q77SCsuhUQEBBg49n7/4Rej03HsJFvQURAXBVY+vw+2P3c+/H0NV/HuBGPoKWXWdWztL4FC5cchaOvv6+BVndNdOb/wzqcoydg00B/Pc5lSjY31MRCbbC3Gs2cTWV0yTqf9TYG1eeTmaUTyA5uoLZm+ePTGZMNLO8Kz9SjWrs+0AiaZKOra9FjQTRWUwtcTVSb+Wvr5cuS81bcy9Qeaeyj+YKUdt5fAuStJJ0JOdmshMjCyEvy11TNvjR5elSuHpM/x9alxqZ65UayE0JtxB/TdyrnEPEpnRtbuYnYILLHi3AGj0yLZWqHkU5GT705ejgkdHSUfuXHd1PGbLMva/aWBUE+BdhBuvEcPXFS7iRU9uj0uZH1lpkdGpbjQx1+Wjm/lWjeDzLj07IjFezJ0yNknGxC2l0Xkagl3gN6N93hOaOkRDIwU92KoNEf6uHNbVfyYrujBdHLO9jSlzdIAjYFlUoF559/PqrV/B9nzj//fPTq1QtPPPEEfv3rX2PixIn48pe/3ElWBgQEBARs6zjwU5Ox18ylmPvq2Vj7bh9EBYn9Rr+Clj+PxOFj52Ddqp549NUpWDH6CTz66hSsW9ULh4+dg4enf6bRpgdsQQSip0Gg3+Ebpb89c0LAnbhREsC3QBB9cyDLBiWX68nSx8vzZPvmdj57qB/wrIGV/KMEjvmrKB47U4290T0Bu9cKRCeINmWN7adk2v29K4heuopWQgxFOm8NX+lLkMeB0klpE0WaWJ0pQRIi2yuKCRT0vln9iqz4pfclIYkkYiEhRfJpdCVEgySkA83NozxQK2tFglxHOvFXWyQhInP96LVMnPKniVYFal0vc60iQnh5Nvdi0guWEF9qFGXdlD6ZWTdeFlnESROqTx1Q4oGuuAXzKYzbmTr1ZaOqfEPWd2Nm2e/TQ3TQN5K0/lpkV9YDxV7ezVQSAlKoVenINUsJOKkM8V1Iek6vqmUNTGOYdwyRA0HIGimNvVa/StO3OjkS05fJvgVsKr72ta8BAL761a/WVf/ee+/F//2//xf//u//jo0bN25J0wICAgICtkNM/K+r0fPrr+ORR49EeUMLmrpXIATQ1FxFn6F7YMjIUTjmkpvQ75ylWLWsP8bt+yha169rtNkBWwiB6GkQ9I+seZOyLaXYN/Gtod43baFBBVmBBbVMyUMWGZUl3zcn5vronJvrSHzwRfQYOscmY+h6WD6JtH2igUeeVJ1adM/QD5QqUtbTDDn2/J22tENAhFqJKuMfJbMAm9CwUkZ7x64iBUxETZx+Il31SlqbmpgLQhIJCGk2CTW3lc5mvTQlTCyRTCOIFDEVp5vWFQvIdAPMdZXpkvLJDSId96yBIwAp3Cgi7wpY/Obgo1ImN6Feqj6PEFIXW8mpJ6KH3xhKRhYBJWA/HBS5BvqJ/BtfuSLN0IAkz708CI8sXxkfEtRsEL31PNQAt6+8ETdpNE8cQyCGkNL4FBFOiDNR+hOMWLEGpyFtQMeRJGVpHUg9bjRZQ3gcvUXk4mo/ffoCGokhQ4bgi1/8In7zm9/gU5/6FIrFYs0ooICAgICAAB9aevTAR2/5Cx5/6TgAgKwK9N1lLUY1X4xXLtoPq/75Clp67ICXiiegpXcRC2ZOa6zBAVsMgehpECTdaQ87spmUO/OXGuq9Py5Dz28yf9yvF1nz2iydPh2U9qCvIeXNea05EZDGtfBol0SKHdUjrTKhZ6P2ZqgZ9x+SWBdN28A6Q30mrykRfW4aZZeIsq9QGtFDfLTr015JYBZft/vUdEVCyNBjSNOLkepRIRCxqB4zKTfkkoSJ5pEpmSO1XLbpCaxISQQVl0SuorADJtQ7YSIir4CpKBUkk3d1Q+TemppcMFdM6Jk+60bfoBW2IPoKW+aNyCfyQPZN59MnmQyfPk4u0Kge5bRqX0c0ER0WIKIdnzh8tnseMnroCWY61VuvDrYyuRttk2wSkYnoESZHlB460upIu++UbN3ndHACiMi7X9oWYiStC0XU+Mgh2r+qQMl0fQpoLH73u9/hc5/7HP72t79hxx13xKpVq8JqWwEBAQEBm4SWeD0AYN4b38T7/xwAEQF7jXwTO/ztUDx2yRewz+emAACaNrzdSDMDtiC2C6LnqquughAC06ZN02VtbW2YOnUqBgwYgF69euGkk07CihUrrHZvvvkmJk+ejB122AEDBw7Et771LVQqvkyhmx+C7uSxF5tZp5o3eF9nyTHBN+nd0hxVe3Ta+k28DXJ84npM7IsvW44tRf010SB0Ju1Gx9BIHpobx2TIoHWz8hBxAsd/ldwonZjY5Hpn96P9QphNBQnrDZZkoi3JBtAQi8RD9cqXZLl5CJegtaRiSUQPtE6bcLCIJ12WRCvFUuUF8kcO0Ve/pHodjNKV6c3Ae5gfa61SphFEOXl6atwsKtdR7q3PiQ+g/ogeCXe4ELmS7JtNkQCUVREWd8E6I1sn4Yccn7LA5ZAIIieSh5ut9n0PJV6W2WeKtaGDlcbwScPdOA9Q4ZevVkmzBpPSA6BKdZJ2oHakHaeieSLf61dpfdb/mnGLbZ8COg/PPvssbr75Ztx666144YUXAAA9e/bEiSeeiOHDhwMAdtllFxx//PGNNDMgICAgYBtHpeeuAICmeD12uuCfmDv/4yitb0FzzxIOG3EPuv/1k1a9gO0P2zzRs3DhQvzkJz/BgQceaJWfe+65+OMf/4i7774b8+bNwzvvvIMTTzxRn69Wq5g8eTJKpRIef/xx/OIXv8Dtt99e9/KnmwrJD3ImgZtTp/6+Lz1bjgmcWsjip+g8tJYbtc63R6c9kBV1InJ9UsdURvKjvh0TY2rbEtzcLsYiOzMPLIlKR6T3XZ18yq88kpkeCUcSf+WKltt1lQbh6DVaFWFD1atoHkLMpESAlB4LrNw8dG7MSDVCHgHSIWgs6im1g7/eJkAjiJRqmyEQoFE9huLjET2+MQOhxkyiJxKpj05USI0tFZrcg9LR5yjnBAqPqsnSSex2nUmL84gOxq5IKsenk59TKn3kUBb4A4X4SoaafxM54vNsj+gnOUFIL5vqoTqoUpkvnxugzheILiubtDD1LNInBmKVo8fnFPWD6I6UbJG+3hXQGbjxxhsxZswYXHTRRbjgggswatQojB49Gs8880yjTQsICAgI2M4wfsYNKK1vwX4t96BaKuK4H/4eyw/6G177xzDIGOg3ZA2kBHq0/ROVcqm2wIBtDts00bN+/XqceuqpuPXWW7Hjjjvq8jVr1uBnP/sZrr/+ehx77LEYO3Ysfv7zn+Pxxx/HE088AQD461//ihdffBH/8z//gzFjxuATn/gEvv/97+Pmm29GqdSJg70Tf0zVX/elPffIm4NSM/UP0nVsPhk+e3zHknzW0mmvmGVLquUTn0eagAEezQNLgp2XR03+qMUuGePm4VFZbEBKqb9Gs/R4Yy+JDlCLs3LvUL32GcDQLT5ugJAoFplgXrFKthgQcTovlsl+qiGGiepxyUVKqgmLQJIQNjmjdCmZqR36FTqq08oHZBI5q30T0RNBSpONSDnpG796zi1V3xNdkO2LsJFGqND9nKGYDwF14am+jubr4bKd13rIpok9YkOWDo9OkaUzD+qBQvzLjObxnPOCnvDZriJvQAerGq0yzc0jrVcP9YMVgP2uGtlidY4YQK9nlbz/JWNLrxXNA8DKz2P5o+qT9qpDFNNm2UQHRcDmxm233YbFixejWCziiiuuwFVXXYUPP/wQH3zwAV5//XV84hOfwFFHHYXHH3+80aYGBAQEBGxH6NGrNxYuOQr9h67C6hv3xbzLvoGWbgW8u8NErFvRW6/jcMjYBVj7w93xjzt+0miTAzYztmmiZ+rUqZg8eTImTpxolS9atAjlctkqHzFiBIYNG4b58+cDAObPn49Ro0Zh0KBBus6kSZOwdu1aHU7NUSwWsXbtWmvbZNQ72dkM0PMYOmdD9pZlZr0EUd70getg817nM2vzBTT4NGfpU5uSZeSpSZ2hd/QkD+5v+pxaoq90caImiXVBui6V3cqV4q4KZcrsQcPJJ6XLH0lkonfs/jakEJ2xm6XMaf/ZkTzJfkQ61kiOYEfW6MTDpK+1Hr6EOiVnHH0wr3jp17BSncK8aiZEnJBAIrZkm2ge129nFNE5vwAkIUEikdpFB2K9N4w02nPhIYhq6kLGeeagpDs81I9WIM+OTL05Op3AE2aH9zgtkzSiB3CjeoRn84iq60GnInoo2ZUV0SO440CSsMejmOY7AqBXzlK6rRw97JN3kJbPHNXXRjAf6EmYshDRs0Vx7bXXYvz48ejVqxc++OADLFy4EDfeeCPmzZuHHXfcEVdddRWuuuoqnH/++Y02NSAgICBgO8PR19+Hxxcdh9791+PI4bdh8PNH4Mjht6F7rxIee/JoPLtgBOJKhL6D12Jk9Tw8+62xaF27utFmB2wmNDXagI7izjvvxOLFi7Fw4ULn3PLly9HS0oJ+/fpZ5YMGDcLy5ct1HUryqPPqnA8zZ87EZZddthmsZ+ikH1Str/O+CZd92nu8uUzlc02RoTNjzufdt6ULp4Tr88mMyVlbtk3p8HacGlD+CN1GOm3UPo/O8RE4dn16ZM4Jx4rkSFr+mEgi1x7/iKD6hZDpa1mpbOHrA8UEmJZJRA/3k1ourBMxrSekO83VBIlhShTPok7G0uiklJWW47x/lM67vb0A3T1CC2TUTISEjag1aLNFuwW+CbwzgDyMRu2bxCKaXEOIRZLVY1xFXToVl5EODcF11joWSALEiDz+PNM6uA28c+2bM9sHwI524fIBOBEyWkcEK85QAFaUD5XpPIyk+2nZpQaiNPZZN4gwbRWLqD6VHUKm5okQ0bOF8eKLL6KtrQ3PPfccjjrqKERRhDvvvBMXXXQR2trasPvuu2OvvfbCokWL8Kc//QkjR47EHnvs0WizAwICAgK2Exx9/X1oXb8Oj8ychqYNb6PSc1eMn3EDjunVGwDw2M0/wIgN16Lfbqsx8qAlaP2ffTB/45cx4fxrGmx5wKZimyR6li1bhnPOOQezZ89G9+7dO03vjBkzMH36dH28du1aDB06tEOyBMh38U6D8Cqtxwyh4vtqwDM/q6krb67Vkd+as9rUMQ/1Ujy2bPqCE9fqavaRKHS+rmIEzLFLCpnz5hWnLAs5EaUkUkSkzO4Tn1Sh9QNIX3myPaGTdrtvTG8WINJXrmDmnmmdhIoyE1YJoUMNdbJmrkNXF/pY9YuqGgmzNpmPYtJ0FfVJQC/rbsEz0fbmm8ldz7sWWLtaJIQeQJ7rRjsjyxw2kPQ9QDuYN5AAUi7LeRzUo9Nnbi1SKpVpiLyMqoIRdak9mQErGS6ac+SZ5+kno4Mssa6K+XOWHut+9wxaywhWh9qlLxYn+TgDRuynnUOJrhDQs8XRvXt3jBs3DkcccQRGjx6Nu+66C3EcY8mSJXjmmWfw8MMP46GHHsJpp52GDz/8EL169do8EcMBAQEBAQFIXuP66BU/8547Yuq5KLV9DY+fNxmHjn0KPfq1Ylzfm/Hqd/8Xfb74Bwzcd2QnWxuwubBNvrq1aNEirFy5EgcffDCamprQ1NSEefPm4aabbkJTUxMGDRqEUqmE1atXW+1WrFiBwYMHAwAGDx7srMKljlUdjm7duqFPnz7W1lFIwHADfo4gEx1oYrR6dNYjT9S5BC+Xw+dSPpd9x/XCV1eSv1l1s3QmZeqvqSXZp9Hgp17yyDMzr7Nf1uJranEbTHv72PZLSaeZeWBtfIUg+5rwf3af8PpOZwrPvBUARGTsdpZY5xZQb1LNAtabLEK/xiM9q66L9HUaX2rtpFySTdvj6Xmrk4XRCUj7jZpUXYffgpGwXmezdNJjfh7ouFIyybeuqT6gF5VUrI/zzVab55fvQaSuecZDQufjMVyhlacnn7Rj4sjgjSwlZqPjMdmnB0oMuylIXwqpEolHerMa+24wpSNKB38Uuf2krhN9b02trgXySZd51xc8oDNw3XXX4ZprrsFXvvIVLF68GMOHD8enP/1p9OrVC0OGDMEHH3yAN998E7/97W8bbWpAQEBAQBdCS/fuOOrmOXh54K/w7suDIASw5/5voc8jR+Cxi05utHkBHcQ2SfQcd9xxeO655/DMM8/o7ZBDDsGpp56q95ubmzFnzhzdZunSpXjzzTcxYcIEAMCECRPw3HPPYeXKlbrO7Nmz0adPH+y///5b3Afrq7V3ZpwNa9LeTq2+CU09Juhz3uW6aNJbs4y2+qVbEBm1NktXHcgMcqijnV93kuZXr8CUbsKT6ZauuZNM1eg5CkrSUBtUPXtRd55AmeqjOrN9FI4FtvPs2gFEj5uq2fw1E2c+XmjOWG//y9RHmWywxoovizBg+pGPL2q+i4R+bAAA0iBJREFUsNKeJPtJslx/quvER5Fu1tilfUuij9yNTKQlsTjehLdgBLlLsm4Ke/CYT66U1s27Ecg5vuqe94FAypzAwDydrK7gZdxHfo6MLd9Dgr+5JlmZw2V4yCLnssnkTrAeXumnNkXCjGHdMYwsQzq+Cdmi/wn1GcN5F06SHTXYdbZ41SGuH04nWn4z4kqzsh0dtAHtxZgxY7Bo0SK88cYbOOyww9C9e3f069cPP/zhD3H11VcDAHbbbTd8/OMfb7ClAQEBAQFdEaM+9Rns9l+vY+6Tn0ZpXTc071DGYSPvx7tX7o5X5vyp0eYFtBPb5KtbvXv3xgEHHGCV9ezZEwMGDNDlU6ZMwfTp09G/f3/06dMH3/jGNzBhwgQcdthhAIDjjz8e+++/P774xS/immuuwfLly/Hd734XU6dORbdu3ba4D9ZXa5F1wg8+92iPVt2G6fT+8u3RSScL0nPenvzbKX3b+7txPX76uo5SLVyG8jPLFhXVIUldTqtI1oLr5PqURipTxYwkZb7F4bN01tOLxnvfq1zJf+aVJyPX88qStpl6YvRoTSqSwcx307+pb0KlgDZ9qvuC5cqhvkqVaDnDbf+LWVHaOonWceexJJIqDcTwzXXpXDgPIvVBpnl07X6tA+lkO7FBZF9ifsOpd5kiQzoZg8CXovPoJOK8NwoTSOr468Pvt+/h4iODfDezgHkbTrC6Ev631jy8l5dYInY4wVSC0OKasCH20IqANYCkkkevJ+snm5sxd6vUMpU+Ss6Qm8vrj8wZgAJ6QPBxsinhWQHtxt57743Zs2djxYoVeOKJJ1AqlTBhwgTstttujTYtICAgICAAAHDcDXfireeew/pfnoJ9Rv8LA/d4HwOWnYInzx+Lg2f+FU3NLY02MaAObJNETz34wQ9+gCiKcNJJJ6FYLGLSpEm45ZZb9PlCoYD7778fX/va1zBhwgT07NkTp59+Or73ve91in3O3KcdE8OO//5q8q2ouUC9siT75BObrPpqfpY1B8zSn1W/nvaKsiCZX5y62fuUhjDyLFLDaUfpIdXar9t8Kj0FqNTI3N6khBNElDyxJ51+Qsr8VYmLTfSK8U/VEh45po7Q/AKghoAv4bHtQ/IRa9KG9wkHJZbU6lhS6yOEmppIk3MJVH/GgIzg3GuQ6cpdfJy5E17p9K85sPwlkUbtJnkAQK0QJrkvTJ7voeFJGFyfTlLk0alHu+fCWqtn1aFT8APaTzX81byFR49+o0xmlwn9BxZJxHXQJeMTdZ6LYT0zzXmLM1E6IiWIEDSqJRMrlD5IJBFu8Edq6c+sJ5+0/ctkMGvUCdjiGDRoED772c822oyAgICAgAAvdhs1CvjvF/DQZd/CITv+Aj132oCxBy/E2h8Ow+v9L8TYL32z0SYG1ICQMnzL6yjWrl2Lvn37YuXS3dGnd/vegovbw7IwVFDV++0RESPOfLWklpyyPb3JJW3okIoBYm02uLxKDJR9smu0lwCqUiBGIVeHb79SBYpohh2lo5BdBgCluAkxohwdgu0nx6WqQBEmgswOwhDeMiW3WE105ukyZca2tkozSoTj1QmPQT/9OjdUmgDmZxJ54+srEzmzsdKCijTXRL1Upcgan/0SgIwFWuNmj00gOgU5NvfhhlILYtrvPMmy9JQhmV+XK01OP5prb8gPrr+t2IRkHXDQCjVumKRttVKDd/fdeBJAWwGa/ON18vTGQFQVhNTJaJNeI9quUIRfZ84NKmTStok/EPIeKPrmjNFc9Mjz2O1E55RjFKrI7hdKCNG+KFXQXCQVY2mfpwwU8z9qqyKqkjs3o38EL28rodBWIu1i0g/pQOHEUXpObtxocvJY/pGOtNom9SrVNswt/R5r1qzZpLxzAQGdBfXdK4zZgICAgM7B+vdXYcn3JmH02CWImmPIqsBLz+2Dvb49Gz132lnXa12/DgvY6l490tW9AhJ05v/DtskcPdsDNiVYXpKtvTrpRmW1V6cAkpVlPHl5uM48WTrtRI0tT149tmfp4OX0yM6Jw8sBYVlnzgGSlXK/k/wxyT8BSfLiZOvzzBSFfT2NZVwfoPLVSEidI0ddO1h6kytichKxPkmTzqpXW6R0X43y9WWi1x4rQrq+Klt0Dwpjm+5baeeYhQRJ8xPrTbIcPHq0Wb5zX0l0Btm3onV0e7WbyIpjPsFG/RCetNsx2zzdlQR/SPfmpM44gt16WmQMONmtpbD1g/SF3XVefXpXZNy/vJ/yHgBED0mTZFdlvgp+QfNvTqi0UdYzTQ1yAZNbR/9zXXf7wXfxzH1hLmZMbkPfeCJCnc5UHZ6eM46Qccv6UzAdAQEBAQEBAQEMvXbqj0NuWohFpavw4bIdIQoS+495Bbh7BB678hwAwMPTPwPcMQxHjbwTEw59BEeNvBO4Y1hSHtAQBKKnQdDf2TuArHlbvTqz5k716tR6yepJalUY35ytPXJrbYDrN9ehXoPgc9+8+a/Z+ApNZnNW0iF0D5dq22r/Tf5F6T+pS8xqXxFMeme1sZV5iOPKT3tNMFqbraNFV71KdSUtI7YZisWaJat5KaS1UpStl/qS7pNxkoyVWv2s5qbsCqXzWKH+CEBEgIgEhFqVCElOIONrZPrVWsKJ9hghpYSZG2vOiXoq3PXJEt0el8D2ybUzxzY1BuhLYG+8LDJ94OjkyCFU9CpVAuSAb0a28NmTo9biUzjnwdvyPss6psXEPEs84Wi0Ab4HCfFLrepmjVeq2Fk2LC2mPIrzsE3bqGvFirRyREmfFqgx1EbPE9VynDgc2febk4unI/8jCAgICAgICOiyOOzMqeg3/Z947PHDUWlrQve+bThsj/+H96/ZFYePnYN1q3rh0VenYMXoJ/Doq1OwblUvHD52TiB7GoTtNkfP1g71vdzs1I+OfjdXqqjKetVnEjjSjl7x6cyyI6uNKs87l2WXKvPNqX3teBnNXeP8+K2PhVXT0DqS2WOy9kinTvK3iiarHT2nvHB9Eea0tHf9ftl2xZJGImSNBJMsms+OhUCaoyUrBxL1R61epSIXqAbXVssOqUgd2wrdB5KWQif/VvolYkj9GpWR6cJlGrRtpKvVa48yJbucRNA8uqXWQLVUJp7RRd0NYZAjR6/CVEN+1s0pyGkyeKzF5T03o5WjJ0s+KbO4iizCy70Z6zqmz1HtEh3WvoEmYN6JJOX2EOUskVNBi3L2fWSSai/JPkhfSsC8qiWtOrYGbhM5JaLUbnpC2vVyPQgICAgICAgIyEZTczOO/tFsvPjXv6DXY1/Hrvsux467rUZcFVi6aiyOvvpGCCEwZORNKLVehVU37otx+z6K1vXrwmtcnYwQ0dMgNOKHVDU3ksI+rgeZAQpZvxYTnfXKrXerJZvSI3xux/W6QRN0z460kY4F9ktVYPVdu4VTp8DaqWgfE2XDXyAjfhGSh1pE/YOWGCFS+yKNdNE9EFl6aTQP1aDpFsPyeSbzQstDKlNH9Qi4EQYsssay3mJD0/PqVaK0WBCZKqJHRSoJ2NE8ApGOZLKjhLQ3jCiyT9hjPzI6lK9ZET3OZB8upPviHu0Ka8CaS5Qd0VPP3J0ospoKwInmici+qkORpTPVQSkH4ThZw1efT6RMm8vc0nyJj/TxyFBlJpCGjk913n3WsdXV2WJl6XjVdYgy770QkYvA7M3K9K2vi4D1jqE1iDz+OwoCAgICAgICAurD/sdPwrDLXsMzT+wPAIgKEoePmY0VV+2Ol/78ewBAS48d8FLxBLT0LmLBzGkNtLZrIhA9DYLIPNiyOq0JDS/LgczY9PmMnN5Zsr2Tsjq3emzNq5kvn+7RLBzumljKD0oV0PpcIphsQKLq1UeTstg6rE9B/aU20R5QsmOTCUjl7YBEkhbcbP500Pa+RWzROao+a+TFVLaaBZNNssmrprbI3JX2l0hPJKtxwZKpc+VIafTKxF+aH4jrtHWnfsG+V+iWtOb9Jk1OldwbxVGb6uDxUZ5O9+XsoRE97dbpq6KcFECcbjxfDwiZUUunJk20m/6HgvDIquUT4TNooJZLWrE2FJwTAR2ehDRRldm7YFq8c5MK0gfSNor6E0uzSQmganQ6kVqKlWI3vu4AaTtNSVR9nzAn43qfqgHbIm6++Wbsscce6N69O8aPH48nn3wyt/7dd9+NESNGoHv37hg1ahT+/Oc/W+ellLjkkkuwyy67oEePHpg4cSJeeeUVq86qVatw6qmnok+fPujXrx+mTJmC9evX6/Nz587FZz/7Weyyyy7o2bMnxowZg1//+tebz+mAgICAgE5FazQAAPDqM8MgY2Dn3T/A3u99CfPP+xjiOMY+n5sCAGja8HYjzeySCERPg+B8te4EsodNWewfmmsg44dwPW/x/jrtydnD7eCyam2qTZ7NgvzNOk83GjhAQwj4P75ylm/+6cvvQ6VR2YBAQVtgR/OYqBiqg+lknWATMa6X2hNh22GiU1T0jT1OLBvUXDKdOMp08pjMfamuCAIFnYlIiMjJ0WPleNI60ggmxTNIcs0Y4eAbd0nEEvFLmEgepQ9EJ/XN9LN0rqsqSObsptdoPiWoiB56QazJfw6ktMaXM7joQCUqRYGc990seTcKOef0horioZE81nGGXq6T8hU2R+LaknXTm5vTNpjwGdby6tLeNHfpuwbcdlJPOKwUG7+Qlm/2Ju2m9JzlXzpuVD6dJDmP0aWNgdZJE0PrG5IOFE3q+AYR7bRUb83BGbAt4q677sL06dNx6aWXYvHixRg9ejQmTZqElStXeus//vjj+I//+A9MmTIFTz/9NE444QSccMIJeP7553Wda665BjfddBNmzZqFBQsWoGfPnpg0aRLa2tp0nVNPPRUvvPACZs+ejfvvvx8PP/wwzjrrLEvPgQceiN///vd49tlnccYZZ+C0007D/fffv+U6IyAgICBgi6HSc1cAwPJeH8Ojy6Zhw3u9UGiOcejYx/He1XvglV9dadUL6DyE5dU3AZuyvLpMJwkdQbmuBctd5C2vrpB1uqSnwD65OfKkRKWGPi5TAohjoOSpn9WGnqPLq9cimmidchUow15W29Tjy5jb62Aly6v72hnygtsAAG3VAipohvVKlv6MWH27Dl1e3dUJjx+Jza3lplQnb6eSPnM7jJyN1ebk9SnSxugzk3oqMwawsdyCqmzy9jtdrj2ZNBv5MYDWajMoJNmREIil3S9AQhBtLDcjlgUtz9It/dcjaStQrjZZdXhfxZ5BL2OBYqkZQOQf1DmDVkIgLvNX5rhSn05AFFX2Xk+brBsg/YzKhBbVZB0B16l4oDYgkhnPPEogMf0iBgplfzOXVSTH1XR5dc8AEr5jIktUYhT4Q4j3j3TloFRGSzE1hulNdEivLAFAtFUR0YctfdhLmEga1Z+qvFhEoVg1NxOoDml/Svuc3NgKezl2Wkeac7S9lKjEJcwth+XVtzeMHz8e48aNw49+9CMAQBzHGDp0KL7xjW/gggsucOqffPLJ2LBhg0W4HHbYYRgzZgxmzZoFKSWGDBmC8847D+effz4AYM2aNRg0aBBuv/12nHLKKXjppZew//77Y+HChTjkkEMAAA8++CA++clP4q233sKQIUO8tk6ePBmDBg3CbbfdVpdvYXn1gICAgK0HrevXAXcMw7pVvdDvnKUobSzi1cuPwwFjX4aIJGRVoFIqQJ78VsjRg7C8epeAnlP5fhHfkkj1WMERyJ+Heppb5vpSaagtT6bnd2b9Az4vz7Ijy0aegDhPJw2SsCJfrH80YMGN5rGn526uHhqzI4nGAtEJS19EdAqtgW/+PjF6bQ+TfRWpwPMCCfI4oMEZrq+mQJKoHrUakcoJlMQrqYgeYW32im1JcIEmo9JxGadl+jUuJ6In9VBAe6AibCJBI5hIEIbO5wPNx7g+uiSs6g9lS4GMFB2BRSN6fAM0bzCTHD2ZF9kT0eO98YBsO3xMm6WHKacRPSqqB9JdtIzrFq40APkRPbQ/fA8E3zmuW8KO6MlQ59jqe2AJUok6LASkSO5j3zWTeRdRESxkLOpoHr1iHLHPZ686SMeNXkKdg5A5tkyiL4qInwHbC0qlEhYtWoSJEyfqsiiKMHHiRMyfP9/bZv78+VZ9AJg0aZKu/89//hPLly+36vTt2xfjx4/XdebPn49+/fppkgcAJk6ciCiKsGDBgkx716xZg/79+7ff0YCAgICAhqNHr95YuOQo9B+6Cqtv3BeLfnQJdj7jNsxfMA7VcgRRkGjuUcH71x+IZU/nv0IcsHkRiJ4Gwfpq3R6mZXPpp/M31OaafBNimiYkzqiTa0OG/NhTlmUHt1F9qil4HqjtxgedyYb8s+3y9ZX0eGO3TyRIprVKaiJDr4+cArL7j7YU7AoJlbfG+Rdb7ZROTuJBTZ7NnJcYZgaVk6NHxslqX9ZmT8o9YvS8VqYnJDlJY6psPxJ9KmrOSoGSdodMu8TtS+UYvKBckxopqn917iOH3YBbxoUKYfEKmYPXm6PHIz9LJ2XwcvWRC2s5rco8OjJ0qmsKcj1t/z1+clnCUyaNXHpzWm/z+VzL6h/ngSK9m5AxRBxDIIaQ0vgUwSYUaf/pT3WOGBJT+TEsckbVsfL2pBdd3YhpcnXvgyKiugQhh5TeDJIoYJvG+++/j2q1ikGDBlnlgwYNwvLly71tli9fnltffdaqM3DgQOt8U1MT+vfvn6n3t7/9LRYuXIgzzjgj059isYi1a9daW0BAQEDA1oOjr78Pjy86Dr37r8eRw2/D4OePwOETnkS1rYD3/jkAUgJD9lmJnRcdj4cvOr3R5nYZBKKnQZDOTucqr2N+ZiHvh3seZFCLNMqSz/fzdPp02NMne8lyn29cjsqM48b0JFJoTA1N0GzWxXKto3E9rtSIpB+xa9G0JHTdLarXnb/bMtwrlEQhmOgXWp/HUSVIKQzyj3eqSHPnmGNI04uR6lWR5s6xNjUxpvRUQubEitSBNCttOUmBUzsl94SsJCZMwIReTSqSEFGa0DndzLVUE+2cCKr0gEZpqZGTuQJW3o0haJJorozAN8TyInr4RuUKsu8liAj5YEKqoN/FytLtsUUNC7Vv+VbvA0fC+5DRQ08w04neTB4jry8BOINH5x2LIEWUfqaRZSnZZFLjEAaO9p2SrfucDk6QCBsyjqiRVuZzpZSQNZnklTT73tW+AgI6H3//+99xxhln4NZbb8XIkSMz682cORN9+/bV29ChQzvRyoCAgICAenD09fcB//kmHnnhFMx/8ig88sIpwKnLMPiiN/Hwi19EcW03NO9QxhEjf4fXv7sv3nvt5UabvN0jED0NgqA7tSaCm1Gnmjd4cyfnmJBHDNUiibJQq357dHL9gu1ndSuVo0gNQ2xQubYU9ZdSPVQaj44xEiltEpO1tWgNuuaWDVHHVXLjgmJik+ud3XcugWTRU5owQTrRlmQD7KiepC8Tska94uWL5lFaUrEpeSRUOY3iEXYfJ3NlciwlYklX3JK2Hvbql5Tpku2Urkyz+/Ie5sdKa6xW9kLsrrxVZ6ibev0s99b3kTZZ8n2bQzCZT0nL9OZcbM2oCO5HLZ2EH7Lcz3OYyyE3Rl5EovUs891E9dgtQVkbOljtu15xN/wBqv7XymWqVdKswaT0AKhSnaSdMlq/j6aIn4iQQ/xpSByV7DO2fQrYvrDTTjuhUChgxYoVVvmKFSswePBgb5vBgwfn1lefterwZM+VSgWrVq1y9M6bNw+f/vSn8YMf/ACnnXZarj8zZszAmjVr9LZs2bLc+gEBAQEBjUGPXr3x0St+hiNveBAfveJnOifPsVfMwodHzMW/XtgNALD7/m+h598Ow6NXnNNIc7d7BKKnQZD8oMZEcHPp1N/3pWfLMYFTC1n8lPOD9SagPTrt+aKZYdbqVtqexIHoSbeR65NgJPOIHmHt22SJigSJIEgQgYqBMZEiXFPymeWRrZtG7bixO7wul8G9JP/IPNRE8xBiRpEo0mOBlZuHzo0ZqUbII8CO6JGS2Z7aYV6dI/77IofYq18mqsfELtHkLr4rrroquVaJHrXSlxsVUmNLhSYqrZgpFz4ChUfVZOkkdmvErDiP6GDsCstlXeuGNC5IcqrWQ4ITW8RXX36xrM0rN8tuK/cROUFILx7Hp3X4iJks+dwgdb5AdNHrqQ74hZJxkhnc6lhqNz2GkavySemVtwK2J7S0tGDs2LGYM2eOLovjGHPmzMGECRO8bSZMmGDVB4DZs2fr+nvuuScGDx5s1Vm7di0WLFig60yYMAGrV6/GokWLdJ2HHnoIcRxj/Pjxumzu3LmYPHkyrr76amtFrix069YNffr0sbaAgICAgG0Lu446EHtfsRTzFn8W5Q0t6Na7iMP2+H9YcsFIrFnxTqPN2y7R1GgDujw68cdUPWeSnrlGBvgPwXw/T1c9dThVYZFRGTpr25JI8sVH+Ags+mN7rGsJVp9SPllys3rGftGKzgdpRA+siSOdzQP+HqU22vJ91ugf81MtUtumzic6BasvSB0h0rl/OuGkizJJYrNQx1JlCUpIA64zaUd1JrN3ayyQsAyB9BopLiaNZBDpsRBSr5alcgIl1W1Om07O6fUUgHkrzHM51bxZxtRnsww86xD/vkeokKpvRaZuawhQffXeHL7B79vnY02SYhJR0xGdKpUM5S5qPlBUg5hZ52vXjmeb10arXxWZYp8TtJ56oFLDkoFoKjkPNeY4vd5V+hpWxqdqYC3/Hptypz6gbxDf64G1lmIM2CYxffp0nH766TjkkENw6KGH4oYbbsCGDRt0LpzTTjsNu+66K2bOnAkAOOecc3DMMcfguuuuw+TJk3HnnXfiqaeewk9/+lMAgBAC06ZNw+WXX47hw4djzz33xMUXX4whQ4bghBNOAADst99++PjHP44zzzwTs2bNQrlcxtlnn41TTjlFr7j197//HZ/61Kdwzjnn4KSTTtK5e1paWkJC5oCAgIAugGOvvQOvPvYous0+DUM+sgLDD/wXWn93IB4vT8Xh0y5rtHnbFQLR02j4Jm9bCGbiTNTVqdOaw9Shp5bojDmapUt4yvLsMp+29DxdVI5ZectM4A2ZIslRlizhlPnOGR5BoACQ5ed577re5M2NOQElyF+JxDcJE9Gj+tf+tHXyOvaKzpxK85NjKrYnFnYNRcgI0ruCz9QlwJdHd8nKNJ5KUp4libJRY14QCk9pMt1t7LEJIAY1V9auCV0psggwENl1ILb7NhN55A+9UFltnH61DwV1niuhnS+SSyJoWZ06HZK5nptTtY1sXsWpn3FzOsV5DyhVmUb0cB8cBWlkGWeMvPLpDaSqkgP1Gpa+6RShQ2X6ZJACPRZ8F0YYfYq1DRE92yVOPvlkvPfee7jkkkuwfPlyjBkzBg8++KBOpvzmm28iigwBfvjhh+OOO+7Ad7/7XVx44YUYPnw47r33XhxwwAG6zre//W1s2LABZ511FlavXo0jjzwSDz74ILp3767r/PrXv8bZZ5+N4447DlEU4aSTTsJNN92kz//iF7/Axo0bMXPmTE0yAcAxxxyDuXPnbsEeCQgICAjYWrDPEUcCR7yOeed8GoeNfhg9dmzFofG1+Mf5f8RHLp2LHr1D5ObmgJCy5vQiIANr165F3759sXLp7ujTu31vwcV8ltUOlFFtXwOtJjYta+jkp4teCqW2jFjKXGuduUqKagyUMvRltdFtpUCskw9nt+XllSrQhma4hAW0LE6mqDqluJDqdM9ltQEEWqsFlNGidcZpuV3fJpBU+2K1CckSz64vvD0t21huRtnxU4D2F/2k+lurzZqg4fZYBI8Ulv/ryk2oSn/fSl1mCAW68lpbtdnxTc1rtW3S1h9LYEOpGbFsYu2I7c77R6luCZQqTc51s+xWc23iSCyBYlszIAuu0LwbJyVAqpUCMqNpfGUSkDEg2pLF3jPbZemOgahqIqjsesQOfoGrQKGIhHmpV2eqQ8RAVIWbO6fWcSVGc6srz6pPeA4qQ1RiFCqw4SF7nD4oVdDSWnXaWPUyEmhHpSqiCg3v8utU7bXMjW0oFCtMdhbBI0k0joTc2JY8OGlbSg5xm9O2lWob5pZ/jzVr1oRXYgK2CajvXmHMBgQEBGz7eO7+ezHw2W9ipz0+AACsf68XXu55IcZ95ZwGW7Zl0Jn/Dws5ehoE/cW+U2k24eiUGZvTUprktHkb0+bAp4frpPv16PTbkDNJ99ihzim6I2uLkC6r7JzzW0Nzx6iNZsYpkJqKhuAZQESqE9oGTt1Qv7gN9ipb9j5v6+8Vao+dyyn1x5Ojx/YZKECkCxjZCZXp1RCIk1w5QiJSG6ROmCxpnp442ac6hT6fei5MImeaiyfZJCIRIxIxhMdnp3etKulVIvmCtP56V93yDtwaJI/vRhGANyOxvWxb9ubjKSRgJ+qVdsUoPeqATrWIV6afvgdQWia5LL5F6UaPhf855OhVsB4iEk5Can7nC3InkmrO/clzHUECiM2+Oh95ki5RBzRpk96Ilm2pbDoeQOzXcohcuhJYQEBAQEBAQEAnY9SnTsCAb72G+fMPQ7VUQK+d12NMy0V4avrhqJSKjTZvm0YgehoEZ6LUDviJjTq1euYQ9cgTdS7BW8smny7fcb3IruvO4gTb9/vtn6FK9mlrEOD68vg7TqHkzcSFx0JNsHj98pEo9uJMnIygyZqpXpcKEk4PqQLpmmm3jmxf7SXW+WTaUD8SNJky2fR82IxpZYORZxZAt61WVFmyRLZOEG31AvPeEpFaxwdtlJBLHYJESoB5dHo7lBz7Xr2ph0BWRBFXpf0k10WSiin/0RGdgo4Vqo/rhl1GcxODV5fk07PvBG7Vd3OmrzgyuwTpGlXkWaJcclaKjG/9T0bJZp23m9kdINIkymqlrdRg6x06cr0oKQRps9nWwO3gmA0ICAgICAgI2Axoam7GkT+cg+eab8Tqt/siapI46JB/YO0P98Szv/tlo83bZrFNEj0zZ87EuHHj0Lt3bwwcOBAnnHACli5datVpa2vD1KlTMWDAAPTq1QsnnXSSsyzom2++icmTJ2OHHXbAwIED8a1vfQuVCo/x3zKwvlrXMylj1fOib/K0qrmB84NzDXnm137p3STbzFLExldfvIjPl/b45Pvxv56pC9Vp57ONyWZqCXasaAiFRKf/nE3SUBtUPSrb1i+9Mq0Zm8c3e0UgyWvya6f1mMgh9Y9HJqj0Ic5YIfNJn6/JayIxIGNIGVtjBlJCSNtP1Z+K9lF1Y9/S7M5AUn5lrT2uorJie/xSCk6Q9NmOCDKRlqQv4uRVxQ6B52Hx3RT24DGfPJmuf+h5dJImTK2jX9jHmXlyfDpZXcHLuI/8nPToo6eFp4mwTfcSS3l9I9PXa+nDK/20h5pK+G3qan3KUakSLNtjTQp672XdPGSw68cC7RD2tLOisBishz91pINjNiAgICAgICBgM2LsqWeg99mvYdGC0YgrEfrusgb7bZiK+eceh0q5rOu1rl+HuRdNwaPTPo65F01B6/p1DbR668U2SfTMmzcPU6dOxRNPPIHZs2ejXC7j+OOPx4YNG3Sdc889F3/84x9x9913Y968eXjnnXdw4okn6vPVahWTJ09GqVTC448/jl/84he4/fbbcckll3SKD9ZXa+cX3Hx0/DfYdPLKAxzqkEd/vVYbDbHwlfnmivVu9frpa+fTSetn2ZKUqwXQ1WLn5qUtWpvSEUanfR7OEbUl0ZOQMCbyJOsVMIBH/yitvj6RzALWYyK5jtITksL91OQg8dM3XHlQg7TsS6IQhIjIJxkvzDfaWyoyQo2viCyXrl4FowYlHIyAFOni9cIXNZX4qaJ5IHi/Q8/brUm73mydOtokEnaUT71IJ9tS3Zx5N4XucFVXdQQ7J2DfCF6dpAnxQ/tsMTu2HY6feTqZ7VnPIP/ASot8dQErckc3YWWauLIMsHU63StURA+9wMQetalxzMVr9ljYxgt1T9mRPVCRPUq5itKhz1SVLZ4ba/mTNQAFrGsJ2EusBwQEBAQEBARsBWjp0QOH3vg4Fq6/DOtX9kbUHOPQcU/gg2v3wtLZf8bD0z8D3DEMR428ExMOfQRHjbwTuGNYUh5gYbtIxvzee+9h4MCBmDdvHo4++misWbMGO++8M+644w58/vOfBwAsWbIE++23H+bPn4/DDjsMDzzwAD71qU/hnXfe0atQzJo1C9/5znfw3nvvoaWlpabeTUnGrH7HJQV1o+PJmKVOAq0mP/WqLVnt7FY+GfRH+iqy55xZ+isxUM44lzVnU8exFIhR8OqU7JPuV6tAUS9EJzz1fMmdk4lVOS6gmvKm0jpn73P9pWoBJTRnygfcOB6kBFExNomRqa9+O42stnJTmow5y1b72NghsLHabHzR0R3cN8HapQmgdZJiAX5t3ITVKekj0wTQVB8hYxxbiIwN5RbEkl8TgjhidhhZpWpTmheI2kTqSSYzDaQolprhJCl2FDPEiR9xJUkqkxstw85JCYi2CEm2J4+eGkRPVBH+m1MTJcLtPAlEbUiItFo6FV+RHooqULDeI/TXd44rMZr5a9qUqPL4oLiSqBIjqrA2Phv5uXIZLW1sAFjtpB54Fv8kAVGsIqqmDXQy5Wz9+o5tK6HQlj75qp6LrRrQB3i6Lze2AnGajFlFeUmaEFq6fkuJSiUkYw7YthCSMQcEBAR0DWxcvRovX3YcDjh4KURBIi5HEE0xVi3rj5dKn8Pwk87EK7+/Ffu13IP+Q1fh8UXH4ejr72u02bkIyZjbiTVr1gAA+vfvDwBYtGgRyuUyJk6cqOuMGDECw4YNw/z58wEA8+fPx6hRozTJAwCTJk3C2rVr8cILL2xxm535V9Yv9x60oyrTyWdC5sfiWlA/JCerDpOGGdE8Rqfx1Rek4MaP5Acz8H7L6ge6HHpW0IA/qMBYxdMw09p2mSFH3Kgbo9f+jCB0FhA7goj3ji2XrLIlvS99ePxWUUppBIEgkSxOolkaNkB1MjKHTrLZBaB6dUkayZNsdJxkj4JkHirMldQ2k6Zp6IbJ2ZMeiNQPx8/IbDqawTOK1OSfD2uy6Ry3QkBEyZY5cLNAOzTrRvFdEqIbXG/dOkkZ9dPqFgkn2bN1nunMGIhUpfPWkXDr8xtGAI4eQTZLJStTnEgm6cV16X7gF5+baPJMaV2EgzHKvYNG54Uyzxx2kQuRm+CaCnb80cwr8c0R4PZFPWMmICAgICAgIKCTsUO/fhjzg0V4/J1paF21A6LmGEIApVITdp90CoaMHIVjLrkJ/c5ZilXL+mPcvo+G17gItnmiJ45jTJs2DUcccQQOOOAAAMDy5cvR0tKCfv36WXUHDRqE5cuX6zqU5FHn1TkfisUi1q5da20dhf7O7ttqoB1VM3UK9UOzRF0pGviP8MkcMGnM8/LQIDE+h+CuZmVRyeqS+uexwjrOztbCy8weX/0KnnPUOmt1qgx/zRYjTuOrkjgrnqvD1uWbnUs2+aQvbFHdiVcm904sY5P2Q9I6UtsCyyL3Sqi5r5SCyHD7krblOZxoXh73aiumxRzTlb9iNW7VsNMXMj2QceIz1amuOB2nUuUlckH5BnsBKtVe6U/zB9GOsC+VH9bAZvSYGpx8Y10lVUfwm5M74SOBOLcloVcz05s6Zg8A2p3ODcpJIdhUg6XW9yDzDAkJOHpYqiSzefRbNtW4OfUQ0gfq4kOTVtL657quL6Ig+2wVM52rhypWzsYAvONJmE/G39hMFYgjanwweZDp/wgQEBAQEBAQELDV4ujvXI6Fb04CkHyd2WWflRj83Ccxb8YXAAAtPXbAS8UT0NK7iAUzpzXQ0q0L2zzRM3XqVDz//PO48847t7iumTNnom/fvnobOnRoh2Wp7+odQXt+vPfpVFON9sjwzhc90Ty+ORuXkyc3b8uS4dNhp9bNDxywNztXjpsa2N0kk6oWWze22n9VHqBIZwFR0TzJr/pSb2Yhd/XTfqxXjBIJuQABs+C7uramXazbRPpTiijNY2NWnorJeSNP2RChmm4AoJY6j9O1q1W/2am31b6JSnJzOGVfBfuVLLvcjjoRJIhHRdWoCCmyopbK2ZOZl4fRU8LMjTXnRK+zMLmL9D9fRE8Ww+HcKFIXM5ftrnSDrUAuQv5NnUOo6Jw2NExGSCCix0a2NxgrR63FrXBOz/dQyPqkupTtcMyjfJxNZEu4csm+Hp5RcmWdUC4JT+NUNOXbpDT1NTGjNtsfYdmTRvEUiDGWygxWRg9h4nBEFPhCN30kW0BAQEBAQEDAVojm0vsAgHn/OAnFtd3RvEMZR466B69cOAJrVryDfT43BQDQtOHtRpq5VWGbJnrOPvts3H///fj73/+O3XbbTZcPHjwYpVIJq1evtuqvWLECgwcP1nX4KlzqWNXhmDFjBtasWaO3ZcuWddj2nHlRTfB5Unt1dkR3xo/fTjRPls6ssky5no228e3TMt+cl7fzb/y3ekqf2JZIXU5LbIulVRNEQxJxUoUgupKwDUPT2DoTv0jcjjD0Cu9X218jX4JF1qR2IEOnak+TO6tlwNl8m/Wt8tLELVnjRGZFS9m9lcyLTT2rP/TENimSsUyjIGLtl/HVRPiYVbn8dxGlgOhk3JAVaQQPHy2xhOOOxXA4qohSATWWLN1cFo/qiVPhWTdLHih/QTiJpF+FZzOyHbKmhk5rjGYRUrV8zfBRm+u6ZfMb3EbPtbGGKOhzzbMpcoj5qe5N15hUmdabjkMemiaR5tjxXUzPE1VVUw5bjJzSKV1ReTIDAgICAgICArYiVHruCgBo6tUPa495FP96Ppn773XAMkS/HY3Xfj7DqhewjRI9UkqcffbZuOeee/DQQw9hzz33tM6PHTsWzc3NmDNnji5bunQp3nzzTUyYMAEAMGHCBDz33HNYuXKlrjN79mz06dMH+++/v1dvt27d0KdPH2vrsA9qp4PfsTeJrBH2cb366NzMmtBkRPMoHfXKrXerBz66wjch868xpfbc6B4uxSQ+ttvYklQrXidKfri3rDFhG4ZeMXl5KD2S8C2UZrJh64z0xnPW6HKm08ikfkpACs1BkPmup+ci/Q9p9A9PesOjeqQlSSkhUVWKdCCidIROJNKVr5RfdjSPgMkP5MulZKbWjAKy5/VpKyWTXO+siB4+bLw3ilnMXuuWcGXwqJ5I+Z+jL1Mn8U3a5EhuZI+uQ2Tl6ZTWrkmc7Buweb769CnXyfVRZJX+5AyQR5cgck13CrjEiSoz9tOoL8stkf6xSDKijCeASsetuQi8PzOe2NQvPXCkbUjmuPDoCQgICAgICAjYyjB+xg0orW9JEi/vsTv2vnIpHnnq46gWC9hhwEYcPvZhVNoKGD/jhkabutWgqXaVrQ9Tp07FHXfcgf/93/9F7969dU6dvn37okePHujbty+mTJmC6dOno3///ujTpw++8Y1vYMKECTjssMMAAMcffzz2339/fPGLX8Q111yD5cuX47vf/S6mTp2Kbt26bXEf9ETZOagP7ayu1WhVAm7C0jr1+eepfglZ0whlR5beekiovPMip07+D/vSKudEEV8ditMEfn2EJLF0yjSiJ4Y7CLL0CKtQklkstYXqNJYlOpIEx5LU9esEjM9Uv0l0LfQ8UcDO9wMgjeRJdSJGshpV1lUT5K/UE9Dkg1goDP8hY/sqWxN89U9HSwjYklx/jTRhjx9SzQzz2O03nq/Gt5/pvkh71r6+xrGMtjH80Rq1blgqX/UpddZnL31eKGIl7wZW4snQ1ryJj+RhC0Q5n9wWQq7Q55l1VR0SxGOnNGMKINfY994XkyH4Dn2o67w+rBH1xbl2VVundY7cbNZKXpJVoTcJzM0JT5ss8iggICAgICAgYCtCj1698fCSo3D42DlYdeO+eKl4AoZ/6WI89vMqDj/wITR1q6KpexXvXDcahc/fiyEjD2y0yQ3HNrm8ushYJurnP/85vvSlLwEA2tracN555+E3v/kNisUiJk2ahFtuucV6LeuNN97A1772NcydOxc9e/bE6aefjquuugpNTfXxX5uyvHrs+3Jd55Xo6PLqyWs0TFXdOjlVYZBnTSylc74elVnLq6u2/vS5yflqury6T1fevLRSBYpoyagv2DzUjmAqx02I01mlOzcmOWfYuXK1gCJayHzWHtuxI88cl6oFxGRZHtu+iOwDdPq7odKEimy2fKM6YmYr1VmsNCX1Pcu6WyuCMV82VFpQlYUMvoMmdLblyjhZRj4p9KWlZn2bnowlsLHckowDSgjQz9gcW3ZLoFRthhXtIO3+UOwCt6dYbAIQZZM8GWSDlEC10uS2yYMiKFrJ8uq0fY4+AECcLq+O+kkb1a7QJpC5jHwGOSRk0rapAhe1btRy9vLqIrXJaZt+RpUYhSo756knuK3lCprbOKNEEMduW6WzWEVEl+CyHljS1a32W8soFEvpOdJefciY2EGIKCkhN7aZLOWSKJAg9dy2lUoRc8u/C0tVB2wzCMurBwQEBHRdPDz9Mxg34hG09CrpstK6Zqxa2Q+D9noPQgDFtd2weN3/xZHfurKBlvrRmf8P2yaJnq0Fm0L0JNEGHdO7SURPDZ1Zp0s5RE8W6QIAUkr45nW12lZjoJRxLu836FpEj0+nqlOuAmU0WQQFJTHsY2HN1Uqa6HHl8tWw6Lm2agHllFxySRKX5KGETWuV6zT7sdbLbQHays0oo9lrJ9cJZntrtUmTPKaNkWOWQ7d1biy3oCqbMubzkd1G2n60Vps9bZDOYYXWafEpEthYbkYsC+DRPFyHJRNALAXKjp923dgzcGUsUCw1wyF6tFFuG+pHXC74qxBixtUJiGIG0cMN95yLypzoYSS672aRgGgDIh/Rw3Q6S5/HQMHH4BLZjt0SQDUlejxEjfAdEzmiEqPAH0Ie8o3LQamMlmJagelNdEirjH5GbVVE9GHL66pzKZmjbS4WUShWCTlDCRppf0r7nNzYaoggSdpoPbHbXkpU4hLmln8fJs0B2wwC0RMQEBDQtdG6fh0WzJyGpg1vo9JzV4yfcQN69OqNv3/3LEzY/bdo7lmGrAo8s3gUxlzzMJqam2sL7SR05v/DtslXt7YH+OZU5sSWg3r1xVLTDp1OigeYRE/tIYFU3bwMEfQcl533tpt6Wcgn3VeqjqP0b55NSd+50TnCkmwmz6qUvvZFyZkCBCqktekXSnbwF8S47Ty6RrC/wrIFQkB4SAz75S8WWeN0hO0jJYhUimhHp4dAs95OUjcFHaPSkAV+Ak75o97+oSuepTrp62XqvArKiX3+uSSs9jPVE1n9k/ZFBPuic0OzXiGir9ZowzzgvIq6+Xw3ZZ4dYGV5DwP+tp0AUM2OquQ6edNMcjvvgZDqzDxPZRAuhDb3yrQMS/fpA0uQTrRvzlSHej1LmrxD3BivjZLoF/a1EhGSDlbN0x2R6hPS2Kg7VLrXUhM6pIDK1A+ljMisgICAgICAgICtFD169cZHr/iZU/5/Lv8pXv77yej7xJew0+6rcNC4Z/HBdXth7YTbsc8xxzXA0sYifMNrEKzJR+ZsegshnTzzVZPteBGnSeYWI3uBnCxwXVweL/PV89moPm1aIdunmG2etZQcnb6+kh5v7PamjGqsejRxnVkLkdMZtb0CmHuFYrUKVbryVC2dlDKim9STRAmpN0AKCZFuSqekPSslYrLal9kMx6H1kLEJQC/6pHTC0gOo1cRipldFzSkdcTrJljHUwlwawtoTmTcCtSXWvZbqk2pVL6sj6ZDIGLgCVnbjvJvRHqz+G4/ry7pZKC9JiQe+0ZW3YlXm0ZGhU11TyMTNuvL/clnCUybhJnZOu5KvZO4F7TvA7SdrNSyzL2ScbHqlOij2j+RqZv1HO9kiyaT9upUeQ8yodIU8JzmSJIrVOb2JdIl1ag/RpVaok1l0fEBAQEBAQEDAtoWP/J/jMOD817FowWjEVYEBw1Zht1dOwryLz2y0aZ2OQPQ0CNLZ6Vy9dczPLGSRQek0w7tATj3zOSqH7/tk+QkPV4aagptj1zcuJ/FBeP5RCZSwMWtg0XgWutE1uOySRFtEykwNN6rIzOm5HYresXXa9BBdWSvSPtpt/JFMkuvUu2lUUPqp9qW0e1P3qhCIhFoFi27QpI1ekl0kmxRUp3A2TeVJob1Ua32ZVbfsiT8EICKZbIycSnpMTbTdhd+l3SnWtVN97F0ByzfYKERKeunJO1dI9PoGbb03CiNEnIcBQAgHsmnWTdryfbo9tqjLB2HEOX5x+Gz36LOWhVebPUz98n196fShb/AISERmUyu4ER4m4Wgy2CjBbACVDyCKiC5iC4x+014pjQkxxPylg1Z6fBJMZkBAQEBAQEDANo6m5mYceuPjeOLdc9C2pjuadyjjiBF34PnzR6N13dpGm9dpCERPg8C/v+dOBDejTpHueL/v56jPI4ZqkURZyKpfDxnFdXL9zlzaU8Z1kDgQ8k/JtXtH/aVUD5VIyRFKYygNKvIkdmqYmtx+RSy4dtD2cRrZwj0zNsVk395zoWggM9FMa1kRPbHeh464MeESavUrmc6CVSSPCZRQWlJNEoY8SskaLdfq19RmYeuJZRq95OiyiSKd34eTU0KkuvzcCR07sY5USiOJVFZpu+tr3ij0/ssErVBLvm/z3RTpp6RleiMEh+432NwF7Yw8nYTnqPs5weUwX7MiEq1nWRbJ49Ph+KEHKOiAte56xd04D1Dhkad8UPcQMUKVValOalt6XidoVvdjZJNDui7pcOqz+oxtnwICAgICAgICtjcc9e0rsObouXhr6WAIAex38Ktou3VfPPu7XzfatE5BIHoaBMkPakwEN5dO/X1ferYc9VmT3jyOqqNu+HTU0mnPYc2spla30vb+iB4qk0uwiQcqTVj7sCQqwibS8nkN+7Uz2we+J5z2kSWFkkLmvN0Gjk7bS2KBDs0wkTx8I+83WToUcWAieQxRZV0jAUMcQSY+EnLGkitBdCGRnfoYcV3W+4rpRN2K6klJOLJ0tY/PUBAACmmkUqRyENGoE5D9vEFr3X85dw01RrXlUS5ZOn0g70gKKt9HdLC+c16/qlMn5X5qPuuoLOn66o3m8WxeuVl2R/STnCCkl6Z5WBJwLzGTJZ8bpM4XiC4VaSOI0fxCyThd/YsPDjYgtKxUV6R0CvJ6V0BAQEBAQEDA9oUhI0dh98tew/wFRyAuR+gzeC1GrPsaHj7/xEabtsURiJ5GoxN/TKVzhFrzTz5Xq2dCxSdWtaYPvrmi0tUenTF4N1JiI3/uSeeRJvWJm5eHSjARPDx7TnaP8HgdlVPGPmsm+9n9yAkUu1UMO8uQOkfrxaS+0ZNHMdAJb1qXRPBItvmiemIAvmgeZR2haiwSSfcrJxqUnwKwE7WYTEt2TiBhRfRIK6IngpSG5stL7qLmxnavpbog2xdhQ+blVm6XrBvHN5Dr1QfySeVxud7XegjZoV/XI3LawbQIWpRHQlEodij1NS+/mBoONfgmVz63V0fe0MFq7iUhFUEIc29Z3Irwy44BhyWjh1Xy/pfKnWPZQRoIARPRwwWRNtQxzuhLoOZSjAEBAQEBAQEB2ziOvPGvWFy8Ahve74lCtyqOOPgveO2ifbH6rWWNNm2LIRA9jUbds5FNh54jpFu980NuZnvIoTxbfHNFJb89OnkQBdecMee0ypUck2uI0jmmtmCbTaQYqygRJJkHKqpHZc3xx9/wvnB1Jv9sS01kji1VEkk8W1AW4eYjiZL/1ACyyRNfVI+yJUoFq8gaIahF5urQKBtN4EhXl9EHQwiR17AEVESP0hsnxFO6CSuiJ9aklLT8dAeMzmErSD/AE9FjX/L8jfIIeci6MflnFsvBy4kcqeVLd6O6TVf75dYotxacYjbkQgIpF+ema1LmCfJJzvlkWT5RWA8VPXgAX0SPGrk0kbaW7RtAMAyVZQ85Vq9hWRE96pg5oEmpVJeQ9nnaFgLueEgLQkRPQEBAQEBAQBfA+LO+ifjzz+C1Z3cHAOwx8i0U7j0YT/z42gZbtmUQiJ6tAZ30gyqfYNVD1pDqdW+5ekmZj9DgOmsFLPAVv2xpnGLx20DLE3kqLa9NQQACMSKHXFFUBbVMWJ/2OZoXx+QF8vchoT8cnSJlCVwSCERP7NTh8T48IipXpyDEDHntSbCcPXaUjdQBEiqyRr32AhjChObMiUnUjYkQkmaiHCWfasUvbg+gInqgCSAVuZNs6TUmZZQEyxq0dj4r28cORfRILdoei3kDlsJiOMjmu6h8gBF5ghuRl6iXchVZOrP8zCKJ8qAuCVttzInqoWXknLcffeXcbrWSlS+iRxGFMtlcYocJpxfWtwqXImPi2NFn2EXVjx7mjD7yrI3KASGPYHSHiJ6AgICAgICALoJ+Q4bgI1e9iEcWT0alrQk79N+IsT0vw/xzj0Ucb18rkQaip0EQQDYzsiW1Mp05c0/esq6Nt1E6KLIJDXe/Xr2uDfaRT5/PZzuax35JC5CIECPSVBDd/NbQuBVK0KijAvQLQzl9SOOC7FW+fJbaeox0E3ckOqwzmc/a/ilyxkT0ROS1K2H8FIC9BLuRn0hVUTcSkdogCVGTTJJlLJJlvtUrRCTCJ6mbaDYRPXbkjiARPZGgdB4hbTIGrZkze/pbL5PuHQo1N0oLap0eG6z91D9HJ88Fk3WjSFe0RRDoHD3kXETqZunM8pGSRD4/fQ8g1ee+HD1qQ3JeeiJ++Pj26vX5I2M1aJMCFdFDX/0TIo3osdsKJ2qHOKLHPyFEVT/rxMpgepkwndCZXVDnfTZqP3WSyAqrbgUEBAQEBAR0MXz02t9iyY63Ys07fRE1xTh03AK8c/neePvZpxtt2mZDU6MN6KrQP8xaBfVBtK+6rYR/x/fXympdt968ej63BduvZUdtXWoW7JeZNbWJyRl7/ik8ZVS63Tu2X8J7TlEclDuWzDIeEwTnrAu7vrFEWSGEsH7Ez9Pp6pVWXwrAitBI5rN230sAiCLEVX+va30O6ZCWC2ZheiD1TeR6muxH6atcvE/SMp4rReaMQVZVpGwStRtRqts3IPNuCM2lSPta+Aatz0Ah7EFE29S6YYXSzcroDh3aAgk/4ZOdp5PwEpp8qffmVzyFfaOY6jKnDPb4tGT7HqTkWEAkETY+nfpYOueBZGx5+1SyQiWD2qJflyOEDpWjOlDl8KED1745zDkqR3jqBwQEBAQEBAR0MYz+3L+jdf0n8PwlR2PkwS9jl31Wojj/ODzyp6/gqBnXAABa16/DgpnT0LThbVR67orxM25Aj169G2x5fQhET4Ogv9d3gLHpGMmTaFVRGR2RmUygsmvyuRn9zNKRV1bPFMSmFPLbZZEXtt2xdWT22CQclMTx22Dn5/HrTQIhzGtUeT5TfTw0w0dK+fwAACml1lkfjE4hhEOeJAcsGoWRbDKOIYTQ805DxpAAChIYocgxrYuQQJSAkGSfQyKGEL6gRSWF+aIJCGG/0eLIFk6ZifSRHYyQUJN3vy9eQkJvnKmxxWZeaErQcT7BlzCYcR6ZbnKdbGBnRvTk+Z1xc1CuT2SUOc2y+pnZLCGTiB56c0pGHKUdpwNrlB0RkqXSSR2vHl9Bep9oxkr3AWOLdD1N4RLyx+Mzt4UTSwEBAQEBAQEBXQw9evXG6OufxtxLvo7xQ3+Dbr2LmLDDLVh07lyslzvj0P0fx1EjS7p+6Y4/4OElR+Ho6+9roNX1Iby61SBYP6jac/aaaEdVR6ueBDGdtWTq6nrJagG+Og8v43MprjZvq9dP3s7odAkYn2xXJ02VnJxJX/KxarupmoVzHs6RTX+INN+PqmXHACgKKKtnANq7dkve89Lql2RSKvTs1KZk8nVagQFUr5NkVuq/EgJCRAnpoj/JeIH7GpT2RCVXTuur17HsJdOhX+tJXtcRiR5EMDNwukWQ6abHLtdNSQJHhHTKhAAQmeTP7UJK1ui+zbsprM5PFaulsvn5LNII7jk795DykVRSxIMw9evW6bXd45vPV3KtfbeAlYsH/rJMXoVeP2anEMkTwBhA7KFN1Tgm9kogJcXo4LA361/6vpmwlCiCh3SAzhTPOlSSncwBKGBdS+WkGkcBAQEBAQEBAV0YH/3eLXhnv//Fe/8cAFGQGDPuBRwxbi42rO6BR1+dghWjn8Cjr07BulW9cPjYOXh4+mcabXJNhIieBoF87U7Qjh9VNzWiR8tg3/vr0Zm0k95zvjI17ag3UIGWt8cuijydPHjAPW9+vqc+JGdsSsS0F7Dz5ij9pr6rT6VDFnrP9kBRJLYc01pNGG1bkVFfEKuktDUaYqy2zmROmOpUc1HNHrrQfSdjkoBZlVIL/JNTlUsn5UJs/6TZV/NiIzlNRC39ET2JG8k5EwNhT9gh/QEQZjZPyilZ05EbVOUTYjqZYnac7sReIz0PGa6TNGMEnmlHK9n1hafc0SlNsVPHdxN69k3/es4RwkefYmUWiWNuA0uW5DJk+izgSZatQ3OxGP9ESClyQTMuU0IeqTuSdYw98H2tbUGQGded3GOCyQsRPQEBAQEBAQEB2PvIo1EZ/xoWnf9RHHzoMxAC6NV/I+Tr6zBk5CgMGXkTSq1XYdWN+2Lcvo+idf26rfo1rhDR0yA4X62zfrn3oB1VvVqtOZz6wbkGVDxL8oMy+ZU5K8KHaOSkD92ijHK+2R4YZPWDmjb5AgGy9Cfn7VKThtgX0WOyzyqKRHqt9kwE9eLqic4o3bhe13Ka8VYtr877xFdCZIrIjsxqh061pLviFnRAgPM+ji1DRfREqW47TMM3CqJ0jmvIIaHHG2mqQjd0RI+AFBGkSMMfnHCQSG8mka5nFCn/SHNvcJBI7gcRCRNZ056bk062s24UHcnhnheiA3o9BItz69L+VeQBke/0BZetBScbVemNXIKnTNjF3De6upav3DIpj4BjN6ca00JddHVtHRPVPWTrMuSn+iQDSFitk39SQGeSpsZEUXrtlR3KuVSwl8zJco7UUUYqOfWO1YCAgICAgICA7RxNzc3Y2GsEhABK61vQvEMZR478LV789ihUymW09NgBLxVPQEvvIhbMnNZoc3MRiJ4GQX3H9m410I6q3sZqImQtrFMDfAnzZA4o0yiGZAM5BqmXZbtE/atRZ8nLcNGqmafHLTd77rLl7jlqnb2Eud9fs8WI01gSmS6wThc89+mkEgVkQnowaoacJf7b3koZ29fNo1Nk6YRI9tO5p1mYye5vs4R76ptUUT1mnAjp71t6Ffk63gIwq02n1aVEkppEsQgyBmSMJKKH+pj2gZRWPeFkMlbXk4wiok+RMkZ/oiNWS2DXC2tgG23WsyFmG+sqqTqC35zcCR8JxAkKdR3pFqtPWDoktY/foJwUIqocM3wPMs9QkICjx1pxi24e/ZZNNW5OPTT0gbrY0KSVpP/MKbakuyR2UDmpBHUPUsXK2RhmSXXaL5o1Ew5BZT0F1I0i1XiH+aQCaX8EBAQEBAQEBASgacPbAIDXd7sdby3ZBUIAPVqKaGpuBgDs87kpVr2tFYHoaRDUd/WOoL1BA1ynmmq0R4Z3vuiJ5vHN2bicPLl5G2+Tp4PSJr422Xoi/Us7pW4AHq1jNkkk0sXWjb+C1DDLnkc6hocudq7yx/h0wtYhTUQP99NuYfIOAcLKkSOEOUd18sge45fUs2spYed1sWygcUoi1RexsZLXp4JcXLuvaWoRFVGRpP/RO9ARU9rPNDdQZl4eMgGHNAQC6VzjY2KAEKZ3BdKonqyBW+tmk0q34RScC0oje2gZPHrrASE3TE4bdZBuET02sp1grDrU6H1O5PgeClmf1D9lOxzzDK9BOBrrwQcmK93XwzNdQc2JCNPXhnZGKp7xbRYLZckyIpwoKqRRPAWQpd099nLoIUzInIg65GnsI9kCAgICAgICAro4Kj13BQC8/9Qc7P69V/HIoknodcYD+vyr9/zMqre1IhA9DUK9czEf+DypvTo7opv/8K3LWDRPls565eZtvE2ezIw5oVXHH+VjomskO+NLcgxNSfA6lGiSpAaNdInTOBoezRMTasXVqWiQZJVlX6JnY53ak2lISBKBEJtoAmnO1aMzIYoMteXrW6UR2sfUczZW/NFSds9B++heg0SMmV3LWEKkUTpKt/Ez1pFMOvKMyLKJKkMBUQLA6k8pEUsrriONroG7+TqJd5hQvcvm5VwWH7RxKizrZrEvCtNpNisyRgLeqB4i2yFrasAao76bk9qY5SvfCBcoyfWxOC+HSPHoIcf0UWZF9KgKgu/zsUOOHfKNdViqQ0ecmVCi1HdPB1uHtAO4w5zUybtQm/J/o4CAgICAgICA7QvjZ9yA0voW7NdyD0qtG/HR6/6AnffeGwBQat2I/brdi9K6bhg/44bGGloDgegBcPPNN2OPPfZA9+7dMX78eDz55JNbXKf+2t3B79ibRNYIu6xefXz6kMwrzC/GPlm+Mt/EqD1bPfKzIl1oHV+AhLD2zD9DqZhZI6UqqBZVn+f/4XKBKI2jodaY6BpDr0TaV0unlJZ03zze1mlHuShdqjxLJ7hOKTQHAWlP4jUZpPUmkUtqyXIrwkC4ET2SSdLEArkGakl0O9jC5MkRkYBaNU1H8yg/nZw9lKZTmx0Npuf1Vt+q/qwjoocPPu+NYkdnqb51ZPBBq1Zh6siNwjgLSo44UT00skfXsTokW6e0+RRhOZkhJ+sGBWujXCfXR5FV+lN62rGbk5JBpjv5WCEVKOkFe1+qPZHWtx72gt0wgm0RUSdMPUUE+dhCWp8SU9xv7zXiBQEBAQEBAQEBXRs9evXGwiVHof/QVVh9476Yd9k38Pazz2DeZd/A6hv3Rf+hq7Bw6ZFbdSJmIBA9uOuuuzB9+nRceumlWLx4MUaPHo1JkyZh5cqVW1SvyDyoD/USNLXU+L77+/Vl/+N5eWrppOV5P9hnbSBt/bbm+5Uv357u02gbnvnG0A92mWTWunuqTowqAOkkYvHn6HFeqhI0GsidyymdJn4o3VO5ckAiX3J0gusUKiWzUajmvjHpOSNZRdcAdsiEL89RSucoTkF3LukDkWy2qWnkTpzkyol5biAS3WOieWzYRBVRzXw0V9D0qoQ0OVVqDVwfhIBDlSojVHtfrh4V+dFefUp++im5s040D9nAOIcaN6ciTrRK381J/aQbj+qh8gmfQfkKi++qRbIpGaTckESUXCEVpbQ4GD1OLZ8io8sidrh/qTyda6lq69R+e5ga3f9pPUhY7KcgRkpaD8ZhyZMsBQQEBAQEBAQEHH39fXh80XHo3X89jhx+GwY/fwSOHH4beu+4AY8vOg5HX39fo02siS6/vPr111+PM888E2eccQYAYNasWfjTn/6E2267DRdccMEW0+t8tRa+ws0gl51TE1xZhz7fvM3XhL42weGu/CzZsR/6F3WPTQJ0SWx/2/acU12RnIsy6vKUvULbU0uDIH/p3E8CKECgkq4wxdsndfj039iRJDM2kz9jnwTS/D+mFZErIggpiC2C2cV9E5ZsARNVY+pJCMYbR0xnVu8L2HNQQJg3hWJiF48+8LAG6iiWiWQh3Am3utZ6rgt+HT00kFS9avrHGQP2gmjufh4Bk0Zn6duS1hPsk9iEQnrC17V5Ny0r51fT0skJixhJZBQv9+ki11Vxc/6HCLKGh6uDHYvUJscE5UqtBw1vKBR5IzwnaP3YJGJ2SCjpPtN9/UUZIwkkF7RKGqQn1FiXsd1U6yFsEw9jkrRBWqAyqUdRQuYFBAQEBAQEBARYOPr6+9C6fh0emTkNTRveRqXnrhg/4wYcvZVH8ih0aaKnVCph0aJFmDFjhi6LoggTJ07E/Pnzt6huOtHsCDrSVCJ/0sNP0XlKLtGTo5NOhT3Tpg7JreV7e8+r41j/FV67PDEXZP6uzrlElr0Ol32uChNdYv81P/8LIgewqSjahpI5SaQJ9YPYxSJapEen8k3p0DoVQSTMVTT2xea8JRdpfpwIbt8b6ZTk0IQNIWf44KXEAeVQpN7ihBySdBSqNjY5ZNskrLHJqidWS04HCXsFKt8gy71RGGnECYis9jqXi0efzw5LZ41Cn860LJbJ21xeeAkkWEEmXrX8InrkWcfSfAhywbSKvAcMHyzsXDJGyEkPcaJ5Ri6XP+gssgbJYKJROJaNVWYXcVIJp9FsoCy7tO5L60ELEJJItZe2joCAgICAgICAAAs9evXGR6/4WaPN6BC6NNHz/vvvo1qtYtCgQVb5oEGDsGTJEqd+sVhEsVjUx2vWrAEArFufF1/iR5z15bqO79zl3HiWbDHmhZbalXlR2deOwWdVLM3v01xDnrw4BspsFlUv6RRLgQoA30w2T2elChRZLU7PZM3lKzFQzViCSNEmiqiJSZ1yHKNERoNvPkrrU/KmWAVoRIuZlgrnk8ouVqqoEubCXDcqx943OqVFntj+URn2+VK1kiYv9kGk143bkPwpVygR5c7NJXlNRteRQLUMxNIefTGzi86zdbkEKpUWx0pr3i3dawkAcTGGNwlWHnGR6owrhcT+eufdMmkn2tQyTdnyvfpjABVhcRBZeixUgagkUJWeN3+5Hk76VIH05szXwY8rMaKSv462PePmjCqxuww816P6gD7EKmVUKi7Bo0ESJgvma1SpIopZA63LQxrpa1KGjMvpvofRgiQ2pgMgJWykLANx1dimzuvGnsEugIqspGYFwidg24Aaq2vXrm2wJQEBAQEBAe2D+n9XZ3zv6tJET3sxc+ZMXHbZZU753mOXNcCagICAgICAzYN169ahb9++jTYjIKAm1q1bBwAYOnRogy0JCAgICAjoGDrje1eXJnp22mknFAoFrFixwipfsWIFBg8e7NSfMWMGpk+fro/jOMaqVaswYMCAdBWjbR9r167F0KFDsWzZMvTp06fR5jQcoT8MQl/YCP1hEPrCxrbUH1JKrFu3DkOGDGm0KQEBdWHIkCFYtmwZevfu7Xz32pbuvc2F4HPX8Bnomn4Hn7uGz0DX8bszv3d1aaKnpaUFY8eOxZw5c3DCCScASMibOXPm4Oyzz3bqd+vWDd26dbPK+vXr1wmWdj769OmzXd9k7UXoD4PQFzZCfxiEvrCxrfRHiOQJ2JYQRRF222233Drbyr23ORF87jroin4Hn7sOuoLfnfW9q0sTPQAwffp0nH766TjkkENw6KGH4oYbbsCGDRv0KlwBAQEBAQEBAQEBAQEBAQEB2wq6PNFz8skn47333sMll1yC5cuXY8yYMXjwwQedBM0BAQEBAQEBAQEBAQEBAQEBWzu6PNEDAGeffbb3Va2uiG7duuHSSy91XlHrqgj9YRD6wkboD4PQFzZCfwQENAZd8d4LPncddEW/g89dB13V7y0JIcOaqgEBAQEBAQEBAQEBAQEBAQHbBaJGGxAQEBAQEBAQEBAQEBAQEBAQsHkQiJ6AgICAgICAgICAgICAgICA7QSB6AkICAgICAgICAgICAgICAjYThCInoCAgICAgICAgICAgICAgIDtBIHo6aKYOXMmxo0bh969e2PgwIE44YQTsHTpUqtOW1sbpk6digEDBqBXr1446aSTsGLFigZZ3Hm46qqrIITAtGnTdFlX6ou3334bX/jCFzBgwAD06NEDo0aNwlNPPaXPSylxySWXYJdddkGPHj0wceJEvPLKKw20eMuhWq3i4osvxp577okePXpg7733xve//33QHPbbc388/PDD+PSnP40hQ4ZACIF7773XOl+P76tWrcKpp56KPn36oF+/fpgyZQrWr1/fiV5sHuT1Rblcxne+8x2MGjUKPXv2xJAhQ3DaaafhnXfesWRsL30RELA14uabb8Yee+yB7t27Y/z48XjyyScbbdJmQ/jO1rW+m3W172Fd5btWV/xOFb47NRaB6OmimDdvHqZOnYonnngCs2fPRrlcxvHHH48NGzboOueeey7++Mc/4u6778a8efPwzjvv4MQTT2yg1VseCxcuxE9+8hMceOCBVnlX6YsPP/wQRxxxBJqbm/HAAw/gxRdfxHXXXYcdd9xR17nmmmtw0003YdasWViwYAF69uyJSZMmoa2trYGWbxlcffXV+PGPf4wf/ehHeOmll3D11VfjmmuuwQ9/+ENdZ3vujw0bNmD06NG4+eabvefr8f3UU0/FCy+8gNmzZ+P+++/Hww8/jLPOOquzXNhsyOuLjRs3YvHixbj44ouxePFi/OEPf8DSpUvxmc98xqq3vfRFQMDWhrvuugvTp0/HpZdeisWLF2P06NGYNGkSVq5c2WjTNgu6+ne2rvTdrCt+D+sq37W64neq8N2pwZABAVLKlStXSgBy3rx5UkopV69eLZubm+Xdd9+t67z00ksSgJw/f36jzNyiWLdunRw+fLicPXu2POaYY+Q555wjpexaffGd73xHHnnkkZnn4ziWgwcPlv/93/+ty1avXi27desmf/Ob33SGiZ2KyZMnyy9/+ctW2YknnihPPfVUKWXX6g8A8p577tHH9fj+4osvSgBy4cKFus4DDzwghRDy7bff7jTbNzd4X/jw5JNPSgDyjTfekFJuv30RELA14NBDD5VTp07Vx9VqVQ4ZMkTOnDmzgVZtOXSl72xd7btZV/we1hW/a3XF71Thu1PnI0T0BAAA1qxZAwDo378/AGDRokUol8uYOHGirjNixAgMGzYM8+fPb4iNWxpTp07F5MmTLZ+BrtUX9913Hw455BD827/9GwYOHIiDDjoIt956qz7/z3/+E8uXL7f6om/fvhg/fvx21xcAcPjhh2POnDl4+eWXAQD/+Mc/8Oijj+ITn/gEgK7XHxT1+D5//nz069cPhxxyiK4zceJERFGEBQsWdLrNnYk1a9ZACIF+/foB6Np9ERCwJVEqlbBo0SLrWRRFESZOnLjdPoe70ne2rvbdrCt+DwvftcJ3KoXw3WnzoqnRBgQ0HnEcY9q0aTjiiCNwwAEHAACWL1+OlpYWfaMpDBo0CMuXL2+AlVsWd955JxYvXoyFCxc657pSX7z++uv48Y9/jOnTp+PCCy/EwoUL8c1vfhMtLS04/fTTtb+DBg2y2m2PfQEAF1xwAdauXYsRI0agUCigWq3iiiuuwKmnngoAXa4/KOrxffny5Rg4cKB1vqmpCf3799+u+6etrQ3f+c538B//8R/o06cPgK7bFwEBWxrvv/8+qtWq91m0ZMmSBlm15dCVvrN1xe9mXfF7WPiuFb5TAeG705ZAIHoCMHXqVDz//PN49NFHG21KQ7Bs2TKcc845mD17Nrp3795ocxqKOI5xyCGH4MorrwQAHHTQQXj++ecxa9as/9/efYdFcb1vA7+X3osgTaq9gIhiQWOLxt6N3dhNVKwYY69RscQUY4vmq8ZEY+wmRqNGRdTYBcGGDUURxEKRDrvz/uHL/FxB2i677O79ua693D07Z+aZcWEenj1zBkOHDlVzdKq3a9cubN++HTt27ECdOnUQHh6OyZMnw8XFRSePBxUtJycHffv2hSAIWL9+vbrDISItoys5m67mZrqYhzHXIuZOZYOXbum48ePH49ChQzh16hRcXV3FdicnJ2RnZyMpKUlu+efPn8PJyUnFUZatq1evIiEhAfXr14eBgQEMDAxw+vRprF69GgYGBnB0dNSZY+Hs7IzatWvLtdWqVQsxMTEAIO7v+3e10MZjAQDTpk3DjBkz0L9/f/j4+OCzzz7DlClTEBwcDED3jse7irPvTk5O+SZDzc3NxevXr7Xy+OQlKo8fP8bx48fFb6QA3TsWRKpib28PfX19nfg9rEs5m67mZrqYhzHX0u2cirlT2WGhR0cJgoDx48dj//79OHnyJLy8vOTeb9CgAQwNDXHixAmxLSoqCjExMQgICFB1uGWqTZs2iIyMRHh4uPjw9/fHoEGDxOe6ciyaNWuW75atd+/ehYeHBwDAy8sLTk5OcsciJSUFFy9e1LpjAby9I4CenvyvSX19fchkMgC6dzzeVZx9DwgIQFJSEq5evSouc/LkSchkMjRu3FjlMZelvETl3r17+Pfff2FnZyf3vi4dCyJVMjIyQoMGDeR+F8lkMpw4cUJrfg/rYs6mq7mZLuZhzLV0N6di7lTG1DsXNKnL2LFjBWtrayEkJESIi4sTH+np6eIyY8aMEdzd3YWTJ08KV65cEQICAoSAgAA1Rq06797ZQRB051hcunRJMDAwEJYsWSLcu3dP2L59u2BmZib89ttv4jLLli0TbGxshIMHDwoRERFC9+7dBS8vLyEjI0ONkZeNoUOHCpUqVRIOHTokREdHC/v27RPs7e2Fr776SlxGm4/HmzdvhLCwMCEsLEwAIHz77bdCWFiYeDeE4ux7hw4dBD8/P+HixYvC2bNnhWrVqgkDBgxQ1y6VWmHHIjs7W+jWrZvg6uoqhIeHy/1OzcrKEtehLceCqLzZuXOnYGxsLGzdulW4deuW8Pnnnws2NjZCfHy8ukNTCuZsb+lCbqaLeZiu5Fq6mFMxd1IvFnp0FIACH1u2bBGXycjIEMaNGyfY2toKZmZmQs+ePYW4uDj1Ba1C7ycTunQs/vrrL8Hb21swNjYWatasKWzcuFHufZlMJsydO1dwdHQUjI2NhTZt2ghRUVFqirZspaSkCJMmTRLc3d0FExMToXLlysLs2bPlTkDafDxOnTpV4O+JoUOHCoJQvH1/9eqVMGDAAMHCwkKwsrIShg8fLrx580YNe6OYwo5FdHT0B3+nnjp1SlyHthwLovLoxx9/FNzd3QUjIyOhUaNGwoULF9QdktIwZ3tLV3IzXcvDdCXX0sWcirmTekkEQRCUP06IiIiIiIiIiIhUjXP0EBERERERERFpCRZ6iIiIiIiIiIi0BAs9RERERERERERagoUeIiIiIiIiIiItwUIPEREREREREZGWYKGHiIiIiIiIiEhLsNBDRERERERERKQlWOghIiIiIiIiItISLPQQEREREREREWkJFnqISKkEQQAALFiwQO41ERERESkfcy8iep9E4G8CIlKidevWwcDAAPfu3YO+vj46duyIli1bqjssIiIiIq3E3IuI3scRPUSkVOPGjUNycjJWr16Nrl27FivRaNWqFSQSCSQSCcLDw8s+yPcMGzZM3P6BAwdUvn0iIiKi0mLuRUTvY6GHiJRqw4YNsLa2xsSJE/HXX3/hzJkzxeo3evRoxMXFwdvbu4wjzO+HH35AXFycyrdLREREpCjmXkT0PgN1B0BE2uWLL76ARCLBggULsGDBgmJfJ25mZgYnJ6cyjq5g1tbWsLa2Vsu2iYiIiBTB3IuI3scRPURUIkuXLhWH2r77+P777wEAEokEwP9NCJj3uqRatWqFCRMmYPLkybC1tYWjoyM2bdqEtLQ0DB8+HJaWlqhatSqOHDmilH5ERERE5RFzLyIqKRZ6iKhEJkyYgLi4OPExevRoeHh44NNPP1X6tn755RfY29vj0qVLmDBhAsaOHYs+ffqgadOmuHbtGtq1a4fPPvsM6enpSulHREREVN4w9yKikuJdt4io1ObOnYtff/0VISEh8PT0LPV6WrVqhXr16onfTOW1SaVS8TpzqVQKa2tr9OrVC9u2bQMAxMfHw9nZGefPn0eTJk0U6ge8/QZs//796NGjR6n3hYiIiKisMPciouLgiB4iKpV58+YpJdEoTN26dcXn+vr6sLOzg4+Pj9jm6OgIAEhISFBKPyIiIqLyirkXERUXCz1EVGLz58/Htm3byjTRAABDQ0O51xKJRK4t7xp0mUymlH5ERERE5RFzLyIqCRZ6iKhE5s+fj19++aXMEw0iIiIiYu5FRCXH26sTUbEtXrwY69evx59//gkTExPEx8cDAGxtbWFsbKzm6IiIiIi0C3MvIioNFnqIqFgEQcDKlSuRkpKCgIAAufcuXbqEhg0bqikyIiIiIu3D3IuISouFHiIqFolEguTkZJVtLyQkJF/bo0eP8rW9f+PA0vYjIiIiKk+YexFRaXGOHiIqF9atWwcLCwtERkaqfNtjxoyBhYWFyrdLREREpC7MvYi0l0RgaZWI1Cw2NhYZGRkAAHd3dxgZGal0+wkJCUhJSQEAODs7w9zcXKXbJyIiIlIl5l5E2o2FHiIiIiIiIiIiLcFLt4iIiIiIiIiItAQLPUREREREREREWoKFHiIiIiIiIiIiLcFCDxERERERERGRlmChh4iIiIiIiIhIS7DQQ0RERERERESkJVjoISIiIiIiIiLSEiz0EBERERERERFpCRZ6iIiIiIiIiIi0BAs9RERERERERERagoUeIiIiIiIiIiItwUIPEREREREREZGWYKGHiIiIiIiIiEhLsNBDRERERERERKQlWOghIiIiIiIiItISLPQQEREREREREWkJFnqIiIiIiIiIiLQECz1ERERERERERFqChR4iIiIiIiIiIi3BQg8RERERERERkZZgoYeIiIiIiIiISEuw0ENEREREREREpCVY6CEiIiIiIiIi0hIs9BARERERERERaQkWeoiIiIiIiIiItAQLPUREREREREREWoKFHiIiIiIiIiIiLcFCDxERERERERGRlmChh4iIiIiIiIhIS7DQQ0RERERERESkJVjoISIiIiIiIiLSEiz0EBERERERERFpCRZ6iIiIiIiIiIi0BAs9RERERERERERagoUeIiIiIiIiIiItwUIPEREREREREZGWYKGHiIiIiIiIiEhLsNBDRERERERERKQlWOghIiIiIiIiItISLPQQEREREREREWkJFnqIiIiIiIiIiLQECz1ERERERERERFqChR4iIiIiIiIiIi1Rrgs9r169goODAx49elTksjNmzMCECRPKPigiIiIiLVVU7hUSEgKJRIKkpCQAwD///IN69epBJpOpLkgiIiIqVLku9CxZsgTdu3eHp6dnkct++eWX+OWXX/Dw4cOyD4yIiIhIC5Uk9wKADh06wNDQENu3by/bwIiIiKjYDNQdwIekp6fjf//7H44ePVqs5e3t7dG+fXusX78eK1euLOPoiKg8kEqlyMnJUXcYRBrJ0NAQ+vr66g6DypGS5l55hg0bhtWrV+Ozzz4ro8iIqDxg3kWkGCMjI+jpqWasTbkt9Bw+fBjGxsZo0qSJ2Hbz5k1Mnz4doaGhEAQB9erVw9atW1GlShUAQNeuXTF79mwWeoi0nCAIiI+PFy8dIKLSsbGxgZOTEyQSibpDoXKgoNzr8OHDmDx5Mp48eYImTZpg6NCh+fp17doV48ePx4MHD8ScjIi0B/MuIuXQ09ODl5cXjIyMynxb5bbQc+bMGTRo0EB8HRsbixYtWqBVq1Y4efIkrKyscO7cOeTm5orLNGrUCE+fPsWjR4+KPeSYiDRPXrLh4OAAMzMz/pFKVEKCICA9PR0JCQkAAGdnZzVHROXB+7nXkydP0KtXLwQGBuLzzz/HlStXMHXq1Hz93N3d4ejoiDNnzrDQQ6SFmHcRKU4mk+HZs2eIi4uDu7t7mf8cldtCz+PHj+Hi4iK+Xrt2LaytrbFz504YGhoCAKpXry7XJ2/5x48fs9BDpKWkUqmYbNjZ2ak7HCKNZWpqCgBISEiAg4MDL+OifLnX+vXrUaVKFaxatQoAUKNGDURGRmL58uX5+rq4uODx48cqi5WIVIN5F5HyVKxYEc+ePUNubq5Y0ygr5XYy5oyMDJiYmIivw8PD0bx580IPSF7Smp6eXubxEZF65F0bbmZmpuZIiDRf3s8R51wgIH/udfv2bTRu3FhumYCAgAL7mpqaMv8i0kLMu4iUJ++SLalUWubbKreFHnt7eyQmJoqv84o4hXn9+jWAt5UyItJuHDZMpDj+HNG73s+9SuL169fMv4i0GM8XRIpT5c9RuS30+Pn54datW+LrunXr4syZM4V+63jjxg0YGhqiTp06qgiRiIiISGu8n3vVqlULly5dklvmwoUL+fplZmbiwYMH8PPzK/MYiYiIqGjlttDTvn173Lx5U/xmafz48UhJSUH//v1x5coV3Lt3D7/++iuioqLEPmfOnEHz5s2LNfqHiEjVQkND0bVrV7i4uEAikeDAgQNq2cawYcMgkUggkUhgaGgIR0dHfPLJJ9i8eTNkMpnSY9ImxT12np6e4nJ5D1dX13zvv/9H8+TJk9GqVSu5tpSUFMyePRs1a9aEiYkJnJyc0LZtW+zbtw+CIIjL3b9/H8OHD4erqyuMjY3h5eWFAQMG4MqVK2VzMEjrvJ97jRkzBvfu3cO0adMQFRWFHTt2YOvWrfn6XbhwAcbGxh+8rIuISF2Ye2k25l2lV24LPT4+Pqhfvz527doFALCzs8PJkyeRmpqKli1bokGDBti0aZPcnD07d+7E6NGj1RUyEVGh0tLS4Ovri7Vr15a4b6tWrQr8A6u02+jQoQPi4uLw6NEjHDlyBK1bt8akSZPQpUsXubsZUn7FPXaLFi1CXFyc+AgLC5Nbj4mJCaZPn17otpKSktC0aVNs27YNM2fOxLVr1xAaGop+/frhq6++QnJyMgDgypUraNCgAe7evYuffvoJt27dwv79+1GzZs0C75JEVJD3cy93d3fs3bsXBw4cgK+vLzZs2IClS5fm6/f7779j0KBBnMODiMod5l6aj3lXKQnl2KFDh4RatWoJUqm0yGUPHz4s1KpVS8jJyVFBZESkLhkZGcKtW7eEjIwMdYeiEADC/v37i718y5YthS1btihlG0OHDhW6d++er/3EiRMCAGHTpk0l2o4uKe6x8/DwEL777rsPrsfDw0OYOHGiYGRkJPz9999i+6RJk4SWLVuKr8eOHSuYm5sLsbGx+dbx5s0bIScnR5DJZEKdOnWEBg0aFHi+TExM/GAc2vLzRMpTktxLEAThxYsXQoUKFYSHDx+WcWREpA7adJ5g7qV5mHeVXrm9vToAdO7cGffu3UNsbCzc3NwKXTYtLQ1btmyBgUG53iUiUjJBENR2pxczMzOtmpzw448/hq+vL/bt24dRo0apJYa0tDQA8sc2OzsbOTk5MDAwgLGxcb5lTU1Noaf3doBqTk4OsrOzoa+vL3f3oIKWVabSHDsvLy+MGTMGM2fORIcOHfLFJZPJsHPnTgwaNEjultd5LCwsAABhYWG4efMmduzYUeC+2djYlHyHSGeVJPcCgEePHmHdunXw8vJSQXREVB4w91Iededeqsy7cnJylHZLceZdRSu3l27lmTx5crESjU8//TTfLUCJSPulp6fDwsJCLQ9tvJVwzZo18ejRI7VtP+/Yvnz5UmxbuXIlLCwsMH78eLllHRwcYGFhgZiYGLFt7dq1sLCwwMiRI+WW9fT0hIWFBW7fvl1msb9/7KZPny73eVm9enW+PnPmzEF0dDS2b9+e772XL18iMTERNWvWLHS79+7dE7dPpAzFzb0AwN/fH/369SvjiIioPGHupVzqzL1UmXcV5zK4kmDeVbhyX+ghItJFS5culTtZnTlzBmPGjJFre/dEqyyCIGjVN2Wq9P6xmzZtGsLDw8XHkCFD8vWpWLEivvzyS8ybNw/Z2dn51lfc7RIREZFimHtpFuZdheN1TkSk0czMzJCamqq2bZeVMWPGoG/fvuLrQYMGoXfv3ujVq5fYVtCwUkXdvn1brZdg5P1fvntsp02bhsmTJ+e7NDchIQEA5O60GBgYiNGjR0NfX19u2bxvfMryrozvHzt7e3tUrVq1yH5BQUFYt24d1q1bJ9desWJF2NjY4M6dO4X2r169OgDgzp07vL01ERGVOeZeyqXO3EuVedewYcOUGTrzriKw0ENEGk0ikcDc3FzdYShdhQoVUKFCBfG1qakpHBwcinUCK62TJ08iMjISU6ZMKbNtFKWg/0sjIyMYGRkVa1lDQ8MCr/8u68+IIsfOwsICc+fOxYIFC9CtWzexXU9PD/3798evv/6K+fPn50suU1NTYWJignr16qF27dpYtWoV+vXrl+968aSkpHJzvTgREWk+5l7Ko+7cS5V5l7Lm5wGYdxUHL90iIlKR1NRUcTgpAERHRyM8PFypw4CLu42srCzEx8cjNjYW165dw9KlS9G9e3d06dKlwKGu9H/K4th9/vnnsLa2xo4dO+TalyxZAjc3NzRu3Bjbtm3DrVu3cO/ePWzevBl+fn5ITU2FRCLBli1bcPfuXTRv3hyHDx/Gw4cPERERgSVLlqB79+7K2G0iIiKNw9xL8zHvKh2O6CEiUpErV66gdevW4uugoCAAwNChQ5U2QV1xt/HPP//A2dkZBgYGsLW1ha+vL1avXo2hQ4eWyV2ptElZHDtDQ0N8/fXXGDhwoFx7hQoVcOHCBSxbtgyLFy/G48ePYWtrCx8fH6xcuRLW1tYAgEaNGuHKlStYsmQJRo8ejZcvX8LZ2RlNmzbF999/r+guExERaSTmXpqPeVfpSARNmU2IiAhAZmYmoqOj4eXlJXcbRyIqOf48ERFRYXieIFIeVf48sXRIRERERERERKQlWOghIiIiIiIiItISLPQQEREREREREWkJFnqIiIiIiIiIiLQECz1ERERERERERFqChR4i0ki8YSCR4vhzRERExcHzBZHiVPlzxEIPEWkUQ0NDAEB6erqaIyHSfHk/R3k/V0RERO9i3kWkPNnZ2QAAfX39Mt+WQZlvgYhIifT19WFjY4OEhAQAgJmZGSQSiZqjItIsgiAgPT0dCQkJsLGxUUnCQUREmod5F5FyyGQyvHjxAmZmZjAwKPsyDAs9RKRxnJycAEBMOoiodGxsbMSfJyIiooIw7yJSDj09Pbi7u6ukWCoReMElEWkoqVSKnJwcdYdBpJEMDQ05koeIiIqNeReRYoyMjKCnp5rZc1joISIiIiIiIiLSEpyMWUlCQ0PRtWtXuLi4QCKR4MCBA2W+zdjYWAwePBh2dnYwNTWFj48Prly5UubbJSIiIlI35l5EREQFY6FHSdLS0uDr64u1a9eqZHuJiYlo1qwZDA0NceTIEdy6dQurVq2Cra2tSrZPREREpE7MvYiIiArGS7fKgEQiwf79+9GjRw+xLSsrC7Nnz8bvv/+OpKQkeHt7Y/ny5WjVqlWptjFjxgycO3cOZ86cUU7QRERERBqKuRcREdH/4YgeFRk/fjzOnz+PnTt3IiIiAn369EGHDh1w7969Uq3vzz//hL+/P/r06QMHBwf4+flh06ZNSo6aiIiISDMx9yIiIl3FET1l4P1vlWJiYlC5cmXExMTAxcVFXK5t27Zo1KgRli5dWuJtmJiYAACCgoLQp08fXL58GZMmTcKGDRswdOhQpewHERERkSZg7kVERPR/DNQdgC6IjIyEVCpF9erV5dqzsrJgZ2cHALhz5w5q1apV6HqmT5+OZcuWAQBkMhn8/f3FRMXPzw83btxgskFEREQ6j7kXERHpMhZ6VCA1NRX6+vq4evUq9PX15d6zsLAAAFSuXBm3b98udD15iQkAODs7o3bt2nLv16pVC3v37lVS1ERERESaibkXERHpMhZ6VMDPzw9SqRQJCQlo3rx5gcsYGRmhZs2axV5ns2bNEBUVJdd29+5deHh4KBQrERERkaZj7kVERLqMhR4lSU1Nxf3798XX0dHRCA8PR4UKFVC9enUMGjQIQ4YMwapVq+Dn54cXL17gxIkTqFu3Ljp37lzi7U2ZMgVNmzbF0qVL0bdvX1y6dAkbN27Exo0blblbREREROUScy8iIqKCcTJmJQkJCUHr1q3ztQ8dOhRbt25FTk4OFi9ejG3btiE2Nhb29vZo0qQJFi5cCB8fn1Jt89ChQ5g5cybu3bsHLy8vBAUFYfTo0YruChEREVG5x9yLiIioYCz0EBERERERERFpCT11B0BERERERERERMrBQg8RERERERERkZbgZMwKkMlkePbsGSwtLSGRSNQdDhERUYkIgoA3b97AxcUFenr87ofKP+ZeRESkqVSZd7HQo4Bnz57Bzc1N3WEQEREp5MmTJ3B1dVV3GERFYu5FRESaThV5l9YUeoKDg7Fv3z7cuXMHpqamaNq0KZYvX44aNWp8sM/WrVsxfPhwuTZjY2NkZmYWa5uWlpYA3v5HWVlZlT54IiIiNUhJSYGbm5t4PiMq75h7ERGRplJl3qU1hZ7Tp08jMDAQDRs2RG5uLmbNmoV27drh1q1bMDc3/2A/KysrREVFia9LMgw4b1krKysmG0REpLF4CQxpCuZeRESk6VSRd2lNoeeff/6Re71161Y4ODjg6tWraNGixQf7SSQSODk5lXV4RERERERERERlTmtnXkxOTgYAVKhQodDlUlNT4eHhATc3N3Tv3h03b9784LJZWVlISUmRexARERERERERlRdaWeiRyWSYPHkymjVrBm9v7w8uV6NGDWzevBkHDx7Eb7/9BplMhqZNm+Lp06cFLh8cHAxra2vxwckAiYiIiIiIiKg8kQiCIKg7CGUbO3Ysjhw5grNnz5ZoNuucnBzUqlULAwYMwNdff53v/aysLGRlZYmv8yZTSk5OVtp14ps3b4aDgwM+/vhjmJmZKWWdREREBUlJSYG1tbVSz2NEZUnZn9nY2FicPn0a/fv3L/Nb3RIRkW5TZd6lNXP05Bk/fjwOHTqE0NDQEt+yzNDQEH5+frh//36B7xsbG8PY2FgZYRYoOzsbo0aNgiAIeP78uVjo2b59O/bu3YuePXvis88+E5fP+4BwEk0iIiKikhs8eDBCQkIQExODGTNmqDscIiIipdCary4EQcD48eOxf/9+nDx5El5eXiVeh1QqRWRkJJydncsgwqKlpqaiR48eaNy4MSpWrCi2X7hwAfv378etW7fEtuzsbNja2sLGxgavXr0S269cuYK9e/fiwYMHKo2diIiISJNkZ2cjJCQEADBr1ixo4SB3IiLSUVpT6AkMDMRvv/2GHTt2wNLSEvHx8YiPj0dGRoa4zJAhQzBz5kzx9aJFi3Ds2DE8fPgQ165dw+DBg/H48WOMGjVKHbuAChUqYN++fbhw4YLcKJ0hQ4ZgzZo16Natm9gWGxsLQRCQnZ0tN+H0tm3b8Omnn+Lnn38W23JyctC5c2eMGzdO7ngQERER6apLly6JzwVBwK+//qrGaIiIiJRHay7dWr9+PQCgVatWcu1btmzBsGHDAAAxMTFy118nJiZi9OjRiI+Ph62tLRo0aID//vsPtWvXVlXYxdKwYUM0bNhQrs3LywupqamIj4+XKwq5u7ujSZMmcpNQP3nyBIcPH4aJiQnWrl0rti9fvhynTp1CYGAgunbtWvY7QkRERFROnDp1Su71mDFjYG9vj06dOqkpIiIiIuXQysmYVUVTJrF8/fo19u3bh5SUFAQFBYntLVu2RGhoKDZu3IjRo0cDAF69eoU1a9agZcuW+YpmRESkXTTlPEaUR5mf2aSkJISGhsLa2horVqzA4cOHAQBffPEFZs+ezburEhGRUqky72KhRwGaniBHRkYiJCQE3bt3h7u7OwDgwIED6NmzJ2rXro2bN2+Ky964cQMeHh6wtLRUV7hERKRkmn4eI91TVp/ZrKwsTJs2DT/++CMAQE9PD61bt0bv3r3RokUL1KpVi3flIiIihfCuW6QSPj4+8PHxkWtzcHDAgAEDULVqVbn2Ll264OnTpzhz5gwCAgIAvL2enXf8IiIiIk1nbGyM1atXo3fv3li4cCFOnTqFEydO4MSJEwAACwsLVK9eHdWqVYOnpyccHBxQsWJFODg4oEKFCjA3N4eZmZn4r6mpKQtDVC5kZ2fj5s2buHbtGq5du4br16/j5cuXePPmDYyNjeHo6Ahvb2/Uq1cPzZs3h7e3Nz+7RFqAI3oUoCvfhCYlJcHPzw9Pnz5FYmIiLCwsAACrV6/G5s2bMW7cOHz++edqjpKIiEpKV85jpD1U9Zl9+PAhdu3ahWPHjuHSpUtIS0sr8TqMjY2hp6cHiUSS71+JRAKZTAZBEMR/330ukUhQsWJFODs7w9nZGS4uLqhUqZL4cHFxgZWVFUxMTMSHgYEBv4DTccnJybh+/br4CAsLQ2RkJHJycoq9DltbW3z00Udo0aIFWrRoAT8/PxgaGha4rCAIyMjIQEZGBmQyGYC3n3tLS0t+FokKwEu3NISuJcjPnz+Ho6Oj+LpXr17Yv38/li1bhunTpwMAMjMzERQUhObNm6Nv377Q19dXV7hERFQEXTuPkeZTx2c2NzcX9+7dw71793D37l08ffoUL168QEJCAhISEvD69WtkZGQgPT1drXc31dPTE4s+xsbGMDY2hkwmQ+3atVGzZk1kZmYiLi5OXFZPTw/6+vowMDCAoaEhDAwM8j3PK0ABEJ+/+6dDXvHqQwWtov7NKwbkPTcwMICNjQ0yMzORlJSEnJycfDG8G0tRzxV9v7iPd5c3MDCAlZWV+LC2toaDgwMcHR3h6OgIBwcHGBsbl/j/NzMzU7yrcN7jyZMnePTokfh49uxZgX1tbGxQv3591K9fH35+fnB1dYWFhQWys7Px+PFjRERE4PLly/jvv//yFTWNjY1hZ2cHKysryGQy5OTkICsrC6mpqUhNTRULPO/S19dHhQoVUKFCBVhYWMDMzEx85I16MzMzEwuU7z/e/QzmPYyMjOT65eTkIDMzE1lZWcjMzERKSgqSk5PFf/OeC4IAQ0NDGBkZwdjYWPw/eff/J++5jY0NXFxcYG1tzUIVlQkWejSErifIcXFxOHPmDOrXry9e6hUaGoqWLVvC0dERcXFx4i/JM2fOwNLSEt7e3jAw4BWDRETlga6fx0jzlPfPrEwmkyv6vDtK5/1/Cyp65D3Pzc1FQkIC4uLiEBcXh2fPniE2NhaxsbF49uwZnj17hrS0NGRnZ6t7l6kUbGxs5Io/jo6OsLe3h1QqFT8/iYmJckWdpKSkYq3b3d0dvr6+8PX1Rb169VC/fn14enoWq3CRk5ODsLAwhIaG4syZMzhz5gwSExMV3FvNY2lpCTc3N3h4eKBq1aqoUqWK+PDy8oKJiYm6QyQNxUKPhijvyYY63LlzBxs3boSpqSmWLFkittevXx9hYWHYtWsX+vTpAwB48+YNsrKyYG9vr65wiYh0Gs9jVFrBwcHYt28f7ty5A1NTUzRt2hTLly9HjRo1Pthn06ZN2LZtG27cuAEAaNCgAZYuXYpGjRoVe7v8zMqTyWTiiIb3H1lZWRAEARcuXEBCQgIMDAxQqVIl6OvrQyaTQSqVQiqVIjc3F7m5ucjJycn3b14BqqDHuyNaZDLZBwtaRf377oiY7OxsJCcnw8TEBDY2NuLIl/dH/xTUVpL3S7rsu48Ptec9srOz8ebNG7x58wYpKSlITEzE8+fPkZCQgOfPnyM3N7fU/9/GxsZwcnKCk5OTeEmfl5cXPD094enpiSpVqsDW1rbU63+fTCZDTEwMEhMTkZKSIo4AMzIygqWlJSwsLGBpaSk3J1VGRgYSExPx6tUrvH79Gunp6XKPtLQ08XlmZiakUqn4mSvskZmZKRbBMjMzYWhoKI5eMzExgaWlJaytrcUROnn/6uvrIzs7G9nZ2eLIn3dH/7z7/PXr10UW1CQSCSpVqiRX/KlatSqcnJzkRiyZm5vD3NwcJiYmHB1EIhZ6NASTjeKRyWTo3Lkzzp07h1u3bsHV1RUA8Msvv2DYsGHo168fdu7cKS7PSZ6JiFSD5zEqrQ4dOqB///5o2LAhcnNzMWvWLNy4cQO3bt2Cubl5gX0GDRqEZs2aoWnTpjAxMcHy5cuxf/9+3Lx5E5UqVSrWdvmZJU0mCIJY+Ml75BWAXr58CUNDQ5iamsLMzAzW1tZiQSevuMNLispeWloanj59ipiYGDx69Aj379/HgwcPxMebN29KtD4DA4N8BagPPWxtbcVHhQoVYGtrCysrK06OrUV41y3SKnp6ejhy5AikUqncnD0PHjwAALi5uYltMplMHCb5+++/w8nJSeXxEhERUeH++ecfuddbt26Fg4MDrl69ihYtWhTYZ/v27XKvf/75Z+zduxcnTpzAkCFDyixWovJCIpGIc9fUqlVL3eFQAczNzVGjRo0CRycKgoCXL1/KFX7yCkGvXr0SRyylpaUhKysLwNs5vl69eoVXr16VKh49PT2xCJQ3iipvtFBRz/NGO5mYmIgFRFNTU5iamopthoaGLB5qKRZ6SGXen5h50aJFmDJlitz15VFRUXj69ClevXoFOzs7sX3t2rW4cOEChg0bhjZt2qgsZiIiIipacnIyAKBChQrF7pOeno6cnJxC+2RlZYl/MAFvvw0lIlKHvLvhVaxYEU2aNCl0WalUirS0NLnJoYt6JCYmyj3y7maW97qs9imvQFSc4pGVlRVcXFzEOYxcXV054qicYqGH1Or964irV6+OGzdu4OHDh3K3cjx48CCOHz+Opk2bioWeV69eYdWqVQgICEDXrl1VGjcRERG9JZPJMHnyZDRr1gze3t7F7jd9+nS4uLigbdu2H1wmODgYCxcuVEaYREQqo6+vL97NK2/aipLKysqSK/zk3eksLS1N/Lew53nzd2VkZIiPvDmO8giCIK63NExMTFCtWjXUrVtXnATc19dX7k7NpB6co0cBvE5cdU6dOoWQkBAMHjwY1apVAwD8/fff6NKlC2rUqIE7d+6Iy8bGxsLZ2ZnVZSKiIvA8RsowduxYHDlyBGfPni32HzTLli3DihUrEBISgrp1635wuYJG9Li5ufEzS0RUSnkTn79b/ClO4Sg1NRXJycl4+vSpOI9RTk5OgdtwdHQUiz55RaCaNWvKfZGvizhHD9F7WrdujdatW8u1OTo6YuTIkfkmcPz444+RkpKCv/76C/7+/qoMk4iISKeMHz8ehw4dQmhoaLGLPN988w2WLVuGf//9t9AiD/D2LkN5d14iIiLFSSQS8XerjY1NqdeTm5uLmJgY3Lx5ExEREbh+/TquX7+Oe/fu4fnz5zh27BiOHTsmLm9kZITatWujVq1a8PT0hJeXl/hwd3cvl0Wg2NhYzJ49G2PHjkXjxo3VHU6JcESPAvhNaPnz/PlzVKtWDZmZmXj58qX4/3LixAk8evQIXbp04VBCIqL/j+cxKi1BEDBhwgTs378fISEh4mjboqxYsQJLlizB0aNHi5zjoiD8zBIRlW9paWm4ceMGrl+/LhaAIiIiCp1jTU9PD66urvkKQHkPFxcXlV6tkZWVJY48TU9PR7NmzXDmzBmFJ67W6hE9QUFBJe4zZ86cEk3uR7rL0dERL1++REREhNwPz5o1a3DgwAEsXLgQ8+bNA/A2SQXAmeaJiEirlUXuFRgYiB07duDgwYOwtLREfHw8AMDa2hqmpqYAgCFDhqBSpUoIDg4GACxfvhzz5s3Djh074OnpKfaxsLCAhYVFiWMkIqLyx9zcHI0bN5YbASMIAh49eoTr16/j/v37iI6OFh+PHj1CZmYmYmJiEBMTg9DQ0HzrtLa2RrNmzfDRRx+hffv28PPzK7O/4WQyGUaMGIEdO3YAAJo1a4Zvv/1W4/5mVPmIHj09PQQEBMDIyKhYy589exZRUVGoXLlyGUdWcvxWSXN88803+OOPP7Bp0ybUq1cPAHDhwgUMGjQIgwYNwqJFi9QbIBGRGvA8phvKIvf6UMK7ZcsWDBs2DADQqlUreHp6YuvWrQAAT09PPH78OF+f+fPnY8GCBcWKjZ9ZIiLtIggC4uPjxaLPu0Wg6OhoxMTEQCqVyvWpXLkyPv30U4wePRpVq1ZVWiwxMTEYOXIk/v33X+jr6+OXX37BwIEDlVbkUeU5TC2Fnvj4eDg4OBRreUtLS1y/fp2FHlK62bNnY+nSpejXrx927twptv/7779o2LAhrK2t1RgdEVHZ43lMNzD3IiIiTZWTk4OIiAicO3cOp06dwtGjR5GRkQHg7ZcO3bt3x8KFC4uc860oL168QNOmTXH//n0YGxtj48aNGDJkiDJ2QaTVl25t2bKlRH9A//TTT5xTRYPIZDKkpqYiKSkJSUlJePPmDYyNjcVh2ebm5rCwsCgXk23NmjUL/v7+cHJyEttevXqFDh06QE9PD0+fPi12UkxE6pN394h37w6Rd6vQrKwsWFlZwcbGRnyYmppq3PBbIkUw9yIiIk1laGiIBg0aoEGDBpg4cSLS0tJw+PBhbN68Gf/88w8OHDiAP//8E2PHjsXixYtLNcF0WloaunTpgvv378PT0xNHjx5F9erVlb8zKqSWyZilUin09fVVvVml08ZvlQRBkCvUvPtITEwssP3dR3JyMmQyWZHbMTQ0zFf8yfv3Q8+L02ZoaKjQH3BhYWEYNGgQDA0Ncf36dbF92bJlePPmDYYPH67U4YGaTBAEZGVl4c2bN0hJScGbN2/knhf0b25urkpiMzAwgJWVFSwtLWFpaSk+L6jN0tISJiYm/MNfBfI+M3m36SyoMFPa5yX5bBkZGckVforzsLW1FZ+bmJiU4VFSLW08j1HBmHsREZG2uXXrFubNm4e9e/cCADw8PLBr1y40atSo2OuQSqXo1asX/vzzT1SoUAH//fcfatSoUSbxavWlWwDg5OSEYcOGYcSIERpdKVN1siGTyZCVlYXMzMxiPTIyMj74Xlpa2geLNcUp1BTFyMgItra2sLCwQFZWlvhHWVn/oa+vrw9zc3OYm5vDzMys1M9lMhkcHR1hbm4OU1NTNG7cGHFxcfj777/RqVOnMt0HZZDJZMjOzkZOTg5ycnLE5wW15f3/FFWkKejfnJwcde+qUhgYGBS7KPTucwsLCxgZGcHQ0FD89/3n779W5R0DSiMnJ0cswqSnp8sVZd5/Xdy2d18r4/dLYfJGEOYVgY2MjPDmzRuxUK2M7efdjvT9h5mZGUxMTJT2ULRwXRz8o1l3MPciIiJtderUKYwaNQoPHz6EoaEh1qxZg88//7xYfcePH4+1a9fCxMQEJ06cQNOmTcssTq0v9Hz99df45ZdfEB0djaZNm2LkyJHo27cvzMzMVB2KQpT5H5WUlIQePXoUWqTJzs5WUuRFMzQ0lPsGu7jfdBf1jfeHLq94v6047727jKqOjZ6eHkxMTKCvr4/c3Fzk5ubC3NwcJiYm0NPTg76+PvT19ZXyXCKRFFmk+VBbWf8x/T5zc/N8BZKCCibFnQhUUdnZ2R8cafTu89TUVJXE8y59ff1iFYTef08QBEilUkilUshksgKfK+M9VX12jIyMCh3JV5xRfgU9NzD48BXJhY1YLO7IRVWeMiUSSYEFIAsLC1y6dEkp2+AfzbqDuRcREWmz5ORkjBo1Cnv27AHw9rw3e/bsQr8027hxI7744gtIJBLs3r0bvXv3LtMYtb7QkyckJARbtmzB3r17oa+vj759+2LUqFFyt2Irz5T5H5WcnFyi6wklEglMTU3FxP/d58V5mJqaFlrI0bQ5LHJycuRGDZT0eVHLafrIFYlE8sFCQt4f3EUVaj70r4WFhcZeDpA3p1RxikIfKhS9X3B7/7Wm+tDouA+1FfX63TYzM7NyMU9XSb0/B9n7RaHCRlEW95GVlVVkHObm5korUvKPZs1ga2tb7HPy69evC32fuRcREWkrQRCwYMEC8Y7KEydOxHfffVfgiPoLFy6gRYsWyMnJwdKlSzFz5swyj09nCj15UlNTsXPnTmzduhX//fcfatWqhZEjRyIoKEjdoRVKmf9RUqkU+/btK3ahxsDAQKMKMZou73KW9PR0ZGVlQSqVIioqCr///js+//xz2NjYQCqV4tq1a9i1axd69eqFOnXqfHAERXGeC4LwwcJMSdqMjIw0thCj6QRBQG5uboEFoJI+19PTkxv5VdioMEVem5iYwMzMDEZGRvwdowZ5l10WVgzKzc1F27ZtlbI9/tGsGX755Rfx+atXr7B48WK0b98eAQEBAIDz58/j6NGjmDt3LqZMmVKsdTL3IiIibbV69WpMmjQJANC/f39s3boVxsbG4vvPnj2Dv78/4uLi0Lt3b+zevVsleW+5LvSUJAH49ttvSxzQ33//jSFDhiApKQlSqbTE/VWJyQa9b+DAgfj9998xevRobNy4Ud3hEBEViucxzdO7d2+0bt0a48ePl2tfs2YN/v33Xxw4cKDE62TuRURE2mbHjh0YNmwYcnJy0KZNG+zZsweWlpY4ePAgpkyZgpiYGHh7e+P8+fOwsLBQSUzl+vbqYWFhcq+vXbuG3NxccWbqu3fvQl9fHw0aNCj2OtPT07Fr1y5s2bIFZ8+eRZUqVTBt2rSShkakdl9++SUsLCwwduxYse3p06eYN28exowZU6IZ4ImIiN539OhRLF++PF97hw4dMGPGjGKvh7kXERFps4EDB8Le3h69evXCiRMnULlyZZiamuLZs2cAAC8vLxw4cEBlRR5VK3Gh59SpU+Lzb7/9FpaWlvjll19ga2sLAEhMTMTw4cPRvHnzItf133//YfPmzdi9ezdyc3Px6aef4uuvv0aLFi1KGhZRuVC/fv18I3l+/vlnbNmyBQ8fPkRISIh6AiMiIq1gZ2eHgwcPYurUqXLtBw8ehJ2dXZH9mXsREZGuaNeuHU6fPo2BAwfi7t27SExMhK2tLcaOHYvZs2dr3A0JSqLEhZ53rVq1CseOHROLPMDbCQMXL16Mdu3a5UtC8qxYsQJbtmzB3bt34e/vj5UrV2LAgAGwtLRUJByicqlz5854+PAhunfvLrZlZWVhxowZGDZsGHx9fdUYHRERaZKFCxdi1KhRCAkJESdQvnjxIv755x9s2rTpg/2YexERkS5q0KABbty4gdDQUMhkMjRv3vyDd4jWJgpNxmxpaYm//voLrVq1kms/deoUunXrhjdv3hTYr2LFihg8eDBGjhwJb2/v0m5e7XidOJXWjh07MGjQILi5uSE6OpqTJRORWvA8ppkuXryI1atX4/bt2wCAWrVqYeLEiYXeOYu5FxERkXqV6zl63tWzZ08MHz4cq1atEuceuXjxIqZNm4ZevXp9sN+zZ8808ta6RMpSvXp19O3bF35+fmKRRxAErFy5Et26dUPNmjXVHCEREZVXjRs3xvbt20vUh7kXERGR7sh/Q/kS2LBhAzp27IiBAwfCw8MDHh4eGDhwIDp06IB169YV2Gf16tUluqPDhg0bPjgyiEhT+fv7448//pCbOPPKlSuYPn066tWrh5SUFDVGR0RE5dmDBw8wZ84cDBw4EAkJCQCAI0eO4ObNmwUuz9yLiIhItyhU6DEzM8O6devw6tUrhIWFISwsDK9fv8a6detgbm5eYJ8pU6aUKHn46quv8OLFC0XCJNIIhoaG6NatG/r37y83lO/y5cvIzc1VY2RERFRenD59Gj4+Prh48SL27t2L1NRUAMD169cxf/78Avsw9yIiItItCl26lScuLg5xcXFo0aIFTE1NIQgCJBJJgcsKgoA2bdrAwKB4m87IyFBGiETlXr169XDw4EHIZDKx7dmzZ2jevDnc3Nxw7tw5ODg4qDFCIiJStxkzZmDx4sUICgqSm0j5448/xpo1awrsw9yLiIhItyhU6Hn16hX69u2LU6dOQSKR4N69e6hcuTJGjhwJW1tbrFq1Kl+fD33b9CHdu3dHhQoVFAmTSKPo6f3fQLvbt2/DwsICjo6OqFixohqjIiKi8iAyMhI7duzI1+7g4ICXL18W2Ie5FxERkW5RqNAzZcoUGBoaIiYmBrVq1RLb+/Xrh6CgIKUUeoh0WZs2bfDo0SMkJCSIo+Sys7PRoUMHDBo0CEOGDOHkmkREOsTGxgZxcXHw8vKSaw8LC0OlSpUK7MPci4iISLcoVOg5duwYjh49CldXV7n2atWq4fHjxwoFRkRvWVhYwMLCQny9fft2nDp1Crdv38bAgQNZ6CEi0iH9+/fH9OnTsXv3bkgkEshkMpw7dw5ffvklhgwZou7wiIiIqBxQaDLmtLQ0mJmZ5Wt//fo1jI2NFVl1iQUHB6Nhw4awtLSEg4MDevTogaioqCL77d69GzVr1oSJiQl8fHxw+PBhFURLVHp9+/bFqlWrsGTJEpiamortf/31F7Kzs9UYGRERlbWlS5eiZs2acHNzQ2pqKmrXro0WLVqgadOmmDNnjrrDIyIionJAoUJP8+bNsW3bNvF13jdLK1asQOvWrRUOriROnz6NwMBAXLhwAcePH0dOTg7atWuHtLS0D/b577//MGDAAIwcORJhYWHo0aMHevTogRs3bqgwcqKSMTc3R1BQEEaMGCG2nT9/Ht26dUPt2rWRmZmpxuiIiKgsGRkZYdOmTXjw4AEOHTqE3377DXfu3MGvv/4KfX19dYdHRERE5YBEEAShtJ1v3LiBNm3aoH79+jh58iS6deuGmzdv4vXr1zh37hyqVKmizFhL5MWLF3BwcMDp06fRokWLApfp168f0tLScOjQIbGtSZMmqFevHjZs2FDkNlJSUmBtbY3k5GS522ETqdqff/6JMWPGoGPHjvjf//4nthd2BzwiIp7HSNPwM0tERJpKlecwhebo8fb2xt27d7FmzRpYWloiNTUVvXr1QmBgIJydnQvtm5OTg5o1a+LQoUNyEzkrS3JyMgAUeteI8+fPIygoSK6tffv2OHDggNLjISpL3bp1wyeffIL09HSx7fnz52jRogUmTpyIMWPG8JteIiIt8H7ekkcikcDExARVq1b94F2zyjr3IiIiovJBoUIPAFhbW2P27Nkl7mdoaFhml5jIZDJMnjwZzZo1g7e39weXi4+Ph6Ojo1ybo6Mj4uPjC1w+KysLWVlZ4uuUlBTlBEykBKampnJz9qxbtw53797Ftm3bMG7cODVGRkREyhIWFoZr165BKpWiRo0aAIC7d+9CX18fNWvWxLp16zB16lScPXsWtWvXlutblrkXERERlR8KFXoiIiIKbM/7Vsnd3b3QSZkDAwOxfPly/PzzzzAwULjmJLfeGzdu4OzZs0pbJ/B2wueFCxcqdZ1EZWXWrFlwdHREzZo1xcu3cnNzsWXLFgwePFiuKERERJohb7TOli1bxGHfycnJGDVqFD766COMHj0aAwcOxJQpU3D06NF8/csq9yIiIqLyQ6E5evT09MQ/IPNW8+58IIaGhujXrx9++uknmJiY5Ovfs2dPnDhxAhYWFvDx8YG5ubnc+/v27StxTOPHj8fBgwcRGhoKLy+vQpd1d3dHUFAQJk+eLLbNnz8fBw4cwPXr1/MtX9CIHjc3N14nThpj69atGD58OHx9fREWFsb5e4h0HOc70TyVKlXC8ePH843WuXnzJtq1a4fY2Fhcu3YN7dq1w8uXL/P1L4vcS5X4mSUiIk2lMXP07N+/H9OnT8e0adPQqFEjAMClS5ewatUqzJ8/H7m5uZgxYwbmzJmDb775Jl9/Gxsb9O7dW5EQRIIgYMKECdi/fz9CQkKKLPIAQEBAAE6cOCFX6Dl+/DgCAgIKXN7Y2Fjlt40nUiZzc3O4u7tj0KBBckUeqVTKOXyIiDRAcnIyEhIS8hV6Xrx4IV5SbmNjg+zs7AL7KzP3IiIiovJJoULPkiVL8MMPP6B9+/Zim4+PD1xdXTF37lxcunQJ5ubmmDp1aoGFni1btiiyeTmBgYHYsWMHDh48CEtLS3GeHWtra/ESlSFDhqBSpUoIDg4GAEyaNAktW7bEqlWr0LlzZ+zcuRNXrlzBxo0blRYXUXnSp08fdO/eHTKZTGwLCwtD7969sXz5cvTp00eN0RERUVG6d++OESNGYNWqVWjYsCEA4PLly/jyyy/Ro0cPAG+/dKtevXqB/ZWZexEREVH5pFChJzIyEh4eHvnaPTw8EBkZCQCoV68e4uLiCl3PixcvEBUVBQCoUaMGKlasWOJY1q9fDwBo1aqVXPuWLVswbNgwAEBMTAz09PTE95o2bYodO3Zgzpw5mDVrFqpVq4YDBw4UOoEzkaYzMjKSe718+XJER0dj//79LPQQEZVzP/30E6ZMmYL+/fsjNzcXAGBgYIChQ4fiu+++AwDUrFkTP//8c6HrUUbuRUREROWTQnP0+Pn5wdfXFxs3bhT/eMzJycHo0aNx/fp1hIWF4dy5cxg8eDCio6Pz9U9LS8OECROwbds2cYSBvr4+hgwZgh9//BFmZmalDU0leJ04aYP09HR88803GD58ONzc3AC8/Wynp6fDyclJzdERUVnieUxzpaam4uHDhwCAypUrw8LColj9mHsRERGphyrPYXpFL/Jha9euxaFDh+Dq6oq2bduibdu2cHV1xaFDh8QRNg8fPvzgrZ2DgoJw+vRp/PXXX0hKSkJSUhIOHjyI06dPY+rUqYqERkTFZGZmhnnz5olFHgBYvHgxqlevjq1bt6ovMCIi+iALCwvUrVsXdevWLXaRB2DuRUREpAsUGtEDAG/evMH27dtx9+5dAG+H/w4cOBCWlpZF9rW3t8eePXvyXW516tQp9O3bFy9evFAktDLHb5VIG0mlUrRq1Qpnz57FoUOH0LlzZ3WHRERlhOcxzXTlyhXs2rULMTEx+SZdLuquWcy9iIiI1ENj7roFAJaWlhgzZkyp+qanp8PR0TFfu4ODA9LT0xUNjYhKQV9fH6dPn8axY8fkJloPDQ2Fra0tfHx81BgdEZFu27lzJ4YMGYL27dvj2LFjaNeuHe7evYvnz5+jZ8+eRfZn7kVERKT9FB7RAwC3bt0q8Fulbt26FdqvTZs2sLOzw7Zt22BiYgIAyMjIwNChQ/H69Wv8+++/ioZWpvitEumKzMxM1K5dG48fP8b+/fuL/NkmIs3A85jmqVu3Lr744gsEBgbC0tIS169fh5eXF7744gs4Oztj4cKFhfZn7kVERKQeGjOi5+HDh+jZsyciIyMhkUiQVzOSSCQA3l4CUpjvv/8eHTp0gKurK3x9fQEA169fh4mJCY4ePapIaESkRKmpqWjQoAGys7Px8ccfqzscIiKd9eDBA/GSWiMjI6SlpUEikWDKlCn4+OOPiyz0MPciIiLSfgpNxjxp0iR4eXkhISEBZmZmuHnzJkJDQ+Hv74+QkJAi+/v4+ODevXsIDg5GvXr1UK9ePSxbtgz37t1DnTp1FAmNiJTI3t4eu3fvRkREhNykn9OmTcPhw4fVGBkRkW6xtbXFmzdvAACVKlXCjRs3AABJSUnFuvSKuRcREZH2U2hEz/nz53Hy5EnY29tDT08Penp6+OijjxAcHIyJEyciLCzsg31zcnJQs2ZNHDp0CKNHj1YkDCJSkQoVKojPQ0JC8M033+Dbb7/F/fv34eXlpcbIiIh0Q4sWLXD8+HH4+PigT58+mDRpEk6ePInjx4+jTZs2hfZl7kVERKQbFCr0SKVS8e5a9vb2ePbsGWrUqAEPDw9ERUUV2tfQ0BCZmZmKbJ6I1MjPzw9ffvklcnNz5Yo8UqkU+vr6aoyMiEh7rVmzRsyfZs+eDUNDQ/z333/o3bs35syZU2hf5l5ERES6QaFLt7y9vXH9+nUAQOPGjbFixQqcO3cOixYtQuXKlYvsHxgYiOXLlyM3N1eRMIhIDaytrbFy5Up89913Ytvz589RrVo1rFu3rsg5uoiIqGRyc3Nx6NAhsZiup6eHGTNm4M8//8SqVatga2tb5DqYexEREWk/hUb0zJkzB2lpaQCARYsWoUuXLmjevDns7Ozwxx9/FNn/8uXLOHHiBI4dOwYfHx+Ym5vLvb9v3z5FwiMiFVuzZg2io6OxZcsWjBkzRt3hEBFpFQMDA4wZMwa3b98u9TqYexEREWk/hQo97du3F59XrVoVd+7cwevXr2FrayveeaswNjY26N27tyIhEFE5Mn/+fDg7O8PPzw96em8HDEqlUsTExHAOHyIiJWjUqBHCw8Ph4eFRqv7MvYiIiLRfqQs9OTk5MDU1RXh4OLy9vcX2dydrLUxubi5at26Ndu3awcnJqbRhEFE5YmBggHHjxsm1bd26FWPHjsXcuXMxd+5cNUVGRKQdxo0bh6CgIDx58gQNGjTINyKnbt26H+zL3IuIiEg3lLrQY2hoCHd391LPw6GM4cdEVP6FhoYiJycn3x8jRERUcv379wcATJw4UWyTSCQQBAESiaTQvIy5FxERkW5QaDLm2bNnY9asWXj9+nWp+jdq1KjQW7ATkebbunUrjh49ivHjx4ttERER+PPPPyEIghojIyLSPNHR0fkeDx8+FP8tirJyr+DgYDRs2BCWlpZwcHBAjx49irzjKgDs3r0bNWvWhImJCXx8fHD48GGFYyEiIiJ5Cs3Rs2bNGty/fx8uLi7w8PDI9439tWvXCu0/btw4TJ06FU+fPi3x8GMi0gwSiQTt2rUTXwuCgMmTJ+PUqVNYvHgxZs+ercboiIg0S2nn5smjrNzr9OnTCAwMRMOGDZGbm4tZs2ahXbt2uHXr1gdHcP73338YMGAAgoOD0aVLF+zYsQM9evTAtWvX5KYBICIiIsVIBAW+Ul+4cGGh78+fP7/Q9/Mma5ULqJjDj8uDlJQUWFtbIzk5GVZWVuoOh0gj5OTkYP78+Vi/fr1CE4oSkeJ4HtNMv/76KzZs2IDo6GicP38eHh4e+P777+Hl5YXu3bsX2rescq8XL17AwcEBp0+fRosWLQpcpl+/fkhLS8OhQ4fEtiZNmqBevXrYsGFDsbbDzywREWkqVZ7DFBrRU1QhpyjR0dEK9ScizWNoaIilS5di1qxZsLCwENvnzZuHrKwszJw5EzY2NuoLkIioHFu/fj3mzZuHyZMnY8mSJWJhxsbGBt9//32RhZ6yyr2Sk5MBFH5TjvPnzyMoKEiurX379jhw4ECZxERERKSrFCr0AEBSUhL27NmDBw8eYNq0aahQoQKuXbsGR0dHVKpUqdC+/CafSHe9W+SJi4vDihUrkJWVhRYtWqBz585qjIyIqPz68ccfsWnTJvTo0QPLli0T2/39/fHll18W2b8sci+ZTIbJkyejWbNmhV6CFR8fD0dHR7k2R0dHxMfHf7BPVlYWsrKyxNcpKSmKB0xERKTlFJqMOSIiAtWrV8fy5cvxzTffICkpCQCwb98+zJw5s1jr+PXXX9GsWTO4uLjg8ePHAIDvv/8eBw8eVCQ0ItIgTk5O2Lt3L7744gt06tRJbH/27BknbCYiekd0dDT8/PzytRsbGyMtLa1Y61B27hUYGIgbN25g586dpepfmODgYFhbW4sPNzc3pW+DiIhI2yhU6AkKCsKwYcNw7949mJiYiO2dOnVCaGhokf3Xr1+PoKAgdOrUCUlJSfmGHxORbpBIJOjcuTM2bNgAiUQC4O23uM2bN0fz5s3x6NEj9QZIRFROeHl5ITw8PF/7P//8g1q1ahXZX9m51/jx43Ho0CGcOnUKrq6uhS7r5OSE58+fy7U9f/4cTk5OH+wzc+ZMJCcni48nT56UOEYiIiJdo1Ch5/Lly/jiiy/ytVeqVKnQYbh58oYfz549G/r6+mK7v78/IiMjFQmNiDTc1atXERcXh4cPH6JixYrqDoeIqFwICgpCYGAg/vjjDwiCgEuXLmHJkiWYOXMmvvrqqyL7Kyv3EgQB48ePx/79+3Hy5El4eXkV2ScgIAAnTpyQazt+/DgCAgI+2MfY2BhWVlZyDyIiIiqcQnP0GBsbF3it9N27d4v1h5kyhh8TkXZq2rQp7t69i4cPH8rdqnfz5s3o2bMnbG1t1RgdEZF6jBo1CqamppgzZw7S09MxcOBAuLi44IcffkD//v2L7K+s3CswMBA7duzAwYMHYWlpKX7BZ21tDVNTUwDAkCFDUKlSJQQHBwMAJk2ahJYtW2LVqlXo3Lkzdu7ciStXrmDjxo3F3i4REREVTaERPd26dcOiRYuQk5MD4O3lFzExMZg+fTp69+5dZH9Fhx8TkXZzdXWVu01vaGgoRo4ciRo1auDNmzdqjIyISH0GDRqEe/fuITU1FfHx8Xj69ClGjhxZrL7Kyr3Wr1+P5ORktGrVCs7OzuLjjz/+EJeJiYlBXFyc+Lpp06bYsWMHNm7cCF9fX+zZswcHDhwodAJnIiIiKjmFRvSsWrUKn376KRwcHJCRkYGWLVsiPj4eAQEBWLJkSZH984YfZ2ZmisOPf//9dwQHB+Pnn39WJDQi0kL6+vrw9vZGs2bNYGlpqe5wiIhUbvHixRg0aBC8vLxgZmYGMzOzEvVXVu5VnInyQ0JC8rX16dMHffr0KUnIREREVEISQQm3tDl79iwiIiKQmpqK+vXro23btsXuu337dixYsAAPHjwAALi4uGDhwoXF/mZKnVJSUmBtbY3k5GReM06kIlKpFBkZGeLt2Z8/f47PPvsMCxYsQNOmTdUcHZFm4XlM8/j6+uLGjRto3LgxBg8ejL59+8Le3r5E62DuRUREpHqqPIcpVOh58uSJ0m5zmZ6ejtTUVDg4OChlfarAZINI/SZMmIA1a9agYcOGuHjxonjXLiIqGs9jmunmzZvYvn07du7ciadPn+KTTz7BoEGD0KNHjxKN8GHuRUREpDqqPIcpNEePp6cnWrZsiU2bNiExMVGhQMzMzDQq0SCi8mHmzJkYNWoUvvnmG7HIk5OTk+8WvkRE2qJOnTpYunQpHj58iFOnTsHT0xOTJ08u9DblBWHuRUREpJ0UKvRcuXIFjRo1wqJFi+Ds7IwePXpgz549yMrKUlZ8RESFcnFxwaZNm+Qmbf7ll19QuXJlrFixQo2RERGVPXNzc5iamsLIyEi8OQYRERHpNoUKPX5+fli5ciViYmJw5MgRVKxYEZ9//jkcHR0xYsQIZcVIRFQiR48eRXp6OoyMjNQdChGR0kVHR2PJkiWoU6cO/P39ERYWhoULF4q3OCciIiLdppTJmN917do1jBw5EhEREZBKpcpcdbnD68SJyidBEHD48GF8/PHHMDU1BfB2BOKJEycwfvx4mJubqzlCovKB5zHN06RJE1y+fBl169bFoEGDMGDAAFSqVEndYakMP7NERKSpVHkOU+j26nmePn2KHTt2YMeOHbhx4wYCAgKwdu3aEq0jMzMTJiYmygiHiHScRCJB586d5dpmz56NY8eO4enTp/jxxx/VFBkRkWLatGmDzZs3o3bt2gqvi7kXERGRdlLo0q2ffvoJLVu2hKenJ7Zt24Z+/frhwYMHOHPmDMaMGVNkf5lMhq+//hqVKlWChYUFHj58CACYO3cu/ve//ykSGhGRSBAEDB48GDVr1kRQUJDYnpqayjktiEijLFmyRKEiD3MvIiIi7adQoWfx4sVo3Lgxrl69ihs3bmDmzJnw8PAoUf+tW7dixYoVcnNpeHt74+eff1YkNCIikUQiwWeffYZbt27By8tLbF+4cCFq1qyJw4cPqzE6IqKSefr0KdatW4cZM2YgKChI7lEU5l5ERETaT6FLt2JiYsTbGZfGtm3bsHHjRrRp00ZuBJCvry/u3LmjSGhERPm8+/sqJycHe/fuRXR0tEK/x4iIVOnEiRPo1q0bKleujDt37sDb2xuPHj2CIAioX79+kf2ZexEREWk/hQo9eX8cpaenIyYmBtnZ2XLv161bt9D+sbGxqFq1ar52mUzGyymIqEwZGhoiMjISe/bsQYcOHcT2v//+G7m5uejWrRsLQERU7sycORNffvklFi5cCEtLS+zduxcODg4YNGiQ3O+yD2HuRUREpP0UunTrxYsX6Ny5MywtLVGnTh34+fnJPYpSu3ZtnDlzJl/7nj17itX/XaGhoejatStcXFwgkUhw4MCBQpcPCQmBRCLJ9+CtSYl0h7m5OYYOHSoWdHJycjBx4kT06NEDmzdvVnN0RET53b59G0OGDAEAGBgYICMjAxYWFli0aBGWL19eZH9l5l5ERERUPik0omfy5MlITk7GxYsX0apVK+zfvx/Pnz/H4sWLsWrVqiL7z5s3D0OHDkVsbCxkMhn27duHqKgobNu2DYcOHSpRLGlpafD19cWIESPQq1evYveLioqSu7WZg4NDibZLRNojJycH/fr1wx9//IH+/fuL7RkZGeJt2omI1Mnc3FwcQe3s7IwHDx6gTp06AICXL18W2V+ZuRcRERGVTwoVek6ePImDBw/C398fenp68PDwwCeffAIrKysEBwfnu73x+7p3746//voLixYtgrm5OebNm4f69evjr7/+wieffFKiWDp27IiOHTuWeB8cHBxgY2NT4n5EpH3MzMywdOlSLFq0CAYG//frsU+fPsjOzsYPP/yAWrVqqTFCItJ1TZo0wdmzZ1GrVi106tQJU6dORWRkJPbt24cmTZoU2V+ZuRcRERGVTwoVetLS0sQRMLa2tnjx4gWqV68OHx8fXLt2rVjraN68OY4fP65IGAqpV68esrKy4O3tjQULFqBZs2YfXDYrKwtZWVni65SUFFWESEQq9m6R5/Hjxzh27BhkMhn09fXVGBUREfDtt98iNTUVwNs7B6ampuKPP/5AtWrV8O233xZrHerOvYiIiKhsKTRHT40aNRAVFQXg7d0afvrpJ8TGxmLDhg1wdnYusn/lypXx6tWrfO1JSUmoXLmyIqEVydnZGRs2bMDevXuxd+9euLm5oVWrVoUWqIKDg2FtbS0+3NzcyjRGIlI/Dw8PREVFYdOmTahevbrY/scff+DWrVtqjIyIdFHlypXFm12Ym5tjw4YNiIiIwN69e+Hh4VGs/urKvYiIiEg1JIIgCKXt/NtvvyE3NxfDhg3D1atX0aFDB7x+/RpGRkbYunUr+vXrV2h/PT09xMfH55sX5/nz53B3d5cbPVMSEokE+/fvR48ePUrUr2XLlnB3d8evv/5a4PsFjehxc3NDcnKy3Dw/RKTdEhISULlyZWRkZODChQto2LChukMiKpWUlBRYW1vzPKahxo0bh0WLFsHe3r7Yfcoq91IVfmaJiEhTqfIcptClW4MHDxafN2jQAI8fP8adO3fg7u5eaNLx559/is+PHj0Ka2tr8bVUKsWJEyfg6empSGil0qhRI5w9e/aD7xsbG8PY2FiFERFReZSZmYl27drh2bNn8Pf3F9sFQeAt2YlIZX777Td8+eWXxSr0lNfci4iIiJRPoULPu86dOwd/f3/Ur1+/yGXzRtpIJBIMHTpU7j1DQ0N4enoW665dyhYeHl6sS86ISLe5u7tj3759yMjIEAs7ubm5aNWqFXr27InAwECYmJioOUoi0nYlGZRdXnMvIiIiUj6lFXo6duyI8PDwYl3fLZPJAABeXl64fPlyiYYcf0hqairu378vvo6OjkZ4eDgqVKgAd3d3zJw5E7Gxsdi2bRsA4Pvvv4eXlxfq1KmDzMxM/Pzzzzh58iSOHTumcCxEpBveveX67t27ce7cOdy+fRujRo1ioYeIypWyyL2IiIiofFJaoac0U/1ER0cra/O4cuUKWrduLb4OCgoCAAwdOhRbt25FXFwcYmJixPezs7MxdepUxMbGwszMDHXr1sW///4rtw4iouLq27cvMjMzIZFI5C6JuH79OurWrctLuohI6d68eVPiPsrMvYiIiKh8Umgy5ndZWlri+vXrJbpjw6JFiwp9f968eYqGVaY4ISARFebq1avw9/dH27ZtcfjwYRgaGqo7JCI5PI9ppgcPHmDLli14+PAhvv/+ezg4OODIkSNwd3dHnTp1Cu3L3IuIiEg9NGYy5nf99NNPcHR0LFGf/fv3y73OyclBdHQ0DAwMUKVKlXKfbBARFSYsLAxGRkZwcnJikYeIlOL06dPo2LEjmjVrhtDQUCxevBgODg64fv06/ve//2HPnj2F9mfuRUREpP2UUui5f/8+7OzsoKenB6D4d54JCwvL15aSkoJhw4ahZ8+eygiNiEhtRo0ahU8++QQGBv/3qzYxMRFLlizBV199le/2xkRERZkxYwYWL16MoKAgWFpaiu0ff/wx1qxZU2R/5l5ERETaT0+Rzq9evULbtm1RvXp1dOrUCXFxcQCAkSNHYurUqaVap5WVFRYuXIi5c+cqEhoRUbng4eGBSpUqia+Dg4OxatUq8Q44REQlERkZWWBBxsHBAS9fvizVOpl7ERERaReFCj1TpkyBgYEBYmJiYGZmJrb369cP//zzT6nXm5ycjOTkZEVCIyIqlzp27IgGDRpg9uzZYptMJkNOTo4aoyIiTWFjYyN+sfausLAwuaJySTH3IiIi0h4KXbp17NgxHD16FK6urnLt1apVw+PHj4vsv3r1arnXgiAgLi4Ov/76Kzp27KhIaERE5VLr1q1x6dIluctbd+3ahfnz5+Obb75B165d1RgdEZV3/fv3x/Tp07F7925IJBLIZDKcO3cOX375JYYMGVJkf+ZeRERE2k+hQk9aWprcSJ48r1+/hrGxcZH9v/vuO7nXenp6qFixIoYOHYqZM2cqEhoRUbmVN59Znh9++AF3797F9evXWeghokItXboUgYGBcHNzg1QqRe3atSGVSjFw4EDMmTOnyP7MvYiIiLSfQrdX79SpExo0aICvv/4alpaWiIiIgIeHB/r37w+ZTFbknR80HW/xSUTKkJKSgjVr1mDSpEkwNzcH8Pb2yZmZmUXeKplIETyPaa4nT54gMjISqamp8PPzQ7Vq1dQdkkrwM0tERJpKlecwhQo9N27cQJs2bVC/fn2cPHkS3bp1w82bN/H69WucO3cOVapUUWas5Q6TDSIqK927d8ehQ4ewZs0ajB07Vt3hkJbieYw0DT+zRESkqVR5DlPo0i1vb2/cvXsXa9asgaWlJVJTU9GrVy8EBgbC2dm5wD69evUq9vr37dunSHhERBopOzsbhoaGkEgkaN26tbrDIaJypHfv3mjUqBGmT58u175ixQpcvnwZu3fvzteHuRcREZFuUajQAwDW1tZyd48pzvJERPRhRkZG2LNnDx49egRPT0+x/fvvvwcAjB07tljzoBGR9gkNDcWCBQvytXfs2BGrVq0qsA9zLyIiIt2icKEnMzMTERERSEhIgEwmk3uvW7du+ZbfsmWLopskItIJ7xZ5nj9/jjlz5iAtLQ0eHh7o2bOn+gIjIrVJTU2FkZFRvnZDQ0OkpKQU2Ie5FxERkW5RqNDzzz//YMiQIXj58mW+9yQSCaRSabHW8+LFC0RFRQEAatSogYoVKyoSFhGR1rGzs8N3332Hv//+Gz169BDbk5OT+W09kQ7x8fHBH3/8gXnz5sm179y5E7Vr1y72eph7ERERaS+FCj0TJkxAnz59MG/ePDg6Opa4f1paGiZMmIBt27aJo4H09fUxZMgQ/PjjjwXeup2ISBcZGBhg9OjRGD16tNgmlUrRrFkzeHp6Yv369XBzc1NjhESkCnPnzkWvXr3w4MEDfPzxxwCAEydO4Pfffy9wfp73MfciIiLSfnqKdH7+/DmCgoJKVeQBgKCgIJw+fRp//fUXkpKSkJSUhIMHD+L06dOYOnWqIqEREWm9S5cuISoqCv/99x8sLCzUHQ4RqUDXrl1x4MAB3L9/H+PGjcPUqVPx9OlT/Pvvv3Kj/T6EuRcREZH2U+j26iNGjECzZs0wcuTIUvW3t7fHnj170KpVK7n2U6dOoW/fvnjx4kVpQ1MJ3uKTiNTt7t27uHv3Lrp06SK27dq1C+3bt+clXVQknsd0D3MvIiIi9dCY26uvWbMGffr0wZkzZ+Dj4wNDQ0O59ydOnFho//T09AJHAzk4OCA9PV2R0IiIdEL16tVRvXp18XVYWBj69esHBwcH3LlzB7a2tmqMjojKG+ZeRERE2k+hQs/vv/+OY8eOwcTEBCEhIZBIJOJ7EomkyEJPQEAA5s+fj23btsHExAQAkJGRgYULFyIgIECR0IiIdFJaWhpq1KgBf39/FnmItJBUKsV3332HXbt2ISYmBtnZ2XLvv379utD+zL2IiIi0n0KFntmzZ2PhwoWYMWMG9PRKPt3PDz/8gPbt28PV1RW+vr4AgOvXr8PExARHjx5VJDQiIp300Ucf4caNG0hNTRXbEhMTMWDAAMyaNQstWrRQY3REpKiFCxfi559/xtSpUzFnzhzMnj0bjx49woEDB/LdiasgzL2IiIi0n0Jz9FSoUAGXL19GlSpVSh1Aeno6tm/fjjt37gAAatWqhUGDBsHU1LTU61QVXidORJpg1qxZCA4ORp06dRAREVGqwjxpJ57HNE+VKlWwevVqdO7cGZaWlggPDxfbLly4gB07dhS5DuZeREREqqcxc/QMHToUf/zxB2bNmlXqdZiZmcndLpiIiJRr4sSJSEpKQpcuXcQijyAISEhIKPVdE4lIPeLj4+Hj4wMAsLCwQHJyMgCgS5cumDt3brHWwdyLiIhIuyn0ta5UKsWKFSvQsmVLTJgwAUFBQXKPovzyyy/4+++/xddfffUVbGxs0LRpUzx+/FiR0IiI6P9zcnLCunXr0KlTJ7Ft9+7dqFy5MpYtW6bGyIiopFxdXREXFwfg7eieY8eOAQAuX74MY2PjIvsz9yIiItJ+ChV6IiMj4efnBz09Pdy4cQNhYWHiIzw8vMj+S5cuFYcJnz9/HmvWrMGKFStgb2+PKVOmKBIaEREV4s8//0R6enq+iVyJqHzr2bMnTpw4AQCYMGEC5s6di2rVqmHIkCEYMWJEkf2VmXuFhoaia9eucHFxgUQiwYEDB4rss337dvj6+sLMzAzOzs4YMWIEXr16VaLtEhERUeEUmqNHUWZmZrhz5w7c3d0xffp0xMXFYdu2bbh58yZatWqFFy9eqCu0YuF14kSkqQRBwMGDB9G2bVtYWFgAAKKiohAREYHevXtzHh8dwfOY5jt//jzOnz+PatWqoWvXrkUur8zc68iRIzh37hwaNGiAXr16Yf/+/ejRo8cHlz937hxatGiB7777Dl27dkVsbCzGjBmD6tWrY9++fcXaJj+zRESkqTRmjh5FWVhY4NWrV3B3d8exY8fEy71MTEyQkZGhztCIiLSaRCLJ9wfZjBkzcODAAUybNg0rVqxQT2BEVCIBAQElui26MnOvjh07omPHjsVe/vz58/D09MTEiRMBAF5eXvjiiy+wfPnyEm2XiIiIClfiQk+vXr2wdetWWFlZoVevXoUuW9S3M5988glGjRoFPz8/3L17V5w/4ubNm/D09CxpaEREVEoymQy+vr44deoUhg8fLtfO0T1E5UtUVBR+/PFH3L59G8Dbu2ZNmDABNWrUKLKvOnOvgIAAzJo1C4cPH0bHjh2RkJCAPXv2yM0f9r6srCxkZWWJr1NSUso0RiIiIm1Q4uzd2toaEolEfF7Yoyhr165FQEAAXrx4gb1798LOzg4AcPXqVQwYMKCkoRERUSnp6elhwYIFiI2NRa1atcT2xYsXo1u3brh586YaoyOiPHv37oW3tzeuXr0KX19f+Pr64tq1a/D29sbevXuL7K/O3KtZs2bYvn07+vXrByMjIzg5OcHa2hpr1679YJ/g4GC53NLNza1MYyQiItIGpZqjZ9GiRfjyyy9hZmZWFjFpDF4nTkTaLCMjA5UqVUJiYiJ27dqFPn36qDskUjKexzRPlSpVMGjQICxatEiuff78+fjtt9/w4MEDtcQlkUiKnKPn1q1baNu2LaZMmYL27dsjLi4O06ZNQ8OGDfG///2vwD4Fjehxc3PjZ5aIiDSOKvOuUhV69PX1ERcXBwcHB4UDSExMxP/+9z+54ccjRoxAhQoVFF53WWOCTETaLioqCps3b0ZwcLB4CVdYWBicnJzg7Oys5uhIUTyPaR4zMzNERESgatWqcu337t2Dr68v0tPTi1xHWeRexSn0fPbZZ8jMzMTu3bvFtrNnz6J58+Z49uxZsX6n8DNLRESaSpXnsFJNvKCsG3WFhobC09MTq1evRmJiIhITE/Hjjz/Cy8sLoaGhStkGERGVXo0aNbB8+XKxyCOVSvHZZ5+hSpUq+Oeff9QcHZHuadWqFc6cOZOvPa9gUhR15l7p6en55vzS19cHoLzckoiIiBS461bePD2KCAwMRL9+/bB+/XrxRC+VSjFu3DgEBgYiMjJS4W0QEZHyvHz5EtbW1jA2Nkbjxo3VHQ6RzunWrRumT5+Oq1evokmTJgCACxcuYPfu3Vi4cCH+/PNPuWXfp8zcKzU1Fffv3xdfR0dHIzw8HBUqVIC7uztmzpyJ2NhYbNu2DQDQtWtXjB49GuvXrxcv3Zo8eTIaNWoEFxeXUh0PIiIiyq9Ul27p6enJTcr8Ia9fvy70fVNTU4SHh+e7S0RUVBTq1atX7m+xzuHDRKSLBEHAo0eP4OXlJbZNmjQJVatWxeeffw5jY2M1RkclwfOY5inuXfAkEgmkUmm+dmXmXiEhIWjdunW+9qFDh2Lr1q0YNmwYHj16hJCQEPG9H3/8ERs2bEB0dDRsbGzw8ccfY/ny5ahUqVKxtsnPLBERaSpVnsNKPaJn4cKFxbqzVmHq16+P27dv50s2bt++DV9fX4XWTUREZUMikcgVeSIjI7F69WoAQIsWLfj7m6gMyWQyhforM/dq1apVoZdcbd26NV/bhAkTMGHChBJth4iIiEqm1IWe/v37l2oy5oiICPH5xIkTMWnSJNy/f19u+PHatWuxbNmy0oZGREQqVLNmTWzYsCHfH4r3799HlSpVlHKpL5GuO3/+PF69eoUuXbqIbdu2bcP8+fORlpaGHj164McffyxwRB1zLyIiIt2i8rtu6enpQSKRFDnp3oeGHJcnHD5MRFSwpKQkVK5cGVWrVsWBAwc4/0Y5xfOY5ujYsSNatWqF6dOnA3g7kq5+/foYNmwYatWqhZUrV+KLL77AggUL8vVl7kVERKR+5f7SLUXujBAdHV3qvkREpBmuXLmC7OxspKWlwdHRUd3hEGm88PBwfP311+LrnTt3onHjxti0aRMAwM3NDfPnzy+w0MPci4iISLeUqtCjyPXhHh4epe5bmNDQUKxcuRJXr15FXFwc9u/fjx49ehTaJyQkBEFBQbh58ybc3NwwZ84cDBs2rEziIyLSJW3btsWDBw8QFxcnd/vkr776CsOGDUOdOnXUHCGRZklMTJQrmp4+fRodO3YUXzds2BBPnjwpsG9Z5V5ERERUPpV6jh5lunXrFmJiYpCdnS3XXtBtQT8kLS0Nvr6+GDFiBHr16lXk8tHR0ejcuTPGjBmD7du348SJExg1ahScnZ3Rvn37Eu8DERHJc3R0lPvDdO/evfjmm2+wadMmxMbGwtzcXI3REWkWR0dHREdHw83NDdnZ2bh27RoWLlwovv/mzRsYGhoWe33KyL2IiIiofFJroefhw4fo2bMnIiMj5a4dz5u4syTXiXfs2FHum62ibNiwAV5eXli1ahUAoFatWjh79iy+++47FnqIiMqAj48PevfuDR8fH7kiT3h4OHx9fTlpM1EhOnXqhBkzZmD58uU4cOAAzMzM0Lx5c/H9iIgIVKlSpcj1KDP3IiIiovJJT50bnzRpEry8vJCQkAAzMzPcvHkToaGh8Pf3R0hISJlu+/z582jbtq1cW/v27XH+/PkP9snKykJKSorcg4iIiqdGjRrYs2cP5s2bJ7bdvn0bfn5+8Pf3R2ZmphqjIyrfvv76axgYGKBly5bYtGkTNm3aBCMjI/H9zZs3o127dkWuR525FxEREamGWkf0nD9/HidPnoS9vT309PSgp6eHjz76CMHBwZg4cSLCwsLKbNvx8fH5Jgh1dHRESkoKMjIyYGpqmq9PcHCw3DBpIiIquXdH7oSHh8PMzAweHh4wMTER26VSqTi3DxEB9vb2CA0NRXJyMiwsLPL9fOzevRsWFhZFrkeduRcRERGphlpH9EilUlhaWgJ4m8A8e/YMwNtJA6OiotQZWoFmzpyJ5ORk8fGhSQ+JiKh4BgwYgJiYGHz77bdiW3JyMry8vPDVV18hIyNDjdERlT/W1tYFFkErVKggN8LnQzQt9yIiIqKSU+uIHm9vb1y/fh1eXl5o3LgxVqxYASMjI2zcuBGVK1cu0207OTnh+fPncm3Pnz+HlZVVgaN5AMDY2BjGxsZlGhcRka6xs7ODnZ2d+Hrnzp148uQJ/v77byxbtkyNkRFpH3XmXkRERKQaai30zJkzB2lpaQCARYsWoUuXLmjevDns7Ozwxx9/lOm2AwICcPjwYbm248ePIyAgoEy3S0REhRs9ejQqVaoEfX196Om9HXgqk8nwxRdfoG/fvmjbti0nbiYqJXXmXkRERKQaEiHvdgvlxOvXr2Fra1viJD41NRX3798HAPj5+eHbb79F69atUaFCBbi7u2PmzJmIjY3Ftm3bALy9vbq3tzcCAwMxYsQInDx5EhMnTsTff/9d7LtupaSkwNraGsnJybCysirZjhIRUbEdOnQIXbt2hZWVFZ4+fSpeekKK4XmMgNLnXurAzywREWkqVZ7D1DqipyAVKlQoVb8rV66gdevW4uugoCAAwNChQ7F161bExcUhJiZGfN/Lywt///03pkyZgh9++AGurq74+eefeWt1IqJyqG7dupg0aRJsbW3lijx79+5F27ZtYW1trcboiDRbaXMvIiIiKp/K3YgeTcJvlYiI1CcqKgo1a9aEtbU1Hj58yD9WS4HnMdI0/MwSEZGm0ukRPURERMXx6tUr1KlTB1WqVJEr8iQkJMDBwUGNkRERERERqQ8LPUREpJGaNm2KyMhIJCcni20pKSmoXr06/P398fvvv6NixYpqjJCIiIiISPX01B0AERFRaUkkEtjY2Iivz549i9TUVDx9+lTulu1Xr15FYmKiGiIkIiIiIlItjughIiKt0alTJzx8+BCxsbHirdkFQUCXLl0QHx+PS5cuoWHDhgDe3rI9bxkiIiIiIm3BDJeIiLSKu7s7AgICxNevXr2CtbU1jI2N4ePjI7avXLkSVapUwZo1a9QRJhERERFRmWChh4iItJq9vT3u3LmD+Ph4mJiYiO1nz57Fw4cPkZubK7alpqaib9++WL16NaRSqTrCJSIiIiJSCC/dIiIinfDuXD4AsH37dvz333+oXbu22Hb+/Hns3r0bFy9exMSJE8X2o0ePwsLCAv7+/jA2NlZVyEREREREJcZCDxER6SQrKyt06NBBrq1KlSpYvHgxjIyM5NqnTp2KmzdvYt++fejZsyeAt6N/BEGApaWlymImIiIiIioKL90iIiL6/ypXrozZs2dj2rRpYltubi5q1qwJBwcHfPTRR2L777//DltbW4wZM0ZuHXFxcZDJZCqLmYiIiIjoXRzRQ0REVAgDAwPs2bMHgiBAIpGI7Tdv3oRUKkXFihXFtpycHLi6usLQ0BAxMTFwcHAAAISHh+Pp06eoW7cu3N3dVb4PRERERKQ7OKKHiIioGN4t8gDA999/j5iYGIwbN05se/bsGfT09KCnpydXANq8eTO6du2KtWvXim25ubkYPnw4vv76a2RmZpb9DhARERGRTmChh4iIqJTc3Nzg7Owsvvbw8EBGRgbu3r0rVxhydnZGvXr1UKdOHbEtJiYGW7duxZIlS+TmBJozZw78/Pywbds2sU0qleLBgwdydwgjIiIiIioIL90iIiJSIgMDA7i6usq1zZw5EzNnzpRrMzMzw+LFi5GWlgY9vf/73iUiIgLh4eFIT08X2548eYKqVavC3NwcKSkpcssTEREREb2LhR4iIiI1cHJywuzZs/O1f/fdd/j888/h4+MjtsXFxcHY2Biurq4s8hARERFRoVjoISIiKkeqVKmCKlWqyLUFBAQgPT0diYmJaoqKiIiIiDQFvxYkIiLSAHp6erCzs1N3GERERERUzrHQQ0RERERERESkJVjoISIiIiIiIiLSEiz0EBERERERERFpCRZ6iIiIiIiIiIi0BO+6pQBBEAAAKSkpao6EiIio5PLOX3nnM6LyjrkXERFpKlXmXSz0KODNmzcAADc3NzVHQkREVHpv3ryBtbW1usMgKhJzLyIi0nSqyLskAr/GKzWZTIZnz57B0tISEolE7r2UlBS4ubnhyZMnsLKyUlOEqqWL+wzo5n5zn3VjnwHd3G9d2mdBEPDmzRu4uLhAT49Xc1P5V1juVRq69PNeHDwe+fGY5MdjIo/HIz8eE3l5xyMmJgYSiUQleRdH9ChAT08Prq6uhS5jZWWlcx9uXdxnQDf3m/usO3Rxv3VlnzmShzRJcXKv0tCVn/fi4vHIj8ckPx4TeTwe+fGYyLO2tlbZ8eDXd0REREREREREWoKFHiIiIiIiIiIiLcFCTxkxNjbG/PnzYWxsrO5QVEYX9xnQzf3mPusOXdxvXdxnIl3Fn3d5PB758Zjkx2Mij8cjPx4Teeo4HpyMmYiIiIiIiIhIS3BEDxERERERERGRlmChh4iIiIiIiIhIS7DQQ0RERERERESkJVjoISIiIiIiIiLSEiz0lIG1a9fC09MTJiYmaNy4MS5duqTukJQmODgYDRs2hKWlJRwcHNCjRw9ERUXJLZOZmYnAwEDY2dnBwsICvXv3xvPnz9UUsfItW7YMEokEkydPFtu0dZ9jY2MxePBg2NnZwdTUFD4+Prhy5Yr4viAImDdvHpydnWFqaoq2bdvi3r17aoxYMVKpFHPnzoWXlxdMTU1RpUoVfP3113h3znpt2OfQ0FB07doVLi4ukEgkOHDggNz7xdnH169fY9CgQbCysoKNjQ1GjhyJ1NRUFe5FyRS2zzk5OZg+fTp8fHxgbm4OFxcXDBkyBM+ePZNbh6btMxEVTpvztXcpK3eLiYlB586dYWZmBgcHB0ybNg25ubmq3JUyUdq8TtuOhzJyPm06TyorJ9TkY6KqfDEiIgLNmzeHiYkJ3NzcsGLFirLetVJRVS6ptOMhkFLt3LlTMDIyEjZv3izcvHlTGD16tGBjYyM8f/5c3aEpRfv27YUtW7YIN27cEMLDw4VOnToJ7u7uQmpqqrjMmDFjBDc3N+HEiRPClStXhCZNmghNmzZVY9TKc+nSJcHT01OoW7euMGnSJLFdG/f59evXgoeHhzBs2DDh4sWLwsOHD4WjR48K9+/fF5dZtmyZYG1tLRw4cEC4fv260K1bN8HLy0vIyMhQY+Slt2TJEsHOzk44dOiQEB0dLezevVuwsLAQfvjhB3EZbdjnw4cPC7Nnzxb27dsnABD2798v935x9rFDhw6Cr6+vcOHCBeHMmTNC1apVhQEDBqh4T4qvsH1OSkoS2rZtK/zxxx/CnTt3hPPnzwuNGjUSGjRoILcOTdtnIvowbc/X3qWM3C03N1fw9vYW2rZtK4SFhQmHDx8W7O3thZkzZ6pjl5SmtHmdth0PZeV82nSeVFZOqMnHRBX5YnJysuDo6CgMGjRIuHHjhvD7778Lpqamwk8//aSq3Sw2VeSSyjweLPQoWaNGjYTAwEDxtVQqFVxcXITg4GA1RlV2EhISBADC6dOnBUF4+yE3NDQUdu/eLS5z+/ZtAYBw/vx5dYWpFG/evBGqVasmHD9+XGjZsqWYEGjrPk+fPl346KOPPvi+TCYTnJychJUrV4ptSUlJgrGxsfD777+rIkSl69y5szBixAi5tl69egmDBg0SBEE79/n9E1Vx9vHWrVsCAOHy5cviMkeOHBEkEokQGxursthLq6Bk5X2XLl0SAAiPHz8WBEHz95mI5Olavvau0uRuhw8fFvT09IT4+HhxmfXr1wtWVlZCVlaWandASRTJ67TteCgj59O286QyckJtOiZllS+uW7dOsLW1lfu5mT59ulCjRo0y3iPFlFUuqczjwUu3lCg7OxtXr15F27ZtxTY9PT20bdsW58+fV2NkZSc5ORkAUKFCBQDA1atXkZOTI3cMatasCXd3d40/BoGBgejcubPcvgHau89//vkn/P390adPHzg4OMDPzw+bNm0S34+OjkZ8fLzcfltbW6Nx48Yau99NmzbFiRMncPfuXQDA9evXcfbsWXTs2BGAdu7z+4qzj+fPn4eNjQ38/f3FZdq2bQs9PT1cvHhR5TGXheTkZEgkEtjY2ADQjX0m0hW6mK+9qzS52/nz5+Hj4wNHR0dxmfbt2yMlJQU3b95UYfTKo0hep23HQxk5n7adJ5WRE2rbMXmXsvb//PnzaNGiBYyMjMRl2rdvj6ioKCQmJqpob8pGaXJJZR4PA8V3gfK8fPkSUqlU7pc+ADg6OuLOnTtqiqrsyGQyTJ48Gc2aNYO3tzcAID4+HkZGRuIHOo+joyPi4+PVEKVy7Ny5E9euXcPly5fzvaet+/zw4UOsX78eQUFBmDVrFi5fvoyJEyfCyMgIQ4cOFfetoM+7pu73jBkzkJKSgpo1a0JfXx9SqRRLlizBoEGDAEAr9/l9xdnH+Ph4ODg4yL1vYGCAChUqaMVxyMzMxPTp0zFgwABYWVkB0P59JtIlupavvau0uVt8fHyBxyvvPU2jaF6nbcdDGTmftp0nlZETatsxeZey9j8+Ph5eXl751pH3nq2tbZnEX9ZKm0sq83iw0EOlFhgYiBs3buDs2bPqDqVMPXnyBJMmTcLx48dhYmKi7nBURiaTwd/fH0uXLgUA+Pn54caNG9iwYQOGDh2q5ujKxq5du7B9+3bs2LEDderUQXh4OCZPngwXFxet3WeSl5OTg759+0IQBKxfv17d4RARKZWu5G6F0dW8rjC6mPMVhTkhlVZ5ySV56ZYS2dvbQ19fP9+s/M+fP4eTk5Oaoiob48ePx6FDh3Dq1Cm4urqK7U5OTsjOzkZSUpLc8pp8DK5evYqEhATUr18fBgYGMDAwwOnTp7F69WoYGBjA0dFR6/YZAJydnVG7dm25tlq1aiEmJgYAxH3Tps/7tGnTMGPGDPTv3x8+Pj747LPPMGXKFAQHBwPQzn1+X3H20cnJCQkJCXLv5+bm4vXr1xp9HPJOzI8fP8bx48fFb2AA7d1nIl2kS/nauxTJ3ZycnAo8XnnvaRJl5HXadDwA5eR82naeVEZOqG3H5F3K2n9t+1lSNJdU5vFgoUeJjIyM0KBBA5w4cUJsk8lkOHHiBAICAtQYmfIIgoDx48dj//79OHnyZL6hZQ0aNIChoaHcMYiKikJMTIzGHoM2bdogMjIS4eHh4sPf3x+DBg0Sn2vbPgNAs2bN8t1+9e7du/Dw8AAAeHl5wcnJSW6/U1JScPHiRY3d7/T0dOjpyf9a1NfXh0wmA6Cd+/y+4uxjQEAAkpKScPXqVXGZkydPQiaToXHjxiqPWRnyTsz37t3Dv//+Czs7O7n3tXGfiXSVLuRr71JG7hYQEIDIyEi5P1Ly/oh5v0BQ3ikjr9Om4wEoJ+fTtvOkMnJCbTsm71LW/gcEBCA0NBQ5OTniMsePH0eNGjU07rItZeSSSj0eJZ6+mQq1c+dOwdjYWNi6datw69Yt4fPPPxdsbGzkZuXXZGPHjhWsra2FkJAQIS4uTnykp6eLy4wZM0Zwd3cXTp48KVy5ckUICAgQAgIC1Bi18r17dwZB0M59vnTpkmBgYCAsWbJEuHfvnrB9+3bBzMxM+O2338Rlli1bJtjY2AgHDx4UIiIihO7du2vcrcbfNXToUKFSpUrirTT37dsn2NvbC1999ZW4jDbs85s3b4SwsDAhLCxMACB8++23QlhYmHhXgOLsY4cOHQQ/Pz/h4sWLwtmzZ4Vq1aqV69uFFrbP2dnZQrdu3QRXV1chPDxc7nfbu3c90LR9JqIP0/Z87V3KyN3ybiferl07ITw8XPjnn3+EihUrauztxN9X0rxO246HsnI+bTpPKisn1ORjoop8MSkpSXB0dBQ+++wz4caNG8LOnTsFMzOzcnl7dVXkkso8Hiz0lIEff/xRcHd3F4yMjIRGjRoJFy5cUHdISgOgwMeWLVvEZTIyMoRx48YJtra2gpmZmdCzZ08hLi5OfUGXgfcTAm3d57/++kvw9vYWjI2NhZo1awobN26Ue18mkwlz584VHB0dBWNjY6FNmzZCVFSUmqJVXEpKijBp0iTB3d1dMDExESpXrizMnj1b7he0NuzzqVOnCvw5Hjp0qCAIxdvHV69eCQMGDBAsLCwEKysrYfjw4cKbN2/UsDfFU9g+R0dHf/B326lTp8R1aNo+E1HhtDlfe5eycrdHjx4JHTt2FExNTQV7e3th6tSpQk5Ojor3pmyUJq/TtuOhjJxPm86TysoJNfmYqCpfvH79uvDRRx8JxsbGQqVKlYRly5apahdLRFW5pLKOh0QQBKFkY4CIiIiIiIiIiKg84hw9RERERERERERagoUeIiIiIiIiIiItwUIPEREREREREZGWYKGHiIiIiIiIiEhLsNBDRERERERERKQlWOghIiIiIiIiItISLPQQEREREREREWkJFnqIiIiIiIiIiLQECz1ERERERERERFqChR4iUipBEAAACxYskHtNREREROrB/IxIt0gE/pQTkRKtW7cOBgYGuHfvHvT19dGxY0e0bNlS3WERERER6SzmZ0S6hSN6iEipxo0bh+TkZKxevRpdu3YtVhLRqlUrSCQSSCQShIeHl32Q7xk2bJi4/QMHDqh8+0RERERlqaT5WWlyM+ZTROUHCz1EpFQbNmyAtbU1Jk6ciL/++gtnzpwpVr/Ro0cjLi4O3t7eZRxhfj/88APi4uJUvl0iIiIiZZoyZQp69eqVr700+VlJczPmU0Tlh4G6AyAi7fLFF19AIpFgwYIFWLBgQbGvATczM4OTk1MZR1cwa2trWFtbq2XbRERERMpy6dIldO7cOV97afKzkuZmzKeIyg+O6CGiElm6dKk4LPfdx/fffw8AkEgkAP5vsr+81yXVqlUrTJgwAZMnT4atrS0cHR2xadMmpKWlYfjw4bC0tETVqlVx5MgRpfQjIiIi0lTZ2dkwNDTEf//9h9mzZ0MikaBJkybi+8rKz/bs2QMfHx+YmprCzs4Obdu2RVpamsLxE5FysdBDRCUyYcIExMXFiY/Ro0fDw8MDn376qdK39csvv8De3h6XLl3ChAkTMHbsWPTp0wdNmzbFtWvX0K5dO3z22WdIT09XSj8iIiIiTWRgYIBz584BAMLDwxEXF4d//vlHqduIi4vDgAEDMGLECNy+fRshISHo1asX7+BFVA6x0ENEJWJpaQknJyc4OTlh7dq1OHbsGEJCQuDq6qr0bfn6+mLOnDmoVq0aZs6cCRMTE9jb22P06NGoVq0a5s2bh1evXiEiIkIp/YiIiIg0kZ6eHp49ewY7Ozv4+vrCyckJNjY2St1GXFwccnNz0atXL3h6esLHxwfjxo2DhYWFUrdDRIpjoYeISmXevHn49ddfERISAk9PzzLZRt26dcXn+vr6sLOzg4+Pj9jm6OgIAEhISFBKPyIiIiJNFRYWBl9f3zJbv6+vL9q0aQMfHx/06dMHmzZtQmJiYpltj4hKj4UeIiqx+fPnY9u2bWVa5AEAQ0NDudcSiUSuLe/6cplMppR+RERERJoqPDy8TAs9+vr6OH78OI4cOYLatWvjxx9/RI0aNRAdHV1m2ySi0mGhh4hKZP78+fjll1/KvMhDRERERMUXGRmJevXqlek2JBIJmjVrhoULFyIsLAxGRkbYv39/mW6TiEqOt1cnomJbvHgx1q9fjz///BMmJiaIj48HANja2sLY2FjN0RERERHpLplMhqioKDx79gzm5uZKv9X5xYsXceLECbRr1w4ODg64ePEiXrx4gVq1ail1O0SkOI7oIaJiEQQBK1euxIsXLxAQEABnZ2fxwUmNiYiIiNRr8eLF2Lp1KypVqoTFixcrff1WVlYIDQ1Fp06dUL16dcyZMwerVq1Cx44dlb4tIlIMR/QQUbFIJBIkJyerbHshISH52h49epSv7f1bepa2HxEREZEmGzx4MAYPHlxm669Vq5bSb9lORGWDI3qIqFxYt24dLCwsEBkZqfJtjxkzhrcGJSIiInpHSXMz5lNE5YdE4NfaRKRmsbGxyMjIAAC4u7vDyMhIpdtPSEhASkoKAMDZ2Rnm5uYq3T4RERFReVKa3Iz5FFH5wUIPEREREREREZGW4KVbRERERERERERagoUeIiIiIiIiIiItwUIPEREREREREZGWYKGHiIiIiIiIiEhLsNBDRERERERERKQlWOghIiIiIiIiItISLPQQEREREREREWkJFnqIiIiIiIiIiLQECz1ERERERERERFqChR4iIiIiIiIiIi3BQg8RERERERERkZb4fy14mWAONTr2AAAAAElFTkSuQmCC",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1300x700 with 5 Axes>"
       ]
@@ -687,7 +676,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1300x700 with 5 Axes>"
       ]
@@ -740,7 +729,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1300x700 with 5 Axes>"
       ]
@@ -839,7 +828,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "dev",
+   "display_name": "venv",
    "language": "python",
    "name": "python3"
   },
@@ -853,7 +842,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.11.6"
   },
   "toc": {
    "base_numbering": 1,
@@ -870,7 +859,7 @@
   },
   "vscode": {
    "interpreter": {
-    "hash": "bca2b99bfac80e18288b793d52fa0653ab9b5fe5d22e7b211c44eb982a41c00c"
+    "hash": "9ff3d0c7e37de5f5aa47f4f719e4c84fc6cba7b39c571a05173422444e82fa58"
    }
   }
  },
diff --git a/docs/source/examples/notebooks/models/thermal-models.ipynb b/docs/source/examples/notebooks/models/thermal-models.ipynb
index 8bcc504af0..599a362b4b 100644
--- a/docs/source/examples/notebooks/models/thermal-models.ipynb
+++ b/docs/source/examples/notebooks/models/thermal-models.ipynb
@@ -1,473 +1,507 @@
 {
-  "cells": [
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Thermal models\n",
-        "\n",
-        "There are a number of thermal submodels available in PyBaMM. In this notebook we give details of each of the models, and highlight any relevant parameters. At present PyBaMM includes an isothermal and a lumped thermal model, both of which can be used with any cell geometry, as well as a 1D thermal model which accounts for the through-cell variation in temperature in a pouch cell, and \"1+1D\" and \"2+1D\" pouch cell models which assumed the temperature is uniform through the thickness of the pouch, but accounts for variations in temperature in the remaining dimensions. Here we give the governing equations for each model (except the isothermal model, which just sets the temperature to be equal to to the parameter \"Ambient temperature [K]\"). \n",
-        "\n",
-        "A more comprehensive review of the pouch cell models, including how to properly compute the effective cooling terms, can be found in references [4] and [6] at the end of this notebook."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "zsh:1: no matches found: pybamm[plot,cite]\n",
-            "Note: you may need to restart the kernel to use updated packages.\n"
-          ]
-        }
-      ],
-      "source": [
-        "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
-        "import pybamm"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Lumped model\n",
-        "\n",
-        "The lumped thermal model solves the following ordinary differential equation for the average temperature, given here in dimensional terms,\n",
-        "\n",
-        "$$\n",
-        "\\rho_{eff} \\frac{\\partial T}{\\partial t} = \\bar{Q} - \\frac{hA}{V}(T-T_{\\infty}),\n",
-        "$$\n",
-        "\n",
-        "where $\\rho_{eff}$ is effective volumetric heat capacity, $T$ is the temperature, $t$ is time, $\\bar{Q}$ is the averaged heat source term, $h$ is the heat transfer coefficient, $A$ is the surface area (available for cooling), $V$ is the cell volume, and $T_{\\infty}$ is the ambient temperature. An initial temperature $T_0$ must be prescribed.\n",
-        "\n",
-        "\n",
-        "The effective volumetric heat capacity is computed as \n",
-        "\n",
-        "$$\n",
-        "\\rho_{eff} = \\frac{\\sum_k \\rho_k c_{p,k} L_k}{\\sum_k L_k},\n",
-        "$$\n",
-        "\n",
-        "where $\\rho_k$ is the density, $c_{p,k}$ is the specific heat, and $L_k$ is the thickness of each component. The subscript $k \\in \\{cn, n, s, p, cp\\}$ is used to refer to the components negative current collector, negative electrode, separator, positive electrode, and positive current collector.\n",
-        "\n",
-        "The heat source term accounts for Ohmic heating $Q_{Ohm,k}$ due to resistance in the solid and electrolyte, irreverisble heating due to electrochemical reactions $Q_{rxn,k}$, and reversible heating due to entropic changes in the the electrode $Q_{rev,k}$:\n",
-        "\n",
-        "$$\n",
-        "Q = Q_{Ohm,k}+Q_{rxn,k}+Q_{rev,k},\n",
-        "$$\n",
-        "\n",
-        "with\n",
-        "\n",
-        "$$ \n",
-        "Q_{Ohm,k} = -i_k \\nabla \\phi_k, \\quad Q_{rxn,k} = a_k j_k \\eta_k, \\quad Q_{rev,k} = a_k j_k T_k \\frac{\\partial U}{\\partial T} \\bigg|_{T=T_{\\infty}}.\n",
-        "$$\n",
-        "\n",
-        "Here $i_k$ is the current, $\\phi_k$ the potential, $a_k$ the surface area to volume ratio, $j_k$ the interfacial current density, $\\eta_k$ the overpotential, and $U$ the open-circuit potential. The averaged heat source term $\\bar{Q}$ is computed by taking the volume-average of $Q$.\n"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The relevant parameters to specify the cooling conditions are:  \n",
-        "\n",
-        "\"Total heat transfer coefficient [W.m-2.K-1]\"  \n",
-        "\"Cell cooling surface area [m2]\"  \n",
-        "\"Cell volume [m3]\"\n",
-        "\n",
-        "which correspond directly to the parameters $h$, $A$ and $V$ in the governing equation.\n",
-        "\n",
-        "The lumped thermal option can be selected as follows\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "options = {\"thermal\": \"lumped\"}\n",
-        "model = pybamm.lithium_ion.DFN(options)"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Pouch cell models"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 1D (through-cell) model\n",
-        "\n",
-        "The 1D model solves for $T(x,t)$, capturing variations through the thickness of the cell, but ignoring variations in the other dimensions. The temperature is found as the solution of a partial differential equation, given here in dimensional terms\n",
-        "\n",
-        "$$\\rho_k c_{p,k} \\frac{\\partial T}{\\partial t} = \\lambda_k \\nabla^2 T + Q(x,t) - Q_{cool}(x,t)$$\n",
-        "\n",
-        "with boundary conditions \n",
-        "\n",
-        "$$ -\\lambda_{cn} \\frac{\\partial T}{\\partial x}\\bigg|_{x=0} = h_{cn}(T_{\\infty} - T) \\quad -\\lambda_{cp} \\frac{\\partial T}{\\partial x}\\bigg|_{x=1} = h_{cp}(T-T_{\\infty}),$$\n",
-        "\n",
-        "and initial condition\n",
-        "\n",
-        "$$ T\\big|_{t=0} = T_0.$$\n",
-        "\n",
-        "Here $\\lambda_k$ is the thermal conductivity of component $k$, and the heat transfer coefficients $h_{cn}$ and $h_{cp}$ correspond to heat transfer at the large surface of the pouch on the side of the negative current collector, heat transfer at the large surface of the pouch on the side of the positive current collector, respectively. The heat source term $Q$ is as described in the section on lumped models. The term $Q_cool$ accounts for additional heat losses due to heat transfer at the sides of the pouch, as well as the tabs. This term is computed automatically by PyBaMM based on the cell geometry and heat transfer coefficients on the edges and tabs of the cell.\n",
-        "\n",
-        "The relevant heat transfer parameters are:\n",
-        "\"Negative current collector surface heat transfer coefficient [W.m-2.K-1]\"\n",
-        "\"Positive current collector surface heat transfer coefficient [W.m-2.K-1]\"\n",
-        "\"Negative tab heat transfer coefficient [W.m-2.K-1]\"\n",
-        "\"Positive tab heat transfer coefficient [W.m-2.K-1]\"\n",
-        "\"Edge heat transfer coefficient [W.m-2.K-1]\"\n",
-        "\n",
-        "The 1D model is termed \"x-full\" (since it fully accounts for variation in the x direction) and can be selected as follows\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "options = {\"thermal\": \"x-full\"}\n",
-        "model = pybamm.lithium_ion.DFN(options)"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Higher dimensional pouch cell models\n",
-        "\n",
-        "These pouch cell thermal models ignore any variation in temperature through the thickness of the cell (x direction), and solve for $T(y,z,t)$. It is therefore referred to as an \"x-lumped\" model. The temperature is found as the solution of a partial differential equation, given here in dimensional terms,\n",
-        "\n",
-        "$$\n",
-        "\\rho_{eff} \\frac{\\partial T}{\\partial t} = \\lambda_{eff} \\nabla_\\perp^2T + \\bar{Q} - \\frac{(h_{cn}+h_{cp})A}{V}(T-T_{\\infty}),\n",
-        "$$\n",
-        "\n",
-        "along with boundary conditions\n",
-        "\n",
-        "$$\n",
-        "-\\lambda_{eff} \\nabla_\\perp T \\cdot \\boldsymbol{n} = \\frac{L_{cn}h_{cn} + (L_n+L_s+L_p+L_{cp})h_{edge}}{L_{cn}+L_n+L_s+L_p+L_{cp}}(T-T_\\infty),\n",
-        "$$\n",
-        "\n",
-        "at the negative tab,\n",
-        "\n",
-        "$$\n",
-        "-\\lambda_{eff} \\nabla_\\perp T \\cdot \\boldsymbol{n} = \\frac{(L_{cn}+L_n+L_s+L_p)h_{edge}+L_{cp}h_{cp}}{L_{cn}+L_n+L_s+L_p+L_{cp}}(T-T_\\infty),\n",
-        "$$\n",
-        "\n",
-        "at the positive tab, and\n",
-        "\n",
-        "$$\n",
-        "-\\lambda_{eff} \\nabla_\\perp T \\cdot \\boldsymbol{n} = h_{edge}(T-T_\\infty),\n",
-        "$$\n",
-        "\n",
-        "elsewhere. Again, an initial temperature $T_0$ must be prescribed.\n",
-        "\n",
-        "Here the heat source term is averaged in the x direction so that $\\bar{Q}=\\bar{Q}(y,z)$. The parameter $\\lambda_{eff}$ is the effective thermal conductivity, computed as \n",
-        "\n",
-        "$$\n",
-        "\\lambda_{eff} = \\frac{\\sum_k \\lambda_k L_k}{\\sum_k L_k}.\n",
-        "$$\n",
-        "\n",
-        "The heat transfer coefficients $h_{cn}$, $h_{cp}$ and $h_{egde}$ correspond to heat transfer at the large surface of the pouch on the side of the negative current collector, heat transfer at the large surface of the pouch on the side of the positive current collector, and  heat transfer at the remaining, respectively.\n",
-        "\n",
-        "The relevant heat transfer parameters are:\n",
-        "\"Negative current collector surface heat transfer coefficient [W.m-2.K-1]\"\n",
-        "\"Positive current collector surface heat transfer coefficient [W.m-2.K-1]\"\n",
-        "\"Negative tab heat transfer coefficient [W.m-2.K-1]\"\n",
-        "\"Positive tab heat transfer coefficient [W.m-2.K-1]\"\n",
-        "\"Edge heat transfer coefficient [W.m-2.K-1]\"\n",
-        "\n",
-        "The \"2+1D\" model can be selected as follows"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 5,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "options = {\n",
-        "    \"current collector\": \"potential pair\",\n",
-        "    \"dimensionality\": 2,\n",
-        "    \"thermal\": \"x-lumped\",\n",
-        "}\n",
-        "model = pybamm.lithium_ion.DFN(options)"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Model usage"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Here we compare the \"full\" one-dimensional model with the lumped model for a pouch cell. We first set up our models, passing the relevant options, and then show how to adjust the parameters to so that the lumped and full models give the same behaviour"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 6,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "full_thermal_model = pybamm.lithium_ion.SPMe(\n",
-        "    {\"thermal\": \"x-full\"}, name=\"full thermal model\"\n",
-        ")\n",
-        "lumped_thermal_model = pybamm.lithium_ion.SPMe(\n",
-        "    {\"thermal\": \"lumped\"}, name=\"lumped thermal model\"\n",
-        ")\n",
-        "models = [full_thermal_model, lumped_thermal_model]"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We then pick our parameter set"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 7,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "parameter_values = pybamm.ParameterValues(\"Marquis2019\")"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "For the \"full\" model we use a heat transfer coefficient of $5\\,  \\text{Wm}^{-2}\\text{K}^{-1}$ on the large surfaces of the pouch and zero heat transfer coefficient on the tabs and edges"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 8,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "full_params = parameter_values.copy()\n",
-        "full_params.update(\n",
-        "    {\n",
-        "        \"Negative current collector\"\n",
-        "        + \" surface heat transfer coefficient [W.m-2.K-1]\": 5,\n",
-        "        \"Positive current collector\"\n",
-        "        + \" surface heat transfer coefficient [W.m-2.K-1]\": 5,\n",
-        "        \"Negative tab heat transfer coefficient [W.m-2.K-1]\": 0,\n",
-        "        \"Positive tab heat transfer coefficient [W.m-2.K-1]\": 0,\n",
-        "        \"Edge heat transfer coefficient [W.m-2.K-1]\": 0,\n",
-        "    }\n",
-        ")"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "For the lumped model we set the \"Total heat transfer coefficient [W.m-2.K-1]\"\n",
-        "parameter as well as the \"Cell cooling surface area [m2]\" parameter. Since the \"full\"\n",
-        "model only accounts for cooling from the large surfaces of the pouch, we set the\n",
-        "\"Surface area for cooling\" parameter to the area of the large surfaces of the pouch,\n",
-        "and the total heat transfer coefficient to $5\\,  \\text{Wm}^{-2}\\text{K}^{-1}$ "
-      ]
-    },
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Thermal models\n",
+    "\n",
+    "There are a number of thermal submodels available in PyBaMM. In this notebook we give details of each of the models, and highlight any relevant parameters. At present PyBaMM includes an isothermal and a lumped thermal model, both of which can be used with any cell geometry, as well as a 1D thermal model which accounts for the through-cell variation in temperature in a pouch cell, and \"1+1D\" and \"2+1D\" pouch cell models which assumed the temperature is uniform through the thickness of the pouch, but accounts for variations in temperature in the remaining dimensions. Here we give the governing equations for each model (except the isothermal model, which just sets the temperature to be equal to to the parameter \"Ambient temperature [K]\"). \n",
+    "\n",
+    "A more comprehensive review of the pouch cell models, including how to properly compute the effective cooling terms, can be found in references [4] and [6] at the end of this notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 9,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "A = parameter_values[\"Electrode width [m]\"] * parameter_values[\"Electrode height [m]\"]\n",
-        "lumped_params = parameter_values.copy()\n",
-        "lumped_params.update(\n",
-        "    {\n",
-        "        \"Total heat transfer coefficient [W.m-2.K-1]\": 5,\n",
-        "        \"Cell cooling surface area [m2]\": 2 * A,\n",
-        "    }\n",
-        ")"
-      ]
-    },
+     "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 is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.2\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"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
+    "import pybamm"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lumped model\n",
+    "\n",
+    "The lumped thermal model solves the following ordinary differential equation for the average temperature, given here in dimensional terms,\n",
+    "\n",
+    "$$\n",
+    "\\rho_{eff} \\frac{\\partial T}{\\partial t} = \\bar{Q} - \\frac{hA}{V}(T-T_{\\infty}),\n",
+    "$$\n",
+    "\n",
+    "where $\\rho_{eff}$ is effective volumetric heat capacity, $T$ is the temperature, $t$ is time, $\\bar{Q}$ is the averaged heat source term, $h$ is the heat transfer coefficient, $A$ is the surface area (available for cooling), $V$ is the cell volume, and $T_{\\infty}$ is the ambient temperature. An initial temperature $T_0$ must be prescribed.\n",
+    "\n",
+    "\n",
+    "The effective volumetric heat capacity is computed as \n",
+    "\n",
+    "$$\n",
+    "\\rho_{eff} = \\frac{\\sum_k \\rho_k c_{p,k} L_k}{\\sum_k L_k},\n",
+    "$$\n",
+    "\n",
+    "where $\\rho_k$ is the density, $c_{p,k}$ is the specific heat, and $L_k$ is the thickness of each component. The subscript $k \\in \\{cn, n, s, p, cp\\}$ is used to refer to the components negative current collector, negative electrode, separator, positive electrode, and positive current collector.\n",
+    "\n",
+    "The heat source term accounts for Ohmic heating $Q_{Ohm,k}$ due to resistance in the solid and electrolyte, irreverisble heating due to electrochemical reactions $Q_{rxn,k}$, and reversible heating due to entropic changes in the the electrode $Q_{rev,k}$:\n",
+    "\n",
+    "$$\n",
+    "Q = Q_{Ohm,k}+Q_{rxn,k}+Q_{rev,k},\n",
+    "$$\n",
+    "\n",
+    "with\n",
+    "\n",
+    "$$ \n",
+    "Q_{Ohm,k} = -i_k \\nabla \\phi_k, \\quad Q_{rxn,k} = a_k j_k \\eta_k, \\quad Q_{rev,k} = a_k j_k T_k \\frac{\\partial U}{\\partial T} \\bigg|_{T=T_{\\infty}}.\n",
+    "$$\n",
+    "\n",
+    "Here $i_k$ is the current, $\\phi_k$ the potential, $a_k$ the surface area to volume ratio, $j_k$ the interfacial current density, $\\eta_k$ the overpotential, and $U$ the open-circuit potential. The averaged heat source term $\\bar{Q}$ is computed by taking the volume-average of $Q$.\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When using the option `{\"cell geometry\": \"arbitrary\"}` the relevant parameters to specify the cooling conditions are:  \n",
+    "\n",
+    "\"Total heat transfer coefficient [W.m-2.K-1]\"  \n",
+    "\"Cell cooling surface area [m2]\"  \n",
+    "\"Cell volume [m3]\"\n",
+    "\n",
+    "which correspond directly to the parameters $h$, $A$ and $V$ in the governing equation.\n",
+    "\n",
+    "When using the option `{\"cell geometry\": \"pouch\"}` the parameter $A$ and $V$ are computed automatically from the pouch dimensions, assuming a single-layer pouch cell, i.e. $A$ is the total surface area of a single-layer pouch cell and $V$ is the volume. The parameter $h$ is still set by the \"Total heat transfer coefficient [W.m-2.K-1]\" parameter.\n",
+    "\n",
+    "The lumped thermal option can be selected as follows\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "options = {\"cell geometry\": \"arbitrary\", \"thermal\": \"lumped\"}\n",
+    "arbitrary_lumped_model = pybamm.lithium_ion.DFN(options)\n",
+    "# OR\n",
+    "options = {\"cell geometry\": \"pouch\", \"thermal\": \"lumped\"}\n",
+    "pouch_lumped_model = pybamm.lithium_ion.DFN(options)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If no cell geometry is specified, the \"arbitrary\" cell geometry is used by default"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Let's run simulations with both options and compare the results. For demonstration purposes we'll increase the current to amplify the thermal effects"
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cell geometry: arbitrary\n"
+     ]
+    }
+   ],
+   "source": [
+    "options = {\"thermal\": \"lumped\"}\n",
+    "model = pybamm.lithium_ion.DFN(options)\n",
+    "print(\"Cell geometry:\", model.options[\"cell geometry\"])"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pouch cell models"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1D (through-cell) model\n",
+    "\n",
+    "The 1D model solves for $T(x,t)$, capturing variations through the thickness of the cell, but ignoring variations in the other dimensions. The temperature is found as the solution of a partial differential equation, given here in dimensional terms\n",
+    "\n",
+    "$$\\rho_k c_{p,k} \\frac{\\partial T}{\\partial t} = \\lambda_k \\nabla^2 T + Q(x,t) - Q_{cool}(x,t)$$\n",
+    "\n",
+    "with boundary conditions \n",
+    "\n",
+    "$$ -\\lambda_{cn} \\frac{\\partial T}{\\partial x}\\bigg|_{x=0} = h_{cn}(T_{\\infty} - T) \\quad -\\lambda_{cp} \\frac{\\partial T}{\\partial x}\\bigg|_{x=1} = h_{cp}(T-T_{\\infty}),$$\n",
+    "\n",
+    "and initial condition\n",
+    "\n",
+    "$$ T\\big|_{t=0} = T_0.$$\n",
+    "\n",
+    "Here $\\lambda_k$ is the thermal conductivity of component $k$, and the heat transfer coefficients $h_{cn}$ and $h_{cp}$ correspond to heat transfer at the large surface of the pouch on the side of the negative current collector, heat transfer at the large surface of the pouch on the side of the positive current collector, respectively. The heat source term $Q$ is as described in the section on lumped models. The term $Q_cool$ accounts for additional heat losses due to heat transfer at the sides of the pouch, as well as the tabs. This term is computed automatically by PyBaMM based on the cell geometry and heat transfer coefficients on the edges and tabs of the cell.\n",
+    "\n",
+    "The relevant heat transfer parameters are:\n",
+    "\"Negative current collector surface heat transfer coefficient [W.m-2.K-1]\"\n",
+    "\"Positive current collector surface heat transfer coefficient [W.m-2.K-1]\"\n",
+    "\"Negative tab heat transfer coefficient [W.m-2.K-1]\"\n",
+    "\"Positive tab heat transfer coefficient [W.m-2.K-1]\"\n",
+    "\"Edge heat transfer coefficient [W.m-2.K-1]\"\n",
+    "\n",
+    "The 1D model is termed \"x-full\" (since it fully accounts for variation in the x direction) and can be selected as follows\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "options = {\"thermal\": \"x-full\"}\n",
+    "model = pybamm.lithium_ion.DFN(options)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Higher dimensional pouch cell models\n",
+    "\n",
+    "These pouch cell thermal models ignore any variation in temperature through the thickness of the cell (x direction), and solve for $T(y,z,t)$. It is therefore referred to as an \"x-lumped\" model. The temperature is found as the solution of a partial differential equation, given here in dimensional terms,\n",
+    "\n",
+    "$$\n",
+    "\\rho_{eff} \\frac{\\partial T}{\\partial t} = \\lambda_{eff} \\nabla_\\perp^2T + \\bar{Q} - \\frac{(h_{cn}+h_{cp})A}{V}(T-T_{\\infty}),\n",
+    "$$\n",
+    "\n",
+    "along with boundary conditions\n",
+    "\n",
+    "$$\n",
+    "-\\lambda_{eff} \\nabla_\\perp T \\cdot \\boldsymbol{n} = \\frac{L_{cn}h_{cn} + (L_n+L_s+L_p+L_{cp})h_{edge}}{L_{cn}+L_n+L_s+L_p+L_{cp}}(T-T_\\infty),\n",
+    "$$\n",
+    "\n",
+    "at the negative tab,\n",
+    "\n",
+    "$$\n",
+    "-\\lambda_{eff} \\nabla_\\perp T \\cdot \\boldsymbol{n} = \\frac{(L_{cn}+L_n+L_s+L_p)h_{edge}+L_{cp}h_{cp}}{L_{cn}+L_n+L_s+L_p+L_{cp}}(T-T_\\infty),\n",
+    "$$\n",
+    "\n",
+    "at the positive tab, and\n",
+    "\n",
+    "$$\n",
+    "-\\lambda_{eff} \\nabla_\\perp T \\cdot \\boldsymbol{n} = h_{edge}(T-T_\\infty),\n",
+    "$$\n",
+    "\n",
+    "elsewhere. Again, an initial temperature $T_0$ must be prescribed.\n",
+    "\n",
+    "Here the heat source term is averaged in the x direction so that $\\bar{Q}=\\bar{Q}(y,z)$. The parameter $\\lambda_{eff}$ is the effective thermal conductivity, computed as \n",
+    "\n",
+    "$$\n",
+    "\\lambda_{eff} = \\frac{\\sum_k \\lambda_k L_k}{\\sum_k L_k}.\n",
+    "$$\n",
+    "\n",
+    "The heat transfer coefficients $h_{cn}$, $h_{cp}$ and $h_{egde}$ correspond to heat transfer at the large surface of the pouch on the side of the negative current collector, heat transfer at the large surface of the pouch on the side of the positive current collector, and  heat transfer at the remaining, respectively.\n",
+    "\n",
+    "The relevant heat transfer parameters are:\n",
+    "\"Negative current collector surface heat transfer coefficient [W.m-2.K-1]\"\n",
+    "\"Positive current collector surface heat transfer coefficient [W.m-2.K-1]\"\n",
+    "\"Negative tab heat transfer coefficient [W.m-2.K-1]\"\n",
+    "\"Positive tab heat transfer coefficient [W.m-2.K-1]\"\n",
+    "\"Edge heat transfer coefficient [W.m-2.K-1]\"\n",
+    "\n",
+    "The \"2+1D\" model can be selected as follows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "options = {\n",
+    "    \"current collector\": \"potential pair\",\n",
+    "    \"dimensionality\": 2,\n",
+    "    \"thermal\": \"x-lumped\",\n",
+    "}\n",
+    "model = pybamm.lithium_ion.DFN(options)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Model usage"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we compare the \"full\" one-dimensional model with the lumped model for a pouch cell. We first set up our models, passing the relevant options, and then show how to adjust the parameters to so that the lumped and full models give the same behaviour"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "full_thermal_model = pybamm.lithium_ion.SPMe(\n",
+    "    {\"thermal\": \"x-full\"}, name=\"full thermal model\"\n",
+    ")\n",
+    "lumped_thermal_model = pybamm.lithium_ion.SPMe(\n",
+    "    {\"thermal\": \"lumped\"}, name=\"lumped thermal model\"\n",
+    ")\n",
+    "models = [full_thermal_model, lumped_thermal_model]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We then pick our parameter set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parameter_values = pybamm.ParameterValues(\"Marquis2019\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the \"full\" model we use a heat transfer coefficient of $5\\,  \\text{Wm}^{-2}\\text{K}^{-1}$ on the large surfaces of the pouch and zero heat transfer coefficient on the tabs and edges"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "full_params = parameter_values.copy()\n",
+    "full_params.update(\n",
+    "    {\n",
+    "        \"Negative current collector\"\n",
+    "        + \" surface heat transfer coefficient [W.m-2.K-1]\": 5,\n",
+    "        \"Positive current collector\"\n",
+    "        + \" surface heat transfer coefficient [W.m-2.K-1]\": 5,\n",
+    "        \"Negative tab heat transfer coefficient [W.m-2.K-1]\": 0,\n",
+    "        \"Positive tab heat transfer coefficient [W.m-2.K-1]\": 0,\n",
+    "        \"Edge heat transfer coefficient [W.m-2.K-1]\": 0,\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the lumped model we set the \"Total heat transfer coefficient [W.m-2.K-1]\"\n",
+    "parameter as well as the \"Cell cooling surface area [m2]\" parameter. Since the \"full\"\n",
+    "model only accounts for cooling from the large surfaces of the pouch, we set the\n",
+    "\"Surface area for cooling\" parameter to the area of the large surfaces of the pouch,\n",
+    "and the total heat transfer coefficient to $5\\,  \\text{Wm}^{-2}\\text{K}^{-1}$ "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = parameter_values[\"Electrode width [m]\"] * parameter_values[\"Electrode height [m]\"]\n",
+    "lumped_params = parameter_values.copy()\n",
+    "lumped_params.update(\n",
+    "    {\n",
+    "        \"Total heat transfer coefficient [W.m-2.K-1]\": 5,\n",
+    "        \"Cell cooling surface area [m2]\": 2 * A,\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's run simulations with both options and compare the results. For demonstration purposes we'll increase the current to amplify the thermal effects"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 10,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "97a1370f6f8745b0a4b2a7bb4df5b477",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "interactive(children=(FloatSlider(value=0.0, description='t', max=1154.7667871396477, step=11.547667871396477)…"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "fb646d540c774a10af2ee25e79251283",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "interactive(children=(FloatSlider(value=0.0, description='t', max=1154.7667871396477, step=11.547667871396477)…"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "text/plain": [
-              "<pybamm.plotting.quick_plot.QuickPlot at 0x14e1456d0>"
-            ]
-          },
-          "execution_count": 10,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "params = [full_params, lumped_params]\n",
-        "# loop over the models and solve\n",
-        "sols = []\n",
-        "for model, param in zip(models, params):\n",
-        "    param[\"Current function [A]\"] = 3 * 0.68\n",
-        "    sim = pybamm.Simulation(model, parameter_values=param)\n",
-        "    sim.solve([0, 3600])\n",
-        "    sols.append(sim.solution)\n",
-        "\n",
-        "\n",
-        "# plot\n",
-        "output_variables = [\n",
-        "    \"Voltage [V]\",\n",
-        "    \"X-averaged cell temperature [K]\",\n",
-        "    \"Cell temperature [K]\",\n",
-        "]\n",
-        "pybamm.dynamic_plot(sols, output_variables)\n",
-        "\n",
-        "# plot the results\n",
-        "pybamm.dynamic_plot(\n",
-        "    sols,\n",
-        "    [\n",
-        "        \"Volume-averaged cell temperature [K]\",\n",
-        "        \"Volume-averaged total heating [W.m-3]\",\n",
-        "        \"Current [A]\",\n",
-        "        \"Voltage [V]\",\n",
-        "    ],\n",
-        ")"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b29f3527b0cb47b888bf748ff800f359",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.0, description='t', max=1154.7660708378553, step=11.547660708378553)…"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## References\n",
-        "\n",
-        "The relevant papers for this notebook are:"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7de290974a0c4649b7edddae4562bf90",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.0, description='t', max=1154.7660708378553, step=11.547660708378553)…"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": 11,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n",
-            "[2] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n",
-            "[3] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n",
-            "[4] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n",
-            "[5] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n",
-            "[6] Robert Timms, Scott G Marquis, Valentin Sulzer, Colin P. Please, and S Jonathan Chapman. Asymptotic Reduction of a Lithium-ion Pouch Cell Model. SIAM Journal on Applied Mathematics, 81(3):765–788, 2021. doi:10.1137/20M1336898.\n",
-            "\n"
-          ]
-        }
-      ],
-      "source": [
-        "pybamm.print_citations()"
+     "data": {
+      "text/plain": [
+       "<pybamm.plotting.quick_plot.QuickPlot at 0x17f501e90>"
       ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
     }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "dev",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.9.16"
-    },
-    "toc": {
-      "base_numbering": 1,
-      "nav_menu": {},
-      "number_sections": true,
-      "sideBar": true,
-      "skip_h1_title": false,
-      "title_cell": "Table of Contents",
-      "title_sidebar": "Contents",
-      "toc_cell": false,
-      "toc_position": {},
-      "toc_section_display": true,
-      "toc_window_display": true
-    },
-    "vscode": {
-      "interpreter": {
-        "hash": "bca2b99bfac80e18288b793d52fa0653ab9b5fe5d22e7b211c44eb982a41c00c"
-      }
+   ],
+   "source": [
+    "params = [full_params, lumped_params]\n",
+    "# loop over the models and solve\n",
+    "sols = []\n",
+    "for model, param in zip(models, params):\n",
+    "    param[\"Current function [A]\"] = 3 * 0.68\n",
+    "    sim = pybamm.Simulation(model, parameter_values=param)\n",
+    "    sim.solve([0, 3600])\n",
+    "    sols.append(sim.solution)\n",
+    "\n",
+    "\n",
+    "# plot\n",
+    "output_variables = [\n",
+    "    \"Voltage [V]\",\n",
+    "    \"X-averaged cell temperature [K]\",\n",
+    "    \"Cell temperature [K]\",\n",
+    "]\n",
+    "pybamm.dynamic_plot(sols, output_variables)\n",
+    "\n",
+    "# plot the results\n",
+    "pybamm.dynamic_plot(\n",
+    "    sols,\n",
+    "    [\n",
+    "        \"Volume-averaged cell temperature [K]\",\n",
+    "        \"Volume-averaged total heating [W.m-3]\",\n",
+    "        \"Current [A]\",\n",
+    "        \"Voltage [V]\",\n",
+    "    ],\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## References\n",
+    "\n",
+    "The relevant papers for this notebook are:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n",
+      "[2] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n",
+      "[3] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n",
+      "[4] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n",
+      "[5] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n",
+      "[6] Robert Timms, Scott G Marquis, Valentin Sulzer, Colin P. Please, and S Jonathan Chapman. Asymptotic Reduction of a Lithium-ion Pouch Cell Model. SIAM Journal on Applied Mathematics, 81(3):765–788, 2021. doi:10.1137/20M1336898.\n",
+      "\n"
+     ]
     }
+   ],
+   "source": [
+    "pybamm.print_citations()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": true
   },
-  "nbformat": 4,
-  "nbformat_minor": 4
+  "vscode": {
+   "interpreter": {
+    "hash": "9ff3d0c7e37de5f5aa47f4f719e4c84fc6cba7b39c571a05173422444e82fa58"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
 }
diff --git a/examples/scripts/thermal_lithium_ion.py b/examples/scripts/thermal_lithium_ion.py
index 68b6cabdf0..c6aa978c99 100644
--- a/examples/scripts/thermal_lithium_ion.py
+++ b/examples/scripts/thermal_lithium_ion.py
@@ -6,11 +6,15 @@
 pybamm.set_logging_level("INFO")
 
 # load models
+# for the full model we use the "x-full" thermal submodel, which means that we solve
+# the thermal model in the x-direction for a single-layer pouch cell
+# for the lumped model we use the "arbitrary" cell geometry, which means that we can
+# specify the surface area for cooling and total heat transfer coefficient
 full_thermal_model = pybamm.lithium_ion.SPMe(
     {"thermal": "x-full"}, name="full thermal model"
 )
 lumped_thermal_model = pybamm.lithium_ion.SPMe(
-    {"thermal": "lumped"}, name="lumped thermal model"
+    {"cell geometry": "arbitrary", "thermal": "lumped"}, name="lumped thermal model"
 )
 models = [full_thermal_model, lumped_thermal_model]
 
@@ -31,27 +35,43 @@
     }
 )
 # for the lumped model we set the "Total heat transfer coefficient [W.m-2.K-1]"
-# parameter as well as the "Cell cooling surface area [m2]" parameter. Since the "full"
-# model only accounts for cooling from the large surfaces of the pouch, we set the
-# "Surface area for cooling" parameter to the area of the large surfaces of the pouch,
-# and the total heat transfer coefficient to 5 W.m-2.K-1
+# parameter as well as the "Cell cooling surface area [m2]" and "Cell volume [m3]
+# parameters. Since the "full" model only accounts for cooling from the large surfaces
+# of the pouch, we set the "Surface area for cooling [m2]" parameter to the area of the
+# large surfaces of the pouch, and the total heat transfer coefficient to 5 W.m-2.K-1
 A = parameter_values["Electrode width [m]"] * parameter_values["Electrode height [m]"]
+contributing_layers = [
+    "Negative current collector",
+    "Negative electrode",
+    "Separator",
+    "Positive electrode",
+    "Positive current collector",
+]
+total_thickness = sum(
+    [parameter_values[layer + " thickness [m]"] for layer in contributing_layers]
+)
+electrode_volume = (
+    total_thickness
+    * parameter_values["Electrode height [m]"]
+    * parameter_values["Electrode width [m]"]
+)
 lumped_params = parameter_values.copy()
 lumped_params.update(
     {
         "Total heat transfer coefficient [W.m-2.K-1]": 5,
         "Cell cooling surface area [m2]": 2 * A,
+        "Cell volume [m3]": electrode_volume,
     }
 )
-params = [full_params, lumped_params]
+
 # loop over the models and solve
+params = [full_params, lumped_params]
 sols = []
 for model, param in zip(models, params):
     sim = pybamm.Simulation(model, parameter_values=param)
     sim.solve([0, 3600])
     sols.append(sim.solution)
 
-
 # plot
 output_variables = [
     "Voltage [V]",
diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py
index 4886251e0a..cbc270653b 100644
--- a/pybamm/models/full_battery_models/base_battery_model.py
+++ b/pybamm/models/full_battery_models/base_battery_model.py
@@ -4,7 +4,6 @@
 
 import pybamm
 from functools import cached_property
-import warnings
 
 from pybamm.expression_tree.operations.serialise import Serialise
 
@@ -683,24 +682,6 @@ def __init__(self, extra_options):
                             f"Possible values are {self.possible_options[option]}"
                         )
 
-        # Issue a warning to let users know that the 'lumped' thermal option (or
-        # equivalently 'x-lumped' with 0D current collectors) now uses the total heat
-        # transfer coefficient, surface area for cooling, and cell volume parameters,
-        # regardless of the 'cell geometry option' chosen.
-        thermal_option = options["thermal"]
-        dimensionality_option = options["dimensionality"]
-        if thermal_option == "lumped" or (
-            thermal_option == "x-lumped" and dimensionality_option == 0
-        ):
-            message = (
-                f"The '{thermal_option}' thermal option with "
-                f"'dimensionality' {dimensionality_option} 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."
-            )
-            warnings.warn(message, pybamm.OptionWarning, stacklevel=2)
         super().__init__(options.items())
 
     @property
diff --git a/pybamm/models/submodels/thermal/base_thermal.py b/pybamm/models/submodels/thermal/base_thermal.py
index b2a79c52d5..808cdefc67 100644
--- a/pybamm/models/submodels/thermal/base_thermal.py
+++ b/pybamm/models/submodels/thermal/base_thermal.py
@@ -179,32 +179,74 @@ def _get_standard_coupled_variables(self, variables):
         Q = Q_ohm + Q_rxn + Q_rev
 
         # Compute the X-average over the entire cell, including current collectors
+        # Note: this can still be a function of y and z for higher-dimensional pouch
+        # cell models
         Q_ohm_av = self._x_average(Q_ohm, Q_ohm_s_cn, Q_ohm_s_cp)
         Q_rxn_av = self._x_average(Q_rxn, 0, 0)
         Q_rev_av = self._x_average(Q_rev, 0, 0)
         Q_av = self._x_average(Q, Q_ohm_s_cn, Q_ohm_s_cp)
 
-        # Compute volume-averaged heat source terms
-        Q_ohm_vol_av = self._yz_average(Q_ohm_av)
-        Q_rxn_vol_av = self._yz_average(Q_rxn_av)
-        Q_rev_vol_av = self._yz_average(Q_rev_av)
-        Q_vol_av = self._yz_average(Q_av)
+        # Compute the integrated heat source per unit simulated electrode-pair area
+        # in W.m-2. Note: this can still be a function of y and z for
+        # higher-dimensional pouch cell models
+        Q_ohm_Wm2 = Q_ohm_av * param.L
+        Q_rxn_Wm2 = Q_rxn_av * param.L
+        Q_rev_Wm2 = Q_rev_av * param.L
+        Q_Wm2 = Q_av * param.L
+        # Now average over the electrode height and width
+        Q_ohm_Wm2_av = self._yz_average(Q_ohm_Wm2)
+        Q_rxn_Wm2_av = self._yz_average(Q_rxn_Wm2)
+        Q_rev_Wm2_av = self._yz_average(Q_rev_Wm2)
+        Q_Wm2_av = self._yz_average(Q_Wm2)
+
+        # Compute total heat source terms (in W) over the *entire cell volume*, not
+        # the product of electrode height * electrode width * electrode stack thickness
+        # Note: we multiply by the number of electrode pairs, since the Q_xx_Wm2_av
+        # variables are per electrode pair
+        n_elec = param.n_electrodes_parallel
+        A = param.L_y * param.L_z  # *modelled* electrode area
+        Q_ohm_W = Q_ohm_Wm2_av * n_elec * A
+        Q_rxn_W = Q_rxn_Wm2_av * n_elec * A
+        Q_rev_W = Q_rev_Wm2_av * n_elec * A
+        Q_W = Q_Wm2_av * n_elec * A
+
+        # Compute volume-averaged heat source terms over the *entire cell volume*, not
+        # the product of electrode height * electrode width * electrode stack thickness
+        V = param.V_cell  # *actual* cell volume
+        Q_ohm_vol_av = Q_ohm_W / V
+        Q_rxn_vol_av = Q_rxn_W / V
+        Q_rev_vol_av = Q_rev_W / V
+        Q_vol_av = Q_W / V
 
         variables.update(
             {
+                # Ohmic
                 "Ohmic heating [W.m-3]": Q_ohm,
                 "X-averaged Ohmic heating [W.m-3]": Q_ohm_av,
                 "Volume-averaged Ohmic heating [W.m-3]": Q_ohm_vol_av,
+                "Ohmic heating per unit electrode-pair area [W.m-2]": Q_ohm_Wm2,
+                "Ohmic heating [W]": Q_ohm_W,
+                # Irreversible
                 "Irreversible electrochemical heating [W.m-3]": Q_rxn,
                 "X-averaged irreversible electrochemical heating [W.m-3]": Q_rxn_av,
                 "Volume-averaged irreversible electrochemical heating "
                 + "[W.m-3]": Q_rxn_vol_av,
+                "Irreversible electrochemical heating per unit "
+                + "electrode-pair area [W.m-2]": Q_rxn_Wm2,
+                "Irreversible electrochemical heating [W]": Q_rxn_W,
+                # Reversible
                 "Reversible heating [W.m-3]": Q_rev,
                 "X-averaged reversible heating [W.m-3]": Q_rev_av,
                 "Volume-averaged reversible heating [W.m-3]": Q_rev_vol_av,
+                "Reversible heating per unit electrode-pair area " "[W.m-2]": Q_rev_Wm2,
+                "Reversible heating [W]": Q_rev_W,
+                # Total
                 "Total heating [W.m-3]": Q,
                 "X-averaged total heating [W.m-3]": Q_av,
                 "Volume-averaged total heating [W.m-3]": Q_vol_av,
+                "Total heating per unit electrode-pair area [W.m-2]": Q_Wm2,
+                "Total heating [W]": Q_W,
+                # Current collector
                 "Negative current collector Ohmic heating [W.m-3]": Q_ohm_s_cn,
                 "Positive current collector Ohmic heating [W.m-3]": Q_ohm_s_cp,
             }
diff --git a/pybamm/parameters/geometric_parameters.py b/pybamm/parameters/geometric_parameters.py
index 8ef6add863..ecc52e30f1 100644
--- a/pybamm/parameters/geometric_parameters.py
+++ b/pybamm/parameters/geometric_parameters.py
@@ -42,8 +42,19 @@ def _set_parameters(self):
         self.r_inner = pybamm.Parameter("Inner cell radius [m]")
         self.r_outer = pybamm.Parameter("Outer cell radius [m]")
         self.A_cc = self.L_y * self.L_z  # Current collector cross sectional area
-        self.A_cooling = pybamm.Parameter("Cell cooling surface area [m2]")
-        self.V_cell = pybamm.Parameter("Cell volume [m3]")
+
+        # Cell surface area and volume (for thermal models only)
+        cell_geometry = self.options.get("cell geometry", None)
+        if cell_geometry == "pouch":
+            # assuming a single-layer pouch cell for now, see
+            # https://github.com/pybamm-team/PyBaMM/issues/1777
+            self.A_cooling = 2 * (
+                self.L_y * self.L_z + self.L_z * self.L + self.L_y * self.L
+            )
+            self.V_cell = self.L_y * self.L_z * self.L
+        else:
+            self.A_cooling = pybamm.Parameter("Cell cooling surface area [m2]")
+            self.V_cell = pybamm.Parameter("Cell volume [m3]")
 
 
 class DomainGeometricParameters(BaseParameters):

From f22c2bcd38c52ca597090f400156213d1809d057 Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Wed, 17 Jan 2024 11:01:49 +0100
Subject: [PATCH 108/109] Improve the release workflow (#3737)

* Try fixing the release workflow

* Turn off safety

* Fix CHANGELOG

* Add OS

* Use regex for better matches

* Update instructions, add safety checks

* checkout to the version branch for the final release
---
 .github/release_workflow.md          |  8 +++---
 .github/workflows/publish_pypi.yml   |  2 +-
 .github/workflows/update_version.yml | 42 +++++++++++++++++++++++-----
 CHANGELOG.md                         | 11 ++------
 scripts/update_version.py            | 15 ++++++++--
 5 files changed, 55 insertions(+), 23 deletions(-)

diff --git a/.github/release_workflow.md b/.github/release_workflow.md
index 690f7fa407..89a22e7d38 100644
--- a/.github/release_workflow.md
+++ b/.github/release_workflow.md
@@ -21,9 +21,9 @@ This file contains the workflow required to make a `PyBaMM` release on GitHub, P
 
 ## rcX releases (manual)
 
-If a new release candidate is required after the release of `rc0` -
+If a new release candidate is required after the release of `rc{X-1}` -
 
-1. Fix a bug in `vYY.MM` (no new features should be added to `vYY.MM` once `rc0` is released) and `develop` individually.
+1. Cherry-pick the bug fix (no new features should be added to `vYY.MM` once `rc{X-1}` is released) commit to `vYY.MM` branch once the fix is merged into `develop`. The CHANGELOG entry for such fixes should go under the `rc{X-1}` heading in `CHANGELOG.md`
 
 2. Run `update_version.yml` manually while using `append_to_tag` to specify the release candidate version number (`rc1`, `rc2`, ...).
 
@@ -36,7 +36,7 @@ If a new release candidate is required after the release of `rc0` -
    - `vcpkg.json`
    - `CHANGELOG.md`
 
-      These changes will be automatically pushed to the existing `vYY.MM` branch and a PR from `vvYY.MM` to `develop` will be created (to sync the branches).
+      These changes will be automatically pushed to the existing `vYY.MM` branch and a PR will be created to update version strings in `develop`.
 
 4. Create a new GitHub _pre-release_ with the same tag (`vYY.MMrcX`) from the `vYY.MM` branch and a description copied from `CHANGELOG.md`.
 
@@ -57,7 +57,7 @@ Once satisfied with the release candidates -
    - `vcpkg.json`
    - `CHANGELOG.md`
 
-      These changes will be automatically pushed to the existing `vYY.MM` branch and a PR from `vvYY.MM` to `develop` will be created (to sync the branches).
+      These changes will be automatically pushed to the existing `vYY.MM` branch and a PR will be created to update version strings in `develop`.
 
 3. Next, a PR from `vYY.MM` to `main` will be generated that should be merged once all the tests pass.
 
diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 8a8126b0e4..ce930733db 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -213,7 +213,7 @@ jobs:
   open_failure_issue:
     needs: [build_windows_wheels, build_macos_and_linux_wheels, build_sdist]
     name: Open an issue if build fails
-    if: ${{ always() && contains(needs.*.result, 'failure') }}
+    if: ${{ always() && contains(needs.*.result, 'failure') && github.repository_owner == 'pybamm-team'}}
     runs-on: ubuntu-latest
     steps:
     - uses: actions/checkout@v4
diff --git a/.github/workflows/update_version.yml b/.github/workflows/update_version.yml
index a6c35c0333..f04b033272 100644
--- a/.github/workflows/update_version.yml
+++ b/.github/workflows/update_version.yml
@@ -29,11 +29,13 @@ jobs:
           echo "VERSION=$(date +'v%y.%-m')${{ github.event.inputs.append_to_tag }}" >> $GITHUB_ENV
           echo "NON_RC_VERSION=$(date +'v%y.%-m')" >> $GITHUB_ENV
 
+      # the schedule workflow is for rc0 release
       - uses: actions/checkout@v4
         if: github.event_name == 'schedule'
         with:
           ref: 'develop'
 
+      # the dispatch workflow is for rcX and final releases
       - uses: actions/checkout@v4
         if: github.event_name == 'workflow_dispatch'
         with:
@@ -49,29 +51,55 @@ jobs:
           pip install wheel
           pip install --editable ".[all]"
 
+      # update all the version strings and add CHANGELOG headings
       - name: Update version
         run: python scripts/update_version.py
 
+      # create a new version branch for rc0 release and commit
       - uses: EndBug/add-and-commit@v9
         if: github.event_name == 'schedule'
         with:
           message: 'Bump to ${{ env.VERSION }}'
           new_branch: '${{ env.NON_RC_VERSION }}'
 
+      # use the already created release branch for rcX + final releases
+      # and commit
       - uses: EndBug/add-and-commit@v9
         if: github.event_name == 'workflow_dispatch'
         with:
           message: 'Bump to ${{ env.VERSION }}'
 
-      - name: Make a PR from ${{ env.NON_RC_VERSION }} to develop
-        uses: repo-sync/pull-request@v2
+      # checkout to develop for updating versions in the same
+      - uses: actions/checkout@v4
         with:
-          source_branch: '${{ env.NON_RC_VERSION }}'
-          destination_branch: "develop"
-          pr_title: "Sync ${{ env.NON_RC_VERSION }} and develop"
-          pr_body: "**Merge as soon as possible to avoid potential conflicts.**"
-          github_token: ${{ secrets.GITHUB_TOKEN }}
+          ref: 'develop'
+
+      # update all the version strings
+      - name: Update version
+        if: github.event_name == 'workflow_dispatch'
+        run: python scripts/update_version.py
+
+      # create a pull request updating versions in develop
+      - name: Create Pull Request
+        id: version_pr
+        uses: peter-evans/create-pull-request@v3
+        with:
+          delete-branch: true
+          branch-suffix: short-commit-hash
+          base: develop
+          commit-message: Update version to ${{ env.VERSION }}
+          title: Bump to ${{ env.VERSION }}
+          body: |
+            - [x] Update to ${{ env.VERSION }}
+            - [ ] Check the [release workflow](https://github.com/pybamm-team/PyBaMM/blob/develop/.github/release_workflow.md)
+
+      # checkout to the version branch for the final release
+      - uses: actions/checkout@v4
+        if: github.event_name == 'workflow_dispatch' && !startsWith(github.event.inputs.append_to_tag, 'rc')
+        with:
+          ref: '${{ env.NON_RC_VERSION }}'
 
+      # for final releases, create a PR from version branch to main
       - name: Make a PR from ${{ env.NON_RC_VERSION }} to main
         id: release_pr
         if: github.event_name == 'workflow_dispatch' && !startsWith(github.event.inputs.append_to_tag, 'rc')
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 0692d152ca..20559a11d4 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,13 +1,5 @@
 # [Unreleased](https://github.com/pybamm-team/PyBaMM/)
 
-## Bug fixes
-
-- Fixed a bug where if the first step(s) in a cycle are skipped then the cycle solution started from the model's initial conditions instead of from the last state of the previous cycle ([#3708](https://github.com/pybamm-team/PyBaMM/pull/3708))
-- Fixed a bug where the lumped thermal model conflates cell volume with electrode volume  ([#3707](https://github.com/pybamm-team/PyBaMM/pull/3707))
-
-## Breaking changes
-- The parameters `GeometricParameters.A_cooling` and `GeometricParameters.V_cell` are now automatically computed from the electrode heights, widths and thicknesses if the "cell geometry" option is "pouch" and from the parameters "Cell cooling surface area [m2]" and "Cell volume [m3]", respectively, otherwise. When using the lumped thermal model we recommend using the "arbitrary" cell geometry and specifying the parameters "Cell cooling surface area [m2]", "Cell volume [m3]" and "Total heat transfer coefficient [W.m-2.K-1]" directly. ([#3707](https://github.com/pybamm-team/PyBaMM/pull/3707))
-
 # [v24.1rc0](https://github.com/pybamm-team/PyBaMM/tree/v24.1rc0) - 2024-01-31
 
 ## Features
@@ -26,6 +18,8 @@
 
 ## Bug fixes
 
+- Fixed a bug where if the first step(s) in a cycle are skipped then the cycle solution started from the model's initial conditions instead of from the last state of the previous cycle ([#3708](https://github.com/pybamm-team/PyBaMM/pull/3708))
+- Fixed a bug where the lumped thermal model conflates cell volume with electrode volume  ([#3707](https://github.com/pybamm-team/PyBaMM/pull/3707))
 - Reverted a change to the coupled degradation example notebook that caused it to be unstable for large numbers of cycles ([#3691](https://github.com/pybamm-team/PyBaMM/pull/3691))
 - Fixed a bug where simulations using the CasADi-based solvers would fail randomly with the half-cell model ([#3494](https://github.com/pybamm-team/PyBaMM/pull/3494))
 - Fixed bug that made identical Experiment steps with different end times crash ([#3516](https://github.com/pybamm-team/PyBaMM/pull/3516))
@@ -38,6 +32,7 @@
 
 ## Breaking changes
 
+- The parameters `GeometricParameters.A_cooling` and `GeometricParameters.V_cell` are now automatically computed from the electrode heights, widths and thicknesses if the "cell geometry" option is "pouch" and from the parameters "Cell cooling surface area [m2]" and "Cell volume [m3]", respectively, otherwise. When using the lumped thermal model we recommend using the "arbitrary" cell geometry and specifying the parameters "Cell cooling surface area [m2]", "Cell volume [m3]" and "Total heat transfer coefficient [W.m-2.K-1]" directly. ([#3707](https://github.com/pybamm-team/PyBaMM/pull/3707))
 - Dropped support for the `[jax]` extra, i.e., the Jax solver when running on Python 3.8. The Jax solver is now available on Python 3.9 and above ([#3550](https://github.com/pybamm-team/PyBaMM/pull/3550))
 
 # [v23.9](https://github.com/pybamm-team/PyBaMM/tree/v23.9) - 2023-10-31
diff --git a/scripts/update_version.py b/scripts/update_version.py
index 1d2d64ce41..dfc6b7f32e 100644
--- a/scripts/update_version.py
+++ b/scripts/update_version.py
@@ -17,7 +17,11 @@ def update_version():
     Opens file and updates the version number
     """
     release_version = os.getenv("VERSION")[1:]
-    last_day_of_month = date.today() + relativedelta(day=31)
+    release_date = (
+        date.today()
+        if "rc" in release_version
+        else date.today() + relativedelta(day=31)
+    )
 
     # pybamm/version.py
     with open(os.path.join(pybamm.root_dir(), "pybamm", "version.py"), "r+") as file:
@@ -72,16 +76,21 @@ def update_version():
         file.write(replace_commit_id)
 
     changelog_line1 = "# [Unreleased](https://github.com/pybamm-team/PyBaMM/)\n"
-    changelog_line2 = f"# [v{release_version}](https://github.com/pybamm-team/PyBaMM/tree/v{release_version}) - {last_day_of_month}\n\n"
+    changelog_line2 = f"# [v{release_version}](https://github.com/pybamm-team/PyBaMM/tree/v{release_version}) - {release_date}\n\n"
 
     # CHANGELOG.md
     with open(os.path.join(pybamm.root_dir(), "CHANGELOG.md"), "r+") as file:
         output_list = file.readlines()
         output_list[0] = changelog_line1
+        # add a new heading for rc0 releases
         if "rc0" in release_version:
             output_list.insert(2, changelog_line2)
         else:
-            output_list[2] = changelog_line2
+            # for rcX and final releases, update the already existing rc
+            # release heading
+            for i in range(0, len(output_list)):
+                if re.search("[v]\d\d\.\drc\d", output_list[i]):
+                    output_list[i] = changelog_line2[:-1]
         file.truncate(0)
         file.seek(0)
         file.writelines(output_list)

From 40be4bc088710dcf80d6e26ba3c88f147dc40425 Mon Sep 17 00:00:00 2001
From: "github-actions[bot]"
 <41898282+github-actions[bot]@users.noreply.github.com>
Date: Wed, 17 Jan 2024 11:14:26 +0100
Subject: [PATCH 109/109] Update version to v24.1rc1 (#3741)

Co-authored-by: Saransh-cpp <Saransh-cpp@users.noreply.github.com>
---
 CHANGELOG.md      | 2 +-
 CITATION.cff      | 2 +-
 pybamm/version.py | 2 +-
 pyproject.toml    | 2 +-
 vcpkg.json        | 2 +-
 5 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 20559a11d4..9cfcc2f3fe 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,6 @@
 # [Unreleased](https://github.com/pybamm-team/PyBaMM/)
 
-# [v24.1rc0](https://github.com/pybamm-team/PyBaMM/tree/v24.1rc0) - 2024-01-31
+# [v24.1rc1](https://github.com/pybamm-team/PyBaMM/tree/v24.1rc1) - 2024-01-17
 
 ## Features
 
diff --git a/CITATION.cff b/CITATION.cff
index 494f226a89..1512a57965 100644
--- a/CITATION.cff
+++ b/CITATION.cff
@@ -24,6 +24,6 @@ keywords:
   - "expression tree"
   - "python"
   - "symbolic differentiation"
-version: "24.1rc0"
+version: "24.1rc1"
 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 b2305df5cb..96e7fef1e7 100644
--- a/pybamm/version.py
+++ b/pybamm/version.py
@@ -1 +1 @@
-__version__ = "24.1rc0"
+__version__ = "24.1rc1"
diff --git a/pyproject.toml b/pyproject.toml
index a39a37ecc4..6bd016bb56 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -13,7 +13,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "pybamm"
-version = "24.1rc0"
+version = "24.1rc1"
 license = { file = "LICENSE.txt" }
 description = "Python Battery Mathematical Modelling"
 authors = [{name = "The PyBaMM Team", email = "pybamm@pybamm.org"}]
diff --git a/vcpkg.json b/vcpkg.json
index 911703e7cf..959964dc7c 100644
--- a/vcpkg.json
+++ b/vcpkg.json
@@ -1,6 +1,6 @@
 {
   "name": "pybamm",
-  "version-string": "24.1rc0",
+  "version-string": "24.1rc1",
   "dependencies": [
     "casadi",
     {