Repository: pybamm-team/PyBaMM
Pull request #4635 (2024-12-02 10:17:23)

BPX example
# Description

Add a BPX example in the example notebooks to replace [this repo](https://github.com/pybamm-team/bpx-example) which isn't tested or maintained. Includes changes from #4634 but made a separate PR since that one is a bug fix.

Fixes # (issue)

## Type of change

Please add a line in the relevant section of [CHANGELOG.md](https://github.com/pybamm-team/PyBaMM/blob/develop/CHANGELOG.md) to document the change (include PR #) - note reverse order of PR #s. If necessary, also add to the list of breaking changes.

- [ ] New feature (non-breaking change which adds functionality)
- [ ] Optimization (back-end change that speeds up the code)
- [ ] Bug fix (non-breaking change which fixes an issue)

# Key checklist:

- [ ] No style issues: `$ pre-commit run` (or `$ nox -s pre-commit`) (see [CONTRIBUTING.md](https://github.com/pybamm-team/PyBaMM/blob/develop/CONTRIBUTING.md#installing-and-using-pre-commit) for how to set this up to run automatically when committing locally, in just two lines of code)
- [ ] All tests pass: `$ python -m pytest` (or `$ nox -s tests`)
- [ ] The documentation builds: `$ python -m pytest --doctest-plus src` (or `$ nox -s doctests`)

You can run integration tests, unit tests, and doctests together at once, using `$ nox -s quick`.

## Further checks:

- [ ] Code is commented, particularly in hard-to-understand areas
- [ ] Tests added that prove fix is effective or that feature works


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Attention: Patch coverage is `96.77419%` with `1 line` in your changes missing coverage. Please review.
> Project coverage is 99.21%. Comparing base [(`72c23ea`)](https://app.codecov.io/gh/pybamm-team/PyBaMM/commit/72c23ea9ecfb81057b7d149e4988000b669b4e6b?dropdown=coverage&el=desc&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=pybamm-team) to head [(`1ead6c5`)](https://app.codecov.io/gh/pybamm-team/PyBaMM/commit/1ead6c512a6cff3effaa35f47efb354ec4a3c8c8?dropdown=coverage&el=desc&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=pybamm-team).
> Report is 2 commits behind head on develop.

| [Files with missing lines](https://app.codecov.io/gh/pybamm-team/PyBaMM/pull/4635?dropdown=coverage&src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=pybamm-team) | Patch % | Lines |
|---|---|---|
| [src/pybamm/parameters/parameter\_values.py](https://app.codecov.io/gh/pybamm-team/PyBaMM/pull/4635?src=pr&el=tree&filepath=src%2Fpybamm%2Fparameters%2Fparameter_values.py&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=pybamm-team#diff-c3JjL3B5YmFtbS9wYXJhbWV0ZXJzL3BhcmFtZXRlcl92YWx1ZXMucHk=) | 95.23% | [1 Missing :warning: ](https://app.codecov.io/gh/pybamm-team/PyBaMM/pull/4635?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=pybamm-team) |

<details><summary>Additional details and impacted files</summary>


```diff
@@             Coverage Diff             @@
##           develop    #4635      +/-   ##
===========================================
- Coverage    99.21%   99.21%   -0.01%     
===========================================
  Files          302      302              
  Lines        22858    22876      +18     
===========================================
+ Hits         22679    22696      +17     
- Misses         179      180       +1     
```

</details>

[:umbrella: View full report in Codecov by Sentry](https://app.codecov.io/gh/pybamm-team/PyBaMM/pull/4635?dropdown=coverage&src=pr&el=continue&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=pybamm-team).   
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kratman: I made this a draft until #4634 is merged

Patch:
diff --git a/docs/source/examples/index.rst b/docs/source/examples/index.rst
index 6ddaf5867..16c977038 100644
--- a/docs/source/examples/index.rst
+++ b/docs/source/examples/index.rst
@@ -88,6 +88,7 @@ The notebooks are organised into subfolders, and can be viewed in the galleries
     notebooks/parameterization/parameter-values.ipynb
     notebooks/parameterization/parameterization.ipynb
     notebooks/parameterization/sensitivities_and_data_fitting.ipynb
+    notebooks/parameterization/bpx.ipynb
 
 .. nbgallery::
     :caption: Simulations and Experiments
diff --git a/docs/source/examples/notebooks/parameterization/bpx.ipynb b/docs/source/examples/notebooks/parameterization/bpx.ipynb
new file mode 100644
index 000000000..902d6af33
--- /dev/null
+++ b/docs/source/examples/notebooks/parameterization/bpx.ipynb
@@ -0,0 +1,341 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using Battery Parameter eXchange (BPX) files\n",
+    "\n",
+    "In this notebook we how to import parameters defined using the [BPX standard](https://github.com/FaradayInstitution/BPX)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
+    "import pybamm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First we load an example BPX file from the [pybamm-data](https://github.com/pybamm-team/pybamm-data/releases) repository using the `DataLoader` class. For more information on the `DataLoader` class, see the [pybamm_data](../pybamm_data.ipynb) notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_to_bpx_file = pybamm.DataLoader().get_data(\"nmc_pouch_cell_BPX.json\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can create a `ParameterValues` object from a BPX file using the `create_from_bpx` method. We pass in the path to the BPX file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/robertwtimms/Documents/PyBaMM/env/lib/python3.11/site-packages/bpx/validators.py:32: UserWarning: The maximum voltage computed from the STO limits (4.201761488607647 V) is higher than the upper voltage cut-off (4.2 V) with the absolute tolerance v_tol = 0.001 V\n",
+      "  warn(\n",
+      "/Users/robertwtimms/Documents/PyBaMM/src/pybamm/parameters/parameter_values.py:166: UserWarning: 'Open-circuit voltage at 0% SOC [V]' not found in BPX file. Using 'Lower voltage cut-off [V]'.\n",
+      "  return ParameterValues._create_from_bpx(bpx, target_soc)\n",
+      "/Users/robertwtimms/Documents/PyBaMM/src/pybamm/parameters/parameter_values.py:166: UserWarning: 'Open-circuit voltage at 100% SOC [V]' not found in BPX file. Using 'Upper voltage cut-off [V]'.\n",
+      "  return ParameterValues._create_from_bpx(bpx, target_soc)\n"
+     ]
+    }
+   ],
+   "source": [
+    "parameter_values = pybamm.ParameterValues.create_from_bpx(path_to_bpx_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Alternatively, we can create a `ParameterValues` object from a BPX JSON object as `pybamm.ParameterValues.create_from_bpx_obj(bpx_obj)`\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can take a look at the parameter values we have loaded, and interact with them like any other `ParameterValues` object. For example, we can search for parameters containing the text \"Negative electrode\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initial concentration in negative electrode [mol.m-3]\t22496.096400000002\n",
+      "Maximum concentration in negative electrode [mol.m-3]\t29730.0\n",
+      "Negative electrode Bruggeman coefficient (electrode)\t0\n",
+      "Negative electrode Bruggeman coefficient (electrolyte)\t1.5\n",
+      "Negative electrode OCP [V]\t<function reconstructed_function at 0x17da30b80>\n",
+      "Negative electrode OCP entropic change [V.K-1]\t<function reconstructed_function at 0x17da31580>\n",
+      "Negative electrode active material volume fraction\t0.6860102133333333\n",
+      "Negative electrode conductivity [S.m-1]\t0.222\n",
+      "Negative electrode density [kg.m-3]\t1847.0\n",
+      "Negative electrode diffusivity [m2.s-1]\tfunctools.partial(<function bpx_to_param_dict.<locals>._diffusivity at 0x17da31800>, D_ref=2.728e-14, Ea=30000.0, constant=True)\n",
+      "Negative electrode diffusivity activation energy [J.mol-1]\t30000.0\n",
+      "Negative electrode exchange-current density [A.m-2]\tfunctools.partial(<function bpx_to_param_dict.<locals>._exchange_current_density at 0x17da316c0>, k_ref=5.33563612557725e-07, Ea=55000.0)\n",
+      "Negative electrode maximum stoichiometry\t0.75668\n",
+      "Negative electrode minimum stoichiometry\t0.005504\n",
+      "Negative electrode porosity\t0.25398416831491194\n",
+      "Negative electrode reaction rate constant [mol.m-2.s-1]\t5.199e-06\n",
+      "Negative electrode reaction rate constant activation energy [J.mol-1]\t55000.0\n",
+      "Negative electrode specific heat capacity [J.kg-1.K-1]\t913.0\n",
+      "Negative electrode surface area per unit volume [m-1]\t499522.0\n",
+      "Negative electrode thermal conductivity [W.m-1.K-1]\t2.04\n",
+      "Negative electrode thickness [m]\t5.62e-05\n",
+      "Negative electrode transport efficiency\t0.128\n"
+     ]
+    }
+   ],
+   "source": [
+    "parameter_values.search(\"Negative electrode\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next we load a model and create a simulation, passing in our parameter values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = pybamm.lithium_ion.DFN()\n",
+    "sim = pybamm.Simulation(model, parameter_values=parameter_values)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, we can solve the model and plot the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f8ff849506fd49a4a507ff056c24af9b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.0, description='t', max=1.0, step=0.01), Output()), _dom_classes=('w…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# solve for 1 hour\n",
+    "sim.solve([0, 3600])\n",
+    "_ = sim.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also plot the parameters some of the parameters we loaded. Here we will plot the electrolyte conductivity and diffusivity, as well as the particle diffusivity, exchange-current density and open-circuit potential for the positive and negative electrodes over their respective stoichiometry ranges."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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rFkVyc3OVmZl50YbLM5lMuq1pFS2M66SnOteWp9lNS3afUPfxS/Xu3J3Kzi0wOiIAAAAcjLvRAYoiLS1NFotFISEhF+0PCQnRzp07L3vOAw88oLS0NLVv3142m00FBQV64oknrjoVVW5urnJzcwt/dpROSKAPU1EBAAAA1yM5OVlDhgzRggUL5O3tXeTzR48erZkzZ2rx4sVXPX/UqFF64403ridqmVPOy13/vjlS97SK0Bu/btPiXSf06eJ9+u+GI3r11ga6rWmYTCaT0TEBAADgAJxqxEZxLF68WCNHjtQnn3yiDRs26Mcff9Tvv/+ut95664rnjBo1SgEBAYVbREREKSa+MqaiAgAAAK5PQkKCjh8/rhYtWsjd3V3u7u5asmSJPvroI7m7u8tiufLi1WPGjNHo0aM1f/78f1xwfOjQocrIyCjckpOTS/qtuKyaQeU0/eHW+rxfK0VU9FFK5jk9891G3T9ltXalZBkdDwAAAA7AqUZsBAUFyWw2KzU19aL9qampCg0Nvew5w4cPV9++ffXoo49Kkpo0aaLs7Gw99thjevXVV+XmdmltZ+jQoYqLiyv8OTMz0yGKGxcWDz/NVFQAAABAsXTt2lVbtmy5aN+AAQMUGRmpl156SWaz+bLnvffee3rnnXc0b948tWrV6h+v4+XlJS8vrxLJXBaZTCbFNgxR+7pBmrx0vyYu2qvV+0+px0fL1C+muv4VW08BPh5GxwQAAIBBnGrEhqenp1q2bKn4+PjCfVarVfHx8YqJibnsOTk5OZcULy50Vmw222XP8fLykr+//0WbIwj8a8RGBlNRAQAAAMXi5+enxo0bX7SVK1dOlSpVUuPGjSVJ/fr109ChQwvPeffddzV8+HBNmzZNNWrUUEpKilJSUnTmDItb25u3h1nPdq2rhXGddHOjUFmsNk1fcVBdxy7W9+uTZbVevk8HAAAA1+ZUhQ1JiouL05QpUzRjxgzt2LFDTz75pLKzszVgwABJl3ZCevbsqU8//VQzZ87UgQMHtGDBAg0fPlw9e/a84tNYjirQh6moAAAAAHtLSkrSsWPHCn/+9NNPlZeXp7vvvlthYWGF25gxYwxMWbZEVPTVpL4t9dXANqpVuZzSzuTpxdmbddekldpyOMPoeAAAAChlTjUVlST16dNHJ06c0GuvvaaUlBQ1a9ZMc+fOLVxQPCkp6aIRGsOGDZPJZNKwYcN05MgRVa5cWT179tQ777xj1FsotsC/pqLKOJsvi9UmsxsL5wEAAADXa/HixVf9+eDBg6WWBVfXoW5lzR3SUV+sPKAPF+7RxqR03T5xue5rXU0vdq+viuU8jY4IAACAUmCyXWk+JhTKzMxUQECAMjIyDJ2WKq/AqnrD/pAkJb7WrbDQAQAAABjNUe6ZHRXtU/JSM89p1Jwd+inxqCQpwMdDcd3q6cHoanI3O93kBAAAAGVeUe6ZudtzIp7ubirneX76rHQWEAcAAABQhoX4e2v8fc31n8djFBnqp4yz+Rrxyzb1+GiZlu9JMzoeAAAA7IjChpO5MErjNAuIAwAAAIDa1Kyo355pr7d7N1YFXw/tTj2jh6au0aAv1+tgWrbR8QAAAGAHFDacTKAvC4gDAAAAwN+5m9300A3VtfiFGzWgXQ2Z3UxasD1VN32wVKP/2KkzuQVGRwQAAEAJorDhZC4UNjKYigoAAAAALhLg66ERPRtp7pAO6lA3SHkWqyYt2acbxyzW9+uTZbWyxCQAAIAroLDhZJiKCgAAAACurm6In758pI2m9m+lGpV8dSIrVy/O3qzen6xQwqHTRscDAADAdaKw4WQCff6aiooRGwAAAABwRSaTSV0bhGjecx31So9Ilfdy1+bDGbrr05X618yNOpZx1uiIAAAAKCYKG06mwl8jNtIZsQEAAAAA/8jL3azHOtbWohc6q0+rCJlM0k+JR9VlzBJ9HL9H5/ItRkcEAABAEVHYcDIsHg4AAAAARVfZz0vv3t1Uvwxur1bVK+hsvkVjF+xW17FLNGfLMdlsrL8BAADgLChsOJkApqICAAAAgGJrUjVA3z8Row/va6awAG8dST+rp77ZoPsmr9bWIxlGxwMAAMA1oLDhZJiKCgAAAACuj8lkUq9m4Yp/vpOGdK0rL3c3rTlwSj0nLNeL329SauY5oyMCAADgKihsOBmmogIAAACAkuHr6a7nutVT/POddHtUFdls0vcJh9X5/cUav3C3cvIKjI4IAACAy6Cw4WQuFDZOZTNiAwAAAABKQtUKvvro/ub68am2alEtUGfzLRq/cI+6jFmiHxIOy2pl/Q0AAABHQmHDyYT4e0uSss4VKDuXp4cAAAAAoKS0qFZBPzzZVhMeaK6qFXyUknlOz3+/SbdPXK7V+08aHQ8AAAB/obDhZPy8PQoXED+SftbgNAAAAADgWkwmk25rWkUL4zrp5Vsi5eflrq1HMnXf5NV67Mv1OpCWbXREAACAMo/ChhMKD/SRJB0+nWNwEgAAAABwTd4eZj3RqbYWvdhZD91QTW4maf72VHUbt0Rv/rpd6TlMDwwAAGAUChtOKLzC+cLGkdOM2AAAAAAAewoq76W3ezfR3H91VOf6lVVgtWnaigPq9P5iTVt+QHkFVqMjAgAAlDkUNpxQ4YgNpqICAAAAgFJRL8RPXwxooy8faaP6IX7KOJuvN3/bru7jl2r+thTZbCwwDgAAUFoobDihqhUuTEVFYQMAAAAASlPHepX1+7PtNerOJgoq76kDadl67KsE3T9ltbYeyTA6HgAAQJlAYcMJVWUqKgAAAAAwjLvZTfe3qaZFL3TW4Btry9PdTav3n9JtHy/XkJkblXyK9RABAADsicKGEwoP9JUkHWEqKgAAAAAwjJ+3h17sHqk/n++kXs2qSJJ+TjyqrmOX6K3ftut0NguMAwAA2AOFDSd0YcTGiaxcncu3GJwGAAAAAMq2qhV89eF9zfXr0+3Vrk4l5Vmsmrr8gDq+v0ifLt5Hvw0AAKCEUdhwQoG+HvL1NEuSjjJqAwAAAAAcQpOqAfp6YLRmPNJGkaF+yjpXoHfn7tSNYxbr+/XJslhZYBwAAKAkUNhwQiaTSeGBf62zQWEDAAAAAByGyWRSp3qV9fuzHTT2nihVCfDWsYxzenH2Zt360TIt2nVcNhsFDgAAgOtBYcNJXZiO6jALiAMAAACAwzG7mXRXy6r684XOGnpLpPy93bUzJUsDpq/Tg5+v0ZbDGUZHBAAAcFoUNpxU+F+FjSMUNgAAAADAYXl7mPV4p9pa+u8bNahDTXma3bRy30n1nLBcz363UcmncoyOCAAA4HQobDip8EBfSUxFBQAAAADOINDXU6/e2lDxz3fSHc3DZTJJv2w6qi5jF+vNX7frVHae0REBAACcBoUNJ1WVERsAAAAA4HQiKvrqgz7N9OvT7dWhbpDyLTZNW3FAnd5bpImL9upsnsXoiAAAAA6PwoaTCi9cY4NhywAAAADgbBqHB+irgdH6amAbNQzzV1Zugd6ft0sd31+kr1YdVF6B1eiIAAAADovChpOqGni+sJGSeU75Fm54AQAAAMAZdahbWb89014f9IlSREUfncjK1fCft6nruMX6ccNhWaw2oyMCAAA4HAobTiqovJc8zW6y2qSUjHNGxwEAAAAAFJObm0l3NK+q+LjOeqtXI1X281LyqbOK+88m3fLhUs3fliKbjQIHAADABRQ2nJSbm+lv01GxzgYAAAAAODtPdzf1jamhJS921r9vri9/b3ftTj2jx75K0B2frNTKvWlGRwQAAHAIFDacWPhf01EdSaewAQAAAACuwtfTXU91rqNlL3XR4Btry8fDrMTkdD3w+Ro99PkabUpONzoiAACAoShsOLHCwgYjNgAAAADA5QT4eOjF7pFa8u/OerhtDXmYTVq+N029Jq7Q41+t157ULKMjAgAAGILChhOrWjgVVY7BSQAAAAAA9hLs563Xb2+kP5/vrLtaVJWbSZq3LVXdxy9V3H8SlXyKPiEAAChbKGw4sQtrbDAVFQAAAAC4voiKvhp7b5Tm/aujbm4UKqtN+nHDEXUZu1gjft6qE1m5RkcEAAAoFRQ2nBhrbAAAAABA2VM3xE+T+rbUz4PbqUPdIOVbbJqx6pA6vrdI787dqdPZeUZHBAAAsCsKG06sakVfSdLR9LOyWm0GpwEAAAAAlKaoiEB9NTBa3w6KVrOIQJ3Nt+jTxfvU/t0/NXb+LmXk5BsdEQAAwC4obDixED8vmd1MyrfYdJwhxwAAAABQJrWtHaT/PtVWn/drpUZV/JWdZ9HHf+5V+/f+1IcL9yjzHAUOAADgWpyysDFx4kTVqFFD3t7eio6O1tq1a696fHp6ugYPHqywsDB5eXmpXr16mjNnTimltR93s5vCArwlSYdOZhucBgAAAABgFJPJpNiGIfrtmfaa9FBLRYb6KetcgT5YuFsd3l2kiYv26kxugdExAQAASoTTFTZmzZqluLg4jRgxQhs2bFBUVJS6d++u48ePX/b4vLw8devWTQcPHtTs2bO1a9cuTZkyReHh4aWc3D4iQ/0lSduPZRqcBAAAAABgNJPJpJsbh2rOsx008YEWqhtcXhln8/X+vF3q8O6fmrRkn3LyKHAAAADn5nSFjXHjxmnQoEEaMGCAGjZsqEmTJsnX11fTpk277PHTpk3TqVOn9NNPP6ldu3aqUaOGOnXqpKioqFJObh+NqpwvbGw9QmEDAAAAAHCem5tJtzYN09x/ddSH9zVTraByOp2Tr9F/7FSHdxfp82X7dTbPYnRMAACAYnGqwkZeXp4SEhIUGxtbuM/NzU2xsbFatWrVZc/55ZdfFBMTo8GDByskJESNGzfWyJEjZbFc+QYuNzdXmZmZF22OqnF4gCRp29EMg5MAAAAAAByN2c2kXs3CNf+5jhp3b5SqV/LVyew8vf37DnV8f5Gmrzigc/kUOAAAgHNxqsJGWlqaLBaLQkJCLtofEhKilJSUy56zf/9+zZ49WxaLRXPmzNHw4cM1duxYvf3221e8zqhRoxQQEFC4RURElOj7KEkXRmzsOX6Gm1EAAAAAwGW5m910Z4uqWhjXSe/d1VRVK/joRFau3vh1uzq+t0hTlx9gBAcAAHAaTlXYKA6r1arg4GBNnjxZLVu2VJ8+ffTqq69q0qRJVzxn6NChysjIKNySk5NLMXHRhAV4q2I5T1msNu1KyTI6DgAAAADAgXmY3XRv6wj9+XxnjbyjiaoEeOt4Vq7e+m27Orz3pyYv3adsFhkHAAAOzqkKG0FBQTKbzUpNTb1of2pqqkJDQy97TlhYmOrVqyez2Vy4r0GDBkpJSVFeXt5lz/Hy8pK/v/9Fm6MymUyFoza2HXXcKbMAAAAAAI7D091ND0RX0+IXb9SoO5uoagUfpZ3J08g5O9XhvUX6ZPFenaHAAQAAHJRTFTY8PT3VsmVLxcfHF+6zWq2Kj49XTEzMZc9p166d9u7dK6vVWrhv9+7dCgsLk6enp90zl4ZGVc6vs7GVdTYAAAAAAEXg6e6m+9tU06IXOuv9u5uqRiVfncrO03tzd6n9u3/q4/g9yjyXb3RMAACAizhVYUOS4uLiNGXKFM2YMUM7duzQk08+qezsbA0YMECS1K9fPw0dOrTw+CeffFKnTp3SkCFDtHv3bv3+++8aOXKkBg8ebNRbKHGNw/8asXGEwgYAAAAAoOg8zG66p1WEFsZ10gd9olSrcjml5+Rr7ILdajf6T41bsFvpOZef9QAAAKC0udvjRX/55Zcin9OtWzf5+Pj843F9+vTRiRMn9NprryklJUXNmjXT3LlzCxcUT0pKkpvb/9drIiIiNG/ePD333HNq2rSpwsPDNWTIEL300ktFzuioLozY2JGSpXyLVR5mp6tXAQAAAHbtRwC4Nu5mN93RvKpujwrX71uO6eP4Pdpz/Iw+it+jacsPqH/b6hrYvpYqlnONGRAAAIBzMtlsNltJv+jfCwvXFMJk0p49e1SrVq2SjlIiMjMzFRAQoIyMDIdcb8NqtanpG/N1JrdAc//VQZGhjpcRAAAArq0k7pmN6EeMHj1aQ4cO1ZAhQzR+/PgrHvf9999r+PDhOnjwoOrWrat3331XPXr0uObrOHqfArgSq9WmudtS9FH8Hu1MyZIk+Xqa9dAN1fVo+5oK9vc2OCEAAHAVRblnttuj/SkpKbJarde0+fr62itGmeDmZlLDvxYQ33qEBcQBAADgvEqzH7Fu3Tp99tlnatq06VWPW7lype6//34NHDhQGzduVO/evdW7d29t3br1uq4POAM3N5N6NAnTnGc7aHLflmoc7q+cPIsmL92v9u8t0iv/3aKkkzlGxwQAAGWMXQob/fv3L9Jw8Iceeoinlq5To78KG9tYQBwAAABOqjT7EWfOnNGDDz6oKVOmqEKFClc99sMPP9TNN9+sF198UQ0aNNBbb72lFi1aaMKECcW6NuCM3NxMuqlRqH59ur2mPdxKrapXUF6BVd+uSVLnMYs0ZOZG7UzhQTsAAFA67FLYmD59uvz8/K75+E8//VRBQUH2iFJmNP5rnY1tjNgAAACAkyrNfsTgwYN16623KjY29h+PXbVq1SXHde/eXatWrbriObm5ucrMzLxoA1yByWRSl8gQzX6yrf7zeIw61assq036OfGobh6/TI/OWKeEQ6eNjgkAAFyc3aaiYlh26Wocfr6wsf1YpqzWEl82BQAAACgVpdGPmDlzpjZs2KBRo0Zd0/EpKSkKCQm5aF9ISIhSUlKueM6oUaMUEBBQuEVERFxXZsARtalZUTMeaaPfnmmvW5uEyWSSFu44rrs+Xan7Jq/S0t0nZIdlPQEAAOxX2GjatKmio6M1ZcoUZWVl2esy+EvtyuXk5e6mM7kFOnSK+U0BAADgnOzdj0hOTtaQIUP0zTffyNvbfoseDx06VBkZGYVbcnKy3a4FGK1xeIAmPthCC+M66d5WVeXuZtLq/afUb9pa3T5hhf7YcowH8AAAQImyW2FjyZIlatSokZ5//nmFhYWpf//+WrZsmb0uV+a5m90UGXZhAXHW2QAAAIBzsnc/IiEhQcePH1eLFi3k7u4ud3d3LVmyRB999JHc3d1lsVguOSc0NFSpqakX7UtNTVVoaOgVr+Pl5SV/f/+LNsDV1a5cXu/dHaWl/75RA9rVkLeHm7YcydCT32xQ7AdL9J/1ycotuPTvGAAAQFHZrbDRoUMHTZs2TceOHdPHH3+sgwcPqlOnTqpXr57efffdqw7bRvE0CT/fWdqQxHymAAAAcE727kd07dpVW7ZsUWJiYuHWqlUrPfjgg0pMTJTZbL7knJiYGMXHx1+0b8GCBYqJibmuLICrqhLooxE9G2nFS130TJc68vd21/4T2fr37M3q8O4ifbJ4rzLO5hsdEwAAODGTrRQnvNy7d6+mT5+ur776SikpKbr55pv1yy+/lNbliy0zM1MBAQHKyMhw6Cetft98TIO/3aC6weW1IK6T0XEAAABQhtjzntne/YjOnTurWbNmGj9+vCSpX79+Cg8PL1yDY+XKlerUqZNGjx6tW2+9VTNnztTIkSO1YcMGNW7c+Jqu4Sx9CsAess7l69s1SZq24oBSM3MlSeU8zbqvTTU90r6mwgN9DE4IAAAcQVHume02YuNy6tSpo1deeUXDhg2Tn5+ffv/999K8vMtrV6eS3EzSnuNndDT9rNFxAAAAgBJR2v2IpKQkHTt2rPDntm3b6ttvv9XkyZMVFRWl2bNn66effrrmogZQ1vl5e+jxTrW17N9dNOaeKNUP8VN2nkVTlx9Qx/cWacjMjUypDAAAiqTURmwsXbpU06ZN0w8//CA3Nzfde++9GjhwoG644YbSuPx1caanq3pPXKHE5HS9d1dT3ds6wug4AAAAKCPsdc/szP2Iv3OmPgVgbzabTUt2n9Dkpfu1ct/Jwv3t6lTSYx1rq2PdIJlMJgMTAgAAIxTlntndnkGOHj2qL774Ql988YX27t2rtm3b6qOPPtK9996rcuXK2fPSZVbHepWVmJyuJXtOUNgAAACAU6IfAbg2k8mkzvWD1bl+sLYeydDkpfv1+5ZjWrH3pFbsPanIUD891rGWbmtaRZ7upTrRBAAAcBJ2G7Fxyy23aOHChQoKClK/fv30yCOPqH79+va4lN0509NV6w+e0t2TVinQ10MJw7rJ7MZTLgAAALC/krpndqV+xN85U58CMELyqRxNW3FAs9YlKyfPIkkK9ffWgHY1dF+bagrw8TA4IQAAsDeHGLHh4eGh2bNn67bbbpPZbLbXZfA/mkUEys/LXek5+dp6JENREYFGRwIAAACuGf0IoGyKqOirET0b6V9d6+nrNYf0xcqDSsk8p1F/7NSH8Xt0d8uqerhtDdWqXN7oqAAAwAHYZUzn5s2b9d///le9evW6ps7Itm3bVFBQYI8oZY672U1t61SSJC3dfcLgNAAAAMC1ox8BIMDXQ4NvrKPlL92o9+5qqshQP+XkWfTlqkPqMnaJBkxfq2V7TqiUlgsFAAAOyi6FjebNm+vUqVPXfHxMTIySkpLsEaVM6livsiRp2Z40g5MAAAAA145+BIALvNzNurd1hP4Y0kHfPhqt2AbBMpmkRbtOqO/Uteo+fqm+W5ukc/kWo6MCAAAD2GUqKpvNpuHDh8vX1/eajs/Ly7NHjDKrY93zhY0NSaeVdS5fft7MRQoAAADHRz8CwP8ymUxqWydIbesE6UBatmasPKj/rE/W7tQzGvrjFr03d6ceiK6mvjfUUGiAt9FxAQBAKbFLYaNjx47atWvXNR8fExMjHx8fe0QpkyIq+qpGJV8dPJmjVftO6qZGoUZHAgAAAP4R/QgAV1MzqJxev72RnutWT9+vT9YXKw/q8Omzmrhonz5bsl+3Ng3TgHY11Yy1JgEAcHkmGxNT/qOirMbuKF77eau+XHVID91QTW/3bmJ0HAAAALg4Z7xnLk20D1DyLFabFmxP1bQVB7T2wP9PY9eiWqD6xdTQLU1C5eX+z+v1AAAAx1CUe2a7rLEB412Yjip+x3FZrdSuAAAAAACuxexm0s2NQ/Wfx2P02zPtdVeLqvI0u2lDUrr+NStRbUf9qffn7dSR9LNGRwUAACWMwoaLal83SH5e7jqWcU4JSaeNjgMAAAAAgN00Dg/Q2HujtPzlGxXXrZ5C/b11MjtPExftU4d3/9SgL9dr2Z4TPPgHAICLoLDhorw9zOrWKESS9NumowanAQAAAADA/oL9vPVs17pa/tKNmvRQC7WtXUlWm7Rge6r6Tl2r2HFLNG35AWWczTc6KgAAuA4UNlxYz6gqkqTftxxTgcVqcBoAAAAAAEqHu9lNNzcO07eDbtDCuI7qH1Nd5b3ctT8tW2/+tl03jIzX0B+3aPvRTKOjAgCAYqCw4cLa1wlSoK+H0s7kac3fFlIDAAAAAKCsqBPspzd6NdbqV7rqrd6NVS+kvM7mW/Td2iT1+GiZ7v50pX5OPKLcAovRUQEAwDVyL+0Lbt++XT///LMCAwPVqFEjNWnSRBUqVCjtGGWCh9lNtzQO1Xdrk/Xb5qNqVyfI6EgAAABAsWzYsEFjxoxRenq6mjRpoieeeEI1a9Y0OhYAJ1Ley119b6iuh6Krae2BU/py9SHN25qi9YdOa/2h06rg66G7WlTVfW2qqU5weaPjAgCAqyj1ERu33367fH19lZ2dralTp6pr166qXbt2accoM3o2PT8d1R9bU5RXwHRUAAAAcE59+vTRbbfdpnfeeUf16tXTnXfeqfnz5xsdC4ATMplMiq5VSRMfaKEVL3fRv2LrKtTfW6dz8vX58gOKHbdE905apf9uPKxz+YziAADAEZlsNputNC/Yvn17LV++/KJ9FotFZrO5NGMUSWZmpgICApSRkSF/f3+j4xSJxWpT9Mh4pZ3J1fSHW+vGyGCjIwEAAMAF2fueuU2bNlq7dm3hzydPnlSXLl20adOmEr+WPThznwIoCwosVi3ZfULfrU3WnztTZf3rX0oCfDx0R/Nw3d+mmuqH+hkbEgAAF1eUe+ZSH7HRtWtXTZ8+/aJ9jlzUcHZmN5NuaxomSfp181GD0wAAAADFU7t2bY0dO1YXnssKDAw0NhAAl+JudlPXBiH6vH8rrXy5q57vVk/hgT7KOJuvL1YeVPfxS3XnJyv0/fpknc1jFAcAAEYr9REbt956q7Zu3So3Nze1bt1aUVFRatq0qXr27FmaMYrE2Z+uWn/wlO6etErlvdy17tVY+XhSSAIAAEDJsvc985133qnNmzcrNzdXjRo10qFDh9SjRw/FxcUpPDy8xK9X0py9TwGURRarTcv2nNDMtclauCNVBX8N4/DzdlfvZuG6r02EGlUJMDglAACuoyj3zKVe2LggKytLW7du1datW7Vt2zaNHz/eiBjXxNk7IVarTR3fX6TDp89qfJ9m6t3c8Tt+AAAAcC6ldc+cnZ2tLVu2aPPmzYXb0aNHtXfvXrtdsyQ4e58CKOuOZ53T7ITDmrk2WUmncgr3Nw73190tqur2ZuGqWM7TwIQAADg/hyhs3HDDDWrZsmXh1rhxY6edcsoVOiEfLtyjDxbuVkytSvrusRuMjgMAAAAXU1L3zK7Uj/g7V+hTADj/4ODKfSf13bokzd+WonzL+X9S8TCb1DUyRPe0qqpO9SrL3VzqM38DAOD0inLP7G6vEC1atFBCQoKmT5+u3NxceXl5qUmTJmrbtq3uu+8+RUdH2+vSuIy7W1XV+PjdWrX/pJJO5qhaJV+jIwEAAACXoB8BwJG5uZnUvm6Q2tcN0unsPP2y6ahmJxzWliMZmrstRXO3pSiovJfuaF5Fd7eMYMFxAADsxO5TURUUFGjbtm1KSEhQQkKCFi5cqL1792rAgAGaMmWKTCaTPS9fIlzl6aq+U9do2Z40PdOljp6/qb7RcQAAAOBCSvqe2RX6EX/nKn0KAJe341imfkg4rJ8SjyjtTF7h/qZVA3R3y6q6PaqKAn2ZqgoAgKtxiKmormbBggV68MEHNXr0aD3yyCOlffkic5VOyK+bjuqZ7zYqLMBby1/qIrObc3UGAQAA4LhK457Z2foRf+cqfQoAV5dvsWrxrhOanZCs+B3HCxcc9zS7KbZhsO5pGaEOdYOYqgoAgMtw+MKGJE2ePFlTpkzRunXrjLh8kbhKJyS3wKLokfFKz8nXFwNaq3P9YKMjAQAAwEWU1j2zM/Uj/s5V+hQArt3JM7n6OfH8VFXbj2UW7q9UzlO3NQ1Tr+bhah4R6HQj0AAAsJei3DMb9ojADTfcoL179xp1+TLJy92s3s3CJUnfrz9scBoAAACg6OhHAHAWlcp76ZH2NTVnSAf9/mx7DWhXQ5XKeepkdp5mrDqkOz9Zqc5jFmvc/F3ad+KM0XEBAHAqditsTJw4UWvXrlVubu5l/zwtLU0eHh72ujyu4N5WEZKk+dtTdCo77x+OBgAAAEoX/QgArqhRlQCN6NlIq1/pqukDWqt3syry9TTr0MkcffTnXnUdu0Q9P16uz5ft1/HMc0bHBQDA4dltKqoKFSooMzNT7u7uatiwoVq1aqVWrVqpUaNGOnPmjIYOHaratWtr9uzZ9rh8iXK1YeO3fbxMW49k6tUeDTSoYy2j4wAAAMAFlNQ9syv1I/7O1foUAK5fTl6BFmxP1U8bj2jpnjRZ/lqPw80kta0dpF7Nqqh741D5e1PMBQCUDQ6zxsa+ffuUkJBQuG3YsEHp6emSpMjISM2ZM0c1atQo8utOnDhR77//vlJSUhQVFaWPP/5Ybdq0+cfzZs6cqfvvv1+9evXSTz/9dM3Xc7VOyLdrkvTKf7eoRiVf/fl8Z7mxiDgAAACuU0neM9urH2EkV+tTAChZJ8/kas6WY/op8agSDp0u3O/p7qbYBsHq2bSKOtcPlo+n2cCUAADYl8MUNi7n8OHDys/PV82aNYt1/qxZs9SvXz9NmjRJ0dHRGj9+vL7//nvt2rVLwcFXXgz74MGDat++vWrVqqWKFSuW6cJGdm6BokfG60xugb4a2EYd6lY2OhIAAACcnL3vma+3H2E0V+tTALCf5FM5+jnxiH5KPKq9x/9/7Q1fT7O6RAbr1iZhFDkAAC7JoQsb1ys6OlqtW7fWhAkTJElWq1URERF65pln9PLLL1/2HIvFoo4dO+qRRx7RsmXLlJ6eXqYLG5L02s9b9eWqQ+reKESf9W1ldBwAAAA4OVe8Zy5JtA+AorLZbNp2NFO/bDqq3zcf05H0s4V/dqHIcVvT80UObw+KHAAA51eUe2a7LR5uD3l5eUpISFBsbGzhPjc3N8XGxmrVqlVXPO/NN99UcHCwBg4cWBoxncJDN1SXJC3ccVwpGSxMBgAAAACAIzGZTGocHqBXejTQ8pdu1M+D2+mxjrUUHuijnDyLftt8TE98vUEt3lqgZ77bqLlbj+lcvsXo2AAAlAp3owMURVpamiwWi0JCQi7aHxISop07d172nOXLl2vq1KlKTEy85uvk5uYqNze38OfMzMxi5XVk9UL81KZmRa09cErfrU3Sc93qGR0JAAAAAABchslkUlREoKIiAjX0lkhtOpyhOVuOFY7k+HXTUf266ah8Pc3q2iBEtzYJVad6TFcFAHBdTlXYKKqsrCz17dtXU6ZMUVBQ0DWfN2rUKL3xxht2TOYYHrqhutYeOKWZ65L0dJc68jA71QAeAAAAAADKHJPJpGYRgWr2tyLH75uPas6WlIuKHN4ebupYt7JuahSqrpHBqlDO0+joAACUGKcqbAQFBclsNis1NfWi/ampqQoNDb3k+H379ungwYPq2bNn4T6r1SpJcnd3165du1S7du1Lzhs6dKji4uIKf87MzFRERERJvQ2HcXOjUAWV91RqZq4Wbk/VLU3CjI4EAAAAAACu0d+LHK/0aKDE5HTN2XKssMgxf3uq5m9PldnNpNY1KuimhqHq1jBEERV9jY4OAMB1carChqenp1q2bKn4+Hj17t1b0vlCRXx8vJ5++ulLjo+MjNSWLVsu2jds2DBlZWXpww8/vGKxwsvLS15eXiWe39F4urupT+sITVy0TzNWHaSwAQAAAACAkzKZTGperYKaV6ugV3o00LajmVrwV2Fjx7FMrd5/Sqv3n9Kbv21XwzB/3dQoRDc1DFWDMD+ZTCaj4wMAUCROVdiQpLi4OPXv31+tWrVSmzZtNH78eGVnZ2vAgAGSpH79+ik8PFyjRo2St7e3GjdufNH5gYGBknTJ/rLqoRuqa9KS/Vq9/5S2Hc1QoyoBRkcCAAAAAADX4cLC443DA/Rct3pKPpVzfvTGthStO3hK249lavuxTI1fuEdVK/ioW8PzRY7WNSrInWmqAQBOwOkKG3369NGJEyf02muvKSUlRc2aNdPcuXMLFxRPSkqSmxtfwtcqLMBHtzQO1W+bj+mLFQf1/j1RRkcCAAAAAAAlKKKirwa2r6mB7WvqVHae/tx5XPO3pWjpnhM6fPqspq84qOkrDsrf210d61VWl8hgdapXWZXKu/5sFgAA52Sy2Ww2o0M4uszMTAUEBCgjI0P+/v5GxylxG5JO685PVsrT7KaVQ7soiBsXAAAAFJGr3zNfL9oHgCM6m2fRsj0nNH97quJ3pOp0Tn7hn5lMUtOqgepSP1g3RlZW4yoBcnNjyioAgP0U5Z7Z6UZsoOS1qFZBzSIClZicrm9WJ2lIbF2jIwEAAAAAADvz8TTrpkahuqlRqCxWmxKT07Vo53Et2nVc245malNyujYlp+uDhbsVVN5LnetX1o31g9W+bpACfDyMjg8AKMMYsXENysLTVb9sOqpnv9uooPJeWvHyjfJyNxsdCQAAAE6kLNwzXw/aB4CzSc08p8W7jmvRzhNatueEsvMshX9mdjOpVfUKujEyWJ3rV1b9EBYgBwBcP0ZsoMhuaRyqUH9vpWSe02+bjumullWNjgQAAAAAAAwS4u+tPq2rqU/rasorsGr9wVP686/RHPtOZGvNgVNac+CURv+xU0HlvdS+TiW1r1tZHeoGKcTf2+j4AAAXx4iNa1BWnq76ZPFevTd3lxqE+WvOs+152gIAAADXrKzcMxcX7QPAlSSdzNGiXeeLHKv3n9S5fOtFf143uLza1w1Sh7pBiq5ZSeW8eK4WAPDPinLPTGHjGpSVTkh6Tp7ajv5TOXkWfTGgtTrXDzY6EgAAAJxEWblnLi7aB4Cryi2wKOHQaS3fk6ble9O05UiG/v4vTR5mk5pXq6AOdYLUvm6QmlYNlJlFyAEAl0Fho4SVpU7I279t1+fLD6hNjYr6zxMxRscBAACAkyhL98zFQfsAKCtOZ+dp5b6TWr73hJbtSdPh02cv+nN/b3fdUKtS4RYZ6ic3Ch0AALHGBq7DoI619OWqQ1p78JTWHjilNjUrGh0JAAAAAAA4iQrlPHVr0zDd2jRMNptNh07maNneNC3fc0Ir951U5rkCzd+eqvnbUyVJAT4ealOzom6oVUnRNSuqQZg/IzoAAP+IwgYuEuLvrbtbVdW3a5L0yeK9alOzjdGRAAAAAACAEzKZTKoRVE41gsqp7w3VVWCxavORDK3ef1Kr95/S+oOnlHE2Xwu2p2rBX4UOf2/3vxU6KqlhFQodAIBLUdjAJZ7oWFsz1yZp8a4T2nokQ43DA4yOBAAAAAAAnJy72U0tqlVQi2oV9FRnKd9i1dYjGVpz4JRW7z+pdQdOKfNcgRbuOK6FO45Lkvy83dW6RkVF16yoVjUqqFGVAHl7mI19IwAAw1HYwCWqVfLV7VFV9FPiUX26eJ8mPtjC6EgAAAAAAMDFeJjd1LxaBTWvVkFPdKqtAotV245mavX+k1pz4JTWHTilrHMF+nPncf2583yhw9Pspsbh/mpVo6JaVKugltUrqLKfl8HvBABQ2ihs4LKe7FxHPyUe1Zytx7QrJUv1Q/2MjgQAAABct08//VSffvqpDh48KElq1KiRXnvtNd1yyy1XPGf8+PH69NNPlZSUpKCgIN19990aNWqUvL29Syk1AJQN7mY3RUUEKioiUI93qi2L1abtfxU61h08pQ1Jp5V2Jk8bktK1ISm98LzqlXzVsloFtah+vtBRL8SP6asAwMVR2MBl1Q/10y2NQ/XH1hS9P2+nPu/f2uhIAAAAwHWrWrWqRo8erbp168pms2nGjBnq1auXNm7cqEaNGl1y/LfffquXX35Z06ZNU9u2bbV79249/PDDMplMGjdunAHvAADKDrObSU2qBqhJ1QAN6lhLNptNSadytP7gaSUkndaGQ6e1KzVLh07m6NDJHP248Ygkyc/LXc2qBapFtQpqFhGoplUDVKk8ozoAwJWYbDabzegQji4zM1MBAQHKyMiQv7+/0XFKzb4TZ3TTB0tlsdr0n8dj1KZmRaMjAQAAwEE58z1zxYoV9f7772vgwIGX/NnTTz+tHTt2KD4+vnDf888/rzVr1mj58uXXfA1nbh8AcGQZZ/OVmJyuhEPnCx0bk04rO89yyXFVK/goqmqgoiIC1LRqoBqHB6i8F8/7AoAjKco9M/8FxxXVrlxe97WO0DdrkjT6jx364cm2MpkYygkAAADXYLFY9P333ys7O1sxMTGXPaZt27b6+uuvtXbtWrVp00b79+/XnDlz1Ldv36u+dm5urnJzcwt/zszMLNHsAIDzAnw81KleZXWqV1mSVGCxaldqlhIOnVZiUroSD6dr/4lsHT59VodPn9XvW45JkkwmqW5weTWtGqioqgGKighUZKi/PN3djHw7AIBrRGEDVzWka139uOGINiSla962VN3cONToSAAAAMB12bJli2JiYnTu3DmVL19e//3vf9WwYcPLHvvAAw8oLS1N7du3l81mU0FBgZ544gm98sorV73GqFGj9MYbb9gjPgDgKtzNbmpUJUCNqgSo318168xz+dp6OEOJh9O1OTlDmw+n62jGOe1OPaPdqWc0O+GwpPMLkzcI81PDKgFqWMVfjar4KzLUT76e/PMZADgapqK6BmV92PjY+bv08Z97VatyOc3/V0e5m3l6AQAAABdzpnvmvLw8JSUlKSMjQ7Nnz9bnn3+uJUuWXLa4sXjxYt133316++23FR0drb1792rIkCEaNGiQhg8ffsVrXG7ERkREhFO0DwCUBcezzhUWORIPn//f9Jz8S45zM0k1g8qpYZUANfqr2NEwzJ81OwDADorSp6CwcQ2cqZNmD1nn8tXxvUU6nZOvN25vpP5taxgdCcD/tXfn0VGVh//HP7NPMtkTshISAQmIgBSUgtpSi/Un/qh2tdYK/f5a/Z6KHo8ef1+1oriCW1tasdbyrUurLf3WWu1iRcXyc6/WgrKGfQtZyD5ZZr+/PyaZJCSEJCYzmcn7dc6cmdx55s5zn5vk3jufeZ4HAIBRJp7PmRctWqRJkybpiSee6PXc+eefr89+9rN6+OGHI8ueffZZXXPNNWppaZHZPLAv/cRz+wDAWGAYho7Ut+uTikbtONas7ceataOyWcfd3j7L56c5I706zihIU1l+qkqyXbKYGcIbAIaKOTYwrFKdNt104RTd8dJ2PbyhXF+anqeC9KRYVwsAAAAYFqFQqEfviu7a2tp6hRcWi0VS+EMwAEBiMJlMmpCdrAnZyfrfMwsjy2vcnh5Bx45jzTpQ26qqZo+qmj16Y1dNpKzDatbk3BSV5aVqSn6qyvJSVZafqoJ0J3OWAsAwI9jAgFw5r0R/2hyea+OOF7dr3dI5HJQBAAAQd2677TZdfPHFmjBhgtxut377299q06ZN2rBhgyRp6dKlKioq0urVqyVJS5Ys0Y9//GPNnj07MhTVHXfcoSVLlkQCDgBA4spNdSq3zKmFZbmRZS3egHZVdoQdHYHHnhq3PP6QtneEIN2lOqyakp+qKXmpKstLiYQeDGcFAENHsIEBMZtNeuBrM3XJz97S6zur9fdtVVo8oyDW1QIAAAAGpaamRkuXLlVlZaXS09M1c+ZMbdiwQRdeeKEk6fDhwz16aKxYsUImk0krVqxQRUWFxo0bpyVLluj++++P1SYAAGIsxWHV3NIszS3NiiwLhgwdbWhTeZVbu6vdKq9u0e4qt/Ydb5HbG9BHhxr00aGGHuvJTLZp0riU8C3XpYk5KZqUm6LizCTmNwWAU2COjQFgPNwuP361XD97Y69yUhzaeNPnlZ5si3WVAAAAMApwztw/2gcAxiZfIKQDta0qr3Zrd5U7fF/t1uH6Np3sEzmbxaTSbFck8OgMPyaOcynVyecwABIXc2xgxFz7hcn669ZK7T/eqrv+sl0//uYshqQCAAAAAADog91qVll+eK4Nzepa3u4L6kBtq/Ydb+m4tWpfTYv217bI4w9pT02L9tS0SNt7ri8nxaHS7GSVZLvC9zmuyM/pSYQeAMYOgg0MitNm0UNfm6lvPvGe/rS5QnNKMvWdz5bEuloAAAAAAABxI8lu0RmFaTqjsOc3kkMhQ8ea2iNBR/fg47jbq9qW8O1fJwxrJYWHtuoMPCZku3oEIFkuO19MBZBQCDYwaHNLs3TL/5qq1X/fpbv/sl3TC9M0e0JmrKsFAAAAAAAQ18xmk8ZnJmt8ZrI+P2Vcj+ea2v06XNemg3WtOlTXqoN1bTpU16pDdW2qcXvV0OZXQ1ujthxp7LXeVIdVJTnJmpCVrOLMZI3PTOp4nyQVZSYp2c5HhADiC/+1MCTXfG6iNh9u1Cvbq3Ttc//WX64/TzkpjlhXCwAAAAAAICGlJ9k0Y3y6ZoxP7/Vcqzegw/VtPQKPg7VtOlzfpmNN7XJ7A9pW0axtFc19rjvbZe8RdhB8ABjt+K+EITGZTHr4GzO1u8at/cdbtfy5f+uZ/3OOnDZLrKsGAAAAAAAwprgcVk0rSNO0gt6T7Xr8QR1taNOB2jYdbWjT0Yb2bvftamr3q67Vp7pWnz4+2tTn+juDj6LMJBWkJ6kg3anCjPB9QXqSxqU6ZDEz1BWA6DEZhmHEuhKj3WBmYx9r9lS7ddlj76jVF9TnpozTL6+aQ7gBAAAwBnHO3D/aBwAwWjW1+1VxQtjRPQBp9gROuQ6r2aS8NKcK0p3KPyH0KMwIL8txOWQm/ADQj8GcM9NjA5/K6XmpevK7Z+u7T32oN3cf1w+e/Ui/uGqOHFbCDQAAAAAAgNEuPcmm9CRbr4nMO50YfFQ1e3SssV2VTR5VNrar2u1VIGSoorFdFY3tJ30fu8WsvHRHpMdH531emkN5aU7lpTk1LtUhm8U8UpsKIIHQY2MA+HbVqb23r07/8fQH8vhD+uLUXK399meUZCfcAAAAGCs4Z+4f7QMASFTBkKHjbq+ONbWrstGjyqaO0KOpXcc6fq5xezWQTyBNpvCwV7mp4cAjP93Z8bhnAJLtstP7A0hAgzlnJtgYAC5CBuadvbX6P09/KG8gpGkFafrFdz6jkmxXrKsFAACAKOCcuX+0DwBgLPMHQ6pxe1XZ2K5jTR5VdYQe1c2dN69q3B75gwP7mNJqNmlcqkO5aU7lpYYDj3AI0hV+5KU5lJ5kk8lEAALEC4KNYcZFyMD9c3+drn3u36pr9SnNadWab52lC6bmxbpaAAAAGGGcM/eP9gEAoH+hkKGGNp+qm72qdntU0+xRVVPX4+pmr6qbPapt8So0wE8zHVZzJOQIhyBdPT9y0xyRniEpDisBCDAKEGwMMy5CBqeyqV3XPvdvbT7cKEn6zmcn6OYvlSkj2R7bigEAAGDEcM7cP9oHAIDhEQiGVNvi6+rt4fZ2BB8eVTV3PW5o8w94ncl2SzjsiPT46Aw/unqE5KY5lGxnumJgJBFsDDMuQgbPFwjpvr/t0K/fOyRJyky26f9eNFWXn10sC2MgAgAAJBzOmftH+wAAEF0ef1DH3eEhrqqawvfVncGHu6sHiNsTGPA6U53WruAjtSP46Nbzo3MCdKeNeWeBoSDYGGZchAzdu/tqddeft2t3dYskadI4l7533kR99TNF/JMHAABIIJwz94/2AQBgdGrzBVTTEXJ07/3RGXzUuMP3bb7ggNeZkWzrCD5O6AFyQgBis5hHcMuA+EOwMcy4CPl0/MGQfv3eIa15fXckBc9y2XX52cVaMrNQ0wpSGccQAAAgznHO3D/aBwCA+GUYhlq8gUjIEQlCmnvPAeINhAa83pwUu3I7A5DO0CPdqcL0JBVkOFWQnqQ0J/N/YOwg2BhmXIQMD7fHr99/eERPvXNQFY3tkeWn5bh08Zn5Om9yjmZPyFSSnZ4cAAAA8YZz5v7RPgAAJD7DMNTcHugY6qpbr48eIUh4WCx/cGAfybrsFhVkJKmgW+DRPfgozHAy9wcSBsHGMOMiZHgFgiG9tqNaL26p0Kby4z2SbJvFpFnjMzSrOENnFKRpelGaJuakyG6lax4AAMBoxjlz/2gfAADQKRQy1Njuj0yA3tkDpKrZo6omj441eVTZ1K7GAU6Anp5kCwcfnQFIx31BepLGZ4YfWxn2CnGAYGOYcREyclq8Ab2xq0Zv7KzW+/vrVdXs6VXGZJIK0pwqzkpWcVayJmQlqzgrScWZycpJcSg7xa4UB93yAAAAYolz5v7RPgAAYLDafAFVNnlU2ejRsaZ2VTaGA49jTR5VNrarssmjFu+pJz+3mE0qSHdqfGb487TxmeHP1sZnJmt8ZpLy0pyymPlcDbGX8MHGY489pocfflhVVVWaNWuWHn30UZ1zzjl9ll23bp1+/etfa9u2bZKkOXPmaNWqVSct3xcuQqLDMAwdrm/TBwfqtf1Ys3Yca9aOyuYB/YO2W83KcdmV3RF0ZLscykmxKzvFriyXQ9kuuzJddmUl25XpshGEAAAADDPOmftH+wAAgJHQ7PH3Dj4i9+HHvmD/837YLCYVZiR1Cz6SVJzVdT8uxcHnaIiKwZwzx90AbL///e9100036Re/+IXmzZunNWvW6KKLLlJ5eblyc3N7ld+0aZOuuOIKLViwQE6nUw8++KC+9KUvafv27SoqKorBFuBkTCaTSrJdKsl26RsdywzDUG2LT4fr23S0oU1H6tt0uL5NR+rbVdHYrroWr1p9QfkCIR3r6Ko3EDaLSZnJdmW5wreu0MOurGRb+L7bLSfFIRtd9gAAAAAAADCKpDltSsu3qSw/tc/nQyFDNW5v+HO1hjYdrW/X0Yb28OOGcPjhDxo6VNemQ3Vtkup6rcNlt6gk26XSnGSVZLt0WrZLJdnJKs1xKTeV0AOxEXc9NubNm6ezzz5ba9eulSSFQiEVFxfr+uuv16233nrK1weDQWVmZmrt2rVaunTpgN6Tb1eNbu2+oOpavapr8amu1avaFl/4cYtXda0+1bZ41djmV32rT/WtPrX7g4N+D5NJynbZlZfmVH6aU3np4fsTH6cn20ZgCwEAAEY/zpn7R/sAAIDRKBgyVN3s0ZH6cNDRFXqEv1hc2dSuUD+fHifZLOGQI9ulkpzkjtAjHILkpTplZogrDELC9tjw+Xz66KOPdNttt0WWmc1mLVq0SO+9996A1tHW1ia/36+srKyRqiaiLMlu0Xh7eHzAgWj3BdXQFg45Ou/rW31qaPWpvs2nhlZ/r+cCoXDPkdoWn7Yfaz7putOcVpVkuzQhK1kTspNV0nmf7VI+4xUCAAAAAABgFLGYw8NQFWYkaV4fz3sDQR1taNehulYdrG3TwbpWHaxr06G6Vh1taFe7P6hdVW7tqnL3eq3TZlZJlkuTcl2aPC5Fk3JTNGlciiaOcynZHlcfS2MUiqvfoNraWgWDQeXl5fVYnpeXp127dg1oHbfccosKCwu1aNGik5bxer3yer2Rn5ubT/5BNuJPkt2iJHv4H/ZAhEKG6tt8qm72qLrZo6omr6qaPapu8oTvO24NbX41ewLaWtGkrRVNvdZjt5g1PitJpdkunZ6Xoim5qSrLT9Xk3BQ5bZbh3kwAAAAAAADgU3FYLZo0LhxInMgXCKmisT0cdtS26lBdOPg4VBceTt7jD6m82q3y6t6hR1FGkiblpnQEHl3BR7bLztBWGJC4CjY+rQceeEDr16/Xpk2b5HQ6T1pu9erVuvvuu6NYM4xmZrNJOSkO5aQ4NL0w/aTl2n1BHWlo6xiTsFWH68OPO+cH8QVD2n+8VfuPt+qNXTWR15lMUklWsqbkpYZv+akqy0vVaTku2a3M6wEAAAAAAIDRx24167Qcl07LcUllPZ/zB0M61tiu/bWt2lfTon3HW7S3pkX7jreqvtWnisbw/Llv7j7e43UZyTZNGpeiKXmpmpof/lJwWV6qMl32KG4Z4kFczbHh8/mUnJys559/Xpdddllk+bJly9TY2KiXXnrppK995JFHdN999+n111/X3Llz+32fvnpsFBcXMx4uhiwYMnSssV1H6tu0r7ZVe6rd2l3tVnmVWw1t/j5fY7OYVJafqhlF6TqzKF1nFqarLD+V3h0AAGBUYg6J/tE+AAAAYfWtPu073qJ9NZ1hR4v2Hm/R0YZ2neyT6txURyTkmJIfDj1Oz01Vkp3PyRJJws6xYbfbNWfOHG3cuDESbIRCIW3cuFHXXXfdSV/30EMP6f7779eGDRtOGWpIksPhkMPhGK5qA7KYTSrOSlZxVrIWTM6JLDeM8Nwdezq65e2ubtHuard2V7nl9ga0raJZ2yqaJR2RJFnNJk3J6ww70nRmUbqmFaQRdgAAAAAAACAuZLnsynJl6ezSnnMge/xB7T/eqj01XV8I3lXl1tGGdtW4vapxe/XWntpI+c5RUDoDjzMK0zS9MF3jM5MYzmoMiKseG5L0+9//XsuWLdMTTzyhc845R2vWrNH//M//aNeuXcrLy9PSpUtVVFSk1atXS5IefPBB3Xnnnfrtb3+rc889N7KelJQUpaT0HhuuL3y7CtFmGIaONrRrW8d8HVsrmrStoqnP3h1Wc7hnx8zxGZo1Pl0zx2doSl6KrBaGsQIAANHDOXP/aB8AAIChafEGIl8E3lUVDjzKq92qb/X1WT49yaYzCtI0vTBN04vCYcfEHBeflcWBwZwzx12wIUlr167Vww8/rKqqKp111ln62c9+pnnz5kmSFi5cqNLSUj399NOSpNLSUh06dKjXOlauXKm77rprQO/HRQhGA8MwVNEYDju2VTRHwo66Pv6JO21mnVkYDjlmFYfvS7OTSasBAMCI4Zy5f7QPAADA8Dru9mp3dTjs2FXZrO3HmrWnxi1/sPfH3U6bWVPzO8KOwnRNL0xjyPdRKOGDjWjjIgSjVWfY8cnRJn18tFGfHAn37mjxBnqVTXNaNXN8hmaOT9es4gzNGp+h/HRnDGoNAAASEefM/aN9AAAARp4vENLuard2HGvW9mNN2n6sWTsqm9XmC/YqazGbVJaXqlnFGZpdnKFZxRmanJsii5kvBscKwcYw4yIE8SQUMrS/tlUfH2nUJ0cb9fHRJu2obJYvEOpVNjfV0TWEVXH4PiPZHoNaAwCAeMc5c/9oHwAAgNgIhgwdrGvV9o6wIxx6NPc5lFWy3aIZRek6qyPomFWcocJ0J6OgRAnBxjDjIgTxrjOt7uzV8fHRRu2udivUx19/SXZyJOw4ozBNU/PTlOUi7AAAAP3jnLl/tA8AAMDoYRiGKps8+uRoo7YcadKWIw3aerRJrX307MhJceis4nTNGh8OOs6akKE0py0GtU58BBvDjIsQJKI2X0DbjzV39Oxo0idHG3Wwrq3PsuNSHZqan6qyvFSV5Ydvp+emKsnOOIQAACCMc+b+0T4AAACjWzBkaN/xFm050qiPjzTq46ON2lXpVuCEbwabTFJZXqrOLs3S3NJMzS3NUlFGUoxqnVgINoYZFyEYK5ra/PqkIhx0fHykUTurmnWkvr3PsiaTVJrtUlleqiblulSS7VJptkul2ckal+qgix4AAGMM58z9o30AAADij8cfjHwx+OOjjdpypFGH+vhicGG6U3NKs3R2aabmlmSpLD+VuTqGgGBjmHERgrGsxRvQ7mq3dle5tavKrfIqt8qr3X2OQ9gp2W7pCDqSVZLt0mk5yZqQ5VJRRpLy0h1yWOnpAQBAouGcuX+0DwAAQGKocXv00cEG/etQg/51sF7bjjUreEKvjlSHVbNLMnV2SabmlGZqdnEmI58MAMHGMOMiBOjJMAwdb/Fqd1WLdlU162Bdqw7VtelgXasqGtr7nLuju2yXXfnpThWkO5Wf7lR+mlP56UkqSHcqL82pbJdd6Uk2mUm2AQCIG5wz94/2AQAASExtvoC2HGnUvw426MOD9dp8uFEt3kCPMnaLWWdNyNCCSdmaPzFbZ03I4Iu/fSDYGGZchAAD5wuEdLQhHHIcrG3TobpWHawL31c2eeQNhAa0HovZpMxkm7Jc9hNuDmW77Mp02ZXqtCrNaVOa06q0JJtSnVYl2SwMgwUAQAxwztw/2gcAAGBsCIYM7apq1r86enV8eKBeVc2eHmWcNrPmlmRp/qRszZ+UrZlF6bJazDGq8ehBsDHMuAgBhodhGGps86uq2aOqJo8qmzyqamoP3zeHf65u9sjtCZx6ZSdhNZvCgUdH0JHisCrZblWS3aJkm0XJdouS7Fa57JbwMru1Y1n4uWS7RUk2q+xWsxxWsxw2sxwWixw2s+wWM71IAAA4Cc6Z+0f7AAAAjE2GYehgXZve21end/fV6v39dapt6TnEe4rDqrNLM3X+6eO0sGycTstxjckv7hJsDDMuQoDo8gVCamjzqa7Fp/pWn+rbfKpv8aq+1ae61vCyhjaf3J6A3J6Amj1+uT2BXuMZjgSbxSSH1dIVfFjNHY8t3R6be5SxWc2ymU2yWXo+tlrMslk6llvMslpMsnfc27o9ZzWbZbeaZDWbey7vKG/r8Rozk1MBAGKCc+b+0T4AAACQwkHHnpoWvbu3Vu/tr9P7++vV1O7vUWZCVrIWloVDjvkTc8bM/BwEG8OMixBg9DMMQ22+YEfY4Vezx6/m9oBavAG1+4Jq8wXU5g+q3RdUqzeodn9Abb5gxy38uL3j53Z/UL5ASN5AUN5ASPH2X9JskqwWc8+QxGySzWqW1dwVgHQPVWwWU+/X9BW6mM2yWU2ymTuet5rDj/sIXvoKYTrDHHsfwQ6BDADEN86Z+0f7AAAAoC/BkKGdlc16Z2+t/t/u4/rwYL38wa4Po+xWs+adlqWFZblaWDZOExO4NwfBxjDjIgQYuwzDUCBkyBsIdYUd/pB8wZC8/vDP4eWdt2C3suGf/QFDgVD4NYGgIX8wJH/HfaDbY38wpEDIkC/Q83Eg1FnW6FhHz9dEoaNKVJhM6hHCDD2Q6SNQOUUgY7eG12G3dj22Wbp64NitXT/bO9afqCcRADBUnDP3j/YBAADAQLR4A3p3b6027T6u/1d+XBWN7T2en5CVrC+dkaf/dWa+PjMhM6GGTSfYGGZchAAYzYKdwUfIkD8Qkj8UDj4CHcGHv1uYEg5KuockXQGJP9gRwAROeH3HegOh8Gu6Pz4xZOl8na/P9+/2viEjKkOHjaQeYYilM/wwyd45DJklHJx0BSaWjqHMupfvGaacGLBEwpUBlreaCVwAxA7nzP2jfQAAADBYhmFob02LNpUf16bdNfrwQIN8wVDk+dxUhy6anq+Lz8zXOadlxf0E5AQbw4yLEAAYfqGQ0SOE8QVPHsj0CkZOCFNODFa6BzJ9hTPdA5nOHjLh0Cb8fGePm87nfcFQXAQxnb1eOud/6TH3i83SbXnXcw5b33PEhJebT5hTpufyHq/pWD/hCjB2cc7cP9oHAAAAn1arN6C39tTqlW2V2rizRm5vIPJclsuui6bn6etzxuszEzLj8tqcYGOYcRECAOjsGdM51Fhn75U+g5DO57uXPyEo6XHfrXznuroHLl2v7xwWLRgJgkZb4GI2qWcY0i046RWU2E62/ITX2CyyW8y9QhWnrXc447Ca4/LkDUgEnDP3j/YBAADAcPIGgnp3X51e2VqlV3dUqaGtawLy03Jc+vqc8frK7CIVZiTFsJaDQ7AxzLgIAQCMVsGOuVgiQUkwJK8/2G0emBPnggn2WB6ZMyYQfl33+WIir/F3zR/TWa77+rt3gx0Nugcizj6ClcgyWx/LOoKSSGjSfVnkNZY+12W3mBNqbFNgsDhn7h/tAwAAgJESCIb0zwP1+tPmCr28tVJtvqCk8MgO503O0dfnjNdF0/PltFliXNP+EWwMMy5CAAA4uVDHEF9ef0je4AnBSbdgpCtc6Rmw+LqXPSF4OfE1Hn/P8p6OMGa0nM30GLarVyDSd4jSa1mfocypAxlCFcQa58z9o30AAAAQDa3egP6+rUrPf3RE7++vjyzPdtl15bwJ+s5nS5Sb5oxhDU+OYGOYcRECAMDoZRhGxzBdPcOO7j1NPP6ePU48ffRQiZT39wxSugc0nu7lOtY7WkYDC09O3zPssPcYsqsrOOnRI8XWx7KTDPV14rLO97IQqkCcM58K7QMAAIBoO1Lfpj/++6j+58MjOtbkkRS+dvzyrCIt/8IkTRyXEuMa9kSwMcy4CAEAACfjP2EorxNDlF7LOsIRzylCFO8JIUpfoUxglKQqVrOpq6dJH/OnOGz9hyh9D/XVO5Rx2nr2anFYzbJZzLHefHTgnLl/tA8AAABiJRAM6dUd1Xry7QP616EGSeE5MpfMKtT1F0zW5NzUGNcwbDDnzNYo1QkAACAh2SzhD9dTHNE/rQoEQ330OukepJxk2SB7tnSfo6UzgPEHu0KVQMhQwBdUa8c4rtFk6QxVuoUoAxn+61QhSvdl9o4AxWo2Rfa31dL52BR5jonrAQAAAIxGVotZi2cUaPGMAm050qi1b+zR6ztr9NKWY/rLx8d0xTkTdNOFU5Sd4oh1VQeMHhsDwLerAAAAeuqcuH4gQ3ZFypzQc8Xj7z986atnS+e8LKNRZ/DRPfSwmsPBiNVsktVilt0SvreaTZHlfYUlVrO563HHz1azSRaLKXxv7lxnz5+LMpN0dmlWTLafc+b+0T4AAAAYTbZVNOmnG/fotR3VkqRUh1XXf3Gyli0olcMam0nG6bEBAACAEWUxm5RktyjJHv0T3h4T1p8YrJxkqK9IcHKK4b88Jw7/1bGeQMiQPxiSPxhSIGj0OQxYIGQoEApK/qg3ScTiGfkxCzbixeOPP67HH39cBw8elCRNnz5dd955py6++OKTvqaxsVG33367XnjhBdXX16ukpERr1qzR4sWLo1RrAAAAYHidWZSudUvn6v39dbr3rzu0/VizVr28SwdqW7X6qzNjXb1TItgAAABAXDGbTXKaLXLaLJJsMalD56T1gVBI/oAhfygceHSGH/6Ox90DEX/QUKDHc12v9Qc6y3Y8FwzJFykfUtAwFOx4PhgKByvBjvfs+tnQ1Hx6ApzK+PHj9cADD+j000+XYRh65plndOmll2rz5s2aPn16r/I+n08XXnihcnNz9fzzz6uoqEiHDh1SRkZG9CsPAAAADLPPTszWX647T3/891H95LXd+t55E2NdpQEh2AAAAAAGyWQyyW41yS6zZI91bTAYS5Ys6fHz/fffr8cff1zvv/9+n8HGk08+qfr6er377ruy2cJBWmlpaTSqCgAAAESF2WzSN+YW6yuzi2S1mGNdnQGJj1oCAAAAwDALBoNav369WltbNX/+/D7L/PnPf9b8+fO1fPly5eXl6cwzz9SqVasUDAajXFsAAABgZMVLqCHRYwMAAADAGLN161bNnz9fHo9HKSkp+tOf/qQzzjijz7L79+/XG2+8oSuvvFIvv/yy9u7dq2uvvVZ+v18rV6486Xt4vV55vd7Iz83NzcO+HQAAAMBYFT8RDAAAAAAMg7KyMm3ZskX//Oc/9YMf/EDLli3Tjh07+iwbCoWUm5urX/7yl5ozZ44uv/xy3X777frFL37R73usXr1a6enpkVtxcfFIbAoAAAAwJhFsAAAAABhT7Ha7Jk+erDlz5mj16tWaNWuWfvrTn/ZZtqCgQFOmTJHFYoksmzZtmqqqquTz+U76HrfddpuampoityNHjgz7dgAAAABjFcEGAAAAgDEtFAr1GDaqu3PPPVd79+5VKBSKLNu9e7cKCgpkt5985niHw6G0tLQeNwAAAADDg2ADAAAAwJhx22236c0339TBgwe1detW3Xbbbdq0aZOuvPJKSdLSpUt12223Rcr/4Ac/UH19vW644Qbt3r1bf/vb37Rq1SotX748VpsAAAAAjHlMHg4AAABgzKipqdHSpUtVWVmp9PR0zZw5Uxs2bNCFF14oSTp8+LDM5q7vfxUXF2vDhg268cYbNXPmTBUVFemGG27QLbfcEqtNAAAAAMY8k2EYRqwrMdo1NTUpIyNDR44coQs5AAAA0Ifm5mYVFxersbFR6enpsa7OqMM1BQAAANC/wVxT0GNjANxut6Twt7UAAAAAnJzb7SbY6APXFAAAAMDADOSagh4bAxAKhXTs2DGlpqbKZDKN+Pt1JlN8m2v40bYjg3YdObTtyKFtRw5tO3Jo25FBuw4PwzDkdrtVWFjYYygnhHW/pnC73fzOJTj+ryQ+9vHYwH5OfOzjxMc+ji+Duaagx8YAmM1mjR8/Purvm5aWxh/cCKFtRwbtOnJo25FD244c2nbk0LYjg3b99OipcXLdryk6vyzF71ziYx8nPvbx2MB+Tnzs48THPo4fA72m4KtUAAAAAAAAAAAgbhBsAAAAAAAAAACAuEGwMQo5HA6tXLlSDocj1lVJOLTtyKBdRw5tO3Jo25FD244c2nZk0K6INn7nEh/7OPGxj8cG9nPiYx8nPvZx4mLycAAAAAAAAAAAEDfosQEAAAAAAAAAAOIGwQYAAAAAAAAAAIgbBBsAAAAAAAAAACBuEGzEyGOPPabS0lI5nU7NmzdPH3zwQb/l//CHP2jq1KlyOp2aMWOGXn755SjVNP4Mpm23b9+ur33tayotLZXJZNKaNWuiV9E4M5h2Xbdunc4//3xlZmYqMzNTixYtOuXv+Fg2mLZ94YUXNHfuXGVkZMjlcumss87Sb37zmyjWNr4M9n9tp/Xr18tkMumyyy4b2QrGscG07dNPPy2TydTj5nQ6o1jb+DLY39vGxkYtX75cBQUFcjgcmjJlCucJfRhMuy5cuLDX76zJZNIll1wSxRoj3nEMSnwcCxMfx+SxgXOExDfYv+U1a9aorKxMSUlJKi4u1o033iiPxxOl2mIoBrOP/X6/7rnnHk2aNElOp1OzZs3SK6+8EsXaYtgYiLr169cbdrvdePLJJ43t27cbV199tZGRkWFUV1f3Wf6dd94xLBaL8dBDDxk7duwwVqxYYdhsNmPr1q1RrvnoN9i2/eCDD4ybb77Z+N3vfmfk5+cbP/nJT6Jb4Tgx2Hb99re/bTz22GPG5s2bjZ07dxrf/e53jfT0dOPo0aNRrvnoN9i2/cc//mG88MILxo4dO4y9e/caa9asMSwWi/HKK69Eueaj32DbttOBAweMoqIi4/zzzzcuvfTS6FQ2zgy2bZ966ikjLS3NqKysjNyqqqqiXOv4MNi29Xq9xty5c43Fixcbb7/9tnHgwAFj06ZNxpYtW6Jc89FtsO1aV1fX4/d127ZthsViMZ566qnoVhxxi2NQ4uNYmPg4Jo8NnCMkvsHu4+eee85wOBzGc889Zxw4cMDYsGGDUVBQYNx4441RrjkGarD7+L/+67+MwsJC429/+5uxb98+4+c//7nhdDqNf//731GuOT4tgo0YOOecc4zly5dHfg4Gg0ZhYaGxevXqPst/85vfNC655JIey+bNm2f853/+54jWMx4Ntm27KykpIdg4iU/TroZhGIFAwEhNTTWeeeaZkapi3Pq0bWsYhjF79mxjxYoVI1G9uDaUtg0EAsaCBQuM//7v/zaWLVvGh0onMdi2feqpp4z09PQo1S6+DbZtH3/8cWPixImGz+eLVhXj0qf9X/uTn/zESE1NNVpaWkaqikgwHIMSH8fCxMcxeWzgHCHxDXYfL1++3Ljgggt6LLvpppuMc889d0TriaEb7D4uKCgw1q5d22PZV7/6VePKK68c0Xpi+DEUVZT5fD599NFHWrRoUWSZ2WzWokWL9N577/X5mvfee69HeUm66KKLTlp+rBpK2+LUhqNd29ra5Pf7lZWVNVLVjEuftm0Nw9DGjRtVXl6uz33ucyNZ1bgz1La95557lJubq+9973vRqGZcGmrbtrS0qKSkRMXFxbr00ku1ffv2aFQ3rgylbf/85z9r/vz5Wr58ufLy8nTmmWdq1apVCgaD0ar2qDccx7Ff/epX+ta3viWXyzVS1UQC4RiU+DgWJj6OyWMD5wiJbyj7eMGCBfroo48iQxnt379fL7/8shYvXhyVOmNwhrKPvV5vr+Egk5KS9Pbbb49oXTH8CDairLa2VsFgUHl5eT2W5+Xlqaqqqs/XVFVVDar8WDWUtsWpDUe73nLLLSosLOwV0I11Q23bpqYmpaSkyG6365JLLtGjjz6qCy+8cKSrG1eG0rZvv/22fvWrX2ndunXRqGLcGkrblpWV6cknn9RLL72kZ599VqFQSAsWLNDRo0ejUeW4MZS23b9/v55//nkFg0G9/PLLuuOOO/SjH/1I9913XzSqHBc+7XHsgw8+0LZt2/T9739/pKqIBMMxKPFxLEx8HJPHBs4REt9Q9vG3v/1t3XPPPTrvvPNks9k0adIkLVy4UD/84Q+jUWUM0lD28UUXXaQf//jH2rNnj0KhkF577TW98MILqqysjEaVMYyssa4AgMT2wAMPaP369dq0aRMTJA6T1NRUbdmyRS0tLdq4caNuuukmTZw4UQsXLox11eKW2+3WVVddpXXr1iknJyfW1Uk48+fP1/z58yM/L1iwQNOmTdMTTzyhe++9N4Y1i3+hUEi5ubn65S9/KYvFojlz5qiiokIPP/ywVq5cGevqJYRf/epXmjFjhs4555xYVwUJimPQ2MCxMPFxTB57OEdITJs2bdKqVav085//XPPmzdPevXt1ww036N5779Udd9wR6+phGPz0pz/V1VdfralTp8pkMmnSpEn6j//4Dz355JOxrhoGiWAjynJycmSxWFRdXd1jeXV1tfLz8/t8TX5+/qDKj1VDaVuc2qdp10ceeUQPPPCAXn/9dc2cOXMkqxm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+      "text/plain": [
+       "<Figure size 1600x1200 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# define stoichiometry ranges\n",
+    "xLi_n_max = parameter_values[\"Negative electrode maximum stoichiometry\"]\n",
+    "xLi_n_min = parameter_values[\"Negative electrode minimum stoichiometry\"]\n",
+    "xLi_n = pybamm.linspace(xLi_n_min, xLi_n_max, 200)\n",
+    "xLi_p_max = parameter_values[\"Positive electrode maximum stoichiometry\"]\n",
+    "xLi_p_min = parameter_values[\"Positive electrode minimum stoichiometry\"]\n",
+    "xLi_p = pybamm.linspace(xLi_p_min, xLi_p_max, 200)\n",
+    "\n",
+    "# define concentration ranges\n",
+    "c_n_max = parameter_values[\"Maximum concentration in negative electrode [mol.m-3]\"]\n",
+    "c_p_max = parameter_values[\"Maximum concentration in positive electrode [mol.m-3]\"]\n",
+    "c_n = xLi_n * c_n_max\n",
+    "c_p = xLi_p * c_p_max\n",
+    "c_e = pybamm.linspace(0, 2000, 200)\n",
+    "\n",
+    "c_e_ref = parameter_values[\"Initial concentration in electrolyte [mol.m-3]\"]\n",
+    "T = parameter_values[\"Reference temperature [K]\"]\n",
+    "\n",
+    "# create figure\n",
+    "fig, ax = plt.subplots(4, 2, figsize=(16, 12))\n",
+    "\n",
+    "ax[0, 0].plot(\n",
+    "    c_e.entries,\n",
+    "    parameter_values.evaluate(\n",
+    "        parameter_values[\"Electrolyte conductivity [S.m-1]\"](c_e, T)\n",
+    "    ),\n",
+    ")\n",
+    "ax[0, 0].set(xlabel=\"$c_e$ [mol/m3]\", ylabel=\"$\\kappa$ [S.m-1]\")\n",
+    "ax[0, 1].plot(\n",
+    "    c_e.entries,\n",
+    "    parameter_values.evaluate(\n",
+    "        parameter_values[\"Electrolyte diffusivity [m2.s-1]\"](c_e, T)\n",
+    "    ),\n",
+    ")\n",
+    "ax[0, 1].set(xlabel=\"$c_e$ [mol/m3]\", ylabel=\"$D_e$ [S.m-1]\")\n",
+    "ax[1, 0].plot(\n",
+    "    xLi_n.entries,\n",
+    "    parameter_values.evaluate(\n",
+    "        parameter_values[\"Negative electrode diffusivity [m2.s-1]\"](xLi_n, T)\n",
+    "    )\n",
+    "    * np.ones_like(xLi_n.entries),\n",
+    ")\n",
+    "ax[1, 0].set(xlabel=\"sto [-]\", ylabel=\"$D_n$ [S.m-1]\")\n",
+    "ax[1, 1].plot(\n",
+    "    xLi_p.entries,\n",
+    "    parameter_values.evaluate(\n",
+    "        parameter_values[\"Positive electrode diffusivity [m2.s-1]\"](xLi_p, T)\n",
+    "    )\n",
+    "    * np.ones_like(xLi_p.entries),\n",
+    ")\n",
+    "ax[1, 1].set(xlabel=\"sto [-]\", ylabel=\"$D_n$ [S.m-1]\")\n",
+    "ax[2, 0].plot(\n",
+    "    xLi_n.entries,\n",
+    "    parameter_values.evaluate(\n",
+    "        parameter_values[\"Negative electrode exchange-current density [A.m-2]\"](\n",
+    "            c_e_ref, c_n, c_n_max, T\n",
+    "        )\n",
+    "    ),\n",
+    ")\n",
+    "ax[2, 0].set(xlabel=\"sto [-]\", ylabel=\"$j_n$ [A.m-2]\")\n",
+    "ax[2, 1].plot(\n",
+    "    xLi_p.entries,\n",
+    "    parameter_values.evaluate(\n",
+    "        parameter_values[\"Positive electrode exchange-current density [A.m-2]\"](\n",
+    "            c_e_ref, c_p, c_p_max, T\n",
+    "        )\n",
+    "    ),\n",
+    ")\n",
+    "ax[2, 1].set(xlabel=\"sto [-]\", ylabel=\"$j_p$ [A.m-2]\")\n",
+    "ax[3, 0].plot(\n",
+    "    xLi_n.entries, parameter_values[\"Negative electrode OCP [V]\"](xLi_n).entries\n",
+    ")\n",
+    "ax[3, 0].set(xlabel=\"sto [-]\", ylabel=\"$U_n$ [V]\")\n",
+    "ax[3, 1].plot(\n",
+    "    xLi_p.entries, parameter_values[\"Positive electrode OCP [V]\"](xLi_p).entries\n",
+    ")\n",
+    "ax[3, 1].set(xlabel=\"sto [-]\", ylabel=\"$U_p$ [V]\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## References\n",
+    "\n",
+    "The relevant papers for this notebook are:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "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] Von DAG Bruggeman. Berechnung verschiedener physikalischer konstanten von heterogenen substanzen. i. dielektrizitätskonstanten und leitfähigkeiten der mischkörper aus isotropen substanzen. Annalen der physik, 416(7):636–664, 1935.\n",
+      "[3] 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",
+      "[4] 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",
+      "[5] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "pybamm.print_citations()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "env",
+   "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": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/source/examples/notebooks/pybamm_data.ipynb b/docs/source/examples/notebooks/pybamm_data.ipynb
index b7111ed99..959443e66 100644
--- a/docs/source/examples/notebooks/pybamm_data.ipynb
+++ b/docs/source/examples/notebooks/pybamm_data.ipynb
@@ -10,7 +10,7 @@
     "This notebook is a reference for using pybamm.DataLoader module for using and fetching data files from the pybamm-data registry.\n",
     "Checkout the [documentation](../../api/pybamm_data.rst) for further implementation details on this module.\n",
     "\n",
-    "The following steps provide an example for using pybamm.DataLoader to download data files from PyBaMM data registry upstream at [pybamm-data](https://github.com/pybamm-team/pybamm-data/releases/tag/v1.0.0).\n"
+    "The following steps provide an example for using pybamm.DataLoader to download data files from PyBaMM data registry upstream at [pybamm-data](https://github.com/pybamm-team/pybamm-data/releases).\n"
    ]
   },
   {
diff --git a/src/pybamm/parameters/parameter_values.py b/src/pybamm/parameters/parameter_values.py
index 72065b4df..9ddbfd2fd 100644
--- a/src/pybamm/parameters/parameter_values.py
+++ b/src/pybamm/parameters/parameter_values.py
@@ -68,41 +68,34 @@ class ParameterValues:
                 pybamm.citations.register(citation)
 
     @staticmethod
-    def create_from_bpx(filename, target_soc: float = 1):
-        """
-        Parameters
-        ----------
-        filename: str
-            The filename of the bpx file
-        target_soc : float, optional
-            Target state of charge. Must be between 0 and 1. Default is 1.
-
-        Returns
-        -------
-        ParameterValues
-            A parameter values object with the parameters in the bpx file
+    def _create_from_bpx(bpx, target_soc):
+        from bpx import get_electrode_concentrations
+        from bpx.schema import ElectrodeBlended, ElectrodeBlendedSPM
+        from .bpx import bpx_to_param_dict
 
-        """
         if target_soc < 0 or target_soc > 1:
             raise ValueError("Target SOC should be between 0 and 1")
 
-        from bpx import parse_bpx_file, get_electrode_concentrations
-        from bpx.schema import ElectrodeBlended, ElectrodeBlendedSPM
-        from .bpx import bpx_to_param_dict
-
-        # parse bpx
-        bpx = parse_bpx_file(filename)
         pybamm_dict = bpx_to_param_dict(bpx)
 
         if "Open-circuit voltage at 0% SOC [V]" not in pybamm_dict:
             pybamm_dict["Open-circuit voltage at 0% SOC [V]"] = pybamm_dict[
                 "Lower voltage cut-off [V]"
             ]
+            warn(
+                "'Open-circuit voltage at 0% SOC [V]' not found in BPX file. Using "
+                "'Lower voltage cut-off [V]'.",
+                stacklevel=2,
+            )
+        if "Open-circuit voltage at 100% SOC [V]" not in pybamm_dict:
             pybamm_dict["Open-circuit voltage at 100% SOC [V]"] = pybamm_dict[
                 "Upper voltage cut-off [V]"
             ]
-            # probably should put a warning here to indicate we are going
-            # ahead with the low voltage limit.
+            warn(
+                "'Open-circuit voltage at 100% SOC [V]' not found in BPX file. Using "
+                "'Upper voltage cut-off [V]'.",
+                stacklevel=2,
+            )
 
         # get initial concentrations based on SOC
         # Note: we cannot set SOC for blended electrodes,
@@ -127,6 +120,48 @@ class ParameterValues:
 
         return pybamm.ParameterValues(pybamm_dict)
 
+    @staticmethod
+    def create_from_bpx_obj(bpx_obj, target_soc: float = 1):
+        """
+        Parameters
+        ----------
+        bpx_obj: dict
+            A dictionary containing the parameters in the `BPX <https://bpxstandard.com/>`_ format
+        target_soc : float, optional
+            Target state of charge. Must be between 0 and 1. Default is 1.
+
+        Returns
+        -------
+        ParameterValues
+            A parameter values object with the parameters in the bpx file
+
+        """
+        from bpx import parse_bpx_obj
+
+        bpx = parse_bpx_obj(bpx_obj)
+        return ParameterValues._create_from_bpx(bpx, target_soc)
+
+    @staticmethod
+    def create_from_bpx(filename, target_soc: float = 1):
+        """
+        Parameters
+        ----------
+        filename: str
+            The filename of the `BPX <https://bpxstandard.com/>`_ file
+        target_soc : float, optional
+            Target state of charge. Must be between 0 and 1. Default is 1.
+
+        Returns
+        -------
+        ParameterValues
+            A parameter values object with the parameters in the bpx file
+
+        """
+        from bpx import parse_bpx_file
+
+        bpx = parse_bpx_file(filename)
+        return ParameterValues._create_from_bpx(bpx, target_soc)
+
     def __getitem__(self, key):
         try:
             return self._dict_items[key]
diff --git a/src/pybamm/pybamm_data.py b/src/pybamm/pybamm_data.py
index 657e13c02..6f9bd4f1f 100644
--- a/src/pybamm/pybamm_data.py
+++ b/src/pybamm/pybamm_data.py
@@ -80,7 +80,7 @@ class DataLoader:
         """
         Create a pooch registry with the following data files available upstream at https://github.com/pybamm-team/pybamm-data/
         """
-        self.version = "v1.0.0"  # Version of pybamm-data release
+        self.version = "v1.0.1"  # Version of pybamm-data release
         self.path = pooch.os_cache("pybamm")
         self.files = {
             # COMSOL results
@@ -112,6 +112,8 @@ class DataLoader:
             "US06.csv": "sha256:5909eb2ec7983fae86a050ff3b35a2041d0ab698710a6b0f95d5816e348077ba",
             "WLTC.csv": "sha256:bb2f95018a44ac1425cb9c787c34721192af502c7385f1358f28e4f75df11fd8",
             "car_current.csv": "sha256:4305b91b9df073cb048c25dd3fae725e06a94fe200e322e5c08db290d6799e36",
+            # BPX files
+            "nmc_pouch_cell_BPX.json": "sha256:27261e2a7012725ed16c0c8b799b870388bd6c77f86bbf1949353286569c7d0d",
         }
         self.registry = pooch.create(
             path=self.path,


Test patch:
diff --git a/tests/unit/test_parameters/test_bpx.py b/tests/unit/test_parameters/test_bpx.py
index 3d4b65afc..0f92653c6 100644
--- a/tests/unit/test_parameters/test_bpx.py
+++ b/tests/unit/test_parameters/test_bpx.py
@@ -263,8 +263,9 @@ class TestBPX:
                 assert isinstance(dUdT, pybamm.Interpolant)
 
     def test_bpx_soc_error(self):
+        bpx_obj = copy.copy(self.base)
         with pytest.raises(ValueError, match="Target SOC"):
-            pybamm.ParameterValues.create_from_bpx("blah.json", target_soc=10)
+            pybamm.ParameterValues.create_from_bpx_obj(bpx_obj, target_soc=10)
 
     def test_bpx_arrhenius(self):
         bpx_obj = copy.copy(self.base)
@@ -500,3 +501,8 @@ class TestBPX:
             assert param[
                 "Negative electrode diffusivity activation energy [J.mol-1]"
             ] == pytest.approx(0.0, rel=1e-12)
+
+    def test_bpx_from_obj(self):
+        bpx_obj = copy.copy(self.base)
+        param = pybamm.ParameterValues.create_from_bpx_obj(bpx_obj)
+        assert isinstance(param, pybamm.ParameterValues)