From 61bf61d3eb99aa4001b3b3a7adcc9092ef907166 Mon Sep 17 00:00:00 2001 From: "arjxn.py" Date: Mon, 17 Jul 2023 22:00:48 +0530 Subject: [PATCH 1/6] Add `nbqa-ruff` as pre-commit hook --- .pre-commit-config.yaml | 7 +++++++ .../notebooks/creating_models/6-a-simple-SEI-model.ipynb | 2 +- .../examples/notebooks/models/jelly-roll-model.ipynb | 4 ++-- docs/source/examples/notebooks/models/latexify.ipynb | 2 +- 4 files changed, 11 insertions(+), 4 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index ba385be222..6eecc02b75 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -13,3 +13,10 @@ repos: hooks: - id: ruff args: [--ignore=E741, --exclude=__init__.py] + + - repo: https://github.com/nbQA-dev/nbQA + rev: 1.7.0 + hooks: + - id: nbqa-ruff + additional_dependencies: [ruff==0.0.276] + args: ["--fix","--ignore=E501,E402"] \ No newline at end of file diff --git a/docs/source/examples/notebooks/creating_models/6-a-simple-SEI-model.ipynb b/docs/source/examples/notebooks/creating_models/6-a-simple-SEI-model.ipynb index e0125ee390..51fa6c5f3d 100644 --- a/docs/source/examples/notebooks/creating_models/6-a-simple-SEI-model.ipynb +++ b/docs/source/examples/notebooks/creating_models/6-a-simple-SEI-model.ipynb @@ -576,7 +576,7 @@ " ax1.set_ylabel(r'SEI thickness [$\\mu$m]')\n", " ax1.set_xlabel(r't [$\\mu$s]') \n", " \n", - " plot_c, = ax2.plot(x * 1e6 * L_out(t_in_seconds), c_out(t_in_seconds, x_in_metres))\n", + " plot_c, = ax2.plot(x * 1e6 * L_out(t_in_seconds), c_out(t_in_seconds, x_in_metres)) # noqa: F821\n", " ax2.set_ylim(0, 1.1)\n", " ax2.set_xlim(0, x[-1] * 1e6 * L_out(solution.t[-1])) \n", " ax2.set_ylabel('Solvent concentration [mol.m-3]')\n", diff --git a/docs/source/examples/notebooks/models/jelly-roll-model.ipynb b/docs/source/examples/notebooks/models/jelly-roll-model.ipynb index 10e9377ddf..7e2fb202c4 100644 --- a/docs/source/examples/notebooks/models/jelly-roll-model.ipynb +++ b/docs/source/examples/notebooks/models/jelly-roll-model.ipynb @@ -79,7 +79,7 @@ "delta = pybamm.Parameter(\"Current collector thickness\")\n", "delta_p = delta # assume same thickness\n", "delta_n = delta # assume same thickness\n", - "l = 1/2 - delta_p - delta_n # active material thickness\n", + "l = 1/2 - delta_p - delta_n # active material thickness # noqa: E741\n", "sigma_p = pybamm.Parameter(\"Positive current collector conductivity\")\n", "sigma_n = pybamm.Parameter(\"Negative current collector conductivity\")\n", "sigma_a = pybamm.Parameter(\"Active material conductivity\")" @@ -332,7 +332,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "
" ] diff --git a/docs/source/examples/notebooks/models/latexify.ipynb b/docs/source/examples/notebooks/models/latexify.ipynb index cb7d3ca833..9a49f238eb 100644 --- a/docs/source/examples/notebooks/models/latexify.ipynb +++ b/docs/source/examples/notebooks/models/latexify.ipynb @@ -1252,7 +1252,7 @@ "source": [ "spme_latex = model_spme.latexify(newline=False)\n", "for line in spme_latex:\n", - " display(line)" + " display(line) # noqa: F821" ] }, { From a63e49ece0f9336d1f5c2562f7459e555c6e6693 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 17 Jul 2023 16:31:44 +0000 Subject: [PATCH 2/6] style: pre-commit fixes --- .../examples/notebooks/batch_study.ipynb | 2 +- .../source/examples/notebooks/callbacks.ipynb | 2 +- ...paring-full-and-reduced-order-models.ipynb | 2 + .../creating_models/5-half-cell-model.ipynb | 1352 +++--- .../6-a-simple-SEI-model.ipynb | 5 + .../tutorial-11-creating-a-submodel.ipynb | 4 +- .../tutorial-4-setting-parameter-values.ipynb | 2 + .../tutorial-9-changing-the-mesh.ipynb | 2 +- ...DFN-with-particle-size-distributions.ipynb | 5 +- .../examples/notebooks/models/MPM.ipynb | 12 +- .../notebooks/models/SEI-on-cracks.ipynb | 3 +- .../examples/notebooks/models/SPM.ipynb | 2248 +++++----- ...ating_mechanical_models_Enertech_DFN.ipynb | 3 +- .../notebooks/models/composite_particle.ipynb | 1825 +++++---- .../models/electrode-state-of-health.ipynb | 1312 +++--- .../notebooks/models/jelly-roll-model.ipynb | 27 +- .../notebooks/models/lithium-plating.ipynb | 4 +- .../notebooks/models/pouch-cell-model.ipynb | 4 +- .../models/submodel_cracking_DFN_or_SPM.ipynb | 8 +- .../submodel_loss_of_active_materials.ipynb | 6 +- .../using-model-options_thermal-example.ipynb | 2 - .../change-input-current.ipynb | 4 +- .../parameterization/parameter-values.ipynb | 4 +- .../parameterization/parameterization.ipynb | 3632 +++++++++-------- .../plotting/customize-quick-plot.ipynb | 2 +- .../simulating-long-experiments.ipynb | 21 +- .../notebooks/solvers/dae-solver.ipynb | 2 - .../notebooks/solvers/ode-solver.ipynb | 2 - .../notebooks/solvers/speed-up-solver.ipynb | 2 +- .../spatial_methods/finite-volumes.ipynb | 3 +- 30 files changed, 5263 insertions(+), 5239 deletions(-) diff --git a/docs/source/examples/notebooks/batch_study.ipynb b/docs/source/examples/notebooks/batch_study.ipynb index d1364bad64..4e79505156 100644 --- a/docs/source/examples/notebooks/batch_study.ipynb +++ b/docs/source/examples/notebooks/batch_study.ipynb @@ -516,7 +516,7 @@ "batch_study.solve(initial_soc=1)\n", "\n", "labels = [f\"Inner SEI open-circuit potential [V]: {inner_sei_oc_v}\" for inner_sei_oc_v in inner_sei_oc_v_values]\n", - "batch_study.plot(labels=labels)\n" + "batch_study.plot(labels=labels)" ] }, { diff --git a/docs/source/examples/notebooks/callbacks.ipynb b/docs/source/examples/notebooks/callbacks.ipynb index 19f6c6a6a2..f5d737f339 100644 --- a/docs/source/examples/notebooks/callbacks.ipynb +++ b/docs/source/examples/notebooks/callbacks.ipynb @@ -151,7 +151,7 @@ ], "source": [ "callback = pybamm.callbacks.LoggingCallback(\"output.log\")\n", - "sim.solve(callbacks=callback);\n", + "sim.solve(callbacks=callback)\n", "\n", "# Read the file that has been written, which was saved to callback.logfile\n", "with open(callback.logfile, \"r\") as f:\n", diff --git a/docs/source/examples/notebooks/creating_models/4-comparing-full-and-reduced-order-models.ipynb b/docs/source/examples/notebooks/creating_models/4-comparing-full-and-reduced-order-models.ipynb index 253bd6af87..7b9e1198f8 100644 --- a/docs/source/examples/notebooks/creating_models/4-comparing-full-and-reduced-order-models.ipynb +++ b/docs/source/examples/notebooks/creating_models/4-comparing-full-and-reduced-order-models.ipynb @@ -348,6 +348,7 @@ "# plot\n", "r = mesh[\"negative particle\"].nodes # radial position\n", "\n", + "\n", "def plot(t):\n", " fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13, 4))\n", " \n", @@ -373,6 +374,7 @@ " plt.tight_layout()\n", " plt.show()\n", " \n", + "\n", "import ipywidgets as widgets\n", "widgets.interact(plot, t=widgets.FloatSlider(min=0,max=3600,step=1,value=0));\n", " " diff --git a/docs/source/examples/notebooks/creating_models/5-half-cell-model.ipynb b/docs/source/examples/notebooks/creating_models/5-half-cell-model.ipynb index bb5acd5df6..5090d1c126 100644 --- a/docs/source/examples/notebooks/creating_models/5-half-cell-model.ipynb +++ b/docs/source/examples/notebooks/creating_models/5-half-cell-model.ipynb @@ -1,686 +1,684 @@ { - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "id": "professional-composer", - "metadata": {}, - "source": [ - "# A half cell model" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "naval-management", - "metadata": {}, - "source": [ - "In the [previous notebook](./4-comparing-full-and-reduced-order-models.ipynb) we saw how to compare full and reduced-order models. Both of these models were posed on a single domain: the negative electrode particle. Here we will see how to create a model which contains multiple domains.\n", - "\n", - "We consider a problem posed on a half-cell geometry, which consists of a separator ($-L_s" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# plot\n", - "pybamm.dynamic_plot(\n", - " solution,\n", - " [\n", - " \"Positive electrode potential [V]\",\n", - " \"Electrolyte potential [V]\",\n", - " \"Positive electrode interfacial current density [A.m-2]\",\n", - " \"Positive particle surface concentration [mol.m-3]\",\n", - " \"Average positive particle surface concentration [mol.m-3]\",\n", - " [\"Positive electrode OCP [V]\", \"Voltage [V]\"],\n", - " ],\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "abandoned-shirt", - "metadata": {}, - "source": [ - "In the [next notebook](./6-a-simple-SEI-model.ipynb) we consider a simple model for SEI growth, and show how to correctly pose the model in non-dimensional form and then create and solve it using pybamm." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "independent-development", - "metadata": {}, - "source": [ - "## References\n", - "\n", - "The relevant papers for this notebook are:" + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "id": "professional-composer", + "metadata": {}, + "source": [ + "# A half cell model" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "naval-management", + "metadata": {}, + "source": [ + "In the [previous notebook](./4-comparing-full-and-reduced-order-models.ipynb) we saw how to compare full and reduced-order models. Both of these models were posed on a single domain: the negative electrode particle. Here we will see how to create a model which contains multiple domains.\n", + "\n", + "We consider a problem posed on a half-cell geometry, which consists of a separator ($-L_s" ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.9.0" - }, - "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 + ], + "source": [ + "# plot\n", + "pybamm.dynamic_plot(\n", + " solution,\n", + " [\n", + " \"Positive electrode potential [V]\",\n", + " \"Electrolyte potential [V]\",\n", + " \"Positive electrode interfacial current density [A.m-2]\",\n", + " \"Positive particle surface concentration [mol.m-3]\",\n", + " \"Average positive particle surface concentration [mol.m-3]\",\n", + " [\"Positive electrode OCP [V]\", \"Voltage [V]\"],\n", + " ],\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "abandoned-shirt", + "metadata": {}, + "source": [ + "In the [next notebook](./6-a-simple-SEI-model.ipynb) we consider a simple model for SEI growth, and show how to correctly pose the model in non-dimensional form and then create and solve it using pybamm." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "independent-development", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "The relevant papers for this notebook are:" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "laden-replica", + "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] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", + "[3] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", + "\n" + ] } + ], + "source": [ + "pybamm.print_citations()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.0" }, - "nbformat": 4, - "nbformat_minor": 5 + "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": 5 } diff --git a/docs/source/examples/notebooks/creating_models/6-a-simple-SEI-model.ipynb b/docs/source/examples/notebooks/creating_models/6-a-simple-SEI-model.ipynb index 51fa6c5f3d..5c5d154089 100644 --- a/docs/source/examples/notebooks/creating_models/6-a-simple-SEI-model.ipynb +++ b/docs/source/examples/notebooks/creating_models/6-a-simple-SEI-model.ipynb @@ -189,6 +189,7 @@ "V_hat = pybamm.Parameter(\"Partial molar volume [m3.mol-1]\")\n", "c_inf = pybamm.Parameter(\"Bulk electrolyte solvent concentration [mol.m-3]\")\n", "\n", + "\n", "def D(cc):\n", " return pybamm.FunctionParameter(\"Diffusivity [m2.s-1]\", {\"Solvent concentration [mol.m-3]\": cc})" ] @@ -487,9 +488,11 @@ " {\"SEI layer\": {x: {\"min\": pybamm.Scalar(0), \"max\": L_0}}}\n", ")\n", "\n", + "\n", "def Diffusivity(cc):\n", " return cc * 10**(-5)\n", "\n", + "\n", "# parameter values (not physically based, for example only!)\n", "param = pybamm.ParameterValues(\n", " {\n", @@ -567,6 +570,7 @@ "L_0_eval = param.evaluate(L_0)\n", "x = np.linspace(0, L_0_eval, 100) # dimensionless space\n", "\n", + "\n", "def plot(t):\n", " t_in_seconds = t / 1e6\n", " f, (ax1, ax2) = plt.subplots(1, 2 ,figsize=(10,5))\n", @@ -585,6 +589,7 @@ " plt.tight_layout()\n", " plt.show()\n", " \n", + "\n", "import ipywidgets as widgets\n", "widgets.interact(plot, t=widgets.FloatSlider(min=0,max=solution.t[-1]*1e6,step=0.1,value=0));" ] diff --git a/docs/source/examples/notebooks/getting_started/tutorial-11-creating-a-submodel.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-11-creating-a-submodel.ipynb index b1dba81c84..72cac0cc24 100644 --- a/docs/source/examples/notebooks/getting_started/tutorial-11-creating-a-submodel.ipynb +++ b/docs/source/examples/notebooks/getting_started/tutorial-11-creating-a-submodel.ipynb @@ -137,7 +137,7 @@ " c0 = pybamm.Parameter(\"Initial concentration\")\n", " \n", " # add the initial conditions to the submodel dictionary\n", - " self.initial_conditions = {c: c0}\n" + " self.initial_conditions = {c: c0}" ] }, { @@ -162,7 +162,7 @@ " # update dictionary of model variables\n", " variables.update({\"Boundary flux\": j})\n", "\n", - " return variables\n" + " return variables" ] }, { diff --git a/docs/source/examples/notebooks/getting_started/tutorial-4-setting-parameter-values.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-4-setting-parameter-values.ipynb index 20fe938fba..59bbac5c9b 100644 --- a/docs/source/examples/notebooks/getting_started/tutorial-4-setting-parameter-values.ipynb +++ b/docs/source/examples/notebooks/getting_started/tutorial-4-setting-parameter-values.ipynb @@ -494,9 +494,11 @@ "source": [ "import numpy as np\n", "\n", + "\n", "def my_current(t):\n", " return pybamm.sin(2 * np.pi * t / 60)\n", "\n", + "\n", "parameter_values[\"Current function [A]\"] = my_current" ] }, diff --git a/docs/source/examples/notebooks/getting_started/tutorial-9-changing-the-mesh.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-9-changing-the-mesh.ipynb index 14bb7e461d..2bfb789a16 100644 --- a/docs/source/examples/notebooks/getting_started/tutorial-9-changing-the-mesh.ipynb +++ b/docs/source/examples/notebooks/getting_started/tutorial-9-changing-the-mesh.ipynb @@ -236,7 +236,7 @@ " model, solver=solver, parameter_values=parameter_values, var_pts=var_pts\n", " )\n", " sim.solve([0, 3600])\n", - " solutions.append(sim.solution)\n" + " solutions.append(sim.solution)" ] }, { diff --git a/docs/source/examples/notebooks/models/DFN-with-particle-size-distributions.ipynb b/docs/source/examples/notebooks/models/DFN-with-particle-size-distributions.ipynb index 3f73593d49..b24414dc7e 100644 --- a/docs/source/examples/notebooks/models/DFN-with-particle-size-distributions.ipynb +++ b/docs/source/examples/notebooks/models/DFN-with-particle-size-distributions.ipynb @@ -44,7 +44,6 @@ "source": [ "%pip install pybamm -q # install PyBaMM if it is not installed\n", "import pybamm\n", - "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, @@ -293,6 +292,8 @@ "\n", "# Set the area-weighted particle-size distribution.\n", "# Choose a lognormal (but any pybamm function could be used)\n", + "\n", + "\n", "def f_a_dist_p_dim(R):\n", " return pybamm.lognormal(R, R_av_p_dim, sd_p_dim)\n", "\n", @@ -397,9 +398,11 @@ "t_rest = 3600 # [s]\n", "I_typ = params[\"Nominal cell capacity [A.h]\"] # current for 1C\n", "\n", + "\n", "def current(t):\n", " return I_typ * pybamm.EqualHeaviside(t, t_cutoff)\n", "\n", + "\n", "params.update({\"Current function [A]\": current})\n", "t_eval = [0, t_cutoff + t_rest]\n", "\n", diff --git a/docs/source/examples/notebooks/models/MPM.ipynb b/docs/source/examples/notebooks/models/MPM.ipynb index c16098ff20..aacb847356 100644 --- a/docs/source/examples/notebooks/models/MPM.ipynb +++ b/docs/source/examples/notebooks/models/MPM.ipynb @@ -105,7 +105,6 @@ "source": [ "%pip install pybamm -q # install PyBaMM if it is not installed\n", "import pybamm\n", - "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, @@ -384,7 +383,6 @@ "t = sim.solution[\"Time [s]\"].entries\n", "\n", "\n", - "\n", "def plot_concentrations(t):\n", " f, axs = plt.subplots(1, 2 ,figsize=(10,3)) \n", " plot_c_n = axs[0].pcolormesh(\n", @@ -477,6 +475,8 @@ "\n", "# Set the area-weighted particle-size distributions.\n", "# Choose a lognormal (but any pybamm function could be used)\n", + "\n", + "\n", "def f_a_dist_n_dim(R):\n", " return pybamm.lognormal(R, R_a_n_dim, sd_a_n_dim)\n", "\n", @@ -484,7 +484,7 @@ "def f_a_dist_p_dim(R):\n", " return pybamm.lognormal(R, R_a_p_dim, sd_a_p_dim)\n", "\n", - "# Note: the only argument must be the particle size R\n" + "# Note: the only argument must be the particle size R" ] }, { @@ -504,7 +504,7 @@ " \"Positive area-weighted \"\n", " + \"particle-size distribution [m-1]\": f_a_dist_p_dim,\n", "}\n", - "params.update(distribution_params, check_already_exists=False)\n" + "params.update(distribution_params, check_already_exists=False)" ] }, { @@ -571,7 +571,6 @@ } ], "source": [ - "\n", "# The discrete sizes or \"bins\" used, and the distributions\n", "R_p = sim.solution[\"Positive particle sizes [m]\"].entries[:,0] # const in the current collector direction\n", "# The distributions\n", @@ -615,9 +614,12 @@ "sd_a_p_dim = pybamm.Parameter(\"Positive electrode area-weighted particle-size standard deviation [m]\")\n", "\n", "# Set the area-weighted particle-size distribution\n", + "\n", + "\n", "def f_a_dist_p_dim(R):\n", " return pybamm.lognormal(R, R_a_p_dim, sd_a_p_dim)\n", "\n", + "\n", "# input to param dictionary\n", "distribution_params = {\n", " \"Positive electrode area-weighted particle-size \"\n", diff --git a/docs/source/examples/notebooks/models/SEI-on-cracks.ipynb b/docs/source/examples/notebooks/models/SEI-on-cracks.ipynb index 065adb42d3..ee2dd91559 100644 --- a/docs/source/examples/notebooks/models/SEI-on-cracks.ipynb +++ b/docs/source/examples/notebooks/models/SEI-on-cracks.ipynb @@ -30,8 +30,7 @@ "source": [ "%pip install pybamm -q # install PyBaMM if it is not installed\n", "import pybamm\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np" + "import matplotlib.pyplot as plt" ] }, { diff --git a/docs/source/examples/notebooks/models/SPM.ipynb b/docs/source/examples/notebooks/models/SPM.ipynb index 1c1a5283a2..eb7f0662e2 100644 --- a/docs/source/examples/notebooks/models/SPM.ipynb +++ b/docs/source/examples/notebooks/models/SPM.ipynb @@ -1,1144 +1,1146 @@ { - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Single Particle Model (SPM) " - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Model Equations" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The SPM consists of two spherically symmetric diffusion equations: one within a representative negative particle ($\\text{k}=\\text{n}$) and one within a representative positive particle ($\\text{k}=\\text{p}$). In the centre of the particle the standard no-flux condition is imposed. Since the SPM assumes that all particles in an electrode behave in exactly the same way, the flux on the surface of a particle is simply the current $I$ divided by the thickness of the electrode $L_{\\text{k}}$. The concentration of lithium in electrode $\\text{k}$ is denoted $c_{\\text{k}}$ and the current is denoted by $I$. The model equations for the SPM are then: \n", - "$$\n", - "\\frac{\\partial c_{\\text{s,k}}}{\\partial t} = -\\frac{1}{r_{\\text{k}}^2} \\frac{\\partial}{\\partial r_{\\text{k}}} \\left(r_{\\text{k}}^2 N_{\\text{s,k}}\\right), \\\\\n", - "N_{\\text{s,k}} = -D_{\\text{s,k}}(c_{\\text{s,k}}) \\frac{\\partial c_{\\text{s,k}}}{\\partial r_{\\text{k}}}, \\quad \\text{k} \\in \\text{n, p},\n", - "$$\n", - "\n", - "$$\n", - "N_{\\text{s,k}}\\big|_{r_{\\text{k}}=0} = 0, \\quad \\text{k} \\in \\text{n, p}, \\quad \\ \\ - N_{\\text{s,k}}\\big|_{r_{\\text{k}}=1} = \n", - "\\begin{cases}\n", - "\t\t \\frac{I}{Fa_{\\text{n}}L_{\\text{n}}}, \\quad &\\text{k}=\\text{n}, \\\\ \n", - "\t\t -\\frac{I}{Fa_{\\text{p}}L_{\\text{p}}}, \\quad &\\text{k}=\\text{p}, \n", - "\\end{cases} \\\\\n", - "c_{\\text{s,k}}(r_{\\text{k}},0) = c_{\\text{s,k,0}}, \\quad \\text{k} \\in \\text{n, p},$$\n", - "where $D_{\\text{s,k}}$ is the diffusion coefficient in the solid, $N_{\\text{s,k}}$ denotes the flux of lithium ions in the solid particle within the region $\\text{k}$, and $r_{\\text{k}} \\in[0,1]$ is the radial coordinate of the particle in electrode $\\text{k}$. \n", - "\n", - "### Voltage Expression\n", - "The voltage is obtained from the expression: \n", - "$$\n", - "V = U_{\\text{p}}(c_{\\text{p}})\\big|_{r_{\\text{p}}=1} - U_{\\text{n}}(c_{\\text{n}})\\big|_{r_{\\text{n}}=1} - \\frac{2RT}{F}\\sinh^{-1}\\left(\\frac{I}{2j_{\\text{0,p}} a_{\\text{p}}L_{\\text{p}}}\\right) - \\frac{2RT}{F}\\sinh^{-1}\\left(\\frac{I}{2j_{\\text{0,n}} a_{\\text{p}}L_{\\text{n}}}\\right)\n", - "$$\n", - "with the exchange current densities given by\n", - "$$j_{\\text{0,k}} = (c_{\\text{k}})^{1/2}(1-c_{\\text{k}})^{1/2} $$\n", - "\n", - "More details can be found in [[3]](#References)." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Example solving SPM using PyBaMM\n", - "\n", - "Below we show how to solve the Single Particle Model, using the default geometry, mesh, parameters, discretisation and solver provided with PyBaMM. In this notebook we explicitly handle all the stages of setting up, processing and solving the model in order to explain them in detail. However, it is often simpler in practice to use the `Simulation` class, which handles many of the stages automatically, as shown [here](../simulation-class.ipynb).\n", - "\n", - "First we need to import `pybamm`, and then change our working directory to the root of the pybamm folder. " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], - "source": [ - "%pip install pybamm -q # install PyBaMM if it is not installed\n", - "import pybamm\n", - "import numpy as np\n", - "import os\n", - "import matplotlib.pyplot as plt\n", - "os.chdir(pybamm.__path__[0]+'/..')" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We then create an instance of the SPM:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "model = pybamm.lithium_ion.SPM()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The model object is a subtype of [`pybamm.BaseModel`](https://docs.pybamm.org/en/latest/source/api/models/base_models/base_model.html), and contains all the equations that define this particular model. For example, the `rhs` dict contained in `model` has a dictionary mapping variables such as $c_n$ to the equation representing its rate of change with time (i.e. $\\partial{c_n}/\\partial{t}$). We can see this explicitly by visualising this entry in the `rhs` dict:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rhs equation for variable ' Throughput capacity [A.h] ' is:\n" - ] - } - ], - "source": [ - "variable = list(model.rhs.keys())[1]\n", - "equation = list(model.rhs.values())[1]\n", - "print('rhs equation for variable \\'',variable,'\\' is:')\n", - "path = 'docs/source/examples/notebooks/models/'\n", - "equation.visualise(path+'spm1.png')" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![spm1](spm1.png)" - ] - }, + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Single Particle Model (SPM) " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Equations" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The SPM consists of two spherically symmetric diffusion equations: one within a representative negative particle ($\\text{k}=\\text{n}$) and one within a representative positive particle ($\\text{k}=\\text{p}$). In the centre of the particle the standard no-flux condition is imposed. Since the SPM assumes that all particles in an electrode behave in exactly the same way, the flux on the surface of a particle is simply the current $I$ divided by the thickness of the electrode $L_{\\text{k}}$. The concentration of lithium in electrode $\\text{k}$ is denoted $c_{\\text{k}}$ and the current is denoted by $I$. The model equations for the SPM are then: \n", + "$$\n", + "\\frac{\\partial c_{\\text{s,k}}}{\\partial t} = -\\frac{1}{r_{\\text{k}}^2} \\frac{\\partial}{\\partial r_{\\text{k}}} \\left(r_{\\text{k}}^2 N_{\\text{s,k}}\\right), \\\\\n", + "N_{\\text{s,k}} = -D_{\\text{s,k}}(c_{\\text{s,k}}) \\frac{\\partial c_{\\text{s,k}}}{\\partial r_{\\text{k}}}, \\quad \\text{k} \\in \\text{n, p},\n", + "$$\n", + "\n", + "$$\n", + "N_{\\text{s,k}}\\big|_{r_{\\text{k}}=0} = 0, \\quad \\text{k} \\in \\text{n, p}, \\quad \\ \\ - N_{\\text{s,k}}\\big|_{r_{\\text{k}}=1} = \n", + "\\begin{cases}\n", + "\t\t \\frac{I}{Fa_{\\text{n}}L_{\\text{n}}}, \\quad &\\text{k}=\\text{n}, \\\\ \n", + "\t\t -\\frac{I}{Fa_{\\text{p}}L_{\\text{p}}}, \\quad &\\text{k}=\\text{p}, \n", + "\\end{cases} \\\\\n", + "c_{\\text{s,k}}(r_{\\text{k}},0) = c_{\\text{s,k,0}}, \\quad \\text{k} \\in \\text{n, p},$$\n", + "where $D_{\\text{s,k}}$ is the diffusion coefficient in the solid, $N_{\\text{s,k}}$ denotes the flux of lithium ions in the solid particle within the region $\\text{k}$, and $r_{\\text{k}} \\in[0,1]$ is the radial coordinate of the particle in electrode $\\text{k}$. \n", + "\n", + "### Voltage Expression\n", + "The voltage is obtained from the expression: \n", + "$$\n", + "V = U_{\\text{p}}(c_{\\text{p}})\\big|_{r_{\\text{p}}=1} - U_{\\text{n}}(c_{\\text{n}})\\big|_{r_{\\text{n}}=1} - \\frac{2RT}{F}\\sinh^{-1}\\left(\\frac{I}{2j_{\\text{0,p}} a_{\\text{p}}L_{\\text{p}}}\\right) - \\frac{2RT}{F}\\sinh^{-1}\\left(\\frac{I}{2j_{\\text{0,n}} a_{\\text{p}}L_{\\text{n}}}\\right)\n", + "$$\n", + "with the exchange current densities given by\n", + "$$j_{\\text{0,k}} = (c_{\\text{k}})^{1/2}(1-c_{\\text{k}})^{1/2} $$\n", + "\n", + "More details can be found in [[3]](#References)." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example solving SPM using PyBaMM\n", + "\n", + "Below we show how to solve the Single Particle Model, using the default geometry, mesh, parameters, discretisation and solver provided with PyBaMM. In this notebook we explicitly handle all the stages of setting up, processing and solving the model in order to explain them in detail. However, it is often simpler in practice to use the `Simulation` class, which handles many of the stages automatically, as shown [here](../simulation-class.ipynb).\n", + "\n", + "First we need to import `pybamm`, and then change our working directory to the root of the pybamm folder. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We need a geometry in which to define our model equations. In pybamm this is represented by the [`pybamm.Geometry`](https://docs.pybamm.org/en/latest/source/api/geometry/index.html) class. In this case we use the default geometry object defined by the model" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install pybamm -q # install PyBaMM if it is not installed\n", + "import pybamm\n", + "import numpy as np\n", + "import os\n", + "import matplotlib.pyplot as plt\n", + "os.chdir(pybamm.__path__[0]+'/..')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We then create an instance of the SPM:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "model = pybamm.lithium_ion.SPM()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model object is a subtype of [`pybamm.BaseModel`](https://docs.pybamm.org/en/latest/source/api/models/base_models/base_model.html), and contains all the equations that define this particular model. For example, the `rhs` dict contained in `model` has a dictionary mapping variables such as $c_n$ to the equation representing its rate of change with time (i.e. $\\partial{c_n}/\\partial{t}$). We can see this explicitly by visualising this entry in the `rhs` dict:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "geometry = model.default_geometry" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "rhs equation for variable ' Throughput capacity [A.h] ' is:\n" + ] + } + ], + "source": [ + "variable = list(model.rhs.keys())[1]\n", + "equation = list(model.rhs.values())[1]\n", + "print('rhs equation for variable \\'',variable,'\\' is:')\n", + "path = 'docs/source/examples/notebooks/models/'\n", + "equation.visualise(path+'spm1.png')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![spm1](spm1.png)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We need a geometry in which to define our model equations. In pybamm this is represented by the [`pybamm.Geometry`](https://docs.pybamm.org/en/latest/source/api/geometry/index.html) class. In this case we use the default geometry object defined by the model" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "geometry = model.default_geometry" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This geometry object defines a number of domains, each with its own name, spatial variables and min/max limits (the latter are represented as equations similar to the rhs equation shown above). For instance, the SPM has the following domains:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This geometry object defines a number of domains, each with its own name, spatial variables and min/max limits (the latter are represented as equations similar to the rhs equation shown above). For instance, the SPM has the following domains:" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "SPM domains:\n", + "1. negative electrode with variables:\n", + " -( 0 ) <= x_n <= ( Negative electrode thickness [m] )\n", + "2. separator with variables:\n", + " -( Negative electrode thickness [m] ) <= x_s <= ( Negative electrode thickness [m] + Separator thickness [m] )\n", + "3. positive electrode with variables:\n", + " -( Negative electrode thickness [m] + Separator thickness [m] ) <= x_p <= ( Negative electrode thickness [m] + Separator thickness [m] + Positive electrode thickness [m] )\n", + "4. negative particle with variables:\n", + " -( 0 ) <= r_n <= ( Negative particle radius [m] )\n", + "5. positive particle with variables:\n", + " -( 0 ) <= r_p <= ( Positive particle radius [m] )\n", + "6. current collector with variables:\n", + "z = 1\n" + ] + } + ], + "source": [ + "print('SPM domains:')\n", + "for i, (k, v) in enumerate(geometry.items()):\n", + " print(str(i+1)+'.',k,'with variables:')\n", + " for var, rng in v.items():\n", + " if 'min' in rng:\n", + " print(' -(',rng['min'],') <=',var,'<= (',rng['max'],')')\n", + " else:\n", + " print(var, '=', rng['position'])" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Both the model equations and the geometry include parameters, such as $L_p$. We can substitute these symbolic parameters in the model with values by using the [`pybamm.ParameterValues`](https://docs.pybamm.org/en/latest/source/api/parameters/parameter_values.html) class, which takes either a python dictionary or CSV file with the mapping between parameter names and values. Rather than create our own instance of `pybamm.ParameterValues`, we will use the default parameter set included in the model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "param = model.default_parameter_values" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then apply this parameter set to the model and geometry" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "param.process_model(model)\n", + "param.process_geometry(geometry)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The next step is to mesh the input geometry. We can do this using the [`pybamm.Mesh`](https://docs.pybamm.org/en/latest/source/api/meshes/index.html) class. This class takes in the geometry of the problem, and also two dictionaries containing the type of mesh to use within each domain of the geometry (i.e. within the positive or negative electrode domains), and the number of mesh points. \n", + "\n", + "The default mesh types and the default number of points to use in each variable for the SPM are:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SPM domains:\n", - "1. negative electrode with variables:\n", - " -( 0 ) <= x_n <= ( Negative electrode thickness [m] )\n", - "2. separator with variables:\n", - " -( Negative electrode thickness [m] ) <= x_s <= ( Negative electrode thickness [m] + Separator thickness [m] )\n", - "3. positive electrode with variables:\n", - " -( Negative electrode thickness [m] + Separator thickness [m] ) <= x_p <= ( Negative electrode thickness [m] + Separator thickness [m] + Positive electrode thickness [m] )\n", - "4. negative particle with variables:\n", - " -( 0 ) <= r_n <= ( Negative particle radius [m] )\n", - "5. positive particle with variables:\n", - " -( 0 ) <= r_p <= ( Positive particle radius [m] )\n", - "6. current collector with variables:\n", - "z = 1\n" - ] - } - ], - "source": [ - "print('SPM domains:')\n", - "for i, (k, v) in enumerate(geometry.items()):\n", - " print(str(i+1)+'.',k,'with variables:')\n", - " for var, rng in v.items():\n", - " if 'min' in rng:\n", - " print(' -(',rng['min'],') <=',var,'<= (',rng['max'],')')\n", - " else:\n", - " print(var, '=', rng['position'])" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "negative electrode is of type Uniform1DSubMesh\n", + "separator is of type Uniform1DSubMesh\n", + "positive electrode is of type Uniform1DSubMesh\n", + "negative particle is of type Uniform1DSubMesh\n", + "positive particle is of type Uniform1DSubMesh\n", + "negative primary particle is of type Uniform1DSubMesh\n", + "positive primary particle is of type Uniform1DSubMesh\n", + "negative secondary particle is of type Uniform1DSubMesh\n", + "positive secondary particle is of type Uniform1DSubMesh\n", + "negative particle size is of type Uniform1DSubMesh\n", + "positive particle size is of type Uniform1DSubMesh\n", + "current collector is of type SubMesh0D\n", + "x_n has 20 mesh points\n", + "x_s has 20 mesh points\n", + "x_p has 20 mesh points\n", + "r_n has 20 mesh points\n", + "r_p has 20 mesh points\n", + "r_n_prim has 20 mesh points\n", + "r_p_prim has 20 mesh points\n", + "r_n_sec has 20 mesh points\n", + "r_p_sec has 20 mesh points\n", + "y has 10 mesh points\n", + "z has 10 mesh points\n", + "R_n has 30 mesh points\n", + "R_p has 30 mesh points\n" + ] + } + ], + "source": [ + "for k, t in model.default_submesh_types.items():\n", + " print(k,'is of type',t.__name__)\n", + "for var, npts in model.default_var_pts.items():\n", + " print(var,'has',npts,'mesh points')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With these defaults, we can then create our mesh of the given geometry:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "mesh = pybamm.Mesh(geometry, model.default_submesh_types, model.default_var_pts)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The next step is to discretise the model equations using this mesh. We do this using the [`pybamm.Discretisation`](https://docs.pybamm.org/en/latest/source/api/spatial_methods/discretisation.html) class, which takes both the mesh we have already created, and a dictionary of spatial methods to use for each geometry domain. For the case of the SPM, we use the following defaults for the spatial discretisation methods:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Both the model equations and the geometry include parameters, such as $L_p$. We can substitute these symbolic parameters in the model with values by using the [`pybamm.ParameterValues`](https://docs.pybamm.org/en/latest/source/api/parameters/parameter_values.html) class, which takes either a python dictionary or CSV file with the mapping between parameter names and values. Rather than create our own instance of `pybamm.ParameterValues`, we will use the default parameter set included in the model" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "macroscale is discretised using FiniteVolume method\n", + "negative particle is discretised using FiniteVolume method\n", + "positive particle is discretised using FiniteVolume method\n", + "negative primary particle is discretised using FiniteVolume method\n", + "positive primary particle is discretised using FiniteVolume method\n", + "negative secondary particle is discretised using FiniteVolume method\n", + "positive secondary particle is discretised using FiniteVolume method\n", + "negative particle size is discretised using FiniteVolume method\n", + "positive particle size is discretised using FiniteVolume method\n", + "current collector is discretised using ZeroDimensionalSpatialMethod method\n" + ] + } + ], + "source": [ + "for k, method in model.default_spatial_methods.items():\n", + " print(k,'is discretised using',method.__class__.__name__,'method')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We then create the `pybamm.Discretisation` object, and use this to discretise the model equations" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "param = model.default_parameter_values" + "data": { + "text/plain": [ + "" ] - }, + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "disc = pybamm.Discretisation(mesh, model.default_spatial_methods)\n", + "disc.process_model(model)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After this stage, all of the variables in `model` have been discretised into `pybamm.StateVector` objects, and spatial operators have been replaced by matrix-vector multiplications, ready to be evaluated within a time-stepping algorithm of a given solver. For example, the rhs expression for $\\partial{c_n}/\\partial{t}$ that we visualised above is now represented by:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "model.concatenated_rhs.children[1].visualise(path+'spm2.png')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![spm2](spm2.png)\n", + "\n", + "Now we are ready to run the time-stepping routine to solve the model. Once again we use the default ODE solver." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can then apply this parameter set to the model and geometry" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Solving using CasadiSolver solver...\n", + "Finished.\n" + ] + } + ], + "source": [ + "# Solve the model at the given time points (in seconds)\n", + "solver = model.default_solver\n", + "n = 250\n", + "t_eval = np.linspace(0, 3600, n)\n", + "print('Solving using',type(solver).__name__,'solver...')\n", + "solution = solver.solve(model, t_eval)\n", + "print('Finished.')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each model in pybamm has a list of relevant variables defined in the model, for use in visualising the model solution or for comparison with other models. The SPM defines the following variables:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "param.process_model(model)\n", - "param.process_geometry(geometry)" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "SPM model variables:\n", + "\t- Time [s]\n", + "\t- Time [min]\n", + "\t- Time [h]\n", + "\t- x [m]\n", + "\t- x_n [m]\n", + "\t- x_s [m]\n", + "\t- x_p [m]\n", + "\t- r_n [m]\n", + "\t- r_p [m]\n", + "\t- Current variable [A]\n", + "\t- Total current density [A.m-2]\n", + "\t- Current [A]\n", + "\t- C-rate\n", + "\t- Discharge capacity [A.h]\n", + "\t- Throughput capacity [A.h]\n", + "\t- Discharge energy [W.h]\n", + "\t- Throughput energy [W.h]\n", + "\t- Porosity\n", + "\t- Negative electrode porosity\n", + "\t- X-averaged negative electrode porosity\n", + "\t- Separator porosity\n", + "\t- X-averaged separator porosity\n", + "\t- Positive electrode porosity\n", + "\t- X-averaged positive electrode porosity\n", + "\t- Porosity change\n", + "\t- Negative electrode porosity change [s-1]\n", + "\t- X-averaged negative electrode porosity change [s-1]\n", + "\t- Separator porosity change [s-1]\n", + "\t- X-averaged separator porosity change [s-1]\n", + "\t- Positive electrode porosity change [s-1]\n", + "\t- X-averaged positive electrode porosity change [s-1]\n", + "\t- Negative electrode interface utilisation variable\n", + "\t- X-averaged negative electrode interface utilisation variable\n", + "\t- Negative electrode interface utilisation\n", + "\t- X-averaged negative electrode interface utilisation\n", + "\t- Positive electrode interface utilisation variable\n", + "\t- X-averaged positive electrode interface utilisation variable\n", + "\t- Positive electrode interface utilisation\n", + "\t- X-averaged positive electrode interface utilisation\n", + "\t- Negative particle crack length [m]\n", + "\t- X-averaged negative particle crack length [m]\n", + "\t- Negative particle cracking rate [m.s-1]\n", + "\t- X-averaged negative particle cracking rate [m.s-1]\n", + "\t- Positive particle crack length [m]\n", + "\t- X-averaged positive particle crack length [m]\n", + "\t- Positive particle cracking rate [m.s-1]\n", + "\t- X-averaged positive particle cracking rate [m.s-1]\n", + "\t- Negative electrode active material volume fraction\n", + "\t- X-averaged negative electrode active material volume fraction\n", + "\t- Negative electrode capacity [A.h]\n", + "\t- Negative particle radius\n", + "\t- Negative particle radius [m]\n", + "\t- X-averaged negative particle radius [m]\n", + "\t- Negative electrode surface area to volume ratio [m-1]\n", + "\t- X-averaged negative electrode surface area to volume ratio [m-1]\n", + "\t- Negative electrode active material volume fraction change [s-1]\n", + "\t- X-averaged negative electrode active material volume fraction change [s-1]\n", + "\t- Loss of lithium due to loss of active material in negative electrode [mol]\n", + "\t- Positive electrode active material volume fraction\n", + "\t- X-averaged positive electrode active material volume fraction\n", + "\t- Positive electrode capacity [A.h]\n", + "\t- Positive particle radius\n", + "\t- Positive particle radius [m]\n", + "\t- X-averaged positive particle radius [m]\n", + "\t- Positive electrode surface area to volume ratio [m-1]\n", + "\t- X-averaged positive electrode surface area to volume ratio [m-1]\n", + "\t- Positive electrode active material volume fraction change [s-1]\n", + "\t- X-averaged positive electrode active material volume fraction change [s-1]\n", + "\t- Loss of lithium due to loss of active material in positive electrode [mol]\n", + "\t- Separator pressure [Pa]\n", + "\t- X-averaged separator pressure [Pa]\n", + "\t- negative electrode transverse volume-averaged velocity [m.s-1]\n", + "\t- X-averaged negative electrode transverse volume-averaged velocity [m.s-1]\n", + "\t- separator transverse volume-averaged velocity [m.s-1]\n", + "\t- X-averaged separator transverse volume-averaged velocity [m.s-1]\n", + "\t- positive electrode transverse volume-averaged velocity [m.s-1]\n", + "\t- X-averaged positive electrode transverse volume-averaged velocity [m.s-1]\n", + "\t- Transverse volume-averaged velocity [m.s-1]\n", + "\t- negative electrode transverse volume-averaged acceleration [m.s-2]\n", + "\t- X-averaged negative electrode transverse volume-averaged acceleration [m.s-2]\n", + "\t- separator transverse volume-averaged acceleration [m.s-2]\n", + "\t- X-averaged separator transverse volume-averaged acceleration [m.s-2]\n", + "\t- positive electrode transverse volume-averaged acceleration [m.s-2]\n", + "\t- X-averaged positive electrode transverse volume-averaged acceleration [m.s-2]\n", + "\t- Transverse volume-averaged acceleration [m.s-2]\n", + "\t- Negative electrode volume-averaged velocity [m.s-1]\n", + "\t- Negative electrode volume-averaged acceleration [m.s-2]\n", + "\t- X-averaged negative electrode volume-averaged acceleration [m.s-2]\n", + "\t- Negative electrode pressure [Pa]\n", + "\t- X-averaged negative electrode pressure [Pa]\n", + "\t- Positive electrode volume-averaged velocity [m.s-1]\n", + "\t- Positive electrode volume-averaged acceleration [m.s-2]\n", + "\t- X-averaged positive electrode volume-averaged acceleration [m.s-2]\n", + "\t- Positive electrode pressure [Pa]\n", + "\t- X-averaged positive electrode pressure [Pa]\n", + "\t- Negative particle stoichiometry\n", + "\t- Negative particle concentration\n", + "\t- Negative particle concentration [mol.m-3]\n", + "\t- X-averaged negative particle concentration\n", + "\t- X-averaged negative particle concentration [mol.m-3]\n", + "\t- R-averaged negative particle concentration\n", + "\t- R-averaged negative particle concentration [mol.m-3]\n", + "\t- Average negative particle concentration\n", + "\t- Average negative particle concentration [mol.m-3]\n", + "\t- Negative particle surface stoichiometry\n", + "\t- Negative particle surface concentration\n", + "\t- Negative particle surface concentration [mol.m-3]\n", + "\t- X-averaged negative particle surface concentration\n", + "\t- X-averaged negative particle surface concentration [mol.m-3]\n", + "\t- Negative electrode extent of lithiation\n", + "\t- X-averaged negative electrode extent of lithiation\n", + "\t- Minimum negative particle concentration\n", + "\t- Maximum negative particle concentration\n", + "\t- Minimum negative particle concentration [mol.m-3]\n", + "\t- Maximum negative particle concentration [mol.m-3]\n", + "\t- Minimum negative particle surface concentration\n", + "\t- Maximum negative particle surface concentration\n", + "\t- Minimum negative particle surface concentration [mol.m-3]\n", + "\t- Maximum negative particle surface concentration [mol.m-3]\n", + "\t- Positive particle stoichiometry\n", + "\t- Positive particle concentration\n", + "\t- Positive particle concentration [mol.m-3]\n", + "\t- X-averaged positive particle concentration\n", + "\t- X-averaged positive particle concentration [mol.m-3]\n", + "\t- R-averaged positive particle concentration\n", + "\t- R-averaged positive particle concentration [mol.m-3]\n", + "\t- Average positive particle concentration\n", + "\t- Average positive particle concentration [mol.m-3]\n", + "\t- Positive particle surface stoichiometry\n", + "\t- Positive particle surface concentration\n", + "\t- Positive particle surface concentration [mol.m-3]\n", + "\t- X-averaged positive particle surface concentration\n", + "\t- X-averaged positive particle surface concentration [mol.m-3]\n", + "\t- Positive electrode extent of lithiation\n", + "\t- X-averaged positive electrode extent of lithiation\n", + "\t- Minimum positive particle concentration\n", + "\t- Maximum positive particle concentration\n", + "\t- Minimum positive particle concentration [mol.m-3]\n", + "\t- Maximum positive particle concentration [mol.m-3]\n", + "\t- Minimum positive particle surface concentration\n", + "\t- Maximum positive particle surface concentration\n", + "\t- Minimum positive particle surface concentration [mol.m-3]\n", + "\t- Maximum positive particle surface concentration [mol.m-3]\n", + "\t- Porosity times concentration [mol.m-3]\n", + "\t- Negative electrode porosity times concentration [mol.m-3]\n", + "\t- Separator porosity times concentration [mol.m-3]\n", + "\t- Positive electrode porosity times concentration [mol.m-3]\n", + "\t- Total lithium in electrolyte [mol]\n", + "\t- Electrolyte flux [mol.m-2.s-1]\n", + "\t- Ambient temperature [K]\n", + "\t- Cell temperature [K]\n", + "\t- Negative current collector temperature [K]\n", + "\t- Positive current collector temperature [K]\n", + "\t- X-averaged cell temperature [K]\n", + "\t- Volume-averaged cell temperature [K]\n", + "\t- Negative electrode temperature [K]\n", + "\t- X-averaged negative electrode temperature [K]\n", + "\t- Separator temperature [K]\n", + "\t- X-averaged separator temperature [K]\n", + "\t- Positive electrode temperature [K]\n", + "\t- X-averaged positive electrode temperature [K]\n", + "\t- Ambient temperature [C]\n", + "\t- Cell temperature [C]\n", + "\t- Negative current collector temperature [C]\n", + "\t- Positive current collector temperature [C]\n", + "\t- X-averaged cell temperature [C]\n", + "\t- Volume-averaged cell temperature [C]\n", + "\t- Negative electrode temperature [C]\n", + "\t- X-averaged negative electrode temperature [C]\n", + "\t- Separator temperature [C]\n", + "\t- X-averaged separator temperature [C]\n", + "\t- Positive electrode temperature [C]\n", + "\t- X-averaged positive electrode temperature [C]\n", + "\t- Negative current collector potential [V]\n", + "\t- Inner SEI thickness [m]\n", + "\t- Outer SEI thickness [m]\n", + "\t- X-averaged inner SEI thickness [m]\n", + "\t- X-averaged outer SEI thickness [m]\n", + "\t- SEI [m]\n", + "\t- Total SEI thickness [m]\n", + "\t- X-averaged SEI thickness [m]\n", + "\t- X-averaged total SEI thickness [m]\n", + "\t- X-averaged negative electrode resistance [Ohm.m2]\n", + "\t- Inner SEI interfacial current density [A.m-2]\n", + "\t- X-averaged inner SEI interfacial current density [A.m-2]\n", + "\t- Outer SEI interfacial current density [A.m-2]\n", + "\t- X-averaged outer SEI interfacial current density [A.m-2]\n", + "\t- SEI interfacial current density [A.m-2]\n", + "\t- X-averaged SEI interfacial current density [A.m-2]\n", + "\t- Inner SEI on cracks thickness [m]\n", + "\t- Outer SEI on cracks thickness [m]\n", + "\t- X-averaged inner SEI on cracks thickness [m]\n", + "\t- X-averaged outer SEI on cracks thickness [m]\n", + "\t- SEI on cracks [m]\n", + "\t- Total SEI on cracks thickness [m]\n", + "\t- X-averaged SEI on cracks thickness [m]\n", + "\t- X-averaged total SEI on cracks thickness [m]\n", + "\t- Inner SEI on cracks interfacial current density [A.m-2]\n", + "\t- X-averaged inner SEI on cracks interfacial current density [A.m-2]\n", + "\t- Outer SEI on cracks interfacial current density [A.m-2]\n", + "\t- X-averaged outer SEI on cracks interfacial current density [A.m-2]\n", + "\t- SEI on cracks interfacial current density [A.m-2]\n", + "\t- X-averaged SEI on cracks interfacial current density [A.m-2]\n", + "\t- Lithium plating concentration [mol.m-3]\n", + "\t- X-averaged lithium plating concentration [mol.m-3]\n", + "\t- Dead lithium concentration [mol.m-3]\n", + "\t- X-averaged dead lithium concentration [mol.m-3]\n", + "\t- Lithium plating thickness [m]\n", + "\t- X-averaged lithium plating thickness [m]\n", + "\t- Dead lithium thickness [m]\n", + "\t- X-averaged dead lithium thickness [m]\n", + "\t- Loss of lithium to lithium plating [mol]\n", + "\t- Loss of capacity to lithium plating [A.h]\n", + "\t- Negative electrode lithium plating reaction overpotential [V]\n", + "\t- X-averaged negative electrode lithium plating reaction overpotential [V]\n", + "\t- Lithium plating interfacial current density [A.m-2]\n", + "\t- X-averaged lithium plating interfacial current density [A.m-2]\n", + "\t- Negative crack surface to volume ratio [m-1]\n", + "\t- Negative electrode roughness ratio\n", + "\t- X-averaged negative electrode roughness ratio\n", + "\t- Positive crack surface to volume ratio [m-1]\n", + "\t- Positive electrode roughness ratio\n", + "\t- X-averaged positive electrode roughness ratio\n", + "\t- Electrolyte transport efficiency\n", + "\t- Negative electrolyte transport efficiency\n", + "\t- X-averaged negative electrolyte transport efficiency\n", + "\t- Separator electrolyte transport efficiency\n", + "\t- X-averaged separator electrolyte transport efficiency\n", + "\t- Positive electrolyte transport efficiency\n", + "\t- X-averaged positive electrolyte transport efficiency\n", + "\t- Electrode transport efficiency\n", + "\t- Negative electrode transport efficiency\n", + "\t- X-averaged negative electrode transport efficiency\n", + "\t- Separator electrode transport efficiency\n", + "\t- X-averaged separator electrode transport efficiency\n", + "\t- Positive electrode transport efficiency\n", + "\t- X-averaged positive electrode transport efficiency\n", + "\t- Separator volume-averaged velocity [m.s-1]\n", + "\t- Separator volume-averaged acceleration [m.s-2]\n", + "\t- X-averaged separator volume-averaged acceleration [m.s-2]\n", + "\t- Volume-averaged velocity [m.s-1]\n", + "\t- Volume-averaged acceleration [m.s-1]\n", + "\t- X-averaged volume-averaged acceleration [m.s-1]\n", + "\t- Pressure [Pa]\n", + "\t- Negative electrode stoichiometry\n", + "\t- Negative electrode volume-averaged concentration\n", + "\t- Negative electrode volume-averaged concentration [mol.m-3]\n", + "\t- Total lithium in primary phase in negative electrode [mol]\n", + "\t- Positive electrode stoichiometry\n", + "\t- Positive electrode volume-averaged concentration\n", + "\t- Positive electrode volume-averaged concentration [mol.m-3]\n", + "\t- Total lithium in primary phase in positive electrode [mol]\n", + "\t- Electrolyte concentration concatenation [mol.m-3]\n", + "\t- Negative electrolyte concentration [mol.m-3]\n", + "\t- X-averaged negative electrolyte concentration [mol.m-3]\n", + "\t- Separator electrolyte concentration [mol.m-3]\n", + "\t- X-averaged separator electrolyte concentration [mol.m-3]\n", + "\t- Positive electrolyte concentration [mol.m-3]\n", + "\t- X-averaged positive electrolyte concentration [mol.m-3]\n", + "\t- Negative electrolyte concentration [Molar]\n", + "\t- X-averaged negative electrolyte concentration [Molar]\n", + "\t- Separator electrolyte concentration [Molar]\n", + "\t- X-averaged separator electrolyte concentration [Molar]\n", + "\t- Positive electrolyte concentration [Molar]\n", + "\t- X-averaged positive electrolyte concentration [Molar]\n", + "\t- Electrolyte concentration [mol.m-3]\n", + "\t- X-averaged electrolyte concentration [mol.m-3]\n", + "\t- Electrolyte concentration [Molar]\n", + "\t- X-averaged electrolyte concentration [Molar]\n", + "\t- Ohmic heating [W.m-3]\n", + "\t- X-averaged Ohmic heating [W.m-3]\n", + "\t- Volume-averaged Ohmic heating [W.m-3]\n", + "\t- Irreversible electrochemical heating [W.m-3]\n", + "\t- X-averaged irreversible electrochemical heating [W.m-3]\n", + "\t- Volume-averaged irreversible electrochemical heating [W.m-3]\n", + "\t- Reversible heating [W.m-3]\n", + "\t- X-averaged reversible heating [W.m-3]\n", + "\t- Volume-averaged reversible heating [W.m-3]\n", + "\t- Total heating [W.m-3]\n", + "\t- X-averaged total heating [W.m-3]\n", + "\t- Volume-averaged total heating [W.m-3]\n", + "\t- Current collector current density [A.m-2]\n", + "\t- Inner SEI concentration [mol.m-3]\n", + "\t- X-averaged inner SEI concentration [mol.m-3]\n", + "\t- Outer SEI concentration [mol.m-3]\n", + "\t- X-averaged outer SEI concentration [mol.m-3]\n", + "\t- SEI concentration [mol.m-3]\n", + "\t- X-averaged SEI concentration [mol.m-3]\n", + "\t- Loss of lithium to SEI [mol]\n", + "\t- Loss of capacity to SEI [A.h]\n", + "\t- X-averaged negative electrode SEI interfacial current density [A.m-2]\n", + "\t- Negative electrode SEI interfacial current density [A.m-2]\n", + "\t- Positive electrode SEI interfacial current density [A.m-2]\n", + "\t- X-averaged positive electrode SEI volumetric interfacial current density [A.m-2]\n", + "\t- Positive electrode SEI volumetric interfacial current density [A.m-3]\n", + "\t- Negative electrode SEI volumetric interfacial current density [A.m-3]\n", + "\t- X-averaged negative electrode SEI volumetric interfacial current density [A.m-3]\n", + "\t- Inner SEI on cracks concentration [mol.m-3]\n", + "\t- X-averaged inner SEI on cracks concentration [mol.m-3]\n", + "\t- Outer SEI on cracks concentration [mol.m-3]\n", + "\t- X-averaged outer SEI on cracks concentration [mol.m-3]\n", + "\t- SEI on cracks concentration [mol.m-3]\n", + "\t- X-averaged SEI on cracks concentration [mol.m-3]\n", + "\t- Loss of lithium to SEI on cracks [mol]\n", + "\t- Loss of capacity to SEI on cracks [A.h]\n", + "\t- X-averaged negative electrode SEI on cracks interfacial current density [A.m-2]\n", + "\t- Negative electrode SEI on cracks interfacial current density [A.m-2]\n", + "\t- Positive electrode SEI on cracks interfacial current density [A.m-2]\n", + "\t- X-averaged positive electrode SEI on cracks volumetric interfacial current density [A.m-2]\n", + "\t- Positive electrode SEI on cracks volumetric interfacial current density [A.m-3]\n", + "\t- Negative electrode SEI on cracks volumetric interfacial current density [A.m-3]\n", + "\t- X-averaged negative electrode SEI on cracks volumetric interfacial current density [A.m-3]\n", + "\t- Negative electrode lithium plating interfacial current density [A.m-2]\n", + "\t- X-averaged negative electrode lithium plating interfacial current density [A.m-2]\n", + "\t- Lithium plating volumetric interfacial current density [A.m-3]\n", + "\t- X-averaged lithium plating volumetric interfacial current density [A.m-3]\n", + "\t- X-averaged positive electrode lithium plating interfacial current density [A.m-2]\n", + "\t- X-averaged positive electrode lithium plating volumetric interfacial current density [A.m-3]\n", + "\t- Positive electrode lithium plating interfacial current density [A.m-2]\n", + "\t- Positive electrode lithium plating volumetric interfacial current density [A.m-3]\n", + "\t- Negative electrode lithium plating volumetric interfacial current density [A.m-3]\n", + "\t- X-averaged negative electrode lithium plating volumetric interfacial current density [A.m-3]\n", + "\t- Negative electrode open-circuit potential [V]\n", + "\t- X-averaged negative electrode open-circuit potential [V]\n", + "\t- Negative electrode bulk open-circuit potential [V]\n", + "\t- Negative particle concentration overpotential [V]\n", + "\t- Negative electrode entropic change [V.K-1]\n", + "\t- X-averaged negative electrode entropic change [V.K-1]\n", + "\t- Positive electrode open-circuit potential [V]\n", + "\t- X-averaged positive electrode open-circuit potential [V]\n", + "\t- Positive electrode bulk open-circuit potential [V]\n", + "\t- Positive particle concentration overpotential [V]\n", + "\t- Positive electrode entropic change [V.K-1]\n", + "\t- X-averaged positive electrode entropic change [V.K-1]\n", + "\t- X-averaged negative electrode total interfacial current density [A.m-2]\n", + "\t- X-averaged negative electrode total volumetric interfacial current density [A.m-3]\n", + "\t- SEI film overpotential [V]\n", + "\t- X-averaged SEI film overpotential [V]\n", + "\t- Negative electrode exchange current density [A.m-2]\n", + "\t- X-averaged negative electrode exchange current density [A.m-2]\n", + "\t- Negative electrode reaction overpotential [V]\n", + "\t- X-averaged negative electrode reaction overpotential [V]\n", + "\t- X-averaged negative electrode surface potential difference [V]\n", + "\t- Negative electrode interfacial current density [A.m-2]\n", + "\t- X-averaged negative electrode interfacial current density [A.m-2]\n", + "\t- Negative electrode volumetric interfacial current density [A.m-3]\n", + "\t- X-averaged negative electrode volumetric interfacial current density [A.m-3]\n", + "\t- X-averaged positive electrode total interfacial current density [A.m-2]\n", + "\t- X-averaged positive electrode total volumetric interfacial current density [A.m-3]\n", + "\t- Positive electrode exchange current density [A.m-2]\n", + "\t- X-averaged positive electrode exchange current density [A.m-2]\n", + "\t- Positive electrode reaction overpotential [V]\n", + "\t- X-averaged positive electrode reaction overpotential [V]\n", + "\t- X-averaged positive electrode surface potential difference [V]\n", + "\t- Positive electrode interfacial current density [A.m-2]\n", + "\t- X-averaged positive electrode interfacial current density [A.m-2]\n", + "\t- Positive electrode volumetric interfacial current density [A.m-3]\n", + "\t- X-averaged positive electrode volumetric interfacial current density [A.m-3]\n", + "\t- Negative particle rhs [mol.m-3.s-1]\n", + "\t- Negative particle bc [mol.m-2]\n", + "\t- Negative particle effective diffusivity [m2.s-1]\n", + "\t- X-averaged negative particle effective diffusivity [m2.s-1]\n", + "\t- Negative particle flux [mol.m-2.s-1]\n", + "\t- X-averaged negative particle flux [mol.m-2.s-1]\n", + "\t- Positive particle rhs [mol.m-3.s-1]\n", + "\t- Positive particle bc [mol.m-2]\n", + "\t- Positive particle effective diffusivity [m2.s-1]\n", + "\t- X-averaged positive particle effective diffusivity [m2.s-1]\n", + "\t- Positive particle flux [mol.m-2.s-1]\n", + "\t- X-averaged positive particle flux [mol.m-2.s-1]\n", + "\t- Negative electrode potential [V]\n", + "\t- X-averaged negative electrode potential [V]\n", + "\t- Negative electrode ohmic losses [V]\n", + "\t- X-averaged negative electrode ohmic losses [V]\n", + "\t- Gradient of negative electrode potential [V.m-1]\n", + "\t- Negative electrode current density [A.m-2]\n", + "\t- Electrolyte potential [V]\n", + "\t- X-averaged electrolyte potential [V]\n", + "\t- X-averaged electrolyte overpotential [V]\n", + "\t- Gradient of electrolyte potential [V.m-1]\n", + "\t- Negative electrolyte potential [V]\n", + "\t- X-averaged negative electrolyte potential [V]\n", + "\t- Gradient of negative electrolyte potential [V.m-1]\n", + "\t- Separator electrolyte potential [V]\n", + "\t- X-averaged separator electrolyte potential [V]\n", + "\t- Gradient of separator electrolyte potential [V.m-1]\n", + "\t- Positive electrolyte potential [V]\n", + "\t- X-averaged positive electrolyte potential [V]\n", + "\t- Gradient of positive electrolyte potential [V.m-1]\n", + "\t- Electrolyte current density [A.m-2]\n", + "\t- Negative electrolyte current density [A.m-2]\n", + "\t- Positive electrolyte current density [A.m-2]\n", + "\t- X-averaged concentration overpotential [V]\n", + "\t- X-averaged electrolyte ohmic losses [V]\n", + "\t- Negative electrode surface potential difference [V]\n", + "\t- Negative electrode surface potential difference at separator interface [V]\n", + "\t- Sum of negative electrode electrolyte reaction source terms [A.m-3]\n", + "\t- Sum of x-averaged negative electrode electrolyte reaction source terms [A.m-3]\n", + "\t- Sum of negative electrode volumetric interfacial current densities [A.m-3]\n", + "\t- Sum of x-averaged negative electrode volumetric interfacial current densities [A.m-3]\n", + "\t- Sum of positive electrode electrolyte reaction source terms [A.m-3]\n", + "\t- Sum of x-averaged positive electrode electrolyte reaction source terms [A.m-3]\n", + "\t- Sum of positive electrode volumetric interfacial current densities [A.m-3]\n", + "\t- Sum of x-averaged positive electrode volumetric interfacial current densities [A.m-3]\n", + "\t- Interfacial current density [A.m-2]\n", + "\t- Exchange current density [A.m-2]\n", + "\t- Sum of volumetric interfacial current densities [A.m-3]\n", + "\t- Sum of electrolyte reaction source terms [A.m-3]\n", + "\t- Positive electrode potential [V]\n", + "\t- X-averaged positive electrode potential [V]\n", + "\t- Positive electrode ohmic losses [V]\n", + "\t- X-averaged positive electrode ohmic losses [V]\n", + "\t- Gradient of positive electrode potential [V.m-1]\n", + "\t- Positive electrode current density [A.m-2]\n", + "\t- Electrode current density [A.m-2]\n", + "\t- Positive current collector potential [V]\n", + "\t- Local voltage [V]\n", + "\t- Terminal voltage [V]\n", + "\t- Voltage [V]\n", + "\t- Contact overpotential [V]\n", + "\t- Positive electrode surface potential difference [V]\n", + "\t- Positive electrode surface potential difference at separator interface [V]\n", + "\t- Surface open-circuit voltage [V]\n", + "\t- Bulk open-circuit voltage [V]\n", + "\t- Particle concentration overpotential [V]\n", + "\t- X-averaged reaction overpotential [V]\n", + "\t- X-averaged solid phase ohmic losses [V]\n", + "\t- Battery open-circuit voltage [V]\n", + "\t- Battery negative electrode bulk open-circuit potential [V]\n", + "\t- Battery positive electrode bulk open-circuit potential [V]\n", + "\t- Battery particle concentration overpotential [V]\n", + "\t- Battery negative particle concentration overpotential [V]\n", + "\t- Battery positive particle concentration overpotential [V]\n", + "\t- X-averaged battery reaction overpotential [V]\n", + "\t- X-averaged battery negative reaction overpotential [V]\n", + "\t- X-averaged battery positive reaction overpotential [V]\n", + "\t- X-averaged battery solid phase ohmic losses [V]\n", + "\t- X-averaged battery negative solid phase ohmic losses [V]\n", + "\t- X-averaged battery positive solid phase ohmic losses [V]\n", + "\t- X-averaged battery electrolyte ohmic losses [V]\n", + "\t- X-averaged battery concentration overpotential [V]\n", + "\t- Battery voltage [V]\n", + "\t- Change in open-circuit voltage [V]\n", + "\t- Local ECM resistance [Ohm]\n", + "\t- Terminal power [W]\n", + "\t- Power [W]\n", + "\t- Resistance [Ohm]\n", + "\t- Total lithium in negative electrode [mol]\n", + "\t- LAM_ne [%]\n", + "\t- Loss of active material in negative electrode [%]\n", + "\t- Total lithium in positive electrode [mol]\n", + "\t- LAM_pe [%]\n", + "\t- Loss of active material in positive electrode [%]\n", + "\t- LLI [%]\n", + "\t- Loss of lithium inventory [%]\n", + "\t- Loss of lithium inventory, including electrolyte [%]\n", + "\t- Total lithium [mol]\n", + "\t- Total lithium in particles [mol]\n", + "\t- Total lithium capacity [A.h]\n", + "\t- Total lithium capacity in particles [A.h]\n", + "\t- Total lithium lost [mol]\n", + "\t- Total lithium lost from particles [mol]\n", + "\t- Total lithium lost from electrolyte [mol]\n", + "\t- Total lithium lost to side reactions [mol]\n", + "\t- Total capacity lost to side reactions [A.h]\n" + ] + } + ], + "source": [ + "print('SPM model variables:')\n", + "for v in model.variables.keys():\n", + " print('\\t-',v)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To help visualise the results, pybamm provides the `pybamm.ProcessedVariable` class, which takes the output of a solver and a variable, and allows the user to evaluate the value of that variable at any given time or $x$ value. These processed variables are automatically created by the solution dictionary." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "voltage = solution['Voltage [V]']\n", + "c_s_n_surf = solution['Negative particle surface concentration']\n", + "c_s_p_surf = solution['Positive particle surface concentration']" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One we have these variables in hand, we can begin generating plots using a library such as Matplotlib. Below we plot the voltage and surface particle concentrations versus time" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The next step is to mesh the input geometry. We can do this using the [`pybamm.Mesh`](https://docs.pybamm.org/en/latest/source/api/meshes/index.html) class. This class takes in the geometry of the problem, and also two dictionaries containing the type of mesh to use within each domain of the geometry (i.e. within the positive or negative electrode domains), and the number of mesh points. \n", - "\n", - "The default mesh types and the default number of points to use in each variable for the SPM are:" + "data": { + "image/png": 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", + "text/plain": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "t = solution[\"Time [s]\"].entries\n", + "x = solution[\"x [m]\"].entries[:, 0]\n", + "f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(13,4))\n", + "\n", + "ax1.plot(t, voltage(t))\n", + "ax1.set_xlabel(r'$Time [s]$')\n", + "ax1.set_ylabel('Voltage [V]')\n", + "\n", + "ax2.plot(t, c_s_n_surf(t=t, x=x[0])) # can evaluate at arbitrary x (single representative particle)\n", + "ax2.set_xlabel(r'$Time [s]$')\n", + "ax2.set_ylabel('Negative particle surface concentration')\n", + "\n", + "ax3.plot(t, c_s_p_surf(t=t, x=x[-1])) # can evaluate at arbitrary x (single representative particle)\n", + "ax3.set_xlabel(r'$Time [s]$')\n", + "ax3.set_ylabel('Positive particle surface concentration')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some of the output variables are defined over space as well as time. Once option to visualise these variables is to use the `interact` slider widget. Below we plot the negative/positive particle concentration over $r$, using a slider to change the current time point" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "c_s_n = solution['Negative particle concentration']\n", + "c_s_p = solution['Positive particle concentration']\n", + "r_n = solution[\"r_n [m]\"].entries[:, 0]\n", + "r_p = solution[\"r_p [m]\"].entries[:, 0]" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "scrolled": true + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3800db8e54f0444c9aed3bd9b06dbfa6", + "version_major": 2, + "version_minor": 0 }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "negative electrode is of type Uniform1DSubMesh\n", - "separator is of type Uniform1DSubMesh\n", - "positive electrode is of type Uniform1DSubMesh\n", - "negative particle is of type Uniform1DSubMesh\n", - "positive particle is of type Uniform1DSubMesh\n", - "negative primary particle is of type Uniform1DSubMesh\n", - "positive primary particle is of type Uniform1DSubMesh\n", - "negative secondary particle is of type Uniform1DSubMesh\n", - "positive secondary particle is of type Uniform1DSubMesh\n", - "negative particle size is of type Uniform1DSubMesh\n", - "positive particle size is of type Uniform1DSubMesh\n", - "current collector is of type SubMesh0D\n", - "x_n has 20 mesh points\n", - "x_s has 20 mesh points\n", - "x_p has 20 mesh points\n", - "r_n has 20 mesh points\n", - "r_p has 20 mesh points\n", - "r_n_prim has 20 mesh points\n", - "r_p_prim has 20 mesh points\n", - "r_n_sec has 20 mesh points\n", - "r_p_sec has 20 mesh points\n", - "y has 10 mesh points\n", - "z has 10 mesh points\n", - "R_n has 30 mesh points\n", - "R_p has 30 mesh points\n" - ] - } - ], - "source": [ - "for k, t in model.default_submesh_types.items():\n", - " print(k,'is of type',t.__name__)\n", - "for var, npts in model.default_var_pts.items():\n", - " print(var,'has',npts,'mesh points')" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With these defaults, we can then create our mesh of the given geometry:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "mesh = pybamm.Mesh(geometry, model.default_submesh_types, model.default_var_pts)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The next step is to discretise the model equations using this mesh. We do this using the [`pybamm.Discretisation`](https://docs.pybamm.org/en/latest/source/api/spatial_methods/discretisation.html) class, which takes both the mesh we have already created, and a dictionary of spatial methods to use for each geometry domain. For the case of the SPM, we use the following defaults for the spatial discretisation methods:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "macroscale is discretised using FiniteVolume method\n", - "negative particle is discretised using FiniteVolume method\n", - "positive particle is discretised using FiniteVolume method\n", - "negative primary particle is discretised using FiniteVolume method\n", - "positive primary particle is discretised using FiniteVolume method\n", - "negative secondary particle is discretised using FiniteVolume method\n", - "positive secondary particle is discretised using FiniteVolume method\n", - "negative particle size is discretised using FiniteVolume method\n", - "positive particle size is discretised using FiniteVolume method\n", - "current collector is discretised using ZeroDimensionalSpatialMethod method\n" - ] - } - ], - "source": [ - "for k, method in model.default_spatial_methods.items():\n", - " print(k,'is discretised using',method.__class__.__name__,'method')" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We then create the `pybamm.Discretisation` object, and use this to discretise the model equations" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "disc = pybamm.Discretisation(mesh, model.default_spatial_methods)\n", - "disc.process_model(model)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After this stage, all of the variables in `model` have been discretised into `pybamm.StateVector` objects, and spatial operators have been replaced by matrix-vector multiplications, ready to be evaluated within a time-stepping algorithm of a given solver. For example, the rhs expression for $\\partial{c_n}/\\partial{t}$ that we visualised above is now represented by:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "model.concatenated_rhs.children[1].visualise(path+'spm2.png')" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![spm2](spm2.png)\n", - "\n", - "Now we are ready to run the time-stepping routine to solve the model. Once again we use the default ODE solver." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Solving using CasadiSolver solver...\n", - "Finished.\n" - ] - } - ], - "source": [ - "# Solve the model at the given time points (in seconds)\n", - "solver = model.default_solver\n", - "n = 250\n", - "t_eval = np.linspace(0, 3600, n)\n", - "print('Solving using',type(solver).__name__,'solver...')\n", - "solution = solver.solve(model, t_eval)\n", - "print('Finished.')" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Each model in pybamm has a list of relevant variables defined in the model, for use in visualising the model solution or for comparison with other models. The SPM defines the following variables:" + "text/plain": [ + "interactive(children=(FloatSlider(value=0.0, description='t', max=3600.0, step=10.0), Output()), _dom_classes=…" ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SPM model variables:\n", - "\t- Time [s]\n", - "\t- Time [min]\n", - "\t- Time [h]\n", - "\t- x [m]\n", - "\t- x_n [m]\n", - "\t- x_s [m]\n", - "\t- x_p [m]\n", - "\t- r_n [m]\n", - "\t- r_p [m]\n", - "\t- Current variable [A]\n", - "\t- Total current density [A.m-2]\n", - "\t- Current [A]\n", - "\t- C-rate\n", - "\t- Discharge capacity [A.h]\n", - "\t- Throughput capacity [A.h]\n", - "\t- Discharge energy [W.h]\n", - "\t- Throughput energy [W.h]\n", - "\t- Porosity\n", - "\t- Negative electrode porosity\n", - "\t- X-averaged negative electrode porosity\n", - "\t- Separator porosity\n", - "\t- X-averaged separator porosity\n", - "\t- Positive electrode porosity\n", - "\t- X-averaged positive electrode porosity\n", - "\t- Porosity change\n", - "\t- Negative electrode porosity change [s-1]\n", - "\t- X-averaged negative electrode porosity change [s-1]\n", - "\t- Separator porosity change [s-1]\n", - "\t- X-averaged separator porosity change [s-1]\n", - "\t- Positive electrode porosity change [s-1]\n", - "\t- X-averaged positive electrode porosity change [s-1]\n", - "\t- Negative electrode interface utilisation variable\n", - "\t- X-averaged negative electrode interface utilisation variable\n", - "\t- Negative electrode interface utilisation\n", - "\t- X-averaged negative electrode interface utilisation\n", - "\t- Positive electrode interface utilisation variable\n", - "\t- X-averaged positive electrode interface utilisation variable\n", - "\t- Positive electrode interface utilisation\n", - "\t- X-averaged positive electrode interface utilisation\n", - "\t- Negative particle crack length [m]\n", - "\t- X-averaged negative particle crack length [m]\n", - "\t- Negative particle cracking rate [m.s-1]\n", - "\t- X-averaged negative particle cracking rate [m.s-1]\n", - "\t- Positive particle crack length [m]\n", - "\t- X-averaged positive particle crack length [m]\n", - "\t- Positive particle cracking rate [m.s-1]\n", - "\t- X-averaged positive particle cracking rate [m.s-1]\n", - "\t- Negative electrode active material volume fraction\n", - "\t- X-averaged negative electrode active material volume fraction\n", - "\t- Negative electrode capacity [A.h]\n", - "\t- Negative particle radius\n", - "\t- Negative particle radius [m]\n", - "\t- X-averaged negative particle radius [m]\n", - "\t- Negative electrode surface area to volume ratio [m-1]\n", - "\t- X-averaged negative electrode surface area to volume ratio [m-1]\n", - "\t- Negative electrode active material volume fraction change [s-1]\n", - "\t- X-averaged negative electrode active material volume fraction change [s-1]\n", - "\t- Loss of lithium due to loss of active material in negative electrode [mol]\n", - "\t- Positive electrode active material volume fraction\n", - "\t- X-averaged positive electrode active material volume fraction\n", - "\t- Positive electrode capacity [A.h]\n", - "\t- Positive particle radius\n", - "\t- Positive particle radius [m]\n", - "\t- X-averaged positive particle radius [m]\n", - "\t- Positive electrode surface area to volume ratio [m-1]\n", - "\t- X-averaged positive electrode surface area to volume ratio [m-1]\n", - "\t- Positive electrode active material volume fraction change [s-1]\n", - "\t- X-averaged positive electrode active material volume fraction change [s-1]\n", - "\t- Loss of lithium due to loss of active material in positive electrode [mol]\n", - "\t- Separator pressure [Pa]\n", - "\t- X-averaged separator pressure [Pa]\n", - "\t- negative electrode transverse volume-averaged velocity [m.s-1]\n", - "\t- X-averaged negative electrode transverse volume-averaged velocity [m.s-1]\n", - "\t- separator transverse volume-averaged velocity [m.s-1]\n", - "\t- X-averaged separator transverse volume-averaged velocity [m.s-1]\n", - "\t- positive electrode transverse volume-averaged velocity [m.s-1]\n", - "\t- X-averaged positive electrode transverse volume-averaged velocity [m.s-1]\n", - "\t- Transverse volume-averaged velocity [m.s-1]\n", - "\t- negative electrode transverse volume-averaged acceleration [m.s-2]\n", - "\t- X-averaged negative electrode transverse volume-averaged acceleration [m.s-2]\n", - "\t- separator transverse volume-averaged acceleration [m.s-2]\n", - "\t- X-averaged separator transverse volume-averaged acceleration [m.s-2]\n", - "\t- positive electrode transverse volume-averaged acceleration [m.s-2]\n", - "\t- X-averaged positive electrode transverse volume-averaged acceleration [m.s-2]\n", - "\t- Transverse volume-averaged acceleration [m.s-2]\n", - "\t- Negative electrode volume-averaged velocity [m.s-1]\n", - "\t- Negative electrode volume-averaged acceleration [m.s-2]\n", - "\t- X-averaged negative electrode volume-averaged acceleration [m.s-2]\n", - "\t- Negative electrode pressure [Pa]\n", - "\t- X-averaged negative electrode pressure [Pa]\n", - "\t- Positive electrode volume-averaged velocity [m.s-1]\n", - "\t- Positive electrode volume-averaged acceleration [m.s-2]\n", - "\t- X-averaged positive electrode volume-averaged acceleration [m.s-2]\n", - "\t- Positive electrode pressure [Pa]\n", - "\t- X-averaged positive electrode pressure [Pa]\n", - "\t- Negative particle stoichiometry\n", - "\t- Negative particle concentration\n", - "\t- Negative particle concentration [mol.m-3]\n", - "\t- X-averaged negative particle concentration\n", - "\t- X-averaged negative particle concentration [mol.m-3]\n", - "\t- R-averaged negative particle concentration\n", - "\t- R-averaged negative particle concentration [mol.m-3]\n", - "\t- Average negative particle concentration\n", - "\t- Average negative particle concentration [mol.m-3]\n", - "\t- Negative particle surface stoichiometry\n", - "\t- Negative particle surface concentration\n", - "\t- Negative particle surface concentration [mol.m-3]\n", - "\t- X-averaged negative particle surface concentration\n", - "\t- X-averaged negative particle surface concentration [mol.m-3]\n", - "\t- Negative electrode extent of lithiation\n", - "\t- X-averaged negative electrode extent of lithiation\n", - "\t- Minimum negative particle concentration\n", - "\t- Maximum negative particle concentration\n", - "\t- Minimum negative particle concentration [mol.m-3]\n", - "\t- Maximum negative particle concentration [mol.m-3]\n", - "\t- Minimum negative particle surface concentration\n", - "\t- Maximum negative particle surface concentration\n", - "\t- Minimum negative particle surface concentration [mol.m-3]\n", - "\t- Maximum negative particle surface concentration [mol.m-3]\n", - "\t- Positive particle stoichiometry\n", - "\t- Positive particle concentration\n", - "\t- Positive particle concentration [mol.m-3]\n", - "\t- X-averaged positive particle concentration\n", - "\t- X-averaged positive particle concentration [mol.m-3]\n", - "\t- R-averaged positive particle concentration\n", - "\t- R-averaged positive particle concentration [mol.m-3]\n", - "\t- Average positive particle concentration\n", - "\t- Average positive particle concentration [mol.m-3]\n", - "\t- Positive particle surface stoichiometry\n", - "\t- Positive particle surface concentration\n", - "\t- Positive particle surface concentration [mol.m-3]\n", - "\t- X-averaged positive particle surface concentration\n", - "\t- X-averaged positive particle surface concentration [mol.m-3]\n", - "\t- Positive electrode extent of lithiation\n", - "\t- X-averaged positive electrode extent of lithiation\n", - "\t- Minimum positive particle concentration\n", - "\t- Maximum positive particle concentration\n", - "\t- Minimum positive particle concentration [mol.m-3]\n", - "\t- Maximum positive particle concentration [mol.m-3]\n", - "\t- Minimum positive particle surface concentration\n", - "\t- Maximum positive particle surface concentration\n", - "\t- Minimum positive particle surface concentration [mol.m-3]\n", - "\t- Maximum positive particle surface concentration [mol.m-3]\n", - "\t- Porosity times concentration [mol.m-3]\n", - "\t- Negative electrode porosity times concentration [mol.m-3]\n", - "\t- Separator porosity times concentration [mol.m-3]\n", - "\t- Positive electrode porosity times concentration [mol.m-3]\n", - "\t- Total lithium in electrolyte [mol]\n", - "\t- Electrolyte flux [mol.m-2.s-1]\n", - "\t- Ambient temperature [K]\n", - "\t- Cell temperature [K]\n", - "\t- Negative current collector temperature [K]\n", - "\t- Positive current collector temperature [K]\n", - "\t- X-averaged cell temperature [K]\n", - "\t- Volume-averaged cell temperature [K]\n", - "\t- Negative electrode temperature [K]\n", - "\t- X-averaged negative electrode temperature [K]\n", - "\t- Separator temperature [K]\n", - "\t- X-averaged separator temperature [K]\n", - "\t- Positive electrode temperature [K]\n", - "\t- X-averaged positive electrode temperature [K]\n", - "\t- Ambient temperature [C]\n", - "\t- Cell temperature [C]\n", - "\t- Negative current collector temperature [C]\n", - "\t- Positive current collector temperature [C]\n", - "\t- X-averaged cell temperature [C]\n", - "\t- Volume-averaged cell temperature [C]\n", - "\t- Negative electrode temperature [C]\n", - "\t- X-averaged negative electrode temperature [C]\n", - "\t- Separator temperature [C]\n", - "\t- X-averaged separator temperature [C]\n", - "\t- Positive electrode temperature [C]\n", - "\t- X-averaged positive electrode temperature [C]\n", - "\t- Negative current collector potential [V]\n", - "\t- Inner SEI thickness [m]\n", - "\t- Outer SEI thickness [m]\n", - "\t- X-averaged inner SEI thickness [m]\n", - "\t- X-averaged outer SEI thickness [m]\n", - "\t- SEI [m]\n", - "\t- Total SEI thickness [m]\n", - "\t- X-averaged SEI thickness [m]\n", - "\t- X-averaged total SEI thickness [m]\n", - "\t- X-averaged negative electrode resistance [Ohm.m2]\n", - "\t- Inner SEI interfacial current density [A.m-2]\n", - "\t- X-averaged inner SEI interfacial current density [A.m-2]\n", - "\t- Outer SEI interfacial current density [A.m-2]\n", - "\t- X-averaged outer SEI interfacial current density [A.m-2]\n", - "\t- SEI interfacial current density [A.m-2]\n", - "\t- X-averaged SEI interfacial current density [A.m-2]\n", - "\t- Inner SEI on cracks thickness [m]\n", - "\t- Outer SEI on cracks thickness [m]\n", - "\t- X-averaged inner SEI on cracks thickness [m]\n", - "\t- X-averaged outer SEI on cracks thickness [m]\n", - "\t- SEI on cracks [m]\n", - "\t- Total SEI on cracks thickness [m]\n", - "\t- X-averaged SEI on cracks thickness [m]\n", - "\t- X-averaged total SEI on cracks thickness [m]\n", - "\t- Inner SEI on cracks interfacial current density [A.m-2]\n", - "\t- X-averaged inner SEI on cracks interfacial current density [A.m-2]\n", - "\t- Outer SEI on cracks interfacial current density [A.m-2]\n", - "\t- X-averaged outer SEI on cracks interfacial current density [A.m-2]\n", - "\t- SEI on cracks interfacial current density [A.m-2]\n", - "\t- X-averaged SEI on cracks interfacial current density [A.m-2]\n", - "\t- Lithium plating concentration [mol.m-3]\n", - "\t- X-averaged lithium plating concentration [mol.m-3]\n", - "\t- Dead lithium concentration [mol.m-3]\n", - "\t- X-averaged dead lithium concentration [mol.m-3]\n", - "\t- Lithium plating thickness [m]\n", - "\t- X-averaged lithium plating thickness [m]\n", - "\t- Dead lithium thickness [m]\n", - "\t- X-averaged dead lithium thickness [m]\n", - "\t- Loss of lithium to lithium plating [mol]\n", - "\t- Loss of capacity to lithium plating [A.h]\n", - "\t- Negative electrode lithium plating reaction overpotential [V]\n", - "\t- X-averaged negative electrode lithium plating reaction overpotential [V]\n", - "\t- Lithium plating interfacial current density [A.m-2]\n", - "\t- X-averaged lithium plating interfacial current density [A.m-2]\n", - "\t- Negative crack surface to volume ratio [m-1]\n", - "\t- Negative electrode roughness ratio\n", - "\t- X-averaged negative electrode roughness ratio\n", - "\t- Positive crack surface to volume ratio [m-1]\n", - "\t- Positive electrode roughness ratio\n", - "\t- X-averaged positive electrode roughness ratio\n", - "\t- Electrolyte transport efficiency\n", - "\t- Negative electrolyte transport efficiency\n", - "\t- X-averaged negative electrolyte transport efficiency\n", - "\t- Separator electrolyte transport efficiency\n", - "\t- X-averaged separator electrolyte transport efficiency\n", - "\t- Positive electrolyte transport efficiency\n", - "\t- X-averaged positive electrolyte transport efficiency\n", - "\t- Electrode transport efficiency\n", - "\t- Negative electrode transport efficiency\n", - "\t- X-averaged negative electrode transport efficiency\n", - "\t- Separator electrode transport efficiency\n", - "\t- X-averaged separator electrode transport efficiency\n", - "\t- Positive electrode transport efficiency\n", - "\t- X-averaged positive electrode transport efficiency\n", - "\t- Separator volume-averaged velocity [m.s-1]\n", - "\t- Separator volume-averaged acceleration [m.s-2]\n", - "\t- X-averaged separator volume-averaged acceleration [m.s-2]\n", - "\t- Volume-averaged velocity [m.s-1]\n", - "\t- Volume-averaged acceleration [m.s-1]\n", - "\t- X-averaged volume-averaged acceleration [m.s-1]\n", - "\t- Pressure [Pa]\n", - "\t- Negative electrode stoichiometry\n", - "\t- Negative electrode volume-averaged concentration\n", - "\t- Negative electrode volume-averaged concentration [mol.m-3]\n", - "\t- Total lithium in primary phase in negative electrode [mol]\n", - "\t- Positive electrode stoichiometry\n", - "\t- Positive electrode volume-averaged concentration\n", - "\t- Positive electrode volume-averaged concentration [mol.m-3]\n", - "\t- Total lithium in primary phase in positive electrode [mol]\n", - "\t- Electrolyte concentration concatenation [mol.m-3]\n", - "\t- Negative electrolyte concentration [mol.m-3]\n", - "\t- X-averaged negative electrolyte concentration [mol.m-3]\n", - "\t- Separator electrolyte concentration [mol.m-3]\n", - "\t- X-averaged separator electrolyte concentration [mol.m-3]\n", - "\t- Positive electrolyte concentration [mol.m-3]\n", - "\t- X-averaged positive electrolyte concentration [mol.m-3]\n", - "\t- Negative electrolyte concentration [Molar]\n", - "\t- X-averaged negative electrolyte concentration [Molar]\n", - "\t- Separator electrolyte concentration [Molar]\n", - "\t- X-averaged separator electrolyte concentration [Molar]\n", - "\t- Positive electrolyte concentration [Molar]\n", - "\t- X-averaged positive electrolyte concentration [Molar]\n", - "\t- Electrolyte concentration [mol.m-3]\n", - "\t- X-averaged electrolyte concentration [mol.m-3]\n", - "\t- Electrolyte concentration [Molar]\n", - "\t- X-averaged electrolyte concentration [Molar]\n", - "\t- Ohmic heating [W.m-3]\n", - "\t- X-averaged Ohmic heating [W.m-3]\n", - "\t- Volume-averaged Ohmic heating [W.m-3]\n", - "\t- Irreversible electrochemical heating [W.m-3]\n", - "\t- X-averaged irreversible electrochemical heating [W.m-3]\n", - "\t- Volume-averaged irreversible electrochemical heating [W.m-3]\n", - "\t- Reversible heating [W.m-3]\n", - "\t- X-averaged reversible heating [W.m-3]\n", - "\t- Volume-averaged reversible heating [W.m-3]\n", - "\t- Total heating [W.m-3]\n", - "\t- X-averaged total heating [W.m-3]\n", - "\t- Volume-averaged total heating [W.m-3]\n", - "\t- Current collector current density [A.m-2]\n", - "\t- Inner SEI concentration [mol.m-3]\n", - "\t- X-averaged inner SEI concentration [mol.m-3]\n", - "\t- Outer SEI concentration [mol.m-3]\n", - "\t- X-averaged outer SEI concentration [mol.m-3]\n", - "\t- SEI concentration [mol.m-3]\n", - "\t- X-averaged SEI concentration [mol.m-3]\n", - "\t- Loss of lithium to SEI [mol]\n", - "\t- Loss of capacity to SEI [A.h]\n", - "\t- X-averaged negative electrode SEI interfacial current density [A.m-2]\n", - "\t- Negative electrode SEI interfacial current density [A.m-2]\n", - "\t- Positive electrode SEI interfacial current density [A.m-2]\n", - "\t- X-averaged positive electrode SEI volumetric interfacial current density [A.m-2]\n", - "\t- Positive electrode SEI volumetric interfacial current density [A.m-3]\n", - "\t- Negative electrode SEI volumetric interfacial current density [A.m-3]\n", - "\t- X-averaged negative electrode SEI volumetric interfacial current density [A.m-3]\n", - "\t- Inner SEI on cracks concentration [mol.m-3]\n", - "\t- X-averaged inner SEI on cracks concentration [mol.m-3]\n", - "\t- Outer SEI on cracks concentration [mol.m-3]\n", - "\t- X-averaged outer SEI on cracks concentration [mol.m-3]\n", - "\t- SEI on cracks concentration [mol.m-3]\n", - "\t- X-averaged SEI on cracks concentration [mol.m-3]\n", - "\t- Loss of lithium to SEI on cracks [mol]\n", - "\t- Loss of capacity to SEI on cracks [A.h]\n", - "\t- X-averaged negative electrode SEI on cracks interfacial current density [A.m-2]\n", - "\t- Negative electrode SEI on cracks interfacial current density [A.m-2]\n", - "\t- Positive electrode SEI on cracks interfacial current density [A.m-2]\n", - "\t- X-averaged positive electrode SEI on cracks volumetric interfacial current density [A.m-2]\n", - "\t- Positive electrode SEI on cracks volumetric interfacial current density [A.m-3]\n", - "\t- Negative electrode SEI on cracks volumetric interfacial current density [A.m-3]\n", - "\t- X-averaged negative electrode SEI on cracks volumetric interfacial current density [A.m-3]\n", - "\t- Negative electrode lithium plating interfacial current density [A.m-2]\n", - "\t- X-averaged negative electrode lithium plating interfacial current density [A.m-2]\n", - "\t- Lithium plating volumetric interfacial current density [A.m-3]\n", - "\t- X-averaged lithium plating volumetric interfacial current density [A.m-3]\n", - "\t- X-averaged positive electrode lithium plating interfacial current density [A.m-2]\n", - "\t- X-averaged positive electrode lithium plating volumetric interfacial current density [A.m-3]\n", - "\t- Positive electrode lithium plating interfacial current density [A.m-2]\n", - "\t- Positive electrode lithium plating volumetric interfacial current density [A.m-3]\n", - "\t- Negative electrode lithium plating volumetric interfacial current density [A.m-3]\n", - "\t- X-averaged negative electrode lithium plating volumetric interfacial current density [A.m-3]\n", - "\t- Negative electrode open-circuit potential [V]\n", - "\t- X-averaged negative electrode open-circuit potential [V]\n", - "\t- Negative electrode bulk open-circuit potential [V]\n", - "\t- Negative particle concentration overpotential [V]\n", - "\t- Negative electrode entropic change [V.K-1]\n", - "\t- X-averaged negative electrode entropic change [V.K-1]\n", - "\t- Positive electrode open-circuit potential [V]\n", - "\t- X-averaged positive electrode open-circuit potential [V]\n", - "\t- Positive electrode bulk open-circuit potential [V]\n", - "\t- Positive particle concentration overpotential [V]\n", - "\t- Positive electrode entropic change [V.K-1]\n", - "\t- X-averaged positive electrode entropic change [V.K-1]\n", - "\t- X-averaged negative electrode total interfacial current density [A.m-2]\n", - "\t- X-averaged negative electrode total volumetric interfacial current density [A.m-3]\n", - "\t- SEI film overpotential [V]\n", - "\t- X-averaged SEI film overpotential [V]\n", - "\t- Negative electrode exchange current density [A.m-2]\n", - "\t- X-averaged negative electrode exchange current density [A.m-2]\n", - "\t- Negative electrode reaction overpotential [V]\n", - "\t- X-averaged negative electrode reaction overpotential [V]\n", - "\t- X-averaged negative electrode surface potential difference [V]\n", - "\t- Negative electrode interfacial current density [A.m-2]\n", - "\t- X-averaged negative electrode interfacial current density [A.m-2]\n", - "\t- Negative electrode volumetric interfacial current density [A.m-3]\n", - "\t- X-averaged negative electrode volumetric interfacial current density [A.m-3]\n", - "\t- X-averaged positive electrode total interfacial current density [A.m-2]\n", - "\t- X-averaged positive electrode total volumetric interfacial current density [A.m-3]\n", - "\t- Positive electrode exchange current density [A.m-2]\n", - "\t- X-averaged positive electrode exchange current density [A.m-2]\n", - "\t- Positive electrode reaction overpotential [V]\n", - "\t- X-averaged positive electrode reaction overpotential [V]\n", - "\t- X-averaged positive electrode surface potential difference [V]\n", - "\t- Positive electrode interfacial current density [A.m-2]\n", - "\t- X-averaged positive electrode interfacial current density [A.m-2]\n", - "\t- Positive electrode volumetric interfacial current density [A.m-3]\n", - "\t- X-averaged positive electrode volumetric interfacial current density [A.m-3]\n", - "\t- Negative particle rhs [mol.m-3.s-1]\n", - "\t- Negative particle bc [mol.m-2]\n", - "\t- Negative particle effective diffusivity [m2.s-1]\n", - "\t- X-averaged negative particle effective diffusivity [m2.s-1]\n", - "\t- Negative particle flux [mol.m-2.s-1]\n", - "\t- X-averaged negative particle flux [mol.m-2.s-1]\n", - "\t- Positive particle rhs [mol.m-3.s-1]\n", - "\t- Positive particle bc [mol.m-2]\n", - "\t- Positive particle effective diffusivity [m2.s-1]\n", - "\t- X-averaged positive particle effective diffusivity [m2.s-1]\n", - "\t- Positive particle flux [mol.m-2.s-1]\n", - "\t- X-averaged positive particle flux [mol.m-2.s-1]\n", - "\t- Negative electrode potential [V]\n", - "\t- X-averaged negative electrode potential [V]\n", - "\t- Negative electrode ohmic losses [V]\n", - "\t- X-averaged negative electrode ohmic losses [V]\n", - "\t- Gradient of negative electrode potential [V.m-1]\n", - "\t- Negative electrode current density [A.m-2]\n", - "\t- Electrolyte potential [V]\n", - "\t- X-averaged electrolyte potential [V]\n", - "\t- X-averaged electrolyte overpotential [V]\n", - "\t- Gradient of electrolyte potential [V.m-1]\n", - "\t- Negative electrolyte potential [V]\n", - "\t- X-averaged negative electrolyte potential [V]\n", - "\t- Gradient of negative electrolyte potential [V.m-1]\n", - "\t- Separator electrolyte potential [V]\n", - "\t- X-averaged separator electrolyte potential [V]\n", - "\t- Gradient of separator electrolyte potential [V.m-1]\n", - "\t- Positive electrolyte potential [V]\n", - "\t- X-averaged positive electrolyte potential [V]\n", - "\t- Gradient of positive electrolyte potential [V.m-1]\n", - "\t- Electrolyte current density [A.m-2]\n", - "\t- Negative electrolyte current density [A.m-2]\n", - "\t- Positive electrolyte current density [A.m-2]\n", - "\t- X-averaged concentration overpotential [V]\n", - "\t- X-averaged electrolyte ohmic losses [V]\n", - "\t- Negative electrode surface potential difference [V]\n", - "\t- Negative electrode surface potential difference at separator interface [V]\n", - "\t- Sum of negative electrode electrolyte reaction source terms [A.m-3]\n", - "\t- Sum of x-averaged negative electrode electrolyte reaction source terms [A.m-3]\n", - "\t- Sum of negative electrode volumetric interfacial current densities [A.m-3]\n", - "\t- Sum of x-averaged negative electrode volumetric interfacial current densities [A.m-3]\n", - "\t- Sum of positive electrode electrolyte reaction source terms [A.m-3]\n", - "\t- Sum of x-averaged positive electrode electrolyte reaction source terms [A.m-3]\n", - "\t- Sum of positive electrode volumetric interfacial current densities [A.m-3]\n", - "\t- Sum of x-averaged positive electrode volumetric interfacial current densities [A.m-3]\n", - "\t- Interfacial current density [A.m-2]\n", - "\t- Exchange current density [A.m-2]\n", - "\t- Sum of volumetric interfacial current densities [A.m-3]\n", - "\t- Sum of electrolyte reaction source terms [A.m-3]\n", - "\t- Positive electrode potential [V]\n", - "\t- X-averaged positive electrode potential [V]\n", - "\t- Positive electrode ohmic losses [V]\n", - "\t- X-averaged positive electrode ohmic losses [V]\n", - "\t- Gradient of positive electrode potential [V.m-1]\n", - "\t- Positive electrode current density [A.m-2]\n", - "\t- Electrode current density [A.m-2]\n", - "\t- Positive current collector potential [V]\n", - "\t- Local voltage [V]\n", - "\t- Terminal voltage [V]\n", - "\t- Voltage [V]\n", - "\t- Contact overpotential [V]\n", - "\t- Positive electrode surface potential difference [V]\n", - "\t- Positive electrode surface potential difference at separator interface [V]\n", - "\t- Surface open-circuit voltage [V]\n", - "\t- Bulk open-circuit voltage [V]\n", - "\t- Particle concentration overpotential [V]\n", - "\t- X-averaged reaction overpotential [V]\n", - "\t- X-averaged solid phase ohmic losses [V]\n", - "\t- Battery open-circuit voltage [V]\n", - "\t- Battery negative electrode bulk open-circuit potential [V]\n", - "\t- Battery positive electrode bulk open-circuit potential [V]\n", - "\t- Battery particle concentration overpotential [V]\n", - "\t- Battery negative particle concentration overpotential [V]\n", - "\t- Battery positive particle concentration overpotential [V]\n", - "\t- X-averaged battery reaction overpotential [V]\n", - "\t- X-averaged battery negative reaction overpotential [V]\n", - "\t- X-averaged battery positive reaction overpotential [V]\n", - "\t- X-averaged battery solid phase ohmic losses [V]\n", - "\t- X-averaged battery negative solid phase ohmic losses [V]\n", - "\t- X-averaged battery positive solid phase ohmic losses [V]\n", - "\t- X-averaged battery electrolyte ohmic losses [V]\n", - "\t- X-averaged battery concentration overpotential [V]\n", - "\t- Battery voltage [V]\n", - "\t- Change in open-circuit voltage [V]\n", - "\t- Local ECM resistance [Ohm]\n", - "\t- Terminal power [W]\n", - "\t- Power [W]\n", - "\t- Resistance [Ohm]\n", - "\t- Total lithium in negative electrode [mol]\n", - "\t- LAM_ne [%]\n", - "\t- Loss of active material in negative electrode [%]\n", - "\t- Total lithium in positive electrode [mol]\n", - "\t- LAM_pe [%]\n", - "\t- Loss of active material in positive electrode [%]\n", - "\t- LLI [%]\n", - "\t- Loss of lithium inventory [%]\n", - "\t- Loss of lithium inventory, including electrolyte [%]\n", - "\t- Total lithium [mol]\n", - "\t- Total lithium in particles [mol]\n", - "\t- Total lithium capacity [A.h]\n", - "\t- Total lithium capacity in particles [A.h]\n", - "\t- Total lithium lost [mol]\n", - "\t- Total lithium lost from particles [mol]\n", - "\t- Total lithium lost from electrolyte [mol]\n", - "\t- Total lithium lost to side reactions [mol]\n", - "\t- Total capacity lost to side reactions [A.h]\n" - ] - } - ], - "source": [ - "print('SPM model variables:')\n", - "for v in model.variables.keys():\n", - " print('\\t-',v)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To help visualise the results, pybamm provides the `pybamm.ProcessedVariable` class, which takes the output of a solver and a variable, and allows the user to evaluate the value of that variable at any given time or $x$ value. These processed variables are automatically created by the solution dictionary." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "voltage = solution['Voltage [V]']\n", - "c_s_n_surf = solution['Negative particle surface concentration']\n", - "c_s_p_surf = solution['Positive particle surface concentration']" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "One we have these variables in hand, we can begin generating plots using a library such as Matplotlib. Below we plot the voltage and surface particle concentrations versus time" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "t = solution[\"Time [s]\"].entries\n", - "x = solution[\"x [m]\"].entries[:, 0]\n", - "f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(13,4))\n", - "\n", - "ax1.plot(t, voltage(t))\n", - "ax1.set_xlabel(r'$Time [s]$')\n", - "ax1.set_ylabel('Voltage [V]')\n", - "\n", - "ax2.plot(t, c_s_n_surf(t=t, x=x[0])) # can evaluate at arbitrary x (single representative particle)\n", - "ax2.set_xlabel(r'$Time [s]$')\n", - "ax2.set_ylabel('Negative particle surface concentration')\n", - "\n", - "ax3.plot(t, c_s_p_surf(t=t, x=x[-1])) # can evaluate at arbitrary x (single representative particle)\n", - "ax3.set_xlabel(r'$Time [s]$')\n", - "ax3.set_ylabel('Positive particle surface concentration')\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Some of the output variables are defined over space as well as time. Once option to visualise these variables is to use the `interact` slider widget. Below we plot the negative/positive particle concentration over $r$, using a slider to change the current time point" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "c_s_n = solution['Negative particle concentration']\n", - "c_s_p = solution['Positive particle concentration']\n", - "r_n = solution[\"r_n [m]\"].entries[:, 0]\n", - "r_p = solution[\"r_p [m]\"].entries[:, 0]" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "3800db8e54f0444c9aed3bd9b06dbfa6", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=3600.0, step=10.0), Output()), _dom_classes=…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "c_s_n = solution['Negative particle concentration']\n", - "c_s_p = solution['Positive particle concentration']\n", - "r_n = solution[\"r_n [m]\"].entries[:, 0, 0]\n", - "r_p = solution[\"r_p [m]\"].entries[:, 0, 0]\n", - "\n", - "def plot_concentrations(t):\n", - " f, (ax1, ax2) = plt.subplots(1, 2 ,figsize=(10,5))\n", - " plot_c_n, = ax1.plot(r_n, c_s_n(r=r_n,t=t,x=x[0])) # can evaluate at arbitrary x (single representative particle)\n", - " plot_c_p, = ax2.plot(r_p, c_s_p(r=r_p,t=t,x=x[-1])) # can evaluate at arbitrary x (single representative particle)\n", - " ax1.set_ylabel('Negative particle concentration')\n", - " ax2.set_ylabel('Positive particle concentration')\n", - " ax1.set_xlabel(r'$r_n$ [m]')\n", - " ax2.set_xlabel(r'$r_p$ [m]')\n", - " ax1.set_ylim(0, 1)\n", - " ax2.set_ylim(0, 1)\n", - " plt.show()\n", - "\n", - "import ipywidgets as widgets\n", - "widgets.interact(plot_concentrations, t=widgets.FloatSlider(min=0,max=3600,step=10,value=0));\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The QuickPlot class can be used to plot the common set of useful outputs which should give you a good initial overview of the model. The method `Quickplot.dynamic_plot` employs the slider widget. " - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "ac9df01028e4489c83eccaa4071aa245", - "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": [ - "quick_plot = pybamm.QuickPlot(solution)\n", - "quick_plot.dynamic_plot()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## References\n", - "\n", - "The relevant papers for this notebook are:" - ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "c_s_n = solution['Negative particle concentration']\n", + "c_s_p = solution['Positive particle concentration']\n", + "r_n = solution[\"r_n [m]\"].entries[:, 0, 0]\n", + "r_p = solution[\"r_p [m]\"].entries[:, 0, 0]\n", + "\n", + "\n", + "def plot_concentrations(t):\n", + " f, (ax1, ax2) = plt.subplots(1, 2 ,figsize=(10,5))\n", + " plot_c_n, = ax1.plot(r_n, c_s_n(r=r_n,t=t,x=x[0])) # can evaluate at arbitrary x (single representative particle)\n", + " plot_c_p, = ax2.plot(r_p, c_s_p(r=r_p,t=t,x=x[-1])) # can evaluate at arbitrary x (single representative particle)\n", + " ax1.set_ylabel('Negative particle concentration')\n", + " ax2.set_ylabel('Positive particle concentration')\n", + " ax1.set_xlabel(r'$r_n$ [m]')\n", + " ax2.set_xlabel(r'$r_p$ [m]')\n", + " ax1.set_ylim(0, 1)\n", + " ax2.set_ylim(0, 1)\n", + " plt.show()\n", + "\n", + "\n", + "import ipywidgets as widgets\n", + "widgets.interact(plot_concentrations, t=widgets.FloatSlider(min=0,max=3600,step=10,value=0));" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The QuickPlot class can be used to plot the common set of useful outputs which should give you a good initial overview of the model. The method `Quickplot.dynamic_plot` employs the slider widget. " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 20, - "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] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", - "[3] 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", - "[4] 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()" + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ac9df01028e4489c83eccaa4071aa245", + "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" } - ], - "metadata": { - "kernelspec": { - "display_name": "pybamm", - "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": "3.10.12" - }, - "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": "187972e187ab8dfbecfab9e8e194ae6d08262b2d51a54fa40644e3ddb6b5f74c" - } + ], + "source": [ + "quick_plot = pybamm.QuickPlot(solution)\n", + "quick_plot.dynamic_plot()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "The relevant papers for this notebook are:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "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] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", + "[3] 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", + "[4] 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": "pybamm", + "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": "3.10.12" + }, + "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": 2 + "vscode": { + "interpreter": { + "hash": "187972e187ab8dfbecfab9e8e194ae6d08262b2d51a54fa40644e3ddb6b5f74c" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 } diff --git a/docs/source/examples/notebooks/models/Validating_mechanical_models_Enertech_DFN.ipynb b/docs/source/examples/notebooks/models/Validating_mechanical_models_Enertech_DFN.ipynb index fcbde8fad2..5551cecef1 100644 --- a/docs/source/examples/notebooks/models/Validating_mechanical_models_Enertech_DFN.ipynb +++ b/docs/source/examples/notebooks/models/Validating_mechanical_models_Enertech_DFN.ipynb @@ -26,7 +26,6 @@ "%pip install pybamm -q # install PyBaMM if it is not installed\n", "import pybamm\n", "import os\n", - "import numpy as np\n", "import matplotlib.pyplot as plt\n", "os.chdir(pybamm.__path__[0]+'/..')" ] @@ -228,7 +227,7 @@ "ax3.text(1.8, -0.0001, r'0.5 C', {'color': 'g', 'fontsize': 14})\n", "\n", "f.tight_layout()\n", - "f.show()\n" + "f.show()" ] }, { diff --git a/docs/source/examples/notebooks/models/composite_particle.ipynb b/docs/source/examples/notebooks/models/composite_particle.ipynb index 62680ef3b4..e697004477 100644 --- a/docs/source/examples/notebooks/models/composite_particle.ipynb +++ b/docs/source/examples/notebooks/models/composite_particle.ipynb @@ -1,987 +1,986 @@ { - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "id": "68feb769", - "metadata": {}, - "source": [ - "# A composite electrode particle model" - ] + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "id": "68feb769", + "metadata": {}, + "source": [ + "# A composite electrode particle model" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "58f614c2", + "metadata": {}, + "source": [ + " A composite electrode particle model is developed for (negative) electrodes with two phases, e.g. graphite/silicon in LG M50 battery cells. The current version is demonstrated for negative composite electrodes only but is easily extended to positive composite electrodes. The reference is at the end of this notebook." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "44c5f462", + "metadata": {}, + "source": [ + "## How to use the model\n", + "\n", + "Let us set up PyBaMM " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "29f9e4d0", + "metadata": {}, + "outputs": [], + "source": [ + "#%pip install pybamm -q # install PyBaMM if it is not installed\n", + "import os\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pybamm\n", + "import timeit\n", + "from matplotlib import style\n", + "style.use('ggplot')\n", + "os.chdir(pybamm.__path__[0]+'/..')\n", + "pybamm.set_logging_level(\"INFO\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "7d4ebc5e", + "metadata": {}, + "source": [ + "Choose the option `{\"particle phases\": (\"2\", \"1\")}` to load the composite electrode particle model by specifying that there are two particle phases (graphite and silicon) in the negative electrode. The parameter set \"Chen2020_composite\" includes parameters for silicon as a secondary particle" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8935004a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-02-21 09:08:46.318 - [INFO] base_model._build_model(550): Start building Doyle-Fuller-Newman model\n", + "2023-02-21 09:08:46.452 - [INFO] base_battery_model.build_model(970): Finish building Doyle-Fuller-Newman model\n" + ] + } + ], + "source": [ + "start = timeit.default_timer()\n", + "model = pybamm.lithium_ion.DFN({\n", + " \"particle phases\": (\"2\", \"1\"),\n", + " \"open-circuit potential\": ((\"single\", \"current sigmoid\"), \"single\")\n", + "})\n", + "param = pybamm.ParameterValues(\"Chen2020_composite\")\n", + "\n", + "param.update({\"Upper voltage cut-off [V]\": 4.5})\n", + "param.update({\"Lower voltage cut-off [V]\": 2.5})\n", + "\n", + "param.update({\n", + " \"Primary: Maximum concentration in negative electrode [mol.m-3]\":28700,\n", + " \"Primary: Initial concentration in negative electrode [mol.m-3]\":23000,\n", + " \"Primary: Negative electrode diffusivity [m2.s-1]\":5.5E-14,\n", + " \"Secondary: Negative electrode diffusivity [m2.s-1]\":1.67E-14,\n", + " \"Secondary: Initial concentration in negative electrode [mol.m-3]\":277000,\n", + " \"Secondary: Maximum concentration in negative electrode [mol.m-3]\":278000\n", + "})" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "10339d40", + "metadata": {}, + "source": [ + "## Single Cycle Simulations" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "cf194af2", + "metadata": {}, + "source": [ + "Define a current loading" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5a0dc425", + "metadata": {}, + "outputs": [], + "source": [ + "C_rate = 0.5\n", + "capacity = param[\"Nominal cell capacity [A.h]\"]\n", + "I_load = C_rate * capacity \n", + "\n", + "t_eval = np.linspace(0,10000,1000)\n", + "\n", + "param[\"Current function [A]\"] = I_load" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "659b97d9", + "metadata": {}, + "source": [ + "It is very easy to vary the relative volume fraction of each phase. The following example shows how to compare the results of batteries with three relative volume fractions (0.001, 0.04, 0.1) of silicon." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "6319cc89", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-02-21 09:08:46.528 - [INFO] parameter_values.process_model(425): Start setting parameters for Doyle-Fuller-Newman model\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "id": "58f614c2", - "metadata": {}, - "source": [ - " A composite electrode particle model is developed for (negative) electrodes with two phases, e.g. graphite/silicon in LG M50 battery cells. The current version is demonstrated for negative composite electrodes only but is easily extended to positive composite electrodes. The reference is at the end of this notebook." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "0.001\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "id": "44c5f462", - "metadata": {}, - "source": [ - "## How to use the model\n", - "\n", - "Let us set up PyBaMM " - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-02-21 09:08:46.741 - [INFO] parameter_values.process_model(527): Finish setting parameters for Doyle-Fuller-Newman model\n", + "2023-02-21 09:08:46.743 - [INFO] discretisation.process_model(147): Start discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:08:46.750 - [INFO] discretisation.remove_independent_variables_from_rhs(1120): removing variable Discharge capacity [A.h] from rhs\n", + "2023-02-21 09:08:47.292 - [INFO] discretisation.process_model(259): Finish discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:08:47.293 - [INFO] base_solver.solve(703): Start solving Doyle-Fuller-Newman model with CasADi solver with 'safe' mode\n", + "2023-02-21 09:08:47.297 - [INFO] base_solver.set_up(111): Start solver set-up\n", + "2023-02-21 09:08:47.433 - [INFO] base_solver.set_up(236): Finish solver set-up\n", + "2023-02-21 09:08:55.129 - [INFO] base_solver.solve(937): Finish solving Doyle-Fuller-Newman model (event: Minimum voltage)\n", + "2023-02-21 09:08:55.130 - [INFO] base_solver.solve(938): Set-up time: 136.675 ms, Solve time: 7.685 s (of which integration time: 6.012 s), Total time: 7.821 s\n", + "2023-02-21 09:08:55.137 - [INFO] parameter_values.process_model(425): Start setting parameters for Doyle-Fuller-Newman model\n", + "2023-02-21 09:08:55.250 - [INFO] parameter_values.process_model(527): Finish setting parameters for Doyle-Fuller-Newman model\n", + "2023-02-21 09:08:55.252 - [INFO] discretisation.process_model(147): Start discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:08:55.260 - [INFO] discretisation.remove_independent_variables_from_rhs(1120): removing variable Discharge capacity [A.h] from rhs\n" + ] }, { - "cell_type": "code", - "execution_count": 1, - "id": "29f9e4d0", - "metadata": {}, - "outputs": [], - "source": [ - "#%pip install pybamm -q # install PyBaMM if it is not installed\n", - "import os\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pybamm\n", - "import pandas as pd\n", - "import timeit\n", - "from matplotlib import style\n", - "style.use('ggplot')\n", - "os.chdir(pybamm.__path__[0]+'/..')\n", - "pybamm.set_logging_level(\"INFO\")" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "0.04\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "id": "7d4ebc5e", - "metadata": {}, - "source": [ - "Choose the option `{\"particle phases\": (\"2\", \"1\")}` to load the composite electrode particle model by specifying that there are two particle phases (graphite and silicon) in the negative electrode. The parameter set \"Chen2020_composite\" includes parameters for silicon as a secondary particle" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-02-21 09:08:55.808 - [INFO] discretisation.process_model(259): Finish discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:08:55.809 - [INFO] base_solver.solve(703): Start solving Doyle-Fuller-Newman model with CasADi solver with 'safe' mode\n", + "2023-02-21 09:08:55.814 - [INFO] base_solver.set_up(111): Start solver set-up\n", + "2023-02-21 09:08:55.947 - [INFO] base_solver.set_up(236): Finish solver set-up\n", + "2023-02-21 09:09:06.233 - [INFO] base_solver.solve(937): Finish solving Doyle-Fuller-Newman model (event: Minimum voltage)\n", + "2023-02-21 09:09:06.234 - [INFO] base_solver.solve(938): Set-up time: 134.145 ms, Solve time: 10.272 s (of which integration time: 8.058 s), Total time: 10.407 s\n", + "2023-02-21 09:09:06.242 - [INFO] parameter_values.process_model(425): Start setting parameters for Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:06.444 - [INFO] parameter_values.process_model(527): Finish setting parameters for Doyle-Fuller-Newman model\n" + ] }, { - "cell_type": "code", - "execution_count": 2, - "id": "8935004a", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-02-21 09:08:46.318 - [INFO] base_model._build_model(550): Start building Doyle-Fuller-Newman model\n", - "2023-02-21 09:08:46.452 - [INFO] base_battery_model.build_model(970): Finish building Doyle-Fuller-Newman model\n" - ] - } - ], - "source": [ - "start = timeit.default_timer()\n", - "model = pybamm.lithium_ion.DFN({\n", - " \"particle phases\": (\"2\", \"1\"),\n", - " \"open-circuit potential\": ((\"single\", \"current sigmoid\"), \"single\")\n", - "})\n", - "param = pybamm.ParameterValues(\"Chen2020_composite\")\n", - "\n", - "param.update({\"Upper voltage cut-off [V]\": 4.5})\n", - "param.update({\"Lower voltage cut-off [V]\": 2.5})\n", - "\n", - "param.update({\n", - " \"Primary: Maximum concentration in negative electrode [mol.m-3]\":28700,\n", - " \"Primary: Initial concentration in negative electrode [mol.m-3]\":23000,\n", - " \"Primary: Negative electrode diffusivity [m2.s-1]\":5.5E-14,\n", - " \"Secondary: Negative electrode diffusivity [m2.s-1]\":1.67E-14,\n", - " \"Secondary: Initial concentration in negative electrode [mol.m-3]\":277000,\n", - " \"Secondary: Maximum concentration in negative electrode [mol.m-3]\":278000\n", - "})" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "0.1\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "id": "10339d40", - "metadata": {}, - "source": [ - "## Single Cycle Simulations" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-02-21 09:09:06.447 - [INFO] discretisation.process_model(147): Start discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:06.453 - [INFO] discretisation.remove_independent_variables_from_rhs(1120): removing variable Discharge capacity [A.h] from rhs\n", + "2023-02-21 09:09:07.047 - [INFO] discretisation.process_model(259): Finish discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:07.048 - [INFO] base_solver.solve(703): Start solving Doyle-Fuller-Newman model with CasADi solver with 'safe' mode\n", + "2023-02-21 09:09:07.052 - [INFO] base_solver.set_up(111): Start solver set-up\n", + "2023-02-21 09:09:07.200 - [INFO] base_solver.set_up(236): Finish solver set-up\n", + "At t = 0.00076482 and h = 3.55373e-22, the corrector convergence failed repeatedly or with |h| = hmin.\n", + "At t = 0.000250492 and h = 1.20507e-16, the corrector convergence failed repeatedly or with |h| = hmin.\n", + "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n", + "At t = 0.000250493 and h = 2.0588e-18, the corrector convergence failed repeatedly or with |h| = hmin.\n", + "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:852: Linear solve failed\n", + "At t = 0.000121913 and h = 6.48118e-25, the corrector convergence failed repeatedly or with |h| = hmin.\n", + "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:852: Linear solve failed\n", + "At t = 5.76225e-05 and h = 3.13053e-22, the corrector convergence failed repeatedly or with |h| = hmin.\n", + "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:852: Linear solve failed\n", + "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:852: Linear solve failed\n", + "At t = 2.54755e-05 and h = 4.28889e-31, the corrector convergence failed repeatedly or with |h| = hmin.\n", + "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:852: Linear solve failed\n", + "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:852: Linear solve failed\n", + "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:852: Linear solve failed\n", + "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:852: Linear solve failed\n", + "At t = 9.40462e-06 and h = 4.82262e-22, the corrector convergence failed repeatedly or with |h| = hmin.\n", + "2023-02-21 09:09:21.630 - [INFO] base_solver.solve(937): Finish solving Doyle-Fuller-Newman model (event: Minimum voltage)\n", + "2023-02-21 09:09:21.631 - [INFO] base_solver.solve(938): Set-up time: 148.688 ms, Solve time: 14.417 s (of which integration time: 9.176 s), Total time: 14.566 s\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "id": "cf194af2", - "metadata": {}, - "source": [ - "Define a current loading" + "name": "stdout", + "output_type": "stream", + "text": [ + "running time: 35.389444837s\n" + ] + } + ], + "source": [ + "v_si=[0.001,0.04,0.1]\n", + "total_am_volume_fraction = 0.75\n", + "solution=[]\n", + "for v in v_si:\n", + " param.update({\n", + " \"Primary: Negative electrode active material volume fraction\": (1-v) * total_am_volume_fraction, #primary\n", + " \"Secondary: Negative electrode active material volume fraction\": v * total_am_volume_fraction,\n", + " })\n", + " print(v)\n", + " sim = pybamm.Simulation(\n", + " model,\n", + " parameter_values=param,\n", + " solver=pybamm.CasadiSolver(dt_max = 5),\n", + " )\n", + " solution.append(sim.solve(t_eval = t_eval))\n", + "stop = timeit.default_timer()\n", + "print(\"running time: \" + str(stop - start) + \"s\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "e87e931c", + "metadata": {}, + "source": [ + "## Results\n", + "Compare the cell voltages of the three cells in this example, to see how silicon affects the output capacity" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ec9bebd1", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "code", - "execution_count": 3, - "id": "5a0dc425", - "metadata": {}, - "outputs": [], - "source": [ - "C_rate = 0.5\n", - "capacity = param[\"Nominal cell capacity [A.h]\"]\n", - "I_load = C_rate * capacity \n", - "\n", - "t_eval = np.linspace(0,10000,1000)\n", - "\n", - "param[\"Current function [A]\"] = I_load" + "data": { + "image/png": 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", + "text/plain": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ltype=['k-','r--','b-.','g:','m-','c--','y-.']\n", + "for i in range(0,len(v_si)):\n", + " t_i = solution[i][\"Time [s]\"].entries / 3600\n", + " V_i = solution[i][\"Voltage [V]\"].entries\n", + " plt.plot(t_i, V_i,ltype[i],label=\"$V_\\mathrm{si}=$\"+str(v_si[i]))\n", + "plt.xlabel('Time [h]')\n", + "plt.ylabel('Voltage [V]')\n", + "plt.legend()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "ee7bc032", + "metadata": {}, + "source": [ + "Results of interfacial current density in silicon" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "9893a85f", + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "id": "659b97d9", - "metadata": {}, - "source": [ - "It is very easy to vary the relative volume fraction of each phase. The following example shows how to compare the results of batteries with three relative volume fractions (0.001, 0.04, 0.1) of silicon." + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Silicon')" ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "code", - "execution_count": 4, - "id": "6319cc89", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-02-21 09:08:46.528 - [INFO] parameter_values.process_model(425): Start setting parameters for Doyle-Fuller-Newman model\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.001\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-02-21 09:08:46.741 - [INFO] parameter_values.process_model(527): Finish setting parameters for Doyle-Fuller-Newman model\n", - "2023-02-21 09:08:46.743 - [INFO] discretisation.process_model(147): Start discretising Doyle-Fuller-Newman model\n", - "2023-02-21 09:08:46.750 - [INFO] discretisation.remove_independent_variables_from_rhs(1120): removing variable Discharge capacity [A.h] from rhs\n", - "2023-02-21 09:08:47.292 - [INFO] discretisation.process_model(259): Finish discretising Doyle-Fuller-Newman model\n", - "2023-02-21 09:08:47.293 - [INFO] base_solver.solve(703): Start solving Doyle-Fuller-Newman model with CasADi solver with 'safe' mode\n", - "2023-02-21 09:08:47.297 - [INFO] base_solver.set_up(111): Start solver set-up\n", - "2023-02-21 09:08:47.433 - [INFO] base_solver.set_up(236): Finish solver set-up\n", - "2023-02-21 09:08:55.129 - [INFO] base_solver.solve(937): Finish solving Doyle-Fuller-Newman model (event: Minimum voltage)\n", - "2023-02-21 09:08:55.130 - [INFO] base_solver.solve(938): Set-up time: 136.675 ms, Solve time: 7.685 s (of which integration time: 6.012 s), Total time: 7.821 s\n", - "2023-02-21 09:08:55.137 - [INFO] parameter_values.process_model(425): Start setting parameters for Doyle-Fuller-Newman model\n", - "2023-02-21 09:08:55.250 - [INFO] parameter_values.process_model(527): Finish setting parameters for Doyle-Fuller-Newman model\n", - "2023-02-21 09:08:55.252 - [INFO] discretisation.process_model(147): Start discretising Doyle-Fuller-Newman model\n", - "2023-02-21 09:08:55.260 - [INFO] discretisation.remove_independent_variables_from_rhs(1120): removing variable Discharge capacity [A.h] from rhs\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.04\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-02-21 09:08:55.808 - [INFO] discretisation.process_model(259): Finish discretising Doyle-Fuller-Newman model\n", - "2023-02-21 09:08:55.809 - [INFO] base_solver.solve(703): Start solving Doyle-Fuller-Newman model with CasADi solver with 'safe' mode\n", - "2023-02-21 09:08:55.814 - [INFO] base_solver.set_up(111): Start solver set-up\n", - "2023-02-21 09:08:55.947 - [INFO] base_solver.set_up(236): Finish solver set-up\n", - "2023-02-21 09:09:06.233 - [INFO] base_solver.solve(937): Finish solving Doyle-Fuller-Newman model (event: Minimum voltage)\n", - "2023-02-21 09:09:06.234 - [INFO] base_solver.solve(938): Set-up time: 134.145 ms, Solve time: 10.272 s (of which integration time: 8.058 s), Total time: 10.407 s\n", - "2023-02-21 09:09:06.242 - [INFO] parameter_values.process_model(425): Start setting parameters for Doyle-Fuller-Newman model\n", - "2023-02-21 09:09:06.444 - [INFO] parameter_values.process_model(527): Finish setting parameters for Doyle-Fuller-Newman model\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.1\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-02-21 09:09:06.447 - [INFO] discretisation.process_model(147): Start discretising Doyle-Fuller-Newman model\n", - "2023-02-21 09:09:06.453 - [INFO] discretisation.remove_independent_variables_from_rhs(1120): removing variable Discharge capacity [A.h] from rhs\n", - "2023-02-21 09:09:07.047 - [INFO] discretisation.process_model(259): Finish discretising Doyle-Fuller-Newman model\n", - "2023-02-21 09:09:07.048 - [INFO] base_solver.solve(703): Start solving Doyle-Fuller-Newman model with CasADi solver with 'safe' mode\n", - "2023-02-21 09:09:07.052 - [INFO] base_solver.set_up(111): Start solver set-up\n", - "2023-02-21 09:09:07.200 - [INFO] base_solver.set_up(236): Finish solver set-up\n", - "At t = 0.00076482 and h = 3.55373e-22, the corrector convergence failed repeatedly or with |h| = hmin.\n", - "At t = 0.000250492 and h = 1.20507e-16, the corrector convergence failed repeatedly or with |h| = hmin.\n", - "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:849: Calculating Jacobian failed\n", - "At t = 0.000250493 and h = 2.0588e-18, the corrector convergence failed repeatedly or with |h| = hmin.\n", - "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:852: Linear solve failed\n", - "At t = 0.000121913 and h = 6.48118e-25, the corrector convergence failed repeatedly or with |h| = hmin.\n", - "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:852: Linear solve failed\n", - "At t = 5.76225e-05 and h = 3.13053e-22, the corrector convergence failed repeatedly or with |h| = hmin.\n", - "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:852: Linear solve failed\n", - "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:852: Linear solve failed\n", - "At t = 2.54755e-05 and h = 4.28889e-31, the corrector convergence failed repeatedly or with |h| = hmin.\n", - "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:852: Linear solve failed\n", - "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:852: Linear solve failed\n", - "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:852: Linear solve failed\n", - "psetup failed: .../casadi/interfaces/sundials/idas_interface.cpp:852: Linear solve failed\n", - "At t = 9.40462e-06 and h = 4.82262e-22, the corrector convergence failed repeatedly or with |h| = hmin.\n", - "2023-02-21 09:09:21.630 - [INFO] base_solver.solve(937): Finish solving Doyle-Fuller-Newman model (event: Minimum voltage)\n", - "2023-02-21 09:09:21.631 - [INFO] base_solver.solve(938): Set-up time: 148.688 ms, Solve time: 14.417 s (of which integration time: 9.176 s), Total time: 14.566 s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "running time: 35.389444837s\n" - ] - } - ], - "source": [ - "v_si=[0.001,0.04,0.1]\n", - "total_am_volume_fraction = 0.75\n", - "solution=[]\n", - "for v in v_si:\n", - " param.update({\n", - " \"Primary: Negative electrode active material volume fraction\": (1-v) * total_am_volume_fraction, #primary\n", - " \"Secondary: Negative electrode active material volume fraction\": v * total_am_volume_fraction,\n", - " })\n", - " print(v)\n", - " sim = pybamm.Simulation(\n", - " model,\n", - " parameter_values=param,\n", - " solver=pybamm.CasadiSolver(dt_max = 5),\n", - " )\n", - " solution.append(sim.solve(t_eval = t_eval))\n", - "stop = timeit.default_timer()\n", - "print(\"running time: \" + str(stop - start) + \"s\")" + "data": { + "image/png": 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", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "attachments": {}, - "cell_type": "markdown", - "id": "e87e931c", - "metadata": {}, - "source": [ - "## Results\n", - "Compare the cell voltages of the three cells in this example, to see how silicon affects the output capacity" + "data": { + "image/png": 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", + "text/plain": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "for i in range(0,len(v_si)):\n", + " t_i = solution[i][\"Time [s]\"].entries / 3600\n", + " j_n_p1_av = solution[i][\"X-averaged negative electrode primary interfacial current density [A.m-2]\"].entries\n", + " plt.plot(t_i, j_n_p1_av,ltype[i],label=\"$V_\\mathrm{si}=$\"+str(v_si[i]))\n", + "plt.xlabel('Time [h]')\n", + "plt.ylabel('Averaged interfacial current density [A/m$^{2}$]')\n", + "plt.legend()\n", + "plt.title('Graphite')\n", + "\n", + "plt.figure()\n", + "for i in range(0,len(v_si)):\n", + " t_i = solution[i][\"Time [s]\"].entries / 3600\n", + " j_n_p2_av = solution[i][\"X-averaged negative electrode secondary interfacial current density [A.m-2]\"].entries\n", + " plt.plot(t_i, j_n_p2_av,ltype[i],label=\"$V_\\mathrm{si}=$\"+str(v_si[i]))\n", + "plt.xlabel('Time [h]')\n", + "plt.ylabel('Averaged interfacial current density [A/m$^{2}$]')\n", + "plt.legend()\n", + "plt.title('Silicon')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "77fa4197", + "metadata": {}, + "source": [ + "Results of interfacial current density in graphite" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "9eafca0e", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 5, - "id": "ec9bebd1", - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ltype=['k-','r--','b-.','g:','m-','c--','y-.'];\n", - "for i in range(0,len(v_si)):\n", - " t_i = solution[i][\"Time [s]\"].entries / 3600\n", - " V_i = solution[i][\"Voltage [V]\"].entries\n", - " plt.plot(t_i, V_i,ltype[i],label=\"$V_\\mathrm{si}=$\"+str(v_si[i]))\n", - "plt.xlabel('Time [h]')\n", - "plt.ylabel('Voltage [V]')\n", - "plt.legend()" + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Silicon')" ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" }, { - "attachments": {}, - "cell_type": "markdown", - "id": "ee7bc032", - "metadata": {}, - "source": [ - "Results of interfacial current density in silicon" + "data": { + "image/png": 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", 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GRVmpX9+KLHMihGe0aNGCY8eOUVRUhPfZ5vTExESWLl3K4sWL0Vbyw3HWrFnMnj37guds2rSJ+vXrO56bTCYSExNLJTsajYbu3buzY8eOcsu4lGuqUq6nSBLkRp9/DrNmVa55s1u3YpYsyXQ8v//+UPLyNGzZcprYWHuysnu3niVLfCssIzDQ/osvJMRGcLCNoCAbcXGl10B6770cFEWlSZNzxx98MI9Ro/LPJmj2hKyEzWb/RV1UpHDmjD3hys3VoNWWnrYYGmrD21ulZctz5S5Z4su77wYSEGCjQwcTXbua6NWriPh4C7Vl/LrFAidPajlyRMfhwzqOHNFy7JiO06ftSU9mpgabrXSyqSgqI0YUOOror7/0rF7tw7XXFtOzp/1YerqG9esvrX9So1GpX99Ko0ZWZs7MJirKBtgT3Vq6+LC4zKmqSmFh4cVPdAGdTldqLTkfH59KrdodHx+P1Wrl0KFDtG7dGoDXXnuNgQMH0rVr13KvSU1NZcqUKcyZM6fMa/feey8333zzBe9Zt27dUs+zsrKwWq1lur3Cw8P5559/yi3jUq6pSrmeIkmQGzVqBB07mjhzxp4w5Obax9pYrRX/x/n3+l9t2pgpKip9fq9exfj7q45Ep+RrSdJzKX/tl7QknM++e0j5azFoNGAw2FuLAgOtnPfHRSnLlmVitVIquSkqUvDzs3HmjIZffvHml1+8mTo1kDp1rPTsWUzPnsVcd10xdevaLh74ZWDnTj2JiXo6dzY5ksG1a715+OELJ8QajUp4uI3wcCvBwfYuR5MJxxisu+8u4Npri+nc+dz+RXFxFt5910hxsb2ei4sV8vMVsrLsrYOZmVoyMzWkpGgpKlI4cUJHaqqWOnXO1fULLwSxY4eBZ545w6BBRa6vECGqSWFhIU2bNvXIvQ8ePIivb8V/jP5bgwYNCAwMZP/+/bRu3ZoVK1aQmJjI5s2bK7wmMjKy3AQIICQkhJCQkErHXdtJEuRGzz4LI0ZkllnkyWoFk8k+ANlqVdDp1FKDj8+3dGkm/9ali4kuXWruRn7/btV96aUzPPfcGfbt0/H7715s2WLg11+9yMzUsny5L8uX2z9IWrY0c911xVx3XRGdOplq5ABsVYXUVA1Hjugcj2PHtHz8cbYj8Zs3z5/vv/dh4sQcRxIUE2PB21ulcWMLMTEWYmMtNG5sJTLSSkSElYgIe9fVhVrE7XVT+lhUlI277rr4RpI2m73V6PhxHZmZGgyGc68lJenZt09fKnHdt0/H99/7cP31RVx1lbnWtNgJUZ2aN2/O/v37KSoq4q233uLxxx93dFfl5+fz0EMPkZqaCsDEiRNp0qQJDz30ED/88EOZsqrSHRYaGopWqyUjI6PUeenp6RWuvHwp11SlXE+RJKgG0GrtrS72MT61Y8VXnQ5at7bQurWF0aPzMZlg504Dv/zixS+/eJGYqGfvXvtj7lx/nnrqDC+8cAaw/wJXFPd12agqpKVpOHbM3m11+HBJF5aOo0e1FBaWzQiSk3Np0MDejNepUzGFhYrjOUDLlhYOHkzxWDKh0UDdujbq1i2bPH/2WRY7dhjo2vVc6+CqVT7MnBnAzJkB1K1r5ZZbCrnttkJatzZL15moMXx8fDh48KBb7lVed1hltWzZkn379jF37lwAHn30UcdrmzZtIiQkhK+++gpVVcnLyyuzg8L5qtIdZjAYaNu2LQkJCY5Z2DabjYSEBEaNGlVuGZdyTVXK9RRJgkSNYDCca9F64YUzZGVp2LLFwKZN3vzyi1ep7rr1670YNy6YoUMLmTw513G8qAinWotOnNCyb5+OevWstGpl/3Dbv1/HwIFhFBVVnK1otSoNG1qJjbW36sTEWPD1Pde9NGpUAaNGlW6dqcktKXXr2hg4sHQ3WIcOJgYNKmTTJi9On9Yyb54/8+b506yZmeHDC7jjjgJCQ2tHAi9qLkVRKtUl5QxX7B3WokULVq9ezdatW3n//fdLJVItWrTgtddeY8qUKQwYMICrr776gklQVbvDxowZwzPPPEPbtm1p37498+bNo7Cw0DGrC+Czzz7jhx9+YMmSJZd8zaWck5+fz5EjRxzPjx8/TlJSEiEhIaVarKqTJEGiRgoNtXHrrUXcemsRqmpvjSmxZYsX2dlaTKZzTRD5+QrNm0cSEWEfExUcXDI+CoqLgwB7y1FhoX2MTF6eQl6ehgULsmjY0N5C89VXvsyeHcCoUXlMmWJPrurWtVJUpHEMIrYnOVZHshMba6Fhwyt/llWfPsX06VOMyQSbNnmxbJkvP/3kzYEDeqZMCeLddwMZMsSe7Lloc2chrnjx8fFkZmbStWvXUht5AzRp0oQff/yR9evXM2nSJIYOHUq/fv1cHsOtt95KVlYW7777Lunp6bRq1Yr//Oc/pbqtsrKyOHbsWKWuuZRz/vrrr1Lr+k2aNAmA22+/nQ8++MDlP2t5FFV23CtXenq6S3cIVhSFqKgoUlJSZJNDJxUXw969evz8VJo2tbfY7N2ro1+/iEqXtXx5Bp062buElizxYcECP/r3L+KZZ/Ic5xw5oqV+fWupcTMCcnIUVq3y4Ysv/Niz51wW2LcvPPpoJl26FEtXmRvU9s+W3NxcAgMD3X7f6t5FPjU1leDgYLy9vVmxYgVbtmxh7NixFY4Jqg1K6ryif3O9Xl/pMUeSBFVAkqDLi6pCdrbCyZM6jEYN2dkKRqMWgyGInJxcbDb7OT4+Kn5+Nvz8VPz9Va66ykRIiPx7OENV4Y8/DHz2mS+rV/tgsdgzn++/T6djx+r7JSHsavtny5WaBG3atIk33ngDjUaDt7c37733Hj4+PpIESRLkHtWSBNWtS0pqqvNDn0v+yc7+ma3k5qLJzkYxmaC4GMVksn9/9qupUyfUs28YXVIShj/+QDGbUcxm+zlmM5jNKCYT+aNGYY2OBsBrwwZ8Fy2yl2WzlbonisKZceMwX3UVAIaEBPy+/BLV2xvVy8v+1cfH/r2PD8X9+mE5O3VVyctDk52NLSgINSCg2kY4X3G/HM5OI1TMZhSLpdS/nRocjO3sEvtKYSG6vXvPjR4/+1DPDkSyhYdjKxkgabOB2WwflOWCf4dTp3R88UUEO3cW8+23mY4iT560t6ZJy5DrXXHv80q6UpMgUVZ1JEEyJsidnn6aqNmzUbVa0OlQdTr7V60W9HoyVqzA2rAhAP4zZuC3YAGKqtp/UdlsYLWiWCwoZjNpv/yCJS7Ofu7cuQTMnFnhbdPXrHEkK96//ELgW29VeG5R//6OJEh74gQ+a9ZUeG7+yJGO77UnT+KzalWF52Y1aOBIgrw2biT0kUcAUHU6rBER2OrWdXwtGD4cc/v2ACgFBSj5+djq1KnZo4krYrPZf4a8PDR5eVhDQ1HPJiuaU6fw/vFHNHl59tfPnEEp+T4vj/z77qPo7DgBw9athA0bVuFtcl94gbynngJAe/gw4ReYJZL36KPkvvKK/dyTJ6l77bWoimJPXP387MlpcDC2oCCKBgyg4O677RdaLHivX481MhJr3brYIiLKrH/QoIGVOXPg1KksR8JjNCrccEM48fFm5s7NJizsylj/SQhx+ZMkyJ3OTqdUrFZ7QlNcdoHCEkphIdrs7IrLOu9a1ccHm68vGAyoZx8YDPZWmJLnZ5mbNqXwppvsx/R6VL3+3PcGA9bzRrWaunTB+NZb9rJKEhBVtSdmgLl583PlXn01xilTUIqKyj4KC7HExJz72YqLUb297a9ZLOiSkyE5+dyP1q2bIwny2riR0IceQtXpsIWHY61b1/4L+OzXooEDsTRrZr+wsBClsNDeulTVkcqqal+0SaNxlKHJzES3Zw+aswmZkp9vT1zOnEHJz6fw1lsxd+wI2JOVoJdesic0+fn2pOa8v86NU6dScN99AOiOHiX4bDJSnuLzFgFS/fzKhqrVour19lac85MRgwFLw4aUjChXSvoCAWw2bMHBjlNL3oOKqqIUFkJhIdrz1vawnLfwnCYtjdAHHjh3f40GW0QE1vr1sTRuTFG/fhTfeqv9XM25W/7xh4HCQvu+dSEhkgAJIWoOSYLcafp0Tj/2GKrJZE+ELBawWOxdGxYL1vPWcMh/6CEKhw9HVRT7bxSNxt6lYd+6Hdt5UyHznnySvCefvKQQim+4geIbbrikcy0tWmBp0eLSzo2Lc7RMXUzhsGEUDhsGRUVoMjPRpqWhTUtDk5qKNi0Nc3y841xNTg6qoqBYLGhTUtCmpJS+b9OmjiTIZ906Qh5/HACbtzeqvz94eRGu0YBWS86kSRSfnV3htX49Qa++au8GLOkatFgciVn2hx9SOHgwAIbffyf0AjscW+LiHEkQViv6ctYpUTUae3JmO5cEWKOiKBw0CJu/P6q/P2pAgON7W0AAllatHOeaW7YkNTHRntjq9fYErYKWMUvTpqRt21ZhvKXObdKElL//Ppew5uWhyclBk5ODkpNTKglSioowtW+PNiUFTXo6itWKNjUVbWoqhh07sIWHO5IgJSuLiL59MbdsydCWLWn/0tWkhsejtdQFrRdFRfDJJ/6MHp2Pv3/t68IRQtQMkgS5k58ftjp1Lqnf3hYejq2Grazpct7e2OrXx1a/PhX1rBeMGEHB8OFo0tPRnj6N9vRpe7J0+jSatLTSv6QLzq3Foykqsi8cxLk3uVJ0bu0bpagI3XlTPv9Nyc93fG8LC8PcsiWqry82Pz9UPz9HoqL6+WE+u+8PgLl1azKWLLEnNH5+qAEB9oe3d5kxN9bYWLI//vhitWSn19u7BF1No0ENCkINCrroqdbYWDJKujytVjQZGfYk6MQJdEePYipJBAH93r2Ofy82baIN0AZ796elWTPeiPmEaau78dlnfrz8ci5DhxZelr2dQojLmwyMroDMDrtMWSyO8TWa/HzCg4PJSE1FNZuxNmniGDysycpC+88/ji5B9HpUnc4+Lsbf3979JFvcV5rjfX74MLo9e9Dv24du7170Zx+anBwAlo5ewnM/D+HoUXuK2iVgN+8NXEXzG+vZB/JfQlIm7Gr7Z4sMjK49ZHaYG7k6CTp58qRjLxWpcvdQFIWwsLBaXeclu1r/e3drRVHKvHapXy/0mkajISIigvT0dFRVLf26quKVlob/vn3kxceTHxLJN9/U5bNPIigwe6HDzAtM42XehJYxFFx9NYWdOlF07bUowcEoioJGo3E8zv8ZajNJgiQJqi0kCXIjVydB48aNY/HixS4rT4grRT3qYWAWR7kNgBbs5VMeoCtbAbgB+Onsuf5AAVAysur8pKgkMdJoNGi12lLPK3r8+/ySh16vR6fTodfry3xf8txgMKDT6Up9/+9z9Ho9Wq3W8bXkfJ1OV+a4wWDAy8sLLy+vMt/7+vqiqaC/UJIgSYJqC5kifxkLDg6mQYMGWK3Wi58sHJz9S1+r1dbaOi/5hfjvX4yqqpZ57VK/VuacSy0jgwxU9S401lux2Wazj5Z0I4G+fMDbTOA3zs2EfBkYA/wIrAHW2Gxk2a78GWdarZbQ0FDq1KlDWFgYTZo0oWXLlnTt2pW4S5yQIIQoS1qCKiBjgi5/Uufu52ydZ2crvPFGIIsX25cEaNHCxIwZqTRtasJms9FgxAh8d+xwnK9qtZzp2JHs664j+7rrKA4Lw2q1YrPZsNlsqKrq+N5qtZZ6XtHDarViNpuxWCyYTCYsFkup781ms+Px7+flXVfysFqtFT4vuba4uJji4mJMJhMmk+mSPoOaN2/Oc889R//+/StsLbqSSUtQ7VFjusO+//77yl7C9ddfX2qH3JpOkqDLn9S5+7mqzn/6yYtnnw0mM1OLt7fKV19l0qWLCSwWDLt24fXzz3j/9BP6vXsd11jr1OH0n39enotqVsBms1FcXExubi4ZGRlkZmZy+vRpDhw4wF9//cXvv//u+Jxq164dH3/8MQ0aNPBw1O4lSVDtUWOSoMr+taEoCgcPHiQ2Nrayt/IYSYIuf1Ln7ufKOk9L0zBuXDCpqVpWrUrH27vsOdqjR/FeuxafH37A3LQpOe++a39BVQl56CFMXbpQOHhw9SwvUAPk5OSwaNEiZs6cSU5ODhEREXz77be1qotMkqDaozqSoCr/yZSamnrRZuWSh6+vb1VvI4SopSIibCxcmMXixZmOBMhqhT17zg1ltDZuTP4jj5CxYgU506Y5juv/+AOfNWsIevVV6nboQOj99+O9apV9NfArSFBQEI888gi7d+8mPj6etLQ0Ro4cSfaFVpsXNULv3r15tyRp/5fZs2fTqlUrsrKy3BLL559/TufOnYmNjWXQoEHs2rXLpdfMmTOH+vXr8+qrr7oybJeoUhI0cuTISnVt3XPPPR7J1IUQlzeNBurUOTfwedYsf268MZwFC8puI3L+uk6WuDiMU6ZgatcOxWLB+6efCH34Yep26YL/Bx+gycx0R/hu07BhQxYtWkTDhg05evQoU6ZM8XRI4iJatGjB/v37yxw/ffo0s2fP5rnnniP07Lpm1WnFihVMmjSJcePGsXbtWuLj47n77rvJOG/7HGeu+fPPP/nPf/5Dy5Ytq/PHqLIqJUGfffYZAQEBl3z+//3f/xEWFlaVWwkhBGDfi+zwYR1Wq0JAwIVnhKkhIRSMGkXG6tWkbdrEmSeewBoRgfb0aQKnT0eflOSmqN0nLCyM2bNnA7Bo0SL++OMPD0ckLqRly5bs27evzPG3336bRo0ace+997oljnnz5jFixAjuuOMOmjVrxttvv42Pjw+LFi1y+pr8/HyeeOIJ3nnnHYLP27OwJrlyRhAKIa5oigKzZhlZsiSD228vdBy/WA+XpWlTzrz0Eqe3byd79mwKbr2V4p49Ha97bdyI9uTJ6grbra655hqGDx8O2LsgaiuloKDCB+dtn3PRcwsLKzyX/Pxz31dBixYtOHbsGEXnxZOYmMjSpUuZPHky2kquWD9r1iyaNm16wcepU6dKXWMymUhMTKRHjx6OYxqNhu7du7PjvFmYVb1mwoQJ9O3bl57n/X+raSq9TlBhYSFZWVnUr1+/1PE9e/bQ6rwNH4UQwtUUBbp1O5f1ZGZquOWWMB5/PI8RIy7yy8hgoHDoUAqHDj1X3pkzhDzxBEpBAXkPPUTeU0/Zt0y5jD3++ON8++23/PTTTxw6dKhWDZIuEXXenoL/VtSnD1lfful4XrdtWzT/SnZKFF97LZlLlzqeR3TujLaccTrJ/0ouLkV8fDxWq5VDhw7R+uz+g6+99hoDBw6ka9eu5V6TmprKlClTyk1w7733Xm6++eYL3rPueZt0A2RlZWG1Wsv01ISHh/PPP/+UW8alXrNixQqSkpJYvXr1BWPytEq1BC1dupSmTZty00030bZtW7Zv3+54zV1Nd0IIUeI///Hl6FEdzz0XzKuvBmKxVO56jdGIOT4exWQiYM4cInr0wGfpUnvf22UqLi6O66+/HuCCXRrCsxo0aEBgYKBjXNCKFStITEy84ODhyMjIClv4QkJCiImJueBDp3PP+sinTp3i1VdfZfbs2XiXN62zBqlUjUyZMoUdO3ZQt25dduzYwciRI5kwYQIjRoyQKchCCLd76qk8VBWmTw/k00/9OXhQx//9XzbBwZf2eWRt2JDMJUvw+ukngiZNQnf0KCFjx+KzbBk577yDtWHDav4Jqsdtt93Gjz/+yJo1a3j55Zdr3R5rKQcPVvia+q8lXk4nJlZ87r/qLe28P/x1Oh2Wymbd/9K8eXP2799PUVERb731Fo8//rijlyU/P5+HHnqI1NRUACZOnEiTJk146KGH+OGHH8qUNWvWLMeYsIps2rSpVC9OaGioY0/L86Wnp1c41fxSrtm9ezcZGRkMGDDA8brVamXbtm18/vnnHDlypNLdfdWlUkmQ2Wx2NKd17NiRzZs3M2TIEA4dOlTr/pMJITxPUeDpp/No1szCU08Fs3mzNzffHM5//pNJdPQlbpeiKBTfcANp112H/yefEPDBB3hv3oyhXz/Sfv0V22U4qaNPnz54e3tz7Ngx9uzZ4+huqS3USizLUuVz9XpUJ9cJKhkcPXfuXAAeffRRx2ubNm0iJCSEr776ClVVycvLw2g0VlhWVbrDDAYDbdu2JSEhwZGw2Gw2EhISGDVqVLllXMo13bt3Z8OGDaWuGzduHE2aNOHxxx+vMQkQVLI7LCIigsTzsubQ0FB++ukn9u7dW+q4EEK408CBRaxYkUH9+hYOH9YxeHAYf/9dyaZ/Ly/ynnyStB9/pLhzZwqHDr0sEyAAX19f+vTpA8CPP/7o4WhERVq0aMGff/7Jhx9+yMSJE0stPdOiRQu2bdvm6IG52IzsqnaHjRkzhq+//polS5Zw8OBBXnzxRQoLC7njjjsc53z22WeOAfeXco2/vz8tWrQo9fD19SUkJIQWLVo4W20uValPiS+//LJMJRoMBr755hueeOIJlwYmhBCV0aqVhZUrM7j77jrs3avnttvC+PzzLDp3rtwCidYmTeyDYc+bdqY9dQr9H39QdOutrg672nTv3p01a9aUGrspapb4+HgyMzPp2rUrgwYNKvVakyZN+PHHH1m/fj2TJk1i6NCh9OvXz+Ux3HrrrWRlZfHuu++Snp5Oq1at+M9//lOqOywrK4tjx45V6prLhWygWgHZNuPyJ3XufjWhznNyFO6/P5Tff/fC21tlwYIsrruu+OIXVsRqpc7w4Xht20bBsGHkvPkmqr+/6wJ2UkV1vm/fPvr27YuPjw979+5Fr9d7MMrqc6Vum5GamkpwcDDe3t6sWLGCLVu2MHbs2ArHBNUG1bFthkuGihcVFZGYmEhaWho2W+lFzG655Ranyl67di0rV67EaDQSHR3N6NGjK5zy+frrr/P333+XOd6+fXteeuklp+IQQlwegoJUvv46k0ceCWX9em9Gjw7liy8y6d69iltmqCqmLl0w/P47vkuXYvjf/8iePRtzx46uDdzFmjVrRnBwMEajkaSkJNq3b+/pkEQl7Nu3jzfeeAONRoO3tzfvvfeep0O6IjndErR27Vruu+++cpfLVhQFq/USByeW47fffmPOnDmMGTOGpk2bsnr1arZt28YHH3xAUFBQmfPz8vJKjdY/c+YMzz33HI888gi9evWq1L2lJejyJ3XufjWpzk0mePDBUDZs8MbHx8aiRZlcfXXV/08bfv+d4CefRHfyJKpOR+7LL5M/Zox9dLYHXajOR44cyfr163n11Vd5+OGHPRRh9bpSW4JEWTVqA9USTz75JLfffjspKSllNk51JgECWLVqFX379qV37940aNCAMWPGYDAY2LhxY7nn+/v7Exwc7HgkJibi5eVFly5dnIpDCHH5MRjgk0+y6NWriJgYKzExzn0emTp1Iv2nnyi85RYUi4WgSZMIefhhlLw8F0Xseu3atQMot4VcCOGCJOj06dOMGzeuzNQ7Z1ksFg4fPkybNm0cxzQaDW3atOHAgQOXVMbPP/9M165da/xiTUKI6uHtDfPnZ/HttxmlNmKtKjUwkOyPPiLnjTdQ9Xp0hw7Zd3mtoUpm4pS3UacQwgVjgoYNG8amTZto0qSJK+JxyM3NxWazldl0LTg4mOTk5Itef+jQIU6cOFFq3YXymM3mUk2aiqI4pim6cu2jkrJkPSX3kTp3v5pY576+9gfYY/rhBy+6djURFFTF7jpFoeCBBzC3a4ctKAj8/PDkT3uhOi9Jgg4ePIjNZqtR67MI4QxXfcY4nQTNmTOH22+/nS1bttCmTZsyMxCeeuopZ29RJT///DONGjW66L45y5cvZ+l5e8PExMQwbdq0apvqFxkZWS3liopJnbtfTa3zDz+EJ56AG2+ElSvBqZzgX1OamTnTXqCHlgspr84jIiLw8fGhsLCQgoICmjVr5oHIqldhYaHHZr5dqTPuajK9Xo/BYCAqKsol5TmdBH3zzTf8+OOPeHt7s2nTplLZmaIoVU6CAgMD0Wg0ZVbINBqNZVqH/q2oqIhff/211GJPFRkyZEip9RlK4k9PT3d6SfTzKYpCZGQkqampHh8wWltInbtfTa/zpk11eHuH0aBBPsnJZ3DVVkq63bsJe+YZFFUlf+dOcidNwmWFX8TF6rxp06YkJiayZcuWiy64dzkymUweGaAsA6Pdr6TOTSYTKSkpZV7X6XTunyL/8ssvM2nSJF588UU0Luwb1+l0xMbGkpSURKdOnQD70txJSUml9iMpz7Zt27BYLPTo0eOi99Hr9RVm89XxIa6qao385XAlkzp3v5pa561bm/nllzQaNLAPknZViOZWrTjz0ksEvvUWfp99hvboUbI//BC1nFms1aWiOm/evDmJiYns27ePG2+80W3xCFGdXPX54nTWYjKZuOOOO1yaAJUYNGgQGzZsYNOmTZw8eZL58+dTXFzsmO4+Z84cvv766zLX/fzzz1xzzTVX5F89QgjnlCRAAGYzJCe74LNLUch7/HGy5s3D5u2N98aNhN90E7oLbOTpLiXjNY8cOeLhSISoeZz+3z9y5EgWL17siljK6Nq1K/feey9Llizh+eef5+jRo0yYMMHRHZaRkUF2dnapa5KTk9m3b59j3xwhhChPerqGO+6owz331KGw0DVlFg0cSOZ332GpVw/dkSOE3XQT3uvWuabwKmrUqBEAx48f92gcQtRETneHWa1W3nnnHdatW0fbtm3LdC3NmDHDqfIHDBhQYffX66+/XuZYvXr1WLJkiVP3FELUDocP60hP1zJpUhBvv53jkjLNbdqQ8cMPhDzyCF5bt6Lk5rqk3Kpq3LgxQKm9n4QQdk4nQbt373Ysx56UlFTqtZo0TVYIIc4XHm5j1iwjd91Vhy+/9KNnz2IGDixySdm2sDAyv/kG38WLKRw2zHHc+/vvQaejqE8f+yJGblDSEpSWlkZhYWGpncqFqO2q3B326quvsmPHDjZu3Fjh4+eff3ZlrEII4VI9exbz2GNnABg/PphTp1y4jo5eT8E995zbVqOoiKBXXyV0zBgi27UjeNw4DL/+CjbnF3G8kODgYMcWA9IlVnP07t2bd999t9zXZs+eTatWrcjKynJLLJ9//jmdO3cmNjaWQYMGsWvXrguev23bNkaOHEmHDh2oX78+a9eudUuc1aHKSdDJkye58cYbadCgAY8++ihr167FZKriBoVCCOEhzz9/hvbtTeTkaHjqqeBqy0mUoiIKhw3DUq8emjNn8F28mLDhw4m49loC3nsPTWZm9dxXUYiOjgakS6wmadGiRbkreZ8+fZrZs2fz3HPPERoaWu1xrFixgkmTJjFu3DjWrl1LfHw8d999d7n7gZYoKCggPj6eN998s9rjq25VToIWLFhAamoq33zzDQEBAYwdO5awsDBuu+02Fi5c6LYMVgghnKHXw4cfZuPra2PbNi/mzfOrlvuowcHkvvIKadu3k/Hf/5J/993YAgLQnTxJwIwZBEybVi33BRkcXRO1bNmSffv2lTn+9ttv06hRI+699163xDFv3jxGjBjBHXfcQbNmzXj77bfx8fFh0aJFFV7Tp08fXnjhhStiyQWnZodpNBp69OjBO++8w/79+9m+fTudO3fm448/pl69evTs2ZN3332XU6dOuSpeIYRwuehoK6+/bh/APG1aIPv3V+NChxoNps6dyXnnHVJ37SL7ww8xtW9PXjXu8l7bWoIKCpRKP85fG9disZfx71mD55+fn3/ueVW0aNGCY8eOUVR0bhxaYmIiS5cuZfLkyZXe4mTWrFk0bdr0go9//y42mUwkJiaWWlNPo9HQvXt3duzYUaWf63Lj0v/pLVu2pGXLljz//POkpaWxatUqVqxYAcD48eNdeSshhHCpESMKWLvWm59/9mbs2GBWrsyg2ndF8PGhcPBgCgcPLnU44K23MLdtS9G/t+aoooYNGwJw4sQJl5RX0zVtWvktFebOzeLmm+0JyQ8/ePPII6Fce20xS5ee66bs3DmCrKyyycmpUxffz/Lf4uPjsVqtHDp0iNatWwPw2muvMXDgQLp27VruNampqUyZMoU5c+aUee3ee+/l5ptvvuA9/73ReVZWFlarlbCwsFLHw8PD+eeffyrz41y2qu3PnYiICDp16sTo0aOr6xZCCOEyigLvvmukT58Idu82MHNmAOPHn3F7HF4bNxLw4YeoWi3ZWi1FLuhyKEmCTp486XRZwjUaNGhAYGAg+/fvp3Xr1qxYsYLExEQ2b95c4TWRkZHlJkAAISEhhISEVFe4VyyXJ0Fnzpzhm2++Yf78+ezYsQOr1Xrxi4QQogaoW9fGW28ZeeyxUGbN8qdfvyLatXPv/lDFPXtSMHw4vkuWEPLYY2R+8w2mLl2cKrMkCTp+/Diqql7xy5ccPFh2X6mLMRjObcNw441FHDyYgqKU3pph+/Y0x/c6nc7p/SWbN2/O/v37KSoq4q233uLxxx+nfv36AOTn5/PQQw+RmpoKwMSJE2nSpAkPPfQQP/zwQ5myZs2axezZsy94v02bNjnKBwgNDUWr1ZYZBJ2enl5tm4jXNC5LgjZv3synn37KsmXLqFevHkOHDuXDDz90VfFCCOEWt95axLp1BaxY4ct77wXw5ZdunuSh1WKcPh0lLw+fNWsIeewx0tetw+bEL6Xzf7FmZ2e7ZdaRJ/n6OrevlE4HOl3ZMs4vV68Hs9m5+5QMjp47dy4Ajz76qOO1TZs2ERISwldffYWqquTl5ZXZUPx8VekOMxgMtG3bloSEBMeixDabjYSEBEaNGlXFn+ry4lQSlJqayueff86nn35Kbm4uw4cPp7i4mO+++474+HhXxSiEEG715ps5REXZeOYZ93eHAaDTYZw5E92hQ+gPHCDo5ZfJ/uSTKhfn4+NDREQEaWlpnDhx4opPgi4XLVq0YPXq1WzdupX333+/1EKWLVq04LXXXmPKlCkMGDCAq6+++oJJUFW7w8aMGcMzzzxD27Ztad++PfPmzaOwsJA77rjDcc5nn33GDz/84NiNIT8/v9RedMePHycpKYmQkJBSLU2XgyrPDrv55psduxN/8MEHJCcnX7QpTgghLgchISoTJ+bi7++ibearQPX1JXvOHFStFp/Vq/H66Senyqttg6MvB/Hx8WRmZtKuXTsG/WsQfJMmTfjxxx9p1qwZkyZN4rPPPquWGG699VYmTpzIu+++yw033MDff//Nf/7zn1LdYVlZWaVmFv7111/079+f/v37AzBp0iT69+/P9OnTqyXG6lTllqAffviBp556ikcffZSmTZu6MiYhhKgxbDZYutSHQYOKnO5mqSxLq1bkP/QQfp9+iu7IEYqdKKthw4bs2LFDBkfXINdcc02FS8ikpqYSHBzM8OHD8fLyYsuWLfTr169a4hg1atQFu7+effZZnn32Wcfzrl27XjFL31S5JSghIYEzZ87QsWNHOnfuzJw5cy64wqQQQlyOnnkmmGeeCWHatACP3P/MU0+RlpBA/kMPOVXO+YOjRc23b98+brrpJq6//nrmz5/PQ07++4vyVbklqEuXLnTp0oUPPviAxYsXs2DBAsaNG4fNZuOnn36iYcOGBAR45kNDCCFcZciQQlav9qZRI8/MdFUDA7Ge3fvLGdIddnnp1asXvXr1KnO8vJlhouqcWjEawM/Pj9GjR5OQkMDu3bt59tlnefvtt4mIiOCWW25xRYxCCOExvXoVs317Gg88kO/pUNDv3o0uKalK18paQUKU5XQSdL7mzZvzzjvvcPLkSb755htXFi2EEB5Tp865XVU9tfSZ36efEj5gAAEffFCl689vCVJVzw34FqImqVISlJiYiO0CWy1rtVoGDx7M999/D8CePXucXlRKCCE8betWA717h/Pnn9W9n0ZZxd26AeC9fj1KFTaorlevHoqiUFhYKOM3hTirSklQ+/btyczMvPiJZ1177bUyGE8Icdn76itf/vlHz7hxwZhM7r23pUULTK1bo5jN+Jz9A7MyvLy8HK1BtWVfKCEupkoDo1VVZeLEifj6+l7S+SZ3f1oIIUQ1mDw5l82bvdi/X8+HH/rzzDN5br1/4dChGJKS8Fm9moL776/09U2bNuX48ePs37+fLk5uxSHElaBKSVDPnj3Zv3//JZ9/7bXXlloJUwghLkehoTYmT87l8cdDmDUrgJtvLiIuzn1d/UX9+xM0eTKG339HyclBDQqq1PXNmjVjw4YNHDhwoJoi9AybzYZG49IhrqKGutBQnKqoUhK0adMmlwYhhBCXi1tvLWTZMh9+/tmb558PYunSTNz1+9fauDHmpk3RHzyI16ZNFN16a6Wub9myJWAf13ml8PX15cyZMwQEBEgidIWz2WycOXMGPz8/l5Xp8l3khRDiSqYoMHVqDr17G9i+3YuvvvLl3nsL3Hb/ouuvR3/wIN4//1zpJKhDhw6AfbJKcXExXl5e1RGiW+l0Ovz8/MjLc2/XpMFgkKEebubt7Y2fnx86netSF0mChBCikho0sPL882d4/fUg3nwzkOuvLyIy0rXN9BUpGDGC4uuuw9yxY6Wvbdy4MXXq1CEzM5M//viDbmdnnF3udDodgS5YUPJSKYpCVFQUKSkpstyAm1RXnUvboRBCVMHo0fm0a2fizBkNEydWbmyOM6wxMZi6d0etwjhLRVHo27cvAGvWrHF1aEJcdiQJEkKIKtBqYfp0Izqdypo1Pqxd6+3pkC7JrWe70JYuXUp2draHoxHCsyQJEkKIKoqPt/DII/axKC+/HERuruKW++r27SPw9dfxnzWr0tded911tGzZkry8PGbMmFEN0Qlx+XA6CRo5ciSbN292RSxCCHHZefrpMzRubCE1VcvUqe4Zl6I7dgz/efOqtGiioii89tprAHzxxRfs27fP1eEJcdlwOgnKycmhX79+NG3alLfeeotTp065Ii4hhLgs+PjAO+8Y8fa20aCBFXeMkzW1aQOA7sABKCys9PU9evTgxhtvxGq1Mm3aNFeHJ8Rlw+kk6LvvvuPUqVM8+uijLF68mMaNG3PjjTeydOlSzGazK2IUQogarVs3E7//nsbjj+ehuKFHzBYVhTUsDMVqRf/331Uq48UXXwRg/fr1pKSkuDI8IS4bLhkTFB4ezrhx4/jrr7/Yvn07cXFx3HvvvdSrV49nnnmGgwcPuuI2QghRY52/03y1twYpCua2bQHQV3Hhw7i4ODp27IjNZuPHH390ZXRCXDZcOjA6JSWFn376iZ9++gmtVsvAgQPZvXs38fHxvP/++668lRBC1Ej/+5+eAQPCSEys3p3mnU2CAPr16wfAxo0bXRKTEJcbp5Mgs9nMsmXLGDRoENHR0Xz77bc8/fTTJCcn88UXX7B+/XqWLFnC5MmTXRGvEELUaB9/7E9SkoG33qreQdLmVq0A+0yxqurRowcA//vf/2TRP1ErOb1idFRUFDabjbvuuovff/+ddu3alTmnd+/eBAcHO3srIYSo8WbMMBIWZuOll3Kr9T7mZs0A0GRkVLn/rVWrVnh5eWE0Gjly5AixsbGuDFGIGs/plqCxY8dy8uRJPvzww1IJkKqqHD9+HIDg4GCOHDni7K2EEKLGCwxUefvtHIKC7ImJzQY7dri+a8waE0Pqn3+S/vvvVHU0tsFgoNXZFqXdu3e7MjwhLgtOJ0Gvv/56uRvXZWVlERMT42zxrF27lscff5y7776bCRMmcOjQoQuen5+fz/z583nooYcYMWIEY8eOZefOnU7HIYQQVbFwoS+33BLOiy8GkZ/vwqljWi228PAqJ0Almp1tUfrnn39cEZUQlxWnu8Mq6kfOy8vD29u5ZeR/++03Fi5cyJgxY2jatCmrV6/mzTff5IMPPiAoqOxePRaLhSlTphAYGMi4ceMIDQ0lIyMDX19fp+IQQoiqSk7WAvDll35s2ODFlCm59O9f5OGozomLiwOQWbyiVqpyEjRu3DjAvvroq6++WirRsFqtbN++vdzxQZWxatUq+vbtS+/evQEYM2YMO3fuZOPGjQwePLjM+T///DN5eXm88cYb6HT2Hy0iIsKpGIQQwhkTJpyhR49inn8+mOPHdYweHUr//oW88UYO9es7t/O8YfNm/D/9FK6+Gp56qkpllCRBF2tlF+JKVOUkaNeuXYC9JWj37t0YDAbHawaDgauuuorx48dXOTCLxcLhw4dLJTsajYY2bdpw4MCBcq/ZsWMHTZs25dNPP+WPP/4gMDCQbt26MXjwYDSa8nv+zGZzqUUdFUXB5+zuzIoLVz0rKcuVZYoLkzp3P6nz8vXsaebnn9OZOTOA//s/P9at82HLFi+eey6PBx7IR1fFT2Jtbi7e69fDmTMoY8dWqYymTZsCcPjwYWw2G1qttmrB1CLyPne/6qrzKidBJetKjBo1ipkzZxIY6NrpoLm5udhstjKzyoKDg0lOTi73mtOnT5Oenk737t156aWXSE1NZf78+VitVm6//fZyr1m+fDlLly51PI+JiWHatGmEh4e77Gc5X2RkZLWUKyomde5+UuflmzULHn7Y/vj1Vw2TJgWyYkUgn38OV11VhQKvucb+9Z9/qlznERERGAwGioqKMJvNNGjQoErl1EbyPnc/V9e502OCPvvsM1fE4RKqqhIYGMjDDz+MRqMhNjaWrKwsvv/++wqToCFDhjBo0CDH85IsMz09HYvF4rLYFEUhMjKS1NRUWY/DTaTO3U/q/OJCQ2HxYvjmGx/efDOQP//U0K2bjXnzsunVy1SpshQ/PyIBMjI4feAAtoCAKsUUGxvLvn37+PXXX50ey1kbyPvc/S6lznU6XaUbMKqUBI0bN4433ngDPz8/x9igisyYMaMqtyAwMBCNRoPRaCx13Gg0VrjmUHBwMDqdrlTXV/369TEajVgsFsc4ofPp9Xr0+vKnr1bHm1tVVflP42ZS5+4ndX5higIjRhRwww1FPP54CAkJXtx3XyiffppFv37Fl1yO6u+PNSwMbUYGmiNHsJ7dWLWyYmJi2LdvH0ePHpV/t0qQ97n7ubrOq5QE7dq1yzGOpmRsUHmc6bvT6XTExsaSlJREp06dALDZbCQlJTFgwIByr2nevDm//vorNpvNkQilpKQQEhJSbgIkhBCeFBZm48svM3n66WBWrPDlxReDSUg4TWUaY6yNG6PNyEB37BjmKiZBDRs2BHCs7SZEbVGlzOD8fWaqc8+ZQYMG8eGHHxIbG0tcXBxr1qyhuLiYXr16ATBnzhxCQ0MZMWIEADfccAPr1q3j888/Z8CAAaSmprJ8+XJuvPHGaotRCCGcYTDAzJlG/PxUxozJr1QCBGBp3BjDH3+gPXq0yjFER0cDcOLEiSqXIcTlyOnmkcLCQlRVdUyRP3bsGMuXLyc+Pp4bbrjBqbK7du1Kbm4uS5YswWg00rhxYyZMmODoDsvIyCjV2hQWFsbLL7/MF198wXPPPUdoaCg33nhjudPphRCiptDrYfr0nCpda23cGAICUIovvRvt36QlSNRWiupk59oNN9zA0KFDeeSRRzAajTRv3hyDwUBGRgYzZszg0UcfdVWsbpWenl5q6ryzFEUhKiqKlJQU6UN2E6lz95M6d40dO/QYjRr69r14YqNYLEQ1bEiKE4N0Dx48SK9evfD392ffvn0y9fsi5H3ufpdS53q9vtIDo53eNmPnzp2OnYiXLl1KZGQkx44dY+HChcyaNcvZ4oUQolb58UcvbrklnOeeCyYv7xKSEb3e6a0zSqbF5+XlkZ2d7VRZQlxOnE6CCgoKCDg7LfPHH39k6NChaDQaunTpwrFjx5wOUAghapPrrismJsZCr17FuLAx+oJ8fHwcq+vLuCBRmzidBMXFxfHdd99x4sQJ1q1b5xgHlJaW5vIFFIUQ4krn5QU//pjOjBlGQkIuoatFVWHwYML69kVz+nSV79uoUSNAxgWJ2sXpJOjVV19l/PjxNG7cmM6dO3PttdcC9lah9u3bOx2gEELUNr6+lRhnoiiwaxf6vXvRnjxZ5XtKEiRqI6eToGHDhnH8+HH++OMP1q5d6zjet29f3n//fWeLF0KIWmvvXh3PPRd08bFBZxMY7alTVb6XzBATtZFLVhCMjIwss59HyQKHQgghKk9V4eGHQ/jnHz0tWlh44IH8ik8uSYIq2FfxUpS0BMmYIFGbuCQJ2rBhAxs2bCAtLQ2bzVbqtQULFrjiFkIIUasoCowenc/LLwfzzTe+jB6dX/EksLOtOK5IgqQlSNQmTneHTZo0iRtuuIENGzaQkZFBdnZ2qYcQQoiqGTy4EC8vlb179SQllb/HIeCS7rCSJOjkyZNYrdYqlyPE5cTplqC5c+fy+eefc++997oiHiGEEGcFB6sMGFDIihW+LFrkS5s2Fawq7YIkKCoqCp1Oh9lsJjU1lfr161e5LCEuF063BJlMJrp27eqKWIQQQvzLHXcUArB8uQ8mUwUnNWyILTAQ1d+/yvfRarWOxEfGBYnawukk6MEHH+Trr792RSxCCCH+pXv3YsLDreTkaNi61av8k9q25fS+fWQuXerUvUpmiMlCt6K2cLo7rKioiE8++YT169fTtm1b9PrS/dYzZsxw9hZCCFFrabVwww1FfPWVH+vWeXPddeXsJ+aivb6aN29OQkICf//9t0vKE6KmczoJSkxMpF27dgAkJSWVek024RNCCOcNGHAuCZoyJQeN02345WvVqhUAe/bsqZ4bCFHDOJ0Ebdy40RVxCCGEqEC3bsX4+dlITdWSmKinXbuym4r5T5uG948/cmbsWIpuvrlK92nbti0Af/31F8XFxXh5VdD9JsQVopr+nhBCCOEqXl7Qp4+9G2zdOu9yz9GmpKDfuxedE+N5mjdvTmRkJAUFBfz2229VLkeIy4VLkqAtW7Zwzz33cO2113Lq7BTNL7/8koSEBFcUL4QQtV6fPkUAbNlSfuuMrW5dAKc2UdVoNPTr1w+w7/8oxJXO6SRo2bJl9O/fHx8fH3bt2kVxsf2vlZycHN566y2nAxRCCAHdutnnxycm6jlzpux4S2tEBABaJ5IggBtuuAGAdevWYbFYnCpLiJrO6SRoypQpzJ07l3nz5pWaGdatWzd27tzpbPFCCCGA+vWtTJ9uZN26dPz8yu4yX9IS5GwS1L17d0JCQjh9+jSvvvoq27dvd6o8IWoyp5Og/fv307NnzzLHg4KCMBqNzhYvhBDirBEjCmjZ0lLu7LCSliBNWppT9/Dy8nLsAPDFF18wdOhQaRESVyynk6DIyEgOHTpU5nhCQgKxsbHOFi+EEOIS2CIjAdCmpdm3oHfCE088Uep5YWGhU+UJUVM5nQSNGTOGsWPHsn37dhRFITk5ma+++orx48fz6KOPuiJGIYQQ2HObRYt8ePLJYLKzS48LskZEYAsOxtKoEYqTSYufn1+p55IEiSuV0+sEvfjii9hsNvr27UtBQQE9e/bEy8uL8ePH8+STT7oiRiGEENgXhp4zJ4AjR3QMHVpI797nrR7t7U1qNS1yWFRUVC3lCuFpTrcEKYrCyy+/TFZWFklJSWzbto309HTeeOMNV8QnhBDiPCNGFDB27BkaNqzecTrvvfee43tpCRJXKqdbgkoYDAbi4+NdVZwQQohyPPZYnlvuc+eddzJ16lQyMjKkJUhcsaqUBI0bN+6Sz5UNVIUQwj38/u//8F22jPx77qHg/vudLi8oKIiMjAxpCRJXrColQbt27Sr1fOfOnVgsFpo3bw7AgQMH0Gq1dOzY0fkIhRBClJKRoeHPP/V06GCiTp1zxzVZWfatM44cccl9Vq9ejcFgwGAwuKQ8IWqaKiVB52+aOmPGDAICAvjiiy8ICQkBIDs7m1GjRtGjRw/XRCmEEMLh7rtDSUoy8MknWQwadG5wtM1Fq0aXCAgIcEk5QtRUTg+Mfu+995g6daojAQIICQlhypQppQbWCSGEcI2rrrLvIv/nn/pSx121YKIQtYXTSVBubi7p6elljqenp3PmzBlnixdCCPEv7drZk6Ddu0t3UzkWTHRRS5AQVzqnk6AhQ4YwatQo/vvf/3Ly5ElOnjzJsmXLeOCBBxg6dKgrYhRCCHGeFi3sSdCBA6VHNFjDwgDQZGS4PSYhLkdOT5GfO3cu48ePZ8SIEZjN9v+YOp2OBx54gOnTpzsdoBBCiNKaNbOvEXT6tJasLIWoKPtxW3g4AJq8PCgsBB8fT4UoxGXB6STI19eXjz76iOnTp/PPP/8A0KRJkzLLrgshhHANf3+Vhg0tnDih48ABPa1a2Y+rAQFYIyOxBQaiycvDJkmQEBfkssUS/fz8aNu2rauKE0IIcQHNm9uToH37zvsYVxRO79jhuaCEuMy4LAmqLmvXrmXlypUYjUaio6MZPXo0cXFx5Z67adMmPvroo1LH9Ho9X331lTtCFUIIt2nRwsz69d7s31/jP8aFqLFq9P+e3377jYULFzJmzBiaNm3K6tWrefPNN/nggw8ICgoq9xofHx9mzpzp5kiFEMK9mje3jwsq1RIkhKgUp2eHHT9+HFVVyxxXVZXjx487VfaqVavo27cvvXv3pkGDBowZMwaDwVBqscZ/UxSF4ODgUg8hhLjSNG9un4iyf7+e8z+C/ebOJbxvX/zmz/dQZEJcPpz+EyImJoaUlBQizi7SVSIrK4uYmBisVmuVyrVYLBw+fJjBgwc7jmk0Gtq0acOBAwcqvK6oqIjHHnsMVVWJiYnhrrvuomHDhhWebzabHbPawJ5E+ZwdTKgoSpViL09JWa4sU1yY1Ln7SZ27T1ycFa1WxWjUkJICGo29zrVGI/p9+9AdOyb/DtVE3ufuV1117nQSpKpquUHl5eXh7e1d5XJzc3Ox2WxlWnKCg4NJTk4u95p69erx6KOPEh0dTUFBAd9//z2vvPIKM2bMoM75G+ycZ/ny5SxdutTxPCYmhmnTphF+dqqpq0WeXcxMuI/UuftJnbtHXBzs3w979sD115+t8yZNAPDLz8evZO68qBbyPnc/V9d5lZOgkp3kFUVh4sSJ+Pr6Ol6zWq1s376ddu3aOR1gZTRr1oxmzZqVev7MM8/w008/ceedd5Z7zZAhQxg0aJDjeUlCl56ejsVicVlsiqIQGRlJampqud2HwvWkzt1P6ty9GjUKIStLz5kzWkede+v1hADFJ06QlZLi6RCvSPI+d79LqXOdTlfpBowqJ0ElO8mrqsru3btL7TJsMBi46qqrGD9+fFWLJzAwEI1Gg9FoLHXcaDRe8jgfnU5HTEwMqampFZ6j1+vR6/XlvlYdb25VVeU/jZtJnbuf1Ll7zJuXhcGgEBUVRUqKvc4dq0anp8u/QTWT97n7ubrOq5wElQxOHjVqFDNnziQwMNBlQYE9gYmNjSUpKYlOnToBYLPZSEpKYsCAAZdUhs1m4/jx47Rv396lsQkhRE1Q3t9vjlWjZesMIS7K6TFBn332mSviKNegQYP48MMPiY2NJS4ujjVr1lBcXEyvXr0AmDNnDqGhoYwYMQKApUuX0rRpUyIjI8nPz+f7778nPT2dvn37VluMQghRk9jOtgRps7PBbC4/UxJCAC5aJ2jDhg1s2LCBtLQ0bDZbqdcWLFhQ5XK7du1Kbm4uS5YswWg00rhxYyZMmODoDsvIyCg1KDsvL4+PP/4Yo9GIn58fsbGxTJkyhQYNGlQ5BiGEqKnOnFF46KFQUlJgwwbQasEWEoI1IgJbSAhKXh5qSIinwxSixlJUJzvXJk2axOTJk7n66quJiooqM1Ns+fLlTgXoKenp6aWmzjtLUUr67VOkD9lNpM7dT+rcvWw2aNo0iqIihd9+SyM62nWTOUTF5H3ufpdS53q93n0Do0vMnTuXzz//nHvvvdfZooQQQlSCRgNz5hhp3jyEunWrtiabELWZ00mQyWSia9eurohFCCFEJQ0cWERUFKSkgDRKCFE5Tm+b8eCDD/L111+7IhYhhBAu4D9nDuF9+uD7+eeeDkWIGs3plqCioiI++eQT1q9fT9u2bcusuTNjxgxnbyGEEKICqakaVq6E3FwfRowoAECTlYV+/350Tu7fKMSVzukkKDEx0bEydFJSUqnXZF8VIYSoXkeO6Hj4YYiJ8XckQdaStYLS0z0ZmhA1ntNJ0IV2dBdCCFG9Gja0D4g+eVKLzWYfLG07u1eiLJgoxIU5PSZICCGE50RGWtHpwGxWSE21f6SXrBqtlSRIiAtySRK0ZcsW7rnnHq699lpOnToFwJdffklCQoIrihdCCFEBnQ4aNrR/f+KEvXHfKltnCHFJnE6Cli1bRv/+/fHx8WHXrl0UFxcDkJOTw1tvveV0gEIIIS4sJsb+9cQJLXBed1hmpn1FRSFEuZxOgqZMmcLcuXOZN29eqZlh3bp1Y+fOnc4WL4QQ4iLKJEFhYVjDwrA0bYqSn+/ByISo2ZweGL1//3569uxZ5nhQUBBGo9HZ4oUQQlxE48b2r8ePn/1I1+s5/ddfHotHiMuF0y1BkZGRHDp0qMzxhIQEYmNjnS1eCCHERfy7JUgIcWmcToLGjBnD2LFj2b59O4qikJyczFdffcX48eN59NFHXRGjEEKICyhpCZIkSIjKcbo77MUXX8Rms9G3b18KCgro2bMnXl5ejB8/nieffNIVMQohhLiAkpag5GQtZjPo9eD//vv4fP89+Q8+SMHdd3s2QCFqKKdagsxmM/369WP48OFkZWWRlJTEtm3bSE9P54033nBVjEIIIS4gMhK8vFRsNoWUFHtrkCYrC/2BA2hl6wwhKuRUS5BerycxMREAg8FAfHy8S4ISQghx6TQaqF/fyuHDOo4f19KokRVbWJj9NVkrSIgKOT0m6J577uHTTz91RSxCCCGq6KqrzLRrZ3I8L0mCtLJ/mBAVcnpMkMViYcGCBaxfv56OHTvi5+dX6nXZRV4IIarfhx8aUVXV8dyxanRmpqdCEqLGczoJSkpKokOHDgAcOHCg1Guyi7wQQniGoztMWoKEqJDsIi+EEFcgR3dYRgaoKsgfpUKU4fTssL59+3Lw4EFXxSOEEKIKEhN1dOsWQf/+9uTHFh5u3zojNhalqMjD0QlRM7lsdpgQQgjP8fdXOXpUh6+vDVUFfHxk6wwhLkJmhwkhxBWgQQMry5Zl8PPPMgZIiEsls8OEEOIKYDBAly6mi58ohHCQ2WFCCHGFCpg+He/Vq8l77DEKhw/3dDhC1DgyO0wIIa4QmzZ5sW2bgW7diunRw4QmIwP9wYPoTpzwdGhC1EhOJ0FCCCFqhvXrvfjsM39UFXr0MGErWTBR1goSolxOJ0GTJ0++4Ouvvvqqs7cQQghxCerVswH23eQBrLJ/mBAX5HQStHz58lLPzWYzR44cQafT0aRJE0mChBDCTerXtwDnkiDZRFWIC3M6Cdq1a1eZY7m5udx///0MGTLE2eKFEEJcopKWoFOnziZBZ7vDZBNVIcrn9DpB5QkMDGTSpElMnDixOooXQghRjnr1rACkpmqx2cBapw4gLUFCVKRakiCAnJwccnJyqqt4IYQQ/1K3rhWNRsVsVkhP19i3zggPx9qwIZjNng5PiBrH6e6wWbNmlXquqiopKSl8+eWX3Hjjjc4WL4QQ4hLpdFC3ro2UFC2nTmmp2yGI03/+6emwhKixnE6C3n///VLPNRoN4eHhjBw5kpdeesnZ4oUQQlRC/fpWUlK0JCdr6dBBWn+EuBCnk6AjR464Io4KrV27lpUrV2I0GomOjmb06NHExcVd9Lpff/2VmTNncvXVV/P8889Xa4xCCFFTlIwLKpkhJoSoWLWNCXKF3377jYULFzJs2DCmTZtGdHQ0b7755kXHGqWlpfHll1/SsmVLN0UqhBA1Q0kSVDJDLGDqVMKvuw6ffy1nIoRwQRI0depUFixYUOb4ggULmDZtmlNlr1q1ir59+9K7d28aNGjAmDFjMBgMF9yqw2azMXv2bIYPH05ERIRT9xdCiMtN/fqlW4K06enoDx1CK1tnCFGG091hH3/8MV9//XWZ461ateLOO+/khRdeqFK5FouFw4cPM3jwYMcxjUZDmzZtymzUer6lS5cSGBhInz592Lt370XvYzabMZ83a0JRFHx8fBzfu0pJWbKprPtInbuf1Ln7/bvOz+8OUxTl3FpBmZny7+Ii8j53v+qqc6eToNTUVKKiosocDw8PJyUlpcrl5ubmYrPZCA4OLnU8ODiY5OTkcq/Zt28fP//8M++8884l32f58uUsXbrU8TwmJoZp06YRfvaDw9UiIyOrpVxRMalz95M6d7+SOr/mGujUCa66ymD/bI6NBcAvLw+/cj6rRdXJ+9z9XF3nTidBDRs25NdffyUmJqbU8V9//ZV69eo5W/wlKywsZPbs2Tz88MMEBgZe8nVDhgxh0KBBjuclWWZ6ejoWi8Vl8SmKQmRkJKmpqaiq6rJyRcWkzt1P6tz9/l3nYWHw3Xf211JSwNtgIAQoPnGCLCf+MBXnyPvc/S6lznU6XaUbMJxOgsaMGcPTTz+N2WymT58+AGzYsIHnn3+eZ599tsrlBgYGotFoMBqNpY4bjcYyrUMAp0+fJj09vdQ4pJKKuvPOO/nggw/KzSD1ej16vb7cGKrjza2qqvyncTOpc/eTOne/iur8/E1U5d/EteR97n6urnOnk6DnnnuOzMxMHnvsMUwmEwDe3t688MILTq0TpNPpiI2NJSkpiU6dOgH2Qc9JSUkMGDCgzPn16tXj3XffLXVs0aJFFBUVcf/99xN29oNACCFqA1UFqxV0JUmQ7B8mRBlOJ0GKojBt2jQmTpzI3r178fHxoWnTpnh5eTkd3KBBg/jwww+JjY0lLi6ONWvWUFxcTK9evQCYM2cOoaGhjBgxAoPBQKNGjUpd7+fnB1DmuBBCXMmeeCKY1at9mDHDyG09I7BG2B/YbKCp0SujCOFWTidBJfz9/bnmmmtcVRwAXbt2JTc3lyVLlmA0GmncuDETJkxwdIdlZGTI6HwhhPgXjQZMJoXkZC22OnU4vWuXp0MSokZSVOnQLFd6enqpqfPOUhSFqKgoUlJSpA/ZTaTO3U/q3P3Kq/OTJ7Woqn1DVYPBwwFegeR97n6XUud6vd79A6OFEELULA0aWD0dghCXBekcFkKIK1zgpEmE9+yJ98qVng5FiBpFWoKEEOIKk52tMHeuPzk5Gt5+OwdNRgb6f/5Be+qUp0MTokapUhI0bty4Sz53xowZVbmFEEIIJ8yZEwDAa6/lEHh2mrxWpskLUUqVkqBdlzjTQGZuCSGE+wUHq/j42Cgs1JCSoiX8vAUThRDnVCkJutAu7kIIITxLUewbqf7zjz0JaiVJkBDlctmYoL///pvjx487Vo0Ge0vQzTff7KpbCCGEuET16tn45x/7bvIlO8lLEiREaU4nQYcPH2bIkCHs3r0bRVEc8/dLusKsVpmqKYQQ7lavnv2zNzlZi6352TFBkgQJUYrTU+THjh1LTEwMaWlp+Pr6smfPHjZv3szVV1/Npk2bXBCiEEKIyoqKOpcEWcPDsdatizUy0r6pmBACcEFL0NatW/n5558JCwtDo9Gg0Wjo3r07U6dO5amnnrrkQdRCCCFcp1RLUFQUp3fu9HBEQtQ8TrcEWa1WAgLsUzHDwsJITk4GIDo6mv379ztbvBBCiCooSYJSUrQejkSImsvplqDWrVvz119/ERMTQ+fOnXnnnXcwGAx88sknxMbGuiJGIYQQlSRJkBAX53RL0CuvvILNZgNg8uTJHDlyhB49erBmzRpmzZrldIBCCCEqryQJMho1FBQoBE6eTESPHnivWOHhyISoOZxuCerfv7/j+7i4OPbt20dWVhYhISGyWKIQQnhIQIBKQICNM2c0JCdrqZeRge7wYXQnT3o6NCFqjGrZQDU0NFQSICGE8LBSM8Tq1gVAc/q0J0MSokap8t5hb7zxBn5+fhfdR0z2DhNCCM/o2bOYuDgL/v42bBERAGjT0jwclRA1R5X3DjObzY7vKyKtQUII4TmTJuU6vreesCdBGkmChHBweu8w2UdMCCFqPtvZ7jCtdIcJ4VAtY4KEEELUDDYb5OYqpccEyarRQgAuSIKmTp3KggULyhxfsGAB06ZNc7Z4IYQQVbR9u4EmTaK49dYwbHXrYo2IwBIXB8XFng5NiBrB6STo448/pkWLFmWOt2rVirlz5zpbvBBCiCqqU8eGyaSQmqpF9fXl9K5dZKxdC97eng5NiBrB6XWCUlNTiYqKKnM8PDyclJQUZ4sXQghRRdHRFrZvP01EhNXToQhRIzndEtSwYUN+/fXXMsd//fVX6tWr52zxQgghqkivhwYNrBgMno5EiJrJ6ZagMWPG8PTTT2M2m+nTpw8AGzZs4Pnnn+fZZ591OkAhhBCu4f/++/guW0begw9ScP/9ng5HCI9zOgl67rnnyMzM5LHHHsNkMqGqKj4+Przwwgu8+OKLrohRCCFEFX37rQ+bN3txyy2F3JaTg+7IEXQnTng6LCFqBKeTIEVRmDZtGhMnTmTv3r34+PjQtGlTvLy8XBGfEEIIJ+zYYeC///WlUSMrg0umycuCiUIALkiCShw/fpzMzExMJhOHDx92HL/llltcdQshhBCVVLKbfHKyFlsPWTBRiPM5nQQdPnyYIUOGsHv3bhRFQT27CFfJlhlWq8xKEEIITzk/CbJGyNYZQpzP6dlhY8eOJSYmhrS0NHx9fUlKSmLz5s1cffXVbNq0yQUhCiGEqKqSneRTUjTnts6QJEgIwAUtQVu3buXnn38mLCwMjUaDVqule/fuTJ06laeeeuqCG6wKIYSoXue3BFnCz7YE5eRAYSH4+HgyNCE8zumWIKvVSkBAAABhYWEkJycDEB0dzf79+50tXgghhBNKWoIKCzVk2YKxNGyI6aqr0OTleTgyITzP6Zag1q1b89dffxETE0Pnzp155513MBgMfPLJJ8TGxroiRiGEEFXk7Q0REVbS0rScOqUjdNs2T4ckRI3hdEvQK6+8gs1mA2Dy5MkcOXKEHj16sGbNGmbNmuV0gEIIIZzToIG9NejECa2HIxGiZqlSS1BiYiKtW7dGo9HQv39/x/G4uDj27dtHVlYWISEhjhlizli7di0rV67EaDQSHR3N6NGjiYuLK/fc7du3s3z5clJTU7FarURGRnLzzTfTs2dPp+MQQojLVYMGVnbulCRIiH+rUktQ+/btycjIACA2NpbMzMxSr4eGhrokAfrtt99YuHAhw4YNY9q0aURHR/Pmm2+Sk5NT7vn+/v4MHTqUKVOmMH36dHr37s1HH33En3/+6XQsQghxuWrY0ALAyZNafP/zHyK6dSNwyhQPRyWE51UpCQoODubIkSMAHD161NEd5mqrVq2ib9++9O7dmwYNGjBmzBgMBgMbN24s9/xWrVrRqVMnGjRoQGRkJAMHDiQ6Opp9+/ZVS3xCCHE5ONcdpgOLBd3Ro2iPHvVsUELUAFXqDrvtttu47rrriIqKQlEUrr76arTa8ptZz189ujIsFguHDx9m8ODBjmMajYY2bdpw4MCBi16vqipJSUkkJydz9913V3ie2WzGbDY7niuKgs/ZaaOuaM06v1xXlykuTOrc/aTO3e9S6rxRI/sfqidParFFRQGgTUmRf6cqkve5+1VXnVcpCfrkk08YOnQohw4d4qmnnmLMmDGOafKukpubi81mIzg4uNTx4OBgxzT88hQUFPDwww9jsVjQaDQ88MADtG3btsLzly9fztKlSx3PY2JimDZtGuHh4U7/DOWJjIyslnJFxaTO3U/q3P0uVOfXXgtDh0J8vJ7Qs5+HhtOniTqbEImqkfe5+7m6zqs8RX7AgAEA7Nixg7Fjx7o8Caoqb29vpk+fTlFREbt372bhwoXUrVuXVq1alXv+kCFDGDRokON5SZaZnp6OxWJxWVyKohAZGUlqaqpjaxFRvaTO3U/q3P0upc69vWHOHPv3p9P11AXU1FRSjx8Hvd59wV4h5H3ufpdS5zqdrtINGE6tE2Q2mzl+/DipqakuT4ICAwPRaDQYjcZSx41GY5nWofNpNBpHpti4cWNOnTrFd999V2ESpNfr0VfwIVAdb25VVeU/jZtJnbuf1Ln7XWqdW0NDUfV6FLMZzenTWOvXd0N0VyZ5n7ufq+vcqXWC9Ho9iYmJroqlFJ1OR2xsLElJSY5jNpuNpKQkmjVrdsnl2Gy2UmN+hBCiNlJVSEvTYMzVYj37h6LmAkMLhKgNnF4s8Z577uHTTz91RSxlDBo0iA0bNrBp0yZOnjzJ/PnzKS4uplevXgDMmTOHr7/+2nH+8uXLSUxM5PTp05w8eZKVK1eyZcsWevToUS3xCSHE5eLJJ4Np3z6SJUt8Mbdujemqq1CkFUPUck5vm2GxWFiwYAHr16+nY8eO+Pn5lXp9xowZVS67a9eu5ObmsmTJEoxGI40bN2bChAmO7rCMjIxSI8WLi4uZP38+mZmZGAwG6tevz5NPPknXrl2rHIMQQlwJIiNtKIqK0aghe/58T4cjRI2gqE52rvXu3bviwhWFn3/+2ZniPSY9Pd2l3WiKohAVFUVKSor0IbuJ1Ln7SZ2736XWeX6+gl6vYjC4MbgrlLzP3e9S6lyv17t3YDRQ4cKFQgghag4/P/llLcS/OT0mCGDLli3cc889dO3alVOnTgHw5ZdfkpCQ4IrihRBCuJBh2zYiunWjzvDhng5FCI9yOglatmwZ/fv3x8fHh507d1JcXAxATk4Ob731ltMBCiGEcJ7VCk89FczQoXUwmv3sW2ec3f5IiNrK6SRoypQpzJ07l3nz5pVab6dbt27s3LnT2eKFEEK4gFYLv/zixfbtXhy2NLIfO33anh0JUUs5nQTt37+fnj17ljkeFBRUZqFDIYQQnhMdbU94juSGo+p0KFYrmvR0D0clhOc4nQRFRkZy6NChMscTEhKIjY11tnghhBAu0rixfSugo8cNWOvWBUArCyaKWszpJGjMmDGMHTuW7du3oygKycnJfPXVV4wfP55HH33UFTEKIYRwgZIk6NgxLdYGDQDQnjzpyZCE8Cinp8i/+OKL2Gw2+vbtS0FBAT179sTLy4vx48fz5JNPuiJGIYQQLlDSHXb0qM6xZ5juxAlPhiSERzmdBCmKwssvv8xzzz3HoUOHyMvLIz4+Hn9/f1fEJ4QQwkWio8+1BJn7xmM6fBjbBTakFuJK53QSdPz4cRo2bIjBYCA+Pr7Ma40aNXL2FkIIIVwgJsbeEpSSoiVz1KN4y5AFUcs5PSYoJiaG9HJmF2RmZhITE+Ns8UIIIVwkNNSGv78NVVU4ccLpv4GFuOw5nQSpqlpqE9MSeXl5eHt7O1u8EEIIF1GU88cFae0HbTaQ/a9ELVXlPwXGjRsH2McETZw4EV9fX8drVquV7du3065dO6cDFEII4TqNG1vYs0fPsaNawq+7Dt3x45z+/Xdsldx4UogrQZWToF27dgH2lqDdu3djOG9rYoPBwFVXXcX48eOdj1AIIYTLONYKOqZDKShAMZnQnjghSZColaqcBJXsHj9q1ChmzpxJYGCgy4ISQghRPUq6w44d02Ft2BBdcjLaEycwd+jg4ciEcD+nxwR99tlnkgAJIcRlon17Ew8+mMfQoYWOBRN1smCiqKVcMj1gw4YNbNiwgbS0NGw2W6nXFixY4IpbCCGEcIH4eAuTJuUCYD3UEADt8eOeDEkIj3E6CZo0aRKTJ0/m6quvJioqqtyZYkIIIWoeS8OzSZC0BIlayukkaO7cuXz++efce++9rohHCCFENTMaFQ4d0tHYpykhgFa2zhC1lNNjgkwmE127dnVFLEIIIdxg/Phgbr01nJUH22C66irMV13l6ZCE8Aink6AHH3yQr7/+2hWxCCGEcIMmTSxERVmxBoeQsWYNxtmzPR2SEB7hdHdYUVERn3zyCevXr6dt27bo9fpSr8+YMcPZWwghhHChF144w0svnfF0GEJ4nNNJUGJiomNl6KSkJGeLE0IIUc00/+4DsNmguBh8fDwSjxCe4nQSVLJoohBCiMtPwLRp+H/8MWeeeIK8s9shCVFbVCkJGjduHG+88QZ+fn6OPcTKoygK7733XpWDE0IIUT0efTSE33838PWA5vQuLkZ3+LCnQxLC7aqUBO3atQuz2ez4viKyZpAQQtRM6ekaUlO17NPE0xvQHTni6ZCEcLsqJUHnd4FJd5gQQlx+4uIsbN3qxf6iaOBsEqSqIH+8ilrE6SnyQgghLj9xcfbd5A9m2neP1+TkoMnO9mRIQridJEFCCFELNWliT4IOHfHCGhUFgFbGBYlaRpIgIYSohUpago4c0VHUOA6QcUGi9nHJLvJCCCEuL/XrW/H2VikqUjjQ5mZahAZiq1vX02EJ4VaSBAkhRC2k0UBsrIW//9bzZ5cHqNv/bk+HJITbSXeYEELUUi1a2Jc62btX/h4WtVOVF0u8VLJ3mBBC1EwtW9rHBe3bpwebDU1qKraoKJkmL2qNKi+WeL6dO3disVho3rw5AAcOHECr1dKxY0enA1y7di0rV67EaDQSHR3N6NGjiYuLK/fc9evXs3nzZk6cOAFAbGwsd911V4XnCyFEbdaypb0laN9eLZEtWqDJzyf1zz+xhYd7ODIh3MPpxRJnzJhBQEAAX3zxBSEhIQBkZ2czatQoevTo4VRwv/32GwsXLmTMmDE0bdqU1atX8+abb/LBBx8QFBRU5vy///6bbt260bx5c/R6PStWrGDKlCnMmDGD0NBQp2IRQogrTUl32OEjegqiovDPP4TuyBFMkgSJWsLpMUHvvfceU6dOdSRAACEhIUyZMsXpfcNWrVpF37596d27Nw0aNGDMmDEYDIYKV6l+6qmn6N+/P40bN6Z+/fo88sgjqKrK7t27nYpDCCGuRJGRNoKDbVitCkkR1wGg++cfD0clhPs4PRouNzeX9PT0MsfT09M5c+ZMlcu1WCwcPnyYwYMHO45pNBratGnDgQMHLqmM4uJiLBYL/v7+FZ5jNpsd+6CBfb8zHx8fx/euUlKW7KfmPlLn7id17n7O1LmiwJQpOYSEqDRe5w07QXfwoPz7XYS8z92vuurc6SRoyJAhjBo1ivfee49OnToBsH37dp577jmGDh1a5XJzc3Ox2WwEBweXOh4cHExycvIllfHVV18RGhpKmzZtKjxn+fLlLF261PE8JiaGadOmEV5NzcGRkZHVUq6omNS5+0mdu19V6/zxx89+k9cWFoL/0aP4n11BWlyYvM/dz9V17nQSNHfuXMaPH8+IESMcLSo6nY4HHniA6dOnOx1gVX333Xf8+uuvvP766xgMhgrPGzJkCIMGDXI8L8ky09PTsVgsLotHURQiIyNJTU1FVVWXlSsqJnXuflLn7ueqOtfXrUsYYN29m7SUFNcFeAWS97n7XUqd63S6SjdgOJ0E+fr68tFHHzF9+nT+OduX3KRJE/z8/JwqNzAwEI1Gg9FoLHXcaDSWaR36t++//57vvvuOiRMnEh0dfcFz9Xo9er2+3Neq482tqqr8p3EzqXP3kzp3v6rWeWEh/PSTN8f2XctUQJucDLm5qAEBrg/yCiPvc/dzdZ27ZLHELVu28PDDD/PII49Qp04d/Pz8+PLLL0lISKhymTqdjtjYWJKSkhzHbDYbSUlJNGvWrMLrVqxYwbJly5gwYQJNmjSp8v2FEKI2sFoVHn00lLdnRnB80P2ceeIJOG+cpBBXMqeToGXLltG/f398fHzYuXMnxcXFAOTk5PDWW285VfagQYPYsGEDmzZt4uTJk8yfP5/i4mJ69eoFwJw5c/j6668d53/33XcsXryYRx99lIiICIxGI0ajkaKiIqfiEEKIK5W/v8qAAYXcdVc+6a9P5cxLL6HKkiKilnC6O2zKlCnMnTuX++67j0WLFjmOd+vWjSlTpjhVdteuXcnNzWXJkiUYjUYaN27MhAkTHN1hGRkZpUaK//TTT1gsljKrVA8bNozhw4c7FYsQQlypPv0029MhCOERTidB+/fvp2fPnmWOBwUFlRnPUxUDBgxgwIAB5b72+uuvl3r+4YcfOn0/IYSotVQVTXo6mrQ0LK1bezoaIaqd091hkZGRHDp0qMzxhIQEYmNjnS1eCCGEGxQXw4FlB4ls3546I0d6Ohwh3MLplqAxY8YwduxYFixYgKIoJCcns3XrVsaPH8/EiRNdEaMQQohqlJur0LZtJGZzPYwEEpSaipKVJWODxBXP6SToxRdfxGaz0bdvXwoKCujZsydeXl6MHz+eJ5980hUxCiGEqEaBgSp161o5eVLH/+oOpN/pRej37MHk5P6PQtR0TneHKYrCyy+/TFZWFklJSWzbto309HTeeOMNV8QnhBDCDdq2tU+L/z3kegD0e/Z4Mhwh3MLplqASBoOB+Ph4VxUnhBDCja66ysyaNT7soCMgSZCoHZxOgsaNG1fucUVR8Pb2Ji4ujltvvZVQ6VsWQogaq21bEwC7smIA0J+3UK0QVyqnk6Bdu3axc+dOrFYrzZs3B+DAgQNotVpatGjBRx99xLPPPktCQoK0FAkhRA1V0h12JC2QbIIJPnTIvqeGj4+HIxOi+jg9JujWW2+lX79+JCcns2PHDnbs2MHJkye5/vrrueuuuzh16hQ9e/bkmWeecUW8QgghqkFwsErjxvZNo7fcMhnju++iXOQaIS53TidB06dP54033iAwMNBxLCgoiNdff5133nkHX19fXn31VXbs2OHsrYQQQlSjktaghOYjKbzjDlRpBRJXOKeToJycHNLS0socT09PJzc3F4Dg4GBMJpOztxJCCFGNrrnG/jn9xx8GD0cihHu4pDts9OjRLF++nJMnT3Ly5EmWL1/OAw88wODBgwH4/fffL7jzuxBCCM8rSYJ2/GFAs/0PfBYv9nBEQlQvpwdGf/zxxzzzzDPceeedWCz2/mSdTsfIkSN5//33AWjRogXz58939lZCCCGqUcuWZnx9beSe0ZA29BXaKEkUDRyIGhDg6dCEqBZOtwT5+/szb948MjMz2bVrF7t27SIzM5NPPvkEPz8/ANq1a0e7du2cvZUQQohqpNNBx472cUGbQwahqCr6P//0bFBCVCOnk6AS/v7+tG3blrZt2+Lv7++qYoUQQrjRNdeYMBhUUiLaAmDYudPDEQlRfVy2YvTff//N8ePHywyAvuWWW1x1CyGEENVszJg8Hn/8DHX+cwBekyRIXNmcToIOHz7MkCFD2L17N4qioKoqYF8xGsBqtTp7CyGEEG4SGGj/DDe1bw+AftcuUFVQZNUgceVxujts7NixxMTEkJaWhq+vL3v27GHz5s1cffXVbNq0yQUhCiGEcDdz69ZYvHzRZmai++cfT4cjRLVwOgnaunUrkydPJiwsDI1Gg0ajoXv37kydOpWnnnrKFTEKIYRwo7Vrvel3UwOeCFkIgOHXXz0ckRDVw+nuMKvVSsDZ6ZNhYWEkJyfTvHlzoqOj2b9/v9MBCiGEcC+NRmXvXj2mejeQsXw5JpndK65QTidBrVu35q+//iImJobOnTvzzjvvYDAY+OSTT4iNjXVFjEIIIdzo2mtNfPRRFt26mTCFdfJ0OEJUG6eToFdeeYWCggIAJk+ezKBBg+jRowd16tRhsaw2KoQQl52AAJVbby3ydBhCVDunkiCz2cw777zD3LlzAYiLi2Pfvn1kZWUREhLimCEmhBDi8mTYtg2fFSso7tKFoltv9XQ4QriUU0mQXq8nMTGxzPHQ0FBnihVCCOFheXkKn33mR9J/m7PiwJdok5MlCRJXHKdnh91zzz18+umnrohFCCFEDWEwqMyc6c+qA61IojVeCQkohYWeDksIl3J6TJDFYmHBggWsX7+ejh07OvYLKzFjxgxnbyGEEMLNDAb7AOmff/ZmXeAw2ua+huHXXynu18/ToQnhMk4nQUlJSXTo0AGAAwcOlHpNxgQJIcTlq2fPYn7+2ZtVvsN4Lvc1vNevlyRIXFGcToI2btzoijiEEELUMNdfX8Trrwfxa1oLsgkmYMMG2UJDXFFctou8EEKIK0vjxlaaNzdjtWlYo78FXXIy+r/+8nRYQriMS5KgLVu2cM8993Dttddy6tQpAL788ksSEhJcUbwQQggPuf56+3pBKyIewNK4MZrsbA9HJITrOJ0ELVu2jP79++Pj48OuXbsoLi4GICcnh7feesvpAIUQQnhOSRK0LrcbJ39OoLh3bw9HJITrOJ0ETZkyhblz5zJv3jz0er3jeLdu3di5c6ezxQshhPCg9u3NhIVZyT2jZfvvXp4ORwiXcjoJ2r9/Pz179ixzPCgoCKPR6GzxQgghPEirhX797K1BP/7oDUVFGH7/3cNRCeEaTidBkZGRHDp0qMzxhIQE2UBVCCGuADfeaE+CVq7wIuzqztS58040WVkejkoI5zk9RX7MmDGMHTuWBQsWoCgKycnJbN26lfHjxzNx4kSnA1y7di0rV67EaDQSHR3N6NGjiYuLK/fcEydOsHjxYo4cOUJ6ejojR47kpptucjoGIYSozXr2LCY42EZ6pp4NMYO5MXs+vl9/Td4TT3g6NCGc4nRL0IsvvsiIESPo27cveXl59OzZkwcffJCHH36YJ5980qmyf/vtNxYuXMiwYcOYNm0a0dHRvPnmm+Tk5JR7fnFxMXXr1mXEiBEEBwc7dW8hhBB2BgPcdJN9y4zv6j8GgO8XX4DF4smwhHCa00mQoii8/PLLZGVlkZSUxLZt20hPT+eNN95wOrhVq1bRt29fevfuTYMGDRgzZgwGg6HCBRrj4uK499576datW6lB2kIIIZzz4IP5fPNNBq8sCMIaGoouORmf5cs9HZYQTnG6O+zBBx/knnvuoVevXsTHx7siJsC+J9nhw4cZPHiw45hGo6FNmzZltudwhtlsxmw2O54rioKPj4/je1cpKUu2EnEfqXP3kzp3P3fVefPmVpo3twI+5D/yCIFvvUXAjBkUDR5sbyqqReR97n7VVedOJ0Hp6ekMGDCA8PBw7rzzTu655x6uuuoqpwPLzc3FZrOV6dYKDg4mOTnZ6fJLLF++nKVLlzqex8TEMG3aNMLDw112j/NFRkZWS7miYlLn7id17n7urHPbSy/DggXojh8natUqePxxt927JpH3ufu5us6dToJWrFhBdnY23377LV9//TUzZsygRYsW3H333YwYMYLGjRu7IMzqM2TIEAYNGuR4XpJlpqenY3Fhf7eiKERGRpKamoqqqi4rV1RM6tz9pM7dz511brHA1KkBfPedD7+MepGmU58mb+9ezqSkVOt9axp5n7vfpdS5TqerdAOG00kQQEhICA899BAPPfQQJ0+e5JtvvmHBggW8+uqrVU4kAgMD0Wg0ZdYaMhqNLh30rNfrKxw/VB1vblVV5T+Nm0mdu5/Uufu5o861Wti2zUBKipav9ffx+PJoTJ062TdVrYXkfe5+rq5zlyRBJcxmM3/88Qfbt2/n6NGj1K1bt8pl6XQ6YmNjSUpKolOnTgDYbDaSkpIYMGCAq0IWQghRCc8/f4aiIujTpxiTrtO5F6xWe5YkxGXEJUnQxo0b+frrr1m2bBk2m42hQ4eyatUq+vTp41S5gwYN4sMPPyQ2Npa4uDjWrFlDcXExvXr1AmDOnDmEhoYyYsQIwD6Y+uTJk47vs7KyOHr0KN7e3tJ3K4QQLtCzZ3GZY5rTpwkdNYq8xx6j6LzhBc7Q79yJ1+bNaE+cQJuZiVJUBKqKzd8fW0gI+Q88gKVlS5fcS9ReTidB9evXJysriwEDBvDJJ59w88034+Xlmv1lunbtSm5uLkuWLMFoNNK4cWMmTJjg6A7LyMgoNVI8KyuL559/3vF85cqVrFy5kvj4eF5//XWXxCSEEMKupPHH78svMfz1FyGPPUZOejoF998PlZjFo0lOxuu33yjq1w/17Oe7V0ICgdOnV3hN4fDhju+9fv4Z7w0bKBw0yN49Jy1S4hIpqpOda/PmzeP2228vd5xOUlISrVu3dqZ4j0lPTy81dd5ZiqIQFRVFSkqK9CG7idS5+0mdu58n6lxVYfZsf774wo///jeD6AYmgp99Ft9vvwWg+NprOTNuHKZrry2bDKkq2uPHMezYgWHrVrx++w3d0aMAZM2bR9HAgQAYEhLwXbYMS6NG2CIiUEuWLsnLQ5ORQf6oUaghIQAETZiA3xdfAGCNiKBg+HAK7rsPa/361fLzy/vc/S6lzvV6vfsHRo8ZM6bU8zNnzvDNN98wf/58duzYgdVqdfYWQgghahBFge3bDaSmavnkE3/efDMH4/vvY27dmoC338Zr61a8br8da2Qk+aNHk3d2Cr1+925C774bbWZmqfJUjQZz27alWnBM3btj6t79kuIpvPlmlMJCvNetQ5uWRsCcOfh/9BFF119P/sMPY+rc2XU/vLiiOL1idInNmzczcuRIoqKiePfdd+nTpw/btm1zVfFCCCFqkEceyQNg0SIfsrI0oCjkP/gg6T//TP7dd2Pz80Obmgo2m+Ma1ccHbWYmql6PqX178h5+mMwvviD177/JWL2aov79qxSL6dprMb7/Pql//knWvHkUd++OYrPhs24dwU88AS5s1RdXFqdaglJTU/n888/59NNPyc3NZfjw4RQXF/Pdd9+5dPVoIYQQNUv37iZatzaRlGTgiy98eeYZe1JkbdSInHfeIXfSJPS7d2ONinJcY2nYkPSVKzHHx4O3t+uDMhgoGjiQooED0R06hN8nn9hbmEqWQbFaMfz+O6YuXSo1ZklcuarcEnTzzTfTvHlzEhMT+eCDD0hOTmb27NmujE0IIUQNpSjw2GP2xGf+fH9yckonFaqPD6ZOnbA2bHjuoJcX5g4dqicB+hdLXBw577xDwT33OI75LF9O2LBh1Bk2DMPWrdUeg6j5qpwE/fDDDzzwwANMmjSJm266Ca2MxhdCiFpl0KAimjY1YzRqmDvX39PhXJQ2LQ3VywuvbdvsydAdd6D/3/88HZbwoConQQkJCZw5c4aOHTvSuXNn5syZQ0ZGhitjE0IIUYNptfbFEwHmz/cjPd1lw0yrRd5jj3E6IYH8++5D1evxSkggfPBgQu++G/2uXZ4OT3hAld+xXbp0Yd68eaSkpPDwww+zaNEi6tWrh81m46effuLMmTOujFMIIUQNdOONRbRrZ6KgQMOsWTW/NchWrx45U6eSlpBA/t13o+p0eG/aRODkyZ4OTXiA02m7n58fo0ePJiEhgd27d/Pss8/y9ttvExERwS233OKKGIUQQtRQigIvvJALwMKFfuzf79LdmKqNtUEDct55h7TNmykYPpwz48c7XlOMRnR//+3B6IS7uLTtsnnz5rzzzjuOTVSFEEJc+Xr2NNG/fyEWi8KECUGX1X6q1uhojO+/j6lbN8cx/48/JuL66wl55BF0+/d7MDpR3aqlA1er1TJ48GC+//776iheCCFEDTN5ci7e3ja2bfPiv//18XQ4TtGkpwPgs3IlEX36UGfYMHy++w6Ky+6bJi5vNXsUmxBCiMtCgwZWnn7aPmV+yRJfD0fjnJx33yXtp58oHDgQVaPBa+tWQh5/nLodO+I/Y4anwxMudHl03gohhKjxHn44D60WRo/O83QoTrPEx5M9bx6aU6fwXbwYv6+/RpuSgiY7+9xJhYX4/ve/FF97LdbGjWUBxsuQJEFCCCFcwmA4t4DilcJWvz5548aRN3YsXps3l9qU1bBzJ8HPPQeANSQES4sWmFu0wNKkCbbISEzt2mErWTHbYrFvIaKqKBYLmEwoZjNKcTFKYSHWunVRg4IA0Jw6hdf27SiFhaUeWK2ovr4U9+mDuU0bAJTcXHT//IPq74/q5WXf3VZRsEZG2v9BxAVJEiSEEMLlrFaYPj2AG28s4qqrroC9u7Rainv3BsDR3qOqFHfqhGHXLrTZ2Wi3bsXrvJWos2fOpHDYMAC8160j9KGHKiw+e84cCocMAcCQmEjIk09WeK4xNNSRBOn/+ouwO+8sc46q02Hq0oW8MWMo7tevwrI0mZnYgoJAVzvTgdr5UwshhKhWc+b4M3t2AEuX+rJ5cxq+vpfRlLFLZOrencxu3aCwEP2hQ+j27kW/fz/a48fRpqZijY52nKsUFpZbhs3bG9Wn9EBya926FPfogc3HB/W8BxoNSkEBlubNS51vadAATV6efeC2oqBYrSjFxXglJFBwNrECUPLyUA0GMBjQpKbi8/33+M+aRf7DD5N3gaTrSiZJkBBCCJcbPTqftWu9eeSRvCsyASrFxwdzmzaY27Sh/FQHCgcPpqhfP9BoUPV6+6auWm2544jMHTqQuWjRJd3a1KMHadu3lz6oqmiPHsVnzRqKbr3Vcdj/44/xnzkTVa9HU1TkOO69bh15jz1mj6eWkSRICCGEywUEqKxenYFG5iDb6XSowcGOpyYT5Bo15OUpZx/274uLFaxWsNkUrrnGRP36VgCSkzXs2GEgIsJG584mRzlHj2rx8VEJDLThaFBSFKwxMeQ9/njpEP7+295KZLWiKgrmq66i4K67KBg+vFYmQCBJkBBCiGpyfgJ06pSG2bMDeO21XHx8ruyWIZMJ8vMVQkLsP2dxMYwbF0xampb0dA3p6VqMxotnhx9/nOVIgnbsMPDII6F06VLMsmWZjnOGDAkjLc2ewHh52ZOhwEAbwcEqERFWwsNt574O/w9170onNiKHwLg6qL6X91IGriBJkBBCiGpltcJ999Vh3z49SUl6Pv88i7Awm6fDclpxMRw8qKN+fSvBwfaE55tvfHn++SAGDChi3jz7dHqDAX74wYfi4rJdX35+Nvz9Vfz8VPz9bXh7q2i19gQyNPRcHQUH2+jSpZj4+NKDzDUa0GhUbDZ7K1J6upb09Au16tRh6lQj97UtAGDfPh0ffeRPmzZmxozJd5xlNtt77K50kgQJIYSoVlotTJ2aw6hRoezaZWDQoDA+/jj7spo1ZjLB33/r2bnTwK5dBvbvh/37I7FYFObOzeLmm+1jbMLCrNhsCqmp5xIRRYHJk3Pw81MJD7cSEWEjLMyeOF1qd2GPHiZ69Mgsc3zHjtPYbPaWp9xcDTk59q+ZmRpHq1N6usbRCpWWpiEy0uq4ft8+PcuW+XLqVHGpJKhLl7pYrdCwoZXoaAuNGp372qiRhchI2xXRgyZJkBBCiGrXqZOJ779P57776nD0qI5bbgnjhRfOOBZYrGmSkzXs3Glg504DO3YY2L1bX05LjkJQkI3c3HOZTPfuJnbuTCUionRL1z33FFRbrBqNfQxWQICV85YxuiTx8WYmTMglPPxcYmQywenTGlTV3rK0c2fZ9YYMBpUGDUonRv37FxETYy1zbk0mSZAQQgi3aNLEyurV6Tz/fDCrV/vw5puBLF/uw5QpOaUG+3rShAlBrFvnXaolp0RwsI0OHUx07GjmuusCqFv3NFFR1lITvHx81MtqzFOzZhaaNSu9wKVeD7t3p3LypI7jx7UcP67l2DGd4+vJk1pMJoXDh3UcPnwujYiNtTiSoB9/9GLmzAD69Cnm2WfPOM45dUpD3bq2GrMsUQ0JQwghRG0QHKzy8cfZLFpUzBtvBPL333qGDg2jZ88inngij65dTW7ZfSI7W+HddwM5eFDH4sWZjntmZGhITdWi1aq0bGmmQwczHTua6NDBREyMPeFRFIWoqABSUmyol0++c8kUBUJCVEJCzLRpU7bL0mKB1FQtx45pOX5cd/arlmbNLI5z9u/X8+efBpo0OXfMbLZ3s735Zg733Vd9LWOVIUmQEEIIt1IUuOuuAm64oYhp0wJYtMiXzZu92bzZm5gYC7fdVsBDD+Xj5+dchmG1wpEjWv7+W8/evXrq1rVy//32X76+vipff+2LyaRw9KjW0YLx2GN5jB6dT9u25it/faMq0unsG+Y2aGClW7fyW/CGDi2kaVMLdeqc6x5LS9Oi00HjxjWny0xR1Ssxj3Veeno6ZrPrBu3Z/3KIIiUlBaly95A6dz+pc/e7Eur8+HEt//d//nz7rQ+FhRp8fGzs2ZOKl5f99aVLfbBaoU+fYsLD7WNtsrMVMjO1mExgNGrIzrYPBD5x4ly3zT//aCkqOjdeJz7ezE8/pTuez5vnR926Vnr3LiYg4NLr7kqoc085u31apceBXUqd6/V6wsPDK1WutAQJIYTwqEaNrEydmsMrr+SyZo03x4/rHAkQwOzZ/hw6pOfbbzMID7e3PCxZ4svkyUEXLdvb20bLlhbi4820alX6D9vzZ0MJ96hpi2dKEiSEEKJG8PNTuf32shtP9OpVTFSUrdTUbm9vleBgG1qt/WtIiEqdOtazM5bss5ViYuwDdWvi7DNRM0gSJIQQokabNCm3zLGRIwsYObJmDK4Vl68a1jAlhBBCCOEekgQJIYQQolaSJEgIIYQQtZIkQUIIIYSolSQJEkIIIUStVONnh61du5aVK1diNBqJjo5m9OjRxMXFVXj+1q1bWbx4Menp6URGRnL33XfToUMHN0YshBBCiMtBjW4J+u2331i4cCHDhg1j2rRpREdH8+abb5KTk1Pu+fv372fmzJn06dOHadOmcc011zB9+nSOHz/u5siFEEIIUdPV6CRo1apV9O3bl969e9OgQQPGjBmDwWBg48aN5Z6/Zs0a2rVrxy233EKDBg248847iY2NZe3atW6OXAghhBA1XY1NgiwWC4cPH6ZNmzaOYxqNhjZt2nDgwIFyrzlw4ECp8wGuuuoqDh48WK2xCiGEEOLyU2PHBOXm5mKz2QgODi51PDg4mOTk5HKvMRqNBAWV3ksmKCgIo9FY4X3MZnOpjVIVRcHHx8fxvauUlOXKMsWFSZ27n9S5+0mdu5/UuftVV53X2CTIXZYvX87SpUsdz2NiYpg2bVqld6K9VJGRkdVSrqiY1Ln7SZ27n9S5+0mdu5+r67zGJkGBgYFoNJoyrThGo7FM61CJ4ODgMoOmc3JyKjwfYMiQIQwaNMjxvCTLTE9Px2KxVCn28iiKQmRkJKmpqaiq6rJyRcWkzt1P6tz9pM7dT+rc/S6lznU6XaUbMGpsEqTT6YiNjSUpKYlOnToBYLPZSEpKYsCAAeVe06xZM3bv3s1NN93kOJaYmEjTpk0rvI9er0ev15f7WnW8uVVVlf80biZ17n5S5+4nde5+Uufu5+o6r7EDowEGDRrEhg0b2LRpEydPnmT+/PkUFxfTq1cvAObMmcPXX3/tOH/gwIH89ddfrFy5klOnTrFkyRL++eefCpMmIYQQQtReNbYlCKBr167k5uayZMkSjEYjjRs3ZsKECY7urYyMjFKDpJo3b85TTz3FokWL+Oabb4iKiuK5556jUaNGlb63Tlc9VVNd5YqKSZ27n9S5+0mdu5/UuftdqM6r8u+hqNKWJ4QQQohaqEZ3h11JCgsLeeGFFygsLPR0KLWG1Ln7SZ27n9S5+0mdu1911bkkQW6iqipHjhyRQXRuJHXuflLn7id17n5S5+5XXXUuSZAQQgghaiVJgoQQQghRK0kS5CZ6vZ5hw4ZVuCaRcD2pc/eTOnc/qXP3kzp3v+qqc5kdJoQQQohaSVqChBBCCFErSRIkhBBCiFpJkiAhhBBC1EqSBAkhhBCiVpKNT1xo7dq1rFy5EqPRSHR0NKNHjyYuLq7C87du3crixYtJT08nMjKSu+++mw4dOrgx4stfZep806ZNfPTRR6WO6fV6vvrqK3eEekX4+++/+f777zly5AjZ2dmMHz+eTp06XfCaPXv2sHDhQk6cOEGdOnW47bbbHJsgi4urbJ3v2bOHSZMmlTn+ySefOPZdFBe2fPlyfv/9d06dOoXBYKBZs2bcc8891KtX74LXyWd61VWlzl3xmS5JkIv89ttvLFy4kDFjxtC0aVNWr17Nm2++yQcffEBQUFCZ8/fv38/MmTMZMWIEHTp0ICEhgenTpzNt2rQqbfhaG1W2zgF8fHyYOXOmmyO9chQXF9O4cWP69OnDu+++e9Hz09LSePvtt7n++ut58sknSUpKYu7cuQQHB9OuXbvqD/gKUNk6L/HBBx/g6+vreB4YGFgd4V2R/v77b/6/vXsLafL/4wD+nlkpzhMbntBS0ynRskVX3nQQtMzCG0MvjIwMnawWknYwrZCgi6LCmw7GlNCUuipNjVgQEWgYHi7SUoYlHlJ7NDMrnf+LcPyW2t/Nx9Z83i8YbF+er89nHx8+++w5LSEhARs2bMD09DQqKytRXFyMq1evws3Nbd45rOlLY0/OgaXXdDZBInn8+DHi4uKwc+dOAEBmZiaam5thNBqRnJw8Z/na2lps2bIF+/fvBwCkpqaira0NdXV1OHr06N8M3WnZmnMAkMlk/Da8BBqNBhqNZtHLNzQ0wM/PDwcPHgQABAcH4+3bt6ipqWETtEi25nyWt7c3PDw8liGile/s2bNWr3NycnDkyBF0d3dj48aN885hTV8ae3IOLL2mswkSwdTUFLq7u60+eF1cXKBWq9HZ2TnvnM7OTiQlJVmNxcTEoKmpaTlDXTHsyTkATE5OQqvVYmZmBmFhYUhLS0NISMhfiFia3r17B7VabTUWExMDg8HgmIAkJC8vDz9//kRISAhSUlIQHR3t6JCc1sTEBABALpcvuAxrurgWk3Ng6TWdJ0aLYGxsDGazeU436uPjA0EQ5p0jCMKcQzbe3t4LLk/W7Ml5UFAQsrOzkZeXB51OB7PZjIKCAgwPDy9/wBK10Hb+7ds3/Pjxw0FRrWy+vr7IzMxEbm4ucnNzoVAocOHCBXR3dzs6NKdkNpthMBgQFRX1x8NarOniWWzOxajp3BNEkqFSqaBSqaxenzhxAk+fPkVqaqoDIyMST1BQkNXJpFFRURgYGEBNTQ10Op0DI3NOpaWl+PDhAy5evOjoUCRjsTkXo6ZzT5AIvLy84OLiMqfjFwRhwWOVPj4+GB0dtRobHR3l+SqLZE/Of+fq6oqwsDD09/eLHyABWHg7d3d3x5o1axwUlfRERERwO7dDaWkpmpubUVRUBIVC8cdlWdPFYUvOf2dPTWcTJAJXV1eEh4ejvb3dMmY2m9He3m7Vpf6XSqVCW1ub1VhraysiIyOXNdaVwp6c/85sNqOnpwe+vr7LFabkRUZGzrudL/Z/ROIwmUzczm0wMzOD0tJSNDY2orCwEH5+fv93Dmv60tiT89/ZU9PZBIkkKSkJz549w/Pnz/Hx40fcuXMH379/t9wPpaSkBBUVFZblExMT0dLSgkePHqG3txfV1dXo6urC7t27HfQOnI+tOX/w4AFaWlowMDCA7u5u3LhxA58+fUJcXJyD3oHzmZychMlkgslkAvDrEniTyYShoSEAQEVFBUpKSizLx8fHY3BwEPfu3UNvby/q6+vx6tUr7N271xHhOyVbc15TU4Ompib09/ejp6cHBoMB7e3tSEhIcET4Tqm0tBQvXrzA8ePH4e7uDkEQIAiC1XlsrOnisifnYtR0nhMkktjYWIyNjaG6uhqCICA0NBRnzpyx7AodGhqCTCazLB8VFYVjx47h/v37qKysRGBgIE6ePMn7SdjA1pyPj4/j5s2bEAQBHh4eCA8PR3FxMYKDgx30DpxPV1eX1Y34ysvLAQDbt29HTk4OPn/+bPlwBgA/Pz+cOnUKZWVlqK2thUKhQFZWFi+Pt4GtOZ+amkJ5eTlGRkawdu1arF+/HufOncOmTZv+euzOqqGhAQBw/vx5q3GtVmv5ksWaLi57ci5GTZfNzMzMLDl6IiIiIifDw2FEREQkSWyCiIiISJLYBBEREZEksQkiIiIiSWITRERERJLEJoiIiIgkiU0QERERSRKbICL6px06dAjJycl/fb0GgwEymQwymQx6vd4yHhoaimvXrv1x7uw8/m4U0b+Nd4wmIof5791f51NUVITr16/DUfd09fLyQkdHBzw8PGya19fXh6qqKhQVFS1TZEQkBjZBROQwfX19ludVVVUoLCxER0eHZUwul0MulzsiNAC/mrSAgACb5wUEBMDb23sZIiIiMfFwGBE5TEBAgOXh7e1taTpmH3K5fM7hsB07dkCn00Gv18PX1xf+/v64ffs2vn79ioyMDHh6eiIiIgJPnjyxWld7ezv27NkDuVwOf39/pKenW/3mli0mJiZw+PBheHp6Yt26dbh169ZS0kBEDsImiIicTllZGZRKJRobG6HT6ZCdnY2UlBTExsaiubkZ8fHxSE9Px8TEBABAEATs2rULGo0Gr1+/Rl1dHQYGBnDgwAG71n/lyhVs27YNb968gVarRXZ2ttUeLCJyDmyCiMjpxMTEoKCgAJGRkTh9+jTc3NygVCqRmZmJyMhIFBYWYnh4GK2trQCAkpISaDQaXLp0CdHR0dBoNLh79y6MRiM6OzttXn9iYiK0Wi0iIiKQn58PpVIJo9Eo9tskomXGc4KIyOls3rzZ8nzVqlVQKBRQq9WWMX9/fwDA4OAgAKClpQVGo3He84u6urqgUqnsXv/sIbzZdRGR82ATREROZ/Xq1VavZTKZ1djsVWdmsxkAMD4+jn379uHy5ctz/lZgYKAo659dFxE5DzZBRLTibd26FQ8fPkRoaChcXVn2iOgXnhNERCteTk4ORkZGkJaWhqamJnR1daG+vh4ZGRmYnp52dHhE5CBsgohoxQsKCsLLly8xPT2N+Ph4qNVq6PV6+Pj4wMWFZZBIqmQzjroVKxHRP8xgMECv10MQBIfMJ6Llx69AREQLGB0dhVwuR35+vk3z5HI5srKylikqIhIL9wQREc3jy5cvGBgYAAD4+PhAqVQueu779+8B/Lp8PywsbFniI6KlYxNEREREksTDYURERCRJbIKIiIhIktgEERERkSSxCSIiIiJJYhNEREREksQmiIiIiCSJTRARERFJEpsgIiIikiQ2QURERCRJ/wNCJCrqs3PfHwAAAABJRU5ErkJggg==", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure()\n", - "for i in range(0,len(v_si)):\n", - " t_i = solution[i][\"Time [s]\"].entries / 3600\n", - " j_n_p1_av = solution[i][\"X-averaged negative electrode primary interfacial current density [A.m-2]\"].entries\n", - " plt.plot(t_i, j_n_p1_av,ltype[i],label=\"$V_\\mathrm{si}=$\"+str(v_si[i]))\n", - "plt.xlabel('Time [h]')\n", - "plt.ylabel('Averaged interfacial current density [A/m$^{2}$]')\n", - "plt.legend()\n", - "plt.title('Graphite')\n", - "\n", - "plt.figure()\n", - "for i in range(0,len(v_si)):\n", - " t_i = solution[i][\"Time [s]\"].entries / 3600\n", - " j_n_p2_av = solution[i][\"X-averaged negative electrode secondary interfacial current density [A.m-2]\"].entries\n", - " plt.plot(t_i, j_n_p2_av,ltype[i],label=\"$V_\\mathrm{si}=$\"+str(v_si[i]))\n", - "plt.xlabel('Time [h]')\n", - "plt.ylabel('Averaged interfacial current density [A/m$^{2}$]')\n", - "plt.legend()\n", - "plt.title('Silicon')" + "data": { + "image/png": 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", + "text/plain": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "for i in range(0,len(v_si)):\n", + " t_i = solution[i][\"Time [s]\"].entries / 3600\n", + " j_n_p1_Vav = solution[i][\"X-averaged negative electrode primary volumetric interfacial current density [A.m-3]\"].entries\n", + " plt.plot(t_i, j_n_p1_Vav,ltype[i],label=\"$V_\\mathrm{si}=$\"+str(v_si[i]))\n", + "plt.xlabel('Time [h]')\n", + "plt.ylabel('Averaged volumetric interfacial current density [A/m$^{3}$]')\n", + "plt.legend()\n", + "plt.title('Graphite')\n", + "\n", + "plt.figure()\n", + "for i in range(0,len(v_si)):\n", + " t_i = solution[i][\"Time [s]\"].entries / 3600\n", + " j_n_p2_Vav = solution[i][\"X-averaged negative electrode secondary volumetric interfacial current density [A.m-3]\"].entries\n", + " plt.plot(t_i, j_n_p2_Vav,ltype[i],label=\"$V_\\mathrm{si}=$\"+str(v_si[i]))\n", + "plt.xlabel('Time [h]')\n", + "plt.ylabel('Averaged volumetric interfacial current density [A/m$^{3}$]')\n", + "plt.legend()\n", + "plt.title('Silicon')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "1d2efd36", + "metadata": {}, + "source": [ + "Results of average lithium concentration" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "302e7bb8", + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "id": "77fa4197", - "metadata": {}, - "source": [ - "Results of interfacial current density in graphite" + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Silicon')" ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "code", - "execution_count": 7, - "id": "9eafca0e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Silicon')" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure()\n", - "for i in range(0,len(v_si)):\n", - " t_i = solution[i][\"Time [s]\"].entries / 3600\n", - " j_n_p1_Vav = solution[i][\"X-averaged negative electrode primary volumetric interfacial current density [A.m-3]\"].entries\n", - " plt.plot(t_i, j_n_p1_Vav,ltype[i],label=\"$V_\\mathrm{si}=$\"+str(v_si[i]))\n", - "plt.xlabel('Time [h]')\n", - "plt.ylabel('Averaged volumetric interfacial current density [A/m$^{3}$]')\n", - "plt.legend()\n", - "plt.title('Graphite')\n", - "\n", - "plt.figure()\n", - "for i in range(0,len(v_si)):\n", - " t_i = solution[i][\"Time [s]\"].entries / 3600\n", - " j_n_p2_Vav = solution[i][\"X-averaged negative electrode secondary volumetric interfacial current density [A.m-3]\"].entries\n", - " plt.plot(t_i, j_n_p2_Vav,ltype[i],label=\"$V_\\mathrm{si}=$\"+str(v_si[i]))\n", - "plt.xlabel('Time [h]')\n", - "plt.ylabel('Averaged volumetric interfacial current density [A/m$^{3}$]')\n", - "plt.legend()\n", - "plt.title('Silicon')" + "data": { + "image/png": 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2NjbZBjJHR0c/MUA8yzLPs97cMtVn3aqCD0CHDh34v//7PypWrEjlypXZt28faWlptGjRAoBly5bh4eFB3759AXjppZfYu3cvFSpUMB7q2rx5My+99JLVXNa8zaiXmHx5D9ODexCc/A7FG89k6u9D5fi8EELkkYODA1euXDHLtlQqVZajAg/HhubGwwHODw9djRgxwvjakSNHcHd355tvvsFgMJCYmJjtCMjj/P396dix41O3V7JkySzPbW1tqVOnDseOHTPuUNDr9Rw7doxBgwbluI5nWeZ51mspVhd8mjRpQnx8PFu2bEGj0VC+fHkmT55sPNQVExOTJTB0794dhULBpk2biI2NxcXFhZdeeom3337bQu8gZ0MXN+FW2HZW/9mVFZFTuPf+GpYu7fDfCwohhHgihUKRq8NNeWGKu7NXr16dvXv38uuvv7Jw4cIs4al69epMnz6d2bNn06ZNGxo0aPDU4OPu7o67e+6HTgQEBDB27Fjq1KlDvXr1WLlyJSkpKfTu3ds4z9q1a/n+++/ZsmXLMy/zLPMkJSVx/fp14/Nbt24RGhqKu7t7lj1T+cnqgg9AmzZtnnhoa8aMGVme29jY0LNnT3r27GmGyvJm5q5GnH/rN34935Bt2/pQvvx3jB/f2tJlCSGEMJOaNWty//59mjRpkmWsKUClSpU4cOAAP/74IzNnzqRbt2688cYbJq+hc+fOxMbGsmDBAqKjo3nhhRfYsGFDlkNSsbGx3Lx5M1fLPMs8Z86cyfL7eubMmQD07NmTRYsWmfy95kRhkAPERtHR0XlO849TKBT4+PgQERFhPDaZng7NmiVz505lFNxk9aRttB7d+z/WJJ5VTj0X+Ut6bn5Fuefx8fG4uLhYZNum2OPzNJGRkbi5uWFvb8/OnTs5evQoo0ePfuIYn6LgYc+f9PeuVqsL9uDmosDWFkJCnGj2yh3iknxZMq8pJcqGUK9bznu4hBBCFA0XL15k1qxZKJVK7O3t+fzzzy1dUqEkwccCPDwM7NySTJf2sZyhAeHvz8Hd14vyL71k6dKEEEJYSIsWLYwn8jyuqO7tyS/WcdpTEVSlrhPL/+8Oq2x6MdywC3WPHtx77HiqEEIIIUxPgo8FvdrFk6Zb+hKvUPByejoRrduS8IS70AshhBAi7yT4WFixV17h1pIlXKYcUxIO8fWrM7JdFl0IIYQQpiHBxwp4dOvGtHob+Yv6bLk/nWkj30ev11u6LCGEEKLQkeBjJT4NrkDn6j+TomzH1/t289lnn1m6JCGEEKLQkeBjJezt4YuDlRn/+RgAvvjiC4JyuBuvEEIIIZ6fnM5uZXr16kVUVBSH517h9KwKHFGuosWwoZYuSwghhCgUZI+PFere7T3+YgNbGcQvM5X8GRJi6ZKEEEKIQkGCjxUqVRrmzogC4AumcmroMS6fPm3ZooQQQohCQIKPleodoGbk22EAfGZYzm9dFnHn1i0LVyWEEEIUbBJ8rNiH8+xp3+gGGdgyN2MTR94chebBA0uXJYQQQhRYEnysmFIJi7+xpW65OzzAgxWJm/ii02BSU1MtXZoQQohcatmyJQsWLMjxtaVLl/LCCy8QGxtrllqCgoJo1KgRFStWpEOHDvz1118mXWbZsmWULl2aadOmmbJsk5DgY+UcHGDdbhtKF4vhGpX5v7DPeffdseh0OkuXJoQQIheqV6/OpUuXsk2Piopi6dKlTJw4EQ8Pj3yvY+fOncycOZNx48YREhJCzZo18fPzIyYmxiTLnD59mg0bNlCjRo38fBvPTYJPAVC8uJ4Nu/Q4OqYDzdi/vxfTpk3HYDBYujQhhBDPqEaNGly8eDHb9Llz51KuXDn8/f3NUsfKlSvp27cvvXv3pmrVqsydOxcHBwc2bdqU52WSkpIYNWoU8+bNw83NLZ/fyfOR4FNAVK2qZfXqeJRKHeDHvaBybJ0509JlCSGEVVAkJz/xh38ND3jqvCkpT5yXpKRHj59D9erVuXnzZpbhCmfPniU4OJhPPvkEGxubXK1vyZIlVKlS5ak/d+7cybJMeno6Z8+e5dVXXzVOUyqVNGvWjD/++CPH7eRmmcmTJ9OqVSuaN2+eq/diTnIBwwKkefN05s5NYNIkN/YxgzdX9mNf2dW0GzLE0qUJIYRF+VSp8sTXUl9/ndivvzY+L1mnDsp/BZyH0ho35n5wsPF5iUaNsMlh3M3dfwWKZ1GzZk10Oh1Xr16lVq1aAEyfPp127drRpEmTHJeJjIxk9uzZLFu2LNtr/v7+dOzY8anbLFmyZJbnsbGx6HQ6ihcvnmW6l5cX165dy3Edz7rMzp07CQ0NZe/evU+tydIk+BQwfn7J3LiQwRdrvYilKq9Nm8YvpUvTtE0bS5cmhBDiKcqUKYOLiwuXLl2iVq1a7Ny5k7Nnz/Lzzz8/cRlvb+8cQw+Au7s77u7u+VVurty5c4dp06bx7bffYm9vb+lynkqCTwH00ScZNKsVSruP5uCaDtHDhhG6axe16ta1dGlCCGEREVeuPPE1gzLrqI6os2efPK9CkeX5vZMnjY9VKhVarfY5K8xUrVo1Ll26RGpqKp9++ikjR46kdOnSQOb4mGHDhhEZGQnAxx9/TKVKlRg2bBjff/99tnUtWbKEpUuXPnV7R44cMa4fwMPDAxsbm2yDkqOjo/Hy8spxHc+yzLlz54iJiaHNY/8J1+l0nDhxgqCgIK5fv57rQ3n5RYJPAaRUwmt9PEiquBm77t1prlMR1HUQxQ5vx7d8eUuXJ4QQZmdwdMz/edVqDBkZuSkrm4cDnJf/cxPqESNGGF87cuQI7u7ufPPNNxgMBhITE9FoNE9c1/Mc6rK1taVOnTocO3bMGFL0ej3Hjh1j0KBBOa7jWZZp1qwZBw8ezLLcuHHjqFSpEiNHjrSa0AMSfAq2hg259Nkq3v2gCup0LZr2XWn/8494enpaujIhhBA5qF69Onv37uXXX39l4cKFODg4ZHlt+vTpzJ49mzZt2tCgQYOnBp/nPdQVEBDA2LFjqVOnDvXq1WPlypWkpKTQu3dv4zxr167l+++/Z8uWLc+0jLOzM9WrV8+yHUdHR9zd3bNNtzQJPgVc7MvtOWvrgjI9lSRNSbYMGMCWLVtwzMX/aIQQQphHzZo1uX//Pk2aNKFDhw5ZXqtUqRIHDhzgxx9/ZObMmXTr1o033njD5DV07tyZ2NhYFixYQHR0NC+88AIbNmzIcqgrNjaWmzdv5mqZgkJhkIvBGEVHR5ORx92Yj1MoFPj4+BAREZGv19w5dswWXdRp3p3WE41GwxtvvMHq1atRqYperjVXz8Uj0nPzK8o9j4+Px8XFxSLbVqvVJv0d8W+RkZG4ublhb2/Pzp07OXr0KKNHj37iGJ+i4GHPn/T3rlarcx2+5Do+hUCzZum81r0mQUFB2Nvbc/vHY/zfO+8UuS9EIYQoyC5evEj79u158803WbVqFcOGDbN0SYVS0dslUIi9/PLLTOnxBcs2tKHx991ZPXMmQ2fMsHRZQgghnkGLFi1o0aJFtulFdW9PfpE9PoXMyXvdicKHYeygwcof2bJypaVLEkIIIayGBJ9CZuH/JVGnWhLRlGA8eyk/YyE/7ttn6bKEEEIIqyDBp5BxdDSwdmMCPp6pXKQGywhGP2wkf/z+u6VLE0IIISxOgk8h5O2tJ2hjPI52GRzkDY4Z/o8LvXpz9epVS5cmhBBCWJRVDm4OCQlh9+7daDQafH19GTx4MJUrV85x3hkzZnD+/Pls0+vVq8dHH32U36VarVq1tHz5VTyDBrqz2jCUgLQrLPfz47tdu7JdyVMIIYQoKqwu+Bw/fpz169cTEBBAlSpV2Lt3L3PmzGHRokW4urpmm3/ChAlZ7p2SkJDAxIkTady4sTnLtkpvvJHGzE8S+PhjV1YSCOHX8Pf357vvvqNYsWKWLk8IIYQwO6s71LVnzx5atWpFy5YtKVOmDAEBAdja2nL48OEc53d2dsbNzc34c/bsWezs7HjllVfMXLl1Gjw4icGDE/959jV//+3IhAEDSE9Pt2hdQgghhCVYVfDRarWEhYVRu3Zt4zSlUknt2rW5fPnyM63j0KFDNGnSBHt7+/wqs8CZMSOeVq1SAQeKsYtpJyOYO2IEer3e0qUJIYQQZmVVh7ri4+PR6/W4ubllme7m5sbdu3f/c/mrV69y+/btLHe7zUlGRkaWy44rFArjjeIUCkXuC3+Ch+sy5Tqfh0oFX36poWsnd/6+6M0g9rImpAkLp09nwuzZFq3N1Kyl50WJ9Nz8pOeiKDLV592qgk9eHTp0iHLlyj1xIPRD27dvJzg42Pi8QoUKBAYG5tvN1ry9vfNlvbkV8gM0fEnHS5rT1E1PJXnNGr6rWZNR48dbujSTs5aeFyXSc/Mrij1PSUlBrVZbbPuW3HZRpVarsbW1xcfHxyTrs6rg4+LiglKpRKPRZJmu0Wiy7QX6t9TUVH755Rd69+79n9vp2rVrlrviPkyR0dHRWQZK55VCocDb25vIyEiruG+WjQ3s26+kVIwP+g5KWqRBxIQJrHB0pFOXLpYuzySsredFgfTc/Ipyz9PT0/P1RqFPk9eblLZs2ZL27dszYcKEbK8tXbqU5cuXc/ToUTw8PPJS5jMJCgriyy+/JDo6mpo1azJr1izq1av3xPlPnDjBl19+yblz54iKimL16tW0adMm3+t82PP09HQiIiKyva5SqXK908Kqgo9KpaJixYqEhobSsGFDAPR6PaGhof/Z4BMnTqDVann11Vf/cztqtfqJqT0/vkQMBoPVfDmVKKFDW6IGievWoejjhzevEj5qFMdLlChUZ8JZU8+LCum5+UnPC5bq1atz6dKlbNOjoqJYunQpkydPNkvo2blzJzNnzmTu3LnUq1ePVatW4efnx88//0zx4sVzXCY5OZmaNWvSp08fhg4dmu815sRUn3WrGtwM0KFDBw4ePMiRI0cIDw9n1apVpKWlGW/ctmzZMjZu3JhtuUOHDvHyyy/LadrPKLHRq3SqeZlWHKSSviOfDxjAhQsXLF2WEEIUWjVq1ODixYvZps+dO5dy5crh7+9vljpWrlxJ37596d27N1WrVmXu3Lk4ODiwadOmJy7z+uuv88EHH9C2bVuz1JifrGqPD0CTJk2Ij49ny5YtaDQaypcvz+TJk42HumJiYrINcLp79y4XL15k6tSpFqi4YFKrwbtBSewu6/i6eFl+jUyiX79+7Nq1i9KlS1u6PCGEyJXk5NwPfLW1NaD657egVgvp6QoUCgP/nOuSbb0qFWi1mc8dHXO/96F69ercvHmT1NRU45nHZ8+eJTg4mM2bN2NjY5Or9S1ZsoSlS5c+dZ4jR45k+U5PT0/n7NmzjBo1yjhNqVTSrFkz/vjjj1xtv6CyuuAD0KZNmyce2poxY0a2aaVKlWLLli35XFXholDArFlxDBigwtt7BOe7HuDy5cv069eP7du3/+eYKiGEsCZVquR+4Ovy5bF07JgKwPff2/POOx40bpxGcPB94zyNGpUgNjZ7ILlz57/PNP63mjVrotPpuHr1KrVq1QJg+vTptGvXjiZNmuS4TGRkJLNnz2bZsmXZXvP396djx45P3ea/r9QfGxuLTqfLdkjLy8uLa9eu5ebtFFhWGXyEeahUUL26FnBjw4YNDG4zlDmXw/i4b1/mb9sm10ISQggTKlOmDC4uLly6dIlatWqxc+dOzp49y88///zEZby9vXMMPQDu7u64u7vnV7mFlgQfAUBsbHmiE35kBb8x/0xnpgwfzrw1a3K961UIISzhypXsZ/z8F1vbR4er2rZN5cqVCBSKrIewTp68Z3ysUqnyfOZvtWrVuHTpEqmpqXz66aeMHDnSeCgqKSmJYcOGERkZCcDHH39MpUqVGDZsGN9//322dT3PoS4PDw9sbGyIiYnJMl90dHS+XdLF2kjwEQDodBBn48m+jPZUZCHv/fg+s6dMYdpnn8lF0oQQVu95xtw8TqUClSr7Oh5fr1oNGRl5287DAc7Lly8HyHLB3SNHjuDu7s4333yDwWAgMTEx2+VdHvc8h7psbW2pU6cOx44dMw4p0ev1HDt2jEGDBj3nuypYJPgIAOrWzWDpUg0BAR4s4z2qcpm3vl7Gl6VL8+5771m6PCGEKBSqV6/O3r17+fXXX1m4cKHxrgEPX5s+fTqzZ8+mTZs2NGjQ4KnB53kPdQUEBDB27Fjq1KlDvXr1WLlyJSkpKVmug7d27Vq+//574/jZpKQkrl+/bnz91q1bhIaG4u7uXuBOiLG609mF5bRrl8qUKfEAjGER9rSj+Ny5Wa5yLYQQ4vnVrFmT+/fvU7du3SwX0gWoVKkSBw4coGrVqsycOZO1a9fmSw2dO3fm448/ZsGCBbz11lucP3+eDRs2ZDnUFRsby82bN43Pz5w5Q+vWrWndujUAM2fOpHXr1syfPz9fasxPCoNc/cooOjrapFcEVSgU+Pj4EBERUWAuMmYwwKRJrmzc6IQTiRyjGV8pz9Hs66+N11KyZgWx5wWd9Nz8inLP4+PjcXFxsci283rl5v8SGRmJm5sb9vb27Ny5k6NHjzJ69OgnjvEpCh72/El/72q1umBfuVlYnkIBn34ax+3bKo4edaatYi8GfUPWBQTw3XffUadOHUuXKIQQhdLFixeZNWsWSqUSe3t7Pv/8c0uXVCjJHp/HyB6fR+LiFHTuXJwrV9Q4OV0mKak+xYs7sGvXLnx9fS1d3hMV5J4XVNJz8yvKPS/Me3xEdvmxx0fG+IgcuboaWL8+Fk9PHUlJVXF23kXzmFim9urF/fv3/3sFQgghhBWS4COeqFw5HWvWxGJnZyAx8XV8mc9X4eGM6duX5ORkS5cnhBBC5JoEH/FUDRpksHDhAwAWMo7f6MNnoaGMCQjI84W8hBBCCHOT4CP+U+fOqUyaFE/rZvd5y/kIDYFhR44wedKkIje+QAghRMEmwUc8k/ffT2TlxjT036xAq1bTCWiyeTOfL1hg6dKEEEKIZybBRzwThQJsbCCjQQPi/u8LFjGarnijXrSIDRs2WLo8IUQRotfrLV2CMCNT/31L8BG5Nvt8b8ayiE7swhk1H330EQcOHLB0WUKIIsDR0ZGEhAQJP0WEXq8nISEBR0dHk61TLmAocq1nz2S++caRnt3UnI7vgf7bbxkxYgSbN2+mQYMGli5PCFGIqVQqnJycSExMNPu2bW1tSU9PN/t2izJ7e3ucnJxQqUwXVyT4iFwrX17HL7/cw8mpBH20c4mKiuKXQ4f4uF8/lu7ZQ+XKlS1dohCiEFOpVGa/iGFRvmikpeRXz+VQl3guTk6ZH0KVSsX8yQv4zK4nuxISGNenD1FRURauTgghhMiZBB+RJ7GxSnoOrcnEtI1c5g1WRkQwvG9fEhISLF2aEEIIkY0EH5En7u566tbXoUNFD77Dlpp8cvEiwwcPlmPhQgghrI4EH5EnCgUsWKChUaM04nGhPfuoRwn8jx9n3NixcuaFEEIIqyLBR+SZnR2sWhVL+fJabuJLZ3bSF3tq7djBp59+aunyhBBCCCMJPsIkPDwMrF9/Hzc3PSd5hQGs4z0UbP3yS1atWmXp8oQQQghAgo8woUqVdKxaFYtabWArvXi77l5igBkzZrBr1y5LlyeEEEJI8BGm1bhxOvPmaQA4eLotTZuuwmAwMPb99zl+/LhlixNCCFHkSfARJterVwrvv595OvvJk4PpW30Ef2RkMG3QIC5cuGDh6oQQQhRlEnxEvpg4MYFOnVLQahXsu/wZSqqxOTGRd/v25c6dO5YuTwghRBElwUfkC6USFi58wEsvpaPRuzJa/QW1gS/v3WOQnx8PHjywdIlCCCGKIAk+It/Y28PatbF065bMom8U6BwdeR344MoVBg8aRGpqqqVLFEIIUcRI8BH5ytNTz9KlGlybVkOzciV6pRJ/oOOpU7z33nvodDpLlyiEEKIIkeAjzCatRQuWdA/hEz5mKmCzbx/Tpk2TOx0LIYQwG5WlC8hJSEgIu3fvRqPR4Ovry+DBg6lcufIT509KSuLbb7/lt99+IzExES8vLwYMGED9+vXNWLX4L6dPqxm79U3gTSrViGL/ha9IDwrCx8eHUaNGWbo8IYQQRYDVBZ/jx4+zfv16AgICqFKlCnv37mXOnDksWrQIV1fXbPNrtVpmz56Ni4sL48aNw8PDg5iYGBwdHS1QvXiaunUzGDUqAXs7Ay3GTGPKmtJMnz6dzz77jJIlS9KzZ09LlyiEEKKQs7rgs2fPHlq1akXLli0BCAgI4M8//+Tw4cN06dIl2/yHDh0iMTGRWbNmoVJlvp0SJUqYs2SRCx9+mIBCAaBg6NChRN29i8+KFSwdPx4vLy9atGhh4QqFEEIUZlY1xker1RIWFkbt2rWN05RKJbVr1+by5cs5LvPHH39QpUoVVq9eTUBAAOPHj2fbtm1yV3ArlRl6MqWkKNAf780IPNit0zFp6FDOnj1rueKEEEIUela1xyc+Ph69Xo+bm1uW6W5ubty9ezfHZaKiooiOjqZZs2Z89NFHREZGsmrVKnQ63RMPnWRkZJCRkWF8rlAocHBwMD42lYfrMuU6C5PRo93Ye64lf9vu4Yf0FmxMSaFvv34E79mDr6/vc61Tem5+0nPzk56bn/Tc/PKr51YVfJ6HwWDAxcWF4cOHo1QqqVixIrGxsezateuJwWf79u0EBwcbn1eoUIHAwEC8vLzypUZvb+98WW9BN3cuHD0KR+MbE6AOYl1GXz6/f5/+/frxy6+/5unvQ3puftJz85Oem5/03PxM3XOrCj4uLi4olUo0Gk2W6RqNJtteoIfc3NxQqVQolY+O2pUuXRqNRoNWqzWO+3lc165d6dChg/H5wzQZHR2NVqvN+xt5bL3e3t5ERkbKKds58PSE5ctt8ff34OuMt6msvMg0/SfcunaNt956i+Dg4FwPUpeem5/03Pyk5+YnPTe/Z+m5SqXK9X+SrSr4qFQqKlasSGhoKA0bNgRAr9cTGhpKmzZtclymWrVq/PLLL+j1emP4iYiIwN3dPcfQA6BWq1Gr1Tm+lh8faIPBIP9QnuC119KYMyeODz90Y7p+JlW4zEQ2cfP0aYYNG8batWuf+Pf4NNJz85Oem5/03Pyk5+Zn6p5b1eBmgA4dOnDw4EGOHDlCeHg4q1atIi0tzXi2z7Jly9i4caNx/rfeeovExESCgoK4e/cuf/75J9u3b6d169YWegcit/z9kxk+PBGAQTbrOapqzj21mkOHDvHBBx/Il4wQQgiTsao9PgBNmjQhPj6eLVu2oNFoKF++PJMnTzYe6oqJicky0Kl48eJMmTKFdevWMXHiRDw8PGjbtm2Op74L6zVlSjw3b9oQEuJAF7cf+eCjXXz3US82bdqEj48PEyZMsHSJQgghCgGFQf47bRQdHZ3lbK+8UigU+Pj4EBERIXstnkFysoLu3T05e9aWSpUy6NfvC9bOHIMKGDZ3Lv7+/v+5Dum5+UnPzU96bn7Sc/N7lp6r1epcj/GxukNdouhydDSwdm0sPj46rl1T8+NOf/5wcCUE+N9HH7F//35LlyiEEKKAk+AjrIq3t5716+/j5KTnl9MeTFQspzKww2Bg3IgR/P7775YuUQghRAEmwUdYnZo1tXz55QOUSgP77LsSXqwajYHVaWkM6t+fq1evWrpEIYQQBZQEH2GVWrVKY9myB+wJeYDDus8wqNV0A6bFxeHXty9RUVGWLlEIIUQBJMFHWK3OnVMpXVpPeqNGPFi8mHTUjAZ63rlDv379SEhIsHSJQgghChgJPqJA2KbuTVW3SG7gi79KxeXz5xk6dCjp6emWLk0IIUQBIsFHWD2dDpYsceamxoO5TYO5v3Urdk5OHDt2jHHjxqHX6y1dohBCiAJCgo+wejY2sGZNLKNHJzB5YylqNmzIypUrUalUHNi+nTlz5li6RCGEEAWEBB9RIJQqpWfSpAQe3rar+avNOfJWa84A25YvZ+XKlRatTwghRMEgwUcUOBkZMGmcE8d/bUwlYA8wf8YMdu3aZenShBBCWDkJPqLACQmxZ+NWNz7QTGabcz9eBr4Fxr3/PsePH7d0eUIIIayYBB9R4HTokEr//kkYDAr6pQfxm7oxHYHPMzIYPGgQ586ds3SJQgghrJQEH1HgKBQwa1YcLVumkpJuQyfHH7lFWUYAIxISaNOmDeHh4ZYuUwghhBWS4CMKJJUKvvzyATVqZBAV50i7kqdIwJm5gOfdu/j5+fHgwQNLlymEEMLKSPARBVaxYgbWrYulRAkdf0eVpHu5E1z9YAqxpUtz5coVBg8eTEpKiqXLFEIIYUUk+IgCrXRpHUFBsdjb6/nh1gvMifqIffu+x8XFhd9++433338fnU5n6TKFEEJYCQk+osB78cUMli3ToFAYCApy4siR2mxYtIgtSiWn9+1j2rRpGAwGS5cphBDCCkjwEYVC27apTJ0aD8DYsZCxJIyeej17ge+Cgli2bJllCxRCCGEVJPiIQmP48CT8/JLR68H/0gx+d2tJXSAYWDB3Llu2bLFwhUIIISxNgo8oNBQK+PTTON58E1RqBbc/+Ay9gwNvASuAiRMmcPjwYUuXKYQQwoIk+IhCRa2GrVth9+77NOpflgdffolBqWQwMFmnY9iwYZw9e9bSZQohhLAQCT6i0HF1hSpVtACkvfkmZ8b/HxmomAl0S07G39+fGzduWLRGIYQQliHBRxRqx4/b0uKrYQx/4TBpZcoSW7kyMTEx+Pn5ERMTY+nyhBBCmJkEH1GoJSYqiI9XcNb+ZcK372fO1q2ULVuWGzduMGDAAJKTky1dohBCCDOS4CMKtbfeSmPdulg2b76PQylXSpQowYYNG+ji7Mz906cZPnw4GRkZli5TCCGEmUjwEYVeq1ZpODg8eu7z+w2+S07me4WCPw4d4sMPP5QLHAohRBEhwUcUGXo9fPZZMV6Z9TZXPV7mBYOBbcC2TZtYsGCBpcsTQghhBhJ8RJGRlgZHj9oRq1HTzv4g0Y5leR1YDSxatIivv/7a0iUKIYTIZxJ8RJHh4ABBQbGULq3lWrgTncr9TqrSgX7AbGDy5Mns37/f0mUKIYTIRxJ8RJFSooSer7+OpVgxPSculsC/7h8YgCnAEL2ed999l1OnTlm6TCGEEPlEgo8ocqpV0/LVV7GoVAaC/6zBh40y9/J0KlWK1NRUBg4cyNWrVy1cpRBCiPygsnQBOQkJCWH37t1oNBp8fX0ZPHgwlStXznHeI0eO8MUXX2SZplar+eabb8xRqiigmjdPJzBQw/jx7sw7+RZlB/9Mmw+9qde7N3/99Rd+fn7s3LkTb29vS5cqhBDChKwu+Bw/fpz169cTEBBAlSpV2Lt3L3PmzGHRokW4urrmuIyDgwOLFy82c6WioOvTJ4WbN1UsWVKMseub4fXWfdatW0e3Tp2Iv3EDf39/tm3bRrFixSxdqhBCCBOxukNde/bsoVWrVrRs2ZIyZcoQEBCAra3tU++qrVAocHNzy/IjxLOYNCmBLl2S0WoVDBvmQWy4CydLl+aYjQ0R588zdOhQ0tPTLV2mEEIIE7Gq4KPVagkLC6N27drGaUqlktq1a3P58uUnLpeamsq7777LiBEjmDdvHrdv3zZHuaIQUCjgf//T0LBhGvHxSvoN8Sb5aiJVdDr2KJWcOnaMsWPHotfrLV2qEEIIE7CqQ13x8fHo9fpse2zc3Ny4e/dujsuUKlWKESNG4OvrS3JyMrt27WLq1Kn873//w9PTM8dlMjIystymQKFQ4PDPpX0VCoVp3sxj6zLlOsXTPU/P7e1hzZoHdOpUnLAwOzpX/5kTyb40SdCwXqGgz44deHt7M23atPwqu0CTz7n5Sc/NT3pufvnVc6sKPs+jatWqVK1aNcvzsWPH8sMPP9CnT58cl9m+fTvBwcHG5xUqVCAwMBAvL698qVEGyJpfbnvu4wP790PLlvDhJy6oi2+D1q3plZHBLWDi8uVUq1aNMWPG5Eu9hYF8zs1Pem5+0nPzM3XPrSr4uLi4oFQq0Wg0WaZrNJpnHrejUqmoUKECkZGRT5yna9eudOjQwfj8YZqMjo5Gq9Xmuu4nUSgUeHt7ExkZKfeCMpO89NzJCX7+OXMPUATVsV+4EPdRo5gA3ATGjh2Lvb09nTt3zpfaCyr5nJuf9Nz8pOfm9yw9V6lUud5pYVXBR6VSUbFiRUJDQ2nYsCEAer2e0NBQ2rRp80zr0Ov13Lp1i3r16j1xHrVajVqtzvG1/PhAGwwG+YdiZs/bczs7eLjYtUbdOdvRg8G7+zJPrWZTRgajR4/G09OTpk2bmrjigk8+5+YnPTc/6bn5mbrnVjW4GaBDhw4cPHiQI0eOEB4ezqpVq0hLS6NFixYALFu2jI0bNxrnDw4O5syZM0RFRREWFsaSJUuIjo6mVatWFnoHojCIjlbSsaMXAXv7sLXDl8Rv20aj9u1JT09nyJAhnD9/3tIlCiGEeA5WtccHoEmTJsTHx7NlyxY0Gg3ly5dn8uTJxkNdMTExWQY6JSYmsmLFCjQaDU5OTlSsWJHZs2dTpkwZC70DURgUL67n9ddT+f13W8pN7YqurI4lS5YQExPDyZMn8ff3Z9euXZQuXdrSpQohhMgFhUH22RlFR0dnOdsrrxQKBT4+PkRERMiuUTMxZc8zMiAlRYGLy6P1pB49yoNBg2iTkoJ7lSps374dd3f3vJZdoMnn3Pyk5+YnPTe/Z+m5Wq3O9RgfqzvUJYS1UKvJEnp+OqzGZep8XkpJIUStJvzKFQYNGkRKSooFqxRCCJEbEnyEeAYbNzri51+cHm4/kOZanJcyMtisUvHHqVO899576HQ6S5cohBDiGUjwEeIZ1K6dgYODgZ9+d2foy6fQ29rRUatliVLJ999/z8cffyy7v4UQogCQ4CPEM6hdO4MvvniAUmlgw4/lmdHuJwwKBSP1esYD69atY9myZZYuUwghxH+Q4CPEM3rzzTQ++SQOgFk7GhHUbRMAC4DWwNy5c9myZYvlChRCCPGfJPgIkQuDBiUzdGgiACP29OSHDp+R+uab1AgIAGDChAkcPnzYkiUKIYR4ijwHn3v37mWbduXKlbyuVgirNW1aPK1bp5CWpqD3LxP5Y2oQ46dNo1u3buh0OoYNG8aZM2csXaYQQogc5Dn4NGvWzHglZYPBwPz58+nWrVueCxPCWtnYwLJlGurWTefBAxv8B5ZAo7Hh8wULWFKxIsWSk+nfvz83btywdKlCCCH+Jc/B5+jRo2zfvp1OnTrRtGlTwsPDOXnypClqE8JqOToaCAqKpUwZLdevqxgyxAOnhV/yXlgYh+ztSYmJwc/Pj5iYGEuXKoQQ4jF5Dj5eXl7Uq1ePK1euEBUVRZs2bXB0dDRFbUJYNS8vPV9/HYuLi57ffrNj2PmJZLh7UjM1lZ329ty+cYMBAwaQlJRk6VKFEEL8I8/Bp3Hjxmg0Gk6fPs3hw4dZvHgxAwYMMEVtQli9qlW1fPVVLCqVgR0Hvfju/b3o7e1plZrKaltbTp8+zTvvvGPSW6EIIYR4fnkOPosXL2bevHnY2dlRrlw5QkJCeO2110xRmxAFwquvpjNvnoY5czQ0H1YWzRdfYFAoGJCezjSVikOHDvHhhx/KBQ6FEMIK5Pnu7K+88goZGRncunWLtLQ04zQhipLevR/dryu1dWviZs3CbepUZmq13FAoWL9pE97e3kycONGCVQohhMjzHp9vv/2WunXrUq9ePQYMGEDdunUZOnSoKWoTokDSaBT0/nkcp3rNxKBS0btnTwAWLVrE119/beHqhBCiaMtz8Pnss884deoUFStW5NSpU/z222+ULVvWFLUJUSDNmuXCgQMODDj7IVG791J34ULGjRsHwOTJkwkJCbFwhUIIUXTlOfjY2dkZz+LKyMigbt26/P3333kuTIiCasqUeJo1S2PpMg36OrUAGDduHO9064a3Xs/IkSM5deqUhasUQoiiKc/Bx9vbG41GQ8eOHWnbti29e/eWPT6iSPPwMLB5831q1NAap6muX2fJ77/zs7Mz6tRUBg4cyNWrVy1YpRBCFE15Hty8e/duAGbNmsWRI0eIj4+nTZs2eS5MiMLi5Elbfv/Bl09SU6mUmMiBYsVortHQt29fdu3ahbe3t6VLFEKIIiPPwedxLVq0MOXqhCjwwsNt6NPHk/R0BSXH/cio5a/wSkICG52d6XnnDv7+/nz33Xe4uLhYulQhhCgS8nyo69q1a4wZM4auXbvSqVMn448QAsqU0fHuu5l3cx+/pBY739+BwcaGHomJzHd05Pz58wwdOtR4KQghhBD5K897fLp27cqoUaPo3bs3SmWec5QQhc6ECQncuGHDjh2ODPqyFaXHfUXD+UOYkJxMmK0tX/7yC+PGjWPp0qXyb0gIIfJZnoOPo6Mjw4YNM0UtQhRKCgV8/rmG8HAVv/9uS68t/TkccI0KKz/lMy8vgiIj2bFjByVLlmTatGmWLlcIIQq1PP/38qOPPuKDDz7ghx9+4Oeffzb+CCEesbeHNWti8fXVcvOmil5/TePehKmkhIQQuHAhACtWrOCrr76ycKVCCFG45XmPz/79+zly5AhXr1417qZXKBQ0b948z8UJUZh4eupZvz6WTp2K8/vvdgwv8xHL3DV0796dqKgoAufMYebMmZQsWZLOnTtbulwhhCiU8hx8fvzxRy5duoRCoTBFPUIUapUrZ97N3c/Pkx07HKlQQceECQmML1aM4e7u1HnwgDFjxlC8eHGaNm1q6XKFEKLQyfOhroYNG3Lt2jVT1CJEkdCsWTqBgRoAFi4sxncbFLgsWkTZBw/42cMD0tMZMmQI58+ft2yhQghRCOU5+ISGhlKrVi1efPFFGjZsyMsvv0zDhg1NUZsQhVafPimMGpUAwPip3uyZuBt9sWLUjo1lt6cniQkJ+Pv7c+fOHQtXKoQQhUueD3Xt3LnTFHUIUeR88EEC16+r2LvXgR3natD0q6/w9Pfnrfv3We7hwfDISPz8/Ni+fTvu7u6WLlcIIQqFPAcfX19fU9QhRJGjVMLixQ9o3jwNP79k0hXN0cyfj/vYsQyLjeWaiwvzrlxh0KBBfPvttzg4OFi6ZCGEKPDkamlCWJCDA/Trl8zDcwOSevQiZuwHAMxNTKSZszOnTp1i1KhR6HQ6C1YqhBCFg8mDT0REhFx+X4jnkJKiYNgwd0bcnkZiXz8SJk1ibFAQtra2hISE8PHHH2MwGCxdphBCFGgmDz7+/v5Ur16dCRMmPPc6QkJCGDlyJH5+fkyePJmrV68+03K//PILvXr1Yt68ec+9bSEs5exZNQcO2LNrlwMnB31O4nvv8UrjxixduhSFQsG6detYunSppcsUQogCzeTB58cff+T69esMHTr0uZY/fvw469evp0ePHgQGBuLr68ucOXOIi4t76nL37t3j66+/pkaNGs+1XSEsrVGjdObP17B5831q1Hx0WKtjy5b89vLLeAKBgYFs3rzZckUKIUQBl+fBzfv27cs2zcXFhZo1az7X+vbs2UOrVq1o2bIlAAEBAfz5558cPnyYLl265LiMXq9n6dKl9OrViwsXLpCUlPRc2xbC0nr3Tsny3GAA99Gj8fntN056e1MrMpKJEydSokQJ478RIYQQzy7Pe3yWLVuGv78/a9euZc2aNfTv358ZM2bQuHFj1q9fn6t1abVawsLCqF279qMClUpq167N5cuXn7hccHAwLi4uvP7668/9PoSwNhcuqGjXrjjn+k1D7+pKpchIDpUqhUGnY9iwYZw+fdrSJQohRIGT5z0+6enpXLx4ES8vLwCio6Pp27cvJ0+epFmzZvTv3/+Z1xUfH49er8fNzS3LdDc3N+7evZvjMhcvXuTQoUO5GteTkZFBRkaG8blCoTCeKmzKW288XJfczsN8ClPPp0935exZW/ymN2D/0q+pMLQHje/e5dvSpel95w79+/dn9+7dlC9f3qJ1FqaeFxTSc/OTnptffvU8z8EnPDwcDw8P43N3d3du376Nm5sbarU6r6t/qpSUFJYuXcrw4cNxcXF55uW2b99OcHCw8XmFChUIDAw0hjdT8/b2zpf1iicrDD3fsgUaNYKrV1UMW9eR71etQ93/bXrducOt0qWZeOcO/v7+HD9+nBIlSli63ELR84JGem5+0nPzM3XP8xx8evbsSdOmTenatSuQeSXnnj17kpSURLVq1XK1LhcXF5RKJRqNJst0jUaTbS8QQFRUFNHR0QQGBhqnPTzdt0+fPixatCjHhnXt2pUOHToYnz9Mk9HR0Wi12lzV/DQKhQJvb28iIyPlNGQzKUw9Vyhg7VoVXbp4cvCgkgElOvHlR5Nx/exTxt+9y2VPT1Zeu8Zbb71FcHAwTk5OFqqz8PS8oJCem5/03PyepecqlSrXOy3yHHxmzZpFp06dOH78OAaDgUWLFhnv1bVp06bcFaNSUbFiRUJDQ43r0Ov1hIaG0qZNm2zzlypVigULFmSZtmnTJlJTUxk4cCDFixfPcTtqtfqJe6Py4wNtMBjkH4qZFZae16yZwZdfPmDgQA++/daRipMn8VH/cOxDQnhnzhyCJ03izJkzDB8+nDVr1uT7XtanKSw9L0ik5+YnPTc/U/c818Hn+PHjuLi4UKtWLeO0l19+mZdfftkkBXXo0IH/+7//o2LFilSuXJl9+/aRlpZGixYtgMzB1B4eHvTt2xdbW1vKlSuXZfmH/+v993QhCqpWrdL45JM4pk51Y86nrvgu/5yOo0fj7e3Nem9vevbsyaFDh/jggw/4/PPPZQyCEEI8Ra7P6ho5ciQnT57MNv3atWskJCTkuaAmTZrg7+/Pli1bmDRpEjdu3GDy5MnGQ10xMTE8ePAgz9sRoiAZNCiZIUMSAXh/jCd/RJQFoH79+gRPmkRJhYLNmzczf/58S5YphBBWT2HI5f4jR0dHzp07R6VKlbJMX7FiBbt372bPnj0mLdCcoqOjs5ztlVcKhQIfHx8iIiJk16iZFOae63QwaJAHBw/a4+WlY8+eGCpd/gGPgAAivbyofPs2ycBnn32Wq7Mp86ow99xaSc/NT3pufs/Sc7VanesxPrne4+Pi4pLjHpdXX32VEydO5HZ1QohnZGMDX3zxgJo1M4iOtqF/fw9iPSpgsLfH5/ZtfqtYERtgypQphISEWLpcIYSwSrkOPm3atMk2oBgyLzSYnp5ukqKEEDlzdjawbt19SpbUcemSmmHzX+Le6nUY7Ox4ISyMkCpV0Ov1jBw5klOnTlm6XCGEsDq5Dj6zZs3ip59+onv37pw7dw6A1NRUAgMDqVOnjskLFEJkVaqUnqCgWBwc9CgUkFD7ZR4sXYpBoeCNK1dYWbmy8czGK1euWLpcIYSwKrkOPmXLluXEiROkpKTw4osv4uDgQLFixdi9e7cMrBTCTOrUyWDHjhiCgmJxcjKQ2r498TNmADD06lU+8vVFo9Hg5+dHZGSkZYsVQggr8szBZ9q0afzxxx8A+Pr6sm/fPq5fv87mzZvZtWsXly9fpnHjxvlWqBAiq1q1tKj+uSCFwQChrYaTGBAAwKQaNahYoQJ37tyhX79+xMfHW7BSIYSwHs98HZ/w8HDatm2Lra0tHTt2pHPnzrz++uv4+vrmZ31CiP+QkQFTpriybZsD24Jn0ahGDVJ69OCbO3fo1KkTFy5cYMiQIWzYsAE7OztLlyuEEBb1zHt81qxZQ2RkJN9++y3FihVj9OjRFC9enO7du7N+/XpiY2Pzs04hxBMoFBAebkNqqoLQ83ak9O4NNjaUK1eODV9/jY+jI8ePH2fs2LHo9XpLlyuEEBaVqzE+SqWSV199lXnz5nHp0iVOnjxJo0aNWLFiBaVKlaJ58+YsWLCAO3fu5Fe9Qoh/Ualg+fIHbNwYS9++yY9eSE+n2apV/O3lhZuNDTt37mT27NmWK1QIIaxArgc3P65GjRpMmjSJX375hdu3bzNgwACOHj3Kt99+a6r6hBDPwMXFQPPmacbnCQkK9BHR2P30E+43b3K6ShVUZF5o9KuvvrJcoUIIYWF5Cj6P8/LyYsiQIezcuZMJEyaYarVCiFy6dcuGTp2KM31VDWLXrUPv4IDvxYuc/OdyEzNnzmTnzp0WrlIIISwjz3dn37dvX7ZpLi4u1KxZEw8Pj7yuXgiRS+fPq7l8OfOnfPkmvLt8OR6DBlH/7Fl21qtH57/+YvTo0Xh6etKsWTNLlyuEEGaV5z0+y5Ytw9/fn7Vr17JmzRr69+/PjBkzaNy4MevXrzdFjUKIXGjTJpUpUzJPX58xw4Xd+vbEffYZAJ3++otFtWuTkZHB0KFD+fvvvy1ZqhBCmF2eg096ejoXL15k69atBAcHc+HCBRQKBSdPnmTevHmmqFEIkUsjRiTi55eEXq9g5Eh3fqs7kIT33wfgvStXaFu/PgkJCfj7+xMeHm7haoUQwnzyHHzCw8OzHNJyd3fn9u3buLm5oVar87p6IcRzUChgzpw4mjdPJTlZyYABnlzq9yFJAwcSGxTEgq+/plq1akRFRdGvX78cbzwshBCFUZ6DT8+ePWnatCmBgYEEBgbSvHlzevbsSVJSEtWqVTNFjUKI56BWw4oVD6haNYPISBsGDCzOnY8+Jf3VV3Fzc2PDhg34+Phw5coVBg4cSEpKiqVLFkKIfJfr4HP8+HFCQ0ONz2fNmsXSpUuxt7fHzs6ORYsWMWvWLJycnNi0aZNJixVC5I6Li4H162MpXlzH+fNqRoxwR6vNfK1ccjLnypWjnIsLv//+O6NGjUKn01m2YCGEyGe5Dj4jR47k5MmTWaa9/PLLtG/fniFDhtCwYUOTFSeEyLuyZXUEBcVib2/g0CF7pk93xaDT4/7OO3iePMnvZctSzNaWkJAQpk6disFgsHTJQgiRb3IdfC5dukSLFi2yTT948CBvv/22KWoSQphYvXoZLFnyAIXCQFCQE6vXFuPB0qXoixXD6++/+fPFF1EC69evZ+nSpZYuVwgh8k2ug4+Li0uOAyFfffVVTpw4YZKihBCm17591tPcD0TUJXblSgxqNZVPneLoP9f0CQwMZPPmzZYsVQgh8k2ug0+bNm1YsGBB9hUplaSnp5ukKCFE/njnnST8/JKoWzeD2rUzSH/1VTT//HtucuwYm5s3B2DixIkcOnTIkqUKIUS+yHXwmTVrFj/99BPdu3fn3LlzAKSmphIYGEidfy6JL4SwTg9Pc9+6NQYvr8w7taf06EH8pEkA9Dx6lFlNm6LT6Rg+fDinT5+2YLVCCGF6uQ4+ZcuW5cSJE6SkpPDiiy/i4OBAsWLF2L17N/Pnz8+PGoUQJqRWg4PDo+d799oTOXg0SX5+pL/8Mv2XLeO1114jOTmZ/v37c/36dcsVK4QQJvZc9+ry9fVl37593Lp1i9OnT6NWq2nUqJHcm0uIAmb5cidmzXLl9ddTWbvyU1RoUdnb89VXX9G9e3dCQ0Pp168fO3fupHjx4pYuVwgh8ixPNyktV64c5cqVM1UtQggza9QoHXt7PS+8kIHSVgXKzK8EZ2dn9vTsSZcHDzh94wb9+/dn69atODk5WbhiIYTImzxfuVkIUXDVq5fBzz9H8+GHCSgf+zZw+vJLfKdP5ydXV0q7u3PmzBneeecdMjIyLFesEEKYgAQfIYq40qUfXa05JQX+/FNN6ptvondzw+X8eX6vWhVHOzsOHTrEpEmT5AKHQogCTYKPEAIAjUZBr17F6dXLk7+SaxAbFITBzg7vkyf5o1kzbJRKtmzZwrx58yxdqhBCPDcJPkIIAJycDBQrpiclRcnAgR5cL9WIB0uWYFAoqH7wIAfbtgVgyZIlrFu3zsLVCiHE85HgI4QAMk9zX778AdWrZxAVZcOAAZ7EtOhI/LRpALy2dy9ft28PwNSpUwkJCbFkuUII8Vwk+AghjFxcDKxbF4uXl44LFzLv5h43eBiJQ4YA0KFBA/z8/NDr9YwcOZJTp05ZuGIhhMgdCT5CiCzKlHl4N3c9hw7ZM22aK3HTphPz3XckDxvGp59+yptvvklqaioDBw7kypUrli5ZCCGemQQfIUQ2detmsGyZBoXCwLp1Tqxa60L6K68AoFKpWD5vHm/Uro1Go6Fv377cvXvXwhULIcSzydMFDPNLSEgIu3fvRqPR4Ovry+DBg6lcuXKO8548eZLt27cTGRmJTqfD29ubjh070vyfmy0KIZ5P27apTJ0az6xZrsyc6UK5cjpat05FGRFBWX9/dgGNypfn3I0btG3bli1btlCsWDFLly2EEE9ldXt8jh8/zvr16+nRoweBgYH4+voyZ84c4uLicpzf2dmZbt26MXv2bObPn0/Lli354osv5OaKQpjA8OFJ+PsnYTAoGDnSjbNn1SjS01FGR+Nw4QI/e3tTysuLs2fPMmTIENLS0ixdshBCPJXVBZ89e/bQqlUrWrZsSZkyZQgICMDW1pbDhw/nOP8LL7xAw4YNKVOmDN7e3rRr1w5fX18uXrxo5sqFKHwUCpg9O46WLVONp7nfUlUgdt069A4OuJ04wcn69Snm7Mwvv/zC2LFj0ev1li5bCCGeyKoOdWm1WsLCwujSpYtxmlKppHbt2ly+fPk/lzcYDISGhnL37l38/PyeOF9GRkaWS+8rFAoc/rldtUKheP438C8P12XKdYqnk56bXuZp7hq6dPHkwgU148e7s3lzPTTLl+M+aBBl9u/nr/79qb5xIzt37sTb25vp06dbuuxCTT7n5ic9N7/86rlVBZ/4+Hj0ej1ubm5Zpru5uT118GRycjLDhw9Hq9WiVCoZMmQIderUeeL827dvJzg42Pi8QoUKBAYG4uXllef3kBNvb+98Wa94Mum5afn4QEgIDBoEq1bZ4ePjA/37Q3IyjBhBpfXr+TkggCYrV7JixQqqVq3KuHHjLF12oSefc/OTnpufqXtuVcHnednb2zN//nxSU1M5d+4c69evp2TJkrzwwgs5zt+1a1c6dOhgfP4wTUZHR6PVak1Wl0KhwNvbm8jISLm/kZlIz/OPWg0bNmQ+joj4Z2LnzhQ7fx7npUt5OSSEGR9+yIy5cxk/fjwODg5Z9t4K05HPuflJz83vWXquUqlyvdPCqoKPi4sLSqUSjUaTZbpGo8m2F+hxSqXSmAjLly/PnTt32LFjxxODj1qtRq1W5/hafnygDQaD/EMxM+l5/jtwwI4bN1QM//BDnN3cuN+5M0O9vbkdHc3q1asZPXo0np6eNGvWzNKlFlryOTc/6bn5mbrnVjW4WaVSUbFiRUJDQ43T9Ho9oaGhVK1a9ZnXo9frs4zhEUKY1oULKoYM8WDmTFeOHrODTz5BX6oUCoWCGTNm0LF9ezIyMhg6dCh///23pcsVQggjqwo+AB06dODgwYMcOXKE8PBwVq1aRVpaGi1atABg2bJlbNy40Tj/9u3bOXv2LFFRUYSHh7N7926OHj3Kq6++aqF3IEThV6OGlsGDk3j77SReeSU9y2uO+/ax9eZN3nz5ZRISEvD39yc8PNxClQohRFZWdagLoEmTJsTHx7NlyxY0Gg3ly5dn8uTJxkNdMTExWUZ4p6WlsWrVKu7fv4+trS2lS5fmvffeo0mTJhZ6B0IUDdOnx6NQgFL56N+jIiUFl5kzUd29y/aGDWlctSrnLl/Gz8+P7du34+HhYcGKhRACFAY5WGkUHR1t0kNkCoUCHx8fIiIi5JiwmUjPzU+hUODl5cPEiQkMGJBEqZhQinftijIxkfutW1PnzBnuRkbSoEEDNm3aZLx0hHh+8jk3P+m5+T1Lz9Vqda4HN1vdoS4hRMEzZgwsWlSM/v09iPN9gQcrV2JQqfDcv5/jLVrg6urK77//zqhRo9DpdJYuVwhRhEnwEULk2Zgx4OGh49w5W0aMcCepSXM08+cD4LtpE0d69cLOzo6QkBCmTp0q/2MWQliMBB8hRJ5Vrgzr1j3A3t7AwYP2TJ3qSnLPXsRPmADAi6tXs2nsWBQKBevXr2fJkiUWrlgIUVRJ8BFCmMRLL2WwbNkDFAoDX3/txBdfOJM4ZgxJffuSNHQoDUeOZNasWQDMmzePzZs3W7hiIURRJMFHCGEybdumMnNmPACffurCjp2OxAUGEj99OiiVDBo0iFGjRgEwceJEDh06ZMlyhRBFkAQfIYRJDRmSREBAIgBjx7rx60n7Ry+mpfFpRgaD2rdHp9MxbNgwTp8+bZlChRBFkgQfIYTJTZsWT7t2KaSnKxgyxIMrVzIvGeb24YcUW7GCL8PDad2sGSkpKfTv35/r169buGIhRFEhwUcIYXJKJSxZ8oCXXkonLk5Jv34e3LunJGHkSHTu7tidOUOwjQ31atXi/v379OvXj5iYGEuXLYQoAiT4CCHyhYMDBAXFUr68lvBwFf37exDvU4XYdevQ29vj/NNP/FCpEuXKluXGjRv079+fpKQkS5cthCjkJPgIIfKNh4eeDRvu4+Gh4/p1FZcvq8h46SUefPklBqUSz507Ofr663h4eHDmzBmGDx8uNxgWQuQrCT5CiHxVoYKOdeti2bYthnr1MkNN2ltvETd3LgDl1q3jcJ8+ODg4cPjwYSZOnCgXOBRC5BsJPkKIfFe/fgYvvKA1PtdoFCT7+ZEwbhx6FxdKvf46y5cvx8bGhq1btzJv3jwLViuEKMwk+AghzOr339U0a1aCzZsdSBg3jnsHD5LeuDFvvPEGc//ZC7RkyRLWrVtn4UqFEIWRBB8hhFkdPmzPgwc2fPutIzq9An2pUsbX+tevz7xBgwCYMmUK33//vaXKFEIUUhJ8hBBmNWFCAjNnxrFxYyw2No+mq//6i+JduzL2wAFGdu2KwWBg1KhRnDp1ynLFCiEKHQk+QgizUihg6NAkHB0fDWBOTFSg8/VF7+mJ6s4d/nfxIl1atiQ1NZWBAwdy+fJlC1YshChMJPgIISzGYIDPPy/Gm296Eaktzv2NG9F5eWF74QIbU1JoVLcuGo0GPz8/IiIiLF2uEKIQkOAjhLCYhAQF27Y5cOuWCn9/DzTuvtzfsAG9kxMOJ04Q4uNDpQoVuHv3Lv7+/sTHx1u6ZCFEASfBRwhhMS4uBr755j7Fi+sIDbUlIMCD5Kq1eLBqFQaVCrfvv+do48aUKFGCCxcuMGTIENLS0ixdthCiAJPgI4SwqPLldaxfH4ujo56jR+0YN86NlGbN0SxcCIDHjRtsWL0aZ2dnjh8/zpgxY9Dr9RauWghRUEnwEUJY3IsvZrBy5QNUKgPbtzsyZ44LKd26cX/tWu5v2MAL9euzcuVK1Go1u3bt4pNPPrF0yUKIAkqCjxDCKrRokcbnn2sAWL7cma++ciLtrbfAzg6A5s2b89XUqQCsXLmSFStWWKpUIUQBJsFHCGE1evRIYfLkzAHMM2e6snOnfeYLej0uM2bQf/58lgUEAPDJJ5+wY8cOC1UqhCioJPgIIazKu+8mMmRIIgCjR7tz9KgtZGSgDg1FmZjIO7t2MalXLwDGjBnDsWPHLFmuEKKAkeAjhLAqCgXMmBFPhw4pZGQoGDrUg9ArTsSuXk1G9erYREUx+48/ePutt8jIyGDIkCH8/fffli5bCFFASPARQlgdpRIWL35A48ZpJCYq6d/fk0SVG/e//hqdjw/qa9dYExPDaw0bkpiYiL+/P+Hh4ZYuWwhRAEjwEUJYJXt7WL06lhdfTGfq1HicnAzoS5Xi/jffoHd1xf7PP9nt7MwL1aoRFRWFn58fsbGxli5bCGHlJPgIIayWq6uB3btj6NYtxThNW60asWvXYrCzw/nwYb4bMwYfHx+uXr3KoEGDSElJecoahRBFnQQfIYRVe/wO7pGRSsaPdyWuzis8WLaMB198QbFOnfjmm29wdXXl999/Z+TIkeh0OssVLISwahJ8hBAFgsEAAwd6sGmTE1OmuJLarh2pnToBUK1aNYJWrsTOzo79+/czZcoUDAbDf6xRCFEUSfARQhQICgXMmhVPjRoZjBmTkOU15Z07tJ82jb0DBqBQKPj6669ZsmSJhSoVQlgzlaULyElISAi7d+9Go9Hg6+vL4MGDqVy5co7z/vjjj/z888/cvn0bgIoVK/L2228/cX4hRMH18svpHDgQjfJf/2Vz2rgR9cWLvH7tGl8PGEC/oCDmzZuHt7c3vXv3tkyxQgirZHV7fI4fP8769evp0aMHgYGB+Pr6MmfOHOLi4nKc//z58zRt2pTp06cze/ZsPD09mT17tpzdIUQh9XjoOXDAjmXLnEkYN46UDh1QZGTw9tatBPbsCcDEiRM5ePCghSoVQlgjqws+e/bsoVWrVrRs2ZIyZcoQEBCAra0thw8fznH+999/n9atW1O+fHlKly7NO++8g8Fg4Ny5c2auXAhhTlevqhg61IPPPnNh3YZiPFiyhLRmzVAmJTH+0CFGtW6NTqdj+PDhnD592tLlCiGshFUd6tJqtYSFhdGlSxfjNKVSSe3atbl8+fIzrSMtLQ2tVouzs/MT58nIyCAjI8P4XKFQ4ODgYHxsKg/XZcp1iqeTnpufpXpepYqO999PZOHCYkyZ4oqHh4HOa9bg2b076nPnWPj339x95RW2nThB//792bVrFxUqVDBrjflFPufmJz03v/zquVUFn/j4ePR6PW5ublmmu7m5cffu3WdaxzfffIOHhwe1a9d+4jzbt28nODjY+LxChQoEBgbi5eX1XHX/F29v73xZr3gy6bn5WaLnn38OaWnwxRcK3nvPnQp73Hnrxx+haVNUV6+yqU4dGr/0En/88Qf+/v4cP36ckiVLmr3O/CKfc/OTnpufqXtuVcEnr3bs2MEvv/zCjBkzsLW1feJ8Xbt2pUOHDsbnD9NkdHQ0Wq3WZPUoFAq8vb2JjIyUU2vNRHpufpbu+UcfQXi4G7t2OdC1q57Nm5U0/PprXD/8EM3s2ayxsaFjx46EhYXRunVrgoODcXJyMnudpmTpnhdF0nPze5aeq1SqXO+0sKrg4+LiglKpRKPRZJmu0Wiy7QX6t127drFjxw4+/vhjfH19nzqvWq1GrVbn+Fp+fKANBoP8QzEz6bn5WarnD+/rFRen4Kef7OnXz4OtWyvywsaNABQnc09w586dOXPmDAEBAQQFBT3xO6Agkc+5+UnPzc/UPbeqwc0qlYqKFSsSGhpqnKbX6wkNDaVq1apPXG7nzp189913TJ48mUqVKpmjVCGEFbG1hVWrHtCgQTpxcUreftuTq1cfXfL5hTNn+KNxYxzs7Tly5AgTJ06UX15CFFFWFXwAOnTowMGDBzly5Ajh4eGsWrWKtLQ0WrRoAcCyZcvY+M//5CDz8NbmzZsZMWIEJUqUQKPRoNFoSE1NtdA7EEJYgqOjgfXr71OrVjr379vQp09xbt+2webmTdxGj6b83r2ceOstbGxs2Lp1K4GBgZYuWQhhAVZ1qAugSZMmxMfHs2XLFjQaDeXLl2fy5MnGQ10xMTFZRnj/8MMPaLVa/ve//2VZT48ePejVq5c5SxdCWJirq4GNG2Pp3t2TK1fU9O7tybZtBuw+/RS3Dz6gzq5dHOjShVY7drB06VK8vb0ZOHCgpcsWQpiRwiD7e42io6OznOaeVwqFAh8fHyIiImS3uplIz83PGnseGamkW7fi3LypomrVDHbujKHU6v/hsmABBoWCb9q3x3/PHhQKBStXrqRt27aWLjlXrLHnhZ303PyepedqtTrXg5ut7lCXEELklbe3ns2b7+PtraNlyzSKFTOQOGYMSQMHojAY8Nu/n89efx2DwcDIkSP57bffLF2yEMJMJPgIIQqlsmV1HDgQzccfx6NQAAoFcZ98QkrHjigyMph04gQDmjUjLS2NQYMGPfNFUoUQBZsEHyFEoeXpqefhkMDUVFi4xJWIeZm3tkgaPJiP167lpZdeQqPR4OfnR0REhGULFkLkO6sb3CyEEPlh1Ch3vv/egcuX1Xz59ddga4sDEBQURJcuXbh27Rr+/v5s27YNFxcXS5crhMgnssdHCFEkDBqUhIeHjn79kjIv/PMPDwcHjr30EjWKF+fChQsMHjyYtLQ0C1YqhMhPEnyEEEVC06bpnDhxj6ZN07NMd5s0Ce8tWzjh5kYpJyd+/fVXxowZg16vt1ClQoj8JMFHCFFkODk9OiX20iUVM2a4EPfeaHSenrhcvcpf5cpRTKVi165dfPLJJxasVAiRXyT4CCGKnORkBW+/7cnKlc6MW/ESMV9/g75YMUpcuMCZ6tVRAStXrmT58uWWLlUIYWISfIQQRY6jo4Hp0+NQKg1s2uTE+G+aEbMmCIO9PRVCQ/m9Vi0UwKxZs9ixY4elyxVCmJAEHyFEkdS5cypLlmhQKg18840TE3e35v7yFRhUKl4MDSWkVi0AxowZw9GjRy1crRDCVCT4CCGKrK5dU1i4UINCYWD9eicm/dSNB4sWo3N3p+6cOXTs2JGMjAyGDh3K33//belyhRAmIMFHCFGk9eiRwuefZ4aftWudmfRXf6J+OY62QQMWL15M48aNSUxMxN/fn9u3b1u6XCFEHknwEUIUeb17pzB/fhwAq1c788niMhgMYGdnxzfjxjGpVCmioqLw8/MjNjbWwtUKIfJCgo8QQgBvv51MYKAGgBUrnJk+3QVl2HV8Bw1ibmQkQ9zduXbtGgMHDiQlJcWyxQohnpsEHyGE+Ee/fsnMnasBMvf8fLDyRZI6dEKh1/NVYiKdnJz4448/GDlyJFqt1rLFCiGeiwQfIYR4jL9/Mp9//uCfAc/OLKy2jJR27VBmZPCdTkdztZr9+/czZcoUDAbDf69QCGFVJPgIIcS/9OmTwuLFGl59NQ0//1QeLFtG6muvoUpN5YBKRX1gw4YNLF682NKlCiFySYKPEELkoHv3FL799j4ODgawsyN25SqSX34Fu5QUjjo6UgWYP38+mzZtsnSpQohckOAjhBBPoFA8ehy4rCS93A+QWKcByoYN6TB8OACTJk3i4MGDFqpQCJFbKksXIIQQ1i4szIbly51JT1fw3f9t4822Wsbb2nL7/n2Cg4MZPnw4W7dupV69epYuVQjxH2SPjxBC/IeKFXWsXRvLjBlxvNnFBuzsUCgULJg/n2WVKuGWkkL//v25fv26pUsVQvwHCT5CCPEMWrRIIyAgyfg8NlaJYvE6Rl67xlE7O5Sxsfj5+REdHW3BKoUQ/0WCjxBC5FJCggI/Pw/a75vA3RK1qZSWxhG1mribN+nfvz9JSUn/vRIhhEVI8BFCiFyKjLTh7l0bQi878artCa571OWFjAx+sLEh7OxZhg0bRkZGhqXLFELkQIKPEELkUpUqWrZvj6F0aS1h4Y40s/mV88VepoFOxz6lkpNHjjBhwgS5wKEQVkiCjxBCPIeKFXXs2BFD5coZ3I22p7niGL87NuNVvZ5tQHBwMIGBgZYuUwjxLxJ8hBDiOZUqpWfbtvvUqZPO/XhbXucQRxxao+jdG4ClS5cSFBRk2SKFEFlI8BFCiDzw9NSzZct9XnkljYRkNW31+4hs+39MmDABgKlTp7Jv3z4LVymEeEiCjxBC5FGxYgY2bLhPq1appKYpGTLEg1KlJvNu9+7MNhgYPXIkv/32m6XLFEIgwUcIIUzCwQFWr46lR49kdDoF48a5U+rnJnwELE9PZ/CAAVy+fNnSZQpR5EnwEUIIE1GrYdEiDe++mwDA1OhxzFdMwh/4PD6efn37EhERYdkihSjirPJeXSEhIezevRuNRoOvry+DBw+mcuXKOc57+/ZtNm/ezPXr14mOjmbAgAG0b9/ezBULIUQmhQKmTEnAy0vPqlVOtB1ZC8NUJYP0ejIiIujfrx/B27bh6upq6VKFKJKsbo/P8ePHWb9+PT169CAwMBBfX1/mzJlDXFxcjvOnpaVRsmRJ+vbti5ubm3mLFUKIJxg2LInDh6PxHNAKzZIlGJRKhqBk5MWLDBk8mLS0NEuXKESRZHXBZ8+ePbRq1YqWLVtSpkwZAgICsLW15fDhwznOX7lyZfz9/WnatClqtdrM1QohxJM5OWVewDCla1eC+u+mMb/SAy9eP3GC0aNHo9frLVyhEEWPVR3q0mq1hIWF0aVLF+M0pVJJ7dq1TTooMCMjI8vl5BUKBQ4ODsbHpvJwXaZcp3g66bn5Sc//W2oqTNnfmghsWOQ8ic2pHxG2ezfe3t7MmDEj172Tnpuf9Nz88qvnVhV84uPj0ev12Q5Zubm5cffuXZNtZ/v27QQHBxufV6hQgcDAQLy8vEy2jcd5e3vny3rFk0nPzU96/nRHjsAXX8CsOaOotbMUfn5+rFy5kmrVqjF+/PjnWqf03Pyk5+Zn6p5bVfAxl65du9KhQwfj84dpMjo6Gq1Wa7LtKBQKvL29iYyMlHv2mIn03Pyk58+mWDH44AO4Fw8tW7Zk8uTp/PrpbhImTOD/7O3p1q3bM69Lem5+0nPze5aeq1SqXO+0sKrg4+LiglKpRKPRZJmu0WhMOnBZrVY/cTxQfnygDQaD/EMxM+m5+UnPn51eD5d+H8EvfMQ7+HPh/ff5uXhxXn311VytR3puftJz8zN1z61qcLNKpaJixYqEhoYap+n1ekJDQ6lataoFKxNCCNPRaiFF5UI6dvRhE876MZz375/lu08IkT+sKvgAdOjQgYMHD3LkyBHCw8NZtWoVaWlptGjRAoBly5axceNG4/xarZYbN25w48YNtFotsbGx3Lhxg8jISAu9AyGEeDpbW1i+/AGDBiViQMkEPud+xucc69aL27dvW7o8IQo1qzrUBdCkSRPi4+PZsmULGo2G8uXLM3nyZOOhrpiYmCwjvGNjY5k0aZLx+e7du9m9ezc1a9ZkxowZZq5eCCGejY0NzJoVT7lyOj6ZWYz/YxQdknwxtOtF75/24uHhYekShSiUFAY5WGkUHR2d5TT3vFIoFPj4+BARESHHhM1Eem5+0vO827vXnvdHOJOqs6U+f1Cz4mhmHFhnvMzGv0nPzU96bn7P0nO1Wp3rwc1Wd6hLCCGKmvbtU9myTYOHXTx/8hIbwr6hf/9Ak55lKoTIJMFHCCGswEsNtOw+mEypUgmAL8ePz2PIoK9l74IQJibBRwghrET5Cnr270+kcuVIwI3DhyYR+NrG/1xOCPHsJPgIIYQV8fAwsH+/nma+x9Gh5ptrgznT932QPT9CmIQEHyGEsDL29rDpl/L0K/clW+lJ25++4/4770j4EcIEJPgIIYQVUihg7vFO3H4xFoDae/bwQ69VnD1jdVchEaJAkeAjhBBWSqFQ0GLnThZXqcJlqvD+8TF07uDOqZM2li5NiAJLgo8QQlgxtVpNu717WetjQwsOU1//GxV1JyxdlhAFlgQfIYSwck5OTvT5fhPengE40I6+M6cSHx9PSgocP25r6fKEKFAk+AghRAHg5eWF/47tnPVUc+7cObp27crn4xPo2bM448a5odEo/nslQggJPkIIUVBUrFiRDRs24OTkxNVDh2DX9yjQs3mzIy1alOC77xzQ6y1dpRDWTYKPEEIUIHXq1GHNmjXYqNUM0o/hKK9SnQtER9vw/vvutGtXnGPH5PCXEE8iwUcIIQqYV199lcBvvuE14BbH+Yt6fMpHFFMlc+6cLb17F6dXL0+OHLGTS/8I8S8SfIQQogDq2bMnH3/2GX2BKaQxyWY+17S+jHRcjcpGzy+/2OHn58lbb3mxaZMDSUkyBkgIkOAjhBAF1oABAxg/fjz/AxrpdKhK2rAseShnek5m6NBEHB31nD+vZvx4d+rWLcmKFU6WLlkIi5PgI4QQBdjYsWMZMGAAfwAV7t8ntFs33GcOZubMeH77LYopw29Q0TeN5GQlJUo8GvkcHm7Drl32xMbKniBRtEjwEUKIAkyhUDBr1iw6duxInFZLo5AQ/rp6FQB3Nz0f/9mLyw9KcqDXEtrVv2lcbscOB0aM8GDSJDfjNL0erl61kTPDRKEmN30RQogCzsbGhsWLF/PgwQOOHTuGv78/27dvp6qHB8r4eGzi43hzy2gM29WkdOhAcr9+FHNuSdWqGTRunG5cT1iYDa+9VhInJz01a2ZQs6aWihW1VKiQ+WfZsjpU8ltDFHDyERZCiELAzs6O1atX07NnT86ePUvv3r0JDg5G/8MP2O/Zg/OaNdj+/juO27fjuH07k7y9eeejj0ju3sO4jjt3VNjb60lKUnLqlB2nTtll2YZKZaBMGR0+Ppk/3t46vL31+PklYW+fOU9ysgJbW4MEJGG15KMphBCFhLOzMxs2bKBHjx5cvnyZnj17EhwcTPnOnUnt3Bn1uXM4BgXhsHcvNpGRGBwdUfwzxEd16RJtr//Kte0NuayqybkLDly6pOL69Uc/qakKbtxQcePGo18dCoWB/v2TjM/HjXNj924HPv1Uw4AByQCcP69i8eJiODkZcHbW//OnASenrI8z/zTg6Jj5p5ubHqUMyBAmJsFHCCEKEU9PT7Zs2ULPnj25cuWKMfz4+vqSUbs2cZ9/TtycOdj/9BOpzZsbl3PYs4di//sfboC3vT2Na9cm48UXyXijJtoqVUir8QIRsQ7cvKkiMtKGqCglERE2JCcrUKsfbV+jyUwqzs6PLiAUHm7Dnj0OuX4vly5FGNcza5YLBw7YM3JkAn36pABw65YN//tfZqByctLj4GD45/HDAKU3hqhHf+pxczNIoCrCJPgIIUQh4+XlZQw/V69epWfPnmzduhVfX9/MGeztSW3dOssy2vLlSWvWDPWZMygTErA7dQq7U6eMr0f9/DOlK1WidOl07H78EdW9q+galUXr64suriwGV1cANmy4j0ajxNHxUfCpXl3L7NkaEhOVJCYqSE5WGB8nJWU+zvwz87XkZCWpqQocHB6t4+5dG8LCVCQlPUoskZE2bN3qmOv+/PZbJKVLZ47gXrjQmW3bHBkwIImhQzP3XEVHK/n0UxfjXihXVz2urgZ8fUGns8XFRY+LS+a0YsX02MqFsgsUCT5CCFEIlShRgi1bttCjRw/CwsLo2rUrGzdupHr16jnOn9K9Oyndu4NejyosDPXp06jPnkV96RI2N26gexiaAIedO3Hcti3L8npXV7Rly6IrVw71vHkYHN0BUMTGUs7LgUGDdLmqX6cDG5tHzz/4IJ5Bg5IoU0ZrnFa6tI7Jk+NJTs4MUJmhKfNxUpLS+PzR65mBysnpUaCKiMgMVAkJj07rj4lRsmXLkwKVZ7YpDg6ZIWjz5vtUrpxZ3/799vz0kx3NmqXRrl0qAOnpcOKEHa6uD4OTHhcXGQ9lbgqDQS5o/lB0dDQZGRkmW59CocDHx4eIiAikzeYhPTc/6bn55abn9+7do2/fvly4cAE3Nzc2bNhAvXr18rR9x2++wfaXX1DduoXN7dvYxMQYXzMoFESEhfFwN4jb++/j+N136EqUQFe2LNpy5dD9E5C0ZcqQ/sorZDlWls90OlAqMY5tun3bhrt3bShVSkfZspnh7N49JVu3Ohr3QsXFKYmPV5Kaak9MTAZxcQri45UkJGQ9XnbyZBRlymSuY/ZsF7780plhwxKZPj0egDt3lDRs6J2tJmfnR3uQMvcuZQaih4/ffjsZH5/MPVRRUUpiY5WULKnHw6NwX3fgWT7narUaLy+vXK1XcqYQQhRiJUqUYOvWrfTv358///yT3r17s2bNGpo1a/bc60z28yPZz8/4XJGUlBmAbt3KDEGPHftRRkcDYHPvHjb37mH7xx/G1wwKBRHXrhmfO//f/6G6ds2450hXtizasmXRlyyJqQblPL4XCaBs2UeB56ESJfSMHJmYZdqjX8Ixxl/COh0kJGSGoPh4BSVLPlpP8+Zp2NkZaNDg0eUCtFoFNWpkoNFkzv/wsF3mYT8ld+/mXHPr1qnG4LN5syOBgS706ZPE55/HAZCUpKB1a69socnN7dFjF5fMsU0P9zQ9DFn/7kdRIMFHCCEKOXd3dzZt2sTgwYM5duwY/fv358svv6T1v8b5PC+DkxPa6tXR5nAYLXbjRhQPHqB6GIzCw417ihSpqWD36JR5u4MHsTt5Mvv6bW3RlitH9KFDxuSi/vNPMBjQlSuHvnjxR7twzMjGBtzcDLi5ZT+M17x5Gs2bp2WZ5uur48cfo43PMzIgIUGJRpMZnuLilMTFKf75MzMcaTSZe3ce36aHhy7L3h6NRsH167n/db51awxNmmQGs9277QkKcqJlyzRGjcoMfQYDrFnj9M+Ypswz8ooVyxxIXqxY5tl4Dg4GS7Q+TyT4CCFEEeDk5MS6desYOXIkISEhBAQE8L///Y8ePXr898J5oVBg8PAgw8ODjBdffOqsScOGkfbqq49C0u3b2Ny9iyI9HWVSUpbdNS6ffordr78CoLe3zzx89s+Ptlw5koYPt0gYyg21Gjw89Hh4ADzbGKiRIxOz7Y3y9NTz3XcxxMdnD02ZgSp7sEpKUuLi8ig8Xb+u4sQJO8qXfzSGKjlZwbRprk+tR6nMDECPQpGB6dPjaNAgc9jImTNq9u+35733EnDI/Yl9+UKCjxBCFBH29vasWLGC8ePHExwczOjRowkPD2f06NEorCAkpLZpA23aZJ2YkYFNZCRKjSbLZL2nJzofH5SRkShTU1FeuYL6yhUAdN7eJL3zjnFezz59sLl5E523N3pvb3SP/5QuTUaDBvn91vKVvT288kr6f8/4GK0269HD9u1TqFBBS6lSuizzdOqUYgxUSUkKEhIyz8ZLTFRgMCjQ6xXEx2cGq4fS0x99lk6fVrN4cTHeey/h+d+giUnwEUKIIkSlUrFw4UI8PT1ZsWIF8+fP5/r168ybNw87O7v/XoG5qdXGPTmPe7BiReaD9HRs7tzB5vZtVOHh2Ny6heFf55fb3LyJ6tYtVLduZVu9zseHqN9/Nz53e+89bO7dQ+flhd7TE33x4ui8vDAULw61aoGPj+nfowX8+0yySpV0VKqUda+Tq6uBL798kOPyBgP/XJZAQULCo8sTJCYqqV790V6jqlW1DBqUaLyytzWQ4COEEEWMUqlk2rRplC9fnqlTpxIcHEx4eDgrV67EI/O4S8Fha4uuQgV0FSrwpH0e97duxebuXZQREdhERhp/lJGRmeODHl/dH3+gunkz5xX5+sI/h9cA3IcPxyYyEl3x4ug9PLL86EqWJP2xC0QWNgoFxgtFliwJTzpU17hxepb7wVkDCT5CCFFE9e/fH19fX4YPH86JEyfo0KEDa9aseeK1fgoqXZky6MqUeaZ5NfPnZ4aimBiU9+9jEx2d+TgmBtvHrmUEoD57Nse9SADacuW491hI8uzeHdXNm4+Ckafno8c+PqT06WOcVxEXh8HR0ayn+RclVhl8QkJC2L17NxqNBl9fXwYPHkzlypWfOP+vv/7K5s2biY6OxtvbGz8/P+rXr2/GioUQomB67bXX2LFjB4MGDeLmzZt07NiRJUuW0LZtW0uXZhHpTZvmOP3h6exERBinPVi6FJt79zKDUWzso5/797PtSbK5cwebiAhsHlv+IW25clmCj+fbb2N75gx6V9ese5E8PdGVLk3iuHHGeVVXr2JQqdB7eGAoVszqB3RbA6sLPsePH2f9+vUEBARQpUoV9u7dy5w5c1i0aBGurtlHl1+6dInFixfTt29f6tevz7Fjx5g/fz6BgYGUK1fOAu9ACCEKlurVq7N3716GDx/O8ePHeeeddzh27Bhl/zWuRmSV0aABz3rJ2/vBwSjv38/8eSwgKR88wODikmVe5YPMcTXKuDiUcXFw/brxNa2vb5bg4/b++9ieOQOAQa3OEpS0ZcsS9/nnxnltf/0VtNrMEFWxIgZrOc3KzKwu+OzZs4dWrVrRsmVLAAICAvjzzz85fPgwXbp0yTb/vn37qFu3Lp06dQKgT58+nDt3jpCQEIYNG2bO0oUQosDy8PBg48aNzJo1i7Jly0roMbHcHG67d+xYZuh5PCT9E5QM/xolbLCzQ+/oiDI5GUVGBjZRUdhERQFgU758lnldPvkE27NnAYjZupX0Jk3y/sYKIKsKPlqtlrCwsCwBR6lUUrt2bS5fvpzjMpcvX6ZDhw5Zpr344ouceuzmev+WkZGR5dYUCoUCh3+SrylP6Xy4Lms4TbSokJ6bn/Tc/PKr57a2tsyaNcuk6ywszPo5V6kweHpmHtrKqZbHHsfu2JH5ICXl0V6kf4ISSmWWenUVKpCRlpYZoIoXt/p/s/nVc6sKPvHx8ej1etzc3LJMd3Nz4+4TruWt0WiyHQJzdXVF869rPjxu+/btBAcHG59XqFCBwMDAXN/v41l5e2e/N4vIX9Jz85Oem5/03PysuucVKz799YchCcif33b5w9Q9t6rgYy5du3bNspfoYZqMjo5Gq9U+abFcUygUeHt7ExkZKTdvNBPpuflJz81Pem5+0nPze5aeq1Sqgn2TUhcXF5RKZba9NRqNJtteoIfc3NyIi4vLMi0uLu6J80Pm3VzVTzhNMD8+0AaDQf6hmJn03Pyk5+YnPTc/6bn5mbrnprndrYmoVCoqVqxIaGiocZperyc0NJSqVavmuEzVqlU5d+5clmlnz56lSpUq+VqrEEIIIQoeqwo+AB06dODgwYMcOXKE8PBwVq1aRVpaGi1atABg2bJlbNy40Th/u3btOHPmDLt37+bOnTts2bKFa9eu0ebf93sRQgghRJFnVYe6AJo0aUJ8fDxbtmxBo9FQvnx5Jk+ebDx0FRMTk2WEd7Vq1Xj//ffZtGkT3377LT4+PkycOFGu4SOEEEKIbBQGOVhpFB0dneU097x6eKXPiIgIOSZsJtJz85Oem5/03Pyk5+b3LD1Xq9W5HtxsdYe6hBBCCCHyiwQfIYQQQhQZEnyEEEIIUWRI8BFCCCFEkSHBRwghhBBFhgQfIYQQQhQZEnyEEEIIUWRI8BFCCCFEkWF1V262JJUqf9qRX+sVTyY9Nz/puflJz81Pem5+T+v58/x9yJWbhRBCCFFkyKGufJSSksIHH3xASkqKpUspMqTn5ic9Nz/puflJz80vv3ouwScfGQwGrl+/Lvd1MSPpuflJz81Pem5+0nPzy6+eS/ARQgghRJEhwUcIIYQQRYYEn3ykVqvp0aMHarXa0qUUGdJz85Oem5/03Pyk5+aXXz2Xs7qEEEIIUWTIHh8hhBBCFBkSfIQQQghRZEjwEUIIIUSRIcFHCCGEEEWG3HQkj0JCQti9ezcajQZfX18GDx5M5cqVnzj/r7/+yubNm4mOjsbb2xs/Pz/q169vxooLvtz0/MiRI3zxxRdZpqnVar755htzlFoonD9/nl27dnH9+nUePHjAhAkTaNiw4VOX+fvvv1m/fj23b9/G09OT7t2706JFC/MUXAjktud///03M2fOzDb9q6++ws3NLR8rLRy2b9/Ob7/9xp07d7C1taVq1ar069ePUqVKPXU5+T5/fs/Tc1N9n0vwyYPjx4+zfv16AgICqFKlCnv37mXOnDksWrQIV1fXbPNfunSJxYsX07dvX+rXr8+xY8eYP38+gYGBlCtXzgLvoODJbc8BHBwcWLx4sZkrLTzS0tIoX748r7/+OgsWLPjP+e/du8fcuXN58803ee+99wgNDWX58uW4ublRt27d/C+4EMhtzx9atGgRjo6OxucuLi75UV6hc/78eVq3bk2lSpXQ6XR8++23zJ49m//973/Y29vnuIx8n+fN8/QcTPN9LsEnD/bs2UOrVq1o2bIlAAEBAfz5558cPnyYLl26ZJt/37591K1bl06dOgHQp08fzp07R0hICMOGDTNn6QVWbnsOoFAo5H+9eVCvXj3q1av3zPMfOHCAEiVK0L9/fwDKlCnDxYsX2bt3rwSfZ5Tbnj/k6uqKk5NTPlRUuE2ZMiXL85EjRzJ06FDCwsKoWbNmjsvI93nePE/PwTTf5xJ8npNWqyUsLCzLL1ulUknt2rW5fPlyjstcvnyZDh06ZJn24osvcurUqfwstdB4np4DpKam8u6772IwGKhQoQJvv/02ZcuWNUPFRdOVK1eoXbt2lmkvvvgiQUFBlimoCJk0aRIZGRmULVuWnj17Ur16dUuXVCAlJycD4Ozs/MR55PvctJ6l52Ca73MZ3Pyc4uPj0ev12ZKnm5sbGo0mx2U0Gk22wzGurq5PnF9k9Tw9L1WqFCNGjGDSpEm899576PV6pk6dyv379/O/4CLqSZ/zlJQU0tPTLVRV4ebu7k5AQADjx49n/PjxeHp6MnPmTMLCwixdWoGj1+sJCgqiWrVqTz1kJd/npvOsPTfV97ns8RGFWtWqValatWqW52PHjuWHH36gT58+FqxMCNMpVapUlkGh1apVIyoqir179/Lee+9ZsLKCZ/Xq1dy+fZtPPvnE0qUUGc/ac1N9n8sen+fk4uKCUqnMlu41Gs0Tjz+6ubkRFxeXZVpcXJyMP3lGz9Pzf1OpVFSoUIHIyEjTFyiAJ3/OHRwcsLW1tVBVRU/lypXlc55Lq1ev5s8//2T69Ol4eno+dV75PjeN3PT83573+1yCz3NSqVRUrFiR0NBQ4zS9Xk9oaGiWRPq4qlWrcu7cuSzTzp49S5UqVfK11sLieXr+b3q9nlu3buHu7p5fZRZ5VapUyfFz/qx/R8I0bty4IZ/zZ2QwGFi9ejW//fYb06ZNo0SJEv+5jHyf583z9Pzfnvf7XIJPHnTo0IGDBw9y5MgRwsPDWbVqFWlpacbrlSxbtoyNGzca52/Xrh1nzpxh9+7d3Llzhy1btnDt2jXatGljoXdQ8OS258HBwZw5c4aoqCjCwsJYsmQJ0dHRtGrVykLvoOBJTU3lxo0b3LhxA8g8Xf3GjRvExMQAsHHjRpYtW2ac/6233uLevXts2LCBO3fusH//fn799Vfat29vifILpNz2fO/evZw6dYrIyEhu3bpFUFAQoaGhtG7d2hLlFzirV6/m6NGjjB49GgcHBzQaDRqNJsuYNPk+N63n6bmpvs9ljE8eNGnShPj4eLZs2YJGo6F8+fJMnjzZuKszJiYGhUJhnL9atWq8//77bNq0iW+//RYfHx8mTpwo13zIhdz2PDExkRUrVqDRaHBycqJixYrMnj2bMmXKWOgdFDzXrl3LcnG89evXA/Daa68xcuRIHjx4YPyFDFCiRAk+/PBD1q1bx759+/D09OSdd96RU9lzIbc912q1rF+/ntjYWOzs7PD19eXjjz+mVq1aZq+9IDpw4AAAM2bMyDL93XffNf6nSr7PTet5em6q73OFwWAw5Kl6IYQQQogCQg51CSGEEKLIkOAjhBBCiCJDgo8QQgghigwJPkIIIYQoMiT4CCGEEKLIkOAjhBBCiCJDgo8QQgghigwJPkIIqzNw4EC6dOli9u0GBQWhUChQKBSMGTPGOL18+fIsWrToqcs+XE7u1SSEdZMrNwshzOrxK7HmZPr06SxevBhLXVvVxcWFS5cu4eTklKvlIiIi2Lx5M9OnT8+nyoQQpiDBRwhhVhEREcbHmzdvZtq0aVy6dMk4zdnZGWdnZ0uUBmQGM29v71wv5+3tjauraz5UJIQwJTnUJYQwK29vb+OPq6urMWg8/HF2ds52qKtFixa89957jBkzBnd3d0qWLMnKlStJSkpi0KBBFCtWjMqVK/P9999n2VZoaCht27bF2dmZkiVL4u/vn+UeV7mRnJzM4MGDKVasGOXKleOrr77KSxuEEBYiwUcIUSCsW7eO4sWL89tvv/Hee+8xYsQIevbsSZMmTfjzzz9566238Pf3Jzk5GQCNRsPrr79OvXr1+P333wkJCSEqKopevXo91/Y///xzGjRowF9//cW7777LiBEjsuypEkIUDBJ8hBAFwosvvsjUqVOpUqUKH330Efb29hQvXpyAgACqVKnCtGnTuH//PmfPngVg2bJl1KtXj08//ZTq1atTr1491qxZw+HDh7l8+XKut9+uXTveffddKleuzAcffEDx4sU5fPiwqd+mECKfyRgfIUSBUKdOHeNjGxsbPD09qV27tnFayZIlAbh37x4AZ86c4fDhwzmOF7p27RpVq1Z97u0/PDz3cFtCiIJDgo8QokBQq9VZnisUiizTHp4tptfrAUhMTKRjx44EBgZmW5ePj49Jtv9wW0KIgkOCjxCiUKpfvz7fffcd5cuXR6WSrzohRCYZ4yOEKJRGjhxJbGwsb7/9NqdOneLatWvs37+fQYMGodPpLF2eEMJCJPgIIQqlUqVK8csvv6DT6XjrrbeoXbs2Y8aMwc3NDaVSvvqEKKoUBktdHlUIIaxMUFAQY8aMQaPRWGR5IUT+k//2CCHEY+Li4nB2duaDDz7I1XLOzs688847+VSVEMJUZI+PEEL8IyEhgaioKADc3NwoXrz4My979epVIPNU+woVKuRLfUKIvJPgI4QQQogiQw51CSGEEKLIkOAjhBBCiCJDgo8QQgghigwJPkIIIYQoMiT4CCGEEKLIkOAjhBBCiCJDgo8QQgghigwJPkIIIYQoMiT4CCGEEKLI+H8YNPo/1RmicQAAAABJRU5ErkJggg==", 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DJ3JMdc5rBr8JPB07dmThwoVHfX7ChAmVHvPMM8/UZFkBxwgNpX77JOq3/9ts/LOh/zXHO7oQj6eQ4mLIzYac1AKirc/xS9ZeStLTqfNTCLf9tpi4uk6uOud5cnNzyd6bx+q315NfGkmBK4pcTwx5nhic2HATQj7xwF+/pXwO4IHbit7moqIsyMpiBhfzJC9x+uYP+XblX124TfkdO0XEk0EUfxBJBvaQbMJDsimMLGZT02ji490kJkKPvF3E1wkhsn4MYfWTsTdsSGT9+sTGxxMTE+OTyYIi4l12u52tW7f65LX+PqRVHe3bt2fTpk288sorAIwfP778udWrVxMfH8+cOXPweDzk5eXhcDiOeq7qDGmFhoZyyimnsGbNGvr16weA2+1mzZo1jBo1qtJznMgx1TmvGfwm8EjtYBgQHn7wp069CKDFoR+4cABcWMkx/3jk8EfFeDxpFBUZ5OQY5GQbZOcYZGa6SNtfRO5Pf5Cd7qSoQXfWFSfiychgz/ZkWu3eRFJcNpd3vxyHw0FeRj4//Nr8yBdzHfopAbL+2lx2bd48hnIFkwH4jHN5jHuJ42tWxE4lNjaWuLg4ztt9FhFhbsKjnUTGe4isYyWmfjj2+onYGjYkvFmz8l6mqKgov5q/JBJMDMOo8tBSdXnjbunt2rVj2bJlrFu3jv/7v/+rEJzatWvHo48+yqRJk+jXrx+nn376MQNPdYe0xo4dy1133cUpp5xC165def311yksLOSaa/76jffNN9/k448/Lu+EOJFjTmSf/Px8duzYUf54165dpKSkEB8fX6EnqqYo8IjPGQbY7R7sdg8VfwEJgxFlk847lG+98dAP9Dn0Ay4XbN60H8feYrJ2F3BgVx7pe4vJy7awf7+T9JJo9pFAXl4oBfnhlBRG4CQUizWHApdBhMfD77TmU/pzOpCdPYns7Gx27drFVtZRSAT87cKASPKIJJMDZAGZwG4uJZM4I5teoe/SKGITxZGRZIU3YJ+1HdZkC3k9GhAdHU1MTAz1CguJSEwkvF49IhMTiY6OJioqSr1LIkGiQ4cOZGRk0KNHjwpXDAO0bNmSFStWsHLlSiZOnMigQYO44IILvF7DlVdeSWZmJlOmTCEtLY2OHTsye/bsCkNPmZmZ/PHHH1U65kT2+emnnxgyZEj547JpLEOGDOGFF17w+nv9O8MT7NP8OTjO6O1LHQ3DoH79+uzbty/or6TwlWO1uccDhYUGVquH0FCguJgdG4tY9z83UVG5tD+/GIfDQVaGg//e15mcwgjySiPJcUaT64k77mvP5xqu4eBvQ+8yiKt4l86s4Wd6le/Tnp8oxU4s2USTTQTZ2HEQRg6F1nzWxBtERDiJinJyYd4eEsKLSUpwEJVkwRIfjyU+HltSErZ69Qhr3JioqChiYmKIiooqv8zV1/Q5971gbvOcnBxiYmJMeW1v9PAcS2pqKnFxcYSHh7NkyRK+/PJL7rjjjqPO4QkGNpuNjIyMSv/ObTZb8FyWLlIVhgEREYf94xAWRvMzwmh+Bhw+hwjgon6HPyrA5SogJ8fA4bCQnW3B4bDgcBikpTlxfL+TzDQnlsad+MbjgYwMtu9sQfKuPcREFTDkwiHk5uaSm5vLD1+1pIDIygt0Aml/Pfzl0H9fZRwjeB2A1ZzHNSygHd/wBZeU7zuMfxNCJGGWbEJD8giz5RMaWkiYvQR3jJvNnZuQkGAhKSmUFgV5RERGElqnDmF162KvU4fomBj1NomYbNOmTTz++ONYLBbCw8N57rnnzC4p4CjwiBxHSAjEx3uIjy+bIHS4Rof+O6Z8y3WHfqAT8EL59t9+yyc3O5+c/cXkpZeQlVpE1oEiHOlOsopC2R+SSE6OQV6eBSO1mPxSOwVxsNmoQ1hREdsLkzngrEd9SwL169UnLy+P3NxcPmc0qTQ8uCaTGygFCgAHB4flytdlchNFLgk4eJSJjOZNXMDPNONp7iaUP5gX+QrR0dFER0dzmaM18RaD0Egn4dEu7PFgTYjBiIsjpE4dSlq2JCYmhubNm1NcXExcXBxxcXEKTSLV0Lt3b3r37n3E9mDt3akJCjwiPtK+fdlq3iGA/dDP0YRzcGXJxw79QK88g093HcBitKRd+4OLSLrdbl5+MI0D+/bhyPSQk2MhJ99KXmEYuSV2cpzR5LijcLlCAQt5xJJHLEXYyivJpDmzuI2W/EJ+/nPk5+eTmprKUhaznY4VqoojiyTSiSaDH0gH0oEvGEYGrUinMetJtG0mPzyc/IgoimPicCXEsOmUU8onWSbGxhKXlERCQgJ16tQhISFB6y+JSI1T4BGpJaKiPHToUPEWKBaLhQmTj7WwZRFQRFER5OZayM42yM210LDBY+yLvhcjJ4eI35zcvngTEWFF9L9lDXl5eeTk5PDsrW7ys/eTWxpJvjsKAAfxOIgHKi72Nu/Qf5/iAcaWpkBpKT/ktqTb/u9ow69s/rpz+b73MIYkItjLPizsw81+LHYHzlgrhQkJpHTpQp06dahbty5169Shbr161KlTh3r16lX7cmAREQUekSBwcCkBNwfn+B0clvMQgScigibJcH8fgIoTA8/5oWzfHJzOHHJyLGRm/vWTlXXwvyUlMezeuIfMA27qdu7Gtthbce7fz/ebG+PZaMEdbmP8qPFkZWWRlZXFkhV3s9XTocJrUQhRhbnUTd3L77/uAg7+/IN1hLMLF7+wjf2k2e0UJCZSWr8+niZNCOnUiYYNG5b/JCYmqrdIRCqlq7TQVVqBQm3ue8dqc5cLMjMtFBQYNG3619ynpyeGsmOzh7T9BvvTrex32Cl0HnuV7Kd4gAcOrZ+0jZbcxzM0ZyPP89fq7A8BsSEhZCUmUty4MSFt2xLfoQMtWrakWbNmNGzYMCDmFwXz5zyQr9KSI+kqLRGpFUJCoE6dI+/+/MCjJYc9KgEKyMszOHDAwr59IezdG8KePQf/u/d3J3t3GyRf1Jv9MSWUbNvGVylNWLxjEO1sbbjikh/Ys2cPe/bsYUnqh5S6omh1YBstD2yn2XfbaMoXRPAyKezhXJuNJk2a0Lx5c9q3bk3r9u1p27YtrVq1Mu2yfhHxHQUeETFdVJSHqCgXLVr8/Sq4Mqfg4hRCgHZ/hvD4p9lERjbkmmteBsDthjbN61DotLGFtkccHUk2paW/sn37r2zf/gtXrfyVdiwlg93MNwwO1K1LYbt2xHTrRsfOnTn11FOpW7cqN/0VEX+nwCMitUrjxi5Gj86vsM0w4H9rM9mxI4Q//rCyc2cIO7aHsG0z7PgznHxXLHD2oR+4+dBxMxjNQ543Yf9+0vc7+eN/+/ie/6MrHho0aMCpp57KqV260OXUUzn11FOJiory6XsVEe9R4BGRWs8woGFDFw0buujZs6TCcyUlsGOHlc2brWzdamPzZitbfjXYsSuUVmPPIn2/A37+mbm/9+MO91TOtH6I4bqCvXv3sn/vXh776CdSeYo3DIPUli2J7tOH0885hzPPPJPY2Fhz3rCIVJkCj4gEtNBQaNvWSdu2Tg5epn9QSQmEhPSnJOTgPY32/F8E9qkuegzpzNsPb+Lnn39my5K1nPn2czRgDz08azln21ectm0tJa/P5D1K2diyJXX69aNv375069YNm81m0rsUkePRVVroKq1AoTb3vUBrc6cTiosNIiMPvpe1X1gYdl1dnK6Kl7qHU0h3vsbgc1bzGfA1ydHhXNmtG20vv5y+559f5StITlSgtXlV6Cqt4KKrtEREaojVClbrXyGix7luNm3ez08/2fj221C+/dbGdxusZGbb+R+9gd7ARCAfV+4amq/+jJar57GWe9jcuhVJV1/NxVdeScOGDc15QyJSTit0iYgcg93u4ayzSrj11jxmzsxi4y9p/O9/B5g82cEVVxSSmOgCIknjYh5gMmP4iJuAF7Zu5a4nnuC3My/m1bPOYsbzz7Nz506T342YpU+fPkyZMqXS56ZOnUrHjh3JzMz0SS0zZ86ke/futGjRgv79+/PDDz949Zhp06bRsGFDHnnkEW+WfdIUeEREqsAwoFUrJ9ddV8DLL2fx00/7WbXqAP/+dzYXX5BPv94O0q8aTE5MDBHAP/mJOX9+xvTnPuScc87hwgsv5LXnn2fP7t1mvxXxoXbt2rF58+Yjtu/fv5+pU6dy7733kpCQUON1LFmyhIkTJ3L33XezfPlyOnTowLXXXkt6erpXjvnxxx+ZPXs27du3r8m3US0KPCIiJ8EwoF07J2PG5PPGW9lMnhOJ88UXyfv1V76f9T9SLQ3YbTSiUY9GhISE8Ouvv5L6XA7zui/mtVNH8/HkZ3FkZZn9NqSGtW/fnk2bNh2x/emnn6ZJkyaMGDHCJ3W8/vrrDB8+nGuuuYY2bdrw9NNPY7fbmT9//kkfk5+fz6233sozzzxDXFxcDb+TqtMcHhGRmmAY1D+/Fb/8doDffrNyxhmzyMzM5JNly3jjgUv5lc6QBnVf2s/ZL31C87rrOXNMI8656VqsutqrSoyCgqM+57FYDt5M7kT2NQw47Aa1Ffa1WjGcTjwREdWqsV27dvzxxx8UFRWVr+y9ceNGFi1axIIFC6p825OXXnqJqVOnHnOf1atXV5g/VlJSwsaNG7n11lvLt1ksFnr27Ml3331X6TmqcsxDDz3E+eefz7nnnstLL71UpffjCwo8IiI1KCrKwxlnHLy6JyEhgaHXjaC0MISVCzazbnMjDnjqsYTr4cD1hD1VxCmTPyP+gnzuvbcdHTokmlx97VC/deujPlfUty+Zb79d/rjeKadgKSysdN/is88mY9Gi8sd1u3cn5G/zavbu2VOtGjt06IDL5WLbtm106tQJgEcffZRLL72UHj16VHpMamoqkyZNYtq0aUc8N2LECC6//PJjvma9evUqPM7MzMTlcpGUlFRhe506ddi+fXul5zjRY5YsWUJKSgrLli07Zk1mUuAREfEhw4Drx7m4flw0JSXZfL8mk6XTdrLqu8b86WzGN+5LYQWsWOGmbuIWxvbazdB/dyQhMbguQQ80jRo1IiYmhs2bN9OpUyeWLFnCxo0b+eKLL456THJycqVhByA+Pp74+PiaKrdK9uzZwyOPPMK8efP8+r50CjwiIiYJDYWz+oZwVt+WTPLAL+s38Z/paaz8IpGCgg4cyGjHE++3Y/L7pXx86ww6/KMfhB37zvLBaN/WrUd9zmOpOFV1/8aNR9/XMCo8PvD11+V/tlqtOJ3OalZ4UNu2bdm8eTNFRUU8+eSTTJgwoXzIKT8/n3HjxpGamgrAww8/TMuWLRk3bhwff/zxEeeqzpBWQkICISEhR0w2TktLO+qaNidyzM8//0x6ejr9+vUrf97lcrF+/XpmzpzJjh07qjxkVxMUeERE/IBhQKezY3j57IOLrH3yyad8cP837Ei7mGwS6DvtFnJnx+J+/HFWtrmFxDoW6tU72s1Wg0tV5tVUe1+bDc9JLjxYNnH5lVdeAWD8+PHlz61evZr4+HjmzJmDx+MhLy8Ph8Nx1HNVZ0grNDSUU045hTVr1pSHE7fbzZo1axg1alSl5ziRY3r27MmqVasqHHf33XfTsmVLJkyY4BdhBxR4RET80sUXd+Tiizuy8csv+fb2x0g94KGBw0Hhbfcy1Hod2Z46vL8kg65dtfpvbdGuXTuWLVvGunXr+L//+z/sh02QbteuHY8++iiTJk2iX79+nH766ccMPNUd0ho7dix33XUXp5xyCl27duX111+nsLCQa665pnyfN998k48//piFCxee0DFRUVG0a9euwutEREQQHx9/xHYzKfCIiPixU3r1ouO3K5jx8ssUTZ7MNe5k2jt/ZpetJZ07eYCDwzAFBQYREZrn4886dOhARkYGPXr0oH///hWea9myJStWrGDlypVMnDiRQYMGccEFF3i9hiuvvJLMzEymTJlCWloaHTt2ZPbs2RWGtDIzM/njjz+qdExtoHtpoXtpBQq1ue+pzX1rw4YNPHjDDUzKzmZ++3N49qPZhIaGUlIC555bl3PPLeb++3NJTHSbXWqNCOR7aaWmphIXF0d4eDhLlizhyy+/5I477jjqHJ5g4O17aWnhQRGRWuLMM89k6qJFjIqOZtFvXzFp0iQANjz7PX/+aWXOnEh69qzLa69Fovtc1i6bNm3isssu48ILL2T69OmMGzfO7JICjnp4UA9PoFCb+57a3PcMw+C7774rn7D6v8ceo9fjj/O/OoO4LfZNUjZHAtCpUwnPPeegU6eTu7LInwRyD48cST08IiJBrn///gwfPhyAx95+G1fDhvROfYdvCzrx7EM7iYtzk5ISymWX1eGZZ6IpLja5YBE/oMAjIlIL/etf/yImJobPt29n7rhxOJs1I+zPndzx/mWs/nAHl15aiNNp8OKL0VxySR1++03XqEhwU+AREamF4uLiyud5TJ47l/T583HVqYPt119p+/BYXn85jVdfzSQpycXmzTb696/DW29FoJFHCVYKPCIitdSoUaMICwvj119/5YfMTDJnzsQdHk74558T89RT9O9fxKpVafTtW0RRkcFDD8Uxdmw8WVnG8U8uEmAUeEREaqm4uDguu+wyAObMmUPpqafiOHSXasPhALebpCQ3b72VyWOPZWOzefj4Yztz5kSaWLWIORR4RERqsaFDhwKwbNkynE4nRZddRtqHH5L93HNw6D5SFguMHZvP0qXpDB5cwM0355lZsogpFHhERGqx7t27ExcXh8Ph4PvvvwegtGvXv3bweCibuNO5cykvveTAemj+ckkJvPxypK7ikqCgwCMiUotZrVb69u0LwMqVKys8Z9m/n4RRo4h8881Kj3388RgmTYpl9OgETWaWgKfAIyJSy5Xdc+nvd6wO/+QTwj/9lOgnnyRk584jjuvbt5i4ODc33JCPoXnMEuAUeEREarmePXsCB29PkJ2dXb694LrrKD77bCyFhcTdfTe4K95jq0+fYtav38+FF/41ppWdreQjgUmBR0SklktMTKRZs2YA/PDDD389YbHgeP553BERhH39NfaFC484Njr6r7GsXbtCOPfcukydGqUhLi/r06cPU6ZMqfS5qVOn0rFjRzIzM31Sy8yZM+nevTstWrSgf//+FT8zlVi/fj0jR47ktNNOo2HDhixfvtwndXqbAo+ISAA47bTTAPjuu+8qbHc1aULuP/4BQMwTT2BkZR31HMuWhZOeHsLTT8dw111xlJTUXL3Bpl27dmzevPmI7fv372fq1Knce++9JCQk1HgdS5YsYeLEidx9990sX76cDh06cO2115Kenn7UYwoKCujQoQNPPPFEjddXkxR4REQCQLdu3YAjAw9A/pgxlLZtS0hmJjGTJx/1HOPH5/PEEw5CQjy8804Ew4YlkpmpIS5vaN++PZs2bTpi+9NPP02TJk0YMWKET+p4/fXXGT58ONdccw1t2rTh6aefxm63M3/+/KMe07dvX+6//34uueQSn9RYU3RzFRGRAHD66acD8NNPP+HxeDAOn4Vss5H9xBMkXXUVYatXY+Tn44msfPHBG24ooFkzFzffHM/69WEMGJDEnDmZNG7s8sXbqJaCgqqHstBQT/nl+U4nlJQYGIYHu73y81qt4HQaRERUb6yvXbt2/PHHHxQVFREeHg7Axo0bWbRoEQsWLCAkJKRK53vppZeYOnXqMfdZvXo1DRs2LH9cUlLCxo0bufXWW8u3WSwWevbsWWlQDjQKPCIiAaBVq1ZYLBYcDgdpaWnUrVu3wvMlZ59N5ssvU3zBBXgiIo55rt69i3n//XRGjEhg+3YbV16ZxNtvZ9Cxo7Mm30K1tW5dv8rHvPJKJpdfXgTAxx+Hc/PNCZx9djGLFmWU79O9e10yMysGkT179larxg4dOuByudi2bRudOnUC4NFHH+XSSy+lR48elR6TmprKpEmTmDZt2hHPjRgxgssvv/yYr1mvXr0KjzMzM3G5XCQlJVXYXqdOHbZv316Vt1MrKfCIiASA8PBwmjZtyo4dO9i8efMRgQeg6IorTvh87do5WbIknREjEtm0ycagQUnMmJFJz56a2FMdjRo1IiYmhs2bN9OpUyeWLFnCxo0b+eKLL456THJycqVhByA+Pp74+PiaKjcgKfCIiASItm3bsmPHDrZu3UqvXr2OvqPLhX3pUgovvRRCQ4+6W4MGbt57L50xYxJYty6M665L5MUXs7jyyqIaqL76tm7dV+VjQkP/Gpq65JIitm7dh2FUHK76+usD5X+2Wq04nSfXw9W2bVs2b95MUVERTz75JBMmTCgfcsrPz2fcuHGkpqYC8PDDD9OyZUvGjRvHxx9/fMS5qjOklZCQQEhIyBETlNPS0qhTp85JvbfaQIFHRCRAtG7dmuXLl1d6NdDhEkaOJPzzz7FMnEj+jTcec9/YWA+zZ2dwxx3xfPihnVtuSSA1NZubbsr3ZuknpbrzaspYrWC1HnmOw89rs0Fp6cm9TtnE5VdeeQWA8ePHlz+3evVq4uPjmTNnDh6Ph7y8PBwOx1HPVZ0hrdDQUE455RTWrFlDv379AHC73axZs4ZRo0ZV813VHrpKS0QkQLRp0waArVu3HnO/okNX20T/3/9hHLZQ4dGEh8PLL2cxZszBm47++9+xLFxoP85R8nft2rXjxx9/5D//+Q8PP/ww9sNmSLdr147169czadIkvvvuO6Kjo495rvj4eJo3b37MH6v1yD6NsWPHMnfuXBYuXMjWrVt54IEHKCws5Jprrinf58033+Tqq68uf5yfn09KSgopKSkA7Nq1i5SUFPbs2XOyTeJT6uEREQkQZYFny5Ytx9yv4JpriJw+HduWLURPnUrOv/513HNbLDBxYg7167tYsSKcyy8v9ErNwaRDhw5kZGTQo0cP+vfvX+G5li1bsmLFClauXMnEiRMZNGhQ+S1DvOnKK68kMzOTKVOmkJaWRseOHZk9e3aFIa3MzEz++OOP8sc//fQTQ4YMKX88ceJEAIYMGcILL7zg9RpriuHxaD3NtLQ0SktLvXpOwzCoX78++/btQ03sG2pz31Ob+96x2jw3N5d27doBsHnzZqKioo56nrCVK0kcORJPeDj7v/oKd3LyCdfgdFJ+SbfHA6Wlx5wK5DU5OTnExMTU/AtVwmazef3ficOlpqYSFxdHeHg4S5Ys4csvv+SOO+446hyeYGCz2cjIyKj079xms1V53pGGtEREAkR0dDRxcXEA7N69+5j7Fp9/PiWnn45RVETUUa4EOprDR0qeeSaa4cMTyc3VAoUnY9OmTVx22WVceOGFTJ8+nXHjxpldUsBR4BERCSCNGjUCjh94MAxy7r0XgMg5cwipxnyMvXstvPFGJOvWhbF6dViVj5e/9O7dm1WrVvHpp5+ydOlS2rRpQ+PGjYO2d6cmaA6PiEgAadSoESkpKccPPEBJz54Un302RnExRm5ulV+rQQM377yTwTffhJYv4ifirxR4REQCSFkPz59//nlC+2fOmIEnJgaM6g1JnXJKKaec8tfclsxMA4fDQosW/nsrCglOGtISEQkgjRs3Bk5gSOsQT2xstcPO3+XlGYwYkciAAUmkpOj3afEvCjwiIgHkhOfw/I2RnU30s88SchL3VCouNnA6ISMjhKuuSuLrr31w6ZbICVLgEREJIFUd0ioT++CDRL/wAtHHuV3BsSQmHpzT0717Mbm5FoYPT9BkZvEbCjwiIgGkQYMGAGRkZFBcXHzCx+WPHQuA/b33CNm1q9qvHxPjYc6cDM4/v4iiIgujRiWwfHl4tc/3d26322vnEv/m7bW9FHhERAJIXFwcNpsN4IibRB5LadeuFJ17LobLRdR//3tSNdjtMH16JpddVkhJicG4cfG8//7J34oiIiKC3NxchZ4gUVBQQFiY93oINatMRCSAWCwWkpKS2LdvH2lpaRXuln08ebffTvgXXxCxYAG5d95ZpdWX/y40FP773yzuvtvDu+9GcOutcRQWGgwbVlDtc1qtViIjI8nLy6v2OaorNDSUkpISn79usPJ4PMTHx+PxeLzW06PAIyISYOrVq8e+ffs4cOBAlY4rOessis88k7ANG4h69VVyHn30pOqwWuGFFxxERHh4++1I7rknjoICgzFjqn+ndavV6vPbS+gWKr5nGAbJycns27fPa+fUkJaISIApu8dQVQMPhkHe7bcDEPH221gyM0+6FosFnnoqm3HjDvbKPPJILFOnHv0eXyI1xa96eJYvX87SpUtxOBw0bdqU0aNH06pVq6Puv2zZMlasWEF6ejoxMTF0796d4cOHE+qLu9iJiPipunXrAgdvjFxVxb17U3LaaThbtz54V1AvMAx45JEcIiM9/N//RfP00zGEhXkYN676PT0iVeU3gWft2rXMmjWLsWPH0rp1a5YtW8YTTzzBCy+8QGxs7BH7r1mzhrlz5zJ+/HjatGnDvn37+O9//4thGIwcOdKEdyAi4h+q3cMDYBikL15c8Q6hXmAYcM89uUREeJg5M4JLL9WtKMS3/GZI68MPP+T888+nT58+NGrUiLFjxxIaGsrnn39e6f6bN2+mbdu29OzZk7p169KlSxfOOecctm3b5uPKRUT8y0kFHvB62DncLbfksWpVGo0a6dYT4lt+0cPjdDr5/fffGTBgQPk2i8VC586d2bJlS6XHtG3bli+//JJt27bRqlUr9u/fzw8//ECvXr2O+jqlpaWUHtZFaxgGdru9/M/eVHY+b59Xjk5t7ntqc987kTZPPnR1VVpa2kn93Vg3byZy+nRyJk7EExFR7fP83cE5xwfr+uijcFasCGPKlOyazFknRZ9z36uJNveLj1dOTg5ut5u4uLgK2+Pi4ti7d2+lx/Ts2ZOcnBwefvhhAFwuFxdeeCGDBg066ussXryYRYsWlT9u3rw5kydPLv9tqCYkn8RlnVI9anPfU5v73rHavH379sDBxQfr169fvRdwu+Hcc2HbNiLOOgtuvbV65zmGtDS44w7Iz4fzzovgllu8/hJepc+573mzzf0i8FTHL7/8wuLFi7nxxhtp3bo1qampvPnmmyxatIirrrqq0mMGDhxI//79yx+XJce0tDScTqdX6yu7pC41NVWXMfqI2tz31Oa+dyJtbrEcnK2wb98+9u7dW+3fkiPGjCH2wQdxPvMMaVdeWSNDXf/5TxjLloVzxRXZePEKZK/S59z3jtfmVqu1yp0VfhF4YmJisFgsOByOCtsdDscRvT5lFixYwLnnnsv5558PQJMmTSgqKuK1115j0KBB5f/DH85ms5WvQPp3NfUh9uaiSXJi1Oa+pzb3vWO1eWJiIgDFxcXk5eURFVW9y8Dzhwwh6tlnsf75J2EffkjRlVdWu96jufDCIi688OAEZo8HnM6DNyGNjPS/z5M+577nzTb3i0nLVquVFi1akJKSUr7N7XaTkpJCmzZtKj2muLj4iN9aKgs5IiLBxm63Ex5+8P5VmSezlo7dTv7o0QBEvfzywURSg9xuuPvuOK65JhGHQ/NlxLv8JiH079+fVatWsXr1anbv3s306dMpLi6md+/eAEybNo25c+eW79+tWzc+/fRTvvrqKw4cOMDGjRtZsGAB3bp1U/ARkaBmGAbx8fHASQYeIH/kSNx2O6E//0zomjXeKO+odu8OYdWqcH74IZSrr04iI0Pf5eI9fjGkBdCjRw9ycnJYuHAhDoeDZs2a8dBDD5UPaaWnp1fo0Rk8eDCGYTB//nwyMzOJiYmhW7duDBs2zKR3ICLiPxISEti3bx9ZWVkndR5PQgIFw4YR9cYbRL38MpnHuBL2ZDVp4mLRonSGDUvkl19sXHVVIvPnZ1Cvnm4WKifPbwIPQL9+/ejXr1+lzz322GMVHoeEhDBkyBCGDBnig8pERGoXb/XwAOSPG4f9o48oOfvsg+NONdiL3r69k0WL0rnmmiS2bLExaFASCxZkaN0eOWnqLxQRCUAJCQmAdwKPq3Fj9m/YQN5tt9Vo2CnTqpWLxYvTadLEyc6dVgYOTGTHjpAaf10JbAo8IiIByJuBB4AQ3waOJk1cvPtuOi1aONm718rgwUls2eJXgxJSyyjwiIgEoLLAc7JzeCpwuQj/6CMi33jDe+c8hgYN3Lz3Xjrt25eyf38IgwcnkpKi0CPVo8AjIhKAvN7DA4R+/TUJY8cS/eSTGF4877HUqePmnXfS6dKlhMzMEIYMSeK77ypfT03kWBR4REQCUE0EnpKzz6a0Y0cshYVEzprltfMeT3y8h/nzMzjjjGJyciwMG5bI2rWhPnt9CQwKPCIiAajsKi2vDmkZBnmHbngV+cYbUFjovXMfR0yMh7lzM+nZsxiXy0D38ZSqUuAREQlANTKHByjs3x9no0aEZGQQcdjNmH0hIsLDW29l8M476Zx9dolPX1tqPwUeEZEAdPg6PF69/5PVSv7YsQBEvfbawXV5fCg8HE47rbT88aZNVj74INynNUjtpMAjIhKAynp4SktLycvL8+q5C4YOxR0Tg/X33wlbudKr566KffssDB2ayC23xLN8uUKPHJsCj4hIAAoPDyc09ODE3uzsbK+e2xMVRf6IEZSceiqeyEivnrsq6tVz069fEe3aOenevdi0OqR20IIGIiIByDAMYmNjSUtLw+Fw0KhRI6+eP/eee8h98EHMnD1sscBTT2WTl2cQHV2zd3KX2k89PCIiASo2Nhbwfg8PAKGhpoadMoZBhbDz5psRTJkSjTenLUlgUA+PiEiAqtHAc4iRnU3knDkU9u+Pq0mTGnudE/Hbb1b+9a84AAoKDB5+OMcfMpn4CfXwiIgEKF8Enri77iLmiSeInD69xl7jRLVv7+Txxw++11dfjeLBB2N9fRGZ+DEFHhGRABUXFwfUbOApuOEGACLmzcNwOGrsdU7U6NH5TJniwDA8vP12JHffHYfTaXZV4g8UeEREAlRZD4+jBoNIca9elLZvj6WggMjZs2vsdapi2LACpk51EBLi4Z13Irj11nhKS49/nAQ2BR4RkQDliyEtDIO8m24CDt1uosQ/VkAeOLCQV1/NwmbzsHSpnbFjEygqMrsqMZMCj4hIgPJJ4AEKr7wSV3IyIfv3Y3///Rp9raq45JIi3nwzk/BwD59+Gs4NNyRSUKBZzMFKgUdEJED5KvAQGkr+6NEARL36Kv50TXifPsW8/XYGERFuvvwyjGuvTSA3V6EnGCnwiIgEKF9MWi6Tf911uGNjKW3fHiM/v8Zfryp69Chh3rwMYmLcbNgQxtChiWRlKfQEGwUeEZEA5YtJy2U8sbHs/+YbHNOm4YmKqvHXq6rTTy9l4cIM4uNd/PhjKJ9/rntvBRstPCgiEqB8NqR1iJn31ToRnTuX8u67GaxdG8qgQYVmlyM+ph4eEZEAdXjg8fhwXo1161bsCxb47PWqom1bJ6NGFZQ/zsoy2L07xMSKxFfUwyMiEqDK5vC4XC7y8/OJ8sFQU8jvv1O3d288VivFvXrhbtCgxl+zunJzDa67LpH9+0NYsCCdli1dZpckNUg9PCIiAcput2Oz2QDfDWu5WrSg+KyzMJxOIt980yevWV0FBQb5+QZFRVBaqknMgU6BR0QkQBmGUd7L44uJy2XKFyKcPRsjL89nr1tV9eq5effdDBYsyKBdO91/ItAp8IiIBDBfT1wGKL7gAkpbtsSSk0PEvHk+e93qSEx007HjX2Fn3bpQvvkm1MSKpKYo8IiIBDAzAg8WC/njxgEQ+frr1Ja7d6akWLn++gSGDUtgzRqFnkCjwCMiEsB8uRbP4QoGD8aVmIh1zx7Cly3z6WtXV8uWLs44o4TCQgvXX5/IqlVhZpckXqTAIyISwHy52nIFdjv5o0bhSkjAKKwda97Y7R7efDOTiy8upLjYYMyYBJYt0wKFgUKBR0QkgJnVwwOQP3YsBzZsoHDoUJ+/dnWFhcGrr2Zx5ZUFlJYajB8fz6JFdrPLEi/QOjwiIgHMlDk8h/jjLSZOhM0GU6c6sNs9zJ8fye23xxESAgMHml2ZnAz18IiIBDAzA085t5uwTz8lZMcO82qoopAQePbZbMaMOXhZ/YQJMHWqf986Q45NgUdEJID5Q+CJffhhEm+4gaiXXzathuqwWGDixBzuvDMXgKeeiuGpp6Lx4V06xIsUeEREAphpk5YPU3jllQBELFqEJT3dtDqqwzDgvvvyePbZg4+nTYvmn/+Mxe02ty6pOgUeEZEAZuak5TIlZ5xBSdeuGMXFRM6caVodJ+Oee+CZZxwYhoe33opkwYIIs0uSKlLgEREJYP4wpIVhlN9uImLmzFpzmfrfXXddIdOmORgwoIAhQwqOf4D4FQUeEZEAdnjg8Zg4+aTokktwNmlCSFYW9oULTavjZA0YcDD0WA9d4+x0HrwJqfg/BR4RkQBWNofH5XKRn59vXiFWK/ljxwIQ9frr4HKZV8tJMg7lG7cb7r03jmHDEsnOVujxdwo8IiIBzG63Y7PZAJOHtYCCa67BHRuLJyICy4EDptbiDX/+GcInn4Tzww82fvxR997yd1p4UEQkgBmGQUxMDBkZGeTk5NCwYUPTavFERpK2fDmuxo3/6iapxZo2dbFoUTpbt1o577xis8uR41DgEREJcNHR0eWBx2yuJk3MLsGrOnRw0qHDX3eD//PPEFwuaNas9g7ZBSoNaYmIBLiyicv+EHjKGHl5hH36qdlleNX+/RaGDk1k0KAkNm1Sf4K/UeAREQlwMTExgP8EHiMzk3pnnknC6NGE/PGH2eV4jcVy8I7r+/eHMHhwEj/+aDO7JDmMAo+ISICLjo4G/CfweBISDi5E6HYTOX262eV4TZ06bt55J52uXUtwOCxcfXUi69ZpMrO/UOAREQlw/jikVb4Q4fz5GFlZJlfjPfHxHubPz6BHj2Ly8y1cd10iq1aFmV2WoMAjIhLw/G1IC6CkVy9KO3TAUlBA5OzZZpfjVVFRHt5+O4MLLyyiqMhg9OgEliwJN7usoKfAIyIS4Pwx8Bx+u4nIN96A4sC6rDs8HF5/PZMBAwpwOg0mTIhn3jzdf8tMCjwiIgHOLwMPUHjFFbiSkwk5cAD7+++bXY7X2Wzw0ksOrr02H4/H4J574njttUizywpaCjwiIgHOXwMPoaHkjxmDx2LB+vvvZldTI0JCYPLkbMaPzwNg4sRYnnsuGhNvaxa0tFCAiEiA89vAA+SPGEHhZZfhatrU7FJqjGHAP/+ZQ3S0m2eeieH556OJj3czerSJ9zYLQgo8IiIBzp8Djyc6Gtehy+YDmWHAHXfkERXlYf78CAYOLDC7pKCjIS0RkQDnz4HncCE7dxKya5fZZdSoMWPyWbYsjfj4v8a03G4TCwoiCjwiIgHOH9fh+bvI11+nbs+eRD/7rNml1LjQw9YifPPNCG64IYHCQvPqCRYKPCIiAa5speWSkhKKiopMrqZyJd27Y3g82D/4AMvevWaX4xMHDlh44okYVq0K54MP7GaXE/AUeEREAlxUVBSGYQD+28tTesopFJ99NobTSdQbb5hdjk/Uretm9uxMbrstl6uvVhdPTVPgEREJcBaLpVbM4ym/3cTs2Ri5uSZX4xtnnVXCAw/kciiPUlBgkJamf5prglpVRCQI+NsNRCtTfP75lLZqhSU3l4h588wux+eKi+HGG+MZMCCJP/8MMbucgKPAIyISBGpDDw8WC/njxgEcvIt6aanJBflWRoaFHTus7NxpZeDAJLZtU+jxJgUeEZEgUBuu1AIoGDwYV1ISlrw8rFu3ml2OTzVo4Oa999Jp3bqUfftCGDQoiZQULZfnLV4JPAcOHDhi29Yg+6CKiPiz2jCkBUB4OJkzZ7L/m29wduhgdjU+V7++m3ffzaBTpxIyMkIYMiSJb7+1mV1WQPBK4OnZsydz584FwOPx8OyzzzJo0KAqn2f58uVMmDCBa6+9loceeoht27Ydc//8/HymT5/OuHHjGD58OHfccQfff/99td6DiEggqxVDWoeUdu2KJzJ4b7KZmOjmnXcyOOOMYnJyLAwblsiXX4Ye/0A5Jq8Eni+//JLFixdzxRVXcM4557B7926+/vrrKp1j7dq1zJo1i6uuuorJkyfTtGlTnnjiCbKzsyvd3+l0MmnSJNLS0rj77rt54YUXuOmmm0hISPDGWxIRCSi1ZUirAo8H288/m12FKWJiPMydm8l55xVRUGDh+usTWbEizOyyajWvBJ46derQtWtXtm7dyv79++nXrx8RERFVOseHH37I+eefT58+fWjUqBFjx44lNDSUzz//vNL9P/vsM/Ly8rj33ntp164ddevWpUOHDjRr1swL70hEJLDUmiGtMkVF1Ln4Yur06xd0c3nKRER4ePPNTC65pJCSEoMbb0xg8WItUFhdXpkNdfbZZ3Peeefx448/sn//fsaNG8f8+fN56623Tuh4p9PJ77//zoABA8q3WSwWOnfuzJYtWyo95rvvvqN169bMmDGDb7/9lpiYGM455xwGDBiAxVJ5jistLaX0sFn/hmFgt9vL/+xNZefz9nnl6NTmvqc2973qtvnhPTy14u/LbsfVuDG2X34h6rXXyJ4yxbRSzPych4fDq686uPtuD4sWRXDbbXEUFBhcd11gL1RYE23ulcDz4osvctZZZwHQpEkTli9fzhtVWCkzJycHt9tNXFxche1xcXHsPcoS4/v37yctLY2ePXvy4IMPkpqayvTp03G5XAwZMqTSYxYvXsyiRYvKHzdv3pzJkydTp06dE661qpKTk2vs3FI5tbnvqc19r6pt3qRJE+Dg7SXq169fEyV537/+BcuXE/Huu0Q8/zzUq2dqOWZ+zhcsgNtug//+1+C+++KIiopj/HjTyvEZb7a5VwLPWWedRWlpKbt27aK4uLh8W03yeDzExMRw0003YbFYaNGiBZmZmXzwwQdHDTwDBw6kf//+5Y/LkmNaWhpOp9Or9RmGQXJyMqmpqXg8nuMfICdNbe57anPfq26buw/dkvvAgQPs27evpsrzrubNSezWjdDvviP36afJu+8+U8rwl8/5P/8JFks0c+ZE0K5dBvv2efffLX9yvDa3Wq1V7qzwSuCZN28ekyZN4s8//6Rt27b89NNPnH766axdu/aEjo+JicFiseBwOCpsdzgcR/T6lImLi8NqtVYYvmrYsCEOhwOn04nVeuRbs9ls2GyVX95XUx9ij8ejfwh8TG3ue2pz36tqmx8+h6c2/V3ljRtHwk03ETlzJnm33orHbt4cFn/4nD/4YA6jR+dRr56bWvTXWG3ebHOvTFp+6qmn+Oabb2jRogXffPMNGzZsoHHjxid8vNVqpUWLFqSkpJRvc7vdpKSk0KZNm0qPadu2LampqeW/tQDs27eP+Pj4SsOOiEgwq5VXaQFFl1yCs0kTLA4H9gULzC7HL9Sr99e/e+vWhfLgg7G4XCYWVEt4JfCEhYWVX5VVWlrKqaeeyi+//FKlc/Tv359Vq1axevVqdu/ezfTp0ykuLqZ3794ATJs2rXytH4CLLrqIvLw8Zs6cyd69e/n+++9ZvHgxF198sTfekohIQKl1V2mVCQkh79DtJsJXrza3Fj+TnW0wZkwCs2ZF8vrrwbtu0YnySldIcnIyDoeDyy+/nEsuuYTExMQq9fAA9OjRg5ycHBYuXIjD4aBZs2Y89NBD5UNa6enpFWZrJyUl8c9//pO33nqLe++9l4SEBC655JIKV3qJiMhBZT08+fn5Rx3291eF11yDq2lTivv0MbsUvxIb6+HZZx3MmxfBDTfkm12O3zM8Xh6QXL16NTk5OfTr14/Q0NqxMmRaWlqFy9W9wTAM6tevz759+0wf8w0WanPfU5v7XnXbvLS0tHydspSUFOLj42uowsDj759zjwfK+gM8HigqMrDb/a/Oqjhem9tsNnMmLR+ubAhKRET8h81mw263U1hYSE5OTq0NPEZhIUZ2Nm4thVDu8LDzxBMxrFsXyttvZ5CQULtDj7d5JfBs376dqVOn8scff+A6bObUBx984I3Ti4iIF8TGxpYHntoofPlyYu+5h5Kzzybr9dfNLsfvHDhgYf58O1lZB286OnduRoUJzsHOK4Fn4MCB3HrrrVxzzTVHXeVYRETMFRUVBUBeXp7JlVSPs3lzQrKyCF++nJCdO3HpVkIV1Kvn5r33Mhg6NJFNm2wMGpTEggUZNGqkS7jAS1dpRUREMG7cOM4++2y6d+9e/iMiIv6j7Eqt3NxckyupHmfbthT17YvhdhM5fbrZ5filNm2cLF6cTuPGTnbutDJgQBLbtoWYXZZf8ErgefDBB7n//vv59NNP+eKLL8p/RETEf5T18OTn194resouUY+YPx8jM9PkavxT06YuFi9Op1WrUvbtC2HQoCR++aX2XJVXU7zSAp988gmrV69m27Zt5UNahmFw7rnneuP0IiLiBbW9hwegpGdPSjt2xPbLL0S+/TZ5d9xhdkl+qX79g8Nbw4cnkJISypAhScyalcHpp3v3iuTaxCuBZ+XKlWzevLl23IFXRCRIRUYeXJyuts7hAcAwyLv5ZuJvu43IN98k7+abISzM7Kr8UmKim4ULMxg5MoFvvglj2LBE3ngjk169SswuzRReGdI688wz2b59uzdOJSIiNSQQengACi+/HFf9+oSkpRG2bp3Z5fi12FgPc+dmcu65RRQUWBg5MpEVK4IzIHqlhyclJYVOnTrRtm1bwsLC8Hg8GIbBhg0bvHF6ERHxgtp+lVY5mw3H88/jbNAAV6tWZlfj9yIiPMycmcktt8SzfLmdG29M4KWXHAwYUGh2aT7llcCzZMkSb5xGRERqUKD08AAUa45olYSFwauvZnHXXR7ef9+OzRZ8ixJ6JfA0bdrUG6cREZEaFDA9PH9jSUvDXcXbDAQjqxVefNHByJH5QTl5WasEiogEiUDq4QHA4yHuttuo160b1pQUs6upFSwWKoSd3btDeOWVSPzwFmFeVyOBZ9++fRQXF9fEqUVEpJoCrofHMMDjwXC5iHr1VbOrqXUKCw2GDk3k8cdjefnlKLPLqXE1EnhGjBhBu3btuOeee2ri9CIiUg0B18MD5N90EwD2JUuw7NljcjW1i93uYcyYPJo0cTJgQIHZ5dS4Ggk8K1euZMeOHdx44401cXoREamGgOvhAUo7d6a4R4+DvTwzZphdTq0zalQBn32WRoMGf91kNFCHt04q8IwYMQKAM844gzPPPPOIn3bt2nmlSBEROXllgSeQeniAg4sPAhFz5mAE2HvzBbv9r4SzZEk4o0YlUBiAV6yf1FVazzzzDACLFi3ySjEiIlJzyoa0CgoKcLlchIQExk0li/v0obR1a2xbtxIxbx75h+63JVXjcBjcf38cubkWRoxIZObMTKKiAqe756R6eOrXrw/Ali1bSExMpGnTpixbtownnniCwkCMhyIitVhZDw/U7huIHsFiIf/QFIqIhQsDd0ymhsXFeXjrrUyiotysWxfG0KGJZGUFzi2jvDKH55577iEqKor169czZ84cLrjgAsaMGeONU4uIiJeEhYURGhoKBN6wVsHgwWRPnEj6e+8dvHpLqqV79xIWLswgPt7FDz+EctVVSRw4EBgr2Hj1Xbz//vvcfPPNXH311RQUBP6MbxGR2iYQJy4DYLeTf+ONeGJizK6k1uvSpZR3382gXj0XmzbZGDgwid27a//wp1cCT4MGDRgxYgTz5s2jf//+FBcX43K5vHFqERHxokC8NP0IHg+Gfuk+KW3bOnnvvXQaN3ayc6eVgQMT2b69docerwSeRYsWMXDgQFauXEl8fDyZmZlMmTLFG6cWEREvCtgenkNs335L0qWXEnfXXWaXUus1a+bivffSadWqlL17rQwalMQvv3jljlSmqFbgWbt2LSmHLeMdGRnJoEGDaN26NXBwMvNFF13knQpFRMRrAr2HxxMZSejGjYR/9BEhf/5pdjm1XoMGbt59N4OOHUtJTw9hyJAkvv/eZnZZ1VKtwDNhwgS+/vrrI7Zv3749YP8nEhEJBIHew+Ns356ic8/FcLuJfOMNs8sJCElJbt55J53TTy8hO9vC0KGJrF0banZZVVatwLN582Z69+59xPaVK1cybNiwk61JRERqSKAuPni4snV4IubO1UKEXhIb62Hu3AzOOaeY/HwL69aFmV1SlVUr8MTExJCVlXXE9l69erF+/fqTLkpERGpGoPfwABT37k1pmzZY8vKImDfP7HICRmSkh1mzMpgyxcHdd9e+IFmtwNOvX79KJyVbLBZKSkpOuigREakZgT6HBwDDIH/sWAAiZ8wAp9PkggJHeDgMG1ZQvtRRYaHBZ5/Vjt6eagWexx9/nP/9738MHjyYn3/+GYCioiImT57MKaec4tUCRUTEe4KhhwegYOBAXImJWHfvJnzVKrPLCUglJTB2bDzXX5/AvHkRZpdzXNW6vqxx48asX7+e8ePH06VLF8LCwnA6ncTGxrJ06VJv1ygiIl4SFD08AHY7OQ8/jCcmhqILLjC7moBktUKzZk7Cw0Np2dL/e9GqfUF906ZN+eijj9i1axc//vgjNpuN7t27k5CQ4M36RETEi4KlhwegcMgQs0sIaBYLPP54DqNG5dOypf8vNlylwPPII49w5ZVX0q1bt/JtTZo0oUmTJl4vTEREvK8s8ATUzUNPhMsFAXJ3eH9iGFQIO7/8YmX5cjt3353rd7c0q9Icnt27d3PJJZfQqFEjxo8fz8cff6xJyiIitUhExMG5FkETeDweov7v/6h35pmE7NpldjUBLSfHYPjwRJ5/Ppp//jMWt9vsiiqqUuB54403SE1NZd68eURHR3PnnXeSlJTE4MGDmTVrFpmZmTVVp4iIeEFkZCQQRIHHMAj95htCUlMPXrElNSYmxsN99+ViGB7eeiuSu++O86sL5Kp8lZbFYqFXr14888wzbN68ma+//pru3bvz6quv0qBBA84991ymTJnCnj17aqJeERE5CWU9PIWFhSZX4jvlCxHOn4+Rk2NyNYHt2msLmDrVQUiIh3feieCWW+Lxl4Ggk755aPv27bnvvvv46quv2LVrFyNHjuTLL79knhZ7EhHxO0HXwwMUn3eeFiL0oYEDC3nttSxCQz189lkYW7f6xw1HvVpF3bp1GTNmDGPGjPHmaUVExEsOn8Pj8Xgw/G1maU0wDPLHjSPunnuInDGD/DFjDl5TLTWmX78iZs7MxDCgY0f/GNc6qb/xESNG8Pbbb3PGGWdU+j/Nhg0bTub0IiLiZWU9PB6Ph6KiIux2u8kV+UbBwIFEP/UU1j17CP/oI4quuMLskgLeeecVm11CBScVeJ555hkAFi1a5JViRESkZh0ecAoKCoIm8BAeTsHIkUQ//zxRr72mwBOETmoOT/369QHYsmULiYmJNG3alGXLlvHEE08E1YQ4EZHaIiQkpDzkBNM8HoD8kSPJHz4cRyX3gpTAd9KTlgHuueceoqKiWL9+PXPmzOGCCy7QPB4RET8VdGvxHOJOSiL72WdxtmtndiliAq8EnjLvv/8+N998M1dffTUFBQXePLWIiHhJMF6pJeKVaeoNGjRgxIgRfPHFF/z4448UFxfjcvn/fTVERIJRWeAJ1l9MQ7ZtI3rqVNx16pDzr3+ZXY74SLV6eNauXUtKSkr540WLFjFw4EBWrlxJfHw8mZmZTNEYqYiIXyob0grWwGPds4eIRYuImDVLCxEGkWoFngkTJvD111+XP46MjGTQoEFYLBZyc3OpX78+F110kdeKFBER7wn2Ia3ic889uBBhfj4R8+ebXY74SLUCz+bNm+ndu/cR21euXMmwYcNOtiYREalBwTppuZxhHFx8EIh8882Dd1KXgFetwBMTE0NWVtYR23v16sX69etPuigREak5wT6kBVA4eDDuuDisu3YR/umnZpcjPlCtwNOvX79K5+hYLBZK/OUuYSIiUqlgn7QM4LHbyb/uOgAip083uRrxhWoFnscff5z//e9/DB48mJ9//hmAoqIiJk+ezCmnnOLVAkVExLuCfQ5PmfyRI/FYrYStW4f1sAtxJDBV67L0xo0bs379esaPH0+XLl0ICwvD6XQSGxvL0qVLvV2jiIh4UdDP4TnE3aAB+aNG4U5MxNWwodnlSA2r9jo8TZs25aOPPmLXrl38+OOP2Gw2unfvTkJCgjfrExERL9OQ1l9yHnvM7BLER0564cEmTZrQpEkTb9QiIiI+oEnLEoy8emsJERHxf5rD8zcuF+HLlhE/ahQUF5tdjdQQBR4RkSCjOTx/43YT+8gj2FeswL5kidnVSA1R4BERCTKaw/M3Nhv5o0YBEDljBng8JhckNUGBR0QkyGgOz5Hyhw/HHR5OaEoKoYfdOkkChwKPiEiQ0RyeI3kSEigcPBjQQoSBSoFHRCTIKPBULv/GGwEI/+QTQnbtMrka8TYFHhGRIFM2pFVYWIjb7Ta5Gv/hbNOGovPOw3C7D95UVALKSa/DIyIitUtZDw8cDD2HPw52+WPGYMnLo+Sss8wuRbxMgUdEJMiEh4djGAYej4f8/HwFnsMU9+1L8fnnm12G1AANaYmIBBnDMDSP52gMw+wKpIYo8IiIBCEtPnhsRlYWUf/9L6Fr1phdiniJ3w1pLV++nKVLl+JwOGjatCmjR4+mVatWxz3uq6++4sUXX+T000/nvvvu80GlIiK11+ETl+VIUS+/TPR//kNxr15k9upldjniBX7Vw7N27VpmzZrFVVddxeTJk2natClPPPEE2dnZxzzuwIEDvP3227Rv395HlYqI1G5lQ1p5eXkmV+KfCkaOxBMSQtiXX2LdtMnscsQL/CrwfPjhh5x//vn06dOHRo0aMXbsWEJDQ/n888+Peozb7Wbq1KlcffXV1K1b14fViojUXna7HYCioiKTK/FProYNKerXD9BChIHCb4a0nE4nv//+OwMGDCjfZrFY6Ny5M1u2bDnqcYsWLSImJoa+ffvy22+/HfM1SktLKS0tLX9sGEb5//SGlyeqlZ3P2+eVo1Ob+57a3Pe81eaHBx79/VUuf+xY7MuWYX/vPUhPVzv5UE18t/hN4MnJycHtdhMXF1dhe1xcHHv37q30mE2bNvHZZ5/xzDPPnNBrLF68mEWLFpU/bt68OZMnT6ZOnTrVrvt4kpOTa+zcUjm1ue+pzX3vZNs8ISEBAJvNRv369b1RUuC54gro1g3ju+/gtddIfughsysKOt78bvGbwFNVhYWFTJ06lZtuuomYmJgTOmbgwIH079+//HFZckxLS8PpdHq1PsMwSE5OJjU1FY/uvOsTanPfU5v7nrfavOz7LzU1lX379nmrvIBjv+EG4r77Dv7zH1Kvuw6PzWZ2SUHheJ9zq9Va5c4Kvwk8MTExWCwWHA5Hhe0Oh+OIXh+A/fv3k5aWxuTJk8u3lTXK0KFDeeGFF45IhjabDdtRPqw19WXt8Xj0D4GPqc19T23ueyfb5mVDWgUFBfq7O4aC/v2JevpprOedBzk5eA71jIlvePO7xW8Cj9VqpUWLFqSkpHDmmWcCByckp6Sk0O/QxLHDNWjQgClTplTYNn/+fIqKirjhhhtISkrySd0iIrVRWeDRZenHERpK2po11G/WDM++faBwWGv5TeAB6N+/P//5z39o0aIFrVq14qOPPqK4uJjevXsDMG3aNBISEhg+fDihoaE0adKkwvFll1n+fbuIiFSkwFMFYWFmVyBe4FeBp0ePHuTk5LBw4UIcDgfNmjXjoYceKh/SStcseRERr1DgqTrrli3YNmyg4NprzS5FqsGvAg9Av379Kh3CAnjssceOeeyECRNqoCIRkcCjwFNFv/9Ond698YSEUNy7N66GDc2uSKrIrxYeFBER3wgPDwcUeE5YixYUn3MOhstFxFtvmV2NVIMCj4hIENJKy1WXP2YMAJFz5mAoKNY6CjwiIkGo7OahBQUFJldSexRfeCHOJk2wOBwHV1+WWkWBR0QkCGkOTzWEhJB/ww0ARM6YoUvUaxkFHhGRIKTAUz0FQ4fijojAtnkzoWvWmF2OVIECj4hIEFLgqR5PbCyFV1+NKzGRkPR0s8uRKvC7y9JFRKTmKfBUX84995D98MNw6Eo3qR0UeEREgpACT/V54uPNLkGqQUNaIiJB6PDAo5uHVpPbTdjKlRh5eWZXIidAgUdEJAiVBR63201JSYnJ1dROCSNHkjhyJPZ33jG7FDkBCjwiIkGoLPCAhrWqq6hvXwCi3ngD3G6Tq5HjUeAREQlCNpsNm80GaPHB6iocMgR3dDTW338nbPVqs8uR41DgEREJUpq4fHI8UVEUDB0KHFqIUPyaAo+ISJBS4Dl5+aNG4TEMwlevxrptm9nlyDEo8IiIBCkFnpPnatqUogsvBCDyjTdMrkaORYFHRCRIKfB4R/7o0QBYf/1V99fyY1p4UEQkSIUfWim4qKjI5Epqt5KePUn74ANKTzsNDMPscuQoFHhERIKUeni8xDAo7dbN7CrkODSkJSISpBR4vM/IyyNk506zy5BKKPCIiASpiIgIQIHHW8JWrKBet27E3Xef2aVIJRR4RESCVFkPjxYe9A5nx44YhYWEffUV1t9+M7sc+RsFHhGRIKUhLe9yNWxIUb9+gC5R90cKPCIiQUqBx/vyx4wBIOK99zAyM02uRg6nwCMiEqQUeLyv5MwzKenUCaOoiMi5c80uRw6jwCMiEqQUeGqAYZT38kTOnAmlpebWI+UUeEREgpQCT80ovOIKXImJWPbvJ/SHH8wuRw7RwoMiIkFKgaeGhIfjeOklnC1b4mrc2Oxq5BAFHhGRIKXAU3OKe/c2uwT5Gw1piYgEKS086Bu6Wss/KPCIiASpspuHKvDUDKOwkPhRo0g+4wwsaWlmlxP0FHhERIKUhrRqlsduJyQtDaOoiIjZs80uJ+gp8IiIBCkFnpqXf+ONAETOmgUlJSZXE9wUeEREgpQCT80rvPRSXPXqEXLgAPYPPzS7nKCmwCMiEqQUeHwgNJT8668HIHLGDPB4TC4oeCnwiIgEqbLAU1paSqlWBK4xBdddhyc0lNAff8T23XdmlxO0FHhERIJUWeABKCoqMrGSwOZOSqJwwAAAIjV52TRaeFBEJEiFhYVhGAYej4fCwkKio6PNLilg5d10EyWnnkrhVVeZXUrQUuAREQlShmEQERFBfn4+BQUFZpcT0Jzt2uFs187sMoKahrRERIKYJi6bwOMBl8vsKoKOAo+ISBBT4PGt8KVLqXPBBdgXLTK7lKCjwCMiEsQUeHwrZPdubJs2ETV9ui5R9zEFHhGRIKbA41sFQ4fiDg/H9uuvhK5fb3Y5QUWBR0QkiJUFHk1a9g1PfHz5lVqRb7xhcjXBRYFHRCSIlQUercPjO/mjRwMQvnw5IX/+aXI1wUOBR0QkiIWHhwMKPL7kbNuW4l69MNxuImfONLucoKHAIyISxMoCj+bw+FbeoV6eiHnzMDSc6BNaeFBEJIhpSMscxRdcQP7QoRRdfjmeQ6FTapYCj4hIEFMPj0ksFrKfe87sKoKKhrRERIKYengkWCjwiIgEMU1aNpclPZ3op54i7s47zS4l4CnwiIgEMQ1pmcvIySF62jTsixYRsmOH2eUENAUeEZEgpiEtc7latKCob18Mj4fIN980u5yApsAjIhLENKRlvvwbbwQgYsECjNxck6sJXAo8IiJBTPfSMl/xuedS2qoVlrw8IhYuNLucgKXAIyISxNTD4wcMo/x2E5FvvAFut8kFBSYFHhGRIKZJy/6h8KqrcMfEYN25k7DPPjO7nICkhQdFRIKYJi37B09kJPk33oglMxNnq1ZmlxOQFHhERIKYhrT8R+4//mF2CQFNQ1oiIkFMQ1oSLBR4RESCmIa0/IzHg+2bb4i77TYMh8PsagKKAo+ISBA7vIfH4/GYXI0AxD34IBHvvUfE/PlmlxJQ/G4Oz/Lly1m6dCkOh4OmTZsyevRoWh1lAtfKlSv54osv+PPPPwFo0aIFw4YNO+r+IiJSUVkPD0BxcXF5ABKTGAb5Y8YQd889RL75Jvljx0JIiNlVBQS/6uFZu3Yts2bN4qqrrmLy5Mk0bdqUJ554guzs7Er3//XXXznnnHN49NFHmTRpEomJiUyaNInMzEwfVy4iUjsdHnA0rOUfCgYMwBUfj3X3bsJXrDC7nIDhV4Hnww8/5Pzzz6dPnz40atSIsWPHEhoayueff17p/rfffjsXX3wxzZo1o2HDhtx88814PB5+/vlnH1cuIlI72Ww2Qg71IGjisp+w2ym49loAImfMMLmYwOE3Q1pOp5Pff/+dAQMGlG+zWCx07tyZLVu2nNA5iouLcTqdREVFVfp8aWkppaWl5Y8NwyjvzjUMo/rFV6LsfN4+rxyd2tz31Oa+VxNtbrfbycvLo7i4WH+XlTDjc15www1EvfwyYevWYfv1V5wdO/rstf1BTbS53wSenJwc3G43cXFxFbbHxcWxd+/eEzrHnDlzSEhIoHPnzpU+v3jxYhYtWlT+uHnz5kyePJk6depUu+7jSU5OrrFzS+XU5r6nNvc9b7Z5REQEeXl5REdHU79+fa+dN9D49HNevz4MHgwLF1Jn3jwI0p4eb7a53wSek/X+++/z1Vdf8dhjjxEaGlrpPgMHDqR///7lj8uSY1paGk6n06v1GIZBcnIyqampuvLBR9Tmvqc2972aaPOy78xdu3aRlJTklXMGErM+57ZrryVu/XryW7akYN8+n72uPzhem1ut1ip3VvhN4ImJicFiseD427oDDofjiF6fv/vggw94//33efjhh2natOlR97PZbNhstkqfq6kPscfj0T8EPqY29z21ue95s80Pv2O6/h6Pztef85Ju3TiwZs3Bq7SC9O/Fm23uN5OWrVYrLVq0ICUlpXyb2+0mJSWFNm3aHPW4JUuW8O677/LQQw/RsmVLX5QqIhJQtNqynzIMXZLuRX4TeAD69+/PqlWrWL16Nbt372b69OkUFxfTu3dvAKZNm8bcuXPL93///fdZsGAB48ePp27dujgcDhwOhy6tFBGpAq227OdKSrC/9x6hX3xhdiW1mt8MaQH06NGDnJwcFi5ciMPhoFmzZjz00EPlQ1rp6ekVZmx/+umnOJ1Onn/++Qrnueqqq7j66qt9WbqISK2lG4j6t6jXXiPmqaco6daN9HPPNbucWsuvAg9Av3796NevX6XPPfbYYxUe/+c///FBRSIigU1DWv6t4OqriZ4yhdDvvsP244+Unnqq2SXVSn41pCUiIr6nIS3/5q5bl8IrrgC0EOHJUOAREQlyGtLyf/ljxgBgX7oUy/79JldTOynwiIgEucMvSxf/VNqlCyWnn45RWkrk7Nlml1MrKfCIiAQ59fDUDnmjRwMQMWsWFBebXE3to8AjIhLkNGm5dii69FJcyck4W7cmJD3d7HJqHb+7SktERHxLk5ZrCZuNA6tW4TnO3QekcurhEREJchrSqj0UdqpPgUdEJMhpSKv2saSlEf7hh2aXUatoSEtEJMhpSKt2saSmUq9HD3A62d+1K+6GDc0uqVZQD4+ISJBTD0/t4k5OpuS00zBcLiJnzTK7nFpDgUdEJMiph6f2yb/xRgAi5swBBdUTosAjIhLkNGm59im68EKcjRsTkpVFxOLFZpdTKyjwiIgEOQ1p1UIhIeTfcAMAkW+8AR6PufXUAgo8IiJBTkNatVPB0KG47XZsv/1G6Nq1Zpfj9xR4RESCnIa0aidPXByFQ4bgCQvDunWr2eX4PV2WLiIS5DSkVXvl3nknuffcgzsx0exS/J4Cj4hIkCsb0nK5XJSWlmKz2UyuSE6Uu149s0uoNTSkJSIS5Mp6eEC9PLWZNSUFIz/f7DL8lgKPiEiQCwsLwzAMQPN4aqu4u+6i7sUXY1+0yOxS/JYCj4hIkDMMQxOXa7nSzp2BQ5eou90mV+OfFHhEREQTl2u5gquvxh0VhW3bNsK++MLscvySAo+IiGgtnlrOExVFwTXXABA5Y4bJ1fgnBR4REdGQVgDIHz0aj2EQ/tlnhGzfbnY5fkeBR0REynt4NKRVe7maNaP4ggsAiJw509xi/JACj4iIqIcnQOSPHg1A2P/+p8nLf6OFB0VERJOWA0Rxr15kzphB0fnng0V9GodT4BEREU1aDhSGQVG/fmZX4ZcU/0RERENagcjpxJKWZnYVfkOBR0RENKQVYEK/+oq6PXoQd+edZpfiNzSkJSIiGtIKMK5GjQjZuxfrnj1Yt23D2aqV2SWZTj08IiKiHp4A42ralKILLwQg8s03Ta7GPyjwiIiIengCUNkl6vZ33sHIyTG5GvMp8IiIiCYtB6CSnj0pbdMGS34+EQsXml2O6RR4RESE8PBwQkJCcGuxusBhGOW9PJFvvhn0CxEq8IiICOPGjWPXrl0899xzZpciXlQ4eDDu2FisO3cSun692eWYSldpiYgIhmGYXYLUAE9EBNlPPomzSRNKTzvN7HJMpcAjIiISwAoHDDC7BL+gIS0REZFgUVJidgWmUeAREREJcEZhIbEPPEC9M87AyM42uxxTKPCIiIgEOE94OKHffENIejoRCxaYXY4pFHhEREQC3aFL1D0WCyF79phdjSk0aVlERCQIFA4aRHHv3rgaNjS7FFOoh0dERCQIeOz2oA07oMAjIiIiQUCBR0RERAKeAo+IiIgEPAUeERERCXgKPCIiIhLwFHhEREQk4CnwiIiISMBT4BEREZGAp8AjIiIiAU+BR0RERAKeAo+IiIgEPAUeERERCXgKPCIiIhLwFHhEREQk4FnNLsAfWK011ww1eW6pnNrc99Tmvqc29z21ue8drc2r83dheDwez8kWJCIiIuLPNKRVQwoLC7n//vspLCw0u5SgoTb3PbW576nNfU9t7ns10eYKPDXE4/GwY8cO1IHmO2pz31Ob+57a3PfU5r5XE22uwCMiIiIBT4FHREREAp4CTw2x2WxcddVV2Gw2s0sJGmpz31Ob+57a3PfU5r5XE22uq7REREQk4KmHR0RERAKeAo+IiIgEPAUeERERCXgKPCIiIhLwdGOQk7B8+XKWLl2Kw+GgadOmjB49mlatWh11/3Xr1rFgwQLS0tJITk7m2muv5bTTTvNhxbVfVdp89erV/Pe//62wzWazMWfOHF+UWuv9+uuvfPDBB+zYsYOsrCzuuecezjzzzGMe88svvzBr1iz+/PNPEhMTGTx4ML179/ZNwQGgqm3+yy+/MHHixCO2v/baa8TFxdVgpYFj8eLFbNiwgT179hAaGkqbNm247rrraNCgwTGP0/d59VWnzb3xfa7AU01r165l1qxZjB07ltatW7Ns2TKeeOIJXnjhBWJjY4/Yf/Pmzbz44osMHz6c0047jTVr1vDss88yefJkmjRpYsI7qH2q2uYAdrudF1980ceVBobi4mKaNWtG3759mTJlynH3P3DgAE8//TQXXnght912GykpKbzyyivExcVx6qmn1nzBAaCqbV7mhRdeICIiovxxTExMTZQXkH799VcuvvhiWrZsicvlYt68eUyaNInnn3+e8PDwSo/R9/nJqU6bw8l/nyvwVNOHH37I+eefT58+fQAYO3Ys33//PZ9//jkDBgw4Yv+PPvqIU089lSuuuAKAoUOH8vPPP7N8+XLGjRvny9Jrraq2OYBhGPpNt5q6du1K165dT3j/FStWULduXa6//noAGjVqxKZNm1i2bJkCzwmqapuXiY2NJTIysgYqCnz//Oc/KzyeMGECN954I7///jsdOnSo9Bh9n5+c6rQ5nPz3uQJPNTidTn7//fcK/8haLBY6d+7Mli1bKj1my5Yt9O/fv8K2Ll268M0339RkqQGjOm0OUFRUxC233ILH46F58+YMGzaMxo0b+6Di4LN161Y6d+5cYVuXLl2YOXOmOQUFkfvuu4/S0lIaN27MkCFDaNeundkl1VoFBQUAREVFHXUffZ9714m0OZz897kmLVdDTk4Obrf7iKQZFxeHw+Go9BiHw3HEsEtsbOxR95eKqtPmDRo0YPz48dx3333cdtttuN1u/vWvf5GRkVHzBQeho33GCwsLKSkpMamqwBYfH8/YsWP5xz/+wT/+8Q8SExOZOHEiv//+u9ml1Uput5uZM2fStm3bYw5N6fvce060zb3xfa4eHglYbdq0oU2bNhUe33XXXXz66acMHTrUxMpEvKNBgwYVJnq2bduW/fv3s2zZMm677TYTK6udZsyYwZ9//sm///1vs0sJGifa5t74PlcPTzXExMRgsViOSPMOh+Oo44txcXFkZ2dX2Jadna35JSeoOm3+d1arlebNm5Oamur9AuWon3G73U5oaKhJVQWfVq1a6TNeDTNmzOD777/n0UcfJTEx8Zj76vvcO6rS5n9Xne9zBZ5qsFqttGjRgpSUlPJtbreblJSUCgn0cG3atOHnn3+usG3jxo20bt26RmsNFNVp879zu93s2rWL+Pj4miozqLVu3brSz/iJ/v2Id+zcuVOf8SrweDzMmDGDDRs28Mgjj1C3bt3jHqPv85NTnTb/u+p8nyvwVFP//v1ZtWoVq1evZvfu3UyfPp3i4uLyNUemTZvG3Llzy/e/9NJL+emnn1i6dCl79uxh4cKFbN++nX79+pn0Dmqfqrb5okWL+Omnn9i/fz+///47L730EmlpaZx//vkmvYPapaioiJ07d7Jz507g4GXnO3fuJD09HYC5c+cybdq08v0vuugiDhw4wOzZs9mzZw+ffPIJ69at47LLLjOj/Fqpqm2+bNkyvvnmG1JTU9m1axczZ84kJSWFiy++2Izya6UZM2bw5Zdfcscdd2C323E4HDgcjgrzzvR97l3VaXNvfJ9rDk819ejRg5ycHBYuXIjD4aBZs2Y89NBD5V2a6enpGIZRvn/btm25/fbbmT9/PvPmzaN+/frce++9WrOhCqra5nl5ebz66qs4HA4iIyNp0aIFkyZNolGjRia9g9pl+/btFRa1mzVrFgDnnXceEyZMICsrq/wfYoC6devywAMP8NZbb/HRRx+RmJjIzTffrEvSq6Cqbe50Opk1axaZmZmEhYXRtGlTHn74YTp16uTz2murFStWAPDYY49V2H7LLbeU/zKl73Pvqk6be+P73PB4PJ6Trl5ERETEj2lIS0RERAKeAo+IiIgEPAUeERERCXgKPCIiIhLwFHhEREQk4CnwiIiISMBT4BEREZGAp8AjIn7jhhtuYMCAAT5/3ZkzZ2IYBoZhcOedd5Zvb9asGS+88MIxjy07TvdREvFvWmlZRHzi8FVTK/Poo4/y4osvYtZaqDExMWzevJnIyMgqHbdv3z4WLFjAo48+WkOViYg3KPCIiE/s27ev/M8LFizgkUceYfPmzeXboqKiiIqKMqM04GAgS05OrvJxycnJxMbG1kBFIuJNGtISEZ9ITk4u/4mNjS0PGGU/UVFRRwxp9e7dm9tuu40777yT+Ph46tWrx+uvv05+fj6jRo0iOjqaVq1a8fHHH1d4rZSUFC655BKioqKoV68eI0aMqHAPqqooKChg9OjRREdH06RJE1577bWTaQYRMYkCj4j4tbfeeoukpCQ2bNjAbbfdxvjx4xkyZAg9evTg+++/56KLLmLEiBEUFBQA4HA46Nu3L127duXbb79l+fLl7N+/n6uvvrpar//cc89x+umn88MPP3DLLbcwfvz4Cj1TIlI7KPCIiF/r0qUL//rXv2jdujUPPvgg4eHhJCUlMXbsWFq3bs0jjzxCRkYGGzduBGDatGl07dqVJ598knbt2tG1a1feeOMNPv/8c7Zs2VLl17/00ku55ZZbaNWqFffffz9JSUl8/vnn3n6bIlLDNIdHRPzaKaecUv7nkJAQEhMT6dy5c/m2evXqAXDgwAEAfvrpJz7//PNK5wNt376dNm3aVPv1y4bhyl5LRGoPBR4R8Ws2m63CY8MwKmwru/rL7XYDkJeXx+WXX87kyZOPOFf9+vW98vplryUitYcCj4gElNNOO413332XZs2aYbXqK05EDtIcHhEJKBMmTCAzM5Nhw4bxzTffsH37dj755BNGjRqFy+UyuzwRMYkCj4gElAYNGvDVV1/hcrm46KKL6Ny5M3feeSdxcXFYLPrKEwlWhsesZU1FRPzEzJkzufPOO3E4HKYcLyI1T7/uiIgA2dnZREVFcf/991fpuKioKG6++eYaqkpEvEU9PCIS9HJzc9m/fz8AcXFxJCUlnfCx27ZtAw5eMt+8efMaqU9ETp4Cj4iIiAQ8DWmJiIhIwFPgERERkYCnwCMiIiIBT4FHREREAp4Cj4iIiAQ8BR4REREJeAo8IiIiEvAUeERERCTgKfCIiIhIwPt/FQH10MeFPn8AAAAASUVORK5CYII=", + "text/plain": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "for i in range(0,len(v_si)):\n", + " t_i = solution[i][\"Time [s]\"].entries / 3600\n", + " c_s_xrav_n_p1 = solution[i][\"Average negative primary particle concentration\"].entries\n", + " plt.plot(t_i, c_s_xrav_n_p1 ,ltype[i],label=\"$V_\\mathrm{si}=$\"+str(v_si[i]))\n", + "plt.xlabel('Time [h]')\n", + "plt.ylabel(\"$c_\\mathrm{g}/c_\\mathrm{g,max}$\")\n", + "plt.legend()\n", + "plt.title('Graphite')\n", + "\n", + "plt.figure()\n", + "for i in range(0,len(v_si)):\n", + " t_i = solution[i][\"Time [s]\"].entries / 3600\n", + " c_s_xrav_n_p2 = solution[i][\"Average negative secondary particle concentration\"].entries\n", + " plt.plot(t_i, c_s_xrav_n_p2,ltype[i],label=\"$V_\\mathrm{si}=$\"+str(v_si[i]))\n", + "plt.xlabel('Time [h]')\n", + "plt.ylabel(\"$c_\\mathrm{si}/c_\\mathrm{si,max}$\")\n", + "plt.legend()\n", + "plt.title('Silicon')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "299bd7ec", + "metadata": {}, + "source": [ + "Results of equilibrium potential" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "cfd2994e", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 8, - "id": "302e7bb8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Silicon')" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure()\n", - "for i in range(0,len(v_si)):\n", - " t_i = solution[i][\"Time [s]\"].entries / 3600\n", - " c_s_xrav_n_p1 = solution[i][\"Average negative primary particle concentration\"].entries\n", - " plt.plot(t_i, c_s_xrav_n_p1 ,ltype[i],label=\"$V_\\mathrm{si}=$\"+str(v_si[i]))\n", - "plt.xlabel('Time [h]')\n", - "plt.ylabel(\"$c_\\mathrm{g}/c_\\mathrm{g,max}$\")\n", - "plt.legend()\n", - "plt.title('Graphite')\n", - "\n", - "plt.figure()\n", - "for i in range(0,len(v_si)):\n", - " t_i = solution[i][\"Time [s]\"].entries / 3600\n", - " c_s_xrav_n_p2 = solution[i][\"Average negative secondary particle concentration\"].entries\n", - " plt.plot(t_i, c_s_xrav_n_p2,ltype[i],label=\"$V_\\mathrm{si}=$\"+str(v_si[i]))\n", - "plt.xlabel('Time [h]')\n", - "plt.ylabel(\"$c_\\mathrm{si}/c_\\mathrm{si,max}$\")\n", - "plt.legend()\n", - "plt.title('Silicon')" + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'NMC811')" ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" }, { - "attachments": {}, - "cell_type": "markdown", - "id": "299bd7ec", - "metadata": {}, - "source": [ - "Results of equilibrium potential" + "data": { + "image/png": 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", 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure()\n", - "for i in range(0,len(v_si)):\n", - " t_i = solution[i][\"Time [s]\"].entries / 3600\n", - " ocp_p1 = solution[i][\"X-averaged negative electrode primary open-circuit potential [V]\"].entries\n", - " plt.plot(t_i, ocp_p1 ,ltype[i],label=\"$V_\\mathrm{si}=$\"+str(v_si[i]))\n", - "plt.xlabel('Time [h]')\n", - "plt.ylabel(\"Equilibruim potential [V]\")\n", - "plt.legend()\n", - "plt.title('Graphite')\n", - "\n", - "plt.figure()\n", - "for i in range(0,len(v_si)):\n", - " t_i = solution[i][\"Time [s]\"].entries / 3600\n", - " ocp_p2 = solution[i][\"X-averaged negative electrode secondary open-circuit potential [V]\"].entries\n", - " plt.plot(t_i, ocp_p2,ltype[i],label=\"$V_\\mathrm{si}=$\"+str(v_si[i]))\n", - "plt.xlabel('Time [h]')\n", - "plt.ylabel(\"Equilibruim potential [V]\")\n", - "plt.legend()\n", - "plt.title('Silicon')\n", - "\n", - "plt.figure()\n", - "for i in range(0,len(v_si)):\n", - " t_i = solution[len(v_si)- 1 - i][\"Time [s]\"].entries / 3600\n", - " ocp_p = solution[len(v_si)- 1 - i][\"X-averaged positive electrode open-circuit potential [V]\"].entries\n", - " plt.plot(t_i, ocp_p,ltype[len(v_si)- 1 - i],label=\"$V_\\mathrm{si}=$\"+str(v_si[len(v_si)- 1 - i]))\n", - "plt.xlabel('Time [h]')\n", - "plt.ylabel(\"Equilibrium potential [V]\")\n", - "plt.legend()\n", - "plt.title('NMC811')" + "data": { + "image/png": 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9PIfq/pybzqy9WBgfX23uw5Vl7rXeZiaTibi4OJKSkhzbbDYbSUlJtGjRotxj8vPzy7TVqcOciEjlnJ3kiA/Kz8fvzPI51vNMzeLLvNpclZiYyBtvvEFcXBzx8fGsXLmS/Px8x4R1s2bNIioqiuHDhwPQpUsXVqxYQbNmzRzNVQsWLKBLly5KdkREnKS1q3yb6eBBDDYbttBQbPXrezscr/BqktOjRw8yMzNZuHAhFouF2NhYxo0b52iuSktLK1Vzc8MNN2AwGJg/fz7p6emEhYXRpUsXbrnlFi/dgYhI9aWaHN9mtFiwNmyILToaqvjIKnfxesfj/v37079//3LfmzhxYqnXfn5+DB06tNQq0yIiUjma8di3FXTvzokffoAaPBhGbTw+pHfv3kyfPr3c915//XXatm1Lenq6R2KZM2cO3bt3Jy4ujsTERLZt2+bSY2bNmkWjRo149tlnXRm2SI2i5qoaogZPa6Ikx4e0atWK3bt3l9l+/PhxXn/9dZ544gmioqLcHseSJUuYNGkSjz76KKtXr6ZNmzaMGDGCtLQ0lxyzfft2PvjgA1q3bu3O2xDxeTabDVCSI75LSY4Pad26Nbt27SqzferUqTRt2pRbb73VI3HMnj2b4cOHM2zYMFq0aMHUqVMJCgpi/vz5F3xMdnY2DzzwAC+++OI5pxoQkYrR2lU+rLCQ+l27UvummzCcYz3ImkBPdgUZcnLO+Z7daITAwIrtazDAmWUlzrevPTjY6RhbtWrFoUOHyMvLI/BMPDt27GDRokUsWLDA6b/WXnvtNV5//fXz7rNu3ToaNWrkeF1QUMCOHTt44IEHHNuMRiOXX345W7ZsKfcczhwzbtw4+vbtS8+ePXnttdecuh8RKU3NVb7LdOAAfsnJGDIzsYeGejscr1GSU0ENEhLO+V5enz6k/+9/jtf1O3TAmJtb7r75l17KybOWmajXvTt+5fSTOXb0qNMxtmnThqKiIvbu3Uu7du0AmDBhAgMHDqRHjx7lHpOSksLkyZOZNWtWmfduvfVWrr322vNes/6fhiWmp6dTVFREnTp1Sm2vW7cu+/btK/ccFT1myZIlJCUlsWLFivPGJCIVo+Yq32U6U6tvbdECavAUK0pyfEjjxo0JCwtj9+7dtGvXjiVLlrBjxw6++eabcx4THR1dboIDEBkZSWRkpLvCdcrRo0d59tlnmTdvnqOWSkQujJqrfJf5TP/MwlatvByJd+nJrqDkM1Njl8f+pyz5+I4d5973T3MVnPjuuwsL7E9atmzJ7t27ycvL44UXXuD+++93NCdlZ2dz9913k5KSAsD48eNp3rw5d999N6tWrSpzrso0V0VFReHn51emw3Bqauo51xypyDE7d+4kLS2t1HQDRUVFbN68mTlz5nDgwAH9NSripJIh5JpM1feYziQ51pYtvRyJdynJqSBn+si4a9+KKOl8/NZbbwFw7733Ot5bt24dkZGRfPjhh9jtdrKyssqsHXa2yjRX+fv706FDBzZs2OBISGw2Gxs2bGDkyJHlnqMix1x++eV8+eWXpY579NFHad68OQ899JASHJFKUE2O7zKfaa5STY74lFatWrFixQo2bdrEK6+8QtBZnZxbtWrFhAkTmDx5Mv3796dr167nTXIq21w1evRoHnnkETp06ECnTp2YPXs2ubm5DBs2zLHPe++9x6pVq1i4cGGFjqlVqxat/vTDGhwcTGRkJK1bt9bK7yKVoMkAfVRuLn4HDwJgVZIjvqRNmzacPHmSHj16kJiYWOq95s2bs2bNGr744gsmTZrEkCFDuOqqq1wew/XXX096ejrTp08nNTWVtm3b8sEHH5RqrkpPT+fQmYXjKnqMiLiWmqt8k9FioaBHD4wnTmD704COmsZgry5rr7tYampquX/9Z2ZmEhYWVunzms3mKlurkJKSQkREBIGBgSxZsoT169fz0EMPnbNPTnVxoWV+od/zmsZgMNCgQQOSk5OpoR8fHueuMm/fvj3p6el89dVXtKzhfTf+TM+55/1VmZvNZqf/8FVNTg2ya9cunn/+eYxGI4GBgbz88sveDklEvEhDyMXXKcmpQXr16kWvXr3KbK/OtTgiUnmaDNA3GXJzsZ/VH7MmU0OsiEgNpdFVvqlejx7FE82eYwLWmkRPtohIDaXmKt9jPHECvxMnsBuN2Bo29HY4XqeaHBGRGkrNVb7HnJQEgDUuTk1WKMkREamR7Ha7oyZHzVW+w/zzzwAUnlm/sKZTkiMiUgOVzJEDqsnxJSVJjrVtWy9HUjUoyRERqYFKmqpASY4vKWmuUk1OMSU5IiI10Nk1OWqu8g2GrCzHcg6FqskBNLpKRKRGUnOV7zHk5ZHz97/jl5yMrXZtb4dTJSjJERGpgdRc5XtsdeqQMXWqt8OoUtRcJSJSA51dk6MFOsVX6cn2Ib1792b69Onlvvf666/Ttm1b0tPTPRLLnDlz6N69O3FxcSQmJrJt27bz7r9582Zuv/12OnfuTKNGjVi9erVH4hSpqUqSHJPJhMFg8HI04gqmX36BvDxvh1GlKMnxIa1atWL37t1lth8/fpzXX3+dJ554gqioKLfHsWTJEiZNmsSjjz7K6tWradOmDSNGjCAtLe2cx+Tk5NCmTRumTJni9vhEpHSSIz4gL4+6AwbQoGVLjCkp3o6mylCS40Nat27Nrl27ymyfOnUqTZs25dZbb/VIHLNnz2b48OEMGzaMFi1aMHXqVIKCgpg/f/45j+nTpw9PPvkkAwYM8EiMIjVdSZ8cNVX5BvMvv2CwWrGFh2OrX9/b4VQZSuErKCenYtW5JhNYrcX7+vvbKfkjyWqFggIDBoOds2faPtd5g4PtTsfYqlUrDh06RF5eHoGBgQDs2LGDRYsWsWDBAqc7F7722mu8/vrr591n3bp1NGrUyPG6oKCAHTt28MADDzi2GY1GLr/8crZs2eLU9UXEfVST41vMP/0EQGHHjqDmRwc93RWUkNDA6WPeeiuda68tbh9dtSqQe+6J4tJL81m06KRjn+7d65GeXjb5OHr0mNPXa9OmDUVFRezdu5d2ZyaCmjBhAgMHDqRHjx7lHpOSksLkyZOZNWtWmfduvfVWrr322vNes/6f/mJIT0+nqKiIOnXqlNpet25d9mlFXJEqoyTJ0cgq3+C/fTsAhRdd5NU4qholOT6kcePGhIWFsXv3btq1a8eSJUvYsWMH33zzzTmPiY6OLjfBAYiMjCQyMtJd4YqIF2lxTt9SUpNT0LGjlyOpWpTkVNBvvyVXaD+TyeT48PD3/6PJacCAPH77LRmDoXQz1HffnXBdkEDLli3ZvXs3eXl5vPDCC9x///2O5qTs7GzuvvtuUs50Shs/fjzNmzfn7rvvZtWqVWXOVZnmqqioKPz8/Mp0Mk5NTaVu3boXensi4iJqrvIdhqwsTHv3AqrJ+TM93RVU0T4yZjMUFpbd12QCk6ns9sr0vTmfks7Hb731FgD33nuv471169YRGRnJhx9+iN1uJysrC4vFcs5zVaa5yt/fnw4dOrBhwwb69+8PgM1mY8OGDYwcObKSdyUirlZYWAgoyfEF5h07MNjtWBs1wvanrgI1nZ5uH9OqVStWrFjBpk2beOWVVwg6q5dzq1atmDBhApMnT6Z///507dr1vElOZZurRo8ezSOPPEKHDh3o1KkTs2fPJjc3l2HDhjn2ee+991i1ahULFy4EimuZDhw44Hj/8OHDJCUlERkZWaqmSERco6TGWUlO9VcUE0PG+PGgkXJl6On2MW3atOHkyZP06NGDxMTEUu81b96cNWvW8MUXXzBp0iSGDBnCVVdd5fIYrr/+etLT05k+fTqpqam0bduWDz74oFRzVXp6OocOHXK8/umnnxg6dKjj9aRJkwAYOnQoM2fOdHmMIjVdSU2O2Wz2ciRyoYoaNSL7nnu8HUaVpCTHx3Tr1o2jR4+W+15KSgoRERHcdNNNBAQEsH79erckOQAjR448b/PUY489xmOPPeZ43aNHj3PGLSKup5ocqQn0dNcgu3bt4vnnn8doNBIYGMjLL7/s7ZBExEtUk+MbjOnpBHz5JQXdulEUG+vtcKqcKpHkrF69mmXLlmGxWIiJiWHUqFHEx8eXu+/EiRP55Zdfymzv1KkTTz/9tLtDrdZ69epFr169ymwvb2SViPg21eT4Bv+NG4l8+GEK27Yldc0ab4dT5Xj96d64cSNz585l9OjRJCQksGLFCqZMmcLMmTMJDw8vs//jjz/u+OEEOH36NE888QSXXnqpJ8MWEanWVJPjG/x//BGAgq5dvRxJ1eT1rtjLly+nb9++9O7dm8aNGzN69Gj8/f1Zu3ZtufvXqlWLiIgIx78dO3YQEBDAJZdc4uHIRUSqL9Xk+AYlOefn1afbarWyf/9+Bg0a5NhmNBpp3749e/bsqdA5vvrqK3r06OFYq+nPCgsLHX+xABgMBsewaoPW95Az9CxUXElZqcw8xx1lXpLkmM1mfS/LUS2e89xczElJABR261a1Y60Ad5S5V5OczMxMbDYbERERpbZHRERw7Nhfr920d+9ejhw5UmrCuz9bvHgxixYtcrxu1qwZ06ZNO+fsu7m5uRdcfavqX8+7kDL39/enQQPn1yar6aKjo70dQo3jyjIPCQlx/K/n/9yq9HO+YQMUFkJ0NPUuvthnFuZ0ZZlX63rKr776iqZNm56zkzLA4MGDS80XU5IhpqamlurbU6KgoKBUzY+zzGbzBR0vzrvQMi8oKCA5uWLLdkjxz1B0dDQpKSnY7a6dsVvK544yL1l6paioSM9/OarDcx6yejVhQG7nzljOLNdTnf1VmZtMJqeXB/JqkhMWFobRaCwz667FYilTu/NneXl5fPvtt6Vm0S2P2Ww+51/553pwbTYbRs0cWSOUPANV9UOsKrPb7So3D3NlmZ/dJ0ffx3Orys+5uaQ/TpcuVTbGynBlmXv1N7nJZCIuLo6kM22KUJxgJCUl0aJFi/Meu3nzZqxWK1dccYVLYwoODub06dPYbDaXnleqppycHAICArwdhojHaXRV9ZcxbRrp77xD3oAB3g6lyvJ6c1ViYiJvvPEGcXFxxMfHs3LlSvLz8x3zucyaNYuoqCiGDx9e6rivvvqKbt26ERoa6tJ4TCYTISEhZGVlVep4f39/CgoKXBqTnF9ly9xut2MymZTkSI2k0VXVn61uXfLOLIQs5fP6092jRw8yMzNZuHAhFouF2NhYxo0b52iuSktLK9PT+tixY+zatYtnnnnGLTGZTCbCwsKcPs5gMNCgQQOSk5N9quqwKlOZi1SOanKkJvB6kgPQv39/+p8jG504cWKZbQ0bNnSsXi0iIs5TTU71VuuVV8BuJ/fGGylq2tTb4VRZerpFRGog1eRUY3Y7IXPm4JeWRsFllynJOQ8NIRIRqYFUk1N9mfbuxS8tDXtgIAUXXeTtcKo0JTkiIjWQanKqL/9NmwAo6NwZNHDivJTkiIjUQKrJqb4CziQ5+T16eDmSqk9JjohIDVRSk6Mkp5qx2/+oydHC1H9JSY6ISA109gKdUn347duHX2oq9oAACjp18nY4VZ5SeBGRGkg1OdWT6dAhbKGhFLZrB4GB3g6nytPTLSJSA6kmp3rK79uXlJ9/xpie7u1QqgU1V4mI1EDqeFyN+flhc3I17ppKSY6ISA2k5qpqqLAQtHyNU5TkiIjUQCVJjr+/v5cjkYqqNWsW9S69lOAPP/R2KNWGkhwRkRqooKAAUJJTnQR88w2mI0e8HUa1oiRHRKQGUpJTvRgyM/HfsgWA/J49vRxN9aEkR0SkBtKyDtVLwMaNGIqKsMbFUdSkibfDqTaU5IiI1ECqyaleAr7+GoC8K6/0ciTVi5IcEZEaSElO9RLwzTeAmqqcpSRHRKQGUpJTffgdOIDp4EHsJhMFWpTTKZogQUSkBtIQ8mrEz4+skSMxnj6NvVYtb0dTrVQoyencubNTJzUYDCxdupRGjRpVKigREXGv/Px8QElOdVDUtCmZkyd7O4xqqUJJzvbt23nssceoVYEM0m63M3XqVMcPkIiIVD0aXSU1QYWbq5544gnq1atXoX1ffvnlSgckIiLupz451YMpKQljZiYF3bqBElKnVajj8YEDB6jrxGJgv/zyCzExMZUOSkRE3MdutzuSnICAAC9HI+dT6z//oc7QoYROn+7tUKqlCiU5MTEx/PzzzxU+aZMmTfDz86t0UCIi4j4lTVWg5qoqzWol8KuvAMjv08fLwVRPFR5C3qFDB7p3787s2bM5ffq0O2MSERE3OjvJUXNV1eW/ZQtGiwVbRAQFXbp4O5xqqcJJztdff03btm157LHHaNCgAbfffjvr1693Z2wiIuIGZw8MUZJTdQV+/jkAeX36gEkzvlRGhZOcK664gnfffZfk5GRef/11Dh48yJVXXkmLFi2YNm0aKSkp7oxTRERcpKQ/jp+fn7oWVFV2O4GrVgGQd/XVXg6m+nJ6xuOQkBBGjhzJ119/zZ49exg6dChvvPEGTZs25brrrnNHjCIi4kIaPl71mX79tXiW48BA9ce5ABe0rEN8fDzjxo3jmWeeITQ0lBUrVrgqLhERcZOS5iqNrKq6AtetAyCvVy/NcnwBKt3I98033/Duu+/y8ccfYzQauemmm7jzzjtdGZuIiLiBanKqvqx77yX/ssvAqCUmL4RTSc6xY8eYM2cOc+bMYe/evfTo0YPXXnuNm266iZCQEHfFKCIiLqSJAKsBg4HCjh29HUW1V+EkZ8CAAXzxxRfUqVOH2267jVGjRtGyZUt3xiYiIm6gJEdqigonOWazmUWLFpGYmKje+CIi1VhJn5zAwEAvRyLlqX3TTRQ1bkzmY49h00LXF6TCSc7SpUvdGYeIiHiIOh5XXX4HDhDw7bfY/fzIeOYZb4dT7VWoR9OQIUPIzMys8ElHjBjBiRMnKh2UiIi4j5Kcqivo008ByL/sMuxRUd4NxgdUqCZnyZIlpKamVuiEdrudZcuW8fzzz1d41XIREfGckiRHfXKqGLudoMWLAcgdPNjLwfiGCiU5drudFi1auCWA1atXs2zZMiwWCzExMYwaNYr4+Phz7p+dnc28efP4/vvvycrKom7dutx+++107tzZLfGJiPga1eRUTeakJMz79mEPDCRvwABvh+MTKpTkrF271ukTN6pAZ6mNGzcyd+5cRo8eTUJCAitWrGDKlCnMnDmT8PDwMvtbrVYmT55MWFgYjz76KFFRUaSlpREcHOx0fCIiNVVeXh6gJKeqCfrkE6B4GQd7aKiXo/ENFUpyrrzySrdcfPny5fTt25fevXsDMHr0aLZu3cratWsZNGhQmf2/+uorsrKyeP755zGdWaxMTWIiIs7R6KoqqKiIoDMDfNRU5TpeW9bUarWyf//+UsmM0Wikffv27Nmzp9xjtmzZQkJCAu+88w4//vgjYWFhXHbZZQwaNAjjOWaFLCwsdMzuCWAwGAgKCnJ87Uol53P1eeXcVOaepzL3PFeXeck8OQEBAfo+noPHn/OCAnJuuYWAtWvJ79OnRn5f3FHmXktyMjMzsdlsRERElNoeERHBsWPHyj3m+PHjpKamcvnll/P000+TkpLC22+/TVFREUOHDi33mMWLF7No0SLH62bNmjFt2jTq1q3rsnv5s+joaLedW8qnMvc8lbnnuarMSzocR0VF0aBBA5ec01d59DmfMQOAmv4dcWWZey3JqQy73U5YWBj/+Mc/MBqNxMXFkZ6eztKlS8+Z5AwePJjExETH65IMMTU1FavV6tL4DAYD0dHRpKSkYLfbXXpuKZ/K3PNU5p7n6jJPS0sDimvUk5OTL/h8vkjPuef9VZmbTCanKyi8luSEhYVhNBqxWCyltlssljK1OyUiIiIwmUylmqYaNWqExWLBarU6+umczWw2n3MROnc9uHa7XT8UHqYy9zyVuee5qszPHl2l7+H5eeI599+wAWNGBnlXXw0a1u/SMvfa8qYmk4m4uDiSkpIc22w2G0lJSeccrt6yZUtSUlKw2WyObcnJyURGRpab4IiISFklo6s0T07VEPrKK0TdfTe13n7b26H4nAplBp06dapwR6CtW7dW+OKJiYm88cYbxMXFER8fz8qVK8nPz6dXr14AzJo1i6ioKIYPHw5Av379+Oyzz5gzZw79+/cnJSWFxYsXM0DzCYiIVJhGV1Udfvv3E7B5M3ajkRyNqnK5CiU55Q3ndoUePXqQmZnJwoULsVgsxMbGMm7cOEdzVVpaWqnkqk6dOvzzn//k/fff54knniAqKooBAwa4LT4REV+kyQCrjuAFCwDI79ULmzqBu1yFkpwJEya4LYD+/fvTv3//ct+bOHFimW0tWrRgypQpbotHRMTXKcmpIqxWgv/v/wDIueUWLwfjm7zWJ0dERLxDa1dVDQFr1+J3/DhFtWuTd9VV3g7HJzndW7eoqIhXXnmFhQsXcvjwYcekUiXS09NdFpyIiLheScfjkolRxTuC588HIPeGGzSqyk2crsmZNGkSM2bMYNiwYWRkZPDoo48yZMgQjEZjuc1LIiJSteTm5gJKcrzKasV46hSgpip3cjrJ+fDDD5k9ezaPPfYYJpOJW265hbfffptnn32WzZs3uyNGERFxoZycHAAtbuxNJhMnP/mEE19/jfUc06bIhXM6yUlJSaF9+/YA1KpVi4yMDKB4OPiKFStcG52IiLicanKqDmt8vLdD8GlOJzmNGzd2TAPevHlz1qxZA8APP/ygnvoiItVASU2OkhzvMO3Zg0H9Vz3C6SRn8ODBfPnllwCMGTOG8ePHk5CQwG233caoUaNcHqCIiLhWSU2Omqu8I+Lxx4nu1o2AM5UE4j5Oj66aOnWq4+thw4bRtGlTNm3aREJCAtdee61LgxMREdey2WwaXeVF5h078N+yBbvZTOFFF3k7HJ93wQs+XXrppVx66aWuiEVERNysJMEBJTneEDJnDgC511yDrV497wZTA1QoyVm6dCkDBgzAbDazdOnS8+573XXXuSQwERFxvZKmKlCS42nGkycJWrIEgOw77vBuMDVEhdeuSklJoV69euddJ8pgMFBUVOSq2ERExMVKkpzAwECMRk1670khc+ZgyMujoEMHCrt29XY4NUKFkhybzVbu1yIiUr2UjKzSCuSeZcjNJfi99wDIuuceOGvxaXEfp9P4uXPnOtY9OVtBQQFz5851SVAiIuIeGlnlHeYtWzBmZ2Nt2pS8a67xdjg1htNJzsiRIx0TAJ7t9OnTjBw50iVBiYiIe2giQO8ouPxyjm/ezKnXXgPTBY/5kQpyuqTtdjuGcqrZfv/9d8LDw10SlIiIuIcmAvQeW/362OrX93YYNUqFk5xOnTphMBgwGAz07dsX01mZaFFREQcOHKB///5uCVJERFxDzVUeZrdj2rsXa0KCtyOpkSqc5JSMqtq+fTt/+9vfqFWrluM9f39/YmNjueGGG1weoIiIuI6aqzzLf/166txyC7n9+nHq3XfV4djDKpzkTJgwAYDY2FiGDRumnvkiItWQViD3ILud0FdeAaCocWMlOF7gdJ+c22+/HSgeTXXixIkyQ8qbNm3qmshERMTlVJPjOf7ffkvA999j9/cn6777vB1OjeR0kvPbb78xatQoNm7cWGp7SYdkTQYoIlJ1qeOx55TU4uQMH46tQQMvR1MzOZ3k3HHHHZhMJpYvX06DBg3KHWklIiJVkxbn9Az/jRsJ2LwZu78/p++/39vh1FhOJznbt29ny5YttGrVyh3xiIiIG6m5yjNCZ8wAIOfmm7E1bOjlaGoupycDbNOmDWlpae6IRURE3EzNVe5nTE/H7/Bh7GYzWQ884O1wajSnk5xp06YxduxY1q1bx8mTJ8nMzCz1T0REqi7Nk+N+tqgoTnzzDSc/+ICiRo28HU6N5nRz1VVXXQVA3759S21Xx2MRkapPzVUeEhhIweWXezuKGs/pJGft2rXuiENERDxAzVVuZLUStHw5uYmJWp+qinD6u3DllVe6Iw4REfEATQboPkGLFhH52GMEv/8+Jz/5RJP/VQFO98kBWL9+PX//+9/p0aMHR48eBeB///sfGzZscGlwIiLiWtnZ2QClluYRF8jNJWz6dADy+vdXglNFOJ3kfPzxx/ztb38jKCiIrVu3kp+fD0BGRgYvvPCCywMUERHXOX36NAChoaFejsS31HrnHfySk7E2bEj2mZUBxPucTnImT57MW2+9xezZszGbzY7tl112GVu3bnVpcCIi4lpZWVmAanJcyXj8OLVeew2A008+CVrbscpwOsnZvXs3PXv2LLM9PDwci8XiiphERMQN7Ha7anLcIGzaNIzZ2RR06kTukCHeDkfO4nSSEx0dzd69e8ts37BhA3FxcS4JSkREXC83N9exqLJqclzD/NNPBC9YAEDGpElgrFRXV3ETp0dXjR49moceeoh3330Xg8HAsWPH2LRpE48//jjjx493R4wiIuICJbU4BoNBo6tcxB4YSP4ll1DUsCGFXbp4Oxz5E6eTnKeeegqbzUbfvn3JycmhZ8+eBAQE8PjjjzNmzBh3xCgiIi5wdlOVFld2DWvLlpxctAjDmYVPpWpxOskxGAz885//5IknnmDv3r1kZWXRpk0bVX2KiFRx6nTsQnb7H8PEDQbsmlyxSnI6yRk1ahSvvvoqoaGhtGnTxrE9OzubMWPG8O677zodxOrVq1m2bBkWi4WYmBhGjRpFfHx8ufuuW7eOf//736W2mc1mPvzwQ6evKyJSk6jTseuETp2KIT+f048/jl1JY5XldJLz/vvvM3Xq1DI/JLm5ucydO9fpJGfjxo3MnTuX0aNHk5CQwIoVK5gyZQozZ84kPDy83GOCgoJ49dVXnQ1dRKRGU02Oa5h+/ZVab76JoaiI/J49ye/Tx9shyTlUOMnJzMzEbrc7hiAGnjUPQFFREStXrqRevXpOB7B8+XL69u1L7969geKOzVu3bmXt2rUMGjSo3GMMBgMRERFOX0tEpCZTTY4L2GxEPPUUhqIicgcOVIJTxVU4yYmIiMBgMGAwGGjRokWZ9w0GA5MmTXLq4larlf3795dKZoxGI+3bt2fPnj3nPC4vL4/77rsPu91Os2bNuOWWW2jSpIlT1xYRqWm0pMOFC54/H/8ff8QWElI8ZFyqtAonOWvXrsVut9OnTx8+/vhjoqKiHO/5+/sTExNDw4YNnbp4ZmYmNputTK1MREQEx44dK/eYhg0bcu+99xITE0NOTg5Lly7lmWeeYcaMGdSuXbvM/oWFhRQWFjpeGwwGx+q7rh5dUHI+jVrwHJW556nMPc9VZV7SXKXRVX+tvDI3njxJ2JQpAGQ98QT2Ro1QKbqOOz5bKpzklKw+fuDAAZo0aYLRSxMetWjRolRNUosWLXjkkUf4/PPPufnmm8vsv3jxYhYtWuR43axZM6ZNm0bdunXdFmN0dLTbzi3lU5l7nsrc8y60zO12u+M8DRo0cEVIPq9UmY8dCxYLdOxI2D//SZjJ6W6tUgGu/Gxx+jsUExODxWLhnXfe4ddffwWgbdu2jBo16pwdhc8lLCwMo9FYZjkIi8VS4T43JpOJZs2akZKSUu77gwcPJjEx0fG6JENMTU3FarU6Fe9fMRgMREdHk5KS4vgwEfdSmXueytzzXFXmJZ+TBoOB5ORkV4Xnk/5c5sbjx6m7bBkGo5GTU6ZQmJrq7RB9zl895yaTyekKCqeTnB9//NGxCvnFF18MwIwZM5gyZQpr1qyhc+fOFb+4yURcXBxJSUmOc9lsNpKSkujfv3+FzmGz2Th8+DCdOnUq932z2VxqIdGzuesDuqSDtniOytzzVOaed6FlXtLxuFatWvreVVBJmRfVq8eJr74iYP16Cjp1Kp4nR9zClZ8tTic5jzzyCNdddx2zZ8/GdKaqzmq1ctddd/Hwww/zzTffOHW+xMRE3njjDeLi4oiPj2flypXk5+fTq1cvAGbNmkVUVBTDhw8HYNGiRSQkJBAdHU12djZLly4lNTWVvn37OnsrIiI1ikZXXRhbdDS5Q4d6OwxxQqVqcs5OcKC4Rmbs2LF07drV6QB69OhBZmYmCxcuxGKxEBsby7hx4xzNVWlpaaU6IWVlZfGf//wHi8VCSEgIcXFxTJ48mcaNGzt9bRGRmkTz5DjP/9tvISuL/H79vB2KVILTSU5YWBiHDx+mVatWpbYfOXKk0n8d9O/f/5zNUxMnTiz1+o477uCOO+6o1HVERGqyzMxMQDU5FXb6NOEPP4zp6FFOzZypWpxqyOkhUsOGDePOO+9kwYIFHDlyhCNHjjB//nzuuusubrnlFnfEKCIiLlAyyEOTqVbQk09iOnoUa9Om5F1zjbejkUpwuiZn+vTpGAwGbrvtNsfoJLPZzL333svUqVNdHqCIiLhGRkYGoCSnIvw3bIA33wTA8tJL2IODvRyRVIbTSY6/vz+vvvoq//rXv9i3bx8AzZs3J1gPgIhIlVVYWOjok6Mk5/wMFgsRDz0EQPZtt1Fw+eVejkgqq9IzGQUHBzt+UJTgiIhUbSW1OAaDgbCwMC9HU7WFP/MMfsnJEB/P6Wef9XY4cgGc7pNjtVoZP3484eHhxMbGEhsbS3h4OM8880yp5RNERKTqKOmPEx4ejp+fn3eDqcLM27YRvHgxdj8/+OADNVNVc07X5IwZM4ZPPvmEF198kUsvvRSATZs2MXHiRE6ePMmbZ9owRUSk6jh16hSgpqq/UtipEyffew/zoUOEde8Omhm6WnM6yfnoo4+YP38+AwYMcGzr0KEDTZo04ZZbblGSIyJSBWlkVcXl9+tHgcGAGvWqP6ebqwICAoiNjS2zvVmzZvj7+7siJhERcTElOecX+NlnGFVr43OcTnIeeOABnn/+efLz8x3b8vPzmTJlCg888IBLgxMREddQknNupt27ibz3XupddRV+Bw96OxxxIaebq7Zt28aXX35J48aN6dixIwA//fQTBQUF9O3blyFDhjj2/eSTT1wXqYiIVNrZHY/lLAUFRI4ZgyE/n/zLLqMoJsbbEYkLOZ3kREREcMMNN5Ta1qRJE5cFJCIirqeanPKFTp+O+eefKYqMxPLyy3DWWolS/Tmd5Lz33nvuiENERNxIsx2X5f/dd9T6978ByHjpJWz16nk5InE1p/vkiIhI9aOanNIMmZlEPPQQBrudnGHDyDtrxLD4DiU5IiI1gJKc0mq98QamI0ewNmlCxqRJ3g5H3KTSyzqIiEj1UTIZYGRkpJcjqRqyHn4YY1YWuYMGYQ8N9XY44iZKckREagDV5JRmDwoiY8oUb4chbqbmKhERH2ez2dTxGMBmI2jxYrDZvB2JeEilanJ++OEH1q5dy4kTJ7D96WGZMWOGSwITERHXyMzMxG63AzV7npyQ2bMJf+45ghYvJv399zVcvAZwOsl54YUXeOaZZ2jZsiX169fHcNZDYtADIyJS5ZQ0VYWEhNTY5XdMv/xC2NSpAOT166cEp4ZwOsl59dVXeffdd7njjjvcEI6IiLhaje+Pk5dXPKtxQQF5V19NzogR3o5IPMTpPjlGo5HLLrvMHbGIiIgb1PQkJ+xf/8K8axdFdepgmT5dtTg1iNNJziOPPMIbb7zhjlhERMQNavK6VQFff02tt98GwDJjBrY6dbwckXiS081Vjz/+ONdccw3NmzenTZs2mM3mUu9rUU4RkaolPT0dgKioKC9H4mFWK+FPPglA9u23k9+3r5cDEk9zuibnwQcfZO3atbRo0YLatWsTHh5e6p+IiFQtJUlO7dq1vRyJh5lMpL/zDrkDB5I5fry3o/F5djtMnx7KO++EeDsUB6drct5//30+/vhjrrnmGnfEIyIiLlZja3IAa9u2nJo929th1AgGA6SmGvnPf0K4/vpc6tTx/nxETic5UVFRNG/e3B2xiIiIG5w8eRKoOUmO8dgxjOnpWNu183YoPu/YMSM2m4HGjYsAePrpTK68Mp/atb2f4EAlmqsmTpzIhAkTyMnJcUc8IiLiYjWqJsduJ+Lxx6l7zTUELVjg7Wh8VlERzJ4dQq9e9Rg7Npwzc00SEWFn4MC8KjOAzemanNdee419+/ZRv359YmNjy3Q83rp1q8uCExGRC1eyOGdNSHKCP/iAwK+/xh4YSGGXLt4OxyclJZl44okIduwonlgyK8vI6dMGwsLsXo6sLKeTnEGDBrkhDBERcZea0lzld+gQYc89B0DmU09hjY/3ckS+JSfHwPTpocyeHYLNZiAszMa4cZmMGJGDsYquhOl0kjNhwgR3xCEiIm5gt9trRnOVzUbEo49izMkh/5JLyL7zTm9H5FO+/DKAcePC+f334rTh2mtzmTQpg/r1q0bfm3Op1AKdIiJSPZw+fRqr1Qr4dpIT8s47BGzejC04GMuMGVTZqoVq5sQJI88+G86yZUEANG5s5YUXMujbN9/LkVVMhZKcqKgo9uzZQ506dYiMjDzvQpwlfzGIiIj3lTRVhYSEEBgY6OVo3MPv8GHH4puZzz5LUUyMlyOq/mw2+OijYF54IYyMDCNGo53Ro7N5/PHTBAdXvb4351KhJOeVV14hNDQUgJkzZ7ozHhERcaGa0FRV1LgxmU8/jf+mTeT8/e/eDqfay8uD4cNr8913AQB06FDAiy9m0L59oZcjc16Fkpzbb7+93K9FRKRqqxGzHRuNZN91V3E/nKoydrkaCwyEpk2L2LnTxtixpxk5MhtTNe3c4nTYhw8fPu/7TZs2rXQwIiLiWr5ck2NMS8MWEgJBxf1FlOBU3s6dZurUKaJBg+KOxM8+m8ETT5ymUaMiL0d2YZxOcmJjY8/bJ6eoqHoXiIiILylJciIjI70ciYvZ7UQ89BCmgwc5NWsWhZ06eTuiauvDD4N5+ulwevfOZ86cdAwGiIqyA9X/97nTSc62bdtKvS4sLGTbtm3MmDGDKVOmVCqI1atXs2zZMiwWCzExMYwaNYr4Csxv8O233/Lqq6/StWtXxo4dW6lri4j4Ml+tyQlasoTAdeuwBwRgCwvzdjjVWteuBfj5QXCwnfz84uYqX+F0ktOxY8cy27p27UrDhg156aWXGDJkiFPn27hxI3PnzmX06NEkJCSwYsUKpkyZwsyZM8+7qvmJEyf43//+R+vWrZ29BRGRGsMX++QYTp0i7MycbafHjKFI6yk6JSfHwIYN/vTrVzwMvGVLK19+eYK4uOpfc/NnLptIoGXLlvzwww9OH7d8+XL69u1L7969ady4MaNHj8bf35+1a9ee8xibzcbrr7/OTTfdRL169S4kbBERn+aLsx2HTZmCX1oahQkJZN1/v7fDqVbWr/enb9+63HlnFNu3/7Esky8mOFCJmpzMzMxSr+12O8nJyUycOJGEhASnzmW1Wtm/f3+ppSKMRiPt27dnz5495zxu0aJFhIWF0adPH3799dfzXqOwsJDCwj+GvRkMBoLOdFI7X9+iyig5n6vPK+emMvc8lbnnXUiZn12T4wvfM//NmwmZNw+AjBdfxBAQ4Jbr+Npznplp4Lnnwvjoo2AAGjWyUlBgqFL3544ydzrJiYiIKBOA3W6nSZMmzJ8/36lzZWZmYrPZiIiIKHONY8eOlXvMrl27+Oqrr3jxxRcrdI3FixezaNEix+tmzZoxbdo06tat61SszoiOjnbbuaV8KnPPU5l7XmXKvOQP0xYtWtCgQQNXh+RZ+fnw9NPFX999N3U8sJaiLzzna9bAqFFw9Gjx6/vvh3/9y0RoaB3vBnYOrixzp5OcPzcjGY1G6tatS3x8PCY3D6TPzc3l9ddf5x//+AdhFexoNnjwYBITEx2vSxK01NRUx1TnrmIwGIiOjiYlJQW7vfrMCFmdqcw9T2XueRdS5idOnACKm/mTk5PdEZ7HGE6eJKJxY8zp6aQ+/DB2N96PLzznOTkGnn8+lPffDwGgWTMrL7+cwSWXFJCVBVlZXg7wT/6qzE0mk9MVFE5lJYWFhbz//vuMHz+eZs2aOXWh8oSFhWE0GrFYLKW2WyyWMrU7AMePHyc1NZVp06Y5tpUUxM0338zMmTPLZIBmsxmz2Ux53PXg2u32avtDUV2pzD1PZe55zpZ5YWEhGRkZQHGfnOr+/bJHRZH+/vsYU1KwhYeDB+6nuj7nP/xg5uGHIzl4sPjX/MiRWYwbV7wkQ1W/HVeWuVNJjtls5uOPP2b8+PGuubjJRFxcHElJSVx88cVA8V8bSUlJ9O/fv8z+DRs2ZPr06aW2zZ8/n7y8PO644w7q1KmaVW8iIt5Q8gek0Wg872jVasVgwFbdm93cKD8fZswI5d//roXNZqBBgyJmzDhFz54F3g7NK5weXTVo0CA+/fRTlwWQmJjIl19+ybp16/j99995++23yc/Pp1evXgDMmjWLjz76CAB/f3+aNm1a6l/JonNNmzZ1e3OZiEh1UtLpOCIiAj8/Py9HU3lBCxYQ8eijGLQA9Hnt2mXimmvqMmtWKDabgRtvzOHLL0/U2AQHKtEnJyEhgeeee45vv/2WLl26EBISUur9Bx980Knz9ejRg8zMTBYuXIjFYiE2NpZx48Y5mqvS0tKqVO9vEZHqwheGjxvT0gh/7jmMFguFbdqQfddd3g6pyrJa4bffTERFFfHiixkMGJDn7ZC8zmB3suHrfH1xDAYD+/fvv+CgPCE1NbXU0HJXMBgMNGjQgOTk5GrZhlsdqcw9T2XueZUt8+XLl/OPf/yDiy++mMWLF7sxQveJeOABghcvpqBdO9JWrMBTK0VWl+c8K8tArVp/xLd8eSDduxdQt67Ni1FVzl+Vudlsdm/HY4ADBw44e4iIiHhBdZ/tOGDdOoIXL8ZuNJLx4oseS3CqA7sd3n8/mBdfDOP//i+Ntm2LRwsnJqr25mwXNONxde11LiJSE1Tn5ipDbi7hZ+bEyR45ksJylhSq6davDyAjw8gHH4T89c41VKWSnHfeeYd27doRGBhIYGAg7dq14+2333Z1bCIicgFOnToFVM8VyGu98gqmw4cpatCA01qAGSiuvckvXm4KgwGmTctgyhQLU6ZkeDewKszpur9nn32WGTNmMGbMGC699FIANm3axCOPPMLhw4d57rnnXB6kiIg4r7o2Vxlycwn++GMALC+8gL1WLS9H5H0nTxp56qlw/P3tvPGGBYA6dWzccUeOdwOr4pxOct58801mz57NLbfc4th23XXX0aFDB8aMGaMkR0SkiqiuzVX2oCBOfPEFQcuWkd+vn7fD8bo1awJ44okI0tL8MJnsPPxwFgkJrp2x31c53VxVWFhI165dy2zv0qWLy5dJEBGRyiupyaluSQ6APTKSnNtu83YYXnX6tIFHH41g5MjapKX50bJlIcuXpynBcYLTSc6tt97Km2++WWb7f//7X0aMGOGSoERE5MJVt+YqY3IyQZ9+6pHlGqq6b7/1p2/fuixYEIzBYOfee7NYuTKV9u1dO/WJr6tQc9Wjjz7q+NpgMPD222+zZs0aLrnkEgC+++47Dh8+zG01POsWEakq7HZ7tavJCR8/nqBVqzAnJZH5zDPeDscrcnNh6tQw3n67uB9S06ZWZs600L17zZ21+EJUKMnZtm1bqdddunQBYN++fQDUqVOHOnXq8PPPP7s4PBERqYzs7Gzy8ornTKkONTmBn31G0KpV2E0mcoYM8XY4XrF9u5mHHopg797iRaVHjMjm2WczS032J86pUJKzdu1ad8chIiIulJaWBkBwcDDBwcFejub8DFlZhP/znwBk3XMP1jZtvByRZxUWwquvhvLaa7UoKjJQv34R06db6NMn39uhVXuaPlJExAeVJDl16tTxciR/LfTFF/FLTsYaE8Pphx/2djgeN3nyH81T11+fw5QpGURGqvbGFSqU5AwZMoQ5c+YQFhbGkL+oRvzkk09cEpiIiFReyfDxqt5UZd62jZB33wUgY+pUCAryckSed889WXzxRSBjx2Zy/fValsGVKpTkhIeHO1YCDw8Pd2tAIiJy4apFTY7NRsSTT2Kw28kZMoT8nj29HZFHHD7sx6pVgfzjH9kANGhg4+uvT2hpLjeoUJG+99575X4tIiJVU7VIcoxGMiZNImzqVDInTPB2NB6Rnm6gX7+6nD5tpFkzK/36Ffe7UYLjHipWEREfVF2aqwouvZS0JUu8HYbHREXZGT48h59+MtOqlSb1c7cKJTmdOnVyNFf9la1bt15QQCIicuGqdE2O3Y7xxAls9et7OxK3s9vh00+D6NChgObNiwB46qlM/PzAz8/LwdUAFUpyBg0a5OYwRETElapykhO4dCkRjz3G6XHjyB41ytvhuE3JoporVwbRuXMBn36ahp8f+Pt7O7Kao0JJzoQa0lYqIuIrqmpzlcFiIXzCBIy5uRgyMrwdjtusXh3I2LHhnDxZvKhmnz55Wq3CC9QnR0TEB1XVmpywyZPxS02lMD6erPvu83Y4LpeRYeDZZ8NZtKh4AsZWrQp59dVTtGun/jfeUKEkJyoqij179lCnTh0iIyPP2z+nZK0UERHxjqKiIsdncVVKcvy//ZaQefMAyJg+HQICvByRa339dQCPPhpBSoofRmPxopqPPXba126zWqlQkvPKK68QGhoKwMyZM90Zj4iIXCCLxYLNZgOq0OKcublEjB0LQPZtt1HQrZuXA3Kd7GwDkyeHMXduCACxsVZmzjxFt25aMdzbKpTk3H777eV+LSIiVU9JU1VkZCSmKjIBS+jMmZgOHqQoOprMp5/2djgu8803ATzxRDi//15cziNHZjFu3GmCg9UBpypw+uk/fPjwed9v2rRppYMREZELVxX749gDA7GbTFj+9S/sYWHeDueC2e0wdmw4H31UXHvTuLGV6dMtXHFFgZcjk7M5neTExsaet09OUVHRBQUkIiIXpiomOVmPPELu0KEUNW7s7VBcwmCAgAA7BoOdkSOzeeqp04SEqPamqnE6ydm2bVup14WFhWzbto0ZM2YwZcoUlwUmIiKVUzJ8vEr0x7HbizMCqPYJTnq6kdxcaNSouL/T00+f5vrrc9X3pgpzOsnp2LFjmW1du3alYcOGvPTSS3+5SrmIiLhXamoq4P2aHL+9e4l85BEsU6dibdvWq7FcqG+/9efeeyNp0cLKwoUnMRohJMSuBKeKM7rqRC1btuSHH35w1elERKSSjh8/DkB9by6bUFRE5COP4L91K2HTpnkvDhdp3LiInBwDp04ZOXnSZb86xc2crsnJzMws9dput5OcnMzEiRNJSEhwWWAiIlI5J06cACA6OtprMdR66y38t27FFhqK5V//8loclWW1wurVUNJ4ERNTxIIFJ2nfvlDLMlQjTic5ERERZToe2+12mjRpwvz5810WmIiIVE5KSgrgvZoc065dhE6fDkDGpEnYGjXyShyVtXWrmSefjOCXX+CTT/zp3j0fgC5d1DRV3Tid5Kxdu7bUa6PRSN26dYmPj68y8zGIiNRkXm2uKiwk4qGHMBQUkHf11eTedJPnY6ikjAwDU6eG8b//BWO3G4iKglOnzj2aWKo+p7OSK6+80h1xiIiICxQUFDiWdPBGkhM6Ywb+SUnYIiKwvPiiY2RVVWa3w6efBjFpUhipqX4A3HRTDrNmBWO15mthzWrM6SRn6dKlFd73uuuuc/b0IiJyAUpGVpnNZiIjIz17casV/61bAbD861/Y6tXz7PUrISnJxPjx4Xz/ffECU82bFzJ1agaXXVZI3brBJCd7OUC5IE4nOYMGDcJgMGD/U2r7520Gg0ETA4qIeNjZ/XHON3GrW5hMnPzoIwK++or8q6/27LWddPKkkWnTQvnoo+KmqaAgGw88kMW992adWVCz6tdAyV9zehzcmjVruOiii1i1ahUWiwWLxcKqVavo3Lkzn332GTabDZvNpgRHRMQLvD583M+vSic4hYXwzjshXHFFPT78MAS73cCgQTl8880JHn44SyuG+xina3Iefvhh3nrrLS6//HLHtr/97W8EBwdz99138+uvv7o0QBERqThvJDkhs2djOnCAjPHjISjIY9etjOefD+Odd2oB0LZtIc8/n0H37lpvylc5neTs27ePiIiIMtvDw8M5ePBgpYJYvXo1y5Ytw2KxEBMTw6hRo4iPjy933++++47FixeTkpJCUVER0dHRXHvttfTs2bNS1xYR8SWeHj7uv3EjYc8/j6GoiIJOncgdOtQj13VGURH4Ffcn5rbbslm2LIhHHz3N8OE5ju3im5xOcrp168ajjz7K//73P8cP0fHjx3niiSe4+OKLnQ5g48aNzJ07l9GjR5OQkMCKFSuYMmUKM2fOJDw8vMz+tWrVYsiQITRs2BCTycTWrVv597//TVhYGBdddJHT1xcR8SVHjhwBoLEH1okyHj1K5D33YCgqImfIEHJvvNHt13TGoUN+TJkSRq1admbMsAAQH1/E5s3H1SxVQzjdJ+fdd98lOTmZpk2bEh8fT3x8PE2bNuXo0aO88847TgewfPly+vbtS+/evWncuDGjR4/G39+/zHw8Jdq2bcvFF19M48aNiY6OZuDAgcTExLBr1y6nry0i4msOHz4MQNOmTd17obw8okaPxu/kSQrbtiWjCg4XP3nSyIoVQXz8cRApKX/8ulOCU3M4XZMTHx/Pjh07+Pzzzx2JRevWrbnqqquc7slvtVrZv38/gwYNcmwzGo20b9+ePXv2/OXxdrudpKQkjh07xogRI8rdp7CwkMLCP2apNBgMBJ1pM3b1yIOS83l8REMNpjL3PJW55zlT5mcnOW77HtnthI8di/9PP2GLjOTUu+9CcLDXxyMdPWrkxx/9uf76PAC6dLHyzDOZ9OmTT4MGdpwZMaXn3PPcUeaVmqLYYDDQr18/+vXrd0EXz8zMxGazlenjExERwbFjx855XE5ODv/4xz+wWq0YjUbuvPNOOnToUO6+ixcvZtGiRY7XzZo1Y9q0adStW/eCYj8fb64XU1OpzD1PZe55f1XmWVlZnDx5EoDu3buX2+TvEuPHw8cfg58fxoULqVeJrgqudOQI/Otf8PbbxZVJiYlQ0lr3/PMXdm49557nyjKvcJIzcOBA5s2b5/ihmTp1Kvfcc48jQTl58iRXXHEFv/zyi8uCO5fAwEBeeukl8vLy2LlzJ3PnzqV+/fq0bdu2zL6DBw8mMTHR8bokQ0xNTcVqtbo0LoPBQHR0NCkpKWXmERL3UJl7nsrc8ypa5iWjWyMiIsjJySEnJ8ct8fi3b09kaCiZkyaR27Yt3pox77ffTLz5ZggffxxEYWHxZ/tll+Wzf38mfn4X9vmu59zz/qrMTSaT0xUUFU5yPvvsM/Lz8x2vX3jhBW666SZHkmO1Wtm9e7dTFw8LC8NoNGKxWEptt1gs5Y7gKmE0Gh2ZXmxsLEePHuXTTz8tN8kxm82YzeZyz+OuB9dut+uHwsNU5p6nMve8vyrzs5uq3Pm9yb/8ck58+y222rXx9JoHdjts3uzPW2/V4osvAh3be/TI59FHT3PppQWO/VxzPT3nnubKMq9wkvPnC7oiAJPJRFxcHElJSY6RWTabjaSkJPr371/h89hstlL9bkREaqJDhw4B0KRJE5efO3DlSqwxMVjP/DFpq13b5dc4n/x8WLUqiNmzQ9i+3R8Ag8FO//55/OMfWXTrpt8BUpbXlw1PTEzkjTfeIC4ujvj4eFauXEl+fj69evUCYNasWURFRTF8+HCguI9N8+bNqV+/PoWFhWzbto3169dz1113efEuRES8ryTJiYmJcel5A5ctI/L++7GHhpK6ahVF7h65dZbkZCPvvx/CvHnBpKUVT2oTEGBn6NAc7r47i+bNNbu+nFuFkxyDwVCmx7MrekD36NGDzMxMFi5ciMViITY2lnHjxjmaq9LS0kpdJz8/n7fffpuTJ0/i7+9Po0aNGDNmDD169LjgWEREqrOSEa8tWrRw2TmD33uP8PHjMdjt5F51FUWNGrns3BWxa5eZ118PBSA6uogRI7K57bYc6tSxeTQOqZ6caq664447CDgzwUBeXh733HMPISEhAKX66zirf//+52yemjhxYqnXN998MzfffHOlryUi4ovsdruj43Hr1q1dcUJCp04ldNYsALJHjCDjX//CnVMEHzjgx/vvhxAZaeOhh7IAuPLKfG64IYf+/fO4+uo8ztHFUqRcFU5ybr/99lKv//73v5fZ57bbbrvwiERExGnHjh3DYrHg5+d3zmVxKsqQkUHEI48Q9NlnAGSOHUvWgw+6ZbI/mw2MZ+bp++03E7Nn16Ju3SLuvz8Lk6n4vddes7j8ulIzVDjJee+999wZh4iIXIAtW7YA0KZNGwIDA/9i7/Or9cYbBH32GXZ/fyzTppF7002uCNHh99/9WLEikBUrgrjkknzGjTsNQO/e+QwdmsPAgblVbfJkqaa83vFYREQu3I8//ghA165dL/hcWY88gum338h6+GEKO3a84PPZ7cW1NJ9/HsiKFYH89JO/472TJ408/fRpDAYwm2HmTMsFX0+khJIcEREf8PXXXwNwySWXOH2sae9eQt57j4znngM/P+xBQZy6wNp7i8XA+vUBfP11AOvWBZKc/EdfHqPRTvfuBSQm5tK/f55qbcRtlOSIiFRz+/fvZ+/evfj5+XHFFVdU+DjD6dPUeu01as2ejaGwEGuzZmRfwHQcyclG5s0LZt26QLZtM2Oz/ZG9BATYueSSfAYOzKN//zyNjhKPUJIjIlLN/d///R8AV155ZcXWq7LZCFq4kLCpU/FLTQUgr08fcgcMcOq6yclGcnMNxMUVz1Vz+rSRl18Oc7yfkFDIlVfm06tXPpdcUkBQkGYOFs9SkiMiUo0VFRU5FiG+qQIdhP1/+IGwZ5/Ff8cOAKzNmpExcSL5ffs6NXrq7bdDmDAhnOuuy+XNN08BkJBgZfjwbDp1Kk5uGjXSRH3iXUpyRESqsZUrV3Ls2DEiIiK4+uqrz7+z3U7oCy/gv2MHttBQTj/8MNmjRoG//zkPOXjQj7VrA/jqq0DuuCObvn2L50Tr0KEQg8FOVtYfiZHBAC+9lOGS+xJxBSU5IiLVlM1m45VXXgHgzjvvLH/oeF4eBpsNe3AwGAxkTppE8P/+x+knn8RWp055u/PddwF8+WUAa9cGsn//H78mGjUqciQ5nTsXsGNHClFRaoKSqktJjohINbVixQp2795NWFgYd955Z+k37XYC16whbOJE8q65hsxnngGgsEMHMl56qdSup08b+OKLQFauDGTt2gByc42O90wmO926FdC7dz79+uWdtR0lOFLlKckREamG8vPzmTp1KgB33XVXqQ7HxtRUwp9+mqBVqwAIXLGCzMcfh7NqeiwWA6tXF0/It2FDAAUFfzQ7RUcX0bt3Hr1753PFFfmEhSmZkepJSY6ISDX03//+l4MHD1KvXj3uvvvu4o12O4FLlxL+z3/id+oUdpOJrHvuKV6S4U9NWfPnB/P8838kRvHxhQwcmMfAgXm0a1eouWvEJyjJERGpZn7//XdeffVVAMaPH09oaCjGkyeLa29WrACgsE0bTr3yCtZ27di1y8RHHwVzySUFDBxY3OQ0aFAun3wSzMCBuQwcmEeLFlav3Y+IuyjJERGpRoqKinjooYfIzc2le/fuDB48GABDbi4B69YV1948+CCnx4xxjJpatiyId96pxZ49+Y4kJzraxpo1qV67DxFPUJIjIlKNvPnmm2zevJmQkBBe/de/MJxpVypq3BjLzJkcCGjBnO87cdnmAnr2LB4JNWJENvv2mRg2LMeboYt4nJIcEZFqYs2aNY7Oxh8NGULXYcM49dpr5F3ek6+/DuD9hX/niy8CsNsN/PxzniPJadjQxltvnfJm6CJeoSRHRKQa+Pbbb7nvvvuob7eztEkTuv3vf6QTyezxFv5TWI9Dh/74OL/iinz+/nfV2ogoyRERqeK+/PJL7hs9mrvy8phsMrH7SD1GGp5jvnE4eXuL+92EhdkYOjSH227LJj5eyymIgJIcEZEqy2q18sorr7B15kzWEEkSI7jKOprv6Q52oAjatCnkjjuyGTw4l+BgzWcjcjYlOSIiVdAPP/zA008/za+//ko//PkbB8ikeF4bf387iYm53HZbNl27ak4bkXNRkiMiUoUkrdvMJ0+s54djTfiVX4mIiOD6yZMJm32CX/MDuH5IAcOG5VCnjs3boYpUeUpyRES8zWbj8PvvUzhrFu1TCriGFKyYua7/Yaa8dBe1a9dm1J0NOHUqGbtdTVIiFaUkR0TECwoLYf2S08yffoSc3618ZX/G8d4N/h8QdlFT7n7mEaKizECZVRlEpAKU5IiIeEhREXz3nT8LFsCKFYHk5jYEWgKwg6Yca26m9lNPMWPg37wbqIiPUJIjIuJGdjts2WJm2Uc2li7150RupOM9P44z2Pgxbbvuxz5zIR1imngxUhHfoyRHRMTF7Hb4+WcTSz4JYNkiI0dOhjreCyGdbD6hefMfeeihjlx77UD8z6wxJSKupSRHRMSF7HYYPKAWP+wMc2yrxWkG8SltWADdLLR/9ik6d37Wi1GK1AxKckRELkBampEvvghg2LBcDAb45eck4nYdZwdDSWQ5A/mI9MB1FNx8Pf0ffJ769et7O2SRGkNJjohIJRUUwOU96nA624Ql5Qu+2PAKmzZt4i4a8BEPsCEmkpwHH+SGwS8TEBDg7XBFahwlOSIiFVBQAOvWBfD99wE889hxglauJHzuXBKzH2Ev8Xz30iw2sQk/Pz9ODOyK35138ljXrhg0HbGI1yjJERE5B5sNfvzRn08+CWLZsiAsFiMAo9/vS6ec7wF4hzv4gkL+HRrKmDvGcNttt9GwYUNvhi0iZyjJERH5k927TXzySRCffhrE77//8TEZTTI3M5+onBQOAW8D3ybEcu299zLruusICgryWswiUpaSHBER4OhRP5YuDeSTT4L55RezY3twsJUmTX7k2LGXePv0p+Rh4z6jEePfBjDyrru4p3t3NUmJVFFKckSkRluwIIj//S+Ebdv+mKvGTAEDWMUtfMiDOcvYvTsPgLvr1+eWv/+dSTffrCYpkWpASY6I1CiHD/vRsGERpjOffr/+WMC2bZEYsHEF67mFeQzl/zCQzjzADPTq1YvbbruNvn37YjLpY1OkuqgSP62rV69m2bJlWCwWYmJiGDVqFPHx8eXu+8UXX/DNN99w5MgRAOLi4rjlllvOub+ISIlbb43iq68CmTcvjZ49CwC4zvwhHdnHED6hHimsAv4BbGnQgGtvvJFFt9xCTEyMV+MWkcrxepKzceNG5s6dy+jRo0lISGDFihVMmTKFmTNnEh4eXmb/X375hcsuu4yWLVtiNptZsmQJkydPZsaMGURFRXnhDkSkqikogM2b/dmwIYCnnjqN0Qh+v/9Ok8zjGOnAT29uZNu2b1mxYgUpP//MIuB5YFVQEN0SExk2dCgzL70Uo9Ho7VsRkQvg9SRn+fLl9O3bl969ewMwevRotm7dytq1axk0aFCZ/R988MFSr++55x6+++47du7cyZVXXumJkEWkCjp50sjatQF8/nkgX38dwOnTxQnK4JPvcsWO/+L/yy88T2NeJJ8936RyxTfFx/n5+THx8su54YYbeHTAAIKDg714FyLiSl5NcqxWK/v37y+VzBiNRtq3b8+ePXsqdI78/HysViu1atUq9/3CwkIKCwsdrw0Gg2OYp6tHRJScTyMtPEdl7nlVpczz8+GHH/z5+usAvvkmgJ07zaXer29MJdG2hOj5/8WfXygCDvA7S4Elfn707tmTxMRE/va3v1X5WuCqUuY1icrc89xR5l5NcjIzM7HZbERERJTaHhERwbFjxyp0jg8//JCoqCjat29f7vuLFy9m0aJFjtfNmjVj2rRp1K1bt9Jx/5Xo6Gi3nVvKpzL3PG+U+S+/wGefweefw7p1kJtb+v02bQpo2nQnp09/xLiNr9KTIlYD04Av/f3pdPXV3Hjjjfzz+uuJjIz0ePwXSs+556nMPc+VZe715qoL8emnn/Ltt98yceJE/P39y91n8ODBJCYmOl6XZIipqalYrVaXxmMwGIiOjiYlJQW73e7Sc0v5VOae56kyt9vh6FEjjRvbHNtGjKjN9u1//KxHG1LoZ/+Mq/mcq/iCbr8cZ/Uvxe/dDZijo7ni6qu56qqrmHDZZY6mqLy8PJKTk90Wu6vpOfc8lbnn/VWZm0wmpysovJrkhIWFYTQasVgspbZbLJYytTt/tnTpUj799FPGjx9/3pEPZrMZs9lc7nvuenDtdrt+KDxMZe557izzzEwDvXrVIzXVyC8/JxMaVrz9Gv/V1Meffqzhaj6nnT2J08BXwHNAjsFA1y5d6Nu3L1dddRWtW7cuVfVd3Z8RPeeepzL3PFeWuVeTHJPJRFxcHElJSVx88cUA2Gw2kpKS6N+//zmPW7JkCZ988gn//Oc/ad68uafCFREXys+HnTvNbNniz5Yt/gQH25n5yin8Dh4kevNmgrNGYLJFsnzacg6E7OD777+n5datLKeILcCnwH1AWvPmXNqzJ1dccQVjLrmk3FGZIlIzeb25KjExkTfeeIO4uDji4+NZuXIl+fn59OrVC4BZs2YRFRXF8OHDgeImqoULF/Lggw9Sr149Ry1QYGAggYGBXroLEfkrR48a2bLFn61bi5OapCQzBQV/1LKE+WUTtaYDgRnpAKzmPzTlMBPn5PPGmX2SgM316tHxiiu44oormHn55TRo0MDzNyMi1YLXk5wePXqQmZnJwoULsVgsxMbGMm7cOEdzVVpaWqnq5s8//xyr1cqMGTNKnefGG2/kpptu8mToInIOp08b2LLFn507zezYYWbrVn9SUvzK7BcamktM2M+MOLqIy4s2YM6wkA98D3zNb3wNpDRrxi2XXMLFF1/MxRdfTExMjEa8iEiFGOw1tLExNTW11NByVzAYDDRo0IDk5GS14XqIytzz/lzmaWlGfvrJTKNGRbRqVdyZf/06P24eUb/UcX5Y6cAOLmUT1oCt/Dd/HbAff+A94DvgBz8/rG3b0ulMUtOtWzfq1Knj4TusevSce57K3PP+qszNZnP16ngsItVHbi7s3Wtm924zd955ZqPdzoznjby/qDYPXL+Hx2b6s3fvXpJ376QFA+nMVrqwha78SDd+wEoO24BF+cUfaPHxCXTs2JEDF13E1R078lCbNmp2FhGXUZIjIqUUFcGhQ37s2mVm1y4Tv/5qZvduEwcOmLDZipuJOn32PO0Ozif4wAGuzB3Gd4zFunIeLVZOddSQfs+jnAS2Af8GUhs3JrRjbzp26kSvjh0Z0749oaGhXrtPEfF9SnJEarDCQvjkkyD27zexb5+JfXv9OHjQREFh+Ws2RXKSDuzEuHI1kRRPSHMrc7iUOawvhLconhqidevWvNmmDa1bt6Z969YMbdVKyyWIiMcpyRGpIX76yczcucFE1y/iibFZABQVFfDPsbXJtQaU2jeQXNrwC7Hs5BN2UjyuaSevkEw4sA6YGxpKVkwMxtataRIfT4sWLfi+bVsaNmyojsEiUiUoyRGp5ux2SEszFtfEOP75sX+PkX/etJ3r635N3o4dnPiuNvP3vkor489s/+kBDhw4wJEjR7jW9m8aUkRLdtOS3TRiN7kcZj82DgYEcKB1a5rFxREXN4KcuDii4+IYc+mlZGdnq0OmiFRpSnJEqoncXDhw4I9EZv9+E/v3wL79ZjKzy5/V+/fpK6jN8wD0I5qJRNDGtpMR69ZRMrYwzP9B6tapQ0pMDCfbtqV22/40i4ujXVwcPaOiuO1P5zQYDISFhZGdne2+mxURcQElOSJVTHZ28RwzFouB667Lc2wf2DOEPcfKn83XgA07B4HdwG5uZzd/ZzcR7OQzYJ/RSHodG9amK9nZsiUvdJhGXHw8zZo1o169empeEhGfpCRHxAtOnzaUal66qE02A+OSMO3bx75vM7jlf2MJM2ayd/d09h86yL59+whLfp5ILnc0K7VgDy3ZTTi7sbKX2/2s1IqJoVmzZkQ0aMDWhF40bXE3cXFxXNewIUZj+Z2JRUR8lZIcETfKyjKwZ4+JPXtM7N5tZs8vsGd/IMeOlf7RG8n/cQfFk890I4DWJNLCtoeFM9/iCLkA3MpN3Es+qeHhZDVsSG5CAnvat6dxy0HExcXxfePG51yMVkSkJlKSI+JiH3wQzOqlRvbs8uPoyZDz7JlCLLu5mt305UtOUdzYtId8/h7UlawGDYjqNJAGLVvSvHlzmjVrRkxMjCbLExGpICU5IpVUUAD33xPGrzsNLF96lNSsZHbv3s3GGZGsPT7UsV80ybTlZ8e/f/MLP/EzgYF5tGrYkNr16rGzbVtS2r1C8/h4usbFcdWZtdtERKTylOSInENhIfz2m4mffzbz889mft1mo75fKm92f438H36g1m+/sT1tO8doxK3d72Jr0XoA+nMZb/AlEfxMET9z3JxFZoMG2Fq2JK1zZ8Z2uI/4+Hgaqp+MiIhbKckRoXiumSNH/Ni61Z8tP5rZshl+3RtcZubfaLKp+91rjtev8SAGLMwp2squ4GBatGhBnbj6nEyIIKzdP2jVsiV9NDmeiIhXKMmRGuvQIT+WLwtk6wYrW38K4kRmecsOZADbiWY7z7OdULbzPnAkPJzc+HjMXerQpNsAnm47mX83aaKaGRGRKkRJjtQIv//ux/ff+9O6VQGt2xRRWFjIxjk/8sJ/r3PsY6KQTmzjUjbRlc1s5Dtm+x2hZcsWtG/dmpRWrYi46Bq6tm7N1ZGRXrwbERGpCCU54nOysw389JOZi7vkEnjgN/y3buWV1zox/0hfbop+h4NN/8uOHTsIyQvhBt6iG5uozWZy2Epy7RCsHTpwrFcvrut2G2NbtSIgIOCvLyoiIlWOkhyp1ux2+O03WLUqiB9/NLP1RxO/7jJjsxvZbOpGd+uPAPTjDg4QQv2UH1iY8j0AAeEBRDb/F2mXXUbDbvdwSadOREVFefN2RETEhZTkSLWSlWVg+3YzW340s/WbQrbuDCY9ByCi1H6NOYLFGkUW8D1w3PA+XRt/ia17d2b0mEGXLl2Ii4tTHxoRER+mJEeqPLsdnhkbxA8b4Ncj4djspRMTf/IoYAuwGdjE3WwmNDSTpR07suGKp+nUtSs3duhAcHB5HYtFRMRXKcmRKuX4cSPz5gWTcTSHpybn8uuvv7J9+3a+X5DIL0UdAGjKIS5lE53ZRDCbOWXcycou7ejYuTOdO/ejU6cnaahh2yIiNZ6SHPGK06cNJCWZ2bHDTPNaKQyM+IbsDRvI3JjCS3s/w0wgHy6sTbY1C4DR/Mj9QBGbSa1dQM5FF1F05ZUkXPwcffr04a60NOx2u3dvSkREqhQlOeJ2mZlnEprtfuxMCmDHTjP79/+xkOQQvuA27qYO0BS4i9m0YidvW+0cj4ykU6dOhLVrQli3blx00XOlOgcbDAYtSikiIuVSkiMulZlpYOdOMzu3QtLGfHb8HMS+k+WPWArlMH3ZwpV8yVZgh58fJxo1Iu6i5dTq25d3u31O06ZN1ewkIiKVoiRHKi0jozih6dixkNBQOzk5OUwbeZg5m/uU2bcph+jCFn5jC0lswWjcTue4UFo1b05Bz56kdf2M3i1bqlZGRERcRkmOVMipUwYOHzbRKfp3TDt3krdxI4lznmJ/fgNGtHiQzUUr2b9/P93sg4ilGV3YQku2EMxWikw7yGsajK1jRy679FLatn+YFi1aEBgY6O3bEhERH6YkR8pITzewc6c/O3aY2b7dwE/f20hOD6cWp8mgC0aKO/hewhXYsZG75yj72AdARu31DG80CEPnzjTp3p227a4hNjZW89GIiIjHKcmp4dLTjezYaiBpXSZbNhaQdKg2x/Kiy923Hic4Rn0ySeEng4F2dZ4gvmVz/C+5hCEXfUDbtm2pV6+eh+9ARESkfEpyapCiIvDzg8zMTNYt+oVJz19KSkFJQlO/1L71+I0TbAG2UCdiPw9HHcLcsSnLLnucFhddxKXNm3Olv7/H70FERKSilOT4MEN2Nlnffsv//mvl7R8H0K7W5+wJm8ihQ4cIIZRsMgFIYA8d2EI9thIR9htR8acJ7NGGeldcQevWt1G7dm0v34mIiIjzlOT4gOxsAzt3mlj/dTa/fPo7u5Ib84rfnQzK+4oGQDy3ksxd1D3VnEOnDgEQ0SiM+w1DadLSRmTPjjS+4gqaNR+DyaRHQkREfIN+o1UzRUWw6ycr384/xA8brOw8EcOR3BjAD6gLxAJwpLAdRr7iKBAc9A3/rH07jS8xE3njfNq1a0dkZKT3bkJERMQDlORUA5+9d5SvP0kjaU8Uv2a1IYdQiucGPtsRDIYtDAz+lQ51DhLS3cDqAe/R/LLL6BESQg9vBC4iIuJFSnKqkKIi2Ln+FD8u3YOhbRLbt29n27ZthBz4H0kMcOxXi9NcxHfE+m+lUaPfCbilBT16JtCiRUcCAi724h2IiIhUHUpyvOi0pYgDn/1A4Kb5BPz4I+bfi7im8BBGWhHEbWRTvDjl31hMjHE/TWvvoXmnPNoNbUNC754EBrXx8h2IiIhUXUpyPOj33wv55JMTrFtXSMr2MH7Pb0U/AlnJ/zn2ac8OanGCxp36EdM3jk6dOtGxY8czfWj6ei94ERGRasbrSc7q1atZtmwZFouFmJgYRo0aRXx8fLn7HjlyhAULFnDgwAFSU1O5/fbbueaaazwcccXY7bDjs72sm7ufnT+F8UtGew7ZE4CYUvv9TkM21golrUUChh49eOfa4zRs2waD4XXvBC4iIuIjvJrkbNy4kblz5zJ69GgSEhJYsWIFU6ZMYebMmYSHh5fZPz8/n/r163PppZfy/vvveyHi89v3w0GmT/mMbTtr83NuV07QE+h51h42/Px+pWHD/fRoe4ohF+fTZcSVBNXadWZMlIiIiLiKV5Oc5cuX07dvX3r37g3A6NGj2bp1K2vXrmXQoEFl9o+Pj3fU8nz00UeeDPUvrVy5kndGf8bms5qezBTQ1rCFFpE7aX1RFpff1YbWPdtjMHTxYqQiIiI1g9eSHKvVyv79+0slM0ajkfbt27Nnzx6XXaewsJDCwkLHa4PBQFBQkONrV+nWrRsP8iBd/L6hXaNfuehKP/rc04W6zWJB9TRuU/I9dOX3Us5PZe55KnPPU5l7njvK3GtJTmZmJjabjYiIiFLbIyIiOHbsmMuus3jxYhYtWuR43axZM6ZNm0bdunVddg2ABg0asO/YPho0aEDpJirxhOjo8hcVFfdRmXueytzzVOae58oy93rHY3cbPHgwiYmJjtclGWJqaipWq9Wl1yo5d0pKCna73aXnlvIZDAaio6NV5h6kMvc8lbnnqcw976/K3GQyOV1B4bUkJywsDKPRiMViKbXdYrGUqd25EGazGbPZXO577npw7Xa7fig8TGXueSpzz1OZe57K3PNcWeZGl5ylEkwmE3FxcSQlJTm22Ww2kpKSaNGihbfCEhERER/h1eaqxMRE3njjDeLi4oiPj2flypXk5+fTq1cvAGbNmkVUVBTDhw8Hijsr//77746v09PTOXjwIIGBgWo3FRERkVK8muT06NGDzMxMFi5ciMViITY2lnHjxjmaq9LS0kr1sk5PT2fs2LGO18uWLWPZsmW0adOGiRMnejh6ERERqcoM9hra2JiamlpqaLkrGAwGGjRoQHJystpwPURl7nkqc89TmXueytzz/qrMzWaz0x2PvdYnR0RERMSdlOSIiIiIT1KSIyIiIj5JSY6IiIj4JCU5IiIi4pOU5IiIiIhPUpIjIiIiPklJjoiIiPgkn1+F/FxMJvfdujvPLeVTmXueytzzVOaepzL3vHOVeWW+FzV2xmMRERHxbWqucqHc3FyefPJJcnNzvR1KjaEy9zyVueepzD1PZe557ihzJTkuZLfbOXDggNY58SCVueepzD1PZe55KnPPc0eZK8kRERERn6QkR0RERHySkhwXMpvN3HjjjZjNZm+HUmOozD1PZe55KnPPU5l7njvKXKOrRERExCepJkdERER8kpIcERER8UlKckRERMQnKckRERERn6RFOZy0evVqli1bhsViISYmhlGjRhEfH3/O/Tdt2sSCBQtITU0lOjqaESNG0LlzZw9GXP05U+br1q3j3//+d6ltZrOZDz/80BOhVnu//PILS5cu5cCBA5w6dYrHH3+ciy+++LzH/Pzzz8ydO5cjR45Qu3ZtbrjhBnr16uWZgH2As2X+888/M2nSpDLb//vf/xIREeHGSH3H4sWL+f777zl69Cj+/v60aNGCv//97zRs2PC8x+nzvPIqU+au+DxXkuOEjRs3MnfuXEaPHk1CQgIrVqxgypQpzJw5k/Dw8DL77969m1dffZXhw4fTuXNnNmzYwEsvvcS0adNo2rSpF+6g+nG2zAGCgoJ49dVXPRypb8jPzyc2NpY+ffowffr0v9z/xIkTTJ06lauvvpoxY8aQlJTEW2+9RUREBBdddJH7A/YBzpZ5iZkzZxIcHOx4HRYW5o7wfNIvv/zC3/72N5o3b05RURHz5s1j8uTJzJgxg8DAwHKP0ef5halMmcOFf54ryXHC8uXL6du3L7179wZg9OjRbN26lbVr1zJo0KAy+69cuZKLLrqI6667DoCbb76ZnTt3snr1au6++25Phl5tOVvmAAaDQX/RVlKnTp3o1KlThfdfs2YN9erV47bbbgOgcePG7Nq1ixUrVijJqSBny7xEeHg4ISEhbojI9/3zn/8s9fr+++/nrrvuYv/+/bRp06bcY/R5fmEqU+Zw4Z/nSnIqyGq1sn///lK/WI1GI+3bt2fPnj3lHrNnzx4SExNLbevYsSM//PCDO0P1GZUpc4C8vDzuu+8+7HY7zZo145ZbbqFJkyYeiLjm+e2332jfvn2pbR07dmTOnDneCagGGTt2LIWFhTRp0oShQ4fSqlUrb4dUbeXk5ABQq1atc+6jz3PXqkiZw4V/nqvjcQVlZmZis9nKZJQRERFYLJZyj7FYLGWaVMLDw8+5v5RWmTJv2LAh9957L2PHjmXMmDHYbDaeeeYZTp486f6Aa6BzPeO5ubkUFBR4KSrfFhkZyejRo3nsscd47LHHqF27NpMmTWL//v3eDq1astlszJkzh5YtW5632Umf565T0TJ3xee5anLEp7Ro0YIWLVqUev3II4/w+eefc/PNN3sxMhHXaNiwYanOmi1btuT48eOsWLGCMWPGeDGy6umdd97hyJEjPPfcc94OpcaoaJm74vNcNTkVFBYWhtFoLJO1WyyWc7YXRkREkJGRUWpbRkaG+otUUGXK/M9MJhPNmjUjJSXF9QHKOZ/xoKAg/P39vRRVzRMfH69nvBLeeecdtm7dyoQJE6hdu/Z599XnuWs4U+Z/VpnPcyU5FWQymYiLiyMpKcmxzWazkZSUVCrTPFuLFi3YuXNnqW07duwgISHBrbH6isqU+Z/ZbDYOHz5MZGSku8Ks0RISEsp9xiv6/RHXOHjwoJ5xJ9jtdt555x2+//57nn32WerVq/eXx+jz/MJUpsz/rDKf50pynJCYmMiXX37JunXr+P3333n77bfJz893zAkya9YsPvroI8f+AwcO5KeffmLZsmUcPXqUhQsXsm/fPvr37++lO6h+nC3zRYsW8dNPP3H8+HH279/Pa6+9RmpqKn379vXSHVQveXl5HDx4kIMHDwLFQ8QPHjxIWloaAB999BGzZs1y7N+vXz9OnDjBBx98wNGjR/nss8/YtGkT11xzjTfCr5acLfMVK1bwww8/kJKSwuHDh5kzZw5JSUn87W9/80b41dI777zD+vXreeihhwgKCsJisWCxWEr1I9PnuWtVpsxd8XmuPjlO6NGjB5mZmSxcuBCLxUJsbCzjxo1zVFempaVhMBgc+7ds2ZIHH3yQ+fPnM2/ePBo0aMATTzyhORWc4GyZZ2Vl8Z///AeLxUJISAhxcXFMnjyZxo0be+kOqpd9+/aVmmhu7ty5AFx55ZXcf//9nDp1yvHLF6BevXo89dRTvP/++6xcuZLatWtzzz33aPi4E5wtc6vVyty5c0lPTycgIICYmBjGjx9Pu3btPB57dbVmzRoAJk6cWGr7fffd5/gDSp/nrlWZMnfF57nBbrfbLzh6ERERkSpGzVUiIiLik5TkiIiIiE9SkiMiIiI+SUmOiIiI+CQlOSIiIuKTlOSIiIiIT1KSIyIiIj5JSY6IeNUdd9zBoEGDPH7dOXPmYDAYMBgMPPzww47tsbGxzJw587zHlhyndYtEqjbNeCwibnP27KXlmTBhAq+++irempM0LCyM3bt3ExIS4tRxycnJLFiwgAkTJrgpMhFxBSU5IuI2ycnJjq8XLFjAs88+y+7dux3batWqRa1atbwRGlCchEVHRzt9XHR0NOHh4W6ISERcSc1VIuI20dHRjn/h4eGOpKLkX61atco0V/Xq1YsxY8bw8MMPExkZSf369Zk9ezbZ2dmMHDmS0NBQ4uPjWbVqValrJSUlMWDAAGrVqkX9+vW59dZbS6355IycnBxGjRpFaGgoTZs25b///e+FFIOIeImSHBGpct5//33q1KnD999/z5gxY7j33nsZOnQoPXr0YOvWrfTr149bb72VnJwcACwWC3369KFTp078+OOPrF69muPHj3PTTTdV6vovv/wyXbt2Zdu2bdx3333ce++9pWqgRKR6UJIjIlVOx44deeaZZ0hISODpp58mMDCQOnXqMHr0aBISEnj22Wc5efIkO3bsAGDWrFl06tSJF154gVatWtGpUyfeffdd1q5dy549e5y+/sCBA7nvvvuIj4/nySefpE6dOqxdu9bVtykibqY+OSJS5XTo0MHxtZ+fH7Vr16Z9+/aObfXr1wfgxIkTAPz000+sXbu23P49+/bto0WLFpW+fkkTW8m1RKT6UJIjIlWO2Wwu9dpgMJTaVjJqy2azAZCVlcW1117LtGnTypyrQYMGLrl+ybVEpPpQkiMi1V7nzp35+OOPiY2NxWTSx5qIFFOfHBGp9u6//37S09O55ZZb+OGHH9i3bx+fffYZI0eOpKioyNvhiYiXKMkRkWqvYcOGfPvttxQVFdGvXz/at2/Pww8/TEREBEajPuZEaiqD3VtTjYqIeNGcOXN4+OGHsVgsXjleRNxPf+KISI2VkZFBrVq1ePLJJ506rlatWtxzzz1uikpEXEU1OSJSI50+fZrjx48DEBERQZ06dSp87N69e4Hi4e3NmjVzS3wicuGU5IiIiIhPUnOViIiI+CQlOSIiIuKTlOSIiIiIT1KSIyIiIj5JSY6IiIj4JCU5IiIi4pOU5IiIiIhPUpIjIiIiPklJjoiIiPik/wewed9RlqEe2AAAAABJRU5ErkJggg==", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "attachments": {}, - "cell_type": "markdown", - "id": "dbc0edb2", - "metadata": {}, - "source": [ - "## Multi-Cycle Simulations\n", - "For multi-cycling, an experiment definition for static C/2 discharge and charge cycling is presented." + "data": { + "image/png": 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", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "for i in range(0,len(v_si)):\n", + " t_i = solution[i][\"Time [s]\"].entries / 3600\n", + " ocp_p1 = solution[i][\"X-averaged negative electrode primary open-circuit potential [V]\"].entries\n", + " plt.plot(t_i, ocp_p1 ,ltype[i],label=\"$V_\\mathrm{si}=$\"+str(v_si[i]))\n", + "plt.xlabel('Time [h]')\n", + "plt.ylabel(\"Equilibruim potential [V]\")\n", + "plt.legend()\n", + "plt.title('Graphite')\n", + "\n", + "plt.figure()\n", + "for i in range(0,len(v_si)):\n", + " t_i = solution[i][\"Time [s]\"].entries / 3600\n", + " ocp_p2 = solution[i][\"X-averaged negative electrode secondary open-circuit potential [V]\"].entries\n", + " plt.plot(t_i, ocp_p2,ltype[i],label=\"$V_\\mathrm{si}=$\"+str(v_si[i]))\n", + "plt.xlabel('Time [h]')\n", + "plt.ylabel(\"Equilibruim potential [V]\")\n", + "plt.legend()\n", + "plt.title('Silicon')\n", + "\n", + "plt.figure()\n", + "for i in range(0,len(v_si)):\n", + " t_i = solution[len(v_si)- 1 - i][\"Time [s]\"].entries / 3600\n", + " ocp_p = solution[len(v_si)- 1 - i][\"X-averaged positive electrode open-circuit potential [V]\"].entries\n", + " plt.plot(t_i, ocp_p,ltype[len(v_si)- 1 - i],label=\"$V_\\mathrm{si}=$\"+str(v_si[len(v_si)- 1 - i]))\n", + "plt.xlabel('Time [h]')\n", + "plt.ylabel(\"Equilibrium potential [V]\")\n", + "plt.legend()\n", + "plt.title('NMC811')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "dbc0edb2", + "metadata": {}, + "source": [ + "## Multi-Cycle Simulations\n", + "For multi-cycling, an experiment definition for static C/2 discharge and charge cycling is presented." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "4cb719b9", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-02-21 09:09:26.957 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Discharge at C/2 until 3.0 V', using temperature from parameter values.\n", + "2023-02-21 09:09:26.959 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", + "2023-02-21 09:09:26.961 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Charge at C/2 until 4.2 V', using temperature from parameter values.\n", + "2023-02-21 09:09:26.963 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", + "2023-02-21 09:09:26.964 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Discharge at C/2 until 3.0 V', using temperature from parameter values.\n", + "2023-02-21 09:09:26.965 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", + "2023-02-21 09:09:26.966 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Charge at C/2 until 4.2 V', using temperature from parameter values.\n", + "2023-02-21 09:09:26.967 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n" + ] + } + ], + "source": [ + "experiment = pybamm.Experiment(\n", + " [\n", + " (\n", + " \"Discharge at C/2 until 3.0 V\",\n", + " \"Rest for 1 hour\",\n", + " \"Charge at C/2 until 4.2 V\",\n", + " \"Rest for 1 hour\",\n", + " ),\n", + " ]\n", + " * 2\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "40467b70", + "metadata": {}, + "source": [ + "The solution is reintroduced, with `calc_esoh=False` passed into the solve function. Currently, composite electrode state of health predictions are not included in this model. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "dac3f3bb", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-02-21 09:09:27.094 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Discharge at C/2 until 3.0 V', using temperature from parameter values.\n", + "2023-02-21 09:09:27.096 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", + "2023-02-21 09:09:27.097 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Charge at C/2 until 4.2 V', using temperature from parameter values.\n", + "2023-02-21 09:09:27.098 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", + "2023-02-21 09:09:27.099 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Discharge at C/2 until 3.0 V', using temperature from parameter values.\n", + "2023-02-21 09:09:27.099 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", + "2023-02-21 09:09:27.100 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Charge at C/2 until 4.2 V', using temperature from parameter values.\n", + "2023-02-21 09:09:27.101 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", + "2023-02-21 09:09:27.115 - [INFO] callbacks.on_experiment_start(166): Start running experiment\n", + "2023-02-21 09:09:27.118 - [INFO] parameter_values.process_model(425): Start setting parameters for Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:27.244 - [INFO] parameter_values.process_model(527): Finish setting parameters for Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:27.246 - [INFO] parameter_values.process_model(425): Start setting parameters for Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:27.362 - [INFO] parameter_values.process_model(527): Finish setting parameters for Doyle-Fuller-Newman model\n" + ] }, { - "cell_type": "code", - "execution_count": 10, - "id": "4cb719b9", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-02-21 09:09:26.957 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Discharge at C/2 until 3.0 V', using temperature from parameter values.\n", - "2023-02-21 09:09:26.959 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", - "2023-02-21 09:09:26.961 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Charge at C/2 until 4.2 V', using temperature from parameter values.\n", - "2023-02-21 09:09:26.963 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", - "2023-02-21 09:09:26.964 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Discharge at C/2 until 3.0 V', using temperature from parameter values.\n", - "2023-02-21 09:09:26.965 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", - "2023-02-21 09:09:26.966 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Charge at C/2 until 4.2 V', using temperature from parameter values.\n", - "2023-02-21 09:09:26.967 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n" - ] - } - ], - "source": [ - "experiment = pybamm.Experiment(\n", - " [\n", - " (\n", - " \"Discharge at C/2 until 3.0 V\",\n", - " \"Rest for 1 hour\",\n", - " \"Charge at C/2 until 4.2 V\",\n", - " \"Rest for 1 hour\",\n", - " ),\n", - " ]\n", - " * 2\n", - ")" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "0.001\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "id": "40467b70", - "metadata": {}, - "source": [ - "The solution is reintroduced, with `calc_esoh=False` passed into the solve function. Currently, composite electrode state of health predictions are not included in this model. " - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-02-21 09:09:27.364 - [INFO] parameter_values.process_model(425): Start setting parameters for Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:27.482 - [INFO] parameter_values.process_model(527): Finish setting parameters for Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:27.486 - [INFO] discretisation.process_model(147): Start discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:27.492 - [INFO] discretisation.remove_independent_variables_from_rhs(1120): removing variable Discharge capacity [A.h] from rhs\n", + "2023-02-21 09:09:28.059 - [INFO] discretisation.process_model(259): Finish discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:28.060 - [INFO] discretisation.process_model(147): Start discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:28.066 - [INFO] discretisation.remove_independent_variables_from_rhs(1120): removing variable Discharge capacity [A.h] from rhs\n", + "2023-02-21 09:09:28.601 - [INFO] discretisation.process_model(259): Finish discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:28.602 - [INFO] discretisation.process_model(147): Start discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:28.610 - [INFO] discretisation.remove_independent_variables_from_rhs(1120): removing variable Discharge capacity [A.h] from rhs\n", + "2023-02-21 09:09:29.139 - [INFO] discretisation.process_model(259): Finish discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:29.140 - [NOTICE] callbacks.on_cycle_start(174): Cycle 1/2 (20.167 us elapsed) --------------------\n", + "2023-02-21 09:09:29.141 - [NOTICE] callbacks.on_step_start(182): Cycle 1/2, step 1/4: Discharge at C/2 until 3.0 V\n", + "2023-02-21 09:09:29.145 - [INFO] base_solver.set_up(111): Start solver set-up\n", + "2023-02-21 09:09:29.290 - [INFO] base_solver.set_up(236): Finish solver set-up\n", + "2023-02-21 09:09:30.657 - [NOTICE] callbacks.on_step_start(182): Cycle 1/2, step 2/4: Rest for 1 hour\n", + "2023-02-21 09:09:30.661 - [INFO] base_solver.set_up(111): Start solver set-up\n", + "2023-02-21 09:09:30.800 - [INFO] base_solver.set_up(236): Finish solver set-up\n", + "2023-02-21 09:09:31.643 - [NOTICE] callbacks.on_step_start(182): Cycle 1/2, step 3/4: Charge at C/2 until 4.2 V\n", + "2023-02-21 09:09:31.647 - [INFO] base_solver.set_up(111): Start solver set-up\n", + "2023-02-21 09:09:31.796 - [INFO] base_solver.set_up(236): Finish solver set-up\n", + "2023-02-21 09:09:33.027 - [NOTICE] callbacks.on_step_start(182): Cycle 1/2, step 4/4: Rest for 1 hour\n", + "2023-02-21 09:09:33.910 - [NOTICE] callbacks.on_cycle_start(174): Cycle 2/2 (4.771 s elapsed) --------------------\n", + "2023-02-21 09:09:33.911 - [NOTICE] callbacks.on_step_start(182): Cycle 2/2, step 1/4: Discharge at C/2 until 3.0 V\n", + "2023-02-21 09:09:35.119 - [NOTICE] callbacks.on_step_start(182): Cycle 2/2, step 2/4: Rest for 1 hour\n", + "2023-02-21 09:09:35.867 - [NOTICE] callbacks.on_step_start(182): Cycle 2/2, step 3/4: Charge at C/2 until 4.2 V\n", + "2023-02-21 09:09:37.032 - [NOTICE] callbacks.on_step_start(182): Cycle 2/2, step 4/4: Rest for 1 hour\n", + "2023-02-21 09:09:37.783 - [NOTICE] callbacks.on_experiment_end(222): Finish experiment simulation, took 8.643 s\n", + "2023-02-21 09:09:37.784 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Discharge at C/2 until 3.0 V', using temperature from parameter values.\n", + "2023-02-21 09:09:37.785 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", + "2023-02-21 09:09:37.785 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Charge at C/2 until 4.2 V', using temperature from parameter values.\n", + "2023-02-21 09:09:37.786 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", + "2023-02-21 09:09:37.788 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Discharge at C/2 until 3.0 V', using temperature from parameter values.\n", + "2023-02-21 09:09:37.789 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", + "2023-02-21 09:09:37.790 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Charge at C/2 until 4.2 V', using temperature from parameter values.\n", + "2023-02-21 09:09:37.791 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", + "2023-02-21 09:09:37.792 - [INFO] callbacks.on_experiment_start(166): Start running experiment\n", + "2023-02-21 09:09:37.794 - [INFO] parameter_values.process_model(425): Start setting parameters for Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:37.912 - [INFO] parameter_values.process_model(527): Finish setting parameters for Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:37.914 - [INFO] parameter_values.process_model(425): Start setting parameters for Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:38.035 - [INFO] parameter_values.process_model(527): Finish setting parameters for Doyle-Fuller-Newman model\n" + ] }, { - "cell_type": "code", - "execution_count": 11, - "id": "dac3f3bb", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-02-21 09:09:27.094 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Discharge at C/2 until 3.0 V', using temperature from parameter values.\n", - "2023-02-21 09:09:27.096 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", - "2023-02-21 09:09:27.097 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Charge at C/2 until 4.2 V', using temperature from parameter values.\n", - "2023-02-21 09:09:27.098 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", - "2023-02-21 09:09:27.099 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Discharge at C/2 until 3.0 V', using temperature from parameter values.\n", - "2023-02-21 09:09:27.099 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", - "2023-02-21 09:09:27.100 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Charge at C/2 until 4.2 V', using temperature from parameter values.\n", - "2023-02-21 09:09:27.101 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", - "2023-02-21 09:09:27.115 - [INFO] callbacks.on_experiment_start(166): Start running experiment\n", - "2023-02-21 09:09:27.118 - [INFO] parameter_values.process_model(425): Start setting parameters for Doyle-Fuller-Newman model\n", - "2023-02-21 09:09:27.244 - [INFO] parameter_values.process_model(527): Finish setting parameters for Doyle-Fuller-Newman model\n", - "2023-02-21 09:09:27.246 - [INFO] parameter_values.process_model(425): Start setting parameters for Doyle-Fuller-Newman model\n", - "2023-02-21 09:09:27.362 - [INFO] parameter_values.process_model(527): Finish setting parameters for Doyle-Fuller-Newman model\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.001\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-02-21 09:09:27.364 - [INFO] parameter_values.process_model(425): Start setting parameters for Doyle-Fuller-Newman model\n", - "2023-02-21 09:09:27.482 - [INFO] parameter_values.process_model(527): Finish setting parameters for Doyle-Fuller-Newman model\n", - "2023-02-21 09:09:27.486 - [INFO] discretisation.process_model(147): Start discretising Doyle-Fuller-Newman model\n", - "2023-02-21 09:09:27.492 - [INFO] discretisation.remove_independent_variables_from_rhs(1120): removing variable Discharge capacity [A.h] from rhs\n", - "2023-02-21 09:09:28.059 - [INFO] discretisation.process_model(259): Finish discretising Doyle-Fuller-Newman model\n", - "2023-02-21 09:09:28.060 - [INFO] discretisation.process_model(147): Start discretising Doyle-Fuller-Newman model\n", - "2023-02-21 09:09:28.066 - [INFO] discretisation.remove_independent_variables_from_rhs(1120): removing variable Discharge capacity [A.h] from rhs\n", - "2023-02-21 09:09:28.601 - [INFO] discretisation.process_model(259): Finish discretising Doyle-Fuller-Newman model\n", - "2023-02-21 09:09:28.602 - [INFO] discretisation.process_model(147): Start discretising Doyle-Fuller-Newman model\n", - "2023-02-21 09:09:28.610 - [INFO] discretisation.remove_independent_variables_from_rhs(1120): removing variable Discharge capacity [A.h] from rhs\n", - "2023-02-21 09:09:29.139 - [INFO] discretisation.process_model(259): Finish discretising Doyle-Fuller-Newman model\n", - "2023-02-21 09:09:29.140 - [NOTICE] callbacks.on_cycle_start(174): Cycle 1/2 (20.167 us elapsed) --------------------\n", - "2023-02-21 09:09:29.141 - [NOTICE] callbacks.on_step_start(182): Cycle 1/2, step 1/4: Discharge at C/2 until 3.0 V\n", - "2023-02-21 09:09:29.145 - [INFO] base_solver.set_up(111): Start solver set-up\n", - "2023-02-21 09:09:29.290 - [INFO] base_solver.set_up(236): Finish solver set-up\n", - "2023-02-21 09:09:30.657 - [NOTICE] callbacks.on_step_start(182): Cycle 1/2, step 2/4: Rest for 1 hour\n", - "2023-02-21 09:09:30.661 - [INFO] base_solver.set_up(111): Start solver set-up\n", - "2023-02-21 09:09:30.800 - [INFO] base_solver.set_up(236): Finish solver set-up\n", - "2023-02-21 09:09:31.643 - [NOTICE] callbacks.on_step_start(182): Cycle 1/2, step 3/4: Charge at C/2 until 4.2 V\n", - "2023-02-21 09:09:31.647 - [INFO] base_solver.set_up(111): Start solver set-up\n", - "2023-02-21 09:09:31.796 - [INFO] base_solver.set_up(236): Finish solver set-up\n", - "2023-02-21 09:09:33.027 - [NOTICE] callbacks.on_step_start(182): Cycle 1/2, step 4/4: Rest for 1 hour\n", - "2023-02-21 09:09:33.910 - [NOTICE] callbacks.on_cycle_start(174): Cycle 2/2 (4.771 s elapsed) --------------------\n", - "2023-02-21 09:09:33.911 - [NOTICE] callbacks.on_step_start(182): Cycle 2/2, step 1/4: Discharge at C/2 until 3.0 V\n", - "2023-02-21 09:09:35.119 - [NOTICE] callbacks.on_step_start(182): Cycle 2/2, step 2/4: Rest for 1 hour\n", - "2023-02-21 09:09:35.867 - [NOTICE] callbacks.on_step_start(182): Cycle 2/2, step 3/4: Charge at C/2 until 4.2 V\n", - "2023-02-21 09:09:37.032 - [NOTICE] callbacks.on_step_start(182): Cycle 2/2, step 4/4: Rest for 1 hour\n", - "2023-02-21 09:09:37.783 - [NOTICE] callbacks.on_experiment_end(222): Finish experiment simulation, took 8.643 s\n", - "2023-02-21 09:09:37.784 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Discharge at C/2 until 3.0 V', using temperature from parameter values.\n", - "2023-02-21 09:09:37.785 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", - "2023-02-21 09:09:37.785 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Charge at C/2 until 4.2 V', using temperature from parameter values.\n", - "2023-02-21 09:09:37.786 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", - "2023-02-21 09:09:37.788 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Discharge at C/2 until 3.0 V', using temperature from parameter values.\n", - "2023-02-21 09:09:37.789 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", - "2023-02-21 09:09:37.790 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Charge at C/2 until 4.2 V', using temperature from parameter values.\n", - "2023-02-21 09:09:37.791 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", - "2023-02-21 09:09:37.792 - [INFO] callbacks.on_experiment_start(166): Start running experiment\n", - "2023-02-21 09:09:37.794 - [INFO] parameter_values.process_model(425): Start setting parameters for Doyle-Fuller-Newman model\n", - "2023-02-21 09:09:37.912 - [INFO] parameter_values.process_model(527): Finish setting parameters for Doyle-Fuller-Newman model\n", - "2023-02-21 09:09:37.914 - [INFO] parameter_values.process_model(425): Start setting parameters for Doyle-Fuller-Newman model\n", - "2023-02-21 09:09:38.035 - [INFO] parameter_values.process_model(527): Finish setting parameters for Doyle-Fuller-Newman model\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.04\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-02-21 09:09:38.037 - [INFO] parameter_values.process_model(425): Start setting parameters for Doyle-Fuller-Newman model\n", - "2023-02-21 09:09:38.341 - [INFO] parameter_values.process_model(527): Finish setting parameters for Doyle-Fuller-Newman model\n", - "2023-02-21 09:09:38.344 - [INFO] discretisation.process_model(147): Start discretising Doyle-Fuller-Newman model\n", - "2023-02-21 09:09:38.349 - [INFO] discretisation.remove_independent_variables_from_rhs(1120): removing variable Discharge capacity [A.h] from rhs\n", - "2023-02-21 09:09:38.914 - [INFO] discretisation.process_model(259): Finish discretising Doyle-Fuller-Newman model\n", - "2023-02-21 09:09:38.915 - [INFO] discretisation.process_model(147): Start discretising Doyle-Fuller-Newman model\n", - "2023-02-21 09:09:38.922 - [INFO] discretisation.remove_independent_variables_from_rhs(1120): removing variable Discharge capacity [A.h] from rhs\n", - "2023-02-21 09:09:39.441 - [INFO] discretisation.process_model(259): Finish discretising Doyle-Fuller-Newman model\n", - "2023-02-21 09:09:39.442 - [INFO] discretisation.process_model(147): Start discretising Doyle-Fuller-Newman model\n", - "2023-02-21 09:09:39.448 - [INFO] discretisation.remove_independent_variables_from_rhs(1120): removing variable Discharge capacity [A.h] from rhs\n", - "2023-02-21 09:09:39.986 - [INFO] discretisation.process_model(259): Finish discretising Doyle-Fuller-Newman model\n", - "2023-02-21 09:09:39.987 - [NOTICE] callbacks.on_cycle_start(174): Cycle 1/2 (16.366 us elapsed) --------------------\n", - "2023-02-21 09:09:39.988 - [NOTICE] callbacks.on_step_start(182): Cycle 1/2, step 1/4: Discharge at C/2 until 3.0 V\n", - "2023-02-21 09:09:39.993 - [INFO] base_solver.set_up(111): Start solver set-up\n", - "2023-02-21 09:09:40.141 - [INFO] base_solver.set_up(236): Finish solver set-up\n", - "2023-02-21 09:09:41.853 - [NOTICE] callbacks.on_step_start(182): Cycle 1/2, step 2/4: Rest for 1 hour\n", - "2023-02-21 09:09:41.858 - [INFO] base_solver.set_up(111): Start solver set-up\n", - "2023-02-21 09:09:41.990 - [INFO] base_solver.set_up(236): Finish solver set-up\n", - "2023-02-21 09:09:42.685 - [NOTICE] callbacks.on_step_start(182): Cycle 1/2, step 3/4: Charge at C/2 until 4.2 V\n", - "2023-02-21 09:09:42.690 - [INFO] base_solver.set_up(111): Start solver set-up\n", - "2023-02-21 09:09:42.834 - [INFO] base_solver.set_up(236): Finish solver set-up\n", - "2023-02-21 09:09:44.378 - [NOTICE] callbacks.on_step_start(182): Cycle 1/2, step 4/4: Rest for 1 hour\n", - "2023-02-21 09:09:45.649 - [NOTICE] callbacks.on_cycle_start(174): Cycle 2/2 (5.662 s elapsed) --------------------\n", - "2023-02-21 09:09:45.650 - [NOTICE] callbacks.on_step_start(182): Cycle 2/2, step 1/4: Discharge at C/2 until 3.0 V\n", - "2023-02-21 09:09:47.096 - [NOTICE] callbacks.on_step_start(182): Cycle 2/2, step 2/4: Rest for 1 hour\n", - "2023-02-21 09:09:47.757 - [NOTICE] callbacks.on_step_start(182): Cycle 2/2, step 3/4: Charge at C/2 until 4.2 V\n", - "2023-02-21 09:09:49.184 - [NOTICE] callbacks.on_step_start(182): Cycle 2/2, step 4/4: Rest for 1 hour\n", - "2023-02-21 09:09:49.955 - [NOTICE] callbacks.on_experiment_end(222): Finish experiment simulation, took 9.968 s\n", - "2023-02-21 09:09:49.956 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Discharge at C/2 until 3.0 V', using temperature from parameter values.\n", - "2023-02-21 09:09:49.957 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", - "2023-02-21 09:09:49.958 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Charge at C/2 until 4.2 V', using temperature from parameter values.\n", - "2023-02-21 09:09:49.959 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", - "2023-02-21 09:09:49.960 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Discharge at C/2 until 3.0 V', using temperature from parameter values.\n", - "2023-02-21 09:09:49.961 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", - "2023-02-21 09:09:49.962 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Charge at C/2 until 4.2 V', using temperature from parameter values.\n", - "2023-02-21 09:09:49.963 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", - "2023-02-21 09:09:49.964 - [INFO] callbacks.on_experiment_start(166): Start running experiment\n", - "2023-02-21 09:09:49.966 - [INFO] parameter_values.process_model(425): Start setting parameters for Doyle-Fuller-Newman model\n", - "2023-02-21 09:09:50.085 - [INFO] parameter_values.process_model(527): Finish setting parameters for Doyle-Fuller-Newman model\n", - "2023-02-21 09:09:50.088 - [INFO] parameter_values.process_model(425): Start setting parameters for Doyle-Fuller-Newman model\n", - "2023-02-21 09:09:50.206 - [INFO] parameter_values.process_model(527): Finish setting parameters for Doyle-Fuller-Newman model\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.1\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-02-21 09:09:50.209 - [INFO] parameter_values.process_model(425): Start setting parameters for Doyle-Fuller-Newman model\n", - "2023-02-21 09:09:50.322 - [INFO] parameter_values.process_model(527): Finish setting parameters for Doyle-Fuller-Newman model\n", - "2023-02-21 09:09:50.326 - [INFO] discretisation.process_model(147): Start discretising Doyle-Fuller-Newman model\n", - "2023-02-21 09:09:50.332 - [INFO] discretisation.remove_independent_variables_from_rhs(1120): removing variable Discharge capacity [A.h] from rhs\n", - "2023-02-21 09:09:50.868 - [INFO] discretisation.process_model(259): Finish discretising Doyle-Fuller-Newman model\n", - "2023-02-21 09:09:50.869 - [INFO] discretisation.process_model(147): Start discretising Doyle-Fuller-Newman model\n", - "2023-02-21 09:09:50.877 - [INFO] discretisation.remove_independent_variables_from_rhs(1120): removing variable Discharge capacity [A.h] from rhs\n", - "2023-02-21 09:09:51.557 - [INFO] discretisation.process_model(259): Finish discretising Doyle-Fuller-Newman model\n", - "2023-02-21 09:09:51.557 - [INFO] discretisation.process_model(147): Start discretising Doyle-Fuller-Newman model\n", - "2023-02-21 09:09:51.563 - [INFO] discretisation.remove_independent_variables_from_rhs(1120): removing variable Discharge capacity [A.h] from rhs\n", - "2023-02-21 09:09:52.101 - [INFO] discretisation.process_model(259): Finish discretising Doyle-Fuller-Newman model\n", - "2023-02-21 09:09:52.101 - [NOTICE] callbacks.on_cycle_start(174): Cycle 1/2 (16.504 us elapsed) --------------------\n", - "2023-02-21 09:09:52.102 - [NOTICE] callbacks.on_step_start(182): Cycle 1/2, step 1/4: Discharge at C/2 until 3.0 V\n", - "2023-02-21 09:09:52.108 - [INFO] base_solver.set_up(111): Start solver set-up\n", - "2023-02-21 09:09:52.250 - [INFO] base_solver.set_up(236): Finish solver set-up\n", - "2023-02-21 09:09:54.101 - [NOTICE] callbacks.on_step_start(182): Cycle 1/2, step 2/4: Rest for 1 hour\n", - "2023-02-21 09:09:54.107 - [INFO] base_solver.set_up(111): Start solver set-up\n", - "2023-02-21 09:09:54.236 - [INFO] base_solver.set_up(236): Finish solver set-up\n", - "2023-02-21 09:09:54.910 - [NOTICE] callbacks.on_step_start(182): Cycle 1/2, step 3/4: Charge at C/2 until 4.2 V\n", - "2023-02-21 09:09:54.914 - [INFO] base_solver.set_up(111): Start solver set-up\n", - "2023-02-21 09:09:55.056 - [INFO] base_solver.set_up(236): Finish solver set-up\n", - "2023-02-21 09:09:56.719 - [NOTICE] callbacks.on_step_start(182): Cycle 1/2, step 4/4: Rest for 1 hour\n", - "2023-02-21 09:09:57.598 - [NOTICE] callbacks.on_cycle_start(174): Cycle 2/2 (5.496 s elapsed) --------------------\n", - "2023-02-21 09:09:57.598 - [NOTICE] callbacks.on_step_start(182): Cycle 2/2, step 1/4: Discharge at C/2 until 3.0 V\n", - "2023-02-21 09:09:59.232 - [NOTICE] callbacks.on_step_start(182): Cycle 2/2, step 2/4: Rest for 1 hour\n", - "2023-02-21 09:09:59.886 - [NOTICE] callbacks.on_step_start(182): Cycle 2/2, step 3/4: Charge at C/2 until 4.2 V\n", - "2023-02-21 09:10:01.516 - [NOTICE] callbacks.on_step_start(182): Cycle 2/2, step 4/4: Rest for 1 hour\n", - "2023-02-21 09:10:02.299 - [NOTICE] callbacks.on_experiment_end(222): Finish experiment simulation, took 10.198 s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "running time: 76.058786603s\n" - ] - } - ], - "source": [ - "solution=[]\n", - "for v in v_si:\n", - " param.update({\n", - " \"Primary: Negative electrode active material volume fraction\": (1-v) * total_am_volume_fraction, #primary\n", - " \"Secondary: Negative electrode active material volume fraction\": v * total_am_volume_fraction,\n", - " })\n", - " print(v)\n", - " sim = pybamm.Simulation(\n", - " model,\n", - " experiment=experiment,\n", - " parameter_values=param,\n", - " solver=pybamm.CasadiSolver(dt_max = 5)\n", - " )\n", - " solution.append(sim.solve(calc_esoh=False))\n", - "stop = timeit.default_timer()\n", - "print(\"running time: \" + str(stop - start) + \"s\")" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "0.04\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "id": "977b4c09", - "metadata": {}, - "source": [ - "## Cycling Results\n", - "The previously displayed single discharge results can be extended to the cycling solution. As an example, voltage is displayed below." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-02-21 09:09:38.037 - [INFO] parameter_values.process_model(425): Start setting parameters for Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:38.341 - [INFO] parameter_values.process_model(527): Finish setting parameters for Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:38.344 - [INFO] discretisation.process_model(147): Start discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:38.349 - [INFO] discretisation.remove_independent_variables_from_rhs(1120): removing variable Discharge capacity [A.h] from rhs\n", + "2023-02-21 09:09:38.914 - [INFO] discretisation.process_model(259): Finish discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:38.915 - [INFO] discretisation.process_model(147): Start discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:38.922 - [INFO] discretisation.remove_independent_variables_from_rhs(1120): removing variable Discharge capacity [A.h] from rhs\n", + "2023-02-21 09:09:39.441 - [INFO] discretisation.process_model(259): Finish discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:39.442 - [INFO] discretisation.process_model(147): Start discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:39.448 - [INFO] discretisation.remove_independent_variables_from_rhs(1120): removing variable Discharge capacity [A.h] from rhs\n", + "2023-02-21 09:09:39.986 - [INFO] discretisation.process_model(259): Finish discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:39.987 - [NOTICE] callbacks.on_cycle_start(174): Cycle 1/2 (16.366 us elapsed) --------------------\n", + "2023-02-21 09:09:39.988 - [NOTICE] callbacks.on_step_start(182): Cycle 1/2, step 1/4: Discharge at C/2 until 3.0 V\n", + "2023-02-21 09:09:39.993 - [INFO] base_solver.set_up(111): Start solver set-up\n", + "2023-02-21 09:09:40.141 - [INFO] base_solver.set_up(236): Finish solver set-up\n", + "2023-02-21 09:09:41.853 - [NOTICE] callbacks.on_step_start(182): Cycle 1/2, step 2/4: Rest for 1 hour\n", + "2023-02-21 09:09:41.858 - [INFO] base_solver.set_up(111): Start solver set-up\n", + "2023-02-21 09:09:41.990 - [INFO] base_solver.set_up(236): Finish solver set-up\n", + "2023-02-21 09:09:42.685 - [NOTICE] callbacks.on_step_start(182): Cycle 1/2, step 3/4: Charge at C/2 until 4.2 V\n", + "2023-02-21 09:09:42.690 - [INFO] base_solver.set_up(111): Start solver set-up\n", + "2023-02-21 09:09:42.834 - [INFO] base_solver.set_up(236): Finish solver set-up\n", + "2023-02-21 09:09:44.378 - [NOTICE] callbacks.on_step_start(182): Cycle 1/2, step 4/4: Rest for 1 hour\n", + "2023-02-21 09:09:45.649 - [NOTICE] callbacks.on_cycle_start(174): Cycle 2/2 (5.662 s elapsed) --------------------\n", + "2023-02-21 09:09:45.650 - [NOTICE] callbacks.on_step_start(182): Cycle 2/2, step 1/4: Discharge at C/2 until 3.0 V\n", + "2023-02-21 09:09:47.096 - [NOTICE] callbacks.on_step_start(182): Cycle 2/2, step 2/4: Rest for 1 hour\n", + "2023-02-21 09:09:47.757 - [NOTICE] callbacks.on_step_start(182): Cycle 2/2, step 3/4: Charge at C/2 until 4.2 V\n", + "2023-02-21 09:09:49.184 - [NOTICE] callbacks.on_step_start(182): Cycle 2/2, step 4/4: Rest for 1 hour\n", + "2023-02-21 09:09:49.955 - [NOTICE] callbacks.on_experiment_end(222): Finish experiment simulation, took 9.968 s\n", + "2023-02-21 09:09:49.956 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Discharge at C/2 until 3.0 V', using temperature from parameter values.\n", + "2023-02-21 09:09:49.957 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", + "2023-02-21 09:09:49.958 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Charge at C/2 until 4.2 V', using temperature from parameter values.\n", + "2023-02-21 09:09:49.959 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", + "2023-02-21 09:09:49.960 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Discharge at C/2 until 3.0 V', using temperature from parameter values.\n", + "2023-02-21 09:09:49.961 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", + "2023-02-21 09:09:49.962 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Charge at C/2 until 4.2 V', using temperature from parameter values.\n", + "2023-02-21 09:09:49.963 - [WARNING] experiment._read_and_drop_temperature(431): Temperature not found on step: 'Rest for 1 hour', using temperature from parameter values.\n", + "2023-02-21 09:09:49.964 - [INFO] callbacks.on_experiment_start(166): Start running experiment\n", + "2023-02-21 09:09:49.966 - [INFO] parameter_values.process_model(425): Start setting parameters for Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:50.085 - [INFO] parameter_values.process_model(527): Finish setting parameters for Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:50.088 - [INFO] parameter_values.process_model(425): Start setting parameters for Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:50.206 - [INFO] parameter_values.process_model(527): Finish setting parameters for Doyle-Fuller-Newman model\n" + ] }, { - "cell_type": "code", - "execution_count": 12, - "id": "15b6f3ee", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ltype=['k-','r--','b-.','g:','m-','c--','y-.'];\n", - "for i in range(0,len(v_si)):\n", - " t_i = solution[i][\"Time [s]\"].entries / 3600\n", - " V_i = solution[i][\"Voltage [V]\"].entries\n", - " plt.plot(t_i, V_i,ltype[i],label=\"$V_\\mathrm{si}=$\"+str(v_si[i]))\n", - "plt.xlabel('Time [h]')\n", - "plt.ylabel('Voltage [V]')\n", - "plt.legend()" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "0.1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-02-21 09:09:50.209 - [INFO] parameter_values.process_model(425): Start setting parameters for Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:50.322 - [INFO] parameter_values.process_model(527): Finish setting parameters for Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:50.326 - [INFO] discretisation.process_model(147): Start discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:50.332 - [INFO] discretisation.remove_independent_variables_from_rhs(1120): removing variable Discharge capacity [A.h] from rhs\n", + "2023-02-21 09:09:50.868 - [INFO] discretisation.process_model(259): Finish discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:50.869 - [INFO] discretisation.process_model(147): Start discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:50.877 - [INFO] discretisation.remove_independent_variables_from_rhs(1120): removing variable Discharge capacity [A.h] from rhs\n", + "2023-02-21 09:09:51.557 - [INFO] discretisation.process_model(259): Finish discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:51.557 - [INFO] discretisation.process_model(147): Start discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:51.563 - [INFO] discretisation.remove_independent_variables_from_rhs(1120): removing variable Discharge capacity [A.h] from rhs\n", + "2023-02-21 09:09:52.101 - [INFO] discretisation.process_model(259): Finish discretising Doyle-Fuller-Newman model\n", + "2023-02-21 09:09:52.101 - [NOTICE] callbacks.on_cycle_start(174): Cycle 1/2 (16.504 us elapsed) --------------------\n", + "2023-02-21 09:09:52.102 - [NOTICE] callbacks.on_step_start(182): Cycle 1/2, step 1/4: Discharge at C/2 until 3.0 V\n", + "2023-02-21 09:09:52.108 - [INFO] base_solver.set_up(111): Start solver set-up\n", + "2023-02-21 09:09:52.250 - [INFO] base_solver.set_up(236): Finish solver set-up\n", + "2023-02-21 09:09:54.101 - [NOTICE] callbacks.on_step_start(182): Cycle 1/2, step 2/4: Rest for 1 hour\n", + "2023-02-21 09:09:54.107 - [INFO] base_solver.set_up(111): Start solver set-up\n", + "2023-02-21 09:09:54.236 - [INFO] base_solver.set_up(236): Finish solver set-up\n", + "2023-02-21 09:09:54.910 - [NOTICE] callbacks.on_step_start(182): Cycle 1/2, step 3/4: Charge at C/2 until 4.2 V\n", + "2023-02-21 09:09:54.914 - [INFO] base_solver.set_up(111): Start solver set-up\n", + "2023-02-21 09:09:55.056 - [INFO] base_solver.set_up(236): Finish solver set-up\n", + "2023-02-21 09:09:56.719 - [NOTICE] callbacks.on_step_start(182): Cycle 1/2, step 4/4: Rest for 1 hour\n", + "2023-02-21 09:09:57.598 - [NOTICE] callbacks.on_cycle_start(174): Cycle 2/2 (5.496 s elapsed) --------------------\n", + "2023-02-21 09:09:57.598 - [NOTICE] callbacks.on_step_start(182): Cycle 2/2, step 1/4: Discharge at C/2 until 3.0 V\n", + "2023-02-21 09:09:59.232 - [NOTICE] callbacks.on_step_start(182): Cycle 2/2, step 2/4: Rest for 1 hour\n", + "2023-02-21 09:09:59.886 - [NOTICE] callbacks.on_step_start(182): Cycle 2/2, step 3/4: Charge at C/2 until 4.2 V\n", + "2023-02-21 09:10:01.516 - [NOTICE] callbacks.on_step_start(182): Cycle 2/2, step 4/4: Rest for 1 hour\n", + "2023-02-21 09:10:02.299 - [NOTICE] callbacks.on_experiment_end(222): Finish experiment simulation, took 10.198 s\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "id": "e9a2ba08", - "metadata": {}, - "source": [ - "## References\n", - "\n", - "The relevant papers for this notebook are:" + "name": "stdout", + "output_type": "stream", + "text": [ + "running time: 76.058786603s\n" + ] + } + ], + "source": [ + "solution=[]\n", + "for v in v_si:\n", + " param.update({\n", + " \"Primary: Negative electrode active material volume fraction\": (1-v) * total_am_volume_fraction, #primary\n", + " \"Secondary: Negative electrode active material volume fraction\": v * total_am_volume_fraction,\n", + " })\n", + " print(v)\n", + " sim = pybamm.Simulation(\n", + " model,\n", + " experiment=experiment,\n", + " parameter_values=param,\n", + " solver=pybamm.CasadiSolver(dt_max = 5)\n", + " )\n", + " solution.append(sim.solve(calc_esoh=False))\n", + "stop = timeit.default_timer()\n", + "print(\"running time: \" + str(stop - start) + \"s\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "977b4c09", + "metadata": {}, + "source": [ + "## Cycling Results\n", + "The previously displayed single discharge results can be extended to the cycling solution. As an example, voltage is displayed below." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "15b6f3ee", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "code", - "execution_count": 13, - "id": "5e9e5819", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1] Weilong Ai, Niall Kirkaldy, Yang Jiang, Gregory Offer, Huizhi Wang, and Billy Wu. A composite electrode model for lithium-ion batteries with silicon/graphite negative electrodes. Journal of Power Sources, 527:231142, 2022. URL: https://www.sciencedirect.com/science/article/pii/S0378775322001604, doi:https://doi.org/10.1016/j.jpowsour.2022.231142.\n", - "[2] Weilong Ai, Ludwig Kraft, Johannes Sturm, Andreas Jossen, and Billy Wu. Electrochemical thermal-mechanical modelling of stress inhomogeneity in lithium-ion pouch cells. Journal of The Electrochemical Society, 167(1):013512, 2019. doi:10.1149/2.0122001JES.\n", - "[3] 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", - "[4] Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050.\n", - "[5] Rutooj Deshpande, Mark Verbrugge, Yang-Tse Cheng, John Wang, and Ping Liu. Battery cycle life prediction with coupled chemical degradation and fatigue mechanics. Journal of the Electrochemical Society, 159(10):A1730, 2012. doi:10.1149/2.049210jes.\n", - "[6] 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", - "[7] 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", - "[8] 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()" + "data": { + "image/png": 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", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.8.10" - }, - "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": "c9bd1bb48411923570c08daa2bc8068dae5eb4b582894c14e668f51575eba180" - } + ], + "source": [ + "ltype=['k-','r--','b-.','g:','m-','c--','y-.']\n", + "for i in range(0,len(v_si)):\n", + " t_i = solution[i][\"Time [s]\"].entries / 3600\n", + " V_i = solution[i][\"Voltage [V]\"].entries\n", + " plt.plot(t_i, V_i,ltype[i],label=\"$V_\\mathrm{si}=$\"+str(v_si[i]))\n", + "plt.xlabel('Time [h]')\n", + "plt.ylabel('Voltage [V]')\n", + "plt.legend()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "e9a2ba08", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "The relevant papers for this notebook are:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "5e9e5819", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] Weilong Ai, Niall Kirkaldy, Yang Jiang, Gregory Offer, Huizhi Wang, and Billy Wu. A composite electrode model for lithium-ion batteries with silicon/graphite negative electrodes. Journal of Power Sources, 527:231142, 2022. URL: https://www.sciencedirect.com/science/article/pii/S0378775322001604, doi:https://doi.org/10.1016/j.jpowsour.2022.231142.\n", + "[2] Weilong Ai, Ludwig Kraft, Johannes Sturm, Andreas Jossen, and Billy Wu. Electrochemical thermal-mechanical modelling of stress inhomogeneity in lithium-ion pouch cells. Journal of The Electrochemical Society, 167(1):013512, 2019. doi:10.1149/2.0122001JES.\n", + "[3] 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", + "[4] Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050.\n", + "[5] Rutooj Deshpande, Mark Verbrugge, Yang-Tse Cheng, John Wang, and Ping Liu. Battery cycle life prediction with coupled chemical degradation and fatigue mechanics. Journal of the Electrochemical Society, 159(10):A1730, 2012. doi:10.1149/2.049210jes.\n", + "[6] 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", + "[7] 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", + "[8] 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": "Python 3 (ipykernel)", + "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": "3.8.10" + }, + "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": 5 + "vscode": { + "interpreter": { + "hash": "c9bd1bb48411923570c08daa2bc8068dae5eb4b582894c14e668f51575eba180" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/docs/source/examples/notebooks/models/electrode-state-of-health.ipynb b/docs/source/examples/notebooks/models/electrode-state-of-health.ipynb index 136459c68a..a0b768ba3a 100644 --- a/docs/source/examples/notebooks/models/electrode-state-of-health.ipynb +++ b/docs/source/examples/notebooks/models/electrode-state-of-health.ipynb @@ -1,658 +1,660 @@ { - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Electrode State of Health" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook demonstrates some utilities to work with electrode State of Health (also sometimes called electrode stoichiometry), using the algorithm from Mohtat et al [1]\n", - "\n", - "[1] Mohtat, P., Lee, S., Siegel, J. B., & Stefanopoulou, A. G. (2019). Towards better estimability of electrode-specific state of health: Decoding the cell expansion. Journal of Power Sources, 427, 101-111." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "ERROR: Invalid requirement: '#'\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], - "source": [ - "%pip install pybamm -q # install PyBaMM if it is not installed\n", - "import pybamm\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create and solve model" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "3b55c57e62d4444fb157f8a1bbdde58c", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=2.32485835391946, step=0.0232485835391946), …" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "spm = pybamm.lithium_ion.SPM()\n", - "experiment = pybamm.Experiment([\n", - " \"Charge at 1C until 4.2V\", \n", - " \"Hold at 4.2V until C/50\",\n", - " \"Discharge at 1C until 2.8V\",\n", - " \"Hold at 2.8V until C/50\",\n", - "])\n", - "parameter_values = pybamm.ParameterValues(\"Mohtat2020\")\n", - "\n", - "sim = pybamm.Simulation(spm, experiment=experiment, parameter_values=parameter_values)\n", - "spm_sol = sim.solve()\n", - "spm_sol.plot([\n", - " \"Voltage [V]\", \n", - " \"Current [A]\", \n", - " \"Negative electrode stoichiometry\",\n", - " \"Positive electrode stoichiometry\",\n", - "])" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Solve for electrode SOH variables" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Given a total amount of cyclable lithium capacity, $Q_{Li}$, electrode capacities, $Q_n$ and $Q_p$, and voltage limits, $V_{min}$ and $V_{max}$, we can solve for the min and max electrode SOCs, $x_0$, $x_{100}$, $y_0$, and $y_{100}$, and the cell capacity, $C$, using the algorithm adapted from Mohtat et al [1].\n", - "First, we find $x_{100}$ and $y_{100}$ using\n", - "\n", - "$$\n", - "Q_{Li} = y_{100}Q_p + x_{100}Q_n,\n", - "\\\\\n", - "V_{max} = U_p(y_{100}) - U_n(x_{100}).\n", - "$$\n", - "\n", - "Note that Mohtat et al use $n_{Li} = \\frac{3600 Q_{Li}}{F}$ instead.\n", - "Then, we find $Q$ using\n", - "\n", - "$$\n", - "V_{min} = U_p(y_{0}) - U_n(x_{0})\n", - "= U_p\\left(y_{100} + \\frac{Q}{Q_p}\\right) - U_n\\left(x_{100} - \\frac{Q}{Q_n}\\right)\n", - "$$\n", - "\n", - "Finally, $x_0$ and $y_0$ are simply defined as\n", - "\n", - "$$\n", - "x_0 = x_{100} - \\frac{Q}{Q_n},\n", - "\\\\\n", - "y_0 = y_{100} + \\frac{Q}{Q_p}.\n", - "$$\n", - "\n", - "We implement this in PyBaMM as an algebraic model." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "param = pybamm.LithiumIonParameters()\n", - "\n", - "Vmin = 2.8\n", - "Vmax = 4.2\n", - "Q_n = parameter_values.evaluate(param.n.Q_init)\n", - "Q_p = parameter_values.evaluate(param.p.Q_init)\n", - "Q_Li = parameter_values.evaluate(param.Q_Li_particles_init)\n", - "\n", - "U_n = param.n.prim.U\n", - "U_p = param.p.prim.U\n", - "T_ref = param.T_ref" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "x_100 : 0.83337428922595\n", - "y_100 : 0.03354553395256055\n", - "Q : 4.968932758817601\n", - "x_0 : 0.0015118453536460735\n", - "y_0 : 0.8908948803914055\n" - ] - } - ], - "source": [ - "# First we solve for x_100 and y_100\n", - "\n", - "model = pybamm.BaseModel()\n", - "\n", - "x_100 = pybamm.Variable(\"x_100\")\n", - "y_100 = (Q_Li - x_100 * Q_n) / Q_p\n", - "\n", - "y_100_min = 1e-10\n", - "\n", - "x_100_upper_limit = (Q_Li - y_100_min*Q_p)/Q_n\n", - "\n", - "model.algebraic = {x_100: U_p(y_100, T_ref) - U_n(x_100, T_ref) - Vmax}\n", - " \n", - "model.initial_conditions = {x_100: x_100_upper_limit}\n", - "\n", - "model.variables = {\n", - " \"x_100\": x_100,\n", - " \"y_100\": y_100\n", - "}\n", - "\n", - "sim = pybamm.Simulation(model, parameter_values=parameter_values)\n", - "sol = sim.solve([0])\n", - "\n", - "x_100 = sol[\"x_100\"].data[0]\n", - "y_100 = sol[\"y_100\"].data[0]\n", - "\n", - "for var in [\"x_100\", \"y_100\"]:\n", - " print(var, \":\", sol[var].data[0])\n", - "\n", - "# Based on the calculated values for x_100 and y_100 we solve for x_0\n", - "model = pybamm.BaseModel()\n", - "\n", - "x_0 = pybamm.Variable(\"x_0\")\n", - "Q = Q_n * (x_100 - x_0)\n", - "y_0 = y_100 + Q/Q_p\n", - "\n", - "model.algebraic = {x_0: U_p(y_0, T_ref) - U_n(x_0, T_ref) - Vmin}\n", - "model.initial_conditions = {x_0: 0.1}\n", - "\n", - "model.variables = {\n", - " \"Q\": Q,\n", - " \"x_0\": x_0,\n", - " \"y_0\": y_0,\n", - "}\n", - "\n", - "sim = pybamm.Simulation(model, parameter_values=parameter_values)\n", - "sol = sim.solve([0])\n", - "\n", - "\n", - "for var in [\"Q\", \"x_0\", \"y_0\"]:\n", - " print(var, \":\", sol[var].data[0])" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is implemented in PyBaMM as the `ElectrodeSOHSolver` class" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "x_100 : 0.833374276202919\n", - "y_100 : 0.03354554737459606\n", - "Q : 4.968932679279884\n", - "x_0 : 0.0015118456462390728\n", - "y_0 : 0.8908948800898482\n" - ] - } - ], - "source": [ - "esoh_solver = pybamm.lithium_ion.ElectrodeSOHSolver(parameter_values, param)\n", - "\n", - "inputs={ \"V_min\": Vmin, \"V_max\": Vmax, \"Q_n\": Q_n, \"Q_p\": Q_p, \"Q_Li\": Q_Li}\n", - "\n", - "esoh_sol = esoh_solver.solve(inputs)\n", - "\n", - "for var in [\"x_100\", \"y_100\", \"Q\", \"x_0\", \"y_0\"]:\n", - " print(var, \":\", esoh_sol[var])" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Check against simulations" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plotting the SPM simulations against the eSOH calculations validates the min/max stoichiometry calculations" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "t = spm_sol[\"Time [h]\"].data\n", - "x_spm = spm_sol[\"Negative electrode stoichiometry\"].data\n", - "y_spm = spm_sol[\"Positive electrode stoichiometry\"].data\n", - "\n", - "x_0 = esoh_sol[\"x_0\"].data * np.ones_like(t)\n", - "y_0 = esoh_sol[\"y_0\"].data * np.ones_like(t)\n", - "x_100 = esoh_sol[\"x_100\"].data * np.ones_like(t)\n", - "y_100 = esoh_sol[\"y_100\"].data * np.ones_like(t)\n", - "\n", - "fig, axes = plt.subplots(1,2)\n", - "\n", - "axes[0].plot(t, x_spm, \"b\")\n", - "axes[0].plot(t, x_0, \"k:\")\n", - "axes[0].plot(t, x_100, \"k:\")\n", - "axes[0].set_ylabel(\"x\")\n", - " \n", - "axes[1].plot(t, y_spm, \"r\")\n", - "axes[1].plot(t, y_0, \"k:\")\n", - "axes[1].plot(t, y_100, \"k:\")\n", - "axes[1].set_ylabel(\"y\")\n", - " \n", - "for k in range(2):\n", - " axes[k].set_xlim([t[0],t[-1]])\n", - " axes[k].set_ylim([0,1]) \n", - " axes[k].set_xlabel(\"Time [h]\")\n", - " \n", - "fig.tight_layout()\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## How does electrode SOH depend on cyclable lithium" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can do a parameter sweep for the amount of cyclable lithium to see how it affects the electrode SOH parameters and cell capacity" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "all_parameter_sets = [\n", - " k\n", - " for k, v in pybamm.parameter_sets.items()\n", - " if v[\"chemistry\"] == \"lithium_ion\" and k not in [\"Xu2019\", \"Chen2020_composite\"]\n", - "]\n", - "\n", - "def solve_esoh_sweep_QLi(parameter_set, param):\n", - " parameter_values = pybamm.ParameterValues(parameter_set)\n", - "\n", - " # Vmin = parameter_values[\"Lower voltage cut-off [V]\"]\n", - " # Vmax = parameter_values[\"Upper voltage cut-off [V]\"]\n", - " Vmin = parameter_values[\"Open-circuit voltage at 0% SOC [V]\"]\n", - " Vmax = parameter_values[\"Open-circuit voltage at 100% SOC [V]\"]\n", - " \n", - " Q_n = parameter_values.evaluate(param.n.Q_init)\n", - " Q_p = parameter_values.evaluate(param.p.Q_init)\n", - " \n", - " Q = parameter_values.evaluate(param.Q/param.n_electrodes_parallel)\n", - " esoh_solver = pybamm.lithium_ion.ElectrodeSOHSolver(parameter_values, param, known_value=\"cell capacity\")\n", - " inputs = {\"V_max\": Vmax, \"V_min\": Vmin, \"Q\": Q, \"Q_n\": Q_n, \"Q_p\": Q_p}\n", - " sol_init_Q = esoh_solver.solve(inputs)\n", - " \n", - " Q_Li_init = parameter_values.evaluate(param.Q_Li_particles_init)\n", - " esoh_solver = pybamm.lithium_ion.ElectrodeSOHSolver(parameter_values, param)\n", - " inputs = {\"V_max\": Vmax, \"V_min\": Vmin, \"Q_Li\": Q_Li_init, \"Q_n\": Q_n, \"Q_p\": Q_p}\n", - " sol_init_QLi = esoh_solver.solve(inputs)\n", - "\n", - " Q_Li_sweep = np.linspace(1e-6, Q_n + Q_p)\n", - " sweep = {}\n", - " variables = [\"Q_Li\", \"x_0\", \"x_100\", \"y_0\", \"y_100\", \"Q\"]\n", - " for var in variables:\n", - " sweep[var] = []\n", - "\n", - " for Q_Li in Q_Li_sweep:\n", - " inputs[\"Q_Li\"] = Q_Li\n", - " try:\n", - " sol = esoh_solver.solve(inputs)\n", - " for var in variables:\n", - " sweep[var].append(sol[var])\n", - " except (ValueError, pybamm.SolverError):\n", - " pass\n", - "\n", - " return sweep, sol_init_QLi, sol_init_Q\n", - " \n", - "for parameter_set in [\"Chen2020\"]:\n", - " sweep, sol_init_QLi, sol_init_Q = solve_esoh_sweep_QLi(parameter_set, param)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "def plot_sweep(sweep, sol_init, sol_init_Q, parameter_set):\n", - " fig, axes = plt.subplots(1,3,figsize=(10,3))\n", - " parameter_values = pybamm.ParameterValues(parameter_set)\n", - " Qn = parameter_values.evaluate(param.n.Q_init)\n", - " Qp = parameter_values.evaluate(param.p.Q_init)\n", - " # Plot min/max stoichimetric limits, including the value with the given Q_Li\n", - " for i,ks in enumerate([[\"x_0\",\"x_100\"],[\"y_0\",\"y_100\"],[\"Q\"]]):\n", - " ax = axes.flat[i]\n", - " for j,k in enumerate(ks):\n", - " if i == 0 and j == 0:\n", - " label1 = \"Stoichiometric envelope\"\n", - " label2 = \"Calculation from cyclable lithium\"\n", - " label3 = \"Calculation from cell capacity\"\n", - " else:\n", - " label1 = label2 = label3 = None\n", - " ax.plot(sweep[\"Q_Li\"], sweep[k],\"b-\", label=label1)\n", - " ax.axhline(sol_init_QLi[k],c=\"k\",linestyle=\"--\", label=label2)\n", - " ax.axhline(sol_init_Q[k],c=\"r\",linestyle=\"--\", label=label3)\n", - " ax.set_xlabel(\"Cyclable lithium [A.h]\")\n", - " ax.set_ylabel(ks[0][0])\n", - " ax.set_xlim([np.min(sweep[\"Q_Li\"]),np.max(sweep[\"Q_Li\"])])\n", - " ax.axvline(sol_init_QLi[\"Q_Li\"],c=\"k\",linestyle=\"--\")\n", - " ax.axvline(sol_init_Q[\"Q_Li\"],c=\"r\",linestyle=\"--\")\n", - " # Plot capacities of electrodes\n", - " # ax.axvline(Qn,c=\"b\",linestyle=\"--\")\n", - " # ax.axvline(Qp,c=\"r\",linestyle=\"--\")\n", - " axes[-1].set_ylabel(\"Cell capacity [A.h]\")\n", - " \n", - " # Plot initial values of stoichometries\n", - " sto_n_init = parameter_values.evaluate(param.n.prim.sto_init_av)\n", - " sto_p_init = parameter_values.evaluate(param.p.prim.sto_init_av)\n", - " # axes[0].axhline(sto_n_init,c=\"g\",linestyle=\"--\")\n", - " # axes[1].axhline(sto_p_init,c=\"g\",linestyle=\"--\")\n", - "\n", - " axes[1].set_title(parameter_set)\n", - " fig.legend(loc=\"center left\", bbox_to_anchor=(1.01,0.5))\n", - " fig.tight_layout()\n", - " return fig, axes\n", - "\n", - "plot_sweep(sweep, sol_init_QLi, sol_init_Q, \"Chen2020\");" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ai2020\n", - "Chen2020\n", - "Ecker2015\n", - "Marquis2019\n", - "Mohtat2020\n", - "NCA_Kim2011\n", - "OKane2022\n", - "ORegan2022\n", - "Prada2013\n", - "Ramadass2004\n" - ] - }, - { - "data": { - "image/png": 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", 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", 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", 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", 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", 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", 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7uyd5TmRkJJGRkdbvg4ODMzxPkc1FR8O8eZjNZuYBFkdH+vTpg7Oz8wOfunw5dOumP+AKkZkepn0EaSNFGj1C+5hW8+fDuHEQFKTb1JiYdH8JIYRIP7HtI0B09+7MW7AAIFVtZGQk9O4NS5ak/uUcHWHoUPj444fOWAghhHgk2apD8WFMnjyZDz/80Og0RHYSFQVvvYUjMBQIA7p162b3ZFApmDQJPvhAf1+7NhQtmvqXlCvMxoqOhlTUPMmRpI0UafIQ7ePD+P576NMn5cednPTonMQ3V9ek3zs6pmtquVJubiOFSLXY9hEgqn173ordflAbeesWtG0Lv/+u26tmzfSuQkMhLEzfEm5HRennmc0wZQp07AhVq2b4TyeEEEIkka06FAsWLMj169dt7rt+/Tre3t4pjr4ZNWoUgwcPtn4fHBxM0bT09AjxAFFR8OabEFdQd/BgfYInH2Kzj+Bg8PExOotH8zDtI0gbKbKe336DN97Q2/36wZAhSTsJnbLV2Uv2lxPaSCGyohMn4MUX4dw5/Te2ciU0bWr/OTExehmCPn30zJhBg2DHDrk4LYQQIvNlq1PyOnXqsHnzZpv7fv75Z+rUqZPic1xdXXF1dc3o1EQudfcuvPqqXjPRwQG++AL69zc6K5EbPUz7CNJGiqzl2DFo00ZfqHn5Zd2mysUZIURO9Msv0K6dXtahZEnYuBHKl3/w85ycwMtLX7xev15fhFmzRp+PCiGEEJnJwcgXDwkJ4eDBgxw8eBCA8+fPc/DgQS5cuADokTNvxA1TAPr27cu5c+cYPnw4J06c4Msvv2TFihW8++67RqQvcrkbN6BuXd2Z6OkJP/wgnYki/Uj7KHKbK1egRQu4dw/q1IHvvpPORCFEzrRiBTRvrjsT69WDvXtT15mYUNGiMGyY3h46VBehEkIIITKToR2KBw4coGrVqlSNXfhj8ODBVK1alTFjxgBw9epV64dngJIlS7Jp0yZ+/vlnKleuzLRp0/j6669p1qyZIfmL3G3wYD1VpUgRve5Ny5ZGZyRyEmkfRW5y/76e9nfhApQpAxs2gJ2Z+iKTbd9udAZC5Bw3b+qlcsxm6NJFj1T093+4fQ0frs9DAwNhxoz0zFIIIYR4MJNSShmdRGYKDg7Gx8eHoKAgvL29jU5HZEWhoXrIIZAHXXQgJCSEPHnyWEN27IBGjfR6Nfv3Q/XqhmQq0om0C/HkWAi7UtE+plV0NLz0Evz0E+TPD3/+CaVKpU+64tGtWAEdOwYD0i5I+yjsStA+hl6/jmeBAkDSNrJXL/jmG6hSBQ4cePSR2N9+C1276pc+dQoKFXq0/Ym0kXZBCJGbGTpCUYjsKDoaBgzQ2/36SWeiEEI8LKV0YYGffgIPD72GmHQmZh1//BFfIEcI8ej27tWdiQCzZ6fPsg6dO0Pt2hASAu+//+j7E0IIIVIrWxVlESJTuLrCxo2YzWZWAsrR0aZoxeef68IBfn7w0UfGpSnST0iI0RkIkU08oH1Mq7FjYeFC/aF6xQqoWTPdMhWP6MwZXSAnMlKvbfnjj0ZnJEQWF9s+Arh6e7Mxbju2jTSb4y9IBwTodbjTg4ODnu5cp45uTwcMkIvdQgghMod0KAqRmJMTvPgijkDiZREvX4Zx4/T2lCmQN28m5yYyxKefGp2BENmEnfYxrebPhwkT9PacOXoNRZE13L6t1wW+dQtq1NAjqgoXNjorIbK42PYR9AesFxM1at98A3/9BT4+8Mkn6fvSzzyj12NcuhQGDdKVn02m9H0NIYQQIjGZ8ixEGgwdqkez1amjry6L7O/sWZg1y+gshMhdNm3SS0YAjB4NvXsbm4+IFxkJbdvC6dNQrBj88AM8whKZQgh0J/2oUXp7/HiIXV4xXU2erItZ/f47rFqV/vsXQgghEpMRikIkFh0NS5cSYzazVCmUkxNdunRh1y5nli/XU0tmz9ZfRfY3bJh+y4UQqZBC++js7JzqXRw4AB066Ol/AQHw4YcZmK9IE6WgRw/YtQu8vWHzZihYEIKDjc5MiGwgtn0EiO7QgaUrVgDQpUsXPvjAmTt3oGJF6N8/Y16+aFEYMULPpBk2TBe7cnfPmNcSQgghQKo8G52OyIqSqWJ6504I9erl4fhxeOst+OILQzMU6eTXX6FxY3BwCMZikXYBpI0UD/CIVZ7PndMjvG/cgBde0MuNpaEvUmSw0aP12sBOTnrNxCZN9P3SLmhyHIRdKVR53rUrhOeey4NSsHMnPPdcxqUQFgZly8KlSzBxIrz3Xsa9ltCkXRBC5GYyxkqIVJg9G44fB3//+DW/RPYWE6PXGQLo1cvQVITIFW7dgubNdWdilSp6Sp50JmYdCxbEFxqbOze+M1EI8WgGD9ajf7t0ydjORAAPj/j1GSdNgitXMvb1hBBC5G7SoShEKkyapL9OmQKPPWZoKiKdzJ8PR46Ar2/8ukZCiIwRFgatWsWvy7d5M3h5GZ2ViLNtG/Tpo7ffe09PexZCpI/9+/XAxalTM+f1OnXSRVpCQ2WEohBCiIwlHYpCpEJ4uL6qLIVYcoa7d/XUPtDrt/n6GpuPEDmZ2axH5uzZoy/IbNkChQoZnZWIc+wYvPqqHrX92msyCl+IjDBhQua1eyYTzJihtxct0uvWCiGEEBkh1xZlCQ0NxdHRMcn9jo6OuLm52cSlxMHBAfcEqx2nJTYsLIyUlq80mUx4eHg8VGx4eDgWiyXFPBKuc5WW2IiICMxmc7rEenh4YDKZAIiMjCQmJiZdYt3d3XGIrZQSFRVFtJ1KG3ZjQ0NJvBqYoyPMmQPR0fb36+bmZv29io6OJioqKsVYV1dXnJyc0hwbExNDZGRkirEuLi7WAglpiTWbzURERKQY6+zsjIuLS5pjLRYL4eHh6RLr5OSEq6srAEopwsLCHir2gw90xcVy5aBrV+z+LEKIh6cUDBwI69aBqyts2KD/7kTWcO0atGwJQUFQr56e9iwFxx5MziHlHDLZ2ATnjwnfzwoV9PrbD9pvep5DVqigLxAsXw4DB7ry++9OmExyDpke55CJyTmkECJXU7lMUFCQAlK8tWzZ0ibew8MjxdgGDRrYxPr5+aUYW6NGDZvY4sWLpxhbvnx5m9jy5cunGFu8eHGb2Bo1aqQY6+fnZxPboEGDFGM9PDxsYlu2bGn3uCXUrl07u7EhISHW2ICAALuxN27csMb279/fbuz58+etsUOHDrUbe/ToUWvs2LFjbX92/RlYqdhtQA0ZonOeMmWK3f1u377dut9Zs2bZjd24caM1dsGCBXZjV6xYYY1dsWKF3dgFCxZYYzdu3Gg3dtasWdbY7du3242dMmWKNXbfvn12Y8eOHWuNPXr0qN3YoUOHWmPPnz9vN7Z///7W2Bs3btiNDQgIsMaGhITYjW3Tpo0CVFBQkMrt4tpIORYiWSEhSdrHhG16YlOm6HCTSamVKzMxT/FAoaFK1ayp35/SpZW6eTPl2JzSLiT+fw+osmXLpvr5cg4ZT84htYS/U8mdPwJq2zZjzyFhhVq+XMfKOaQm55BCCJE+cu0IRSHSYsQIozMQQojsZdkyGD5cb0+fDu3aGZuPiBc3DX3/fsiXT69p6edndFaZ4+mnn+aXX36xfh83ykuIjFK7ttEZ6La4dWujsxBCCJHTmJRKYR5EDhUcHIyPjw9XzpzBO5kV4R1dXHBLUHUj9MaNFPfl4OSEe4LF19ISG3brFiqFqSImBwc8EpzZpyU2/M4dLHamdOTJn/+hYiPu3cNsZ+pFWmI9/PwwxU4ViQwOJsbOVIG0xLr7+uIQ+8EgKiSEaDvTE+zGxsRwdd5mPvzQzGrg7aHeTJzcHicnpwfu1+2xx3CMnXoRHRZGVEhIirGu3t44xU6NSktsTEQEkcHBKca6eHriHDuFKS2x5qgoIu7dSzHW2cMDF0/PNMdaYmIIv3MnXWKd3Nxw9fYGQFkshN26labYX36Bzl3AyRF+/x1KldKxoRERFChenKCgILxjn5NbxbWRcixEsmJiYO1azGYzawHl6Ejbtm2TdMps3w7NmkF0NLz7ru5QFFnH4MHw2Wfg4qILsjz7rP34nNIujBs3jnXr1nHw4MGHer71HPLKlWSPg0x5Tj4210x5jonBccMGNm+GDstb4u65mc8+c6Fbt1f1OWQmTnmOExYG1aq5cumSExMmwMiRMuUZSNcpz6GhoRQoUCDbt49CCPFQjBweaQTrtJ0E0xJsbommqygPj+TjQKlE01WUn1/KsYmmq6jixVOOTTRdRZUvn3JsoukqqkaNlGMTTVdRDRqkHJtouopq2TLl2MS/Ru3a2Y9NOD0uIMB+bILpKqp/f/uxCaarqKFD7ccmmK6ixo61H7tvX3xs3Py9lG4JpquoWbPsxyaYrqIWLLAfm2DKs1qxwn5sgukqauNG+7EJpquo7dvtxyaYrqL27bMfm2C6ijp61H5sgukq6vx5+7EJpquoGzfsxyaYrpJwmmZytyCZrmKVU6Y2CuMcPqyUj4/+82rXTimz2eiMREIJ/zUtW5a65+SUdmHs2LHKw8NDFSpUSJUsWVJ17txZ/ffff6l+vvU4XLmi/68kvoWH2z4huZi4W1jYw8eGhqYcGxr68LFhYfbzeNjY8PD0i7VY4mMjItIvNmFDFRn50LEXT4Qof48Q5UGIWvRliFIxManfb8LYqCj7sdHRqY5dtiTaemp/KTDa/n6jouL3G52G2JgY+7GRkQ8XazanX2xERHysxZJusUHXr+eI9lEIIR6GLL0thBBCiHRx6VJ8kY/69WHJEinykZVs3AjvvKO3J07URRtyk9q1a7Nw4UK2bNnCnDlzOH/+PPXr1+f+/fvJxkdGRhIcHGxzA6BwYfD0THp79VXbHeTPn3ycpye0aGEbW6JEyrHPPWcbW758yrE1a9rG1qyZcmz58raxzz2XcmyJEraxLVqkHJtg1gqgj0tKsbEzFKy6drUfm3Ck2Jtv2o9NOIth8GD7sRcuxMe+/7792OPH42MnTbJ57PGnPLkR5kkonrzR3xP+/js+duZM+/vdtSs+dt48+7Fbt8bHLl1qN7ajy1rq1NGHbs0ba+3vd+nS+P1u3Wo/dt68+Nhdu+zHzpwZH/v33/ZjJ02Kjz1+3H7s++/Hx164YD928OD42Fu37Me++WZ8bFiY/dg+fRBCiNwq9y4cc+UKJDcsPXHVPjvTmJN8SgoMTH3ssWN6gEByYqdnWO3fn/rY334DO1NQbPz4Y+pjV6/Wiy6lxpIlsHBhyo8nmF7D3Lkwe3bqYqdPhylTUo5NMB2IiRNh3LjUxb73HgwbBsDNm1CjSgz1gzbQ6TUL4c0U6tw52latqqeWDBwI/funvN8EU53o0we6dUs5NnYqBaAXs2rfPnWxbduCnenRxE79APScw9TG1q9vPzZ2WgsA1aqlPrZcudTHFitmPzbhtEo/v1TH3gz1oLJ3CEHB8OVsCAhIFBsaCuvXp7wvIYRmZ8pzUJDuTLx0Sf/Zr1tn2yQKY/39t+5AtFigZ08YNcrojDJfiwSdeJUqVaJ27doUL16cFStW0LNnzyTxkydP5sMPP8zMFEUOEQOsBfjll/hzSIOYTLo/r1Yt/THhbcMyEUIIkdPk2jUUZZ0LkZw+fWDp/FBC0VfM8wBhQEhIiM0aPyJ76dtX911Xrar75xNfN5B2IZ4cC2FXaKh1RFHC9tHZOQ8tWsCvv0LBgrBnDxQvbmimIoGLF3VhiKtXoUkTXYQl4bWcB8nJ7ULNmjVp0qQJkydPTvJYZGSkzRpywcHBFC1alKAU1lDE0dG2F93Ouog4ONhe3ExLbFiY/QvNCS/GpiU2PNz+heaE50FpiY2IsH9ROi2xHh7xF9MjI/VFjvSIdXePv/gfFaUXgE1DbFQU1K8Wyt7AAgCEnjuHZ+xCzdZzyAft180t/gQlOlrHp8TVNf7CaSpjAwJg6eIYnqsVybZtScckAPpCc1zjEBOjj1tKEsaazfq9S4mzc/xF7LTEWiz6dy09Yp2c4i/QK2U72vURYoNDQ/GRNRSFELlU7h2hKEQif/0FX38N7g8OFdnIoUMwf77enjkzaWeiEOLRWCzQo4fuTPT01J1V0pmYdQQHw4sv6s7EChVg1aq0dSbmZCEhIZw9e5auXbsm+7irq6u1MIONPHlsO8FSkpYLkWmJTdgJmJ6x7mk4A0pLbFqGKqcl1tXVdgZHesW6uNjO4EhF7Mwv4WhggvuTez/Tsl9n59T/oaYydtIkWLXKie37nFj+A3Tq9IAnODnZzgyxx9Ex9b/DaYl1cMiYWJMp/WJTO4NLCCFyIFnZSAj0xce339ZfO3YwOhuRXpSCQYN0h0eHDnpWtxAifY0bp5fdcnLSq2NUrWp0RiJOdLRu+44c0SNHN20CHx+jszLO0KFD2blzJ4GBgfzxxx+0bdsWR0dHOj2wZ0WIlF27BhMmGJ3FgxUpEr/UwfDh9gfoCSGEEKkhIxSFQH8Y/vNPfQHyo4+AFUZnJNLDmjWwY4ce8GBv+U2RVGhoKI7JDOd0dHTELcEIklA70/QcHBxwTzCKJS2xYWFhpLQih8lkwiPBqJu0xIaHh2OxM00v4dIGaYmNiIjAbGeUQlpiPTw8MMXORYuMjCTGzjS9tMS6u7vjEDtNLyoqimg7U+9SjA0NJfE4jenT9c8yfz40bBhFaGjK+3Vzc7P+XkVHRxNlZ5qeq6urdd2xtMTGxMTYTFNNzMXFBefY0TxpiTWbzUTYmabn7OyMS+zoo7TEWiwWwu1M00tLrJOTk3VEncWiePPNGLZudcbDQ7FyZQT58lmsM2sTxiqlCLPTu2DvZ8lOLl26RKdOnbh9+zb+/v48++yz7NmzB39/f6NTE9nYe+/B/ftQvxrw9wPDDTVkiG6rL1yATz+FMWOMzkgIIUS2ZmiNaQMEBQUpQAUFBRmdisgigoOVKlRIKVBq0iSlVEiI/gaUByhAhYSEGJ2mSKPwcKVKlNBv5ejR9mOlXYgXdyxSurVs2dIm3sPDI8XYBg0a2MT6+fmlGFujRg2b2OLFi6cYW758eZvY8uXLpxhbvHhxm9gaNWqkGOvn52cT26BBgxRjPTw8bGJbtmxp97gl1K5dO7uxCdubgIAAu7E3btywxvbv399u7Pnz562xQ4cOtRt79OhRa+zYsWPjf+7YtjFh+wg71YQJOnbKlCl297t9+3brfmfNmmU3duPGjdbYBQsW2I1dsWKFNXbFihV2YxcsWGCN3bhxo93YWbNmWWO3b99uN3bKlCnW2H379tmNHTt2rDX26NGjdmOHDh1qjT1//rzd2P79+1tjR4++H/tWmRW0ShIbEBBgjQ0JCbG73zZt2iiQNlL+V4jE9u2zNolq76/x548h168n26ZnBcuX6zQ9PJS6eNHobLI/aReEELmZTHkWud6kSXptqSeegHffNTobkV6mT9eF14sUgREjjM5GiJzrpZfg/feNzkIktHIlTJjgGfvdIOAHA7MRImdSCt55R2937aqrKGcHHTpAvXp6yvPIkUZnI4QQIjuTKs8iVzt9Wi9SHxUFGzZAq1akWMVUqjxnH1euwJNP6rdy6VLo3Nl+vLQL8eKOxZUzZ/D28kryuKOLC26PPWb9PvTGjRT35eDkhLuv70PFht26hUphurHJwQEPP7+Hig2/cweLnWnBefLnf6jYiHv3MNuZkpuWWA8/P0yx040jg4OJsTPdNC2x7r6+OMROC44KCSHazhTXFGNDQ8kTW7k0rn28feMOvv55U7Vft8cewzF2+m50WBhRISEpxrp6e+MUO70+LbExERFEBgenGOvi6Ylz7DT4tMSao6KIuHcvxVhnDw9cYv93pCXWEhND+J076RLr5ObG3/9606iRLs7ar1cYUycmf9yc3NxwjW3vlMVC2K1bKe43NCKCAsWL5/o2Uv5XiIS+/VZ3JObJA6dOQWGf+PPH0OvX8SygKz5nxXPIAwegZk29vWePrgIvHo60C0KIXM3oIZKZTYali4ReeklP+2jeXCmLJfbOqCilFixQ0V9/rRbOn68WLFigoqKiDM1TpM0bb+j3tU6dBO+rHdIuxLMeiwRTW21uiaY8Kw+P5ONAqURTnpWfX8qxiaY8q+LFU45NNOVZlS+fcmyiKc+qRo2UYxNNeVYNGqQcm2jKs2rZMuXYxP9q27WzH5twelxAgP3YBFOeVf/+9mMTTHlWQ4faj00w5VmNHZvk8WhQC0EtABX1xx/xsVOm2N9vginPatYs+7EJpjyrBQvsxyaY8qxWrLAfm2DKs9q40X5sginPavt2+7EJpjzbzINM7pZgyrM6etR+bIIpz+r8ebuxZ7qMsf6ZtW4eoWJwSDk+wZTnhEt9JHcLkinPSin5XyHi3b+vVOHC+k9k0qTYO2PPH9WCBSoqNFQtWLAgS59Dxp0rPfts6s6VRPKkXRBC5GZSlEXkWps3w8aNujLpjBkQW9cAnJ2hWzecgAAD8xMPZ98+WLxYb8+cmeB9FUKkG5v20UlOJbKCO+TlxU39uHUPqleH7+YE41gy5aJCQoiHN3myng1RqlSC5XJizx8BnIFusdtZ1cSJenmE33+HtWvhlVeMzkgIIUR2I1OeRa4UFQUVK+opKkOHwtSpRmck0oNSULeunr4TEAALF6buedIuxLMeiytXkj8Wjo66bHYcO5WbcXCABJWb0xQbFqbf0OSYTJCgcnOaYsPDwU7lZhJOS0tLbEQE2KncnKZYD4/4nvDISLAz7TpNse7u+jiDbgTtVHlOHBsTHk2HDrD1J8jnC7/+CqVLx8a6uenfi9TsN2FsdLSOT4mra3xnZVpiY2L0sUiJi4v+4J/WWLNZv3cpcXbW8WmNtVj079ojxEZGwgut3fhttyPFiuk2sFBBpf82UuLkpI8b6L8fO7HBoaH4FCiQaW2kb4LlD1LDZDLx999/U7x48QzKSJP/FQLg/HkoV07/3a1bB23aGJ3Rwxs9Gj76SLfn//4b39SI1JN2QQiRmxk+rGD27NlMnTqVa9euUblyZb744gtq2VnVeMaMGcyZM4cLFy7g5+dHu3btmDx5Mm4JP+AK8QCzZunOxPz59cmUjZgY2LoVs9nMVkA5OtKsWTOcZBROlvfdd/qDtKenHj2QExjWRubJY9sJZi8uLftMrYSdgOkZm7DTMj1j03J80xLr6hrf6ZOesS4uqf7kqJxd6P+WC2t/gjyuMfz03lZK/mdm838J2seH2C/OzvGddekZ6+SU+lGTaYl1dEz973BaYh0cHilWKejVF37bDd7esGkTFCoEYEr9fk0PiLXXAZ4B7t27x4wZM/Dx8XlgrFKK/v37Y87kHEXuNXy47kxs3Bhat07wQOz5I0BM48Zs3bYNIEufQw4fDvPnw5kzMGcODBxodEZCCCGyFSPnWy9fvly5uLio//3vf+rff/9VvXv3Vo899pi6fv16svFLly5Vrq6uaunSper8+fNq69atqlChQurdd99N9WvKOhfixg2lfHz0ujFff51MQIK1pDxAASok4ZpmIksKCVGqSJFE6xmlUlZtF6SNFFnBhAn678rBQakflkv7mNWMGaPfEicnpX76KWNeI7PbBZPJlGI7lxxPT0919uzZDMxIk/ZR/PZbfHt4+HCiBxOcP4Zcv67IJm3k3Lk6bV9fpe7cMTqb7EfaBSFEbuZgVEcmwPTp0+nduzfdu3enfPnyfPXVV3h4ePC///0v2fg//viDevXq0blzZ0qUKMELL7xAp06d2LdvXyZnLrKz0aMhKAiqVbMudSNygE8+gcuXoWTJBOsZZXPSRgqjLVwYP4p71ix46SVD0xGJLFoE48fr7a++gqZNjc0nvVgsFvInqIz+IPfv36dUbPVxITKKxQKDBunt3r310jk5QY8e8PTTcOeOXldRCCGESC3DOhSjoqL466+/aNKkSXwyDg40adKEP//8M9nn1K1bl7/++sv64fjcuXNs3ryZli1bZkrOIvs7dEhP7QBdiCVuKS+Rvf33X/w6mJ9+mrYZpVmVtJHCaD/9pD80A4wcCf36GZuPsLV9e/z789570LOnsfkIkdMtXgx//62XFojryM8JnJziz6G++ALOnTM2HyGEENmHYQt63Lp1C7PZTIECBWzuL1CgACdOnEj2OZ07d+bWrVs8++yzKKWIiYmhb9++vPfeeym+TmRkJJEJFlwPDg5Onx9AZDtK6SvLFgt06AD16xudkUgvw4fr+geNGkHbtkZnkz6kjRRG+ucfePVVvSRYly4yaiWrOX5ct3XR0fDaazBhgtEZZazTp0+zfft2bty4gSVRoaQxY8YYlJXITUJCYNQovT16tF6DOydp3lyPcP75Z/1zfv+90RkJIYTIDgyd8pxWO3bsYNKkSXz55Zf8/fffrFmzhk2bNjHBzpn05MmT8fHxsd6KFi2aiRmLrGTtWtixQ49emzLF6GxEevntN1ixQtcqmDEjvuBtbiRtpEgP//0HLVvqD9DPPw//+198wWdhvOvX9fsTFAT16sGCBTn7/Zk/fz7lypVjzJgxrFq1irVr11pv69atMzo9kUt88glcuwZPPAFvv210NunPZNIzPEwmfU6VwkQIIYQQwoZhIxT9/PxwdHTk+vXrNvdfv36dggULJvuc0aNH07VrV3r16gVAxYoVCQ0NpU+fPrz//vs4JHNGPWrUKAYPHmz9Pjg4WD4w50IRETB0qN4eNgyKFzc2H5E+zOb4ioR9+kClSsbmk56kjRRGuHNHj1S5dk2vD7ZmTeqLNouMFxamq8oGBkLp0rBuXc5Y4sGejz76iIkTJzJixAijUxG51H//6c420FODXV2NzSejVKoE3bvri0iDB8Mff+Tui7RCCCEezLBr2i4uLlSvXp1t27ZZ77NYLGzbto06deok+5ywsLAkH4gdYxfBU0ol+xxXV1e8vb1tbiL3+ewzOH8eihQB+UyScyxYAAcPgo9PzlrPCKSNFJkvIgLatIETJ+Dxx2HzZv23JbIGsxlefx327YN8+fT74+dndFYZ7+7du7Rv397oNEQuNnKkbh8bNoSXXzY6m4w1YQJ4eMCePbBqldHZCCGEyOoMG6EIMHjwYAICAqhRowa1atVixowZhIaG0r17dwDeeOMNihQpwuTJkwFo1aoV06dPp2rVqtSuXZszZ84wevRoWrVqZf3QLERiV6/CpEl6+5NPIE+eBzzBxQVmzcJsNvMpYHF0xEWG6GQ5QUG6EAHAuHHg729oOhlC2kiRWSwW3Vn1+++6E/HHH3WnYhLSPhpm+HC9dIeLix6ZWKaM0Rlljvbt2/PTTz/Rt29fo1MRudAff8Dy5Xqk3mefPWDEXmz7CODi6cmsuO1s1EYWLqzbmnHj9AX41q1z7ohMIYQQj87QDsWOHTty8+ZNxowZw7Vr16hSpQpbtmyxFiG4cOGCzWibDz74AJPJxAcffMDly5fx9/enVatWTJTV4oUdI0fqtcCeeQY6d07FE5ydYcAAHAEpapp1ffQR3LwJZcvCgAFGZ5MxpI0UmUEpPb1t9er4zqoKFVIIlvbREF9+CdOn6+1Fi+DZZ43NJ6N9/vnn1u3SpUszevRo9uzZQ8WKFXF2draJfeeddzI7PZFLWCzxy6r06AFVqjzgCbHtI4AzMCCbnpwMHQpz5+qZPbNmwZAhRmckhBAiqzKplObB5VDBwcH4+PgQFBQkU/tygb17dUci6GliNWsam49IH6dPw9NP6wqnmzdDixaPtj9pF+LJsch9pk+P/8C4bJmuGiyyjk2b9Cghi0VX27ZTtD3DZHa7ULJkyVTFmUwmzp07l8HZxJP2MXdZuFCvKejlpc87Yq/l5Qr/+x/07AmPPQZnzuhlFkTypF0QQuRmho5QFCIjWSzxlfi6d09DZ6LZDLt2YTab2QXg6Ej9+vVlymgWMmSI7kxs0eLROxOFyM2+/z6+M3Hq1FR0Jkr7mKn++Qc6dtT/z3r2hFGjjM4oc5w/f97oFEQud/9+/N/b6NGp7EyMbR8BzHXrsuuPPwCyZRsZEAAzZ8Lhw3pdxRkzjM5ICCFEViQjFEWOlfDK8qlTkEJh3KRCQ8HTE4A8QBgQEhJCngcuvigyw08/QbNm4OQER47AU089+j6lXYgnxyL32LkTXngBoqL0xZeZM1NR0VPax0xz6RLUrg1XrkCTJno0dqLZvplG2gVNjkPuMWoUfPyxrqZ+9Ggq1xFM0D6GXr+OZ2wvZHZtI3/+Wf+PcHKCY8dyz7qtaSXtghAiNzOsyrMQGSk4WK+dCPrKcqo7E0WWFh0N776rt996K306E4XIjf79V1crjYqCV15JRbEBkamCg+HFF3Vn4tNP62qrRnUmZlXr169n8eLFRqchcqCzZ+PXLJ0+PfcWJWnaVM8CiYnRBVqEEEKIxKRDUeRIH30E16/rq6lxC2qL7O+rr/RVcj8/GDPG6GyEyJ6uXNEfEu/dg3r14NtvIZvNxsvRoqOhQwc91bBgQb2Goo+P0VllPSNGjLBWvBciPQ0dqi+2NG0KL71kdDbGmjoVHBx0hfnY2dxCCCGElXQoihzn1Kn4tV4++0xXLRXZ3+3bMHas3p4wAfLmNTYfIbKj4GDdmXjxoq6Qvn49uLsbnZWIo5Qefb11K3h4wA8/QPHiRmeVNZ04cQKz2Wx0GiKH2bZNV7p3dJSR26BHSPfqpbeHDNHruQohhBBxpENR5DiDB8cX7HjxRaOzEell7Fi4excqVYLevY3ORojsJyoKXn1Vj3wrUAB+/FEqd2Y1U6fCvHm6E+O776BGDaMzyrru3bvHrFmzjE5D5CAxMTBokN7u3193pgkYP14vDbl/PyxfbnQ2QgghshLpUBQ5yo8/6ulhTk76yrLIGY4e1dOdQY8+lemZQqSNUnqUyS+/QJ48usBHyZJGZyUSWrkyfp2yzz6DNm2MzSer2rZtG507d6ZQoUKMjRu2/hA+/vhjTCYTg+J6kESuN2+ePt/w9YVx44zOJusoUCB+XfJRoyAiwth8hBBCZB3SoShyjMjI+PUSBw7U0/lE9qeULsRiNuviEY0aGZ2RENnPBx/AkiW6M37VKqhWzeiMREJ//gldu+rtt9+WtX8Tu3jxIuPHj6dkyZK88MILmEwm1q5dy7Vr1x5qf/v372fu3LlUqlQpnTMV2dWdO7qIH+gReb6+xuaT1bz7Ljz+OFy4ADNnGp2NEEKIrMLJ6ASESC+ffQanT+srqY9UsMPZGaZMISYmhvGAxckJZymvaZgfftCjqlxd9XRAIUTafPUVTJqkt+fPh+bNH2Fn0j6mu7NnoXVrfVGsVSsZXR8nOjqadevW8fXXX7Nr1y6aN2/O1KlT6dSpE++//z7ly5d/qP2GhITQpUsX5s+fz0cffZTOWYvsavRo3alYoQK8+eZD7iS2fQRw9vBgStx2DmgjPTxg4kQICND/T3r0AH9/o7MSQghhNJNSShmdRGYKDg7Gx8eHoKAgvL29jU5HpJNLl/SIxLAwWLw4fqSHyN4iI/UaRmfP6mk2cZ0i6U3ahXhyLHKW9ev1yF6LBT78UKqjZzV37kDdunDypB41unOnXqssqzGiXcifPz9PPfUUr7/+Ou3btydvbCUuZ2dnDh069NAdigEBAfj6+vLZZ5/RsGFDqlSpwoy4Sm4PIO1jznTokP77s1hg+3Zo2NDojLImiwVq1oS//4YBA0CWMNWkXRBC5GYy5VnkCEOG6M7EZ5+F1183OhuRXj7/XHcmFiqkOxSFEKm3Zw906qQ/BPbqFT+dT2QNkZG6s/fkSShaFDZuzJqdiUaJiYnBZDJhMplwTKeFc5cvX87ff//N5MmTUxUfGRlJcHCwzU3kLErpZQYsFujQQToT7XFwgE8/1dtffQUnThibjxBCCONJh6LI9n79FVas0Cc6X3yhq2M+ErMZ9u/HvGcP+/fsYf/+/ZjN5nTJVaTe9eswYYLenjwZvLyMzUeI7OT0aT19NjwcWraEOXPSoW0EaR/TiVK6Wv3Onbpt27RJXzgR8a5cuUKfPn1YtmwZBQsW5NVXX2Xt2rWYHvIX+eLFiwwcOJClS5fi5uaWqudMnjwZHx8f661o0aIP9doi61q2DHbt0lN64zrLHlps+8j+/Zijoti/f3+OayMbNdL/W8zm+CJSQgghci+Z8iyytehoqFIFjh1Lx+kXoaHWYSJ5gDD0mkt58uRJh52L1OrVC775Rk+v2bNHdxhnFGkX4smxyP6uX9fTaM+dgxo19BS+dBv5Ju1juhg3Tk9Bd3LSFbebNjU6I/uMbhfOnj3LggULWLRoEZcvX6ZTp05069aN559/PtWjF9etW0fbtm1t4s1mMyaTCQcHByIjI5PsKzIyksjISOv3wcHBFC1aVNrHHCIkRC+Xc+UKfPQRvP/+I+4wQfsYev06ngUKxL5OzmojT5zQa02azTJFHIxvH4UQwkgyQlFka7Nm6c5EPz9dlU/kDH/9Bf/7n96eOTNjOxOFyElCQ+Gll3RnYsmSMo02K1q8WHcmgp42mNU7E7OCJ554go8++oj//vuPTZs2ERkZyUsvvUSB2A6b1GjcuDFHjhzh4MGD1luNGjXo0qULBw8eTLZj0tXVFW9vb5ubyDk++kh3JpYqpZfOEanz1FPQt6/eHjJETxcXQgiRO0mVZ5FtXb0KY8fq7cmTwdfX2HxE+lAKBg7UX7t0gTp1jM5IiOwhJkavAXbgAOTLB1u26Kr3IuvYvl2Pvga9LmzPnsbmk904ODjQokULWrRowc2bN1myZEmqn+vl5UWFChVs7suTJw/58uVLcr/I+U6fhunT9faMGZDKWfAi1tixsGSJLtCydKkUQxRCiNxKxv2IbGvECLh/X0+J7dHD6GxEelmxAnbv1usZffyx0dkIkT0oBf366emzbm56ZOKTTxqdlUjo+HFo21Yv1dGxox4dJR6ev78/gwcPNjoNkU0NGqT/Flu00KO6Rdr4+8N77+nt997ThRGFEELkPtKhKLKl33/XV0ZNJpg9W6bE5hRhYTBsmN4eORIef9zYfITILj76CL7+WreFy5fDM88YnZFI6Pp1XRwnKEivb7lwofzfssfX15dbt26lOr5YsWL8999/aX6dHTt2MGPGjDQ/T2RvGzfqiy/Oznp0YroUrMqFBg6EYsXg0iV9HIUQQuQ+MuVZZDsxMboAC+jpYjVrGpuPSD+ffgoXL+oT1KFDjc5GiOxhwQIYM0Zvz5oFbdoYm4+wFRYGrVtDYCA88QSsXy/TKx/k3r17/Pjjj/j4+KQq/vbt2zmqkq7IOBERuiMMYPBgGcn9KNzc9JJDXbrorz17yjIbQgiR20iHosh2vvwSDh+GvHn1CYzIGS5ejJ/iPHUquLsbm48Q2cGWLdC7t94eOVJPexZZh8Wi1xbbt0+v87t5sy4iJh4sICDA6BREDjR1qi5aVaQIfPCB0dlkf6+9pkcn7t+v11X86iujMxJCCJGZpENRZCvXr8Po0Xp78uQM+mDm7Axjx2I2mxkJmB0dcXZ2zoAXEgmNHAnh4VC/PrRvb3Q2QmR9f/0F7dqB2Qyvvw6TJmXCi0r7mCbDh8OaNeDiAuvWyWio1LJI2ViRAQID49vJadPA0zOdXyC2fQRw9vBgbNx2Dm4jHRz0sXzuOZg/H955B8qXNzorIYQQmcWklFJGJ5GZgoOD8fHxISgoCG9vb6PTEWnUrRssWgTVq8PeveDoaHRGIj388QfUq6fXMTpwAKpVy9zXl3YhnhyL7OHcOV0B/cYNaNIENm3SnVYi65gzB/r319tLl0Lnzsbm8yikXdDkOGRvr7wCa9dCw4bw66+ydmJ6iju2LVvq/0e5ibQLQojcTJYEF9nG7t26M9Fk0tOepTMxZ7BY4tcz6tEj8zsThchubt2C5s11Z2LlyrB6tXQmZjWbN8Nbb+ntCROyd2eiEDnB1q26w8vREb74QjoT09snn4CTk277fvnF6GyEEEJkFpnyLLKFxIVYatXKwBezWOD4cSwWC8cBHBwoV64cDlKSM0MsXqxHJXp5wcSJRmcjRNYWFgatWsHp07p40ebNkKkDIqR9fKCDB6FjR32ouneH9983OiMhcrfISD0VF+Dtt6FChQx6odj2EcBStizHT54EyBVtZJkyekT255/DkCHw999y4V8IIXID6VAU2cKcOXDoUCYVYgkPhwoVcABqAWFASEgIefLkyeAXzn3u34dRo/T26NFSHVAIe8xmPdJtzx547DH48UcoXDiTk5D20a5Ll+DFFyEkBBo3hrlzZSSUEEabMQNOndLnGOPGZeALxbaPAOHXr1Mhdju3tJFjxuiLxIcP66/duxudkRBCiIyWsy+XiRwhYSGWSZOkQmZOMmkSXLsGpUvHjx4QQiSllB5Zs349uLrChg2y8H1Wc/8+vPQSXLmi35tVq3SNBiGEcS5d0ssOAEyZAj4+xuaTk+XLF185+4MPIDTU2HyEEEJkPOlQFFmaUtC3LwQF6UIsvXsbnZFIL+fOwfTpenvaNN1JIoRI3qRJeqS2yQTffquroYusIyZGT3M+dEiPgtq8WY8iFY+uQYMGLF68mPDwcKNTEdmMUvDuu7pjq25deP11ozPK+d56C0qW1BdWpk0zOhshhBAZzfApz7Nnz2bq1Klcu3aNypUr88UXX1DLzgJ59+7d4/3332fNmjXcuXOH4sWLM2PGDFq2bJmJWYvMMn06rFunCw7MmyfrseQkQ4dCVBQ0barXhBPJkzZSLFwYP+pj5kxo187QdEQicaNHf/wR3N3hhx+geHGjs8o5qlatytChQ3n77bfp0KEDPXv25JlnnjE6LZENzJunRwo7OsKsWZDDlzHMElxd4eOP9QWWKVP0QIBChYzOKndQShETE4PZbDY6FSFENufo6IiTkxOmVKzbY2iH4vfff8/gwYP56quvqF27NjNmzKBZs2acPHmS/PnzJ4mPioqiadOm5M+fn1WrVlGkSBH+++8/HpNhADnSrl0wYoTenjlTqv/mJL/+Gl9t8bPPZI2xlEgbKX78EXr10tvDh+uOK5G1TJsGX32l27Fly6BmTaMzyllmzJjBp59+yoYNG1i0aBHPPfccpUuXpkePHnTt2pUCsviuSMaBA/FLqUyaBFWrGptPbtK+vT6327NHL1n09ddGZ5TzRUVFcfXqVcLCwoxORQiRQ3h4eFCoUCFcXFzsxpmUUiqTckqidu3a1KxZk1mzZgFgsVgoWrQob7/9NiNHjkwS/9VXXzF16lROnDiB80MuTBQcHIyPjw9BQUF4Z2ppTJEW16/rk7+rV6FLF1iyJBM7nUJDwdMTgDxI0YH0FhOjO4ePHNFTY774wuiMsm67IG1k7rZ/PzRqpJuk11+HRYuywAgbaR9trF4dP2L0s89g0CBD08kwWalduHHjBvPmzWPixImYzWZatmzJO++8w/PPP5/hr52VjoNI2Z07+jzjv/+gdWs90yVTziETtI+h16/jGdvZnRvbyD/+gHr19HE/eBAqVTI6o4xjdLtgsVg4ffo0jo6O+Pv74+LikqpRRUIIkRylFFFRUdy8eROz2UyZMmVwsPMBxLARilFRUfz111+MiivxCjg4ONCkSRP+/PPPZJ+zYcMG6tSpw4ABA1i/fj3+/v507tyZESNG4ChzYXMMsxk6ddKdieXLx4/8EDnD11/rzsS8eTO42mI2J21k7nb2rK4WHBqqlwX45pss0JkobOzZE78m21tvwcCBxuaTG+zbt48FCxawfPly8ufPT7du3bh8+TIvvfQS/fv359NPPzU6RWEwiwW6dtWdiaVK6Qsxcg6Z+erW1SMVV66EYcNg61ajM8q5oqKirBecPTw8jE5HCJEDuLu74+zszH///UdUVBRubm4pxhrWoXjr1i3MZnOSqSoFChTgxIkTyT7n3Llz/Prrr3Tp0oXNmzdz5swZ+vfvT3R0NGPHjk32OZGRkURGRlq/Dw4OTr8fQmSIsWNh+3bIk0evfRN7sTfzODvD0KGYzWbeBsyOjg892kvYuns3fi248eN1RUCRPGkjc68bN6B5c7h5U4/UXr1aryObJUj7COiiUq1bQ0SEruw8Y4Z0WmSUGzdusGTJEhYsWMDp06dp1aoVy5Yto1mzZtZRON26daN58+bSoSj4+GNdFMnVVZ9DZuqKH7HtI4CzhwdD47ZzYRsJ+r1Ytw5++gm2bNH/10TGsTeCSAgh0iq1bYrhRVnSwmKxkD9/fubNm4ejoyPVq1fn8uXLTJ06NcUPy5MnT+bDDz/M5EzFw9q0CSZO1Ntffw3lyhmQhIsLTJ2KI/CxAS+fk334Idy+rUee9u1rdDY5j7SR2V9ICLRsCWfOQIkS+oOxl5fRWSUg7SN37uj3KK7Dd9kyKRiWkR5//HGeeOIJevToQbdu3fD3908SU6lSJWrK4pW53rZtes0+gNmzDVg3MbZ9BHABpsZu51alSul1f6dP1/2sTZqAU7b65CmEEOJBDLuU4efnh6OjI9evX7e5//r16xQsWDDZ5xQqVIgnn3zSZupeuXLluHbtGlFRUck+Z9SoUQQFBVlvFy9eTL8fQqSrW7fgjTf09ltvwWuvGZuPSF/Hj+sTfNBrjclJpX3SRuY+UVHw6qvw11/g56eniKXwVguDREbCK6/AyZNQtChs3GjAKPpcZtu2bRw/fpxhw4Yl25kI4O3tzfbt2zM5M5GVXL0KnTvrKc/dukGPHkZnJEDPSvH1hX//hQULjM5G5BYmk4l169alKnbcuHFUqVLFbkzDhg0ZlM0WSQ4MDMRkMnHw4EGjU3kkO3bswGQyce/ePaNTESkwrEPRxcWF6tWrs23bNut9FouFbdu2UadOnWSfU69ePc6cOYPFYrHed+rUKbvVZ1xdXfH29ra5iaxp1Cg98qNSJTB01pLFAoGBWM6dI/DcOQIDA21+58TDGTxYF2Rp1QpeeMHobLI+aSNzF4tFfwD+6Sfw8NCjtZ980uiskpGL20eloHdv2LlTjxrduBEKFzY6q5xv7NixyX6QCA4OzpRCLCJ7GDJELxdRqZK+eGnIEgSx7SOBgVhiYggMDMxVbWRy8uaFMWP09ujRcP++sfmIrOPmzZv069ePYsWK4erqSsGCBWnWrBm7d++2xqSlYzChq1ev0qJFi3TLdc2aNUyYMCHd9vewFi5cyGOpXMehaNGiXL16lQoVKmRsUiLXM3SxhcGDBzN//nwWLVrE8ePH6devH6GhoXTv3h2AN954w6YgQb9+/bhz5w4DBw7k1KlTbNq0iUmTJjFgwACjfgSRTvbt00UHAL78Uq99Y5jwcChZEocnnuDpJ56gZMmShIeHG5hQ9rd5s14/x9kZpk0zOpvsQ9rI3GPECFi6VI/cXb0aatUyOqMU5OL28cMPYckSPb151aqcXbU0K9m5c2eyI6wjIiLYtWuXARmJrGbHDr30gMmkR8EZVpcitn2kZEnC79yhZMmSuaqNTEm/flC6NFy/DlOmGJ2NyCpeffVV/vnnHxYtWsSpU6fYsGEDDRs25Pbt24+874IFC+Kajh8mfX198cpS68/YFxUVhaOjIwULFsRJpoSJDGZoh2LHjh359NNPGTNmDFWqVOHgwYNs2bLFWoTgwoULXL161RpftGhRtm7dyv79+6lUqRLvvPMOAwcOZOTIkUb9CCIdmM0wYIAe/fHGG1CvntEZifQUFQXvvqu3Bw6EMmWMzSc7kTYyd5g+PX5U9jffyML1WdHixbpDEWDOHBllnRkOHz7M4cOHUUpx7Ngx6/eHDx/mn3/+4ZtvvqFIkSJGpykMFh2tzyFBr81crZqx+YikXFzgk0/09rRpcOmSsfkI4927d49du3bxySef0KhRI4oXL06tWrUYNWoUrVu3BqBEiRIAtG3bFpPJZP0eYM6cOTzxxBO4uLhQtmxZlixZYrP/xCMbL126RKdOnfD19SVPnjzUqFGDvXv32jxnyZIllChRAh8fH1577TXuJxhOm3jK8927d3njjTfImzcvHh4etGjRgtOnT1sfjxtJuHHjRsqWLYuHhwft2rUjLCyMRYsWUaJECfLmzcs777yD2Wy2Pi8yMpKhQ4dSpEgR8uTJQ+3atdmxYwegp/52796doKAgTCYTJpOJcePGWY/VhAkTeOONN/D29qZPnz7JTnn+999/eemll/D29sbLy4v69etz9uzZFN+no0eP0qJFCzw9PSlQoABdu3bl1q1bNsflnXfeYfjw4fj6+lKwYEFrTgCdO3emY8eONvuMjo7Gz8+PxYsXA3r21eTJkylZsiTu7u5UrlyZVatWpZgTwOrVq3n66adxdXWlRIkSTEs0WiXueHTq1Ik8efJQpEgRZsetuxXr3r179OrVC39/f7y9vXn++ec5dOiQ3dcVKVC5TFBQkAJUUFCQ0amIWHPnKgVKeXsrde2a0dkopUJCdEKgPEABKiQkxOissq3p0/XhzJ9fqXv3jM4medIuxJNjkbmWLrU2N+qTT4zOJhVyYfu4fbtSzs76xx4xwuhsjGFEu2AymZSDg4NycHBQJpMpyc3Dw0N98803mZaPUtI+ZkXTpum/zXz5lLp92+BkErSPIdevK3JJG5kaFotSzz6rD09AgNHZpC+j24Xw8HB17NgxFR4ebr3PYtG/jpl9s1hSl3N0dLTy9PRUgwYNUhEREcnG3LhxQwFqwYIF6urVq+rGjRtKKaXWrFmjnJ2d1ezZs9XJkyfVtGnTlKOjo/r111+tzwXU2rVrlVJK3b9/X5UqVUrVr19f7dq1S50+fVp9//336o8//lBKKTV27Fjl6empXnnlFXXkyBH122+/qYIFC6r33nvPur8GDRqogQMHWr9v3bq1KleunPrtt9/UwYMHVbNmzVTp0qVVVFSUUkqpBQsWKGdnZ9W0aVP1999/q507d6p8+fKpF154QXXo0EH9+++/6ocfflAuLi5q+fLl1v326tVL1a1bV/3222/qzJkzaurUqcrV1VWdOnVKRUZGqhkzZihvb2919epVdfXqVXX//n2llFLFixdX3t7e6tNPP1VnzpxRZ86cUefPn1eA+ueff5RSSl26dEn5+vqqV155Re3fv1+dPHlS/e9//1MnTpxI9vjfvXtX+fv7q1GjRqnjx4+rv//+WzVt2lQ1atTI5rh4e3urcePGqVOnTqlFixYpk8mkfvrpJ6WUUhs3blTu7u7WPJVS6ocfflDu7u4qODhYKaXURx99pJ566im1ZcsWdfbsWbVgwQLl6uqqduzYoZRSavv27QpQd+/eVUopdeDAAeXg4KDGjx+vTp48qRYsWKDc3d3VggULrK9RvHhx5eXlpSZPnqxOnjypPv/8c+Xo6GjNSymlmjRpolq1aqX279+vTp06pYYMGaLy5cunbhv+jyTrSK5tSY50KApD3bqllK+vPsGYMcPobGLlwg/MGeXGDaV8fPTh/Ppro7NJmbQL8eRYZJ6tW5VyctJ/HwMHpv5E3FC5rH08dkypxx7TP3KHDkqZzUZnZAwj2oXAwEB1/vx5ZTKZ1P79+1VgYKD1duXKFRUTE5NpucSR9jFruXJFKS8v/fc5f77R2SjpUHyAvXv14TGZlPr7b6OzST9GtwvJfehP8KuYqbe0/KqvWrVK5c2bV7m5uam6deuqUaNGqUOHDtnEJOwYjFO3bl3Vu3dvm/vat2+vWrZsmezz5s6dq7y8vFLsKBo7dqzy8PCwdnAppdSwYcNU7dq1rd8n7FA8deqUAtTu3butj9+6dUu5u7urFStWKKV0hyKgzpw5Y4158803lYeHh03nWrNmzdSbb76plFLqv//+U46Ojury5cs2+TVu3FiNGjXKul8fH58kP0Px4sXVyy+/bHNf4g7FUaNGqZIlS1o7PR9kwoQJ6oUXXrC57+LFiwpQJ0+etB6XZ5991iamZs2aakTs1dfo6Gjl5+enFi9ebH28U6dOqmPHjkoppSIiIpSHh4e1czdOz549VadOnZRSSTsUO3furJo2bWoTP2zYMFW+fHmb49G8eXObmI4dO6oWLVoopZTatWuX8vb2TtKZ/cQTT6i5c+c+4MjkHqntUDR0yrMQ77+vC7FUrBg/ZUXkHKNHQ1AQVK2qqy4KIbT9+3W14JgY6NRJT3s2pIiASNGNG/Dii3DvHtSpAwsXgoOcNWWa4sWLU6JECSwWCzVq1KB48eLWW6FChWyq2YvcadgwXeSjVi2p6pwd1Kql/98pBUOH6q8i93r11Ve5cuUKGzZsoHnz5uzYsYNq1aqxcOFCu887fvw49RKtj1WvXj2OHz+ebPzBgwepWrUqvr6+Ke6zRIkSNmskFipUiBs3bqT4+k5OTtSuXdt6X758+ShbtqxNDh4eHjzxxBPW7wsUKECJEiXw9PS0uS/udY4cOYLZbObJJ5/E09PTetu5c6fdaclxatSoYffxgwcPUr9+fZydnR+4L4BDhw6xfft2m1yeeuopAJt8KiVaUDrhsXNycqJDhw4sXboUgNDQUNavX0+XLl0AOHPmDGFhYTRt2tTmdRYvXpziz5zS+3/69Gmb6eOJC1jWqVPH+v4cOnSIkJAQ8uXLZ/O658+fT9WxFrZklU5hmAMHYN48vT1rli5GIHKOQ4dg/ny9PXOmLmQghIDTp6FlSwgNhSZNpKMqKwoPh9at4fx5KFUK1q8Hd3ejs8o9NmzYQIsWLXB2dmbDhg12Y+PW20qNOXPmMGfOHAIDAwF4+umnGTNmTLpWAxWZ47ffdCErk0lXdZY2NHuYNAnWrIFff9UF+1580eiMciYPDwgJMeZ108LNzY2mTZvStGlTRo8eTa9evRg7dizd0nEUgnsq/nkn7mQzmUyPXJ09uX3ae52QkBAcHR3566+/klwwS9gJmZI8efLYfTw1xyGhkJAQWrVqxSdxC6AmUKhQIev2g45dly5daNCgATdu3ODnn3/G3d2d5rGLhYfE/pJu2rQpyZrI6VlUJ7GQkBAKFSpkXZ8yodRW0RbxpAtHGMJigbfe0lcnu3SB554zOiORnpSCQYP0+9yhA9Svb3RGQmQNV69Cs2Zw6xZUr64/WLm4GJ2VSMhiga5dYe9eyJtXf+j19zc6q9zl5Zdf5tq1a+TPn5+XX345xTiTyWQzIuFBHn/8cT7++GPKlCmDUopFixbRpk0b/vnnH55++ul0yFxkhpgYfQ4J0KcPPGBgjshCSpTQBfqmTNEjTJs1kwEFGcFkggf0L2VJ5cuXtymm4uzsnKSNL1euHLt37yYgIMB63+7duylfvnyy+6xUqRJff/01d+7csTtKMbXKlStHTEwMe/fupW7dugDcvn2bkydPpphDalStWhWz2cyNGzeon8IHJxcXlzT9z0uoUqVKLFq0iOjo6FSNUqxWrRqrV6+mRIkSj1Qpum7duhQtWpTvv/+eH3/8kfbt21tfv3z58ri6unLhwgUaNGiQqv3Fvf8J7d69myeffNKmI3bPnj02MXv27KFcuXLWn+3atWs4OTnZFPsRD0eacGGIhQv1hzUvL5g61ehsEnFygv79MZvN9AJiHB0fqSHNjdasgR07wM1NnzQKIfT0/xYt9Ki30qV1R1WCGTbZQy5oH0eMgNWrdUfvunVQtqzRGeU+CUc3POookYRatWpl8/3EiROZM2cOe/bskQ7FbGT2bDhyBHx9YeJEo7NJILZ9BHByc6N/3HYOayMf1ahR8M03cPy4nsnSr5/RGYnMdvv2bdq3b0+PHj2oVKkSXl5eHDhwgClTptCmTRtrXIkSJdi2bRv16tXD1dWVvHnzMmzYMDp06EDVqlVp0qQJP/zwA2vWrOGXX35J9rU6derEpEmTePnll5k8eTKFChXin3/+oXDhwkmmxaZGmTJlaNOmDb1792bu3Ll4eXkxcuRIihQpYpN7Wj355JN06dKFN954g2nTplG1alVu3rzJtm3bqFSpEi+++CIlSpQgJCSEbdu2UblyZTw8PPBI5bDQt956iy+++ILXXnuNUaNG4ePjw549e6hVqxZlkznRGTBgAPPnz6dTp07WKs5nzpxh+fLlfP3112ladqRz58589dVXnDp1iu3bt1vv9/LyYujQobz77rtYLBaeffZZgoKC2L17N97e3jadxnGGDBlCzZo1mTBhAh07duTPP/9k1qxZfPnllzZxu3fvZsqUKbz88sv8/PPPrFy5kk2bNgHQpEkT6tSpw8svv8yUKVN48sknuXLlCps2baJt27YPnD4uEsmcJR2zDqMXzhVK3b2rlL+/Xrx32jSjsxHpLTxcqRIl9Ps7erTR2aSOtAvx5FhkjPBwpRo21H8XBQoodfas0RmJ5MyZE7+4/NKlRmeTdeTEdiEmJkYtW7ZMubi4qH///TdVz8mJxyG7uXZNKW9v/Tcqa+dnX198od9Df3+lsvufk9HtQmoLJ2QlERERauTIkapatWrKx8dHeXh4qLJly6oPPvhAhYWFWeM2bNigSpcurZycnFTx4sWt93/55ZeqVKlSytnZWT355JM2RT+USlrMJTAwUL366qvK29tbeXh4qBo1aqi9e/cqpXRRlsqVK9s8/7PPPrN5vcRVnu/cuaO6du2qfHx8lLu7u2rWrJk6deqU9fHkiqck9zoBAQGqTZs21u+joqLUmDFjVIkSJZSzs7MqVKiQatu2rTp8+LA1pm/fvipfvnwKUGPHjlVK6SIkn332mc2+ExdlUUqpQ4cOqRdeeEF5eHgoLy8vVb9+fXXWzgnpqVOnVNu2bdVjjz2m3N3d1VNPPaUGDRqkLLFVBBMfF6WUatOmjQpIVMr92LFjClDFixe3PjeOxWJRM2bMUGXLllXOzs7K399fNWvWTO3cuVMplbQoi1K6oE/58uWVs7OzKlasmJo6darNPosXL64+/PBD1b59e+Xh4aEKFiyoZs6caRMTHBys3n77bVW4cGHl7OysihYtqrp06aIuXLiQ4vHIbVLbtpiUyl1L4gYHB+Pj40NQUBDe3t5Gp5MrvfsuzJgB5crpdfZSuTasyCYmTdLFdooUgZMns8eUC2kX4smxSH9mM7z2GqxapUck7typCxWJrOXHH+Gll/SU5wkT4IMPjM4o6zCyXXjnnXcoXbo077zzjs39s2bN4syZM8yYMSNN+zty5Ah16tQhIiICT09PvvvuO1q2bJlsbGRkJJGRkdbvg4ODKVq0qLSPBureXc9yqVED9uyR9Zmzq+hoqFABTp3SIxYnTTI6o4dn9HlTREQE58+fp2TJkri5uWX66wuR1ZQoUYJBgwYxaNAgo1PJ1lLbtsgSxiJTHTsGX3yht2fOzKKdiUrBzZuoGze4eeMGN2/eJJf1uz+0K1fiTwo/+SR7dCYKkZHi1hNdtUq3d+vWZfPOxBzaPh46pNd7tVh0Rfr33zc6IxFn9erVSSo6gl6XadWqVWneX9myZTl48CB79+6lX79+BAQEcOzYsWRjJ0+ejI+Pj/VWtGjRNL+eSD979ujORNDF/LJcZ2Js+8jNmyiLhZs3b+aYNjK9OTvHL4nz2Wdw4YKx+QghhHg40qEoMo1S8M47erTOyy9D06ZGZ5SCsDDInx9TgQKUKFCA/PnzExYWZnRW2cKoUbpybZ060Lmz0dkIYbyPP9YffAGWLIHnnzc2n0eWA9vHS5d0pdGQEP3+zJ2rF7QXWcPt27fx8fFJcr+3tze3bt1K8/5cXFwoXbo01atXZ/LkyVSuXJmZM2cmGztq1CiCgoKst4sXL6b59UT6MJvjC7F07w61axubT7Ji20fy5yfs1i3y58+fI9rIjNK6NTRoABERchFHCCGyK+lQFJlm7VrYtg1cXWHaNKOzEelt3z5YvFhvz5wpH8iFWLgQ3ntPb8+YAR07GpmNSM79+3qa8+XLUL58fDEWkXWULl2aLVu2JLn/xx9/pFSpUo+8f4vFYjOtOSFXV1e8vb1tbsIY33wDf/0FPj4webLR2Yj0YDLBp5/q7W+/hQMHjM1HCJEzBAYGynTnTJTmDsWElXkSmzt37iMlI3Ku8HAYPFhvDx8O6fAZQGQhSsHAgXo7IABq1jQ2HyMFBATw22+/GZ2GMNjmzdCrl94eMSL+70NkHTExupP30CEoUAA2bYLHHjM6K5HY4MGDGT58OGPHjmXnzp3s3LmTMWPGMHLkSN5999007WvUqFH89ttvBAYGcuTIEUaNGsWOHTvo0qVLBmUv0sOdO/EXZz78UP+9ipyhRg14/XW9PWSIPp8UQgiRfaS5Q7F58+YMGzaM6Oho6323bt2iVatWjBw5Ml2TEznH1Knw339QtCjIr0nO8913em2jPHmy98La6SEoKIgmTZpQpkwZJk2axOXLl41OSWSyvXuhfXs9Re+NN2Q0TVakFLz9ti7E4u4OP/wAJUoYnZVITo8ePZg2bRrffPMNjRo1olGjRnz77bfMmTOH3r17p2lfN27c4I033qBs2bI0btyY/fv3s3XrVppm2TVYBMDo0XD7ti7iMWCA0dmI9DZxIri5wW+/wYYNRmcjhBAiLR5qhOLatWupWbMmx44dY9OmTVSoUIHg4GAOHjyYASmK7O6//+I/UH/6KXh4GJuPSF+hoXoEFug1cAoXNjYfo61bt47Lly/Tr18/vv/+e0qUKEGLFi1YtWqVzYUYkTOdOqXX4wsLg+bN4euvZfp/VjRtGnz1lX5vvvsud4+qzg769evHpUuXuH79OsHBwZw7d4433ngjzfv55ptvCAwMJDIykhs3bvDLL79IZ2IWd/Cg/lsFXdTPycnQdEQGKFYM4gYbDx+uK0ALIYTIHtLcoVi3bl0OHjxIhQoVqFatGm3btuXdd99lx44dFC9ePCNyFNnc0KF6weWGDfWoHZGzfPKJXn+sZMn4E8Lczt/fn8GDB3Po0CH27t1L6dKl6dq1K4ULF+bdd9/l9OnTRqcoMsC1a9CsmR5JU6MGrFyZRSvZ53KrV8OwYXp72jRdJExkD/7+/nh6ehqdhsgkSulCLBaLXp6gYUOjMxIZZeRIXc/m1Kn4DmQhhBBZ30MVZTl16hQHDhzg8ccfx8nJiZMnT0oFM5Gs7dth1SpwcIDPP5eROjnNf//p6eygv7q5GZtPVnP16lV+/vlnfv75ZxwdHWnZsiVHjhyhfPnyfPbZZ0anJ9JRcDC0bAmBgfDEE3o9Pun3yHr27Ilfr2vAAJA1u7OHVatW0aFDB5555hmqVatmcxM519KlsHu3ntkSV7xD5Eze3np9TNBf790zNB0hhBCplOYOxY8//pg6derQtGlTjh49yr59+/jnn3+oVKkSf/75Z0bkKLKpmJj4QgT9+kHFisbmk2pOThAQgPn11+n0+usEBATgJHNskjV8ePzo01deMTqbrCE6OprVq1fz0ksvUbx4cVauXMmgQYO4cuUKixYt4pdffmHFihWMHz/e6FRFOomKgldfhX/+AX9/2LpVj7TIkbJx+3juHLRurdusF1/UlbflIlfW9/nnn9O9e3cKFCjAP//8Q61atciXLx/nzp2jRYsWRqcnMsj9+/ocA+CDD+Dxx43NJ1Vi20cCAnBycyMgICBbtZFG69ULypXTo/xz+3rcQgiRbag0KliwoNq8ebPNfVFRUWro0KHKxcUlrbvLdEFBQQpQQUFBRqeS482erRQo5eur1O3bRmcj0tvOnfr9dXBQ6uBBo7N5NOnZLuTLl0/lzZtX9e/fX/3zzz/Jxty9e1eVKFHikV8rI0gbmTZms1Kvv67/FvLkUWr/fqMzEsm5c0epp57S71PVqkrdv290RtmLke1C2bJl1XfffaeUUsrT01OdPXtWKaXU6NGj1YABAzI1F2kfM8+wYfrvtXRppSIijM5GZJaNG/X77uKi1LlzRmeTOka3C+Hh4erYsWMqPDzckNc32oIFC5SPj0+67e/8+fMKSPEcPrP3kxpjx45V+fPnV4Bau3Zthr+ekbZv364Adffu3VQ/p0GDBmrgwIF2Y4oXL64+++yzh84r8fud2jwf9LqZ+XuUWGrbljSPUDxy5EiSK8LOzs5MnTqVn3766VH6NkUOcueOrsoHMH48+Poam49IX2Zz/FTB3r2hcmVD08lSPvvsM65cucLs2bOpUqVKsjGPPfYY58+fz9zERIYYNQq+/VYPTFm1Sq+dKLKWqCg9gvrECT3KaeNGmY6enVy4cIG6desC4O7uzv379wHo2rUry5YtMzI1kUFOntQjiEF/dXU1MhuRmVq2hMaNdbv93ntGZyMy2rVr13j77bcpVaoUrq6uFC1alFatWrFt2zajU0uTbt268XKiBZmLFi3K1atXqVChQoa+9vHjx/nwww+ZO3cuV69elZH7WUTdunW5evUqPj4+ACxcuJDHHnsszfvJrN+jR5HmDkU/P78UH2vQoMEjJSNyjjFjdKdixYrw5ptGZ5NGSkFoKCokhNCQEEJDQ1FKGZ1VlrJggZ7e6eMDEyYYnU3W0rVrV9xkMclc4fPPYcoUvf3117qqc46XzdpHpfQ0uh07wMtLr22Z2yvRZzcFCxbkzp07ABQrVow9e/YAcP78+Sz9uycejlJ6uZzoaL00wYsvGp1RGsS2j4SGoiwWQkNDs3wbmdWYTHq9TJMJli+HvXuNzkhklMDAQKpXr86vv/7K1KlTOXLkCFu2bKFRo0YMGDDA6PQemaOjIwULFszwJQ/Onj0LQJs2bShYsCCuyVyBiYqKytAcRFIuLi4ULFgQ0yOurZNZv0eP4qGKsghhz5EjMGeO3p45U4/cyVbCwsDTE5OXF/m9vPD09JSiQwkEBcH77+vtsWP1mnFC5DYrVsSP0p00SS+blStks/Zx/HhYsgQcHXXV7UqVjM5IpNXzzz/Phg0bAOjevTvvvvsuTZs2pWPHjrRt29bg7ER627BBr0Pr4hI/SjHbiG0f8fQk7NYtPD09s3wbmRVVqRL/P3XwYN1PK3Ke/v37YzKZ2LdvH6+++ipPPvkkTz/9NIMHD7ZeOAKYPn06FStWJE+ePBQtWpT+/fsTEhJid98//PADNWvWxM3NDT8/P5v/FSaTiXXr1tnEP/bYYyxcuDDZfZnNZnr27EnJkiVxd3enbNmyzJw50/r4uHHjWLRoEevXr8dkMmEymdixYweBgYGYTCYOHjxojd25cye1atXC1dWVQoUKMXLkSGJiYqyPN2zYkHfeeYfhw4fj6+tLwYIFGTduXIo/57hx42jVqhUADg4O1s6ruBGTEydOpHDhwpQtWxbQM02ff/553N3dyZcvH3369LE5lnHPmzRpEgUKFOCxxx5j/PjxxMTEMGzYMHx9fXn88cdZsGCB3eNvsViYMmUKpUuXxtXVlWLFijFx4kRA/09/6623bOJv3ryJi4uLdWRqZGQkI0aMoGjRori6ulK6dGm++eabZF/r9u3bdOrUiSJFiuDh4UHFihWTnb0QExPDW2+9hY+PD35+fowePdruxZ579+7Rq1cv/P398fb25vnnn+fQoUN2f+6EduzYgclk4t69e+zYsYPu3bsTFBRk/R1J+L6GhYXRo0cPvLy8KFasGPPmzbM+lvj3KLmRjuvWrbPpuBw3bhxVqlThf//7H8WKFcPT05P+/ftjNpuZMmUKBQsWJH/+/Nb35FFlt64ekcXFXVm2WHSRgkaNjM5IpLePPoIbN6BsWV0lVYjcZscO6NpVt3cDBsDIkUZnJJKzZAnEna99+SU0a2ZoOuIhzZs3D4vFAsCAAQPIly8ff/zxB61bt+bNbDcFQtgTEQHvvqu3hwyB0qWNzUcY56OP9IW7P/6A1auhXTujM8qeQkNDU3zM0dHRZkaNvVgHBwfc3d3txubJkyfVed25c4ctW7YwceLEZJ+XsMPEwcGBzz//nJIlS3Lu3Dn69+/P8OHD+fLLL5Pd96ZNm2jbti3vv/8+ixcvJioqis2bN6c6t8QsFguPP/44K1eutP7/6dOnD4UKFaJDhw4MHTqU48ePExwcbO1o8/X15cqVKzb7uXz5Mi1btqRbt24sXryYEydO0Lt3b9zc3Gw6lxYtWsTgwYPZu3cvf/75J926daNevXo0bdo0SW5Dhw6lRIkSdO/enatXr9o8tm3bNry9vfn5558B/Z41a9aMOnXqsH//fm7cuEGvXr146623bDpTf/31Vx5//HF+++03du/eTc+ePfnjjz947rnn2Lt3L99//z1vvvkmTZs25fEUqmWNGjWK+fPn89lnn/Hss89y9epVTpw4AWB9zWnTpllHU3777bcUKVKE559/HoA33niDP//8k88//5zKlStz/vx5bt26lexrRUREUL16dUaMGIG3tzebNm2ia9euPPHEE9SqVcvmuPbs2ZN9+/Zx4MAB+vTpQ7Fixejdu3ey+23fvj3u7u78+OOP+Pj4MHfuXBo3bsypU6fwTeNabnXr1mXGjBmMGTOGkydPAuCZYO2dadOmMWHCBN577z1WrVpFv379aNCggbUj+GGcPXuWH3/8kS1btnD27FnatWvHuXPnePLJJ9m5cyd//PEHPXr0oEmTJtSuXfuhXwdIe1GW7M7ohXNzulWr9GLKbm5KnT9vdDYPKSRE/xCgPEABKiQkxOissoRTp5RydtaHZ9Mmo7NJP9IuxJNjYd/hw0r5+Oi/gVdeUSomxuiMMlk2aR+3b49vq0aMMDqb7E/aBU2OQ8aaMEH/zRYpkk0LJyVoH0OuX1dk4TYyOxgzRh/OUqWUiow0OpuUGd0u2CucEPc7mNytZcuWNrEeHh4pxjZo0MAm1s/PL0lMWuzdu1cBas2aNWn+eVeuXKny5ctn/T5xUZY6deqoLl26pPh8kilc4uPjoxYsWKCUSl0RjAEDBqhXX33V+n1AQIBq06aNTUzi/bz33nuqbNmyymKxWGNmz56tPD09ldlsVkrp4iHPPvuszX5q1qypRtg5kVm7dm2S4x8QEKAKFCigIhP84cybN0/lzZvXpj3atGmTcnBwUNeuXbM+r3jx4tZ8lNKF0erXr2/9PiYmRuXJk0ctW7Ys2XyCg4OVq6urmj9/frKPh4eHq7x586rvv//eel+lSpXUuHHjlFJKnTx5UgHq559/Tvb5qSl28uKLL6ohQ4ZYv2/QoIEqV66czbEfMWKEKleunPX7hMVRdu3apby9vVVEoopgTzzxhJo7d26yr/mgoiwpFQ8qXry4ev31163fWywWlT9/fjVnzpxk95vcfhL/DowdO1Z5eHio4OBg633NmjVTJUqUSPLeTp48OdmfR6kMLMoiRErCw/UVZYBhw6BECUPTERlgyBC9rlGLFnrhbCFykwsX9DqJQUHw7LO6GIujo9FZicROnIC2bXVb1aGDnpIusre7d+/y6aef0rNnT3r27Mm0adOs6yqKnOHChfi/1U8/lcJJQn+WKFgQzp2D2bONzkakJ5WGeey//PILjRs3pkiRInh5edG1a1du376d4lICBw8epHHjxumVKgCzZ8+mevXq+Pv74+npybx587hw4UKa9nH8+HHq1KljMzW1Xr16hISEcOnSJet9lRKtzVKoUCFu3LiR5pwrVqyIi4uLzetXrlzZZkRovXr1sFgs1lFzAE8//TQODvFdRAUKFKBixYrW7x0dHcmXL1+KOR0/fpzIyMgU3wM3Nze6du3K//73PwD+/vtvjh49Srdu3QD9/jk6Oqa6NofZbGbChAlUrFgRX19fPD092bp1a5L355lnnrE59nXq1OH06dOYzeYk+zx06BAhISHky5fPunSFp6cn58+ft65ZmZ4Svucmk4mCBQs+1HueUIkSJfDy8rJ+X6BAAcqXL5/kvX3U1wGZ8izS0bRp8N9/uormiBFGZyPS208/wQ8/6DUxp083OhshMtedO7oz8coVKF9er/OVYPaPyCJu3NAXO+7dgzp1YOFCcJBLp9nab7/9RuvWrfH29qZGbBn1zz//nPHjx/PDDz/w3HPPGZyhSA9Dh+oL0w0aQMeORmcjsgJPT134r3dv/TUgANI40zDXs7fWoGOiK6L2OhYcEv0jDQwMfKS8ypQpg8lksk6DTUlgYCAvvfQS/fr1Y+LEifj6+vL777/Ts2dPoqKi8PDwSPIc9wecnJlMpiQdmtHR0SnGL1++nKFDhzJt2jTq1KmDl5cXU6dOZW8GVQxydnZOkm/csh9pkZYp6A96/bTk9KDjD3rac5UqVbh06RILFizg+eefp3jx4ql+fkJTp05l5syZzJgxw7rW5qBBgx6pEE1ISAiFChVix44dSR57mErND5KW4+vg4JCq399HfR/TQk6zRbq4fBkmT9bbn3wCD9mGiSwqOjp+XaMBA+Cpp4zNR4jMFB4OrVvD8eNQpAhs2QJ58xqdlUgsPBzatIHz56FUKVi/Xjp9c4IBAwbQoUMHzp8/z5o1a1izZg3nzp3jtddeyxGVQIVel3blSt35//nnusKvEADdu0OFCnD3rl5XUaRNnjx5UrwlXD/xQbGJO3mSi0kLX19fmjVrxuzZs5Ndj/HevXsA/PXXX1gsFqZNm8YzzzzDk08+mWRtwsQqVapkLe6RHH9/f5v1Bk+fPm23cNLu3bupW7cu/fv3p2rVqpQuXTrJKDUXF5dkR7olVK5cOf7880+bzqDdu3fj5eWV4lqE6alcuXIcOnTI5njv3r0bBweHR1qrL7EyZcrg7u5u9z2oWLEiNWrUYP78+Xz33Xf06NHD5jGLxcLOnTtT9Xq7d++mTZs2vP7661SuXJlSpUpx6tSpJHGJO4D37NlDmTJlknSsA1SrVo1r167h5ORE6dKlbW5+fn6pyiux1PyOpIa/vz/379+3eR8TFv4xgnQoinQxcqQublevHnTqZHQ2Ir199RUcOwZ+frqysxC5RUyMbtN27wYfH92ZWLSo0VmJxCwWXShnzx7d2bt5s1SgzynOnDnDkCFDbE76HR0dGTx4MGfOnDEwM5EeYmJ0MT+Avn2lEruw5eiop8ADzJoF8iefc8yePRuz2UytWrVYvXo1p0+f5vjx43z++efUqVMHgNKlSxMdHc0XX3zBuXPnWLJkCV999ZXd/Y4dO5Zly5YxduxYjh8/zpEjR/jkk0+sjz///PPMmjWLf/75hwMHDtC3b98kI7cSKlOmDAcOHGDr1q2cOnWK0aNHs3//fpuYEiVKcPjwYU6ePMmtW7eSHTHWv39/Ll68yNtvv82JEydYv349Y8eOZfDgwUlGgGaELl264ObmRkBAAEePHmX79u28/fbbdO3alQIFCqTb67i5uTFixAiGDx/O4sWLOXv2LHv27ElSpblXr158/PHHKKVsqnCXKFGCgIAAevTowbp16zh//jw7duxgxYoVyb5emTJl+Pnnn/njjz84fvw4b775JtevX08Sd+HCBQYPHszJkydZtmwZX3zxBQPj/vkk0qRJE+rUqcPLL7/MTz/9RGBgIH/88Qfvv/8+Bw4ceKjjUqJECUJCQti2bRu3bt2y24ltT+3atfHw8OC9997j7NmzfPfddylWKM8s0qEoHtmff+q1xEwmmDkzB1xZdnSEdu0wt21Lm7ZtadeuXbJXL3KL27fjOxEnTJCRWSL3iKvivH49uLrqac4VKhidlcGyaPs4cqSuBOriAuvW6Sr0ImeoVq0ax48fT3J/3HpQInubPx8OH9bnFuPHG53NI4ptH2nXDkcXF9q1a5dl2sjsrFkzfYuO1m29yBlKlSrF33//TaNGjRgyZAgVKlSgadOmbNu2jTlz5gBQuXJlpk+fzieffEKFChVYunQpk+OmxKWgYcOGrFy5kg0bNlClShWef/559u3bZ3182rRpFC1alPr169O5c2eGDh2a7NTpOG+++SavvPIKHTt2pHbt2ty+fZv+/fvbxPTu3ZuyZctSo0YN/P392b17d5L9FClShM2bN7Nv3z4qV65M37596dmzJx988EFaDttD8/DwYOvWrdy5c4eaNWvSrl07GjduzKxZs9L9tUaPHs2QIUMYM2YM5cqVo2PHjkmm1Hfq1AknJyc6deqUZLTsnDlzaNeuHf379+epp56id+/eKVYh/+CDD6hWrRrNmjWjYcOGFCxYkJdffjlJ3BtvvEF4eDi1atViwIABDBw4kD59+iS7T5PJxObNm3nuuefo3r07Tz75JK+99hr//fffQ3e+1q1bl759+9KxY0f8/f2ZMmXKQ+3H19eXb7/9ls2bN1OxYkWWLVtmUyXcEHZLtmSSWbNmqeLFiytXV1dVq1YttXfv3lQ9b9myZQpIUlXJHqMrceU0ZrNSNWvqKmw9ehidjcgIAwbo97dSpZxb0TYrtwuZ2T4qlbWPRWYbN07/7js4KPUQhQhFJpkzx1pYVX37rdHZ5ExGtgvLly9XxYoVU1OnTlW7du1Su3btUlOnTlUlSpRQy5cvV4cOHbLeMpq0j+nr9m2lfH313+6sWUZnI7KyI0f0/2JQ6vffjc7GltHtQmorsQqRlZw/f145ODiov/76y+hURApS27YYXpTl+++/Z/DgwXz11VfUrl2bGTNm0KxZM06ePEn+/PlTfF5gYCBDhw6lfv36mZitSGzJEti/H7y8YOJEo7MR6e3oUT3dGWDGDKlom9mkfTTOvHkQd8Fv9mxdNVhkPT/+qEeRgh7d1KWLsfmI9Ncpdh2V4cOHJ/tY3AL7JpMpXdYnEpln7Fhd8KpiRXjzTaOzEVlZhQrQowd8/TUMGaJnR2X7GVFC5ELR0dHcvn2bDz74gGeeeYZq1aoZnZJ4RIZPeZ4+fTq9e/eme/fulC9fnq+++goPDw9rKfHkmM1munTpwocffkipUqUyMVuR0P378VMPRo+GggWNzUekL6V0IRazGV55BRo1Mjqj3EfaR2OsXw/9+unt0aP1ul4i6zl0CDp00OsndusGmTRrSGSy8+fP272dO3fO+lVkH0ePQuysRmbOBCfDhziIrG7CBF30ce9e+P57o7MRQjyM3bt3U6hQIfbv3//A9TBF9mDov++oqCj++usvRo0aZb3PwcGBJk2a8Oeff6b4vPHjx5M/f3569uzJrl277L5GZGQkkZGR1u+Dg4MfPXEBwKRJcO0alC4N77xjdDbpKDQUPD0ByAOEocvHp7WCWXb3ww/wyy967bipU43OJvfJjPYRpI1MbPdueO013UnVqxd8+KHRGWUxWaR9vHQJXnwRQkLg+edh7lwZrZJTFS9e3OgURDpTShdiMZvh1Vdz0AXLBO1j6PXreMautZUbzyEzQsGCMGIEjBmjBzS8/DIkWnpNCJHFNWzY0KbStcj+DO1QvHXrFmazOcnilgUKFODEiRPJPuf333/nm2++SXV57MmTJ/OhfCJMd2fPwvTpenv6dN3pJHKOyEgYPFhvDx4MMtAt82VG+wjSRib077/w0ksQEQGtWunRM9JJlfXcv6/fp8uXoVy5+GIsImc7duwYFy5cICoqyub+1q1bG5SReFhr1sCvv+rOoLgKvkKkxuDBeime//6DL76AYcOMzkgIIXK3bDXB4P79+3Tt2pX58+fj5+eXqueMGjWKwXE9I+jRN0WLFs2oFHONwYMhKgpeeEF/sBM5y8yZutO4UCFIMEBOZGEP0z6CtJFxLl6E5s3h3j2oUweWL5cpeFlRTAx07KinO+fPD5s3w2OPGZ2VyEjnzp2jbdu2HDlyxLpeIugqjICsm5jNhIfD0KF6e9gwKFHC0HRENpMnj16zvXv3+K9pOOURQgiRzgz9uOTn54ejoyPXr1+3uf/69esUTGZBvrNnzxIYGEirVq2s91ksFgCcnJw4efIkTzzxhM1zXF1dcZXhc+lq9WrYsEF/2P7sMxnBk9NcuwYffaS3J0/WBXdE5suM9hGkjQS4e1d3Jl66pEe8bdwIHh5GZyUSU0ovr/Hjj+DurpdlkM6InG/gwIGULFmSbdu2UbJkSfbt28ft27cZMmQIn8rwtmxn3DgIDITHH9fTV4VIq65d9YXvgwd1Ma7PPzc6IyGEyL0MLcri4uJC9erV2bZtm/U+i8XCtm3bqFOnTpL4p556iiNHjnDw4EHrrXXr1jRq1IiDBw/mylE1me3uXXjrLb09ciSUL29sPiL9vf++nlJYs6Y+aRPGkPYxc4SHQ+vWcOwYFCkCW7aAr6/RWYnkTJ8ePw196VKoVcvojERm+PPPPxk/fjx+fn44ODjg4ODAs88+y+TJk3knRy3gnPMdOBA/xfnLL/VoMyHSytEx/vdozhw4dcrYfIQQIjczfELX4MGDCQgIoEaNGtSqVYsZM2YQGhpK9+7dAXjjjTcoUqQIkydPxs3NjQoVKtg8/7HYuU6J7xcZY+hQPYLtqaekomZO9PffsGCB3p45ExwMrwOfu0n7mLFiYqBTJ/j9d/Dx0Z2JxYoZnZVIzurV8WtlffoptG1rbD4i85jNZrxih8r7+flx5coVypYtS/HixTl58mSq9zN58mTWrFnDiRMncHd3p27dunzyySeULVs2o1IXCURHQ8+euuBVp056nVohHlbjxrow16ZNeqTr2rVGZySEELmT4R2KHTt25ObNm4wZM4Zr165RpUoVtmzZYi1EcOHCBRykVyNL2LYN/vc/PTrk66+lEEtOE1d1USno0kWvIyeMJe1jxlEK3n4b1q/XbdmGDSD9rlnT3r3w+uv6PRswAN591+iMRGaqUKEChw4domTJktSuXZspU6bg4uLCvHnzKJWGimE7d+5kwIAB1KxZk5iYGN577z1eeOEFjh07JhV4M8GUKXD4MOTLpy9YCvGopk7VFwLXrYOdO6FBA6MzEkKI3Mekclnd7uDgYHx8fAgKCsLb29vodLKN0FCoWBHOn9cf6GbNMjqjDBQRAa++itli4VWliHZ0ZPXq1bi5uRmdWYb6/nt47TW9dtzJk3p9o9xC2oV4ueVYTJ4M772nL5CsWgWvvGJ0RtlEJreP589D7dpw86YejbJunRTLMYKR7cLWrVsJDQ3llVde4cyZM7z00kucOnWKfPny8f333/P8888/1H5v3rxJ/vz52blzJ88991yqnpNb2sf0dvw4VKmii/ktXQqdOxudUQaJbR8BIpYu5dUuXQByxTmkUfr101Wfa9TQF5+MuMZqdLsQERHB+fPnKVmyZK78PVu4cCGDBg3i3r176bK/wMBASpYsyT///EOVKlUM309qjBs3jjlz5nDjxg3Wrl3Lyy+/nKGvl9G6devGvXv3WLduHQANGzakSpUqzJgxw9C8HkVm/j6kl9S2LXJaLlJlzBj9wa5oUf1BPEdzc4NNm3AE1hmdSyYJC4Phw/X2yJG5qzNR5D5LlujORNAjZaQzMQ0ysX28exdattSdiVWrSuXt3KpZs2bW7dKlS3PixAnu3LlD3rx5rZWeH0ZQUBAAvnYWTY2MjCQyMtL6fXBw8EO/Xm5lNuupzlFR+qJAp05GZ5SBYttHADdgU+y2yDgffqg7qQ8cgGXL9AwbkX1cu3aNiRMnsmnTJi5fvkz+/PmpUqUKgwYNonHjxkanl2qJO8AAihYtytWrV/HL4DLkx48f58MPP2Tt2rU888wz5M2bN0NfTzycxL8PO3bsoFGjRty9e9e6RFV2JXPlxAPt3w9xFwTmzpWqvznRp5/ChQt6/bihQ43ORoiM8/PP0KOH3h42TE97FllPVJQe6HPihL7AsXEjeHoanZUwQlBQEHfu3LG5z9fXl7t37z50B5/FYmHQoEHUq1fP7hqzkydPxsfHx3qT4lZp9+WX8Oef+twxrqiSEOklf34YNUpvjxqlC62J7CEwMJDq1avz66+/MnXqVI4cOcKWLVto1KgRAwYMMDq9R+bo6EjBggVxyuAroWfPngWgTZs2FCxYENdk1iSLiorK0BzEg2XW74MRpENR2BUVFb+Idpcu0KKF0RmJ9HbxInz8sd6eOhXc3Y3NR4iMcvCg7qSKidHT++N+70XWohT07g3bt+tOiE2boHBho7MSRnnttddYvnx5kvtXrFjBa6+99lD7HDBgAEePHk12vwmNGjWKoKAg6+3ixYsP9Xq5VWBgfGfPlCl6losQ6W3QIP27dfGirM+ZnfTv3x+TycS+fft49dVXefLJJ3n66acZPHgwe/bsscZNnz6dihUrkidPHooWLUr//v0JCQmxu+8ffviBmjVr4ubmhp+fH20TVHIzmUw2IwlBFzFcuHBhsvsym8307NmTkiVL4u7uTtmyZZmZ4Bdt3LhxLFq0iPXr12MymTCZTOzYsYPAwEBMJhMHDx60xu7cuZNatWrh6upKoUKFGDlyJDExMdbHGzZsyDvvvMPw4cPx9fWlYMGCjBs3LsWfc9y4cbSKrXDl4OBgHbXfrVs3Xn75ZSZOnEjhwoWtxceOHDnC888/j7u7O/ny5aNPnz42xzLueZMmTaJAgQI89thjjB8/npiYGIYNG4avry+PP/44C+IqeKbAYrEwZcoUSpcujaurK8WKFWPixInWxy9evEiHDh147LHH8PX1pU2bNgQGBtrd54PYe8+XLFlCjRo18PLyomDBgnTu3JkbN25YH9+xYwcmk4lNmzZRqVIl3NzceOaZZzh69Kg15vbt23Tq1IkiRYrg4eFBxYoVWbZsWap/7oS/D4GBgTRq1AjAOtuiW7duLF68mHz58tnMjAB4+eWX6dq16yMdn4wkHYrCrkmT4MgR8POLH6WY44WGQp48qDx58PfwIE+ePISGhhqdVYYZOVJf0a1fH9q3NzobITLGf//p6bP370PDhrBwoVQxfyiZ0D5OmACLF4OjI6xcCZUqpevuRTazd+9e64l3Qg0bNmTv3r1p3t9bb73Fxo0b2b59O48/YH0PV1dXvL29bW4idSwW6NVLNxnPPQd9+hidUSaIbR/Jk4fQGzfIkydPjj+HzArc3fXnFdBfE/QTiNDQlG8REamPTTz0M7mYNLhz5w5btmxhwIAByRbFSjgF1MHBgc8//5x///2XRYsW8euvvzI8bp2mZGzatIm2bdvSsmVL/vnnH7Zt20atWrXSlF9CFouFxx9/nJUrV3Ls2DHGjBnDe++9x4oVKwAYOnQoHTp0oHnz5ly9epWrV69St27dJPu5fPkyLVu2pGbNmhw6dIg5c+bwzTff8NFHH9nELVq0iDx58rB3716mTJnC+PHj+fnnn5PNbejQodbOvbjXjrNt2zZOnjzJzz//zMaNGwkNDaVZs2bkzZuX/fv3s3LlSn755Rfeeustm33++uuvXLlyhd9++43p06czduxYXnrpJfLmzcvevXvp27cvb775JpcuXUrxmI0aNYqPP/6Y0aNHc+zYMb777jtrQcno6GiaNWuGl5cXu3btYvfu3Xh6etK8efOHHkn5oPc8OjqaCRMmcOjQIdatW0dgYCDdunVLsp9hw4Yxbdo09u/fj7+/P61atSI6OhrQ6wlWr16dTZs2cfToUfr06UPXrl3Zt29fqn7uhIoWLcrq1asBOHnyJFevXmXmzJm0b98es9nMhg0brLE3btxg06ZN9IibXpUVqVwmKChIASooKMjoVLK8/fuVcnRUCpRavtzobDJRSIj+oUF5gAJUSEiI0VlliD/+0D+qyaTUX38ZnY1xpF2IlxOPxZ07SpUvr3/XK1RQ6u5dozPKxjK4fVyyxLp7NXduuu1WPCIj2wUPDw91+PDhJPcfPnxYubu7p3o/FotFDRgwQBUuXFidOnXqoXLJie1jRvniC/137O6u1EMe7uwnQfsYcv26IoefQ2YlZrNS1avrw9+vX+a+ttHtQnh4uDp27JgKDw9P+mDcP9Tkbi1b2sZ6eKQc26CBbayfX9KYNNi7d68C1Jo1a9L2wyqlVq5cqfLly2f9fsGCBcrHx8f6fZ06dVSXLl1SfD6g1q5da3Ofj4+PWrBggVJKqfPnzytA/fPPPynuY8CAAerVV1+1fh8QEKDatGljE5N4P++9954qW7asslgs1pjZs2crT09PZTablVJKNWjQQD377LM2+6lZs6YaMWJEirmsXbtWJe7SCQgIUAUKFFCRkZHW++bNm6fy5s1r0x5t2rRJOTg4qGvXrlmfV7x4cWs+SilVtmxZVb9+fev3MTExKk+ePGrZsmXJ5hMcHKxcXV3V/Pnzk318yZIlSY5DZGSkcnd3V1u3brXmkfB4NmjQQA0cODDFY/Cg9zyx/fv3K0Ddv39fKaXU9u3bFaCWJ+jwuH37tnJ3d1fff/99ivt58cUX1ZAhQ5RSD/65E/8+xL3m3UQfSvr166datGhh/X7atGmqVKlSNscrs9htWxKQ8RkiWRER8MYbejHtjh31TeQsFgu8847e7tEDqlUzNh8hMkJkpC66cuyYnja7eTNk87WPc6ydO+PXtxw+PJeMaBIPVKtWLebNm5fk/q+++orq1aunej8DBgzg22+/5bvvvsPLy4tr165x7do1wmXRtXR36lR8obepU6FMGWPzETmfgwNMm6a3583TlcVF1qWUSnXsL7/8QuPGjSlSpAheXl507dqV27dvExYWlmz8wYMH072gy+zZs6levTr+/v54enoyb948Lly4kKZ9HD9+nDp16tgUE6tXrx4hISE2o/0qJZqWUahQIZvpualVsWJFXFxcbF6/cuXKNiNC69Wrh8Vi4eTJk9b7nn76aRwSTOEpUKAAFStWtH7v6OhIvnz5Uszp+PHjREZGpvgeHDp0iDNnzuDl5YWnpyeenp74+voSERFhXQ8yrR70nv/111+0atWKYsWK4eXlRYMGDQCSvId16tSxbvv6+lK2bFmOxzYmZrOZCRMmULFiRXx9ffH09GTr1q3WfTzo506t3r1789NPP3H58mVAVzHv1q3bIxWhy2g5b1VIkS5Gj9b/jAsUgNmzjc5GZIQlS3RVPC8vSLCshRA5hlK6g2rHDv17vnmzrOGVVZ08CW3bQnS0Xnph8mSjMxJZxUcffUSTJk04dOiQ9UR927Zt7N+/n59++inV+5kzZw6gp0ontGDBgmSnPomHExOjL0iHh0OTJtCvn9EZidyiQQNo0wbWr9cd2j/8YHRGWYC9tQYdHW2/t9dplXiNmEdc765MmTKYTCZOnDhhNy4wMJCXXnqJfv36MXHiRHx9ffn999/p2bMnUVFReHh4JHmO+wMWgzeZTEk6NOOmtSZn+fLlDB06lGnTplGnTh28vLyYOnXqQy25kRrOzs5J8rVYLGneT3JTyR/29dOS04OOf0hICNWrV2fp0qVJHvP3909jtg9+zbip3s2aNWPp0qX4+/tz4cIFmjVrlqYp1lOnTmXmzJnMmDHDuqbnoEGDrPt40M+dWlWrVqVy5cosXryYF154gX///ZdNmzaly74zioxQFEn8/nv8Vb758yFfPmPzEenv/n29diLozuNklncQItv74AP47jtwcoJVq6ByZaMzEsm5eVOvb3n3LtSpA4sWyfqWIl69evX4888/KVq0KCtWrOCHH36gdOnSHD58mPr166d6P0qpZG/SmZi+PvkE9u4FHx/43//kb1lkrk8+0f/zN26EX381OpssIHZNz2Rvbm6pj03cWZJcTBr4+vrSrFkzZs+enewao/fu3QP0yDKLxcK0adN45plnePLJJ7ly5YrdfVeqVIlt27al+Li/v7/NWoOnT59OcbQjwO7du6lbty79+/enatWqlC5dOslIOhcXF8xms928ypUrx59//mnTmbl79268vLweuJ5veihXrhyHDh2yOd67d+/GwcHBWrQlPZQpUwZ3d/cU34Nq1apx+vRp8ufPT+nSpW1uPj4+D/Wa9t7zEydOcPv2bT7++GPq16/PU089leLoyoTFgO7evcupU6coV64coI9VmzZteP3116lcuTKlSpXi1KlTqf65E4sbPZrc702vXr1YuHAhCxYsoEmTJhTN4qMh5N+8sBESAgEBemRP9+4QWzhK5DCTJ8O1a1C6dPy0ZyFyknnz4hdpnzcPXnjB2HxE8sLDoXVrOHcOSpXSI0uk0rxIrEqVKixdupR///2XAwcO8L///Y8yMo82y/nnH4grSPrFFzIiXGS+smWhb1+9PWSIXt5HZE2zZ8/GbDZTq1YtVq9ezenTpzl+/Diff/65depp6dKliY6O5osvvuDcuXMsWbKEr776yu5+x44dy7Jlyxg7dizHjx/nyJEjfPLJJ9bHn3/+eWbNmsU///zDgQMH6Nu3b5IReAmVKVOGAwcOsHXrVk6dOsXo0aPZv3+/TUyJEiU4fPgwJ0+e5NatW8mOeOzfvz8XL17k7bff5sSJE6xfv56xY8cyePBgmynGGaVLly64ubkREBDA0aNH2b59O2+//TZdu3ZNtnDIw3Jzc2PEiBEMHz6cxYsXc/bsWfbs2cM333xjzcPPz482bdqwa9cuzp8/z44dO3jnnXfsFnqxx957XqxYMVxcXKy/Qxs2bGDChAnJ7mf8+PFs27aNo0eP0q1bN/z8/Hj55ZcB/Xvw888/88cff3D8+HHefPNNrl+/nuqfO7HixYtjMpnYuHEjN2/etKm23blzZy5dusT8+fOzdjGWWNKhKGwMH64/2BUtCp99ZnQ2IiOcOxc/AnXaNHB1NTYfIdLb5s3Qv7/eHjtWXxwRWY/FoqdG7tkDefPq9+0hZ7sIIQwWt/Z2TIxet/b1143OSORWY8eCtzccPKiX9xFZU6lSpfj7779p1KgRQ4YMoUKFCjRt2pRt27ZZl6ioXLky06dP55NPPqFChQosXbqUyQ9YE6Vhw4asXLmSDRs2UKVKFZ5//nmbSrzTpk2jaNGi1K9fn86dOzN06NBkp07HefPNN3nllVfo2LEjtWvX5vbt2/SPO8mM1bt3b8qWLUuNGjXw9/dn9+7dSfZTpEgRNm/ezL59+6hcuTJ9+/alZ8+efPDBB2k5bA/Nw8ODrVu3cufOHWrWrEm7du1o3Lgxs2bNSvfXGj16NEOGDGHMmDGUK1eOjh07WkcFenh48Ntvv1GsWDFeeeUVypUrR8+ePYmIiMDb2/uhXs/ee+7v78/ChQtZuXIl5cuX5+OPP+bTTz9Ndj8ff/wxAwcOpHr16ly7do0ffvjBOpLwgw8+oFq1ajRr1oyGDRtSsGBBa2djan7uxIoUKcKHH37IyJEjKVCggE21bR8fH1599VU8PT2TvEZWZFJpWRU1BwgODsbHx4egoKCH/qXNqX7+OX4Uz88/67VvcqXwcGjRArPFQguliHJ05Mcff0y3tRGM9uqrsGYNNG0KW7dCFl7jNdNIuxAvux+LAwegYUMIDYVu3fSUO/kdT0fp2D4OH64LNjg76/85sWtkiywou7cL6UWOQ8ri/p7z54ejR3PpxYHY9hEgfM0aWrzyCkCOOofMLqZMgREjoEgRXSTITn/RIzO6XYiIiOD8+fOULFkSt8TTmIUQD7Rjxw4aNWrE3bt3eSyLVG5s3LgxTz/9NJ9//rlhOaS2bZGiLALQ6/AGBOjtAQNycWci6Pl2O3bgCKR+uffsYft23Zno6KhHoEpHi8hJTp3Sn+VCQ3WH+bx58jue7tKpfZw7V3c+gO70lc5EIbKvX36BuAEf8+fn0s5EsLaPAO7oD6nCGO+8A19+Cf/9B9On6zWVhRAiq7t79y47duxgx44dfPnll0ankyoy5VlYp51dvQrlyukFjUXOExMDgwbp7X794OmnDU1HiHR15YoeYX3rFlSvDqtX65FvIuvZskVfuAL48EOZGilEdnb5MnTurNfe7t1br4kqhNHc3ODjj/X2xx/rdcOFECKrq1q1Kt26deOTTz5J12I5GUlGKAqmTNFTX93dYcWKNBfqEtnE11/D4cN6rbK4RdOFyAnu3YPmzfVIhNKl9Vp8Xl5GZyWSs307tG8PZrMeFT96tNEZCSEeVkwMvPaartReuTLMnGl0RkLE69hRz8bZt0+vqzh3rtEZCSGyooYNG5JVVgEMDAw0OoU0kw7FXO733+OnAXzxBVSoYGw+WUJoKJQogVKKEkCYyURgYCB5snFP69278e/zhx9CvnzG5iNEeomrEnzkCBQsCD/9pNfwEhnkIdvHixdh2DD4/nv9faNGMiVdpOyV2LXnUmPNmjUZmImw5/339XmklxesXCkV2uPaR4DQf/+lROxUkOx+DpldmUx6uvOzz+qL6m+/LZ9zhBAivUmHYi52+zZ06qRHinTuDNmgKnnmuXULE3ALCDM6l3Qwfrx+v8uX19OdhcgJYmJ0G7Zrl67ouGULlCxpdFa5QBrax/Bwvbba5Ml628EB+vTRS2vEFs4TIgkfHx+jUxAPsGGDnuECeh3UMmWMzSfLuHUrweYtO4EiM9Srp4sRrl6tL2r9+KPRGQkhRM4iHYq5lFK6AuqlS/Dkk/DVVzJSJKc6cQJmzdLbM2aAk/zVi1QIvXkTx4iIJPc7urjglqACWuiNGynuw8HJCXdf34eKDbt1C2WxJBtrcnDAPZ8f/frB+vXg43yLlYsslC4EoTeSxnr4+Vm/D79zB0tMTIp55EkwvDEtsRH37mGOikqXWA8/P0wOeonjyOBgYpJ5Hx4m1t3XF4fYBiAqJITosJS7A1OMDQ0l8Tibi4FRRJnzEBwM926EcP9OGCEheir6N9/AxUtgAp6vDVM+f4zqtXRPYnRYGFEhISnm4OrtjVNsVbm0xMZERBAZHJxirIunJ86xJT/TEmuOiiLi3r0UY509PHDx9ExzrCUmhvA7d9Il1snNDdfYKqPKYiHMTodGWmIj7PxeZYQFCxZk6uuJtAkMjC/k98470K6doekI8f/27ju+yXL9H/gnTXdLF6WTMmUoS2QJCqhUEBREKyJ6RBCQo6AgIOucUlCRLUN6UPmyHIhwGP4EBKFSRPYWKSAgLRztoEJbkk6S+/fH3SYtNGla2jwZn/frlVefJHeeXH0aLp5czz3Mmj1bFsB37JCjGHr2VDqimmErQzaJyDFYnFOEk8nOzhYARHZ2ttKhKGr+fCEAITw8hDh1SulobIxGIw8OILwBAUBoNBqlo6qy3r3lr9O3r9KR2C7mBSPDsSj+N3Dn7UidOmXaa0y0E4A46e9fpu11lcpk27Pe3mXaXlOrTba96OEh4uLkXRcXIS64ephse02tLrPfs97eJtteV6nKtD3p72+yreaO/z6P1Kljsq24o+2ByEizbTXp6Ya2+xo3Ntv2elKSuHZNiKNHhdjWoKXZtuNj9okRI4QYOlSIFbXbm207ossWMWCAEM89J8SSoO7ltinJj23xmeHhCehjdr8nFy40/G6JAwaYbXskLs54HIYNM9v2wLvvGo/vu++abbtv2DDj363kg2TiljhggPHzsHCh2bZ7+vQxfs5Wrzbftnt3Q9uLW7aYb9u+vfHfxb595uNt2dLQ9npSkvnj0Lixoa0mPd1s213h4YI5kv9XCCFEfr4Q7YvTR8eOQhQUKB2RDSl1/qhJTxeA/Z9DOoqxY+WfplUrIW7frt59K50Xbt++LZKSkkRmZqYi709EjikzM1MkJSWJ2xUkTfZVckK//AJMniy3Fy2SE2mTY9q+XQ7vcHMDFixQOhqi6qHTyblAASA+HnAZC8B0R0KH9vTTwJErcntpBW3/uxFIKd6+v4K2+w8AScXbrSto6wI5b6WfH+CXAcB0hz8ii7Rt2xYqC4dNnDhxooajoRJCyHnojh2TC7ytX8+pC8g+xMYCq1fL+ZZXrwaGDVM6ouqjVqsREBCAjOJRIN7e3hbnTyKiOwkhkJubi4yMDAQEBECtVpttrxJCCCvFZhNycnLg7++P7Oxs+BUP9XEmKSlAhw5yRb6XXgLWruVQ57totUDx8DIfyDnCNBqN3U2oXVgItG4NXLgATJgAzJundES2y9nzQmklx+KvS5fgV85SyUoPed65E3h1sAtyEYx//xv44IOKh0dbc8izEHKob2qqLHyWdFdxDwwxbOdnZeF2QSH0ekCvl+2KiuStsBAQXsEouu2CvDxAeyMH2ux85OUBubnA1avyolBuXvFxQjDUaheEhwP+njnw886Hjw/g7S1vXl6Ap2fxz8AgeHq7ws0NUBVpoNbnQq2W8xoWj5o2xKjyDoJK7QpXV0Ct08ANuXB1BTx0Wrw4upH8/SHzY9bfN+AfFAig4qHUngEBULtzyLO9DXnW5ucjtH59q+XIGSVXDCwQFxdXg5GU5ez/VyxcCIwbJ88bt24F+vRROiIbU+r8UZueDt/QUAD2eQ7piD7+GBg/HggPB37/3fCnume2kBeEEEhLS0OWmf93iIgqIyAgAGFhYRVeoGBB0YlotXJy4tOngbZt5UIGPL8ph4MUFEtO/ENC5IkT57g3zZnzwp1s+VgcOgQ88YRc3OP11+WqjUpdELl+XfZ0OHMGuHhRzimWkiJ/mql7VZuwMKB3b/mFPjoaKFXjrVkOkh+pcmw5L1iTMx+HrVuBfv3kBYcFC+T5Bd2BBUWbVlAgFyf84w8gLg6YPr169mtLeUGn06GoqEjRGIjI/rm5uVXYM7EEhzw7Cb1eTqB9+rQsMG3ZwmKiSS4uQPv20Ov1aAugwMUFLiXdd+zE9evGIaEzZ7KYSPbvwgXgmWdkMbFPH+suJHXrFrB3r7ydPi2LiGlp5l8THCyHAqpUZW8lvQFLegaW/HR3l1MTuLkZtz09ZZ4u6XHo4wMEBcmiaps2xl6FVuUA+ZHsT1ZWFv773//i8uXLeO+99xAUFIQTJ04gNDQUkZGRSofn8H79FRg0SBYTR4wA3n1X6YhsVHF+BGSv+/Yl28yRNsHDQy7Q8uKLctTOG28AERFKR1W91Gq1xUUAIqLqwIKik/jwQ2DjRvkldfNmoF49pSOyYV5ewNGjcAHwi9KxVFFsLJCdLXuiDh2qdDRE9yYtDXjqKeDvv4GOHeW8XW5uNfd+hYWyN+Tu3UBCAnD4sByWfKdGjYBWrYD77wcaNgTq1wcaNJD51cur5uJTlAPkR7Ivv/76K6Kjo+Hv74/k5GSMGDECQUFB2LRpE65evYovvvhC6RAdWno60Lev7Hn9xBNy3lpOlWNCcX4EAC8AR4u3yXa88ALQpQtw4IA8V16xQumIiIjsGwuKTmDTJtm1H5C9erp0UTYeqlmnTwPLl8vtxYtlDygie6XVyi+zycnAfffJYXc10bv69m0gMRFYt05efLlzGqJGjYAePeQctK1aAS1aAOVMMUlE1WzcuHEYMmQI5s6di1ql/tH16dMHL7/8soKROb68PKB/fzl3a5MmwIYNNXsxh6imqVRyyH7nzsCqVcA773BxSiKie8GCooM7fRp49VW5PXasnHeMHJcQ8u+s1wMDBgBduyodEVHV6XTAK6/IFUWDg+WK5XXqVM++Cwpkz8c//pAXXdavB0qvGRMSIguIJbcGDarnfYmoco4ePYrPPvvsrscjIyORVtHcA1RlQgDDh8ve2oGB8mJOqXWziOzWww/LYc/r18tFC3/8kb1uiYiqigVFB1ZYCLz8slwZ9MknucqvxXJzgQcegF4IPCAE8l1ckJSUBO/ilT5t2aZNspeVpyf/3mT/JkwAvvtOznv03Xeyh2JVaLVymN6PP8rVl1NTgZs3725Xu7YcDjVoEPDoo+zdWy47zo9knzw8PJBTzkrcv//+O+pU1xUGust//wusXQu4uspe202bKh2RHSjOjwCQe+wYHiieQ5E50vbMni3nk9+9G9ixQy5yRkRElWcTswTHx8ejQYMG8PT0RKdOnXDkyBGTbZcvX46uXbsiMDAQgYGBiI6ONtvemc2dCyQlyZ4233wjTwrJAkIAKSlwuXoV165dQ0pKCuxhMfT8fFmAAYD33pPzuZH9c9b8+MknwKJFcvuLL6o2VUNBgdxP48bApElyPsSkJGMx0d1dznc4eLDs/ZiaKqeF6N6dxUST7DQ/kv3q168f3n//fcPKpSqVClevXsWkSZMQExOjcHSOKTsbGDNGbk+dCjz+uLLx2I3i/IiUFAi9HikpKcyRNqphQzncGZDnzrdvKxsPEZG9Uryg+O2332LcuHGIi4vDiRMn0KZNG/Tq1QsZpceelZKYmIhBgwZhz549OHjwIKKiotCzZ0/8+eefVo7ctv3+u1yIBQAWLpQ9b8ixffyxnGcuMlIWT8j+OWt+/P57OXQfAGbNkkOTKuP2bTk3UtOm8gtDerqcAzE+XvZG+O03ucBLfr787rdmjVz0hXODEdmeBQsWQKPRICQkBHl5eejevTvuu+8+1KpVCzNnzlQ6PIf0r3/JCyxNmgBTpigdDVHNmDpVDuNPSgJWrlQ6GiIi+6QSCl8269SpEzp06IClS5cCAPR6PaKiovD2229j8uTJFb5ep9MhMDAQS5cuxeDBgytsn5OTA39/f2RnZ8PPz++e47dFQsg5v/bsAXr2lF35OTdIJWi1gK8vAMAHQC4AjUYDn5pYCaKa/PWXLJ5otcBXX8l558hytpoXrJ0fAeWPxfHjQLducuTY8OHA559XLn8dPy7njT13Tt6PiJArOb7+uuyRSPfIDvMj3Tul8wIA7N+/H6dPn4ZGo8FDDz2E6Ohoq8dgC8ehph05IueYE0JegOnRQ+mI7Eip/KhNT4dvaCgA5khbtmSJ7I0bEgJculS1xdacIS8QEZmi6CDYwsJCHD9+HFNKXf50cXFBdHQ0Dh48aNE+cnNzUVRUhCATM0UXFBSgoKDAcL+8eXgczRdfyGKilxewbBmLic5g6lR5Hvvww3LeTLJ/1siPgG3lyKtXgWeekcXEnj2B//zH8vwlhCw+vvOOnD+2dm3Zs+att2QuJCL79sgjj+CRRx5ROgyHdvs28MYbMp+++iqLieT4/vlPOTXKpUvAnDnG0V1ERGQZRYc8Z2ZmQqfTIbT4Cl6J0NBQi1fumzRpEiIiIkxeqZ41axb8/f0Nt6ioqHuO25ZlZgLjx8vtuDg5zI8c25EjcsgmACxezAKyo7BGfgRsJ0dmZwNPPy1XXm7VCtiwwfIhyFot8Npr8otBYSHQrx9w8aLMhSwmEtmnn376CQ888EC5Fzmys7PRokUL7Nu3r1L7/Pnnn9G3b19ERERApVJhy5Yt1RStY1i8GDh9Wg4DXbBA6WiIap67u5xzHpCf+f/9T9l4iIjsjeJzKN6L2bNnY926ddi8eTM8PT3LbTNlyhRkZ2cbbteuXbNylNY1YYKcG6xVK2DcOKWjoZomhHHi9NdeAzp2VDYesh2W5EfANnJkUREwYICc2zA8HNi2DbB01NCFC0CnTsCXX8qFVObMkSs3BgbWaMhEVMMWLVqEESNGlDuE0N/fHyNHjsTHH39cqX1qtVq0adMG8fHx1RWmw0hJAaZNk9vz5gFcQJucRf/+QNeucl7lf/1L6WiIiOyLokOeg4ODoVarkZ6eXubx9PR0hIWFmX3t/PnzMXv2bOzevRutW7c22c7DwwMeHh7VEq+t++kn2VNNpZJD/7jAQBWpVMADD0AvBJoLgXwXF6hstNvf2rXAoUOAjw/w0UdKR0PVyRr5EVA+RwohhyXv2iU/x1u3ApZ2kty8Wa7QrNEAYWHAunVyhWaqQXaUH8m+nT59GnPmzDH5fM+ePTF//vxK7bN3797o3bv3vYbmcIQARo+W00106wYMHap0RHaqOD8CgMrFBQ+UbDNH2jSVSvZO7NhRThs1Zgzw0ENKR0VEZB8U7aHo7u6Odu3aISEhwfCYXq9HQkICOnfubPJ1c+fOxQcffIAdO3agffv21gjV5t28KYf7AcCbb8q59KiKvL2Bs2fhkpSE4+fO4ezZs/D29lY6qrvcugVMnCi3p06Vi0+Q43CW/DhnDvB//we4uMiCoKUn8UuWADExspj42GPAyZMsJlqFneRHsn/p6elwM3Nl1NXVFdevX6/RGAoKCpCTk1Pm5ohWrpQXc9zcgE8/5dQpVVacH3H2LLyDg3H27FnmSDvRoYNxDvLx42WRnYiIKqb4kOdx48Zh+fLlWLNmDc6dO4c333wTWq0WQ4svjw4ePLjMogRz5sxBbGwsVq5ciQYNGiAtLQ1paWnQaDRK/QqKy8gAHn9czhkWEcGeas7iww/l6s6NGxvnzSTH4uj58dtv5cIpgJy765lnKn6NXi8L6WPGyBP+N9+UvRsr6LRJRHYmMjISv/32m8nnf/31V4SHh9doDLYyx2xN+vxzYMQIuT11KnD//crGQ6SUjz4CPDyAxERZYCciooopXlAcOHAg5s+fj2nTpuHBBx/EqVOnsGPHDsNCBFevXkVqaqqh/bJly1BYWIgXXngB4eHhhltlh704ij//lL1yTp8GQkKAH34A/P2Vjopq2oULwMKFcnvxYnkCRI7HkfPj/v1y3k8AGDtWDrerSGGhXHl03jx5/6OPgPh4wFXRyTuIqCb06dMHsbGxyM/Pv+u5vLw8xMXF4RlLrkLcA1uYY7YmLVoEjBwpL868/bZxDkUiZ1S/vjwfAYD33pPzOxMRkXkqIZyrU3dOTg78/f2RnZ1d7kTf9uTKFaBHD/mzbl0gIQFo2lTpqBxAbi7QoQP0QqBD8RxhR48etZkhK0IATz0F/PijXBWXV1HvnSPlhXtljWNx6ZKcluHvv4FnnwU2bpQLqpiTnS2HOCckyALiihVy/kSyMhvPj1QzlMiR6enpeOihh6BWqzF69Gg0a9YMAHD+/HnEx8dDp9PhxIkThgsslaVSqbB582b079/f4tc40v8VH31kXIBi4kRg9mwOdb5nxfkRAHL37kWH4nk4mCPtR3Y2cN99QGamvGD51lsVv8aR8gIRUWWxX4edOn8eiI6WPRQbNZJfshs0UDoqByEEkJQEFwDnAeQCsKW6+3ffyWKiu7vsXUBkT/7+G+jTR/5s3x74+uuKi4lpabKIfvo04OsrC5A9e1onXrqDjedHchyhoaE4cOAA3nzzTUyZMsXwOVOpVOjVqxfi4+OrXEx0ZkIAsbHAzJny/owZ8j6LidWgOD8CgNDrkVSyzRxpN/z95b+JUaOAuDjglVc48ouIyBwWFO3QgQNA//7A9etyrpvdu7kgh7PIywPefVduv/eevIpKZC8KCoDnnpPzvdavD3z/vVzZ2ZyUFHnx5NIlIDRUTuvQtq114iUiZdWvXx/bt2/HzZs3cenSJQgh0KRJEwQGBlZpfxqNBpcuXTLcv3LlCk6dOoWgoCDUq1evusK2WZmZcp7E5cvl/blz5bkEERmNGCEXfrtwAZg1S/beJSKi8ik+hyJZLjUVGDIEeOQRWUxs2xbYu5fFRGcydy6QnAxERRkXsyCyB3o9MHQosG+fvNq/bVvFC6n8/jvQtassJjZoIOddZDGRyPkEBgaiQ4cO6NixY5WLiQBw7NgxtG3bFm2LE8m4cePQtm1bTHPwyQOzsuT8iA0bGouJS5eymEhUHjc341zNixbJC5tERFQ+FhTtQGEhMH8+0KwZsGaNfGzIEOCnn4A6dRQNjawoOdl4lXTBgop7dhHZkmnTgG++kfMfbtwItGhhvv3p07KYeO0a0Lw58MsvckVzIqKqeuyxxyCEuOu2evVqpUOrERqNnCuxUSPggw/k/bZtgZ075ZBOIirfM88Ajz0mR1ZMnap0NEREtosFRRum1wP/7/8BrVrJq8i3bgEdOwKHDgGrVgEBAUpHSNY0bhyQnw88/jjwwgtKR0NkuVWrjPN1ffaZXEzKnEOH5Il8Rob88vvzz0BkZI2HSURk94QAjh2T06M0bCgXXrl5E3jgAeC//5XPcQ5aIvNUKnnxXqUC1q4Fjh5VOiIiItvEORRtUFoasHKlHJaSnCwfCwkB5syRq5q6sAzsdH78Edi8WS5e8cknnDyd7Mfu3cAbb8jtqVOB11833/6nn4B+/QCtVk7vsHUrL54QEVXk8mW5yNXatXLutxKNG8tFJl56qeIFsIjI6KGHgFdfBb74Ahg/Xk4zxfNvIqKyWFC0ETodsGeP7L2zZQtw+7Z8PCBAfhmfOpWrjFmNSgXUrw+9EIgSAvkuLlApeAZRWAi8847cfvvtioeKEtmKs2eBmBiZzwYNkkPuzNm5Uy44lZ8PPPmkLKJzaL+NsbH8SOTMrlyRvQ43bCjbg8rTU16YeeUVoHdvOSccWUFxfgQAlYsL6pdsM0farQ8/BNavl/M/b9kiF5YjIiIjFhQVVFgIJCYCmzbJ/6TS043Pde4MjBwJDBgAeHsrFaGT8vYGkpPhAuC80rEAWLxY9jYICQGmT1c6GiLLpKUBffoAOTnAo4/KYc/meld//70cyl9YCPTtK78ge3hYL16ykI3lRyJnc/myLCKWDF8u4eIip5N45RVZ9PDzUy5Gp1WcHwHAG0ByyTAjsltRUbJ34syZwMSJwNNPA+7uSkdFRGQ7WFC0sqwsICEB+O47+QU6K8v4XEAA8PLLspDYurVCAZJN+esv4P335fbcueylSvZBq5VFwatXgSZN5AUTc8XBTZuAgQNlT8aYGDlkjyfsRESyx/bPPwM//CBvpYczu7gA3bvLi8/PPQeEhSkXJ5GjmjRJTkN16RLw6afGUUNERMSCYo27fVsOQ/nxRzmc7/BhudhKiZAQeRL4/PNyEQJ+iabSJk6UqzI+/LCcx4XI1ul0sofMsWNA7drA9u3ypynr1gH/+Id83UsvAV9+KVeCJiJyVn/8YSwg7tkD5OYan1Or5fnigAFyiojQUKWiJHIOtWrJi/v//Kecj/TVV4HAQKWjIiKyDfzaVgNSUmQB8ccf5YIEpXshAkDz5sBTT8kiYpcunCTb5uTlAd26Qa/XoxuAAhcX/Pzzz/Dy8rJqGPv2yQnWVSpg6VIuxkP2YcIE2QPbw0P+vO8+022//BIYMkReZBk8WC5GxXxo42wkPxI5krw8ueBDSRHx4sWyz0dEyPPG3r2B6GguVGWzivMjAOTt3IluvXoBAHOkAxg2DFiyBEhKksOf589XOiIiItvAgmI1+N//ZM/DxERZRPz997LPBwbKE8CePeVCA8VzNJOt0uuBY8fgAuAkgFwA+tLdSq1Ap5MLsADAiBFAu3ZWfXuiKlm6FFi0SG6vWSNXaTZlxQr52RYCGD5cLkjForkdsIH8SGTPhJDTQRw6JG8HDwInT8r5Y0u4usr8WVJEbN2aq8vaheL8CAD627dxrGSbOdLuuboC8+bJORQ/+QQYNQpo2FDpqIiIlMeCYiXl58uTv8OH5e3IETnPXWlqtRyi2rOnvHXowF43VDmffQacPi2L0TNnKh0NUcW2bgXGjJHbH30k50Q0Zdky4K235Pabb7IHLhE5tsxMOe3NDz8AP/0EpKbe3aZuXVk8fOopubgK50wmsi0lPYR37wYmTwa+/VbpiIiIlMeCYgWEkBNg79wpb4mJckRDaWo10LKlXJm5Z0/giSd4IkhVl5kJ/PvfcvvDD4HgYGXjIarIiROygKjXy2FBkyebbrt4MTB2rNweOxb4+GP2vCEix5OUJFdi/uEHeQFaCONzrq7Agw/Ki88lt0aNmAuJbJlKJYc6t20LrF8vz2E6d1Y6KiIiZbGgaEZ8vOzenpJS9vGwMDlFSseO8vbQQ4CPjzIxkuP517+Amzfll42RI5WOhsi8a9eAZ56RiwY8+aTsfWjqS/H8+cB778ntiROB2bP5BZqIHM+mTXLRlNIjXVu3Bvr0AXr1kueO3t7KxUdEVdOmDTB0qJzzefx4YP9+pSMiIlIWC4omlP7i6+4OdO0qTwJ79QJateKXYKoZx44By5fL7U8+4VB5sm05OXI+odRU2Ut7wwbAza38tjNnGnvexsbKlRKZR4nI0ezeDQwaJIuJTzwht596Sg5pJiL798EHwLp1cgqsjRvl6DQiImfFgmI54uONxcTYWGDSJPZApJqn1wOjR8thUf/4B/Doo0pHRGRaUZHsgXPmjOy1vW2b6ake3n8fiIszbsfGWi9OIiJrOXQI6N9fLrASEyPnWOOFQSLHEhEhvyfOmCG/I3bvrnRERETKYUHxDitWyKIOAEydKr/8khMKDoYQAsEAcq3UjeqLL+Q8S76+wNy5VnlLoioRQq5w+OOPctje1q1AvXrlt50501hMnD1bnnyTnVMgPxLZujNn5JBmrVZO//D11ywmOqVSE18HcxJsh/Xee8DnnwN//GEcWURE5IxYUCxl7VpgxAi5/e67ckEMckI+PsD161ABSKmwcfXIzjYWWuLigPBwK70xURXMnStPoFUq4JtvgHbtym83Z45xmPOcOXLeRLJzCuRHIlt3+bIc9njzplxgZdMmwMND6ajI6orzIwD4ALhevE2Ox8dHDn0ePpydAIjIubkoHYCt2LgRGDxY9rx5801gwQLO70XWM306kJEBNG8OvPOO0tEQmbZ+vXEV50WLgH79ym+3YIGx3cyZLCYSkWP66y/ZIzEtTc6xvX27HGlARI5tyBC52FJ2ttKREBEphwVFyIUwBg0CdDq5ctfSpSwmkvX89ptcgAUAliyRiwAR2aIDB+SFF0AWvk0VvxcvBiZMkNszZsjpI4iIHNGwYcCVK0DjxsDOnUBgoNIREZE1qNVyEU8iImfm9EOehQDeflsuMNC/vxzG58Iyq3PLywN694ZOr0dvIVCoVuOHH36Al5dXtb+VELIoo9MBzz8vezkQ2aLLl4FnnwUKCmSvxI8/Lr9dfDwwdqzcjo0Fpk2zWohkDVbMj0S2budOYMcOubr9tm2crsTpFedHAMjbtAm9n38eAJgjHdiTT8q5U7dvVzoSIiJlOH1Bce1auSqfj4/8IswJtAl6PbB3L9QA9gPIBaDX66tl10LIgkxurrzt2AHs2QN4esohokS26O+/5QlzZibQvr3Mm+XlysWLjcXEKVNk70RyMDWYH4nsiU4nF2YA5CJVzZopGw/ZgOL8CAD627ext2SbOdKhrV0LBAQoHQURkTKctqCovX4d+X/nY+J7tQGo8d4YDfxdc5Gf5Q7PUv8raDMyTO7DxdUVXkFBVWqbm5kJYeIEQ+XiAu9SK8NVpm3ejRvQ375tMg6fkJAqtc3PyoKusLBa2noHB0NV3A20ICcHt/Pzq6WtV1AQXFzlR7pQo0FRbm6l2ur18uJy/t9a3Llg7Y8/yvNEzQ0NtFm5yM2VbUtuublAfj6guR0Abb47cnOBAk0uCjUaw3O5uUBunnGfBfCDDp4AgMnjc1HHWwOtiY+Qh58fXD1l29v5+SjIyTH5u7n7+sLN27vSbXWFhcjPyjLZ1s3bG+7FE0NVpq3+9m3k3bhRLW1dPT3h4ecHABB6PXIzM6ulbb6Zz5WzKygAnnsO+P13uZLz99/LCzB3mjvXuLDQpEly3kROHUFEjmrVKrmyc0CA7I1NRM6J5zpE5NSEk8nOzhYARDYgYjFDAEI0xGWRBw8hAHGkTp0y7TWyU1m5t5P+/mXaXlepTLY96+1dpu01tdpk24seHmXaXvTwMNn2mlpdpu1Zb2+Tba+rVGXanvT3N9lWc8dH40idOibbijvaHoiMNNtWk55uaLuvcWOzba8nJRnaJrZoabbtqrh94v/+T4glS4RYG9nebNu+jbaI5s2FqFdPiA9du5ts5w0IAALQCECICehjdr/dsdBw9y0MMB+DS5zw8xPi8ceF2PPaMLNtD7z7rvH4vvuu2bb7hg0z/t3i4sy2TRwwwPh5WLjQbNs9ffoYP2erV5tv27278fO7ZYv5tu3bG/9d7NtnPt6WLQ1tryclmT8OjRsb2mrS08223RUeLgCI7Oxs4ewMOTI7W+j1Qrz8sjxMfn5CnDlT/mvef994OKdNE0Kvt27MZEUazV35UaPRKB0V1bDSecGZlRyHP//MFmFh8p/CggVKR0U2o1R+1KSnF58/Mkc6OuZHInJmTttD8SrqYh7kWJX5mABPFCgckfPYvx+4pQdu3ADCs8y37dkT+LMQ0GiAublAdzNtp88AUoq351YQw+U/gPPF20UWxNymjVy1MfgCANMd3TDgBWBYP8DLC7i9BMA+021jY4H/N11u/zLcgiCIrGzaNDmUx9UV2LgRaNmy7PNCyDYffijvf/gh8K9/WT9OIiJrWrJErurcqJEc7kxERETkjFRCCKF0EPHx8Zg3bx7S0tLQpk0bfPLJJ+jYsaPJ9hs2bEBsbCySk5PRpEkTzJkzB3369LHovXJycuDv74++vdPx/Q8h6NqlENs3ZRm6q6vdOeS5vLb5WVm4XVCI/Hzg1i0gJwfIzpa3mzeBHF0IbtyQRcIbf2XhxvVCZGbC8FheqRGluQhGyQLj7siBK0wPNzXV1stTDrv08pI/PT0Bd/8g+NRyhY8P4OOmgY97Lry9jW28vGC4XyskCL5+rvD2BjxUGrirco3PCy1qNW4kjwHkHGEajQY+Pj4VDqX2DAiAuniZ5qJcOeTZlNLDmCvTlkOeq3/IszY/H6H16yM7Oxt+xa+xFdbMj4AxR8bHZ2PUKHksVqwAXn+9bDsh5NDmefPk/XnzjCs7kwPTauXVFdydH8lxleQFW8yRlVXZnFpayXHw9MxGfr4fNmwAXnihhgMm+1EqP2rT0+EbGgqAOdLROVJ+JCKqLMULit9++y0GDx6MTz/9FJ06dcKiRYuwYcMGXLhwASGlClolDhw4gG7dumHWrFl45plnsHbtWsyZMwcnTpxAyzu7z5SjJOkD2XBx8cOJE7L3mbPQ6WQR8O+/ZSHQUAS8YbxfUijMyir7MztbroZdVWo1ULs2EBQkfwYGyrmH/PwAf3/5088PqFVL3nx9ZSHwzp9eXjW8Eje/MDsdWz0ZtHZ+BIzHQq3Ohk7nh6lT5XyIJfR64LvvgNmzgSNH5GOLF8vVyskJMD86JVvNkZVV2Zx6p9LnkF26+OGXXzh/GpXCgqJTcpT8SERUFYoXFDt16oQOHTpg6dKlAORKaFFRUXj77bcxefLku9oPHDgQWq0WW7duNTz28MMP48EHH8Snn35a4fuVPhkcOdIPFrxEcfri4cEZGfKWni4LgoWFZW8FBXKBEK227E2jMRYNs7Jkz6J7oVIZi4AlhcGgIHkLDASCg4E6deTPku3ateVr7OLEW6sFQkIgAIQIgVyVChkZGTwZdGC2ejJo7fwIlM2RL73kh6+/lgX8ggLgq69kT8QLF2RbT0859G/EiHv+VcleMD86JVvNkZVV2Zx6p9L58eBBPzz8cA0HTPalOD8CgPbKFYQ0bAgAzJEOzlHyIxFRVSg6h2JhYSGOHz+OKVOmGB5zcXFBdHQ0Dh48WO5rDh48iHHjxpV5rFevXtiyZUu57QsKClBQYJwfMad4+Kebmxy6O1yhuevKK+rpdGWHEpfcbtyQz1UnX9+yxcCSnwEBxpu/v/FnyS0gQL62RnsIKs3HB9BqoQJwXelYyGlZIz8CpnNkcLAsGL7xhryosXMn8Ndfsk1AgJw37J13DN+dyFkwP5KdqkpONZUfn38eLCbS3YrzIyB7cGuLt4mIiByVogXFzMxM6HQ6hBYPCSgRGhqK8+fPl/uatLS0ctunpaWV237WrFmYMWPGXY8XFcnFBuxJYCAQGiq/wNeuLYf+ursbb25ucg5Ab295TlNyKykelu5F6Oam9G9DROZYIz8CpnNkZiawenXZxyIjgXfflUXGWrUs+z2IiGxBVXKqqfwYF1cjIRIRERHZFYdf5XnKlClleuzk5OQgKioKU6capjlRzJ3Df0sPJS59CwqSw4aL1/ogIqo2pnLktGmyh2KJBg2AmBjmISJyHqbyY4MGysVEREREZCsULSgGBwdDrVYjPT29zOPp6ekICwsr9zVhYWGVau/h4QEPD4+7Hp80SRbviO6Snw/ExECn1yNGCBSp1di4cSM8S1dXiGqYNfIjYDpHjh/PHEnlYH4kO1WVnGoqPxKVqzg/AkD+118j5pVXAIA5koiIHJaiM+G5u7ujXbt2SEhIMDym1+uRkJCAzp07l/uazp07l2kPALt27TLZnqjSdDpg+3aod+zArp07sX37duiqexJLogowP5JNYn4kO1WVnEpUKcX5Edu3Q1dYiO3btzNHEhGRQ1N8yPO4cePw2muvoX379ujYsSMWLVoErVaLoUOHAgAGDx6MyMhIzJo1CwAwZswYdO/eHQsWLMDTTz+NdevW4dixY/j888+V/DWIiKod8yMRUfWpKKcSERERkeUULygOHDgQ169fx7Rp05CWloYHH3wQO3bsMEyaffXqVbiUWlK4S5cuWLt2Lf79739j6tSpaNKkCbZs2YKWLVsq9SsQEdUI5kcioupTUU4lIiIiIsuphBBC6SCsKScnB/7+/sjOzoYfJwij8mi1hhV7fADkAtBoNPDx8VE0LKo5zAtGPBZkFvOjU2JekHgcyKxS+VGbng7f4kI1c6RjY14gImem6ByKREREREREREREZF9YUCQiIiIiIiIiIiKLKT6HorWVjPDOyclROBKyWVqtYbNkPoCcnByu0ufASvKBk80AUS7mSDKL+dEpMUdKzI9kVqn8qL11y7DNHOnYmB+JyJk5XUHxVvF/8FFRUQpHQvYkIiJC6RDICm7dugV/f3+lw1AUcyRVFvOj83D2HMn8SBa77z7DJnOkc3D2/EhEzsnpFmXR6/X466+/UKtWLahUKqXDsVhOTg6ioqJw7do1u5rwl3FbF+OuGiEEbt26hYiIiDKrJjsj5kjrsceYAcZtbbYQN3OkxPxoXYzbuhh31TA/EpEzc7oeii4uLqhbt67SYVSZn5+fXf0nX4JxWxfjrjxeVZaYI63PHmMGGLe1KR03cyTzo1IYt3Ux7spjfiQiZ8XLKERERERERERERGQxFhSJiIiIiIiIiIjIYiwo2gkPDw/ExcXBw8ND6VAqhXFbF+MmZ2WPnyF7jBlg3NZmr3GT7bDXzxDjti7GTUREleV0i7IQERERERERERFR1bGHIhEREREREREREVmMBUUiIiIiIiIiIiKyGAuKREREREREREREZDEWFG3ArFmz0KFDB9SqVQshISHo378/Lly4YPY1q1evhkqlKnPz9PS0UsTS9OnT74qhefPmZl+zYcMGNG/eHJ6enmjVqhW2b99upWiNGjRocFfcKpUKo0aNKre9Usf6559/Rt++fREREQGVSoUtW7aUeV4IgWnTpiE8PBxeXl6Ijo7GxYsXK9xvfHw8GjRoAE9PT3Tq1AlHjhyxWtxFRUWYNGkSWrVqBR8fH0RERGDw4MH466+/zO6zKp81chzMkdbFHFlzOZL5kaob86N1MT/yHJKIiIxYULQBe/fuxahRo3Do0CHs2rULRUVF6NmzJ7RardnX+fn5ITU11XBLSUmxUsRGLVq0KBPDL7/8YrLtgQMHMGjQIAwbNgwnT55E//790b9/f/z2229WjBg4evRomZh37doFABgwYIDJ1yhxrLVaLdq0aYP4+Phyn587dy6WLFmCTz/9FIcPH4aPjw969eqF/Px8k/v89ttvMW7cOMTFxeHEiRNo06YNevXqhYyMDKvEnZubixMnTiA2NhYnTpzApk2bcOHCBfTr16/C/Vbms0aOhTmSObI89pgjmR+pujE/Mj+Wxx7zY0VxM0cSEdkgQTYnIyNDABB79+412WbVqlXC39/fekGVIy4uTrRp08bi9i+++KJ4+umnyzzWqVMnMXLkyGqOrHLGjBkjGjduLPR6fbnP28KxBiA2b95suK/X60VYWJiYN2+e4bGsrCzh4eEhvvnmG5P76dixoxg1apThvk6nExEREWLWrFlWibs8R44cEQBESkqKyTaV/ayRY2OOtC7myJrJkcyPVBOYH62L+ZHnkEREzow9FG1QdnY2ACAoKMhsO41Gg/r16yMqKgrPPvsszp49a43wyrh48SIiIiLQqFEjvPLKK7h69arJtgcPHkR0dHSZx3r16oWDBw/WdJgmFRYW4quvvsLrr78OlUplsp0tHOvSrly5grS0tDLH09/fH506dTJ5PAsLC3H8+PEyr3FxcUF0dLSif4Ps7GyoVCoEBASYbVeZzxo5NuZI62GOVDZHMj9SZTE/Wg/zI88hiYicHQuKNkav12Ps2LF45JFH0LJlS5PtmjVrhpUrV+K7777DV199Bb1ejy5duuB///uf1WLt1KkTVq9ejR07dmDZsmW4cuUKunbtilu3bpXbPi0tDaGhoWUeCw0NRVpamjXCLdeWLVuQlZWFIUOGmGxjC8f6TiXHrDLHMzMzEzqdzqb+Bvn5+Zg0aRIGDRoEPz8/k+0q+1kjx8UcaV3Mkcr9DZgfqbKYH62L+ZHnkEREzs5V6QCorFGjRuG3336rcG6Pzp07o3Pnzob7Xbp0wf3334/PPvsMH3zwQU2HCQDo3bu3Ybt169bo1KkT6tevj/Xr12PYsGFWieFerVixAr1790ZERITJNrZwrB1RUVERXnzxRQghsGzZMrNtHeGzRtWDOdK6mCOVwfxIVcH8aF3Mj8phjiQisg3soWhDRo8eja1bt2LPnj2oW7dupV7r5uaGtm3b4tKlSzUUXcUCAgLQtGlTkzGEhYUhPT29zGPp6ekICwuzRnh3SUlJwe7duzF8+PBKvc4WjnXJMavM8QwODoZarbaJv0HJiWBKSgp27dpl9spyeSr6rJFjYo60LubIil9TE5gfqSqYH62L+bHi19QU5kgiItvBgqINEEJg9OjR2Lx5M3766Sc0bNiw0vvQ6XQ4c+YMwsPDayBCy2g0Gly+fNlkDJ07d0ZCQkKZx3bt2lXmyq01rVq1CiEhIXj66acr9TpbONYNGzZEWFhYmeOZk5ODw4cPmzye7u7uaNeuXZnX6PV6JCQkWPVvUHIiePHiRezevRu1a9eu9D4q+qyRY2GOZI6sLHvNkcyPVFnMj8yPlWWv+RFgjiQisjlKrghD0ptvvin8/f1FYmKiSE1NNdxyc3MNbV599VUxefJkw/0ZM2aInTt3isuXL4vjx4+Ll156SXh6eoqzZ89aLe7x48eLxMREceXKFbF//34RHR0tgoODRUZGRrkx79+/X7i6uor58+eLc+fOibi4OOHm5ibOnDljtZhL6HQ6Ua9ePTFp0qS7nrOVY33r1i1x8uRJcfLkSQFAfPzxx+LkyZOGlexmz54tAgICxHfffSd+/fVX8eyzz4qGDRuKvLw8wz6eeOIJ8cknnxjur1u3Tnh4eIjVq1eLpKQk8cYbb4iAgACRlpZmlbgLCwtFv379RN26dcWpU6fKfN4LCgpMxl3RZ40cG3Mkc2R57DFHMj9SdWN+ZH4sjz3mx4riZo4kIrI9LCjaAADl3latWmVo0717d/Haa68Z7o8dO1bUq1dPuLu7i9DQUNGnTx9x4sQJq8Y9cOBAER4eLtzd3UVkZKQYOHCguHTpksmYhRBi/fr1omnTpsLd3V20aNFCbNu2zaoxl9i5c6cAIC5cuHDXc7ZyrPfs2VPu56IkNr1eL2JjY0VoaKjw8PAQPXr0uOv3qV+/voiLiyvz2CeffGL4fTp27CgOHTpktbivXLli8vO+Z88ek3FX9Fkjx8YcaX3MkTWTI5kfqboxP1of8yPPIYmISFIJIUQVOzcSERERERERERGRk+EcikRERERERERERGQxFhSJiIiIiIiIiIjIYiwoEhERERERERERkcVYUCQiIiIiIiIiIiKLsaBIREREREREREREFmNBkYiIiIiIiIiIiCzGgiIRERERERERERFZjAVFIiIiIiIiIiIishgLilRlycnJUKlUOHXqlMWvGTJkCPr372+2zWOPPYaxY8feU2wqlQpbtmwBYHmclrxv6f1a0/Tp06FSqaBSqbBo0aJ72tfq1asREBBgtfcjclbMkdbDHElkX5gfrYf5kYiIagoLig4sLS0Nb7/9Nho1agQPDw9ERUWhb9++SEhIUDo0q4qKikJqaipatmwJAEhMTIRKpUJWVlal95WamorevXtXc4SWadGiBVJTU/HGG2/c9dysWbOgVqsxb968anmvCRMmIDU1FXXr1q2W/RHZIuZIiTmy8pgjydExP0rMj5XH/EhE5DxYUHRQycnJaNeuHX766SfMmzcPZ86cwY4dO/D4449j1KhRSodnVWq1GmFhYXB1db3nfYWFhcHDw6Maoqo8V1dXhIWFwdvb+67nVq5ciYkTJ2LlypXV8l6+vr4ICwuDWq2ulv0R2RrmSCPmyMpjjiRHxvxoxPxYecyPRETOgwVFB/XWW29BpVLhyJEjiImJQdOmTdGiRQuMGzcOhw4dAgC8/vrreOaZZ8q8rqioCCEhIVixYgUAQK/XY+7cubjvvvvg4eGBevXqYebMmeW+p06nw7Bhw9CwYUN4eXmhWbNmWLx4cbltZ8yYgTp16sDPzw///Oc/UVhYaPJ3KSgowIQJExAZGQkfHx906tQJiYmJFh+L0sNVkpOT8fjjjwMAAgMDoVKpMGTIEENbvV6PiRMnIigoCGFhYZg+fXqZfZUerlLeVepTp05BpVIhOTkZgHFoyNatW9GsWTN4e3vjhRdeQG5uLtasWYMGDRogMDAQ77zzDnQ6ncW/U2l79+5FXl4e3n//feTk5ODAgQMWvW7nzp24//774evri6eeegqpqalVen8ie8QcacQcWT7mSHJWzI9GzI/lY34kIiIAuPfLbWRzbty4gR07dmDmzJnw8fG56/mSuU+GDx+Obt26ITU1FeHh4QCArVu3Ijc3FwMHDgQATJkyBcuXL8fChQvx6KOPIjU1FefPny/3ffV6PerWrYsNGzagdu3aOHDgAN544w2Eh4fjxRdfNLRLSEiAp6cnEhMTkZycjKFDh6J27domTzJHjx6NpKQkrFu3DhEREdi8eTOeeuopnDlzBk2aNKnUsYmKisLGjRsRExODCxcuwM/PD15eXobn16xZg3HjxuHw4cM4ePAghgwZgkceeQRPPvlkpd6ntNzcXCxZsgTr1q3DrVu38Pzzz+O5555DQEAAtm/fjj/++AMxMTF45JFHDMe9MlasWIFBgwbBzc0NgwYNwooVK9ClS5cKY5o/fz6+/PJLuLi44B//+AcmTJiAr7/+uqq/JpHdYI40jTnSGBNzJDkj5kfTmB+NMTE/EhERAECQwzl8+LAAIDZt2lRh2wceeEDMmTPHcL9v375iyJAhQgghcnJyhIeHh1i+fHm5r71y5YoAIE6ePGly/6NGjRIxMTGG+6+99poICgoSWq3W8NiyZcuEr6+v0Ol0QgghunfvLsaMGSOEECIlJUWo1Wrx559/ltlvjx49xJQpU0y+LwCxefPmcuPcs2ePACBu3rxZ5jXdu3cXjz76aJnHOnToICZNmlTufsvbz8mTJwUAceXKFSGEEKtWrRIAxKVLlwxtRo4cKby9vcWtW7cMj/Xq1UuMHDnS5O8TFxcn2rRpc9fj2dnZwsvLS5w6dcrw/r6+vmX2fafyYoqPjxehoaF3ta1fv75YuHChyX0R2SPmSOZI5kii8jE/Mj8yPxIRkaU45NkBCSEsbjt8+HCsWrUKAJCeno4ffvgBr7/+OgDg3LlzKCgoQI8ePSzeX3x8PNq1a4c6derA19cXn3/+Oa5evVqmTZs2bcrM4dK5c2doNBpcu3btrv2dOXMGOp0OTZs2ha+vr+G2d+9eXL582eK4LNW6desy98PDw5GRkXFP+/T29kbjxo0N90NDQ9GgQQP4+vqWeawq7/PNN9+gcePGaNOmDQDgwQcfRP369fHtt99WKqbq+D2J7AVzZNUxRxI5NubHqmN+JCIiZ8Mhzw6oSZMmUKlUJoeVlDZ48GBMnjwZBw8exIEDB9CwYUN07doVAMoM47DEunXrMGHCBCxYsACdO3dGrVq1MG/ePBw+fLhKvwcAaDQaqNVqHD9+/K7JnUufTFUXNze3MvdVKhX0en25bV1cZD2+9Ml3UVGRRfuszPuYs2LFCpw9e7bMZOF6vR4rV67EsGHDTL6uvPevzJcIInvGHFl1zJFEjo35seqYH4mIyNmwoOiAgoKC0KtXL8THx+Odd965aw6crKwswxw4tWvXRv/+/bFq1SocPHgQQ4cONbRr0qQJvLy8kJCQgOHDh1f4vvv370eXLl3w1ltvGR4r7wrw6dOnkZeXZzjZPHToEHx9fREVFXVX27Zt20Kn0yEjI8Nwknqv3N3dAaDKE1iXqFOnDgAgNTUVgYGBAOSE2tZy5swZHDt2DImJiQgKCjI8fuPGDTz22GM4f/48mjdvbrV4iOwFc6R5zJFEzov50TzmRyIiIiMOeXZQ8fHx0Ol06NixIzZu3IiLFy/i3LlzWLJkCTp37lym7fDhw7FmzRqcO3cOr732muFxT09PTJo0CRMnTsQXX3yBy5cv49ChQ4bV++7UpEkTHDt2DDt37sTvv/+O2NhYHD169K52hYWFGDZsGJKSkrB9+3bExcVh9OjRhqu1pTVt2hSvvPIKBg8ejE2bNuHKlSs4cuQIZs2ahW3btlXp2NSvXx8qlQpbt27F9evXodFoqrSf++67D1FRUZg+fTouXryIbdu2YcGCBVXaV1WsWLECHTt2RLdu3dCyZUvDrVu3bujQoYPh77R06dJKDTkicgbMkaYxRxI5N+ZH05gfiYiIjFhQdFCNGjXCiRMn8Pjjj2P8+PFo2bIlnnzySSQkJGDZsmVl2kZHRyM8PBy9evVCREREmediY2Mxfvx4TJs2Dffffz8GDhxocp6UkSNH4vnnn8fAgQPRqVMn/P3332WuNJfo0aMHmjRpgm7dumHgwIHo168fpk+fbvJ3WbVqFQYPHozx48ejWbNm6N+/P44ePYp69epV/sAAiIyMxIwZMzB58mSEhoZi9OjRVdqPm5sbvvnmG5w/fx6tW7fGnDlz8OGHH1ZpX5VVWFiIr776CjExMeU+HxMTgy+++AJFRUXIzMyskbmCiOwZc6RpzJFEzo350TTmRyIiIiOV4KQXTk+j0SAyMhKrVq3C888/r3Q4VI7p06djy5YtVh0OAwANGjTA2LFjMXbsWKu+L5EtYY60fcyRRMpgfrR9zI9ERFRT2EPRien1emRkZOCDDz5AQEAA+vXrp3RIZMaZM2fg6+uL//znPzX+Xh999BF8fX3vWl2RyJkwR9oX5kgi62F+tC/Mj0REVBPYQ9GJJScno2HDhqhbty5Wr17NOVJs2I0bN3Djxg0AciJvf39/h3o/IlvEHGk/mCOJrIv50X4wPxIRUU1hQZGIiIiIiIiIiIgsxiHPREREREREREREZDEWFImIiIiIiIiIiMhiLCgSERERERERERGRxVhQJCIiIiIiIiIiIouxoEhEREREREREREQWY0GRiIiIiIiIiIiILMaCIhEREREREREREVmMBUUiIiIiIiIiIiKyGAuKREREREREREREZLH/D8BQdK+ElC/vAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for parameter_set in all_parameter_sets:\n", - " print(parameter_set)\n", - " try:\n", - " sweep, sol_init_QLi, sol_init_Q = solve_esoh_sweep_QLi(parameter_set, param)\n", - " fig, axes = plot_sweep(sweep, sol_init_QLi, sol_init_Q, parameter_set)\n", - " except ValueError:\n", - " pass\n", - " # print(\"success\")\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## References\n", - "\n", - "The relevant papers for this notebook are:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1] Weilong Ai, Ludwig Kraft, Johannes Sturm, Andreas Jossen, and Billy Wu. Electrochemical thermal-mechanical modelling of stress inhomogeneity in lithium-ion pouch cells. Journal of The Electrochemical Society, 167(1):013512, 2019. doi:10.1149/2.0122001JES.\n", - "[2] 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", - "[3] Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050.\n", - "[4] Rutooj Deshpande, Mark Verbrugge, Yang-Tse Cheng, John Wang, and Ping Liu. Battery cycle life prediction with coupled chemical degradation and fatigue mechanics. Journal of the Electrochemical Society, 159(10):A1730, 2012. doi:10.1149/2.049210jes.\n", - "[5] Madeleine Ecker, Stefan Käbitz, Izaro Laresgoiti, and Dirk Uwe Sauer. Parameterization of a Physico-Chemical Model of a Lithium-Ion Battery: II. Model Validation. Journal of The Electrochemical Society, 162(9):A1849–A1857, 2015. doi:10.1149/2.0541509jes.\n", - "[6] Madeleine Ecker, Thi Kim Dung Tran, Philipp Dechent, Stefan Käbitz, Alexander Warnecke, and Dirk Uwe Sauer. Parameterization of a Physico-Chemical Model of a Lithium-Ion Battery: I. Determination of Parameters. Journal of the Electrochemical Society, 162(9):A1836–A1848, 2015. doi:10.1149/2.0551509jes.\n", - "[7] Alastair Hales, Laura Bravo Diaz, Mohamed Waseem Marzook, Yan Zhao, Yatish Patel, and Gregory Offer. The cell cooling coefficient: a standard to define heat rejection from lithium-ion batteries. Journal of The Electrochemical Society, 166(12):A2383, 2019.\n", - "[8] 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", - "[9] Gi-Heon Kim, Kandler Smith, Kyu-Jin Lee, Shriram Santhanagopalan, and Ahmad Pesaran. Multi-domain modeling of lithium-ion batteries encompassing multi-physics in varied length scales. Journal of the Electrochemical Society, 158(8):A955–A969, 2011. doi:10.1149/1.3597614.\n", - "[10] Michael J. Lain, James Brandon, and Emma Kendrick. Design strategies for high power vs. high energy lithium ion cells. Batteries, 5(4):64, 2019. doi:10.3390/batteries5040064.\n", - "[11] 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", - "[12] Peyman Mohtat, Suhak Lee, Jason B Siegel, and Anna G Stefanopoulou. Towards better estimability of electrode-specific state of health: decoding the cell expansion. Journal of Power Sources, 427:101–111, 2019.\n", - "[13] Peyman Mohtat, Suhak Lee, Valentin Sulzer, Jason B. Siegel, and Anna G. Stefanopoulou. Differential Expansion and Voltage Model for Li-ion Batteries at Practical Charging Rates. Journal of The Electrochemical Society, 167(11):110561, 2020. doi:10.1149/1945-7111/aba5d1.\n", - "[14] Andreas Nyman, Mårten Behm, and Göran Lindbergh. Electrochemical characterisation and modelling of the mass transport phenomena in lipf6–ec–emc electrolyte. Electrochimica Acta, 53(22):6356–6365, 2008.\n", - "[15] Simon E. J. O'Kane, Ian D. Campbell, Mohamed W. J. Marzook, Gregory J. Offer, and Monica Marinescu. Physical origin of the differential voltage minimum associated with lithium plating in li-ion batteries. Journal of The Electrochemical Society, 167(9):090540, may 2020. URL: https://doi.org/10.1149/1945-7111/ab90ac, doi:10.1149/1945-7111/ab90ac.\n", - "[16] Kieran O'Regan, Ferran Brosa Planella, W. Dhammika Widanage, and Emma Kendrick. Thermal-electrochemical parameters of a high energy lithium-ion cylindrical battery. Electrochimica Acta, 425:140700, 2022. doi:10.1016/j.electacta.2022.140700.\n", - "[17] Simon E. J. O'Kane, Weilong Ai, Ganesh Madabattula, Diego Alonso-Alvarez, Robert Timms, Valentin Sulzer, Jacqueline Sophie Edge, Billy Wu, Gregory J. Offer, and Monica Marinescu. Lithium-ion battery degradation: how to model it. Phys. Chem. Chem. Phys., 24:7909-7922, 2022. URL: http://dx.doi.org/10.1039/D2CP00417H, doi:10.1039/D2CP00417H.\n", - "[18] Eric Prada, D. Di Domenico, Y. Creff, J. Bernard, Valérie Sauvant-Moynot, and François Huet. A simplified electrochemical and thermal aging model of LiFePO4-graphite Li-ion batteries: power and capacity fade simulations. Journal of The Electrochemical Society, 160(4):A616, 2013. doi:10.1149/2.053304jes.\n", - "[19] P Ramadass, Bala Haran, Parthasarathy M Gomadam, Ralph White, and Branko N Popov. Development of first principles capacity fade model for li-ion cells. Journal of the Electrochemical Society, 151(2):A196, 2004. doi:10.1149/1.1634273.\n", - "[20] Giles Richardson, Ivan Korotkin, Rahifa Ranom, Michael Castle, and Jamie M. Foster. Generalised single particle models for high-rate operation of graded lithium-ion electrodes: systematic derivation and validation. Electrochimica Acta, 339:135862, 2020. doi:10.1016/j.electacta.2020.135862.\n", - "[21] 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", - "[22] 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", - "[23] Yan Zhao, Yatish Patel, Teng Zhang, and Gregory J Offer. Modeling the effects of thermal gradients induced by tab and surface cooling on lithium ion cell performance. Journal of The Electrochemical Society, 165(13):A3169, 2018.\n", - "\n" - ] - } - ], - "source": [ - "pybamm.print_citations()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "pybamm", - "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": "187972e187ab8dfbecfab9e8e194ae6d08262b2d51a54fa40644e3ddb6b5f74c" - } - } + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Electrode State of Health" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook demonstrates some utilities to work with electrode State of Health (also sometimes called electrode stoichiometry), using the algorithm from Mohtat et al [1]\n", + "\n", + "[1] Mohtat, P., Lee, S., Siegel, J. B., & Stefanopoulou, A. G. (2019). Towards better estimability of electrode-specific state of health: Decoding the cell expansion. Journal of Power Sources, 427, 101-111." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ERROR: Invalid requirement: '#'\n" + ] }, - "nbformat": 4, - "nbformat_minor": 4 + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install pybamm -q # install PyBaMM if it is not installed\n", + "import pybamm\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create and solve model" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3b55c57e62d4444fb157f8a1bbdde58c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(FloatSlider(value=0.0, description='t', max=2.32485835391946, step=0.0232485835391946), …" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "spm = pybamm.lithium_ion.SPM()\n", + "experiment = pybamm.Experiment([\n", + " \"Charge at 1C until 4.2V\", \n", + " \"Hold at 4.2V until C/50\",\n", + " \"Discharge at 1C until 2.8V\",\n", + " \"Hold at 2.8V until C/50\",\n", + "])\n", + "parameter_values = pybamm.ParameterValues(\"Mohtat2020\")\n", + "\n", + "sim = pybamm.Simulation(spm, experiment=experiment, parameter_values=parameter_values)\n", + "spm_sol = sim.solve()\n", + "spm_sol.plot([\n", + " \"Voltage [V]\", \n", + " \"Current [A]\", \n", + " \"Negative electrode stoichiometry\",\n", + " \"Positive electrode stoichiometry\",\n", + "])" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Solve for electrode SOH variables" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Given a total amount of cyclable lithium capacity, $Q_{Li}$, electrode capacities, $Q_n$ and $Q_p$, and voltage limits, $V_{min}$ and $V_{max}$, we can solve for the min and max electrode SOCs, $x_0$, $x_{100}$, $y_0$, and $y_{100}$, and the cell capacity, $C$, using the algorithm adapted from Mohtat et al [1].\n", + "First, we find $x_{100}$ and $y_{100}$ using\n", + "\n", + "$$\n", + "Q_{Li} = y_{100}Q_p + x_{100}Q_n,\n", + "\\\\\n", + "V_{max} = U_p(y_{100}) - U_n(x_{100}).\n", + "$$\n", + "\n", + "Note that Mohtat et al use $n_{Li} = \\frac{3600 Q_{Li}}{F}$ instead.\n", + "Then, we find $Q$ using\n", + "\n", + "$$\n", + "V_{min} = U_p(y_{0}) - U_n(x_{0})\n", + "= U_p\\left(y_{100} + \\frac{Q}{Q_p}\\right) - U_n\\left(x_{100} - \\frac{Q}{Q_n}\\right)\n", + "$$\n", + "\n", + "Finally, $x_0$ and $y_0$ are simply defined as\n", + "\n", + "$$\n", + "x_0 = x_{100} - \\frac{Q}{Q_n},\n", + "\\\\\n", + "y_0 = y_{100} + \\frac{Q}{Q_p}.\n", + "$$\n", + "\n", + "We implement this in PyBaMM as an algebraic model." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "param = pybamm.LithiumIonParameters()\n", + "\n", + "Vmin = 2.8\n", + "Vmax = 4.2\n", + "Q_n = parameter_values.evaluate(param.n.Q_init)\n", + "Q_p = parameter_values.evaluate(param.p.Q_init)\n", + "Q_Li = parameter_values.evaluate(param.Q_Li_particles_init)\n", + "\n", + "U_n = param.n.prim.U\n", + "U_p = param.p.prim.U\n", + "T_ref = param.T_ref" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x_100 : 0.83337428922595\n", + "y_100 : 0.03354553395256055\n", + "Q : 4.968932758817601\n", + "x_0 : 0.0015118453536460735\n", + "y_0 : 0.8908948803914055\n" + ] + } + ], + "source": [ + "# First we solve for x_100 and y_100\n", + "\n", + "model = pybamm.BaseModel()\n", + "\n", + "x_100 = pybamm.Variable(\"x_100\")\n", + "y_100 = (Q_Li - x_100 * Q_n) / Q_p\n", + "\n", + "y_100_min = 1e-10\n", + "\n", + "x_100_upper_limit = (Q_Li - y_100_min*Q_p)/Q_n\n", + "\n", + "model.algebraic = {x_100: U_p(y_100, T_ref) - U_n(x_100, T_ref) - Vmax}\n", + " \n", + "model.initial_conditions = {x_100: x_100_upper_limit}\n", + "\n", + "model.variables = {\n", + " \"x_100\": x_100,\n", + " \"y_100\": y_100\n", + "}\n", + "\n", + "sim = pybamm.Simulation(model, parameter_values=parameter_values)\n", + "sol = sim.solve([0])\n", + "\n", + "x_100 = sol[\"x_100\"].data[0]\n", + "y_100 = sol[\"y_100\"].data[0]\n", + "\n", + "for var in [\"x_100\", \"y_100\"]:\n", + " print(var, \":\", sol[var].data[0])\n", + "\n", + "# Based on the calculated values for x_100 and y_100 we solve for x_0\n", + "model = pybamm.BaseModel()\n", + "\n", + "x_0 = pybamm.Variable(\"x_0\")\n", + "Q = Q_n * (x_100 - x_0)\n", + "y_0 = y_100 + Q/Q_p\n", + "\n", + "model.algebraic = {x_0: U_p(y_0, T_ref) - U_n(x_0, T_ref) - Vmin}\n", + "model.initial_conditions = {x_0: 0.1}\n", + "\n", + "model.variables = {\n", + " \"Q\": Q,\n", + " \"x_0\": x_0,\n", + " \"y_0\": y_0,\n", + "}\n", + "\n", + "sim = pybamm.Simulation(model, parameter_values=parameter_values)\n", + "sol = sim.solve([0])\n", + "\n", + "\n", + "for var in [\"Q\", \"x_0\", \"y_0\"]:\n", + " print(var, \":\", sol[var].data[0])" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is implemented in PyBaMM as the `ElectrodeSOHSolver` class" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x_100 : 0.833374276202919\n", + "y_100 : 0.03354554737459606\n", + "Q : 4.968932679279884\n", + "x_0 : 0.0015118456462390728\n", + "y_0 : 0.8908948800898482\n" + ] + } + ], + "source": [ + "esoh_solver = pybamm.lithium_ion.ElectrodeSOHSolver(parameter_values, param)\n", + "\n", + "inputs={ \"V_min\": Vmin, \"V_max\": Vmax, \"Q_n\": Q_n, \"Q_p\": Q_p, \"Q_Li\": Q_Li}\n", + "\n", + "esoh_sol = esoh_solver.solve(inputs)\n", + "\n", + "for var in [\"x_100\", \"y_100\", \"Q\", \"x_0\", \"y_0\"]:\n", + " print(var, \":\", esoh_sol[var])" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Check against simulations" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plotting the SPM simulations against the eSOH calculations validates the min/max stoichiometry calculations" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "t = spm_sol[\"Time [h]\"].data\n", + "x_spm = spm_sol[\"Negative electrode stoichiometry\"].data\n", + "y_spm = spm_sol[\"Positive electrode stoichiometry\"].data\n", + "\n", + "x_0 = esoh_sol[\"x_0\"].data * np.ones_like(t)\n", + "y_0 = esoh_sol[\"y_0\"].data * np.ones_like(t)\n", + "x_100 = esoh_sol[\"x_100\"].data * np.ones_like(t)\n", + "y_100 = esoh_sol[\"y_100\"].data * np.ones_like(t)\n", + "\n", + "fig, axes = plt.subplots(1,2)\n", + "\n", + "axes[0].plot(t, x_spm, \"b\")\n", + "axes[0].plot(t, x_0, \"k:\")\n", + "axes[0].plot(t, x_100, \"k:\")\n", + "axes[0].set_ylabel(\"x\")\n", + " \n", + "axes[1].plot(t, y_spm, \"r\")\n", + "axes[1].plot(t, y_0, \"k:\")\n", + "axes[1].plot(t, y_100, \"k:\")\n", + "axes[1].set_ylabel(\"y\")\n", + " \n", + "for k in range(2):\n", + " axes[k].set_xlim([t[0],t[-1]])\n", + " axes[k].set_ylim([0,1]) \n", + " axes[k].set_xlabel(\"Time [h]\")\n", + " \n", + "fig.tight_layout()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## How does electrode SOH depend on cyclable lithium" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can do a parameter sweep for the amount of cyclable lithium to see how it affects the electrode SOH parameters and cell capacity" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "all_parameter_sets = [\n", + " k\n", + " for k, v in pybamm.parameter_sets.items()\n", + " if v[\"chemistry\"] == \"lithium_ion\" and k not in [\"Xu2019\", \"Chen2020_composite\"]\n", + "]\n", + "\n", + "\n", + "def solve_esoh_sweep_QLi(parameter_set, param):\n", + " parameter_values = pybamm.ParameterValues(parameter_set)\n", + "\n", + " # Vmin = parameter_values[\"Lower voltage cut-off [V]\"]\n", + " # Vmax = parameter_values[\"Upper voltage cut-off [V]\"]\n", + " Vmin = parameter_values[\"Open-circuit voltage at 0% SOC [V]\"]\n", + " Vmax = parameter_values[\"Open-circuit voltage at 100% SOC [V]\"]\n", + " \n", + " Q_n = parameter_values.evaluate(param.n.Q_init)\n", + " Q_p = parameter_values.evaluate(param.p.Q_init)\n", + " \n", + " Q = parameter_values.evaluate(param.Q/param.n_electrodes_parallel)\n", + " esoh_solver = pybamm.lithium_ion.ElectrodeSOHSolver(parameter_values, param, known_value=\"cell capacity\")\n", + " inputs = {\"V_max\": Vmax, \"V_min\": Vmin, \"Q\": Q, \"Q_n\": Q_n, \"Q_p\": Q_p}\n", + " sol_init_Q = esoh_solver.solve(inputs)\n", + " \n", + " Q_Li_init = parameter_values.evaluate(param.Q_Li_particles_init)\n", + " esoh_solver = pybamm.lithium_ion.ElectrodeSOHSolver(parameter_values, param)\n", + " inputs = {\"V_max\": Vmax, \"V_min\": Vmin, \"Q_Li\": Q_Li_init, \"Q_n\": Q_n, \"Q_p\": Q_p}\n", + " sol_init_QLi = esoh_solver.solve(inputs)\n", + "\n", + " Q_Li_sweep = np.linspace(1e-6, Q_n + Q_p)\n", + " sweep = {}\n", + " variables = [\"Q_Li\", \"x_0\", \"x_100\", \"y_0\", \"y_100\", \"Q\"]\n", + " for var in variables:\n", + " sweep[var] = []\n", + "\n", + " for Q_Li in Q_Li_sweep:\n", + " inputs[\"Q_Li\"] = Q_Li\n", + " try:\n", + " sol = esoh_solver.solve(inputs)\n", + " for var in variables:\n", + " sweep[var].append(sol[var])\n", + " except (ValueError, pybamm.SolverError):\n", + " pass\n", + "\n", + " return sweep, sol_init_QLi, sol_init_Q\n", + " \n", + "\n", + "for parameter_set in [\"Chen2020\"]:\n", + " sweep, sol_init_QLi, sol_init_Q = solve_esoh_sweep_QLi(parameter_set, param)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_sweep(sweep, sol_init, sol_init_Q, parameter_set):\n", + " fig, axes = plt.subplots(1,3,figsize=(10,3))\n", + " parameter_values = pybamm.ParameterValues(parameter_set)\n", + " parameter_values.evaluate(param.n.Q_init)\n", + " parameter_values.evaluate(param.p.Q_init)\n", + " # Plot min/max stoichimetric limits, including the value with the given Q_Li\n", + " for i,ks in enumerate([[\"x_0\",\"x_100\"],[\"y_0\",\"y_100\"],[\"Q\"]]):\n", + " ax = axes.flat[i]\n", + " for j,k in enumerate(ks):\n", + " if i == 0 and j == 0:\n", + " label1 = \"Stoichiometric envelope\"\n", + " label2 = \"Calculation from cyclable lithium\"\n", + " label3 = \"Calculation from cell capacity\"\n", + " else:\n", + " label1 = label2 = label3 = None\n", + " ax.plot(sweep[\"Q_Li\"], sweep[k],\"b-\", label=label1)\n", + " ax.axhline(sol_init_QLi[k],c=\"k\",linestyle=\"--\", label=label2)\n", + " ax.axhline(sol_init_Q[k],c=\"r\",linestyle=\"--\", label=label3)\n", + " ax.set_xlabel(\"Cyclable lithium [A.h]\")\n", + " ax.set_ylabel(ks[0][0])\n", + " ax.set_xlim([np.min(sweep[\"Q_Li\"]),np.max(sweep[\"Q_Li\"])])\n", + " ax.axvline(sol_init_QLi[\"Q_Li\"],c=\"k\",linestyle=\"--\")\n", + " ax.axvline(sol_init_Q[\"Q_Li\"],c=\"r\",linestyle=\"--\")\n", + " # Plot capacities of electrodes\n", + " # ax.axvline(Qn,c=\"b\",linestyle=\"--\")\n", + " # ax.axvline(Qp,c=\"r\",linestyle=\"--\")\n", + " axes[-1].set_ylabel(\"Cell capacity [A.h]\")\n", + " \n", + " # Plot initial values of stoichometries\n", + " parameter_values.evaluate(param.n.prim.sto_init_av)\n", + " parameter_values.evaluate(param.p.prim.sto_init_av)\n", + " # axes[0].axhline(sto_n_init,c=\"g\",linestyle=\"--\")\n", + " # axes[1].axhline(sto_p_init,c=\"g\",linestyle=\"--\")\n", + "\n", + " axes[1].set_title(parameter_set)\n", + " fig.legend(loc=\"center left\", bbox_to_anchor=(1.01,0.5))\n", + " fig.tight_layout()\n", + " return fig, axes\n", + "\n", + "\n", + "plot_sweep(sweep, sol_init_QLi, sol_init_Q, \"Chen2020\");" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ai2020\n", + "Chen2020\n", + "Ecker2015\n", + "Marquis2019\n", + "Mohtat2020\n", + "NCA_Kim2011\n", + "OKane2022\n", + "ORegan2022\n", + "Prada2013\n", + "Ramadass2004\n" + ] + }, + { + "data": { + "image/png": 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", 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OxkeAxkiimb5/5yttHj0C7O0BPz+aTCSEaBfGgI4d+XkkAJQvDyxbBri5KXd/fX3A0pJfVq8GqlYFjhwBTp4E2rTJu7wJIYToDj0hHzw6Ohr379/H/fv3AQCBgYG4f/8+3qUUQpo8eTL69Okjix86dCjevHmDCRMm4NmzZ1i7di327t2LsWPHCpE+IT8UEQEMHswnE11dgcePAR8fmkwkP0bjIyFZi4sDfv8duHkTsLYGzp0DSpcWOitCCMldu3bxyURjY2D5cuDBA+UnEzOqUAEYPZpfHzWKangTQgjJHYJOKN6+fRvVq1dH9erVAQCenp6oXr06pk+fDgAIDg6WvXkGgBIlSuDEiRM4d+4cqlatiiVLluB///sf3HL615WQPDZ1KhASAri4AMePA2XLCp0R0RQ0PhKSWWIi0KkT4O/Pu5eeOQNUqiR0VoQQkrsiI3nzFQCYNo1PBv5sCVpvb6BIEeD1a2Dx4p/PkRBCCBExxpjQSahTVFQUrKysEBkZCUtLS6HTIZooJka2xDAmNBTmKZ11o6OjYWZmJgu7fZs3XWGMb8dr1kyQbIkSaFxIQ88F+WlKjpGqkkiAHj2AffsAU1M+mdioUa5kTH6AxgWOngfy05QcH0eN4g1YypYFHj7kqxRzw65dQM+egIkJEBAAODvnznF1GY0LhBBdJugKRUK0lUQCDB3KJxN79aLJREII+RlSKS8fsW8fYGTEmwrQZCIhRBvdvQusWcOvr1mTe5OJANC9O9CkCd/yTBVRCCGE/CyNaspCSL5gbMz3LwMwtrTE8dTr6c741q0D7tzhXTGXLBEkS0IIEYYSY6QqGOPNrLZsAfT0+Aqbli1zLVtCCFGfH4yPUikwbBj/t3t3Xn87N4lEvEFLtWr8g5lTp3iDK0IIISQnaMszIbksOBgoVw6IigLWruUnhiR/o3EhDT0XJL+ZMQOYOZNf37oVcHcXMhvdROMCR88DyWsbNwJDhvAasc+e8ZqHecHLi3/gXbo0bxiYm6sgdQ2NC4QQXUZbngnJZZ6efDKxdm2+RY8QQkjOLFuWNpm4ahVNJhJCtNfnz8CkSfz6nDl5N5kI8AYtDg7Aq1fUoIUQQkjO0YQiIapKSuLLZLZuRVJsLLZu3YqtW7ciKSkJ584Bu3fzbXnr1wP6+kInSwghaqZgjFTF5s1pXU7nzAFGjMj9VAkhRK0UjI8TJwLfvvHtyB4eeZuGhUXaROLcucDbt3n7eIQQQrQTbXkmRFXZdOgLD49GvXpmePmSd+dbsULIJIkqaFxIQ88F+Wm50OV5715eP4wxYMIEYP58XvuLCIPGBY6eB/LTshkfz56NRsuWfHy8dg345Ze8T4UxoGlT4OJF4I8/gAMH8v4xtRGNC4QQXUYrFAnJJUuXAi9f8i0ks2cLnQ0hhGimkyeBXr34m90hQ2gykRCi/VI7Lg8apJ7JRCCtQYu+PnDwIHD2rHoelxBCiPagCUVCcknq1pHlywH6gJIQQlR38SLQqROQnAz07MkbW9FkIiFE2z15AhQqBPj4qPdxK1UCRo7k10eOBBIT1fv4hBBCNJuB0AkIJSYmBvpZFLjT19eHiYmJXFx29PT0YGpqmqPY2NhYZLfbXCQSQSwW5yg2Li4OUqk02zzSbzdTJTY+Ph4SiSRXYsViMUQp7xATEhKQnJycK7GmpqbQ0+Nz5ImJiQrrdakSa2JiIvtdSUxMRFJMDFJ/2vSveWJiDFq0MEGXLjw2KSkJiQrOzIyNjWFgYKBybHJyMhISErKNNTIygqGhocqxEokE8fHx2cYaGhrCyMhI5VipVIq4uLhciTUwMIBxSitCxhhiY2NzJVbRz0IIUY/bt4F27YD4eP7v1q28Hi0h+Q2dQ9I5ZG6fQwIx8PExQaFC6j+HHD8e+Ocf4MULYPFiI0yZQueQdA5JCCFKYjomMjKSAcj20qZNG7l4sVicbWzjxo3lYm1sbLKNrVWrllysk5NTtrEVKlSQi61QoUK2sU5OTnKxtWrVyjbWxsZGLrZx48bZxorFYrnYNm3aKHze0uvcubPC2OjoaFmsu7u7wtiwsDBZrIeHh8LYwMBAWayXl5fC2MePH8tivb29FcbevHlTFrtw4UIm5jvxGAOYOEPs339fkMWuXr1a4XGPHz8ui/X19VUYu3fvXlns3r17Fcb6+vrKYo8fP64wdvXq1bLYCxcuKIxduHChLPbmzZsKY729vWWxjx8/Vhjr5eUliw0MDFQY6+HhIYsNCwtTGOvu7i6LjY6OVhjbvn17BoBFRkYyXZc6RtJzQXIsOlo2RkaHhmY59mf0+DFj1tb8bk2bMhYXp8Z8yQ/RuMDROWQaOofkcvMc0s/vgixWqHNIIyNf9v49j6VzSI7OIQkhJHv02T8huahoUaEzIIQQzfLmDdCiBfD1K1CnDnDkCJBukRchhOiE/LAiOzERGDdO6CwIIYRoCp3t8vzp06csO3HRdpWsY2m7SrrtKhERMEvpyufR5w3WbS8JAHj3LhRFihSSxdKWZ83ZrhITEwM7Ozvq0AfqVkhygQpdnj9+BBo1AgIDeS2vixcBa2u1Z0x+QFvGhRkzZmDmzJlyt7m4uODZs2dK3Z/OIekcUtVYReeQFZ3e4Olbfg4ZGhqKQoWEPYd8+BBo0MAIjBni/HmgcWM6h1Qmls4hCSG6TGdrKJqZmWV6Y5NdnCrHVFb6E7jcjE1/wpmbsSYqLBdRJdbY2Fj2Bzs3Y42MjGQnGLkea20N7N2L12+ATZMcAOzFtGmAg4O1XE0lQ0ND2YnWj6gSa2BgIDsxzM1YfX19pX+HVYnV09PLk1iRSJRrsYrevBBCVGRsDOzdy69aWmJv6vUM43d4ONCyJZ9MLFWKdxilyUSS1ypWrIjz58/Lvlb2b2R6dA5J55A5jk05hzxwAHi+xwEFCuzF8uWAtbXw55D16gEeHsCaNbxBy4MHBjAzo3NIOockhJDs6eyEImJigCwKakNfX36vlYJPjKGnB6Q/oVIlNjaWV1DJikgEpD8BVCU2Lg5Q8Ikx0v9BVCU2Ph5Q9AdTlVixOK1tZ0ICb+eZG7Gmpmn7RRITAQWfGKsUa2KS9ruSEitxa4O+TQAjSPBnlzaYNTElP5EoLTYpSXG7PGNjIPWkTpXY5GT+WNkxMgJSTyxViZVI+GuXHUNDHq9qrFTKf9dyI9bAgD8XAP8/oeATY5ViqaA2IbnHwADo0oVfBdAl5Xp6UVFAq1bA06e8VMT584CDg5rzJDrJwMAA9vb2P3cQOoekc8ifOIcMLN8Ggw8DxpBg47I26NIZ+eYccvZ0I+zda4iAAGDVsmR4DqdzSDqHJIQQBYQq3igUWWHxdEWR5S4ZCmozsTjrOICxDAW1mY1N9rEZCmozJ6fsYzMU1GYVKmQfm6GgNqtVK/vYDAW1WePG2cdmKKjN2rTJPjbjr1Hnzopj0xfmd3dXHJuuoDbz8FAcm66gNvPyUhybrqA28/ZWHJuuoDZbuFBx7IULabGrVyuOTVdQm/n6Ko5NV1Cb7d2rODZdUxZ2/Lji2HQFtdmFC4pj0xXUZjdvKo5NV1CbPX6sODZdQW0WGKg4Nl1BbRYWpjg2XUHt9E0isrpEUkFtGWq+QPJabCxjv/6a9mcpIEDojMiPaMu44O3tzcRiMXNwcGAlSpRgPXv2ZG/fvs02Pj4+nkVGRsou79+/Z3QOmYLOITktPIfcvJlf7WRC55CMMTqHJIQQBfJB+V9CNFcygH0pFwWfexNCiO5ITgb27QP27UNyfDz27duHffv2ITk5GYmJQOfOwKVLgKUlcOYMUK6c0AkTXVG3bl1s3boVp0+fxrp16xAYGIhGjRrh+/fvWcb7+PjAyspKdnF0dFRzxkSb5ddzyL59gbp1gThaeEcIIeQHdLYpS2Q2BbVpu0o2sbRdRRY7tEcE1h/kBbWjXr2BVWleUDs6NBRmhQoJvl2FtjyrHhsVEwMrKqgNQHuaLxABZdOUJTIyGoMHm2HPHj4Enz0LNGwoZKJEWdo6LkRERMDJyQlLly7FgAEDMn0/ISFBrilFVFQUHB0d6RxS1Vg6hwQAxHxLRIOKEbgfzMfEmDdvYF4yf55D3rkD/FIrGUZIwOlTvHlWdrEA6BySziEJITpKd2sompnJn8AoilPlmMpSoUi2SrEqFMlWKVaFItkqxRobp/3Bzs1YI6O0E4xcjPW7bIQdB82wPuVrfYt0r7mZmXxNJUPDtBOtH1El1sAg7cQwN2P19ZX/HVYlVk8vb2JFotyLpYLahOS50aOBPXv4UHfwIE0mapIzZ4TOIG8UKFAAZcuWxatXr7L8fraNPOgcks4hcxA7Z6ERXgZnOG9Mfz0fnUPWrAkMGGKADRsM4DEeuHv3Bw9B55CEEKKTaMszIUpKSODd7wghhKhu61b+fu+ff3hDFqIZfH2Brl2FziJvREdH4/Xr13CgjkAkjz19CixeLHQWqpk7F7C2Bh4/BtauFTobQggh+ZHurlAkREULFwIvXgAlCgMIEzobQvJWTEwM9LPoYqqvrw+TdCtIYhRs09PT04NpulUsqsTGxsYiu4ocIpEI4nSrblSJjYuLg1TBNj2zdKsQVImNj4+HRMEqBVVixWIxRCnb9BISEpCsYJueKrGmpqbQS9mml5iYiCQF2/RUiTUxMZH9riQmJiIpJgapP638ax6DDRtM0Lkzj01KSkKigm16xsbGMEhZSaNKbHJystw21YyMjIxgmLLURpVYiUSCeAXb9AwNDWGUslJJlVipVIo4Bdv0VIk1MDCQrahjjCFWwTY9ZWKPHdPHwIFKrurSAF5eXmjXrh2cnJzw6dMneHt7Q19fHz169BA6NaLFGAOGD+e7jTu0BnBK6IyUU6gQ4OMDDBkCTJ8OdOsG/GyDdEIIIVpGyI4wQtCWToVEvV6+ZMzYOKVZnm9at7fo0FAGgAFg0ek7DxKNQuNCmtTnIrtLmwxdTMVicbaxjTN0MbWxsck2tlaGLqZOTk7ZxlbI0MW0QoUK2cY6ZehiWqtWrWxjbTJ0MW3cuHG2seIMXUzbtGmj8HlLr3Pnzgpj048l7u7uCmPD0nUx9fDwUBgbmK6LqZeXl8LYx+m6mHp7eyuMvZmui+nChQuZOF33S3GG2AvpupiuXr1a4XGPp+ti6uvrqzB2b7oupnv37lUY6+vrK4s9fvy4wtjV6bqYXrhwQWHswnRdTG/evKkw1jtdF9PHjx8rjPVK18U0MDBQYaxHui6mYWFhCmPd03UxjY6OziKmMQPiGMCYo+MxBmj+GNmtWzfm4ODAjIyMWNGiRVm3bt3Yq1evlL4//a0gOfH333xINDVlLPCxZp1DJienNf/u00fobPInGhcIIbqMVigS8gOMASNG8C3Prq68Qyn6CZ0VIYQQkleqATgKwATAYVSrth3v3wubUW7YvXu30CkQHRMZCYwbx6//9Rfg7CxoOirT1wdWrwZ++QXYvh0YPBho0EDorAghhOQXutvlmTpxESXt28frRxkZAY8eAWWLZt3BNDo6Wm5bI9EcNC6kSX0uPr16BUsLi0zf1zcygkmBArKvY8Ky3/+vZ2AAU2vrHMXGhoeDZbPdWKSnB7GNTY5i475+hVTBtmCzwoVzFBsfEQGJgi25qsSKbWwgStlunBAVhWQFW2dViTW1toZeyrbgxOhoJCnYDqtKrEmBAtBP2ZKbGB2NpM+fYZbSudQGj/EFlQAAoW/eoFDRorLYpNhYJEZHZ3tcY0tLGKRsr1clNjk+HglRUdnGGpmbwzBlG7wqsZLERMRHRGQbaygWwyjlb4MqsdLkZMR9/ZorsQYmJjBOGcOYVIrY8HCVY1++1keLdgUR/kUPDesn4vCuCEgQDzsnJ50fI+lvBVHVqFHAqlVA2bLAw4eAcbJmnkMOHAhs3gxUrQrcvq18vz9dQOMCIUSnCb1EUt1oWTpRRWQkYw4OfKuHbIdaYiJjvr6M+fqyxJgY5uvry3x9fVliYqKAmZKfQeNCGtlzkW7bqtwlw5ZnJhZnHQcwlmHLM7OxyT42w5Zn5uSUfWyGLc+sQoXsYzNseZbt3crqkmHLM2vcOPvYDFueWZs22cdm/FPbubPi2PRb39zdFcem2/LMPDwUx6bb8sy8vBTHptvyzLy9Fcem2/LMFi6U+14iwHxTLokAY+m2PLPVqxUfN92WZ+brqzg23ZZntnev4th0W57Z8eOKY9NteWYXLiiOTbflmd28qTg23ZZn9vix4th0W55ZYKDi2HRbnllYmOLYdFueWTTfhvkBRZgTAhnAWA3cZpGwYAxgke3bMxoj6W8FUc2dO4zp6fH/bufOpdyooeeQYWGMFSyYeVgkNC4QQnQbfb5EiALTpwPBwUDp0sCkSSk3GhoCffvyqwD6plwnhBBd9/Yt4JTua0MAfQXKhajmKwrCDWfwFs4ogxc4hdawxHeh0yJEI0mlgIcH/7d7d14yB4DGnkPa2gJz5vDmMlOn8p07trZCZ0UIIURotOWZkGzcuwfUqsVPBs+cAVq2FDojkldoXEgjey4+fcr6udDXB9J1eYaCzs3Q0wPSdW5WKTY2lq+jyopIBKTr3KxSbFwc/0+dnfRbzlSJjY8HFHRuVilWLOZ5A7x4q4Jt1yrFmpry5xkAEhMBBZ2bVYo1MQH09fHgAdCicSJiIpPQvBkvF2GcsUFwSiwAfkwFW79hbJy2r06V2ORk/lxkx8iIv6lXNVYi4a9ddgwNebyqsVIp/13LjVgDg7QnnTH+f0OJ2JhoBtfmUly/qY8iDlJcOR8PZ6e0/1NRMTGwsrNT2xhpna78gTJEIhHu3r0LJyenHwf/BPpbQZS1aROvN2hhATx7BhQpInRGP08iAWrX5ufH/fvzLdCExgVCiG4TfIXimjVrsGjRIoSEhKBq1apYtWoV6tSpk2388uXLsW7dOrx79w42Njbo3LkzfHx8YJL+DS4hP0kiAYYO5e/dunbNMJmYnMxnGAEkN2+OM35+AAA3NzcYUFEZkssEGyPNzOQnwRTFqXJMZaWfBMzN2PSTlrkZq8rzq0qssXEWs3K5EGtklDZJlQuxL17wcfJzpBF+ra+HvR5nYHwtizEydTIR4BNlqZN1P6JKrIGB8gW+VInV11f+d1iVWD29vIkViZSKTUwEOnUW4fpNfRQsCJw9pwfnChn+TymaAM8DERERWL58OaysrH4YyxiDh4cHJGrOkZDshIen7WqZOTPDZKIGn0OmNmhp0ADYsgUYNIg3ayGEEKLDhNxvvXv3bmZkZMS2bNnCnjx5wgYNGsQKFCjAQkNDs4zfuXMnMzY2Zjt37mSBgYHszJkzzMHBgY0dO1bpx6Q6F0QZ69bxOjEWFox9/Jjhmym1phjAokNDGQAGgEWnr3tGNEp+HRdojCSa4O1bxhwd+bBYvTpjER9pjNQUycmMdeuWVhb02rWs49Q9LohEomzHuayYm5uz169f52FGHI2PRBkDBvD/U1WqMJaUlOGbWnAOmVrat0YNPoboOhoXCCG6TE+YaUxu6dKlGDRoEPr164cKFSpg/fr1EIvF2LJlS5bxV69eRYMGDdCzZ084OzujZcuW6NGjB27evKnmzIk2Cw1N+2R5zhzt2KZCNBONkSS/CwsDWrQA3r8HXFyA06cBJRaVkXyAMd6Bds8evvjz4MH8s9pIKpWicLrO6D/y/ft3lEzpLE6IkK5eTdsKvG6ddnZDXrCAj/N37wL/+5/Q2RBCCBGSYBOKiYmJuHPnDlxlVYoBPT09uLq64tq1a1nep379+rhz547szfGbN29w8uRJtGnTRi05E93g5QVERgI1avCC2oQIgcZIkt9FRABubny7c/HiwLlzgApzQERgM2YAa9fyndE7dvDXkhCSc8nJwLBh/Hr//kD9+sLmk1fs7IBZs/j1KVOAL1+EzYcQQohwBPvcLDw8HBKJBHZ2dnK329nZ4dmzZ1nep2fPnggPD0fDhg3BGENycjKGDh2KKVOmZPs4CQkJSEhXcD0qKip3fgCilS5cAP7+m7/BWr9eOz9ZJplduCB0BpnRGEnys5gYoG1b4P59/uby/HnA0VHorIiyVq1KmxBYswbo1k3YfH7k5cuXuHDhAsLCwiDN0Chp+vTpAmVFiLw1a4CHD4GCBYH584XOJm95ePCVmA8f8knFDRuEzogQQogQBN3yrCp/f3/MmzcPa9euxd27d3Hw4EGcOHECs2fPzvY+Pj4+sLKykl0c6R0PyUZCQtony0OH8k52RPvFxwOenkJnkTtojCTqkJAA/PEHcOUKUKAAcPYsUKaM0FkRZe3cybc6A3xSMfXvXn61adMmlC9fHtOnT8f+/ftx6NAh2eXw4cNCp0cIAODTJ2DaNH59/nzA1lbYfPKagQFv0ALwjta3bwubDyGEEGEItv7KxsYG+vr6CA0Nlbs9NDQU9vb2Wd5n2rRp6N27NwYOHAgAqFy5MmJiYjB48GD89ddf0NPLPD86efJkeKabLYiKiqI3zCRLixcDz5/zLXvz5gmdDVGXBQuAN2+EziIzGiNJfpScDPTqxScRzcyAU6eAKlWEzooo6+RJoG9ffn3kSGDqVEHTUcqcOXMwd+5cTJw4UehUCMmWlxfw/TtQpw6Q8idY6zVqBPz5J9/ZM3w4cO0ab0RPCCFEdwg27BsZGaFmzZrw8/OT3SaVSuHn54d69epleZ/Y2NhMb4j19fUBAIyxLO9jbGwMS0tLuQshGb15wxuwAMDSpXzVDdF+L18CPj5CZ5E1GiNJfiOVAoMGAQcOAEZGwOHD+aeJB/mxK1eAzp3TJoWXL+flPfK7b9++oUuXLkKnQUi2/PyAXbv4ZNq6dbo1qbZwIWBhAdy8Cfj6Cp0NIYQQdRO0Qpynpyfc3d1Rq1Yt1KlTB8uXL0dMTAz69esHAOjTpw+KFi0Kn5R3/O3atcPSpUtRvXp11K1bF69evcK0adPQrl072ZtmQlTFGDBiBN/62qwZ0LPnD+5gZCTb52Fkbo7VqdeNjPI4U5KbGOOfqCck8Nf933+FzigzGiNJfsEYLw2wdSugrw/s3g2k6xckj8bIfOfhQ17zMi4O+O03/sZfUyY9unTpgrNnz2Lo0KFCp0JIJomJ/FwC4HUFa9T4wR20bHx0cOANnsaNAyZNAjp2BKythc6KEEKIugg6oditWzd8/vwZ06dPR0hICKpVq4bTp0/LmhC8e/dObrXN1KlTIRKJMHXqVHz8+BG2trZo164d5s6dK9SPQLTAwYN8256hIS+o/cMVG4aGsrNHQwDDU88kiUbZu5d3pTU25tvdf/gmQAA0RpL8YtYsYMUKfn3LFv6mMVs0RuYrb97wDs4REUCDBnzsMzQUOivFVq5cKbteunRpTJs2DdevX0flypVhmCH5UakFIQkRwJIlaeVyFJQrTqOF4+PIkfzvwpMnvI7kmjVCZ0QIIURdRCy7fXBaKioqClZWVoiMjKStfQTfvwPlywMfP/JaUkqdDBKNFxnJX/fgYP7J+tixNC6kojGSZLRiBTBmDL++ciV/80g0Q0gI0LAh8Po1ULkycPEi70CrKnWPCyVKlFAqTiQS4Y0ai+DS+EjSCwoCKlTgK3937OD1BHWVvz/QtClf+Xz7NlC9utAZqQ+NC4QQXSboCkVChObtzScTS5YEpkxR8k4SCXD5Mr9avz4uX70KAGjUqBFtK9UQ06bxycQyZYCJE/mWJUJIZr6+aZOJs2YpOZlIY2S+EBEBtGrFJxNLlADOnMnZZKIQAgMDhU6BkB8aM4ZPJjZuzOuSKkVLx8cmTYDu3Xk5jOHDgf/+05yyCoQQQnKOVigSnXX/PlCrFj+3O3WKv/FSSkwMYG7Or4aGwjxl+2l0dDTMzMzyJlmSa+7eBWrX5g0mzp3jdeBoXEhDzwVJdeAA0LUr/7/i6clLAyjVxIPGSMHFxvJtzv/9B9jZ8YYspUrl/Hg0LnD0PJBUx48D7doBBgbAgwd8paJStHh8/PgRKFcOiI7mH0aldpTXdjQuEEJ0GX12RHSSVAoMG8YnEzt3VmEykWg0iQQYOpS//t27K2gqQYiOO3sW6NGD/18ZMECFyUQiuKQkoFs3PploZcVXJv7MZGJ+deTIEWzfvl3oNIgOio1NW63t6anCZKKWK1oUmD6dX58wga+SJoQQot1oQpHopP/9D7h+nX9IvHy50NkQddmwAbh1C7C0BJYuFTobQvKnq1d505WkJKBLF/7/hiYTNUPqBPDx44CJCXDsGFC1qtBZ5Y2JEyfKOt4Tok4+Prx+YrFivIQKSTN6NF+l+PkzLytECCFEu9GEItE5YWHApEn8+uzZ/BNVov1CQtLqZM6dCzg4CJsPIfnRgwdAmzZ8BU6rVsDffwMaXNZLpzAGjBvHm0Po6wP79gGNGgmdVd559uwZJBKJ0GkQHfPiBbBwIb++YoVs9zJJYWQErFrFr69eDTx8KGw+hBBC8hZNKBKdM2EC8O0bUK0aMGKE0NkQdfHy4t2da9bk290JIfJevABatuT/Txo25DUUjYyEzoooy8cnbcW9ry/Qtq2g6eS5iIgIrF69Wug0iA5hjJ83JibyD1w6dhQ6o/zJ1ZWXE5JKeYMW3arWTwghuoUmFIlOuXgR2LaNb99bv54X0ybaz88P2Lkz7XWnFVeEyHv3jr8JDAsDqlfnW2bFYqGzIsrauBH46y9+fdkyoHdvYfPJS35+fujZsyccHBzgTXsqiRrt38+buRkb81V4VAoie0uX8r8h//3Hz78IIYRoJ5pQJDojMTFtZdrgwUDdusLmQ9QjIQHw8ODXPTx4Z29CSJqwMKBFC+D9e8DFBTh9mjfzIJph/37ebArgZR3GjBE0nTzx/v17zJo1CyVKlEDLli0hEolw6NAhhISE5PiY8+fPh0gkwhhtfMJIrvv+HRg7ll+fNAkoXVrYfPI7R0dg6lR+ffx4ICpK2HwIIYTkDVqfRXTG4sVAQABQuDDfGpZjhoayAjqGYjEWpl43NMyFLEluW7SIb+W0t+e1EwkhaSIiADc3/n+keHG++qZw4Z88KI2RanPuHNCzJ99SOGQIMGeO0BnlnqSkJBw+fBj/+9//cPnyZbRq1QqLFi1Cjx498Ndff6HCT7TWvXXrFjZs2IAqVarkYsZEm82YAXz8CJQsCUyc+BMH0qHx0dOTl194+ZI/f9QMjxBCtI+IMd2qbBEVFQUrKytERkbC0tJS6HSImrx5A1SsCMTH8yYDvXoJnRFRh9ev+euekAD88w/Qo0fWcTQupKHnQnfExvKaiVeuAHZ2wOXLQJkyQmdFlHXzJtCsGRATw+uV7d6dd+UchBgXChcujHLlyuHPP/9Ely5dULBgQQB84uXBgwc5nlCMjo5GjRo1sHbtWsyZMwfVqlXD8tTikz9A46NuevgQqFEDkEiAU6d4/USinDNn+POlrw/cvw9UqiR0RrmPxgVCiC6jLc9E6zHGi0LHxwPNm/PVHET7pRZPT0jgr3v37kJnREj+kZgI/PEHn0wsUAA4e5YmEzVJQADQujWfTHR11c5u3MnJyRCJRBCJRNDPxR9u+PDh+O233+Dq6vrD2ISEBERFRcldiG6RSnm5HImET9zTZKJq3NyADh348zdiBDVoIYQQbUMTikTr7d/Pa4IZGQFr1+ZCEW2JBLh1C7h1C5LERNy6dQu3bt2CRCLJlXxJ7jhwIJdfd0K0hEQC/PknXzkiFgMnTwK5uvOTxsg89fYtr3n59StQpw5w6BBvEqFtPn36hMGDB2PXrl2wt7dHp06dcOjQIYh+YjDfvXs37t69Cx8l6574+PjAyspKdnF0dMzxYxPN5OsLXL0KmJvzhkc/TQfHx2XLABMT3hhxzx6hsyGEEJKbaEKRaLXISGD0aH598mSgbNlcOGh8PH8XV6cO4iMiUKdOHdSpUwfx8fG5cHCSG75/T3vdJ03KpdedEC2QWmtv3z4+2X74MFCvXi4/CI2ReebzZ75N/eNHoFw54MQJPtGhjUxMTNCrVy/8+++/ePToEcqXL49Ro0YhOTkZc+fOxblz51SahHn//j1Gjx6NnTt3wsTERKn7TJ48GZGRkbLL+/fvc/rjEA0UHg5MmMCvz5wJFCuWCwfVwfHR2Zk3jAKAceP4ORohhBDtQBOKRKtNmwYEB/OtfJMmCZ0NUZfp04FPn4BSpfhEMiGETyZ6eQGbNwN6esCuXXylG9EM37/zbc4vXvAOqmfPAjY2QmelHqVKlcKcOXPw9u1bnDhxAgkJCWjbti3s7OyUPsadO3cQFhaGGjVqwMDAAAYGBrh48SJWrlwJAwODLCcnjY2NYWlpKXchumPyZL4SuHJlYORIobPRbOPH83OyT5+A2bOFzoYQQkhuoS7PRGvdvg2sXs2vr13Lt1sQ7XfvHrByJb++Zg297oSkmjs3rcvm5s28hiLRDPHxvA7ZnTt8EvHsWT6pqGv09PTQunVrtG7dGp8/f8aOHTuUvm/z5s3x6NEjudv69euHcuXKYeLEiblap5FovqtXgf/9j19ft443ZyY5Z2ICrFgBtG3Lt0D36weULy90VoQQQn4WTSgSrSSR8G19jPGOzkrUXidaILV4ulQKdO3Ki4ETQoBVq/iKbQBYvhzo21fIbIgqkpN5M7F//+Xbm0+d4tuddZ2trS08PT2VjrewsEClDC1mzczMUKhQoUy3E92WnMzPJQCgf3+gQQNh89EWv/3GJxSPH+crPs+do/rWhBCi6WjLM9FKK1cCd+/y7qVLlgidDVGXTZuAGzcAC4tcKp5OiBbYvh0YNYpfnzEjrb4oyf8YA4YO5Y1XjIyAI0eAWrWEzirvWVtbIzw8XOn44sWL4+3bt3mYEdElq1YBDx8C1tbAggVCZ6NdVqzgTaT8/HjTREIIIZqNVigSrfPiRVrx54ULARVKLBENFhaWVidzzhygSBFh8yEkPzh8mK+wAYAxY3h9UaI5pkyRr3nZrJnQGalHREQETp06BSsrK6Xiv3z5kqMuuf7+/irfh2i3N2+AqVP59fnzdadOqbqULAlMnAjMmgV4evK6sNraWIoQQnQBTSgSrSKR8K188fG82cDAgUJnRNTFywuIiACqVwc8PITOhhDh+fkB3brxcbFfP75am7aXaY4lS/iEBgBs3Kh7NS/d3d2FToHoGKkUGDAAiI0FmjTh10numzSJr5wPCgLmzeMXQgghmokmFIlWWbYMuHaNb3n93//y6M2zoSHg7c2visXwTr1OFbsFc+ECsGMHf73XrwcMaGQjOu76daB9eyAxkU9EbdzIV7mpBY2RP23rVv4hCcC3XOraxIZUKhU6BaKD1q8H/P0BsThtZXCuo/ERpqa8lm+HDsDixXwhQNmyAidFCCEkR0SMMSZ0EuoUFRUFKysrREZGwtLSUuh0SC4KCOCr0xIS+GSirr0B01WJiUDVqsCzZ7yI+tq1qh+DxoU09FxovocPgcaN+Yrdli2Bo0d5zSqiGY4cATp14itLvbyARYuEzojGhVT0PGivwECgcmUgJobXUBwxQuiMtBtjvEnLqVO8gd6pU5q7gp7GBUKILqOmLEQrJCfzTzgTEoBWrdJqhhHtt3gxn0wsXJi2zRDy6hWfRIyIAOrXBw4epMlETXLxovw29YULhc6IEO2XutU5Jgb49Vcqm6IOIhFv0GJkBJw5w+v9EkII0Tw0oUi0wpIlwM2bgJUV7/Sbp59ySqXAkyfAkyeQJifjyZMnePLkCW3REkBgIDB7Nr++dCnv6k2IrvrwAXB1BUJDgWrVgBMnADMzARKhMTJH7t0D2rXjH4y1b8+3qWvqih1CNMnGjbx0iqkpsGVLHpeHoPFRpkyZtNIOY8bw2pWEEEI0C1UaIxrvyZO0zqXLlwPFiuXxA8bFAZUq8auhoaiUcj06Ohpmgrx7102M8S1J8fFA06ZAz55CZ0SIcD5/5o2o3r7lb9JOnxZwgp3GSJW9eMG3/X3/zrer795NtWAJUYegIGD8eH59/nygVKk8fkAaH+VMmcJrYL97B/j4pH1ITAghRDPQCkWi0ZKTAXd3Xkfvt9/4daIbDh0CTp7k9c3XrqWVPER3RUbyyahnzwBHR+D8ecDOTuisiLI+fuTb1D9/BmrU4DUvTUyEzooQ7ccYMHAgEB0NNGpEdROFYGbGGyoCvMTDq1fC5kMIIUQ1NKFINNqUKcCdO3wlDm0P0x3fvwOjR/PrEycC5coJmw8hQomNBdq25dtlbW2Bc+eA4sWFzooo6+tXPhmcurL01CmAavrLa9y4MbZv3464uDihUyFaZv16wM+Pb3XOs67O5If++IOvsE9M5FufCSGEaA7BN9SsWbMGixYtQkhICKpWrYpVq1ahTp062cZHRETgr7/+wsGDB/H161c4OTlh+fLlaNOmjRqzJvnBoUNp3S83bQKKFBE2H6I+M2bwenElS/JJZW1GYyTJTmIi7wb833+8fuzZs4CLi9BZEWXFxPCV9U+e8L9fZ8/y5lJEXvXq1eHl5YWRI0eia9euGDBgAH755Reh0yIa7u5dYOxYfn3ePD6hT4QhEvHO2pUr89q/x47xerJENYwxJCcnQyKRCJ0KIUTD6evrw8DAACIlVmsJOqG4Z88eeHp6Yv369ahbty6WL18ONzc3PH/+HIWzOKtOTExEixYtULhwYezfvx9FixbF27dvUYA6Meicly95V2cA8PQEOncWNB2iRg8f8s6AALBmDV9ZoK1ojCTZkUiAXr14rUSxmG//r1ZN6KyIslIng69fBwoW5JOJzs5CZ5U/LV++HIsXL8bRo0exbds2/PrrryhdujT69++P3r17w4729xMVffvGzxsTEoDffwdGjRI6I+Liws/nFyzgO1BcXbX7/C63JSYmIjg4GLHU2YYQkkvEYjEcHBxgZGSkME7EGGNqyimTunXronbt2li9ejUAQCqVwtHRESNHjsSkSZMyxa9fvx6LFi3Cs2fPYGhomKPHjIqKgpWVFSIjI2FJ+4o0Umws8MsvwKNHQMOGwL//8jp6ahMTA5ib86uhoTBPeTOjqwW11Ukq5a/5tWv8zcC+fblz3Pw6LtAYSbIilQKDBvFupEZGwPHjfLtYvkFjpEKpk8F79vDJYD8//jctP8tP40JYWBg2btyIuXPnQiKRoE2bNhg1ahSaNWuW54+dn54HkjNSKdChA18FV6IEL5tTsKAaE6DxMVvR0byEzcePwMyZaQ0X8zuhxwWpVIqXL19CX18ftra2MDIyUmpVESGEZIUxhsTERHz+/BkSiQRlypSBnoKaIIKtUExMTMSdO3cwefJk2W16enpwdXXFtWvXsrzP0aNHUa9ePQwfPhxHjhyBra0tevbsiYkTJ0JfX19dqRMBMQYMHconEwsX5m/I1DqZSAS1eTOfTDQ35x29tRmNkSQrjAHjxvHJRD09YNeufDaZSBRijK+GSv3bdfBg/p9MzE9u3rwJX19f7N69G4ULF0bfvn3x8eNHtG3bFh4eHli8eLHQKZJ8btEiPplobAzs36/myUSikLk5sGQJ0L077/jcuzef9CWKJSYmyj5wFovFQqdDCNECpqamMDQ0xNu3b5GYmAgTBd0CBZtQDA8Ph0QiybRVxc7ODs+ePcvyPm/evMG///6LXr164eTJk3j16hU8PDyQlJQEb2/vLO+TkJCAhIQE2ddRUVG590MQtdu4Edixg7+R3rNHoLqJhoaAlxe/KhbDK/U6zWzmqc+feQMWAJg9GyhaVNh88hqNkSQrs2alTaZv2cKL2ec7NEZma8aMtK70O3bwhixEsbCwMOzYsQO+vr54+fIl2rVrh127dsHNzU22Cqdv375o1aoVTSgShfz90+our1zJu6qrHY2PCnXtys/1//2X17g8fFjojDSHohVEhBCiKmXHFMGbsqhCKpWicOHC2LhxI/T19VGzZk18/PgRixYtyvbNso+PD2bOnKnmTEleuHUrrc6Njw/QpIlAiRgZybrBGAFYlNoZhuSpCRN43aNq1YARI4TOJn+iMVK7LV3KJ6QAXkfU3V3QdLJHY2SWVq3iE8IAr//arZuw+WiKYsWKoVSpUujfvz/69u0LW1vbTDFVqlRB7dq1BciOaIrgYL7yTSoF+vThZSMEQeOjQqkNWqpWBY4c4Z3vW7cWOitCCCHZEeyjDBsbG+jr6yM0NFTu9tDQUNjb22d5HwcHB5QtW1Zu61758uUREhKCxMTELO8zefJkREZGyi7v37/PvR+CqE1kJK+Zl5jIa9+MHy90RkSdLl0Ctm7lJ5rr1gEGGvVRSM7QGEnS27SJb3UGgDlzqImAptm5M+01mz0bGDZM2Hw0iZ+fHwICAjB+/PgsJxMBwNLSEhcuXFBzZkRTJCfzycTQUKBSpbRVwiR/qlAhbbwcNYo3zyFEFSKRCIeVXN46Y8YMVPtBV7smTZpgzJgxP52XOgUFBUEkEuH+/ftCp/JT/P39IRKJEBERIXQqJBuCTSgaGRmhZs2a8PPzk90mlUrh5+eHevXqZXmfBg0a4NWrV5BKpbLbXrx4obD7jLGxMSwtLeUuRPOMGwe8eweULJk2sSQYqRQICgKCgiBNTkZQUBCCgoLkfi9J7klMTHvzPXiw7tQbozGSpNq9GxgyhF+fMCFty16+RWOknJMngb59+fVRo4C//hI0HY3j7e2d5RuJqKgotTRiIZpv3jz+waSFBa+bKGjvExofleLtDdjbA69e8bqKRPt8/vwZw4YNQ/HixWFsbAx7e3u4ubnhypUrshhVJgbTCw4ORutcXNp68OBBzJ49O9eOl1Nbt25FgQIFlIp1dHREcHAwKlWqlLdJEZ0naLEFT09PbNq0Cdu2bUNAQACGDRuGmJgY9OvXDwDQp08fuYYEw4YNw9evXzF69Gi8ePECJ06cwLx58zB8+HChfgSiBmfO8GYcIhGfTLSyEjihuDheJbpECcR9/YoSJUqgRIkSiIuLEzgx7bR0KfD0KWBry7e66xIaI8nx47wwfWpDqvnzNWBlDY2RMleu8NX1ycm8s/OyZRrw+uUzFy9ezHKFdXx8PC5fvixARkSTPHzIVwUDfIeDi4uw+dD4qBxLSyC1JOqcOXxRAdEunTp1wr1797Bt2za8ePECR48eRZMmTfDly5efPra9vT2MjY1zIUvO2toaFhYWuXa8vJaYmAh9fX3Y29vDQBe2dRFBCTqh2K1bNyxevBjTp09HtWrVcP/+fZw+fVrWhODdu3cIDg6WxTs6OuLMmTO4desWqlSpglGjRmH06NGYNGmSUD8CyWNRUWl1bkaOBBo1EjYfol5BQWk1x5Ys0b1ujDRG6rYLF9Imo/78k9fdo8kozfHoEdC2LZ8/aNMG8PXlDcWIch4+fIiHDx+CMYanT5/Kvn748CHu3buHzZs3o6i2d+ciPyU5Gejfn//bvj3Qs6fQGRFV9OwJ/PorH0NTS34Q7RAREYHLly9jwYIFaNq0KZycnFCnTh1MnjwZv//+OwDA2dkZANCxY0eIRCLZ1wCwbt06lCpVCkZGRnBxccGOHTvkjp9xZeOHDx/Qo0cPWFtbw8zMDLVq1cKNGzfk7rNjxw44OzvDysoK3bt3x/fv32Xfy7jl+du3b+jTpw8KFiwIsViM1q1b4+XLl7Lvp64kPH78OFxcXCAWi9G5c2fExsZi27ZtcHZ2RsGCBTFq1ChIJBLZ/RISEuDl5YWiRYvCzMwMdevWhb+/PwC+9bdfv36IjIyESCSCSCTCjJTC2s7Ozpg9ezb69OkDS0tLDB48OMstz0+ePEHbtm1haWkJCwsLNGrUCK9fv872dXr8+DFat24Nc3Nz2NnZoXfv3ggPD5d7XkaNGoUJEybA2toa9vb2spwAoGfPnuiWoWB0UlISbGxssH37dgB895WPjw9KlCgBU1NTVK1aFfv37882JwA4cOAAKlasCGNjYzg7O2NJhmXMqc9Hjx49YGZmhqJFi2LNmjVyMRERERg4cCBsbW1haWmJZs2a4cGDBwofl2SD6ZjIyEgGgEVGRgqdClHC4MGMAYyVLMlYdLTQ2aSIjuZJASw6NJQBYABYdL5JUDtIpYy1bcuf6iZN+Nd5hcaFNPRc5A/XrzNmZsZ//9u3ZywpSeiMVEBjJHv9mjEHB/40NGjAWEyM0Bn9HCHGBZFIxPT09Jienh4TiUSZLmKxmG3evFlt+TBG46Om8fHh/wcLFGDs0yehs0lB46NKHjxgTF+fP2XnzgmdTdaEHhfi4uLY06dPWVxcnOw2qZT/qqn7ouy5elJSEjM3N2djxoxh8fHxWcaEhYUxAMzX15cFBwezsLAwxhhjBw8eZIaGhmzNmjXs+fPnbMmSJUxfX5/9+++/svsCYIcOHWKMMfb9+3dWsmRJ1qhRI3b58mX28uVLtmfPHnb16lXGGGPe3t7M3Nyc/fHHH+zRo0fs0qVLzN7enk2ZMkV2vMaNG7PRo0fLvv79999Z+fLl2aVLl9j9+/eZm5sbK126NEtMTGSMMebr68sMDQ1ZixYt2N27d9nFixdZoUKFWMuWLVnXrl3ZkydP2LFjx5iRkRHbvXu37LgDBw5k9evXZ5cuXWKvXr1iixYtYsbGxuzFixcsISGBLV++nFlaWrLg4GAWHBzMvn//zhhjzMnJiVlaWrLFixezV69esVevXrHAwEAGgN27d48xxtiHDx+YtbU1++OPP9itW7fY8+fP2ZYtW9izZ8+yfP6/ffvGbG1t2eTJk1lAQAC7e/cua9GiBWvatKnc82JpaclmzJjBXrx4wbZt28ZEIhE7e/YsY4yx48ePM1NTU1mejDF27NgxZmpqyqKiohhjjM2ZM4eVK1eOnT59mr1+/Zr5+voyY2Nj5u/vzxhj7MKFCwwA+/btG2OMsdu3bzM9PT02a9Ys9vz5c+br68tMTU2Zr6+v7DGcnJyYhYUF8/HxYc+fP2crV65k+vr6srwYY8zV1ZW1a9eO3bp1i7148YKNGzeOFSpUiH358iXL50MXZTW2ZIUmFEm+de6c7JyLXbggdDbp0MmgWhw6xJ9mQ0PGnj7N28eicSENPRfCu3+fvwEGGHN1ZewHf8fzHx0fI4ODGStVij8FlSsz9vWr0Bn9PCHGhaCgIBYYGMhEIhG7desWCwoKkl0+ffrEkpOT1ZZLKhofNcfTp4wZGfH/h1u3Cp1NOjo+PubEqFH8KXNxYSwhQehsMhN6XMjqTX+6XzO1XlT5Nd6/fz8rWLAgMzExYfXr12eTJ09mDx48kItJPzGYqn79+mzQoEFyt3Xp0oW1adMmy/tt2LCBWVhYZDtR5O3tzcRisWyCizHGxo8fz+rWrSv7Ov2E4osXLxgAduXKFdn3w8PDmampKdu7dy9jjE8oAmCvXr2SxQwZMoSJxWK5yTU3Nzc2ZMgQxhhjb9++Zfr6+uzjx49y+TVv3pxNnjxZdlwrK6tMP4OTkxPr0KGD3G0ZJxQnT57MSpQoIZv0/JHZs2ezli1byt32/v17BoA9f/5c9rw0bNhQLqZ27dps4sSJjDE+cWxjY8O2b98u+36PHj1Yt27dGGOMxcfHM7FYLJvcTTVgwADWo0cPxljmCcWePXuyFi1ayMWPHz+eVahQQe75aNWqlVxMt27dWOvWrRljjF2+fJlZWlpmmswuVaoU27Bhww+eGd2h7IQibb4h+dL378DAgfz68OFAkyaCpkPULDo6rcPf+PFA+fLC5kOIujx/DrRsCUREAPXrA4cPAyYmQmdFlBURAbRqBbx+zcuknTmje6UacouTkxOcnZ0hlUpRq1YtODk5yS4ODg5y3ewJSU8i4VudExOB1q2BPn2Ezoj8jJkzgcKF+d/H5cuFzobklk6dOuHTp084evQoWrVqBX9/f9SoUQNbt25VeL+AgAA0aNBA7rYGDRogICAgy/j79++jevXqsLa2zvaYzs7OcjUSHRwcEBYWlu3jGxgYoG7durLbChUqBBcXF7kcxGIxSpUqJfvazs4Ozs7OMDc3l7st9XEePXoEiUSCsmXLwtzcXHa5ePGiwm3JqWrVqqXw+/fv30ejRo1gaGj4w2MBwIMHD3DhwgW5XMqVKwcAcvlUqVJF7n7pnzsDAwN07doVO3fuBADExMTgyJEj6NWrFwDg1atXiI2NRYsWLeQeZ/v27dn+zNm9/i9fvpTbPp6xgWW9evVkr8+DBw8QHR2NQoUKyT1uYGCgUs81kUdVOkm+NHEi8PYt4OzMmxAQ3TJzJvD+PX/9qSMq0RVv3wKurkBYGFC9OnDihMDdSIlKYmOBdu2ABw8AOzvg7FnAwUHorDTT0aNH0bp1axgaGuLo0aMKY1PrbSlj3bp1WLduHYKCggAAFStWxPTp03O1GygR3ooVwPXrvLHHxo1Ue1bTFSgALFwI9O3L62r37AkUKyZ0VvmbWMw/nBficVVhYmKCFi1aoEWLFpg2bRoGDhwIb29v9O3bN9dyMjU1/WFMxkk2kUj0053XszqmoseJjo6Gvr4+7ty5k+kDs/STkNkx+8EJozLPQ3rR0dFo164dFixYkOl7DulObn703PXq1QuNGzdGWFgYzp07B1NTU7Rq1Ur2GABw4sSJTDWRc7OpTkbR0dFwcHCQ1adMT9ku2iQNTSiSfOfff3knPoB3d1ZiDCVa5NEj3gkV4E0oVD05IUQTBQcDzZsDHz4A5crxlW10TqM5kpKAbt2A//4DrKz461e6tNBZaa4OHTogJCQEhQsXRocOHbKNE4lEcisSfqRYsWKYP38+ypQpA8YYtm3bhvbt2+PevXuoWLFiLmROhPbyZdoHkYsX08STtujdm08OX70KeHkBu3cLnVH+JhJp5geSFSpUkGumYmhomGmML1++PK5cuQJ3d3fZbVeuXEGFChWyPGaVKlXwv//9D1+/flW4SlFZ5cuXR3JyMm7cuIH69esDAL58+YLnz59nm4MyqlevDolEgrCwMDTKpgupkZGRSn/z0qtSpQq2bduGpKQkpVYp1qhRAwcOHICzs/NPdYquX78+HB0dsWfPHpw6dQpdunSRPX6FChVgbGyMd+/eoXHjxkodL/X1T+/KlSsoW7as3ETs9evX5WKuX7+O8ilb3mrUqIGQkBAYGBjINfshOUMTiiRfiY1N2+o8dCjQrJmw+WTJwADw8OBXTUzgkXr9JwZbwkmlwLBhfLvSH3/wzqiEaLsvX4AWLdK2yZ4/D9jaCp3VT9CxMVIqBQYMAI4f59vTjx0DqlYVOivNln51w8+uEkmvXbt2cl/PnTsX69atw/Xr12lCUQtIpfwcMj6er/ZOPZ/MV3RsfMwtenr8Q+aaNYE9e4AhQ4CmTYXOiuTUly9f0KVLF/Tv3x9VqlSBhYUFbt++jYULF6J9+/ayOGdnZ/j5+aFBgwYwNjZGwYIFMX78eHTt2hXVq1eHq6srjh07hoMHD+L8+fNZPlaPHj0wb948dOjQAT4+PnBwcMC9e/dQpEiRTNtilVGmTBm0b98egwYNwoYNG2BhYYFJkyahaNGicrmrqmzZsujVqxf69OmDJUuWoHr16vj8+TP8/PxQpUoV/Pbbb3B2dkZ0dDT8/PxQtWpViMViiJVceTFixAisWrUK3bt3x+TJk2FlZYXr16+jTp06cHFxyRQ/fPhwbNq0CT169JB1cX716hV2796N//3vfyqVHenZsyfWr1+PFy9e4MKFC7LbLSws4OXlhbFjx0IqlaJhw4aIjIzElStXYGlpKTdpnGrcuHGoXbs2Zs+ejW7duuHatWtYvXo11q5dKxd35coVLFy4EB06dMC5c+ewb98+nDhxAgDg6uqKevXqoUOHDli4cCHKli2LT58+4cSJE+jYseMPt4+TDNRT0jH/ELpwLlFs8mRe1LdYMcbS1cYlOuJ//+Ovv7k5Y+/eqe9xaVxIQ8+FekVGMlarFv+9d3Dg3YGJ5pBKGRszhr9++vqMHT0qdEZ5QxvHheTkZLZr1y5mZGTEnjx5otR9tPF50CYbNvD/i2ZmjAUGCp0NyQvDh/PXuEIFxpTsLZHnhB4XlG2ckJ/Ex8ezSZMmsRo1ajArKysmFouZi4sLmzp1KouNjZXFHT16lJUuXZoZGBgwJycn2e1r165lJUuWZIaGhqxs2bJyTT8Yy9zMJSgoiHXq1IlZWloysVjMatWqxW7cuMEY401ZqlatKnf/ZcuWyT1exi7PX79+Zb1792ZWVlbM1NSUubm5sRcvXsi+n1XzlKwex93dnbVv3172dWJiIps+fTpzdnZmhoaGzMHBgXXs2JE9fPhQFjN06FBWqFAhBoB5e3szxngTkmXLlskdO2NTFsYYe/DgAWvZsiUTi8XMwsKCNWrUiL1WcOL54sUL1rFjR1agQAFmamrKypUrx8aMGcOkKe28Mz4vjDHWvn175u7uLnfb06dPGQDm5OQku28qqVTKli9fzlxcXJihoSGztbVlbm5u7OLFi4yxzE1ZGOMNfSpUqMAMDQ1Z8eLF2aJFi+SO6eTkxGbOnMm6dOnCxGIxs7e3ZytWrJCLiYqKYiNHjmRFihRhhoaGzNHRkfXq1Yu9U+cb0HxO2bFFxBhjAs1lCiIqKgpWVlaIjIyEpaWl0OmQdJ48AapVA5KTgUOHAAW7nIgWCg8HXFyAr1+BJUsAT0/1PTaNC2nouVCf2FjeMODSJaBQIf7vT+yWIQKYNy9te+W2bdrb/EHIcWHUqFEoXbo0RqV26kqxevVqvHr1CstV7NLw6NEj1KtXD/Hx8TA3N8c///yDNtksh09ISEBCQoLs66ioKDg6OtL4mA+FhfFziIgI3rhj9GihMyJ54ds3oGxZfs6o7nPF7Ah93hQfH4/AwECUKFECJtTFjRA4OztjzJgxGDNmjNCpaDRlxxbq8kzyBcb4VtfkZOD33/P5ZCJjwOfPwOfPYFIpPn/+jM+fP0PH5uZz3cSJfDKxSpW0Ds+EaKvERKBzZz6JaGnJa+5pzWSijoyRGzakTSYuW6a9k4lCO3DgQKaOjgCvy7R//36Vj+fi4oL79+/jxo0bGDZsGNzd3fH06dMsY318fGBlZSW7ODo6qvx4RD28vPhkYvXqwPDhQmejgI6Mj3mlYMG0Zo0zZvD6w4QQQoRDE4okX9i6Fbh8mTfgWLlS6Gx+IDYWKFwYKFwYseHhKFy4MAoXLozY2FihM9NY//0HbNnCr69bx0sMEaKtJBLgzz+BU6cAU1PezblmTaGzykU6MEbu28c/BAOAKVMA+hA873z58gVWVlaZbre0tER4eLjKxzMyMkLp0qVRs2ZN+Pj4oGrVqlixYkWWsZMnT0ZkZKTs8v79e5Ufj+S9CxeAHTt4I4r16/P5OYQOjI95rV8/oG5d4Pt3YPx4obMhhBDdRhOKRHDh4WknBDNmAE5OgqZD1CwpKe2N+cCBQErDNEK0EmO8mPy+fYChIS/v0LCh0FkRVZw7B/TqxV/LwYOBOXOEzki7lS5dGqdPn850+6lTp1CyZMmfPr5UKpXb1pyesbExLC0t5S4kf0lISDuHGDYMqFNH2HxI3tPTA1av5hPIO3fylf6EEJIqKCiItjurkcoTiuk782S0YcOGn0qG6KaJE3mX08qVaZWHLlq+HHj8GLCxSdvGosnc3d1xic5uSRYYA8aNAzZv5m+Idu0C3NyEzoqo4uZNoGNH/kFI587A2rX8TS3JO56enpgwYQK8vb1x8eJFXLx4EdOnT8ekSZMwduxYlY41efJkXLp0CUFBQXj06BEmT54Mf39/9OrVK4+yJ3lt8WLg+XPAzg6YO1fobIi61KrFP9ABgBEjeMkkQggh6qfyhGKrVq0wfvx4JCUlyW4LDw9Hu3btMGnSpFxNjmi/y5fTtrquX89X7BDd8fYtX5UKAIsW8cYUmi4yMhKurq4oU6YM5s2bh48fPwqdEsknZs3itfYAPqnYqZOw+RDVBATwJjoxMYCrK/D334C+vtBZab/+/ftjyZIl2Lx5M5o2bYqmTZvi77//xrp16zBo0CCVjhUWFoY+ffrAxcUFzZs3x61bt3DmzBm0aNEij7Ineen167QVwkuXAgUKCJoOUbO5cwFra+DRI2DNGqGzIYQQ3ZSjFYqHDh1C7dq18fTpU5w4cQKVKlVCVFQU7t+/nwcpEm2VmAgMHcqvDxpEW1110ejRvJxQo0aAu7vQ2eSOw4cP4+PHjxg2bBj27NkDZ2dntG7dGvv375f7IIboluXL0ybPV6wA+vYVMBmisnfvgJYteeOoOnX4VnVjY6Gz0h3Dhg3Dhw8fEBoaiqioKLx58wZ9ctAFZ/PmzQgKCkJCQgLCwsJw/vx5mkzUUIzxlWnx8UDz5kCPHkJnRNStUCFg3jx+ffp0ICRE2HwIIUQXqTyhWL9+fdy/fx+VKlVCjRo10LFjR4wdOxb+/v5wouJ3RAVLlwJPnwK2ttqx1ZWo5tgx4MgRXjx93Trt2jZoa2sLT09PPHjwADdu3EDp0qXRu3dvFClSBGPHjsXLly+FTpGoka8vkLozc9Ys6mKuaT5/5pOJHz4A5cvzJjrm5kJnpZtsbW1hTk8+AXDgAHD6NGBkRKUHdNnAgbypWVQUQBvlCCFE/XLUlOXFixe4ffs2ihUrBgMDAzx//py6kxGVBAbyN9YAr39jbS1sPkS9YmL4ygIA8PQEKlYUNp+8EhwcjHPnzuHcuXPQ19dHmzZt8OjRI1SoUAHLUve+Eq22fz9/wwPw+olTpwqbD1HN9+98m/Pz50Dx4sDZs7zeK1Gv/fv3o2vXrvjll19Qo0YNuQvRPVFRfIcDAEyeDJQtK2w+RDj6+mnbnbdtA65eFTYfQgjRNSpPKM6fPx/16tVDixYt8PjxY9y8eRP37t1DlSpVcO3atbzIkWiZ1G0qcXFA06ZA795CZ6QiAwO+P9fdHQYmJnB3d4e7uzsMDAyEzkxjzJ7NtxA6OfFtKtokKSkJBw4cQNu2beHk5IR9+/ZhzJgx+PTpE7Zt24bz589j7969mJU6o0601pkzQM+egFQKDBjA64TqxCoaLRkj4+OBDh2AO3f4JOLZs0CxYkJnpXtWrlyJfv36wc7ODvfu3UOdOnVQqFAhvHnzBq1btxY6PSKA6dOBT5+A0qU1cFWaloyP+UnduvxvLAAMHw5IJMLmQwghOoWpyN7enp08eVLutsTERObl5cWMjIxUPZzaRUZGMgAsMjJS6FR01v79jAGMGRkx9uyZ0NkQdXv8mDEDA/47cOSI0NlwuTkuFCpUiBUsWJB5eHiwe/fuZRnz7ds35uzs/NOPlRdojMwdly8zZmrKf8+7dmUsOVnojIgqkpMZ++MP/vqZmzN2+7bQGQlLyHHBxcWF/fPPP4wxxszNzdnr168ZY4xNmzaNDR8+XK250PgovDt3GNPT4/83z54VOhuSX4SFMVagAP+9WL1avY8t9LgQFxfHnj59yuLi4gR5fKH5+voyKyurXDteYGAgA5DtOby6j6MMb29vVrhwYQaAHTp0KM8fT0gXLlxgANi3b9+Uvk/jxo3Z6NGjFcY4OTmxZcuW5TivjK+3snn+6HHV+XuUkbJji8orFB89epTpE2FDQ0MsWrQIZ8+e/Zm5TaIDvn9Pqx82cSLg4iJsPkS9GAOGDQOSk4H27YHffxc6o9y3bNkyfPr0CWvWrEG1atWyjClQoAACAwPVmxhRm7t3gd9+46uwW7cGduygbsCahDHeMOzgQV6f7cgRXqOLCOPdu3eon9K1zdTUFN+/fwcA9O7dG7t27RIyNaJmEgn/vymVAt27A9RPh6SytU3r+D11KhAWJmw+RDkhISEYOXIkSpYsCWNjYzg6OqJdu3bw8/MTOjWV9O3bFx06dJC7zdHREcHBwahUqVKePnZAQABmzpyJDRs2IDg4mFbu5xP169dHcHAwrKysAABbt25FgQIFVD6Oun6PfobKE4o2CooHNW7c+KeSIdovdZtKqVK87o1GYowXAYyJAZNKERMTg5iYGDDGhM4s39u2Dbh8GRCLgZUrhc4mb/Tu3RsmJiZCp0EE8uwZ0KoVr/HVqBGvoWhkJHRWaqbhY+SUKcD//gfo6QG7dgHNmgmdkW6zt7fH169fAQDFixfH9evXAQCBgYEa8ztFcsfGjcCtW4ClJW/sp5E0fHzMz4YOBapVAyIiNPg9hg4JCgpCzZo18e+//2LRokV49OgRTp8+jaZNm2L48OFCp/fT9PX1YW9vn+flDF6/fg0AaN++Pezt7WFsbJwpJjExMU9zIJkZGRnB3t4eop+sdaSu36OfkaOmLITkxL17aZNIa9YApqbC5pNjsbG8xae5OWLDw2Fubg5zc3NqTPQDX74A48fz6zNm8AYHhGiTt2/5ipnPn4EaNXgnc7FY6KwEoMFj5OLFwPz5/PrGjcAffwibDwGaNWuGo0ePAgD69euHsWPHokWLFujWrRs6duwocHZEXUJC0iaJ5s0DHByEzSfHNHh8zO/SN2jZsgW4cUPYfIhiHh4eEIlEuHnzJjp16oSyZcuiYsWK8PT0lH1wBABLly5F5cqVYWZmBkdHR3h4eCA6OlrhsY8dO4batWvDxMQENjY2cn8rRCIRDh8+LBdfoEABbN26NctjSSQSDBgwACVKlICpqSlcXFywYsUK2fdnzJiBbdu24ciRIxCJRBCJRPD390dQUBBEIhHu378vi7148SLq1KkDY2NjODg4YNKkSUhOTpZ9v0mTJhg1ahQmTJgAa2tr2NvbY8aMGdn+nDNmzEC7du0AAHp6erLJq9QVk3PnzkWRIkXgkrIl8NGjR2jWrBlMTU1RqFAhDB48WO65TL3fvHnzYGdnhwIFCmDWrFlITk7G+PHjYW1tjWLFisHX11fh8y+VSrFw4UKULl0axsbGKF68OObOnQuA/00fkdqZM8Xnz59hZGQkW5makJCAiRMnwtHREcbGxihdujQ2b96c5WN9+fIFPXr0QNGiRSEWi1G5cuUsdy8kJydjxIgRsLKygo2NDaZNm6bwg5yIiAgMHDgQtra2sLS0RLNmzfDgwQOFP3d6/v7+EIlEiIiIgL+/P/r164fIyEjZ70j61zU2Nhb9+/eHhYUFihcvjo0bN8q+l/H3KKuVjocPH5abuJwxYwaqVauGLVu2oHjx4jA3N4eHhwckEgkWLlwIe3t7FC5cWPaa/Kz8O9VJtIpEAgwZwrepdOsGuLkJnRFRt0mTgPBwoFIlYMwYobMhJHeFhgKursCHD0D58rwhS8ouB6IhfH3TPvSYPz+tyD8R1saNGyGVSgEAw4cPR6FChXD16lX8/vvvGDJkiMDZEXUZNw6IjARq1eIr0QjJSv36vOfNtm28QcuNG7pdciQmJibb7+nr68vtqFEUq6enB9N0K0GyijUzM1M6r69fv+L06dOYO3dulvdLP2Gip6eHlStXokSJEnjz5g08PDwwYcIErF27NstjnzhxAh07dsRff/2F7du3IzExESdPnlQ6t4ykUimKFSuGffv2yf7+DB48GA4ODujatSu8vLwQEBCAqKgo2USbtbU1Pn36JHecjx8/ok2bNujbty+2b9+OZ8+eYdCgQTAxMZGbXNq2bRs8PT1x48YNXLt2DX379kWDBg3QIosaD15eXnB2dka/fv0QHBws9z0/Pz9YWlri3LlzAPhr5ubmhnr16uHWrVsICwvDwIEDMWLECLnJ1H///RfFihXDpUuXcOXKFQwYMABXr17Fr7/+ihs3bmDPnj0YMmQIWrRogWLZdKqbPHkyNm3ahGXLlqFhw4YIDg7Gs2fPAED2mEuWLJGtpvz7779RtGhRNEvZEtKnTx9cu3YNK1euRNWqVREYGIjw8PAsHys+Ph41a9bExIkTYWlpiRMnTqB3794oVaoU6tSpI/e8DhgwADdv3sTt27cxePBgFC9eHIMGDcryuF26dIGpqSlOnToFKysrbNiwAc2bN8eLFy9gbW2d5X2yU79+fSxfvhzTp0/H8+fPAQDm5uay7y9ZsgSzZ8/GlClTsH//fgwbNgyNGzeWTQTnxOvXr3Hq1CmcPn0ar1+/RufOnfHmzRuULVsWFy9exNWrV9G/f3+4urqibt26OX4cAKo3ZdF0QhfO1VVr1/JCyZaWjH36JHQ2Pyk6mv8wAIsODWUAGAAWHR0tdGb51pUrsqeMXb4sdDaZ0biQhp4L1X39yliVKvz329mZsQ8fhM5IYBo4Rh46lNboYdw4xqRSoTPKX2hc4Oh5EMa5c/z/pp6eFjRI0sDxUdOEhDBmZcWf5vXr8/7xhB4XFDVOSP39yurSpk0buVixWJxtbOPGjeVibWxsMsWo4saNGwwAO3jwoMo/7759+1ihQoVkX2dsylKvXj3Wq1evbO+PLBqXWFlZMV9fX8aYck0whg8fzjp16iT72t3dnbVv314uJuNxpkyZwlxcXJg03QnGmjVrmLm5OZNIJIwx3jykYcOGcsepXbs2mzhxYra5HDp0KNPz7+7uzuzs7FhCQoLsto0bN7KCBQvKjTUnTpxgenp6LCQkRHY/JycnWT6M8cZojRo1kn2dnJzMzMzM2K5du7LMJyoqihkbG7NNmzZl+f24uDhWsGBBtmfPHtltVapUYTNmzGCMMfb8+XMGgJ07dy7L+yvT7OS3335j48aNk33duHFjVr58ebnnfuLEiax8+fKyr9M3R7l8+TKztLRk8fHxcsctVaoU27BhQ5aP+aOmLNk1D3JycmJ//vmn7GupVMoKFy7M1q1bl+VxszpOxt8Bb29vJhaLWVRUlOw2Nzc35uzsnOm19fHxyfLnYSwPm7IQoiqt2aZCciQ5mTdiAYD+/YGGDYXNh5DcFB3NG7A8fAjY2wPnzgFFiwqdFVGFvz9v8CCVAv36AYsWAT9Z8obksm/fvmHx4sUYMGAABgwYgCVLlsjqKhLtFh8PeHjw68OHU4Mk8mN2dsCsWfz6lCm85A7JX5gKNUPPnz+P5s2bo2jRorCwsEDv3r3x5cuXbMsE3L9/H82bN8+tVAEAa9asQc2aNWFrawtzc3Ns3LgR7969U+kYAQEBqFevntzW1AYNGiA6OhofPnyQ3ValShW5+zk4OCAsB12GKleuDKN0RbwDAgJQtWpVuRWhDRo0gFQqla2aA4CKFStCTy9tisjOzg6VK1eWfa2vr49ChQplm1NAQAASEhKyfQ1MTEzQu3dvbNmyBQBw9+5dPH78GH379gXAXz99fX2le3NIJBLMnj0blStXhrW1NczNzXHmzJlMr88vv/wi99zXq1cPL1++hEQiyXTMBw8eIDo6GoUKFZKVpTA3N0dgYKCsZmVuSv+ai0Qi2Nvb5+g1T8/Z2RkWFhayr+3s7FChQoVMr+3PPg5AW56JGowdS9tUdNnKlXyyxdoaWLBA6GwIyT0JCUDHjsC1a0DBgsDZs0Dp0kJnRVRx7x7vNp+QwDvPb9xIk4n5zaVLl/D777/D0tIStWrVAgCsXLkSs2bNwrFjx/Drr78KnCHJSwsXAi9f8g+jZ88WOhuiKTw8eHOtR4/4pOKGDUJnJAxFtQb1M+wFVzSxkH4SAuB13X5GmTJlIBKJZNtgsxMUFIS2bdti2LBhmDt3LqytrfHff/9hwIABSExMhDiLQtWmPyjSLxKJMk1oJiUlZRu/e/dueHl5YcmSJahXrx4sLCywaNEi3MijIp2GhoaZ8k0t+6EKVbag/+jxVcnpR88/wLc9V6tWDR8+fICvry+aNWsGJycnpe+f3qJFi7BixQosX75cVmtzzJgxP9WIJjo6Gg4ODvD398/0vZx0av4RVZ5fPT09pX5/f/Z1VAWtUCR56vRpYPdu3i1zwwbdrmOii96/5529Af6mQEGTeEI0SnIy0KMHcP48r69/+jSQ7gNcogFevOD1fL9/Bxo35n+r8nETPZ01fPhwdO3aFYGBgTh48CAOHjyIN2/eoHv37lrRCZRk78ULvrMFAJYvp7q0RHkGBsDq1fz6pk3A7dvC5iMUMzOzbC/p6yf+KDbjJE9WMaqwtraGm5sb1qxZk2U9xoiICADAnTt3IJVKsWTJEvzyyy8oW7ZsptqEGVWpUkXW3CMrtra2cvUGX758qbAp0pUrV1C/fn14eHigevXqKF26dKZVakZGRlmudEuvfPnyuHbtmtxk0JUrV2BhYZFtLcLcVL58eTx48EDu+b5y5Qr09PR+qlZfRmXKlIGpqanC16By5cqoVasWNm3ahH/++Qf9+/eX+55UKsXFixeVerwrV66gffv2+PPPP1G1alWULFkSL168yBSXcQL4+vXrKFOmTKaJdQCoUaMGQkJCYGBggNKlS8tdbHL4ZlaZ3xFl2Nra4vv373KvY/rGP0KgCUWSZ2Jj07a6jh7Nu54S3TJmDBATAzRowLcSEqINpFK+ff/QIcDYGDhyBEhX95logI8fgZYteUfu6tX5a5jhvRXJJ169eoVx48bJnfTr6+vD09MTr169EjAzkpcY4+eQCQl84r9LF6EzIprm11+BXr3479KIEfxvN8k/1qxZA4lEgjp16uDAgQN4+fIlAgICsHLlStSrVw8AULp0aSQlJWHVqlV48+YNduzYgfXr1ys8rre3N3bt2gVvb28EBATg0aNHWJBui1SzZs2wevVq3Lt3D7dv38bQoUMzrdxKr0yZMrh9+zbOnDmDFy9eYNq0abh165ZcjLOzMx4+fIjnz58jPDw8yxVjHh4eeP/+PUaOHIlnz57hyJEj8Pb2hqenZ6YVoHmhV69eMDExgbu7Ox4/fowLFy5g5MiR6N27N+zs7HLtcUxMTDBx4kRMmDAB27dvx+vXr3H9+vVMXZoHDhyI+fPngzEm14Xb2dkZ7u7u6N+/Pw4fPozAwED4+/tj7969WT5emTJlcO7cOVy9ehUBAQEYMmQIQkNDM8W9e/cOnp6eeP78OXbt2oVVq1Zh9OjRWR7T1dUV9erVQ4cOHXD27FkEBQXh6tWr+Ouvv3A7h59OODs7Izo6Gn5+fggPD1c4ia1I3bp1IRaLMWXKFLx+/Rr//PNPth3K1YUmFEmemTkTCAoCihdPq2WiFfT1gc6dgc6doW9khM6dO6Nz585ZfsKhy06cAA4e5E/XunV8lSohmo4xYNQoYMcO/ru9dy+Q0pSOpMrnY+TXr3wy8e1boEwZvrqUVj7lXzVq1EBAQECm21PrQRHt9PffwL//8on+tWu1qBRBPh8ftc2iRYCFBe/2nNKAl+QTJUuWxN27d9G0aVOMGzcOlSpVQosWLeDn54d169YBAKpWrYqlS5diwYIFqFSpEnbu3AkfHx+Fx23SpAn27duHo0ePolq1amjWrBlu3rwp+/6SJUvg6OiIRo0aoWfPnvDy8spy63SqIUOG4I8//kC3bt1Qt25dfPnyBR6phV1TDBo0CC4uLqhVqxZsbW1x5cqVTMcpWrQoTp48iZs3b6Jq1aoYOnQoBgwYgKlTp6rytOWYWCzGmTNn8PXrV9SuXRudO3dG8+bNsTp1KW8umjZtGsaNG4fp06ejfPny6NatW6Yt9T169ICBgQF69OiRabXsunXr0LlzZ3h4eKBcuXIYNGhQtl3Ip06diho1asDNzQ1NmjSBvb09OnTokCmuT58+iIuLQ506dTB8+HCMHj0agwcPzvKYIpEIJ0+exK+//op+/fqhbNmy6N69O96+fZvjydf69etj6NCh6NatG2xtbbFw4cIcHcfa2hp///03Tp48icqVK2PXrl1yXcIFobBli5qsXr2aOTk5MWNjY1anTh1248YNpe63a9cuBiBTVyVFhO7EpSvu32dMX593WDt2TOhsiLrFxPButwBj48cLnc2P5edxQZ3jI2P5+7nID6ZM4b/XIhFjO3cKnQ1RVXQ0Y7/8wl/DIkUYCwwUOiPNIOS4sHv3bla8eHG2aNEidvnyZXb58mW2aNEi5uzszHbv3s0ePHggu+Q1Gh/VIzycMRsb/v9UQQNKQpSyZAn/XbKxYezLl9w/vtDjgrKdWAnJTwIDA5menh67c+eO0KmQbCg7tgheLWjPnj3w9PTE+vXrUbduXSxfvhxubm54/vw5ChcunO39goKC4OXlhUaNGqkxW6IMiQQYPJj/27kz0Lat0BkRdZs7l69OdXRMq6FIVEfjY/6ycGFaPa9164CePYXNh6gmMRHo1Am4fj2tiY6zs9BZkR/p0aMHAGDChAlZfi+1wL5IJMqV+kREeBMnAuHhQKVKwLhxQmdDNN3IkcCWLcCTJ8C0acCaNUJnRIjuSkpKwpcvXzB16lT88ssvqEE10TSe4JsQly5dikGDBqFfv36oUKEC1q9fD7FYLGslnhWJRIJevXph5syZKFmypBqzJcpYvx64eROwtARWrBA6G6JuAQF8iwnAOzybmwubjyaj8TH/WL+ev8kFeLfyIUOEzYeoRiIB+vQBzpwBxGJekqFiRaGzIsoIDAxUeHnz5o3sX6L5Ll0CUkttbdgAKChtRohSDA3TGrSsXw/cuydsPoTositXrsDBwQG3bt36YT1MohkEXaGYmJiIO3fuYPLkybLb9PT04OrqimvXrmV7v1mzZqFw4cIYMGAALl++rPAxEhISkJCQIPs6Kirq5xMn2fr4EUh9OX18gCJFhM0nT8TEyGbJYkJDYZ5SSyE6OlrlLmfaJrWIelIS0K4d0L690BlpLnWMjwCNkcr45x8gtVzOlClAFgulSHr5bIxMrXu5Zw9/Y3nwIJBS751oACcnJ6FTIGqSkJD2Yc3gwUD9+sLmkyfy2fioK5o0Abp3B3bvBoYPB/77j2p7EyKEJk2ayHW6JppP0AnF8PBwSCSSTMUt7ezs8OzZsyzv899//2Hz5s1Kt8f28fHBzJkzfzZVoqRRo4Dv34FffgGGDhU6G6JuO3YAFy8CpqZ8daLWFFEXgDrGR4DGyB85epSvbGOMvwmZM0fojIiqZs5Ma+qwfTvvGEs0z9OnT/Hu3TskJibK3f77778LlBHJbYsWAc+eAYULA/PnC50N0TaLFgHHjgHXrvHzVXd3oTMihBDNJ3gNRVV8//4dvXv3xqZNm2BjY6PUfSZPngxPT0/Z11FRUXB0dMyrFHXakSN85YeBAbBxI33yp2u+fQO8vPj16dOpNpm65WR8BGiMVOTff4GuXdO2y9IkueZZtYpPKAK8blb37sLmQ1T35s0bdOzYEY8ePZLVSwR4F0YAVDdRS7x6lfaBzbJlvM4pIbmpWDF+fjpxIt9p0L49UKCA0FkRQohmE3RC0cbGBvr6+ggNDZW7PTQ0FPb29pniX79+jaCgILRr1052m1QqBQAYGBjg+fPnKFWqlNx9jI2NYWxsnAfZk/S+fk1bkejlBVSuLGw+RP0mTwY+fwYqVADSzU+RHFLH+AjQGJmdGzeA33/nW/A6duQ1vehDEs2ycydfNQ/wScVhw4TNh+TM6NGjUaJECfj5+aFEiRK4efMmvnz5gnHjxmHx4sVCp0dyAWN8q3NCAtCiBZDSh4eQXDdmDODry1fCentTrXdCCPlZgr49MjIyQs2aNeHn5ye7TSqVws/PD/WyKHBUrlw5PHr0CPfv35ddfv/9dzRt2hT379+nVTUCGjMGCAkBypXjf6CJbrl+nRdPB3j3WyMjYfPRBjQ+CufhQ6B1a17qqkULYNcuvvKaaI6TJ4G+ffn1kSN5Z0+ima5du4ZZs2bBxsYGenp60NPTQ8OGDeHj44NRqTPGRKNt2sRXhJua8nMIWglO8oqREV+5DvBGLQ8fCpsPIYRoOsHfInl6esLd3R21atVCnTp1sHz5csTExKBfv34AgD59+qBo0aLw8fGBiYkJKlWqJHf/Ailr1TPeTtTn2DFei0RPj3/qZ2IidEZEnZKT01anursDv/4qbD7ahMZH9Xv5EmjZkm/hr18fOHQIoAWcmuXKFaBzZz429ewJLF9OExSaTCKRwMLCAgBfuf3p0ye4uLjAyckJz58/V/o4Pj4+OHjwIJ49ewZTU1PUr18fCxYsgIuLS16lTpTw/n1auZS5c4EsFtITkqtcXfnfiP37eW3kS5fobwQhhOSU4BOK3bp1w+fPnzF9+nSEhISgWrVqOH36tKwRwbt376BH+8zyrW/f0jryjRvHm7EQ3bJ6NfDgAa93tGiR0NloFxof1evDB/5GIzQUqFoVOHECoKabmuXhQ6BtWyAujq8y3bqVtqprukqVKuHBgwcoUaIE6tati4ULF8LIyAgbN25EyZIllT7OxYsXMXz4cNSuXRvJycmYMmUKWrZsiadPn1J3XYGkbnX+/p13XqcFp0Rdli7lK9n/+4+Xx/jzT6EzIoQQDcV0TGRkJAPAIiMjhU5FK7i7MwYwVrYsY7GxQmejJnFxjLVpw1ibNizu2zfWpk0b1qZNGxYXFyd0Zmr3/j1j5ub8d2DjRqGzyTkaF9Lo6nMRFsZYuXJp41loqNAZaTCBxsjXrxmzt+evYYMGjMXE5OnD6RQhx4XTp0+zAwcOMMYYe/nyJXNxcWEikYjZ2NgwPz+/HB83LCyMAWAXL15U+j66Oj7mla1b+f9XY2PGAgKEzkZN6Bwy35g3j//+2dsz9jP/pYUeF+Li4tjTp0919nfI19eXWVlZ5drxAgMDGQB27969fHEcZXh7e7PChQszAOzQoUN5/nh5zd3dnbVv3172dePGjdno0aMFyyc3qPP3IbcoO7YIvkKRaK4TJ4Bt2/g2AV9fXvtGJ5iY8B8egAmAEynXddHYsUB0NF9ZMGCA0NkQkjNRUXw127NngKMjcO4cULiw0FlpMAHGyJAQXu8yJIQ3BTt2DBCL8/xhiRq4ubnJrpcuXRrPnj3D169fUbBgQVmn55yIjIwEAFhbW/90jkR1wcG8/jYAzJjBa3DrBDqHzDc8Pfn7l5cveeOuJUuEzkj3hISEYO7cuThx4gQ+fvyIwoULo1q1ahgzZgyaN28udHpK69u3LyIiInD48GHZbY6OjggODoaNjU2ePnZAQABmzpyJQ4cO4ZdffkHBggXz9PFIzmT8ffD390fTpk3x7ds3WYkqTUUTiiRHIiKAwYP59bFjea0xoltOneL1Z/T1gfXraVsh0Uxxcbyb8507gK0tn0wsXlzorIgqIiKAVq2AN2+AEiWAM2d4CQaiHSIjIyGRSOQm/qytrfH161cYGBjA0tJS5WNKpVKMGTMGDRo0UFhjNiEhAQkJCbKvo6KiVH4skhljvOt6RARQs2ZaDUVC1MnYGFi5kn+guGIF0L8/ULGi0FnpjqCgIDRo0AAFChTAokWLULlyZSQlJeHMmTMYPnw4nj17JnSKP0VfXx/29vZ5/jivX78GALRv3z7bD9kSExNhRB0zBaWu3wch0BQAyRFPT+DTJ6BMGWD2bKGzIeoWFweMGMGvjx4NVKkibD6E5ERSEtC1K3DxImBpCZw+DVB/Bs0SGwu0a8fruNrZ8QlhBwehsyK5qXv37ti9e3em2/fu3Yvu3bvn6JjDhw/H48ePszxuej4+PrCyspJdHB0dc/R4RN6ePcCRI4ChIV8hZkDLG4hAWrUCOnQAJBJ+XsuY0BnpDg8PD4hEIty8eROdOnVC2bJlUbFiRXh6euL69euyuKVLl6Jy5cowMzODo6MjPDw8EB0drfDYx44dQ+3atWFiYgIbGxt07NhR9j2RSCS3khDgTQy3bt2a5bEkEgkGDBiAEiVKwNTUFC4uLlixYoXs+zNmzMC2bdtw5MgRiEQiiEQi+Pv7IygoCCKRCPfv35fFXrx4EXXq1IGxsTEcHBwwadIkJCcny77fpEkTjBo1ChMmTIC1tTXs7e0xY8aMbH/OGTNmoF27dgAAPT092YRi37590aFDB8ydOxdFihSRNR979OgRmjVrBlNTUxQqVAiDBw+Wey5T7zdv3jzY2dmhQIECmDVrFpKTkzF+/HhYW1ujWLFi8PX1Vfj8S6VSLFy4EKVLl4axsTGKFy+OuXPnyr7//v17dO3aFQUKFIC1tTXat2+PoKAghcf8EUWv+Y4dO1CrVi1YWFjA3t4ePXv2RFhYmOz7/v7+EIlEOHHiBKpUqQITExP88ssvePz4sSzmy5cv6NGjB4oWLQqxWIzKlStj165dSv/c6X8fgoKC0LRpUwCQ7bbo27cvtm/fjkKFCsl9kAkAHTp0QO/evX/q+clLNKFIVHb4MD8BFImALVt0cFtZTAzv1GBmhpiwMJiZmcHMzAwxMTFCZ6Y28+bx1UDFivGtSoRoGqkU6NsXOH6c70A7dgyoUUPorLSEmsbIpCSgWzdeVN/Kiq9MpA6x2ufGjRuyE+/0mjRpghs3bqh8vBEjRuD48eO4cOECihUrpjB28uTJiIyMlF3ev3+v8uMReZ8/AyNH8ut//cVLFOgUOofMd5Yt4+cB/v7A3r1CZ5PLYmKyv8THKx8bF/fjWBV8/foVp0+fxvDhw7NsipV+C6ienh5WrlyJJ0+eYNu2bfj3338xYcKEbI994sQJdOzYEW3atMG9e/fg5+eHOnXqqJRfelKpFMWKFcO+ffvw9OlTTJ8+HVOmTMHelF8WLy8vdO3aFa1atUJwcDCCg4NRP4utex8/fkSbNm1Qu3ZtPHjwAOvWrcPmzZsxZ84cubht27bBzMwMN27cwMKFCzFr1iycO3cuy9y8vLxkk3upj53Kz88Pz58/x7lz53D8+HHExMTAzc0NBQsWxK1bt7Bv3z6cP38eI1JXiKT4999/8enTJ1y6dAlLly6Ft7c32rZti4IFC+LGjRsYOnQohgwZgg8fPmT7nE2ePBnz58/HtGnT8PTpU/zzzz+yhpJJSUlwc3ODhYUFLl++jCtXrsDc3BytWrVCYmKiEq9IZj96zZOSkjB79mw8ePAAhw8fRlBQEPr27ZvpOOPHj8eSJUtw69Yt2Nraol27dkhKSgIAxMfHo2bNmjhx4gQeP36MwYMHo3fv3rh586ZSP3d6jo6OOHDgAADg+fPnCA4OxooVK9ClSxdIJBIcPXpUFhsWFoYTJ06gf//+OXpu1EJNNR3zDaEL52q6oCDGChTgRYzHjRM6G4FER/MnAGDRoaEMAAPAoqOjhc5MLQICGDMy4k9BSp18jUfjQhpdeC6kUsaGD+e/wwYGjB0/LnRGWkYNY6REwljv3vxhTEwYu3Qp1w5NsiDkuCAWi9nDhw8z3f7w4UNmamqq9HGkUikbPnw4K1KkCHvx4kWOctGF8TEvSaWM/f47/39bpQpjCQlCZyQAHT+HzK9mzuQvS9GijH3/rtp9hR4XFDZOSPldy/LSpo18rFicfWzjxvKxNjaZY1Rw48YNBoAdPHhQtR+WMbZv3z5WqFAh2dcZm7LUq1eP9erVK9v7I4vGJVZWVszX15cxplzzjOHDh7NOnTrJvs7YRCSr40yZMoW5uLgwqVQqi1mzZg0zNzdnEomEMcabjzRs2FDuOLVr12YTJ07MNpdDhw6xjFM67u7uzM7OjiWkG2Q3btzIChYsKDfWnDhxgunp6bGQkBDZ/ZycnGT5MMaYi4sLa9Sokezr5ORkZmZmxnbt2pVlPlFRUczY2Jht2rQpy+/v2LEj0/OQkJDATE1N2ZkzZ2R5qNKU5UeveUa3bt1iANj3lP/sFy5cYADY7t27ZTFfvnxhpqambM+ePdke57fffmPjUiZEfvRzZ/x9SH3Mb9++ycUNGzaMtW7dWvb1kiVLWMmSJeWeL3VRtikLrVAkSktKArp35zVv6tThq9SIbmEM8PAAEhOBNm2AdKvJCdEY3t7AmjV8lfX27cBvvwmdEVEFY7zm2o4dvIbrvn1Ao0ZCZ0XySp06dbBx48ZMt69fvx41a9ZU+jjDhw/H33//jX/++QcWFhYICQlBSEgI4jKuvCF5ZtUq4OhRwMgI2LqV/0tIfjBhAlCyJPDxI5VyUgemwt7y8+fPo3nz5ihatCgsLCzQu3dvfPnyBbGxsVnG379/P9cbuqxZswY1a9aEra0tzM3NsXHjRrx7906lYwQEBKBevXpydQ4bNGiA6OhoudV+VTLUkXJwcJDbnqusypUry9VNDAgIQNWqVeVWhDZo0ABSqRTPnz+X3VaxYkXopSuMb2dnh8rplpLr6+ujUKFC2eYUEBCAhISEbF+DBw8e4NWrV7CwsIC5uTnMzc1hbW2N+Ph4WT1IVf3oNb9z5w7atWuH4sWLw8LCAo0bNwaATK9hvXr1ZNetra3h4uKCgIAAAHzr++zZs1G5cmVYW1vD3NwcZ86ckR3jRz+3sgYNGoSzZ8/i48ePAICtW7eib9++P9WELq9R1RKitL/+Aq5f51vL9uyhE0Fd9M8/wIULvKP36tV8QoYQTbJsWdqbhTVrgB49hM2HqG7+fP46Arz8Rtu2wuZD8tacOXPg6uqKBw8eyE7U/fz8cOvWLZw9e1bp46xbtw4A3yqdnq+vb5Zbn0juunMnrfnK4sVA9erC5kNIeiYmvDFLu3bA0qVAv35a0nlcUa1BfX35rxVNWmXsvPiT9e7KlCkDkUj0w8YrQUFBaNu2LYYNG4a5c+fC2toa//33HwYMGIDExESIs6i7ZWpqqvCYIpEo04Rm6rbWrOzevRteXl5YsmQJ6tWrBwsLCyxatChHJTeUYWhomClfqVSq8nGy2kqe08dXJacfPf/R0dGoWbMmdu7cmel7tra2Kmb748dM3ert5uaGnTt3wtbWFu/evYObm5tKW6wXLVqEFStWYPny5bKanmPGjJEd40c/t7KqV6+OqlWrYvv27WjZsiWePHmCEydO5Mqx8wqtUCRKOXkSWLSIX/f1BZydBU2HCODbN96MBwCmTuXdVAnRJNu2pf0Oz5nDu4wSzbJxIzBlCr++bBmQj2tUk1zSoEEDXLt2DY6Ojti7dy+OHTuG0qVL4+HDh2ikwtJUxliWF5pMzHtRUbzeaVISb4CRoWQXIflC27Z8x0JyMq/zqRUNWlLqdWZ5MTFRPjbjZElWMSqwtraGm5sb1qxZk2X90IiICAB8ZZlUKsWSJUvwyy+/oGzZsvj06ZPCY1epUgV+fn7Zft/W1lau1uDLly+zXe0IAFeuXEH9+vXh4eGB6tWro3Tp0plW0hkZGUEikSjMq3z58rh27ZrcZOaVK1dgYWHxw3q+uaF8+fJ48OCB3PN95coV6OnpyZq25IYyZcrA1NQ029egRo0aePnyJQoXLozSpUvLXaysrHL0mIpe82fPnuHLly+YP38+GjVqhHLlymW7ujJ9M6Bv377hxYsXKF++PAD+XLVv3x5//vknqlatipIlS+LFixdK/9wZpa4ezer3ZuDAgdi6dSt8fX3h6uqa7xvC0YQi+aEPH4A+ffj1kSNpm6uu+usv/uFl+fJpqwwI0RSHDwMDBvDrnp5pk1JEc+zfDwwdyq9PmQKMGSNoOkSNqlWrhp07d+LJkye4ffs2tmzZgjJlygidFlECY8CQIcDr10Dx4ryZH+1uIPnVihWAsTFw/jxw8KDQ2Wi3NWvWQCKRoE6dOjhw4ABevnyJgIAArFy5Urb1tHTp0khKSsKqVavw5s0b7NixA+vXr1d4XG9vb+zatQve3t4ICAjAo0ePsGDBAtn3mzVrhtWrV+PevXu4ffs2hg4dmmkFXnplypTB7du3cebMGbx48QLTpk3DrVu35GKcnZ3x8OFDPH/+HOHh4VmuePTw8MD79+8xcuRIPHv2DEeOHIG3tzc8PT3lthjnlV69esHExATu7u54/PgxLly4gJEjR6J3795ZNg7JKRMTE0ycOBETJkzA9u3b8fr1a1y/fh2bN2+W5WFjY4P27dvj8uXLCAwMhL+/P0aNGqWw0Ysiil7z4sWLw8jISPY7dPToUczOpq7BrFmz4Ofnh8ePH6Nv376wsbFBhw4dAPDfg3PnzuHq1asICAjAkCFDEBoaqvTPnZGTkxNEIhGOHz+Oz58/y3Xb7tmzJz58+IBNmzbl72YsKWhCkSiUnAz07Al8+cI7oKauUiS65eZNIPXv99q1tN2daJZ//+WrYyQSvo1p8WJ6Q6tpzp3jf4tSJycyNEUkhORTmzcDu3fz3ZW7dgEFCwqdESHZK1WK11MEgLFjVW5eTFRQsmRJ3L17F02bNsW4ceNQqVIltGjRAn5+frISFVWrVsXSpUuxYMECVKpUCTt37oSPj4/C4zZp0gT79u3D0aNHUa1aNTRr1kyuE++SJUvg6OiIRo0aoWfPnvDy8spy63SqIUOG4I8//kC3bt1Qt25dfPnyBR4eHnIxgwYNgouLC2rVqgVbW1tcuXIl03GKFi2KkydP4ubNm6hatSqGDh2KAQMGYOrUqao8bTkmFotx5swZfP36FbVr10bnzp3RvHlzrF69Otcfa9q0aRg3bhymT5+O8uXLo1u3brJVgWKxGJcuXULx4sXxxx9/oHz58hgwYADi4+NhaWmZo8dT9Jrb2tpi69at2LdvHypUqID58+dj8eLFWR5n/vz5GD16NGrWrImQkBAcO3ZMtpJw6tSpqFGjBtzc3NCkSRPY29vLJhuV+bkzKlq0KGbOnIlJkybBzs5Ortu2lZUVOnXqBHNz80yPkR+JmCpVUbVAVFQUrKysEBkZmeNfWl0ydSowdy5gYQHcvQuULi10RvlAXBzQujW/evAgWv/xBwDg1KlTuVY/IT9JTuZNeO7d4ytVt20TOqPcR+NCGm17Lm7dApo142WEOnYE9u4FDKh6cN7K5THy5k3+GsbEAF268EmJjKWfSN7StnEhp+h5UM2TJ0Dt2nxI8PEBJk0SOqN8QMfOITVRbCxQoQLw9i1fDT93ruJ4oceF+Ph4BAYGokSJEjDJuI2ZEPJD/v7+aNq0Kb59+4YCBQoInQ4AoHnz5qhYsSJWrlwpWA7Kji30topk68iRtD+imzbRZKKMqSng78+vgg9C2mztWj6ZWLAgrVAlmuXJE/6+LToaaN6cNxWiyUQ1yMUxMiCAv4YxMUCLFmmdnQkh+VtsLNC1K58/a9kybdWXztOxc0hNJBbzGr1//MF3NPTtC1CFBUKIOnz79g3+/v7w9/fH2rVrhU5HKbTlmWTpyRPgzz/59REj+HZBons+feKrVAG+uqBwYWHzIURZr14Brq68XEOdOsChQ5nrj5P87e1bPon49St/DQ8e5LWtCCH5G2PAwIHA06eAvT3/IEANJcIIyTUdOgBubkBiIjBqlJY0aCGE5HvVq1dH3759sWDBglxtlpOXaK0GyeTrV6B9e76qp2lTYOlSoTMiQhk7Fvj+HfjlF2DQIKGzIUQ579/zFYkhIUDlysCpU7xsA9EcQUH8zdzHj7wR1MmTgLm50FkRQpSxaBEvTWBgwOsn0oeRRNOIRMDKlUClSsDp08DRo/y9ESFE+zRp0gT5pQpgUFCQ0CmojCYUiZzkZKB7d96Nz9mZ1xtT0PhKN8XE8CcHQMyTJ3CuWBEAHwDMzMwETCx3nTnDX389PWDdOlpdQDRDaChfmfjuHd+idO4cYG0tdFY65ifGSMaArVuB0aP5hxnFiwNnzwKFCuVxziRf+SOlrpwyDlIr1nzl5Mm0WokrVgCNGwubT76jI+eQ2qBsWcDLi+/QGTOGb92nMpeEECKPJhSJnAkT+BtwMzNeQ9HGRuiM8qnw8HRXwxUEaqa4OGD4cH591CigWjVB0yFEKV+/8hP+Fy/4RNT584CdndBZ6agcjJGhocDgwXwlCAA0aAD8/TdQrFheJEjyMysrK6FTIDnw/DnQowf/YGDwYGDYMKEzyqe0/BxSm/z1F/87FBQEzJ8PzJwpdEaEEJK/0IQikdm2jRchTr1epYqw+RDhzJ/PV6kWLQrMmiV0NkQIMZ8/Qz8+PtPt+kZGMEnXAS0mLCzbY+gZGMA03fJAVWJjw8PBpNIsY0V6ehCn+7QjNjwc36Ok6NQJePUQcLYFju4BCpkAseHysXFfv0KanJxtHmbp9uapEhsfEQFJYmKuxIptbCBKWRKcEBWF5Cxeh5zEmlpbQy+lK01idDSSYmNzJdakQAHoGxmlxX7+jNR1NjGfP8viYsLCYFK0qCw2KTYWidHRAIATJ4Dx44HwL4CVATB5MjBmoiWMzUwyxWbF2NISBilFMpPj45EQFZVtrJG5OQzFYpVjJYmJiI+IyDbWUCyGUcq+bFVipcnJiPv6NVdiDUxMYJzSZZRJpYhVMFmhSmy8gt+rvODr66vWxyM/LyIC+P13ICqKfxiwahXfNkqIJjMz46WfunQBFiwA+vQBSpUSOqus5Zctm4QQ7aD0mMJ0TGRkJAPAIiMjhU4lX7l+nTFjY8YAxqZPFzqbfC46mj9RAIsODWUAGAAWHR0tdGa54vlzxoyM+I+4b5/Q2agHjQtpZM9Fyu94xstNW1u5+Ohs4hjA7llZycV+FomyjX0iFsvFvtfXzzb2pbGxXOxLI+NsY9/r68vFPhGLs439LBLJxd6zsso2NjrDn8+btrbZxrIMsVeLFlUYGx0aKou9XKqUwtjPT5/KYv0rVVIYu2/RZebry5iPD2Pb7GopjPVocpj17s1Y796MrS3cWGHsqHpb2Z9/Mvbnn4wtK9Im0/MkGyMB5lljmSx2QfEuCo9709s77XkYMEBh7NWxY9Oe37FjFcZeHjAg7XXz9lYY69+lS9rvw7JlCmMvtGmT9nu2davi2MaN035/Dx9WHFurVtr/i8uXFedbqZIs9vPTp4qfh1KlZLHRoaEKY885ODAaI+lvRXaSkxlrk/Jfv1gxxkJChM4oH9Pyc0htJJUy5urKX7Z27TJ/X+hxITk5mT19+pSFh4cL8viEEO0UHh7Onj59ypKTkxXG0QpFgpAQ4I8/gIQE3tXM21vojIhQGAM8PHhXu1atgE6dhM6IEMUSEoCk7BcRknS8xgNvU64v/EGsvz/wNOV6yR/EXr0G3L7Gr9v/IPbOXeDiXX7d4wexRHdVr14dIiWXt929ezePsyE/8tdfvHaiiQlw+DCVmiDaJbVBS5UqwLFjfEX9b78JnVUafX19FChQAGEpu0DEYrHS4ychhGTEGENsbCzCwsJQoEAB6OvrK4wXMcaYmnLLF6KiomBlZYXIyEhYpmz10WVJSbwb6uXLQIUKwPXr1A31h2JiZO1GY0JDYZ5y5hwdHa3xBbV37QJ69uRvCh4/zr/bOnIbjQtpUp+LT69ewTKLwSA/bXlOTga6dgVOHwqH2ESKPXt4R/KsYlMJteXZ1KYwIiOB6GggOjwC8TGJSEriY3BiIiCV8gl9qRQwsLQBRHqQSoGk6ChIEuLlvp+UBHz7Bnz5AoTH2uDLVz2EhwNBz6MQ9Drz1lQ9EWBrC1jZW8O+iAHs7AC7gtEwM4qFnh7kLrL3IKZpW56l8dFAUvZbnkWmBSAyMJLFGsZ8xhgfPg25cNxjTFxSCQAwZ/IbGFsVhZ4hj2WJsWAJ0RCJgPr1M5fZSL+NmbY8C7vlOSY+HnZOTmobI2eqUKjMW42fgtLfisz27gW6dePX//mH11AkCmjxOaS2mzCBdzAvWRJ48oSfKwP5Y1xgjCEkJAQRCv7uEEKIKgoUKAB7e/sffkBBE4o6bswY3oXP0hK4eRNwcRE6Iw2gpSeDERFAuXK8McLs2cDUqUJnpD40LqTRlOdCIgHc3YGdOwEjI75iwNVVuHykUuDlS+D+fX55/Rr4/JnX3k/9VyJRTy5lywL16qVdKlYEfvDhYu7S0jFSl2nKuJDX6HmQ9+QJULcu/y8/fjyw8EdLnwmNjxrs+3d+nvzpE68vPm0avz0/jQsSiQRJSUmC5kAI0XyGhoY/XJmYirY867CdO/lkIgBs306TiUrT0wNq1eJXDQxQK/V6SmMETTV1Kp9MdHHhbwwIya8Y491Dd+4EDAyA/fvVP5kYGgr4+QH//ccnEB8+5O8Tf8TQEDA25pOghoZp/+rrp60QTL9aMKvr+vpAoUKAjQ1feWhjwy9FigC1a/PvCUpLx0ginIiICOzfvx+vX7/G+PHjYW1tjbt378LOzg5FixYVOj2dFBkJdOzIx73mzYF584TOSEPQ+KixLCyAJUv4Ktx584DevQFnZ6Gzkqevr6/0JAAhhOQGWqGoox484CtX4uL4RNLs2UJnRIR06xZfZcAYnyRp1kzojNSLxoU0+f25YAzw9ASWL+fvy/75J227XV6KjgYuXQLOn+eXR48yx5iaAlWrAtWq8VUMhQunTfil/mtsnPe5EpLbhBwXHj58CFdXV1hZWSEoKAjPnz9HyZIlMXXqVLx79w7bt29XWy75fXxUF6mUTyYePQo4OgJ37vAxjhBtxxifQL9wgdedP3SIxgVCiG6jFYo66OtXfiIYF8cbb8yYIXRGREgSCV/txRivn6hrk4lEs0yfzicTAWDz5rybTPz2DbhyhdeXvXQJuH0byFhOsXp1oGlTvtikenWgTBk1by0mRAd4enqib9++WLhwISzS1XVt06YNevbsKWBmusvHh08mGhsDBw/SZCLRHSIRsGoV/+Dw8GHg9GleA5gQQnQVTSjqGIkE6NULCAwESpTgWwbpDbBuW7eOry6wsuJbOQjJrxYuBObM4ddXrwb69s2d4yYnAwEB/P/BrVt8EvHxYz7Jnp6zM9CiBd9e3bQpvYkmRB1u3bqFDRs2ZLq9aNGiCAkJESAj3Xb6dFrtuLVrZbt3CdEZFSsCo0YBS5cCI0cCV68KnREhhAiHJhR1zKxZ/GTQ1JQv00/XVJUoKzaWt8QGEHv7NiqknE0/ffoU4pRuoJoiOBj46y9+fd48wN5e2HwIyc769cDEifz6/PnA8OE5P1ZCAnDkCK9/ePs2r4EYF5c5rmxZoFEj4Ndf+b8lSuT8MXWKFo2RRHjGxsaIyqIT94sXL2BLs/pqFRjIdzIwBgweDPTvL3RGGojGR63g7c1Lrrx6xVcsEkKIrsoXFYDXrFkDZ2dnmJiYoG7durh582a2sZs2bUKjRo1QsGBBFCxYEK6urgrjSZqzZ9NqJW7cyGt9kRxgDHj7Fnj7Fkwqxdu3b/H27VtoYjlST08gKoqvMBgyROhsyP/bu+/wqMq0f+DfSW8kEEoaIRQDKE2lZANKkWikikSJqBtAeGWFoAj8BNyFgMpG2sIiAdSXpihNJe4LEcRAUCH0oBRBwFDUNBZSJp2Z5/fHQyYZMhMmIZkz5fu5rnNxZuaZM/eZTG4m93mKIcyPwKZNwKRJcv/ttysLi7WVlQXMnw+EhMih0h98AKSmymKil5csHL75JrB9O5CZCVy4APzv/wIxMSwm1ooN5UhS3vDhw/HOO+/oVi5VqVS4du0aZs6ciaioKIWjsx8lJcDIkXI6iF69gBUrlI7ISjE/2gRvb2DxYrlf8S8RkT1SvKC4detWTJs2DXFxcTh58iS6deuGyMhIZGdnG2yfkpKC0aNHY//+/UhNTUVwcDCeeuop/PHHH2aO3Lr88Ycc6iyELBy9/LLSEZHS9u4FtmyRC1usWcOh75aI+VH2pB47Vuau2NjKIc+1ceIEMGYM0KqVnDM2K0uuiDx1qixWnj8vVyw9cEAOYXruOcDPr55PhIjqZOnSpVCr1WjRogWKi4vRr18/PPDAA2jUqBEWLFigdHh2Y/p02Zu7eXPgyy+5wBTRSy/J0QslJUpHQkSkHMVXeQ4LC0PPnj2xcuVKAIBWq0VwcDCmTJmCWbNm3fP5Go0GTZo0wcqVKxETE3PP9va4Etft23KhjR9+kJMIp6YCbm5KR2XFCgtldyYAhVlZ8LpTeVCr1fD09FQyMpOVlABdusihGlOmsKeBpeYFc+dHwLLei2+/BYYNA8rKZEFw3TpZADfV9evA+PGyeF4hPFzOfRQVBTg713/MBJvIkaTPEvLCwYMH8dNPP0GtVuPRRx9FRESE2WOwhPdBCV98ATz/vNzfswd46ill47FqzI825eefgUceyYdWa395gYgIUHgOxbKyMpw4cQKzZ8/W3efg4ICIiAikpqaadIyioiKUl5fD18hkgKWlpSgtLdXdNjQPj62bM0cWExs1kkP5WEykhQtlMTEgoHIYPFkWc+RHwHJz5MGDwIgRspgYFSWHHtemmLhtm+yNnZsrC4ejRgFvvAH07NlQERNRQ+rTpw/69OmjdBh257ff5IUZAJg9m8VEoqq6dpVzkfO7NBHZK0WHPN+4cQMajQZ+d40t8/PzM3nlvpkzZyIwMNDoler4+Hj4+PjotuDg4PuO25okJckFDABg7VrggQeUjYeUd/EiEB8v95ctk6s7k+UxR34ELDNHpqUBgwfLuQ2fflpOfO5k4uWvggJg3Dg5R2Jurpzr69w5ObSZxUQi67Fv3z489NBDBi9y5OXloVOnTvjhhx9qdczvv/8ew4YNQ2BgIFQqFRITE+spWttUViZzaX4+0KePXNiPiPTNmKF0BEREylF8DsX78f7772PLli3YsWMH3Ix0u5s9ezby8vJ02/Xr180cpXKuXwf++le5P3ly5XAVsl9CyM9CaSnw5JOy1xbZJlPyI2B5OfLCBSAyUv4B+/jjcq4uFxfTnnv0KPDII8CGDYBKJXsN/PgjL6QQWaPly5fjf/7nfwwOIfTx8cHEiRPxr3/9q1bHLCwsRLdu3ZCQkFBfYdq0WbOA48cBX19g82bTL+wQERGRfVD0q0GzZs3g6OiIrKwsvfuzsrLg7+9f43OXLFmC999/H9999x26du1qtJ2rqytc7XDm6PJyeVX55k2ge3dg6VKlI7IhKhXw0ENy18EBD1Xsq1RKRmWSbdvkfHKurkBCgjwVskzmyI+AZeXIa9dkoTsnB3j0UeD//g/w8Lj384QAliyRK0Dfvi0XX/n0U7lqMynAinMkWY6ffvoJCxcuNPr4U089hSVLltTqmIMGDcKgQYPuNzS78J//yFEMgLxIYwGd120D8yMREdkQRQuKLi4u6N69O5KTkzFixAgActGB5ORkxMbGGn3eokWLsGDBAuzZswc9evQwU7TWQ6OR892kpsrhrNu2cTW+euXhAZw9K3cBnL2zb+ny8oA335T7s2cDoaHKxkM1s7f8mJUli4nXrwMdOwK7d5s2HL+0FHj1VeCTT+Tt6Gi5annjxg0aLtXESnMkWZasrCw417BykpOTE3JycswYkf24dg0YO1buv/mmXByL6gnzIxER2RDFBy9MmzYNY8aMQY8ePdCrVy8sX74chYWFGDduHAAgJiYGQUFBiL8z6dvChQsxd+5cfP7552jdurVuLjEvLy943Vk1zZ7dvg3ExMihKY6O8o/stm2VjooswZw5QEaGLCTOnKl0NGQKe8mPublymPOvvwIhIbIXbfPm935eTg7w7LNyARdHR+Df/wYmTWLPWyJbEBQUhDNnzuABI3MW/PzzzwgICGjQGCx10aqGlJsLjBwJ3LoF9OhROQ83ERER0d0ULyhGR0cjJycHc+fORWZmJh5++GHs3r1btxDBtWvX4FBlac/Vq1ejrKwMzz33nN5x4uLiMG/ePHOGbnHKy4EXXwS++ELOc7NlCzB8uNJRkSU4eVIOcQaAVau40re1sIf8WFgIDBkC/PQT4Ocni4ktW977eWfPAkOHAleuyJ6M27fLHo5EZBsGDx6MOXPm4Omnn642D2xxcTHi4uIwdOjQBo0hPj4e8+fPb9DXsCS5uXIV5xMngKZNga1bTZ/DloiIiOyPSgghlA7CnPLz8+Hj44O8vDyDE31bq4qV+BITAWdnWVRkMbGBFBXplostOnAAPfv1AwAcO3YMHqZM+GZmGg0QHg4cOwa88ILsvUr6bDUv1IU534uyMpmn9uyRQ5QPHADuMeUjAOCbb2S+KygA2rWTcy0++GCDhkq1YWU5ku5NiRyZlZWFRx99FI6OjoiNjUWHDh0AAOfPn0dCQgI0Gg1Onjypu8BSWyqVCjt27NBNKWGIoR6KwcHBNvl/xa1bsph4/DjQrBmQnGxaPqZaYn60OfwOSUT2TPEeinT/SkqA554Ddu2ScyV+9RUweLDSUdkwIYBz5+SuVotzFfsWWpv/8ENZTPT2Bmq5ICZRg9FogJdflsVEDw8gKcm0P14TEoDXXwe0WrnoyldfyZ40ZEGsLEeSZfLz88OhQ4fw2muvYfbs2brPj0qlQmRkJBISEupcTDSVJS1a1ZBu3ZI9vE+ckMXEffuALl2UjspGMT8SEZENYUHRyt28KYc579kjh7F+/bW8wkwEAJmZcuVbAFiwAGjg6aaITCIEMHGiHKbs7Cx7VoeH3/s5b79dOZ/XuHFy8RUOxyOyXSEhIUhKSsKtW7dw6dIlCCEQGhqKJk2a1Ol4arUaly5d0t1OT0/HqVOn4Ovri1atWtVX2Fbl5k1ZTDx5Us5du28f0Lmz0lERERGRNWBB0UqVlsqeOu++K+e88fCQw/6eeELpyMiSzJghV3fu3h147TWloyGShcG33gLWrgUcHOQQ/HvNfVhWBkyYAHz6qbz97rvA3//OxVeI7EWTJk3Q884w0ftx/PhxDBgwQHd72rRpAIAxY8Zgw4YN9318a3PjhrwInZYGtGghi4mdOikdFREREVkLFhStjBCyV8+sWUB6uryvSxfgo4+Av/xF2djIsiQnA599Josua9bIVXCJlPb++8CSJXL/44+BqKia2xcUyCkdvv1WfoY//lj2TiQiqq3+/ftzaCnkYlgrVwILF8rhzn5+spj40ENKR0ZERETWhAVFKyEE8OOPsmfP4cPyvoAA4L33gDFjWCwifaWlwKRJcn/SJKBHD2XjIQKA1asrh+AvXQq88krN7TMz5XywaWmyF/YXXwCDBjV8nEREtqisTF6Uee89mV8BWUT84gsubEVERES1x4Kihbt+Hdi0CfjkE+D8eXmfh4csLE6fDnh5KRsfWabFi4FffwX8/eXciURK27wZmDxZ7v/978CdkYZG/forEBkJXLki5/XatUu3MCYREdVCSQmwZQswf77MqQDQpo28/eKLvChNREREdcOCogVSq+XKpZ98IoegVIzOcXOTq6LOnw8EBiobo11TqYCQELnr4ICQin0LmdDt8mXZ+wCQqzr7+CgbD1FSEhATI3PZpElyDsSaHD8ueyLeuAG0aycXnWrXzjyxUj2w8BxJZA8KCmTu3bFDXpBRq+X9AQHAnDnA+PFc1EoRzI9ERGRDWFC0EBcvyi9+SUlASoocllKhXz/5x/hzzwHe3oqFSBU8PHSX+D0AXKm43G8BhABiY+WQ54EDgRdeUDoisnc//CDnSbx9W/aE+eCDmhdT+e474Nln5R+/3bvLnNiihfnipXpgwTmSyJZdvSpzaGIisHev/C5QISgIeP11+R3Bw0OxEIn5kYiIbAgLigpRq+WciHv2yCvHFy/qPx4aCvz1r7JHYps2ysRI1ueLL4Ddu2Wvg1WruAouKSstDRg6VA63GzIE2LBBruxszLZtMueVl8uC+I4dQKNGZguXiMiq/PknsH9/5fbbb/qPP/CAvKDz7LNyyoia8i8RERFRbbGgaCZlZXIxlX375Oq7hw/LHjsVnJyAxx+Xf3QPGQJ06MBiENVOfj4wdarcnzULaN9e0XDIzlXMgZifD/TtK1end3Y23j4hAZgyRfayHTVKTvng6mq+eImILJkQsmPbDz/I7fvvZZ6tytFRLsI2ZIgsInbqxO+SRERE1HBYUGwgpaXAsWPAgQNyCPOhQ0BRkX6bVq2AiAi5iumTT3I4s9UoLpYVEgDFe/agb2QkAOD777+Hu7u7YmHFxcneCu3aAbNnKxYGEX7/Xea0nBzgkUeA//wHMParIQQwbx7wzjvy9qRJwIoVXCTAqllojiSyJlotcPasHM1SUUD84w/9NiqVzLFPPAEMGCAvTLNXt4VjfiQiIhvCgmI9KSwEjhyRX/oOHABSU+Uwv6qaN5df+gYOlP+2bcsrx1ZJq5WrRgDQ3r6N4xX7Wq1iIaWlySIMIHt6ubkpFgrZuRs3ZDHx2jXZS3b3buMLA2k0cj6vNWvk7XnzgLlzmRetngXmSCJLV1wsf21+/FFuBw8CeXn6bZycZA/Exx+X22OPAU2aKBMv1RHzIxER2RAWFOsoM1N+2av44peWJv84rqpFC3kRsl8/uXXuzD+Uqf5ptcBrr8l/R42Sw0yJlJCfL1dnPn8eaNlSLgpgbEGVsjI5T+y2bTIvJiTIzzERka0TArh+XV58Tk2Vo1jS0vSnwgEALy8gPBzo00d+nwwL44IqREREZDlYUDRBYSFw4gRw9KjshXj0qOx9c7dWrSq/9PXrB3TsyAIiNbyPP5afy0aNgGXLlI6G7FVJCTBihOx40ayZLCa2amW4rVotFwr49ls5r+KmTbIYTkRki27dkrnx2DH579Gj1YcvA0BAgOx1WLF17Sp7JRIRERFZIn5NqcGNG8BLLwHffSd7f1WlUskven36yC99ffoY/+OZqKFkZ8sFWADgvfeAwEBl4yH7dPs28MILcpXRRo3kMOeOHQ23/e9/5YIBR47InjY7dgBPPWXeeImIGtr168A//iFHs1y+XP1xR0fg4YeB3r1lL8TeveX3SF6IJiIiImvBgqIRN27IuQ5//lneDgyUQ03CwoBeveQcNpz4mpQ2YwaQmysnZZ80SeloyB5ptcCECcDXX8tVmf/v/4Du3Q23/eMPWTw8dw7w9QV27QL+8hfzxktE1NCuXQP69wfS0yvva9cO6NlTbj16yI3Dl4mIiMiasaBoQE6OLCaePg34+wPffCOvIhNZkv37gU8/lb0Z1qzhsCgyPyGAadOAjRtlb5vt2+V0D4ZcvCgXa7l6VV6g+fZboFMn88ZLRNTQfv9drricni6LiAkJsojo66t0ZERERET1iyWIu9xdTNy/3/jQPbJjzZpV2W1WQ8OGUVpauYDFa6/JXrNE5vbee8C//y33N2wAhg0z3O7UKblYUHY2EBoqi4mtW5spSFKGwjmSSAl//CGLib/9BrRtC6SkyAWqiPQwPxIRkY1gQbGK7GxZTDxzRk6MvX8/0KGD0lGRxfH0lJVnAJ4Acu7sm9PixcCFC4CfH7BggdlfnggrVwJz58r9FSuAl1823O7HH4GhQ4G8PNnTe88e4ys/k42wgBxJZG5//gk88QRw6RLQpo38DsliIlXD/EhERDbEQekALEV2tvwieOaMHI6XksJiIlmmy5dlzzBArurcuLGi4ZAd+uwzYMoUuT9vXuX+3b75Rs6ZmJcHPP64zKssJhKRrcnIkN8hf/0VCAmRxUQu1EdERES2jgVFAOXlwNNPA2fPAkFB8o/e9u2VjoqoOiGA2Fg55DkiQq6sS2ROO3cCY8bI/ddfr+yleLctW4Dhw4HiYmDwYLnys4+P+eIkIjKHW7fk6JYLF2QRcf9+WVQkIiIisnUsKAJYvhxISwOaNpVfBENDlY6ILFpxsVy+sX9/FN+8if79+6N///4oLi5u8Jf+4gtZmHF1BVatkguyEJnL998Dzz8PaDRyiPOyZYY/gx9+CLz4InD7NjB6NJCYyNVM7YqCOZLI3P7f/wN++UUOb96/Xw53JjKK+ZGIiGyI3c+heO2aHLIHAEuWsJhIJtBqgQMH5O7t2zhQsa/VNujL5ucDb7wh92fP5meVzOvkSbnoSkmJ/HfdOsDBwCWpRYuAmTPl/qRJwAcfGG5HNkyhHElkbikpwNq1cn/zZrkQC1GNmB+JiMiG2H1B8fXXgaIioG/fymF8ROak1coiTXGx3EpK5GeyoABQq+VWUADs2iXnaQoNrSzYEJnDhQtyWoj8fKBfP2DrVsDZWb+NEMA//gH885/y9ttvy7k+2YuWiGxRcTHw6qty/29/Ax57TNl4iIiIiMzNbguKhTk52PO1Fl9/3RhOTgJL3r2JohwNHF1c4FZllYvC7Gyjx3BwcoK7r2+d2hbduAFh5GqkysEBHs2a1alt8c2b0N6+bTQOzyorItSmbUluLjRlZfXS1qNZM6judFkqzc/H7ZKSemnr7usLByf5kS5Tq1FeVGRS25J8NdS3ilBWJucmvHvTODdGudZFFvpy1SjPyUHFgrYJiypX5/v7m9nQOAWhpFy2LVUXoUytRmlpZcGwrEz+W1oK5JV4o7DUDaWlgBOK4AK10XhL4Q0N3AAAK5eXQJOfj8J8w21dvLzgfGd86e2SEpTmG2l4V1tNWRlKcnONtnX28ICLl1et22pv30bxzZv10tbJzQ2u3t4AAKHVoujGjXppW1LD58reXbokFxvIyQEefRT4z38Ad3f9Nlqt7D27cqW8vXAh8NZb5o+ViMhcFiwALl4EAgKA999XOhoiIiIiBQg7k5eXJwCIP+EuQpAuACFmIl4I2cFGHG3eXK+9+s79hrY0Hx+9tjkqldG2Zz089Nped3Q02vaiq6te24uurkbbXnd01Gt71sPDaNsclUqvbZqPj9G26rs+GkebNzfaVtzV9lBQUI1t1VlZurY/tGtXY9ttCefE1q1CbNwoxFeBnWts+7enfxAvvCDEs88K8aFPjxrb9vJMFJ6eQjg5CRGHfjW27YENupszMLja+4Q7mxoQ/bBM9/AkPF/jcQcjTndzDMbX2HZG6zfF888LkZAgxKE336yx7Q/jx1f+3OLiamyb8vzzlZ+HZctqbLt/8ODKz9mGDTW37dev8vObmFhz2x49Kn8vfvih5ng7d678fTt3rub3oV07XVt1VlaNbfcGBAgAIi8vT9i7ihyZl5cnfvtNiOBg+TZ16iREdnb19uXlQowZI9uoVEKsWmX2kMnSqNV6+V6XI9VqpSOjOqqaF+xZxftw6FCecHKSH/Mvv1Q6KrIqzI82h/mRiOyZRcxslZCQgNatW8PNzQ1hYWE4evRoje23b9+Ojh07ws3NDV26dEFSUlKtX3MR3sJVtEYIrmAO3q1r6DbtyhU50XhamlwJuyaLFgHvvCOHOebm1dy2Rw+gRQvA0xO4dLnmtpMmA9HRcjj6n3/W3Pab3XJl2R07gLx7xKAuBAoL5aIR9+LXAujSBejZE/BtUnPbJyPkMM8lS4BHH6m5bexk+R5nZQFjYmpuO/JZYNs2OScd2Rcl8iMg55cdMAC4fh3o2BFITgaaN9dvU1oqfz83bgQcHYFPPwVee61OL0dEZBa1zamGTJkivz+MGAGMHFn/MRIRERFZA5UQQigZwNatWxETE4M1a9YgLCwMy5cvx/bt23HhwgW0qDKMtsKhQ4fQt29fxMfHY+jQofj888+xcOFCnDx5Ep07d77n6+Xn58PHxweOjrnQaHyw7ZNcDI6sHJ5rKUOe3XybIT9fFsZyrt5AXq5WN6deYWHlpi50gFrbDOXlcuVVjfomhOY2NBr5Zff2bVkMvH1bPl6kaoGyMjkE93bBTZQW39Ybkqut8mkoQuX774pcOML4MObatW2GigXGXZAPJ8jhpi7OciVYd/fKDR7N4OHpADc3wN0hHx4uJXBxAVxcADc36PZdXQEXH1+4ezrB1RVwFmo4iSLdY66u0Nv3al7ZVlWuhqNWtnV2rj7nm1vjxnB0cQFwZyh1Tg4878y8nn3mDPzufO6yfvsNTYOCdG3Li+SQZ2Ncvb3h5OZW67a1GcbMIc+mtS0sKYFfSAjy8vLgfec5lsDc+RGozJGtW+fhyhVvhIbK+eMDAirbFBTI4uEHHwDnz8vfrW3bgGeeqa8zJ6tWWAjc+Z0uzMqCl58fAECtVsPT01PJyKiOKvKCpeXI2qptTr1bxfsAyPfh3DkgKKjh4yYbwvxoc2wlPxIR1YXiBcWwsDD07NkTK+9MvqXVahEcHIwpU6Zg1qxZ1dpHR0ejsLAQO3fu1N33l7/8BQ8//DDWrFlzz9er+mXwmWe8kZgo/2//809ZvKsovFX8W3W/okBXdf/u2+XlxjdD8/OVlFQvElZsSnN3l0U7N7fKfVdX/X+rbhX3VS0IVt08PeXm5VW5X7G5u8seTlahsFB2sQRQmJ6OFm3aAACys7P5ZdBKWeqXQXPnR0A/R7Zr540DByr/YD5/Hli1CtiwQRYVAcDHB/jiCyAi4n7OlGwKc6TNsdQcWVu1zal3q5ofV63yZo9sqj3mR5tjK/mRiKguFF2UpaysDCdOnMDs2bN19zk4OCAiIgKpqakGn5Oamopp06bp3RcZGYnExESD7UtLS1FaWqq7nX+nt5ZKBZw5I/8YrqEDl+JcXGSMFVujRnLz8tLfXFxkQc7JSf9fZ2e57+ysv1+10GeoCOjqytVZjfL01FV8PQEUWkL1l2yOOfIjYDxHenrK6Qni4uT96enAvn2Vz+vQAYiNBWJiAH5/Jj3MkWSB6pJTjeXHsDBg4sSGjZdsFPMjERHZEEULijdu3IBGo4Hfne7+Ffz8/HD+/HmDz8nMzDTYPjMz02D7+Ph4zJ8/v9r9QgCXq8zf5+kJNGkii21VC3IV+3ffrvi3okhXdbu7eOfsrD/cturm5la9t15FLz4fH/k4Edkfc+RHwHiOLCwEtm7Vv8/BARg2TBYSBw7kRQcish51yanG8uOKFTIfEhEREdkzRQuK5jB79my9Hjv5+fkIDg7G6tWyh01goNwaNVIwSCIihRjLkXPn6l/QcHeXCxC0bm32EImIFGEsP3bsqGBQRERERBZC0YJis2bN4OjoiKysLL37s7Ky4O/vb/A5/v7+tWrv6uoKV1fXave/+CKH6VEdlZQAUVFy97PPEPXSSwCAL7/8Em7sUkr1xBz5ETCeI6dPZ46kOmKOJAtUl5xqLD8S1RnzIxER2RBFB2y4uLige/fuSE5O1t2n1WqRnJyM8PBwg88JDw/Xaw8Ae/fuNdqeqN5pNEBSEpCUBE1ZGZKSkpCUlASNRqN0ZGRDmB/JajFHkgWqS04lqnfMj0REZEMUH/I8bdo0jBkzBj169ECvXr2wfPlyFBYWYty4cQCAmJgYBAUFIT4+HgDwxhtvoF+/fli6dCmGDBmCLVu24Pjx4/joo4+UPA0ionrH/EhEVH/ulVOJiIiIyHSKFxSjo6ORk5ODuXPnIjMzEw8//DB2796tmzT72rVrcKgy83Xv3r3x+eef4x//+AfefvtthIaGIjExEZ07d1bqFIiIGgTzIxFR/blXTiUiIiIi06mEEELpIMwpPz8fPj4+yMvLgzcnCKO6KCyUy3ADKMzKgtedP0TUajU8PT2VjIzqiHmhEt8Lum/MkTaHeUHi+0D3jfnR5jAvEJE9U3QORSIiIiIiIiIiIrIuLCgSERERERERERGRyRSfQ9HcKkZ45+fnKxwJWa3CwsrdggLdfn5+Plfps1IV+cDOZoAwiDmS7htzpM1hjpSYH+m+MT/aHOZHIrJndjeH4u+//47g4GClwyAiC3T9+nW0bNlS6TAUxRxJRMbYe45kfiQiY+w9PxKRfbK7gqJWq8Wff/6JRo0aQaVSKR1OneTn5yM4OBjXr1+32sl/eQ6WgecgCSFQUFCAwMBAvVWT7RFzpGXgOVgGnoPEHCkxP1oGnoNl4DlIzI9EZM/sbsizg4ODzVw98vb2ttr/wCvwHCwDzwHw8fGpx2isF3OkZeE5WAaeA3MkwPxoaXgOloHnwPxIRPaLl1GIiIiIiIiIiIjIZCwoEhERERERERERkclYULRCrq6uiIuLg6urq9Kh1BnPwTLwHMgW2cJngudgGXgOZGts4fPAc7AMPAciIrK7RVmIiIiIiIiIiIio7thDkYiIiIiIiIiIiEzGgiIRERERERERERGZjAVFIiIiIiIiIiIiMhkLihYmPj4ePXv2RKNGjdCiRQuMGDECFy5cqPE5GzZsgEql0tvc3NzMFHF18+bNqxZPx44da3zO9u3b0bFjR7i5uaFLly5ISkoyU7SGtW7duto5qFQqTJ482WB7S/gZfP/99xg2bBgCAwOhUqmQmJio97gQAnPnzkVAQADc3d0RERGBixcv3vO4CQkJaN26Ndzc3BAWFoajR4820BnUfA7l5eWYOXMmunTpAk9PTwQGBiImJgZ//vlnjcesy+eRLBdzJHNkXTFHGsYcaTuYH5kf64r50TDmRyKimrGgaGEOHDiAyZMn4/Dhw9i7dy/Ky8vx1FNPobCwsMbneXt7IyMjQ7ddvXrVTBEb1qlTJ714fvzxR6NtDx06hNGjR2P8+PFIS0vDiBEjMGLECJw5c8aMEes7duyYXvx79+4FADz//PNGn6P0z6CwsBDdunVDQkKCwccXLVqEFStWYM2aNThy5Ag8PT0RGRmJkpISo8fcunUrpk2bhri4OJw8eRLdunVDZGQksrOzzX4ORUVFOHnyJObMmYOTJ0/iq6++woULFzB8+PB7Hrc2n0eybMyRzJF1xRxpHHOkbWB+ZH6sK+ZH45gfiYhqIMiiZWdnCwDiwIEDRtusX79e+Pj4mC+oe4iLixPdunUzuf2oUaPEkCFD9O4LCwsTEydOrOfI6u6NN94Q7dq1E1qt1uDjlvYzACB27Nihu63VaoW/v79YvHix7r7c3Fzh6uoqNm/ebPQ4vXr1EpMnT9bd1mg0IjAwUMTHxzdI3FXdfQ6GHD16VAAQV69eNdqmtp9Hsi7MkZaBOVJijiRLwvxoGZgfJeZHIiLbwx6KFi4vLw8A4OvrW2M7tVqNkJAQBAcH45lnnsHZs2fNEZ5RFy9eRGBgINq2bYuXXnoJ165dM9o2NTUVERERevdFRkYiNTW1ocM0SVlZGTZt2oRXXnkFKpXKaDtL+xlUlZ6ejszMTL332cfHB2FhYUbf57KyMpw4cULvOQ4ODoiIiLCYn01eXh5UKhUaN25cY7vafB7JujBHKo85kjmSLBPzo/KYH5kfiYhsGQuKFkyr1WLq1Kno06cPOnfubLRdhw4dsG7dOnz99dfYtGkTtFotevfujd9//92M0VYKCwvDhg0bsHv3bqxevRrp6el4/PHHUVBQYLB9ZmYm/Pz89O7z8/NDZmamOcK9p8TEROTm5mLs2LFG21jaz+BuFe9lbd7nGzduQKPRWOzPpqSkBDNnzsTo0aPh7e1ttF1tP49kPZgjlf89BJgjLfVnwxxp35gflf8dBJgfLfVnw/xIRFQ/nJQOgIybPHkyzpw5c8+5OsLDwxEeHq673bt3bzz44IP48MMP8e677zZ0mNUMGjRIt9+1a1eEhYUhJCQE27Ztw/jx480ez/1au3YtBg0ahMDAQKNtLO1nYOvKy8sxatQoCCGwevXqGtva2ueRKjFHWgbmSMvDHEnMj5aB+dHyMD8SEdUf9lC0ULGxsdi5cyf279+Pli1b1uq5zs7OeOSRR3Dp0qUGiq52GjdujPbt2xuNx9/fH1lZWXr3ZWVlwd/f3xzh1ejq1av47rvvMGHChFo9z9J+BhXvZW3e52bNmsHR0dHifjYVXwSvXr2KvXv31nhl2ZB7fR7JOjBHMkfWJ+bISsyR1o/5kfmxPjE/VmJ+JCLSx4KihRFCIDY2Fjt27MC+ffvQpk2bWh9Do9Hg9OnTCAgIaIAIa0+tVuPy5ctG4wkPD0dycrLefXv37tW7WquU9evXo0WLFhgyZEitnmdpP4M2bdrA399f733Oz8/HkSNHjL7PLi4u6N69u95ztFotkpOTFfvZVHwRvHjxIr777js0bdq01se41+eRLBtzpMQcWb+YIysxR1ov5keJ+bF+MT9WYn4kIrqLkivCUHWvvfaa8PHxESkpKSIjI0O3FRUV6dr89a9/FbNmzdLdnj9/vtizZ4+4fPmyOHHihHjhhReEm5ubOHv2rBKnIKZPny5SUlJEenq6OHjwoIiIiBDNmjUT2dnZBuM/ePCgcHJyEkuWLBG//PKLiIuLE87OzuL06dOKxF9Bo9GIVq1aiZkzZ1Z7zBJ/BgUFBSItLU2kpaUJAOJf//qXSEtL061e9/7774vGjRuLr7/+Wvz888/imWeeEW3atBHFxcW6YzzxxBPigw8+0N3esmWLcHV1FRs2bBDnzp0Tr776qmjcuLHIzMw0+zmUlZWJ4cOHi5YtW4pTp07p/X6UlpYaPYd7fR7JujBHMkfWFXOk4XNgjrQdzI/Mj3XF/Gj4HJgfiYhqxoKihQFgcFu/fr2uTb9+/cSYMWN0t6dOnSpatWolXFxchJ+fnxg8eLA4efKk+YO/Izo6WgQEBAgXFxcRFBQkoqOjxaVLl3SP3x2/EEJs27ZNtG/fXri4uIhOnTqJXbt2mTnq6vbs2SMAiAsXLlR7zBJ/Bvv37zf42amIU6vVijlz5gg/Pz/h6uoqBg4cWO3cQkJCRFxcnN59H3zwge7cevXqJQ4fPqzIOaSnpxv9/di/f7/Rc7jX55GsC3Mkc2RdMUcaPgfmSNvB/Mj8WFfMj4bPgfmRiKhmKiGEqGPnRiIiIiIiIiIiIrIznEORiIiIiIiIiIiITMaCIhEREREREREREZmMBUUiIiIiIiIiIiIyGQuKREREREREREREZDIWFImIiIiIiIiIiMhkLCgSERERERERERGRyVhQJCIiIiIiIiIiIpOxoEhEREREREREREQmY0GR6uzKlStQqVQ4deqUyc8ZO3YsRowYUWOb/v37Y+rUqfcVm0qlQmJiIgDT4zTldase15zmzZsHlUoFlUqF5cuX39exNmzYgMaNG5vt9YjsFXOk+TBHElkX5kfzYX4kIqKGwoKiDcvMzMSUKVPQtm1buLq6Ijg4GMOGDUNycrLSoZlVcHAwMjIy0LlzZwBASkoKVCoVcnNza32sjIwMDBo0qJ4jNE2nTp2QkZGBV199tdpj8fHxcHR0xOLFi+vltWbMmIGMjAy0bNmyXo5HZImYIyXmyNpjjiRbx/woMT/WHvMjEZH9YEHRRl25cgXdu3fHvn37sHjxYpw+fRq7d+/GgAEDMHnyZKXDMytHR0f4+/vDycnpvo/l7+8PV1fXeoiq9pycnODv7w8PD49qj61btw5vvfUW1q1bVy+v5eXlBX9/fzg6OtbL8YgsDXNkJebI2mOOJFvG/FiJ+bH2mB+JiOwHC4o2atKkSVCpVDh69CiioqLQvn17dOrUCdOmTcPhw4cBAK+88gqGDh2q97zy8nK0aNECa9euBQBotVosWrQIDzzwAFxdXdGqVSssWLDA4GtqNBqMHz8ebdq0gbu7Ozp06IB///vfBtvOnz8fzZs3h7e3N/72t7+hrKzM6LmUlpZixowZCAoKgqenJ8LCwpCSkmLye1F1uMqVK1cwYMAAAECTJk2gUqkwduxYXVutVou33noLvr6+8Pf3x7x58/SOVXW4iqGr1KdOnYJKpcKVK1cAVA4N2blzJzp06AAPDw8899xzKCoqwsaNG9G6dWs0adIEr7/+OjQajcnnVNWBAwdQXFyMd955B/n5+Th06JBJz9uzZw8efPBBeHl54emnn0ZGRkadXp/IGjFHVmKONIw5kuwV82Ml5kfDmB+JiAgA7v9yG1mcmzdvYvfu3ViwYAE8PT2rPV4x98mECRPQt29fZGRkICAgAACwc+dOFBUVITo6GgAwe/ZsfPzxx1i2bBkee+wxZGRk4Pz58wZfV6vVomXLlti+fTuaNm2KQ4cO4dVXX0VAQABGjRqla5ecnAw3NzekpKTgypUrGDduHJo2bWr0S2ZsbCzOnTuHLVu2IDAwEDt27MDTTz+N06dPIzQ0tFbvTXBwML788ktERUXhwoUL8Pb2hru7u+7xjRs3Ytq0aThy5AhSU1MxduxY9OnTB08++WStXqeqoqIirFixAlu2bEFBQQFGjhyJZ599Fo0bN0ZSUhJ+++03REVFoU+fPrr3vTbWrl2L0aNHw9nZGaNHj8batWvRu3fve8a0ZMkSfPrpp3BwcMDLL7+MGTNm4LPPPqvraRJZDeZI45gjK2NijiR7xPxoHPNjZUzMj0REBAAQZHOOHDkiAIivvvrqnm0feughsXDhQt3tYcOGibFjxwohhMjPzxeurq7i448/Nvjc9PR0AUCkpaUZPf7kyZNFVFSU7vaYMWOEr6+vKCws1N23evVq4eXlJTQajRBCiH79+ok33nhDCCHE1atXhaOjo/jjjz/0jjtw4EAxe/Zso68LQOzYscNgnPv37xcAxK1bt/Se069fP/HYY4/p3dezZ08xc+ZMg8c1dJy0tDQBQKSnpwshhFi/fr0AIC5duqRrM3HiROHh4SEKCgp090VGRoqJEycaPZ+4uDjRrVu3avfn5eUJd3d3cerUKd3re3l56R37boZiSkhIEH5+ftXahoSEiGXLlhk9FpE1Yo5kjmSOJDKM+ZH5kfmRiIhMxSHPNkgIYXLbCRMmYP369QCArKwsfPPNN3jllVcAAL/88gtKS0sxcOBAk4+XkJCA7t27o3nz5vDy8sJHH32Ea9eu6bXp1q2b3hwu4eHhUKvVuH79erXjnT59GhqNBu3bt4eXl5duO3DgAC5fvmxyXKbq2rWr3u2AgABkZ2ff1zE9PDzQrl073W0/Pz+0bt0aXl5eevfV5XU2b96Mdu3aoVu3bgCAhx9+GCEhIdi6dWutYqqP8ySyFsyRdcccSWTbmB/rjvmRiIjsDYc826DQ0FCoVCqjw0qqiomJwaxZs5CamopDhw6hTZs2ePzxxwFAbxiHKbZs2YIZM2Zg6dKlCA8PR6NGjbB48WIcOXKkTucBAGq1Go6Ojjhx4kS1yZ2rfpmqL87Oznq3VSoVtFqtwbYODrIeX/XLd3l5uUnHrM3r1GTt2rU4e/as3mThWq0W69atw/jx440+z9Dr1+aPCCJrxhxZd8yRRLaN+bHumB+JiMjesKBog3x9fREZGYmEhAS8/vrr1ebAyc3N1c2B07RpU4wYMQLr169Hamoqxo0bp2sXGhoKd3d3JCcnY8KECfd83YMHD6J3796YNGmS7j5DV4B/+uknFBcX675sHj58GF5eXggODq7W9pFHHoFGo0F2drbuS+r9cnFxAYA6T2BdoXnz5gCAjIwMNGnSBICcUNtcTp8+jePHjyMlJQW+vr66+2/evIn+/fvj/Pnz6Nixo9niIbIWzJE1Y44ksl/MjzVjfiQiIqrEIc82KiEhARqNBr169cKXX36Jixcv4pdffsGKFSsQHh6u13bChAnYuHEjfvnlF4wZM0Z3v5ubG2bOnIm33noLn3zyCS5fvozDhw/rVu+7W2hoKI4fP449e/bg119/xZw5c3Ds2LFq7crKyjB+/HicO3cOSUlJiIuLQ2xsrO5qbVXt27fHSy+9hJiYGHz11VdIT0/H0aNHER8fj127dtXpvQkJCYFKpcLOnTuRk5MDtVpdp+M88MADCA4Oxrx583Dx4kXs2rULS5curdOx6mLt2rXo1asX+vbti86dO+u2vn37omfPnrqf08qVK2s15IjIHjBHGsccSWTfmB+NY34kIiKqxIKijWrbti1OnjyJAQMGYPr06ejcuTOefPJJJCcnY/Xq1XptIyIiEBAQgMjISAQGBuo9NmfOHEyfPh1z587Fgw8+iOjoaKPzpEycOBEjR45EdHQ0wsLC8N///lfvSnOFgQMHIjQ0FH379kV0dDSGDx+OefPmGT2X9evXIyYmBtOnT0eHDh0wYsQIHDt2DK1atar9GwMgKCgI8+fPx6xZs+Dn54fY2Ng6HcfZ2RmbN2/G+fPn0bVrVyxcuBDvvfdenY5VW2VlZdi0aROioqIMPh4VFYVPPvkE5eXluHHjRoPMFURkzZgjjWOOJLJvzI/GMT8SERFVUglOemH31Go1goKCsH79eowcOVLpcMiAefPmITEx0azDYQCgdevWmDp1KqZOnWrW1yWyJMyRlo85kkgZzI+Wj/mRiIgaCnso2jGtVovs7Gy8++67aNy4MYYPH650SFSD06dPw8vLC6tWrWrw1/rnP/8JLy+vaqsrEtkT5kjrwhxJZD7Mj9aF+ZGIiBoCeyjasStXrqBNmzZo2bIlNmzYwDlSLNjNmzdx8+ZNAHIibx8fH5t6PSJLxBxpPZgjicyL+dF6MD8SEVFDYUGRiIiIiIiIiIiITMYhz0RERERERERERGQyFhSJiIiIiIiIiIjIZCwoEhERERERERERkclYUCQiIiIiIiIiIiKTsaBIREREREREREREJmNBkYiIiIiIiIiIiEzGgiIRERERERERERGZjAVFIiIiIiIiIiIiMhkLikRERERERERERGSy/w+RAKCcTB2y2AAAAABJRU5ErkJggg==", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for parameter_set in all_parameter_sets:\n", + " print(parameter_set)\n", + " try:\n", + " sweep, sol_init_QLi, sol_init_Q = solve_esoh_sweep_QLi(parameter_set, param)\n", + " fig, axes = plot_sweep(sweep, sol_init_QLi, sol_init_Q, parameter_set)\n", + " except ValueError:\n", + " pass\n", + " # print(\"success\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "The relevant papers for this notebook are:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] Weilong Ai, Ludwig Kraft, Johannes Sturm, Andreas Jossen, and Billy Wu. Electrochemical thermal-mechanical modelling of stress inhomogeneity in lithium-ion pouch cells. Journal of The Electrochemical Society, 167(1):013512, 2019. doi:10.1149/2.0122001JES.\n", + "[2] 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", + "[3] Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050.\n", + "[4] Rutooj Deshpande, Mark Verbrugge, Yang-Tse Cheng, John Wang, and Ping Liu. Battery cycle life prediction with coupled chemical degradation and fatigue mechanics. Journal of the Electrochemical Society, 159(10):A1730, 2012. doi:10.1149/2.049210jes.\n", + "[5] Madeleine Ecker, Stefan Käbitz, Izaro Laresgoiti, and Dirk Uwe Sauer. Parameterization of a Physico-Chemical Model of a Lithium-Ion Battery: II. Model Validation. Journal of The Electrochemical Society, 162(9):A1849–A1857, 2015. doi:10.1149/2.0541509jes.\n", + "[6] Madeleine Ecker, Thi Kim Dung Tran, Philipp Dechent, Stefan Käbitz, Alexander Warnecke, and Dirk Uwe Sauer. Parameterization of a Physico-Chemical Model of a Lithium-Ion Battery: I. Determination of Parameters. Journal of the Electrochemical Society, 162(9):A1836–A1848, 2015. doi:10.1149/2.0551509jes.\n", + "[7] Alastair Hales, Laura Bravo Diaz, Mohamed Waseem Marzook, Yan Zhao, Yatish Patel, and Gregory Offer. The cell cooling coefficient: a standard to define heat rejection from lithium-ion batteries. Journal of The Electrochemical Society, 166(12):A2383, 2019.\n", + "[8] 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", + "[9] Gi-Heon Kim, Kandler Smith, Kyu-Jin Lee, Shriram Santhanagopalan, and Ahmad Pesaran. Multi-domain modeling of lithium-ion batteries encompassing multi-physics in varied length scales. Journal of the Electrochemical Society, 158(8):A955–A969, 2011. doi:10.1149/1.3597614.\n", + "[10] Michael J. Lain, James Brandon, and Emma Kendrick. Design strategies for high power vs. high energy lithium ion cells. Batteries, 5(4):64, 2019. doi:10.3390/batteries5040064.\n", + "[11] 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", + "[12] Peyman Mohtat, Suhak Lee, Jason B Siegel, and Anna G Stefanopoulou. Towards better estimability of electrode-specific state of health: decoding the cell expansion. Journal of Power Sources, 427:101–111, 2019.\n", + "[13] Peyman Mohtat, Suhak Lee, Valentin Sulzer, Jason B. Siegel, and Anna G. Stefanopoulou. Differential Expansion and Voltage Model for Li-ion Batteries at Practical Charging Rates. Journal of The Electrochemical Society, 167(11):110561, 2020. doi:10.1149/1945-7111/aba5d1.\n", + "[14] Andreas Nyman, Mårten Behm, and Göran Lindbergh. Electrochemical characterisation and modelling of the mass transport phenomena in lipf6–ec–emc electrolyte. Electrochimica Acta, 53(22):6356–6365, 2008.\n", + "[15] Simon E. J. O'Kane, Ian D. Campbell, Mohamed W. J. Marzook, Gregory J. Offer, and Monica Marinescu. Physical origin of the differential voltage minimum associated with lithium plating in li-ion batteries. Journal of The Electrochemical Society, 167(9):090540, may 2020. URL: https://doi.org/10.1149/1945-7111/ab90ac, doi:10.1149/1945-7111/ab90ac.\n", + "[16] Kieran O'Regan, Ferran Brosa Planella, W. Dhammika Widanage, and Emma Kendrick. Thermal-electrochemical parameters of a high energy lithium-ion cylindrical battery. Electrochimica Acta, 425:140700, 2022. doi:10.1016/j.electacta.2022.140700.\n", + "[17] Simon E. J. O'Kane, Weilong Ai, Ganesh Madabattula, Diego Alonso-Alvarez, Robert Timms, Valentin Sulzer, Jacqueline Sophie Edge, Billy Wu, Gregory J. Offer, and Monica Marinescu. Lithium-ion battery degradation: how to model it. Phys. Chem. Chem. Phys., 24:7909-7922, 2022. URL: http://dx.doi.org/10.1039/D2CP00417H, doi:10.1039/D2CP00417H.\n", + "[18] Eric Prada, D. Di Domenico, Y. Creff, J. Bernard, Valérie Sauvant-Moynot, and François Huet. A simplified electrochemical and thermal aging model of LiFePO4-graphite Li-ion batteries: power and capacity fade simulations. Journal of The Electrochemical Society, 160(4):A616, 2013. doi:10.1149/2.053304jes.\n", + "[19] P Ramadass, Bala Haran, Parthasarathy M Gomadam, Ralph White, and Branko N Popov. Development of first principles capacity fade model for li-ion cells. Journal of the Electrochemical Society, 151(2):A196, 2004. doi:10.1149/1.1634273.\n", + "[20] Giles Richardson, Ivan Korotkin, Rahifa Ranom, Michael Castle, and Jamie M. Foster. Generalised single particle models for high-rate operation of graded lithium-ion electrodes: systematic derivation and validation. Electrochimica Acta, 339:135862, 2020. doi:10.1016/j.electacta.2020.135862.\n", + "[21] 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", + "[22] 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", + "[23] Yan Zhao, Yatish Patel, Teng Zhang, and Gregory J Offer. Modeling the effects of thermal gradients induced by tab and surface cooling on lithium ion cell performance. Journal of The Electrochemical Society, 165(13):A3169, 2018.\n", + "\n" + ] + } + ], + "source": [ + "pybamm.print_citations()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pybamm", + "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": "187972e187ab8dfbecfab9e8e194ae6d08262b2d51a54fa40644e3ddb6b5f74c" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 } diff --git a/docs/source/examples/notebooks/models/jelly-roll-model.ipynb b/docs/source/examples/notebooks/models/jelly-roll-model.ipynb index 7e2fb202c4..bc00edeb7d 100644 --- a/docs/source/examples/notebooks/models/jelly-roll-model.ipynb +++ b/docs/source/examples/notebooks/models/jelly-roll-model.ipynb @@ -261,13 +261,16 @@ "phi_n = solution[\"Negative potential\"]\n", "phi_p = solution[\"Positive potential\"]\n", "\n", + "\n", "def alpha(r):\n", " return 2 * (phi_n(x=r) - phi_p(x=r))\n", "\n", + "\n", "def phi_am1(r, theta):\n", " # careful here - phi always returns a column vector so we need to add a new axis to r to get the right shape \n", " return alpha(r) * (r[:,np.newaxis]/eps - r0/eps - delta - theta / 2 / pi) / (1 - 4*delta) + phi_p(x=r)\n", "\n", + "\n", "def phi_am2(r, theta):\n", " # careful here - phi always returns a column vector so we need to add a new axis to r to get the right shape \n", " return alpha(r) * (r0/eps + 1 - delta + theta / 2 / pi - r[:,np.newaxis]/eps) / (1 - 4*delta) + phi_p(x=r)" @@ -281,17 +284,25 @@ "outputs": [], "source": [ "# define spiral \n", - "spiral_pos_inner = lambda t : r0 - eps * delta + eps * t / (2 * pi)\n", - "spiral_pos_outer = lambda t : r0 + eps * delta + eps * t / (2 * pi)\n", + "def spiral_pos_inner(t):\n", + " return r0 - eps * delta + eps * t / (2 * pi)\n", + "def spiral_pos_outer(t):\n", + " return r0 + eps * delta + eps * t / (2 * pi)\n", "\n", - "spiral_neg_inner = lambda t : r0 - eps * delta + eps/2 + eps * t / (2 * pi)\n", - "spiral_neg_outer = lambda t : r0 + eps * delta + eps/2 + eps * t / (2 * pi)\n", + "def spiral_neg_inner(t):\n", + " return r0 - eps * delta + eps / 2 + eps * t / (2 * pi)\n", + "def spiral_neg_outer(t):\n", + " return r0 + eps * delta + eps / 2 + eps * t / (2 * pi)\n", "\n", - "spiral_am1_inner = lambda t : r0 + eps * delta + eps * t / (2 * pi)\n", - "spiral_am1_outer = lambda t : r0 - eps * delta + eps/2 + eps * t / (2 * pi)\n", + "def spiral_am1_inner(t):\n", + " return r0 + eps * delta + eps * t / (2 * pi)\n", + "def spiral_am1_outer(t):\n", + " return r0 - eps * delta + eps / 2 + eps * t / (2 * pi)\n", "\n", - "spiral_am2_inner = lambda t : r0 + eps * delta + eps/2 + eps * t / (2 * pi)\n", - "spiral_am2_outer = lambda t : r0 - eps * delta + eps + eps * t / (2 * pi)" + "def spiral_am2_inner(t):\n", + " return r0 + eps * delta + eps / 2 + eps * t / (2 * pi)\n", + "def spiral_am2_outer(t):\n", + " return r0 - eps * delta + eps + eps * t / (2 * pi)" ] }, { diff --git a/docs/source/examples/notebooks/models/lithium-plating.ipynb b/docs/source/examples/notebooks/models/lithium-plating.ipynb index d9c4fcf842..c8c195d76d 100644 --- a/docs/source/examples/notebooks/models/lithium-plating.ipynb +++ b/docs/source/examples/notebooks/models/lithium-plating.ipynb @@ -27,7 +27,6 @@ "%pip install pybamm -q # install PyBaMM if it is not installed\n", "import pybamm\n", "import os\n", - "import numpy as np\n", "import matplotlib.pyplot as plt\n", "os.chdir(pybamm.__path__[0]+'/..')" ] @@ -142,6 +141,7 @@ "\n", " return sims\n", "\n", + "\n", "sims_reversible = define_and_solve_sims(models[\"reversible\"], experiments, parameter_values)" ] }, @@ -176,6 +176,7 @@ " \"Sum of x-averaged negative electrode volumetric interfacial current densities [A.m-3]\"\n", "]\n", "\n", + "\n", "def plot(sims):\n", " fig, axs = plt.subplots(2, 2, figsize=(13,9))\n", " for (C_rate,sim), color in zip(sims.items(),colors):\n", @@ -215,6 +216,7 @@ "\n", " return fig, axs\n", "\n", + "\n", "plot(sims_reversible);" ] }, diff --git a/docs/source/examples/notebooks/models/pouch-cell-model.ipynb b/docs/source/examples/notebooks/models/pouch-cell-model.ipynb index 7add73fcb8..3da4b56415 100644 --- a/docs/source/examples/notebooks/models/pouch-cell-model.ipynb +++ b/docs/source/examples/notebooks/models/pouch-cell-model.ipynb @@ -56,7 +56,6 @@ "source": [ "%pip install pybamm -q # install PyBaMM if it is not installed\n", "import pybamm\n", - "import sys\n", "import pickle\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -505,7 +504,7 @@ " mode=\"expand\",\n", " )\n", "\n", - " leg2 = ax[0, 1].legend(\n", + " ax[0, 1].legend(\n", " [comsol_p, pybamm_p, dfncc_p],\n", " [\"COMSOL\", r\"$1+1$D\", \"DFNCC\"],\n", " bbox_to_anchor=(0, 1.5, 1.0, 0.102),\n", @@ -642,6 +641,7 @@ "dfncc_var = dfncc_vars[var]\n", "V_dfncc = dfncc_vars[\"Voltage [V]\"]\n", "\n", + "\n", "def dfncc_var_fun(t, z):\n", " return dfncc_var(t=t, z=z) - V_dfncc(t)\n", "\n", diff --git a/docs/source/examples/notebooks/models/submodel_cracking_DFN_or_SPM.ipynb b/docs/source/examples/notebooks/models/submodel_cracking_DFN_or_SPM.ipynb index c8f4c6913f..762420d814 100644 --- a/docs/source/examples/notebooks/models/submodel_cracking_DFN_or_SPM.ipynb +++ b/docs/source/examples/notebooks/models/submodel_cracking_DFN_or_SPM.ipynb @@ -26,7 +26,6 @@ "%pip install pybamm -q # install PyBaMM if it is not installed\n", "import pybamm\n", "import os\n", - "import numpy as np\n", "import matplotlib.pyplot as plt\n", "os.chdir(pybamm.__path__[0]+'/..')" ] @@ -71,7 +70,7 @@ "source": [ "param = pybamm.ParameterValues(\"Ai2020\")\n", "## It can update the speed of crack propagation using the commands below:\n", - "# param.update({\"Negative electrode Cracking rate\":3.9e-20*10})\n" + "# param.update({\"Negative electrode Cracking rate\":3.9e-20*10})" ] }, { @@ -152,6 +151,8 @@ "r_n = solution[\"r_n [m]\"].entries[:, 0, 0]\n", "\n", "# plot\n", + "\n", + "\n", "def plot_concentrations(t):\n", " f, (ax1, ax2, ax3, ax4) = plt.subplots(1, 4 ,figsize=(20,4))\n", " ax1.plot(x, stress_t_n_surf(t=t,x=x) / E_n)\n", @@ -180,6 +181,7 @@ " ax4.grid()\n", " plt.show()\n", " \n", + "\n", "import ipywidgets as widgets\n", "widgets.interact(plot_concentrations, t=widgets.FloatSlider(min=0,max=3600,step=10,value=0));" ] @@ -221,7 +223,7 @@ " \"X-averaged positive particle crack length [m]\"\n", "]\n", "quick_plot = pybamm.QuickPlot(solution, output_variables, label,variable_limits='tight')\n", - "quick_plot.dynamic_plot();\n" + "quick_plot.dynamic_plot();" ] }, { diff --git a/docs/source/examples/notebooks/models/submodel_loss_of_active_materials.ipynb b/docs/source/examples/notebooks/models/submodel_loss_of_active_materials.ipynb index 0f01da45b8..857b54a6bd 100644 --- a/docs/source/examples/notebooks/models/submodel_loss_of_active_materials.ipynb +++ b/docs/source/examples/notebooks/models/submodel_loss_of_active_materials.ipynb @@ -26,7 +26,6 @@ "%pip install pybamm -q # install PyBaMM if it is not installed\n", "import pybamm\n", "import os\n", - "import numpy as np\n", "import matplotlib.pyplot as plt\n", "os.chdir(pybamm.__path__[0]+'/..')\n", "# Here the model is applicable to SPM, SPMe and DFN\n", @@ -93,7 +92,6 @@ } ], "source": [ - "\n", "# ploting the results\n", "f, (ax1, ax2, ax3) = plt.subplots(1, 3 ,figsize=(18,4))\n", "\n", @@ -235,7 +233,6 @@ } ], "source": [ - "\n", "t_all2 = solution2[\"Time [h]\"].entries\n", "t_all3 = solution3[\"Time [h]\"].entries\n", "LAM_n_all2 = solution2[\"X-averaged negative electrode active material volume fraction\"].entries\n", @@ -324,7 +321,6 @@ } ], "source": [ - "\n", "v_all = solution[\"Voltage [V]\"].entries\n", "v_all2 = solution2[\"Voltage [V]\"].entries\n", "v_all3 = solution3[\"Voltage [V]\"].entries\n", @@ -362,7 +358,7 @@ "#ax3.set_xlim(2,2.8)\n", "#ax3.set_ylim(2.492,2.494)\n", "ax3.set_ylim(-2.494,-2.492)\n", - "plt.tight_layout(pad=1.0)\n" + "plt.tight_layout(pad=1.0)" ] }, { diff --git a/docs/source/examples/notebooks/models/using-model-options_thermal-example.ipynb b/docs/source/examples/notebooks/models/using-model-options_thermal-example.ipynb index 85c91ec0d0..a98409adb7 100644 --- a/docs/source/examples/notebooks/models/using-model-options_thermal-example.ipynb +++ b/docs/source/examples/notebooks/models/using-model-options_thermal-example.ipynb @@ -35,8 +35,6 @@ "%pip install pybamm -q # install PyBaMM if it is not installed\n", "import pybamm\n", "import os\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", "os.chdir(pybamm.__path__[0]+'/..')" ] }, diff --git a/docs/source/examples/notebooks/parameterization/change-input-current.ipynb b/docs/source/examples/notebooks/parameterization/change-input-current.ipynb index 4240b70f1a..527c61d2fc 100644 --- a/docs/source/examples/notebooks/parameterization/change-input-current.ipynb +++ b/docs/source/examples/notebooks/parameterization/change-input-current.ipynb @@ -235,6 +235,8 @@ "outputs": [], "source": [ "# create user-defined function\n", + "\n", + "\n", "def my_fun(A, omega):\n", " def current(t):\n", " return A * pybamm.sin(2 * np.pi * omega * t)\n", @@ -265,7 +267,7 @@ "# set user defined current function\n", "A = model.param.I_typ\n", "omega = 0.1\n", - "param[\"Current function [A]\"] = my_fun(A,omega)\n" + "param[\"Current function [A]\"] = my_fun(A,omega)" ] }, { diff --git a/docs/source/examples/notebooks/parameterization/parameter-values.ipynb b/docs/source/examples/notebooks/parameterization/parameter-values.ipynb index 4b10e9579c..1775cae09d 100644 --- a/docs/source/examples/notebooks/parameterization/parameter-values.ipynb +++ b/docs/source/examples/notebooks/parameterization/parameter-values.ipynb @@ -33,11 +33,9 @@ "source": [ "%pip install pybamm -q # install PyBaMM if it is not installed\n", "import pybamm\n", - "import tests\n", "import numpy as np\n", "import os\n", "import matplotlib.pyplot as plt\n", - "from pprint import pprint\n", "os.chdir(pybamm.__path__[0]+'/..')" ] }, @@ -130,6 +128,8 @@ "source": [ "def cubed(x):\n", " return x ** 3\n", + "\n", + "\n", "parameter_values.update({\"cube function\": cubed}, check_already_exists=False)\n", "print(\"parameter values are {}\".format(parameter_values))" ] diff --git a/docs/source/examples/notebooks/parameterization/parameterization.ipynb b/docs/source/examples/notebooks/parameterization/parameterization.ipynb index 211884f19d..e2f915b1c1 100644 --- a/docs/source/examples/notebooks/parameterization/parameterization.ipynb +++ b/docs/source/examples/notebooks/parameterization/parameterization.ipynb @@ -1,1817 +1,1821 @@ { - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Parameterisation\n", - "\n", - "In this notebook, we show how to find which parameters are needed in a model and define them.\n", - "\n", - "For other notebooks about parameterization, see:\n", - "\n", - "- The API documentation of [Parameters](https://pybamm.readthedocs.io/en/latest/source/api/parameters/index.html)\n", - "- [Setting parameter values](https://github.com/pybamm-team/PyBaMM/blob/develop/examples/notebooks/Getting%20Started/Tutorial%204%20-%20Setting%20parameter%20values.ipynb) can be found at `pybamm/examples/notebooks/Getting Started/Tutorial 4 - Setting parameter values.ipynb`. This explains the basics of how to set the parameters of a model (in less detail than here).\n", - "- [parameter-values.ipynb](https://github.com/pybamm-team/PyBaMM/blob/develop/examples/notebooks/parameterization/parameter-values.ipynb) can be found at `pybamm/examples/notebooks/parameterization/parameter-values.ipynb`. This explains the basics of the `ParameterValues` class.\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding your own parameter sets (using a dictionary)\n", - "\n", - "We will be using the model defined and explained in more detail in [3-negative-particle-problem.ipynb](https://github.com/pybamm-team/PyBaMM/blob/develop/examples/notebooks/Creating%20Models/3-negative-particle-problem.ipynb) example notebook. We begin by importing the required libraries" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], - "source": [ - "%pip install pybamm -q # install PyBaMM if it is not installed\n", - "import pybamm\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setting up the model\n", - "\n", - "We define all the parameters and variables using `pybamm.Parameter` and `pybamm.Variable` respectively." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "c = pybamm.Variable(\"Concentration [mol.m-3]\", domain=\"negative particle\")\n", - "\n", - "R = pybamm.Parameter(\"Particle radius [m]\")\n", - "D = pybamm.FunctionParameter(\"Diffusion coefficient [m2.s-1]\", {\"Concentration [mol.m-3]\": c})\n", - "j = pybamm.InputParameter(\"Interfacial current density [A.m-2]\")\n", - "c0 = pybamm.Parameter(\"Initial concentration [mol.m-3]\")\n", - "c_e = pybamm.Parameter(\"Electrolyte concentration [mol.m-3]\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we define our model equations, boundary and initial conditions. We also add the variables required using the dictionary `model.variables`" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "model = pybamm.BaseModel()\n", - "\n", - "# governing equations\n", - "N = -D * pybamm.grad(c) # flux\n", - "dcdt = -pybamm.div(N)\n", - "model.rhs = {c: dcdt} \n", - "\n", - "# boundary conditions \n", - "lbc = pybamm.Scalar(0)\n", - "rbc = -j\n", - "model.boundary_conditions = {c: {\"left\": (lbc, \"Neumann\"), \"right\": (rbc, \"Neumann\")}}\n", - "\n", - "# initial conditions \n", - "model.initial_conditions = {c: c0}\n", - "\n", - "model.variables = {\n", - " \"Concentration [mol.m-3]\": c,\n", - " \"Surface concentration [mol.m-3]\": pybamm.surf(c),\n", - " \"Flux [mol.m-2.s-1]\": N,\n", - "}" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We also define the geometry, since there are parameters in the geometry too" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "r = pybamm.SpatialVariable(\"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\")\n", - "geometry = pybamm.Geometry({\"negative particle\": {r: {\"min\": pybamm.Scalar(0), \"max\": R}}})" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Finding the parameters required" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To know what parameters are required by the model and geometry, we can do" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "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", - "Particle radius [m] (Parameter)\n" - ] - } - ], - "source": [ - "model.print_parameter_info()\n", - "geometry.print_parameter_info()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This tells us that we need to provide parameter values for the initial concentration and Faraday constant, an `InputParameter` at solve time for the interfacial current density, and diffusivity as a function of concentration. Since the electrolyte concentration does not appear anywhere in the model, there is no need to provide a value for it." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding the parameters\n", - "\n", - "Now we can proceed to the step where we add the `parameter` values using a dictionary. We set up a dictionary with parameter names as the dictionary keys and their respective values as the dictionary values." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "def D_fun(c):\n", - " return 3.9 #* pybamm.exp(-c)\n", - "\n", - "values = {\n", - " \"Particle radius [m]\": 2,\n", - " \"Diffusion coefficient [m2.s-1]\": D_fun,\n", - " \"Initial concentration [mol.m-3]\": 2.5,\n", - "}" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can pass this dictionary in `pybamm.ParameterValues` class which accepts a dictionary of parameter names and values. We can then print `param` to check if it was initialised." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Diffusion coefficient [m2.s-1]': ,\n", - " 'Initial concentration [mol.m-3]': 2.5,\n", - " 'Particle radius [m]': 2}" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "param = pybamm.ParameterValues(values)\n", - "\n", - "param" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Updating the parameter values\n", - "\n", - "The parameter values or `param` can be further updated by using the `update` function of `ParameterValues` class. The `update` function takes a dictionary with keys being the parameters to be updated and their respective values being the updated values. Here we update the `\"Particle radius [m]\"` parameter's value. Additionally, a function can also be passed as a `parameter`'s value which we will see ahead, and a new `parameter` can also be added by passing `check_already_exists=False` in the `update` function." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Diffusion coefficient [m2.s-1]': ,\n", - " 'Initial concentration [mol.m-3]': 1.5,\n", - " 'Particle radius [m]': 2}" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "param.update({\"Initial concentration [mol.m-3]\": 1.5})\n", - "param" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Solving the model " - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Finding the parameters in a model\n", - "\n", - "The `parameter` function of the `BaseModel` class can be used to obtain the parameters of a model." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[Parameter(-0x7c3ebfeae2200290, Initial concentration [mol.m-3], children=[], domains={}),\n", - " InputParameter(-0x4a08933302b1e44e, Interfacial current density [A.m-2], children=[], domains={}),\n", - " FunctionParameter(0x66f7cbc27c44053b, Diffusion coefficient [m2.s-1], children=['Concentration [mol.m-3]'], domains={'primary': ['negative particle']})]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "parameters = model.parameters\n", - "parameters" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As explained in the [3-negative-particle-problem.ipynb](https://github.com/pybamm-team/PyBaMM/blob/develop/examples/notebooks/Creating%20Models/3-negative-particle-problem.ipynb) example, we first process both the `model` and the `geometry`." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "param.process_model(model)\n", - "param.process_geometry(geometry)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now set up our mesh, choose a spatial method, and discretise our model" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "submesh_types = {\"negative particle\": pybamm.Uniform1DSubMesh}\n", - "var_pts = {r: 20}\n", - "mesh = pybamm.Mesh(geometry, submesh_types, var_pts)\n", - "\n", - "spatial_methods = {\"negative particle\": pybamm.FiniteVolume()}\n", - "disc = pybamm.Discretisation(mesh, spatial_methods)\n", - "disc.process_model(model);" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We choose a solver and times at which we want the solution returned, and solve the model. Here we give a value for the current density `j`." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# solve\n", - "solver = pybamm.ScipySolver()\n", - "t = np.linspace(0, 3600, 600)\n", - "solution = solver.solve(model, t, inputs={\"Interfacial current density [A.m-2]\": 1.4})\n", - "\n", - "# post-process, so that the solution can be called at any time t or space r\n", - "# (using interpolation)\n", - "c = solution[\"Concentration [mol.m-3]\"]\n", - "c_surf = solution[\"Surface concentration [mol.m-3]\"]\n", - "\n", - "# plot\n", - "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13, 4))\n", - "\n", - "ax1.plot(solution.t, c_surf(solution.t))\n", - "ax1.set_xlabel(\"Time [s]\")\n", - "ax1.set_ylabel(\"Surface concentration [mol.m-3]\")\n", - "\n", - "rsol = mesh[\"negative particle\"].nodes # radial position\n", - "time = 1000 # time in seconds\n", - "ax2.plot(rsol * 1e6, c(t=time, r=rsol), label=\"t={}[s]\".format(time))\n", - "ax2.set_xlabel(\"Particle radius [microns]\")\n", - "ax2.set_ylabel(\"Concentration [mol.m-3]\")\n", - "ax2.legend()\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Using pre-defined models in `PyBaMM`\n", - "\n", - "In the next few steps, we will be showing the same workflow with the Single Particle Model (`SPM`). We will also see how you can pass a function as a `parameter`'s value and how to plot such `parameter functions`.\n", - "\n", - "We start by initializing our model" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "spm = pybamm.lithium_ion.SPM()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Finding the parameters in a model\n", - "\n", - "We can print the `parameters` of a model by using the `get_parameters_info` function." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Maximum concentration in positive electrode [mol.m-3] (Parameter)\n", - "Initial concentration in electrolyte [mol.m-3] (Parameter)\n", - "Separator thickness [m] (Parameter)\n", - "Positive electrode Bruggeman coefficient (electrode) (Parameter)\n", - "Negative electrode thickness [m] (Parameter)\n", - "Electrode height [m] (Parameter)\n", - "Negative electrode Bruggeman coefficient (electrode) (Parameter)\n", - "Number of cells connected in series to make a battery (Parameter)\n", - "Negative electrode Bruggeman coefficient (electrolyte) (Parameter)\n", - "Maximum concentration in negative electrode [mol.m-3] (Parameter)\n", - "Positive electrode Bruggeman coefficient (electrolyte) (Parameter)\n", - "Lower voltage cut-off [V] (Parameter)\n", - "Nominal cell capacity [A.h] (Parameter)\n", - "Typical electrolyte concentration [mol.m-3] (Parameter)\n", - "Upper voltage cut-off [V] (Parameter)\n", - "Positive electrode electrons in reaction (Parameter)\n", - "Negative electrode electrons in reaction (Parameter)\n", - "Initial temperature [K] (Parameter)\n", - "Reference temperature [K] (Parameter)\n", - "Positive electrode thickness [m] (Parameter)\n", - "Number of electrodes connected in parallel to make a cell (Parameter)\n", - "Electrode width [m] (Parameter)\n", - "Separator Bruggeman coefficient (electrolyte) (Parameter)\n", - "Positive particle radius [m] (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", - "Positive electrode OCP [V] (FunctionParameter with input(s) 'Positive particle stoichiometry')\n", - "Separator porosity (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", - "Current function [A] (FunctionParameter with input(s) 'Time[s]')\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", - "Ambient temperature [K] (FunctionParameter with input(s) 'Time [s]')\n", - "Negative particle radius [m] (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\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", - "Negative electrode active material volume fraction (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", - "Positive electrode active material volume fraction (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", - "Negative electrode porosity (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", - "Negative electrode diffusivity [m2.s-1] (FunctionParameter with input(s) 'Negative particle stoichiometry', 'Temperature [K]')\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", - "Initial concentration in negative electrode [mol.m-3] (FunctionParameter with input(s) 'Radial distance (r) [m]', 'Through-cell distance (x) [m]')\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", - "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", - "\n" - ] - } - ], - "source": [ - "spm.print_parameter_info()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that there are no `InputParameter` objects in the default SPM. Also, note that if a `FunctionParameter` is expected, it is ok to provide a scalar (parameter) instead. However, if a `Parameter` is expected, you cannot provide a function instead." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Another way to view what parameters are needed is to print the default parameter values. This can also be used to get some good defaults (but care must be taken when combining parameters across datasets and chemistries)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'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]': ,\n", - " 'Negative electrode OCP [V]': ,\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 electrons in reaction': 1.0,\n", - " 'Negative electrode exchange-current density [A.m-2]': ,\n", - " 'Negative electrode OCP entropic change [V.K-1]': ,\n", - " 'Maximum concentration in positive electrode [mol.m-3]': 51217.9257309275,\n", - " 'Positive electrode diffusivity [m2.s-1]': ,\n", - " 'Positive electrode OCP [V]': ,\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 electrons in reaction': 1.0,\n", - " 'Positive electrode exchange-current density [A.m-2]': ,\n", - " 'Positive electrode OCP entropic change [V.K-1]': ,\n", - " 'Separator porosity': 1.0,\n", - " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n", - " 'Typical electrolyte concentration [mol.m-3]': 1000.0,\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,\n", - " 'Initial temperature [K]': 298.15}" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "{k: v for k,v in spm.default_parameter_values.items() if k in spm._parameter_info}" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now define a dictionary of values for `ParameterValues` as before (here, a subset of the `Chen2020` parameters)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Ambient temperature [K]': 298.15,\n", - " 'Current function [A]': 5.0,\n", - " 'Electrode height [m]': 0.065,\n", - " 'Electrode width [m]': 1.58,\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]': ,\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]': ,\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": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def graphite_mcmb2528_diffusivity_Dualfoil1998(sto, T):\n", - " D_ref = 3.9 * 10 ** (-14)\n", - " E_D_s = 42770\n", - " arrhenius = exp(E_D_s / constants.R * (1 / 298.15 - 1 / T))\n", - " return D_ref * arrhenius\n", - "\n", - "neg_ocp = np.array([[0. , 1.81772748],\n", - " [0.03129623, 1.0828807 ],\n", - " [0.03499902, 0.99593794],\n", - " [0.0387018 , 0.90023398],\n", - " [0.04240458, 0.79649431],\n", - " [0.04610736, 0.73354429],\n", - " [0.04981015, 0.66664314],\n", - " [0.05351292, 0.64137149],\n", - " [0.05721568, 0.59813869],\n", - " [0.06091845, 0.5670836 ],\n", - " [0.06462122, 0.54746181],\n", - " [0.06832399, 0.53068399],\n", - " [0.07202675, 0.51304734],\n", - " [0.07572951, 0.49394092],\n", - " [0.07943227, 0.47926274],\n", - " [0.08313503, 0.46065259],\n", - " [0.08683779, 0.45992726],\n", - " [0.09054054, 0.43801501],\n", - " [0.09424331, 0.42438665],\n", - " [0.09794607, 0.41150269],\n", - " [0.10164883, 0.40033659],\n", - " [0.10535158, 0.38957134],\n", - " [0.10905434, 0.37756538],\n", - " [0.1127571 , 0.36292541],\n", - " [0.11645985, 0.34357086],\n", - " [0.12016261, 0.3406314 ],\n", - " [0.12386536, 0.32299468],\n", - " [0.12756811, 0.31379458],\n", - " [0.13127086, 0.30795386],\n", - " [0.13497362, 0.29207319],\n", - " [0.13867638, 0.28697687],\n", - " [0.14237913, 0.27405477],\n", - " [0.14608189, 0.2670497 ],\n", - " [0.14978465, 0.25857493],\n", - " [0.15348741, 0.25265783],\n", - " [0.15719018, 0.24826777],\n", - " [0.16089294, 0.2414345 ],\n", - " [0.1645957 , 0.23362778],\n", - " [0.16829847, 0.22956218],\n", - " [0.17200122, 0.22370236],\n", - " [0.17570399, 0.22181271],\n", - " [0.17940674, 0.22089651],\n", - " [0.1831095 , 0.2194268 ],\n", - " [0.18681229, 0.21830064],\n", - " [0.19051504, 0.21845333],\n", - " [0.1942178 , 0.21753715],\n", - " [0.19792056, 0.21719357],\n", - " [0.20162334, 0.21635373],\n", - " [0.2053261 , 0.21667822],\n", - " [0.20902886, 0.21738444],\n", - " [0.21273164, 0.21469313],\n", - " [0.2164344 , 0.21541846],\n", - " [0.22013716, 0.21465495],\n", - " [0.22383993, 0.2135479 ],\n", - " [0.2275427 , 0.21392964],\n", - " [0.23124547, 0.21074206],\n", - " [0.23494825, 0.20873788],\n", - " [0.23865101, 0.20465319],\n", - " [0.24235377, 0.20205732],\n", - " [0.24605653, 0.19774358],\n", - " [0.2497593 , 0.19444147],\n", - " [0.25346208, 0.19190285],\n", - " [0.25716486, 0.18850531],\n", - " [0.26086762, 0.18581399],\n", - " [0.26457039, 0.18327537],\n", - " [0.26827314, 0.18157659],\n", - " [0.2719759 , 0.17814088],\n", - " [0.27567867, 0.17529686],\n", - " [0.27938144, 0.1719375 ],\n", - " [0.28308421, 0.16934161],\n", - " [0.28678698, 0.16756649],\n", - " [0.29048974, 0.16609676],\n", - " [0.29419251, 0.16414985],\n", - " [0.29789529, 0.16260378],\n", - " [0.30159806, 0.16224113],\n", - " [0.30530083, 0.160027 ],\n", - " [0.30900361, 0.15827096],\n", - " [0.31270637, 0.1588054 ],\n", - " [0.31640913, 0.15552238],\n", - " [0.32011189, 0.15580869],\n", - " [0.32381466, 0.15220118],\n", - " [0.32751744, 0.1511132 ],\n", - " [0.33122021, 0.14987253],\n", - " [0.33492297, 0.14874637],\n", - " [0.33862575, 0.14678037],\n", - " [0.34232853, 0.14620776],\n", - " [0.34603131, 0.14555879],\n", - " [0.34973408, 0.14389819],\n", - " [0.35343685, 0.14359279],\n", - " [0.35713963, 0.14242846],\n", - " [0.36084241, 0.14038612],\n", - " [0.36454517, 0.13882096],\n", - " [0.36824795, 0.13954628],\n", - " [0.37195071, 0.13946992],\n", - " [0.37565348, 0.13780934],\n", - " [0.37935626, 0.13973714],\n", - " [0.38305904, 0.13698858],\n", - " [0.38676182, 0.13523254],\n", - " [0.3904646 , 0.13441178],\n", - " [0.39416737, 0.1352898 ],\n", - " [0.39787015, 0.13507985],\n", - " [0.40157291, 0.13647321],\n", - " [0.40527567, 0.13601512],\n", - " [0.40897844, 0.13435452],\n", - " [0.41268121, 0.1334765 ],\n", - " [0.41638398, 0.1348317 ],\n", - " [0.42008676, 0.13275118],\n", - " [0.42378953, 0.13286571],\n", - " [0.4274923 , 0.13263667],\n", - " [0.43119506, 0.13456447],\n", - " [0.43489784, 0.13471718],\n", - " [0.43860061, 0.13395369],\n", - " [0.44230338, 0.13448814],\n", - " [0.44600615, 0.1334765 ],\n", - " [0.44970893, 0.13298023],\n", - " [0.45341168, 0.13259849],\n", - " [0.45711444, 0.13338107],\n", - " [0.46081719, 0.13309476],\n", - " [0.46451994, 0.13275118],\n", - " [0.46822269, 0.13443087],\n", - " [0.47192545, 0.13315202],\n", - " [0.47562821, 0.132713 ],\n", - " [0.47933098, 0.1330184 ],\n", - " [0.48303375, 0.13278936],\n", - " [0.48673651, 0.13225491],\n", - " [0.49043926, 0.13317111],\n", - " [0.49414203, 0.13263667],\n", - " [0.49784482, 0.13187316],\n", - " [0.50154759, 0.13265574],\n", - " [0.50525036, 0.13250305],\n", - " [0.50895311, 0.13324745],\n", - " [0.51265586, 0.13204496],\n", - " [0.51635861, 0.13242669],\n", - " [0.52006139, 0.13233127],\n", - " [0.52376415, 0.13198769],\n", - " [0.52746692, 0.13254122],\n", - " [0.53116969, 0.13145325],\n", - " [0.53487245, 0.13298023],\n", - " [0.53857521, 0.13168229],\n", - " [0.54227797, 0.1313578 ],\n", - " [0.54598074, 0.13235036],\n", - " [0.5496835 , 0.13120511],\n", - " [0.55338627, 0.13089971],\n", - " [0.55708902, 0.13109058],\n", - " [0.56079178, 0.13082336],\n", - " [0.56449454, 0.13011713],\n", - " [0.5681973 , 0.129869 ],\n", - " [0.57190006, 0.12992626],\n", - " [0.57560282, 0.12942998],\n", - " [0.57930558, 0.12796026],\n", - " [0.58300835, 0.12862831],\n", - " [0.58671112, 0.12656689],\n", - " [0.59041389, 0.12734947],\n", - " [0.59411664, 0.12509716],\n", - " [0.59781941, 0.12110791],\n", - " [0.60152218, 0.11839751],\n", - " [0.60522496, 0.11244226],\n", - " [0.60892772, 0.11307214],\n", - " [0.61263048, 0.1092165 ],\n", - " [0.61633325, 0.10683058],\n", - " [0.62003603, 0.10433014],\n", - " [0.6237388 , 0.10530359],\n", - " [0.62744156, 0.10056993],\n", - " [0.63114433, 0.09950104],\n", - " [0.63484711, 0.09854668],\n", - " [0.63854988, 0.09921473],\n", - " [0.64225265, 0.09541635],\n", - " [0.64595543, 0.09980643],\n", - " [0.64965823, 0.0986612 ],\n", - " [0.653361 , 0.09560722],\n", - " [0.65706377, 0.09755413],\n", - " [0.66076656, 0.09612258],\n", - " [0.66446934, 0.09430929],\n", - " [0.66817212, 0.09661885],\n", - " [0.67187489, 0.09366032],\n", - " [0.67557767, 0.09522548],\n", - " [0.67928044, 0.09535909],\n", - " [0.68298322, 0.09316404],\n", - " [0.686686 , 0.09450016],\n", - " [0.69038878, 0.0930877 ],\n", - " [0.69409156, 0.09343126],\n", - " [0.69779433, 0.0932404 ],\n", - " [0.70149709, 0.09350762],\n", - " [0.70519988, 0.09339309],\n", - " [0.70890264, 0.09291591],\n", - " [0.7126054 , 0.09303043],\n", - " [0.71630818, 0.0926296 ],\n", - " [0.72001095, 0.0932404 ],\n", - " [0.72371371, 0.09261052],\n", - " [0.72741648, 0.09249599],\n", - " [0.73111925, 0.09240055],\n", - " [0.73482204, 0.09253416],\n", - " [0.7385248 , 0.09209515],\n", - " [0.74222757, 0.09234329],\n", - " [0.74593034, 0.09366032],\n", - " [0.74963312, 0.09333583],\n", - " [0.75333589, 0.09322131],\n", - " [0.75703868, 0.09264868],\n", - " [0.76074146, 0.09253416],\n", - " [0.76444422, 0.09243873],\n", - " [0.76814698, 0.09230512],\n", - " [0.77184976, 0.09310678],\n", - " [0.77555253, 0.09165615],\n", - " [0.77925531, 0.09159888],\n", - " [0.78295807, 0.09207606],\n", - " [0.78666085, 0.09175158],\n", - " [0.79036364, 0.09177067],\n", - " [0.79406641, 0.09236237],\n", - " [0.79776918, 0.09241964],\n", - " [0.80147197, 0.09320222],\n", - " [0.80517474, 0.09199972],\n", - " [0.80887751, 0.09167523],\n", - " [0.81258028, 0.09322131],\n", - " [0.81628304, 0.09190428],\n", - " [0.81998581, 0.09167523],\n", - " [0.82368858, 0.09285865],\n", - " [0.82739136, 0.09180884],\n", - " [0.83109411, 0.09150345],\n", - " [0.83479688, 0.09186611],\n", - " [0.83849965, 0.0920188 ],\n", - " [0.84220242, 0.09320222],\n", - " [0.84590519, 0.09131257],\n", - " [0.84960797, 0.09117896],\n", - " [0.85331075, 0.09133166],\n", - " [0.85701353, 0.09089265],\n", - " [0.86071631, 0.09058725],\n", - " [0.86441907, 0.09051091],\n", - " [0.86812186, 0.09033912],\n", - " [0.87182464, 0.09041547],\n", - " [0.87552742, 0.0911217 ],\n", - " [0.87923019, 0.0894611 ],\n", - " [0.88293296, 0.08999555],\n", - " [0.88663573, 0.08921297],\n", - " [0.89033849, 0.08881213],\n", - " [0.89404126, 0.08797229],\n", - " [0.89774404, 0.08709427],\n", - " [0.9014468 , 0.08503284],\n", - " [1. , 0.07601531]])\n", - "\n", - "pos_ocp = np.array([[0.24879728, 4.4 ],\n", - " [0.26614516, 4.2935653 ],\n", - " [0.26886763, 4.2768621 ],\n", - " [0.27159011, 4.2647018 ],\n", - " [0.27431258, 4.2540312 ],\n", - " [0.27703505, 4.2449446 ],\n", - " [0.27975753, 4.2364879 ],\n", - " [0.28248 , 4.2302647 ],\n", - " [0.28520247, 4.2225528 ],\n", - " [0.28792495, 4.2182574 ],\n", - " [0.29064743, 4.213294 ],\n", - " [0.29336992, 4.2090373 ],\n", - " [0.29609239, 4.2051239 ],\n", - " [0.29881487, 4.2012677 ],\n", - " [0.30153735, 4.1981564 ],\n", - " [0.30425983, 4.1955218 ],\n", - " [0.30698231, 4.1931167 ],\n", - " [0.30970478, 4.1889744 ],\n", - " [0.31242725, 4.1881533 ],\n", - 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" [0.88686877, 3.5801177 ],\n", - " [0.88959124, 3.5778842 ],\n", - " [0.89231371, 3.5763381 ],\n", - " [0.8950362 , 3.5737801 ],\n", - " [0.89775868, 3.5721002 ],\n", - " [0.90048116, 3.5702102 ],\n", - " [0.90320364, 3.5684922 ],\n", - " [0.90592613, 3.5672133 ],\n", - " [1. , 3.52302167]])\n", - "\n", - "from pybamm import exp, constants\n", - "\n", - "\n", - "def graphite_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, c_n_max, T):\n", - " m_ref = 6.48e-7 # (A/m2)(m3/mol)**1.5 - includes ref concentrations\n", - " E_r = 35000\n", - " arrhenius = exp(E_r / constants.R * (1 / 298.15 - 1 / T))\n", - "\n", - " return (\n", - " m_ref * arrhenius * c_e ** 0.5 * c_s_surf ** 0.5 * (c_n_max - c_s_surf) ** 0.5\n", - " )\n", - "\n", - "def nmc_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, c_p_max, T):\n", - " m_ref = 3.42e-6 # (A/m2)(m3/mol)**1.5 - includes ref concentrations\n", - " E_r = 17800\n", - " arrhenius = exp(E_r / constants.R * (1 / 298.15 - 1 / T))\n", - "\n", - " return (\n", - " m_ref * arrhenius * c_e ** 0.5 * c_s_surf ** 0.5 * (c_p_max - c_s_surf) ** 0.5\n", - " )\n", - "\n", - "\n", - "values = {\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", - " 'Typical current [A]': 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]': ('graphite_LGM50_ocp_Chen2020', neg_ocp),\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)': 1.5,\n", - " 'Negative electrode electrons in reaction': 1.0,\n", - " 'Negative electrode exchange-current density [A.m-2]': graphite_LGM50_electrolyte_exchange_current_density_Chen2020,\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]': ('nmc_LGM50_ocp_Chen2020', pos_ocp),\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)': 1.5,\n", - " 'Positive electrode electrons in reaction': 1.0,\n", - " 'Positive electrode exchange-current density [A.m-2]': nmc_LGM50_electrolyte_exchange_current_density_Chen2020,\n", - " 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n", - " 'Separator porosity': 0.47,\n", - " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n", - " 'Typical electrolyte concentration [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]': 4.4,\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", - "}\n", - "param = pybamm.ParameterValues(values)\n", - "param" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we would have got the same result by doing" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'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]': ,\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 electrons in reaction': 1.0,\n", - " 'Negative electrode exchange-current density [A.m-2]': ,\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]': ,\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 electrons in reaction': 1.0,\n", - " 'Positive electrode exchange-current density [A.m-2]': ,\n", - " 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n", - " 'Separator porosity': 0.47,\n", - " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n", - " 'Typical electrolyte concentration [mol.m-3]': 1000.0,\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,\n", - " 'Initial temperature [K]': 298.15}" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "param_same = pybamm.ParameterValues(\"Chen2020\")\n", - "{k: v for k,v in param_same.items() if k in spm._parameter_info}" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Updating a specific parameter" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once a parameter set has been defined (either via a dictionary or a pre-built set), single parameters can be updated" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Using a constant value:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current function [A]\t5.0\n" - ] - }, - { - "data": { - "text/plain": [ - "4.0" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "param.search(\"Current function [A]\")\n", - "\n", - "param.update({\"Current function [A]\": 4.0})\n", - "\n", - "param[\"Current function [A]\"]" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Using a function:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def curren_func(time):\n", - " return 1 + pybamm.sin(2 * np.pi * time / 60)\n", - "\n", - "param.update({\"Current function [A]\": curren_func})\n", - "\n", - "param[\"Current function [A]\"]" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plotting parameter functions\n", - "\n", - "As seen above, functions can be passed as parameter values. These parameter values can then be plotted by using `pybamm.plot`" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plotting \"Current function \\[A]\"" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "currentfunc = param[\"Current function [A]\"]\n", - "time = pybamm.linspace(0, 120, 60)\n", - "evaluated = param.evaluate(currentfunc(time))\n", - "evaluated = pybamm.Array(evaluated)\n", - "pybamm.plot(time, evaluated)\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Taking another such example:\n", - "\n", - "### Plotting \"Negative electrode exchange-current density \\[A.m-2]\"" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "negative_electrode_exchange_current_density = param[\"Negative electrode exchange-current density [A.m-2]\"]\n", - "x = pybamm.linspace(3000,6000,100)\n", - "c_n_max = param[\"Maximum concentration in negative electrode [mol.m-3]\"]\n", - "evaluated = param.evaluate(negative_electrode_exchange_current_density(1000,x,c_n_max,300))\n", - "evaluated = pybamm.Array(evaluated)\n", - "pybamm.plot(x, evaluated)\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulating and solving the model\n", - "\n", - "Finally we can simulate the model and solve it using `pybamm.Simulation` and `solve` respectively." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "ename": "KeyError", - "evalue": "\"'Initial concentration in electrolyte [mol.m-3]' not found. Best matches are ['Initial concentration in positive electrode [mol.m-3]', 'Initial concentration in negative electrode [mol.m-3]', 'Maximum concentration in positive electrode [mol.m-3]']\"", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: PrimaryBroadcast(0x55db2b43f3b99d37, broadcast, children=['(0.00017234666524563961 * Ambient temperature [K] / Positive electrode electrons in reaction) * arcsinh(-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m]) / (2.0 * Positive electrode exchange-current density [A.m-2])) + Positive electrode OCP [V] + 1e-06 * (1.0 / (maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10)) + 1.0 / (-1.0 + maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10))) + (Ambient temperature [K] - Reference temperature [K]) * Positive electrode OCP entropic change [V.K-1] - ((0.00017234666524563961 * Ambient temperature [K] / Negative electrode electrons in reaction) * arcsinh(Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Negative electrode thickness [m] / x-average(3.0 * Negative electrode active material volume fraction / Negative particle radius [m]) / (2.0 * Negative electrode exchange-current density [A.m-2])) + Negative electrode OCP [V] + 1e-06 * (1.0 / (maximum(minimum(boundary value(X-averaged negative particle concentration [mol.m-3]) / Maximum concentration in negative electrode [mol.m-3], 0.9999999999), 1e-10)) + 1.0 / (-1.0 + maximum(minimum(boundary value(X-averaged negative particle concentration [mol.m-3]) / Maximum concentration in negative electrode [mol.m-3], 0.9999999999), 1e-10))) + (Ambient temperature [K] - Reference temperature [K]) * Negative electrode OCP entropic change [V.K-1])'], domains={'primary': ['positive electrode'], 'secondary': ['current collector']})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: Subtraction(0x6e57fdf0f90fdbb0, -, children=['(0.00017234666524563961 * Ambient temperature [K] / Positive electrode electrons in reaction) * arcsinh(-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m]) / (2.0 * Positive electrode exchange-current density [A.m-2])) + Positive electrode OCP [V] + 1e-06 * (1.0 / (maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10)) + 1.0 / (-1.0 + maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10))) + (Ambient temperature [K] - Reference temperature [K]) * Positive electrode OCP entropic change [V.K-1]', '(0.00017234666524563961 * Ambient temperature [K] / Negative electrode electrons in reaction) * arcsinh(Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Negative electrode thickness [m] / x-average(3.0 * Negative electrode active material volume fraction / Negative particle radius [m]) / (2.0 * Negative electrode exchange-current density [A.m-2])) + Negative electrode OCP [V] + 1e-06 * (1.0 / (maximum(minimum(boundary value(X-averaged negative particle concentration [mol.m-3]) / Maximum concentration in negative electrode [mol.m-3], 0.9999999999), 1e-10)) + 1.0 / (-1.0 + maximum(minimum(boundary value(X-averaged negative particle concentration [mol.m-3]) / Maximum concentration in negative electrode [mol.m-3], 0.9999999999), 1e-10))) + (Ambient temperature [K] - Reference temperature [K]) * Negative electrode OCP entropic change [V.K-1]'], domains={'primary': ['current collector']})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: Addition(-0x524f51ed4f620efd, +, children=['(0.00017234666524563961 * Ambient temperature [K] / Positive electrode electrons in reaction) * arcsinh(-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m]) / (2.0 * Positive electrode exchange-current density [A.m-2]))', 'Positive electrode OCP [V] + 1e-06 * (1.0 / (maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10)) + 1.0 / (-1.0 + maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10))) + (Ambient temperature [K] - Reference temperature [K]) * Positive electrode OCP entropic change [V.K-1]'], domains={'primary': ['current collector']})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: Multiplication(-0x36e2f7f52718fd84, *, children=['0.00017234666524563961 * Ambient temperature [K] / Positive electrode electrons in reaction', 'arcsinh(-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m]) / (2.0 * Positive electrode exchange-current density [A.m-2]))'], domains={'primary': ['current collector']})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: Arcsinh(-0x70bcbae05a17171a, function (arcsinh), children=['-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m]) / (2.0 * Positive electrode exchange-current density [A.m-2])'], domains={'primary': ['current collector']})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: Division(0x2d06e4ce68936693, /, children=['-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m])', '2.0 * Positive electrode exchange-current density [A.m-2]'], domains={'primary': ['current collector']})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: Multiplication(-0x533055788c0787e9, *, children=['2.0', 'Positive electrode exchange-current density [A.m-2]'], domains={'primary': ['current collector']})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: FunctionParameter(0x79a3a2c645f54668, Positive electrode exchange-current density [A.m-2], children=['maximum(Initial concentration in electrolyte [mol.m-3], 1e-08)', 'maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]), 0.99999999 * Maximum concentration in positive electrode [mol.m-3]), 1e-08 * Maximum concentration in positive electrode [mol.m-3])', 'Maximum concentration in positive electrode [mol.m-3]', 'broadcast(Ambient temperature [K])'], domains={'primary': ['current collector']})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: Maximum(-0x10b1c06354524acc, maximum, children=['Initial concentration in electrolyte [mol.m-3]', '1e-08'], domains={})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: Parameter(-0x23a8868071606836, Initial concentration in electrolyte [mol.m-3], children=[], domains={})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/util.py:58\u001b[0m, in \u001b[0;36mFuzzyDict.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 58\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49m\u001b[39m__getitem__\u001b[39;49m(key)\n\u001b[1;32m 59\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: 'Initial concentration in electrolyte [mol.m-3]'", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[22], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m sim \u001b[39m=\u001b[39m pybamm\u001b[39m.\u001b[39mSimulation(spm, parameter_values\u001b[39m=\u001b[39mparam)\n\u001b[1;32m 2\u001b[0m t_eval \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marange(\u001b[39m0\u001b[39m, \u001b[39m3600\u001b[39m, \u001b[39m1\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m sim\u001b[39m.\u001b[39;49msolve(t_eval\u001b[39m=\u001b[39;49mt_eval)\n\u001b[1;32m 4\u001b[0m sim\u001b[39m.\u001b[39mplot()\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/simulation.py:559\u001b[0m, in \u001b[0;36mSimulation.solve\u001b[0;34m(self, t_eval, solver, check_model, save_at_cycles, calc_esoh, starting_solution, initial_soc, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m 556\u001b[0m logs \u001b[39m=\u001b[39m {}\n\u001b[1;32m 558\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39moperating_mode \u001b[39min\u001b[39;00m [\u001b[39m\"\u001b[39m\u001b[39mwithout experiment\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mdrive cycle\u001b[39m\u001b[39m\"\u001b[39m]:\n\u001b[0;32m--> 559\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbuild(check_model\u001b[39m=\u001b[39;49mcheck_model, initial_soc\u001b[39m=\u001b[39;49minitial_soc)\n\u001b[1;32m 560\u001b[0m \u001b[39mif\u001b[39;00m save_at_cycles \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 561\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m 562\u001b[0m \u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39msave_at_cycles\u001b[39m\u001b[39m'\u001b[39m\u001b[39m option can only be used if simulating an \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 563\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mExperiment \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 564\u001b[0m )\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/simulation.py:449\u001b[0m, in \u001b[0;36mSimulation.build\u001b[0;34m(self, check_model, initial_soc)\u001b[0m\n\u001b[1;32m 447\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_built_model \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel\n\u001b[1;32m 448\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 449\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mset_parameters()\n\u001b[1;32m 450\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_mesh \u001b[39m=\u001b[39m pybamm\u001b[39m.\u001b[39mMesh(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_geometry, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_submesh_types, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_var_pts)\n\u001b[1;32m 451\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_disc \u001b[39m=\u001b[39m pybamm\u001b[39m.\u001b[39mDiscretisation(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_mesh, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_spatial_methods)\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/simulation.py:399\u001b[0m, in \u001b[0;36mSimulation.set_parameters\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 397\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_model_with_set_params \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_unprocessed_model\n\u001b[1;32m 398\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 399\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_model_with_set_params \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_parameter_values\u001b[39m.\u001b[39;49mprocess_model(\n\u001b[1;32m 400\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_unprocessed_model, inplace\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m\n\u001b[1;32m 401\u001b[0m )\n\u001b[1;32m 402\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_parameter_values\u001b[39m.\u001b[39mprocess_geometry(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mgeometry)\n\u001b[1;32m 403\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_model_with_set_params\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:465\u001b[0m, in \u001b[0;36mParameterValues.process_model\u001b[0;34m(self, unprocessed_model, inplace)\u001b[0m\n\u001b[1;32m 462\u001b[0m new_initial_conditions[new_variable] \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(equation)\n\u001b[1;32m 463\u001b[0m model\u001b[39m.\u001b[39minitial_conditions \u001b[39m=\u001b[39m new_initial_conditions\n\u001b[0;32m--> 465\u001b[0m model\u001b[39m.\u001b[39mboundary_conditions \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_boundary_conditions(unprocessed_model)\n\u001b[1;32m 467\u001b[0m new_variables \u001b[39m=\u001b[39m {}\n\u001b[1;32m 468\u001b[0m \u001b[39mfor\u001b[39;00m variable, equation \u001b[39min\u001b[39;00m unprocessed_model\u001b[39m.\u001b[39mvariables\u001b[39m.\u001b[39mitems():\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:541\u001b[0m, in \u001b[0;36mParameterValues.process_boundary_conditions\u001b[0;34m(self, model)\u001b[0m\n\u001b[1;32m 539\u001b[0m sides \u001b[39m=\u001b[39m [\u001b[39m\"\u001b[39m\u001b[39mleft\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mright\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mnegative tab\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mpositive tab\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mno tab\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[1;32m 540\u001b[0m \u001b[39mfor\u001b[39;00m variable, bcs \u001b[39min\u001b[39;00m model\u001b[39m.\u001b[39mboundary_conditions\u001b[39m.\u001b[39mitems():\n\u001b[0;32m--> 541\u001b[0m processed_variable \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(variable)\n\u001b[1;32m 542\u001b[0m new_boundary_conditions[processed_variable] \u001b[39m=\u001b[39m {}\n\u001b[1;32m 543\u001b[0m \u001b[39mfor\u001b[39;00m side \u001b[39min\u001b[39;00m sides:\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:731\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 729\u001b[0m \u001b[39m# Unary operators\u001b[39;00m\n\u001b[1;32m 730\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mUnaryOperator):\n\u001b[0;32m--> 731\u001b[0m new_child \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mchild)\n\u001b[1;32m 732\u001b[0m new_symbol \u001b[39m=\u001b[39m symbol\u001b[39m.\u001b[39m_unary_new_copy(new_child)\n\u001b[1;32m 733\u001b[0m \u001b[39m# ensure domain remains the same\u001b[39;00m\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:722\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 718\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(function_out)\n\u001b[1;32m 720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m 721\u001b[0m \u001b[39m# process children\u001b[39;00m\n\u001b[0;32m--> 722\u001b[0m new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mleft)\n\u001b[1;32m 723\u001b[0m new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mright)\n\u001b[1;32m 724\u001b[0m \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:722\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 718\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(function_out)\n\u001b[1;32m 720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m 721\u001b[0m \u001b[39m# process children\u001b[39;00m\n\u001b[0;32m--> 722\u001b[0m new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mleft)\n\u001b[1;32m 723\u001b[0m new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mright)\n\u001b[1;32m 724\u001b[0m \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:723\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m 721\u001b[0m \u001b[39m# process children\u001b[39;00m\n\u001b[1;32m 722\u001b[0m new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mleft)\n\u001b[0;32m--> 723\u001b[0m new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mright)\n\u001b[1;32m 724\u001b[0m \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n\u001b[1;32m 725\u001b[0m new_symbol \u001b[39m=\u001b[39m symbol\u001b[39m.\u001b[39m_binary_new_copy(new_left, new_right)\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:748\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 746\u001b[0m \u001b[39m# Functions\u001b[39;00m\n\u001b[1;32m 747\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mFunction):\n\u001b[0;32m--> 748\u001b[0m new_children \u001b[39m=\u001b[39m [\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(child) \u001b[39mfor\u001b[39;00m child \u001b[39min\u001b[39;00m symbol\u001b[39m.\u001b[39mchildren]\n\u001b[1;32m 749\u001b[0m \u001b[39mreturn\u001b[39;00m symbol\u001b[39m.\u001b[39m_function_new_copy(new_children)\n\u001b[1;32m 751\u001b[0m \u001b[39m# Concatenations\u001b[39;00m\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:748\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 746\u001b[0m \u001b[39m# Functions\u001b[39;00m\n\u001b[1;32m 747\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mFunction):\n\u001b[0;32m--> 748\u001b[0m new_children \u001b[39m=\u001b[39m [\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(child) \u001b[39mfor\u001b[39;00m child \u001b[39min\u001b[39;00m symbol\u001b[39m.\u001b[39mchildren]\n\u001b[1;32m 749\u001b[0m \u001b[39mreturn\u001b[39;00m symbol\u001b[39m.\u001b[39m_function_new_copy(new_children)\n\u001b[1;32m 751\u001b[0m \u001b[39m# Concatenations\u001b[39;00m\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:723\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m 721\u001b[0m \u001b[39m# process children\u001b[39;00m\n\u001b[1;32m 722\u001b[0m new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mleft)\n\u001b[0;32m--> 723\u001b[0m new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mright)\n\u001b[1;32m 724\u001b[0m \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n\u001b[1;32m 725\u001b[0m new_symbol \u001b[39m=\u001b[39m symbol\u001b[39m.\u001b[39m_binary_new_copy(new_left, new_right)\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:723\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m 721\u001b[0m \u001b[39m# process children\u001b[39;00m\n\u001b[1;32m 722\u001b[0m new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mleft)\n\u001b[0;32m--> 723\u001b[0m new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mright)\n\u001b[1;32m 724\u001b[0m \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n\u001b[1;32m 725\u001b[0m new_symbol \u001b[39m=\u001b[39m symbol\u001b[39m.\u001b[39m_binary_new_copy(new_left, new_right)\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:657\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 655\u001b[0m new_children\u001b[39m.\u001b[39mappend(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(new_child))\n\u001b[1;32m 656\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 657\u001b[0m new_children\u001b[39m.\u001b[39mappend(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(child))\n\u001b[1;32m 659\u001b[0m \u001b[39m# Create Function or Interpolant or Scalar object\u001b[39;00m\n\u001b[1;32m 660\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(function_name, \u001b[39mtuple\u001b[39m):\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:722\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 718\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(function_out)\n\u001b[1;32m 720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m 721\u001b[0m \u001b[39m# process children\u001b[39;00m\n\u001b[0;32m--> 722\u001b[0m new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mleft)\n\u001b[1;32m 723\u001b[0m new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mright)\n\u001b[1;32m 724\u001b[0m \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:620\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 617\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"See :meth:`ParameterValues.process_symbol()`.\"\"\"\u001b[39;00m\n\u001b[1;32m 619\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mParameter):\n\u001b[0;32m--> 620\u001b[0m value \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m[symbol\u001b[39m.\u001b[39;49mname]\n\u001b[1;32m 621\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(value, numbers\u001b[39m.\u001b[39mNumber):\n\u001b[1;32m 622\u001b[0m \u001b[39m# Check not NaN (parameter in csv file but no value given)\u001b[39;00m\n\u001b[1;32m 623\u001b[0m \u001b[39mif\u001b[39;00m np\u001b[39m.\u001b[39misnan(value):\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:139\u001b[0m, in \u001b[0;36mParameterValues.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__getitem__\u001b[39m(\u001b[39mself\u001b[39m, key):\n\u001b[0;32m--> 139\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_dict_items[key]\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/util.py:73\u001b[0m, in \u001b[0;36mFuzzyDict.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[39mif\u001b[39;00m key \u001b[39min\u001b[39;00m k \u001b[39mand\u001b[39;00m k\u001b[39m.\u001b[39mendswith(\u001b[39m\"\u001b[39m\u001b[39m]\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[1;32m 70\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(\n\u001b[1;32m 71\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mkey\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m not found. Use the dimensional version \u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mk\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m instead.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 72\u001b[0m )\n\u001b[0;32m---> 73\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mkey\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m not found. Best matches are \u001b[39m\u001b[39m{\u001b[39;00mbest_matches\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n", - "\u001b[0;31mKeyError\u001b[0m: \"'Initial concentration in electrolyte [mol.m-3]' not found. Best matches are ['Initial concentration in positive electrode [mol.m-3]', 'Initial concentration in negative electrode [mol.m-3]', 'Maximum concentration in positive electrode [mol.m-3]']\"" - ] - } - ], - "source": [ - "sim = pybamm.Simulation(spm, parameter_values=param)\n", - "t_eval = np.arange(0, 3600, 1)\n", - "sim.solve(t_eval=t_eval)\n", - "sim.plot()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## References\n", - "The relevant papers for this notebook are:" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "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] Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050.\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] 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" - ] - } - ], - "source": [ - "pybamm.print_citations()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "pybamm", - "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": "187972e187ab8dfbecfab9e8e194ae6d08262b2d51a54fa40644e3ddb6b5f74c" - } - } + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Parameterisation\n", + "\n", + "In this notebook, we show how to find which parameters are needed in a model and define them.\n", + "\n", + "For other notebooks about parameterization, see:\n", + "\n", + "- The API documentation of [Parameters](https://pybamm.readthedocs.io/en/latest/source/api/parameters/index.html)\n", + "- [Setting parameter values](https://github.com/pybamm-team/PyBaMM/blob/develop/examples/notebooks/Getting%20Started/Tutorial%204%20-%20Setting%20parameter%20values.ipynb) can be found at `pybamm/examples/notebooks/Getting Started/Tutorial 4 - Setting parameter values.ipynb`. This explains the basics of how to set the parameters of a model (in less detail than here).\n", + "- [parameter-values.ipynb](https://github.com/pybamm-team/PyBaMM/blob/develop/examples/notebooks/parameterization/parameter-values.ipynb) can be found at `pybamm/examples/notebooks/parameterization/parameter-values.ipynb`. This explains the basics of the `ParameterValues` class.\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding your own parameter sets (using a dictionary)\n", + "\n", + "We will be using the model defined and explained in more detail in [3-negative-particle-problem.ipynb](https://github.com/pybamm-team/PyBaMM/blob/develop/examples/notebooks/Creating%20Models/3-negative-particle-problem.ipynb) example notebook. We begin by importing the required libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install pybamm -q # install PyBaMM if it is not installed\n", + "import pybamm\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting up the model\n", + "\n", + "We define all the parameters and variables using `pybamm.Parameter` and `pybamm.Variable` respectively." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "c = pybamm.Variable(\"Concentration [mol.m-3]\", domain=\"negative particle\")\n", + "\n", + "R = pybamm.Parameter(\"Particle radius [m]\")\n", + "D = pybamm.FunctionParameter(\"Diffusion coefficient [m2.s-1]\", {\"Concentration [mol.m-3]\": c})\n", + "j = pybamm.InputParameter(\"Interfacial current density [A.m-2]\")\n", + "c0 = pybamm.Parameter(\"Initial concentration [mol.m-3]\")\n", + "c_e = pybamm.Parameter(\"Electrolyte concentration [mol.m-3]\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we define our model equations, boundary and initial conditions. We also add the variables required using the dictionary `model.variables`" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "model = pybamm.BaseModel()\n", + "\n", + "# governing equations\n", + "N = -D * pybamm.grad(c) # flux\n", + "dcdt = -pybamm.div(N)\n", + "model.rhs = {c: dcdt} \n", + "\n", + "# boundary conditions \n", + "lbc = pybamm.Scalar(0)\n", + "rbc = -j\n", + "model.boundary_conditions = {c: {\"left\": (lbc, \"Neumann\"), \"right\": (rbc, \"Neumann\")}}\n", + "\n", + "# initial conditions \n", + "model.initial_conditions = {c: c0}\n", + "\n", + "model.variables = {\n", + " \"Concentration [mol.m-3]\": c,\n", + " \"Surface concentration [mol.m-3]\": pybamm.surf(c),\n", + " \"Flux [mol.m-2.s-1]\": N,\n", + "}" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We also define the geometry, since there are parameters in the geometry too" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "r = pybamm.SpatialVariable(\"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\")\n", + "geometry = pybamm.Geometry({\"negative particle\": {r: {\"min\": pybamm.Scalar(0), \"max\": R}}})" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Finding the parameters required" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To know what parameters are required by the model and geometry, we can do" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "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", + "Particle radius [m] (Parameter)\n" + ] + } + ], + "source": [ + "model.print_parameter_info()\n", + "geometry.print_parameter_info()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This tells us that we need to provide parameter values for the initial concentration and Faraday constant, an `InputParameter` at solve time for the interfacial current density, and diffusivity as a function of concentration. Since the electrolyte concentration does not appear anywhere in the model, there is no need to provide a value for it." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding the parameters\n", + "\n", + "Now we can proceed to the step where we add the `parameter` values using a dictionary. We set up a dictionary with parameter names as the dictionary keys and their respective values as the dictionary values." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def D_fun(c):\n", + " return 3.9 #* pybamm.exp(-c)\n", + "\n", + "\n", + "values = {\n", + " \"Particle radius [m]\": 2,\n", + " \"Diffusion coefficient [m2.s-1]\": D_fun,\n", + " \"Initial concentration [mol.m-3]\": 2.5,\n", + "}" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can pass this dictionary in `pybamm.ParameterValues` class which accepts a dictionary of parameter names and values. We can then print `param` to check if it was initialised." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Diffusion coefficient [m2.s-1]': ,\n", + " 'Initial concentration [mol.m-3]': 2.5,\n", + " 'Particle radius [m]': 2}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "param = pybamm.ParameterValues(values)\n", + "\n", + "param" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Updating the parameter values\n", + "\n", + "The parameter values or `param` can be further updated by using the `update` function of `ParameterValues` class. The `update` function takes a dictionary with keys being the parameters to be updated and their respective values being the updated values. Here we update the `\"Particle radius [m]\"` parameter's value. Additionally, a function can also be passed as a `parameter`'s value which we will see ahead, and a new `parameter` can also be added by passing `check_already_exists=False` in the `update` function." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Diffusion coefficient [m2.s-1]': ,\n", + " 'Initial concentration [mol.m-3]': 1.5,\n", + " 'Particle radius [m]': 2}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "param.update({\"Initial concentration [mol.m-3]\": 1.5})\n", + "param" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Solving the model " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Finding the parameters in a model\n", + "\n", + "The `parameter` function of the `BaseModel` class can be used to obtain the parameters of a model." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[Parameter(-0x7c3ebfeae2200290, Initial concentration [mol.m-3], children=[], domains={}),\n", + " InputParameter(-0x4a08933302b1e44e, Interfacial current density [A.m-2], children=[], domains={}),\n", + " FunctionParameter(0x66f7cbc27c44053b, Diffusion coefficient [m2.s-1], children=['Concentration [mol.m-3]'], domains={'primary': ['negative particle']})]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parameters = model.parameters\n", + "parameters" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As explained in the [3-negative-particle-problem.ipynb](https://github.com/pybamm-team/PyBaMM/blob/develop/examples/notebooks/Creating%20Models/3-negative-particle-problem.ipynb) example, we first process both the `model` and the `geometry`." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "param.process_model(model)\n", + "param.process_geometry(geometry)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now set up our mesh, choose a spatial method, and discretise our model" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "submesh_types = {\"negative particle\": pybamm.Uniform1DSubMesh}\n", + "var_pts = {r: 20}\n", + "mesh = pybamm.Mesh(geometry, submesh_types, var_pts)\n", + "\n", + "spatial_methods = {\"negative particle\": pybamm.FiniteVolume()}\n", + "disc = pybamm.Discretisation(mesh, spatial_methods)\n", + "disc.process_model(model);" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We choose a solver and times at which we want the solution returned, and solve the model. Here we give a value for the current density `j`." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# solve\n", + "solver = pybamm.ScipySolver()\n", + "t = np.linspace(0, 3600, 600)\n", + "solution = solver.solve(model, t, inputs={\"Interfacial current density [A.m-2]\": 1.4})\n", + "\n", + "# post-process, so that the solution can be called at any time t or space r\n", + "# (using interpolation)\n", + "c = solution[\"Concentration [mol.m-3]\"]\n", + "c_surf = solution[\"Surface concentration [mol.m-3]\"]\n", + "\n", + "# plot\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13, 4))\n", + "\n", + "ax1.plot(solution.t, c_surf(solution.t))\n", + "ax1.set_xlabel(\"Time [s]\")\n", + "ax1.set_ylabel(\"Surface concentration [mol.m-3]\")\n", + "\n", + "rsol = mesh[\"negative particle\"].nodes # radial position\n", + "time = 1000 # time in seconds\n", + "ax2.plot(rsol * 1e6, c(t=time, r=rsol), label=\"t={}[s]\".format(time))\n", + "ax2.set_xlabel(\"Particle radius [microns]\")\n", + "ax2.set_ylabel(\"Concentration [mol.m-3]\")\n", + "ax2.legend()\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using pre-defined models in `PyBaMM`\n", + "\n", + "In the next few steps, we will be showing the same workflow with the Single Particle Model (`SPM`). We will also see how you can pass a function as a `parameter`'s value and how to plot such `parameter functions`.\n", + "\n", + "We start by initializing our model" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "spm = pybamm.lithium_ion.SPM()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Finding the parameters in a model\n", + "\n", + "We can print the `parameters` of a model by using the `get_parameters_info` function." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Maximum concentration in positive electrode [mol.m-3] (Parameter)\n", + "Initial concentration in electrolyte [mol.m-3] (Parameter)\n", + "Separator thickness [m] (Parameter)\n", + "Positive electrode Bruggeman coefficient (electrode) (Parameter)\n", + "Negative electrode thickness [m] (Parameter)\n", + "Electrode height [m] (Parameter)\n", + "Negative electrode Bruggeman coefficient (electrode) (Parameter)\n", + "Number of cells connected in series to make a battery (Parameter)\n", + "Negative electrode Bruggeman coefficient (electrolyte) (Parameter)\n", + "Maximum concentration in negative electrode [mol.m-3] (Parameter)\n", + "Positive electrode Bruggeman coefficient (electrolyte) (Parameter)\n", + "Lower voltage cut-off [V] (Parameter)\n", + "Nominal cell capacity [A.h] (Parameter)\n", + "Typical electrolyte concentration [mol.m-3] (Parameter)\n", + "Upper voltage cut-off [V] (Parameter)\n", + "Positive electrode electrons in reaction (Parameter)\n", + "Negative electrode electrons in reaction (Parameter)\n", + "Initial temperature [K] (Parameter)\n", + "Reference temperature [K] (Parameter)\n", + "Positive electrode thickness [m] (Parameter)\n", + "Number of electrodes connected in parallel to make a cell (Parameter)\n", + "Electrode width [m] (Parameter)\n", + "Separator Bruggeman coefficient (electrolyte) (Parameter)\n", + "Positive particle radius [m] (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", + "Positive electrode OCP [V] (FunctionParameter with input(s) 'Positive particle stoichiometry')\n", + "Separator porosity (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", + "Current function [A] (FunctionParameter with input(s) 'Time[s]')\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", + "Ambient temperature [K] (FunctionParameter with input(s) 'Time [s]')\n", + "Negative particle radius [m] (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\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", + "Negative electrode active material volume fraction (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", + "Positive electrode active material volume fraction (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", + "Negative electrode porosity (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", + "Negative electrode diffusivity [m2.s-1] (FunctionParameter with input(s) 'Negative particle stoichiometry', 'Temperature [K]')\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", + "Initial concentration in negative electrode [mol.m-3] (FunctionParameter with input(s) 'Radial distance (r) [m]', 'Through-cell distance (x) [m]')\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", + "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", + "\n" + ] + } + ], + "source": [ + "spm.print_parameter_info()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that there are no `InputParameter` objects in the default SPM. Also, note that if a `FunctionParameter` is expected, it is ok to provide a scalar (parameter) instead. However, if a `Parameter` is expected, you cannot provide a function instead." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Another way to view what parameters are needed is to print the default parameter values. This can also be used to get some good defaults (but care must be taken when combining parameters across datasets and chemistries)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'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]': ,\n", + " 'Negative electrode OCP [V]': ,\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 electrons in reaction': 1.0,\n", + " 'Negative electrode exchange-current density [A.m-2]': ,\n", + " 'Negative electrode OCP entropic change [V.K-1]': ,\n", + " 'Maximum concentration in positive electrode [mol.m-3]': 51217.9257309275,\n", + " 'Positive electrode diffusivity [m2.s-1]': ,\n", + " 'Positive electrode OCP [V]': ,\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 electrons in reaction': 1.0,\n", + " 'Positive electrode exchange-current density [A.m-2]': ,\n", + " 'Positive electrode OCP entropic change [V.K-1]': ,\n", + " 'Separator porosity': 1.0,\n", + " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n", + " 'Typical electrolyte concentration [mol.m-3]': 1000.0,\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,\n", + " 'Initial temperature [K]': 298.15}" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "{k: v for k,v in spm.default_parameter_values.items() if k in spm._parameter_info}" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now define a dictionary of values for `ParameterValues` as before (here, a subset of the `Chen2020` parameters)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Ambient temperature [K]': 298.15,\n", + " 'Current function [A]': 5.0,\n", + " 'Electrode height [m]': 0.065,\n", + " 'Electrode width [m]': 1.58,\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]': ,\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]': ,\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": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def graphite_mcmb2528_diffusivity_Dualfoil1998(sto, T):\n", + " D_ref = 3.9 * 10 ** (-14)\n", + " E_D_s = 42770\n", + " arrhenius = exp(E_D_s / constants.R * (1 / 298.15 - 1 / T))\n", + " return D_ref * arrhenius\n", + "\n", + "\n", + "neg_ocp = np.array([[0. , 1.81772748],\n", + " [0.03129623, 1.0828807 ],\n", + " [0.03499902, 0.99593794],\n", + " [0.0387018 , 0.90023398],\n", + " [0.04240458, 0.79649431],\n", + " [0.04610736, 0.73354429],\n", + " [0.04981015, 0.66664314],\n", + " [0.05351292, 0.64137149],\n", + " [0.05721568, 0.59813869],\n", + " [0.06091845, 0.5670836 ],\n", + " [0.06462122, 0.54746181],\n", + " [0.06832399, 0.53068399],\n", + " [0.07202675, 0.51304734],\n", + " [0.07572951, 0.49394092],\n", + " [0.07943227, 0.47926274],\n", + " [0.08313503, 0.46065259],\n", + " [0.08683779, 0.45992726],\n", + " [0.09054054, 0.43801501],\n", + " [0.09424331, 0.42438665],\n", + " [0.09794607, 0.41150269],\n", + " [0.10164883, 0.40033659],\n", + " [0.10535158, 0.38957134],\n", + " [0.10905434, 0.37756538],\n", + " [0.1127571 , 0.36292541],\n", + " [0.11645985, 0.34357086],\n", + " [0.12016261, 0.3406314 ],\n", + " [0.12386536, 0.32299468],\n", + " [0.12756811, 0.31379458],\n", + " [0.13127086, 0.30795386],\n", + " [0.13497362, 0.29207319],\n", + " [0.13867638, 0.28697687],\n", + " [0.14237913, 0.27405477],\n", + " [0.14608189, 0.2670497 ],\n", + " [0.14978465, 0.25857493],\n", + " [0.15348741, 0.25265783],\n", + " [0.15719018, 0.24826777],\n", + " [0.16089294, 0.2414345 ],\n", + " [0.1645957 , 0.23362778],\n", + " [0.16829847, 0.22956218],\n", + " [0.17200122, 0.22370236],\n", + " [0.17570399, 0.22181271],\n", + " [0.17940674, 0.22089651],\n", + " [0.1831095 , 0.2194268 ],\n", + " [0.18681229, 0.21830064],\n", + " [0.19051504, 0.21845333],\n", + " [0.1942178 , 0.21753715],\n", + " [0.19792056, 0.21719357],\n", + " [0.20162334, 0.21635373],\n", + " [0.2053261 , 0.21667822],\n", + " [0.20902886, 0.21738444],\n", + " [0.21273164, 0.21469313],\n", + " [0.2164344 , 0.21541846],\n", + " [0.22013716, 0.21465495],\n", + " [0.22383993, 0.2135479 ],\n", + " [0.2275427 , 0.21392964],\n", + " [0.23124547, 0.21074206],\n", + " [0.23494825, 0.20873788],\n", + " [0.23865101, 0.20465319],\n", + " [0.24235377, 0.20205732],\n", + " [0.24605653, 0.19774358],\n", + " [0.2497593 , 0.19444147],\n", + " [0.25346208, 0.19190285],\n", + " [0.25716486, 0.18850531],\n", + " [0.26086762, 0.18581399],\n", + " [0.26457039, 0.18327537],\n", + " [0.26827314, 0.18157659],\n", + " [0.2719759 , 0.17814088],\n", + " [0.27567867, 0.17529686],\n", + " [0.27938144, 0.1719375 ],\n", + " [0.28308421, 0.16934161],\n", + " [0.28678698, 0.16756649],\n", + " [0.29048974, 0.16609676],\n", + " [0.29419251, 0.16414985],\n", + " [0.29789529, 0.16260378],\n", + " [0.30159806, 0.16224113],\n", + " [0.30530083, 0.160027 ],\n", + " [0.30900361, 0.15827096],\n", + " [0.31270637, 0.1588054 ],\n", + " [0.31640913, 0.15552238],\n", + " [0.32011189, 0.15580869],\n", + " [0.32381466, 0.15220118],\n", + " [0.32751744, 0.1511132 ],\n", + " [0.33122021, 0.14987253],\n", + " [0.33492297, 0.14874637],\n", + " [0.33862575, 0.14678037],\n", + " [0.34232853, 0.14620776],\n", + " [0.34603131, 0.14555879],\n", + " [0.34973408, 0.14389819],\n", + " [0.35343685, 0.14359279],\n", + " [0.35713963, 0.14242846],\n", + " [0.36084241, 0.14038612],\n", + " [0.36454517, 0.13882096],\n", + " [0.36824795, 0.13954628],\n", + " [0.37195071, 0.13946992],\n", + " [0.37565348, 0.13780934],\n", + " [0.37935626, 0.13973714],\n", + " [0.38305904, 0.13698858],\n", + " [0.38676182, 0.13523254],\n", + " [0.3904646 , 0.13441178],\n", + " [0.39416737, 0.1352898 ],\n", + " [0.39787015, 0.13507985],\n", + " [0.40157291, 0.13647321],\n", + " [0.40527567, 0.13601512],\n", + " [0.40897844, 0.13435452],\n", + " [0.41268121, 0.1334765 ],\n", + " [0.41638398, 0.1348317 ],\n", + " [0.42008676, 0.13275118],\n", + " [0.42378953, 0.13286571],\n", + " [0.4274923 , 0.13263667],\n", + " [0.43119506, 0.13456447],\n", + " [0.43489784, 0.13471718],\n", + " [0.43860061, 0.13395369],\n", + " [0.44230338, 0.13448814],\n", + " [0.44600615, 0.1334765 ],\n", + " [0.44970893, 0.13298023],\n", + " [0.45341168, 0.13259849],\n", + " [0.45711444, 0.13338107],\n", + " [0.46081719, 0.13309476],\n", + " [0.46451994, 0.13275118],\n", + " [0.46822269, 0.13443087],\n", + " [0.47192545, 0.13315202],\n", + " [0.47562821, 0.132713 ],\n", + " [0.47933098, 0.1330184 ],\n", + " [0.48303375, 0.13278936],\n", + " [0.48673651, 0.13225491],\n", + " [0.49043926, 0.13317111],\n", + " [0.49414203, 0.13263667],\n", + " [0.49784482, 0.13187316],\n", + " [0.50154759, 0.13265574],\n", + " [0.50525036, 0.13250305],\n", + " [0.50895311, 0.13324745],\n", + " [0.51265586, 0.13204496],\n", + " [0.51635861, 0.13242669],\n", + " [0.52006139, 0.13233127],\n", + " [0.52376415, 0.13198769],\n", + " [0.52746692, 0.13254122],\n", + " [0.53116969, 0.13145325],\n", + " [0.53487245, 0.13298023],\n", + " [0.53857521, 0.13168229],\n", + " [0.54227797, 0.1313578 ],\n", + " [0.54598074, 0.13235036],\n", + " [0.5496835 , 0.13120511],\n", + " [0.55338627, 0.13089971],\n", + " [0.55708902, 0.13109058],\n", + " [0.56079178, 0.13082336],\n", + " [0.56449454, 0.13011713],\n", + " [0.5681973 , 0.129869 ],\n", + " [0.57190006, 0.12992626],\n", + " [0.57560282, 0.12942998],\n", + " [0.57930558, 0.12796026],\n", + " [0.58300835, 0.12862831],\n", + " [0.58671112, 0.12656689],\n", + " [0.59041389, 0.12734947],\n", + " [0.59411664, 0.12509716],\n", + " [0.59781941, 0.12110791],\n", + " [0.60152218, 0.11839751],\n", + " [0.60522496, 0.11244226],\n", + " [0.60892772, 0.11307214],\n", + " [0.61263048, 0.1092165 ],\n", + " [0.61633325, 0.10683058],\n", + " [0.62003603, 0.10433014],\n", + " [0.6237388 , 0.10530359],\n", + " [0.62744156, 0.10056993],\n", + " [0.63114433, 0.09950104],\n", + " [0.63484711, 0.09854668],\n", + " [0.63854988, 0.09921473],\n", + " [0.64225265, 0.09541635],\n", + " [0.64595543, 0.09980643],\n", + " [0.64965823, 0.0986612 ],\n", + " [0.653361 , 0.09560722],\n", + " [0.65706377, 0.09755413],\n", + " [0.66076656, 0.09612258],\n", + " [0.66446934, 0.09430929],\n", + " [0.66817212, 0.09661885],\n", + " [0.67187489, 0.09366032],\n", + " [0.67557767, 0.09522548],\n", + " [0.67928044, 0.09535909],\n", + " [0.68298322, 0.09316404],\n", + " [0.686686 , 0.09450016],\n", + " [0.69038878, 0.0930877 ],\n", + " [0.69409156, 0.09343126],\n", + " [0.69779433, 0.0932404 ],\n", + " [0.70149709, 0.09350762],\n", + " [0.70519988, 0.09339309],\n", + " [0.70890264, 0.09291591],\n", + " [0.7126054 , 0.09303043],\n", + " [0.71630818, 0.0926296 ],\n", + " [0.72001095, 0.0932404 ],\n", + " [0.72371371, 0.09261052],\n", + " [0.72741648, 0.09249599],\n", + " [0.73111925, 0.09240055],\n", + " [0.73482204, 0.09253416],\n", + " [0.7385248 , 0.09209515],\n", + " [0.74222757, 0.09234329],\n", + " [0.74593034, 0.09366032],\n", + " [0.74963312, 0.09333583],\n", + " [0.75333589, 0.09322131],\n", + " [0.75703868, 0.09264868],\n", + " [0.76074146, 0.09253416],\n", + " [0.76444422, 0.09243873],\n", + " [0.76814698, 0.09230512],\n", + " [0.77184976, 0.09310678],\n", + " [0.77555253, 0.09165615],\n", + " [0.77925531, 0.09159888],\n", + " [0.78295807, 0.09207606],\n", + " [0.78666085, 0.09175158],\n", + " [0.79036364, 0.09177067],\n", + " [0.79406641, 0.09236237],\n", + " [0.79776918, 0.09241964],\n", + " [0.80147197, 0.09320222],\n", + " [0.80517474, 0.09199972],\n", + " [0.80887751, 0.09167523],\n", + " [0.81258028, 0.09322131],\n", + " [0.81628304, 0.09190428],\n", + " [0.81998581, 0.09167523],\n", + " [0.82368858, 0.09285865],\n", + " [0.82739136, 0.09180884],\n", + " [0.83109411, 0.09150345],\n", + " [0.83479688, 0.09186611],\n", + " [0.83849965, 0.0920188 ],\n", + " [0.84220242, 0.09320222],\n", + " [0.84590519, 0.09131257],\n", + " [0.84960797, 0.09117896],\n", + " [0.85331075, 0.09133166],\n", + " [0.85701353, 0.09089265],\n", + " [0.86071631, 0.09058725],\n", + " [0.86441907, 0.09051091],\n", + " [0.86812186, 0.09033912],\n", + " [0.87182464, 0.09041547],\n", + " [0.87552742, 0.0911217 ],\n", + " [0.87923019, 0.0894611 ],\n", + " [0.88293296, 0.08999555],\n", + " [0.88663573, 0.08921297],\n", + " [0.89033849, 0.08881213],\n", + " [0.89404126, 0.08797229],\n", + " [0.89774404, 0.08709427],\n", + " [0.9014468 , 0.08503284],\n", + " [1. , 0.07601531]])\n", + "\n", + "pos_ocp = np.array([[0.24879728, 4.4 ],\n", + " [0.26614516, 4.2935653 ],\n", + " [0.26886763, 4.2768621 ],\n", + " [0.27159011, 4.2647018 ],\n", + " [0.27431258, 4.2540312 ],\n", + " [0.27703505, 4.2449446 ],\n", + " [0.27975753, 4.2364879 ],\n", + " [0.28248 , 4.2302647 ],\n", + " [0.28520247, 4.2225528 ],\n", + " [0.28792495, 4.2182574 ],\n", + " [0.29064743, 4.213294 ],\n", + " [0.29336992, 4.2090373 ],\n", + " [0.29609239, 4.2051239 ],\n", + " [0.29881487, 4.2012677 ],\n", + " [0.30153735, 4.1981564 ],\n", + " [0.30425983, 4.1955218 ],\n", + " [0.30698231, 4.1931167 ],\n", + " [0.30970478, 4.1889744 ],\n", + " [0.31242725, 4.1881533 ],\n", + " [0.31514973, 4.1865883 ],\n", + " [0.3178722 , 4.1850228 ],\n", + " [0.32059466, 4.1832285 ],\n", + " [0.32331714, 4.1808805 ],\n", + " [0.32603962, 4.1805749 ],\n", + " [0.32876209, 4.1789522 ],\n", + " [0.33148456, 4.1768146 ],\n", + " [0.33420703, 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+ " [0.89775868, 3.5721002 ],\n", + " [0.90048116, 3.5702102 ],\n", + " [0.90320364, 3.5684922 ],\n", + " [0.90592613, 3.5672133 ],\n", + " [1. , 3.52302167]])\n", + "\n", + "from pybamm import exp, constants\n", + "\n", + "\n", + "def graphite_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, c_n_max, T):\n", + " m_ref = 6.48e-7 # (A/m2)(m3/mol)**1.5 - includes ref concentrations\n", + " E_r = 35000\n", + " arrhenius = exp(E_r / constants.R * (1 / 298.15 - 1 / T))\n", + "\n", + " return (\n", + " m_ref * arrhenius * c_e ** 0.5 * c_s_surf ** 0.5 * (c_n_max - c_s_surf) ** 0.5\n", + " )\n", + "\n", + "\n", + "def nmc_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, c_p_max, T):\n", + " m_ref = 3.42e-6 # (A/m2)(m3/mol)**1.5 - includes ref concentrations\n", + " E_r = 17800\n", + " arrhenius = exp(E_r / constants.R * (1 / 298.15 - 1 / T))\n", + "\n", + " return (\n", + " m_ref * arrhenius * c_e ** 0.5 * c_s_surf ** 0.5 * (c_p_max - c_s_surf) ** 0.5\n", + " )\n", + "\n", + "\n", + "values = {\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", + " 'Typical current [A]': 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]': ('graphite_LGM50_ocp_Chen2020', neg_ocp),\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)': 1.5,\n", + " 'Negative electrode electrons in reaction': 1.0,\n", + " 'Negative electrode exchange-current density [A.m-2]': graphite_LGM50_electrolyte_exchange_current_density_Chen2020,\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]': ('nmc_LGM50_ocp_Chen2020', pos_ocp),\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)': 1.5,\n", + " 'Positive electrode electrons in reaction': 1.0,\n", + " 'Positive electrode exchange-current density [A.m-2]': nmc_LGM50_electrolyte_exchange_current_density_Chen2020,\n", + " 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n", + " 'Separator porosity': 0.47,\n", + " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n", + " 'Typical electrolyte concentration [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]': 4.4,\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", + "}\n", + "param = pybamm.ParameterValues(values)\n", + "param" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we would have got the same result by doing" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'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]': ,\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 electrons in reaction': 1.0,\n", + " 'Negative electrode exchange-current density [A.m-2]': ,\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]': ,\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 electrons in reaction': 1.0,\n", + " 'Positive electrode exchange-current density [A.m-2]': ,\n", + " 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n", + " 'Separator porosity': 0.47,\n", + " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n", + " 'Typical electrolyte concentration [mol.m-3]': 1000.0,\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,\n", + " 'Initial temperature [K]': 298.15}" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "param_same = pybamm.ParameterValues(\"Chen2020\")\n", + "{k: v for k,v in param_same.items() if k in spm._parameter_info}" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Updating a specific parameter" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once a parameter set has been defined (either via a dictionary or a pre-built set), single parameters can be updated" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Using a constant value:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current function [A]\t5.0\n" + ] }, - "nbformat": 4, - "nbformat_minor": 2 + { + "data": { + "text/plain": [ + "4.0" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "param.search(\"Current function [A]\")\n", + "\n", + "param.update({\"Current function [A]\": 4.0})\n", + "\n", + "param[\"Current function [A]\"]" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Using a function:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def curren_func(time):\n", + " return 1 + pybamm.sin(2 * np.pi * time / 60)\n", + "\n", + "\n", + "param.update({\"Current function [A]\": curren_func})\n", + "\n", + "param[\"Current function [A]\"]" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting parameter functions\n", + "\n", + "As seen above, functions can be passed as parameter values. These parameter values can then be plotted by using `pybamm.plot`" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotting \"Current function \\[A]\"" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "currentfunc = param[\"Current function [A]\"]\n", + "time = pybamm.linspace(0, 120, 60)\n", + "evaluated = param.evaluate(currentfunc(time))\n", + "evaluated = pybamm.Array(evaluated)\n", + "pybamm.plot(time, evaluated)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Taking another such example:\n", + "\n", + "### Plotting \"Negative electrode exchange-current density \\[A.m-2]\"" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "negative_electrode_exchange_current_density = param[\"Negative electrode exchange-current density [A.m-2]\"]\n", + "x = pybamm.linspace(3000,6000,100)\n", + "c_n_max = param[\"Maximum concentration in negative electrode [mol.m-3]\"]\n", + "evaluated = param.evaluate(negative_electrode_exchange_current_density(1000,x,c_n_max,300))\n", + "evaluated = pybamm.Array(evaluated)\n", + "pybamm.plot(x, evaluated)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simulating and solving the model\n", + "\n", + "Finally we can simulate the model and solve it using `pybamm.Simulation` and `solve` respectively." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "\"'Initial concentration in electrolyte [mol.m-3]' not found. Best matches are ['Initial concentration in positive electrode [mol.m-3]', 'Initial concentration in negative electrode [mol.m-3]', 'Maximum concentration in positive electrode [mol.m-3]']\"", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", + "\u001b[0;31mKeyError\u001b[0m: PrimaryBroadcast(0x55db2b43f3b99d37, broadcast, children=['(0.00017234666524563961 * Ambient temperature [K] / Positive electrode electrons in reaction) * arcsinh(-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m]) / (2.0 * Positive electrode exchange-current density [A.m-2])) + Positive electrode OCP [V] + 1e-06 * (1.0 / (maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10)) + 1.0 / (-1.0 + maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10))) + (Ambient temperature [K] - Reference temperature [K]) * Positive electrode OCP entropic change [V.K-1] - ((0.00017234666524563961 * Ambient temperature [K] / Negative electrode electrons in reaction) * arcsinh(Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Negative electrode thickness [m] / x-average(3.0 * Negative electrode active material volume fraction / Negative particle radius [m]) / (2.0 * Negative electrode exchange-current density [A.m-2])) + Negative electrode OCP [V] + 1e-06 * (1.0 / (maximum(minimum(boundary value(X-averaged negative particle concentration [mol.m-3]) / Maximum concentration in negative electrode [mol.m-3], 0.9999999999), 1e-10)) + 1.0 / (-1.0 + maximum(minimum(boundary value(X-averaged negative particle concentration [mol.m-3]) / Maximum concentration in negative electrode [mol.m-3], 0.9999999999), 1e-10))) + (Ambient temperature [K] - Reference temperature [K]) * Negative electrode OCP entropic change [V.K-1])'], domains={'primary': ['positive electrode'], 'secondary': ['current collector']})", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", + "\u001b[0;31mKeyError\u001b[0m: Subtraction(0x6e57fdf0f90fdbb0, -, children=['(0.00017234666524563961 * Ambient temperature [K] / Positive electrode electrons in reaction) * arcsinh(-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m]) / (2.0 * Positive electrode exchange-current density [A.m-2])) + Positive electrode OCP [V] + 1e-06 * (1.0 / (maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10)) + 1.0 / (-1.0 + maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10))) + (Ambient temperature [K] - Reference temperature [K]) * Positive electrode OCP entropic change [V.K-1]', '(0.00017234666524563961 * Ambient temperature [K] / Negative electrode electrons in reaction) * arcsinh(Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Negative electrode thickness [m] / x-average(3.0 * Negative electrode active material volume fraction / Negative particle radius [m]) / (2.0 * Negative electrode exchange-current density [A.m-2])) + Negative electrode OCP [V] + 1e-06 * (1.0 / (maximum(minimum(boundary value(X-averaged negative particle concentration [mol.m-3]) / Maximum concentration in negative electrode [mol.m-3], 0.9999999999), 1e-10)) + 1.0 / (-1.0 + maximum(minimum(boundary value(X-averaged negative particle concentration [mol.m-3]) / Maximum concentration in negative electrode [mol.m-3], 0.9999999999), 1e-10))) + (Ambient temperature [K] - Reference temperature [K]) * Negative electrode OCP entropic change [V.K-1]'], domains={'primary': ['current collector']})", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", + "\u001b[0;31mKeyError\u001b[0m: Addition(-0x524f51ed4f620efd, +, children=['(0.00017234666524563961 * Ambient temperature [K] / Positive electrode electrons in reaction) * arcsinh(-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m]) / (2.0 * Positive electrode exchange-current density [A.m-2]))', 'Positive electrode OCP [V] + 1e-06 * (1.0 / (maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10)) + 1.0 / (-1.0 + maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10))) + (Ambient temperature [K] - Reference temperature [K]) * Positive electrode OCP entropic change [V.K-1]'], domains={'primary': ['current collector']})", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", + "\u001b[0;31mKeyError\u001b[0m: Multiplication(-0x36e2f7f52718fd84, *, children=['0.00017234666524563961 * Ambient temperature [K] / Positive electrode electrons in reaction', 'arcsinh(-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m]) / (2.0 * Positive electrode exchange-current density [A.m-2]))'], domains={'primary': ['current collector']})", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", + "\u001b[0;31mKeyError\u001b[0m: Arcsinh(-0x70bcbae05a17171a, function (arcsinh), children=['-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m]) / (2.0 * Positive electrode exchange-current density [A.m-2])'], domains={'primary': ['current collector']})", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", + "\u001b[0;31mKeyError\u001b[0m: Division(0x2d06e4ce68936693, /, children=['-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m])', '2.0 * Positive electrode exchange-current density [A.m-2]'], domains={'primary': ['current collector']})", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", + "\u001b[0;31mKeyError\u001b[0m: Multiplication(-0x533055788c0787e9, *, children=['2.0', 'Positive electrode exchange-current density [A.m-2]'], domains={'primary': ['current collector']})", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", + "\u001b[0;31mKeyError\u001b[0m: FunctionParameter(0x79a3a2c645f54668, Positive electrode exchange-current density [A.m-2], children=['maximum(Initial concentration in electrolyte [mol.m-3], 1e-08)', 'maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]), 0.99999999 * Maximum concentration in positive electrode [mol.m-3]), 1e-08 * Maximum concentration in positive electrode [mol.m-3])', 'Maximum concentration in positive electrode [mol.m-3]', 'broadcast(Ambient temperature [K])'], domains={'primary': ['current collector']})", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", + "\u001b[0;31mKeyError\u001b[0m: Maximum(-0x10b1c06354524acc, maximum, children=['Initial concentration in electrolyte [mol.m-3]', '1e-08'], domains={})", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", + "\u001b[0;31mKeyError\u001b[0m: Parameter(-0x23a8868071606836, Initial concentration in electrolyte [mol.m-3], children=[], domains={})", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/util.py:58\u001b[0m, in \u001b[0;36mFuzzyDict.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 58\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49m\u001b[39m__getitem__\u001b[39;49m(key)\n\u001b[1;32m 59\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", + "\u001b[0;31mKeyError\u001b[0m: 'Initial concentration in electrolyte [mol.m-3]'", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[22], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m sim \u001b[39m=\u001b[39m pybamm\u001b[39m.\u001b[39mSimulation(spm, parameter_values\u001b[39m=\u001b[39mparam)\n\u001b[1;32m 2\u001b[0m t_eval \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marange(\u001b[39m0\u001b[39m, \u001b[39m3600\u001b[39m, \u001b[39m1\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m sim\u001b[39m.\u001b[39;49msolve(t_eval\u001b[39m=\u001b[39;49mt_eval)\n\u001b[1;32m 4\u001b[0m sim\u001b[39m.\u001b[39mplot()\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/simulation.py:559\u001b[0m, in \u001b[0;36mSimulation.solve\u001b[0;34m(self, t_eval, solver, check_model, save_at_cycles, calc_esoh, starting_solution, initial_soc, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m 556\u001b[0m logs \u001b[39m=\u001b[39m {}\n\u001b[1;32m 558\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39moperating_mode \u001b[39min\u001b[39;00m [\u001b[39m\"\u001b[39m\u001b[39mwithout experiment\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mdrive cycle\u001b[39m\u001b[39m\"\u001b[39m]:\n\u001b[0;32m--> 559\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbuild(check_model\u001b[39m=\u001b[39;49mcheck_model, initial_soc\u001b[39m=\u001b[39;49minitial_soc)\n\u001b[1;32m 560\u001b[0m \u001b[39mif\u001b[39;00m save_at_cycles \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 561\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m 562\u001b[0m \u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39msave_at_cycles\u001b[39m\u001b[39m'\u001b[39m\u001b[39m option can only be used if simulating an \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 563\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mExperiment \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 564\u001b[0m )\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/simulation.py:449\u001b[0m, in \u001b[0;36mSimulation.build\u001b[0;34m(self, check_model, initial_soc)\u001b[0m\n\u001b[1;32m 447\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_built_model \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel\n\u001b[1;32m 448\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 449\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mset_parameters()\n\u001b[1;32m 450\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_mesh \u001b[39m=\u001b[39m pybamm\u001b[39m.\u001b[39mMesh(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_geometry, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_submesh_types, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_var_pts)\n\u001b[1;32m 451\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_disc \u001b[39m=\u001b[39m pybamm\u001b[39m.\u001b[39mDiscretisation(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_mesh, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_spatial_methods)\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/simulation.py:399\u001b[0m, in \u001b[0;36mSimulation.set_parameters\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 397\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_model_with_set_params \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_unprocessed_model\n\u001b[1;32m 398\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 399\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_model_with_set_params \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_parameter_values\u001b[39m.\u001b[39;49mprocess_model(\n\u001b[1;32m 400\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_unprocessed_model, inplace\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m\n\u001b[1;32m 401\u001b[0m )\n\u001b[1;32m 402\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_parameter_values\u001b[39m.\u001b[39mprocess_geometry(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mgeometry)\n\u001b[1;32m 403\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_model_with_set_params\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:465\u001b[0m, in \u001b[0;36mParameterValues.process_model\u001b[0;34m(self, unprocessed_model, inplace)\u001b[0m\n\u001b[1;32m 462\u001b[0m new_initial_conditions[new_variable] \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(equation)\n\u001b[1;32m 463\u001b[0m model\u001b[39m.\u001b[39minitial_conditions \u001b[39m=\u001b[39m new_initial_conditions\n\u001b[0;32m--> 465\u001b[0m model\u001b[39m.\u001b[39mboundary_conditions \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_boundary_conditions(unprocessed_model)\n\u001b[1;32m 467\u001b[0m new_variables \u001b[39m=\u001b[39m {}\n\u001b[1;32m 468\u001b[0m \u001b[39mfor\u001b[39;00m variable, equation \u001b[39min\u001b[39;00m unprocessed_model\u001b[39m.\u001b[39mvariables\u001b[39m.\u001b[39mitems():\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:541\u001b[0m, in \u001b[0;36mParameterValues.process_boundary_conditions\u001b[0;34m(self, model)\u001b[0m\n\u001b[1;32m 539\u001b[0m sides \u001b[39m=\u001b[39m [\u001b[39m\"\u001b[39m\u001b[39mleft\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mright\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mnegative tab\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mpositive tab\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mno tab\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[1;32m 540\u001b[0m \u001b[39mfor\u001b[39;00m variable, bcs \u001b[39min\u001b[39;00m model\u001b[39m.\u001b[39mboundary_conditions\u001b[39m.\u001b[39mitems():\n\u001b[0;32m--> 541\u001b[0m processed_variable \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(variable)\n\u001b[1;32m 542\u001b[0m new_boundary_conditions[processed_variable] \u001b[39m=\u001b[39m {}\n\u001b[1;32m 543\u001b[0m \u001b[39mfor\u001b[39;00m side \u001b[39min\u001b[39;00m sides:\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:731\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 729\u001b[0m \u001b[39m# Unary operators\u001b[39;00m\n\u001b[1;32m 730\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mUnaryOperator):\n\u001b[0;32m--> 731\u001b[0m new_child \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mchild)\n\u001b[1;32m 732\u001b[0m new_symbol \u001b[39m=\u001b[39m symbol\u001b[39m.\u001b[39m_unary_new_copy(new_child)\n\u001b[1;32m 733\u001b[0m \u001b[39m# ensure domain remains the same\u001b[39;00m\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:722\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 718\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(function_out)\n\u001b[1;32m 720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m 721\u001b[0m \u001b[39m# process children\u001b[39;00m\n\u001b[0;32m--> 722\u001b[0m new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mleft)\n\u001b[1;32m 723\u001b[0m new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mright)\n\u001b[1;32m 724\u001b[0m \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:722\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 718\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(function_out)\n\u001b[1;32m 720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m 721\u001b[0m \u001b[39m# process children\u001b[39;00m\n\u001b[0;32m--> 722\u001b[0m new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mleft)\n\u001b[1;32m 723\u001b[0m new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mright)\n\u001b[1;32m 724\u001b[0m \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:723\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m 721\u001b[0m \u001b[39m# process children\u001b[39;00m\n\u001b[1;32m 722\u001b[0m new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mleft)\n\u001b[0;32m--> 723\u001b[0m new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mright)\n\u001b[1;32m 724\u001b[0m \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n\u001b[1;32m 725\u001b[0m new_symbol \u001b[39m=\u001b[39m symbol\u001b[39m.\u001b[39m_binary_new_copy(new_left, new_right)\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:748\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 746\u001b[0m \u001b[39m# Functions\u001b[39;00m\n\u001b[1;32m 747\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mFunction):\n\u001b[0;32m--> 748\u001b[0m new_children \u001b[39m=\u001b[39m [\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(child) \u001b[39mfor\u001b[39;00m child \u001b[39min\u001b[39;00m symbol\u001b[39m.\u001b[39mchildren]\n\u001b[1;32m 749\u001b[0m \u001b[39mreturn\u001b[39;00m symbol\u001b[39m.\u001b[39m_function_new_copy(new_children)\n\u001b[1;32m 751\u001b[0m \u001b[39m# Concatenations\u001b[39;00m\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:748\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 746\u001b[0m \u001b[39m# Functions\u001b[39;00m\n\u001b[1;32m 747\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mFunction):\n\u001b[0;32m--> 748\u001b[0m new_children \u001b[39m=\u001b[39m [\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(child) \u001b[39mfor\u001b[39;00m child \u001b[39min\u001b[39;00m symbol\u001b[39m.\u001b[39mchildren]\n\u001b[1;32m 749\u001b[0m \u001b[39mreturn\u001b[39;00m symbol\u001b[39m.\u001b[39m_function_new_copy(new_children)\n\u001b[1;32m 751\u001b[0m \u001b[39m# Concatenations\u001b[39;00m\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:723\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m 721\u001b[0m \u001b[39m# process children\u001b[39;00m\n\u001b[1;32m 722\u001b[0m new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mleft)\n\u001b[0;32m--> 723\u001b[0m new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mright)\n\u001b[1;32m 724\u001b[0m \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n\u001b[1;32m 725\u001b[0m new_symbol \u001b[39m=\u001b[39m symbol\u001b[39m.\u001b[39m_binary_new_copy(new_left, new_right)\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:723\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m 721\u001b[0m \u001b[39m# process children\u001b[39;00m\n\u001b[1;32m 722\u001b[0m new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mleft)\n\u001b[0;32m--> 723\u001b[0m new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mright)\n\u001b[1;32m 724\u001b[0m \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n\u001b[1;32m 725\u001b[0m new_symbol \u001b[39m=\u001b[39m symbol\u001b[39m.\u001b[39m_binary_new_copy(new_left, new_right)\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:657\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 655\u001b[0m new_children\u001b[39m.\u001b[39mappend(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(new_child))\n\u001b[1;32m 656\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 657\u001b[0m new_children\u001b[39m.\u001b[39mappend(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(child))\n\u001b[1;32m 659\u001b[0m \u001b[39m# Create Function or Interpolant or Scalar object\u001b[39;00m\n\u001b[1;32m 660\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(function_name, \u001b[39mtuple\u001b[39m):\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:722\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 718\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(function_out)\n\u001b[1;32m 720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m 721\u001b[0m \u001b[39m# process children\u001b[39;00m\n\u001b[0;32m--> 722\u001b[0m new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mleft)\n\u001b[1;32m 723\u001b[0m new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mright)\n\u001b[1;32m 724\u001b[0m \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:620\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 617\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"See :meth:`ParameterValues.process_symbol()`.\"\"\"\u001b[39;00m\n\u001b[1;32m 619\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mParameter):\n\u001b[0;32m--> 620\u001b[0m value \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m[symbol\u001b[39m.\u001b[39;49mname]\n\u001b[1;32m 621\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(value, numbers\u001b[39m.\u001b[39mNumber):\n\u001b[1;32m 622\u001b[0m \u001b[39m# Check not NaN (parameter in csv file but no value given)\u001b[39;00m\n\u001b[1;32m 623\u001b[0m \u001b[39mif\u001b[39;00m np\u001b[39m.\u001b[39misnan(value):\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:139\u001b[0m, in \u001b[0;36mParameterValues.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__getitem__\u001b[39m(\u001b[39mself\u001b[39m, key):\n\u001b[0;32m--> 139\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_dict_items[key]\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/util.py:73\u001b[0m, in \u001b[0;36mFuzzyDict.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[39mif\u001b[39;00m key \u001b[39min\u001b[39;00m k \u001b[39mand\u001b[39;00m k\u001b[39m.\u001b[39mendswith(\u001b[39m\"\u001b[39m\u001b[39m]\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[1;32m 70\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(\n\u001b[1;32m 71\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mkey\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m not found. Use the dimensional version \u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mk\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m instead.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 72\u001b[0m )\n\u001b[0;32m---> 73\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mkey\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m not found. Best matches are \u001b[39m\u001b[39m{\u001b[39;00mbest_matches\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n", + "\u001b[0;31mKeyError\u001b[0m: \"'Initial concentration in electrolyte [mol.m-3]' not found. Best matches are ['Initial concentration in positive electrode [mol.m-3]', 'Initial concentration in negative electrode [mol.m-3]', 'Maximum concentration in positive electrode [mol.m-3]']\"" + ] + } + ], + "source": [ + "sim = pybamm.Simulation(spm, parameter_values=param)\n", + "t_eval = np.arange(0, 3600, 1)\n", + "sim.solve(t_eval=t_eval)\n", + "sim.plot()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "The relevant papers for this notebook are:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "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] Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050.\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] 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" + ] + } + ], + "source": [ + "pybamm.print_citations()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pybamm", + "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": "187972e187ab8dfbecfab9e8e194ae6d08262b2d51a54fa40644e3ddb6b5f74c" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 } diff --git a/docs/source/examples/notebooks/plotting/customize-quick-plot.ipynb b/docs/source/examples/notebooks/plotting/customize-quick-plot.ipynb index c9ea0e7581..db017c75eb 100644 --- a/docs/source/examples/notebooks/plotting/customize-quick-plot.ipynb +++ b/docs/source/examples/notebooks/plotting/customize-quick-plot.ipynb @@ -327,7 +327,7 @@ "pybamm.settings.max_words_in_line = 4\n", "\n", "plot = pybamm.QuickPlot(sims, figsize=(14,7))\n", - "plot.plot(0.5); # time in hours\n", + "plot.plot(0.5) # time in hours\n", "\n", "# Move title to ylabel\n", "for ax in plot.fig.axes:\n", diff --git a/docs/source/examples/notebooks/simulating-long-experiments.ipynb b/docs/source/examples/notebooks/simulating-long-experiments.ipynb index 9bdfd40b54..d7e5b5b531 100644 --- a/docs/source/examples/notebooks/simulating-long-experiments.ipynb +++ b/docs/source/examples/notebooks/simulating-long-experiments.ipynb @@ -35,8 +35,7 @@ "source": [ "%pip install pybamm -q # install PyBaMM if it is not installed\n", "import pybamm\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np" + "import matplotlib.pyplot as plt" ] }, { @@ -121,10 +120,10 @@ "pybamm.set_logging_level(\"NOTICE\")\n", "\n", "experiment = pybamm.Experiment([\n", - " (f\"Discharge at 1C until 3V\",\n", + " (\"Discharge at 1C until 3V\",\n", " \"Rest for 1 hour\",\n", - " f\"Charge at 1C until 4.2V\", \n", - " f\"Hold at 4.2V until C/50\"\n", + " \"Charge at 1C until 4.2V\", \n", + " \"Hold at 4.2V until C/50\"\n", " )\n", "])\n", "sim = pybamm.Simulation(spm, experiment=experiment, parameter_values=parameter_values)\n", @@ -460,10 +459,10 @@ ], "source": [ "experiment = pybamm.Experiment([\n", - " (f\"Discharge at 1C until 3V\",\n", + " (\"Discharge at 1C until 3V\",\n", " \"Rest for 1 hour\",\n", - " f\"Charge at 1C until 4.2V\", \n", - " f\"Hold at 4.2V until C/50\")\n", + " \"Charge at 1C until 4.2V\", \n", + " \"Hold at 4.2V until C/50\")\n", "] * 500,\n", "termination=\"80% capacity\"\n", ")\n", @@ -1749,10 +1748,10 @@ ], "source": [ "experiment = pybamm.Experiment([\n", - " (f\"Discharge at 1C until 3V\",\n", + " (\"Discharge at 1C until 3V\",\n", " \"Rest for 1 hour\",\n", - " f\"Charge at 1C until 4.2V\", \n", - " f\"Hold at 4.2V until C/50\")\n", + " \"Charge at 1C until 4.2V\", \n", + " \"Hold at 4.2V until C/50\")\n", "] * 10,\n", "termination=\"80% capacity\"\n", ")\n", diff --git a/docs/source/examples/notebooks/solvers/dae-solver.ipynb b/docs/source/examples/notebooks/solvers/dae-solver.ipynb index 6b15849239..73d6398ea0 100644 --- a/docs/source/examples/notebooks/solvers/dae-solver.ipynb +++ b/docs/source/examples/notebooks/solvers/dae-solver.ipynb @@ -27,11 +27,9 @@ "source": [ "%pip install pybamm -q # install PyBaMM if it is not installed\n", "import pybamm\n", - "import tests\n", "import numpy as np\n", "import os\n", "import matplotlib.pyplot as plt\n", - "from pprint import pprint\n", "os.chdir(pybamm.__path__[0]+'/..')" ] }, diff --git a/docs/source/examples/notebooks/solvers/ode-solver.ipynb b/docs/source/examples/notebooks/solvers/ode-solver.ipynb index d0592a2707..423e4b28ab 100644 --- a/docs/source/examples/notebooks/solvers/ode-solver.ipynb +++ b/docs/source/examples/notebooks/solvers/ode-solver.ipynb @@ -27,11 +27,9 @@ "source": [ "%pip install pybamm -q # install PyBaMM if it is not installed\n", "import pybamm\n", - "import tests\n", "import numpy as np\n", "import os\n", "import matplotlib.pyplot as plt\n", - "from pprint import pprint\n", "os.chdir(pybamm.__path__[0]+'/..')" ] }, diff --git a/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb b/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb index ec33199274..cf899a20e0 100644 --- a/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb +++ b/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb @@ -198,7 +198,7 @@ "\n", "plt.plot(safe_sol[\"Time [h]\"].data, safe_sol[\"Voltage [V]\"].data, \"r-\", label=\"Safe\")\n", "plt.plot(safe_sol[\"Time [h]\"].data, cutoff * np.ones_like(safe_sol[\"Time [h]\"].data), \"k--\", label=\"Voltage cut-off\")\n", - "plt.legend();\n", + "plt.legend()\n", "\n", "try:\n", " sim.solve([0,4500], solver=fast_solver, inputs={\"Crate\": 1})\n", diff --git a/docs/source/examples/notebooks/spatial_methods/finite-volumes.ipynb b/docs/source/examples/notebooks/spatial_methods/finite-volumes.ipynb index 9b4ff12986..e1d5cd2f0f 100644 --- a/docs/source/examples/notebooks/spatial_methods/finite-volumes.ipynb +++ b/docs/source/examples/notebooks/spatial_methods/finite-volumes.ipynb @@ -67,7 +67,6 @@ "import numpy as np\n", "import os\n", "import matplotlib.pyplot as plt\n", - "from pprint import pprint\n", "os.chdir(pybamm.__path__[0]+'/..')" ] }, @@ -1155,6 +1154,7 @@ "model.boundary_conditions = {c: {\"left\": (0, \"Dirichlet\")}}\n", "model.variables = {\"c\": c}\n", "\n", + "\n", "def solve_and_plot(model):\n", " model_disc = disc.process_model(model, inplace=False)\n", "\n", @@ -1165,6 +1165,7 @@ " plot = pybamm.QuickPlot(solution,[\"c\"],spatial_unit=\"m\")\n", " plot.dynamic_plot()\n", " \n", + "\n", "solve_and_plot(model)" ] }, From c7d247aba099d4bd31fd2f572dfd3aeaea1deddf Mon Sep 17 00:00:00 2001 From: "arjxn.py" Date: Mon, 17 Jul 2023 22:06:54 +0530 Subject: [PATCH 3/6] Add pre-commit hash --- .git-blame-ignore-revs | 1 + 1 file changed, 1 insertion(+) diff --git a/.git-blame-ignore-revs b/.git-blame-ignore-revs index ee879ab82a..8f9ad5affc 100644 --- a/.git-blame-ignore-revs +++ b/.git-blame-ignore-revs @@ -1,2 +1,3 @@ # activated pre-commit - https://github.com/pybamm-team/PyBaMM/pull/2272 0054efe388d2d17301f7e0554449eac9a7d3b7fc +a63e49ece0f9336d1f5c2562f7459e555c6e6693 From 9c1be1871df12b98f292ac7c59a4a76ec02cadb0 Mon Sep 17 00:00:00 2001 From: Arjun Date: Wed, 19 Jul 2023 15:32:37 +0530 Subject: [PATCH 4/6] Apply suggestions from code review Co-authored-by: Saransh Chopra --- .git-blame-ignore-revs | 1 + .pre-commit-config.yaml | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/.git-blame-ignore-revs b/.git-blame-ignore-revs index 8f9ad5affc..e0aa2cf0ab 100644 --- a/.git-blame-ignore-revs +++ b/.git-blame-ignore-revs @@ -1,3 +1,4 @@ # activated pre-commit - https://github.com/pybamm-team/PyBaMM/pull/2272 0054efe388d2d17301f7e0554449eac9a7d3b7fc +# activated pre-commit for notebooks - https://github.com/pybamm-team/PyBaMM/pull/3110 a63e49ece0f9336d1f5c2562f7459e555c6e6693 diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 6eecc02b75..dcb717cb3f 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -19,4 +19,4 @@ repos: hooks: - id: nbqa-ruff additional_dependencies: [ruff==0.0.276] - args: ["--fix","--ignore=E501,E402"] \ No newline at end of file + args: ["--fix","--ignore=E501,E402"] From 77e085f1f7ad418108de6db4370e7ff4366e6458 Mon Sep 17 00:00:00 2001 From: "arjxn.py" Date: Sat, 22 Jul 2023 00:43:49 +0530 Subject: [PATCH 5/6] Try fixing breaking notebook --- .../notebooks/creating_models/6-a-simple-SEI-model.ipynb | 5 ----- 1 file changed, 5 deletions(-) diff --git a/docs/source/examples/notebooks/creating_models/6-a-simple-SEI-model.ipynb b/docs/source/examples/notebooks/creating_models/6-a-simple-SEI-model.ipynb index bd097b6c4b..8d46012aec 100644 --- a/docs/source/examples/notebooks/creating_models/6-a-simple-SEI-model.ipynb +++ b/docs/source/examples/notebooks/creating_models/6-a-simple-SEI-model.ipynb @@ -199,7 +199,6 @@ "V_hat = pybamm.Parameter(\"Partial molar volume [m3.mol-1]\")\n", "c_inf = pybamm.Parameter(\"Bulk electrolyte solvent concentration [mol.m-3]\")\n", "\n", - "\n", "def D(cc):\n", " return pybamm.FunctionParameter(\"Diffusivity [m2.s-1]\", {\"Solvent concentration [mol.m-3]\": cc})" ] @@ -486,11 +485,9 @@ " {\"SEI layer\": {xi: {\"min\": pybamm.Scalar(0), \"max\": pybamm.Scalar(1)}}}\n", ")\n", "\n", - "\n", "def Diffusivity(cc):\n", " return cc * 10**(-12)\n", "\n", - "\n", "# parameter values (not physically based, for example only!)\n", "param = pybamm.ParameterValues(\n", " {\n", @@ -568,7 +565,6 @@ "L_0_eval = param.evaluate(L_0)\n", "xi = np.linspace(0, 1, 100) # dimensionless space\n", "\n", - "\n", "def plot(t):\n", " _, (ax1, ax2) = plt.subplots(1, 2 ,figsize=(10,5))\n", " ax1.plot(solution.t, L_out(solution.t) * 1e6)\n", @@ -585,7 +581,6 @@ " plt.tight_layout()\n", " plt.show()\n", " \n", - "\n", "import ipywidgets as widgets\n", "widgets.interact(plot, t=widgets.FloatSlider(min=0,max=solution.t[-1],step=0.1,value=0));" ] From d7b7caf552273d3b0bb49d649b4bc3c117e2b8c2 Mon Sep 17 00:00:00 2001 From: Arjun Date: Sat, 22 Jul 2023 01:31:39 +0530 Subject: [PATCH 6/6] Fix `parameterization.ipynb` --- .../parameterization/parameterization.ipynb | 330 +----------------- 1 file changed, 2 insertions(+), 328 deletions(-) diff --git a/docs/source/examples/notebooks/parameterization/parameterization.ipynb b/docs/source/examples/notebooks/parameterization/parameterization.ipynb index d1266bb203..097be2dcce 100644 --- a/docs/source/examples/notebooks/parameterization/parameterization.ipynb +++ b/docs/source/examples/notebooks/parameterization/parameterization.ipynb @@ -1812,332 +1812,6 @@ } } }, - { - "data": { - "text/plain": [ - "4.0" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "param.search(\"Current function [A]\")\n", - "\n", - "param.update({\"Current function [A]\": 4.0})\n", - "\n", - "param[\"Current function [A]\"]" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Using a function:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def curren_func(time):\n", - " return 1 + pybamm.sin(2 * np.pi * time / 60)\n", - "\n", - "\n", - "param.update({\"Current function [A]\": curren_func})\n", - "\n", - "param[\"Current function [A]\"]" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plotting parameter functions\n", - "\n", - "As seen above, functions can be passed as parameter values. These parameter values can then be plotted by using `pybamm.plot`" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plotting \"Current function \\[A]\"" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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Y/ES+s/fKnZOV1XulL7c6E5Ztx8+hud0sczREV+5z59TK7JHxMISFyBzN5SUZ9LhurKP4lvsLUSBgwuIHzjS1o6isEQDwHYWuDrrQqMRIjE2KhMUmcg6dAoKUsCzMTpY5koG7d7pjJGjDXhbfkv9jwuIHNjiXMs8cHqeoJlWXIxXffrqf00Lk3yoa2nCk1gS1SsD8LPl2ZXbX3FFDkBoTClOnFZ/tr5U7HKIrwoRF4URRdDWLuytP2cW2F1o00ZGwFJ1qRH1Lp8zREA2eNLoyY3gsYsK1MkczcGqVgO9Nk4pvT8scDdGVYcKicMWnm1DR2I4wrRo3KngZ5aWkxYYhNz0adhH4F6/uyI/927lC70Y/mg6S3D0lFSoB2FvZjKrz3OOL/BcTFoXLd46uLMxORrhCdoR1h1R8yyZy5K+qmzuw74wRggDcMN5/poMkCZF6TBkWCwBsM0B+ze2EpbCwEIsWLUJKSgoEQcDHH3/c7/H3338/BEG46DZ+/HjXMWvXrr3kMZ2dwT2N0Gmx4bN9zt4ref5RbHuhmyckQyUAJby6Iz8lFY1PzYhFQqTy9u8aiAXOuhsmLOTP3E5Y2trakJOTg1dffXVAx//xj39EbW2t61ZVVYXY2FjcfffdvY6LiorqdVxtbS30ev/8cPCUzYfq0NJlxdDoUMzIjJM7nEFJiNJjxnBH7Cy+JX/UPR3kX1OyPc0b50hYdpadh7HDInM0RIPj9hzDwoULsXDhwgEfbzAYYDAYXD9//PHHaGpqwgMPPNDrOEEQkJTkvx8I3tBzo0OVSvm9V/pya04KdpxqxMbSGvz0mpFyh0M0YPUtndhzugmAfycsw+LDMSohAifqW7H1WD1um+SfI7YU3Hxew/LWW29h3rx5yMjI6HV/a2srMjIykJqailtuuQUlJSX9Pk5XVxdMJlOvWyCpN3Vi+wnHbsd3KLwV/+XcmJ2EELWAo3Ut7LhJfmXzobMQRSAnLRop0aFyh3NF5jmnhQoOc1qI/JNPE5ba2lp8/vnnePDBB3vdP3bsWKxduxYbN27EunXroNfrcdVVV+HEiRN9Ptbq1atdozcGgwFpaWneDt+n/nO0HnYRmJQWjWHx4XKHc0Wiw7SYO2oIALbqJ/8iTQfd5MejKxKpf8y2Y+dgttpljobIfT5NWNauXYvo6Gjcfvvtve6fMWMG7rvvPuTk5GDOnDn48MMPMXr0aPzpT3/q87FWrVoFo9HoulVVVXk5et/68mg9AOB6Z2ttfyc1kdu4rwaiKMocDdHlnW8z49uy8wD8q7ttXyalRiM+QoeWLit2ljfKHQ6R23yWsIiiiLfffhtLliyBVtt/4yWVSoWpU6f2O8Ki0+kQFRXV6xYouqw2fHOyAQBwbYAkLPPGJUIfokJFYzsOVBvlDofosgoO18FmF5GVHIX0OP/pMN0XlUrAvHGOz5MtnBYiP+SzhGXbtm04efIkli1bdtljRVFEaWkpkpP9/6pmMHaVn0e72YaESB3GpwRGIhau0+B650oFTguRP+jeO8j/p4Mk0mqhgsNnOdJJfsfthKW1tRWlpaUoLS0FAJSXl6O0tBSVlY7dQFetWoWlS5de9HdvvfUWpk+fjuzs7It+99xzz2Hz5s0oKytDaWkpli1bhtLSUixfvtzd8AKCNB10zZghfrEz80BJTeQ+218Lu50flqRcxg6La5Rz4YTASVhmj4qHPkSFGmMnDtcG1kIFCnxuJyx79uxBbm4ucnNzAQArV65Ebm4unn32WQCOwlopeZEYjUbk5+f3ObrS3NyMhx56COPGjcOCBQtQXV2NwsJCTJs2zd3wAsLWY47VQdcFyHSQ5OrRQxCp06DW2IndFeflDoeoT18ePQuLTcSohAiMTIiUOxyP0YeoMcdZAM/VQuRv3O7Dcs011/Q7lLh27dqL7jMYDGhv77vL6csvv4yXX37Z3VACUnlDG8ob2hCiFnDVyHi5w/EofYga87MSsaGkGl8eq8f04f7ZDI8C3+cHAm86SDI/KxEFh89iy5GzeGzeaLnDIRow7iWkMF85p4OmDotFpD5E5mg87+oxjqu7bc5RJCKlaeuyYttxx79Pf9zs8HKuG5sAQQAOVptQ09whdzhEA8aERWG+OuZIWK4dE1jTQZLZI+MhCMDRuhbUm4J7ryhSpq3HzqHLakdGXBjGJQfOdJAkPkKHvPQYAMB/uLcQ+REmLArS1mXFTmffh0BZznyhuAgdslMcWzUUnmiQORqii23qsXdQIBW99yR1vf2CdSzkR5iwKMg3JxtgttmRFhuKEUP8u7ttf+aOdtTmFB7ntBApS6fF5pqWvSkAp4MkUtfbb8sa0dLJzRDJPzBhUZCvpNVBYxIC9soOgKtN//aTDVzeTIpSePwc2s02DI0OxcRUw+X/wE+NGBKB4fHhsNhEFB7nSCf5ByYsCiGKIrY661euCdDpIMnkjBhE6DQ432bGwRp2vSXl2OKs6VgwPjGgLxqA7lGWgsN1MkdCNDBMWBTiaF0Lao2d0IeoMDPAl/uGqFWYNcLxGjktREohiiK2O+uqArXovSepjuXLo/Ww2LgZIikfExaFkLrbzhoRD32IWuZovG/uaOfyZiYspBAVje2oMXZCq1Zh6rBYucPxusnpMYgN18LUaWUjR/ILTFgUQir0C9TVQRe62pmw7K1sholFf6QA2084kue8jBiEagP/okGtElzdtLccrpc5GqLLY8KiAM3tZuytbAIAXOtsrBbo0mLDkBkfDptdxI6T3Oqe5LfduXfQ7FGB1WG6P646liN13AyRFI8JiwJsO34OdhEYnRiB1Bj/38Z+oOY6vxgKT3BaiORls4vYccqROAfalhj9mTMqHjqNClXnO3D8bKvc4RD1iwmLAkibHQZDoV9PUh1L4fFzvLojWR2oNqKl04oovQYThgbucuYLhWk1mO1M0LhaiJSOCYvMbPbu5czBUr8imTE8DiFqAWeaOlDe0CZ3OBTEpPqVWSPioVYF9nLmC10/zjEtxH4spHRMWGS270wzmtotiNRrkJcRI3c4PhWu02BKhmM1Bpc3k5yk+pWrgqh+RXLVSEeLgZKqJnSYbTJHQ9Q3Jiwyk1YHzR01BCHq4PvP4dq9mQkLyaTdbMXe080A4JoeCSbpsWEYGh0Ki03EntNc3kzKFXzfkAoj7c58TZCsDrqQ1Kb/27Lz6LLy6o58b3dFE8w2O4ZGh2JYXPAUvUsEQcBMZyNHqfCYSImYsMio3tSJg9UmAMA1QVZwKxmXHIkhkTp0WGzYU9EkdzgUhKT6ldkj4wO+HX9fpO7aTFhIyZiwyEhaHTQx1YAhkTqZo5GHIAiYM4q7N5N8tjv7AAVj/YpEGmE5cIaNHEm5mLDISGrHH2zLmS90Ndv0k0waWrtwpNYxyintbxWMUqJDkRkfDrsI7C5nHQspExMWmdjsIr455ViZEKz1KxLHULxjA8h6U6fc4VAQkaZAxiVHIT4iOEc5JaxjIaVjwiKTI7UmtHRaEaELrkZVlxIXoUN2iuMcFJ5gLwjyne76leAdXZGwjoWUjgmLTL4tc3woTBkWA00QLme+EKeFyNdEUcT2E9L+QcE9ygk4GjkCjouppjazzNEQXYzflDLZ6Zwnlj4kgp3Upn/7iXOw2dmmn7yvorEdNcZOaNUqTB0WXE0bL2VIpA5jEiMBdF9QESkJExYZ2O0idjkTlumZsTJHowy56dGI0GnQ1G7BwWqj3OFQEJC6207OiEaYViNzNMrAOhZSMiYsMjha1wJjhwXhWjWyg7x+RRKiVrlWaXB5M/lCz/4r5CAlLEUcYSEFYsIiA2m4NW9YbFC24++La/fmE0xYyLtsdtE1inAVExaXGZlxEATgZH0rV+yR4vDbUgZSwjJjOKeDepIKb/dWNqO1yypzNBTIDlQb0dJpRaReg4mp0XKHoxiGsBDXij2OspDSMGHxMbtdxK4KqX6FBbc9pTk3YbPZRew9zTb95D3fOOtXZo2Ig1oVnO34++KqYznJhIWUhQmLjx0724LmdgtCQ9SYmMr6lQtJRci72G2TvOhr1q/0iXUspFRMWHxsZ4/+K6xfudg0KWGpYMJC3tFutmLv6WYArF+5lKnDYqFRCag8346q8+1yh0Pkwm9MH/u2jP1X+iMlLKVVzei02GSOhgLR7oommG12pBj0yIwPlzscxYnQaVyjvxxlISVxO2EpLCzEokWLkJKSAkEQ8PHHH/d7/NatWyEIwkW3o0eP9jouPz8fWVlZ0Ol0yMrKwkcffeRuaIrXs36FBbeXlhkfjvgILcxWO/afYT8W8jypfmX2qHgIAutXLmXWCMfIUxH7sZCCuJ2wtLW1IScnB6+++qpbf3fs2DHU1ta6bqNGjXL9rqioCIsXL8aSJUuwb98+LFmyBPfccw927tzpbniKdqK+FefbzNCHqDBhaLTc4SiSIAiuUZbdnBYiL/ja2Y6f00F9k3oiFZ1qhCiy8zQpg9vtHRcuXIiFCxe6/UQJCQmIjo6+5O/WrFmD+fPnY9WqVQCAVatWYdu2bVizZg3WrVvn9nMp1c5yZ/1KRiy0Gs7G9WXasFhsOlCHneXn8fC1ckdDgaShtQtHak0AukcR6GKTM2KgVatQZ+pEeUMbhg+JkDskIt/VsOTm5iI5ORnXX389vvrqq16/KyoqwoIFC3rdd8MNN2DHjh19Pl5XVxdMJlOvm9JJ/VfYjr9/U53np7jiPKw2u8zRUCDZ7Vx9NiYxEkMidTJHo1z6EDUmZ0QDYJt+Ug6vJyzJycl48803kZ+fjw0bNmDMmDG4/vrrUVhY6Dqmrq4OiYmJvf4uMTERdXV1fT7u6tWrYTAYXLe0tDSvvQZPEEURO6WC2xEsuO3P2KQoROo1aDPbcLhW+Yko+Y/dFY7+PtN40XBZrjoWFt6SQng9YRkzZgx+9KMfYfLkyZg5cyZee+013Hzzzfj973/f67gLi99EUey3IG7VqlUwGo2uW1VVlVfi95ST9a1odNavsP9K/9QqAVOHsR8Led6e045/T1O4O/NlSXUs355qhJ07qJMCyFJIMWPGDJw4ccL1c1JS0kWjKfX19ReNuvSk0+kQFRXV66Zk0nTQ5PQY6DRqmaNRvmlsIEce1tZlxaEax4idlBBT3yamRiM0RI3GNjOO17fIHQ6RPAlLSUkJkpOTXT/PnDkTBQUFvY754osvMGvWLF+H5jXflrP/ijukL5TdFed5dUceUVLZDJtdxNDoUKREh8odjuJpNSpXPRnb9JMSuL1KqLW1FSdPnnT9XF5ejtLSUsTGxiI9PR2rVq1CdXU13n33XQCOFUDDhg3D+PHjYTab8d577yE/Px/5+fmux1ixYgXmzp2LF198Ebfddhs++eQTbNmyBdu3b/fAS5Sfo36FBbfumDDUAH2ICk3tFpw614pRiZFyh0R+Tlomz+mggZs1Ig6Fx8+hqKwRP5ydKXc4FOTcTlj27NmDa6/tXmu6cuVKAMAPfvADrF27FrW1taisrHT93mw24+c//zmqq6sRGhqK8ePH41//+hduuukm1zGzZs3CBx98gGeeeQa//OUvMWLECKxfvx7Tp0+/ktemGKfOtaGh1QydRoWctGi5w/ELWo0Kk9NjsONUI3aWn2fCQlesu36FFw0DNdM5IvxtWSNsdpEbRZKs3E5Yrrnmmn4bCa1du7bXz0888QSeeOKJyz7uXXfdhbvuusvdcPyCVL+Smx4NfQjrVwZq6rBY7DjViF3l53HfjAy5wyE/ZrHZUVLZDMDR54cGZnyKY8VeS6cVh2qMmJgaLXdIFMTYvcwHdrJ+ZVB67tzMbpt0JY7UmtButiFKr8GoBDZBGyiNWuVK8KQl4URyYcLiZaIoukZYmLC4Jzc9BhqVgDpTJ840dcgdDvkx6ct2yrBYqDit4ZY8Z81P8Wmu2CN5MWHxsvKGNpxr6YJWo8Ik1q+4JVSrdvWs2cnlzXQF9rDgdtDy0h3nbE9FE0c6SVZMWLzsW2d329w01q8MxlTXtBCXVdLgiKLoGmFh/xX35aRFI0QtoL6liyOdJCsmLF4mbXg4ndNBgzI9k/PndGUqGtvR0NoFrVqFCUPZZdpd+hA1xqc4ztseTguRjJiweFHv+hVe2Q1GXkYsBMExtVZv6pQ7HPJDUv+ViakGjnIO0pSM7mkhIrkwYfGiisZ2nDU5ruwmp3PufDAMoSEYm+TYdmFXBa/uyH3d9Su8aBisvAyp8JYJC8mHCYsXSd1tJ7F+5Yq4poVYeEuDsMe1QzMvGgZLWil07GwLTJ0WmaOhYMWExYv2OK9GpvKD8opIGyFypRC5q6G1C2UNbQCAvHSOsAxWQqQe6bFhEEW4GvAR+RoTFi/a60xYpmTwg/JKSCs7jp1tQXO7WeZoyJ9IoytjEiNhCAuRORr/JtWxFHNqlmTChMVLzreZXVd2uenR8gbj54ZE6jA8PhyiyKI/cg/7r3iONC20h3UsJBMmLF5SUul4U49MiEB0mFbmaPzfNNfyZl7d0cDtPs3+K54iFd6WVjXDarPLHA0FIyYsXiJV00/m6IpHsI6F3NVutuJQtREAR1g8YXRCJCL1GrSbbTha1yJ3OBSEmLB4iZSwSFcldGWkK+SD1Ua0m60yR0P+oLSqGVa7iGSDHkOjQ+UOx++pVIKrPcMejnSSDJiweIHFZsf+M44rOyYsnpEaE4oUgx5Wu8hVCjQgu8u7p4MEgRseeoKrgRzrWEgGTFi84GhtCzosjq3sh8dzK3tPEASB00LkFqmN/FROB3lM987NTFjI95iweIG0DfvkjBhuZe9B0kaIHI6my7Ha7N1tBVhw6zGT0qKhVgmoNXaippkbIZJvMWHxgr3OKYs8tuP3KGn+fF9VM2x2bnNPfTta14I2sw2Reg1GJ0bKHU7ACNNqkJXs2CqD00Lka0xYvMC1Qoj1Kx41OjESEToN2sw2HD/LVQrUN2n5e15GDNQc5fSoPDaQI5kwYfGws6ZOVDd3QCUAOWnRcocTUNQqATlpjm3u91by6o76JjUYZP8Vz5vCBnIkEyYsHibNm49NikKETiNzNIFHmhbae7pZ3kBIsURRdI2wTOEop8dJIyxHak1o7WKLAfIdJiwe1j0dFC1vIAFK2uaghCMs1Ieq8x2ob+lCiFrgKKcXJBtCMTQ6FHYRKGWLAfIhJiweVlzJhnHelJvmOK9lDW1oauNGiHQxaXRlYmo09CFqmaMJTK46Fk4LkQ8xYfGgTosNh6pNALiVvbfEhGsxPD4cgKOTKdGFdnPDQ6/rrmNh4S35DhMWDzpUY4TZZkd8hA5psWwF7i25Uh0Lp4XoEqRi0CkZvGjwFmmEpaSSLQbId5iweFDPDQ/ZCtx7pDoWJix0IWO7BSfrWwFwWtabxiRGIlyrRmuXFce4ESL5CBMWD5JWrvCD0ru6G8gZeXVHvZSeaQYADIsLQ2y4Vt5gAphGrXKNdBZzWoh8hAmLh4iiyIJbHxmT1H11d6KeV3fUTVo9lssu017HwlvyNSYsHnKmqQPnnEsps4ca5A4noDkayEUDYD8W6k3ayVuaNiTvYQM58jUmLB4i1VOMTzFwKaUPsI6FLmS3i66VY9Lyd/KeSWnRUAmOi7Wzpk65w6EgwITFQ6RhUU4H+YZUx8IGciQpb2yDscMCnUaFscnc8NDbIvUhGJPk3Aixgu9D8j63E5bCwkIsWrQIKSkpEAQBH3/8cb/Hb9iwAfPnz8eQIUMQFRWFmTNnYvPmzb2OWbt2LQRBuOjW2ek/WXv3CiEmLL4g1SicOteG5nY2kKPu6aCJqQaEqHkt5gvS1gfsx0K+4Pa7uq2tDTk5OXj11VcHdHxhYSHmz5+PTZs2obi4GNdeey0WLVqEkpKSXsdFRUWhtra2102v17sbnizauqw46lzax5b8vhEbrkWms4FcCRvIEVhwKwepjmUv61jIB9zenW/hwoVYuHDhgI9fs2ZNr59/+9vf4pNPPsGnn36K3Nxc1/2CICApKcndcBRh3xlH86Sh0aFINrBhnK/kpkWjvKENJaebcO2YBLnDIZm5Cm65f5DPSFPgh2pM6LTYWL9HXuXzcVO73Y6WlhbExvbuQtna2oqMjAykpqbilltuuWgE5kJdXV0wmUy9bnKRri64MsG3cqVumxxhCXrtZiuO1jk+AzjC4jtDo0MRH6GD1S7iYLVR7nAowPk8YfnDH/6AtrY23HPPPa77xo4di7Vr12Ljxo1Yt24d9Ho9rrrqKpw4caLPx1m9ejUMBoPrlpaW5ovwL2mv88qOBbe+NdmZIJZWNsPOBnJBbf8ZI+wikGzQI8ngH1PJgUAQBNeFGvf2Im/zacKybt06/PrXv8b69euRkNA9hD9jxgzcd999yMnJwZw5c/Dhhx9i9OjR+NOf/tTnY61atQpGo9F1q6qq8sVLuIjdLrqW1jJh8a0xiZEI06rR0mXFCWc7dgpO7L8in0nOKTjpvwGRt/gsYVm/fj2WLVuGDz/8EPPmzev3WJVKhalTp/Y7wqLT6RAVFdXrJoeyhjY0t1ugD1FhXLI8MQQrjVqFnNRoAOzHEuxcBbfsv+JzHGEhX/FJwrJu3Trcf//9eP/993HzzTdf9nhRFFFaWork5GQfRHdlpC/KianRXEopA+nDkv1Ygpcoiq46Jo6w+N7E1GgIAlDd3IF6NpAjL3L7G7a1tRWlpaUoLS0FAJSXl6O0tBSVlZUAHFM1S5cudR2/bt06LF26FH/4wx8wY8YM1NXVoa6uDkZjd4HWc889h82bN6OsrAylpaVYtmwZSktLsXz58it8ed63lw3jZCX1vdnL4eigVd3s2BZDo+K2GHKI0GkwJtHRqI8F8ORNbicse/bsQW5urmtJ8sqVK5Gbm4tnn30WAFBbW+tKXgDgL3/5C6xWKx5++GEkJye7bitWrHAd09zcjIceegjjxo3DggULUF1djcLCQkybNu1KX5/XuTrccmWCLKQr6pP1rTC2W+QNhmQh1U5kpURxWa1MWMdCvuB2H5ZrrrkGotj3ioy1a9f2+nnr1q2XfcyXX34ZL7/8sruhyM7YYXEVe3IoWh5xEToMiwtDRWM7SqqacA37sQQd9l+RX256ND7YXYXSKk7Nkvew6OIK7HMOfw6LC0NchE7eYIJYrmtfoWZ5AyFZlFSxw63cJjmLnfefMcLGFgPkJUxYroBUFT+JV3aymsydm4NWl9WGQ9VSw7hoeYMJYiMTIhCh06DdbMPxsy1yh0MBignLFZBWpjBhkZd0ZV1axQZyweZQjQlmmx2x4Vqkx4bJHU7QUqsETEx1FDxzpJO8hQnLIImi2D3CwqFoWY1NikRoiBotnVacPMcGcsGkZ/2KIAjyBhPkpAs31rGQtzBhGaTK8+1oardAq1ZhXHKk3OEENY1a5bq6466xwaV7h+ZoeQMh1pKR1zFhGSRpdCUrJQo6DZdSym1yBj8sg1F3S36OcspNGmE5ea4Vpk62GCDPY8IySNIHJetXlKG7gRxHWIJFvakT1c0dEAS4RthIPkMidUiNCYUoAvuruHMzeR4TlkEqZStwRZH+O5yob4Wxg1d3wUDqqjo6IRKR+hB5gyEArGMh72LCMghdVhsO1ziWUnKERRniI3TIiHOsEuEmbMGBOzQrD+tYyJuYsAzCkdoWLqVUICl53MeEJSiw4FZ5ukdYmvvtiE40GExYBqG0R/8VLqVUjp4flhTYrDY79p9x1Emw4FY5xqdEIUQtoLHNjKrzHXKHQwGGCcsgsMOtMvHqLngcO9uCDosNkToNRg6JkDscctKHqJGVHAWge8sEIk9hwjIITFiUKSslClq1Cud5dRfwXKv00qOhUnGUU0lYx0LewoTFTU1tZlQ0tgMAcpiwKIpOo8a4FF7dBQPu0KxcnJolb2HC4qbSM80AgOFDwmEI5VJKpZnk7MfBD8vAxh2alUsqgj5cY0KX1SZvMBRQmLC4iQ3jlG2S88OSCUvgam43o+xcGwC+D5UoPTYMseFamG12V/sHIk9gwuImV8M4flAq0qQ0xxX3oRoTzFa7zNGQN0jvwcz4cMSEa+UNhi4iCAKnhcgrmLC4QRRFV48P6YuRlGVYXBiiw0JgttpxtI5Xd4GIo5zKJ/23YeEteRITFjeUN7TB2GGBTqPCWO7QrEiCICAnNRoAr+4CFVfpKR9HWMgbmLC4QXrzZQ81IETNU6dUrg9LXt0FHFEUsc9Z+M6ERbmkFZSV59vR2NolbzAUMPit6wZe2fkHXt0FrorGdjS3W6DVqDDO2aCMlMcQGoIRQ8IB8H1InsOExQ1MWPyDdHVX1tAGYzt3bg4k0i7A41OioNXw40vJ2ECOPI3v+AHqtNhwpJY7NPuD2HBt987NzukDCgz7qhz7B/E9qHwc6SRPY8IyQIdqTLDYRMRHaJEaEyp3OHQZ3Lk5MJVwlNNvSA3k9lU1w27n3l505ZiwDFDP6SDu0Kx8vLoLPF1WG47UcJTTX4xJjERoiBotXVacOtcqdzgUAJiwDBDrV/wLd24OPIdrTDDb7IgN1yI9NkzucOgyNGoVJji3ymAdC3kCE5YBKuXeJX5lXHIUQtQCd24OINL0Xk6qgaOcfkLqCF7CkU7yACYsA9DQ2oWq8x0QBGCi84qBlE0fokZWMnduDiTSKCd3SfcfnJolT2LCMgBSA7KRQyIQqecOzf6CH5aBhdOy/kfajPRYnQntZqu8wZDfY8IyAPyg9E+TeqxSIP/W1GZGRWM7AL4P/UmyIRSJUTrYReDAGaPc4ZCfY8IyAK6ExfkFSP5B2qDyIHdu9ntSO/7M+HBEh3GHZn/CkU7yFLcTlsLCQixatAgpKSkQBAEff/zxZf9m27ZtyMvLg16vx/Dhw/HGG29cdEx+fj6ysrKg0+mQlZWFjz76yN3QvMJu77lDc7SssZB7hsWFwRDKnZsDAUc5/ZdUc7SPTRzpCrmdsLS1tSEnJwevvvrqgI4vLy/HTTfdhDlz5qCkpAS/+MUv8LOf/Qz5+fmuY4qKirB48WIsWbIE+/btw5IlS3DPPfdg586d7obncWUNrWjpsiI0RI0xidyh2Z8IguD6sOTVnX8r7bFCiPwLNyMlT9G4+wcLFy7EwoULB3z8G2+8gfT0dKxZswYAMG7cOOzZswe///3vceeddwIA1qxZg/nz52PVqlUAgFWrVmHbtm1Ys2YN1q1b526IHiX1D5gw1AANd2j2O5PSolF4/BxKK5uxdKbc0dBgiGKPUU62FfA7E1OjIQhAjbET9aZOJETp5Q6J/JTXv4GLioqwYMGCXvfdcMMN2LNnDywWS7/H7Nixo8/H7erqgslk6nXzBtav+LdcjrD4vcrz7Whqt0CrVmFcMkc5/U2EToPRCY7/buzH4r/+uOUE/u/mo6hoaJMtBq8nLHV1dUhMTOx1X2JiIqxWKxoaGvo9pq6urs/HXb16NQwGg+uWlpbm+eDBuXN/x52b/Z/0HsxKiYJOo5Y3GBoUFt76v/d3ncafvzqF+pYu2WLwyRzHhV0ppVbpPe+/1DH9dbNctWoVjEaj61ZVVeXBiLstm52Je6enIy+DQ9H+qOfOzSz680/StCwvGvwXWwz4t1pjB86auqBWCZgwVL46MrdrWNyVlJR00UhJfX09NBoN4uLi+j3mwlGXnnQ6HXQ6necDvsAdk1Nxx+RUrz8Pec+ktGicbmxHaVUz5o4eInc45CaOcvq/nNRoAMD+M0bY7CLUKm6t4E+kgukxiZEI1co3yun1EZaZM2eioKCg131ffPEFpkyZgpCQkH6PmTVrlrfDoyAgfVhyONr/mK12HOYOzX5vdGIEQkPUaOXOzX6pRCG1nG4nLK2trSgtLUVpaSkAx7Ll0tJSVFZWAnBM1SxdutR1/PLly3H69GmsXLkSR44cwdtvv4233noLP//5z13HrFixAl988QVefPFFHD16FC+++CK2bNmCxx577MpeHRG632Tcudn/HKl17NAcExbimtoj/9Nz52Yub/Y/0n+zXJkvGtxOWPbs2YPc3Fzk5uYCAFauXInc3Fw8++yzAIDa2lpX8gIAmZmZ2LRpE7Zu3YpJkybhf/7nf/DKK6+4ljQDwKxZs/DBBx/gnXfewcSJE7F27VqsX78e06dPv9LXR4Qs7tzst3pueMgdmv0bd272T1abHQeqHdsq5Mo8wuJ2Dcs111zT71Xq2rVrL7rv6quvxt69e/t93Lvuugt33XWXu+EQXZa0c/O+M0aUVDUhnVfqfqO7YVy0rHHQleNKIf907GwLOiw2ROo1GB4fIWss7IRGQYEflv5pn0LmzunKSf8Nj59t4c7NfqTnRYNK5mJpJiwUFLis0v8Y2y0oczapmsQRFr8n7dxss4s4WM29vfxFqYLaCjBhoaAgTSlw52b/UersmzMsLgwx4dyhORB0r9hrkjcQGjAltRVgwkJBITM+3LVz85FaXt35A+nKLkcBH5TkGT1X7JHytXRacNK5DF0J07JMWCgoCILAOhY/I3UmVsKVHXkGd272L/vPGCGKQGpMKOIjvN+o9XKYsFDQkJbklVRyOFrpRFFU1FA0ecaFOzeTsintPciEhYIGR1j8R9X5DpxvM0OrViErJUrucMhDeu7czPeh8kkXd0xYiHxMetNVNLbjfJtZ3mCoXyXOosxx3KE54PDCwT/0HOWUu2GchAkLBY3oMC2Gx4cD4PJmpdtX5eisOSlVvp1hyTtYeOsfzjR1oKHVDI1KwPgUZbwPmbBQUJE+LNkeXNmkZa9KWJlAnnXhzs2kTFJCmZUSBX2IMkY5mbBQUHHtZ8LCW8UyW+046NqhOUbmaMjTuHOzf1BawS3AhIWCTG664wtwX1Uz7Ly6U6SjdY7mfobQEAzjvk8Bhzs3+wcmLEQyG5MUCZ1GBVOn1dX2nZSlRNrKPp07NAcqaaRT6mZMymKx2XHQuUMzExYimYSoVZgoXd2xjkWRpOm6XE4HBSw2kFO2o7Ut6HKOcmY6FyooARMWCjqTWMeiaCUKW0pJnicVUx8724IOs03eYOgiUtF7TpqyRjmZsFDQkepYOMKiPI2tXTjd2A6AewgFsqQoPRIiHTs3H3BOPZBylCiwfgVgwkJBSHoTHq3j1Z3SSEnkyIQIGEJD5A2GvKb33l4c6VQaV8M4JixE8ko28OpOqVwFtwr7oCTPk6aFpCaBpAzGdgvKzjkWJChtlJMJCwUdQRC4EaJCSS35pWk7Clxs0a9M0sqtjLgwxIZr5Q3mAkxYKChJDcn4YakcNrvoutpmwW3gk3Zurm7u4M7NCiKt3FJa/QrAhIWCVPcIS7OscVC3k/WtaO2yIkyrxujESLnDIS/ruXMzt8pQDte2GExYiJRhwlADVAJQZ+pErbFD7nAI3dNzOanRUKuUs5SSvGdyRjQAYC+nZhWh5w7NTFiIFCJcp8GYpCgAbF6lFD073FJwkJoDcqRTGSrPt6Op3QKtWoWslCi5w7kIExYKWiz6UxYW3AYfaYRl/5lmWGx2eYOhXjs06zTK2KG5JyYsFLRcdSxMWGRn6rTgRL1j514lDkWTdwyPj0CUXoNOix1Ha1vkDifolSi44BZgwkJBTOr1ceCMEVZe3clqf5URogikxYZiSKRO7nDIR1QqwTWixjoW+ZUqfFsMJiwUtEYMiUCkToMOiw3HzvLqTk7c8DB4SV+OTFjk1WW14XCNCQBHWIgUR6USXJ0cWfQnL254GLwmp7PwVgkO15hgttkRG65FemyY3OFcEhMWCmrSFyQLb+UjimL3CAsLboPOpHRHA7nK8+1oaO2SO5ygJX0G5qQaFLVDc09MWCioTXKNsHA4Wi6nG51LKTUqZCUrbykleVeUPgSjEiIAAHtP830ol+62Asq9aGDCQkFNSlhOnWuDscMibzBBSlrOnJ0SBa2GH0nBSKpd2stpIdkUO5PFvIwAS1hee+01ZGZmQq/XIy8vD19//XWfx95///0QBOGi2/jx413HrF279pLHdHZyfwnyrrgInWu+dh+nhWQhXdlNVvCVHXmX1I+FI53yOGvqRHVzB1SC8nZo7snthGX9+vV47LHH8PTTT6OkpARz5szBwoULUVlZecnj//jHP6K2ttZ1q6qqQmxsLO6+++5ex0VFRfU6rra2Fnq9fnCvisgNbCAnL38YiibvkpLV/WwxIAtpKm5MUhQidBqZo+mb2wnLSy+9hGXLluHBBx/EuHHjsGbNGqSlpeH111+/5PEGgwFJSUmu2549e9DU1IQHHnig13GCIPQ6LikpaXCviMhN3Rsh8urO1zrMNhypdSyl5Aqh4DViSAQi9Y4WA0fr2GLA17qng6LlDeQy3EpYzGYziouLsWDBgl73L1iwADt27BjQY7z11luYN28eMjIyet3f2tqKjIwMpKam4pZbbkFJSUm/j9PV1QWTydTrRjQYPUdYRFGUN5ggc7DGCKtdRGKUDskGjqgGK5VKcL0P2Y/F94qd51zp07JuJSwNDQ2w2WxITEzsdX9iYiLq6uou+/e1tbX4/PPP8eCDD/a6f+zYsVi7di02btyIdevWQa/X46qrrsKJEyf6fKzVq1fDYDC4bmlpae68FCKXrJQoaNUqNLVbcLqxXe5wgkrPhnFKXUpJvsF+LPLotNhwqNpxwa/kgltgkEW3F36wiKI4oA+btWvXIjo6Grfffnuv+2fMmIH77rsPOTk5mDNnDj788EOMHj0af/rTn/p8rFWrVsFoNLpuVVVVg3kpRNBp1K6dSVnH4lvcoZkkkzPYol8Oh2qMMNvsiI9QbsM4iVsJS3x8PNRq9UWjKfX19ReNulxIFEW8/fbbWLJkCbRabf9BqVSYOnVqvyMsOp0OUVFRvW5Eg8X24L4niqLrfLPglqQpodONbCDnS1L9Sm668kc53UpYtFot8vLyUFBQ0Ov+goICzJo1q9+/3bZtG06ePIlly5Zd9nlEUURpaSmSk5PdCY9o0KTh6GI2rvKZWmMnzpq6oFYJmDDUIHc4JDNDaHcDOU4L+c7e080AlD8dBAxiSmjlypX429/+hrfffhtHjhzB448/jsrKSixfvhyAY6pm6dKlF/3dW2+9henTpyM7O/ui3z333HPYvHkzysrKUFpaimXLlqG0tNT1mETeNmWY4816pNaEti6rzNEEB+lLaVxyJEK1anmDIUXgij3fEkXRVXDrDwmL2wuuFy9ejMbGRjz//POora1FdnY2Nm3a5Fr1U1tbe1FPFqPRiPz8fPzxj3+85GM2NzfjoYceQl1dHQwGA3Jzc1FYWIhp06YN4iURuS/ZEIqh0aGobu5AaVUzrhoZL3dIAY87NNOFJqfH4MM9Zzg16yNnmjpwrqULGj8Z5RxUh5if/vSn+OlPf3rJ361du/ai+wwGA9rb+1598fLLL+Pll18eTChEHpOXEYPq5g7sqWhiwuID3KGZLiQV3u6rcjSQ06i5VYM3SYnh+KEG6EOUP8rJfw1ETtKQ6J7T52WOJPCZrXYcqDYCYMEtdRs5JAKROjaQ8xVXwzg/eQ8yYSFykhKW0spm2OxsIOdNR2pNMFvtiA4LwbA4ZS+lJN9RqQRMYh2Lz/jDhoc9MWEhchqbFIlwrRotXVYcP8urO2/qrl+JVvxSSvKtXDaQ84m2LqtrFGuywlvyS5iwEDlp1CrXh+UeLm/2qu76Ff+4siPfmcyeSD6x74xjJDnFoEeyIVTucAaECQtRD9LQaHEF61i8qbtZVbS8gZDiSKvGKhrb0cgGcl4j7dA82U+mgwAmLES9dBfe8urOW2qNHTjT1AGVwBEWupghLAQjhoQD4LSQN+11nlulb3jYExMWoh5y06OhEhz9Cc6aOuUOJyDtqXAkg1kpUYjQDaqzAgU410aIVbxw8Aa7vXtbDH8puAWYsBD1EqkPwZgkx75UbNPvHXuc021TMmJljoSUyrURorNtPHlWWUMbmtst0IeoXBu/+gMmLEQXmCJNC1UwYfGG3c7zOnUYExa6NGmEZd+ZZlhtdpmjCTzS6MrEodEI8aPmfP4TKZGPSPsKFbOBnMeZOi04WmcCAEwd5j9D0eRbIxMiEKHToN1swzG2GPA4fyy4BZiwEF1Euro7VGNCh9kmczSBZe/pJthFICMuDAlRernDIYVSqwRMSosGwMJbb/DH+hWACQvRRVJjQpEYpYPVLmLfmWa5wwko0jQb61foctiPxTuMHRYcP9sKwP/aCjBhIbqAIAiuL1QW3nrWbmfBLaeD6HJyXYW3fA96ktRlelhcGOIjdDJH4x4mLESX4OrHwgZyHmO22lHq7HA7hQW3dBmT02MgCI4GcvUtbDHgKa7+K342HQQwYSG6pO7C2ybYuRGiRxysMaLLakdMj8ZgRH0xhIZgrLPFwO5yjrJ4iqvg1o8axkmYsBBdwrjkKISGqGHqtOLkuVa5wwkIrv4rw2K54SENyPRMx0jcrvJGmSMJDDa76JoS8reCW4AJC9ElhahVyEkzAGAdi6dI/VemcTqIBmiaM2HZWc6pWU84frYFbWYbInQajE6MlDsctzFhIeqDVHjLBnJXThTFHiMs/ndlR/KQmgseO9sCY7tF5mj8n3TxNSktGmqV/41yMmEh6kMeG8h5zKlzrWhytgIfn2KQOxzyE0MidRgeHw5RBPbwfXjF/LVhnIQJC1Efeq5SONfCbe6vhDQdNCktGloNP3Zo4Ka56liYsFwpf20YJ+EnB1EfDKEhGJ3gmOdl86or091/hfUr5B7p3wzrWK5MQ2sXKhrbAcDVRdjfMGEh6oc0dMrC2yvj6nDLhIXcJI2wHKw2ot1slTka/yWNUI1JjIQhNETmaAaHCQtRP6awgdwVO2vqROX5dqiE7nbrRAOVGhOKFIMeVrvIfYWuwM4yx9LwGcP996KBCQtRP6QVLQerTei0cCPEwZBGV8YlRyFS759XdiQfQRC4vNkDvi1znLsZw+NkjmTwmLAQ9SM91rHfhtlmx4Fqo9zh+CXWr9CVmsoGclfkfJsZx862AOieYvNHTFiI+uHYCJF1LFdiN/uv0BWSOt6WVDbDbLXLHI3/kRK90YkRiPOzDQ97YsJCdBndGyEyYXFXS6cFR2pNALob8RG5a8SQCMSGa9FlteNAdbPc4fidQJgOApiwEF2W1EBub2UTRJEbIbqjpLIZdhFIiw1FkkEvdzjkpwRBwFTn+5B1LO771llwOz2TCQtRQMtOMUCnUeF8mxllDW1yh+NXpNVVUzm6QldomvPLlg3k3NPUZsbROkf9ynQ/XiEEMGEhuiytRuVqtCRdqdDA7Gb/FfIQqY6luKIJNjtHOgdql/OiYVRCBOL9uH4FYMJCNCCzRsQDAIpOMWEZKIvNjpIqR8IylQW3dIXGJUchQqdBS5fVVRdFl+eaDvLz0RVgkAnLa6+9hszMTOj1euTl5eHrr7/u89itW7dCEISLbkePHu11XH5+PrKysqDT6ZCVlYWPPvpoMKERecWskY7h6KJTjaxjGaBDNSZ0WuyICQvByIQIucMhP6dWCa4CeE4LDdzOACm4BQaRsKxfvx6PPfYYnn76aZSUlGDOnDlYuHAhKisr+/27Y8eOoba21nUbNWqU63dFRUVYvHgxlixZgn379mHJkiW45557sHPnTvdfEZEX5KRGIzREjcY2M46fbZU7HL8g1a/kZcRCEPxvK3tSHqmHyG52nh6Q5nYzjtQ5RqP8uf+KxO2E5aWXXsKyZcvw4IMPYty4cVizZg3S0tLw+uuv9/t3CQkJSEpKct3UarXrd2vWrMH8+fOxatUqjB07FqtWrcL111+PNWvWuP2CiLxBq1G5+ojsONUgczT+QboK5nQQecr0Hjs3c6Tz8hznCRgxJBwJkf6/Ss+thMVsNqO4uBgLFizodf+CBQuwY8eOfv82NzcXycnJuP766/HVV1/1+l1RUdFFj3nDDTdc9jGJfIl1LAMniiL2nGbBLXnWhFQDtBoVGtvMOHWOK/YuR1oCPj0ApoMANxOWhoYG2Gw2JCYm9ro/MTERdXV1l/yb5ORkvPnmm8jPz8eGDRswZswYXH/99SgsLHQdU1dX59ZjAkBXVxdMJlOvG5E3zRzheNN/W9bIVQqXUdbQhvNtZug0KmQPjZI7HAoQOo0auc4Ve6xjubxvXRseBkbCohnMH104Hy2KYp9z1GPGjMGYMWNcP8+cORNVVVX4/e9/j7lz5w7qMQFg9erVeO655wYTPtGgZKdEIVKnganTisM1JkxINcgdkmLtdn6Z5KRFQ6dRX+ZoooGbnhmLneXnsbviPO6dni53OIpl7LDgsHM11YwAqF8B3BxhiY+Ph1qtvmjko76+/qIRkv7MmDEDJ06ccP2clJTk9mOuWrUKRqPRdauqqhrw8xMNhkatci0NLCpjHUt/vnFOmwXKByUpx9QedSzUt93O+pXh8eFIiPL/+hXAzYRFq9UiLy8PBQUFve4vKCjArFmzBvw4JSUlSE5Odv08c+bMix7ziy++6PcxdTodoqKiet2IvG2ms45lB+tY+mS3i9hx0pHQzR41ROZoKNBMTo+BWiWgurkDZ5ra5Q5HsXaWS/1XAmM6CBjElNDKlSuxZMkSTJkyBTNnzsSbb76JyspKLF++HIBj5KO6uhrvvvsuAMcKoGHDhmH8+PEwm8147733kJ+fj/z8fNdjrlixAnPnzsWLL76I2267DZ988gm2bNmC7du3e+hlEnnGzOHd7cEtNjtC1Oy9eKGjdS1obDMjTKt2dQgm8pRwnQbZQw3YV9WM3RXnkRoTJndIitS94WHgjHK6nbAsXrwYjY2NeP7551FbW4vs7Gxs2rQJGRkZAIDa2tpePVnMZjN+/vOfo7q6GqGhoRg/fjz+9a9/4aabbnIdM2vWLHzwwQd45pln8Mtf/hIjRozA+vXrMX36dA+8RCLPGZsUiZiwEDS1W7D/TDPyuEfORbafPAfAUein1TChI8+bnhmLfVXN2FV+Ht/JTZU7HMUxdVpwqMYIwP83POxJEANkMbvJZILBYIDRaOT0EHnVT/9ejE0H6vDzBaPxyHWjLv8HQWbp27tQePwcfnlLFpbNzpQ7HApABYfP4kfv7sHwIeH48r+ukTscxfny6Fn8cO0eDIsLw9b/vlbucC5roN/fvPwhcpM0LcQ6lot1WW3Y5Zw7nz0yXuZoKFBJzQjLzrXhXEuXzNEoz7cB1I6/JyYsRG6SCm/3nG5Cp8UmczTKsvd0MzotdgyJ1GF0IvcPIu+IDtNibFIkgO4tIKjbzgDrvyJhwkLkJkebax3MVjtKKpvlDkdRpPqV2SPjuX8QeZW0Nw5HOntr6bTgQLWzfiWACm4BJixEbhMEAbNGSLs3sx9LT9tPOr48ruJ0EHmZNOVYeOKczJEoy57TTbCLQEZcGJINoXKH41FMWIgGQWrTz6u7bsZ2Cw6caQYAXDUysIaiSXlmjoiDRiXgdGM7TjdyXyGJ1I5/egA2bWTCQjQI0kaIpVXNaOuyyhyNMhSVNcLu3Bk20K7sSHki9SGYnOEovi08zlEWSaAW3AJMWIgGJS02DKkxobDau3clDnbfSN1tOR1EPnL1aEcn5W3HOTULAK1dVhx01a8wYSEip+7lzfywBIDtbMdPPjbX+W+t6FQDzFa7zNHIb0/FedjsItJiQzE0OvBGOZmwEA3SrJFS4S3rWM40taO8oQ1qlRBwKxNIucanRCEuXIs2sw3FHOnETueGkDMCqLttT0xYiAZp5nDH1MfBaiOMHRaZo5HXDufqoJxUA6L0ITJHQ8FCpRIwZxRXC0mkRQCBOB0EMGEhGrQkgx7Dh4TDLnKr++2sXyGZzHXWsQR74W1jaxf2O1fpSUlcoGHCQnQFWMcC2O2iq+CW/VfI1+Y461gO1ZiCuk3/tuPnIIpAVnIUEqP0cofjFUxYiK6AtLw5mOtYjta1oLHNjDCtGrnpMXKHQ0FmSKQOWcmODfOkTsvB6Ktjjtd+3dgEmSPxHiYsRFdghrPA9GhdCxpbg/PqThpdmZ4ZC62GHynke93TQsE50mm12bHtWD0A4NqxgbtKj58uRFcgLkLn2oRNatgUbLZzOohkNne0s/D2+DnY7aLM0fheSVUzTJ1WRIeFYFJa4I5yMmEhukLdbfqD7+quy2rDznLHdNjsAC30I+WbkhGLMK0ajW1mHK41yR2Oz3151DG6cvXoIVCrAnfTUSYsRFdIqmORpkaCyd7Tzei02BEfocOYxEi5w6EgpdWoXBuSbgvC1UJfOROWQK5fAZiwEF2xmSPioFWrUNHYjpP1rXKH41Pd7fjjIAiBe2VHyhesy5trmjtwtK4FKqG782+gYsJCdIUidBrMcF7dbTlyVuZofIv1K6QU0pd18ekmtAbRhqRbnauDctNjEBOulTka72LCQuQB87MSAQAFh4MnYTG2W1yNqpiwkNyGxYcjPTYMVrsYVG0GpPqVa8cE9ugKwISFyCPmjXPMHe+tbEJDkCxvLiprhF0Ehg8JR0oAbrRG/qfnaqFg0GmxuaZlrw3w+hWACQuRRyQbQjFhqAGiCHx5pF7ucHxC+qCcw9EVUghpWihYCm93lZ9Hh8WGxKju5nmBjAkLkYfMG+ecFgqSOha24yelmTUyHhqVgMrz7ahoaJM7HK/rng5KCIqidyYsRB4i1bF8feIcOi02maPxrtONbShraINaJbgKjonkFqHTIC/D0TgtGHZv3ursbnvNmMCfDgKYsBB5zLjkSAyNDkWnxY7tJwK7J8vnB+sAOLYmiNKHyBwNUbdgWd5cdq4VFY3tCFELQdO0kQkLkYcIguAqvg305c1SwnJjdrLMkRD1drUzYSk61Qiz1S5zNN4jbXY4LTMWETqNzNH4BhMWIg+an5UEANhypD5g9zSpae7AvqpmCAJww/hEucMh6iUrOQpx4Vq0mW0oPt0kdzhe81WP+pVgwYSFyIOmZcYiUqdBQ2sXSp09SgLNv52jK1MyYpAQqZc5GqLeVCoBc5xTJIFax9LWZXXt4RUMy5klTFiIPEirUeEa5wdIoDaRkxKWhZwOIoWS6li2HQvMhGX7yQZYbCIy4sIwPD5c7nB8hgkLkYe56lgCMGGpb+nE7tPnAQA3ZifJHA3Rpc1x9mM5XGtCvalT5mg8T1odFCzLmSVMWIg87JoxCdCoBJyobw24XhCbD52FKAI5adHsbkuKNSRSh0lp0QCATQdq5Q3Gw0RRxFdHHSNHwTQdBAwyYXnttdeQmZkJvV6PvLw8fP31130eu2HDBsyfPx9DhgxBVFQUZs6cic2bN/c6Zu3atRAE4aJbZ2fgZcYU+AyhIZg+PBZA4K0W+vdBx4f/Qo6ukMItykkBAGzcVyNzJJ51pLYFdaZOhIaoMT0zVu5wfMrthGX9+vV47LHH8PTTT6OkpARz5szBwoULUVlZecnjCwsLMX/+fGzatAnFxcW49tprsWjRIpSUlPQ6LioqCrW1tb1uej0L+sg/zXd2vf0igKaFmtrM+LbMMR3EhIWU7paJyRAEYG9lM6rOt8sdjsd85ZwOumpkHPQhapmj8S23E5aXXnoJy5Ytw4MPPohx48ZhzZo1SEtLw+uvv37J49esWYMnnngCU6dOxahRo/Db3/4Wo0aNwqefftrrOEEQkJSU1OtG5K/mObve7qk4j6Y2s8zReEbB4bOw2UWMS45CRlzwFPqRf0qM0rtGID7bHzjTQtJy5mDpbtuTWwmL2WxGcXExFixY0Ov+BQsWYMeOHQN6DLvdjpaWFsTG9h7Kam1tRUZGBlJTU3HLLbdcNAJD5E9SY8IwLjkKdrF7vw9/97lzOugmjq6Qn7g1ZyiAwJkWamozY2+lo7dMsNWvAG4mLA0NDbDZbEhM7N0sKjExEXV1dQN6jD/84Q9oa2vDPffc47pv7NixWLt2LTZu3Ih169ZBr9fjqquuwokTJ/p8nK6uLphMpl43IiWZH0Bdb02dFmx3bna4cAITFvIPC7OToFEJOFJrwsn6FrnDuWLbjp+DXQTGJDq2AQk2gyq6vXAZlSiKA1patW7dOvz617/G+vXrkZDQnR3OmDED9913H3JycjBnzhx8+OGHGD16NP70pz/1+VirV6+GwWBw3dLS0gbzUoi8Rup6u+24/2+G+J8jZ2GxiRiZEIGRCZFyh0M0IDHhWlcTuY37/H9a6OPSagDdG60GG7cSlvj4eKjV6otGU+rr6y8adbnQ+vXrsWzZMnz44YeYN29e/0GpVJg6dWq/IyyrVq2C0Wh03aqqqgb+Qoh8IHtoFJKi9Gg321BU1ih3OFfk8wNSsziOrpB/uXWSY7XQZ/tqIIr+u11GvanTtaHjHZOHyhyNPNxKWLRaLfLy8lBQUNDr/oKCAsyaNavPv1u3bh3uv/9+vP/++7j55psv+zyiKKK0tBTJyX130tTpdIiKiup1I1ISQRAwL8v/u962dVmxzflByWZx5G/mZyVBp1GhrKENh2r8t3Tgo5Jq2EUgLyMGw4dEyB2OLNyeElq5ciX+9re/4e2338aRI0fw+OOPo7KyEsuXLwfgGPlYunSp6/h169Zh6dKl+MMf/oAZM2agrq4OdXV1MBqNrmOee+45bN68GWVlZSgtLcWyZctQWlrqekwifzXPubz5P0fO+u1miFuPnUOX1Y702DBkJfPCgPxLhE6D6531ZP5afCuKIvL3ngEA3Dk5VeZo5ON2wrJ48WKsWbMGzz//PCZNmoTCwkJs2rQJGRkZAIDa2tpePVn+8pe/wGq14uGHH0ZycrLrtmLFCtcxzc3NeOihhzBu3DgsWLAA1dXVKCwsxLRp0zzwEonkM3NEHMK1apw1deFAtfHyf6BA0uqghROSgqoNOAWOW3O6p4X88cLhYLUJx8+2QqtR4eaJwbuHlyD686ReDyaTCQaDAUajkdNDpCg//XsxNh2ow4+vHo5VC8fJHY5bOi025P1PAdrMNnz88FWududE/qTTYsOU32xBa5cV/1g+E1OH+VeH2F9vPIS1Oypwy8RkvHrvZLnD8biBfn9zLyEiL7ttkqNALr/4DMxWu8zRuKfw+Dm0mW1IMeiRk2qQOxyiQdGHqLFgvGN6dmOpf00Lma12fOJcHXRXXvBOBwFMWIi87vqxCUiM0qGh1YwvDg+sX5FS/PugI94bsjkdRP5NmhbadKAWVpv/XDh8ebQeTe0WJETqXLtQBysmLEReplGrsHiKo0/Q37+99J5bSmS22lHgbHq3MDt4580pMFw1Mh6x4Vo0tpmx45T/tBmQim2/kzsUalVwXzQwYSHygcXT0qESgKKyRpSda5U7nAHZcaoBLZ1WxEfokJcRI3c4RFckRK1y9RHyl9VCja1drr2D7gzy6SCACQuRTwyNDsW1zs3K1u3yj1GWTQccq4NuzE4M+is7CgzStNDmg3Xosiq/+/QnpTWw2kVMTDVgdCI7TDNhIfKRe6enAwD+WXxG8a36m9rMrqvQRRNTZI6GyDOmDotFUpQeLV1WbD12Tu5wLou9V3pjwkLkI9eMSUCKQY+mdourmFWp3t9ViU6LHVnJUZiW6V9LQIn6olIJuMXZx0Tp00JHak04VGNCiFpwjQwFOyYsRD6iVglYPNUxyvL+TuVOC5mtdrxbVAEAWDY7k6uDKKBIewv958hZtHVZZY6mb/nFjtGV68cmIiZcK3M0ysCEhciHFk9Ng1olYFfFeZw4q8zt7v91oAZnTV0YEqnDIl7ZUYCZMNSAYXFh6LTYseWIMvf4strs+NjZL4bFtt2YsBD5UJJBj+vHOopv/67AURZRFPHW9nIAwNIZGdBq+BFBgUUQBFcivmFvtczRXFrhiXNoaO1CXLgW14wJ7t4rPfHTiMjHpOLbDXuVV3y7q/w8DlaboNOo8P0ZGXKHQ+QVd0xOhSAA246fw2EF7uCcX+xIpG6bNBQhan5NS3gmiHxs7qghSI0JhanTis/218odTi/S6Modk4cilvPmFKAy48Nxi3P126tfnZA5mt6a280oOOyYqrozb6jM0SgLExYiH1OpBHxvmlR8e1rmaLqdbmxzdbb94VWZMkdD5F2PXDsSALDpQB2OK6ie7NP9tTDb7BibFInxKdy/qycmLEQyuHtKKjQqAXsrm3GkVhlD0u98UwFRBOaOHoJRbFJFAW5MUiRuHO/ofPvqlydljqabtDoo2Dc6vBQmLEQySIjUu3aPVcISZ1OnBf/YUwXAsZSZKBg8er1jlOWz/TWK2DJjd8V5lFY1Q60SXLu8UzcmLEQyuXeao6j145JqtJvl7QexflcV2sw2jEqIwNxR8bLGQuQr41MMmDcuAXYR+PNXp2SNRRRFvPCvIwAc7Q+GROpkjUeJmLAQyWTWiDgMiwtDS5cVn8rYddNqs2PtjgoAwA/ZKI6CzKPXjQIAfFxajcrGdtni2HSgDqVVzQjTqvHYvFGyxaFkTFiIZNKz+FbOniybD51FdXMHYsO1+E4uh6EpuOSkRWPu6CGw2UW8tlWeWhaz1Y7/s/koAOChucOREKmXJQ6lY8JCJKO78lKhVauw/4wRW4/VyxLDW9vLAADfn54OfYhalhiI5LTCWcuSv/cMqps7fP787317Gqcb2zEkUocfzRnu8+f3F0xYiGQUF6HDkpmOWpZnPj7o81qWksom7K1sRohawBI2iqMglZcRi1kj4mCxiXhjq29rWYwdFrzypaMXzMr5oxGu0/j0+f0JExYima2cPxpDo0NxpqkDf9zi2yZWUqO4RTkpSIjiMDQFL6mWZf3uKtQZO332vK9tPYnmdgtGJUTgbi5l7hcTFiKZhes0eP628QCAv20vx6Eao0+e90xTOz4/WAeAS5mJZgyPxdRhMTDb7PhLoW9GWc40teOdbyoAAE8tHAsN2/D3i2eHSAGuH5eImyYkwWYXsWrDAdjsolefz2YX8d//2A+bXcTM4XHsqElBTxAE1yjL+zsrca6ly+vP+dIXx2G22jFjeCyuc26KSn1jwkKkEL9eNB6Reg32nzHi3aIKrz7Xq1+eRFFZI8K0avzmO9lefS4ifzFnVDwmpUWjy2rH374u8+pzHaw24qNSxyaHT9+UxXYCA8CEhUghEqL0ePLGsQCA328+hhovrVb4tqwRf/zPcQDAb27PxoghEV55HiJ/IwgCfuZcMfS/357G+TazV55HFEWs/vwIRBG4bVIKJqRyhHMgmLAQKci909KRlxGDNrMNz35yCKLo2amhxtYurPigBHbRsaT6jsks8iPq6doxCcgeGoV2sw0rPyyFxWb3+HNsPX4O35xshFatws8XjPH44wcqJixECqJSCVh9xwSEqAVsOXIWmw/Veeyx7XYR//WPfThr6sKIIeGuQl8i6iYIAl64fQJCQ9TYeuwcnszf79ELB5tdxO82OZrE3X/VMKTFhnnssQMdExYihRmdGIkfzx0BAHj2k0MwdVo88rh/216GrcfOQadR4c/fn4wwLfs9EF1KTlo0/vz9XKhVAjbsrcaL/z7mscd+f1cljp1tgSE0BA9fM9JjjxsMmLAQKdAj143EsLgw1Ld04f964MNyb2UT/o/zcX61aDzGJkVd8WMSBbLrxibid3dMAAC8se0U3nb2LLoS/29HBX71yUEAwKPXjYQhLOSKHzOYMGEhUiB9iBq//Y7jw/K9nadRfPr8oB/L2G7Bo++XwGoXccvEZHxvWpqnwiQKaHdPScMTNzpqTJ7/7DA2DnKTUptdxPOfHsavNh6CXQS+OzUN988a5sFIgwMTFiKFmjUyHndOToUoAve/vRv/W1Thdn8WURTxZP5+VDd3ID02DKvvmMDlk0Ru+MnVI1zJxX99WIrtJxrc+vsOsw0/ea8Yb3/jGKF54sYxWH3HBDaJG4RBnbHXXnsNmZmZ0Ov1yMvLw9dff93v8du2bUNeXh70ej2GDx+ON95446Jj8vPzkZWVBZ1Oh6ysLHz00UeDCY0ooPzylnHISYtGS5cVv/zkEO54fceAO+FabXb8pbAM/z5UhxC1gFfvzUWknkPQRO4QBAHP3pKFmycmw2IT8eP/3YOD1QN7D55r6cJ3//otvjh8FlqNCn/6Xi5+es1IXjQMktsJy/r16/HYY4/h6aefRklJCebMmYOFCxeisrLykseXl5fjpptuwpw5c1BSUoJf/OIX+NnPfob8/HzXMUVFRVi8eDGWLFmCffv2YcmSJbjnnnuwc+fOwb8yogAQHabFhp/MwnO3jkeEToN9Vc249dVv8JvPDqOt69IbJR4/24LVm45g1u++xO8+d6xGWLVwHCamRvswcqLAoVIJeOmeHMwaEYc2sw33v7MblY3t/f7NyfoWfOe1b7CvqhnRYSH4+4PTsSgnxUcRByZBdHO91vTp0zF58mS8/vrrrvvGjRuH22+/HatXr77o+CeffBIbN27EkSNHXPctX74c+/btQ1FREQBg8eLFMJlM+Pzzz13H3HjjjYiJicG6desGFJfJZILBYIDRaERUFAsKKfCcNXXi+U8P418HagEAyQY9fn3reNwwPglNbWZ8ur8G/yw+g/1nuq/+YsJCsHTmMDw2bxSv6oiuUEunBff85VscqTUhNESNjLgwpMaEIjVG+t9QDI0OQ0NbF1asK4Gp04phcWF454FpyIwPlzt8xRro97db6xrNZjOKi4vx1FNP9bp/wYIF2LFjxyX/pqioCAsWLOh13w033IC33noLFosFISEhKCoqwuOPP37RMWvWrHEnPKKAlhilx5+/Pxl3HavHs58cRNX5Dvz4f4sxPiUKJ862wuxscKVRCbh2bALuykvFtWMSoNVwrpzIEyL1Ifh/D0zFvX/biZP1rTha14KjdS19Hp+XEYO/Lp2C2HCtD6MMXG4lLA0NDbDZbEhMTOx1f2JiIurqLt3gqq6u7pLHW61WNDQ0IDk5uc9j+npMAOjq6kJXV/fmVCaTyZ2XQuS3rh2TgC8euxp/+vIE3iwsw6Eax7/98SlRuCsvFbfmpCAuQidzlESBKSFKj3+vmIOKxnacaWrHmaYO560d1c2O/9/UZsatOSn47R0ToA9Ryx1ywBhU56gLh5ZFUex3uPlSx194v7uPuXr1ajz33HMDjpkokIRq1XjixrG4Y/JQFJ1qxJRhsRiXzKlQIl/QqFUYmRCBkQmX3ofLbhehUnEK1tPcGiuOj4+HWq2+aOSjvr7+ohESSVJS0iWP12g0iIuL6/eYvh4TAFatWgWj0ei6VVVVufNSiALCyIRILJk5jMkKkYIwWfEOtxIWrVaLvLw8FBQU9Lq/oKAAs2bNuuTfzJw586Ljv/jiC0yZMgUhISH9HtPXYwKATqdDVFRUrxsREREFJrenhFauXIklS5ZgypQpmDlzJt58801UVlZi+fLlABwjH9XV1Xj33XcBOFYEvfrqq1i5ciV+9KMfoaioCG+99Vav1T8rVqzA3Llz8eKLL+K2227DJ598gi1btmD79u0eeplERETkz9xOWBYvXozGxkY8//zzqK2tRXZ2NjZt2oSMjAwAQG1tba+eLJmZmdi0aRMef/xx/PnPf0ZKSgpeeeUV3Hnnna5jZs2ahQ8++ADPPPMMfvnLX2LEiBFYv349pk+f7oGXSERERP7O7T4sSsU+LERERP5noN/fbNBAREREiseEhYiIiBSPCQsREREpHhMWIiIiUjwmLERERKR4TFiIiIhI8ZiwEBERkeIxYSEiIiLFY8JCREREiud2a36lkhr2mkwmmSMhIiKigZK+ty/XeD9gEpaWlhYAQFpamsyREBERkbtaWlpgMBj6/H3A7CVkt9tRU1ODyMhICILgscc1mUxIS0tDVVUV9yi6DJ4r9/B8DRzP1cDxXA0cz9XAefNciaKIlpYWpKSkQKXqu1IlYEZYVCoVUlNTvfb4UVFR/Ac9QDxX7uH5Gjieq4HjuRo4nquB89a56m9kRcKiWyIiIlI8JixERESkeExYLkOn0+FXv/oVdDqd3KEoHs+Ve3i+Bo7nauB4rgaO52rglHCuAqboloiIiAIXR1iIiIhI8ZiwEBERkeIxYSEiIiLFY8JCREREiseE5TJee+01ZGZmQq/XIy8vD19//bXcIclu9erVmDp1KiIjI5GQkIDbb78dx44d63WMKIr49a9/jZSUFISGhuKaa67BoUOHZIpYGVavXg1BEPDYY4+57uN56q26uhr33Xcf4uLiEBYWhkmTJqG4uNj1e54vB6vVimeeeQaZmZkIDQ3F8OHD8fzzz8Nut7uOCdZzVVhYiEWLFiElJQWCIODjjz/u9fuBnJeuri48+uijiI+PR3h4OG699VacOXPGh6/CN/o7VxaLBU8++SQmTJiA8PBwpKSkYOnSpaipqen1GD49VyL16YMPPhBDQkLEv/71r+Lhw4fFFStWiOHh4eLp06flDk1WN9xwg/jOO++IBw8eFEtLS8Wbb75ZTE9PF1tbW13H/O53vxMjIyPF/Px88cCBA+LixYvF5ORk0WQyyRi5fHbt2iUOGzZMnDhxorhixQrX/TxP3c6fPy9mZGSI999/v7hz506xvLxc3LJli3jy5EnXMTxfDr/5zW/EuLg48bPPPhPLy8vFf/zjH2JERIS4Zs0a1zHBeq42bdokPv3002J+fr4IQPzoo496/X4g52X58uXi0KFDxYKCAnHv3r3itddeK+bk5IhWq9XHr8a7+jtXzc3N4rx588T169eLR48eFYuKisTp06eLeXl5vR7Dl+eKCUs/pk2bJi5fvrzXfWPHjhWfeuopmSJSpvr6ehGAuG3bNlEURdFut4tJSUni7373O9cxnZ2dosFgEN944w25wpRNS0uLOGrUKLGgoEC8+uqrXQkLz1NvTz75pDh79uw+f8/z1e3mm28Wf/jDH/a674477hDvu+8+URR5riQXfgkP5Lw0NzeLISEh4gcffOA6prq6WlSpVOK///1vn8Xua5dK7i60a9cuEYDrot3X54pTQn0wm80oLi7GggULet2/YMEC7NixQ6aolMloNAIAYmNjAQDl5eWoq6vrde50Oh2uvvrqoDx3Dz/8MG6++WbMmzev1/08T71t3LgRU6ZMwd13342EhATk5ubir3/9q+v3PF/dZs+ejf/85z84fvw4AGDfvn3Yvn07brrpJgA8V30ZyHkpLi6GxWLpdUxKSgqys7OD+twBjs96QRAQHR0NwPfnKmA2P/S0hoYG2Gw2JCYm9ro/MTERdXV1MkWlPKIoYuXKlZg9ezays7MBwHV+LnXuTp8+7fMY5fTBBx9g79692L1790W/43nqraysDK+//jpWrlyJX/ziF9i1axd+9rOfQafTYenSpTxfPTz55JMwGo0YO3Ys1Go1bDYbXnjhBXzve98DwH9bfRnIeamrq4NWq0VMTMxFxwTzZ39nZyeeeuop3Hvvva7ND319rpiwXIYgCL1+FkXxovuC2SOPPIL9+/dj+/btF/0u2M9dVVUVVqxYgS+++AJ6vb7P44L9PEnsdjumTJmC3/72twCA3NxcHDp0CK+//jqWLl3qOo7nC1i/fj3ee+89vP/++xg/fjxKS0vx2GOPISUlBT/4wQ9cx/FcXdpgzkswnzuLxYLvfve7sNvteO211y57vLfOFaeE+hAfHw+1Wn1RllhfX39Rdh6sHn30UWzcuBFfffUVUlNTXfcnJSUBQNCfu+LiYtTX1yMvLw8ajQYajQbbtm3DK6+8Ao1G4zoXwX6eJMnJycjKyup137hx41BZWQmA/656+u///m889dRT+O53v4sJEyZgyZIlePzxx7F69WoAPFd9Gch5SUpKgtlsRlNTU5/HBBOLxYJ77rkH5eXlKCgocI2uAL4/V0xY+qDVapGXl4eCgoJe9xcUFGDWrFkyRaUMoijikUcewYYNG/Dll18iMzOz1+8zMzORlJTU69yZzWZs27YtqM7d9ddfjwMHDqC0tNR1mzJlCr7//e+jtLQUw4cP53nq4aqrrrpoefzx48eRkZEBgP+uempvb4dK1fvjW61Wu5Y181xd2kDOS15eHkJCQnodU1tbi4MHDwbduZOSlRMnTmDLli2Ii4vr9XufnyuPl/EGEGlZ81tvvSUePnxYfOyxx8Tw8HCxoqJC7tBk9ZOf/EQ0GAzi1q1bxdraWtetvb3ddczvfvc70WAwiBs2bBAPHDggfu973wuKJZWX03OVkCjyPPW0a9cuUaPRiC+88IJ44sQJ8e9//7sYFhYmvvfee65jeL4cfvCDH4hDhw51LWvesGGDGB8fLz7xxBOuY4L1XLW0tIglJSViSUmJCEB86aWXxJKSEtfKloGcl+XLl4upqanili1bxL1794rXXXddQC5r7u9cWSwW8dZbbxVTU1PF0tLSXp/1XV1drsfw5bliwnIZf/7zn8WMjAxRq9WKkydPdi3dDWYALnl75513XMfY7XbxV7/6lZiUlCTqdDpx7ty54oEDB+QLWiEuTFh4nnr79NNPxezsbFGn04ljx44V33zzzV6/5/lyMJlM4ooVK8T09HRRr9eLw4cPF59++uleXyTBeq6++uqrS34+/eAHPxBFcWDnpaOjQ3zkkUfE2NhYMTQ0VLzlllvEyspKGV6Nd/V3rsrLy/v8rP/qq69cj+HLcyWIoih6ftyGiIiIyHNYw0JERESKx4SFiIiIFI8JCxERESkeExYiIiJSPCYsREREpHhMWIiIiEjxmLAQERGR4jFhISIiIsVjwkJERESKx4SFiIiIFI8JCxERESkeExYiIiJSvP8Pdhd2+6691ikAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "currentfunc = param[\"Current function [A]\"]\n", - "time = pybamm.linspace(0, 120, 60)\n", - "evaluated = param.evaluate(currentfunc(time))\n", - "evaluated = pybamm.Array(evaluated)\n", - "pybamm.plot(time, evaluated)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Taking another such example:\n", - "\n", - "### Plotting \"Negative electrode exchange-current density \\[A.m-2]\"" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "negative_electrode_exchange_current_density = param[\"Negative electrode exchange-current density [A.m-2]\"]\n", - "x = pybamm.linspace(3000,6000,100)\n", - "c_n_max = param[\"Maximum concentration in negative electrode [mol.m-3]\"]\n", - "evaluated = param.evaluate(negative_electrode_exchange_current_density(1000,x,c_n_max,300))\n", - "evaluated = pybamm.Array(evaluated)\n", - "pybamm.plot(x, evaluated)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulating and solving the model\n", - "\n", - "Finally we can simulate the model and solve it using `pybamm.Simulation` and `solve` respectively." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "ename": "KeyError", - "evalue": "\"'Initial concentration in electrolyte [mol.m-3]' not found. Best matches are ['Initial concentration in positive electrode [mol.m-3]', 'Initial concentration in negative electrode [mol.m-3]', 'Maximum concentration in positive electrode [mol.m-3]']\"", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: PrimaryBroadcast(0x55db2b43f3b99d37, broadcast, children=['(0.00017234666524563961 * Ambient temperature [K] / Positive electrode electrons in reaction) * arcsinh(-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m]) / (2.0 * Positive electrode exchange-current density [A.m-2])) + Positive electrode OCP [V] + 1e-06 * (1.0 / (maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10)) + 1.0 / (-1.0 + maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10))) + (Ambient temperature [K] - Reference temperature [K]) * Positive electrode OCP entropic change [V.K-1] - ((0.00017234666524563961 * Ambient temperature [K] / Negative electrode electrons in reaction) * arcsinh(Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Negative electrode thickness [m] / x-average(3.0 * Negative electrode active material volume fraction / Negative particle radius [m]) / (2.0 * Negative electrode exchange-current density [A.m-2])) + Negative electrode OCP [V] + 1e-06 * (1.0 / (maximum(minimum(boundary value(X-averaged negative particle concentration [mol.m-3]) / Maximum concentration in negative electrode [mol.m-3], 0.9999999999), 1e-10)) + 1.0 / (-1.0 + maximum(minimum(boundary value(X-averaged negative particle concentration [mol.m-3]) / Maximum concentration in negative electrode [mol.m-3], 0.9999999999), 1e-10))) + (Ambient temperature [K] - Reference temperature [K]) * Negative electrode OCP entropic change [V.K-1])'], domains={'primary': ['positive electrode'], 'secondary': ['current collector']})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: Subtraction(0x6e57fdf0f90fdbb0, -, children=['(0.00017234666524563961 * Ambient temperature [K] / Positive electrode electrons in reaction) * arcsinh(-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m]) / (2.0 * Positive electrode exchange-current density [A.m-2])) + Positive electrode OCP [V] + 1e-06 * (1.0 / (maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10)) + 1.0 / (-1.0 + maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10))) + (Ambient temperature [K] - Reference temperature [K]) * Positive electrode OCP entropic change [V.K-1]', '(0.00017234666524563961 * Ambient temperature [K] / Negative electrode electrons in reaction) * arcsinh(Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Negative electrode thickness [m] / x-average(3.0 * Negative electrode active material volume fraction / Negative particle radius [m]) / (2.0 * Negative electrode exchange-current density [A.m-2])) + Negative electrode OCP [V] + 1e-06 * (1.0 / (maximum(minimum(boundary value(X-averaged negative particle concentration [mol.m-3]) / Maximum concentration in negative electrode [mol.m-3], 0.9999999999), 1e-10)) + 1.0 / (-1.0 + maximum(minimum(boundary value(X-averaged negative particle concentration [mol.m-3]) / Maximum concentration in negative electrode [mol.m-3], 0.9999999999), 1e-10))) + (Ambient temperature [K] - Reference temperature [K]) * Negative electrode OCP entropic change [V.K-1]'], domains={'primary': ['current collector']})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: Addition(-0x524f51ed4f620efd, +, children=['(0.00017234666524563961 * Ambient temperature [K] / Positive electrode electrons in reaction) * arcsinh(-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m]) / (2.0 * Positive electrode exchange-current density [A.m-2]))', 'Positive electrode OCP [V] + 1e-06 * (1.0 / (maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10)) + 1.0 / (-1.0 + maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10))) + (Ambient temperature [K] - Reference temperature [K]) * Positive electrode OCP entropic change [V.K-1]'], domains={'primary': ['current collector']})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: Multiplication(-0x36e2f7f52718fd84, *, children=['0.00017234666524563961 * Ambient temperature [K] / Positive electrode electrons in reaction', 'arcsinh(-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m]) / (2.0 * Positive electrode exchange-current density [A.m-2]))'], domains={'primary': ['current collector']})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: Arcsinh(-0x70bcbae05a17171a, function (arcsinh), children=['-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m]) / (2.0 * Positive electrode exchange-current density [A.m-2])'], domains={'primary': ['current collector']})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: Division(0x2d06e4ce68936693, /, children=['-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m])', '2.0 * Positive electrode exchange-current density [A.m-2]'], domains={'primary': ['current collector']})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: Multiplication(-0x533055788c0787e9, *, children=['2.0', 'Positive electrode exchange-current density [A.m-2]'], domains={'primary': ['current collector']})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: FunctionParameter(0x79a3a2c645f54668, Positive electrode exchange-current density [A.m-2], children=['maximum(Initial concentration in electrolyte [mol.m-3], 1e-08)', 'maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]), 0.99999999 * Maximum concentration in positive electrode [mol.m-3]), 1e-08 * Maximum concentration in positive electrode [mol.m-3])', 'Maximum concentration in positive electrode [mol.m-3]', 'broadcast(Ambient temperature [K])'], domains={'primary': ['current collector']})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: Maximum(-0x10b1c06354524acc, maximum, children=['Initial concentration in electrolyte [mol.m-3]', '1e-08'], domains={})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: Parameter(-0x23a8868071606836, Initial concentration in electrolyte [mol.m-3], children=[], domains={})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/util.py:58\u001b[0m, in \u001b[0;36mFuzzyDict.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 58\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49m\u001b[39m__getitem__\u001b[39;49m(key)\n\u001b[1;32m 59\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: 'Initial concentration in electrolyte [mol.m-3]'", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[22], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m sim \u001b[39m=\u001b[39m pybamm\u001b[39m.\u001b[39mSimulation(spm, parameter_values\u001b[39m=\u001b[39mparam)\n\u001b[1;32m 2\u001b[0m t_eval \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marange(\u001b[39m0\u001b[39m, \u001b[39m3600\u001b[39m, \u001b[39m1\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m sim\u001b[39m.\u001b[39;49msolve(t_eval\u001b[39m=\u001b[39;49mt_eval)\n\u001b[1;32m 4\u001b[0m sim\u001b[39m.\u001b[39mplot()\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/simulation.py:559\u001b[0m, in \u001b[0;36mSimulation.solve\u001b[0;34m(self, t_eval, solver, check_model, save_at_cycles, calc_esoh, starting_solution, initial_soc, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m 556\u001b[0m logs \u001b[39m=\u001b[39m {}\n\u001b[1;32m 558\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39moperating_mode \u001b[39min\u001b[39;00m [\u001b[39m\"\u001b[39m\u001b[39mwithout experiment\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mdrive cycle\u001b[39m\u001b[39m\"\u001b[39m]:\n\u001b[0;32m--> 559\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbuild(check_model\u001b[39m=\u001b[39;49mcheck_model, initial_soc\u001b[39m=\u001b[39;49minitial_soc)\n\u001b[1;32m 560\u001b[0m \u001b[39mif\u001b[39;00m save_at_cycles \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 561\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m 562\u001b[0m \u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39msave_at_cycles\u001b[39m\u001b[39m'\u001b[39m\u001b[39m option can only be used if simulating an \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 563\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mExperiment \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 564\u001b[0m )\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/simulation.py:449\u001b[0m, in \u001b[0;36mSimulation.build\u001b[0;34m(self, check_model, initial_soc)\u001b[0m\n\u001b[1;32m 447\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_built_model \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel\n\u001b[1;32m 448\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 449\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mset_parameters()\n\u001b[1;32m 450\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_mesh \u001b[39m=\u001b[39m pybamm\u001b[39m.\u001b[39mMesh(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_geometry, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_submesh_types, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_var_pts)\n\u001b[1;32m 451\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_disc \u001b[39m=\u001b[39m pybamm\u001b[39m.\u001b[39mDiscretisation(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_mesh, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_spatial_methods)\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/simulation.py:399\u001b[0m, in \u001b[0;36mSimulation.set_parameters\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 397\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_model_with_set_params \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_unprocessed_model\n\u001b[1;32m 398\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 399\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_model_with_set_params \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_parameter_values\u001b[39m.\u001b[39;49mprocess_model(\n\u001b[1;32m 400\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_unprocessed_model, inplace\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m\n\u001b[1;32m 401\u001b[0m )\n\u001b[1;32m 402\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_parameter_values\u001b[39m.\u001b[39mprocess_geometry(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mgeometry)\n\u001b[1;32m 403\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_model_with_set_params\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:465\u001b[0m, in \u001b[0;36mParameterValues.process_model\u001b[0;34m(self, unprocessed_model, inplace)\u001b[0m\n\u001b[1;32m 462\u001b[0m new_initial_conditions[new_variable] \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(equation)\n\u001b[1;32m 463\u001b[0m model\u001b[39m.\u001b[39minitial_conditions \u001b[39m=\u001b[39m new_initial_conditions\n\u001b[0;32m--> 465\u001b[0m model\u001b[39m.\u001b[39mboundary_conditions \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_boundary_conditions(unprocessed_model)\n\u001b[1;32m 467\u001b[0m new_variables \u001b[39m=\u001b[39m {}\n\u001b[1;32m 468\u001b[0m \u001b[39mfor\u001b[39;00m variable, equation \u001b[39min\u001b[39;00m unprocessed_model\u001b[39m.\u001b[39mvariables\u001b[39m.\u001b[39mitems():\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:541\u001b[0m, in \u001b[0;36mParameterValues.process_boundary_conditions\u001b[0;34m(self, model)\u001b[0m\n\u001b[1;32m 539\u001b[0m sides \u001b[39m=\u001b[39m [\u001b[39m\"\u001b[39m\u001b[39mleft\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mright\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mnegative tab\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mpositive tab\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mno tab\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[1;32m 540\u001b[0m \u001b[39mfor\u001b[39;00m variable, bcs \u001b[39min\u001b[39;00m model\u001b[39m.\u001b[39mboundary_conditions\u001b[39m.\u001b[39mitems():\n\u001b[0;32m--> 541\u001b[0m processed_variable \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(variable)\n\u001b[1;32m 542\u001b[0m new_boundary_conditions[processed_variable] \u001b[39m=\u001b[39m {}\n\u001b[1;32m 543\u001b[0m \u001b[39mfor\u001b[39;00m side \u001b[39min\u001b[39;00m sides:\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:731\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 729\u001b[0m \u001b[39m# Unary operators\u001b[39;00m\n\u001b[1;32m 730\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mUnaryOperator):\n\u001b[0;32m--> 731\u001b[0m new_child \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mchild)\n\u001b[1;32m 732\u001b[0m new_symbol \u001b[39m=\u001b[39m symbol\u001b[39m.\u001b[39m_unary_new_copy(new_child)\n\u001b[1;32m 733\u001b[0m \u001b[39m# ensure domain remains the same\u001b[39;00m\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:722\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 718\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(function_out)\n\u001b[1;32m 720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m 721\u001b[0m \u001b[39m# process children\u001b[39;00m\n\u001b[0;32m--> 722\u001b[0m new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mleft)\n\u001b[1;32m 723\u001b[0m new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mright)\n\u001b[1;32m 724\u001b[0m \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:722\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 718\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(function_out)\n\u001b[1;32m 720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m 721\u001b[0m \u001b[39m# process children\u001b[39;00m\n\u001b[0;32m--> 722\u001b[0m new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mleft)\n\u001b[1;32m 723\u001b[0m new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mright)\n\u001b[1;32m 724\u001b[0m \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:723\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m 721\u001b[0m \u001b[39m# process children\u001b[39;00m\n\u001b[1;32m 722\u001b[0m new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mleft)\n\u001b[0;32m--> 723\u001b[0m new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mright)\n\u001b[1;32m 724\u001b[0m \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n\u001b[1;32m 725\u001b[0m new_symbol \u001b[39m=\u001b[39m symbol\u001b[39m.\u001b[39m_binary_new_copy(new_left, new_right)\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:748\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 746\u001b[0m \u001b[39m# Functions\u001b[39;00m\n\u001b[1;32m 747\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mFunction):\n\u001b[0;32m--> 748\u001b[0m new_children \u001b[39m=\u001b[39m [\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(child) \u001b[39mfor\u001b[39;00m child \u001b[39min\u001b[39;00m symbol\u001b[39m.\u001b[39mchildren]\n\u001b[1;32m 749\u001b[0m \u001b[39mreturn\u001b[39;00m symbol\u001b[39m.\u001b[39m_function_new_copy(new_children)\n\u001b[1;32m 751\u001b[0m \u001b[39m# Concatenations\u001b[39;00m\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:748\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 746\u001b[0m \u001b[39m# Functions\u001b[39;00m\n\u001b[1;32m 747\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mFunction):\n\u001b[0;32m--> 748\u001b[0m new_children \u001b[39m=\u001b[39m [\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(child) \u001b[39mfor\u001b[39;00m child \u001b[39min\u001b[39;00m symbol\u001b[39m.\u001b[39mchildren]\n\u001b[1;32m 749\u001b[0m \u001b[39mreturn\u001b[39;00m symbol\u001b[39m.\u001b[39m_function_new_copy(new_children)\n\u001b[1;32m 751\u001b[0m \u001b[39m# Concatenations\u001b[39;00m\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:723\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m 721\u001b[0m \u001b[39m# process children\u001b[39;00m\n\u001b[1;32m 722\u001b[0m new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mleft)\n\u001b[0;32m--> 723\u001b[0m new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mright)\n\u001b[1;32m 724\u001b[0m \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n\u001b[1;32m 725\u001b[0m new_symbol \u001b[39m=\u001b[39m symbol\u001b[39m.\u001b[39m_binary_new_copy(new_left, new_right)\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:723\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m 721\u001b[0m \u001b[39m# process children\u001b[39;00m\n\u001b[1;32m 722\u001b[0m new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mleft)\n\u001b[0;32m--> 723\u001b[0m new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mright)\n\u001b[1;32m 724\u001b[0m \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n\u001b[1;32m 725\u001b[0m new_symbol \u001b[39m=\u001b[39m symbol\u001b[39m.\u001b[39m_binary_new_copy(new_left, new_right)\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:657\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 655\u001b[0m new_children\u001b[39m.\u001b[39mappend(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(new_child))\n\u001b[1;32m 656\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 657\u001b[0m new_children\u001b[39m.\u001b[39mappend(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(child))\n\u001b[1;32m 659\u001b[0m \u001b[39m# Create Function or Interpolant or Scalar object\u001b[39;00m\n\u001b[1;32m 660\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(function_name, \u001b[39mtuple\u001b[39m):\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:722\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 718\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(function_out)\n\u001b[1;32m 720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m 721\u001b[0m \u001b[39m# process children\u001b[39;00m\n\u001b[0;32m--> 722\u001b[0m new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mleft)\n\u001b[1;32m 723\u001b[0m new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mright)\n\u001b[1;32m 724\u001b[0m \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:620\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 617\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"See :meth:`ParameterValues.process_symbol()`.\"\"\"\u001b[39;00m\n\u001b[1;32m 619\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mParameter):\n\u001b[0;32m--> 620\u001b[0m value \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m[symbol\u001b[39m.\u001b[39;49mname]\n\u001b[1;32m 621\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(value, numbers\u001b[39m.\u001b[39mNumber):\n\u001b[1;32m 622\u001b[0m \u001b[39m# Check not NaN (parameter in csv file but no value given)\u001b[39;00m\n\u001b[1;32m 623\u001b[0m \u001b[39mif\u001b[39;00m np\u001b[39m.\u001b[39misnan(value):\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:139\u001b[0m, in \u001b[0;36mParameterValues.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__getitem__\u001b[39m(\u001b[39mself\u001b[39m, key):\n\u001b[0;32m--> 139\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_dict_items[key]\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/util.py:73\u001b[0m, in \u001b[0;36mFuzzyDict.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[39mif\u001b[39;00m key \u001b[39min\u001b[39;00m k \u001b[39mand\u001b[39;00m k\u001b[39m.\u001b[39mendswith(\u001b[39m\"\u001b[39m\u001b[39m]\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[1;32m 70\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(\n\u001b[1;32m 71\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mkey\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m not found. Use the dimensional version \u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mk\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m instead.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 72\u001b[0m )\n\u001b[0;32m---> 73\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mkey\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m not found. Best matches are \u001b[39m\u001b[39m{\u001b[39;00mbest_matches\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n", - "\u001b[0;31mKeyError\u001b[0m: \"'Initial concentration in electrolyte [mol.m-3]' not found. Best matches are ['Initial concentration in positive electrode [mol.m-3]', 'Initial concentration in negative electrode [mol.m-3]', 'Maximum concentration in positive electrode [mol.m-3]']\"" - ] - } - ], - "source": [ - "sim = pybamm.Simulation(spm, parameter_values=param)\n", - "t_eval = np.arange(0, 3600, 1)\n", - "sim.solve(t_eval=t_eval)\n", - "sim.plot()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## References\n", - "The relevant papers for this notebook are:" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "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] Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050.\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] 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" - ] - } - ], - "source": [ - "pybamm.print_citations()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "pybamm", - "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": "187972e187ab8dfbecfab9e8e194ae6d08262b2d51a54fa40644e3ddb6b5f74c" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 + "nbformat": 4, + "nbformat_minor": 2 }