From 758a0522af7e7c742b6378bba7131d5b0a9d3c6f Mon Sep 17 00:00:00 2001 From: Saransh Chopra Date: Sat, 23 Dec 2023 21:32:56 +0530 Subject: [PATCH 1/5] Move to ruff format --- .pre-commit-config.yaml | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 9b3a8f9d4b..5c63ec7b76 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -9,12 +9,14 @@ repos: - id: ruff args: [--fix, --show-fixes] types_or: [python, pyi, jupyter] + - id: ruff-format + types_or: [python, pyi, jupyter] - repo: https://github.com/adamchainz/blacken-docs rev: "1.16.0" hooks: - id: blacken-docs - additional_dependencies: [black==22.12.0] + additional_dependencies: [black==23.*] - repo: https://github.com/pre-commit/pre-commit-hooks rev: v4.5.0 From c60fd5053b243d8abfc7dfcf4a365febcf816ec1 Mon Sep 17 00:00:00 2001 From: Saransh Chopra Date: Sat, 23 Dec 2023 21:33:32 +0530 Subject: [PATCH 2/5] Fix config --- .pre-commit-config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 5c63ec7b76..5cfbdf4710 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -9,7 +9,7 @@ repos: - id: ruff args: [--fix, --show-fixes] types_or: [python, pyi, jupyter] - - id: ruff-format + - id: ruff-format types_or: [python, pyi, jupyter] - repo: https://github.com/adamchainz/blacken-docs From d2edbcaafa8970ac3a82212cf4d641c51a831ed2 Mon Sep 17 00:00:00 2001 From: Saransh Chopra Date: Sat, 23 Dec 2023 21:40:37 +0530 Subject: [PATCH 3/5] Ignore F821 for lithium-plating.ipynb --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index e95017eb75..12134e966c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -223,6 +223,7 @@ ignore = [ "docs/*" = ["T20"] "examples/*" = ["T20"] "**.ipynb" = ["E402", "E703"] +"docs/source/examples/notebooks/models/lithium-plating.ipynb" = ["F821"] # NOTE: currently used only for notebook tests with the nbmake plugin. [tool.pytest.ini_options] From 60ebd4148059a95428a496f4f55c1175ead362d3 Mon Sep 17 00:00:00 2001 From: Saransh Chopra Date: Sat, 23 Dec 2023 21:40:45 +0530 Subject: [PATCH 4/5] Format everything --- .../examples/notebooks/batch_study.ipynb | 54 +- .../examples/notebooks/change-settings.ipynb | 23 +- .../creating_models/1-an-ode-model.ipynb | 2 +- .../creating_models/2-a-pde-model.ipynb | 4 +- .../3-negative-particle-problem.ipynb | 12 +- ...paring-full-and-reduced-order-models.ipynb | 42 +- .../creating_models/5-half-cell-model.ipynb | 67 +- .../6-a-simple-SEI-model.ipynb | 56 +- .../expression_tree/broadcasts.ipynb | 8 +- .../expression_tree/expression-tree.ipynb | 21 +- .../tutorial-10-creating-a-model.ipynb | 40 +- .../tutorial-11-creating-a-submodel.ipynb | 59 +- .../tutorial-3-basic-plotting.ipynb | 1811 +++++++++-------- .../tutorial-4-setting-parameter-values.ipynb | 17 +- .../tutorial-5-run-experiments.ipynb | 25 +- ...torial-6-managing-simulation-outputs.ipynb | 10 +- .../tutorial-7-model-options.ipynb | 6 +- .../tutorial-9-changing-the-mesh.ipynb | 12 +- .../initialize-model-with-solution.ipynb | 14 +- ...DFN-with-particle-size-distributions.ipynb | 118 +- .../examples/notebooks/models/DFN.ipynb | 6 +- .../examples/notebooks/models/MPM.ipynb | 129 +- .../examples/notebooks/models/MSMR.ipynb | 18 +- .../notebooks/models/SEI-on-cracks.ipynb | 53 +- .../examples/notebooks/models/SPM.ipynb | 94 +- .../examples/notebooks/models/SPMe.ipynb | 2 +- ...ating_mechanical_models_Enertech_DFN.ipynb | 215 +- .../compare-comsol-discharge-curve.ipynb | 10 +- .../notebooks/models/compare-ecker-data.ipynb | 29 +- .../models/compare-lithium-ion.ipynb | 19 +- .../compare-particle-diffusion-models.ipynb | 63 +- .../notebooks/models/composite_particle.ipynb | 218 +- .../models/coupled-degradation.ipynb | 57 +- .../models/electrode-state-of-health.ipynb | 102 +- .../examples/notebooks/models/half-cell.ipynb | 70 +- .../notebooks/models/jelly-roll-model.ipynb | 85 +- .../examples/notebooks/models/lead-acid.ipynb | 5 +- .../notebooks/models/lithium-plating.ipynb | 133 +- .../models/loss_of_active_materials.ipynb | 141 +- .../notebooks/models/pouch-cell-model.ipynb | 44 +- .../notebooks/models/rate-capability.ipynb | 15 +- .../notebooks/models/saving_models.ipynb | 1 + ...simulating-ORegan-2022-parameter-set.ipynb | 4 +- .../models/submodel_cracking_DFN_or_SPM.ipynb | 82 +- .../models/unsteady-heat-equation.ipynb | 14 +- .../using-model-options_thermal-example.ipynb | 13 +- .../notebooks/models/using-submodels.ipynb | 80 +- .../change-input-current.ipynb | 11 +- .../parameterization/parameter-values.ipynb | 15 +- .../parameterization/parameterization.ipynb | 1122 +++++----- .../plotting/customize-quick-plot.ipynb | 13 +- .../callbacks.ipynb | 6 +- .../custom-experiments.ipynb | 8 +- .../experiments-start-time.ipynb | 4 +- .../rpt-experiment.ipynb | 95 +- .../simulating-long-experiments.ipynb | 59 +- .../simulation-class.ipynb | 4 +- ...olution-data-and-processed-variables.ipynb | 47 +- .../notebooks/solvers/dae-solver.ipynb | 40 +- .../notebooks/solvers/ode-solver.ipynb | 24 +- .../notebooks/solvers/speed-up-solver.ipynb | 198 +- .../spatial_methods/finite-volumes.ipynb | 111 +- .../scripts/compare_comsol/discharge_curve.py | 2 +- examples/scripts/heat_equation.py | 4 +- pybamm/discretisations/discretisation.py | 12 +- pybamm/expression_tree/binary_operators.py | 6 +- pybamm/expression_tree/concatenations.py | 8 +- pybamm/expression_tree/functions.py | 3 +- .../expression_tree/independent_variable.py | 4 +- pybamm/expression_tree/interpolant.py | 8 +- .../operations/convert_to_casadi.py | 4 +- .../operations/evaluate_python.py | 16 +- pybamm/expression_tree/parameter.py | 8 +- .../printing/sympy_overrides.py | 1 + pybamm/expression_tree/scalar.py | 1 + pybamm/expression_tree/state_vector.py | 7 +- pybamm/expression_tree/unary_operators.py | 3 +- pybamm/expression_tree/variable.py | 8 +- pybamm/expression_tree/vector.py | 4 +- pybamm/input/parameters/lithium_ion/Ai2020.py | 8 +- .../input/parameters/lithium_ion/Chen2020.py | 8 +- .../lithium_ion/Chen2020_composite.py | 12 +- .../input/parameters/lithium_ion/Ecker2015.py | 8 +- .../Ecker2015_graphite_halfcell.py | 4 +- .../parameters/lithium_ion/Marquis2019.py | 8 +- .../parameters/lithium_ion/Mohtat2020.py | 14 +- .../parameters/lithium_ion/NCA_Kim2011.py | 16 +- .../input/parameters/lithium_ion/OKane2022.py | 8 +- .../OKane2022_graphite_SiOx_halfcell.py | 4 +- .../input/parameters/lithium_ion/Prada2013.py | 8 +- .../parameters/lithium_ion/Ramadass2004.py | 8 +- pybamm/install_odes.py | 4 +- pybamm/meshes/one_dimensional_submeshes.py | 4 +- pybamm/models/base_model.py | 50 +- .../full_battery_models/base_battery_model.py | 46 +- .../through_cell/explicit_convection.py | 6 +- .../interface/lithium_plating/base_plating.py | 12 +- .../interface/total_interfacial_current.py | 2 +- .../submodels/particle/base_particle.py | 4 +- .../submodels/particle/msmr_diffusion.py | 4 +- .../particle/x_averaged_polynomial_profile.py | 3 +- .../particle_mechanics/base_mechanics.py | 2 +- .../models/submodels/thermal/base_thermal.py | 9 +- pybamm/parameters/bpx.py | 12 +- pybamm/parameters/parameter_values.py | 27 +- pybamm/parameters/process_parameter_data.py | 6 +- pybamm/plotting/plot2D.py | 2 +- pybamm/plotting/plot_voltage_components.py | 11 +- pybamm/settings.py | 12 +- pybamm/simulation.py | 4 +- pybamm/solvers/base_solver.py | 36 +- pybamm/solvers/casadi_algebraic_solver.py | 4 +- pybamm/solvers/casadi_solver.py | 4 +- pybamm/solvers/idaklu_solver.py | 8 +- pybamm/solvers/jax_bdf_solver.py | 14 +- pybamm/solvers/scipy_solver.py | 2 +- pybamm/spatial_methods/finite_volume.py | 3 +- pybamm/spatial_methods/spectral_volume.py | 12 +- pybamm/util.py | 10 +- scripts/fix_casadi_rpath_mac.py | 14 +- scripts/update_version.py | 5 +- setup.py | 45 +- .../base_lithium_ion_tests.py | 21 +- .../test_lithium_ion/test_mpm.py | 10 +- .../unit/test_experiments/test_experiment.py | 1 + .../test_binary_operators.py | 4 +- .../test_operations/test_evaluate_python.py | 16 +- .../test_operations/test_jac.py | 4 +- .../test_meshes/test_scikit_fem_submesh.py | 2 +- .../test_base_battery_model.py | 19 +- .../test_lithium_ion/test_mpm.py | 4 +- .../test_lithium_ion/test_mpm_half_cell.py | 4 +- tests/unit/test_parameters/test_bpx.py | 9 +- .../test_parameter_sets/test_Ecker2015.py | 2 +- .../test_Ecker2015_graphite_halfcell.py | 2 +- .../test_size_distribution_parameters.py | 1 - tests/unit/test_simulation.py | 10 +- tests/unit/test_solvers/test_idaklu_solver.py | 26 +- .../test_processed_variable_computed.py | 12 +- tests/unit/test_solvers/test_solution.py | 27 +- .../test_scikit_finite_element.py | 8 +- tests/unit/test_util.py | 21 +- 142 files changed, 3617 insertions(+), 3028 deletions(-) diff --git a/docs/source/examples/notebooks/batch_study.ipynb b/docs/source/examples/notebooks/batch_study.ipynb index f02d1154ad..0c0d216763 100644 --- a/docs/source/examples/notebooks/batch_study.ipynb +++ b/docs/source/examples/notebooks/batch_study.ipynb @@ -136,7 +136,9 @@ "parameter_values = {\"Chen2020\": pybamm.ParameterValues(\"Chen2020\")}\n", "\n", "# creating a BatchStudy object and solving the simulation\n", - "batch_study = pybamm.BatchStudy(models=models, parameter_values=parameter_values, permutations=True)\n", + "batch_study = pybamm.BatchStudy(\n", + " models=models, parameter_values=parameter_values, permutations=True\n", + ")\n", "batch_study.solve(t_eval=[0, 3600])\n", "batch_study.plot()" ] @@ -195,13 +197,17 @@ "# different values for \"Current function [A]\"\n", "current_values = [4.5, 4.75, 5]\n", "\n", - "# changing the value of \"Current function [A]\" in all the parameter values present in the \n", + "# changing the value of \"Current function [A]\" in all the parameter values present in the\n", "# parameter_values dictionary\n", - "for k, v, current_value in zip(parameter_values.keys(), parameter_values.values(), current_values):\n", - " v[\"Current function [A]\"] = current_value \n", + "for k, v, current_value in zip(\n", + " parameter_values.keys(), parameter_values.values(), current_values\n", + "):\n", + " v[\"Current function [A]\"] = current_value\n", "\n", "# creating a BatchStudy object with permutations set to True to create a cartesian product\n", - "batch_study = pybamm.BatchStudy(models=model, parameter_values=parameter_values, permutations=True)\n", + "batch_study = pybamm.BatchStudy(\n", + " models=model, parameter_values=parameter_values, permutations=True\n", + ")\n", "batch_study.solve(t_eval=[0, 3600])\n", "\n", "# generating the required labels and plotting\n", @@ -474,19 +480,19 @@ "# using the cccv experiment with 10 cycles\n", "cccv = pybamm.Experiment(\n", " [\n", - " (\"Discharge at C/10 for 10 hours or until 3.3 V\",\n", - " \"Rest for 1 hour\",\n", - " \"Charge at 1 A until 4.1 V\",\n", - " \"Hold at 4.1 V until 50 mA\",\n", - " \"Rest for 1 hour\")\n", + " (\n", + " \"Discharge at C/10 for 10 hours or until 3.3 V\",\n", + " \"Rest for 1 hour\",\n", + " \"Charge at 1 A until 4.1 V\",\n", + " \"Hold at 4.1 V until 50 mA\",\n", + " \"Rest for 1 hour\",\n", + " )\n", " ]\n", " * 10,\n", ")\n", "\n", "# creating the experiment dict\n", - "experiment = {\n", - " \"cccv\": cccv\n", - "}\n", + "experiment = {\"cccv\": cccv}\n", "\n", "# populating a dictionary with 3 same parameter values (Mohtat2020 chemistry)\n", "parameter_values = {\n", @@ -499,23 +505,31 @@ "inner_sei_oc_v_values = [2.0e-4, 2.7e-4, 3.4e-4]\n", "\n", "# updating the value of \"Inner SEI open-circuit potential [V]\" in all the dictionary items\n", - "for k, v, inner_sei_oc_v in zip(parameter_values.keys(), parameter_values.values(), inner_sei_oc_v_values):\n", + "for k, v, inner_sei_oc_v in zip(\n", + " parameter_values.keys(), parameter_values.values(), inner_sei_oc_v_values\n", + "):\n", " v.update(\n", - " {\n", - " \"Inner SEI open-circuit potential [V]\": inner_sei_oc_v\n", - " },\n", + " {\"Inner SEI open-circuit potential [V]\": inner_sei_oc_v},\n", " )\n", "\n", "# creating a Single Particle Model with \"electron-mitigation limited\" SEI\n", "model = {\"spm\": pybamm.lithium_ion.SPM({\"SEI\": \"electron-migration limited\"})}\n", "\n", "# creating a BatchStudy object with the given experimen, model and parameter_values\n", - "batch_study = pybamm.BatchStudy(models=model, experiments=experiment, parameter_values=parameter_values, permutations=True)\n", + "batch_study = pybamm.BatchStudy(\n", + " models=model,\n", + " experiments=experiment,\n", + " parameter_values=parameter_values,\n", + " permutations=True,\n", + ")\n", "\n", - "#solving and plotting the result\n", + "# solving and plotting the result\n", "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", + "labels = [\n", + " f\"Inner SEI open-circuit potential [V]: {inner_sei_oc_v}\"\n", + " for inner_sei_oc_v in inner_sei_oc_v_values\n", + "]\n", "batch_study.plot(labels=labels)" ] }, diff --git a/docs/source/examples/notebooks/change-settings.ipynb b/docs/source/examples/notebooks/change-settings.ipynb index c54da8754c..1a23da86fc 100644 --- a/docs/source/examples/notebooks/change-settings.ipynb +++ b/docs/source/examples/notebooks/change-settings.ipynb @@ -48,7 +48,8 @@ "import numpy as np\n", "import os\n", "import matplotlib.pyplot as plt\n", - "os.chdir(pybamm.__path__[0]+'/..')\n", + "\n", + "os.chdir(pybamm.__path__[0] + \"/..\")\n", "\n", "# create the model\n", "model = pybamm.lithium_ion.SPM()\n", @@ -378,9 +379,9 @@ } ], "source": [ - "format_str = '{:<75} {:>20}'\n", - "print(format_str.format('PARAMETER', 'VALUE'))\n", - "print(\"-\"*97)\n", + "format_str = \"{:<75} {:>20}\"\n", + "print(format_str.format(\"PARAMETER\", \"VALUE\"))\n", + "print(\"-\" * 97)\n", "for key, value in model.default_parameter_values.items():\n", " try:\n", " print(format_str.format(key, value))\n", @@ -417,8 +418,8 @@ "old_value = param[variable]\n", "param[variable] = 1.4\n", "new_value = param[variable]\n", - "print(variable,'was',old_value)\n", - "print(variable,'now is',param[variable])" + "print(variable, \"was\", old_value)\n", + "print(variable, \"now is\", param[variable])" ] }, { @@ -514,8 +515,8 @@ } ], "source": [ - "print(format_str.format('DOMAIN', 'DISCRETISED BY'))\n", - "print(\"-\"*82)\n", + "print(format_str.format(\"DOMAIN\", \"DISCRETISED BY\"))\n", + "print(\"-\" * 82)\n", "for key, value in model.default_spatial_methods.items():\n", " print(format_str.format(key, value.__class__.__name__))" ] @@ -553,7 +554,9 @@ "outputs": [], "source": [ "submesh_types = model.default_submesh_types\n", - "submesh_types[\"negative particle\"] = pybamm.MeshGenerator(pybamm.SpectralVolume1DSubMesh)" + "submesh_types[\"negative particle\"] = pybamm.MeshGenerator(\n", + " pybamm.SpectralVolume1DSubMesh\n", + ")" ] }, { @@ -621,7 +624,7 @@ } ], "source": [ - "print('Default solver for SPM model:',type(model.default_solver).__name__)" + "print(\"Default solver for SPM model:\", type(model.default_solver).__name__)" ] }, { diff --git a/docs/source/examples/notebooks/creating_models/1-an-ode-model.ipynb b/docs/source/examples/notebooks/creating_models/1-an-ode-model.ipynb index a610700887..dd97a4aad8 100644 --- a/docs/source/examples/notebooks/creating_models/1-an-ode-model.ipynb +++ b/docs/source/examples/notebooks/creating_models/1-an-ode-model.ipynb @@ -117,7 +117,7 @@ "metadata": {}, "outputs": [], "source": [ - "model.rhs = {x: dxdt, y: dydt} " + "model.rhs = {x: dxdt, y: dydt}" ] }, { diff --git a/docs/source/examples/notebooks/creating_models/2-a-pde-model.ipynb b/docs/source/examples/notebooks/creating_models/2-a-pde-model.ipynb index c427fd4fe6..4926d19432 100644 --- a/docs/source/examples/notebooks/creating_models/2-a-pde-model.ipynb +++ b/docs/source/examples/notebooks/creating_models/2-a-pde-model.ipynb @@ -180,7 +180,9 @@ "r = pybamm.SpatialVariable(\n", " \"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\"\n", ")\n", - "geometry = {\"negative particle\": {r: {\"min\": pybamm.Scalar(0), \"max\": pybamm.Scalar(1)}}}" + "geometry = {\n", + " \"negative particle\": {r: {\"min\": pybamm.Scalar(0), \"max\": pybamm.Scalar(1)}}\n", + "}" ] }, { diff --git a/docs/source/examples/notebooks/creating_models/3-negative-particle-problem.ipynb b/docs/source/examples/notebooks/creating_models/3-negative-particle-problem.ipynb index b04616c5f9..c14c1279e6 100644 --- a/docs/source/examples/notebooks/creating_models/3-negative-particle-problem.ipynb +++ b/docs/source/examples/notebooks/creating_models/3-negative-particle-problem.ipynb @@ -106,14 +106,14 @@ "# governing equations\n", "N = -D * pybamm.grad(c) # flux\n", "dcdt = -pybamm.div(N)\n", - "model.rhs = {c: dcdt} \n", + "model.rhs = {c: dcdt}\n", "\n", - "# boundary conditions \n", + "# boundary conditions\n", "lbc = pybamm.Scalar(0)\n", "rbc = -j / F / D\n", "model.boundary_conditions = {c: {\"left\": (lbc, \"Neumann\"), \"right\": (rbc, \"Neumann\")}}\n", "\n", - "# initial conditions \n", + "# initial conditions\n", "model.initial_conditions = {c: c0}" ] }, @@ -193,7 +193,9 @@ "metadata": {}, "outputs": [], "source": [ - "r = pybamm.SpatialVariable(\"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\")\n", + "r = pybamm.SpatialVariable(\n", + " \"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\"\n", + ")\n", "geometry = {\"negative particle\": {r: {\"min\": pybamm.Scalar(0), \"max\": R}}}" ] }, @@ -305,7 +307,7 @@ "ax1.set_xlabel(\"Time [s]\")\n", "ax1.set_ylabel(\"Surface concentration [mol.m-3]\")\n", "\n", - "r = mesh[\"negative particle\"].nodes # radial position\n", + "r = mesh[\"negative particle\"].nodes # radial position\n", "time = 1000 # time in seconds\n", "ax2.plot(r * 1e6, c(t=time, r=r), label=f\"t={time}[s]\")\n", "ax2.set_xlabel(\"Particle radius [microns]\")\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 15d9e8e027..f180d16f0d 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 @@ -144,11 +144,11 @@ "# governing equations for full model\n", "N = -D * pybamm.grad(c) # flux\n", "dcdt = -pybamm.div(N)\n", - "full_model.rhs = {c: dcdt} \n", + "full_model.rhs = {c: dcdt}\n", "\n", "# governing equations for reduced model\n", "dc_avdt = -3 * j / R / F\n", - "reduced_model.rhs = {c_av: dc_avdt} \n", + "reduced_model.rhs = {c_av: dc_avdt}\n", "\n", "# initial conditions (these are the same for both models)\n", "full_model.initial_conditions = {c: c0}\n", @@ -157,7 +157,9 @@ "# boundary conditions (only required for full model)\n", "lbc = pybamm.Scalar(0)\n", "rbc = -j / F / D\n", - "full_model.boundary_conditions = {c: {\"left\": (lbc, \"Neumann\"), \"right\": (rbc, \"Neumann\")}}" + "full_model.boundary_conditions = {\n", + " c: {\"left\": (lbc, \"Neumann\"), \"right\": (rbc, \"Neumann\")}\n", + "}" ] }, { @@ -186,7 +188,7 @@ "# reduced model\n", "reduced_model.variables = {\n", " \"Concentration [mol.m-3]\": pybamm.PrimaryBroadcast(c_av, \"negative particle\"),\n", - " \"Surface concentration [mol.m-3]\": c_av, # in this model the surface concentration is just equal to the scalar average concentration \n", + " \"Surface concentration [mol.m-3]\": c_av, # in this model the surface concentration is just equal to the scalar average concentration\n", " \"Average concentration [mol.m-3]\": c_av,\n", "}" ] @@ -239,7 +241,9 @@ "outputs": [], "source": [ "# geometry\n", - "r = pybamm.SpatialVariable(\"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\")\n", + "r = pybamm.SpatialVariable(\n", + " \"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\"\n", + ")\n", "geometry = {\"negative particle\": {r: {\"min\": pybamm.Scalar(0), \"max\": R}}}\n", "param.process_geometry(geometry)\n", "\n", @@ -273,7 +277,7 @@ "\n", "# process models\n", "for model in models:\n", - " disc.process_model(model);" + " disc.process_model(model)" ] }, { @@ -346,38 +350,38 @@ "c_av_reduced = solutions[1][\"Average concentration [mol.m-3]\"]\n", "\n", "# plot\n", - "r = mesh[\"negative particle\"].nodes # radial position\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", + "\n", " # Plot concetration as a function of r\n", - " ax1.plot(r * 1e6, c_full(t=t,r=r), label=\"Full Model\")\n", - " ax1.plot(r * 1e6, c_reduced(t=t,r=r), label=\"Reduced Model\") \n", + " ax1.plot(r * 1e6, c_full(t=t, r=r), label=\"Full Model\")\n", + " ax1.plot(r * 1e6, c_reduced(t=t, r=r), label=\"Reduced Model\")\n", " ax1.set_xlabel(\"Particle radius [microns]\")\n", " ax1.set_ylabel(\"Concentration [mol.m-3]\")\n", " ax1.legend()\n", - " \n", + "\n", " # Plot average concentration over time\n", " t_hour = np.linspace(0, 3600, 600) # plot over full hour\n", - " c_min = c_av_reduced(t=3600) * 0.98 # minimum axes limit \n", - " c_max = param[\"Initial concentration [mol.m-3]\"] * 1.02 # maximum axes limit \n", - " \n", + " c_min = c_av_reduced(t=3600) * 0.98 # minimum axes limit\n", + " c_max = param[\"Initial concentration [mol.m-3]\"] * 1.02 # maximum axes limit\n", + "\n", " ax2.plot(t_hour, c_av_full(t=t_hour), label=\"Full Model\")\n", - " ax2.plot(t_hour, c_av_reduced(t=t_hour), label=\"Reduced Model\") \n", + " ax2.plot(t_hour, c_av_reduced(t=t_hour), label=\"Reduced Model\")\n", " ax2.plot([t, t], [c_min, c_max], \"k--\") # plot line to track time\n", " ax2.set_xlabel(\"Time [s]\")\n", - " ax2.set_ylabel(\"Average concentration [mol.m-3]\") \n", + " ax2.set_ylabel(\"Average concentration [mol.m-3]\")\n", " ax2.legend()\n", "\n", " plt.tight_layout()\n", " plt.show()\n", - " \n", + "\n", "\n", "import ipywidgets as widgets\n", - "widgets.interact(plot, t=widgets.FloatSlider(min=0,max=3600,step=1,value=0));\n", - " " + "\n", + "widgets.interact(plot, t=widgets.FloatSlider(min=0, max=3600, step=1, value=0));" ] }, { 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 b28d6add1a..685ac0b8d1 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 @@ -114,7 +114,9 @@ "outputs": [], "source": [ "phi_e_s = pybamm.Variable(\"Separator electrolyte potential [V]\", domain=\"separator\")\n", - "phi_e_p = pybamm.Variable(\"Positive electrolyte potential [V]\", domain=\"positive electrode\")" + "phi_e_p = pybamm.Variable(\n", + " \"Positive electrolyte potential [V]\", domain=\"positive electrode\"\n", + ")" ] }, { @@ -231,7 +233,9 @@ "source": [ "c_surf = pybamm.surf(c) # get the surface concentration\n", "inputs = {\"Positive particle surface concentration [mol.m-3]\": c_surf}\n", - "j0 = pybamm.FunctionParameter(\"Positive electrode exchange-current density [A.m-2]\", inputs)\n", + "j0 = pybamm.FunctionParameter(\n", + " \"Positive electrode exchange-current density [A.m-2]\", inputs\n", + ")\n", "U = pybamm.FunctionParameter(\"Positive electrode OCP [V]\", inputs)" ] }, @@ -252,7 +256,7 @@ "outputs": [], "source": [ "j_s = pybamm.PrimaryBroadcast(0, \"separator\")\n", - "j_p = 2 * j0 * pybamm.sinh((F / 2 / R / T) * (phi - phi_e_p - U))\n", + "j_p = 2 * j0 * pybamm.sinh((F / 2 / R / T) * (phi - phi_e_p - U))\n", "j = pybamm.concatenation(j_s, j_p)" ] }, @@ -272,7 +276,7 @@ "metadata": {}, "outputs": [], "source": [ - "# charge conservation equations \n", + "# charge conservation equations\n", "i = -sigma * pybamm.grad(phi)\n", "i_e = -kappa * pybamm.grad(phi_e)\n", "model.algebraic = {\n", @@ -282,23 +286,25 @@ "# particle equations (mass conservation)\n", "N = -D * pybamm.grad(c) # flux\n", "dcdt = -pybamm.div(N)\n", - "model.rhs = {c: dcdt} \n", + "model.rhs = {c: dcdt}\n", "\n", - "# boundary conditions \n", + "# boundary conditions\n", "model.boundary_conditions = {\n", - " phi: {\"left\": (pybamm.Scalar(0), \"Neumann\"), \"right\": (-I_app / A / sigma, \"Neumann\")},\n", - " phi_e: {\"left\": (pybamm.Scalar(0), \"Dirichlet\"), \"right\": (pybamm.Scalar(0), \"Neumann\")},\n", - " c: {\"left\": (pybamm.Scalar(0), \"Neumann\"), \"right\": (-j_p / F / D, \"Neumann\")}\n", + " phi: {\n", + " \"left\": (pybamm.Scalar(0), \"Neumann\"),\n", + " \"right\": (-I_app / A / sigma, \"Neumann\"),\n", + " },\n", + " phi_e: {\n", + " \"left\": (pybamm.Scalar(0), \"Dirichlet\"),\n", + " \"right\": (pybamm.Scalar(0), \"Neumann\"),\n", + " },\n", + " c: {\"left\": (pybamm.Scalar(0), \"Neumann\"), \"right\": (-j_p / F / D, \"Neumann\")},\n", "}\n", "\n", "# initial conditions\n", "inputs = {\"Initial concentration [mol.m-3]\": c0}\n", "U_init = pybamm.FunctionParameter(\"Positive electrode OCP [V]\", inputs)\n", - "model.initial_conditions = {\n", - " phi: U_init,\n", - " phi_e: 0,\n", - " c: c0\n", - "}" + "model.initial_conditions = {phi: U_init, phi_e: 0, c: c0}" ] }, { @@ -322,9 +328,11 @@ " \"Electrolyte potential [V]\": phi_e,\n", " \"Positive particle concentration [mol.m-3]\": c,\n", " \"Positive particle surface concentration [mol.m-3]\": c_surf,\n", - " \"Average positive particle surface concentration [mol.m-3]\": pybamm.x_average(c_surf),\n", + " \"Average positive particle surface concentration [mol.m-3]\": pybamm.x_average(\n", + " c_surf\n", + " ),\n", " \"Positive electrode interfacial current density [A.m-2]\": j_p,\n", - " \"Positive electrode OCP [V]\":pybamm.boundary_value(U, \"right\"),\n", + " \"Positive electrode OCP [V]\": pybamm.boundary_value(U, \"right\"),\n", " \"Voltage [V]\": pybamm.boundary_value(phi, \"right\"),\n", "}" ] @@ -356,20 +364,20 @@ "source": [ "from pybamm import tanh\n", "\n", - "# both functions will depend on the maximum concentration \n", + "# both functions will depend on the maximum concentration\n", "c_max = pybamm.Parameter(\"Maximum concentration in positive electrode [mol.m-3]\")\n", "\n", "\n", "def exchange_current_density(c_surf):\n", - " k = 6 * 10 ** (-7) # reaction rate [(A/m2)(m3/mol)**1.5]\n", + " k = 6 * 10 ** (-7) # reaction rate [(A/m2)(m3/mol)**1.5]\n", " c_e = 1000 # (constant) electrolyte concentration [mol.m-3]\n", - " return k * c_e** 0.5 * c_surf ** 0.5 * (c_max - c_surf) ** 0.5\n", + " return k * c_e**0.5 * c_surf**0.5 * (c_max - c_surf) ** 0.5\n", "\n", "\n", "def open_circuit_potential(c_surf):\n", " stretch = 1.062\n", " sto = stretch * c_surf / c_max\n", - " \n", + "\n", " u_eq = (\n", " 2.16216\n", " + 0.07645 * tanh(30.834 - 54.4806 * sto)\n", @@ -400,7 +408,7 @@ "source": [ "param = pybamm.ParameterValues(\n", " {\n", - " \"Surface area per unit volume [m-1]\":0.15e6,\n", + " \"Surface area per unit volume [m-1]\": 0.15e6,\n", " \"Positive particle radius [m]\": 10e-6,\n", " \"Separator thickness [m]\": 25e-6,\n", " \"Positive electrode thickness [m]\": 100e-6,\n", @@ -437,18 +445,19 @@ "outputs": [], "source": [ "r = pybamm.SpatialVariable(\n", - " \"r\", \n", - " domain=[\"positive particle\"], \n", - " auxiliary_domains={\n", - " \"secondary\": \"positive electrode\"\n", - " },\n", - " coord_sys=\"spherical polar\")\n", + " \"r\",\n", + " domain=[\"positive particle\"],\n", + " auxiliary_domains={\"secondary\": \"positive electrode\"},\n", + " coord_sys=\"spherical polar\",\n", + ")\n", "x_s = pybamm.SpatialVariable(\"x_s\", domain=[\"separator\"], coord_sys=\"cartesian\")\n", - "x_p = pybamm.SpatialVariable(\"x_p\", domain=[\"positive electrode\"], coord_sys=\"cartesian\")\n", + "x_p = pybamm.SpatialVariable(\n", + " \"x_p\", domain=[\"positive electrode\"], coord_sys=\"cartesian\"\n", + ")\n", "\n", "\n", "geometry = {\n", - " \"separator\": {x_s: {\"min\": -L_s, \"max\": 0}}, \n", + " \"separator\": {x_s: {\"min\": -L_s, \"max\": 0}},\n", " \"positive electrode\": {x_p: {\"min\": 0, \"max\": L_p}},\n", " \"positive particle\": {r: {\"min\": 0, \"max\": R_p}},\n", "}" 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 e383498065..fbbe0cac5e 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 @@ -127,7 +127,8 @@ "import pybamm\n", "import numpy as np\n", "import os\n", - "os.chdir(pybamm.__path__[0]+'/..')" + "\n", + "os.chdir(pybamm.__path__[0] + \"/..\")" ] }, { @@ -201,7 +202,9 @@ "\n", "\n", "def D(cc):\n", - " return pybamm.FunctionParameter(\"Diffusivity [m2.s-1]\", {\"Solvent concentration [mol.m-3]\": cc})" + " return pybamm.FunctionParameter(\n", + " \"Diffusivity [m2.s-1]\", {\"Solvent concentration [mol.m-3]\": cc}\n", + " )" ] }, { @@ -249,8 +252,8 @@ "R = k * pybamm.BoundaryValue(c, \"left\")\n", "\n", "# solvent concentration equation\n", - "N = - 1/L * D(c) * pybamm.grad(c)\n", - "dcdt = (V_hat * R) / L * pybamm.inner(xi, pybamm.grad(c)) - 1/L * pybamm.div(N)\n", + "N = -1 / L * D(c) * pybamm.grad(c)\n", + "dcdt = (V_hat * R) / L * pybamm.inner(xi, pybamm.grad(c)) - 1 / L * pybamm.div(N)\n", "\n", "# SEI thickness equation\n", "dLdt = V_hat * R" @@ -305,7 +308,9 @@ "metadata": {}, "outputs": [], "source": [ - "D_left = pybamm.BoundaryValue(D(c), \"left\") # pybamm requires BoundaryValue(D(c)) and not D(BoundaryValue(c)) \n", + "D_left = pybamm.BoundaryValue(\n", + " D(c), \"left\"\n", + ") # pybamm requires BoundaryValue(D(c)) and not D(BoundaryValue(c))\n", "grad_c_left = R * L / D_left" ] }, @@ -351,7 +356,9 @@ "metadata": {}, "outputs": [], "source": [ - "model.boundary_conditions = {c: {\"left\": (grad_c_left, \"Neumann\"), \"right\": (c_right, \"Dirichlet\")}}" + "model.boundary_conditions = {\n", + " c: {\"left\": (grad_c_left, \"Neumann\"), \"right\": (c_right, \"Dirichlet\")}\n", + "}" ] }, { @@ -437,7 +444,11 @@ "metadata": {}, "outputs": [], "source": [ - "model.variables = {\"SEI thickness [m]\": L, \"SEI growth rate [m]\": dLdt, \"Solvent concentration [mol.m-3]\": c}" + "model.variables = {\n", + " \"SEI thickness [m]\": L,\n", + " \"SEI growth rate [m]\": dLdt,\n", + " \"Solvent concentration [mol.m-3]\": c,\n", + "}" ] }, { @@ -488,7 +499,7 @@ "\n", "\n", "def Diffusivity(cc):\n", - " return cc * 10**(-12)\n", + " return cc * 10 ** (-12)\n", "\n", "\n", "# parameter values (not physically based, for example only!)\n", @@ -510,7 +521,7 @@ "submesh_types = {\"SEI layer\": pybamm.Uniform1DSubMesh}\n", "var_pts = {xi: 100}\n", "mesh = pybamm.Mesh(geometry, submesh_types, var_pts)\n", - " \n", + "\n", "spatial_methods = {\"SEI layer\": pybamm.FiniteVolume()}\n", "disc = pybamm.Discretisation(mesh, spatial_methods)\n", "disc.process_model(model)" @@ -524,7 +535,7 @@ "source": [ "# solve\n", "solver = pybamm.ScipySolver()\n", - "t = [0, 100] # solve for 100s\n", + "t = [0, 100] # solve for 100s\n", "solution = solver.solve(model, t)\n", "\n", "# post-process output variables\n", @@ -566,28 +577,31 @@ "# plot SEI thickness in microns as a function of t in microseconds\n", "# and concentration in mol/m3 as a function of x in microns\n", "L_0_eval = param.evaluate(L_0)\n", - "xi = np.linspace(0, 1, 100) # dimensionless space\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, ax2) = plt.subplots(1, 2, figsize=(10, 5))\n", " ax1.plot(solution.t, L_out(solution.t) * 1e6)\n", - " ax1.plot(t, L_out(t) * 1e6, 'r.')\n", - " ax1.set_ylabel(r'SEI thickness [$\\mu$m]')\n", - " ax1.set_xlabel(r't [s]') \n", - " \n", + " ax1.plot(t, L_out(t) * 1e6, \"r.\")\n", + " ax1.set_ylabel(r\"SEI thickness [$\\mu$m]\")\n", + " ax1.set_xlabel(r\"t [s]\")\n", + "\n", " ax2.plot(xi * L_out(t) * 1e6, c_out(t, xi))\n", " ax2.set_ylim(0, 1.1)\n", - " ax2.set_xlim(0, L_out(solution.t[-1]) * 1e6) \n", - " ax2.set_ylabel('Solvent concentration [mol.m-3]')\n", - " ax2.set_xlabel(r'x [$\\mu$m]')\n", + " ax2.set_xlim(0, L_out(solution.t[-1]) * 1e6)\n", + " ax2.set_ylabel(\"Solvent concentration [mol.m-3]\")\n", + " ax2.set_xlabel(r\"x [$\\mu$m]\")\n", "\n", " plt.tight_layout()\n", " plt.show()\n", - " \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));" + "\n", + "widgets.interact(\n", + " plot, t=widgets.FloatSlider(min=0, max=solution.t[-1], step=0.1, value=0)\n", + ");" ] }, { diff --git a/docs/source/examples/notebooks/expression_tree/broadcasts.ipynb b/docs/source/examples/notebooks/expression_tree/broadcasts.ipynb index 035fe77ed7..466baa3c7a 100644 --- a/docs/source/examples/notebooks/expression_tree/broadcasts.ipynb +++ b/docs/source/examples/notebooks/expression_tree/broadcasts.ipynb @@ -102,7 +102,7 @@ "T = pybamm.Variable(\"T\", domain=\"negative electrode\")\n", "disc.set_variable_slices([T])\n", "disc_T = disc.process_symbol(T)\n", - "disc_T.evaluate(y=np.linspace(0,1,5))" + "disc_T.evaluate(y=np.linspace(0, 1, 5))" ] }, { @@ -145,7 +145,7 @@ "source": [ "primary_broad_T = pybamm.PrimaryBroadcast(T, \"negative particle\")\n", "disc_T = disc.process_symbol(primary_broad_T)\n", - "disc_T.evaluate(y=np.linspace(0,1,5))" + "disc_T.evaluate(y=np.linspace(0, 1, 5))" ] }, { @@ -192,7 +192,7 @@ "c_s = pybamm.Variable(\"c_s\", domain=\"negative particle\")\n", "disc.set_variable_slices([c_s])\n", "disc_c_s = disc.process_symbol(c_s)\n", - "disc_c_s.evaluate(y=np.linspace(0,1,3))" + "disc_c_s.evaluate(y=np.linspace(0, 1, 3))" ] }, { @@ -235,7 +235,7 @@ "source": [ "secondary_broad_c_s = pybamm.SecondaryBroadcast(c_s, \"negative electrode\")\n", "disc_broad_c_s = disc.process_symbol(secondary_broad_c_s)\n", - "disc_broad_c_s.evaluate(y=np.linspace(0,1,3))" + "disc_broad_c_s.evaluate(y=np.linspace(0, 1, 3))" ] }, { diff --git a/docs/source/examples/notebooks/expression_tree/expression-tree.ipynb b/docs/source/examples/notebooks/expression_tree/expression-tree.ipynb index b15c8b1d32..a5b38efd9e 100644 --- a/docs/source/examples/notebooks/expression_tree/expression-tree.ipynb +++ b/docs/source/examples/notebooks/expression_tree/expression-tree.ipynb @@ -39,10 +39,10 @@ "import pybamm\n", "import numpy as np\n", "\n", - "y = pybamm.StateVector(slice(0,1))\n", + "y = pybamm.StateVector(slice(0, 1))\n", "t = pybamm.t\n", - "equation = 2*y * (1 - y) + t\n", - "equation.visualise('expression_tree1.png')" + "equation = 2 * y * (1 - y) + t\n", + "equation.visualise(\"expression_tree1.png\")" ] }, { @@ -90,7 +90,7 @@ "outputs": [], "source": [ "diff_wrt_equation = equation.diff(t)\n", - "diff_wrt_equation.visualise('expression_tree2.png')" + "diff_wrt_equation.visualise(\"expression_tree2.png\")" ] }, { @@ -152,11 +152,11 @@ "metadata": {}, "outputs": [], "source": [ - "D = pybamm.Parameter('D')\n", - "c = pybamm.Variable('c', domain=['negative electrode'])\n", + "D = pybamm.Parameter(\"D\")\n", + "c = pybamm.Variable(\"c\", domain=[\"negative electrode\"])\n", "\n", "dcdt = D * pybamm.div(pybamm.grad(c))\n", - "dcdt.visualise('expression_tree3.png')" + "dcdt.visualise(\"expression_tree3.png\")" ] }, { @@ -183,9 +183,9 @@ "metadata": {}, "outputs": [], "source": [ - "parameter_values = pybamm.ParameterValues({'D': 2})\n", + "parameter_values = pybamm.ParameterValues({\"D\": 2})\n", "dcdt = parameter_values.process_symbol(dcdt)\n", - "dcdt.visualise('expression_tree4.png')" + "dcdt.visualise(\"expression_tree4.png\")" ] }, { @@ -210,13 +210,14 @@ "source": [ "# Here, we import a dummy discretisation from the PyBaMM tests directory.\n", "import sys\n", + "\n", "sys.path.insert(0, pybamm.root_dir())\n", "from tests import get_discretisation_for_testing\n", "\n", "disc = get_discretisation_for_testing()\n", "disc.y_slices = {c: [slice(0, 40)]}\n", "dcdt = disc.process_symbol(dcdt)\n", - "dcdt.visualise('expression_tree5.png')" + "dcdt.visualise(\"expression_tree5.png\")" ] }, { diff --git a/docs/source/examples/notebooks/getting_started/tutorial-10-creating-a-model.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-10-creating-a-model.ipynb index 8744e94f7e..c788a773b8 100644 --- a/docs/source/examples/notebooks/getting_started/tutorial-10-creating-a-model.ipynb +++ b/docs/source/examples/notebooks/getting_started/tutorial-10-creating-a-model.ipynb @@ -107,7 +107,7 @@ "N = -pybamm.grad(c) # define the flux\n", "dcdt = -pybamm.div(N) # define the rhs equation\n", "\n", - "model.rhs = {c: dcdt} # add the equation to rhs dictionary with the variable as the key " + "model.rhs = {c: dcdt} # add the equation to rhs dictionary with the variable as the key" ] }, { @@ -126,12 +126,12 @@ "metadata": {}, "outputs": [], "source": [ - "# boundary conditions \n", + "# boundary conditions\n", "c_surf = pybamm.surf(c) # concentration at the surface of the sphere\n", "j = j0 * (1 - c_surf) ** (1 / 2) * c_surf ** (1 / 2) # prescribed boundary flux\n", "model.boundary_conditions = {c: {\"left\": (0, \"Neumann\"), \"right\": (-j, \"Neumann\")}}\n", "\n", - "# initial conditions \n", + "# initial conditions\n", "model.initial_conditions = {c: c0}" ] }, @@ -177,7 +177,9 @@ "metadata": {}, "outputs": [], "source": [ - "r = pybamm.SpatialVariable(\"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\")" + "r = pybamm.SpatialVariable(\n", + " \"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\"\n", + ")" ] }, { @@ -219,7 +221,7 @@ "submesh_types = {\"negative particle\": pybamm.Uniform1DSubMesh}\n", "var_pts = {r: 20}\n", "# create a mesh of our geometry, using a uniform grid with 20 volumes\n", - "mesh = pybamm.Mesh(geometry, submesh_types, var_pts) " + "mesh = pybamm.Mesh(geometry, submesh_types, var_pts)" ] }, { @@ -240,10 +242,12 @@ "metadata": {}, "outputs": [], "source": [ - "parameter_values = pybamm.ParameterValues({\n", - " \"Initial concentration\": 0.9,\n", - " \"Flux parameter\": 0.8,\n", - "})" + "parameter_values = pybamm.ParameterValues(\n", + " {\n", + " \"Initial concentration\": 0.9,\n", + " \"Flux parameter\": 0.8,\n", + " }\n", + ")" ] }, { @@ -282,13 +286,13 @@ "outputs": [], "source": [ "sim = pybamm.Simulation(\n", - " model,\n", - " geometry=geometry,\n", - " parameter_values=parameter_values,\n", - " submesh_types=submesh_types,\n", - " var_pts=var_pts,\n", - " spatial_methods=spatial_methods,\n", - " solver=solver,\n", + " model,\n", + " geometry=geometry,\n", + " parameter_values=parameter_values,\n", + " submesh_types=submesh_types,\n", + " var_pts=var_pts,\n", + " spatial_methods=spatial_methods,\n", + " solver=solver,\n", ")" ] }, @@ -373,8 +377,8 @@ } ], "source": [ - "# pass in a list of the variables we want to plot \n", - "sim.plot([\"Concentration\", \"Surface concentration\", \"Flux\", \"Boundary flux\"]) " + "# pass in a list of the variables we want to plot\n", + "sim.plot([\"Concentration\", \"Surface concentration\", \"Flux\", \"Boundary flux\"])" ] }, { 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 a38c0c90ee..45dd6a7702 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 @@ -90,14 +90,14 @@ " c = pybamm.Variable(\"Concentration\", domain=\"negative particle\")\n", "\n", " # define concentration at the surface of the sphere\n", - " c_surf = pybamm.surf(c) \n", + " c_surf = pybamm.surf(c)\n", "\n", - " # define flux \n", + " # define flux\n", " N = -pybamm.grad(c)\n", "\n", " # create dictionary of model variables\n", " variables = {\n", - " \"Concentration\": c, \n", + " \"Concentration\": c,\n", " \"Surface concentration\": c_surf,\n", " \"Flux\": N,\n", " }\n", @@ -105,7 +105,7 @@ " return variables\n", "\n", " def get_coupled_variables(self, variables):\n", - " return variables \n", + " return variables\n", "\n", " def set_rhs(self, variables):\n", " # extract the variables we need\n", @@ -115,8 +115,8 @@ " # define the rhs of the PDE\n", " dcdt = -pybamm.div(N)\n", "\n", - " # add it to the submodel dictionary \n", - " self.rhs = {c: dcdt} \n", + " # add it to the submodel dictionary\n", + " self.rhs = {c: dcdt}\n", "\n", " def set_algebraic(self, variables):\n", " pass\n", @@ -127,7 +127,9 @@ " j = variables[\"Boundary flux\"]\n", "\n", " # add the boundary conditions to the submodel dictionary\n", - " self.boundary_conditions = {c: {\"left\": (0, \"Neumann\"), \"right\": (-j, \"Neumann\")}}\n", + " self.boundary_conditions = {\n", + " c: {\"left\": (0, \"Neumann\"), \"right\": (-j, \"Neumann\")}\n", + " }\n", "\n", " def set_initial_conditions(self, variables):\n", " # extract the variable we need\n", @@ -135,7 +137,7 @@ "\n", " # define the initial concentration parameter\n", " c0 = pybamm.Parameter(\"Initial concentration\")\n", - " \n", + "\n", " # add the initial conditions to the submodel dictionary\n", " self.initial_conditions = {c: c0}" ] @@ -183,7 +185,10 @@ "metadata": {}, "outputs": [], "source": [ - "model.submodels = {\"Particle\": Particle(None, \"Negative\"), \"Boundary flux\": BoundaryFlux(None, \"Negative\")}" + "model.submodels = {\n", + " \"Particle\": Particle(None, \"Negative\"),\n", + " \"Boundary flux\": BoundaryFlux(None, \"Negative\"),\n", + "}" ] }, { @@ -285,12 +290,14 @@ "metadata": {}, "outputs": [], "source": [ - "r = pybamm.SpatialVariable(\"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\")\n", + "r = pybamm.SpatialVariable(\n", + " \"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\"\n", + ")\n", "geometry = {\"negative particle\": {r: {\"min\": 0, \"max\": 1}}}\n", "spatial_methods = {\"negative particle\": pybamm.FiniteVolume()}\n", "submesh_types = {\"negative particle\": pybamm.Uniform1DSubMesh}\n", "var_pts = {r: 20}\n", - "mesh = pybamm.Mesh(geometry, submesh_types, var_pts) " + "mesh = pybamm.Mesh(geometry, submesh_types, var_pts)" ] }, { @@ -309,21 +316,23 @@ "metadata": {}, "outputs": [], "source": [ - "parameter_values = pybamm.ParameterValues({\n", - " \"Initial concentration\": 0.9,\n", - " \"Flux parameter\": 0.8,\n", - "})\n", + "parameter_values = pybamm.ParameterValues(\n", + " {\n", + " \"Initial concentration\": 0.9,\n", + " \"Flux parameter\": 0.8,\n", + " }\n", + ")\n", "\n", "solver = pybamm.ScipySolver()\n", "\n", "sim = pybamm.Simulation(\n", - " model,\n", - " geometry=geometry,\n", - " parameter_values=parameter_values,\n", - " submesh_types=submesh_types,\n", - " var_pts=var_pts,\n", - " spatial_methods=spatial_methods,\n", - " solver=solver,\n", + " model,\n", + " geometry=geometry,\n", + " parameter_values=parameter_values,\n", + " submesh_types=submesh_types,\n", + " var_pts=var_pts,\n", + " spatial_methods=spatial_methods,\n", + " solver=solver,\n", ")" ] }, @@ -354,7 +363,7 @@ } ], "source": [ - "sim.solve([0, 1]) " + "sim.solve([0, 1])" ] }, { @@ -406,8 +415,8 @@ } ], "source": [ - "# pass in a list of the variables we want to plot \n", - "sim.plot([\"Concentration\", \"Surface concentration\", \"Flux\", \"Boundary flux\"]) " + "# pass in a list of the variables we want to plot\n", + "sim.plot([\"Concentration\", \"Surface concentration\", \"Flux\", \"Boundary flux\"])" ] }, { diff --git a/docs/source/examples/notebooks/getting_started/tutorial-3-basic-plotting.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-3-basic-plotting.ipynb index 40a02f682a..022a11e48a 100644 --- a/docs/source/examples/notebooks/getting_started/tutorial-3-basic-plotting.ipynb +++ b/docs/source/examples/notebooks/getting_started/tutorial-3-basic-plotting.ipynb @@ -1,942 +1,947 @@ { - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tutorial 3 - Basic plotting" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In [Tutorial 2](./tutorial-2-compare-models.ipynb), we made use of PyBaMM's automatic plotting function when comparing models. This gave a good quick overview of many of the key variables in the model. However, by passing in just a few arguments it is easy to plot any of the many other variables that may be of interest to you. We start by building and solving a model as before:" - ] - }, - { - "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" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%pip install \"pybamm[plot,cite]\" -q # install PyBaMM if it is not installed\n", - "import pybamm\n", - "import matplotlib.pyplot as plt\n", - "\n", - "model_dfn = pybamm.lithium_ion.DFN()\n", - "sim_dfn = pybamm.Simulation(model_dfn)\n", - "sim_dfn.solve([0, 3600])" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now want to plot a selection of the model variables. To see a full list of the available variables just type:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['Time [s]',\n", - " 'Time [min]',\n", - " 'Time [h]',\n", - " 'x [m]',\n", - " 'x_n [m]',\n", - " 'x_s [m]',\n", - " 'x_p [m]',\n", - " 'r_n [m]',\n", - " 'r_p [m]',\n", - " 'Current variable [A]',\n", - " 'Total current density [A.m-2]',\n", - " 'Current [A]',\n", - " 'C-rate',\n", - " 'Discharge capacity [A.h]',\n", - " 'Discharge energy [W.h]',\n", - " 'Throughput energy [W.h]',\n", - " 'Throughput capacity [A.h]',\n", - " 'Porosity',\n", - " 'Negative electrode porosity',\n", - " 'X-averaged negative electrode porosity',\n", - " 'Separator porosity',\n", - " 'X-averaged separator porosity',\n", - " 'Positive electrode porosity',\n", - " 'X-averaged positive electrode porosity',\n", - " 'Porosity change',\n", - " 'Negative electrode porosity change [s-1]',\n", - " 'X-averaged negative electrode porosity change [s-1]',\n", - " 'Separator porosity change [s-1]',\n", - " 'X-averaged separator porosity change [s-1]',\n", - " 'Positive electrode porosity change [s-1]',\n", - " 'X-averaged positive electrode porosity change [s-1]',\n", - " 'Negative electrode interface utilisation variable',\n", - " 'X-averaged negative electrode interface utilisation variable',\n", - " 'Negative electrode interface utilisation',\n", - " 'X-averaged negative electrode interface utilisation',\n", - " 'Positive electrode interface utilisation variable',\n", - " 'X-averaged positive electrode interface utilisation variable',\n", - " 'Positive electrode interface utilisation',\n", - " 'X-averaged positive electrode interface utilisation',\n", - " 'Negative particle crack length [m]',\n", - " 'X-averaged negative particle crack length [m]',\n", - " 'Negative particle cracking rate [m.s-1]',\n", - " 'X-averaged negative particle cracking rate [m.s-1]',\n", - " 'Positive particle crack length [m]',\n", - " 'X-averaged positive particle crack length [m]',\n", - " 'Positive particle cracking rate [m.s-1]',\n", - " 'X-averaged positive particle cracking rate [m.s-1]',\n", - " 'Negative electrode active material volume fraction',\n", - " 'X-averaged negative electrode active material volume fraction',\n", - " 'Negative electrode capacity [A.h]',\n", - " 'Negative particle radius',\n", - " 'Negative particle radius [m]',\n", - " 'X-averaged negative particle radius [m]',\n", - " 'Negative electrode surface area to volume ratio [m-1]',\n", - " 'X-averaged negative electrode surface area to volume ratio [m-1]',\n", - " 'Negative electrode active material volume fraction change [s-1]',\n", - " 'X-averaged negative electrode active material volume fraction change [s-1]',\n", - " 'Loss of lithium due to loss of active material in negative electrode [mol]',\n", - " 'Positive electrode active material volume fraction',\n", - " 'X-averaged positive electrode active material volume fraction',\n", - " 'Positive electrode capacity [A.h]',\n", - " 'Positive particle radius',\n", - " 'Positive particle radius [m]',\n", - " 'X-averaged positive particle radius [m]',\n", - " 'Positive electrode surface area to volume ratio [m-1]',\n", - " 'X-averaged positive electrode surface area to volume ratio [m-1]',\n", - " 'Positive electrode active material volume fraction change [s-1]',\n", - " 'X-averaged positive electrode active material volume fraction change [s-1]',\n", - " 'Loss of lithium due to loss of active material in positive electrode [mol]',\n", - " 'Separator pressure [Pa]',\n", - " 'X-averaged separator pressure [Pa]',\n", - " 'negative electrode transverse volume-averaged velocity [m.s-1]',\n", - " 'X-averaged negative electrode transverse volume-averaged velocity [m.s-1]',\n", - " 'separator transverse volume-averaged velocity [m.s-1]',\n", - " 'X-averaged separator transverse volume-averaged velocity [m.s-1]',\n", - " 'positive electrode transverse volume-averaged velocity [m.s-1]',\n", - " 'X-averaged positive electrode transverse volume-averaged velocity [m.s-1]',\n", - " 'Transverse volume-averaged velocity [m.s-1]',\n", - " 'negative electrode transverse volume-averaged acceleration [m.s-2]',\n", - " 'X-averaged negative electrode transverse volume-averaged acceleration [m.s-2]',\n", - " 'separator transverse volume-averaged acceleration [m.s-2]',\n", - " 'X-averaged separator transverse volume-averaged acceleration [m.s-2]',\n", - " 'positive electrode transverse volume-averaged acceleration [m.s-2]',\n", - " 'X-averaged positive electrode transverse volume-averaged acceleration [m.s-2]',\n", - " 'Transverse volume-averaged acceleration [m.s-2]',\n", - " 'Negative electrode volume-averaged velocity [m.s-1]',\n", - " 'Negative electrode volume-averaged acceleration [m.s-2]',\n", - " 'X-averaged negative electrode volume-averaged acceleration [m.s-2]',\n", - " 'Negative electrode pressure [Pa]',\n", - " 'X-averaged negative electrode pressure [Pa]',\n", - " 'Positive electrode volume-averaged velocity [m.s-1]',\n", - " 'Positive electrode volume-averaged acceleration [m.s-2]',\n", - " 'X-averaged positive electrode volume-averaged acceleration [m.s-2]',\n", - " 'Positive electrode pressure [Pa]',\n", - " 'X-averaged positive electrode pressure [Pa]',\n", - " 'Negative particle stoichiometry',\n", - " 'Negative particle concentration',\n", - " 'Negative particle concentration [mol.m-3]',\n", - " 'X-averaged negative particle concentration',\n", - " 'X-averaged negative particle concentration [mol.m-3]',\n", - " 'R-averaged negative particle concentration',\n", - " 'R-averaged negative particle concentration [mol.m-3]',\n", - " 'Average negative particle concentration',\n", - " 'Average negative particle concentration [mol.m-3]',\n", - " 'Negative particle surface stoichiometry',\n", - " 'Negative particle surface concentration',\n", - " 'Negative particle surface concentration [mol.m-3]',\n", - " 'X-averaged negative particle surface concentration',\n", - " 'X-averaged negative particle surface concentration [mol.m-3]',\n", - " 'Negative electrode extent of lithiation',\n", - " 'X-averaged negative electrode extent of lithiation',\n", - " 'Minimum negative particle concentration',\n", - " 'Maximum negative particle concentration',\n", - " 'Minimum negative particle concentration [mol.m-3]',\n", - " 'Maximum negative particle concentration [mol.m-3]',\n", - " 'Minimum negative particle surface concentration',\n", - " 'Maximum negative particle surface concentration',\n", - " 'Minimum negative particle surface concentration [mol.m-3]',\n", - " 'Maximum negative particle surface concentration [mol.m-3]',\n", - " 'Positive particle stoichiometry',\n", - " 'Positive particle concentration',\n", - " 'Positive particle concentration [mol.m-3]',\n", - " 'X-averaged positive particle concentration',\n", - " 'X-averaged positive particle concentration [mol.m-3]',\n", - " 'R-averaged positive particle concentration',\n", - " 'R-averaged positive particle concentration [mol.m-3]',\n", - " 'Average positive particle concentration',\n", - " 'Average positive particle concentration [mol.m-3]',\n", - " 'Positive particle surface stoichiometry',\n", - " 'Positive particle surface concentration',\n", - " 'Positive particle surface concentration [mol.m-3]',\n", - " 'X-averaged positive particle surface concentration',\n", - " 'X-averaged positive particle surface concentration [mol.m-3]',\n", - " 'Positive electrode extent of lithiation',\n", - " 'X-averaged positive electrode extent of lithiation',\n", - " 'Minimum positive particle concentration',\n", - " 'Maximum positive particle concentration',\n", - " 'Minimum positive particle concentration [mol.m-3]',\n", - " 'Maximum positive particle concentration [mol.m-3]',\n", - " 'Minimum positive particle surface concentration',\n", - " 'Maximum positive particle surface concentration',\n", - " 'Minimum positive particle surface concentration [mol.m-3]',\n", - " 'Maximum positive particle surface concentration [mol.m-3]',\n", - " 'Negative electrode potential [V]',\n", - " 'X-averaged negative electrode potential [V]',\n", - " 'Negative electrode ohmic losses [V]',\n", - " 'X-averaged negative electrode ohmic losses [V]',\n", - " 'Gradient of negative electrode potential [V.m-1]',\n", - " 'Positive electrode potential [V]',\n", - " 'X-averaged positive electrode potential [V]',\n", - " 'Positive electrode ohmic losses [V]',\n", - " 'X-averaged positive electrode ohmic losses [V]',\n", - " 'Gradient of positive electrode potential [V.m-1]',\n", - " 'Porosity times concentration [mol.m-3]',\n", - " 'Negative electrode porosity times concentration [mol.m-3]',\n", - " 'Separator porosity times concentration [mol.m-3]',\n", - " 'Positive electrode porosity times concentration [mol.m-3]',\n", - " 'Total lithium in electrolyte [mol]',\n", - " 'Electrolyte potential [V]',\n", - " 'X-averaged electrolyte potential [V]',\n", - " 'X-averaged electrolyte overpotential [V]',\n", - " 'Gradient of electrolyte potential [V.m-1]',\n", - " 'Negative electrolyte potential [V]',\n", - " 'X-averaged negative electrolyte potential [V]',\n", - " 'Gradient of negative electrolyte potential [V.m-1]',\n", - " 'Separator electrolyte potential [V]',\n", - " 'X-averaged separator electrolyte potential [V]',\n", - " 'Gradient of separator electrolyte potential [V.m-1]',\n", - " 'Positive electrolyte potential [V]',\n", - " 'X-averaged positive electrolyte potential [V]',\n", - " 'Gradient of positive electrolyte potential [V.m-1]',\n", - " 'Ambient temperature [K]',\n", - " 'Cell temperature [K]',\n", - " 'Negative current collector temperature [K]',\n", - " 'Positive current collector temperature [K]',\n", - " 'X-averaged cell temperature [K]',\n", - " 'Volume-averaged cell temperature [K]',\n", - " 'Negative electrode temperature [K]',\n", - " 'X-averaged negative electrode temperature [K]',\n", - " 'Separator temperature [K]',\n", - " 'X-averaged separator temperature [K]',\n", - " 'Positive electrode temperature [K]',\n", - " 'X-averaged positive electrode temperature [K]',\n", - " 'Ambient temperature [C]',\n", - " 'Cell temperature [C]',\n", - " 'Negative current collector temperature [C]',\n", - " 'Positive current collector temperature [C]',\n", - " 'X-averaged cell temperature [C]',\n", - " 'Volume-averaged cell temperature [C]',\n", - " 'Negative electrode temperature [C]',\n", - " 'X-averaged negative electrode temperature [C]',\n", - " 'Separator temperature [C]',\n", - " 'X-averaged separator temperature [C]',\n", - " 'Positive electrode temperature [C]',\n", - " 'X-averaged positive electrode temperature [C]',\n", - " 'Negative current collector potential [V]',\n", - " 'Inner SEI thickness [m]',\n", - " 'Outer SEI thickness [m]',\n", - " 'X-averaged inner SEI thickness [m]',\n", - " 'X-averaged outer SEI thickness [m]',\n", - " 'SEI [m]',\n", - " 'Total SEI thickness [m]',\n", - " 'X-averaged SEI thickness [m]',\n", - " 'X-averaged total SEI thickness [m]',\n", - " 'X-averaged negative electrode resistance [Ohm.m2]',\n", - " 'Inner SEI interfacial current density [A.m-2]',\n", - " 'X-averaged inner SEI interfacial current density [A.m-2]',\n", - " 'Outer SEI interfacial current density [A.m-2]',\n", - " 'X-averaged outer SEI interfacial current density [A.m-2]',\n", - " 'SEI interfacial current density [A.m-2]',\n", - " 'X-averaged SEI interfacial current density [A.m-2]',\n", - " 'Inner SEI on cracks thickness [m]',\n", - " 'Outer SEI on cracks thickness [m]',\n", - " 'X-averaged inner SEI on cracks thickness [m]',\n", - " 'X-averaged outer SEI on cracks thickness [m]',\n", - " 'SEI on cracks [m]',\n", - " 'Total SEI on cracks thickness [m]',\n", - " 'X-averaged SEI on cracks thickness [m]',\n", - " 'X-averaged total SEI on cracks thickness [m]',\n", - " 'Inner SEI on cracks interfacial current density [A.m-2]',\n", - " 'X-averaged inner SEI on cracks interfacial current density [A.m-2]',\n", - " 'Outer SEI on cracks interfacial current density [A.m-2]',\n", - " 'X-averaged outer SEI on cracks interfacial current density [A.m-2]',\n", - " 'SEI on cracks interfacial current density [A.m-2]',\n", - " 'X-averaged SEI on cracks interfacial current density [A.m-2]',\n", - " 'Lithium plating concentration [mol.m-3]',\n", - " 'X-averaged lithium plating concentration [mol.m-3]',\n", - " 'Dead lithium concentration [mol.m-3]',\n", - " 'X-averaged dead lithium concentration [mol.m-3]',\n", - " 'Lithium plating thickness [m]',\n", - " 'X-averaged lithium plating thickness [m]',\n", - " 'Dead lithium thickness [m]',\n", - " 'X-averaged dead lithium thickness [m]',\n", - " 'Loss of lithium to lithium plating [mol]',\n", - " 'Loss of capacity to lithium plating [A.h]',\n", - " 'Negative electrode lithium plating reaction overpotential [V]',\n", - " 'X-averaged negative electrode lithium plating reaction overpotential [V]',\n", - " 'Lithium plating interfacial current density [A.m-2]',\n", - " 'X-averaged lithium plating interfacial current density [A.m-2]',\n", - " 'Negative crack surface to volume ratio [m-1]',\n", - " 'Negative electrode roughness ratio',\n", - " 'X-averaged negative electrode roughness ratio',\n", - " 'Positive crack surface to volume ratio [m-1]',\n", - " 'Positive electrode roughness ratio',\n", - " 'X-averaged positive electrode roughness ratio',\n", - " 'Electrolyte transport efficiency',\n", - " 'Negative electrolyte transport efficiency',\n", - " 'X-averaged negative electrolyte transport efficiency',\n", - " 'Separator electrolyte transport efficiency',\n", - " 'X-averaged separator electrolyte transport efficiency',\n", - " 'Positive electrolyte transport efficiency',\n", - " 'X-averaged positive electrolyte transport efficiency',\n", - " 'Electrode transport efficiency',\n", - " 'Negative electrode transport efficiency',\n", - " 'X-averaged negative electrode transport efficiency',\n", - " 'Separator electrode transport efficiency',\n", - " 'X-averaged separator electrode transport efficiency',\n", - " 'Positive electrode transport efficiency',\n", - " 'X-averaged positive electrode transport efficiency',\n", - " 'Separator volume-averaged velocity [m.s-1]',\n", - " 'Separator volume-averaged acceleration [m.s-2]',\n", - " 'X-averaged separator volume-averaged acceleration [m.s-2]',\n", - " 'Volume-averaged velocity [m.s-1]',\n", - " 'Volume-averaged acceleration [m.s-1]',\n", - " 'X-averaged volume-averaged acceleration [m.s-1]',\n", - " 'Pressure [Pa]',\n", - " 'Negative electrode open-circuit potential [V]',\n", - " 'X-averaged negative electrode open-circuit potential [V]',\n", - " 'Negative electrode entropic change [V.K-1]',\n", - " 'X-averaged negative electrode entropic change [V.K-1]',\n", - " 'Positive electrode open-circuit potential [V]',\n", - " 'X-averaged positive electrode open-circuit potential [V]',\n", - " 'Positive electrode entropic change [V.K-1]',\n", - " 'X-averaged positive electrode entropic change [V.K-1]',\n", - " 'Negative electrode effective conductivity',\n", - " 'Negative electrode current density [A.m-2]',\n", - " 'Positive electrode effective conductivity',\n", - " 'Positive electrode current density [A.m-2]',\n", - " 'Electrode current density [A.m-2]',\n", - " 'Positive current collector potential [V]',\n", - " 'Local voltage [V]',\n", - " 'Voltage [V]',\n", - " 'Contact overpotential [V]',\n", - " 'Electrolyte concentration concatenation [mol.m-3]',\n", - " 'Negative electrolyte concentration [mol.m-3]',\n", - " 'X-averaged negative electrolyte concentration [mol.m-3]',\n", - " 'Separator electrolyte concentration [mol.m-3]',\n", - " 'X-averaged separator electrolyte concentration [mol.m-3]',\n", - " 'Positive electrolyte concentration [mol.m-3]',\n", - " 'X-averaged positive electrolyte concentration [mol.m-3]',\n", - " 'Negative electrolyte concentration',\n", - " 'Negative electrolyte concentration [Molar]',\n", - " 'X-averaged negative electrolyte concentration',\n", - " 'X-averaged negative electrolyte concentration [Molar]',\n", - " 'Separator electrolyte concentration',\n", - " 'Separator electrolyte concentration [Molar]',\n", - " 'X-averaged separator electrolyte concentration',\n", - " 'X-averaged separator electrolyte concentration [Molar]',\n", - " 'Positive electrolyte concentration',\n", - " 'Positive electrolyte concentration [Molar]',\n", - " 'X-averaged positive electrolyte concentration',\n", - " 'X-averaged positive electrolyte concentration [Molar]',\n", - " 'Electrolyte concentration [mol.m-3]',\n", - " 'X-averaged electrolyte concentration [mol.m-3]',\n", - " 'Electrolyte concentration',\n", - " 'Electrolyte concentration [Molar]',\n", - " 'X-averaged electrolyte concentration',\n", - " 'X-averaged electrolyte concentration [Molar]',\n", - " 'Electrolyte current density [A.m-2]',\n", - " 'X-averaged concentration overpotential [V]',\n", - " 'X-averaged electrolyte ohmic losses [V]',\n", - " 'Negative electrode surface potential difference [V]',\n", - " 'X-averaged negative electrode surface potential difference [V]',\n", - " 'Positive electrode surface potential difference [V]',\n", - " 'X-averaged positive electrode surface potential difference [V]',\n", - " 'Ohmic heating [W.m-3]',\n", - " 'X-averaged Ohmic heating [W.m-3]',\n", - " 'Volume-averaged Ohmic heating [W.m-3]',\n", - " 'Irreversible electrochemical heating [W.m-3]',\n", - " 'X-averaged irreversible electrochemical heating [W.m-3]',\n", - " 'Volume-averaged irreversible electrochemical heating [W.m-3]',\n", - " 'Reversible heating [W.m-3]',\n", - " 'X-averaged reversible heating [W.m-3]',\n", - " 'Volume-averaged reversible heating [W.m-3]',\n", - " 'Total heating [W.m-3]',\n", - " 'X-averaged total heating [W.m-3]',\n", - " 'Volume-averaged total heating [W.m-3]',\n", - " 'Current collector current density [A.m-2]',\n", - " 'Inner SEI concentration [mol.m-3]',\n", - " 'X-averaged inner SEI concentration [mol.m-3]',\n", - " 'Outer SEI concentration [mol.m-3]',\n", - " 'X-averaged outer SEI concentration [mol.m-3]',\n", - " 'SEI concentration [mol.m-3]',\n", - " 'X-averaged SEI concentration [mol.m-3]',\n", - " 'Loss of lithium to SEI [mol]',\n", - " 'Loss of capacity to SEI [A.h]',\n", - " 'X-averaged negative electrode SEI interfacial current density [A.m-2]',\n", - " 'Negative electrode SEI interfacial current density [A.m-2]',\n", - " 'Positive electrode SEI interfacial current density [A.m-2]',\n", - " 'X-averaged positive electrode SEI volumetric interfacial current density [A.m-2]',\n", - " 'Positive electrode SEI volumetric interfacial current density [A.m-3]',\n", - " 'Negative electrode SEI volumetric interfacial current density [A.m-3]',\n", - " 'X-averaged negative electrode SEI volumetric interfacial current density [A.m-3]',\n", - " 'Inner SEI on cracks concentration [mol.m-3]',\n", - " 'X-averaged inner SEI on cracks concentration [mol.m-3]',\n", - " 'Outer SEI on cracks concentration [mol.m-3]',\n", - " 'X-averaged outer SEI on cracks concentration [mol.m-3]',\n", - " 'SEI on cracks concentration [mol.m-3]',\n", - " 'X-averaged SEI on cracks concentration [mol.m-3]',\n", - " 'Loss of lithium to SEI on cracks [mol]',\n", - " 'Loss of capacity to SEI on cracks [A.h]',\n", - " 'X-averaged negative electrode SEI on cracks interfacial current density [A.m-2]',\n", - " 'Negative electrode SEI on cracks interfacial current density [A.m-2]',\n", - " 'Positive electrode SEI on cracks interfacial current density [A.m-2]',\n", - " 'X-averaged positive electrode SEI on cracks volumetric interfacial current density [A.m-2]',\n", - " 'Positive electrode SEI on cracks volumetric interfacial current density [A.m-3]',\n", - " 'Negative electrode SEI on cracks volumetric interfacial current density [A.m-3]',\n", - " 'X-averaged negative electrode SEI on cracks volumetric interfacial current density [A.m-3]',\n", - " 'Negative electrode lithium plating interfacial current density [A.m-2]',\n", - " 'X-averaged negative electrode lithium plating interfacial current density [A.m-2]',\n", - " 'Lithium plating volumetric interfacial current density [A.m-3]',\n", - " 'X-averaged lithium plating volumetric interfacial current density [A.m-3]',\n", - " 'X-averaged positive electrode lithium plating interfacial current density [A.m-2]',\n", - " 'X-averaged positive electrode lithium plating volumetric interfacial current density [A.m-3]',\n", - " 'Positive electrode lithium plating interfacial current density [A.m-2]',\n", - " 'Positive electrode lithium plating volumetric interfacial current density [A.m-3]',\n", - " 'Negative electrode lithium plating volumetric interfacial current density [A.m-3]',\n", - " 'X-averaged negative electrode lithium plating volumetric interfacial current density [A.m-3]',\n", - " 'Negative electrode interfacial current density [A.m-2]',\n", - " 'X-averaged negative electrode interfacial current density [A.m-2]',\n", - " 'X-averaged negative electrode total interfacial current density [A.m-2]',\n", - " 'X-averaged negative electrode total volumetric interfacial current density [A.m-3]',\n", - " 'Negative electrode exchange current density [A.m-2]',\n", - " 'X-averaged negative electrode exchange current density [A.m-2]',\n", - " 'Negative electrode reaction overpotential [V]',\n", - " 'X-averaged negative electrode reaction overpotential [V]',\n", - " 'Negative electrode volumetric interfacial current density [A.m-3]',\n", - " 'X-averaged negative electrode volumetric interfacial current density [A.m-3]',\n", - " 'SEI film overpotential [V]',\n", - " 'X-averaged SEI film overpotential [V]',\n", - " 'Positive electrode interfacial current density [A.m-2]',\n", - " 'X-averaged positive electrode interfacial current density [A.m-2]',\n", - " 'X-averaged positive electrode total interfacial current density [A.m-2]',\n", - " 'X-averaged positive electrode total volumetric interfacial current density [A.m-3]',\n", - " 'Positive electrode exchange current density [A.m-2]',\n", - " 'X-averaged positive electrode exchange current density [A.m-2]',\n", - " 'Positive electrode reaction overpotential [V]',\n", - " 'X-averaged positive electrode reaction overpotential [V]',\n", - " 'Positive electrode volumetric interfacial current density [A.m-3]',\n", - " 'X-averaged positive electrode volumetric interfacial current density [A.m-3]',\n", - " 'Negative particle rhs [mol.m-3.s-1]',\n", - " 'Negative particle bc [mol.m-2]',\n", - " 'Negative particle effective diffusivity [m2.s-1]',\n", - " 'X-averaged negative particle effective diffusivity [m2.s-1]',\n", - " 'Negative particle flux [mol.m-2.s-1]',\n", - " 'Negative electrode stoichiometry',\n", - " 'Negative electrode volume-averaged concentration',\n", - " 'Negative electrode volume-averaged concentration [mol.m-3]',\n", - " 'Total lithium in primary phase in negative electrode [mol]',\n", - " 'Positive particle rhs [mol.m-3.s-1]',\n", - " 'Positive particle bc [mol.m-2]',\n", - " 'Positive particle effective diffusivity [m2.s-1]',\n", - " 'X-averaged positive particle effective diffusivity [m2.s-1]',\n", - " 'Positive particle flux [mol.m-2.s-1]',\n", - " 'Positive electrode stoichiometry',\n", - " 'Positive electrode volume-averaged concentration',\n", - " 'Positive electrode volume-averaged concentration [mol.m-3]',\n", - " 'Total lithium in primary phase in positive electrode [mol]',\n", - " 'Electrolyte flux [mol.m-2.s-1]',\n", - " 'Electrolyte diffusion flux [mol.m-2.s-1]',\n", - " 'Electrolyte migration flux [mol.m-2.s-1]',\n", - " 'Electrolyte convection flux [mol.m-2.s-1]',\n", - " 'Sum of negative electrode electrolyte reaction source terms [A.m-3]',\n", - " 'Sum of x-averaged negative electrode electrolyte reaction source terms [A.m-3]',\n", - " 'Sum of negative electrode volumetric interfacial current densities [A.m-3]',\n", - " 'Sum of x-averaged negative electrode volumetric interfacial current densities [A.m-3]',\n", - " 'Sum of positive electrode electrolyte reaction source terms [A.m-3]',\n", - " 'Sum of x-averaged positive electrode electrolyte reaction source terms [A.m-3]',\n", - " 'Sum of positive electrode volumetric interfacial current densities [A.m-3]',\n", - " 'Sum of x-averaged positive electrode volumetric interfacial current densities [A.m-3]',\n", - " 'Interfacial current density [A.m-2]',\n", - " 'Exchange current density [A.m-2]',\n", - " 'Sum of volumetric interfacial current densities [A.m-3]',\n", - " 'Sum of electrolyte reaction source terms [A.m-3]',\n", - " 'X-averaged open-circuit voltage [V]',\n", - " 'Measured open-circuit voltage [V]',\n", - " 'X-averaged reaction overpotential [V]',\n", - " 'X-averaged solid phase ohmic losses [V]',\n", - " 'X-averaged battery open-circuit voltage [V]',\n", - " 'Measured battery open-circuit voltage [V]',\n", - " 'X-averaged battery reaction overpotential [V]',\n", - " 'X-averaged battery solid phase ohmic losses [V]',\n", - " 'X-averaged battery electrolyte ohmic losses [V]',\n", - " 'X-averaged battery concentration overpotential [V]',\n", - " 'Battery voltage [V]',\n", - " 'Change in measured open-circuit voltage [V]',\n", - " 'Local ECM resistance [Ohm]',\n", - " 'Terminal power [W]',\n", - " 'Power [W]',\n", - " 'Resistance [Ohm]',\n", - " 'Total lithium in negative electrode [mol]',\n", - " 'LAM_ne [%]',\n", - " 'Loss of active material in negative electrode [%]',\n", - " 'Total lithium in positive electrode [mol]',\n", - " 'LAM_pe [%]',\n", - " 'Loss of active material in positive electrode [%]',\n", - " 'LLI [%]',\n", - " 'Loss of lithium inventory [%]',\n", - " 'Loss of lithium inventory, including electrolyte [%]',\n", - " 'Total lithium [mol]',\n", - " 'Total lithium in particles [mol]',\n", - " 'Total lithium capacity [A.h]',\n", - " 'Total lithium capacity in particles [A.h]',\n", - " 'Total lithium lost [mol]',\n", - " 'Total lithium lost from particles [mol]',\n", - " 'Total lithium lost from electrolyte [mol]',\n", - " 'Total lithium lost to side reactions [mol]',\n", - " 'Total capacity lost to side reactions [A.h]']" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model_dfn.variable_names()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There are a _lot_ of variables. You can also search the list of variables for a particular string (e.g. \"electrolyte\")" - ] - }, + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tutorial 3 - Basic plotting" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In [Tutorial 2](./tutorial-2-compare-models.ipynb), we made use of PyBaMM's automatic plotting function when comparing models. This gave a good quick overview of many of the key variables in the model. However, by passing in just a few arguments it is easy to plot any of the many other variables that may be of interest to you. We start by building and solving a model as before:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Electrolyte concentration\n", - "Electrolyte concentration [Molar]\n", - "Electrolyte concentration [mol.m-3]\n", - "Electrolyte concentration concatenation [mol.m-3]\n", - "Electrolyte convection flux [mol.m-2.s-1]\n", - "Electrolyte current density [A.m-2]\n", - "Electrolyte diffusion flux [mol.m-2.s-1]\n", - "Electrolyte flux [mol.m-2.s-1]\n", - "Electrolyte migration flux [mol.m-2.s-1]\n", - "Electrolyte potential [V]\n", - "Electrolyte transport efficiency\n", - "Gradient of electrolyte potential [V.m-1]\n", - "Gradient of negative electrolyte potential [V.m-1]\n", - "Gradient of positive electrolyte potential [V.m-1]\n", - "Gradient of separator electrolyte potential [V.m-1]\n", - "Loss of lithium inventory, including electrolyte [%]\n", - "Negative electrolyte concentration\n", - "Negative electrolyte concentration [Molar]\n", - "Negative electrolyte concentration [mol.m-3]\n", - "Negative electrolyte potential [V]\n", - "Negative electrolyte transport efficiency\n", - "Positive electrolyte concentration\n", - "Positive electrolyte concentration [Molar]\n", - "Positive electrolyte concentration [mol.m-3]\n", - "Positive electrolyte potential [V]\n", - "Positive electrolyte transport efficiency\n", - "Separator electrolyte concentration\n", - "Separator electrolyte concentration [Molar]\n", - "Separator electrolyte concentration [mol.m-3]\n", - "Separator electrolyte potential [V]\n", - "Separator electrolyte transport efficiency\n", - "Sum of electrolyte reaction source terms [A.m-3]\n", - "Sum of negative electrode electrolyte reaction source terms [A.m-3]\n", - "Sum of positive electrode electrolyte reaction source terms [A.m-3]\n", - "Sum of x-averaged negative electrode electrolyte reaction source terms [A.m-3]\n", - "Sum of x-averaged positive electrode electrolyte reaction source terms [A.m-3]\n", - "Total lithium in electrolyte [mol]\n", - "Total lithium lost from electrolyte [mol]\n", - "X-averaged battery electrolyte ohmic losses [V]\n", - "X-averaged electrolyte concentration\n", - "X-averaged electrolyte concentration [Molar]\n", - "X-averaged electrolyte concentration [mol.m-3]\n", - "X-averaged electrolyte ohmic losses [V]\n", - "X-averaged electrolyte overpotential [V]\n", - "X-averaged electrolyte potential [V]\n", - "X-averaged negative electrolyte concentration\n", - "X-averaged negative electrolyte concentration [Molar]\n", - "X-averaged negative electrolyte concentration [mol.m-3]\n", - "X-averaged negative electrolyte potential [V]\n", - "X-averaged negative electrolyte transport efficiency\n", - "X-averaged positive electrolyte concentration\n", - "X-averaged positive electrolyte concentration [Molar]\n", - "X-averaged positive electrolyte concentration [mol.m-3]\n", - "X-averaged positive electrolyte potential [V]\n", - "X-averaged positive electrolyte transport efficiency\n", - "X-averaged separator electrolyte concentration\n", - "X-averaged separator electrolyte concentration [Molar]\n", - "X-averaged separator electrolyte concentration [mol.m-3]\n", - "X-averaged separator electrolyte potential [V]\n", - "X-averaged separator electrolyte transport efficiency\n" - ] - } - ], - "source": [ - "model_dfn.variables.search(\"electrolyte\")" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have tried to make variables names fairly self explanatory." + "data": { + "text/plain": [ + "" ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As a first example, we choose to plot the voltage. We add this to a list and then pass this list to the `plot` method of our simulation:" - ] - }, + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%pip install \"pybamm[plot,cite]\" -q # install PyBaMM if it is not installed\n", + "import pybamm\n", + "import matplotlib.pyplot as plt\n", + "\n", + "model_dfn = pybamm.lithium_ion.DFN()\n", + "sim_dfn = pybamm.Simulation(model_dfn)\n", + "sim_dfn.solve([0, 3600])" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now want to plot a selection of the model variables. To see a full list of the available variables just type:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "8c87342bdc1e425ba87715d286d799d0", - "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" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "output_variables = [\"Voltage [V]\"]\n", - "sim_dfn.plot(output_variables=output_variables)" + "data": { + "text/plain": [ + "['Time [s]',\n", + " 'Time [min]',\n", + " 'Time [h]',\n", + " 'x [m]',\n", + " 'x_n [m]',\n", + " 'x_s [m]',\n", + " 'x_p [m]',\n", + " 'r_n [m]',\n", + " 'r_p [m]',\n", + " 'Current variable [A]',\n", + " 'Total current density [A.m-2]',\n", + " 'Current [A]',\n", + " 'C-rate',\n", + " 'Discharge capacity [A.h]',\n", + " 'Discharge energy [W.h]',\n", + " 'Throughput energy [W.h]',\n", + " 'Throughput capacity [A.h]',\n", + " 'Porosity',\n", + " 'Negative electrode porosity',\n", + " 'X-averaged negative electrode porosity',\n", + " 'Separator porosity',\n", + " 'X-averaged separator porosity',\n", + " 'Positive electrode porosity',\n", + " 'X-averaged positive electrode porosity',\n", + " 'Porosity change',\n", + " 'Negative electrode porosity change [s-1]',\n", + " 'X-averaged negative electrode porosity change [s-1]',\n", + " 'Separator porosity change [s-1]',\n", + " 'X-averaged separator porosity change [s-1]',\n", + " 'Positive electrode porosity change [s-1]',\n", + " 'X-averaged positive electrode porosity change [s-1]',\n", + " 'Negative electrode interface utilisation variable',\n", + " 'X-averaged negative electrode interface utilisation variable',\n", + " 'Negative electrode interface utilisation',\n", + " 'X-averaged negative electrode interface utilisation',\n", + " 'Positive electrode interface utilisation variable',\n", + " 'X-averaged positive electrode interface utilisation variable',\n", + " 'Positive electrode interface utilisation',\n", + " 'X-averaged positive electrode interface utilisation',\n", + " 'Negative particle crack length [m]',\n", + " 'X-averaged negative particle crack length [m]',\n", + " 'Negative particle cracking rate [m.s-1]',\n", + " 'X-averaged negative particle cracking rate [m.s-1]',\n", + " 'Positive particle crack length [m]',\n", + " 'X-averaged positive particle crack length [m]',\n", + " 'Positive particle cracking rate [m.s-1]',\n", + " 'X-averaged positive particle cracking rate [m.s-1]',\n", + " 'Negative electrode active material volume fraction',\n", + " 'X-averaged negative electrode active material volume fraction',\n", + " 'Negative electrode capacity [A.h]',\n", + " 'Negative particle radius',\n", + " 'Negative particle radius [m]',\n", + " 'X-averaged negative particle radius [m]',\n", + " 'Negative electrode surface area to volume ratio [m-1]',\n", + " 'X-averaged negative electrode surface area to volume ratio [m-1]',\n", + " 'Negative electrode active material volume fraction change [s-1]',\n", + " 'X-averaged negative electrode active material volume fraction change [s-1]',\n", + " 'Loss of lithium due to loss of active material in negative electrode [mol]',\n", + " 'Positive electrode active material volume fraction',\n", + " 'X-averaged positive electrode active material volume fraction',\n", + " 'Positive electrode capacity [A.h]',\n", + " 'Positive particle radius',\n", + " 'Positive particle radius [m]',\n", + " 'X-averaged positive particle radius [m]',\n", + " 'Positive electrode surface area to volume ratio [m-1]',\n", + " 'X-averaged positive electrode surface area to volume ratio [m-1]',\n", + " 'Positive electrode active material volume fraction change [s-1]',\n", + " 'X-averaged positive electrode active material volume fraction change [s-1]',\n", + " 'Loss of lithium due to loss of active material in positive electrode [mol]',\n", + " 'Separator pressure [Pa]',\n", + " 'X-averaged separator pressure [Pa]',\n", + " 'negative electrode transverse volume-averaged velocity [m.s-1]',\n", + " 'X-averaged negative electrode transverse volume-averaged velocity [m.s-1]',\n", + " 'separator transverse volume-averaged velocity [m.s-1]',\n", + " 'X-averaged separator transverse volume-averaged velocity [m.s-1]',\n", + " 'positive electrode transverse volume-averaged velocity [m.s-1]',\n", + " 'X-averaged positive electrode transverse volume-averaged velocity [m.s-1]',\n", + " 'Transverse volume-averaged velocity [m.s-1]',\n", + " 'negative electrode transverse volume-averaged acceleration [m.s-2]',\n", + " 'X-averaged negative electrode transverse volume-averaged acceleration [m.s-2]',\n", + " 'separator transverse volume-averaged acceleration [m.s-2]',\n", + " 'X-averaged separator transverse volume-averaged acceleration [m.s-2]',\n", + " 'positive electrode transverse volume-averaged acceleration [m.s-2]',\n", + " 'X-averaged positive electrode transverse volume-averaged acceleration [m.s-2]',\n", + " 'Transverse volume-averaged acceleration [m.s-2]',\n", + " 'Negative electrode volume-averaged velocity [m.s-1]',\n", + " 'Negative electrode volume-averaged acceleration [m.s-2]',\n", + " 'X-averaged negative electrode volume-averaged acceleration [m.s-2]',\n", + " 'Negative electrode pressure [Pa]',\n", + " 'X-averaged negative electrode pressure [Pa]',\n", + " 'Positive electrode volume-averaged velocity [m.s-1]',\n", + " 'Positive electrode volume-averaged acceleration [m.s-2]',\n", + " 'X-averaged positive electrode volume-averaged acceleration [m.s-2]',\n", + " 'Positive electrode pressure [Pa]',\n", + " 'X-averaged positive electrode pressure [Pa]',\n", + " 'Negative particle stoichiometry',\n", + " 'Negative particle concentration',\n", + " 'Negative particle concentration [mol.m-3]',\n", + " 'X-averaged negative particle concentration',\n", + " 'X-averaged negative particle concentration [mol.m-3]',\n", + " 'R-averaged negative particle concentration',\n", + " 'R-averaged negative particle concentration [mol.m-3]',\n", + " 'Average negative particle concentration',\n", + " 'Average negative particle concentration [mol.m-3]',\n", + " 'Negative particle surface stoichiometry',\n", + " 'Negative particle surface concentration',\n", + " 'Negative particle surface concentration [mol.m-3]',\n", + " 'X-averaged negative particle surface concentration',\n", + " 'X-averaged negative particle surface concentration [mol.m-3]',\n", + " 'Negative electrode extent of lithiation',\n", + " 'X-averaged negative electrode extent of lithiation',\n", + " 'Minimum negative particle concentration',\n", + " 'Maximum negative particle concentration',\n", + " 'Minimum negative particle concentration [mol.m-3]',\n", + " 'Maximum negative particle concentration [mol.m-3]',\n", + " 'Minimum negative particle surface concentration',\n", + " 'Maximum negative particle surface concentration',\n", + " 'Minimum negative particle surface concentration [mol.m-3]',\n", + " 'Maximum negative particle surface concentration [mol.m-3]',\n", + " 'Positive particle stoichiometry',\n", + " 'Positive particle concentration',\n", + " 'Positive particle concentration [mol.m-3]',\n", + " 'X-averaged positive particle concentration',\n", + " 'X-averaged positive particle concentration [mol.m-3]',\n", + " 'R-averaged positive particle concentration',\n", + " 'R-averaged positive particle concentration [mol.m-3]',\n", + " 'Average positive particle concentration',\n", + " 'Average positive particle concentration [mol.m-3]',\n", + " 'Positive particle surface stoichiometry',\n", + " 'Positive particle surface concentration',\n", + " 'Positive particle surface concentration [mol.m-3]',\n", + " 'X-averaged positive particle surface concentration',\n", + " 'X-averaged positive particle surface concentration [mol.m-3]',\n", + " 'Positive electrode extent of lithiation',\n", + " 'X-averaged positive electrode extent of lithiation',\n", + " 'Minimum positive particle concentration',\n", + " 'Maximum positive particle concentration',\n", + " 'Minimum positive particle concentration [mol.m-3]',\n", + " 'Maximum positive particle concentration [mol.m-3]',\n", + " 'Minimum positive particle surface concentration',\n", + " 'Maximum positive particle surface concentration',\n", + " 'Minimum positive particle surface concentration [mol.m-3]',\n", + " 'Maximum positive particle surface concentration [mol.m-3]',\n", + " 'Negative electrode potential [V]',\n", + " 'X-averaged negative electrode potential [V]',\n", + " 'Negative electrode ohmic losses [V]',\n", + " 'X-averaged negative electrode ohmic losses [V]',\n", + " 'Gradient of negative electrode potential [V.m-1]',\n", + " 'Positive electrode potential [V]',\n", + " 'X-averaged positive electrode potential [V]',\n", + " 'Positive electrode ohmic losses [V]',\n", + " 'X-averaged positive electrode ohmic losses [V]',\n", + " 'Gradient of positive electrode potential [V.m-1]',\n", + " 'Porosity times concentration [mol.m-3]',\n", + " 'Negative electrode porosity times concentration [mol.m-3]',\n", + " 'Separator porosity times concentration [mol.m-3]',\n", + " 'Positive electrode porosity times concentration [mol.m-3]',\n", + " 'Total lithium in electrolyte [mol]',\n", + " 'Electrolyte potential [V]',\n", + " 'X-averaged electrolyte potential [V]',\n", + " 'X-averaged electrolyte overpotential [V]',\n", + " 'Gradient of electrolyte potential [V.m-1]',\n", + " 'Negative electrolyte potential [V]',\n", + " 'X-averaged negative electrolyte potential [V]',\n", + " 'Gradient of negative electrolyte potential [V.m-1]',\n", + " 'Separator electrolyte potential [V]',\n", + " 'X-averaged separator electrolyte potential [V]',\n", + " 'Gradient of separator electrolyte potential [V.m-1]',\n", + " 'Positive electrolyte potential [V]',\n", + " 'X-averaged positive electrolyte potential [V]',\n", + " 'Gradient of positive electrolyte potential [V.m-1]',\n", + " 'Ambient temperature [K]',\n", + " 'Cell temperature [K]',\n", + " 'Negative current collector temperature [K]',\n", + " 'Positive current collector temperature [K]',\n", + " 'X-averaged cell temperature [K]',\n", + " 'Volume-averaged cell temperature [K]',\n", + " 'Negative electrode temperature [K]',\n", + " 'X-averaged negative electrode temperature [K]',\n", + " 'Separator temperature [K]',\n", + " 'X-averaged separator temperature [K]',\n", + " 'Positive electrode temperature [K]',\n", + " 'X-averaged positive electrode temperature [K]',\n", + " 'Ambient temperature [C]',\n", + " 'Cell temperature [C]',\n", + " 'Negative current collector temperature [C]',\n", + " 'Positive current collector temperature [C]',\n", + " 'X-averaged cell temperature [C]',\n", + " 'Volume-averaged cell temperature [C]',\n", + " 'Negative electrode temperature [C]',\n", + " 'X-averaged negative electrode temperature [C]',\n", + " 'Separator temperature [C]',\n", + " 'X-averaged separator temperature [C]',\n", + " 'Positive electrode temperature [C]',\n", + " 'X-averaged positive electrode temperature [C]',\n", + " 'Negative current collector potential [V]',\n", + " 'Inner SEI thickness [m]',\n", + " 'Outer SEI thickness [m]',\n", + " 'X-averaged inner SEI thickness [m]',\n", + " 'X-averaged outer SEI thickness [m]',\n", + " 'SEI [m]',\n", + " 'Total SEI thickness [m]',\n", + " 'X-averaged SEI thickness [m]',\n", + " 'X-averaged total SEI thickness [m]',\n", + " 'X-averaged negative electrode resistance [Ohm.m2]',\n", + " 'Inner SEI interfacial current density [A.m-2]',\n", + " 'X-averaged inner SEI interfacial current density [A.m-2]',\n", + " 'Outer SEI interfacial current density [A.m-2]',\n", + " 'X-averaged outer SEI interfacial current density [A.m-2]',\n", + " 'SEI interfacial current density [A.m-2]',\n", + " 'X-averaged SEI interfacial current density [A.m-2]',\n", + " 'Inner SEI on cracks thickness [m]',\n", + " 'Outer SEI on cracks thickness [m]',\n", + " 'X-averaged inner SEI on cracks thickness [m]',\n", + " 'X-averaged outer SEI on cracks thickness [m]',\n", + " 'SEI on cracks [m]',\n", + " 'Total SEI on cracks thickness [m]',\n", + " 'X-averaged SEI on cracks thickness [m]',\n", + " 'X-averaged total SEI on cracks thickness [m]',\n", + " 'Inner SEI on cracks interfacial current density [A.m-2]',\n", + " 'X-averaged inner SEI on cracks interfacial current density [A.m-2]',\n", + " 'Outer SEI on cracks interfacial current density [A.m-2]',\n", + " 'X-averaged outer SEI on cracks interfacial current density [A.m-2]',\n", + " 'SEI on cracks interfacial current density [A.m-2]',\n", + " 'X-averaged SEI on cracks interfacial current density [A.m-2]',\n", + " 'Lithium plating concentration [mol.m-3]',\n", + " 'X-averaged lithium plating concentration [mol.m-3]',\n", + " 'Dead lithium concentration [mol.m-3]',\n", + " 'X-averaged dead lithium concentration [mol.m-3]',\n", + " 'Lithium plating thickness [m]',\n", + " 'X-averaged lithium plating thickness [m]',\n", + " 'Dead lithium thickness [m]',\n", + " 'X-averaged dead lithium thickness [m]',\n", + " 'Loss of lithium to lithium plating [mol]',\n", + " 'Loss of capacity to lithium plating [A.h]',\n", + " 'Negative electrode lithium plating reaction overpotential [V]',\n", + " 'X-averaged negative electrode lithium plating reaction overpotential [V]',\n", + " 'Lithium plating interfacial current density [A.m-2]',\n", + " 'X-averaged lithium plating interfacial current density [A.m-2]',\n", + " 'Negative crack surface to volume ratio [m-1]',\n", + " 'Negative electrode roughness ratio',\n", + " 'X-averaged negative electrode roughness ratio',\n", + " 'Positive crack surface to volume ratio [m-1]',\n", + " 'Positive electrode roughness ratio',\n", + " 'X-averaged positive electrode roughness ratio',\n", + " 'Electrolyte transport efficiency',\n", + " 'Negative electrolyte transport efficiency',\n", + " 'X-averaged negative electrolyte transport efficiency',\n", + " 'Separator electrolyte transport efficiency',\n", + " 'X-averaged separator electrolyte transport efficiency',\n", + " 'Positive electrolyte transport efficiency',\n", + " 'X-averaged positive electrolyte transport efficiency',\n", + " 'Electrode transport efficiency',\n", + " 'Negative electrode transport efficiency',\n", + " 'X-averaged negative electrode transport efficiency',\n", + " 'Separator electrode transport efficiency',\n", + " 'X-averaged separator electrode transport efficiency',\n", + " 'Positive electrode transport efficiency',\n", + " 'X-averaged positive electrode transport efficiency',\n", + " 'Separator volume-averaged velocity [m.s-1]',\n", + " 'Separator volume-averaged acceleration [m.s-2]',\n", + " 'X-averaged separator volume-averaged acceleration [m.s-2]',\n", + " 'Volume-averaged velocity [m.s-1]',\n", + " 'Volume-averaged acceleration [m.s-1]',\n", + " 'X-averaged volume-averaged acceleration [m.s-1]',\n", + " 'Pressure [Pa]',\n", + " 'Negative electrode open-circuit potential [V]',\n", + " 'X-averaged negative electrode open-circuit potential [V]',\n", + " 'Negative electrode entropic change [V.K-1]',\n", + " 'X-averaged negative electrode entropic change [V.K-1]',\n", + " 'Positive electrode open-circuit potential [V]',\n", + " 'X-averaged positive electrode open-circuit potential [V]',\n", + " 'Positive electrode entropic change [V.K-1]',\n", + " 'X-averaged positive electrode entropic change [V.K-1]',\n", + " 'Negative electrode effective conductivity',\n", + " 'Negative electrode current density [A.m-2]',\n", + " 'Positive electrode effective conductivity',\n", + " 'Positive electrode current density [A.m-2]',\n", + " 'Electrode current density [A.m-2]',\n", + " 'Positive current collector potential [V]',\n", + " 'Local voltage [V]',\n", + " 'Voltage [V]',\n", + " 'Contact overpotential [V]',\n", + " 'Electrolyte concentration concatenation [mol.m-3]',\n", + " 'Negative electrolyte concentration [mol.m-3]',\n", + " 'X-averaged negative electrolyte concentration [mol.m-3]',\n", + " 'Separator electrolyte concentration [mol.m-3]',\n", + " 'X-averaged separator electrolyte concentration [mol.m-3]',\n", + " 'Positive electrolyte concentration [mol.m-3]',\n", + " 'X-averaged positive electrolyte concentration [mol.m-3]',\n", + " 'Negative electrolyte concentration',\n", + " 'Negative electrolyte concentration [Molar]',\n", + " 'X-averaged negative electrolyte concentration',\n", + " 'X-averaged negative electrolyte concentration [Molar]',\n", + " 'Separator electrolyte concentration',\n", + " 'Separator electrolyte concentration [Molar]',\n", + " 'X-averaged separator electrolyte concentration',\n", + " 'X-averaged separator electrolyte concentration [Molar]',\n", + " 'Positive electrolyte concentration',\n", + " 'Positive electrolyte concentration [Molar]',\n", + " 'X-averaged positive electrolyte concentration',\n", + " 'X-averaged positive electrolyte concentration [Molar]',\n", + " 'Electrolyte concentration [mol.m-3]',\n", + " 'X-averaged electrolyte concentration [mol.m-3]',\n", + " 'Electrolyte concentration',\n", + " 'Electrolyte concentration [Molar]',\n", + " 'X-averaged electrolyte concentration',\n", + " 'X-averaged electrolyte concentration [Molar]',\n", + " 'Electrolyte current density [A.m-2]',\n", + " 'X-averaged concentration overpotential [V]',\n", + " 'X-averaged electrolyte ohmic losses [V]',\n", + " 'Negative electrode surface potential difference [V]',\n", + " 'X-averaged negative electrode surface potential difference [V]',\n", + " 'Positive electrode surface potential difference [V]',\n", + " 'X-averaged positive electrode surface potential difference [V]',\n", + " 'Ohmic heating [W.m-3]',\n", + " 'X-averaged Ohmic heating [W.m-3]',\n", + " 'Volume-averaged Ohmic heating [W.m-3]',\n", + " 'Irreversible electrochemical heating [W.m-3]',\n", + " 'X-averaged irreversible electrochemical heating [W.m-3]',\n", + " 'Volume-averaged irreversible electrochemical heating [W.m-3]',\n", + " 'Reversible heating [W.m-3]',\n", + " 'X-averaged reversible heating [W.m-3]',\n", + " 'Volume-averaged reversible heating [W.m-3]',\n", + " 'Total heating [W.m-3]',\n", + " 'X-averaged total heating [W.m-3]',\n", + " 'Volume-averaged total heating [W.m-3]',\n", + " 'Current collector current density [A.m-2]',\n", + " 'Inner SEI concentration [mol.m-3]',\n", + " 'X-averaged inner SEI concentration [mol.m-3]',\n", + " 'Outer SEI concentration [mol.m-3]',\n", + " 'X-averaged outer SEI concentration [mol.m-3]',\n", + " 'SEI concentration [mol.m-3]',\n", + " 'X-averaged SEI concentration [mol.m-3]',\n", + " 'Loss of lithium to SEI [mol]',\n", + " 'Loss of capacity to SEI [A.h]',\n", + " 'X-averaged negative electrode SEI interfacial current density [A.m-2]',\n", + " 'Negative electrode SEI interfacial current density [A.m-2]',\n", + " 'Positive electrode SEI interfacial current density [A.m-2]',\n", + " 'X-averaged positive electrode SEI volumetric interfacial current density [A.m-2]',\n", + " 'Positive electrode SEI volumetric interfacial current density [A.m-3]',\n", + " 'Negative electrode SEI volumetric interfacial current density [A.m-3]',\n", + " 'X-averaged negative electrode SEI volumetric interfacial current density [A.m-3]',\n", + " 'Inner SEI on cracks concentration [mol.m-3]',\n", + " 'X-averaged inner SEI on cracks concentration [mol.m-3]',\n", + " 'Outer SEI on cracks concentration [mol.m-3]',\n", + " 'X-averaged outer SEI on cracks concentration [mol.m-3]',\n", + " 'SEI on cracks concentration [mol.m-3]',\n", + " 'X-averaged SEI on cracks concentration [mol.m-3]',\n", + " 'Loss of lithium to SEI on cracks [mol]',\n", + " 'Loss of capacity to SEI on cracks [A.h]',\n", + " 'X-averaged negative electrode SEI on cracks interfacial current density [A.m-2]',\n", + " 'Negative electrode SEI on cracks interfacial current density [A.m-2]',\n", + " 'Positive electrode SEI on cracks interfacial current density [A.m-2]',\n", + " 'X-averaged positive electrode SEI on cracks volumetric interfacial current density [A.m-2]',\n", + " 'Positive electrode SEI on cracks volumetric interfacial current density [A.m-3]',\n", + " 'Negative electrode SEI on cracks volumetric interfacial current density [A.m-3]',\n", + " 'X-averaged negative electrode SEI on cracks volumetric interfacial current density [A.m-3]',\n", + " 'Negative electrode lithium plating interfacial current density [A.m-2]',\n", + " 'X-averaged negative electrode lithium plating interfacial current density [A.m-2]',\n", + " 'Lithium plating volumetric interfacial current density [A.m-3]',\n", + " 'X-averaged lithium plating volumetric interfacial current density [A.m-3]',\n", + " 'X-averaged positive electrode lithium plating interfacial current density [A.m-2]',\n", + " 'X-averaged positive electrode lithium plating volumetric interfacial current density [A.m-3]',\n", + " 'Positive electrode lithium plating interfacial current density [A.m-2]',\n", + " 'Positive electrode lithium plating volumetric interfacial current density [A.m-3]',\n", + " 'Negative electrode lithium plating volumetric interfacial current density [A.m-3]',\n", + " 'X-averaged negative electrode lithium plating volumetric interfacial current density [A.m-3]',\n", + " 'Negative electrode interfacial current density [A.m-2]',\n", + " 'X-averaged negative electrode interfacial current density [A.m-2]',\n", + " 'X-averaged negative electrode total interfacial current density [A.m-2]',\n", + " 'X-averaged negative electrode total volumetric interfacial current density [A.m-3]',\n", + " 'Negative electrode exchange current density [A.m-2]',\n", + " 'X-averaged negative electrode exchange current density [A.m-2]',\n", + " 'Negative electrode reaction overpotential [V]',\n", + " 'X-averaged negative electrode reaction overpotential [V]',\n", + " 'Negative electrode volumetric interfacial current density [A.m-3]',\n", + " 'X-averaged negative electrode volumetric interfacial current density [A.m-3]',\n", + " 'SEI film overpotential [V]',\n", + " 'X-averaged SEI film overpotential [V]',\n", + " 'Positive electrode interfacial current density [A.m-2]',\n", + " 'X-averaged positive electrode interfacial current density [A.m-2]',\n", + " 'X-averaged positive electrode total interfacial current density [A.m-2]',\n", + " 'X-averaged positive electrode total volumetric interfacial current density [A.m-3]',\n", + " 'Positive electrode exchange current density [A.m-2]',\n", + " 'X-averaged positive electrode exchange current density [A.m-2]',\n", + " 'Positive electrode reaction overpotential [V]',\n", + " 'X-averaged positive electrode reaction overpotential [V]',\n", + " 'Positive electrode volumetric interfacial current density [A.m-3]',\n", + " 'X-averaged positive electrode volumetric interfacial current density [A.m-3]',\n", + " 'Negative particle rhs [mol.m-3.s-1]',\n", + " 'Negative particle bc [mol.m-2]',\n", + " 'Negative particle effective diffusivity [m2.s-1]',\n", + " 'X-averaged negative particle effective diffusivity [m2.s-1]',\n", + " 'Negative particle flux [mol.m-2.s-1]',\n", + " 'Negative electrode stoichiometry',\n", + " 'Negative electrode volume-averaged concentration',\n", + " 'Negative electrode volume-averaged concentration [mol.m-3]',\n", + " 'Total lithium in primary phase in negative electrode [mol]',\n", + " 'Positive particle rhs [mol.m-3.s-1]',\n", + " 'Positive particle bc [mol.m-2]',\n", + " 'Positive particle effective diffusivity [m2.s-1]',\n", + " 'X-averaged positive particle effective diffusivity [m2.s-1]',\n", + " 'Positive particle flux [mol.m-2.s-1]',\n", + " 'Positive electrode stoichiometry',\n", + " 'Positive electrode volume-averaged concentration',\n", + " 'Positive electrode volume-averaged concentration [mol.m-3]',\n", + " 'Total lithium in primary phase in positive electrode [mol]',\n", + " 'Electrolyte flux [mol.m-2.s-1]',\n", + " 'Electrolyte diffusion flux [mol.m-2.s-1]',\n", + " 'Electrolyte migration flux [mol.m-2.s-1]',\n", + " 'Electrolyte convection flux [mol.m-2.s-1]',\n", + " 'Sum of negative electrode electrolyte reaction source terms [A.m-3]',\n", + " 'Sum of x-averaged negative electrode electrolyte reaction source terms [A.m-3]',\n", + " 'Sum of negative electrode volumetric interfacial current densities [A.m-3]',\n", + " 'Sum of x-averaged negative electrode volumetric interfacial current densities [A.m-3]',\n", + " 'Sum of positive electrode electrolyte reaction source terms [A.m-3]',\n", + " 'Sum of x-averaged positive electrode electrolyte reaction source terms [A.m-3]',\n", + " 'Sum of positive electrode volumetric interfacial current densities [A.m-3]',\n", + " 'Sum of x-averaged positive electrode volumetric interfacial current densities [A.m-3]',\n", + " 'Interfacial current density [A.m-2]',\n", + " 'Exchange current density [A.m-2]',\n", + " 'Sum of volumetric interfacial current densities [A.m-3]',\n", + " 'Sum of electrolyte reaction source terms [A.m-3]',\n", + " 'X-averaged open-circuit voltage [V]',\n", + " 'Measured open-circuit voltage [V]',\n", + " 'X-averaged reaction overpotential [V]',\n", + " 'X-averaged solid phase ohmic losses [V]',\n", + " 'X-averaged battery open-circuit voltage [V]',\n", + " 'Measured battery open-circuit voltage [V]',\n", + " 'X-averaged battery reaction overpotential [V]',\n", + " 'X-averaged battery solid phase ohmic losses [V]',\n", + " 'X-averaged battery electrolyte ohmic losses [V]',\n", + " 'X-averaged battery concentration overpotential [V]',\n", + " 'Battery voltage [V]',\n", + " 'Change in measured open-circuit voltage [V]',\n", + " 'Local ECM resistance [Ohm]',\n", + " 'Terminal power [W]',\n", + " 'Power [W]',\n", + " 'Resistance [Ohm]',\n", + " 'Total lithium in negative electrode [mol]',\n", + " 'LAM_ne [%]',\n", + " 'Loss of active material in negative electrode [%]',\n", + " 'Total lithium in positive electrode [mol]',\n", + " 'LAM_pe [%]',\n", + " 'Loss of active material in positive electrode [%]',\n", + " 'LLI [%]',\n", + " 'Loss of lithium inventory [%]',\n", + " 'Loss of lithium inventory, including electrolyte [%]',\n", + " 'Total lithium [mol]',\n", + " 'Total lithium in particles [mol]',\n", + " 'Total lithium capacity [A.h]',\n", + " 'Total lithium capacity in particles [A.h]',\n", + " 'Total lithium lost [mol]',\n", + " 'Total lithium lost from particles [mol]',\n", + " 'Total lithium lost from electrolyte [mol]',\n", + " 'Total lithium lost to side reactions [mol]',\n", + " 'Total capacity lost to side reactions [A.h]']" ] - }, + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_dfn.variable_names()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are a _lot_ of variables. You can also search the list of variables for a particular string (e.g. \"electrolyte\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Alternatively, we may be interested in plotting both the electrolyte concentration and the voltage. In which case, we would do:" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Electrolyte concentration\n", + "Electrolyte concentration [Molar]\n", + "Electrolyte concentration [mol.m-3]\n", + "Electrolyte concentration concatenation [mol.m-3]\n", + "Electrolyte convection flux [mol.m-2.s-1]\n", + "Electrolyte current density [A.m-2]\n", + "Electrolyte diffusion flux [mol.m-2.s-1]\n", + "Electrolyte flux [mol.m-2.s-1]\n", + "Electrolyte migration flux [mol.m-2.s-1]\n", + "Electrolyte potential [V]\n", + "Electrolyte transport efficiency\n", + "Gradient of electrolyte potential [V.m-1]\n", + "Gradient of negative electrolyte potential [V.m-1]\n", + "Gradient of positive electrolyte potential [V.m-1]\n", + "Gradient of separator electrolyte potential [V.m-1]\n", + "Loss of lithium inventory, including electrolyte [%]\n", + "Negative electrolyte concentration\n", + "Negative electrolyte concentration [Molar]\n", + "Negative electrolyte concentration [mol.m-3]\n", + "Negative electrolyte potential [V]\n", + "Negative electrolyte transport efficiency\n", + "Positive electrolyte concentration\n", + "Positive electrolyte concentration [Molar]\n", + "Positive electrolyte concentration [mol.m-3]\n", + "Positive electrolyte potential [V]\n", + "Positive electrolyte transport efficiency\n", + "Separator electrolyte concentration\n", + "Separator electrolyte concentration [Molar]\n", + "Separator electrolyte concentration [mol.m-3]\n", + "Separator electrolyte potential [V]\n", + "Separator electrolyte transport efficiency\n", + "Sum of electrolyte reaction source terms [A.m-3]\n", + "Sum of negative electrode electrolyte reaction source terms [A.m-3]\n", + "Sum of positive electrode electrolyte reaction source terms [A.m-3]\n", + "Sum of x-averaged negative electrode electrolyte reaction source terms [A.m-3]\n", + "Sum of x-averaged positive electrode electrolyte reaction source terms [A.m-3]\n", + "Total lithium in electrolyte [mol]\n", + "Total lithium lost from electrolyte [mol]\n", + "X-averaged battery electrolyte ohmic losses [V]\n", + "X-averaged electrolyte concentration\n", + "X-averaged electrolyte concentration [Molar]\n", + "X-averaged electrolyte concentration [mol.m-3]\n", + "X-averaged electrolyte ohmic losses [V]\n", + "X-averaged electrolyte overpotential [V]\n", + "X-averaged electrolyte potential [V]\n", + "X-averaged negative electrolyte concentration\n", + "X-averaged negative electrolyte concentration [Molar]\n", + "X-averaged negative electrolyte concentration [mol.m-3]\n", + "X-averaged negative electrolyte potential [V]\n", + "X-averaged negative electrolyte transport efficiency\n", + "X-averaged positive electrolyte concentration\n", + "X-averaged positive electrolyte concentration [Molar]\n", + "X-averaged positive electrolyte concentration [mol.m-3]\n", + "X-averaged positive electrolyte potential [V]\n", + "X-averaged positive electrolyte transport efficiency\n", + "X-averaged separator electrolyte concentration\n", + "X-averaged separator electrolyte concentration [Molar]\n", + "X-averaged separator electrolyte concentration [mol.m-3]\n", + "X-averaged separator electrolyte potential [V]\n", + "X-averaged separator electrolyte transport efficiency\n" + ] + } + ], + "source": [ + "model_dfn.variables.search(\"electrolyte\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have tried to make variables names fairly self explanatory." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As a first example, we choose to plot the voltage. We add this to a list and then pass this list to the `plot` method of our simulation:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "a6cc55c5b66b4ce78ca2cae475435df1", - "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" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "output_variables = [\"Electrolyte concentration [mol.m-3]\", \"Voltage [V]\"]\n", - "sim_dfn.plot(output_variables=output_variables)" + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8c87342bdc1e425ba87715d286d799d0", + "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" }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can also plot multiple variables on the same plot by nesting lists" + "data": { + "text/plain": [ + "" ] - }, + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output_variables = [\"Voltage [V]\"]\n", + "sim_dfn.plot(output_variables=output_variables)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, we may be interested in plotting both the electrolyte concentration and the voltage. In which case, we would do:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "974616973e534d219b0d196b893f522b", - "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" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sim_dfn.plot([[\"Electrode current density [A.m-2]\", \"Electrolyte current density [A.m-2]\"], \"Voltage [V]\"])" + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a6cc55c5b66b4ce78ca2cae475435df1", + "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" }, { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "467c303add6f439fa35d549653026823", - "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" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sim_dfn.plot()" + "data": { + "text/plain": [ + "" ] - }, + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output_variables = [\"Electrolyte concentration [mol.m-3]\", \"Voltage [V]\"]\n", + "sim_dfn.plot(output_variables=output_variables)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also plot multiple variables on the same plot by nesting lists" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For plotting the voltage components you can use the `plot_votage_components` function" + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "974616973e534d219b0d196b893f522b", + "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" }, { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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, )" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pybamm.plot_voltage_components(sim_dfn.solution)" + "data": { + "text/plain": [ + "" ] - }, + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim_dfn.plot(\n", + " [\n", + " [\"Electrode current density [A.m-2]\", \"Electrolyte current density [A.m-2]\"],\n", + " \"Voltage [V]\",\n", + " ]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And with a few modifications (by creating subplots and by providing the axes on which the voltage components have to be plotted), it can also be used to compare the voltage components of different simulations" + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "467c303add6f439fa35d549653026823", + "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" }, { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# simulating and solving Single Particle Model\n", - "model_spm = pybamm.lithium_ion.SPM()\n", - "sim_spm = pybamm.Simulation(model_spm)\n", - "sim_spm.solve([0, 3700])\n", - "\n", - "# comparing voltage components for Doyle-Fuller-Newman model and Single Particle Model\n", - "fig, axes = plt.subplots(1, 2, figsize=(15, 6), sharey=True)\n", - "\n", - "pybamm.plot_voltage_components(sim_dfn.solution, ax=axes.flat[0])\n", - "pybamm.plot_voltage_components(sim_spm.solution, ax=axes.flat[1])\n", - "\n", - "axes.flat[0].set_title(\"Doyle-Fuller-Newman Model\")\n", - "axes.flat[1].set_title(\"Single Particle Model\")\n", - "\n", - "plt.show()" + "data": { + "text/plain": [ + "" ] - }, + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim_dfn.plot()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For plotting the voltage components you can use the `plot_votage_components` function" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this tutorial we have seen how to use the plotting functionality in PyBaMM.\n", - "\n", - "In [Tutorial 4](./tutorial-4-setting-parameter-values.ipynb) we show how to change parameter values." + "data": { + "image/png": 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", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## References\n", - "\n", - "The relevant papers for this notebook are:" + "data": { + "text/plain": [ + "(
, )" ] - }, + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pybamm.plot_voltage_components(sim_dfn.solution)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And with a few modifications (by creating subplots and by providing the axes on which the voltage components have to be plotted), it can also be used to compare the voltage components of different simulations" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ { - "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] 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", - "[4] 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", - "[5] 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", - "[6] 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", - "[7] 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": "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" - }, - "vscode": { - "interpreter": { - "hash": "187972e187ab8dfbecfab9e8e194ae6d08262b2d51a54fa40644e3ddb6b5f74c" - } + ], + "source": [ + "# simulating and solving Single Particle Model\n", + "model_spm = pybamm.lithium_ion.SPM()\n", + "sim_spm = pybamm.Simulation(model_spm)\n", + "sim_spm.solve([0, 3700])\n", + "\n", + "# comparing voltage components for Doyle-Fuller-Newman model and Single Particle Model\n", + "fig, axes = plt.subplots(1, 2, figsize=(15, 6), sharey=True)\n", + "\n", + "pybamm.plot_voltage_components(sim_dfn.solution, ax=axes.flat[0])\n", + "pybamm.plot_voltage_components(sim_spm.solution, ax=axes.flat[1])\n", + "\n", + "axes.flat[0].set_title(\"Doyle-Fuller-Newman Model\")\n", + "axes.flat[1].set_title(\"Single Particle Model\")\n", + "\n", + "plt.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this tutorial we have seen how to use the plotting functionality in PyBaMM.\n", + "\n", + "In [Tutorial 4](./tutorial-4-setting-parameter-values.ipynb) we show how to change parameter values." + ] + }, + { + "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] 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", + "[4] 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", + "[5] 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", + "[6] 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", + "[7] 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": "3.9.16" }, - "nbformat": 4, - "nbformat_minor": 2 + "vscode": { + "interpreter": { + "hash": "187972e187ab8dfbecfab9e8e194ae6d08262b2d51a54fa40644e3ddb6b5f74c" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 } 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 64a345c312..8ac3cf2eda 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 @@ -35,7 +35,8 @@ "%pip install \"pybamm[plot,cite]\" -q # install PyBaMM if it is not installed\n", "import pybamm\n", "import os\n", - "os.chdir(pybamm.__path__[0]+'/..')" + "\n", + "os.chdir(pybamm.__path__[0] + \"/..\")" ] }, { @@ -323,8 +324,8 @@ "outputs": [], "source": [ "parameter_values[\"Current function [A]\"] = 10\n", - "parameter_values[\"Open-circuit voltage at 100% SOC [V]\"]=3.4\n", - "parameter_values[\"Open-circuit voltage at 0% SOC [V]\"]=3.0" + "parameter_values[\"Open-circuit voltage at 100% SOC [V]\"] = 3.4\n", + "parameter_values[\"Open-circuit voltage at 0% SOC [V]\"] = 3.0" ] }, { @@ -366,8 +367,8 @@ } ], "source": [ - "sim = pybamm.Simulation(model,parameter_values=parameter_values)\n", - "sim.solve([0, 3600],initial_soc=1)\n", + "sim = pybamm.Simulation(model, parameter_values=parameter_values)\n", + "sim.solve([0, 3600], initial_soc=1)\n", "sim.plot()" ] }, @@ -401,10 +402,12 @@ "metadata": {}, "outputs": [], "source": [ - "import pandas as pd # needed to read the csv data file\n", + "import pandas as pd # needed to read the csv data file\n", "\n", "# Import drive cycle from file\n", - "drive_cycle = pd.read_csv(\"pybamm/input/drive_cycles/US06.csv\", comment=\"#\", header=None).to_numpy()\n", + "drive_cycle = pd.read_csv(\n", + " \"pybamm/input/drive_cycles/US06.csv\", comment=\"#\", header=None\n", + ").to_numpy()\n", "\n", "# Create interpolant\n", "current_interpolant = pybamm.Interpolant(drive_cycle[:, 0], drive_cycle[:, 1], pybamm.t)\n", diff --git a/docs/source/examples/notebooks/getting_started/tutorial-5-run-experiments.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-5-run-experiments.ipynb index 3aad616445..9ec8d79cf1 100644 --- a/docs/source/examples/notebooks/getting_started/tutorial-5-run-experiments.ipynb +++ b/docs/source/examples/notebooks/getting_started/tutorial-5-run-experiments.ipynb @@ -62,12 +62,15 @@ "source": [ "experiment = pybamm.Experiment(\n", " [\n", - " (\"Discharge at C/10 for 10 hours or until 3.3 V\",\n", - " \"Rest for 1 hour\",\n", - " \"Charge at 1 A until 4.1 V\",\n", - " \"Hold at 4.1 V until 50 mA\",\n", - " \"Rest for 1 hour\"),\n", - " ] * 3\n", + " (\n", + " \"Discharge at C/10 for 10 hours or until 3.3 V\",\n", + " \"Rest for 1 hour\",\n", + " \"Charge at 1 A until 4.1 V\",\n", + " \"Hold at 4.1 V until 50 mA\",\n", + " \"Rest for 1 hour\",\n", + " ),\n", + " ]\n", + " * 3\n", ")" ] }, @@ -196,7 +199,9 @@ } ], "source": [ - "[(\"Discharge at 1C for 0.5 hours\", \"Discharge at C/20 for 0.5 hours\")] * 3 + [(\"Charge at 0.5 C for 45 minutes\",)]" + "[(\"Discharge at 1C for 0.5 hours\", \"Discharge at C/20 for 0.5 hours\")] * 3 + [\n", + " (\"Charge at 0.5 C for 45 minutes\",)\n", + "]" ] }, { @@ -224,7 +229,9 @@ } ], "source": [ - "pybamm.step.string(\"Discharge at 1C for 1 hour\", period=\"1 minute\", temperature=\"25oC\", tags=[\"tag1\"])" + "pybamm.step.string(\n", + " \"Discharge at 1C for 1 hour\", period=\"1 minute\", temperature=\"25oC\", tags=[\"tag1\"]\n", + ")" ] }, { @@ -336,7 +343,7 @@ "source": [ "t = np.linspace(0, 1, 60)\n", "sin_t = 0.5 * np.sin(2 * np.pi * t)\n", - "drive_cycle_power = np.column_stack([t,sin_t])\n", + "drive_cycle_power = np.column_stack([t, sin_t])\n", "experiment = pybamm.Experiment([pybamm.step.power(drive_cycle_power)])\n", "sim = pybamm.Simulation(model, experiment=experiment)\n", "sim.solve()\n", diff --git a/docs/source/examples/notebooks/getting_started/tutorial-6-managing-simulation-outputs.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-6-managing-simulation-outputs.ipynb index 3599c37abb..f2e1b9be75 100644 --- a/docs/source/examples/notebooks/getting_started/tutorial-6-managing-simulation-outputs.ipynb +++ b/docs/source/examples/notebooks/getting_started/tutorial-6-managing-simulation-outputs.ipynb @@ -44,6 +44,7 @@ "source": [ "%pip install \"pybamm[plot,cite]\" -q # install PyBaMM if it is not installed\n", "import pybamm\n", + "\n", "model = pybamm.lithium_ion.SPMe()\n", "sim = pybamm.Simulation(model)\n", "sim.solve([0, 3600])" @@ -387,8 +388,12 @@ "source": [ "sol.save_data(\"sol_data.csv\", [\"Current [A]\", \"Voltage [V]\"], to_format=\"csv\")\n", "# matlab needs names without spaces\n", - "sol.save_data(\"sol_data.mat\", [\"Current [A]\", \"Voltage [V]\"], to_format=\"matlab\",\n", - " short_names={\"Current [A]\": \"I\", \"Voltage [V]\": \"V\"})" + "sol.save_data(\n", + " \"sol_data.mat\",\n", + " [\"Current [A]\", \"Voltage [V]\"],\n", + " to_format=\"matlab\",\n", + " short_names={\"Current [A]\": \"I\", \"Voltage [V]\": \"V\"},\n", + ")" ] }, { @@ -414,6 +419,7 @@ "outputs": [], "source": [ "import os\n", + "\n", "os.remove(\"SPMe.pkl\")\n", "os.remove(\"SPMe_sol.pkl\")\n", "os.remove(\"sol_data.pkl\")\n", diff --git a/docs/source/examples/notebooks/getting_started/tutorial-7-model-options.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-7-model-options.ipynb index 96f6e203f2..8969afc15a 100644 --- a/docs/source/examples/notebooks/getting_started/tutorial-7-model-options.ipynb +++ b/docs/source/examples/notebooks/getting_started/tutorial-7-model-options.ipynb @@ -70,7 +70,7 @@ } ], "source": [ - "model = pybamm.lithium_ion.SPMe(options=options) # loading in options\n", + "model = pybamm.lithium_ion.SPMe(options=options) # loading in options\n", "\n", "sim = pybamm.Simulation(model)\n", "sim.solve([0, 3600])" @@ -115,7 +115,9 @@ } ], "source": [ - "sim.plot([\"Cell temperature [K]\", \"Total heating [W.m-3]\", \"Current [A]\", \"Voltage [V]\"])" + "sim.plot(\n", + " [\"Cell temperature [K]\", \"Total heating [W.m-3]\", \"Current [A]\", \"Voltage [V]\"]\n", + ")" ] }, { 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 ee4cdc7f63..7cee8dd679 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 @@ -101,10 +101,10 @@ "metadata": {}, "outputs": [], "source": [ - "# create our dictionary \n", + "# create our dictionary\n", "var_pts = {\n", " \"x_n\": 10, # negative electrode\n", - " \"x_s\": 10, # separator \n", + " \"x_s\": 10, # separator\n", " \"x_p\": 10, # positive electrode\n", " \"r_n\": 10, # negative particle\n", " \"r_p\": 10, # positive particle\n", @@ -219,7 +219,7 @@ "model = pybamm.lithium_ion.DFN()\n", "parameter_values = pybamm.ParameterValues(\"Ecker2015\")\n", "\n", - "# choose solver \n", + "# choose solver\n", "solver = pybamm.CasadiSolver(mode=\"fast\")\n", "\n", "# loop over number of mesh points\n", @@ -227,11 +227,11 @@ "for N in npts:\n", " var_pts = {\n", " \"x_n\": N, # negative electrode\n", - " \"x_s\": N, # separator \n", + " \"x_s\": N, # separator\n", " \"x_p\": N, # positive electrode\n", " \"r_n\": N, # negative particle\n", " \"r_p\": N, # positive particle\n", - " } \n", + " }\n", " sim = pybamm.Simulation(\n", " model, solver=solver, parameter_values=parameter_values, var_pts=var_pts\n", " )\n", @@ -278,7 +278,7 @@ } ], "source": [ - "pybamm.dynamic_plot(solutions, [\"Voltage [V]\"], time_unit=\"seconds\", labels=npts) " + "pybamm.dynamic_plot(solutions, [\"Voltage [V]\"], time_unit=\"seconds\", labels=npts)" ] }, { diff --git a/docs/source/examples/notebooks/initialize-model-with-solution.ipynb b/docs/source/examples/notebooks/initialize-model-with-solution.ipynb index 8691439334..aa7bea4d5c 100644 --- a/docs/source/examples/notebooks/initialize-model-with-solution.ipynb +++ b/docs/source/examples/notebooks/initialize-model-with-solution.ipynb @@ -23,8 +23,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001B[33mWARNING: You are using pip version 21.0.1; however, version 21.1 is available.\n", - "You should consider upgrading via the '/Users/vsulzer/Documents/Energy_storage/PyBaMM/.tox/dev/bin/python -m pip install --upgrade pip' command.\u001B[0m\n", + "\u001b[33mWARNING: You are using pip version 21.0.1; however, version 21.1 is available.\n", + "You should consider upgrading via the '/Users/vsulzer/Documents/Energy_storage/PyBaMM/.tox/dev/bin/python -m pip install --upgrade pip' command.\u001b[0m\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } @@ -36,7 +36,7 @@ "import pandas as pd\n", "import os\n", "\n", - "os.chdir(pybamm.__path__[0]+'/..')" + "os.chdir(pybamm.__path__[0] + \"/..\")" ] }, { @@ -265,8 +265,12 @@ ], "source": [ "pybamm.dynamic_plot(\n", - " [sol_US06_1, sol_US06_2, sol_US06_3], \n", - " labels=[\"Default initial conditions\", \"Fully charged (from DFN)\", \"Fully charged (from SPM)\"]\n", + " [sol_US06_1, sol_US06_2, sol_US06_3],\n", + " labels=[\n", + " \"Default initial conditions\",\n", + " \"Fully charged (from DFN)\",\n", + " \"Fully charged (from SPM)\",\n", + " ],\n", ")" ] }, 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 59e1e47e97..d3553ed278 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 @@ -214,43 +214,93 @@ ], "source": [ "# The discrete sizes or \"bins\" used\n", - "R_p = sim.solution[\"Positive particle sizes [m]\"].entries[:,0,0] # const in the x and current collector direction\n", - "R_n = sim.solution[\"Negative particle sizes [m]\"].entries[:,0,0]\n", + "R_p = sim.solution[\"Positive particle sizes [m]\"].entries[\n", + " :, 0, 0\n", + "] # const in the x and current collector direction\n", + "R_n = sim.solution[\"Negative particle sizes [m]\"].entries[:, 0, 0]\n", "\n", "# The distributions (number, area, and volume-weighted)\n", - "f_a_p = sim.solution[\"X-averaged positive area-weighted particle-size distribution [m-1]\"].entries[:,0]\n", - "f_num_p = sim.solution[\"X-averaged positive number-based particle-size distribution [m-1]\"].entries[:,0]\n", - "f_v_p = sim.solution[\"X-averaged positive volume-weighted particle-size distribution [m-1]\"].entries[:,0]\n", - "f_a_n = sim.solution[\"X-averaged negative area-weighted particle-size distribution [m-1]\"].entries[:,0]\n", - "f_num_n = sim.solution[\"X-averaged negative number-based particle-size distribution [m-1]\"].entries[:,0]\n", - "f_v_n = sim.solution[\"X-averaged negative volume-weighted particle-size distribution [m-1]\"].entries[:,0]\n", + "f_a_p = sim.solution[\n", + " \"X-averaged positive area-weighted particle-size distribution [m-1]\"\n", + "].entries[:, 0]\n", + "f_num_p = sim.solution[\n", + " \"X-averaged positive number-based particle-size distribution [m-1]\"\n", + "].entries[:, 0]\n", + "f_v_p = sim.solution[\n", + " \"X-averaged positive volume-weighted particle-size distribution [m-1]\"\n", + "].entries[:, 0]\n", + "f_a_n = sim.solution[\n", + " \"X-averaged negative area-weighted particle-size distribution [m-1]\"\n", + "].entries[:, 0]\n", + "f_num_n = sim.solution[\n", + " \"X-averaged negative number-based particle-size distribution [m-1]\"\n", + "].entries[:, 0]\n", + "f_v_n = sim.solution[\n", + " \"X-averaged negative volume-weighted particle-size distribution [m-1]\"\n", + "].entries[:, 0]\n", "\n", "# plot\n", - "f, axs = plt.subplots(1, 2 ,figsize=(10,4))\n", + "f, axs = plt.subplots(1, 2, figsize=(10, 4))\n", "\n", "# negative electrode\n", - "width_n = (R_n[-1] - R_n[-2])/ 1e-6\n", - "axs[0].bar(R_n / 1e-6, f_a_n * 1e-6, width=width_n, alpha=0.3, color=\"tab:blue\",\n", - " label=\"area-weighted\")\n", - "axs[0].bar(R_n / 1e-6, f_num_n * 1e-6, width=width_n, alpha=0.3, color=\"tab:red\",\n", - " label=\"number-weighted\")\n", - "axs[0].bar(R_n / 1e-6, f_v_n * 1e-6, width=width_n, alpha=0.3, color=\"tab:green\",\n", - " label=\"volume-weighted\")\n", - "axs[0].set_xlim((0,25))\n", + "width_n = (R_n[-1] - R_n[-2]) / 1e-6\n", + "axs[0].bar(\n", + " R_n / 1e-6,\n", + " f_a_n * 1e-6,\n", + " width=width_n,\n", + " alpha=0.3,\n", + " color=\"tab:blue\",\n", + " label=\"area-weighted\",\n", + ")\n", + "axs[0].bar(\n", + " R_n / 1e-6,\n", + " f_num_n * 1e-6,\n", + " width=width_n,\n", + " alpha=0.3,\n", + " color=\"tab:red\",\n", + " label=\"number-weighted\",\n", + ")\n", + "axs[0].bar(\n", + " R_n / 1e-6,\n", + " f_v_n * 1e-6,\n", + " width=width_n,\n", + " alpha=0.3,\n", + " color=\"tab:green\",\n", + " label=\"volume-weighted\",\n", + ")\n", + "axs[0].set_xlim((0, 25))\n", "axs[0].set_xlabel(\"Particle size $R_{\\mathrm{n}}$ [$\\mu$m]\", fontsize=12)\n", "axs[0].set_ylabel(\"[$\\mu$m$^{-1}$]\", fontsize=12)\n", "axs[0].legend(fontsize=10)\n", "axs[0].set_title(\"Discretized distributions (histograms) in negative electrode\")\n", "\n", "# positive electrode\n", - "width_p = (R_p[-1] - R_p[-2])/ 1e-6\n", - "axs[1].bar(R_p / 1e-6, f_a_p * 1e-6, width=width_p, alpha=0.3, color=\"tab:blue\",\n", - " label=\"area-weighted\")\n", - "axs[1].bar(R_p / 1e-6, f_num_p * 1e-6, width=width_p, alpha=0.3, color=\"tab:red\",\n", - " label=\"number-weighted\")\n", - "axs[1].bar(R_p / 1e-6, f_v_p * 1e-6, width=width_p, alpha=0.3, color=\"tab:green\",\n", - " label=\"volume-weighted\")\n", - "axs[1].set_xlim((0,25))\n", + "width_p = (R_p[-1] - R_p[-2]) / 1e-6\n", + "axs[1].bar(\n", + " R_p / 1e-6,\n", + " f_a_p * 1e-6,\n", + " width=width_p,\n", + " alpha=0.3,\n", + " color=\"tab:blue\",\n", + " label=\"area-weighted\",\n", + ")\n", + "axs[1].bar(\n", + " R_p / 1e-6,\n", + " f_num_p * 1e-6,\n", + " width=width_p,\n", + " alpha=0.3,\n", + " color=\"tab:red\",\n", + " label=\"number-weighted\",\n", + ")\n", + "axs[1].bar(\n", + " R_p / 1e-6,\n", + " f_v_p * 1e-6,\n", + " width=width_p,\n", + " alpha=0.3,\n", + " color=\"tab:green\",\n", + " label=\"volume-weighted\",\n", + ")\n", + "axs[1].set_xlim((0, 25))\n", "axs[1].set_xlabel(\"Particle size $R_{\\mathrm{p}}$ [$\\mu$m]\", fontsize=12)\n", "axs[1].set_ylabel(\"[$\\mu$m$^{-1}$]\", fontsize=12)\n", "axs[1].set_title(\"Positive electrode\")\n", @@ -297,6 +347,7 @@ "def f_a_dist_p_dim(R):\n", " return pybamm.lognormal(R, R_av_p_dim, sd_p_dim)\n", "\n", + "\n", "# Note: the only argument must be the particle size R" ] }, @@ -310,8 +361,7 @@ "distribution_params = {\n", " \"Positive minimum particle radius [m]\": R_min_p,\n", " \"Positive maximum particle radius [m]\": R_max_p,\n", - " \"Positive area-weighted \"\n", - " + \"particle-size distribution [m-1]\": f_a_dist_p_dim,\n", + " \"Positive area-weighted \" + \"particle-size distribution [m-1]\": f_a_dist_p_dim,\n", "}\n", "params.update(distribution_params, check_already_exists=False)" ] @@ -353,10 +403,12 @@ "output_variables = [\n", " \"X-averaged negative area-weighted particle-size distribution [m-1]\",\n", " \"X-averaged positive area-weighted particle-size distribution [m-1]\",\n", - " \"Voltage [V]\"\n", + " \"Voltage [V]\",\n", "]\n", "quickplot = pybamm.QuickPlot(\n", - " [sim, sim_custom], output_variables=output_variables, labels=[\"default lognormals\", \"custom\"]\n", + " [sim, sim_custom],\n", + " output_variables=output_variables,\n", + " labels=[\"default lognormals\", \"custom\"],\n", ")\n", "quickplot.plot(0)" ] @@ -386,15 +438,15 @@ "models = [\n", " pybamm.lithium_ion.DFN(options={\"particle size\": \"distribution\"}, name=\"MP-DFN\"),\n", " pybamm.lithium_ion.MPM(name=\"MPM\"),\n", - " pybamm.lithium_ion.DFN(name=\"DFN\")\n", + " pybamm.lithium_ion.DFN(name=\"DFN\"),\n", "]\n", "\n", "# parameters\n", "params = pybamm.ParameterValues(\"Marquis2019\")\n", - "params = pybamm.get_size_distribution_parameters(params) \n", + "params = pybamm.get_size_distribution_parameters(params)\n", "\n", "# define current function\n", - "t_cutoff = 3450 # [s]\n", + "t_cutoff = 3450 # [s]\n", "t_rest = 3600 # [s]\n", "I_typ = params[\"Nominal cell capacity [A.h]\"] # current for 1C\n", "\n", @@ -433,7 +485,7 @@ } ], "source": [ - "# plot current, voltage \n", + "# plot current, voltage\n", "qp = pybamm.QuickPlot(sims, output_variables=[\"Current [A]\", \"Voltage [V]\"])\n", "qp.plot(0)" ] diff --git a/docs/source/examples/notebooks/models/DFN.ipynb b/docs/source/examples/notebooks/models/DFN.ipynb index d77a0856e3..a6237a2f3f 100644 --- a/docs/source/examples/notebooks/models/DFN.ipynb +++ b/docs/source/examples/notebooks/models/DFN.ipynb @@ -197,7 +197,7 @@ "source": [ "# solve model\n", "solver = model.default_solver\n", - "t_eval = np.linspace(0, 3600, 300) # time in seconds\n", + "t_eval = np.linspace(0, 3600, 300) # time in seconds\n", "solution = solver.solve(model, t_eval)" ] }, @@ -230,7 +230,9 @@ } ], "source": [ - "quick_plot = pybamm.QuickPlot(solution, [\"Positive electrode interfacial current density [A.m-2]\"])\n", + "quick_plot = pybamm.QuickPlot(\n", + " solution, [\"Positive electrode interfacial current density [A.m-2]\"]\n", + ")\n", "quick_plot.dynamic_plot();" ] }, diff --git a/docs/source/examples/notebooks/models/MPM.ipynb b/docs/source/examples/notebooks/models/MPM.ipynb index c7e1068dc2..82e5a9502d 100644 --- a/docs/source/examples/notebooks/models/MPM.ipynb +++ b/docs/source/examples/notebooks/models/MPM.ipynb @@ -180,7 +180,9 @@ } ], "source": [ - "c_n_R_dependent = model.variables[\"X-averaged negative particle concentration distribution [mol.m-3]\"]\n", + "c_n_R_dependent = model.variables[\n", + " \"X-averaged negative particle concentration distribution [mol.m-3]\"\n", + "]\n", "c_n_R_dependent.domains" ] }, @@ -282,9 +284,9 @@ ], "source": [ "for k, t in model.default_submesh_types.items():\n", - " print(k,'is of type',t.__name__)\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')" + " print(var, \"has\", npts, \"mesh points\")" ] }, { @@ -371,38 +373,42 @@ ], "source": [ "# Concentrations as a function of t, r and R\n", - "c_s_n = sim.solution[\"X-averaged negative particle concentration distribution [mol.m-3]\"]\n", - "c_s_p = sim.solution[\"X-averaged positive particle concentration distribution [mol.m-3]\"]\n", + "c_s_n = sim.solution[\n", + " \"X-averaged negative particle concentration distribution [mol.m-3]\"\n", + "]\n", + "c_s_p = sim.solution[\n", + " \"X-averaged positive particle concentration distribution [mol.m-3]\"\n", + "]\n", "\n", "# r_n, r_p\n", - "r_n = sim.solution[\"r_n [m]\"].entries[:,0,0]\n", - "r_p = sim.solution[\"r_p [m]\"].entries[:,0,0]\n", + "r_n = sim.solution[\"r_n [m]\"].entries[:, 0, 0]\n", + "r_p = sim.solution[\"r_p [m]\"].entries[:, 0, 0]\n", "# dimensional R_n, R_p\n", - "R_n = sim.solution[\"Negative particle sizes [m]\"].entries[:,0]\n", - "R_p = sim.solution[\"Positive particle sizes [m]\"].entries[:,0]\n", + "R_n = sim.solution[\"Negative particle sizes [m]\"].entries[:, 0]\n", + "R_p = sim.solution[\"Positive particle sizes [m]\"].entries[:, 0]\n", "t = sim.solution[\"Time [s]\"].entries\n", "\n", "\n", "def plot_concentrations(t):\n", - " f, axs = plt.subplots(1, 2 ,figsize=(10,3)) \n", + " f, axs = plt.subplots(1, 2, figsize=(10, 3))\n", " plot_c_n = axs[0].pcolormesh(\n", " R_n, r_n, c_s_n(r=r_n, R=R_n, t=t), vmin=0.15, vmax=0.8\n", " )\n", " plot_c_p = axs[1].pcolormesh(\n", " R_p, r_p, c_s_p(r=r_p, R=R_p, t=t), vmin=0.6, vmax=0.95\n", " )\n", - " axs[0].set_xlabel(r'$R_n$ [$\\mu$m]')\n", - " axs[1].set_xlabel(r'$R_p$ [$\\mu$m]')\n", - " axs[0].set_ylabel(r'$r_n / R_n$')\n", - " axs[1].set_ylabel(r'$r_p / R_p$')\n", - " axs[0].set_title('Concentration in negative particles [mol.m-3]')\n", - " axs[1].set_title('Concentration in positive particles [mol.m-3]')\n", + " axs[0].set_xlabel(r\"$R_n$ [$\\mu$m]\")\n", + " axs[1].set_xlabel(r\"$R_p$ [$\\mu$m]\")\n", + " axs[0].set_ylabel(r\"$r_n / R_n$\")\n", + " axs[1].set_ylabel(r\"$r_p / R_p$\")\n", + " axs[0].set_title(\"Concentration in negative particles [mol.m-3]\")\n", + " axs[1].set_title(\"Concentration in positive particles [mol.m-3]\")\n", " plt.colorbar(plot_c_n, ax=axs[0])\n", " plt.colorbar(plot_c_p, ax=axs[1])\n", - " \n", + "\n", " plt.show()\n", - " \n", - " \n", + "\n", + "\n", "# initial time\n", "plot_concentrations(t[0])" ] @@ -464,7 +470,7 @@ "R_a_p_dim = params[\"Positive particle radius [m]\"]\n", "\n", "# Standard deviations (dimensional)\n", - "sd_a_n_dim = 0.2 * R_a_n_dim \n", + "sd_a_n_dim = 0.2 * R_a_n_dim\n", "sd_a_p_dim = 0.6 * R_a_p_dim\n", "\n", "# Minimum and maximum particle sizes (dimensional)\n", @@ -484,6 +490,7 @@ "def f_a_dist_p_dim(R):\n", " return pybamm.lognormal(R, R_a_p_dim, sd_a_p_dim)\n", "\n", + "\n", "# Note: the only argument must be the particle size R" ] }, @@ -499,10 +506,8 @@ " \"Positive minimum particle radius [m]\": R_min_p,\n", " \"Negative maximum particle radius [m]\": R_max_n,\n", " \"Positive maximum particle radius [m]\": R_max_p,\n", - " \"Negative area-weighted \"\n", - " + \"particle-size distribution [m-1]\": f_a_dist_n_dim,\n", - " \"Positive area-weighted \"\n", - " + \"particle-size distribution [m-1]\": f_a_dist_p_dim,\n", + " \"Negative area-weighted \" + \"particle-size distribution [m-1]\": f_a_dist_n_dim,\n", + " \"Positive area-weighted \" + \"particle-size distribution [m-1]\": f_a_dist_p_dim,\n", "}\n", "params.update(distribution_params, check_already_exists=False)" ] @@ -572,22 +577,48 @@ ], "source": [ "# 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", + "R_p = sim.solution[\"Positive particle sizes [m]\"].entries[\n", + " :, 0\n", + "] # const in the current collector direction\n", "# The distributions\n", - "f_a_p = sim.solution[\"X-averaged positive area-weighted particle-size distribution [m-1]\"].entries[:,0]\n", - "f_num_p = sim.solution[\"X-averaged positive number-based particle-size distribution [m-1]\"].entries[:,0]\n", - "f_v_p = sim.solution[\"X-averaged positive volume-weighted particle-size distribution [m-1]\"].entries[:,0]\n", + "f_a_p = sim.solution[\n", + " \"X-averaged positive area-weighted particle-size distribution [m-1]\"\n", + "].entries[:, 0]\n", + "f_num_p = sim.solution[\n", + " \"X-averaged positive number-based particle-size distribution [m-1]\"\n", + "].entries[:, 0]\n", + "f_v_p = sim.solution[\n", + " \"X-averaged positive volume-weighted particle-size distribution [m-1]\"\n", + "].entries[:, 0]\n", "\n", "\n", "# plot\n", - "width_p = (R_p[-1] - R_p[-2])/ 1e-6\n", - "plt.bar(R_p / 1e-6, f_a_p * 1e-6, width=width_p, alpha=0.3, color=\"tab:blue\",\n", - " label=\"area-weighted\")\n", - "plt.bar(R_p / 1e-6, f_num_p * 1e-6, width=width_p, alpha=0.3, color=\"tab:red\",\n", - " label=\"number-weighted\")\n", - "plt.bar(R_p / 1e-6, f_v_p * 1e-6, width=width_p, alpha=0.3, color=\"tab:green\",\n", - " label=\"volume-weighted\")\n", - "plt.xlim((0,30))\n", + "width_p = (R_p[-1] - R_p[-2]) / 1e-6\n", + "plt.bar(\n", + " R_p / 1e-6,\n", + " f_a_p * 1e-6,\n", + " width=width_p,\n", + " alpha=0.3,\n", + " color=\"tab:blue\",\n", + " label=\"area-weighted\",\n", + ")\n", + "plt.bar(\n", + " R_p / 1e-6,\n", + " f_num_p * 1e-6,\n", + " width=width_p,\n", + " alpha=0.3,\n", + " color=\"tab:red\",\n", + " label=\"number-weighted\",\n", + ")\n", + "plt.bar(\n", + " R_p / 1e-6,\n", + " f_v_p * 1e-6,\n", + " width=width_p,\n", + " alpha=0.3,\n", + " color=\"tab:green\",\n", + " label=\"volume-weighted\",\n", + ")\n", + "plt.xlim((0, 30))\n", "plt.xlabel(\"Particle size $R_{\\mathrm{p}}$ [$\\mu$m]\", fontsize=12)\n", "plt.ylabel(\"[$\\mu$m$^{-1}$]\", fontsize=12)\n", "plt.legend(fontsize=10)\n", @@ -611,7 +642,9 @@ "outputs": [], "source": [ "# Define standard deviation in negative electrode to vary\n", - "sd_a_p_dim = pybamm.Parameter(\"Positive electrode area-weighted particle-size standard deviation [m]\")\n", + "sd_a_p_dim = pybamm.Parameter(\n", + " \"Positive electrode area-weighted particle-size standard deviation [m]\"\n", + ")\n", "\n", "# Set the area-weighted particle-size distribution\n", "\n", @@ -624,8 +657,7 @@ "distribution_params = {\n", " \"Positive electrode area-weighted particle-size \"\n", " + \"standard deviation [m]\": \"[input]\",\n", - " \"Positive area-weighted \"\n", - " + \"particle-size distribution [m-1]\": f_a_dist_p_dim,\n", + " \"Positive area-weighted \" + \"particle-size distribution [m-1]\": f_a_dist_p_dim,\n", "}\n", "params.update(distribution_params, check_already_exists=False)" ] @@ -666,7 +698,7 @@ "\n", "sim = pybamm.Simulation(model, parameter_values=params, experiment=experiment)\n", "solutions = []\n", - "for sd_a_p in [0.4, 0.6, 0.8]: \n", + "for sd_a_p in [0.4, 0.6, 0.8]:\n", " solution = sim.solve(\n", " inputs={\n", " \"Positive electrode area-weighted particle-size \"\n", @@ -679,7 +711,7 @@ "pybamm.dynamic_plot(\n", " solutions,\n", " output_variables=output_variables,\n", - " labels=[\"MPM, sd_a_p=0.4\", \"MPM, sd_a_p=0.6\", \"MPM, sd_a_p=0.8\"]\n", + " labels=[\"MPM, sd_a_p=0.4\", \"MPM, sd_a_p=0.6\", \"MPM, sd_a_p=0.8\"],\n", ")" ] }, @@ -711,7 +743,7 @@ ], "source": [ "print(\"The mean of the input lognormal was:\", R_a_p_dim)\n", - "print(\"The means of discretized distributions are:\") \n", + "print(\"The means of discretized distributions are:\")\n", "for solution in solutions:\n", " R = solution[\"Positive area-weighted mean particle radius [m]\"]\n", " print(\"Positive area-weighted mean particle radius [m]\", R.entries[0])" @@ -742,7 +774,7 @@ "print(0.4 * R_a_p_dim)\n", "print(0.6 * R_a_p_dim)\n", "print(0.8 * R_a_p_dim)\n", - "print(\"The standard deviations of discretized distributions are:\") \n", + "print(\"The standard deviations of discretized distributions are:\")\n", "for solution in solutions:\n", " sd = solution[\"Positive area-weighted particle-size standard deviation [m]\"]\n", " print(\"Positive area-weighted particle-size standard deviation [m]\", sd.entries[0])" @@ -788,11 +820,7 @@ } ], "source": [ - "models = [\n", - " pybamm.lithium_ion.SPM(),\n", - " pybamm.lithium_ion.MPM(),\n", - " pybamm.lithium_ion.DFN()\n", - "]\n", + "models = [pybamm.lithium_ion.SPM(), pybamm.lithium_ion.MPM(), pybamm.lithium_ion.DFN()]\n", "\n", "# solve\n", "sims = []\n", @@ -856,8 +884,7 @@ "source": [ "model_Fickian = pybamm.lithium_ion.MPM(name=\"MPM Fickian\")\n", "model_Uniform = pybamm.lithium_ion.MPM(\n", - " name=\"MPM Uniform\",\n", - " options={\"particle\": \"uniform profile\"}\n", + " name=\"MPM Uniform\", options={\"particle\": \"uniform profile\"}\n", ")\n", "\n", "sim_Fickian = pybamm.Simulation(model_Fickian)\n", @@ -914,7 +941,7 @@ " options={\n", " \"current collector\": \"potential pair\",\n", " \"dimensionality\": 1,\n", - " \"particle\": \"uniform profile\", # to reduce computation time\n", + " \"particle\": \"uniform profile\", # to reduce computation time\n", " }\n", ")\n", "\n", diff --git a/docs/source/examples/notebooks/models/MSMR.ipynb b/docs/source/examples/notebooks/models/MSMR.ipynb index 6dbe14f484..3f009a045a 100644 --- a/docs/source/examples/notebooks/models/MSMR.ipynb +++ b/docs/source/examples/notebooks/models/MSMR.ipynb @@ -373,7 +373,7 @@ " \"Negative particle stoichiometry\",\n", " \"Positive particle stoichiometry\",\n", " \"X-averaged negative electrode open-circuit potential [V]\",\n", - " \"X-averaged positive electrode open-circuit potential [V]\", \n", + " \"X-averaged positive electrode open-circuit potential [V]\",\n", " \"Negative particle potential [V]\",\n", " \"Positive particle potential [V]\",\n", " \"Current [A]\",\n", @@ -422,8 +422,12 @@ } ], "source": [ - "xns = [f\"Average x_n_{i}\" for i in range(6)] # negative electrode reactions: x_n_0, x_n_1, ..., x_n_5\n", - "xps = [f\"Average x_p_{i}\" for i in range(4)] # positive electrode reactions: x_p_0, x_p_1, ..., x_p_3\n", + "xns = [\n", + " f\"Average x_n_{i}\" for i in range(6)\n", + "] # negative electrode reactions: x_n_0, x_n_1, ..., x_n_5\n", + "xps = [\n", + " f\"Average x_p_{i}\" for i in range(4)\n", + "] # positive electrode reactions: x_p_0, x_p_1, ..., x_p_3\n", "sim.plot(\n", " [\n", " xns,\n", @@ -483,9 +487,7 @@ " bottom = top\n", "ax[0].set_xlabel(\"Time [h]\")\n", "ax[0].set_ylabel(\"x_n [-]\")\n", - "ax[0].legend(\n", - " loc=\"upper center\", bbox_to_anchor=(0.5, -0.15), ncol=3\n", - ")\n", + "ax[0].legend(loc=\"upper center\", bbox_to_anchor=(0.5, -0.15), ncol=3)\n", "ax[1].plot(time, sol[\"Average positive particle stoichiometry\"].data, \"k-\", label=\"x_p\")\n", "bottom = 0\n", "for xp in xps:\n", @@ -494,9 +496,7 @@ " bottom = top\n", "ax[1].set_xlabel(\"Time [h]\")\n", "ax[1].set_ylabel(\"x_p [-]\")\n", - "ax[1].legend(\n", - " loc=\"upper center\", bbox_to_anchor=(0.5, -0.15), ncol=3\n", - ")" + "ax[1].legend(loc=\"upper center\", bbox_to_anchor=(0.5, -0.15), ncol=3)" ] }, { diff --git a/docs/source/examples/notebooks/models/SEI-on-cracks.ipynb b/docs/source/examples/notebooks/models/SEI-on-cracks.ipynb index d70c7032a3..f9d41ffc54 100644 --- a/docs/source/examples/notebooks/models/SEI-on-cracks.ipynb +++ b/docs/source/examples/notebooks/models/SEI-on-cracks.ipynb @@ -21,8 +21,8 @@ "output_type": "stream", "text": [ "\n", - "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip available: \u001B[0m\u001B[31;49m22.3.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m23.0.1\u001B[0m\n", - "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.0.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } @@ -48,12 +48,16 @@ "metadata": {}, "outputs": [], "source": [ - "model1 = pybamm.lithium_ion.DFN({\"SEI\": \"solvent-diffusion limited\", \"particle mechanics\": \"swelling only\"})\n", - "model2 = pybamm.lithium_ion.DFN({\n", - " \"particle mechanics\": \"swelling and cracking\",\n", - " \"SEI\": \"solvent-diffusion limited\",\n", - " \"SEI on cracks\": \"true\",\n", - "})" + "model1 = pybamm.lithium_ion.DFN(\n", + " {\"SEI\": \"solvent-diffusion limited\", \"particle mechanics\": \"swelling only\"}\n", + ")\n", + "model2 = pybamm.lithium_ion.DFN(\n", + " {\n", + " \"particle mechanics\": \"swelling and cracking\",\n", + " \"SEI\": \"solvent-diffusion limited\",\n", + " \"SEI on cracks\": \"true\",\n", + " }\n", + ")" ] }, { @@ -74,7 +78,7 @@ "param = pybamm.ParameterValues(\"OKane2022\")\n", "var_pts = {\n", " \"x_n\": 20, # negative electrode\n", - " \"x_s\": 20, # separator \n", + " \"x_s\": 20, # separator\n", " \"x_p\": 20, # positive electrode\n", " \"r_n\": 26, # negative particle\n", " \"r_p\": 26, # positive particle\n", @@ -107,10 +111,16 @@ } ], "source": [ - "exp = pybamm.Experiment([\"Hold at 4.2 V until C/100\", \"Rest for 1 hour\", \"Discharge at 1C until 2.5 V\"])\n", - "sim1 = pybamm.Simulation(model1, parameter_values=param, experiment=exp, var_pts=var_pts)\n", + "exp = pybamm.Experiment(\n", + " [\"Hold at 4.2 V until C/100\", \"Rest for 1 hour\", \"Discharge at 1C until 2.5 V\"]\n", + ")\n", + "sim1 = pybamm.Simulation(\n", + " model1, parameter_values=param, experiment=exp, var_pts=var_pts\n", + ")\n", "sol1 = sim1.solve(calc_esoh=False)\n", - "sim2 = pybamm.Simulation(model2, parameter_values=param, experiment=exp, var_pts=var_pts)\n", + "sim2 = pybamm.Simulation(\n", + " model2, parameter_values=param, experiment=exp, var_pts=var_pts\n", + ")\n", "sol2 = sim2.solve(calc_esoh=False)" ] }, @@ -128,7 +138,10 @@ "lithium_pos1 = sol1[\"Total lithium in positive electrode [mol]\"].entries\n", "t2 = sol2[\"Time [s]\"].entries\n", "V2 = sol2[\"Voltage [V]\"].entries\n", - "SEI2 = sol2[\"Loss of lithium to negative SEI [mol]\"].entries + sol2[\"Loss of lithium to negative SEI on cracks [mol]\"].entries\n", + "SEI2 = (\n", + " sol2[\"Loss of lithium to negative SEI [mol]\"].entries\n", + " + sol2[\"Loss of lithium to negative SEI on cracks [mol]\"].entries\n", + ")\n", "lithium_neg2 = sol2[\"Total lithium in negative electrode [mol]\"].entries\n", "lithium_pos2 = sol2[\"Total lithium in positive electrode [mol]\"].entries" ] @@ -153,14 +166,14 @@ } ], "source": [ - "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(18,4))\n", - "ax1.plot(t1,V1,label=\"without cracking\")\n", - "ax1.plot(t2,V2,label=\"with cracking\")\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(18, 4))\n", + "ax1.plot(t1, V1, label=\"without cracking\")\n", + "ax1.plot(t2, V2, label=\"with cracking\")\n", "ax1.set_xlabel(\"Time [s]\")\n", "ax1.set_ylabel(\"Voltage [V]\")\n", "ax1.legend()\n", - "ax2.plot(t1,SEI1,label=\"without cracking\")\n", - "ax2.plot(t2,SEI2,label=\"with cracking\")\n", + "ax2.plot(t1, SEI1, label=\"without cracking\")\n", + "ax2.plot(t2, SEI2, label=\"with cracking\")\n", "ax2.set_xlabel(\"Time [s]\")\n", "ax2.set_ylabel(\"Loss of lithium to SEI [mol]\")\n", "ax2.legend()\n", @@ -196,8 +209,8 @@ ], "source": [ "fig, ax = plt.subplots()\n", - "ax.plot(t2,lithium_neg2+lithium_pos2)\n", - "ax.plot(t2,lithium_neg2[0]+lithium_pos2[0]-SEI2,linestyle=\"dashed\")\n", + "ax.plot(t2, lithium_neg2 + lithium_pos2)\n", + "ax.plot(t2, lithium_neg2[0] + lithium_pos2[0] - SEI2, linestyle=\"dashed\")\n", "ax.set_xlabel(\"Time [s]\")\n", "ax.set_ylabel(\"Total lithium in electrodes [mol]\")\n", "plt.show()" diff --git a/docs/source/examples/notebooks/models/SPM.ipynb b/docs/source/examples/notebooks/models/SPM.ipynb index e373bdafb5..9b01b13a80 100644 --- a/docs/source/examples/notebooks/models/SPM.ipynb +++ b/docs/source/examples/notebooks/models/SPM.ipynb @@ -78,7 +78,8 @@ "import numpy as np\n", "import os\n", "import matplotlib.pyplot as plt\n", - "os.chdir(pybamm.__path__[0]+'/..')" + "\n", + "os.chdir(pybamm.__path__[0] + \"/..\")" ] }, { @@ -122,9 +123,9 @@ "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')" + "print(\"rhs equation for variable '\", variable, \"' is:\")\n", + "path = \"docs/source/examples/notebooks/models/\"\n", + "equation.visualise(path + \"spm1.png\")" ] }, { @@ -186,14 +187,14 @@ } ], "source": [ - "print('SPM domains:')\n", + "print(\"SPM domains:\")\n", "for i, (k, v) in enumerate(geometry.items()):\n", - " print(str(i+1)+'.',k,'with variables:')\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", + " if \"min\" in rng:\n", + " print(\" -(\", rng[\"min\"], \") <=\", var, \"<= (\", rng[\"max\"], \")\")\n", " else:\n", - " print(var, '=', rng['position'])" + " print(var, \"=\", rng[\"position\"])" ] }, { @@ -282,9 +283,9 @@ ], "source": [ "for k, t in model.default_submesh_types.items():\n", - " print(k,'is of type',t.__name__)\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')" + " print(var, \"has\", npts, \"mesh points\")" ] }, { @@ -336,7 +337,7 @@ ], "source": [ "for k, method in model.default_spatial_methods.items():\n", - " print(k,'is discretised using',method.__class__.__name__,'method')" + " print(k, \"is discretised using\", method.__class__.__name__, \"method\")" ] }, { @@ -382,7 +383,7 @@ "metadata": {}, "outputs": [], "source": [ - "model.concatenated_rhs.children[1].visualise(path+'spm2.png')" + "model.concatenated_rhs.children[1].visualise(path + \"spm2.png\")" ] }, { @@ -414,9 +415,9 @@ "solver = model.default_solver\n", "n = 250\n", "t_eval = np.linspace(0, 3600, n)\n", - "print('Solving using',type(solver).__name__,'solver...')\n", + "print(\"Solving using\", type(solver).__name__, \"solver...\")\n", "solution = solver.solve(model, t_eval)\n", - "print('Finished.')" + "print(\"Finished.\")" ] }, { @@ -906,9 +907,9 @@ } ], "source": [ - "print('SPM model variables:')\n", + "print(\"SPM model variables:\")\n", "for v in model.variables.keys():\n", - " print('\\t-',v)" + " print(\"\\t-\", v)" ] }, { @@ -925,9 +926,9 @@ "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']" + "voltage = solution[\"Voltage [V]\"]\n", + "c_s_n_surf = solution[\"Negative particle surface concentration\"]\n", + "c_s_p_surf = solution[\"Positive particle surface concentration\"]" ] }, { @@ -957,19 +958,23 @@ "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", + "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", + "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", + "ax2.plot(\n", + " t, c_s_n_surf(t=t, x=x[0])\n", + ") # 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", + "ax3.plot(\n", + " t, c_s_p_surf(t=t, x=x[-1])\n", + ") # 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()" @@ -989,8 +994,8 @@ "metadata": {}, "outputs": [], "source": [ - "c_s_n = solution['Negative particle concentration']\n", - "c_s_p = solution['Positive particle concentration']\n", + "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]" ] @@ -1016,27 +1021,34 @@ } ], "source": [ - "c_s_n = solution['Negative particle concentration']\n", - "c_s_p = solution['Positive particle concentration']\n", + "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", + " f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))\n", + " (plot_c_n,) = ax1.plot(\n", + " r_n, c_s_n(r=r_n, t=t, x=x[0])\n", + " ) # can evaluate at arbitrary x (single representative particle)\n", + " (plot_c_p,) = ax2.plot(\n", + " r_p, c_s_p(r=r_p, t=t, x=x[-1])\n", + " ) # 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));" + "\n", + "widgets.interact(\n", + " plot_concentrations, t=widgets.FloatSlider(min=0, max=3600, step=10, value=0)\n", + ");" ] }, { diff --git a/docs/source/examples/notebooks/models/SPMe.ipynb b/docs/source/examples/notebooks/models/SPMe.ipynb index a9542d89ec..1e60568055 100644 --- a/docs/source/examples/notebooks/models/SPMe.ipynb +++ b/docs/source/examples/notebooks/models/SPMe.ipynb @@ -177,7 +177,7 @@ ], "source": [ "# solve simulation\n", - "simulation.solve([0, 3600]) # time interval in seconds" + "simulation.solve([0, 3600]) # time interval in seconds" ] }, { 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 8bdfa76f60..8e1b742c15 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 @@ -27,7 +27,8 @@ "import pybamm\n", "import os\n", "import matplotlib.pyplot as plt\n", - "os.chdir(pybamm.__path__[0]+'/..')" + "\n", + "os.chdir(pybamm.__path__[0] + \"/..\")" ] }, { @@ -45,10 +46,10 @@ "outputs": [], "source": [ "model = pybamm.lithium_ion.DFN(\n", - " options = {\n", - " \"particle\": \"Fickian diffusion\", \n", - " \"cell geometry\": \"arbitrary\", \n", - " \"thermal\": \"lumped\", \n", + " options={\n", + " \"particle\": \"Fickian diffusion\",\n", + " \"cell geometry\": \"arbitrary\",\n", + " \"thermal\": \"lumped\",\n", " \"particle mechanics\": \"swelling only\",\n", " }\n", ")" @@ -87,34 +88,32 @@ "# update parameters, making C-rate and input\n", "param = pybamm.ParameterValues(\"Ai2020\")\n", "capacity = param[\"Nominal cell capacity [A.h]\"]\n", - "param.update({\n", - " \"Current function [A]\": capacity * pybamm.InputParameter(\"C-rate\")\n", - "})\n", + "param.update({\"Current function [A]\": capacity * pybamm.InputParameter(\"C-rate\")})\n", "\n", "# update the mesh\n", "var = pybamm.standard_spatial_vars\n", "var_pts = {\n", - " var.x_n: 50,\n", - " var.x_s: 50,\n", - " var.x_p: 50,\n", - " var.r_n: 21,\n", - " var.r_p: 21,\n", + " var.x_n: 50,\n", + " var.x_s: 50,\n", + " var.x_p: 50,\n", + " var.r_n: 21,\n", + " var.r_p: 21,\n", "}\n", "\n", "# define the simulation\n", "sim = pybamm.Simulation(\n", - " model,\n", - " var_pts=var_pts,\n", - " parameter_values=param,\n", - " solver=pybamm.CasadiSolver(mode=\"fast\")\n", - " )\n", + " model,\n", + " var_pts=var_pts,\n", + " parameter_values=param,\n", + " solver=pybamm.CasadiSolver(mode=\"fast\"),\n", + ")\n", "\n", "# solve for different C-rates\n", "Crates = [0.5, 1, 2]\n", "solutions = []\n", "for Crate in Crates:\n", " print(f\"{Crate} C\")\n", - " sol = sim.solve(t_eval=[0, 3600/Crate*1.05], inputs={\"C-rate\": Crate})\n", + " sol = sim.solve(t_eval=[0, 3600 / Crate * 1.05], inputs={\"C-rate\": Crate})\n", " solutions.append(sol)\n", "\n", "# unpack solutions\n", @@ -137,18 +136,27 @@ "source": [ "# load experimental results\n", "import pandas as pd\n", + "\n", "path = \"pybamm/input/discharge_data/Enertech_cells/\"\n", - "data_Disp_01C=pd.read_csv (path + \"0.1C_discharge_displacement.txt\", delimiter= '\\s+',header=None)\n", - "data_Disp_05C=pd.read_csv (path + \"0.5C_discharge_displacement.txt\", delimiter= '\\s+',header=None)\n", - "data_Disp_1C=pd.read_csv (path + \"1C_discharge_displacement.txt\", delimiter= '\\s+',header=None)\n", - "data_Disp_2C=pd.read_csv (path + \"2C_discharge_displacement.txt\", delimiter= '\\s+',header=None)\n", - "data_V_01C=pd.read_csv (path + \"0.1C_discharge_U.txt\", delimiter= '\\s+',header=None)\n", - "data_V_05C=pd.read_csv (path + \"0.5C_discharge_U.txt\", delimiter= '\\s+',header=None)\n", - "data_V_1C=pd.read_csv (path + \"1C_discharge_U.txt\", delimiter= '\\s+',header=None)\n", - "data_V_2C=pd.read_csv (path + \"2C_discharge_U.txt\", delimiter= '\\s+',header=None)\n", - "data_T_05C=pd.read_csv (path + \"0.5C_discharge_T.txt\", delimiter= '\\s+',header=None)\n", - "data_T_1C=pd.read_csv (path + \"1C_discharge_T.txt\", delimiter= '\\s+',header=None)\n", - "data_T_2C=pd.read_csv (path + \"2C_discharge_T.txt\", delimiter= '\\s+',header=None)" + "data_Disp_01C = pd.read_csv(\n", + " path + \"0.1C_discharge_displacement.txt\", delimiter=\"\\s+\", header=None\n", + ")\n", + "data_Disp_05C = pd.read_csv(\n", + " path + \"0.5C_discharge_displacement.txt\", delimiter=\"\\s+\", header=None\n", + ")\n", + "data_Disp_1C = pd.read_csv(\n", + " path + \"1C_discharge_displacement.txt\", delimiter=\"\\s+\", header=None\n", + ")\n", + "data_Disp_2C = pd.read_csv(\n", + " path + \"2C_discharge_displacement.txt\", delimiter=\"\\s+\", header=None\n", + ")\n", + "data_V_01C = pd.read_csv(path + \"0.1C_discharge_U.txt\", delimiter=\"\\s+\", header=None)\n", + "data_V_05C = pd.read_csv(path + \"0.5C_discharge_U.txt\", delimiter=\"\\s+\", header=None)\n", + "data_V_1C = pd.read_csv(path + \"1C_discharge_U.txt\", delimiter=\"\\s+\", header=None)\n", + "data_V_2C = pd.read_csv(path + \"2C_discharge_U.txt\", delimiter=\"\\s+\", header=None)\n", + "data_T_05C = pd.read_csv(path + \"0.5C_discharge_T.txt\", delimiter=\"\\s+\", header=None)\n", + "data_T_1C = pd.read_csv(path + \"1C_discharge_T.txt\", delimiter=\"\\s+\", header=None)\n", + "data_T_2C = pd.read_csv(path + \"2C_discharge_T.txt\", delimiter=\"\\s+\", header=None)" ] }, { @@ -178,59 +186,116 @@ "source": [ "t_all2C = solution2C[\"Time [h]\"].entries\n", "V_n2C = solution2C[\"Voltage [V]\"].entries\n", - "T_n2C = solution2C[\"Volume-averaged cell temperature [K]\"].entries - param[\"Initial temperature [K]\"]\n", + "T_n2C = (\n", + " solution2C[\"Volume-averaged cell temperature [K]\"].entries\n", + " - param[\"Initial temperature [K]\"]\n", + ")\n", "L_x2C = solution2C[\"Cell thickness change [m]\"].entries\n", "\n", "t_all1C = solution1C[\"Time [h]\"].entries\n", "V_n1C = solution1C[\"Voltage [V]\"].entries\n", - "T_n1C = solution1C[\"Volume-averaged cell temperature [K]\"].entries - param[\"Initial temperature [K]\"]\n", + "T_n1C = (\n", + " solution1C[\"Volume-averaged cell temperature [K]\"].entries\n", + " - param[\"Initial temperature [K]\"]\n", + ")\n", "L_x1C = solution1C[\"Cell thickness change [m]\"].entries\n", "\n", "t_all05C = solution05C[\"Time [h]\"].entries\n", "V_n05C = solution05C[\"Voltage [V]\"].entries\n", - "T_n05C = solution05C[\"Volume-averaged cell temperature [K]\"].entries - param[\"Initial temperature [K]\"]\n", + "T_n05C = (\n", + " solution05C[\"Volume-averaged cell temperature [K]\"].entries\n", + " - param[\"Initial temperature [K]\"]\n", + ")\n", "L_x05C = solution05C[\"Cell thickness change [m]\"].entries\n", "\n", - "f, (ax1, ax2,ax3) = plt.subplots(1, 3 ,figsize=(14,4))\n", + "f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(14, 4))\n", "\n", - "ax1.plot(t_all2C, V_n2C,'r-',label=\"Simulation\")\n", - "ax1.plot(data_V_2C.values[::30,0]/3600, data_V_2C.values[::30,1],'ro',markerfacecolor='none',label=\"Experiment\")\n", - "ax1.plot(t_all05C, V_n05C,'g-')\n", - "ax1.plot(data_V_05C.values[::100,0]/3600, data_V_05C.values[::100,1],'go',markerfacecolor='none')\n", - "ax1.plot(t_all1C, V_n1C,'b-')\n", - "ax1.plot(data_V_1C.values[::50,0]/3600, data_V_1C.values[::50,1],'bo',markerfacecolor='none')\n", + "ax1.plot(t_all2C, V_n2C, \"r-\", label=\"Simulation\")\n", + "ax1.plot(\n", + " data_V_2C.values[::30, 0] / 3600,\n", + " data_V_2C.values[::30, 1],\n", + " \"ro\",\n", + " markerfacecolor=\"none\",\n", + " label=\"Experiment\",\n", + ")\n", + "ax1.plot(t_all05C, V_n05C, \"g-\")\n", + "ax1.plot(\n", + " data_V_05C.values[::100, 0] / 3600,\n", + " data_V_05C.values[::100, 1],\n", + " \"go\",\n", + " markerfacecolor=\"none\",\n", + ")\n", + "ax1.plot(t_all1C, V_n1C, \"b-\")\n", + "ax1.plot(\n", + " data_V_1C.values[::50, 0] / 3600,\n", + " data_V_1C.values[::50, 1],\n", + " \"bo\",\n", + " markerfacecolor=\"none\",\n", + ")\n", "ax1.legend()\n", "ax1.set_xlabel(\"Time [h]\")\n", "ax1.set_ylabel(\"Voltage [V]\")\n", - "ax1.text(0.1, 3.2, r'2 C', {'color': 'r', 'fontsize': 14})\n", - "ax1.text(1.1, 3.2, r'1 C', {'color': 'b', 'fontsize': 14})\n", - "ax1.text(1.6, 3.2, r'0.5 C', {'color': 'g', 'fontsize': 14})\n", + "ax1.text(0.1, 3.2, r\"2 C\", {\"color\": \"r\", \"fontsize\": 14})\n", + "ax1.text(1.1, 3.2, r\"1 C\", {\"color\": \"b\", \"fontsize\": 14})\n", + "ax1.text(1.6, 3.2, r\"0.5 C\", {\"color\": \"g\", \"fontsize\": 14})\n", "\n", - "ax2.plot(t_all2C, T_n2C,'r-',label=\"Simulation\")\n", - "ax2.plot(data_T_2C.values[0:1754:50,0]/3600, data_T_2C.values[0:1754:50,1],'ro',markerfacecolor='none',label=\"Experiment\")\n", - "ax2.plot(t_all05C, T_n05C,'g-')\n", - "ax2.plot(data_T_05C.values[0:7301:200,0]/3600, data_T_05C.values[0:7301:200,1],'go',markerfacecolor='none')\n", - "ax2.plot(t_all1C, T_n1C,'b-')\n", - "ax2.plot(data_T_1C.values[0:3598:100,0]/3600, data_T_1C.values[0:3598:100,1],'bo',markerfacecolor='none')\n", + "ax2.plot(t_all2C, T_n2C, \"r-\", label=\"Simulation\")\n", + "ax2.plot(\n", + " data_T_2C.values[0:1754:50, 0] / 3600,\n", + " data_T_2C.values[0:1754:50, 1],\n", + " \"ro\",\n", + " markerfacecolor=\"none\",\n", + " label=\"Experiment\",\n", + ")\n", + "ax2.plot(t_all05C, T_n05C, \"g-\")\n", + "ax2.plot(\n", + " data_T_05C.values[0:7301:200, 0] / 3600,\n", + " data_T_05C.values[0:7301:200, 1],\n", + " \"go\",\n", + " markerfacecolor=\"none\",\n", + ")\n", + "ax2.plot(t_all1C, T_n1C, \"b-\")\n", + "ax2.plot(\n", + " data_T_1C.values[0:3598:100, 0] / 3600,\n", + " data_T_1C.values[0:3598:100, 1],\n", + " \"bo\",\n", + " markerfacecolor=\"none\",\n", + ")\n", "ax2.legend()\n", "ax2.set_xlabel(\"Time [h]\")\n", "ax2.set_ylabel(\"Temperature rise [K]\")\n", - "ax2.text(0.5, 8, r'2 C', {'color': 'r', 'fontsize': 14})\n", - "ax2.text(0.8, 4.4, r'1 C', {'color': 'b', 'fontsize': 14})\n", - "ax2.text(1.5, 2, r'0.5 C', {'color': 'g', 'fontsize': 14})\n", + "ax2.text(0.5, 8, r\"2 C\", {\"color\": \"r\", \"fontsize\": 14})\n", + "ax2.text(0.8, 4.4, r\"1 C\", {\"color\": \"b\", \"fontsize\": 14})\n", + "ax2.text(1.5, 2, r\"0.5 C\", {\"color\": \"g\", \"fontsize\": 14})\n", "\n", - "ax3.plot(t_all2C, L_x2C,'r-',label=\"Simulation\")\n", - "ax3.plot(data_Disp_2C.values[0:1754:5,0]/3600, data_Disp_2C.values[0:1754:5,1]-data_Disp_2C.values[0,1],'ro',markerfacecolor='none',label=\"Experiment\")\n", - "ax3.plot(t_all05C, L_x05C,'g-')\n", - "ax3.plot(data_Disp_05C.values[0:1754:10,0]/3600, data_Disp_05C.values[0:1754:10,1]-data_Disp_05C.values[0,1],'go',markerfacecolor='none')\n", - "ax3.plot(t_all1C, L_x1C,'b-')\n", - "ax3.plot(data_Disp_1C.values[0:1754:10,0]/3600, data_Disp_1C.values[0:1754:10,1]-data_Disp_1C.values[0,1],'bo',markerfacecolor='none')\n", + "ax3.plot(t_all2C, L_x2C, \"r-\", label=\"Simulation\")\n", + "ax3.plot(\n", + " data_Disp_2C.values[0:1754:5, 0] / 3600,\n", + " data_Disp_2C.values[0:1754:5, 1] - data_Disp_2C.values[0, 1],\n", + " \"ro\",\n", + " markerfacecolor=\"none\",\n", + " label=\"Experiment\",\n", + ")\n", + "ax3.plot(t_all05C, L_x05C, \"g-\")\n", + "ax3.plot(\n", + " data_Disp_05C.values[0:1754:10, 0] / 3600,\n", + " data_Disp_05C.values[0:1754:10, 1] - data_Disp_05C.values[0, 1],\n", + " \"go\",\n", + " markerfacecolor=\"none\",\n", + ")\n", + "ax3.plot(t_all1C, L_x1C, \"b-\")\n", + "ax3.plot(\n", + " data_Disp_1C.values[0:1754:10, 0] / 3600,\n", + " data_Disp_1C.values[0:1754:10, 1] - data_Disp_1C.values[0, 1],\n", + " \"bo\",\n", + " markerfacecolor=\"none\",\n", + ")\n", "ax3.legend()\n", "ax3.set_xlabel(\"Time [h]\")\n", "ax3.set_ylabel(\"Thickness change [m]\")\n", - "ax3.text(0.1, -0.0001, r'2 C', {'color': 'r', 'fontsize': 14})\n", - "ax3.text(0.9, -0.0001, r'1 C', {'color': 'b', 'fontsize': 14})\n", - "ax3.text(1.8, -0.0001, r'0.5 C', {'color': 'g', 'fontsize': 14})\n", + "ax3.text(0.1, -0.0001, r\"2 C\", {\"color\": \"r\", \"fontsize\": 14})\n", + "ax3.text(0.9, -0.0001, r\"1 C\", {\"color\": \"b\", \"fontsize\": 14})\n", + "ax3.text(1.8, -0.0001, r\"0.5 C\", {\"color\": \"g\", \"fontsize\": 14})\n", "\n", "f.tight_layout()\n", "f.show()" @@ -266,22 +331,32 @@ "\n", "cs_n_xav = solution2C[\"X-averaged negative particle concentration [mol.m-3]\"].entries\n", "cs_p_xav = solution2C[\"X-averaged positive particle concentration [mol.m-3]\"].entries\n", - "st_surf_n = solution2C[\"Negative particle surface tangential stress [Pa]\"].entries / E_n\n", - "st_surf_p = solution2C[\"Positive particle surface tangential stress [Pa]\"].entries / E_p\n", + "st_surf_n = solution2C[\"Negative particle surface tangential stress [Pa]\"].entries / E_n\n", + "st_surf_p = solution2C[\"Positive particle surface tangential stress [Pa]\"].entries / E_p\n", "\n", - "data_st_n_2C=pd.read_csv (path + \"stn_2C.txt\", delimiter= ',',header=3)\n", - "data_st_p_2C=pd.read_csv (path + \"stp_2C.txt\", delimiter= ',',header=3)\n", + "data_st_n_2C = pd.read_csv(path + \"stn_2C.txt\", delimiter=\",\", header=3)\n", + "data_st_p_2C = pd.read_csv(path + \"stp_2C.txt\", delimiter=\",\", header=3)\n", "\n", - "f, (ax1, ax2) = plt.subplots(1, 2 ,figsize=(10,3.5))\n", + "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 3.5))\n", "\n", - "ax1.plot(t_all2C, st_surf_n[-1,:],'ro',markerfacecolor='none',label=\"Current\")\n", - "ax1.plot(data_st_n_2C.values[:,0]/3600, data_st_n_2C.values[:,1],'r-',label=\"Ai et al. 2020\")\n", + "ax1.plot(t_all2C, st_surf_n[-1, :], \"ro\", markerfacecolor=\"none\", label=\"Current\")\n", + "ax1.plot(\n", + " data_st_n_2C.values[:, 0] / 3600,\n", + " data_st_n_2C.values[:, 1],\n", + " \"r-\",\n", + " label=\"Ai et al. 2020\",\n", + ")\n", "ax1.legend()\n", "ax1.set_xlabel(\"Time [h]\")\n", "ax1.set_ylabel(\"$\\sigma_{t,n}/E_n$\")\n", "\n", - "ax2.plot(t_all2C, st_surf_p[0,:],'ro',markerfacecolor='none',label=\"Current\")\n", - "ax2.plot(data_st_p_2C.values[0:3601,0]/3600, data_st_p_2C.values[0:3601,1],'r-',label=\"Ai et al. 2020\")\n", + "ax2.plot(t_all2C, st_surf_p[0, :], \"ro\", markerfacecolor=\"none\", label=\"Current\")\n", + "ax2.plot(\n", + " data_st_p_2C.values[0:3601, 0] / 3600,\n", + " data_st_p_2C.values[0:3601, 1],\n", + " \"r-\",\n", + " label=\"Ai et al. 2020\",\n", + ")\n", "ax2.legend()\n", "ax2.set_xlabel(\"Time [h]\")\n", "ax2.set_ylabel(\"$\\sigma_{t,p}/E_p$\")\n", diff --git a/docs/source/examples/notebooks/models/compare-comsol-discharge-curve.ipynb b/docs/source/examples/notebooks/models/compare-comsol-discharge-curve.ipynb index 462f03827b..2faac3bb1d 100644 --- a/docs/source/examples/notebooks/models/compare-comsol-discharge-curve.ipynb +++ b/docs/source/examples/notebooks/models/compare-comsol-discharge-curve.ipynb @@ -38,6 +38,7 @@ "import os\n", "import pickle\n", "import matplotlib.pyplot as plt\n", + "\n", "os.chdir(pybamm.__path__[0] + \"/..\")" ] }, @@ -160,16 +161,15 @@ "plt.grid(True)\n", "plt.xlabel(r\"Discharge Capacity (Ah)\")\n", "plt.ylabel(r\"$\\vert V - V_{comsol} \\vert$\")\n", - "colors = iter(plt.cycler(color='bgrcmyk'))\n", + "colors = iter(plt.cycler(color=\"bgrcmyk\"))\n", "\n", "# loop over C_rates dict to create plot\n", "for key, C_rate in C_rates.items():\n", - "\n", " # load the comsol results\n", " comsol_results_path = pybamm.get_parameters_filepath(\n", " f\"input/comsol_results/comsol_{key}C.pickle\",\n", " )\n", - " comsol_variables = pickle.load(open(comsol_results_path, 'rb'))\n", + " comsol_variables = pickle.load(open(comsol_results_path, \"rb\"))\n", " comsol_time = comsol_variables[\"time\"]\n", " comsol_voltage = comsol_variables[\"voltage\"]\n", "\n", @@ -178,7 +178,9 @@ "\n", " # solve model at comsol times\n", " solver = pybamm.CasadiSolver(mode=\"fast\")\n", - " solution = solver.solve(model, comsol_time, inputs={\"Current function [A]\": current})\n", + " solution = solver.solve(\n", + " model, comsol_time, inputs={\"Current function [A]\": current}\n", + " )\n", " time_in_seconds = solution[\"Time [s]\"].entries\n", " # discharge capacity\n", " discharge_capacity = solution[\"Discharge capacity [A.h]\"]\n", diff --git a/docs/source/examples/notebooks/models/compare-ecker-data.ipynb b/docs/source/examples/notebooks/models/compare-ecker-data.ipynb index 05a375fa45..b0db095926 100644 --- a/docs/source/examples/notebooks/models/compare-ecker-data.ipynb +++ b/docs/source/examples/notebooks/models/compare-ecker-data.ipynb @@ -38,7 +38,8 @@ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "os.chdir(pybamm.__path__[0]+'/..')" + "\n", + "os.chdir(pybamm.__path__[0] + \"/..\")" ] }, { @@ -55,8 +56,12 @@ "metadata": {}, "outputs": [], "source": [ - "voltage_data_1C = pd.read_csv(\"pybamm/input/discharge_data/Ecker2015/Ecker_1C.csv\", header=None).to_numpy()\n", - "voltage_data_5C = pd.read_csv(\"pybamm/input/discharge_data/Ecker2015/Ecker_5C.csv\", header=None).to_numpy()" + "voltage_data_1C = pd.read_csv(\n", + " \"pybamm/input/discharge_data/Ecker2015/Ecker_1C.csv\", header=None\n", + ").to_numpy()\n", + "voltage_data_5C = pd.read_csv(\n", + " \"pybamm/input/discharge_data/Ecker2015/Ecker_5C.csv\", header=None\n", + ").to_numpy()" ] }, { @@ -127,7 +132,7 @@ "metadata": {}, "outputs": [], "source": [ - "sim = pybamm.Simulation(model, parameter_values=parameter_values, var_pts=var_pts)" + "sim = pybamm.Simulation(model, parameter_values=parameter_values, var_pts=var_pts)" ] }, { @@ -147,15 +152,19 @@ "C_rates = [1, 5] # C-rates to solve for\n", "capacity = parameter_values[\"Nominal cell capacity [A.h]\"]\n", "t_evals = [\n", - " np.linspace(0, 3800, 100), \n", - " np.linspace(0, 720, 100)\n", - "] # times to return the solution at\n", + " np.linspace(0, 3800, 100),\n", + " np.linspace(0, 720, 100),\n", + "] # times to return the solution at\n", "solutions = [None] * len(C_rates) # empty list that will hold solutions\n", "\n", "# loop over C-rates\n", "for i, C_rate in enumerate(C_rates):\n", " current = C_rate * capacity\n", - " sim.solve(t_eval=t_evals[i], solver=pybamm.CasadiSolver(mode=\"fast\"),inputs={\"Current function [A]\": current})\n", + " sim.solve(\n", + " t_eval=t_evals[i],\n", + " solver=pybamm.CasadiSolver(mode=\"fast\"),\n", + " inputs={\"Current function [A]\": current},\n", + " )\n", " solutions[i] = sim.solution" ] }, @@ -193,7 +202,7 @@ "# plot the 1C results\n", "t_sol = solutions[0][\"Time [s]\"].entries\n", "ax1.plot(t_sol, solutions[0][\"Voltage [V]\"](t_sol))\n", - "ax1.plot(voltage_data_1C[:,0], voltage_data_1C[:,1], \"o\")\n", + "ax1.plot(voltage_data_1C[:, 0], voltage_data_1C[:, 1], \"o\")\n", "ax1.set_xlabel(\"Time [s]\")\n", "ax1.set_ylabel(\"Voltage [V]\")\n", "ax1.set_title(\"1C\")\n", @@ -202,7 +211,7 @@ "# plot the 5C results\n", "t_sol = solutions[1][\"Time [s]\"].entries\n", "ax2.plot(t_sol, solutions[1][\"Voltage [V]\"](t_sol))\n", - "ax2.plot(voltage_data_5C[:,0], voltage_data_5C[:,1], \"o\")\n", + "ax2.plot(voltage_data_5C[:, 0], voltage_data_5C[:, 1], \"o\")\n", "ax2.set_xlabel(\"Time [s]\")\n", "ax2.set_ylabel(\"Voltage [V]\")\n", "ax2.set_title(\"5C\")\n", diff --git a/docs/source/examples/notebooks/models/compare-lithium-ion.ipynb b/docs/source/examples/notebooks/models/compare-lithium-ion.ipynb index 74157628f8..36dbe5a6af 100644 --- a/docs/source/examples/notebooks/models/compare-lithium-ion.ipynb +++ b/docs/source/examples/notebooks/models/compare-lithium-ion.ipynb @@ -51,7 +51,8 @@ "%pip install \"pybamm[plot,cite]\" -q # install PyBaMM if it is not installed\n", "import pybamm\n", "import os\n", - "os.chdir(pybamm.__path__[0]+'/..')\n", + "\n", + "os.chdir(pybamm.__path__[0] + \"/..\")\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt" @@ -138,7 +139,11 @@ "metadata": {}, "outputs": [], "source": [ - "geometry = {\"DFN\": dfn.default_geometry, \"SPM\": spm.default_geometry, \"SPMe\": spme.default_geometry}" + "geometry = {\n", + " \"DFN\": dfn.default_geometry,\n", + " \"SPM\": spm.default_geometry,\n", + " \"SPMe\": spme.default_geometry,\n", + "}" ] }, { @@ -207,7 +212,9 @@ "source": [ "mesh = {}\n", "for model_name, model in models.items():\n", - " mesh[model_name] = pybamm.Mesh(geometry[model_name], model.default_submesh_types, model.default_var_pts)" + " mesh[model_name] = pybamm.Mesh(\n", + " geometry[model_name], model.default_submesh_types, model.default_var_pts\n", + " )" ] }, { @@ -402,9 +409,11 @@ "source": [ "# update parameter values and solve again\n", "# simulate for shorter time\n", - "t_eval = np.linspace(0,800,300)\n", + "t_eval = np.linspace(0, 800, 300)\n", "for model_name, model in models.items():\n", - " solutions[model_name] = model.default_solver.solve(model, t_eval, inputs={\"Current function [A]\": 3})\n", + " solutions[model_name] = model.default_solver.solve(\n", + " model, t_eval, inputs={\"Current function [A]\": 3}\n", + " )\n", "\n", "# Plot\n", "list_of_solutions = list(solutions.values())\n", diff --git a/docs/source/examples/notebooks/models/compare-particle-diffusion-models.ipynb b/docs/source/examples/notebooks/models/compare-particle-diffusion-models.ipynb index da6f05870e..2d74940e3d 100644 --- a/docs/source/examples/notebooks/models/compare-particle-diffusion-models.ipynb +++ b/docs/source/examples/notebooks/models/compare-particle-diffusion-models.ipynb @@ -40,7 +40,8 @@ "import os\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "os.chdir(pybamm.__path__[0]+'/..')" + "\n", + "os.chdir(pybamm.__path__[0] + \"/..\")" ] }, { @@ -57,8 +58,16 @@ "metadata": {}, "outputs": [], "source": [ - "particle_options = [\"Fickian diffusion\", \"uniform profile\", \"quadratic profile\", \"quartic profile\"]\n", - "models = [pybamm.lithium_ion.DFN(options={'particle': opt}, name=opt) for opt in particle_options]" + "particle_options = [\n", + " \"Fickian diffusion\",\n", + " \"uniform profile\",\n", + " \"quadratic profile\",\n", + " \"quartic profile\",\n", + "]\n", + "models = [\n", + " pybamm.lithium_ion.DFN(options={\"particle\": opt}, name=opt)\n", + " for opt in particle_options\n", + "]" ] }, { @@ -156,14 +165,18 @@ ], "source": [ "plt.figure(figsize=(15, 15))\n", - "style = ['k', 'r*', 'b^', 'g--']\n", + "style = [\"k\", \"r*\", \"b^\", \"g--\"]\n", "for i in range(len(models)):\n", - " plt.plot(solutions_1C[i]['Time [s]'].entries,\n", - " solutions_1C[i]['Voltage [V]'].entries, style[i], label=particle_options[i])\n", + " plt.plot(\n", + " solutions_1C[i][\"Time [s]\"].entries,\n", + " solutions_1C[i][\"Voltage [V]\"].entries,\n", + " style[i],\n", + " label=particle_options[i],\n", + " )\n", "plt.legend()\n", - "plt.title('Model Comparison 1C')\n", - "plt.xlabel('Time [s]')\n", - "plt.ylabel('Voltage [V]')\n", + "plt.title(\"Model Comparison 1C\")\n", + "plt.xlabel(\"Time [s]\")\n", + "plt.ylabel(\"Voltage [V]\")\n", "plt.grid()" ] }, @@ -184,7 +197,7 @@ "t_eval = np.linspace(0, 1800, 72)\n", "solutions_2C = []\n", "for sim in simulations:\n", - " sim.solve(t_eval, inputs={\"Current function [A]\": 2*0.68})\n", + " sim.solve(t_eval, inputs={\"Current function [A]\": 2 * 0.68})\n", " solutions_2C.append(sim.solution)" ] }, @@ -209,12 +222,16 @@ "source": [ "plt.figure(figsize=(15, 15))\n", "for i in range(len(models)):\n", - " plt.plot(solutions_2C[i]['Time [s]'].entries,\n", - " solutions_2C[i]['Voltage [V]'].entries, style[i], label=particle_options[i])\n", + " plt.plot(\n", + " solutions_2C[i][\"Time [s]\"].entries,\n", + " solutions_2C[i][\"Voltage [V]\"].entries,\n", + " style[i],\n", + " label=particle_options[i],\n", + " )\n", "plt.legend()\n", - "plt.title('Model Comparison 2C')\n", - "plt.xlabel('Time [s]')\n", - "plt.ylabel('Voltage [V]')\n", + "plt.title(\"Model Comparison 2C\")\n", + "plt.xlabel(\"Time [s]\")\n", + "plt.ylabel(\"Voltage [V]\")\n", "plt.grid()" ] }, @@ -235,7 +252,7 @@ "t_eval = np.linspace(0, 360, 72)\n", "solutions_6C = []\n", "for sim in simulations:\n", - " sim.solve(t_eval, inputs={\"Current function [A]\": 6*0.68})\n", + " sim.solve(t_eval, inputs={\"Current function [A]\": 6 * 0.68})\n", " solutions_6C.append(sim.solution)" ] }, @@ -260,12 +277,16 @@ "source": [ "plt.figure(figsize=(15, 15))\n", "for i in range(len(models)):\n", - " plt.plot(solutions_6C[i]['Time [s]'].entries,\n", - " solutions_6C[i]['Voltage [V]'].entries, style[i], label=particle_options[i])\n", + " plt.plot(\n", + " solutions_6C[i][\"Time [s]\"].entries,\n", + " solutions_6C[i][\"Voltage [V]\"].entries,\n", + " style[i],\n", + " label=particle_options[i],\n", + " )\n", "plt.legend()\n", - "plt.title('Model Comparison 6C')\n", - "plt.xlabel('Time [s]')\n", - "plt.ylabel('Voltage [V]')\n", + "plt.title(\"Model Comparison 6C\")\n", + "plt.xlabel(\"Time [s]\")\n", + "plt.ylabel(\"Voltage [V]\")\n", "plt.grid()" ] }, diff --git a/docs/source/examples/notebooks/models/composite_particle.ipynb b/docs/source/examples/notebooks/models/composite_particle.ipynb index 59fa9c957e..5057d57589 100644 --- a/docs/source/examples/notebooks/models/composite_particle.ipynb +++ b/docs/source/examples/notebooks/models/composite_particle.ipynb @@ -36,15 +36,16 @@ "metadata": {}, "outputs": [], "source": [ - "#%pip install \"pybamm[plot,cite]\" -q # install PyBaMM if it is not installed\n", + "# %pip install \"pybamm[plot,cite]\" -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", + "\n", + "style.use(\"ggplot\")\n", + "os.chdir(pybamm.__path__[0] + \"/..\")\n", "pybamm.set_logging_level(\"INFO\")" ] }, @@ -74,23 +75,27 @@ ], "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", + "model = pybamm.lithium_ion.DFN(\n", + " {\n", + " \"particle phases\": (\"2\", \"1\"),\n", + " \"open-circuit potential\": ((\"single\", \"current sigmoid\"), \"single\"),\n", + " }\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", - "})" + "param.update(\n", + " {\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", + " }\n", + ")" ] }, { @@ -120,9 +125,9 @@ "source": [ "C_rate = 0.5\n", "capacity = param[\"Nominal cell capacity [A.h]\"]\n", - "I_load = C_rate * capacity \n", + "I_load = C_rate * capacity\n", "\n", - "t_eval = np.linspace(0,10000,1000)\n", + "t_eval = np.linspace(0, 10000, 1000)\n", "\n", "param[\"Current function [A]\"] = I_load" ] @@ -242,21 +247,25 @@ } ], "source": [ - "v_si=[0.001,0.04,0.1]\n", + "v_si = [0.001, 0.04, 0.1]\n", "total_am_volume_fraction = 0.75\n", - "solution=[]\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", + " param.update(\n", + " {\n", + " \"Primary: Negative electrode active material volume fraction\": (1 - v)\n", + " * total_am_volume_fraction, # primary\n", + " \"Secondary: Negative electrode active material volume fraction\": v\n", + " * total_am_volume_fraction,\n", + " }\n", + " )\n", " print(v)\n", " sim = pybamm.Simulation(\n", " model,\n", " parameter_values=param,\n", - " solver=pybamm.CasadiSolver(dt_max = 5),\n", + " solver=pybamm.CasadiSolver(dt_max=5),\n", " )\n", - " solution.append(sim.solve(t_eval = t_eval))\n", + " solution.append(sim.solve(t_eval=t_eval))\n", "stop = timeit.default_timer()\n", "print(\"running time: \" + str(stop - start) + \"s\")" ] @@ -301,13 +310,13 @@ } ], "source": [ - "ltype=['k-','r--','b-.','g:','m-','c--','y-.']\n", - "for i in range(0,len(v_si)):\n", + "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.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()" ] }, @@ -359,24 +368,28 @@ ], "source": [ "plt.figure()\n", - "for i in range(0,len(v_si)):\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", + " j_n_p1_av = solution[i][\n", + " \"X-averaged negative electrode primary interfacial current density [A.m-2]\"\n", + " ].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", + "plt.title(\"Graphite\")\n", "\n", "plt.figure()\n", - "for i in range(0,len(v_si)):\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", + " j_n_p2_av = solution[i][\n", + " \"X-averaged negative electrode secondary interfacial current density [A.m-2]\"\n", + " ].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')" + "plt.title(\"Silicon\")" ] }, { @@ -427,24 +440,28 @@ ], "source": [ "plt.figure()\n", - "for i in range(0,len(v_si)):\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", + " j_n_p1_Vav = solution[i][\n", + " \"X-averaged negative electrode primary volumetric interfacial current density [A.m-3]\"\n", + " ].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", + "plt.title(\"Graphite\")\n", "\n", "plt.figure()\n", - "for i in range(0,len(v_si)):\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", + " j_n_p2_Vav = solution[i][\n", + " \"X-averaged negative electrode secondary volumetric interfacial current density [A.m-3]\"\n", + " ].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')" + "plt.title(\"Silicon\")" ] }, { @@ -495,24 +512,28 @@ ], "source": [ "plt.figure()\n", - "for i in range(0,len(v_si)):\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", + " c_s_xrav_n_p1 = solution[i][\n", + " \"Average negative primary particle concentration\"\n", + " ].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", + "plt.title(\"Graphite\")\n", "\n", "plt.figure()\n", - "for i in range(0,len(v_si)):\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", + " c_s_xrav_n_p2 = solution[i][\n", + " \"Average negative secondary particle concentration\"\n", + " ].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')" + "plt.title(\"Silicon\")" ] }, { @@ -573,34 +594,45 @@ ], "source": [ "plt.figure()\n", - "for i in range(0,len(v_si)):\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", + " ocp_p1 = solution[i][\n", + " \"X-averaged negative electrode primary open-circuit potential [V]\"\n", + " ].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", + "plt.title(\"Graphite\")\n", "\n", "plt.figure()\n", - "for i in range(0,len(v_si)):\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", + " ocp_p2 = solution[i][\n", + " \"X-averaged negative electrode secondary open-circuit potential [V]\"\n", + " ].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", + "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", + "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][\n", + " \"X-averaged positive electrode open-circuit potential [V]\"\n", + " ].entries\n", + " plt.plot(\n", + " t_i,\n", + " ocp_p,\n", + " ltype[len(v_si) - 1 - i],\n", + " label=\"$V_\\mathrm{si}=$\" + str(v_si[len(v_si) - 1 - i]),\n", + " )\n", + "plt.xlabel(\"Time [h]\")\n", "plt.ylabel(\"Equilibrium potential [V]\")\n", "plt.legend()\n", - "plt.title('NMC811')" + "plt.title(\"NMC811\")" ] }, { @@ -840,18 +872,22 @@ } ], "source": [ - "solution=[]\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", + " param.update(\n", + " {\n", + " \"Primary: Negative electrode active material volume fraction\": (1 - v)\n", + " * total_am_volume_fraction, # primary\n", + " \"Secondary: Negative electrode active material volume fraction\": v\n", + " * total_am_volume_fraction,\n", + " }\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", + " solver=pybamm.CasadiSolver(dt_max=5),\n", " )\n", " solution.append(sim.solve(calc_esoh=False))\n", "stop = timeit.default_timer()\n", @@ -896,13 +932,13 @@ } ], "source": [ - "ltype=['k-','r--','b-.','g:','m-','c--','y-.']\n", - "for i in range(0,len(v_si)):\n", + "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.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()" ] }, diff --git a/docs/source/examples/notebooks/models/coupled-degradation.ipynb b/docs/source/examples/notebooks/models/coupled-degradation.ipynb index 00b524c041..1551a79a64 100644 --- a/docs/source/examples/notebooks/models/coupled-degradation.ipynb +++ b/docs/source/examples/notebooks/models/coupled-degradation.ipynb @@ -80,7 +80,7 @@ "param = pybamm.ParameterValues(\"OKane2022\")\n", "var_pts = {\n", " \"x_n\": 5, # negative electrode\n", - " \"x_s\": 5, # separator \n", + " \"x_s\": 5, # separator\n", " \"x_p\": 5, # positive electrode\n", " \"r_n\": 30, # negative particle\n", " \"r_p\": 30, # positive particle\n", @@ -104,16 +104,23 @@ "source": [ "cycle_number = 10\n", "exp = pybamm.Experiment(\n", - " [\"Hold at 4.2 V until C/100\",\n", - " \"Rest for 4 hours\",\n", - " \"Discharge at 0.1C until 2.5 V\", # initial capacity check\n", - " \"Charge at 0.3C until 4.2 V\",\n", - " \"Hold at 4.2 V until C/100\",]\n", - " + [(\"Discharge at 1C until 2.5 V\", # ageing cycles\n", - " \"Charge at 0.3C until 4.2 V\",\n", - " \"Hold at 4.2 V until C/100\",)] * cycle_number\n", + " [\n", + " \"Hold at 4.2 V until C/100\",\n", + " \"Rest for 4 hours\",\n", + " \"Discharge at 0.1C until 2.5 V\", # initial capacity check\n", + " \"Charge at 0.3C until 4.2 V\",\n", + " \"Hold at 4.2 V until C/100\",\n", + " ]\n", + " + [\n", + " (\n", + " \"Discharge at 1C until 2.5 V\", # ageing cycles\n", + " \"Charge at 0.3C until 4.2 V\",\n", + " \"Hold at 4.2 V until C/100\",\n", + " )\n", + " ]\n", + " * cycle_number\n", " + [\"Discharge at 0.1C until 2.5 V\"], # final capacity check\n", - " period=\"5 minutes\"\n", + " period=\"5 minutes\",\n", ")\n", "sim = pybamm.Simulation(model, parameter_values=param, experiment=exp, var_pts=var_pts)\n", "sol = sim.solve()" @@ -152,13 +159,15 @@ "Q_SEI_cr = sol[\"Loss of capacity to negative SEI on cracks [A.h]\"].entries\n", "Q_plating = sol[\"Loss of capacity to negative lithium plating [A.h]\"].entries\n", "Q_side = sol[\"Total capacity lost to side reactions [A.h]\"].entries\n", - "Q_LLI = sol[\"Total lithium lost [mol]\"].entries * 96485.3 / 3600 # convert from mol to A.h\n", + "Q_LLI = (\n", + " sol[\"Total lithium lost [mol]\"].entries * 96485.3 / 3600\n", + ") # convert from mol to A.h\n", "plt.figure()\n", - "plt.plot(Qt,Q_SEI,label=\"SEI\",linestyle=\"dashed\")\n", - "plt.plot(Qt,Q_SEI_cr,label=\"SEI on cracks\",linestyle=\"dashdot\")\n", - "plt.plot(Qt,Q_plating,label=\"Li plating\",linestyle=\"dotted\")\n", - "plt.plot(Qt,Q_side,label=\"All side reactions\",linestyle=(0,(6,1)))\n", - "plt.plot(Qt,Q_LLI,label=\"All LLI\")\n", + "plt.plot(Qt, Q_SEI, label=\"SEI\", linestyle=\"dashed\")\n", + "plt.plot(Qt, Q_SEI_cr, label=\"SEI on cracks\", linestyle=\"dashdot\")\n", + "plt.plot(Qt, Q_plating, label=\"Li plating\", linestyle=\"dotted\")\n", + "plt.plot(Qt, Q_side, label=\"All side reactions\", linestyle=(0, (6, 1)))\n", + "plt.plot(Qt, Q_LLI, label=\"All LLI\")\n", "plt.xlabel(\"Throughput capacity [A.h]\")\n", "plt.ylabel(\"Capacity loss [A.h]\")\n", "plt.legend()\n", @@ -206,9 +215,9 @@ "LAM_neg = sol[\"Loss of active material in negative electrode [%]\"].entries\n", "LAM_pos = sol[\"Loss of active material in positive electrode [%]\"].entries\n", "plt.figure()\n", - "plt.plot(Qt,LLI,label=\"LLI\")\n", - "plt.plot(Qt,LAM_neg,label=\"LAM (negative)\")\n", - "plt.plot(Qt,LAM_pos,label=\"LAM (positive)\")\n", + "plt.plot(Qt, LLI, label=\"LLI\")\n", + "plt.plot(Qt, LAM_neg, label=\"LAM (negative)\")\n", + "plt.plot(Qt, LAM_pos, label=\"LAM (positive)\")\n", "plt.xlabel(\"Throughput capacity [A.h]\")\n", "plt.ylabel(\"Degradation modes [%]\")\n", "plt.legend()\n", @@ -252,12 +261,12 @@ ], "source": [ "eps_neg_avg = sol[\"X-averaged negative electrode porosity\"].entries\n", - "eps_neg_sep = sol[\"Negative electrode porosity\"].entries[-1,:]\n", - "eps_neg_CC = sol[\"Negative electrode porosity\"].entries[0,:]\n", + "eps_neg_sep = sol[\"Negative electrode porosity\"].entries[-1, :]\n", + "eps_neg_CC = sol[\"Negative electrode porosity\"].entries[0, :]\n", "plt.figure()\n", - "plt.plot(Qt,eps_neg_avg,label=\"Average\")\n", - "plt.plot(Qt,eps_neg_sep,label=\"Separator\",linestyle=\"dotted\")\n", - "plt.plot(Qt,eps_neg_CC,label=\"Current collector\",linestyle=\"dashed\")\n", + "plt.plot(Qt, eps_neg_avg, label=\"Average\")\n", + "plt.plot(Qt, eps_neg_sep, label=\"Separator\", linestyle=\"dotted\")\n", + "plt.plot(Qt, eps_neg_CC, label=\"Current collector\", linestyle=\"dashed\")\n", "plt.xlabel(\"Throughput capacity [A.h]\")\n", "plt.ylabel(\"Negative electrode porosity\")\n", "plt.legend()\n", 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 54b71157f7..5528b74830 100644 --- a/docs/source/examples/notebooks/models/electrode-state-of-health.ipynb +++ b/docs/source/examples/notebooks/models/electrode-state-of-health.ipynb @@ -76,22 +76,26 @@ ], "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", + "experiment = pybamm.Experiment(\n", + " [\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", + ")\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", - "])" + "spm_sol.plot(\n", + " [\n", + " \"Voltage [V]\",\n", + " \"Current [A]\",\n", + " \"Negative electrode stoichiometry\",\n", + " \"Positive electrode stoichiometry\",\n", + " ]\n", + ")" ] }, { @@ -179,16 +183,13 @@ "\n", "y_100_min = 1e-10\n", "\n", - "x_100_upper_limit = (Q_Li - y_100_min*Q_p)/Q_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", + "\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", + "model.variables = {\"x_100\": x_100, \"y_100\": y_100}\n", "\n", "sim = pybamm.Simulation(model, parameter_values=parameter_values)\n", "sol = sim.solve([0])\n", @@ -204,7 +205,7 @@ "\n", "x_0 = pybamm.Variable(\"x_0\")\n", "Q = Q_n * (x_100 - x_0)\n", - "y_0 = y_100 + Q/Q_p\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", @@ -250,7 +251,7 @@ "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", + "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", @@ -298,23 +299,23 @@ "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", + "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", + "\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", + "\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_xlim([t[0], t[-1]])\n", + " axes[k].set_ylim([0, 1])\n", " axes[k].set_xlabel(\"Time [h]\")\n", - " \n", + "\n", "fig.tight_layout()" ] }, @@ -341,8 +342,13 @@ "all_parameter_sets = [\n", " k\n", " for k, v in pybamm.parameter_sets.items()\n", - " if v[\"chemistry\"] == \"lithium_ion\" and k not in [\n", - " \"Xu2019\", \"Chen2020_composite\", \"Ecker2015_graphite_halfcell\", \"OKane2022_graphite_SiOx_halfcell\"\n", + " if v[\"chemistry\"] == \"lithium_ion\"\n", + " and k\n", + " not in [\n", + " \"Xu2019\",\n", + " \"Chen2020_composite\",\n", + " \"Ecker2015_graphite_halfcell\",\n", + " \"OKane2022_graphite_SiOx_halfcell\",\n", " ]\n", "]\n", "\n", @@ -350,19 +356,21 @@ "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[\"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", + "\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", + "\n", + " Q = parameter_values.evaluate(param.Q / param.n_electrodes_parallel)\n", + " esoh_solver = pybamm.lithium_ion.ElectrodeSOHSolver(\n", + " parameter_values, param, known_value=\"cell capacity\"\n", + " )\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", + "\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", @@ -384,7 +392,7 @@ " pass\n", "\n", " return sweep, sol_init_QLi, sol_init_Q\n", - " \n", + "\n", "\n", "for parameter_set in [\"Chen2020\"]:\n", " sweep, sol_init_QLi, sol_init_Q = solve_esoh_sweep_QLi(parameter_set, param)" @@ -408,33 +416,33 @@ ], "source": [ "def plot_sweep(sweep, sol_init, sol_init_Q, parameter_set):\n", - " fig, axes = plt.subplots(1,3,figsize=(10,3))\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", + " 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", + " 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.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", + " 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", + "\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", @@ -442,7 +450,7 @@ " # 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.legend(loc=\"center left\", bbox_to_anchor=(1.01, 0.5))\n", " fig.tight_layout()\n", " return fig, axes\n", "\n", diff --git a/docs/source/examples/notebooks/models/half-cell.ipynb b/docs/source/examples/notebooks/models/half-cell.ipynb index 7eda7e2491..2085162694 100644 --- a/docs/source/examples/notebooks/models/half-cell.ipynb +++ b/docs/source/examples/notebooks/models/half-cell.ipynb @@ -77,13 +77,15 @@ } ], "source": [ - "exp_slow = pybamm.Experiment([\"Discharge at C/25 until 3.5 V\", \"Charge at C/25 until 4.2 V\"])\n", + "exp_slow = pybamm.Experiment(\n", + " [\"Discharge at C/25 until 3.5 V\", \"Charge at C/25 until 4.2 V\"]\n", + ")\n", "sim1 = pybamm.Simulation(model, parameter_values=param_nmc, experiment=exp_slow)\n", "sol1 = sim1.solve()\n", "t = sol1[\"Time [s]\"].entries\n", "V = sol1[\"Voltage [V]\"].entries\n", "plt.figure()\n", - "plt.plot(t,V)\n", + "plt.plot(t, V)\n", "plt.xlabel(\"Time [s]\")\n", "plt.ylabel(\"Voltage [V]\")\n", "plt.show()" @@ -124,13 +126,15 @@ } ], "source": [ - "exp_fast = pybamm.Experiment([\"Discharge at 1C until 3.5 V\", \"Charge at 1C until 4.2 V\"])\n", + "exp_fast = pybamm.Experiment(\n", + " [\"Discharge at 1C until 3.5 V\", \"Charge at 1C until 4.2 V\"]\n", + ")\n", "sim2 = pybamm.Simulation(model, parameter_values=param_nmc, experiment=exp_fast)\n", "sol2 = sim2.solve()\n", "t = sol2[\"Time [s]\"].entries\n", "V = sol2[\"Voltage [V]\"].entries\n", "plt.figure()\n", - "plt.plot(t,V)\n", + "plt.plot(t, V)\n", "plt.xlabel(\"Time [s]\")\n", "plt.ylabel(\"Voltage [V]\")\n", "plt.show()" @@ -164,25 +168,34 @@ } ], "source": [ - "model_with_degradation = pybamm.lithium_ion.DFN({\n", - " \"working electrode\": \"positive\",\n", - " \"SEI\": \"reaction limited\", # SEI on both electrodes\n", - " \"SEI porosity change\": \"true\",\n", - " \"particle mechanics\": \"swelling and cracking\",\n", - " \"SEI on cracks\": \"true\",\n", - " \"lithium plating\": \"partially reversible\",\n", - " \"lithium plating porosity change\": \"true\", # alias for \"SEI porosity change\"\n", - "})\n", + "model_with_degradation = pybamm.lithium_ion.DFN(\n", + " {\n", + " \"working electrode\": \"positive\",\n", + " \"SEI\": \"reaction limited\", # SEI on both electrodes\n", + " \"SEI porosity change\": \"true\",\n", + " \"particle mechanics\": \"swelling and cracking\",\n", + " \"SEI on cracks\": \"true\",\n", + " \"lithium plating\": \"partially reversible\",\n", + " \"lithium plating porosity change\": \"true\", # alias for \"SEI porosity change\"\n", + " }\n", + ")\n", "param_GrSi = pybamm.ParameterValues(\"OKane2022_graphite_SiOx_halfcell\")\n", "param_GrSi.update({\"SEI reaction exchange current density [A.m-2]\": 1.5e-07})\n", "var_pts = {\"x_n\": 1, \"x_s\": 5, \"x_p\": 7, \"r_n\": 1, \"r_p\": 30}\n", - "exp_degradation = pybamm.Experiment([\"Charge at 0.3C until 1.5 V\", \"Discharge at 0.3C until 0.005 V\"])\n", - "sim3 = pybamm.Simulation(model_with_degradation, parameter_values=param_GrSi, experiment=exp_degradation, var_pts=var_pts)\n", + "exp_degradation = pybamm.Experiment(\n", + " [\"Charge at 0.3C until 1.5 V\", \"Discharge at 0.3C until 0.005 V\"]\n", + ")\n", + "sim3 = pybamm.Simulation(\n", + " model_with_degradation,\n", + " parameter_values=param_GrSi,\n", + " experiment=exp_degradation,\n", + " var_pts=var_pts,\n", + ")\n", "sol3 = sim3.solve()\n", "t = sol3[\"Time [s]\"].entries\n", "V = sol3[\"Voltage [V]\"].entries\n", "plt.figure()\n", - "plt.plot(t,V)\n", + "plt.plot(t, V)\n", "plt.xlabel(\"Time [s]\")\n", "plt.ylabel(\"Voltage [V]\")\n", "plt.show()" @@ -221,10 +234,10 @@ "Q_SEI_cr = sol3[\"Loss of capacity to positive SEI on cracks [A.h]\"].entries\n", "Q_pl = sol3[\"Loss of capacity to positive lithium plating [A.h]\"].entries\n", "plt.figure()\n", - "plt.plot(t,Q_SEI_n,label=\"Negative SEI\")\n", - "plt.plot(t,Q_SEI_p,label=\"Positive SEI\")\n", - "plt.plot(t,Q_SEI_cr,label=\"SEI on cracks\")\n", - "plt.plot(t,Q_pl,label=\"Lithium plating\")\n", + "plt.plot(t, Q_SEI_n, label=\"Negative SEI\")\n", + "plt.plot(t, Q_SEI_p, label=\"Positive SEI\")\n", + "plt.plot(t, Q_SEI_cr, label=\"SEI on cracks\")\n", + "plt.plot(t, Q_pl, label=\"Lithium plating\")\n", "plt.xlabel(\"Time [s]\")\n", "plt.ylabel(\"Loss of lithium inventory [A.h]\")\n", "plt.legend()\n", @@ -260,12 +273,17 @@ ], "source": [ "param_GrSi.update({\"SEI reaction exchange current density [A.m-2]\": 6e-07})\n", - "sim4 = pybamm.Simulation(model_with_degradation, parameter_values=param_GrSi, experiment=exp_degradation, var_pts=var_pts)\n", + "sim4 = pybamm.Simulation(\n", + " model_with_degradation,\n", + " parameter_values=param_GrSi,\n", + " experiment=exp_degradation,\n", + " var_pts=var_pts,\n", + ")\n", "sol4 = sim4.solve()\n", "t = sol4[\"Time [s]\"].entries\n", "V = sol4[\"Voltage [V]\"].entries\n", "plt.figure()\n", - "plt.plot(t,V)\n", + "plt.plot(t, V)\n", "plt.xlabel(\"Time [s]\")\n", "plt.ylabel(\"Voltage [V]\")\n", "plt.show()" @@ -296,10 +314,10 @@ "Q_SEI_cr = sol4[\"Loss of capacity to positive SEI on cracks [A.h]\"].entries\n", "Q_pl = sol4[\"Loss of capacity to positive lithium plating [A.h]\"].entries\n", "plt.figure()\n", - "plt.plot(t,Q_SEI_n,label=\"Negative SEI\")\n", - "plt.plot(t,Q_SEI_p,label=\"Positive SEI\")\n", - "plt.plot(t,Q_SEI_cr,label=\"SEI on cracks\")\n", - "plt.plot(t,Q_pl,label=\"Lithium plating\")\n", + "plt.plot(t, Q_SEI_n, label=\"Negative SEI\")\n", + "plt.plot(t, Q_SEI_p, label=\"Positive SEI\")\n", + "plt.plot(t, Q_SEI_cr, label=\"SEI on cracks\")\n", + "plt.plot(t, Q_pl, label=\"Lithium plating\")\n", "plt.xlabel(\"Time [s]\")\n", "plt.ylabel(\"Loss of lithium inventory [A.h]\")\n", "plt.legend()\n", diff --git a/docs/source/examples/notebooks/models/jelly-roll-model.ipynb b/docs/source/examples/notebooks/models/jelly-roll-model.ipynb index 557366099a..43e65fbe7d 100644 --- a/docs/source/examples/notebooks/models/jelly-roll-model.ipynb +++ b/docs/source/examples/notebooks/models/jelly-roll-model.ipynb @@ -58,9 +58,9 @@ "source": [ "%pip install \"pybamm[plot,cite]\" -q # install PyBaMM if it is not installed\n", "import pybamm\n", - "import numpy as np \n", + "import numpy as np\n", "from numpy import pi\n", - "import matplotlib.pyplot as plt " + "import matplotlib.pyplot as plt" ] }, { @@ -84,7 +84,7 @@ "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\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\")" @@ -114,11 +114,11 @@ "phi_p = pybamm.Variable(\"Positive potential\", domain=\"cell\")\n", "phi_n = pybamm.Variable(\"Negative potential\", domain=\"cell\")\n", "\n", - "A_p = (2 * sigma_a / eps ** 4 / l) / (delta_p * sigma_p / 2 / pi ** 2)\n", - "A_n = (2 * sigma_a / eps ** 4 / l) / (delta_n * sigma_n / 2 / pi ** 2)\n", + "A_p = (2 * sigma_a / eps**4 / l) / (delta_p * sigma_p / 2 / pi**2)\n", + "A_n = (2 * sigma_a / eps**4 / l) / (delta_n * sigma_n / 2 / pi**2)\n", "model.algebraic = {\n", - " phi_p: pybamm.div((1 / r ** 2) * pybamm.grad(phi_p)) + A_p * (phi_n - phi_p),\n", - " phi_n: pybamm.div((1 / r ** 2) * pybamm.grad(phi_n)) - A_n * (phi_n - phi_p),\n", + " phi_p: pybamm.div((1 / r**2) * pybamm.grad(phi_p)) + A_p * (phi_n - phi_p),\n", + " phi_n: pybamm.div((1 / r**2) * pybamm.grad(phi_n)) - A_n * (phi_n - phi_p),\n", "}\n", "\n", "model.boundary_conditions = {\n", @@ -129,7 +129,7 @@ " phi_n: {\n", " \"left\": (0, \"Dirichlet\"),\n", " \"right\": (0, \"Neumann\"),\n", - " } \n", + " },\n", "}\n", "\n", "model.initial_conditions = {phi_p: 1, phi_n: 0} # initial guess for solver\n", @@ -165,7 +165,7 @@ "source": [ "params = pybamm.ParameterValues(\n", " {\n", - " \"Number of winds\":20,\n", + " \"Number of winds\": 20,\n", " \"Inner radius\": 0.25,\n", " \"Current collector thickness\": 0.05,\n", " \"Positive current collector conductivity\": 5e6,\n", @@ -218,7 +218,7 @@ "metadata": {}, "outputs": [], "source": [ - "# solver \n", + "# solver\n", "solver = pybamm.CasadiAlgebraicSolver()\n", "solution = solver.solve(model)" ] @@ -253,7 +253,7 @@ "metadata": {}, "outputs": [], "source": [ - "# post-process homogenised potential \n", + "# post-process homogenised potential\n", "phi_n = solution[\"Negative potential\"]\n", "phi_p = solution[\"Positive potential\"]\n", "\n", @@ -263,13 +263,17 @@ "\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(r=r)\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) / (\n", + " 1 - 4 * delta\n", + " ) + phi_p(r=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(r=r)" + " # 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) * (\n", + " r0 / eps + 1 - delta + theta / 2 / pi - r[:, np.newaxis] / eps\n", + " ) / (1 - 4 * delta) + phi_p(r=r)" ] }, { @@ -279,7 +283,7 @@ "metadata": {}, "outputs": [], "source": [ - "# define spiral \n", + "# define spiral\n", "\n", "\n", "def spiral_pos_inner(t):\n", @@ -324,22 +328,22 @@ "# Setup fine mesh with nr points per layer\n", "nr = 10\n", "rr = np.linspace(r0, 1, nr)\n", - "tt = np.arange(0, (N+1)*2*pi, 2*pi)\n", + "tt = np.arange(0, (N + 1) * 2 * pi, 2 * pi)\n", "# N+1 winds of pos c.c.\n", - "r_mesh_pos = np.zeros((len(tt),len(rr)))\n", + "r_mesh_pos = np.zeros((len(tt), len(rr)))\n", "for i in range(len(tt)):\n", - " r_mesh_pos[i,:] = np.linspace(spiral_pos_inner(tt[i]), spiral_pos_outer(tt[i]), nr)\n", + " r_mesh_pos[i, :] = np.linspace(spiral_pos_inner(tt[i]), spiral_pos_outer(tt[i]), nr)\n", "# N winds of neg, am1, am2\n", - "r_mesh_neg = np.zeros((len(tt)-1, len(rr)))\n", - "r_mesh_am1 = np.zeros((len(tt)-1, len(rr)))\n", - "r_mesh_am2 = np.zeros((len(tt)-1, len(rr)))\n", - "for i in range(len(tt)-1):\n", - " r_mesh_am2[i,:] = np.linspace(spiral_am2_inner(tt[i]), spiral_am2_outer(tt[i]), nr)\n", - " r_mesh_neg[i,:] = np.linspace(spiral_neg_inner(tt[i]), spiral_neg_outer(tt[i]), nr)\n", - " r_mesh_am1[i,:] = np.linspace(spiral_am1_inner(tt[i]), spiral_am1_outer(tt[i]), nr)\n", - "# Combine and sort \n", - "r_total_mesh = np.vstack((r_mesh_pos,r_mesh_neg,r_mesh_am1, r_mesh_am2))\n", - "r_total_mesh = np.sort(r_total_mesh,axis=None)" + "r_mesh_neg = np.zeros((len(tt) - 1, len(rr)))\n", + "r_mesh_am1 = np.zeros((len(tt) - 1, len(rr)))\n", + "r_mesh_am2 = np.zeros((len(tt) - 1, len(rr)))\n", + "for i in range(len(tt) - 1):\n", + " r_mesh_am2[i, :] = np.linspace(spiral_am2_inner(tt[i]), spiral_am2_outer(tt[i]), nr)\n", + " r_mesh_neg[i, :] = np.linspace(spiral_neg_inner(tt[i]), spiral_neg_outer(tt[i]), nr)\n", + " r_mesh_am1[i, :] = np.linspace(spiral_am1_inner(tt[i]), spiral_am1_outer(tt[i]), nr)\n", + "# Combine and sort\n", + "r_total_mesh = np.vstack((r_mesh_pos, r_mesh_neg, r_mesh_am1, r_mesh_am2))\n", + "r_total_mesh = np.sort(r_total_mesh, axis=None)" ] }, { @@ -362,17 +366,22 @@ } ], "source": [ - "# plot homogenised potential \n", - "fig, ax = plt.subplots(1, 1, figsize=(8,6))\n", + "# plot homogenised potential\n", + "fig, ax = plt.subplots(1, 1, figsize=(8, 6))\n", "\n", - "ax.plot(r_total_mesh, phi_n(r=r_total_mesh), 'b', label=r\"$\\phi^-$\")\n", - "ax.plot(r_total_mesh, phi_p(r=r_total_mesh), 'r', label=r\"$\\phi^+$\")\n", + "ax.plot(r_total_mesh, phi_n(r=r_total_mesh), \"b\", label=r\"$\\phi^-$\")\n", + "ax.plot(r_total_mesh, phi_p(r=r_total_mesh), \"r\", label=r\"$\\phi^+$\")\n", "for i in range(len(tt)):\n", - " ax.plot(r_mesh_pos[i,:], phi_p(r=r_mesh_pos[i,:]), 'k', label=r\"$\\phi$\" if i ==0 else \"\")\n", - "for i in range(len(tt)-1):\n", - " ax.plot(r_mesh_neg[i,:], phi_n(r=r_mesh_neg[i,:]), 'k')\n", - " ax.plot(r_mesh_am1[i,:], phi_am1(r_mesh_am1[i,:], tt[i]), 'k')\n", - " ax.plot(r_mesh_am2[i,:], phi_am2(r_mesh_am2[i,:], tt[i]), 'k')\n", + " ax.plot(\n", + " r_mesh_pos[i, :],\n", + " phi_p(r=r_mesh_pos[i, :]),\n", + " \"k\",\n", + " label=r\"$\\phi$\" if i == 0 else \"\",\n", + " )\n", + "for i in range(len(tt) - 1):\n", + " ax.plot(r_mesh_neg[i, :], phi_n(r=r_mesh_neg[i, :]), \"k\")\n", + " ax.plot(r_mesh_am1[i, :], phi_am1(r_mesh_am1[i, :], tt[i]), \"k\")\n", + " ax.plot(r_mesh_am2[i, :], phi_am2(r_mesh_am2[i, :], tt[i]), \"k\")\n", "ax.set_xlabel(r\"$r$\")\n", "ax.set_ylabel(r\"$\\phi$\")\n", "ax.legend();" diff --git a/docs/source/examples/notebooks/models/lead-acid.ipynb b/docs/source/examples/notebooks/models/lead-acid.ipynb index 0dd20126a6..ccfa35b091 100644 --- a/docs/source/examples/notebooks/models/lead-acid.ipynb +++ b/docs/source/examples/notebooks/models/lead-acid.ipynb @@ -37,7 +37,8 @@ "import numpy as np\n", "import os\n", "import matplotlib.pyplot as plt\n", - "os.chdir(pybamm.__path__[0]+'/..')" + "\n", + "os.chdir(pybamm.__path__[0] + \"/..\")" ] }, { @@ -223,7 +224,7 @@ "source": [ "timer = pybamm.Timer()\n", "solutions = {}\n", - "t_eval = np.linspace(0, 3600 * 17, 100) # time in seconds\n", + "t_eval = np.linspace(0, 3600 * 17, 100) # time in seconds\n", "for model in models:\n", " solver = pybamm.CasadiSolver()\n", " timer.reset()\n", diff --git a/docs/source/examples/notebooks/models/lithium-plating.ipynb b/docs/source/examples/notebooks/models/lithium-plating.ipynb index 1e14513620..e84fdbb1ac 100644 --- a/docs/source/examples/notebooks/models/lithium-plating.ipynb +++ b/docs/source/examples/notebooks/models/lithium-plating.ipynb @@ -18,7 +18,8 @@ "%pip install \"pybamm[plot,cite]\" -q # install PyBaMM if it is not installed\n", "import pybamm\n", "import os\n", - "os.chdir(pybamm.__path__[0]+'/..')" + "\n", + "os.chdir(pybamm.__path__[0] + \"/..\")" ] }, { @@ -36,16 +37,18 @@ "source": [ "# choose models\n", "plating_options = [\"reversible\", \"irreversible\", \"partially reversible\"]\n", - "models = {option: pybamm.lithium_ion.DFN(options={\"lithium plating\": option}, name=option) \n", - " for option in plating_options}\n", + "models = {\n", + " option: pybamm.lithium_ion.DFN(options={\"lithium plating\": option}, name=option)\n", + " for option in plating_options\n", + "}\n", "\n", "# pick parameters\n", "parameter_values = pybamm.ParameterValues(\"OKane2022\")\n", "parameter_values.update({\"Ambient temperature [K]\": 268.15})\n", "parameter_values.update({\"Upper voltage cut-off [V]\": 4.21})\n", - "#parameter_values.update({\"Lithium plating kinetic rate constant [m.s-1]\": 1E-9})\n", + "# parameter_values.update({\"Lithium plating kinetic rate constant [m.s-1]\": 1E-9})\n", "parameter_values.update({\"Lithium plating transfer coefficient\": 0.5})\n", - "parameter_values.update({\"Dead lithium decay constant [s-1]\": 1E-4})" + "parameter_values.update({\"Dead lithium decay constant [s-1]\": 1e-4})" ] }, { @@ -67,14 +70,18 @@ "s = pybamm.step.string\n", "experiment_discharge = pybamm.Experiment(\n", " [\n", - " (s(\"Discharge at C/20 until 2.5 V\", period=\"10 minutes\"),\n", - " s(\"Rest for 1 hour\", period=\"3 minutes\")),\n", + " (\n", + " s(\"Discharge at C/20 until 2.5 V\", period=\"10 minutes\"),\n", + " s(\"Rest for 1 hour\", period=\"3 minutes\"),\n", + " ),\n", " ]\n", ")\n", "\n", "sims_discharge = []\n", "for model in models.values():\n", - " sim_discharge = pybamm.Simulation(model, parameter_values=parameter_values, experiment=experiment_discharge)\n", + " sim_discharge = pybamm.Simulation(\n", + " model, parameter_values=parameter_values, experiment=experiment_discharge\n", + " )\n", " sol_discharge = sim_discharge.solve(calc_esoh=False)\n", " model.set_initial_conditions_from(sol_discharge, inplace=True)\n", " sims_discharge.append(sim_discharge)" @@ -97,12 +104,14 @@ "experiments = {}\n", "for C_rate in C_rates:\n", " experiments[C_rate] = pybamm.Experiment(\n", - " [\n", - " (f\"Charge at {C_rate} until 4.2 V\",\n", - " \"Hold at 4.2 V until C/20\",\n", - " \"Rest for 1 hour\")\n", - " ]\n", - ")" + " [\n", + " (\n", + " f\"Charge at {C_rate} until 4.2 V\",\n", + " \"Hold at 4.2 V until C/20\",\n", + " \"Rest for 1 hour\",\n", + " )\n", + " ]\n", + " )" ] }, { @@ -121,14 +130,18 @@ "def define_and_solve_sims(model, experiments, parameter_values):\n", " sims = {}\n", " for C_rate, experiment in experiments.items():\n", - " sim = pybamm.Simulation(model, experiment=experiment, parameter_values=parameter_values)\n", + " sim = pybamm.Simulation(\n", + " model, experiment=experiment, parameter_values=parameter_values\n", + " )\n", " sim.solve(calc_esoh=False)\n", " sims[C_rate] = sim\n", "\n", " return sims\n", "\n", "\n", - "sims_reversible = define_and_solve_sims(models[\"reversible\"], experiments, parameter_values)" + "sims_reversible = define_and_solve_sims(\n", + " models[\"reversible\"], experiments, parameter_values\n", + ")" ] }, { @@ -138,7 +151,7 @@ "outputs": [ { "data": { - "image/png": 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", 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lj1Dv168ftWrVYvHixfj6+mIwGGjcuHGJG3OsWLGCKVOmMHfuXNq1a4ejoyMffvgh+/btK/U8FVWez+pG13ptdOL1D36vrQX5X//9HIsry8zMpF+/fsyePbtIXR8fH9OfK/L3fStIktBC3Gy9gDhylUT0BQbUmiq9PKQQQghxyymKUqFpv7eT0WjkxRdfZNWqVWzfvt30BeCanj174uHhwQcffMCqVauKtE9NTZV1CSncJbp169YUFBTw6quv0rNnT06cOGG6uZ44cSLr1q3jl19+wdnZmbFjx/LII4+we/du4Oqi6H379sXb25s9e/YQGxvLkCFD0Gq1pik9UVFR9O3bl+eff57vv/+eLVu28Mwzz+Dj42NaTPynn35i0qRJfP7557Rt25Z58+bRq1cvTp06hZeXl2U+oDKKO5cGRjAajJIgFELcccpzT3Cr6lbE1q1bOXr0KBMnTgSgRYsWxMXFodFoKnXDCSsrK9Oagde0aNGCEydOlHt9vaZNm7JlyxazKcLXJCUlcerUKRYvXkynTleTsdfPoijO7t27ad++PaNHjzaV/XfUZHHx/1eDBg1YuXKl2cjM3bt34+joaPaQtjwcHR0JDAxky5Yt3HfffUXevza1NzY2ltDQUACzTUzKq0WLFqxcuZLAwMCb2rVZq9Xe8POqDJKZspBgz2AAcqzSSbuSbeFohBBCCFEWY8aM4bvvvuOHH37A0dGRuLg44uLiyMnJAa4+PV6yZAnr1q3jwQcf5M8//+T8+fMcPHiQl19+meeff97CV1A13GiX6LS0NJYuXcpHH31E165dadmyJcuWLWPPnj38/fffAGzatIkTJ07w3Xff0bx5c/r06cO7777LwoULTSMbPv/8c4KCgpg7dy4NGjRg7NixPProo3z88cemWD766CNGjRrF8OHDadiwIZ9//jl2dnZ89dVXJcafl5dHenq62csSWvSqxcOTW9BhYIipzKA3YNDf/pEHQghxN8rLyyMuLo6YmBgOHz7Me++9x0MPPcQDDzzAkCFDAOjevTvt2rWjf//+bNq0ifPnz7Nnzx5ee+01Dh48WOFzBwYG8tdffxETE2PaKfeVV15hz549jB07lrCwMCIjI1mzZk2RjUv+a9q0aRw4cIDRo0cTHh5OREQEixYtIjExEVdXV9zd3fnyyy85c+YMW7duZdKkSaX2FxISwsGDB9m4cSOnT5/mjTfeKLLZRmBgIOHh4Zw6dYrExMRiR+uNHj2aixcv8uKLLxIREcGaNWt48803mTRpkmnEX0W89dZbzJ07l/nz5xMZGcnhw4f59NNPgasjDe+55x7ThiQ7duzg9ddfr/C5xowZQ3JyMk888QQHDhzg7NmzbNy4keHDh5cr6XctsRkXF2fadfpWkCShhTTyvzpvP802j3OX0ywcjRBCCCHKYtGiRaSlpdGlSxd8fHxMr59++slU56GHHmLPnj1otVqefPJJ6tevzxNPPEFaWlqR3fjEVf/dJfrQoUPodDq6d+9uqlO/fn0CAgLYu3cvAHv37qVJkyZm04979epFeno6x48fN9W5vo9rda71kZ+fz6FDh8zqqFQqunfvbqpTnFmzZuHs7Gx6+fv738zl3xTfEBesbQtHJuz65Qy/LwwnN6v4qVFCCCEqz4YNG/Dx8SEwMJDevXuzbds25s+fz5o1a0zrCSqKwh9//MG9997L8OHDqVu3LoMGDeLChQtmv8PK65133uH8+fPUrl3bNPqtadOm7Nixg9OnT9OpUydCQ0OZPn06vr6+pfZVt25dNm3axJEjR2jTpg3t2rVjzZo1aDQaVCoVK1as4NChQzRu3JiJEyfy4Ycfltrfc889xyOPPMLjjz9O27ZtSUpKMhtVCDBq1Cjq1atHq1at8PT0NM0UuJ6fnx9//PEH+/fvp1mzZjz//POMHDnyppJ2AEOHDmXevHl89tlnNGrUiAceeMC0+Rxc3VikoKCAli1bMmHChJu6f/P19WX37t3o9Xp69uxJkyZNmDBhAi4uLuVKdM6dO5fNmzfj7+9vGuF4KyhGWWG7iPT0dJydnUlLS8PJyemWnOO38MNM2/oZzqozvOQ1k4GDOtyS8wghhBBVUW5uLlFRUQQFBZl22hPmSvuMbse9yu1iMBh48MEHSU1NNU1f+uGHHxg+fDh5eXlmddu0acN9993H7NmzefbZZ7lw4QIbN240vZ+dnY29vT1//PEHffr0oW7dugwfPpxp06aZ6vzxxx/07duX7OxsUlJS8PPzY8+ePWZrSL788svs2LGjxLWT8vLyzGJLT0/H39/f4n8f6Yk5/PjOPgryDTwwthm1GrvfuJEQQliY3BMIUTkq495R1iS0kLWH8smLe5g071XEXowAJEkohBBCiLtPcbtEV3XW1tZYW1tbOowinDxseeSllsSdTZMEoRBCCCHKTaYbW0iwx9XMrSHfg8S00xaORgghhBDi9ru2S/S2bdvMFiD39vYmPz+f1NRUs/rx8fF4e3ub6sTHxxd5/9p7pdVxcnLC1tYWDw8P1Gp1sXWu9VGVXMjJI6Og9PWLPP0dadKl8LPMyyng+M4YZPKQEEIIIW5EkoQWUsvdDgBjvhtZeRctHI0QQgghxO1jNBoZO3Ysq1atYuvWrUV2iW7ZsiVarZYtW7aYyk6dOkV0dLRpWnC7du04evQoCQkJpjqbN2/GycmJhg0bmupc38e1Otf6sLKyomXLlmZ1DAYDW7ZsMZt+XBVk6w0MORpFj4OniMjKKVMbo8HI5qXH2f79Kfb+dvbGDYQQQghxV5PpxhYS8G+S0KBzJ09JQF9gQK2RnK0QQggh7nxjxozhhx9+YM2aNaZdogGcnZ2xtbXF2dmZkSNHMmnSJNzc3HBycuLFF1+kXbt23HPPPQD07NmThg0b8vTTT/PBBx8QFxfH66+/zpgxY0xTgZ9//nkWLFjAyy+/zIgRI9i6dSs///wz69atM8UyadIkhg4dSqtWrWjTpg3z5s0jKyuL4cOH3/4PphSX8/LJLNCTbzTiri3jLbwCAY3cuHwmlTqtvG5tgEIIIYSo9iRJaCG13P5NEua7ka1OIz0xB1dvewtHJYQQQghx6y1atAiALl26mJUvW7aMYcOGAfDxxx+jUqkYMGAAeXl59OrVi88++8xUV61W8/vvv/PCCy/Qrl077O3tGTp0KO+8846pTlBQEOvWrWPixIl88skn1KxZkyVLltCrVy9Tnccff5wrV64wffp04uLiaN68ORs2bLipHSdvhTp2NmxpXY/o3Hw8rbSm8jyDAesSdkdUFIWm9/kT0roGtg5WpnKD3oBKLQ+nhRBCCGFOkoQWUtPVDgUjRqMVybZaLsSmS5JQCCGEEHeFsqyPZ2Njw8KFC1m4cGGJdWrVqsUff/xRaj9dunThn3/+KbXO2LFjGTt27A1jsjQXrQaX60YR7kvN5IUTF5hXP4B73RxLbHd9gjA9MYe1n4TR6fG6srmJEEIIIczII0QLsdKo8HS6epMXZ+/G2bPRFo5ICCGEEEJUJ59ciOdyno6f45LL3ObwxgukXclh39pzGA2ymYkQQgghCslIQgsKdHcgIT2NVK07cRcigbaWDkkIIYQQQlQTSxoH8emFeMYElH29wU6P10VrraZZtwAUlXILoxNCCCFEdSMjCS2otqcTAIZ8dxJTTlk4GiGEEEIIUZUZsrMxGgymYzu1ileCfXDQqE1lC6MTOJyWVWIfao2KDo+G4OBqbSqLPZNKgU5/a4IWQgghRLUhSUILquV+dQ1Cg86dzLwLFo5GCCGEEEJUVcaCAi69OI6YCRMx5OQUW2fdlVTePXuZR8LOcCk3v0z9xp5NY828MNZ8HEZeTkFlhiyEEKIcunTpwoQJEyq1z/Pnz6MoCmFhYZXar7hzSZLQgq7f4TiXBPR6ww1aCCGEEMLS/vrrL/r164evry+KorB69eoidc6cOcPw4cOpWbMm1tbWBAUF8cQTT3Dw4MHbH7C4I+QeO0bW/v1k7txJfvTFYut0cXWku7sTz/l7UdPGqtg6/2UoMKDWqrBx0KK1Vt+4gRBCCACuXLnCCy+8QEBAANbW1nh7e9OrVy92795tqlPSfUJxfvvtN959991KjdHf35/Y2FgaN25cqf3eCYYNG0b//v0tHUaVI2sSWlCA+9UkoVHnTq4mhYykXFy87CwclRBCCCFKk5WVRbNmzRgxYgSPPPJIkfcPHjxIt27daNy4MV988QX169cnIyODNWvWMHnyZHbs2GGBqEV1Z9u8ObWWfYU+PQObenWLrWOvUbO8SRDXrzSYqzegVhS0Jaw/6FfPlYFTW2HvYo1K1igUQogyGzBgAPn5+Sxfvpzg4GDi4+PZsmULSUlJ5eonPz8fKysr3NzcKj1GtVqNt7d3pfd7O1z7XK6n1+tRFAWVSsa73SryyVrQtenGRr09aVZ6LsVlWjgiIYQQQtxInz59mDFjBg8//HCR94xGI8OGDSMkJISdO3fSt29fateuTfPmzXnzzTdZs2aNBSI2t3bt2nK/ckqY3ipuL7tWrXDsep/pOP/SJTK2bjOro1YUVMrVZJ/BaOTFk9EMDj9Lmq7kqcQuNezMRhEe3nSBM4cSKjl6IYS4c6SmprJz505mz57NfffdR61atWjTpg3Tpk3jwQcfBCAwMBCAhx9+GEVRTMdvvfUWzZs3Z8mSJQQFBWFjYwMUnW4cGBjIu+++yxNPPIG9vT1+fn4sXLjQLA5FUVi0aBF9+vTB1taW4OBgfv31V9P7/51uvH37dhRFYcuWLbRq1Qo7Ozvat2/PqVPmeyTMmDEDLy8vHB0deeaZZ5g6dSrNmzcv9TM5fvw4DzzwAE5OTjg6OtKpUyfOnj1b7LUB9O/fn2HDhhW53iFDhuDk5MSzzz7L119/jYuLC2vXrqVhw4ZYW1sTHR1NXl4eU6ZMwc/PD3t7e9q2bcv27dtNfV1rt3HjRho0aICDgwO9e/cmNjbW9HewfPly1qxZg6IoKIpi1v5uJiMJLcjBWoOzrUJajpFYezfOnLtI46Zl351OCCGEuJMYjUayDbd/6Q07lQpFqZwRVGFhYRw/fpwffvih2KfcLi4ulXKem1HeqTWKohAZGUlwcPCtCUhUiD4zk4vPP0/+2XP4zn4f53+/lF4vMjuPLcnp6AxGTmfn0dr5xrf+lyKS2fvbWVDApUZrPGo63orwhRDihnR5VzdU0lgV/p7WFxgw6I2oVApqrapoXa3KtHO7Xm/AUGBEUYFGq75hXbW67GOoHBwccHBwYPXq1dxzzz1YW1sXqXPgwAG8vLxYtmwZvXv3Rq0ujOHMmTOsXLmS3377zaz8vz788ENeffVV3n77bTZu3Mj48eOpW7cuPXr0MNV54403eP/99/nkk0/49ttvGTRoEEePHqVBgwYl9vvaa68xd+5cPD09ef755xkxYoRpmvT333/PzJkz+eyzz+jQoQMrVqxg7ty5BAUFldhfTEwM9957L126dGHr1q04OTmxe/duCgrKt9btnDlzmD59Om+++SYAO3fuJDs7m9mzZ7NkyRLc3d3x8vJi7NixnDhxghUrVuDr68uqVavo3bs3R48eJSQkBIDs7GzmzJnDt99+i0ql4qmnnmLKlCl8//33TJkyhZMnT5Kens6yZcsAbslIzupIkoQWFuBux9FLWSRZexB3/izQ0tIhCSGEEBaRbTBQ+6+jt/28Z+9tgn0pN+jlERkZCUD9+vUrpb9bJS4uDi+vsj2YdHSUJFFVpLKxwa51awzpGdi1bVtsnXr2NqwJrcO5nDxaO9uXqV/fuq406VITtVYlCUIhhEV9Of7q8hwjPuyIrePVaaf/bIpm39pzNOzgw31PFybBvnppJwX5Bp6e0Q4nD1sAjm2PYdcvkYS0rkHPkY1Mdb95bQ+5mToGTW+Du68DABF7YmnUya/MsWk0Gr7++mtGjRrF559/TosWLejcuTODBg2iadOmAHh6egJXHxD+d8pvfn4+33zzjalOSTp06MDUqVMBqFu3Lrt37+bjjz82SxIOHDiQZ555BoB3332XzZs38+mnn/LZZ5+V2O/MmTPp3LkzAFOnTqVv377k5uZiY2PDp59+ysiRIxk+fDgA06dPZ9OmTWRmljzzceHChTg7O7NixQq0Wq0p3vLq2rUrkydPNh3v3LkTnU7HZ599RrNmzQCIjo5m2bJlREdH4+vrC8CUKVPYsGEDy5Yt47333gNAp9Px+eefU7t2bQDGjh3LO++8A1xN8tra2pKXl1dtp2PfKjLd2MJqezgDUFDgzpWkUzeoLYQQQoiqzGg0WjqEGxo6dCi2trZlrv/UU0/h5OR0CyMSFaFoNHhPn07Q6lVoa9QwlRv/Mxq3iaMdD3m5mo6v5Ov4Mym9xH5VKoVOj4fQ/pHapjK9zoAuX1+J0QshRPU3YMAALl++zNq1a+nduzfbt2+nRYsWfP311zdsW6tWrRsmCAHatWtX5PjkyZPlrvNf1xKZAD4+PgAkJFxdZuLUqVO0adPGrP5/j/8rLCyMTp06mRKEFdWqVasiZVZWVmbxHj16FL1eT926dU0jOh0cHNixY4dpejOAnZ2dKUEIV6/z2jWKkslIQgszrUuY705m7nnLBiOEEEJYkJ1Kxdl7m1jkvJXl2lPziIgIQkNDK63fynRtWk1ZLVq06BZFIsprf+x+3GzcqONaB7g6FVxz3fSo7H/+If7dGdT8dD5av6IjYrL0egaHn+NYRg7zGwTwqHfxU6uun35vNBrZ8s1J0hNz6Du6qWk0jxBC3GrPfnJ1pJvGqvD3dGjPAJp18y+y0dKIDztdrXvdFOTGXfxo2NEX5T+/5ofMbF+kbv32PhWK0cbGhh49etCjRw/eeOMNnnnmGd58802ztfaKY29fttHdt8r1ybxr/+YbbmLJlxs9fFSpVEUepOp0uiL1ivtcbG1tzX4vZWZmolarOXToUJGp2g4ODqY//zdhqShKtXiYa2kyktDCav27w7FB50aeMQ6D/vavxSSEEEJUBYqiYK9W3/ZXZa1HCNC8eXMaNmzI3Llzi73ZTk1NrbRzibtLZEok47eNZ8j6IZxKLjr7xGg0EvfOu+SeOMGVhcVPMbNWVDR1sMNFq6aFU9m+oGYk5RJ9IokrFzJIipFN9oQQt4/WWo3W2vz3tFqjQmutNluP0KzudclDtfpq3evXIyytbmVo2LAhWVlZhefSatHrKz4S+++//y5y/N+1BstSpzzq1avHgQMHzMr+e/xfTZs2NU0NLo6np6dp0xC4ukvxsWPHKhRfaGgoer2ehIQE6tSpY/Yqz9RhKyurm/q7uVNJktDCro0kNOS7k6dOISM518IRCSGEEKI0mZmZhIWFmXYKjIqKIiwsjOjoaBRFYdmyZZw+fZpOnTrxxx9/cO7cOcLDw5k5cyYPPfSQZYO/zpIlSxg6dKhpZOFPP/1EgwYNCA4ONi0YLqoOD1sPQlxDqOdWjyDnoovHK4qC/6LPcO7fH+/XXyu2D41K4cN6Ndncqh7BdkUX2S+Ok4ctA15qSfcRDalZXxZ1F0IIgKSkJLp27cp3331HeHg4UVFR/PLLL3zwwQdmv+sDAwPZsmULcXFxpKSklPs8u3fv5oMPPuD06dMsXLiQX375hfHjx5vV+eWXX/jqq684ffo0b775Jvv372fs2LEVvrYXX3yRpUuXsnz5ciIjI5kxYwbh4eGlPlQdO3Ys6enpDBo0iIMHDxIZGcm3335r2jW5a9eurFu3jnXr1hEREcELL7xQ4QendevWZfDgwQwZMoTffvuNqKgo9u/fz6xZs1i3bl2Z+wkMDCQ8PJxTp06RmJhYYoLzbiPTjS3s2khCY4ET6VZ5xMZl4expZ+GohBBCCFGSgwcPct9995mOJ02aBFxd6+/rr7+mTZs2HDx4kJkzZzJq1CgSExPx8fGhffv2zJs3z0JRm5s3bx6vv/46vXr14rXXXuPy5ct8/PHHTJw4Eb1ez9y5c/Hz8+PZZ5+1dKjiX642rizuuZg8fR5W6sIpv0aj0fTFTevtje/7s8za5Z8/j1VgoOlYURT8bArbn8zMYc75OObVD8BRU/wGPq7e9rh6F448zMnIJy4qnaCmHpVxaUIIUe04ODjQtm1bPv74Y86ePYtOp8Pf359Ro0bx6quvmurNnTuXSZMmsXjxYvz8/Dh//ny5zjN58mQOHjzI22+/jZOTEx999BG9evUyq/P222+zYsUKRo8ejY+PDz/++CMNGzas8LUNHjyYc+fOMWXKFHJzc3nssccYNmwY+/fvL7GNu7s7W7du5aWXXqJz586o1WqaN29Ohw4dABgxYgRHjhxhyJAhaDQaJk6caHYvVV7Lli1jxowZTJ48mZiYGDw8PLjnnnt44IEHytzHqFGj2L59O61atSIzM5Nt27bRpUuXCsd0p1CMMim7iPT0dJydnUlLS7vlC3UbjUYaTP+DXB3Udp7L+Caf8OADt389JiGEEOJ2ys3NJSoqiqCgIGxsbCwdTpVU2md0s/cqDRo04I033uDJJ5/kn3/+oU2bNnz++eeMHDkSgKVLl7Jo0SIOHjxYKddyp7ud947X+yniJ44mHuXNdm+iVRddLD5t7VouT51GjWnTcHv6qSLv641GOu+P4Ex2HkN83fmgnv8Nz1mQr2fNvH+Ii0rnvsH1adjRt1KuRQhx95J7guIFBgYyYcIEJkyYUGIdRVFYtWoV/fv3v6Wx9OjRA29vb7799ttbeh5xcyrj3lGmG1uYoijUdL36l5dg50Zc1NkbtBBCCCGEuDkXLlygY8eOwNW1fdRqNffcc4/p/c6dO5vtECiqnviseGYfmM2as2vYcH5DsXVyjx8HgwFdTEyx76sVhYUNa9HRxYFXg8u2aL9Ko8IzwAlrWw3etZ0rHL8QQoiqKTs7m48++ojjx48TERHBm2++yZ9//snQoUMtHZq4DWS6cRUQ7OnEmYRc8g0eJCaetnQ4QgghhLjD2dnZmS2s7unpabYjIEBBQcHtDkuUQw37Gnxy3yfsvrybB4KLn17lNXUqdq1b49C1a4n9NHO049fQOmZlifkFeFgV/zVBpVLo9HgILXoF4OAqI36EEOJOoygKf/zxBzNnziQ3N5d69eqxcuVKunfvbunQxG0gScIqIMjdAUjAkO9GRs45S4cjhBBCiDtc/fr1CQ8PN+1+ePHiRbP3IyIiCLxuHTtRNXWq2YlONTuZjvUGPRczLhLoHAhc/aLneN2XOqPRSMo33+Dy6KOo7Ivf3XhjYhrPH7/A/AYB9PNyKbaOoihmCcLky1ns/Pk03Yc3xN65bBuiCCGEKF1Z1i+8FavH2dra8ueff1Z6v6J6kOnGVcD1OxznGmMx6A0WjkgIIYQQd7LZs2dTr169Et+Pjo7mueeeu40RiZtlNBqZfWA2g9YNYn9s8YvLJ376KfGz3id65DMY9fpi66xNSCXHYGB7cnqZz7tl+QkuRaSw+9czFY5fCCGEEJYnIwmrANMOxzo38lVJZKbk4eRha+GohBBCCHGnurbbYElGjx59myIRlUVn0HEm9QzZumyScpOKrePQuTMpP67AZeBAFHXxOxnPbxBAG2d7nvRxL9N5FUWhx8hG7Polknsfr1vh+IUQQghheTKSsAoIcLuaJDTku5GlzSI+PusGLYQQQgghKtfo0aNJTEy0dBiigqzUVizqvogF3RbQJ6hPsXVsmzWj9ob1uAx4pMR+1IrCUD8PtCoFuDpS8Oe4ZPIMJc90cfGy44ExzbBxKNxhOe1KdgWvRAghhBCWIknCKsDXxRa1yghoSHCw5/SFeEuHJIQQQoi7zHfffUd6etmmmIqqyVptzb017zUdZ+myWBW5ymzNKrVz4Y7EhtxcLj7/AtkHD5bY56KLVxh3MponjpxDX8a1r6KPJ/HDm/vY979zt2S9LCGEEELcGjLduApQqxS8na2ISdERa+tO7PnzQCNLhyWEEEKIu4gkc+4seoOeCdsm8Hfs38RmxTK6edEp5IlffEHm9u3kHj9O7c2bUNkU3a24iYMtjmoVPd2dUCtKmc4dfz4dg8FIRlLuTV+HEEIIIW4fSRJWEcEejsSkJJOtuJMUL4s+CyGEEEKIilOr1Nxb816OJh6lk1+nYut4PPccugsXcH3yyWIThACd3BzZ2bYB3tbaYt8vTuu+Qbj7OVCrsTtKGROLQgghhLA8mW5cRQR7OAJg1LmTniVJQiGEEELcXhkZGQQHB1s6DFGJnm74NOseXkcTzybFvq+yscHvo4+wa9XKVFaQklJkVOn1CUKdwcgzx6LYkZxR6rmDm3ui1hR+1Ti88QKpCbJOoRBC3AxFUVi9erWlwxB3MEkSVhEB7vbA1c1Lcg2XMRhkyo8QQgghbo+EhASOHTtGeHi42UtUf+62hbsUx2TGMGn7JNLy0oqtq4tP4PyAR4l/dwZGvb7YOosvXeH3K2mMOh5Fmq6gTDGc2H2ZvavO8tuHh8jN0pX/IoQQoopRFKXU11tvvVVi2/Pnz6MoCmFhYbct3qpKPouqR6YbVxG1TDscu5OvSiQzORcnD1sLRyWEEEKI4sTFxTFz5kzWrVtHTEwMXl5eNG/enAkTJtCtWzdTvaCgIBYvXoxGo+Hjjz9m//79pKenExISwksvvcTgwYMteBVw6NAhhg4dysmTJ02jxxRFwWg0oigK+hISRcIyIuLScbWzooZT8VODS2M0Gnl5x8uEJ4ajUlTM6TynSJ3s/fvQxcaStXs3hsxMs01OrhlZ04MTmTk86OWCs7ZsXyVqNXbHq5YjwaGe2NiXfdqyEEJUVbGxsaY///TTT0yfPp1Tp06ZyhwcHCwRVqXT6/UoioJKZT6+LD8/HysrKwtFJW4lGUlYRdRy/zdJqHMnW5NBokzHEEIIIaqk8+fP07JlS7Zu3cqHH37I0aNH2bBhA/fddx9jxowx1QsPDyclJYXOnTuzZ88emjZtysqVKwkPD2f48OEMGTKE33//3YJXAiNGjKBu3brs2bOHc+fOERUVZfZfUXVcTM7m6aX7eeSzPZxPzCp3e0VReLP9mzTzbMZLrV4qto5zv374zZuH/9IlxSYIAaxVKhY0rEVPj8L34/N05BsMJZ7b3tmah6e0oEWvWqayAp1eNssRQlRb3t7eppezszOKopiOvby8+Oijj6hZsybW1tY0b96cDRs2mNoGBQUBEBoaiqIodOnSBYADBw7Qo0cPPDw8cHZ2pnPnzhw+fLhccRkMBj744APq1KmDtbU1AQEBzJw5E4Dt27ejKAqpqamm+mFhYSiKwvnz5wH4+uuvcXFxYe3atTRs2BBra2uio6MJDAzk3XffZciQITg5OfHss88CsGvXLjp16oStrS3+/v6MGzeOrKzC31GBgYG89957jBgxAkdHRwICAvjyyy9v+FkIy5GRhFWE/78jCTHYkGSvJTImieCG7qU3EkIIIe4gRqMRY07ObT+vYmtbrs0VRo8ejaIo7N+/H3t7e1N5o0aNGDFihOl4zZo19O7dG61Wy6uvvmrWx/jx49m0aRO//fYbDzzwwM1fRAWdO3eOlStXUqdOHYvFIMrGaAQHaw3WGhVuDhUbvVHXtS7f9vnW7P/3nIIcbDWFs1ecevU0a5N94ADagAC0NWoU22eqroBHw87gY61lSeMgnDTqYutptIXlBr2BDV8cw9peQ9enGqDWyrgFIUQho9FIQX7JDx5uJY2V6qY3XPrkk0+YO3cuX3zxBaGhoXz11Vc8+OCDHD9+nJCQEPbv30+bNm34888/adSokWlEXkZGBkOHDuXTTz/FaDQyd+5c7r//fiIjI3F0dCzTuadNm8bixYv5+OOP6dixI7GxsURERJQr/uzsbGbPns2SJUtwd3fHy8sLgDlz5jB9+nTefPNNAM6ePUvv3r2ZMWMGX331FVeuXGHs2LGMHTuWZcuWmfqbO3cu7777Lq+++iq//vorL7zwAp07d6ZevXolfhbCciRJWEXYaNW4O6hJytRz2d6dS+eigLqWDksIIYS4bYw5OZxq0fK2n7fe4UModnZlqpucnMyGDRuYOXOmWYLwGhcXF9Of165dy6RJk0rsKy0tjQYNGpQ73srUrVs3jhw5IknCaiDA3Y6VL7Qnv8CAk03Fp+xe/+X3cPxhJm2fxIedP6S1d+sidXOOHefic8+jcnEm8Ntv0fr5FalzKiuXmDwdWXoDWXp9iUnC68VHpRN9Ihm1WiG0Ry08at4Z0/KEEJWjIN/Al+N3WOTcz37SGa31jf8dK82cOXN45ZVXGDRoEACzZ89m27ZtzJs3j4ULF+Lp6QmAu7s73t7epnZdu3Y16+fLL7/ExcWFHTt2lOmBYkZGBp988gkLFixg6NChANSuXZuOHTuWK36dTsdnn31Gs2bNzMq7du3K5MmTTcfPPPMMgwcPZsKECQCEhIQwf/58OnfuzKJFi7Cxubo0xv3338/o0aMBeOWVV/j444/Ztm0b9erVK/GzEJYjScIqJMjDnqTMdNI17iTHRlk6HCGEEEL8x5kzZzAajdSvX7/UejExMYSHh9OnT59i3//55585cOAAX3zxxa0Is8yWLFnC0KFDOXbsGI0bN0arNU8+PfjggxaKTBTHzd58hMXG43EkpOfydLvACvW3/PhyknKT+DHix2KThGoXFzReXmh8vFH/+0Xuv9q6OLAmtA4aRcHHumwjQHzquNBvbDN0eXpJEAoh7ijp6elcvnyZDh06mJV36NCBI0eOlNo2Pj6e119/ne3bt5OQkIBeryc7O5vo6OgynfvkyZPk5eWZrY1cEVZWVjRt2rRIeatWrcyOjxw5Qnh4ON9//72pzGg0YjAYiIqKMj0Ivb6va9OyExISbipGcetIkrAKCfZw4uD5dAz57qRlnbZ0OEIIIcRtpdjaUu/wIYuct6zKuoba2rVr6dixo9nIwmu2bdvG8OHDWbx4MY0aNSrzuW+FvXv3snv3btavX1/kPdm4pGqLSsxi3I//kFdgwMvJhl6Nyj8C44POH/Bl+Jc80+SZYt+3qulHrR9/QNFqUZUyBayJo/lI3P2pmWTqDXR1dyqxjX9DN7PjjORcUhOy8a/vVkILIcTdQmOl4tlPOlvs3JYydOhQkpKS+OSTT6hVqxbW1ta0a9eO/Pz8MrW3vcH9zLXNR66/l9Hpiu44b1vCMiz/nUGRmZnJc889x7hx44rUDQgIMP35vw8gFUXBUMo6tsKyJElYhdRyv/pDZ9C5kVtwHKPBiKK6ufUQhBBCiOpCUZQyT/u1lJCQEBRFueH6PmvXri12FN6OHTvo168fH3/8MUOGDLlVYZbZiy++yFNPPcUbb7xBjRLWnBNVU6C7HWPvq8ORS6l0q+9VoT6s1da8GPqiWdmxxGM09mhsOta4upq9n/zDDygaDa6PPVZsn9E5eQw7FkWqTs+3TYPpVkqi8Jr83ALWLTxCcmw2PUY0JKSV/L8oxN1MUZSbnvJrKU5OTvj6+rJ79246dy5MdO7evZs2bdoAmNbd+++DuN27d/PZZ59x//33A3Dx4kUSExPLfO6QkBBsbW3ZsmULzzxT9OHPtam9sbGxuP77b3tYWFjZL+4/WrRowYkTJ25qyZKSPgthObJKcBUS8O/mJYZ8d3SqK2Sm5lk4IiGEEEJcz83NjV69erFw4UKz3fuuSU1NJTMzk23btvHQQw+Zvbd9+3b69u3L7NmzTbsCWlpSUhITJ06UBGE1pCgKL3YL4YunW6FRF44OydVV/IvW6jOreWLdE8w5MKfYUbM54eHEvzuDuOlvkn3wYLF9eFtr6eHuTFNHO9q5lG0qsVqtwr2mA7YOWryDi99VWQghqouXXnqJ2bNn89NPP3Hq1CmmTp1KWFgY48ePB8DLywtbW1s2bNhAfHw8aWlpwNUk37fffsvJkyfZt28fgwcPvuHowOvZ2Njwyiuv8PLLL/PNN99w9uxZ/v77b5YuXQpAnTp18Pf356233iIyMpJ169Yxd+7cCl/nK6+8wp49exg7dixhYWFERkayZs0axo4dW+Y+SvoshOVIkrAKqeV+NUlozHcnV5NGUnzRLx9CCCGEsKyFCxei1+tp06YNK1euJDIykpMnTzJ//nzatWvHhg0bqFu3LoGBgaY227Zto2/fvowbN44BAwYQFxdHXFwcycnJlrsQ4JFHHmHbtm0WjUHcHPV1s04W7TjLo5/v4UpGxR40p+elA6BSit/d06ZJE9yfexaXJwZh27L4TYasVCrm1ffn1+a1sVMXftUoMJQ8VV+tVdF9WEMGTmuNo5uNqbys0/uFEKIqGTduHJMmTWLy5Mk0adKEDRs2sHbtWkJCQgDQaDTMnz+fL774Al9fX9NDxaVLl5KSkkKLFi14+umnGTdunGln4bJ64403mDx5MtOnT6dBgwY8/vjjpvX/tFotP/74IxERETRt2pTZs2czY8aMCl9n06ZN2bFjB6dPn6ZTp06EhoYyffp0fH19y9xHSZ+FsBzFKL99i0hPT8fZ2Zm0tDScnG48RaKypGXraPbOJgDaWL/BqHt/oUe32rft/EIIIcTtkpubS1RUFEFBQabd76qT2NhYZs6cye+//05sbCyenp60bNmSiRMnsnTpUmrVqmV24z1s2DCWL19epJ/OnTuzffv2Ys9R2mdUWfcqM2fOZN68efTt25cmTZoUWTeouHWGRFGWune8Xlq2jm4fbScxM5/ZA5rweOuAGzcqxqH4Q4R6haJSSh5LYDQaTUlEY0EBxrw8VMXs9n3NL3HJLL50hW+aBONtXbadmeOi0ti54jS9RjXGyaPsI2mEENVPdb8nEKKqqIx7xyozkvD9999HURTT9tnFOX78OAMGDCAwMBBFUZg3b16ROtfe++9rzJgxty74SuJsp8XB5uoNV4yjG5eizls2ICGEEEIUy8fHhwULFnD+/Hny8vK4dOkSa9asoWPHjvzxxx9F1iP8+uuvMRqNRV4lJQhvlyVLluDg4MCOHTtYsGABH3/8selV3H2WqLqc7bT8+nx7Xru/QYUThAAta7Q0JQgNRgNfHPmC5FzzEa+mBKHRSNy7Mzj/9NPoStipMkdvYNa5WMIzcvgptmwjZ41GI3/9eJqECxkc+D2qwtcihBBCiPKpEhuXHDhwgC+++KLYbbavl52dTXBwMAMHDmTixIkl9nX9opfHjh2jR48eDBw4sFJjvlUC3Ow4cTmLZCt3Ui6ft3Q4QgghhCiH5ORkJk6cSOvWrS0dSplERUkC5k4S6GHPqHuDTcf5BQaOX04jNMC1lFYl+/zI5yw6soj1Uev5pd8vaNXmowALEq6QsXkz+pQUco8fR1vMtDhbtYpVoXVYHpPEi7XKNm1OURTuf6Epf68+S6dBdSsUuxBCCCHKz+IjCTMzMxk8eDCLFy827bBTktatW/Phhx8yaNAgrK2ti63j6emJt7e36fX7779Tu3Zts52F/isvL4/09HSzl6XU9rg67NNQ4E5aRqTF4hBCCCFE+Xl5efH6668Xu56bELeTwWDk5V+PMPDzvawJi6lQH32C+uDn4MfwxsOLJAgBtDW8CFzxI77vz8LxvvtK7KeWrTXT6/ii+vfnwmA0sjkxrdQ1Bx1crek+vCFWNoVjGqJPJGEoZW1DIYQQQtwciycJx4wZQ9++fenevXul952fn893333HiBEjSr1ZnzVrFs7OzqaXv79/pcdSVrXcr67nYsh3J7fgEka5ERJCCCHEbbZmzRq++eYbS4chboLeaOTabaSzbdnWAfyvIOcgVj20iofqFC4k/9/EnlVAAM7XLTSvz8ggc8eOUvv9MCqOp49GMf1M2ZOXZw4l8L/5R1i3MBy93lDmdkIIIYQoO4smCVesWMHhw4eZNWvWLel/9erVpKamMmzYsFLrTZs2jbS0NNPr4sWLtySesgj4d4djg86dfOLJTK3Y7nRCCCGEEBX1yiuvMHz4cEuHIW6CVq1i3uPN+fWF9nSpV77dMa9nqyncNCSnIIdnNj3DpvObiq1rzM/n0tgXufj8C6T89HOJfTpo1ChAYwe7csWi0apw9bZDrbb4OAchxC0ge6oKcXMq42fIYmsSXrx4kfHjx7N58+ZbtoPR0qVL6dOnzw234La2ti5x+vLtFmgaSehGnjaNlIRsHN1khychhBBC3D4RERGWDkFUApVKobm/i+k4OSufxTvPMbF7Xaw05U+0rYhYwf64/USmRNLetz0OVg7mFdRqrOvUIffoUWybNimxnzEBXvRwd6Kufdnvceu09MLN1x6XGoWJxet3WRZCVF9a7dXRztnZ2djaym7mQlRUdnY2UPgzVREWSxIeOnSIhIQEWrRoYSrT6/X89ddfLFiwgLy8PNRqdYX7v3DhAn/++Se//fZbZYR729T6dyShUedCmnUuZ+PSCKjvZuGohBBCCHE3SU1N5bvvvmPs2LGWDkVUEqPRyPPfHmL/+WQS0vOY+1izcvcxpOEQErIT6FGrR9EEIaCo1dR4/TXchg/DqmbNUvu6PkGYozcw4lgUYwK86OjqWGIbNx97s+vZuvwknrUcadKlpiQLhajG1Go1Li4uJPy7S7qdnZ38TAtRDkajkezsbBISEnBxcbmpXJrFkoTdunXj6NGjZmXDhw+nfv36vPLKKzd1UQDLli3Dy8uLvn373lQ/t5uXozVWGsgvUBPr6MqFqIvQJcjSYQkhhBDiLrBlyxaWLl3KqlWrsLOzkyThHURRFMZ0rUPMb0d5oUvwjRsUQ61S80qbV8zKLmVcwtPOE2u1tek81ycI86OjiXvrbXxmvYe2Ro1i+10QHc+25AyOZ+bw9z0NsSvDdOILR5OI+DuO0/vj8W/ghqu3/Q3bCCGqLm9vbwBTolAIUX4uLi6mn6WKsliS0NHRkcaNG5uV2dvb4+7ubiofMmQIfn5+pjUL8/PzOXHihOnPMTExhIWF4eDgQJ06dUz9GAwGli1bxtChQ9FoLHaJFaIoCn6uNkRdySXBxp2Uy+eBey0dlhBCCCHuUBcvXmTZsmUsW7aM6OhoBg0axKpVq+jWrZulQxOVrHNdT7ZO6Yy1pvBhvE5vQFvBNf4SshMYvnE4fg5+fHLfJzhbOxepE/va62QfOEDcW2/jv+izYvt5MaAGl3J1PO7tVqYEIUCtJu60H1AHrZVKEoRC3AEURcHHxwcvLy90Op2lwxGi2tFqtTc92A4smCQsi+joaFSqwhuFy5cvExoaajqeM2cOc+bMoXPnzmzfvt1U/ueffxIdHc2IESNuZ7iVpraHE1FXctHp3UlLOWPpcIQQQghxh9HpdKxevZolS5awc+dOevfuzYcffsgTTzzBa6+9RsOGDS0dorhFrk8QRsZnMHL5QeYMbEaboPIvbxOTGUNmfiZJOUklLpbuM2sWcW+/jffbb5XYj41axScNAszKTmflUsNKg7O2+K8riqIQ2sO8TXZ6PpkpuXjVcirfhQghqgy1Wl0piQ4hRMVUqSTh9Ym+4o4DAwPLtFtLz549q/XOSLWubV6icydXF01Gcq5sXiKEEEKISuPn50f9+vV56qmnWLFiBa6urgA88cQTFo5M3E7zt54hOjmbjzef5odRbcu9BlioVyhf9/4ae609LjYuxdaxqulHwOIvzcryo6OxCggotj7AlXwdg46cxUal4sdmwdSyvfEGg/oCAxu+OEpCdAY9RzYiuLlnua5FCCGEEFCxuQXilgq8tnlJvjs6VTwXTyRbOCIhhBBCXC8uLo4XX3yR4OBgrK2t8ff3p1+/fmzZssWsXlBQEH/++adZ2ZkzZ3B0dMTFxeU2RmyuoKAARVFQFEVGbNzFPny0KcM7BLJwcIsKbxJQz60eNR0L1yDcHbObPZf3lFg/++BBzvV9gPjZH2DU64utk6QrQAFUCriWMJLwv/QFBqzttajVCq7edjduIIQQQogiqtRIQnFVgGkkoRs6JYnwiEQadvS1cFRCCCHErWU0GskpyLnt57XV2JYrQXL+/Hk6dOiAi4sLH374IU2aNEGn07Fx40bGjBlDREQEAOHh4aSkpNC5c2dTW51OxxNPPEGnTp3Ys6fkRMqtdvnyZVauXMnSpUsZP348ffr04amnnpLdJO8yNlo1b/ZrZFb2T3QKzWq6oFKV//+FMylnmLh9Ijq9jiW9ltCyRssidXKOHsOo06G7fBlK+P+tvr0tG1rVJbPAgJOmbElsKxsN9z/fhJT4bLM1Co0GI0oFrkUIIYS4G0mSsAqq5Xb16ach340sqywOX77IY/rGqCq4qLQQQghRHeQU5ND2h7a3/bz7ntyHnbbsI49Gjx6Noijs378fe/vCZESjRo3M1kNes2YNvXv3RqvVmspef/116tevT7du3SyaJLSxsWHw4MEMHjyYs2fPsmzZMsaNG0dBQQEzZ85k2LBhdO3aVUYZ3mV2nL7CiK8P0LNhDT4ZFIqVpnz3ngFOAXTx70JybjJNPZsWW8d9+DCsggKxb9sWRVVy/55WWjytCo83JaaxJiGVD+v5l7i5iaJScPMp/JlMvpzFpqXH6T68AR41Hct1LUIIIcTdSLJOVZCfqy0qBTBaEe3qhDblBPFR6ZYOSwghhLjrJScns2HDBsaMGWOWILzm+inEa9eu5aGHHjIdb926lV9++YWFCxfejlDLrHbt2syYMYMLFy6wbt068vLyeOCBB6hRo4alQxO3WVqODpUCtlo1WnX5R99Zqa14v9P7zL9vPlrV1eS40WhEZzDfqdSxSxdUtram46QlS8g5cqTEfjML9Iw/Gc3K+BSWXrpS5nh2r4wkKSaTfWvOlfNKhBBCiLuTjCSsgrRqFT7O1sSk5nHJyZM+J45w/kQSPnVcLB2aEEIIccvYamzZ9+Q+i5y3rM6cOYPRaKR+/fql1ouJiSE8PJw+ffoAkJSUxLBhw/juu+9wcqqaO6+qVCr69OlDnz59uHLlCt9++62lQxK32YPNfAl0t6Oet2OFp56rFJXZyNzvTn7HpvObmN91Pq42rkXqZ/z5Jwlz5qJYW1P7j3Vo/fyK1HHQqFnaOIivYq7wvL9XmWPpMaIRu1eeof3DtSt0LUIIIcTdRpKEVVSQhyMxqXkY891Q54ez51wS7ZAbHCGEEHcuRVHKNe3XEoxGY5nqrV27lo4dO5pGFo4aNYonn3ySe++99xZGV3k8PT2ZNGmSpcMQFtC0povZ8UebT9Mu2J12td3L3VdaXhpfhH9BWl4amy9s5rF6jxWpY3dPOxzuuw/rkJBiE4TXtHd1oL2rg+nYaDSyOzWTjq4lTyO2sdfSbUgDs7IzhxLwq+uCraNVCa2EEEKIu5dMN66iarlfW5fQnTO+ecRciSQnM9/CUQkhhBB3t5CQEBRFMW1OUpK1a9fy4IMPmo63bt3KnDlz0Gg0aDQaRo4cSVpaGhqNhq+++upWh23Gzc2NxMTEMtcPCAjgwoULtzAiUVX9Hn6Z+VsiGfrVfmJSy7+pkLO1M9/0/obRzUYzsO7AYuuoHeypueBTPCeMN5UZcnMx5OaW2vfSmEQeDTvLy6culjl5fykimU1Lj/PzewfITpf7aiGEEOK/ZCRhFWVKEurcOBKk0P3ESS6eTKZua28LRyaEEELcvdzc3OjVqxcLFy5k3LhxRdYlTE1NRaPRsG3bNhYtWmQq37t3L3q93nS8Zs0aZs+ezZ49e/ArZfTUrZCamsr69etxdnYuU/2kpCSz2MXdo3uDGtzfxJtGvs74uZR9Wv71gl2CeaH5C6ZjnUHHgdgDtPdrbypTrtsgx2gwcPmVqejiYqn56adovYqfXpxvMKICgm2tyzw12s7JGicPG7yDnLF11N64gRBCCHGXkSRhFRXgdvVLhzHfkxRHBdeUfzh1IkmShEIIIYSFLVy4kA4dOtCmTRveeecdmjZtSkFBAZs3b2bRokW8++671K1bl8DAQFObBg3MpzwePHgQlUpF48aNb3P0Vw0dOtQi5xXVi41WzYInWnB9Di47vwCNSlXunY/h6hThWftm8cvpXxjfYjzPNHmmSB3dpUtk/f03huxsdJdiSkwSjg7w4l5XBxo5FCYvDUYjqlIShm6+9gyc1hq1WjElFvUFBjCCWisTrIQQQghJElZRjf2uLmpuyPXBqLcmR3uBPTHxPGBsWOGFpIUQQghx84KDgzl8+DAzZ85k8uTJxMbG4unpScuWLVm0aBFLly41m2pc1RgMBkuHIKoRlarwvlNvMPLiD/+QmVfA50+1xNW+/Ov6OVo5oqBQx6VOse9bBQQQ9PNP5J6MwK5FaKl9NXYsXMO0wGDkyfCzdHd3YlRNzxLvl61tzb/+7F55hvhzafR6tjFO7hUbLSmEEELcKeSRWRVV09WOIA97jKgoyK7NsUAjBemnSYrJtHRoQgghxF3Px8eHBQsWcP78efLy8rh06RJr1qyhY8eO/PHHHzdMEg4bNozU1NTbE6wQleRMQib7opIJu5jKxZTscrdXFIWJLSfy24O/0cW/S4n1rGrVwql3L9OxLiGBK599hrGUae9rElL4KyWT2VFxxObpyhRPdno+p/fHkXAhg+TLWWW+DiGEEOJOJSMJq7COdTyISsxCn1WHE/7HabzvGNHHk/GoWfIubkIIIYSwnOTkZCZOnEjr1q0tHYoQla6etyMrX2hPdHJ2kV2Qy6OOa+EowvT8dCZtm8SElhNo7FF0+r1RryfmxXHkHDmCPjkF79dfK7bPR2q4klKgx8dai69N2UY42jlZ8dirrbl4IpnAJh4VuxghhBDiDiIjCauwjiFXb1aMWfXIs1Kwzwjj4JkkC0clhBBCiJJ4eXnx+uuvy9Ig4o5Vz9uRHg1rmI5jUnP4cX90hfv79PCn7Ivbx9SdUykwFBR5X1GrcR3yNJoaNXB7+qkS+1EUhWdqetLX08VUdjorl02JaaWe38ndlkadCjcPysvWse27CHIyZfdjIYQQdx8ZSViFtavtjlqlUJDvjkHnTJxLChlXLpGf2xQrG/mrE0IIIYQQlpOr0/PM8oOcjE0nLUfH851rl7uPCS0nkJKXwsjGI9Goir+/de7bF8fu3VFZW5vKClJS0Li6lthvVoGekceiiMzOY159fwb5uJcpnh0/nibyQDyp8dk8PLlF+S5GCCGEqOZkJGEV5mSjpVlNZwAKskIID1KwTz9JzOlUywYmhBBCCCHuetYaFQ8198XL0Zp+zXwr1Ie91p45nefQwL1wB/BTyafI1pmveXh9gjD31CnO9uhJ0pIlGI3GYvvVqhQ6uTribaWlm7tTmeNp2bsWbr72dHi0+I1VhBBCiDuZJAmruI4hngDos0KI8gavhHDOHU+0cFRCCCGEuBN07tyZb775hpycHEuHIqohRVF4vnNttkzujJ9L4c7AubqSNxi5kYsZF3lm0zMM2zCMxJzi73nTN2zAkJlJ1p69UMJu3VYqFe/VrcnWNvXwtNKayhNusKmJu58Dg15vg1etwsRiwoV08nOKToUWQggh7jSSJKziOl1blzC7LgZUqHUn2HUp2cJRCSGEEOJOEBoaypQpU/D29mbUqFH8/ffflg5JVEOONoVJuLCLqXT+cBs7I69UqK+U3BRUigqVosJB61BsHc9x4/B57z18585BUatL7c9NWziFeW9qJm3+PsGi6IQSRyACKKrCNUUzU3L536dH+Om9A6RdkWS6EEKIO5skCau45v4uOFhr0BfYYsj1IdI3n+T0c6QmZN+4sRBCCCFEKebNm8fly5dZtmwZCQkJ3HvvvTRs2JA5c+YQHx9v6fBENbRo+xni0/P47u8LFWrf1LMpP/b9kXn3zcNGY1NsHUVRcHnkYbM1CZO/+ZaMbdtK7Xv9lTRyDUaOZZY92ZebpUNjpcLKRo29S9l2TRZCCCGqK0kSVnFatYp7gt2Aq+sSHglScEs+wcUTMppQCCGEEDdPo9HwyCOPsGbNGi5dusSTTz7JG2+8gb+/P/3792fr1q2WDlFUI58MCuXFrnWYM7BZhfvwdfDF297bdLz6zGre2P0Gefq8YutnH/6H+Pff59ILo8k5drzEft+u48unDQL4oF7NMu9A7lHTkcdfa0Of55ug0RaOWizIr/iUaiGEEKKqkiRhNdCxztUpx4asEJKcFTyTwjgeIesSCiGEEKLy7N+/nzfffJO5c+fi5eXFtGnT8PDw4IEHHmDKlCmWDk9UEzZaNZN71jObgrxifzSXUys2VTc5N5n39r3H6jOrWXNmTbF1bBs3wnXQ4zgPeASbRg1L7EtRFAZ6u2F/3RTlt8/EsCwmsdTpxzb2WpzcC9dcPLknlhUz9pN4KaMCVySEEEJUXZobVxGWdm3zEkNOEEaDhmzNBSITrzBQZ0CtlTyvEEIIISomISGBb7/9lmXLlhEZGUm/fv348ccf6dWrl2mk1bBhw+jduzdz5syxcLSiOtp4PI6pvx3Fw8GKjRPuxd3B+saNruNm48a8++axPmo9j9Z9tNg6ipUV3tOnY9TrTf/fGvLz0V2KwTo4qMS+96Vmsuji1bUTQx3taO5kd8N4DHoDhzdeIC0hh/NHk/Co6Viu6xFCCCGqMskwVQO1Pe3xcbbBYFSjzw7iaCCoMyOJPZtq6dCEEEKIu1JcXBwvvvgiwcHBWFtb4+/vT79+/diyZYtZvaCgIP78808ANm7cyD333IOjoyOenp4MGDCA8+fPWyD6QjVr1mTJkiUMHTqUS5cu8euvv9K7d2+zqZhNmzaldevWFoxSVGcNfZxo4ONEv2a+5U4QXtPetz3vdngXlXL1q4vBaOCvS38VqXf9Jibx771H1IABpK9fX2K/bZzteau2L+Nr1ShTghBApVYx4KWWtH4giJa9apXzSoQQQoiqTUYSVgOKotCxjge/HLpEQVYIJwJOM3DfMS4cT6JmfTdLhyeEEEJUCqPRSI7u9q/zZatVl3l9MoDz58/ToUMHXFxc+PDDD2nSpAk6nY6NGzcyZswYIiIiAAgPDyclJYXOnTsTFRXFQw89xKRJk/j+++9JS0tj4sSJPPLIIxw+fPhWXdoNbdmyhU6dOpVax8nJiW032BBCiJL4u9mx8oV2WKkLxybk6vSoFAUrTcXGKyw6sojPj3zOk/WfZFrbaUXeN+Tno4uOxpibi8qh+B2S4eo99vMBXmZl6QV61iSk8JSPe4n/Ltg4aGnzQOEIRaPByLbvImjY0RfvYOcKXZMQQghRFUiSsJroVNeTXw5dgqx65Nb4A+e0MP6+kEoHSwcmhBBCVJIcnZ6G0zfe9vOeeKcXdlZlvyUaPXo0iqKwf/9+7O3tTeWNGjVixIgRpuM1a9bQu3dvtFothw4dQq/XM2PGDFSqq4mRKVOm8NBDD6HT6dBqtUXOczu8+eab/Pbbb7i4uJiVp6eny6YlotJc//NlNBqZ9ttRzidl8dngFvg425bSsnjWamsUFBp7NC72fZWVFf6LF5N94AD299xjdu7SHggYjUYmRUTz+5U0TmTmMqtuzTLFc+yvGE7uieXs4QSGvNceazvL/DwLIYQQN0umG1cTHWq7A6DLq4GhwIHLbmlEZcSQlVr8Lm9CCCGEqHzJycls2LCBMWPGmCUIr7k+2bZ27VoeeughAFq2bIlKpWLZsmXo9XrS0tL49ttv6d69u8UShAA7duwgPz+/SHlubi47d+60QETiTncpJYctJ+MJv5RGdFJ2hfp4pskzrH5oNf1q9zOV5evN/z9W1GqzBKE+LY3zAx4lY2vpo2I7uDpir1Yx0Nu1zPHUa+tNSOsadHg0RBKEQgghqjUZSVhNuDtY08jXieOX09Fn1SE86B/qX4kg+kRbGrT3sXR4QgghxE2z1ao58U4vi5y3rM6cOYPRaKR+/fql1ouJiSE8PJw+ffoAV9cm3LRpE4899hjPPfccer2edu3a8ccff9xU7BUVHh4OXB05deLECeLi4kzv6fV6NmzYgJ+fn0ViE3c2fzc7fn+xE4eik2kb7F7hfoJdgk1/zi3IZcj6Idxb815eaPYCalXRn+mkpV+Re+IE8bNmYd+xAyorqyJ1FEVhuJ8H/b1ccNUWfk2KzMolyNYajar4UYhWthp6jDDfVTk1IZvUuGwCm3pU9BKFEEKI205GElYjHUOu3mQUZIVw1gd8448QeTLRwlEJIYQQlUNRFOysNLf9VZ71CI1GY5nqrV27lo4dO5pGFsbFxTFq1CiGDh3KgQMH2LFjB1ZWVjz66KNl7rMyNW/enNDQUBRFoWvXrjRv3tz0atmyJTNmzGD69Om3NIa//vqLfv364evri6IorF692ux9o9HI9OnT8fHxwdbWlu7duxMZGWlWJzk5mcGDB+Pk5ISLiwsjR44kMzPTrE54eDidOnXCxsYGf39/PvjggyKx/PLLL9SvXx8bGxuaNGliseTt3SLA3Y6HQwun8l7JyGPab+Gk5+oq1N/W6K2cTD7Jz6d+JiUvpdg6nmPH4DZsGH4ff1xsgvB61ycI4/J0PPRPJI+EneFKfsnxKYpi+rdEX2Bg89LjrPssnGM7LlXgioQQQgjLkJGE1UinOp58seMcZNfDgIIm/yS7ElJ4wGBEVcKTTSGEEEJUnpCQEBRFMW1OUpK1a9fy4IMPmo4XLlyIs7OzWYLqu+++w9/fn3379nHPddMib4eoqCiMRiPBwcHs378fT09P03tWVlZ4eXmhVpd9hGVFZGVl0axZM0aMGMEjjzxS5P0PPviA+fPns3z5coKCgnjjjTfo1asXJ06cwMbGBoDBgwcTGxvL5s2b0el0DB8+nGeffZYffvgBuLq2Ys+ePenevTuff/45R48eZcSIEbi4uPDss88CsGfPHp544glmzZrFAw88wA8//ED//v05fPgwjRsXv+adqFxTfjnCjtNXuJKRx5Kh5d9J+/7g+zFgwMPWAw/b4kfuKVZW1Jj6illZ5u7dKIqCffv2JfZ9OiuXfIORbL0BJ00ZfyaM4FPbhfTEXBlJKIQQolpRjJZ4fF3Fpaen4+zsTFpaGk5OTpYOxyRXp6fZ25vIKzBgF/wRPU/EoXeayrSn++Eb4mLp8IQQQogyy83NJSoqiqCgIFPCp7ro06cPR48e5dSpU0XWJUxNTUWj0eDh4UFERASBgYEATJ48mV27drFv3z5T3djYWHx9fdm9ezfti0lSlPYZVdV7lYpSFIVVq1bRv39/4OooQl9fXyZPnsyUKVMASEtLo0aNGnz99dcMGjSIkydP0rBhQw4cOECrVq0A2LBhA/fffz+XLl3C19eXRYsW8dprrxEXF4fVv6PHpk6dyurVq02J3scff5ysrCx+//13Uzz33HMPzZs35/PPPy823ry8PPLyCteFTk9Px9/f/475+7jdjlxM5aVfj/DZ4BbU8XKslD6PJR7jWOIxHq/3eLGjhXWxsUT1fxh9ejr+ny/CoXPnEvu6kJOH3gjBdtbA1f8/cw1GbNWlT8rKyczH1qFw1GLChXQ8/R1R5OG+EEKI26ys944y3bgasdGqaRPkBoA+K4TwIAWPpOOc3HPZwpEJIYQQd4+FCxei1+tp06YNK1euJDIykpMnTzJ//nzatWvHhg0bqFu3rilBCNC3b18OHDjAO++8Q2RkJIcPH2b48OHUqlWL0NDQ2xr/2rVr0el0pj+X9rKUqKgo4uLi6N69u6nM2dmZtm3bsnfvXgD27t2Li4uLKUEI0L17d1QqlSkZu3fvXu69915TghCgV69enDp1ipSUFFOd689zrc618xRn1qxZODs7m17+/v43f9F3sWb+LmwYf69ZgvDopTTyCvQV6i8jP4NJ2ycxc99Mfoj4odg6ahcXHHv1wqZhQ+zatSu1v1q21qYEIcBPccl0O3CKsPTSN165PkF4JTqDlR8cYu38MHR5FbsuIYQQ4laT6cbVTMc6HuyMTMSQFcIV/934xIdx4MT9dMzWyW5qQgghxG0QHBzM4cOHmTlzJpMnTyY2NhZPT09atmzJokWLWLp0qdlUY4CuXbvyww8/8MEHH/DBBx9gZ2dnSija2tre1vj79+9PXFwcXl5eppF7xVEUBb3eMsmMaxup1KhRw6y8Ro0apveuXcP1NBoNbm5uZnWCgoKK9HHtPVdXV+Li4ko9T3GmTZvGpEmTTMfXRhKKirt+6ZyzVzJ5YvHfBHrYsWxYGzwdrUtpWZSD1oGnGjzFysiVPFT7oeLPZ2uLzztvY8jONq1RaDQayT16FNumTUvsW280sjA6gXM5efyVkkFzJ7syxZSakI1KpWBlq0FjJeM0hBBCVE2SJKxmOoZ4wHowZNfGaFSTaXWRCPdcTu2Lp+l9NW/cgRBCCCFumo+PDwsWLGDBggVm5QUFBQwYMID169cXaTNo0CAGDRp0u0IskcFgKPbPouysra2xti5f4kqUXUJ6HlYaFXZWGlwr8BBcURSGNBrCE/WfQKsubH8w7iAta7Q0m36ssitM8qX99huxr72O65Cn8X711WL7VisK/2sRwlcxiYwJ8Cq2TnFCWtXAo6YDNg5asw1O9AUGrGzkK5kQQoiqQR5jVTMNvJ1wt7dCb9CizwngaBDYZxzl2K4Yi+yOKIQQQohCycnJTJw4kdaty7/5QlWRmppq6RDw9vYGID4+3qw8Pj7e9J63tzcJCQlm7xcUFJCcnGxWp7g+rj9HSXWuvS9uv3a13fljXCc+fSIUzb/r/hmNRnJ15RvZen2CcMfFHQzfOJxx28ZRYCgotr4u5uoSPhoPz2Lfv8ZFq2FSoDfqf5N9BqOR4Uej+CE2qdT7cVdve7MpyPv/d46fZuwn7lxama9JCCGEuJUkSVjNqFQKHepc3SVNn1mH4wEK9x7azsGCPOKj0i0cnRBCCHF38/Ly4vXXXy92o4SqaPbs2fz000+m44EDB+Lm5oafnx9HjhyxWFxBQUF4e3uzZcsWU1l6ejr79u2j3b/rx7Vr147U1FQOHTpkqrN161YMBgNt27Y11fnrr79MazACbN68mXr16uHq6mqqc/15rtVpd4N16sSt5e1sQw2nwg17lu6Kov/C3Zy9klmh/pJzk9GqtNR0qIlGVfzIPc9xLxK44kfcnxlpKjNkZ9/wQfzahFTWJ6bx2ulLxObpSq17jS5Pz5lDCaQn5pKdnl/2CxFCCCFuIUkSVkMdQ64mCcmpT46Ngkp/ljinRI7vkg1MhBBCCFF2n3/+uWktvc2bN/Pnn3+yYcMG+vTpw0svvXRLz52ZmUlYWBhhYWHA1c1KwsLCiI6ORlEUJkyYwIwZM1i7di1Hjx5lyJAh+Pr6mtZRbNCgAb1792bUqFHs37+f3bt3M3bsWAYNGoSvry8ATz75JFZWVowcOZLjx4/z008/8cknn5itJzh+/Hg2bNjA3LlziYiI4K233uLgwYOMHTv2ll6/KLtcnZ6lu6KIiMtgz9mkCvXxcMjD/PTAT0xsObGw34JcDEbzKfe2zZujqP4dvWgwcHHMGGLGT6Dg341uitPPy4U3avvyTogfvjZWJda7ntZazWOvteG+p+oT3Lxw5KJeJ0sACCGEsBxZAKMa6vRvklCX7YuV3pZtTbNocGYXYbHudMwJwdpW/lqFEEJUD7JURslux2cTFxdnShL+/vvvPPbYY/Ts2ZPAwEDTaLxb5eDBg9x3332m42uJu6FDh/L111/z8ssvk5WVxbPPPktqaiodO3Zkw4YN2NgUji77/vvvGTt2LN26dUOlUjFgwADmz59vet/Z2ZlNmzYxZswYWrZsiYeHB9OnT+fZZ5811Wnfvj0//PADr7/+Oq+++iohISGsXr2axo0b39LrF2Vno1WzZkwHfj54kafaBlS4nxDXENOfjUYjb+55k8ScRGZ2nIm3fdHp5bnHj5N94CCKVos+JRXNv6NP/0utKEXWJzybncsXF6/wRm1fHDXqYttZ22po2NHXdKzL0/PLrAPUbulFqz6BqDUynkMIIcTtJdmkasjH2ZbanvacvZJFQVYwf9c7xpylf/H1gIc4vS+OJl1kAxMhhBBVm1Z7da2w7Ozs2767b3WRnZ0NFH5Wt4KrqysXL17E39+fDRs2MGPGDOBqAuVW72zcpUuXUhOhiqLwzjvv8M4775RYx83NjR9++KHU8zRt2pSdO3eWWmfgwIEMHDiw9ICFRXk52TC2a2GST6c3MOWXIzzTMZgmNZ3L3d/FjItsu7iNfH0+8dnxxSYJbZs0IfCnFeiio7EOLtwl22g0lrqkgNFoZPzJaA6mZ5NjMPBpg1pliunMoXhS4rKJ2BNL8+4BkiQUQghx20mSsJrqFOLJ2StZ2Otaku90nKOB2WhzjnNspy2NO/tVm7WQhBBC3J3UajUuLi6mjSfs7Ozkd9e/jEYj2dnZJCQk4OLiglpd/CikyvDII4/w5JNPEhISQlJSEn369AHgn3/+oU6dOrfsvELcrC//OseasMvsPpPIrle6YqMt389JgFMAPz/wM/8k/EMzz2am8v8mAG0bNcK2USPTcV5UFLFTp+H9zjvY1KtbbN+KovB6bV/eiIxhapBPmWNq0N4XjZUaGwet2cygGyUlhRBCiMoiScJqqlOIB1/vOY8hux64w9ZmKgbs28E/jRvT9XwGNYKcLB2iEEIIUapru8f+d4dacZWLi8st32H3448/JjAwkIsXL/LBBx/g4OAAQGxsLKNHj76l5xbiZjzZJoCjl9Lo18y33AnCawKdAwl0DjQdJ+YkMmHbBF5q/ZJZ4vB6Ce/PJufIERLmziHgyy9L7PseFwc2taprltz7JiaRYDtrOro6ltgupFUNs+OLJ5PZ/79z3Pd0A9x87Mt4ZUIIIUTFSJKwmmob7I5GpZCSqcZJ58U5nwQ8ko9y3j2d47tiJEkohBCiylMUBR8fH7y8vMx2nxVXpxjfyhGE159nypQpRconTpxYTG0hqg5XeysWPdXCLAl3LCaN6ORs7m9S9tF715t/eD5Hrhxh5t8z+emBn4odveczcwbx78/Ga/KkYnowd337k5k5vBYZg85o5I+WIbRwunHCz2g0svvXSJJisji+M4ZOjxU/clEIIYSoLJIkrKYcrDW0DXZj95kk/HiISyxmW1OF2hf2cuSiIx0fDcFKNjARQghRDajV6tuSEBPFi4yMZNu2bSQkJGAwmO+sOn36dAtFJcSNXZ+Ey8nXM27FP5y7ksW7/Rvz9D1lWwfwelNaT8FgNDCs0bASp/dqPDzwm/OhWVnyN9+gsnfA+ZGHS2znb2PFkz5uxOfrCHW0K1M8iqLQd0wzDqyLou2DwaZymX4shBDiVpEsUjU2sKU/u88kkZhQB6Ovws5GCu9+t53vHurN6QPxNL7Xz9IhCiGEEKIKW7x4MS+88AIeHh54e3ubJR4URZEkoag2NGqFPo29+e1wDA9UcCShk5UTMzrOMCv739n/kVOQw8C6A4tNzOWdO0f8h3NAp0PjXQOHDh2K7dtBo2Z2PX8KDIUJvjyDgfkX4nne36vEHZAd3Wzo+nQDs7Kt30Zg52hF676BaKzkAYsQQojKI0nCaqx3Y28c12i4kl6AX42WpNse5IJnCnpDJMd2WtOok688ZRRCCCFEiWbMmMHMmTN55ZVXLB2KEDdFq1bxUq/6jO5SB3vrwq842yISaF/HHesSknClSchOYOa+mWTpsnDQOnB/8P1F6lgFBOA57kVyT5zAvn37G/apURXem887H8/HF+LZkpTB+pYhZbpvvxKdQcSeWBQFgkM9qREoSwwJIYSoPCpLByAqzkar5qHmvgA45fUGYGszhY5hOziSncuV6AxLhieEEEKIKi4lJYWBAwdaOgwhKs31CcL9UcmMWH6AhxbsJju/oNx9edh6MKb5GNr6tKVXYK9i6ygaDR6jRuH30UemJJ8xP5/492dTkJhYav+dXB2pZWPF6ACvMj/Y9wxwpM/zTWj7ULBZgtBoNJbxqoQQQoiSSZKwmnuslT8AZ2McMeptORqkIuT8IU7XyOP4zssWjk4IIYQQVdnAgQPZtGmTpcMQ4pbIyi/A1c6KpjWdsbMq/wQqlaLi6YZPs7jHYtSqqyMRDUYDC/5ZwJXsK2Z1r0/yXVm0iOSvv+bC0GEY/7PO5/Xauzqwo019+nk6m8r2p2by7eVEDKUk/YKbe9Kyd6DpODs9n59mHuBc2JUS2wghhBBlIdONq7kmfs7U93YkIi6DIB7iMivY1diI/+V9hF+wosOjdbCykb9mIYQQQhRVp04d3njjDf7++2+aNGmCVqs1e3/cuHEWikyIm3dfPS82TbwXa03huIjMvAJiU3MIqeFY5n6uTwCuiFjBF+FfsObsGtY9vA4rtVWR+k69epG5dRseo0ejqEofk2GjLnw/z2Bg8qmLRGbnkabTM7ZWjTLFd3jTBZIuZbL/9ygCm3qgUslyQ0IIISpGskfVnKIoPNbKn3d+P0FOcnPwXMG2Jgovr9rKr326EHkgnkadZAMTIYQQQhT15Zdf4uDgwI4dO9ixY4fZe4qiSJJQVHseDtZmx+/9cZJfD17i3f6NeLx1QLn7a+3dmgZuDXg45OFiE4QANvXrE7TyVxRN4Vet7MOHyTt7FpdHHy1552RF4Wlfd5bFJPKUr3uZY2r7YDBqjYrgZp6mBKHRaMRoMKJSy8QxIYQQZSdJwjtA/1A/Zq0/SXSiES/X2iQ5nyXNJo4MTTTHd1pLklAIIYQQxYqKirJ0CELcNjq9gbi0XPL1Bvzd7CrUR4hrCN/3/R61UrgRytnUs4RfCad/nf6mBOD1CUJDXh6xr79B/rlzGNLTcR85sti+1YrCs/5eDPfzRHvdaMBPzsfTxsWedi4OxbbTWqlp17+2WVnE3jjCt12ky5P1qREkm5sIIYQomyrzaOn9999HURQmTJhQYp3jx48zYMAAAgMDURSFefPmFVsvJiaGp556Cnd3d2xtbWnSpAkHDx68NYFXAW72VvRs6A2Al6E/cHUDk3ZH/yI8I5uEC+kWjE4IIYQQVV1+fj6nTp2ioKD8mzsIUV1o1SqWDm3Fyhfa0762h6n8TEIG+QUlrx1YpB+VFpVy9WuUwWjgzT1vMn3PdD478lmx9RWNBpeBA9H6++Py6KNl6L8wQfh3aiazomJ55J8zRGXnlSk+o8HI4Y0XSLyYSczplDK1EUIIIaCKJAkPHDjAF198QdOmTUutl52dTXBwMO+//z7e3t7F1klJSaFDhw5otVrWr1/PiRMnmDt3Lq6urrci9CpjYKuaAFyIqYHRoOZAXYWWJ/Zyws/AsR0xFo5OCCGEEFVRdnY2I0eOxM7OjkaNGhEdHQ3Aiy++yPvvv2/h6ISofIqi0LJW4feCtBwdTy7eR79Pd3ExObvc/RmNRroHdMfdxp0BIQOKP6dajfvwYdT+Yx1q58JNSpKWLiXn2PFS+69nb8MQX3ee9HEjyM661Lqm86kUHpnSgpa9a9Gsu7+pPDdLJ7sgCyGEKJXFk4SZmZkMHjyYxYsX3zCR17p1az788EMGDRqEtXXxvyRnz56Nv78/y5Yto02bNgQFBdGzZ09q165dbH2AvLw80tPTzV7VTacQT3ycbcjINVCDHujVCgfq6vC8coijB+JIT8qxdIhCCCGEqGKmTZvGkSNH2L59OzY2Nqby7t2789NPP1kwMiFuj7NXMikwGNHpDXg6li0Jdz21Ss2wxsPYMGAD3vaFgxhWRa4iIjnCrK5y3cZA2QcPkvDhHM4//jj5ly6V2L+rVsMH9fyZU68w2ZeqK2BQ2FkOpWWV2M7W0Yp7+tdG/e+ahEajkQ1fHmXVnMOkxJXcTgghxN3N4knCMWPG0LdvX7p3714p/a1du5ZWrVoxcOBAvLy8CA0NZfHixaW2mTVrFs7OzqaXv79/qfWrIrVK4dGWV0cTqjI6ArC1qYqe+7ZxrKaWwxujLRmeEEIIIaqg1atXs2DBAjp27Gi2mUKjRo04e/asBSMT4vZoEeDKn5M6s+iplthoC9cZPHoprVz92GgKk+xnUs7wzt/vMOj3QZxOOV1sfaugIJz69sXlkYexqlnzhv1f//P58fl4tqdkMOnURQxlHBmYGp9NfFQ6CdEZqDUW/woohBCiirLob4gVK1Zw+PBhZs2aVWl9njt3jkWLFhESEsLGjRt54YUXGDduHMuXLy+xzbRp00hLSzO9Ll68WGnx3E4DW15Nbp6J1aAp8OSil4JV3jliHRM4sfsyGcm5Fo5QCCGEEFXJlStX8PLyKlKelZVV4g6sQtxp3OytqOftaDpefzSWfgt2MeWXIxWanuti40JX/6508utEiEtIsXU07u74zZ2D95tvmsr0aWnETJ5C/oULpfb/Yq0aDPJ2Y2aIHyqlcDfjAkPJsbp62zP47XvoMaIhTh62pvKEC+kYSmknhBDi7mKxJOHFixcZP34833//vdn0lptlMBho0aIF7733HqGhoTz77LOMGjWKzz//vMQ21tbWODk5mb2qowB3O9oFu2M0gp9ydVHkrc1UtD+yg5PeGg5vLP2GQwghhBB3l1atWrFu3TrT8bXE4JIlS2jXrp2lwhLCos5eyUSlgI+zTYWS5R62HsztMpc5XeaY2ufp83h377tcyjCfWnz9LshX5n9K+rp1XJo4sdTkpIeVhnkNAujoWpjYXJ2QSo+Dp9idklFiOwdXG2qHFj4USLuSzcoPD/Hr+wfJzdKV+zqFEELceTQ3rnJrHDp0iISEBFq0aGEq0+v1/PXXXyxYsIC8vDzUanUpPRTPx8eHhg0bmpU1aNCAlStX3nTM1cFjrWuy91wS8XFBGH0VdjdUmP/5Vl5u/yANdl+mZe9AHFzLv96KEEIIIe487733Hn369OHEiRMUFBTwySefcOLECfbs2cOOHTssHZ4QFjG2awid63oRUsPBVBaTmkNSZh5Na7qUuR9rdeE991fHvuLn0z/zd+zfrO2/FrWq6Pcct6efIv/CBdxHjSpXctJoNLIgOp6TWbkcSMuiw3XJw9KkxGaj0aqxsddgbWexr4VCCCGqEIuNJOzWrRtHjx4lLCzM9GrVqhWDBw8mLCysQglCgA4dOnDq1CmzstOnT1OrVq3KCLvK69PYB0cbDVfSDbgZ25JjrfBPbT1dDm3klJeGw5tkNKEQQgghrurYsSNhYWEUFBTQpEkTNm3ahJeXF3v37qVly5aWDk8Ii2lS09lsjcK31x7noYW7Wb7nfIX661mrJ61qtOLF0BeLTRACWAUGErBkMfZt25jK0tev5+LYsaVubqIoCr80r8OEWjV4zr9wpGBcno6MAn2J7QKbejD47Xvo8lR9U1KyQKfn8MYL5OcWlPcShRBC3AEs9sjI0dGRxo0bm5XZ29vj7u5uKh8yZAh+fn6mNQvz8/M5ceKE6c8xMTGEhYXh4OBAnTp1AJg4cSLt27fnvffe47HHHmP//v18+eWXfPnll7fx6izHRqvmwWa+fL8vGsfcnqTY/c36Viqm/7CJiVP6UH/nZVr2qoW9i4wmFEIIIQTUrl37hpu8CXE3yy8wYGelRq0otKvtXqE+arvU5qteX5mV7Y/dz8+nf+alVi9Rw75GkTbGggISPpyD7vJlbBs3xuP550vs302rYWqwj1nZ5IiLhGdms6BBLTq7FT+60M7Jyuw4fNsl9q46y5lDCQyc1krWJhVCiLtMld7aKjo6mtjYWNPx5cuXCQ0NJTQ0lNjYWObMmUNoaCjPPPOMqU7r1q1ZtWoVP/74I40bN+bdd99l3rx5DB482BKXYBGPt766gUnkJXusFBeivBVO++XT4Z8/Oe2h5p9NstOxEEIIcbdKT08v80sIAVYaFfMGhbJtShfq1ihMtv1xNJaTsWX/OVEUxZR0MxqNvH/gfTae38g3J74pvr5GQ83PF+H0YD/chg0zlevT0jAaDKWeK1lXQFROHqk6PTVttGWO0c3bHidPW5p0qSkJQiGEuAspxops2XWHS09Px9nZmbS0tGq5iYnRaKTPJzuJiMvg3uax/JP3CfUuGXnpV2smT/qYwfsNDJnRDntnGU0ohBBCVEc3c6+iUqnK/OVfry95qqIoVN3vHUX5xaXl0m3udnJ0ela+0J7QANdy9xGRHMGn/3zKex3fw9naGYCcghxs1KVvmBL93HPok5LxmTkDm3r1SqyXbzAUWaNwZVwyDRxsaehgW2I7fYEBlUpBUV2NIeZUCv/8GU37R+rg5mNf3ssUQghRBZT1XkVWqL0DKYrCY638eef3EyTE18bKzYpTNfO56JlDm2NbOePWlX82R9Px0RBLhyqEEEKI22zbtm2mP58/f56pU6cybNgw027Ge/fuZfny5ablXoQQRakU6FzPk5jUXJqVYzOT69V3q8/CbgvNymb+PZML6Rd4/Z7XqedWNAGou3yZnEOHMeTloVhZFXn/elYqlVmCMC5Px5RTl8g1GFjfsi7NneyKbafWmE8227v6LPFR6Th52HLv43XLenlCCCGqIUkS3qH6h/oxa/1JImKzebjek/wZ/zW/tVd48fc/mDKhOyE7YmjRs1aRdUiEEEIIcWfr3Lmz6c/vvPMOH330EU888YSp7MEHH6RJkyZ8+eWXDB061BIhClHleTnZ8NnglmTnF6D6d8Sd3mBkzPeHeaSFHz0a1ij3dN3k3GQ2X9hMdkE2efq8YutofX2pvWE92QcPYR0UZCrP3L0b26ZNUTuWvLOxwWikh4cTsbk6mjkWjiQ0Go2lxtp9WEP2/e8crfoEmspyMvJBAVsH+S4hhBB3kiq9JqGoODd7K3o29AZAyeiIRtFwNEjFFacMQiP+4qyLirDNsjahEEIIcTfbu3cvrVq1KlLeqlUr9u/fb4GIhKhe7KwKx1ysPHyJDcfjmPLLEdIrsDuwm40ba/uvZXq76TT1bGoqP550nGxdtulY4+GBU+9epmNdfAKXxozlbI+e5F+KKbF/XxsrvmwUyC/NaxfuZmww0u9wJJ9FJ5BXwjqHLjXs6PVMY7PBBX+vOce3r+/l1L64cl+nEEKIqkuShHewwW0DAFh3JJmufv0B+K29isc3/4+9ddUc3XHp6lNAIYQQQtyV/P39i93ZeMmSJfj7+1sgIiGqr/ub+DDmvtq81KsezraFm4Vk5OrK3EcN+xoMrDvQdJyen84Lm1+g76q+nEs7V2ybgitX0Pr6YhUUhNbP94bnsFEXfgVck5DCwfRsFkYnUGAo21L1er2BxIsZ6HL1OHmUvLahEEKI6kemG9/B2tV2p5m/C0cupmKV0QuFlRwKUTFoRyoNz+7hnFNbwv68SLuHa1s6VCGEEEJYwMcff8yAAQNYv349bdu2BWD//v1ERkaycuVKC0cnRPXiYK3hpV71zcqOxaTx+Bd7GdkxiIk96pZ7CvKljEvYae2wUlsR4BhQbB3bxo0IXruGguTkwt2TdTpiJk/BZeCj2HfsWOJ5+9dwJf/ffSztNWpT+fbkdDq6OKJRFW2nVqt49JVWXI5Mxae2s6n89P44CnQG6t3jjVotY1GEEKI6kn+972CKojD2vjoA/O+fNO7z6wvAqvYqnti0lj31tBzdfonczLI/3RRCCCHEneP+++8nMjKSBx98kOTkZJKTk+nXrx+nT5/m/vvvt3R4QlR7vx2OIStfz4Xk7HInCAEaujfkf/3/x4KuC9Coro7vMBqNvLXnLfZe3muqp2g0aL28TMepv60iY9MmLr8yFWNOTon9qxWFJ3zcecLH3VR2OD2LQUfOcd+BCHL1xU9BVlQKfvUKd3TW5enZ9esZtn0bQeSB+HJfpxBCiKpBRhLe4brV96K+tyMRcRk45TwI/M7eBioe25lI7eh9RDmEErYlmnsektGEQgghxN2oZs2azJw509JhCHFHeuOBBrQJcqWhT+GIu7RsHZtPxvNwqB/qYkbq/ZdWrSXAqXAU4ZboLayMXMkfUX+w+dHNOFs7F2nj2LMH+VFRWAUGorIr3MU4Pzoaq4DiRyReE5enw02rprmTndnU5NIoKmjRM4DIgwmEtKphKs9KzcPGUSsjC4UQopqQf63vcCqVwhjTaMIMOvp0w6jA6nYqBm9Yw6761oRvu0R2uqxNKIQQQgghRGVSFIXejX0IcC9M1C3acZYpvxzhxR8PV6jPUK9QBjcYzIjGI8wShPFZhSP4NK6u1Jj6Cq6DHjeV5YSFcbZnLy5NmIixhE1KAO73dOHAPQ2ZXrtwfcOk/AL6HYpkdXwKBmPRtQs1WjXNuwfw6CstUWuufsU0Go1sXHKMH97aR9y5tApdqxBCiNtLkoR3gfub+BDsYU9qtg7vgqsLIe9spMIqPw7/2ENcsIX9/yt+IWQhhBBCCCFE5fFxtsHZVssjoTVNZQaDEWMxybfiuNu6M7XNVJ5v9ryp7HzaeXqv7M2k7ZPQGYpfSij70GFQFFR2diiq0r8G2mvUeFoVbr7yxcUEDqRnsehiAqWNfbx+SnVWaj6p8dlkpeTh6GZTpmsTQghhWZIkvAuoVQrPd7k6nfh/h3No7dUevRrW3qPiqfWr2dXQmhO7LpN4KdPCkQohhBBCCHFnG9o+kF2v3Ee3BoVrCP4v/DIPf7aHXZGJFepzb+xe9EY9Or0OrUpbbB33kSMI/t9aPF8cayorSEoi5qWXyTtzptT+Rwd48VKgNy8H+ZgSgQUGIz9cTiKnhHULHVyteXpGex4Y2xR7F2tT+b615wj7M5r83ILyXqYQQohbTJKEd4mHQ/3wc7ElMTOPINWTAGxrqsIl/RKeiUeJdtOw+9fIMj/BFEIIIYQQQlSMo43WbNTd4p3nCLuYStjFlAr190T9J1j54EomtppoKsvWZfP6rtc5nXLaVGZdpw5aHx/TcdKSpaT/739cfu21Uvt30WqYHORNN3cnU9nqhBQmnbpIn0OnS/wOobVWU7O+m+k4MyWPwxsvsPvXMyTFZJX7OoUQQtxakiS8S2jVKp7rHAzA+n8KaObRAp0G/tdWxdN/rGJzqC0XI1I4H16xp5dCCCGEqH66du1KampqkfL09HS6du16+wMS4i61bFgbXuhSm6HtA01lp+Mz2BoRX+aH+CGuIQQ7B5uOV5xawZqza5i8fTIGY/Gj/Zz7P4Rjjx54jhljKjPm55O1f/8Nz2ulUlHTRsvDXq5mCc9kXckjBG0dtHR+oh7123njU7twPcVLp1LISM694TUKIYS4tSRJeBd5rJU/Hg7WXE7NpYF2CACbW6jwToyibtRBjtWyYvevZ9AXlLyQsRBCCCHuHNu3byc/v+jmZbm5uezcudMCEQlxd/J0tOaV3vVxtCmcKjx30ylGfH2QOZtOVajPe/3upVdgL55t+iwqpXAzkYNxB00JQJt69aj56Xwc7r3X1C7t93VEDxnKpRdfLLX/B71c2Nu2Ic/6e5rKjmRk03z3cV4+dbHYJKNaq6JhR1+6DW1oKtPrDPz51XG+e30vlyMrNpJSCCFE5ZAk4V3ERqtmVKcgADYfUVHftSF5WvijtYpRq39kV0MNick5HN1+ycKRCiGEEOJWCg8PJzw8HIATJ06YjsPDw/nnn39YunQpfn5+Fo5SiLuX0Wgk0MMeeys1D4cW/ixm5hWQq9OXqY86rnWY03kO/Wr3M5XtjNnJ8I3DGbFxRIkjBQsSE1GsrLBr3tw8Jn3R82pVCrbqwq+UmxLTyDcaydYbzEYXliYnMx+XGnbYOllRI7BwdGF2ej5GgyyFJIQQt5PG0gGI22vwPbVYtOMs55Oy6Wk/goiUKWxopabvgSt0/3sTe+t1x37deeq19cbW0crS4QohhBDiFmjevDmKoqAoSrHTim1tbfn0008tEJkQAq7uEjytTwPGdQ3B3rrwK9vn28+y4sBFXutbn4ev2x25rGIzY7HV2NLYo7FZEq/AUIBGdfU8Hs+OwuWRh1FsbE3v5xw5wqUJE3EfPgy3IUNK7P+lIB+6ujnholWbyq7k6xh6NIrhfh4MqOGK6j/JQwdXG/pPakFORj5qbWHCccMXR8nOyKf78IZ4BzkjhBDi1pORhHcZB2sNw9tfHU3411Fb6rrUI9vayMoOKgavX80J/3wS0bP/f1EWjlQIIYQQt0pUVBRnz57FaDSyf/9+oqKiTK+YmBjS09MZMWKEpcMU4q53fYLQaDSyJSKBxMw8bK9LwpXH4/UfZ9OATYxsPNJUdiH9Aj1+7cGiI4tMows1Hh6oHexNdVJ+XEFBbCy5p2489bmlsz217WxMx9/EJHE4PZsll65Q2tjC6wcoZKXlkXQ5i4ykXBzdCvuSTRaFEOLWkpGEd6Fh7QNZvPMcEXEZTGn1IqdTx7KxpYqeh3MYvH4lW9o9hfPOGBp39sPdz8HS4QohhBCiktWqVQsAg0HWIRaiulAUhbVjO7DpeDw9Gnqbytceucy2iARGdgyisd+NR9y52LiYHa+MXEliTiInEk+UOEXY++23sGvTBtumTUxluoQE4t6YjutTT2HfsUOJbUfU9ECrUqhvb2OqozMYmXb6EgO8XbnH2b5IW3tna4bOak/cuTTsna1N5Tt+OEVupo7WDwTJ9xQhhLgFJEl4F3K20/LUPbX4fMdZ/jyioVO9e9kZ8xffd1ExafVWVnXpRYyrI7t+ieTB8c3LvJ6IEEIIIaqfyMhItm3bRkJCQpGk4fTp0y0UlRCiOFq1ir5NfUzHRqORz7adISIug7o1HMuUJPyvF0NfpIFbA/wcCtc+zNJlMXHbRB4OeZietXqitrbG5ZGHzdqlrlhB5o4d6DMycOjUscT+XbUaxtWqYVa2PjGN72KT2JiUxqF2DbEq5vuGlY2GgIbupuO8bB0Re+PQFxho3iOg3NcphBDixiRJeJca2TGIZbujCLuYylMdnmfP5d0cqAcRNfU8/9v3fPb4ZHy2pHDhaBKBTT0sHa4QQgghboHFixfzwgsv4OHhgbe3t9mDQUVRJEkoRBWnKAofPNqUb/ZeYFBrf1N52MVUtkUkMLhtAF5ONqX0AFqVlj5BfczKVp9Zzd7YvcRlx9E7sHex7Zz798eQlYVd27amMkN+Plc++hjnhx/Gpl7dEs/ZwN6Gp33dCbCxwkpVuALWFxcTuNfVkQYOtkXaWNtpGTitFVFHrlAjyKnwWv+MJikmk+bdA2R0oRBC3KQyJQnd3NzK1amiKBw+fNg0lUVUPZ6O1jzRJoCv95zn1/2ZPNr0UX469RPfdFcz66swfu16ghP+wbiuPIN/QzfUGlm+UgghhLjTzJgxg5kzZ/LKK69YOhQhRAU1renCnIEuZmVLdp7j9/BY4tNzeX9A03L32SuwF+l56dR0rGl6eGAwGnh///t0C+hGa+/WWAUEUGPaNLN2GRs3kfz116Rv2ECdLX+iqItfOzHE3oYP6/mblR3LyObNM5fRKgpHOzTCRVv0q6q7n4NZItBgMHJk60Uyk/Pwq+cqSUIhhLhJZUoSpqamMm/ePJydbzx83Wg0Mnr0aPR6/U0HJ26tZ+8N5vt9F/j7XDJPt3+Sddp1RNXI5K8mCi+s/I6XJ7xHvfXpHNsRQ7Nu/jfuUAghhBDVSkpKCgMHDrR0GEKISnZ/Ex9i03IZ0i7QVBaTmsPm43E8HFoTZzttqe09bD14ofkLZmX7YvfxY8SP/O/s/9j62FZsNUVH+1kF1sKxVy9sGjY0SxAmLf0Kh/vuwzo4qMRzqhSF+z2csVYpZgnC7y8nUd/ehhZOdkWWQVIU6PVMY07ujaVOCy9T+dnDCUQdSaRxZz+8g2VnZCGEKCvFWIYtolQqFXFxcXh5ed2oKgCOjo4cOXKE4ODgmw7QEtLT03F2diYtLQ0nJ6cbN6jG3v39BEt3RVGvhiOPdTvLvH8+wi1TYd7nOj59bBQ5TvfQ7UIBg9+5B1sHqxt3KIQQQohbrrLuVUaOHEnr1q15/vnnKzG6u8/ddO8oqq+PNp9m/pZI7q3ryTcj2pS7/fm083x74lscrByY2HKiqfzL8C9p7NGYe3zuQaUUnX2Uc/QY5wcORLG2pu6e3ajs7YvUuZ7RaDQlA1N1BTTbc5w8g5E/W9WlsaNdmWJdM+8fLkWk0KpvIG37Vc/vpEIIUZnKeq9SppGE5d35LiMjo1z1heWM6xrCysOXOBWfgTbrXvwcfiKGGH5vozDyfz8z8o17aB6lZ++qs3R9uoGlwxVCCCFEJapTpw5vvPEGf//9N02aNEGrNR9dNG7cOAtFJoSobMEe9tT3dmRAi8INSnJ1er77+wL9Q/3wcLAupTUEOgfyRrs3zMouZVzi038+BWD9I+up6VizSDtFo8bhvvtQOTiYJQiTv/0O67p1sWvdCuW6dQmvHy2YrTfwoJcLUdl5NLpuncJf45LRKAo9PZyxUxdNTLZ9MBgnz1gatCvc5CXhQjp7V52l8b1+1G5RtsEvQghxtynTSEKA33//nf+zd9/xURVrA8d/Z3vJpvdeqaGE3hFBQVCk2BABETsKKqLXLl4Ve39VRBFBERQboIIU6b33UEII6XWTbHaz9bx/LG7IpQiKhjLfz4cbMjs759nkss55dmae/v37o1Bc+mfTXW6fBn++5giT5u8l1E/DUze5eHbdY2hdEu9+5OTnboPY2uJ6rttkZdAjGcQ0CmrocAVBEAThsne+5ipJSaff+idJEllZWX957MvJ5TZ3FC5esiwjy6BQeBNxP27L46E520kJM7J0whXnPF5hTSHTdk+j1FbKW1e85Wv/Ys8XGNVG+ib2xaQxea/tdvu2ILsqKjjYoyc4nSTPn4c2Le2M1/HIMgrf2Ygy7dftJc/uZErzBK4PP7v7kxWzMtm9Mo9GHSO4anRzX7vskZEUJ1dXFgRBuJSc15WEAIMGDSIiIoLbb7+d0aNHk5qael4CFRrebZ0SmLnuKFmlNRzMSqFVWCt2lOxgTg8Fdyz+mQXd+lAQqGL5V5nc/HR7VOpTH0AsCIIgCMLF5ciRIw0dgiAI/yJJkjjxWL8AvZpWsQH0bFy3sk6WZd787QA9G4fRLiHopHMATxRpjOTJjk/Wa7O77UzZMYVqZzXxpng6RHm3Np94RqHscBA4aBCOY8fqJQgrvvkGPB5MffuiCqpL/ilOiMHukbkpMpjfyiq5OqTuvMGfS8xsrqxhWFQIjYwnV3TOuDoevUlNTOO6ca1VDma/uJGU1mF0vzkNxSlWJQqCIFxOzvpd8MiRI9xzzz3Mnj2bxo0b07NnT2bOnInNZvsn4xP+BWqlgif6e7cSf7r6CKMae88YWd5SQUGQkzHzZrOwox/lxVa2LDzakKEKgiAIgvAPcDgcZGZm4nK5GjoUQRD+Rb2ahPPTA90Yd2XdApCduZV88PshRny2AYv93N8T3B43d7e8m67RXWkX2c7X/t2B73h5w8tklmeijogg6r8vEP/5NN/jsttN6Qf/R+Hzk7Bt23ba8fVKBY8nR7G0fRP0JyT1pueV8tGxEhaWVtaNKcv8sXHOP1RPh+uS6+2Mytpegq3KQfHRqnoJQkuFnbPccCcIgnBJOeskYVxcHM8++yyHDx9myZIlJCYmct999xEVFcW9997Lpk2b/sk4hX9Yn6bhdE4OweHyMH+jgmsSr0GWYEZvJVdtXI2f+QibU7VsXXiU8vyahg5XEARBEITzwGq1MmbMGAwGA82bNycnJweABx98kFdeeaWBoxME4d+iOiFBZtQquaFtLIMzYjDp6s4p/e+CvXy84jBlFvsZxzKoDdyefjsfX/VxvUIms/bP4uv9X7O7dLevzSN78Mje8+9lh4PgUSMxtG+PsVs3Xx/zd99x7IEHsKxZc8brjokJY0BYANeFBfra1lfW0GXDPt4/WnTK5zTrGsV141rR8fq64iYet4c5L23ky2fWUVliPeM1BUEQLjV/aT11r169+OKLLygoKOD1119n165ddOrUiVatWp3v+IR/iSRJPDWgKZIE83bk0zviTjQKDbsTJbamSjw8axor03VUaCSWf7Uf2SM+WRMEQRCEi90TTzzBjh07WL58OTpd3fa8Pn36MGfOnAaMTBCEhpIabuKNG1sxeUhLX5vZ6uCLtdm88ut+Km1OX7vLfXYFLmVZZnyb8QxMGUjv+N6+9t+P/U7f7/ry2a7PUOj1hIwZQ8LMGSg0Gl+fyh9/wrJkKfb9mXXjuVy4/6dYZr+wAD5LTyLJUFeA5ecSM0dsDg5aa+v13Wexec85VCqIbxZCfLMQ32MVhVZcdjd2mwtTcN37Yu7+cvIyK/CI+yBBEC5hf+vQBZPJRO/evenVqxeBgYHs3bv3fMUlNID0mABuaOOtSDZlWQm3NR0BwMw+KpLzjtB/1SIWtTeSf7iSPavzGzJUQRAEQRDOgx9//JEPPviAbt261Tt3rHnz5hw+fLgBIxME4UKiUip4fmBzbmkfR3KYn6/9pV/20f/dVSzZe+qVen+QJIkesT14qdtLBOoCfe3LcpZRWFOI2W72tcmyzKbCTTg93mRkxNNPEXLPPfj3v8bXx7pxIwe6dCXvscfOeN0nkqL4uFkCd8SE+doK7U56bcqkzdq91LjdJz0nJMaPO97ozsBxrettQd4wL4sf397GXnEfJAjCJewvJQltNhszZszgiiuuIC0tjdmzZ/PII4+QnZ19nsMT/m2P9m2MXq1ka46ZOMVAgnXB5AfJLGorMWb+N1RoK9kfq2bd94eoMZ95q4EgCIIgCBe2kpISwsPDT2qvqak5Y7ECQRAuL35aFbd1SuCVoXWrC2VZZvHeIvYWVHHi2royi53N2eW4z2LF3XNdnuOdXu8wJG2Ir+2Q+RB3LLqDq+dejcvjQte4MeEPP4Q6KsrXx7plKzidSCesOAQwf/8D9iNHfOcJGlVKBkUE0drf4OuTWVOLn1JBtE6N8YRiKp/mlvB1QRkVThdqrZLwhLrqnx6PTFCUEZ2fmqSWob727F2lzH9/Bwc3nTlJKgiCcLE4pyTh+vXrufvuu33nEMbGxrJkyRIOHTrEU089RUxMzD8Vp/AvifDXcW/PFADeWZzNvS0eBGDOFWpqtHbGzf6c39oZqXJ7WPXNgYYMVRAEQRCEv6ldu3b8/PPPvu//SAx++umndO7cuaHCEgThIiBJEvMe6MarQ1vQPa0ucfbL7kJu+Hgdd37x52fWa5Vaesf3JikgydeWb8knWBdMs5BmqBQqX/v/bf8/Zu+fTaW9krAHHyD5l58JvfNO3+POwkIKnnySrP4DcJvNp71mz2ATe7ul83GzBF+byyPzVnYhD+8/xj5L3dZkq9uDW5ZRKCSuHNGU0a91wxhYt535yPYScvaUUZhVWe8axUerxLZkQRAuSqo/7+LVrFkzMjMzycjIYPLkydx6660EBAT8+ROFi85dPZL4emMOuRU2zMWtyQjPYFvxNj7rq+Kxb7fSes8mlrVqjW5zCUd2lJDUKuzPBxUEQRAE4YLz8ssvc80117B3715cLhfvvvsue/fuZe3ataxYsaKhwxME4QIXbNRwc/v4em1Wuwt/nYqOyXXn/DndHh6ctY1OycEM6xiPVqX836F8esb1ZNmNy6h01CXerE4r03ZNw+Fx0CGyAwHaALTJyZhrzXhctehUOtyVVRi7dEZ2OFEF1VUwLnjmGVzFJYTedy/61q0B0CgUxOvrkn122cOYmDDWmC10CDD62qceK+GjY8WMS4jg/vhwFIr6K6xbXxVPQLihXsVkc5GVbydvRu+vYdTkLiiVf+uEL0EQhH/VWb9j9enTh61bt7J582buu+8+kSC8hBk0Kib2bQzAh78fZlzLp1ApVGxJhQ2NJR78Zjr7o93khKpYOfsAjlpXA0csCIIgCMJf0a1bN3bs2IHL5aJFixb89ttvhIeHs27dOtq2bdvQ4QmCcBG6p2cKW565ipGd61bqbc6uYOGeQt5fdgi1ou4W9FBxdb1CKH9QKpQE64J937tlN2MzxtInvk+9VYdTd02l2+xufLHnC3SNGxE/bRrxM77wPS673VT/thjLihXIJ5w/6MjJoXrpUl/xE6NSyYSkSL7PSEV1QiJwQ6UFs8uN4YREX43LzQuH8llaVkVghIE2fROISKrbmmwutqI1qAiOMtRLEK6YlcnyWZlUFNac/Q9TEAThX3bWScL33ntPVC++jAzOiCE9xp9qu4sfNjq4I/0OAD7vp8ZQa2bMT3P4tZMf5ko7G37KauBoBUEQBEE4V06nkzvuuANJkpg6dSobN25k7969fPnll7Ro0aKhwxME4SKmViowaOo2rSWFGvnPNU24o1tSvdV4E77ZQZv/LmbpvjOf6WfSmLgj/Q7e7vV2vfNSMysysbvtRBgjfG1FtmKeWv0U8w/PB4WC+BkzCJ84Ef0J97JVv/xC7tgHKHjyqXrXkZ31E5YzWiQzv00aA8LqFsisMVv48FgxTx/MrRfLIWstNreHxBah3PF6N666o7nvMbfLw/4NhexZmYfLUVcRuqKwhqN7ysSiC0EQLhhnlSRs06YNFRUVZz1ot27dyMvL+8tBCQ1PoZB4ekAzAL7emMMVEbeS4J9AhcHDrJ4Krl+5mJCiw6xtomPn8lyKjlQ1cMSCIAiCIJwLtVrNd99919BhCIJwGYgM8J57PrZXqq/N7nJjdbhxe2TSY+qScL/uKmDktI18vzX3T8edetVUfhj4A91juvvaNhRsYN7heczePxtJktA1bkTImDvYUrqdYmsxAAqjH5rERAydO/me57ZYONCxE0dHj8Zj9xZoVCkk2gcYCdOoff3CNWqGRQUzOKJuizHAiJ1ZNF61i41mCwqlAmNA3XZmJLh6THNaXxVPaGxddej96wtZ8P4OVs2uf9a7035y1WVBEIR/w1mdSbh9+3Z27NhBcHDwn3c+3t9uF5VvL3adkkPo2zyCRXuKeGFeJk8NfJq7F9/F4jYKeuxxMWHWp4z9z0s0O+Zg2cx93PREe5RqceaGIAiCIFwsBg0axI8//sjDDz/c0KEIgnCZ0aqULH6kJ0VVtUT463ztKw6UsPJACU0iTb42WZb5bPUR2iYE0TI2EOXx1YiSJJEalFpv3KYhTbmzxZ1EGeuqIXtkDw8tf4hKeyWzB8ym+YjbCB5xG3ZnLbIsI0kStq1b8VitOPPyUWjrEnxln32G21xJwKDr0aak0NrfQGv/+ucwVrnc1HpkXLJMEz+9r/2LvFJmFZRxW3QII1qG1quMDKDRKfEP1RHdKNDXZq1yMP0/awhPMDHk0TYoxJmGgiD8i866cEnv3r19peT/zInLroWL2zPXNmPVwVI2ZVdw5FgLrk+5np8O/8QnAzS88mkOg5f9wq8d+xOypIqNC7LoPDj1zwcVBEEQBOGCkJaWxgsvvMCaNWto27YtRqOx3uPjxo1roMgEQbhcnJggBLizexKNI020ia9bqXe4xMKLP+9Dr1ay8/mrUeK938wqsRCgVxPiV5fUaxTUiEZBjeqNWV5bToxfDG6Pm0bBdY9N2/M5szNnMyZ9DCO6jyD55wW4iovrPdf87Vwc2dnoMzLQpqQA4MzLw7plC/qMDDRxcfirlGzt3IxcuxP/E4qyrDVb2FFto5+jbjux3ePhscxc2vgbGH51Am37JSKfUAm5KLsK2SPjcrjrJQhXfXMAa5WD1n3iiUisOwNREAThfDqrJOGRI0fOeeDY2Nhzfo5w4YkNMjDh6sb8d8FeJv+6j+/HjmNl7kpyQipY0EFi1ILvWN6mE9uTjSh+yyGxRShRqYENHbYgCIIgCGfhs88+IzAwkC1btrBly5Z6j0mSJJKEgiD861LDTaSGm+q1uTwyfZtHICGhPiFx9ty8Paw6WMpbN7ViSBvv/afT7UECVCf0C9WHMufaOdhcNtSKuq3DO0t3Ul5bjkapQZIktCkp1MaGMuKXEbQIa8GEthMIufturJs3Y2iT4XueZdUqCp+fhLFLF+KnfQZ43zNDM/fhTkxEebzI57Mp0fQNDaDFCasLd1XbmFNYzpKyKkZGeytASwqJn0vMOD0yXZsEMGpyF2oqHfV+BlnbSrBU2GnePcbXVnKsmsz1hcQ0DjpplaIgCMJfcVZJwoSEhD/vJFyybu+SyE/b89iZW8nbvx1jYqeJPLn6Seb2UNFpv4OHv/6M5+//D8kFTpZ8sY+bn2qPRnfWi1QFQRAEQWgAsiyzfPlywsPD0ev1f/4EQRCEBtIk0p8pI9qd1G6xu3yP/2F5ZgkPfr2V/i2ieOum1r52WZbRq+q/173b6132lu0l1lS3wGVHyQ62l2zHbDfzWPvHCBwymMAhg/ls12fUZNVwbcq1hPiZ0LdujaFD+7rxnU6OjrodubaWlN8WoYmPJ0anYWBNNZLbAXhXTIZpVDyc4C20cuIOvA+OFrOt2srHzRIYFBGEX5COIruTjZU1tPTT0fv2ZhQeriQ8oS6BmpdZwY6lx6gqtdVLEu5Yegy/IC1xzYLFfZkgCOdEHHAg/CmlQuKVIS1RKiR+2VWItrY9HaM64lDKTL1GRfu9O+m0dR2/dDVRWWJj3feHGzpkQRAEQRD+hCzLpKWlkZv758UBBEEQLkQ/3N+VHc9eTeMTzi/ck19JrdOD4n+OwLrm3VXcPGUdR8tqfG0apYbW4a0J1dcl2JqFNOPlbi9zd8u76z3/+4PfM3XXVAosBQRcO4DE2V9TcXNv3tryFiuOrcBVXIwqPByFvz/qE3bVlU39lAMdOlLy3nsAJOi1PJYUyQR/db3xOwUayTAZyPA3+NpWV1Rz155sHth3jNjGQbTrn4hGp+L3sir2WWwEx/vR4opYkjPCfM9xOdys+e4QCz/Zjd1at8254HAl+9YWYC6yntPPWBCEy4v4WEE4K82i/bmrezIfrzjMc/P2MO3OpxhRfCO7Eu2sai4x7tsvGP10czalaZFW5pHUKpT45iENHbYgCIIgCKehUChIS0ujrKyMtLS0hg5HEAThLwkw1E+2jbsyjetaRXNiirDMYmd/YTUAQUaNr33a6iN8uyWXYR3iGNk5EfBuTb4u5bp6Y8qyzKjmo9hdupvmIc197ZsKN/H57s/Jis2iZ+8PSP1tEW5LDVN3f4pJY+KaxGtwFRcBoI6pSxy68vM51LsPqshIUpctRVIoeC41BmdRMUo8vn5qhYKWJj1tAwz1Yrlv71HMLjdL2jWixy3eMxYP1tRyxGYnVVLRtEsUVaU2/ILqzmo8sKGQ3SvzyLg6ni5DvOfIe9weti89RnCUkfjmISgUoraAIFzuxEpC4ayN751GfLCBgspa5qyzck/LewCY0VeN0lXFhFmfsqy1gVKTgmUz9lFb42zgiAVBEARBOJNXXnmFiRMnsnv37oYORRAE4bxQKCRSwvxIDvPztQUaNCx6qAf/d2sb/HUnnEmYa2ZfQRWV1rr7FqvDRceXlzD80/XUOt2Ad1vw4NQbeKHrCwTqAn1904LSuKnRTVwRd4WvzWPQ8PGOj3l5w8tYnBZi3nqLRuvXsaOVickbJrMydyX2rCyQJJSBgUiKulvywueeI7NNW8w//AjAwPBAfm0Sy+O1ZjxW7wpAi9tDE6OOIJWSVENd0Zefis2M3HWE94rL6HVbE65/KANJkvg0t4TfSisxRumJaRRIZHKA7zl/7AJb9OmeeknVA5sK2frbUcrzaxAE4fIiVhIKZ02vUfLS4HRGfLaRL9ZlM6fFIFIDf+GQ+RAz+qgYO38zV25YyYJu3Rmx0MzK2Qe4ekzzPx9YEARBEIQGMXLkSKxWK61atUKj0Zx0NmF5eXkDRSYIgnD+KBUSjSNN9bYlAzzWrwn9W0SREl6XUDxcXENRlR2XW0anrqtU/MyPu1myr4jH+jbhpvZxALQOa0uYqhmxQXXvnXaXnZHNRpJdlU20X7T3+oGBrD+wlVn7Z6FUKOnR/TEab9mMo6yUOxfdSYwphontJuIqKQFZRh0T7RvPtn07x+68E01KCik/L8CkUvJjmzSqV63CvXkz7ubNUPr5EahW0txPR0tTXSwVThdPH8zzvq7uLWjXKx6An0vM7Ky20QE1qe3CkSQJ6YRVhPvXFnBsXwU6o5rgaG/V+6oyG2vnHiI0zo92/ZP+3i9EEIQL1l9KEprNZubOncvhw4eZOHEiwcHBbN26lYiICGJiYv58AOGi1T0tjCFtYvh+ax7P/LiPl255ljt+G8WKdJmO+yQe/PYL7mjcnDXNDCg3FZHcOozUtuENHbYgCIIgCKfwzjvvNHQIgiAIDSY6UE90YP0PR9Ii/PhxbFcqaupXFz5YbKHU4kCnqUscHiq2cM27qwj107L56T4A+Gn86BI8kvYBHiqtLt/25u4x3VFKSjpHdwZAYTBQ5lGyoXAD6mI1z3R6hqTv5uIqLeXD7C9Z/MN/Gdl8JH0tJpRBQaiTE8mqzCLWLxaNUkPxy5NxHDlC/OfTMHbuzJ2xYdxmKaPy6+lUtWiJf7++2Nwerg8PpNLpxqiqi/vXkkrmFlXwZHIU4+5MB6DS6eKmHYdJ0mu5u0UIWoOa8AQTVrcHvUKiPL+Gw9tKMBfb6iUJF3++h6oSG52uTyGmcRDgPRfRbnNh8NfUK84iCMKF75yThDt37qRPnz4EBASQnZ3NXXfdRXBwMN9//z05OTnMmDHjn4hTuIA8PaAZyzNLyCyqZsP+aEY1H8X0PdP5ZKCGNz6y8fiMj3nswSdJzXeyYlYmUakBGAO0fz6wIAiCIAj/qlGjRjV0CIIgCBcUnVpJ67jAk9pnjunAoWILcUF15wMWV9vRqhTEBddPNL61OJP1WeW8c3NrBmV4F9HE6FpRctSPAw4TXY+vq/HX+DO52ytUOsyoFN5bc1VoKAd3Hia7Khun24l/v3749+tHfkUOA34cgEahYcOtG9AkJSF73Gz1K6Xi8DwywjLw27aN8s+m4XfFFfj360u0TsOU5onk3HkXOR43EU8/jTY5mT4h/gTU2ujotiPLMpIkkV3rYEe1jQK7k4+vTIcrvTE+sPcoC0rM/CcynK43pKLWKqlxu1lcWkW8XkNhVhVVJbZ6rz/voJkF7+8gPMHEjU/UVYA+tKUYSYLotED0Jg2CIFx4zvlMwkceeYTbb7+dgwcPotPVnYHQv39/Vq5ceV6DEy5MwUYNz1zbFIB3lx7k2rg7SA1Mxax1M+VaNRmZe7h+xW8s6OZPda2T37/cjyzLDRy1IAiCIAj/Kycn54x/BEEQBC+DRkXL2MB6hU96Ngpj3wv9mH57h3p9Y4MMpIQZSQw1+tr2F1YzZ/MxftyW52szaUzMWR7Kx/OjWH2w1Nf+QIv/MLbRByQbOvvayt1VGFQGIo2RqJQq4j78P1IXLeL70mU8tfopVuatRJuWRtCIEdh6teP2hbfz/NrnkT0erBs3UrN2HSWeSipqK7g+PJBHD2zHv39f8saNByBRp2F6ehLvbV1FxZxvcJvNAOTWOqj1yEQG6mjdJ57m3WPIstq5d+9RRu48Qr+70+l7VzohsX68m13EA3uPsr7CgiSBMVCL0yNT7nQhyzIb5mWx8JPdlJ1w1mH+ITPz39vO5l+O1PsZVpXaqK1xivtIQfiXnfNKwk2bNjFlypST2mNiYigsLDwvQQkXvkGtvVuOVx0s5fmfMpk8dDLDfhnGplQXK1pI3PPjLDY1a8HvrUJQby1j35oCmnWL/vOBBUEQBEH41yQmJp5xK5jb7f4XoxEEQbj4KBTSSRWW37ix1Un9kkONPNQnjRBj/RV02WU1HCu3oVPXrd85WqzklZ8stIrL56ex3q296aHpXGOaRonFyr6CKppG+QOQZGpKiwAPscYUDE3bYWjXjryiLWxZ+A7F1mKQZeI+nYozN4/nj37O0jW/81THp+hjNoMkURlp4utNrxPjF8OwJsPI/HQKhXY76k7tMAYE8G3rVHK+no37lf9Q3P8awsePxy1DxwAj7bZvwqirJSQ9HYVOze+ZVayvrKFPswTuef8KnLVu9tXYuHrzAeJ1GianBKAzqvAP1TGnoJxih5PkvFpy9pajUCnwyDIyoJQk5r23ncpiG4MeySCmkXcbc/HRKg5sLCIs3kTjjpG+n5fT7kalUYitzYJwHpzzSkKtVktVVdVJ7QcOHCAsLOy8BCVc+CRJ4qVBLdCpFazLKmPnESNjW48F4PN+GswGJ09O/5DNySqOhKtY/e1BKkusDRy1IAiCIAgn2rZtG1u3bvX92bBhAx9//DGNGjXi22+/bejwBEEQLhlpESYe6tOIEZ0T67XPubszc+/t7Ev6gfdeq3GEibQTCqoALN1fxM87i6mxu3xtjXTXsXb91by7oO5DnUT/RLrqXiG05n6yym0YO3QgcMhgqh0ePPZQTMpwQu+9l8Y7tmMdfg0z9s7gy31fIjscBAwehF/Pnjx58C06fNWB37J/wVRchPvoUUosRby39T0OFf7Cjxmp3PjOqxy9bQTVRbm4PC7ujw/ng8M7aTL2HszTp6HzU1Nk91aO7rFjE53SzAwel45/iJ6vC8p4KasAc5SWXiOa0Lx7NNurrcQt38HVmzJxOz0A+AVp+bawnA+OFrEpu4IdS49xaEsxTo9MlcuNLMvMfnEjU8atoCi7Lk9RfLSKdT8c5uDmono/Q5vF4RtbEISTnXOScODAgbzwwgs4nd5/7JIkkZOTw+OPP87QoUP/ciCvvPIKkiTx0EMPnbbPnj17GDp0qO9T71Mdtv388897qzOd8KdJkyZ/OS7h9OJDDDzcpxEA/12wl74xt5ARnoFN5ebD6zU0ysli+MKf+LmriWqPh98+3YPbJd6QBUEQBOFC0apVq3p/2rVrx1133cUbb7zBe++919DhCYIgXPKiA/W0SwzGqK3b5HdVswgWPdzjpBWJ/+nXlCeuaUJyWF3y0Opwo1crCTPVnQEfog9hz1E9K/dImK11BVhuiH2KmqxHmbbE21eh0RAWFEtC7eO4C0aTWW4n6vnniZvyMfnVVizmRErMGoJvG07CzBlU9+vE1F1T+Xz358i1tehbtkQdH8+TB94kY2YGNvNyelSXI+/eRV7xYZ5e/TSHcmdxpHsLxnzwBjmj7+DAsW3sK9vHFQFKntuxlowHRhG29XsSW4RS6nDhAXr9+iPXtTjKnS9m4B+i56v8Mt7YewSzppbWvWNJbh3KrmorjVbtosuGfVirvIk/rV7FB0eLmJh5jJVHyti66CgHNxVhcbnZYLZwsKaWua9s5uMHl1N4pNL3c8k/ZGbJ9L3sWp5b7+ddmFVJ8dEqnHaxql64fJzzduM333yTG264gfDwcGw2Gz179qSwsJDOnTvz0ksv/aUg/tjC3LJlyzP2s1qtJCcnc+ONN/Lwww+ftl/z5s1ZsmSJ73uV6i8VcRbOwphuSfyyu5Adx8w8Nnc3r9z8IjcuuIG9MTYWtJcY8esPrE/PYHH7WK5bW836n7LoOjS1ocMWBEEQBOEMGjduzKZNmxo6DEEQBOEEA1pGndQ2pE0sQ9rE4vifxRj39kwhr8JGXHBdoRW7y4NJqyLUry6hmBiQSGVlFsfKbVgddSsUhyc9x8MbdjN/g57b24ShCgsj3JyFYdnTHM3Wsam1lY5fzgQg79v7qS0cyIZMHdcMGYy+RTo7/cx8v/0zIg0FDEscgaF1a1zmCt488gWr1q7hhS4v0FV2UJaTQ5Ylh9u/H0CMXyzbe7xPxYMjKHK5yGzioSCvmgxtS67dsYE206eiGdifmitGsd9cBsBD779G50A/TOMnYgrVsWRHHkV793FFSTFtUsIIaNGEfTW1XL/tEOkOG8OrLCAr0RnVjNt3lK1VVkbYNLjWF+KsdRPWOYKpuSVEatRoPz1IdXktQx9vS3W4FrvHg+tANbt+OExs02B6Da9bjLTttxycDjdNu0RhCvbWbrBZHFSV1KI3qfEPrV/cRhAuVOecPQsICGDx4sWsXr2anTt3YrFYaNOmDX369PlLAVgsFoYPH87UqVN58cUXz9i3ffv2tG/vrY70n//857T9VCoVkZGRp338f9ntdux2u+/7U22nFk5NpVTwzs2t6f/uKtZllfHbjnAea/8Yk9ZNYnYvFa2PeLcd3/3EyyTHaWBxDrGNg0hID2no0AVBEAThsve/cx5ZlikoKOD5558nLS2tgaISBEEQzpVGVX+T4PCOCSf1GZQRw6CMGNye+sVAnurflKIqO4khdYVW/LU6WsQE0CgiwNeWHJiMRs6myGZDpay73ui0Jxm/ZQeZOUY0AxLQJCSQaMnDf4WKrGw12zpa6XU8oVjz3StY9vfjkyot1956K37durJTWcDBpTK5ciyFaZVEDB6E21zJT7mbWXu4lluaR3G9UUMpUBQscecPDxGgCWbHDV9R/uAWalwuPhmkZ92urXRNHUOrwlKSP/0I96Crme5eQ+0OA4n6m3hl4t0YLdUUTnuBpeaFHKoIJHrdQdr+/iuK5FaYWwxga1k2/5dTw4O//UT3LBu5Ud3RGdU8djif7QezeCS/FCnbQW1sBgdqarlu60Eau+zctKSKykoPCc1DmFVTxa5qG10rofyLw8Q0DuTKB1vxfVEFASolni+yqC6z0feudFyxBmxuD4oiG1m/5xMSY6Rtv0Tfz/bw1mIctW7imgbjF+RN7jpqXVgq7Gh0Kl+bIJwvf3mJXbdu3ejWrdvfDmDs2LEMGDCAPn36/GmS8GwdPHiQ6OhodDodnTt3ZvLkycTHx5+2/+TJk5k0adJ5ufblKCnUyDPXNuPJH3bx+qJMfhx7FT1jl7MidwUfDNbw0md5jJk3h+lDRhBV7l3KfcvTHTAGijc0QRAEQWhIgYGBJx30LssycXFxzJ49u4Giahj/93//x+uvv05hYSGtWrXi/fffp0OHDn/+REEQhIuMUlH/fb9f+skrFHs3jaB304iT2mfd2Qmz1UlyWF1CsVGEPw/0SiUqUOdri/GLIT0yH7VUTaC+rrDLzY1GsWLTVvzUAagjwlFHhJPusBC7XEtWsYsKu0zr//4XgJRl3/F7kY4VHjWTJjxAyD13s69kN/L0NAqqY9l4qJQer7yCu6qSw+bdHN46Hmu2xKMtQ6js2ZPCJnH8sKEMhT2Kj28Kxc/lRAbmlmzit5U6Wkc353E9GA5lYm0Rw9jtr6HYHMZNnScwYOmvaKsqWTwK3vhhFsFh3bnygJ2OX3yMo2sn/hu6AvciPZV+o5nw3wmElxSz+N7b+CR3I1ukRig3m0n7fhbVhiRWdEynIPMQk4qSeeT7r+mSWUZOUjt2VbuZs0/Pzv0lPL5jG/J+JQVXtEfbSce12wvodmAvt26uJNcVTb9HuzO9ysyqvEKuLTTj+KkY/yaJ9J/YhkmH8zE6HLRaWEppvo0+d6RTEKfjQE0tCTUyFfOPERCup+dtTVhTYUGjkNBuK6em1E7jTpGoI/XYPB6UVjclu8vRGdWktAn3/c7K8iw4at0EhuvRm7yFd9xuD/YaF0qVhPZ/CvcIF7dzThKe7nwaSZLQ6XSkpqbSo0cPlErln441e/Zstm7del63s3Ts2JHp06fTuHFjCgoKmDRpEt27d2f37t2YTKZTPueJJ57gkUce8X1fVVVFXFzceYvpcjCsQxxL9xWxdH8xj3yzg8/GPMuOkqFkh5j5tpuCW5b9yob0DOb1bMaIhWYWf76XgeNbo1CIClSCIAiC0FCWLVtWL0moUCgICwsjNTX1sjquZc6cOTzyyCN8/PHHdOzYkXfeeYe+ffuSmZlJeHj4nw8gCIJwmYgLNhAXXL+taZR/vcIrf/h0VPuT2q5sEs6qx3rVa/PT+PHY1S0prKylUWTdPfugxn0oKsoiKsCbfFRoNDSPaUP3OA9bc8yY9GoCrh0AwK27D7Buy0EMSj3+/a7Ev19fDLYy4qasIqtcTZXNyRXbtiLX1hK5/luc5hCOuCVajOqGvWkj8vycsHgPNVWN6CRriLntVtzmSkodDsqP3EptvofvO2so29QMV+M4th/Q4KrK4KGrTIR7vFu9cxKtzP25E2q1zGtxSozFBRiuTGJWaR7OOUG0aOmh+57tBBbkU3JHGPfNl9FpQ+igttD8x69wpKVzs2Ifrg/TcCZ2Y9i3X5J0LJulo6/grZ83YjAkEu3W0O7DV7H4xzOufQyvT4kmN6wX78x4n8aHMtlyVW/uXPs7br94qDHx1tQ30SjCefPWJnw2N5zf1W158JcfuXrfYdY2bc0MZzDl/tFkVQfw8rw5qAo1fH9NCvHWaD6ojOP6LZu5ZVcO2/zjsF0XQUlkBLvM/ty1ehmabTY2dUik8/BUPi0LpEnmYfpszuGQw5/AwbHUNopgu9lAj+1bUa01U9A0nl6jG/NtmRJTXjEZe4oor1QTfmU8ntQg9lRJJB09inpzBbVRkXS5MY3fK2qRKy0kHq3CVg7RrWNQJZo4YKklyFKDtLcKyWCkefcY9ltrcbrd+Jc7cZY5CI4yoovQU+p0oZMknNkWJIVEVEoAVR4PMiDVuHBZnOj9NOgDNLhlUEpgKatFUkgYA7W+nIXL6cbtklGqJFTqunyX7JGRLqG8hiTLsvzn3eokJSVRUlKC1WolKMhbiryiogKDwYCfnx/FxcUkJyfz+++/nzHRduzYMdq1a8fixYt9ZxFeccUVtG7d+pQFSf5XYmIiDz300BkLnQCYzWYSEhJ46623GDNmzFm9xqqqKgICAqisrMTf/+Q3O+HUSi12+r2zklKLgzu7JdGpRT4PL38YhQzPz3QRUR3AmKdeoUmxnj5baug4MJl2/RMbOmxBEARBuOiIucr51bFjR9q3b88HH3wAgMfjIS4ujgcffPCMR9z84d/4fdz++ptYHM5/ZGxBEISLnQdwySpAQiPVvVda3Hqcshqj0uZrd8hqSh1BKCU3EZoyX98CRyg1bgPh6jL8VTUA1MgqsqwJqCQ3TQ3Z3mtJHg7WRlHtCCVeW0i4pgxJ9lAieciuzkDCRTvDbpQuBy4l7PLEYLfHEKMtIMFxGMnt5miQitzKroCHjprNaKsrcWpUbDTE47alEK4ponHVDlROJ4diDByr8iZWWyvXE1JagFujZVV4Cu6axgRqimhZthGtvZaDiYFkW64CoKlyE7FF2bjUWlZFp+KqaYZRXUzrik0Yaq0cjvMny9YbUJGo2kpa4WFcSjWr41JwWlqgU5fSunIjJmsNR6L9OeS8AmQtcZqdNMnPxCMpWZWQgsPSCq2qjNZVm/C3VnM0wp8DcnfwGIjW7qV53h5kScHqhCRqLS3RqCppVbOZwOoqcsNM7Fe1B5eRcE02LfN2AbAmKR6rNR2twka6ZRvB1WbyQ0zs07VAdvkTqi6gde4OANYnxWKxNUGjcNKsZiehlRUUB5rYbWoMzkAC1WW0zN0FSGyLi6DamYYKD43sewg3V1BhMrLLPwWPK5AAZTVNijORPB72R4VgdiWjQibVuZ8wcwVVeh17ApNwuwMxSTZSyg+jdHs4HBaAWY5HKUskefYTWlmJVa1hb1ACblcQJslOXOVR1E43OSFGKohD6VGQIGcSZKnEo9Bgik3kswmnr73xd53tXOWck4Rff/01n3zyCZ9++ikpKSkAHDp0iHvuuYe7776brl27cssttxAZGcncuXNPO86PP/7I4MGD6604dLvdSJKEQqHAbrefcTXi2SYJwXuWYZ8+fZg8efJZvUYx8f7rlu4rYswXmwH46s6O/FL4NvMOzyPSouLVKbXsSU7nsQef4KY1NTQudDHokQyiUwMbNmhBEARBuMicr7nK5MmTiYiI4I477qjXPm3aNEpKSnj88cf/bqgXPIfDgcFgYO7cuQwaNMjXPmrUKMxmMz/99NNJzznVedZxcXH/6Nwx7anpON1h/8jYgiAIgiA0LK2qgMwX7/zHxj/bueM57yN5+umn+e6773wJQoDU1FTeeOMNhg4dSlZWFq+99hpDhw494zi9e/dm165d9dpGjx5NkyZNePzxx89qu/LZsFgsHD58mBEjRpyX8YQz6900gls7xjNrQw4TvtnB3LET2FS4iQIK+GSAmnE/7ObWRfP46epBRPxsZvFne7j56Q7ojOIcA0EQBEH4t02ZMoVZs2ad1N68eXNuueWWyyJJWFpaitvtJiKi/tlbERER7N+//5TPaYjzrEMVBVil8n/1msL5IAHntCZDEITz7o+toPLx/1XgkTWAhIx0/KvC+3dZQinZUUq1x/uqsHsCjz92vA/4nqdWWFArqkACt6zC5ooBWTph7Lq+KqUZjboYAI+sotZ++gJhCmUFam0+MiDLSpy2JqftK6kqUepyfddyW5qdvq+yCqX+mO97l6UpoDh1Z6UFlf7oCX2bAKfJ0yisqAxHTujbCDjNPb5Ui8p4uK5vTRrImtP0daAyHjyhbyrIp6tt4ELll3lC3xSQdafp60Hlt6+urzUZPP9bgbruvVvlt/eEvongMZzw+B9fvb9vpd9+pONtLls8uA3HH5VP6H28r/EAkuRB6YYwd1118YZ0zknCgoICXK6Tg3e5XBQWFgIQHR1NdXX1GccxmUykp6fXazMajYSEhPjaR44cSUxMjG8FoMPhYO/evb6/5+XlsX37dvz8/EhNTQXg0Ucf5brrriMhIYH8/Hyee+45lEolw4YNO9eXKvxFTw9oyrrDZRwpreHVX47yau9XGb1wNGuauGneSuKO+d+yM7UJ83qkMfy3SpbN2Mc197Y46eB0QRAEQRD+WYWFhURFnXxgfVhYGAUFBQ0Q0cWhIc6zXvffJ/7R8QVBEBqCxyNjc7qpcbiwOdwEGTX467zJpcLKWtYeLqXG4cbmcGFzeLA6XdQ63Nicbga1jqFLaigA24+ZeeL7Xdid3sdqj3+1O71nz43q4UfvlhI2l429ebW8O//0qZDU2F3ERm6h1lVLpSuEfbtvPG1fKWArqohfvd84gvAcPv2Ha5L/AZSR3tXpkssPDj7te0zhcaOSPTjUbpDcKP33oYmcB4DsUeM+8iAKhRa120BscSEqj5sDsTJIHpSGw2jDlnn7ylCbNxwlfmg8MXTYtxOF7GFdU/AoPGjUBQQY1qP0gEdppFx5NR5FEEpFCgPWLEMC1jbz4FDJ6CQzoZ7daDwSdk0YBSYLVm0gNkNTBq5agixJ7I234FR5UEs2gpzHCHYqsEkh5BvsVBhMVASmc83635GROBZagVsno5bcBEgVRNjAZguk0KjAbNJTFpBO551rkSSJElM5qkAJjVLCT20nqgbcVj/KQ82Ua9WUmdJokrkNSZKo1FZgiFSjViswalRE1oBk0VEbq6VcBaW6OKKy96FQKKhRVuMfo0ar06JTJxNS40FpUSEHR1GlgHJ1KKbCHJRKJTZFDcGxBnQ6NQpFdwKsMiqLhDIwBCtQrtCjqTKjUEh4JCeBkQZ0Rg0KqRUGu4yy2o3CaMIJmGVQuV0oJFAqFJhC9ej0aiSpLyqHB4VTJiDU8Cf/Wv4d55wk7NWrF/fccw+ffvopGRkZAGzbto377ruPK6+8EoBdu3aRlJT0t4PLyclBoajLaufn5/uuCfDGG2/wxhtv0LNnT5YvXw5Abm4uw4YNo6ysjLCwMLp168b69esJCxPbM/4tBo2Kt29uzdCP1jJ/Rz59mrZmXJtxvL3lbT7vpya1wMGz095nzFOTWdlCj2JHKbtX5NHiitiGDl0QBEEQLitxcXGsWbPmpHnbmjVriI6ObqCo/l2hoaEolUqKiorqtRcVFREZGXnK52i1WrTa061kEARBuLyYrQ4Ol9RQXeukqtZFda2T6hO+3tA2lpaxgQCsPFDCpPl7sNhd1Ni9ycETD0B7YVAavdONWBwWVmSW8PJPlae97p7qxYQf2IWltoqS6mCyC4actu/X+77j+5LfAXDXRiCpRuOvMxDqVKAoLADJSVa0GxQucj27KarxriT3uEpRB68m3JREUqmRlE1rcSndzO0hg+RCpclHZ5fRuEClUxCUNI9abRpReQGMmf8tssLNV1fKaFxu9KVW9MUyao8SS2ACtsBlZEUk4mcP4Z4fv8auVrE5tQYkFXKtEk9JGilVSiq0jajV17KpkT9l/npabCnEpVARhQ2VVodCikZhHkO3LJnK6lAc2nC2JxvIDfbgVjfGJSnpoFSR0i4KlVqDSj2O1M3V1OS5Ueh15PgrKDQqkIeMRqWQ6KNU0P6qeDQKCZUkYdhbiaPEjlKtwKKCWpWEplkztCoFQ1VKmrQNR61QoJYkzAUWHDY3SqUChVJCoZJQXtfF+1WpwOCv8RX5kD0ySPzPYqGe5/D/vE7n0LflOfQ9/QrPk52HDwj/dwFjAzvnMwkLCwsZMWIES5cuRa32ZvhdLhe9e/dm5syZRERE8Pvvv+N0Orn66qv/kaD/aeJMwvPj3SUHeXvJAUw6Fb+M78bkLRNZlbeK6CoVk6fWsr1RBk/dN5FbV1aTWubmhsfbERZ36grUgiAIgiDUOV9zlddee43XXnuN119/3fdh79KlS3nssceYMGECTzxxeaxe69ixIx06dOD9998HvIVL4uPjeeCBBy6YwiWCIAj/BFmWsdhdmK1Ogo0ajFrvOqK9+VUs3F2A2eak8n/+VNmcvDq0Jb2beo9p+Gl7HuNnbz/tNUZdoSA92UKlvZId2W7mrznN4hDJjjZiAZqgTQC4a6OwF19DanA8abVaFEezsGFhRVwpKJyojAdR6vO8r8Otw22Lo3fSVVy5X43h6y+pMjp59UY3ercTvdOJ3ulB54SApi0whUZxgOYkrqxg7KzPsGlgYRsFClkNkhoFasJlHa6W1+DyT2F2qAllWQW9N63BptVQo1XgVBlwaPXUarQM3itToo7HoQ3kh45GCgKdhJnNyJIGpVZLeGIIGr0WvVpNl7mFWAqsAByIVlNmUqJxy+gUCgL9NPQakopeqUCvUFC9KI9aswO1VoFbq0CjVaLXKNGoVWiNKpp1rftArySnGrfLg0qjQKVWolQrUGkUKFUKVBqlr0qvcHn6xwqX/GH//v0cOHAAgMaNG9O4ceO/FukFSEz0zg+X28ONU9axLcdMx6Rg/m9EY275+SaKrEV03QfjfnTx0dDbWHTFAMb8XEGMSceNT7ZHqz/nBa6CIAiCcFk5X3MVWZb5z3/+w3vvvYfD4QBAp9Px+OOP8+yzz56vcC94c+bMYdSoUUyZMoUOHTrwzjvv8M0337B///6Tzio8FTF3FAThQmJ1uCiptlNSbSc13I9Ag/e8t/VZZXy1IYfyGjtlFgdlNQ7MVgdOtzclMHVkO65q5n3P+3FbHg/N2X7aa1zfuZLY6DzKa8s5mK9k1950wk0mYvyDMWmU2BwlbKhYg6SoRWXag1KfD4DsMuC2RzK82RD673Jh/2oGBXozz46SkSRvHGqnjMEOgaEx+PuF0C72OtotLSbsk4+p0sPCdgr0dhm9HQwOMLhVJDz6BCHpbRh3oArDhp30W7eSGr0ei96AVaenRm/AbTBwS7mBcv9ULC4DU9LVHPN3oXU6sOp0KCUVIUYtfiolASol188porbS+9/GXfEazH4KtE4ZjUsm2KTlmlsa46dUYlQqODg9E7vFhVavRKNXodEd/6NXYgzU0rx7jO9nV5pbDUiotUo0OiVqrTehJ47fEv5J/3iS8FImJnrnT3ZpDf3fW4XV4eaRqxrRvYWF0QtH45bd3P2rm167FDzw6PO4TEncvLSSlJahXHNPC98SZEEQBEEQTna+5yoWi4V9+/ah1+tJS0u7LLfSfvDBB7z++usUFhbSunVr3nvvPTp27HhWzxVzR0EQ/mmyLFNtd1FUWUtkgA7T8XP71h4u5av1ORRX1/oSgzUOt+95Jyb+zrTiT62UGdi5msiIXEpsJRwpdnI4J4r20c1oF90Mf72KgspMphz4AElpQ6EuR1La640xvs14Bi61UPbxFI6GwYvDlJis4FcLfjYZv1qIvuo6gqOSCPJvReSPGwmb8hEOJRQHKlAqjbjVJlwGE+nRkYQ/OgFtWhrXbD5Axf5MUnOzsRiMVBv8sBgMVBuMuI0m3tinorrCjq3KwcxWWvJDlOgcMga7TJBaRXp6KIEqJYFqFYYPD2I1e+OuMHqPNtO5ZAJUKiIT/bnuwVa+17NlYTZul4zOqEJrUKM1qNAZ1Wj03q8G/9MU2xCEC9A/miTMzc1l3rx55OTk+D51/sNbb7117tFeYMRE7/yauyWXR7/dgSTBzDs6csA+j7e3vI3ao+Clzx3o3GHc+eRk2h9R0H23jU6DkmnbL7GhwxYEQRCEC5aYq1xYxO9DEIS/S5Zl30qy3XmV/LyrgMLKWgoraymqqqWwqhbr8eTf2ST+9GoFgUYFN3dVExNRRlFNEQeLK9l/TEmPxDZ0j88g2Kgh27Kb/6x9AElx6sqq4zLGMXidTOmUKRzTW3l/oBJ/q0xADd4/Vpnk0WMJT0kn0pSEZ9Yv8N67eJRK7IFBVAcGUelnwuofQK+UBIJuG44mNpb+Ww6QlVuAsdaK2c8fq06PfLwegV4h8elRNdVldmrMdubESxT5eVcPGu0ewg0auvaMI0StIkStIuv1ndRU1CUsJQl0Jg0Gk5qweBO9R9VV+z20pRiFQkLnp0bnp0ZvUqM1qMVWXOGSd7ZzlXPe17l06VIGDhxIcnIy+/fvJz09nezsbGRZpk2bNn8raOHSdEPbWDZnlzN70zHGzd7G/AdvZkvRFlbmruTtG7W8MrWEiV9+wqS7HiK61IX0Uxbhif7ENQlu6NAFQRAEQRAEQRD+thq7i8MlFo6V28gpt5JntpJXYSPfXEu+2cY7t7T2nfGXVVrDR8sPn3Icf50Km9NNpb2SPEseNYo8+revoUVEAm1j0ggzaTli2c74FfdiAT7LArJOGEANIWGBtD7kwvzd95gsOcR2chFY5SGoBgItEGSRSRsznphGGSQFJCFtXoBstRJrhVe+1eMICcUSFExNSCgZLeMJTL0eTWws/TYf4GBiS5Svf0K1wehL+gFoFRK/6cI5trOW6t/34a+rJVjlh8GuJ7bCQ5QRrugTR5hGTZhGxdaF23yJv77H60ppdEoMAVrCE7RcFVtXmDRheBMUKgmDSYPBX4PWePqkX2rb8L/8OxSEy8E5JwmfeOIJHn30USZNmoTJZOK7774jPDyc4cOH069fv38iRuES8PzA5uzMrWRvQRXjvt7BxyP/y7BfbqKAIj7pr2Lcjxu5fsVv/NS9L6N/NfPbp3u46cn2mIJ1DR26IAiCIAiCIAjCGXk8MvmVNo6U1nCktIacMivXt46hRWwAAEv2FZ2xuEe+2eb7e7MoE6M6JxDuryEm0EiEvw63opS5Rz6jwHqUV/Ye46nt1fWe3yL1ftqYUqjdt43K7K0A+DtVhFgkgitcBJvdpFw7jNikFrQIbYFz20pq1qwhHHh7h3cMRVAQcmQkxuhowiJ6ootqwtMHc9ka1wTzC2+T7x9Irbbu/kwjSWT3bInb4aEsz0JgrQeFzkiQCxItMmEo6Ng6gkitmiiNmrWv78R6PPHX/cTgJQiPV3JjVEjd2NcnI0kSfoFajIFajEFa1BrlKX92Cekhp2wXBOHcnXOScN++fXz99dfeJ6tU2Gw2/Pz8eOGFF7j++uu57777znuQwsVPp1by0W1tuPb91Ww5WsGU3wt5o+cb3L7wdtY0ddM8W2Ls3C85FJfID1ekMWKhmYWf7GbIhDYo1Yo/v4AgCIIgCIIgCMI/zOORfavUdudV8v6ygxwprSG7zIrD5anXNz7E4EsSxgcbCDdpiQs2EB9sIDZIT0ygnkA/GY+iFKu0k/e2ziO7KpsjlUc4Vn2MuxLvYlCrewA4UlnF0pW/1hs/yK0jJiiB2OAkUgJSqPxpHsWvv45aghkq0DlP2EIsScRe3xVTai9ybHZ2pLfGPOFxsgOD2W8MZKfBj6OyAq1CIqtHSxTHtz4X2J1sVekhTI8CiFQqSfTTEafXEKfT8NXLm6g6ZgGgqwTdTzjMzC9Iy6jrW9Y1tA7DYXdjCtZhCtbhH6LDFKLDGKRFqax/z9ekU9Rf/h0JgvDXnXOS0Gg0+s4hjIqK4vDhwzRv3hyA0tLS8xudcElJCDHyxo2tuGfmFj5dfYR2iW0Y32Y8b215i8/7qknNd/DC1He46z8vsbCjH9eurWL1twfpeeulUzlbEARBEARBEIQLmyzLlNU4OFBUzaFiCweLLBws9v79kasac2vHeADsLg+L9hT5nqdWSiSEGEkMMZIQYqBpVN25X/FhHj64wx9/rT9NgpsAkGXO4vqfrj9tHMeqj2Hdtg3znG9wHj3EKKOK8CI7ERUy4ZWgc1qIeecu/Hv2BaA6ZTnatFTUCQlIsXGUR0SRFxrG4cBQ7uvQEuXxolTPHcrn12ogtfUJL9r7RSVJZGabkQprKc+vobXZQmyVA22+Db8aN4GBOka+3ML3tB90BVQBWoOKgDA9/mF6AkKPfw3T13s93W9udI6/CUEQ/m3nnCTs1KkTq1evpmnTpvTv358JEyawa9cuvv/+ezp16vRPxChcQvo2j+TuHsl8sjKLid/u5McHbmBz7GZW5q7krZt1vDzVzKSp7/DwQ88QmaZFWplHRLK/+CRJEARBEM6TefPmnXXfgQMH/oORCIIgNLxapxu700OAwVsteHdeJSM+20CF1XnK/geL67b5pkX48dx1zUgKNZIc6kd0oA4ZNztLd3KoYhe/Ff7Ah/sPcdh8mAp7BQBD0oYwqcskAGJMMShQEKw0EevyJ9qsICrPSvM+N9Gk8wCi/KKwLllG5Y8/AjAAQKFAExePpmUKmsRENPFxAKyuqGZlXCP2v/Qu+2pqOVZ7QoFRF9yAgsjj36b76cmx2YmTVITXyoTVylzRIYZEvYZQtYrvXttC0ZEqABTAH6f4qTQKdH5qPG4PiuMr/3rf3hSNzlvtVxCEi985JwnfeustLBbvcuJJkyZhsViYM2cOaWlpl0RlY+GfN7FvY7blVLApu4KxX21j+pgXuN08nDzyeOcGDU98dZAHvv2C94bdSWSFm+VfZRIS40dYnKmhQxcEQRCEi96gQYPOqp8kSbjd7n82GEEQhH9RqcXOrrxK9hVUsa+gmn0FVWSVWLirRzJPXNMUgMgAHRVWJ5IEcUEG0sL9SIswHf/qR0qYH+BdbWh1l5Ecn4NaoSY+pAsAFoeV2xfeftK1JSRiTbEEaAOw7dpF8ZtvYT9wgOnVDnTOMqDM1zcsVUFoX2/yT9+iBaHjHkSbnII1PoH9waHssrvZY7ExuVEsOrX3ln5JWRUfHyupd81wjYpUg45Ug5aio1UUZNdQmltNdK6FoYVWZI93+aAkQZsrUn3HPEWnBqJSKwiOMhIUZSQw0kBQhAFjoNZXgfkP/iH1VwsKgnBxO+ckYXJysu/vRqORjz/++LwGJFz61EoFH9zahgHvrWJ/YTVvL8rj3SvfZcSvI9gZZ+OrKxWMXLqUA/FJ/NCjN6N/qWDhlF3c+ER78QmVIAiCIPxNHo/nzzsJgiBc5FxuD6rjq91KLXYGvr+a/MraU/Y9Vm71/T3EqGHBg91ICfNDf7xQhizLFNYUsrtsE0t372Zv2V4yyzN9qwPbRbSjS4w3Sein8aNdeFu0dg8JVj2xBU6iMstpfuWNRA0ZDkBtZibW9esB0EkSmsQEtI0aHf+Thr5VKwB2Vlv51Qa7u/Vjt8VGQVEtFOX6Yh0VE0rnQG/SsnuQCavbQ5KkIrzSjX9hLX0GpCAdPz9x8bQ9HNhYtzUaQGtUERLtR3C0EafD7UsSdhma+ld/7IIgXOT+UpJw06ZNhITUryBkNptp06YNWVlZp3mmINSJ8Nfx7i0Z3PbZBuZsPkbbxJa82PVFJqyYwIIOChKLZMbPmU5WdDw/9kzm1sWVLJm+lwH3tfT9h04QBEEQBEEQBMHucrMnv4qtRyvYfszMztxKWsQE8H/D2wDexJ/F7kKSIDnUSLPoAJpGmWga5U+zKH/CTVrfWJIkERsik119gKYhTX3tNy+42ZcU/INSUpIUkERqYCqusjJKp0zBtmMHj+/dh+ysv11ZDtkDt3j/rk1OJuqlF9E2aow2NQWzSs32KivbqqwMjQwiUe+NZ0uVlbeP1k/sJeu1pJv0tPDTE+qWOLq7jOKjVdQerabp0SqslQ4KgUKgXfsogiKNAMQ2CcLtkgmN9SM0zo/QWL9TrgwUBOHyds5Jwuzs7FNuPbHb7eTl5Z2XoITLQ9fUUB7p04g3Fx/gmR9388P9XbmrxV1M3TWVKQPURJc5+O/Ut7nrPy+zJMNI3y1lbJiXRadBKQ0duiAIgiBcMmpqalixYgU5OTm+4nR/GDduXANFJQiCcGayLPPaokw2HilnV17lSZWFZerK7EqSxJx7OhMXbMBPW/8W2CN7OGw+zLaSbWwv3s724u3kVOcQbghn6Y1Lfc9vFd6KopoimgU0Is3iR2KOndTQxkRc7838uS0WKmZ+CbL3uoqAAHRNm6Jr1gxds2boW3mr/Do8HnZZHWzp0J1tVVa27ThCtq3uvTdKq/YlCTsGGLklMph0k57mBh1R1R6iI4xodN7XsGFeFpt/ya73eiQJgqONhCf410sANu0STdMu0X/55y0IwuXhrJOEJx5yvWjRIgICAnzfu91uli5dSmJi4nkNTrj0je2VypacCpZnlnDPl5v54f67OVBxgBW5K3jzJi2TP63g+anvMOGhp4kq1cDCowTHGGnUPvLPBxcEQRAE4Yy2bdtG//79sVqt1NTUEBwcTGlpKQaDgfDwcJEkFAShwcmyzNEyK+uzysg323jk6saAN3G3+mApu/IqAe9qwYz4IDLiA2kVG0iLmIB645xYafgPr296nR8P/UiVo+qkx/QqPRaHBV1VLdbNW3hySyS2LXnY9/8Ax49tsLVpAzd4k4RKPz/Cxo9DHROLvlVL1HFxSJKE2enCIcsEaLzHJq2ssHDbzpN336XotWT4G4jXawCorXFizLYyLMtFweFcdmZXsdXuZsDYliS2CAUgItGfwAgD4QkmwhP8CU8wERpvQn18m7QgCMK5Ousk4R+HXEuSxKhRo+o9plarSUxM5M033zyvwQmXPoVC4u2bWnP9/60hp9zK/V9t4+MRLzFq0W1kk81bN6h5dmYm9303kyk3jSa0upJlM/YTGG4gPOHk/9ALgiAIgnD2Hn74Ya677jo+/vhjAgICWL9+PWq1mttuu43x48c3dHiCIFyGTkwKev+UU1jlPUtQpZC474pU31mBd/dIxuHy0DYhiIQQwym3ztrddnYU72Bj4Ua2FW/jwz4folV6V+q5ZTdVjip0Sh0twlrQOqw1rYyNaJ3YmQBtALIsc3BgX9zl5fXGVMfGom/VCkP7dvXaQ++9l/xaB4vNFjYeyGVDZQ2ZNbWMiw/nyRTvKr52/gZC1Cra+hto428gw99IK5OewOMFSAoOmZn9wQbK8mpOei0anRJrVd2qw8SWoSS2DP2rP2pBEISTnHWS8I9DrpOSkti0aROhoeLNSDg/gowaPhvVjiEfrmXjkXJe+/Uo7/Z+l+G/DGd/tIVpVyu4e+FvHIhPZu4VPbn91wp++WgXNz7RDmOA9s8vIAiCIAjCKW3fvp0pU6agUChQKpXY7XaSk5N57bXXGDVqFEOGDGnoEAVBuMw8/t1OvtmcW69NrZTIiAuiY3IwDrcHPd4k4XWtTt4+63Q72V22mw0FG9hUuIntxdtxeOoSazuKd9AhqgMAtzS+hWtCuhO7rwz7qo1Y181DdjjwX7kC8C6QMbRrh+PoUQxt22Jo1xZ923aoI8LrXbPa5eaFw/msrqjmiK3+sQ0Aufa68wkD1Sp2d22OrdpJ/kEz+esLKE0LJLCtd0ydn9qXIAyMMBCZ5E9kSgCRyQEERRlRiPPZBUH4B53zmYRHjhz5J+IQLnNpESbeuzWDMdM3MXvTMRpFNOPVHq/ywNIHWJLhLWQy4evPeDg8iu96pTJ8kZlfPtrF4AkZqNRiOb0gCIIg/BVqtRqFwlvNMjw8nJycHJo2bUpAQADHjh1r4OgEQbhU1TrdbDhSzqoDJaw8WMJno9oTF2wAvNuC/0gKdkoOplNyCBnxQb7Vg/9LlmU8sgelwvv4rP2zeGPzG/X6hOnDaB/Zng6RHUgJTMG6eTPVixfjWbcezYEDFJ/YWaXCVVyMOiICgJi330JS1l3b7HSxutiM1ePhpshgAIxKBQuKzVS43CiAliYDnQKNdAww0i7ASJhGjdPuJv+gmWP7yjm2r5zy/LqVgjaLg9TjScLACAP97kknKiUQg7/m7/yYBUEQztlZJQnfe++9sx5QnF0j/FW9GofzZP+mvPjzPl78eS/Tbm/PuDbjeHfru3x+tYq4EicvTXmTex/7Lwu6BDF4VRXLv8yk9+1NRVUuQRAEQfgLMjIy2LRpE2lpafTs2ZNnn32W0tJSZs6cSXp6ekOHJwjCJUKWZQ4WW1iR6U0KbjhSXq/QyKqDpdzaMR6Am9rFcUv7+NMmBQEsDgsbCjewJm8Na/LW8EDGA1yXch0AHSI7EKwLpl1EOzpEdqB9ZHuiipzokpKQNN6kW9HiTyj/YoZvPG2TJhg7d8bYuROGtm1RGI2+xzwKBTsqa1hWXs3y8iq2VlnxANFaNTdGBCFJEgpJ4tnUaELUKjoF+uGvqh+70+Fm2qOrcDnrF1cJiTES3SiIhPQQX5skSaRk1F+pKAiC8G+RZFmW/6xTUlLS2Q0mSWRlnXwI68WmqqqKgIAAKisr8fcX5979m2RZ9m0xMGlV/HB/Fz7eP4lF2YsIsCt56TM7Nn0MYydOok22git22eg8JIU2Vyc0dOiCIAiC8K85X3OVzZs3U11dTa9evSguLmbkyJGsXbuWtLQ0pk2bRqtWrc5j1JcuMXcUhDNbuLuQe7/cUq8t0l9Hj0ah9GgURvfUMAIM6tM+X5ZlDpsPszx3OWvy1rC9eDsu2eV7/Nrka5ncfbKvr6emBuuGDVhWrMSyciWuwkLip0/H2KkjADUbNlK1YAHGLp0xdOyIKjj4lNd98XA+X+WXUeFy12tPM2jpHmTi2ZRodEqFr91mcZCzu4zs3WW4HB4G3N/S99jcVzdTU2knvmkwsU2DiW0ShN5PrBQUBOHfcbZzlbNaSSi2GAv/FkmS+O+gdI6U1rApu4I7Z2xm1t3PcLTqKPvL9/PKMC0vTM/juU/f5YmxjxFS6YYfDhMcZfRV+RIEQRAE4ey0a1d36H54eDgLFy5swGgEQbjYVVqdLD9QzOK9RWTEBzGmm3exSZfUEPy0KtokBNEjLZSejcJIDfc7691ApbZSBs8bXK8twT+BrtFd6RrTlXYR7XCVlVG1YAHVy5dj3bwFnHXnAEpaLY5jOb4kobFjB4wdO/ge98gyO6ttLC6rZHxCBJrjxzA4PDIVLjf+KgU9gkz0CvbnimATMTpvck+WZcryLGTvKiV7ZxmFRyrh+BIcSSFht7nQ6r233Nc92AqNXiV2QAmCcEE7q5WEp/PHUy+1NzrxaXDDK7PYuf7/1pBbYaNLSgiv35zAyEW3UWwtpvkxiadmOfmla28+uGUMty2rIqkGbni8HcFRxj8fXBAEQRAucmKucmERvw/hclZYWcuvuwtYtKeQTdkVuD3ee8RWcYH8NLarr5/D5UGjUpxuGACsTitr89fy+7HfcXlcvNrjVd9jo34dhVFtpEdsD7pGdyXWFItcW4tCrwfAtnsP2Tfc4OuvjovDr2dP/Hr2wNC+PQqdrt61at0eVpst/FZayW+lVRQ6vEnFOa1S6BlsAuCwtZYyh4s2/kZUpygYsnTGPvavLajXFhLrR2J6CPHpIUQm+aNQnvk1C4Ig/BvO60rC/zVjxgxef/11Dh48CECjRo2YOHEiI0aM+GvRCsL/CPHT8umodgz9cC1rD5fx8TI/Puz9IaMWjmJPXA0fD1Awdv5ScsOj+K5nf25fWMnPH+7kxv+0Q2c8/VYFQRAEQbjctWnThqVLlxIUFERGRsYZP+zdunXrvxiZIAgXE1mWGTltI6sOltZrbxxhonfTcPo0i6jXfroEocVhYXnuchZlL2Jt3lpfJWKVQsUzjmfw0/gB8Hm/z5FksG3fTvVHX3N4yRIM7doRPfllAHTNm2G6ph/6Vq3w69kTTWLiKd/f9lhsvJVdyO/l1VjddWcEGpUKegWbMJ2Q1Esx6EgxgNPu5tDuMrK2FdN5SCqmYG/CMTLJn4ObiohtEkRii1AS0kN8jwmCIFyMzjlJ+NZbb/HMM8/wwAMP0LWr95Oh1atXc++991JaWsrDDz983oMULk9NIv1595YM7pq5mZnrj9Ioojlv9nyTsUvHsjIdwivg3u+/Ij80nG+vaMvI38z8+vEuBo5rjVItPrETBEEQhFO5/vrr0Wq1AAwaNKhhgxEE4aKRZ7ax+mAJN7f3FhiRJAl/vffD+bYJQVyTHknf5pG+KsVn44NtH/D57s99iUGAWL9YesX3oldcL/QqPbLHg237dqp+/oWq3xbhLqlLStY4HMiyjCRJSJJE7Ntvn3SNcqeLGreHOF3d+X8/l1QCEKVVc3WIP31DA+ga5IdWUXcPYbe5yN5ZyuGtxeTsLcd9vOhIRHIAra6MA6BRh0gadYxEfYYiK4IgCBeTc95unJSUxKRJkxg5cmS99i+++ILnn3/+kji/UGwZubB8vOIwr/y6H6VCYurItpQrVjFp3SQA7l/gplOmhvGPPItCE8cNK6tp3CGCPqObXXLb4AVBEAThD2KucmERvw/hUlVe42DBznx+2JbHthwzAIsf7kFahHc77pHSGnRqBVEB+j8dy+q0sjJvJR0jOxKkCwLg6/1f8/KGl0n0T6RvYl+uTryatMC0evP4o7ePxrp+ve97hcmE3xVXYOrTB79uXetVIvbF7XTxa0kl84vNrDZXMyg8iA+aeQsdyrLM/+UU0yPYRAs//Un3DJUlNlbOPkDu/nI87rpbZf9QHckZ4TTpFElIjN9Z/gQFQRAuDP/YduOCggK6dOlyUnuXLl0oKCg4xTME4e+5p0cyh4stfLsll/u/2srXd/XmzhZ5fLrrU6b0VxFS5eDlj97g3sf/y5LWRqSNRfiH6uk4MLmhQxcEQRCEC9qmTZvweDx07NixXvuGDRtQKpX1CpsIgnB5qHW6WbqvmB+25bI8swSX549z6KF9YjA1jrpKv0mhZz4P3O1xs7FwI/MPz2dJzhJsLhvPdn6WGxvdCED/pP60CW9Do6BGSJKE/dAhSr/6kJC77kRxfMWzoU0Gtbt2YerTB/8B/TF26oSkObkqcJXLzYISM/OKzKwyV3NCfo/c2vorDh9IqNsK7XK4sVTYCYzwroDU+6nJzfQmCIMiDaS0CSc5I4zQ2LMvtCIIgnCxOuckYWpqKt988w1PPvlkvfY5c+aQlpZ23gIThD9IksTLQ1pQYrGzPLOEO6Zv4tt77yCvOo9fs3/lrRtUvPBFBZM/fJ0HJzyPv1ULv2RjCtHRrGt0Q4cvCIIgCBessWPH8thjj52UJMzLy+PVV19lw4YNDRSZIAgNZeWBEsbOqjuPND3Gn8EZsVzXMopw/7M7b+9QxSHmZ81nQdYCiq3FvvZYv1hUUt0taIA2AGONm4qZMzH/8CP2ffsA0DVpjKlPHwCCR48m5J57fEnD0xmy7RC7Lba6uP30XBcWyHXhgSQb6j/X4/aQu7+CA5uKyNpWQlCkgRufaA+ARq+iz6hmhMT6iaKIgiBcds45SThp0iRuvvlmVq5c6TuTcM2aNSxdupRvvvnmvAcoCABqpYIPh7dh2Cfr2ZFbye2fb+Lru5+myFrE1uKtTL5FzUufH2XS1Ld56r6JmKweFF9lYgrSEdcsuKHDFwRBEIQL0t69e2nTps1J7RkZGezdu7cBIhIE4d+UZ7bx7eZjBBk0jOqSCMAVjcNpFuVPz8ZhDMmI8W0tPlsVtRXcMP8G3LJ3xaG/xp9rkq7hupTraBnaEkmSkJ1OLCtXYv7hByzLV4DL5X2yWo1ft24og4J84ylN9a8vyzJbqqz8WFzBU8nR6I8XGhkQFoDDIzM0IuiUiUFZlinOriZzQyGHthRhq3b6HrNVO7FbnWgN3jMW09rXL7oiCIJwuTjrMwl3795Neno6AFu2bOHtt99m3/FPepo2bcqECRPIyMj45yL9F4lzZS5cZRY7Qz9aS3aZlaZR/kwd1ZT7fh9NdlU2SUUwaaaLVRldeWPk/QxbbiHFIjN0YltxboggCIJwSTlfc5WQkBAWLFhA586d67WvXbuWAQMGUFFR8XdDvSyIuaNwMXG6PSzdV8zsTTmsOFCCLENcsJ6VE3ud83Zat8fN2vy17Cnbw72t7vW1j1s2DoCBKQPpEdsDjbL+9mD7oUNkXXud73tdejoBgwfh378/qhMShCfKsdmZU1jOd0UVZNu8hU4+bpbAoAhvf6dHRiVx2tew8utMdq3Iq7umn5q0tuGktY8gMjkASSG2EguCcOk627nKWScJFQoF7du358477+SWW27BZDq3T5QuJmKid2HLKbMy5KO1lFrsdE0N4YUhUYz+bQQV9gpaHYHHv3HxQ6/+TL9+OCOXVJGkVHPD4+0wBp55i4IgCIIgXCzO11xl2LBhFBQU8NNPPxEQEACA2Wxm0KBBhIeHi10iZ0nMHYWLwdGyGmZvOsbcLbmUVNt97Z2SgxnWIZ5rW0ajPMtEWVFNET8c+oHvD35PQU0BEhK/Dv2VGL8YADyyB4XkXeHnttRQtWA+ruJiwsaN841x7L770SQlETDoenSNGp3yOnaPh4WllczKL2dlRTV/3LgalAr6hwZwV1wYrUwnV1N2uz0c3VVGeIIJvyDvFumsbSX8Nm0Pya3DaNwpktgmQSiVipOeKwiCcCk670nCVatW8fnnnzN37lw8Hg833HADY8aMoXv37uct6AuFmOhd+HbnVXLzlHXUONxc1yqaO3pL3LP4bmwuG533ehg/z8PUQcNY1OM6Ri0ykxRmZPCENmh057zDXhAEQRAuOOdrrpKXl0ePHj0oKyvz7QjZvn07ERERLF68mLi4uPMV8iVNzB2Fi8HEb3fw7ZZcAEL9NAxtG8st7eP/tPjIH1weF2vy1jD3wFxW5q3EI3sA73bigSkDub357UQY67bp1u7dS8XsOVQuWIBstSJptaStWonyHP6NHLLW0m3Dft/3PYL8uDkymH5hARiVypP6l+fXsG9tPpkbCrFVO2k/IJEO13mLGbrdHlx2t29LsSAIwuXkvCcJ/1BTU8M333zD9OnTWbVqFampqYwZM4ZRo0YRGRn5twO/EIiJ3sVh1cESRn++CZdH5s5uSVyZYWbssrG4PC76bPNw10IPr468l53NuzFicSVpjUMYcH8LFOITQ0EQBOEidz7nKjU1NXz11Vfs2LEDvV5Py5YtGTZsGGq1uJE+W2LuKFxoyix2vt6Yw9XNI2l0/EzBrTkVvLvkIMM6xNG7aQTqc5wTzzs8j6dWP+X7vm1EW25odANXJVyFVundseOx2aj65Vcq5syhdudOX19NUhJBt9xM4A03oDCeOilZ43LzU7GZXLuDx5KifO337skmSa/llqhgEvQn7wxyOz0c3l7MnpX55B80+9r1/hoy+sSTcXX8Ob1OQRCES9E/liQ80aFDh/j888+ZOXMmhYWF9OvXj3nz5v3V4S4YYqJ38fhxWx4PzdkOwFP9m5IQf4iJKyfikT0MWuvh5lXw9L0TKA5vwS0rq2nRNZorbm18zuetCIIgCMKFRMxVLizi9yFcKHblVjJ9bTbzd+bjcHkY1iGeyUNa/KWxdpfupsZZQ8cob/Vzq9PK0HlD6R3fmyGNhpAckHzSc8o+/ZTiN970fqNW439VHwJvvgVDh/annX8fstbyeW4p3xSWU+32oJYktnVpTqjmz3cAeTwyXz27jqrSWgAkhURiixCado0mvnmw2E4sCIJw3L+SJIS6T5+feOIJzGYzbrf77wx3QRATvYvLJysP8/Iv3m0Ib97YCkwbeH7d8wAMX+am71Y1j45/CoUyges31NDhhG0HgiAIgnAxOt9zlb1795KTk4PD4ajXPnDgwL899uVAzB2FhuR0e/h1dyFfrM1my9G6YkMtYwO4u0cy17aMPvuxPE4WZy/mq/1fsbNkJ6mBqXw/8Htfgk+W5Xp/r1m7FoXegKGN97gCV2kpR28bQcCQIQQOGYwqNPSU13HLMotLq/g8r5QVFdW+9mS9luHRIYyIDsFfdfJ2Yo9HJv+gmZhGgb44fv9yP0d3ldKsWzTNusXgFyTOIRcEQfhfZztX+csHtK1cuZJp06bx3XffoVAouOmmmxgzZsxfHU4Q/rK7uidTVGXns9VHmDh3B+8N68wjbR/hrS1v8dWVSoy1Tl7+8DXGT3iOZS1CkX7OxuCvIb1nbEOHLgiCIAgNKisri8GDB7Nr1y4kSeKPz47/uPm+FD78FYRLmSzLDPxgDfsKqgBQKSQGtIxiVJdEMuICz3r3TJmtjLkH5vJN5jcU24qPj6WiaXBTrC4rRrV3i7AkSXhsNirnz6di5kzsBw9haN+ehJkzvM8JDSX511/+9LrT80p56qC30rAEXB3qzx0xYXQP8kNxiufarU72ri5g1/JcqstrueHxdkQkeW9yOw9OocewRmLVoCAIwnlwTknC/Px8pk+fzvTp0zl06BBdunThvffe46abbsJ4mrMlBOGfJkkST/VvSo3dxexNxxg/ezsfDr+GMemVfLb7M6Zeo8Rot/LqB6/wwKMvYHDoYfYBdH4aUtuGN3T4giAIgtBgxo8fT1JSEkuXLiUpKYmNGzdSVlbGhAkTeOONNxo6PEEQTqG4upZQoxaFQkKSJPo2j6Ck2s7wjvEM7xhPuL/unMb7at9XvLX5LRwe70riUH0oNzW+iRsb3Uiovm4loLOwkIpZX2OeMwd3ZSUACoMBbZMmyC4Xksp7a3mqBOEeiw27x0Mbf+894+CIIP4vp5ghEUGMjA4h/hRnDQKYi63sXJbLvnUFuOzeDy20BhVVZTZfklBnFOenCoIgnC9nvd34mmuuYcmSJYSGhjJy5EjuuOMOGjdu/E/H1yDElpGLk9sjM/HbHXy/LQ+1UmLKbW1ZZf6YuQfmonLD49+6CbJG8dAjz9B1v4q2x5xc90ArYpsEN3TogiAIgnBOztdcJTQ0lGXLltGyZUsCAgLYuHEjjRs3ZtmyZUyYMIFt27adx6gvXWLuKPwbDpdYmLoyi++35vHRbW3o3dRbSdjqcKFUSGhPsT33dNweN0qFt//a/LXcs/ge0kPSGd5sOH0T+qJW1k+8lbz3PqWffAIuFwDq2FiCR9xGwJAhKE2mU15DlmWWl1fz0bFiVlZYaOdvYEHbRnUxyDLK06w4tFY5+P3L/WTvKoXjd6vB0UZa9Y6jUfsIVJqzf62CIAjCP7DdWK1WM3fuXK699lqUpyg3LwgNTamQeO2GlthdHn7eVcC9X23l05H3UZ1YzaLsRbwxVMkzX+fz+nsvM2H8M2idKpQf7WLwhDaExZ96ciMIgiAIlzK3243p+A1+aGgo+fn5NG7cmISEBDIzMxs4OkEQALYcrWDKisMs3lfEH8s7Vh4o8SUJDWdR4AO8Sbt1+euYtnsaLcNaMq7NOAA6R3Xm6wFf0zykeb1VgLLHg6TwbuHVJCWBy4WhfXuCR43Er1cvpNPcE9o9Hn4oquDjYyXsr/EWFFFKEKPTYHN70B/fFny6BCGAzqiiNLcaZEhID6FV7zhimwSJ4oOCIAj/sLNOEl4KVYuFS59KqeCdW1rjcHtYvLeIu2duYdrtj2FxWliTt4aXb1bz9NfHeO39yUwc9yTaLQrmv7+dIRPbEhhuaOjwBUEQBOFflZ6ezo4dO0hKSqJjx4689tpraDQaPvnkE5KTRZEvQWhI67PKeHfJQdZllfna+jSN4N6eybRLPPudMC6Pi8VHF/P57s/ZV74PgAMVB7iv1X2olWokSSI9NB3wJhItK1ZQ9slU/PteTfCoUQD4X9MPbWoKuqZNz3ituYXlvHA4n2KHd8WhUalgeFQId8aGnnZLsaPWxZ6V+WRtL2bQhDYolQoUSgVXjmiKX5CWoEhxrJUgCMK/5W9XN74UiS0jFz+7y83dM7aw4kAJRo2ST29vxceZ/2Fr8VYMdnhmlotafRpPjv0PQ9a7aI6KIRPbYgwQ1dAEQRCEC9/5mqssWrSImpoahgwZwqFDh7j22ms5cOAAISEhzJkzhyuvvPI8Rn3pEnNH4XzzeGSueXcVmUXVqJUSg1rHcE/PZFLDz373i8vj4pcjvzBlxxRyqnMA0Kv0DE0byshmI4nyi/L1lV0uqn5dSNnUqdgPHABAHR9PyqKF57R6b16xmbv3ZBOlVXNnbBi3RQUToD71uhRbtYOdv+eya3kudqs3qXjVmGY0ah951tcTBEEQzs7ZzlVEkvAUxETv0lDrdHPH9E2sPVyGSafi89GteX/vY2wt3oqx1psorPZvwnP3PM5Nq+0099MzeEIbtPq/XPRbEARBEP4Vf2eusnPnTtLT01EoTl0JtLy8nKAgsa3vXIi5o/B3ybLM+qxyWsUF+LYPL9xdyOpDJdx3RSoxgfpzHvPtLW8zbfc0AAK1gdza9FaGNR5GoC6w7rpOJ5U//UTplE9wHjsGeIuRBA67heCRo1BHnL7IX7bNzgdHi2nip+PO2DAAXB6ZBSVm+ocFoDnNe0x1eS3bFuewb3U+LqfHG1+EgYyr42ncMRKlSlQpFgRBON9EkvBvEBO9S4fV4WLUtI1syq4g0KDm89GteGvXo2wv2Y6xFp6d5aIsKJ2Xx0xg2IpaWkT5c92DrcRhyIIgCMIF7e/MVZRKJQUFBYSHh5OcnMymTZsICQn5hyK9PIi5o/BXybLMusNlvLPkIBuzy3mqf1Pu6vHXtvq7PC4sDosvCXis+hijfh3Fbc1u45bGt2BQn3y0TsGzz2H+5hsAlEFBBI8cQdCtt6IMCDjtdfbX2Hj/aDE/FFXgAcI1KjZ1bob2NEnBE1WV2vjq2fV4PN5b0LB4E237JZDUOgyFQnwwIQiC8E8574VLBOFiZNComHZ7e0Z8tpHtx8yMmb6TT0a+zjt7HmVHyQ7+O0zFs7N2M/GL93hzxEOollehnrqba+5pIT7FFARBEC5JgYGBHDlyhPDwcLKzs/F4PA0dkiBcljZnl/Pawkw2ZpcDoFEpsNhd5zyOy+Pi56yfmbJzCk2Dm/LmFW8CEGeKY9ENi1Ar6ioVyy4Xnlo7Sj/vOX+BN99E9bJlhIwZQ9DNN6EwnP6M7h3VVt7NLuKX0kpf25XBJh5KiDhjgrDW4kTn543BP1RPVGoAkkKiTd8EUYxEEAThAiNWEp6C+DT40lNpczLisw3szK3EX6diysgWfLB/IjtLduJn864oPBrTgQ9vGstty2to2TyMq8c0Q6EUiUJBEAThwvN35ip33303M2bMICoqipycHGJjY1GepkppVlbW+Qj3kifmjsK52F9YxRuLMlmyrxjwJgdv7RDPvT1TiAzQnfU4HtnDwiML+b/t/+c7czBYF8z8wfPx19T//6HsdlP1yy+U/t+HGLt0JvLZZ+secziQNJozXuvt7EJePVIIgAT0DwtgfEIELU2nTyqW5VvY/HM2R3eXcdt/O2Pw917D6XCjFrt2BEEQ/lViJaEgnCBAr+bLOzsy+vNNbDlawV1f7OKD4a8yVX6cnaU7j68o3Mhd36v5dPA9sKIE1cz99B7ZFElsfRAEQRAuIZ988omvUMm4ceO46667MJnOvhiCIAh/z1u/HWDJvmKUComb2sUxvnfaOSUHZVlmXf463tn6jq9acZA2iNvTbz9pW7Hs8VC9aBEl73+A43jS31NTQ/hjj6HQea95ugShR5ZRHF/ld3VoAG9lFzEoIpAH4yNoZDx9vGX5Fjb/ks2hLcVwfDlKzt4ymnTyFkoRCUJBEIQLl1hJeAri0+BLV43dxZ1fbGZdVhl6tZJ3b23G9Kz/sKt0FyYrPDfLxYHErky7/i5uXWmlQ6doegxrJLZBCIIgCBeU8zVXGT16NO+9955IEv5NYu4onEmZxY4MhPppAThQVM27Sw7yyNWNSAnzO+fx5h+ez5OrnwTAqDYyuvloRjQbcdKZgzXr11P8xpvU7t4NgCIggJDRowm67TbfduNT2V9j4/UjhYSqVbzaOM7XXuJwEqZRn/Z55fk1bPrlSL3kYEpGGO0GJBEae+6vUxAEQTh/ROGSv0FM9C5ttU439365heWZJWhUCt66uSlfHv0Pe8r2YLLCM1+7yI7tyNTB93LrKhude8bRZUiKSBQKgiAIF4zzMVdxOp3o9Xq2b99Oenr6eY7w8iLmjsKp1NhdfLrqCFNXZXFdqygmD2n5l8dyepy+swWtTiuDfxrMlfFXcnfLuwnSBZ3Uv3zWLIpe+C/grVYcfMcdBN8+CqXf6ZN1h621vJldxA9FFciAViGxrUtzgtV/vvms1uJk+hNrcB+vViySg4IgCBcWsd1YEE5Dp1YyZURbxn29jUV7injo6728csNLyDzJXvYyabiKJ+Zs4P5vXXw8dCwsP4Zaq6TDtUkNHbogCIIgnDdqtZr4+HjcbndDhyIIlxSPR+b7bXm8tnA/xdV2APYWVON0e1Cf43nXJdYSPtzxIfvK9jFrwCwUkgKD2sD8wfPRKOtvE5Y9HqTjBUT8+/Wj9L338b/2WkLvuxfVGSqYH6t18MaRQr4tLOePMkYDwgKYmBR5xgShw+ZCo/c+rvNT06xbNDVmO+0HJBIaK1YnC4IgXIzESsJTEJ8GXx6cbg8TvtnBvB35KCR4cUhjFpa+wLbibWgd8NhcNzV+rfjoxge5eY2dK69LIePq+IYOWxAEQRDO21zls88+4/vvv2fmzJkEBwefxwgvL2LuKPxhU3Y5/12wl5253grAccF6HuvbhAEtolCcwznXta5aZuydwae7PsXmsgHw2dWf0SGqw0l9XRUVlH70Ec6jOcRN+djX7qmpQWE8/bZigEWlldy1OxvH8VvCq0L8eSwpkhZnKEhitzrZ9lsOO3/PZfCENoTFexOCskcWZ3kLgiBcoMRKQkH4E2qlgrdvbo1OreCbzbk89X0mkwY+i075GusK1jH5JiWP/LCDB2e/zf/d9BDMP4xKo6DFFbENHbogCIIgnBcffPABhw4dIjo6moSEBIz/k1DYunVrA0UmCBefbzcfY+LcnQD4aVU8eGUqt3dNRKs6+0Idsiyz6Ogi3t78Nvk1+QC0DG3JhHYTaBPRpl5fj8NBxcyZlH70MR6LBQDbzp3oW3q3Nf9ZghCgnb8RrUKivcnIU8lRtAk4/XNcDjc7l+eydeFR7FYXAJkbC31JQpEgFARBuPhdMEnCV155hSeeeILx48fzzjvvnLLPnj17ePbZZ9myZQtHjx7l7bff5qGHHvpbYwqXN6VC4pUhLdGplcxYd5Rnf8pkYt9H0cd9yLJjS3ljqJIH5u/hoa/f4P2bH0GaewCVRknTLlENHbogCIIg/G2DBg1q6BAE4ZJxVbMIQowarm4eySNXNSLMpD2n51fUVjD+9/FsK94GQIQhgofbPkz/pP71zsaWZRnL0qUUvfY6zpwcALTNmhI+YYIvQXgqblnmu6IKVldU826TeCRJIkSjYmn7xsTpNKc9f9vj9rBvbQGbFhyhptIBQFCUkU7XJ5PUKvScXqMgCIJwYbsgkoSbNm1iypQptDzDf9QArFYrycnJ3HjjjTz88MPnZUxBUCgkJg1sjl6jZMqKLF5fdJBRnccwIMnAz0fm8/5AJWMWZfLQrNd5/5YJ8PU+AJEoFARBEC56zz33XEOHIAgXJY9H5rutuaw6WMq7t7RGkiQCDRqWT7wCk+70FYDPJEAbgNPtRK/SMzp9NLc3vx29Sl+vj7O4mPyJj2HdsAEAVVgYYY88QsD1A33nEf4vWZb5vbyaFw/ns7emFoChEcH0DPauAIzXnzmZOe+9HeRlVgBgCtbR4bokGnWMPKft04IgCMLF4dxOzv0HWCwWhg8fztSpUwkKOrky14nat2/P66+/zi233IJWe/r/mJ3LmIIAIEkST1zTlKcHNAXgi3U5VOYM5YbUYcgSfNpPyaHwQzz01ev81E7BL7P3s29tfgNHLQiCIAh/n9ls5tNPP+WJJ56gvLwc8G4zzsvLa+DIBOHCtDe/ihunrGPi3J3M25HP75nFvsfOJUFod9uZvns6VqcVAIWk4MVuLzJv0Dzua3XfSQlCAFVgIM7CAiSNhpB77iFl4a8EDh502gThPouNW3ZkcevOLPbW1OKvUvB0chQdzrCt+H+lZIShNajodmMawyd1oknncztfURAEQbh4NPhKwrFjxzJgwAD69OnDiy++2CBj2u127Ha77/uqqqrzEodw8bmzezIR/jomfLODX3cX0qGmFyNa+TMzcwqzeikZvOYIj854ibduexx5TiYATbtEN3DUgiAIgvDX7Ny5kz59+hAQEEB2djZ33XUXwcHBfP/99+Tk5DBjxoyGDlEQLhjVtU7eWnyAL9Zm45HBoFEyvncaXVPPfcvt6rzVTN4wmZzqHKqd1TyY8SAAKYEp9frJDgfmH38kcPBgJLUaSaMh5rXXUIaEoIk9/TnZFpebF7MKmJFXigfQSBKjY0MZnxBxxorF1ioHmxYcIbZpECkZ4QA07x5NWvsIdMa/tkJSEARBuHg0aJJw9uzZbN26lU2bNjXomJMnT2bSpEnnLQbh4nZdq2hC/DTcM2MLG4+UY7a24I4ujzBt/1v80FVBjT6fpz6bxGujHsf9TSayDM26ikShIAiCcPF55JFHuP3223nttdcwmUy+9v79+3Prrbc2YGSCcOGQZZl5O/J58ed9lFR7FxYMaBHF09c2JSrg5NV+Z1JYU8hrm15j8dHFAITpw0gLSjtl35r1Gyh84QUcWVnItXaCR44AQN+q1Z9eR6OQWFlejQcYEBbAsynRJJxhW7HL6Wbnsly2/JqNo9ZNzt4yklqGolAqUCgV6IwNvgFNEARB+Bc0WJLw2LFjjB8/nsWLF6PT6Rp0zCeeeIJHHnnE931VVRVxcXHnJSbh4tQlJZRv7u3M7Z9v5ECRhepl8dx5xbN8duC//NZGQblfOU9Me4E3RjyG5zvvikKRKBQEQRAuNn+c4fy/YmJiKCwsbICIBOHC43TLvLv0ICXVdpJCjUwa2JwejcLObQyPky/3fslHOz7C5rKhlJTc2vRW7m91P34av/p9i4spfu11qhYsAEAZEoLyLI5QWlVeTadAP9QKCY1CweuNvSsNuwaZTvscWZbJ2l7CmrmHqC7znlcYFm+i242pKJQiMSgIgnC5abAk4ZYtWyguLqZNmza+NrfbzcqVK/nggw+w2+0olcp/ZUytVnvGMw6Fy1PTKH++v78ro6Zt5FCxhc8W+XNP39eZdugJNjdyUmm0MmHmS3x40yO4v/eeyyIShYIgCMLFRKvVnvKYlQMHDhAWdm5JEEG4lNgcbtRKCZVSgUal4MXr09lytIK7eyajVZ3bPQrA21veZubemQC0DmvN052epnFw43p9ZJeLillfU/Lee3gsFpAkgoYNI+yh8Sj9/U879sGaWp47lMey8mpeTIvhzljvv90zJQcBKgprWDn7ALn7vUVJjIFaOg9KplGHSCRx5qAgCMJlqcGShL1792bXrl312kaPHk2TJk14/PHHzzlB+E+NKVzeYgL1zL23M3d+sZnNRyt4d4GCcf0+YHbuRA7GVPHyzU4mzn2NrwaM5dsf4UYZmnUTiUJBEATh4jBw4EBeeOEFvvnmG8BbyCsnJ4fHH3+coUOHNnB0gtAwVh0s4ckfdjGqcyJ3dk8GoEtqKF3+wtmDfxjZbCTLcpZxT8t7uD71ehTSyav0Cl/4L+bj/xZ1LVoQ+dxz6NObn3bMGpebN7OL+CS3GJcMakmi0uk+65iqy2rJ3V+BUqUg4+p42vRNQK0V90uCIAiXswZLEppMJtLT0+u1GY1GQkJCfO0jR44kJiaGyZMnA+BwONi7d6/v73l5eWzfvh0/Pz9SU1PPakxBOFeBBg1f3tmR8bO3sWhPEW/8XMHt3d5mre0ZCshn0nCZx+a+x+Iuo5k9X+JmWaZ595iGDlsQBEEQ/tSbb77JDTfcQHh4ODabjZ49e1JYWEjnzp156aWXGjo8QfhXVdQ4ePHnfXy3NReAWRtzuL1LIqpz3HYryzLzDs9jf/l+Hu/wOACRxkgWDF6ASnH626+g4cOpXrKEsHHjCLzxBqTTLHCQZZkFJZU8dyiPfLsTgKtC/JmUGkOy4fS7o2RZxlxkJSjSW9k4vnkInQenkNImnICwcztbURAEQbg0NXh14zPJyclBoaj7j3J+fj4ZGRm+79944w3eeOMNevbsyfLlyxsgQuFyoVMr+XB4W17+ZR+frT7C9NUlXNX8efwD3ieTXbwwTMH4nz5nf1oVs36Wudkl07LX6SvOCYIgCMKFICAggMWLF7NmzRp27NiBxWKhTZs29OnTp6FDE4R/jSzLLNhZwKT5eyi1OJAkGNkpgYn9mpxzgjC3OpcX1r3AuoJ1APSO7027yHYA9RKEsixTNX8+zvwCQu+9BwBd40ak/r4MxZ8cg/RyVgHv5xQDEK/T8FJaDFeFBpzxOSU51ayac4DSPAvDJ3XCGOC9Rpu+Cef0+gRBEIRLmyTLstzQQVxoqqqqCAgIoLKyEv8znP8hXJ6+2XSMp37chdMt0yzaj4jkn9hcthhJlhn9mwe731WUhw/m5m6JtO2X2NDhCoIgCJeg8zVXmTFjBjfffPNJZzM7HA5mz57NyJEj/26olwUxd7x4FVTaePqH3Szd7026pYb78erQFrRNCD6ncVweF1/t+4r/2/5/2Fw2tEot97e+n5HNRp60etCRm0fh889Ts3o1KBQkff8duiZNzvpa+yw2Bmw9yL1xYTwYH4H+DIlMu83F+h8Ps2dlHrIMKo2Cvnelk9jir2+dFgRBEC4+ZztXEUnCUxATPeHPbDxSzr1fbqG8xkGYSUPH1jtYUfoFANet95Bc2prdzUdzc6skOg1KRpLE4c+CIAjC+XO+5ipKpZKCggLCw8PrtZeVlREeHo7bffbnm13OxNzx4pVZWM21768CYGyvVO67IuWcC5Nklmfy3Nrn2FO2B4D2ke15rvNzJPjXX6Unu91UfPklxe+8i2yzIWk0hI4dS8gdo5HU6tOO/1tpJZk1tTyYEOFrMztdBKpPvylMlmUObSlm9TcHsVY5AEhrF06Xoan4BenO6fUJgiAIF7+znatc0NuNBeFC1SEpmJ/GduXOLzaTWVTNb+uac037x1lmfpX5nRS0Pbidaza+zizG4bS76X5TmqgSJwiCIFxwZFk+5QdZubm5BAScefuiIFysLHYXflrvbVDjSBMvD25B67hA0iLOXA34VJweJw8se4DCmkJMahOPtn+UwamDT/p3VZuZScHTz1B7vMiioV07Iv/7AtqkpNOOnV/r4KmDefxaWolSgt4h/jTz854deMYEoUfm5492cnRXmbdvhIGewxoR2+TcVkcKgiAIlx+RJBSEvygu2MB393fhodnbWbKviJ/WBXF161fYbH+WLWkOigPzGPPbi3wrP4RjppMrb2uK4hzPtREEQRCEf0JGRgaSJCFJEr1790alqpsSut1ujhw5Qr9+/RowQkE4/zwema82HOW1RZl8fVcn0mO8ifAb28X95THVCjWPtX+MX4/8yhMdniDMEHbydW02ckaOwl1ZicLPj/CJE72FSRSnnhe6ZZkv8kp5OasAi9uDSoJ748JJ0GvOKiZJIREYZuCYqpy2/RJp2zcBpVrMQQVBEIQ/J7Ybn4LYMiKcC49H5vXfMvlo+WEAOqboKdRPptxdiJ9NZuwCJVtb3E+fpM70vSMdpUpM0gRBEIS/5+/OVSZNmuT7OmHCBPz8/HyPaTQaEhMTGTp0KBrN2SUlLndi7njhyzPbeGzuDtYc8q6uG94xnpcGtzjncRxuBx/t+IjGQY3pl3T2ifTyWbOoWbuWyGeeRR0Rftp++yw2Hs08xpYqKwDt/A283jiOpn5nrj6cf8iM3k/tq1zsqHVhrXQQGGE46xgFQRCES5c4k/BvEBM94a/4YVsuj3+3C4fLQ1ywlsDYb8l2rkXhkRm1WKYydDitGvVn4N0tUGnO7awbQRAEQTjR+ZqrfPHFF//P3n2HR1WlDxz/Ti+Z9B5SSOi9C4gFAUXF/dnWrrj2ggV117L2XV10ddcusrqKu6tiWwuuDRGwIdJ7CRASEtLrZPrMvb8/JkyICSEhA0nI+3me+9x25syZCyQv75zCRRddhNksc5R1hMSOXZeqqry/qpA/fbaFeo8fs0HLfWcM4ooJWWjbORXMlsot3P/D/eys2Um0KZrPz/ucKGPzP2/F46H8+eexnXACERMnhtpxqDmqnQGFscs3U+ULYNNp+WNOKr/rlYC2lde5HT5++u9Otv5YTGrfaM69c7RMcSOEEKIZmZNQiKPs3FHpZCfYmPXWGvZWuSi3n8OIgf3Z4p/PG9M1nLrmLfasLubdF6/kgptHYjTLPz8hhBCd68orrwSCqxmXlZWhKEqT+5mZmZ3RLCHCorTOzb0fbmDJ9nIAxmTF8vQFI8hOiGhXPT7Fx2sbXuMfG/6BX/UTZ47joQkPtZggdG3YwL5778O7ezf2L74k54vP0ZpMbVrEzqrTclfvFL6vtvOXfumkmVvvybtrbRnfvbMjtDBJbLKVgF+RL6OFEEIcNulJ2AL5Nlh0RLXDy+x317FsRzAgHdXHyU7dX0DnZ3C+ypTNg9GPuJVLbhmPNUqGcQkhhGi/cMUqubm5XH311fz0009Nru/v9SSrG7eNxI5d07+W7+GhTzZj1Gv5/Wn9ueaEHHTt7GWXW53L/T/cz9aqrQCcmnUqD0x4gDhz00VAFK+XipdepvK11yAQQJeYQOqjfyJyyikHrbvOH+DRnUX8X1IsJ8cFF03Z/1+z1pKKzjov3y3Yzq41wVgzJtnKKZcPJK1fTLs+mxBCiJ5Dhht3gAR6oqMUReXFJTt5X/k/iQABAABJREFU5psdqCpkJWqpj/o7XmMJiTUqly1NwDnoTmbeMY3oRJkrRgghRPuEK1aZNGkSer2ee++9l9TU1GaJiREjRnS0qT2CxI5dk6Ko/OmzLVw2PvOwVi4ucZQw478z8Cpeok3R3D/+fk7vfXrzlYu3bGHfvffh2bEDgKizziL5/j+ij409aN2LK+v4/fa9FHt89LYY+eG4QejbkMCsLKrno7+vwePwo9FqGH1aJmNn9EZvkN6DQgghDk6ShB0ggZ4Il+9zy7l9wTqqHF5sJi2Rqf+l3vIzBp/K5UsN6OJu4tLfX0JiZvsDVyGEED1XuGKViIgIVq9ezcCBA8PYup5HYseu4Ze8Kl74Npd5V4zBagzPtC6P/fwYpY5SHpr4UIsrF3tyc9l97nng96OLiyPl4YeJmn7aQeur9fl5eOc+FpRUAZBtMfLMwEwmxNgO+poDBQIK789ZhUYDU2YOIjFDYkghhBCHJknCDpBAT4TTvhoXs95ew9qCGgB6p2+jwvYvNBqFyesVBlSfw//94U4yB8d3bkOFEEJ0G+GKVcaNG8czzzzDCSecEMbW9TwSO3YuX0DhuW9yeXnpThQVbp7ch7tPP7zE9+e7P2dU0ihSbanBuhUfeo3+oMN/VVWl6LbbQasl5eGH0MfFtVgOYFFFLX/YXkiJ14cGuD49kXtyUrHqtAd9jaqo7PilhL5jk9Hpg+Xqqz1YogzoWnmdEEIIcSBJEnaABHoi3Lx+hTlfbOWNH/cA0CveQ230s2hM1WSVqpy5bgin3PQEg07M7tyGCiGE6BbCFat8++23PPDAA/zlL39h2LBhGAyGJvclDmobiR07T16Fg9kL1rK+sBaA345J55H/G4LN1L6ehHXeOh7/+XE+z/ucscljee2019Bpmw/hVVWV2g8/JHLaNHQxMUBwPkKNwdDqPIKrah2ctSYXgD4WE88OymRcdOsLqNSWu/j2X1vZl1vDuBm9Oe43Oe36TEIIIcR+srqxEF2IUa/l4d8MYUxWLPd+uJGiSrDY78UY/yH5yb8w/+TNOP8xk7p9TzP+ovGd3VwhhBA9xLRp0wCYOnVqk+uycIno6lRV5f1VhTyycDNOb4Aos56/nDeMs4antbuu1aWrue/7+yh2FKPT6Dgu9ThUmvej8FdUUHz/A9QvW0b99z/Q69ln0Gg0aI2HXohuTJSVsxKjyTAbuTs7FUtrvQdVlc3f7+PHD3fi9wTQm3RYo03t/lxCCCFEe0mSUIij6KzhaYxIj2H2u+tYnV+Nq/g8YlzDcCb9hzen1VD84w3UFNzDab+/uNVvo4UQQohwWLJkSWc3QYjDMnfZLv765XYAxmfH8cxFI0mLsbSrDp/iY+66ufxz0z9RVIV0WzpPnPQEIxKbL9hjX7KE4vsfIFBVhcZoxDpmdKt11/kD/DWvmDt7pxBnCA5X/seQ3mgPEd/Zq9ws+c829m4JzlmY1i+GKTMHEZ3Yvs8mhBBCHA4ZbtwCGTIijjR/QOHlpbt4bnEuAUXFavagJr+O3prPgL0qv604m98+9SdZqU4IIUSLJFbpWuTP4+grrXPzmxd+4HeTenPDSX3QtWFl4Cavd5Qye8lsNlVuAuDsPmdz3/j7iDA0HQKsuFyUPvkkNQveBcDUvz9pTz2FeUD/g9b9Y7Wd27YWUOTxcXZSDPOG9G5Tm/I3VfL1PzfjdfnRGbRMPKcPw09JR9POzyaEEEL8mgw3FqIL0+u03Da1Hyf0S2D2gnUUVIGm4EbMsUvZlr6I5+M/ofjm9Vz/6Hwi0hI6u7lCCCGOIRs2bGDo0KFotVo2bNjQatnhw4cfpVYJ0TpFUflpVyUn9AvGRclRZpb+YfJhr2IcZYrCHXATZYzioYkPMb339GZlPLvzKJw1C29eHgBxv/sdiXfMRmtqeeivO6AwJ6+YeXvLAcgyG7mmV9vjuOgkC0pAITk7iqlXDiI2pfU5C4UQQohwk56ELZBvg8XRVO/x8/Anm/lwTSEARtM+DOlvoTVWcvImI78/7e/0PvXkTm6lEEKIrqQjsYpWq6WkpISkpCS0Wi0ajYaWwkGZk7DtJHY8sqocXu56bx1Ltpcz97LRnDEs9bDqcfvdGHVGtJrgfID5dfmYdCZSIlJaLB+orWX3OedCIEDaE3OIOP74g9a90e7klq0FbHe4Abg8NZ5H+6YRoW99VEhFoZ2E9MjQeVl+HQnpNrSycrEQQogwkp6EQnQTNpOev104gskDErn/o43UudPw592BIfFzlg5ZTu7GWcze8FtOv/NhmadQCCFEh+Xl5ZGYmBg6FqIr+yWvitveWUtJnRuTXovDe3iJ6101u/j9st9zZvaZXDf8OgCyorKalQvY7WhtNjQaDbroaDJefgl9Sgr62NiD1v11RS3XbNqDT1VJNOr5+4AMTk2IbrU9Hpef7xZsZ8cvpZx75yjS+gXrT8qSJLMQQojOI19RCdFF/GZEGl/OPomJOfEoih5P6f/hybuRQlsi98V+wON/PBNfdVVnN1MIIUQ3l5WVFfrSKSsrq9XtSHn88cc5/vjjsVqtxMTEtFimoKCAGTNmYLVaSUpK4g9/+AN+v79JmaVLlzJ69GhMJhN9+/Zl/vz5zep56aWX6N27N2azmfHjx/PLL780ue92u5k1axbx8fHYbDbOP/98SktLw/VRxWFSFJUXv83l4n8sp6TOTU5iBB/PmsRvx6S3qx5VVfko9yMu/uxidtbs5N3t7+L0OVss6/j5Z3afOYPa//43dM08aFCrCUKA8dERJBr1zEiMZum4gYdMEO7LrWbBn1ewY0UpGqCisL5dn0kIIYQ4UiRJKEQXkhZj4a1rx/PYOUOJMOrwebJw5s3GVXMyCwYUccXLU8n/RVaiFEII0b15vV4uuOACbrrpphbvBwIBZsyYgdfr5aeffuLNN99k/vz5PPTQQ6EyeXl5zJgxg1NOOYV169Yxe/Zsrr32Wr766qtQmXfffZc777yThx9+mDVr1jBixAimT59OWVlZqMwdd9zBwoULef/991m2bBn79u3jvPPOO3IfXhxSud3DlW/8wtNf70BR4bxRvVh4ywkMSm1fLzuHz8Eff/gjD/30EO6Am4mpE1lw1gKsBmuTcqrfT9lzz1Fw1dX4y8upXvAuqqIctF5VVfm+yh4aph9t0PPlmP68NqQ38a3MkRjwKyz/eBcf/X0t9VUeohLMnPeHMQw/JaNdn0sIIYQ4UmROwhbIvDKiKyisdnLffzfyfW4FADpTAaZeHxCtlPIH/W849+rH0Wglzy+EED3RsRKrzJ8/n9mzZ1NTU9Pk+hdffMFZZ53Fvn37SE5OBuCVV17hnnvuoby8HKPRyD333MP//vc/Nm3aFHrdxRdfTE1NDV9++SUA48ePZ9y4cbz44osAKIpCRkYGt956K/feey+1tbUkJiby9ttv89vf/haAbdu2MWjQIJYvX86ECRPa9DmOlT+PruKrzSXc8O/VmA1a/nT2UC4Yk97uKVe2VW3jD8v+wJ66Peg0Om4ZdQtXD706NB/hfr7iYop+/wdcq1cDEHPBb0n+4x/RWiwt1lvr83P3jkI+KavhbwMyuCwtvk3tqS5xsOj1LZQX2AEYeHwqJ17YD6NZZn8SQghx5LU1VpEMgxBdVHqslX9dfRx/PX84kWY9AU8mzt23U+mYykP6z7ntr1OoLNzV2c0UQgghwm758uUMGzYslCAEmD59OnV1dWzevDlUZtq0aU1eN336dJYvXw4EeyuuXr26SRmtVsu0adNCZVavXo3P52tSZuDAgWRmZobKtMTj8VBXV9dkE+EzfUgKD8wYxMJbTuDCsRntThDWeeu46sur2FO3h2RrMq9Pf51rh13bLEFoX7yY3eeci2v1arQREaT97WlS//zngyYIV9TUM2Xldj4pq0GnAbu/7fMjluyuo7zAjilCz+nXD2XqzEGSIBRCCNHlSJJQiC5Mo9Fw4bgMFt1xMlMHJgE6vOWn4cy7hcUxFs799Fw+/+Tvnd1MIYQQIqxKSkqaJAiB0HlJSUmrZerq6nC5XFRUVBAIBFosc2AdRqOx2byIB5ZpyZw5c4iOjg5tGRkyXLQjHB4/93+0kZJad+jatSfm0C85spVXHVyUMYrZo2dzUvpJfPCbDxidPLpZGc/uPApvvQ2lthbz0KFkf/RfomfMaLE+v6LyVF4x567dSZHHR5bZyMJR/bgxM6nVdhw4YGvgxBQmnJPDJQ+Op8/o1l8nhBBCdBZJEgrRDaREm3ntyrE8e9FIIkx6FE8azj03U1x3NndXLmD286dTVVXU2c0UQgjRzaxcuZIVK1Y0u75ixQpWrVrVrrruvfdeNBpNq9u2bdvC1fROdd9991FbWxva9u7d29lN6rZ2ltk5+6UfeWtFAbcvWMvhzoS0176XHdU7QucXDriQF6a8QIw5psXyppxsEm6+mbirrqL3229hzMxssVyBy8O5a3fytz2lKMAFKbEsHjeA0dERrbanYEslHzyxCrfDBwS/+B1zem8iYkyH9fmEEEKIo0H6uAvRTWg0Gs4Z1YtJfRN48JNNfLmpBF/NBPz2oXyZ9AWrFpzJ/QNnccaU6zu7qUIIIbqJWbNmcffddzN+/Pgm14uKinjyySdbTCAezF133cXvfve7Vsvk5OS0qa6UlJRmqxDvX3E4JSUltP/1KsSlpaVERUVhsVjQ6XTodLoWyxxYh9frpaampklvwgPLtMRkMmEySbKno/63oZi7P1iPwxsgOcrEH6YPaPfQYoBle5dx3w/3EWWM4t2z3iXaFB1MTNO0rvrvf8CYlRlKCCbeMuuQdRd5fKyucxCp0/LXARmcm9z6SscBv8KKT3azdlEBAKu/2MOk3/Zr92cSQgghOoMkCYXoZhIjTbxy+Rh+yC3n5nfWUue04S6+gGLLWO7SvcdXuf/j4UtfJTZShrIIIYRo3ZYtWxg9uvlQzFGjRrFly5Z21ZWYmEhiYmJY2jVx4kQef/xxysrKSEoK/j5btGgRUVFRDB48OFTm888/b/K6RYsWMXHiRACMRiNjxoxh8eLFnHPOOUBw4ZLFixdzyy23ADBmzBgMBgOLFy/m/PPPB2D79u0UFBSE6hHh5wsoPPHFNv75Qx4AE3LieOGS0SRGti/xGlACvLTuJV7d+CoA2dHZeAKeZuXUQICKl16iYu4rmAYNpPc776BtJcmrqmooWTkxxsbTAzM4IcZGpqX19tWWO/n6tc2U5QcXJxl6ci/G/1/bEuNCCCFEVyDDjYXopk7ol8iqP07jzMEpoIWAKxtn3m38r3YQM/5zFv/7+V+HPWRHCCFEz2AymZr1tAMoLi5Grz9y3yUXFBSwbt06CgoKCAQCrFu3jnXr1lFfXw/AaaedxuDBg7niiitYv349X331FQ888ACzZs0K9eC78cYb2b17N3fffTfbtm3j5Zdf5r333uOOO+4Ivc+dd97Jq6++yptvvsnWrVu56aabcDgcXHXVVQBER0dzzTXXcOedd7JkyRJWr17NVVddxcSJE9u8srFon8p6D5e9uiKUILzx5D7855rx7U4QVrmruOGbG0IJwksHXsr86fNJsjb9ktRfWUnBtddS8fJcUFUsw4e3Wu82h4sZa3LZ5WycH/HS1PhDJgi3ryjh3cdXUpZvx2TVc8YNwzj5kgHojbp2fS4hhBCiM2lUySI009aloYXoKpZtKOGGLzbhrg5+e67R12JK+pxJEdU8ct6LZMRmd3ILhRBChFO4YpVLLrmE4uJiPvnkE6KjowGoqanhnHPOISkpiffeey9cTW7id7/7HW+++Waz60uWLGHy5MkA5Ofnc9NNN7F06VIiIiK48soreeKJJ5okL5cuXcodd9zBli1bSE9P58EHH2w25PnFF1/kqaeeoqSkhJEjR/L88883GV7tdru56667eOedd/B4PEyfPp2XX3651eHGvyaxY9vVe/yc9/KPFNe4efrCEUwf0vbnvN/G8o3csfQOSp2lWPQWHp74MDNymi864lyzhqLZd+AvK0NjsZD6p0eJ/s1vWqxTVVXeKani/h2FuBSVU+IieWdEnza1Z9OyQpa9E5wPMbVvNKdePYTIOHO7P5cQQghxpLQ1VpEkYQsk0BPdUX2dh0vfXsPG4irUhpE2Wks+toTPuXHABK495W6MOmPnNlIIIURYhCtWKSoq4qSTTqKyspJRo0YBsG7dOpKTk1m0aJGs2ttGEju2T36lg4CikpNoO6zX37L4FpYVLqN3VG+emfwMfWP7NrmvqipV89+k7OmnIRDAmJND+vPPYerbt8X66v0B7tlRyIel1QBMjo3khcGZJBoNbWqP2+Hj/TkrGTAhlbFnZKHVyWAtIYQQXYskCTtAAj3RXQUCCs9+tJUXyyvQ5NeiEBzioo9cT3bCjzx2xh8Znzmpk1sphBCio8IZqzgcDt566y3Wr1+PxWJh+PDhXHLJJRgMbUuQCIkdW+MPKMz5YhtJkSZuOLltPfMOpcZdw4vrXmT26NnYjM0TjYrXS/6ll+HetImoGTNI/dOjaCNaXo14c72L6zftYZfLg04D92SncktmEtpWFlBRVZW9W6vIGBQXmrvQ5w1gkKHFQgghuihJEnaABHqiu/txeSF37SykvKyGQJkfNBrQ+DHE/shZfZ08cMajxFviO7uZQgghDpPEKl2L/Hm0rNrhZdbba/hpVyU6rYZv7zqZrPiWk3WtqXRV8uWeL7ls0GVtfo1v3z7qv/+BmAsvOOiKyatqHZy/biceRSXVZOCVwVmMj2m9d6PH5WfJv7exa00Zp1w+kMEnpLXrswghhBCdoa2xiqxuLMQxaNLEdBZmRPPA59v4LEclbm0+dq8VX9XJfLy6nm/33M89U0/gomGXoNPKt95CCNGTfPrpp5xxxhkYDAY+/fTTVsv+3//931FqlTjWbC2u47p/raKw2oXVqOPvF444rATh5srNzF4ymxJHCWadmfP7n99iOfuSJXhyd5Jw/XUAGNLSiL3owlbrHhFpZZjNQoxBz3MDM4k3tv5fo/ICO1++uom6chdarQa/T2n35xFCCCG6MulJ2AL5NlgcK7xuP2++u5Xnoty47PVEbCqmThucmF5rLCMrZRVzzpnJhLTxh6hJCCFEV9KRWEWr1VJSUkJSUhJa7cHnTtNoNAQCgY42tUeQ2LGp/20o5vfvr8flC5AZZ+XVmWMZkBLZ7noW7lrIo8sfxRPw0DuqN89NeY6c6JwmZVRFoeLluVS8+CIAmfPfIKKVlal3Oz1kmI0YtMHehbU+P5F63SGHF2/+fh8/vJdLwK9gizMx/dqhpOREt/szCSGEEJ1BehIKITCa9Vw7cygjlxby5/Iifpk+iOyV26iu0uH2JpFXcCaXztvA2L7/46n/u4as6KzObrIQQogjTFGUFo+FCIdnFu3gucW5AJzYL4EXLhlFjLV9C6f5FT/PrH6Gf235FwAnpZ/EEyc+QaSxaaIxUFfHvrvvoX7pUgBiL70U6+jRB633w5Iqfr+9kCt7xfNI314ARBta/++Q1+VnyVvb2LmqDIDewxOYeuUgzBEyZ6cQQohjjyy9JcQxTqPRMO6UDP5x6iCuWu+hbMQA3CemM9S/C73qRvGk8cvm8Ux55nNuW/gitZ7azm6yEEKIo8Dn8zF16lRyc3M7uyniGBJvCyYErz8phzd+N67dCcJaTy03fXNTKEF43bDreGHKC80ShJ7cXPZccCH1S5eiMRpJnTOHlIceRGNs/n4eReGe7XuZtbUAl6Kwtd6NT2nbYKqKQjs7V5eh1Wo4/vy+nHnTMEkQCiGEOGZJT0IheoiU7Ggevn4MQ/+1mVei9KyacRKZBXvp+8tafo4cQsCdyac/wldr3+KqkxP5/YnnotfKjwghhDhWGQwGNmzY0NnNEMeYmRN7M7RXNKMzYw/r9ZsqNrGieAUWvYXHJj3Gab1Pa1am7uuv2XfvfahOJ/q0VNKffwHL0CEt1rfX7eW6TXtYZ3cCcEdWMr/PTkHXyvDiA6X1i+XEC/uRlBUlw4uFEEIc86QnoRA9iNlm4NIbR/D3+ETO/sVBaUovvj1/BuOjSxhfuwINfjzOLF75wsqoJ1/l9VVLkGlLhRDi2HX55Zfzz3/+s7ObIbqx9XtruOQfP1Pr9IWuHW6CEGBSr0k8OPFB/n3Gv1tMEAIoDieq04l1wgSyP/jgoAnCbyrrOHXldtbZncTqdbw1PId7clJbTRD6vQG+W7CDmjJn6NrwUzIkQSiEEKJHkIVLWiCTT4ueoGR3LR+8tYV3c3TkphmJcDm57qvP+CGgY5NtHBBc9TgmupTbpw3kd2Mnomnjt+5CCCGOrHDFKrfeeiv/+te/6NevH2PGjCEiounqs3//+9872tQeoafGjgvX7+P376/H41e4fEImj50z7LDq+WDHB0xMm0gvW682v8a+eDG2k09Go2951EO1z8+45VuoDyiMjLTy6tDeZJhbH/pcW+7ky39somJvPQkZNi68bxwarcQ+Qgghur+2xiqSJGxBTw30RM/jdflZ+s42Piir4etRVtxGLQMK87nhiw95LSWH3cbR7E8WRkdVM3vqIK4cNxqtBMxCCNGpwhWrnHLKKa3eX7JkyWHX3ZP0tNhRVVWe/SY3tEDJlIFJPHfxSCLN7Zurz6f4ePKXJ3l3+7v0i+3HW2e+hUVvaVbOW1hI6WOPk/r4Y+jj49tc/8el1fxc6+DRvmmYWlnJGyBvfTnfzN+K1+XHEmng1GuGkDEwrl2fRwghhOiqJEnYAT0t0BNi+4oSFn60g0+GmsntZUSjKFy98RfGfbuQF/oPZ5fhOFCD375H2RzcNmUAvxs/DL1OZiwQQojOILFK19KT/jzcvgB3vb+e/20oBuC6E7O594xB6Nr5BWKtp5a7lt3FiuIVaNAwe8xsrhpyVbNRC44Vv1B0++0EamqInD6d9OeePWidm+tduAIKY6MjDlrm15SAwoqFeaz5Mh+AlJwopl83FFusuV2fRwghhOjK2hqryP/whRAMGJ/CdXeN5bYiLWf/XI/JD/8cMYE7Zz3EjbGxPLfyb+Soi0Hrpq4+gsc+LWTk4x/z8rKNOL3+zm6+EEKIw3T11Vdjt9ubXXc4HFx99dWd0CLRlZXZ3Vw0bzn/21CMXqvhyfOHcf+Mwe1OEObV5nHZ55exongFVr2V5055jquHXt0kQaiqKlVvv03BNdcQqKnBPGQIyffde9A6Pyqt5qzVO7hqUx7FHm+b2uF2+Pj0+fWhBOHwKemcc+doSRAKIYTosaQnYQt60rfBQhwoEFBY+VkeS5cW8OWoCLZlBHsPDnXZeeLbheSv+YYXJhxPvuZE1IANAJPBz4XjenHTSUNIi2k+REgIIUT4hStW0el0FBcXk5SU1OR6RUUFKSkp+P3yRVBb9JTYsczu5tyXfsLh9fPK5WOYkNP2ob/7/Vz8M3cuvRO7105qRCovTHmBAXEDmpRRvV5KHnucmvfeAyDqrLNIfezPaM3Nk3d+ReXPu/Yxr7AcgFPiInl5cBaxhpbnKjxQwK/w0d/WULnPwZQrBtJvbHK7P48QQgjRHchw4w7oKYGeEAdTtL2ab97cwhqTwpdjrNRGBOclvKq+gqvf+xdrStfx0oQJ7FVPQPUF/4Og0aicMjCGW08ZwqgOrGoohBDi0Doaq9TV1aGqKrGxseTm5pKYmBi6FwgEWLhwIffeey/79u0LZ7OPWT0pdswttWPQaemd0PYhvfupqsrln1/OhooNjEwcybOnPEu8pWmi0V9dTdGtt+FctQo0GpLuupO4a65pcfG0cq+PGzbn81NNPQC3ZyVzd3ZKq6sXq6oKKqEFSexVbnyeAHGp7f88QgghRHchScIO6EmBnhAH43X5+eGDXNb/XMx3Qy2sGGBG0WiwaTU8VZbHsH/OY20gn39OGEKu4UQCzj6h1w5Os3DTyQM5fWgKBpm3UAghwq6jsYpWq211xXqNRsOjjz7K/fff35Fm9hjHcuz43sq96HUazhudHpb6ypxlvLHpDWaPmY1JZ2p2P1Bby56LLsZfUUHa008ROXlyi/WsqXNw7aY97PP4iNBpeX5QJjMSY1p9b78vwHfv7MASaWTiuX1aLSuEEEIcSyRJ2AHHcqAnRHvt2VjBkv9sY7cmwOdjIyiKDw7fGW018dfctZj++RpbtMW8ObEXGyJOxF83EtRgmYRIPTMn5HDRuAySo2R+HyGECJeOxirLli1DVVWmTJnChx9+SFxc4yquRqORrKws0tLSwtnkY9qxGDuqqsrfvt7Bi0t2YtBp+OzWExmQEtnuejwBDz8U/cDUzKltfo13zx5Uvx9T374HLXPLlnw+KK2mj8XE68OyGRDRepxhr3Lz5byNlOXb0Wg1XPrIeGKSrG1ukxBCCNGdSZKwA47FQE+IjnA7fHy3YAfbV5Wypo+JJSOsuPUatMCVCZHcvOoHXK+9Sq6hkncmRvFL/PH4qsejBoL/mdBqYNqgZC4dn8lJ/RLRtnOCcyGEEE2FK1bJz88nMzOz1V6F4tCOtdjR4w/wh/c38On64HDz26b05Y5T+7f770mVu4rbv72ddeXr+OtJf+WM7DNaLvfWW2i0WmIvuaTNdTv8Af6aV8Jd2SlE6XWtli3aUc1Xr27CZfdhitAz/dqhZAyKa/U1QgghxLFEkoQdcKwFekKEy641ZSx9ezvlfj/fjIpgU2ZwYZNovY67k6M469svqXn9dfJMdbx/vJEf04bjqxlPwJUdqiM91swlx2Vxwdh0kiKld6EQQhyOcMYq33//PfPmzWP37t28//779OrVi3//+99kZ2dzwgknhKnFx7ZjKXasdni5/t+rWLmnGr1Ww1/OG8aFYzPaXU9ebR43f3MzhfWFRBojeXbysxyXelyTMqrfT+mcJ6h+6y3Q6cj+74eYBwxosb4qn5+39lVyS2ZSm5OVqqqy4dtCfvxwJ6qikpBh44wbhhGVIAutCSGE6FnaGqscetkvIYRo0Gd0Eql9Y1j2znYil5czeqeexcfZKLLB/UXVzB91Eo+9N4PRH31A73//m8uNq/h83BoWDUrFYT8OX+1oCqvhqa+288yiHZw6OJmLxmVwYr9EdNK7UAghjroPP/yQK664gssuu4w1a9bg8XgAqK2t5S9/+Quff/55J7dQHE17KhxcNX8leRUOIs16Xrl8DJP6JrS7npUlK5m9ZDZ13jp62Xrx8rSXyYnOaVImYLdTdMedOH74AYDE227D1L9/i/VtrXfxu4155Lu9ANya1bZViJe+vZ0t3wd7Q/Yfn8zkywZiMLbe61AIIYToyaQnYQuOpW+DhTgSVFUld1UpP7yXi6Pex7ocE9+NjsDesEbJ9IQoHo4xE7HgLWreWUCd4mTRKA2fj7NS4RuCt2Y8iisrVF9SpIlzR/Xi/DHp9E9u/3xHQgjR04QrVhk1ahR33HEHM2fOJDIykvXr15OTk8PatWs544wzKCkpCWOrj13HSuz44re5PP31DnrFWJh/1Tj6Hcbv5IW7FvLQTw/hV/yMSBzB81OeJ87cdGivt7CQvTfeiHfnLjRmM2lPPknU9NNarO/L8lpmbc3HEVDINBt5c1g2g2xt6wm4Y2UJi9/YyqQL+jJscroMqxdCCNFjtTVW6TLLjj7xxBNoNBpmz5590DKbN2/m/PPPp3fv3mg0Gp599tlmZebOncvw4cOJiooiKiqKiRMn8sUXXxy5hgvRA2k0GvqPS+HShycweEIKo3d5uOHjaibt8aEDvqqoY/Kucl497zKSvv6a3lffzPmbI3n5JQe3/7CSwdGvYs1+FkPsT2h0TsrsHuZ9t5vTnvmOs174njd+zKOy3tPZH1MIIY5527dv56STTmp2PTo6mpqamqPfINGpZp3SlztP7c9Hs44/rATh1sqt/PGHP+JX/EzvPZ3XTnutWYLQuWYtey64EO/OXeiTksj6z39aTBCqqsrf95Twu015OAIKk2JsfDGm/yEThH5fIHTcf1wKlz46geGnZEiCUAghhGiDLpEkXLlyJfPmzWP48OGtlnM6neTk5PDEE0+QkpLSYpn09HSeeOIJVq9ezapVq5gyZQpnn302mzdvPhJNF6JHM9sMTL1yMP83eyTJMWamrLBz3ec1DHWAV1V5saCMSdv28dG5F5H5zSLSZt/J1KJY/vqKm4c+LmKi7wsi+j2Oude/0ds2gybApqI6Hl24hfF/Wcy1b67ii43FuA8I+IUQQoRPSkoKO3fubHb9hx9+ICcnp4VXiGPNl5saf89qNBpum9rvsOcMHhQ/iGuGXsNVQ67iryf9FbO+eT2utWsIVFdjHjyY3u+/h2XokGZlHIEA123ew1/zgj1Zr+6VwIIRfYg3HnymJFVV2bi0kLcfWYGjtvGLxuhEmX9QCCGEaKtOH25cX1/P6NGjefnll3nssccYOXJkiz0Ef613797Mnj271Z6H+8XFxfHUU09xzTXXtKlNx8qQESGOJr83wMrP97Du6wICikpebzPfjY9kL8H/eGSYjdybncLZUWbqPviQyn/+E39pKSWx8M04I0tG6alV9PjrRuKvHU3AnR6qO8Ko49TBycwYnsZJ/RMwHWIVQyGEONaFK1aZM2cO//nPf3j99dc59dRT+fzzz8nPz+eOO+7gwQcf5NZbbw1jq49d3TF2VFWVvy/awQvf7mT6kGRevmzMYc0P7PQ58Sk+ok3RoXpb67Wnqio1775H9P/9Bq3V2mKZNXUOzl4TTF4/0T+dy9LiW21DwK/w3bs7QvMPjv+/bMaemd3qa4QQQoiepNssXDJr1ixmzJjBtGnTeOyxx8JadyAQ4P3338fhcDBx4sSDlvN4PKGJuiH48IQQ7aM36ph4Th/6jU1myb+3otljp3e+m11jo1ncz8het5dZWwt42WbmgRlnc9JFF2JfuBDj/Plc/vUuLvzWy/JBWr45ZRPbs38i4EnCXzsa1T4GhzeSj9ft4+N1+4g06zltcApnDU9lUt8EjPou0SFaCCG6pXvvvRdFUZg6dSpOp5OTTjoJk8nE73//e0kQHsN8AYV7P9zIh2sKARiYEsXhrB9W6ark1m9vRa/V8+ppr2LSmZolCFWfj8rXXiP2ipnobBFoNBpiL76o1XpHR0Xw94EZZJqNjI+xtVrWWefly3kbKd5VCxqYeE4fRp2W2f4PI4QQQojOTRIuWLCANWvWsHLlyrDWu3HjRiZOnIjb7cZms/HRRx8xePDgg5afM2cOjz76aFjbIERPlZBu4/x7xrJxSSErFu6m38paeq+FvGlJfB6nsLnezSUbdnNCjI0HTjuTEeefj+OHH6maP5+Tf/yRkzdWsTsZvp3mZVnmYtyJX2FwZRCwj0TjGI3dbebDNYV8uKaQaIuB04ekcPqwFI7vEy89DIUQop00Gg33338/f/jDH9i5cyf19fUMHjwYm631xIzovuo9fm76z2q+z61Ap9Xw+DlDufi49ifV8uvyuembm9hr30u0KZq9dXvpG9u3SZmA3U7R7Dtw/PgjrvUbSJ/78kF7GX5UWs0gm5mBEcHhwRekxLVY7kBl+XV88cpG6qs9GC16Tr16ML2HtX81ZiGEEEIEddpw47179zJ27FgWLVoUmotw8uTJYRlu7PV6KSgooLa2lg8++IDXXnuNZcuWHTRR2FJPwoyMjG41ZESIrshR62H5f3exfUVwTqFAtIHtpyeyUOPB2/CjZ0ZiNL/vncIgmwX3jh1UvfkmdZ8uRPX5cJhg+fExLDvOynZtGaqqIeDKQu84joB9OE5P4/ccEUYdkwckcergZE4ZkES01dApn1kIIY6G7ji89VjWXf48yurcXDV/JZv31WEx6Hj5stGcMjCp3fWsL1/PrYtvpdpTTS9bL+ZOm0t2dNPhvb59+9h7w414cnPRWCz0+tvfiJxySrO6FFXlr3klPJtfSqbZyNdj+xNjOHQ/hqLt1Sx8cT0Bn0JMspUzbxpGbEpEuz+LEEII0RO0NVbptCThxx9/zLnnnotO19jzJxAIoNFo0Gq1eDyeJvd+rT1zEk6bNo0+ffowb968NrWtuwR6QnQX+3bW8N2CHVQW1gOg6Wtj9YlxfO6sZ/8PoN8kxnBn72QG2Sz4KyqofmcB1W+/TaC6GoCCFD0/zchkSWo11QF7MGHozCbKOxlXXV/qnI3DjvVaDeNz4jhtcAqnDk4mLUYmLRdCHFvCFau43W5eeOEFlixZQllZGYqiNLm/Zs2ajja1R+gOsaOqqpz90o9sKKwlPsLI678bx4iMmHbXs6RgCXd/dzfugJvB8YN5aepLJFia9t5zbd5M4Y034S8vR5eYQMbcVw66QMltWwv4X3ktALdkJvHHnFS0bViJ2Ovy88GTq4hKsHDqNUMwWTp9FiUhhBCiy+rySUK73U5+fn6Ta1dddRUDBw7knnvuYejQoa2+vj1JwilTppCZmcn8+fPb1LbuEOgJ0d0oAYXN3+9jxae78Tj9AFhPTOL7wRY+r7GHyh2YLFTcbuq+/JKadxbgWr8eAL8WNkxK4fsTYvhZk0dADaCqGvBkkqROxVnTj5Kapv+5GJwaxeQBiUwekMTozBj0OpnHUAjRvYUrVrnsssv4+uuv+e1vf0tycnKzoaAPP/xwR5vaI3SX2HF1fjX3f7SReVeMISu+/b3uPtv9Gff/cD+KqnBirxN5+uSnsRqaLj5iX7KEort+j+p0YurXj4x5r2BIS2tWV7HHy5Ub8thQ78Kg0fD0gAwuSm19iLHfG0Bn0Ib+njrrvJhtBrSHM6GiEEII0YN0+SRhS3493HjmzJn06tWLOXPmAMFhxFu2bAHgzDPP5LLLLuOyyy7DZrPRt29wDpT77ruPM844g8zMTOx2O2+//TZPPvkkX331Faeeemqb2tFdAj0huiOX3cvPH+9iy0/FoILepCNuei++StfxWWVtqNyByUIA99atVC94l9qFC1GdTgBq4kysOncg32c52erKC71W50shXTcDd21/dpWqHPhTLtKs58R+CUzun8TJAxJJjjIfnQ8uhBBhFK5YJTo6ms8//5xJkyaFsXU9T1eOHevcPqLMjVNwKIp62Em13bW7ufKLK5mSOYUHJzyIXtu0957idrPr9DPwl5QQcfzx9HruWXSRkc3qWVfn5MqNuyn1+okz6HhjaPYhFyipq3Tx+csbGDgxlZHTZGESIYQQoj2OiSTh5MmT6d27d6gH4J49e8jOzm72upNPPpmlS5cCcM0117B48WKKi4uJjo5m+PDh3HPPPW1OEELXDvSEOFaU5tXx3bs7KNsTXE08ItpIwlmZfBIbYGHDsCMNcFZiDLdmJTE8MthTIVBfT+2nn1LzzgI8ubmh+iqGpfPL6VksjS5mj6MgdN2sSaSv4TcozkFsK9RQ6/I3aceg1ChO7p/Iif0SGJMVi9kgi58IIbq+cMUqgwcPZsGCBaH5ocXh6aqx4werC/nzZ1t469rxDO0VHZY6i+uLSYlIOegCJO4tW6j58L8k33sPGkPL8wNfvmE331TWMSDCzL+GZZNlMbX+njtr+GLeRlx2HxHRRi59dAJGswwvFkIIIdqqWyYJu4quGugJcaxRVZWdq8tY/tEu7JVuAOJ7RZD0f5m8o/OwsLwmVPakWBuzMpM5KdaGRqNBVVVca9dS8+671H31Nao7+HpVq6X0tJGsOD6WxZptlDhLQ3WYdVaG2M7A6h1HQWkkm/fZm/QyNOq1jMmMZVLfeI7vm8DwXtEyNFkI0SWFK1b54osveP7553nllVfIysoKYwt7lq4WO6qqyivLdvPkl9sAuGpSbx7+TfM5AQ/FE/Dw4A8Pcm6/c5mYNrHl9/J6cW/bhqUdieZqn585u4t5sE8akfrWv5zb+lMxS9/ahhJQSciwceZNw4mMk1EAQgghRHtIkrADulqgJ8SxLuBT2LC0kNVf7AnNV5g5OI6EszL4j7uej8uqCTT8pBpmszArM4mzEmPQNwyXCtTXY//qK2o++gjXqtWNFUdFUnjeeH4ZbmaZcz3FjuLQLYPWwKj4k0jRTKamOpVVe+yU1jWucg4QadIzPieO4/skMCEnngEpkehk3iMhRBcQrlilvLycCy+8kO+++w6r1YrhVz2/qqqqOtrUHqErxY6KovLn/23hjR/3AHD9STnce/rAdg8xrvPWcdu3t7G6dDUxphi+Ov+rZvMPBurrKbrtNpyr15A5/w2so0a1WJdPUfm8ooazk2Lb9TmWf7SLdYuCowNyRiUy7XeDMZikx78QQgjRXpIk7ICuFOgJ0ZO4HT5Wfb6HjUsLUQIqGg0MnJhK2mnp/Mdey1v7qnA1rLyZaTZyY0YiF6fGYz2gt583P5+ajz+m9uNP8Bc3JgX16b0o/c14fhmsZ6ljLXm1jXMYajVahsUPZ0jUFPSewewq1rF8dxW1Ll+T9kWa9YzNimVs7ziOy45jeHo0pkP0gBBCiCMhXLHKtGnTKCgo4Jprrmlx4ZIrr7yyo03tEbpK7OjxB7jrvfV8tiH4+++BGYO49sScdtdT5izjpm9uYkf1DmwGG8+d8hzHpR7XpIyvtJS919+AZ/t2NFYr6c89h+3EE5rVVevzc82mPfxQU89f+vXi6vTEQ76/qqp88cpG8tZXADD2zN4cd1Y2GvmiTgghhDgskiTsgK4S6AnRU9WWO1n+0W52rSkDQKvXMOSEXuSc2ot36+z8s6icKl8AgDiDjivTEriyVwIppsYeMKqi4Fyxgpr/foR98eLQYicAxr59qJ4xkZWD9SypW83Wqq1N3j/ZmsykXifS23gC9XVprNxjZ/WeKhzeQJNyRr2WEenRjOsdx7jecYzKjCHGajxSj0UIIULCFatYrVaWL1/OiBEjwti6nqcrxI4Oj5/r/72KH3dWYtBpePqCEZw9sle769lTu4cbFt3APsc+EiwJzJ02l4FxA5uU8eTmUnD9DfiLi9ElJJDxyitYhjYfzpzv8nD5ht3kOj1E6LS8MjiLUxPaNjfihiV7+em/u5g6cxD9xiW3+3MIIYQQopEkCTugKwR6Qggo2V3Lz5/spmh7NQA6g5Zhk9MZNDWdj+vtvLK3nAK3FwC9JrjIybXpiYyJsjbpDaO4XNQvXUrd559Tv3QZqq+xh6B5yBDcZ57IuiEWfnJs5Ofin3EH3KH7Rq2RcanjmJhyAkn6URRXWFi5p5qVe6qoqPc2a3NOQgQjM2MYlRnLqIwYBqZEyryGQoiwC1esMnr0aF5++WUmTJgQxtb1PF0hdvQFFK55cxWr91TxyhVjOLHfoXvs/dqmik3c/M3NVHuqyYzMZN6p80iPTG9SxrHiFwpvuQXFbseYnU3Gq//AmJ7erK7VtQ5mbsyj0ucn1WTgP8NzGGKztPr+B668rKoq9ko3UQmtv0YIIYQQhyZJwg7oCoGeEKJR4bYqVnyaR8nu4KrHepOOEVPSGTo1g2+dTl4vLOfnWkeo/PBIC9emJ3J2UgwmbdMEXcBux/7NYur+9z8cy5dDoLF3oGngQIynTmb7mGR+ZhffF31PUX1Rk9cnWZKYkDaB8SkTSDONYHeJhl/2VLE6v5q8Cge/ZjZoGd4rhlGZMQxPj2F4ejTpsZaDrgophBBtEa5Y5euvv+bRRx/l8ccfZ9iwYc3mJJQ4qG26Suzo8PgpqHIyKPXw2vDo8kf5YMcHDIkfwktTXyLeEt/kvmvjJvIvvRTV58MyejTpL72IPrb5PIOfltVw29Z83IrKMJuFfw3PJtXUek/7HStLWPt1AefcMQqTteVVkYUQQghxeCRJ2AFdJdATQjRSVZWCLVWs+GQ35QV2AIwWPSOnZTBiSgbb/V5eL6rgv6XVeJTgj7V4g56ZafHM7BXf4n9O/FVV2L/+mrrPv8C5ahU0zHcIYMjMxDZtKhUnDGZlZDnLi5ezpmwNnkDTxU36xvRlQuoEJqZNJMc2jNxSH2sLalhbUM26vTXY3f5m7xtjNTCsV3Tjlh5NrxhJHAoh2i5csYq24YuUX//8UVUVjUZDIBBo6WXiVzordly1p4pvtpZxz+kDwvI7xKf4eG3Da8wcMpMIQ0Sz+2ogQNHsOwBIe+qvaM3NVxne7fRwwoqtKMBp8VHMHZxFRCvz96qqysr/7WHlZ8G5go/7TTbjZmR3+LMIIYQQopEkCTtAkoRCdF2qqpK3voJfFu6msijYc89k1TNscjrDp6TjNGp5u7iSN4oq2OcJDivWaWBafBSXpcYzJS4qtCrygfzV1dR/uwT7N9/g+PFHVG/jUGJdYgKRk0/BcNJEcvtY+Ll6Lcv3LWdb1TZUGn+EajVaBsYNZGzyWMaljGNk4igq6nSsLahm7d4aNhbWsq2kDl+g+Y/dWKuBob2iGZwaxeC0KAalRpGTECFDlYUQLQpXrLJs2bJW75988smHXXdP0hmx45ebSrh9wVo8foW//nY4F47NOKx6fiz6kQmpE9BpW07kqaoKgQAavR4AxetFo9Oh0R088fdSQRklHi+P9O2FrpXkpd8X4Nt/bSN3ZSkAI0/NZOK5fdq9ErMQQgghWidJwg6QJKEQXZ+qqOxcU8bKz/KoLgkuSqI3ahlyQi9GnpqBOdrEFxW1/PNXQ5FTTQYuTonjktQ4Mi2mFutWHA7qv/8e+6JvqF+2DKW+PnRPYzBgHTeWiJNOwj9xFGuNxfxc/DMrildQWF/YpB4NGgbEDWBs8ljGJo9lVPIoIvTR7CipZ0NRDZuKatlQWMv2Ejt+pfmPYqNey4DkSAalRjI4NZg4HJASKYujCCEkVulijvafx7+W7+HhTzejqjBtUBIvXDIai/HgSbuWqKrKvA3zeGndS1zY/0IemPBA8x6lPh/FDzwIQOoTcw7aW9HhD1AfUEhuWEBsf0/U1jjrvHzxygZKdteh1Wo4+dIBDD4hrV2fQQghhBBtI0nCDpDAW4juQ1VUdq8vZ/UX+aFhyFqdhoETUhg1PYuYJCu5DjdvFVfyXklVaFVkDXBybCSXpcUzPSEKo7blHnuK14tzxQrql31H/bJl+PbubXLfkJGB7aSTsJ18EvbBGayu28yqklWsLl3Nnro9zerLjMxkZNJIRiSOYGTSSPpE98EXgO0ldjbvq2NrcR1biuvYVlzXbDXl/ZIiTfRPjqRfso0ByZH0S46kf7KNSLPM4SRETxGuWOW7775r9f5JJ5102HX3JEcrdlRVlb9+tZ25S3cBcMlxGfz57KHt7nWuqApP/vIkb297G4Abht/ArJGzmi765XBQOPsOHN9/DzodvRe8g2XYsGZ1lXp8XLFhNz5V5ZPR/YhqZWjxftUlDhY+vx57lRuTVc/p1w8lfWBcuz6DEEIIIdpOkoQdIElCIbofVVXZu7WK1V/ksy+3BgCNBvqOSWL06VkkpEfiURS+rKjlrX2VfFfd2Dsw3qDnvOQYfpsSx3DbwecGVFUVb94e6r9bhuO773CsXAUHrJSMwYB15Egijp9IxMSJ2Psks6ZiPatKV7GqZBW7anc1q9NmsDEsYVgocTgkfggx5hgURaWgysnW4sbE4dZiO0U1roM+g7RoM32TI+mTGEGfRFvDFkFipEnmOxTiGBPuOQkPdODPC5mTsG2ORuzo9Svc++EG/rs2uKDWnaf259Ypfdv9890X8PHAjw/wed7nANx73L1cNuiyJmX8VVXsveFG3Bs3ojGb6fXsM0ROntysrm0OF5et302Rx0e8Qc8HI/sw6BArGEOwF+H7T6xEp9MyY9ZwYlOaz38ohBBCiPCRJGEHSJJQiO6teGcNq7/KJ39jZeharwGxjJyaQdbQeDRaDfkuD28XV7GguJJSb+PiIv2sJi5IieO85FjSza0P61UcDhw//0z90mXU//gD/n3FTe5rbTas48cTMXEiEcdPxJ0Wz8bKjawrW8e68nVsLN+I0+9sVm+6LZ2hCUMZmjCUIfFDGBw/GKvBCkC9x09uqZ0dpXZ2lNY37O2U1nma1bNfpElPTpItlDzsHR9B7wQrveMjiDDp2/RMhRBdS7hildra2ibnPp+PtWvX8uCDD/L4448zderUjja1RzgasePKPVVcNG85Go2GOecO48Jx7Z+D0OlzcueyO/mx6Ef0Gj2PnfAYM3JmNCnj3buXgmuvxZdfgC4mhox5r2AZMaJZXT9U27l6Ux51foW+VhNvDc8h6yDTeLSkusSBxWbEbJNe8EIIIcSRJknCDpAkoRDHhvK9dtZ8lc+uNeWoDXP+xSRbGX5KOgMnpmIw6fArKt9W1fFhaTVfVdTiPmBuwIkxEVyQHMdZSTGHHD6lqiq+ggIcy5fj+Gk5jhUrUH71n29dYgLWsWOxjhuHdexY9DnZ7KrbzbqydawtX8umik3k1+U3q1ur0ZITncOQ+CEMih/EwLiBDIgdgM1oC5WpdfrYUWZnV1k9u8rr2VXuYFd5PXurnLQw3WFIYqSJ3vHWhsRhBL3jI8iKt5IRayXaKv9xE6KrOtKxyrJly7jzzjtZvXp12Os+Fh2t2PHdlQUkRZo5ZWBSu1+rqirXfX0dK0pWYNaZ+fvkv3Ni+olNyri3bKHg+hsIVFRg6NWLjFdfxZTTfKXhD0qquGPbXnyqyvjoCOYPyybWcPAvnVRFZfnHu4jvZWPA+JR2t10IIYQQHSNJwg6QJKEQxxZ7lZsNSwrZ8sM+vK5gr0GTVc/gE9IYNjmdyDgzAHX+AJ+V1/BBSTU/1TQORzZrNUyNj+I3iTGcGh9FRBvmW1IDAdxbtgaThst/wrV6TZMVkwF00dFYxo4NJQ7NAwdQF3CwpXILmys3s6liE5sqNlHqLG3xPTIiMxgYN7DJlmhJbDL0zOMPkF/pDCUPd5c72FPpYE+lkyqHt8V694sy68lsSBhmxllJjwvuM2ItpMVYMBvaN0m+ECJ8jnSssm3bNsaOHUv9AQs3iYPrLrHj0r1Lefinh3nulOcYmTSy2X3HTz9RcMONmPr0IeMf8zAkNU9GvrWvkru2B+fnPTsphucGZmJuZU5EvzfAN29sYdfacrR6DZf/aWLo964QQgghjg5JEnZAdwn0hBDt43X72ba8hA3f7qW2PDi3n0aroe/oRIZOTie1T3QowVbo9vLf0mreL6ki19k4lNei1TClnQlDAMXjwb1hA46VK3GtWoVz7TpUV9P5BTVWK5Zhw7CMHIll5AgsI0eij42l3FnO5srNbK7czLbKbWyr3kaJo6TF94k1xdI3ti/9Yvo17mP6Nul1uF+ty0d+pYO8Cgd7KpzB40oHe6ucVNS3nkAESLAZ6RVjoVesJbiPCSYPe8VaSIu2EGM1yFyIQhwh4YpVNmzY0ORcVVWKi4t54okn8Pv9/PDDDx1tao/QlWNHRVXQahqTeE6fMzSFRUscP/2EedgwdJGRLd7f5/YyY00u5yXHcn9OKtpWfs4767x8PncDpXl1aPUaplwxSHoSCiGEEJ1AkoQd0JUDPSFExymKSv7GCtYv3kvRjprQ9bi0CIae1Iv+41MwWYLDplRVZVO9i0/LalhYXsMeV2Py7HB6GO6n+ny4N2/GuWoVzpWrcK5Zg2K3NytnzMoKJg1HjcQyciSmvn3R6PVUu6vZXr09lDTcVrmNvLo8FFVp8f1SI1LpFxtMGOZE55AdnU12dDaRxpb/E+j0+tlb5WJvlZOCKid7q52h48JqF86DrLx8ILNBS2q0hZQoM6nRZlKig/vUaAsp0WaSokzER5jQaSWRKER7hXPhEo1Gw6/DwQkTJvD6668zcODAjja1R+iqsePmys089ONDPHfKc6RHprdYpvq997COHdfisOL9AqqK7oBkYLXP3+rwYoCqfQ4+e2k99ko3pgg9Z944jLR+sYf3QYQQQgjRIZIk7ICuGugJIcKvvMDOxqWF5K4sxe8LJtj0Ri39xyUz5KReJGU1/gzYnzBc2JAwzDsgYWjSajgxNpLpCVGcFh9Nsql98/mpgQCenbtwrVsX2rx5ec3KacxmzIMGYR46FMvQIZiHDsXYuzcanQ63382u2l3srN7Jzpqd5FbnkluTS5mz7KDvm2hJbJI0zInJoXdUb5KsSU16njRpq6pS6/JRWO2iqMZF0a/2+2pcVB5iKPN+Oq2GpEgTSVFmkiNNJEeZSY4KnidFmkhs2CSZKERT4YpV8vObzoOq1WpJTEzEbJbhoO3RFWPHFcUruO3b23D6nZyWdRp/m/y3JvdVVaXihReoeHku+rRUcv77X3QxMc3qKff6uHJjHjdlJPGbpOb3W7J3WxVfztuE1+UnOtHCWbeMICb54L0XhRBCCHFkSZKwA7pioCeEOLI8Th/bV5Sw6bt9VBc7QteTsiIZclIv+o1NxmBq7Cmoqiqb610sLK9lYVkNu11NVxceFWllekIU0xOiGRhhPqxht4GaGlwbNjQmDjdsRGlhfjCt1Yp58GDMQ4ZgHjIY08CBmLKz0RiCicpaTy07a3ays3onuTW57Kndw+7a3ZS7yg/63madmYyoDLIis8iKCm6ZUZlkRWURb44/5Odx+wKU1rkprnVTUhvcF9e6QvvSOg8V9R7a+htIq4F4m4lEWzBpmBRpIiHSRHyEkQSbiQSbiXhb8DjWakDfyvxYQhwLJFbpWrran8ei/EXc8909+BQf41PG8+wpzzaZdkINBCh59E/UvPceAAm33ELCrJub/Wzf7fRwyfpd5Lu9pJoM/DR+EJY2/HxdsXA3q/63h9Q+0Zxx0zAsNmN4P6AQQggh2kWShB3Q1QI9IcTRo6oqxTtr2fRdEbvWlqH4gz8iDSYdfcckMXBiCql9Y5r8R0pVVbY73XxdUceXFbWsqXM2qTPTbGR6QhTT4qMZHx3R6gTvrbZNUfDm5+PetBn3pk24Nm/CvWUrqtPZrKzGYMDUrx+mgQMxDxzQsB+I7oCfaXavnbzaPHbX7m6yL7IX4Vf9B22HVW8lPTKddFt6cB+ZTkZkBum2dNJsaRh1bfvPoD+gUFHvpbTOfcDmCe7tHsobtkpH25OJABoNxFqNxEcYiTtg238eG2EkPsLUcGwg1mqURVhEt9ORWOX5559vc9nbbrutvU3rkbpS7Pj+jvf58/I/o6IyLXMaT5z0BCadKXRf8XjY9/vfY1/0DWg0pDz8ELEXX9ysnjW1Di7fuJsqX4BMs5F3RuTQx9q2HqaqqrJpWRGDJqWil5+vQgghRKeTJGEHdKVATwjReVx2L1uXF7P5+33UlTcuMhKVYGbgxFQGjE8hKsHS7HVlHh+LKoMJw++r7biVxh+zFq2GiTE2psRHcUpcJDkWU4cW91ADAbx5ebg2bcK9cRPubdvwbNuG4nC0WF6flhpMHvbt27Dvh6lPDlpL4+fwKT6K64vJr8sPbQX2AvLr8tlXvw+Vg//a0KAhyZpEL1svUm2ppEWkNdmnRqRi0Td/Zq3xBxSqHF7K7B7K6xuThxX1HirqvVTWe6is91JR76HK6W1XQnE/s0FLrNVIjNVIrNXQcNy4j7IYiLEYiGk4j7YEN0kuis7SkVglO/vgc88dSKPRsHv37sNpXo/TFWJHVVV5deOrvLD2BQB+2/+3PDD+AXTaxp9Tgbo6Cm+ehXPVKjQGA2lPP03U9NOa1fVVRS03bt6DS1EZHmnhreE5JBoPPo1GwK+w9usCRk7LQG+Un4tCCCFEVyNJwg7oCoGeEKLr2N+7cNvPxexcXYbP3bhoR6/+MQyYkEqf0YkYzc0ncXcEAnxfVc/XlbUsqbJT7PE1uZ9hNnJKXCSnxEVyQmwkke1Y/OSg7VUUfEVFwYTh1m24t2/Hs3Urvn37Wn6BRoMhIyOYOOzbF1PfPhizszFmZ6OzNV0V2RvwUlRfRKG9kML6Qgrthey17w0du/yult/jAHHmOFIiUki2JpMSkdLsOMmShEHXvjkd9wsoKtVOb2PS0OE96Fbp8FLj9OJXDv/XoEmvDSUMoywGosz60HG0xUCU2UCkWU+UJbiPNO/f64kyGzDptbICtDgsEqt0LV3hz8Mb8HLlF1eyqXIT1w27jltH3drs50vxQw9T8957aG020l96iYjxxzWr582iCu7bUYgCTImL5NUhvVtdmMvj9PHFvI0Uba+h39gkTrt2aLg/mhBCCCE6SJKEHdAVAj0hRNfk8wTYva6cbcuLKdxezf5OdXqjluzhCfQdm0zWkHh0huZDilVVZZvDzZIqO0ur6vi5xoH3gB/BOg2MjLRyQmwkk2JsjI2OwBrGufUCtbV4duzAs3MnntydDftcAtXVB32NPjERY04OxuzemHJyQslDQ2oqGl3T/zSqqkqVu4rC+kKK64vZ59jHvvp9FDuKQ3uHr+UejgfSoCHeEk+SNYkkSxKJ1sTgccOWaEkk2ZpMtCm6wwk2VVWp9/ipcfqodgaTh/uPq50+ap1ealw+al0+apzBffDYSwdyiyEGnYZIswGbSR/czHoiG/YHnkfs34x6Ikw6bA3nttA9HaYwJJhF93EkYpX9IaEkrtuvq8SOFa4Klu1dxvn9z2/xfqC2lqI77iDp7rsxH2Tl6od3FjFvbzmXpMbx1/4ZGFpZNKqu0sVnL26gutiBwaTj9OuHkjkkPiyfRQghhBDhI0nCDugqgZ4QomuzV7nZvqKEbcuLqS1r7EFntOjJGZlAv7HJpA+MRXuQRJ8jEOCn6nqWVNlZUlXXZLVkAKNGw5hoK5NiIjkh1sboKCtGbfgX5PBXVuLJzQ0mDnNz8ebl4cnLI1BRcfAXGQwY09MxZmZiyMrEmJmFMSsLY1YmhrQ0NPrmvSpVVaXOW0exo5gSRwkljhJKnaVNjksdpXiVtq2MrNfqSbAkkGBOIMGaEDw+4DzeHE+8JZ54czxWQ3hX1VQUlXqvn9qGxGGd20edy0edy0+du+FaQ0LR7g5es7v9oeN6j/+whkW3xqDTYDXqiTDqsJoa9g1Jxf17i0GP1ajDatJhNQTLWY06Iox6zAZd8J5RFzq2GHWY9Tq0srJ0lxPOWOVf//oXTz31FLm5uQD079+fP/zhD1xxxRXhaGqP0FmxY7mznO8KvztoUhDAt28fhrS0NtepqCqfldfym8TWv4gpy6/jfy9twFnnJSLGxFm3DCchPbJd7RdCCCHE0SFJwg6QJKEQoj1UVaUs307uqlJ2rirDUdO40rHZZqDv6CT6jUsitU8MmlaSLXvdXn6stvNDdT0/1tQ3G5ps0WoYHRXBcdERjI+JYExURFiGJx9MoK4ulDD07s5rON6NL78A1ec7+Av1egwpKRjS0zGk98KYno6hV/DY0KsX+sTEg/7HU1VVqj3VlDhKKHeWU+Yqo8xZRrmznFJnafCas4xqz8F7P7bEorcQZ44jzhxHvDmeOEvwONYUS6w5ljhzHDHmGOJMccSaYzHr2zY5/+Han2QMJg59ODzB43qPn/qGfZNzrx+Hx4/TE6De48fRcF7v8eP2KUe0rQAWQzBhaDHoMBu0BxzrfnVv/6YN7vXBsmZDsKdj6LpBh0mvDZUN3ZOEZJuFK1b5+9//zoMPPsgtt9zCpEmTAPjhhx946aWXeOyxx7jjjjvC1eRjWmfEjjurd3Lz4pspdhQz58Q5nJVzVrMy9m+/pejOu0i66y7irri8xXrq/QGezy/lruwUTG38ImrPhgq+em0Tfq9CfC8bZ90yHFvskf25KYQQQojDJ0nCDpAkoRDicKmKSvGuWnJXlbJrTRkue2MyzRJlJHtEAjkjE0kfEItOf/D/jKmqSp7Lyw/Vdn6sqeeH6noqfU1XHNYCQ2wWjouO4LiYCMZH20gxHd5cfu2hBgL4S0rwFhTgzS9o2OfjK8jHW7AX1eNp9fUakwlDWlrDloo+NRVDauO5ITkZjbH1FZK9AS+VrkoqXBVUuCood5U3Od+/VbmrcAfc7f6MFr2FWFMsMeYYYkwxRJuiiTE1PQ7tjdFEmaKINEai1YS/p+eh+AMKTl8ApycQSh46PAGcXj8Ob6Dh3I/LG8DhDeBquO7yNpZxeoP3Xd4ALl8ApzeAx3/kk48tMeg0mPSNSUSTXotRr8XUcLx/M+q1GHXBBKPxwGtN7h14rmty78C9Qaf51Xlwr9dquuzQ23DFKtnZ2Tz66KPMnDmzyfU333yTRx55hLy8vI42tUc42rHjz8U/c8eSO6j31dM7qjcvT32ZjKiMJmWq33uPkkceBUXBNnky6S+/hOZXScBSj4/LNuxmU72Li1PieHZQ5iHf2+cJ8O8Hl+Oq85IxKJbTrx+G0dK897gQQgghug5JEnaAJAmFEOGgBBSKtteQu6qU3evK8Tgbk3xGs46sofFkj0wka2h8i4ueHEhVVbY73aysdbCixsEvtQ4K3M2H5aabDYyKjGB0lJXRUVaGRVrDOq/hoaiKgr+sDF9hId7CQnyFRfiKioLnRYX4S0pBOUTySaNBn5CAPiUFQ0oy+uT9++BmSElBn5yM1mQ6dHtUFZffRaWrkkp3JVXuquDeFdzXuGuo8lRR466h2l1NlacKv+I/ZL0tNhsNkcZIooxRRJuiiTJGEWWKCu6NwSTi/vsH7m1GG5HGSEy6Q3+eo0lRVFy+YNIwmFAM4N5/7gvg9gaa3Hd5A7j9Adw+JVTO03Ds9jfc9yl4/I17j0/B7Q/gC3TdUMSoCyYRDQ1JRMMBiUXD/nOdFoO+8Xz/vb9fOBLdEeoZGa5YxWw2s2nTJvr27dvkem5uLsOGDcPtbn+SvSc6mrHjJzs/4ZGfHsGv+hmdNJrnTnmOGHNM6L6qqlS8/DIVL7wIQPR555H66CNoDE2/RMp1uLlkwy4K3T7iDXr+MzyHUVFtm5qheFctO1aUcMJF/dAdxd8xQgghhDg8kiTsAEkSCiHCLeBXKNpRTd66CnavL8dZ25jg0+m1pA+KJXt4AllDE7DFti1ZVOzx8kutg19qHKysdbCp3sWv0286DQyKsDA6ysqohq2f1Yyuk3pHqT4fvuJifPuK8e3bh694H77iYvyh8+JD9kTcTxcdjT4pEX1iEvrExMbjpET0SUnBRGNCAlpr2+cjVFUVh88RShjWemqp8dRQ466hxlMTOg9d99RQ561r06rOh2LQGoJJQ4MNm9EW3B9wHGGIIMIQgc1gw2qwhu5ZDVYi9BGh+2a9uVN6NHZEQFGbJQ89/oZjv9JwHrzvDQTvewPKAfsAngPOvf4DtgPOPQ3Hvl/tvQfswxkV5c0584j1RAxXrDJ06FAuvfRS/vjHPza5/thjj/Huu++ycePGjja1RzgasaOqqsxdP5e56+cCcEbvM/jzCX9u8gWD6vdT8qc/U/PeewDE33gDibff3uzv4S819Vy5MY9qf4Aci4m3R+TQ23Lw3z1+X4DqYieJmTLnoBBCCNEdSZKwAyRJKIQ4klRFpXRPHbvXlbN7XXmTRU8A4tNtZA2NJ2toPCnZUQdd+OTX6v0B1tmdrK1zsqbOyZo6B6Xe5r3iLFotQ2xmhkVaGRZpYbjNQv8I8xFZFKW9VFUlUF2Nr2gf/rJSfKWl+EtK8ZeW4CspxV9Sgq+0FLUdvZs0Viv6+PiGpGE8uvh49AmJweO4OPRxceji4tHHxaKNimo2HK8tfAEftd5a6jx1Tfa1nlrsXjt2r506b12Lx/W++na/X6ufFw0WvSWUNNx/bDVYseqtLe4tegtWfXBv0VuwGCzNrhm0hi479Dac/IFgstAXUEOJxAOTifuvH1huf5kDj/0BlatPyD5i7QxXrPLhhx9y0UUXMW3atNCchD/++COLFy/mvffe49xzzw1Xk49pRyN2XFWyiqu+ugqAa4ddy62jbm3yhYCqKBTedhv13ywGjYbkBx8g7tJLm9Xzv/Iabt6Sj0dRGR1l5V/DckgwHrw3u9vh4/O5G6jYW8+5vx9NYoYkCoUQQojuRpKEHSBJQiHE0aKqKlXFDvLWlbNnYyWle+rggJ/KJquejMFxZA2NJ3NwPNao1ufq+3Xd+zy+JknDDfUunIHmw32NGg0DbWaG26wMtpkZbLMwKMJMtKHrzTOlqipKbS3+8nJ8ZWX4y8uDW9n+feO19iQTAdDr0cXGoI+NQxcfF9zHxKCLjQ1uMTHoYmPQxcSgbzjXWCwdSp4FlAAOvwOH14HdZ6feW0+9rz60t3vtOHwOHD4H9b56nD4n9b76xmveepx+Jw6fA5Uj9ytdp9Fh0Vsw681N9hZd02smnan5sc6MSW8K7nWm0D2z3tzsmlFn7HY9ITtDR2OVTZs2MXToUABWr17NM888w9atWwEYNGgQd911F6NGjQprm49lRyt2nLt+LgmWBC7of0GL9ytff4PyZ58l7emniDrttObt9AeY8PMWqnwBpidEMXdw71anpKircLHwhfXUlDoxmnWcedNweg2IDdvnEUIIIcTRIUnCDpAkoRCis7jqvRRsriJ/UyUFWyrxOJr2BEzIsJE+MI70gbGk9Y3BYGrf6sYBVWW308Omehcb7E422l1srHdR6w+0WL6XycCghoThYJuFQTYzfSxmDN1gBVpVVVEcTgIV5fgrK/FXVOKvKCdQWYm/vAJ/ZSWBqir8VVUEqqpQ6g+vR5/GaEQXHY0uJhptdDS66JjgeXQ0uugotFFR6KKi0UVFoovafx7caw+xQEt7P6/L78Lpd+L0OUNJxP3nh9q7/K7Q5vQ3nh/uHI0dYdQaMelMmPSm4P5Xm1FnDG0HXjPpTBi1Rgw6Q6gOo67x3KgzNt5vOP71NYO2cd+Vk5UdjVW0Wi3jxo3j2muv5eKLLyYyUnqHdURnxo6qqjb5osK7dy/GjIyDll9eU89nZTU82rcX+lZ+lpfl1/HZi+tx2X3YYk2cdcsI4nvZwtp2IYQQQhwdkiTsAEkSCiG6AkVRKdtTR/6mSvI3VVJeYG9yX6vTkJITTfrAWDIGxZGUFdnmockHUlWVArc3lDDc0rAVeXwtljdoNGRbTPSPMNE/wkx/q5n+EWZyLCbM3XgCe8XrJdCQMPRXVhGoriJQXY2/uppATQ2B6hoCoeNq/DU14Gv5GbWVxmRCGxWJzhaJNjISnc2GNjISbaSt4ZoNXWQk2ggbWpsNrS0CXUREw7ENbUQEWqsVja59yeL28Cm+YMLQ58IdcOPyu3D73aEkYpPjgBu3340n4MHld+EJeHD7g9cOvOcOuPH4G/YBDx6/B7969JORh6LX6DHoDE0Sh78+1mv1Tc73lzdoDTw88WF02iPzZ9PRWOX777/njTfe4IMPPkBRFH77299yzTXXcOKJJx6B1h77Oit2tH+7hIp5r5D52mvoDpLodfgD5Do9jGzjoiQAezZU8NVrm/B7FeLTbZw1a0Sb58sVQgghRNcjScIOkCShEKIrctZ5KdxeReG2avZuraK+qukCHwazjrR+MaT1jSGtXwyJmZHo9IeftKv1+dnmcLPF4WZrvYut9W62OFw4WhiuDKAFeltM9Isw0cdiJsdqIsdiIsdqItmoP+bms9vfU1GprSFQV0egtpZATW1wX1tLoLaGQE0NSp2dgN1OoK42eFxXh2K3E84VMjRWK9oIKzprBJqGvTZi/2ZFa92/twbLhraIhr0FrcWCxmIJnlssRzTx2BK/4g8mDBuShqHjAze/B6/ixRPw4A14Q9f3Hx+49ypefAEfXsUbPA948Sk+PAEPPsUXPD/wvuINe6/JDTM3dPmFSxwOB++99x7z58/n+++/p2/fvlxzzTVceeWVpKSkhLHFx7ajHTuqqkrVG/Mpe+opUFUSbr6JxNtua1au2ONl5oY89rg8LBzTj4ERlkPWXbi9mk+fXYuqQubgOKZfPxSjuetNPSGEEEKItpMkYQdIklAI0dWpqkptuYvCbdUUbquicHt1s6HJeoOW5JyoUNIwOScag7FjiR+lYZ7DHQ43Oxxucp1udjg87HC6DzpkGSBCpyXHYiLbaqKPxURvi4neFiO9LSaSjsEE4qGoioLicBCorUOx1xGw21Hq61HsdgL2epT6YGJR2X/scKDUO4JlGraAwwH+I9f7TmM0BpOFDUnDYBLRjNZiRWs2B4/NB1w3W9BazGhMZjRmE1qzpWFvDpY3m4M9JxuOtSYTGpPpqCcjW6OoSmMCUfGFkoih44br+xOKvoAveO/A1yg+/Iofv+LnmmHXHLG2HolYZefOnbzxxhv8+9//pqSkhNNPP51PP/00LHUf645m7Kj6fMEVjN9/H4CYiy4i5YH70RgMTcptsju5YmMexR4f8QY9/x6ezeioiEPWHwgofP7SBiJiTJx82QB03biHuBBCCCGCJEnYAZIkFEJ0N6qiUlFYz77cmtDmdjQdCqvVakjMiiQlO5rknChScqKxxZrCkqBTVZVyr58dTjfbHW7yXB52OT3kuTwUuLy03PcwyKLVkNmQNMwym8iyGMmymMgwG0k3G4joQkmkrkRVVVSvF8VuR3E6URyOxu3A8/3HTieK09Ww/9XmcKC6XCguV1h7OLaJwRBMGB6YODSZ0BqNDYlFY/C6seGe0RBMYJpMaAxGNMaGzRTca40HXDMaG8sYDA3XDMG5IA3B/f7r6LtXsvpIxSoOh4O33nqL++67j5qaGgKBgyf/RaOjFTsGamspvH02zp9/Bq2W5HvuJnbmzGZ/dxdV1HLDlnycAYV+VhP/GZ5DluXgw4X9vgBarSY0ZYXfF0Cn13arfxNCCCGEOLi2xioydkAIIY4BGq2GxMxIEjMjGTE1A1VVqS52sm9nMGFYvLOG+moPpXl1lObVwbfB11mjjY1Jw+xoErMiD6u3oUajIclkIMlk4ITYpvNieRWFfJc3lDjc7fKwx+Vhj8tLkduLS1HZ7ggmF1sSb9CTbjaQYTY2JA4b92kmA9F6XY/8j6xGowkm00zhmydMVVVUjwfF5UJ1OlEaEoeK04XicqK63SguN6rbheJyo7hdDclFd8N9D4rHjepyB/duT7CM24Pidgdf7/E0ncvR50Px+aC+ns5OR4WSib/ejAb49TX9gcf6A+4Fj9HrSbrzzi7VU7I13333Ha+//joffvghWq2WCy+8kGuuOXI9IUX7eQsK2Hv9DXj37EFrtZL2978ROXlys3KvFZbzUG4RCnBirI3XhvRudaV6V72Xz1/eSHy6jZMv6Y9Go0Fv6B5/b4UQQggRXtKTsAXSk1AIcaxRVRV7pZviXbWU7q6lJK+OisJ6VKXprwCtVkNsWgRJDQnHpKwo4tMjjth/GH2KSqHbS747mDTMd3nIdwXPC92+Vocw72fVaellMpBqMpBmMpJmNtDLFEwgpjRsMT00kdhVqYFAMBnp8QQTh243qtcbSiKqHi+q19NQxovq8aB6PShuT7Ccp2Hv8wbLe32N1xo2xecNXt+/P+Ce6gueH+lekwO3bunScxLu27eP+fPnM3/+fHbu3Mnxxx/PNddcw4UXXkhExKGHpYpGRyN29JWVsefCi0CrIWPuXMwDBjQr81FpNTdtyQfg0tQ4nuyf0epq9DWlTj57cT215S5MVj0XPXAckXHmI9J+IYQQQnQe6UkohBAiRKPREJVgISrBwoDxwcUIfN4A5fl2SvJqKc2ro2R3Lc5aL5WF9VQW1rP1p2KgeeIwMTOS+F42DKaOJw4NWg3Z1uBchS2p9fkp9PgodHvZ6/ay1+Wl0OOlwOWlyOOlyhfAGVDIdXrIdXparAPArNWQbAwmDJNNBlKNwX2yUU+S0UCiKbiPlWTiUaHR6UILqHQm1e8PJQxVn6/x2OttvNdkO6C83x9MQPr9sP++3x9MSPr9qAF/l/67dMYZZ/DNN9+QkJDAzJkzufrqqxnQQtJJdB2GpCQyX/0HupgY9ImJLZaZkRjNCTE2To6L5JbMpFb/Du7bWcPnczfgcfiJjDdz1i0jJEEohBBC9HCSJBRCiB7KYGxYDblfDBDsbVhf7aE8305ZQR3lBXbKC+y47L5miUM0EJ1oIaGXjfh0G/G9bCSk24iMN4c1MRJt0BNt0DPE1vKKnK6AQrHHxz6PlyJ3cL/P46PI7aXY46PU66PKF8CtqOS7veS7va0/E42GRKOexP3JQ6OeBIOeBKOeBKOh8digJ86gR99KDx3R9Wn0ejR6PVgOveLrscZgMPDBBx9w1llnoesmQ6IFmPr1a3btuyo7E2IiMGq1GLVa3h3ZB90hfg7nrirlm/lbUPwqSVmRzJg1AmuU8Ug1WwghhBDdhAw3boEMNxZCiKCWEocVe+tx1rWcbDOadcSn24hLjSAuLYLY1AjiUiOwRhk7rVeVO6BQ5vVR4vFR4vVT6vGFEohlXh9lXj/lDcnE9orV64g3BhOGcQZdw77xPN6gJ9agJ9agI0avJ1qvk8SiCAuJVbqWzvjzcAcU/rRrH68XVXBjRiKP9O3Vptet+6aAHz/YCUD2iAROvWbIYc1FK4QQQojuQ4YbCyGE6DCNRkNknJnIODM5oxqHtznrgsOSK4qCPQwrCuupLnHgdQco3llL8c7aJvWYrHpiUyKIS7USmxpBbEoEMclWIuPNaI9w0sys05JpMZHZysqeEFxgpdzrDyUNS70+Krz+4OZruq/y+VGBan+Aan8AOPhQ51+L0muJ1euJMehC+yi9jhh9w96g/9W5jkhd8Li1ucWEED1HrsPNjVv2sLk+uOCThuCXOm35MiYmyYpGA8NPyeD43/Y94j+DhRBCCNF9SE/CFsi380II0X4Bv0J1iZPKonqqix1UNWx15a6Drg+h1WuITrQSk2QhNsVKdJKV2GQrMclWzDZDl53TLaCqVPsClHt9VPsCVPn8B2zB88qG82pfgBq/nzq/0uH3tWg1RDUkDyP1OqJ0DXu9Fps+mEyM1GuJ1OkazoPXbQfudZJsPBZIrNK1HK0/D1VVeaekivt3FOFSFOINep4flMnU+Nbf89cJxMqieuJ72Y5YO4UQQgjRtUhPQiGEEEeVTq8lIT04N+GB/L4ANaWuUOKwuthBdamT2jJXMLHYcC1vfdP6DGYd0YkWohMsRCcFF12JTrQQlWjBFnvkeyC2RqfRNMxT2PZfo35FpcYfTBjW+AJU+/xU+wPU+PzU+gONmy9AnT9Ajb9x7wwEE4wuRcXl9VPq9Xeo/UaNBpteS4SuMXEYodMSoddi0WqDxzodVt3+Yy3WAzaLtuVjg0bTZRO7QnR3df4Ad2/fy8dlNQCcGGvjxUFZJJsMrb6uorCeb/+1lenXDSU6MTj/piQIhRBCCNESSRIKIYQ4ovQGXYvJQ0VRqa9yU1PqpKbMSU1JcF9d6qS+yoPPHaBibz0Ve+ub1anVNw6Djoo3ExlvITK+8Tgi2oimi/WW02vbn1jcz6+o2APBpOH+ze5XqAvsPw6e1wcC1AeUhvMDjgMBHAEFjxLs0ulV1YYej+2fh7E1Og1YtFrMWi2WhgSiRadp2Aevm7UazAceh+4Fj01aLaaGY7NO0+TcpNVg1GoxaTSNx1oNWklMih6gyufnm8o6dBq4JzuVWzKTDvl3f/fachbN34LfE+DHD3I586bhR6m1QgghhOiOJEkohBCiU2i1GqISgj0EM4fEN7nn9wWoq3BTV+6ittxFbYUrdFxX6ULxq9SWuagtc7Vct16DLdZMZKwJW6wZW6wJW1zDvuGayarvNr3e9FoNsdrgIigd4VPUUCLREQjg8CvUB4LJRUdAwRlQcDTccx5w7my45gqoOJXGey4luPc3DCcPqDTUp4AvDB+8jfQaQslEo0aLUasJbhoNBm0w0WjQBK+ZtBoMDWX2X2s8bixnaHhtk30Lx/pfXdcfcL2XqesOmRfdT2+LiRcGZZJoNDA2OqLVsqqisuqLPfyyMA+A9IGxTJk56Gg0UwghhBDdmCQJhRBCdDl6gy64QnJq8/8IK4qKo8aDvdJFXaUbe6W7Ye/CXunGXuVB8avUlQcTiwd9D5OOiGgjthgT1mgTthgTEfu3aCMRMSas0Ub0hmNn1U9DKNkY3nq9DclCt6LiCii4FQVXQMHZsHcrKq7QsYI7oAbLKMF7HkXB3VDOrQR7PHoO2IfKKApeRcWtNJ3k0q+CP6DgCACEt3dkR+ybPAJJETa3Z88e/vznP/Ptt99SUlJCWloal19+Offffz9GozFUbsOGDcyaNYuVK1eSmJjIrbfeyt13392krvfff58HH3yQPXv20K9fP5588knOPPPM0H1VVXn44Yd59dVXqampYdKkScydO5d+/fqFylRVVXHrrbeycOFCtFot559/Ps899xw2W9cbkntGYswhy/g8ARa/uYVda8oBGD4lnUnn90Wr0x7h1gkhhBCiu5MkoRBCiG5Fq20capzWr/l9JaDgqPVir3RTX+2mvtpDfZUbe7UneF7lwe3w4fcEWu2NuJ/JqscaZcQSacQabcQatX8zNVw3YIkM7o+lhGJ7GLVajNqjl4BQVRWfquJV1FAi0asGj70NiURvw31vwz1fQ1mvGrzvUxrr2L8P3VMb7zfbN5T1Nxz7G97L31Bm/3VFVWUY9EFs27YNRVGYN28effv2ZdOmTVx33XU4HA6efvppIDi59mmnnca0adN45ZVX2LhxI1dffTUxMTFcf/31APz0009ccsklzJkzh7POOou3336bc845hzVr1jB06FAA/vrXv/L888/z5ptvkp2dzYMPPsj06dPZsmULZrMZgMsuu4zi4mIWLVqEz+fjqquu4vrrr+ftt9/unAfUAY5aDwtfWE9lYT1anYaTLx3A4Elpnd0sIYQQQnQTsrpxC2TFQCGEOLb5vAEc1R4cNR4ctR4cNd6GvadxX+Ml0M4ViQ1mXTBhaAsmDq2RBsw2I2abAXOEAYvNEDo22wyYLPouN3ei6B6OtVjlqaeeYu7cuezevRuAuXPncv/991NSUhLqXXjvvffy8ccfs23bNgAuuugiHA4Hn332WaieCRMmMHLkSF555RVUVSUtLY277rqL3//+9wDU1taSnJzM/Pnzufjii9m6dSuDBw9m5cqVjB07FoAvv/ySM888k8LCQtLSWk6weTwePB5P6Lyuro6MjIxO//Pw+wJ89Le12CtdnHHDMFL7xnRaW4QQQgjRdcjqxkIIIcRBGIw6YpKtxCRbD1pGVVW8Lj/OOi/OWm9wX+fFWecJnTvqvLjtXlx2H4qi4nMH8LlbH+Z8II1WgzlCj8lqwGTVY44wYIrQY244N0UEE4omix6TVY/RqsdkCd7TG7Uy3504ZtTW1hIXFxc6X758OSeddFKT4cfTp0/nySefpLq6mtjYWJYvX86dd97ZpJ7p06fz8ccfA5CXl0dJSQnTpk0L3Y+Ojmb8+PEsX76ciy++mOXLlxMTExNKEAJMmzYNrVbLihUrOPfcc1ts75w5c3j00UfD8dE7zOPyo9Nr0Bt06A06zrxxGIqiEhln7uymCSGEEKKbkSShEEII0QKNRtOQvDMQm3KIRQJUFY/Tj7veh9PuxW0P7l12L+56H26HD3e9D9cBxz5PAFVRcdl9uOztX+VDq9UEk4ZWPSaLHqNFj9Gsx2jRBY8bzhvv6TCY9+91wbJmncxTJjrdzp07eeGFF0JDjQFKSkrIzs5uUi45OTl0LzY2lpKSktC1A8uUlJSEyh34uoOVSUpKanJfr9cTFxcXKtOS++67r0mCcn9PwqOtYEslS/69jX5jkzn+/L4ARMSYjno7hBBCCHFs6DJJwieeeIL77ruP22+/nWeffbbFMps3b+ahhx5i9erV5Ofn88wzzzB79uwmZebMmcN///tftm3bhsVi4fjjj+fJJ59kwIABR/5DCCGE6JE0Gk1wCHGEodXeiQcK+BTcjmDi0OP0BZOMDh8ehx+P04fb6cfjCCYVvS4/Hqcfj8uP1+lHUVQURQ0mIOs7toyw3qDFYNFjNAWThwbT/k3/q3MdBmPjsd6kw2DUNuwbrhl16I1aDEadDKPuge69916efPLJVsts3bqVgQMHhs6Lioo4/fTTueCCC7juuuuOdBPDxmQyYTJ1XjLO6/Lz44c72fLDPgDyNlRw3G+y0Rt75ryoQgghhAiPLpEkXLlyJfPmzWP48OGtlnM6neTk5HDBBRdwxx13tFhm2bJlzJo1i3HjxuH3+/njH//IaaedxpYtW4iIaL0niBBCCHG06Aza0GrK7aGqKn6vEkwaNiQXva5gAtHnbkgkugKha1538L7XHcDn3r8PhOZb9PsU/D4vbRsg3b7Ptz9huD95qDfs32vRGYJJRp1Rh76hrN6gC77OoA3t918LXdcHj3X6X59rpFdkJ7vrrrv43e9+12qZnJyc0PG+ffs45ZRTOP744/nHP/7RpFxKSgqlpaVNru0/T0lJabXMgff3X0tNTW1SZuTIkaEyZWVlTerw+/1UVVWFXt/V7N1axbf/3kp9VXBOxOGnpDPhnD6SIBRCCCFEh3V6krC+vp7LLruMV199lccee6zVsuPGjWPcuHFA8Nvqlnz55ZdNzufPn09SUhKrV6/mpJNOCk+jhRBCiE6i0WhCvflssYffkyngV/C5A8EkojuAzxPA5/EH51X0NG5etz907PcE8HmVYDmPgt/bcD20b1zoJeBTCPgUPA5/OD52m2i0GnR6TTBxqNeiPeC4cdM03GtMLDa5ptM02Wt1GnQNZQ481+o0DVvDNX3jsVanQavVEptq7VHzRiYmJpKYmNimskVFRZxyyimMGTOGN954A+2vVseeOHEi999/Pz6fD4PBAMCiRYsYMGAAsbGxoTKLFy9uMqpk0aJFTJw4EYDs7GxSUlJYvHhxKClYV1fHihUruOmmm0J11NTUsHr1asaMGQPAt99+i6IojB8//rCfxZHgdfv56cOdbP4+2HswKsHMlJmD6NU/tpNbJoQQQohjRacnCWfNmsWMGTOYNm3aIZOEh6O2thagyWTYv9bSCnVCCCHEsUyn16KzaTHbDGGrU1XVYM9EbzBheGDyMHTN9+t9wz2fQsAbwO9XCHiV4N6n4PcqBPzBcgFfIHjNrxDwqwR8CqqiNr6/ouL3qk2SlZ3p5pdPgZ6TI2yzoqIiJk+eTFZWFk8//TTl5eWhe/t771166aU8+uijXHPNNdxzzz1s2rSJ5557jmeeeSZU9vbbb+fkk0/mb3/7GzNmzGDBggWsWrUq1CtRo9Ewe/ZsHnvsMfr160d2djYPPvggaWlpnHPOOQAMGjSI008/neuuu45XXnkFn8/HLbfcwsUXX3zQlY07i8fpZ8fKYM/JYSf3YsK5fTCaOz2UF0IIIcQxpFMjiwULFrBmzRpWrlx5ROpXFIXZs2czadIkhg4detByXWmFOiGEEKK70mg0wfkJj+KwRyXQkDBsSCoG/AdsPpVAQAndU/aXCxxw7FdQAmrj3td4TQkoBAIqyv57DceBwP776gFlg+X3l1MVVeZlPIhFixaxc+dOdu7cSXp6epN7qhpM+kZHR/P1118za9YsxowZQ0JCAg899BDXX399qOzxxx/P22+/zQMPPMAf//hH+vXrx8cff9wk5rv77rtxOBxcf/311NTUcMIJJ/Dll19iNjeu/PvWW29xyy23MHXqVLRaLeeffz7PP//8EX4K7RcZZ+aUywdiiTSSPkB6DwohhBAi/DTq/mjsKNu7dy9jx45l0aJFobkIJ0+ezMiRIw+6cMmBevfuzezZs5stXHKgm266iS+++IIffvihWRB6oJZ6EmZkZFBbW0tUVFSbP5MQQgghxNFQV1dHdHS0xCpdhPx5CCGEEKIra2us0mk9CVevXk1ZWRmjR48OXQsEAnz33Xe8+OKLeDwedLrD74lwyy238Nlnn/Hdd9+1miCEzl+hTgghhBBCCCGEEEKIztRpScKpU6eycePGJteuuuoqBg4cyD333HPYCUJVVbn11lv56KOPWLp0KdnZ2eForhBCCCGEEEIIIYQQx6xOSxJGRkY2mycwIiKC+Pj40PWZM2fSq1cv5syZA4DX62XLli2h46KiItatW4fNZqNv375AcCGUt99+m08++YTIyEhKSkqA4Nw2FovlaH08IYQQQgghhBBCCCG6DW1nN6A1BQUFFBcXh8737dvHqFGjGDVqFMXFxTz99NOMGjWKa6+9NlRm7ty51NbWMnnyZFJTU0Pbu+++2xkfQQghhBBCCCGEEEKILq9TVzf+taVLl7Z63rt3bw61zkonrcMihBBCCCGEEEIIIUS31aV7EgohhBBCCCGEEEIIIY48SRIKIYQQQgghhBBCCNHDSZJQCCGEEEIIIYQQQogeTpKEQgghhBBCCCGEEEL0cJIkFEIIIYQQQgghhBCih5MkoRBCCCGEEEIIIYQQPZwkCYUQQgghhBBCCCGE6OEkSSiEEEIIIYQQQgghRA8nSUIhhBBCCCGEEEIIIXo4fWc3oCtSVRWAurq6Tm6JEEIIIURz+2OU/TGL6FwSOwohhBCiK2tr7ChJwhbY7XYAMjIyOrklQgghhBAHZ7fbiY6O7uxm9HgSOwohhBCiOzhU7KhR5SvoZhRFYd++fURGRqLRaI7Ie9TV1ZGRkcHevXuJioo6Iu/Rk8jzDD95puElzzO85HmGlzzP8Doaz1NVVex2O2lpaWi1MntMZ5PYsfuR5xle8jzDS55n+MkzDS95nuHVlWJH6UnYAq1WS3p6+lF5r6ioKPlHFUbyPMNPnml4yfMML3me4SXPM7yO9POUHoRdh8SO3Zc8z/CS5xle8jzDT55peMnzDK+uEDvKV89CCCGEEEIIIYQQQvRwkiQUQgghhBBCCCGEEKKHkyRhJzGZTDz88MOYTKbObsoxQZ5n+MkzDS95nuElzzO85HmGlzxPcSTI36vwkucZXvI8w0ueZ/jJMw0veZ7h1ZWepyxcIoQQQgghhBBCCCFEDyc9CYUQQgghhBBCCCGE6OEkSSiEEEIIIYQQQgghRA8nSUIhhBBCCCGEEEIIIXo4SRIKIYQQQgghhBBCCNHDSZJQCCGEEEIIIYQQQogeTpKEneSll16id+/emM1mxo8fzy+//NLZTeoWvvvuO37zm9+QlpaGRqPh448/bnJfVVUeeughUlNTsVgsTJs2jdzc3M5pbDcwZ84cxo0bR2RkJElJSZxzzjls3769SRm3282sWbOIj4/HZrNx/vnnU1pa2kkt7trmzp3L8OHDiYqKIioqiokTJ/LFF1+E7suz7JgnnngCjUbD7NmzQ9fkmbbdI488gkajabINHDgwdF+eZfsVFRVx+eWXEx8fj8ViYdiwYaxatSp0X34niXCS2PHwSOwYXhI7hpfEjkeWxI4dI7Fj+HWH2FGShJ3g3Xff5c477+Thhx9mzZo1jBgxgunTp1NWVtbZTevyHA4HI0aM4KWXXmrx/l//+leef/55XnnlFVasWEFERATTp0/H7XYf5ZZ2D8uWLWPWrFn8/PPPLFq0CJ/Px2mnnYbD4QiVueOOO1i4cCHvv/8+y5YtY9++fZx33nmd2OquKz09nSeeeILVq1ezatUqpkyZwtlnn83mzZsBeZYdsXLlSubNm8fw4cObXJdn2j5DhgyhuLg4tP3www+he/Is26e6uppJkyZhMBj44osv2LJlC3/729+IjY0NlZHfSSJcJHY8fBI7hpfEjuElseORI7FjeEjsGD7dJnZUxVF33HHHqbNmzQqdBwIBNS0tTZ0zZ04ntqr7AdSPPvoodK4oipqSkqI+9dRToWs1NTWqyWRS33nnnU5oYfdTVlamAuqyZctUVQ0+P4PBoL7//vuhMlu3blUBdfny5Z3VzG4lNjZWfe211+RZdoDdblf79eunLlq0SD355JPV22+/XVVV+fvZXg8//LA6YsSIFu/Js2y/e+65Rz3hhBMOel9+J4lwktgxPCR2DD+JHcNPYseOk9gxPCR2DK/uEjtKT8KjzOv1snr1aqZNmxa6ptVqmTZtGsuXL+/ElnV/eXl5lJSUNHm20dHRjB8/Xp5tG9XW1gIQFxcHwOrVq/H5fE2e6cCBA8nMzJRnegiBQIAFCxbgcDiYOHGiPMsOmDVrFjNmzGjy7ED+fh6O3Nxc0tLSyMnJ4bLLLqOgoACQZ3k4Pv30U8aOHcsFF1xAUlISo0aN4tVXXw3dl99JIlwkdjxy5N9px0nsGD4SO4aPxI7hI7Fj+HSX2FGShEdZRUUFgUCA5OTkJteTk5MpKSnppFYdG/Y/P3m2h0dRFGbPns2kSZMYOnQoEHymRqORmJiYJmXlmR7cxo0bsdlsmEwmbrzxRj766CMGDx4sz/IwLViwgDVr1jBnzpxm9+SZts/48eOZP38+X375JXPnziUvL48TTzwRu90uz/Iw7N69m7lz59KvXz+++uorbrrpJm677TbefPNNQH4nifCR2PHIkX+nHSOxY3hI7BheEjuGj8SO4dVdYkf9UXsnIUSXNmvWLDZt2tRkngnRfgMGDGDdunXU1tbywQcfcOWVV7Js2bLObla3tHfvXm6//XYWLVqE2Wzu7OZ0e2eccUboePjw4YwfP56srCzee+89LBZLJ7ase1IUhbFjx/KXv/wFgFGjRrFp0yZeeeUVrrzyyk5unRBCHHkSO4aHxI7hI7FjeEnsGF7dJXaUnoRHWUJCAjqdrtmqP6WlpaSkpHRSq44N+5+fPNv2u+WWW/jss89YsmQJ6enpoespKSl4vV5qamqalJdnenBGo5G+ffsyZswY5syZw4gRI3juuefkWR6G1atXU1ZWxujRo9Hr9ej1epYtW8bzzz+PXq8nOTlZnmkHxMTE0L9/f3bu3Cl/Pw9DamoqgwcPbnJt0KBBoWE48jtJhIvEjkeO/Ds9fBI7ho/EjuEjseORJbFjx3SX2FGShEeZ0WhkzJgxLF68OHRNURQWL17MxIkTO7Fl3V92djYpKSlNnm1dXR0rVqyQZ3sQqqpyyy238NFHH/Htt9+SnZ3d5P6YMWMwGAxNnun27dspKCiQZ9pGiqLg8XjkWR6GqVOnsnHjRtatWxfaxo4dy2WXXRY6lmd6+Orr69m1axepqany9/MwTJo0ie3btze5tmPHDrKysgD5nSTCR2LHI0f+nbafxI5HnsSOh09ixyNLYseO6Tax41FbIkWELFiwQDWZTOr8+fPVLVu2qNdff70aExOjlpSUdHbTujy73a6uXbtWXbt2rQqof//739W1a9eq+fn5qqqq6hNPPKHGxMSon3zyibphwwb17LPPVrOzs1WXy9XJLe+abrrpJjU6OlpdunSpWlxcHNqcTmeozI033qhmZmaq3377rbpq1Sp14sSJ6sSJEzux1V3Xvffeqy5btkzNy8tTN2zYoN57772qRqNRv/76a1VV5VmGw4Er1KmqPNP2uOuuu9SlS5eqeXl56o8//qhOmzZNTUhIUMvKylRVlWfZXr/88ouq1+vVxx9/XM3NzVXfeust1Wq1qv/5z39CZeR3kggXiR0Pn8SO4SWxY3hJ7HjkSex4+CR2DK/uEjtKkrCTvPDCC2pmZqZqNBrV4447Tv355587u0ndwpIlS1Sg2XbllVeqqhpcNvzBBx9Uk5OTVZPJpE6dOlXdvn175za6C2vpWQLqG2+8ESrjcrnUm2++WY2NjVWtVqt67rnnqsXFxZ3X6C7s6quvVrOyslSj0agmJiaqU6dODQV5qirPMhx+HejJM227iy66SE1NTVWNRqPaq1cv9aKLLlJ37twZui/Psv0WLlyoDh06VDWZTOrAgQPVf/zjH03uy+8kEU4SOx4eiR3DS2LH8JLY8ciT2PHwSewYft0hdtSoqqoevX6LQgghhBBCCCGEEEKIrkbmJBRCCCGEEEIIIYQQooeTJKEQQgghhBBCCCGEED2cJAmFEEIIIYQQQgghhOjhJEkohBBCCCGEEEIIIUQPJ0lCIYQQQgghhBBCCCF6OEkSCiGEEEIIIYQQQgjRw0mSUAghhBBCCCGEEEKIHk6ShEIIIYQQQgghhBBC9HCSJBRCdFu/+93vOOecc476+86fPx+NRoNGo2H27NkdrismJiYs7TrSJk+eHPrc69at6+zmCCGEEEK0i8SOR5fEjkJ0P/rOboAQQrREo9G0ev/hhx/mueeeQ1XVo9SipqKioti+fTsREREdqueiiy7izDPPDFOrGmk0Gj766KOwBsL//e9/2bVrF8cdd1zY6hRCCCGECAeJHTtGYkchBEiSUAjRRRUXF4eO3333XR566CG2b98eumaz2bDZbJ3RNCAYSKWkpHS4HovFgsViCUOLjry4uDjq6uo6uxlCCCGEEM1I7Nj1SOwoRPcjw42FEF1SSkpKaIuOjg4FVvs3m83WbMjI5MmTufXWW5k9ezaxsbEkJyfz6quv4nA4uOqqq4iMjKRv37588cUXTd5r06ZNnHHGGdhsNpKTk7niiiuoqKhod5t79+7NY489xsyZM7HZbGRlZfHpp59SXl7O2Wefjc1mY/jw4axatSr0ml8PGXnkkUcYOXIk//73v+nduzfR0dFcfPHF2O32Ju/z7LPPNnnvkSNH8sgjj4TuA5x77rloNJrQOcAnn3zC6NGjMZvN5OTk8Oijj+L3+wFQVZVHHnmEzMxMTCYTaWlp3Hbbbe1+DkIIIYQQR5vEjhI7CiE6TpKEQohjyptvvklCQgK//PILt956KzfddBMXXHABxx9/PGvWrOG0007jiiuuwOl0AlBTU8OUKVMYNWoUq1at4ssvv6S0tJQLL7zwsN7/mWeeYdKkSaxdu5YZM2ZwxRVXMHPmTC6//HLWrFlDnz59mDlzZqtDXXbt2sXHH3/MZ599xmeffcayZct44okn2tyGlStXAvDGG29QXFwcOv/++++ZOXMmt99+O1u2bGHevHnMnz+fxx9/HIAPP/yQZ555hnnz5pGbm8vHH3/MsGHDDus5CCGEEEJ0BxI7SuwohGgkSUIhxDFlxIgRPPDAA/Tr14/77rsPs9lMQkIC1113Hf369eOhhx6isrKSDRs2APDiiy8yatQo/vKXvzBw4EBGjRrF66+/zpIlS9ixY0e73//MM8/khhtuCL1XXV0d48aN44ILLqB///7cc889bN26ldLS0oPWoSgK8+fPZ+jQoZx44olcccUVLF68uM1tSExMBCAmJoaUlJTQ+aOPPsq9997LlVdeSU5ODqeeeip//vOfmTdvHgAFBQWkpKQwbdo0MjMzOe6447juuuva/QyEEEIIIboLiR0ldhRCNJIkoRDimDJ8+PDQsU6nIz4+vsk3msnJyQCUlZUBsH79epYsWRKap8ZmszFw4EAg+K1sR95//3u19v4t6d27N5GRkaHz1NTUVsu31fr16/nTn/7U5LNed911FBcX43Q6ueCCC3C5XOTk5HDdddfx0UcfhYaTCCGEEEIciyR2PDiJHYXoeWThEiHEMcVgMDQ512g0Ta7tX/lOURQA6uvr+c1vfsOTTz7ZrK7U1NQOvf/+92rt/Q9Vx/7XHFheq9U2G3Li8/kO2bb6+noeffRRzjvvvGb3zGYzGRkZbN++nW+++YZFixZx880389RTT7Hs/9m77/goyvyB45/Zmk3vlZCE3ntHUWyo2MXCKSIqFhQLp6dYz3KivzsVTlEsgKBiBw8LAtIs9N5DqCG9Z1M2W+f3xyZLIoEsCNmEfN+vm3tmnnmeyXcJhsl3nnmeVauOi0kIIYQQ4lwg944nJveOQrQ8kiQUQrRoffr04dtvvyU5ORmdrnn8SIyKiqqzgp/ZbObQoUN12uj1epxOZ526Pn36kJqaSrt27U54bZPJxNVXX83VV1/Ngw8+SKdOndixYwd9+vQ5sx9CCCGEEKIZknvHuuTeUYhzi7xuLIRo0R588EGKiooYPXo0GzZs4MCBAyxevJhx48Ydd6PUVFx00UV88skn/Pbbb+zYsYOxY8ei1WrrtElOTmbZsmXk5ORQXFwMwPPPP8/cuXN58cUX2bVrF3v27OGLL77g2WefBdyr5c2cOZOdO3dy8OBBPv30U0wmE0lJSY3+GYUQQgghmiK5d5R7RyHOZZIkFEK0aPHx8fzxxx84nU4uu+wyunfvzqOPPkpoaCgaTdP8ETl58mQuuOACrrrqKkaOHMl1111H27Zt67R54403WLp0KYmJifTu3RuAESNG8MMPP7BkyRL69+/PoEGDeOuttzw3cqGhoXz44YcMHTqUHj168Msvv/D9998TERHR6J9RCCGEEKIpkntHuXcU4lymqCdbS10IIcRxPv74Yx599FFKSkp8HUqjO3z4MCkpKWzZsoVevXr5OhwhhBBCiCZP7h3l3lGI5qJpPuoQQogmrrS0lMDAQJ588klfh9JorrjiCrp27errMIQQQgghmh25dxRCNAcyklAIIU5RWVkZubm5gPs1i8jISB9H1DgyMzOxWCwAtG7dGoPB4OOIhBBCCCGaPrl3lHtHIZoLSRIKIYQQQgghhBBCCNHCyevGQgghhBBCCCGEEEK0cJIkFEIIIYQQQgghhBCihZMkoRBCCCGEEEIIIYQQLZwkCYUQQgghhBBCCCGEaOEkSSiEEEIIIYQQQgghRAsnSUIhhBBCCCGEEEIIIVo4SRIKIYQQQgghhBBCCNHCSZJQCCGEEEIIIYQQQogWTpKEQgghhBBCCCGEEEK0cJIkFEIIIYQQQgghhBCihZMkoRBCCCGEEEIIIYQQLZwkCYUQQgghhBBCCCGEaOEkSSiEEEIIIYQQQgghRAun83UATZHL5SIrK4ugoCAURfF1OEIIIYQQdaiqSllZGfHx8Wg08szX1+TeUQghhBBNmbf3jpIkrEdWVhaJiYm+DkMIIYQQ4qSOHj1Kq1atfB1Giyf3jkIIIYRoDhq6d5QkYT2CgoIA9x9ecHCwj6MRQgghhKjLbDaTmJjouWdpKTIzM3nyySdZtGgRlZWVtGvXjtmzZ9OvX7962995553MmTPnuPouXbqwa9cuAKZMmcL8+fPZu3cvJpOJIUOG8Prrr9OxY0ev45J7RyGEEEI0Zd7eO0qSsB41r4kEBwfLjZ4QQgghmqyW9GprcXExQ4cOZfjw4SxatIioqCjS0tIICws7YZ9p06bx2muveY4dDgc9e/bkpptu8tStWrWKBx98kP79++NwOHj66ae57LLL2L17NwEBAV7FJveOQgghhGgOGrp3lCShEEIIIYRo8l5//XUSExOZPXu2py4lJeWkfUJCQggJCfEcf/fddxQXFzNu3DhP3c8//1ynz8cff0x0dDSbNm1i2LBhZyh6IYQQQoimT2a6FkIIIYQQTd7ChQvp168fN910E9HR0fTu3ZsPP/zwlK4xc+ZMLrnkEpKSkk7YprS0FIDw8PATtrFarZjN5jqbEEIIIURzJ0lCIYQQQgjR5B08eJD33nuP9u3bs3jxYh544AEefvjheuccrE9WVhaLFi3innvuOWEbl8vFo48+ytChQ+nWrdsJ202ZMsUzSjEkJEQWLRFCCCHEOUFRVVX1dRBNjdlsJiQkhNLSUplXRgghhBBNTku8VzEYDPTr14/Vq1d76h5++GE2bNjAmjVrGuw/ZcoU3njjDbKysjAYDPW2eeCBB1i0aBG///77SVf+s1qtWK1Wz3HNZOAt6fshhBBCiObD23tHGUkohBBCCCGavLi4OLp06VKnrnPnzqSnpzfYV1VVZs2axZgxY06YIHzooYf44YcfWLFixUkThABGo9GzSIksViKEEEKIc4UsXCKEEEIIIZq8oUOHkpqaWqdu3759J51fsMaqVavYv38/d99993HnVFVl4sSJLFiwgJUrVza4GIoQQgghxLlKRhIKIYQQQogm77HHHmPt2rW8+uqr7N+/n3nz5vHBBx/w4IMPetpMnjyZO+6447i+M2fOZODAgfXOM/jggw/y6aefMm/ePIKCgsjJySEnJweLxXJWP48QQgghRFMjSUIhhBBCCNHk9e/fnwULFvD555/TrVs3Xn75ZaZOncptt93maZOdnX3c68elpaV8++239Y4iBHjvvfcoLS3lwgsvJC4uzrN9+eWXZ/XzCCGEEEI0NbJwST1a4mTgQgghhGg+5F6laZHvhxBCCCGaMlm4RAghhBBCCCGEEEII4RVJEgohhBBCCCGEEEII0cJJklAIIYQQQgghhBBCiBZOkoRCCCGEEEIIIYQQQrRwOl8HADB9+nT+/e9/k5OTQ8+ePXn77bcZMGDACdt//fXXPPfccxw+fJj27dvz+uuvc+WVV3rO33nnncyZM6dOnxEjRvDzzz+ftc9wqpwOF0d2FpLYORy9UevrcIQQQgghhBBCCCGaBFVVcanuUgVUFVRUd1l7n7ptOMk5lepKOP6atb5u7eV9Pf08+7Xa1XzN6iuq9VybWm2Ona9bD+Cn19AuOuiv/rH9ZT5PEn755ZdMmjSJGTNmMHDgQKZOncqIESNITU0lOjr6uParV69m9OjRTJkyhauuuop58+Zx3XXXsXnzZrp16+Zpd/nllzN79mzPsdFobJTP460Fb2wm95CZS8Z1oePAWF+HI4QQQgghhBBCiGpOl4rd6cLhUnF4ShWHy1V9TvW0cbpUHC61uqx17FRxqmqt8y6cLv5UqjjVP9eBU1Vxudz9XTX7LnBVX8+pqqg1+9X1NedU9Vi7mkSbS3V/HdVzPXebmrYutXZb/nRcK2FX61xNf09Cj+PbqYDLdSwR5/rTOWrt11ynJeocF8yiR873dRi+TxK++eabjB8/nnHjxgEwY8YMfvzxR2bNmsVTTz11XPtp06Zx+eWX88QTTwDw8ssvs3TpUt555x1mzJjhaWc0GomNbbrJt6RuEeQeMrN3TbYkCYUQQgghhBBCtDiqqmJ1uKo3J1Z7rX2Hq/rYia26jc3hwuZ0VR+7620OF1anC7tDxeZ0Yne4E3c17exOF3anis3p3nc4j52vc+w4lgi0u1x1RnmJc4eigAIoioJSp859Qqk+Bndd7fbuupr/q3WdWsfUc23P1Y5rV/11gchAw5n8mKfNp0lCm83Gpk2bmDx5sqdOo9FwySWXsGbNmnr7rFmzhkmTJtWpGzFiBN99912dupUrVxIdHU1YWBgXXXQRr7zyChEREfVe02q1YrVaPcdms/k0P5H3Og6MZf33h8hILaasqIqgcL+z/jWFEEIIIYQQQghv2Z0uKm1OKm0OKqzHSov92LHF5sRid2GxObDYnVTanFjsTqrszupz7vPW6roqu4sqx7H95kSrUdBqFPQ1pVaDVqOg0yhotQp6jcbTRqdV0Go07nM1bWo2RUFTXVdT/rlOqyhoFDz72tr1fzqvURS0GtAoNft1z2mUY+c0tdrVnFNqt9G4E1cazbG6mmRYzTUVapXV11EUd9LLs8+xayt1ruPeh/rboxxLztXuo6CgaOom+JRabaH2tf7U5limTjTAp0nCgoICnE4nMTExdepjYmLYu3dvvX1ycnLqbZ+Tk+M5vvzyy7nhhhtISUnhwIEDPP3001xxxRWsWbMGrfb4+f+mTJnCiy++eAY+kfeCI03Etw8lK62Efetz6Ht5cqN+fSGEEEIIIYQQ5yabw4W5yk5ZlYOyWqW5ykFZlQOzxU651UF5lYNym4OKmn2rg4rqRGC51YHN0XhJPEUBo06DUad1l3r3vkFbs6/BUHOsqzmu3rTuUu8pFQxaDfqaOq271GuV6tK9r6s+p9Mq7mNNzb47uaeraadxl5JsEuc6n79ufDbceuutnv3u3bvTo0cP2rZty8qVK7n44ouPaz958uQ6oxPNZjOJiYlnPc6Og2LJSishdW0OfUYkyQ8cIYQQQgghhBCA+1XcSpuT4kobJZV2iittFFfaKam0UVxhp9RybDNb6h5b7M4zGotBq8Fk0BJg0LpLow5/gxZ/gw6TQYtJr8W/uvSr2Te492vq/PQad6nTYjK4E4B+ei1GvQY/nVaScEI0AT5NEkZGRqLVasnNza1Tn5ube8L5BGNjY0+pPUCbNm2IjIxk//799SYJjUajTxY2adcnmt++2EdxTiV5h8uISQlu9BiEEEIIIYQQQpx9qqpSYXOSX2aloNxKYbmNogobheVWCivc+0UVNgrKre6EYIUdm/OvjeQLNOoI8qvZ9H8qdQQZdQQYdQRWbwFGHYF+x/YDqhOBBp3mDP0pCCGaMp8mCQ0GA3379mXZsmVcd911ALhcLpYtW8ZDDz1Ub5/BgwezbNkyHn30UU/d0qVLGTx48Am/TkZGBoWFhcTFxZ3J8P8yg0lHm95R7Fufy9612ZIkFEIIIYQQQohmRlVViips5JiryDVXkVNqJb/MSn55lbsss5JfbqWgzHZaI/wMWg2h/nrC/A2eMixAT4jJQIhJf9wWbNIRYtIT5KdHq5GReUII7/n8deNJkyYxduxY+vXrx4ABA5g6dSoVFRWe1Y7vuOMOEhISmDJlCgCPPPIIF1xwAW+88QYjR47kiy++YOPGjXzwwQcAlJeX8+KLL3LjjTcSGxvLgQMH+Mc//kG7du0YMWKEzz7niXQcFMu+9bmkbczlvFHt0erlCY0QQgghhBBCNAU1CcDMEgtZJRYyS6rIKrG4E4KlVeSYq8gzW09pxF+AQUtEoJGIQAMRAQbCAwzu41r74dWJwDB/A/4GrbyGK4RoFD5PEt5yyy3k5+fz/PPPk5OTQ69evfj55589i5Okp6ej0RxLnA0ZMoR58+bx7LPP8vTTT9O+fXu+++47unXrBoBWq2X79u3MmTOHkpIS4uPjueyyy3j55Zd98kpxQ1p1Cicg1EhFiZXDOwto2zva1yEJIYQQQgghRItQkwQ8UlTJ0aJK0gsrySi2kFVq8SQGvV2BNzLQQEywH7HBfkQHG4kKNBIVZCSyuqzZDzD6/NdwIYSol6KqqurrIJoas9lMSEgIpaWlBAef/VeA1yzYz+bF6ST3iGTkhB5n/esJIYQQonlr7HsVcXLy/RCiaXO5VLLNVRzKr+BQQTnpRZUcKawkvToxWGFr+BXg6CAj8aEmEsJMxIf4ERtiIjbYj9gQIzHBfkQH+cm8fUKIJsvbexV5hNEEdBwYx+bF6aTvLMRSZsMUZPB1SEIIIYQQQgjRrJRa7BzIL+dQfgUHC8o5VFDBwfwKDhdWNDgaMC7Ej8RwfxLD/EkMN5EQWr2FmYgN8cOo0zbSpxBCCN+RJGETEB4fQHRSEHlHyti3IZeeFyX6OiQhhBBCCCGEaJLMVXbScstJyy1jX245aXll7MstI9dsPWEfvVahdbg/KZEBJEUE0Drcn9bh/iSG+9MqzISfXpKAQgghScImouOgOPKOlJG6NkeShEIIIYQQQogWz+lSOVxYwe4sM7uyzOzJNrMvt4zs0qoT9okN9qNNVAApke6tbVQgKZEBtAozodPK68BCCHEykiRsItr3j+aPb9LITy+jMLOciIRAX4ckhBBCCCGEEI2iyu5kb04Zu7JK2Z1lZne2mb3ZZVjs9c8XGBNspENMEO2jg+gYG0j7mCDaRwcS5Kdv5MiFEOLcIUnCJsIUaCCpWwSHthWQujaHITe283VIQgghhBBCCHHGOV0q+/PK2Xa0hK0ZJWw7WkJqThkO1/Frapr0WjrFBdElLpjOccF0ig2ifUwQISZJBgohxJkmScImpNPgOHeScH0Og65rg0aGwwshhBBCCCGauTxzFRuPFLP1aAlbj5awM7OUynpWFI4MNNAlPoQuccF0iQ+mS1wwKZEBaDWKD6IWQoiWR5KETUhStwj8AvRUlto4ureYpK4Rvg5JCCGEEEIIIbymqioH8ivYeLiIDYeL2XikiCOFlce1CzBo6d4qhJ6JofRqFUrPxFDiQvxQFEkICiGEr0iSsAnR6jS07x/DjpUZpK7JliShEEIIIYQQoklzulR2Z5lZe7CQ9YeL2HSkmKIKW502igKdYoPp3TqUXonurW1UoIwQFEKIJkaShE1Mp8Gx7FiZwcFtBVgtDowm+RYJIYQQQgghmgb3SMFy/thfyOoDBaw9WESpxV6njVGnoVdiKP2Tw+mXHEafpDCCZUERIYRo8iQD1cREtQ4iLC6A4uwKDmzKo8t58b4OSQghhBBCCNGCZZVY+H1/Aav3F7D6QCF5ZdY654OMOgakhDMgJZz+KeF0iw/BoJP51YUQormRJGEToygKnQbFsmbBAfauzZYkoRBCCCGEEKJR2Z0uNh8pZkVqPiv25pGaW1bnvFGnoV9yGEPaRjKkbQTdE0LQyaKLQgjR7EmSsAnqMCCWtd8dIHt/KaX5lYRE+fs6JCGEEEIIIcQ5LL/Myqp97qTgr2n5lFU5POc0CvRMDGVo20iGtIugT+sw/PRaH0YrhBDibJAkYRMUGGakVedwju4uInVtDgOubuPrkIQQQgghhBDnmP155SzelcOSXTlsyyitcy7MX8+FHaO5sGMUF3SIItTf4KMohRBCNBZJEjZRnQbFcnR3EXvX5tB/ZAqKrPwlhBBCCCGE+AtUVWV3tpmfd+bw884c0vLK65zvlhDMRR2jubBTND1bhcrqw0II0cJIkrCJSukVhcFPS1lhFZn7imnVKdzXIQkhhBBCCCGaGZdLZcvREhbvcicG04sqPef0WoUhbSO5vFssF3eKJjrYz4eRCiGE8DVJEjZReoOW9gNi2fVrJntWZ0uSUAghhBBCCOG1vTlmvtuSxcKtmWSVVnnq/fQaLugQxRXd4hjeKZoQk96HUQohhGhKJEnYhHUeEseuXzM5sCWfYZV2jP7yD7gQQgghhBCiflklFhZuy+K7LZnszTm2InGgUcdFnaK5olssF3SMwt8gvwYKIYQ4nvzr0IRFJwURkRBAYWYFaRty6XZBK1+HJIQQQgghhGhCSi12Fu3I5rutmaw7VISquusNWg0Xdozi+t4JDO8ULasRCyGEaJAkCZswRVHoPCSe379OY/cf2ZIkFEIIIYQQQqCqKmsPFvHFhnQW7czB5nB5zg1MCee63glc2S2OEHkTSQghxCmQJGET12FgDKvn7yc/vYyCjDIiWwX5OiQhhBBCCCGED+SXWflmUwZfbkjncOGxBUg6xgRxXe8ErukVT0KoyYcRCiGEaM4kSdjEmQINpPSM5MDmfPaszub8myVJKIQQQgghREvhdKn8lpbPF+uP8sueXBwu9/vEAQYt1/RKYPSARLonhKAoio8jFUII0dxJkrAZ6DwkngOb80ldl8OQ69uh1Wt8HZIQQgghhBDiLCqusDFvfTrz1qWTWWLx1PdKDGX0gESu6hFPgFF+nRNCCHHmyL8qzUBil3ACQo1UlFg5tL2Adn2jfR2SEEIIIYQQ4ixIzSlj9h+HWLAlE2v1XIPBfjpu6NOKWwck0ik22McRCiGEOFdJkrAZ0GgUOg2OZdOiI+z5I0uShEIIIYQQQpxDXC6V5XvzmL36EH/sL/TUd40PZtzQFK7qESerEwshhDjrJEnYTHQaHMemRUdI31NEWVEVQeF+vg5JCCGEEEII8ReUWx18vfEoc1Yf9ixEolFgRNdYxg1NoX9ymMw1KIQQotFIktBHFuaV8GlWAa93SCTF39hg+9Bof+Lbh5KVVkLq2mz6XZnSCFEKIYQQQgghzrSiChuzfj/EnDWHKatyABDkp2P0gNaMGZREYri/jyMUQoi/RlVVVFRcqgtVVXHh8uw7VScu1T2dgkutrq/Vtma/pr7OtWqOq69Zc67mGJVj7Wr1rX2N2vHVvt6f+9RpV+u8+38nqa/dv/q4of0QQwgXJ13cqN+j+kiS0Ec+zy7k1+JyPs8u5Om28V716TI0jqy0Evaszqbv5ckoGnmqKIQQQgghRHORU1rFB78e5PP16VjsTgDaRAYwbmgyN/RpJQuRCNEMOV1O7C67Z3O4HJ6t9rFnX3XgdDmPtVOPtXeqzrqly+lpX/tczfGf912qC4fLgUt1ec7V7LtUFw61+pzLiUp1ss7lPl/7uCah9+d2NfU111TVY4k7p8vpSdrV1AnvdQzrKEnCluz2+AhWFJXxRU4RT6TEofci4demTzT6L/ZhLqgiM62EVh3DGiFSIYQQQgghxF9xpLCCGasO8M2mDOxO9y/O3RKCeWh4Oy7rEotGHv4LccqcLidVziqqHFVUOauwOq1YHVZ3WWurclRhc9qwOq3YnDZsLve+3Wn3tLG77J46m8t2XGlz2uokAT3nXXbPyDTx1ygoaBQNiqKgobpUNCgonjoUPHUaRQNQp43nGtXH9Z33lLXa1P6aCsfqardTUHD/70911W1rPkPtfidqX7u+pn+rwFaN9md9MpIk9JFLI4KJ1OvIszn4pbCUK6JCG+yjN2hp3z+G3b9lsWd1liQJhRBCCCGEaMJSc8p4b+V+Fm7LwlU9qGZAcjgPXtSOYe0jZb5Bcc5zuBxUOiqptFdvjmNlhb3Cc2xxWLA4LFQ5qjz7deqc7rImIVjlqMLusvv649VLo2jQKTr0Wj06jQ69xl3qFJ27rK7TKlrPsVajrdNGq2jddcqx8zXta65fU1fTtqZeo2g8xzXn/1xXu6zZatcrilLnfO26moSaRqNBw7Fzta9Vk8CruYanj1I3+VfTtnbCTviWJAl9xKDRcEtcONPT8/g0q8irJCFAlyHx7P4tiwOb8xl2ix2jv/7sBiqEEEIIIYQ4JQfyy3lz6T5+3J7tqbugQxQPDm/HgJRwH0YmhPfsTjtmm5lyezlltjL3vs29X2Yro8zuLivsFZTbyt2lvW5pcVgaJVaj1ohRa8RP64dBa8BP5+epq9kMWoOnrNnXa/R16vQavXtfY0Cv1ddfavTotXp3WbNpjyUCtRpZiVw0X5Ik9KHb4iKYnp7HiiIzmVU2EvwMDfaJTg4iPD6AoqwK0jbm0W1YQiNEKoQQQgghhGhIZomF//6SxjebM3C6VBQFLu8ay4PD29EtIcTX4YkWyqW6KLOVUVxVTIm15FhpLabUWkqptRSzzYzZaqbUVuqpq3RUnrEYdIoOf70/AfoA/HX++Ourt+p9k86ESWfCT+vnOfbT+rlLnd+x89XJv5rzNccyCk2IM0OShD7Uxt/I4NAA1pRU8EV2EX9PiW2wj6IodB4Sxx/f7GfPH1mSJBRCCCGEEMLHCsqtTF+xn8/WpmNzuucnu6RzNH+/rCOd44J9HJ04F1XaKym0FFJYVUhRVZG7tBQdO7YUUlxV7EkEOlXnaX+tAH0AQYYg96YPItgQTKAhkCBDEIF6dxmgDyBQH0igIdCzH6AP8OzrtfIGnBDNgSQJfez2uAjWlFQwL7uQR5Nj0HrxBKTjwFjWzD9A3pEyCjLKiWwV2AiRCiGEEEIIIWozV9n58NeDzPz9EJU2dxJmUJtwnhjRib5JMn+4OHVVjipyK3PJrcgl35JPfmW+u6zeL7AUkG/Jp8JeccrXDtQHEmoMJcwvjFBjKKHGUEKMIYQYQwg2BHv2QwwhBBuDCTGEEGgIRKeRtIEQLYX81+5jI6NCeSYtk0yrnV+Lyhge0fCTRlOQgeSekRzcks/e1dmcd3P7RohUCCGEEEIIAWB1OJmz+jDTVxyg1OJePKFHqxCeGNGR89rJgiSifnaXndyKXLIrssmuyCanIofcilxyK3Pd+5W5lFhLvL6en9aPCFMEEX4RhPuFE2GqW4b7hRPmF0aY0Z0UlNF8QoiGSJLQx/y0GkbFhvFRRgGfZhd6lSQE6DwkjoNb8kldl8Pg69ui1WvOcqRCCCGEEEK0bKqqsnR3Lv/6aQ9HCt3ztbWPDuTvl3VkRNcYSQ62cE6Xk9zKXI6WHSWrPIvM8kxPmV2RTW5lLi7V1eB1TDoTMf4xRPlHEWWq3vyPlZGmSKJMUQQa5I0yIcSZJUnCJuC2uAg+yihgcUEp+TY7UYaGn/C07hJOQIiBilIbB7fl075fTCNEKoQQQgghRMu0N8fMyz/s5o/9hQBEBRl5YkRHbuzTCq1GkoMthd1lJ6s8i3RzOkfLjnK07CjpZemkm9PJLM/E7rKftL9BYyAuMI7YgFhi/WOJDYglJiCGGP8Y975/DMGGYEk4CyF8QpKETUDnQBN9gv3ZbK7ky+wiHkpqOOGn0WroPDSejT8dZtdvWZIkFEIIIYQQ4iwoqrDx5tJU5q1Lx6WCQafhnvNSmDC8HYFG+XXqXGW2mTlcephDpYeObeZDHDUfxaE6TthPp9HRKrAVCUEJxAfEEx8YT0JgAnEBcSQEJhBhikCjyFtgQoimSf5VayJuj4tgs7mSedlFPNg62qsnR52HxrFx0WEyU4spya0kNMa/ESIVQgghhBDi3Gd3upi75gjTftmHucqdFLqiWyxPX9mZxHC57z5XVNgrSCtOI60kjf3F+9lfsp+DpQcpsBScsI9JZ6JVUCtaB7WmdVBr935waxKDEon1j0Wr0TbiJxBCiDNHkoRNxLXRoTy3P5ODFitrSioYEtbw/BLBESZad4kgfVchu3/PYsiN7RohUiGEEEIIIc5tqw8U8Nx3OzmQ715BtnNcMC9c3YVBbSJ8HJk4XU6XkyNlR9hbuJe0kjTSitPYX7KfzPLME/aJ9o8mJSSFlOAUkkOSSQlJoU1IG6L9o2U0oBDinCRJwiYiQKfl+ugwPs0u5LPsQq+ShABdz48nfVche9ZkM/CaNrKAiRBCCCGEEKepqMLGv37cw7ebMwCIDDTw+GUdualfosw72IzYXXYOlhxkd+Fu9hTtYU/hHlKLU7E4LPW2jzZF0z6sPe1C29E+rD1tQ9uSHJwsC4MIIVocSRI2IbfFR/BpdiE/5Jfwij2BMH3D357k7hEEhBqpKLFycGs+7fvL3IRCCCGEEEKcClVV+WZTBq/+tIfiSjuKAmMGJfH4iI4E+zW8qKDwHZfq4nDpYbYXbGd7/nZ2Fe4irTit3gVETDoTHcI60CGsA+3D2tM+tD3tw9oTYgzxQeRCCNH0SJKwCekVZKJLgB+7K6r4NreYe1pFNdjHvYBJHBt/PMyu3zIlSSiEEEIIIcQpOJBfztPzd7DuUBEAnWKDmHJDd3q3DvNxZKI+JVUlnoTgjoId7MjfQZm97Lh2QfogOkd0pnN4Z0+ZFJwk8wUKIcRJSJKwCVEUhdviI3gmLZPPsgq5OyHSqwVMugyNZ9NPh8ncV0JxTgVhsQGNEK0QQgghhBDNV5XdyXsrD/DeygPYnC789Boeu6QDd52Xgl4rU/g0BaqqklmeyabcTWzK3cTmvM0cMR85rp2f1o8uEV3oEdWDbpHd6BLRhVaBrbz6XUoIIcQxkiRsYm6MCePlA1nsqahii7mSPiENJ/yCwv1I6hbB4R3uBUyGjmrfCJEKIYQQQgjRPK07WMjk+Ts4WOBemOTCjlG8fG03WbXYx1RV5VDpITbmbvQkBnMrc49rlxycTI+oHvSI7EGPqB60C2uHXiOvhQshxF8lScImJlSv46qoUL7JLebT7EKvkoQAXc5P4PCOQvauyWHgtW3Q6WUYvRBCCCGEELVV2Z38e3Eqs/44hKpCVJCRf17dlSu7x8qoMx/JLM9kTdYa1mStYWPuRoqqiuqc1yk6ukZ2pW9MX/rG9KVnVE+ZQ1AIIc4SSRI2QbfFR/BNbjHf5ZXwUrsEAnUNJ/ySuoYTGGakvNjKwS35dBgQ2wiRCiGEEEII0TxsO1rCpK+2ciDfPXrwln6JPD2yMyEmGYHWmMw2MxuyN7Am250YTC9Lr3PeqDXSM6qnJynYPbI7/noZ4SmEEI1BkoRN0KCQANr5G9lfaeW7vBJuj49osI97AZN4NvxwiF2/ZUmSUAghhBBCCMDmcPH28jTeXXkAp0slKsjI6zd256JOsuBfY3CpLvYU7mFVxipWZ61mZ8FOnKrTc16raOke2Z3B8YMZFDeIbpHdMGgNPoxYCCFaLpmRtwlSFIW/xbkTg59kFXjdr8vQOBQFstJKKMquOFvhCSGEEEL4RGZmJrfffjsRERGYTCa6d+/Oxo0bT9j+zjvvRFGU47auXbvWaTd9+nSSk5Px8/Nj4MCBrF+//mx/FNFIUnPKuP7dP3h7+X6cLpWre8az5NFhkiA8yyrtlSxPX84/V/+Ti7++mFt/vJX3tr3HtvxtOFUnycHJ3NrxVqYNn8Zvt/7GJ1d+woReE+gT00cShEII4UMykrCJujk2nNcOZrOtzMK2skp6BjU8xD4wzI+k7pEc3l7A7t+zOO8mWcBECCGEEOeG4uJihg4dyvDhw1m0aBFRUVGkpaURFhZ2wj7Tpk3jtdde8xw7HA569uzJTTfd5Kn78ssvmTRpEjNmzGDgwIFMnTqVESNGkJqaSnR09Fn9TOLscbpUPvztIG8u2YfN6SLMX8/L13Xjqh7xvg7tnJVTkcOqo6tYmbGS9dnrsblsnnP+On+GJgzlvITzGBw3mLjAOB9GKoQA90JBOByoLhc4nahOp7t0uVAdDqipr690uVCdLnA53ft/qqs5rnPO5QKnC1TXn86r7n1PvVr/vkqtdn/qU3MO9eTHqoqqVh+r6vFtVNW9obr/fFzqsbra9bXbnla9+1ilpg4MiYnEPv9c4/4lqIckCZuoSIOOq6JDmZ9bzJzMAt7s1Nqrfl3Pj+fw9gL2rs1m0HWygIkQQgghGt/ChQtPuc+ll16KyWQ64fnXX3+dxMREZs+e7alLSUk56TVDQkIICTm2wMF3331HcXEx48aN89S9+eabjB8/3lM3Y8YMfvzxR2bNmsVTTz11yp9D+F5GcSWPfrGVjUeKAbi4UzRTbuxOdJCfjyM79xw1H2XJkSUsPbKUXYW76pxLCEzgglYXcEHiBfSL6ScjBMU5Q1VVVLsd1WZDtVrdZc1mtx/b/nxc55wd1eFw7zuO1eNwoNod7nMOB6rjT3VOh/vY4U7quc/XJPiq2zmd7nZOl7u+JhHoqD5Xve9JVokmwdGpk69DACRJ2KSNjY9gfm4xC3KLeb5tPKH6hr9drbtGEBhupLzIyoHN+XQcKHMTCiGEEKJxXXfddafUXlEU0tLSaNOmzQnbLFy4kBEjRnDTTTexatUqEhISmDBhAuPHj/f668ycOZNLLrmEpKQkAGw2G5s2bWLy5MmeNhqNhksuuYQ1a9ac8DpWqxWr1eo5NpvNXscgzq6fd+bwj2+2Ya5yEGjU8fzVXbipbytZufgMOmI+wtIjS1lyeAl7ivZ46jWKhp5RPRnWahgXtrqQtqFt5c9dNArV6cRlsaBaLLiqqnBVWlCrLLgsVbgslahVVe79KgtqlRXVWoWryuqut1ahVlndpbU66We14rLV3rdW97N6En/nPEUBrRZFozm+1GhAq0HRaOuWSt22aBT3OY3G/bPgz3UaBTTVdUr1dWvqFQ0oCopWAyjV52q3U9xfp865Y/08xyc6X18bxf25lZrznrYcq0M51r92vaI59uf25+ugnKCeOtfT1nqo6UuSJGzCBoQE0CnAj70VVXydU8z4xKgG+2g0Cl2GxrP++0Ps+i1TkoRCCCGE8ImcnByvX9cNCgpqsM3Bgwd57733mDRpEk8//TQbNmzg4YcfxmAwMHbs2Ab7Z2VlsWjRIubNm+epKygowOl0EhNTd366mJgY9u7de8JrTZkyhRdffLHBrykaT5XdyZSf9jBnzREAeiWG8vbo3iSGy6q4Z8Lh0sMsPryYJUeWsK94n6deq2jpH9ufS5Mu5aLWFxFpivRhlKK5UG02nOXluMrK3GV5Ba6KE2yVlcdKi6W6rESttHjq1FoPbXxCr0ej16MYDO5Nrz+21T42GFB0OhTDsfPodO59XXWdToei17nrddXHNXVabXWdFkVX00aHotWCVueur7Xvbl9z3n2u9n6dsr5koGiRJEnYhCmKwtiESCbvy2BuVgH3tIr06mlcl6HxbPjxMNn7SynMKiciPrARohVCCCGEcBs7duxJXx3+s9tvv53g4OCTtnG5XPTr149XX30VgN69e7Nz505mzJjhVZJwzpw5hIaGnvIox/pMnjyZSZMmeY7NZjOJiYl/+bri9BwqqOCheZvZleUe0XnfsDY8PqIjeq38kvtXlFSVsOjwIr4/8D07CnZ46rWKloFxA7ks6TKGtx5OuF+4D6MUvqA6nTjNZpwlJbhKS9375jKc5lJc5jKcZjOuMjPOUjPOMrO7rrzMnQwsK0O12Rr+IqdDUdCYTCgmExo/PxSTHxqTv3vfz69WaUQxHisVPyMaox+K0Vhd59401aViMKIYDWgMhupjQ92EoCTUxDlEkoRN3KiYMF45kEVapZU/Sso5L6zhJ+0BoUaSu0dwaJt7AZPzb+7QCJEKIYQQQrjVnjfQG++9916DbeLi4ujSpUudus6dO/Ptt9822FdVVWbNmsWYMWMwGI7NixYZGYlWqyU3N7dO+9zcXGJjT/w2htFoxGg0Nvh1xdn3v62ZPD1/BxU2J+EBBt64uSfDO8qCM6fL5rTxa8avfH/ge37N/BWHywG4E4OD4gcxImkEwxOHE+oX6ttAxRmjulw4S0txFhfjLCrCUVSEs6gYZ3ERjqJinCUl7q201FO6zOYzMp+d4u+PNjAQTWAgmoCAWpu/u/R3l9qAABR/f/exqbr0N1UfmzznFINBXnEX4i+SJGETF6TTcmNMGHOzCpmTWehVkhCg67AEDm0rIHVtDoOva4vOIAuYCCGEEKL5Gjp0KKmpqXXq9u3b55lf8GRWrVrF/v37ufvuu+vUGwwG+vbty7JlyzwjDF0uF8uWLeOhhx46Y7GLM89ic/LPhbv4cuNRAAamhDPt1t7EhsjiJKdKVVW2F2zn+wPfs+jQIsy2Y3Nsdg7vzNVtr+aKlCvkVeJmRFVVXKWl2PPycBYW4igowJFfgKOwAKdn313vLCkBp/O0vo4mMBBtSAiakGC0QcFog4PRBAehDQ5BGxyEJri6LjAQbVAQmqAgNAGBaIPcSUFFJ+kIIZoa+a+yGbgzIZK5WYUsKigh12onxqhvsE/rzuEEhftRVlTF/s15dBoU1wiRCiGEEELUVVFRwWuvvcayZcvIy8vD5XLVOX/w4EGvrvPYY48xZMgQXn31VW6++WbWr1/PBx98wAcffOBpM3nyZDIzM5k7d26dvjNnzmTgwIF069btuOtOmjSJsWPH0q9fPwYMGMDUqVOpqKioswKyaFr255XxwKebScsrR1Hg4Yva8/DF7dFqZATRqTDbzHx/4Hu+2fcN+0v2e+qjTdGMbDuSq9tcTfuw9j6MUNRHtdmw5+XhyM7GnpOLIy8PR14e9rxcHHn5OHLddaf6Sq8mKAhteBi60DC04eHu/fBwtKFhaEND0YaFog0Jce+HhqINDnbPqSeEOKdIkrAZ6BJoon9wABvMFczLLuSx5IYXI1E0Cl3Oi2fdwoPsXJUpSUIhhBBC+MQ999zDqlWrGDNmDHFxcaf9Klj//v1ZsGABkydP5qWXXiIlJYWpU6dy2223edpkZ2eTnp5ep19paSnffvst06ZNq/e6t9xyC/n5+Tz//PPk5OTQq1cvfv755+MWMxFNw+JdOUz6cisVNifRQUam3tqLIW1lhJu3VFVlR8EOvkr9isWHF1PlrALAT+vHpUmXcnXbqxkQOwCtRt5C8hVneTn2jAz3lpWNPTsbe042jup9R36+16/6akND0UVFoo2IRBcZiS4iou5xZATa8Ah0YaEotaZiEEK0XIqqnoHJBM4xZrOZkJAQSktLG5xE+3SVr1pF8edfEPv8c+jj4xts/01OEQ/tSSfeqGf9oC7ovHhSWmm2MWfyH7icKjdN7kd00tn5LEIIIYRoXI1xr3KmhIaG8uOPPzJ06FBfh3LWNKfvR3PlcqlM/WUf/13uHvE2uE0Eb/+tN5GBMjekN8pt5fx48Ee+3vc1qcXHXttvF9qOmzrcxFVtryLYIH93G4PqcGDPzMSWfhR7pjsZaDua4UkMOktLG7yGoteji4tDHxODLiYGXUw0+uho9350NLroGHTRUWgk8SeEqObtvYqMJPSRwtkfU7l2LcWdOhL96KMNtr8qKpTn92eSZbXzS6GZy6NCGuzjH2ygXd9o9q3PZceqTC6+Q/7hF0IIIUTjCgsLIzxcVj8Vp89cZeexL7aybG8eAHcNTeHpKzuhk9WLG3So9BCf7v6U7w9+j8VhAcCoNTIieQQ3dbiJnlE9ZaGHs0B1OrFn52A7chjbkSPYjxzBevgw9sNHsGVmgsNx0v7asDD0rVqhj49HHxuLPj7OnRSs3rTh4bKirhDirJAkoY+EjR5N5dq1lHz9DVETJjQ4vNtPq2F0XATT0/OYk1XgVZIQoPuFrdi3Ppe09bkMvaEdfoEyb4QQQgghGs/LL7/M888/z5w5c/D39/d1OKKZ2Z9Xxr1zN3GwoAKjTsOUG7pzQ59Wvg6rSVNVlfU565m7ey6/ZvzqqU8JSeGmDjdxTdtrCDF697uEODnVZsOWno71wEGsB/Zj238A64ED2A4dOumcgIrRiD6xFYZWiehbtcKQ2MqdFGyViD4hAW1gQCN+CiGEOEaShD4SdNFwdNHROPLyMC9dSsjIkQ32uSM+gnfT81hRVMZhi5VkU8OvV8SkBBPVOoj89DJ2/5FFnxENrwAohBBCCPFX9O7du87opP379xMTE0NycjL6P010v3nz5sYOTzQTS3fn8tiXWym3OogP8eP9Mf3o3kqSWydic9pYdGgRc3fPZV/xPgAUFC5IvIA7utxBv5h+MmrwNKkuF/aMDKpSU7HuTcWaluZOBh45cuJRgXo9hsREDElJ7i05ybOvi42VkYBCiCZJkoQ+ouj1hN50EwXTp1P8+edeJQmTTEYuDA9iRVEZczMLeb5dw3MZKopC9wsTWD53LztXZdLr0tZoZOU3IYQQQpxF1113na9DEM2Yy6UybVka05alATAwJZzpt/WR+QdPoLiqmK/3fc3nez+nwFIAgEln4tq213J7l9tJCpZBAqfCVVFBVeo+rPtSqdq7F2vqPqypqbgqK+ttr/H3x9CuHcY2bTC2a4uhTVuMbdugT0hA0cmv20KI5kV+avlQ6M03UTBjBpaNm6hK3Ydfxw4N9rkzIZIVRWV8kVPIP1Ji8fNiLpb2/WL449v9lBVVcWRHASk9o85E+EIIIYQQ9XrhhRd8HYJopiptDh75YitLd+cCcOeQZJ4Z2Rm9zD94nLzKPGbvnM03+77xrFIcbYpmdOfR3NThJnml2Asui4WqPXup2rmTql07sezche3gwXpXD1YMBozt2mHs1Aljh/YY27XH2K4tupgYGaEphDhnSJLQh/QxMQRdfDFlS5ZQ8uUXxD7/fIN9LokIJsGoJ9Nq54f8EkbFNjwRuM6gpcvQeLYsSWfHqkxJEgohhBBCiCYnz1zF3XM2siOzFINOw5Tru3NjX5l/8M+yy7OZuXMmC9IWYHO5573rHN6ZO7rewYikEei1Mgd5fVSHg6rUVCzbtlG1cxdVO3di3b8fXK7j2uqiozF26ohfx04YO3bEr1NHDMnJMjJQCHHOk59yPhb2t9GULVlC6Xf/I2rS3xucpFarKIyJj+C1Qzl8nFngVZIQoNuwBLYsTefo7iKKcyoIi5XJcIUQQgjhO2PHjuXo0aMsX77c16GIJmBfbhnjZm8gs8RCRICBD8f2o0/rMF+H1aQcNR9l5s6Z/O/A/3C43PPg9Y7uzX097mNI/BAZzfYnjuJiLNu2YdmyFcuWLVh27EC1WI5rp4uKwq9bN/y6dcXUrRt+Xbuii4z0QcRCCOF7kiT0Mf+BAzGkpGA7dAjz9wsJGz26wT5/i4vgP4dz2GiuZFe5ha6Bpgb7BEeaSO4eyeHtBexclcn5tzT8arMQQgghxNkSHx+PRibuF8DvaQU88OkmyqwO2kQGMHtcf5Ii5IF2jUOlh/hw+4f8dOgnnKoTgIGxA7mv532yGEk1VVWxZ2ZSuW49lZs3Ydmy1f3a8J9ogoMx9eyJqXt3d2Kwa1f0MdE+iFgIIZqmJnFnNn36dJKTk/Hz82PgwIGsX7/+pO2//vprOnXqhJ+fH927d+enn346Ydv7778fRVGYOnXqGY76zFAUxZMYLJ73OWo981/8WbRRz5VRoQDMySzw+mt1vzABgL1rsrFVnWAVLiGEEEKIRjBlyhRmz57t6zCEj3218Sh3zl5PmdXBgORw5k8YIgnCahllGUz+bTLXfnct3x/8HqfqZGjCUD654hM+GvER/WP7t+gEoT0zk5L5C8h6ajL7L76YA5dcSvYzz1D67XxPgtCQkkLIDTcQ+/JLtPnhezqsXUPrDz8g6uGJBF00XBKEQgjxJz4fSfjll18yadIkZsyYwcCBA5k6dSojRowgNTWV6Ojjf2ivXr2a0aNHM2XKFK666irmzZvHddddx+bNm+nWrVudtgsWLGDt2rXExze8CrAvhVx3LXlvvYU1LQ3Lpk349+vXYJ+x8REszCvhm9xinmsbT5BO22CfxE7hhMb4U5Jbyb51OXS7QOZ4EUIIIUTjM5vNfPbZZ8ycOZONGzf6OhzhA6qq8ubSfby9fD8A1/aK5/9G9cDoxT3tua7QUsgH2z/gq31feV4rvjDxQu7rcR/dIrs10Pvc5cjPp2L1airWrady3TrsmZl1G+h0mLp3x79fP0x9emPq1QtdmLyyLoQQp8LnScI333yT8ePHM27cOABmzJjBjz/+yKxZs3jqqaeOaz9t2jQuv/xynnjiCQBefvllli5dyjvvvMOMGTM87TIzM5k4cSKLFy9m5MiRJ43BarVitVo9x2az+Ux8NK9pg4MJuWokJV9/Q/G8z71KEg4JDaS9v5G0Sitf5hRxT6uGFyNRNArdLkjg96/S2LEqk67DElr000chhBBCNK4VK1Ywa9Ys5s+fT0hICNdff72vQxI+YHU4efKb7Xy3NQuAiRe1Y9KlHVr8fWmFvYI5u+YwZ9ccKh2VAAyOG8wjfR+ha0RXH0fX+FSbjcotW6n4/TfKf/sd6969dRtotZi6dcN/4ED8BwzAv09vNP7+vglWCCHOEV4lCbdv337KF+7SpQu6BlZ/stlsbNq0icmTJ3vqNBoNl1xyCWvWrKm3z5o1a5g0aVKduhEjRvDdd995jl0uF2PGjOGJJ56ga9eG/0GdMmUKL774YoPtzqbQW2+l5OtvMC9dSkxBQYOT5SqKwt2tonhqXwazMgq4KyESjRc3Vp0Gx7H2fwcpyqoga18JCR3l6ZoQQgghzp7MzEw+/vhjZs+eTUlJCcXFxcybN4+bb765xSeFWqLSSjvjP9nI+kNF6DQKr17fnZv7J/o6LJ+yOW18ve9rPtj+AUVVRQB0jejKo30fZVDcIB9H17hsR49S/ttvVPz+B5Vr1+KqrDx2UlHw69qVgMGD8B8w0J0UDJBX04UQ4kzyKknYq1cvFEXxar48cCf69u3bR5s2bU7arqCgAKfTSUxMTJ36mJgY9v75SVG1nJycetvn5OR4jl9//XV0Oh0PP/ywV/FOnjy5TuLRbDaTmNi4Nyumrl0x9eyJZds2Sr75lsj772uwz00xYbx6MIuDFivLi8q4JCK4wT5Gk46OA2PZ9WsmO1ZmSJJQCCGEEGfFt99+y8yZM/n111+54ooreOONN7jiiisICAige/fukiBsgfLKqrhj5nr25pQRZNTx3u19Oa99y11FVlVVfjr0E29veZvMcvers0nBSUzsPZHLki5rEf+NqC4Xlm3bKF++nLJly49bbEQbEUHgeUMJOO98AoYOQRce7qNIhRCiZfD6deN169YRFdXwK62qqh43N2Bj2rRpE9OmTWPz5s1e/8NqNBoxGo1nObKGhf1tNJZt2yj+8ksixt+Doj35nCwBOi1/i4tgxtF8Pjqa71WSEKD7BQns+jWTg9sKKCuqIijc70yEL4QQQgjhccstt/Dkk0/y5ZdfEhQU5OtwhI8dLark9pnrOFJYSVSQkbl3DaBznHf3rueiXQW7mLJ+CtvytwEQZYri/p73c33769Fr9D6O7uxyWa1UrFlD+bLllK1YgbOg1kKMOh3+vXsTcP75BJ43FGOnTiiyCroQQjQar5KEF1xwAe3atSM0NNSriw4bNgyTydRgu8jISLRaLbm5uXXqc3NziY2NrbdPbGzsSdv/9ttv5OXl0bp1a895p9PJ3//+d6ZOncrhw4e9+gy+EHT55WinvIYjO5vyVasIuuiiBvuMS4jkg6P5rCwuY19FFR0CGk74RSQEktAhlMx9Jez6LZNB17Y9E+ELIYQQQnjcfffdTJ8+nZUrVzJmzBhuueUWwmQRgRYpLbeM22euI9dsJTHcxKd3D2yxKxgXWgr575b/siBtASoqJp2J8d3Hc3uX2zHpGv79qblylpe7Rwv+sozy339HrfUasSYwkMBhwwi65GICzj8frTxUEEIIn/HqscyKFSu8ThAC/PTTT8TFxTXYzmAw0LdvX5YtW+apc7lcLFu2jMGDB9fbZ/DgwXXaAyxdutTTfsyYMWzfvp2tW7d6tvj4eJ544gkWL17s9WfwBY3RSMiNNwBQPO9zr/okmYyMiAwBYGZGvtdfq2Zl492/Z+G0u04xUiGEEEKIk3v//ffJzs7m3nvv5fPPPycuLo5rr70WVVVxueTeo6XYdrSEm99fQ67ZSoeYQL65f0iLTBDaXXbm7prLVQuuYn7afFRUrm5zNT9c/wPje4w/JxOELosF86JFZEycSNqQoWT940nKlixBraxEFxND2N9GkzjzIzqs/oOEN98g+MorJUEohBA+5vPVjSdNmsTYsWPp168fAwYMYOrUqVRUVHhWO77jjjtISEhgypQpADzyyCNccMEFvPHGG4wcOZIvvviCjRs38sEHHwAQERFBREREna+h1+uJjY2lY8eOjfvhTkPYLbdQNGs2Fb//ji09HUOtEZEncnerSBYVlPJVTjFPt4kjRN/wtzWlVyQBoUYqSqzs35xHx4H1j9wUQgghhDhdJpOJsWPHMnbsWNLS0pg9ezYbN25k6NChjBw5klGjRnHDDTf4Okxxlqw+UMD4ORupsDnpmRjKx3f2JyzA4OuwGt3qzNW8vuF1Dpa659vrEtGFyQMm0yu6l28DOwtcNhsVv/+O+cefKFuxos6IQUNKCkGXjyDo4kvw69qlRcy5KIQQzc0pJwmdTicff/wxy5YtIy8v77gnwcuXLz+l691yyy3k5+fz/PPPk5OTQ69evfj55589i5Okp6ejqTUPxZAhQ5g3bx7PPvssTz/9NO3bt+e7777z6TyIZ5KhdWsCzj+Pil9/o/iLL4n5xxMN9hkaGkinAD/2VlTxeXYR97eObrCPVquh27B41i08xI6VGZIkFEIIIcRZ1b59e1599VVeeeUVfvzxR2bOnMno0aOxWq2+Dk2cBUt25fDQ51uwOVwMbRfBB2P6EWD0+fiERpVZnsnr619nxdEVAIT7hfNIn0e4rt11aJRzZ5491eWicv16Shd+T9nSpbjKyjzn9K1aEXzFFQSPvBJjx46SGBRCiCZOUb1dsrjaQw89xMcff8zIkSOJi4s77gf9W2+9dUYD9AWz2UxISAilpaUEBzf+hMplK1aQ8cAEtCEhtFu1Eo1fw/MMfpZVyN9Tj5LoZ2DtoM5ovfgHuNJsY87kP3A5VUY92Y+YlJY7ebQQQgjRnPj6XuVMycvLIzq64YebTd258v04U+ZvzuCJb7bjdKlc1iWG/47ujZ/+5AvynUscLgef7fmM6VunY3FY0Ck6Rncezf097yfYcO78/bAdPUrpgu8o/e477FlZnnpddLQnMegnK5kLIUST4O29yik/zvviiy/46quvuPLKK/9SgOLEAocNQx8fjz0rC/Oinwm9/roG+1wfE8YrB7I4WmVjaYGZy6NCGuzjH2ygff8YUtfmsG1ZOpfdc26MxhRCCCGEby1cuJArrrgCvf7kq7TWJAh/+uknhg8f7tXCd6Jpm7vmMM//bxcAo/q24rUbuqPTnjuj5hqyt2gvL6x+gd2FuwHoG9OX5wY9R9vQc2OhQFdFBebFSyidP5/KjRs99ZqgIIKvuIKQq6/C1LevrEgshBDN1CknCQ0GA+3atTsbsYhqilZL6K23kv/mmxR/9hkh113b4BM4f62G2+MjeDs9jw8z8r1KEgL0vDiR1LU57N+cz+CiKoLCGx61KIQQQghxMtdffz05OTlERUV51f7WW29l69attGnT5ixHJs6mOasP88JCd4LwrqEpPDuyMxpNyxhFZnFYeG/be8zdNRen6iRIH8SkfpO4of0Nzf7VYlVVsWzaRMm38zEvXnxsnkFFIWDIEEJuuJ6giy/26u0nIYQQTdspJwn//ve/M23aNN555x0ZOn4WhY66kYJ33qFq504sW7fi37t3g33GJkTy7tE8/igpZ0+5hc6BDT+Nj0oMIqFjKJmpJexYkcGQGyUBLIQQQoi/RlVV7rzzToxGo1ftq6qqznJE4myrnSCccGFbnhjRcuafW5O1hpfWvERGeQYAlyVdxlMDniLK37skeVPlLK/A/P1Ciud9jjUtzVNvSEoi5PrrCbnuWvSxMq+5EEKcS7xKEv551bnly5ezaNEiunbtetxrJPPnzz9z0bVguvBwgq+6itL58yn+5BOvkoSt/AxcERnCD/mlfJSRzxudGl4ZGaDnxa3JTC1h1+9Z9BuZjMGvZU0qLYQQQogza+zYsafU/rbbbpO5/JqxlpogLKkq4d8b/83CAwsBiPGP4dlBz3Jh4oW+Dewvsu7fT/G8zyn93/9wVVQAoJhMBI+8ktAbbsDUu3eL+P4KIURL5FU2KCSk7qur119//VkJRtQVfscYSufPx7x4CdE5OV49qRvfKoof8kv5NreYZ9rGE65v+Fuc3C2CkGgTpXkW9q7JpsfwxDMRvhBCCCFaqNmzZ/s6BNFIaicIH2hBCcJVR1fxwuoXKKwqREHh1k638nDvhwk0BPo6tNOi2u2ULVtO8bx5VK5f76k3JCcT9rfRhFx3HVpJ5AshxDnPqySh3Oided/t/45Pd3/Kfy74D8khyfW28evUCf/+/ancsIHieZ8TPemxBq87ICSA7oEmdpRb+CyrkIlJMQ32UTQKPS9K5Ncv9rFteQbdLmjVYuaPEUIIIYQQp2fumroJwn+0gARhhb2Cf2/4N9+mfQtA25C2/HPIP+kV3cu3gZ0mp9lM8RdfUvzppzjy8tyVGg2BFw0n/G9/w3/w4HP+eyqEEOKY5j2LbjP2y5FfSC1OZd7eeSdtF3bHGABKvvoKlxfz9SiKwj2t3POffJxZgMOlehVPp8FxGP11mPMtHN5e4FUfIYQQQgjRMtVexfj+C1pGgnBL3hZGLRzFt2nfoqBwR5c7+PLqL5tlgtCelUXulNfYf+Fw8t98E0deHtqICCLuv492y34h8Z13CBgy5Jz/ngohhKjrjCUJn376ae66664zdblz3m2dbwPcIwrNNvMJ2wVddBH6hAScJSWUfv+9V9e+NjqUCL2OTKudRQWlXvXRG7V0PT8BgG3LjnrVRwghhBBCtDx/ThA+efm5nSC0O+1M2zyNO3++k4zyDOIC4pg5YiZP9H8Co9a7xXmaiqq9e8l84h/sv/QyiubMwVVZibF9e+KmTKHdiuVEP/oo+rg4X4cphBDCR85YkjAjI4NDhw6dqcud8wbFDaJdaDssDgsL0hacsJ2i1RJ2mzuhWDz3E1S14ZGBfloNd8RHAPBRRr7XMXW/0P2acVZaCXlHTpy4FEIIIYQQLVPtBOF9F7Q55xOEacVp/O2nv/HRjo9wqS6uaXsN317zLf1j+/s6NK+pqkr5H3+QftfdHLrueszffw9OJ/4DB5L4wfukLPwfoddfh8Zg8HWoQgghfOyMJQnnzp3LihUrztTlznmKonhGE36+93OcLucJ24aOuhHF3x9rWhqVa9d6df2xCZHoFFhXWsH2skqv+gSGGWnbNxqQ0YRCCCGEODMOHjzo6xDEGfLVhqN1EoRPXd7pnE0QulQXc3bN4ZYfbmFv0V5CjaG8eeGb/Ou8fxFkCPJ1eF5RVZWy5Ss4POomjt59DxWrV4NGQ/CVV5L8zTckzfmYwGHDztnvoRBCiFN3RpKEqqqyaNEiRo0adSYu12KMbDOSEGMImeWZrMxYecJ22uBgQq+7DoCiuZ94de1Yo56ro0IB+PAURhP2usS9svH+jXmUF1u97ieEEEIIUZ927doxfPhwPv30U6q8mF9ZNE0/7cjmqfnbAbjnvJRzOkFYVFXEhGUT+M/G/2B32Tk/4XwWXLuAS5Mu9XVoXlFVlbIV7uRgxoQJVO3ahWIyETZmDG2XLCHhzTcwdevq6zCFEEI0QX8pSXjo0CGee+45WrduzfXXXy83fqfIpDMxqr07sfrp7k9P2jbs9tsBKF+5Elt6ulfXvzfRPSrwu9wSsq02r/pEJwUT1y4El0tlx6oMr/oIIYQQQpzI5s2b6dGjB5MmTSI2Npb77ruP9evX+zoscQp+S8vnkS+24FLh1v6JPDOy8zmbINyUu4mbFt7EH5l/YNQaeW7Qc0y/eDqRpkhfh9agOsnBB6qTg/7+RIy/h3bLfiH2macxtErwdZhCCCGasFNOElqtVj777DMuuugiOnbsyKuvvsqkSZPIy8vjhx9+OBsxntNu7XQrWkXLxtyN7C3ae8J2xjYpBAw7H1SVok9PnlCs0TvYn0EhAdhVlY8yvF+xuNfFrQHY9WsmduuJX4MWQgghhGhIr169mDZtGllZWcyaNYvs7GzOO+88unXrxptvvkl+vvdvPIjGt+lIMffO3YTdqTKyexz/ur77OZkgdKkuPtrxEXcvvps8Sx4pISnMGzmPmzve3OQ/r6qqlK1cyeGbbj4+OfjLUqL//nd04eG+DlMIIUQzoKjerIQBbNq0iZkzZ/L555/Trl07xowZwy233EKrVq3Ytm0bXbp0OduxNhqz2UxISAilpaUEBwef9a/3xKon+Pnwz1zX7jpeHvryCduV//Y7R8ePRxMQQLtVK9EGBjZ47SUFpdyx4xBBWg2bh3QlSKdtsI/LpfLZ82swF1RxwegOdLug1Sl9HiGEEEKcXY19r3ImWa1W3n33XSZPnozNZsNgMHDzzTfz+uuvE9dMV1Vtzt+Pk9mTbeaW99dgrnJwfvtIPhrbD6MX95LNTVFVEU//9jR/ZP0BwFVtruK5Qc/hr/f3cWQNq1i7jrw33qBqxw4AFJOJ8Nv+Rvhdd0liUAjRJKiqilMFJ+7Spao4VRUn4FRV1FrnnKqKWl3vVMGFiqu6jwtwqu7ruarbuHCfU8HdrqZ9nfqadniO65S19tWaNtXXqd3u2PljX6+m3lWdVqvdxlXrejV9arJvx65X3Q6INegZnxh11r4P3t6r6Ly94MCBA5k4cSJr166lY8eOZyRI4XZb59v4+fDP/HjwRx7t8ygRpoh62wWcNxRDmzbYDh6kdP4Cwu8Y0+C1L4kIpr2/kbRKK59lFXJ/6+gG+2g0Cj0uSuT3r9LYtjyDrucnoGia9hNUIYQQQjRtGzduZNasWXzxxRcEBATw+OOPc/fdd5ORkcGLL77ItddeK68hNyGHCyq4Y9Z6zFUO+iaF8f6YvudkgnBT7ib+seof5FnyMGqNPDPwGa5rd12THz1YtW8feW+8QcWqXwFJDgpxLlBVFZuqYnepWF0qdlXF5nJ56jxlrXOO6j6OWuftqoqjet+h1hzzp+Nam8t9vnads7qPs04dtc65z7tqnXNxbN9Zq3T5+g+2mega6HdWk4Te8jpJePHFFzNz5kzy8vIYM2YMI0aMaPL/eDYXPaN60j2yOzsKdvD1vq+5v+f99bZTFIXwMbeT8+JLFH32KWG334aiOfkb4xpF4f7EaP6eepQPM/K5u1UUei8Sfp2HxLF+4UFKcis5srOQ5B5Nfx4WIYQQQjQ9b775JrNnzyY1NZUrr7ySuXPncuWVV6KpvodJSUnh448/Jjk52beBCo+c0ipun7mO/DIrnWKDmDW2P/4Gr39taBZcqotZO2fx9pa3cakuUkJS+M8F/6FDWAdfh3ZS9txc8t9+m9L5C8DlAp2OsFtuIXLCA+gi6h9oIIQ4NWp1Iq3S6cLicmFxqlQ6nVhcKlU1dS4XVU61unRR5XJR5VKpcrmw1iqt1e2stY6tqru0VScDbdXnbN695HnO0QAaBbSKggYFrVJ9jIJGUdAoNW2UOqVWUVCq+x475+6nKKCpLo8dg6KAwrG2Cu5r1G5f+1xN6qTmnELNdRTP3H2evp7rV7erjo/qY6X62LPV6ofiHknYFHj9r/3ixYs5evQos2fP5oEHHsBisXDLLbcASLLwL1IUhds638ZTvz3Fl6lfcne3u9Fr6/8LEnLtteS9NRX7kXTKV60iaPjwBq9/Y0wYrx3KJtNqZ2FeMTfGNvx00eCno8v5CWxdms7WZUclSSiEEEKI0/Lee+9x1113ceedd57wdeLo6GhmzpzZyJGJ+hRX2Bgzcx0ZxRaSI/yZe/cAQvybxi8uZ0qZrYynfnuKXzPco/Caw+vFzvJyCj/6iKKP56BWLxYZNGIE0Y89ikES7KKFq3K6KHM6qXC6KHc4KasuK5wuyqv3y50uKqrbVFZvFZ7SSaXrWL3F5cLZBPJ1WgUMigaDRkGvKBg0imdfryjoNQoGRYNO426nq26jqz6nU0Cv1D5Wjjt2b9TaV9BpFLSArlYbba12WqX6vOJO4umqE3zu7Vi9lurE35/6aarbaasTbZJPalq8npPwz5YuXcrs2bNZsGABiYmJjBo1ilGjRtGnT58zHWOj88W8MnannRHfjiDfks+r573K1W2vPmHb3P/7N0WzZuE/eBBJs2d7df2ph3N47VAO3QJNLO3Xwav/EMuKqvjk2TWoLpVbnu1PZKsgrz+PEEIIIc6e5jQH3uHDh2ndurVn5GANVVU5evQorVu39lFkZ05z+n6cTLnVwW0frmVbRimxwX58ff9gEsObbuLsdBwqPcTDyx/msPlws3i9WLXbKf7qKwqmv4uzqAgAU58+RD/xOP69e/s4OiH+OlVVqXC6KHE4MTuclNjdZanDSanDQanj2HGZw4XZ4aTM6aTM4cTscFHmcJ7VEXg6Bfy1GkwaDabq0k+jwU+r4Kc5Vu+nUarrNRir940aBaOm/mODomDUatylxp0IrDlvqE7iaZvozyXRPHl7r3LaScIaxcXFfPrpp8yaNYvt27fjdDb/1XB9daP3wfYPeHvL23SN6MrnIz8/4c2KPTOT/ZdeBi4XKQv/h1+Hhl+LKLI76Lt6NxaXi697tuX8cO8Sfos/3Mn+TXl0GBjDpeO6ntLnEUIIIcTZ0ZySUlqtluzsbKKj686LXFhYSHR0tNw7NhF2p4u7Pt7Ab2kFhPnr+eq+wbSPObceEP+a8StP/vok5fZyYvxjmHbRNLpGNN3724q1a8l5+RVsBw4AYEhOJvrxvxN48cVNNqkpWjZVVSl1OCm0Oyi0Odyl3UmR3UGx3UGx3Umxw0GJp85JicOB4wzl+Py1GgK1GgK1WgJ11aVWQ6DOXfprNQRoNQRotbX2a+rddf7VSb+axKA3U3UJ0Ryc8YVLTiQsLIyJEycyceJENm/e/Fcv16KN6jCK97e9z67CXWzL30av6F71ttMnJBB0ySWULVlC8SefEvfySw1eO1yvY3RcOLMyC3j3aJ7XScLel7Vm/6Y80jbkMfCaNgRHmE7lIwkhhBCihTvR8+jy8nL8/PwaORpRH1VVeWbBDn5LK8Ck1/LxuAHnVIJQVVVm7pzJfzf/FxWVPtF9eOPCN4g0Nc3pdOw5OeS+/jpli34GQBseTtTEhwgdNQpFf269+i2aPodLpdDuIN9mJ8/mIN/mIM9mp6C6zPckAx0U2U8/4adXFEJ0WkL1WoJ1WkJqbcF/2gK1Gs9+kE5LUHUiUEbeCfHXeZUk3L59O926dTvuNZE/q3nVeNeuXXTs2BGd7tya4PhsC/cLZ2SbkSzYv4BPdn9ywiQhQPgdYyhbsoTShQuJmvQYurCwBq9/X2IUH2cWsKKojN3lFroENpzwi04KplWnMDL2FrP1l6MMu6VpT+YshBBCiKZh0qRJgHuuoeeffx5//2OvrTqdTtatW0evXr18FJ2o7Z3l+/lqYwYaBd75W296Job6OqQzxuKw8Pwfz/PzYXfC7aYONzF5wOQTzv/tS6rNRuGcORS8NwO1shI0GsJGjybq4YloQ0J8HZ44x6iqSqHdSa7NTrbVTq61urTZybFWb9XJwFPN+wVqNUTodUQYdETodYTrdYTptYTrdYTqtIRVH4fpdYTptITqdZg0ioyQFaIJ8CqL17t3b3JycoiK8m455sGDB7N161batGnzl4JriW7rfBsL9i9gWfoyssuziQusf4JvU9+++HXpQtXu3RR//jlREyY0eO0kk5GRUaF8n1/CjKN5/Ldzklcx9RmRRMbeYvb8nkX/kcmYAg2n9JmEEEII0fJs2bIFcP8iumPHDgyGY/cPBoOBnj178vjjj/sqPFFtwZYM3li6D4AXr+3GxZ1jfBzRmZNVnsUjKx5hb9FedIqOyQMnc3PHm30dVr3K//iD3Ff+he3QIcA972Dsc8/i17mzjyMTzZXF6SLTaiOzyk5mle3YvtVGVnVZ5fIu/acBIgw6og06ovR6oozuMtqgI8pwLBlYkxg0NjC4SAjRdHmVJFRVleeee67OE+CTsdlsfymolqxjeEcGxA5gfc56Pk/9nEl9J9XbTlEUwu+6i6zHH6f408+IuOsuNF68svNA6yi+zy9hQW4Jk9vEEWdsOOHXqlMYUa2DyE8vY8eKDAZcLclfIYQQQpzcihUrABg3bhzTpk1rtnP1nctWHyjgH99sB+C+YW0YM8i7B8jNwYacDfx95d8pthYT7hfOmxe+Sd+Yvr4O6zj2rCxyX3udsiVLANBGRBD9xOOEXHutjKoSJ+VUVbKsdtItVtKrbKRbbLVKK7k2h1fXidDriDPqiTHoiTXqiDXq3ZvBXcYY9EQYdPIqrxAthFdJwmHDhpGamur1RQcPHozJJHPXna7bOt/G+pz1fLvvW+7vcT/++vqTs8GXjyD/zTexZ2VR+t3/CLv1lgav3Sc4gEEhAawtreCjjAKeaxvfYB9FUeh9WWuWfLSL7Ssz6H1ZEnqj9pQ/lxBCCCFantmzZ/s6BFGPfbll3PfJJuxOlZE94njy8k6+DumMWXhgIS/88QIO1UHn8M5MGz7thG/n+IrqclH82Tzy3nrr2KvFt91G1MSH0EpCXVRzqSqZVjuHKq0ctFiPlRYrRyw27A2sQRqg1dDKz0C8UU8rPwMJRj3x1WWCn4E4o15G/Qkh6vAqSbhy5cqzHIao7YJWF9AqsBUZ5Rn8cPCHE74Woeh0hN85ltxXp1A0ezahN41C0TacvJvQOpq1Ow4xN7OAR5NiCNI13Kdtn2iCow5izrew+/csel6ceMqfSwghhBAtww033MDHH39McHAwN9xww0nbzp8/v5GiEjXyzFWMm72BsioH/ZLCeOOmnmjOgRU8VVVlxvYZvLv1XQAuT76cl4a+hEnXtAYvWA8cIPvZ57BUv5Jv6tuX2Oefw69jRx9HJnylyunigMVKWkUVqRVV7KusYn+llcMWK9aTvBJsUBRa+Rlo7WegtammNHqOw3RaGZEqhDglsrJIE6TVaPlb57/xfxv+j0/3fMqoDqPQKPU/4Qm98Ubyp7+L7cgRypYtI/iyyxq8/iURwbT3N5JWaeWzrELubx3dYB+NRqH3pa1ZNS+Vrb+k0+3CBLRaeeokhBBCiOOFhIR4fjENkQUXmpQKq4O75mwgs8RCSmQAH97RDz99839DxO6y89Kal/hu/3cA3N3tbh7u8/AJ76F9QbXbKZw5i4Lp01HtdjT+/kQ/8Tiht9yCIqO5WgSry8X+Siu7yy3sragirTohmG6x4TpBH50CSX5G2vgbaWMyklKrjDfq5TVgIcQZpahqA2OUWyCz2UxISAilpaU+mz+n3FbOpd9cSrm9nLcvepsLEy88Ydu8qVMpnPE+pp49Sfric6+eFn2WVcjfU4+SYNSzdlAX9F48PXbYncx9Zg0Ws42L7+xMp0FN67UNIYQQoqVoCvcq4pjm8v1wOF3c+8kmlu/NIyLAwPwJQ0iKCPB1WH9Zua2cSSsnsSZ7DRpFwzMDn2lyC5RU7d5N1jPPYt2zB4CAYecT989/oo9veOof0fyoqkqOzc7u8ip2l1vYU+Eu91dW4TjBb98hOi0d/P1oH2CsLv1o62+kldGA7hwY6SuE8C1v71VkJGETFWgI5KaONzF752xm75x90iRh+O23UzRrNpZt27Bs3ox/34YnZb4xJozXDmWTabXzfX4JN8SENdhHp9fS86JWrP3uIFuWpNNxQCyK/IMlhBBCiJM4dOgQDoeD9u3b16lPS0tDr9eTnJzsm8BaGFVV+ef3u1i+Nw+jTsOHY/udEwnC3IpcJiybwL7ifZh0Jv5zwX8Y1mqYr8PycFmtFEx/l8KZM8HpRBsSQswzTxN89dXyGug5QlVVjlbZ2F5mYXtZJdvKLOwor6TI7qy3fYhOS+cAPzoFmujgb6RDgB8d/P2IMujk74QQwuckSdiE3d75dj7Z/Qmb8zazNW8rvaJ71dtOFxlJyLXXUvL11xTOnOVVktBPq+HuhEheO5TDu+l5XB8d6tU/St2GJbDp5yMUZVVweGchKT0iT/VjCSGEEKIFufPOO7nrrruOSxKuW7eOjz76SOa+biSfrD3Cp2vTURSYdmtv+rRu+AFxU7eveB8TfplAbmUuEX4RTL94Ol0ju/o6LA/Ltm1kPTUZ26FDAARdfjmxzz6DLlLun5uzbKuNLWZ3MnCbuZLtJ0gIahVoa/KjS6AfXQJNdA5wl/FGvSQDhRBN1iknCSsqKggIaP5PHZuDaP9ormpzFd/t/46Pd33M1OipJ2wbPm4cJd98Q/ny5VgPHMDYtm2D178jIZJpR/LYWW5hZVEZwyMafj3G6K+n27AEtixJZ8viI5IkFEIIIcRJbdmyhaFDhx5XP2jQIB566CEfRNTyrN5fwIvf7wbgycs7cXm3WB9H9NetzV7LYyseo9xeTkpICu9d8h4JgQm+DgsA1eGg4P33KXj3PffowahIYp9/nuBLL/V1aOIU2VwudpZZ2GiuYKO5kk2lFWRa7ce10ysKnQP86BHkT48gEz2C/OkU4IefzOEuhGhmTjlJGBMTw80338xdd93FeeeddzZiErXc2fVOvtv/HcvTl3O49DDJIcn1tjO2SSHwoosoX7aMwtmziX/llQavHa7XcUd8BO9n5DPtSK5XSUKAnhcnsm35UbIPlJK1v4T4dqGn8ImEEEII0ZIoikJZWdlx9aWlpTid9b+OJ86c9MJKJszbjNOlcn3vBO4b1sbXIf1lPx78kWd/fxaH6qBvTF+mDZ9GiLFpLJBjO3qUrCf+gWXrVgCCr7yS2BeeRysL+DQLRXYHa0vK2VBawSZzJdvKKo9bXVirQOcAEz2DTPQM8qdHkD+dA/0wyuIzQohzwCn/JPv0008pKirioosuokOHDrz22mtkZWWdjdgE0Da0LRe0ugAVlTm755y0bcTddwNg/t9C7Hl5Xl3/gdbRGBSFtaUVrC0p96pPQIiRTgPdT6C3LD7iVR8hhBBCtEzDhg1jypQpdRKCTqeTKVOmyAPns6zc6mD83I2UVNrp2SqEKTd0b/avOX6590sm/zYZh+rgiuQr+ODSD5pEglBVVUrmL+DQtddh2boVTWAg8f/+PxLefEMShE1Ygc3BD3klPLMvg+Hr99Ll953ctfMw7x3NZ31pBVaXSrhey6URwUxOiePbXm3Zd153funfkTc6teaOhEh6BftLglAIcc447dWN8/Pz+eSTT/j444/Zs2cPI0aM4K677uKaa65Bp2veUx02tRXqNuZsZNzicRg0BhaPWkyk6cSv+B4e/TcsW7YQce+9RE96zKvr/yP1KHOzChkeHsTnPRt+TRmgJLeSz/65FlS49bkBRCQEetVPCCGEEH9dU7tXOZndu3czbNgwQkNDOf/88wH47bffMJvNLF++nG7duvk4wr+uKX4/XC6V+z/dxJLduUQHGVn40HnEhvj5Oqy/5KMdHzFt8zQAbu14K5MHTkaj+D454ywpIfuFf1K2eDEApn59SXj9dfQJTeP1Z3FMkd3BH8XlrC5xb6kVVce16eDvx6DQAPoGB9A/JIAUk6HZJ9eFEMLbe5XTThLW9vbbb/PEE09gs9mIjIzk/vvv56mnnsLf3/+vXtonmtqNnqqq3P7T7Wwv2M747uN5uM/DJ2xb9ssvZDw0EU1QEO1WrEAb2PD8kUcsVoas24NThZ/7dqBXsHfft5/f38GBLfl0HBjLJeO6eP15hBBCCPHXNLV7lYZkZWXxzjvvsG3bNkwmEz169OChhx4iPDzc16GdEU3x+/HmklT+u3w/Bp2GL+8dRO9mvFCJqqpM2zyNmTtnAjC++3gm9p7YJBI3FWvWkPXUZBy5uaDTEfXww0TcfReKVuvr0ARgd6lsNFewqqiMlUVlbCur5M+//HYK8GNIaCCDQwMZFBpAlEHvk1iFEOJs8vZe5bSH/OXm5jJnzhw+/vhjjhw5wqhRo7j77rvJyMjg9ddfZ+3atSxZsuR0Ly9qURSFO7vdyaSVk/gy9Uvu6X4P/vr6E3mBF12EITkZ2+HDlHzzNRF33tng9ZNMRq6PDuOb3GL+eySXWd1TvIqr94gkDmzJJ21DLgOvbUNQePN+Oi2EEEKIsyM+Pp5XX33V12G0GD9uz+a/y/cDMOX67s06QehSXby67lW+TP0SgMf6PsZd3e7ycVSg2u3kTZ1K0cxZABhSUoj/978xdWs6qyu3RKqqcshiY2WRmVXFZfxeXE6F01WnTccAP84LDWRIWCCDQgKJMDTvt+CEEOJMOuWfiPPnz2f27NksXryYLl26MGHCBG6//XZCQ0M9bYYMGULnzp3PZJwt3kWJF9E6qDXpZenMT5vP7V1ur7edotEQftc4cp5/gaI5cwm/7TYUfcNPwyYmxfBNbjE/FZSSWlFFx4CGE34xycEkdAwjM7WYrUvTOf+WDqf8uYQQQgjRMlRWVpKeno7NZqtT36NHDx9FdG7alVXK419vA2D8+Snc2LeVjyM6fQ6Xg+f/eJ7vD36PgsKzg57l5o43+zos7Lm5ZD42CcvmzQCE3noLMf/4B5pm+hZVc2d3qawrLWdJgZklhaUcttT9GROu13JBWBAXhgdzQXgQsUYZKSiEECdyyknCcePGceutt/LHH3/Qv3//etvEx8fzzDPP/OXgxDFajZaxXcfy8tqX+WT3J9za6VZ0mvq/fSHXXkv+f9/GkZ2N+eefCbn66gav3zHAj5FRIfyYX8rbR3J5p0uSV3H1GdGazNRidv+eRd8rkvEPNpzS5xJCCCHEuS0/P59x48axaNGies/LCsdnTkG5lXvnbsJidzKsQxRPXdF8H9rbnDaeWPUEy48uR6to+dd5/2Jkm5G+DouK1avJfPwJnEVFaIKCiHv1XwRfeqmvw2pxzA4nywvNLCk0s6zQTKnj2M8RvaLQPySAC8ODuCA8iO6BJjRN4NV0IYRoDk55pt/s7Gzef//9EyYIAUwmEy+88MJfCkwc75q21xDuF05WRRZLDp/4VW6N0Uj47bcBUPjRTLyddvLhpBgAFuQVc8Ri9apPYudwopOCcNhdbP0l3as+QgghhGg5Hn30UUpKSli3bh0mk4mff/6ZOXPm0L59exYuXOjr8M4ZNoeLBz7dRGaJhTaRAbw9ujdaTfNMjFTaK3lw2YMsP7ocg8bAWxe+5fMEoepykT99Oul334OzqAhjl86kfPuNJAgbUbbVxkcZ+dy8dT9dft/B/buPMD+3mFKHk3C9lptjw5jZLZk953Vjfu92PJwUQ88gf0kQCiHEKTjlJGFQUBB5eXnH1RcWFqKVCXrPKj+dH6M7jQZg9q7ZJ03+hd16K4q/P9bUVCr+WO3V9XsG+TM8PAinCu+kH/89ro+iKPQf6Z7DcMeqTCzltgZ6CCGEEKIlWb58OW+++Sb9+vVDo9GQlJTE7bffzv/93/8xZcoUX4d3zvjXj7vZcLiYID8dH47tR4ipeb5SWWGv4P5f7mdt9lpMOhPTL5nO8NbDfRqTo7iYo/feR8Hb74CqEnrzzSR//jmG1q19GldLkGO181FGPtduTqP36t08m5bJr8XlOFRo72/kwdbRLOzdjh1Du/HfzkmMjAolUCe/kwohxOk65SThiRJTVqsVg0FeNT3bbu14Kyadib1Fe1mbvfaE7bShoYSOuhGAwg8/9Pr6j1SPJvwyu4hsq3cJv6TuEUS1DsJhdbLtl6Nefy0hhBBCnPsqKiqIjo4GICwsjPz8fAC6d+/O5uo53cRf87+tmcxZcwSAabf2om1UoI8jOj0V9goe+OUBtuRtIcgQxIeXfciguEE+jalyyxYOXX8DFb//juLnR9xrU4h76UU0RqNP4zqX5VntzMrI57rNafRevYtn0zJZV1oBQP/gAF5oG88fAzvx28DOPNc2ngGhgWhltKAQQpwRXs9J+N///hdwjxz76KOPCAw8dvPhdDr59ddf6dSp05mPUNQR6hfK9e2uZ97eeczeOZvB8YNP2Dbizjsp/vwLKteto3LzFvz79G7w+oNCAxkUEsDa0gpmpOfzYvuEBvsoikK/K5NZNGMH21dm0OvS1vgFNM+n10IIIYQ4szp27EhqairJycn07NmT999/n+TkZGbMmEFcXJyvw2v20nLLeOrbHQBMvKgdF3WK8XFEp6fSXsmEXya4E4R6d4Kwa4TvVgpWVZXiuXPJ/fd/wOHAkJJCwrSp+HWQhfrOhlK7g4X5JSzILWFNSTm1h6X0C/bnmuhQrooKJd5PBqUI0VypqorLpaK6VFxOd6m6OFZXXapqdb1T9fRBPdaupo3LRXVbFVXlT6X7Gqr6p/3qErXmXK1+f67/0zGe+hOf89QBuEDlWL2nP8f6164LDDMy8Oo2vvr2eHidJHzrrbcA94eZMWNGnVeLDQaD52ZPnH1juozhi9QvWJO9hr1Fe+kUXn9yVh8fT+h111Ly9TcUzHiP1h984NX1H0mKYe32g8zNKuThpBgiDA3/NUnpGUlEq0AKM8rZtvxok/jLLYQQQgjfe+SRR8jOzgbghRde4PLLL+ezzz7DYDDw8ccf+za4Zq7c6uD+T90LlZzXLpJHL2meCayaOQg3520mUB/IB5d94NMEoctqJfu55zAv/B6A4CuvIPall9EGBvgspnORw6WysriMr3KKWFxQitV1LDXYJ9ifa6JCuSo6lFaSGBQCcCfJnHYXTsexzeVQax2r1XUunE61ujzWxuU81tblVKu36rbV7WvXu5wqTmdNUq/WOZdaZ7/O+VpJQJdLRXXWTv75+k+waYtoFdgk8iheJwkPHToEwPDhw5k/fz5hYWFnLShxcq2CWjEiaQSLDi9i9s7ZvD7s9RO2jRg/npJv51Px629Ydu7C1K3hG64Lw4PoEWRie5mFDzPyeapNw0/5FUWh/5XJ/PzBTrYvz6DXxYkY/WU0oRBCCNHS3X777Z79vn37cuTIEfbu3Uvr1q2JjIz0YWTNm6qqPPXtdg7kVxAb7Me0W3s1y4VKLA4LE5dPZGPuRgL1gbx/6ft0i+zms3jsuXlkTJxI1fbtoNUS89RThN1+G4q8znrG7C638GVOEfNzi8m3OTz1HQP8uCkmjGuiQ2ltkte5RdPncqk4bE7sVvfmsDlx2FzYq0vHn0t7dRu7C4fdhdNWXdqd1aXr2DmH+9hpd+FwuHDZXe4RdecoRaOgaECjKCgaBY1WQVHcdYrm2L7Gs1+9Ke7zmlr7x8o/1bn/D40GqH2OY2089dUlNefr7Ndti+fa1ecARQNQz3lqfw2oqVQUMAU1jQciXicJa6xYseJsxCFO0Z3d7mTR4UUsPryYh/s8TEJg/a8FG1q3JviqkZgXfk/h+zNo9fbbDV5bURQeTYrhrp2HmZWZz4TW0QR7MQFwm15RhMcHUJRVwfYVGZ4FTYQQQgghwJ3YMplM9OnT57T6Z2Zm8uSTT7Jo0SIqKytp164ds2fPpl+/fifsY7Vaeemll/j000/JyckhLi6O559/nrvuusvTZurUqbz33nukp6cTGRnJqFGjmDJlCn5+fqcVZ2P4ePVhftiejU6jMP22PkQENr+kSpWjionLJ7I+Zz3+On/eu+Q9ekT18Fk8lh07yXjwQRx5eWhDQkiYNo2AQQN9Fs+5pNju4OucIr7KKWZnucVTH67XckNMGDfHhtM90CTJWHFWqS4Vu9WJrcqBzVJTOrBVHdu3W53Yq5zYrE7sVgf2Kmd1n5pkoAOH1Z0IdNpdvvswCmh1GrRaBa1eg1anQaNzl1qd4j7WKmi07uM6pVZBo9eg1biPNTqlbltNTd/aW606Ta1jjYLiqatO7tUk7TTH2tdO/NWuq5PAE02CV0nCSZMm8fLLLxMQEMCkSZNO2vbNN988I4GJk+sS0YWBcQNZl72O2Ttn8+ygZ0/YNvLeezF//wNlS3+hat8+r+ZSuTwyhA7+fuyrrGJ2RgGPJDc8v42icc9NuOSjXWxbdpSeFyViMJ1yHloIIYQQ55iZM2fy1ltvkZaWBkD79u159NFHueeee7y+RnFxMUOHDmX48OEsWrSIqKgo0tLSGny75eabbyY3N5eZM2fSrl07srOzcbmO/WI3b948nnrqKWbNmsWQIUPYt28fd955J4qiNNn72k1HivnXj3sAeGZkZ/omNb83fKocVTy8/GHWZa/DpDMx49IZ9Iru5bN4Sn/4kexnnkG1WjG0a0viu+/K6sV/kaqqbDJXMiergIV5JZ7XiQ2KwqWRwdwcG85F4cHom+EIWOE7DrsTa4WDqkq7u6ywY620Y610uDeLA1t1aa20Y7M4sVrs2Cod2KxOOBuD8RTQG7TojFr0Bg06g9a96d37x+o06PRatAYNOr0Grd59rDO4k3vHSq27rE7+afUKWp22unT3qxlRJ8SZ5lUGZ8uWLdjtds/+ichf0sZ1X4/7WJe9jvlp87m3x71E+0fX287Yrh1Bl11G2eLFFL7/AQlv/KfBa2sUhYeTonloTzrvZ+QxPjEKf23Di2G37RNNWOwhinMq2b4yg35XJJ/qxxJCCCHEOeT555/nzTffZOLEiQwe7F5wbc2aNTz22GOkp6fz0ksveXWd119/ncTERGbPnu2pS0k5+VsLP//8M6tWreLgwYOEh4cDkJycXKfN6tWrGTp0KH/7298850ePHs26deu8/YiNqrDcyoOfbcbhUhnZI447hyT7OqRTZnVaeWTFI6zJXoNJZ+K9S96jd3TDC+ydDarLRf60/1L4/vsABF54IfH/+TfawOa5QnRTUO5w8m1uMXOzCthVXuWp7xZo4rb4CK6LDiVMLwMJBDjtLizlNixldizlNqrK7VjK7VRVb5ZyO1UV7vqqCgfWCjuOMzB6T6NRMJh0GExa9H46jCYdBj/3vt5Pi96oxWDUoje6jw3VdfrqOp1Rg95wrE6r10guRJwzFFWV6SP/zGw2ExISQmlpKcHBwb4O54RUVeXOn+9kc95mbu98O08OePKEbav27OHQ9TeARkObH3/A2MBNNbgnEx66bg9Hqmy82C6e+xLrT0L+Weq6HH6ZvRtjgI47/jUEg5/cBAghhBBnUnO5VwGIioriv//9L6NHj65T//nnnzNx4kQKCgq8uk6XLl0YMWIEGRkZrFq1ioSEBCZMmMD48eNP2GfChAns27ePfv368cknnxAQEMA111zDyy+/jMlkAtwjCSdMmMCSJUsYMGAABw8eZOTIkYwZM4ann3663utarVasVqvn2Gw2k5iYeNa/H06Xyh2z1vHH/kLaRgXwv4fOI9DYvO6zbE4bj6x4hN8zf8ekM/Huxe/SL/bEr4ufTc7yCrKefJLyZcsAiLjnbqIeewxF2/A0O+J4O8sqmZtVyLe5xVQ43YkcP43CddFh3JEQQe8gf0mktAAupwtLmZ2KUisVpTYqS61Umm1YzDYqy2zu/TI7ljIb1kpHwxesh6KAMUCPX4Aeo78Oo7+79PPXYfDXYTC5E39Gf707AehfnQisTgxqdZLUEy2Pt/eOp3xXUVpaitPp9DyNrVFUVIROp2vyN6pNxeGCCj787SD/uLwTIabTW+BDURTu7XEv9/9yP9/s+4Z7ut9DhCmi3rZ+nTsTeOGFlK9cSeGHHxH/6r8avL5Oo/BwUgx/Tz3K20fyuD0+ggAvbpra94tmw4+HKM2zsHNVJn1GJJ3yZxNCCCHEucFut9c7Z2Dfvn1xOLz/BfHgwYO89957TJo0iaeffpoNGzbw8MMPYzAYGDt27An7/P777/j5+bFgwQIKCgqYMGEChYWFnhGJf/vb3ygoKOC8885DVVUcDgf333//CROEAFOmTOHFF1/0OvYzZeov+/hjfyEmvZb3bu/b7BKEDpeDJ399kt8zf8dP68f0i6f7LEFoy8gg44EJWNPSUAwG4l55mZBrrvFJLM2ZU1VZXFDK+0fzWVda4alv729kbEIko2LCCJVRg+cMm8VBebGV8uIqykuslBdbqSiuoqLU5kkKWspsp/RKr0aj4BekxxRowC9QjylQj1/15tkPOLYZA/QYjFoUeU1diLPilEcSXnHFFVx99dVMmDChTv2MGTNYuHAhP/300xkN0BfO9tN5VVW58r+/syfbzKRLO/Dwxe3/0rVG/ziaXYW7uLvb3Tza99ETtrVs3crhW0eDTkfbn3/G0Kr+xU5qs1ePJkyvsvF823gmtPZuNOHeNdksm7MHU5CeMa8MQW+UJ7JCCCHEmdKcRhJOnDgRvV5/3Px+jz/+OBaLhenTp3t1HYPBQL9+/Vi9erWn7uGHH2bDhg2sWbOm3j6XXXYZv/32Gzk5OYSEhAAwf/58Ro0aRUVFBSaTiZUrV3LrrbfyyiuvMHDgQPbv388jjzzC+PHjee655+q9ri9GEq7Ym8e4jzcAMO3WXlzbq+H7uKZEVVX+ueafzE+bj16jZ/rF0xkcP9gnsVh27uLofffhLCxEGxVJ4jvvYOrZ0yexNFflDidf5BTx4dF8jlTZANArCiOjQrgjPpLBoQEyUquZUV0qlWYb5sIqygot1WUV5UVVlFUnA21VTq+upSjgH2zAP8RIQKgR/yA9pmAD/sEGTEEG/IMM7uMgA0Z/nST8hGgEZ20k4bp16+qdxPnCCy/kmWeeOdXLtUiKovDAhW15+PMtzPz9EOOGJhPkd/qjCe/rcR8Pr3iYz/d+zrhu4wgxhtTb1tSrFwFDBlOxeg2FMz8i7oUXGry+XqMwKTmGR/ce5Z30XMbGRxDgxUrHHQbEsOHHQ5gLqtj1Wya9LpGJn4UQQoiWaubMmSxZsoRBgwYB7vvJ9PR07rjjjjqL4p1soZC4uDi6dOlSp65z5858++23J+2TkJDgSRDW9FFVlYyMDNq3b89zzz3HmDFjPIuodO/enYqKCu69916eeeYZNJrj52Q2Go0YjY23mnBxhY1Hv9wKwB2Dk5pdghDgrc1vMT9tPhpFw/8N+z+fJQjLf/2VjEcfQ62sxNipE4kz3kMfG+uTWJqjjCobMzPy+Sy7ELPD/UpxuF7LHfGRjEuIJMZ4er/TiMZhq3JgLrBQmmehNN+9eRKCRVW4HA2PHzKYdASGGas3PwLDjASEGPEPMXhKU5ABjST+hGiWTjlJaLVa6301xG63Y7FY6ukh6jOyexxTf9nHwfwKPll7hAkXtjvta12YeCEdwjqwr3gfn+35jAm9JpywbcT991Oxeg2l33xL5P0PoI9peGTgqJhwph3J5ZDFxqzMAiYmNbzSsUaroe8Vyaz4ZC+bl6TTbVgCOoOMJhRCCCFamp07d9KnTx8ADhw4AEBkZCSRkZHs3LnT066hUUdDhw4lNTW1Tt2+fftISjrxtCZDhw7l66+/pry8nMDqhSj27duHRqOhVatWAFRWVh6XCNRWT6/SVKbuDgsw8OzIzny7OYNnRnb2dTinbNbOWcze6X69+4XBL3BJ0iU+iaPk2/lkP/88OJ0EDBlCwn+nyQIlXtpqrmTG0Ty+zy/BWf2fRTt/I/e2imJUbLhXCxyKxuGwOSnJs1CcU0FpXmWdhGCl2XbSvooCgWF+BEX4ERzhLgPD/QgK8yMw3D0qUOabF+LcdsqvGw8fPpxu3brx9ttv16l/8MEH2b59O7/99tsZDdAXGusVnvmbM5j01TbCAwz8/uRw/A2n/wN38eHFPL7qcYIMQSy5cQmBhvpveFRV5cjtY7Bs2kT42LHETH7Kq+t/k1PEQ3vSCdNpWT+4C0FejCZ0Ol189txayoqqOO/m9vS8KPGUPpMQQggh6tecXjc+UzZs2MCQIUN48cUXufnmm1m/fj3jx4/ngw8+4LbbbgNg8uTJZGZmMnfuXADKy8vp3LkzgwYN4sUXX6SgoIB77rmHCy64gA8//BCAf/7zn7z55pt88MEHnteNH3jgAfr27cuXX37pVWyN9f1QVbXZvcI5P20+L6x2v70yqe8kxnUb1+gxqKpKwbvvUvD2OwCEXHsNcS+/jGIwNHoszc26knLeOpzLyuIyT915oYHclxjFxRHBaJrZ38dziaXMRnFOBcU5lZ6tJLcCc2HVSecE9AvQExxlIqR6C440eRKCAWFGtJLwFeKcdNZeN37llVe45JJL2LZtGxdffDEAy5YtY8OGDSxZsuT0I26BrukZz7RlaRwprOSztemMH9bmtK91SetLSAlJ4VDpIb5I/YJ7ut9TbztFUYi8/36Ojh9P8ZdfEnHfvej+tAhNfa6PCWPqkVz2V1r5KCOfx5Ibfi1Dq9XQ94okVn6WypbFR+h6fjw6vYwmFEIIIcSp69+/PwsWLGDy5Mm89NJLpKSkMHXqVE+CECA7O5v09HTPcWBgIEuXLmXixIn069ePiIgIbr75Zl555RVPm2effRZFUXj22WfJzMwkKiqKq6++mn/9q+FF3hpbc0sQ/nLkF15c417gZVy3cb5JEDoc5Lz4EiVffw1AxL33EvXYo83uz7IxqarK78XlvHkkhzUl7sVItApcHx3GA62j6Rpo8nGELYvd6qQoq4LCrHKKMt1lYVYFlpOMCjT66wiN8Scsxp+QaH9Coo8lBP0C5JVwIcSJnfJIQoCtW7fy73//m61bt2IymejRoweTJ0+mffvTX4CjKWnMp/NfbkjnyW93EBlo5Pcnh+P3F5Jo3x/4nqd/f5owYxg/3/gz/nr/etupqsrhm26maudOIu69l+hJj3l1/e9yi7l/9xFCdFrWD+pMiBcrlTkdLj59bg3lxVbOu6k9PS+W0YRCCCHEX9XcRhJu3LiRr776ivT0dGy2ur/Yzp8/30dRnTnN7fvRGNZmr2XCLxOwu+zc2P5GXhj8QqMn5lyVlWQ+NonyVatAoyH2uWcJGz26UWNoTlRVZXlRGW8dzmGjuRJwL0Zya1w4D7WOJsnUePNwtkSqqlJRYiU/vYz8o+UUHC2jMLP8xCMDFQgK9yMsNoCwWH/PFhoTgClIL4lwIUQd3t6rnFaS8FzXmDd6NoeL4f9ZSWaJhX9e3YU7h6ac9rUcLgdXL7iajPIMHu/3OGO7jj1h27Jly8h48CE0AQG0W74MbUj9i53U5lRVLtqQSmpFFX9PjuGJlDiv4tr1WyYrP0uVlY6FEEKIM6Q5JaW++OIL7rjjDkaMGMGSJUu47LLL2LdvH7m5uVx//fXMnj3b1yH+Zc3p+9EYdhbs5O7Fd1PpqOSS1pfwnwv+g1bTuPd/jsJCjt7/AFU7dqAYjSS88R+CLvHNXIhNnaqq/FxQyltHctle5p5j3k+jcFtcBBNaR5PgJ69ln2mqqmIusJCfXk5+ehkFR8vIP1qGpcxeb3tTsIGI+AAi4gMJT6gu4wPk9yohhNfO2uvGtVVVVR33NFhujE6NQafhgQvb8ux3O5mx6iCjB7bG6MV8f/XRaXTc0/0e/rnmn3y862Nu7XQrRm39T/wChw/H2KED1n37KPr0U6IefLDB62sVhceTYxm/6zAfHM3nnlZRhHkxmrDTkDg2L0nHnG9h+4qj9L08+VQ/mhBCCCGaqVdffZW33nqLBx98kKCgIKZNm0ZKSgr33XcfcXHePXAUzcfBkoM88MsDVDoqGRg3kNeHvd7oCUJbRibpd9+F/Ug62pAQWr33Hv59ejdqDM3Fr0Vl/OtgFtuqk4MmjYaxCRFMSIwmWlYqPmMs5TZyD5nd22EzeYfNWCuPXwxU0SiEx/kTmRhEVGIQEQkBRCQEYgqSRK0QonGc8kjCyspK/vGPf/DVV19RWFh43Hmn03nGgvOVxn4abHU4ueD/VpJjruKV67px+6ATr9LXELvTzpULriSnIoenBz7N6E4nfqXC/NNPZE76O5qQENot+8Wr1d1cqsolG1LZXVHFo0kxPNXGu5v71HU5/DJ7N0Z/HWNeGYzRX246hBBCiNPVnEauBQQEsGvXLpKTk4mIiGDlypV0796dPXv2cNFFF5Gdne3rEP+y5vT9OJvyK/O57afbyK7IpltENz4a8REB+oBGjcF66BDp4+7CkZODPiGBxA8/xNjm9N/UOVdtMVfy6sEsfisuByBAq+HuhEjuTYwm8i8spijcizcWpJeTc6jUkxQ051uOa6fRKUQmBBLZ2p0QrEkK6gwyOlAIceadtZGETzzxBCtWrOC9995jzJgxTJ8+nczMTN5//31ee+21vxR0S2XUabnvgja8+P1u3lt5gJv7JWLQnd6qUnqtnru63cWr615l1s5ZjGo/Cr22/oRc0IgRGNpMx3bwIEVz5ng1mlCjKDyREsu4nYf5MCOf8a2iiPDiRqJ9/xg2Lz5CUVYFW5amM+jatqf82YQQQgjR/ISFhVFW5l4ZNSEhgZ07d9K9e3dKSkqorKz0cXTiTKm0V/LgsgfJrsgmOTiZdy95t9EThFWp+0i/+26cBQUY2rSh9exZ6GNiGjWGpi6toorXDmXzY34pAAZFYWxCBA8nxRBlkIf4p8Nuc5J7sJSs/aVk7y8h52ApDpvruHahMf7EpAQTkxxMTEowEa0CZSVhIUSTc8pJwu+//565c+dy4YUXMm7cOM4//3zatWtHUlISn332WZ0V5oT3Rg9ozfQVB8gssbBgSwa39G992te6of0NfLD9A3Iqcvj+4Pfc0P6GetspWi1REx8i87FJFM3+mPDbbkMbGtrg9S+PDKF7oIkd5RbeO5rHs23jG+yj0SgMvLoNi97fwbblGfQYnoh/sAybF0IIIc51w4YNY+nSpXTv3p2bbrqJRx55hOXLl7N06VIuvvhiX4cnzgCny8mTvz7JnqI9hBnDePfidwnzC2vUGCw7dpB+z3hcpaUYO3em9cyP0IWHN2oMTVlWlY3/HM7hi+wiXIAC3BQbxuPJsbSWBUlOidXiIDuthKy0ErL2l5B/pAyXq+7LecYAHbEpIe6kYEow0UnBsqqwEKJZOOUkYVFREW3atAHc8w8WFRUBcN555/HAAw+c2ehaED+9lvuGteFfP+1h+ooD3NinFbrTfLJk1Bq5s+ud/Gfjf/hw+4dc0/YadJr6v9VBI0Zg7NgRa2oqhbM/JvqxRxu8vlI9mvCOHYeYmVHAfYlRXj15TOkVSXRSEHlHyti8+Ajn3XRurIYthBBCiBN75513qKr6f/buOzzKYnvg+Hc3u+k9pPcQCB1CFxBFQAEpigUEBZFrAemogBQFBASliogKgl1E6SCKFKVJC50kEJKQ3nvf9vuDKz+5ErIJJJvA+TzPPpe8mXfmrMslk/POzCkBYPr06ajVao4cOcJTTz3FjBkzTByduFMGg4GFJxZyIOEAFmYWrHhkBb72vjUaQ9HJk8S/+hr6wkKsWrbE97NPjSrKdz/I1+pYdi2VNQnplP43kfVYPXumBnrS2NbKxNHVDY7mWYIAAM+4SURBVDqNnpSYXBIisokPzyItNo//PbDLxtECrwaOeAU74NnAEWcPGxRKqS4shKh7Kp0kDAoKIiYmBj8/Pxo1asSPP/5I+/bt2b59O45GrEIT5Rva0Y9P/rhKXFYR284mMbC1T5X7eqbhM6w9v5aEggR+ifmFfvX73bKdQqnEddxYEl4fQ9bXX+M8fJhRT117utjTys6aM/lFfByXxrvB3hXeo1Ao6DAgiO0rznLhj0Ra9fDF1smy0u9NCCGEEHWH8z/mFUqlkqlTp5owGnG3fRP+Dd9HfA/A/C7zaeXWqkbHLzh0mIQxYzCUlGDdvj0+q1ZhZluz25xrI73BwIaULOZHJ5Nedr1ARkcHG6bX96Kdg/z3uR2D3kBmUgHx4dkkRGSRdCXnX9uHHVyt8Gro+N/EoCN2LpYoFJIUFELUfZVOEo4YMYKzZ8/y0EMPMXXqVPr168fKlSvRaDQsWbKkOmK8b1ibq/jPg4Es2h3Jyn1RDGjljVkVn0BZq60Z1nQYy8OW8+m5T+kd2Lvc1YS2jzyCZbNmlFy4QObna3Cf8laF/SsUCt4K9GDIuWjWJ2YwytcNdyMqoPk2dsargSNJV3I4sSuWbkMbVfq9CSGEEKLu2LVrF2ZmZjz22GM3Xf/tt9/Q6XT07t3bRJGJO7U3bi8fnPgAgMltJvNowKM1On7+77+TOHESBo0Gm4e64rN8OUpLeQB9IreQGVcSblQsDrQyZ3awNz1d7CWRVY6yYi1xl7K4diGDaxezKM4ru+n7VnZqfBo549vYCZ9Gztg5y98zIcS9qdLVjf9XbGwsYWFhBAcH06JFi7sVl0mZskJdQamWzu/vI7dYw4rnQunfsuLz/spTqCmk98+9yS7NZk6nOTzZ4Mnyxz14kPiXX0FhYUH9335D7e5WYf8Gg4F+YVc4mVfEf3zq8V4D41Y+JkXlsPnDMJRKBc+92wFHN2uj35MQQggh6lY13RYtWvD+++/Tp0+fm67v3r2bKVOmcPbsWRNFdvfUpc/jbjmffp6Xfn2JEl0JzzZ8lhkdZ9RoAip3+w6Spk4FnQ67Rx/F+8MPUJjf3+ddJ5WU8V50MptSswGwNVMyKcCD//jUw1wpBTL+V05qEbHnM4g9n0lyVA563f//WqyyMMO7gSM+jZzwbeyMs5eNJFiFEHVatVU3/l8BAQEEBATcaTfiv2wtVLzUOZClv19m5b4r9G3uibKKqwlt1DaMbD6SD09+yOqzq+kb1LfcSsc2XbpgFRpK8enTZH72GR4zKz4j6PpqQk+ePXuVrxIzedXXDV/LiidnXsGO+DV1Ie5iJid2xNDzpaaVfm9CCCGEqBuuXLlCkyZN/nW9UaNGREVFmSAicacS8hMYs28MJboSunh3YVqHaTWaQMnZtJnk6dPBYMBhQH88581DobrjX2vqrGKdntXxaay4lkaxXo8CeM7TmWlBnlKx+B/0egPJUTnEnMkg9kIGuWnFN33f0d0a/+YuBDRzwTPYETOVJFaFEPefKv3Lt3fvXvr27Uv9+vWpX78+ffv25ffff7/bsd23XuwcgJ2FisupBfx6MeWO+no25FlcrVxJKkxi05VN5bZTKBS4jh8PQPaPP6JJTDSq/wedbOniaEuZwcAHMclGx9VxwPXiN5dPpJKZWGD0fUIIIYSoWxwcHIiOjv7X9aioKGxs5Gy0uia3NJfX975OVkkWjZwb8eFDH5Z7pE21jL91640EoePgQXguWHBfJwj3ZOTS9XgEC2NSKNbrae9gw+62DVnSyE8ShIBOpyf+UhYHvo1g/ZRDbFlymrP74slNK0ZppsCnkRNdnmnA0DkdGTq7I12eboBPI2dJEAoh7luV/tdv1apV9OrVCzs7O8aPH8/48eOxt7enT58+fPzxx1UK4uOPPyYgIABLS0s6dOjA8ePHb9t+48aNNGrUCEtLS5o3b86uXbtu+v67775Lo0aNsLGxwcnJiR49enDs2LEqxWYKDlZqXuwcAMDyvVfQ66u+I9xKZcXLLV4G4LNzn1GiLSm3rU3HDlh36AAaDRmrVxvVv0KhYHr961uiN6ZkE15QXMEd17n62VE/1BUMcHx7jFH3CCGEEKLuGTBgABMmTODq1as3rkVFRTF58mT69+9vwshEZWl0GiYdmER0bjRu1m6sfGQlNuqaS/Tm7thJ0rS3rycInxuMxzvvoLhPt9Gmlmp4+UIsL5yPIb6kDC8LNaub+LM1NJiWdvf3UT46jZ7Y8xns/SqcdW8dYtuKM1w8mERxvgYLaxWNOnrQ+9XmjFz8IAMmhNKyu68cfySEEP9V6TMJfXx8mDp1KmPGjLnp+scff8z8+fNJNHIF2t82bNjAsGHDWL16NR06dGDZsmVs3LiRyMhI3Nz+fS7ekSNH6Nq1KwsWLKBv37589913LFy4kLCwMJo1awbAd999h5ubG0FBQRQXF7N06VI2btxIVFQUrq6uFcZUG86VyS4so+ui/eSXalk5JJS+Lap+NmGZroy+m/uSXJjMG23fYHjT4eW2LQoL49qQoWBmRv1dOzH39zdqjFcuxrItLYceLvZ80yLIqHuykgr5Ye4xDAZ4empb3APujzN8hBBCiDtVG+YqxsrNzaVXr16cPHkSH5/r5xcnJCTw4IMPsmnTJhwdHU0b4F1Qlz6POzH36Fx+vPwjNmobvuz1JSHOITU2dt7uX0mcPBl0OhyfeRqP2bPvywSh3mDg66RM5kUnkafVY6aAV3xceSPQAxszM1OHZzJ6nZ6EiGwuH08l5mw6ZSW6G9+zslMT2MqV4FA3vEIcMTO7//7eCCGEsXOVSicJbW1tOXPmDMHBwTddv3LlCqGhoRQUVG7raIcOHWjXrh0rV64EQK/X4+vry9ixY5k6deq/2g8aNIjCwkJ27Nhx41rHjh1p1aoVq8tZ/fb3f4zff/+d7t27VxhTbZnoLfv9Mst+v0KQqw2/TeiK6g5+oG2+splZR2bhZOHE7qd2Y60u/2lZ3MuvUHjwIA4D+uO1cKFR/UcXldL1eDhaA2xqFUwnJ1uj7vt9/SUi/0rBt4kz/ce1MuoeIYQQ4n5XW+YqxjIYDOzZs4ezZ89iZWVFixYt6Nq1q6nDumvq2udRFT9G/sjcv+aiQMHK7ivp6lNzn1/+3r0kjJ8AWi0OTzyB5/x592WCMLygmLciEziRVwhASzsrFof40uw+XTloMBhIj8vn8rFULp9MvakisY2DOUGhbtRv7YpnsGOVz3gXQoh7hbFzlUr/dO3fvz+bN2/+1/WtW7fSt2/fSvVVVlbGqVOn6NGjx/8HpFTSo0cPjh49est7jh49elN7gMcee6zc9mVlZXz22Wc4ODjQsmXLW7YpLS0lLy/vpldtMLJLIE7WaqLTC9l8unIrNP9Xv/r98LPzI7s0m2/Dv71tW9dx44DrVeNK/7E16HaCrC0Y6ukCwHvRSRibe273eCBKpYL4S1kkXs426h4hhBBC1C0KhYJHH32UN998kzFjxtxTCcL7wanUUyw4tgCAca3H1WyCcP9+EiZMBK0W+3798Jz33n2XICzW6Zl/NYmeJyM5kVeIjZmS9xp4s6tNw/syQZiXUczJXbF8P/sYGxec5Oy+eIrzyrC0UdP8IW8GvtmG4Qs603VwQ7wbOkmCUAghKqHSp/w2adKEefPmceDAAR544AEA/vrrLw4fPszkyZNZsWLFjbbj/ptsKk9GRgY6nQ53d/ebrru7uxMREXHLe1JSUm7ZPiXl5gIfO3bsYPDgwRQVFeHp6cmePXuoV6/eLftcsGABs2fPvm2spmBnqea1h+qz4JcIlu+9woBW3phX8RBdlVLF6FajmXpwKusurmNQo0HYm986e2zVvBm23btTsHcv6StX4rN0qVFjTA7w4MeUbMLyitiVkcvjro4V3uPgakWTLl5c+DORo5uv8tRbbWq0Op4QQgghhChfckEykw5MQmvQ0iugFyObjayxsQsOHiJx3HjQaLDv0xuvBfNR3Gdbag9l5/NGZDyxxddXyfWqZ8+8Bj54W5qbOLKapSnVEXUqlfAjySRH5d64bqZWEtSyHg3be+Db1Fm2EgshxB2qdJJw7dq1ODk5cenSJS5dunTjuqOjI2vXrr3xtUKhqDBJWJ26devGmTNnyMjI4PPPP+fZZ5/l2LFjtzzncNq0aUyaNOnG13l5efj6+tZkuOUa9kAAaw7FkJBdzIYTcbzwQECV++oV0Is159cQlRPFVxe/YkzomHLbuo4bS8HeveT/spuSV1/FslGjCvt3s1Dzmq8rS6+lsiA6mcdcHFAZ8eSu7eMBRPyVTGpMHlfD0glu8+/PSAghhBBC1KxibTHj948nqySLxs6NmdN5To09zC08epSEMWMwaDTY9eyJ18KF91UV4yKdnnlXk1ibmAGAp4WaeQ286WPEQ/h7SXpcPhcPJXHleMr/nzOoAJ8QJ0I6eBDUyhVzq/vn74UQQlS3Sv+LGhNz9yrR1qtXDzMzM1JTU2+6npqaioeHxy3v8fDwMKq9jY0NwcHBBAcH07FjRxo0aMDatWuZNm3av/q0sLDAwsLiDt9N9bAyN2PsI8HM2nqRj/ZF8XQbX6zMq/YE1UxpxuutXmfigYl8felrhjYeipOl0y3bWoaEYNe7F/m/7Cb9o5X4frzSqDFG+7nxZVIGUUWl/JCSxfNeLhXeY+NgQWhPP07sjOXolqsEtqyHWRVXTAohhBBCiDtnMBh45/A7hGeF42zpzPJuy7FSWdXI2IXHjhM/ajSG0lJsu3XDe/GHKNTqGhm7NjiZW8i48Diii0sBeMHLhVn1vbBT3R+rKMuKtVw+kcqlQ0mkx+XfuG7vakWTzp6EdPDE1ql2/u4mhBB1nUkzMebm5rRp04a9e/feuKbX69m7d++Nrcz/64EHHripPcCePXvKbf/PfktLS+88aBMY3M4Pb0cr0vJL+epo7B311d2vO42dG1OkLWLdhXW3bes6ZgwolRTs3Uvx+fNG9W+nMmNSwPWE7QcxyRTp9Ebd16qnH9b25uSlF3Phjzs7f1EIIYQQQtyZLy58wS+xv6BSqFj80GI8bT1rZNzi8xdIGDUKQ0kJNg91xXv5MhTm98fW2lL99dWD/cOuEF1ciqeFmu9bBPFBiO99kSBMjc1j71fhrJtyiD++iyQ9Lh+lSkGDtm4MmNCK52d3pE2vAEkQCiFENarS2uyEhAS2bdtGXFwcZWVlN31vyZIllepr0qRJDB8+nLZt29K+fXuWLVtGYWEhI0aMAGDYsGF4e3uzYMH1w5LHjx/PQw89xOLFi3n88cf54YcfOHnyJJ999hkAhYWFzJs3j/79++Pp6UlGRgYff/wxiYmJPPPMM1V5uyZnrlIyvkcD3vrpHJ/8cZUhHfyws6za01SFQsHY0LGM3jua7yO+54UmL+Bq7XrLthb16+PQrx+5W7eS9uFi/NavM2qLyQteLnwWn05cSRlrEtIZ5+9e4T3mlira9wvkwLeRnNgVQ6MHPLCwvn+eGAshhBD3urS0NNLS0tDrb36A2KJFCxNFJMrzZ8KfLA9bDsC0DtNo69G2RsYtjY4m/pVX0BcVYf1AR3xWrEB5nyQIL+QXMTY8jvDCEgCednfivQbeOKrv7a20Op2e6LB0zu6LJzXm/4tHOnlY06SLFyEdPbCyvT/+DgghRG1Q6Z86e/fupX///gQFBREREUGzZs2IjY3FYDDQunXrSgcwaNAg0tPTmTVrFikpKbRq1Yrdu3ffKE4SFxeH8h8VzDp16sR3333HjBkzePvtt2nQoAFbtmyhWbNmAJiZmREREcGXX35JRkYGLi4utGvXjoMHD9K0adNKx1dbDAz1ZvUfV4lOL2TtoRgm9GhY5b66eHehlWsrzqSf4fPzn/N2h7fLbVtv7Fjydu2i6NgxCg8exNaIaoQWSiVTAj14PTyOj66l8ryXC85GTHAad/Lk7L4EspMLObX7Gp0GBlfqfQkhhBCi9jl16hTDhw8nPDwcg8EAXH9oaTAYUCgU6HQ6E0co/ikmN4Ypf07BgIFnGj7DsyHP1si4mpQU4kb+B112NpbNmuHz0UqUtfQ4oLtJqzfwUVwqi2NT0BrARa1iUYiPUQUA67LigjIuHkziwh+JFOZc3+2lVCkIbuNGswe98ajvIMUMhRDCBBSGv2drRmrfvj29e/dm9uzZ2NnZcfbsWdzc3Bg6dCi9evVi1KhR1RVrjcnLy8PBwYHc3Fzs7W9dAdgUtp9NYuz3p7G1UHHwrW442VT9qdrx5OOM/G0kKqWKnU/uxMvWq9y2qQsXkbVuHRYNGhC4ZbNRVeX0BgM9T0ZysaCEV31dmR3sbVRcsecz2PnxOcxUSobM7oC9S82cfSOEEELUJbV1rnIrLVu2pH79+kyZMgV3d/d//eLv7+9vosjunrr0edxOXlkeQ3cOJTYvltZurVnz6BrUZtW/s0Obnc2151+g7OpVzAMD8f/2G1TOztU+rqnFFpcy6uI1TucXAdCnngMLQ3xwNb93d9NkJBRwbn88l4+notNcX1VsZW9Os67eNOvqjbW9rBoUQojqYOxcpdJnEoaHhzNs2DAAVCoVxcXF2NraMmfOHBYuXFj1iEWFHm/uSWNPewpKtaz+8+od9dXesz0dPDqg1Wv57Nxnt21b79VXUNrbU3rlCrlbthrVv1KhYEbQ9cTjuoQM4kvKKrjjOv9mLniHOKLT6jm2Ndqoe4QQQghRe0VHR7No0SI6dOhAQEAA/v7+N71E7aA36Jl+cDqxebF42Hiw+OHFNZIg1BcWEv/aa5RdvYrK3R2/tWvuiwThltRsepyI5HR+EfYqJSsb+7G2WcA9mSA0GAzEXcxky9IwNrx3nPDDyeg0etz87egxognD53Wifd9ASRAKIUQtUOkkoY2NzY1zCD09Pbl69f+TVRkZGXcvMvEvSqWCyT2vbzP+8kgsaXkld9TfmNAxAGyJ2kJ0bvkJOTNHR+q9+ioA6StWoC8xbtyHne3o4mhLmcHABzHJRt2jUCjo/FQDAC4fTyXtWl4FdwghhBCiNuvevTtnz541dRiiAusurONAwgHMleYs67aMelb1qn1MQ1kZCeMnUHL2HGYODvitXYPaq/zdLfeCIp2eyRFxvHbpGgU6PR0cbNjXrhFPezjfc9tr9XoDUafS2LjgJNs/OktiZA4K5fUtxQPfbMPTU9sS0sEDM7VJa2kKIYT4h0qfSdixY0cOHTpE48aN6dOnD5MnT+b8+fNs2rSJjh07VkeM4h+6N3Yj1M+R03E5fLw/itkDmlW5r1ZurXjY92EOxB9g+anlLH9kebltnZ4fSta336BNSibrq6+p98rLFfavUCiYXt+L3qcuszElm9d83WhiW/H2YVc/Oxp2cOfysVSO/BzFgImh99ykSQghhLhfrFmzhuHDh3PhwgWaNWuGWn3zSqn+/fubKDLxtxMpJ1hxegUAUztMpalL9Z/jbdDrSZr2NoWHDqGwssL309VYBN/b51GHFxTz6sVrXC4qQQFM8HdncoAHKuW9Nc/VafVEHkvh9G9x5KRe30qtMlfS9EFvWnb3xc7Z0sQRCiGEKE+lk4RLliyhoKAAgNmzZ1NQUMCGDRto0KBBpSsbi8pTKBS8+WgIQ9Yc47vjcbzcNQgfJ+sq9zex9UQOJhxkX/w+TqWeoo17m1u2U1pY4DZhAklvTSHzs89wfOZpVE5OFfYfam/NADdHtqblMOtKIhtb1Tcq4dehfxBXT6WTeDmHa+czCWhR/U+zhRBCCHH3HT16lMOHD/PLL7/863tSuMT0MoozeOvPt9Ab9PQL6sfTDZ6u9jENBgOp8+aTt3MnqFT4rFiOVatW1T6uqRgMBr5JzmTmlURK9AbczFWsauJPFyc7U4d2V2lKdVw6lMSZ3+MoyL5ejMTCWkWLbj606OaLpe29t5VaCCHuNZVe2x0UFESLFi2A61uPV69ezblz5/j555/lXJka0im4Hp3qu6DRGVix98od9RXkGMTABgMBWHJyCberY2Pfty8WjRujLygg45NPjB5jepAnFkoFh3IK+DXDuO3D9i5WtOzuA8CRTVHodXqjxxNCCCFE7TF27Fief/55kpOT0ev1N70kQWhaWr2Wt/58i4ziDIIdg5nRcUaN7N7IWLWK7G+/BYUCr/ffx/bBB6t9TFPJ1Wh55eI13oxMoERvoJuzHXvbhdxTCcKyEi2ndsfy1dtHOLTxCgXZpVg7mNPpqWCGze9E+35BkiAUQog6otJJwhMnTnDs2LF/XT927BgnT568K0GJir3xWAgAP4clcjW94I76Gt1qNFYqK85lnOPXa7+W206hVOL+5hsAZH//A2VxcUb172dlwWu+bgC8ezWRUr1xCb/WvQKwtFGTnVJE+BHjzjQUQgghRO2SmZnJxIkTcXd3N3Uo4n98fOZjTqScwFplzZKHl2CtrvruFGNlb9xIxkcrAXCfPh2Hvo9X+5imciaviB4nL7M9PQeVAmbV9+LbFkH3THESrUbHmd/j+GbmUf7aEk1JoQZ7VyseHhrCsPc6EdrTD3PLSm9cE0IIYUKVThK+/vrrxMfH/+t6YmIir7/++l0JSlSstZ8TPRq7odMbWPhLxB31Vc+qHiOajQBg+anllOnKr0Rs06kTNl26gEZD+rJlRo8xzs8NN3MVscVlrE0wrsCNhZWKto8HAHBsewxlJVqjxxNCCCFE7TBw4ED2799v6jDE//gj/g/WnF8DwOxOswl0CKz2MQsOHybl3dkAuIx6Defnh1b7mKbyfXImA05fIb6kDD9Lc7a1bsBoPzeU98A52zqtngt/JvLNzL84/FMUxfnXk4M9RjRh6LsdaPqgtxQjEUKIOqrSj3YuXbpE69at/3U9NDSUS5cu3ZWghHGm9m7E/sh0fruUyrHoTDoEuVS5r+FNhvNj5I8kFCTwY+SPPN/k+XLbur0xmZjDh8nb9QvOI0Zg1bx5hf3bqMx4O8iTCRHxLI1N4RkPJ6Oeojbr6s35/Qnkphdzek8cHfoFVep9CSGEEMK0GjZsyLRp0zh06BDNmzf/V+GScePGmSiy+1dCfgLTDk0DYEijIfQK7FXtY5Zcvkzi+Amg02Hfvx+u9+jnXqbXM/NKIl8mZQLwqIs9HzX2w0Fd91fU6XV6Io+lcmJnDPmZJQDYOlvQ7vFAQjp6YGYmiUEhhKjrFIbbHUJ3Cy4uLuzYsYMHHnjgputHjhzh8ccfJzs7+64GaAp5eXk4ODiQm5uLvb29qcO5rembz/PtsTha+jiweXRnlHdQHe2nyz8x++hsHCwc2DVwF/bm5b/3pKnTyN2yBet27fD76kujzq/RGwz0OnWZc/nFvODlwgchvkbFdTUsjd2fXUBlrmTo7I7YOklFNCGEEPe3ujRXCQwsf4WaQqEgOjq6BqOpHnXp8yjTlTHsl2FczLxIi3otWN9rPWqz6t3+qk1PJ2bQILRJyVi3bYvvF2tRmptX65imkFKq4eULsZzIK0QBvBHgwcQA9zq/etBgMBB1Ko3j22NuVCu2tjenbZ8AmnT2klWDQghRBxg7V6n0v+iPPvoo06ZNIzc398a1nJwc3n77bXr27Fm1aEWVTejREBtzM84m5LL9XNId9fVE8BPUd6hPbmnuje0n5XEdPw6FuTlFJ05Q8McfRvWvVCiYG+wNwLdJmVwsKDbqvqBQVzyDHdCW6Tmy6apR9wghhBCidoiJiSn3dS8kCOuaRScWcTHzIg4WDnz40IfVniDUFxURP2o02qRkzAMC8Fn50T2ZIDyeU8CjJyM5kVeIvUrJV80DmRzoUecThCnRufy86BS/rblITmoRljZqOg0M5vn3HqD5wz6SIBRCiHtMpf9V//DDD4mPj8ff359u3brRrVs3AgMDSUlJYfHixdURo7gNVzsLRj1cH4BFuyMp0VS9SqBKqWJS20kAfHvpW5IKyk86qj09cR72AgBpH36IQWvceYEdHG3p7+aIHph1JfG21ZT/plAoePDZhqCAKydSSbqSY9RYQgghhBDi/+2M3smGyA0oUPD+g+/jaetZreMZdDoS33yLkgsXMHN0xPfT1Zg5OlbrmDXNYDCwLjGDgWeiSCvT0sjGkl/bhNCznoOpQ7sjeRnF/LrmAj8vOkVqTB4qCzPa9wvkhXkPEPqoH2pzM1OHKIQQohpU+nAMb29vzp07x7fffsvZs2exsrJixIgRPPfcc/86Y0bUjJFdgvjmrzgSc4r58kgsrz5Uv8p9Pej9IO082nEi5QQfnf6IBQ8uKLetyyuvkLPxJ8qirpK7ZQuOTz9t1Bgz63vxa0Yuh3MK2J2RS29XxwrvcfWzo2kXLy4eTOLPDZd59u12d7S1WgghhBA146WXXrrt97/44osaiuT+Fpsby+yj14uGvNLiFbp4d6n2MdM++JCCvXtRqNX4rPoYc3//ah+zJhXr9Ey9nMCGlCwA+rs5sjTEFxtV3U2glRZrCdsdy9m9Cei0elBAk06etO8fhI2DhanDE0IIUc2qdIKujY0Nr7zyyt2ORVSRlbkZbzwWwhsbz7JyfxTPtvXFyaZq2zgUCgWT205m8I7B7IjewbAmw2js0viWbc3s7XEZ9Rpp7y8kffkK7Hr1xszWpsIxfC3NGeXrxrJrqcy+msQjLvZYKCte1NphQBBRp9LITCjg0qEkmnX1rvT7E0IIIUTN+t/zqjUaDRcuXCAnJ4dHHnnERFHdXzQ6DVMOTqFYW0w7j3aMajmq2sfM+u47stavB8BzwQKsb1H4sC5LLdUw7Hw0Z/OLUQIz6nsxytfVqHO6ayO9Ts+lw8kc3x5Ncb4GAJ9GTnR+Oph6PnYmjk4IIURNkUMk7hFPhnrT2NOe/BItK/ZduaO+mro0pU9gHwAWn1p82y3BTkOGoPb3Q5ueTuannxo9xlg/N9zNVcQWl7EmIcOoe6xszWn/3+rGf229SkmhxujxhBBCCGEamzdvvum1Y8cOoqOjGTRoEB07djR1ePeFFadXcCnzEg4WDizosgAzZfWudCv44w9S35sHgOuE8Tj0fbxax6tpFwuK6XPqMmfzi3FWm7GhZX1G+7nV2QRhQkQWG+ad4I/vIinO1+Dobs3jr7eg//hWkiAUQoj7jCQJ7xFmSgXT+1xf8ff10WvEZBTeUX/jWo9DrVRzLPkYhxIPldtOaW6O+5SpAGStX0/ZtWtG9W+jMuPtIC8AlsamkF5mXMKvWVcvnL1sKC3UcmybHHYuhBBC1EVKpZJJkyaxdOlSU4dyzzuSeIT1F9cDMKfTHNxt3Kt1vJKICBInTgK9Hocnn8Tl1Verdbya9ntmHv3DrpBYqqGBtQW72jTkQee6mUgrzC3lt7UX2brsDFlJhVjaqHlwUEMGz2pPQPN6dTbpKYQQouokSXgP6dKgHg+HuKLVG1i0O+KO+vK29WZIoyEALDm1BJ2+/IIott0exqZLFwwaDakLFxk9xjMeTrS0s6JAp2dhdIpR9yjNlHQd1BCAi38mkpGQb/R4QgghhKg9rl69itbIwmeiajKLM3n70NsADAoZxCN+1bu9W5uZSfyo0eiLirDu2BHP2e/eU4mmNQnpDDsXTaFOTxdHW7a3bkCAVd07p0+vN3BufwLfvfMXV06kggKaP+TN0DkdadHNBzMz+RVRCCHuV1U6k1DUXtN6N+bPy+n8ciGFk7FZtA1wrnJfL7d4mc1Rm4nKiWJz1GaebnjrwiQKhQL3t6cR3X8ABfv2UXDwELYPVnwYtlKhYG6wN/1PR/FtciYvervQzM66wvu8Q5wIbuNG1Kk0Dm64whOTQu+pCagQQghxL5k0adJNXxsMBpKTk9m5cyfDhw83UVT3PoPBwMzDM8ksySTYMZg32r5RveOVlZEwfjza5GTM/f3xWb4MhXnVzsiubbR6A7OiEvki8foROUM8nVnY0Bd1HSyilxqTxx/fR5Ied/1Bu5u/HQ8NCcHN397EkQkhhKgN7ugxUUFBAXl5eTe9hGmFeNgxqJ0vAO/tDL/teYIVcbBw4LWWrwHw0emPyCsr//O1CArCeehQAFIXLMCgMW77cHtHWwa4OWIAZlxJNDreTk8Fo1IrSbqSQ9SpNKPuEUIIIUTNO3369E2vc+fOAbB48WKWLVtm2uDuYd+Gf8vBxIOYK81Z2HUhlirLah0vZcECik+eQmljg8+qjzFzcKjW8WpKvlbHsPPRNxKEM4I8WRxS9xKEJYUaDnwXyU+LTpIel4+5lYqHnmvIU1PaSoJQCCHEDZVeSRgTE8OYMWM4cOAAJSUlN64bDAYUCgU6XfnbUkXNmNijIVvPJHEmPoed55Pp28Kryn0NbjSYny7/RHRuNJ+c+YQp7aeU27be66PJ3b6dsuhosr79FpcXXzRqjJn1vfgtI4+/cgvZmJrNsx4Vr360c7akdS9/jm+P4cjPUQQ0r4faonoP4RZCCCFE5e3fv9/UIdx3IrIiWHJqCQBvtHuDhk4Nq3W87B9/JOf7H0ChwOuDD7CoX79ax6spCSVlvHAumvDCEqyUClY28edxV0dTh1UpBoOBy8dTOfzTlRtVi0M6eNDpqWCs7e+NlZ5CCCHunkqvJHz++efJzs7miy++YO/evezbt499+/axf/9+9u3bVx0xikpys7fkla7XqwAv2h1JqbbqiVu1Un0jMfh9xPdEZUeV29bM3h7XiRMAyFj5MdrMTKPG8LE0Z3LA9UO0Z0clkaMx7nyi0J5+2LlYUpBdyqndsUbdI4QQQghxLyvSFPHWn2+h0Wt42OdhBocMrt7xwsJImfseAK7jxmL3SLdqHa+mnM8vos+py4QXluBmrmJTaIM6lyAszCll16pz/L7uEsX5Gpw8rHliYig9RjSRBKEQQohbqvRKwrNnz3Lq1ClCQkKqIx5xl7zSNYjvjsURl1XEV0eu8fJ/k4ZV0cmrE939urM3bi/vn3ifz3t+Xu4ZgI4DB5Lz/Q+UXLpE+rJleM6da1y8vq78mJLN5aISFkQnszDEt8J7VOZmdHm6Ab98ep7Te+Jo3MkTB9eKzzQUQgghRPVq3bo1e/fuxcnJidDQ258dHBYWVoOR3fsWnVhETG4MrlauzOk8p1rPbdakpJAwbjxoNNg9+igur71WbWPVpEPZ+bx4PoYCnZ7GNpZ83SIIH8u6k1QzGAxE/pXCoY1XKC3SojRT0O7xAEIf9cdMJUVJhBBClK/SScJ27doRHx8vScJaztpcxeRHGzLl5/Os2HuFJ0K9cbWrevW1N9q+wcGEgxxLPsbvcb/T07/nLdspzMxwnzGda0OGkvPTzzgOGoxVs6YV9m+uVPJ+Qx8Gnoniq6RMBnu6EGpfccIvsFU9fBo5kRCRzeGfougzqkWl35sQQggh7q4BAwZgYXF93vHEE0+YNpj7yG+xv/HzlZ9RoGD+g/NxsnSqtrH0paUkjB2HLiMDi4YN8Vow/54oJLczPYdRF69RZjDQydGW9c0DsVfVnSNtCrJL2P9NJHEXr+/ocfO345FhjXHxtjVxZEIIIeoChaGSlS2uXr3Ka6+9xvPPP0+zZs1Qq9U3fb9Fi7qfpMnLy8PBwYHc3Fzs7evuQb46vYEnVx3mXEIuT7fx4cNnWt5RfytPr+TTc5/iaePJ1ie2YqWyKrdt4htvkrdjB1atW+P/7TdGTxrHXLrGT6nZtLCz4pc2DTEz4r6s5EI2zD2OXm+g79iW+Dd1Mfo9CSGEEHXRvTJXuVfUhs8jpTCFgdsGkl+Wz0vNXmJim4nVNpbBYCB56lRyt27DzMGBgJ9/wtzHp9rGqylfJ2UwJTIBPdCnngOrmvhjaVY3Vt4ZDAbCjyRzeOMVykp0KFUKOvQLolUPX5R15D0IIYSoPsbOVSr9EyM9PZ2rV68yYsQI2rVrR6tWrQgNDb3xv6L2MFMqmN3/+iq+n04lcOpa9h31N7L5SDxsPEguTGb9hfW3bev2xmQUVlYUh4WRt2On0WO8E+yFvUrJufxivkoy7kxDZ08bmne7PjH98/tItGVSPEcIIYSobcrKykhISCAuLu6ml7hzeoOeGYdnkF+WTzOXZowJHVOt42V9+SW5W7eBmRney5bW+QShwWBgaWwKb/43Qfi8pwufNwuoMwnC/KwStn90lv1fR1BWosM90J5Bb7en9WP+kiAUQghRKZX+qfHSSy8RGhrK0aNHiY6OJiYm5qb/FbVLqJ8Tz7S5PnF7d9tFdPpKLRy9iZXKijfavgHA2gtrSSxILLet2sODeq++AkDahx+iLyoyagxXczXTgq5XY14QnUR6mcao+9r3DcTG0YK8jBJO7oo16h4hhBBCVL/Lly/z4IMPYmVlhb+/P4GBgQQGBhIQEEBgYKCpw7snfB/xPceSj2FpZsn7Xd9HrVRXfFMVFR45QtqiDwBwf+tNbB54oNrGqgl6g4HpVxJZGJMCwER/dz4I8TFqN4upXT97MJnv5xwj/lIWZiolnQYGM/DNNjh72Zg6PCGEEHVQpZOE165dY+HChXTo0IGAgAD8/f1veona561ejbCzVHE+MZcNJ+LvqK9H/R+lnUc7SnWlLD65+LZtnUeMQO3jgzY1lYzPPjN6jGFeLrSwsyJPq2d2VJJR95hbqeg6uCEAp3+LIzOpwOjxhBBCCFF9RowYgVKpZMeOHZw6dYqwsDDCwsI4ffq0FC25C2JzY1l2ahkAE9tMxN+++ubjZQmJJE6cBHo9Dk88gdOwYdU2Vk0o0+sZfekaXyRmAPBeA2+mBHnWibMVS4u17PniEr+vD0dTosMjyJ5BM9oR+qgfSmXtj18IIUTtVOkk4SOPPMLZs2erIxZRTVztLJjY43oC7YNfI8gpKqtyXwqFgqntp2KmMGPPtT38lfxXuW2VFha4TXkLgKwv1lFm5JYiM4WChQ19UQA/pWZzJNu4hF9QK1cCW9ZDrzdw4JtIDHewalIIIYQQd8eZM2f49NNP6d27N61ataJly5Y3vUTVafVaph+aTomuhI6eHRncaHC1jaUvKyNxwgR0ublYNmuGx+x360QyrTyFWh3DzsWwJS0HtULBJ038+Y+Pq6nDMkpyVA4b3jvOlROpKJQKOvQP5Mk32uDkIasHhRBC3JlKJwn79evHxIkTeffdd/n555/Ztm3bTS9ROw17wJ8QdzuyizQs/u3yHfXV0Kkhg0IGAfD+sffR6MvfEmzXowc2nR7AUFZGypy5GFsnJ9TemmFe1wuQTL2cQJleb9R9Dw5qiNrCjJToXC4dNm4VohBCCCGqT5MmTcjIyDB1GPekdRfWcS7jHHZqO+Z2notSUX3nz6W9v5CSCxdQOjjgs3wZyv9Wr66LcjVanj17lQPZ+VibKfm6RSBPuldfJei7Ra/Tc3x7NJsXh5GfWYJ9PUsGvtGatn0CZfWgEEKIu6LS1Y2VyvInHwqFAp2u7heNqA0V6qrD0auZPPf5XygVsH1sF5p6OVS5r9zSXPpu7ktOaQ5T2k3h+SbPl9u2NCaGmP4DMGg0eC9ZjH2fPkaNkaPR0vlYBJkaLTOCPBnj727UfWf3xnNo4xUsrFU8904HbBzq7iRWCCGEuJXaPlfJy8u78eeTJ08yY8YM5s+fT/PmzVGrbz4vrzbGX1mm+DwisiJ4budzaPVa5nWZR//6/attrNydO0mafP1cap/Vn2D38MPVNlZ1y9ZoGXz2Kmfzi3FSmfFtiyBaO9T+FXh5GcXs+eISKdG5AIR08KDr4IaYW6lMHJkQQoi6oNqqG+v1+nJf90KC8F72QH0X+rbwRG+Ad7ZeNHpV3604WDgwrvU4AFadWUVmcfmViC0CA3F57VUAUuYvQPePXxxux1Gt4p3g60VMFsemklBi3Dbp5t18cPWzo7RIy+GNV4y6RwghhBB3j6OjI05OTjg5OdGzZ0/++usvunfvjpub243rf7cRlVemK2PawWlo9Vq6+3WnX1C/ahurNDqa5JmzAHB59dU6nSDMLNPyzJnrCUJntRk/hQbXiQTh5eMpbHjvOCnRuZhbmtHzpSb0GNFEEoRCCCHuOvnJcp+Z/nhj9oancfJaNlvOJPJkqE+V+xoYPJCNkRsJzwpnedhy5nSeU25bl5dfJm/HTspiYkhbuhTPd94xaoxn3J34LimTv3ILmXklkXXNK66CqFQqeHhoCD+9f5IrJ9MIeSAT/6YuRr8vIYQQQtyZ/fv3mzqEe9rHZz4mKicKZ0tnZj0wq9rOBtQXFZE4fjyGoiKs27fHdeyYahmnJqSXaXjmzFUiCktwNVexsVV9GtlYmTqs29KU6fjz+0gijl6vvOwR5EDPl5pgX692xy2EEKLuqvR24zlzyk8EAcyaNeuOAqoNavsWnjv18f4oPvg1Elc7C/ZNfgg7S3XFN5XjTNoZXvjlBQDW91pPG/c25bYt/OsYcS++CAoFAd9/h1WrVkaNEV5QTM+TkWgNsK5ZAL1dHY2679CPVzi7Lx77epYMntUBtbmZUfcJIYQQtV1dmqvExcXh6+v7r0SWwWAgPj4ePz8/E0V299Tk53E67TQv7n4RvUHPsm7L6O7XvVrGMRgMJE+dRu7WrZi51iNo0yZUrnWjsMf/Si3V8PSZKK4UleJuruKnVsE0sLE0dVi3lZNWxO5PL5CZWIBCAW37BNC2TwBKs+o7d1IIIcS9q9q2G2/evPmm148//sjChQtZvHgxW7ZsuZOYRQ35z4OBBLhYk55fykf7ou6or1ZurRjYYCAAc47OQaMrv4iJTccOOAwYAAYDye+8i0FTftt/amxrxWhfNwCmXE4gR6M16r72/QOxdbIgL6OEkztjjLpHCCGEEHdXYGAg6enp/7qelZVFYGDFOwTE/yvSFDH90HT0Bj396/evtgQhQM5PP5G7dSsolXh/uLjOJgiTSsp48vT1BKGXhZrNoQ1qfYIw+kw6G+efIDOxACs7Nf0nhNK+X5AkCIUQQlS7Sv+kOX369E2vCxcukJycTPfu3Zk4cWJ1xCjuMguVGe/0awrAF4diiEoruKP+JrWZhLOlM9G50Xxx4YvbtnWb8hZmDg6URkaS9dXXxo8R4EGwtQVpZVpmXzWuarG5pYqugxsCcGZPPJmJd/Y+hRBCCFF5BoPhltthCwoKsLSs3cma2mbJqSXE58fjYePB1PZTq22ckvBwUue+B4DrhAnYdGhfbWNVp/j/Jgiji0vxsVSzOTSYIOvaW9BOr9NzZFMUv6w+T1mJDo8gB559uz0+IXJ2pxBCiJpR6e3G5Tl//jz9+vUjNjb2bnRnUnVpC8+dGLn+BHsj0ugSXI+vR7a/o/NsdkbvZOrBqZgrzfm5/88EOASU2zbn559Jnj4DhZUV9XdsR+3tbdQYx3MKGHA6CgPwY8v6dHW2M+q+X1afJ/pMOu6B9jz1ZhsUyuo5t0cIIYSoKXVhrjJp0iQAli9fzssvv4y1tfWN7+l0Oo4dO4aZmRmHDx82VYh3TU18HocTD/Pa768B8Pmjn9PRs2O1jKPLzyfmqafRxMVh+/DD+Kz6GIWy7q1gu1ZcylNnokgo0eBvac5PocH4WpqbOqxyFeaWsmftRRIv5wDQsrsvDwysj5msHhRCCHEXVNt24/Lk5uaSm5t7t7oTNWBWvyaYq5Qcispg21njVueVp09gHzp5daJMX8bcv+betnKyw8CBWLVtg6G4mJS57xldZbm9oy0jvOsBMDkynkKtcdW0HxzUELWlGakxeVw8dGfvUwghhBDG+XvXicFg4Pz58zftRImIiKBly5asX7/e1GHWCXllecw6cv3c7yGNhlRbgtBgMJD89tto4uJQe3nh9f6COpsgHHj6eoIwyMqCzbU8QZgUlcOP80+QeDkHtYUZj73cjC7PNJAEoRBCiBpX6erGK1asuOlrg8FAcnIyX3/9Nb17975rgYnq5+9iw5huwSzZc5k52y/xUENXHK2rNoFSKBTM6DiDgVsHcjzlONuubmNA8IBy23q++y7RTw6k4MAB8vfswf7RR40aZ3qQJ79m5BJfUsb7McnMbVBxdWZbJws6Dgji4IYrHNkUhX8zF+ycZXuTEEIIUZ3+rnA8YsQIli9fXmtXPNYFNiobnm/8PNujtzOhzYRqGyf766/J3/M7qNV4L1+GmaNjtY1VXZJKynjmzFUSSzU0sLbgp1bBuFtUvUhfdTIYDJzbl8Dhn6Mw6A04edrQ+9VmOHnYmDo0IYQQ96lKbzf+3wOmlUolrq6uPPLII0ybNg07O+O2gNZmdWELz91SptXz+IqDXEkr4Nm2Pix6uuUd9bf2/FqWhS3D0cKRbU9sw8my/DNU0pYtI3P1p6jc3QnauQMzW1ujxjiQlcfgs9EogG2tG9DOoeKJlF5vYNMHp0iNycOviTN9x7a8o+3VQgghhCndT3OVuqCmPg+tXotKWeln/EYpuXSJmEGDQaPBfcYMnJ8fWi3jVKf0Mg1Pno4iqqiUACtztoQ2wKOWJgh1Gj0Hvo0g4q8UABq0c+fhoSGYW1bP5yuEEOL+Zuxc5a6dSXgvud8m3qeuZfHUJ0cB+O7lDnSqX6/KfWn0GgbvGMzl7Mv0r9+feV3mldtWX1JCdP8BaOLicHrhBTymv230OOPD49iQkkUDawv2tA3B0ojtGNkphWx47wQ6rZ5uLzSiSWcvo8cTQgghapPaPlcZOHAg69evx97enoEDB9627aZNm2ooqupT2z+PiuiLioh56mnKYmKw7d4dn5Uf1bmHqVkaLU+djiK8sARvCzVbWjeotVuMi/LK+GX1eVKic1EoFXR+OpgW3Xzq3H9zIYQQdUeNn0ko6q42/s4839EPgLc3nadEY9xZf7eiVqp554F3UKBg29Vt/JX8V7ltlZaWeMy6fr5O9rffUnzhotHjvBvshau5iitFpSy7lmrUPU4eNnToHwTA4Y1XyM8qMXo8IYQQQhjPwcHhRsLDwcHhti9heqkL3qcsJgaVmxue782tc8mqPK2O585eJbywBHdzFT+1qr1nEGYkFLDx/ROkROdibqWi35iWtHzEt879NxdCCHFvMmoloTwNvvfllWjoueQPUvNKGdMtmDceC7mj/ub9NY8fIn/Az86PTQM2YWFmUW7bxMlvkLdzJxaNGhG48UcUauO2hexIy+E/F2NRKWB3m4Y0s7Ou8B7ZdiyEEOJecD/OVQASExOZMmUKv/zyC0VFRQQHB7Nu3Tratm1b7j2lpaXMmTOHb775hpSUFDw9PZk1axYvvfTSjTY5OTlMnz6dTZs2kZWVhb+/P8uWLaNPnz5GxVWXP4+83b+SOGECKBT4rfsCm47VUxSluhTqdDx3NprjuYU4q83YHNqAEJvaefZ09Jl09qy7hLZUh4ObFY+PbiHnDwohhKgRd3UloTwNvvfZW6qZ3b8ZAKv/uEpESt4d9Teu9ThcrVyJy4/js3Of3bat+7SpmDk4UBoRQcant2/7T33dHHnc1QGtASZFxKPVV7xzXqlU0H14Y8xUSuIuZRF+JNno8YQQQghhOtnZ2XTu3Bm1Ws0vv/zCpUuXWLx4MU5O5Z9/DPDss8+yd+9e1q5dS2RkJN9//z0hIf//MLSsrIyePXsSGxvLTz/9RGRkJJ9//jne3t7V/ZZMTpOcTPJ/d3W4/Oc/dS5BWKzTM/xcDMdzC3FQmbGhZf1amSA0GAyc2h3LL5+eR1uqw6eRE09PaSsJQiGEELWOnEl4C3X5afCdeuWrk/x2KZVWvo78PKoTZsqqr7Lbc20Pkw5MQqVU8VO/n6jvWL/ctrk7dpL0xhugUhH400YsGzUyaoy0Ug1dj0eQo9UxPciTsf7uRt13+rc4jmyKwtzSjMGzOki1YyGEEHVKbZ+rhIaGGr1SPywszKh2U6dO5fDhwxw8eNDoOHbv3s3gwYOJjo7G2dn5lm1Wr17NBx98QEREBGojdzP8r9r+edyKQacjbviLFJ08iWXz5gR8963RuzlqgzK9nhHnY9mblYeNmZKNLevT2ohidjVNq9Gx/+sILh+/fjxO84e86fxsA8yMOE9bCCGEuFukcMkdqIsTvbslObeYnkv+pKBUy5wBTRn2QECV+zIYDIzbN44DCQcIdQtlfa/1KBW3nhAZDAYSx40jf8/vWDRuTOCPG4yeqG5IzmJ8RBwWSgV724UQbF1xwk+2HQshhKjLavtcZfbs2Ua3feedd4xq16RJEx577DESEhL4448/8Pb2ZvTo0bz88svl3jN69GguX75M27Zt+frrr7GxsaF///7MnTsXKysrAPr06YOzszPW1tZs3boVV1dXhgwZwpQpUzAzM7tlv6WlpZSWlt74Oi8vD19f31r7edxKxiefkL58BUprawK3bMbcz8/UIRlNqzfw6qVYdqbnYqVU8G2L+nRysjV1WP9SmFvKL6vPkxqTh0KpoOugBjR7yMfUYQkhhLgPGTt3VFW249TUVN544w327t1LWloa/5tj1OmqXvRCmJ6ngxVv9Qph1taLLNodSc8m7ng6WFWpL4VCwdsd3uZYyjFOp51mQ+QGnmv0XLltPWbNouj4CUrDw8n47DNcX3/dqHGe9XBiS1o2+7PyGXspju2tG6CqYAXk39uON7x34vq248PJNOki1Y6FEEKIu8HYxF9lREdH88knnzBp0iTefvttTpw4wbhx4zA3N2f48OHl3nPo0CEsLS3ZvHkzGRkZjB49mszMTNatW3ejzb59+xg6dCi7du0iKiqK0aNHo9Foyn0fCxYsqFQitLYpOn2a9JUfA+Dxzqw6lSA0GAxMjoxnZ3ou5goF65oH1soEYU5qEdtWnCE/swQLaxW9XmmGT6Nbr2YVQgghaotKryTs3bs3cXFxjBkzBk9Pz3+tvhowYMBdDdAUavvT+eqm1xt4evURwuJy6NHYnc+HtbmjVXbfhX/HguMLsFJZ8VO/n/CzL38iWtVtx4klZXQ7EUGeVs+bAR5MDvQw6r6/tx2rLc14TrYdCyGEqCPux7mKubk5bdu25ciRIzeujRs3jhMnTnD06NFb3vPoo49y8OBBUlJSbpydvWnTJp5++mkKCwuxsrKiYcOGlJSUEBMTc2Pl4JIlS/jggw9ITr712cV1eSWhLj+fmCeeRJOYiH2/fnh/sMjUIVXK/KtJrIhLw0wBa5sG0su19p2JnhKdy86Pz1FSqMHe1Yp+Y1ri6F5xgT0hhBCiutzVwiX/dOjQIb799ltGjRrFE088wYABA256ibpPqVSwYGALVEoFv4en8uvFlDvqb3CjwbT3aE+xtpiZh2eiN+jLbWv/eB/sevYArZakaW9j0GiMGsPb0pz3G/oCsORaCqfzioy6r2UPX9wD7dGU6DjwTcS/VsYKIYQQonbw9PSkSZMmN11r3LgxcXFxt73H29v7puJ6jRs3xmAwkJCQcKNNw4YNb9pa3LhxY1JSUigrK7tlvxYWFtjb29/0qgsMBgMp77yLJjERtY8PHu/MMnVIlbImIZ0VcWkAfNDQt1YmCGPOprN16WlKCjW4+dvx1JttJEEohBCizqh0ktDX11cSKfeBEA87XnvoeqGRWVsvkltsXLLuVpQKJXM6z8FaZU1YWhjfXPqm3LZ/bzs2c3C4se3YWE+6OTLAzRGdAcZcukaRrvxk5I3Y/rfa8WGpdiyEEELURp07dyYyMvKma5cvX8bf3/+29yQlJVFQUHDTPUqlEh8fnxttoqKi0Ov1N7Xx9PTE3Nz8Lr8L08rdspW8XbvAzAzvDz/AzLb2bdMtz9a0bGZeSQRgaqAHQ7xcTBzRv134M5FfVp9Hq9Hj38yFARNDsba/t/4OCSGEuLdVOkm4bNkypk6dSmxsbDWEI2qTMY8EE1jPhrT8UubtvHRHfXnbevNGuzcAWHF6BTG5MeW2Vbm64j5zJgAZn6ymJCLCqDEUCgXvN/TBw1zN1eJS5l5NMuo+Jw8bOvQPAuDQT1fIzyox6j4hhBBC1JyJEyfy119/MX/+fKKiovjuu+/47LPPeP0fZxhPmzaNYcOG3fh6yJAhuLi4MGLECC5dusSff/7Jm2++yUsvvXSjcMmoUaPIyspi/PjxXL58mZ07dzJ//vyb+r0XlCUkkDp3LgCuY8dg1aqVaQOqhEPZ18+dNgAjvOsx3t/d1CHdxGAwcGxbNH98F4nBAI07edJnVHPMLSt9/LsQQghhUkYlCZ2cnHB2dsbZ2ZnBgwdz4MAB6tevj52d3Y3rf7/EvcNSbcbCp1qgUMCPJxPYF5F6R/093eBpOnl1olRXyoxDM9DqteW2tX+8D7Y9uld627GTWsXyxtfPPFyXmMG+zDyj7vvntuPf111Cr5fVskIIIURt0q5dOzZv3sz3339Ps2bNmDt3LsuWLWPo0KE32iQnJ9+0/djW1pY9e/aQk5ND27ZtGTp0KP369WPFihU32vj6+vLrr79y4sQJWrRowbhx4xg/fjxTp06t0fdXnQx6PclTp6EvKsKqbRtcblMRura5kF/Ei+djKDMYeNzVgfcaeN/RWdl3m06nZ9/XEZzcFQtA28cD6PZCI5RmlV6LIYQQQpicUYVLvvzyS6M7LK+6XF1yPx4Gfjvv7bjEmkMxuNpZ8NuErjjZVH3bREphCk9ufZICTQETWk9gZPOR5bbVpqcT3bcfutxc6o0dY3S1Y4DplxNYm5iBu7mK/e0b4ayu+EluTloRP847gaZUR4cBQbTtHWD0eEIIIURNqu1zlUmTJhnddsmSJdUYSc2o7Z9H5rr1pC1ciMLamqCtWzD39TV1SEa5VlxKv7ArpJVpecDRhu9b1MeyFiXfykq0/Pr5ReIuZqJQwENDQmj6oLepwxJCCCH+xdi5SqWrG98PavtEr6aVaHT0/egQUWkF9G3hycohre+ovy1RW5h5eCZqpZoNfTfQwKlBuW2rWu24SKfnsZORXCkq5XFXB9Y0DTDqqXP4kWT2fRWOUqlg4JttcA+Uz18IIUTtU9vnKt26dbvp67CwMLRaLSEhIcD1M//MzMxo06YN+/btM0WId1Vt/jxKo6KIGfgUhrIyPGbPxmnQs6YOySgZZVr6h10huriUxjaWbAkNxsGIh741paRQw/YVZ0i7lo9KreTRl5sR2KKeqcMSQgghbqnaqhubmZmRlpb2r+uZmZk3VYUT9w5LtRlLnm2JmVLBjnPJbD9r3Fl/5RlQfwAP+TyERq9h+qHpaPTlbyX+17bjcqoM/i9rMyUrm/ijUsDO9Fx+Ss026r5GD3hQv7Uber2BPV9cpKyk/C3RQgghhLi1/fv333j169ePhx56iISEBMLCwggLCyM+Pp5u3brx+OOPmzrUe5pBoyFpylQMZWXYdH0Qx2efMXVIRinU6nj+XDTRxaV4W6j5vmX9WpUgLMorY8uSMNKu5WNpo2bAxFBJEAohhLgnVDpJWN7Cw9LS0nuuApz4fy18HHm9WzAAM7deIC2v6sU9FAoF7zzwDvbm9oRnhbP2/NrbtvV8550b1Y7TP/rI6HFa2lnzRoAHAG9fTiC+pOIEo0Kh4OGhIdg6WZCbXsyhH68YPZ4QQggh/m3x4sUsWLAAJyenG9ecnJx47733WLx4sQkju/dlfPoZJRcvonRwwHPue7XqLL/yaPUGXrl4jTP5RTirzfihZX08LNSmDuuG/KwSNi8OIzOxEGsHc56YHIpHkIOpwxJCCCHuCqMfyf19wLNCoWDNmjXY2tre+J5Op+PPP/+kkZFbQUXdNKZbMHvDU7mYlMe0TedZM7xtlSebrtauvN3hbaYenMqnZz/lYd+HaeR8678/KldXPN6bS+LYcWSuWYtN5y7YdOxgXMx+7vyemcfJvCLGhV/j51bBKCuI2dJGTY8RTdiy9DThR5Lxa+pCcBu3Sr9HIYQQQlzf3pKenv6v6+np6eTn55sgovtD8YWLZKxeDYDHzJmo3evGXGZmVCJ7s/KwUir4unkQDWwsTR3SDbnpRWxdeob8rBJsnS0YMCEURzdrU4clhBBC3DVGryRcunQpS5cuxWAwsHr16htfL126lNWrV1NUVMTq/05ExL3JXKVkybOtMDdTsjcijY0nE+6ovz6Bfejh1wOtQXt927HuNtuOe/bE8ZlnwGAgacoUtNnGbR9WKRV81NgfazMlR3MKWR3/719SbsW7oROtH/MH4MC3EeRnVX3lpBBCCHE/e/LJJxkxYgSbNm0iISGBhIQEfv75Z0aOHMnAgQNNHd49SV9aStLUKaDVYterF/aP9zF1SEZZk5DOusQMAFY28aeNg42JI/p/WUmFbPowjPysEhzcrBj4RhtJEAohhLjnGJ0kjImJISYmhoceeoizZ8/e+DomJobIyEh+/fVXOnQwbnWXqLtCPOyY9GhDAObsuERCdlGV+1IoFMzoOAMnCycuZ19m1dlVt23vPm0q5oGBaFNTSZk1q9yt7/8r0NqCOcHXK80tiE7mTJ5xMbfvF4ibvx2lRVr2rr+EXi81foQQQojKWr16Nb1792bIkCH4+/vj7+/PkCFD6NWrF6tW3f5nv6ia9GXLKYu6ilm9eni8M6tObDP+PTOPWVcSAZge5Mnjro6mDegf0uPy2bwkjKLcMpy9bHhycmvsnGvPCkchhBDibqn0mYT79++/6UwZcf95+cEg2vg7UVCq5c2N5+4oeeZi5cLMB2YCsPb8Wk6knCi3rdLaGq8PPwC1mvw9v5OzcaPR4wz1dOZxVwc0BgOvXowlT6ur8B4zMyU9X2qKysKMxMs5nNkTZ/R4QgghhLjO2tqaVatWkZmZyenTpzl9+jRZWVmsWrUKG5vas1LsXlF04gRZ69cD4Dl3Dqo6MG8PLyjmtYux6IHnPJ0Z41d7tkanROeyZelpSgo0uPnb8eSk1tg4WJg6LCGEEKJaKAxGLMeaNGkSc+fOxcbGhkmTJt227ZIlS+5acKZibGno+1lsRiG9lx+kWKPj3X5NeLFz4B31986Rd9h0ZRNuVm781P8nnCzLn9Bmrl1L2gcforCyIvDnn7EIMm7sXI2WHicvE19SRl9XBz5vGmDUk/VLh5PY/3UESqWCp6a0wc1f/k4IIYQwrbo4V4mKiuLq1at07doVKysrDAZDnVjhZoza8nnoCgqJeeIJNAkJODw1EK9580wWi7HSSjX0PnWZxFINnRxt+aFlEObKSq9jqBYJEVns/OQ82lIdnsEO9H29JeZWtafKshBCCGEsY+cqRv0EPn36NBqN5safy3udOXPmrgQvar+AejZM63O90Mj7uyOITi+4o/6mtJtCgH0AacVpzDpy+63EziNGYP1ARwzFxSS98QaGsoqrFgM4qFV82sQflQJ2pOeyPinTqPsad/Kkfqgrer2BPV9cQlNa8SpEIYQQQlyXmZlJ9+7dadiwIX369CE5ORmAkSNHMnnyZBNHd29J++ADNAkJqL28cJ82zdThVKhYp+fFCzEklmoIsrJgbbOAWpMgjLuUyY6V59CW6vBt7ES/sa0kQSiEEOKeZ9RP4f379+Po6Hjjz+W99u3bV52xilrm+Q7+dAmuR4lGz8Qfz6LR6avcl7XamkVdF6FWqjkQf4AfIn8ot61CqcTr/YWYOTpScukSacuXGz1OawcbZgR5AfDOlUTO51d8PqFCoeDh5xth42hBTmoRBzdcNno8IYQQ4n43ceJE1Go1cXFxWFv/f6GHQYMGsXv3bhNGdm8pOHiInA0bAPCcPx8zW1sTR3R7eoOB8RFxhOUV4agy45sWQTipa0cSLj48i12fnEen1RPQoh6Pj26J2sLM1GEJIYQQ1a52PKoTdZJSqWDh0y2ws1RxNj6Hxb/dWfKssUtjJrW5vp39wxMfcjm7/P7U7m54znsPgKy1X1B45IjR47zq68qjLvaUGQy8cjGWAiPOJ7S0UdNjRBNQQPiRZMKPJBs9nhBCCHE/++2331i4cCE+Pj43XW/QoAHXrl0zUVT3Fl1BIcnvzALA6YUXsOlY+4sJfhCTwra0HNQKBWubBRBkXTvO+UuIyGLXqnPoNNcThL1eaYaZWn5lEkIIcX8w6nHdwIEDje5w06ZNVQ5G1D3ejlYsfKoFo78NY/UfV+lU34WuDV2r3N/QxkM5mnyUPxP+5K0/3uL7vt9jpbK6ZVu77t1xHDSInA0bSJoylcBtW406nFuhULC8sR89TkQSU1zGm5HxrGriX+G5SD4hTnToF8ixbTH8+X0krn521POp3U/phRBCCFMrLCy8aQXh37KysrCwqB2JoboufckStEnJqH18cJs4wdThVGhjShZLr6UCsCjEh85OdiaO6LrEy9nsXHUOrUaPf3MXer3cDDOVJAiFEELcP4z6qefg4GD0S9x/+jT3ZGgHPwAm/XiGtPySKvelUCiY23ku9azqcTX3Kh+c+OC27d2nTsE8KAhtejrJM2be9izDf3JSq1jdNAAzBWxOy+Hb5Cyj7mvTKwC/ps5oNXp2f3aesmKtUfcJIYQQ96sHH3yQr7766sbXCoUCvV7PokWL6NatmwkjuzcUnTxJ9nffAderGStvkZCtTU7kFjI5Ih6AMX5uPOfpYuKIrkuKymHHx+fQlunxa+osKwiFEELcl4yqbny/qS0V6uqSEo2OASsPE5maz4MN6vHliPYolVWvWHg06Siv7nkVAwaWPLyEnv49yx/70iViBg0GjQb3WTNxHjLE6HFWXkvlvehkLJUKdrVpSBPbW69a/KfigjJ+nHeCguxS6rd25bGXm90z1RmFEELUDXVprnLhwgW6d+9O69at2bdvH/379+fixYtkZWVx+PBh6tevb+oQ75ipPg99SQkxA56g7No1HJ95Gs+5c2ts7KpIKdXw6MlI0sq09KnnwJpmAShrwRwq+Wou21ecQfPfIiV9RrdApZYzCIUQQtw77mp1YyEqYqk2Y+WQUCzVSg5eyeDTP6PvqL8HvB5gRLMRALxz5B2SC8o/A9CySRPcJl8/yzBtwfsUnz9v9Dij/dx4xNmOEv318wkLjTif0MrWnMdebobSTMHVsHTO7UswejwhhBDiftOsWTMuX75Mly5dGDBgAIWFhQwcOJDTp0/fEwlCU8r4+GPKrl1D5eaG25tvmjqc2yrV6xl5IYa0Mi2NbCz5qLFfrUgQpsTksv2j6wlC7xAn+oySBKEQQoj7V61IEn788ccEBARgaWlJhw4dOH78+G3bb9y4kUaNGmFpaUnz5s3ZtWvXje9pNBqmTJlC8+bNsbGxwcvLi2HDhpGUlFTdb+O+18Ddjtn9mwLw4W+RnLqWfUf9jQkdQ/N6zckvy2fqwalo9eVv7XUePhy7nj0waDQkjB+PNtu4sZUKBR819sfTQk1UUSlTLicYtWXZI8iBzk8HA3Dk5yhSonONe1NCCCHEfSYuLg57e3umT5/Ojz/+yK5du3jvvffw9PQkLi7O1OHVWcUXLpL5xToAPN59B7NavqJ0+uVETuUV4aAyY33zQGxUpk/EpcbmsX35GTQlOrwbOvL46y1QmZs+LiGEEMJUTJ4k3LBhA5MmTeKdd94hLCyMli1b8thjj5GWlnbL9keOHOG5555j5MiRnD59mieeeIInnniCCxcuAFBUVERYWBgzZ84kLCyMTZs2ERkZSf/+/Wvybd23nm3rS7+WXuj0BsZ9f5rcYk2V+1Ir1Sx8cCE2ahvC0sL4/Nzn5bZVKBR4zp+P2t8PbVIySW9NwaDXGzWOi7mKT5r4owR+Ss3mm+RMo+5r/rAPwW3c0OsN/Pr5BYrzy4y6TwghhLifBAYGkp6e/q/rmZmZBAYGmiCius+g0ZA8YwbodNj36YPdI4+YOqTb+ioxg2+SM1EAnzTxJ8DK9AVr0uPy2b7iDGUlOjyDHXj89ZaoJUEohBDiPmfyMwk7dOhAu3btWLlyJQB6vR5fX1/Gjh3L1KlT/9V+0KBBFBYWsmPHjhvXOnbsSKtWrVi9evUtxzhx4gTt27fn2rVr+Pn5/ev7paWllJaW3vg6Ly8PX1/fOnHOT22UV6Kh74pDxGUV8XhzT1YOCb2jM/t2RO9g2sFpKBVKVvdYzQNeD5TbtiQykthBgzGUlFBv7BhcX3/d6HE+upbKvOhk1AoFm0KDaedgU+E9ZSVaNi44SU5qEb5NnOk7puUdncUohBBCGKMunUmoVCpJTU3F1dX1puvXrl2jSZMmFBYWmiiyu6emP4+MTz4hffkKzBwdCdq1E5Wzc7WPWVUncgsZeDoKjcHA20GejPN3N3VIZKcUsunDMEoKNHjWd6Dv2JaYW6pMHZYQQghRberEmYRlZWWcOnWKHj163LimVCrp0aMHR48eveU9R48evak9wGOPPVZue4Dc3FwUCgWOjo63/P6CBQtuqtDs6+tb+TcjbrC3VPPRc6GolAp2nk/m++Pxd9Rf36C+PBH8BHqDnil/Trn9+YQhIXi8+w4AGSs/puDgIaPHGePnRl9XBzQGAyMvxJBSWvEqSHNLFb1eaYZKrST+UhYnd8UaPZ4QQghxL5s0aRKTJk1CoVAwc+bMG19PmjSJ8ePHM2jQIFq1amXqMOuc0qgoMlZ9AoD79Om1OkGYUqph5IUYNAYDfV0dGOvnZuqQyM8qYdvyM5QUaHDzt6PvGEkQCiGEEH8z6ifiihUrjO5w3LhxRrfNyMhAp9Ph7n7zE0V3d3ciIiJueU9KSsot26ekpNyyfUlJCVOmTOG5554rN1s6bdo0Jk2adOPrv1cSiqpr6evIlF6NmLcrnNnbL9LG34kQD7sq9ze9w3QisyIJzwpn0oFJrO+9HguzW29VcXziCYrDTpPz448kvfkmgZt+Ru3lVeEYCoWC5Y38iCq6QkRhCSMvxLApNBgL5e1z6S7etjw8NITf14dzYmcMHkH2+DVxqdL7FEIIIe4Vp0+fBsBgMHD+/HnMzc1vfM/c3JyWLVvyxhtvmCq8Osmg05E8fQYGjQbbhx7Cvu/jpg6pXP8sVBJiY8nyRn53tLPkbijOL2Pb8jMUZJfi5GF9fQWhlSQIhRBCiL8Z9VNx6dKlN32dnp5OUVHRjZV5OTk5WFtb4+bmVqkkYXXTaDQ8++yzGAwGPvnkk3LbWVhYYGFh+rNR7jUjuwRy+GoGByLTGfNdGFvHdMbavGoTMUuVJUu7LWXQjkFcyLzA+8ff550H3im3vfv0tym5cIGSS5dImDAR/2++RvmPX07KY/Pfw7QfO3mZU3lFTL+cyIeNKk4Yh3T0JOlqLpcOJrFn7SWemdYW+3pWlXqPQgghxL1k//79AIwYMYLly5fX+m3RdUH2t99SfPYsShsbPGa/a/Kk2+3MuPKPQiXNTF+opKxEy/aPzpKTWoStkwX9xrXCyrbiuaEQQghxPzFqu3FMTMyN17x582jVqhXh4eFkZWWRlZVFeHg4rVu3Zu7cuZUavF69epiZmZGamnrT9dTUVDw8PG55j4eHh1Ht/04QXrt2jT179sjE1ASUSgUfPtMSNzsLrqQVMG3TeaMqB5fH29abhQ8uRIGCny7/xOYrm8sf28IC7xXLUTo4UHLuHGnvLzR6nAArCz5p4o8C+CY5k68SM4y678FnG+DqZ0dJoYZdn5ynrKT8asxCCCHE/WLdunUyD7sLyuLjSVu6DAC3N99EXc5cuTb4OimDr5OuFypZ1cSfQGvTPozXanTs+uQ86XH5WNqq6T++FXbOliaNSQghhKiNKn0m4cyZM/noo48ICQm5cS0kJISlS5cyY8aMSvVlbm5OmzZt2Lt3741rer2evXv38sADty5O8cADD9zUHmDPnj03tf87QXjlyhV+//13XFxk66ep1LO1uHE+4dYzSXxxOPaO+uvs3ZnXW10vRvLeX+9xMfNiuW3NfXzwWvg+ANnffUfu9h3ltv1fj7jY83aQJwDTryRyPKegwntUajN6v9YcKzs1mYkF7P0yHIPepHWBhBBCiFrh5MmTvPXWWwwePJiBAwfe9BIVMxgMJM+ahaG4GOv27XF89hlTh1Suk7mFvH05EYCpgZ50dzFtgliv07Nn7SUSI7NRW5jRb2xLnDwqLk4nhBBC3I8qnSRMTk5Gq/33CimdTvevFX7GmDRpEp9//jlffvkl4eHhjBo1isLCQkaMGAHAsGHDmDZt2o3248ePZ/fu3SxevJiIiAjeffddTp48yZgxY4DrCcKnn36akydP8u2336LT6UhJSSElJYWysrJKxyfuXIcgF6Y/3hiA+bvC+Ss68476e7nFyzzs8zBl+jIm7Z9ETklOuW3tHn4Yl1GvAZA8axalV64YPc4YPzf6uTpeL2RyMZbk0or//tg5W9L7tRYoVQqiT6dzYmeM0eMJIYQQ96IffviBTp06ER4ezubNm9FoNFy8eJF9+/bh4OBg6vDqBH1eHoaSUhSWlnjOnYOigvOSTSW9TMN/LsSiMRh43NWBcf6mLVRiMBg48G0k0WfSUaoU9BndAjd/WdUqhBBClKfSM4zu3bvz6quvEhYWduPaqVOnGDVq1L+qDhtj0KBBfPjhh8yaNYtWrVpx5swZdu/efaM4SVxcHMnJ/1/NtlOnTnz33Xd89tlntGzZkp9++oktW7bQrFkzABITE9m2bRsJCQm0atUKT0/PG68jR45UOj5xd7zYKYAnQ73R6Q2M+S6M5NziKvelVCiZ9+A8/Oz8SCpMYsrBKej0unLbu44Zg02nBzAUF5Mwdhy63FyjxlEoFCxr5EtjG0vSy7SMvBBLqV5f4X2e9R14eMj1lbYndsYSdSrNuDcmhBBC3IPmz5/P0qVL2b59O+bm5ixfvpyIiAieffZZ/Pz8TB1enWDm4ID/t98Q8N23mPv7mzqcW9IbDIy9FEdKmYZga4taUajk6KarhB9JRqGAx/7TDJ8QJ5PGI4QQQtR2CkMlD4lLT09n+PDh7N69G7VaDYBWq+Wxxx5j/fr1uLmZ9onh3ZCXl4eDgwO5ublyhs5dVFym46lPjnApOY+Wvo78+GpHLO7gEOvL2ZcZunMoJboSXmnxCmNDx5bbVpuVRcxTT6NNTsamc2d8P12NQmVcEZVrxaU8dvIyOVodQzydWRzia9Sk99DGK5zdG49KrWTgm21w9at6dWchhBDin+rSXMXGxoaLFy8SEBCAi4sLBw4coHnz5oSHh/PII4/c9DC4rqpLn0d1WRqbwsKYFKyUCna1aUhjW9MWcAv79RpHN18F4JFhjWjcycuk8QghhBCmZOxcpdIrCV1dXdm1axcRERFs3LiRjRs3Eh4ezq5du+6JBKGoPlbmZnz6QhscrdWcjc/hna3lnydojIZODXm307sAfHbuM/bH7S+3rcrZGd9VH6OwsqLw8GFSK1HIxN/KgtVN/VEC3yVnsT7JuO3SnQbWx6+JM1qNnl2fnKMoT7a7CyGEuP84OTmRn58PgLe3NxcuXAAgJyeHoqIiU4Ym7pJD2fl8EJMCwPyGPiZPEEYeS7mRIOw0MFgShEIIIYSRqnygSUBAACEhIfTp04eGDRvezZjEPczX2ZoVg0NRKuCHE/F8dyzujvp7POhxhjYeCsDbh97mWt61cttaNm78/4VMvvmG7B82GD3Ow87/X8hkxpUE9mXmVXiP0kzJo/9piqO7NQXZpez+9Dw6TcXblYUQQoh7SdeuXdmzZw8AzzzzDOPHj+fll1/mueeeo3v37iaOTtyptFINoy5dQw8M8nDmOU/TFgxMiMxm31fhALTq6Ufoo7KlXQghhDBWpZOERUVFjBw5Emtra5o2bUpc3PUkz9ixY3n//ffveoDi3tO1oStvPHb9zL53tl0gLC77jvqb3GYyoW6hFGgKGLN3DLml5Z85aP/oo7hOGA9AynvvUfjXMaPHed3PjUEezugM8PLFWC4WVHyuooW1mj6jmmNupSL5ai5/fB9JJXf4CyGEEHXaypUrGTx4MADTp09n0qRJpKam8tRTT7F27VoTRyfuhM5gYNSla6SXaQmxsWRBQx+TxpOVVMgvq8+j1xkIbuNGpyfrmzQeIYQQoq6pdJJw2rRpnD17lgMHDmBpaXnjeo8ePdiwwfiVWeL+Nuqh+vRu5oFGZ2D0N2Gk55dWuS+1mZolDy/Bw8aD2LxYJh+YjEavKbe9y6uvYt+3L2i1JIwfT9m18lcf/pNCoeCDEB+6ONpSqNPz/LlooyoeO3nY8Nh/mqJQQPiRZM7tSzD6vQkhhBB1nbOzM15e17d7KpVKpk6dyrZt21i8eDFOTlJIoi77MCaFwzkFWJsp+bxpANZmpqu6XJhbyo6VZykr1uIR5ED3FxujUJq2cIoQQghR11T6J/mWLVtYuXIlXbp0ual4Q9OmTbl69epdDU7cuxQKBR8805JgN1tS8kp4/dswNLqqb8WtZ1WPlY+sxFplzbGUY8z7a165K/YUCgWe783FskUL9Lm5xI8aje6/ZyVVxFypZG2zABpYW5BcquGFczEUaMuvrPw3v6YudHoqGIDDP10h7qJx5xoKIYQQdVFeXp7RL1E3HcjKY9m1VAA+aOhDQxvLCu6oPppSHbtWnSM/qwQHNyv6jG6OSl314nhCCCHE/arSScL09PRbFigpLCw0quKrEH+ztVDx6QttsLNQcTw2iznbL93RVtwQ5xA+eOgDlAolP1/5mS8vflluW6WlJT4rP0Ll7k5ZdDSJkyZj0GqNGsdBreLbFkHUU6u4UFDMKxdj0eorjrtld18adfLEYIDdn10gPc64xKQQQghR1zg6OuLk5HTb199tRN2TXFrG65fiMAAveLnwlIezyWLR6w38tvYiadfysbRR03dMS6xszU0WjxBCCFGXVTpJ2LZtW3bu3Hnj678Tg2vWrOGBBx64e5GJ+0J9V1uWDGqFQgFf/3WNdYdj76i/rj5debPtmwAsObWEvXF7y22rdnPDZ9XHKCwtKTx4kLQPPjB6HD8rC75qEYiVUsG+rHymX0moMMGpUCh4eEgI3iFOaEp17Fh5lryMis81FEIIIeqa/fv3s2/fvtu+/m4j6hat3sCoi9fI1GhpamvJnGBvk8ViMBg49OMVYs9lYKZS0md0CxzdrE0WjxBCCFHXKQyVXLp16NAhevfuzfPPP8/69et59dVXuXTpEkeOHOGPP/6gTZs21RVrjcnLy8PBwYHc3Fzs7e1NHc594bM/rzJ/VwQKBXz2Qlt6NnGvcl8Gg4F5x+axIXIDVior1vdaTxOXJuW2z9v9K4kTJgDgMXcOTs88Y/RYu9JzGHkhFgPwTn0vRvn9e5Xt/yot1rL5wzAyEwtwdLfmqTfbYGmrNnpMIYQQQuYqtcv99HnMu5rER3Fp2Jop+a1tCEHWFiaL5czvcRz+KQqAx15uRnCbiudhQgghxP3I2LlKpVcSdunShTNnzqDVamnevDm//fYbbm5uHD169J5IEArTePnBIIZ08MNggHHfn+Z8QvkViiuiUCiY2n4qnbw6UawtZuzesaQWppbb3r7XY9QbOwaAlNlzKDx61Oix+rg68k7964exz7maxI60nArvsbBS0XdMS2ydLMhJLWLnqnNoyyo+11AIIYSoy4qKioiIiODcuXM3vUTdcSArj4/i0gBY0sjPpAnCq6fTOPzz9QRhp4HBkiAUQggh7oJKryS8H9xPT4NrE41Oz0vrT3DwSgZudhZseb0zXo5WVe4vvyyfF3a9wNXcqzR2bsz6XuuxVt96C4rBYCBp8hvk7dqF0sYG/6+/wrJJ+asP//feaVcSWZ+YgaVSwaZWwbR2sKnwvsykAjZ/GEZpkZagVq489kozlFKFTwghhBHq0lwlPT2dESNG8Msvv9zy+zpd3X9QVpc+j6rKKNPyyIkI0sq0DPdyYWGIr8liSYnJZcuS0+g0epo95E3XwQ3lbHQhhBDiNqptJaGZmRlpaWn/up6ZmYmZmVQRE1WnNlPy8dDWhLjbkZZfykvrT1BQalwxkVuxM7fjo+4f4WThRHhWONMOTkNvuHUFZYVCgeeC+Vi3b4++sJC4V16lLCHBqHEUCgXvBXvT3dmeEr2BF87HEF1UWuF9Ll629H6tOUqVgugz6Rz68codFW4RQgghaqMJEyaQk5PDsWPHsLKyYvfu3Xz55Zc0aNCAbdu2mTo8YQSDwcDEiDjSyrQ0tLbkXROeQ1iQXcovn5xHp9Hj39yFB59tIAlCIYQQ4i6pdJKwvCRGaWkp5uZSSUzcGXtLNV+MaIernQURKfmM+S4Mre7WiT1j+Nr5svyR5aiVavbF72PZqWXltlVaWODz8UosQkLQZWQQP/I/aLOyjBpHpVTwWVN/mttakanR8syZKJJKyiq8z7uhEz1evL5i8fyBBE7/FmfUeEIIIURdsW/fPpYsWULbtm1RKpX4+/vz/PPPs2jRIhYsWGDq8IQR1idlsiczD3OFgtVN/bEyq/SvEHeFtkzHL6vPUZRXhou3DY+ObIrSRLEIIYQQ9yKVsQ1XrFgBXF81tWbNGmxtbW98T6fT8eeff9KoUaO7H6G473g7WrF2eFue/fQoByLTeXf7ReYOaFblp8ShbqHM6TyHaQense7iOupZ1WNY02G3bGtmZ4fvZ59x7bnnKLt2jfhXX8N//TqUNhVvH7ZRmfFdyyAGhEURXVzKoLNX2RzagHrmt/+/WYO27hTmlHL4pyiObr6KrZMFDdt7VOm9CiGEELVNYWEhbm7Xz4tzcnIiPT2dhg0b0rx5c8LCwkwcnahIRGExs6MSAZhR35MmtlU/CuZOGAwG9n0dQdq1fCxt1PQZ1QJzS6N/lRFCCCGEEYz+ybp06VLg+g/o1atX37S12NzcnICAAFavXn33IxT3pRY+jiwbFMqob0/xzV9xBLjY8J8Hg6rcX9+gvqQUprA8bDkfnPwAR0tH+tfvf8u2anc3fNes4dqQIZScP0/ChIn4rvoYhbriCsSu5mp+bFWf/mFXuFJUypBzV/m5VTB2qttvxW/Vw4+C7FLO7o1n75fhWNmb49vIuUrvVQghhKhNQkJCiIyMJCAggJYtW/Lpp5/emDd6enqaOjxxGyU6PaMvXqNEb6Cbsx3/8XE1WSynf4vjyolUlEoFvV5phn090yQrhRBCiHuZ0evzY2JiiImJ4aGHHuLs2bM3vo6JiSEyMpJff/2VDh06VGes4j7Tq5kH0/s0BmDernB+vZhyR/2NbDaSF5q8AMCsw7P4I/6PcttaBAXiu/oTFJaWFB48SPKMmUafF+hjac6GlvVxVptxLr+YYeejKTZiy3Tnp65X5tPrDPzyyXlSY/KMe2NCCCFELTZ+/HiSk5MBeOedd/jll1/w8/NjxYoVzJ8/38TRiduZH53MpcISXNQqVjT2Q2mis/9iz2VwdMtVAB4c3BDvECeTxCGEEELc66S68S3cDxXq6gqDwcDMrRf45q84LNVKvhnZgbYBVV9hpzfomXFoBtujt2NhZsFnPT+jtXvrctvnHzhAwutjQKfD5eX/4DZ5stFjncsv4qnTUeTr9PR0seeLZoGoK6herNXo2LHyHImR2VhYqxgwMRRXXzujxxRCCHF/qMtzlaKiIiIiIvDz86NevXqmDueuqMufR3n2ZeYx5Fw0AF83D6RnPQeTxJGVVMhPi06iKdHRrKs3Dw0JMUkcQgghRF1m7FylSknChIQEtm3bRlxcHGVlNxdnWLJkSeWjrWXuxYleXabV6Xn5q5Psj0zHzlLFhlceoIlX1T8XjV7DhP0T+DPhT+zUdqzrtY4Q5/InnDk/byJ5+nQA3N+ehvOwW59neCtHcwp47uxVSvQGBro7sdKIp/BlJVq2rzhLSnQulrZqnpzcGmfPis9EFEIIcf+QuUrtcq99HullGh45EUl6mZaXvOsxv6GPSeIoKdSw8f2T5KUX493QkX7jW2EmhUqEEEKISjN2rlLpn7J79+4lJCSETz75hMWLF7N//37WrVvHF198wZkzZ+4kZiFuSWWmZNXQNrQLcCK/RMuwL44Tk1FY5f7USjUfPvQhrd1ak6/J57XfXyM+P77c9o5PDcR1wgQAUhe8T+7OnUaP9YCjLWuaBaJSwKbUbN6+kljhtmVzSxV9x7bE1c+OkgINW5edJje9yOgxhRBCiNrkqaeeYuHChf+6vmjRIp555hkTRCRux2AwMCkinvQyLY1sLJlZ38skceh1en79/AJ56cXYuVjy2CvNJEEohBBCVLNK/6SdNm0ab7zxBufPn8fS0pKff/6Z+Ph4HnroIZnoiWpjZW7GmuHtaOJpT0ZBKc+vOUZybnHV+1NZ8VH3j2jg1ICM4gxe3fMqGcUZ5bZ3efUVnIYMAYOBpLemkLdnj9Fj9XCxZ2VjfxTA+sQMFsZUfLaihZWK/uNa4exlQ1FuGVuXniE/q8ToMYUQQoja4s8//6RPnz7/ut67d2/+/PNPE0QkbmddYgZ7MvOwUCr4pIk/ViZKzB3+KYqEiGxUFmY8ProFVrbmJolDCCGEuJ9U+qd+eHg4w/673VKlUlFcXIytrS1z5sy55VNiIe4WBys1X77UnsB6NiTmFPPC2uNkFZZVfGM57M3t+bTHp3jbehOfH89re14jvyz/lm0VCgXu09/Gvn8/0OlInDSZ/P37jR7rCXcn3v/vVp1l11L56FpqhfdY2qrpP74Vju7W5GeVsHXpaQpzS40eUwghhKgNCgoKMDf/d4JHrVaTlydFumqTiMJi5lxNAmBmfS8a25qmgvClQ0mc258AQM8RTXDxtjVJHEIIIcT9ptJJQhsbmxvnEHp6enL16tUb38vIKH8llhB3g6udBV+PbI+ngyVRaQW8uO44+SWaqvdn7cpnPT/D2dKZyOxIxu4bS4n21iv2FGZmeM2fj32f3qDRkDhuPAUHDxk91nDvekwP8gRgXnQyy2MrThTaOFjQf3wr7FwsyU0vZuuyMxQXVD0xKoQQQtS05s2bs2HDhn9d/+GHH2jSpIkJIhK3UqbXM+ZSHCV6A4842zHS2zRFZVJj8vjj+0gAOvQPJKiVq0niEEIIIe5HlU4SduzYkUOHridG+vTpw+TJk5k3bx4vvfQSHTt2vOsBCvG/fJys+XpkB5xtzDmXkMvLX52kRKOrcn9+9n6s7rEaW7Utp1JPMW7fuPIThSoVXgsXYtezJwaNhoQxYyg8etToscb6uzM10AOABTHJLImteOuxnbMlAyaEYuNoQXZyIduWn6G0qOqJUSGEEKImzZw5k7lz5zJ8+HC+/PJLvvzyS4YNG8a8efOYOXOmqcMT/7U0NpULBcU4q81Y1sgPRQWF1qpDcUEZuz87j15nICjUlTa9A2o8BiGEEOJ+Vukk4ZIlS+jQoQMAs2fPpnv37mzYsIGAgADWrl171wMU4laC3Wz5ckR7bC1U/BWdxZjvwtDo9FXur7FLY1Z2X4mVyoqjyUdvnyhUq/Fe/CG23bphKC0lftRoCo8fN3qsCQEeN1YULopJ4UMjzih0cLViwIRWWNmpyYgvYPtHZykr0Ro9phBCCGEq/fr1Y8uWLURFRTF69GgmT55MQkICv//+O0888YSpwxPAmbwiVsRd3+HwfkNf3CzUNR6DXm9gz9qLFGSX4uBmRfdhjU2SqBRCCCHuZwpDRaVW70PGloYWpvdXdCbDvjhOmVbPk6HeLH6mJUpl1SeUJ1NOMnrvaIq1xXTy6sTybsuxVFnesq2+rIyE18dQePAgCmtr/NZ8jnXr1kaPtfJaKu9FJwMw0d+dtwI9KpwMZyQUsGVJGKVFWtwD7ek3tiUW1jU/kRdCCGFadWWuotVqmT9/Pi+99BI+Pj6mDqfa1JXP41ZKdHp6nozkSlEpT7g5srppgEniOLYtmpO7YlGZK3l6Sls5h1AIIYS4i4ydq5imXJkQd0nHIBdWDWmNmVLB5tOJTN9yHr2+6nnvth5tWdV9FVYqK44kHWHC/gmU6m5dLERpbo7PRyuw6fQAhqIi4l9+heJz54wea4y/O+/U9wJg6bVUFsakUFHOvp6PLf3Ht8LCRkVqTB5blp6WMwqFEELUWiqVikWLFqHVyur32mphTDJXikpxM1cxv6FpErmx5zM4uSsWgIeHNpIEoRBCCGEiRiUJnZyccHZ2NuolRE3r0cSdJc+2RKGA74/HM3XTubuWKDycdJjx+8aXnyi0tMTn44+xbt8efWEhcSP/Q/HFi0aPNcrPjdnB1xOFy66lMj86ucJEoZu/PU9MbH1j6/GWJVL1WAghRO3VvXt3/vjjD1OHIW7hWE4Bq+PTAfgwxBdntarGY8jLKOb3dZcAaP6QNyEdPGo8BiGEEEJcZ9R24y+//NLoDocPH35HAdUGdXnLyP1sy+lEJv14Br0Bnmrtw6KnW2B2B1uPT6Sc4PW9r1OsLaazV2eWP7IcCzOLW7bVFxYS9/IrFIeFoXRwwG/N51g1b270WGsS0plxJRGA0b5uzKzvWeHW4+yUQrYuPU1hbhkOblYMmBCKnfOtt0YLIYS4t9Slucrq1auZPXs2Q4cOpU2bNtjY2Nz0/f79+5sosrunLn0efyvU6njkRCTXSsoY7OHMssZ+NR6DVqPj50WnyIgvwD3Qnicnt8ZMJRudhBBCiLvN2LmKnEl4C3Vxoieu2342iQkbzqDTG3iilRcfPtMSlVnVJ5s3JQq9O7O8W/mJQl1BAfEj/0Px2bMora3xWf0JNu3bGz3WFwnpvP3fROGrPq68G+xVYaIwN72IrUvPkJ9Vgp2LJU9MDMW+npXxb1AIIUSdVJfmKkpl+T+HFQoFOp2uBqOpHnXp8/jb1MsJrE/MwNtCzf72jbBXmdV4DPu+Dif8cDKWtmqefbudPOwUQgghqsldP5NQr9ezcOFCOnfuTLt27Zg6dSrFxcV3JVgh7pZ+Lb346LlQVEoFW84kMfHHs2jvoOpxO492fNz94+tbjxMP3/aMQjNbW3zXrsW6Qwf0/z2jsKAS26te8nHl/f+eBfRpQjoTIuLRVrBt2sHVmiffaI2DqxX5mSVs+jCMnNQi49+gEEIIUc30en25r3shQVgX/ZGVz/rEDACWNvIzSYLw0uEkwg8no1DAoyObSoJQCCGEqAWMThLOmzePt99+G1tbW7y9vVm+fDmvv/56dcYmRJX0ae7JyiGtUSkVbD+bxPgfzqC5C4lCSzNLDiUeYuzesRRpbp2IM7O1wfezT7Ht1g1DaSnxr48hb9cuo8d60bseyxr5YqaADSlZjLgQQ1EFsds5W/LkG61x8rShMKeUTYvDyEwsqNR7FEIIIWpCSUmJqUO47+VpdUyMiANghHc9ujrb1XgM6XH5/Pn9ZQDa9w/Ct7Gcay6EEELUBkYnCb/66itWrVrFr7/+ypYtW9i+fTvffvsten3Vky9CVJdezTz45Pk2qM0U7DyfzNjvTlOmvbNE4aoe14uZHE0+ysu/vUxOSc4t2yotLPBZsRz7xx8HrZbEyW+QvXGj0WMN9nRhXbNALJUK9mTmMejMVbI1t68KaeNgwZOTQnHxsaU4r4wtS06Tdi2vMm9RCCGEqBY6nY65c+fi7e2Nra0t0dHRAMycOZO1a9eaOLr7z6wriSSVagiwMmdGfc8aH7+kUMMvn55Hp9UT0NyFNo/513gMQgghhLg1o5OEcXFx9OnT58bXPXr0QKFQkJSUVC2BCXGnejZx59MX2mBupmT3xRRe/y7sjhOFax5dg4OFA+cyzjF893BSClNu2VahVuO1aCGOgwaBwUDKzFlkrltv9FiP1nPgx5b1cVCZcSKvkCdOR5FUUnbbe6zszHliYihuAfaUFGrYsuQ08ZeyKvMWhRBCiLtu3rx5rF+/nkWLFmFubn7jerNmzVizZo0JI7v//JaRyw8pWSiAFY38sDGr2W3GBoOB/V9HkJ9Zgn09S7q/2ATFHRSZE0IIIcTdZXSSUKvVYml581kharUajUZz14MS4m55pJE7nw1rg7lKyZ5Lqbz69UmKy6p+/lEL1xZ82etL3KzdiM6NZtgvw4jJjbllW4WZGR7vvoPLf0YCkLZwIekrPsLYWkHtHW3ZEhqMh7mayMIS+oVd4Urh7bdpWdqoGTC+Fd4hjmhKdexYeZaIv5Ir9yaFEEKIu+irr77is88+Y+jQoZj9IynVsmVLIiIiTBjZ/SVHo+WNyHgAXvN1pb2jbY3HcOGPRKLPpKM0U/DYy82wtFHXeAxCCCGEKJ/RSUKDwcCLL77IwIEDb7xKSkp47bXXbromRG3zcIgba4e3xUKlZH9kOkPX/EVO0e1X5d1Ofcf6fN37awLsA0guTGb4L8O5mHnxlm0VCgWukyfjOmECABmrVpG6YAEGI7fpN7a1YnubBtS3siCxVMOA01cIyyu87T3mVir6jWlFg3bu6PUG9q4P5+QvsUYnJ4UQQoi7KTExkeDg4H9d1+v18rC5Br0blURamZYG1hZMCaz5bcYZCfkc/ikKgE4Dg3HzrxtVoIUQQoj7idFJwuHDh+Pm5oaDg8ON1/PPP4+Xl9dN14SojR5s4Mq3/+mAg5WasLgcnl59lKScqlfn9rL14sveX9LEpQnZpdm8tPsljicfv2VbhUJBvddexX3GDACyv/qapClT0ZcZl6j0tTRna+sGtLSzIkuj4+kzVzmQdfvzBs3USnqOaELoo34AHNsazR/fRaK/gwIuQgghRFU0adKEgwcP/uv6Tz/9RGhoqAkiuv/8kZV/Y5vxkkZ+WJoZ/SvAXaEp1fHr5xdvnEPY4hGfGh1fCCGEEMZRGGR50b/k5eXh4OBAbm4u9vbylPNecjk1n+FfHCc5twRPB0u+fKk9Dd2rXtWvoKyACfsncCzlGGqlmkVdF9HDv0e57XO2bCF5+gzQ6bBu2xaflR9h5uho3FhaHS9diOHP7AJUCvgwxJfBni4V3ndufwIHf7wMBghoUY9HRzZFbVGzZxAJIYS4u+rSXGXr1q0MHz6cadOmMWfOHGbPnk1kZCRfffUVO3bsoGfPnqYO8Y7V5s+jUKvj4RORxJeUMdK7HvMa1nyCbu9X4UQcScbGwZxBM9tjZWte8U1CCCGEuGuMnavU7GNEIUysobsdP4/qRLCbLcm5JTz9yRFOxla9uIetuS0f9/iYHn490Og1TP5jMj9f/rnc9o5PPIHvZ5+itLWl6ORJYgc/R1lcnHFjqcz4pkUQT7g5ojXAhIh45l5NQldBnr9FNx96vdIMM7WS2HMZbFl6muL8qm+3FkIIISpjwIABbN++nd9//x0bGxtmzZpFeHg427dvr3SCMDExkeeffx4XFxesrKxo3rw5J0+evO09paWlTJ8+HX9/fywsLAgICOCLL764ZdsffvgBhULBE088Uam4arOFMSnEl5ThbaHm7aCa32YceSyFiCPJKBTQc2RTSRAKIYQQtZisJLyF2vw0WNwdOUVlvLT+BGFxOViolKwc0pqeTdyr3J9Or2PuX3P5+cr1BOF/mv+HsaFjUSpunYcvuXyZ+NdeQ5uUjJmTEz6rPsbayC1XeoOBD2JSWHotFYDH6tmzqrE/Nqrbrw5Mjsph5yfnKC3U4uBqRb9xLXFwta7EuxRCCFFb3I9zlezsbEJDQ+nWrRujRo3C1dWVK1euUL9+ferXr1/ufQMGDCA1NZX33nuP4OBgkpOT0ev1dO7c+aZ2sbGxdOnShaCgIJydndmyZYvRsdXWz+NUbiF9w65gAL5rEcQjLjUbW05aET/OO4GmVEe7xwNo3y+oRscXQgghxHXGzlUkSXgLtXWiJ+6u4jIdY74LY29EGkoFLBjYnEHt/Krcn8Fg4KPTH/H5+c8B6OHXg3ld5mGtvnUiTpOWRsKo0ZRcvIjC3ByvRQux79XL6PE2pWYzMSKOUr2BJjaWfNUiCB/L2z+dz04pZPtHZ8nPLMHKTk2vV5rj1cDR6DGFEELUDnVprhIUFMSJEydwcbn5iIycnBxat25NdHS0Uf1MnTqVw4cP3/J8w/Ls3r2bwYMHEx0djbOzc7ntdDodXbt25aWXXuLgwYPk5OTU+SRhqV5PzxOXuVxUwtPuTqxs4l+j4+s0en7+4BTpcfl4NXBkwIRWKGv4LEQhhBBCXCfbjYWogJW5GZ++0IZn2/qgN8CUn8+zct+VKlcBVigUjGs9jnld5qFWqvk97nde3P0iqYWpt2yvdnPD/+uvsH3kEQxlZSROmEjG558bPf5Adyc2tQrG1VzFpcISep+6zKnc21c+dvKw4am32uDqZ0dxvoatS09z4c/ESr9XIYQQwlixsbHodLp/XS8tLSUx0fifQdu2baNt27Y888wzuLm5ERoayueff27UPYsWLcLb25uGDRvyxhtvUFx8c/GyOXPm4ObmxsiRI42KpbS0lLy8vJtetc2Ka6lcLirBRa1iTgPvGh//6JarpMflY2GjoudLTSRBKIQQQtQBKlMHIIQpqcyULHyqBa52Fny8/yof/naZa5lFvPdkMywq2L5bnv71++Nj68OE/RMIzwpnyM4hrOi+gqYuTf/VVmltjc9HK0hduJDsr74mffESNHHxeMyaiUKtrnCsNg42/NKmIcPPR3OxoISBZ6JY2siPge5O5d5j42DBk5Nbs++rcKJOpfHHd5FkJBTw4LMNMFPJBF4IIcTdsW3btht//vXXX3FwcLjxtU6nY+/evQQEBBjdX3R0NJ988gmTJk3i7bff5sSJE4wbNw5zc3OGDx9e7j2HDh3C0tKSzZs3k5GRwejRo8nMzGTdunUAHDp0iLVr13LmzBmjY1mwYAGzZ882un1NCy8oZsW1NADmN/TGWV2zU/7Ycxmc3RsPQPfhTbB1sqzR8YUQQghRNbLd+BZq45YRUf2+OhrLu9suojdAW38nVr/Qhnq2FlXuLz4/nrF7x3I19yqWZpYseHDBbSsfZ339DakLFoBej02nTngt/hCVU/nJvn8q1OoYHX6NXzOur2SY6O/Om4EeKBWKcu8xGAyE/XqNv7ZGgwE8gx3o9UpzrO3lQHEhhKjt6sJcRam8/uBJoVD8a5W8Wq0mICCAxYsX07dvX6P6Mzc3p23bthw5cuTGtXHjxnHixAmOHj16y3seffRRDh48SEpKyo0k5aZNm3j66acpLCxEq9XSokULVq1aRe/evQF48cUXK9xuXFpaSmlp6Y2v8/Ly8PX1rRWfh85goO+pK5zOL+KxevasbxaI4jbzgbutILuUDe8dp6RQQ4tHfHjw2YY1NrYQQgghbk22GwtRScMeCGD9iPbYWao4eS2bASsPcymp6tuHfO18+brP13T26kyJroSJByay5vyacrcTO7/wPD4rV6KwsqLwyBFin36GkkuXjBrLRmXGF80Ced3PDYCl11L5z4VY8rT/3t71N4VCQZteATw+ugXmlmYkR+WyccEJ0uPyK/9mhRBCiP+h1+vR6/X4+fmRlpZ242u9Xk9paSmRkZFGJwgBPD09adKkyU3XGjduTFxc3G3v8fb2vmkVY+PGjTEYDCQkJHD16lViY2Pp168fKpUKlUrFV199xbZt21CpVFy9evWW/VpYWGBvb3/Tq7ZYk5DO6fwi7MyUvN/Qp0YThAa9gd/XX6SkUEM9X1s6PRlcY2MLIYQQ4s5JklCIf+ja0JUtr3cmqJ4NiTnFPPXJEXZfSKlyf3bmdqzsvpIhjYYAsDxsOTMOz6BMV3br9o90I+CH71H7+aFJTCT2uSHkbt1q1FhmCgUz63uxrJEvaoWCXRm5PHYykosFxbe9L6B5PZ6e2hZHd2sKskvZ9MEprpy49TmKQgghRGXFxMRQr169O+6nc+fOREZG3nTt8uXL+PuXX5Cjc+fOJCUlUVBQcNM9SqUSHx8fGjVqxPnz5zlz5syNV//+/enWrRtnzpzB19f3juOuSdeKS3k/+vq8ZVawF54WNbs74Oy+eBIjc1CZK3nsP80wU8uvGkIIIURdItuNb6EubOER1Su3SMOY78M4eCUDgEk9GzL2keA7ehr/Q8QPvH/8fXQGHc1cmrH44cV42Xrdsq0uN5fEt96i8I8/AXAaOhT3KW+hMDdush+WV8jLF2JJLNVgqVSwoKEPz3m63Pae0iINv629RNzFTABaP+ZPh/9r7z7D46jOv49/t+9KK616s2XJveDesekYOxSHEmocIJCQggmQ8gRI6B0SSkjyJ0ACDtVU00IzNhiDjbtx711dVl+tts7zQrKwsGxkSZZW9u9zXXNp98yZmbNnZOn2rXPmnNsLs7njRiCIiEjLdLVYZc6cOcyZM6dxROH+nn322RadY8mSJUyYMIG77rqLiy++mMWLF3PNNdfw9NNPM23aNABuueUW8vLyeP755wGoqalh4MCBjB8/nrvuuovS0lJ+/vOfc/LJJx900ZOWTDf+rmi4H4ZhcPE3W5lfXsOEBDdvDO99yMeOtLe9eTW89sASIiGDU6b157gTO36xFBEREWmephuLtIEnxsZzPx3DVRNzAXh09iZ+88oKfIGDT9/9PpcOuJT/O/3/iLfHs2bvGi5+/2Lm75nfbF2Lx0P2k0+SMn06AOUvvcTOK39KsKi4RdcaGR/L7DH9OS0pjrqIwW837Oa3G3bhC0cOeowjxsbZ04cyckoPAJZ/vJP3nlhJbVXzox5FRERa4q677mLy5MnMmTOH0tJSysvLm2wtNWbMGGbNmsUrr7zC4MGDueeee3j88ccbE4QABQUFTaYfu91uZs+eTUVFBaNHj2batGlMnTqVJ554ol0/YzR4tbCM+eU1OM0mHumf3aEJwnAwwuxn1xEJGeQOSWbQCc3/EVRERESim0YSNiMa/hos0eOVxbu47e01hCIGQ7p5ePqKUWR6XK0+X15NHr///Pes3bsWgF8M/QXXDrsWi7n51ZSrP/uM/D/eRKS6GktqCt0ff5yYUaNadK2IYfDEziIe3l5IBDjO7eTfx/WkZ8yhF2TZtKSQz17YQCgQISbezhlXD6L7gKTD+pwiInLkdKVYJTMzk4cffpjLL7+8s5tyxHT2/dgbCHHi4vWUBcP8uVcmv8lJ79DrL3hrCys+2YXTbeOy28dpETQREZEoo5GEIu3ksrE9eOnn40iKtbM6r5JznviSLzaVtPp83dzdeP7M57mk/yUAPL3qaX716a8oqytrtn7cqafS8/XXcPTtS7iklJ1X/pSyF1866AIo+zObTNyYm8Grw3qTbLOytqaOyUs38kFJxSGP6zcmg4tuHkNSViy1VQHe+dtKFr23jUhEf1MQEZHDEwgEmDBhQmc346h2z9Z8yoJhBsY6+VV2WodeO29TOStm14/ePPUnA5QgFBER6cKUJBRpgXG9knln+kQGZcaz1xvgyucW88gnGwm3Mmlmt9i5dfytPHDiA7isLr4u+JqL3ruIlcUrm6+fm0vuzFeIP+tMCIUouvde8m78LeHKyhZd78SkOD4d04+xnliqwxGuXrODO7fkEYgcfPpxUlYsF948moETM8GApf/bwbuPr8Bb6W/NRxYRkWPUz3/+c15++eXObsZRa2FFDTML6//Q+HD/bGwd+Cxhvy/EpzPWgQEDJ2bSa3hqh11bRERE2p+mGzejs6eMSPSqC4a5+/11vLyo/i/m43sl8cSlI0iLd7b6nFvKt/Dbz3/LjqodWE1Wfjf6d/xk4E+aXSTFMAzKZvyX4kcegVAIa2Ym3f7yMDGjR7foWsGIwb3b8nlqd/1IyCFuF/8YlEP/2EO3f+OiQj5/eSMhfxhXnI0zrjqO7EGafiwi0lm6Uqxyww038PzzzzN06FCGDh2KzWZrsv/RRx/tpJa1n866H4FIhNOXbGRzrZ+fZCbz1wEduxrzpzPWsfHrQuJTnFxy61jsTmuHXl9ERERapqWxipKEzehKgbd0jndW5vGnt1bjDYRJcTv426XDmdgnpdXn8wa93LngTj7a8REAk3pM4o7j7yDBmdBsfd/qNeT94fcEd+4Cs5mUX/2SlGuvxWRtWXD+YUkFv9+4m7JgGKfZxK29s7i6W8ohH3JeXujl42fWsjevBkww6gc5jD2nJ2aLBiSLiHS0rhSrnHrqqQfdZzKZmDt3bge25sjorPvxxM4i7t9WQLLNypfjBpBo67gk3ZZlxXz8zBpMJjj/9yPJ7JPQYdcWERGRw6MkYRt0pcBbOs/Wkhqmv7ScDYXVmExww+l9+c1pfbG0cpqPYRi8vOFl/rr0r4QiIVJdqdw78V4mdGv+OU4Rr5fC++6n8q23AHANH07WX/+CvXv3Fl2vyB/kxg27+KysGoCTE+N4fGA2mY6DP0soFAjz5eubWTs/H4DM3h5O/+kgPKmtX8hFREQOn2KV6NIZ92Onz8/JizdQFzH4+8AeXJTRcSP8vZV+Xrl7EX5viFE/yGH8eb077NoiIiJy+LRwicgR1jvVzdvTJ3LpmGwMAx7/dDNXPLuIkurWPbPPZDIxbeA0XjzzRXLjcynxlfDLT3/JA4seoC5Ud0B9c2wsWfffR9Yjf8XsduNbuZLt551P5fv/a9H10h02Xh7ai/v7dsNpNjGvvJpTF2/k3eKKgx5jtVs4ZdoAJv/8OGxOCwVbK5l572LWfJHXooVUREREpO0Mw+DmTXuoixhMTHBzYXpih1577vPr8XtDpPaIY8w5PTvs2iIiInJkaSRhM/TXeTlcs1bs4U9vrcEXrJ9+/PCFQzhtQHqrz+cL+Xh06aPM3DgTgF6eXjxw4gMMSh7UbP3Anjzy/9//w7diBQCec88l/bbbsLhjW3S9zd46pq/fyapqHwAXpidyf7/uxFstBz2mqtTHnP+uJ39zBQA9BiVx6uUDcSc6WvoxRUSklbpCrHLBBRe0qN5bDSPiu7KOvh/vFlfwi7U7sJtMzB3bnz4xrX828uFa/fkevpi5CYvNzMW3jCEpq2WxhoiIiHQejSQU6UDnj+jOe7+ZSL90N6U1fq6esZSb31xFjT/UqvO5rC7+PP7PPDnpSVJcKWyr3Ma0D6bx79X/JhwJH1Df3r0bOS88T8r06WA2U/nOO2w/91y8Cxa06Hp9Y528P7IvN+akYwbeKCrntCUb+LK8+qDHxKe4OO+3I5h4YR8sVjO71pUx855FbFxUqFGFIiKCx+Np0SaHpzoU5rbNewC4LietQxOEFUW1LHhzCwDHn99bCUIREZGjjEYSNqMr/HVeolNdMMxfP97If77ajmFA90QXf71oGON7Jbf6nOV15dy98G4+3fUpACPTRnLfCffRPa75Zw/WLltG/v/7I8H8+ucGei78Eel//COWFn4vL66o4br1u9hVFwDgsswk7uidRcIhHoZeVuBlzox1FO+sTyr2GpHKKT/ujyvu4M83FBGR1lOsEl068n78edMe/pNXSk+Xnc/GDMDZQQuIRSIGbz+ynIKtlXQfkMgPrx+OqZXPYRYREZGOpZGEIp3AabNw6zmDeOWa8XRPdLGn3Mdlz3zNve+voy544AjAlkh0JvLoKY9yz8R7iLHGsLx4ORe+dyGvbniViBE5oH7MqFH0eu9dEqdNA6DyjTfZds5Uqj/7rEXXG5vgZu6Y/vy0W/1qza8UlHHi4g28W1xx0BGCSZmx/OiPoxj3w56YzSa2rSjhlbsXsW1lSas+s4iIiBxoZVUtz+WVAvBQv+wOSxACrP5sDwVbK7E5LJx6+QAlCEVERI5CGknYDP11XtpDjT/Eve+vY+aS3QD0SXPz6MXDGNo9odXn3F29mz9/+WdWFNc/e3BE2gjuOP4Oeic0v6pg7dKlFPz5VgI7dwIQP3Uq6X+6BWtiyx5wvqiihj9s3M3m2vrFWKakxPNA3+5kOQ8+QrBkVzWfzlhHWb4XgN4j0zjx4r7EJuhZhSIi7UWxSnTpiPsRNgzOXLaJVdU+LkhP5P8G5RyR6zSnoriWV+9ZTCgY4eQf92fwSd067NoiIiLSdi2NVZQkbIYCb2lPczcUcdObqymp9mMxm7ju1D5cd1ofbK386384Embmxpn8bfnf8IV8WM1WrhlyDT8f8nPslgOTd5G6Okr+/nfKnpsBkQiWpCQybr+NuClTMJm+fxSAPxLhbzuL+PvOYoKGgdti5s+9s7gyKxnzQY4PByMsfn8bK2bvxogY2J0Wxp3bm8End8OskQciIm2mWCW6dMT9+PeeEm7dnEe81cyXYweS5rAdket8lxExmPXocgq2VNKtfyLn3ji8RfGDiIiIRA8lCdtAgbe0t3JvgFvfWcP/VhUAMCAjjvvOH8yonKRWn7OgpoD7Ft3HvD3zAOjp6cmdx9/JyPSRzdb3rVpFwZ//jH9z/QPH486YRPqf/oQtM7NF11tf4+P3G3ezvKoWgLGeWP7SP5v+sQd/YHrJ7mo+f2kjxTuqAEjLieOUaQNI7RHX4s8pIiIHUqwSXY70/agIhhi9cB014QgP9evOlQ2PBOkIqz7bzfxXN2N1WLjstrHEp7g67NoiIiLSPpQkbAMF3nKkvPtNPne8s4by2iAAl43twc0/GIAnpnWjAQzD4OOdH/PgogfZW7cXgIv6XcSNo24k3n7g924kEGDvv56i9OmnIRTC5HKR8stfknT1VZjt37/ISNgweC6vlPu3FVAbjmA1wc+7p/K73AzirZZmj4lEDNZ+kcfXb28lUBfGZIKhp2Yz9oc9sTsPvhiKiIgcnGKV6NIR9+OzvVW8VljGPwflHHQkf3urLPEx855FhAIRTrq0H0NOaX7RNBEREYluShK2gQJvOZLKvAEe/HA9ry3dA0CK286tZw/i3OFZrZ6+U+mv5LFlj/Hm5jcBSHWl8scxf2RKbvNTius2bqTwnnvwLV0GgC2nBxl/+hPuk09u0fX21AW4ZdMeZu+tHyGYYrPyp96ZXJqRdND/uHgr/Xz5+ma2LC0GwJ3o4MSL+9FzeIqmLYmIHCbFKtHlaLwfRsTgncdXkLepgm79Ejj3xhFarERERKSLUpKwDY7GQE+iz6Jte/nz22vYUlwDwAl9UrjnvMH0TIlt9TmXFC7h7oV3s6NqBwAj00Zy89ibGZg88IC6hmFQ9f7/KH74YUIl9asQu089lfRbbsbeo0eLrjdnbxW3b85jq69+YZNhcS7u69ud0Z6Df4Zda/cy75WNVJXWAdDjuGQmXtiHpMzWf24RkWONYpXocjTej9Wf7+GLmZuw2s1cets4PKmaZiwiItJVKUnYBkdjoCfRKRCK8Mz8bTwxZzP+UAS71cz0U/rwq1N64TjI9N3v4w/7eXbNszy7+lnqwnWYMHFB3wu4bsR1pLgOfIZRuMZL6ZP/R9l/n6+fgmy3k/zzn5F8zTWYXd//H4JAJMJ/9pTyyI5CasIRAC5MT+TW3llkHOSh6qFAmKUf7GDF7F1EwgYms4nBJ2YxZmpPXO7vn/YsInKsU6wSXY62+1FV6uOVexYT8oc58ZK+DD01u7ObJCIiIm2gJGEbHG2BnkS/nXu93Pr2GuZvLgUgNzmGW84ayORB6a2eilvoLeSxZY/xwfYPAIi1xfKrob9i2sBp2CwHJu/827ZRdO99eBcsAMCalUna739P/JlnYjJ//0rMJYEg928r4JWCMgBiLGZ+m5PONd1TcR5kJefyQi8L3trKjlX1n9vusjLm7FyGnNIdi7V1qz+LiBwLFKtEl6PpfhiGwTuPryRvYzmZfTyc/7uRmmYsIiLSxbU0VomK/4X/85//JDc3F6fTybhx41i8ePEh67/++usMGDAAp9PJkCFD+OCDD5rsf+utt5g8eTLJycmYTCZWrlx5BFsv0nY5ybE8f/VY/n7ZCFLjHOzYW8svX1jGpU9/zZq8yladMyM2g4dOeogXznyB45KPwxv08siyRzjvnfP4fPfnfPfvA45evcj+z7/p9vcnsGZlEsovIP/3f2DHhRc1Jg4PJdVu47EBPfhwVD9GxcdQG45w37YCJixaz0v5ewlFDvx7RGJGLGdfO5Qf3jic5O5uAr4QX72xhZfvWsS2FSUHtFFERESOrLXz88nbWI7VZua0KwYqQSgiInIM6fQk4auvvsrvfvc77rjjDpYvX86wYcOYMmUKxcXFzdZfsGABl112GT/72c9YsWIF5513Hueddx5r1qxprOP1ejnhhBN46KGHOupjiLSZyWRi6rAsPvvDKVx3ah8cVjOLtpcx9R9f8rvXVlJQ6WvVeYenDefls1/mnon3kOJKYVf1Ln4z9zf8YvYvWLt37QFtiD/jDHr/73+k3nA95thY6tatY9fVP2PX1T/Dt3btQa7yrRHxMbw3si9/H9iDbg4b+f4gv9+4m1OWbODd4goizST+sgckcfGfxnDq5QNwxdupKvHx4VOrefvRFZTsqm7V5xYREZHDU7XXx4I3twAw/rzeJKTFdHKLREREpCN1+nTjcePGMWbMGP7xj38AEIlEyM7O5je/+Q0333zzAfUvueQSvF4v77//fmPZ+PHjGT58OP/617+a1N2xYwc9e/ZkxYoVDB8+vMVtOpqmjEjXlVfh4y8fbeDtlfkAOG1mfnFSb355Ui9iHdZWndMb9PLMqmd4ft3zBCNBACb1mMT04dPpk9jngPqhsjJK//Uvyl+ZCcH6+vFnn03qjTdgz/7+5xPVhSM8n1/K4zuLKAuGARjqdnFLr0xOSYprdip1oC7E8o93svLT3YSD9c847D0yjbFTe2pxExGRBopVosvRcD8Mw+D9f3zDrrVlZPb2cP7vNc1YRETkaNElphsHAgGWLVvGpEmTGsvMZjOTJk1i4cKFzR6zcOHCJvUBpkyZctD6LeH3+6mqqmqyiXS2bgkuHr90BG9Pn8jonETqghGemLOZU//6Oa8t2U24mem73yfWFsuNo27knfPeYWqvqZgw8emuT7ng3Qu4Zf4t7Kra1aS+NSmJjD/9id4ffkD81KkAVP3vf2w962wK772P0N69h7ye02LmF9lpLB4/iD/kZuC2mFlV4+OyVdv40cqtLKv0HnCM3Wll/Lm9mXbXePqOSQdg6/JiZt69iNnPraWiuPawP7eIiIgc2ualRexaW4bFaubUywcoQSgiInIM6tQkYWlpKeFwmPT09Cbl6enpFBYWNntMYWHhYdVviQceeACPx9O4ZbdghJRIRxmencDrvzqeJ6eNpEdSDMXVfv745irOeGwe76zMa1WyMDsum/tPvJ9Z587ijJwzMDB4f9v7/PDtH3Lngjsp9Db992Tv3p1uf3mYnm+9SewJJ0AwSPmLL7Jl0hkUPfQwoZKSQ17PbbXwh54ZfD1+EL/snorDbGJBRQ1nL9/MT1ZtY2kzycK4JCeTf3Ycl9w6lp7DUjAM2LSoiJfvXMTcF9ZTtbd1069FRESkqTpvkC9f2wzA6LNySMzQyH0REZFjUac/kzAa3HLLLVRWVjZuu3fv7uwmiTRhMpk4c0gms393En8+ayAJMTa2lXi5YeZKpjz+Be+vyifSimRh74TePHrKo8w8ZyYndDuBsBHmzc1vcvZbZ/PQ4oco9ZU2qe8cNIge/36GHs89i3PwYAyfj7LnnmPLpDMovPc+gt+TrE+xW7mrbzcWjBvIjzOTMAOf7q3inOWb+dGKLXxRVn3AYiUp3d2c9euhXHTLaHIGJ2NEDNZ/VcBLt3/NvJc3UlPuP+zPLSIiIt9a8OYWfNVBEjNjGTE5p7ObIyIiIp2kU5OEKSkpWCwWioqKmpQXFRWRkZHR7DEZGRmHVb8lHA4H8fHxTTaRaOSwWrjmpF7M/+Op/GFyP+KdVrYU13Ddyys482/z+WhNQauShcclH8eTk57k+TOfZ3T6aAKRAC+uf5Epb0zh7oV3s7uqaeI89vjjyX39NbKf+heuYcMw/H7KX3yRrWdMpuCOOwnsyTvk9bo57Tw6oAdfNiQLbSYTX1XUcPE3Wzlr2WY+Kqk8YIGTtJx4zrluGD/64yi6D0gkEjZY80UeL962kHmvbKSyRCMLRUREDteejeWsX1AAwKnT+mOxagyBiIjIsSoqFi4ZO3Ysf//734H6hUt69OjBddddd9CFS2pra3nvvfcayyZMmMDQoUO1cIkcc6rqgjz75Xb+M3871f4QAIMy4/ntGf2YNDCt2YVBvo9hGHxd8DX/t/L/WFmyEgCzycyUnCn8bMjP6J/U/4D6tQsXUvp/T1K7dGl9odWK54c/JOWXv8Ce8/0jEvLqAjy5u5iX8vfia0hyDoh1cn1OOj9MTcDazHOR8jaVs+jdbRRsqQTAZII+o9IYMTmH1B5xh/25RUS6EsUq0aWr3o9QMMzMexZTWezjuJO6ccqP+3//QSIiItLltDRW6fQk4auvvsqVV17JU089xdixY3n88cd57bXX2LBhA+np6VxxxRV069aNBx54AIAFCxZw8skn8+CDD3L22Wczc+ZM7r//fpYvX87gwYMBKCsrY9euXeTn5zfW6d+/PxkZGS0acdhVAz05dlXWBvnPl9t49qsd1DQkCwdmxvOLk3pyztAsbJbDHxVgGAbLi5fz79X/5su8LxvLT+x2Ij8b8jNGpY864JjaJUsoffJJvAsaFhIym4k7/XSSfnolrpEjvzdpWRII8szuEp7LK6U6XL+ycY7Tzs+7p3JpZhJxVssBbczbVMGKj3eya11ZY3n2wERGTM6h+4DEViVKRUSinWKV6NJV78eid7ex9IMdxHjs/PiOcThibJ3dJBERETkCukySEOAf//gHf/nLXygsLGT48OE88cQTjBs3DoBTTjmF3NxcZsyY0Vj/9ddf59Zbb2XHjh307duXhx9+mLPOOqtx/4wZM7jqqqsOuM4dd9zBnXfe+b3t6aqBnki5N8Az87cxY8EOagNhALI8Tq4+oSeXju2B22Ft1Xk3lG3g2dXP8vHOj4kY9cm7EWkjuOq4qzip+0lYzE2Td76VKyl58km8875oLHMOHkzSlVcS/4MpmGyH/k9IZTDEc3mlPL2nhLJg/eeItZi5NCOJn3VPpVeM44BjSvdUs/zjXWxZVozRMBoxtUccIyb3oPeIVMytSJSKiEQrxSrRpSvej735Nbx23xIiYYMf/GIwvUemdXaTRERE5AjpUknCaNMVAz2R/VXUBnjx653MWLCT0pr6hT3inFamjcvhqom5pMc7W3XeXVW7eG7tc7yz5R2CkSAA3dzduGzAZZzX5zw8Dk+T+nWbNlH+wgtUvvMuRiAAgDU9ncRp00i8+CIsCQmHvJ43HOaNwnL+vaeEzbX1n8MEnJ4cz8+7p3ByYtwBIwWrSn2snLOb9V/mEwrWJzTjkp0MPrkbgyZk4XRrlISIdH2KVaJLV7sfRsTgrb8up3BbJblDUzjr10M08l5EROQopiRhG3S1QE/kYOqCYd5ekcfT87exrcQLgM1i4rzh3bjmpF70S2/ds/tKakt4Yf0LvLnpTaoCVQA4LU7O7nU2lw247IDnFobKyiifOZPyl18hXFq/YrLJ6cRz3rkk/eQnOPr0OeT1DMPgi/IantlTwqd7qxrL+8Y4+Hn3VC5MTyT2O1ORfTUBVn+ex+rP9lDnrU9oWmxm+o5OY8gp3UnL0b9tEem6FKtEl652P9Z8kce8lzdic1i47I5xxCW17o+HIiIi0jUoSdgGXS3QE/k+kYjBnA3FPP3FVpbsKG8sH98riZ+Mz2HyoAzsrVjN0Bfy8cG2D3h5w8tsKt/UWD4qfRQ/HvBjTutxGlbzt1OcI4EAVR98QNl/n8e/fn1juWvkSBIuuoj4H0zB7HId8prbav38Z08JMwvL8DY8tzDWYub8tER+nJXEiLiYJqMhgoEwm5cUsWZeHiW7qhvL03vGM+TkbvQelYbVZjngOiIi0UyxSnTpSvfDW+Hn5Tu/JlAX5oSL+zLstOzObpKIiIgcYUoStkFXCvREDtfyXeU8PW8bn6wrpOHRfaS4HVw6JptLx2bTPTHmsM+5b5GTl9e/zJxdcwgb9c8RTItJ4/w+53Nun3PJjstuUr92yRLKX3iB6rmfQbi+vjkuDs/UqSRcfBHOAQMOec2qUJhXC8p4Nq+E7b5AY/nAWCfTspL5UXoiibZvE5SGYVC0vYrVn+9hy/JiIqH6D+902xg0MYuBEzNJSDv8zy4i0hkUq0SXrnQ/Pnp6NVuXl5CWE8ePbhqN2axpxiIiIkc7JQnboCsFeiKtlV/hY+biXcxcspvi6vrn/ZlNcGr/NH4yPoeT+qViacV/HIq8Rby26TXe2PQGZXXfrjg8JmMM5/c5n0k5k3BZvx0tGCwupvKtWVS88QbBPXsay51Dh5Jw0YV4zjoLc2zsQa9nGAYLK7y8XLCX90sqqGvIfDrMJs5K8TAtK5kJCW7M+40urK0KsO6rfNZ+kUdNub+xPLOPh4ETMuk9Mg27s3WLvIiIdATFKtGlq9yP7d+U8MGTqzGZTVz8p9GkdG/dY0dERESka1GSsA26SqAn0h6C4QifrivixUU7+WrL3sby7okuLhqVzQUju5GddPgj7ALhAHN3zeXtLW+zIH8BBvU/amJtsfwg9wec3/d8hqYMbZwabEQieBcupOL1N6ieMweC9c8RNMXEEDfpdDxTf0js8eMxWQ+evKsIhnirqJyXCvaytqausTzbaeeC9ETOT09gQOy3CcpIOMKOVXtZ+2Ueu9eVse+nodVhoc+IVAYcn0lW3wRMGmUhIlFGsUp06Qr3I1AX4pW7FlFT7mfklB4cf/6hnwcsIiIiRw8lCdugKwR6IkfC1pIaXl60izeW7aHSF2wsH9cziR+N7M6ZQzKIcx7+6sCF3kLe3fouszbPYk/Nt6MFe3p68sPeP2RK7pQm05FDe/dS+fbbVLz2OoGdOxvLLcnJxJ91Fp6p5+AccvCVGA3DYFWNj5fy9zKrqJzqhmcXAgyKdXJ+eiLnpSeS7bQ3lteU+9m4qIANCwupKKptLI9PcdJ/fCb9x2XgST308xJFRDqKYpXo0hXux4K3trDik13Epzi59PZx2Ox6Hq+IiMixQknCNugKgZ7IkVQXDPPB6gLeWp7HV1tLG0fYOW1mfnBcBheM7M7EPimHPR05YkRYVrSMt7e8zeyds/GFfI37hqQMYUruFKbkTiEjNgOoT/b5Vqyk6v33qPrwI8Ll3y66Ys/JIf6cc/BMPQd7bu5Br1kbjvBJaSWzisuZu7ea4H4/8sZ5Yjk/PZGpqQkk262N1yzcVsWGhQVsXlpEsC7cWD8tJ44+o9LpMzpNK0GKSKdSrBJdov1+lOV7efXexUQiBmdfO5TcoSmd3SQRERHpQEoStkG0B3oiHSm/wsesFXm8uXwP20q8jeUZ8U7OHZ7FWUMyGdrdc9BRfQdTE6jhk52f8OH2D1lcuJiI8e1ov5FpI/lBzx9wRs4ZpLjq/yNjBIPUfPUVVe+9T/WcORh1304ndgwaSPwZZxB3xhnYe/c+aFvKgyH+V1LJW0XlLKyoYd8PP4sJJiS4OTPFw5mpHjId9SMMg4Ew21aUsGFhAXkby9n/p2VGr/j6hOGoNGITHIf12UVE2kqxSnSJ5vthGAbvPLaCvE0V5A5N4exrh3Z2k0RERKSDKUnYBtEc6Il0FsMw+GZPJW8u28O73+Q3mY7cLcHFWUMyOGtIJsOzEw47YVjqK+XTnZ/y4fYPWV68vLHcbDIzJn0Mp/Y4lVOzTyXLnQVAxOules4cKt97H++CBY2rIwPYe/YkriFh6Bx83EHbkl8X4J3iCmYVlbOqxtdk34i4GM5K9XBWqofeMfUjBmurAmxdXsyWZcXkb6mgMcNogqw+CfQZlUbPYSm4EzXCUESOPMUq0SWa78emxYXMfnYdVpuZy+4YR3yKHp0hIiJyrFGSsA2iOdATiQb+UJjPNpTw/qp85m4opjbwbZIuy+PkzCGZnDUkkxHZCZgPc0pyobeQT3Z8wkc7PmJ16eom+/on9ufUHqdySvYpDEoahMlkIlReTs3cuVR/MhvvggUYwW+Tl9bMTOImTSLu9NOJGTUSk6355ylur/XzQWklH5ZUsLSqtsm+fjFOzkr1MDklnmFxMVhMJrwVfrYsL2bL0mIKt1U2qZ/aI47coSn0HJpCSrb7sBOmIiItoVglukTr/fD7Qrx8x9fUVgUY98NejD4rt7ObJCIiIp1AScI2iNZATyQa+QJh5m0q5oPVhcxZX4R3v4RhpsfJ6QPTOH1AOsf3TsZpO7yHpO+u3s3cXXP5bPdnrChe0WRKclpMGqdm148wHJ0xGofFQbimBu8XX1A1ezY1877AqP024WeOjSV2wvHEnngi7pNOwpaR0ew1i/xBPiqt5MOSSr6sqCa030/IJJuFU5LiOS0pjpOT4ki126guq2PLsmK2ryyhYFvltyMMAXeig55DU8gdlkK3folYrObD+vwiIgejWCW6ROv9mP/aJlbN3YMnzcVlt43DYtPvIRERkWORkoRtEK2Bnki0qwuGmbephA9WFzBnfTE1/lDjPqfNzAl9UjhtQDqnDUgjw3N403LL68r5Ys8XfL77c77K/6rJoidOi5NRGaOYmDWRCVkT6OXpheH3412wkOrZs6n54gvCe/c2OZ+jXz/cJ51I7IknETNyRLOjDCuDIT7dW8WHpZXMK6tuskoywLA4F6clxXNacjwj4mII1ATZuaaU7d+Usnt9GaHAt/VtTgvZA5LIHpREj0FJmu4lIm2iWCW6ROP9KN1TzWv3LcEwYOr1w+gxKLmzmyQiIiKdREnCNojGQE+kq6kLhlmwtZS5G4qZu76Y/Mq6JvsHZcZz+sA0Tu6XyrDsBGyWlo9u8If9LCpYxGe7P2Pe7nmU+Eqa7E+PSWdC1gQmZE1gfOZ4PPZ46tauo2b+F3i/mI/vm2/YfxUSc2wsMWPGEDN+HLHjxuHo3x+TuWl7ghGDZVVe5u6t4rOyalZ/5zmGHquF8QmxnJAQx8REN31sNvI3VrB9VSk7VpVSWxVoWj/V1Zgw7NYvEbvL2uLPLyKiWCW6RNv9MCIGsx5ZTsHWSnqPTOUHvxjS2U0SERGRTqQkYRtEW6An0tUZhsGGwmrmbihmzvoiVuyuaLJScKzdwvheyUzsk8LEPin0S2/5s/wMw2BzxWYW5i9kQf4ClhUtwx/2N+43YWJg8kBGp49mdPpoRqaPJLY2gverBdR8MQ/v/C8Jl5c3OafF4yFm7Nj6pOH48dh79TqgPUX+IJ+XVTO3rIp5ZdVUhMJN9ifZLExIcDMxMY4J8bF4SgPsWV/GrnVlFG2rIhLZL0lpNpHeK57sgUl065dAWm481sOcmi0ixxbFKtEl2u7H+gUFzH1+PVaHhWl3jtOiWiIiIsc4JQnbINoCPZGjzd4aP/M2lTBnQzELtpRSXhtssj81zsGE3t8mDbsltHxqbl2ojuVFy1mQv4Cv8r9iS8WWJvtNmOiX2I/RGaMZlT6KkakjiNlWRO2iRXgXfY1v6TIitU0XL7GkphAzejQxI0bgGjES54D+TaYnhyIGq2t8fFlezYKKGhZVeqn9ztTkNLuVcR43YzwxDHc6ScyrI399ObvXlVFZ0nRUosVqJr1nPFl9E8jql0BGLw82u5KGIvItxSrRJZruR503yMt3fo2vOsjxF/Rm5OScTm2PiIiIdD4lCdsgmgI9kaNdJGKwrqCKr7aU8tXWvSzevpe6YNMEW7cEF6NzExmTm8SY3CT6prlbvGpycW0xSwqXsLRoKcuKlrG9cvsBdXp7ejM8bThDU4cyJGEQmbu91C1egnfRInzLV2D4/U3qm1wuXEOG4Bo5oj5xOHw4Fo+ncX8wYrCyupavyqv5qqKGJZVe6iJNf9S6zCaGxcUw1hPLIGyk7qmjZlMleZsr8H1narLZYiItJ56sfglk9vKQ3jMeV5y9RZ9fRI5OilWiSzTdj3kvb2TNF3kkZsZyya1jsBzG4zxERETk6KQkYRtEU6Ancqzxh8Is31nBV1tK+XJLKavzKgl/J8HmcdkYnZPImJ5JjMlN5LgsT4tXTi71lbKsaBlLC5eytGjpASMNAdw2N4NTBjcmDfvmg2P1ZmqXL8e38hsiVVUHHGPv0xvX4CE4Bw/GNWQwjgEDMDsc9Z8pEmF5VS1LKr0sqfSytNJL+XemJwP0djkYFuein8lKZmmImC017N1UibfCf0BdT6qL9F7xZPT0kNHLQ1K3WP1HUOQYolglukTL/SjeWcXrDy4FA8777Qi69U/stLaIiIhI9FCSsA2iJdATEfD6Q6zYVcGSHWUs3VnG8p0V+IJNE2w2i4kBGfEMy/YwrHsCw7MT6JXqxtKC0YbldeWsKF7B6tLVfFPyDWtK1zRZOXmfbu5uDEoexIDEAfQNJJCzzYtz5WZ8y5cT2LnzwBNbrTj69cV13GCcQwbjGjwYR58+mOx2DMNgS62fJVXfJg031x6YCDQD/WKdDLLZ6V4dITmvDsfmGrz5tQdezmYmNSeO9Nx4UnvEkdojjoS0GEwtHHEpIl2LYpXoEg33IxIxePOhpRTvrKbvmHQm/+y4TmmHiIiIRB8lCdsgGgI9EWleMBxhXX4VS3aUsWRHGct2llNaEzigXqzdwpDuHoZlJzC0WwKDsuLJSYr53mnKoUiIrRVb+abkG1aVrOKbkm/YUbWj2bppMWkMShpE/5hcepZZ6b7di2f1DgKr1xIuKzvwAJsNR69eOPr3w9l/AI4B/XH27481JYW9gRDfVNfyTXUtK6tr+abKR2EgeMApzECu005Pw0JmdYSEfD/OLdU49wb57iezOSykZLtJzY5rTBwmZsRg1ohDkS5PsUp0iYb7sXZ+Hp+/tBG708KP7xpPrMfRKe0QERGR6KMkYRtEQ6AnIi1jGAZ5FT6+2V3JN3sqWLm7gjV5ldQGDpzOG2O3MDAznkGZ8QzKqv/aPyPue6cqV/or2VC2gfV717OubB3r965nZ9VODA788emyuuiT0Ife9m7k1DjI3u0nc20hjhWbmp2mDGBJScHZvz+Ofv1w9OmNo3dv7L17U2J31icNq2obEog+9gZDzZ4j3mwm17CQ6TVIKPbj2uUjsSxIrN9okjy0WM0kZsaQ3M1NcpabpG6xJGe5iU2wt3hFaRHpfIpVoktn3486b5CX7viaupogJ1zUl2GnZ3d4G0RERCR6KUnYBp0d6IlI24QjBluKa/hmdwUrdlewLr+SDYXV+EORA+qaTdAr1U3/9Dj6pLnplx5Hv3Q3uSmx2A4x4s4b9LKxbCPry9azfu96NpZvZGvFVoKRA0f/ASQ5k+jl6k52II6scsjY5SVtbQGe9fmYD/Jj2JqWhqNPb+y9++Do3Qt7r95UZ3VjvTOWdbV+1tX4WFvjY0ttHaGD/CSPw0S3oInkyjBxBXUkloVIrg7jqY1g3u8YR4y1IXEYS2JmLIkZMSRmxBLjUfJQJBopVokunX0/5r+2iVVz95CYEcMlt43VM2pFRESkCSUJ26CzAz0RaX+hcITtpV7WFVSxLr+KdQVVrM2vosx74FRlAKvZRM+UWPo1JA/7pLnpmRJLbkosboe1+WtEQuyq2sXmis1sLm/YKjazp3pPs6MOARxmBz3s6XQPuMkqN5FWUEvK5lJSt5fj8XLAFGIAc0wMtpwc7A2buUc2hemZbEhKZ73dyaZaP5tr69jpCxzkqmA1ICUISdVh4vYGSaoKk1wdJqkmQpzv2wSizWkhMb0+YZiQEUNiRgwJ6TF4UlxY7S1bLEZE2p9ilejSmfejLN/LzHsXY0QMfnj9cLIHJXXo9UVERCT6KUnYBgq8RY4NhmFQXO1nXUEVm4uq2VxUw6biGrYUVeNtZrryPqlxDnqmxNIzOZaeqbHkJsfSMyWW7CQXMfYDE4i1wVq2V25nW+U2tlduZ0fVDrZXbmdn1c6DjjwEcJkdZJFAZq2DtL0hUnfXkLyrgrTyCClVYG9m5rE5NhZbt27YunfHlJVFZVo6+SlpbPYksyo+kXURMzvr/PgjB//RbzEg0RchvipMgjdMQk2EBG+k/rU3QkzDFObYBAeeVFf9luYiPsVFQloM8akuHK7mE6ki0j4Uq0SXzrofhmHw3hMr2b2+nJ7DUjjr10M77NoiIiLSdShJ2AYKvEWObYZhkF9Z923isKia7aVetpd62XuQkYf7pLgdZCe5yE6MoUdSTP3rpBiyE2PI9Dix7jcFLBwJk1+Tz/aq7Y1Jw13Vu9hTvYcCbwER48Dp0ftLNGJI8ztIq4Lk4jqS87ykVBkkVxkkVUOc78CRiJbERKwZGYTS06lJSaUkMZk9niS2xCewNjaeVU43deZDjxC0hQziayN4aiPE14b3ex1pfO12WIlLdhKX5Gz8Gp/sanztiLVqGrNIGyhWiS6ddT+2rSzhw3+txmw18eM7xuFJjemwa4uIiEjXoSRhGyjwFpGDqfQF2VHqZcdeL9tK6r9uL/Wyo9RLVV3zi4rsYzZBeryTrARXw+akW4KLLE/9+0yPk4QYGyaTiWA4SF5NHrurdzcmDndV7yK/Jp/8mnxqQ7Xf21abYSbF7yC5BhLLAiTtDZJUbZBYAwk1BoleSKgBx/7NNpsxJScTTE7Bm5REeUISRfEJ7I7zsDUmns2xcZTFJ1AZF0/EfPBnXjkDEdy+CHE+gzhfw+u6b8sSIpDutJPkceBOcBCb6MCd6NzvtQNnrE2JRJGDUKwSXTrjfoSCYV65axFVpXWM/EEOx5/Xu0OuKyIiIl1PS2MVzQcTETkMHpeNYdkJDMtOOGBfZW2Q3eW17C6rZXd5LbvKatld5mN3WS17yn0EwhEKKusoqKxj2c7yZs/vsJpJj3eSHu8gPd5JRnwy6fHdGOhxcmqqk9Q4B8mxNiKmWvK9+RTUFJBXk0eBt/5robeQotoiyurKCJoiFDh9FDiBFIDmk3oxQTOJtSYSKsN4aiIk1OzFU1uKpwbiiyGp1qCnF86pBWfD7GjDZCLs8eBLSKQqPoGyuHiK3PHsjo2jyB1PpTuOSnc8Fe44ijLiqHW6oJmEnz1oEFvnw13nJXaHQaw/QmxdBHedQWzQIMlqIdlhI81lI93twO1xEOtxEOOxExNvxxVnxxVnw/o9K1SLiBxtvpmzm6rSOmI9dkb9IKezmyMiIiJHASUJRUTaiSfGhifGw+BungP2RSIGpV4/+RV15Ff4yK/wkbff17xyH+W1QfyhCLvK6hOMh+K0mUmNc5DidpLqHkRK3Ah6uh2MSbGTFOsg3mUCaw1ByvFFSijxFVNUW0RRbRGlvlJKakso9ZVSF66j1hah1gN5HjhYInEfRxDiaw3ctRDvq8LtqyLOt7N+xGAp9NsFo3wQW2cQWwfuOojxg2Gx4ouLpyounvJYN3tdbipiY6mOcVMV66Y6Npaq2DiqYtzsTYqlOqZ+X8Bma0guGmD4iPHX4tprEJsXwRUwcAUiuPwG7gh4LBYSrRaS7DaSnVaSY+ykxNpJiLXVJxPddpxuG063DZsWXRGRLqym3M/SD3cCcPwFfbA7FdKLiIhI2ymiEBHpAGazibQ4J2lxToY3MwoRoC4YprjKT1F1HYWVdRRV1W+FVf7G16XVfryBMHXBSMMoRd/3XttkAo8ri6TYXJJj7STE2BnsspGQaCPGYWCx+sHiJUwVAcqpo5za8F6qQyWU15VRVlfG3rq9+MN+/DYo8Zgo8UDzay83L6bOILausmGrTyDG+Ou35GLI9jcti/EbuAJgC1uJmGOos8XgjXFTExNDjSuWmpgYvK4YvE4XXlcMtU4XBU4XW50uvLYYvLiojbjwBZwEInacZeAMGjgDBs6ggStkEGuYcJvMxJpNxFssxFktxNuteBwWEp02Elw2El02EmPsxMdYccbasbssGrUoIp1u4dtbCPnDZPSKp9/Y9M5ujoiIiBwllCQUEYkSTpuFHskx9Eg+9IPnawMhSqsDlNT4Kan2U9rwtaTGT1lNgDJvgL1eP2XeABW+IIYBFbVBKmqDbCvxfk8rEhu2XphNEO+yEe+0keG04nZacNgj2K0hzJYAZosfw+QjbPISMlUTMKqoi5RTGy6jNlKGN1xGXaQKkylCrdNErRNKgMNJLjZ8YkwRL65ACS4/uAL7NgNnAJxeSK2A7AD1ScBA/bRoRxAcgYZVoE12IjgwLE4iZgchawxBSwxBu5M6u5M6h4M6u4M8h5OtDgc+u6OxrM7uIGCzEzbbiFgcYLJhstgx2RxYLXZcZjMuk5kYswmXxUysxUys1YLbZiHWZsFtt+K2W3A7LMQ5bLidVuKdVuJcNtx2KzaznrsoIi1XuK2STYuKwAQnXtJPz24VERGRdqMkoYhIFxNjt9Ij2fq9yUSAUDhChS9YnzisCVBeG6CiNkh5bYBKX5DyhkRiRWN5kCpfkEA4QmS/5GLzzEBsw5Z20DY4rCZcdjMOG9itBjZrGKsljNkSBHMATAEipjoi+AjhJWR48VNDMFKDP1ID5gAmc5BqU4AaVxBigpjMATCHgHBzjzpsRhiobdj2+wQRA0ewPpFo3++rIwT2WgNPJaSGwNaw2Rs2W9jAHgRbGEyGhfpfp1YwWQEbhtmGgZVqs51Ksx3DbCNssRM22xu+OghZHQStdsIWGxGzFcNiI2KxYljqX5usNkxWKyarHbPVhtlmxWyzYbHasdpt2Ow2bHZ742Z32nE4bDhtVpw2M06rBZfdgtNmJsZmwWExYzObsJv2fTVhb/hqM5uwmUxKNohEOSNi8MXMTQAMPD6TtBwtWiMiIiLtR0lCEZGjmNViJsXtIMXtgBbOSDMMA38oQpUvSFVdkEpfkCpfiKq6+gRipS9IjT9MdV2QGn+ImroQ1XUhqv2hxjKvP0QwbADgDxn4Q+H9rmCiMamGq02fz2QysFrAaolgMUcwm8OYLSFMphCYgvUJSAIYRoCIyU+Y+oSkYQqCKQymEHWmEJhCYApj2pcJNIUbzlFfx2QKf8/rCFDXwoRlU9aQgSUC1jBY930NU18WAou/vtwSAUvY2O91Q539XlsiYDJM9RtmTIYZEybAgskwg8kMDeX1ry0YDWUGFjCZMWEBzGC21tc1WRo3TJb685qt9e/NFswmCyazFbPZgtlsxWSyYrbUvzZbrFjMFswWG2azBavVitlsxWK1YrHUbyarBYvVgsVqxWSpf222mrFYLPXnsVgwmUyYLRYslvpyTGbMFjMWsxmz2YzJbG7cbzaZMVnqyy0mE2ZLw36zBbPZVN8es6nxnGaTqf48FjMmkxmL2YQZMJvqv5pMYMaEueGrifqVyk2gpKp0uPULCyjZVY3daWG8VjMWERGRdqYkoYiINGEymXDaLDhtFtLina0+TyAUwesP4Q2E8PrD1PhD1AbqE4hef5jaYBhfwz5fMExtIERtIExtw766hs0XqN+/73VtMIxRn3/EMEwEQxAMWQALYGuXPmgtE5GGxGHk2wQi+xKJYQxTGIg0JCEj+yUY9x0Tadhfv5kayjE3ZADtBvWjJyOA0XDs/vW/U0YEk8nY773R5GvT+kZ9OZH6BCjBhrJ99Y0m9UyNZfudc9/WUNdk+m7Zga/NgQjmQP1HNBv1m6lha/Z9pL6vv1u+/+sm+/l2n+k7ZWbDgO8cA/WJVhrq0JAYZN9xmOqb3jBt3rzf633fBfvq/+ueJVitCrWkffh9Ib5+eysAY87pSUy8vZNbJCIiIkcbRa4iInJE2K1m7FY7ibHt+x/ZfSMd/cEIdaF9ycQIdcEw/lCkMblYF4oQCEXwh8L4g5H6Y0LhhrL6esFwfZ1AONJYvv/7YDhCMGw0vg6EIwRDDWXhyIFtw4xhmBuSSHJ49ks0mvZ14HeSiw3vTfv28d16+5WbaHocNBy7fz2+cxxNjzV/5xrfqW9qctyB5zKbD71auMjhWPK/7fiqgySkxzDklO6d3RwRERE5CilJKCIiXcr+Ix09nThy0DAMgmGDUKQ+aRgMRwg1fA2GI4Qi9cnFcOTbOqGG+qH9jgtFIoQjEI7UHxOO1J933/tQuL4sYhiN+/ff6svqzxExvrN/v/eR5l4bENmvbF+5YdB4rGHQeC7DoLFeZL969e/BwCDSUP/w7ZdQ2//wZk7VlhxsR+ZvlSRsf3l5edx00018+OGH1NbW0qdPH5577jlGjx590GP8fj933303L774IoWFhWRmZnL77bdz9dVXA/DMM8/w/PPPs2bNGgBGjRrF/fffz9ixYzvkM7WEvzbIuvn5AJxwcV8sVn1viYiISPtTklBERKQVTCYTdqsJO/rPenMMoz5huC+puC/ZaFBfZuyXVNy/jnGQ9wb7ko8N5fvOY9CkroHR5Jj6qen7HRP5ttygvnD/90bje+Pbr43n3r+8/ryw/76m55D2VV5ezsSJEzn11FP58MMPSU1NZfPmzSQmJh7yuIsvvpiioiL+85//0KdPHwoKCohEvh0J/Pnnn3PZZZcxYcIEnE4nDz30EJMnT2bt2rV069btSH+sFnHE2Ljk1rFsWVZEznHJnd0cEREROUqZDMNQHPsdVVVVeDweKisriY/XqnEiIiISXY7FWOXmm2/mq6++Yv78+S0+5qOPPuLSSy9l27ZtJCUlteiYcDhMYmIi//jHP7jiiitadMyxeD9ERESk62hprKLhDyIiIiIS9d59911Gjx7NRRddRFpaGiNGjOCZZ55p0TEPP/ww3bp1o1+/fvzhD3/A5/Md9Jja2lqCweAhk4p+v5+qqqomm4iIiEhXpyShiIiIiES9bdu28eSTT9K3b18+/vhjfv3rX3P99dfz3//+95DHfPnll6xZs4ZZs2bx+OOP88Ybb3Dttdce9JibbrqJrKwsJk2adNA6DzzwAB6Pp3HLzs5u02cTERERiQaabtwMTRkRERGRaHYsxip2u53Ro0ezYMGCxrLrr7+eJUuWsHDhwmaPmTx5MvPnz6ewsBCPxwPAW2+9xYUXXojX68XlcjWp/+CDD/Lwww/z+eefM3To0IO2xe/34/f7G99XVVWRnZ19TN0PERER6To03VhEREREjhqZmZkMGjSoSdnAgQPZtWvXIY/p1q1bY4Jw3zGGYbBnz54mdf/617/y4IMP8sknnxwyQQjgcDiIj49vsomIiIh0dUoSioiIiEjUmzhxIhs3bmxStmnTJnJycg55TH5+PjU1NU2OMZvNdO/evbHs4Ycf5p577uGjjz5i9OjR7d94ERERkS5ASUIRERERiXq//e1v+frrr7n//vvZsmULL7/8Mk8//TTTp09vrHPLLbc0WZH4xz/+McnJyVx11VWsW7eOL774gv/3//4fV199deNU44ceeojbbruNZ599ltzcXAoLCyksLGySWBQRERE5FihJKCIiIiJRb8yYMcyaNYtXXnmFwYMHc8899/D4448zbdq0xjoFBQVNph+73W5mz55NRUUFo0ePZtq0aUydOpUnnniisc6TTz5JIBDgwgsvJDMzs3H761//2qGfT0RERKSzaeGSZhyLDwMXERGRrkOxSnTR/RAREZFopoVLREREREREREREpEWUJBQRERERERERETnGKUkoIiIiIiIiIiJyjFOSUERERERERERE5BinJKGIiIiIiIiIiMgxTklCERERERERERGRY5y1sxsQjQzDAOqXiBYRERGJNvtilH0xi3QuxY4iIiISzVoaOypJ2Izq6moAsrOzO7klIiIiIgdXXV2Nx+Pp7GYc8xQ7ioiISFfwfbGjydCfoA8QiUTIz88nLi4Ok8l0RK5RVVVFdnY2u3fvJj4+/ohc41ii/mx/6tP2pf5sX+rP9qX+bF8d0Z+GYVBdXU1WVhZms54e09kUO3Y96s/2pf5sX+rP9qc+bV/qz/YVTbGjRhI2w2w207179w65Vnx8vP5RtSP1Z/tTn7Yv9Wf7Un+2L/Vn+zrS/akRhNFDsWPXpf5sX+rP9qX+bH/q0/al/mxf0RA76k/PIiIiIiIiIiIixzglCUVERERERERERI5xShJ2EofDwR133IHD4ejsphwV1J/tT33avtSf7Uv92b7Un+1L/SlHgr6v2pf6s32pP9uX+rP9qU/bl/qzfUVTf2rhEhERERERERERkWOcRhKKiIiIiIiIiIgc45QkFBEREREREREROcYpSSgiIiIiIiIiInKMU5JQRERERERERETkGKckYSf55z//SW5uLk6nk3HjxrF48eLOblKX8MUXXzB16lSysrIwmUy8/fbbTfYbhsHtt99OZmYmLpeLSZMmsXnz5s5pbBfwwAMPMGbMGOLi4khLS+O8885j48aNTerU1dUxffp0kpOTcbvd/OhHP6KoqKiTWhzdnnzySYYOHUp8fDzx8fEcf/zxfPjhh4371Zdt8+CDD2Iymbjxxhsby9SnLXfnnXdiMpmabAMGDGjcr748fHl5efzkJz8hOTkZl8vFkCFDWLp0aeN+/U6S9qTYsXUUO7YvxY7tS7HjkaXYsW0UO7a/rhA7KknYCV599VV+97vfcccdd7B8+XKGDRvGlClTKC4u7uymRT2v18uwYcP45z//2ez+hx9+mCeeeIJ//etfLFq0iNjYWKZMmUJdXV0Ht7RrmDdvHtOnT+frr79m9uzZBINBJk+ejNfrbazz29/+lvfee4/XX3+defPmkZ+fzwUXXNCJrY5e3bt358EHH2TZsmUsXbqU0047jXPPPZe1a9cC6su2WLJkCU899RRDhw5tUq4+PTzHHXccBQUFjduXX37ZuE99eXjKy8uZOHEiNpuNDz/8kHXr1vHII4+QmJjYWEe/k6S9KHZsPcWO7UuxY/tS7HjkKHZsH4od20+XiR0N6XBjx441pk+f3vg+HA4bWVlZxgMPPNCJrep6AGPWrFmN7yORiJGRkWH85S9/aSyrqKgwHA6H8corr3RCC7ue4uJiAzDmzZtnGEZ9/9lsNuP1119vrLN+/XoDMBYuXNhZzexSEhMTjX//+9/qyzaorq42+vbta8yePds4+eSTjRtuuMEwDH1/Hq477rjDGDZsWLP71JeH76abbjJOOOGEg+7X7yRpT4od24dix/an2LH9KXZsO8WO7UOxY/vqKrGjRhJ2sEAgwLJly5g0aVJjmdlsZtKkSSxcuLATW9b1bd++ncLCwiZ96/F4GDdunPq2hSorKwFISkoCYNmyZQSDwSZ9OmDAAHr06KE+/R7hcJiZM2fi9Xo5/vjj1ZdtMH36dM4+++wmfQf6/myNzZs3k5WVRa9evZg2bRq7du0C1Jet8e677zJ69Gguuugi0tLSGDFiBM8880zjfv1Okvai2PHI0b/TtlPs2H4UO7YfxY7tR7Fj++kqsaOShB2stLSUcDhMenp6k/L09HQKCws7qVVHh339p75tnUgkwo033sjEiRMZPHgwUN+ndrudhISEJnXVpwe3evVq3G43DoeDX/3qV8yaNYtBgwapL1tp5syZLF++nAceeOCAferTwzNu3DhmzJjBRx99xJNPPsn27ds58cQTqa6uVl+2wrZt23jyySfp27cvH3/8Mb/+9a+5/vrr+e9//wvod5K0H8WOR47+nbaNYsf2odixfSl2bD+KHdtXV4kdrR12JRGJatOnT2fNmjVNnjMhh69///6sXLmSyspK3njjDa688krmzZvX2c3qknbv3s0NN9zA7NmzcTqdnd2cLu/MM89sfD106FDGjRtHTk4Or732Gi6XqxNb1jVFIhFGjx7N/fffD8CIESNYs2YN//rXv7jyyis7uXUiIkeeYsf2odix/Sh2bF+KHdtXV4kdNZKwg6WkpGCxWA5Y9aeoqIiMjIxOatXRYV//qW8P33XXXcf777/PZ599Rvfu3RvLMzIyCAQCVFRUNKmvPj04u91Onz59GDVqFA888ADDhg3jb3/7m/qyFZYtW0ZxcTEjR47EarVitVqZN28eTzzxBFarlfT0dPVpGyQkJNCvXz+2bNmi789WyMzMZNCgQU3KBg4c2DgNR7+TpL0odjxy9O+09RQ7th/Fju1HseORpdixbbpK7KgkYQez2+2MGjWKOXPmNJZFIhHmzJnD8ccf34kt6/p69uxJRkZGk76tqqpi0aJF6tuDMAyD6667jlmzZjF37lx69uzZZP+oUaOw2WxN+nTjxo3s2rVLfdpCkUgEv9+vvmyF008/ndWrV7Ny5crGbfTo0UybNq3xtfq09Wpqati6dSuZmZn6/myFiRMnsnHjxiZlmzZtIicnB9DvJGk/ih2PHP07PXyKHY88xY6tp9jxyFLs2DZdJnbssCVSpNHMmTMNh8NhzJgxw1i3bp3xi1/8wkhISDAKCws7u2lRr7q62lixYoWxYsUKAzAeffRRY8WKFcbOnTsNwzCMBx980EhISDDeeecdY9WqVca5555r9OzZ0/D5fJ3c8uj061//2vB4PMbnn39uFBQUNG61tbWNdX71q18ZPXr0MObOnWssXbrUOP74443jjz++E1sdvW6++WZj3rx5xvbt241Vq1YZN998s2EymYxPPvnEMAz1ZXvYf4U6w1CfHo7f//73xueff25s377d+Oqrr4xJkyYZKSkpRnFxsWEY6svDtXjxYsNqtRr33XefsXnzZuOll14yYmJijBdffLGxjn4nSXtR7Nh6ih3bl2LH9qXY8chT7Nh6ih3bV1eJHZUk7CR///vfjR49ehh2u90YO3as8fXXX3d2k7qEzz77zAAO2K688krDMOqXDb/tttuM9PR0w+FwGKeffrqxcePGzm10FGuuLwHjueeea6zj8/mMa6+91khMTDRiYmKM888/3ygoKOi8Rkexq6++2sjJyTHsdruRmppqnH766Y1BnmGoL9vDdwM99WnLXXLJJUZmZqZht9uNbt26GZdccomxZcuWxv3qy8P33nvvGYMHDzYcDocxYMAA4+mnn26yX7+TpD0pdmwdxY7tS7Fj+1LseOQpdmw9xY7tryvEjibDMIyOG7coIiIiIiIiIiIi0UbPJBQRERERERERETnGKUkoIiIiIiIiIiJyjFOSUERERERERERE5BinJKGIiIiIiIiIiMgxTklCERERERERERGRY5yShCIiIiIiIiIiIsc4JQlFRERERERERESOcUoSioiIiIiIiIiIHOOUJBSRLuunP/0p5513Xodfd8aMGZhMJkwmEzfeeGObz5WQkNAu7TrSTjnllMbPvXLlys5ujoiIiMhhUezYsRQ7inQ91s5ugIhIc0wm0yH333HHHfztb3/DMIwOalFT8fHxbNy4kdjY2Dad55JLLuGss85qp1Z9y2QyMWvWrHYNhN966y22bt3K2LFj2+2cIiIiIu1BsWPbKHYUEVCSUESiVEFBQePrV199ldtvv52NGzc2lrndbtxud2c0DagPpDIyMtp8HpfLhcvlaocWHXlJSUlUVVV1djNEREREDqDYMfoodhTpejTdWESiUkZGRuPm8XgaA6t9m9vtPmDKyCmnnMJvfvMbbrzxRhITE0lPT+eZZ57B6/Vy1VVXERcXR58+ffjwww+bXGvNmjWceeaZuN1u0tPTufzyyyktLT3sNufm5nLvvfdyxRVX4Ha7ycnJ4d1336WkpIRzzz0Xt9vN0KFDWbp0aeMx350ycueddzJ8+HBeeOEFcnNz8Xg8XHrppVRXVze5zuOPP97k2sOHD+fOO+9s3A9w/vnnYzKZGt8DvPPOO4wcORKn00mvXr246667CIVCABiGwZ133kmPHj1wOBxkZWVx/fXXH3Y/iIiIiHQ0xY6KHUWk7ZQkFJGjyn//+19SUlJYvHgxv/nNb/j1r3/NRRddxIQJE1i+fDmTJ0/m8ssvp7a2FoCKigpOO+00RowYwdKlS/noo48oKiri4osvbtX1H3vsMSZOnMiKFSs4++yzufzyy7niiiv4yU9+wvLly+nduzdXXHHFIae6bN26lbfffpv333+f999/n3nz5vHggw+2uA1LliwB4LnnnqOgoKDx/fz587niiiu44YYbWLduHU899RQzZszgvvvuA+DNN9/kscce46mnnmLz5s28/fbbDBkypFX9ICIiItIVKHZU7Cgi31KSUESOKsOGDePWW2+lb9++3HLLLTidTlJSUrjmmmvo27cvt99+O3v37mXVqlUA/OMf/2DEiBHcf//9DBgwgBEjRvDss8/y2WefsWnTpsO+/llnncUvf/nLxmtVVVUxZswYLrroIvr168dNN93E+vXrKSoqOug5IpEIM2bMYPDgwZx44olcfvnlzJkzp8VtSE1NBSAhIYGMjIzG93fddRc333wzV155Jb169eKMM87gnnvu4amnngJg165dZGRkMGnSJHr06MHYsWO55pprDrsPRERERLoKxY6KHUXkW0oSishRZejQoY2vLRYLycnJTf6imZ6eDkBxcTEA33zzDZ999lnjc2rcbjcDBgwA6v8q25br77vWoa7fnNzcXOLi4hrfZ2ZmHrJ+S33zzTfcfffdTT7rNddcQ0FBAbW1tVx00UX4fD569erFNddcw6xZsxqnk4iIiIgcjRQ7HpxiR5FjjxYuEZGjis1ma/LeZDI1Kdu38l0kEgGgpqaGqVOn8tBDDx1wrszMzDZdf9+1DnX97zvHvmP2r282mw+YchIMBr+3bTU1Ndx1111ccMEFB+xzOp1kZ2ezceNGPv30U2bPns21117LX/7yF+bNm3dAm0RERESOBoodD06xo8ixR0lCETmmjRw5kjfffJPc3Fys1q7xIzE1NbXJCn5VVVVs3769SR2bzUY4HG5SNnLkSDZu3EifPn0Oem6Xy8XUqVOZOnUq06dPZ8CAAaxevZqRI0e274cQERER6YIUOzal2FHk6KLpxiJyTJs+fTplZWVcdtllLFmyhK1bt/Lxxx9z1VVXHRAoRYvTTjuNF154gfnz57N69WquvPJKLBZLkzq5ubnMmTOHwsJCysvLAbj99tt5/vnnueuuu1i7di3r169n5syZ3HrrrUD9ann/+c9/WLNmDdu2bePFF1/E5XKRk5PT4Z9RREREJBopdlTsKHI0U5JQRI5pWVlZfPXVV4TDYSZPnsyQIUO48cYbSUhIwGyOzh+Rt9xyCyeffDLnnHMOZ599Nueddx69e/duUueRRx5h9uzZZGdnM2LECACmTJnC+++/zyeffMKYMWMYP348jz32WGMgl5CQwDPPPMPEiRMZOnQon376Ke+99x7Jyckd/hlFREREopFiR8WOIkczk3GotdRFROQAM2bM4MYbb6SioqKzm9LhduzYQc+ePVmxYgXDhw/v7OaIiIiIRD3FjoodRbqK6PxTh4hIlKusrMTtdnPTTTd1dlM6zJlnnslxxx3X2c0QERER6XIUO4pIV6CRhCIih6m6upqioiKgfppFSkpKJ7eoY+Tl5eHz+QDo0aMHdru9k1skIiIiEv0UOyp2FOkqlCQUERERERERERE5xmm6sYiIiIiIiIiIyDFOSUIREREREREREZFjnJKEIiIiIiIiIiIixzglCUVERERERERERI5xShKKiIiIiIiIiIgc45QkFBEREREREREROcYpSSgiIiIiIiIiInKMU5JQRERERERERETkGPf/AcJ2KHGnlwcKAAAAAElFTkSuQmCC", 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" ] @@ -148,7 +161,6 @@ } ], "source": [ - "\n", "colors = [\"tab:purple\", \"tab:cyan\", \"tab:red\", \"tab:green\", \"tab:blue\"]\n", "linestyles = [\"dashed\", \"dotted\", \"solid\"]\n", "\n", @@ -160,42 +172,49 @@ "currents = [\n", " \"X-averaged negative electrode volumetric interfacial current density [A.m-3]\",\n", " \"X-averaged negative electrode lithium plating volumetric interfacial current density [A.m-3]\",\n", - " \"Sum of x-averaged negative electrode volumetric interfacial current densities [A.m-3]\"\n", + " \"Sum of x-averaged negative electrode volumetric interfacial current densities [A.m-3]\",\n", "]\n", "\n", "\n", "def plot(sims):\n", " import matplotlib.pyplot as plt\n", - " fig, axs = plt.subplots(2, 2, figsize=(13,9))\n", - " for (C_rate,sim), color in zip(sims.items(),colors):\n", + "\n", + " fig, axs = plt.subplots(2, 2, figsize=(13, 9))\n", + " for (C_rate, sim), color in zip(sims.items(), colors):\n", " # Isolate final equilibration phase\n", " sol = sim.solution.cycles[0].steps[2]\n", "\n", " # Voltage vs time\n", " t = sol[\"Time [min]\"].entries\n", - " t = t-t[0]\n", + " t = t - t[0]\n", " V = sol[\"Voltage [V]\"].entries\n", - " axs[0,0].plot(t, V, color=color, linestyle=\"solid\", label=C_rate)\n", + " axs[0, 0].plot(t, V, color=color, linestyle=\"solid\", label=C_rate)\n", "\n", " # Currents\n", - " for current, ls in zip(currents,linestyles):\n", + " for current, ls in zip(currents, linestyles):\n", " j = sol[current].entries\n", - " axs[0,1].plot(t, j, color=color, linestyle=ls)\n", + " axs[0, 1].plot(t, j, color=color, linestyle=ls)\n", "\n", " # Plated lithium capacity\n", " Q_Li = sol[\"Loss of capacity to negative lithium plating [A.h]\"].entries\n", - " axs[1,0].plot(t, Q_Li, color=color, linestyle='solid')\n", + " axs[1, 0].plot(t, Q_Li, color=color, linestyle=\"solid\")\n", "\n", " # Capacity vs time\n", - " Q_main = sol[\"Negative electrode volume-averaged concentration [mol.m-3]\"].entries * F * A * L_n / 3600\n", - " axs[1,1].plot(t, Q_main, color=color, linestyle='solid')\n", + " Q_main = (\n", + " sol[\"Negative electrode volume-averaged concentration [mol.m-3]\"].entries\n", + " * F\n", + " * A\n", + " * L_n\n", + " / 3600\n", + " )\n", + " axs[1, 1].plot(t, Q_main, color=color, linestyle=\"solid\")\n", "\n", - " axs[0,0].legend()\n", - " axs[0,0].set_ylabel(\"Voltage [V]\")\n", - " axs[0,1].set_ylabel(\"Volumetric interfacial current density [A.m-3]\")\n", - " axs[0,1].legend(('Deintercalation current','Stripping current','Total current'))\n", - " axs[1,0].set_ylabel(\"Plated lithium capacity [A.h]\")\n", - " axs[1,1].set_ylabel(\"Intercalated lithium capacity [A.h]\")\n", + " axs[0, 0].legend()\n", + " axs[0, 0].set_ylabel(\"Voltage [V]\")\n", + " axs[0, 1].set_ylabel(\"Volumetric interfacial current density [A.m-3]\")\n", + " axs[0, 1].legend((\"Deintercalation current\", \"Stripping current\", \"Total current\"))\n", + " axs[1, 0].set_ylabel(\"Plated lithium capacity [A.h]\")\n", + " axs[1, 1].set_ylabel(\"Intercalated lithium capacity [A.h]\")\n", "\n", " for ax in axs.flat:\n", " ax.set_xlabel(\"Time [minutes]\")\n", @@ -228,7 +247,9 @@ "metadata": {}, "outputs": [], "source": [ - "sims_irreversible = define_and_solve_sims(models[\"irreversible\"], experiments, parameter_values)" + "sims_irreversible = define_and_solve_sims(\n", + " models[\"irreversible\"], experiments, parameter_values\n", + ")" ] }, { @@ -238,22 +259,7 @@ "outputs": [ { "data": { - "text/plain": [ - "(
,\n", - " array([[,\n", - " ],\n", - " [,\n", - " ]],\n", - " dtype=object))" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -263,7 +269,7 @@ } ], "source": [ - "plot(sims_irreversible)" + "plot(sims_irreversible);" ] }, { @@ -279,7 +285,9 @@ "metadata": {}, "outputs": [], "source": [ - "sims_partially_reversible = define_and_solve_sims(models[\"partially reversible\"], experiments, parameter_values)" + "sims_partially_reversible = define_and_solve_sims(\n", + " models[\"partially reversible\"], experiments, parameter_values\n", + ")" ] }, { @@ -289,22 +297,7 @@ "outputs": [ { "data": { - "text/plain": [ - "(
,\n", - " array([[,\n", - " ],\n", - " [,\n", - " ]],\n", - " dtype=object))" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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62lV47KWXXuLiiy/Gy8vrX/s5HA7efvvt4z7v2LFjGTt27HHv31AcDgdGo/Gwy/WxcOFCfHx86Nu3b0OH16SkB/QQ/D00zppAVq5d7u5QhBDitDdw4EACAwMPaHvyySeZNm0aNpsNAJvNxuTJk90RnqiHQdGDeGvkWwxrMYzAK64g4vHHUWZz3UijzzPzGbliC0sLSzEYFD4BVgDW/55Bzs5id4YuhGggmZmZBAcHY7G4vlwKDg4mMjKSqVOnsmfPHoYMGcKQIUMA8PHx4Y477qBLly789ddfDB48mBUrVtStu+222+jQoQPDhg1j31RRgwcP5pZbbiEpKYmOHTuybNkyAN5//31uvPFGwPWF5s0330zfvn2Jj49n5kzX1MhOp5Prr7+edu3aMWLECEaPHl23bn/btm1j+PDhdOnShW7durF9+3YWLlzImDFj6ra58cYbef/99wGIjY3lnnvuoVu3bnz55Zf/Wv7ll1/o06cP3bp1Y+LEiZSWltbt99BDD9GtWzc6derEpk2bSEtL44033uDFF18kKSmJ33//vaH/EzUZ6QE9hEh/b9L2eLExbR2jcP83JkIIcTJ4YGs660orGvSYHX08eax19DHtU1xcTElJCfHx8Q0ay9EopY6WCSkgU2vdpiniOZUopegd0RuArLIsCioLaOUIIuPmWwi9607GJnWlxOGgh5933T7VlXZWzEkjpl0gQy+1uSt0IZqtWc+vol2fCNr3jcDhcPLtSykk9o+kba9waqodfP/KGjoOiqJ1chhVFXZ+fH0tnYdGk9A1lIrSan56cx1JI1oQ1zmYsqKqo45aGDlyJI8++iht2rRh+PDhTJo0iUGDBnHzzTfzwgsvsGDBAoKDgwEoKyujV69ePP/8v2eeKisrIzk5mRdffJFHH32URx55hFdffRWA8vJyUlJSWLRoEVdccQXr1q371/6ZmZn88ccfbNq0ibFjx3Luuefy9ddfk5aWxoYNG8jJyaF9+/ZcccUV/9r3oosu4t5772X8+PFUVlbidDrZvXv3v7bbX1BQEKtWrQLg3nvvrVvOzc3lnHPOYd68eXh7ezNlyhReeOEFHnzwQcCVoK9atYrXX3+d5557jrfffptrr70WHx8f7rzzziOe82QnPaCH0KFFNGAgNT/b3aEIIYQ4eWzXWtuO8OMLlLk7yJOZ1pq7fruLuxfdjTYb0XY7zrIyPI0GrooOwaAUhTV29lRW42E1cc6d3Rl8UVt3hy2EaAA+Pj6sXLmS6dOnExISwqRJk+p6Cg9mNBqZMGHCIdcZDAYmTZoEwMUXX8wff/xRt+6CCy4AXCNniouLKSws/Nf+48aNw2AwkJiYSHa267P+H3/8wcSJEzEYDISHh9f1xO6vpKSEjIwMxo8fD4DVaj3kkOGD7Yv14OUlS5awYcMG+vXrR1JSEh988AE7d+6s2+6cc84BoHv37qSlpR31PKcS6QE9hD5t43hryWqyZC5QIYSoc6w9lY3FZrPh4+NDampqU/eCHvrT0LFvc9pSSvFQn4dQSuHhH0TsF5+jDP98F6615oI1qTjRzOneBt9A11Dcqgo7a+btInl0LAajfHcuREMYf0e3utdGo+GAZbOH8YBli6fpgGVPH48Dluv7zLbRaGTw4MEMHjyYTp068cEHH3DZZZf9azur1Vrv5yP3Vdc++PWhloG6IcBAgxQcNZlMOJ3OuuXKg/IHb2/vQy5rrRkxYgSffvrpIY+7L06j0Yjdbj/hOE8m8lf8EFqF+gOQr+XtEUKIk9F9993HDTfcQHGxa1RsaWlpo1fB1VqnHtymlAo82jbiQK0CWpHgnwDA6r0paK0pnjOHjNtvB6eT/8ZH8EirKAz7fXDctT6PlXN2krVDngcV4lS1efNmtm7dWreckpJCy5au6ZZ8fX0pKSmp13GcTmfd85mffPIJ/fv3r1v3+eefA64eTT8/P/z8/Op1zH79+vHVV1/hdDrJzs5m4cKF/9rG19eX6OhovvnmGwCqqqooLy+nZcuWbNiwgaqqKgoLC5k/f369ztm7d28WL17Mtm3bANfQ4i1bthxxn2N5n05mkmEdQt1coOro3epCCCEa1wUXXECfPn3YvHkz0dHRvPPOO1x33XUMGTKEHj160LFjRwYMGIDB0Li3NKVUP6XURqXUeqVUL6XUXGC5Umq3UqpPo568GVqZvZLJP03mm23fYM/LpyYrG2d5OQMCfent7wPA1rJKtNa0Tg7jwkd6EdnK371BCyGOW2lpKZMnTyYxMZHOnTuzYcMGHn74YQCuvvpqRo0adcihrwfz9vZm2bJldOzYkV9//bXumUlw9Zx27dqVa6+9lnfeqf9UxxMmTCA6OprExEQuvvhiunXrdsjkdcaMGUydOpXOnTvTt29fsrKyiImJ4bzzzqNjx46cd955dO3atV7nDAkJ4f333+eCCy6gc+fO9OnTh02bNh1xn7POOotZs2ad8kWIVHOZ6zI5OVnvq47VENrc9xkmzy2sf+CBQ3bfCyHE6WDjxo20b9/e3WE0mENdj1JqpdY6+Wj7KqWWAVcCPsB3wDit9R9KqW7AK1rrfo0Rs7s1xP21uLKGF37Zwp1ntMXH4nr6R2vNN9u+YUz8GEwGE9jtKLO5bp+VRWWMXb2VF9u14LzwfzqaM7YUUFlWQ0LX0BOKSYjTTXP5e+7j41NXLXZ/gwcP5rnnniM5+ah/zg+ptLQUHx8f8vLy6NmzJ4sXLyY8PPxEwz1tHMv9VXpAD8PLWEEVAezNyjz6xkIIIU4HZq3131rrv4C9Wus/ALTWqwBP94Z2ctuwp5jPlu9iRVp+XZtSivGtx2M2mql0VJJekYWzspI9995HRUoKXW1e3BsXwZnB//RCaK1Z/sMOVvyYhtPZPL5AF0KcHMaMGUNSUhIDBgzggQcekOSzEUkRosPwt2iKqgNYsWYZoyPGuTscIYQQ7rf/l7b3HbTOoykDOdX0jg/i97uHEuJ76EIldy+6m60FW/l60AeUr16FtXMnPJOSuKllGAB2p2ZPVTUtPC2M+k8nUGAwyOgkIU5Hh+r9BA753OaxONH9Rf1JD+hhRNl80DU2Nu/a4O5QhBBCnBweUMpVHEBr/c2+RqVUAtC4FZCagX3J59LUPL5amX7Aums6X8O9Pe/FKziM+NmzCbzwwgPW371lN2ev3kaJ3YHVx4zV24zTqVnxYxoVJdVNdg1CCCFOnCSgh5HYMgowkponc4EKIYQArfW3Wuvy/duUUuFa6+1a62fcFdep5q3fd/DW76nUOP6ZtqBjcEcGxwwGIMuej9aayg0byHzkEbTTyVXRIdwVG46v6Z9pGQqzylkxJ42tK+Q+LYQQpxJJQA+jb1vX3HJZVTIXqBBCiMP60d0BnGpemNSFz6/pg/kQ83luzt/M2d+czaxtsyhfuYrShb9hz8kh0ceTCyODAEivrMahNYGR3lzwYE86D4lp6ksQQghxAiQBPYz4UBsABTIXqBBCiMOTBxGPkc1qxs/TjMOpmbZwO0XlNXXrWge05tIOlzIweiABF19E/LezMe9XCCSnqoaRKzYzJdVVINAvxDVdWnFuBat/2dW0FyKEEOK4SHZ1GBF+noCmWOYCFUIIt8rKyuL8888nISGB7t27M3r06LrJus8880zS09O56KKLaNu2LR07duSKK66gpqbmKEdtMG811Ymamy3ZJbwwdzPfrt1T12ZQBm7qehPBnsEAFJlr0FqTO/0typcvJ9Ri5oYWYVwQEXTAsTb+mcnKn9IoLahq0msQQtRfXl4eSUlJJCUlER4eTlRUVN1ydfWBz3K/9NJLlJeXH+ZI/xg8eDANOQ1jQ/nmm2/YsEHqyByOJKCH4WEy4KHKKDX40VzmShVCiFON1prx48czePBgtm/fzsqVK3nqqafIzs6moqKCvLw8oqOjueiii9i0aRN///03FRUVvP32240al1IqQCnVGViilOpWOxeoOAbtI2z8dOtALund8pDrn1r2FJfOuZTSor0UffMNxXN+AuCGFqHEebkKGu2scCWcPcbEcd5/e+ATcOgqu0II9wsKCiIlJYWUlBSuvfZabrvttrplD48DC4nXNwFtDA6H44jL9SEJ6JE1egKqlDIqpVYrpb4/xLqBSqlVSim7Uurc/dqHKKVS9vupVEqNa+xYD+aaC9SfnD0ZTX1qIYQQwIIFCzCbzVx77bV1bV26dGHAgAEsXLiQwYMHAzB69GiUUiil6NmzJ+np6Yc54olTSj0GrAWmAs/X/jzXaCdsxhJCfADYnV/O7JQD77WjYkcxrtU4fPxCaPnxR4Q9cP8B6z/IyGXQsk1sLK3AYFDYgl1TsW78M5Pdm/IRQpz85s+fT9euXenUqRNXXHEFVVVVTJ06lT179jBkyBCGDBkCwHXXXUdycjIdOnTgoYceOupxly9fTt++fenSpQs9e/akpKSE999/nxtvvLFumzFjxtRNveLj48Mdd9xBly5d+Ouvv/61/NFHH9GzZ0+SkpK45ppr6pJSHx8f/ve//9GlSxd69+5NdnY2f/75J99++y133XUXSUlJbN++veHfuFNcU8wDeguwEbAdYt0u4DLgzv0btdYLgCQApVQgsA34pTGDPJS6uUDXLuf/oqKb+vRCCHFSyXrySao2bmrQY1ratyP8v/897Pp169bRvXv3Q66bM2cO48aNO6CtpqaGGTNm8PLLLzdkmAc7D0jQWsv8Hw1k6vytzNuYzZB2odisZgC6hXWjW5irY7nc24gvYM/NJe+ddwm9/TbGhPizt9pOay9r3XEcdidr5u/CP9SLmHaB7rgUIU4Jv3+xhdzdh55P83gFx/gw4Lw29d6+srKSyy67jPnz59OmTRsuvfRSpk2bxq233soLL7zAggULCA52Dcd/4oknCAwMxOFwMGzYMNauXUvnzp0Pedzq6momTZrE559/To8ePSguLsbT0/OIsZSVldGrVy+ef/75fy1v3LiRKVOmsHjxYsxmM9dffz0ff/wxl156KWVlZfTu3ZsnnniCu+++m7feeov777+fsWPHMmbMGM4999wjnvd01ag9oEqpaOD/gEOOhdJap2mt1wLOQ62vdS4w5+DS900hys8bXeMnc4EKIcRJaPHixfTv3/+Atuuvv56BAwcyYMCAxjz1OsC/MU9wunl4bAe+vr5fXfK5v+yybCZ8O4GPNn5E2dKlFHz2GZWbtxDkYeLOuHBMBkWx3UFetR2jycDYW7oy4soObrgKIcSxcDgcxMXF0aaNK2mdPHkyixYtOuS2X3zxBd26daNr166sX7/+iMNbN2/eTEREBD169ADAZrNhMh25z81oNDJhwoRDLs+fP5+VK1fSo0cPkpKSmD9/PqmpqQB4eHgwZswYALp3705aWlr9Lv4019g9oC8BdwO+J3CM84EXDrVCKXU1cDVAixYtTuAUh9ahZTSL9xSwI1/mGBNCiCP1VDaWDh06MHPmzH+1p6amEhMTc8BzQ4888gh79+7lzTffbOywngJWK6XWAXVVb7TWYxv7xM2Vt8VEnMX1keT7tXvolxBMgLfrv22IVwhDY4aSHJaMX2J7vJJ7YA4LrdvXqTWTUrZjNSq+TmqFl821X02Vg7++3kbPs+Kx+vw7sRXidHYsPZXutmPHDp577jmWL19OQEAAl112GZWVxz5Noslkwun8p89r/2NYrVaMRuMhl7XWTJ48maeeeupfxzSbzSjlKoZuNBqx2+3HHNfpqNF6QJVSY4AcrfXKEzhGBNAJ+PlQ67XW07XWyVrr5JCQkOM9zWH1kblAhRDCrYYOHUpVVRXTp0+va1u7di0zZsxg1KhRdW1vv/02P//8M59++ikGQ6OXN/gAmAI8zT/PgD7f2Cc9HewprOD2L9bwxqJ/npkyKAP39bqP9kHtAagJdD03WvLrrxR9/wMGpbi5ZSi3tgyv+yAIkJ9ZxsYlWezZWtik1yCEqB+j0UhaWhrbtm0DYMaMGQwaNAgAX19fSkpKACguLsbb2xs/Pz+ys7OZM2fOEY/btm1bMjMzWb58OQAlJSXY7XZiY2NJSUnB6XSye/duli1bVq84hw0bxsyZM8nJyQEgPz+fnTt3HnGf/eMX/9aYPaD9gLFKqdGAFbAppT7SWl98DMc4D5iltW6yevr7iwt2ddwWaONRthRCCNEYlFLMmjWLW2+9lSlTpmC1WomNjcXpdDJt2rS67a699lpatmxJnz59ADjnnHN48MEHGyuscq311MY6+Oks0t+Tz67uTacov0Ou/3zT57yz7h0+OvMjKmbMQFdVYxt9JmeG+Ndts7mskjZeFsJibVzyWJ+6HlEhxMnFarXy3nvvMXHiROx2Oz169KgrOHf11VczatQoIiMjWbBgAV27dqVdu3bExMTQr1+/Ix7Xw8ODzz//nJtuuomKigo8PT2ZN28e/fr1Iy4ujsTERNq3b0+3bvUrXp6YmMjjjz/OyJEjcTqdmM1mXnvtNVq2PHQFb4Dzzz+f//znP0ydOpWZM2eSkJBQ/zfmNKCaYooRpdRg4E6t9ZjDrH8f+F5rPfOg9iXAfbVFiY4oOTlZN/Q8QFV2B23v/4lQz/kse+iQo4CFEKJZ27hxI+3bt3d3GAeoqqqiX79+xzX326GuRym1UmudXN9jKKVewDX09lsOHIK76pgDOgU0xv21Psqq7CzaspczO0XUtW3K38THGz/m/t73YyqrQplMGLz+ma97Y2kFI1ds4aFWkVwV/c/IqKzUIjYtyWLQ+W1QBoUQp6OT8e+5aD6O5f7aFFVwDw7kUWCF1vpbpVQPYBYQAJyllHpEa92hdrtYIAb4ralj3MdiMmJWpZTVzgW6/9AeIYQQ7mGxWNw98XjX2t+992vTwFA3xNJsvfnbdl5fuJ1fI/1oEeRKMtsFtuOxfo8BUOXtGp6rqqvJefllgi6/nHZBQfwvPoJzwwIOOFbmtiJ2b8ynorRGekSFEMLNmiQB1VovBBbWvn5wv/blwCHnN9FapwFRjR/dkXkbKyjRAWTu3k1kIxQ6EkIIcWrRWg9xdwyngxuGtqJ/65C65HN/NY4arvr5KhL8E7g3+EIKPvkUS3wC/hPO4doWrgJFDq1ZX1pBZ18vkkbE0GFgJB7WJv/eXQghxEEavVLDqc7fAs4af1b+7dZv24UQQpzElFL1e5hI1JvFZKRnnGsuz1W7CtiRW1a3zmw00zeqL30i+2Bt04aEn37Cf8I5B+z/QloWZ63ays6KKpRSeFhNaK358+ttrPl1d5NeixAni6Z49E6cfo7135UkoEfhmgvUn827ZS5QIYQQh3WduwNorqrsDm78eBUPf7v+gPbrulzHGbFnAFAd6O3adutWsp99Fq01V0aH8HSbaFp6Wur20RoKs8spyi6XD+LitGO1WsnLy5N/+6JBaa3Jy8vDarXWex8Zi3IUHWOjWbwnn7QCmQtUCCHEYV3v7gCaK4vJyPRLk4nwO/SHm5ScFG789UZeHPwicQtTKP72OwIvvZTAsDAuiAgCIK2iCofWJHhZOePqjhgMCqUU2qmlKJE4bURHR5Oens7evXvdHYpoZqxWK9HRh3yq8pAkAT2K3q3jePPPfHJkLlAhhBD7Ua7KdEOBC4ExQJh7I2q+OtZOy+J0ar5bu4ezOkdiqE0c4/3j6RvZl5a2lgRdlYz/hAmYAgPr9nVqzZXrdmBE8XNyG4xG1+CvsqIqfnhtLb3HxdMiMajpL0qIJmY2m4mLi3N3GELIENyjiQ1xzQWaL3OBCiGEW2RlZXH++eeTkJBA9+7dGT16NFu2bAHgzDPPJD09vW7bm2++GR8fn0aNRynVWyk1FdgJzAYWAe0a9aQCgF835XDLZyn8vD6rrs3mYeOZgc8Q6uUqPlTq5fpok/fuexT98AMGpXi5XQteSWx5QDV7o8mAycOAwSgfhYQQoinJX92j2Dfkp9jw7yp8QgghGpfWmvHjxzN48GC2b9/OypUreeqpp8jOzqaiooK8vLy6YT8rVqygoKCg0WJRSj2plNoKPAGsxTUdy16t9Qda68Y7sagzrH0oH17Rk1Edww+5/vkVz3PxnIspLsun9NdfKV3omsmto68Xbb1d9/PZOQUU2x1Yvc2Mv6Mb0W1dU7bUVDma5iKEEOI0JwnoUVjN++YCtclD20II0cQWLFiA2Wzm2muvrWvr0qULAwYMYOHChQwePBgAh8PBXXfdxTPPPNOY4VwFZAPTgBla6zxc8382GKXUu0qpHKXUuv3aApVSc5VSW2t/B9S2K6XUVKXUNqXU2v0r8SqlJtduv1UpNXm/9u5Kqb9r95mqTrEJrpVSDGwTglKKPYUV/LQu64D1w1sOZ3TcaHy9Aoh58w0in37qgPW7Kqq4ccMuXt+VU3c8gB1rc5lx/5/k7SltmgsRQojTmDwDWg/exkpKdAB7du0iqmVLd4cjhBBuMWXZFDblb2rQY7YLbMc9Pe857Pp169bRvXv3Q66bM2cO48aNA+DVV19l7NixRERENGh8B4kARgAXAC8ppRYAnkopk9ba3kDneB94Ffhwv7Z7gfla66eVUvfWLt8DnAm0rv3phSsx7qWUCgQeApJxJcgrlVLf1vbSTgP+AywFfgRGAXMaKPYm9dwvm/l1Uw59WwVhs5oBSApNIik0CYB8QyW+mDEWlbLnnnsJveN2WrRuzVdJCXS1eR9wrKBIb6LaBuATUP8qjkIIIY6P9IDWg2su0ACZC1QIIU4iixcvpn///uzZs4cvv/ySm266qVHPp7V2aK1/0lpPBhKAb4DFQIZS6pMGOsciIP+g5rOBD2pffwCM26/9Q+2yBPBXSkUAZwBztdb5tUnnXGBU7Tqb1nqJdg3p+XC/Y51yHj27I59f3acu+dxfhb2CS+ZcwqN/PYqjpISqrVup3u16Vrinvw9mg6LM7uC5HVnUODW2YE/OuKojFk8TDoeT0gIpPCiEEI1FekDrIcrfm7QMD7bsWgNMcHc4QgjhFkfqqWwsHTp0YObMmf9qT01NJSYmBg8PD1avXs22bdto1aoVAOXl5bRq1Ypt27Y1Wlxa6yrgK+ArpZSNxk3kwrTWmbWvs/in2m4UsHu/7dJr247Unn6I9n9RSl0NXA3QokWLEwy/cfhYTLQNdxUK/G7NHhIjbSSEuApQeZo8uSTxEjoGdcQjJJr4OT9i8PAAQDudKIOB+fklvLgzi34BPvTx/6dw1aLPtrBrXR7nP9gLi6d8TBJCiIYmPaD10DkuBjCSlrv7qNsKIYRoOEOHDqWqqorp06fXta1du5YZM2YwatQoAP7v//6PrKws0tLSSEtLw8vLq1GST6XUmEO1a62LtdYfHmmbhlLbc9noBQm01tO11sla6+SQkJDGPt0JKa2y8+j3G3h9wfYD2i9odwGdQjoBsLV0h2vbxYtJO28S9oICxob680fP9gcknwCdBkXT7YyWknwKIUQjkb+u9dC7dSzT/shlt73Q3aEIIcRpRSnFrFmzuPXWW5kyZQpWq5XY2FicTifTpk1r6nCeVUplAEcq3PMk8H0DnzdbKRWhtc6sHUabU9ueAcTst110bVsGMPig9oW17dGH2P6U5mMx8dnVvYny9zzk+pXZK7n8p8t5ov8TDDWHoYxGcLgq3sZ5WQBYWljK7JxCHm8dRXC0D8HRrqS0IKsMg9GAX8ihjy2EEOLYSQJaDzGBrmIFGV5WqsvL8fCSKVmEEKKpREZG8sUXX9QtV1VV0a9fP2JjYw+5fWlpo1UyzQZeOMo2WxvhvN8Ck4Gna3/P3q/9RqXUZ7iKEBXVJqk/A0/uq5YLjATu01rnK6WKlVK9cRUhuhR4pRHibXL7ht5W1jh47ufN3DS0NX5etYWJQpK4I/kOhrccjmeCJ16ffYpSCu10omtqMFgsLCks47f8EgrtDgLNro9G2qn5afo6TB5Gzr2nO6dYwWAhhDhpSQJaD9EBXhiUgyJDJHN/+o7/O2eSu0MSQojTlsViYcWKpi8Kp7Ue3NjnUEp9iqv3MlgplY6rmu3TwBdKqSuBncB5tZv/CIwGtgHlwOW1ceYrpR4Dltdu96jWel9ho+txVdr1xFX99pSsgHs4GzKLmbFkJ8mxAYzq6KqIbDQYmdzBNRNNtaOa5VnL6RfVj6zHHqN6Rxotpr/JzS1DuTI6GB+TsW7KNWVQDL88EaPRIMmnEEI0IElA68HDZKBVsAfbSuJYsuFnSUCFEEI0Cq31BYdZNewQ22rghsMc513g3UO0rwA6nkiMJ7NuLQJYdPcQwmyHnk7l7b/fZvra6Xw77lv8unbF6O8PZjNKqbrk86Fte7BrzROtowiJ8a3bd8Mfe4huF4AtWIbjCiHEiZAiRPU0qlMszspIdlTvdHcoQgghhDiMfcnn3+lF3PHFGmoczrp1l3e8nJeHvEwLWwv8xo4l9JZbUEpRk52Ds6oKAKXAeFCHZ0VpNX/O2kbKfClGKIQQJ0oS0HrqHR8MGNjmHYC9UuYHE0IIIU5m6/YUsSQ1j9zSqro2T5Mng2IGAbAxbyO/p/+Os7KSnRdfTOZ//4dSiocTInm0VRRKKfJr7Di1xtPHg3PvSabfua3cdTlCCNFsSAJaT11bBKBwkm+IY+G8n90djhBCCDdRSq1USt2wX5EfcRK6oGcLfrltIBF+nmitcToPnL3mpVUvMWX5FBweRoKvvYbAy1zPiSqlUEpRZncwdtVW/rfVVSjYP9QLo9FAdaWdH15fy95dJU1+TUII0RxIAlpPnh5GYgON2Mvj+H3tD+4ORwghhPtMAiKB5Uqpz5RSZyipUnNS8ra4Sl28OG8rd3y5Bsd+SegzA5/hzRFvYjaY8Z8wAc9OrjlDy5YsQVdX420ycl54IGeH+h9wzMqyGvIzyyjJk9FQQghxPCQBPQZndIzFWRFNWuX2o28shBCiQWRlZXH++eeTkJBA9+7dGT16NFu2bAHgzDPPJD09nfnz59OtWzeSkpLo378/27Zta7R4tNbbtNb/A9oAn+Aq9rNTKfWIUiqw0U4sjpuHUWE2qgMmcPWz+BHlEwXA9LXTWbh7IVWpqey64kry3nkHgJtbhtHb3zXFy9LCUmqcGluQJxc+2Iv4riEAVFfam/JShBDilCcJ6DHo0yoEMLLNy4azutrd4QghRLOntWb8+PEMHjyY7du3s3LlSp566imys7OpqKggLy+P6OhorrvuOj7++GNSUlK48MILefzxxxs1LqVUZ+B54FngK2AiUAz82qgnFsflxqGtmTKhMwaDoriy5oCe0CpHFQt2LWDh7oVY4uOJevklAi+77ID9d5RXcU7KNl7amQWA0ez6+JSVWsSM+/8iY3NBU12KEEKc8mQalmPQvaXrOdC9xjj+WDifgSPPdHdIQgjRrC1YsACz2cy1115b19alSxcA5syZw+DBgwHXc3vFxcUAFBUVERkZ2WgxKaVWAoXAO8C9Wut9VW6WKqX6NdqJxQlRSlFZ42DSm0tIivHnqXNcQ24tRgtvn/E2VqOreq7P8GEYlAFnVRW5r71O0NVXE+fjzbTEWIYG+h5wTL8QT6LaBBAQ4d3k1yOEEKcqSUCPgY/FRLSfgT3l8fy28jtJQIUQp5VHvlvPhj3FDXrMxEgbD53V4bDr161bR/fu3Q+5bs6cOYwbNw6At99+m9GjR+Pp6YnNZmPJkiUNGudBJmqtU/dvUErFaa13aK3PacwTixNjNRsZ0zmCTlF+B7R7m10JZFFVEdfPu57LOl5G3yw/8t57D8+kLvgOHcrY2mdBa5yap1IzuaFFKEG+Hoy62jWtqtaa3RvzaZEY1KTXJIQQpxoZgnuMRnRsiaMihtSKze4ORQghTmuLFy+mf//+ALz44ov8+OOPpKenc/nll3P77bc35qln1rNNnIRuGNKKgW1cz2+u3lVwwDyhBmXA0+SJl8kL7149afXTHHyHDgVcCSbA+tIK3s3Yy8L8A7+M2bw0i++mriF9U34TXYkQQpyapAf0GPVNCOHdxTvZ7umN027HYJK3UAhxejhST2Vj6dChAzNn/ju3S01NJSYmBg8PD/bu3cuaNWvo1asXAJMmTWLUqFENHotSqh3QAfBTSu3f02kDrA1+QtGoMgormDR9CVf2j+OeUe0A8PXw5a2Rb7GvqHG2TRMNVG7YQPaTTxH14gskhYTwZ6/2RFo9AHBqjUEp2vQMx2BURLWV2XmEEOJIpAf0GPWICwQ0WeZ4lv2+yN3hCCFEszZ06FCqqqqYPn16XdvatWuZMWNGXZIZEBBAUVFRXWXcuXPn0r59+8YIpy0wBvAHztrvpxvwn8Y4oWg8Uf6ePHtuZ64ZGH9A+77kc0PeBsZ+M5ZZW2fhKC7Gnp+Ptrsq3u5LPreUVTJk+WbWl1ZgMCja9AhHKUVpQRWLPtuCvcbRtBclhBCnAOm+O0Z+nmYifDU55bHMXz6b3kOGujskIYRotpRSzJo1i1tvvZUpU6ZgtVqJjY3F6XQybdo0AEwmE2+99RYTJkzAYDAQEBDAu+++2+CxaK1nA7OVUn201n81+AlEkzs7yTUNi8OpeX3BNi7tG4ufpxmANgFtuLLTlQxtMRTv1n7EfzsbZTKhtcaenY05PByDAk+DAW/jgd/np2/OZ/OSTDoOiiJQChQJIcQBJAE9DsMTWzBjmZ3Umu/dHYoQQjR7kZGRfPHFF3XLVVVV9OvXj9jY2Lq28ePHM378+EaNQyl1t9b6GeBCpdQFB6/XWt/cqAGIRrN+TxFTf91KmJ+V85JjADAZTNyQdAMADqeDb3d8x9iEsRR99Al7X36Z2Jlf0ioujjndW9f1mq4pKaeLrxftekfQIjEIL5urp7S60o6HVT5yCSEESAJ6XPq1DmXG0nR2WK1opxNlkJHMQgjRVCwWCytWrHDHqTfW/nbLyUXj6Rztzy+3DSIu2NVbqbWuSyoBfkv/jQf/fJAAawD9Ro7AXpCPR8uWwD9Ddn/YW8iV69L4pHM8Q4NsdcnntpU5LPp8C+Nu6yq9oUIIgTwDelx6xAYCkGGJZfWfMgpLCCFOB1rr72p/f7DvB5gBzKp9LU5h+5LP3fnlnP3aYjZnldStG9piKO+d8R6DYwZjDg8n9JZbUAYD9oICir7/AYARQTYebx3FoIPmCg2K8iamfQC2YKlTJYQQIAnocQnysRDs5cBeEc/cv752dzhCCNGo9k0/caprqOtQSn2ilLIppbyBdcAGpdRdDXJw4XZl1XaqapwY1IHtyeHJAGSUZnDVz1eRWZpJ3ttvk3n//dRk5+BhMHBVdAhGpSiqsfPA1nTK7A4Cwr0ZcXkHTGYj9moHW5ZnueGqhBDi5CEJ6HEamhiDozyWbcVr3R2KEEI0GqvVSl5e3imfhGqtycvLw2ptkF6oRK11MTAOmAPEAZc0xIGF+7ULtzHnlgG0DnP1ZG7LKT1gfU55Duml6ZTbywm99VZafvgB5rBQ4J8vOf4qLGPGnjw2llUesO+6RRnMfXcDuekHHlMIIU4n8gzocerfOowvVuxhh9X8r2dFhBCiuYiOjiY9PZ29e/e6O5QTZrVaiY6ObohDmZVSZlwJ6Kta6xql1KmdoYsDGGq7P+duyObqGSt4//KeDGoTAkDX0K58N/47zAZXtdyC+GA8gZIFC8h/732ip77MqBB/lvZOJMxSu02NnQCzic5DYwiO9iE42gcA7dSog7tahRCimZME9Dj1jnM9B5pubcmGFSvp0CPZzREJIUTDM5vNxMXFuTuMk82bQBqwBliklGoJFLs1ItEoBrQO5o4RbegTH3RA+77k8+e0n7n393t594x3SaiqQtvtKA9X8aF9yefSwlIuWpvKux3jGBjoS3Q71+eHvbtLmP/+RkZd3RH/MK8mvCohhHAvGYJ7nEJtVgKsDmoq4vlp0ZfuDkcIIUQT0VpP1VpHaa1Ha5edwBB3xyUantVs5MahrfEwGSirsjPlp01UVDvq1veJ7MPkxMl0DO6IbdQoWn40A4OXF7q6msoNGwBo7W3l7FB/kmwHJpkOuxOjSWG2Gpv0moQQwt0kAT0Bg9pF4iyPY2ueVMIVQojThVLKopS6UCn1X6XUg0qpB4H/ujsu0bj+3J7H27+nkrK7sK7N5mHj1u63YjaYKa8p57mVz1NWU8be118n7fwLqMnMJNBs4vl2LbCZjDi05pWd2ZTZHYTH+XHuvcl4+1nQWrNjbe4p/6y1EELUhySgJ2Bgm3C005M1wRZyd+52dzhCCCGaxmzgbMAOlO33I5qxEYlhLLhzMH0SXMNxS6vsB6xfkb2CTzd9yvrc9QRddhnhDz2EOSLigG2WFZXxVGomc/NcI7b31Y/YkZLLj6+vJe3vvCa4EiGEcK9GT0CVUkal1Gql1PeHWDdQKbVKKWVXSp170LoWSqlflFIblVIblFKxjR3rsepV+0xIvkrg489fdXM0Qgghmki01nqS1voZrfXz+37cHZRofNEBrmG0a3YX0n/Kryzellu3bmD0QOacM4eeET0x+vtjOGs4AFXbt7P7+huw5+fTx9+HBT3bMS4sAHAVJwKISwrmjP90JLaT63OF0yk9oUKI5qspekBvATYeZt0u4DLgk0Os+xB4VmvdHugJ5DRKdCcgyt+TVqHeOIo6sa5okbvDEUII0TT+VEp1cncQwn2iAjwZ2DqE9hG2A9rDvMMAWJe7jlEzR/F7+u9UpaZStWkTuroagLberqmAMiqr6b90E+9l5KKUolX3UJRSVJRU89ljy0j7OxchhGiOGjUBVUpFA/8HvH2o9VrrNK31WsB50H6JgElrPbd2u1KtdXljxnq8xneNxl4ZS0q4iZ3r17s7HCGEEI2vP7BSKbVZKbVWKfW3UkomhT6NBPtYmHpBVwK9PXA6NW8tSqWksqZufYxvDENbDHUVJxoxgvif5mAOD0drTcWaNQAEmU2MC/VnYIDPAcd22DVevma8/SxNek1CCNFUGrsH9CXgbg5KMOuhDVColPq6dvjus0qpf5WJU0pdrZRaoZRa4a456sZ2iQSgqLoLn8163S0xCCGEaFJnAq2BkcBZwJja3+I0tDajiKd/2sSPf2fWtflZ/Hi8/+MEWANwaidT/36djNIMSn7+hbRJ51O6eDFWo4En2kST4OXqEX1lZzYpxeX4BFg4+7auhLTwBWDz0izKiqrccm1CCNEYGi0BVUqNAXK01iuPY3cTMAC4E+gBxOMaqnsArfV0rXWy1jo5JCTkRMI9bjGBXnSJ9sNRlMTGqqVSwU4IIZq52mlXYoChta/LkaJ+p62kGH9+vHkA5yXHAFBYXn3A+t0lu/li8xcsSl+E79AhhD/0IN59+gDUfWYoqrHzfkYus7ILgH+KE1WUVPPbJ5tZ+dPOprocIYRodI15w+wHjFVKpQGfAUOVUh/Vc990IEVrnaq1tgPfAN0aJcoGML5rFI7qCFLC/Vj/1xJ3hyOEEKIRKaUeAu4B7qttMgP1vb+JZqhtuC9KKXJLqzjjpUW88dv2unUtbS2ZPW4257c9H+XhQflZA9EKHMXFpJ07kdLf/8DPbGJej7b8N8FVNXdnRRW51XY8fT04995kep8dD7gSUkfNsQ4qE0KIk0ujJaBa6/u01tFa61jgfOBXrfXF9dx9OeCvlNrXrTkU2NAIYTaI/+sciUJTXp7E1z+/4e5whBBCNK7xwFhqp17RWu8BfN0akTgp2KxmzuocyaA2B47KCvEKQSlFcXUxl/x4CU8ufRJnaSnKZMLo63oGNMBswmIwoLXmug07OS9lG06tCYzwxsNqQjs1c978m+9eSZHRVkKIU5qpqU+olHoUWKG1/lYp1QOYBQQAZymlHtFad9BaO5RSdwLzlWscykrgraaOtb5CfC30bRXMkrQkNhtfQDudKIOMxhJCiGaqWmutlVIaQCnl7e6AxMnBw2Tg/jGJdctv/Lad1qE+DGvvqo7ra/bl+qTr6RjcEXNgJC0+/QRD7eeFou9/wKtbV8yRkTzTJpqCGgcGpdBa4wSMBkXS8BY4HbpuiK4QQpyKmiRL0lov1FqPqX39oNb629rXy2t7Sb211kFa6w777TNXa91Za91Ja32Z1rr6cMc/GYxLisJhD2RNaDhLfvnZ3eEIIYRoPF8opd7ENVLnP8A8muBLUqVUWm3F3RSl1IratkCl1Fyl1Nba3wG17UopNVUpta22Um+3/Y4zuXb7rUqpyY0d9+mq2u7k+7V7+GV9dl2bUopz25xLu8B2ALy25jWmLJtCTXER2Y89xt7XXcUMO/p6MSDQ1an+eVY+Y1dtJa/aTnxSCK26hwKQunovv7y9jupKexNfmRBCnBjppmsgZ3QMx2SAqtKufP/He+4ORwghRCPRWj8HzAS+AtoCD2qtX2mi0w/RWidprZNrl+8F5mutWwPza5fhn0q9rYGrgWngSliBh4BeuObYfmhf0ioalofJwFfX9eXhsa7v1nfnl7M1u6Ruvdaa8ppyymrKMNv8iJ35JWF33QWAPTcXR1ERAF5GI6EeZgLMB04GUJJfSUl+JUazfJQTQpxa5K9WA7FZzQxvH4ajqDNbPLbgrD6pO2yFEEKcgNpROndpre/cN2e1m5wNfFD7+gNg3H7tH2qXJbh6ayOAM4C5Wut8rXUBMBcY1cQxnzYsJiOeHq7E8ZHvNnDh20uprHEArt7Qe3rew8N9HwYgN8DIN9nz0Fqz53//I+38C9B2O2ND/XmvUxwGpSixO7h5404yKqvpMiyG8Xd2x2g0YK92sOSb7VRXSG+oEOLkJwloAxqbFIXT6cv6oFh++Wamu8MRQgjRgJRSJUqp4sP9NEEIGvhFKbVSKXV1bVuY1nrfBJRZQFjt6yhg9377pte2Ha79ACfDPNvNzZPndOTlSUlYa3syi8prADAo10exTzd9yrMrniW3IpeQG28i5OabUCZXqQ7tcCWta0vKmbO3iKyq2n0NrmdB0zcXsOqXXeTsbIp/hkIIcWIkAW1AQ9uFYjUrqou7Mn/1p+4ORwghRAPSWvtqrW3Ay7iGukYB0bimZHmpCULor7Xuhmt47Q1KqYEHxadxJakn7GSYZ7u5CfW10rdVMABz/s5k4LML2Jj5T8J4W/fb+GT0J4R4heDZqSObuwajtab0999JHXs21bt30y/Al5V9O9Ddz1X36pvsAjIqq4ntFMzFj/Umul0gALs35FNVm+AKIcTJRhLQBmQ1GxndKRJncUc2+6ZTU17u7pCEEEI0vLFa69e11iVa62Kt9TRcQ14bldY6o/Z3Dq4K8j2B7NqhtdT+zqndPAOI2W/36Nq2w7WLJtQ23JfRnSJoFeqagkVrjUEZiPd3zfe5PGs5V/x8Bd+nfo8ymzFHRWIKc3Vu20yuHtQSu4N7tqTzfFqWqz3IE4CqCjtzpv/N4pnbmvqyhBCiXiQBbWBnJ0Xh1Fa2+Lblm0/fd3c4QgghGl6ZUuoipZRRKWVQSl1E7ZygjUUp5a2U8t33GhgJrAO+BfZVsp0MzK59/S1waW013N5AUe1Q3Z+BkUqpgNriQyNr20QTig/x4alzOmE2GqiscTDxjb+Yu+Gfarndw7rzeL/HGRU7Cu/evfF6+UmU2Yyurmb3dddTtmQJviYjvyS34b/xkQBkVFazsbQCi6eJcbd1pedZcQBUlFRTkl/plusUQohDkQS0gfVLCMLP04i9uAt/bv7S3eEIIYRoeBcC5wHZtT8Ta9saUxjwh1JqDbAM+EFr/RPwNDBCKbUVGF67DPAjkApswzVFzPUAWut84DFgee3Po7Vtwk0Ky2twaI3nflVuDcrA2a3Oxmw0U+Oo4aqfr+J/f/yPmpy9VO/cibO8AoCWnhaCPVzPiT6Vmsm41dsotTsIbWnDJ8AKwJ9fb+PzJ5bJdC1CiJOGcj0ycupLTk7WK1ascHcYADw4ex0z/tpOVOSjvNdnOq17dHd3SEIIIQ5DKbVyv2lNxEFOpvtrc6W1RilXQaEP/0qjvNrB1QPiMRgUTu1k1tZZhHuH0y+qH/aqSuxGsJqsFM76Bl1Tjf+551LgcLKmuJwhQTYAVheXk+TrSUl+Jdk7immd7BrCW5hdjn+Yl9uuVQhx+jjc/VV6QBvB2C6RaEzk6o588tXTR99BCCGEEKetfcknwKqdBSzfkc++JoMyMKHNBPpF9QNg5o5vOOfbc8ityKVk3jyK58wBpQg0m+qSz2WFpZy5cgtfZhdgC/KsSz6z04r5+OElbF6a1bQXKIQQ+5EEtBF0axFAi0AvnHl9WOW/ieKs7KPvJIQQQojT3kvnd+W1i7qhlCK3tIqHZq+joOyfucUT/BPoHdGbIGsQ0a++QugLz6KUwlFURPZTT2EvKKCrzZtn20ZzVog/AKnlVRTbHQRGetP77HjiOruq8ZbkV2KvdrjjMoUQpzFJQBuBwaC4vF8s1dUxbPWP4aO3pBdUCCGaC6VUXH3ahDhe++YKXZKax+crdpNf/k8C2iO8Bw/2eRClFCU1JZw173xmbZ1F2bJlFHz6GfasLMwGxSWRwXgaDWituXZDGhNTtmEyG+g+KhYPTxNaa+a+s55ZL6ymuTyOJYQ4NUgC2kgmJsfgYzHiyO3PsqoFOKqq3B2SEEKIhvHVIdpmNnkUotkb0zmSxfcMJSHENV3LK/O3HlAt1+600zO8J+0C22EbMYKYuXMwt20DQN4771Iybx5KKZ5tG8P98ZGunlKtWZDnmn+059h4uo9qiVIKrTVZqUVNf5FCiNOOJKCNxMdi4vweLagq7cSKFt58+8Hb7g5JCCHECVBKtVNKTQD8lFLn7PdzGWB1c3iimQrysQBQZXfw/dpMFm/LrVsXaA3kyQFP0j6oPQBv7P6UC3+8kKqqcoq+/57S3xYB0MXXiwGBvgD8sLeIC9am8mt+CdFtA4hPCgFgx5pcvnpmJWl/5yKEEI1JEtBGNLlvLAZloKawLwu2fSRDXIQQ4tTWFhgD+ANn7ffTDfiP+8ISpwOLycj3N/fn7lFtAdicVcIdX6xhb8k/I6w6BXeiX2Q/LBYv4r78AsdNriliq3bsIP3W26jJzmZ0sB9vdmjJkNqEdGF+MRtLK2jRIZDBF7WlRWIgAJnbCinaW97EVymEOB1IAtqIYgK9OKNDOPaC3iyJrSRl3lx3hySEEOI4aa1na60vB8ZorS/f7+dmrfWf7o5PNH9mowGv2nk/16YX8vvWvXgYXR/ltNaMjB3Jzd1uBiCjIouzfj6Xzzd9TtWWrZSvWIEymTAZFGODbBhqh90+uHUP925Jx2Q20mFAFIba50Z/+3Qzv7y93m3XKoRovkzuDqC5u7J/HHPWZVFU3Z2Zv7xE1xEj3R2SEEKIE7NNKfVfIJb97qNa6yvcFpE47UxMjmFsUiQWk6tg0VUfrKBztD+3DG8NuIbnXt/legbHDMbWLozCHq3ZZSwiniAybr0VU3gE4f/7L7O7tSKvxg5Amd3BPVvSubllGGfdnERFiav4kb3GweIvt9FleAz+oTKHqBDixBw2AVVKTa3H/sVa6/sbMJ5mp3vLADpH+bExeyDLwp8le/t2whIS3B2WEEKI4zcb+B2YB8gcFsJt9iWf1XYnwT4WbJ6uj3Vaa0orDfyn8z8jw19f/yaLdi9i3rlzMce0wBTsmoolwGzCJycbvKJYX1rB3LxirogKxtvPG4uvBwB7d5WyaUkmCd1D8Q/1Qmt9wNylQghxLNThnktUSu0EHjzK/vdqrds3eFTHITk5Wa9YscLdYRzS7JQMbvksBc+Y97gi08Zdj7/r7pCEEELUUkqt1FonH8P2KVrrpEYM6aRyMt9fxaEt2JzDNR+u5NOre9G9peuZzryKPDbmb6R/VH8AXln9CgOiBtB6RzW7LruMmDem4TNoEGUOB95GV2L7xPY9LC8q48ukVjgq7Fi8TCilWPHjDjK3FzH6us4YTfI0lxDi0A53fz3SENwXtdYfHOWgAScc2WngzI4RPOG7kcK9A1hpfoeq4hIsNl93hyWEEOL4fK+UGq21/tHdgQhxKK1DfbiifxydovwB13yiPhZTXfJZWFnIV1u+wsvkRcdW4wi86QYsPWo/Iy5fTnFJCb7DhhHraaHS6cRsUJi9zfxRUEI3mzcWLzNefpa65DMrtYiQGF+MZklGhRBHd6S/FIuPtrPW+qWGC6X58jAZmNw3lurKVqwND+WLt15wd0hCCCGO3y24ktBKpVSxUqpEKVXs7qCE2Cc6wIt7z2yHR22C+OzPm7nzyzV11fj9rf78NOEnLmx/IaagILadncRZcyaQWpRKwSefkvPcc6A1F0UG8WhCJAC51XYuWJPKlB2ZdBoczbBLXQPgKstqmP3iahZ/vc09FyuEOOUcKQGdrpTaqpR6TCmV2GQRNVMX9myBxWTAntefP/Z+g6Oiwt0hCSGEOA5aa1+ttUFrbdVa22qXbe6OS4jDee/yHky9oCtKKartTsa/vpiFmwrwNHkCYDVZaRPQhhifGKJeeJ6C529nR+lOtMPBjnHjyf/oY4LMRr5MSuCqaNe8oRtLK7hkbSqZysHo6zrTeXA0AIU55fw0fZ1M4SKEOKzDJqBa66645juzAzOVUmuUUvcqpWKbKrjmJMDbgwndo6kp6cafCR7MnPacu0MSQghxHJTLxUqpB2qXY5RSPd0dlxCHY7OaaRPmevQnr6wKi8mA1ex6zrOovAaLvRVTh07FbDSjTCZe2vkhd/12F47SUqyJiZjCQlFK0cOk8Pt1Hrq6mt2V1Wwqq8TPbCImMZACXyN7q2soyCpnz9YCTB6u45fkV1JZVuO2axdCnHyOOFhfa71Za/2I1joRuBTwA+YrpY46PFf82xX9YtHaiL2gL3P3foW9tNTdIQkhhDh2rwN9gAtrl0uB19wXjhD1F+HnyWdX92Fw21AAPl+xizGv/MGO3LK6baYOncrj/R7H5OdHyJOPMrn8dWZtnUXJL7+QcfsdVG7axMhgP5Z0b0Wg2VVO5KFtGYxasYXYTkFc9nQ/vP0sAPz51TY+e2wZ2nnoopdCiNNPvZ4WV0oZgFAgDPAGchozqOaqVagvIxLDqC4cxNJWZj577Wl3hySEEOLY9dJa3wBUAmitCwAP94YkxPGZlNyCVy7oSlywNwBP/biRJ79Lp11gOwBKqktI8E8g2DMYv3Fn4/fOq3xn2URJdQl5r7zKjgnnomtqeCAhkqfbRKOUQhkU41dv5c3dOXQb1ZL+E1ujDK5pW36ctpa1C3a77XqFEO53xARUKTVAKfU6kA7ciWves7Za6/FNEVxzdPuINjicZuy5g5hX8h3VxVK3QgghTjE1SikjoAGUUiGA070hCXF8/LzMnNUlsm7ZYjLg5WGsm+dzzpoSrmzzIAOiB6AMBpaGFvPYksfIKsvC0qY1Nb06U0ENrb2tdJ31BSXz5lHh1IR7mPE1GQmJ8SU6KZjnd2SRVlKB1rBvBkCnw0nKvF2UFlS549KFEG5y2ARUKbUbeArYACRprc/QWr+ntS5qsuiaofYRNsZ0jqC6cADL4zz55JXH3B2SEEKIYzMVmAWEKqWeAP4AnnRvSEI0jNtHtuWxcR0BKK2y89j3G/huzR4AtNZ0sg3n67Ff0zqgNX5nncWswRZGfjWSquoKir76mrK/luBlNDCtQyxnbVqLs6yMlJJynkvLYkd1Df93fWei+0ewpaySnJ0lLJ65jZydri/jq8prKM6VIo1CNHdH6gHtr7Xur7V+VWstQ24b0G0j2qAx4cgZwrzqn6nKL3B3SEIIIeqh9pGUHcDduL6kzQTGaa2/dGtgQjQCH4uJJfcN48r+cQCsTS9i+IuL2J7hA4Dd4WRk7BnclHQTFg9P4n/8gef65jFl2RSqUneQft31FH71Fb39fUhJbk0f5QDgi6x8Bi7bRE2kJ5c83oeAtn44tWbLsmxm3P8XhTmuCrr2akfd1DFCiObjSAno5UfbWSn1cMOFcvpICPHhnG7RVBf3ZXWMLzNefdjdIQkhhKgHrbUTeE1rvUlr/Vrtl7Qb3R2XEI3F38uDIB9XQaEWgV48fFYiveKCAPjh70yufTuXfmFnAaAMBkJ8wwm0BuIR25IWH37AC9Hr+D39d7xWLGNH//5UpKRwTlgAr7WJIsbqgS3Yk6d2ZdNv6UZadApi0IVt8QtxTQ/z1zfb+eThpTilgJEQzYrpCOuuOsrE2go4H3i4QSM6TdwyrDXfrM7AmT2M+XoWF+3NxTMk2N1hCSGEOLr5SqkJwNdaumfEaSTA24PL+sXVLYfbrAxoHUKknythnLZwO6m7h3HXRd1RBkV159as/nE9nUq70yuhD97XXcUrlT9xbqWNwXMXsf2zz4n7aibDg2y08vTAL8gTv4FRXLBmOy09LfynlT9WbzOG2gJGP01fh5efBwMntXHL9QshGsaRekDfAnyP8ONTu404DjGBXlzQswWVJT1YGxnIB6884O6QhBBC1M81wJdAlVKqWClVcpQvbIVolnrFB/HcxC51CaLRAB4mY93ya/MyGe77AhPaTMCjRQvyJw3h861fklOeg0dMDAX9Evlwx5f08HEy9v3p7L7uerTWdPDxpJWXhYRuoSSPjmXosk28k74XH38L3n6ugtNaa75+biV/L0x32/ULIY7PYXtAtdaPNGUgp6Mbh7biixW7cWQPZ57HF1ycmYlPRIS7wxJCCHEYtc+AjtJay3zYQhzk6oEJByznl1VT7TBhMrg+br4118nNrT4nOSwWU4SJnTGFPP/Xw5wZdybmmBjW2YpYuPEj7mwzkZzrbiYrLhafu+8h0ceTEJOR/ue1pqDGTvc/1/NQbARevh4YPQxorampcjDz6RX0PjuB+K4hdc+O7qvmK4Q4edRrHlDROMJsVi7t05LK0q5sCA3h/Vf+6+6QhBBCHEHtM6CvujsOIU4FL0xK4tGzXRV1axxOCiuqqbEbMBlMVNudfDg3nP91mEmYdxiBl01m3YAYXk95HZPBhCUhgV+jCpmxfjpT28XQ/sJzyX1zOqUOJz38vImuLmPUNZ2o7OBHh8XrWLK3BL9QL5yeBkrsDvIySvnwv3+Sua0QAHuNA3uNw43vhhBiH0lA3ezaQQl4eZhwZp/BL37Lyf77b3eHJIQQ4sjmK6UmKOlaEaLezEYDH1/Vm6sGxANQUF6NzWoi2MsfgJ155bz3fXv+2/FjTAYTfnfcydoW/izavQiqq7GNPIO3g9fx9YZpvBrph+eokez94AOsRgOjfD2J37mRMycnsNJP0eb3v9lZXUN4vB95XooVRWVsTdnLW7cuIj+zDICS/Epy00ukwJEQbiAJqJsF+Vi4sn8clWUd2eYfydvv3CElx4UQ4uQmz4AKcYLCbFZmXNmL4YlhdW1ndgynfXgoAH9tz+Ozn7twS+JUDFYr1VffxGbiyC8vQxkMhN13L9d7f81Xfz/HE5RRctWVLF/4GfGWKh7ygphfvmDo2BC+ryzn7NVb8Qv3ImlEC5YZa3hzdw4b/9rD508sx17t6hXdvSmfdYsy0JKQCtHojpqAKqXaKKXmK6XW1S53Vkrd3/ihnT6uGhCPzWpCZ57Nj633sHr2V+4OSQghxGForX211gattYfW2la7bHN3XEKcymKDvXl6QmdahbrmGG0V6sMDYxLpGBUAwNwN2SxY3oYbutyO0c+PlT1GUZl/Ie0DkvBIaEXwtGnckP8Gv277kAuLcsh96WVe+XsavSwZfFWaRfW915Pc05P5BSV8umkHbdpaOPOajjyxO5ur1u1g28oclv+wg9TKKnKqalgyezs/v72uLr7ivAqqymvc8t4I0dzUpwf0LeA+oAZAa70W1/QrooH4eZq5ZXgbKipjyXd2YMbvU3BWVro7LCGEEIeglBp4qB93xyVEcxIT6MWV/ePwsbgKGF3QswXf3NCPkNo5SfcUVlBUGMo5bcZg9PHmrYpQKrc/wrhW47GNHMmuT77m3c3byCzeQgcfKyUmO8MXnc9Qj3V8smMtaeOGs8NzOd6U0m19Ch2LFzDp3m7csWk3V/+9HZPZgNnDyMtp2XyamceCGZv49uUUSuwOtNas+XU3m5dm1cXrcDjd8j4JcSo60jyg+3hprZcd9KiLvZHiOW1d2qclny3bxc6sc/i1/VN8+9qTjLvjUXeHJYQQ4t/u2u+1FegJrASGuiccIZo/Tw8jSTH+dcuT+8YyuW9s3XKfhGD8PD1oG9gKgG83lhNcfiUT2vTF0t7Ce3u90BmbifCOIHBQAj/g4IVfp/Hq/91Gp93bWTPvCz6JW84F7W6g9duzYfWf+L7zFD/vqmbYlq2Mt1RjGDaSocs309/Xk75Lc7GFePJjGCT5erFn6gYiWvnR44I2+BgN/L0wncBIH6LbunpwnQ4nBqM8+SYE1C8BzVVKJQAaQCl1LpBZ3xMopYzACiBDaz3moHUDgZeAzsD5WuuZ+61zAPsq8uzSWo+t7zlPRWajgUfGduDCt0sx5Q/my/JZjMy4Fq+oSHeHJoQQYj9a67P2X1ZKxeC6lwkh3GRI21CGtA2tW35+YhK5pVVYjK4e0zahQYT59iAptB2EwvfzsmmtWpEclozfncOY7pvE7s1/c28vX8IH9OcpfxvffP0kMyc9ju2DBfxelcr3MT9wQdt7GPzM01SST/65l/F6WjmP/L2aRIPGq/142v/xN3d6KXy+zKT10Hie1yWMC/Fn86Or6TQ8Gu/BEbS0evD3NzuI6xxMVNsAtNYU5VTgHWDB7GF011soRJOpTwJ6AzAdaKeUygB2ABcfwzluATYCh3o+ZhdwGXDnIdZVaK2TjuE8p7y+rYIZ3Smcn9YNJSVhBe9MvYObpnzq7rCEEEIcWTrQ3t1BCCH+4elhJCbQq2752kEHzlE67aJkKmoc+Flcz5x2bxnNcK842gQkwBlt+HWlokdIF+L94rG+/DLPPvkzXtvTeG14DOrMUVy6OZctcz7nr6teoOTdV3mphT+/r5nNHYNfZfhtd7C+nQe/hHXg19yzGf/qC8R4W9kZPIJblxfzRtoGKn6roDpwBJcUZXO3t4XUl7fR6fw2pCRYGebtzcZ3N9NxVAssrW0EOBU7l+XQokMg/qFeOBxOKktr8PQxS6+qOCUdNQHVWqcCw5VS3oBBa11S34MrpaKB/wOeAG4/xLHTareTgfO1/vd/ify6MQfnnrP5NvwDxi39i5hefdwdlhBCiFpKqVeoHRWEq5ZCErDKbQEdI6XUKOBlwAi8rbV+2s0hCdHkIv09D1i+Z1S7A5YX3DmEarsTT5MHADcNTSQuuAcB1gD0+HFUv7yIi1p3JtAzkID3P+Db//1IN2M3bomPo+jGGxjzaxX+q34n5eYupH1t5GKvVhSvepd3J75K1AO38NjoIWTuvoeEbk8SfuVFLBrZlVXlgXyfOoHebzzD3ugwvsrvwdQliXzx20+sSzPhe9UQpqd68lT6bjb+UEbCjb351dfBRO3B5q920e7cePYEmOhgN5C+JIf4fuEY/S1YKhwUZJQSHueHh6cJR40Th8OJ2WLkdJxNqsbhun6nBo3GbrdTU1OJ1azQDjuF5VXUmD2wGj2hopqK8mIq7aX4WxVOp4OsMjtOmx++HgFQWEppST5ljgICrBqlNXtKNM6gELxMwZCVR1HpXioNRYT6asxodhWa0MGhWMwRGDIyKSzNwWEuJNTmxApsL/KEkDCsHi0w7thBQWUuWAsI83XiqWB7sR+OwGCs1ng8tm0l356D2bOIYF8HntpAamkA9sBgPD3bYNm8jr2OXDx9CgnytuOpjWyvCMEREILFoxVeG9eQY8jHx7eAAK8arA4zLSJ7ccaIEY363+CoCahS6vaDlgGKgJVa65Sj7P4ScDfgexyxWZVSK3A9b/q01vqbQ8R2NXA1QIsWLY7jFCefKH9Prh/SihfmOkkPbsX0T+/m0R6/oQzyDZcQQpwkVuz32g58qrVe7K5gjkXtYzGvASNw9dwuV0p9q7Xe4N7IhDi5eHmY8PL4Z/mK/nF1r5VS/HTroLplp9b8fOtAbFYzBmXAc8hQbjOk0iehL0opAh9/Gv93/mBix7aMDg1g9yefs2LaKkb4xPFW53jWX3czn2z0pd3e5Wwb1ZGUz8KYYh5K5Na5TD1jGDV3/8j00ZPx3fYmHTrdhv2JR/jgnLF4bZ5HTtClnHn3zXwybiReGy0sNw7h/ddf5K3E1nh7JfBLTXs++G4Wsyw++Ezqya+Vobzw5xLmZToIvmYAy6osXLUujZXbamhxZQ822Z0M2V3I6o2V+A+JZldNDa3KIW1HNl0HBVOtqihJKyAlvRJbWAA+eFKdU8DmojQGxpZipJINOQa22MOwWgPxqrZSlZXNNkcWPUJWA9XsKophp26NyRyAtcKEzs8l01JGl6Cv0TjJKO1OtrMDRoMf5kowlJdS5FVB6+DpaAWZRWdQVN0Bpbww1WgM9mqqraVERLyEVpCbewEVFe1QmFBOjQaURz7+sc/hVFCS/h8c5Qf2iBsse/COnwpA2Y4bcFbGHLDe6LkDr9g3Xeu3346zOvTA9d6b8GrxPgClW+9D26tw/Yl1MfluwjPaNaqxZPPD4CwACmrXmjH7pWGNdM2CUbLxSaAC2Fy73oY5cC3WsO/RTjOlmx8DnMA2QAFheASvw2KYi9PuQ9nWfZOVbMNVpiAaS+haPMyLcFYHUbZ9XxmDHbgGqNqwGFfjYVmKozKSbktMjZ6AqqPNOamU+gRIBr6rbRoDrAVigS+11s8cZr8xwGit9fVKqcHAnQc/A7rftu8D3x/0DGiU1jpDKRUP/AoM01pvP1ycycnJesWKFYdbfUqprHEw/IXfyClJxy/mOaZ63USfS/7j7rCEEKJZUkqt1FonH8P23kCl1tpRu2wELFrr8saKsaEopfoAD2utz6hdvg9Aa/3U4fZpiPtr0gMvUGSPPaFjCNGcaFypwz/LBsCJql2HNoJyotBoFGgTKPt+y2bAAUqDNgBGUDW4EhND7fr9Bxga/lmvja7t4cAoVA0oJziNHLKPSlW7zuc07bf//uurXIdzmjjkRBuqunb9vvMflIOofdPcGGuv6eD1+2qgGmrX70+Dcuy3/qCeXQWw33rUgYdXmn/er8N1+hxpvebAgTEnsv5wvdJHWn/AxZzgeriru4Ebzh13mDjq73D31/o8AxoNdNNal9Ye6CHgB2Agrqp/h0xAgX7AWKXUaFzpt00p9ZHWul7Pj2qtM2p/pyqlFgJdgcMmoM2J1WzkwTGJXD2jgpLSvry/41WSc8djDg52d2hCCCFgPjAcKK1d9gR+Afq6LaL6iwJ277ecDvQ6eKOGHmGUYK0ip2rTCR9HiFOJK6Uw4NRGnBgx4MCkanBioNQRhBMjunadAwNWUy4exgLs2oOCyvagza5ks5bFkobRIxu705fq0iTAdEDeYPTahsGSi7M6AEdZK5SyY3KC0WmnymjC6LUdk7kIR1Uw9uoYjIZSrHaNwa4oNftgtWzBQjnVNWGUO6KxWPbgXe3EafekwBCKj3UtXo4aKmoiKNEt8Pb8G1sVVNYEUGBsia/XanwrocIZQYGhJb4+S/EvM1BWE0q+KQY/7xT8yhQljggKjJH4+azErwyK7ZEUGsPw91qDrRwK7dEUm4Lx90rBVgYFzhaUGP0J8PobW5mTPGcspSYbAZ7rsJU5ydVxlJm8CLBswq/czl4dT7nZgr/HVvzK7eToeCpMJgI8UrGV2clR8VSaFQGmXfhW1JBDPFUmjb8pHd+KGrJJoNqjBn9DFr6V1WSTQI1HNf6GbHwrq8kigRpLJQFqLz6V1WSSgMNSjr/Kx6eyikwScFrK8FMFrmXVCqelGH9djHdVJZmqFdpShJ8uwbuykkxDK7AU4qdL8apbzsdPl+NVWcUeQwIGSx5+ugJrZTVZhniUJRc/XYm1soYsYywGj1xsugpLpZ1sY0uMHnvxdVZjqbKTbWyByZKLr6MajyoH2cYYTB578XXWYK5ykGOKweyRg4/DjrlKU2H0olVUv0b9f6M+CWgoULXfcg0QprWuUEpVHWYftNb34Zo/lP16QOuVfCqlAoByrXWVUioYVzJ7uES3WRqRGMbA1sEs3jaKP1qn8P5zt/Cfpz92d1hCCCHAuu9LWQCtdalSyutIO5xqtNbTcRUgJDk5+chDperhq//dd8IxCXGy0FqzalcBe0uqyC2tJr/M9dM1xp+hHWzsLsrh4je2UFzpxO74Z7/WYesIC1pMXpWDjO03AU78PC34FOVR4VFKVfBynH4pOO3emHNL8TBprAH/x3kz3+PvuCpKLbuwVRTi5fSmzLqdwoBWpCQM5cavPiQ9pJxyuwGDwZv+3p7k7kojLbIV62NDaJ25E+VhIabDUIIiA+hv8GXPknyKfI3stSp8PIz4eJho2/H/CPG3Yi13UJpbgdliwuRhwGwxYvIwYvEcjzLs33vmmqCixuGkssaBj2U0SimKK2soLKshJtC1nFVUQWpeHkkxw/DUJrbt3sua4lyGtR+ArcZEysadrKgpZFzS9QSUKv5K2cpfhjLG97iSwHwHS5dv4g+vaib0vpjgrCoW/7WBv/ydnDPwfCLTSlj453qWh9YwbvAEWmzJY+6SLayM0Jw9dBwJ67KYszyVNVFGRg8dS+LqNL5ZncHGKAMjh4+h61+b+Wx9LtsiNMOG/R99Fq3h/W0lpIXaGTh0NEPmL+ONXaXsCamk99DRjP7xN17KKWNvUAldh4xm4je/MKW4goKgAjoOGs0lX87m0aoqSgPzaTXw/7j248+5T1VTGZ5L3IDx3PLe+9zh6cAekENM/0ncPX0aN/oloAOzCO93EQ9MfZ7/hLXBGJhFSN/LeeC5x7i6ZVvMQRn497mGh5+8n/+0bos1eA++va7m4Ufu4+qObfEOycSzx3949IG7uaZrG3xDMzF3/w+P3Hcn1/Zsi2/YHgxJV/Pg3bdyQ98r8IvIxNH5Kv53523cPPBK/COzcLSdzD3/vY9VI8ZzRp9/fS/ZoOozBPcBYDwwu7bpLOBb4Hlgutb6oqOeZL8huEqpR4EVWutvlVI9gFlAAFAJZGmtOyil+gJvUjeOgJe01u8c6RzNaQjuPttySjnjxUV4eKUQYfuE6e2fpdXIM90dlhBCNCvHMQR3MXCT1npV7XJ34FWt9UlfMc5dQ3CFONnVOJyUVtoJ8HY9+DljyU525ZWRVVxFdlElOSWVdG5h5dJBZnLKc7jlXQc19n+GUloMNRj8/8IU9iNaQ1XmBDCWM6LtRFp+/BGpAZlsjc8huDoXn3Lw9InB2aYXs4LGcPeMt8n3riDfZqba7MfAkGjsSzzZHd6Wn7r7EJ6Xh8JKXGI47VoH0dvgwe5vd+L0MVHqYyLI00Swlwfx7YPwC/GkptpBRXE1Fm8zHhbjQUmjEE3ncPfXoyagtTv34J+hRYu11ifdnai53iCf+WkTry/cjmfMO4xK3cVzj/yG0cfb3WEJIUSzcRwJaA/gM2AProdpwoFJWuuVjRRig1FKmYAtwDAgA1gOXKi1Xn+4fZrr/VWcXuwOJwXlNYT4uuYFfeePHaxNLySjoIKMwgqyiyvpFOPJbWcZ2FO6h2e+slJcbiLcZiVCOaiqTGerbRkegUtcxyuLw8ti4Nzu99L38ZfZW7yG5W2MWJxe+Jv8ifSNp7rLOdwdHEDvdWtxKgMOX3+8osI4t1UsuVM2Um2Cda08CTYaCfUwkdQ5lB49IvB0alJX7cXLZsHTZsbTxwOrjxmjSQpSilPLCSWgtQcIxfUsJwBa610NF96Ja643yMoaB6Nf/p3dBTlY46dwf0ZvJj34mrvDEkKIZuNYE9DafcxA29rFzVrrmiNtfzKprc3wEq4qIO9qrZ840vbN9f4qmh+tdd20InP+zuTP7Xmk5ZWxK7+cjIIKogKsvHVVNDuKdvDst6VkFUJsoI0EDzM1RVv41boAs20dAKZqDyKDIji30z3Ev/Ql9tXzWd3OF2X0x887gh4BbanpeT4TPQrxLczFpM1YvWx0aBHAqGAbhc+tp6bSQX5LK5GeFqL8LMQlBtOqu6t6an5mGd7+Fjysp+dUKOL0cNwJqFJqLK7htpFADtAC2KS17tAYgR6v5nyDXLmzgHOn/YnVtoxYj694b8B0wnudCrUuhBDi5Hc8CejppDnfX8WpK6uokpTdhWzOKmFLTgnbc0rJKq5k1f0jqHJWcuvny/htcwlRAR60CwnEV+czO/9jTP6uKXu1hhDPIG5LvovWX6yj5Iv3WdXeCw9TMFafaAbFJ2I8+3LGZWSQXVEKRgshTgOdov3o5++Dz4dp7N1VQlWohSiblaBgTyJb+ZPYPxKAytIaLF4mGf4qTmsnUgX3MaA3ME9r3VUpNQSoVzEh0TC6twzgiv5xvPMHpLVIYernd/B4198weHgcfWchhBBCiFNUWZWdjZnF/J1RxIY9xdz/f4n4eZn5dNkuXp6/FaUgJsCL+BAv7NZ1jPnyVdIr03A6DZjiHYxMvJj/bIkm46nH8exnIDIfgo1hdAhrS9z9j3BpZgmrOviiH+1LqbcvcV4Wevp74/drIWnPpzDKZsDXqYgKMBIWa2PIKNd8oMVX+2D1MeNhPfRHaauPuSnfJiFOKfVJQGu01nlKKYNSyqC1XqCUeqmxAxMHunNkW35Zl0VWxvn8kPgMA199lFG3P+7usIQQQgghGkRljatkrNVsZPG2XB6cvY7U3DL2DdYL8DbRISGHErWJ1ZWphLXJoFeLFjwbdx05L77IHS0W47urml450DLHTs8bHmJry+HcX7ABfeXNrA2NZFdYBNrDg5+9w/n+o3Siqyrwx0BIkYOQ8gKufX4gZg8j6cqX9n0jCIryxjfIE8NBPZm2YM+mfnuEaDbqk4AWKqV8gEXAx0qpHKCsccMSB/P0MPLseV04f/oSqveeyZt8Q9+NF2Jrn+ju0IQQ4rSglOp2pPX7quIKIY5Oa016QQWrdhWwelchq3YVsGFPMS9MSmJsl0h8rQaCbU7atzQwLrEbHcO9uWneRTz/dyoGrWhRaGJwUHsGxA+mQhko2rKV6+x92RwTyx+9ornirGGE+Abw44YMVhn9iIzrQ7uMSh7tn0CvGH92Lc3GYXcyJjaYkBa+BMf4EBTpg9HsKvQT3TbAze+QEM1XfZ4B9QYqcE2HchHgB3yktc5v/PDq73R5RuX+WX/z0dKdeLWcxgVbSrn/mXkoswzzEEKI41XfZ0CVUguOsFprrYc2YFgnjdPl/ioal9aatLxyHE5Nq1AfdueXM+AZ1/9SnmYjnaP9aBHqwOq3jl1Vf7E+bz1Vjio8TZ78MW4+OwYPY51/CSYnxNUE4NshieCJE1nSuRuXrduBo/bjbEuLB939vbmwxsKaaRtcjQqCIr0Ji7XR9YyW+Ic2q2l7hThpncgzoA9qre/BNSfnB7UHmwLc07Ahivq4d3R75q3PJjfjAr5u/xy9X3mUEbc/5u6whBCi2dNaD3F3DEKcSrKKKlm0ZS+Lt+eyJDWP7OIqxnaJZOoFXQm1Gbl2uC8Vxs3c3ud8bB7evDLvUd7Z9TWti70Ysa2aTl5tGPbA61RavMg773zy/IP5JTKWxZ6+PNk2hvNtNrw27GVMkZGgtHL8U8sZeVYCSX1bUFFSjXVsPOHxNkJjbYd9VlMI0fTq83/jCP6dbJ55iDbRBHwsJp6d1IVL3qmiMn8ULxtm0XXpaIJ7nfTznwshRLOhlOoIJHLg9GQfui8iIdyvssZB6t4yEiNtAFz67lK2ZJcS7GOhT0IQnWOsVHv8zU3z32Vp1lIq7BUYlIGzE7sT/OCb9Fm6iP5O8IsLx5CcTPCA/mAJJvGPddQkD8XToOjm5cndITZ6eHny/r2LcTo0PbxMRLTyJ3JMBC07BgHg6etB8uhYN74bQojDOWwCqpS6DrgeiFdKrd1vlS+wuLEDE4c3oHUI53WL5otV/dneYhNTvryZpxN/xejr6+7QhBCi2VNKPQQMxpWA/ojrS9k/AElAxWlnb0kV8zdmM3dDNou352IyGFj94AjMRgMPjGnHnoqtdIkKpl1QOzbkrmfSD48R4fBl8A4Tnf6Gs977Gf+AcPZMOJfCAUP4Pr4dcxwGutu8+bRLAmWFVVyrvPHcUYpldQGRLR2Mv6MNAMaL2hEY6U1IC99/FQkSQpy8jtQD+gkwB3gKuHe/9pKT7fnP09HD4zqwZHseGekX82P7Z+n6zE1c+Nj77g5LCCFOB+cCXYDVWuvLlVJhwEdujkmIJqO1RinFB3+m8fB369Eaovw9mZQcQ+9Wfvy661cWZSxg4e6FFFcXM7FsIrdVDcR4//285LQTUVSMd3IyPmcPxtfsw31b0vnAHIIzKoRQbWJMiI0RQTZ++2Qz6xZl4A/4BlmJ7RtJy05BdXG07xvhrrdACHECjpSAGoFi4IaDVyilAiUJdS8vDxPTJndn7Ct/YE8/n2mh79Dz2y9oNfY8d4cmhBDNXYXW2qmUsiulbEAOEOPuoIRoTDvzyvhuzR6+W5PJg2cl0q9VMMmxAdw6rA3DE0NIjPBDKcVZs84irTgNX+VJz/wAhredxNDkqzCmZeDTsyfRfQfwU5uO/FDt5OukVhhNRrr4VHFVQAAJuyoxrCxg4t1tsXqb2dqmCp9AC7GdggmM9EYp6eUUojk4UgK6EthXIvfg/+M1EN8oEYl66xDpx32j2vH4HNhb3Y8nlj/Bm8n98YiMdHdoQgjRnK1QSvkDb+G6V5YCf7k1IiEaQWWNg4+W7OS7NXtYk14EQI/YAIwGRY2jhr2O1WR4/Mi9Szcy66yvqfj9Dy7Y4I95hZl2m0uw2IyE3hlJqdPMx2Zfvj3/ajaWVaLyyujl501abjklS3KoWZlD2N4Kyg2K6PYBVJbWYPU20zo5zM3vgBCiMRw2AdVaxzVlIOL4XDkwnl/XZfPX7jEsjdvB6y9cyS3P/IAyGNwdmhBCNEta6+trX76hlPoJsGmt1x5pHyFOFZU1DtLyymgXbsNkUExbuJ0Ifyv3ndmOMV0iqdAZfLzpVe5Y+gvF1cX4mX0ZGTeKCkcl2Y89TvfSUnxHnAk3j8SenIy/rzcbSyuYsiOLXn7ePBgVxjAvb9pG2yjMKeeTX3YR3S6A7qNaEt8lBKuPTC0nRHN31HlAAZRSY4GBtYsLtdbfN2pUx+F0nqcsr7SKoVMWUKb34hfzAq+q8+lztRQpFkKI+jiGeUDbaa03KaW6HWq91npVw0fnfqfz/fV0obXm74wivlixm9kpe7BZzSy6ewhGg6KgrBqHoRiFIsgziMUZi7l1wa30r4mlz58FdNhQRuKC31AeHpRu387vnja+yCtlXl4xZ4f682piS6qr7KxYnU3usr2kb8wnLimEM6/pBEBlmau3UwjR/Bz3PKBKqaeBHsDHtU23KKX6aq3/28AxiuMU5GPhlQu7cekHyynNPZunTDN4f/lAAnvI1CxCCNGAbgeuBp4/xDoNDG3acIQ4cfM3ZvPsz5vZlFWCxWRgdKcIJiZHo7WDRel/MmvrLBbuXsglHS7hes9RRL32CdMXV2CtXIe1S2f8b7wSrTUvpWXxVmY5+TUlhHiYuCI6mPPDA1n2XSop83dTU+nAJ9BC9zNjadsrvO78knwKcfqpzzygo4EkrbUTQCn1AbAakAT0JDKwfSiTk6L5IAW2RG3hkZk38nz8L5iCgo66rxBCiKPTWl9d+3uIu2MR4kRsyynFZjURarNiMCg8TAaeGN+Rs7pEYrOa+WjDRzwyawZ7yvYQ4OHPBbHnMK7VOJxb8qhenULUxZfhPW48v/sFMyLYhkEpqp2avv4+TAzxJ2ZnJW3jwjAaDazyMBKfFEK7PhFEtfZHyXQpQpz26pOAAvgD+6re+jVOKOJE3T+xE4u35bJ9z3nMTZjK9CmTue6p2Sij0d2hCSFEs6GUugH4WGtdWLscAFygtX7drYEJcQQOp2bBphw++CuN37fmcs2geO47sz2D24QwpG0o6SXp2Kyu3sh1eesIN/hzRUYkiV+uIuQcRfjAeHRyHN4//cx7uSV8nJlHzu4dfNo5niFBNq6z+fP36gw2vbeObWU1eFnNxHYOptsZLd185UKIk019EtCngNVKqQW4quEO5MB5QcVJwmw08N51vTnjud+o3n05b7V6iY6vPsTAWx53d2hCCNGc/Edr/dq+Ba11gVLqP4AkoOKk9PnyXUxbuJ20vHLCbVbuHNmG83u2QGvN7xm/8+GGD1mauZSZZ80kamMuV72bSc1fa1FeXviNHY//eRMpqLFz/9YMZucU4NAwLMjG5Mggelqs/PTm36Sm7AWliO8STIdBUUS3DXD3ZQshTlKHTUCVUq8Bn2itP1VKLcT1HCjAPVrrrKYIThy7mCBvXpmYxFVfrKY863we8f2QGfP7EjlstLtDE0KI5sKolFK6toqfUsoIeLg5JiEOUFnjwGp2jYD6c3seNk8zr17YlTM6hKOUk1/SfuGdde+wpWALoZ6h3NLtFsK9wyn56SN06k5C77oT28SJ7DFbsHpaMDk1G0oruCIqhCsig/AtshMU7IN2asqKquh6Rks6DYrGJ8Di5isXQpzsDlsFVyl1C3A+EAF8AXyqtV7dhLEdE6nSd6Apn65l2prdWIJ/oXfpQt688nusMTJPuhBCHKy+VXD32/5ZoCXwZm3TNcBurfUdjRGfu8n99dSSW1rFe4t3MOOvnXx+TR/aR9gor7bjaTailOv5y+LqYkbOHEmYZyiTCtvS9YNlxL48Fc8uXXAUFuKwevJ1QSmv7cqh0O5gee9ErEYD1dV2Nv+ZRcq8XVSV25n8VD/MFiNa67pjCyHEPsdcBVdr/TLwslKqJa5E9F2llCfwKa5kdEujRStO2N3nd2J1Wj5LckewLDqD51+5hPse+xmDRb6ZFEKIE3QPrqTzutrlucDb7gtHCCgoq+bNRal88GcalXYHZ3YMx8PkmhPcYLAzY8MnLMlcwmvDXsPHYea1qol4v/Y1Ou97vHr1AqOJaqeTL8sdvLxxO7sqq+ngY+XhhEi03cmahRms+mUn5UXVhMf70W9Ca4xm1/El+RRCHIt6zQNat7FSXYF3gc5a65Oqso18Q/tvxSXVjHjqV3J0OV6xL/NkXnvO/u8b7g5LCCFOKsfaA3q6kfvrya/a7qT/lF/ZW1rF2V0iuWlYaxJCfKhx1jBr6yzeXPsmOeU59AzvyfMDnyPvnIuo3rED7759CL7hBry6dwfgj4ISzk3ZTpKvF7fHhjEiyIZSiqzUIr56ZiVRbf3pMTqOyDb+knQKIY7qcPfXoyagSikTcCauXtBhwEJcPaCzGyHO4yY3yENbuyGXcz9citO8l+Dwqbxlu4JOl9zo7rCEEOKkcRxDcFvjKtCXCFj3tWut4xshPLeT++vJqbzazvdrM5nYPRqlFLNTMmgXbqNtuC8AO4p2cP2860kvTScpuAtXlHZl8MTbUAYDRd99jzkyAnPXbnySmUex3cFNLcPQWrOsqIxunlb+XphBVYWdPuMSAMhNLyE42tedlyyEOMUc8xBcpdQI4AJc84AuAz4DrtZalzValKLBdU4M5sEe8dy/XJOffz53VrzJ+7+1JWLQCHeHJoQQp6r3gIeAF4EhwOWAwa0RidOGw6mZuXI3z/2yhb0lVbQK9aFbiwDOTopCa012WTZh3mFE+0TTyr8Vt6jhJLzwI/aMlZS37I93717Yxvwf3+0t4qllG9lRUc2gAF9ubBGKdmp81xXz8fdrKCusIrZzMNqpUQYlyacQosEcaRqW+4BPgDu01gVNFI9oBBed047V2/L4qqADaR5ncseiO3kr6ku8W7Vxd2hCCHEq8tRaz6+thLsTeFgptRJ40N2Biebtz+25PP79RjZkFtOthT/TLupGtxau6U425m3kmeXPsKtkF9+N+w69ah23T8uict08jO3aEfH2o3j37sXfJeXctTmdlJJy2nlbmdEpjuFBNnLSSpj/wQYKssoJi7Mx4opEotrIVCpCiIZ3pCJEQ5syENF4lFI8cWNPdj2xiOUFg1gZVsAD717ClDvnYA4MdHd4QghxqqlSShmArUqpG4EMwMfNMYlmrsru4PbP12A0KF65oCtjOkeglCK3IpdXVr/CrK2z8LP4cWPSjZi1gZ3/+x/a4SDi6afwO+sstMHVSW82KPJr7LzcrgXnhgeAw1XB1upjxmA0cOY1nYhLCpZnPIUQjeaYihCdzOQZlaPL3lXCpNcWk6bseEZ/yLV7irn54W9R/8/efcfHUZwNHP/N9SKdepdc5N6bbLrp3WB6C6GGGhIIIQkJqfASCIQWAoTeS4BQTO8d3HvvsorV++n6zvvHnWTZ2OAi6VSeb7Kf3Z3ZvXsGyZp7bndnbDJ9nRCi/9qLZ0CnAquAZOAWwAPcqbWe3TURxpf0r/HT7A/x1DebueLQIdgsJlZXNDEozd0+v+eWpi2c9fZZBMIBzh16JmcudDDwZ1djcjgIrF+PNT+fBrOFf2zcSlM4wkNjBgEQ0ZrW+gDfvbaeoD/CjGsmAMh0KkKITrWr/lWeWelHsgYkct/J40jBjL/sPB4p0Lxx1zX0lS8hhBCiKymlno1tHqi1btFal2qtL9Zan95Xk08RH1pr3l22laPu/oK7P17L7I21AIzM9uCwmqnwVgBQkFjAT0b+hGcSf8Epv30P/wOP4f32OwDMhUN4qqaZA2ev4rmttaTbLES0JhyMsPDdzbzw19lsXFJDxsBEDCP6OUCSTyFEd/ihZ0BFHzThoDxu3tTIb1ZuxldyCbfk/Zv8Z+5i6oU3xDs0IYTo6aYopXKBS5RSzwDbfVrXWtfFJyzRl2ypbeXPs5bz+ZpqRud4+M/5U5gUe86zurWaf8z7B1+WfsmbM98kpayJk+6eg2/+AqzjxlHw0IM4x41jtdfHVSuKWeX1c3ByAv83PI+RbifVJc28959lNNf6GTI5gwNPG4on3RnnFgsh+htJQPuhE84ZSemdzdzZrGmsuJTr0x7gyXcKGHri2fEOTQgherL/AJ8AhcACtk9AdaxciH1y/cuLWbW1iT/NGM2FBwzEYjYRMSK8vPZl/rXwXwQjQS4bfxlpzjS23vJbgus3kH3LzSSffjrErmCmW62YleLxsYM4IT2JthudPOlOkjKcHHHBKPJHyABDQoj4kGdA+ylvY4Db/v4dz5lasNg3U5j4OE/tfye5Bx0V79CEEKJb7cUzoA9pra/qyph6Eulfu96SkgYGpbtJclpZV9lMgsNCTlL0ymQwEuTi9y9mac1S9s/Zn+utxzNs/KFY0tIIlpRgTkxEJSXxwtY63q1u4NnxhZiVQmuNEdEs+nALm5bWcNpvJmM2y5NXQojuI8+Aiu24k+xcfdlEjg04CPsHsdF7Pld99Rvqli2Md2hCCNFjKaXMROf+FGKfBcIR7nh/Nac++A3/+mQdAMOyEslJcraPz2Az25iaPZW/T/kzf/0sHa66idpHH4vWFRSw0ergtEXruWFNCX5D0xiOAFCxsYmX/z6PObM2kphqJxyIxKeRQgixA0lA+7Hcocn8/KQRHBS2E24dyYrw2Vzzxs9o2bQ+3qEJIUSPpLWOAGuUUgO64/2UUn9VSpUppRbHlhM61P1eKbVeKbVGKXVsh/LjYmXrlVI3digfrJSaEyv/r1JKhkCPo6WlDZx0/9c8+PkGzpiSz7VHDWuvW1O3hnPeOYflNcsBuKRmNCOu/BdNs94i7coryPjVdQQNg7s3V3DkvDWs9vq5e2QB/5s4hERD8fkLa3jtzgUEfWFOvHo8x10+DrvLGq+mCiHEduQZ0H5u3GH5XLa1Fd+yLSxsGcfcpBDXP30e9189C3tmdrzDE0KInigFWKGUmgt42wq11id30fvdo7X+Z8cCpdRo4BxgDJALfKyUGh6rfgA4GigF5imlZmmtVwL/iL3WS0qp/wCXAg91UcziB7y+qJQbXllKeoKNJy+ayuEjMwEIGSEeX/Y4Dy95mCR7Ei2hFmoff4KqO+/EMXo0Ax59BMeoUQBEIgavVtRzfEYS/zcsjwxbNME0WRSVmxqZcGQB004ajM0hH/WEED2L/FXq55RSHHL2MBqrWvHV1bKqcTKfJQe46d+nc/sN72HxeOIdohBC9DR/incAwEzgJa11ANiklFoPTIvVrddabwRQSr0EzFRKrQKOAM6LHfM08FckAe1WbfNs7jc4jbOnFvC7Y0eSFLsyua5+HTd9fROr6lZxwuAT+N2kX5OamEn4lGFgMpH60/MJm8w8WlLNT3LTcJlNvDdlGElWC0F/mG//t54pxw/E7rJyxu+KMFvkJjchRM8kf50EZrOJ4y4fy/nKzaAEB6GGA3gz4SBu++dpGD5fvMMTQogeRWv9BbAZsMa25wFd+QD9NUqppUqpJ5RSbUOX5gElHY4pjZXtqjwNaNBah3co3yml1OVKqflKqfnV1dWd1Y5+S2vNc7OLueLZBRiGJjfZyd9PHdeefAJ8suUTKlsruevA2/nV526arrweHYlgSUsj7eKLWBcIM2PhWv60voy3qhoASLJaKF1Tz0u3zGXxx1soWVUPIMmnEKJHk79QAgC7y8qMayZwfr2VrBQnobpDedYzln/+41SMQCDe4QkhRI+hlLoMeBV4OFaUB7yxD6/3sVJq+U6WmUSvUA4BJgJbgbv2KfjdpLV+RGtdpLUuysjI6I637LPqvUGueHYBf3xjOf6wQWto22BA9f56llUvA+CycZfx0vDbGPLLB6h/4UWc48ZBJIKhNY+UVHHM/DWU+IM8MmYQZ+ekEgpG+Oq/a3nznkWYTIpTb5jC0CmZ8WqmEELsti6/BTc2YuB8oExrPWOHuunAvcB44Byt9as71HuAlcAbWutrujrW/i4pw8UJV44jcO8iHh3spKHmaB5LN6FvO5Xf/OENTDYZr0IIIYCfE73ddQ6A1nqdUmqvP/lrrXdr/iul1KPA27HdMqCgQ3V+rIxdlNcCyUopS+wqaMfjRRf5bkMtv/rvYmq9Af544iguOWgwJlN0rs45W+fw+69+j8Vk4e2TZ9H0xFM0/PsBLGlpDHjyCdwHHADAn9aV8lhpDUenebhrRAGZ9uhV06/+u5ZV32xl3OH5HHDKEKx2c9zaKYQQe6I7roBeC6zaRd0W4CLghV3U3wJ82QUxiV3IHZbCET8ZxUUbNZ5sF8GaI3nMNZa7bjsVHQzGOzwhhOgJAlrr9j+ISikL0CWTaiulcjrsngosj23PAs5RStmVUoOBYcBcorcDD4uNeGsjOlDRLB2d0+Mz4IzY+RcCb3ZFzCIqGDa44ZUluGxmXr/6IH52SCEmkyJkhLhv4X1c9uFlJNgSuP+I+zEHIzS89jqeY46mcNabuA84gEhsGpaf5Wdw98gCnhk3mAxb9HlPgP1OKmTmdROZfvZwST6FEL1Kl14BVUrlAycCtwLX71ivtd4cO87YyblTgCzgfWC3JwgX+27UgTk0VLaiP97Mk6PcNJUeyiMpVvjHGdxw4/9QVhnKXQjRr32hlPoD4FRKHQ1cDbzVRe91h1JqItEEdzNwBYDWeoVS6mWidwmFgZ/HpohBKXUN8AFgBp7QWq+IvdbvgJeUUv8HLAIe76KY+7U6bxCPw4LNYuLJi6eSl+zEbY9+3GoKNnHVx1extHoppw87nV/ajyPZPQiT3c6g/76EOTmZoNb8bV0ZZYEgj40ZxCCnnUFOO61NQT59ZhWRsMHJv5yIO9mOO9ke59YKIcSe6+oroPcCvwW+l2D+EKWUiehzLjf8yHEySEIX2X9mIeOKcrhwdYTEQYmE6g/kEctk7vrHmehQKN7hCSFEPN0IVAPLiCaE72qtb+qKN9Ja/1RrPU5rPV5rfbLWemuHulu11kO01iO01u91KH9Xaz08Vndrh/KNWutpWuuhWuszYyPoik60cEs9J9z3Ff/8cC0Aw7MS25NPgERrIoVJhdx5yB1cszSHyp9eSu3DjwBgSUlhky/ISQvW8UhpNVk2K+HYdfWSVXX89//mUrq6nsKJGaC6vWlCCNFpuiwBVUrNAKq01gv24vSriXbopT90kAyS0HWUSXH4BSMZPiKNC1aFSRiaSKhxKv8xFXHXnWdLEiqE6M9+obV+NJbEnaG1flQpdW28gxLxo7Xm2dnFnP3wd1gtipMmbLtz2tAGTy5/kpLmEpRS/GX09Yy+7XWq770Xz/HHk3rJJQC8VlnP0fPXsMUf5Kmxg/n78HzMWvPdGxuY9a/F2F0WzrixiHGH5aOUZKBCiN6rK2/BPQg4WSl1AuAAPEqp57TW5+/GuQcAhyilrgYSAJtSqkVrfWMXxit2YDabOPaysfjvXcyFa7w8OcpD69pJ/CfRSssdp/GnX7+C2eGId5hCCNHdLgTu26Hsop2UiX7AF4xw0xvLeG1hGYePyODesye1T6/SFGzipq9v4vOSz2kNt3KJ5TBKr7mGSF0d2X/9K8lnn4VSisZQmD+tK2NsgpMHRw8kzxEd9C/oj7B2bgWjD8zh4LPkWU8hRN/QZQmo1vr3wO8BlFKHATfsZvKJ1vonbdtKqYuAIkk+48PmsDDjmvG8dudCLt4Q4vFRyfhXjeVZl4v6u07hzl++jC3RE+8whRCiyymlzgXOAwYrpWZ1qEoE6uITlYi34jov7y2r4FdHDecXRwxtH+V2Td0afvX5r9jaspUbp93IeSPPI7hpE+bkZAoefADH6NFUB0OkWS0kWS28OXkogxx2LCZF9ZZmUnPdONxWzr5pGg63jL0ghOg7un0eUKXUzUqpk2PbU5VSpcCZwMNKqRU/fLaIB2eCjZN+OYH0sOLSYoVzYhqR1oG8FTiLy/91Nv46ef5WCNEvfEt0fILVsXXb8mvg2DjGJeKgrMEHwMhsD1/89jCuPWpYe/K5sHIh5797PoFwgMePeoQZ6xIBsBcWMvj113CMHs13DS0cMW8N9xVXAjDU5cCsYMknJbx6+3wWflAMIMmnEKLP6ZYEVGv9edscoFrrP2utZ8W252mt87XWbq11mtZ6zE7OfUrmAI0/T5qTk34xkeT6MFduViTsnwvhVL7wXcC5D11CY3lxvEMUQogupbUujvVnB2itv+iwLIzNrSn6idcWlnL4nZ8za0k5AJmJ2z+OMjJ1JDOGzOD5g/9D2u/up/y3v8M3f357/cMlVZyxeD0es5kTMpIBCPrDfPj4Cr5+ZR0DxqYx/vD8bmuPEEJ0p26/Aip6r/T8BE68ejyOMh9XrjVIPGQgZuws8l7EGU/dQPWmXU33KoQQfYdS6jSl1DqlVKNSqkkp1ayUaop3XKLrGYbmjvdXc/3LS5gyMIXpw9Lb6/xhP/ctvA9vyIvL6uK3njNoPv8KfEuXknvHP3BNnUprxOCqlcX8ZX05x6Yl8X7RcEa4HTRUtfLqPxawYUEV+59SyAlXjsPukiufQoi+SRJQsUdyhyVzwtXjsWxp5aoVYTyHD8Fm1axr+ikzX/oHm5Z8Fe8QhRCiq90BnKy1TtJae7TWiVpreRi+j2sNhrnq+QU8+PkGzp02gGcunUayKzpYUHVrNRe/fzGPL3uc78q/o+mjjyg+7yegYeDzz5N08skArG7x8X5NI38ozOHxsYNItEQHFQoHI4QCYU66diJTjhuEMskot0KIvksSULHHCkalctwVY9GbWrhqSYjkI0didwcobzyTk995k+8+eDreIQohRFeq1FrLLR/9zDfra/l4VRV/OWk0fz91LFZz9CPUytqVnPPOOWxo3MA9h9/DUQOPwmSz4Rg7lsGvvoJz7BiqAtGpyyYnuZm9/2h+OTALgLK19QCk5ydy/s0HUDAyNT6NE0KIbiQJqNgrg8alc+xlYwmvb+bKRQHSjhyDPSNIc9PhXDCnhuefuTneIQohRFeZr5T6r1Lq3NjtuKcppU6Ld1Cia/hDEQCOHp3FJ9cfysUHDW6fh/Pb8m+56P2LMCkTzxz9JPuXJwCQcOihDHzuWSxpabxRWc9+s1fyXnUDANl2K5GwwRcvrOGNuxexeVkNAGaLfCQTQvQP8tdO7LXCiRkcfekY/KsbuWJBgLxDRmEaaiLUOow/rh/An+77OUYkEu8whRCis3mAVuAY4KTYMiOuEYkuMW9zHdPv+Ix5m6Oz7AxKd29XP9AzkP2y9+O5Qx/F9ft72HLJJQQ2bgJAA3ds2sqVK4sZn+hialI0OfW1BJl132JWfFXO5OMGMnBMWre2SQgh4q3L5gEV/cPQKZlEwqP5+KmVXKEULx0wlLlJW3EtCvBs5RGsu+NqnvrlnTjc8niUEKJv0FpfHO8YRNd7f/lWfvnSYvKTnWR7to1yq7Xmg+IPOGbgMeQl5HH3yBspueQqAps2kXPLzdgLB+OLGPxy1Rbeqm7gnOxU/jEiH7vJRN1WL+88sARvQ5CjLh7NiP2y49hCIYSID0lAxT4bsV82RsTg02dXc15Yk3J4Pu+6nGTOXs3sxhkcdffNvHrRZWQXjIh3qEIIsdeUUr/VWt+hlLqf6AWu7WitfxmHsEQXeOa7zfxl1gomFiTz+IVTSXVHBxsKGSFu/u5m3lj/BhwKh7UOYMsVV6D9AQY8+gjuAw4A4KPaJt6ubuDPQ3K5qiCj/ZbdunIv4aDBqb+eTNZg+WJWCNE/SQIqOsWoA3MxmU188tRKjg8apJ6YyXN2C0PmLKa0+XAOe/wd/j19GUcdcUa8QxVCiL3VNvDQ/B88SvRqn66u5M9vruCoUVncf+4knLboSLWtoVau/+J6vin7hqsmXMWxA4+l7smnUBYrA194AvuwYYQNjcWkODkzmWGuEYxKcALQVOPDk+5k6JRMBoxJxeaQj19CiP5Laf29L3F7paKiIj1/vnwmiLcNC6v48PEVpOYlsPmMXO4prWLCmjWsKXaizF5+OmATf7v8D+3fBgshRLwppRZorYviHUdP1d/6V8PQvDy/hDOm5GOJjXRb66vl6k+uZnXdav60/584NfsYzB4PWmuMpibMSUksaW7lihWb+c/oQUz0uIDo7boL3tvMvHc2c+oNk8kenBTPpgkhRLfaVf8qgxCJTjVkcibHXzmO+q1eBrxUyv8NzGHZyJEU7peAgzDPbBrPCX//C00tDfEOVQghhAAgFDG45e2VlNa3YjIpzpk2oD35BNjctJnS5lL+dfi/OHKxwYZjjiWwYQNKKcxJSXxS28Spi9YT1hp37LxIxOCz51YzZ9YmhhZlkpGfGK/mCSFEjyIJqOh0g8alM+OaCTTV+nE8tYlHB+dTkpaB+cgxFDq3sKp5Pw6481nmLe8/36gLIYTomfyhCFc/v5DHv97El2trtqtrDbUCMCVrCu+f9j5j3llFxZ//gmP8OKw5OQC8UF7LBcs2MsRp593JwxnmdhD0h3nngaWs+mYrRScM4qiLRmO2ykcuIYQASUBFF8kfkcLMayfiawlR8/AaXhiYj9vpZMOhB3FMQSneYD5nv7ieW59/PN6hCiGE6Kdag2F+9vR8PlpZyS0zx3DefgPa69bVr+Ok10/irQ1voQ0D7z/vp/re+/CcfBIFDzyAyeXiw5pGrl9TwvSURF6fNJRMuxWA1d9VULq6nsN/OpL9Ti6Ux06EEKIDSUBFl8kuTOKUX00iEjZY8q/lPJmaxSSPm1mj9+OsaRbsqoVHl2Vz2P/dQ0VdbbzDFUKI3aKUelopldxhP0Up9UQcQxJ7ockf4oLH5/LthhruOnMCPz1gUHvd8prlXPzBxWg0o9NGU//SS9Q/+yypF15I7u23o6zRRPOIVA+3DM3jmXGFJFjMtI2rMe6wPM68sYjRB+XGo2lCCNGjSQIqulTGgERO/20RDreVL+9fxu14ODcnlWeSCzjopGlMdC1ic8sQDr7rA578+KN4hyuEELtjvNa6oW1Ha10PTIpfOGJvaA0RrXngvMmcPiW/vXxexTwu/eBSEqwJPH380wxJHkLyGWeQe+cdZN74Owyl+MfGrVQFQlhMissKMrCaFLXlLbxy23waKltRSpExQJ75FEKInZEEVHS5pAwnp/92CukFCXzy6Aou2gp/GZLLO60GdceewXUjy1AqwN8+DnLKXY/Q5AvEO2QhhPghJqVUStuOUioVmdas12hoDeIPRUhyWvnflQdy/Lic9rqyljKu+vgqst3ZPHnkY9geeolwfT0mm42kk04iqDVXrNjMPcWVvFPT2H5excZGXv/nQrwNASJhIx7NEkKIXkMSUNEtnAk2Zv5qEoPHp/P1f9czYV4TL40vpCYU4v7Cg7j5pNEMss9mcXUO0259ldfnL413yEIIsSt3Ad8ppW5RSv0f8C1wR5xjEruh3hvk3Efn8MsXFwFgMm3/bGZeQh5/2O8PPHH4w4R+ezN1TzyB95tvAfBGIly4dBNvVzfy1yG5XJyXDkDxilrevHcRdreV034zhbS8hO5tlBBC9DKSgIpuY7WZOe6KcYydnseij7YQeKOE9ycOY6Tbwa9a7Rx69qVcnP4dIcL86tUSTr7neSqbfPEOWwghtqO1fgY4DagEKoDTtNbPxjcq8WPqvUHOe2wOG6tbOH//gdvVfVP2DStqVgBwSt5xtFz7e7xffU32LTeTNONEGkNhzlm8kS/rm7l7ZAFXDsgEoHR1He8+sJTkLBen/2YKSRnObm+XEEL0NpKAim5lMimmnzuc/U8pZN28SuY9uJznCgdwaV46D1c2sOCIi3n28BQK7J+xtCqBA29/l/s/nodh6HiHLoTo55RSntg6lWji+UJsqYiViR6qLfncUN3CoxcUMX14RnvdV6Vf8YtPf8E9C+4h3NzMlp9dRuvcueT+43ZSzjwTgJAGv2Hw8JhBnJeT1n5u1uAkxh6WxynXT8blsXV7u4QQojdSbSO29XZFRUV6/nyZV7I3Wb+gik+eXonDbeX4K8fxjSPC9atLcJtN3D8onfkv3MYjrUMI+YeQ7/HyyEXHMDo3Od5hCyH6GKXUAq110W4c97bWeoZSahPQsfNUgNZaF3ZZkHHUF/rX8x+bw9zNdTy2Q/L5ZemXXPfZdQxNHsqjxzyKqyHAlksvIeOaa/AcdxwNoTAuswmbyUREa8yx6VQ2LKqiYFQqNoc8+iuEELuyq/5VElARV9Ulzbz70FJ8zSGOvGAUxugkLlu+mbWtfi7Pz+D88iVc++mrrA4fi464mDnRw99OPoBkl3zTLIToHLubgMaOVUCB1npLF4fVY/SF/nVleRM1LYFdJp8PH/IvkhMzUGYzOhxGWSxUB0OctXgDoxOcPDB62y27Sz4t4euX1zHl+IHsP3NIPJojhBC9wq76V7kFV8RVRkEiZ944lcyBiXz4+ArqPyrj/cnDuCQvnUdKq7nCWchdV9/BTUmf40qYy5uLm5h26zv858vVhCIy0qAQonvp6Le278Q7DvHjWgJh/jtvC1prRud6tks+Ad5c/yZDk4fyyCH/ouma37D1pj8CoCwWqgIhTl+0gc2+AGdnb7u7euEHxXz98joKJ2Uw9cTB3doeIYToKyQBFXHn8tiYed0kRh+cy4L3i/ns0RX8pSCb58YXUh0Mc/zqMtR5N/PxkUdwjOVRIvZibn93Awfd/jYfr6ygr1zFF0L0GguVUlPjHYTYNV8wwqVPzeMPry9nfVXLdnVtfcbth9zOI9P/TeO1N9K6cCHugw4CoCIQ4rTF6ynxB3lufCHTUxPRWjPnrY189/oGhk3N4tifjcFskY9QQgixN+Svp+gRzBYTh/1kBNPPGU7x8lpe/vs8JngVn00bwcHJidy0rozfuAZy840v8XR2C4Xup6gO1PGzZxZw+n8+ZXlZ44+/iRBCdI79iE7DskEptVQptUwpJXNH9RDBsMFVzy9g7uY67j5rAsOyEtvrllQv4aL3L6LWV4s5FKHpVzdGBxy6/TaSTpqB1pqLlm1iayDEixMKOSgleq7fG2LV1+WMOjCHoy4ejcksH5+EEGJvyTOgosfZur6BDx5bgb8lxCFnD2PUQTk8VV7LzRvKsZtM/G1oLjPDXp555DoeSEinufFotOHiyNGp/P64sQzNTPzxNxFCiJg9eQY0dvzAnZVrrYs7L6qeozf1r+GIwS9fWsS7yyq47bRxnDttQHvd6rrVXPLBJSTbk3n6uKcJ/O4Wmj/+mJy//53kU09pP25BoxcDmJrkbr9aqpTC2xjAlWhD7TB3qBBCiJ2TQYhEr+JrDvLRkyspWVnH8P2yOPTcEZQYYX69uoTZjV4OT03kjuH5mL56nzs//SfvpRYRrD8EtI2TJ+bw66NHMSDNFe9mCCF6gb1IQJ/VWv/0x8r6it7Uv87ZWMu5j87mDyeM4meHbBuUeGPDRi56/yLsFjtPH/c0uQm5eOfOJVRSSvLpp1ETDPNRbSPndphiRWvNnFkbCfojHHLWMJSSxFMIIfaEDEIkehVnoo2TrpnAficPZt3cSl69fT7JDWFemzSUvw/LY06jl8PmreGjMQfxz798yEtJWRxivhNL6lfMWlLCYf/8lD+8tpTyBl+8myKE6HvGdNxRSpmBKXGKRXSwX2EaH1w3fbvks6SphJ99+DNMysSjRz1C0vISANzTppF8+mnUhcKctXg9f1hbSpk/CESTz7lvbWLBe8VEgpHtJ90RQgixTyQBFT2WMimKThjMyddOxN8a5tXb57Pq63Iuzkvn86kjKPK4uXFtKWesKiXxp9fxxLWzeCjUzBj3PzAnzebFuZs55B+fcsMri783CIUQQuwppdTvlVLNwHilVJNSqjm2XwW8uY+vfaZSaoVSylBKFe1Q93ul1Hql1Bql1LEdyo+Lla1XSt3YoXywUmpOrPy/SilbrNwe218fqx+0LzH3JPd/so5PV1cCbPfMJ4DVbCUvIY9Hjn4E+33PsOWii/AtjT6y2xAKc/biDWzwBXh6XCF5Dlt78jn/3c2MPiiHw34yUm67FUKITiQJqOjx8kemcvZNU8kuTOLz59fw7kPLSA8pXppQyN0jC1jp9XH43DXc0RTigBvv55XTn+CvdcsYmH4HpuRv+N/CzRx99xdc+ex8lpY2xLs5QoheSmt9m9Y6EbhTa+3RWifGljSt9e/38eWXA6cBX3YsVEqNBs4hetX1OOBBpZQ5dtX1AeB4YDRwbuxYgH8A92ithwL1wKWx8kuB+lj5PbHjer1nZxdz10dr+Whl1XblLcEWDG2Q7c7mmeOfIfnJt2l48SXSfnYpjnHjaA5HOHfJRtZ4/TwxdjDTU6OJ6/x3NzP/3c2MkuRTCCG6hCSgoldwJ9k5+ZcTOfjMYZSsrOOlW+aweVkt5+Wk8c1+ozg1K5l/bali+pzVfJqew7m3v84bU2/hd+VzGZRxG9a0T/lo1RZO/vc3/OSx2Xy5tlqmbxFC7K2blFLnK6X+BKCUKlBKTduXF9Rar9Jar9lJ1UzgJa11QGu9CVgPTIst67XWG7XWQeAlYKaKPqh4BPBq7PyngVM6vNbTse1XgSNVL3+w8b1lW/nzm8s5alQmt8zcdmd0a6iVKz6+gj998ycAah99jNpHHyX57LPJ+PWvUUrxRV0zK1p8PDp2EEemedrPTS9IZMwhuRwuyacQQnQJSUBFr6FMiglHFnDm74twJdl598GlfPb8apIMxb9GDeTNSUNJspi5dPlmfrJsE3VTD+TSez/if2P/xg3FCxmYfiu2zHeZu3kLFzwxl6Pv+YLnZhfTGgzHu2lCiN7lAeAA4LzYfkusrCvkASUd9ktjZbsqTwMatNbhHcq3e61YfWPs+O9RSl2ulJqvlJpfXV3dSU3pXN9tqOXalxYzqSCZ+8+djCU2NUrICPHrL37N8prlHFFwBL4VK6i++248M2aQ/ec/tQ8mNCMzmW/2H8Wx6UkANFa3AjB4fLpc+RRCiC4kCajoddLyEjjzd0VMOmYAK78u5+Vb57F1fQP7JSfwYdEI/m9YHvMbvRw2dzW3bNiK+fCjuOxfn/DK8L/w6/XLKEi7FUfOy5Q2beCPbyxn/79/wm3vrqK0vjXeTRNC9A77aa1/DvgBtNb1gO3HTlJKfayUWr6TZWZXB7yntNaPaK2LtNZFGRkZ8Q5npz5bU8WANBdPXDQVp80MgKEN/vzNn/m67Gv+tP+fOHLgkTjHjCH/wQfJve3vRJSJa1YW83V9MwAFjuiPbcVXZTz/lzmUrqmPW3uEEKK/kARU9Epmq4kDTxvKKb+ahBHRvPbPhXzx4hqMQISf5WfwzX6jOCUrmYdKqth/9ioeLash8YQZXP6vT3l50J/5/aotDLPfg3PgQ4StS3nkqw1Mv+MzLntmPp+triJiyO25QohdCsWewdQASqkMwPixk7TWR2mtx+5k+aEBjMqAgg77+bGyXZXXAslKKcsO5du9Vqw+KXZ8r/T740fyv6sOJNm1Lfe/b+F9vL3xba6ZeA3H1+TiW7IEgMQjDkdbLPxqzRZeraxnrdfffs6aORV8/sIaBoxJJWdIUre3Qwgh+htJQEWvljc8hXP+PI3xR+Sz/MsyXrx5DpuX1ZBpt/KvUQP5qGg44xKd/Hl9OYfMWc2sumbSTjmNCx78hOeL7uG2JZrJwadwDbkdR8qXfL2+lIufmsch//iUez5aK9O4CCF25l/A60CmUupW4Gvg7130XrOAc2Ij2A4GhgFzgXnAsNiItzaiAxXN0tGH2z8DzoidfyHbRuidFdsnVv+p7mUPw9d5g/z08Tmsr2pBKUWS07pd/fT86fxs3M/4qekgSq/5BZV/vw2tNVpr/rSujFcq6vnt4GwuyY9e1d2wsIpPnlpJ3vAUjrtsLGaLfCwSQoiupnpZ37NLvWmibNE1KjY28tlzq6kr9zKsKJODzxqOyxMdUv/zumZu3lDOKq+fyR4XNxXmcFBKIlprWmfPZt4L9/GyYxnfjrTibx2F23sktQ05mBQcOjyDs4oKOGJUJnaLOd7NFEJ0sl1NlP0j54wEjgQU8InWetU+xnAqcD+QATQAi7XWx8bqbgIuAcLAdVrr92LlJwD3AmbgCa31rbHyQqKDEqUCi4DztdYBpZQDeBaYBNQB52itN/5YbD2lf/WHIpz36GxWlDfx3M/2Y+qg1Pa6spYy8hKij7oGi4vZfO55mBwOBr74ItasTO7YtJW7N1dyRX4Gfx2ai1KKuq1e/vt/c8kc6OGkX07A5rDs6q2FEELshV31r5KAij4lEjZY+EEx89/djM1h4YDThjDqgByUSRHRmpcr6rhjUwVbAyEOSk7gN4Oz2T85AQDfsuVsfPEx3qj6hA8naGocqdibphNsmUaLz4zHYeHE8bmcOimPooEpmGSACiH6hL1MQFOI3s7anrVorRd2dmw9QU/oXw1Dc82LC3lveQUPnjeZ48fltNd9V/4dV39yNbcedCvHeKax+dzzMJqbGfjCC9gLB2NozbWrt2BRirtHFLQPQqS1ZsVX5QwrysTusu7qrYUQQuwlSUBFv1JX7uXz51ezdUMjmQMTmX7OCLIGR4fZ90cMnttay33FlVQHw0xPSeA3g3OYmuQGIFxdTc1/X+KD2c/x3vAWlg+wgHcYKaFjqazJJRiG/BQnp0zM45RJuQzNTPyhUIQQPdyeJqBKqVuAi4ANxJ4DBbTW+oguCC/uekL/ett7q3j4i43cdMIoLpte2F6+pm4NF75/IbkJuTx93NP47n+EuueeZ+BTT+KcMIGwobGYFIbWaMCsFNVbmjFbTKTmuuPXICGE6AckARX9jtaatXMr+fa19bQ2Bhl5YA4HnDIElyc6YEVrxOCZshru31JFbSjM4amJXD8ouz0RNYJBmt9/n8WvP857rvV8OdZEk8OG0zsNV+gISqpcGBqGZSZwwrgcThiXw/CshPZv14UQvcNeJKBrgHGx+Tf7vHj3r4FwhJ8+PpfhWQncMnNs+9/YSm8l5717Hmh4/sTnyXZno8NhAuvX4xg5ko9rm/jb+jJemDCkfbTb+govr/1zIYmpDs78fZH8vRZCiC4UtwQ0NlLgfKBMaz1jh7rpRJ9fGU/0WZRXY+UDiQ7wYAKswP1a6//80PvEu4MUPVfQH2b+O5tZ8mkJFquJqTMGM+7wfMyxOeO8kQhPltbwYEkVdaEI05LcXDMgk6PSPJhiH078q1ZR/cp/+XTlW3wyzM+SQhPhSCLZkWPQvslsqjCjgSEZbk4cl8OxY7MZneORDzdC9AJ7kYD+D7hKa13VhWH1GD2hfw2EI5iV2jbXZyTEue+cS2lLKU8d+xRpL3xM8llnYs3KAmBho5fTF29gmMvOa5OGkmAx01zn57U7FxAJG5x2wxSSs1zxbJIQQvR58UxArweKAM9OEtBBgAe4gejofW0JqC0WW0AplQAsBw7UWpfv6n16Qgcperb6Ci9fv7yOLSvrSM5ysf/MQgonZbQnid5IhBe31vHQlirKAiGGuxxcPSCD07JSsJmiH3oMn4+m9z9gw6wXeJ/lfDXWRFm6QoU9FKgZhFrGsX6rwtCQm+TgiFGZHDUqi/0L03BYZQAjIXqivUhAi4iOLLscCLSVa61P7oLw4i5e/evK8ibu+nANd501YbupVtq8svYVct25DHt1PrUP/YesP/ye1AsuYH2rn5MXriPRbObtKcPIsFnxtQR5/Z8L8TYEOOX6yWQMkEcnhBCiq8UlAVVK5QNPA7cC1++YgHY47ing7bYEdIe6NKKj+O0vCajYV1prNi+r5bvXN1C/1UvWYA8HnjaU3GHJ7ceEDM2sqnoe2FLFSq+fHLuVS/PSOS83jVTrtlESAxs20DBrFku+fp0vMmr5ZoyJWg9YI8kMtp5M2DuatWUmfCEDl83MIcPSOWJkJtOHZ5CT5IxD64UQO7MXCegK4GFgGR3m/9Raf9EF4cVdPPrXrY0+Tn3gW5SC168+iOwkBxD9G17aUkpBYnQK1Ib//Y+tN/2RpDNOJ+eWW6gMhpmxcC3+iOatycMY7LID8NXLa1nxZTkn/XICecNTurUtQgjRX8UrAX0VuA1IBG7YkwRUKVUAvAMMBX6jtX5gJ+ddDlwOMGDAgCnFxcWd3gbRNxkRg9WzK5g7ayPexiCDxqdzwClDthuUQmvNZ3XNPLClim8aWnCYFKdmpXBpXjpjE7fduqUNA9/ChdTPmsW8Je/y1SAfc0eaaXBpLIaDoY4TsAWmsL7cSWVT9JGxYZkJHDIsg+nD09lvcBpOm1wdFSJe9iIBnae1ntqVMfUk3Z2AegNhzvzPd2ypa+WVKw9gVI6nve6RpY/w6NJHeWnGS2StqKDkiitx77cfBf95CGW1UhMMc9XKzfxxSC4TOvydDoci1JS0kF2Y1G3tEEKI/q7bE1Cl1AzgBK311Uqpw9jDBLRDXS7wBnCS1rpyV+8nV0DF3ggFIyz9tISF7xcTCkQYsV82U04YRHLm9s8GrWrx8WRZDa9U1OMzDKYlubkkL50TM5KxdpiOxQgGafniCxree4+Faz9j9sAgc0eaqfZoTNrEcPehpOiDqK3LYlmpj2DYwGYxMXVQCgcUpnHAkDTG5SVjk8nQheg2e5GA3k301ttZbH8LrkzDso8MQ3Plcwv4eFUlj180lcNHZLbXfbD5A2744gZmFM7g1oNuZct5P8FobWXgC88TdrlQgM1kQmuNUgptaBa8v5mxh+bjcMs0K0II0d3ikYDeBvyU6MTZDqLPer6mtT5/J8c+xS4S0Fj9E8C7u6oHSUDFvvG1BFnwXjHLvyzDCBsM3y+bouMHfW+QioZQmJe21vFkWQ3F/iAZNgtnZqVyXm4qQ12O7Y41/H6833xD4wfvs2zZp8wu8LNguJnijOi/uRxHPkOcJ6BbR7KpwsaaihYAnFYzRYNSOGBIGvsXpjE2N0kSUiG60F4koJ/tpFimYekEFY1+Tn/oWy49eDCXHDy4vXx5zXIuev8iRqeN5rFjHsNmthFpasLw+TBnZnLVymKawhGeG1+IWSm01nz9yjqWflrK4eePZPTBud0SvxBCiG3iOg3Lnl4BjT07Wqu19sUm+54DnK61Xrar95AEVHQGb2OARR9tYcUXZUTCBsOnZVN0wvcTUUNrPq1r5vnyWj6qbSSsYVqSm/NyUjkpMxm3eftbao1gEO+339Ly6WdsnvcJ85LqWDDMxPLBJoJmjV3ZGJu2P5nqIHzNBawpN1hbGU1I7RYTE/KTmTIohamDUpg8IGWnA3IIIfbOniag/U1396/N/hAJdkv7AHHVrdWc9fZZ2M12nj3sMfTTr5D+86sx2aPPd96yoZwHtlTxx8IcrhkYGwX3w2K+e20DE44o4KAzh8qI5EIIEQc9JgFVSt0MzNdaz1JKTSU63UoK4AcqtNZjlFJHA3cRneBbAf/WWj/yQ+8hCajoTK1NQRZ9WMzyWCI6ZEomk44eQOZAz/eOrQqEeLmijhe31rHBFyDBbOLkzGROy0rhgOQEzDt88NFa41+xkpbPP6f2i09Y6F3NkkLFsiEWSlKj45kk25KYmDadVIrwteSypjzMivImwkb03+vQzAQm5CczsSCJ8fnJjMxJxG6R50iF2Bt7cQX0zzsr11rf3HlR9Rzd0b8uKK7jrSVbuenEUVjN29/xETbC3LfwPk4ePAPHH+6h5euvGfj0U7iKiniqrIYb15ZyYW4atw/PRynFmjkVfPzkSoYWZXLMJWNQJkk+hRAiHuKagHYHSUBFV2htCrL4oy0s/6qMkD9C3ohkJh41gIFj0r73oUZrzdxGLy9srePt6ga8EYNsm5WZWcmcnpXCuATnTr+FD1VW4f3mG7zffEPJ4q9ZktLE0kGKZUOt1DsjAKTb05iQOY0MNZVw6wA2VymWlTVS0xId1MhmNjEq18OE/CTG5iYxJs/DsMxEuXVXiN2wFwnorzvsOoAZwCqt9SWdHlwP0NX9a2l9KzP//Q2JDgtv/vxgklzR5zUjRoSmYBMpjuiotZW33U7d00+T/Zc/k3LuuXxY08hFyzZxZJqHJ8cOxmJSRMIGL948h4QUOyddMxGzVf4GCiFEvEgCKsQ+CPjCrPyqnCWfluBtCJCS42biUQWMmJa90w84rRGDj2obea2ynk9rmwlpzVCXnZmZyczISGak27HTZFQbBv5Vq/B+8y0tX3/FhuJFrMyJsHKAiVWFVmqdYQCSrB7GZUxgsHsytvAwGhuTWVneyvKyRrzBaNJqM5sYnp0QTUhzPYzM8TAiOxGPQwbjEKKjfb0FVyllBz7QWh/WeVH1HF3Zv7YEwpzx0LeUNfh44+cHMSQjob3urvl38f7m93l5xsvwxodU/PWvpPz0p2Tf9AcAlje3csemCh4aM3C7xx68jQEsNjN2p+V77yeEEKL7SAIqRCeIhA3WL6hi0YdbqC1rwZloZdRBuYydnkdiqmOn59SHwrxT3cj/KuuY3eBFA4VOOydmJHFiRjITEnd+ZRSiAxn5Fi+hde5cvHPnULx5CStzIqwuUKwbbKfEEwJAoRiaPJRx6ePJto1DBQdQ2+hiVXkLK8obqW8Ntb9mXrKTkdmJjMxJZES2h2GZCRRmuOUWXtFvdUICmgLM01oP7cSweoyu6l8jhubyZ+bz+dpqnrp4KocMy2ive33d6/z52z9z9oiz+f3YX7Hh2GNxjBlNwYMP4kWRsMPfq6YaH8u/KGP/UwoxmeWqpxBC9ASSgArRibTWlK6qZ+nnpRQvqwFg0Ph0xh6aR8HI1F0+c1QVCPFeTSPvVjfydUMzEQ15divHZyRxdFoS+ye7sZt2/eGpPSGdPx/f4sXUrFrMGo+XtXmKdYOsbMhRtFiiV0ltJhsjU0cyKnU0uY7RWMIDaGpOZG2ll9UVTWys9rY/U2pSMCjNzdDMBIZlJTAsM5HCDDeFGQkk2OUqgujb9uIW3GVExygAMAMZwM1a6393RXzx1lX966qtTZz24Lf84YSR/PSAQe3lCysXcumHlzI1ayoPHvUgFpOFYHEx5rQ0mu0OTlq4jhkZyfyuMAcAf0uI/925AF9zkLP+MBVPurPTYxVCCLHnJAEVoos01fpY8VU5q74px9ccIinTyZiD8xi+XxbuJPsuz6sPhfmgppF3qhv5qr4Zv6Fxm00cmpLIUekejkr1kGn/4dtltWEQ3LCB1kWL8C1aTOuSxZTUb2ZDNmzIUWwa5GBjegSfOXpbrkVZGJI8hBGpIxiaNBK3HkIkkEF5vcG6yhbWVTWzubaViLHt70Jmop3CDDeD0xMYkuFmUJqbgWkuClJdOKxy1VT0fnuRgA7ssBsGKrXW4c6PrGfoyv51a6OPnKRtCWN5SznnvnMuibZEnjnwQfj4K1LOOw+lFEHD4NwlG5nb6OW/E4ZwYEoC4VCEWfctpnJzEzOvnUTusOQuiVMIIcSekwRUiC4WCRlsWFTF8i/K2LqhEWVSDBybxqgDchg4Lg3zDwwI1Box+Lq+mY9rm/i4tonyQPSW2fEJTqanJnJoSiJTk9w4duPWskhLC/7lK/AvX4Zv2XJaly1li38rxVmKzZmK4gF2Nmcq6m3B9nPSHGkMSxnG0OShDPYMw6UHEgqkUl4fYVONl43VLWys8dLQ4VZepSDH42BAmotBaW4KUl3kpzgpSHVRkOIiPcEmUx+IXmF3E1ClVOoP1Wut6zovqp6js/vX7zbUsqG6hfP3H/i9ugZ/A3/97q/8ctxVmH91K77Fixn85pvYBg/iV6tLeKmijn+PGsAZ2aloQ/PhEytYP7+KY342hmFFWZ0WoxBCiH0nCagQ3ai+wsvq77ayenYFrY1BHAlWRkzLZsT+2aQXJPxgYqa1ZpXXz0c1TXxW18T8Ji9hDU6TYr+kBKanJjI9JYFRCc7vTfGyK+G6OvyrVhFYtQr/ylX4V62iqnozmzOgNB1KcqyU5Nko8YQImCLt52U4MyhMKmRQ0iAKkwpJtw3EFM6iudXBllofxXVeimtbKa71to/I28ZpNZOf4iQvxUluspO82JKbHC3LSrRjkWe1RA+wBwnoJrZND7YjrbUu7PTgeoDO7F831Xg55YFvyEi08/YvDm6/i8LQBhEjgtVsRWvN1j/9icZX/0funXeQdNJJ3F9cya0bt3L9oCx+Ozh6621teQuv3j6fqScOZvKx309mhRBCxJckoELEgREx2LKyjtXfbWXTkhqMiCYl28XQoiyGFWWSku3+0dfwhiN829DCl/XNfFHXwtpWPwBJFjPTktzsn5zAAcluxiW4sO7BfHeG14t/7VoC69YRWLuOwLp1+NauYatuoCRDUZYG5dk2tmZHE9NW87Y7DO1mOwWJBQxIHMBAz0AGeAaQ4cjHHMnA53NRVu+npN5HSV0r5Y0+yhv81Hm3T1BNCjIS7WQnOcn22MlJcpKd5CDb4yAz0U6mx0GWx77dhPRCdIV9HYSor+us/rWxNcSpD35DfWuQN35+EAPTtv39e2jxQ3y39Tv+c9R/8L/wCpW33U7alVeQed11AMyqauCzuibuHlGw3d+DphofiWk7H1VcCCFEfEkCKkSc+VtCbFhUxbp5lZStawAN6QUJDCvKYuiUzN0eOGNrIMg39S3MbvDyXUMLG3wBAFxmE0UeF0VJboo8bqZ4XCRZ92wAIa01kZqaaFK6YSOBjRsIbtiIf+MGan21lKUrtqbA1jQTlblOKlIVFc4AIWW0v4ZFmclNyCM/MZ/8hHxyE3LJTcgl1ZaFKZJOq89OeWOArY1+Khp9sXV0aQ58/zE6l80cTUgTHWQk2r+/JNhJS7CR5rbLvKdir+xNAqqUOhmYHtv9XGv9dudH1jN0Rv8aihhc9ORc5m6q4/mf7c+0wdvuZv6o+COu//x6Zg6ZyZ+H/JyNxxxLwmGHknffffg1OHe4U2Lzshpa6gOMnZ63TzEJIYToWpKACtGDeBsCrF9Qxbr5lVRuagKiyejgCRkUTswgLc+929/oVwVCzG6MJqNzGlpY7fXTlg4OdzkoSnJR5HEz0eNiuMuBZQ+uknYUaWwksGEjweJigsWbCW4uJlhcjK94EzVWP1XJispkqEwxUZPtoDLNTIU7RLM5tN3rWE1Wctw55LhzyHJnkeXKItudTbY7G48lExVJwuuzUtUcoLLJT1VzgIomP9XNAWqaA1Q3B3aaqAJ4HBbSE+2ku6NJaYrbRprbRmpsSXPbSXFbSXHZSHHZcNpkECWxV4MQ3Q5MBZ6PFZ1LdBqWP3RFfPHWGf3rZ2uquPjJedx5xnjOLCpoL19dt5oL3ruA4SnDeeLYJ7CZbbTOm4djzBhKlZlTFq3nr0PzODkzGYDqLc28dtdCUrJcnP7bKT/4bL0QQoj4kgRUiB6qsdrHxkXVbFpSzdaNjaDBk+5g8MQMCiekk1WYhHkPnpVsCUdY1NTK/CYv8xq9LGxqpSEcfa7TaTIxLtHJxEQXExKdTPS4GOy0Y9qH29e01oSrqgmVlhDcUkKopIRgyba1t6mW6iSoTlLRdbKZ2iwHtclmatwR6iwBDLX93yG72U6GM4NMVyaZrkwyXBlkODNId6aT4cogwZyKMpLw+a3UeoPUtgSpaQlQ2xKgxhukpjlAnTdInTdIfWsQYxd/5hxWEykuG8kuGymuaGKa5LKS7LSS7LKS7IzuJzmteBzW9m23zSy3/PUhe5GALgUmaq2N2L4ZWKS1Ht9VMcZTZ/WvqyuaGJntad+v8dVw7jvnorXm+UMexr1uKwmHHAxAUzjCjAXrqAyGeHvyMIa5HTTX+Xn19vmYLIozflf0g6OMCyGEiL9d9a8ywZ8QcZaU4WTSMQOYdMwAvI0BNi+tYePiGpZ9XsqSj0uwOS0UjEphwJg0Bo5Jw538wx+6EixmDklN5JDURAAMrdnoC7C4qZUlza0sbvLxbHkNj8SyMpfZxBi3kzGJTsYmOBmT4GSk2/G92952RSmFNSsTa1YmrilTvldv+HyEtm4lVFZOqKyMUHlsvbaC8Nat+KuraHCEqfVATaKiLhHqkwI0plVTl1zPMtcqam1B/B0GR2pjNVlJc6aR6kglzZFGWnoaQ/NT2c+RRqozlVR7Kh5bHhY8GGEXzT5NfWuQ+tZQdO2NbXuDNPhCrK5ootEXoqE11D5H6s5YTAqP00qiw4LHYcXjtJBoj60d0fJEh5VEu4VEh4UEh4WEtm27FbfdjNtmwbSXV6NFj5AMtI16mxTHOHqNjsknQHOwmURbIrfs91f8v/s/6hctYujHH6HT0rl8+WY2+vy8NGEIw9wOgr4w7zywhHAwwmnXTZHkUwghejFJQIXoQdxJdsYckseYQ/II+sKUrK6jeHktW5bXsmFhNRC9VXfA6DTyR6aQMyQJy4/cRmpSiqEuB0NdDs7Ijj53FTY0a1v9LG5uZUWzjxUtPl6tqOOpSPTmXRNQ6LIzwu1gpNvBSHc0KR3stO/xLbwmpxN7YSH2wp0PEKojEcK1tYQrKghtrSBcWUm4uopQZSXh4mrClZWEKivwhgM0JECDGxoSFPUJUJ8IzSl1NCY1Ue4qZpUjQoMlSLjDM6nb/fe1ukm2J0cXRzLJnmQyMlIYak9qL/fYPXhsHmwkog0XwZCFZl+ERl+ofWnyR9fN/jBNvhBN/jDVzS00+cI0+UO0Br+fLO80HpsZtz2anLrtlvbE1N22bzPjalvbzLhs0WOctmiZ0xY93hXbdtksmCWp7Q63AYuUUp8RHRF3OnBjfEPqPdruvBqcNJhXT3qVqlv+j/rZs8m57TbM6en8bm0pn9c3c/eIAg5OiX6RVryilvqKVmZcM4G03IR4hi+EEGIfyS24QvQCWmtqy1ooXl5L8fJaKjY2oQ2N2WIie4iH/BGp5I9MIXNgIqa9nNrE0JoSf5DlLT6WN/tY4/Wz2utnky9A218Jm1IUuuwMczkY6rIz3B1dD3E5cHXxlCqG10u4pia6VFcTro6ta2uI1NYRrq0lUltLqLaGFhWkyUVsUdu2Ey20JNloSTDT7IRmu6bJGqLVtPNnSgFMykSCNQGPzYPH7iHRlhjdtkW3E22JJFgTttt2WRLAcKC0k3DYii8Izf4QLYEw3kCElkCIlkCEFn8YbyBMSzBMa6zOG4yVBSL4gmG8u5nMtrFZTDit0YTVaY0mpm1rhzW23WHfYTVF1xZTe5ndEi1vW0eP21Zmt0TL+kqyuwfTsDwAvKC1/kYplUP0OVCAuVrrii4NMo46u399esXTbGzcyB/3/yPNL71C5c23kHrpJWT95jdorbljUwUhrfnjkNztzmuq8e32YG1CCCHiT54BFaIPCfrDlK9roHRNPWVr6qkpaQHAajeTXeghZ2gyuUOTyRrs+dErpD+mNWKwvjWajK5u8bOu1c/6Vj/FviAdrzPm2q0MdtopdNmja6edwS47Ax02HN0436fWGsPrJVJXR6SujnBdPZH6OsJ1dUTq6onU1xNpaIguse1ASxMtdk2LE1oc0OJU2217Eyz4Eqx4XSa8ToXXpvFaDbzm8Hbzpu6Kw+zAbXW3Ly6rK7pt2bbtsrpwWaLbTosTl9WF0+LEYXZi1g7QdrRhJxKxEIlY8IcMWoMRfKFo4uoLRvCFItGyYDhWt63cF4rgDxn4Y2WtwTD+sEEwvPOrxbvDYlLYLSbs1mhSareYsFm2Jam29n0TNosZm7njvql9f7vt2Nravlbt5daOdWYTLruZ9IR9vxVzDxLQa4FzgBzgZeBFrfWifQ6gh+vM/vXL0i/5xae/4MgBR3Jr7hVsPvV0Eg45hPwH/k1YmdqnktJao5Ri6WelpOe7yR2W0invL4QQovtIAipEH+ZrCVK2poGytfVsXd9AbbkXNJjMisyBieQMSSa7MImswZ4ffYZ0d/kjBpt8Ada1Bljf6mdja4BNvuhSF9o+Kcu2WRnotDHAaWOgw85Ap418h40Ch41sm3WvR+btLDoSwWhuJtLYuG1piK2bGjGamok0NWE0NxFpbCLS3IzR2EikpYVgawutVgOvA1rt4HUo/Lbodtvisyv8Lgt+lwWfw4QvdozPqvGZDXzmyE6fcf0hDrMDl9WFw+zAaXFGF6sTh9mBw+KIJa/RbbvZjtPixG6247BsK3OYHVjNNkzaDoYNra2grRiGGR2xYBhmQhFFIBxNXANhg0DbOlbmDxkEIxECIYNgxCAQMgiEo8cEw9vKgpHo/o51+9IFTRmYwv+uOnDvXyBmLwYhGkg0ET0HcAIvEk1G1+5zMD1QZ/WvGxo2cP6755OfmM/Txz2N0+Kk8fU3SDzmGFZoxc+Wb+bRsYOYkOgCYP2CKj54dDnD98vi6IvH7PP7CyGE6F6SgArRj/i9ISo2NrJ1fQPl6xqpKm7CiET/rSek2Mka7CFrUDQhTS9IwObo3MfBG0JhNvoCbGoNsMUfpNgXpNgfYIsvSHkgRMe/OmYFOXYr+XYbBU4beXYbuXYrOXYreQ4bOXYryZaeO+qsNgyM1tZoAtvcjNHcjOH1YrS0EGlpwWiJbhveFiJeb7TO68Xwtm7b9vkIt3oJhHz4bESTUxsErBCwKfzWaFnbOmiNlgXsJoIOMwG7maBdEbCZCFggaNUEzBq/2SBgNgiqCMZe/udTKOxmG3ZzNGm1mq3YzXbsZjs2sy26mGzt5TZTtMxqsrbXt23vuLYoCxZlQ2EFbYkuWNCGBbQZtBmtzRiGCa1NaMNExDBhGIqwASkuG0eOytrnn+HezAPa4dxJwBPAeK11n5zXpzP61wZ/A+e9ex6toVaePeghMv1W7EOHAlDuD3LCgnWYFbw3ZTiZdisVmxp54+5FZBQkMPO6Sft8J4cQQojuJ6PgCtGPONxWBo1LZ9C4dADCoQg1JS1UbmqiclMjlZub2gc1QkFKlouMAYlkDEgkc2Ai6QWJ+5SUJlstTLZamOxxf68uYBiU+IOU+oOU+kOU+oPt+9/Wt1ARDBHZ4Xsxp8lEtt1Ctt1Kts1KVixBzYptZ9osZNmsuM2mbk9UlcmEOSEBc0IC1pycfXotbRhony+a0La2YrRv+zBavdvqfH4Mvy+67/Nj+FrRTT4MfwDtj5X5/dF6f/TYYDhAIBwgYNEELRC0El1bIGhRBC0Qiu2HLB3qrIqQ2UfQ6iNkUYRsZsJWRchqImRVtFgVYXP0nJAZwiZNyKwJmTQhpQmZDCKq87/oVCgmpY7nyFHPdfpr/+h7K2UBjid6BfRI4HPgr90eSC+yoXEDLcEW7jvkLiI33EJxSQlDP/oQn9XGBcs20RKJ8NbkYWTarTRW+3j3waW4k2yccNV4ST6FEKKPkQRUiH7AYjWTXZhEdmESEJ0EvrUpSNXmJqq2NFO9pZmytQ2snVvZfo4n3UF6fiJpeW7S8hNIy0sgKd2J2sfbZe0mU/uovDsT0ZqqYIit/hBlgRBbA0HKAiEqAyEqAiEWNbdSURPCv5NpUpwmE1l2C5k2Kxk2C+lWCxk2K+nt2xbSbBbSrBaSLOZ9mv+0KyiTCeV2Y3J/P3HvDFprdCgUS0wD6GAA7fdjBILogB8dCGwrDwQwAgF0IIgOBqIJbTCEDgbRgQA6FMRoCWwr23EJBaN1oRDhUIBwJETQCBHUIcJGmLAZwqZo4ho2xxaT2rYdW0JmiLTXbyuPmBRZKdVwUpf8p9oppdTRwLnACcBc4CXgcq21t/ui6J2mZE3hvdPeo+EPf6Vp0SLy7r0Xw+7giuWbWNni47nxhYxKiA4wtOKrMgxDM+OaCTgTbXGOXAghRGeTBFSIfsrlsTFofDqDxqe3l7U2BakqbqKmpJnaMi81pS1sWlLd/pyexWYiNcdNSo67w9pFYpqz0+a0NCtFjt1Gjt3G5F0co7WmMRxhayBEdTBMVTBEZWxdFYhur/MG+C7U8r3nUbe9D6Rao8lo2zrFaibVaiHVaibFaiHFaiHVYibZaiHZaibJYsbcw5LWPaGUQtlsYLNhjuPMlVprCEWT0/YlGESHw9v227aDQXQojA5Hy2ivC2NO6faBaX4PvAD8Wmtd391v3tu1PvoUTW+/TcZ11+E57lh8EQOLgtuH53NE2rY5Qg84ZQhjDsklKcMVx2iFEEJ0FUlAhRDtXB7bdrfuAoSCEeq3RpPR2rIW6itaKVtTz5rZ22adMFtNJGc6Sc50kZTlIiXLRXKWi+RMF44Ea6fHqZSKJYUWRv3IsSFDUxcKUx2MJqu1oehSF4pQ22F/lddHXShMQyjCD40Lm2Qxk2wxkxRLSNsWj8VMssWCx2rGYzaR2KE8MbZOMJt63FXXeFBKgc0WTYZ7Ea31EV312kqpM4nexjsKmKa1nh8rHwSsAtbEDp2ttb4yVjcFeIroQEjvAtdqrbVSKhX4LzAI2AycpbWuV9H70+8jegW3FbhIa72wq9rUUcvX31Bz/79JmjmTtCsuJ2AYOM0mnhw7GKUU2tDMfnMjYw7JxZPulORTCCH6MElAhRA/yGozkznQQ+ZAz3blAV+Y+gov9Vu91G1tpaGyldpyL5uW1GB0uD3W7rLEPlA68WRE10npThLTHSQk2/d63tLdjt+kyLJHnxXdHUbs6mp9KEJ9KExdKExjOEJDOLrfEIpuN4QiNIbDVARCNIYjNIUjO70teEcJseS04zrBbMZtMZFoju1bzLjNJtxtdW3bsXJX22Lq/mdeRZdZDpwGPLyTug1a64k7KX8IuAyYQzQBPQ54D7gR+ERrfbtS6sbY/u+IPrc6LLbsFzt/v85txs65pk0l49fXk3rhhTxVXstz5TW8PGEoabbox5DZb25g4QdbcCfbGH94QXeEJIQQIk4kARVC7BW700L24CSyB29/L2ckYtBc46ehKpqUNlb7aKr2Ub2lmY2LqrdLTpVJkZBix5PmIDHNQWKak4QUO4kpDhJS7SSkOLDau3cAEpNS7bffwp5NWeOPGDSFIzRFoglpdDFoDkdoDEdoiURoCRs0RyI0h7dtVwbDtIQjeCPR/R0HYdoVBThjyanL1CExNZtwmrbfdu6w3bZ2mBQuswmHKbrtbN+O7jvMpl5923FvobVeBez2FwpKqRzAo7WeHdt/BjiFaAI6EzgsdujTRAdJ+l2s/BkdHf5+tlIqWSmVo7Xe2mkN2QWTzUb6ZZfxTnUDf1hbytFpHpIs0X/by78sY+EHWxg7PY9xh+V3dShCCCHiTBJQIUSnMptN0dtvs1wwbvs6I2LQXBegqdpHc52fplofzbV+mmv9lKyqx9tYATskX3aXhYQUB+5kOwnJNtzJ9u2XJDvOBOs+D47UGRxmEw6ziUz2/rZjrTV+Q9MSidAaMfDGlrYEta28ra41YtBqRLd9bfsRg7pQEF9E0xox8BnRuuBeTrtlVao9GbWbVHuCajcp7KZtZXaTwrbdtgmbUtu2TbFtte3YtmNsbftq27E2pbDG3sMaO6afJsODlVKLgCbgj1rrr4A8oLTDMaWxMoCsDkllBdA2V00eULKTc76XgCqlLgcuBxgwYECnNOK7hhauXlnMZI+L/4wZhMWk2Ly0hi9fXMPAcWkccvYwuaIvhBD9gCSgQohuYzKborfgZjh3Wh8JG3gbArTU+2mui65b6gOxxU91STO+5uD3klRlUrgSrbiS7LiSbLg80cWZ2GEd27a7LD0iWd0VpRROc/RKZGcLGxq/EU1ItyWmsbKI0WGt8RkG/ti234jWBWLbAUMTMAz8sXPrQwZBIxQt19vqg4YmYOgdf1z7xATYTAqrUkxNcvPChCGd+OpdSyn1MZC9k6qbtNZv7uK0rcAArXVt7JnPN5RSY3b3PWPPhO7xj0Br/QjwCETnAd3T83e0qsXHhcs2UuCw8ez4QlxmE1prFn5YTHpBIsdcOqbLb8cXQgjRM0gCKoToMcwWE550J570nSeoEL3Ft7UxiLchEF0ag7Q2BmhtCuKNlVcXRxPVnV3wUyaFw23BmWjD4bbiTLTiTLDhSLDicFtxuC3Y3dYO+1bszp6dtO4ui0mRYDKTQPfd1qy1JqR1ezIaMAxCOrodjCWpwfZ6o/3YoNaEdihrrzM0IW2QZ+91gxgdtRfnBIBAbHuBUmoDMBwoAzrer5ofKwOobLu1NnarblWsvIy2eZi+f06X8ljMTEp088+RBaRaox89lFKc+PMJGBFjn+YdFkII0bvIX3whRK9iNptITHWQmLrzeUTbaEPjbw3R2hTE1xzC1xSMbrcE8bWE8LeE8DUHqSv34mtuwN8a+t6V1XYq+syr3WXB7oolqS4rNpdlW7nTgs1lweaIbccWu9OC1W7uEwns3lCq7bZaSIh3ML2QUioDqNNaR5RShUQHENqota5TSjUppfYnOgjRBcD9sdNmARcCt8fWb3Yov0Yp9RLRwYcau+P5T4A8h43/Toxera4ta2HBe5s5/IJR2J3yMUQIIfob+csvhOiTlEnhTLDhTNi9q2SGoQm2hvF7Q9GlJdS+HWgNx5YQfm903VwXIOCLbhvhH7lDUYHNbsYWS0atDgs2hxmbw4LVYcZmN2N1mKN1dsu2bVt0bbG31W0rN1mUPC/XhyilTiWaQGYA7yilFmutjwWmAzcrpUKAAVypta6LnXY126ZheS+2QDTxfFkpdSlQDJwVK3+X6BQs64lOw3JxV7drRw2Vrbx532JMJoWvOYg1bdd3OwghhOibJAEVQgjAZFLR2273Yt7ScChCoDVM0BfetvaFCfkjBHzR/aA/ut9x3doUJOSPEApECAbCP57IdqBMCovNhNUWS1BtJiw2M5a2tXVbmdlmwmI1YbHG6q0mzNttR+uia9N2a7MlujaZJOHtSlrr14HXd1L+P+B/uzhnPjB2J+W1wJE7KdfAz/c52L3UXOfnzXsXoQ3NzOsn4ZHkUwgh+iVJQIUQYh9ZrGYsSWbcSXs2bcuOImGDUCCWkPrDhAMGoWB0PxwrDwUihIIRwsFItD60rS4Sih7f2hgkHGw7ziAcMogEIzt9JnZ3KcW2ZNRiwmJpS1BVtNxiwtRx22zCbFWYzdHjzWa13dpkbqtT3y/bbl9haitrW0zRfatj3/+bi+7hbQzw5j2LCPojnPKrSaRku+MdkhBCiDiRBFQIIXqItuTN4d77aVx2RWuNEdGEQ0Y0eQ0aREIG4VAktjY6rCNEwrq9LBKORLfDmki4rczosB0tNyIGIX+ISERjxOqNiN5uHQlrtNE54+JmFyZx+m+ndMpria7lbwmhteakX0wgY0BivMMRQggRR5KACiFEP6CUar9aGe+BX7QRTYYjke0TVKN9f9u2ETGiCW37YrRvd0WiLrpGWl4C5/1tf8wy1YoQQvR7koAKIYToVsqkMJsUZqskI/2JJJ9CCCEgOqe3EEIIIYQQQgjR5SQBFUIIIYQQQgjRLSQBFUIIIYQQQgjRLSQBFUIIIYQQQgjRLSQBFUIIIYQQQgjRLbo8AVVKmZVSi5RSb++kbrpSaqFSKqyUOqND+USl1HdKqRVKqaVKqbO7Ok4hhBBCCCGEEF2rO66AXgus2kXdFuAi4IUdyluBC7TWY4DjgHuVUsldFaAQQgghhBBCiK7XpQmoUiofOBF4bGf1WuvNWuulgLFD+Vqt9brYdjlQBWR0ZaxCCCGEEEIIIbpWV18BvRf4LTskmHtCKTUNsAEbdlJ3uVJqvlJqfnV19V4HKYQQQgghhBCi63VZAqqUmgFUaa0X7MNr5ADPAhdrrb+XxGqtH9FaF2mtizIy5AKpEEIIIYQQQvRkli587YOAk5VSJwAOwKOUek5rff7unKyU8gDvADdprWf/2PELFiyoUUoV71PE26QDNZ30Wr1Bf2sv9L8297f2Qv9rc39rL3Remwd2wmv0WZ3Yv8rvaN/X39oL/a/N/a290P/a3Jnt3Wn/qrTWnfT6u6aUOgy4QWs9Yxf1TwFva61fje3bgPeAt7TW93Z5gN+PZ77Wuqi73zde+lt7of+1ub+1F/pfm/tbe6F/trk3648/r/7W5v7WXuh/be5v7YX+1+buaG+3zwOqlLpZKXVybHuqUqoUOBN4WCm1InbYWcB04CKl1OLYMrG7YxVCCCGEEEII0Xm68hbcdlrrz4HPY9t/7lA+D8jfyfHPAc91R2xCCCGEEEIIIbpHt18B7SUeiXcA3ay/tRf6X5v7W3uh/7W5v7UX+mebe7P++PPqb23ub+2F/tfm/tZe6H9t7vL2dsszoEIIIYQQQgghhFwBFUIIIYQQQgjRLSQBFUIIIYQQQgjRLSQB7UApdZxSao1Sar1S6sZ4x9MVlFJPKKWqlFLLO5SlKqU+Ukqti61T4hljZ1JKFSilPlNKrVRKrVBKXRsr78ttdiil5iqllsTa/LdY+WCl1JzY7/d/Y9Md9RlKKbNSapFS6u3Yfl9v72al1LLYKOHzY2V9+fc6WSn1qlJqtVJqlVLqgL7c3r6mr/ev/a1vhf7Xv0rfKn1rX/udbhOP/lUS0BillBl4ADgeGA2cq5QaHd+ousRTwHE7lN0IfKK1HgZ8EtvvK8LAr7XWo4H9gZ/Hfq59uc0B4Ait9QRgInCcUmp/4B/APVrroUA9cGn8QuwS1wKrOuz39fYCHK61nthhvq6+/Ht9H/C+1nokMIHoz7ovt7fP6Cf961P0r74V+l//Kn1rVF9vL/SvvhXi0L9KArrNNGC91nqj1joIvATMjHNMnU5r/SVQt0PxTODp2PbTwCndGVNX0lpv1VovjG03E/1HlUffbrPWWrfEdq2xRQNHAK/GyvtUm5VS+cCJwGOxfUUfbu8P6JO/10qpJKJzQz8OoLUOaq0b6KPt7YP6fP/a3/pW6H/9q/St0rfGtvtUm+PVv0oCuk0eUNJhvzRW1h9kaa23xrYrgKx4BtNVlFKDgEnAHPp4m2O3zCwGqoCPgA1Ag9Y6HDukr/1+3wv8FjBi+2n07fZC9IPPh0qpBUqpy2NlffX3ejBQDTwZuxXsMaWUm77b3r6mv/av/eb3s7/0r9K3St9KH/udJk79qySgYjs6Oi9Pn5ubRymVAPwPuE5r3dSxri+2WWsd0VpPBPKJXn0YGd+Iuo5SagZQpbVeEO9YutnBWuvJRG9r/LlSanrHyj72e20BJgMPaa0nAV52uB2oj7VX9DF9+fezP/Wv0rf2C/2pb4U49a+SgG5TBhR02M+PlfUHlUqpHIDYuirO8XQqpZSVaOf4vNb6tVhxn25zm9htFJ8BBwDJSilLrKov/X4fBJyslNpM9Na+I4g+z9BX2wuA1rostq4CXif6Yaiv/l6XAqVa6zmx/VeJdph9tb19TX/tX/v872d/7V+lb+2T7QX6Xd8KcepfJQHdZh4wLDa6lw04B5gV55i6yyzgwtj2hcCbcYylU8WeV3gcWKW1vrtDVV9uc4ZSKjm27QSOJvpszmfAGbHD+kybtda/11rna60HEf13+6nW+if00fYCKKXcSqnEtm3gGGA5ffT3WmtdAZQopUbEio4EVtJH29sH9df+tU//fva3/lX6VulbY4f1qTbHq39V0auqAkApdQLR+93NwBNa61vjG1HnU0q9CBwGpAOVwF+AN4CXgQFAMXCW1nrHwRR6JaXUwcBXwDK2PcPwB6LPqfTVNo8n+sC4meiXTC9rrW9WShUS/RYzFVgEnK+1DsQv0s6nlDoMuEFrPaMvtzfWttdjuxbgBa31rUqpNPru7/VEogNh2ICNwMXEfr/pg+3ta/p6/9rf+lbof/2r9K3St9LHfqfbxKN/lQRUCCGEEEIIIUS3kFtwhRBCCCGEEEJ0C0lAhRBCCCGEEEJ0C0lAhRBCCCGEEEJ0C0lAhRBCCCGEEEJ0C0lAhRBCCCGEEEJ0C0lAhRBCCCGEEEJ0C0lAhehESqk0pdTi2FKhlCqLbbcopR7sgvd7Sim1SSl15R6e927bhNp78Z4TY3P67c25zth/j6BSKn1vXkMIIUT/I/3rj54r/avoNSzxDkCIvkRrXQtMBFBK/RVo0Vr/s4vf9jda61f35ASt9V51cDETgSLg3T09UWvtAyYqpTbvw/sLIYToZ6R//dH3lf5V9BpyBVSIbqCUOkwp9XZs+69KqaeVUl8ppYqVUqcppe5QSi1TSr2vlLLGjpuilPpCKbVAKfWBUipnN97nKaXUQ0qp2UqpjbH3fUIptUop9VSH4zYrpdKVUoNidY8qpVYopT5USjljx3yulCqKbafHzrEBNwNnx75pPVsp5Y69x1yl1CKl1MzYOWNiZYuVUkuVUsM6/T+sEEKIfk36V+lfRe8jCagQ8TEEOAI4GXgO+ExrPQ7wASfGOsn7gTO01lOAJ4Bbd/O1U4ADgF8Bs4B7gDHAOKXUxJ0cPwx4QGs9BmgATt/VC2utg8Cfgf9qrSdqrf8L3AR8qrWeBhwO3KmUcgNXAvdprScS/Ua3dDfjF0IIIfaW9K9C9HByC64Q8fGe1jqklFoGmIH3Y+XLgEHACGAs8JFSitgxW3fztd/SWuvYa1dqrZcBKKVWxF578Q7Hb9Jat5UtiB2zJ44BTlZK3RDbdwADgO+Am5RS+cBrWut1e/i6QgghxJ6S/lWIHk4SUCHiIwCgtTaUUiGttY6VG0T/XSpghdb6gL197dhrBTqUt732ro4HiADO2HaYbXdJOH7g/RRwutZ6zQ7lq5RSc4ATgXeVUldorT/djfiFEEKIvSX9qxA9nNyCK0TPtAbIUEodAKCUsiqlxnRzDJuBKbHtMzqUNwOJHfY/AH6hYl8lK6UmxdaFwEat9b+AN4HxXR2wEEII8SOkfxUiziQBFaIHij0LcgbwD6XUEqK39RzYzWH8E7hKKbUI6Dik+2fA6LZBEoBbACuwNHYb0i2x484CliulFhO93emZbotcCCGE2AnpX4WIP7XtzgQhRG8TG3nv7T0dJj7eVHSY+CKtdU28YxFCCCF2JP2rEF1HroAK0bs1AreoPZwoO15UbKJsot/oGnEORwghhNgV6V+F6CJyBVQIIYQQQgghRLeQK6BCCCGEEEIIIbqFJKBCCCGEEEIIIbqFJKBCCCGEEEIIIbqFJKBCCCGEEEIIIbqFJKBCCCGEEEIIIbqFJKBCCCGEEEIIIbqFJKBCCCGEEEIIIbqFJKBCCCGEEEIIIbqFJd4BdJb09HQ9aNCgeIchhBCil1mwYEGN1joj3nH0VNK/CiGE2Bu76l/7TAI6aNAg5s+fH+8whBBC9DJKqeJ4x9CTSf8qhBBib+yqf5VbcIUQQgghhBBCdAtJQIUQQgghhBBCdAtJQIUQQgghhBBCdAtJQIUQQgghhBBCdAtJQIUQQgghhBBCdAtJQIUQQgghhBBCdAtJQIUQQgghhBBCdAtJQIUQQgghhBBCdAtJQIUQQgghhBBCdAtLvAPoaSo2NpI5yIPJpOIdihBCCCGE6KO01kQMTURrtIaIoTG0xjDA0NFyQ2vQYOhomQYMQwOx/Wg1OlanNURLaK/bEx0//Sq1fWnbvgKUUrE1KFR7ncn0/fK2bZOKnmdqqzOBKfY6JrXtWJNSsSV6vOh7JAHtoHpLM/+7cwFTjhvI/jOHxDscIYQQQgixDwxD4w9HaA1G8AUj+ELb1oGwgT8UwR+KEAgZ+MMRgmGDQGwJxpZAOEIoYhCKaIKRaFl03yAU1oQMg3BEE4oYhI1oUhmKGEQM3b4f7rBvxBJPY0+zw35qW1K6LYFtS1I71plNapf10bptx7YnuabtE95o2bbX61jX9vrmHc7bdlzsHNMO76EUZtO21zZ3XKto0m6O7auO7xsrM5tof9+21/+h8o6vuS2+bf8dvhfvDuVmk8LjsGAxd92NspKAdpBekMCoA3NY8F4xGQMSGTIpM94hCSGEEEL0O4ahafaHafSF2pcmf4hmf4hmf5hmf5iWQJhmf4iWQBhvIII3EC1rDUa3vcEw/pCx1zHYLCbsZhM2S3SxmjuszQqL2YTVrEiwWrCYtu1bTCYsZoXFpDCbTLF1dOm43fGDf8ckwdQhieiYULVdKST6/+2uGna8Ctl2dTK6Ha3bHbrD9VKt28ra9rev08Suvu5wBZYOV2q3P27b1VtN9Iqujl3Bbb+yq7cd23bF12jfbzt2274Ru2oMHa4e621Xltte0zA6vl7Hq82xY2PHb/c6BtEvDdpi6fDFgda0X502Ylew269a7/BeHd870ou+fHjv2kMYlePpsteXBLQDpRSHnjOCunIvHz+1iuQsF2m5CfEOSwghhBCiV/OHItR6g9Q0B6hpiS613iD13iB13hD1rcHo4g1S3xpNNvWPfEh328wkOqwkOCy47RbcNjMFbhdumxlXbN9ps+CymXHZzDis0bXTGt12WE3YLTtuxxJOs0lu/xRdpi35jnRITNsS6o63ZhtGLNndLnnVRIydJbXbXqMtMe6YUEc6JsyxpHrb+3RYG5osj6NL2y8J6A7MVhPHXT6OV26bx3sPLeOMG4twuK3xDksIIYQQoseJGJqalgBlDT62NvipbPJT2eynqilAZZOfiiY/1U0BmgPhnZ7vsJpIddlIcdtIddsoSHGR4rKS5LTicW6/TnJaSXRYSLRHk06zjNcheinVdnswCqs53tF0P0lAdyIhxc5xl4/ljXsW8dETKznx5+NlUCIhhBBC9DuGoalo8rOlrpUtda2U1LVSWu+jrMFHeYOPikY/4R3uJ7SZTWR67GR5HIzK9jB9mJ2MRDtpbhvpCXbSEqLr9AQ7Tls//PQtRD8nCegu5AxN5pCzh/PFC2uY+9ZGGZRICCGEEH2S1prq5gAbqr1srGlhY7WXjdUtFNdGk81gZNtzlGaTItvjIC/ZSdHAFHKTneQkO8lLdpDtcZKd5CDFZZXbV4UQuyQJ6A8Yc0gu1cVNMiiREEIIIXq9tkRzTWUzayqaWVvZzJrKFjZUtdDS4RZZh9XE4PQERmQncvSYLAakutqX3GQn1i4cHVMI0fdJAvoDlFJMP2cEtW2DEmW6SMuTQYmEEEII0bNFDM2mmhaWlzWxvKyRFeVNrK5oor411H5MeoKN4VmJnD45j8KMBAoz3BRmJJDjccijR0KILiMJ6I8wW00cf8U4Xr5tHu88uJQzfleEy2OLd1hCCCGEEED0ymZpvY+FW+pZtKWBZWWNrCxvwheKANHpREbleDh2TDYjshMZkZ3I8KxE0hPscY5cCNEfSQK6G9zJdk68ejyv/3Mh7/1nKTN/NQlLfxyySgghhBBx5w9FWFrayILi+vaks6YlAIDTamZsnoezpxYwNi+JsXkehmQkyG2zQogeo0sTUKXUccB9gBl4TGt9+w7104F7gfHAOVrrV2PlE4GHAA8QAW7VWv+3K2P9MZkDPRx50Wg+eHQ5nz6zmqMvGS0P2AshhBCiy/lDERYW1zN7Ux1zNtayqKSBYDg6MNDgdDfTh6czaUAKkwckMyIrEYskm0KIHqzLElCllBl4ADgaKAXmKaVmaa1XdjhsC3ARcMMOp7cCF2it1ymlcoEFSqkPtNYNXRXv7hg6JZPG6kJmv7GR5CwX02YMjmc4QgghhOiDDEOzvLyRL9dW8+W6GhZvaSAYMTApGJ3r4YL9B7JfYRpTBqaQ6pbHgoQQvUtXXgGdBqzXWm8EUEq9BMwE2hNQrfXmWJ3R8USt9doO2+VKqSogA2jownh3y+RjB9JQ0cq8tzeRnOVk+NTseIckhBBCiF6uqtnPl2tr+HJtNV+vr6HOGwRgTK6Hiw4axP6FqRQNSsXjsMY5UiGE2DddmYDmASUd9kuB/fb0RZRS0wAbsGEndZcDlwMMGDBg76Lc83g47Ccjaazx8enTq/GkOckuTOqW9xZCCCFE36C1ZkN1Cx+sqOSjlZUsLmkAoiPTHjo8g+nD0zl4aAYZiTJQkBCib+nRgxAppXKAZ4ELtdbGjvVa60eARwCKiop0d8Vltpo4/spxvPqPBbz7UHRkXE+6s7veXgghhBC9kGFoFpXU8+GKSj5cWcmmGi8A4/OT+PXRwzl8ZCajczwyBYoQok/rygS0DCjosJ8fK9stSikP8A5wk9Z6difHts+cCTZm/Hw8/7tjAW/dv4TTfjMZZ4I8hyGEEKJrKaWSgceAsYAGLtFaf7fDMYcRHeTPCtRorQ/d3XNF59Jas7ysibeWlvP2knLKG/1YzYr9C9O45ODBHD0qi+wkR7zDFEKIbtOVCeg8YJhSajDRxPMc4LzdOVEpZQNeB55pGxm3J0rJdnPCVeOZdd9i3nlgKTOvm4TVLtOzCCGE6FL3Ae9rrc+I9ZeujpWxJPNB4Dit9RalVObunis6z7rKZmYtKeetJeVsrm3FalZMH5bBb44bwZGjsuRZTiFEv9VlCajWOqyUugb4gOg0LE9orVcopW4G5mutZymlphJNNFOAk5RSf9NajwHOAqYDaUqpi2IveZHWenFXxbu3coclc8zPxvD+w8v44NHlHH/VOMwy/LkQQoguoJRKIto/XgSgtQ4CwR0OOw94TWu9JXZM1R6cK/ZBoy/EW0vKeWV+CUtKGzEpOHBIOlcdNoRjx2ST7JI7pYQQokufAdVavwu8u0PZnztszyN6a+6O5z0HPNeVsXWmwokZHHreCD5/fg2fP7uaIy4cJXOECiGE6AqDgWrgSaXUBGABcK3W2tvhmOGAVSn1OZAI3Ke1fmY3zxV7yDA0szfW8vL8Et5bXkEgbDAyO5E/zRjNSRNyyEyU22uFEKKjHj0IUW8y5pA8vI1B5r29CVeSnQNOHRLvkIQQQvQ9FmAy8Aut9Ryl1H3AjcCfdjhmCnAk4AS+U0rN3s1zgfiMMt/b1HuD/Hd+Cc/PKaakzkeiw8JZRQWcVVTA2DyPfBEthBC7IAloJ5p64iC8jQEWflCMK8nGhCMKfvwkIYQQYveVAqVa6zmx/VeJJpE7HlMbu7LpVUp9CUwAvtqNc4H4jTLfG6wob+Tpbzfz5uJyAmGD/QancsMxIzh2TDYOq4wDIYQQP0YS0E6klOLQc0fgawry9SvrcCZaGT41O95hCSGE6CO01hVKqRKl1Ait9RqiVzlX7nDYm8C/lVIWovNo7wfcs5vnip0IRwzeW17B099uZn5xPU6rmdOn5HPhAYMYkZ0Y7/CEEKJXkQS0k5lMimMuHcNb9y/h4ydXYbGaKZyYEe+whBBC9B2/AJ6PjWK7EbhYKXUlgNb6P1rrVUqp94GlgAE8prVevqtzuz/83sMXjPDKghIe+XIjpfU+Bqa5+OOJozizqIAkp4xiK4QQe0Np3TfurCkqKtLz58+Pdxjtgv4ws+5bTHVJMydcNZ6BY9LiHZIQQoidUEot0FoXxTuOnqqn9a/dobE1xDPfbeapbzdT6w0yeUAyVx02lCNHZmIyybOdQgixO3bVv8oV0C5ic1g46RcTeOOeRbz3n2XMuGYC+SNS4h2WEEIIIXahqtnPo19u5IU5W/AGIxw+IoOrDhvK1EEpMqiQEEJ0EklAu5DdZeXkayfyxt2LeOfBpZz8iwnkDE2Od1hCCCGE6KDOG+ThLzfw9LebCUU0J43P4YpDhzAqxxPv0IQQos+RBLSLORNsnHztRF6/ayFv/3sJM381icyB0qEJIYQQ8dboC/H4Vxt5/OtNtIYinDIxj2uPHMagdHe8QxNCiD5LEtBu4E6yM/O6Sbx+10Jm3beYU66fTHp+QrzDEkIIIfql1mCYJ7/ZzMNfbKDJH+bEcTlcd9QwhmXJiLZCCNHVTPEOoL9ITHVwyq8mYbWbeeOehVRvaY53SEIIIUS/YhiaV+aXcNidn3PnB2uYNjiVd355MA/8ZLIkn0II0U0kAe1GnnQnp1w/GZvdwhv3LKJiU2O8QxJCCCH6he821HLSv7/mN68uJTfZyf+uOoDHLpzKmNykeIcmhBD9iiSg3Swpw8kpv56EI8HKrHsXU76uId4hCSGEEH3Wphovlz8zn3MfnU1Da4j7zpnI61cfyJSBqfEOTQgh+iVJQOPAk+bk1Osnk5Bi5637F1O6ui7eIQkhhBB9SmswzG3vreKYe77gm/U1/ObYEXzy60OZOTFPplQRQog4kgQ0ThJS7Jxy/WQ86U7efmApxStq4x2SEEII0Sd8vLKSo+/+koe/2MjMiXl89pvD+PnhQ3FYzfEOTQgh+j1JQOPI5bFxyvWTSMl28e5DS9m4uDreIQkhhBC9VlmDj8ufmc/PnpmP227mlSsP4J9nTiAz0RHv0IQQQsRIAhpnzgQbM6+bREZBIu8/vIyVX5fHOyQhhBCiVwlFDB75cgNH3fUFX66r5sbjR/LOLw9h6iB5zlMIIXoamQe0B3C4rcy8bhLvP7Kcz55bjbcxQNEJg+QZFSGEEOJHrKlo5tevLGZ5WRNHjcrkLyeNoSDVFe+whBBC7IIkoD2E1W7mhKvH8fmzq5n71iZaG4Mccs5wTCZJQoUQQogdhSMGD3+5kXs/XovHYeWhn0zm+HE58Q5LCCHEj5AEtAcxm00cceEoXEl2Fn5QTGtzkKMvGY1FBk0QQggh2q2rbObXryxhaWkjJ47P4eaTx5CWYI93WEIIIXaDJKA9jFKKA04dgivJxtevrOOtfy3hhKvGYXdZ4x2aEEIIEVfhiMGjX23ino/WkuCw8MB5kzlxvFz1FEKI3kQS0B5qwhEFuDw2Pn5yJf+7YwEn/nw8SRnyTIsQQoj+qbS+leteWsz84nqOG5PN/506lnS56imEEL2OjILbgw0ryuLkX06ktTnIq7cvoGxtfbxDEkIIIbrde8u2csJ9X7G6opl7zp7AQ+dPluRTCCF6KUlAe7i8ESmc8bsinIlWZt23mJXfyDQtQggh+gdfMMIfXl/GVc8vZHC6m3d+eTCnTsqXUeKFEKIXkwS0F0jOdHH6b6eQNyKFz55dzdevrsMwdLzDEkIIIbrMmopmZj7wNS/M2cIVhxbyypUHMjDNHe+whBBC7KMuTUCVUscppdYopdYrpW7cSf10pdRCpVRYKXXGDnXvK6UalFJvd2WMvYXdZWXGz8cz7vB8lnxcwrsPLiXoC8c7LCGEEKJTaa15ce4WTv7319R5QzxzyTR+f/wobBb5zlwIIfqCLvtrrpQyAw8AxwOjgXOVUqN3OGwLcBHwwk5e4k7gp10VX29kMpuYfvZwDj1vBFtW1vHqP+ZTt9Ub77CEEEKITuEPRfjd/5by+9eWMW1wKu9dewjTh2fEOywhhBCdqCtHwZ0GrNdabwRQSr0EzARWth2gtd4cqzN2PFlr/YlS6rAujK/XGjs9j+QsFx8+tpxXbp/PEeePZNjUrHiHJYQQ4kcopZbuxmHVWusjuzyYHqaswcdVzy1gaWkjvzhiKNcdNRyzSZ71FEKIvqYrE9A8oKTDfimwX2e+gVLqcuBygAEDBnTmS/d4+SNSOOsP0/jg0eV8+PgKtm5s5KDTh2KWW5SEEKInMwMn/EC9AmZ1Uyw9xjfra/jFi4sIhQ0e+ekUjhmTHe+QhNgprTVhI0wgEiAQCRAyQgQjwZ2uw0aYsBHetq3D7WVhI0xER7ZbG9ogoiNEjEj7dsf1jotGb9vWGoNt2xq9bY0m+v/o+CFtdW3b7W1j98YXUbH/QXT++vZtFNH/f7/+e+u27R33UZiUCZOKfp7d2XbbMe1rte0ckzJhYvsyhcKszN8r2+6cnZS1ndNx3fE9TKbYeofX+cHzYvU7O2fHczvu9zW9eh5QrfUjwCMARUVF/W5UnoQUO6f8ehLfvbaBJZ+UULW5ieMuH0tCiiPeoQkhhNi5K7TWxT90gFLq6u4KJt601jz85UbueH81QzISePinUyjMSIh3WKKXM7SBN+SlJdgSXYdaaA214g178Ya8tIZaaQ234gv72pfWUHQ/EAngD/vxR/wEwoHoOpZsBiNBApFAl8a+Y0JiUZadJjHtCU6HpKljAgfsNLlr873EsUP5D2lLamH7RLbj/k4T3Vi5oY3t6toS6bZjDGIJdMdt9HYJd8fzOu63JeB9TcefccfE1GTaPpnd422T+fuvGVvfMPUG8hLyuqxNXZmAlgEFHfbzY2WiE5nNJg4+cxjZhUl8+swq/nvrPI65ZAwFo1PjHZoQQogdaK2/7oxj+gJ/KMINryzh7aVbOXFcDnecMR63vVd/Ly46WSASoN5f3740BBpoDDbSGIguTcEmmgJN0XWwiZZQS3vSuTtX8xQKp8W5bbE6cZqd2C120qxpOCwO7GY7drMdh8WBzWTDZrZhN9uxmaPbbWVWkxWr2YrNZMNqtmI1WbGYLFhMlvZtq9pWZjaZo9squm1W5j57tau7aa2J6Eh7khvRkfYkNaIjALu8otx2bMd1+zEYGMa2RLfj8oPndThmV+fsav+Hynd83V1dOd/l6xgRQjq002NDkVCX/oy68i/9PGCYUmow0cTzHOC8Lny/fm3olEzS8ty89/ByZt2/mElHDWC/kwsxW+WWXCGE6GmUUgcBfwUGEu2LFaC11oXxjKu7VDX7ueyZBSwtbeDG40dyxfRC+eDdT4SMEDWtNVS2VlLtq6bWV0uNr4Zaf2ztq6XOX0edvw5f2LfL13FZXCTZk0iyJ+GxeRiQOIBEWyKJtkQSbAkkWBNItCXisrpIsCbgtrpxWVzRtdWFy+LCbrbL710fpJTCouTLrJ6sy346WuuwUuoa4AOiz7w8obVeoZS6GZivtZ6llJoKvA6kACcppf6mtR4DoJT6ChgJJCilSoFLtdYfdFW8fUFKtpszf1/EN6+uZ9FHWyhZXcfRl4whNUfmTRNCiB7mceBXwAIgEudYutXqiiYufWo+dd4g/zl/CsfK8559hqENan21lHvL2dqyla3erZS3lFPhraCytZKq1irq/HXfuzqpUKQ6UklzppHuTGeQZxApjhRSHCkk25Oj2/bodpI9CY/dg9VkjVMrhRD7SnW8d7s3Kyoq0vPnz493GD3GpiXVfPrsasKBCAedOYwxh+TKt3xCCLETSqkFWuuibn7POVrrTh2Yr6t0Zv/62ZoqfvHCItx2M49fOJWxeUmd8rqi+wQjQUqbS9nSvIWS5pL2pbS5lLKWMkLG9rfuJdoSyXHnkOnKJMuVRaYrc7sl3ZlOsj0Zi0muWAnR1+yqf5V/7X3U4AkZnDPIwydPr+KLF9ZQvLyWI346EmeiLd6hCSFEv6WUmhzb/EwpdSfwGtA+qonWemFcAusGT32ziZvfXsmoHA+PXziV7CQZMK8naww0srFxI5saN7GpcVP7dllL2XYDvbitbgoSCxiWMozDCw4nJyGHXHdu+zrBJoNKCSG2JwloH+ZOsnPSNRNY+lkp376+nhdvnsP0c0YwdEpmvEMTQoj+6q4d9jt+M6yBI7oxlm6hteZvb63kqW83c9SoLO47Z6IMNtSDBCNBNjZuZF39OtbVr2Ntw1rW1a2jylfVfozdbGegZyCj00ZzYuGJDPQMpCCxgILEAlLsKXKHlRBij0gP0Mcpk2LCkQXkj0zhk6dX8cGjy1k3P4Pp5wzHnWSPd3hCCNGvaK0P31WdUiqrO2PpLkopkl1WLjtkMDcePwqzSZKVeAlGgqyrX8eK2hWsrF3JytqVrGtYR9gIA2A1WRmSPIT9cvZjeMpwCpMLKUwqJMedg9lkjnP0Qoi+QhLQfiItL4EzfjeFxR+XMPetTby4Zg4HnzWMEftlyzeXQggRJ0qpZOB0oqPEjwJy4xpQF7n2yGHS13QzrTVbvVtZUr2ExVWLWVK9hDX1a9qTTY/Nw+i00Vww+gJGpo5keMpwBngGyOA+QoguJwloP2Iym5h87EAGT0jns2dX88lTq1g3r5LDfjKSxFR5FkcIIbqDUsoJzCSadE4CEoFTgC938/xk4DFgLNHbdi/RWn+3wzGHAfcCVqBGa31ohzozMB8o01rP2Je27C5JPrueoQ3W1q9lXsU8FlUtYknVkvbbaJ0WJ2PTx3LB6AsYnTaa0WmjyU/Il5+LECIuJAHth1Ky3Zz668ks+6KU797YyAt/m8PUEwYx4cgCzBaZN1QIIbqKUuoF4BDgQ+B+4FNgvdb68z14mfuA97XWZyilbIBrh/dIBh4EjtNab1FK7fjg/7XAKsCzV40QPYLWmvUN65lbMZd5FfOYXzmfxkAjALnuXIqyi5iQMYGJmRMZnjJcRpkVQvQY8teon1ImxfjDCxg0Lp2vXl7Hd69vYNW3W5l+9nAKRqfGOzwhhOirRgP1RBPAVVrriFJqt+dDU0olAdOBiwC01kEguMNh5wGvaa23xI6p6nB+PnAicCtw/d43Q8RDvb+e78q/4+uyr/mm/Bvq/HUA5CXkcXjB4UzLnsbU7Klku2VuVSFEz7XLBFQptTtZiKG1bui8cER386Q7OfHq8WxeVsNXL69j1r8WM2RSBgedOUxuyxVCiE6mtZ6olBoJnAt8rJSqARKVUlla68rdeInBQDXwpFJqArAAuFZr7e1wzHDAqpT6nOjtvfdprZ+J1d0L/DZWLno4Qxssr1nO12Vf83XZ1yyvWY5Gk2xP5sDcA9k/Z3+m5UwjLyEv3qEKIcRu+6EroOWx5YceEDADAzo1ojhb3NTK+EQnpn72XMSgcenkj0xh8UdbWPBeMcUraply3CAmHlWAxSYj3wkhRGfRWq8G/gL8RSk1hWgyOk8pVaq1PvBHTrcAk4FfaK3nKKX+n737DqyjuBYw/s3ert4lW8Vy7zY2xvTeCSXhQWihEwKEGkhCEtJIQkvDCQm919BCD71XYxtcwL1Lli1ZktWlW/a8P/ZKvpKbbEu6Kuf33mZ3Z2f3nmsZr87OzswM4Hrg1x3q7AkcDgSAz4wxn+MkpuUiMjvaR3SbjDEXAxcDFBX1q9t8rxeKhPhyw5e8u+Zd3lvzHuVN5RgME7Mncukel3LA4AMYlzlOR6VVSvVZ20tAF4rIlO2dbIz5qovjiat5dY0cN3sJVw3J5efDBsU7nB7n9riYdtxQRu2dxyfPLOOLl1bwzUelTD9hKKP3GYSlQ+crpVSXEpHZwGxjzE9x+obuSAlQIiJfRPefxUlAO9apjLaKNhhjPgQm4ySuJxpjjgP8QIox5jER+cFW4roHuAdg2rRpnX5FWO2alkgLH5V8xNtr3ubDtR9SF6oj4A6w/+D9OazoMA7MP5A0f1q8w1RKqS6xvQR0306c35k6fcbEpACnDcrg76s3MDLRz8m56fEOKS5SMgMce8lESpdU8+lzy3j3kUV8/fZa9jt5BEXjM3TUPKWU2kXGmIujyV07IiJER8HdVp1ovfXGmLXGmNEishinlfPbDtVeBO4wxrgBL7A38HcReQb4RfQzDgGu21ryqXpG2A4zc/1MXlvxGu+seYf6UD2pvlQOKzqMw4oOY7/B++F3a1cYpVT/s80EVESaW7ejQ7bnxtYXkTWxdfoDYwy3jipgZWML1yxaQ7Hfy9TUxHiHFTf5o9I55fppLJ9TwWcvLOeVO+aSPzqN/U4eQc4QHTxRKaV2wfXRfp/bYnBGqd1qAhp1BfB4dATcFcD5xphLAETkLhFZaIx5HZgH2MB9IrKga8JXu0NEWLBxAa+ufJXXV75OZXMlSZ4kDi86nOOGHsf0QdN1tFqlVL9nnIeu26lgzBU4fVU24NzIwHlYO6mbY9sp06ZNk1mzZnXJtSqDYY6bvYRG2+Z/e46iwO/tkuv2ZZGwzTcflfLlq6torg9RPCmL6ccPJbtIx7FQSvVtxpjZIjKthz7rwU5UqxGRq7s7ls7qyvvrQFXVXMXLy1/mv0v/y/Ka5XgtLwcXHsxxQ4/jwIID8bl88Q5RKaW63Lbur51JQJcBe4tIZXcF1xW6+ga5uKGZ42cvoSjg5aUpI0l0a2d/gJamMPPeXcvcd9bS0himeGImex0/VFtElVJ9Vk8moH2RJqC7JmJH+Lzsc55b+hzvrX2PsB1mUvYkTh5xMkcVH0WyVx/gKqX6t23dXzvznsdaoKbrQ+rdRif6uXt8MT+Yt4IfL1zNAxOGDriRcbfGF3Cz13eGMumwQua/t5av317LMzfPYsjETPY6bii5QzURVUopNXBVN1fz3NLneHrx05Q1lJHmS+OMMWdw8oiTGZE+It7hKaVU3G1vHtDWCapXAO8bY14FWlqPi8jfujm2uDssM4UbR+Zzw9JSbl5Rxq+GD453SL2GL+Bm2nFDmXRoIfPeL+Hrt9fw7K2zyB+Vxh5HFDFkQiZGR81VSik1QCyuWswTi57g1RWv0hJpYe+8vbl22rUcWngoXpd25VFKqVbbawFtfTdkTXTxRheAATMk+4X5WSxpaOafa8oZluDjjEGZ8Q6pV/EG3Ew7tphJhxbwzUfrmPfuWl799zzS8xKYfHgho/fJw+3R15eVUiqWMcYlIpF4x6F2T8SO8N7a93h84ePM2jALv8vPicNP5IwxZzAyfWS8w1NKqV5pe6Pg/n5r5cYYP3BCt0XUyxhj+NPIAlY3Bblu8VpyvB4Oz9TXTDvy+t1MObKISYcVsHx2OV+/vZb3H1/MFy+tYMLBBYw/cDCJqTrIglJKRS01xjwHPCgiHadRUb1cKBLi5RUv8+CCB1lVu4rBiYO5ds9r+d7I75HqS413eEop1at1aqzv6DQsRwNnAEcCHwPPdGNcvYrHMtw/oZjvfrWMH36ziuf3GMEeKQnxDqtXcrksRk3PY+Reuaxbsomv3l7Dl6+sZPZrqxg6OYvxB+VTMDpdX89VSg10k4HTgfuMMRbwAPCUiNTGNyy1PY2hRp5d8iwPf/sw5Y3ljM0Yy18O/gtHFB2By9K3fZRSqjO2OwquMeZg4EzgOGAmsD8wTEQaeya8zuuJUfo2tIT4zpwlNEeEV/ccyZCAtuh1xqYNjXzz8ToWfVpGc0OI1JwA4w/MZ+y+g/AneeIdnlJqgIv3KLjRe+0TQBrwLPAHEVkWr3g60lFwoS5Yx2MLH+OJhU+wqWUT03Kn8cOJP2TfwftidIBCpZTaqp2ehsUYU4LT9/NO4AURqTPGrBSRod0b6q7pqRvk0oZmTpyzlHSPm5emjiTLqxNGd1Y4FGH5nAq++aiUsmU1WG7D0ElZjN47j6IJmbhcVrxDVEoNQPFIQKNvFn0HOB8oBh4FHgcOBG4SkVE9Gc/2DOQEtDHUyBOLnuDBBQ9SG6zlkIJDuHDiheyRs0e8Q1NKqV5vV6ZheRb4LnAaEDHGvMgAGnxoW0Ym+nlk0jBO/XoZ58xfwbN7jCBBE6dOcXtcjN47j9F751FZWs+3n6xj6ZcbWD6nAn+Sh5F75TJ67zxyhiTrE2WlVH+3FHgP+LOIfBpT/qwx5qA4xaSiWiItPLP4Ge6dfy9VzVUcVHAQl+9xOWMzx8Y7NKWU6vN29AquAQ7B6ft5HJAKXAi8JiL1PRFgZ/X0E9r/VWziwgWrOCIzhQcmDMWtfRp3SSRis/bbKhZ/vp6VczcSCduk5yUwYs8chk/NIWNwoiajSqluFacW0ANE5OMOZfuLyCc9GUdnDKQW0LAd5sVlL3LXvLtY37Ce6XnTuWLKFdriqZRSu2CnX8HdygU8bB6I6GgRyeraEHdPPG6QD5Vu5PolJZyel8HfxhRiaaK0W1oaQyybXc6SmRtYt2wTCKTlJjB8ajYj9swhMz9Jk1GlVJeLUwI6R0Sm7qisNxgoCegnpZ/wl1l/YdmmZUzKmsQVU69gn0H7xDsspZTqs3blFdx2RCQEvAK8YowJdPJDjwFmAC7gPhG5pcPxg4DbgUnA6SLybMyxc4Ebort/FJGHOxtrTzkvP4uKYIi/rtpAosvijyPzNUHaDb4ED+MPzGf8gfk01LSw8usKls2pYM7rq5n9v9WkZgconpxF8YRMBo1Iw+XWV5+VUn2LMWZfYD8g2xjzk5hDKTj3StXDVtSs4C9f/oWPSj+iMLmQ2w+5ncOKDtP7uVJKdZNtJqDGmHtE5OKtHRORph3ViQ6w8C+caVtKgC+NMS91mO9sDXAecF2HczOA3wLTcPqdzo6eW93ZL9ZTrivOoz5ic/faCpLcLn4xbFC8Q+oXElN9TDi4gAkHF9BUF2TF1xWs+KqC+e+XMPfttXj9LgrHZTBkQhZDJmSSkOKNd8hKKdUZXiAJ5/6bHFNeC5wSl4gGqE3Nm7hz7p38Z/F/CLgDXLvntZw59ky8Lr2fKKVUd9peC+h3jTHN2zlugEO3c3w6sExEVgAYY54CTgLaElARWRU9Znc492jgLRGpih5/CzgGeHI7nxcXxhh+N3wwTRGbGas3kGBZXFWcG++w+pVAsretZTTYHKZkUTWr529k9YJKls+pAANZBUkUjMmgYEw6g0ek4fFpQ4JSqvcRkQ+AD4wxD4nI6njHMxDZYvPskmeZMWcG9aF6Th11KpftcRkZ/ox4h6aUUgPC9hLQn3bi/I+2cywfWBuzXwLs3ZmgtnFufifP7XHGGG4ZVUBDxObmlWUkui0uKsiOd1j9ktfvZtge2QzbIxsRYePaelYv2EjJomrmvbeWr99ag+Uy5A1LbUtGc4am4PFqQqqUij9jzO0icjVwhzFmi0EYROTEno9q4FhctZgbP7uReRvnMT1vOtdPv56R6SPjHZZSSg0o20xAe2Ofy46MMRcDFwMUFRXFNRbLGGaMKaIxYnPD0lISXBZnDsqMa0z9nTGG7KJksouSmXbcUELBCGXLNlGyqJqSRdXMfGUlCFiWIasomUEjUhk0PJVBw9P0lV2lVLw8Gl3/Ja5RDDCNoUb+/fW/eWzhY6T6UrnpgJs4ftjx2s9TKaXioNODEO2CUqAwZr8gWtbZcw/pcO77HSuJyD3APeCM0rcrQXYlt2W4a/wQzp23kmsXrcVvWZycmx7vsAYMj9dF0bhMisY5iX9zQ4j1K2ooW15D2bJNLHi/lLlvOw3ryZl+coakkFucQk6xk8R6/d35n4NSSoGIzI5uzgKaRMSGtnETfHELrB97b8173DTzJtY3rOeUUadw9dSrSfWlxjsspZQasLrzN+4vgZHGmKE4CeXpwJmdPPcN4CZjTGv2dhTwi64Psev5LIsHJg7lrHnLufzb1dginJKn/UriwZ/ooXhiFsUTnRmDIiGbirV1lC2rYcOqWspX17J8TrlT2UB6XiLZRUlk5SeTWZBIVkGytpQqpbrLO8ARQOuc2gHgTZwRclUXqG6u5o+f/5E3V7/JiLQRPHrsozqfp1JK9QI7TECNMRNFZP7OXlhEwsaYy3GSSRfwgIh8Y4y5EZglIi8ZY/YC/gukAycYY34vIuNFpMoY8wecJBbgxtYBifqCBJfFY5OGce68lVyxcA0hEc7Q13HjzuWxyBuWSt6wzU++m+qC0WS0jvLVtZQu3sSSLza0HQ+keMkqSCJjcCIZgxJJz0skPS8Bf6InHl9BKdV/+EWkNflEROqNMQnxDKg/eWf1O9z4+Y3UBmu5csqVnDfhPDyW/rutlFK9QWdaQP9tjPEBDwGPi0hNZy8uIq8Br3Uo+03M9pc4r9du7dwHgAc6+1m9TaLLxSOThnH+/JVcs2gtYRHOHpwV77BUB4Fkb7tWUoCm+iCVJfVsLKl31qX1LPhgE5HQ5sGaE1K8pA9KIC03kdTsAGk5AVKzE0jJ9uP26IBHSqkdajDGTBWROQDGmD2BpjjH1OfVtNRw88ybeXXFq4zNGMu9R93LqPRR8Q5LKaVUjB0moCJyoDFmJHABznycM4EHReStbo+uj0twWTw8cSgXLFjJTxeXELKFC3R03F4vkOSNTumy+dVp2xbqKpuoLmukan0D1WUNVK9vZNmsDbQ0hjefbCAp3UdqVoDkTD/JGX6SMwOkZPpJzvSTmO7D5bLi8K2UUr3M1cAzxph1ONOa5QGnxTWiPu7Dkg/53ae/o7q5mssmX8ZFky7SVk/VLUQEwmHslhakpQUJBpGWFuxgEGkJIsEWJBTavARb10EkHEbCzj7hsLMfCiORMEQiSDjibIcjSCS6HbHBjh6zIxCxnbUtYHfYFrttGxEn1ug2IggCQts+Iq1fylnRut/JPwwTu2nAxCwQ3d7Ksdhyy2pfZqzNZZZx9k1rvfbHjWUguh+73XZe67bl2qKOcVnR7c3Ht1rXZWEsCyxXzHGDcbnARK9jtcYQU8/lcmJ0ucByOdezXO3rx17D5YqeH3Osrb6rfZ2Yuq37WFafGVitU31ARWSpMeYGnEET/gFMMc43/KWIPN+dAfZ1TtZLqAAAdo5JREFUfpfFgxOH8sMFq/jl0lIiAj8s1CS0r7EsQ2p2AqnZCRRPat+S3dwQoqa8iZqKRjaVN1FT3khdZTNrF1bTUNPS/h9x47SeJqX5SEzzkZTuJyndR2Kql4QUHwmpXhJSvPgTPc4/VEqpfklEvjTGjAFGR4sWi0gonjH1Vc3hZm778jaeWfIMI9JGcMfhdzAuc1y8w1K9hN3Sgl1fj11XR6S+Abu+ztlvaMBubIxZNzrrpibspiakqQm7uXnzdksL0tzctsbuOIX9bnK5nITD7ca0bm+xtpzkp23tchIOK5oktSZqLqstsTNtSZuBLZJAnASpNYs021hvi0j77bYENyaJbZfoRhPijuW2HS2XtqS5LZHueNy2QaL7W0u0W+tGIpvLI5EtyzvW7eqfZ7xYW0lSt5KsbrGOJrmtf7fy//JnvMXF3RZmZ/qATgLOB74DvAWcICJzjDGDgc8ATUB3wGdZ3DehmEu/Xc2vl5XSbNtcXpTTZ55SqO3zJ3rwD/WQOzRli2ORkE1ddTN1ldGlupmG6hYaNrVQU9FE6ZJNBJvCW5xnLENCsodAipdAspdAkodAkhd/kodAsrPtS3TjS/DgT3TjS/Tg9vSdJ19KKcBJPscBfmCqMQYReSTOMfUpy6qX8dMPf8qyTcs4f/z5XD7lcrwuHTyuP7Kbm4lUVhKu3kRkU3Sprt68XVNDpLYGu6aWSK2z2LW1TktjJ1gJCZjEBKxAAlYggOX3YwIBPKmpGL8Pyx9w1j5/dN+P8fqcba8X4/NhPF6M11ksnxfj8YDHg+m4uN2b1263k3Ra+nZUvG0tWZXW1uNIpH1ZawLbuo4mts5xcVqsI06yvLUyaU16bdsp28oxiXRs7Y60r9tuHVN3O2vn+vbm77HVdQTc3fv2SGdaQP8J3IfT2tnWP0VE1kVbRVUneC2Lu8YVc8XC1fxpRRkbQ2F+O3wwliYM/ZrLY5GWk0BazrbHFgk2h2msCdJY27q00FgTpKE2SFNtkKb6EDXljTTVhQi1RLb9WW4LX4IbX4Ibb8CNL+CsvQlufH43Hr8Lb3Tt8cVse519j8+F2+fC47Ww9DVh1YuJLdi28xTd5embf1eNMb/FmW5sHM5YCccCHwOagHaCiPDMkme47cvbSPQkcvcRd7Nfvg4g3NdIJEKkqopQeTnh8nLC5RXOuqKCcFUlkY2VhKuqiGzciN3YuM3rWKmpuFJSnCU1BfegQbiSk3GlpmAlJWMlJ+FKSsJKTsZKTMJKSsSVmIiVmOgknoGAJoDKeYjvdlIj/e28e3UmAf2viDwaW2CMuUpEZnQsV9vnsQz/HjeEDI+bu9dWUBkM8/cxRXj0VcsBzet34/W7Scvd8QCY4VCE5voQTXUhWhpDNDeEo+sQLQ1hmhtDBJvCBJvCNDeGqa1spiW6HzuI0o5YboPH68LlsXB7LNxeV9va5bFwuZ1yl9ty9j0WLpfB5baw3BYud3Tb5WxbLoPlsqLrmG3LWUx027TuW86NYGv7xrTf3vxqkdNybGhfRvQto819UGK+aGf+03PeGmrrNyMx5RLzmpFA9HWfzfUl+kfe+sqRRI9v3u9wzG6tE7vd/hjRbVsEYuvEJGW2LVtex26/7bzF5NSx7fbH2+3b0vZZdtt1oteIfm7rZ7eeY8fWabeOOSaCHWndBjtib/78SPvjdmws9uZXvgYNT+Xkn+7Z6b/XvcwpwGTgKxE53xiTCzwW55j6hNpgLb/79He8tfot9hu8H3864E9kBXSQv97IbmggWFJKqLSEUFkZ4bIyQuvKCK1f7+yXlzstTh24MjJwZ2biyswkMHEirswM3JlZuDLScWdk4EpLw5We7qxTUpxWRKVUn9GZ/2LPAW7vUHYeMKOrgxkILGP408h8crxublm5nqpQmHsnFJPo0pFT1Y65PS6S0l0kpft3+txIxCbUHCHUEiHYFHbWzWHCLTahoFMejlmHg7azDtnOEi0LNoWJhG0i0fJIyHb2wzZ2uH2CoPouY6IJfWvS3/owwEQfCMTutz5AMGC5Ws9p/wDBsgzGvflBQ7uHC9H61hYPImLOtbY8NznDF+8/pt3RJCK2MSZsjEkByoHCeAfV282rmMdPP/gp5Y3lXLPnNZw3/jwsoy1X8RSuria4chXBVasIrllNaG0JwZK1hNaWEKlqP4Oe8XhwDxqEJy+PxOnTcefl4c7NwZOTgzs7G3dODu7MTIxXX6NWqj/bZgJqjDkDOBMYaox5KeZQMtBn5uTsjYwxXF2cR5bXw88Wr+XUr5fz2KRhZHj0CZ7qPi6XhSvR6vY5TG1bsCM2kbAQCdnYEWffWQu2HbstSHQdu93Wcmd32JfNLXyIRMcMiGl1jG1hdA45q9bR/WJz43Z5cuvONppE2w3oZ9qXY7Y83tpK21re2kLbmtRFm2SNFR0DIpqAtZ5nWdHt2Bbf1lZdy3mQFdsqHHteW3mHOrFJpDM2hWmXYHbc1/7E3W6WMSYNuBeYDdTjjKugtqL1ldubZ95MbkIuDx/7MJOyJ8U7rAFDbJtQaSktS5fRsnQpwRUrnIRz1SoiNTGz87lceAYPxltYgP/ww/EUFuItLMCTn49n0CBcmZn6qqtSarstoJ8CZUAW8NeY8jpgXncGNVD8YHAmGR4Xl367mpPmLOWpycPJ9+tTP9W3Oa/Vupz+64F4R6NU7yQil0U37zLGvA6kiIjeW7eiJdLCTV/cxPNLn2f//P259cBbSfWlxjusfitSU0PzwoU0L1xEy+LFtCxbRsvy5UjT5mlq3bm5eIcOJfnYY/ANHYq3uBhvcTGe/Hx9HVYptUPb/FdCRFYDq4F9ey6cgee47DSenOTm3PkrOG72Eh6eOIw9UnbcF1AppVTfZow5GTgApwn+Y/Th7hbK6su45v1r+KbyGy6edDGXTb4Ml6VdVrpKuLqa5nnzaPrmG1oWLqT524WESkvbjruys/CPHEn690/FO2IEvujiSk6OY9RKqb5ue6/gfiwiBxhj6thiJkNERLacc6KPi9TUUHbDr8m+5hp8w4b22Oful57ES1NHcvb8FXzvq6X8c+wQjs9J67HPV0op1bOMMf8GRgBPRot+ZIw5QkR+HMewepUvyr7gpx/8lKAdZMahMzis6LB4h9SnSTBI8+LFNH09l6Z582iaN5fQ6jVtx71DhhCYPIm000/DP3Yc/rFjcGdmxjFipVR/tb0W0AOi6wHzmCtSV0fj7Nms/dGPKH7qyR79h3dsUoD/7TmK8+av5KJvVvHLpkFcoXOFKqVUf3UYMFaiHZSNMQ8D38Q3pN5BRHhs4WP8ZdZfKE4p5vZDb2doas89FO4v7KYmmubOpfHLWTTOmkXT3LlIczMA7uxsAntMJu2UUwhMnox/3HhcSYlxjlgpNVDs8EV9Y8w+wDciUhfdTwbGicgX3R1cT/MWFFB4579Zfc65rL3sMoY8/DCWf+dHG91V2V4Pz+0xgmsWreGmFWUsbWzmL6ML8WmHfaWU6m+WAUU4XV3AGQF3WfzC6R1CdohbvriFp5c8zeFFh/OnA/5EokcTo86wg0Ga5nxFw6ef0jhzJk3ffAOhEFgW/jFjSD/t+wSmTCUweRLuvDx9wK2UipvO9BS/E5gas9+wlbJ+IzB5MoP/8mdKr7yKdT/9Gfm3/x3Tg1Ok+F0W/x43hBEJfv68aj1rmoLcP2EoWV7t1K+UUv1IMrDQGDMTp5vLdJyRcV8CEJET4xlcPNQF67jug+v4dN2nXDDhAq6aepVOsbIdIkJw+XIaPvmE+k8/pXHml85AQW43gfHjyTzvXBKmTSMwdar22VRK9SqdyWqMyObJC6LzlvXrbCjlyCMJX/9zNtx8C+W3/ZncX1zfo59vjOHaoXkMT/Bx9aI1HDN7MfeNH6qDEymlVP/xm109MTp9y33ABJzk9QIR+axDnUNw5vD2ABtF5GBjTCHwCJAbPe8eEekVc3qX1pfy47d/zOra1fx+v99z8siT4x1Sr2QHgzR+8QV1775L/XvvE16/HgBvcTFpJ59M4v77kTB9Oq6kpDhHqpRS29aZRHKFMeZKnFZPgMuAFd0XUu+Qfs45BNeWUPXww3gKCsg4+wc9HsN3c9MZEvBx0YKVnDhnKTePKuCswToggFJK9XUi8sFunD4DeF1ETjHGeIF2TyejCeq/gWNEZI0xJid6KAxcKyJzot1pZhtj3hKRb3cjlt02t2IuV757JSE7xF1H3sXeg/aOZzi9Tri6mvr3P6D+3Xep/+QTpLERk5BA0v77kXjZpSTtvz+e/Px4h6mUUp3WmQT0EuAfwA04T0zfAS7uzqB6A2MMub+4nlBZGRtuvhlP/mCSD+v5EfimpCTw5rTRXPbtaq5dvJZZtQ3cNLKAgEtfS1JKqb4qOr7CP4GxgBdwAQ07GmHeGJMKHAScByAiQSDYodqZwPMisiZapzy6LsOZ3xsRqTPGLATygbgloG+uepNffvxLsgPZ/OuIfzEsdVi8QulVwtXV1L31FnWvv07D51+AbePOzSX1pBNJPvRQEvbeG8vni3eYSim1S3aYgEZvXKf3QCy9jnG5yP/zbaw+9zxKf3ItQx55mMCkST0eR6bXzROTh/Hnleu5ffUGvqlr4t4JxQwJ6M1HKaX6qDtw7q3PANOAc4BRnThvKFABPGiMmQzMBq4SkYaYOqMAjzHmfZy+pjNE5JHYixhjioEpwFYHFDTGXEz0YXNRUVGnv9TOeGrRU9z0xU1Mzp7MPw77B+n+9G75nL4iUlND3dtvU/u/12n47DOIRPAMKSLz4h+SctRR+MaO1YGDlFL9gonp3rn1Csb4gQuB8UDbkLAickH3hrZzpk2bJrNmzeqWa4c3bmTV6Wdg19Ux5PHH8I0Y0S2f0xlvbqzh8oWrsTDcMW4IR2T2u+lYlVKqRxljZovItB7+zFkiMs0YM09EJkXLvhKRKTs4bxrwObC/iHxhjJkB1IrIr2Pq3IGT1B4OBIDPgO+IyJLo8STgA+BPIvL8jmLt6vuriHDn3Du5c+6dHFJwCH8++M/43T034nxvIqEQ9R9/TM1/X6DuvfcgFMJTWEjKMceQctyx+MaM0aRTKdVnbev+2plXcB8FFgFHAzcCZwELuza83s2dlUXRA/ez6qyzWHPBhQx54gm8BfHpb3FUVipv7DmaCxes5AfzVnBxQTa/HDYIv76Sq5RSfUljtP/m18aY23Beje3MP+QlQEnMVGjPAh1HyisBKqOtog3GmA+BycASY4wHeA54vDPJZ1eL2BFunnkz/1n8H7474rv8dt/f4rb69biGW9W8eAk1//0vNa+8QmTjRlwZGWSceQYpJ5yIf/w4TTqVUv1aZ252I6JPVhtE5GHgO8CAGyHAW1RE0X33Yzc3s+bCCwhXVMQtlqEJPl7dcxQX5GdxT0kFx81ewuKG5rjFo5RSaqedjXMPvhxnerNC4P92dJKIrAfWGmNGR4sOZ8s+nC8CBxhj3MaYBJx79kLjZDX3AwtF5G9d8zU6LxgJ8rMPf8Z/Fv+HCyZcwI373Tigkk+7sZHqp59m5cn/x8qTTqLq8cdJmDKFgn//m5EfvE/uL35BYMJ4TT6VUv1eZ/7lD0XXm4wxE4D1QM526vdb/tGjKLz7LtZccCFrLvohQx59BFdKfF6BDbgsbhpVwKEZyVy9aC1Hz1rM70bkc+7gTL15KaVU77cRCIpIM/B7Y4wL6GzH/iuAx6MtqCuA840xlwCIyF0istAY8zowD7CB+0RkgTHmAJzEd74x5uvotX4pIq913dfauoZQA1e9dxVflH3BddOu49zx53b3R/YaLcuXU/3kU9S88AJ2fT2+UaPI/dWvSDn+O7jTB3a/V6XUwNSZPqAX4byuMwl4EEgCfi0id3d/eJ3XnX1AO6r/+BPWXnopgQkTKLr/PqyE+M7PWd4S4qpFa3ivqo6js1L42+giMr0D56myUkrtjjj1Af0cOEJE6qP7ScCbIrJfT8bRGbt7fxURLnzzQuZsmMMf9v8DJww/oQuj650kHKbu7XeofvJJGr/4AuPxkHzMMaSfcTqBKVP0QbFSakDY1v11hwloX9GTCShA7etvUPqTn5C4//4U/usOjNfbY5+9NbYI95VU8MflZaR6XNw6qoDjstPiGpNSSvUFcUpAvxaRPXZU1ht0xf31s3WfEbJDHFRwUBdF1TvZDQ1seu55qh5+mFBpKZ78fNJOP420//s/3BkZ8Q5PKaV61C4PQmSMyQR+B+yPMw/oR8AfRKSyq4PsS1KOOZpI3e9Y/+vfUHrtteT/7W8Yjydu8VjGcHFhDvunJ3P1wjVcsGAVJ+akcdPIArK0NVQppXqbBmPMVBGZA2CM2RNoinNM3WbfwfvGO4RuFa6ooOqxx6l+6insmhoCU6eS+4vrSTr0UIzLFe/wlFKqV+lMZvIU8CGbB0c4C/gPcER3BdVXpJ96KtLUxIabbqb0J9eS/7e/xjUJBRifFOC1PUfxrzUb+NuqDXxcXcdNIws4KSdNX/lRSqne42rgGWPMOsAAecBpcY1I7bTg6tVU3ncfNS+8iITDJB95JBnnn0fClO3OpqOUUgNaZxLQQSLyh5j9PxpjOnWTNMYcA8wAXDiDINzS4bgPeATYE6gEThORVdGBFe7GmcfMxplk+/3OfGZPyzjnHBBhw8239Jok1GMZri7O45jsVK5ZuJZLvl3NC+XV3DqqkFxffGNTSikFIvKlMWYM0Dqa7WIRCW3vHNV7BFevZuNdd1Pz0ksYt5vUU/6PzPPOwztkSLxDU0qpXq8zCeibxpjTgaej+6cAb+zopOiIfv8CjsSZk+xLY8xLIhI7XPyFQLWIjIh+xq04T4B/CCAiE40xOcD/jDF7iYjd2S/WkzLOdUbz23DzLZReex35f/1L3JNQgDGJAV6eOpJ7Siq4bWUZB85cyM+HDuLcwVm4LW0NVUqpeIomnAviHYfqvI6JZ8YPziLjwgvx5AzIyQGUUmqXdGYe0B8CTwDB6PIU8CNjTJ0xpnY7500HlonIChFpPe+kDnVOAh6Obj8LHB6dp2wc8C6AiJQDm3BaQ3utjHPPJfcX11P35puUXnsdEuodD7LdluGyohze3WsMU5IT+dXSUo6ZvYQvaxriHZpSSinVJ4RKS1n3i1+y/LjvUPvaa2T84CyGv/Umub/4hSafSim1k3aYgIpIsohYIuKOLla0LFlEtjcJZj6wNma/JFq21ToiEgZqgExgLnBidBLtoTiv6BZ2/ABjzMXGmFnGmFkVFRU7+irdLuPcc8m5/udOEnrdT3tNEgowLMHHU5OHcd/4YqpCYU6Ys5SrF66hIth7YlRKKaV6k0hNDRtu+zPLjzlWE0+llOoinRoe1RiTDowE/K1lIvJhdwUFPACMBWYBq4FPgUjHSiJyD3APOMPEd2M8nZZ53nkAlN9yKyWRMPl//SuWr7Nzi3cvYwzH56RxaEYyf1+9gbvWlvO/jTX8bGge5wzOwqOv5SqlVI8xxkwCiom5F4vI83ELSLWxW1qofvwJNt59N3ZtLanf/S7ZV16BZ9CgeIemlFJ9XmemYbkIuAooAL4G9gE+Aw7bwamltG+1LIiWba1OiTHGDaQCleJMTnpNTAyfAkt2FGtvkXneeRi3hw1//CNrf3QJBXfcgSspMd5htUl0u7hh+GC+n5fBr5aW8KulpTxQspFfDR/EsVmpOlquUkp1M2PMA8Ak4BucwfbAmepME9A4Etum9tXXqPj73wmtW0figQeSc921+EeP3vHJSimlOqUzLaBXAXsBn4vIodFR+27qxHlfAiOjr9CWAqcDZ3ao8xJwLk5CewrwroiIMSYBMCLSYIw5Egh3GLyo18v4wVm4UpJZ94tfsub88ym8527c6enxDqudUYl+np48nLcqa/nD8nVcsGAVe6Uk8psRg9krtfckzEop1Q/tIyLj4h2E2qzpm2/Y8Ic/0vT11/jGjaXoj38gcb/94h2WUkr1O50ZhKhZRJrBmTZFRBaxedj4bYr26bwcZ8TchcDTIvKNMeZGY8yJ0Wr3A5nGmGXAT4Dro+U5wBxjzELg58DZO/OleovUE0+k4J//pGXxYlb/4GxC69fHO6QtGGM4KiuV9/Yaw19GF7K6uYUT5izlwgUrWd7YHO/wlFKqv/rMGKMJaC8Qqalh/Y03surU7xNcs4ZBN93E0Gef1eRTKaW6iXHedt1OBWP+C5yPM2n2YUA14BGR47o9up0wbdo0mTVrVrzD2KqGmTMpufQyXKmpFD1wP97i4niHtE0NkQh3r63gX2vKabZtTsnN4OohuQxN6B39WJVSqqsZY2aLSI+OtG6MORjnLaD1QAtgABGRST0ZR2f05vvr7hDbpub55yn/69+I1NSQfuaZZF95Ba6U7Y2vqJRSqrO2dX/dYQLa4SIH4/TTfD06tUqv0dtvkE0LvmHtD38IlkXRfffiHzs23iFtV0UwxB2ry3l43UZCIvxfbjrXDMnTRFQp1e/EKQFtffNnPpv7gCIiq3syjs7o7ffXXdG8cCFlv/sdzXPnEdhzT/J+fQP+MWPiHZZSSvUr27q/7vAVXGPMPsaYZAAR+QB4H5jS5RH2c4EJ4xny+GMYr5fVZ/2A+g+7cxDh3Zft9fD7kfnM3GccF+Vn81L5Jg6YuZArFq5mRWNLvMNTSqm+rkJEXhKRlSKyunWJd1D9nd3SQvnfb2flKacSKill8K23MOSxRzX5VEqpHtSZPqB3AvUx+/XRMrWTfMOGUfzUU3iKh7D2kkupfvLJeIe0Qzm+mES0IJtXyjdxwBcLuXDBSmbXNMQ7PKWU6qu+MsY8YYw5wxhzcusS76D6s8Y5X7HyeydTeffdpJ5wAsNffYXUk07Skd+VUqqHdWYUXCMx7+mKiB2dMkXtAk9uDsWPPkrptdex/vc3Ely9hpyfXodxueId2nbl+Dz8fkQ+Py7M4b6SCh5eV8mrFTVMT03k0sJsjspKxaU3caWU6qwATt/Po2LKdBqWbmA3NFB++wyqH3sM96A8Cu+9l6QDD4h3WGqAERGCIgRtocUWQmITtJ39ULQ8HLMOiRCKbscuESFm29lvW+OU2eK81x+J1rEBEbDZfMyO/mpvR8slpk7rdlvs0aX1e8SWd0brb4etD3vMFuWb9w3GWZvW/WhZzL5lTHQd3cfZaN02xmlhs4xpa2mzjHMsdm1i6lgm9roxZR2u54r5fJdpvZ6z3Xquq8NnuNqut/nzXSb2c5x9V2t80XJXtNxE67s61I+9Tl/TmURyhTHmSja3el4GrOi+kPo/KzGRgn/dwYZbbqXqoYcIlqwl/7bbsBIS4h3aDuX4PPxy+GCuGpLLk+uruHttBecvWMWwgI+LC7M5NTedRHfvTqaVUireROT8eMcwEDR8/gVlv/oVoXXrnEGGrrmmV83LrXqPkC3URSLUhyM0RGwaIjb1EWe7PmzTaNs0RmwaI5Ho2qbJtmmKCM227SwRocm2abFtmm2hxbZpia6b7c6PubK7WpOn2IQlNhEy0aTG2krSRszx1oRvc1l0m80Jz45Sn7bENbq1OZHtkNgibUlva7lTR9rt22xOnAUnkRZoS7Jb69vR7daEu7/bVsIamwxvL4F1sTmhdhnDv8YO6dZxXzqTgF4C/AO4Aedn/Q5wcbdFNEAYl4u8X/0Sb2EhG265hdXnnEvhnf/GnZ0d79A6JdHt4qKCbM4bnMWrGzdx55oKrl9Swh+Xr+OUvAzOHZzJ2KRAvMNUSqleyRjzIFtpQBCRC+IQTr9jB4NUzJhB1QMP4i0qYshjj5Kw557xDkt1s5AtVIfCVIbCVIXCVIcibApH2BQKUxOOUBOOUB2KUBvdro8423XhCE07kSAGLEPAZRGwLBJcFn7LWQIuQ7rHg99l4bcMPsvCF7s2Fl7LbF6i+x5j2tYey+A2zrbbMniNwWWcMpfBKTcGyxjchnbbmxPMvtci1t0kmqhGOiSmEm0hjsjmxNWOJsPtyyXacuwcj8jm5DcSe6xtO1qnw7Vaj9nRFmtb2pe3nhNbvrlVe3OLd+vnbq9+RITIdj6v/XU3X9sW5wFGd9phAioi5cDp3RvGwJVxztl4CgoovfZaVp5yKgX/mEFg8uR4h9VpbstwUk46J2anMae2kYfWbeTJskoeKt3I9NREzh2cyXey0/C7OtPdWCmlBoxXYrb9wPeAdXGKpV9pWbaM0p/+jJaFC0k7/TRyf/azPvGGkdq6FttmQ0uIDcEw61tCVARDVATDbAyFN28HnYSzLrLtti6PMaS6XaR5XKS6XaR7XBQFvKS4XCS7LVLcLpLdLpJcFomu1rVFkttFostJNBMsi4DL6pOvPA50Jua13R2326rutlPTsPRmfX2Y+OZFiyi5/ArCGzaQ++sbSP/+9+Md0i6rCoV5uqyKR9ZVsqKphQyPi+/mpPP9vAwmJwf0yZxSqleJxzQsW4nBAj4Wkf3iGcfW9JX7q4hQ/fgTlP/5z1iJiQz64x9JPuzQeIeltqPFtilrCVHSHKS0OURpS5DS5iDrWkKsbwmxIRiiKhTZ4jwDZHjcZHudJcvjJtPrJsMTu7hI97hJc7tIc7tIcFn6+4dSPWxb91cdTKiX8I8Zw9Bnn3EGJ/rNb2mev4DcX9+A5fXGO7SdluFxc0lRDhcXZvNxdT2PlVXyeFklD5RuZGSCj+/nZXBybjr5/r733ZRSqpuMBHLiHURfFd64kXW/+CUNH31E4sEHMfiPf+wzXVr6MxFhYyjMysYW1jQHWd0UZHVzC2uagqxuDlLWEtrinGyvm8E+D0MCXqanJpLn85Dr85DnddY50URTBz5Uqu/SBLQXcaWlUXjP3VTM+AeV99xD85LFFMyYgScvL96h7RLLGA7KSOagjGRqQmFerqjhmfVV/GlFGTetKOOA9CROyknnmKxUsrz6V1EpNXAYY+pw+oCa6Ho98PO4BtVHNcycSem112LX1jlvEJ15prZ09bCQLSxvamZpQwvLG5tZ1tjC8sYWVjS1UBNu34I52OehyO/loPRkCv1eCvweCvxe8n1eBvk82mVHqQFgh6/gGmPSgHOAYmISVhG5sjsD21l95RWhzqp9803Krv8FJhAg/+9/I3H69HiH1GVWNbXw7PpqnttQxcqmIC4D+6UlcXx2Gsdlp5Lt9cQ7RKXUANIbXsHtzXrr/VVsm8p776Nixgy8RUXkz5iBf/SoeIfVr9kirG4K8m1DE4sbmlnU0MzihmaWNzYTjvl1crDPw7CAj+EJzjI04KM44KPQ79UEU6kBZFv3184koJ8CnwPziRnJWEQe7uogd0dvvUHujpZlyyi5/AqCa9aQdemlZF16Ccbdf1oKRYRvG5p5uXwTL5dvYnlTCxawd1oiR2emcmRWCsMT/PEOUynVz/VkAmqMGSMii4wxU7d2XETm9EQcO6M33l8jmzax7ufXU//BB6Qcdyx5N/5Bp1fpYiFbWNrYzPy6JhbUNzK/rolv6pvaDfQzxO9ldKKfMYl+Rif6GZXoZ1jAp9OxKaWA3UtA54jIVm+UvUlvvEF2hUh9Axv+cCM1L75EYNqe5P/5z3gGDYp3WF1ORFjU0MzLFZt4taKGxQ3NAAwNeDkyM5UjMlPYJy0Rr6VPTpVSXauHE9B7RORiY8x7WzksInJYT8SxM3rb/bVp/nxKr7qaUEUFudf/XF+57QIiwrqWELNrG5lT28BXtY3Mq2tsm5okYBnGJQWYkBRgUnIC45ICjEr0kejSRFMptW27k4BeA9TjDBnf0louIlVdHeTu6G03yK5W8+KLrP/9jeDxMOiPfyDlyCPjHVK3WtPUwtuVtbxdWcsnm+ppsYVEl8X+aUkcmJ7MgRlJjE7w6y8dSqndpq/gbl9vur9WP/Uf1v/pT7izsyi4/XYCkybFO6Q+KSLCwvomPq9p4LNN9cyqaWBDMAyAzzJMSAowNSWBPZITmJicwPAEnw76o5TaabszCm4Q+DPwKzZPmi3AsK4LT+1I6kknEZg8mdJrr6P0iitpOON0cn/+cyx//3xFtSjg44KCbC4oyKYhEuGT6nrerqzlo+o63qysBSDH6+aA9GQOSE9iv7Qkhvi9mpAqpfoMY8x+bDm+wiNxC6gXk1CI9TfdxKYnnyLxwAMZfNutuNPT4x1WnxERYW5dI59W1/N5TQMza+qpDTuv0hb6vRyQnszUlASmpiQyPsmvbxsppbpVZxLQa4ERIrKxu4NR2+ctLqb4ySco//vtVD34II1ffsngm28mMHFivEPrVokuF0dlpXJUVioAa5uDfFRdx0dVdXxYVcfzG6oBJyGdnprI3qlJTE9LZHxiALelCalSqvcxxjwKDAe+BlqHCRVAE9AOwtXVlF51NY0zZ5Jx4QXk/OQnGH31c4dWN7XwQVUdH1TX8XF1fdtotCMTfJyUk84+qYnsnZZEgU6JppTqYZ1JQJcBjd0diOoc4/WS+/OfkbjffpTdcAOrTj+DzAsvJOvyH/fJOUN3RaHfy5mDMjlzUGZb39GZNQ3MrGngi5p6XqmoASDRZTEpOcAeyQnsEX2VqEhbSZVSvcM0YJzsqB/MANe8ZAkll/2YcHk5g2+9hdSTTop3SL1Wc8Tmk031vLmxhver6ljdHAScEWmPy07l4PRk9k9P0pHmlVJx15kEtAH4OjpgQmwf0F41DUtXCEVCzJgzg/MmnEdWICve4WxX0oEHMOzll9hwy61U3nMPde++MyBaQzsyxjA2KcDYpADn5js/s9LmIF9GE9Kvahu5v2QjwejveBkeF5OTE6KDKPgZnxRgaED7tiiletwCIA8oi3cgvVXdu++y7rqfYiUmMuTRRwhMnhzvkHqdimCItytreWtjLe9X19EYsQlYFgekJ/HDwmwOTk9mRIJPH7wqpXqVziSgL0SXfm9V7SqeXvI0M9fP5MFjHiTR07uHdHelpDD4pj+RcvRRlP3mt05r6EUXkfXjywZMa+jW5Pu95Pu9fDfX6R8UtG0WNjTzdW0jX9c18nVtIx9Vb2ibsyxgWYxJdJLRMUl+RiX4GZnoI8/r0Zu2UqpLGWNexnnVNhn41hgzk/YPd0+MV2y9hYhQdf/9lP/1b/jHj6fgX3fgyc2Nd1i9xrrmIK9UbOLl8hpm1TYgwCCfh1Ny0zk6K5X905J0rk2lVK+2w1Fw+4quGqXvw5IPufLdK5meN51/Hf4vPK6+8apKpLaWDbfcSs3zz+MdPpy83/yGxL2nxzusXqvFtlnS0Mw39U18W9+6bqI6HGmrk+yyGJnoZ2SCnxHRibSHJvgoDnh16Hml+pEenobl4O0dF5EPeiKOndGTo+BKJMKGP/2J6ieeJOW4Yxl00039drC9nVHWEuSV8hpertjEzJoGAMYn+TkuK42jslKYkBTQB6ZKqV5nd6ZhWcnm0W/biEivGgW3K2+QLyx7gV9/8mu+M+w73HTATVim7zxJrP/wQ9b//kZCpaWknHgCuT/7Ge6s3v06cW8hIlQEwyxpbGZJQzNLG1tY0tDMksZmKqLD07fK83ooDngpDvgo8HspbF0CXgZ5PTr4kVJ9SDymYTHG3CoiP99R2TbOTQPuAybg3J8vEJHPOtQ5BLgd8AAbReTgaPkxwAzABdwnIrfs6PN6KgG1m5oove6n1L/zDpkXXUj2T36CGcCjsdaGI7xcvoln1lfxeTTpHJfo58ScNE7ISWN4gibmSqnebXemYYk9yQ+cCmR0VWC90XdHfJeNTRuZMWcG2YFsrp12bbxD6rSkgw5i2Csvs/Huu6m8/wHq33uf7KuvIv3003XUwB0wxpDj85Dj83BAenK7Y3XhCKuaWljR1MKqxiArmlpY2dTCB9V1rG8JtXtC4zJOgjrY5yXP52Gwz8Mgn4dBfg+DvM71s71ubUVVamA7EuiYbB67lbKtmQG8LiKnGGO8QELswWiC+m/gGBFZY4zJiZa7gH9FP7sE+NIY85KIfLtb36QLhKuqWHvppTTPm0/ur28g46yz4h1SXERE+LCqjqfXV/G/jTU028KIBB8/Lc7jxJw0RiZq0qmU6vt2mICKSGWHotuNMbOB33RPSL3DhRMupLyxnIe+eYisQBbnjj833iF1mhUIkHP11aSeeBLr/3AjG/7wR2qe/y95v/2NTtq9i5LdLiZGJ+TuqMW2WdccYm1zkLXNQdY0ByltDrK+JcS39U28XVlDk73lmwaJLoscr5tcr4dMr5tMT3TxusmK2U5zu0jzuAlYRl+xUqqPM8ZcClwGDDPGzIs5lAx80onzU4GDgPMARCSIM193rDOB50VkTbROebR8OrBMRFZEr/UUcBIQ1wQ0uHo1ay6+mPD6DRT88x8kH3FEPMOJi1VNLTy2rpJn11ezPhgize3itLwMTsvLYEpKgv7br5TqV3aYgBpjpsbsWjgtop1pOe3TjDH8fK+fs7FpI3+Z9ReyA9kcN+y4eIe1U3zDhlL0wAPU/e9/bLj5FlZ9/zRSTjiBnKuvwpOfH+/w+g2fZTE0wekfujUiQk04wrqWEBtaQpQHw5QHQ1QEw2wIhigPhljS0ExlKEx1KLLl++5tn2PaktE0t4tkt4sUt4tkl0VqzH6SyyLJ7SLRZZHcuu9ykeCySHBZOuKvUvH1BPA/4Gbg+pjyOhGp6sT5Q4EK4EFjzGRgNnCViDTE1BkFeIwx7+MktjNE5BEgH1gbU68E2HtrH2KMuRi4GKCoqKgTYe2apnnzWPujS0CEooceJGHKlG77rN4mbAtvV9by8LqNvFdVh8vAYRkp/CEvn6OyUvAN4NePlVL9W2cSyb/GbIeBVcD3O3PxHfU1Mcb4cCbd3hOoBE4TkVXGGA9O/5ap0RgfEZGbO/OZXcllubj5wJupbq7mV5/8ilRfKvvn79/TYewWYwwpxx1H4kEHUXnvfVQ99BB1b7xBxrnnkHnxxbiSk3d8EbVbjDFO0uhxMy4psN26ERGqQmEqQ2Eqg2GqQhFqwhGqQ2E2hSNsaltHKG8JsbShmbqIUyfSyfHEfJYh0WURsJyENBDdDljOtt8y+F0WPsvCZxn80XXrvtcYPNF9rzF4LWdxG4Mnurit6Dpa12UMbgPuaJnbGCwDLpxjLgPWAEuMRQQbsAVshIhsLovEHmvbFiLRtbTWEYggiDj7kZhzIrHXitazhXblkS32N9dtO7aVOpEO14u0fX7MdrQ8vLVzd3COU7Zl3dZrhaN190hO4InJw+Py89sNEr3P/bjjAWNMRieSUDfOvfEKEfnCGDMDJ5H9dYc6ewKHAwHgM2PM5zsZ5D3APeD0Ad2Zczur4fPPWXvZj3FnZFB47z34hg7tjo/pdda3hHiirJLH1lWyriVEntfDdcV5nDU4g0G+gTuCvVJq4OjMK7iH7sqFO9nX5EKgWkRGGGNOB24FTsPpZ+oTkYnGmAScoeqfFJFVuxLL7vC5fMw4bAYXvH4BV793Nf8+4t/slbdXT4ex21xJSeRcczXpp59Gxe0zqLz3PjY9+xxZP/4x6ad9H+PpG6P99ncuY8j2epyJwndiFiARodG2qQvb1Eci1EfXDRGbunCE+ohNY+xi2zRGjzdHhGbbpi4SoTwYotkWmmybFtumxRZabLttypru5oompZYBQ2tiCla0DJxtY5zXMSxjaE1b29bRc7dWvi2tY7G1fk0BJLoXe0yi+xI9asvmuiKb67QmjU79zXXtaNJm7+KfT29jQdvDBSv6IMFtDFb0Z+c8XDBtP9e27dh1TF2fZbY4x4p5cNG63VpnaKBP/rL+BHA8TsulsPmvKNH9HQ3wVwKUiMgX0f1nad+S2lqnMtoq2mCM+RCYHC0vjKlXAJTuypfYXXXvvkfp1VfjHTKEwvvvw5OTE48wetSCukbuWlvBC+XVhAUOTk/mjyPzOSozVQeuU0oNKNtMQI0xPxCRx4wxP9nacRH52w6u3Zm+JicBv4tuPwvcYZyODgIkGmPcOE9vg0Dtjr9O90jxpnDPUfdw/uvnc/k7l3P3kXezR84e8Qpnt3gGDWLwrbeQfs7ZlN96Gxv++EeqH32UrMsuJeX443Wgoj7KGEOiyxUd2KjrHyaEbaFFnIQ0FE1KQyIEbSEYXYdsp4UqKELYFkLiLK0tV87itGSFbGFbLXF2TLnEtg6yufWvNRGMxCSJbYkgm5PGVrGJ5fZ+zYs95iSyreVOQttaZqJlbUlxNBFuXVymff3W4y5jsKLlVluivbl+63ErmtC1Jtku0z7xbq3nMk5Za6Lm2so5reWuaHnsvsuYtrqx19i833qdDslk2/fQX5p3logcH13vUnOfiKw3xqw1xowWkcU4rZwd+3C+iHM/dQNenNds/w4sAkYaY4biJJ6n4/QX7VE1r7zKup//HP+4cRTeczfu9PSeDqHH2CK8W1XHXWvK+XhTPYkui/Pzszg/P5th2+i2oZRS/d32WkBb21929R3NzvQ1aasjImFjTA2QiZOMngSU4Yzud83WXkvqqT4qABn+DO476j7Oe/08Lnv7Mu47+j7GZY7r1s/sToHx4yl6+CHq33+fittnsO7n17PxzrvI+vFlpBx3nCaiqh23ZXDjIlH/Wii1WzqMq7AFEZnTictcATweHQF3BXC+MeaS6Pl3ichCY8zrwDycBvf7RGRB9PMvB97A6RrzgIh8s+vfZudV/+dp1v/udyRMm0bBnXfiStqJVz36kKBt8+z6au5cW87SxhYG+TzcMGwQZw/OJNXT74fRUEqp7drhPKC7fGFjTsEZAv6i6P7ZwN4icnlMnQXROiXR/eU4SeponFECzwPSgY+AY1tbU7emp+YpK6sv47zXz6Mx3MgDRz/AyPSR3f6Z3U1sm7q332bjHf+iZckSvMOGkXXZZaQce4wmokqpfq8n5wE1xry3ncMiIof1RBw7o6vur5X3P0D5n/9M0sEHkz/jdix//5tSpMW2ebKsin+u3kBpS4gJSQEuKczmxJw0vDqokFJqgNnleUCNMdnAD4Hi2PoicsEOTi1lx31NWuuURF8VSsUZjOhMnDnOQkC5MeYTnNF3t5mA9pRBSYPaWkIvevMiHjrmIYam9u2BE4xlkXLUUSQfcQR1b77Fxn/9i3XXXcfGf/+bzAvOJ+XEE7G8fbKvlVJK9Sq7Oq5CXyYibPznP9n47ztJOe5YBt9yC6af3VOaIzaPlVXyrzXllLWE2Cslkb+MLuSQjGR9VV0ppTrozOO4F3ESw7eBV2OWHfmSaF+T6GtCpwMvdajzEtA6weYpwLviNMmuAQ4DMMYkAvvg9F3pFQpTCrn36HsBuOjNi1hbu3YHZ/QNxrJIOeZohr74Avl//xvG56Pshl+z7PDD2Xj3PURqauIdolJKqT7Ibmwi7dRTGPznP/er5LM5YnPP2nL2/vxbblhayhC/l2cmD+elqSM4NDNFk0+llNqKHb6Ca4z5WkT22KWLG3MccDub+5r8yRhzIzBLRF4yxviBR4EpQBVwuoisMMYkAQ8C43DG6HhQRP68vc/qqVdwYy2pXsIFb1yAz+XjvqPu6/MtoR2JCI2ffUbl/Q/Q8MknmIQE0k75PzLPPVfnEVVK9Rs9+QpuX9QV99fW3zX6S0IWEeHZ9dXctrKM0pYQ+6cl8ZPiXPZP16nNlFKq1bbur51JQP8IfCoir3VXcF0hHgkowOKqxVz81sUYDPcedW+/6BO6Nc2LF1P1wAPUvPoa2DZJhxxC+hlnkLj/fhjt16KU6sM0Ad2+eN1feyMR4e3KWv60ooxFDc1MTg7w6+GDOUATT6WU2sJOJ6DGmDo2z1qQCLQAoei+iEhK94W78+J5g1xRs4IfvvFDgnaQu4+8u0+PjrsjobIyqp98ik3PPUekshJPURHpp32f1JNP7tdD6Sul+q8eHoSoK0bB7VGagDpm1zTwh+Xr+LymgaEBL78YNpgTslP7TauuUkp1tV1uAe0r4n2DXFu7lovevIi6YB13Hnknk7Mnxy2WniDBILVvvUX1k0/SNGs2xusl+ZijSTv5ZBKmT9dWUaVUnxGnUXD9OIPrzcV5sDsJp3vKvj0Rx86I9/013spagvxheRnPb6gm2+vm2uI8zhqUicfSxFMppbZnW/fXHWYJxph3OlM20BWmFPLQMQ+R7k/n4jcvZtb6/n2zNl4vqd/5DsWPPcbQl14k7ZT/o/7d91hz3vksO/wIyv9+Oy0rVsY7TKWU6lVE5NDoSLhlwFQRmSYie+KMhdBxpHgVRy22zT9Xb2D/LxbxasUmrhmSy+d7j+W8/CxNPpVSajdsMwE1xviNMZlAljEm3RiTEV2KAR2BZisGJQ3iwWMeJC8xj0vfvpRPSj+Jd0g9wj9qFHm/+Q0jP/6IwX/9C74RI6i8915WHHccK087jaonniBcWRnvMJVSqjcZLSLzW3dEZAEwNo7xqBhvbazhkJmL+NOKMg5OT+bD6WP4+bBBJLp1bmyllNpd2+sDehVwNTAYWBdzqBa4V0Tu6PbodkJvekWoqrmKH731I5ZVL+P3+/+eE4efGO+QelyovJzal1+h5oUXaFm6FCyLhL32IuWYo0k+8kjcWVnxDlEppYD4DEJkjHkSaAAeixadBSSJyBk9GUdn9Kb7a3db3dTCL5eU8k5VLSMTfPxhZD6HZPSqIS+UUqrP2J1RcK8QkX92W2RdpLfdIOuD9Vz9/tV8UfYFV029igsnXDggByoQEVqWLKXujdepff0NgitWOMnotGkkH30UyYcdhmfQoHiHqZQawOKUgPqBS4GDokUfAneKSHNPxtEZve3+2h3CtnBPSQV/XlmGyxiuK87jgoIsvDqegVJK7bJdGQX3MBF51xhz8taOi8jzXRzjbumNN8hQJMQNn9zAaytf4/TRp3P99OtxWQP39R0RoWXpUupef4PaN94guHw5AL7Ro0k6+GCSDjmEwORJGNfA/TNSSvW8eE3DYowJAEUisrinP3tn9Mb7a1eaX9fItYvWMq++iaOzUrh5ZAGD/d54h6WUUn3etu6v7u2cczDwLnDCVo4J0KsS0N7I4/Jw84E3k5uQy4PfPEhFUwW3HHgLfrc/3qHFhTEG/6hR+EeNIvvKK2hZsYL6996n/oMPqLz/firvuQdXWhqJBx1I4n77kbjvvnhyc+MdtlJKdTljzInAnwEvMNQYswdwo4gMvD4bcdIYsfnrqvXctbacDI+be8cXc7xOq6KUUt1Op2HpIY99+xi3fXkbe+TswT8P+yepvtR4h9SrRGprafj4Y+o/+ID6Dz8iUl0NgHfYMBL33ZfEffchYfp0XCnaF0cp1bXi9ArubOAw4H0RmRItmy8iE3syjs7o7ffXXfHZpnquXriG1c1BzhyUwW+GDybNs71n8koppXbWTreAGmN+sr0LisjfuiKwgeIH435AVkIWv/zol5z12ln847B/MCx1WLzD6jVcKSmkHHccKccdh9g2LYsX0/DpZzR8/jmbnn+e6scfB8vCN2Y0CVOmkrDnVAJ77qktpEqpviokIjUdWtv6xxPhXqw5YnPryjLuWlvBkICXZ/cYzgHpyfEOSymlBpTtPe7Tf5G72DHFx5ATyOGa96/hB6/+gNsOvo0D8g+Id1i9jrEs/GPH4h87lswLL0CCQZrmzqXh8y9onDN7c0IKePLzCUydSmDSJAITJ+AbOxbL54vzN1BKqR36xhhzJuAyxowErgQ+jXNM/do39U38+NvVLGpo5pzBmfx2+GCdVkUppeJAX8GNg3X167jy3StZumkpP9nzJ5wz7hztc7ITJBSiedFimubMpnHOVzTOmU2kYqNz0O3GN2okgQkT8U+cgH/sOHwjR2hSqpTapji9gpsA/Ao4Klr0BvAHEWnpyTg6oy/dX7cmIsK/15Rz28r1pHtc/G1MEUdkancOpZTqbrs8DUtf0ddukI2hRn718a94e83bnDT8JH6z72/wunTUvV0hIoQ3bKBp/nya5y+gecF8mhZ8g11b61RwufAWF+MfPRrf6NH4x4zGN2IE7kGDMDrEvlIDXpwS0FNF5JkdlfUGfe3+Gmt1UwtXLFzDzJoGjs9O5dZRhWR6ta+nUkr1BE1AeyFbbO6aexd3zr2TydmTuf3Q28kKZMU7rH5BbJvQmjU0L1pMy5LFznrxYkKlpW11TCCAb+hQvMOH4xs+HO/wYfiKi/EUFmL5B+ZIxUoNRHFKQOeIyNQdlfUGffH+CvBqxSauWbQGEbh5VAH/l5uubxsppVQP2pVpWFQ3s4zFZXtcxvC04dzw8Q2c+vKp3HbQbeyVt1e8Q+vzjGXhLS7GW1wMxxzdVh6pq6NlyRJali0nuGI5LctX0DhrFrUvv9zufHdeHt4hQ/AWFeEdUoSnoABPfj6ewYNxZWToLzFKqV1ijDkWOA7IN8b8I+ZQChCOT1T9S4tt8/tl63igdCN7JCdw9/ghDAloNwyllOotdBTcXuDo4qMpTinm2g+u5aI3L+KKKVdwwYQLsIy+HtrVXMnJJOy5Jwl77tmu3G5ooGXFSoKrVxNcs5rQ6jUE16yh7t13iVRWtqtr/H48gwc7y6BBuHNz8eTl4s51Fk9uLlZKiiapSqmtWQfMAk4EZseU1wHXxCWifmRVUwsXf7OKeXVNXFyQzQ3DB+HVrhZKKdWrdGYU3NHAXsBL0f0TgJndGdRANDpjNP85/j/8/tPfM2PODGZvmM1NB9xEuj893qENCFZiIoGJEwhMnLDFsUh9PaHS0uiyzlmvW0eopITmhQu3SFABjM+HOysLd1YWrujanZWFKzMDd0YGrvQMXOlpznZaGsatLyMoNRCIyFxgrjHmCREJxTue/uSl8k1cu2gNljE8NGEox2TrfNtKKdUb7bAPqDHmQ+A7IlIX3U8GXhWRg3ogvk7rq31UOhIRnl78NLd+eSsZ/gz+cvBf2CNnj3iHpbbDDgYJl1cQLt9AeMMGQus3EK6oILyxgsjGjYQ3VhLeuJFIVdU2r2GlpuJKScGVmhpdUqJlqbiSk7CSkrGSk3AlJzvbSYm4EhOxoovxeHrwGyvVv8SpD+hI4GZgHNDW6VxEet0E0b39/hqyhd8vL+W+ko1MTUng7vHFFPp1UD+llIq33ekDmgsEY/aD0TLVDYwxnDbmNCZmT+Ta96/l/NfP54qpV3DuuHNxWTpfWW9keb14C/LxFuRvt56EQoSrq4lUbyJSXU2kusrZr6p29mtridTWYG+qIVRaSqSmhkhtLUQiO4zBeDxOMpqQgEkIYAUSsAIBTMDvbPv9zrbPj/H7nH2fH8vvw3h9GK8X4/NivF4sX3Tf68V4PFsubje4PRiPG+N2Y1z697IriW1DJIKIOOuIDbKVMjt2HQHb3mJNJLL5ejHnSCS8uV7Ha4UjiO2Uix2B2P1IpK1Ou3XrNdutI9HPjbS/bru1DeEwEokpiz0vum5XFg4j0dgDkyYx5KEH4/0j21UPAr8F/g4cCpwP6LuiO2ljMMzF36zi0031/LAgi18PH6yv3CqlVC/XmQT0EWCmMea/0f3vAg93W0QKgHGZ4/jPCf/hd5/+jr/P/jsflnzInw74E/lJ209yVO9lPB48OTl4cnI6fY6IIE1NROrqsetqidTVYdfXO0tDA3ZjY/t1QyN2U1N0acTeWEmoqQS7qQlpbsZuaUGam53kpMu+mIkmpU4yalwuaF273c5UNy7XFmssgzEWWFb7bWPAgME42zFlzscZYnacZdt/gO3XSNufKxItj1mEbZSLOH9mIojYYEeP2fbm/bbj0XI70lbeVqc1iYxNMm27LUns0p9LT2j9+UbX7X72W6wtjMvdtm47z+XCeNxYlgvcLufYVs4z7mhZTD1PYUG8/wR2R0BE3jHGGBFZDfzOGDMb+E28A+sr5tU1cv78lWwMhfnn2CJOzcuId0hKKaU6YYcJqIj8yRjzP+DAaNH5IvJV94alAFK8Kfz14L/y0vKXuHnmzfzfS//H9dOv56ThJ+kANwOEMQaTkICVkAC5nU9ct0dEIBRqS0YlGMRuCSLBFiQYRFpanP1wCAmFnLrBIBKK7ofDSCiMhMNOnXA42oplQyTstHS1toKFwx1a6mLWW0ngJJrEtUv+ImHnuBM8EpNUCtvoQiBszlFjk9WO6w7JrjFW+/LWZNfliu7HJsnGSaLaEmgD0fONy3K2XZZTJ7bcikm+W69rRRMtywWW1eF81+brtJ4bW98VTfysba1dbZ9rXNbmxK5jueVykrwt9mOu445JKrWVaXe1GGMsYKkx5nKgFEiKc0x9xnPrq7h28VoyPG5enDKSPVIS4h2SUkqpTursyCcJQK2IPGiMyTbGDBWRld0ZmHIYYzhpxElMy5vGrz7+Fb/+5Ne8v/Z9frPvb8jw69NetfOMMeD14vJ6ITl5xycopbrDVTj31iuBPwCHAefGNaI+IGwLf1yxjrvWVrBPaiL3Tigm26t94JVSqi/Z4SNsY8xvgZ8Dv4gWeYDHujMotaX8pHzuP+p+rt3zWj4s+ZCTXzyZ99a8F++wlFJK7QIR+VJE6kWkRETOF5GTReTzeMfVm9WFI5w9fwV3ra3ggvwsntljhCafSinVB3WmBfR7wBRgDoCIrIuOhKt6mMtycd6E89gvfz9+8dEvuPK9KzlqyFFcP/16shOy4x2eUkqpHTDGvAzbenccROTEHgynzyhtDnL2vBUsbmzmL6ML+cHgzHiHpJRSahd1JgENiogYYwTAGJPYzTGpHRiVPoqnvvMUD37zIHfPvZvP1n3GT6b9hJNHnoxltF+WUkr1Yn+JdwB9zfy6Rs6et5KGSIQnJg3n4Ax9Bq6UUn1ZZxLQp40xdwNpxpgfAhcA93Xm4saYY4AZgAu4T0Ru6XDchzPK7p5AJXCaiKwyxpwF/DSm6iRgqoh83ZnPHQg8Lg8XT7qYo4YcxY2f38jvP/s9Ly9/md/u91uGpfa6aeSUUkoBIvJBvGPoS97aWMOPvl1NutvFS1NHMjYpEO+QlOoxti3YYRs7ItgRIRLZvG1HbGxbEFs2l9mCtK7tzWuxnWu1jgAvEi1v3Rba9oF2x2gb9y96XNg8AGDMuxxtAwTuhC0G1IwdPDBmkPu2em1lZvMg+Ma0r9c6jmBruTFt+8SUd6zrDAjoDFtojAGrfV0TPd56DcvqcD0rpq7VoSw6UOFWy6wO5wyQQUZNZ/7CGGOOBI7C+avxhoi81YlzXMAS4EigBPgSOENEvo2pcxkwSUQuMcacDnxPRE7rcJ2JwAsiMnx7n9fbJ8ruTiLCC8te4C+z/kJTuIkLJ17IBRMuIODWG7VSSu3ItibK7ubPHAncDIwD/K3lItLrniDG6/76QEkFNywtZUJygEcnDiPXp/09VXzZthBqiRBqDhNsjhBqiRBuiRAKRreDEcJBe/N2yCYcsokEI4SCNpHW/bCzHQk7SzhkY4db952kMxJxkkQ1wLQlse0TU2tbCavVoc4WCa6JDt4fPWbFJtDRY23nbT6+7/eGk5zh33G8O/o627i/7rAF1Bhzq4j8HHhrK2XbMx1YJiIrouc8BZwEfBtT5yTgd9HtZ4E7onOixf4Xdwbw1I7iHMiMMXxv5Pc4sOBAbvvyNu6aexcvLHuBn+z5E44pPmbAPE1RSqk+5EHgt8DfgUOB8+nEwIADgYhw4/J13Lm2gqMyU7hz/BASXa54h6X6gUjEpqUhTHN9iObGEC2NYVoaQ7Q0RNdNYYJNYYJNkc3bzc461OwklDvD5bZwey1cHgu3x8LtdeH2RPe9Fr5ENy635SweC5fL4HJbWG4Llzu67TJYLmftitluK7cMpnXfak04NicbVofkojWRIZqwQPsWuq21ErbVgQ5zckfrt37hnfl1s2MjakxLa+u+OAXtpvJuna+7rYW2tVzat9o6h5zW39brir35nNb91m3bjjkv5lpiR9t77fatxbYtW1y33bYdc370mnaHMrE7ntO+NTr2uC0CtjjTkLeVtz/Xtls/J+Z8u/2+HRHEttt9j46fb9tCOBjZiR/mzuvMK7hH4oyCG+vYrZR1lA+sjdkvAfbeVh0RCRtjaoBMYGNMndNwEtUtGGMuBi4GKCoq2kE4/V9WIIvbDrqN00afxq0zb+VnH/6MJxc9yc+n/5zxmePjHZ5SSqnNAiLyTvSh62rgd8aY2cBvdnSiMSYNpyvMBJzf0S4Qkc9ijh8CvAi0Tpf2vIjcGD12DXBR9Lz5OHN7N3fVl9pdYVu4dvFa/rO+ivPzs/jjyHxc+hBVbYOI0NIYpqGmhabaII11QZpqQzTWBmmqc/ab60PO0uAknNvj9bvwBtx4A258ATcJKV7ScgJ4Am68fjdevwuPL7r4XXh8bmfb68Lts5y114Xb6ySbrQmeUqq9bSagxphLgcuAYcaYeTGHkoFPujuwaAx7A40ismBrx0XkHuAecF4R6omY+oI9c/fkye88yYvLX2TGnBmc8coZfHfEd7ly6pVkBbLiHZ5SSiloMcZYwFJjzOVAKZDUyXNnAK+LyCnGGC/OfKIdfSQix8cWGGPyceYdHSciTcaYp4HTgYd29Ut0paaIzSXfruKNjbX8tDiPnxTn6hs8A5jYQmNtkLrqZuqrWqiraqa+upmGTUEaa1poqGmhYVOQSHjLVknLMgSSPQRSvASSPKRkBfAnefAneghE175EN74ED74EN/4ED96AC8ulLyEo1RO21wL6BPA/nD4q18eU14lIVSeuXQoUxuwXRMu2VqfEGOMGUnEGI2p1OvBkJz5LdeCyXJw88mSOHHIk98y7h8cWPsbrq17n7HFnc+74c0nxpsQ7RKWUGsiuwkkcrwT+gPMa7rk7OskYkwocBJwHICJBILgTn+sGAsaYUPTz1+1U1N2kNhzh3Pkr+HxTAzeNzOeCAp1arL8TEZobQtRWNFO7sYnayiZqK5qo2dhMXWUT9dUt2JH2bQtun4ukNB+JqV7yhqWSmOojMc1HQqqXhGQvgRQvCSlefAG3M2CMUqpX6tQgRADGmBzaD5SwZgf13TiDEB2Ok2h+CZwpIt/E1PkxMDFmEKKTReT70WMWzuu5B7b2I92egTwIUWesrl3NHV/dweurXifFm8IFEy7gzLFn6kBFSqkBLx6DEO0qY8weOG/+fAtMBmYDV4lIQ0ydQ4DncLq+rAOua733GmOuAv4ENAFvishZ2/ic2C4ue65evbp7vhBQEQxx5twVLGxo4p9jh/C93PRu+yzV88KhCJs2NLFpQ+PmpdxZd3wlNpDiJTXLT3JmgOQMP8kZPpLS/SRFt70Bt7aKK9WHbOv+usME1BhzAvA3YDBQDgwBForIDjsVGmOOA27HmYblARH5kzHmRmCWiLxkjPEDjwJTgCrg9JhBiw4BbhGRfTrzBTUB7ZxFVYv451f/5MOSD8kKZHHxpIs5ZeQpeFw6uqBSamCK0yi4bwGnisim6H468JSIHL2D86YBnwP7i8gXxpgZQK2I/DqmTgpgi0h99D48Q0RGRj/jOZyxFTYBzwDPishj2/vM7ry/rmlq4fS5KyhrCXLfhKEcnqlv5/RVkYjNpg2NVK1r2LyUNVBT3kjsr5pJ6T7SchNIy0kgNSdAanaAlCxn8fh0sCml+pPdSUDnAocBb4vIFGPMocAPROTC7gl112gCunO+Kv+KGXNmMHvDbAYlDuK88edx8siT8bt3f8hlpZTqS+KUgH4lIlN2VLaV8/KAz0WkOLp/IHC9iHxnO+esAqbhvOZ7TOv92xhzDrCPiFy2vc/srvvrysYW/u/rZTREbB6bNIy9UhO7/DNU9wi1RKgsradiTR0b19ZRsbaeqnUNbf0xjYHUnAQyBic6y6DEtqRTk0ylBo5dnoYFCIlIpTHGMsZYIvKeMeb2rg8x/uqaQ/z8uXlcd9RohmV3diyIvmlKzhQePPpBPl33KXfNvYubZ97M3fPu5gdjf8BpY07TPqJKKdW9bGNMUWt3FmPMENpN6751IrLeGLPWGDNaRBbjdHOJnd6sNUndICJijJmOM71LJbAG2McYk4DzCu7hQFye3C5rbOaUr5YTFJvn9hjOhOStjaOkegM7YlNV1sCGlbVsWFnL+pW1VK9vaPvb6kt0k12YzMRDC8gqSCIz30k23R5NNJVSW9eZBHSTMSYJ+BB43BhTDjTs4Jw+aWN9kC9WVHHmvV/w9I/2pSizf98QjTHsn78/+w3ej9kbZnPfgvv4x1f/4IEFD3Da6NP4wbgf6Ki5SinVPX4JfGyM+QBn9rwDifa57IQrcO7HXmAFcL4x5hIAEbkLOAW41BgTxkk0T4/Or/2FMeZZYA4QBr4iOpJ8T1rc0MypXy8jIvDcHiMYm6RjEfQmLU1h1i+vYd3STaxfUUP56lrCQadl05/oIXdoCiOmZpNdlExWYTJJ6T7tl6mU2imdeQU3EWjGuUGehTNS7eMiUrndE3tYV70itLCsljPu/Zwkn5v//Ghf8tMG1o1xYeVC7l9wP2+uehOP5eGYocdw1tizGJc5Lt6hKaVUt+jpV3Cjg+ydArwLtI5z8LmIbNz2WfHTla/gLqxv4pSvl2MZeHaPEYxO1G4f8dZUH2Td0k1tS2VJPSLOVCZZRcnkDU0hN7qkZAU02VRKddou9wHtK7ryBjm/pIYz7/2czCQv//nRvuSmDLwb5KqaVTy28DFeWv4STeEmpuRM4cwxZ3L4kMPxWDpgkVKq/4hTH9BZfWXk3a66v35T38SpXy/DayyenTKcEQkD797aG4RDEcqW11CysIo131axcW09AG6PRe6wVAaPTGPwiFRyh6Xi8eprtEqpXbfTCagxpg7nDf/WR12tFQ0gItKrOgl29SAJs1dXc/b9XzA4LcBTF+9DVpKvy67dl9QGa3lx2Ys8sfAJSupLyAnkcOroU/neiO+Rm5gb7/CUUmq3xSkBvQXYCPyHmG4tnZxnu0d1xf11Xl0jp329nIDL4rk9RjA0YWDeU+Nl04ZGVs3fyJpvq1i3dBORkI3lMuQNS6VwbDr5ozPIGZKMy23FO1SlVD+iLaC74PMVlZz34EyKMxN56uJ9SEvwdun1+xJbbD4u/ZjHFz7Op+s+xTIW+w3ej5NHnswhBYfoNC5KqT4rTgnoyq0Ui4gM68k4OmN3768iwolzlrGuJcjzU0YwJKDJZ3ezIzZly2tYNW8jq+ZXsmlDIwDpeQkUjsugcGwGg0em4fV3ZigQpZTaNbvSAuoHLgFGAPNw5vEMb7VyL9Bdw8R/tLSCCx+exejcZB67aG9SA5poraldwwvLXuDF5S9S3lhOui+d44cfz3dHfJdR6aPiHZ5SSu2UeCSgfUlX3F83tIQIilDoH7gPcrtbOBRh7bdVLJtTzur5lbQ0hrFchvzR6RRPzKJ4YiYpWQNrXAulVHztSgL6HyAEfAQcC6wWkau6Ncrd0J3zgL67aAM/enQ2Y/JSeOSC6aQn6g0UIGJH+HTdp/x32X95b+17hO0wI9NHcmzxsRwz9BgKkwvjHaJSSu1QnFpAE4CfAEUicrExZiQwWkRe6ck4OkPn2e69YpPOlXM3EmqO4EtwM3RSFsWTsigcl6GtnEqpuNmVBHS+iEyMbruBmSIytXvD3HXdfYN8d9EGLnlsDsOyEnn0wr3JTtZXiGJVNVfxv5X/4/WVr/N1xdcATMyayDHFx3B08dHaX1Qp1WvFKQH9DzAbOEdEJkQT0k9FZI+ejKMzNAHtXWxbKF1czeLP17NibkVb0jlsj2xG7JlD/ph0XC7ty6mUir9dSUDnxCacHfd7m564QX6ybCMXPTyLQWl+nrhoH/JSdQS/rVlXv443Vr3B/1b+j4VVCwGYlDWJQ4sO5dDCQxmWOkyHcVdK9RrxHAXXGPOViEyJls0Vkck9GUdnaALaO1Svb2DRZ+tZMnM99dUteANuhk1xks4CTTqVUr3QriSgETaPzGeAANDIABkFd1u+XFXF+Q9+SUail8cv2pvCjIRu/8y+bGXNSt5a/RbvrXmPBZULAChKLuLQwkM5tOhQJmdPxm3p60FKqfiJUwL6KXA48ImITDXGDAeeFJHpPRlHZ2gCGj8tTWGWfLGeRZ+VUb66DmMZisZlMHqfPIZOysKt06QopXoxHQW3C81du4lzHphJgtfF4xftzbDspB753L5uQ8MGPij5gHfXvsvMspmE7BBJniSm501n//z92XfwvtpvVCnV4+KUgB4F/AoYB7wJ7A+cLyLv9WQcnaEJaM+rWFPHgg9KWPLlBsJBm8z8JMbsm8fIvXJJTNUuQEqpvkET0C727bpafnD/F1jG8NhF0xmT16sahHu9+mA9n5V9xqfrPuXT0k9Z17AOgMLkQvYbvB/T8qYxLXcaWYGsOEeqlOrv4jUKrjEmE9gH582iz0VkY0/H0BmagPaMUDDC0i838M2HpZSvrsPtsRg5PZcJB+WTM0R/x1BK9T2agHaDZeV1nHXfFzQGI9xz9jT2HZ7Zo5/fX4gIa+rW8EnpJ3y27jNmrp9JY9iZs6w4pZg9c/dkz9w9mZY7jUFJg+IcrVKqv4lTC+g7InL4jsp6A01Au1d9dQvz31/LNx+to6UxTPqgRCYcNJjRe+fhS9Cp35RSfZcmoN2kdFMT5z4wkzWVjfz1+5M5YfLgHo+hvwnbYRZVLWLW+lnM3jCb2eWzqQvWAZATyGFi9kQmZk1kUvYkxmeOJ8Gj/XCVUruuJxPQ6BzbCcB7wCE4rZ8AKcDrIjKmJ+LYGZqAdo+KtXV8/fYaln1ZjogwbEo2kw4tYNCINB2oTynVL2zr/qqjv+ym/LQAz16yLxc/MpsrnvyKDbXNXHTgsHiH1ae5LTcTsiYwIWsC5004D1tsllYvZfaG2czbOI/5FfN5Z807AFjGYnjacMZljGNs5ljGZIxhdPpokrzaL1cp1Sv9CLgaGIwzDUtrplEL3BGnmFQPERFWL6jk67fXUrq4GrfPxYRD8pl8WCEpWYF4h6eUUj1CW0C7SHMowk+e/prX5q/ngv2HcsN3xmJZ+gSzu1Q3VzN/43xnqZjPwqqFVDVXtR0vTC5kTMYYRqaPZETaCIanDqcwpRCPpa8zKaXai9MruFeIyD978jN3Vbzvr/2B2MKKuRXMem0VG9fWk5jmY9JhBYw/YLC+ZquU6re0BbSb+T0u7jhjKn9I+ZYHPlnJhtpm/vr9yfg9OkR6d0j3p3NQwUEcVHAQ4DxVrmiqYFHVorZlYeVC3l79NoLzkMVtuSlOKWZE2giKU4sZkjKE4pRiilKKSPHqAA9KqZ4jIv80xuwHFBNzLxaRR+IWlOpyti0sn1POrNdWUbWugdScAIedM5ZRe+fqvJ1KqQFLE9AuZFmG354wnvy0AH98dSFlNU3cdfae5CT74x1av2eMISchh5yEnLakFKAp3MTKmpUs37ScZZuWsWLTChZsXMCbq9/EFrutXoY/gyEpQyhIKiA/OZ/8JGcpSCogJyEHl6UPEpRSXccY8ygwHPgaiESLBdAEtB+wbWHplxuY/b9VVK9vJD0vgSMvGMeIPXOwNPFUSg1wmoB2g4sOHMbgtADXPj2Xk+74hHvOnsbEgtR4hzUgBdwBxmWOY1zmuHblwUiQkroSVtWuYk3tGlbVrmJ17Wq+3PAlr6x4pa3VFJyW09yEXHITcslLzCMvMa9tOzchl8xAJpmBTH29Vym1M6YB46S/9INRQLSP5/xKPnthOVXrGsjMT+ToH05g+JRsjHbLUUopQBPQbnPcxEEMyUzg4kdmc8pdn3LbKZM4aY/8eIelorwuL8PShjEsbcsBo0KREGUNZZTUl1BaX0ppXSllDWVsaNzA3Iq5vLn6TcJ2uN05BkO6P52sQBbZgWwyA5lk+DPI8GeQ7k9vt53qTSXRk6ijHCo1sC0A8oCyeAeiukbZsk189sJyypbVkJoT4KiLxjNiao4mnkop1YEmoN1o/OBUXrx8fy57bA5XPfU1i9bXcd1Ro3HpzahX87g8FKUUUZRStNXjtthUNVexoWED5Y3lVDRVsLFpo7NudNYralZQ2VRJ0A5u9Rpu4ybFl0KaL41UXyqp3lSSvclbLEmeJJI8SSR6E0l0J5LkTSLBk0CiO1FfC1YDkogQkQgRiSAi+N19totDFvCtMWYm0NJaKCInxi8ktSsqS+v5/MUVrJq3kYQULwefOZqx+w/SPp5KKbUNmoB2s6wkH49dtDe/e/kb7nx/OYvX13H76XuQ4tfXNfsqy1hkBbLICmQxnvHbrCciNIYbqWqucpamKja1bKKmpYaaYM3m7ZYayhrKWLppKbXBWuqD9e1eAd4Wr+UlwZNAwB1ot/jdfvwuv7OObvtcPnxuHz6XD6/lxevyOmUuHx7Lg8flwevy4rE8eC0vHpcHj+XBbblxW+52227jrF3GpUnwThIRbLGxsZ212G0JVet+7HbsfmvCFbtv2+2Pdzw/Ym/9WNgOb/Ocjsda922xCUu47brbu2br8dbtrZ0be3xnzontuz0lZwqPHNtnu0z+Lt4BqN3TVB/ki5dW8u1HpXh8LvY+aRiTDyvE49N/F5VSans0Ae0BXrfFTd+byNhBKfz+pW846Y5PuOPMKYwfrP1C+zNjDImeRBI9iRQmF3b6PFtsGkIN1AXrqAvW0RBqaLfUh+ppDDXSFG6iMeys27ZDTVQ3V9MSaaEp3ERzuJmWSAvN4WbCEt7xh+/sd8Tgsly4jRvLWLiMC8ty1i7jwjLW1hcsjDHOgsEyFobN+yY6NWLrvvP/pt3ndhSbtLdty+ZtEaHt/6Ld7lq3O5bbYrc71pqAtR6LPd663TGhbC1rTRxb6/U1rT+z1p/p1n7Gbsvdvo7Vvm7r3w+vy4vL7dpmPZdxbXGt1ut3rO+yXOQl5sX7j2eXicgH8Y5B7Ro7YrPgw3XMfHkFweYIEw4pYPp3huJP0gfLSinVGd2agBpjjgFmAC7gPhG5pcNxH86If3sClcBpIrIqemwScDeQAtjAXiLS3J3xdrez9xnC6NxkrnhyDt/796f89oRxnDm9SPsCqnYsY7W9gtuVInaEoB0kGAnSEmmhJdJCMBIkZIcIRoJt2637YTtMyA4RtsOEJUwo4my3tkSF7FBby1RrecdWt9YyEXFa/ezNrX+xSVnHZA6iiaPQLjFsK4/Z3lZi2vrfVWsC27q9RaJr2pdbxmr7ObSWW5a11UQ5NqluOx69hsu4nG2stvKtJeWt523tWGxZa5JmsbncZbm2qNtubW1Z7jbuduXtjsUkf7HfQXUdY0wdbPVJhAFERHROqF6sZFEVHz29lKp1DRSMSeeA748kc3BSvMNSSqk+pdsSUGOMC/gXcCRQAnxpjHlJRL6NqXYhUC0iI4wxpwO3AqcZY9zAY8DZIjLXGJMJhLor1p40fWgGr115INc8PZdf/XcBX6yo4qaTJ5Lk08Zo1b1clouA5bymq5SKDxHp2idLqkfUVTXzyTNLWf5VBcmZfo790USG7pGlD2iUUmoXdGfWMx1YJiIrAIwxTwEnAbEJ6Els7gfzLHCHcf41PwqYJyJzAUSkshvj7HGZST4eOm8v7vxgOX99czHzS2v415lTGTdYH3wrpZRSvYVtCws+KOHzF1YgtrD3iUPZ44gi3F7t56mUUruqO4doywfWxuyXRMu2WkdEwkANkAmMAsQY84YxZo4x5mdb+wBjzMXGmFnGmFkVFRVd/gW6k2UZfnzoCJ784T40tIT53r8/4dHPV7d71VAppZRS8bGxpJ7nbpvNR/9ZyqARqZzx272ZdtxQTT6VUmo39dYxwt3AAcBZ0fX3jDGHd6wkIveIyDQRmZadnd3TMXaJvYdl8tpVB7L3sEx+/cICzn3wS9bX9OmurkoppVSfFQ5G+Oy/y3nmpi+pq2ziyAvHcfzlk0nJ0u4LSinVFbozAS0FYof+LIiWbbVOtN9nKs5gRCXAhyKyUUQagdeAqd0Ya1xlJfl4+Py9+MNJ45m5spKjb/+Ql+aui3dYSiml1IBSuqSaJ/8wkzlvrGb0Pnmc+bt9GLVXnvb1VEqpLtSdCeiXwEhjzFBjjBc4HXipQ52XgHOj26cA74rzDuobwERjTEI0MT2Y9n1H+x1jDGfvW8z/rjqIYdmJXPnkV1z+xByqG4LxDk0ppZTq18KhCB8/u5QX/v4VACddM4XDzhmLP1GnVlFKqa7WbYMQiUjYGHM5TjLpAh4QkW+MMTcCs0TkJeB+4FFjzDKgCidJRUSqjTF/w0liBXhNRF7trlh7k6FZiTzzo325+8MV3P72Er5YWcUtJ0/k8LG58Q5NKaWU6nfKV9fy9oPfUr2+kQkH57PfySPw+LSfp1JKdRfTXwa9mTZtmsyaNSveYXSpb9bV8JP/zGXxhjq+M3EQvz1hHDkp/niHpZRS/YoxZraITIt3HL1Vf7y/AkQiNrP/t5pZr60iIcXLYeeMoWhcZrzDUkqpfmNb91edfLIXGz84lZevOIB7PlzOP95dxodLKvjZsWM4a3oRlqX9UZRSSqldUb2+gbcf/Jby1XWMmp7LgaeN0tdtlVKqh2gC2st53RaXHzaS4ycN5lcvzOfXLyzg+Tkl3HzyRMbk6byhSiml1M5Y/HkZ7z+5BJfbcPQPJzBiz5x4h6SUUgNKb52GRXVQnJXIYxfuzd++P5nVlY0c/4+Pufm1hdQ1h+IdmlJKqR5kjEkzxjxrjFlkjFlojNm3w/FDjDE1xpivo8tvOntufxZsDvPOQ9/y9kMLySlK5vQbpmvyqZRScaAtoH2IMYaTpxZw6Ogcbv7fQu7+cAXPzSnhuqNGc+q0Qlz6Wq5SSg0EM4DXReSU6CjzCVup85GIHL+L5/Y7G0vqeOPeb9hU3si07xSz13HFWC59Bq+UUvGg//r2QemJXm47ZTIvXb4/xZmJXP/8fI7/58d8trwy3qEppZTqRsaYVOAgnFHkEZGgiGzq7nP7KhFhwQclPHvLbILNYU66egp7nzBMk0+llIoj/Re4D5tUkMYzl+zLHWdOobYpxBn3fs6PHp3Fqo0N8Q5NKaVU9xgKVAAPGmO+MsbcZ4xJ3Eq9fY0xc40x/zPGjN/JczHGXGyMmWWMmVVRUdE936SbhVoivPXAt3zw5BLyR6Vx2q+mUzA6Pd5hKaXUgKcJaB9njOH4SYN559qDue6oUXy0dCNH/O0DfvH8fMpqmuIdnlJKqa7lBqYCd4rIFKABuL5DnTnAEBGZDPwTeGEnzgVARO4RkWkiMi07O7vrv0U3q6lo5LnbZrN01gb2PmkYx18+mYQUb7zDUkophSag/Ybf4+Lyw0by/nWHcObeRTw7ey0H//l9fv/yN1TUtcQ7PKWUUl2jBCgRkS+i+8/iJJVtRKRWROqj268BHmNMVmfO7Q9Wf1PJMzfPor66meMvn8y0Y4sxOkaCUkr1GpqA9jM5KX5uPGkC7113CN/bI59HPlvNQbe9x62vL2JTYzDe4SmllNoNIrIeWGuMGR0tOhz4NraOMSbPGGOi29Nx7vWVnTm3LxMRZv1vFa/cMZekdD+n/mIvhozPjHdYSimlOtBRcPupgvQEbj1lEj86eBgz3lnKXR8s55FPV3HWPkO4YP+h5KX64x2iUkqpXXMF8Hh0FNsVwPnGmEsAROQu4BTgUmNMGGgCThcR2da5PR59Nwg2h3nn4YWs+KqCkXvlcugPxuDxueIdllJKqa0wm+9Jfdu0adNk1qxZ8Q6j11q8vo5/v7+Ml+euw2UZTp5SwMUHD2N4dlK8Q1NKqbgyxswWkWnxjqO36u3317qqZl7911yqyhrZ7+ThTD68kGgDsFJKqTja1v1VW0AHiNF5ycw4fQrXHTWaez9awX++XMvTs9dy9Lg8fnTwMKYU6ciASiml+pYNq2p59d/ziAQjHH/5JIrG6Su3SinV22kCOsAUZiRw40kTuPLwkTz86Soe/nQVr3+znskFqZy9bzHHTxqE36OvLSmllOrdln9VztsPfEsgxct3r55CxuCtziijlFKql9FBiAaorCQf1x41mk9/cTg3njSe+pYw1z0zl31vfodb/reIkurGeIeolFJKbUFEmPPGal6/ewGZBUmc8vNpmnwqpVQfoi2gA1ySz805+xZz9j5D+HR5JY98top7PlzOPR8u57AxOZw6rZDDxuTgcemzCqWUUvEVidh88MRiFn5SxohpORx+zljcXn1rRyml+hJNQBUAxhj2H5HF/iOyKN3UxBNfrObpWSW8vbCczEQv352Sz6nTChiTlxLvUJVSSg1AweYwr9+zgLXfVjHtuGKmHz9U5/dUSqk+SBNQtYX8tAA/PXoM1xwxig+WVPDMrBIe+WwV93+8kon5qfzf1HyOmzSInGSdykUppVT3a6oL8sodc6lYW8+hZ49h3P6D4x2SUkqpXaQJqNomt8vi8LG5HD42l6qGIC9+Xcozs0r43cvfcuMr37L30EyOnzyIY8bnkZnki3e4Siml+qHayiZe/sdc6qqaOfaSiQydlBXvkJRSSu0GnQdU7bQlG+p4Ze46XplXxoqNDbgsw77DMvnOpEEcPjZHW0aVUn2KzgO6ffG8v1aW1vPyP74mHLI57rJJDB6RFpc4lFJK7TydB1R1mVG5yfzkqNFcc+QoFpbV8ep8Jxn9xfPzAZhcmMYRY3I4YlwuY/KSdUJwpZRSO23dsk289u95uD0W37t2Kpn5SfEOSSmlVBfQBFTtMmMM4wanMG5wCtcdNZqFZXW8s3ADby8q569vLeGvby0hPy3A4WNzOHBkNvsMyyDZ74l32EoppXq5VfM28vq9C0jO8HPClZNJyQzEOySllFJdRBNQ1SVik9ErDh9JeW0z7y4q5+2F5Tw9ay2PfLYal2WYUpjG/iOyOHBkFpML03R6F6WUUu0sn1POm/d9Q1ZhEsdfPplAsjfeISmllOpCmoCqbpGT4uf06UWcPr2IlnCE2aur+WTZRj5eupF/vLuUGe8sJcnnZuqQdPYaks5eQzPYozANv0fnc1NKqYFqycz1vP3QQnKLUzj+isn4AvprilJK9Tf6L7vqdj63i/2GZ7Hf8Cx+ejRsagzy2fJKPlm+kS9XVvPXt5YA4HVZTCxIZVpxOlMK09mjMI28VB3QSCmlBoJvP1nHe48tIn9kGsddNgmvX39FUUqp/qhb/3U3xhwDzABcwH0ickuH4z7gEWBPoBI4TURWGWOKgYXA4mjVz0Xkku6MVfWctAQvx04cxLETBwFOQjprVTVfrqriy1VVPPDxSkKRFQDkpviYXJDG5MI0JhekMX5wCumJ+jqWUkr1Jws+LOWDJxZTOC6DYy+ZiMerb8MopVR/1W0JqDHGBfwLOBIoAb40xrwkIt/GVLsQqBaREcaY04FbgdOix5aLyB7dFZ/qPdISvBwxLpcjxuUC0ByK8G1ZLXPXbmLu2k3MK6nhzW83tNUflOpn7KAUxg5KZtygVMYOSmZIZiIuS0fbVUqpvmbuO2v5+JmlFE/K4ugfjsetXTGUUqpf684W0OnAMhFZAWCMeQo4CYhNQE8Cfhfdfha4w+icHQOe3+NialE6U4vS28pqGkPMK93EwrJavl1Xy8KyOj5YUkHEduax9bothmUlMjI3mRHZSYzMTWJEThJDMhPwufWXGaWU6o3mvLmaz55fzvAp2Rx54Xhcbh2YTiml+rvuTEDzgbUx+yXA3tuqIyJhY0wNkBk9NtQY8xVQC9wgIh91Y6yql0tN8HDgyGwOHJndVtYcirCsvJ5vy2pZVl7P0g11fL22mpfnrmurYwwMTg1QnJVAcWais2QlUpgRoCA9gSSf9jFSSql4+PrtNXz2/HJGTsvhiPPHYemo6EopNSD01t++y4AiEak0xuwJvGCMGS8itbGVjDEXAxcDFBUVxSFMFU9+j4sJ+alMyE9tV94YDLOiooFl5fWs3NjAqsoGVlU28ur8MjY1htrVTUvwUJAeoCAtgYL0AIPSAgxK9ZOX6mdQqp/sJB9u/aVIKaW61Pz3S/jk2WUMn5qtyadSSg0w3ZmAlgKFMfsF0bKt1SkxxriBVKBSRARoARCR2caY5cAoYFbsySJyD3APwLRp06Q7voTqexK87q0mpuAMeLRyYwMl1U2UbmqipLqRkuomllXU8/6ScppDdrv6loHsZB+5KU4ymp0csyT5yEzykZHoISPRR1rAg6X9UJVSaru++aiUD59awtDJWRx54XhNPpVSaoDpzgT0S2CkMWYoTqJ5OnBmhzovAecCnwGnAO+KiBhjsoEqEYkYY4YBI4EV3RirGiDSErxMKfIyJaZ/aSsRoaYpRFlNM+trmqPrJspqmimva6Gsppl5pTVU1rdgb+Vxh2Wc62ckeslI8JKa4CE14CEt4CEtwUNqgpcUv5uUgIcUv5tkv4fk6DrR60K7Pyul+ruFn5bx/uOLGTIhk6MvmoBLk0+llBpwui0BjfbpvBx4A2calgdE5BtjzI3ALBF5CbgfeNQYswyowklSAQ4CbjTGhAAbuEREqrorVqUAjDGkJXhJS/AydlDKNutFbKGqIUh5XTNVDcF2S2VDkKr6IJuagqytamRBU4iaphCNwch2P9sykOh1k+hzk+BzkeRzR/ddBLxuEjwuAl4XCdHF74ldLPzuzds+twuv28LnttqtvW4Lj2VpK20fJiJEbCEigm3jrEWw7fblYdtuOx6xnToRe/N22N7ynEj0OmF7y3Na60W2dywi7a8Rs922FiEc2fJa4cjmuCIdl46fFVM+YXAq/zprarx/LKqTFn+xnncfXUjh2HSO+dEEXB5NPpVSaiDq1j6gIvIa8FqHst/EbDcDp27lvOeA57ozNqV2lcsyba/hdlZLOEJNY4ja5hC1zWHqmsPUNYeoaw5T2xSiviVMfUuYhpYwDS2Rtu3STSGagmEagxGaghEaQ5G2kX93ldsyeFwWHpfB67ZwWxYuy+BxGdwuq+2422VwWwaXZdrquCyDZQwuy/lzMMbgMia6DS7jHLcsJ6G3DBii62gLr2Wcuq1psIk5Ztr+Zztky00RQcTZd9bOftuxmHJbiB5zEq/WurbEbjv17OhF7dZET5zrtR6L2LH1o+WtCVpbXSFix5Y757QmUiKtSRYx2zF1WhNM2fydeju3ZbCs6N8fY3C5Nv89af075N5WmbW53Ove/PfOHVPHMs7+sOykeH9V1UnLZpfzzkPfkj8qjWMvnaRTrSil1ADWWwchUqpf8bld5KS4yEnx79Z1RIRgxKY5aNMcjtAcitAcsmkJO+vmUIRg2KYlbBOMxGyHbYIRm1BYCEYihCLSVhaO2IQjQsgWIrZNKCJOWUxrU3Mo0m6/rQUsJjmy7fbJWeu6NQG0bWfdmtBFNzcnimw7wRLa56Wxbyub6JHWpNYY05bItu3HbFvRg05S7JzfetyKJjmWcRLl1mu0Jkgm5piJrp1jm6/tjiZNrfuu6Hkdk3bLbP48l3ESNpdF9PM3J/pWTEJmdUjYYuu7okmfK5qcxV6vNalrTQq3fk1wWVa0Hu2Svo5J49auE/sZSsUSERZ/Xkbe8FSOu3QSHq8mn0opNZBpAqpUH2KMwed24XO7SMUT73CUUmqHjDEcffEE7Ijg9euvHUopNdDpnUAppZRS3crtcaHPzJRSSgHoCABKKaWUUkoppXrE/7d3r6GWlXUcx78/xhkcLLIckWgytYZEy44XwqkQnUgsQyNFiwKJoAsRFll0gTLFF5V0xYQyG6ObYpkm5oWcyhelOTk16iSWjqSok5WVIZrNvxf7Ocxp8DJn2metOWt9P3DYaz177bWe39nP4X+evdde2wmoJEmSJKkTTkAlSZIkSZ1wAipJkiRJ6oQTUEmSJElSJ5yASpIkSZI64QRUkiRJktQJJ6CSJEmSpE44AZUkSZIkdcIJqCRJkiSpE6mqvvswFUn+DNwzpd2tAB6a0r4Wg7HlhfFlHlteGF/mseWF6WV+UVXtPYX9DNIU66tjdPjGlhfGl3lseWF8maeZ90nr62AmoNOU5OaqOqLvfnRlbHlhfJnHlhfGl3lseWGcmRezMT5fY8s8trwwvsxjywvjy9xFXk/BlSRJkiR1wgmoJEmSJKkTTkCf3Nf67kDHxpYXxpd5bHlhfJnHlhfGmXkxG+PzNbbMY8sL48s8trwwvswLntfPgEqSJEmSOuE7oJIkSZKkTjgBlSRJkiR1wgnoHEmOS3JHkj8k+Wjf/VkISS5MsiXJrXPanpfkuiR3ttvn9tnHaUrywiTrktye5LYkp7f2IWfePclNSX7bMn+6te+f5MY2vi9Osqzvvk5TkiVJbklyZVsfet7NSTYm2ZDk5tY25HG9Z5JLk/w+yaYkq4ecd2iGXl/HVlthfPXV2mptHdqYntVHfXUC2iRZApwHvB44CHhrkoP67dWCWAsct13bR4GfVtUq4KdtfSieAD5UVQcBRwLva8/rkDM/BqypqlcAM8BxSY4EPgN8oapeAvwNeGd/XVwQpwOb5qwPPS/AMVU1M+f7uoY8rr8EXF1VBwKvYPJcDznvYIykvq5lXLUVxldfra0TQ88L46qt0EN9dQK6zSuBP1TVXVX1OPB94MSe+zR1VfUL4K/bNZ8IXNSWLwLe1GWfFlJV3V9Vv2nL/2TyR/UChp25quqRtrq0/RSwBri0tQ8qc5KVwPHABW09DDjv0xjkuE7yHOAo4BsAVfV4VT3MQPMO0ODr69hqK4yvvlpbra1teVCZ+6qvTkC3eQHwpznr97a2Mdinqu5vyw8A+/TZmYWSZD/gUOBGBp65nTKzAdgCXAf8EXi4qp5omwxtfH8R+Aiwta3vxbDzwuQfn2uTrE/yrtY21HG9P/Bn4JvtVLALkuzBcPMOzVjr62jG51jqq7XV2srAxjQ91VcnoPofNflensF9N0+SZwE/AD5QVf+Ye98QM1fVf6pqBljJ5N2HA/vt0cJJ8kZgS1Wt77svHXtNVR3G5LTG9yU5au6dAxvXuwGHAedX1aHAv9judKCB5dXADHl8jqm+WltHYUy1FXqqr05At7kPeOGc9ZWtbQweTPJ8gHa7pef+TFWSpUyK43eq6oetedCZZ7XTKNYBq4E9k+zW7hrS+H41cEKSzUxO7VvD5PMMQ80LQFXd1263AJcx+WdoqOP6XuDeqrqxrV/KpGAONe/QjLW+Dn58jrW+WlsHmRcYXW2FnuqrE9Btfg2salf3Wga8Bbii5z515QrgtLZ8GnB5j32ZqvZ5hW8Am6rq83PuGnLmvZPs2ZaXA69j8tmcdcDJbbPBZK6qj1XVyqraj8nf7fVV9TYGmhcgyR5Jnj27DBwL3MpAx3VVPQD8KclLW9NrgdsZaN4BGmt9HfT4HFt9tbZaW9tmg8rcV33N5F1VASR5A5Pz3ZcAF1bVOf32aPqSfA84GlgBPAh8CvgRcAmwL3APcEpVbX8xhUUpyWuAG4CNbPsMw8eZfE5lqJkPYfKB8SVMXmS6pKrOSnIAk1cxnwfcAry9qh7rr6fTl+Ro4IyqeuOQ87Zsl7XV3YDvVtU5SfZiuON6hsmFMJYBdwHvoI1vBph3aIZeX8dWW2F89dXaam1lYGN6Vh/11QmoJEmSJKkTnoIrSZIkSeqEE1BJkiRJUiecgEqSJEmSOuEEVJIkSZLUCSegkiRJkqROOAGVJEmSJHXCCag0RUn2SrKh/TyQ5L62/EiSry7A8dYmuTvJe+b5uKtmv1B7J445077Tb2ceu7z9Ph5PsmJn9iFJGh/r6zM+1vqqRWO3vjsgDUlV/QWYAUhyJvBIVZ27wIf9cFVdOp8HVNVOFbhmBjgCuGq+D6yqR4GZJJv/j+NLkkbG+vqMx7W+atHwHVCpA0mOTnJlWz4zyUVJbkhyT5I3J/lsko1Jrk6ytG13eJKfJ1mf5Jokz9+B46xNcn6SXyW5qx33wiSbkqyds93mJCuS7Nfu+3qS25Jcm2R52+ZnSY5oyyvaY5YBZwGntldaT02yRzvGTUluSXJie8zBrW1Dkt8lWTX1X6wkadSsr9ZXLT5OQKV+vBhYA5wAfBtYV1UvBx4Fjm9F8ivAyVV1OHAhcM4O7vu5wGrgg8AVwBeAg4GXJ5l5ku1XAedV1cHAw8BJT7Xjqnoc+CRwcVXNVNXFwCeA66vqlcAxwOeS7AG8B/hSVc0weUX33h3svyRJO8v6Ku3iPAVX6sdPqurfSTYCS4CrW/tGYD/gpcDLgOuS0La5fwf3/eOqqrbvB6tqI0CS29q+N2y3/d1VNdu2vm0zH8cCJyQ5o63vDuwL/BL4RJKVwA+r6s557leSpPmyvkq7OCegUj8eA6iqrUn+XVXV2rcy+bsMcFtVrd7Zfbd9PTanfXbfT7U9wH+A5W35CbadJbH70xwvwElVdcd27ZuS3AgcD1yV5N1Vdf0O9F+SpJ1lfZV2cZ6CK+2a7gD2TrIaIMnSJAd33IfNwOFt+eQ57f8Enj1n/Rrg/WkvJSc5tN0eANxVVV8GLgcOWegOS5L0DKyvUs+cgEq7oPZZkJOBzyT5LZPTel7VcTfOBd6b5BZg7iXd1wEHzV4kATgbWAr8rp2GdHbb7hTg1iQbmJzu9K3Oei5J0pOwvkr9y7YzEyQtNu3Ke1fO9zLxfcvkMvFHVNVDffdFkqTtWV+lheM7oNLi9nfg7Mzzi7L7kvZF2Uxe0d3ac3ckSXoq1ldpgfgOqCRJkiSpE74DKkmSJEnqhBNQSZIkSVInnIBKkiRJkjrhBFSSJEmS1In/AkC1A3VXzKRGAAAAAElFTkSuQmCC", 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", "text/plain": [ "
" ] @@ -314,7 +307,7 @@ } ], "source": [ - "plot(sims_partially_reversible)" + "plot(sims_partially_reversible);" ] }, { diff --git a/docs/source/examples/notebooks/models/loss_of_active_materials.ipynb b/docs/source/examples/notebooks/models/loss_of_active_materials.ipynb index 4ec9f4cc65..127763ec35 100644 --- a/docs/source/examples/notebooks/models/loss_of_active_materials.ipynb +++ b/docs/source/examples/notebooks/models/loss_of_active_materials.ipynb @@ -36,17 +36,16 @@ "import pybamm\n", "\n", "model = pybamm.lithium_ion.DFN(\n", - " options=\n", - " {\n", - " \"SEI\":\"solvent-diffusion limited\", \n", - " \"SEI porosity change\":\"false\", \n", - " \"particle mechanics\":\"swelling only\",\n", - " \"loss of active material\":\"stress-driven\",\n", + " options={\n", + " \"SEI\": \"solvent-diffusion limited\",\n", + " \"SEI porosity change\": \"false\",\n", + " \"particle mechanics\": \"swelling only\",\n", + " \"loss of active material\": \"stress-driven\",\n", " }\n", ")\n", "param = pybamm.ParameterValues(\"Ai2020\")\n", - "param.update({\"Negative electrode LAM constant proportional term [s-1]\": 1e-4/3600})\n", - "param.update({\"Positive electrode LAM constant proportional term [s-1]\": 1e-4/3600})\n", + "param.update({\"Negative electrode LAM constant proportional term [s-1]\": 1e-4 / 3600})\n", + "param.update({\"Positive electrode LAM constant proportional term [s-1]\": 1e-4 / 3600})\n", "total_cycles = 2\n", "experiment = pybamm.Experiment(\n", " [\n", @@ -54,13 +53,14 @@ " \"Rest for 600 seconds\",\n", " \"Charge at 1C until 4.2 V\",\n", " \"Hold at 4.199 V for 600 seconds\",\n", - " ] * total_cycles\n", + " ]\n", + " * total_cycles\n", ")\n", "sim = pybamm.Simulation(\n", - " model, \n", - " experiment = experiment,\n", - " parameter_values = param,\n", - " solver = pybamm.CasadiSolver(\"fast with events\")\n", + " model,\n", + " experiment=experiment,\n", + " parameter_values=param,\n", + " solver=pybamm.CasadiSolver(\"fast with events\"),\n", ")\n", "solution = sim.solve(calc_esoh=False)" ] @@ -103,16 +103,18 @@ } ], "source": [ - "sim.plot([\n", - " \"Voltage [V]\",\n", - " \"Current [A]\",\n", - " \"Sum of x-averaged positive electrode volumetric interfacial current densities [A.m-3]\",\n", - " \"Sum of x-averaged negative electrode volumetric interfacial current densities [A.m-3]\",\n", - " \"X-averaged positive electrode active material volume fraction\",\n", - " \"X-averaged negative electrode active material volume fraction\",\n", - " \"X-averaged positive particle surface tangential stress [Pa]\",\n", - " \"X-averaged negative particle surface tangential stress [Pa]\",\n", - "])" + "sim.plot(\n", + " [\n", + " \"Voltage [V]\",\n", + " \"Current [A]\",\n", + " \"Sum of x-averaged positive electrode volumetric interfacial current densities [A.m-3]\",\n", + " \"Sum of x-averaged negative electrode volumetric interfacial current densities [A.m-3]\",\n", + " \"X-averaged positive electrode active material volume fraction\",\n", + " \"X-averaged negative electrode active material volume fraction\",\n", + " \"X-averaged positive particle surface tangential stress [Pa]\",\n", + " \"X-averaged negative particle surface tangential stress [Pa]\",\n", + " ]\n", + ")" ] }, { @@ -157,18 +159,18 @@ "solutions = []\n", "\n", "for k in ks:\n", - " param.update({\"Positive electrode LAM constant proportional term [s-1]\": k/3600})\n", - " param.update({\"Negative electrode LAM constant proportional term [s-1]\": k/3600})\n", + " param.update({\"Positive electrode LAM constant proportional term [s-1]\": k / 3600})\n", + " param.update({\"Negative electrode LAM constant proportional term [s-1]\": k / 3600})\n", "\n", " sim = pybamm.Simulation(\n", - " model, \n", + " model,\n", " experiment=experiment,\n", " parameter_values=param,\n", " solver=pybamm.CasadiSolver(\"fast with events\"),\n", " )\n", " solution = sim.solve(calc_esoh=False)\n", " solutions.append(solution)\n", - " \n", + "\n", "pybamm.dynamic_plot(\n", " solutions,\n", " output_variables=[\n", @@ -181,7 +183,7 @@ " \"X-averaged positive electrode surface area to volume ratio [m-1]\",\n", " \"X-averaged negative electrode surface area to volume ratio [m-1]\",\n", " ],\n", - " labels=[f\"k={k:.0e}\" for k in ks]\n", + " labels=[f\"k={k:.0e}\" for k in ks],\n", ")" ] }, @@ -226,14 +228,17 @@ ], "source": [ "model = pybamm.lithium_ion.DFN(\n", - " options=\n", - " {\n", - " \"SEI\":\"solvent-diffusion limited\", \n", - " \"loss of active material\":\"reaction-driven\",\n", + " options={\n", + " \"SEI\": \"solvent-diffusion limited\",\n", + " \"loss of active material\": \"reaction-driven\",\n", " }\n", ")\n", "param = pybamm.ParameterValues(\"Chen2020\")\n", - "param.update({\"Negative electrode reaction-driven LAM factor [m3.mol-1]\": 1e-3,})\n", + "param.update(\n", + " {\n", + " \"Negative electrode reaction-driven LAM factor [m3.mol-1]\": 1e-3,\n", + " }\n", + ")\n", "total_cycles = 2\n", "experiment = pybamm.Experiment(\n", " [\n", @@ -241,24 +246,27 @@ " \"Rest for 600 seconds\",\n", " \"Charge at 1C until 4.2 V\",\n", " \"Hold at 4.199 V for 600 seconds\",\n", - " ] * total_cycles\n", + " ]\n", + " * total_cycles\n", ")\n", "sim = pybamm.Simulation(\n", - " model, \n", - " experiment = experiment,\n", - " parameter_values = param,\n", - " solver = pybamm.CasadiSolver(\"fast with events\")\n", + " model,\n", + " experiment=experiment,\n", + " parameter_values=param,\n", + " solver=pybamm.CasadiSolver(\"fast with events\"),\n", ")\n", "solution = sim.solve(calc_esoh=False)\n", "\n", - "sim.plot([\n", - " \"Voltage [V]\",\n", - " \"Current [A]\",\n", - " \"Sum of x-averaged negative electrode volumetric interfacial current densities [A.m-3]\",\n", - " \"X-averaged negative electrode active material volume fraction\",\n", - " \"Negative total SEI thickness [m]\",\n", - " \"X-averaged negative total SEI thickness [m]\",\n", - "])" + "sim.plot(\n", + " [\n", + " \"Voltage [V]\",\n", + " \"Current [A]\",\n", + " \"Sum of x-averaged negative electrode volumetric interfacial current densities [A.m-3]\",\n", + " \"X-averaged negative electrode active material volume fraction\",\n", + " \"Negative total SEI thickness [m]\",\n", + " \"X-averaged negative total SEI thickness [m]\",\n", + " ]\n", + ")" ] }, { @@ -313,16 +321,18 @@ "\n", "\n", "model = pybamm.lithium_ion.DFN(\n", - " options=\n", - " {\n", - " \"loss of active material\":\"current-driven\",\n", + " options={\n", + " \"loss of active material\": \"current-driven\",\n", " }\n", ")\n", "param = pybamm.ParameterValues(\"Chen2020\")\n", - "param.update({\n", - " \"Positive electrode current-driven LAM rate\": current_LAM,\n", - " \"Negative electrode current-driven LAM rate\": current_LAM,\n", - "}, check_already_exists=False)\n", + "param.update(\n", + " {\n", + " \"Positive electrode current-driven LAM rate\": current_LAM,\n", + " \"Negative electrode current-driven LAM rate\": current_LAM,\n", + " },\n", + " check_already_exists=False,\n", + ")\n", "total_cycles = 2\n", "experiment = pybamm.Experiment(\n", " [\n", @@ -330,22 +340,25 @@ " \"Rest for 600 seconds\",\n", " \"Charge at 1C until 4.2 V\",\n", " \"Hold at 4.199 V for 600 seconds\",\n", - " ] * total_cycles\n", + " ]\n", + " * total_cycles\n", ")\n", "sim = pybamm.Simulation(\n", - " model, \n", - " experiment = experiment,\n", - " parameter_values = param,\n", - " solver = pybamm.CasadiSolver(\"fast with events\")\n", + " model,\n", + " experiment=experiment,\n", + " parameter_values=param,\n", + " solver=pybamm.CasadiSolver(\"fast with events\"),\n", ")\n", "solution = sim.solve(calc_esoh=False)\n", "\n", - "sim.plot([\n", - " \"Voltage [V]\",\n", - " \"Current [A]\",\n", - " \"X-averaged positive electrode active material volume fraction\",\n", - " \"X-averaged negative electrode active material volume fraction\",\n", - "])" + "sim.plot(\n", + " [\n", + " \"Voltage [V]\",\n", + " \"Current [A]\",\n", + " \"X-averaged positive electrode active material volume fraction\",\n", + " \"X-averaged negative electrode active material volume fraction\",\n", + " ]\n", + ")" ] }, { diff --git a/docs/source/examples/notebooks/models/pouch-cell-model.ipynb b/docs/source/examples/notebooks/models/pouch-cell-model.ipynb index 2c58b1861f..69cfbfec40 100644 --- a/docs/source/examples/notebooks/models/pouch-cell-model.ipynb +++ b/docs/source/examples/notebooks/models/pouch-cell-model.ipynb @@ -132,10 +132,12 @@ "outputs": [], "source": [ "param = dfn.default_parameter_values\n", - "I_1C = param[\"Nominal cell capacity [A.h]\"] # 1C current is cell capacity multipled by 1 hour\n", + "I_1C = param[\n", + " \"Nominal cell capacity [A.h]\"\n", + "] # 1C current is cell capacity multipled by 1 hour\n", "param.update(\n", " {\n", - " \"Current function [A]\": I_1C * 3, \n", + " \"Current function [A]\": I_1C * 3,\n", " \"Negative electrode diffusivity [m2.s-1]\": 3.9 * 10 ** (-14),\n", " \"Positive electrode diffusivity [m2.s-1]\": 10 ** (-13),\n", " \"Negative current collector surface heat transfer coefficient [W.m-2.K-1]\": 10,\n", @@ -213,14 +215,16 @@ " sim = pybamm.Simulation(model, parameter_values=param, var_pts=var_pts)\n", " simulations[name] = sim # store simulation for later\n", " if name == \"Current collector\":\n", - " # model is independent of time, so just solve arbitrarily at t=0 using \n", + " # model is independent of time, so just solve arbitrarily at t=0 using\n", " # the default algebraic solver\n", " t_eval = np.array([0])\n", - " solutions[name] = sim.solve(t_eval=t_eval) \n", + " solutions[name] = sim.solve(t_eval=t_eval)\n", " else:\n", " # solve at COMSOL times using Casadi solver in \"fast\" mode\n", - " t_eval = comsol_variables[\"time\"] \n", - " solutions[name] = sim.solve(solver=pybamm.CasadiSolver(mode=\"fast\"), t_eval=t_eval)" + " t_eval = comsol_variables[\"time\"]\n", + " solutions[name] = sim.solve(\n", + " solver=pybamm.CasadiSolver(mode=\"fast\"), t_eval=t_eval\n", + " )" ] }, { @@ -265,7 +269,7 @@ "\n", "def get_interp_fun_curr_coll(variable_name):\n", " \"\"\"\n", - " Create a :class:`pybamm.Function` object using the variable (interpolate in space \n", + " Create a :class:`pybamm.Function` object using the variable (interpolate in space\n", " to match nodes, and then create function to interpolate in time)\n", " \"\"\"\n", "\n", @@ -275,10 +279,7 @@ "\n", " # Make sure to use dimensional time\n", " fun = pybamm.Interpolant(\n", - " comsol_t,\n", - " variable.T,\n", - " pybamm.t,\n", - " name=variable_name + \"_comsol\"\n", + " comsol_t, variable.T, pybamm.t, name=variable_name + \"_comsol\"\n", " )\n", " fun.domains = {\"primary\": \"current collector\"}\n", " fun.mesh = mesh.combine_submeshes(\"current collector\")\n", @@ -302,7 +303,7 @@ "outputs": [], "source": [ "comsol_voltage = pybamm.Interpolant(\n", - " comsol_t, \n", + " comsol_t,\n", " comsol_variables[\"voltage\"],\n", " pybamm.t,\n", " name=\"voltage_comsol\",\n", @@ -338,7 +339,7 @@ " \"Current collector current density [A.m-2]\": comsol_current,\n", " \"X-averaged cell temperature [K]\": comsol_temperature,\n", " # Add spatial variables to match pybamm model\n", - " \"z [m]\": simulations[\"1+1D DFN\"].built_model.variables[\"z [m]\"], \n", + " \"z [m]\": simulations[\"1+1D DFN\"].built_model.variables[\"z [m]\"],\n", "}" ] }, @@ -356,7 +357,9 @@ "metadata": {}, "outputs": [], "source": [ - "comsol_solution = pybamm.Solution(solutions[\"1+1D DFN\"].t, solutions[\"1+1D DFN\"].y, comsol_model, {})" + "comsol_solution = pybamm.Solution(\n", + " solutions[\"1+1D DFN\"].t, solutions[\"1+1D DFN\"].y, comsol_model, {}\n", + ")" ] }, { @@ -386,9 +389,7 @@ "V_av = solutions[\"Average DFN\"][\"Voltage [V]\"]\n", "I_av = solutions[\"Average DFN\"][\"Total current density [A.m-2]\"]\n", "\n", - "dfncc_vars = cc_model.post_process(\n", - " solutions[\"Current collector\"], param, V_av, I_av\n", - ")" + "dfncc_vars = cc_model.post_process(solutions[\"Current collector\"], param, V_av, I_av)" ] }, { @@ -417,7 +418,6 @@ " param,\n", " cmap=\"viridis\",\n", "):\n", - "\n", " fig, ax = plt.subplots(2, 2, figsize=(13, 7))\n", " fig.subplots_adjust(\n", " left=0.15, bottom=0.1, right=0.95, top=0.95, wspace=0.4, hspace=0.8\n", @@ -462,9 +462,9 @@ " )\n", " ax[0, 1].plot(z_plot * 1e3, dfncc_var_slice, \":\", color=color)\n", " # add dummy points for legend of styles\n", - " comsol_p, = ax[0, 1].plot(np.nan, np.nan, \"ko\", fillstyle=\"none\")\n", - " pybamm_p, = ax[0, 1].plot(np.nan, np.nan, \"k-\", fillstyle=\"none\")\n", - " dfncc_p, = ax[0, 1].plot(np.nan, np.nan, \"k:\", fillstyle=\"none\")\n", + " (comsol_p,) = ax[0, 1].plot(np.nan, np.nan, \"ko\", fillstyle=\"none\")\n", + " (pybamm_p,) = ax[0, 1].plot(np.nan, np.nan, \"k-\", fillstyle=\"none\")\n", + " (dfncc_p,) = ax[0, 1].plot(np.nan, np.nan, \"k:\", fillstyle=\"none\")\n", "\n", " # compute errors\n", " dfn_var = dfn_var_fun(t=t_plot, z=z_plot)\n", @@ -650,7 +650,7 @@ " return dfn_var(t=t, z=z) - V(t=t)\n", "\n", "\n", - "dfncc_var = dfncc_vars[var]\n", + "dfncc_var = dfncc_vars[var]\n", "V_dfncc = dfncc_vars[\"Voltage [V]\"]\n", "\n", "\n", diff --git a/docs/source/examples/notebooks/models/rate-capability.ipynb b/docs/source/examples/notebooks/models/rate-capability.ipynb index 056362b8f9..ef09a37909 100644 --- a/docs/source/examples/notebooks/models/rate-capability.ipynb +++ b/docs/source/examples/notebooks/models/rate-capability.ipynb @@ -97,13 +97,10 @@ "\n", "for i, C_rate in enumerate(C_rates):\n", " experiment = pybamm.Experiment(\n", - " [f\"Discharge at {C_rate:.4f}C until 3.2V\"],\n", - " period=f\"{10 / C_rate:.4f} seconds\"\n", + " [f\"Discharge at {C_rate:.4f}C until 3.2V\"], period=f\"{10 / C_rate:.4f} seconds\"\n", " )\n", " sim = pybamm.Simulation(\n", - " model,\n", - " experiment=experiment,\n", - " solver=pybamm.CasadiSolver(dt_max=120)\n", + " model, experiment=experiment, solver=pybamm.CasadiSolver(dt_max=120)\n", " )\n", " sim.solve()\n", "\n", @@ -118,13 +115,13 @@ "\n", "plt.figure(1)\n", "plt.scatter(C_rates, capacities)\n", - "plt.xlabel('C-rate')\n", - "plt.ylabel('Capacity [Ah]')\n", + "plt.xlabel(\"C-rate\")\n", + "plt.ylabel(\"Capacity [Ah]\")\n", "\n", "plt.figure(2)\n", "plt.scatter(currents * voltage_av, capacities * voltage_av)\n", - "plt.xlabel('Power [W]')\n", - "plt.ylabel('Energy [Wh]')\n", + "plt.xlabel(\"Power [W]\")\n", + "plt.ylabel(\"Energy [Wh]\")\n", "\n", "plt.show()" ] diff --git a/docs/source/examples/notebooks/models/saving_models.ipynb b/docs/source/examples/notebooks/models/saving_models.ipynb index 91a6f2ae5c..57bda3ef85 100644 --- a/docs/source/examples/notebooks/models/saving_models.ipynb +++ b/docs/source/examples/notebooks/models/saving_models.ipynb @@ -307,6 +307,7 @@ "outputs": [], "source": [ "import os\n", + "\n", "os.remove(\"example_model.json\")\n", "os.remove(\"sim_model_example.json\")\n", "os.remove(\"sim_model_variables.json\")" diff --git a/docs/source/examples/notebooks/models/simulating-ORegan-2022-parameter-set.ipynb b/docs/source/examples/notebooks/models/simulating-ORegan-2022-parameter-set.ipynb index f20f385601..9965a78563 100644 --- a/docs/source/examples/notebooks/models/simulating-ORegan-2022-parameter-set.ipynb +++ b/docs/source/examples/notebooks/models/simulating-ORegan-2022-parameter-set.ipynb @@ -131,7 +131,9 @@ } ], "source": [ - "sim.solve([0, 10]) # solving time kept short for testing purposes, feel free to extend it\n", + "sim.solve(\n", + " [0, 10]\n", + ") # solving time kept short for testing purposes, feel free to extend it\n", "sim.plot()" ] }, 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 b3725fd36f..ac92c06d15 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 @@ -23,7 +23,8 @@ "import pybamm\n", "import os\n", "import matplotlib.pyplot as plt\n", - "os.chdir(pybamm.__path__[0]+'/..')" + "\n", + "os.chdir(pybamm.__path__[0] + \"/..\")" ] }, { @@ -47,9 +48,9 @@ "outputs": [], "source": [ "model = pybamm.lithium_ion.DFN(\n", - " options = {\n", - " \"particle\": \"Fickian diffusion\", \n", - " \"particle mechanics\": \"swelling and cracking\", # other options are \"none\", \"swelling only\"\n", + " options={\n", + " \"particle\": \"Fickian diffusion\",\n", + " \"particle mechanics\": \"swelling and cracking\", # other options are \"none\", \"swelling only\"\n", " }\n", ")" ] @@ -87,12 +88,12 @@ }, { "cell_type": "markdown", - "source": [ - "Depending on the parameter set being used, the particle cracking model can require a large number of mesh points inside the particles to be numerically stable." - ], "metadata": { "collapsed": false - } + }, + "source": [ + "Depending on the parameter set being used, the particle cracking model can require a large number of mesh points inside the particles to be numerically stable." + ] }, { "cell_type": "code", @@ -107,7 +108,7 @@ "source": [ "var_pts = {\n", " \"x_n\": 20, # negative electrode\n", - " \"x_s\": 20, # separator \n", + " \"x_s\": 20, # separator\n", " \"x_p\": 20, # positive electrode\n", " \"r_n\": 26, # negative particle\n", " \"r_p\": 26, # positive particle\n", @@ -189,43 +190,52 @@ "E_n = param[\"Negative electrode Young's modulus [Pa]\"]\n", "stress_t_n_surf = solution[\"Negative particle surface tangential stress [Pa]\"]\n", "x = solution[\"x [m]\"].entries[0:19, 0]\n", - "c_s_n = solution['Negative particle concentration']\n", + "c_s_n = solution[\"Negative particle concentration\"]\n", "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", - " ax1.set_xlabel(r'$x_n$ [m]')\n", - " ax1.set_ylabel('$\\sigma_t/E_n$')\n", - " \n", - " plot_c_n, = ax2.plot(r_n, c_s_n(r=r_n,t=t,x=x[0])) # can evaluate at arbitrary x (single representative particle)\n", - " ax2.set_ylabel('Negative particle concentration')\n", - " ax2.set_xlabel(r'$r_n$ [m]')\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", + " ax1.set_xlabel(r\"$x_n$ [m]\")\n", + " ax1.set_ylabel(\"$\\sigma_t/E_n$\")\n", + "\n", + " (plot_c_n,) = ax2.plot(\n", + " r_n, c_s_n(r=r_n, t=t, x=x[0])\n", + " ) # can evaluate at arbitrary x (single representative particle)\n", + " ax2.set_ylabel(\"Negative particle concentration\")\n", + " ax2.set_xlabel(r\"$r_n$ [m]\")\n", " ax2.set_ylim(0, 1)\n", - " ax2.set_title('Close to current collector')\n", + " ax2.set_title(\"Close to current collector\")\n", " ax2.grid()\n", - " \n", - " plot_c_n, = ax3.plot(r_n, c_s_n(r=r_n,t=t,x=x[10])) # can evaluate at arbitrary x (single representative particle)\n", - " ax3.set_ylabel('Negative particle concentration')\n", - " ax3.set_xlabel(r'$r_n$ [m]')\n", - " ax3.set_ylim(0, 1) \n", - " ax3.set_title('In the middle')\n", + "\n", + " (plot_c_n,) = ax3.plot(\n", + " r_n, c_s_n(r=r_n, t=t, x=x[10])\n", + " ) # can evaluate at arbitrary x (single representative particle)\n", + " ax3.set_ylabel(\"Negative particle concentration\")\n", + " ax3.set_xlabel(r\"$r_n$ [m]\")\n", + " ax3.set_ylim(0, 1)\n", + " ax3.set_title(\"In the middle\")\n", " ax3.grid()\n", "\n", - " plot_c_n, = ax4.plot(r_n, c_s_n(r=r_n,t=t,x=x[-1])) # can evaluate at arbitrary x (single representative particle)\n", - " ax4.set_ylabel('Negative particle concentration')\n", - " ax4.set_xlabel(r'$r_n$ [m]')\n", - " ax4.set_ylim(0, 1) \n", - " ax4.set_title('Close to separator')\n", + " (plot_c_n,) = ax4.plot(\n", + " r_n, c_s_n(r=r_n, t=t, x=x[-1])\n", + " ) # can evaluate at arbitrary x (single representative particle)\n", + " ax4.set_ylabel(\"Negative particle concentration\")\n", + " ax4.set_xlabel(r\"$r_n$ [m]\")\n", + " ax4.set_ylim(0, 1)\n", + " ax4.set_title(\"Close to separator\")\n", " ax4.grid()\n", " plt.show()\n", - " \n", + "\n", "\n", "import ipywidgets as widgets\n", - "widgets.interact(plot_concentrations, t=widgets.FloatSlider(min=0,max=3600,step=10,value=0));" + "\n", + "widgets.interact(\n", + " plot_concentrations, t=widgets.FloatSlider(min=0, max=3600, step=10, value=0)\n", + ");" ] }, { @@ -263,12 +273,14 @@ "source": [ "label = [\"Crack model\"]\n", "output_variables = [\n", - " \"Negative particle crack length [m]\", \n", + " \"Negative particle crack length [m]\",\n", " \"Positive particle crack length [m]\",\n", " \"X-averaged negative particle crack length [m]\",\n", - " \"X-averaged positive particle crack length [m]\"\n", + " \"X-averaged positive particle crack length [m]\",\n", "]\n", - "quick_plot = pybamm.QuickPlot(solution, output_variables, label,variable_limits='tight')\n", + "quick_plot = pybamm.QuickPlot(\n", + " solution, output_variables, label, variable_limits=\"tight\"\n", + ")\n", "quick_plot.dynamic_plot();" ] }, diff --git a/docs/source/examples/notebooks/models/unsteady-heat-equation.ipynb b/docs/source/examples/notebooks/models/unsteady-heat-equation.ipynb index cf7bef3b47..5e3d11a0ee 100644 --- a/docs/source/examples/notebooks/models/unsteady-heat-equation.ipynb +++ b/docs/source/examples/notebooks/models/unsteady-heat-equation.ipynb @@ -138,7 +138,7 @@ "metadata": {}, "outputs": [], "source": [ - "model.initial_conditions = {T: 2 * x - x ** 2}" + "model.initial_conditions = {T: 2 * x - x**2}" ] }, { @@ -331,24 +331,26 @@ "outputs": [], "source": [ "N = 100 # number of Fourier modes to sum\n", - "k_val = param[\"Thermal diffusivity\"] # extract value of diffusivity from the parameters dictionary\n", + "k_val = param[\n", + " \"Thermal diffusivity\"\n", + "] # extract value of diffusivity from the parameters dictionary\n", "\n", "\n", "# Fourier coefficients\n", "def q(n):\n", - " return (8 / (n ** 2 * np.pi ** 2)) * np.sin(n * np.pi / 2)\n", + " return (8 / (n**2 * np.pi**2)) * np.sin(n * np.pi / 2)\n", "\n", "\n", "def c(n):\n", - " return (16 / (n ** 3 * np.pi ** 3)) * (1 - np.cos(n * np.pi))\n", + " return (16 / (n**3 * np.pi**3)) * (1 - np.cos(n * np.pi))\n", "\n", "\n", "def b(n):\n", - " return c(n) - 4 * q(n) / (k_val * n ** 2 * np.pi ** 2)\n", + " return c(n) - 4 * q(n) / (k_val * n**2 * np.pi**2)\n", "\n", "\n", "def T_n(t, n):\n", - " return (4 * q(n) / (k_val * n ** 2 * np.pi ** 2)) + b(n) * np.exp(\n", + " return (4 * q(n) / (k_val * n**2 * np.pi**2)) + b(n) * np.exp(\n", " -k_val * (n * np.pi / 2) ** 2 * t\n", " )\n", "\n", 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 0c97752792..1f6250f760 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 @@ -35,7 +35,8 @@ "%pip install \"pybamm[plot,cite]\" -q # install PyBaMM if it is not installed\n", "import pybamm\n", "import os\n", - "os.chdir(pybamm.__path__[0]+'/..')" + "\n", + "os.chdir(pybamm.__path__[0] + \"/..\")" ] }, { @@ -147,10 +148,12 @@ } ], "source": [ - "simulation.plot([\n", - " \"Voltage [V]\",\n", - " \"X-averaged cell temperature [K]\",\n", - "])" + "simulation.plot(\n", + " [\n", + " \"Voltage [V]\",\n", + " \"X-averaged cell temperature [K]\",\n", + " ]\n", + ")" ] }, { diff --git a/docs/source/examples/notebooks/models/using-submodels.ipynb b/docs/source/examples/notebooks/models/using-submodels.ipynb index 211e3346d8..d02e5489c7 100644 --- a/docs/source/examples/notebooks/models/using-submodels.ipynb +++ b/docs/source/examples/notebooks/models/using-submodels.ipynb @@ -142,7 +142,9 @@ "metadata": {}, "outputs": [], "source": [ - "model.submodels[\"negative primary particle\"] = pybamm.particle.XAveragedPolynomialProfile(\n", + "model.submodels[\n", + " \"negative primary particle\"\n", + "] = pybamm.particle.XAveragedPolynomialProfile(\n", " model.param, \"negative\", options={**model.options, \"particle\": \"uniform profile\"}\n", ")" ] @@ -365,7 +367,9 @@ "metadata": {}, "outputs": [], "source": [ - "model.submodels[\"external circuit\"] = pybamm.external_circuit.ExplicitCurrentControl(model.param, model.options)" + "model.submodels[\"external circuit\"] = pybamm.external_circuit.ExplicitCurrentControl(\n", + " model.param, model.options\n", + ")" ] }, { @@ -427,11 +431,19 @@ "outputs": [], "source": [ "options = {**model.options, \"particle\": \"uniform profile\"}\n", - "model.submodels[\"negative primary particle\"] = pybamm.particle.XAveragedPolynomialProfile(model.param, \"negative\", options)\n", - "model.submodels[\"positive primary particle\"] = pybamm.particle.XAveragedPolynomialProfile(model.param, \"positive\", options)\n", + "model.submodels[\n", + " \"negative primary particle\"\n", + "] = pybamm.particle.XAveragedPolynomialProfile(model.param, \"negative\", options)\n", + "model.submodels[\n", + " \"positive primary particle\"\n", + "] = pybamm.particle.XAveragedPolynomialProfile(model.param, \"positive\", options)\n", "\n", - "model.submodels[\"negative total particle concentration\"] = pybamm.particle.TotalConcentration(model.param, \"negative\", options)\n", - "model.submodels[\"positive total particle concentration\"] = pybamm.particle.TotalConcentration(model.param, \"positive\", options)" + "model.submodels[\n", + " \"negative total particle concentration\"\n", + "] = pybamm.particle.TotalConcentration(model.param, \"negative\", options)\n", + "model.submodels[\n", + " \"positive total particle concentration\"\n", + "] = pybamm.particle.TotalConcentration(model.param, \"positive\", options)" ] }, { @@ -457,14 +469,10 @@ "] = pybamm.open_circuit_potential.SingleOpenCircuitPotential(\n", " model.param, \"positive\", \"lithium-ion main\", options=model.options\n", ")\n", - "model.submodels[\n", - " \"negative interface\"\n", - "] = pybamm.kinetics.InverseButlerVolmer(\n", + "model.submodels[\"negative interface\"] = pybamm.kinetics.InverseButlerVolmer(\n", " model.param, \"negative\", \"lithium-ion main\", options=model.options\n", ")\n", - "model.submodels[\n", - " \"positive interface\"\n", - "] = pybamm.kinetics.InverseButlerVolmer(\n", + "model.submodels[\"positive interface\"] = pybamm.kinetics.InverseButlerVolmer(\n", " model.param, \"positive\", \"lithium-ion main\", options=model.options\n", ")\n", "model.submodels[\n", @@ -498,18 +506,30 @@ "metadata": {}, "outputs": [], "source": [ - "model.submodels[\n", - " \"Negative particle mechanics\"\n", - "] = pybamm.particle_mechanics.NoMechanics(model.param, \"negative\", model.options)\n", - "model.submodels[\n", - " \"Positive particle mechanics\"\n", - "] = pybamm.particle_mechanics.NoMechanics(model.param, \"positive\", model.options)\n", - "model.submodels[\"Negative sei\"] = pybamm.sei.NoSEI(model.param, \"negative\", model.options)\n", - "model.submodels[\"Positive sei\"] = pybamm.sei.NoSEI(model.param, \"positive\", model.options)\n", - "model.submodels[\"Negative sei on cracks\"] = pybamm.sei.NoSEI(model.param, \"negative\", model.options, cracks=True)\n", - "model.submodels[\"Positive sei on cracks\"] = pybamm.sei.NoSEI(model.param, \"positive\", model.options, cracks=True)\n", - "model.submodels[\"Negative lithium plating\"] = pybamm.lithium_plating.NoPlating(model.param, \"Negative\")\n", - "model.submodels[\"Positive lithium plating\"] = pybamm.lithium_plating.NoPlating(model.param, \"Positive\")" + "model.submodels[\"Negative particle mechanics\"] = pybamm.particle_mechanics.NoMechanics(\n", + " model.param, \"negative\", model.options\n", + ")\n", + "model.submodels[\"Positive particle mechanics\"] = pybamm.particle_mechanics.NoMechanics(\n", + " model.param, \"positive\", model.options\n", + ")\n", + "model.submodels[\"Negative sei\"] = pybamm.sei.NoSEI(\n", + " model.param, \"negative\", model.options\n", + ")\n", + "model.submodels[\"Positive sei\"] = pybamm.sei.NoSEI(\n", + " model.param, \"positive\", model.options\n", + ")\n", + "model.submodels[\"Negative sei on cracks\"] = pybamm.sei.NoSEI(\n", + " model.param, \"negative\", model.options, cracks=True\n", + ")\n", + "model.submodels[\"Positive sei on cracks\"] = pybamm.sei.NoSEI(\n", + " model.param, \"positive\", model.options, cracks=True\n", + ")\n", + "model.submodels[\"Negative lithium plating\"] = pybamm.lithium_plating.NoPlating(\n", + " model.param, \"Negative\"\n", + ")\n", + "model.submodels[\"Positive lithium plating\"] = pybamm.lithium_plating.NoPlating(\n", + " model.param, \"Positive\"\n", + ")" ] }, { @@ -525,12 +545,12 @@ "metadata": {}, "outputs": [], "source": [ - "model.submodels[\"electrolyte diffusion\"] = pybamm.electrolyte_diffusion.ConstantConcentration(\n", - " model.param\n", - ")\n", - "model.submodels[\"electrolyte conductivity\"] = pybamm.electrolyte_conductivity.LeadingOrder(\n", - " model.param\n", - ")" + "model.submodels[\n", + " \"electrolyte diffusion\"\n", + "] = pybamm.electrolyte_diffusion.ConstantConcentration(model.param)\n", + "model.submodels[\n", + " \"electrolyte conductivity\"\n", + "] = pybamm.electrolyte_conductivity.LeadingOrder(model.param)" ] }, { diff --git a/docs/source/examples/notebooks/parameterization/change-input-current.ipynb b/docs/source/examples/notebooks/parameterization/change-input-current.ipynb index 4b3ef7846e..28fc476e16 100644 --- a/docs/source/examples/notebooks/parameterization/change-input-current.ipynb +++ b/docs/source/examples/notebooks/parameterization/change-input-current.ipynb @@ -45,7 +45,8 @@ "import pybamm\n", "import numpy as np\n", "import os\n", - "os.chdir(pybamm.__path__[0]+'/..')\n", + "\n", + "os.chdir(pybamm.__path__[0] + \"/..\")\n", "\n", "# create the model\n", "model = pybamm.lithium_ion.DFN()\n", @@ -151,12 +152,14 @@ "metadata": {}, "outputs": [], "source": [ - "import pandas as pd # needed to read the csv data file\n", + "import pandas as pd # needed to read the csv data file\n", "\n", "model = pybamm.lithium_ion.DFN()\n", "\n", "# import drive cycle from file\n", - "drive_cycle = pd.read_csv(\"pybamm/input/drive_cycles/US06.csv\", comment=\"#\", header=None).to_numpy()\n", + "drive_cycle = pd.read_csv(\n", + " \"pybamm/input/drive_cycles/US06.csv\", comment=\"#\", header=None\n", + ").to_numpy()\n", "\n", "# load parameter values\n", "param = model.default_parameter_values\n", @@ -267,7 +270,7 @@ "# set user defined current function\n", "A = model.param.I_typ\n", "omega = 0.1\n", - "param[\"Current function [A]\"] = my_fun(A,omega)" + "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 6d2b6f707f..b13084b166 100644 --- a/docs/source/examples/notebooks/parameterization/parameter-values.ipynb +++ b/docs/source/examples/notebooks/parameterization/parameter-values.ipynb @@ -36,7 +36,8 @@ "import numpy as np\n", "import os\n", "import matplotlib.pyplot as plt\n", - "os.chdir(pybamm.__path__[0]+'/..')" + "\n", + "os.chdir(pybamm.__path__[0] + \"/..\")" ] }, { @@ -93,8 +94,10 @@ ], "source": [ "chem_parameter_values = pybamm.ParameterValues(\"Marquis2019\")\n", - "print(\"Negative current collector thickness is {} m\".format(\n", - " chem_parameter_values[\"Negative current collector thickness [m]\"])\n", + "print(\n", + " \"Negative current collector thickness is {} m\".format(\n", + " chem_parameter_values[\"Negative current collector thickness [m]\"]\n", + " )\n", ")" ] }, @@ -127,7 +130,7 @@ ], "source": [ "def cubed(x):\n", - " return x ** 3\n", + " return x**3\n", "\n", "\n", "parameter_values.update({\"cube function\": cubed}, check_already_exists=False)\n", @@ -325,9 +328,9 @@ "###################################################################\n", "\n", "# Plot\n", - "t_fine = np.linspace(0,t_eval[-1],1000)\n", + "t_fine = np.linspace(0, t_eval[-1], 1000)\n", "\n", - "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13,4))\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13, 4))\n", "ax1.plot(t_fine, 2 * np.exp(-3 * t_fine), t_sol1, u1(t_sol1), \"o\")\n", "ax1.set_xlabel(\"t\")\n", "ax1.legend([\"2 * exp(-3 * t)\", \"u1\"], loc=\"best\")\n", diff --git a/docs/source/examples/notebooks/parameterization/parameterization.ipynb b/docs/source/examples/notebooks/parameterization/parameterization.ipynb index 50be5e8ed9..9c060ed1ff 100644 --- a/docs/source/examples/notebooks/parameterization/parameterization.ipynb +++ b/docs/source/examples/notebooks/parameterization/parameterization.ipynb @@ -76,7 +76,9 @@ "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", + "D = pybamm.FunctionParameter(\n", + " \"Diffusion coefficient [m2.s-1]\", {\"Concentration [mol.m-3]\": c}\n", + ")\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]\")" @@ -106,14 +108,14 @@ "# governing equations\n", "N = -D * pybamm.grad(c) # flux\n", "dcdt = -pybamm.div(N)\n", - "model.rhs = {c: dcdt} \n", + "model.rhs = {c: dcdt}\n", "\n", - "# boundary conditions \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", + "# initial conditions\n", "model.initial_conditions = {c: c0}\n", "\n", "model.variables = {\n", @@ -142,8 +144,12 @@ }, "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}}})" + "r = pybamm.SpatialVariable(\n", + " \"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\"\n", + ")\n", + "geometry = pybamm.Geometry(\n", + " {\"negative particle\": {r: {\"min\": pybamm.Scalar(0), \"max\": R}}}\n", + ")" ] }, { @@ -220,7 +226,7 @@ "outputs": [], "source": [ "def D_fun(c):\n", - " return 3.9 #* pybamm.exp(-c)\n", + " return 3.9 # * pybamm.exp(-c)\n", "\n", "\n", "values = {\n", @@ -319,11 +325,11 @@ "cell_type": "code", "execution_count": 9, "metadata": { - "scrolled": true, "ExecuteTime": { "end_time": "2023-12-10T12:14:18.891821400Z", "start_time": "2023-12-10T12:14:18.864911Z" - } + }, + "scrolled": true }, "outputs": [ { @@ -411,8 +417,8 @@ "outputs": [ { "data": { - "text/plain": "
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" 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", + "text/plain": "
" }, "metadata": {}, "output_type": "display_data" @@ -436,7 +442,7 @@ "ax1.set_xlabel(\"Time [s]\")\n", "ax1.set_ylabel(\"Surface concentration [mol.m-3]\")\n", "\n", - "rsol = mesh[\"negative particle\"].nodes # radial position\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=f\"t={time}[s]\")\n", "ax2.set_xlabel(\"Particle radius [microns]\")\n", @@ -566,11 +572,11 @@ "cell_type": "code", "execution_count": 15, "metadata": { - "scrolled": true, "ExecuteTime": { "end_time": "2023-12-10T12:14:19.401195400Z", "start_time": "2023-12-10T12:14:19.232194200Z" - } + }, + "scrolled": true }, "outputs": [ { @@ -583,7 +589,7 @@ } ], "source": [ - "{k: v for k,v in spm.default_parameter_values.items() if k in spm.get_parameter_info()}" + "{k: v for k, v in spm.default_parameter_values.items() if k in spm.get_parameter_info()}" ] }, { @@ -621,495 +627,503 @@ " 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", + "neg_ocp = np.array(\n", + " [\n", + " [0.0, 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, 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4.0050669],\n", + " [0.47849828, 4.0023184],\n", + " [0.48122074, 3.9995501],\n", + " [0.48394321, 3.9969349],\n", + " [0.48666569, 3.9926589],\n", + " [0.48938816, 3.9889555],\n", + " [0.49211064, 3.9834003],\n", + " [0.4948331, 3.9783037],\n", + " [0.49755557, 3.9755929],\n", + " [0.50027804, 3.9707632],\n", + " [0.50300052, 3.9681098],\n", + " [0.50572298, 3.9635665],\n", + " [0.50844545, 3.9594433],\n", + " [0.51116792, 3.9556634],\n", + " [0.51389038, 3.9521511],\n", + " [0.51661284, 3.9479132],\n", + " [0.51933531, 3.9438281],\n", + " [0.52205777, 3.9400866],\n", + " [0.52478024, 3.9362304],\n", + " [0.52750271, 3.9314201],\n", + " [0.53022518, 3.9283848],\n", + " [0.53294765, 3.9242232],\n", + " [0.53567012, 3.9192028],\n", + " [0.53839258, 3.9166257],\n", + " [0.54111506, 3.9117961],\n", + " [0.54383753, 3.90815],\n", + " [0.54656, 3.9038739],\n", + " [0.54928247, 3.8995597],\n", + " [0.55200494, 3.8959136],\n", + " [0.5547274, 3.8909314],\n", + " [0.55744986, 3.8872662],\n", + 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3.7794874],\n", + " [0.64456893, 3.7769294],\n", + " [0.6472914, 3.773608],\n", + " [0.65001389, 3.7695992],\n", + " [0.65273637, 3.7690265],\n", + " [0.65545884, 3.7662776],\n", + " [0.65818131, 3.7642922],\n", + " [0.66090379, 3.7626889],\n", + " [0.66362625, 3.7603791],\n", + " [0.66634874, 3.7575538],\n", + " [0.66907121, 3.7552056],\n", + " [0.67179369, 3.7533159],\n", + " [0.67451616, 3.7507198],\n", + " [0.67723865, 3.7487535],\n", + " [0.67996113, 3.7471499],\n", + " [0.68268361, 3.7442865],\n", + " [0.68540608, 3.7423012],\n", + " [0.68812855, 3.7400677],\n", + " [0.69085103, 3.7385788],\n", + " [0.6935735, 3.7345319],\n", + " [0.69629597, 3.7339211],\n", + " [0.69901843, 3.7301605],\n", + " [0.7017409, 3.7301033],\n", + " [0.70446338, 3.7278316],\n", + " [0.70718585, 3.7251589],\n", + " [0.70990833, 3.723861],\n", + " [0.71263081, 3.7215703],\n", + " [0.71535328, 3.7191267],\n", + " [0.71807574, 3.7172751],\n", + " [0.72079822, 3.7157097],\n", + " [0.72352069, 3.7130945],\n", + " [0.72624317, 3.7099447],\n", + " [0.72896564, 3.7071004],\n", + " [0.7316881, 3.7045615],\n", + " [0.73441057, 3.703588],\n", + " [0.73713303, 3.70208],\n", + " [0.73985551, 3.7002664],\n", + " [0.74257799, 3.6972122],\n", + " [0.74530047, 3.6952841],\n", + " [0.74802293, 3.6929362],\n", + " [0.7507454, 3.6898055],\n", + " [0.75346787, 3.6890991],\n", + " [0.75619034, 3.686522],\n", + " [0.75891281, 3.6849759],\n", + " [0.76163529, 3.6821697],\n", + " [0.76435776, 3.6808143],\n", + " [0.76708024, 3.6786573],\n", + " [0.7698027, 3.6761947],\n", + " [0.77252517, 3.674763],\n", + " [0.77524765, 3.6712887],\n", + " [0.77797012, 3.6697233],\n", + " [0.78069258, 3.6678908],\n", + " [0.78341506, 3.6652565],\n", + " [0.78613753, 3.6630611],\n", + " [0.78885999, 3.660274],\n", + " [0.79158246, 3.6583652],\n", + " [0.79430494, 3.6554828],\n", + " [0.79702741, 3.6522949],\n", + " [0.79974987, 3.6499848],\n", + " [0.80247234, 3.6470451],\n", + " [0.8051948, 3.6405547],\n", + " [0.80791727, 3.6383405],\n", + " [0.81063974, 3.635076],\n", + " [0.81336221, 3.633549],\n", + " [0.81608468, 3.6322317],\n", + " [0.81880714, 3.6306856],\n", + " [0.82152961, 3.6283948],\n", + " [0.82425208, 3.6268487],\n", + " [0.82697453, 3.6243098],\n", + " [0.829697, 3.6223626],\n", + " [0.83241946, 3.6193655],\n", + " [0.83514192, 3.6177621],\n", + " [0.83786439, 3.6158531],\n", + " [0.84058684, 3.6128371],\n", + " [0.84330931, 3.6118062],\n", + " [0.84603177, 3.6094582],\n", + " [0.84875424, 3.6072438],\n", + " [0.8514767, 3.6049912],\n", + " [0.85419916, 3.6030822],\n", + " [0.85692162, 3.6012688],\n", + " [0.85964409, 3.5995889],\n", + " [0.86236656, 3.5976417],\n", + " [0.86508902, 3.5951984],\n", + " [0.86781149, 3.593843],\n", + " [0.87053395, 3.5916286],\n", + " [0.87325642, 3.5894907],\n", + " [0.87597888, 3.587429],\n", + " [0.87870135, 3.5852909],\n", + " [0.88142383, 3.5834775],\n", + " [0.8841463, 3.5817785],\n", + " [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", + " [1.0, 3.52302167],\n", + " ]\n", + ")\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", + "def graphite_LGM50_electrolyte_exchange_current_density_Chen2020(\n", + " c_e, c_s_surf, c_n_max, T\n", + "):\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", + " return 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", @@ -1117,55 +1131,53 @@ " 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", + " return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_p_max - c_s_surf) ** 0.5\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", + " \"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" @@ -1200,7 +1212,7 @@ ], "source": [ "param_same = pybamm.ParameterValues(\"Chen2020\")\n", - "{k: v for k,v in param_same.items() if k in spm.get_parameter_info()}" + "{k: v for k, v in param_same.items() if k in spm.get_parameter_info()}" ] }, { @@ -1328,8 +1340,8 @@ "outputs": [ { "data": { - "text/plain": "
", - "image/png": 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" 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", + "text/plain": "
" }, "metadata": {}, "output_type": "display_data" @@ -1373,8 +1385,8 @@ "outputs": [ { "data": { - "text/plain": "
", - "image/png": 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" + "image/png": 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", + "text/plain": "
" }, "metadata": {}, "output_type": "display_data" @@ -1389,10 +1401,14 @@ } ], "source": [ - "negative_electrode_exchange_current_density = param[\"Negative electrode exchange-current density [A.m-2]\"]\n", - "x = pybamm.linspace(3000,6000,100)\n", + "negative_electrode_exchange_current_density = param[\n", + " \"Negative electrode exchange-current density [A.m-2]\"\n", + "]\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 = param.evaluate(\n", + " negative_electrode_exchange_current_density(1000, x, c_n_max, 300)\n", + ")\n", "evaluated = pybamm.Array(evaluated)\n", "pybamm.plot(x, evaluated)" ] @@ -1419,12 +1435,12 @@ "outputs": [ { "data": { - "text/plain": "interactive(children=(FloatSlider(value=0.0, description='t', max=3599.0, step=35.99), Output()), _dom_classes…", "application/vnd.jupyter.widget-view+json": { + "model_id": "e3e2a10c3de140de8cc785ae5421b534", "version_major": 2, - "version_minor": 0, - "model_id": "e3e2a10c3de140de8cc785ae5421b534" - } + "version_minor": 0 + }, + "text/plain": "interactive(children=(FloatSlider(value=0.0, description='t', max=3599.0, step=35.99), Output()), _dom_classes…" }, "metadata": {}, "output_type": "display_data" diff --git a/docs/source/examples/notebooks/plotting/customize-quick-plot.ipynb b/docs/source/examples/notebooks/plotting/customize-quick-plot.ipynb index d7a5fda2da..67b81b4ae6 100644 --- a/docs/source/examples/notebooks/plotting/customize-quick-plot.ipynb +++ b/docs/source/examples/notebooks/plotting/customize-quick-plot.ipynb @@ -164,6 +164,7 @@ ], "source": [ "import matplotlib.pyplot as plt\n", + "\n", "plt.style.available" ] }, @@ -279,10 +280,10 @@ "\n", "mpl.rcParams[\"axes.labelsize\"] = 12\n", "mpl.rcParams[\"axes.titlesize\"] = 12\n", - "mpl.rcParams[\"xtick.labelsize\"] = 12\n", - "mpl.rcParams[\"ytick.labelsize\"] = 12\n", - "mpl.rcParams[\"legend.fontsize\"] = 12\n", - "mpl.rcParams[\"axes.prop_cycle\"] = cycler('color', [\"k\", \"g\", \"c\"])\n", + "mpl.rcParams[\"xtick.labelsize\"] = 12\n", + "mpl.rcParams[\"ytick.labelsize\"] = 12\n", + "mpl.rcParams[\"legend.fontsize\"] = 12\n", + "mpl.rcParams[\"axes.prop_cycle\"] = cycler(\"color\", [\"k\", \"g\", \"c\"])\n", "pybamm.dynamic_plot(sims)" ] }, @@ -326,8 +327,8 @@ "source": [ "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 = pybamm.QuickPlot(sims, figsize=(14, 7))\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/simulations_and_experiments/callbacks.ipynb b/docs/source/examples/notebooks/simulations_and_experiments/callbacks.ipynb index 366d99c1f8..f6fb7609ae 100644 --- a/docs/source/examples/notebooks/simulations_and_experiments/callbacks.ipynb +++ b/docs/source/examples/notebooks/simulations_and_experiments/callbacks.ipynb @@ -52,7 +52,8 @@ "import pybamm\n", "\n", "model = pybamm.lithium_ion.DFN()\n", - "experiment = pybamm.Experiment([\n", + "experiment = pybamm.Experiment(\n", + " [\n", " (\n", " \"Discharge at C/5 for 10 hours or until 3.3 V\",\n", " \"Charge at 1 A until 4.1 V\",\n", @@ -156,9 +157,10 @@ "# Read the file that has been written, which was saved to callback.logfile\n", "with open(callback.logfile) as f:\n", " print(f.read())\n", - " \n", + "\n", "# Remove the log file\n", "import os\n", + "\n", "os.remove(callback.logfile)" ] }, diff --git a/docs/source/examples/notebooks/simulations_and_experiments/custom-experiments.ipynb b/docs/source/examples/notebooks/simulations_and_experiments/custom-experiments.ipynb index 888c002c31..4dfa8c8c72 100644 --- a/docs/source/examples/notebooks/simulations_and_experiments/custom-experiments.ipynb +++ b/docs/source/examples/notebooks/simulations_and_experiments/custom-experiments.ipynb @@ -77,6 +77,7 @@ "def anode_potential_cutoff(variables):\n", " return variables[\"Anode potential [V]\"] - 0.02\n", "\n", + "\n", "# The CustomTermination class takes a name and function\n", "anode_potential_termination = pybamm.step.CustomTermination(\n", " name=\"Anode potential cut-off [V]\", event_function=anode_potential_cutoff\n", @@ -103,7 +104,7 @@ "sim = pybamm.Simulation(model, parameter_values=parameter_values, experiment=experiment)\n", "\n", "# for a charge we start as SOC 0\n", - "sim.solve(initial_soc=0)\n" + "sim.solve(initial_soc=0)" ] }, { @@ -133,7 +134,6 @@ } ], "source": [ - "\n", "# Plot\n", "plot = pybamm.QuickPlot(\n", " sim.solution,\n", @@ -141,13 +141,13 @@ " \"Current [A]\",\n", " \"Voltage [V]\",\n", " \"Anode potential [V]\",\n", - " ]\n", + " ],\n", ")\n", "plot.plot(0)\n", "\n", "# Plot the limits used in the termination events to check they are not surpassed\n", "plot.axes.by_variable(\"Voltage [V]\").axhline(4.2, color=\"k\", linestyle=\":\")\n", - "plot.axes.by_variable(\"Anode potential [V]\").axhline(0.02, color=\"k\", linestyle=\":\")\n" + "plot.axes.by_variable(\"Anode potential [V]\").axhline(0.02, color=\"k\", linestyle=\":\")" ] }, { diff --git a/docs/source/examples/notebooks/simulations_and_experiments/experiments-start-time.ipynb b/docs/source/examples/notebooks/simulations_and_experiments/experiments-start-time.ipynb index 4af1bd6201..60699ab6b2 100644 --- a/docs/source/examples/notebooks/simulations_and_experiments/experiments-start-time.ipynb +++ b/docs/source/examples/notebooks/simulations_and_experiments/experiments-start-time.ipynb @@ -101,7 +101,9 @@ } ], "source": [ - "experiment = pybamm.Experiment([\"Discharge at 1C for 20 minutes\", \"Charge at C/3 for 10 minutes\"])\n", + "experiment = pybamm.Experiment(\n", + " [\"Discharge at 1C for 20 minutes\", \"Charge at C/3 for 10 minutes\"]\n", + ")\n", "sim = pybamm.Simulation(model, experiment=experiment)\n", "sim.solve()\n", "sim.plot()" diff --git a/docs/source/examples/notebooks/simulations_and_experiments/rpt-experiment.ipynb b/docs/source/examples/notebooks/simulations_and_experiments/rpt-experiment.ipynb index cbe07e3a55..fe06dadffe 100644 --- a/docs/source/examples/notebooks/simulations_and_experiments/rpt-experiment.ipynb +++ b/docs/source/examples/notebooks/simulations_and_experiments/rpt-experiment.ipynb @@ -37,7 +37,7 @@ "import matplotlib.pyplot as plt\n", "import os\n", "\n", - "os.chdir(pybamm.__path__[0]+'/..')" + "os.chdir(pybamm.__path__[0] + \"/..\")" ] }, { @@ -79,21 +79,26 @@ "outputs": [], "source": [ "N = 10\n", - "cccv_experiment = pybamm.Experiment([\n", - " (\"Charge at 1C until 4.2V\", \n", - " \"Hold at 4.2V until C/50\",\n", - " \"Discharge at 1C until 3V\",\n", - " \"Rest for 1 hour\",\n", - " )\n", - "] * N)\n", - "charge_experiment = pybamm.Experiment([\n", - " (\"Charge at 1C until 4.2V\", \n", - " \"Hold at 4.2V until C/50\",\n", - " )\n", - "])\n", - "rpt_experiment = pybamm.Experiment([\n", - " (\"Discharge at C/3 until 3V\",)\n", - "])" + "cccv_experiment = pybamm.Experiment(\n", + " [\n", + " (\n", + " \"Charge at 1C until 4.2V\",\n", + " \"Hold at 4.2V until C/50\",\n", + " \"Discharge at 1C until 3V\",\n", + " \"Rest for 1 hour\",\n", + " )\n", + " ]\n", + " * N\n", + ")\n", + "charge_experiment = pybamm.Experiment(\n", + " [\n", + " (\n", + " \"Charge at 1C until 4.2V\",\n", + " \"Hold at 4.2V until C/50\",\n", + " )\n", + " ]\n", + ")\n", + "rpt_experiment = pybamm.Experiment([(\"Discharge at C/3 until 3V\",)])" ] }, { @@ -111,11 +116,17 @@ "metadata": {}, "outputs": [], "source": [ - "sim = pybamm.Simulation(model, experiment=cccv_experiment, parameter_values=parameter_values)\n", + "sim = pybamm.Simulation(\n", + " model, experiment=cccv_experiment, parameter_values=parameter_values\n", + ")\n", "cccv_sol = sim.solve()\n", - "sim = pybamm.Simulation(model, experiment=charge_experiment, parameter_values=parameter_values)\n", + "sim = pybamm.Simulation(\n", + " model, experiment=charge_experiment, parameter_values=parameter_values\n", + ")\n", "charge_sol = sim.solve(starting_solution=cccv_sol)\n", - "sim = pybamm.Simulation(model, experiment=rpt_experiment, parameter_values=parameter_values)\n", + "sim = pybamm.Simulation(\n", + " model, experiment=rpt_experiment, parameter_values=parameter_values\n", + ")\n", "rpt_sol = sim.solve(starting_solution=charge_sol)" ] }, @@ -214,11 +225,17 @@ "M = 5\n", "for i in range(M):\n", " if i != 0: # skip the first set of ageing cycles because it's already been done\n", - " sim = pybamm.Simulation(model, experiment=cccv_experiment, parameter_values=parameter_values)\n", + " sim = pybamm.Simulation(\n", + " model, experiment=cccv_experiment, parameter_values=parameter_values\n", + " )\n", " cccv_sol = sim.solve(starting_solution=rpt_sol)\n", - " sim = pybamm.Simulation(model, experiment=charge_experiment, parameter_values=parameter_values)\n", + " sim = pybamm.Simulation(\n", + " model, experiment=charge_experiment, parameter_values=parameter_values\n", + " )\n", " charge_sol = sim.solve(starting_solution=cccv_sol)\n", - " sim = pybamm.Simulation(model, experiment=rpt_experiment, parameter_values=parameter_values)\n", + " sim = pybamm.Simulation(\n", + " model, experiment=rpt_experiment, parameter_values=parameter_values\n", + " )\n", " rpt_sol = sim.solve(starting_solution=charge_sol)\n", " cccv_sols.append(cccv_sol)\n", " charge_sols.append(charge_sol)\n", @@ -310,18 +327,30 @@ "cccv_capacities = []\n", "rpt_cycles = []\n", "rpt_capacities = []\n", - "for i in range (M):\n", + "for i in range(M):\n", " for j in range(N):\n", - " cccv_cycles.append(i*(N+2)+j+1)\n", - " start_capacity = rpt_sol.cycles[i*(N+2)+j].steps[2][\"Discharge capacity [A.h]\"].entries[0]\n", - " end_capacity = rpt_sol.cycles[i*(N+2)+j].steps[2][\"Discharge capacity [A.h]\"].entries[-1]\n", - " cccv_capacities.append(end_capacity-start_capacity)\n", - " rpt_cycles.append((i+1)*(N+2))\n", - " start_capacity = rpt_sol.cycles[(i+1)*(N+2)-1][\"Discharge capacity [A.h]\"].entries[0]\n", - " end_capacity = rpt_sol.cycles[(i+1)*(N+2)-1][\"Discharge capacity [A.h]\"].entries[-1]\n", - " rpt_capacities.append(end_capacity-start_capacity)\n", - "plt.scatter(cccv_cycles,cccv_capacities,label=\"Ageing cycles\")\n", - "plt.scatter(rpt_cycles,rpt_capacities,label=\"RPT cycles\")\n", + " cccv_cycles.append(i * (N + 2) + j + 1)\n", + " start_capacity = (\n", + " rpt_sol.cycles[i * (N + 2) + j]\n", + " .steps[2][\"Discharge capacity [A.h]\"]\n", + " .entries[0]\n", + " )\n", + " end_capacity = (\n", + " rpt_sol.cycles[i * (N + 2) + j]\n", + " .steps[2][\"Discharge capacity [A.h]\"]\n", + " .entries[-1]\n", + " )\n", + " cccv_capacities.append(end_capacity - start_capacity)\n", + " rpt_cycles.append((i + 1) * (N + 2))\n", + " start_capacity = rpt_sol.cycles[(i + 1) * (N + 2) - 1][\n", + " \"Discharge capacity [A.h]\"\n", + " ].entries[0]\n", + " end_capacity = rpt_sol.cycles[(i + 1) * (N + 2) - 1][\n", + " \"Discharge capacity [A.h]\"\n", + " ].entries[-1]\n", + " rpt_capacities.append(end_capacity - start_capacity)\n", + "plt.scatter(cccv_cycles, cccv_capacities, label=\"Ageing cycles\")\n", + "plt.scatter(rpt_cycles, rpt_capacities, label=\"RPT cycles\")\n", "plt.xlabel(\"Cycle number\")\n", "plt.ylabel(\"Discharge capacity [A.h]\")\n", "plt.legend()" diff --git a/docs/source/examples/notebooks/simulations_and_experiments/simulating-long-experiments.ipynb b/docs/source/examples/notebooks/simulations_and_experiments/simulating-long-experiments.ipynb index 890107e421..c7f1f0e634 100644 --- a/docs/source/examples/notebooks/simulations_and_experiments/simulating-long-experiments.ipynb +++ b/docs/source/examples/notebooks/simulations_and_experiments/simulating-long-experiments.ipynb @@ -119,13 +119,16 @@ "source": [ "pybamm.set_logging_level(\"NOTICE\")\n", "\n", - "experiment = pybamm.Experiment([\n", - " (\"Discharge at 1C until 3V\",\n", - " \"Rest for 1 hour\",\n", - " \"Charge at 1C until 4.2V\", \n", - " \"Hold at 4.2V until C/50\"\n", - " )\n", - "])\n", + "experiment = pybamm.Experiment(\n", + " [\n", + " (\n", + " \"Discharge at 1C until 3V\",\n", + " \"Rest for 1 hour\",\n", + " \"Charge at 1C until 4.2V\",\n", + " \"Hold at 4.2V until C/50\",\n", + " )\n", + " ]\n", + ")\n", "sim = pybamm.Simulation(spm, experiment=experiment, parameter_values=parameter_values)\n", "sol = sim.solve()" ] @@ -458,13 +461,17 @@ } ], "source": [ - "experiment = pybamm.Experiment([\n", - " (\"Discharge at 1C until 3V\",\n", - " \"Rest for 1 hour\",\n", - " \"Charge at 1C until 4.2V\", \n", - " \"Hold at 4.2V until C/50\")\n", - "] * 500,\n", - "termination=\"80% capacity\"\n", + "experiment = pybamm.Experiment(\n", + " [\n", + " (\n", + " \"Discharge at 1C until 3V\",\n", + " \"Rest for 1 hour\",\n", + " \"Charge at 1C until 4.2V\",\n", + " \"Hold at 4.2V until C/50\",\n", + " )\n", + " ]\n", + " * 500,\n", + " termination=\"80% capacity\",\n", ")\n", "sim = pybamm.Simulation(spm, experiment=experiment, parameter_values=parameter_values)\n", "sol = sim.solve()" @@ -1389,7 +1396,7 @@ "# With integer\n", "sol_int = sim.solve(save_at_cycles=5)\n", "# With list\n", - "sol_list = sim.solve(save_at_cycles=[30,45,55])" + "sol_list = sim.solve(save_at_cycles=[30, 45, 55])" ] }, { @@ -1573,7 +1580,7 @@ } ], "source": [ - "sol_list.cycles[44].plot([\"Current [A]\",\"Voltage [V]\"])" + "sol_list.cycles[44].plot([\"Current [A]\", \"Voltage [V]\"])" ] }, { @@ -1594,7 +1601,7 @@ } ], "source": [ - "fig, ax = plt.subplots(1,2,figsize=(10,5))\n", + "fig, ax = plt.subplots(1, 2, figsize=(10, 5))\n", "for cycle in sol_int.cycles:\n", " if cycle is not None:\n", " t = cycle[\"Time [h]\"].data - cycle[\"Time [h]\"].data[0]\n", @@ -1747,13 +1754,17 @@ } ], "source": [ - "experiment = pybamm.Experiment([\n", - " (\"Discharge at 1C until 3V\",\n", - " \"Rest for 1 hour\",\n", - " \"Charge at 1C until 4.2V\", \n", - " \"Hold at 4.2V until C/50\")\n", - "] * 10,\n", - "termination=\"80% capacity\"\n", + "experiment = pybamm.Experiment(\n", + " [\n", + " (\n", + " \"Discharge at 1C until 3V\",\n", + " \"Rest for 1 hour\",\n", + " \"Charge at 1C until 4.2V\",\n", + " \"Hold at 4.2V until C/50\",\n", + " )\n", + " ]\n", + " * 10,\n", + " termination=\"80% capacity\",\n", ")\n", "sim = pybamm.Simulation(spm, experiment=experiment, parameter_values=parameter_values)\n", "sol = sim.solve()" diff --git a/docs/source/examples/notebooks/simulations_and_experiments/simulation-class.ipynb b/docs/source/examples/notebooks/simulations_and_experiments/simulation-class.ipynb index df82fa8175..db2a0fb20d 100644 --- a/docs/source/examples/notebooks/simulations_and_experiments/simulation-class.ipynb +++ b/docs/source/examples/notebooks/simulations_and_experiments/simulation-class.ipynb @@ -134,7 +134,9 @@ "source": [ "# using less number of images in the example\n", "# for a smoother GIF use more images\n", - "simulation.create_gif(number_of_images=5, duration=0.2, output_filename=\"simulation.gif\")" + "simulation.create_gif(\n", + " number_of_images=5, duration=0.2, output_filename=\"simulation.gif\"\n", + ")" ] }, { diff --git a/docs/source/examples/notebooks/solution-data-and-processed-variables.ipynb b/docs/source/examples/notebooks/solution-data-and-processed-variables.ipynb index 2849fca58c..3379b05991 100644 --- a/docs/source/examples/notebooks/solution-data-and-processed-variables.ipynb +++ b/docs/source/examples/notebooks/solution-data-and-processed-variables.ipynb @@ -47,7 +47,8 @@ "import numpy as np\n", "import os\n", "import matplotlib.pyplot as plt\n", - "os.chdir(pybamm.__path__[0]+'/..')\n", + "\n", + "os.chdir(pybamm.__path__[0] + \"/..\")\n", "\n", "# load model\n", "model = pybamm.lithium_ion.SPMe()\n", @@ -106,7 +107,7 @@ } ], "source": [ - "solution.data['Negative particle surface concentration [mol.m-3]'].shape" + "solution.data[\"Negative particle surface concentration [mol.m-3]\"].shape" ] }, { @@ -210,7 +211,7 @@ } ], "source": [ - "solution['Time [h]']" + "solution[\"Time [h]\"]" ] }, { @@ -268,7 +269,7 @@ } ], "source": [ - "solution['Time [h]'].entries" + "solution[\"Time [h]\"].entries" ] }, { @@ -284,7 +285,7 @@ "metadata": {}, "outputs": [], "source": [ - "time_in_seconds = np.array([0, 600, 900, 1700, 3000 ])" + "time_in_seconds = np.array([0, 600, 900, 1700, 3000])" ] }, { @@ -304,7 +305,7 @@ } ], "source": [ - "solution['Time [h]'](time_in_seconds)" + "solution[\"Time [h]\"](time_in_seconds)" ] }, { @@ -331,7 +332,7 @@ } ], "source": [ - "var = 'X-averaged negative electrode temperature [K]'\n", + "var = \"X-averaged negative electrode temperature [K]\"\n", "solution[var](time_in_seconds)" ] }, @@ -359,17 +360,21 @@ "source": [ "# to a pickle file (default)\n", "solution.save_data(\n", - " \"outputs.pickle\", [\"Time [h]\", \"Current [A]\", \"Voltage [V]\", \"Electrolyte concentration [mol.m-3]\"]\n", + " \"outputs.pickle\",\n", + " [\"Time [h]\", \"Current [A]\", \"Voltage [V]\", \"Electrolyte concentration [mol.m-3]\"],\n", ")\n", "# to a matlab file\n", "# need to give variable names without space\n", "solution.save_data(\n", - " \"outputs.mat\", \n", - " [\"Time [h]\", \"Current [A]\", \"Voltage [V]\", \"Electrolyte concentration [mol.m-3]\"], \n", + " \"outputs.mat\",\n", + " [\"Time [h]\", \"Current [A]\", \"Voltage [V]\", \"Electrolyte concentration [mol.m-3]\"],\n", " to_format=\"matlab\",\n", " short_names={\n", - " \"Time [h]\": \"t\", \"Current [A]\": \"I\", \"Voltage [V]\": \"V\", \"Electrolyte concentration [mol.m-3]\": \"c_e\",\n", - " }\n", + " \"Time [h]\": \"t\",\n", + " \"Current [A]\": \"I\",\n", + " \"Voltage [V]\": \"V\",\n", + " \"Electrolyte concentration [mol.m-3]\": \"c_e\",\n", + " },\n", ")\n", "# to a csv file (time-dependent outputs only, no spatial dependence allowed)\n", "solution.save_data(\n", @@ -430,7 +435,7 @@ "step_simulation = pybamm.Simulation(model)\n", "while time < end_time:\n", " step_solution = step_simulation.step(dt)\n", - " print('Time', time)\n", + " print(\"Time\", time)\n", " print(step_solution[\"Voltage [V]\"].entries)\n", " time += dt" ] @@ -483,25 +488,25 @@ }, { "cell_type": "markdown", - "source": [ - "As a final step, we will clean up the output files created by this notebook:" - ], "metadata": { "collapsed": false - } + }, + "source": [ + "As a final step, we will clean up the output files created by this notebook:" + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "os.remove(\"outputs.csv\")\n", "os.remove(\"outputs.mat\")\n", "os.remove(\"outputs.pickle\")" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", diff --git a/docs/source/examples/notebooks/solvers/dae-solver.ipynb b/docs/source/examples/notebooks/solvers/dae-solver.ipynb index 324d500df3..cb8293c676 100644 --- a/docs/source/examples/notebooks/solvers/dae-solver.ipynb +++ b/docs/source/examples/notebooks/solvers/dae-solver.ipynb @@ -30,7 +30,8 @@ "import numpy as np\n", "import os\n", "import matplotlib.pyplot as plt\n", - "os.chdir(pybamm.__path__[0]+'/..')" + "\n", + "os.chdir(pybamm.__path__[0] + \"/..\")" ] }, { @@ -57,8 +58,8 @@ "model = pybamm.BaseModel()\n", "u = pybamm.Variable(\"u\")\n", "v = pybamm.Variable(\"v\")\n", - "model.rhs = {u: -v} # du/dt = -v\n", - "model.algebraic = {v: 2 * u - v} # 2*v = u\n", + "model.rhs = {u: -v} # du/dt = -v\n", + "model.algebraic = {v: 2 * u - v} # 2*v = u\n", "model.initial_conditions = {u: 1, v: 2}\n", "model.variables = {\"u\": u, \"v\": v}\n", "\n", @@ -103,9 +104,9 @@ "v = solution[\"v\"]\n", "\n", "# Plot\n", - "t_fine = np.linspace(0,t_eval[-1],1000)\n", + "t_fine = np.linspace(0, t_eval[-1], 1000)\n", "\n", - "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13,4))\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13, 4))\n", "ax1.plot(t_fine, np.exp(-2 * t_fine), t_sol, u(t_sol), \"o\")\n", "ax1.set_xlabel(\"t\")\n", "ax1.legend([\"exp(-2*t)\", \"u\"], loc=\"best\")\n", @@ -182,10 +183,10 @@ "model = pybamm.BaseModel()\n", "u = pybamm.Variable(\"u\")\n", "v = pybamm.Variable(\"v\")\n", - "model.rhs = {u: -v} # du/dt = -v\n", - "model.algebraic = {v: 2 * u - v} # 2*v = u\n", + "model.rhs = {u: -v} # du/dt = -v\n", + "model.algebraic = {v: 2 * u - v} # 2*v = u\n", "model.initial_conditions = {u: 1, v: 2}\n", - "model.events.append(pybamm.Event('v=0.2', v - 0.2)) # adding event here\n", + "model.events.append(pybamm.Event(\"v=0.2\", v - 0.2)) # adding event here\n", "\n", "model.variables = {\"u\": u, \"v\": v}\n", "\n", @@ -205,14 +206,23 @@ "v = solution[\"v\"]\n", "\n", "# Plot\n", - "t_fine = np.linspace(0,t_eval[-1],1000)\n", + "t_fine = np.linspace(0, t_eval[-1], 1000)\n", "\n", - "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13,4))\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13, 4))\n", "ax1.plot(t_fine, np.exp(-2 * t_fine), t_sol, u(t_sol), \"o\")\n", "ax1.set_xlabel(\"t\")\n", "ax1.legend([\"exp(-2*t)\", \"u\"], loc=\"best\")\n", "\n", - "ax2.plot(t_fine, 2 * np.exp(-2 * t_fine), t_sol, v(t_sol), \"o\", t_fine, 0.2 * np.ones_like(t_fine), \"k\")\n", + "ax2.plot(\n", + " t_fine,\n", + " 2 * np.exp(-2 * t_fine),\n", + " t_sol,\n", + " v(t_sol),\n", + " \"o\",\n", + " t_fine,\n", + " 0.2 * np.ones_like(t_fine),\n", + " \"k\",\n", + ")\n", "ax2.set_xlabel(\"t\")\n", "ax2.legend([\"2*exp(-2*t)\", \"v\", \"v = 0.2\"], loc=\"best\")\n", "\n", @@ -275,10 +285,10 @@ "model = pybamm.BaseModel()\n", "u = pybamm.Variable(\"u\")\n", "v = pybamm.Variable(\"v\")\n", - "model.rhs = {u: -v} # du/dt = -v\n", - "model.algebraic = {v: 2 * u - v} # 2*v = u\n", - "model.initial_conditions = {u: 1, v: 1} # bad initial conditions, solver fixes\n", - "model.events.append(pybamm.Event('v=0.2', v - 0.2))\n", + "model.rhs = {u: -v} # du/dt = -v\n", + "model.algebraic = {v: 2 * u - v} # 2*v = u\n", + "model.initial_conditions = {u: 1, v: 1} # bad initial conditions, solver fixes\n", + "model.events.append(pybamm.Event(\"v=0.2\", v - 0.2))\n", "model.variables = {\"u\": u, \"v\": v}\n", "\n", "# Discretise using default discretisation\n", diff --git a/docs/source/examples/notebooks/solvers/ode-solver.ipynb b/docs/source/examples/notebooks/solvers/ode-solver.ipynb index 992dae5980..156b7b2ada 100644 --- a/docs/source/examples/notebooks/solvers/ode-solver.ipynb +++ b/docs/source/examples/notebooks/solvers/ode-solver.ipynb @@ -30,7 +30,8 @@ "import numpy as np\n", "import os\n", "import matplotlib.pyplot as plt\n", - "os.chdir(pybamm.__path__[0]+'/..')" + "\n", + "os.chdir(pybamm.__path__[0] + \"/..\")" ] }, { @@ -103,9 +104,9 @@ "v = solution[\"v\"]\n", "\n", "# Plot\n", - "t_fine = np.linspace(0,t_eval[-1],1000)\n", + "t_fine = np.linspace(0, t_eval[-1], 1000)\n", "\n", - "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13,4))\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13, 4))\n", "ax1.plot(t_fine, 2 * np.cos(t_fine) - np.sin(t_fine), t_sol, u(t_sol), \"o\")\n", "ax1.set_xlabel(\"t\")\n", "ax1.legend([\"2*cos(t) - sin(t)\", \"u\"], loc=\"best\")\n", @@ -184,7 +185,7 @@ "v = pybamm.Variable(\"v\")\n", "model.rhs = {u: -v, v: u}\n", "model.initial_conditions = {u: 2, v: 1}\n", - "model.events.append(pybamm.Event('v=-2', v + 2)) # New termination event\n", + "model.events.append(pybamm.Event(\"v=-2\", v + 2)) # New termination event\n", "model.variables = {\"u\": u, \"v\": v}\n", "\n", "# Discretise using default discretisation\n", @@ -203,14 +204,23 @@ "v = solution[\"v\"]\n", "\n", "# Plot\n", - "t_fine = np.linspace(0,t_eval[-1],1000)\n", + "t_fine = np.linspace(0, t_eval[-1], 1000)\n", "\n", - "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13,4))\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13, 4))\n", "ax1.plot(t_fine, 2 * np.cos(t_fine) - np.sin(t_fine), t_sol, u(t_sol), \"o\")\n", "ax1.set_xlabel(\"t\")\n", "ax1.legend([\"2*cos(t) - sin(t)\", \"u\"], loc=\"best\")\n", "\n", - "ax2.plot(t_fine, 2 * np.sin(t_fine) + np.cos(t_fine), t_sol, v(t_sol), \"o\", t_fine, -2 * np.ones_like(t_fine), \"k\")\n", + "ax2.plot(\n", + " t_fine,\n", + " 2 * np.sin(t_fine) + np.cos(t_fine),\n", + " t_sol,\n", + " v(t_sol),\n", + " \"o\",\n", + " t_fine,\n", + " -2 * np.ones_like(t_fine),\n", + " \"k\",\n", + ")\n", "ax2.set_xlabel(\"t\")\n", "ax2.legend([\"2*sin(t) + cos(t)\", \"v\", \"v = -2\"], loc=\"best\")\n", "\n", diff --git a/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb b/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb index 80dc0ddb88..c49c8926fb 100644 --- a/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb +++ b/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb @@ -98,7 +98,9 @@ "sim = pybamm.Simulation(model, parameter_values=param)\n", "\n", "# Set up solvers. Reduce max_num_steps for the fast solver, for faster errors\n", - "fast_solver = pybamm.CasadiSolver(mode=\"fast\", extra_options_setup={\"max_num_steps\": 1000})\n", + "fast_solver = pybamm.CasadiSolver(\n", + " mode=\"fast\", extra_options_setup={\"max_num_steps\": 1000}\n", + ")\n", "safe_solver = pybamm.CasadiSolver(mode=\"safe\")" ] }, @@ -134,8 +136,8 @@ } ], "source": [ - "safe_sol = sim.solve([0,3700], solver=safe_solver, inputs={\"Crate\": 1})\n", - "fast_sol = sim.solve([0,3700], solver=fast_solver, inputs={\"Crate\": 1})\n", + "safe_sol = sim.solve([0, 3700], solver=safe_solver, inputs={\"Crate\": 1})\n", + "fast_sol = sim.solve([0, 3700], solver=fast_solver, inputs={\"Crate\": 1})\n", "\n", "timer = pybamm.Timer()\n", "print(\"Safe:\", safe_sol.solve_time)\n", @@ -144,7 +146,12 @@ "cutoff = param[\"Lower voltage cut-off [V]\"]\n", "plt.plot(fast_sol[\"Time [h]\"].data, fast_sol[\"Voltage [V]\"].data, \"b-\", label=\"Fast\")\n", "plt.plot(safe_sol[\"Time [h]\"].data, safe_sol[\"Voltage [V]\"].data, \"r-\", label=\"Safe\")\n", - "plt.plot(fast_sol[\"Time [h]\"].data, cutoff * np.ones_like(fast_sol[\"Time [h]\"].data), \"k--\", label=\"Voltage cut-off\")\n", + "plt.plot(\n", + " fast_sol[\"Time [h]\"].data,\n", + " cutoff * np.ones_like(fast_sol[\"Time [h]\"].data),\n", + " \"k--\",\n", + " label=\"Voltage cut-off\",\n", + ")\n", "plt.legend();" ] }, @@ -196,16 +203,21 @@ } ], "source": [ - "safe_sol = sim.solve([0,4500], solver=safe_solver, inputs={\"Crate\": 1})\n", + "safe_sol = sim.solve([0, 4500], solver=safe_solver, inputs={\"Crate\": 1})\n", "\n", "print(\"Safe:\", safe_sol.solve_time)\n", "\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.plot(\n", + " safe_sol[\"Time [h]\"].data,\n", + " cutoff * np.ones_like(safe_sol[\"Time [h]\"].data),\n", + " \"k--\",\n", + " label=\"Voltage cut-off\",\n", + ")\n", "plt.legend()\n", "\n", "try:\n", - " sim.solve([0,4500], solver=fast_solver, inputs={\"Crate\": 1})\n", + " sim.solve([0, 4500], solver=fast_solver, inputs={\"Crate\": 1})\n", "except pybamm.SolverError as e:\n", " print(\"Solving fast mode, error occurred:\", e.args[0])" ] @@ -238,13 +250,17 @@ } ], "source": [ - "fast_sol = sim.solve([0,4049], solver=fast_solver, inputs={\"Crate\": 1})\n", - "fast_sol.plot([\n", - " \"Minimum negative particle surface concentration\",\n", - " \"Electrolyte concentration [mol.m-3]\",\n", - " \"Maximum positive particle surface concentration\",\n", - " \"Voltage [V]\",\n", - "], time_unit=\"seconds\", figsize=(9,9));" + "fast_sol = sim.solve([0, 4049], solver=fast_solver, inputs={\"Crate\": 1})\n", + "fast_sol.plot(\n", + " [\n", + " \"Minimum negative particle surface concentration\",\n", + " \"Electrolyte concentration [mol.m-3]\",\n", + " \"Maximum positive particle surface concentration\",\n", + " \"Voltage [V]\",\n", + " ],\n", + " time_unit=\"seconds\",\n", + " figsize=(9, 9),\n", + ");" ] }, { @@ -285,9 +301,16 @@ } ], "source": [ - "safe_sol_160 = sim.solve([0,160], solver=safe_solver, inputs={\"Crate\": 10})\n", - "plt.plot(safe_sol_160[\"Time [h]\"].data, safe_sol_160[\"Voltage [V]\"].data, \"r-\", label=\"Safe\")\n", - "plt.plot(safe_sol_160[\"Time [h]\"].data, cutoff * np.ones_like(safe_sol_160[\"Time [h]\"].data), \"k--\", label=\"Voltage cut-off\")\n", + "safe_sol_160 = sim.solve([0, 160], solver=safe_solver, inputs={\"Crate\": 10})\n", + "plt.plot(\n", + " safe_sol_160[\"Time [h]\"].data, safe_sol_160[\"Voltage [V]\"].data, \"r-\", label=\"Safe\"\n", + ")\n", + "plt.plot(\n", + " safe_sol_160[\"Time [h]\"].data,\n", + " cutoff * np.ones_like(safe_sol_160[\"Time [h]\"].data),\n", + " \"k--\",\n", + " label=\"Voltage cut-off\",\n", + ")\n", "plt.legend();" ] }, @@ -315,10 +338,25 @@ } ], "source": [ - "safe_sol_150 = sim.solve([0,150], solver=safe_solver, inputs={\"Crate\": 10})\n", - "plt.plot(safe_sol_150[\"Time [h]\"].data, safe_sol_150[\"Voltage [V]\"].data, \"r-\", label=\"Safe [0,150]\")\n", - "plt.plot(safe_sol_160[\"Time [h]\"].data, safe_sol_160[\"Voltage [V]\"].data, \"b.\", label=\"Safe [0,160]\")\n", - "plt.plot(safe_sol_150[\"Time [h]\"].data, cutoff * np.ones_like(safe_sol_150[\"Time [h]\"].data), \"k--\", label=\"Voltage cut-off\")\n", + "safe_sol_150 = sim.solve([0, 150], solver=safe_solver, inputs={\"Crate\": 10})\n", + "plt.plot(\n", + " safe_sol_150[\"Time [h]\"].data,\n", + " safe_sol_150[\"Voltage [V]\"].data,\n", + " \"r-\",\n", + " label=\"Safe [0,150]\",\n", + ")\n", + "plt.plot(\n", + " safe_sol_160[\"Time [h]\"].data,\n", + " safe_sol_160[\"Voltage [V]\"].data,\n", + " \"b.\",\n", + " label=\"Safe [0,160]\",\n", + ")\n", + "plt.plot(\n", + " safe_sol_150[\"Time [h]\"].data,\n", + " cutoff * np.ones_like(safe_sol_150[\"Time [h]\"].data),\n", + " \"k--\",\n", + " label=\"Voltage cut-off\",\n", + ")\n", "plt.legend();" ] }, @@ -329,7 +367,7 @@ "outputs": [], "source": [ "safe_solver_2 = pybamm.CasadiSolver(mode=\"safe\", dt_max=30)\n", - "safe_sol_2 = sim.solve([0,160], solver=safe_solver_2, inputs={\"Crate\": 10})" + "safe_sol_2 = sim.solve([0, 160], solver=safe_solver_2, inputs={\"Crate\": 10})" ] }, { @@ -365,18 +403,22 @@ } ], "source": [ - "for dt_max in [10,20,100,1000,3700]:\n", + "for dt_max in [10, 20, 100, 1000, 3700]:\n", " safe_sol = sim.solve(\n", - " [0,3600], \n", + " [0, 3600],\n", " solver=pybamm.CasadiSolver(mode=\"safe\", dt_max=dt_max),\n", - " inputs={\"Crate\": 1}\n", + " inputs={\"Crate\": 1},\n", + " )\n", + " print(\n", + " f\"With dt_max={dt_max}, took {safe_sol.solve_time} \"\n", + " + f\"(integration time: {safe_sol.integration_time})\"\n", " )\n", - " print(f\"With dt_max={dt_max}, took {safe_sol.solve_time} \"+\n", - " f\"(integration time: {safe_sol.integration_time})\")\n", "\n", - "fast_sol = sim.solve([0,3600], solver=fast_solver, inputs={\"Crate\": 1})\n", - "print(f\"With 'fast' mode, took {fast_sol.solve_time} \"+\n", - " f\"(integration time: {fast_sol.integration_time})\")" + "fast_sol = sim.solve([0, 3600], solver=fast_solver, inputs={\"Crate\": 1})\n", + "print(\n", + " f\"With 'fast' mode, took {fast_sol.solve_time} \"\n", + " + f\"(integration time: {fast_sol.integration_time})\"\n", + ")" ] }, { @@ -429,15 +471,19 @@ } ], "source": [ - "for dt_max in [10,20,100,1000,3600]:\n", + "for dt_max in [10, 20, 100, 1000, 3600]:\n", " # Reduce max_num_steps to fail faster\n", " safe_sol = sim.solve(\n", - " [0,4500], \n", - " solver=pybamm.CasadiSolver(mode=\"safe\", dt_max=dt_max, extra_options_setup={\"max_num_steps\": 1000}),\n", - " inputs={\"Crate\": 1}\n", + " [0, 4500],\n", + " solver=pybamm.CasadiSolver(\n", + " mode=\"safe\", dt_max=dt_max, extra_options_setup={\"max_num_steps\": 1000}\n", + " ),\n", + " inputs={\"Crate\": 1},\n", " )\n", - " print(f\"With dt_max={dt_max}, took {safe_sol.solve_time} \"+\n", - " f\"(integration time: {safe_sol.integration_time})\")" + " print(\n", + " f\"With dt_max={dt_max}, took {safe_sol.solve_time} \"\n", + " + f\"(integration time: {safe_sol.integration_time})\"\n", + " )" ] }, { @@ -763,7 +809,7 @@ "print(f\"Exact maximum: {pybamm.maximum(x,y)}\")\n", "\n", "# Softplus\n", - "print(\"Softplus (k=10): \", pybamm.softplus(x,y,10))\n", + "print(\"Softplus (k=10): \", pybamm.softplus(x, y, 10))\n", "\n", "# Changing the setting to call softplus automatically\n", "pybamm.settings.min_max_mode = \"soft\"\n", @@ -804,9 +850,9 @@ "a = pybamm.InputParameter(\"a\")\n", "pybamm.settings.max_smoothing = 20\n", "# Both inputs are constant so uses exact maximum\n", - "print(\"Exact:\", pybamm.maximum(0.999,1).evaluate())\n", + "print(\"Exact:\", pybamm.maximum(0.999, 1).evaluate())\n", "# One input is not constant (InputParameter) so uses softplus\n", - "print(\"Softplus:\", pybamm.maximum(a,1).evaluate(inputs={\"a\": 0.999}))\n", + "print(\"Softplus:\", pybamm.maximum(a, 1).evaluate(inputs={\"a\": 0.999}))\n", "pybamm.settings.set_smoothing_parameters(\"exact\")" ] }, @@ -836,11 +882,29 @@ "source": [ "pts = pybamm.linspace(0, 2, 100)\n", "\n", - "fig, ax = plt.subplots(figsize=(10,5))\n", - "ax.plot(pts.evaluate(), pybamm.maximum(pts,1).evaluate(), lw=2, label=\"exact\")\n", - "ax.plot(pts.evaluate(), pybamm.softplus(pts,1,5).evaluate(), \":\", lw=2, label=\"softplus (k=5)\")\n", - "ax.plot(pts.evaluate(), pybamm.softplus(pts,1,10).evaluate(), \":\", lw=2, label=\"softplus (k=10)\")\n", - "ax.plot(pts.evaluate(), pybamm.softplus(pts,1,100).evaluate(), \":\", lw=2, label=\"softplus (k=100)\")\n", + "fig, ax = plt.subplots(figsize=(10, 5))\n", + "ax.plot(pts.evaluate(), pybamm.maximum(pts, 1).evaluate(), lw=2, label=\"exact\")\n", + "ax.plot(\n", + " pts.evaluate(),\n", + " pybamm.softplus(pts, 1, 5).evaluate(),\n", + " \":\",\n", + " lw=2,\n", + " label=\"softplus (k=5)\",\n", + ")\n", + "ax.plot(\n", + " pts.evaluate(),\n", + " pybamm.softplus(pts, 1, 10).evaluate(),\n", + " \":\",\n", + " lw=2,\n", + " label=\"softplus (k=10)\",\n", + ")\n", + "ax.plot(\n", + " pts.evaluate(),\n", + " pybamm.softplus(pts, 1, 100).evaluate(),\n", + " \":\",\n", + " lw=2,\n", + " label=\"softplus (k=100)\",\n", + ")\n", "ax.legend();" ] }, @@ -902,7 +966,7 @@ " exact_sol = solver.solve(model_exact, [0, 2])\n", " # Report integration time, which is the time spent actually doing the integration\n", " time += exact_sol.integration_time\n", - "print(\"Exact:\", time/100)\n", + "print(\"Exact:\", time / 100)\n", "sols = [exact_sol]\n", "\n", "ks = [5, 10, 100]\n", @@ -912,10 +976,12 @@ " for _ in range(100):\n", " sol = solver.solve(model_smooth, [0, 2], inputs={\"k\": k})\n", " time += sol.integration_time\n", - " print(f\"Soft, k={k}:\", time/100)\n", + " print(f\"Soft, k={k}:\", time / 100)\n", " sols.append(sol)\n", "\n", - "pybamm.dynamic_plot(sols, [\"x\", \"max(x,1)\"], labels=[\"exact\"] + [f\"soft (k={k})\" for k in ks]);" + "pybamm.dynamic_plot(\n", + " sols, [\"x\", \"max(x,1)\"], labels=[\"exact\"] + [f\"soft (k={k})\" for k in ks]\n", + ");" ] }, { @@ -962,7 +1028,7 @@ "print(f\"Exact maximum: {pybamm.maximum(x,y)}\")\n", "\n", "# Smooth plus can be called explicitly\n", - "print(\"Smooth plus (k=100): \", pybamm.smooth_max(x,y,100))\n", + "print(\"Smooth plus (k=100): \", pybamm.smooth_max(x, y, 100))\n", "\n", "# Smooth plus and smooth minus will be used when the mode is set to \"smooth\"\n", "pybamm.settings.min_max_mode = \"smooth\"\n", @@ -1004,11 +1070,29 @@ "source": [ "pts = pybamm.linspace(0, 2, 100)\n", "\n", - "fig, ax = plt.subplots(figsize=(10,5))\n", - "ax.plot(pts.evaluate(), pybamm.maximum(pts,1).evaluate(), lw=2, label=\"exact\")\n", - "ax.plot(pts.evaluate(), pybamm.smooth_max(pts,1,5).evaluate(), \":\", lw=2, label=\"smooth_max (k=5)\")\n", - "ax.plot(pts.evaluate(), pybamm.smooth_max(pts,1,10).evaluate(), \":\", lw=2, label=\"smooth_max (k=10)\")\n", - "ax.plot(pts.evaluate(), pybamm.smooth_max(pts,1,100).evaluate(), \":\", lw=2, label=\"smooth_max (k=100)\")\n", + "fig, ax = plt.subplots(figsize=(10, 5))\n", + "ax.plot(pts.evaluate(), pybamm.maximum(pts, 1).evaluate(), lw=2, label=\"exact\")\n", + "ax.plot(\n", + " pts.evaluate(),\n", + " pybamm.smooth_max(pts, 1, 5).evaluate(),\n", + " \":\",\n", + " lw=2,\n", + " label=\"smooth_max (k=5)\",\n", + ")\n", + "ax.plot(\n", + " pts.evaluate(),\n", + " pybamm.smooth_max(pts, 1, 10).evaluate(),\n", + " \":\",\n", + " lw=2,\n", + " label=\"smooth_max (k=10)\",\n", + ")\n", + "ax.plot(\n", + " pts.evaluate(),\n", + " pybamm.smooth_max(pts, 1, 100).evaluate(),\n", + " \":\",\n", + " lw=2,\n", + " label=\"smooth_max (k=100)\",\n", + ")\n", "ax.legend();" ] }, @@ -1072,7 +1156,7 @@ " exact_sol = solver.solve(model_exact, [0, 2])\n", " # Report integration time, which is the time spent actually doing the integration\n", " time += exact_sol.integration_time\n", - "print(\"Exact:\", time/100)\n", + "print(\"Exact:\", time / 100)\n", "sols = [exact_sol]\n", "\n", "ks = [10, 50, 100, 1000, 10000]\n", @@ -1082,10 +1166,12 @@ " for _ in range(100):\n", " sol = solver.solve(model_smooth, [0, 2], inputs={\"k\": k})\n", " time += sol.integration_time\n", - " print(f\"Smooth, k={k}:\", time/100)\n", + " print(f\"Smooth, k={k}:\", time / 100)\n", " sols.append(sol)\n", "\n", - "pybamm.dynamic_plot(sols, [\"x\", \"max(x,1)\"], labels=[\"exact\"] + [f\"soft (k={k})\" for k in ks]);" + "pybamm.dynamic_plot(\n", + " sols, [\"x\", \"max(x,1)\"], labels=[\"exact\"] + [f\"soft (k={k})\" for k in ks]\n", + ");" ] }, { diff --git a/docs/source/examples/notebooks/spatial_methods/finite-volumes.ipynb b/docs/source/examples/notebooks/spatial_methods/finite-volumes.ipynb index a0725d4dd3..849f1bdf47 100644 --- a/docs/source/examples/notebooks/spatial_methods/finite-volumes.ipynb +++ b/docs/source/examples/notebooks/spatial_methods/finite-volumes.ipynb @@ -67,7 +67,8 @@ "import numpy as np\n", "import os\n", "import matplotlib.pyplot as plt\n", - "os.chdir(pybamm.__path__[0]+'/..')" + "\n", + "os.chdir(pybamm.__path__[0] + \"/..\")" ] }, { @@ -199,7 +200,7 @@ } ], "source": [ - "# Set up \n", + "# Set up\n", "macroscale = [\"negative electrode\", \"separator\", \"positive electrode\"]\n", "x_var = pybamm.SpatialVariable(\"x\", domain=macroscale)\n", "r_var = pybamm.SpatialVariable(\"r\", domain=[\"negative particle\"])\n", @@ -214,7 +215,7 @@ "x = x_disc.evaluate()\n", "r = r_disc.evaluate()\n", "\n", - "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(13,4))\n", + "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(13, 4))\n", "\n", "ax1.plot(x, \"*\")\n", "ax1.set_xlabel(\"index\")\n", @@ -242,7 +243,7 @@ "metadata": {}, "outputs": [], "source": [ - "y_macroscale = x ** 3 / 3\n", + "y_macroscale = x**3 / 3\n", "y_microscale = np.cos(r)\n", "y_scalar = np.array([[5]])\n", "\n", @@ -271,11 +272,17 @@ "metadata": {}, "outputs": [], "source": [ - "u = pybamm.Variable(\"u\", domain=macroscale) # u is a variable in the macroscale (e.g. electrolyte potential)\n", - "v = pybamm.Variable(\"v\", domain=[\"negative particle\"]) # v is a variable in the negative particle (e.g. particle concentration)\n", - "w = pybamm.Variable(\"w\") # w is a variable without a domain (e.g. time, average concentration)\n", + "u = pybamm.Variable(\n", + " \"u\", domain=macroscale\n", + ") # u is a variable in the macroscale (e.g. electrolyte potential)\n", + "v = pybamm.Variable(\n", + " \"v\", domain=[\"negative particle\"]\n", + ") # v is a variable in the negative particle (e.g. particle concentration)\n", + "w = pybamm.Variable(\n", + " \"w\"\n", + ") # w is a variable without a domain (e.g. time, average concentration)\n", "\n", - "variables = [u,v,w]" + "variables = [u, v, w]" ] }, { @@ -304,7 +311,7 @@ "source": [ "try:\n", " u.evaluate()\n", - "except NotImplementedError as e: \n", + "except NotImplementedError as e:\n", " print(e)" ] }, @@ -342,7 +349,7 @@ "v_disc = disc.process_symbol(v)\n", "w_disc = disc.process_symbol(w)\n", "\n", - "# Print the outcome \n", + "# Print the outcome\n", "print(f\"Discretised u is the StateVector {u_disc}\")\n", "print(f\"Discretised v is the StateVector {v_disc}\")\n", "print(f\"Discretised w is the StateVector {w_disc}\")" @@ -376,8 +383,8 @@ "x_fine = np.linspace(x[0], x[-1], 1000)\n", "r_fine = np.linspace(r[0], r[-1], 1000)\n", "\n", - "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13,4))\n", - "ax1.plot(x_fine, x_fine**3/3, x, u_disc.evaluate(y=y), \"o\")\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13, 4))\n", + "ax1.plot(x_fine, x_fine**3 / 3, x, u_disc.evaluate(y=y), \"o\")\n", "ax1.set_xlabel(\"x\")\n", "ax1.legend([\"x^3/3\", \"u\"], loc=\"best\")\n", "\n", @@ -484,7 +491,9 @@ "source": [ "macro_mesh = mesh.combine_submeshes(*macroscale)\n", "print(\"gradient matrix is:\\n\")\n", - "print(f\"1/dx *\\n{macro_mesh.d_nodes[:,np.newaxis] * grad_u_disc.children[0].entries.toarray()}\")" + "print(\n", + " f\"1/dx *\\n{macro_mesh.d_nodes[:,np.newaxis] * grad_u_disc.children[0].entries.toarray()}\"\n", + ")" ] }, { @@ -512,7 +521,7 @@ } ], "source": [ - "x_edge = macro_mesh.edges[1:-1] # note that grad_u_disc is evaluated on the node edges\n", + "x_edge = macro_mesh.edges[1:-1] # note that grad_u_disc is evaluated on the node edges\n", "\n", "fig, ax = plt.subplots()\n", "ax.plot(x_fine, x_fine**2, x_edge, grad_u_disc.evaluate(y=y), \"o\")\n", @@ -600,9 +609,11 @@ "\n", "micro_mesh = mesh[\"negative particle\"]\n", "print(\"\\n gradient matrix is:\\n\")\n", - "print(f\"1/dr *\\n{micro_mesh.d_nodes[:,np.newaxis] * grad_v_disc.children[0].entries.toarray()}\")\n", + "print(\n", + " f\"1/dr *\\n{micro_mesh.d_nodes[:,np.newaxis] * grad_v_disc.children[0].entries.toarray()}\"\n", + ")\n", "\n", - "r_edge = micro_mesh.edges[1:-1] # note that grad_u_disc is evaluated on the node edges\n", + "r_edge = micro_mesh.edges[1:-1] # note that grad_u_disc is evaluated on the node edges\n", "\n", "fig, ax = plt.subplots()\n", "ax.plot(r_fine, -np.sin(r_fine), r_edge, grad_v_disc.evaluate(y=y), \"o\")\n", @@ -655,7 +666,12 @@ } ], "source": [ - "disc.bcs = {u: {\"left\": (pybamm.Scalar(1), \"Dirichlet\"), \"right\": (pybamm.Scalar(2), \"Dirichlet\")}}\n", + "disc.bcs = {\n", + " u: {\n", + " \"left\": (pybamm.Scalar(1), \"Dirichlet\"),\n", + " \"right\": (pybamm.Scalar(2), \"Dirichlet\"),\n", + " }\n", + "}\n", "grad_u_disc = disc.process_symbol(grad_u)\n", "print(\"The gradient object is:\")\n", "(grad_u_disc.render())\n", @@ -699,7 +715,9 @@ } ], "source": [ - "disc.bcs = {u: {\"left\": (pybamm.Scalar(3), \"Neumann\"), \"right\": (pybamm.Scalar(4), \"Neumann\")}}\n", + "disc.bcs = {\n", + " u: {\"left\": (pybamm.Scalar(3), \"Neumann\"), \"right\": (pybamm.Scalar(4), \"Neumann\")}\n", + "}\n", "grad_u_disc = disc.process_symbol(grad_u)\n", "print(\"The gradient object is:\")\n", "(grad_u_disc.render())\n", @@ -739,7 +757,9 @@ } ], "source": [ - "disc.bcs = {u: {\"left\": (pybamm.Scalar(5), \"Dirichlet\"), \"right\": (pybamm.Scalar(6), \"Neumann\")}}\n", + "disc.bcs = {\n", + " u: {\"left\": (pybamm.Scalar(5), \"Dirichlet\"), \"right\": (pybamm.Scalar(6), \"Neumann\")}\n", + "}\n", "grad_u_disc = disc.process_symbol(grad_u)\n", "print(\"The gradient object is:\")\n", "(grad_u_disc.render())\n", @@ -779,7 +799,9 @@ "metadata": {}, "outputs": [], "source": [ - "disc.bcs = {u: {\"left\": (pybamm.Scalar(-1), \"Neumann\"), \"right\": (pybamm.Scalar(1), \"Neumann\")}}" + "disc.bcs = {\n", + " u: {\"left\": (pybamm.Scalar(-1), \"Neumann\"), \"right\": (pybamm.Scalar(1), \"Neumann\")}\n", + "}" ] }, { @@ -849,9 +871,12 @@ ], "source": [ "print(\"div(grad) matrix is:\\n\")\n", - "print(\"1/dx^2 * \\n{}\".format(\n", - " macro_mesh.d_edges[:,np.newaxis]**2 * div_grad_u_disc.right.left.entries.toarray()\n", - "))" + "print(\n", + " \"1/dx^2 * \\n{}\".format(\n", + " macro_mesh.d_edges[:, np.newaxis] ** 2\n", + " * div_grad_u_disc.right.left.entries.toarray()\n", + " )\n", + ")" ] }, { @@ -892,10 +917,12 @@ "print(f\"int(u) = {int_u_disc.evaluate(y=y)} is approximately equal to 1/12, {1/12}\")\n", "\n", "# We divide v by r to evaluate the integral more easily\n", - "int_v_over_r2 = pybamm.Integral(v/r_var**2, r_var)\n", + "int_v_over_r2 = pybamm.Integral(v / r_var**2, r_var)\n", "int_v_over_r2_disc = disc.process_symbol(int_v_over_r2)\n", - "print(\"int(v/r^2) = {} is approximately equal to 4 * pi * sin(1), {}\".format(\n", - " int_v_over_r2_disc.evaluate(y=y), 4 * np.pi * np.sin(1))\n", + "print(\n", + " \"int(v/r^2) = {} is approximately equal to 4 * pi * sin(1), {}\".format(\n", + " int_v_over_r2_disc.evaluate(y=y), 4 * np.pi * np.sin(1)\n", + " )\n", ")" ] }, @@ -999,7 +1026,7 @@ " c_e: {\"left\": (np.cos(0), \"Neumann\"), \"right\": (np.cos(10), \"Neumann\")},\n", " c_s: {\"left\": (0, \"Neumann\"), \"right\": (-1, \"Neumann\")},\n", "}\n", - "model.initial_conditions = {c_e: 1 + 0.1 * pybamm.sin(10*x_var), c_s: 1}\n", + "model.initial_conditions = {c_e: 1 + 0.1 * pybamm.sin(10 * x_var), c_s: 1}\n", "\n", "# Create a new discretisation and process model\n", "disc2 = pybamm.Discretisation(mesh, spatial_methods)\n", @@ -1035,8 +1062,8 @@ "c_s_0 = model.initial_conditions[c_s].evaluate()\n", "y0 = model.concatenated_initial_conditions.evaluate()\n", "\n", - "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(13,4))\n", - "ax1.plot(x_fine, 1 + 0.1*np.sin(10*x_fine), x, c_e_0, \"o\")\n", + "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(13, 4))\n", + "ax1.plot(x_fine, 1 + 0.1 * np.sin(10 * x_fine), x, c_e_0, \"o\")\n", "ax1.set_xlabel(\"x\")\n", "ax1.legend([\"1+0.1*sin(10*x)\", \"c_e_0\"], loc=\"best\")\n", "\n", @@ -1044,7 +1071,7 @@ "ax2.set_xlabel(\"r\")\n", "ax2.legend([\"1\", \"c_s_0\"], loc=\"best\")\n", "\n", - "ax3.plot(y0,\"*\")\n", + "ax3.plot(y0, \"*\")\n", "ax3.set_xlabel(\"index\")\n", "ax3.set_ylabel(\"y0\")\n", "\n", @@ -1081,8 +1108,8 @@ "rhs_c_s = model.rhs[c_s].evaluate(0, y0)\n", "rhs = model.concatenated_rhs.evaluate(0, y0)\n", "\n", - "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(13,4))\n", - "ax1.plot(x_fine, -10*np.sin(10*x_fine) - 5, x, rhs_c_e, \"o\")\n", + "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(13, 4))\n", + "ax1.plot(x_fine, -10 * np.sin(10 * x_fine) - 5, x, rhs_c_e, \"o\")\n", "ax1.set_xlabel(\"x\")\n", "ax1.set_ylabel(\"rhs_c_e\")\n", "ax1.legend([\"1+0.1*sin(10*x)\", \"c_e_0\"], loc=\"best\")\n", @@ -1091,7 +1118,7 @@ "ax2.set_xlabel(\"r\")\n", "ax2.set_ylabel(\"rhs_c_s\")\n", "\n", - "ax3.plot(rhs,\"*\")\n", + "ax3.plot(rhs, \"*\")\n", "ax3.set_xlabel(\"index\")\n", "ax3.set_ylabel(\"rhs\")\n", "\n", @@ -1147,8 +1174,12 @@ "model = pybamm.BaseModel()\n", "\n", "# Define concentration and velocity\n", - "c = pybamm.Variable(\"c\", domain=[\"negative electrode\", \"separator\", \"positive electrode\"])\n", - "v = pybamm.PrimaryBroadcastToEdges(1, [\"negative electrode\", \"separator\", \"positive electrode\"])\n", + "c = pybamm.Variable(\n", + " \"c\", domain=[\"negative electrode\", \"separator\", \"positive electrode\"]\n", + ")\n", + "v = pybamm.PrimaryBroadcastToEdges(\n", + " 1, [\"negative electrode\", \"separator\", \"positive electrode\"]\n", + ")\n", "model.rhs = {c: -pybamm.div(c * v) + 1}\n", "model.initial_conditions = {c: 0}\n", "model.boundary_conditions = {c: {\"left\": (0, \"Dirichlet\")}}\n", @@ -1158,13 +1189,13 @@ "def solve_and_plot(model):\n", " model_disc = disc.process_model(model, inplace=False)\n", "\n", - " t_eval = [0,100]\n", + " t_eval = [0, 100]\n", " solution = pybamm.CasadiSolver().solve(model_disc, t_eval)\n", "\n", " # plot\n", - " plot = pybamm.QuickPlot(solution,[\"c\"],spatial_unit=\"m\")\n", + " plot = pybamm.QuickPlot(solution, [\"c\"], spatial_unit=\"m\")\n", " plot.dynamic_plot()\n", - " \n", + "\n", "\n", "solve_and_plot(model)" ] @@ -1198,7 +1229,7 @@ } ], "source": [ - "model.rhs = {c: -pybamm.div(pybamm.upwind(c) * v) + 1} \n", + "model.rhs = {c: -pybamm.div(pybamm.upwind(c) * v) + 1}\n", "solve_and_plot(model)" ] }, @@ -1231,7 +1262,7 @@ } ], "source": [ - "model.rhs = {c: -pybamm.div(pybamm.downwind(c) * (-v)) + 1} \n", + "model.rhs = {c: -pybamm.div(pybamm.downwind(c) * (-v)) + 1}\n", "model.boundary_conditions = {c: {\"right\": (0, \"Dirichlet\")}}\n", "solve_and_plot(model)" ] diff --git a/examples/scripts/compare_comsol/discharge_curve.py b/examples/scripts/compare_comsol/discharge_curve.py index 7544730eea..9e437ce301 100644 --- a/examples/scripts/compare_comsol/discharge_curve.py +++ b/examples/scripts/compare_comsol/discharge_curve.py @@ -53,7 +53,7 @@ plt.grid(True) plt.xlabel(r"Discharge Capacity (Ah)", fontsize=20) plt.ylabel(r"$\vert V - V_{comsol} \vert$", fontsize=20) -colors = iter(plt.cycler(color='bgrcmyk')) +colors = iter(plt.cycler(color="bgrcmyk")) for key, C_rate in C_rates.items(): current = 24 * C_rate diff --git a/examples/scripts/heat_equation.py b/examples/scripts/heat_equation.py index 20f9601090..fd01b37f97 100644 --- a/examples/scripts/heat_equation.py +++ b/examples/scripts/heat_equation.py @@ -119,9 +119,7 @@ def T_exact(x, t): color=color, label="Numerical" if i == 0 else "", ) - plt.plot( - xx, T_exact(xx, t), "-", color=color, label=f"Exact (t={plot_times[i]})" - ) + plt.plot(xx, T_exact(xx, t), "-", color=color, label=f"Exact (t={plot_times[i]})") plt.xlabel("x", fontsize=16) plt.ylabel("T", fontsize=16) plt.legend() diff --git a/pybamm/discretisations/discretisation.py b/pybamm/discretisations/discretisation.py index c250d06e9c..7be3b2bc53 100644 --- a/pybamm/discretisations/discretisation.py +++ b/pybamm/discretisations/discretisation.py @@ -175,13 +175,9 @@ def process_model( self.set_variable_slices(variables) # set boundary conditions (only need key ids for boundary_conditions) - pybamm.logger.verbose( - f"Discretise boundary conditions for {model.name}" - ) + pybamm.logger.verbose(f"Discretise boundary conditions for {model.name}") self._bcs = self.process_boundary_conditions(model) - pybamm.logger.verbose( - f"Set internal boundary conditions for {model.name}" - ) + pybamm.logger.verbose(f"Set internal boundary conditions for {model.name}") self.set_internal_boundary_conditions(model) # set up inplace vs not inplace @@ -898,9 +894,7 @@ def _process_symbol(self, symbol): No key set for variable '{}'. Make sure it is included in either model.rhs or model.algebraic in an unmodified form (e.g. not Broadcasted) - """.format( - symbol.name - ) + """.format(symbol.name) ) # Add symbol's reference and multiply by the symbol's scale # so that the state vector is of order 1 diff --git a/pybamm/expression_tree/binary_operators.py b/pybamm/expression_tree/binary_operators.py index 7348e08712..3d70741785 100644 --- a/pybamm/expression_tree/binary_operators.py +++ b/pybamm/expression_tree/binary_operators.py @@ -1336,8 +1336,8 @@ def smooth_min(left, right, k): Smooth_min approximation to the minimum function. k is the smoothing parameter, set by `pybamm.settings.min_max_smoothing`. The recommended value is k=100. """ - sigma = (1.0 / k)**2 - return ((left + right) - (pybamm.sqrt((left - right)**2 + sigma))) / 2 + sigma = (1.0 / k) ** 2 + return ((left + right) - (pybamm.sqrt((left - right) ** 2 + sigma))) / 2 def smooth_max(left, right, k): @@ -1346,7 +1346,7 @@ def smooth_max(left, right, k): set by `pybamm.settings.min_max_smoothing`. The recommended value is k=100. """ sigma = (1.0 / k) ** 2 - return (pybamm.sqrt((left - right)**2 + sigma) + (left + right)) / 2 + return (pybamm.sqrt((left - right) ** 2 + sigma) + (left + right)) / 2 def sigmoid(left, right, k): diff --git a/pybamm/expression_tree/concatenations.py b/pybamm/expression_tree/concatenations.py index afd9bdc1d5..40cfe617ac 100644 --- a/pybamm/expression_tree/concatenations.py +++ b/pybamm/expression_tree/concatenations.py @@ -191,7 +191,7 @@ def __init__(self, *children): *children, name="numpy_concatenation", check_domain=False, - concat_fun=np.concatenate + concat_fun=np.concatenate, ) @classmethod @@ -201,7 +201,7 @@ def _from_json(cls, snippet: dict): *snippet["children"], name="numpy_concatenation", domains=snippet["domains"], - concat_fun=np.concatenate + concat_fun=np.concatenate, ) return instance @@ -280,7 +280,7 @@ def _from_json(cls, snippet: dict): instance = super()._from_json( *snippet["children"], name="domain_concatenation", - domains=snippet["domains"] + domains=snippet["domains"], ) def repack_defaultDict(slices): @@ -415,7 +415,7 @@ def __init__(self, *children): *children, name="sparse_stack", check_domain=False, - concat_fun=concatenation_function + concat_fun=concatenation_function, ) def _concatenation_new_copy(self, children): diff --git a/pybamm/expression_tree/functions.py b/pybamm/expression_tree/functions.py index d8248eabe8..f95f190b43 100644 --- a/pybamm/expression_tree/functions.py +++ b/pybamm/expression_tree/functions.py @@ -10,6 +10,7 @@ import pybamm from pybamm.util import have_optional_dependency + class Function(pybamm.Symbol): """ A node in the expression tree representing an arbitrary function. @@ -412,7 +413,7 @@ def _from_json(cls, snippet: dict): def _function_diff(self, children, idx): """See :meth:`pybamm.Function._function_diff()`.""" - return 2 / np.sqrt(np.pi) * exp(-children[0] ** 2) + return 2 / np.sqrt(np.pi) * exp(-(children[0] ** 2)) def erf(child): diff --git a/pybamm/expression_tree/independent_variable.py b/pybamm/expression_tree/independent_variable.py index ee8afac38e..dccb627eed 100644 --- a/pybamm/expression_tree/independent_variable.py +++ b/pybamm/expression_tree/independent_variable.py @@ -145,9 +145,7 @@ def __init__( elif name in ["x", "y", "z", "x_n", "x_s", "x_p"] and any( ["particle" in dom for dom in domain] ): - raise pybamm.DomainError( - f"domain cannot be particle if name is '{name}'" - ) + raise pybamm.DomainError(f"domain cannot be particle if name is '{name}'") def create_copy(self): """See :meth:`pybamm.Symbol.new_copy()`.""" diff --git a/pybamm/expression_tree/interpolant.py b/pybamm/expression_tree/interpolant.py index 5de21da089..7efe10413c 100644 --- a/pybamm/expression_tree/interpolant.py +++ b/pybamm/expression_tree/interpolant.py @@ -243,7 +243,13 @@ def entries_string(self, value): def set_id(self): """See :meth:`pybamm.Symbol.set_id()`.""" self._id = hash( - (self.__class__, self.name, self.entries_string, *tuple([child.id for child in self.children]), *tuple(self.domain)) + ( + self.__class__, + self.name, + self.entries_string, + *tuple([child.id for child in self.children]), + *tuple(self.domain), + ) ) def _function_new_copy(self, children): diff --git a/pybamm/expression_tree/operations/convert_to_casadi.py b/pybamm/expression_tree/operations/convert_to_casadi.py index 6461a9267f..d29ae994f2 100644 --- a/pybamm/expression_tree/operations/convert_to_casadi.py +++ b/pybamm/expression_tree/operations/convert_to_casadi.py @@ -209,7 +209,5 @@ def _convert(self, symbol, t, y, y_dot, inputs): """ Cannot convert symbol of type '{}' to CasADi. Symbols must all be 'linear algebra' at this stage. - """.format( - type(symbol) - ) + """.format(type(symbol)) ) diff --git a/pybamm/expression_tree/operations/evaluate_python.py b/pybamm/expression_tree/operations/evaluate_python.py index f65ecc7159..9c6f734553 100644 --- a/pybamm/expression_tree/operations/evaluate_python.py +++ b/pybamm/expression_tree/operations/evaluate_python.py @@ -203,7 +203,9 @@ def find_symbols(symbol, constant_symbols, variable_symbols, output_jax=False): dummy_eval_right = symbol.children[1].evaluate_for_shape() if scipy.sparse.issparse(dummy_eval_left): if output_jax and is_scalar(dummy_eval_right): - symbol_str = f"{children_vars[0]}.scalar_multiply({children_vars[1]})" + symbol_str = ( + f"{children_vars[0]}.scalar_multiply({children_vars[1]})" + ) else: symbol_str = f"{children_vars[0]}.multiply({children_vars[1]})" elif scipy.sparse.issparse(dummy_eval_right): @@ -215,7 +217,9 @@ def find_symbols(symbol, constant_symbols, variable_symbols, output_jax=False): dummy_eval_right = symbol.children[1].evaluate_for_shape() if scipy.sparse.issparse(dummy_eval_left): if output_jax and is_scalar(dummy_eval_right): - symbol_str = f"{children_vars[0]}.scalar_multiply(1/{children_vars[1]})" + symbol_str = ( + f"{children_vars[0]}.scalar_multiply(1/{children_vars[1]})" + ) else: symbol_str = f"{children_vars[0]}.multiply(1/{children_vars[1]})" else: @@ -226,12 +230,16 @@ def find_symbols(symbol, constant_symbols, variable_symbols, output_jax=False): dummy_eval_right = symbol.children[1].evaluate_for_shape() if scipy.sparse.issparse(dummy_eval_left): if output_jax and is_scalar(dummy_eval_right): - symbol_str = f"{children_vars[0]}.scalar_multiply({children_vars[1]})" + symbol_str = ( + f"{children_vars[0]}.scalar_multiply({children_vars[1]})" + ) else: symbol_str = f"{children_vars[0]}.multiply({children_vars[1]})" elif scipy.sparse.issparse(dummy_eval_right): if output_jax and is_scalar(dummy_eval_left): - symbol_str = f"{children_vars[1]}.scalar_multiply({children_vars[0]})" + symbol_str = ( + f"{children_vars[1]}.scalar_multiply({children_vars[0]})" + ) else: symbol_str = f"{children_vars[1]}.multiply({children_vars[0]})" else: diff --git a/pybamm/expression_tree/parameter.py b/pybamm/expression_tree/parameter.py index 5fcb8c8ec9..787b7b5007 100644 --- a/pybamm/expression_tree/parameter.py +++ b/pybamm/expression_tree/parameter.py @@ -163,7 +163,13 @@ def input_names(self, inp=None): def set_id(self): """See :meth:`pybamm.Symbol.set_id`""" self._id = hash( - (self.__class__, self.name, self.diff_variable, *tuple([child.id for child in self.children]), *tuple(self.domain)) + ( + self.__class__, + self.name, + self.diff_variable, + *tuple([child.id for child in self.children]), + *tuple(self.domain), + ) ) def diff(self, variable): diff --git a/pybamm/expression_tree/printing/sympy_overrides.py b/pybamm/expression_tree/printing/sympy_overrides.py index 1898822ea8..58ac356399 100644 --- a/pybamm/expression_tree/printing/sympy_overrides.py +++ b/pybamm/expression_tree/printing/sympy_overrides.py @@ -8,6 +8,7 @@ class CustomPrint(LatexPrinter): """Override SymPy methods to match PyBaMM's requirements""" + def _print_Derivative(self, expr): """Override :meth:`sympy.printing.latex.LatexPrinter._print_Derivative`""" eqn = super()._print_Derivative(expr) diff --git a/pybamm/expression_tree/scalar.py b/pybamm/expression_tree/scalar.py index 64a3893fc9..73dccf7d6c 100644 --- a/pybamm/expression_tree/scalar.py +++ b/pybamm/expression_tree/scalar.py @@ -6,6 +6,7 @@ import pybamm from pybamm.util import have_optional_dependency + class Scalar(pybamm.Symbol): """ A node in the expression tree representing a scalar value. diff --git a/pybamm/expression_tree/state_vector.py b/pybamm/expression_tree/state_vector.py index 2f51d4bda1..348f908b45 100644 --- a/pybamm/expression_tree/state_vector.py +++ b/pybamm/expression_tree/state_vector.py @@ -118,7 +118,12 @@ def set_evaluation_array(self, y_slices, evaluation_array): def set_id(self): """See :meth:`pybamm.Symbol.set_id()`""" self._id = hash( - (self.__class__, self.name, tuple(self.evaluation_array), *tuple(self.domain)) + ( + self.__class__, + self.name, + tuple(self.evaluation_array), + *tuple(self.domain), + ) ) def _jac_diff_vector(self, variable): diff --git a/pybamm/expression_tree/unary_operators.py b/pybamm/expression_tree/unary_operators.py index 435bd5dce2..950ac16318 100644 --- a/pybamm/expression_tree/unary_operators.py +++ b/pybamm/expression_tree/unary_operators.py @@ -1115,8 +1115,7 @@ def __init__(self, name, child): ) if child.evaluates_on_edges("primary") is True: raise TypeError( - f"Cannot upwind '{child}' since it does not " - + "evaluate on nodes." + f"Cannot upwind '{child}' since it does not " + "evaluate on nodes." ) super().__init__(name, child) diff --git a/pybamm/expression_tree/variable.py b/pybamm/expression_tree/variable.py index 3916ff5249..35193782e3 100644 --- a/pybamm/expression_tree/variable.py +++ b/pybamm/expression_tree/variable.py @@ -103,7 +103,13 @@ def bounds(self, values): def set_id(self): self._id = hash( - (self.__class__, self.name, self.scale, self.reference, *tuple([(k, tuple(v)) for k, v in self.domains.items() if v != []])) + ( + self.__class__, + self.name, + self.scale, + self.reference, + *tuple([(k, tuple(v)) for k, v in self.domains.items() if v != []]), + ) ) def create_copy(self): diff --git a/pybamm/expression_tree/vector.py b/pybamm/expression_tree/vector.py index 66fe7d8c12..641c098f79 100644 --- a/pybamm/expression_tree/vector.py +++ b/pybamm/expression_tree/vector.py @@ -29,9 +29,7 @@ def __init__( raise ValueError( """ Entries must have 1 dimension or be column vector, not have shape {} - """.format( - entries.shape - ) + """.format(entries.shape) ) if name is None: name = f"Column vector of length {entries.shape[0]!s}" diff --git a/pybamm/input/parameters/lithium_ion/Ai2020.py b/pybamm/input/parameters/lithium_ion/Ai2020.py index 31b9ab228d..d13f7fb0db 100644 --- a/pybamm/input/parameters/lithium_ion/Ai2020.py +++ b/pybamm/input/parameters/lithium_ion/Ai2020.py @@ -69,9 +69,7 @@ def graphite_electrolyte_exchange_current_density_Dualfoil1998( E_r = 5000 # activation energy for Temperature Dependent Reaction Constant [J/mol] arrhenius = np.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T)) - return ( - m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 - ) + return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 def graphite_entropy_Enertech_Ai2020_function(sto, c_s_max): @@ -272,9 +270,7 @@ def lico2_electrolyte_exchange_current_density_Dualfoil1998(c_e, c_s_surf, c_s_m E_r = 5000 arrhenius = np.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T)) - return ( - m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 - ) + return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 def lico2_entropic_change_Ai2020_function(sto, c_s_max): diff --git a/pybamm/input/parameters/lithium_ion/Chen2020.py b/pybamm/input/parameters/lithium_ion/Chen2020.py index e526b480c4..0b5420baaf 100644 --- a/pybamm/input/parameters/lithium_ion/Chen2020.py +++ b/pybamm/input/parameters/lithium_ion/Chen2020.py @@ -70,9 +70,7 @@ def graphite_LGM50_electrolyte_exchange_current_density_Chen2020( E_r = 35000 arrhenius = np.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T)) - return ( - m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 - ) + return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 def nmc_LGM50_ocp_Chen2020(sto): @@ -141,9 +139,7 @@ def nmc_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, c_s_m E_r = 17800 arrhenius = np.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T)) - return ( - m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 - ) + return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 def electrolyte_diffusivity_Nyman2008(c_e, T): diff --git a/pybamm/input/parameters/lithium_ion/Chen2020_composite.py b/pybamm/input/parameters/lithium_ion/Chen2020_composite.py index f7e27c8d52..69767cbddf 100644 --- a/pybamm/input/parameters/lithium_ion/Chen2020_composite.py +++ b/pybamm/input/parameters/lithium_ion/Chen2020_composite.py @@ -37,9 +37,7 @@ def graphite_LGM50_electrolyte_exchange_current_density_Chen2020( E_r = 35000 arrhenius = np.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T)) - return ( - m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 - ) + return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 def silicon_ocp_lithiation_Mark2016(sto): @@ -167,9 +165,7 @@ def silicon_LGM50_electrolyte_exchange_current_density_Chen2020( E_r = 35000 arrhenius = np.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T)) - return ( - m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 - ) + return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 def nmc_LGM50_ocp_Chen2020(sto): @@ -238,9 +234,7 @@ def nmc_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, c_s_m E_r = 17800 arrhenius = np.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T)) - return ( - m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 - ) + return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 def electrolyte_diffusivity_Nyman2008(c_e, T): diff --git a/pybamm/input/parameters/lithium_ion/Ecker2015.py b/pybamm/input/parameters/lithium_ion/Ecker2015.py index 3f37db6e61..28b2ca21e4 100644 --- a/pybamm/input/parameters/lithium_ion/Ecker2015.py +++ b/pybamm/input/parameters/lithium_ion/Ecker2015.py @@ -147,9 +147,7 @@ def graphite_electrolyte_exchange_current_density_Ecker2015(c_e, c_s_surf, c_s_m E_r / (pybamm.constants.R * 296.15) ) - return ( - m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 - ) + return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 def nco_diffusivity_Ecker2015(sto, T): @@ -292,9 +290,7 @@ def nco_electrolyte_exchange_current_density_Ecker2015(c_e, c_s_surf, c_s_max, T E_r / (pybamm.constants.R * 296.15) ) - return ( - m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 - ) + return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 def electrolyte_diffusivity_Ecker2015(c_e, T): diff --git a/pybamm/input/parameters/lithium_ion/Ecker2015_graphite_halfcell.py b/pybamm/input/parameters/lithium_ion/Ecker2015_graphite_halfcell.py index f6bc8e4d93..441ae95b8b 100644 --- a/pybamm/input/parameters/lithium_ion/Ecker2015_graphite_halfcell.py +++ b/pybamm/input/parameters/lithium_ion/Ecker2015_graphite_halfcell.py @@ -177,9 +177,7 @@ def graphite_electrolyte_exchange_current_density_Ecker2015(c_e, c_s_surf, c_s_m E_r / (pybamm.constants.R * 296.15) ) - return ( - m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 - ) + return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 def electrolyte_diffusivity_Ecker2015(c_e, T): diff --git a/pybamm/input/parameters/lithium_ion/Marquis2019.py b/pybamm/input/parameters/lithium_ion/Marquis2019.py index d3bddc6e30..1664e6b1b2 100644 --- a/pybamm/input/parameters/lithium_ion/Marquis2019.py +++ b/pybamm/input/parameters/lithium_ion/Marquis2019.py @@ -90,9 +90,7 @@ def graphite_electrolyte_exchange_current_density_Dualfoil1998( E_r = 37480 arrhenius = np.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T)) - return ( - m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 - ) + return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 def graphite_entropic_change_Moura2016(sto, c_s_max): @@ -221,9 +219,7 @@ def lico2_electrolyte_exchange_current_density_Dualfoil1998(c_e, c_s_surf, c_s_m E_r = 39570 arrhenius = np.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T)) - return ( - m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 - ) + return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 def lico2_entropic_change_Moura2016(sto, c_s_max): diff --git a/pybamm/input/parameters/lithium_ion/Mohtat2020.py b/pybamm/input/parameters/lithium_ion/Mohtat2020.py index 29535b9f3d..86f14e39a2 100644 --- a/pybamm/input/parameters/lithium_ion/Mohtat2020.py +++ b/pybamm/input/parameters/lithium_ion/Mohtat2020.py @@ -86,9 +86,7 @@ def graphite_electrolyte_exchange_current_density_PeymanMPM(c_e, c_s_surf, c_s_m E_r = 37480 arrhenius = np.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T)) - return ( - m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 - ) + return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 def graphite_entropic_change_PeymanMPM(sto, c_s_max): @@ -208,9 +206,7 @@ def NMC_electrolyte_exchange_current_density_PeymanMPM(c_e, c_s_surf, c_s_max, T E_r = 39570 arrhenius = np.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T)) - return ( - m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 - ) + return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 def NMC_entropic_change_PeymanMPM(sto, c_s_max): @@ -244,9 +240,9 @@ def NMC_entropic_change_PeymanMPM(sto, c_s_max): - 0.5623 * 10 ** (-4) * np.exp(109.451 * sto - 100.006) ) - du_dT = ( - -800 + 779 * u_eq - 284 * u_eq**2 + 46 * u_eq**3 - 2.8 * u_eq**4 - ) * 10 ** (-3) + du_dT = (-800 + 779 * u_eq - 284 * u_eq**2 + 46 * u_eq**3 - 2.8 * u_eq**4) * 10 ** ( + -3 + ) return du_dT diff --git a/pybamm/input/parameters/lithium_ion/NCA_Kim2011.py b/pybamm/input/parameters/lithium_ion/NCA_Kim2011.py index b5543ea6c2..123714a9da 100644 --- a/pybamm/input/parameters/lithium_ion/NCA_Kim2011.py +++ b/pybamm/input/parameters/lithium_ion/NCA_Kim2011.py @@ -105,11 +105,7 @@ def graphite_electrolyte_exchange_current_density_Kim2011(c_e, c_s_surf, c_s_max arrhenius = np.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T)) return ( - m_ref - * arrhenius - * c_e**alpha - * c_s_surf**alpha - * (c_s_max - c_s_surf) ** alpha + m_ref * arrhenius * c_e**alpha * c_s_surf**alpha * (c_s_max - c_s_surf) ** alpha ) @@ -177,18 +173,12 @@ def nca_electrolyte_exchange_current_density_Kim2011(c_e, c_s_surf, c_s_max, T): c_e_ref = pybamm.Parameter("Initial concentration in electrolyte [mol.m-3]") alpha = 0.5 # charge transfer coefficient - m_ref = i0_ref / ( - c_e_ref**alpha * (c_s_max - c_s_ref) ** alpha * c_s_ref**alpha - ) + m_ref = i0_ref / (c_e_ref**alpha * (c_s_max - c_s_ref) ** alpha * c_s_ref**alpha) E_r = 3e4 arrhenius = np.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T)) return ( - m_ref - * arrhenius - * c_e**alpha - * c_s_surf**alpha - * (c_s_max - c_s_surf) ** alpha + m_ref * arrhenius * c_e**alpha * c_s_surf**alpha * (c_s_max - c_s_surf) ** alpha ) diff --git a/pybamm/input/parameters/lithium_ion/OKane2022.py b/pybamm/input/parameters/lithium_ion/OKane2022.py index e3718fb9ee..930848268f 100644 --- a/pybamm/input/parameters/lithium_ion/OKane2022.py +++ b/pybamm/input/parameters/lithium_ion/OKane2022.py @@ -162,9 +162,7 @@ def graphite_LGM50_electrolyte_exchange_current_density_Chen2020( E_r = 35000 arrhenius = np.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T)) - return ( - m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 - ) + return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 def graphite_volume_change_Ai2020(sto, c_s_max): @@ -343,9 +341,7 @@ def nmc_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, c_s_m E_r = 17800 arrhenius = np.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T)) - return ( - m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 - ) + return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 def volume_change_Ai2020(sto, c_s_max): diff --git a/pybamm/input/parameters/lithium_ion/OKane2022_graphite_SiOx_halfcell.py b/pybamm/input/parameters/lithium_ion/OKane2022_graphite_SiOx_halfcell.py index e13d27fad0..31081af14a 100644 --- a/pybamm/input/parameters/lithium_ion/OKane2022_graphite_SiOx_halfcell.py +++ b/pybamm/input/parameters/lithium_ion/OKane2022_graphite_SiOx_halfcell.py @@ -192,9 +192,7 @@ def graphite_LGM50_electrolyte_exchange_current_density_Chen2020( E_r = 35000 arrhenius = pybamm.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T)) - return ( - m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 - ) + return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 def graphite_volume_change_Ai2020(sto, c_s_max): diff --git a/pybamm/input/parameters/lithium_ion/Prada2013.py b/pybamm/input/parameters/lithium_ion/Prada2013.py index 2d3d0e7ceb..421256af2a 100644 --- a/pybamm/input/parameters/lithium_ion/Prada2013.py +++ b/pybamm/input/parameters/lithium_ion/Prada2013.py @@ -70,9 +70,7 @@ def graphite_LGM50_electrolyte_exchange_current_density_Chen2020( E_r = 35000 arrhenius = np.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T)) - return ( - m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 - ) + return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 def LFP_ocp_Afshar2017(sto): @@ -131,9 +129,7 @@ def LFP_electrolyte_exchange_current_density_kashkooli2017(c_e, c_s_surf, c_s_ma E_r = 39570 arrhenius = np.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T)) - return ( - m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 - ) + return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 def electrolyte_conductivity_Prada2013(c_e, T): diff --git a/pybamm/input/parameters/lithium_ion/Ramadass2004.py b/pybamm/input/parameters/lithium_ion/Ramadass2004.py index 13aa86fe8e..4269acf1e9 100644 --- a/pybamm/input/parameters/lithium_ion/Ramadass2004.py +++ b/pybamm/input/parameters/lithium_ion/Ramadass2004.py @@ -89,9 +89,7 @@ def graphite_electrolyte_exchange_current_density_Ramadass2004( E_r = 37480 arrhenius = np.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T)) - return ( - m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 - ) + return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 def graphite_entropic_change_Moura2016(sto, c_s_max): @@ -227,9 +225,7 @@ def lico2_electrolyte_exchange_current_density_Ramadass2004(c_e, c_s_surf, c_s_m E_r = 39570 arrhenius = np.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T)) - return ( - m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 - ) + return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 def lico2_entropic_change_Moura2016(sto, c_s_max): diff --git a/pybamm/install_odes.py b/pybamm/install_odes.py index a51c9eea76..0680c5acdb 100644 --- a/pybamm/install_odes.py +++ b/pybamm/install_odes.py @@ -100,13 +100,11 @@ def update_LD_LIBRARY_PATH(install_dir): if export_statement not in fh.read(): fh.write(export_statement) print( - f"Adding {install_dir}/lib to LD_LIBRARY_PATH" - f" in {script_path}" + f"Adding {install_dir}/lib to LD_LIBRARY_PATH" f" in {script_path}" ) def main(arguments=None): - log_format = "%(asctime)s - %(name)s - %(levelname)s - %(message)s" logger = logging.getLogger("scikits.odes setup") diff --git a/pybamm/meshes/one_dimensional_submeshes.py b/pybamm/meshes/one_dimensional_submeshes.py index d68745daec..d6c3c7f78e 100644 --- a/pybamm/meshes/one_dimensional_submeshes.py +++ b/pybamm/meshes/one_dimensional_submeshes.py @@ -302,9 +302,7 @@ def __init__(self, lims, npts, edges=None): """User-suppled edges has should have length (npts + 1) but has length {}.Number of points (npts) for domain {} is {}.""".format( len(edges), spatial_var.domain, npts - ).replace( - "\n ", " " - ) + ).replace("\n ", " ") ) # check end points of edges agree with spatial_lims diff --git a/pybamm/models/base_model.py b/pybamm/models/base_model.py index 3da6b53618..6b45aeb083 100644 --- a/pybamm/models/base_model.py +++ b/pybamm/models/base_model.py @@ -437,13 +437,19 @@ def get_parameter_info(self): if not input_param.domain: parameter_info[input_param.name] = (input_param, "InputParameter") else: - parameter_info[input_param.name] = (input_param, f"InputParameter in {input_param.domain}") + parameter_info[input_param.name] = ( + input_param, + f"InputParameter in {input_param.domain}", + ) function_parameters = self._find_symbols(pybamm.FunctionParameter) for func_param in function_parameters: if func_param.name not in parameter_info: - input_names = "', '".join(func_param.input_names) - parameter_info[func_param.name] = (func_param, f"FunctionParameter with inputs(s) '{input_names}'") + input_names = "', '".join(func_param.input_names) + parameter_info[func_param.name] = ( + func_param, + f"FunctionParameter with inputs(s) '{input_names}'", + ) return parameter_info @@ -454,21 +460,35 @@ def print_parameter_info(self): max_param_type_length = 0 for param, param_type in info.values(): - param_name_length = len(getattr(param, 'name', str(param))) + param_name_length = len(getattr(param, "name", str(param))) param_type_length = len(param_type) max_param_name_length = max(max_param_name_length, param_name_length) max_param_type_length = max(max_param_type_length, param_type_length) - header_format = f"| {{:<{max_param_name_length}}} | {{:<{max_param_type_length}}} |" - row_format = f"| {{:<{max_param_name_length}}} | {{:<{max_param_type_length}}} |" + header_format = ( + f"| {{:<{max_param_name_length}}} | {{:<{max_param_type_length}}} |" + ) + row_format = ( + f"| {{:<{max_param_name_length}}} | {{:<{max_param_type_length}}} |" + ) - table = [header_format.format("Parameter", "Type of parameter"), - header_format.format("=" * max_param_name_length, "=" * max_param_type_length)] + table = [ + header_format.format("Parameter", "Type of parameter"), + header_format.format( + "=" * max_param_name_length, "=" * max_param_type_length + ), + ] for param, param_type in info.values(): - param_name = getattr(param, 'name', str(param)) - param_name_lines = [param_name[i:i + max_param_name_length] for i in range(0, len(param_name), max_param_name_length)] - param_type_lines = [param_type[i:i + max_param_type_length] for i in range(0, len(param_type), max_param_type_length)] + param_name = getattr(param, "name", str(param)) + param_name_lines = [ + param_name[i : i + max_param_name_length] + for i in range(0, len(param_name), max_param_name_length) + ] + param_type_lines = [ + param_type[i : i + max_param_type_length] + for i in range(0, len(param_type), max_param_type_length) + ] max_lines = max(len(param_name_lines), len(param_type_lines)) for i in range(max_lines): @@ -612,9 +632,7 @@ def build_model_equations(self): ) submodel.set_initial_conditions(self.variables) submodel.set_events(self.variables) - pybamm.logger.verbose( - f"Updating {submodel_name} submodel ({self.name})" - ) + pybamm.logger.verbose(f"Updating {submodel_name} submodel ({self.name})") self.update(submodel) self.check_no_repeated_keys() @@ -1360,9 +1378,7 @@ def check_and_convert_bcs(self, boundary_conditions): raise pybamm.ModelError( """ boundary condition types must be Dirichlet or Neumann, not '{}' - """.format( - bc[1] - ) + """.format(bc[1]) ) return boundary_conditions diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py index dea066db08..4886251e0a 100644 --- a/pybamm/models/full_battery_models/base_battery_model.py +++ b/pybamm/models/full_battery_models/base_battery_model.py @@ -631,28 +631,26 @@ def __init__(self, extra_options): value = (value,) else: if not ( - - option - in [ - "diffusivity", - "exchange-current density", - "intercalation kinetics", - "interface utilisation", - "lithium plating", - "loss of active material", - "number of MSMR reactions", - "open-circuit potential", - "particle", - "particle mechanics", - "particle phases", - "particle size", - "SEI", - "SEI on cracks", - "stress-induced diffusion", - ] - and isinstance(value, tuple) - and len(value) == 2 - + option + in [ + "diffusivity", + "exchange-current density", + "intercalation kinetics", + "interface utilisation", + "lithium plating", + "loss of active material", + "number of MSMR reactions", + "open-circuit potential", + "particle", + "particle mechanics", + "particle phases", + "particle size", + "SEI", + "SEI on cracks", + "stress-induced diffusion", + ] + and isinstance(value, tuple) + and len(value) == 2 ): # more possible options that can take 2-tuples to be added # as they come @@ -1071,9 +1069,7 @@ def build_model_equations(self): ) submodel.set_initial_conditions(self.variables) submodel.set_events(self.variables) - pybamm.logger.verbose( - f"Updating {submodel_name} submodel ({self.name})" - ) + pybamm.logger.verbose(f"Updating {submodel_name} submodel ({self.name})") self.update(submodel) self.check_no_repeated_keys() diff --git a/pybamm/models/submodels/convection/through_cell/explicit_convection.py b/pybamm/models/submodels/convection/through_cell/explicit_convection.py index 7d83c550ec..33b58e2b23 100644 --- a/pybamm/models/submodels/convection/through_cell/explicit_convection.py +++ b/pybamm/models/submodels/convection/through_cell/explicit_convection.py @@ -39,11 +39,7 @@ def get_coupled_variables(self, variables): x_p = pybamm.standard_spatial_vars.x_p DeltaV_k = param.p.DeltaV p_k = ( - -DeltaV_k - * a_j_k_av - / param.F - * ((x_p - 1) ** 2 - param.p.L**2) - / 2 + -DeltaV_k * a_j_k_av / param.F * ((x_p - 1) ** 2 - param.p.L**2) / 2 + p_s ) v_box_k = -DeltaV_k * a_j_k_av / param.F * (x_p - param.L_x) diff --git a/pybamm/models/submodels/interface/lithium_plating/base_plating.py b/pybamm/models/submodels/interface/lithium_plating/base_plating.py index 5b7a7a5b7f..ebfbe46831 100644 --- a/pybamm/models/submodels/interface/lithium_plating/base_plating.py +++ b/pybamm/models/submodels/interface/lithium_plating/base_plating.py @@ -111,10 +111,14 @@ def _get_standard_concentration_variables(self, c_plated_Li, c_dead_Li): f"X-averaged {domain} lithium plating thickness [m]": L_plated_Li_av, f"{Domain} dead lithium thickness [m]": L_dead_Li, f"X-averaged {domain} dead lithium thickness [m]": L_dead_Li_av, - f"Loss of lithium to {domain} lithium plating " - "[mol]": (Q_plated_Li + Q_dead_Li), - f"Loss of capacity to {domain} lithium plating " - "[A.h]": (Q_plated_Li + Q_dead_Li) * param.F / 3600, + f"Loss of lithium to {domain} lithium plating " "[mol]": ( + Q_plated_Li + Q_dead_Li + ), + f"Loss of capacity to {domain} lithium plating " "[A.h]": ( + Q_plated_Li + Q_dead_Li + ) + * param.F + / 3600, } return variables diff --git a/pybamm/models/submodels/interface/total_interfacial_current.py b/pybamm/models/submodels/interface/total_interfacial_current.py index a9094c4448..79e13e37f6 100644 --- a/pybamm/models/submodels/interface/total_interfacial_current.py +++ b/pybamm/models/submodels/interface/total_interfacial_current.py @@ -110,7 +110,7 @@ def _get_coupled_variables_by_phase_and_domain(self, variables, domain, phase_na new_variables[ f"Sum of {domain} electrode {phase_name}" "electrolyte reaction source terms [A.m-3]" - ] += (s_k * a_j_k) + ] += s_k * a_j_k new_variables[ f"Sum of x-averaged {domain} electrode {phase_name}" "electrolyte reaction source terms [A.m-3]" diff --git a/pybamm/models/submodels/particle/base_particle.py b/pybamm/models/submodels/particle/base_particle.py index 0f46615724..dab48b5f79 100644 --- a/pybamm/models/submodels/particle/base_particle.py +++ b/pybamm/models/submodels/particle/base_particle.py @@ -199,9 +199,7 @@ def _get_distribution_variables(self, R): f_v_dist = R * f_a_dist / pybamm.Integral(R * f_a_dist, R) # [m-1] # Number-based particle-size distribution - f_num_dist = (f_a_dist / R**2) / pybamm.Integral( - f_a_dist / R**2, R - ) # [m-1] + f_num_dist = (f_a_dist / R**2) / pybamm.Integral(f_a_dist / R**2, R) # [m-1] # True mean radii and standard deviations, calculated from the f_a_dist that # was given, all have units [m] diff --git a/pybamm/models/submodels/particle/msmr_diffusion.py b/pybamm/models/submodels/particle/msmr_diffusion.py index 65ab913e97..c53f313ab4 100644 --- a/pybamm/models/submodels/particle/msmr_diffusion.py +++ b/pybamm/models/submodels/particle/msmr_diffusion.py @@ -262,9 +262,7 @@ def get_coupled_variables(self, variables): N_s = c_max * x * (1 - x) * f * D_eff * pybamm.grad(U) variables.update( { - f"{Domain} {phase_name}particle rhs [V.s-1]": -( - 1 / (R_broad_nondim**2) - ) + f"{Domain} {phase_name}particle rhs [V.s-1]": -(1 / (R_broad_nondim**2)) * pybamm.div(N_s) / c_max / dxdU, diff --git a/pybamm/models/submodels/particle/x_averaged_polynomial_profile.py b/pybamm/models/submodels/particle/x_averaged_polynomial_profile.py index 45497c4c54..8b4b7ffe7c 100644 --- a/pybamm/models/submodels/particle/x_averaged_polynomial_profile.py +++ b/pybamm/models/submodels/particle/x_averaged_polynomial_profile.py @@ -203,8 +203,7 @@ def get_coupled_variables(self, variables): # The flux may be computed directly from the polynomial for c N_s_xav = -D_eff_xav * ( (-70 * c_s_surf_xav + 20 * q_s_av * R + 70 * c_s_av) * r / R**2 - + (105 * c_s_surf_xav - 28 * q_s_av * R - 105 * c_s_av) - * (r**3 / R**4) + + (105 * c_s_surf_xav - 28 * q_s_av * R - 105 * c_s_av) * (r**3 / R**4) ) N_s = pybamm.SecondaryBroadcast(N_s_xav, [f"{domain} electrode"]) diff --git a/pybamm/models/submodels/particle_mechanics/base_mechanics.py b/pybamm/models/submodels/particle_mechanics/base_mechanics.py index 35adadf47d..4e25becbab 100644 --- a/pybamm/models/submodels/particle_mechanics/base_mechanics.py +++ b/pybamm/models/submodels/particle_mechanics/base_mechanics.py @@ -50,7 +50,7 @@ def _get_mechanical_results(self, variables): ) eps_s = variables[f"{Domain} electrode active material volume fraction"] - #use a tangential approximation for omega + # use a tangential approximation for omega sto = variables[f"{Domain} particle concentration"] Omega = pybamm.r_average(domain_param.Omega(sto, T)) R0 = domain_param.prim.R diff --git a/pybamm/models/submodels/thermal/base_thermal.py b/pybamm/models/submodels/thermal/base_thermal.py index 27e52d6638..b2a79c52d5 100644 --- a/pybamm/models/submodels/thermal/base_thermal.py +++ b/pybamm/models/submodels/thermal/base_thermal.py @@ -117,7 +117,7 @@ def _get_standard_coupled_variables(self, variables): # Total Ohmic heating Q_ohm = Q_ohm_s + Q_ohm_e - num_phases = int(getattr(self.options, 'positive')["particle phases"]) + num_phases = int(getattr(self.options, "positive")["particle phases"]) phase_names = [""] if num_phases > 1: phase_names = ["primary ", "secondary "] @@ -135,8 +135,7 @@ def _get_standard_coupled_variables(self, variables): dUdT_p = variables[f"Positive electrode {phase}entropic change [V.K-1]"] Q_rev_p += a_j_p * T_p * dUdT_p - - num_phases = int(getattr(self.options, 'negative')["particle phases"]) + num_phases = int(getattr(self.options, "negative")["particle phases"]) phase_names = [""] if num_phases > 1: phase_names = ["primary", "secondary"] @@ -156,7 +155,9 @@ def _get_standard_coupled_variables(self, variables): a_j_n = variables[ f"Negative electrode {phase}volumetric interfacial current density [A.m-3]" ] - eta_r_n = variables[f"Negative electrode {phase}reaction overpotential [V]"] + eta_r_n = variables[ + f"Negative electrode {phase}reaction overpotential [V]" + ] # Irreversible electrochemical heating Q_rxn_n += a_j_n * eta_r_n diff --git a/pybamm/parameters/bpx.py b/pybamm/parameters/bpx.py index 8efd26cd57..a97288b062 100644 --- a/pybamm/parameters/bpx.py +++ b/pybamm/parameters/bpx.py @@ -261,12 +261,12 @@ def _positive_electrode_entropic_change(sto, c_s_max): "Maximum concentration in " + negative_electrode.pre_name.lower() + "[mol.m-3]" ] k_n_norm = pybamm_dict[ - negative_electrode.pre_name - + "reaction rate constant [mol.m-2.s-1]" + negative_electrode.pre_name + "reaction rate constant [mol.m-2.s-1]" ] Ea_k_n = pybamm_dict.get( negative_electrode.pre_name - + "reaction rate constant activation energy [J.mol-1]", 0.0 + + "reaction rate constant activation energy [J.mol-1]", + 0.0, ) # Note that in BPX j = 2*F*k_norm*sqrt((ce/ce0)*(c/c_max)*(1-c/c_max))*sinh(...), # and in PyBaMM j = 2*k*sqrt(ce*c*(c_max - c))*sinh(...) @@ -292,12 +292,12 @@ def _negative_electrode_exchange_current_density(c_e, c_s_surf, c_s_max, T): "Maximum concentration in " + positive_electrode.pre_name.lower() + "[mol.m-3]" ] k_p_norm = pybamm_dict[ - positive_electrode.pre_name - + "reaction rate constant [mol.m-2.s-1]" + positive_electrode.pre_name + "reaction rate constant [mol.m-2.s-1]" ] Ea_k_p = pybamm_dict.get( positive_electrode.pre_name - + "reaction rate constant activation energy [J.mol-1]", 0.0 + + "reaction rate constant activation energy [J.mol-1]", + 0.0, ) # Note that in BPX j = 2*F*k_norm*sqrt((ce/ce0)*(c/c_max)*(1-c/c_max))*sinh(...), # and in PyBaMM j = 2*k*sqrt(ce*c*(c_max - c))*sinh(...) diff --git a/pybamm/parameters/parameter_values.py b/pybamm/parameters/parameter_values.py index be842a7bca..5dcb3c950a 100644 --- a/pybamm/parameters/parameter_values.py +++ b/pybamm/parameters/parameter_values.py @@ -295,8 +295,7 @@ def set_initial_stoichiometry_half_cell( { "Initial concentration in {} electrode [mol.m-3]".format( options["working electrode"] - ): x - * c_max + ): x * c_max } ) return parameter_values @@ -392,9 +391,7 @@ def process_model(self, unprocessed_model, inplace=True): `model.variables = {}`) """ - pybamm.logger.info( - f"Start setting parameters for {unprocessed_model.name}" - ) + pybamm.logger.info(f"Start setting parameters for {unprocessed_model.name}") # set up inplace vs not inplace if inplace: @@ -414,18 +411,14 @@ def process_model(self, unprocessed_model, inplace=True): new_rhs = {} for variable, equation in unprocessed_model.rhs.items(): - pybamm.logger.verbose( - f"Processing parameters for {variable!r} (rhs)" - ) + pybamm.logger.verbose(f"Processing parameters for {variable!r} (rhs)") new_variable = self.process_symbol(variable) new_rhs[new_variable] = self.process_symbol(equation) model.rhs = new_rhs new_algebraic = {} for variable, equation in unprocessed_model.algebraic.items(): - pybamm.logger.verbose( - f"Processing parameters for {variable!r} (algebraic)" - ) + pybamm.logger.verbose(f"Processing parameters for {variable!r} (algebraic)") new_variable = self.process_symbol(variable) new_algebraic[new_variable] = self.process_symbol(equation) model.algebraic = new_algebraic @@ -443,17 +436,13 @@ def process_model(self, unprocessed_model, inplace=True): new_variables = {} for variable, equation in unprocessed_model.variables.items(): - pybamm.logger.verbose( - f"Processing parameters for {variable!r} (variables)" - ) + pybamm.logger.verbose(f"Processing parameters for {variable!r} (variables)") new_variables[variable] = self.process_symbol(equation) model.variables = new_variables new_events = [] for event in unprocessed_model.events: - pybamm.logger.verbose( - f"Processing parameters for event '{event.name}''" - ) + pybamm.logger.verbose(f"Processing parameters for event '{event.name}''") new_events.append( pybamm.Event( event.name, self.process_symbol(event.expression), event.event_type @@ -462,9 +451,7 @@ def process_model(self, unprocessed_model, inplace=True): interpolant_events = self._get_interpolant_events(model) for event in interpolant_events: - pybamm.logger.verbose( - f"Processing parameters for event '{event.name}''" - ) + pybamm.logger.verbose(f"Processing parameters for event '{event.name}''") new_events.append( pybamm.Event( event.name, self.process_symbol(event.expression), event.event_type diff --git a/pybamm/parameters/process_parameter_data.py b/pybamm/parameters/process_parameter_data.py index 8998c6e583..03b3c2b54d 100644 --- a/pybamm/parameters/process_parameter_data.py +++ b/pybamm/parameters/process_parameter_data.py @@ -35,7 +35,7 @@ def process_1D_data(name, path=None): """ filename, name = _process_name(name, path, ".csv") - data = np.genfromtxt(filename, delimiter=',', skip_header=1) + data = np.genfromtxt(filename, delimiter=",", skip_header=1) x = data[:, 0] y = data[:, 1] @@ -88,7 +88,7 @@ def process_2D_data_csv(name, path=None): filename, name = _process_name(name, path, ".csv") - data = np.genfromtxt(filename, delimiter=',',skip_header=1) + data = np.genfromtxt(filename, delimiter=",", skip_header=1) x1 = np.unique(data[:, 0]) x2 = np.unique(data[:, 1]) @@ -135,7 +135,7 @@ def process_3D_data_csv(name, path=None): filename, name = _process_name(name, path, ".csv") - data = np.genfromtxt(filename, delimiter=',',skip_header=1) + data = np.genfromtxt(filename, delimiter=",", skip_header=1) x1 = np.unique(data[:, 0]) x2 = np.unique(data[:, 1]) diff --git a/pybamm/plotting/plot2D.py b/pybamm/plotting/plot2D.py index d4f6d31e3a..3a69bab803 100644 --- a/pybamm/plotting/plot2D.py +++ b/pybamm/plotting/plot2D.py @@ -54,7 +54,7 @@ def plot2D(x, y, z, ax=None, testing=False, **kwargs): z.entries, vmin=ax_min(z.entries), vmax=ax_max(z.entries), - **kwargs + **kwargs, ) plt.colorbar(plot, ax=ax) diff --git a/pybamm/plotting/plot_voltage_components.py b/pybamm/plotting/plot_voltage_components.py index a681094bea..ef95f7016f 100644 --- a/pybamm/plotting/plot_voltage_components.py +++ b/pybamm/plotting/plot_voltage_components.py @@ -12,7 +12,7 @@ def plot_voltage_components( show_legend=True, split_by_electrode=False, testing=False, - **kwargs_fill + **kwargs_fill, ): """ Generate a plot showing the component overpotentials that make up the voltage @@ -105,14 +105,14 @@ def plot_voltage_components( initial_ocv - delta_ocp_n, initial_ocv, **kwargs_fill, - label="Negative open-circuit potential" + label="Negative open-circuit potential", ) ax.fill_between( time, initial_ocv - delta_ocp_n + delta_ocp_p, initial_ocv - delta_ocp_n, **kwargs_fill, - label="Positive open-circuit potential" + label="Positive open-circuit potential", ) ocv = initial_ocv - delta_ocp_n + delta_ocp_p top = ocv @@ -138,8 +138,9 @@ def plot_voltage_components( ax.set_xlim([time[0], time[-1]]) ax.set_xlabel("Time [h]") - y_min, y_max = 0.98 * min(np.nanmin(V), np.nanmin(ocv)), 1.02 * ( - max(np.nanmax(V), np.nanmax(ocv)) + y_min, y_max = ( + 0.98 * min(np.nanmin(V), np.nanmin(ocv)), + 1.02 * (max(np.nanmax(V), np.nanmax(ocv))), ) ax.set_ylim([y_min, y_max]) diff --git a/pybamm/settings.py b/pybamm/settings.py index 30b4c3aa0a..2ccd9bcd13 100644 --- a/pybamm/settings.py +++ b/pybamm/settings.py @@ -66,9 +66,7 @@ def min_max_mode(self): @min_max_mode.setter def min_max_mode(self, mode): if mode not in ["exact", "soft", "smooth"]: - raise ValueError( - "Smoothing mode must be 'exact', 'soft', or 'smooth'" - ) + raise ValueError("Smoothing mode must be 'exact', 'soft', or 'smooth'") self._min_max_mode = mode @property @@ -78,13 +76,9 @@ def min_max_smoothing(self): @min_max_smoothing.setter def min_max_smoothing(self, k): if self._min_max_mode == "soft" and k <= 0: - raise ValueError( - "Smoothing parameter must be a strictly positive number" - ) + raise ValueError("Smoothing parameter must be a strictly positive number") if self._min_max_mode == "smooth" and k < 1: - raise ValueError( - "Smoothing parameter must be greater than 1" - ) + raise ValueError("Smoothing parameter must be greater than 1") self._min_max_smoothing = k @property diff --git a/pybamm/simulation.py b/pybamm/simulation.py index 5c2cf0bff1..c95ab3039c 100644 --- a/pybamm/simulation.py +++ b/pybamm/simulation.py @@ -577,9 +577,7 @@ def solve( capture the input. Try refining t_eval. Alternatively, passing t_eval = None automatically sets t_eval to be the points in the data. - """.format( - dt_eval_max, dt_data_min - ), + """.format(dt_eval_max, dt_data_min), pybamm.SolverWarning, ) diff --git a/pybamm/solvers/base_solver.py b/pybamm/solvers/base_solver.py index 76cf3e9367..69de3be968 100644 --- a/pybamm/solvers/base_solver.py +++ b/pybamm/solvers/base_solver.py @@ -684,9 +684,7 @@ def calculate_consistent_state(self, model, time=0, inputs=None): try: root_sol = self.root_method._integrate(model, np.array([time]), inputs) except pybamm.SolverError as e: - raise pybamm.SolverError( - f"Could not find consistent states: {e.args[0]}" - ) + raise pybamm.SolverError(f"Could not find consistent states: {e.args[0]}") pybamm.logger.debug("Found consistent states") self.check_extrapolation(root_sol, model.events) @@ -1044,9 +1042,7 @@ def _get_discontinuity_start_end_indices(self, model, inputs, t_eval): # remove any discontinuities after end of t_eval discontinuities = [v for v in discontinuities if v < t_eval[-1]] - pybamm.logger.verbose( - f"Discontinuity events found at t = {discontinuities}" - ) + pybamm.logger.verbose(f"Discontinuity events found at t = {discontinuities}") if isinstance(inputs, list): raise pybamm.SolverError( "Cannot solve for a list of input parameters" @@ -1205,9 +1201,7 @@ def step( isinstance(old_solution, pybamm.EmptySolution) and old_solution.termination is None ): - pybamm.logger.verbose( - f"Start stepping {model.name} with {self.name}" - ) + pybamm.logger.verbose(f"Start stepping {model.name} with {self.name}") if isinstance(old_solution, pybamm.EmptySolution): if not first_step_this_model: @@ -1236,9 +1230,7 @@ def step( self._check_events_with_initial_conditions(t_eval, model, model_inputs) # Step - pybamm.logger.verbose( - f"Stepping for {t_start_shifted:.0f} < t < {t_end:.0f}" - ) + pybamm.logger.verbose(f"Stepping for {t_start_shifted:.0f} < t < {t_end:.0f}") timer.reset() solution = self._integrate(model, t_eval, model_inputs) solution.solve_time = timer.time() @@ -1475,10 +1467,8 @@ def report(string): jacp = None if model.calculate_sensitivities: report( - - f"Calculating sensitivities for {name} with respect " - f"to parameters {model.calculate_sensitivities} using jax" - + f"Calculating sensitivities for {name} with respect " + f"to parameters {model.calculate_sensitivities} using jax" ) jacp = func.get_sensitivities() if use_jacobian: @@ -1496,10 +1486,8 @@ def report(string): # to python evaluator if model.calculate_sensitivities: report( - - f"Calculating sensitivities for {name} with respect " - f"to parameters {model.calculate_sensitivities}" - + f"Calculating sensitivities for {name} with respect " + f"to parameters {model.calculate_sensitivities}" ) jacp_dict = { p: symbol.diff(pybamm.InputParameter(p)) @@ -1602,11 +1590,9 @@ def jacp(*args, **kwargs): casadi_expression = casadi.vertcat(x0, Sx_0, z0, Sz_0) elif model.calculate_sensitivities: report( - - f"Calculating sensitivities for {name} with respect " - f"to parameters {model.calculate_sensitivities} using " - "CasADi" - + f"Calculating sensitivities for {name} with respect " + f"to parameters {model.calculate_sensitivities} using " + "CasADi" ) # Compute derivate wrt p-stacked (can be passed to solver to # compute sensitivities online) diff --git a/pybamm/solvers/casadi_algebraic_solver.py b/pybamm/solvers/casadi_algebraic_solver.py index cdde5bb99c..ec7305906a 100644 --- a/pybamm/solvers/casadi_algebraic_solver.py +++ b/pybamm/solvers/casadi_algebraic_solver.py @@ -153,9 +153,7 @@ def _integrate(self, model, t_eval, inputs_dict=None): Could not find acceptable solution: solver terminated successfully, but maximum solution error ({}) above tolerance ({}) - """.format( - casadi.mmax(casadi.fabs(fun)), self.tol - ) + """.format(casadi.mmax(casadi.fabs(fun)), self.tol) ) # Concatenate differential part diff --git a/pybamm/solvers/casadi_solver.py b/pybamm/solvers/casadi_solver.py index 6ee8758de3..02ff4a2cd9 100644 --- a/pybamm/solvers/casadi_solver.py +++ b/pybamm/solvers/casadi_solver.py @@ -183,9 +183,7 @@ def _integrate(self, model, t_eval, inputs_dict=None): t = t_eval[0] t_f = t_eval[-1] - pybamm.logger.debug( - f"Start solving {model.name} with {self.name}" - ) + pybamm.logger.debug(f"Start solving {model.name} with {self.name}") if self.mode == "safe without grid": # in "safe without grid" mode, diff --git a/pybamm/solvers/idaklu_solver.py b/pybamm/solvers/idaklu_solver.py index d9819f1608..6c81bf91e7 100644 --- a/pybamm/solvers/idaklu_solver.py +++ b/pybamm/solvers/idaklu_solver.py @@ -281,10 +281,14 @@ def resfn(t, y, inputs, ydot): # Convert derivative functions for sensitivities if (len(inputs) > 0) and (model.calculate_sensitivities): self.dvar_dy_idaklu_fcns.append( - idaklu.generate_function(self.computed_dvar_dy_fcns[key].serialize()) + idaklu.generate_function( + self.computed_dvar_dy_fcns[key].serialize() + ) ) self.dvar_dp_idaklu_fcns.append( - idaklu.generate_function(self.computed_dvar_dp_fcns[key].serialize()) + idaklu.generate_function( + self.computed_dvar_dp_fcns[key].serialize() + ) ) else: diff --git a/pybamm/solvers/jax_bdf_solver.py b/pybamm/solvers/jax_bdf_solver.py index 8f5b8ed817..b193b945e7 100644 --- a/pybamm/solvers/jax_bdf_solver.py +++ b/pybamm/solvers/jax_bdf_solver.py @@ -217,9 +217,7 @@ def _bdf_init(fun, jac, mass, t0, y0, h0, rtol, atol): state["rtol"] = rtol state["M"] = mass EPS = jnp.finfo(y0.dtype).eps - state["newton_tol"] = jnp.maximum( - 10 * EPS / rtol, jnp.minimum(0.03, rtol**0.5) - ) + state["newton_tol"] = jnp.maximum(10 * EPS / rtol, jnp.minimum(0.03, rtol**0.5)) scale_y0 = atol + rtol * jnp.abs(y0) y0, not_converged = _select_initial_conditions( @@ -645,7 +643,8 @@ def while_body(while_state): # try again (state, updated_jacobian) = tree_map( partial( - jnp.where, not_converged * (updated_jacobian == False) # noqa: E712 + jnp.where, + not_converged * (updated_jacobian == False), # noqa: E712 ), (_update_jacobian(state, jac), True), (state, False + updated_jacobian), @@ -883,7 +882,12 @@ def arg_dicts_to_values(args): """ return sum((tuple(b.values()) for b in args if isinstance(b, dict)), ()) - aug_mass = (mass, mass, onp.array(1.0), *arg_dicts_to_values(tree_map(arg_to_identity, args))) + aug_mass = ( + mass, + mass, + onp.array(1.0), + *arg_dicts_to_values(tree_map(arg_to_identity, args)), + ) def scan_fun(carry, i): y_bar, t0_bar, args_bar = carry diff --git a/pybamm/solvers/scipy_solver.py b/pybamm/solvers/scipy_solver.py index e0065cf4ec..fb320f558d 100644 --- a/pybamm/solvers/scipy_solver.py +++ b/pybamm/solvers/scipy_solver.py @@ -123,7 +123,7 @@ def event_fn(t, y): t_eval=t_eval, method=self.method, dense_output=True, - **extra_options + **extra_options, ) integration_time = timer.time() diff --git a/pybamm/spatial_methods/finite_volume.py b/pybamm/spatial_methods/finite_volume.py index 84f76a2bbd..11313a1450 100644 --- a/pybamm/spatial_methods/finite_volume.py +++ b/pybamm/spatial_methods/finite_volume.py @@ -1395,8 +1395,7 @@ def upwind_or_downwind(self, symbol, discretised_symbol, bcs, direction): if symbol not in bcs: raise pybamm.ModelError( - "Boundary conditions must be provided for " - f"{direction}ing '{symbol}'" + "Boundary conditions must be provided for " f"{direction}ing '{symbol}'" ) if direction == "upwind": diff --git a/pybamm/spatial_methods/spectral_volume.py b/pybamm/spatial_methods/spectral_volume.py index a10422813f..50e1cadf25 100644 --- a/pybamm/spatial_methods/spectral_volume.py +++ b/pybamm/spatial_methods/spectral_volume.py @@ -527,8 +527,7 @@ def replace_dirichlet_values(self, symbol, discretised_symbol, bcs): lbc_vector = pybamm.Vector(np.zeros(n * second_dim_repeats)) else: raise ValueError( - "boundary condition must be Dirichlet or Neumann, " - f"not '{lbc_type}'" + "boundary condition must be Dirichlet or Neumann, " f"not '{lbc_type}'" ) if rbc_type == "Dirichlet": @@ -543,8 +542,7 @@ def replace_dirichlet_values(self, symbol, discretised_symbol, bcs): rbc_vector = pybamm.Vector(np.zeros(n * second_dim_repeats)) else: raise ValueError( - "boundary condition must be Dirichlet or Neumann, " - f"not '{rbc_type}'" + "boundary condition must be Dirichlet or Neumann, " f"not '{rbc_type}'" ) bcs_vector = lbc_vector + rbc_vector @@ -621,8 +619,7 @@ def replace_neumann_values(self, symbol, discretised_gradient, bcs): lbc_vector = pybamm.Vector(np.zeros(n * second_dim_repeats)) else: raise ValueError( - "boundary condition must be Dirichlet or Neumann, " - f"not '{lbc_type}'" + "boundary condition must be Dirichlet or Neumann, " f"not '{lbc_type}'" ) if rbc_type == "Neumann": @@ -637,8 +634,7 @@ def replace_neumann_values(self, symbol, discretised_gradient, bcs): rbc_vector = pybamm.Vector(np.zeros(n * second_dim_repeats)) else: raise ValueError( - "boundary condition must be Dirichlet or Neumann, " - f"not '{rbc_type}'" + "boundary condition must be Dirichlet or Neumann, " f"not '{rbc_type}'" ) bcs_vector = lbc_vector + rbc_vector diff --git a/pybamm/util.py b/pybamm/util.py index 71883e3d27..8f76566171 100644 --- a/pybamm/util.py +++ b/pybamm/util.py @@ -271,10 +271,9 @@ def have_jax(): def is_jax_compatible(): """Check if the available version of jax and jaxlib are compatible with PyBaMM""" - return ( - importlib.metadata.distribution("jax").version.startswith(JAX_VERSION) - and importlib.metadata.distribution("jaxlib").version.startswith(JAXLIB_VERSION) - ) + return importlib.metadata.distribution("jax").version.startswith( + JAX_VERSION + ) and importlib.metadata.distribution("jaxlib").version.startswith(JAXLIB_VERSION) def is_constant_and_can_evaluate(symbol): @@ -346,6 +345,7 @@ def install_jax(arguments=None): # pragma: no cover ] ) + # https://docs.pybamm.org/en/latest/source/user_guide/contributing.html#managing-optional-dependencies-and-their-imports def have_optional_dependency(module_name, attribute=None): err_msg = f"Optional dependency {module_name} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details." @@ -360,7 +360,7 @@ def have_optional_dependency(module_name, attribute=None): return imported_attribute # Return the imported attribute else: # Raise an ModuleNotFoundError if the attribute is not available - raise ModuleNotFoundError(err_msg) # pragma: no cover + raise ModuleNotFoundError(err_msg) # pragma: no cover else: # Return the entire module if no attribute is specified return module diff --git a/scripts/fix_casadi_rpath_mac.py b/scripts/fix_casadi_rpath_mac.py index 3f7f71e834..32f98d2945 100644 --- a/scripts/fix_casadi_rpath_mac.py +++ b/scripts/fix_casadi_rpath_mac.py @@ -57,15 +57,17 @@ # This is needed for the casadi python bindings to work while repairing the wheel subprocess.run( - ["cp", - os.path.join(casadi_dir, libcasadi_37_name), - os.path.join(os.getenv("HOME"),".local/lib") + [ + "cp", + os.path.join(casadi_dir, libcasadi_37_name), + os.path.join(os.getenv("HOME"), ".local/lib"), ] ) subprocess.run( - ["cp", - os.path.join(casadi_dir, libcpp_name), - os.path.join(os.getenv("HOME"),".local/lib") + [ + "cp", + os.path.join(casadi_dir, libcpp_name), + os.path.join(os.getenv("HOME"), ".local/lib"), ] ) diff --git a/scripts/update_version.py b/scripts/update_version.py index 8cbc51f1ee..30d2240e9c 100644 --- a/scripts/update_version.py +++ b/scripts/update_version.py @@ -19,7 +19,6 @@ def update_version(): release_version = os.getenv("VERSION")[1:] last_day_of_month = date.today() + relativedelta(day=31) - # pybamm/version.py with open(os.path.join(pybamm.root_dir(), "pybamm", "version.py"), "r+") as file: output = file.read() @@ -33,9 +32,7 @@ def update_version(): # pyproject.toml with open(os.path.join(pybamm.root_dir(), "pyproject.toml"), "r+") as file: output = file.read() - replace_version = re.sub( - '(?<=version = ")(.+)(?=")', release_version, output - ) + replace_version = re.sub('(?<=version = ")(.+)(?=")', release_version, output) file.truncate(0) file.seek(0) file.write(replace_version) diff --git a/setup.py b/setup.py index 2c89603b74..b53ed50c39 100644 --- a/setup.py +++ b/setup.py @@ -17,27 +17,30 @@ # ---------- set environment variables for vcpkg on Windows ---------------------------- + def set_vcpkg_environment_variables(): if not os.getenv("VCPKG_ROOT_DIR"): raise OSError("Environment variable 'VCPKG_ROOT_DIR' is undefined.") if not os.getenv("VCPKG_DEFAULT_TRIPLET"): - raise OSError( - "Environment variable 'VCPKG_DEFAULT_TRIPLET' is undefined." - ) + raise OSError("Environment variable 'VCPKG_DEFAULT_TRIPLET' is undefined.") if not os.getenv("VCPKG_FEATURE_FLAGS"): - raise OSError( - "Environment variable 'VCPKG_FEATURE_FLAGS' is undefined." - ) + raise OSError("Environment variable 'VCPKG_FEATURE_FLAGS' is undefined.") return ( os.getenv("VCPKG_ROOT_DIR"), os.getenv("VCPKG_DEFAULT_TRIPLET"), os.getenv("VCPKG_FEATURE_FLAGS"), ) + # ---------- CMakeBuild class (custom build_ext for IDAKLU target) --------------------- + class CMakeBuild(build_ext): - user_options = [*build_ext.user_options, ("suitesparse-root=", None, "suitesparse source location"), ("sundials-root=", None, "sundials source location")] + user_options = [ + *build_ext.user_options, + ("suitesparse-root=", None, "suitesparse source location"), + ("sundials-root=", None, "sundials source location"), + ] def initialize_options(self): build_ext.initialize_options(self) @@ -95,9 +98,7 @@ def run(self): f"-DSuiteSparse_ROOT={os.path.abspath(self.suitesparse_root)}" ) if self.sundials_root: - cmake_args.append( - f"-DSUNDIALS_ROOT={os.path.abspath(self.sundials_root)}" - ) + cmake_args.append(f"-DSUNDIALS_ROOT={os.path.abspath(self.sundials_root)}") build_dir = self.get_build_directory() if not os.path.exists(build_dir): @@ -110,7 +111,7 @@ def run(self): if os.path.isfile(os.path.join(build_dir, "CMakeError.log")): os.remove(os.path.join(build_dir, "CMakeError.log")) -# ---------- configuration for vcpkg on Windows ---------------------------------------- + # ---------- configuration for vcpkg on Windows ---------------------------------------- build_env = os.environ if os.getenv("PYBAMM_USE_VCPKG"): @@ -123,13 +124,16 @@ def run(self): build_env["vcpkg_default_triplet"] = vcpkg_default_triplet build_env["vcpkg_feature_flags"] = vcpkg_feature_flags -# ---------- Run CMake and build IDAKLU module ----------------------------------------- + # ---------- Run CMake and build IDAKLU module ----------------------------------------- cmake_list_dir = os.path.abspath(os.path.dirname(__file__)) print("-" * 10, "Running CMake for IDAKLU solver", "-" * 40) subprocess.run( - ["cmake", cmake_list_dir, *cmake_args], cwd=build_dir, env=build_env - , check=True) + ["cmake", cmake_list_dir, *cmake_args], + cwd=build_dir, + env=build_env, + check=True, + ) if os.path.isfile(os.path.join(build_dir, "CMakeError.log")): msg = ( @@ -193,7 +197,11 @@ def move_output(self, ext): class CustomInstall(install): """A custom install command to add 2 build options""" - user_options = [*install.user_options, ("suitesparse-root=", None, "suitesparse source location"), ("sundials-root=", None, "sundials source location")] + user_options = [ + *install.user_options, + ("suitesparse-root=", None, "suitesparse source location"), + ("sundials-root=", None, "sundials source location"), + ] def initialize_options(self): install.initialize_options(self) @@ -217,7 +225,11 @@ def run(self): class bdist_wheel(orig.bdist_wheel): """A custom install command to add 2 build options""" - user_options = [*orig.bdist_wheel.user_options, ("suitesparse-root=", None, "suitesparse source location"), ("sundials-root=", None, "sundials source location")] + user_options = [ + *orig.bdist_wheel.user_options, + ("suitesparse-root=", None, "suitesparse source location"), + ("sundials-root=", None, "sundials source location"), + ] def initialize_options(self): orig.bdist_wheel.initialize_options(self) @@ -270,6 +282,7 @@ def compile_KLU(): return CMakeFound and PyBind11Found + idaklu_ext = Extension( name="pybamm.solvers.idaklu", sources=[ diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py index 6e3beeb1fc..6694248b5d 100644 --- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py +++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py @@ -272,10 +272,9 @@ def test_negative_cracking(self): "r_n": 26, # negative particle "r_p": 26, # positive particle } - self.run_basic_processing_test(options, - parameter_values=parameter_values, - var_pts=var_pts - ) + self.run_basic_processing_test( + options, parameter_values=parameter_values, var_pts=var_pts + ) def test_positive_cracking(self): options = {"particle mechanics": ("none", "swelling and cracking")} @@ -287,10 +286,9 @@ def test_positive_cracking(self): "r_n": 26, # negative particle "r_p": 26, # positive particle } - self.run_basic_processing_test(options, - parameter_values=parameter_values, - var_pts=var_pts - ) + self.run_basic_processing_test( + options, parameter_values=parameter_values, var_pts=var_pts + ) def test_both_cracking(self): options = {"particle mechanics": "swelling and cracking"} @@ -302,10 +300,9 @@ def test_both_cracking(self): "r_n": 26, # negative particle "r_p": 26, # positive particle } - self.run_basic_processing_test(options, - parameter_values=parameter_values, - var_pts=var_pts - ) + self.run_basic_processing_test( + options, parameter_values=parameter_values, var_pts=var_pts + ) def test_both_swelling_only(self): options = {"particle mechanics": "swelling only"} diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py index 18d773bed2..e217a11d75 100644 --- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py +++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py @@ -56,10 +56,12 @@ def test_current_sigmoid_ocp(self): parameter_values = pybamm.get_size_distribution_parameters(parameter_values) parameter_values.update( { - "Negative electrode lithiation OCP [V]" - "": parameter_values["Negative electrode OCP [V]"], - "Negative electrode delithiation OCP [V]" - "": parameter_values["Negative electrode OCP [V]"], + "Negative electrode lithiation OCP [V]" "": parameter_values[ + "Negative electrode OCP [V]" + ], + "Negative electrode delithiation OCP [V]" "": parameter_values[ + "Negative electrode OCP [V]" + ], }, check_already_exists=False, ) diff --git a/tests/unit/test_experiments/test_experiment.py b/tests/unit/test_experiments/test_experiment.py index ec1a1cbeae..78ca39e6e8 100644 --- a/tests/unit/test_experiments/test_experiment.py +++ b/tests/unit/test_experiments/test_experiment.py @@ -227,6 +227,7 @@ def test_set_next_start_time(self): # TODO: once #3176 is completed, the test should pass for # operating_conditions_steps (or equivalent) as well + if __name__ == "__main__": print("Add -v for more debug output") import sys diff --git a/tests/unit/test_expression_tree/test_binary_operators.py b/tests/unit/test_expression_tree/test_binary_operators.py index ab582ade12..b6cbe093eb 100644 --- a/tests/unit/test_expression_tree/test_binary_operators.py +++ b/tests/unit/test_expression_tree/test_binary_operators.py @@ -104,9 +104,7 @@ def test_diff(self): self.assertEqual((a**b).diff(b).evaluate(y=y), 5**3 * np.log(5)) self.assertEqual((a**b).diff(a).evaluate(y=y), 3 * 5**2) self.assertEqual((a**b).diff(a**b).evaluate(), 1) - self.assertEqual( - (a**a).diff(a).evaluate(y=y), 5**5 * np.log(5) + 5 * 5**4 - ) + self.assertEqual((a**a).diff(a).evaluate(y=y), 5**5 * np.log(5) + 5 * 5**4) self.assertEqual((a**a).diff(b).evaluate(y=y), 0) # addition diff --git a/tests/unit/test_expression_tree/test_operations/test_evaluate_python.py b/tests/unit/test_expression_tree/test_operations/test_evaluate_python.py index 426e7811f6..552e79bc7e 100644 --- a/tests/unit/test_expression_tree/test_operations/test_evaluate_python.py +++ b/tests/unit/test_expression_tree/test_operations/test_evaluate_python.py @@ -45,9 +45,7 @@ def test_find_symbols(self): var_a = pybamm.id_to_python_variable(a.id) var_b = pybamm.id_to_python_variable(b.id) - self.assertEqual( - list(variable_symbols.values())[2], f"{var_a} + {var_b}" - ) + self.assertEqual(list(variable_symbols.values())[2], f"{var_a} + {var_b}") # test identical subtree constant_symbols = OrderedDict() @@ -65,14 +63,10 @@ def test_find_symbols(self): # test values of variable_symbols self.assertEqual(next(iter(variable_symbols.values())), "y[0:1]") self.assertEqual(list(variable_symbols.values())[1], "y[1:2]") - self.assertEqual( - list(variable_symbols.values())[2], f"{var_a} + {var_b}" - ) + self.assertEqual(list(variable_symbols.values())[2], f"{var_a} + {var_b}") var_child = pybamm.id_to_python_variable(expr.children[0].id) - self.assertEqual( - list(variable_symbols.values())[3], f"{var_child} + {var_b}" - ) + self.assertEqual(list(variable_symbols.values())[3], f"{var_child} + {var_b}") # test unary op constant_symbols = OrderedDict() @@ -107,9 +101,7 @@ def test_find_symbols(self): self.assertEqual(list(variable_symbols.keys())[1], expr.id) self.assertEqual(next(iter(variable_symbols.values())), "y[0:1]") var_funct = pybamm.id_to_python_variable(expr.id, True) - self.assertEqual( - list(variable_symbols.values())[1], f"{var_funct}({var_a})" - ) + self.assertEqual(list(variable_symbols.values())[1], f"{var_funct}({var_a})") # test matrix constant_symbols = OrderedDict() diff --git a/tests/unit/test_expression_tree/test_operations/test_jac.py b/tests/unit/test_expression_tree/test_operations/test_jac.py index 503e7321ea..d3572cafdc 100644 --- a/tests/unit/test_expression_tree/test_operations/test_jac.py +++ b/tests/unit/test_expression_tree/test_operations/test_jac.py @@ -77,9 +77,7 @@ def test_nonlinear(self): np.testing.assert_array_equal(jacobian, dfunc_dy.toarray()) func = 2**v - jacobian = np.array( - [[0, 0, 2**3 * np.log(2), 0], [0, 0, 0, 2**4 * np.log(2)]] - ) + jacobian = np.array([[0, 0, 2**3 * np.log(2), 0], [0, 0, 0, 2**4 * np.log(2)]]) dfunc_dy = func.jac(y).evaluate(y=y0) np.testing.assert_array_equal(jacobian, dfunc_dy.toarray()) diff --git a/tests/unit/test_meshes/test_scikit_fem_submesh.py b/tests/unit/test_meshes/test_scikit_fem_submesh.py index 1e0839250e..83c0192d30 100644 --- a/tests/unit/test_meshes/test_scikit_fem_submesh.py +++ b/tests/unit/test_meshes/test_scikit_fem_submesh.py @@ -280,7 +280,7 @@ def test_to_json(self): new_submesh = pybamm.ScikitUniform2DSubMesh._from_json(submesh) - for x, y in zip(mesh['current collector'].edges, new_submesh.edges): + for x, y in zip(mesh["current collector"].edges, new_submesh.edges): np.testing.assert_array_equal(x, y) diff --git a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py index 34c4b8b969..c56cd2304c 100644 --- a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py +++ b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py @@ -361,10 +361,7 @@ def test_options(self): # thermal half-cell with self.assertRaisesRegex(pybamm.OptionError, "X-full"): pybamm.BaseBatteryModel( - { - "thermal": "x-full", - "working electrode": "positive" - } + {"thermal": "x-full", "working electrode": "positive"} ) with self.assertRaisesRegex(pybamm.OptionError, "X-lumped"): pybamm.BaseBatteryModel( @@ -451,9 +448,7 @@ def test_option_type(self): self.assertEqual(model.options, options) def test_save_load_model(self): - model = ( - pybamm.lithium_ion.SPM() - ) + model = pybamm.lithium_ion.SPM() geometry = model.default_geometry param = model.default_parameter_values param.process_model(model) @@ -463,13 +458,15 @@ def test_save_load_model(self): disc.process_model(model) # save model - model.save_model(filename="test_base_battery_model", mesh=mesh, - variables=model.variables) + model.save_model( + filename="test_base_battery_model", mesh=mesh, variables=model.variables + ) # raises error if variables are saved without mesh with self.assertRaises(ValueError): - model.save_model(filename="test_base_battery_model", - variables=model.variables) + model.save_model( + filename="test_base_battery_model", variables=model.variables + ) os.remove("test_base_battery_model.json") diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py index 442817e354..88049c0c63 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py @@ -21,9 +21,7 @@ def test_default_parameter_values(self): # check default parameters are added correctly model = pybamm.lithium_ion.MPM() self.assertEqual( - model.default_parameter_values[ - "Negative minimum particle radius [m]" - ], + model.default_parameter_values["Negative minimum particle radius [m]"], 0.0, ) diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm_half_cell.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm_half_cell.py index ebd19ba614..77d51f6cf7 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm_half_cell.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm_half_cell.py @@ -21,9 +21,7 @@ def test_default_parameter_values(self): # check default parameters are added correctly model = pybamm.lithium_ion.MPM({"working electrode": "positive"}) self.assertEqual( - model.default_parameter_values[ - "Positive minimum particle radius [m]" - ], + model.default_parameter_values["Positive minimum particle radius [m]"], 0.0, ) diff --git a/tests/unit/test_parameters/test_bpx.py b/tests/unit/test_parameters/test_bpx.py index 2559641d7e..e131e906c4 100644 --- a/tests/unit/test_parameters/test_bpx.py +++ b/tests/unit/test_parameters/test_bpx.py @@ -9,6 +9,7 @@ import pybamm import copy + class TestBPX(TestCase): def setUp(self): self.base = { @@ -180,7 +181,6 @@ def check_constant_output(func): check_constant_output(kappa) check_constant_output(De) - def test_table_data(self): bpx_obj = copy.copy(self.base) data = {"x": [0, 1], "y": [0, 1]} @@ -255,7 +255,6 @@ def test_bpx_arrhenius(self): pv = pybamm.ParameterValues.create_from_bpx(tmp.name) - def arrhenius_assertion(pv, param_key, Ea_key): sto = 0.5 T = 300 @@ -269,11 +268,10 @@ def arrhenius_assertion(pv, param_key, Ea_key): eval_ratio = ( pv[param_key](c_e, c_s_surf, c_s_max, T).value / pv[param_key](c_e, c_s_surf, c_s_max, T_ref).value - ) + ) else: eval_ratio = ( - pv[param_key](sto, T).value - / pv[param_key](sto, T_ref).value + pv[param_key](sto, T).value / pv[param_key](sto, T_ref).value ) calc_ratio = pybamm.exp(Ea / pybamm.constants.R * (1 / T_ref - 1 / T)).value @@ -301,6 +299,7 @@ def arrhenius_assertion(pv, param_key, Ea_key): for param_key, Ea_key in zip(param_keys, Ea_keys): arrhenius_assertion(pv, param_key, Ea_key) + if __name__ == "__main__": print("Add -v for more debug output") import sys diff --git a/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015.py b/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015.py index a537afc93d..894213f92d 100644 --- a/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015.py +++ b/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015.py @@ -29,7 +29,7 @@ def test_functions(self): "Positive electrode OCP [V]": ([sto], 3.9478), # Electrolyte "Electrolyte diffusivity [m2.s-1]": ([1000, T], 2.593e-10), - "Electrolyte conductivity [S.m-1]": ([1000, T], 0.9738) + "Electrolyte conductivity [S.m-1]": ([1000, T], 0.9738), } for name, value in fun_test.items(): diff --git a/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015_graphite_halfcell.py b/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015_graphite_halfcell.py index 6dde10cd9c..f548030f26 100644 --- a/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015_graphite_halfcell.py +++ b/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015_graphite_halfcell.py @@ -22,7 +22,7 @@ def test_functions(self): "Positive electrode OCP [V]": ([sto], 0.124), # Electrolyte "Electrolyte diffusivity [m2.s-1]": ([1000, T], 2.593e-10), - "Electrolyte conductivity [S.m-1]": ([1000, T], 0.9738) + "Electrolyte conductivity [S.m-1]": ([1000, T], 0.9738), } for name, value in fun_test.items(): diff --git a/tests/unit/test_parameters/test_size_distribution_parameters.py b/tests/unit/test_parameters/test_size_distribution_parameters.py index e633b2764a..5deeaa62be 100644 --- a/tests/unit/test_parameters/test_size_distribution_parameters.py +++ b/tests/unit/test_parameters/test_size_distribution_parameters.py @@ -37,7 +37,6 @@ def test_parameter_values(self): np.testing.assert_almost_equal(values.evaluate(param.n.prim.R_max), 2.5e-5, 3) np.testing.assert_almost_equal(values.evaluate(param.p.prim.R_max), 2.5e-5, 3) - # check function parameters (size distributions) evaluate R_test = pybamm.Scalar(1.0) values.evaluate(param.n.prim.f_a_dist(R_test)) diff --git a/tests/unit/test_simulation.py b/tests/unit/test_simulation.py index 4375e745ad..ef055fdc97 100644 --- a/tests/unit/test_simulation.py +++ b/tests/unit/test_simulation.py @@ -208,14 +208,14 @@ def test_solve_with_initial_soc(self): options = {"working electrode": "positive"} model = pybamm.lithium_ion.DFN(options) sim = pybamm.Simulation(model) - sim.solve([0,1], initial_soc = 0.9) + sim.solve([0, 1], initial_soc=0.9) self.assertEqual(sim._built_initial_soc, 0.9) # Test whether initial_soc works with half cell (build) options = {"working electrode": "positive"} model = pybamm.lithium_ion.DFN(options) sim = pybamm.Simulation(model) - sim.build(initial_soc = 0.9) + sim.build(initial_soc=0.9) self.assertEqual(sim._built_initial_soc, 0.9) # Test whether initial_soc works with half cell when it is a voltage @@ -227,7 +227,7 @@ def test_solve_with_initial_soc(self): options = {"working electrode": "positive"} parameter_values["Current function [A]"] = 0.0 sim = pybamm.Simulation(model, parameter_values=parameter_values) - sol = sim.solve([0,1], initial_soc = f"{ucv} V") + sol = sim.solve([0, 1], initial_soc=f"{ucv} V") voltage = sol["Terminal voltage [V]"].entries self.assertAlmostEqual(voltage[0], ucv, places=5) @@ -302,12 +302,10 @@ def test_save_load(self): sim = pybamm.Simulation(model) sim.solve([0, 600]) with self.assertRaisesRegex( - NotImplementedError, - "Cannot save simulation if model format is python" + NotImplementedError, "Cannot save simulation if model format is python" ): sim.save(test_name) - def test_load_param(self): # Test load_sim for parameters imports filename = f"{uuid.uuid4()}.p" diff --git a/tests/unit/test_solvers/test_idaklu_solver.py b/tests/unit/test_solvers/test_idaklu_solver.py index cc54f3dfd5..604f559049 100644 --- a/tests/unit/test_solvers/test_idaklu_solver.py +++ b/tests/unit/test_solvers/test_idaklu_solver.py @@ -87,7 +87,7 @@ def test_model_events(self): # Check invalid atol type raises an error with self.assertRaises(pybamm.SolverError): - solver._check_atol_type({'key': 'value'}, []) + solver._check_atol_type({"key": "value"}, []) # enforce events that won't be triggered model.events = [pybamm.Event("an event", var + 1)] @@ -566,9 +566,9 @@ def test_with_output_variables(self): t_eval = np.linspace(0, 3600, 100) options = { - 'linear_solver': 'SUNLinSol_KLU', - 'jacobian': 'sparse', - 'num_threads': 4, + "linear_solver": "SUNLinSol_KLU", + "jacobian": "sparse", + "num_threads": 4, } # Use a selection of variables of different types @@ -587,7 +587,8 @@ def test_with_output_variables(self): # Use the full model as comparison (tested separately) solver_all = pybamm.IDAKLUSolver( - atol=1e-8, rtol=1e-8, + atol=1e-8, + rtol=1e-8, options=options, ) sol_all = solver_all.solve( @@ -599,7 +600,8 @@ def test_with_output_variables(self): # Solve for a subset of variables and compare results solver = pybamm.IDAKLUSolver( - atol=1e-8, rtol=1e-8, + atol=1e-8, + rtol=1e-8, options=options, output_variables=output_variables, ) @@ -640,9 +642,9 @@ def test_with_output_variables_and_sensitivities(self): t_eval = np.linspace(0, 3600, 100) options = { - 'linear_solver': 'SUNLinSol_KLU', - 'jacobian': 'sparse', - 'num_threads': 4, + "linear_solver": "SUNLinSol_KLU", + "jacobian": "sparse", + "num_threads": 4, } # Use a selection of variables of different types @@ -656,7 +658,8 @@ def test_with_output_variables_and_sensitivities(self): # Use the full model as comparison (tested separately) solver_all = pybamm.IDAKLUSolver( - atol=1e-8, rtol=1e-8, + atol=1e-8, + rtol=1e-8, options=options, ) sol_all = solver_all.solve( @@ -668,7 +671,8 @@ def test_with_output_variables_and_sensitivities(self): # Solve for a subset of variables and compare results solver = pybamm.IDAKLUSolver( - atol=1e-8, rtol=1e-8, + atol=1e-8, + rtol=1e-8, options=options, output_variables=output_variables, ) diff --git a/tests/unit/test_solvers/test_processed_variable_computed.py b/tests/unit/test_solvers/test_processed_variable_computed.py index c8b1f2597d..b5f105b34b 100644 --- a/tests/unit/test_solvers/test_processed_variable_computed.py +++ b/tests/unit/test_solvers/test_processed_variable_computed.py @@ -169,11 +169,15 @@ def test_processed_variable_1D(self): np.testing.assert_array_equal(processed_var.unroll(), y_sol) # Check no error when data dimension is transposed vs node/edge - processed_var.mesh.nodes, processed_var.mesh.edges = \ - processed_var.mesh.edges, processed_var.mesh.nodes + processed_var.mesh.nodes, processed_var.mesh.edges = ( + processed_var.mesh.edges, + processed_var.mesh.nodes, + ) processed_var.initialise_1D() - processed_var.mesh.nodes, processed_var.mesh.edges = \ - processed_var.mesh.edges, processed_var.mesh.nodes + processed_var.mesh.nodes, processed_var.mesh.edges = ( + processed_var.mesh.edges, + processed_var.mesh.nodes, + ) # Check that there are no errors with domain-specific attributes # (see ProcessedVariableComputed.initialise_1D() for details) diff --git a/tests/unit/test_solvers/test_solution.py b/tests/unit/test_solvers/test_solution.py index 9fc93dfb26..c7dfb716de 100644 --- a/tests/unit/test_solvers/test_solution.py +++ b/tests/unit/test_solvers/test_solution.py @@ -279,15 +279,16 @@ def test_save(self): solution.save_data(f"{test_stub}.mat", to_format="matlab") # Works if providing alternative name solution.save_data( - f"{test_stub}.mat", to_format="matlab", - short_names={"c + d": "c_plus_d"} + f"{test_stub}.mat", + to_format="matlab", + short_names={"c + d": "c_plus_d"}, ) data_load = loadmat(f"{test_stub}.mat") np.testing.assert_array_equal(solution.data["c + d"], data_load["c_plus_d"]) # to csv with self.assertRaisesRegex( - ValueError, "only 0D variables can be saved to csv" + ValueError, "only 0D variables can be saved to csv" ): solution.save_data(f"{test_stub}.csv", to_format="csv") # only save "c" and "2c" @@ -319,19 +320,23 @@ def test_save(self): np.testing.assert_array_almost_equal(json_data["d"], solution.data["d"]) # raise error if format is unknown - with self.assertRaisesRegex(ValueError, - "format 'wrong_format' not recognised"): + with self.assertRaisesRegex( + ValueError, "format 'wrong_format' not recognised" + ): solution.save_data(f"{test_stub}.csv", to_format="wrong_format") # test save whole solution solution.save(f"{test_stub}.pickle") solution_load = pybamm.load(f"{test_stub}.pickle") - self.assertEqual(solution.all_models[0].name, - solution_load.all_models[0].name) - np.testing.assert_array_equal(solution["c"].entries, - solution_load["c"].entries) - np.testing.assert_array_equal(solution["d"].entries, - solution_load["d"].entries) + self.assertEqual( + solution.all_models[0].name, solution_load.all_models[0].name + ) + np.testing.assert_array_equal( + solution["c"].entries, solution_load["c"].entries + ) + np.testing.assert_array_equal( + solution["d"].entries, solution_load["d"].entries + ) def test_get_data_cycles_steps(self): model = pybamm.BaseModel() diff --git a/tests/unit/test_spatial_methods/test_scikit_finite_element.py b/tests/unit/test_spatial_methods/test_scikit_finite_element.py index 657d896dfd..05b424e053 100644 --- a/tests/unit/test_spatial_methods/test_scikit_finite_element.py +++ b/tests/unit/test_spatial_methods/test_scikit_finite_element.py @@ -203,7 +203,7 @@ def test_manufactured_solution(self): u = np.sin(np.pi * z_vertices) mass = pybamm.Mass(var) mass_disc = disc.process_symbol(mass) - soln = -np.pi**2 * u + soln = -(np.pi**2) * u np.testing.assert_array_almost_equal( eqn_zz_disc.evaluate(None, u), mass_disc.entries @ soln, decimal=3 ) @@ -226,7 +226,7 @@ def test_manufactured_solution(self): u = np.cos(np.pi * y_vertices) * np.sin(np.pi * z_vertices) mass = pybamm.Mass(var) mass_disc = disc.process_symbol(mass) - soln = -np.pi**2 * u + soln = -(np.pi**2) * u np.testing.assert_array_almost_equal( laplace_eqn_disc.evaluate(None, u), mass_disc.entries @ soln, decimal=2 ) @@ -287,7 +287,7 @@ def test_manufactured_solution_cheb_grid(self): u = np.cos(np.pi * y_vertices) * np.sin(np.pi * z_vertices) mass = pybamm.Mass(var) mass_disc = disc.process_symbol(mass) - soln = -np.pi**2 * u + soln = -(np.pi**2) * u np.testing.assert_array_almost_equal( laplace_eqn_disc.evaluate(None, u), mass_disc.entries @ soln, decimal=1 ) @@ -350,7 +350,7 @@ def test_manufactured_solution_exponential_grid(self): u = np.cos(np.pi * y_vertices) * np.sin(np.pi * z_vertices) mass = pybamm.Mass(var) mass_disc = disc.process_symbol(mass) - soln = -np.pi**2 * u + soln = -(np.pi**2) * u np.testing.assert_array_almost_equal( laplace_eqn_disc.evaluate(None, u), mass_disc.entries @ soln, decimal=1 ) diff --git a/tests/unit/test_util.py b/tests/unit/test_util.py index 730e4cc08d..d0ac5337bf 100644 --- a/tests/unit/test_util.py +++ b/tests/unit/test_util.py @@ -12,7 +12,8 @@ from io import StringIO from tempfile import TemporaryDirectory -anytree = sys.modules['anytree'] +anytree = sys.modules["anytree"] + class TestUtil(TestCase): """ @@ -31,7 +32,7 @@ def test_rmse(self): pybamm.rmse(np.ones(5), np.zeros(3)) def test_is_constant_and_can_evaluate(self): - sys.modules['anytree'] = anytree + sys.modules["anytree"] = anytree symbol = pybamm.PrimaryBroadcast(0, "negative electrode") self.assertEqual(False, pybamm.is_constant_and_can_evaluate(symbol)) symbol = pybamm.StateVector(slice(0, 1)) @@ -92,13 +93,17 @@ def test_git_commit_info(self): self.assertEqual(git_commit_info[:2], "v2") def test_have_optional_dependency(self): - with self.assertRaisesRegex(ModuleNotFoundError, "Optional dependency pybtex is not available."): - pybtex = sys.modules['pybtex'] - sys.modules['pybtex'] = None + with self.assertRaisesRegex( + ModuleNotFoundError, "Optional dependency pybtex is not available." + ): + pybtex = sys.modules["pybtex"] + sys.modules["pybtex"] = None pybamm.print_citations() - with self.assertRaisesRegex(ModuleNotFoundError, "Optional dependency anytree is not available."): + with self.assertRaisesRegex( + ModuleNotFoundError, "Optional dependency anytree is not available." + ): with TemporaryDirectory() as dir_name: - sys.modules['anytree'] = None + sys.modules["anytree"] = None test_stub = os.path.join(dir_name, "test_visualize") test_name = f"{test_stub}.png" c = pybamm.Variable("c", "negative electrode") @@ -106,7 +111,7 @@ def test_have_optional_dependency(self): sym = pybamm.div(c * pybamm.grad(c)) + (c / d + c - d) ** 5 sym.visualise(test_name) - sys.modules['pybtex'] = pybtex + sys.modules["pybtex"] = pybtex pybamm.util.have_optional_dependency("pybtex") pybamm.print_citations() From d8eedf13d2e461644afe3f91254a552c9c86d061 Mon Sep 17 00:00:00 2001 From: Saransh Chopra Date: Sat, 23 Dec 2023 21:41:52 +0530 Subject: [PATCH 5/5] Migrate to ruff-format --- .git-blame-ignore-revs | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/.git-blame-ignore-revs b/.git-blame-ignore-revs index ec0f52cbfd..b38e6697cb 100644 --- a/.git-blame-ignore-revs +++ b/.git-blame-ignore-revs @@ -4,7 +4,9 @@ a63e49ece0f9336d1f5c2562f7459e555c6e6693 # activated standard pre-commits - https://github.com/pybamm-team/PyBaMM/pull/3192 5273214b585c5a4286609aed40e0b092d0e05f42 -# migrate config to pyproject.toml - https://github.com/pybamm-team/PyBaMM/pull/3557 +# migrated config to pyproject.toml - https://github.com/pybamm-team/PyBaMM/pull/3557 12c5d77203bd93542785d237bac00bad5ed5469a # activated pyupgrade - https://github.com/pybamm-team/PyBaMM/pull/3579 ff6d81c01331c7d269303b4a8321d9881bdf98fa +# migrated to ruff-format - https://github.com/pybamm-team/PyBaMM/pull/3655 +60ebd4148059a95428a496f4f55c1175ead362d3