From c95c8cef7a50b1670093ab9f5f1912d4cf237077 Mon Sep 17 00:00:00 2001 From: NicolaCourtier <45851982+NicolaCourtier@users.noreply.github.com> Date: Mon, 29 Jan 2024 18:25:09 +0000 Subject: [PATCH 01/64] Update plotting, descriptions and examples --- .../equivalent_circuit_identification.ipynb | 2 +- examples/notebooks/spm_Adam.ipynb | 4 +- examples/notebooks/spm_CMAES.ipynb | 4 +- examples/notebooks/spm_nlopt.ipynb | 4 +- .../spm_scipy_DifferentialEvolution.ipynb | 4 +- examples/scripts/ecm_CMAES.py | 7 +- examples/scripts/spm_CMAES.py | 7 +- examples/scripts/spm_IRPropMin.py | 7 +- examples/scripts/spm_SNES.py | 7 +- examples/scripts/spm_XNES.py | 7 +- examples/scripts/spm_adam.py | 7 +- examples/scripts/spm_descent.py | 7 +- examples/scripts/spm_nlopt.py | 7 +- examples/scripts/spm_pso.py | 7 +- examples/scripts/spm_scipymin.py | 7 +- examples/scripts/spme_max_energy.py | 4 +- pybop/__init__.py | 9 +- pybop/_costs.py | 3 +- pybop/_dataset.py | 39 ++- pybop/_optimisation.py | 2 +- pybop/plotting/plot_convergence.py | 53 --- pybop/plotting/plot_cost.py | 103 ++++++ pybop/plotting/plot_cost2d.py | 159 --------- pybop/plotting/plot_dataset.py | 57 ++++ pybop/plotting/plot_optimisation.py | 214 ++++++++++++ pybop/plotting/plot_parameters.py | 158 --------- pybop/plotting/plot_problem.py | 101 ++++++ pybop/plotting/quick_plot.py | 246 -------------- pybop/plotting/standard_plot.py | 321 ++++++++++++++++++ 29 files changed, 868 insertions(+), 689 deletions(-) delete mode 100644 pybop/plotting/plot_convergence.py create mode 100644 pybop/plotting/plot_cost.py delete mode 100644 pybop/plotting/plot_cost2d.py create mode 100644 pybop/plotting/plot_dataset.py create mode 100644 pybop/plotting/plot_optimisation.py delete mode 100644 pybop/plotting/plot_parameters.py create mode 100644 pybop/plotting/plot_problem.py delete mode 100644 pybop/plotting/quick_plot.py create mode 100644 pybop/plotting/standard_plot.py diff --git a/examples/notebooks/equivalent_circuit_identification.ipynb b/examples/notebooks/equivalent_circuit_identification.ipynb index c1a09d988..a393a2eba 100644 --- a/examples/notebooks/equivalent_circuit_identification.ipynb +++ b/examples/notebooks/equivalent_circuit_identification.ipynb @@ -9508,7 +9508,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/examples/notebooks/spm_Adam.ipynb b/examples/notebooks/spm_Adam.ipynb index 312e492c9..b042c8c96 100644 --- a/examples/notebooks/spm_Adam.ipynb +++ b/examples/notebooks/spm_Adam.ipynb @@ -9633,7 +9633,7 @@ "pybop.plot_cost2d(cost, steps=15)\n", "# Plot the cost landscape with optimisation path and updated bounds\n", "bounds = np.array([[0.6, 0.9], [0.5, 0.8]])\n", - "pybop.plot_cost2d(cost, optim=optim, bounds=bounds, steps=15);" + "pybop.plot_optim2d(optim, bounds=bounds, steps=15);" ] }, { @@ -9665,7 +9665,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.10.12" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/examples/notebooks/spm_CMAES.ipynb b/examples/notebooks/spm_CMAES.ipynb index 0b568dc26..675dd69dd 100644 --- a/examples/notebooks/spm_CMAES.ipynb +++ b/examples/notebooks/spm_CMAES.ipynb @@ -11277,7 +11277,7 @@ "pybop.plot_cost2d(cost, steps=15)\n", "# Plot the cost landscape with optimisation path and updated bounds\n", "bounds = np.array([[0.6, 0.9], [0.5, 0.8]])\n", - "pybop.plot_cost2d(cost, optim=optim, bounds=bounds, steps=15);" + "pybop.plot_optim2d(optim, bounds=bounds, steps=15);" ] }, { @@ -11309,7 +11309,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.10.12" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/examples/notebooks/spm_nlopt.ipynb b/examples/notebooks/spm_nlopt.ipynb index 04f684b23..154a7e63d 100644 --- a/examples/notebooks/spm_nlopt.ipynb +++ b/examples/notebooks/spm_nlopt.ipynb @@ -34070,7 +34070,7 @@ "pybop.plot_cost2d(cost, steps=15)\n", "# Plot the cost landscape with optimisation path and updated bounds\n", "bounds = np.array([[0.6, 0.9], [0.5, 0.8]])\n", - "pybop.plot_cost2d(cost, optim=optim, bounds=bounds, steps=15);" + "pybop.plot_optim2d(optim, bounds=bounds, steps=15);" ] }, { @@ -34100,7 +34100,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.10.12" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/examples/notebooks/spm_scipy_DifferentialEvolution.ipynb b/examples/notebooks/spm_scipy_DifferentialEvolution.ipynb index 2003c9d46..9db8da2b3 100644 --- a/examples/notebooks/spm_scipy_DifferentialEvolution.ipynb +++ b/examples/notebooks/spm_scipy_DifferentialEvolution.ipynb @@ -9505,7 +9505,7 @@ "pybop.plot_cost2d(cost, steps=15)\n", "# Plot the cost landscape with optimisation path and updated bounds\n", "bounds = np.array([[0.6, 0.9], [0.5, 0.8]])\n", - "pybop.plot_cost2d(cost, optim=optim, bounds=bounds, steps=15);" + "pybop.plot_optim2d(optim, bounds=bounds, steps=15);" ] }, { @@ -9544,7 +9544,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.10.12" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/examples/scripts/ecm_CMAES.py b/examples/scripts/ecm_CMAES.py index d60a8a8c8..da5835ade 100644 --- a/examples/scripts/ecm_CMAES.py +++ b/examples/scripts/ecm_CMAES.py @@ -84,8 +84,11 @@ "examples/scripts/parameters/fit_ecm_parameters.json", fit_params=parameters ) +# Plot the time series +pybop.plot_dataset(dataset) + # Plot the timeseries output -pybop.quick_plot(x, cost, title="Optimised Comparison") +pybop.quick_plot(problem, parameter_values=x, title="Optimised Comparison") # Plot convergence pybop.plot_convergence(optim) @@ -98,4 +101,4 @@ # Plot the cost landscape with optimisation path and updated bounds bounds = np.array([[1e-4, 1e-2], [1e-5, 1e-2]]) -pybop.plot_cost2d(cost, optim=optim, bounds=bounds, steps=15) +pybop.plot_optim2d(optim, bounds=bounds, steps=15) diff --git a/examples/scripts/spm_CMAES.py b/examples/scripts/spm_CMAES.py index e24eb7a15..e3e69bfb1 100644 --- a/examples/scripts/spm_CMAES.py +++ b/examples/scripts/spm_CMAES.py @@ -44,8 +44,11 @@ x, final_cost = optim.run() print("Estimated parameters:", x) +# Plot the time series +pybop.plot_dataset(dataset) + # Plot the timeseries output -pybop.quick_plot(x, cost, title="Optimised Comparison") +pybop.quick_plot(problem, parameter_values=x, title="Optimised Comparison") # Plot convergence pybop.plot_convergence(optim) @@ -58,4 +61,4 @@ # Plot the cost landscape with optimisation path and updated bounds bounds = np.array([[0.55, 0.75], [0.48, 0.68]]) -pybop.plot_cost2d(cost, optim=optim, bounds=bounds, steps=15) +pybop.plot_optim2d(optim, bounds=bounds, steps=15) diff --git a/examples/scripts/spm_IRPropMin.py b/examples/scripts/spm_IRPropMin.py index a72bc92c8..daadac10b 100644 --- a/examples/scripts/spm_IRPropMin.py +++ b/examples/scripts/spm_IRPropMin.py @@ -43,7 +43,7 @@ print("Estimated parameters:", x) # Plot the timeseries output -pybop.quick_plot(x, cost, title="Optimised Comparison") +pybop.quick_plot(problem, parameter_values=x, title="Optimised Comparison") # Plot convergence pybop.plot_convergence(optim) @@ -51,8 +51,5 @@ # Plot the parameter traces pybop.plot_parameters(optim) -# Plot the cost landscape -pybop.plot_cost2d(cost, steps=15) - # Plot the cost landscape with optimisation path -pybop.plot_cost2d(cost, optim=optim, steps=15) +pybop.plot_optim2d(optim, steps=15) diff --git a/examples/scripts/spm_SNES.py b/examples/scripts/spm_SNES.py index 97112dca2..e36639760 100644 --- a/examples/scripts/spm_SNES.py +++ b/examples/scripts/spm_SNES.py @@ -42,7 +42,7 @@ print("Estimated parameters:", x) # Plot the timeseries output -pybop.quick_plot(x, cost, title="Optimised Comparison") +pybop.quick_plot(problem, parameter_values=x, title="Optimised Comparison") # Plot convergence pybop.plot_convergence(optim) @@ -50,8 +50,5 @@ # Plot the parameter traces pybop.plot_parameters(optim) -# Plot the cost landscape -pybop.plot_cost2d(cost, steps=15) - # Plot the cost landscape with optimisation path -pybop.plot_cost2d(cost, optim=optim, steps=15) +pybop.plot_optim2d(optim, steps=15) diff --git a/examples/scripts/spm_XNES.py b/examples/scripts/spm_XNES.py index fb79b1029..3321eb9ba 100644 --- a/examples/scripts/spm_XNES.py +++ b/examples/scripts/spm_XNES.py @@ -43,7 +43,7 @@ print("Estimated parameters:", x) # Plot the timeseries output -pybop.quick_plot(x, cost, title="Optimised Comparison") +pybop.quick_plot(problem, parameter_values=x, title="Optimised Comparison") # Plot convergence pybop.plot_convergence(optim) @@ -51,8 +51,5 @@ # Plot the parameter traces pybop.plot_parameters(optim) -# Plot the cost landscape -pybop.plot_cost2d(cost, steps=15) - # Plot the cost landscape with optimisation path -pybop.plot_cost2d(cost, optim=optim, steps=15) +pybop.plot_optim2d(optim, steps=15) diff --git a/examples/scripts/spm_adam.py b/examples/scripts/spm_adam.py index c9d4b3431..b8f3d2f50 100644 --- a/examples/scripts/spm_adam.py +++ b/examples/scripts/spm_adam.py @@ -45,7 +45,7 @@ print("Estimated parameters:", x) # Plot the timeseries output -pybop.quick_plot(x, cost, title="Optimised Comparison") +pybop.quick_plot(problem, parameter_values=x, title="Optimised Comparison") # Plot convergence pybop.plot_convergence(optim) @@ -53,8 +53,5 @@ # Plot the parameter traces pybop.plot_parameters(optim) -# Plot the cost landscape -pybop.plot_cost2d(cost, steps=15) - # Plot the cost landscape with optimisation path -pybop.plot_cost2d(cost, optim=optim, steps=15) +pybop.plot_optim2d(optim, steps=15) diff --git a/examples/scripts/spm_descent.py b/examples/scripts/spm_descent.py index 1b1ba0e8d..68d6a0208 100644 --- a/examples/scripts/spm_descent.py +++ b/examples/scripts/spm_descent.py @@ -47,7 +47,7 @@ print("Estimated parameters:", x) # Plot the timeseries output -pybop.quick_plot(x, cost, title="Optimised Comparison") +pybop.quick_plot(problem, parameter_values=x, title="Optimised Comparison") # Plot convergence pybop.plot_convergence(optim) @@ -55,8 +55,5 @@ # Plot the parameter traces pybop.plot_parameters(optim) -# Plot the cost landscape -pybop.plot_cost2d(cost, steps=15) - # Plot the cost landscape with optimisation path -pybop.plot_cost2d(cost, optim=optim, steps=15) +pybop.plot_optim2d(optim, steps=15) diff --git a/examples/scripts/spm_nlopt.py b/examples/scripts/spm_nlopt.py index 3aa0900d7..4de29b07f 100644 --- a/examples/scripts/spm_nlopt.py +++ b/examples/scripts/spm_nlopt.py @@ -44,7 +44,7 @@ print("Estimated parameters:", x) # Plot the timeseries output -pybop.quick_plot(x, cost, title="Optimised Comparison") +pybop.quick_plot(problem, parameter_values=x, title="Optimised Comparison") # Plot convergence pybop.plot_convergence(optim) @@ -52,8 +52,5 @@ # Plot the parameter traces pybop.plot_parameters(optim) -# Plot the cost landscape -pybop.plot_cost2d(cost, steps=15) - # Plot the cost landscape with optimisation path -pybop.plot_cost2d(cost, optim=optim, steps=15) +pybop.plot_optim2d(optim, steps=15) diff --git a/examples/scripts/spm_pso.py b/examples/scripts/spm_pso.py index 1a51d80d0..819a7454c 100644 --- a/examples/scripts/spm_pso.py +++ b/examples/scripts/spm_pso.py @@ -43,7 +43,7 @@ print("Estimated parameters:", x) # Plot the timeseries output -pybop.quick_plot(x, cost, title="Optimised Comparison") +pybop.quick_plot(problem, parameter_values=x, title="Optimised Comparison") # Plot convergence pybop.plot_convergence(optim) @@ -51,8 +51,5 @@ # Plot the parameter traces pybop.plot_parameters(optim) -# Plot the cost landscape -pybop.plot_cost2d(cost, steps=15) - # Plot the cost landscape with optimisation path -pybop.plot_cost2d(cost, optim=optim, steps=15) +pybop.plot_optim2d(optim, steps=15) diff --git a/examples/scripts/spm_scipymin.py b/examples/scripts/spm_scipymin.py index 1c57a7358..759f8c2e6 100644 --- a/examples/scripts/spm_scipymin.py +++ b/examples/scripts/spm_scipymin.py @@ -44,7 +44,7 @@ print("Estimated parameters:", x) # Plot the timeseries output -pybop.quick_plot(x, cost, title="Optimised Comparison") +pybop.quick_plot(problem, parameter_values=x, title="Optimised Comparison") # Plot convergence pybop.plot_convergence(optim) @@ -52,8 +52,5 @@ # Plot the parameter traces pybop.plot_parameters(optim) -# Plot the cost landscape -pybop.plot_cost2d(cost, steps=15) - # Plot the cost landscape with optimisation path -pybop.plot_cost2d(cost, optim=optim, steps=15) +pybop.plot_optim2d(optim, steps=15) diff --git a/examples/scripts/spme_max_energy.py b/examples/scripts/spme_max_energy.py index 96c0eeaf4..dee3fda49 100644 --- a/examples/scripts/spme_max_energy.py +++ b/examples/scripts/spme_max_energy.py @@ -189,8 +189,8 @@ def _evaluate(self, x, grad=None): # Plot the timeseries output nominal_capacity(x, cost.problem._model) -pybop.quick_plot(x, cost, title="Optimised Comparison") +pybop.quick_plot(problem, parameter_values=x, title="Optimised Comparison") # Plot the cost landscape with optimisation path if len(x) == 2: - pybop.plot_cost2d(cost, optim=optim, steps=3) + pybop.plot_optim2d(optim, steps=3) diff --git a/pybop/__init__.py b/pybop/__init__.py index 8781b93dc..c8dae8afe 100644 --- a/pybop/__init__.py +++ b/pybop/__init__.py @@ -81,11 +81,12 @@ # # Plotting class # -from .plotting.plot_cost2d import plot_cost2d -from .plotting.quick_plot import StandardPlot, quick_plot -from .plotting.plot_convergence import plot_convergence -from .plotting.plot_parameters import plot_parameters from .plotting.plotly_manager import PlotlyManager +from .plotting.standard_plot import StandardPlot, StandardSubplot, plot_trajectories +from .plotting.plot_cost import plot_cost2d +from .plotting.plot_dataset import plot_dataset +from .plotting.plot_optimisation import plot_convergence, plot_parameters, plot_optim2d +from .plotting.plot_problem import quick_plot # # Remove any imported modules, so we don't expose them as part of pybop diff --git a/pybop/_costs.py b/pybop/_costs.py index 65bf2662f..f2928e996 100644 --- a/pybop/_costs.py +++ b/pybop/_costs.py @@ -29,9 +29,10 @@ def __init__(self, problem): self.problem = problem if problem is not None: self._target = problem._target + self.parameters = problem.parameters self.x0 = problem.x0 self.bounds = problem.bounds - self.n_parameters = problem.n_parameters + self.n_parameters = len(self.parameters) def __call__(self, x, grad=None): """ diff --git a/pybop/_dataset.py b/pybop/_dataset.py index 1263ace3e..f48846025 100644 --- a/pybop/_dataset.py +++ b/pybop/_dataset.py @@ -10,22 +10,13 @@ class Dataset: Parameters ---------- - name : str - The name of the dataset, providing a label for identification. - data : array-like - The actual experimental data, typically in a structured form such as - a NumPy array or a pandas DataFrame. - + data_dictionary : dict or instance of pybamm.solvers.solution.Solution + The experimental data to store within the dataset. """ def __init__(self, data_dictionary): """ - Initialize a Dataset instance with a name and data. - - Parameters - ---------- - data_dictionary : dict or instance of pybamm.solvers.solution.Solution - The experimental data to store within the dataset. + Initialize a Dataset instance with data and a set of names. """ if isinstance(data_dictionary, pybamm.solvers.solution.Solution): @@ -64,3 +55,27 @@ def Interpolant(self): self.Interpolant = pybamm.Interpolant(self.x, self.y, pybamm.t) else: NotImplementedError("Only time interpolation is supported") + + def check(self, signal=["Voltage [V]"]): + """ + Check the consistency of a PyBOP Dataset against the expected format. + + Returns + ------- + bool + If True, the dataset has the expected attributes. + """ + if isinstance(signal, str): + signal = [signal] + + # Check that the dataset contains time and chosen signal + for name in ["Time [s]"] + signal: + if name not in self.names: + raise ValueError(f"expected {name} in list of dataset") + + # Check for consistent data + for s in signal: + if len(self.data[s]) != len(self.data["Time [s]"]): + raise ValueError(f"Time data and {s} data must be the same length.") + + return True diff --git a/pybop/_optimisation.py b/pybop/_optimisation.py index 6ac2a4959..176506cd2 100644 --- a/pybop/_optimisation.py +++ b/pybop/_optimisation.py @@ -488,7 +488,7 @@ def store_optimised_parameters(self, x): x : array-like Optimized parameter values. """ - for i, param in enumerate(self.cost.problem.parameters): + for i, param in enumerate(self.cost.parameters): param.update(value=x[i]) def check_optimal_parameters(self, x): diff --git a/pybop/plotting/plot_convergence.py b/pybop/plotting/plot_convergence.py deleted file mode 100644 index 81e9ef65c..000000000 --- a/pybop/plotting/plot_convergence.py +++ /dev/null @@ -1,53 +0,0 @@ -import pybop - - -def plot_convergence( - optim, xaxis_title="Iteration", yaxis_title="Cost", title="Convergence" -): - """ - Plot the convergence of the optimisation algorithm. - - Parameters - ----------- - optim : optimisation object - Optimisation object containing the cost function and optimiser. - xaxis_title : str, optional - Title for the x-axis (default is "Iteration"). - yaxis_title : str, optional - Title for the y-axis (default is "Cost"). - title : str, optional - Title of the plot (default is "Convergence"). - - Returns - --------- - fig : plotly.graph_objs.Figure - The Plotly figure object for the convergence plot. - """ - - # Extract the cost function from the optimisation object - cost_function = optim.cost - - # Compute the minimum cost for each iteration - min_cost_per_iteration = [ - min(cost_function(solution) for solution in log_entry) - for log_entry in optim.log - ] - - # Generate a list of iteration numbers - iteration_numbers = list(range(1, len(min_cost_per_iteration) + 1)) - - # Create the convergence plot using the StandardPlot class - fig = pybop.StandardPlot( - x=iteration_numbers, - y=min_cost_per_iteration, - cost=cost_function, - xaxis_title=xaxis_title, - yaxis_title=yaxis_title, - title=title, - trace_name=optim.optimiser.name(), - )() - - # Display the figure - fig.show() - - return fig diff --git a/pybop/plotting/plot_cost.py b/pybop/plotting/plot_cost.py new file mode 100644 index 000000000..073398249 --- /dev/null +++ b/pybop/plotting/plot_cost.py @@ -0,0 +1,103 @@ +import pybop +import numpy as np + + +def plot_cost2d(cost, bounds=None, steps=10, show=True, **layout_kwargs): + """ + Plot a 2D visualisation of a cost landscape using Plotly. + + This function generates a contour plot representing the cost landscape for a provided + callable cost function over a grid of parameter values within the specified bounds. + + Parameters + ---------- + cost : callable + The cost function to be evaluated. Must accept a list of parameter values and return a cost value. + bounds : numpy.ndarray, optional + A 2x2 array specifying the [min, max] bounds for each parameter. If None, uses `get_param_bounds`. + steps : int, optional + The number of intervals to divide the parameter space into along each dimension (default is 10). + show : bool, optional + If True, the figure is shown upon creation (default: True). + **layout_kwargs : optional + Valid Plotly layout keys and their values, + e.g. `xaxis_title="Time [s]"` or + `xaxis={"title": "Time [s]", "titlefont_size": 18}`. + + Returns + ------- + plotly.graph_objs.Figure + The Plotly figure object containing the cost landscape plot. + + Raises + ------ + ValueError + If the cost function does not return a valid cost when called with a parameter list. + """ + + # Set up parameter bounds + if bounds is None: + bounds = get_param_bounds(cost) + else: + bounds = bounds + + # Generate grid + x = np.linspace(bounds[0, 0], bounds[0, 1], steps) + y = np.linspace(bounds[1, 0], bounds[1, 1], steps) + + # Initialize cost matrix + costs = np.zeros((len(y), len(x))) + + # Populate cost matrix + for i, xi in enumerate(x): + for j, yj in enumerate(y): + costs[j, i] = cost([xi, yj]) + + # Import plotly only when needed + import plotly.graph_objects as go + + # Set default layout properties + layout_options = dict( + title="Cost Landscape", + title_x=0.5, + title_y=0.9, + width=600, + height=600, + xaxis=dict(range=bounds[0]), + yaxis=dict(range=bounds[1]), + ) + if hasattr(cost, "parameters"): + layout_options["xaxis_title"] = cost.parameters[0].name + layout_options["yaxis_title"] = cost.parameters[1].name + layout = go.Layout(layout_options) + + # Create contour plot and update the layout + fig = go.Figure(data=[go.Contour(x=x, y=y, z=costs)], layout=layout) + fig.update_layout(**layout_kwargs) + if show: + fig.show() + + return fig + + +def get_param_bounds(cost): + """ + Retrieve parameter bounds from a cost object's associated problem parameters. + + Parameters + ---------- + cost : object + The cost object with an associated 'problem' attribute containing 'parameters'. + + Returns + ------- + bounds : numpy.ndarray + An array of shape (n_parameters, 2) containing the bounds for each parameter. + """ + if hasattr(cost, "problem"): + bounds = np.empty((len(cost.parameters), 2)) + for i, param in enumerate(cost.parameters): + bounds[i] = param.bounds + return bounds + + return None diff --git a/pybop/plotting/plot_cost2d.py b/pybop/plotting/plot_cost2d.py deleted file mode 100644 index ee43d9d09..000000000 --- a/pybop/plotting/plot_cost2d.py +++ /dev/null @@ -1,159 +0,0 @@ -import numpy as np - - -def plot_cost2d(cost, bounds=None, optim=None, steps=10): - """ - Plot a 2D visualization of a cost landscape using Plotly. - - This function generates a contour plot representing the cost landscape for a provided - callable cost function over a grid of parameter values within the specified bounds. - - Parameters - ---------- - cost : callable - The cost function to be evaluated. Must accept a list of parameters and return a cost value. - bounds : numpy.ndarray, optional - A 2x2 array specifying the [min, max] bounds for each parameter. If None, uses `get_param_bounds`. - optim : object, optional - An optimiser instance which, if provided, overlays its specific trace on the plot. - steps : int, optional - The number of intervals to divide the parameter space into along each dimension (default is 10). - - Returns - ------- - plotly.graph_objs.Figure - The Plotly figure object containing the cost landscape plot. - - Raises - ------ - ValueError - If the cost function does not return a valid cost when called with a parameter list. - """ - - if bounds is None: - # Set up parameter bounds - bounds = get_param_bounds(cost) - else: - bounds = bounds - - # Generate grid - x = np.linspace(bounds[0, 0], bounds[0, 1], steps) - y = np.linspace(bounds[1, 0], bounds[1, 1], steps) - - # Initialize cost matrix - costs = np.zeros((len(y), len(x))) - - # Populate cost matrix - for i, xi in enumerate(x): - for j, yj in enumerate(y): - costs[j, i] = cost([xi, yj]) - - # Create figure - fig = create_figure(x, y, costs, bounds, cost.problem.parameters, optim) - - # Display figure - fig.show() - - return fig - - -def get_param_bounds(cost): - """ - Retrieve parameter bounds from a cost function's associated problem parameters. - - Parameters - ---------- - cost : callable - The cost function with an associated 'problem' attribute containing 'parameters'. - - Returns - ------- - numpy.ndarray - An array of shape (n_parameters, 2) containing the bounds for each parameter. - """ - bounds = np.empty((len(cost.problem.parameters), 2)) - for i, param in enumerate(cost.problem.parameters): - bounds[i] = param.bounds - return bounds - - -def create_figure(x, y, z, bounds, params, optim): - """ - Create a Plotly figure with a 2D contour plot of the cost landscape. - - Parameters - ---------- - x : numpy.ndarray - 1D array of x-coordinates for the meshgrid. - y : numpy.ndarray - 1D array of y-coordinates for the meshgrid. - z : numpy.ndarray - 2D array of cost function values corresponding to the meshgrid. - bounds : numpy.ndarray - A 2x2 array specifying the [min, max] bounds for each parameter. - params : iterable - An iterable of parameter objects with 'name' attributes for axis labeling. - optim : object - An optimiser instance with 'log' and 'x0' attributes for plotting traces. - - Returns - ------- - plotly.graph_objs.Figure - The Plotly figure object with the contour plot and optimization traces. - """ - - # Import plotly only when needed - import plotly.graph_objects as go - - fig = go.Figure(data=[go.Contour(x=x, y=y, z=z)]) - if optim is not None: - optim_trace = np.array([item for sublist in optim.log for item in sublist]) - optim_trace = optim_trace.reshape(-1, 2) - - # Plot initial guess - fig.add_trace( - go.Scatter( - x=[optim.x0[0]], - y=[optim.x0[1]], - mode="markers", - marker_symbol="x", - marker=dict( - color="red", - line_color="midnightblue", - line_width=1, - size=12, - showscale=False, - ), - showlegend=False, - ) - ) - - # Plot optimisation trace - fig.add_trace( - go.Scatter( - x=optim_trace[:, 0], - y=optim_trace[:, 1], - mode="markers", - marker=dict( - color=[i / len(optim_trace) for i in range(len(optim_trace))], - colorscale="YlOrBr", - showscale=False, - ), - showlegend=False, - ) - ) - - # Set figure properties - fig.update_layout( - title="Cost Landscape", - title_x=0.5, - title_y=0.9, - xaxis_title=params[0].name, - yaxis_title=params[1].name, - width=600, - height=600, - xaxis=dict(range=bounds[0]), - yaxis=dict(range=bounds[1]), - ) - - return fig diff --git a/pybop/plotting/plot_dataset.py b/pybop/plotting/plot_dataset.py new file mode 100644 index 000000000..d9afc1493 --- /dev/null +++ b/pybop/plotting/plot_dataset.py @@ -0,0 +1,57 @@ +import pybop + + +def plot_dataset( + dataset, signal=["Voltage [V]"], trace_names="Data", show=True, **layout_kwargs +): + """ + Quickly plot a PyBOP Dataset using Plotly. + + Parameters + ---------- + dataset : object + A PyBOP dataset. + signal : list or str, optional + The name of the time series to plot (default: "Voltage [V]"). + trace_names : list or str, optional + Name(s) for the trace(s) (default: "Data"). + show : bool, optional + If True, the figure is shown upon creation (default: True). + **layout_kwargs : optional + Valid Plotly layout keys and their values, + e.g. `xaxis_title="Time [s]"` or + `xaxis={"title": "Time [s]", "titlefont_size": 18}`. + + Returns + ------- + plotly.graph_objs.Figure + The Plotly figure object for the scatter plot. + """ + + # Get data dictionary + dataset.check(signal) + data = dataset.data + + # Compile ydata and labels or legend + y = [data[s] for s in signal] + if len(signal) == 1: + yaxis_title = signal[0] + else: + yaxis_title = None + if trace_names is None: + trace_names = signal + + # Create the figure + fig = pybop.plot_trajectories( + x=data["Time [s]"], + y=y, + trace_names=trace_names, + show=False, + xaxis_title="Time [s]", + yaxis_title=yaxis_title, + ) + fig.update_layout(**layout_kwargs) + if show is True: + fig.show() + + return fig diff --git a/pybop/plotting/plot_optimisation.py b/pybop/plotting/plot_optimisation.py new file mode 100644 index 000000000..6ec75aee0 --- /dev/null +++ b/pybop/plotting/plot_optimisation.py @@ -0,0 +1,214 @@ +import pybop +import numpy as np + + +def plot_convergence(optim, show=True, **layout_kwargs): + """ + Plot the convergence of the optimisation algorithm. + + Parameters + ----------- + optim : object + Optimisation object containing the cost function and optimiser. + show : bool, optional + If True, the figure is shown upon creation (default: True). + **layout_kwargs : optional + Valid Plotly layout keys and their values, + e.g. `xaxis_title="Time [s]"` or + `xaxis={"title": "Time [s]", "titlefont_size": 18}`. + + Returns + --------- + fig : plotly.graph_objs.Figure + The Plotly figure object for the convergence plot. + """ + + # Extract the cost function and log from the optimisation object + cost = optim.cost + log = optim.log + + # Compute the minimum cost for each iteration + min_cost_per_iteration = [ + min(cost(solution) for solution in log_entry) for log_entry in log + ] + + # Generate a list of iteration numbers + iteration_numbers = list(range(1, len(min_cost_per_iteration) + 1)) + + # Create a plotting dictionary + plot_dict = pybop.StandardPlot( + x=iteration_numbers, + y=min_cost_per_iteration, + layout_options=dict( + xaxis_title="Iteration", yaxis_title="Cost", title="Convergence" + ), + trace_names=optim.optimiser.name(), + ) + + # Generate and display the figure + fig = plot_dict(show=False) + fig.update_layout(**layout_kwargs) + if show: + fig.show() + + return fig + + +def plot_parameters(optim, show=True, **layout_kwargs): + """ + Plot the evolution of parameters during the optimization process using Plotly. + + Parameters + ---------- + optim : object + Optimisation object containing the history of parameter values and associated cost. + show : bool, optional + If True, the figure is shown upon creation (default: True). + **layout_kwargs : optional + Valid Plotly layout keys and their values, + e.g. `xaxis_title="Time [s]"` or + `xaxis={"title": "Time [s]", "titlefont_size": 18}`. + + Returns + ------- + plotly.graph_objs.Figure + A Plotly figure object showing the parameter evolution over iterations. + """ + + # Extract parameters and log from the optimisation object + parameters = optim.cost.parameters + log = optim.log + + # Create a list of sequential integers for the x-axis + x = list(range(len(log[0]) * len(log))) + + # Determine the number of elements in the smallest arrays + num_elements = len(log[0][0]) + + # Initialize a list of lists to store our columns + y = [[] for _ in range(num_elements)] + + # Loop through each numpy array in y + for array in log: + # Loop through each item (which is a n-element array) in the numpy array + for item in array: + # Loop through each element in the item and append to the corresponding column + for i in range(num_elements): + y[i].append(item[i]) + + # Create lists of axis titles and trace names + axis_titles = [] + trace_names = [] + for param in parameters: + axis_titles.append(("Function Call", param.name)) + trace_names.append(param.name) + + # Set subplot layout options + layout_options = dict( + title="Parameter Convergence", + width=1024, + height=576, + legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1), + ) + + # Create a plotting dictionary + plot_dict = pybop.StandardSubplot( + x=x, + y=y, + axis_titles=axis_titles, + layout_options=layout_options, + trace_names=trace_names, + trace_name_width=50, + ) + + # Generate the figure and update the layout + fig = plot_dict(show=False) + fig.update_layout(**layout_kwargs) + if show: + fig.show() + + return fig + + +def plot_optim2d(optim, bounds=None, steps=10, show=True, **layout_kwargs): + """ + Plot a 2D visualization of a cost landscape using Plotly with the optimisation trace. + + This function generates a contour plot representing the cost landscape for a provided + callable cost function over a grid of parameter values within the specified bounds. + + Parameters + ---------- + optim : object + Optimisation object which provides a specific optimisation trace overlaid on the cost landscape. + bounds : numpy.ndarray, optional + A 2x2 array specifying the [min, max] bounds for each parameter. If None, uses `get_param_bounds`. + steps : int, optional + The number of intervals to divide the parameter space into along each dimension (default is 10). + show : bool, optional + If True, the figure is shown upon creation (default: True). + **layout_kwargs : optional + Valid Plotly layout keys and their values, + e.g. `xaxis_title="Time [s]"` or + `xaxis={"title": "Time [s]", "titlefont_size": 18}`. + + Returns + ------- + plotly.graph_objs.Figure + The Plotly figure object containing the cost landscape plot. + + Raises + ------ + ValueError + If the cost function does not return a valid cost when called with a parameter list. + """ + # Extract the cost function from the optimisation object + cost = optim.cost + + # Create the cost landscape + fig = pybop.plot_cost2d(cost, bounds=bounds, steps=steps, show=False) + + # Import plotly only when needed + import plotly.graph_objects as go + + # Plot the initial guess + fig.add_trace( + go.Scatter( + x=[optim.x0[0]], + y=[optim.x0[1]], + mode="markers", + marker_symbol="x", + marker=dict( + color="red", + line_color="midnightblue", + line_width=1, + size=12, + showscale=False, + ), + showlegend=False, + ) + ) + + # Plot the optimisation trace + optim_trace = np.array([item for sublist in optim.log for item in sublist]) + optim_trace = optim_trace.reshape(-1, 2) + fig.add_trace( + go.Scatter( + x=optim_trace[:, 0], + y=optim_trace[:, 1], + mode="markers", + marker=dict( + color=[i / len(optim_trace) for i in range(len(optim_trace))], + colorscale="YlOrBr", + showscale=False, + ), + showlegend=False, + ) + ) + + # Update the layout and display the figure + fig.update_layout(**layout_kwargs) + if show: + fig.show() + + return fig diff --git a/pybop/plotting/plot_parameters.py b/pybop/plotting/plot_parameters.py deleted file mode 100644 index 84307a72a..000000000 --- a/pybop/plotting/plot_parameters.py +++ /dev/null @@ -1,158 +0,0 @@ -import pybop -import math - - -def plot_parameters( - optim, xaxis_titles="Iteration", yaxis_titles=None, title="Convergence" -): - """ - Plot the evolution of parameters during the optimization process using Plotly. - - Parameters - ---------- - optim : object - The optimization object containing the history of parameter values and associated cost. - xaxis_titles : str, optional - Title for the x-axis, defaulting to "Iteration". - yaxis_titles : list of str, optional - Titles for the y-axes, one for each parameter. If None, parameter names are used. - title : str, optional - Title of the plot, defaulting to "Convergence". - - Returns - ------- - plotly.graph_objs.Figure - A Plotly figure object showing the parameter evolution over iterations. - """ - - # Extract parameters from the optimisation object - params = optim.cost.problem.parameters - - # Create the traces from the optimisation log - traces = create_traces(params, optim.log) - - # Create the axis titles - axis_titles = [] - for param in params: - axis_titles.append(("Function Call", param.name)) - - # Create the figure - fig = create_subplots_with_traces(traces, axis_titles=axis_titles) - - # Display the figure - fig.show() - - return fig - - -def create_traces(params, trace_data, x_values=None): - """ - Create traces for plotting parameter evolution. - - Parameters - ---------- - params : list - List of parameter objects, each having a 'name' attribute used for labeling the trace. - trace_data : list of numpy.ndarray - A list of arrays representing the historical values of each parameter. - x_values : list or numpy.ndarray, optional - The x-axis values for plotting. If None, defaults to sequential integers. - - Returns - ------- - list of plotly.graph_objs.Scatter - A list of Scatter trace objects, one for each parameter. - """ - - # Attempt to import plotly when an instance is created - go = pybop.PlotlyManager().go - - traces = [] - - # If x_values are not provided: - if x_values is None: - x_values = list(range(len(trace_data[0]) * len(trace_data))) - - # Determine the number of elements in the smallest arrays - num_elements = len(trace_data[0][0]) - - # Initialize a list of lists to store our columns - columns = [[] for _ in range(num_elements)] - - # Loop through each numpy array in trace_data - for array in trace_data: - # Loop through each item (which is a n-element array) in the numpy array - for item in array: - # Loop through each element in the item and append to the corresponding column - for i in range(num_elements): - columns[i].append(item[i]) - - # Create a trace for each column - for i in range(len(columns)): - wrap_param = pybop.StandardPlot.wrap_text(params[i].name, width=50) - traces.append(go.Scatter(x=x_values, y=columns[i], name=wrap_param)) - - return traces - - -def create_subplots_with_traces( - traces, - plot_size=(1024, 576), - title="Parameter Convergence", - axis_titles=None, - **layout_kwargs, -): - """ - Create a subplot with individual traces for each parameter. - - Parameters - ---------- - traces : list of plotly.graph_objs.Scatter - Traces to be plotted, one trace per subplot. - plot_size : tuple of int, optional - The size of the plot as (width, height), defaulting to (1024, 576). - title : str, optional - The title of the plot, defaulting to "Parameter Convergence". - axis_titles : list of tuple of str, optional - A list of (x_title, y_title) pairs for each subplot. If None, titles are omitted. - **layout_kwargs : dict - Additional keyword arguments to customize the layout. - - Returns - ------- - plotly.graph_objs.Figure - A Plotly figure object with subplots for each trace. - """ - - # Attempt to import plotly when an instance is created - make_subplots = pybop.PlotlyManager().make_subplots - - num_traces = len(traces) - num_cols = int(math.ceil(math.sqrt(num_traces))) - num_rows = int(math.ceil(num_traces / num_cols)) - - fig = make_subplots(rows=num_rows, cols=num_cols, start_cell="bottom-left") - - for idx, trace in enumerate(traces): - row = (idx // num_cols) + 1 - col = (idx % num_cols) + 1 - fig.add_trace(trace, row=row, col=col) - - if axis_titles and idx < len(axis_titles): - x_title, y_title = axis_titles[idx] - fig.update_xaxes(title_text=x_title, row=row, col=col) - fig.update_yaxes(title_text=y_title, row=row, col=col) - - if plot_size: - layout_kwargs["width"], layout_kwargs["height"] = plot_size - - if title: - layout_kwargs["title_text"] = title - - # Set the legend above the subplots - fig.update_layout( - legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1), - **layout_kwargs, - ) - - return fig diff --git a/pybop/plotting/plot_problem.py b/pybop/plotting/plot_problem.py new file mode 100644 index 000000000..4bc943942 --- /dev/null +++ b/pybop/plotting/plot_problem.py @@ -0,0 +1,101 @@ +import pybop +import numpy as np + + +def quick_plot(problem, parameter_values=None, show=True, **layout_kwargs): + """ + Quickly plot the target dataset against optimised model output. + + Generates an interactive plot comparing the simulated model output with + an optional target dataset and visualises uncertainty. + + Parameters + ---------- + problem : object + Problem object with dataset and signal attributes. + parameter_values : array-like + Optimised (or example) parameter values. + show : bool, optional + If True, the figure is shown upon creation (default: True). + **layout_kwargs : optional + Valid Plotly layout keys and their values, + e.g. `xaxis_title="Time [s]"` or + `xaxis={"title": "Time [s]", "titlefont_size": 18}`. + + Returns + ------- + plotly.graph_objs.Figure + The Plotly figure object for the scatter plot. + """ + if parameter_values is None: + parameter_values = problem.x0 + + # Extract the time data and evaluate the model's output and target values + time_data = problem.time_data() + model_output = problem.evaluate(parameter_values) + target_output = problem.target() + + # Ensure outputs have the same length + len_diff = len(target_output) - len(model_output) + if len_diff > 0: + model_output = np.concatenate( + (model_output, np.full([len_diff, np.shape(model_output)[1]], np.nan)), + axis=0, + ) + elif len_diff < 0: + target_output = np.concatenate( + (target_output, np.full([-len_diff, np.shape(target_output)[1]], np.nan)), + axis=0, + ) + + # Create a plot for each output + figure_list = [] + for i in range(0, problem.n_outputs): + default_layout_options = dict( + title="Scatter Plot", xaxis_title="Time [s]", yaxis_title=problem.signal[i] + ) + + # Create a plotting dictionary + plot_dict = pybop.StandardPlot( + x=time_data, + y=model_output[:, i], + layout_options=default_layout_options, + trace_names="Model", + ) + + # Add the data as markers + target_trace = plot_dict.create_trace( + x=time_data, y=target_output[:, i], name="Target", mode="markers" + ) + plot_dict.traces.append(target_trace) + + # Compute the standard deviation as proxy for uncertainty + plot_dict.sigma = np.std(model_output[:, i] - target_output[:, i]) + + # Convert x and upper and lower limits into lists to create a filled trace + x = time_data.tolist() + y_upper = (model_output[:, i] + plot_dict.sigma).tolist() + y_lower = (model_output[:, i] - plot_dict.sigma).tolist() + fill_trace = plot_dict.create_trace( + x=x + x[::-1], + y=y_upper + y_lower[::-1], + fill="toself", + fillcolor="rgba(255,229,204,0.8)", + line=dict(color="rgba(255,255,255,0)"), + hoverinfo="skip", + showlegend=False, + ) + plot_dict.traces.append(fill_trace) + + # Reverse the order of the traces to put the model on top + plot_dict.traces = plot_dict.traces[::-1] + + # Generate the figure and update the layout + fig = plot_dict(show=False) + fig.update_layout(**layout_kwargs) + if show: + fig.show() + + figure_list.append(fig) + + return figure_list diff --git a/pybop/plotting/quick_plot.py b/pybop/plotting/quick_plot.py deleted file mode 100644 index 56593efec..000000000 --- a/pybop/plotting/quick_plot.py +++ /dev/null @@ -1,246 +0,0 @@ -import numpy as np -import textwrap -import pybop - - -class StandardPlot: - """ - A class for creating and displaying Plotly figures for model output comparison. - - Generates interactive plots comparing simulated model output with an optional target dataset and visualizes uncertainty. - - Parameters - ---------- - x : list or np.ndarray - X-axis data points. - y : list or np.ndarray - Primary Y-axis data points for simulated model output. - cost : float - Cost associated with the model output. - y2 : list or np.ndarray, optional - Secondary Y-axis data points for the target dataset (default: None). - title : str, optional - Title of the plot (default: None). - xaxis_title : str, optional - Title for the x-axis (default: None). - yaxis_title : str, optional - Title for the y-axis (default: None). - trace_name : str, optional - Name for the primary trace (default: "Simulated"). - width : int, optional - Width of the figure in pixels (default: 1024). - height : int, optional - Height of the figure in pixels (default: 576). - """ - - def __init__( - self, - x, - y, - cost, - y2=None, - title=None, - xaxis_title=None, - yaxis_title=None, - trace_name=None, - width=1024, - height=576, - ): - """ - Initialize the StandardPlot object with simulation and optional target data. - - Parameters - ---------- - x : list or np.ndarray - X-axis data points. - y : list or np.ndarray - Primary Y-axis data points for simulated model output. - cost : float - Cost associated with the model output. - y2 : list or np.ndarray, optional - Secondary Y-axis data points for target dataset (default: None). - title : str, optional - Plot title (default: None). - xaxis_title : str, optional - X-axis title (default: None). - yaxis_title : str, optional - Y-axis title (default: None). - trace_name : str, optional - Name for the primary trace (default: "Simulated"). - width : int, optional - Figure width in pixels (default: 1024). - height : int, optional - Figure height in pixels (default: 576). - """ - self.x = x if isinstance(x, list) else x.tolist() - self.y = y - self.y2 = y2 - self.cost = cost - self.width = width - self.height = height - self.title = title - self.xaxis_title = xaxis_title - self.yaxis_title = yaxis_title - self.trace_name = trace_name or "Simulated" - - if self.y2 is not None: - self.sigma = np.std(self.y - self.y2) - self.y_upper = (self.y + self.sigma).tolist() - self.y_lower = (self.y - self.sigma).tolist() - - # Attempt to import plotly when an instance is created - self.go = pybop.PlotlyManager().go - - @staticmethod - def wrap_text(text, width): - """ - Wrap text to a specified width with HTML line breaks. - - Parameters - ---------- - text : str - The text to wrap. - width : int - The width to wrap the text to. - - Returns - ------- - str - The wrapped text. - """ - wrapped_text = textwrap.fill(text, width=width, break_long_words=False) - return wrapped_text.replace("\n", "
") - - def create_layout(self): - """ - Create the layout for the Plotly figure. - - Returns - ------- - plotly.graph_objs.Layout - The layout for the Plotly figure. - """ - return self.go.Layout( - title=self.title, - title_x=0.5, - xaxis=dict(title=self.xaxis_title, titlefont_size=12, tickfont_size=12), - yaxis=dict(title=self.yaxis_title, titlefont_size=12, tickfont_size=12), - legend=dict(x=1, y=1, xanchor="right", yanchor="top", font_size=12), - showlegend=True, - autosize=False, - width=self.width, - height=self.height, - margin=dict(l=10, r=10, b=10, t=75, pad=4), - ) - - def create_traces(self): - """ - Create traces for the Plotly figure. - - Returns - ------- - list - A list of plotly.graph_objs.Scatter objects to be used as traces. - """ - traces = [] - - wrapped_trace_name = self.wrap_text(self.trace_name, width=40) - simulated_trace = self.go.Scatter( - x=self.x, - y=self.y, - line=dict(width=4), - mode="lines", - name=wrapped_trace_name, - ) - - if self.y2 is not None: - target_trace = self.go.Scatter( - x=self.x, y=self.y2, mode="markers", name="Target" - ) - fill_trace = self.go.Scatter( - x=self.x + self.x[::-1], - y=self.y_upper + self.y_lower[::-1], - fill="toself", - fillcolor="rgba(255,229,204,0.8)", - line=dict(color="rgba(255,255,255,0)"), - hoverinfo="skip", - showlegend=False, - ) - traces.extend([fill_trace, target_trace]) - - traces.append(simulated_trace) - - return traces - - def __call__(self): - """ - Generate the Plotly figure. - - Returns - ------- - plotly.graph_objs.Figure - The generated Plotly figure. - """ - layout = self.create_layout() - traces = self.create_traces() - fig = self.go.Figure(data=traces, layout=layout) - return fig - - -def quick_plot(params, cost, title="Scatter Plot", width=1024, height=576): - """ - Quickly plot the target dataset against minimized model output. - - Parameters - ---------- - params : array-like - Optimized parameters. - cost : object - Cost object with problem, dataset, and signal attributes. - title : str, optional - Title of the plot (default: "Scatter Plot"). - width : int, optional - Width of the figure in pixels (default: 1024). - height : int, optional - Height of the figure in pixels (default: 576). - - Returns - ------- - plotly.graph_objs.Figure - The Plotly figure object for the scatter plot. - """ - - # Extract the time data and evaluate the model's output and target values - time_data = cost.problem.time_data() - model_output = cost.problem.evaluate(params) - target_output = cost.problem.target() - - # Ensure outputs have the same length - len_diff = len(target_output) - len(model_output) - if len_diff > 0: - model_output = np.concatenate( - (model_output, np.full([len_diff, np.shape(model_output)[1]], np.nan)), - axis=0, - ) - elif len_diff < 0: - model_output = model_output[0:len_diff] - - for i in range(0, cost.problem.n_outputs): - # Create the figure using the StandardPlot class - fig = pybop.StandardPlot( - x=time_data, - y=model_output[:, i], - cost=cost, - y2=target_output[:, i], - xaxis_title="Time [s]", - yaxis_title=cost.problem.signal[i], - title=title, - trace_name="Model", - width=width, - height=height, - )() - - # Display the figure - fig.show() - - return fig diff --git a/pybop/plotting/standard_plot.py b/pybop/plotting/standard_plot.py new file mode 100644 index 000000000..fe29a5df4 --- /dev/null +++ b/pybop/plotting/standard_plot.py @@ -0,0 +1,321 @@ +import numpy as np +import textwrap +import math +import pybop + + +DEFAULT_LAYOUT_OPTIONS = dict( + title=None, + title_x=0.5, + xaxis=dict(title=None, titlefont_size=12, tickfont_size=12), + yaxis=dict(title=None, titlefont_size=12, tickfont_size=12), + legend=dict(x=1, y=1, xanchor="right", yanchor="top", font_size=12), + showlegend=True, + autosize=False, + width=1024, + height=576, + margin=dict(l=10, r=10, b=10, t=75, pad=4), +) +DEFAULT_SUBPLOT_OPTIONS = dict( + start_cell="bottom-left", +) +DEFAULT_TRACE_OPTIONS = dict(line=dict(width=4), mode="lines") + + +class StandardPlot: + """ + A class for creating and displaying interactive Plotly figures. + + Parameters + ---------- + x : list or np.ndarray + X-axis data points. + y : list or np.ndarray + Primary Y-axis data points for simulated model output. + layout : Plotly layout, optional + A layout for the figure, overrides the layout options (default: None). + layout_options : dict, optional + Settings to modify the default layout (default: DEFAULT_LAYOUT_OPTIONS). + trace_options : dict, optional + Settings to modify the default trace type (default: DEFAULT_TRACE_OPTIONS). + trace_names : str, optional + Name(s) for the primary trace(s) (default: None). + trace_name_width : int, optional + Maximum length of the trace names before text wrapping is used (default: 40). + + Returns + ------- + plotly.graph_objs.Figure + The generated Plotly figure. + """ + + def __init__( + self, + x, + y, + layout=None, + layout_options=DEFAULT_LAYOUT_OPTIONS, + trace_options=DEFAULT_TRACE_OPTIONS, + trace_names=None, + trace_name_width=40, + ): + self.x = x + self.y = y + self.layout = layout + self.layout_options = layout_options + self.trace_options = DEFAULT_TRACE_OPTIONS + if trace_options is not None: + for arg, value in trace_options.items(): + self.trace_options[arg] = value + if isinstance(trace_names, str): + self.trace_names = [trace_names] + else: + self.trace_names = trace_names + self.trace_name_width = trace_name_width + + # Check type and dimensions of data + # What we want is a list of 'things plotly can take', e.g. numpy arrays or lists of numbers + if isinstance(self.x, list): + # If it's a list of numpy arrays, it's fine + # If it's a list of lists, it's fine + # If it's neither, it's a list of numbers that we need to wrap + if not isinstance(self.x[0], np.ndarray) and not isinstance( + self.x[0], list + ): + self.x = [self.x] + elif isinstance(self.x, np.ndarray): + self.x = np.squeeze(self.x) + if self.x.ndim == 1: + self.x = [self.x] + else: + self.x = self.x.tolist() + if isinstance(self.y, list): + if not isinstance(self.y[0], np.ndarray) and not isinstance( + self.y[0], list + ): + self.y = [self.y] + if isinstance(self.y, np.ndarray): + self.y = np.squeeze(self.y) + if self.y.ndim == 1: + self.y = [self.y] + else: + self.y = self.y.tolist() + if len(self.x) > 1 and len(self.x) != len(self.y): + raise ValueError( + "Input x should have either one data series or the same number as y." + ) + + # Attempt to import plotly when an instance is created + self.go = pybop.PlotlyManager().go + + # Create layout + if self.layout is None: + self.layout = self.go.Layout(self.layout_options) + if self.layout_options is not None: + for arg, value in self.layout_options.items(): + self.layout[arg] = value + + # Wrap trace names + if self.trace_names is not None: + for i, name in enumerate(self.trace_names): + self.trace_names[i] = self.wrap_text(name, width=self.trace_name_width) + + # Create a trace for each trajectory + self.traces = [] + x = self.x[0] + for i in range(0, len(self.y)): + if len(self.x) > 1: + x = self.x[i] + if self.trace_names is not None: + self.trace_options["name"] = self.trace_names[i] + else: + self.trace_options["showlegend"] = False + trace = self.create_trace(x, self.y[i], **self.trace_options) + self.traces.append(trace) + + def __call__(self, show=True): + """ + Generate and show the figure. + + Parameters + ---------- + show : bool, optional + If True, the figure is shown upon creation (default: True). + """ + fig = self.go.Figure(data=self.traces, layout=self.layout) + if show is True: + fig.show() + + return fig + + def create_trace(self, x, y, **trace_options): + """ + Create a trace for the Plotly figure. + + Returns + ------- + plotly.graph_objs.Scatter + A trace for a Plotly figure. + """ + + return self.go.Scatter( + x=x, + y=y, + **trace_options, + ) + + @staticmethod + def wrap_text(text, width): + """ + Wrap text to a specified width with HTML line breaks. + + Parameters + ---------- + text : str + The text to wrap. + width : int + The width to wrap the text to. + + Returns + ------- + str + The wrapped text. + """ + wrapped_text = textwrap.fill(text, width=width, break_long_words=False) + return wrapped_text.replace("\n", "
") + + +class StandardSubplot(StandardPlot): + """ + A class for creating and displaying a set of interactive Plotly figures in a grid layout. + + Parameters + ---------- + x : list or np.ndarray + X-axis data points. + y : list or np.ndarray + Primary Y-axis data points for simulated model output. + num_rows : int, optional + Number of rows of subplots, can be set automatically (default: None). + num_cols : int, optional + Number of columns of subplots, can be set automatically (default: None). + layout : Plotly layout, optional + A layout for the figure, overrides the layout options (default: None). + layout_options : dict, optional + Settings to modify the default layout (default: DEFAULT_LAYOUT_OPTIONS). + trace_options : dict, optional + Settings to modify the default trace type (default: DEFAULT_TRACE_OPTIONS). + trace_names : str, optional + Name(s) for the primary trace(s) (default: None). + trace_name_width : int, optional + Maximum length of the trace names before text wrapping is used (default: 40). + + Returns + ------- + plotly.graph_objs.Figure + The generated Plotly figure. + """ + + def __init__( + self, + x, + y, + num_rows=None, + num_cols=None, + axis_titles=None, + layout=None, + layout_options=DEFAULT_LAYOUT_OPTIONS, + subplot_options=DEFAULT_SUBPLOT_OPTIONS, + trace_options=DEFAULT_TRACE_OPTIONS, + trace_names=None, + trace_name_width=40, + ): + super().__init__( + x, y, layout, layout_options, trace_options, trace_names, trace_name_width + ) + self.num_traces = len(self.traces) + self.num_rows = num_rows + self.num_cols = num_cols + if self.num_rows is None and self.num_cols is None: + # Work out the number of subplots + self.num_cols = int(math.ceil(math.sqrt(self.num_traces))) + self.num_rows = int(math.ceil(self.num_traces / self.num_cols)) + elif self.num_rows is None: + self.num_rows = int(math.ceil(self.num_traces / self.num_cols)) + elif self.num_cols is None: + self.num_cols = int(math.ceil(self.num_traces / self.num_rows)) + self.axis_titles = axis_titles + self.subplot_options = DEFAULT_SUBPLOT_OPTIONS + if subplot_options is not None: + for arg, value in subplot_options.items(): + self.subplot_options[arg] = value + + # Attempt to import plotly when an instance is created + self.make_subplots = pybop.PlotlyManager().make_subplots + + def __call__(self, show): + """ + Generate and show the set of figures. + + Parameters + ---------- + show : bool, optional + If True, the figure is shown upon creation (default: True). + """ + fig = self.make_subplots( + rows=self.num_rows, cols=self.num_cols, **self.subplot_options + ) + fig.update_layout(self.layout_options) + + for idx, trace in enumerate(self.traces): + row = (idx // self.num_cols) + 1 + col = (idx % self.num_cols) + 1 + fig.add_trace(trace, row=row, col=col) + + if self.axis_titles and idx < len(self.axis_titles): + x_title, y_title = self.axis_titles[idx] + fig.update_xaxes(title_text=x_title, row=row, col=col) + fig.update_yaxes(title_text=y_title, row=row, col=col) + + if show is True: + fig.show() + + return fig + + +def plot_trajectories(x, y, trace_names=None, show=True, **layout_kwargs): + """ + Quickly plot one or more trajectories using Plotly. + + Parameters + ---------- + x : list or np.ndarray + X-axis data points. + y : list or np.ndarray + Y-axis data points for each trajectory. + trace_names : str or list of strings, optional + Name(s) for the trace(s) (default: None). + **layout_kwargs : optional + Valid Plotly layout keys and their values, + e.g. `xaxis_title="Time [s]"` or + `xaxis={"title": "Time [s]", "titlefont_size": 18}`. + + Returns + ------- + plotly.graph_objs.Figure + The Plotly figure object for the scatter plot. + """ + # Create a plotting dictionary + plot_dict = pybop.StandardPlot( + x=x, + y=y, + trace_names=trace_names, + ) + + # Generate the figure and update the layout + fig = plot_dict(show=False) + fig.update_layout(**layout_kwargs) + if show: + fig.show() + + return fig From c8dc7d783adf39f92c5f610b332bc51ac21e79a2 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 29 Jan 2024 18:26:04 +0000 Subject: [PATCH 02/64] style: pre-commit fixes --- pybop/plotting/plot_cost.py | 1 - 1 file changed, 1 deletion(-) diff --git a/pybop/plotting/plot_cost.py b/pybop/plotting/plot_cost.py index 073398249..54f0e86aa 100644 --- a/pybop/plotting/plot_cost.py +++ b/pybop/plotting/plot_cost.py @@ -1,4 +1,3 @@ -import pybop import numpy as np From 1b86e6441f8f503fa6c1344f5a5b841167ebb168 Mon Sep 17 00:00:00 2001 From: NicolaCourtier <45851982+NicolaCourtier@users.noreply.github.com> Date: Mon, 29 Jan 2024 18:42:41 +0000 Subject: [PATCH 03/64] Update notebooks --- examples/notebooks/equivalent_circuit_identification.ipynb | 2 +- examples/notebooks/spm_Adam.ipynb | 2 +- examples/notebooks/spm_CMAES.ipynb | 2 +- examples/notebooks/spm_nlopt.ipynb | 2 +- examples/notebooks/spm_scipy_DifferentialEvolution.ipynb | 2 +- 5 files changed, 5 insertions(+), 5 deletions(-) diff --git a/examples/notebooks/equivalent_circuit_identification.ipynb b/examples/notebooks/equivalent_circuit_identification.ipynb index a393a2eba..039b7418f 100644 --- a/examples/notebooks/equivalent_circuit_identification.ipynb +++ b/examples/notebooks/equivalent_circuit_identification.ipynb @@ -4890,7 +4890,7 @@ } ], "source": [ - "pybop.quick_plot(x, cost, title=\"Optimised Comparison\");" + "pybop.quick_plot(cost, parameter_values=x, title=\"Optimised Comparison\");" ] }, { diff --git a/examples/notebooks/spm_Adam.ipynb b/examples/notebooks/spm_Adam.ipynb index b042c8c96..44567fe2a 100644 --- a/examples/notebooks/spm_Adam.ipynb +++ b/examples/notebooks/spm_Adam.ipynb @@ -4908,7 +4908,7 @@ } ], "source": [ - "pybop.quick_plot(x, cost, title=\"Optimised Comparison\");" + "pybop.quick_plot(cost, parameter_values=x, title=\"Optimised Comparison\");" ] }, { diff --git a/examples/notebooks/spm_CMAES.ipynb b/examples/notebooks/spm_CMAES.ipynb index 675dd69dd..2c524788d 100644 --- a/examples/notebooks/spm_CMAES.ipynb +++ b/examples/notebooks/spm_CMAES.ipynb @@ -4923,7 +4923,7 @@ } ], "source": [ - "pybop.quick_plot(x, cost, title=\"Optimised Comparison\");" + "pybop.quick_plot(cost, parameter_values=x, title=\"Optimised Comparison\");" ] }, { diff --git a/examples/notebooks/spm_nlopt.ipynb b/examples/notebooks/spm_nlopt.ipynb index 154a7e63d..5a473e27e 100644 --- a/examples/notebooks/spm_nlopt.ipynb +++ b/examples/notebooks/spm_nlopt.ipynb @@ -34027,7 +34027,7 @@ } ], "source": [ - "pybop.quick_plot(x, cost, title=\"Optimised Comparison\");" + "pybop.quick_plot(cost, parameter_values=x, title=\"Optimised Comparison\");" ] }, { diff --git a/examples/notebooks/spm_scipy_DifferentialEvolution.ipynb b/examples/notebooks/spm_scipy_DifferentialEvolution.ipynb index 9db8da2b3..e278866eb 100644 --- a/examples/notebooks/spm_scipy_DifferentialEvolution.ipynb +++ b/examples/notebooks/spm_scipy_DifferentialEvolution.ipynb @@ -4906,7 +4906,7 @@ } ], "source": [ - "pybop.quick_plot(x, cost, title=\"Optimised Comparison\");" + "pybop.quick_plot(cost, parameter_values=x, title=\"Optimised Comparison\");" ] }, { From dd228ebc0ec6652236a7b3279d18ff26d0865578 Mon Sep 17 00:00:00 2001 From: NicolaCourtier <45851982+NicolaCourtier@users.noreply.github.com> Date: Mon, 29 Jan 2024 18:50:48 +0000 Subject: [PATCH 04/64] Update notebooks --- examples/notebooks/equivalent_circuit_identification.ipynb | 2 +- examples/notebooks/spm_Adam.ipynb | 2 +- examples/notebooks/spm_CMAES.ipynb | 2 +- examples/notebooks/spm_nlopt.ipynb | 2 +- examples/notebooks/spm_scipy_DifferentialEvolution.ipynb | 2 +- 5 files changed, 5 insertions(+), 5 deletions(-) diff --git a/examples/notebooks/equivalent_circuit_identification.ipynb b/examples/notebooks/equivalent_circuit_identification.ipynb index 039b7418f..ffde7e5c8 100644 --- a/examples/notebooks/equivalent_circuit_identification.ipynb +++ b/examples/notebooks/equivalent_circuit_identification.ipynb @@ -4890,7 +4890,7 @@ } ], "source": [ - "pybop.quick_plot(cost, parameter_values=x, title=\"Optimised Comparison\");" + "pybop.quick_plot(problem, parameter_values=x, title=\"Optimised Comparison\");" ] }, { diff --git a/examples/notebooks/spm_Adam.ipynb b/examples/notebooks/spm_Adam.ipynb index 44567fe2a..35be8b828 100644 --- a/examples/notebooks/spm_Adam.ipynb +++ b/examples/notebooks/spm_Adam.ipynb @@ -4908,7 +4908,7 @@ } ], "source": [ - "pybop.quick_plot(cost, parameter_values=x, title=\"Optimised Comparison\");" + "pybop.quick_plot(problem, parameter_values=x, title=\"Optimised Comparison\");" ] }, { diff --git a/examples/notebooks/spm_CMAES.ipynb b/examples/notebooks/spm_CMAES.ipynb index 2c524788d..82813081c 100644 --- a/examples/notebooks/spm_CMAES.ipynb +++ b/examples/notebooks/spm_CMAES.ipynb @@ -4923,7 +4923,7 @@ } ], "source": [ - "pybop.quick_plot(cost, parameter_values=x, title=\"Optimised Comparison\");" + "pybop.quick_plot(problem, parameter_values=x, title=\"Optimised Comparison\");" ] }, { diff --git a/examples/notebooks/spm_nlopt.ipynb b/examples/notebooks/spm_nlopt.ipynb index 5a473e27e..c10173612 100644 --- a/examples/notebooks/spm_nlopt.ipynb +++ b/examples/notebooks/spm_nlopt.ipynb @@ -34027,7 +34027,7 @@ } ], "source": [ - "pybop.quick_plot(cost, parameter_values=x, title=\"Optimised Comparison\");" + "pybop.quick_plot(problem, parameter_values=x, title=\"Optimised Comparison\");" ] }, { diff --git a/examples/notebooks/spm_scipy_DifferentialEvolution.ipynb b/examples/notebooks/spm_scipy_DifferentialEvolution.ipynb index e278866eb..1b399872c 100644 --- a/examples/notebooks/spm_scipy_DifferentialEvolution.ipynb +++ b/examples/notebooks/spm_scipy_DifferentialEvolution.ipynb @@ -4906,7 +4906,7 @@ } ], "source": [ - "pybop.quick_plot(cost, parameter_values=x, title=\"Optimised Comparison\");" + "pybop.quick_plot(problem, parameter_values=x, title=\"Optimised Comparison\");" ] }, { From 4b4a1f134648f6442205ce9b87fc9c74de1a4a83 Mon Sep 17 00:00:00 2001 From: NicolaCourtier <45851982+NicolaCourtier@users.noreply.github.com> Date: Mon, 29 Jan 2024 19:57:57 +0000 Subject: [PATCH 05/64] Update legends --- pybop/plotting/plot_dataset.py | 6 ++++-- pybop/plotting/plot_problem.py | 6 +++++- pybop/plotting/standard_plot.py | 2 +- 3 files changed, 10 insertions(+), 4 deletions(-) diff --git a/pybop/plotting/plot_dataset.py b/pybop/plotting/plot_dataset.py index d9afc1493..edbe73d0e 100644 --- a/pybop/plotting/plot_dataset.py +++ b/pybop/plotting/plot_dataset.py @@ -2,7 +2,7 @@ def plot_dataset( - dataset, signal=["Voltage [V]"], trace_names="Data", show=True, **layout_kwargs + dataset, signal=["Voltage [V]"], trace_names=None, show=True, **layout_kwargs ): """ Quickly plot a PyBOP Dataset using Plotly. @@ -36,8 +36,10 @@ def plot_dataset( y = [data[s] for s in signal] if len(signal) == 1: yaxis_title = signal[0] + if trace_names is None: + trace_names = ["Data"] else: - yaxis_title = None + yaxis_title = "Output" if trace_names is None: trace_names = signal diff --git a/pybop/plotting/plot_problem.py b/pybop/plotting/plot_problem.py index 4bc943942..b16c21b76 100644 --- a/pybop/plotting/plot_problem.py +++ b/pybop/plotting/plot_problem.py @@ -65,7 +65,11 @@ def quick_plot(problem, parameter_values=None, show=True, **layout_kwargs): # Add the data as markers target_trace = plot_dict.create_trace( - x=time_data, y=target_output[:, i], name="Target", mode="markers" + x=time_data, + y=target_output[:, i], + name="Target", + mode="markers", + showlegend=True, ) plot_dict.traces.append(target_trace) diff --git a/pybop/plotting/standard_plot.py b/pybop/plotting/standard_plot.py index fe29a5df4..1861041a0 100644 --- a/pybop/plotting/standard_plot.py +++ b/pybop/plotting/standard_plot.py @@ -293,7 +293,7 @@ def plot_trajectories(x, y, trace_names=None, show=True, **layout_kwargs): X-axis data points. y : list or np.ndarray Y-axis data points for each trajectory. - trace_names : str or list of strings, optional + trace_names : list or str, optional Name(s) for the trace(s) (default: None). **layout_kwargs : optional Valid Plotly layout keys and their values, From 90ddac3b5678936af7f4bb8d5bf0155a802a9783 Mon Sep 17 00:00:00 2001 From: NicolaCourtier <45851982+NicolaCourtier@users.noreply.github.com> Date: Mon, 29 Jan 2024 20:33:34 +0000 Subject: [PATCH 06/64] Add getitem and update dataset checks --- pybop/_dataset.py | 40 +++++++++++++++++++++++++++++++++++++++- pybop/_problem.py | 15 ++------------- 2 files changed, 41 insertions(+), 14 deletions(-) diff --git a/pybop/_dataset.py b/pybop/_dataset.py index f48846025..cf33d7120 100644 --- a/pybop/_dataset.py +++ b/pybop/_dataset.py @@ -1,4 +1,5 @@ import pybamm +import numpy as np class Dataset: @@ -36,6 +37,30 @@ def __repr__(self): A string that includes the type and contents of the dataset. """ return f"Dataset: {type(self.data)} \n Contains: {self.names}" + + def __getitem__(self,key): + """ + Return the data corresponding to a particular key. + + Parameters + ---------- + key : str + The name of a data series within the dataset. + + Returns + ------- + list or np.ndarray + The data series corresonding to the key. + + Raises + ------ + ValueError + The key must exist in the dataset. + """ + if key not in self.data.keys(): + raise ValueError(f"The key {key} does not exist in this dataset.") + + return self.data[key] def Interpolant(self): """ @@ -64,6 +89,11 @@ def check(self, signal=["Voltage [V]"]): ------- bool If True, the dataset has the expected attributes. + + Raises + ------ + ValueError + If the time series and the data series are not consistent. """ if isinstance(signal, str): signal = [signal] @@ -73,9 +103,17 @@ def check(self, signal=["Voltage [V]"]): if name not in self.names: raise ValueError(f"expected {name} in list of dataset") + # Check for increasing times + time_data = self.data["Time [s]"] + if np.any(time_data < 0): + raise ValueError("Times can not be negative.") + if np.any(time_data[:-1] >= time_data[1:]): + raise ValueError("Times must be increasing.") + # Check for consistent data + n_time_data = len(time_data) for s in signal: - if len(self.data[s]) != len(self.data["Time [s]"]): + if len(self.data[s]) != n_time_data: raise ValueError(f"Time data and {s} data must be the same length.") return True diff --git a/pybop/_problem.py b/pybop/_problem.py index dc576d04f..30534bcc1 100644 --- a/pybop/_problem.py +++ b/pybop/_problem.py @@ -148,22 +148,11 @@ def __init__( self._dataset = dataset.data # Check that the dataset contains time and current - for name in ["Time [s]", "Current function [A]"] + self.signal: - if name not in self._dataset: - raise ValueError(f"expected {name} in list of dataset") + dataset.check(self.signal + ["Current function [A]"]) + # Unpack time and target data self._time_data = self._dataset["Time [s]"] self.n_time_data = len(self._time_data) - if np.any(self._time_data < 0): - raise ValueError("Times can not be negative.") - if np.any(self._time_data[:-1] >= self._time_data[1:]): - raise ValueError("Times must be increasing.") - - for signal in self.signal: - if len(self._dataset[signal]) != self.n_time_data: - raise ValueError( - f"Time data and {signal} data must be the same length." - ) target = [self._dataset[signal] for signal in self.signal] self._target = np.vstack(target).T From 8c4eb77d49708d232491460f24697f8aac5455ca Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 29 Jan 2024 20:34:06 +0000 Subject: [PATCH 07/64] style: pre-commit fixes --- pybop/_dataset.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/pybop/_dataset.py b/pybop/_dataset.py index cf33d7120..a084063e0 100644 --- a/pybop/_dataset.py +++ b/pybop/_dataset.py @@ -37,8 +37,8 @@ def __repr__(self): A string that includes the type and contents of the dataset. """ return f"Dataset: {type(self.data)} \n Contains: {self.names}" - - def __getitem__(self,key): + + def __getitem__(self, key): """ Return the data corresponding to a particular key. @@ -59,7 +59,7 @@ def __getitem__(self,key): """ if key not in self.data.keys(): raise ValueError(f"The key {key} does not exist in this dataset.") - + return self.data[key] def Interpolant(self): From 7c3c8e6e614fcfdd18795922ad0c6bf73e2dfcd1 Mon Sep 17 00:00:00 2001 From: NicolaCourtier <45851982+NicolaCourtier@users.noreply.github.com> Date: Mon, 29 Jan 2024 20:55:54 +0000 Subject: [PATCH 08/64] Update test_dataset.py --- tests/unit/test_dataset.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/tests/unit/test_dataset.py b/tests/unit/test_dataset.py index b5cfa96dc..31715a721 100644 --- a/tests/unit/test_dataset.py +++ b/tests/unit/test_dataset.py @@ -38,3 +38,11 @@ def test_dataset(self): # Test conversion of pybamm solution into dictionary assert dataset.data == pybop.Dataset(solution).data assert dataset.names == pybop.Dataset(solution).names + + # Test getitem and missing key + dataset["Time [s]"] + with pytest.raises(ValueError): + dataset["Time"] + + # Test conversion of single signal to list + assert dataset.check(signal="Terminal voltage [V]") From 048ecf66852ba0e2f1b54ac19ff74a9cfe006b17 Mon Sep 17 00:00:00 2001 From: NicolaCourtier <45851982+NicolaCourtier@users.noreply.github.com> Date: Mon, 29 Jan 2024 23:26:18 +0000 Subject: [PATCH 09/64] Add plotting tests --- tests/unit/test_plotting.py | 59 +++++++++++++++++++++++++++++++++++++ 1 file changed, 59 insertions(+) diff --git a/tests/unit/test_plotting.py b/tests/unit/test_plotting.py index 92a327c61..c720b49c9 100644 --- a/tests/unit/test_plotting.py +++ b/tests/unit/test_plotting.py @@ -4,6 +4,8 @@ import subprocess import plotly import pytest +import pybop +import numpy as np # Find the Python executable python_executable = find_executable("python") @@ -111,3 +113,60 @@ def test_post_install_setup(plotly_installed): def is_package_installed(package_name): """Check if a package is installed without raising an exception.""" return any(d.metadata["Name"] == package_name for d in distributions()) + + +@pytest.fixture +def dataset(plotly_installed): + # Construct and simulate model + model = pybop.lithium_ion.SPM() + model.parameter_set = model.pybamm_model.default_parameter_values + solution = model.predict(t_eval=np.linspace(0, 10, 100)) + + # Form dataset + data_dictionary = { + "Time [s]": solution["Time [s]"].data, + "Current [A]": solution["Current [A]"].data, + "Terminal voltage [V]": solution["Terminal voltage [V]"].data, + } + return pybop.Dataset(data_dictionary) + + +@pytest.mark.unit +def test_standard_plot(dataset, plotly_installed): + # Check the StandardPlot class + pybop.StandardPlot(dataset.data["Time [s]"], dataset.data["Terminal voltage [V]"]) + + # Check the StandardSubplot class + pybop.StandardSubplot( + dataset.data["Time [s]"], + [dataset.data["Terminal voltage [V]"], dataset.data["Current [A]"]], + num_rows=1, + ) + pybop.StandardSubplot( + dataset.data["Time [s]"], + [dataset.data["Terminal voltage [V]"], dataset.data["Current [A]"]], + num_cols=1, + ) + + # Check plotting numpy arrays, lists, and lists of numpy arrays + pybop.plot_trajectories( + dataset.data["Time [s]"], dataset.data["Terminal voltage [V]"] + ) + pybop.plot_trajectories( + dataset.data["Time [s]"].tolist(), dataset.data["Terminal voltage [V]"].tolist() + ) + pybop.plot_trajectories( + [dataset.data["Time [s]"]], + [dataset.data["Terminal voltage [V]"], dataset.data["Current [A]"]], + ) + pybop.plot_trajectories( + [dataset.data["Time [s]"], dataset.data["Time [s]"]], + [dataset.data["Terminal voltage [V]"], dataset.data["Current [A]"]], + ) + + # Test incorrect dimensions + with pytest.raises(ValueError): + pybop.plot_trajectories( + [dataset.data["Time [s]"], dataset.data["Current [A]"]], + dataset.data["Terminal voltage [V]"], + ) From a0632b51dd19b19705013f20e55ae6d56c320fb8 Mon Sep 17 00:00:00 2001 From: NicolaCourtier <45851982+NicolaCourtier@users.noreply.github.com> Date: Mon, 29 Jan 2024 23:41:58 +0000 Subject: [PATCH 10/64] Add test_plot_dataset --- tests/unit/test_plotting.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/tests/unit/test_plotting.py b/tests/unit/test_plotting.py index c720b49c9..474f44f6c 100644 --- a/tests/unit/test_plotting.py +++ b/tests/unit/test_plotting.py @@ -170,3 +170,10 @@ def test_standard_plot(dataset, plotly_installed): [dataset.data["Time [s]"], dataset.data["Current [A]"]], dataset.data["Terminal voltage [V]"], ) + + +@pytest.mark.unit +def test_plot_dataset(dataset, plotly_installed): + # Test plotting of a dataset + pybop.plot_dataset(dataset, signal=["Terminal voltage [V]"]) + pybop.plot_dataset(dataset, signal=["Terminal voltage [V]", "Current [A]"]) From c8d6c5195fc0ca4ffd97d6cd87a512cd6f5fe1b1 Mon Sep 17 00:00:00 2001 From: NicolaCourtier <45851982+NicolaCourtier@users.noreply.github.com> Date: Thu, 8 Feb 2024 16:37:52 +0000 Subject: [PATCH 11/64] Update dataset check --- pybop/observers/unscented_kalman.py | 14 +------------- tests/unit/test_cost.py | 3 ++- 2 files changed, 3 insertions(+), 14 deletions(-) diff --git a/pybop/observers/unscented_kalman.py b/pybop/observers/unscented_kalman.py index ae13acc8e..62b9d0a79 100644 --- a/pybop/observers/unscented_kalman.py +++ b/pybop/observers/unscented_kalman.py @@ -58,22 +58,10 @@ def __init__( self._dataset = dataset.data # Check that the dataset contains time and current - for name in ["Time [s]", "Current function [A]"] + self.signal: - if name not in self._dataset: - raise ValueError(f"expected {name} in list of dataset") + dataset.check(self.signal + ["Current function [A]"]) self._time_data = self._dataset["Time [s]"] self.n_time_data = len(self._time_data) - if np.any(self._time_data < 0): - raise ValueError("Times can not be negative.") - if np.any(self._time_data[:-1] >= self._time_data[1:]): - raise ValueError("Times must be increasing.") - - for signal in self.signal: - if len(self._dataset[signal]) != self.n_time_data: - raise ValueError( - f"Time data and {signal} data must be the same length." - ) target = [self._dataset[signal] for signal in self.signal] self._target = np.vstack(target).T diff --git a/tests/unit/test_cost.py b/tests/unit/test_cost.py index 94d5e890c..520c85772 100644 --- a/tests/unit/test_cost.py +++ b/tests/unit/test_cost.py @@ -38,6 +38,7 @@ def problem(self, request): signal = ["Voltage [V]"] model.parameter_set.update({"Lower voltage cut-off [V]": cut_off}) problem = pybop.FittingProblem(model, parameters, dataset, signal=signal, x0=x0) + problem.dataset = dataset # add this to pass the pybop dataset to cost return problem @pytest.fixture( @@ -59,7 +60,7 @@ def cost(self, problem, request): process_diag[1] = 1e-4 sigma0 = np.diag(sigma_diag) process = np.diag(process_diag) - dataset = type("dataset", (object,), {"data": problem._dataset})() + dataset = problem.dataset return cls( pybop.UnscentedKalmanFilterObserver( problem.parameters, From 292086546cf36ae6b5e47cad17bc5989d9b48cff Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 8 Feb 2024 16:40:27 +0000 Subject: [PATCH 12/64] style: pre-commit fixes --- tests/unit/test_cost.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/unit/test_cost.py b/tests/unit/test_cost.py index 520c85772..81b77b32d 100644 --- a/tests/unit/test_cost.py +++ b/tests/unit/test_cost.py @@ -38,7 +38,7 @@ def problem(self, request): signal = ["Voltage [V]"] model.parameter_set.update({"Lower voltage cut-off [V]": cut_off}) problem = pybop.FittingProblem(model, parameters, dataset, signal=signal, x0=x0) - problem.dataset = dataset # add this to pass the pybop dataset to cost + problem.dataset = dataset # add this to pass the pybop dataset to cost return problem @pytest.fixture( From 1cdea9e18d41f3fc69531894ceb3736b461c6c68 Mon Sep 17 00:00:00 2001 From: NicolaCourtier <45851982+NicolaCourtier@users.noreply.github.com> Date: Thu, 8 Feb 2024 16:43:58 +0000 Subject: [PATCH 13/64] Update UKF example plotting --- examples/scripts/exp_UKF.py | 7 ++----- examples/scripts/spm_UKF.py | 7 ++----- 2 files changed, 4 insertions(+), 10 deletions(-) diff --git a/examples/scripts/exp_UKF.py b/examples/scripts/exp_UKF.py index e4cf24f3e..965775ecd 100644 --- a/examples/scripts/exp_UKF.py +++ b/examples/scripts/exp_UKF.py @@ -101,7 +101,7 @@ print("Estimated parameters:", x) # Plot the timeseries output (requires model that returns Voltage) -pybop.quick_plot(x, cost, title="Optimised Comparison") +pybop.quick_plot(observer, parameter_values=x, title="Optimised Comparison") # Plot convergence pybop.plot_convergence(optim) @@ -109,8 +109,5 @@ # Plot the parameter traces pybop.plot_parameters(optim) -# Plot the cost landscape -pybop.plot_cost2d(cost, steps=15) - # Plot the cost landscape with optimisation path -pybop.plot_cost2d(cost, optim=optim, steps=15) +pybop.plot_optim2d(optim, steps=15) diff --git a/examples/scripts/spm_UKF.py b/examples/scripts/spm_UKF.py index 7128293cd..067a554d7 100644 --- a/examples/scripts/spm_UKF.py +++ b/examples/scripts/spm_UKF.py @@ -67,7 +67,7 @@ print("Estimated parameters:", x) # Plot the timeseries output (requires model that returns Voltage) -pybop.quick_plot(x, cost, title="Optimised Comparison") +pybop.quick_plot(observer, parameter_values=x, title="Optimised Comparison") # # Plot convergence # pybop.plot_convergence(optim) @@ -75,8 +75,5 @@ # # Plot the parameter traces # pybop.plot_parameters(optim) -# # Plot the cost landscape -# pybop.plot_cost2d(cost, steps=5) - # # Plot the cost landscape with optimisation path -# pybop.plot_cost2d(cost, optim=optim, steps=5) +# pybop.plot_optim2d(optim, steps=15) From a1897965ffbc88825b585e9bbc20fc3721b35e2b Mon Sep 17 00:00:00 2001 From: NicolaCourtier <45851982+NicolaCourtier@users.noreply.github.com> Date: Fri, 9 Feb 2024 17:02:49 +0000 Subject: [PATCH 14/64] Revert file names --- pybop/__init__.py | 6 +++--- .../plotting/{plot_optimisation.py => plot_convergence.py} | 0 pybop/plotting/{plot_cost.py => plot_cost2d.py} | 0 pybop/plotting/{standard_plot.py => quick_plot.py} | 0 4 files changed, 3 insertions(+), 3 deletions(-) rename pybop/plotting/{plot_optimisation.py => plot_convergence.py} (100%) rename pybop/plotting/{plot_cost.py => plot_cost2d.py} (100%) rename pybop/plotting/{standard_plot.py => quick_plot.py} (100%) diff --git a/pybop/__init__.py b/pybop/__init__.py index a514ab4cf..a1904d1e3 100644 --- a/pybop/__init__.py +++ b/pybop/__init__.py @@ -89,10 +89,10 @@ # Plotting class # from .plotting.plotly_manager import PlotlyManager -from .plotting.standard_plot import StandardPlot, StandardSubplot, plot_trajectories -from .plotting.plot_cost import plot_cost2d +from .plotting.quick_plot import StandardPlot, StandardSubplot, plot_trajectories +from .plotting.plot_cost2d import plot_cost2d from .plotting.plot_dataset import plot_dataset -from .plotting.plot_optimisation import plot_convergence, plot_parameters, plot_optim2d +from .plotting.plot_convergence import plot_convergence, plot_parameters, plot_optim2d from .plotting.plot_problem import quick_plot # diff --git a/pybop/plotting/plot_optimisation.py b/pybop/plotting/plot_convergence.py similarity index 100% rename from pybop/plotting/plot_optimisation.py rename to pybop/plotting/plot_convergence.py diff --git a/pybop/plotting/plot_cost.py b/pybop/plotting/plot_cost2d.py similarity index 100% rename from pybop/plotting/plot_cost.py rename to pybop/plotting/plot_cost2d.py diff --git a/pybop/plotting/standard_plot.py b/pybop/plotting/quick_plot.py similarity index 100% rename from pybop/plotting/standard_plot.py rename to pybop/plotting/quick_plot.py From ac10023b1380e618c8d445e13676d0028f72aa51 Mon Sep 17 00:00:00 2001 From: NicolaCourtier <45851982+NicolaCourtier@users.noreply.github.com> Date: Fri, 9 Feb 2024 17:09:41 +0000 Subject: [PATCH 15/64] Re-implement change to BaseCost --- pybop/costs/base_cost.py | 1 + 1 file changed, 1 insertion(+) diff --git a/pybop/costs/base_cost.py b/pybop/costs/base_cost.py index 171e09b37..bdafec3c4 100644 --- a/pybop/costs/base_cost.py +++ b/pybop/costs/base_cost.py @@ -28,6 +28,7 @@ def __init__(self, problem): self.problem = problem if problem is not None: self._target = problem._target + self.parameters = problem.parameters self.x0 = problem.x0 self.bounds = problem.bounds self.n_parameters = problem.n_parameters From 93095b698bfdbf72cf66ed30b7c1adeee6d9f08e Mon Sep 17 00:00:00 2001 From: NicolaCourtier <45851982+NicolaCourtier@users.noreply.github.com> Date: Fri, 9 Feb 2024 17:14:42 +0000 Subject: [PATCH 16/64] Separate off plot_parameters --- pybop/plotting/plot_convergence.py | 76 ----------------------------- pybop/plotting/plot_parameters.py | 78 ++++++++++++++++++++++++++++++ 2 files changed, 78 insertions(+), 76 deletions(-) create mode 100644 pybop/plotting/plot_parameters.py diff --git a/pybop/plotting/plot_convergence.py b/pybop/plotting/plot_convergence.py index 6ec75aee0..6bf1bf2eb 100644 --- a/pybop/plotting/plot_convergence.py +++ b/pybop/plotting/plot_convergence.py @@ -54,82 +54,6 @@ def plot_convergence(optim, show=True, **layout_kwargs): return fig -def plot_parameters(optim, show=True, **layout_kwargs): - """ - Plot the evolution of parameters during the optimization process using Plotly. - - Parameters - ---------- - optim : object - Optimisation object containing the history of parameter values and associated cost. - show : bool, optional - If True, the figure is shown upon creation (default: True). - **layout_kwargs : optional - Valid Plotly layout keys and their values, - e.g. `xaxis_title="Time [s]"` or - `xaxis={"title": "Time [s]", "titlefont_size": 18}`. - - Returns - ------- - plotly.graph_objs.Figure - A Plotly figure object showing the parameter evolution over iterations. - """ - - # Extract parameters and log from the optimisation object - parameters = optim.cost.parameters - log = optim.log - - # Create a list of sequential integers for the x-axis - x = list(range(len(log[0]) * len(log))) - - # Determine the number of elements in the smallest arrays - num_elements = len(log[0][0]) - - # Initialize a list of lists to store our columns - y = [[] for _ in range(num_elements)] - - # Loop through each numpy array in y - for array in log: - # Loop through each item (which is a n-element array) in the numpy array - for item in array: - # Loop through each element in the item and append to the corresponding column - for i in range(num_elements): - y[i].append(item[i]) - - # Create lists of axis titles and trace names - axis_titles = [] - trace_names = [] - for param in parameters: - axis_titles.append(("Function Call", param.name)) - trace_names.append(param.name) - - # Set subplot layout options - layout_options = dict( - title="Parameter Convergence", - width=1024, - height=576, - legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1), - ) - - # Create a plotting dictionary - plot_dict = pybop.StandardSubplot( - x=x, - y=y, - axis_titles=axis_titles, - layout_options=layout_options, - trace_names=trace_names, - trace_name_width=50, - ) - - # Generate the figure and update the layout - fig = plot_dict(show=False) - fig.update_layout(**layout_kwargs) - if show: - fig.show() - - return fig - - def plot_optim2d(optim, bounds=None, steps=10, show=True, **layout_kwargs): """ Plot a 2D visualization of a cost landscape using Plotly with the optimisation trace. diff --git a/pybop/plotting/plot_parameters.py b/pybop/plotting/plot_parameters.py new file mode 100644 index 000000000..570be6fdf --- /dev/null +++ b/pybop/plotting/plot_parameters.py @@ -0,0 +1,78 @@ +import pybop +import numpy as np + + +def plot_parameters(optim, show=True, **layout_kwargs): + """ + Plot the evolution of parameters during the optimization process using Plotly. + + Parameters + ---------- + optim : object + Optimisation object containing the history of parameter values and associated cost. + show : bool, optional + If True, the figure is shown upon creation (default: True). + **layout_kwargs : optional + Valid Plotly layout keys and their values, + e.g. `xaxis_title="Time [s]"` or + `xaxis={"title": "Time [s]", "titlefont_size": 18}`. + + Returns + ------- + plotly.graph_objs.Figure + A Plotly figure object showing the parameter evolution over iterations. + """ + + # Extract parameters and log from the optimisation object + parameters = optim.cost.parameters + log = optim.log + + # Create a list of sequential integers for the x-axis + x = list(range(len(log[0]) * len(log))) + + # Determine the number of elements in the smallest arrays + num_elements = len(log[0][0]) + + # Initialize a list of lists to store our columns + y = [[] for _ in range(num_elements)] + + # Loop through each numpy array in y + for array in log: + # Loop through each item (which is a n-element array) in the numpy array + for item in array: + # Loop through each element in the item and append to the corresponding column + for i in range(num_elements): + y[i].append(item[i]) + + # Create lists of axis titles and trace names + axis_titles = [] + trace_names = [] + for param in parameters: + axis_titles.append(("Function Call", param.name)) + trace_names.append(param.name) + + # Set subplot layout options + layout_options = dict( + title="Parameter Convergence", + width=1024, + height=576, + legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1), + ) + + # Create a plotting dictionary + plot_dict = pybop.StandardSubplot( + x=x, + y=y, + axis_titles=axis_titles, + layout_options=layout_options, + trace_names=trace_names, + trace_name_width=50, + ) + + # Generate the figure and update the layout + fig = plot_dict(show=False) + fig.update_layout(**layout_kwargs) + if show: + fig.show() + + return fig From bfe5bb98139d85ab1211d10f55fef255be1f9141 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 9 Feb 2024 17:15:59 +0000 Subject: [PATCH 17/64] style: pre-commit fixes --- pybop/plotting/plot_parameters.py | 1 - 1 file changed, 1 deletion(-) diff --git a/pybop/plotting/plot_parameters.py b/pybop/plotting/plot_parameters.py index 570be6fdf..cc7b7141a 100644 --- a/pybop/plotting/plot_parameters.py +++ b/pybop/plotting/plot_parameters.py @@ -1,5 +1,4 @@ import pybop -import numpy as np def plot_parameters(optim, show=True, **layout_kwargs): From 1ccfda1f2ea00a3720e0032c37c0cff8b99f6570 Mon Sep 17 00:00:00 2001 From: NicolaCourtier <45851982+NicolaCourtier@users.noreply.github.com> Date: Fri, 9 Feb 2024 17:21:00 +0000 Subject: [PATCH 18/64] Move plot_parameters in init --- pybop/__init__.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/pybop/__init__.py b/pybop/__init__.py index 2077ea71c..1fe3774f8 100644 --- a/pybop/__init__.py +++ b/pybop/__init__.py @@ -102,7 +102,8 @@ from .plotting.quick_plot import StandardPlot, StandardSubplot, plot_trajectories from .plotting.plot_cost2d import plot_cost2d from .plotting.plot_dataset import plot_dataset -from .plotting.plot_convergence import plot_convergence, plot_parameters, plot_optim2d +from .plotting.plot_convergence import plot_convergence, plot_optim2d +from .plotting.plot_parameters import plot_parameters from .plotting.plot_problem import quick_plot # From 993aa91c2ada1ac32a0756325cbcf38baff9051e Mon Sep 17 00:00:00 2001 From: NicolaCourtier <45851982+NicolaCourtier@users.noreply.github.com> Date: Fri, 9 Feb 2024 17:30:39 +0000 Subject: [PATCH 19/64] Update spm_electrode_design.ipynb --- examples/notebooks/spm_electrode_design.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/notebooks/spm_electrode_design.ipynb b/examples/notebooks/spm_electrode_design.ipynb index 9b378388a..f931aced6 100644 --- a/examples/notebooks/spm_electrode_design.ipynb +++ b/examples/notebooks/spm_electrode_design.ipynb @@ -13341,8 +13341,8 @@ ], "source": [ "if cost.update_capacity:\n", - " cost.problem._model.approximate_capacity(x)\n", - "pybop.quick_plot(x, cost, title=\"Optimised Comparison\");" + " problem._model.approximate_capacity(x)\n", + "pybop.quick_plot(problem, parameter_values=x, title=\"Optimised Comparison\")" ] }, { @@ -14417,7 +14417,7 @@ ], "source": [ "if len(x) == 2:\n", - " pybop.plot_cost2d(cost, optim=optim, steps=3)" + " pybop.plot_optim2d(optim, steps=3)" ] } ], From 541ac057b652b846e1edb0cb5b4366ada798fc65 Mon Sep 17 00:00:00 2001 From: NicolaCourtier <45851982+NicolaCourtier@users.noreply.github.com> Date: Fri, 9 Feb 2024 17:59:01 +0000 Subject: [PATCH 20/64] Re-implement design changes to quick_plot --- pybop/plotting/plot_problem.py | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/pybop/plotting/plot_problem.py b/pybop/plotting/plot_problem.py index b16c21b76..e0ab1a89b 100644 --- a/pybop/plotting/plot_problem.py +++ b/pybop/plotting/plot_problem.py @@ -56,18 +56,26 @@ def quick_plot(problem, parameter_values=None, show=True, **layout_kwargs): ) # Create a plotting dictionary + if isinstance(problem, pybop.DesignProblem): + trace_name = "Optimised" + else: + trace_name = "Model" plot_dict = pybop.StandardPlot( x=time_data, y=model_output[:, i], layout_options=default_layout_options, - trace_names="Model", + trace_names=trace_name, ) # Add the data as markers + if isinstance(problem, pybop.DesignProblem): + name = "Initial" + else: + name = "Target" target_trace = plot_dict.create_trace( x=time_data, y=target_output[:, i], - name="Target", + name=name, mode="markers", showlegend=True, ) From a00a2be458b38db4cb46670868eb31604d08cd87 Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Fri, 16 Feb 2024 13:53:05 +0000 Subject: [PATCH 21/64] Update subplot widths, test_plotting dataset access, remove square brackets from plots as per SI standard, quick plot for varying length signals, add pybop.is_numeric and restore design cost exception for optimisation --- pybop/__init__.py | 7 +++- pybop/_utils.py | 8 ++++ pybop/costs/design_costs.py | 24 +++++++++-- pybop/plotting/plot_cost2d.py | 4 +- pybop/plotting/plot_dataset.py | 13 +++--- pybop/plotting/plot_problem.py | 73 +++++++++++++++------------------- pybop/plotting/quick_plot.py | 24 +++++++++-- tests/unit/test_plotting.py | 28 ++++++------- 8 files changed, 108 insertions(+), 73 deletions(-) create mode 100644 pybop/_utils.py diff --git a/pybop/__init__.py b/pybop/__init__.py index 1fe3774f8..0aacb79f3 100644 --- a/pybop/__init__.py +++ b/pybop/__init__.py @@ -24,7 +24,12 @@ script_path = path.dirname(__file__) # -# Cost function class +# Utilities +# +from ._utils import is_numeric + +# +# Cost class # from .costs.base_cost import BaseCost from .costs.fitting_costs import ( diff --git a/pybop/_utils.py b/pybop/_utils.py new file mode 100644 index 000000000..6fbfeaab5 --- /dev/null +++ b/pybop/_utils.py @@ -0,0 +1,8 @@ +import numpy as np + + +def is_numeric(x): + """ + Check if a variable is numeric. + """ + return isinstance(x, (int, float, np.number)) diff --git a/pybop/costs/design_costs.py b/pybop/costs/design_costs.py index 297697e39..710e63c2d 100644 --- a/pybop/costs/design_costs.py +++ b/pybop/costs/design_costs.py @@ -1,3 +1,4 @@ +import pybop import numpy as np import warnings @@ -29,7 +30,7 @@ def __init__(self, problem, update_capacity=False): problem : object The problem instance containing the model and data. """ - super().__init__(problem) + super(DesignCost, self).__init__(problem) self.problem = problem if update_capacity is True: nominal_capacity_warning = ( @@ -92,7 +93,7 @@ class GravimetricEnergyDensity(DesignCost): """ def __init__(self, problem, update_capacity=False): - super().__init__(problem, update_capacity) + super(GravimetricEnergyDensity, self).__init__(problem, update_capacity) def _evaluate(self, x, grad=None): """ @@ -110,6 +111,9 @@ def _evaluate(self, x, grad=None): float The negative gravimetric energy density or infinity in case of infeasible parameters. """ + if not all(pybop.is_numeric(i) for i in x): + raise ValueError("Input must be a numeric array.") + try: with warnings.catch_warnings(): # Convert UserWarning to an exception @@ -126,10 +130,16 @@ def _evaluate(self, x, grad=None): return negative_energy_density + # Catch infeasible solutions and return infinity except UserWarning as e: print(f"Ignoring this sample due to: {e}") return np.inf + # Catch any other exception and return infinity + except Exception as e: + print(f"An error occurred during the evaluation: {e}") + return np.inf + class VolumetricEnergyDensity(DesignCost): """ @@ -142,7 +152,7 @@ class VolumetricEnergyDensity(DesignCost): """ def __init__(self, problem, update_capacity=False): - super().__init__(problem, update_capacity) + super(VolumetricEnergyDensity, self).__init__(problem, update_capacity) def _evaluate(self, x, grad=None): """ @@ -160,6 +170,8 @@ def _evaluate(self, x, grad=None): float The negative volumetric energy density or infinity in case of infeasible parameters. """ + if not all(pybop.is_numeric(i) for i in x): + raise ValueError("Input must be a numeric array.") try: with warnings.catch_warnings(): # Convert UserWarning to an exception @@ -176,6 +188,12 @@ def _evaluate(self, x, grad=None): return negative_energy_density + # Catch infeasible solutions and return infinity except UserWarning as e: print(f"Ignoring this sample due to: {e}") return np.inf + + # Catch any other exception and return infinity + except Exception as e: + print(f"An error occurred during the evaluation: {e}") + return np.inf diff --git a/pybop/plotting/plot_cost2d.py b/pybop/plotting/plot_cost2d.py index 54f0e86aa..c6cefe66a 100644 --- a/pybop/plotting/plot_cost2d.py +++ b/pybop/plotting/plot_cost2d.py @@ -98,5 +98,5 @@ def get_param_bounds(cost): for i, param in enumerate(cost.parameters): bounds[i] = param.bounds return bounds - - return None + else: + return None diff --git a/pybop/plotting/plot_dataset.py b/pybop/plotting/plot_dataset.py index edbe73d0e..459c31cc2 100644 --- a/pybop/plotting/plot_dataset.py +++ b/pybop/plotting/plot_dataset.py @@ -19,8 +19,8 @@ def plot_dataset( If True, the figure is shown upon creation (default: True). **layout_kwargs : optional Valid Plotly layout keys and their values, - e.g. `xaxis_title="Time [s]"` or - `xaxis={"title": "Time [s]", "titlefont_size": 18}`. + e.g. `xaxis_title="Time / s"` or + `xaxis={"title": "Time / s", "titlefont_size": 18}`. Returns ------- @@ -30,10 +30,9 @@ def plot_dataset( # Get data dictionary dataset.check(signal) - data = dataset.data # Compile ydata and labels or legend - y = [data[s] for s in signal] + y = [dataset[s] for s in signal] if len(signal) == 1: yaxis_title = signal[0] if trace_names is None: @@ -41,15 +40,15 @@ def plot_dataset( else: yaxis_title = "Output" if trace_names is None: - trace_names = signal + trace_names = pybop.StandardPlot.remove_brackets(signal) # Create the figure fig = pybop.plot_trajectories( - x=data["Time [s]"], + x=dataset["Time [s]"], y=y, trace_names=trace_names, show=False, - xaxis_title="Time [s]", + xaxis_title="Time / s", yaxis_title=yaxis_title, ) fig.update_layout(**layout_kwargs) diff --git a/pybop/plotting/plot_problem.py b/pybop/plotting/plot_problem.py index e0ab1a89b..032571387 100644 --- a/pybop/plotting/plot_problem.py +++ b/pybop/plotting/plot_problem.py @@ -19,8 +19,8 @@ def quick_plot(problem, parameter_values=None, show=True, **layout_kwargs): If True, the figure is shown upon creation (default: True). **layout_kwargs : optional Valid Plotly layout keys and their values, - e.g. `xaxis_title="Time [s]"` or - `xaxis={"title": "Time [s]", "titlefont_size": 18}`. + e.g. `xaxis_title="Time / s"` or + `xaxis={"title": "Time / s", "titlefont_size": 18}`. Returns ------- @@ -31,73 +31,62 @@ def quick_plot(problem, parameter_values=None, show=True, **layout_kwargs): parameter_values = problem.x0 # Extract the time data and evaluate the model's output and target values - time_data = problem.time_data() + reference_time_data = problem.time_data() model_output = problem.evaluate(parameter_values) target_output = problem.target() - # Ensure outputs have the same length - len_diff = len(target_output) - len(model_output) - if len_diff > 0: - model_output = np.concatenate( - (model_output, np.full([len_diff, np.shape(model_output)[1]], np.nan)), - axis=0, - ) - elif len_diff < 0: - target_output = np.concatenate( - (target_output, np.full([-len_diff, np.shape(target_output)[1]], np.nan)), - axis=0, - ) - # Create a plot for each output figure_list = [] for i in range(0, problem.n_outputs): default_layout_options = dict( - title="Scatter Plot", xaxis_title="Time [s]", yaxis_title=problem.signal[i] + title="Scatter Plot", + xaxis_title="Time / s", + yaxis_title=pybop.StandardPlot.remove_brackets(problem.signal[i]), ) # Create a plotting dictionary if isinstance(problem, pybop.DesignProblem): trace_name = "Optimised" + opt_time_data = model_output[:, -1] else: trace_name = "Model" + opt_time_data = reference_time_data + plot_dict = pybop.StandardPlot( - x=time_data, + x=opt_time_data, y=model_output[:, i], layout_options=default_layout_options, trace_names=trace_name, ) - # Add the data as markers - if isinstance(problem, pybop.DesignProblem): - name = "Initial" - else: - name = "Target" target_trace = plot_dict.create_trace( - x=time_data, + x=reference_time_data, y=target_output[:, i], - name=name, + name="Reference", mode="markers", showlegend=True, ) plot_dict.traces.append(target_trace) - # Compute the standard deviation as proxy for uncertainty - plot_dict.sigma = np.std(model_output[:, i] - target_output[:, i]) - - # Convert x and upper and lower limits into lists to create a filled trace - x = time_data.tolist() - y_upper = (model_output[:, i] + plot_dict.sigma).tolist() - y_lower = (model_output[:, i] - plot_dict.sigma).tolist() - fill_trace = plot_dict.create_trace( - x=x + x[::-1], - y=y_upper + y_lower[::-1], - fill="toself", - fillcolor="rgba(255,229,204,0.8)", - line=dict(color="rgba(255,255,255,0)"), - hoverinfo="skip", - showlegend=False, - ) - plot_dict.traces.append(fill_trace) + if isinstance(problem, pybop.FittingProblem): + # Compute the standard deviation as proxy for uncertainty + plot_dict.sigma = np.std(model_output[:, i] - target_output[:, i]) + + # Convert x and upper and lower limits into lists to create a filled trace + x = reference_time_data.tolist() + y_upper = (model_output[:, i] + plot_dict.sigma).tolist() + y_lower = (model_output[:, i] - plot_dict.sigma).tolist() + + fill_trace = plot_dict.create_trace( + x=x + x[::-1], + y=y_upper + y_lower[::-1], + fill="toself", + fillcolor="rgba(255,229,204,0.8)", + line=dict(color="rgba(255,255,255,0)"), + hoverinfo="skip", + showlegend=False, + ) + plot_dict.traces.append(fill_trace) # Reverse the order of the traces to put the model on top plot_dict.traces = plot_dict.traces[::-1] diff --git a/pybop/plotting/quick_plot.py b/pybop/plotting/quick_plot.py index 1861041a0..d482c10ae 100644 --- a/pybop/plotting/quick_plot.py +++ b/pybop/plotting/quick_plot.py @@ -20,6 +20,7 @@ start_cell="bottom-left", ) DEFAULT_TRACE_OPTIONS = dict(line=dict(width=4), mode="lines") +DEFAULT_SUBPLOT_TRACE_OPTIONS = dict(line=dict(width=2), mode="lines") class StandardPlot: @@ -184,6 +185,23 @@ def wrap_text(text, width): wrapped_text = textwrap.fill(text, width=width, break_long_words=False) return wrapped_text.replace("\n", "
") + @staticmethod + def remove_brackets(s): + """ + Remove square brackets from a string and replace with forward slashes + as per section 7.1 of the SI Handbook + """ + # If s is an iterable (but not a string), apply the function recursively to each element + if hasattr(s, "__iter__") and not isinstance(s, str): + return type(s)(StandardPlot.remove_brackets(i) for i in s) + elif isinstance(s, str): + start = s.find("[") + end = s.find("]") + if start != -1 and end != -1: + char_in_brackets = s[start + 1 : end] + return s[:start] + " / " + char_in_brackets + s[end + 1 :] + return s + class StandardSubplot(StandardPlot): """ @@ -226,7 +244,7 @@ def __init__( layout=None, layout_options=DEFAULT_LAYOUT_OPTIONS, subplot_options=DEFAULT_SUBPLOT_OPTIONS, - trace_options=DEFAULT_TRACE_OPTIONS, + trace_options=DEFAULT_SUBPLOT_TRACE_OPTIONS, trace_names=None, trace_name_width=40, ): @@ -297,8 +315,8 @@ def plot_trajectories(x, y, trace_names=None, show=True, **layout_kwargs): Name(s) for the trace(s) (default: None). **layout_kwargs : optional Valid Plotly layout keys and their values, - e.g. `xaxis_title="Time [s]"` or - `xaxis={"title": "Time [s]", "titlefont_size": 18}`. + e.g. `xaxis_title="Time / s"` or + `xaxis={"title": "Time / s", "titlefont_size": 18}`. Returns ------- diff --git a/tests/unit/test_plotting.py b/tests/unit/test_plotting.py index 474f44f6c..08cc8e256 100644 --- a/tests/unit/test_plotting.py +++ b/tests/unit/test_plotting.py @@ -134,41 +134,39 @@ def dataset(plotly_installed): @pytest.mark.unit def test_standard_plot(dataset, plotly_installed): # Check the StandardPlot class - pybop.StandardPlot(dataset.data["Time [s]"], dataset.data["Terminal voltage [V]"]) + pybop.StandardPlot(dataset["Time [s]"], dataset["Terminal voltage [V]"]) # Check the StandardSubplot class pybop.StandardSubplot( - dataset.data["Time [s]"], - [dataset.data["Terminal voltage [V]"], dataset.data["Current [A]"]], + dataset["Time [s]"], + [dataset["Terminal voltage [V]"], dataset["Current [A]"]], num_rows=1, ) pybop.StandardSubplot( - dataset.data["Time [s]"], - [dataset.data["Terminal voltage [V]"], dataset.data["Current [A]"]], + dataset["Time [s]"], + [dataset["Terminal voltage [V]"], dataset["Current [A]"]], num_cols=1, ) # Check plotting numpy arrays, lists, and lists of numpy arrays + pybop.plot_trajectories(dataset["Time [s]"], dataset["Terminal voltage [V]"]) pybop.plot_trajectories( - dataset.data["Time [s]"], dataset.data["Terminal voltage [V]"] + dataset["Time [s]"].tolist(), dataset["Terminal voltage [V]"].tolist() ) pybop.plot_trajectories( - dataset.data["Time [s]"].tolist(), dataset.data["Terminal voltage [V]"].tolist() + [dataset["Time [s]"]], + [dataset["Terminal voltage [V]"], dataset["Current [A]"]], ) pybop.plot_trajectories( - [dataset.data["Time [s]"]], - [dataset.data["Terminal voltage [V]"], dataset.data["Current [A]"]], - ) - pybop.plot_trajectories( - [dataset.data["Time [s]"], dataset.data["Time [s]"]], - [dataset.data["Terminal voltage [V]"], dataset.data["Current [A]"]], + [dataset["Time [s]"], dataset["Time [s]"]], + [dataset["Terminal voltage [V]"], dataset["Current [A]"]], ) # Test incorrect dimensions with pytest.raises(ValueError): pybop.plot_trajectories( - [dataset.data["Time [s]"], dataset.data["Current [A]"]], - dataset.data["Terminal voltage [V]"], + [dataset["Time [s]"], dataset["Current [A]"]], + dataset["Terminal voltage [V]"], ) From bc48170f6fbbccdc0560941b4d02a1ffc9ffa840 Mon Sep 17 00:00:00 2001 From: NicolaCourtier <45851982+NicolaCourtier@users.noreply.github.com> Date: Fri, 16 Feb 2024 14:29:02 +0000 Subject: [PATCH 22/64] Update pybop/plotting/plot_convergence.py Co-authored-by: Brady Planden <55357039+BradyPlanden@users.noreply.github.com> --- pybop/plotting/plot_convergence.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pybop/plotting/plot_convergence.py b/pybop/plotting/plot_convergence.py index 6bf1bf2eb..9dfc469c1 100644 --- a/pybop/plotting/plot_convergence.py +++ b/pybop/plotting/plot_convergence.py @@ -93,7 +93,7 @@ def plot_optim2d(optim, bounds=None, steps=10, show=True, **layout_kwargs): fig = pybop.plot_cost2d(cost, bounds=bounds, steps=steps, show=False) # Import plotly only when needed - import plotly.graph_objects as go + go = pybop.PlotlyManager().go # Plot the initial guess fig.add_trace( From b5ebd429e130f14ae9879ebe524f2af07f0536ad Mon Sep 17 00:00:00 2001 From: NicolaCourtier <45851982+NicolaCourtier@users.noreply.github.com> Date: Fri, 16 Feb 2024 14:29:09 +0000 Subject: [PATCH 23/64] Update pybop/plotting/plot_cost2d.py Co-authored-by: Brady Planden <55357039+BradyPlanden@users.noreply.github.com> --- pybop/plotting/plot_cost2d.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pybop/plotting/plot_cost2d.py b/pybop/plotting/plot_cost2d.py index 54f0e86aa..61c351125 100644 --- a/pybop/plotting/plot_cost2d.py +++ b/pybop/plotting/plot_cost2d.py @@ -53,7 +53,7 @@ def plot_cost2d(cost, bounds=None, steps=10, show=True, **layout_kwargs): costs[j, i] = cost([xi, yj]) # Import plotly only when needed - import plotly.graph_objects as go + go = pybop.PlotlyManager().go # Set default layout properties layout_options = dict( From b26ac6bc99ab5699aa6a1977e88f81febd16f38a Mon Sep 17 00:00:00 2001 From: NicolaCourtier <45851982+NicolaCourtier@users.noreply.github.com> Date: Fri, 16 Feb 2024 14:40:57 +0000 Subject: [PATCH 24/64] Update plot_cost2d.py --- pybop/plotting/plot_cost2d.py | 1 + 1 file changed, 1 insertion(+) diff --git a/pybop/plotting/plot_cost2d.py b/pybop/plotting/plot_cost2d.py index 61c351125..9bd9428f2 100644 --- a/pybop/plotting/plot_cost2d.py +++ b/pybop/plotting/plot_cost2d.py @@ -1,3 +1,4 @@ +import pybop import numpy as np From 2c5fbd7d7057aca3cbe137099d1c9ec1479d1cda Mon Sep 17 00:00:00 2001 From: NicolaCourtier <45851982+NicolaCourtier@users.noreply.github.com> Date: Mon, 19 Feb 2024 13:32:10 +0000 Subject: [PATCH 25/64] Remove duplicate getitem --- pybop/_dataset.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/pybop/_dataset.py b/pybop/_dataset.py index e564624a4..a084063e0 100644 --- a/pybop/_dataset.py +++ b/pybop/_dataset.py @@ -27,9 +27,6 @@ def __init__(self, data_dictionary): self.data = data_dictionary self.names = self.data.keys() - def __getitem__(self, key): - return self.data[key] - def __repr__(self): """ Return a string representation of the Dataset instance. From 89e2932b58aaacc9de8648fe00703952cd39dc3d Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Thu, 22 Feb 2024 09:12:12 +0000 Subject: [PATCH 26/64] Add plotting support for notebook rendering, adds kaleido as dependancy --- .../equivalent_circuit_identification.ipynb | 9183 +--------- examples/notebooks/spm_Adam.ipynb | 9281 +--------- examples/notebooks/spm_CMAES.ipynb | 10913 +----------- examples/notebooks/spm_electrode_design.ipynb | 14155 +--------------- .../spm_scipy_DifferentialEvolution.ipynb | 9187 +--------- pybop/plotting/plot_convergence.py | 9 +- pybop/plotting/plot_cost2d.py | 5 +- pybop/plotting/plot_dataset.py | 5 +- pybop/plotting/plot_parameters.py | 5 +- pybop/plotting/plot_problem.py | 5 +- pybop/plotting/plotly_manager.py | 4 +- pybop/plotting/quick_plot.py | 13 +- setup.py | 2 +- 13 files changed, 382 insertions(+), 52385 deletions(-) diff --git a/examples/notebooks/equivalent_circuit_identification.ipynb b/examples/notebooks/equivalent_circuit_identification.ipynb index ffde7e5c8..3362de471 100644 --- a/examples/notebooks/equivalent_circuit_identification.ipynb +++ b/examples/notebooks/equivalent_circuit_identification.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 1, "id": "dd0e1a20-1ba3-4ff5-8f6a-f9c6f25c2a4a", "metadata": {}, "outputs": [ @@ -24,27 +24,27 @@ "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: pip in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (23.3.2)\n", - "Requirement already satisfied: ipywidgets in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (8.1.1)\n", - "Requirement already satisfied: comm>=0.1.3 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipywidgets) (0.2.0)\n", - "Requirement already satisfied: ipython>=6.1.0 in 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"Note: you may need to restart the kernel to use updated packages.\n" ] @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 2, "id": "d6afb8f9-3872-4a7e-a76d-0b50855fe089", "metadata": {}, "outputs": [], @@ -88,7 +88,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 3, "id": "734d6d86-61e3-4125-bcea-e83b3235814b", "metadata": {}, "outputs": [], @@ -110,7 +110,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 4, "id": "8d4a0635-51da-4998-8b48-deda13a49e39", "metadata": {}, "outputs": [], @@ -155,7 +155,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 5, "id": "e84b6dd0-8f9e-4b68-b7cb-f3bcb9988802", "metadata": {}, "outputs": [], @@ -175,7 +175,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 6, "id": "e75da7e3-8815-4159-a5ad-600a235b028c", "metadata": {}, "outputs": [], @@ -219,7 +219,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 7, "id": "c346b106-99a9-46bc-8b5d-d330ed911660", "metadata": {}, "outputs": [], @@ -249,7 +249,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 8, "id": "62369a4d-96e5-49d2-8951-4468b3fc5831", "metadata": {}, "outputs": [], @@ -268,17 +268,17 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 9, "id": "f69b34f5-0b46-4646-acbe-991046997b98", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.02481396482148223" + "0.024944621550803514" ] }, - "execution_count": 28, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -297,7 +297,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 10, "id": "6244882e-11ad-4bfe-a512-f1c687a06a08", "metadata": {}, "outputs": [ @@ -305,10 +305,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "Initial parameters: [2.82225575e-04 1.72658962e-04 9.33921482e-05 1.06071260e+04\n", - " 4.57347160e+03]\n", - "Estimated parameters: [1.12930671e-03 6.47383206e-05 2.94920796e-04 1.06071280e+04\n", - " 4.57347320e+03]\n" + "Initial parameters: [2.29696565e-04 3.53341865e-05 1.63145688e-05 1.07259649e+04\n", + " 9.73352990e+03]\n", + "Estimated parameters: [8.44919651e-04 4.36094658e-04 1.99997447e-04 1.07259693e+04\n", + " 9.73353443e+03]\n" ] } ], @@ -332,4557 +332,14 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 15, "id": "2cec5659-31fa-4164-82f0-4467a4894729", "metadata": {}, "outputs": [ { "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "fill": "toself", - "fillcolor": "rgba(255,229,204,0.8)", - "hoverinfo": "skip", - "line": { - "color": "rgba(255,255,255,0)" - }, - "showlegend": false, - "type": "scatter", - "x": [ - 0, - 2, - 4, - 6, - 8, - 10, - 12, - 14, - 16, - 18, - 20, - 22, - 24, - 26, - 28, - 30, - 32, - 34, - 36, - 38, - 40, - 42, - 44, - 46, - 48, - 50, - 52, - 54, - 56, - 58, - 60, - 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/35tDvHBR1y07vpFFgLlPjm7gKAcWG1yQ16g927yBjMCwJF5U/ZcXn2lCJURNG32LEee2EX04I1440ufRtv3NonysfQDV9ShBoj126MQltGnbXik0WGxYYqeibLTiRQkRXkuO9Ogl0mp2ff2TWTqTvRysiRcustzyleGyMPHaxaOtihd3jNd31Dza9tUPpMTLK30n03eiEDNfI1+rUbu3UUcZBTSga1vD91lA1qt1L74nQQAEQAAEQAAEQAAEQAAEQAAE3EggV4kXa2kYHGXC0Sb6raXVA7U94kXJG04F4hQQbavWpItbxcsX+w5Qz2FzZGQHiwZrzZJ44QgTjjR5c3xvavhQdYtdOCNeuJBr9aavysKtCyb3pYcfvM+wf67NwoVwVT0WlkC805O2mdJqPpwha+c4Il64H5Vis3rBSGrXbawphUqNYS8Ha+KF05n+9/hrMsXo4PZlZkVq9REv7hhPL17Gz36XPthgfS2VFPzuk4WmnZzc+F2CrkAABEAABEAABEAABEAABEAABAwIQLz8B2Xp+1tp1uK1Mh2Foy5U4+K7XITXHvHCqUrPdBklC5t+umqqqQ/e2eapV8wL3roa8aIkDz/o81i8w5JqXG9kq4iO4Tlze777ODr0+9+04z9xoY7jrYc5LYrnu2nlRFm8VzUu8PudKJDLQsYZ8cL98PbcE+euktsUc1FhfSFeTkcaMHY+7Vo7S6bANBQFhDm9aff6N8UOTXnkVE6fvUhNnxsoX2dhECIKFzsqXpRU4+gfHodr2rRt2TD7UjPF7k1f28eBD7eUasTvKbExdmBHevrJh2X3XFR34LiFclxV48Ve7tbG04sX3pp6kBiH09O4ULK2xswWsfU5F80dM2Mlrdl8s5i09vuAi/ByLSIu9owGAiAAAiAAAiAAAiAAAiAAAiDgPgIQL/+x/OWPYzIagh9YH61XU4qMfT/8atqe2R7xwtEdtZp1l1EevJvPvaIeyR9HT8iiuNzsqfFib6oR9zd/5QZ6S/zHc24nRAIXw+Vdjb7cd1BuQc1bCHObNn81rVzzmUxBad64Dp05f4k6PfekFC58zXztvEsP75yTPyaafhY/c0TNXeIhnuuqOCteWAC17Zq9fTE3LgR85+23yoLDLIJ4Jx5uqvCtEgPMvkObpoJjOs1/e4MsjjtmwCsyGombo+KF16OmiEbhNBtm9e2W+XJ3IG2zhwMfb028sGThHYy4sSjiHa54TNW020m7Op5evHCEUUsh95gpbxPOaXUZYsci3qL7n9Pn5b3A69jg6dfl/ckpUU0b/I9S09LpwC9H5b3e45VW1F3sWIUGAiAAAiAAAiAAAiAAAiAAAiDgPgK5QryoaBbesYV3brHUeIcf3nFGNX545l15Vn28g1o0qUuThmXvaqS2oeZaHvwAq21cvLXn8Dny4VY1jqL5UBTDLVwwv6nGi6U+RkxZJndR0m6pzP2oorubVk6QD9bc+GH7o61fyQKyLCdUY7nQ7NHapl2Y+IF7zIy3aceeH0wiQG1BzefNXvIRrd6400wScJRKj5efErway+2sawuh5EhxXTUXli8fbNghdhv6OEdhV94y+qVnm0pJwJEsnHY0bf4Hkrf2Wgb3eN5s3SyxW7d1N42ctpxG9esgJZK28TbKXAPmyUYP0tQ3uua4BezhwCdZEy/8Pm+jvVjsrvSXECAskHiXpu8OHpZ1ZfZseFMKLm6ujtdv9Hz6/MvvZLQTCzRuXNiZt47m17X8ePvoGaO6y5dYeo2Yuox4Fylt43tqhIj0srRLVw5geAEEQAAEQAAEQAAEQAAEQAAEQMAuArlCvNhF4r+DropiusdOnpVFaW8XD6OhoSGOnC6PTUpOpb//yY7m4CK++ugKhzu04wR+6P733CWKLVKIihcrJEWGvrEM4mKzJUUNGr4+bWOJw9tnX7maIIRBrEwPcndLTEqmE6fOUXh4OJUpVcwiF57n3/+cEceFCX63mKXNuHtO+v48wcGarPHEeOki0uX0mYvy3r1FyJ/wsLAc2PgYvhc4KobXmyUjGgiAAAiAAAiAAAiAAAiAAAiAgPsJQLy4nyl6zKUEWNi9L6J1HnqgKpUTaV83RF2XVet3yO3BLUXa5FJUuGwQAAEQAAEQAAEQAAEQAAEQyDUEIF5yzVLjQj1N4IdDf1KHPpNyDCO3/545iIoWLuDpKaB/EAABEAABEAABEAABEAABEAABPyMA8eJnC4LpBC4BLqb73YHDosDxKYpPuE6xRQvRHRXLyELLaCAAAiAAAiAAAiAAAiAAAiAAArmTAMRL7lx3XDUIgAAIgAAIgAAIgAAIgAAIgAAIgIAXCEC8eAEyhgABEAABEAABEAABEAABEAABEAABEMidBCBecue646pBAARAAARAAARAAARAAARAAARAAAS8QADixQuQMQQIgAAIgAAIgAAIgAAIgAAIgAAIgEDuJADxkjvXHVcNAiAAAiAAAiAAAiAAAiAAAiAAAiDgBQIQL16AjCFAAARAAARAAARAAARAAARAAARAAARyJwGIl9y57rhqEAABEAABEAABEAABEAABEAABEAABLxCAePECZAwBAiAAAiAAAiAAAiAAAiAAAiAAAiCQOwlAvOTOdcdVgwAIgAAIgAAIgAAIgAAIgAAIgAAIeIEAxIsXIGMIEAABEAABEAABEAABEAABEAABEACB3EkA4iV3rjuuGgRAAARAAARAAARAAARAAARAAARAwAsEIF68ANkbQ2SKQUK9MRDGAAEQAAEQAAEQAAEQAAEQAAEQAAEQsJsAxIvdqHAgCIAACIAACIAACIAACIAACIAACIAACDhGwDviBeEYjq0KjgYBEAABEAABEAABEAABEAABEAABEAgKAt4RL0GBChcBAiAAAiAAAiAAAiAAAiAQ+ASyxCWEBP5l4ApAAAQChgDES8AsFSYKAiAAAiAAAiAAAiAAAiAAAiAAAiAQaAQgXgJtxTBfEAABEAABEAABEAABEAABEAABEACBgCEA8RIwS4WJggAIgAAIgAAIgAAIgAAIgAAIgAAIBBoBiJdAWzHMFwRAAARAAARAwO8JxCfcoLT0dCpWpKDVuf79zxn66Zcj8pjYIoWoQZ37zY5PTU2jcxfjqFSJYhQRHmb23rqtu6l+7ftsjmEPrO27f6CrCdflobWqVaFypYvbc1pQHJOekUGhIaEUGmq55kfC9UT6/MvvTNf7zJP1KSTk5vGZmVl0+uxFKlq4AMVER5pxOfzXSTp/8YpcK1fb2fOX6evvf5Hd5IuOoscb1nK1y4A4PzEpmS7FxVOZUsWtrhNfzOZt+yg5NVVeV/0H76fixQqZXeOFS1dlH/rP5uUrCbRr70/0bLMGLjNJS8+gDZ/tMfXzZKPaFB2V1+V+/aUDZpWVlSXvd+3ngOfHr6dnZOb4vtLP3dZ3n/74Q7//TUeOnZIvVyx3C9W4t5K/4HB5Hny/XLh0hYoUKkBRkXly9GfPd5Q9976242C/R11eFA90APHiAajoEgRAAARAAARAIDgJ/O/x1ygxKUVeXFhYKN1712007Y2uQowUla9dv5FEnQdMo1/+OCZ/Ll2yGL3z5jAqGVvEEMjKNZ/RWyvW06P1alKFciXp1Reay+P44bD/mPkmKcOvPVijCk0d0VU+7HC7u8HLtHT6QKpd826XYb+5/GM6c+4yfSbkwvDeL9Azzeq73KcvO+g+dBZ99c0h0xRYJI3s2yEHK36gb/rcQOr+8lP03FONLE75yLHT1KrjCGrRpK48ZsKQzvLhncXYlLc+oNUbd5nO5TWfPbYnValUXr42e8lH9OW+g7RhxXiXkbDEeXvN5/TP6XPED677ty5wuU9fdnD85Flq9tJQ0xTy5Imgxx95gMYMeIUiIsLl6+26jTV9nvj9Jxs9SOMHd7I47WpNutD9d98mPnNFqXP7J+m2W2+Rx67d8iVNnLtKrhk3FiF8TzRvUkf+/P3Bw/Ty65Pp1y9W5JAJjjJKSk6lsTPflvL10137afvq6XSLuC+cb77fIjZDyJSl72+lhe9uMjHk70D+TGjX4/31O2nW4jX0/aeLrF6u0Xef/n5QHfD30QPV7qKv9/8i1ukPqnpXRZo1pqfzOP3kzL9P/EuDxi8i/lyrdlv50uK+fIlq3ldZvmTvdxQfa3Tv678L1Ti7PppNsxevdeM96idQ/XgaEC9+vDiYGgiAAAiAAAiAgH8RYPHSvnVjeuqxh4ijD4ZMXCx/c75u6Vg50ekLP6S1m7+k9cvGiciHKPHQOEYIlVI0f1Jfwwvhhw/+Db06nw/i6Ikm7fpTpvjN8ZyxvejO28vRL4ePU5835lLBAvloyzuTZF/uFC9qco+3H0SdnnsyKMTLtetJUpDEi0gelh/f/vQ7/bRtCeUVD+/chk1aQhs/3yv/POL1F+0SL799udJsHUdMWUabtu2VQqx+7fvp4uWrNHzyUjr0+1/0hXiwYUnmTvFiemj6+icaKuYfLOJl4ZT+VLpUMSlYeF26tG9Gr3d5Rl7uuFnvUKsn6skohy/2HaBB4xbSytlD6H/332n4meKHz0Wivweq3Xz/k537aeC4BdT1pRb00jNNxWdLSIRVW4k/fwsm96WHH7zPreJFTYwlLX9nuC5efP89OG3+anp33TYa0edFekLIr6TkFLEeB2na/A+kZGGJ8HyP8VI+s9SyR7zov/uUeJk7rjeV1UTdFS6Yj2KLZkcujZ/9Ll2+Eh/w4iXu6jVq8HQfKWgnCJFY5pbidPT4aZqzZJ0QSxWod6enHfqOYjZG9z6Ll6vx12m0kJnadrsQPCyPg+ke9f2nxPoMIF78fYUwPxAAARAAARAAAUHA97/x5WXghyh+IGT5wm3hO5to0Xub6YB4oOfW8Nm+Mv1jYLd28mdOBxo5bbnF36IbiRf1kLhxxQS6vUJp0+rzQyn/9p8lDqetsHhp/cTDtFeknnAaBv/meVjv9uKhJ5J+P3KCeg6fQ80erU0fbNgpQ/953pFCOswTETbXE5PpuZYNaVCP58zurmASL8JbyYdqboqdlikzS05JlZEs/V5r47B4YcnS4OnXJXN1P/BY/CBTt2VPkbJSX7z3ghQvLGfuq3Ib7RTCpEypWBrU/TlTWlmTdgPEn6vJVKYr8deo8cM1qZ1YmzEiYuLkv+epYd3qUgxpU2N2BZl42SxkYkUhKLm90HOCTNdaNLW/4TcffwY5HUh/76qDjR4++XNZ+bZypvtBHftir4nyIf6T96aYxAtHnb2/foc8pPPzT0oJxI2FT1hYmEx34QiFOyqUoYlDO9OMRSK6Q0TL3HX7rXKdOBpDtWB5qGVJUO+pXvI7RPHQXiOLFk5dOS/SIjdv30fLP/jEJfGivR/0N0GwiJcxM1bSBiF+9258U35naxvfN8zUke8oPt+SeNF+F+p5Bss9avhl4WcvQrz42YJgOiAAAiAAAiAAAjcJ7NrDwsX7rWG9UMNBteKFaxl07j+NSJT6WDZjkDz+3kYdaXT/bCHC7adfjtKLvSbQ1+If14UL5s/Rp5F4mSpSV7bu/Ja++nhOjuNrPdmNOrR5jLp3aCnFS5FC+amX+M0oP6gOFdE3HHnxmEjVUGkT1e65QxzfVP686uMdVCK2MPXq2JrS0tLlg/365eOpUsUypnGcFS/pB76ljPP/en2hIqrXptDi2akk2sa/5dU+bCxZtYXmv72RfvxscY4aIcyUHyjtSTXSRrzs/f5XenXgdNqz4U25DtrWW0QnXbocT+/Pf0OKFx6/ZdO61Lh+TVq3ZTf9fvQE7Vo7S57C68hSpU/np+WcWdRxCgevE6fJDJ6wiHq80opeFuuumrPi5c/kq7Tj2mmvr9OdkYWoUf6b95magIpwUA/aN4QQ5CiAHi+L621783rV8RwR8NQrI0R6XzcRdWFc30b/8Mn3+v2NO9NUkRLIaUratmbTF/JzwOlFPxz6U6YasTh5RYx9TKRBzVu+3rS+z746mv78+6Sc2yPXj0kAACAASURBVB1CiHLEEUd3sATi2kwLxP1VIH8MLZk+wDSEKw+1f+/y/ndfqMjuqvBwzu++b3/8nTr1nyrvWf4OsdY4pYu/w1yJeGGmSjTmzRthJnucFi+X/iK6lJ0C6tUWewdR0Qo5hmzd6Q2qKD7f00d2szkde76juBNL4oVFYevHs/9O4lbvwXulCObmyj1qc+I4wIwAxAtuCBAAARAAARAAAb8l0LlPdj0Gb7elc7LTUfSNxQsLFA6D/+t4tmhYPnMQcV4+i5h7HnnF7KHwj6P/0DNdRtGnq6YaFqw1Ei99R80TkQ4XzNKP1DxYjNS4t7KsqaBPNeIHo+JFC9OkYV1ypE3wAyL/4/1dUW+metXsopT1W/ehjs89QR2ebWq6TGfFy43pwyjtu93eXiaKGTCRIh64+UChJsDihWUTX+vZC3F04tRZmjikCzVrXDvHHO15qFE1XrTi5aMtX9Go6StIn37EA3B6DKfF8IOqPtXoz79PET907V4/V6Yi6dfx0Tb9qKVIZWPxwm3oxCXyN9/aB3pnxcu7l4/QSyd2en2dXipaid4un7OGjhIv/BDINV1+FlFd9wrx8dbE1ylfTJTZPLnAcXNRD4Yl4+a3J0k5ZdT0D59nzl2ixiKqaMWsIWbpR3wuc+w1Yq6IOpgn0zz0NV64r3GDOsrIMRYvnL6kotlGT19J/Pn+cNEoOY0tO76hN6YuN0W/8WvOPtSmizJSG3p4/7svXNT/feqtnN996l7/ZdcKm8WN3SFeOD0z5r8oEL4PlonvWNWcFi8H1xL9vMHr9z7d/6ww8k8Zfu+0af4I9e/axuac7PmO4k4siZdvhDirJCK0VHul3eNS0Ltyj9qcNA7IQcBQvAjZzr+8QQMBEAABEAABEAABnxJ4/6MMn4z//DPmOwipSbB44Zx8fqBf9sFW+ZturkegGke8cGHQVo/Xky85E/HCtRT4Ic4o4oXHZ1nS7aXsiBdtcd1+o+eLlKJ0OR99oVAujMlze2fuMNNuICxZWjZ9SNa9UM1Z8ZK6YxNlnPzb62uVp1FzCrv19hzjsnjh3/LyOmz87GuZBvHpqik5Qvr5RHseaozEi4p4UQJFOwl+mL8sZImKeNEW1+XaQI+27U+ffzBNph3p17HFy8NFseXqssYDtwlz3hUP+CfpvXnDTUM4K16+u3GB3r38p9fX6YGYEvSikC/6psQLRziEiHoTHIEyesDLOXYW4gKjnILE6V0cpWVttzBLES9TRrwmBYq2fSiKIo8VkozlmVFx3ToteohoqGepTfMGOcQLR3X8+PMRk3hh0dZz2BwzEeeseOE5Hnjf+999YSLi5d42Ob/7VMTLjg9nmAqJW7qJ3CFePJJqdPoA0ZmfvX7vU5lqRLfcm2Nclq/ly5aimaO725yTPd9R3AlSjWyi9OkBiHjxKX4MDgLBQwDCNnjWElcCAiBgmYA21WjP/p+p6+CZMg2EU3+4cS0JLjw5oGtb+bP6TbGlnVKMIl54F5QBYxfkSAM6+Ntf1F4Ur+RCpPVqVXVIvHDB3qoNX/GYePG3e0abasTbcT/efjDdLYQZiyf9ttH2PNQYiReOQuGoocGiTs5LmqghlgR1W/aSdVr4PX3Eiy3xwqk0DR+q5hHx4m/rpE81mrZgNa388DOz6BQuDPqCSNdLFsVcVy8cZXP7dEs1Xrgmi75uzPPdx8l6R5tWTnBYvLAg5fQkFfHibvHiT2vF20c/3Kq32Xedmh+nh2m3UPdb8eJPQMVcODqKBfseEfmmj+7SM7XnOwrixc8W2GA6EC/+v0aYIQiAAAiAAAiAgJ8Q0BfXXbH6U7mT0YxR3WXoNj84smzZIH4rHy1SItp1dX5XI47SmDu+N1W541aZgtFrxBzx0FlIPiRycyTiJTeLF2alpBVvS8sRSdzSMzIoU0QC1RMPlD2FPGvb4hHi7YqNmpF44ePUrkaThr0qi+ByRMYQUZPl1z+P05fr5sjaLxAvlj+8evHC6XqvDpxB+w/8LnfvKlq4ID32/EDKyMyUOxXlzxctOwsNDTVM3bP08KkKVnNhWK6VwzuGLRZFsd/9aJsQmf2EyLwX4sXGdyyn+HCh7iE9n5eFvBOFCNvz7SH5ncf1XHjtuJ7OWvH9N3vJWpm+FSrSwcJFQWKjZiSd9feD0XlOpxr5yd8hahr8XdFIpBXy7kITh3aR9/PfJ87Q3GUfm3Y1cuQ7ytK9r693pcfgSlSWnyH1++lAvPj9EmGCIAACIAACIAAC/kJAL154XrzbCRfD/WjJGLElbix16jdV7irErVTxIqKuynCL4flGDx983oVLV6n/mPkiVemI6dIfrFFF1o9RhVxZvHBRX36dG6caZYoH1Nlje8rfxHfoM8m0m5KReHnihcEy1ei1F5ubxnA21chf1kfNw+hhY4NIOeKtnoeL7XCfb9VI8mFO2qYvNqzesyReUlPTaIpIOVktUlZU4zWXwkxE2HCbs3QdffXNQfpYbDHOjWvOcB2XbaunU+mSxaRA066jPuJl4tz3ZKoR1+dRzdlUI39bJ6MHbX4QbNFhKKUItivEttEtReqVvrEgUzuJ6d8zinjhYzgSY+LcVcRrxo37GDdQ1G/5r+6P/jPDx3CqUd9Xn5WpT1zjpVb1u0zRbPqIF04n46LKP+9cbppSMD3UslR5a+UGWr76E+LURW5cZ4clDNec4u88ZqRtdWreY1abSPtebhcvzIIF7ZAJi4k/B6rdWqaEqCvUSaaEOvIdxecj1cjfvuHM5wPx4t/rg9mBAAiAAAiAAAgEIAHefjU1LY1KxhaxOntL4kWdxA+fXByUH9AtRWO4E0+wiBd3MuG+LIkXNQ6LrdNnL1AhUXi5wH9RGe6eg7a/YBEvnmBkSbyosTj1LDQklIoXK+SJ4c36DCbxor0wFsMZImKshPh+06fu2QvV1nefpX6CJeJFe338Pc9bcRcpVCBH2pG9PC2JF1vnB+s9auu6ffE+xIsvqGNMEAABEAABEAABEBAE+OGDIyKqi22f7xDbOnMYvy/amBkr5U5K3x86TCP7diBOyUG7SUCJlwerZ0cXLZk+0OkHTle4csrUmyIV4fylKzKtaf/WBa50F5Tnsni5tXQJuWPUwO7t6M7by3n9OrnOT6/hc4V8TZdRa9tFdNMtQp6i3STg6HcfF0Le9tUP9Mdf/1CtanfRrDE9gVNHwJF7H/eo928fiBfvM8eIIAACIAACIAACICAJ/CuiWf4Uu+9wK1Qwn2mrZ2/j+e7AYbp+I1EOW6VyeZuROt6en6/H44cU3tlFtUfqVqOQEO/vAcoFfX/+PXv3qKjIvFS75t2+RuN343PRa06L4Vat6h1y+3dvN67NsfubQ6Zh6z5QlfJaqB/k7bn5y3iOfvfxdt+nhBzmxhLLF0LNX9hZmocj9z7uUe+vJsSL95ljRBAAARAAARAAARAAARAAARAAARAAgVxCAOIllyw0LhMEQAAEQAAEQAAEQAAEQAAEQAAEQMD7BCBevM8cI4IACIAACIAACIAACIAACIAACIBATgK8aVQowAQbAYiXYFtRXA8IgAAIgAAIgAAIgAAIgAAIgAAIgIDfEIB48ZulwERAAARAAARAAARAAARAAARAAARAAASCjQDES7CtKK4HBEAABEAABEAABEAABEAABPyEADJn/GQhMA2fEoB48Sl+DA4CIAACIAACIAACIAACIAACIAACIBDMBCBegnl1cW0gAAIgAAIgAAIgAAIgAAIgAAIgAAI+JQDx4lP8GBwEQAAEQAAEQAAEQAAEQAAEQAAEQCCYCUC8BPPq4tpAAARAAARAAARAAARAAARAAARAAAR8SgDixaf4MTgIgAAIgAAIgAAIgAAIgAAIgAAIgEAwE4B4CebVxbWBAAiAAAiAAAiAAAiAAAiAAAiAAAj4lADEi0/xY3AQAAEQAAEQAAEQAAEQAAEQAAEQAIFgJgDxEsyri2sDARAAARAAARAAARAAARAAARAAARDwKQGIF5/ix+AgAAIgAAIgAAIgAAIgAAIgAAIgAALBTADiJcBX98zlpAC/AvPp540IpZioCIpLSAmq6woNISpeOIrOxQXXevEilSgcSZfiUygjMyuo1qxI/jyUmJJByakZAX9dEWEhFFsoMuCvw5kLuJGcTvE30pw51W/PkfemuK7ktEy/naMzE8sXFU4hISF0LTG41ov/Xssn/l67HGR/r4mlopLi77WzQfL3WqGYCIqODHfm1sU5IAACIAACIGCTAMSLTUT+fQDEi3+vj5odxEtgrJN2lhAvgbdmRjNmQXEV4iUgFhPiJSCWyTRJiJfAWi/MFgRAAARAwLcEIF58y9/l0SFeXEbolQ4gXryC2a2DQLy4FafPOoN48Rl6hweGeHEYmU9PgHjxKX4MDgIgAAIgEGAEIF4CbMH004V4CYwFhHgJjHXSzhLiJfDWzGjGEC+Bs44QL4GzVjxTiJfAWi/MFgRAAARAwLcEIF58y9/l0SFeXEbolQ4gXryC2a2DQLy4FafPOoN48Rl6hweGeHEYmU9PgHjxKX4MDgIgAAIgEGAEIF4CbMH004V4CYwFhHgJjHXSzhLiJfDWzGjGEC+Bs44QL4GzVjxTiJfAWi/MFgRAAARAwLcEIF58y9/l0SFeXEbolQ4gXryC2a2DQLy4FafPOoN48Rl6hweGeHEYmU9PgHjxKX4MDgIgAAIgEGAEIF4CbMH004V4CYwFhHgJjHXSzhLiJfDWzGjGEC+Bs44QL4GzVjxTiJfAWi/MFgRAAARAwLcEIF58y9/l0Z0VLxd+DKWUKyFUvEYm5S2c5fI83NVB3ohQiomKoLiEFHd16Rf9QLz4xTI4NAmIF4dw+e3BEC9+uzQ5JmZNvOS9/CflPXeAMmOK0vXyjQPnosRM+e+1fOLvtctB9vcaxEtA3YaYLAiAAAiAgI8JQLz4eAFcHd5Z8fLLwjC6djyE7nktkwpUzHR1Gm47H+LFbSi91lGJwpF0KT6FMjL9R+C54+IhXtxB0fd9QLz4fg3snYE18ZL/yEbKf3QzpRSpRJdrD7K3S784DuLFL5bB5iQKxURQdGS4zeNwAAiAAAiAAAg4QwDixRlqfnQOxIsfLYaVqSDiJTDWSTtLiJfAWzOjGUO8BM46QrwEzlrxTBHxEljrhdmCAAiAAAj4lgDEi2/5uzw6xIvLCL3SAcSLVzC7dRCIF7fi9FlnEC8+Q+/wwBAvDiPz6QkQLz7Fj8FBAARAwIsEODsi1IvjBedQEC8Bvq4QL4GxgBAvgbFO2llCvATemhnNGOIlcNYR4iVw1opnCvESWOuF2YIACIAACPiWQECIl7T0DIoIDzMkde5iHBXIF03RUZF2k0y4nkhX469TmVKxFMpPxP+1TFGj4sz5S1QitojF8awNwudnZWVRWFhOIxh/7QZdvpJAxYoUlPPVNr6+s+cvU2zRQhQVmcfsvQ2ffU21qlehUsWLGA4N8WL3svv0QIgXn+J3anCIF6ew+d1JEC9+tyQWJwTxEjhrBfESWGuF2YIACIAACPiegN+Ll7//OUMtOgyjT1dNoXKlS5iI/XX8X3qpz0SKT7ghX6tf+z6aM7YXRURYLoz26a79NG72O6ZzPlw0iu6pXEGez+8NnrCIMjKyC832ffVZ6vz8k/LP1Zp0odTUtByrtXbxaKpSqbx8nYVLr+Fz5Z/nTexjOvb6jSR68sUhdCku3vRa0wYP0LQ3ukpBM23Balr54Wem96pXrUSzx/akooULyNfubdSRZozqTo0frml4t0C8+P5DZM8MIF7soeRfx0C8+Nd6ODsbiBdnyXn/PHvES0ZUUTrfcIr3J+fCiCiu6wI8L56K4rpehI2hQAAEQCAXEvBr8dKk3QD699wluSx68dK60xsUEx1Fi6b2o1NnLtKzr46iIT3b0/OtGhku49ad39KgcQvpmWb1qX3rxlSkUH4RJZNXRsokJiXTg826S9HSrcNTtHXHNzR88lLa8s4kqlCuFB07eVYImQxTvwd/+4tGT19J21ZPp9Ili9G6rbtp/Jx3pZx5pE41M/HCkS5zl66j9k83pluFONq++wfqP2Y+LZ42gOr+7x5a9sEnVPHWUlSr2l1ynBd7TaQOzzal17s84xXxcnubTLmltL807GrkLyth/zywq5H9rHx1ZERYCMUWsj8q0Ffz9MS4EC+eoOqZPu0RLzzymSeXOjSBiIRTVOC31ZRWoAwl3P2cQ+e642CIF3dQ9HwfEC+eZ4wRQAAEQCA3E/Br8XJGSJfTZy/RK30nm4mXuKvXqN5TvWjp9IFUu+bdcv36jZ4v04RWLxiZYz05GqXB069TpYplacn0ATne/2Tnfho4bgH9tG0J5c0TId+v06IHvSAETfeXn8px/NOdR4p0pMI0f1Jf+d6NxGS6En+NRk1fQVF585qJF/3Jfxz9h57pMoq00TLaY7oPnSWvedPKCfJlbcTLhUtXqevgGSK6537q0/lp+b6rES9lG2VQ2Sb+sw0wxEvgfR1BvPj/mkG85IxY9P9VszxDGY2VnE7Jaf4jzd3B01PiJe/lP6not9N8thU1xIs77g7P9wHx4nnGGAEEQAAEcjMBvxYvvDAc8cKRL9qIFyUvdqyZaap9Mm/5evr40920a+2sHOt58fJVKV6q3lWR0tLSKSk5RUab9H21jYx6Wfr+Vlq++hPat+kt07ntuo2l28uXpvGDO5n1t/vbQ9RtyKwcETh80Osj51G6qNeiTTVSJx8X0Swc3bJr70/0+CO16I2+L+WYJ8+NhQ+nIqlxlXipeV9leuqVETLdavmswaYaNBfjU5y6fw++FULxx0Lo1saZVP4xp7rwyEl5wkOFvAqn+BupHunfV51yqlGR/HnpUoJz6+WredszbtECeejq9TTKEDWOgqkVjI4QD7YZlBIED7dcdorvv9zYEPESOKsO8RI4a8UzRXHdwFovzBYEQAAEQMC3BAJSvHz74+/Uqf9U+nrjm1S4YH5JkOXJonc30fefLspBlFOD2vcYTw0fqk4N61aThXVnLVkrI0feHN+bpi/8kD4RqUhaacNRNvliouX7qnHkTOO2/anGvZVpyojXcoxjTbwc+PUozVi4hn47coIeFMVy54g6Lnn+i65RHfUcNod27z9En78/jUqVKCpfZvEyZsArtPi9zfJa354z1KyOTVq6c7/x3Ng1O3Wq8pMhdGdz/9kejP8hxwWPMzKC6yGeWYcLqZTu5Hr59mvC+ujh4qk+/b/aSP48T0fnFibSczLFx4s/94HeWIpF5jEuUB7o12Zr/hAvtgj5z/vWxEuRH+ZR5PmDcrKOphoh4sUzawzx4hmu6BUEQAAEQCA4CQSkeFERLzvXzqSSYgcibtYiXpR4+XLdbLlzELeVaz6jmYvW0KEdy2Qkij0RL7zDENd+0Y6rvS2siRd1HKck1W/dhwb3eE7WmlFtgqgR8/76nVKscHSLaixeuHHR340rJtDtFUqb3YnOphrtG5xdhBipRt75YKO4rnc4u3MUFNd1J03f9QXx4jv2jo5sTbwU/WYq5Y07Irt0Vrw4c66j12B0PFKN3EHR830g1cjzjDECCIAACORmAgEpXlSNl2UzBtGDNarI9es7ah6dvRBnWOOFZcdDLXvRillD6IFqd8rjl6zaQrOXfES/7FpBn33xnazxckDUeFFRKLWe7CaL3KoaL7zlc/3WvWUa0Kh+HQzvGXvEC5/I6UStHq9HA7u1E79Rz5IyZ+vOb2jl7KFUveodZn2zeGlYt7pMufr33EVR+2Wi3JJaNYiXwPj4QrwExjppZwnxEnhrZjRjiJfAWUd7xcvFeqNEodyydl+YiniBeLEbmV0HIuLFLkw4CARAAARAAAQkAfeKF47IF6ki7mpc8+TU2YvU/KWhtGHFeCpfpqQpzYbrnRQsEEMLJveVxWif6TJSRJE8L6JIHpXDd+gzSe44NHFoF/kzF8TllIF35g6j85eu0GsDp1PJ4kXpvXnDZXHcB57oSl1faiH+a5ljVyM+X0XIfLlujtwRSds4GoV3PeICv+kZ6TR3XG+RVhIuU2Y4LYojblo2rUtFxBbRqzfuoqlvfUALp/SnerWqynoxXDdm5ugeVPm2m/+QLF0qVtZxUTVe6tS8h1p1HCGHXb98vNjRKXuHEogXd91tnu0H4sWzfD3RO8SLJ6h6v0+IF+8zd3ZEe8XL5QcHUkrRm5GhtsaL/ncfFTq4PPvvTAd3RLLVtz3vI+LFHkq+PwYRL75fA8wABEAABIKZgHvFi5tJsXRgqaEaR6NwVAq3I8dOi62XJ9D1G0nyZ5YYLDxUxAqn83Ah2nffHCbf5+K2XBfm/MUr8ufbROHcxdP6m1KVtmz/hgZPuFkfpnenp+m1F5vLY5OSU2WUCm9VzVEq+qaiZ7SvD+zejl5u8xh9f/AwvTpohtxqWjUWPL06tpY/cmSNugbt+Sya7qhQRoqXWaN7UqN61elSXDy1eHmYKChclNYsGk1horYGxIubbzoPdQfx4iGwHuwW4sWDcL3YNcSLF2G7OJSnxEv+Ixsp/9HNcnYQLy4ukuZ0RLy4jyV6AgEQAAEQCH4Cfi1e7MHPKTj580VTAfGfPe3s+csiGiXMVOtFew5LnlNnLtAtorCtvvCtPX1bOoYjbS5fSaCE64lUliNZIrLrq7ijQby4g6Ln+4B48Txjd48A8eJuor7pD+LFN9ydGRXixRlqvjsH4sV37DEyCIAACIBA4BEIePESeMjdO2OIF/fy9FRvEC+eIuu5fiFePMfWmz1DvHiTtmNjhaYlUsS1U5QZHi1rtkC8OMbP10dDvPh6BTA+CIAACIBAIBGAeAmk1TKYK8RLYCwgxEtgrJN2lhAvgbdmRjOGePHfddRv82yveImr0YOSS1YzXZhKI7pRvhFlRuSMfkWqkWfuAYgXz3BFryAAAiAAAsFJAOIlwNcV4iUwFhDiJTDWCeIl8NbJ1owhXmwR8t37zoqXa3c0p2uVWpomfsvWzvLPloruQrx4Zo0hXjzDFb2CAAiAAAgEJwGIlwBfV4iXwFhAiJfAWCeIl8BbJ1szhnixRch37/uLeIk8d0CkPJ2m1CKVHNotyV5y2NXIXlK+PQ67GvmWP0YHARAAgWAnAPES4CsM8RIYCwjxEhjrBPESeOtka8YQL7YI+e59JV7S8peliw+PslrjJXb3GFkPhpu7I16KfjOV8sYdydGvo2T0IkmdD/HiKEnfHA/x4hvuGBUEQAAEcgsBiJcAX2mIl8BYQIiXwFgniJfAWydbM4Z4sUXId+/nO7GdCvz2oZwAb/NsrcaLSidyVbyoFCWWN6q5W7xkRBWl8w2nmPqHePHdPebIyBAvjtDCsSAAAiAAAo4SgHhxlJifHQ/x4mcLYmE6EC+BsU4QL4G3TrZmDPFii5Dv3tfXXnFGvEQknKLYPWPkRdhT40VdLYsed4sXTlkq8uNbJpGk+od48d095sjIEC+O0MKxIAACIAACjhKAeHGUmJ8d76p4yV8hi6p2zfCbq+J/oMZERVBcQorfzMkdE4F4cY1iqDg907UuHD4buxo5jMwvT4B48ctlkZNyVrwklqlNV+/rJPtQ6T38Z0vipcgP8yjy/EEzEI6KF7VzUp5Lf8i0JKOxLBXxhXjx33tQOzOIl8BYJ8wSBEAABAKVAMRLoK7cf/N2p3g5tS2ETu0Mo5J1M6liC28/5mZfEMRL4N2QJQpH0qX4FMrIzAq8yVuZMcRLcCwnxIv/rqOz4iVFFMG9XHuQ3eJFpRJpSWjFi6ofo68do42mUedyGlFY0mWIFwEEuxr572cLM/NTAvxPa/5NEhoIgECuJADxEuDL7k7x8uPkMEq5EkK+jIKBeAm8GxLixf/XLCIshGILRfr/RD0wQ4gXD0DVdMkRJ3lEBIgzOwL5i3hR9WOSS9xPcTV7mq5OG02jp+hMxEvWrhky8kYveDy7Qp7rHeLFc2zRMwiAAAiAQPARgHgJ8DV1l3iJ+y2UDr+TreEhXtx/UyDVyP1MPd0jIl48Tdg7/UO8eJazkifOyARr4kVFqSjBoS2u6+6IF9W3tl+mZk28JNzdlq6Xb2wGt8BvH1C+Ezvla9qIGpVqRJ+Pc8vuSZ5dUft7h3ixnxWOBAEQAAEQAAGIlwC/B5wRLxlJIbR/dJiZZFFpRpbEy4UfQ+nCDyFUoXkmxdziuZQSRLwE3g2JiBf/XzNEvKT5/yI5MEMpBZPTKTnNNymh2qkqeaKPFrHncuwRL3E1elByyWpkSbxo+7BU48VWqpEz4sVINGnHOd9wMmVEFZMYlHgJ3TJMbol9vXwjSrj7uRyI8sb9KV9LKVLZHnw+PwbixedLgAmAAAiAAAgEEAGIlwBaLKOpOiNeEo6F0q+LzKNbtOIlulQW3f+6ecHdXxaG0bXjIXTPa5lUoKLn/rEP8RJ4NyTEi/+vGcQLxIun7lIlPvTRIvaMp48Q0e5qVPLzXhSanmRKy3G3eNFKGk+IF23/Srzk/eDF/8TKzRo1Wk5qHtpoGXs4+uoYiBdfkce4IAACIAACgUgA4iUQV00zZ0+IF+6+zpR0MzIHZ4dR4lmIF2dvF6QaOUvOd+ch1ch37N05MlKN3EkzZ1+uiBdthMi5JnMpukABUbA1hK4lppkiXFRkCcSLZ9fRmd4hXpyhhnNAAARAAARyKwGIlwBfeW+Jl32DwyUpRLw4d8NAvDjHzZdnQbz4kr77xoZ4cR9Lo57cJV44QiSizN12iReeh4oK0aYaJZapQxnRscTbTas0Hz7WKNVIRaRo67hkhkdRWoFylFSuLiWWriPqtWynAr99aAhQCSE+P9+RTZRS6n6KOrlPphJx00e8FEg+SxGfDZfvpeUvSxcfHmXWr3YHJUS8ePaetdQ7tpP2DXeMCgIgAAK5hQDES4CvtLfFy+1tMql4DaQaOXrbQLw4Ssz3x0O8+H4N3DEDiBd3ULTch6fFC9eO92IqNwAAIABJREFUuVapJcXuGSNqn1SSxWlVY1HCskUVtFWv62u92Cte1PlKqqhrUyIn/9HNprH5tav3dSJ1DMsUJV0Mxcu1vyhi50TT+Xq5ohVAlmrVeHYlHe8dES+OM8MZdhDAlst2QMqth3CNyZDcevG47iAgYCxecF8HzNJ6W7yUbZRBZZuguK6jNwjEi6PEfH88xIvv18AdM4B4cQdFz4sXFihhBUpQ6t1PUeqpX6not9PkoCxbrgvxwj/rxYt6Xytj9NKDf47dPcZMimiPMdq5SC9e+OfUonea5qTGvVx7kEm86AnliHgxEC9hSZco+vQ3IgKmDGVFRJv6h3jx7D1rqXdEvPiGO0YFARAAgdxCABEvAb7S7hIvqniuwqGv8aJSjSBenLthIF6c4+bLsyBefEnffWNDvLiPpVFPKprEWnFdLqIbkXBa7OTTTqTylDV1YxSJcu3Ztx0SL0Zz0u84pK0Po443SjVS7+mjWZwRL9rtprm4bsG/NlP4r+tN0+WaNjHHtxNH0WjlkpE44tdYEOUR0T6pQkSlFPWPXY8Q8eLZzxZ6BwEQAAEQCC4CEC8Bvp6eEi/39ckw2zYa4sW1GwXixTV+vjgb4sUX1N0/JsSL+5lqe7RHvKhj7EkB0osXTuFhYWMp4sUT4kVJJJVG5Ix40cqf6LQ4KnBwOYVeOGyaLrMo8NtqGYmTEVVUpC11tBrx4kpKl6fuAIgXT5FFvyAAAiAAAsFIAOIlwFfVU+JFX0QX4sW1GwXixTV+vjgb4sUX1N0/JsSL+5k6Kl5Uqo+94iXt2HdU5Me3zCSFp8SLtjivGtBIvHCdGaOdlYr8MI8izx/MAVkrXmK/nUoRl2/WpuGD42r0MLxGfs8o1QjixbP3MfeOVCPPM8YIIAACIJCbCUC8BPjqQ7wExgJCvATGOmlnCfESeGtmNGOIl5tU8sb9KX9IKeK+VBV7Il6UsNALhZKf96LQ9CSzZeOIFzr4kUzBUY3PU+IlPOkyhYn/rDVHUo2siZdCh5bJGixX7+8odznSiheOUjnfcIppx6TM8EhxLcmmaV0v30imV3FqVczpfRSSnigid9pS3rMHZIFgTmfivlXjOatrtiZe+Hh/2fUIES/B8R2JqwABEAABEPAOgYAXL5mZWZSVlUVhYaE5iPF7Z85fohKxRSgiPMxuohcuXZXHFi9WyOyc+IQblJaeTsWKFLS7L+2B6RkZFBoSSqH8FK5p/Pq5C3HE8y1dspjZtWz47GuqVb0KlSpexHBMb4uX/BWyqGrXDKeu356TOBc+JiqC4hJS7Dk8YI6BeAmYpTJNFOIl8NbMaMYQL9lUQtMSqeS23vLP7nxwd0a8qHolLD30zZZ44eP1xXT1fbAUSY8qZtoW2qjGi5Ip1sSLPkVK3w9zVMdwf5lhUZTn8mG5yxLvuKSXSny80Xg8f6140Ysjfl97njvXz5VPOcSLK/RwLgiAAAiAQG4jENDihYVLr+Fz5ZrNm9jHbO0+3bWfBk9YRBkZ2Vsf9331Wer8/JMW15elx4yFH9L7G3ZSamqalB8/71wuj79+I4k6D5hGv/xxTP7McuSdN4dRSSF0jh4/TU+9MiJHv9rz+c3EpGRq+txA6v7yU/TcU41Mxy9ZtYVmL/nI9HN0VF5aOKU/1bi3knzt3kYdacao7tT44ZqGc3dVvIRFZlGtMRmkL65rKdUI4sW5rwiIF+e4+fIsiBdf0nff2BAv2Sy1u/e488HdGfFiST7Ivysbj6OME9+ZRbxwpEiB3z6URWjltWi2lLZ2pyiBYSRe9DsXaftRqUZ68cJpRSFCYKnxteJFRalYuzY+Pt+J7fJa9I23zVYpS0biRZvSpCSNirpx5dNiKQ3Mnj4hXuyhhGNAAARAAARAIJtAwIqXdVt30/g570pJ8kidambihSXHg826S9HSrcNTtHXHNzR88lLa8s4kqlCulOHaj5iyjLbu/Ja6tG9GzzarTymi3zKlYuWx04WQWbv5S1q/bBzFREdRu25jZD/zJ/WltLR0OnH6nFmfS1dtpT3f/Uz7NmXnqA+btIQ2fr5X/nnE6y+aiZf31++kooXz00MP3Cv76thvCqULWbRp5QR5vKfFC4/BOxj9ODmMUq6EUN7CWfL/WvGScCyUfl2UHVEE8eLcVwfEi3PcfHkWxIsv6btvbIiXbJaeFi8c4XGu6ZuGC6fEhxIKluqi8MlJDYZQ1l9fGabh6MUL/5wqdvhR2zLrB3e3eFH9q+sxEi+R5w6Y1W5R52QWr0zn/jdQzrXEriGmqaotsrVbZfO80wqUo6w80WKr6bKUyVtNfzPVJHy00THcER9z8eFRTn1o1LWoCCBHOoF4cYQWjgUBEAABEMjtBAJWvNxITKYr8ddo1PQVFJU3r5l4+WTnfho4bgH9tG0J5c0TIde4Tose9ELrxjLiRN/OijSfR9v0o0E9nqMOzzbN8X7DZ/vS4w1r0cBu7eR7LH1GTltOv36xgkL4Xx6alnA9kR5q2ZOG9GxPz7fKjmy5FBdPySmp1KrjCOr3Whsz8aIf7JW+k2XK0dtzhsq3tOKFU6C6Dp5B9WvfT306Py3fdzXiRbIR4kVbPPfUzjDSbhsN8eL61wTEi+sMvd0DxIu3iXtmPIiXbK6eFi/y7yMR0aFvWtGgRIjRNtLWVl+JBr14sRa1wv1xFElczZ5mtVnUONYkkKWIF3WuNfGi5ay9JiVe+LXY3aPFbkai/kv+MlKqcASNXrzEHN8hU5VUJI2WmfZYNYYzUUzauRpF2UQknJI7L2VFREmO+gbx4pnvLPQKAiAAAiAQnAQCVryo5Xh95DxKT88wEy9L399Ky1d/Yoo44WPbdRtLt5cvTeMHd8qxkhyNwlEpTerXpD//PkV5IiLo2eb1qb0QNdxYfozu/zK1fuJh+fNPvxylF3tNoK83vkmFC+Y362/i3FW0Zcc+2r1+LoWHmdeVqfVkN3q9yzOG4mXVx9vp013f0bGTZ2jB5H50X5XbTGNzqlHN+yrLlKZypUvQ8lmDTTVrIF4C44MJ8RIY66SdJcRL4K2Z0YwhXrKpeEq8qFQVHsPo4d/o4d4o9cdo7VTBWiUauCBtWOJlU+QH7wyUXLKaxbopSqBYSzUykkC2xEvJz3vKQrrnG06mYt9Mk8V++c8Zoq6MRfFSujqdu7+7vMzof/dRnkt/CDFUXUT27JUpRpw2pIoGawvvqmvUztOofowSNCxLQkUhXxUpo6KBMqKLygLB3LjeD48Vee4nU0qXkXixdc9AvATHdySuAgRAAARAwDsEglK8cGrQJyJtaNfaWSaKHEmSLyaa3hyfXVxQ2+a/vZHeWrFeipaqd1agXw4fJxYhI/u+RG1aPEL3PPIKTXujGz3RqJY87Y+j/9AzXUbRp6umChFS3NTV+YtXiKNjJg7tQi2b1s0xjjXxwpE73x88TJevJNDUEV1FVMt98nyWPmMGvEKL39ssJQ9HwkREhJv6vuxEEdqrf4fQgbduRuo8MjOTvuiXnUpUvkkmndgWKv9f4bHsYbTHi40bqN7E7Lo5nmgR4aEUmSecriWmeqJ7n/XJ/0AtnC8vxV0LrqLBDLRw/jwUfz2NMkXNpWBq+aMjRMphJqUKsRvoLVTcgLxO3mzx127I7zMuRl4gX7Q3hzYbC+IlG4eth2hnF0grNdwpXlgE6FOI+LU8l/80iRdbdVVYoCTc/RzF7hmT4/L00TfaKBIlXkrsGiwFxcV6o+TuRKppa7/wbkvc1LVrOWvrtqTf04ou3JqzzpxRTRiOhOGIGG72RgnpI2NY3iSVfYiiTn0t07b0LFjMRFw7ZbomPv7qfea/mLJ1z2jFi4qOIeK/B0IsRsnwgLyuvDtVupBNLKv8pWE7aX9ZCcwDBEAABIKTQFCKF0cjXli8fLB+B+3ZcDM/vcuA6ZSUnELvzRtukh+tHq8n7wJLES+Dxi0U7x2h7R/OyJGCxOdZEy/q9pry1ge0euMuOiDSpLixeOHGRYI3rphAt1cobXYnpqQ5LkEuHyXaN/Pmw2TzBWG0uVv2z5WeCKEjn2TJ/1duni1jjI639XG4fCT7iKLZtRDtbhwZEhYWInaPCq6HeNZcEWLHplQn1stueD46MI+QZWni/gwy70IR4j7MEBeV6fhHzEcrYXnYTHERUXmzhS0XHh8wdgFNH9lNplAatYuXr1KDp1/P8RYXMefaV9YKinMx8idfHCJTLFVr2uABIa+7Gu4+px3kxKlz1OLlYdSiSV1TdGK1Jl1kLS99W7t4NFWpVJ4Gj18kogxvbsvLx7EQZzHODeIlm5yth2hnb1pHxUtq0Tvl1tC2GosMjgwpdDC7yD03lhBc3JZ3DeJmj3i5XqmlXeNpxYuqmaJNKdLO117xoq3F4oh40Y7lrHjR8+XrSyr3kBlP7TFKNmlf0/I3kmpa8WIkkCylP6ljjWSPrfvCk+9DvHiSLvoGARAAARAISvGiarywvMjzX40Xlh5cv8Woxos6/uD2paZoEo6QSUxMoQ8XjZJRLE80epAGdG0r75iPtnwla8toa7wcO3mWmr80lOaO602N6lU3vLPsES/rP91DXOj30M5lMlWJxUvDutXp33OXxH8XRdHdiWbbWXs71YgvjGvC2GqqZsx9fTIo5hZjicJFfC/8GErhUVlUqm720y22k7ZF1v/eL1E4ki7Fp1CGqE0UTC0YU414Z7b2PcdLkWtNvHA9qUeeeZ1mju5BFW+9WZCcd3SLCA+3WlCcI13mLl1H7Z9uTLeK1Mjtu3+g/mPm0+JpA6ju/+6xeIvEJ9ygZi8Nobir14glt0oL5e/WjIybovjgb3/R6Okradvq6XKHORbe/5w+T+OH3PxtfVRkXlNxdIiXbOT+IF54HtqUGmvfF/zQrk/bYQnBLf/RzfL/SrzoBY3ql8e6el9Hu8SL6ksrW1wVL5x+pArpZtRoT+dLPpLjkq3tgsQHWysQrO2Md366Xr6xYT0bPo7Tk5JL3m9WtJhlC0eecFSPVryo6JVwEZmi0p9UKpUa01Qg+X8v0JniDQzTvSyJF0s7Yal1Vevs7b9PIF68TRzjgQAIgEDuIhCw4oUfHPgf4/1Gzxe7AKVL4REuHghCRcgEF9594Imu1PWlFuK/ljl2Ndr97SEaPWMlLZran+6oUIauxl+nh1v3FrsZNaBhvV+g7w8dpk79plKvjq1lH9MWrJayZcPy8RQdHUntut7c1UjdLhwhc/bCZblzkr6li3lmivnWa9Wber7SitqK9CUlhCaInZka1atB9951G124dIVeHTidIsVDg35Xozo175HFebmtF/OIEfPg5ox4Ydnx15rsaBZu2uK6t7fJlO/F1sikO8SfuWmL66rjbX1MlHjRb0tt9g9FzW5JSuZAvNgi63/vQ7z435roZ8TRO+mpidSiw1BZ4HvSm6toyvDXLEa8KPHC3zWVKpaxeoFGBcW1J6jUTBWhYtRZmkjn4u/VW4RISRDipuwtxQ3rcfG5T3ceSSViC8td5bixeLkivsOXTB9gOM9gEC8sFqJO7hURC3VlnQ4pBZPTKdmBCDpfiRdL2ydbu6nUNsn6HYAsiRdLdVV4jGsi4oXlhq12rslcWejWHvGipAPvBKQicpRk4PopMce3y+F4bNVfWqNhdDGyYo5pWJJG6kAVFWKrLo49gkafXsT1Y9IKlpVySLsrlZEMUmJKzUvJk6x7W9PZsk8Yihe9rNGfqyKLeJ1D05JMKWGWzrO1hq6+D/HiKkGcDwIgwE9ON5+wwAMEzAkErHhZsmoLzV7ykdnVDOzejl5uk12YZMv2b2jwhEWm93t3eppeezH7t2Ucls7h6asXjKSqd2X/Q2jnnp+o7+h58jfB3Dg0fuobr8moE36wYBHz+5ET8r1SxYvQu28Op1Ilisqf+bfIXLx36fSBVLvm3TnusQ59JtEPh/40e1090PAuRXv2/2J6j0Pk+YFCbXvNES+zRveUUTQcus9h+KWKF6U1i0bLsH1nxMupbSHEOxepxnKEt4uOLplFFVtmyT9rt42GeHH9awPFdV1n6O0eginiJT01hTr2GSt3RHtD1K7i7xV7xAt/P3JtqSqVbqUXRASLvpg4r4mlguLHRaTKsg8+oV17f6LHH6klx7XUOPXpyLHT9JFIH+oi5LMl8cLSvNuQWSKNaIosNM6NxcsX+w5Q9aqVqEihAjI6sV6tqqahgkG86FMznBEv2m2OndkBx9LaGaUa5Y3L/vsupUhlwwdyFivcVDSFvm9t9IW2fxYFEQknTREvtgraOvKdoZjYI16UTNIWwbXE1JZ4sSaNshlWosu1B1mMZFHXyOIlsWxds62qLV2/Elgshrhpt5RmwaeNdFF96MWLaV1K3Elnag4goy3C9eeovvQSiQVMwt3tTJFJqqCwI+vnjmMhXtxBEX2AAAg4TAC2xmFkgXpCwIoXe4CzRDl15gLdIgSJijCxdh5Hppw+c1Gm8uSLicpxKIfAp6alUcnYIvYMb/cxKaJ+wRmRSpRfFKDksR1pjogXTu1JOBZCyZezDMULy5Zbm2SLl7DILKrQIouKi8gXvXipNTqDwkR6kLWGiBdzOhAvjtzV/nFssIgX3p5+0OhZIkUolFbMGiKjAm2JF04XGjPjbSmZ+c9bRbFyLpDL6T15/0vf5FWyVlD8wK9HacbCNfSbENYPVq9Cc8b2NPweXvTuZrkL3SfvTaGihQsQi2oj8ZIl6u00btufatxbmaaMeM10k7y/ficdPX5a1LDJQ5yGdOj3v2nswI709JPZu9ClpmdSUkpgF0jO+/t6ivxjI6UXq0w36g+lmMhw8XdRhqitZH96n+qDmcQ/vdJtH7KC61429cX9hl09Sfl2jpSv8c/acdWBSf/rTCHXL8prMmrqOvm9fDveoLD47CKwCS3eojxHt5nOU9cRfvEwxeye7NI1qb7U9aRUaSXnbsRLXWOm2CkoVOyyZI0pHxuekUjhxSvQjYy8OeZoa+6KhZpXlkgZChHbTOtb6q11Ke3WejY5aNmqPlTfyXe1tLgmNx4eQumxd4qyudnlc03rHlOM4h+bTjFfTaLwS+a/YFLn8DiRh94T98ZpSqnylOEctWPzn5m/t1teLu6f13w3Sm/PAeOBAAiAAAgEL4GgFi/Bu2w3r8xe8bJ/VBhlJGfvZFS2UYZF8VKxRSYdmpP9Dw8V9XL261A6vvlm4Jy19CE1M4gX87sP4iXwPo3BIl7OixTIF7qNoEfqVKN8+bKF8uZt++ieyhWobctHqPUT2YLCWuNoFE51ZHHzQLU7TYfaKijOB16Jv0b1W/ehwT2ekzvH6RvXvuJaLZVuy9415ou9B2QqJUfJcBSjahs++5qGT15KO9fOtCq/Oe0z7moCrVs6Vp6aLgS8M0XIbTHx5vsRv62nPL9toIzYypT8yDBZCytdSBdH6iqpPnjeN9q87bbpx6zpYOqL+w278AdFfpktQfhn7bjqwOQGQgAKWcLXZNTUdfJ7kV9MpLCLf1L6LdUo5aHXzfpT16Ed06g/lhVZQhCE/idwjI5RfWmvh4/jcxNbL8xxSvTHXU0CRDtfo77DxF8AESJCNVnIMn0LvXGJorb2t7geqm81L/6ZeXBLrfa8/H+eA+/LeyPt7lYm9pY6VBy170dv6UchQiBZkjpyrAe6UFr5h+Rpet7MTq2Ttl+eX9odTeVLRvPXHsvzCj9zQL6UJYRWZkysuNeHWuTiiTd4nSLzuFu8ZO/yhAYCIAACIAACEC8Bfg/YK16UCOHLtSZeqnbNoBtnQqR8yVs4i2oMEZLGIDWpQEXrW73kVvHCEUXcClQ0/000xEvgfdCCRbzcSEyi99duNe1qxCvx9trP6cEaVahN8wYyrdJW43TL2s2601sTX6cGde6Xh9tTUFz1W6dFD1kwd2C3myJFvcdpo1dENKFqGz7/mgrmj5FCqEv7ZvJlrgFTX9Th4rmO6nfzQd9o3lz4/PuDh2UEDbdgSjVSWwIXPfwhpYoth+PuysnT0lpq63Z4MtVIn9JkqV4I705U5Me3DKerTTXiVJw8lw8T74aUUrSy2NFoOxX47UN5ntEWzkYdcsoLN2u7KelTjVQ/Rrv98Hva1BpLx6g+WJTli4qgywkphtcbu3u0afto7bh5447IYsTnG06R6UCZ4ZFiW+typu20tQWIOV0nm88Oqx9nVQtGe5Cq12LtRO15+vQo3m6beehTx9Q5ttKpLI3raK0XrpcTJgQSp4A5s001Uo2s3jp4EwRAAARAwEUCEC8uAvT16Z4QL3xNSpxw0VuIF/tWmVO5fpyc/dsy/c5PEC/2MfSno4JFvDBTLq4bWyi7IDc3farRtz/9TsMmLaEJgzvLOlW85XRiUgo9Urca5YkIl8XIt331PX318RxTnRdLBcW//fF3mfLTsmldKiJSh1Zv3EVT3/qAFk7pL2uv6Iub69fcKNVo5ZrPaOaiNfTlujmijkt+s1PemLqcnm/ViG4vX5p+FvW2eEc6jqzhCBtuwSRetLUw0opWoosPDrL7I6MVIPygnFYgO8LIlaYvfssiQC94jB7qtbU/jB7KrYkMoyLBth7s+QE+XDyQWxIvSm4wC239EX49qcT9ov5I9r2kbdrrdFW8FPjtA9MW2WoM7VbUzIvnzuNkiGiQ6NPZ26cb7fzEr1vbglrtfqS9FnvFS2qxO0WNnVMUknrDVGdHfsbK1BFz2peDkZqHrZ2bLN2DlmrEWDq+xK7BUv44ep7qD+LFlW8DnAsCIAACIGCLAMSLLUJ+/r494kVfo8VWxAtfsjXxcudLmVTkbkS86G8NLWeIFz//4NgxvdwkXsbPfpfWfbKbvtu6gCKEaOG0npHTlpuKjXONrBkju1HDh6pLctYKinO0yauDZlCqqF2lGu8Ox7vEcTMqbq5dDr14SUpOJY6YYbliFDHzaJt+Yke5OFMXLHdmioLk0VHZ9TSCSbzw9fCDM0d8uCJenH0w1X9s9MKD++XoFLUtMIsBZ8TL9fKNDGUHj6/G5OiPc03nySnZEi+WBIW6HkvFfI2iQ9Q52t2IXBUv2igh1b92xyS1GxGPkyqiWrR8+Xh9sVolV4yEjtHaGx2nX2tek3wndsqXeQekUIM6M/pzkoW0iqvZ0/Ae4PULTU/O8U3MzFniOCNQFAdn72+IlxzLgRdAAARAAATcSADixY0wfdGVM+KlSJVMivv9Zs0WFimH3wklfv3ODtlCJVu8hIjIjbQcES8sbsqKIrzWWm5MNYJ4sb/Qpy8+K46OGcziRc+iSbsB1KTB/2hA17amtzi9h7e4lw92okB5SIj9dQq4EO7lKwlyR7iypWKlzPFki0+4IXZ9uyp3mouOuhnZw2MGm3hRkRDpIuXkQr3sIrb2NG3UgbMPpvpxjMQLb6Ucef6gPNRZ8WJNeKgx9elIttKIrMkZZ8SLtj9XxQuz4v64qetQUS5a5lrxohVPevGi1tco0sQo2smeiBQj2SIlYHQE0ffvGd6CiouKRGERw/cG/z+5VHWxRfrXlCW28GZZxxJGXZMSQdbuA6MBFQdtVA9H6PCYaSI1L7lkNasfFYgXe75JcAwIgAAIgICzBCBenCXnJ+c5I164aO614zcfokrWzaRze0Nl7RclVIwiXkLFTkeZokBvrNjp6I42liNeMpJCaP/o7JQba4V4jUQF58LHiFz4OAu58H6C3XAa2uvh2jhcI+fYplBKPBtCt4mixRXuFr+hjcu5G4U/X5M9cytROJIuxac4VOjTnn59fUxuES9c/Pahlr1EGs9sii1ayNfY3T6+J8QLP8yFpieKh7mylCkeHD3dtPVEtJEHjtRqcUW8yPodIlUn7n89zGpnGImXfEc2mmqQnGsylwqLc7lWibbZSjVyp3hRD//uFi98D8TuGSMvyx3iRfHhCCFuV/6LFIm4dtqEjse5UaGxrI2jHVOJDebGtXA4jYzvS04F4xSrqNNfm6Un6e9XWxFDlu7vuNoDqXC5Oyjkw1cND9Fvhc33a6io7cNN+7nh16L/3SvSuqrJ+0vdq46IF+16aM8rdGiZvHb9GvGY+s8uxIunv8nQPwiAAAjkbgIQLwG+/u4QL0rE2BIv6jj+f6W2YqcQ8cvwvIX5P/NIB62AyK3iRV23ElhVuwpZVSMvxEsAfd5yi3jhlKBzF+OoXOkSAbQ69k/VE+JFpc848mBo/4xzHqlN1+EHSCUyvCVeVCSB/nptiRcWLAV+Wy0Kx2ZvB62aK+LFiKO9UoXPtVVTRlvo1tb6Ki5czPXqfZ0sLrGt4rqWTtSnaaniyhwRwnLFqPaMNT7aKBntcVppYes+5RQoWWtGCBIOgitZOIrSNww2Kw6s0og4SuZGxSZSpFga22g8VUDZWsqZ/jztumrXreTnvWRalLaOT+zuMfKe5GtJLF3H1BXEi63Vx/sgAAIgAAKuEMghXha+s4l+O3LCoT65qGGfzk87dA4Odg8Bb4oXjnS5+GMohYnIl1tElMypnWFmUTLqipwRL0pURIqIl+gAjXjRbrvN18NCShXbhXhxz/3uzV6cFS+r1n5CR46ddGiqt5a9hTo+38Khcxw5WF9c15FzA/3YYBMv2toYjogXbR0PR1KNtA+02odXI4mhT48xSpfh87wpXvQP70biRStOtLLDlngp+XlPmSJj6zh3iRdVM8XRzySLFVuyRp+uZDSGXp4o8ZL0xTxTRA2fp5WDqh9bUUHa8YzSydT7+vfyxmWnaJH4/Y9K01Lroa+dw58Xo4LQqm+IF0fvLBwPAv5NAJu5+/f65MbZ5RAv7bqNpbPnL8tceXsa1wAokC+GNqwYb8/hOMbNBLwpXrRFeZWEMar34op4CeRUI+3uTyxeuP26KLuWDsSLm298L3TnrHjpNWQKXRARJMVji9g1S65Nkj8mhhbPGmHX8c4cBPFys9CvM/z057gS8cJFWaNOirSKcnXNfttubV6Wdp1xRLxo+7AlXniO+f9Z0CdKAAAgAElEQVTcKHf0SSlZ3Ww3IG2NEKOIF22tFXvECz8IR5/aKx+I1W49tkSGnpWRTOE+OC0nRKSEabcW1j94c1/a8bSc4mr0sFoXxN77wFnxwuIgIuGkqdiso1wcuddVupL+HJZtapto/fhKvMQdPSjrtPBW16qOCsueQgeXmSJhnBUvSeUekp+XGxUelX2raBiOppHpWGK3J26qfgz/meecLiJywsU9pd3imu9dTjHS36O8DTc3iBdH7hgcCwIgAAIg4CgBQ/HyWIMH6OW2j9nV15pNX9D763dCvNhFy/0HeUO8HF0TKiNdbhd1Xc7sDqHEczfrw/havNw4E0IZYmOEmFJEYVG+Le6qFy83zhAd35wtXqqIosV3PoRUI/d/AjzXoyvipX7tGvRMy0ftmtyWbXto06dfQbzYRcvxg/wt4sWZ+hXeFi8qFYMflq9Xamn2oKqVEXrhod2Jh1fKkngxKvCq7cuW8NDfBUapMtYkhT66w5J4sSWoVO0dW0LEWfGirtNeweP4p+PmGWoMVcBXK1H4ns0jiv+y6NDWRVHi5ayF2mWqvgqPYouRdu5KjrFc4ZQqTq1T0VbaWkXaLbft2WmJ15ObVrxo040gXly5g3AuCIAACAQzAf6F+s2NaZy9UogXZ8n5yXneEC+/LAyTxXg5iuOfbSFmhXl9LV72jwoT4iV7bgUqWt/i2tNLphUvFZpnUpLY4ZaLFnMr92gG1WyL4rqeXgN39g/x4k6avusL4iWbva2IF/VAq40c4IfwlFLV5PbVqmkfoPXiRfsgzMdbEi9GkTravmwJD6O7yZpM0R/vLvHCkUGRZ3+SkTUqasJobq6KF2d3+XHkU6fkCkeYaOueWOvDlnhR0SnchyPihY9Xa6SNuNFvV659z9o81XG82xE3S/czxIsjdwyOBQEQAAEQcJRADvFy/UYS5RFbf+bJI7YItKNxYcZk8V+BfJ7f2cGO6eS6Q5wRL9Els8yiVtTPLAtKPaTdTprEdtLppBUv8X9lydouqnGh3apdM8y425tqFPdbqNzGmpsSJ/pUI+4r/lgIFayYZShW7Nm22ls3hVa8sJC6Kuaudo+CePHWKrhvHGfFy43EJIoId+w7NCUtTaQbee47FKlG/pNq5EjES/bD8BGZbsIFQvXNnalGal7ah1lZHFWkeOQ/ullGHHDahrbOiPbBWj1c87GqBbt4sffbxlXxYu843j7OlnhxJYrJqOYM33vp4j7Md2KnQ5eqas6w/OHG96h6TZsCBfHiEFYcDAIgAAIg4CCBHOLl5L8X6L1120Sx3GcoJjrSwe5wuLcJOCNeLM1RGzWi3U5aK1606TPyHzA2xAunJxUXRXmNmj41hyNW9OJFHcNbXlcUWzLrmz+JF8WJ58ji5YyIduFoHG4QL97+ZLg+nrPi5fDREzR5zgqaPWEAFSqYn7KyhOhMShZFoyPFLiA30/Rcn6H9PUC8+J94sadQqqUUI7XynhAv+rtKRbGoSBjeQvviw6OyH2CFGNKKFn3Ei/5na/N2V8SLSpVJFrVpOE3FqOkf6jkS4nr5xvJQW5FB9n/qbh6ZW8UL11Mpua23BOFoFJN2HbQyUB/lkpa/jOxfu+22fo3UFux8Lt8TkecPEhdU5ppC2oLREC/O3N04BwRAAARAwF4COcTL3u9/pVcHTqeda2dSSVEc8vjJs9RlwDRaOmMQlS9b0t5+cZyXCNgjXrSCw9q07BEvfL4qGCv/4W1DvBilIqk5OCJejMbhfpR4ufOlTCpyt29TjbTipUiVTIr7/WYuIMSLlz4QbhzGWfHyw8Hfaei4N2nV4olUvGhh+kvscNRt4CRaOH0Y3VbB+EHQjdM27Arixf/Eiz3FRr0pXiyNpYQL110p8uNb8v5SwkcvXtTDrLoJHREvfI4SIo4+pGvPtSelRS9etONBvNj/bWQr4kX+G0HIOW6JZeuaFTm2NYq2Pozcvjq6qNnOSep8lio3KjamYt9ME+lelQyP4TouBX59X+5ApZo2Gutck7mydg3Ei61VwfsgAAIgAAKuELApXv44+g8902UUrVs6lu68vZwrY+FcDxDwtnjRbpEs/1HlhHjRpiIpJJZSjZScsSVerAkeW9hTroRQyhUS2z/zf84X6NWKF+6H+1UN4sXWKvjf+xAv/rcmzszIX2u8BIp4USkZ/KDKtUY4skAVx9WLF44+0EYe6EWMWj9LkTreEi8sV7iFi9QpTp+yJF7ON5zskCywdH/m1ogXZz6v6hyun1Po4HL5I99HqcXuMv2s7VdfiNkoRYnvN230DZ/Pa55PSCEu3JtYpg5FxJ+i9Ab9KapwrCvTxrkgAAIgAAIgYJEAxEuA3xzeFi+cDqSiTIJFvCi544q8YRZa8aJuq9DILMoU6UaBLF5+XRRGBW8T6VOiQLBRK1E4ki7Fp1BGpvPSyh8/hhAv/rgqjs8p2MSLNtXCnalGliJetOIl5vh2mabB6UYcIcAPsxHXTllcFHWu/gBb4sWR61J9qwdueyJe1Dnqmi2JF2fmYQQD4sXxz612229e09Sid5rtRqR6VNEq+vtAO6JaR+09zlItNC2JYveMMR2a/OgoirylkuOTxRkgAAIgAAIgYAcBiBc7IPnzIb4QLz9ODjOL5tALGH2R2bJNzB/IL4itqf8SW1Rrm6WIl2ObQuXOQBxBUmNIzgd/JYFckSbuEi8HZ4dR4lnzGh4cqcMFdgNZvGjr/UC82P42QKqRbUbePMKT4sWeOi36a1VRIs5EvPBv9zOii5keFvViQEakJJymhLvb5ahvoraI5vkYpfJYEi9K9PCDavSpvWY1XdS1aXdC0l6vv4sX3oaa5RHX/VDbJKstovk6glm88N/Aribn2pNq5MpnvcSuwTIiie/71KKVTfViuE+VfnT1vk5mQ1iKeJH/TtHUJFJry5E1MX9vk5Faia0XU3S+GFemjHNBAARAAARAwCIBi+Kld6enqVCBGDp19iKtWP0pdWz3BJUpVcyso1aP17N79yOsgWcI+EK8GEV2aFOBbIkXo5ozakelm8V1eQePULMoEt5hSd/8SbxoI4HUPFkI8S5QgSZeMpJC6MZZorD/s3ce4FEUbRx/00iDAAm9CQioiIWiUsRCsSIqCiIo2FC6gCAIKChFKdJF6YoNQUWpioJ8qBQrqFhAQaUlhISeQto375xzmZvbvd29vuGd51GS26n/md1kf3kLi6+9a6YjixWCLy1XLLJ4cT2VArw0vvxiiI+LhZOnzsDu3/+CRpfUg6Qyrr/Ujxj4EMSzoLuBLhTjJTAxXszAE3VvRewKM21VGCJeFqt8/TxEnjjgdPkRY1T5dADPfqQFVuQXUox5oaYMNhNPJi71R2ecFzEmuhflVGnsAmQKo+N4PA3xrwpm9IBGuTN/sgDUAMcT61m+JbyxeNEaROvl3PJklAZk8eKdggjGCmPine5e8hk2spqSz2f6dWP5t3IAZ7U9ArikcmUhIS7au8lSK1KAFCAFSAFSwEABXfBiRrkvP5oNyeXKmKlKdQKkQKDAi5zJaN+qCG7JccWTBZBYrQiOfBUJad+yz1KLrTt8BS/CYkXNaiRDHk/gRS8GjBnZxRi+9IHjqOAF+6veuoinzMZguzcMjoXUTPeUsGbmGOw6nuLwqHNJKoiD3zbnQUy5It0MVsGevz/G89bV6Jff/oTRE+eamsLSuePcYIyphhYrEXgJH/AiAIcZ8CJbqeCWO8HLN1MgMv0PF8CCL6nCbcIIvGi545gBL/IYaAmDsTHQBaRUxu8u4EW1dFGD7Oq9NJeOj+aZv05nWd8vAi8WHwp+qB5oixd1iuKMypmI1DoVt4x1iTOk3mcIX7DkMgsatVBwXT8cCuqCFCAFSAFSQFcBN/CSey4P0tJZpFETpUbVihAZGZr0qCamd15UsQJeEqqwtLYSLFEFumZsAUTFO9yCZPAishjJ4EN9MS+p4MXIzUbWUAUvmAK7QiNHFqgkBmHajSxVIsFLVHocfDk1XzPQsp1vQm/BSziumcCL9Rd5T/soXCDMwBO9l0czbVW3CU/gRQ5GKqdHFuPLfWmBF7EmUV9Yq+D3+HXqzXP4JS3AoQbZDSV40bLmsXJPksWLebVCBV483TsqQDRzn4kVE3gxv/dUkxQgBUgBUsC6Am7gxXoX1CKUClgBLyLeiJiv+r0MVgIBXjC2y9HvHBmE5Iw/OB8zFi+qqwu6w+wY63CD8cVaxZPFi4ApMpTS228VvGCK6+h4e4KXzN2R3FJHLnK6cfnzwn/iYPtcAi+hfA4YjU3gxb/gRcAHKy91Yo+sWLxYAS9y+l0tsGIEXtSxZHgir1NYFMjZZKyAF0+a+cPixZtU1PL9Q+DF6GlSfD3Y4MVMfCQVvGBGJDUOjN4KCbyY33uqSQqQAqQAKWBdATfwgvFcLql/ATRv2tBUbz/8vAe++fF36N2jo6n6VMm/CoQbeMEMODJYkYPeasV2EWqYAS/qi79sdRNo8KIHHcT8ESRh0GE5jTTCmrPMRcuOFi9ae6WnQeaXMfD7miKf4Jd/7wr/9OatxcuKjz+DenVqAsZ4MVPQNWnXL3uge+fbzFT3qg6BFwd4Sdk2hf+b0WKYVzqKRuEIXuQXTivgBS1lorIyeOBRueiBFy3hSiJ48Qaq6R0qivHi0+3mbFz6789YSvP3eGDdjBZPa3bKz/PZdOb+9gdPFW0lyxWBF//sE/VCCpACpAApoK2AG3jp2ucFuOWGq+Gh+24xpdnyVV/AOys3wkdLxpuq702lvPwCiIl2WDaoJTU9E5JKJ0CChQCVp85kwYmTZ1iwYFdXqUKWDvdw2jGoXDFZdzxP88f2RUVFEBXlaimAbY6fPA3Z2blQrYprgGLR35mz2YBuXinlk1yG+OiTr+CaJg2haqVkzaFDBV5kaxOcGIKPhj0LnRYoYrLBAi96WY/OHo6A/asjIaFqEdTtqJ3DQc/iRQY7RuBF1FUBkPhcz9UIr+9dHgFJdYugfhdfc0x4c3dpt/EGvGBPWnF4/Der4PbkLXgZMGISXN+iKdx7ZztTE16z4UtYtf5/MH/6aFP1valE4CWPZ6+psmEgl8/XbDUlCbyocWTE+UJLgYSD2/i3RtmbVPCixnRB959yOxfzvuxi8ULgxfhJE2yLF+MZFddAABP/71eQXetat0DSev0QeLGiMNUlBUgBUoAUsKqAJni56MKa0KFdC1N9fbblO27xEijw8tc/h6Fjz5Gw/u1JUKt6Zeec/tx/CHo8OZFlDDnLP7u+xRUw84UBEBOjH5F+/aYdMG7GUmeb9+aNgUYX1eHt8drwCfOgoMDx8jv48c7wWLfb+deNb+oF5xgUUcuK+WOhYYPa/GMELgNGzeJfz5n4pLMqgqFOjz7rHBODEffpeRd0u7str3Mo9Rg89fxc+HXP3/x7hEETn+kFV17qyOpwedtH4OUxfaH9dc3cxscPzIAXARZEhh3RkS+uRtiH7FqDfV3A0kaLeDBiDBm8aGVDUut5Cq7ryeIF+6nYtBBqtS9yybwjwAeClysHuaejxnZiXmodGbyg21DypfpgxBvwgvBq58xIp9tVOEELkcZbPnRGFi9YN5zWoHnDWPjQF/BSt3YNaHPtVaZG+2rHj8ziZS+BF1NqWa8k0kl7ymhitdeSBF600u9iTJezddo7A+YaWQ2o4AXdkJK/f8UpK7r/pGx3WBsFCrwIix9/uRoReDG+K8IZvBjP3r0GgRdvVKM2pAApQAqQAmYV0AQvP/+2z2x7Xq9+nRoBAS83dR3KwQQWFbwgzEhMiId5k4fAgcPp0PnxMTCif3cn0FAXsHbjdnh63Gtwb4froXun9jwbU0J8LLeUycrOgeYd+nLQglBk7efbYNRLC2HN0hehTq2qsO/fIwzIFL+079z9J4yd+jpsWDYVqjMLlg/WboHxM9/kcObGlo1dwMuRtAx48/0N0K1TOyiTmABzlnwI7zEroe1r5vKxHx0ymVvDLHv1OYhkljIDR8/iwY0/WPgCX4JZ8IIWCizSMSQywFCWWU+IILnYhx54wUw7mb8WW+dYifGC/QYDvKD7jogHI1JOi73Vy7xz8s8insIZoU/Zeg5XHyx6UEAvc5LcvwyQtG4Ob8CLCjfCCVpoQTI9DYSrkSeNLT1QwqSyL+Dl971/W1pFnVrVCbxYUsx8ZSPwIl7Y5ZglRr2XFPASlc0sPDeNcFsuQodzLOtLmb2r+TWr4EUGLdiewEsklI6PgYxTuUZHy1bXCbzYartosqQAKUAKkAIhVsANvGSeOA25uecsTatUqRg3FxlLHehUPsygy8Ejx+DhwS+5gBecY+u7BsDCqcOgRbNLeeshY+dyNyEEGGpBa5Qb7hkEDerWhAVTh7pdX7dxBwwb9yr8sGEBxLK1YGnZsR88wABN34fucqt/z2PPMXek8jD3xcH82tmsHA5PxkxdAvGxsS7gRW28n0GcDj2egflThkKrqxrBrd2fhgtqVIbXJj3Fqy5d8SnMXvwhfLt+Hv9eBi9Hj52A3sNfZtY9V8KTj93DrwuLF/lFWQUUeuBFtYAJR/Aiwx31xV8LvKBlytlDDvCCFjCVmaFQOIIXDDT853JXlzQr4OXA5w7Xu5rttK14fL3//AVe0NUrrjy4gEBf5xas9t6ClxPsWaBlIedp3mipV76cq5uhP9dJrkZ5oGfxIsCLEVyQ98Mf4CWvTE1Iv26Mx222ElxXdhlS1yK7WeGAeB0tWjA9tLBEkSdC4GW1R8scq/cmxXixqlho6pPFS2h0p1FJAVKAFDhfFAj7rEZo8YKWL7LFy297/4F7e42Bz5dPc8Y+mbN4JXy4fgtsWjHdbe/SM05w8HLZJXUhLy8fsnNyOfQY/HgXbvWy8J21sHjZOti6qtg0GmPd1KtdHcYPf9Slvy3bd0GfEdPdLHCw0qDn5kA+i0cjuxqpk3mDgZXJr7wLmz+YARVTysHqDVthxMT5cEXDC7lVzMRZb0Gvbh3g4a638qYCvDS74iK46+HR3N1q8fThzhg0WuBFBRTiJRqBDMY7EUUGL5FxRdD8+eKXeKOsRthHoCxeCo7FQGbGOWa9Ay4xY8yAF6yDBcGL6v6kBzZ2jImCghxHWnS9lNlGFi9Hvork2mIKaTmWDIKHXTOjeIyZW15wpJMWn+F4uCdp30bwNN9GcWTEvolAvup8/fnQ2jkjCrJYYGC56GmQuiEG9m10pCGX9RO6ml2XP+fvj768BS/+GNvffRB4CQx4KYhPgbQ2kyxtlxwE11OsGYQiFb983qVvT+mkPWUtkqETdojxW+JSd0Jkfrbm3FXwYuS+I6eyzitTA05der8L0AmGxUvS7nc5SMKx85JqWtoTubKZzDlWOyfwYlWx0NQn8BIa3WlUUoAUIAXOFwVsCV62f/8rPPrUZPjq49lQvmwZvlcIT+a9ucppKSJvILoGde83nsVcaAJtWjXmgXWnL1jBLUdmjx8IU197D9YxVyQZ2qCVTWnmGoTXRUHLmfb3PQVNL78IJo1+wu2MGIGXX/7YD936joPOHW6AZwf34O3/PpAK3fqNg7q1qsFPv/3FAvNGwdtzRjljxyB4eX7owzD/rdV8rW/MfMYljk3GaYd10g9zAE7+5XhRrn5dETS4u3h64lrj/gA/snqi1GYxWf5GFyVWyl5YBE3YdVG02tw43fFyLcoXg4tfzLF93VsjXPrHejhGHQdDgi+fiYD8HDfZ+AcVGhXBZYxxlYqKgC3sXSM7w7GOQ1uKx5D7wjYn2Hrl9Yjx8F9cF86p0mURsPcjx5jy/H/8j7HVZwZN304tHkOuI/ev6qOuYv96x5jqHLGe0OmehQwosf069nME/MziTIo1y1qXY3M2KvK81D0xamv2ury3oo3W2vDaT69GQsae/8DLswCxyY6vRR+X3A9Q5WrjdZmdW7DqlYmPhty8QjiXHz5Bj71dO7utoFzpUt42t3U7s65G3li8oDBWA/WaBS8qLMG4K6k3Ox7gVb6ZApHpf3AXnlzmEoRFBi9ZNVpCftlagFY1eF3ty2hDrYIXuX9se6bBnS7gJb31GCdEClSMF6M1mb2O1kEIcApjEnwCOPJ4BF7Mqh/aegReQqs/jU4KkAKkQElXwJbgRVi8bFwxDaqwDERYPFm8CPAirEyw/uvLP4Fp85bDrs8XwaJ315myeMEMQxj7RR5XPiCewAsCFoxDczmzbJk/eagz81G7LkM4EBo58AEegHfgs7PgRxZoE92eohmEQfCCBYP+frxkAktTW93lTOaec1h4fD2NxWv503EpmcXlbTWk2LJFXGs5OBK2Ti9+iWzAstfuWee5jaij9omtVvct7guvV2hQ3J+YZJ0bARp1dsxFrq/eWKL/HJaW+bNRjjXFs63NziyuWeVygKt6F68rYy+zumHrwXr4H64f53vqIEDqTw4d5DndMbe4rZjLlT0iYefS4nXIddJ2AXwzz3FNa/3yGv5YU8i1xPEv6uDqQiTGumdBDJzLKwC1rrw/KfVVZdy/F+vGK+3GRUF8iv+hhtZeaa0N57BtRhEcE+CFnTGxBtGHVjv2XgO/vF8ISTWKz4fxyoNbIyY6EgpYpjLMVmb3UsigcXysfuBxu6/P0/ztAF5ErBXZ/UiFJTKwMAIvQg8Bk/TAC1rsYIlC0s0Kwp28pFo8E0xhVLwzQK6RxYsReEE4ZcY9qzSDnREscMjpLPdg9nY+owRe7LF7BF7ssU80S1KAFCAF7KqALcGLiPGy6OWnoXnThlz7wWPmwJGjmZoxXjD+yrV3DoAl00fA1Y0v5vUXvL0GZix4H37etAQ++eIbHuPlRwY7MF4Nlmtu7wM9O9/sjPGCKa2v7zQQbmaptscM6am533rgBTMWde8/AVo1awQzxvXnQAULprVuwYL6vjTycbjjppb8sx9+3gMPDpgIIuMSgpc2rZrwIMOHUtNh1esToUJyWef4Wq5GakpjLbch7OCKJwu4GwwWvTbCHUm9jm1UV6NydQu5i49c5HZyfVVAUS99W7TTQkWrToP7Cln6ZQfYwAxGGL8F24qxcb4nWIrm0/sjXD7nezq2gMcakV191Dg3TUcUOLMiySmVtdYvz0/U1XLHEevuxMALuhqpdeX9SWIaqkXEg8GU042eKAAraa41D6rBh2qqcFFdz9XoD2bJk7HXASdktyKxbi3txBqimIvbNZKLmzfzDVQbcjUKlLLB7Tf3wC9wKr8UxGb+Dkm73+ODyyDBlxgv2Jc/LF7krECiP0/gpfLOuRB16AeQAwJrZScSsEYPvOBYskuTDHesZIHCPuJSf4CCxIrcygatRuTYMQReKLhucO9670Yj8OKdbtSKFCAFSAFSwJwCYQ1eMB7LgSPpcAcLRovpqmvXqOJ0s8F4J2WTEuHVlwbzALz39noOhvfrxjIWteMr7/nkizzjEKZmxoIBcdFVaOmskZB27Dg8MWwqVKmUAm8xtx4Mjnv1bb2hd4+O7L873bIaYXthIbP5g5k8I5Jc0BoFsx5hgN/8gnyYNW4gREdHsyRDzKWEZYjCeDHNmzSEUYMehEhMA8BK6cR4DlAQ8GAK6QUsUHDphDgWoPd1+N/2nbBl5SynxQumk27JoM3dj4zmbVcuHs8yOsXxr7XAS2x5fEEvBHxhx68xhgiCCPxMBJpNqMLSKw8ucMKTQIEXjG2CMU/UeC3q8RTj71kaBcd2R/B5i2xGoq4as0Wsxyx4EVAgc3ck/L7UAW8wAG8600kUGRzI4AWvewp+6wt4+f2NSJ5dql6XQjhziMVfYLFVMPZLYjUHzJDngfPLOQ7OwLyBiJ+iFbQY54FZsC7u6Q6GjMAL7iUCLbnIeyCAmHomQv09gZdQ74Cfxl/anQdKlbP0aLnohNLVqPKm4U6rEzPgpdI/ayD6l49csg1pgReEIKcadWVw5V8ndJJVFWNpWaNYAS/qTsltMeZL+nVjoeKWsRBz+iCPL3PiCtfYaaI9Wbz46cwHqRvKahQkoWkYUoAUIAVIgRKhQFiDF7T2QKghClqjoFUKlj37DjLLkAlw5qwjOGDray7jwENYrFzf6UkeiPbN2SP5dcwmhHFhMFUzlgtZ4Nz5U55yuiqt+WwbDJ/gyCSEZeCj98ATD97Bv87OOcezHHW7uy0M69PVbeOF9Yx8YVjfrvBQl1tgxZrNPPW0WhCkYIYldCuaMncZ7PoV47tEwkUX1oKnWdurrnRY5qAG08f2h7atm8CxzJPQ8aGRLKBwCiyfN5bXF+BFDYaK2X0QLiCUwKKCFxEEVs8qQc2EZGTxgmOo1iPymhEqqFl85Oui/93zouDkPoe1Cs5ZrXMBixkj4JEIFoxriWGWEyKFtLB4wRf+Sk2KrXAEpFAtWeRxvAUvnqxWjCxeZGhz9IdIDpzkecgZhhB+YMpwYVmkB17QaiXz1wiIYnwu+VJrMUr0wIue1Y8MXhC0RccXWyOJ/VOhlQqTtCx93G6aIH9A4CXIggdquDADLzIgQfChpnQuZDcQBr1FECTSOaM0sjWKWfBiJGkwwIuYtxnLIgIvRjsWXtcJvITXftBsSAFSgBQgBcJbAa/AC1qOYDyScmVLh3x16IJTpnQCJLH/zJQjaRnMGoWlGmYZhdSCkOfA4aNQrXKKE+CY6dMfddDtCC18UsqbTyn7vyn5UP8Rhy+86sYjuwjhdRW8IJjBF/JggRdPUAbnJ17qd82MZq5Aju8FEMGMS4Us65DDeqYYvIg+5UxGsqsR9iuPKyCFDDKwTzl7jxF4QTelAhYgGC140G1JFH+BFy2gIs9Xtu7BsRFoVWJWO2jdhJgquWERn5cMNkQds+dUWKMI3UU7PfCyc3I0ZDlCRDiLbF2FH6qAyJf5mV2Hr/UCCV7wGXr69FlISgrOM/R8zmoEXoIXHmT1NAtGxG5zEcBWnCkVnlg5a2pbPTegUICXM7Xb8qxAWNB9CDMFFSSk6FqoeFq3WKewJCLwwv8JnZ0AACAASURBVFwyT+VaOSphX5fAS9hvEU2QFCAFSAFSIIwU8Aq8HGawoz1L8fzlR7Pd3G7CaG0lfirvP5bndH8RAAVdiDA1MVpGoPuKDDDQ6gCtGbAICwNfwMv3L0W5uANpWanIL+2qBYu8QeKlXsxHTn0t9yu/0OuBlwy2bgFTtMCLOm95HjKwUmPWoEvMr8wtCNehwgwr4AVj1KB7k+hDtnjRAi+eYungfKu1Lk67LQCHcF/CtRnFp1FvFDEfdT/1+tGK3WMFvBil6g7VjayCF0wZnsHc4GQ3ML25HfjcEeuoZjtXFytRP+1oBjzQZzSsWDyZAWxX18VArJfAi3VXIz1XGwQyVTYUZ7uzGuPFLHhBl5yEg9ucx8EfFi/YR2zmHt6n3J9wdbLibmV0Tgm8FCtEwXWNTkt4XKcYL+GxDzQLUoAUCIACaPzumvsjAINQl0YKEHgxUiiMr2uBF/GyrP6Ly9CKUbJzehQHNcL1SCxXwAERA0XrpVu2xBAv+HpwxROUEW0v610cc0Z+cZfbyiBFBi+xKRHclUmN2aJCGwROnoL8egIvOKd/WMpoXKMKC6yAF7WuFfCC7lPoaoRQjb/YM/ASnRAB+1c7vhfgRXY9Q6ueKwdpAwDX4+14KgcbvFgFQ8G6JVXwsmNMFLN2coduWvMRZwwDWItYPXI9Ai/B2kU2DrN4wSJbkJiJ8aIHXlQLlUCBFxmS4PwDCV7MWKNY3TEBXoTWZsYgVyOrKoe2Plm8hFZ/Gp0UIAVIAVLAXgq4gZdNX/0AA0bPMrUKsngxJVPAKvkDvMixXGqy+CmiqC/fvoIXrWC5sjCqxYtV8FK2XoQzw5EMf2Twgu5VGH8EY8SgZRDLluoWR0a2FIphLjsCcAiooQdeBOjQetEWFja3vBgDpyKzQQUvwrVHnSu6gol4K/I1FUSJmDY4RwGEVLjkKTCwekD3rYqE1K8jOYzDf0WxYvEiYgyJtqqFkJWMUQG7gXQ6xvg4Z48wt63kGIiuVAA5/6VsF5oaWeh8vuonmPTGq6amHUiLF9w5Ed3nfLd4wc1AN5rSf2/k+6IFXmSwgXXsBF5UKxy9w6dn8WIGipg60FIlAV7SW49hKaprgpkxCLxYVTm09Qm8hFZ/Gp0UIAVIAVLAXgq4gRdM1Txx1luwftMOuL1tc6hRraLbijC+y7KPN5GrUYj3Wgu8qLFUVFcjdcrBAi9GUgUDvMjWIWgZk5PpsF6Ri4hrIgclFtdlixfVQki8lGsBDqHxdcOiIb9Cjht4EXBFjqkiXu6NwIu6DmyX3Ah4mnAZdmEaakxH7angVVRDL/24VnYi7E/Lgki1PFJhhbCowvahtHgRa0X3oarXOjCF0Dy5HsCV/YrcwItedieh7YGfzsK0l9+DX858Cy0uuhrqXJbilB1d4DCGUVH5s/D5D/8jVyOjB4M/rv9n8SJDBwFe5MC2oQIvCYe2Qrmdi91WasXiRS9OjNppMMELxofBODEZLZ7m0yDwQjFe/HE7B7IPcjUKpLrUNylACpACpICmq1HuuTxoclMvlhFoFDS5rL6bShTjJTwOTjDAi4ABKmhABay4GhkpJl7qZYAhx585d4KZ2rNsP/ILvexqpGfxosKMPOYqglYc2FZ8rTc31T1KBi8qLPAHeJHngXrElmcv6IyGCNcmEf9FtX7JYtYZ6AKDBdeVWD3CLaOVFcCh5TblaX1a4EXVTs81S6zZikWO3n6JeaN1TVkGmXJZAjPUELWUi5gvprj+jcXsQRiC56o+a4dFzurUZro7eDFy3cJAx78tK4Dx+/rCiI7DoG3Pus7hhTURXHAUxmwcReDF6MHgj+sewIsMLLwFL7L1jJnpqjFeyuz52CV7keijID7FmWIaP/PkamQWvMhxY+T+Sv/9GcQe+RGya10LWdVbmlmG5ToEXgi8WD40QW5A4CXIgtNwflVA/PHMr51SZ6QAKeBXBXRjvEyY+SY8eO/NLCVzJbcB0eIFr48d+hAkxLN8tVRCokAwwItYmJZ7hQpefBUBg9fuGBvFXYCuGZvvknEJ+0YAIYIH4/dmwIs8JznjEUKUk38Wp2XWmrsWeNm7PIIDoECDF3U+OPdDDBhhdqcoluVJgBYtwIFt1dTaoj8zgEPLbUoPvKAW6EqllmCCF5FpCiEK6iJnudKyThFrUd3ZMMYQFgQnIvW5AC84BloRmdFRuFGtTX8HbmnaFlr3K7Z4EfdMVPUzsKngHRjUuxvEB+EZSq5GDnAhAssKWGIWvIj6aMURm7HHke3ovxIo8KLeUzmVr4TMZv35x2o6abPgRY5zo4ImX5/fRu0JvBB4MTojob6uBV4oHmWod4XGJwVIAVKg5CjgVXDdkrN8/ZXYgRwjeMGXRywYtwRfdtVMPEauRuIv8CpYkWNwYP/BAC/iRTiZGVld/Jg2eJF3TFi/4NyExYunWDJy6mi0djj6rQNQ6BWhnegTLSnEC7kv4EWFGOpLvdZ81DTZoo4ReFGtesyAFy3IogdeZOsQed5iXsLiSLYowXoqtDMzL7190kqlLuLeCOsUBETo3oTl1D6HdZAah0bMQT77LZ9lFUs7soHhPSaKXtBcvC63R0hW9sIilgkJLZiKnOsOtnsVgReAvDI1GDA5yLfwxJWPcMsOq+BFwAP5LPoCXjD+SVzqD5oWL+p5lzMOmQUvOLfy382GyPwc3p0MXuTU0boPQT9eIPBC4MWPxykgXZHFS0BkpU5JAVKAFCAF/lNAE7xgnJdTp89CtcopUKpUjItYZ7NyID3jBFRMKQeJCWTtEsqTpAVeqrcu4m4mohiBF3whxf8cri3uwXVFP1bAC9bNORHBUyZbKSp4EUFpjaCDDF7MjCfcpmTLBq12Qjvxrxo/R7yoC6sPvRgoaowXT2BDb/6ym5NcR7Z+wc9l+CZbfog2ZgCHEXjB9SK8OHOIuUKxjuUAvGIcrbTmwqIE6/gLvGhBK4Q8mSzts7AKwjWLAMaydiKQsvhMwBQZnDRhBgZxNd3BC0IbDH6sVXBtR/aegZzCs1AuOgWiI2Oc2abwTJ86lgtZ0SfgxjGlg2YxSODFdacExLAKXqp8OoBBjGyXznwBL9i2VMbvAQUvpZkrk7D0kcGLP1NHm3nuCvBy6tL7WKDj9ppNKLiuGSXDpw4F1w2fvaCZkAKkAClACoS/Aprg5dbuT0Nq+nHY/P4MKJuU6LKK3//8F+557Dlo27oJzBo3MPxXWIJnqAVeLmCZieS/zMsWIGZeuoVcvli84Dh6lhCetkMFL3LgXzlzj/pybwW8IBC4crDDpcRojsJCxgi8yAFwZbgg5hlI8KLqqYIXEe9F1DM6A5jRB9290FKl+fPFKagFjEFNkLZgCnIsqsWNOh85PXcgwMuRryKdqbTF2KrVEwYVxngvCIzkIrut4ee4tlrti+DsoWIXND3w4imzEe73+M2j4VT+cXiq9hRIiHJ9hh7JOQCvHXwBWl1zBYx9undQnlAEXlxl9ha8yPFZRI9a4CU28w/IZzFaCuIruO2v3Ae2jT/4FSQc3MbqusZ0URt6a/ESLuBFBNs9den9PMuRViHwEpTHgd8GIfDiNympI1KAFCAFSIHzQAE38LJ3/0G46+HR8NzgHnDfnW00JRg79XVYsWYz7PxsIcTERHslkx1cebxaWBAbmQEv8nSMXrrluoECL55e1D2BF5ybnluQ7G5lJL/s4iGDFzkIrx480LN4CRZ4OfwlONNbq9BAzFnOxoS6oEUMupOJ7E1GZ0BvLdvHRPH4MmqR3bESGbg4ywLVyoBDBi8X3MTi6jCXHay3f7VrPa15IQSKYim91SK7DSFksWpZ5emMqFZCdRjIrMosuMT9IKCOVuwYnG/mrxHwzarDMOOX5+GhNt3g8qgb3DJn4fir0t6E709vgXXLZnv9DDU66/J1Ai+uamXVaAnZNVtxiJiyfQq/6Cm4bmbTfpBTpTGYBS+i3uHbF7ptkwpeBBhRsxipDWXwUjHjG4jZPh8w7kte2QsgKiudwZutbmOhKxOPS5O5h19Da5Ok3e/xr4Nt8WLmvBJ4MaNS+NQh8BI+e0EzIQVIAVKAFAh/BdzAy8effg0jX1wAW1e94mbtIpaz7bvd8NjQKfDevDHQ6KI6JlZJ4clMiGS5ihXwYjWmhLfgRYxjFPtDXqyAHiLmhojxIlu8YH1P4AVf5tFSw6jIlgqym4onICQH8ZXnIFxThBuLXpphoeUlHSKhfOtzzqDBMmzYOT3KaUWitQYcK/OXYg305quCl6S6DncYT1mJ5PH0wIteIGXh6oRWJZiyWuvcqPsmAyyxDmGVgrBIuLzppTqXXcQECJHXbXQG1OtqJqi45GKYo4IX2f1MtW4S2u08tQ1WHl0MH7w+FU5uK615bv86+yssPTId5kweARddeIHhlB0ugcBAGkBiNc9pwbU6I/CiLbFeemWsLbshCUhhBrzI7ayAFznjkDxbMUcZlFTI/gtKbXrRJWCw1gpxfDkuDVrYCNBE4MXwtvNbhdiYSCgdTzFe/CZogDqiGC8BEpa6JQVIAVKAFOAKuIGXN1Z8Ci+/9h78tHGxrkQHj6TDzfcPgyXTR8DVjS8mKUOkQKjBy+8si0zmr65xXIzAi2o1gtKJl1kRLLdG8wiodXee8yVeK1aJLDm+rCNg0EprrG5NnTsKoeq1xbE55JTVwipE68VcZAmSIYIYV8AGPfcT4Q5Tq0UkJNTP5zF4VFceowxRCGlkqIFjHf0h0s19JlDgBa05fmX7radRpaZFUK+LO3iRMwcJXbXAi7gmAA5+L8f4qcksT0TBILmqlYuAWPIZUK2C9KyaZIspdDdCuCHWWeWqIqjQpBDSvnPELJLnrlrpiP3ZeuIz+Czjffj0/VdAyxUK15GZlw4z/xkJU54fDFc2auD2BBFxl8QF0bdVgCraE3jRfkgHC7wkHNrKrFIyeIDf5O9fcU4GQYiweJHjr8iz9QReCqPjnIFztVaI4AUtXkr/vZFfJvASmh/WBF5Co7vVUQm8WFWM6pMCpAApQApYUcANvHy542foPfxl2LJyFqSUT9Lsa/PWndBv5AzYuGIaVKmYbGU8qutHBUINXlTrBlyaeDEUsULU5XoCLwLANLgtAipcn+fSVGssUcEKeBF1RVsz4EVkM1Jf5Ot0ZBCnVSEIAIX1KrHArmrRsv5RX6C1YILcjwpecKy0byOcgEDMDS1QEqo6wIG8VrMWL54gkic4VKtdIdRoX+hm8YLZo9R0057AC64Z04pjERZMuLboBOYScilz+2HQTAAZoY+spQxe0EoIA+yiJmglojd/nGNsebQKclhMydZEmJHo5F/FLlZyWm9Hu2IgJM7BmSY/w5Tls2D5okkQlVnOJeaSmPMfZ3bBO6lz4O35E6FSChv8v4Jn5fBXDMzsjuQ6CFcrsS+4lmuk2DtmHycEXrSVksELxlhJazPJWdGfFi9yRp8ye1c7xzADXhDIlMr4A7JrXcszMWERFi9G+4/gpQwLrivGlMGLyOxk1Ecwr5OrUTDV9n0scjXyXUPqgRQgBUq2AhRao2Tvr9XVuYEXzGjU+q4BusFzz53Lgw49noHjJ0/Dt+vnWR2P6vtRAQFezh4GHmAUs/VcwIKDarncWP1LuQo6VEsRXIYn8ILXtSxQvAUvnjIQWQEv6suyiF2iZriRt0nLakNclzMuqVBH1BFZj+Q+1f3wBJawHYIX2XpCzXIkwwLxtb/Bi0g9rnWEG9xXyC1D1HXgvLXSPQvLIS2XKb2YPVgXx9ECOcIiRoYrqkWK1vzlTFTiLMjBeeMYWMmRgvLKuqv7vXMGcxdjMW5qdj8Jjzw/lAfPHXJvXzfwkl+YB7P/fQ5yIk7D6mUzXOTcwWLpiGxMMshzSXHNNLVaCLxoK6bGVZFdg7wFL2jdUm6nw2JU9CfAizoegpByuxZDVHYGT3Et2smz1XJX0gMvqtWMJ/BiNRuT1TPnTX0CL96oFro2BF5Cpz2NTAqQAqQAKWA/BTSzGr269GOYs3glXFi7Ojz+QAeox/7NZcDl1z1/w+zFH8LJU2dh0ugnoEO7FvZbcQmasQAvJ/90ZGERri5awMNX8KIFFYIJXjxlILICXtSXcTmOiKcYMmqmKIwpg25WMjjwFLhW3RN1P/RcUvC4Cjgga4BjyZBBnofIxiRi0MgQTI6lgllxTzHLmCQGNApyHDeG7FIju/fgNU9w6PI+hVC6NqZxjnRJZ+4NeEHgkJtRnFlI3LK4RjVrF16TUzsLTbTOu9b8RWpx7MfI3Qvr4Fk7xmLtYAptcb8JtyDZquitFWvhjWVroGbVanBV4W1QqVQ1yC/KgyO5/8CmjI8hm6Wa7tXmEejS7yqXJ5J8TtD1rj7TQp2bHuDz9Ggj8KKtjifwUvrvz5yBaM/Ubgtn67aHyptGuHWkpkeWLUzMgBcRc0W2RpEHIfBi/x/a5Gpkjz0kVyN77BPNkhQgBUgBuyqgCV4KC4tg0btrOWQpKHB1nShVKgZGDXwA7u1wvV3XXGLmbTfwIiwW8EU17TtwxulQrWC0XI18AS/ihR0BlQoTjMALurnUvdM1RTf2V5e5Ge2aGcWhCK5HjdmiHjJhDSFDBDk4q6f1qXFzBIiRY+xoWY7IIEjL6kQrRbeYn1kLJ1G/yaBCiKta6JKiW6TuVgMHC83welR8cTwV0RcCBwx8jNYjahHWR7Lbl+ySI/ZTBiqiDy3wIkMbI3cvAV6EhZnoF2EWpqFGOCfWjM/Q9z76lMGX1W7P0JjoGLi1/P3QvkUrqNbaEVNGBMyV90l2K5KhkJ5LG4EXHQWWdteVRo2RIkMOGaAgoDnT4E5nYFq5QzVIrRZ4qfLpABaLJdstGK4MWwIFXgRAwjWgVY2AR2TxErxfBQi8BE9rX0Yi8OKLetSWFCAFSAFSwEgBTfAiGqFly297/wFMMV2KpY2uV6cGXFK/FiTEszcFKiFXwI7gRcAGNVCsbG2iBV5QbPFiLLuCiJdhNbiu6nqjZqARm2cEXrSsLAQIkV+SjSyKfnktiluXiKLWNwNe1APnSUOs6wm8oIZo8SLcWrQAh8iIpAcuZI1bT2HxVBhskNch1ihbocjBefE6Age0HsGi7qtWMFwBXvT0RgiG0CaaAR1P8zdjwRXPwldlZ7oqo+cGhaAH1yFbqWDLU6fPwqqRhyHt3CGoeHEkNL65GlSKqAV/vc6C1vxXBGARLmkibgx+LyCLDF70gjh7eiCRxYu5x7W/wUtam5egIL6CMw21lquRsHhJvWkWVNkw0GWiCIZSb57jNnkrrkbCZUqkyxaZmQi8mDsT/qhF4MUfKga+DwIvgdeYRiAFSAFS4HxWwCN4OZ+FscPaAwleVNcXMy+qqJleoFP1midocFXvKIipk6u5BfgyevRb19TSWq5G+Nne5ZiCN8JlTmqnjjS9EfylX40dIuasureINcpBXrUsLOSx8tgY8QWx8Numc9zSR6u+XlYmPcigapjB3HyyUovhjgxeRPwSs+fazH4L8IKQo8UL7uBFAAIBDdQ4Oti+HMtGpRXvBaFLgy5FLm5LOHe0xMF4RkagS2udMhTSWp8Kv5LrsSC3fzp6EnNFiym9wNFYT8saRWgvAxN1rzH2EJ5DtJpRgRTO9bc3IpyQTG/tCJzQZSyRBVcWQXmFDgReHEpgVqGY0wd1bwNfwEvMqQPMNWkZxJz6l1u3YBFwQysNtbguwAuOrdYTsESdMIEXs0+y8KhH4CU89sFoFgRejBSi66QAKUAKkAK+KEDgxRf1Qtw2kOBFfQk18yIuXlCFdYmnuCaewEuLIVEQUVkbvOAYqsuIHnjJOc5ilrDMP5VZSmCtbEPq9mmBD4QFdTq6QhnVkgP7keOpaB2LSMZDKpWPh3/+ynGCHjkjDrbRAy96UEfVUHUdksGLUfwS1bpEa79VGCdASkr9CGj4eL6bxYtwV3IAgQhuYYOptEVRwYuwGsHr2He11uAWmFa4pQUCvOA80X1MFBm8qFYmWoF4sZ0avBk/E9prxaIRY+E1BFgCvOA9JCy8VFc8jOFz5SBH5ie5aI0jrhN4cSiRVaMFJBzcpvvk9gW8yMF4xQBG4AXjwyTtfo8DofTrxgYEvKiLFXAnvfUYyEuqqatFKC5QcN1QqO79mBRc13vtqCUpQAqQAqSApMB5kv6JwIuNTz2CF3xhO3vINbiumm4Xl2j1RTUQ4EUGCDI0ULMGWQUv4mVXhgtGIERr27V00wpYLD4Ta5Az4+gdJwFeUjMdfwn3VLTisaixabC9P8ALzr0Sy0YUmxIBfy4vhiJaAEE9EwIIyOBFzuCkwhu1vQpeZMCAliNxLMuyHNQY1ywAkZGFkZa+RhYv2EbWvsFtAHvWOXqSoQl+L1vx5GU70nrrnQPMyIXBgiuxOLoCtqkZlnDtYg+Eu5LI5IXfo5WUXLQCOYuMSFquSAReHOrpZQ4S2uqBl7wyNeHUpV09xngxAi9av1Ng0N7Sf2/ksV8yWjwdVPCiFbTX6NkU6OsEXgKtsH/7J/DiXz2pN1KAFCAFSIGSrQCBFxvvL4IXfMnKORHBX8yEhYGWdYPVuBCBAC/yHPwJXsRLqKdUwma22ZNu8gu5WAdqhAF7E6tHQPKlrkGo1fECAV7kPZLTWgtA0fz5YqsIPYsXPdcwrRd7eTwMIlvtuiJuUdTg+mhIuPQct3iR4YUKb4zAC55fDFSL1jGYNeosC66rghehq9XzjO1kixYtsIR1BHzD9dVoEuEELypEwvOLFkZoTYWACDMdla7OrJoYJDFTUIt/WB8FjMOhexgCGVw73sfy+cL1i9g3cuwXLbAoziiBF2UH/guui/FSjjcboAlPjMALXleD3xbEp/A00DmVr4TMZv3BCLxonYtCZuaEbkloiXPiikd5H1iE+5HoW21rxtUI55fWZpLbsMLihcCLmTvVP3XI1cg/Oga6F3I1CrTC1D8pQAqQAue3AgRebLz/ArwIFxPxckjgxTWwrNkttgpezPaL9bwBL8IipGw9lvKZxUJRiyfwolo46YEXORisDJeMwIvcf+XycXDsZK4TvOBYWNSAxip4QauVGBbLRcR4MbKQkdfvDXjB9nLKZ639kwMB120dBbuWOnT3JoWzmfMhWwiJ+mJtaiwZ1BwLWtegBU5ZBmoQTuHZkOsSeFGUZ+AFXXoKYxKhIC7Fa/CS2bQfJH//irNzEShXWKvogRdsIECK3plQA90KOKJmTBLtZfCCcCYubafjbNS/A8rsXc2/1osPU+LACxqDmeOdZm7JgNQh8BIQWf3eKYEXv0tKHZICpAApQApICtgCvOTlF0BMdHHsBXkHU9MzIal0gqVMS6fOZMGJk2egRtWKEIlvxP8VTAF7OO0YVK6YrDuep9OD7YuKWHrYKFe3AGxz/ORpyM7OhWpVKuh2ce5cHhs/A6pWToFYlrYby0effAXXNGkIVSuxFCtKCTV4EdORX+o9BdcNlsWLGdcfrU3QghMiWKqWxYuVJ4k34MUILqiuM2h1ITIEmQUv8hhGFkNaGYtQAxW86OmixlCR3WuwTTDAC1qqYNFy3cLPhQsQ6tforijYNt3xRienrLay72bqInz5cUYkFDJLHyxyKm85KLLsmoVxXnJZDCOMC4PWO/LeYHyci3u6vome765GAmxowRHUXKSVxsxChTGOjFNyWmj8XoYa+L2/wIuWZYoReEkuUwqiPhsPcC6LQaX7nWDHDHgRVjW5KReZOZ5BrUOuRkGV2+fByNXIZwmpA1KAFCAFSIHzSIGwBy9//XMYOvYcCevfngS1qld2bs2f+w9BjycnAqa8xnJ9iytg5gsDIIalvdYr6zftgHEzljrbvDdvDDS6qA6vjteGT5gHBQWOF5bBj3eGx7rdzr9ufFMvQCiilhXzx0LDBrX5xwhcBoyaxb+eM/FJZ1UEQ50efdY5ZnK5MtCn513Q7e62zjp79h2EQc/Nhn8OpvHPnnzsHnj8gTv415e3fQReHtMX2l/XzG38YIIXLQsIMSH5hV3LgkLLckMO1IovtTvGOsBaFMtUfuv0KMg8ZT64rupqZDWejdY6xGdagXuNgIjbRrEPrICXnWz96H5iNI4KXtDtSc4QJFucyHskp23WAi/oZnPlYPfgrb6CF9RFBVhozSPciVTwomUNIrQ10kZrD8x8JlzgVPDi6fyb6deozu9vRELmrw5gK+sg7xsCleRG4BKLB+ur8XC0zj+Bl2GAoEEPvAiIIluemAUvPP5Lo648mxEGypUL9ofFk8WLljuRGfCSlZMPOXksPhNzTxL9y+5QehYvRmcxlNcJvIRSfetjl2zwcp5EerS+7dSCFCAFSAFSwEsFAgpeEGG4236Yn+lNXYfCodRjvIEKXhBmJCbEw7zJQ+DA4XTo/PgYGNG/uwvQkEdau3E7PD3uNbi3w/XQvVN7QACSEB/LLWWysnOgeYe+HLQgFFn7+TYY9dJCWLP0RahTqyrs+/cIAzLFL6I7d/8JY6e+DhuWTYXqzILlg7VbYPzMNzmcubFlYxfwcoRZsLz5/gbo1qkdlElMgDlLPoT3Vn0B29fM5WPj+nCdVze+mI3fARo3qsfmkwsVksvy6dsNvGilzNV6SZZf4vGlVryQV2lWBM0ejQ478CJgCO6JNy/9VsCLp+w08plWA9nKWYM8WbyINNDYlxw0VmTR0QNX8nieXI083eFWwAv2o5fpyZs9MPPkwTVm7kb3nSKoeVEM7P6oAPIZjNWzkDHTp5k6aswj4VomB+HFNSN4wcxQeSw2zAXti3hq7SjmrlXrv69xLAIviuLM1ciTxQsCCiyxmXuc9biOez52uu3g9yIQruhddvGRR8R4LTEnD/C01WbAi5Y7kQAv6CJ1pnZ7tyOEFi8EXszcWeFRh1yNwmMfjGZBrkZGCtF1UoAUIAVIAV8UCCh48WVi2PYwgxIHjxyDhwe/5AJeMk+chtZ3DYCFU4dBi2aX1yd1XwAAIABJREFU8mGGjJ3L3YSWvfqc27BojXLDPYOgQd2asGDqULfr6zbugGHjXoUfNixwuvi07NgPHmCApu9Dd7nVv+ex55g7UnmY++Jgfu1sVg53JRozdQnEx8a6gBe18X4GcTr0eAbmTxkKra5qBCMmzodPN38L365/DaKj3N2pZPBy9NgJ6D38ZWbdcyW3itGzeBEv0PLYVl9U1XgcZi1erIIXzFKDQWDFC/Zlj7AXyqvMW7zIbkUCWHiT8Qa1UjPN4GdacXNkVxCzZ9wb8GImrojQDetiEdYjRuDl3AnmKsEggzyGbO2hxmcR6xTjBQK8aJ2xYIMXeT/5y20uC159zt36x+y+m60n329y4NzM3ZFw6EtH1iT53sKYLlHxjjTnuI8itTeOp+VqRxYv+hYvZsGLsIoRe6q6Hsmfl2JWKALk4OeeLF60wEvKtsluIMjtbJLFi9nbK+T1CLyEfAtMTYDAiymZqBIpQAqQAqSAlwqENXjBNQmLENni5be9/8C9vcbA58unOWOfzFm8Ej5cvwU2rZjuJkV6xgkOXi67pC7k5eVDdk4uhx6DH+/CrV4WvrMWFi9bB1tXFQdO7NrnBahXuzqMH/6oS39btu+CPiOmu1ngYKVBz82BfBaPRnY1UifzxopPYfIr78LmD2ZAxZRyHCBhPJcqlVIg9WgGXMJcl4b3u5/Hn8EiwEuzKy6Cux4ezd2tFk8fzmPQ6IEX+a/nYvxwBS/yCzy+fFZkYQcS42NMW7zI7QV4sbpWoZGWbiK2h55Fgtn7zgp4QQCE8VDqdiyExGqOgKp6RQYv+MKNL+JYZJcv/F4vDo/crxXwImtsNsYLjiWDFLS2wRglAhZZAS9qemez+2ClXjDBixwcVw9yCtgir0ELFuJ1tQ8CL+ELXtTAurh/VsBLVDaLS7ZpBD8W2Fe5XYt5tiVyNbJytwe2LoGXwOrrr94JvPhLSeqHFCAFSAFSQEsBW4KX7d//Co8+NRm++ng2lC9bhq8L4cm8N1cxy5F5butE16Du/cZDm2ubQJtWjXlg3ekLVnDLkdnjB8LU196DdcwVSYY2aGVTmrkG4XVR0HKm/X1PQdPLL4JJo59wG8cIvPzyx37o1nccdO5wAzw7uAdvf+kND8GFDPDc1/FGDmDmvvER5OSeg83vz4BS7HsEL88PfRjmv7War/WNmc8449ggeKlzUxEc/wvgxF8R0LQ/QLkLi2DfeoD9/wURFZPEenVvNX8TYH/fzymu33a6PgDAelgfizzOxsGOz65gViwVLnNtL/rH+eK8RYmOjoS4UlFwJss9po6oI89Nbr/7HRZclqU3trpW0a+WbmLd8phCZ/NqAqAvfNnSpeDE6XNWmhnWFRqLOYnvVQ3kPVI1F4PkZEZADgvYiumR45K191urf1zXabZfGFzaqIj2WE/suzhnWmdMri/37c0eGM1NvY7xJnLzCxmsDd+UKcd+jgD2nu1WUJ9DO1iKapbqvOb1RTzGEO7TeVkkV6OYUweg4pfPu8iAFieyhYoIOqu6Gnlr8VIq43cXlyV1D7TAS8KhrRB1Nh2yaraCgnj3gOyyqxH2J1yTsK/SzEUKrW0IvITPaSfwEj574WkmBF7ssU80S1KAFCAF7KqALcGLsHjZuGIaVGEZiLB4sngR4EVYmWD915d/AtPmLYddny+CRe+uM2XxghmGMPaLPK688Z7Ay98HUnkcmssbXgjzJw91Zj5C8DLxmV5w582teFcYaPfuR0Zzlym00EHwggWD/n68ZALUq1PdOSSCl/q3Aexd5/jo9rmOiDp71hQ6PxOVsV6DDuYj7mTuBWdGl3gmcZvx+m23TSuEzD8dI8njbBpdCNmZAC0GR0JyfddbRPSfXI9dH1LcdxR7QcSsUOfYC69ekeemtvflRtTSTWianwXw6VDHnLTWYzQuIqjYmCgWDNO/bis4Zyxib4Xm6n7Le+SLZmv7OsaT+4+LiYRcBieMsQuAaC90jGbBlL980dGn0FrWUq4vf+7NHhjtkXq9FIOABQVFUMCAa7gW+VzKc6x8OUDaT46sR23HOeLAJMTpBx4P1/X5ZV4SeMH+BKQQfQcCvETkZUHpvzfyNNYRLPOQSPGstZ60Ni9pwhVPayfw4peTEbROCLwETWqfBiLw4pN81JgUIAVIAVLAQAFbghcR42XRy09D86YN+RIHj5kDR45masZ4wfgr1945AJZMH8GD2GJZ8PYamLHgffh50xL45ItveIyXH1mMF7QywXLN7X2gZ+ebnTFeMKX19Z0Gws03XA1jhvTUlFUPvPy652/o3n8CtGrWCGaM6+8Sy6VN58HQ8aZWMKjXvbxPAZXQsgXdixC8tGnVhLtcHUpNh1WvT3QG3hWuRiKTjXAv8LerkVGWINmNRStLjlasEhHTQu0bf0E1cjUySp3r7V2v6ibiz4j+MHYOWoWYcQFS52DF1cjb+WM7PXcrM65GZsYVrkL+cDWyEsNGnZuZtmbW46lOMF2NfJkrBn7OYTF76nYsgjRm8YXxYOSCWY+qX10EFcsxynU+Fi/BS9Ludzk8EammVekym/bjAXgxiK5cEORgQdgif60n/eHbF1reFQIvliULaQMCLyGV3/TgBF5MS0UVSQFSgBQgBbxQIKzBC8ZjOXAkHe5gwWg/WjIeateo4nSzwXgnZZMS4dWXBvMAvPf2eo7FRunGMha14zL0fPJFnnEIrUmwYEBcdBVaOmskpB07Dk8Mm8rjqrw1ZxQPjnv1bb2hd4+O7L873bIaYXthIbP5g5k8I5Jc0BoFsx5hgN/8gnyYNW4gREdHQyR72/75t32A8WKaN2kIowY9yEz+HS9FpRPjOUBBq5s3P/gMVi4aB0llEnmA3q+++Rm+Zm5UmPVIxHhpyaANWsJgWbl4PMvoFOeM8aKCl6PfR/IXsLNHAApzHONZfVHVSx2sdcZCCV68jeeitQ4BXhC4JFZlsWZYfBWELP4owQIvIrCyqoucrtgIpHlarxbYsRLjRc4OZeZMbh8Txc+wnALbm/PszR7aBbzg/Z5YlZ1Zdl7le1GsGbW7gGWvv/yWEuJqZDXLqwF4kd1zZLcfEWdFdTESusrt5POlB14K4lN47BW1EHgpVoTSSXvzpApdm5KdTjp0utLIpAApQAqQAiVTgbAGLwgdEGqIgtYoaJWCBV1yHhwwAc6cZXlVWWl9zWUceAiLles7PckD0b45eyS/jtmEMC5MWjoLYsEKxlWZP+Upp6vSms+2wfAJxfFhBj56DzzxoOMvl9k55wCzHHW7uy0M69PV7SQI6xn5wrC+XeGhLrfAijWbeepptSBIwQxLuSwFNWYq+ubH33kVDPb7ysTBTssc1GD62P7QtnUTOJZ5Ejo+NJIFFE6B5fPGwsonCnimExW8iLHklzAzL7nyHGXwYpQlyBvwgoFCzx5hmVkQckgBZK1avPgTvGBAWwxW6k/gIjQNFnjRe0zJ1jxq4F0rjzax13JmJyvgRT4rTUcUcKDiqYj6CItEJiasb/U8W1mjqGsX8CKvTQu84PXSNYrglrElBLxY3Uwd8JJXpoYz5bOIi4IporGcrdPeJVYKxkxRi1XwgmmmEw5uc+nG2zgsqsVLlU/7Q2R+Dg+uK9aC6a9PXXq/VbVCWp/AS0jltzw4gRfLklEDUoAUIAVIgfNYgbAGL2b2BV1wypROgCT2n5lyJC2DWaNE8YxCakHIc+DwUahWOcUJcMz06Y86GPD31JksqFmtIgvE6uoqoNe/nquRqO8v8GIEN7wBL3prCiV48cc+6vURTuDFaD896SD2WgYf3oIXTynK1TOM4AWLcKMh8KK9S3KWI7TcEhZvBF4cWY2wiBgv6a3HQCSLxZKXVBPKfzeHB6QVRU4x7avFixzvJXHf5y5WL/4CLxg0WF2LVprqQD7j/NE3gRd/qBi8Pgi8BE9rGokUIAVIAVLA/grYHrzYfwu8XwGCF7RGSf3aEZxWfZH1F3iRrRu0ZqsHXrxZGYEXb1QzbiNbvPgCXrRSXYcCvIg038Yr976GHS1e0JLs25ciOXCRreEIvBSDF4zLwiFegzudh0MrgxFeFNmB9Cxe4g9+5WbForoayRmTMMMRfp9d61ooiEuBwpgEDn6sFtXiRW4vXKQIvFhVNXD1KcZL4LT1Z88U48WfalJfpAApQAqQAqoCBF5sfCYQvKAlgLACUMGLiPeBS7RqIYAvcDvGRnF1jNqGErwYzS1ctrekWLxo6RkK8GLGWsbXvbcjeME1o5tgPvPARJe5719y3MPnO3hB6xZPgMMTeFFdhNBFCYEJuvHEpf7glrEI3X1ECumsGi0hKusYBzhaaaO9PaMEXrxVLjTtCLyERneroxJ4saoY1ScFSAFSgBSwogCBFytqhVldBC/CnSChShFcOdg1VbFs5eANoBAZbIzaEngxPhgEXhwayWfFDDyRY7xgez3IaLwD1mvYFbzIKxXBjM938GIUwFaAF5HBSHY1QssROR20DFBUYIPay+BF3gsCL8b3ILkaGWsUTjXI1SicdoPmQgqQAqQAKRDuChB4Cfcd8jA/BC+iaGWq8Rd4MQqCSuDF+BAReHEFL2qqbj0FZfASl1wE6SyDDxYz0MZ4VzzXKAngRehH4MU4ZTPGSEE3oOTvX4G8MjWZVUs8t1RRwUvqTbO4xQuWWFZfWLeI00Tgxfs7j8CL99qFoiWBl1CoTmOSAqQAKUAK2FUBAi923Tk272CBF6OXXBm81OtSCJWaep+C2UyMFytuUOGyvQReXMGL2ZTWIg02xivBopfBKxD7XBLAi9CPwIsxeBFnSATfFUF1ZfCCFjGpN89xO26iDV7QAi/Y7nizAc4Av76eV3I18lXB4LYnV6Pg6u3taORq5K1y1I4UIAVCpQC+cTn+JEnFDgoQeLHDLunMMdDgBS1m8liQzrodPYMUX2LJqEszA16wjXCDCkaQVX8cEQIvDhWFFZZZ8CLqE3jx7hQK/Qi8+Ae86GUhMgIvaCWDRVjKeLebxa0IvPiqYHDbE3gJrt7ejkbgxVvlqB0pQAqQAqSAGQUIvJhRKUzrBBq8mF22ry5N8jhWwYuRNY7ZNQS6HoEXAi+BPmNa/Yt7s2zdImg/slQopqA5Zn5BAaQezYTCwiKoXqUCREUF8O81S7uDUYwXeZKqxcuJKx+BcjsX8ypnarflQXXVooKXmNP/QtLu95zVrIxvZpM8gZdklho7Lm0nd5GSMzeZ6TfUdcjVKNQ7YG18cjWyphfVJgVIAVKAFDi/FSDwYuP9twJeAgkoCLwYHyICL96Bl6MspkvatxFQ+aoiyM0o4q5GWoGkjXfAeo2S4GqE+v25PBKqXF0I1z4eayhC7rk8uPuR0ZCTew42rZiuW3/4+Hmw5vNtLtdrVa8E69+eDG+s+BQmv/KuW9tmV1wEb8x8Bha8vQZmLHjfeT0hPhZem/QUNL28geH8np28GD5ctwW+/ngOlCtbGvbuPwh3PTzarR2CnJ82OmAJ+Ahe0HUoZfsU3tWpS+9j8KW923gqeMEKog1+HUzwIgL+EngxPE5Bq0AWL0GT2qeByOLFJ/moMSlACpACpICBAgRebHxEzmfwYrdtI/Di2DGrrkbyPh/5KhL2r47kKdQv6+2awSsQ56EkgJfc4xFw9FuAchdEwkUtPVu8FBUVQZ8R0+DLHT9D5YrlPYKXp8e9Bv8cTIPxIx51Sh8fFws1qlaEk6fPwtFjx122pN8zM6DRxXVh2ti+8M7KjZBSvgxce/XlkJeXD48MmQT5BYWw6vUJHrdxKQM6k/4DOgK8YPu/D6a6tFv49lr48pufYOuqVxyfWwQvlTcNh6jsDCiIT+H/yuBFLzNRxS1jIeb0QT4c1sEiwAv2k9Zmkl+PqCeLF78OFOTOyOIlyIL7OBxZvPgoIDUnBUgBUoAUOK8UIPBi4+2WwUtFFtC2PgtsKxfZEqWkWbzYbdvCCbwYpQe3qm3l8nFw7GQuFDC3EaMix2ypeZNxfbm/U/si4Zd5BF6MNNa6HhMVARXLxXlsOmXuMm7F0vGmVrB24zZD8HL85BlYMHWo4XS++fF3eHjwS7DqjYlw4QXV3OrjNXQ5QmsYvbJl+y7oP2omPDu4B4yd+rrT4kWtf+pMFlx7Z38Y0b87dLu7reOyRfCSsm0yz2YkCoKUxP2fQQTLepTR4mnNKcptVPCiFxfGUDgPFQi8+KJe8NuSxUvwNfdmRLJ48UY1akMKkAKkAClgVgECL2aVCmo9fCGNMBxRBi8YfFR9kSXwYihh0CqEGrwIlxNccCjBC8KTk38WQdl6EZBU11r2KwIv3h9XI/Cycv2X8ML0pbCawZH1m3bAux9tNAQvX2z9EZpc1gCSyyXBbW2bQ+trLtOcYIcez0D9OtVh+vP9Xa6//eFnbKxvYN+/h+HVl4bAFQ0v1GyP7kT3PPYcvDymL48H0/nxsbrgZeKstxk82gpbVs6C6KgoJ3hJ7bDItHjJWydBKQm8ZLYYBudSLvbYXm6D9bEkb3O4J51LbgCZLYebHt9MxfKlYyArNx9y86zBSzN9h7JOYnwURDAzijNZ+aGcht/HLhUTAYlxMXD89Dm/9x3KDtHipRIDumnHc0I5Db+NnZQQDQlx0X7rjzoiBUgBUoAUIAVkBQi82Pg8EHixz+aFGrwIaIGKhRK82GfH2ItzmVLs5bYAcs4F3q0p0Lp4Ai/f7fqDu/ssnjYcMA4LxmAxAi/oLoRAJD62FOzc/Sfs+vUveGHYI3DP7de5LOXTzd/CkLGvwIZlUzk0kcuYqUvg252/Q8bxUzB5dG+4vsUVbjLgtdseGA4P33cr9O7REX7d87cueElLPw5tOg+Gic/0gjtvblXcF7N4KXzwLdMSR2wYDxFpvzvrF900CooqX+Kxvdym8L55EHH8X4jY4HCdKqp8MRTd5B6HxvSENCoinADGXIrwfyWoRPz3B4eSuC7cskLmzlfSSiRbWElZV35+EZSKCWCg75K2+bQeUoAUIAVIAUsKEHixJFd4VSbwEl774Wk2BF7ss1dipucLeBk8Zg7s+PE3uK65A3z88vt++PdQGrdieYa57JRNSjTcvF5Dp0LmiVPwwcIXnHULWNwWBCGtr7kcxg8vjgWjdoZxW5Z9vAl+3LDAbZz31/wPENB0aNcCIthNlJF5CrZ+9wu0v64Z9OxyMzRuVN/ZBuPO/PDzHvjsvZe51YSz+OhqhKmgjdJAy65GGEg3NuMPZ4wXcjUyPD7OChTjxbxW4VCTYryEwy7QHEgBUoAUIAXsogCBF7vslMY8CbzYZ/MIvNhnr8438PLp5m9g1+6/nBv0/U97YA+zZrn/zjbQ96G7oHRivOHmCeuVdW8VB5F9j8EUdF/634czoUJyWd0+0M1p9KRFsGvjomL3oP9q/7b3H1i9YauzbRoL2vvJF99AlztugC4db4RL6l/Ar+379wjcwVyaZo0bCG1bN3EdyyJ4Sdr9LpT+e6OzDzMZiTyBl5zKV0JmM1c3K0NBDSpQjBdfFQxue4rxEly9vR2NYrx4qxy1IwVIAVKAFDCjAIEXMyqFaR0j8CKywOD0KbhuaDeRwEto9fdm9PPF4kXVRsvV6LWlq+D9NZvhg0XjoGyZRMC0zhi8tl7t6vDTb/t48NzundrD8H738+4wLTUGub371tYwcuADLkNMmPkmgyNN4fJLLuTZjx4fNhXiWEYkkdVo5sIPAGGQDHFEB3quRmhxc+RoBqxZ+qL7VlsELyIds+jIV/ASiLTOBF68uaND14bAS+i0tzIygRcralFdUoAUIAVIAasKEHixqlgY1TcCL3JcDwIvod04Ai+h1d+b0Qm8THfKdmv3p6Fa5QqwaJojq0+7LkMY6Mh0XsfAutPG9oeE+Fj+2fy3VsOcJSvhy5Wz3VyVeg9/maesFqVW9Uow98XBUKdWVf7RMxMX8KxKP21c7LZtWuDlZwZ+uvZ5ARZOHQYtml1K4MWbwx4mbcjVKEw2wuQ0yNXIpFBUjRQgBUgBUoAUYAoQeLHxMZDBS507CqHqta5ZYgi8hM/mEngJn70wO5PzFbyo+pw8fRZa3tEPFr38NDRv2tB5+eSps3As8wRUrZzCgIvnVNVqn2gRczj1GJQpneDRDcnsXnmsFwSLl+Tv5kBc2k4+DTXGC1m8mN9FAi/mtQqHmgRewmEXaA6kAClACpACdlGAwItddkpjnjJ40cpUE6z0u3Laal8z5qBJdmJ8DGSeyrXxzrhPncCL/baTwItjzzCmyuS573pMLx3WuxsE8CK7JxF48f40EHjxXrtQtCTwEgrVaUxSgBQgBUgBuypQ8sELGoGU0OyA4Qhemo4ogNjy3qfMJPASmEcJpZO2riuBF4dmJ06egbz8fKiYUs66iOHQwiJ4kTMS4fTNxHhRwUtU9jGovGkEXz1ZvJg/BARezGsVDjUJvITDLtAcSAFSgBQgBeyigA3ASwkmJz6eknAEL77GkiHw4uOh0Gkugxdf4Zg6ROXycXDsZC4UFHoP3AKzat96JfDim37h0rpg/QuQdvUw09PxB3jBwaqtfYyPmdF8GOSmXGR6fDMVKbiuGZXCpw4F1w2fvfA0Ewqua499olmSAqQAKWBXBWwAXuwqbeDnTeAl8Br7a4RwcjXyFY4RePHXqQhePzFREVCxnLU4LMGbXWBHIvASWH392TtZvPhTzcD3RRYvgdeYRiAFSAFSgBQoOQoQeLHxXhqBl7OHI2DfKhYzpVoR1O3oGnjXn8uWY7z4+lJPFi/+3JnivgIZaJksXgKzZ/7stWSDF89WkVbBS8ypA1Dxy+ed8ltxNSqIT4G0NpN4W7J4sX6CCbxY1yyULQi8hFJ9GpsUIAVIAVLAbgrYArzk5RdATHSUprap6ZmQxDJjWMmqcepMFo9bUKNqRYhEU4T/SiFzlTicxnzzKybrjudpg7F9UVERREW5B5U5fvI0ZGfnQrUqFSydkY8++QquadIQqlZKdmtnBF4sDeRDZQIvxuKRxYuxRuFWg1yNwm1HvJuPVfAiQxP82gp4yU1uABktHCm3CbxY3y8CL9Y1C2ULAi+hVJ/GJgVIAVKAFLCbAmEPXv765zB07DkS1r89CWpVr+zU98/9h6DHkxMBU5piub7FFTDzhQEQExOtuwfrN+2AcTOWOtu8N28MNLqoDq+P14ZPmAcFBQ7LkMGPd4bHut3Ov258Uy84x9KfqmXF/LHQsEFt/jEClwGjZvGv50x80lkVwVCnR591jplcrgz06XkXdLu7rVt/z05eDB+u2wJffzwHypUtza9f3vYReHlMX2h/XTO3+gK8RMYVQfPnC0J29gi8GEsfavBSkB0BO8Y64KWvVknqasnixXj/Q12jZFu8eFaXwEuoT5/58Qm8mNcqHGoSeAmHXaA5kAKkAClACthFgbAGLzd1HQqHUo9xLVXwgjAjMSEe5k0eAgcOp0Pnx8fAiP7dNYEGtl+7cTs8Pe41uLfD9dC9U3tAAJIQH8stZbKyc6B5h74ctCAUWfv5Nhj10kJYs/RFqFOrKuz79wgDMsVgY+fuP2Hs1Ndhw7KpUJ1ZsHywdguMn/kmhzM3tmzsAl6OpGXAm+9vgG6d2kGZxASYs+RDeG/VF7B9zVwXK52lKz6FSa+8y9dqFbxUbFoI9bsEzpXI6DATeDFSiCXWYoZVlcrHQ2pmtnHlANXYOtwBJQm8mBOYLF7M6RTutUINXlJvmgWFMQl+lYmC6/pVzoB3RsF1Ay6xXwag4Lp+kZE6IQVIAVKAFNBRIKzBy2EGXQ4eOQYPD37JBbxknjgNre8aAAunDoMWzS7lSxsydi53E1r26nNuS0VrlBvuGQQN6taEBVOHul1ft3EHDBv3KvywYQHElopxvJx27AcPMEDT96G73Orf89hzzB2pPMx9cTC/djYrB9CVaMzUJRAfG+sCXtTG+xnE6dDjGZg/ZSi0uqoRv7xl+y7oP2omPDu4Bwc6euDl6LET0Hv4y8y650p48rF7YOucfMj4pxAu7lHI47iEqhz5KhL2r3a4V/n6Uk8xXgK3iwRerGlL4MWaXuFa2xvwkrJtMsRm7uFLMuNqFJf6IyR//wpouRqZaW9VOwIvVhULbX0CL6HV3+zoBF7MKkX1SAFSgBQgBbxRIKzBCy4ILV7Q8kW2ePlt7z9wb68x8Pnyac7YJ3MWr4QP12+BTSumu+mQnnGCg5fLLqkLeXn5kJ2Ty6HH4Me7cKuXhe+shcXL1sHWVa8423bt8wLUq10dxg9/1KU/hCR9Rkx3s8DBSoOemwP5LB6N7GqkTuYNZtkymVm2bP5gBlRMKQd79x8EBDnoToTWM50fH6sJXppdcRHc9fBo7m61ePpwZwyawxmhs6AQa/Nn4FYCL97cxubaEHgxp5OoReDFml7hWjsY4EWkoJbBSyD1IPASSHX93zeBF/9rGogeCbwEQlXqkxQgBUgBUkAoYEvwsv37X+HRpybDVx/PhvJly/C1IDyZ9+Yq+Hb9PLfdRdeg7v3GQ5trm0CbVo15YN3pC1Zwy5HZ4wfC1Nfeg3XMFUmGNmhlU5q5BuF1UdBypv19T0HTyy+CSaOfcBvHCLz88sd+6NZ3HHTucAO3bsk4fgpue2A4PHzfrdC7R0f4dc/fmuDl+aEPw/y3VvO1vjHzGZc4NifOuMeeCfbxPv5nBHw/x2Fx026Gb6NHR0dAbEwUnM3O962jMGuNvvBJiTFwMgz2y9/S4LrOZOdBYei83fy9JN5fYnwUnMsrgrx8+y8Mz19Ztk/nY/EFvBRGx0HqzXMMZSPwYiiRqQoU48WUTGFTiWK8hM1W0ERIAVKAFCAFbKCALcGLsHjZuGIaVGEZiLB4sngR4EVYmWD915d/AtPmLYddny+CRe+uM2XxghmGMPaLPK68x57Ay98HUnkcmssbXgjzJw/lmY/eX/M/7p7UoV0LiGBBQDIyT8HW737hgXR7drkZGjeqz4PrYsE8QP3ZAAAgAElEQVSgvx8vmQD16lR3OVZZuaEHFJl7AbbPcICX214pzhLlzfmPYr/JRUdHQm5e6IIFezNvozaoSlypaMg+F/r9Mpqr1evxpaIg51whsJxeVpuGdf1Ylkktn9GkApatzPaFLSEhTj/wuO3X52EB3oCXpN3vQum/N7q4DnnSKDIvCzANNcZyyUuqGXA5yeIl4BL7dQCyePGrnAHrjCxeAiYtdUwKkAKkACnAFLAleBExXha9/DQ0b9qQb+TgMXPgyNFMzRgvGH/l2jsHwJLpI+Dqxhfz+gveXgMzFrwPP29aAp988Q2P8fIji/FS6r8YL9fc3gd6dr7ZGeMFU1pf32kg3HzD1TBmSE/Nw6MHXtCSpXv/CdCqWSOYMa4/REc5sssgQFq9Yauzr7Rjx/lcutxxA3TpeCNcUv8CDl7atGrCXa4OpabDqtcnQoXkss425Gpkj/s4HILrBkopymoUKGX91y9lNRpmScwyez6GMntXmwYvljr3Q2UCL34QMYhdEHgJotg+DEXgxQfxqCkpQAqQAqSAoQJhDV4wHsuBI+lwBwtG+9GS8VC7RhWnmw3GOymblAivvjSYB+C9t9dzMLxfN5axqB1fdM8nX+QxUyY+04t/j3FU0FVo6ayRgIDjiWFToUqlFHhrzigeHPfq23pzd5/ePe50y2qE7YWFzOYPZvKMSHJBaxTMeoQBfvML8mHWuIHMaiMaItnb9s+/7QOMF9O8SUMYNehBlt3GYRFSOjHeBaDgZ3quRhj/pSWDNnc/Mpq3Xbl4PMvoFMe/JvBieMbDogKBl7DYBkuToBgvluQK28reWLwQeAnNdpKrUWh093ZUcjXyVjlqRwqQAqQAKXA+KhDW4AWtPRBqiILWKGiVgmXPvoPw4IAJcOasI7hs62su48BDWKxc3+lJHoj2zdkj+XXMJoRxYdLSj/PvL2SBc+dPecrpqrTms20wfEJxfJiBj94DTzx4B6+bnXOOZznqdndbGNanq9s5EdYz8oVhfbvCQ11ugRVrNvNMRWpBkKJmWNIDL9PH9oe2rZvAscyT0PGhkSygcAosnzeWuysReLHHbUvgxR77JM+SwIv99kxrxt6Al4RDW6HczsVk8RLkI0DgJciC+zgcgRcfBaTmpAApQAqQAueVAmENXszsBLrglCmdAEnsPzPlSFoGs0aJ4hmF1IKQ58Dho1CtcooT4JjpM5R1wgm8RMYVQfPnfYvNQlmNQnmavBubXI280y2YrcjVyJqrUbCD5Vo9C+RqZFWx0NYnV6PQ6m92dHI1MqsU1SMFSAFSgBTwRgHbgxdvFl2S2oQTeClTpwgu603gRet8kcWL/e46snix355pzdgbi5eo7GOQcOBrKEisCFnVW4adEARewm5LPE6IwIs99ovAiz32iWZJCpACpIBdFSDwYted+2/eBF7ssYEEXuyxT/IsCbzYb8/8BV7CfeUEXsJ9h1znR+DFHvtF4MUe+0SzJAVIAVLArgoQeLHrzhF4sdXOEXix1XbxyRJ4sd+eEXix955RjBd77R/FeLHXftFsSQFSgBQgBUKrAIGX0Orv8+hk8eKzhEHpgMBLUGT26yAEXvwqZ8g6y9v2OqQ3uC9k4wdiYLJ4CYSqgeuTLF4Cp60/eyaLF3+qSX2RAqQAKUAKqAoQeLH5mSDwIjYQ03QXhe1uEngJ263RnRiBF/vtmdaM875fAem1O5SMxfy3CgIv9tpOAi/22C8CL/bYJ5olKUAKkAJ2VYDAi1137r95hwN48aeElNXIn2oGpy/KahQcnX0Z5XzOapSVkw8nzub5Il/YtSXwEnZb4nFCBF7ssV8EXuyxTzRLUoAUIAXsqgCBF7vuHIEXW+0cWbzYarv4ZMnixX57pjVjAi/22UeK8WKfvcKZUowXe+0XzZYUIAVIAVIgtAoQeAmt/j6PThYvPksYlA4IvARFZr8OQuDFr3KGrDMCLyGT3vLABF4sSxbSBgReQio/DU4KkAKkAClgMwUIvNhsw9TpEnixxwYSeLHHPsmzJPBivz3TmjGBF/vsI4EX++wVzpTAi732i2ZLCpACJVyBQra+yBK+Rpsvj8CLzTeQwIs9NpDAiz32icCL/fbJaMYEXowUCp/rBF7CZy/MzITAixmVqA4pQAqQAqQAKeBQgMCLzU8CgRd7bCCBF3vsE4EX++2T0YwJvBgpFD7XCbyEz16YmQmBFzMqUR1SgBQgBUgBUoDAS4k4AwRe7LGNBF7ssU8EXuy3T0YzJvBipFD4XPcKvKBZNZpXh3GhrEZhvDnS1CirkT32iWZJCsgKFLFvIkgSUsAmCpDFi002Sm+aBF7ssYEEXuyxTwRe7LdPRjMm8GKkUPhc9wq8WJl+iCANgRcrmxS6ugReQqc9jUwKkAKkwPmgAIEXG+/yubxCKCxC1luyCpovl8BlQSRbWEncr5K6rpJ0DiMZ+SsVff5FXCtkj8f8/JL3nCxJZ1P+6cX/allCn/8ldc9K0vM/hj0jo/CvJFRIAVKAFCAFSIEAKEDgJQCiUpekAClACpACpAApQAqQAqQAKUAKkAKkAClACvC/LRWxQlKQAqQAKUAKkAKkAClACpACpAApQAqQAqQAKUAK+F8BAi/+1zQoPZ48dRby8vOhQnLZoIwXqkHstM5C5teAHDMqyt2lI/dcHhzLPAnVKqdABNqcKyWc14lryi8ohJjoKMvHwGjdljv0UwPcq/SMExATEw3J5cpo9pqanglJpRMgIT7O7Tq2P5x2DCpXTPZKFz8tg7rxoMD5skd2W2defoHuPePpORju68wvKGDupJGAbn1Wi6dnjdW+/Fk/KzuH/9yqUbWS5rqMnu/h/HPNnzpRX6QAKUAKkAKkgBkFCLyYUSmM6pw5mw2PDZ0CP/+2j8+qepUKsHT2SKjCXgDtVPCl94Z7BrlNec7EJ+HGlo3BbutEODFg1Cy+HlyDKPj5i7Pfhrc//Jx/VKpUDLw2aQhc0/gS/r0d1vnOyo0wff5y+Hb9PJf9an3XAMg8cdrls253t4VRTz7IAZSndYfyrG766gcYNGYOFDCYhKVOrarw/NCHoenlDfj3f+4/BD2enAj40oDl+hZXwMwXBnBIg2X9ph0wfMI8Z/vBj3eGx7rdHsol0diKAiVljz7+9GsY+eICt/397pP5EB9XynZn8a9/DkPHniNh/duToFb1ys51GT0Hw30/EVDcfP8w6PvQXXD/XW2d6zL6OWf0rAnljd21zwvO3zPw59btbZvD+OGP8ikZPd+N9jOU66KxSQFSgBQgBUiBUClA4CVUyns57tTX3oMVqzfDykXjIDEhHrr2eZ6/OM59cbCXPYam2dFjJ+DGewfBtLH9oO4FVZ2TQJCEFgZ2WucHa7fA+Jlvwjlm1YLQSAYv27//FR59ajLMnzIUrrriInhh+lL4dPM3sGPta/wviOG8zr/+PgTd+o3ncCghPlYTvHRo3xI63dbauX/ly5bhVlhG6w7NqXOM+sXWH+Fwagbc1vYayMrOhSFjXuFBj1fMH8uvd3r0WX5vzZs8BA4cTofOj4+BEf27A0IlfMFq3qEvBy19et4Faz/fBqNeWghrlr7I70MqoVegJO3RR598Bc9NWQwfLHzBRdh6tatDdk6urc7iTV2HwqHUY3wdKnjx9BwM9/1EMIaADMvoQQ+6gBejn3OenjWhvpPGsZ9Vd7Nne91a1fgz8+lxr8HrM0bAVVdebPh8D+efa6HWlcYnBUgBHxTAv5edf3kCfBCMmoabAgRewm1HDObTpvNguLXNNTCsT1deE1/68RfzX75YounCEq7LE7+Qrlw8HhrUreE2TTut82xWDhw/eRrGTF0C8bGxLuBl9KRF8Mvv++GjJeP5Go+kZUC7+56Ct+aMgsaN6kM4rxNdAtKYu83qz7bC4nfXaYKXRxmAeKjLLW77Z7TucDqXaI00cdZbsGvjIjh1OgvQkmfh1GHQotmlfJpDxs7lbkXLXn0O1m3cAcPGvQo/bFgAseyvwFhaduwHD3Rqz//aTSX0CpSkPULw8vy0N+BHdt7UYrd1HmbQ5eCRY/Dw4JfcwIun5+D6Td+E9T2Hrjg5uefg7kdGw5AnumiCF62fc2gt6OlZE/o7yXUGV936BHTucAM83e9+MHq+h/PPtXDTleZDCpACpAApcP4oQODFZnt9edtHYOxTDzErg+v4zH/4eS88OGACfPXxbEBrA7sUAV4uu6Qun3fDBhfAA/e0d67Bjusc9Nwclrq2wAW84EtG+bJJzLKnr3NrLr3hIZjybB9ucWGHda5Ysxkmv/KuJniJj4uFC9lf32tUrQBd72zDv8ZitO5wOqe9hk6Fv/45BJtWTIff9v4D9/YaA58vnwZVKznc9+YsXgkfrt/Cry98Zy0sXrYOtq56xbkENMlHCwRhhh9Oazsf51KS9gjBC1pUXXv1ZRAbGwOtmjWCTrdfz2Ok2HGdaPGCli+qxYun5yD+ccEO99w1t/eBQb3u1QQvWj/njJ414XTv7t1/EO56eLTz55bR890OP9fCSV+aCylACpACpMD5oQCBFxvtM/pVN7rxYecvPzh18cvb+rcnM5/5SrZZzcnTZ+H5l9/gL7f49dqN23kg0w3LpkIpFkvDjuvUAi9oSt6wQW2Xl3L8pXTUwAegS8cbbbFOPfAyfsabzF0KbT6L4PMvv+dBGD9cOA7q1anO3XX01n0fAzThUjB+zQTmJjZr3EBo27qJ04ReBpn4gjvvzVUcPKEJ/Tp2VhHCiIIvIaUTE2D2+IHhsqzzeh4laY++2/UHfLhuC5RnAaAPHD4KG7/8AW6+4WoOcu24Ti3wYvRzbfnqL2xxz2mBF08/535kfzRBN1S9Z0243MSnzmTBHT2eYe6XcbD6jRd58PiS8HMtXPSleZACpAApQAqcPwoQeLHZXuNLOwYCvftWR1wNu1q8qLLv2XeQm2ovmT4Crm58MbcEsds69Sxeksslwctj9C1ewn2deuBF3kOMb9Pqzv7Qnbnc4F99EUZ4Wnc43HafbfkOcM9wvr26d+BTEiBz44ppzoDVZPESDrtlfg52tAQxu7rXl38CU+Yug52fL4I32Nd2sASR1+bJ4kXvOWhnixdPP+fKlI7n1nV6zxqzZyKQ9TC+zgP9J/AMcOguJbIoGj3f7fjzO5A6Ut+kAClACpACpAAqQODFZucAfadvY9kFhva+j8/8/TX/47FF7BbjRZUd/6rWggUtfWXiILih5ZU89ond1qkFXtAX/tc9f8OHLBgyFoxz0J6Z2ssxXsJ9nWbAC66tXZch0ObaJjCSWfMYrTvUt52IjTScxSvo0flm53RE3IVFLz8NzZs25J8PZhmQjhzNdInxgjE3MNMHFvxLd0/WB8V4CfWuOsYXsU9K4h59uvlbFnPoFW59tXnrTh77xE7r1AMvnp73IsZLuK9Ty+LF08+5yxteyGO86D1rQn03nTh5Bh5gbsw5LIjzstfGOKELzsvo+W7Hn9+h1pvGJwVIAVKAFCj5ChB4sdkeT3l1GYctH7G/PiUw09+uve2Z1QjTg2JGmRtbNeauRWNffh02/O9b+N+HM3mcFzutE9MSFxQU8CCs+QX53G0lOjqaZy3a9t1unv4bsxpdzbJBICRDSwuR1Sic14kuAHl5+bCCnbcZC1bA1x/PgUhmZh4dFQWY8WjN59vhntuvg8oVysP7a/8H6Hr02qSnoPU1lxmuO5S33dsffsaC6b4NfXrcCR3at3BOBf+aWzoxnscyKJuUCK++NJgHA72313MwvF83Zs3TDjCQ8tW39YbePTqy/+6krEah3EidsUvSHr269GNodFFdluq8Pk/d/viwqfzZsur1CbY7i/gsOXAknbutYLDx2jWqOFO0e3oOhvt+5rNnfyH7GdD67oHQ/+G74T7mQiqgrNHPOU/PmlDeWqj5Ld2GQUFhIcxjz/QyzA0YC7qWokuznX+uhVJXGpsUIAVIAVLg/FaAwIvN9h8tQx4dMplbUWDBGClvzh4FVSun2GolIk0qQgss+Ivqy8/14RYTWOy0zgVvr2Fg4n0X/Yf17cqz/SC8wBTSy1d9wa+jfzz+Iisy5oTzOvGMdX58rMu6WrLgngumDuXgBYPKIjwTBWHEgEc68W+N1h3Kw4qWSQi/1ILZOtByBd3eMGA1ptHGgiAJYZp4mVrz2TYYPmGes/nAR++BJx68I5RLorEVBUrKHqFlwcr1XzpXV71KBZbm/Cln6nI7rRPdT8TzXjzzRbYmo+dgOK+z55MvAsbikYvIYmT0c87oWROqG1tYJqnj4zMQ98zo+W60n6FaF41LCpACpAApQAqEUgECL6FU34ex8a+f5/LynHEofOgqZE0xXfHRY8f5+NUYOIqIiHCbS0lYJy4KAcWxzBMs+08lbgmjFjuuE3/5Ts84CWeysqFmtUo804pajNYdssNnYmB8+cC/9GLQZ7XgCyQGO8VzK4CMiS6pShAVKCl7hPdQKkvrjudQxNiQZSwp68Q1eXoO2nWdZn7OeXrWBPGWsTyU0fPdjj/XLItADUgBUoAUIAVIAZMKEHgxKRRVIwVIAVKAFCAFSAFSgBQgBUgBUoAUIAVIAVLAqgIEXqwqRvVJAVKAFCAFSAFSgBQgBUgBUoAUIAVIAVKAFDCpAIEXk0JRNVKAFCAFSAFSgBQgBUgBUoAUCAcFMEZgZDhMhOZACpACpIApBQi8mJKJKpECpAApQAqQAqQAKUAKkAKkAClACpACpAApYF0BAi/WNaMWpAApQAqQAqQAKUAKkAKkAClACpACpAApQAqYUoDAiymZqBIpQAqQAqQAKUAKkAKkAClACpACpAApQAqQAtYVIPBiXTNqQQqQAqQAKUAKkAKkAClACpACpAApQAqQAqSAKQUIvJiSiSqRAqQAKUAKkAKkAClQghWgWKUleHNpaaQAKUAKkAKhVoDAS6h3gMYnBUgBUoAUIAVIAVKAFCAFSAFSgBQgBUiBEqsAgZcSu7W0MFKAFCAFSAFSgBQgBUgBUoAUIAVIAVKAFAi1AgReQr0DND4pQAqQAqQAKUAKkAKkAClACpACpAApQAqUWAUIvJTYraWFkQKkAClACpACpAApQAqQAqQAKUAKkAKkQKgVIPAS6h2g8UkBUoAUIAVIAVKAFCAFSAFSgBQgBUgBUqDEKkDgpcRuLS2MFCAFSAFSgBQgBUgBUoAUIAVIAVKAFCAFQq0AgZdQ7wCNTwqQAqQAKUAKkAKkAClACpACpAApQAqQAiVWAQIvJXZraWGkAClACpACpAApQAqQAqQAKUAKkAKkACkQagUIvIR6B2j8sFbg5KmzsGX7Ls05XnpxHahbq2rI5r/tu92w798j0L1Tu6DMISs7F77d+Tv88de/kJN7DmpVrww3tmwMZZMSTY3/zY+/w+hJC+GNWSOhaqVkWPD2Gvji6x/hnbnPmmpPlUgBUiA8Ffjrn/+zdx5wTlTbH/9tr3Rhlw4qKqAg6p/ieyiCFRGRJqJSBJUFEWmiovQmVQFBlI4FAUWQJgoqKqA+RVBAQRDpdenbsuV/z8QJs8kkmSQzyUz23Pfx89jNrb9zc3fyzTnnHsWuPw+oTu6ORnVRopi2M0Lv1V3OyMInazah3k01cOP11fXuXrW/U2fO4X/b/8Se/YeREB+La6tVROOGdRETHaVp/OdeeVOcreXwYq/HpPrOP2vqhCuxAqwAK8AKsAKsgOkUYPBiOpPwhMykwK87/8LjvUapTqlP9zZ45omHDJ9uZlYObrv/Gbzy/OMCstzjGK/3q1Ox8btfsPPr+YbP4fuffke/YW/h0uVMJCbEITcvHzk5NmncQeIDQqd293mdwxeb/ocXhkzHqoVjUV0Aq+GT5mPVl1vw09pZXttyBVaAFTCvAjMWrMBb85arTvCjWUODAj3GTH0PS1d9g23r33XM4+CRk3jg8Rfx7JMP4flubQwXcN7itZj49kfSOASk6bzME2dlbGwM5k0ZhJtrX+t1Dvd2GIBrq1fEjLF9pbrOP3vtgCuwAqwAK8AKsAKsgCkVYPBiSrPwpMyigAxeJg5Jw92Nby00raioKERGRhg+VfI0+b8HnsVLz3XEk23vdYxH3+ZmC/hRumQxQ+dw7MQZ3P1of6SULYW3X++P666uJI135PhpjJyyQPy+NIYP6Op1DgxevErEFVgBSyogg5fvVkxDcmJCoTXExEQHZU2j3liEj4V3ixK85OcXIP3cBSSJOZH3iZFl3Vc/ov/wGWh4Sy1MGtoTJUsko6CgANt+/wsvjZ6F/j0exX1N/s/rFBi8eJWIK7ACrAArwAqwApZUgMGLJc3Gkw6WAjJ4eWPEc7jnjttchj2dfh5P9h6NIX07o9FttaXXc/Py8FCnl9Gr6yNocXcjbP1ll+Td0bVDcyxc+jkOHjmB/6t7g3i9FW656TpHn/sOHMGIKQuxa88ByaOk9nXVMLBnB4x58z38/uffSE5KcECWj2ePwIefbsTXm3/FommvSH2QB8rYae9jzcYfpG9arxEu7sP6d3aMoXUezot8/rWp2PDtL1g5f7TUp3MhAJSUGI833l2GJZ99JY1N5eoqFfBS747SBxEqDF6CtWt5HFYguArI4OXHNW9LZ4FzobPhz32HMHOc3YuDyqJl67Hqiy0gjxgqPV+egnJlSknn5+df/yigdiQea9UMaZ0fRpzwGKFC3iMUorhs1dc4KUJ6ripdAvfe+X+SJ8nLY9+VzkAK06HSuvkdkodgm+6vod+z7R3n9/FT6Rg8djZ++X2v6C8P/3fzDRjz0tMSWKZC/W/augNNbr9ZOq/Pnr+IB5s1Eh4zrVE+pYxbYRs8mIaoqEh88/GbcIZNNG+C5OQt2LnPWPzx10HpnCRPmNvqXI9hA7qgYupVUt8MXoK7d3k0VsCyCuSLmUdadvY8cVagSCrA4KVImp0XrVUBGbw8/XgL6QFdLuRlUrNGVcnrgx6UJ7yWhubNGkgv23LzcPPd3TBAfMPZtcMDkL8JpdcIxFCYzbyP1qKYAClfLpkstZH7oQfxR1vehTKliuOjFRtFnw2Fy3oyJs9aIgGM+vVqSvWfEv2Sa70yVEcOPaK8K3VqXY35S9aBctR8JkJ7KBeNlnmo6dK4VW+kliuDpe8M8yjb8MkLUCC+Ya51fTXkCg3mfrha+nD07afTpBwPDF607jquxwpYSwEZvEwd+TziFZ4l1SunooIAChRiuGP3PmxcOsWxsHHTP8AHy7/Ejg1zpd81bdcXJ06dlcAJnXt/HzwuAZjJw3o5PEVefX0Olq/9Vgpdannf7fj9jwNYuf57EIh+RYAXgjtySFHdWtfgpppXo37zHiJM8wkpF5bNlos72/SRoEeHh5tK4y4W5yxBbRmYyGMQJGnz4J0STJn/0Tq0bXGnW88+yuvSpM0LoL8TLzzd1qPxOvYcidvqXi/9HSBwT9pVFfmyPp1nD2ll8GKtvc+zZQXCSwGmOeFlT16N2RRg8GI2i/B8TKWAuxwv9OBP39T6Al4oiSx9GKCy4vPvpQ8KX340SfoWddCoWRJEWfv+eMc3tuQmTw/0xZITVUONlDlSjgoAdI8AQI880BijBnWTxqCH+jtb98H9d9WXXN9l8OJpHs7i07fPdZt1k4DR668+q8k21IbG/vaHHRg2cb7IbfCSAEY3MHjRpB5XYgWsp4C7HC89OrVE76daawYvFVKukjz4IiLsIZwEff/v5poCvvQEeao0a9cPzRrfAgI8cqFQSDpD1UKNyBtPCV4o0e5r4+cWAuWrN2zFiyPfls5NOj8JvNDvvhdhU4kJdu8dOp+//XEHNq98S9U45E3Yrd94jHvlGTx07+2aDEi5u86cPS9y43wqwSMCUAR5GLxoko8rsQKsACvACrACllOAwYvlTMYTDqYCMngZ2r8L7hQ3U8iFXN8pht8X8KKEKnTrBbmcL3jzZenbz+ZPDAJ9SPjmkzddlucux4sSvFDIUa9X3pA+oNzXpL6jD/oWOT4uFmvee90BXjzNQ03bOs2ewn/r3+RI9uhO/+279mHohHnY+/fhQlUoSeSd4mYT9ngJ5s7lsViB4Ckgg5eVC8YUyvFSLDlBghdaPV5uFDfFKaFKu2eGSYsgbzv5jHt98LNocU8jl8VpAS/kJfj+J18KqDJdOr+pUCjRfx/uLXnEkGcMgRc6q35YPdMxxpuzP8Y7733mNpH5b7v3o0PaCAzu8yQ6PtLMo/DLRALgaXM/keC0slBuGvJ4ZPASvH3LI7ECrAArwAqwAsFUgMFLMNXmsSyngLccLzJ4IW8Q8gqh4i7USAk8ton8Ak88N9oBXu5u30+46MdJN/44Fy3g5fOvf5JuHXpnwgD85/9udHTRQuSaobwH6xdPVAUvzvNQMxDdCkJzUINCcv30cxelb6cpT8GgXh1xQ40q0geaR58dLgEbBi+W2/o8YVZAswLecrwQeKGzRnmGqIUaOYMXghmUH4XAC4Ud9Rs2Q8oTc4cCgsuT1AJehk6cJ/LDfIP/rXvHkWxXPl/bixDPof06q4KXGfM/xVviP3c3yGVkZgmvxB5oee9/MPaVp93qJsPnxg1uEuGiD4pwo1Th7bJZCiVl8KJ5u3FFVoAVYAVYAVbAkgoweLGk2XjSwVLAG3g5f/Eybn+oV6Ebhwh01Lv3aZccL57Ay7MvTsJ3P/5W6JtYWiN96MgXN2NQzhjKHUA5BOSi9HghL5NWXV+VkknStdNU5A8D5K0ya3x/v8HLhJmLpRwHsiu+UnvKIUN5FQiyEPiZM+lFNLzVnkz3r7+P4OGugxm8BGuz8jisQIgU8AZeKP/Tp+u+K3TjEHmfUH4VZY4XT+BFPk+cc63QGUkhOnSNMyXDlfsjKZxDjRaI18e/9WEhePPNlu1SYt9BvR5Dp3b3+QVeaCw6f+kcXvv+6yJcNKWQJWjuUq4YkXeLwM+vX8x2JOClOb8u5sTgJUSbl4dlBVgBVoAVYI9ovY8AACAASURBVAWCpACDlyAJzcNYUwFv4EV+4L5w6TJGD+ouJZN9a95yKQTJObmuJ/Cy9WeRI6D/eOnWoJ7iFg/KWbD0s69R7qqSUrLINt2HSCBlrMghILnG168jbjtaVCi5Lj34/yNuTKL2lFRypkjaSCFNMgyRc7x4moealSgXQbP2faVEveRGf7e43YmS51JoEd0A0lLkNHhO3OBEySUJ8nR77EEcPXEaU+d8LCXLZI8Xa+59njUroFUBb+Bly/92ovuACUjr9LAUWrnx+1+kkB+CEVrBC82FzkGCG5QYl/KxEPSd8s5SyZPmx21/oGvfcRIEp1uOIiIjUL1y+UI5Xs6dv4Q7Wj8v3YZE1ztHiP9NfHuxFPaz6ZOpUviRWqiRN48Xmtue/YfxyFOvSmvq90x71K19jdTv9z/+jqXiFiZKEkyQaODImRJAb9ygDn75bY+UXJdgPYMXrbuN67ECrAArwAqwAtZUgMGLNe3Gsw6SAjJ4obwDlNRRrWz87heQCzuF21ChDwR088bAtA7o8uj9Dhf5zz+cgErly0p15H4XTn0Ft9axXym9RIAWug6aHsKp0K0aY15+WroGla5zHjbpyhiUf2CScE9f9cVm/LR2llSfkkw+O2gy6FpqubwovsXtLL7FpSK76nubh9oa6QMLXXVNfSgLAR76prjejTWk66TnLl4jfbigQu703/7wm+PbZVoDXU29etE4VBO3ndC34Mr5B8mkPAwrEAQFCsQY9gSxRaHI4OWntW87EtIq100Jt3sPniquad4u/ZrAR2rZ0tj91z8O8ELhlnQjmjLHC90AROeJfOU0gdwBI2ZKwEIulLCcEoZTvR7i/Nv8v9+llyh0aGDao1IIkDL3yg/bduM5kQ+LQoyo0Dn71pi+UgJwKpR8d/03PxXK8SKvz12okTwXgi8vj3lHui5aLgRi7m58m3ROlildHM8OnARKxkuFcrpcf01lUI4YBi9F4Z3Ca2QFWAFWgBUoygoweCnK1ue166rAvn+Ogm7lSFBcp+rPAHR7B5WUq0o5bveQ+6Hbi+hbWfm2DbX+6VtWAiVVBdyIiY7yZwpu2xSIsKfjJ9ORmZ0j5XOhJMPKQt4xB4XXTeUKZT3OUddJcWesACtgCQXoXLosPPfo7Aik0Dlz/OQZpAh4Q+BEWeiq6EsZmarnp1yPzrFDR09JP9JZJd+iFMiclG0pz9fhoyelcCL6mxApvG+U5czZC+KMvigAdHnJQ4YLK8AKsAKsACvACoS/Agxewt/GvEJWgBVgBVgBViD4ChQtx5/g68sjsgKsACvACrACrIBlFGDwYhlT8URZAVaAFWAFWAFWgBVgBVgBVoAVYAVYAVbAagoweLGaxXi+rAArwAqwAqwAK8AKsAKsACvACrACrAArYBkFGLxYxlQ8UVaAFWAFWAFWgBVgBVgBVoAVYAVYAVaAFVAqYIXoZgYvvGdZAVaAFWAFWAFWgBVgBVgBVoAVYAVYAVaAFTBIAQYvBgnL3bICrAArwAqwAqwAK8AKsAKsACvACrACrAArwOCF9wArwAqwAqwAK8AKsAKsACvACrACrAArwAqwAgYpwODFIGG5W1aAFWAFWAFWgBVgBVgBVoAVYAVYAVaAFWAFGLzwHmAFWAFWgBVgBVgBVoAVYAVYAVaAFWAFWAFWwCAFGLwYJCx3ywqwAqwAK8AKsAKsACvACrACrAArwAqwAqwAgxfeA6wAK8AKsAKsACvACrACrAArwAqwAqwAK8AKGKQAgxeDhOVuWQFWgBVgBVgBVoAVYAVYAVaAFdCiQL6oFKmlItdhBVgBSyrA4MWSZuNJswKsACvACrACrAArwAqwAqwAK8AKgKkVbwILKMDgxQJG4imyAqwAK8AKsAKsACvACrACrAArwAqwAmGlQIFYTURYrcjtYhi8FA078ypZAVaAFWAFWAFWgBVgBVgBVoAVYAVYAVYgBAoweAmB6DwkK8AKsAKsACvACrACrAArwAqwAqwAK8AKFA0FGLwUDTvzKlkBVoAVYAVYAVaAFWAFWAFWgBVgBVgBViAECjB4CYHoeg2ZY8tHfgEFxoVXiRBxfmG4LESKhYWjvcJ1XeG0DyMjIxAbXfTuSsgXx2Nubvidk+G0N5V/vWhdVMLz/Ic4/8PrbzWths6W/DBZWIw4I6PEeopayc0rQF5ePsJte1ry2YT+TFOSWA/FkuvS8Kbiv2saRDJBlfjYKBPMwrpTYPBiXdtJMz96JtPiKyg8/biYSCQlxCD9QnZYrYue5cqVSsDxdJ3tpeGPtNFCppSKx+nz2cgLk4dvWa/SxWKRkZ2HrJw8oyU0vP+YqAiULRlv+DhmHCAjKxfnLtvMODW/5yTtTbGuLAHfw6kkJ0QjQjx9X8wIL3vR37Vk8XftTJj9XaMPSqni79oxvf+uhWhTl0yKQWJ8tK6jE5Q6euI0UsqWRky09w8s3uqfv3AZttxcXFW6hOo8qX2BIJdRUb6B9lPnsmATACZcimHPXCYQiJ65TolnrnABnuH4zKXcJvR3jWDZhTD5u1ahTIIJ3gXWnQKDF+vajsGLhWwX7g8BDF7MvRkZvITXB3kGL+Z+vznPjsGLNeylN3hZu/EHDBo9S/ImodL3mXbo3vFBt2J4qn/pcia6D5iA33bvl9pXTL0KC6e9glQBdORCwKX34KnSj9PH9HH8/tSZc2jS5gWXcanOXbfXk37P4MUae5RmyeDFOraimTJ4sZa9jJ4tgxejFTa4f/Z4MVhgnbpn8KKTkEHshj1egii2gUOxx4uB4urcNXu86Cyowd2xx4t7gTMys9CwRU8JtKR1boXVX27B4HGzsWrhWFSvUt6lobf6E9/+CEs/+xrL54xEUmICOqQNl/qZMbav1NfHqzdh1JuLkJNjk2CKErycPH0Od7V9AZOH9cLVVa+MTfAmMcHuCcngxeA3i47dM3jRUcwgdMXgJQgiW2gIBi8WMpbaVBm8WMOADF6sYSflLBm8WM9majNm8GIdOzJ4sY6taKYMXtzba82GHzBw5Ez8sv5dxMXGSBVvb9kLT7S+Bz27tHJp6K1+03Z98UDTBhiY1sEBWoZMmIvfv5onheddzsjC2fMXMXTiPCTExamCl+VzR+G6qyupTprBi3XeewxerGMrmimDF2vZy+jZMngxWmGD+2fwYrDAOnXP4EUnIYPYDYOXIIpt4FAMXgwUV+euGbzoLKjB3TF4cS/w7A9WY+7iNdi88i1HpQ5pI3BttYoYNaibS0Nv9es0ewrD+ndB6+Z3SG1/+W0vnuw9Gt+tmIZSJYo5+nthyHSRUDxPFbzcVPNqqW6t66riiTb3FGpH+YcoyW64FHrmKlNC5EIRuWvCrVxVIg7pF3PCLsdLyeQYZObkIzsM8uop91ySyBtFOV4uZoZHyDOBPy7+K8DgxX/tTNGSwYspzOB1EgxevEpkugoMXkxnEr8mxODFL9lC0ojBS0hk93tQBi/upaPQoDUbtmLj0imOSl37jkNyUiKmjXrepaGn+lNH9saNd3XFhNfS0LxZA6nt7r3/oO3TQ7H2/fGoUrGcR/By/uJlDJ+0AOXLlQb9e7WYV/HkRKxfPNHhjRNuyfFJELqhitfl99s76A0JTlCeovDBf3YJw+22vqJ485uebwYGL3qqGYK+GLyEQHQ/hmTw4odoIW7C4CXEBtBpeAYvOgkZhG4YvARBZB2HYPDiXkxvHizOLb3VJ4+X4QO64pEHGktNffF4cR5rz/7DeOSpVzFvykuoX+8G6WUONdLxjWFwVxxqZLDAOnfPoUY6C2rm7iiPupcL5Ri8mNmAGubG4EWDSCaowuDFBEbwcQoMXnwUzKTVGbyY1DAq02LwYh1b0UwZvLi3l5yzZZvI8RL7b46XBg+moXO7+zzmeHFXn3K8NG/WEAN6PCoNumzVN1I+FznHizwTtVAj51leuJSBRiLx71tjXkCT229m8GKttx3famQxezF4sZjBDJ4ugxeDBTa6e0/g5cCBSMxdGImqVQrQrUue0VPRpX+6djMpIQbpIt44nAqDF+tZk8GL9WymNuNwAS9Re7YjbsoA5NWog6Sh00DryrLZr6kNl8LgxVqWZPDi3l6U7LZ+8x7o0aml+O9hl1uNNm3djmGT5mPW+P6oUb2SlBzXU/0JMxdLsOVTkSA3MTEeHXoUvtWIrqzOy8tDv2EzRK6WXEwd+Tyio0VuCfHwQddUZ2Rm467/1ENsTLQ07vpvfsI3n7zpyPPCHi/mfO+1ObYOW7OPY1nqA2gUnyJNkj1ezGkrd7Ni8GItexk9WwYvRitscP8MXgwWWKfuGbzoJGQQu2HwEkSxDRwqXMBLzMZPELN0JoMXA/eKUV3TFwrJ4gsFSmAaToXBi2drrvpiCwaNnuWo9Hy3Nnj2yYekn1eJ66UHjZqFxTOHgJLeSr/zUJ+8VLr1G49dew5IdSlfy6Jpg1E+pYz087vvr8Ib7y4rNKGBPTugS/v78em670A3IBGcoUIeOJOGpKHpf29x1GfwYs53JoMXc9rFl1kxePFFrfCvy+DF4jZm8GINAzJ4sYadlLNk8GI9m6nNOGzAy2cLELPmPQYvFtyWDF6sYbSSSTFIFDeQ6FkIdhw6ehIVBCCRQ4489e+tfvq5i8ix2ZBatrRP07SJm45Onj4rtaG50BXUysLgxSc5g1aZwUvQpDZsIAYvhklryY6tD14o/XXhvx+WNIS/k2bw4q9ywW3H4CW4eusxGoMXPVQMfR8MXkJvA60z4FAjrUqZox57vJjDDnrMgsGLHirq34cMXuaUa4r7E6tIA3Cokf46G9kjgxcj1bVe39YHLwrNiyKDYfBijTcdgxdr2Ek5SwYv1rOZ2oz1BC8xS2Yg8vB+2NqnIb/SNUEVKIY9XoKqt56DsceLnmoa15cRHi/GzVbfnhm86KunXr3J4KVfibroX6oegxe9hA1iPwxegii2BYYKK/BiAb11nyKDF90lNaRDBi+GyGpopwxeDJU3aJ3rCV7iJvdH1N4dyO47EXnX1Q3aGmggBi9BlVvXwRi86CqnYZ0xeKGvL8OjhMszF4MX6+9HBi/Wt6GeK2DwoqeaIeiLwUsIRPdjyHB5CFBbOrm9nj6fjbz88Hloo3UyePFjo5uwCYMXExrFzZQ41Mg6tqKZcqiRtezlabbs8WJOWzJ4MaddfJkVgxdf1Ar/ugxeLG5jBi/WMCCDF2vYSTlLBi/Ws5najBm8WMeODF6sYysGL9aylbfZMnjxplBoXmfwEhrd9RyVwYuealq/LwYvFrchgxdrGJDBizXsxODFenbyNmMGL94UMs/rDF7MYwstM2GPFy0qWaMOgxdz2imY4GXS2W2YfH47lPlkgqlKOH3ZpdSNwUswd5H5x2LwYn4beZwhgxdrGJDBizXsxODFenbyNmMGL94UMs/rDF7MYwstM2HwokUla9Rh8GJOOwUTvKiNFUxVGLwEU23/x6pQJsH/xtwSDF4svgkYvFjDgAxerGEnBi/Ws5O3GTN48aaQeV5n8GIeW2iZCYMXLSpZow6DF3PaicGLOe3iy6zY48UXtcK/LoMXi9uYwYs1DMjgxRp2YvBiPTt5mzGDF28Kmed1Bi/msYWWmTB40aKSNeoweDGnnRi8mNMuvsyKwYsvaoV/XQYvFrcxgxdrGJDBizXsxODFenbyNmMGL94UMs/rDF7MYwstM2HwokUla9Rh8GJOOzF4MaddfJkVgxdf1Ar/ugxeLG5jLeClZMkC9Hs+zxIrjYuJRFJCDNIvZFtivlonyeBFq1LmqRdO8cYxUREoWzLePOIGcSZ6gpeEV59AxJkTyO47EXnX1TVsFZGH9yEi8xLyK16DgsRkaZyYzxYgZs17yKtRB0lDp4HWlWXLN2wOoeiYwUsoVPd/TAYv/mtntpYMXsxmEft87j2yAjttZ9Eu+Rq8cVVj6XcppeJx6nw28vMLdJ0053jRVU5HZwxejNHVqr0yeLGq5f6dtxbwQlVHDMm1xEoZvFjCTIUmSQ8Bp8VDQJ7ODwGhViI8wQt9UI8MtbRBHV9P8JKYdo80d6PBS9zk/ojau6PQOAxegrptdB2M/q4liy8UzoTZFwoMXnTdJiHtjMFLSOV3O3jFA/Ol1xrGpeLj8vczeDGnmTzOisGLBY1m4JQZvBgobjC6ZvDiRmX6bGmiL4PZ4yUY7wZ9xwhP8KKvRlbojcGLFaxknyN7vFjHVjRTBi/Wspen2VoFvOy0pWPomR9RK6YURpRp4HZJ4fLMxeDF+u8xBi/Wt6GeK2DwoqeaIejLE3jZ/UckPlxi/3abPV5CYBzFkOHyEKCmInu8hHZvaRmdQ41sWmTyWkf2eLG1S4OtaWuv9f2twB4v+tjLX/31bsceL3orakx/JZNikBgfbUznJu/VKuBlS9YJtD2+tpAHiJq04fLMxeDF5G8cDdNj8KJBpCJUhcGLxY3tCbxs/DoCX2+KYvBiAhuHy0MAgxcTbCY/psDgRZ8P8g7w0vwJ2B7q7IcltDVh8KKPvbSpbXwtBi/Ga6zHCAxe9M0ZoodNnPtYl3EQ3U5uZPDCOV6M2F6G9MngxRBZLdspgxfLms4+cQYv1jAggxdr2Ek5Sw41sp7N1GZsRKiRjcGLIZuDQ40MkdWwTjnUyDBpg96xVTxeJp3dhsnntzN4YfAS9PeIvwMyePFXufBsx+DF4nZl8GINAzJ4sYadGLxYz07eZhxu4KUgIQlRVWsgr+LVyGyb5m35lnqdwYulzMU5XqxlLo+zZfBiTmOGItRIeYNSMFUJpy+7lLoxeAnmLjL/WAxezG8jjzNk8GINAzJ4sYadGLxYz07eZhxu4EVeb8F1dZDZd5K35VvqdQYvljIXgxdrmYvBiwXtFQrworxBKZiSMXgJptr+j1WhTIL/jbklGLxYfBMweLGGARm8WMNODF6sZydvM7YieIkf/SwiD+9XvU6awYs3i5vvdc7xYj6bqM2Ic7yYP8cLhxoBdKHBKQNDjRi86HtesceLvnpavTcGLxa3IIMXaxiQwYs17MTgxXp28jZjK4IXOYlvdt+JyLuurrTEmM8WIGbNe47lsseLN8ub53UGL+axhaeZMHhh8GK2ncoeL2aziO/zYfDiu2bh3ILBi8Wty+DFGgZk8GINOzF4sZ6dvM2YwYs3hczzOocamccWWmbCyXW1qGSNOpzjxZx2ksFL5ehkbK3UVpoke7yY01buZsXgxVr2Mnq2DF78VPj4qXQUT05EYkK8ph481c/PL8DRE6eRUrY0YqLt1z9rLQxetCoV2noMXkKrvz+jh1O8MV8nrc/1xMG6Tpo9XvSxlz/veyPasMeLEarq3yd7vLDHi/67KrAeZfBCvRyp1oXBS2ByhqQ1g5eQyG7aQRm8+Giav/4+gk59xuD8hctSyzsb1cWbI3ojJiZatSdv9ddu/AGDRs9CXl6+1L7vM+3QveODLn29Nn4uPlmzCd+vmI6SJZIdrzN48dGAIarO4CVEwgcwLIOXAMQzUdOw9XipfA0yX3nbNErTR7aIAGfDHi8BChjk5uzxEmTBDRyOPV4MFDeArhm8BCCeSZoyeDGJIUwyDQYvPhqidbfXkJSYgFnj++HQ0VNo98xQvPTc4+j4SDPVnjzVz8jMQsMWPSXQkta5FVZ/uQWDx83GqoVjUb1KeUd/C5d+jtff+lD6OezAS6RYlJ05SYW+GUxKiEH6hWwfLWPu6gxezG0ftdkxeLGezdRmHK7ghdaaMfOL8DDSv6tg8GItczJ4sZa9PM3WCPCyNfu4NCQla9WrFNXkuqSfLx4v6zIOYpftLBoJ7RvFp2iSv82xdSCbcXJdTXJprsTgRbNURaIigxcfzJx+7iIat+qN2RMHotFttaWW/YbNkMKEFs8c4tKTt/prNvyAgSNn4pf17yIuNkZqf3vLXnii9T3o2aWV9POmrdvx3OA38VrfThg2cX74gRcn1Ri8+LAhTVKV4o1Piwz7eSJkLpwKg5fwsGbYgJclMxDz1fJCRmHwYo09yqFG1rAThxrp+zdc9taQgYEeu4DBi7YcLzJE6VeiLvqXqqdJegYvmmTyuRKDF58lC+sGDF58MO/uvf+g7dND8eWSyShfrrTUcvrc5fhk7SZsXDrFpSdv9Wd/sBpzF6/B5pVvOdp2SBuBa6tVxKhB3bD378No030IJg3tiYqpVwnvmmEMXnywl5mqsseLmayhbS4MXrTpZPZa4QJe4ib3R9TeHQxezL7hVObH4MUaRmPwwuDFbDvV31Ajf8DLvUdWYKfwkmGPF313AYMXffW0em8MXnyw4Nafd6Fb//H4bsU0lCpRTGpJ8GTWopX4ae0sl5681Z/49kdYs2FrIWjTte84JCclYlj/Lmj+xCB0ffQB9OjUErv2HFAFL9m2PLcrWPV5Ptb+64n+1kSK6TF/iRS+y1GCUtj+zXlj/hlrn2GsSJyck+veXtp7MlfN2OhI2HILQP8LpxITFSl58eQXWH9dlMA7IU49D1U42UxtLQxerqgSN3mA9EN2v4mmNDuHGpnSLG4nxaFGdmkoWtoaT1ju95cRoUbs8RL4+zmY4EXt6urAV6C9h3D6sku5agYv2vdAUajJ4MUHK8seLBuWTkaquIGIihaPF3f1PXm83Fz7WgydOA8t7m6ECAEizqRfwOb//Y577rgNndvfh3o31pDGP3Mhx+0K1m8ENnxlT3f4+khrfHiMiY5AvPiAePGy+W+1IGW1qkoeLyWLxYrcNe7t5cNWNFXVUmJd5y/nIF+Rq8dUE/RzMsUSo5Fty0eO+M/qRTAklEyOtfoy/Jq/lcGLrV0abE1bS+vWw+NFvi3JrCFKDF782uIha8TgJWTS6z4wgxfdJdWlQwYvusgY0k4YvIRUftMNzuDFB5PIOVvmTHoRDW+tJbXsO3Q6jp1M95jjxV19OcfLNpHjJfbfHC8NHkxD53b34a7/1MNn6zc7Znfi9Fms++pHtH+oCdq3vAs1a1SVXuNbjXwwYAirFs1QI2t/DxhO377wddL6gNxgXydta/4EbA91ZvASwrNbj6E51EgPFY3vg0ONtH6VpM0W7PGiTSdPtUIBXmrHlML6ig8HPnkfewinZy7l0hm8+LgRwrw6gxcfDdyq66soUTwJM8f1xeFjp0XOlyEY1KsjHm99t9RT5z5jpXwsY15+WvrZU/3LGVmo37yHFErUo9PDbm81on7chRoxePHRgCGqXjTBS4jE1mnYcHoIYPDC4CUi4xIS+j8ivTvY40WnQ0JjNwxeNAoV4moMXhi8hHgLugwfCvBCk9AzIbJWTcPpmYvBi1arF716DF58tPme/YfxZO/RuHQ5U2rZuMFNmDryeYfHyp2t+6BKxRQsmvaK9Lq3+qu+2IJBo6/kh3m+Wxs8++RDLrNi8OKjoUxWncGLyQyiYTrh9BDA4EVf8JJXpxGyRSJ0o4qaZ02goUZRe7Yjboo9xwuDF6Msp94vg5fg6u3vaAxe9AMvW7JOoO3xtZIp9PwQX5RvNVpfsSVqx5QG3SR5StwkSbnb3BV/kuuqQR5/30v+tAunZy4GL/7sgKLRhsGLn3Y+cvw0iiUnorj4T0vxVD9PJJI9dPQkKqSUcQAcLX1SHfZ40apUaOsxeAmt/v6MHk4PAQxedAYvNeqIBLWT/NlWmtoweNHHXprEDkIlBi9BEFmHIRi8MHjRYRvp2oUShixLfQCN4lMYvOiqsPGdcaiR8RpbaQQGL1aylspcGbxYw4AMXqxhJ+UsGbxYz2ZqMzYiuW4egxdDNgcn1zVEVsM65eS6hkkb9I71Tq7LHi/6mJDBiz46hrIXBi+hVN98YzN4MZ9NfJoRgxef5ApZZQYvIZPe74EZvPgtnakaMnixm4NDjUK3LdnjJXTa+zIye7ywx4sv+8Xoujtt6bj3yErHMFo8Xoac+QG7bGexJeu41K5fibroX6qepqlyqJEmmXyuxODFZ8nCugGDF4ubl8GLNQzI4MUadlLOksFLYDaj+POjJ04jpWxpxERHee3MW/3zFy7DlpuLq0qXUO2L2hcUFCCK7s5WFAYvDF68bj6DKzB4MVhgnbpn8MLgRaetpEs3Sq8h6lALeJFzu8gTYPCiiykC6oTBS0DyhV1jBi8WNymDF2sYkMGLNezE4EUfO63d+IOUNJzyV1Hp+0w7dO/4oNvOPdWnRObdB0zAb7v3S+3p1riFInl5qgA6ciHg0nvwVOnH6WP6FBqHwQuDF312tf+9MHjxX7tgtmTwwuAlmPvN21gMXrwpZI3XGbxYw07BmiWDl2ApbdA4DF4MElbnbhm86CxoELpjjxf/RM7IzELDFj0l0JLWuRVWf7kFg8fNxqqFY1G9SnmXTr3Vn/j2R1j62ddYPmckkhIT0CFtuNTPjLF9pb4+Xr0Jo95chJwcG+66vR6DFzdm41Aj//azHq0YvOihovF9MHhh8GL8LtM+AoMX7VqZuSaDFzNbJ/hzY/ASfM11HZHBi65yGtYZgxfDpDWsYwYv/km7ZsMPGDhyJn5Z/y7iYmOkTm5v2QtPtL4HPbu0cunUW/2m7frigaYNMDCtgwO0DJkwF79/NQ8RIrvn5YwsnD1/EUMnzkNCXByDFwYv/m1cA1sxeDFQXB27ZvDC4EXH7RRwVwxeApbQFB0weDGFGUwzCQYvpjGFfxNh8OKfbsFuxeAl2IoHPh6DF/80nP3BasxdvAabV77l6KBD2ghcW60iRg3q5tKpt/p1mj2FYf27oHXzO6S2v/y2F0/2Ho3vVkxDqRLFHP29MGQ6cnPzXMBLVk4eLmbk+rcYp1YxzzSTflNwXR3kDpiiS59qncjj5LV4Evktu0hVoif2RcSeHYWq297ZoHkOkX/+iqhJ/aX6vrTTPIAOFRPjRS4gAdMyMvWxlw5T0qWLmJgIJMbF4PylHF36M00nEcBVxeNw+ny2aaYUyESSE6IEvI0OpAvLtuVbjcxnOgYv5rOJPzNisOtiuQAAIABJREFU8OKPauHbxtTg5e2FK7FzzwGf1KeH+z7d2/jUxsqVGbxYw3oMXqxhJ+UszQJe3l+6Bnv2H/RJwKqVK+Cpji0dbWKiIlC2ZLxPffhbmUKD1mzYio1Lr4CJrn3HITkpEdNGPe/Sraf6U0f2xo13dcWE19LQvFkDqe3uvf+g7dNDsfb98ahSsZyjP3fgJV/kf8kTiXf1KJcfs8OfqJp1ET9kml9d5u/ahrzdAoLUvBmRtdRvm5DHiWndBbHtnpLGyRrRW7TbXmjMpA83aZ4DjZs50p7/xpd2mgfQoWKkgC7iszzyhM3CqUSKVUWKnM+5Ou1Ds2hDtooWyaxt/+ZyCuW83ln0mXQ2+FKuqVYBzz3V2tGkQKSkio0pnJzbl/6sXNdq4KV4ZAx2V3ncreTh8MzF4MXK76grc2fwEh521GsVpgYv9C3psRNnUD6ljKb1njx9FsWTk/DpvFGa6odDJa3gJe3ZPKGj+R9mySU7KSEG6RfC4xs0eY+Fw0OAu/dLSql46RtPvT7cmuV9aRbw0vul13HyVDrKKZLJetLodPo5FEtKwjtTXnVUCyZ48ebB4jx3b/XJ42X4gK545IHGUlNfPV7Mllw35rMFiFnzHmzNn4Dtoc4upow8vA/xo3tIv1fWiZvcH1F7C3u8ZMz8QvPbhXO8aJZK94ocaqS7pC4d6nFOhiLUyNttbc4L9Vb/uPhbUTw5EYkJ6qDdJrwC1W6Zsxp4IV2OVOtSpMDL8DL10b1YLdAz1ynxzEU3+TmXmgffx4V8m+PXfKuR8WePtxEYvHhTqGi9bnrwcn+T+ujy6P2arLJk5Vf4YPkGBi//qrXx6wh8vcl+jetTnfJRrZr9hhEzFwYvZraO+twYvBhrM/pAcWejW9H24bs1DbRq/bdYufabkIEXOWfLNpHjJfbfHC8NHkxD53b3eczx4q4+5Xhp3qwhBvR4VFr/slXfSPlc5BwvsijuPF6sBl6UgITBi6Ytb/pKDF6MN5Ee52QwwYuW29qUqnmr/9ffR9CpzxgQmKFyZ6O6eFN4ycXEXAmd2vfPUbTs/IrwFnxdeAumFDKKkeBla+W2qByVrMsmmHR2Gyaft3v+FTXwIkMUT+Cl4oH5hXT2F7ysr9gStWOu3Byoi/G8dGKWL7v0XiuDF70VtXZ/DF6sbT9o9Xhh8BJaQ7PHS2j192d0szwE6PGBIpgeL5Tstn7zHujRqaX472GXW402bd2OYZPmY9b4/qhRvZKUHNdT/QkzF0uw5dO5o5CYGI8OPQrfakRXVufl5aHfsBnIzcvF1JHPIzo6WoR2UCAEoBd4ici4hIT+j0h95tWog+x+k/zZVvDm8cLgJULk5Lnyja1fIpusEYMX4w2ixzkZTPDi7bY2Z8W81W/d7TXp1rdZ4/vh0NFTaPfMULz03OPo+Ig9L9W9HQbgyPHT0r+DDV6WpT6ARvGFQY+/O4LBSz2PHi96gRd/bCbbpl3yNXjjKruHqi/FLM9cvsxZS10GL1pUKjp1TA1eiPDHClovf2vqzSx0nWiW+I/cLItKYfBiDUszeLGGnZSzNMtDwOWMTOEa7ts5mG2ziXCjK+dgMMELabjqiy0YNHqWQ87nu7XBs08+JP28SlwvPWjULCyeOQQ31bza/jsP9S9cykC3fuOx6998X+XLlcaiaYMdIajvvr8Kb7y7rNAGG9izA7q0t3tK6gVelECEwYsx72d6QKWbqhi8GKOv3r0KUyG1VAKOpWfq3bXP/elxTgYTvHi7rc1ZAE/1z56/hMatemP2xIFodFttqSmB6KMnTkvnLJWjArocPnYalG+LwYtCXeLzJo3Cd87x4o/HS7diNTGijD0/mreihDb+gJd7j6zATttZNIxLxcfltUUqKOdklmcubzr5+jqDF18VC+/6pgYv4S29Pqtj8KKPjkb3wuDFaIX17z+cHgKCDV7IGuSJcujoSVQQObq0wHNv9dPPXUSOAEqpGnPdyDuCwYtdCc7xov8ZobVH9njRqlRo6wUTvGi9rU1WxFP94yfTpYTjXy6ZDALTVKbPXY5P1m4qlOScPF7I88XK4GXImR8w5+JuaY0cauRKjJw9XnyBIIGAl0N5l9DwkP0LEF/GZPAS2jPPn9ErlEnwpxm3+VcBS4GXfQeOYOP32/DP4RMuBnzl+cfdJhMLZ2t7Ai9z5kfhn4N2d3sONQrtLmDwElr9/RndrODln0NHsfmnHThy7JTLsno91Q4JKkkVQwFe/NHciDYMXuyqMngxYndp65PBizad9KzlzzkZLPBSIG7t0npbG2nirT55s3TrPx7frZiGUiWKSTJS0vJZi1bip7VXPA89gZeLGbm6Jsj/LuM4Hjq0SprLZ5Vb4L+JqbqYt8XB1fg+85jU19nru7vtk7yxiifRFe7WDVtUakgLHVSmHl666lZpXRczbaBbuJxLqT9nF/rVfxLKY1WVB71qfzD3EuruW+yo56vNZp7biVdObJHaax3TeVJJ4jr3HFsBbLnmz0fpVVBFhfhYca+d2JCZ2Xm+NDNt3ZLJMaadmxUmZhnwMn/JOkyYYT8UEhPiRPx+4Sv/1n84ESWKJ1lBc13nyOBFVzkN64zBi2HSGtaxGcHLspVfYtaCj6U1J8S7noMLZ4xE8WKu5yCDl8AfvnUPNbrrEdja93TZv77keMka/DbyK12j6T2g7Ddz0nIUJOqT7FLT4BorcaiRRqFMUs1MoUZKSfw9J4MFXmiuWm9rk9flqb7s8bJh6WSHR6CvHi9ZObnQ87bzby8fw/1/28HLuuot0DipvC679r79q/Bdhh28XL7xabd90leO8bHRyBTrsmpRakhreKXsLRiccisSYqNEWoV8ESHl6vGS9Pu7hZb738Ty+PzqFl4lcB7Lvc0IirheuT76xM8Yc+oXaRytYzpPKi46Crn5ImebnhvR68qNrxAdJcCLGMYmvIDDoSTGXUnYHQ7rCfYa9AMv9P63O1cYUuhWDMrC/v70wZrc1g2ZhAk7ZfBiQqOoTInBizXspJylGcFLqyf7oUJqWbwxeoBP5yCDF33BS0FCEjInf+rXppaT67rLE+MLeMnuOxF519XVNA9lv76009S5TpUYvOgkZJC6MSt48fecDCZ40Xpbm2xKT/XlHC9zJr2IhrfWkpr0HTodx0QIkpzjhX4XzFAjZRJcf/KFuNvCbY6tw9bs49LLRTHUqH3xGshIyEFyVhwqRrp+yeIcalQ5Ohnti9VAI5F3xVOCY+d8Mr7aTGlvDjUqvHs5x0uQ/iBZZBj9wIvBC76zdR/c3fhWvNa3k8EjWat7Bi++2ctgPuh2MgxefLOTGWqbEbw82m0Q/tPgZjz/zGM+ScTgRV/wQuJnzPzCJxvIlRm8uJeNwYtfWypkjcwKXvw9J4MJXrzd1vbm7I/x+dc/Ys17r0v29Va/VddXJa/vmeP6Skl02z49BIN6dcTjre+W2ttsuTgkwlMf6vQyPp03CtUqpRa6alrv66QZvAT+tlRLrku90nXajeJTsSzVNYGtM3iRZ+HtpqFAwYsy9w6DFwYvge/+8O3BMuBl5JSF+PLbn0GulNFRUeFrER9XxuDFR8FCVJ3BS4iED2BYM4KXqe98iO9/+BXvvzPGp3OQwQuDF/Z4CeAwCLAp53gJUEAfm/t7TgYTvHi7re3lMe9i9YYt2LFhrrR6b/X37D+MJ3uPBt0GSqVxg5swdeTzDs9IClWiBOZyoYTn29ZfCUth8OLjJgtCdT3BizcYEih4UXoieRvLnXRmfObSw8zs8aKHiuHTh2XAS0ZmFhq26Inrr6mCsmVKuFhg4pA0Tq7rpAon1zXPG5XBi3lsoXUmZnwIyBTn4COd++PqqpVQulRxl6UM7tuNk+s6qWJEcl0agj1etL6TtNdjjxftWpmhplk9Xvw9J4MJXmT7+Xpbm7f6FE5ULDkRxcV/vhQGL76oFZy6DF6Co7PRozB4MVpha/VvGfAy6e0lmLt4DaJEkqKrSpdAJP3FV5SP54xECZWkktYyh++zZY8X3zULRQsGL6FQPbAxzQhe3l24HEtWrJfOwVIli7ucgzMnDRYP3JxcV2n5cAEviWn3uGxoX3K1sMdLYOdBIK3Z4yUQ9Xxv6+85GQrw4vvqjGnB4MUYXQPpVU/wUjumFNZXfNjtdNjjJRBLeW7L4MU4ba3Ys2XAS+NWvVGtcnksnPqydC0XF7sCDF6ssRMYvFjDTspZmhG8tHvqRVSqkILJI/v5dA5yqJH1Q40YvFjvDJFnzOAluLbz95xk8OJ6S46/luMcL/4qd6WdnuCFevWUjDhQ8HLvkRXYaTsrTZ5DjQrbnsFL4O+FcOrBMuDl7vb9RMxqHQzt3yWc9A94LQxeApYwKB0weAmKzLoOYkbw8vizr6B+vdro0+Nxn9bK4MWa4CW34T3I6fyiZGsGLz5teVNVZvASXHP4e04yeGHwEtyd6nk0NfByKO8Sll7a53NyXV/By5Sy/0X7pGs1y6FM6svghcGL5o1TBCt6BS/qN7YHX6lpcz/Bh59uwFfL3kCcSArGxa4Agxdr7AQGL9awk3KWZgQv8xd/hpXrvsGH74z16Rxk8GJN8KK8cprBi/XOEHnGDF6Cazt/z0lLgBeDHso51Ci4e1TLaGrgZXPWCek6bV9vNfIVvPQrURf9S9XTMk2pjhK8FI+Mwe4qvn05RH2Y8ZlLswAeKrLHix4qhk8fXsGLWZY6Zup7eP+TL1GlYjmUKlHMZVrvTBiA5KQEs0w3aPMIBXg5cCASfx+MQPUqBahW7UqWfD0WTQ+oSQkxSL+QrUd3pumDwYtpTKF5ImZ8CHhrzkf4dM3XqFi+LIoXS3ZZy9jXeiMp0fUcZPDC4IVzvGh+6+tekcGL7pIW6jBS/KR8EvH3nLQEeDFISgYvBgkbQLfrMg6i28mNjh4IhijBy/Ay9TH0zI8oERmLOeWaugAQ56GXpT4ggE2K6ozUII+/4IUG8BTW5E4SMz5zBWA+R1MGL3qoGD59WAi8vI9tv+91q/y8KYMYvDipY9StRhu/jsDXm6LQ5I48NG2in2sqTZ/Bi/UOl5RS8Th9Pht5+fruhVArYcaHgLfmLMHOP/a5lWbC8BcYvDipY7bkunEzhyBqxxYovVmUU1YCEj09XqK3rkfsggnSUL4k5Q3m+7Do3GpEeeqsf16a9VYjf89JBi/67UnO8RL4yanUkHpzBi/9S9ZD2+NrpYFk0KH0PNETvEw+v13qjubgXC7k56DmwQ9QTHi6XMy3f8nB4OWKSgxeAn8vhFMPlgEv4SS6nmsJhccLgxffLcgeL75rFuoWZgQv/mrCHi/m8XiJm9wfUXt3BB28xHy2ADFr3mPw4u+bKIB27PESgHhBbMrghcFLELeb16GMAi8UqkSFcrHIxZPHi/I1NaAiv079yX0zeGHw4nWDF9EKlgMv+eJb9UsZmS7mKp6cWCRNGArw8sFHkfjjz0j2ePFhxzF48UEsk1Q1M3ihc/Bypus5WCxJ/Rxk8MLghcFL6A4WBi+h0d7Xc5LBi/nAy05buvCiyEGtmNIoLkJq2hxbp+nDfTg8czmDl27FamJnzllp/ZWjkzHlqsY+ebxQaFLtmDIubejdyeDFuDOKPV6M09aKPVsGvBw9fhoTZi7G11u2IyfH9SH620+noXRJ19wvVjSKL3MOBXiRQ5g8hRod+Md+5Xe1qr79IedQI1+sb466HGoUPDucOHkG7yz8GFt//l31HFw6dzxKquTAYvDC4IXBS/Dep84jMXgJrvb+npMMXnx7XvNkVb1CjWTQQtCge7FaRRK8yJ4k9P9HxK1Gh3IvSdJTzhZfQo0oTOj2hAohAy9LLv+Fw7mX0S75GlSOcs1RZ+YvuwI5wRi8BKJe+LW1DHjpP3wG1n31Ix5vfTeqVkpBdFRUIWs88kBjxBbB247MCl6GjIiW7PNUp3yfEvAyeLHeIcPgJXg2GzV5Nr75/me0at5EJNgthyinc/C+uxqpnoMMXhi8MHgJ3vuUwUvotKaR/T0nGbyYF7zIt+wURY8XJXiRQ3nMBF7kJMD3JVbG5xmHpDe/WqiRbDt3SX4ZvIT23NQ6eoUyRe8iG63aaKlnGfBS796n0eq+/2Bo/y5a1lVk6jB4sYapw8Ht1Z3SDF6CtwcffOx53HtnA/Tp4dtVjQxeGLwweAne+5TBS+i0ppH9PScZvJgXvBB8+Lj8/UXe40UJXqaU/S/6nvrOATooLOveIyvdvvkIjDxd/EbdPV5k7yaCY3ISXgYvV8zAHi+h/XtgttEtA17adB8iPF1SMXlYT7NpGNL5MHgJqfyaB2fwolkq01Q047cvPQaMkTxdXuvf3SedGLxYB7woAYmetxoxePHpLaNrZQ410lVOr535e04yeDEHeFHeoCN7STB4OS4lw1WCF2fQ4ZynxfmNQu0HlLpyE9L6ii1FzpfSUrVAcrwwePF8JDF48XpkF6kKlgEvX2z6HyjcaP3iiUgtaz8ouAAMXqyxCxi8WMNOylmaEbx8u3UbRotwo4UzR6FcmVKaRWXwwuCFwYu2t4usk7vrvrX1UrgWgxd/VPO/jb/nJIMXc4AX+Upk8ppwDi3iUCP7jURUAgUvypAfreBFLUxIK3iR7cqhRv6fbWZoyaFGgVnB1OClc5+x+N/2PzWt8LsV01BKJamkpsYWrsTgxRrGY/BiDTuZEbz0HzIZO3bu1STgsnnjUaK4a5JxBi/6g5fsvhORd11dRG9dj6jN65Ff4ybYHurs1U7erpNmj5cIXMwI3F5eDeGmAoMX7cpFiBz6qaUScCzd9YY17b3oU1OPc5LBi37gRQlI3H3Qdmd5Bi92ZWSg4S7HizvwUik6Ce2TrnWE/cg6O3u86AVehpz5AXMu7gYlQB565kdpOLVQIwYvofu7ps8pa++FwUtgapoavKz4/Hv8ffCYphU++2RLJMTHaqobTpUYvFjDmgxerGEnM4KX9V9vxeEjJzQJ2LHNA4hXOQcZvAT+wBO1Zzvipgxw2EEGL75+UDcSvMh9y3NTbhr2eNH0FoJWe0acOY7I9BPIL52CgjKpHjtnjxdt2gdSS49zksGLucELgYKJZ7cVueukZfBCQIVuBZKLO/Aih2XJoEMLeJl9cZcDmlB9OZmxEgDRv9UgmjJprvMtS8r3tDwf+YYq5/e7Gb2MAzmT5LYcaqSHiuHTh6nBS/jIbNxKGLwYp62ePTN40VPN4PQVTg8BDF4YvDB40XZuaAUvMuSyNX/Cq6cTgxdt2oe6FoMX84GXe4+swE7bWWlrFHXw4vz+CBS8zCnXFPcnVpG6lb1rasWUwi6htwxvjAAvSqijXFM4PXMp18XgJdQnu7nGtwx4+enXP/Dzjj3Y/89R6brUa6pWwH/q34Trrq5kLkWDPBsGL0EW3M/hGLz4KVwIm5nxIWD773vw++6/cPDwccSIc5ASjt96cy1cXbWiR6UYvDB4YfCi7TBh8KJNJ6plplAj5az9PScZvJgLvOyu0hE1D37gMC19YN+cdaLIerzoDV7UvFqUYU10i5Sv4GXIma0SuFEm7pXnLXu8MHjRfsaasSaHGgVmFdODlzNnL6DPa9Ow7Xf1HAct7m6E0S93R3RUVGBKWLQ1gxdrGI7BizXsZNZvX86eu4DhE97Bzj/2qQrZrHF9DOjdye05yODFd/ASkXEJkUf2oSAhGfmVroGVQo1yOg9EbsN7C+0VBi/aziAGL9p0MiN4CfScZPBiLvBCHi5y6ArtNwYvhd+bsncK/ZYg1c6cs5JeWkON9AIvslcSwZYhp3+UwJhaSBKDF9+fQ7SfxsGryeAlMK1ND17kBLud2t2HJ9vcg/IpZZCXn48DB49jxoIV+PzrH9FZvPZir8cCU8KirbWCl8fa56PmDfm6rHL0+ChkZ0WgyR15aNpE/Q/1kBHR0lhPdcpHtWraxyWX7KSEGKRfyNZlrmbphMGLWSyhfR5m8niRE0e2fqgZWje/C+XEzW50DlLul0VL12DT5p/RRrzWo0tb1QUyePH9gUcGLQUJScic/KmlwIta+AuDF23vfQYv2nQyI3gJ9Jxk8GIMeFGGtGjZXcokrJ7Ai6ekveHwzOWcXNeTdqQFFV/AS+XoZFQS/y1LvV81ka9Wjxe1ZMgMXq5Yi0ONtLzri04dU4OXP/46iDbdh+Cl5zriybaFv72TTfTSmHfw2frN2Lb+XSkEqagVreDFEyTxVTMZqtxwfT46PmqHKsdORGDtukikphSg+f35YPBSWNVweAhwt09SSsXj9PlsAQL0e2jzdU8aUd8s4GXf34fQY8AYpHVth9Ytmqou9fWp8/HlNz9g9YdTVc9BBi/+gxcSPGPmF17Bi1zP216Uc4MUlElB5qj3XKr7cquRM2DxlHdE2a+tXRpsTVt7m2rQX6cH1AgRv2KFW404x4u5Qo30OCcZvOj3N1x5q5G70BJ3B4xW8FJb5CNpGJ+KEWUauHQVDs9cRoCX2+NTXG47ohuI1G5QUgMvarZk8OL5TyWDl6A/Sph6QFODl49Xb8KQCXPx6xezERNj96BwLjKcWfrOMNS6rpqpxTZicqEEL1WrFKBblzxpWQcORGLuwkjp3yOG5DJ4cTJ2ODwEMHgx4h3svc+1X36PyTPfw5rF09yeg/KHjhnjX0KNa6q6fpiPikDZkvHeB7NMDQK+9vPGW8nIysW5y+YDLzRvAjrOJRDwkvDqE4g4cwLePF60JIT1pqsRrzN4MUJV4/o0U44XPc5JBi/WAi+0s5VJYJU7PRyeucwMXgisHcm7hDkpTXHvkZWS9ARwlDccNRKQRy5bRG4e2XuJc7wYdyYHo2cONQpMZVODl/lL1mHyrCXY/uUc6VswtXL42Cnc99hAzJvyEurXuyEwNSzY2ozgJe3ZPMycZc+542uIE4caWW8TsseLsTZbtvJLzH5vOdZ+NN3tOXjsxGl06vkaJgzvi5tvvM71w3zYgRftmhsNXmTPB3cgxXmm3uoHAl4S0+6RhmPwon1/ONfUGmqU0K8VIjIvq2rtYnMRQpssQmjPhFkIrZnAix7nJIMX64EXCpfZWsk1xJbBy/1wvk6aPITuE7cYTT6/vdARtbVyWyy5sFf6vbfkujI0aXh4GQ7lCvAibkbqdnIjikXG4I8qjzN4UfnTwx4v/v89DseWpgYvP277A137jsOiaYNxy001VPVftGw9xk3/AJs/ewsliiWFo408rsmM4IXyusjeL76GODF4sd4WZvBirM1+FTcZDRw6BVNGDcCNNa9RHeyTVRsxc95SfLxgIoonu56DHGpknMeLN5DC4EX7+8MMHi+xC8YjeusXyKtRB9n9JrmdvCfIxeBFu831qqnHOcngxXrghfYPeVo4FwYvruCFNFJeQS1rRvlYNmcedQte1MLGlIlyZWBDoUns8eJ6ojF40euUD49+TA1esnNsuKvtC8jLy8c7Ewagbq3CHzq+2PQ/9B8+QwoxWjxzSHhYxMdVMHjxUbAQVQ+HhwB30jF4MXZT0Tn42DMvI1+cg2Ne6y3Ou+qFBvx26zaMnjwbNa6ugmnjBqlOhsELg5dCOV6aPwHbQ52N3bh+9G4G8CKDNAYv3g1oJo8XPc5JBi8MXrzven1qUOjNFnH7T6O4VChDcpS9+xJqRN4sVHaKq5zd3WpkBHjpVqwm5lzc7RiTwQuDF33eIeHbi6nBC8n+y2970eWFsRJ8KV+uNKpXqYAcmw37/zmK9HMXkZyUgJXzxyClrP3QKWqFwYs1LM7gxRp2Us7SLMl1aU6/796HAUMnS+dguatKoXLF8rDl2nDw8HGcO38RSYkJePeNIShbpiSDFycF9Ao1itn4CWKWznT0nt13IvKuqwulx0vmqEUoKJPqcbN785DhUKPQJtdl8KL9rDYTeNHjnGTwEnrwciE/BzUPfiBtQrXrpJ3DZKieFT1eZKiilu+k7fF10vrpumgZatAVzVqL0eClffEaaHhomTQd59AkLeCFYI1aQmQzPXNp1VpLPfZ40aJS0aljevBCpjgrPlhMn7scP23/E4eOnkR0VCSqVExBk9tvRveOLZAQH1t0LOa0Uk/gZfLUKJw7Z8+N42vIjydB5RuL3CXX5VAjV/UYvFjvLWq2h4DzFy5iweJV2LHrL3GL2ClERUaiQvlyaHjbTejQ6j7EezgH2eMlcI8XJRCh3awGXuTfedrtDF7cq8MeL9Y6J80GXki9QM5JBi+hBy/KJKxy/hD5XaEWJhOO4EUO41FCDTOBl9sTKjgS5RIc2iW8bO5LrIy55ZppCjVylxDZ+ZlryJkfsDXrOIaXaejWK8gKJyaDFytYKXhztAR4CZ4c1hvJE3iRAQmDl9DblcFL6G3g6wzMBl58nb+yPoMXO3iR83Ko3SbkrG/Unu2ImzJA+jXVDyV4iThzHAmvPumyBZyT6HJy3UDeJfa2wfZ4kfeZt9CmwFemfw9mBC+BrJLBi7nAizNoKWrgRd7LBDU+zzikeWtr8Xhpl3wNtgiocTj3suRZpDXHC/U9oFQ9B3hRQrH+4vdaPF6c28g/Oz9z1Tz4Pi7k26T5uQvH0ixKCCsyeAmh+CYc2tTg5fyFy9L1qYkJcZqky8zKQXZ2DkqWSNZUPxwqmQ280J/tboEk142LRFJcDNLD7PYHBi/We7eZBbxcuCjOwehoJGg8B7PEOZiTk4Pixa+cgwxerA1elBBI+U5i8KL/uaIFvERkXEJC/0ekwbVczU1J493daiTbtqBMCjJHvaf/ggzs0UzgRY9zksELgxcD3y4i/0o6LopQqloxpfHu+Z1SMlu1UCPn24icPX+8zVELeKFxN4s8M+RJowQvBGSWXtqH4uKWot3iliIqyuS61PfTJWpJNxkpi7wOPcGLrAODF28WD+7rfJ10YHqbGrx0SBuB+5vUR5dH79e0yiUrv8IHyzfg03mjNNUPh0pmAy+kKYcaue4sBi/We7eZBbz0ful13NnoVrS3E8RYAAAgAElEQVR9+G5NIq5a/y1Wrv0G70x51VGfwYs1wUt+pauRNXgWGLxo2vq6VJLBiycQorSHFk8VLeCFJq/FE0uXRerUiZnAix7nJIMXBi86vTVUu1FCCdnDRA7RUTZwBi/OuW68zTEQ8KL0KpJz5ziDl9vjU1yupPYGXuScNmqwRv6d8zOXrMOUsv9F+6RrvS3btK+zx4tpTROSiTF4CYns+g0abPBy7EQEZs6KkhbAOV6021ENvESK5vnauzBtTb7VyFjT6PGBgsFLYOCFcrdE/vkrYtZc8UiQ87nI4T20C7TkeIkf/SwiD++XNo3aB23nkCaqowW8KOuQFwYVmq/skcG3Gml7n3rLwUO9MHixa8ngRdueskKtU+eyYMtj8GKkrZTgZeLZbZK3iVq+E2/gpWpsMv7JueR2qqEAL8PL1Ef3YrVAeVkoITAl0H1UJOGtLbx7qAQCXtS8goy0k959M3jRW1Fr92d68HLk2CmkliujSeWTp8+iVIli7PHyr1pG5Hg5cCAScxcSMmDwomlT/luJPV58Ucscdc3k8XL85BlxY5G2m9vOpJ9DieLF2OPl320k32oUeXgf4kf3kH6rxbNA+eFaDbzIQEMJXmzt0mBr2trjBlbWDwZ4kT0yGLxoO1cYvGjTyYzgJdBzkj1e9AMvSnjgywdnZXJd55wu7nKdmO1Wo0N5l6RwnUrRSYU8NeScJcowH3/Ayx1JFbDp8tGggRelLWm+tWPtty0pixwO5AxYdlfpKMKWYhm8CEp9IcP3JP/aT+Pg1eRQo8C0NjV4eff9Vdi996BPK7y2WgX07NLKpzZWrhxsjxd34GX3H5H4cIkdyHCokeuOYvBivXeZWcDLh5+sw19/H/ZJwGqVUvHkoy0cbdjjxVbIS8Ew8CI8TWwPdWbw4tNuvVLZTLcaeQJ07PFit5mZPF70OCcZvJgbvMi3/DgfL/KHe+XvQ/nMJcOjytHJ2FqprWNaMsDwBF4oD8y9R1a6QI22x9c6fucNvMjXNTt7zlAHsoYESj66tEcCRBTKcyjnoiPnjHxltwy0nMEL9eN8y5I78CKHCfnq8aK8VtwXcOfnnx5Dm7HHi6HyWq5zU4MXM6t5/FQ6iicnisS/8Zqm6al+fn4Bjp44jZSypUUSTXsYj1xsuXni6lj6truk6rXZZgEvG7+OwNeb7HNn8OK6JUL5EKBpgwZQiUONAhAvSE0ZvFgHvMQuGI/orV84dkagoUayx0vczCGI2rFF6ldLQtggbc1CwwQbvERvXY+IMyeQ2/AeFJRJleYSbI8XpSeSFiAYCru4G9NM4EUPXRi8WBO8qCVfDeUzl9JrR+mNowW8KNvKe9o5x4s38CKDCm/gRc4zQ/WpyMl+9QQvtcV10+srPuyzx4tSBxkk6fEeD0UfDF5Cobp5x2Tw4qNt/vr7CDr1GQO6cYnKnY3q4s0RvaXbl9SKt/prN/6AQaNnIS/Pnu2j7zPt0L3jg9K/J8xcjPkfrXN0e8tN1+GNEc+hTKnijt+ZEbw88nA+lq+we780uSMPTZto/2NOSQiTEvhWIx+3ZUirM3gJqfyaBmfwYh3wovzgT8b1B7wQbMkX/1GOFwd4mdwfUXt3SPuFwYv9bSNrrczN4y0UjNrp6fGiBC+ZoxY5AJCmN3aIKzF4CbEBdBxe7xwvRoQaufN4YfBSeCPI4KXB4aXSddFUKOyJ/i3/v/ImI6p/Xty2ROFDlKtl6JkfpTZqHi/0ezU7rK/YUsrnoubZQv2o/d45sbDSy1gJXtTCsXTc+oZ3xeDFcIktNQCDFx/N1brba0hKTMCs8f1w6OgptHtmKF567nF0fKSZak+e6mdkZqFhi54SaEnr3Aqrv9yCweNmY9XCsahepTzmfLgGV1ctjwb1amL/wWN4svcYdG53H154+orrolbwUrJkgbhmW3ijdM7zuOKsrAgcPyEeSIUjT/kUV2DiLtRI6fFyw/X5+ONPO3i5uW4+WgsQo7UweNGqlJZ6pLvdDkYWBi9GqqtP3wxefAcvyg/EmnO86BBqxOAlAheDFAvvL3hR7o1AbzVy3md519m/fbZCYfBiBStpmyOBl02Xj2OLSPjaSOTxaCRurgmk+A1eck6g7VF7WI1zjhcrgxfK+9Lw0DLHuuSrnJ2hgp4eL863ESnDg5zBi/JqaTmsyR14oaumL+QXzlci1/UFvDivncFLIO+44LXlHC+Bac3gxQf90s9dRONWvTF74kA0uq22/QAdNkMKE1os3Lidi7f6azb8gIEjZ+KX9e8iLjZGan57y154ovU9qnlqer48BYePncbK+aMdQ2kFL3KDEUNyPa5YBivKG4uUDbSAF2r7z8EIqZm7ftxNgsGLDxvSJFUZvJjEEB6mweAlSODlrkdga9/T44bw5lGhN3ihyZDXjLJf9nixmyihXytEZF4udBuVN/tQOwYvdv0YvJj/7Nc6QwIv407/4gg36V+qntamqvX8Bi9ZArz8m88knMCLEqi0S74Gh2yXVW81sgJ4UTO4N/Ai33akbMvgJaC3WMgaM3gJTHoGLz7ot3vvP2j79FB8uWQyypezX5E2fe5yfLJ2EzYuneLSk7f6sz9YjbmL12DzyrccbTukjcC11Spi1KBuhfqz2XIlKHNfk/qFXmPw4oMBQ1g1lPHGRi+bwYvRCgfeP4OX4IAXLd4P3j7YBwu85IkkwNp9EQPfg1p6CHaOF9kWvoYaMXhh8KJlP1upjhnBixwWI3u6WNnjxTl0hvaG2nXSvoKXWiKHyi7b2UJbTQ41kj1eqE7xyLhCCXGVHi+UBPhQrv16amU+GXceL/6AF6X3jdxeK3iR88RY6f2knCuHGlnVcsbMm8GLD7pu/XkXuvUfj+9WTJOuraZC8GTWopX4ae0sl5681Z/49kdYs2FrIWjTte84JCclYtqo5wv199wrb2LTD9vx+QcTRAjQleu1z1zIdruCQa+5hpm8PtLzo/Z3WyLx2RqgetUC9OjuGmq0/+8IzJpr92ZR1lm/EdjwlX08+v3f/7jW0SJ1THQk4mOjhat5jpbqlqlD3wyWSo5D+kX39rLMYpwmWqpYLM5fsiG/QHsuHyustVhiDLJz8pEjElxbvUSKDUh2KopFvk5amZdDSxJTLaFG+dffjLgpAxyyhhK8UKLY2AUTpLkoc7zQz+zxor7zGbwEdiKwx0tg+pmptRnBi6xPKMELhQjRDTuUv0RrUUuu6wxeLuZnY6cAJoGGGqnBKGfwQnWouAs1Uq7LHXgpJsKLLjqFF8nt6LU/qjwu/egu1EgreDmWmYFtl0/joABBfU9955ia2pXhWu0R6noMXkJtAXONbynwQl4fFL5DpVSJZMT+G54TLEllD5YNSycjVdxAREWLx4u7+lo9Xka/uQgfLN+ABW++jNvqXl9oudk29yCl1wDXD4wjBkeJ5LzuFVv1eT7WflGAa68WiX57Fr5hiVrt3Qe8MdPer7KO3E7+/V/77WO468fdDMgzJCoqArbc8PoQTxgqRiQOzvFgr2DtY73HiRWwzCaSQ4cZdwF5ieSJReWbzS3ADwPmi0UkxKknAPejO0eTYN3uRgNSQnNbbi6uKi2SVflQ1MCL0sPBXVdWAy/OXhi0LjmZLoMXdSszePHhjaRS1WrgJSsrBydOn0HlCqmIpIcNp1LUbzV68tiX+DzjkJRbxQyhRrJ5QgVeZFjia3LXJZf/ckADGRgof6eEJf6Al06lr8PC9D2SPBS2RFdCK4ue4EWpgfMV0kr7fFz+funHQMBLVGIBUnctkPpxBkpKHZdc/Avti12L9knXBnaABak1g5cgCW2RYUwPXs6cvYAZ8z/FyvXfIyOzsLdAStlSaNuiCbo++oDqVct620DO2TJn0otoeGstqfu+Q6fj2Ml0jzle3NWXc7xsEzleZIjU4ME0KYFuzy6txAe+AinZ7uoNWzD/jZdxy001XJZEoUbkZ6L22XDICNcPWnTVc7Vq7j9JyklyteR4ocnIOWOUyXU5x4vrzuNQI73fjf/2527z6zCcMtGbDt0Z2sWFS5fxzoJP0OupdkhQueJe71Ajb7e1OS/WW31Pt7tdupyJ7gMm4LfddppbMfUqLJz2igN+Dxo1C6tEYnJlqVKxHNa+P176lR7gJafzQEScOibdEiQXypNiJo8XBi++v8VMAV6WzEDMV8ulydM+y214r+8LCVELs4GX9LPnsXDxKhw6dgKVy6fgkYeaomql8g51tu34Ay8OfxNL544Xlw3YvZaVpaiDl5aH16qGv/izvfTI8SKPazXwooQParlPtIAXOcyKNHC+Tvq11Fsx8vjPkjzOeXDk3xE4k71MtHq8yLdDybbbXaUjduaclXLuUB8XhJeOc1gTjaeER2rghW48GnL6x0IeN87t6OffI8/gvv2fqW43Zx2db0SSG8lrVrvpyp99rEcbBi96qBg+fZgavNAVy3Rr0J/7DuH2225E7euriTCcBPHNegHOX7yM/23/U3oYr1/vBsyb8lJQrNKq66soUTwJM8f1lRLdtn16CAb16ojHW98tjd+5z1jpg8GYl5+WfvZU/3JGFuo374EenVqK/x52udUo7aUp2LR1OyYP64Xrr6nsWF/F8mURE233RvE1x4tW8JIqbjTq+ayrx8yWHyKx9vMrIUwMXrRtOwYv2nQyUy0rgZcTJ8/gibRX3X6g0Bu8BPN2NwrJXPrZ11g+Z6R0o1yHtOHSrW8zxvaVtsuLI9/GP4dPYNRLV/JiJcTHoZI4J6noAV4IslCxKnghD5/oVQv5OmmnA8Zf8BInkulH7bDDPi3hZZQ0PjkhBmqhwVZIeuzuXDYbeHn82Vdw8vRZ4TUbCXp+pNLn2Y5ocW9j6d8MXtz/haVQI6PAS6P4VNyeUF7yzqgclezxz7xajhM9wMtOW7p0TTLlOxlRpoGmRw15LnSLz+5/Q2m0NPQFvDj3reZh4gxePr76frTZvw4U4vN0sVpSQmRleUF4LA30A7zIcEOGFyPE1dLke066OcMbZeiRN/BC8594dpuu4MWdF1LNg+9LNy4xeNGyU/2rw8l1/dNNbmVq8CJ7hNCNQTfVFLEvKmXZqm8wdOI8fPnRpEK5TwKTxX3rPfsPi2udR4O+haXSuMFNmDryeYfHyp2t+6BKxRQsEt/IUvFWf9UXWzBo9JX8MM93a4Nnn3xIakveL/I4yhl9Om8UalSvJP1Kb/DywUeRjqug1W5AUnq20PgMXrTtNAYv2nQyUy2zgJfDR05g3cbNHqW5eDkDa774Lijgxdttbc4T9Vbf2+1uTdv1xQNNG2BgWgep649Xb8KQCXPx+1fzxK0qERJ4OXv+Et6deCXXinIOVgEvMgSgD/JyiBCtg8KElPlplGtT3k7kyeOFwYv628dv8DK5v8NGDF4ScCzd/jwUynJM3C7Zqedr6NWtPR5+oAkOHDqKSdMXiS/u/pF+16r5XSEBL+S5TDdfpojwdPkLM086eavvLeTS1/BPeS5Gghd5DC0fho0CL/6EDanlatGyx72BF+c+lPlLtICXDTUeRrO9KyQYcru49tsZvGgNNTqUd1E1j4q7fCw0bzncSOm1o/Q+Ua5d9tpxB16ck+Z68niR947cvzvwInvrDBfQqLuAUmYo7PFiBiuYZw6mBi+UA+UtEWZEoTjuCoUi3fHI83hnwgD85/9uDJqyR46fRrHkRBQX/2kpnurTNzOHjp5EBZE019e8NXqDlznzoxxXQTN40WJZbXUYvGjTyUy1zAJeftv1FwYOm+L4BteTRu5c6PX0ePF2W5vz/LzV95brqk6zpzCsfxe0bn6H1PUvv+2V4Lec5JzAy1ebt4lQzOtQumRxNG/WUALicvEVvEScOY7orV8g8s/t4sO1/ZvEYHi8+AVeFNdXM3jx/fSQNbe1S4OtaWupA2+3TlEdpZdKQUISMid/6nFwXz1eKFFyxJkTIuzoHhSUsSfGNGMxk8fLJ6s2Yua8pVizeJrIp2YPs87Ny8PYKXOwacs2vNSnq3Q+BDPUyFMIpZo9Awm59BTOeerMOTRp84LLkNPH9MFdt9fDk39vQNv4azDujN0rwdecJmprUYYaya+7BS+UbufftH7+gBe1nDTOz1yhAi9bK7eVvHzUrlOWdVGCFzkXDHkHrcs4KCW0dfZ4ya3XA7+cOYNzedlYd+kfv8ELjS9f3U3/dvZ4UdrV2eNF+bNSf5pzt5Pitg1RZDjjDrwox6R/z8jYgdEnf1E96nwFL3rkKdLrzGXwopeS4dGPqcHLtz/8hh6DJmH2xIFodFttVcXnf7QOE2YuFvH8r0ueJkWtMHixhsUZvFjDTspZmgW80Jz2im9te744DlNGDcCNNa9xETOYoUbebmtznpy3+p5ud5s6sjduvKsrJryWJoCK3T1cBjmUw4VyuVDi8b1/HxbJg2Px686/sH3XPowY+BTaPGgHNXLJ3fkLLg233xaXPHQqomvforoplfXkCvFtu0r/zFo2z9GGfhddu56jT3ohutbNSB423eNmP9f+v47Xi09fishy9hwUjt8nJgk3l8uOOiWXfAe1OTmPl7VkjmN+NA8qubt+daw3a+lcx89a5ulxEWHyoqw52TK+fTfknzyGC8+1K6S92lIvDXvOoSW9Tjbytyj7kudxoVdb5J867nGf+jteuLZbsvIrDJ+8AL9+MdsBXuS19hs2A59//SOefrwF3n1/Fb79dJqAMK45Xij5fawIC9OjZGRmoWGLnuje8UGkdW7lEkruPIa3+t5CLj2Ff548fQ53tX1BClu/uuqVnDcUFp8ocoJpAS8EZHbmpKObRi8Cn8CLQgy1HCHeQo18AS++hA2pea5o2RvKdjIwUPMikftSghe5La2pcmwxHMq5iPbFa6DhoWWOoQm8nDqfLeWBVNNLi8cLjekMufQAL863N9G+IQ36nv7WcWW1UkPl2hm8aNldoa/DoUaB2cDU4CUnx4a2zwzDvgNHcMO1VVDrumqShwnleLlwKUN887lHiu3v+EgzDO7zZGBKWLS1r+ClyR15aNrE/Y1B7PFizEZg8GKMrkb2aibwQuu8v30vTBzeL+TgJdi3u5HHy/ABXfHIA/Y8Dc4eL8574OkBE8Xtdxfw8ewR0ku+eryohfUY5fGivF1J6WmhXJOnUCNlmIsvHi9awmOMfG+565u+GaTwsYsZNsOHV9pZDtlytr27a8eVHi/SHhPhYJ6Krx4vaiFQhgvixwBm8njZtedv9Hl5PF5+4Sk0bfx/hVZDXsUDhk7B77v/kn4fjOS63kIoneX2Vt9TyCWFWjZu1bvQl5QEmyjEiUL1ZfCyfO4oXHe1PUxdWQi81I8WEPv8X249XmSQ4vzh3J0Xi1nBC61b69XEoQYvypullHoqwYuah5A/4IXCgn6oZIfOapCoW7GaUqJdZagR1aWfleDLHXhRetYo956v4OUFAXDoFidvoUbuku/6ccwF3IQ9XgKWMKw6MDV4IaUJsHy0YiM+WbMJB4+cdIhPydPIw+XRlnfhMQFeoqNcrz4OK0u5WYze4GX0+ChkZ9mvWfQl1OiTFZH4dbv9myK+1cjVWAxerPduNBt4oRs7ihdPVj3r6Jsvgg1lShWXPrg6Fz1DjYJ9uxt94KDwoQE9HpWWJef1knO8OK+Vcn799OsfWPPe69JLvoIXCvOIXTChULfuwAtVooS7NhHyI99M4+1DuBKwyODFXQ4Xaf4ecrxoBS80/8i9O3zKS6LlHSvDHmWuGS3t3NUxG3jJGvw28iu5epgZBV4otCin84uOcCct154Honegbc0EXugLudEirKh6lYp4vO0DLkvLFB4ofV6ZiCPHT+LDWWOks9S56HmrkbcQSuexvdX3FHJ5XNys2fbpofhyyWSUL1da6nr63OX4ZO0mbFw6xQFeKFdiKXGbU63rquKJNvdI/6ZC4CW1IBHfZRyXPkireYXIH/zl0Jl7j6zATttZuMulYTbwMvviLilJLBV/wIuW/DSyTfXyeFGClwaHl+Jwrt0T0jN4iRAgpI50HbjyVqPasaUw5+Jux7Zz9nhRggw18EJw5VDeJcfV1cpQI+UekMELJd+tHVNG2k/KUCllUl5nWww6txnvndsD5Y1OlAyZblKSx1CuSb7CWvlekvedHuFygZ6PcnsGL3opGR79mB68OMucLbxgIiMjCyUpy8zKQVxsjPi96weO8DCT+1XoDV6UV1D7Al6UnjIMXlztxeDFeu9Es4EXbwpmiXOQckSpnYN6gheaRzBvd6NQUoItn4pvaxMT49GhR+FbjV4bP1fyery2WkXsELfcde07Ttwyd4+4be4xSTIZvCiBiqcPtErPEVlzr+BFgA35xiMjwUt+gvD4rHQtorMvo+DgvkI36ihhAAEZKnKSXsPBiyLXjLd96ul1s4EXd/vEKPAigzT2eAlkF3lu6+mc1BO8eAqhnDbKHvKoLIGEXB4VOQe79R/vyHtF/RLImbVoJX5aO0u6BXT4pAUSlKF/r96wVfIeX794ovTsTOClanQyvrl0TMCXY9K0Lt9ov5mTynlxjXCFXQulf6+r3gKNk8oj6Xd77sVXyt6CwSm3uqxHfl35gtzWk4VGn/gZY04VzvMxq9KdePbwN26bqc2BPg3Ex0YjMydXaqfsV7k2rXNxnvvAY5vxW1Y6xldohDpxZQp1oxxLbnff/lUObdXG/G9ieXx+dQvHPJ3XpGyff0sPZOXki7Q4Bfj28jHc//eqQl3KbeU21PcdwmZKXUkDZVt5fOpIba7UJxW5D6pfJ6E0dmSmY0LFKxrIfdLrVWOT8f65vZJGLx613wJHv5f3GP1Me+mm+NKYUP52PPD3amy6fFTaU8pxqL7zmqitsx2V+1S5Hk82DsZr0cJRgPaj7d+b1oIxppFjJMbZc2hx8U8By4EX52WSC+UTz43C4reHqsbs+ieLdVqZEbwo1YuLL8DgF12vpXanMLlkJ4lrN9MvZFvHCBpmyuBFg0gmq2Il8HI6/Tz6Dp6AaeMGoeS/32Iq5dQbvHi7rU3P293I67Fbv/HYteeAtCT68LBo2mDHLXZ3t++HY+IbX7lQYt3JIgdHYkKc9CsZvCiBSiDgRb51iP4/X/wnebwECbzIH8xLHt6JnNEvmAa86BW6ZBXw4hwW5g22eQo1Snj1CSmRLhVn8KKXJ5FRR6uZPF60rNHbOaknePHmweI8X2/1PYVcyh4vG5ZORqq4PYmK0uPFeSw6vx956lXMm/IS6te7QQIvxRAjfYj+PtMOXs7e0N3RjDxhHjpo/3D/WZUW4sNzKkr9MVv6eVCZenipbGHwoqyvHFtuK//ugwt70OvoJvxHXDW9quqD0q/HnfoZr4skv87t5PHV7Kw2B/qgWzwpBucv28MWlf0q1+Zp3yjbOM+9xT+rJa2cf0/9vXxiM94+u0tVL1qrrLFybFkDeUznNcnjUZv8W9JwIdMm0i5A8lJy1kZuK7ehvslmSl1JA2VbpQ2UY8lzpD6pyH30KFULY1Nud5FP7lM5JrWV26mtXx774UNrJPCiVt95TTSwsx2dtdBqZ097QI/X4sXnGvJEzszR/llIj3GN6oPOSS7+K2AJ8ELJEn//Yz/qiKSSN95Q3eFK/8dfB9G5z1jpyuUtq2ZovmHIf7nM19Ls4IUUU/OccadkMMDLsRMRIpwKSBW5mOMFGApGYfASDJX1HcNs4IVyGOz56wBuqFEd119b1XEO/vX3IQwYMgWXMzLxycJJKJbketOa3uBFVjqYt7tRiFOOzeb4YKG0Nl2xejr9nARjKFmkshRF8FJQJgX5pVOC5vHC4MX/HC9KiMPgJfAzPJBzUk/wIudsoVs55dsqGzyYhs7t7kPPLq1cFuqtvqeQSznHy5xJL6LhrfYrdPsOnS4Bacrx4lwIZjcSiX/fGvMCmtx+swRe9mdcgIhYdeTwULvimPqZUva/qB1bGvceWSl1S7k/RpSxJz6Xi1reEXrNOSxJ7WpgtWSxzrf6OK9HS3Jdf/K1qIUMyWPLIS9qIUjKUB35dWUIjJwnRbmOysLjaGulto5kuc5rUvbpT44X52un/Qk1ovnKV1druTVI1o/2CIU5KcOPlGuXw4I6nPwc3wrvFqW9KVfL5xmHHHlklDo4h4y5SxYc+KkSWA8cahSYfuHW2vTgZcaCFXhr3nKH7tWrlAclCNv80+/o/eqbiBK5Xd4VV0nfVvf6cLONpvX4Cl5uuD4fHR/NV+2bgMTMWVdy5fgbauTcub/gZc26SBwXc2p6ZwGqVVOfsyaRnCpNnhqFc+ci8FSnfF379TQXBi/+WCq0bcwEXhYtWY2FH11xJ65cMRWzJr+Kn3/dhWHj35bOwbGv9kad2jVURTMKvITWQtpGL4rghZTJr3Q1Ig/vl0SiHDSRh/fpnuNFDrlh8BI88BKRcQmRR/ahICFZNf+MtneFPrXM5vES6DmpJ3i5nJGF+s17oEenluK/h11uNdq0dTuGTZqPWeP7o0b1SgKce67vLeTSU/gnXVOdkZmNu/5TT9zaFC2Nu/6bn/DNJ29KeV58AS/0gfv2hAqOa4jpQ7P8oV7+MO4OvDh/WA8VeFlfsaXIP2L3DPJUPIGXhoeXSbf06AVeaB4EEpS3GilzvFgZvChvpaJ1OoOn2iKPy/qKD0OZwJnWS+XR4tei76nvICfLDSZ4kXPaUM6Z9knXetsubl/3F7zstKWL68RzMCH9V4dmc8o1xf2JVfyeix4N+VajwFQ0NXih6/X+74EeoIRgg59/QiRFO42Xx76L6pVT8ee+Q7iqdAnJ5ZyuFC2qxVfwQvlXunVRd3c7cCAScxdeuUoxlOBl36EcTHnTDoG83cSk1fYH/rHnAJq7wN4vgxetynmul1IqHqfF1YZ59HVZGBWzgBdKCtnyib7C06UaenV7FHR19PjpC1CpQgr2HzgsQiyLY7K4Zrpi+bJu1WfwYkMgoUZ5dRpJH3IprKhQqBH97qvlsDCKo8cAACAASURBVLVLQ8zSmZL+3sJOAkmuqzXUyHkjOOd80QuUyOCFPGwyR70X8Lu/KIYaOXu82Nr3RPzoHpKWaqFGciJmvWwYiNHMBF70OCf1BC+k66r/Z+884KQqljVeu8sCS1RBBNPFrBjxqgT1GjBhBhUwi6iImSQ+QUAEE4JZwYARr4piFjFgBgxXzJgTKjkLy7Lp9ddDzdb09gmTdmZ2u9+7P9mZczpU9zlz+n+qvnpjFg0ZMzFq4sv6nER9zzxO//3ym+q70RO1RwqecYOODwq59Av/fP61D2j42EmE7E4o8MAZN7wfHXpARLejNoEX3pg/26YrndimLS1YVqzH6AdRvK4BeY4JjRgS2MSFE/F4QR8AWmaq8KVZ62IzBeG7dIMXmQXIJq4LwDSz+O+EPF6CwAvGB+hkZs7C5wzx2CsmHvASjyCybQ2wIDODoUTvlYmCF5tAdRhPo0T7GfY8B17CWsp+XFaDl6+//5V69r1WpwVFOmkUxMHeet8U/UP1wC2DqUnjouQskONn1zR4eeKpfPru++pwRorrmiZNxOPllTdKadr0SDupAi9SOBj1OvCSmsXvwEtq7OhVy/c//06XXHkjTbjlatpum630YU8+N50efPx5DWNuHH4ZNW7kfx904KU6eMn//EPtEVLao1+M54BNXFfquUjwgrmAgC00YxrcOkjPTV0EL2HGHeYqqevgBQBr/VmDo2vJCl7Uum0wcWSMvg/btvDpe6xrOoztwx7DsA1rvmKnPan1xkU0f8PmNmwd6TguFffJVIMXjBOwY97fi2hzFQbJIUd+4w863i/kEvV6hX+WlpWr7EbLddPoi8x+B/Dy0opfVTajBtqLA4WzF+Hf0oMFG3R4i3DISbZ5vNQ0eLFthE3wAhsinTJstVVh42hmIL914BVqhDo+aHciLVYvu5DNMEw6adSFwnOm14kCHfCo4JAx2Z4XeJlXvlp7n6CEAQDSowleLjITkjn2bAQvErx1atiamufXJ3icxFsceInXYrX7+KwGL7M+/YbOGzSWPnzhLiUYGUn99+6sL+ii/7tVKbVPqBbLX7unyj66mgYvJmBhqJJq8DL2jjL6/Y+Ih0oqwIsZRoV6HXhJzRWTSvACn5lsyU2WLR4vn30xl4aMuoOefWhsNAXq7E+/omtuuIdefPxWKjI0TWyz6sBLdfBS/9GxWtTUFNrNSvDCm23leVPSbxQFieuaayDdHi9oLwg4hbnb1Ch42WBT9AuAo2Knvahg9nSqN6sqbMhLhDmV4rpmXRLiwdMK8y0Lr0+Ekq0bWuVNgWMajumrwUtJXwVm9to/jMn1MQxsyg7tFnhetI0sAy+puE+mA7yEnoQMHgjw8viyH2J6IL0F5OZeghacgNTT5zVtpzf17DVR06FGNm+NVHi8nLvoLa0vguLl8RIveDG1VrymPRnwwnPHAMULvKBt9qqQ7Q1f+lFM6mkchzpRAJBs9rCNIx7wAtDXcd4ztJUK65m95SnR6kyPF+kFMnfr09T6q1/tWP4gWY8Xmx3CpiKX9rCBF9SNNNk9mm7vGcbkPF4yeFNMY9NZDV4+VDouFwy+hZ6aOEIJ5zbWZvjfl9/TsJsepOcfGq3S4FVdcFsqN3uXTjp2pZgeHvjWL9Rozhf59NwL/qFGuQpezDAq2MKBl9TcWVIJXlLTo9TUki3g5VOl4/J/191Jd918FTVpFBHO/Xruj3TL3Y/RfbcOUzH7VffBNpu1rJF00qmxcM3U4qXxwh4quQBeeLPNHhC1Cbxw6AxAQr3RD+o38avXRjKRpLNIwGZLF462Mw5eVNaskgHjYswg+23CLoY48WZDYi+WMOfJVNfZ5PGSivukAy9VS80PvJheG9i0A7xwOEhNgxduF71PpceL9PwIAi9Pr/mJnl79Ex3VeGua9s8fUU0OiBFvVdA06vESFryYIUzclzAeL8mCFy+BY9iXwYstxMq8X8Mm8JCBcDA8qfw8XngNdS5qTVM2OypaFXRWIkAmIj4sYYQJVsx+o84ezSK6d1sVRF7ex1Nsnj8SvDA8gS38NINs4EVCManlw/0Lew3FM55UHetCjZKzZE6AlzBDfP/5O+t8OunlK/No5Qqi5huREkyrJBt4ab1ZJV3U167xMuOdPHrnPX9xXbPOmvB46bhfBR19VHLiuqkALwBTK5SN99qzQts3nuLEdeOxVnYcm23gJYxVpky6uUbSSYfpS7YcYwMvMv2z6R2QTo8XhgxsG97Ym5/D06Hgy1n6MGyuw4CXogEnUl7xGqvZa8LjZd3QCQmJvcqx5z/8tgW84N5f9ULAa11BPDiv+B+q2GI7qmwU/JCdKHhBO9BgqdhCiRf/FREvDvL28Usn7evxsgG8oK/5P35F5Z2PoLzF87XWkK1dB17uDHXb8bpP1lXw0vf3d+m+JZH0x1y8wAs8XNoVtogRSI0FL0eo8JfF0Q26rDNd4roSvBzx1wsqhGY5pcLjJR7wIr07MGYWkJVixOjnIKXjwvDCb7GaUCER8CKFetGWGWqEz2weL2HASxhvEhMeILtRTwVCVpWX0vCls7XHBxcv8CL7KMOR8HkQeOG6ESb0TOsqmBPqJqEOCgIv/H0QhLKBF7a79NaS/fICL17Hhx1TKo5z4CU5K2Y1eFm8dAW9/eGcUCM88agDQsXPhqoshw6SoUYMTjg0xwZeMDQvzRUTvJgeIevW5dH1N1eBGVlXOkON/Lx0wk7V3O/y6b9Pxz68x+vxwmOM9zz00YGXsDOVPcdlC3hZqtIkz1KhRWHKEQd3tN4HXahRbKiRBC/mW/54wEtks7+Gisc9R0UDu+kp8tuEhwUv688eTAUzp0f1Y/K//1xvtv08XswNvFwvNQFevLxDgtZtMHgJqiHyvdQeKd8xomngVxIFL1LcFvo+QXOu+1aYT02KCmnpqpKYLkW9fQTEiQk12gBeioadocPitGeOWm8QdLa1GwUvKosVRHrDlqjHS4jzuA2s0fJOR2SNxksq7pN1FbyMmf8/Gvb3xzHLxQu84CAWS+UTOF1wNni88IbWBC8ybAReKGGy1MiNt4Q7Nm0UP/DSvF4DGrH0Y516u2vjthkHL3IsYcELMkEBmDA0SgS8+OnI8Jq6cpP2dHmz2Pu3FN2Nx+NFLugw/TXvl5y5Sn4utY92+WMyraooDdS7McGLXD9yLmQ7XuDF63iz72G9ccL+RsjjHHhJxGpV52Q1eEluaHXjbAleGAwAvHTuSNUgCVskUfDCXiMAIay/ki6Pl4HDyqhEgR6UVIAXEyqh3ni1Y8Yo6IQ+OfASe225UKPsv9c48BIevNR/5GaqNzs2PTDABYRP8XlZx8P1f1lklzfAYTb+XuClcMZUnRUJXhTw1sCmtv4jY2scvKB/+QomVKjxhoEX0ssm0+CF+xK2HyZ4wbgZpPAVbatLgpdIaudflNaKv7dPEHiRa8kEL1gLRcPO1F0CeJH9lJBPri3OeBR2PuNJCy69asqOPztrwEsq7sIOvFRZUW5UObsLf2uCF6Tb/bNsTWCo0SlNtqPbWh4YbYRhiNxM2rwtWP/Da45tIGHgRnvSzdt2jmY18vNe8arXC7zITTHDBAleVleUaK8bFKmvwqEvCJ0JKqn2eGH4g3Zt9pJeG7Y5gLeJHHcYkGHCgyABX/QtFeDFXJ+oF9BrVIsOQWaP+d6msSLHbYNWq1T652+VaHFTpT3D4UcmeHlt7R/UZ9EM3ZYXSDGvOXntPdsm2HsnKJQpLkMYBzvwkoz1lI5lpSrJVeHOzqQFvMDLtm3zYlJDyz4yLHn1tXxasDCPuqownjYqBGmq0nf5XIXTcDEBA3uN7LxTRTSzUbrAyyWDqsKhagK8ACoVq5eRrVtXeoYRsQdR1yMrqFOH+EKfnMdLJq+SxNrOFo+XxHofe5YDL7HgRYbymB4vvAmVFpQeI+wtk1LwokJJpEcL2pYgp6Y8XhhGAC6tP/vKwKVnS43NJzUYH8nyVDLgFt96UuXxIrVHwkAjCV6wHuBJAvCC1ODwNgJQCQIvGBhntfJrMx7wIr2xAPtKjz1LQzgUL/ACW+cVr9bCuigMXniMQamn2aMm6DjU7cBL4GWRcwfYPF6QveWoRpFsouZG3LaxxXG8ieSNZVMVlrRaeQRwMTeZMnwGHipEldXawrlmiIlp4GwDLxxmhH6a4AV6HrYNvTmmsOAFG/1d/ngi5nQ+V4YadS7aPOqtYrOXbM8LvLDeChpLF3h5bPPD6ND6W8aMZ2flWYJ1ZAI4r1Aj2/oM6ymChgFFsIa5YA6hVwO4aAMvZqYveS3g3xK8YL7OVfUjZTgXm2CvbQ7Mev1uNA68ZO9t2IGX7J2bUD1LBLz0UxovAC3sIcMgwQwXMsGLDGWa9VE+lZQo74+zy7WmzNTn8qNeMGbHE0knXdPghdNk++nJMHiJ11MG9nDgJdRyzqqDHHjJqulIuDM2jRcJTeoCeKksaqz1V9ijw2uDHXajzpMhwYtpR/4uSP8kK8CL8vBBYYhS7+VHPYGK9HiR5yQLXrguuTbxGXtY4d8SEOFvhLjBQ8oMMwOwKR79uAI2Ee+tIKDC5wcdhzYdeEn4VpS1J9rAi/ROMDeB0HlBiEW7wo1jdDp4c2tms+GBe4EXfM+bT69Nvx+syCR4Yc0N7jfbhsecbvCCdkzbpAu8yLaSBS9XLHnfmlb7xa2OoX8XbBpzrTBEQJtSHyce8IJ5mbv16YHXoPRGwcG8trz6wMfgvxK4yTUpwUuQbgx30LwOsM6QYWtXdc29vsUJgePgMKhEPH2CKnceL0EW8v/egZfk7Jfxs+MBLw0aVGpY0u2ECmqvBGL9dFkwMBO8sEcMQM23c/OioEWGHtkMkg3ghcGK7B8DFHj5YGwoft41DrzYl7sLNcr4bSCwA87jxfB4URtthhCmd0dYj5fKTVpR3rJFVFHUiNaNfyGUxohnqJGPx0tpj36U98dPegOdqMaLuUAceFEplDfYHLaRHk3wcskm8CJBDP4NAWH2bEFfAV4g9msWAC9ey7bU0/J4CW78QBlCq1jLCGsRoUatSlfQkm+UMKuCe2E8jQJvVmk+QI7BHKsLNaoyvh944aMQOjTln5+jJ9Vm8CI3vGZ6bYR+2DbUMAzsCE8R2Im1ZbyOlUs/rMcLzokXvMi55XPDeLzIthIBL1Jbx8ujY862PalVRVHMXYDthfORJYmLqdXDMEcehzC4lcrLhD1mbNmN4OGCY3BenwVvRcPE0A6vaQ6LQ0gW9HpkwTF/qTlG5iZZABPh4TJo+Yd0UOPN6fSGO0XXCTzKONzI5vEiU5nzOmJxZBzPGaPM8D1un+c1Hk+fsLdeB17CWsp+nAMvydkv42fHA14YkDBwuGdigQ418iomeJHism+pDEis8+IFXjZrVUkLF8WniQKX7HXrCmnEmHChRm+rLEy//krRcCmvsdggE+yALEUyvMoLvMisSM7jJdbKDrxk/DYQ2AEHXrzBiwkhbOAFG9fKoiaRUBS14eSsMjA8n5+UxosPeJGTm2rwUvj0PXoTD7gDj5hMeLxIAGLPahS4vPUBUU8MFSpUemj3wJOyAbywtk+pErWFUDPWl+nxIgdifgfwgsKp0eWxgAoNx/SNQho/oBIWvEhwyOBlozeeoJJnH9IaSPCysZV6s1+ngs8/1GFTWGeZLHIMZihZXQUvU1f8Qif9PD1mWsKAF85EwyfWZvCCMfIGOV7wMnPdQu0NYaZ5lgbHhv6pVT9GPYjSCV6kQGyi4GXu1qdRM6Vj4lekiCyOs8Ed9uTgepbu2IfWrY/NvCrDZmRmJjNLls0rBWsSBfaX4XPcHgMM/M3rGSFyCLMDLDM9mkzYiPOkl5O8JrBeOMsWjoPNOvw5RXuL4d+9F86IWRfcJ9it/6L3YwCQWS9DJi+w4sBLJn9l/Nt24CV75yZUz+IBLwAGSBfNcMEr6xF7xiQLXhjIcD3IivTr73lU1ICobVu7RgrAy99/F9Lt94YDLzyGU3tU0C47e+uueIGXX5W2CwMkGDxZ8IIxvjo9j7ZtSzrtNBcXahRqOWfVQS7UKKumI+HOBIUahQEvaJw3vdkCXkrGXKFFcEsGjNO2sWU1Yq8c03hesCgd4KV49GNqUx55+LWVVIAX0xOj9LizA9dLg3uHR1N2V/N4eetZ/Z2ZahyV2mBJkKAva7ys+PTjGPFimSacRXPjBi/KC6XBxJHVxgu7sygvf+nl6ZQIeEHY0/qLRlHzF+6j9dOm6CYgBFzW8YhqfYlX+Dhw8pI4ABAoqpljQDoHXqoMm07w4hVOhNZtISjYwLLOh23q4w018vISMOs2s9oweJHCp2YoilkH7GiCF9sYAUPuX/ENPbh6rq7CC7wABLyy0zG0eGUJVVREJDphT2zWEYpiOxefwfPiteKIbonM6MR2tXmwcD/hNfLRlqfoc2WGoTCXoVcWIqk/g/AepJZuVlCfft3uzGrghfthQho/8NJ70Zvay0Xqr5hhN/1VuNPTwmOLx2NbH17hc9IGvBZ4zMgEdcRfL0YPkaFSWEsSFHVquFmMfU3bmufKtYm6AKSaK2DUp2m7mHq2qteEZm95cpipCn2M83gJbSrrgQ68JGe/jJ8twQt7sACw2MR1EWL0nAqpadCwkoZeWU5e4MUEJjxIL48X1McZiKRBZD3zFxJNmx4J5/HzGJHghQHQRhtV0oDLYgk46pHprb3qnK88eqYpEWG0b/YR5zB4ASSB5wvbxpxY6fECceHTetohj/QikiFWDrxk/FKJuwMOvMRtsqw8wQZeZEdzFbysV+CF+87gAWFP+cVro8Pz2sR7gReZ0QkeEqi3/oSRuj6bSK7csEs7+nkVmIskFeDF9MQIBV7GD4zRvEG/WOPFFDOWfbbBEnhElXU5yQodcC6Dl39GXhL1mkIfGf7I9OHxghfuq2lXmR1JfmfzfPETSZbnmpmT1g8cR03vULDl28/1YYAxJf1GVbsPSF2YMHOTzhtJTDYrI322Ay9Vls8UePHSwPALz5H6Hbzp9ctqFDb8wgzhsenQIARpVMsONHzJRzEeCmxJG3hB6NGfpWto+NLZUQ8X1C2BlBd4QX03b9spBrxwWzbvlaBrCR4f89avph7NdiAzDIe9QcLay9aWF3hhGzQrKNRCswjfObBRG3p+y6OrgRfuhynW7AdeblEwCl4uOIaFhbFOAJDYU0d6hUh9FgATzkjEYwoDXhjY8Fo1vWM4vAjj+E7pzfBxWENHKg8bKYDM7XLGMAleTJFh+beZfQr12EKZgtaF3/cOvCRjPZfVKDnrZcHZErwwSAHw6HJwZbWsRv0vL6dbby/QvQYUSBV48TKDBC/ffqdc/T6OD7zY0lbLtiQM8fJUmaNgCmCTrbAHkGkPU5Nm0iMFCvJQNCzLqy0JglCn9Bhy4CULLpY4u5Bt4GX5hrC4jZpXao2meIoLNSqN6l2YdosbvKi35Ej9zCUbQo2k4KtMiRwEXnhDzJ4KMswKG3S50ZYbdi8dHPa+SRi8jLqf8v61A61eW5UJJcw6zzR4QR8RLlTa4yJrdz3Bywb4I3VlkFIcGZVk4c/M77T+zzyl/zMrNv257o8REsf1meAFIU5SI8bPeycQvAgPLNn/bAIvpqcTr1n014GXqllLBLzw2/UgcV0vrxa0ngh4wXm8uaxp8BJ0f4Idp6z5Wet/yBAfc6yZAi9+/eeQqnSAF9kuvHG+Wb+cdttoY9pU6buYoUYym5I8Dx4sRzf5VzR1s/QgkeAFmaRsWX6k9w7/G4LRb1jEa83U2Da7cUpubgvXA+YdoAdjZI8dMyQPdQG+DFD9hO4L+tAsvwHtWl9BPZEC+/C/XtCgjuvlPkgoA2i0qrw0RoTYgZegq7Rmv3ceLzVr75S3Fg94AVC4e0JBVHdl0qN2IOHl8cKgBvUECfNioLIeUxOmzznlygslj95RWjGt2+TRIcr7BEV6vMQDXkxPFWxSUebMIR1eZStm/XJ88ngTUIUJR8L5DrykfLnXaIXZBl44qxjW+tFHVtIuO1VSQ/XvMMWBF2/wAvsFQQUcwxDD9CRg3ZVMarzYMu3IPptrhGGRuSHOOHi56lbK26V9NfCiNUJmvk7lnY+wepVkA3iR3kf13n5OxX41VjAmojXjBV44jTPCggpVJiUIKNuKnxeMebwXpOHj1g2dQAVfzNR/lh5zZgxcw2cQm85bqkSjd9idTO8UG3hpfM2ZVLE4khrVpvMiwU6YrElh7mfJHCPXOLJ9FY9/PlqdAy9VlpVhGV5CqKbGC86WAMFM68sCtX6ZXZIBLzK1cio9XtjTgnVNvOxhW5cyPMbcAEsAFQReZGhOKj1e/K4lhC8NX/JxNQAQz/Un5zNIkNfvmcsWaiazapmhO/PKV2tPnqMUmIH3CmcrArTo0XQHPQTYlMOoGGowPDHHGAa8MFgz1/DRTbemV1f/oYV64VljAy9oj68n05OH+yLrxdhXVa7XKa7lGoONUWT2J5u2TTxzaB7rPF6SsZ7zeEnOellwdrzghYGJ9PaQw0B4T2sVagjdE1PjJdXgRXqssJcJHlABSqa9UalDkhia2DIj8UaU+w+Pno2VNwB/DkCCIjVc5FhZ/BdjHjqkKvTqahWGxRta2Uc+Nyx44exROM95vGTBxRJnF2oSvPz3qXylCUS+OkWvqpA59hoDfGmjrlOkc/cq8MBaoELs2v6rkhx4ceBFrpOw4EXqYZTvoBaoKgg5SpvHiwd4keE9tlCVdIGXsk6HU9mhJ8UIwtpCjWAXtqnpJQTwUH/+r9Roj71pzd1jIgK6G0JyZMptGQJjXtNeYUPyOACTyk02o8pN20Q1TGz3Bng3scYJIAzCyaQ4L4erQR+ofKfInK8/60r9Xxt4Kbrw8JhmTI8a8xzpYRLnLTnwcLRF6p1LxRbb6WxPtsI253FySm4c68BLlcWklwNndDHtGS94wflygyzrYyiRDHiRm+NUghfeMGMD+8Cqb9Vmt3oGG6/FKeGTCV4kwDHBi9exqQ41CryokjxAitcGeV74PXOZWX7MbgE4jFChW98ojxBbqBCOt6UkD+vNEwReZD0mmLuxdSe6asGsqAiv1JAB3Dp34VsaoDBIMrM18VjltYG1eP/Kb6Mgh0OlYAdAJ5n9Ced71ZnI9DrwkojVqs5xHi/J2S/jZ6cavEhYkQx4MQEOBGc5gxKDCxkGlArwwgK7EshAH2bFikgK7YZK1Pe/T1f38uH+SA0bFv+NB7zM/S4/pn6pO/P77/m0aFEhbbppqaewcMYXU4IdcFmNEjTchtPgncUhgIAkXjDFSyD6UBVWaCuAOXO/z1cgtZIuv6iCNt2oYXIdzdGzWePFBgt4SGE8XvjYTHq8sNjrJvkltK7vsSrTUuSNfbweL+yZ4OfxAi+MejOnx2Rwgg2wWUc2JBnShM9TovGSQfDC48FaMDMeSVgQCF5U9h4Wu0VdbOMGw26ntU89GM1cBAAC8VtOR26CFw75wdwiExCncfa6DDEvnDGo4YATolo/fro/pSpsDufYsiLJdhhO+IEXG8hAHWYKdb/sSsneYoJEfPOWLojavHLL7aOaPpwG24GXqhmQG0m/VMkyywzO9vN44e/j8Xgx9TC81oipbXFes11o4vYH04JlxfqUXf6YrLPJoITdbJsaILaMNkFrNh7wwl4ZbCdZdyY8XoLGFvZ79H3XBi10piC/4gdepKCxrQ4z64/tGPZqkd+FXQtmhiauA2sC5ahG/4qOT4IXeKNctumedMzvL0ebNT1aTKjk5Rkkrxt411yx6AMNXmRB3SjmdQl9m2fadK2mXcPnApAB/mA8tpTbsg0HXsKufPtxDrwkZ7+Mn52t4AUwo03rSv2GvuuRFVFhXRgMG8GL+pZHPVPwWT/1dxv1eTweL0+ojeV3amMJzQukhWbQwZ/LyQHYsUEUHOMHXkyvGhzvJcBrHivBy6SHC+g35UWUSCrqjC+ygA448BLfDMETpbiEtHcWirluADwpL+J5tnwl6TWOMnN2dYFofA4PLRxXonSIcA68tcy1PvAylfVrO0Ue62CJF7zIFLw2c3mBlyCvDNRlbkKx8UXqY9adkFl0bKCIN+N4QF13xkG6e1KLRWbnwXf4Gx4NpmYIn+cHXtCWTbhV6pFwGxJamOMMyvgTI64bAF68QlU40xDa9tNakfMp7StDeUzwYoajBIEXOZ4Y8NJ/DK179A4VxrNQz0uZSq0M4MFj8gIv3Gdb1io5Hgk0WCSZvWBk+nN5Djxvyjodac2KJI9jDSCzj+sUnGuo4BG8YypU5ioWJ2aQgTrMc9IFXmRmK1s2Kt2XGVO1PpO2i0oPX6hCwjhUEN/XVfDy2qo/qOuPr8Tc7mzghYU+cSC+76wysaQbvARlDeJOm+Clk+rfB7t2i4IX6e0QNtOLl/hqPD+j7MXAAEmea3q8SK+K2uLxEo+t/MCLBB9mCBvaQBgbvF1sduY+2Dy3woIX1GHzmLFBEjmvCF3ap0krX/BiesiYWkDcfwYvPEbbeCR4wdiaK/Hib9Yvi0KV21oeaJ0SBpNeYU7yJAde4lnV1Y914CU5+2X87ETBC2fxMQeQKo8X1LONShmNUCFbWBNAiAydYO8aL/ByiHqrb4qKsgdAx/0qNOAxAQqPDSFFF19Y7glecP7RR1XE6NZwXTbwgnr9Qp9YO0aGJDF4gd27K++bbCs8zkTAkAMv8c0me0YB4B16UCXN+ihPe2WZBV5acz7Po99UCnZZADJRPpuTp/Wa8PeMd/N01i4Ob+PsWpwZbP9OFdS7lwMvppcG21WmPDY3uKbHQErBixJA1dlthMgqb1pTBV4wRtu4JRQwdWpwDoRb8+fMVOeq8A1RUg1e5DjzEwUvLz0S9cwx4Yz0BpKeK2HBC4ZugxoyExGOYS8ieANhQ48iUzrX63YOlT33sP4cfazYa38NAZIFLxIc8DQVKK8baMasGzYxChviu0vFQo//ZgAAIABJREFUHg1Qsf7sK6tBlPUq7Kz++Ag84nXGoA0gBFo3+Ur8lzVlcAwyQKEu9s5Jpl/y3KBws8K3pyqdoOmU/+cvOk14/rKFUQiD/qDUVfAyd91yavfNkzFTYQMvcsOLf5/fvJ0WA5UFwIC1S2weIvGGGqUDvKC/trAX9jzgzWcqwAvbxrbB5w0364w48FKf1paUVxPXZRvyfEB/qLkSrAX0MyGMH0iRYU9cp5li2u9+JD1mvMKZcL6cRxzXRmUb3P2nquvLhDXS08lrbeJzKVr9bJujYrJgyfGwbXgdB4kkm1ALdeOz1UoQeAuliWN6wDjwktyvlgMvydkv42czeJHhCl5ZjQALTC8RcwBIlQyPFAYmMowhHo0XWQ+DEd4Eok1ToJfDhABeHn8yn776hgifydAgU1tl/B0FesPKx2Eji2xOADoo6MOvv+UpAFSp0z97ebwwbJiqsh8hpTQKe7XYvGe4/6btpLYMdGVkf7mvXvowZl0YA0KzsDHnsCfzmLcV1NrmXxVawyPZwhDML1W2VxuJghesWfb6SLb/QecjrC0eMVrUly6NF3OdAZgAKrZV65TXH9rnMDlcNyglJbFratZH+dqTTKZzxzmHqDWDTF6os/sJkexmO+1YQYMvduDFC7xIrwwTvJjZYUzwwm/XE/F4YQ0RDt2R/QgCL8XnH015xWsIYSDQEWHvCawVHqfpASOvEzkO3lj7hWPxuTivcMo9egOLYnqL6PZVeAyHrwR5vIQBL3yMp8eLH3jZEPojz2VRWx6Tn8cLjrGFopkAio+T45E2luAFkIYUmMD8sX0S9XhhrynzHgjwAZ0T7fGk1gf+J7NxwUslb9mi6Gl+Ar5RqCTsjBMleKlQ8AWeNfAQWn/WYCUUrASRN4j4mn3jPgPqwfNEQhj0t+DLmZ6pub3u9TFeTxuAJh8LEMXhXxzaZQNyDrxUWTcMeBmksrBIEU+cLcGKlwaMLdTIS5g0CLywFw7OR4pm7o+fxwv301xL5lt/3uhL8VKv9Rf0uR944e/8wAunX96ysDHt07JlytJJB/W7Jr8PeuYyMxMBDswr/SeqdYK++nlscPYkmcY7jIcH24Dnh0GZl234OPZMaVJUj5rPfSB6uAle0K+T5k/TGYuCwNFTq36ins22px6Nt7eCF/Zymb52HrGorhSdtgFHE0jhGIaQtv448JLcVeHAS3L2y/jZDF7M1Mq2dNKAHV4eHBgI3pzvvHNlNBOQ9H7g+tl7JCirEc5FAcCRHiAsdGuCF24L4OX+h/LpJ/VMDy+YqS8ob4ANGYo4RImNLkHQmJsK9MZUFojtFqm9JsIwEMaEYkuhzW2btpF9xLgbKokM2X9z8tkm8DrAxleGJMlU38joFFSkCLJNw0OCNikGHFSv1/fcXlgwJOtJFLyMublA6ZlUROfGr++AFcjkA7HYBmoeeD7DjJdtBSjR/XhvkMV1sShtq00KaZMW5fTrvApasCCvWgpnhlWsy4LzZinPK8Ar9A9/I+zHhGgM4SSIBJzEsbYMXFhPy5dXEjxldtm5gng9yDWAvsv68Dd7vyB72Nabk9N42eBVYlszgCfUWG0AlTgpdDdkCQQvCkTASyUR8IJ2bN4Cej4t/ZWhRsWjLqW8H77UG3d9/IawFfw7XvBiiu36XVcmeAoCL15ggNtIN3jheZHgheEaw4dEwUvBrOkxmYgAaGSoGsbOsCN/lz2pYm6s9xCL7MIWJniRXlj43ivUKAhssZ1ldiGMG55WBXM+UJBjlj4kKHOSGYKFc3h8ZkgT4AugUsx1pNJt5xUrLxiVuUl+z6Fh7FkFCAmoF29IEodX6X4Z4IVD+ZDxqWKr7bTHjbQpt+XAS9WMYcN1W6sDaMo/P9PTq3/UaXFNj5dEwYtfyIYJZYLAC/fJzOYSL3iRb/2lxws2z+c3bRcNqbKFudjulzIsC98nC15kG3jmWryyhCoqYl+6sV2Dsgf53d8z+V0QeAEgQKaizkWbUycV5sZF6r+EyeAj11g84AXtAWIgPXQ8RYIXL7CCegHXkIEpbJGeMrzeUD8KtF/kOujw5xQdboTPZqnvABO5LTNkCUK8LM4LbZi5W58e0yUHXsLOkP04B16Ss1/Gz04leOENvC3shMGLVziPaQgv8IKNMwAJQofeVqmkuXiBF5mGGsdyiA82k/dOLNBv9QcowGIDQbZwIBt44TAnE7xAd2bSI/k6hIMzJnmlnEbfpDgvp+rmPsh2sVFvqzxVvERRURegBNqV8AsbbXhEYMwbNyftyYBiiiAnsijTAV78PFpYWBlwKgi+8LFRDxB1DjRLglIpc/umh0lQewxUGhYR7bNXJX0wK09DNG4TAGSBCvGZ8zlFQ4TMsCD2QpFeW+j/qcrzCusWkKT9nhENJBSsrwXz86qJPweBMKSHBxDcuz1AD8VoKUkg57Ia2bMacRgRbzqxEeaNKF9HuQBe2JsBm1j8uybBC+xToMI48ovXau8beFlIiGALhZH3qJoCLwhxWTd0om6aIQZDpETBi6mBg/FLEVy5dmzgRdomSIRWghe5TsOCF4xbZ/1RBXMELxM5T4AnXumscY5OQ61AjdSLKT3mDCp85XENOiq22r6aVgyntsb56GeF8vQxwSZsz0LDcl2Y4Cnody0mdGxD1iicw4K6+LfMYIS/i4aerr1+WJjYgZcqK2MDZ4KV2gxe5OYdm3Fs7uE9Y2rZsEfByvLSasKmco3KzSs+DwNesPHuOO8ZXY1fBqC6Cl687gESmnnpo8hzZRrveMFL0H3I9j3Ai4Yey37WnirxghuvNqWHFLRkRiz9mKBfhAJQKsELw6ZODVurUKgFOsMSAxX+jvWI8B0LUaMurOVmefWVplNrmqnOPXernRIxgztngwUceMnxpWADL/AMgXcAb8wxRAYUfh4vDAlSAV7gPbORggMIFeJNKDwBiovzrOmdWfsEHi83jc+nZcsjm9Gpz+XHHM99NEEQ+ozQC+n1kix4kZtmrgsbXehqsBiwXD428ILNL7wl5Fzo+VCb8AGXVfd8AVCCSOqkRwp01RK8sDYIzt1rj4h+DgqDF/awSGRJsxcG94u9PlBXUCiTl8cLwrQQCoa1aIZLcXuo3/Rkkv1HP8bdEYFfskhvDpv3C58HDxcOV8P602LMyn5Ym6Z2CurnsDibDXkuGIqFsbMMA5LHY7136qi8Yabl08YbIyQoNhSONZJ22cXfu0fCLT3m2yPXgKkl5MCLHbwEveXHnFULLVIbSJkFxgwV8QMN5uYa9ePtPzbBpjBpoMfLUw9Q3kuP6U0vCjbE+He+SlecTvAi0xGjXdmmLWwmLvBy6XWUt8+BtHptJPsIl6BQI+ntYIYjmWK37PkBr4/iMZOreZLYPDvkhl3q8djEh2W/AcJY7yVPwY5KFUYjixSBDQIvcj1IoBMvoJDty3ThJmA07282seWyQ7tRvRlVArUa+im4Ay8fQM0SBboYtPDaYNjB9SOMqVQJDSPFtdRTYtugjxAkRiiTFO2NiEZHQqjKOhwRA7zkGuD553A627qCnhGuwaa7t6dGjeteSKaXxksi4AUbu2/WL9UbN69Qo2z0eJGZZfzAi0wFbI4DG9eezXbQG+utCprGhGHZNvimZgfWJteZCHjBBhplVMv94vKcCPMsUxPHBHm8+PUBEAJFesJ4HS/FbMN4yCQ7doCX/Lw8WmX8riVbL0DdiKUf6WomteoSI/5rhkOZAr44h9cYrzkTFppi2qsrSrSAceW/+yXb9Tp9vgMvOT79NvCCIWEzjs0+p3VGhiEIyNYUeNGZWXAz2OCVgX9j4/rrb7Eghc3Pb/YBXoZcE/ECMMOReFzYxPM4WBhXAwsFdbg9jHvokOpgw+bxwt4s2MTOmUPRPnKIlNQ9keFE7dXmWRbekAO2TFY6NQhLstmBz7GFCJmbeoyv3c6qT6ouhJuwBogUR2YgIENfWBsmLIyRdjEzQNkAFsDXt9+pzDwbE514VH1aotxey4XbK9q9XnntcME44OEDL5UoQNqQjQrHeIVL8Zg4jCb63w2eMvAe4bX1m0rZjdLtxAqapbxU2JsEnzGIYHAWdNkzpIEGTUcFSFhLZVvlrYQUzVxMmIiQNIA5s6D9+cqjBd/hmGOOsoc88Twk6sUE7yCsExPYOPBiBy9Bm03MY7rBC9pgABSXxksI8GJ6wMh1KccVT6iRabNUgpe8E86i/O7nxg1eTKFcW/pnjN3MAIXjzBAeG3ixzYtX1idp4yCwJ+uV4IWhkKzLC7zEG5Ij6zQFaW3Zj9hrRQI+8/7m53XDXjYMTWTGJfawYW8bhC7RmtURrRgFZMr26Ky1YhC2ZIKTmL4LwMXXE68B1vOx9dEM76p/02PUaJttgn4eat33ErxIrxa8MZcaLmE8XqRxbODFrJOP57fz6Q41QujQagWF5m59WozngWwXgqtdG7e1erxIgGKCF+nVIr0RMMaw4CXM4vLyeAlzbjYfkwx4iWdcEkLURFhWusCLOWa5Hk3RYJuwMLyDEOJ0xF8vEiAL0k2zxxWvWc5axl4wOG7enmfFY253rGEBB15yfEkEgRczVCGd4EVuOsOAF3jFtFFhmoAl8YIX3pCboTi33h7ZiAeFaJigQS4D00bsXYFjGLywJwOAwuZtSMEE9b8NG3IJjGAHaMxA88Usts21CYYwDnhESNFV1AMvEYjvorANZLgVPtu2bR5NfTGvmq4JPFEARlAHwxA/8AIwJYEPYIS0w43DC2nt+ljwEg0PEnAF8AEFnibwhMH8fzs34gHF4sr4Hud+p+zZtm1V6Iz0MGJ44udNYtqL4Zr0CjG9lqJjUtBu4OUVtGaV0nhpWU55+eVRUWqeQ9b8QYgPRHHvmZAfDR0DpEHB+JYrz63vvs9T6dODQ6NwDuYGXmHdu1WkVHjYgZfcAy9YD6YXBHs34AG1eNa7lHf3cELYiQ4bsXi8mB4w8h4EjwIWHLWBF3gfVOy5v+6DFGHNJHhB/22gwRe8iCxDqQAvRQNOjIgaq1TK9VSWHBNWwJb5Sxdom5kZsczfAOlJI+faJiKcDvAiw3AAPQpefkSHjMnCXjtBEE96o1T7sbN8gLYbKh0j2Ik1XxDyAw8WeL+YhQV+dVjd0/eqOVgdFXjmYxngcFgZi+raQJa+voToLv4uHH43Nd5tzzDdr1XHeIEXW7gMtCNQbKFIplFqGrysVG/kOcuSl8aL1ISR3hESvMix4d8QM2XNCwlQWDeDx+3AS3KXRU2BFxlWVpvAixyXmXUJgAXw5euSpQRtGMBHpOBGgRcLe3LxmsZahucURH9xrFzjs3brltxE1/GzHXjJ8QUQL3jxyuwDM9hCjdiLAB4UEpDITT4DF/YQQV3Y1EoPFHyGjeh89ZvNm2IcA60S9oxA+0uXFtDtd0e8ApAC2tRuQR2dOlSlfjbhRVgRWz/wwtCAlwZv2vG32R/WHZHLSKbKRn+Li+2iqQxMACIAUVq3ivUQQp2w6cpVVVoituXKHjmybzivs/LU4DAb6XEix47+KQ/IaCYo1I/xwvNHhjJJPZnmCgTdM7Eq/KfHiQX0/uwy6q0gEzxa4Dn0X+XxgzEBWpnaI2gDniuAGzNnR9phzyV8Z8In+R2+Bzx58OF8q2eJtA+nRkdac6mnA+8om1gu+r1yeR5hfIBL5kMAzps1O09Btjzdd6kxg+sKnljy+pLrJtO3GQdeEgcvMu0y5tHmAZOMuC7qtHm84HOv8BMNXr74lPLGDoykJt6QUcYELX7gRQIUbHyLxz8f4/3hleY428ELj4WvOQksUgFe2EMGdenMPQq+5P35UxRY+HmGmPcBCZHkXPuFxaAOTmUNGMFCsYneY6TeTT2VgtrM/GWud1s7iXrdyLlhOCJFgM22NOxSmZJkdiZ5DMBNwzEX6o/QJxbVrRJ4xsuKKkFSs63CMQ9S4x3qnn7BkrJ1tOkXD2m7Sa8WE5zgLfqDq+fq47BR69lkx2pZjeR8mOAG36XT42Vm8d9REdxUghcZcoUxQacDxcs7B99JvRb87efxIsOXwlzHzuMljJW8j5HeSLUJvGDE0LuBRw9Cj7yKLasYNGLOUyLSOHemCtvq2mRr/TfW8bkL3tIZl3gdj9u+c3ITUMfPduAlxxcAgxdOLcvD4VAj0/MjXvDCm2/WvuBNsNzk83cSvJjhKugX+vTLb1UQwhSexd9//VlAEydVpc01QQfDCtYIMTe37A0R1uOFAY9cBtJGZnrloGxOXJ/UyUHdMlsNh8ugbogMc7gMh7fIvtjAjrlkeaz3qLAb9oLBMbCVbBceLgjD4fa8lr45T3JebecUKRHa4uKIFw6EXqV2CkKIikuUONcGTyQ+X4aIabFZBWygx7KRgh6yf7Bnn3Oqe4uw50qRao+zXqFOeJfgb2gahfUy8bJDom9fMPcofuLJNX3bceDFA7yIrDNecxIEXkxB2Xg1XtAuv/E3wyGkRwKO4w2uXpt//kn5V50eCcnocLinxwsp7wzWGZFjNAGKHCf6YRNgxfk28JK3bKEWZgUQKOt4RKy4rgoDqWi/P1VssZ2u0ywy1Mcr1Eimf0Y/Wd+DUxGbeji29M9sPzP7VIxorQJYCFHxS+0swQuPRbYPG+Qtnl/NE8Yct+mFYYb9IOuQLCZAStU9pOHovkor5RcNFG3gRcIMbjPGk6dRY1o77vmEuoMMRrw2ObsRKmIbI8wJUBO2QR9tUAufYf3lz/uZ1g2bGD1X9tsU1ZWd5bbQfuPel1OjhvUSGkuun5T3v3v1EPzAi4QmUgfFa+w2yAJNDfZKkeclGmrEcAj/ReHQCAleOJ0uwox2LWxRLeMLzuNU0mwDhi2mZ49NsJTHYQroytCPVG7wHXhJ7mrzS9udXM32s2sq1Chs3216L37rU+ofATxesfXuYZtyx7EFwPs3qBA48JLjy4LBixkek2rwwptvW1gLp082wYup8wEPl7nqZQlgAIv9wvycChqb9KVL8n3BC2t1eGUXCkrDzNPt5xmDfsOeK1SIUPcTIrokXPAdvDRMIV9s+rdpS+p/keMZhKG/y5UXBcJpjj0yn/4pLlWhQ3nRdNPwJOLQlHiXIgMcFsTlMbEHEv9X6ouEaQNrR2aT8oI/mEOGHrJe9GsXlZYc4rEsfMtAjO0hQ2kkzIKQL8AN+o5igy7cFjxQsJ6KiqB/o0TVOqnwK6WjMn9BpQ6zMgV9w4xdHpMoeIm3nZo4vi6Dl5XDL6H1bbaJyfYDm8Ojo0yJekqhXNtcBIEX3uQnmk5atmnToZBgIAa8rCuj/PMib7UYhtg8XvC9TbtDCr/iGN6oYrOLDSwXE0JEwzlUKNK68S/ow6pSNu9J5Z2PsIIHhjKmjcOAF9MGElJozwYj9bYXeJHhQQzIUg1eZHpvv2vbDCeSkM1mq3SBF62hokBRWecjqb4CITKrF8Ohwhkqa5UKy4mKNovsX/Xa7UWrLq0eGhTmvibXlhQJjvapy0ka1vF8SyFqrj8o7bbNe0j2LQqeVOhd0/0PrvPgBWlmp6+dp01keryY4GXgxu1jBD3NObeBF1v4Ec7zAi+sVSEz0ch2vMALwii+3L0XLVhWrLK4LIzqteBcM9UuPpOQxAw1kh4vfuDF9Fxx4CXMXaDqmJp65nLgZU5MevTOKjU3rmWvIkENQpgOb71FfBPrjo6xgAMvOb4gkgEvvHFnE5ihRtIDwwQv0KJAhhgU9mpAeIZZzJAeBkTSI8XmRcKAR6YCRt047+iuEa8Nm7eKTfvFNsXcL9OjJZ7lIMdmhjyx14zUvbntBpWiDa4hqjBsCvIm8euP9GjB3HF/pPAuzue+2YSFbfVDb2WmCqkBLArbPo4DiEHbnTtStVTPZhiPWa/psYUQKKRbDkoZHc98xXtsTT0ExNuvRI6vy+BlRY8DNGRBkWEU8YAXuek1Qy94k8+bQ5s+B8+ZzCJjm0fbm3k/8EKXHqdDXKLipEZWI5uHQDR1trKJtIdMrewlTos+s9iqLXMMvpceOHKMXhvgVIAXm0Aut23OHWci8gMv5jxxqAqHpphwSmZVsoIXlUGJlJaJLNDmKek3KuYzGfZjaqZw2AxOSDS0J+jeYUI2rzmGPTjcp/A/XWnlqQOCqrZ+D9gE2AO4EhQyZcvyZbMFgxR8h/6X9rhIayCFKXU1nTRswx4vEoxkA3jhEB3bW3r02wu86Pnfu1/C4OX85u20Z47p8SJFec2QDTOcyIGXMFdd1TE1+cwVJntUfL33PjrbPF6k0G6YrE7QhGEvNWRC2ryFcnN3JWELOPCSsOmy48RkwIvc9EsQIuEIb74Z0jAQkR42tqw3bB0TvPAGvEFRlTeEH3gxPXkQznKI8hKBdklQOJHfDI1XoS/w1pDivPHOaBjwwnW2VcDo6gH19UMAijlmCblsoMSWLUemu0bIFYfzsAcSt80isjxm2zhRf4kKCWKbyBAl83gvaOaVmSisXVlbB2vtXBVeZEsTHbauVBxXkw8BqeivXx11HbzANhzOw3bKBHgxN7bmnNk21H7gpfKm/hqeRDPPqA1x3ryfdNgPihW8bPBWACBBml4uYcGLtB8DGnNctmxRpvYK15MIeJFgxPRIQr3Sjg3HqFCaP3/RzWGMBbOma/swTLF5vJjaOgxpvOCambLaPL/iuDMpX6X+lsUWksZ9sQE4bgNhPuxplOr7BmvWoF6sKwle5Jhgx8IJwzX0a3BSb1p+2Gmp7kq1+mxzUrlpGx3aJgsDGi9BXb+OOvAS6+WSLHgBpNjljye0yTmESYYyyblgT5Ij/npBi31ySTV4aV5QqD16zA2n6fECLwCELaH9HipFNGd7kWmeg8CL/N6FGgXfImrTM5ccbbaBF+nxY4rw2mYJujHDl3xMWxU2pttaHujAS/BS9j3CgZckDZjp0+MFLzL8Jx7wwuM0wYsMGbLZIozYrenVgnpYRNcGgdjTIxloEjYkyW9+JTwx4ZMZZnW08uA4sWuDKHgxgRKgBUSGAUBOOzWS0UaCHZtnDIDKq9MiaavZJjgOYTxSU4X7JvsrvZ0YpEhdGgYvtnallxB7GAWtg7DXCcKHMg1cuK+16SHAgZfqK5DfhrMYp1fqXynSiVrkphN/x+PxknLwcsewmNAQM8UxMhflKxAjQ428UminErwwCDKtbguhSQS8SDtyiBQ22px9SYIXWb/UMeGwrpoGL34aQBgXiqnvgs+qwrkiOjTpLFpDR2UPYp0ftCVDofQ6Q+iROqbpCb1o8Xb7prM7um70qWhgVTYNL68fBi9+dvbqrAMvseAFoTNT/vk5aq54Q40AKRhohAUvZormsOClh+orytOivzaPFwlUOLyChXCRKvfPsjW0Vb0mdErj7aLgBcfB4wZFhmQwWGGh4S3VxnSrgiodK5llxkxfncwF4zRekrFezZ+bbeAFIAUppFEkSAxrGefxEtZS9uMceEnOfhk/O17wgg7LMBsOFwryeOGBmuAlyOskDHixpbhmrw8beOFQGoYziUwCQwiZKjreemT64aFDyqudLsHJQOWRstM2DaPgxU/AlyvicCT8DciyQmnFQHeGNWFk2moJXvqcUx4NZQJg4b5J8CLDkVg3h20tNWIAgQBxJICRwAvjWLiwgBoWldOee0TSRdeW4sBL7ZhJhBrZiplC2Q+8yI2+KUKaSfBSPvWhGKjCMAGeC5WbbKaFSU3YA/hhS9fL4SNmqJQXLJLHBYVQSfsDlMjQj3jBC84vmPNBdNw8Hjl/mQIvMgRJjqti8DjKVxmoUBIBAjivJsGL15UPqIGy/sJrNZRBRrzWGxfR/A2enOm+Y3AYkRnqZWsXoMYm5uzXRwdeIuAFoQXIYgIxWplKNp3ghQFLPOAF/TtfZV5hQV1zbsOCF6kBI9Nl499+oRjDl35E36xfTtCAkampuR/JbnC91qoDL+m+06S2/mwDL8mOzoGX5CzowEty9sv42Qxe2POAO8RhKDYwwkAAG2gWiQ0LXhhU2LRabMbgzb6ZElgeK8ELPD1atMijww8v154P/B28LCBSu3BRns6AU6JEbk1dlXgmg/uVTB1scy/4JMOZDjukklqpB1QONYJHDFIyI6tRe6WL4mc7fMdeKwjVQqpnCMoirTb3Qab0BnhhDR7ZN7lG5NwzSIG3icwoxECG+8YgCGtrl52r+oyHgCUrS6i8orrGTzxzkm3HOvCSbTOSWH+8wAtnUeENcqrBiy37Tao9XrzAi7SULUOPTVCYPWFM8GKGeHDd8jj2kIDWCXudyD5wdhpksKlK7Rs5IkafpX1nKrjielq9tjRmsqt5rbz1bNTTh/VtoJmS96PKfqPCXxCqA4+NfBUuU6hSJHNJ1uPFS0DZBkViwMuI+yj/2gt0N3IZvJhXYE2DF86C5KejlNhdInKWAy8R8IJ0sgwgpD2zDbzAi6Zns+2p/+IPrNOeCvCSbIgQoI7pCZPMGsW5Drwka8GaPd+Bl5q1d7a35sBLts9QQP8YvJiaIaYYrqxGgpdflccCQlXkBn3ud/laQ8VWbJ4o2Oh7lTAhPdL7o8shFdTtmEJatkoJjqgiw1+4r9yWmUq6pqdSQqHTelaHJ4AkyLKDTEeNlKaNBC9h+sq28/MqMr2FGKJAL+WzOWi7IprWWB6L49ieEqRIHRgTsKDO5csVKFLi51JI2YGXMLOZ2WNcqFF1+5viqtjMRdIUR/RAuMB7AlldWExUbt5tGhj4DB4BHBZR1ulwJTgLLYrDqd7M6b5phv00XmRbGgqqrEal38yJycpky4pkghdACRmyweNMBrxwHX7eMRXKLgh5QjvQ5iiY+TpV7LB7rD12UqmDr749Cl5sgqqmxxEDM5nRyQy5io5RaeAgZXKeSrHNOiqhQo1UqmGItHqBF0AeeOEgdTeOQ2HBXWiyVN75EhWOuoAq/vi5GngKe2eIwh2LKG/YOlJ9XE2DF51W+vvP9foxtV1SMba6DF62+uJRHWarVJIBAAAgAElEQVSTLvDCYUsI45lX9k+16UrE48UUvjUrTQa8NFPeNKsqSimVIUKpWKOow4GXVFmyZupx4KVm7JwrrTjwkiszZenn9z9VUtON1+lvEgEv8F7B5twELxKEmM0mCl7MTbysV7bXo3sl/adzvSh4kceZWjB+or41Ma1BmXpkH/KVS3Y6wAsL0nJbfro3MnOQV5gWw5l4NFsceKmJ1ZZcGw68VLcfg5eioadrLw0zFTOfARhiCoti8+4lPuol2usFA2TPagK8oI2GA07QniGysOeImX0ojMcL1xMWvOB4W4prShV4gbaN2qTDw0YWqW/DtraBF+iZAJJpoPLlrKjIbJiU4bK9AqWDUqHCveo3b0aF4wZQxdwvtEaQmbEozNXN0KFip70SOj9MG/EeU9PgJd7+xXt8qsALXsPYX13F26OaO37/b55Tni4LfMGLFMvlEBwzNEj2WGq8eKWR5uOh0XJe812j2hP8OQMZqZfSTqWKRihUGPDyyeLFNHLpxzp8CsfbNF44a4tMpc3tJ6KBke5Zc+Al3RZObf0OvKTWnrlemwMvOTyD511eGg1BYS8WGXLCoquHqnAWWdirARDlrXfSC14QvlJSnEetW3unBpYhLn3PraTd29nBi/TYCNKWybZpTQS8cLiQH0wxIVnYY71CrBACtWCBCucSWaeCbOnAS5CFMv+9Ay/V54DBixTjRGiKTLGMs1IGXixCt2avbOCF+2fzeFlXWhETqmPb1GPz32DiSN0UZ8SRHjzcB876ZIbCeIIXi+eFH6QpO/Ys7Z2DcbD3S7VZMcBL0bAzYjIv4XiMsf6EEdprRRbTE8Z21YUFL3yumcWIQ13iDRdqUJhPjf9ZQssabJL5m0EKe+DASwqNmeGq/MDLtS32UxomrWnXwk2iYrkcgrPzH5NjdGDkMOIBLzjP5g3D4EVmYpFCvdBXOXnBNKv14PEy+OeZUQ0YgJXORW1ohAIxfZruQqNadCDUO27FHPXfCHSSejEQ2/1oy1MyPDPVm3fgJeumxLdDDrzk1nylu7cOvKTbwmmsH+AFG+jmG1dqAVQIqbbejLQHCzwWkBrYJh4LrY/5anN9dFelEbIhK47MVJNKj5eww2dwdPnFlbTDNnbwEsZjI2x7NX1cIuAF8/DLbyrtduu8GE0V2XdTl8VPs0bOazLaNqbtHHip6dUUf3sOvFS3WRjwwiKe8Hzg7EdeoUacdQUAA+EmpoAt2stbvpDqzYqkeraVtIAX5fnBmi4S3gC+sPguvDq4hAUvNvjgBV7gRVPe6cgoeEFbJuDS7RvgJUb/ZUMHOfuSab8w4IXDkqQ4q/T+8Ro7240hWLxeKwAvTYoKaemGENr4r+DsPMOBl+ycl0R65QdeZMpZ9nBh8GKmVJZtxwtebP1m8MKZh3BMPOCl1w+v68xMJsBBHc+2OUqlu56sQ4pQTPDCxyRiz3Se48BLOq2b+rodeEm9TXO5Rgdecnj2GLxgCJMezdc6LSgAL1yCNtg2DRYv8AKYc2qvihjR21R5nsCbBaXr4XnUWD2gssaLnB7Zr0zru8S7bBIBL2HbkNmTkP3IKx0zQqM4zbTfcWHb5eMceInXYjV/vAMv1W3OIq/S4wVHyRAYCSoYAniBF5zLx3ilbDZ7IVMg47tEwAsASsHLj+jQIavHiwd4kX2RYCkZ8GKm/OU2UGd5+wM0vKrYcluqLGqScvACzZaCL2dSvTefrabTw/1grx4ZTiV1ZBx4ie/e5MBLfPbK5qP9wIsMt0kVeAlKE822grfNeSpzEYotNXWQxwvGBaFgBkXsObOrClca1bIDARxxMfVt4CEzqVWXrJs2B16ybkp8O+TAS27NV7p768BLui2cxvoBXuDRAsHTd94rIGQOgidLqsELstt06qg8L1SWIS5S9NYMZUpmyNol2wO8cEYfhMMcfVRupS6uCfASBoIxpEmlPo4DL8ms+Jo514GX6nZmSAHoAGCAFMf1VLaclIAXJcZq6ovYZhpgp7LFZlRvdsQLJhHwgvPQ/8Ip9yjh1ouqpdCVXih+2WAgBps/T4m/qpCg8r32j3bXy4vFK9zG5qViEzKWHi8IgdKaM8LjxS/ESXrocEfNtN5+V5bMrOTAS+L3IAdeErddtp2ZbvAiMyJh7GHBi8wqdPhfL0S1XQBTgjReft7tDGr/7VPao2X2VifTVgVNdGiRV2iSCV64j9k2Vw68ZNuM+PcnY+AF77PTkGzUpZNObv058JKc/TJ6NsALND0AImZ/nE8QTP12burBi81rJhPgJaPGTrLxdIIXhI79+lseHXNUJbVVWYxqujjwUtMWj7+9ug5eoht7YbowGYAkqMAGHaVkwDiy6a7gO/7c1p5t1nQGpLMHK+AzVYuwSuDBxwdpvAStBunNkmgaXj+YYrbfcHTfah4nJnhhzxM+N5rKOwx4UTYzw5Rk6JAXsJH9lHPvB16kB0/x6MeoodKoyVu6kNYNnaBBXdjiQo3CWiqzx6VKXDezo0isdQleENaD8BwuqfB4SQV44bAmGWqEcCEvgd8ZO55Ah/7wgh4Gj8EPvKCPtyyfE02l7cBLYmsp0bN0tr4StadZ750pNdG6M3lexsBLmgbtwEtyhnXgJTn7ZfRsgBfp5SLFcrljQZ4NYUKNbOCF0xXv3V6lF1YeMakqfh4vqWojE/WkE7xkYjyyTQdeMj0Dwe3XdfAS3dgLU9nAS73Zr6s0x9OjXiheoEKGJ5Ued3a0Vlv6Y7/ZCQNCkgUvaN8LFAWvnMgR8YAXmw3MDFJmu+VKqFd7sWy9HeW1bE3rt99Dv6njFN62fgK2cOpvW1pvPqcEosbLFsbUxamkpW3wb5sXD6eFLt9Bpbr+8YuoQHFY2+k2nMZLPObK2LEOvCwghPasLCuJEZlNBLwg89AbW5wQI8YrPU3Yk2WcAh1S0NacfOnxwsciBGhleSntWl+FCymBXC/wMn6r/WnAvA9JiuTGA15k2xlblJaGncdLNs1GcF8ceAm2UV06woGXHJ5tgBeElyxYpN7CKq+Xq68sp8lP5seEGgWBF5vniqnxEqQTk0oTOvCSSmvWTF0OvNSMnZNpxYGX6l4SfgKpZjYb0/Ze4IU36UFzVapCkSpUOE9loyaBnhPRttQ5EO1F0W8G15URshqFKchsVKhSYJfv2ZkkKApzLo5h8CIBlleokc0GbGsvMIXQHxOy2GCZ7C+nv7YBE+4vZ3GSXjDQ1SkeMzlalZ/HCw5CaumiYWdGjzfTbYexoQMvYayU+WPqMnhZvGIdlZZH4hJMGJIIeGFhWoYiCPXpOO+Z6CRz6I8NvHC6aBws4Qc8cf4sXUNbFjbWYUNcvMDL8Nb70KgFn+qQJHjGoNjAyykqlXXPJjtqkNN74Yyox4sDLzV7TTqPl5q1d6KtOY+XRC0XOc+Bl+Tsl9GzAV44fTQyGg0dUk4IO0HYEZcg8GLzXHHgJfXT6jxeUm/TdNdYmx4C6jJ4WdmvO5UqbwXWUeF14wdeEJ6D1MEIJ2HYIdebF3gpfOmRGI0YrzUaTzpiW1vxgpdkrxWMC0Wm2/Yag80GQeAFoTucNSpsXyWsMfsiQRFCwwBPGqpQscrif2j9gPExsCsIvKA/EiYhNKys4xFhu6mPc+AlLnNl7GAHXqqDl6b5hfTd1qdH54TFaEe13E+nl/bKamSCF8AbTj0t63xg9bc6vbMsSPX84Oq5+qOw8MMGVI5v3pZeXPkbAazc1vLAaBMmqJEhRXI8EjhlbFFaGnYeL9k0G8F9cR4vwTaqS0c48JLDsw3wwoWFVdmDBZ8Dylx8YfyxkiZ4SWUGnCBzO4+XIAtl3/fO4yX75sTsUV0ALwsWL6NmTRpRo6KGMcNfOfxiWr/t7tWAiE3INuxM1iR44U2/hAs1DV7YLoBRLBrsBSAQrmWm0mbw4uURhLlgWJLXvjNVzpkZOBUyrbQJ0ThFtBnKBc0WeBnJEga8sNcLPGhKhk5UoWitA/sX04YLNYrLXpk6uLaDl5L1pbRk2UrafLMWlAdlZFG8PF6CUiqb4AVhPT0ab09b1W+q/8uQAxBDarT4eaA82OpQ6rNohu5dWPCCY78pXUarVAjS8KWztQjvRgX1aUX5+qiQrxzv02t+ov6LP9AfycxJ5y56i6avnac/d+ClZq/E2vSyS1rOgZeaXUfZ3poDL9k+Qz79k+AFmYe6qwxHEryEyXJjq94EL0FeM6k0oQMvqbRmzdTlwEvN2DmZVmozePnp17/orMuvp5Wr1mgTHdRpT7p91KVUWFhP/12T4CUmg5DSLaHiSJ9YDBZhLhVq017e+cjQXhPsQZIN4EVCFS+PIZu4LR+L1NdmSBGHA6HuBisWUv6BR1P5oF7R5W4TKgZQgScSe8mYYrfxaNqEAS/JXHs413m8JGvBmjm/toKXyspKuuHOyTR56pvakPXrF9KEmwZQh/a7RA2bKHi5Ysn7MUK8JqixgRd4tECbBcXmqTJ369Nolz+e0N/HA154MBIGwbvm4y1PoWb59WMWkWzXpiPDGjU1s/Lia8V5vMRnr0wf7cBLpmcgu9p34CW75sPaG2woSsvKqOUmzWO+HzKylJYuj3yE7EZI6+zAS3ZOqAs1ys558etVbXr7UpvBS/c+11DjRkU08eYBNO/vxXTKBSPoqktOp9O6ddHTC/Cy7qBu1GDiSC2MWnrhKP15ucoilGjx8niR0EF6hBQNPZ3yli2yircG9SGbwIvMkOQFXswsSnl//kQloydrTxNTL0XPg4IoCAdCwQMq3sSX9e8RsZfStUF9AFeAVvgMhXVW2EvG9F7ymh+brR14CVqB3t+7dNKJ264mz5z9v2+pz8Cb6b6xg2jfPXeiUbc+StPf+Zg+emUC5ePhRJVEwYup0WKCFwAOlE4NN4t6vEgPEwYgACQPtTpMea0spfOatqMOf06hP8vWJA1ebt30AO15Y5ZVFeutcIfHE+TpU5PzZ7blwEsmrR9/2w68xG+z2nyGAy9ZPLv/rCmm8waNpa/m/qJ7uUXrlvTonVdT60030X/P+bKS7n6wTP+bBXAleNl5pwo6rWc48UVpBqSnvmdiPq1YGflBdh4vyS8SB16St2FN1+DAS01bPP72lq1YTQeeeCk9cMtg6rTPrrqCASPvob8XLqEn7x2u/9bgpeuZ1EClAg6TRShML7w29qwNg5CWdcMmRquKHq9EZEsP7R6miegxgDn5339OFTvtFYVFmQo1QqcYdgSJE+NYG9zidNOAKqQyGAHIsGYKg5fVa0sJ44bGDsKTkPEoHl0cW3iWl9EleIk3TXTYiXQeL2EtldnjaqvHy7CbHqSvv/uVnn9otDbw/IVL6bCeA+nxu4ZS+9120J+lC7yYMwqB3OZ59aMeKAxeTNDBXivJeLzsUdSCpm12nOeiYm8ceNiwRwyDF1MXJrMrM7Z1B16yaTaC++LAS7CN6tIRDrxk8WzfMuEpmvLSO/Tcg9fpN7q9+l1L22zdhu65oX+018++WkINlaTBLjtVqv/GerywF0wiQ+Rwo0ThTSJt4hwXapSo5TJ3ngs1ypztw7ZcWz1e5v74O518/gh68+nx1KZVBEjfNek5mjrtPZox5Vb9d/EXn1Jx01ZEH75GeZu2oQoV5pNsyZv3ExH0QrbaTpGIpoHV5f33LsI5ladeos6p/vY1sALjgKaN6lHJ+nJaXxYRw6zJoseCsZ9wDlUqcBJvyVMQib5T/9v7gGq2KGpQoDxelGnXVWmT5SO99+8/Ro5X8ClMibax816B5+j6ixqr45QHVIi5DNO+eUxhvTwqalCPVq2p0mVLpJ5sOwdzBQi4dNX6bOtaQv1p1CBfz1NtK73730gbN29G40dGsqKh7HrwOTT2mn50dJdIyM/Kf9ZT2Yb3dK+u+Z0uWfCe/vz6Vh2pV9MInLGVD4sX0Adr/45+9S+l7eJ3vFnHvLJ/6ImVP5B53tfrl+qU0bs12Jia5zeIa0pw7qurf6derbenrUubxXUuj+eARpvT/kXx39/iaizBgzdW19yKNeupMv73qgm2WDOn6d81lalvfchsfTXTq+Rbsf2uJV9r5mpo0Sw2bC9zPcnNlh14yeJ5O/SU/tT10A40uF8k3v3ZV96j4WMn0ddvPxQVRvt7aXHMCGZ9lE/zFxBt07YKxmTxEKt1zYGXXJqtSF8deMn+Oaut4IVd6D944U61sYgAkAeeeIUmPvYifTKtyuMk+2fI9dBZwFkg0xZYX1pO9QsLMt2NlLePcMx2O7al0UP6ROveo8u5NPSyM6jnCYda21tRXqKFadsqkOKKs4CzgLOAs0BqLODAS2rsmJZa8MM4cuA51P3o/+j6P/vqRzrz0jEkNxm15U0TGxBvBhuqN06ra+GbQbylWFZL3gzKBY9xrVRvXyrc25e03AdSUSlC3TBPta2wx8tbU8ZHQzBNj5fyikoqK695z5B02hogDeNS/1+rSoFaqPCiqG3zheuvQM1ZaQY8lNK5QBCMXF9lbMJb6tpRKpXXbe0DL/B42WSjZjRuhLfHS6lyd6lN9xOszUK1Nmub9wSus/r18qm0vIKUZnKtKvp3TQ2qtj1L1rbfNbwgdyVxCzjwkrjt0nomVOh3O6R3jCsobzKmTb6Ztt5Cuc674izgLOAsEMIC2MjWUw81ta2wxsuD466kjv9up4fXf8RdNH/RsqjGy9p1Zcotu3aFeGRS4yWda0hqvKSznZqu22m81LTFE2uvNmu8fPvDbzRVha2j/L1gCR3ea5Cnxkti1suus2q7rt7ilSUKUNQu8lKbdPXk1eA0XrLr3pDp3jjwkukZ8GkfHi/XDupN3boeqI+yebzUnjdNEUPU5jeD7u1LFl9slq7VprcvFeoVUm3ULsC0ndh7GDVv1pjuvbE//Tl/idJ8GU5DLj6NTu9+mJ5VB15y57pz4CV35go9dVmNcmO+Zn36jU7UgKxG++21M4245SF6471PPbMa5cao/HvpwEvuzaIDL7kxZ5u3KMqNjmZpLx14ydKJQbeg8XJ0l4406MKeupfPvPyu/sH003jJ4uGE6prTeAllpqw6yGm8ZNV0WDtTWzVeMNgffvlTh2AiCxzKgR12pzuuu4zq1y/MUfCCsA1/V17n8ZL915zsofN4yY35qq0eL/CgRgrpp198W09EQUE+TbxpYDQTHD6TWY1yY7b8e+nAS+7NogMvuTFnDrwkN08OvCRnv7SePfbeJzVseX7SaGrUqCH1urB6ViNTXDetHaqByh14qQEjp7gJB15SbNA0VFebwQub6y/lPt+0SSNqpv4ni/N4ScOCSlOVzuMlTYZNU7XO4yVNhk1TtWuLS2jJshW0ZZtWlA8yIYoDL2kyehqqdemk02DUNFbpQo3SaNwcrNqBlyyetFX/rKU+A24mxOaiIF3qY3cOpTabtYj22oGXLJ5A0bXa/vZliYo3hthnbSq16e1LXQAvXmvPgZfcuSodeMmduUJPHXjJrfny660DL7kzlw685M5coacOvOTWfKW7tw68pNvCKagfApLrS0ujWTtklQ68pMDANVCFAy81YOQUN+HAS4oNmqHq1ihx3ZVOXDdD1o+vWQde4rNXpo924CXTM5C69iHWisxGtaXU9mcuJ66bOys1V8ALApzD3AFcqFFya8+Bl+Ts5852FnAWcBZwFnAWcBZwFnAWcBZwFnAWcBZwFnAWcBbwtIADL25xOAs4CzgLOAs4CzgLOAs4CzgLOAs4CzgLOAs4CzgLpMkCDrykybCuWmcBZwFnAWcBZwFnAWcBZwFnAWcBZwFnAWcBZwFnAQdecnQNrFy1RsXjllHLTZrn6AjCdTuXxlmhxGWRthGpGs1Ssr5UZRNYSZsrYeQ8BMYbJZvHiTGVlVdQYb2CcJMmjgoad9wVpugEzNXipSuosLAebbJRU2utCxYv0xlyGhU1rPY9zv974RLabNNNErJLiobhqvGxQF2Zo1wbZ2lZuec143cfzPZxlpWXU35efrVsMWEuUr97TZjz03XM2uJ1+nfLlgUHbQbd37P5dy1dNsu1eoPmMNfG49XfXBqn3zNX0H0w28fpd//3W2tB487UOg3zLJnLv2uZsmttbteBlxyb3X/WFNN5g8bSV3N/0T3fonVLevTOq63Cu9k8NGx6Dz7pimpdvOv6y+mQzu0p18aJH8pLh96hx4MxcMHnN9w5mSZPfVN/VL9+IU24aQB1aL+L/jsXxvnEc2/Rrfc9TZ9MmxgzXweeeClB+FmW07p1oaGXn6kBlN+4M7k2Z3zwGV0x4i4qVzAJZZut29C1g3rTv/fYUf/9069/0VmXX0/4sUQ5qNOedPuoSzWkQZk24yMaMmZi9Pz+F5xC5512TCaH5No2LFBb5uiF6R/S1TfcX21+P33tPipqWD/n1uLPv/9Nx599NU2bfBNtvcVm0XEF3QezfT4BKI48dTBddM6JdOqJXaLjCvqdC7rXZPLC7tVvVPQ5A79bx3TpSKOH9NFdCrq/B81nPONCrrzqryriqaGWHIufq+rvdBIeXNAcJlxxBk7M1WcRL1N5PXP53QdzYT697v9DRk+kl9+cFWOOrbdopX4nbs7qZ66gZ8mg+2C2/65l4FKuE0068JJj03zLhKdoykvv0HMPXkeNGxVRr37X6o3jPTf0z6mRLFqygg45+QoaP/Ji2vZfbaJ9B0iCh0EujfPZV96j0bc/RuuVVwugkQQvs//3LfUZeDPdN3YQ7bvnTjTq1kdp+jsf00evTNBvRrN5nD//9heddvFoDYcaFTWwgpdjD+9M3Y8+MDp/Gzdvqr2wgsadycX69sw59PeCpXR0lw60triEBoy4myoUKJpy30jdre59rtHX1sSbB9C8vxfTKReMoKsuOZ0AlbDB6njsRRq09Dv7RHpFPSwMvfEBevnRG/R16ErmLVCb5uj51z6g4WMn0bMPjIox7PZtt6DidSU5tRaP6DWI/lqwJPIgbYAXv/tgts8nwBgAGcqwK86MAS9Bv3N+95pMX0nXqd+qburevu3WmxPumVdeN4Eevu0q2nevnQPv79n8u5Zpu2ZL+9n8Gx2vjQBecvFZxByn3zNX0H0w2+fT7/6Pe8vvfy6k0VdFwC5KUcMGytNu06x+5gp6lszl37V4r0F3fHgLOPAS3lZZceShp/Snrod2oMH9eun+YNOPB/Ov337IGsKSFZ22dIIfSJ+bNJp23HbLakfk0jjXrF1Hy1euphG3PERFDRrEgJdhNz1IX3/3Kz3/0Gg9xvkLl9JhPQfS43cNpfa77UDZPE64hC5U4TYvvTGTJv33VSt46aMAxDk9jqo2f0HjzqZ1CW+k6+94nL5460FatXot4SHugVsGU6d9dtXdHDDyHh1W9OS9w+nVtz6iwdfdS5+9fj81UG+BUToffzGd0f1w/bbblcxboDbNEcDLteMfoTlqvZkl18b5t4Iuf85fQr3731gNvPjdB6fN+DirrzmE4qwrWU/dzh1GA/r2sIIX2+8cvAX97jWZv5Jie7Bv1750yrEH05UXn0pB9/ds/l3LNrtmqj9Bc5ipfiXSLq6j2vAs4vfMFXS/z/b59Lv/A7wsX/kP3X/LoJz+nZPPkvUKCnyf77P9dy2R69CdE84CDryEs1PWHLVHl3Np5MBzlJfBf3SfPvvqRzrz0jH0wQt3ErwNcqUweNl9l211v9vt+C8646TDo2PIxXFeMfwuKlOwQnq8YJOxcfNmyrPnoujU7HrwOTT2mn7a4yIXxjnl5Xfo5rv/awUveCuxnXr7vmWbltTrhEP1v1GCxp1N6/T8QbfQz7//RTOm3Epzf/ydTj5/BL359Hhq02oT3c27Jj1HU6e9p79/4IlXaNKTr9LMF++ODgEu+fBAYDf8bBpbXexLbZojgBd4VB2w3+7UoEEh7b/PbtT9mIO0RkoujhMeL3jzaXq8+N0H8XIhF665Dsf0oyvOP9kKXmy/c0H3mmy6dn/89U86sfew6O9W0P09F37Xssm+mehL0Bxmok+JtgnwUhueRXj8tmeuoPt9Lsyn1/0f4AXeI3vvvqPS3Gumno070oEddtfmCBp3omsmHefJZ0nUXxt+19Jhp7pepwMvObQCEMO52yG9ow8/6Do/vCEWEjGRuVJWrl5D1457RG9u8e9X3pqthUxff/IWqq+0NHJxnDbwAlfydju2jdmU42Y89LIzqMfxh+TEOL3Ay+jbHlPhUgg6r6Q33/+fFmGc+sB1tP02W+hwHa9x91SAJlsKYqnHqDCxO667jLocuHfUhV6CTPzwT3zsRQ2e4Dr6qlqrgDBc8MDTpHEjunP0ZdkyrDrdj9o0R59+8T1NffU92lgJQM/7exG99f5ndOTB+2mQm4vjtD14B/2uPf3S2zlxzdnAi9/v3Bz10gRhqF73mmy5iFf9s5aOO+v/VPhlQ3rpkRu0eHxt+F3LFvtmqh+58hsdxj614VlEjtP2zBV0v8+F+fQCL3gOA9wtalCfPv/mJ/ri259p1OBz6aRj/pMzv3Pms2Rt+V0Lc/25Y+KzgAMv8dkr40dj0w4h0G5dI7oauerxYhryh1/+1K7aD916Fe3XfmdNinNtnF4eLyD440Z4e7xk+zi9wIucQ+jb7H/CJXS6CrnBW1/ACL9xZ/xCUh14471PCXOG/p5/+rG6Swwy35oyPipY7TxesmG2wvchl96QhR9V5MiHn36Nxt7zJH3+5oP0iPp3LniCyDH6ebx43Qdz2ePF73euaZMi7V3nda+Jd22k43joSpxxyRidAQ7hUpxFMej+nou/3+mwXzbXGTSH2dx3v77l4rOIOZ5EPV6y/ZnL6/5vjh+eI8tWrNLaZrnwe257lsSY/O6DufK7lqv3gWzutwMv2Tw7lr4hdhpueIMu7Km/febld7W2SK5pvJhDw1u1Tkq09O7rr6CDO++lYyNzbZw28IK4229/+I2mKjFkFMS5Hq5c7aXGS7aPMwx4wdgO6zGADj1gb7paedh3fFMAAByNSURBVPMEjTvTlx1rIw1RegVnnXJktDusu/DguCup47/b6c/7qwxI8xcti9F4geYGMn2g4E332aoOp/GS6VmNtM+x8LVxjqa/84nSHLpbe1+9M/NzrX2SS+P0evD2u99zLHy2j9Pm8eL3O7dHu+20xovXvSbTV9MKpblwhgpjXqdEnJ+cMCIKXdCvoPt7Lv5+Z9reNd1+0BzWdH9S2V4uPYvYxm175gr6XcuF+QwLXrCn+eTz7+jVx2/K+t9zr2dJzGtt+F1L5XXp6opYwIGXHFsJY+99UsOW59Xbp0bK9bfXhbmZ1Qhp1JBR5pD92+vQopHjHqbX3/2E3p16u9Z5yaVxIi1xeXm5FmEtKy/TYSv16tXTWYtmffqNTv+NrEb7qWwQ+EEBHeesRtk8TrhKlpaW0RS13m67fwp9+MJdlK/czCEaBvX9l9+crV1BN2u5MT3zyrsEd98JNw3UsblB487kZTd56htKTHcy9TvrBJUJoVO0K3ib26RxkdYyaN6sMd17Y38tBnry+cNpyMWnKW+ewwhCyvsdfSFdeNbx6n8nuKxGmZxIj7Zr0xzd++gLtNtO26pU5zvo1O0XDL5F31tefHhMzq1F3EvmzV+sw1YgNt52y9bRFO1+98Fsn88yde+vUL8BB3a7jC7p3Y16qhBShrJBv3N+95pMXlqw+VGnDabyigqaqO7pTVUYMApCSxHSHHR/z+bftUzaNZvaDprDbOqrX19y+VnEHJffM1fQfTDb59Pv/n/NzZN01kho5X059xftMQ3vabwYCxp3Jtdp0LNkLv+uZdKutb1tB15ybIbhGdJnwM3aiwIFGimP3TmU2mzWIqdGwmlSAS1Q8KA6bng/7TGBkkvjvH/yywpMPBNj/8EX9dLZfvBDihTST7/4tv4e8fF4kOWMOdk8TqyxUy4YGTOuzkrcE8rzeNiBqCzgGRfAiEvP7a7/DBp3JhcrPJMAv8yCbB3wXEHYGwSrkUYbBSAJMI03Uy+/MYuGjJkYPf2yPidR3zOPy+SQXNuGBWrLHOEt5nPT3o+ObovWLVWa84HR1OW5NE64XfP9nu/5nK0p6D6YzeM8+/IbCFo8snAWo6DfuaB7TaYubH4zbbaPeyDmLOj+HjSfmRqXa7fKAkFzmCu2yuVnEdPGfs9cONbvPpjt8+l3/4eHEryKueCZa/zIS6hRUQP9Ubbe/4OeJYPug9k6rly59nO1nw685OjM4e3n+tLSqA5FLg4DqfMWLVmuu765Akd5eXnVhlEbxolBAVAsWbZCZf9ppT1hzJKL48QP/eKlK+mftcW01eatdKYVswSNO5vXLTYfeNML0WezYAMJsVOsWwYy2TyWuti32jJHuIYWqLTuWIessSHns7aME2Pyuw/m6jjD/M753Wuy+doNur/n4u9aNts7HX0LmsN0tJnqOmv7s0g89/tcnc+Vq9boZ2S8RG5U1LBWPXPVxt+1VF/Ddam+Ogpe4GWBbCyuOAs4CzgLOAs4CzgLOAs4CzgLOAs4CzgLOAs4CzgLpM8CdRS8pM+grmZnAWcBZwFnAWcBZwFnAWcBZ4FabQH3DrNWT68bnLOAs0DqLeDAS+pt6mp0FnAWcBZwFnAWcBZwFnAWcBZwFnAWcBZwFnAWcBbQFnDgxS0EZwFnAWcBZwFnAWcBZwFnAWcBZwFnAWcBZwFnAWeBNFnAgZc0GdZV6yzgLOAs4CzgLOAs4CzgLOAs4CzgLOAs4CzgLOAs4MCLWwPOAs4CzgLOAs4CzgLOAs4CzgLOAs4CzgLOAs4CzgJpsoADL2kyrKvWWcBZwFnAWcBZwNcCTpzSLRBnAWcBZwFnAWcBZwFngTphAQde6sQ0u0E6CzgLOAs4CzgLOAs4CzgLOAs4CzgLOAs4CzgLZMICDrxkwuquTWcBZwFnAWcBZwFnAWcBZwFnAWcBZwFnAWcBZ4E6YQEHXurENLtBOgs4CzgLOAs4CzgLOAs4CzgLOAs4CzgLOAs4C2TCAg68ZMLqrk1nAWcBZwFnAWcBZwFnAWcBZwFnAWcBZwFnAWeBOmEBB17qxDS7QToLOAs4CzgLOAs4CzgLOAs4CzgLOAs4CzgLOAtkwgIOvGTC6q5NZwFnAWcBZwFnAWcBZwFnAWcBZwFnAWcBZwFngTphAQde6sQ0u0E6CzgLOAs4CzgLOAs4CzgLOAs4CzgLOAs4CzgLZMICDrxkwuquTWcBZwFnAWcBZwFnAWcBZwFnAWcBZwFnAWcBZ4E6YQEHXurENLtBOgs4CzgLOAs4CzgLOAs4CwRaoFIdkRd4lDvAWcBZwFnAWcBZIC4LOPASl7ncwXXNAitXraH3Zn9hHfauO29D227dJmMmmfXpN/TLH/Pp9O6H1Ugf1haX0Ceff0ff//wHrStZT1tvsRkd0rk9NW/WOFT7H8/5jobd9AA9csfV1KbVJnT/5Jfp7Q/n0BP3XBPqfHeQs4CzQHZa4Off/6Zvv//N2rn/dNqTmjcNd49I9ejWrF1HU199j9rvvgPtttM2qa7eWt/ipSvo0y++px9++ZOKGtan7dtuQQd23JMK6xWEav+Sq29X99ZWdOXFp+rjzb9DVeIOchZwFsg6C7z61kdUXl5erV+t1fPQvnvtnLH+4v4985OvqeuhHajlJs3T3o/Kykr67Ksf6dsffqNFS1bQ5q1b0P777qafKcMUPIue2HsoDbywBx158H5kPluGqcMd4yyQKQs48JIpy7t2c8ICn3/zE51+8WhrXy8/7yS64Izj0j6O4nXraZ+jLqCrLztdQZbDo+1dOuwOmvHBZ/TNOw+nvQ8fqh/lASPvpn/WFFOjogZUVl5B69eX6naHqA3CWaccGdiHN977lK4Yfhe9/OgNtI0CVteOe5hefnMWfTJtYuC57gBnAWeB7LXAPY+8QHc/9Jy1g09NHFEj0OP6Ox6nKS+/S3Nevz/ajz/+WkRdT7+S+p55HF3W56S0G/ChJ6fRLROe0u0ASON+Wa7ulfXrF9JDtw6hvXbdPrAPR/QaRNtvswXdc0N/faz5d2AF7gBnAWeBrLTArgefY+3X3rvvSI/deXXa++z1LPnUCzNo1K2P0qPqpdi/99gxrf3APfmK4XeqF3jzqKAgX8HpBvo+iXLoAXvTnaMvC2wfQH2/oy9Uz8Rn6BeP5rNlYAXuAGeBDFrAgZcMGt81nf0WYPByy/B+dNiB/47pcEFBAeXnp98fGXR/36596apLTqMzTz4i2gf8+JQo+LHJRk3Tasj5C5fSYT0H0mabbkwTbhpIO267pW7vrwVL6LpbH1Gfb0LXDuod2AcHXgJN5A5wFshJCzB4+eCFO6lJo6KYMRQW1quRMY2+7TF6Vnm3SPBSUVFJy1asosaqT/A+SWd57e2PaeC191DHvdvRuBEX0UbNmxDe7M75+ie6asxE9Xa2p3o7u29gFxx4CTSRO8BZICctAPDS/ej/0PD+Z8X0P089R9ZTz5PpLl7PkniJtuqftfqelc5+AEIfeeogWqS8Asdec6H2VkEBeJnw2Iv6ReKrj98UaAYHXgJN5A7IYgs48JLFk+O6lnkLMHi5bdQldPh/9qnWoSXLVtKZl45RP6RnU6d9dtXflylX0uPO+j+6uHc3OvawTjT7s2+1d0fvXkfTo1Om0x9/LaR999xZfX8i4U0Hl59/+0u/dYD7JTxKdt2xLQ2+qBddf/vj9PX3v1KTxkVRyPLsA6Pov8/PoHdmfh59U4IfzxvunEyvzvhI/5Btp1zcRw48W7dRoRr5OGQ/zEFeds0d9Nb7n9GLD4/RdZoFP4KNGzWk2+5/hp5+6e3o24ttt96crrr0NL0RQXHgJfPr2fXAWSAdFmDw8vGrE/S9wCy4N+AN5703Rrw4UB575nV6+Y1ZBI8YlIv+71Zq1WJjff+c/s7HCmrn06kndqF+Z59ADZTHCAoe3BGi+MzL7+iHd7jFH3HQvtqT5P9uuF974SFMBwUbHHgInnTeNTSgb4/o/XvB4mU09IYH6LOvf9Ru/3Dxv/6q8zVYRkH9783+kg7uvJe+Xy9fuZqO6dJJecx0pzabtfA0X4dj+uk3uO8+ezuZsAn9BiSHt+DZl99A3/30h75PwhNmnz12opGDzqEtWrfUdTvwko4V6up0Fsi8BQBeehx3MI0YeE61zoR5llxbvE7dz4ZTt64H0qz/fUP/+/IH2rLNpnT+6cfqz7jE+yz5jQoTHT52Ek269SodBo7yxHNv0UQFQ9Av3GcvPOt4fT9GCdsPc5BPv/SOfhYeevmZdFq3SF2y8LMknjfH3PGYbhv3TrR/wRnHRj2+HXjJ/Fp2PUjcAg68JG47d2YdsACDF/ywyRhceJnsssO/tNcHHpTHXtOPju7SQVuktKyc9jqsDw1Sbzh79+pK/CYU3wHEIMzmoaemUVMFUt58erw+h+vBg3jP4w+hFhs3I7h/Ht2lo3JZb0LjJz6tAcZ+7XfRx5+r6oVrvQzV4dAj6K7s0W5bevjp1wgaNS+p0B5o0YTph21KDzzxUmrdqgVNuW+k74xfO/4RqlRvmNvt1JbKlA0m/fcVvTl6//k7tcaDAy914IJxQ6yTFmDwcsd1l1FD4VmyzVatVfx+Sx1i+OXcn2nGlFuj9rnxrifUw/2b9OVbk/Rnh57SnxYuXq7BCe57v/6xQAOY8SMvjnqKDLvpQXpu2vs6dOn4IzvT19/9Ri++/iEBRF+twAvgDocU7dluO9p9l21jXNJLS8vooJMu19Cj1wmH6nafVPdZQG0GJtwGIMlJxxykYcrDT71GJx97kKdnH3RdDj7pCr0BuuL8k33XwGkXXUf77LmT/h3AxgK2+5fSNnj+oUhIqwMvdfIScoOuAxYAeOm8z250Ts+joqPF/QXPdmGeJfE81/n4i/W5/Dz4+rufaJD74Qt3aY+VRJ4l35n1eUwYOIce7bTdVuo+uz+9OP1DfW8dMeBs6qGeT8P0wzadQ0ZP1M+suOdj3F7l2Vfeo3dnf04d1PMuQpFeeWs2zf7ft3Tf2EFaC8aBlzpwsdTiITrwUosn1w0teQt4abzgwR9vasP8WDLwgIgsNgMoL6gfMmwU3nxqnH6Lyj9I0ybfHH1jCzd5PNA3bdLIGmokNVL+VgDocAWA8NZj9JA+ug081B/U/XI66pD9tOt7mH6YFsPb5z279NHA6KZhfUMZFOeg7fc/+pJG3vKw0ja4SgGjnR14CWU9d5CzQO5ZwEvjBW9JLz23e2jwsvlmLbUHX15eJIQT0HffvXZR8OUigqdKl1MGUJcD9yYAHi4IhcQ91BZqZD6gQ2j3mpsnxYByPNRfed0Efd/E/RPgBZ99qMKmGhVFvHdwf37/4y9p5ot3WycHXo19BtxMN159AR13ROdQEwi9haXLVyptnOc1POLNiAMvocznDnIWyDkLeGm8QKcvzLMkA49Lzu1G/c46QY+foe91V56rvfwSeZY0X4oB7jRsUD8GlONZsrSsTN8Dw/TDNjnd+1yj+4uXcWEKQjWXrVitz4GnD4NtB17CWM8dk60WcOAlW2fG9SsrLMDgBa6hB6nMFFzg+i7fLoTxeJFQBVkv4HL+yO3/p99+Hn3GEE3x3516e7Vxe8XlSvCCkKOLr75Nb1A4bhYV4S0yfkARN8vgxa8fNqPv0eVcOmC/3aNij14T88W3P9OIsQ/Rj7/+GXMIRCIPUplNnMdLVixp1wlngZRbgMHLi49cH6Px0rRJkYYXYT1edlOZ4iRUOeWCkbqv8Lbje9xNQ/vSsYd3qjaGMOAFXoKTp74ZfTuMShBKdMAJl2qRRog1ArzgXvXRK/dG27j9gWfpvsdf8hQy/2ruL9Sr3yhPF3rZ2WeUAPCdk6ZqOC0LtGng8ejAS8qXp6vQWSArLADwgsxBg/v1ivYHkLlVy43iAi/secKVoF4kekDCh0SeJeWzGbybkcwBEAcwhwt7An6m7lPrFDQGnPHrh83gZ1wyRmfFDEqogHsjMmDOVJk7EWrEhe/RDrxkxXJ2nUjQAg68JGg4d1rdsECQxgu/pYA3CLxCULxCjSTwmKP0BfAjxODlsB4DlIt+A53xxyxhwMv0dz7RWYfYFZPrOFZpzUD34PUnb7GCF7MftllFVhD0wQaF+Hi8lcDbaegUDLn4NNp5h631hqZn32s1sHHgpW5cL26UddMCQRovAC+418h7iC3UyAQvgBl48AZ4QdjRgJH3aJ2Y/wgIzhYPA15G3PKQ0od5lz597b6o2C7fX+FCj42EDbzc8/DzdLf6n1cGOWge7Nv1Qjr+iP3phqvP91wEvME5sMPuKlz0GBVu1Fp5u8zUoaQOvNTNa8eNuu5YwE/jJcyzJHua+AGPRJ4lJXhpofRUOh17EZ2tMlVySnvM0PV3TFbQ+g0FTSZQaWl5QuDlOqVhiNDO9567Q4fTexXA58UKvlylMmbixSRg0AHq+fIUFe4JOO7AS925ZmrjSB14qY2z6saUMgsEgZeVq1XM7XEXx2QcAuhof8T51TRe/MBL3yvH0QcffxXzJhaDwKajQrlbQjMG2gFwteQiPV7gZXJi72FafAxpp1F4MwBvlYk3D0wYvIy990mtccCu+NK4eBBA7C8gC8DPg+OupI7/jojp/vTrX3RC76G1E7xUqgGmP6FVytaxqyizFqjtyyUIvED/6fnXPojJOATvEzyES40XP/DC9xNTawX3SOgFII0zxHC5Psy4+YD+iPr+5rv/GwNv3p31hRb2HaIe8s9Sm41EwAvawv0X9+Fpk29S4aKbxSw49F1rxSjdLYCfz994ICrAiz7fpPrkwEtmr1HXurNAui3gB17CPEuGAS+JPEua3sh4fsVLNPkiEJ408xct0/epMP2w2fLDT76mCwbfEg1/l8cgRP09dS/+twIteKaGPiJ0ErmgTw68pHuFuvprwgIOvNSElV0bOWuBIPDCD9yr/llDY4acp8Vk737oOe02aorr+oEXCIf1GXizzhp0kcriAc2CKUoBHi6oEItEfCtAyg1KQ0C7xu+3h8p29FiMuC4e/H9XGZNwPkQl71WijQhpYhhiCzUK4/ECLYIuPfrrH1so0R+msjtBPBehRcgAcrzSNLhEZXCCuCQgT59Tj6G/Fy6hOx58VotlOo+XnF3+ruPOAqEsEAReZimX8fMGjdW6BHiDOePDz3TID2BEWPCCjuA+CLgBYVzosQD63nrfFO1J8/Gc76h3/xs1BEeWI6Ro3WarNjHiuitW/kP/6X6ZzpKB9M556v9umfCkDvt5b+odOnw0UfDywy9/Urdzh+kxDbigB+2563a63g8//pqmqCxMEAkGJBp83b0aoB/YYQ/67KsftLguYL0DL6GWmjvIWSBnLeAHXsI8S4YBHok8S74zc06MuC6/bEMGpuOUF99Lb8ykp198OwpDwvTDa5IAuQG7IQ58xsmH68QLc3/8nR56cpo+BQknECKfr0KwrlHZQpF57rFnX9f3dxdqlLNL33VcWMCBF7ccnAV8LMDgBboDEHW0lRkffEZwYUe4DQo2BMi8gTheqNezi/z0/47Vqf9QuN5H77ia/r1HJKU0Uu0hHTQewlGQVeP6/ztfp0FFer2R46ragP7AOOWe/rL6QeR4WYhM9h0ynpBKkAtcReEyihK2H7YxYsOCVNeoQxYAHrwpbr/bDjqd9KQnX43G5MKd/v2Pvoq+XcYYkJr6lcdupLYq2wnegsv+u4XoLOAskJsWYPACN3QWpJUjwdvMS4feodI0f6E/BvhovekmNPen36PgBS7yyIgmNV6QAQiwglN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(2.4.1)\n", + "Requirement already satisfied: pure-eval in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (0.2.2)\n", + "Requirement already satisfied: six>=1.12.0 in /Users/engs2510/.pyenv/versions/3.11.7/envs/pybop/lib/python3.11/site-packages (from asttokens>=2.1.0->stack-data->ipython>=6.1.0->ipywidgets) (1.16.0)\n", "Note: you may need to restart the kernel to use updated packages.\n", "Note: you may need to restart the kernel to use updated packages.\n" ] @@ -76,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 2, "metadata": { "id": "SQdt4brD04p1" }, @@ -103,7 +103,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -122,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 4, "metadata": { "id": "sBasxv8U04p3" }, @@ -143,7 +143,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -180,7 +180,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 6, "metadata": { "id": "zuvGHWID04p_" }, @@ -208,7 +208,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 7, "metadata": { "id": "WPCybXIJ04qA" }, @@ -241,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 8, "metadata": { "id": "etMzRtx404qA" }, @@ -274,7 +274,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 9, "metadata": { "id": "-9OVt0EQ04qB" }, @@ -296,7 +296,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -308,10 +308,10 @@ { "data": { "text/plain": [ - "array([0.77574635, 0.66084088])" + "array([0.71969102, 0.67039216])" ] }, - "execution_count": 24, + "execution_count": 10, "metadata": {}, "output_type": 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OPNm6+wj1Gz1f5u5g8YTzknDoBofw2HKBJFy9JYsVpNdfzZ6keGI7RpgFiw/efSvRxV/309LSu8VV8YQ7+6BaJxlGYvO2sffpsl2PTeSIW3fbXiEoiYS1Nk+fpPjbvF3iiicc8lS8akcZvnJs+xyRJPYHGiFs1LuD8OypH+PZ4woHe+KJLX/OT3sXxeYh4f4TiidqjLd+23c0dOISsoknC1dupynz18mEuY1rlU8UtS0sqolItPtixudz0KQUdq4n7I0CAiAAAiAAAiAAAiAAAiAAAiDwPAGfEU8SHlUcFwWH1nxUq1uixxbzQ7Gz4oktSWpCwaBdn0l0+PgvqoknHBb0XuWYHBgcJmKv2DxPbPkxbHU5dIVDWNg7hR/C7RV3xJNWPUUOkx/PSXGH87IkVljciLZYZM6O9yq3l6frbFs2Jl5VW4jKiN4tZX4ZV8QT7sgWrsLCwrqtB+jipat0ZOssmT+Fiysc7IknNvHNFg5ku4iE4oka4yUUT/YfPkVdBk2nBiJXzGARlpNYsYU/zRjVNd6pP9gUQQAEQAAEQAAEQAAEQAAEQAAEHBOAeCIYcX6SOm2GPidG8IN2jRbxk6fa8zwpWK6lDN05skU8nIvTYrg8ePiE6rUbJhPSquV5wv1WFDlFuM9JQzvSx2WKxrM0ixa5xIkzmTOmoznLNtPMxRvFEcgx4oOt2AQjTn7KAgyfIGMrnFOFvTQ4iSgXd8QTW+JZbp8wrwz/jo+E5jCpDs2qy6OIy9btLk9++XrhCMrzek45LidH5bF5rluFqJJLiCuuiid8xHI5kbCXPWjYA4WPC2YBwR0O9sSTUVOX06pN+6QtOD8LJ/DlE4R6iyOb+dQm22k7rnBParyE4glzY35sywNfT5OeT7ZyWhz7nFl4mpwTxzYzbxbctiwdHXtsNtdjLsdF3pS4Ryk73jpQAwRAAARAAARAAARAAARAAAR8hwDEE2Fr9nwoJsI7+H/59JaCIpHo+YuXZbJRLs7mPOEErJwzhR9Qy5d8Vx5Vu1skpuV+uagpnthOAuJ+K370rjyZ54Y4TpjDg/hhfcWsQfT2W6/H5v3gnBbN6n5MYRERVPit3DLXy+ylm2iW+I8futlrgZOGsuhx4MhPUrD49cBSOW93xBNuN3neWhkWw4WTsxYVJ8PwEcGcWJUf6rnYhBXb6UecJ6VD02pC7EghQ6D4VBm+vinDY5LauiqecBs+MYl5cUks2auzHOyJJ8yLk+SyrZknJ9plDyFbiXtUsdLxEoonPAaf6MMn+7BIxKE76dKkkuv3yMmzsddsS2rL4kq9amUotWD8s7AFe8PkFYIVe82ggAAIgAAIgAAIgAAIgAAIgAAIPE8g2YsnNq+SxPJpxMXByUU7D5wWK3Tw3xrVLEdrRGLVF9Kmjs15YnvIT3hSDtfnE3qadPmCrgsRw1ZYjLktHqxZBDi4cTpleCGNPOmHvQAS9rFz/zHqJTwV4ubk4H5siWT7dW5EnLPCVn78+QINGrdQ5mqJW3jMMeLYWfY84TJ1wXrpFcEeBlxsiUU5bGb99u9ovDiqNu6DPj/8Vy3/Po3u30bWd1c84bY/nPxV5udgL5m4hQUpPpKYc7ikTBEs/8QhLkMmLJbeO7ZSvVIJGtqzeeyRyEmxY/bl6/eUotD0kf95lnA/uw6coB7DZkmPjISn+fDfneUwbOJSWrftAK2bPyzecdS2ufIcxs9eJcWhNCIsiMUizpvDItL4we2pSrnisqrS8b7eflByGtqjmRRBuPCJRQtWbKM5X26Ox49Fq0nDOsmQNLYxr4XVm/fFq8NeUp2a10gyf048w+EHEAABEAABEAABEAABEAABEPBBAslePHHFpqFhESLU4ppswiEiKUJiHupdKRzywn3wyS4vZcssxRJPF34o5jAYnm+2LBnI38/vuSH5gf2SONWG87ewcJGw8GlDLHBkSp9Oii4cdqJm4RCcv8X4EZGRlCVzhnihJXHHYX6X/rkuT6557eXsFBIcqOY0HPalNgcWjthTZPWcIVQgby6Pc2c7swcSr79sIhSLPY4SFllHhPrcF8d2Z8+SKTbEzCEcVAABEAABEAABEAABEAABEAABHyUA8cRHDY/LVp8An4j0YdEC9Por2WXoznc/nJGn4LBYtX/dFPk7VQs76KjcparzQ2cgAAIgAAIgAAIgAAIgAAIgkEwIQDxJJobEZehP4K3SzZ+bBOcgmTuuh8xJgwICIAACIAACIAACIAACIAACIOCdBCCeeKfdMGsDEvjp1z/o198v0e27D2Uel1deykIl3iuQaOiMAaePKYEACIAACIAACIAACIAACIAACCRBAOIJlgYIgAAIgAAIgAAIgAAIgAAIgAAIgAAI2CEA8QTLAwRAAARAAARAAARAAARAAARAAARAAARUFU+QpBILCgRAAARAAARAAARAAARAAARAAARAwIcIwPPEKMaGKGUUS2AeIAACIAACIAACIAACIAACIAACIBCPAMQTLAgQAAEQAAEQAAEQMBoBq5iQyWiTwnxAAARAAARAwHcJQDzxXdvjykEABEAABEAABEAABEAABEAABEAABJwgAPHECUioAgIgAAIgAAIgAAIgAAIgAAIgAAIg4LsEIJ74ru1x5SAAAiAAAiAAAiAAAiAAAiAAAiAAAk4QgHjiBCRUAQEQAAEQAAEQAAEQAAEQAAEQAAEQ8F0CEE981/a4chAAARAAARAAARAAARAAARAAARAAAScIQDxxAhKqgAAIgAAIgAAIgAAIgAAIgAAIgAAI+C4BiCe+a3tcOQhoR8AihjJrNxxGAgEQAAEQAAEQAAEQAAEQAAE1CUA8UZMm+gIBEAABEAABEAABEAABEAABEAABEEh2BCCeJDuT4oJAAARAAARAAARAAARAAARAAARAAATUJADxRE2a6AsEQAAEQAAEQAAEQAAEQCABAav42QQqIAACIODVBCCeeLX5MHkQAAEQAAEQAAEQAAEQAAEQAAEQAAFPE4B44mnC6B8EQAAEQAAEQAAEQAAEQAAEQAAEQMCrCUA88WrzYfIgAAIgAAIgAAIgAAIgAAIgAAIgAAKeJgDxxNOE0T8IgAAI+AiByMgoioyKphQhQZpd8aFjv1CGF1JTvjde0WxMZwY6+/slevjoKZV4L78z1T1S51loOPn7mSkwMMAj/Ruh0+hoC4WFR1DKFMEOp+OptbJhx0H6sGhBypwxncM5eLrClWu36NCxM/TaK9mpeJF8HhvOF9aWx+Al0bGj/dPR3z0xX099ZpTOFfurUoLOtcf+Gp8T9lfn1k1yrwXxJLlbGNcHAiCQrAm8V7kd8YPM9JFdqVzJIvJad+4/RkMmLKYTO+d57NovXbkuxlhCS6b2FQ/ofnKckVOW0d5DP9J3G6Z5bNyEHddvN5yKFHyD+nZq6PaYrXqOp+5t61L+N191u4+EDUdP/4rO/naJVs4erFqf9jpim5888zsN7t40tlrlxn2o0Fuv09gBbTWZg6uDJLaGXO1j/+FT1GXQdLnmMqZPa7e5GmslsQHeKt2c5o7rSSWLFXB1+qrWX7r2G5owe7Vcx4UL5KZ+nRup0r83ri1VLtxBJ7a9l6uxQMn2H92vDaVKGeLW8HH3T+yv9hFif3W8xLC/OmbkSg3sr67QSt51IZ4kb/vi6kAABJI5Ab6BjxJv3zOkS0171kwik8lEO/Ydo6ETPSue/PTrH9S40yg6vXtBrGfDvzfu0MPHTylv7pc1o67GAzE//M4e050+er+QavPW+uZ+9pebadueI7Tjq3Gx1/D7n/9Ij4wcWTOpdl1qdpTYGnK1f15vf16+RgXzvRYr4iXVhxprxcjiSdWm/alaxQ+o7WefuorRbn1vXFuqAkiiM957G9eqQNUqlZBrcMCYBfRuoTw0Z2x3t4aPu39ifzWWeOKNnwHsr259DJNshP1VXZ7e3BvEE2+2HuYOAiDg8wT4Br5jsxo0ce4a6WHwqXh4SiieXLl2k/qMmkfnLlymXDmzUdO6FanWJ6UkOw4t6T9mPh0+/ov8+XXh7p/n9Zw0un8b4f7/s3wg4AdULu+JB4OJQzvQC2lTU9m63enm7fv0YqYXyCwEmy9E/cvCG+WHH8/R1BGdqVm3MVSqeCFq1fCTWBs16DCC6lYtTbWrlJJ9D5+0lG7dfUDvv/MWtWlcVTx4vJmoPZ+FhtGwiUtpj/BqSSXEgIY1ylHz+h+L8KBgSvhAbK/f70+cpS+mLaer12/La2jRoDL9cekabdx5SL4tTi3+q1etjAjBKEBdhTfDyD6tiG+a79x7QDtXjKff/rgiOfKDUnohVvVoV49qVi4p5/zkaajguIC+++En8hOeOIEB/vTay9liPU9cuV72hDn1y0WKiIiU47BXjM1ezGL09BW05+BJihBhUuxl0KFZdeo0YKqsnzVzejmfLV+OoRFTvpS2/LTCB4LTMBo3qD29I7x0uFy/dY+adB4lPSZeeyUbLVy5nZas2UmhYRFUtfz71K7Jp0mKLhUb9KLSHxSmXQeO0/2Hj6lCqXepQfWyNHzyl8RrrWyJIjTo8ybSE8TVNVT07TxJzoXXb0K7TBnemTqLa9+1aqLgbiZ77OyJJ47W67a9P9C4mSvp3oPH9HKOF2n84PaxnkpxPU/YC6ZU8YJynXPhdry+Fk3qI392hR3Xd3bdsNfC6s37Y9fxxKEdaeWGvZRLrEGe8+HjP1Pzeh/TrTsP6Mt130hvNfaWqCM+iwO6fiZFV3fWFvfJrv2T562ltVu/pXCxBosVzkdjB7YV4XRpYq+5fMl36BuxXu7ceygEnhJizMby8+vNhffez9vUkQIKl+mLvqbl63dLjz97ewV7S7G4ynsf7zs1Py5JvTs2oDXCfrb904j7q9VqtbtPKN1f7X3GfGV/5T2u88Bpcg9etWmffDHCayxYfFZnLtlIT56FUUOx1/b5v6elmvvryo376MRP5+X38NfbD9Kbr+WkZvUqJdv91dF6jrs3Obu/7vruhKrf3Y72V9t6qV7pQ1q5ca+ccutGVeT9FIrnCEA88Rxb9AwCIJAMCew/ZNHlqsqWNCc6Lt/AD+/VUnxhX5APswe+nib+90Ss5wnHyX9UuxsVyJOL2jetRheFWMCixfblY+mVl7JQuz6TiN9QdWtdRwon42atpCBxo8bhJj/+fEEKLvxW/5m4aeszaq7wznibRvVtJR7AdtH4Watoxqiu5O/vR/lF/ys27BEP9T/SlqVf0KS5MQ9TR7fNlg9mPL8mXUbT3rWT5UNatWYDpLBSsfR7tHHHIdqy+3s6vmOurJuw8APpeTGPgeKB3CT+b8DYBdS5RS1qVLNcPPHkz7//TbLfPy//S9VbDJTeJU1qVxQcrtLx079R+2bVZB8sFrwtQlxyZMtMd8UDXvPPx8rcLXxTEhIcRB2b16BSNbvIXBKdxL8PHj0jblb20VczB1Lh/Lmp6+Dp9MPJX8XfalLeN16miXPWUIDgwhztzSux6529dJPM4cI5NHaIcJwlq3fSwY3T5cNox/5TxA3ub9S1VW35YLx8/S7BoQJt+uawvPH9ol9ria/EewWoUceRsSFNtVoNppzZX5TCFpcZizfQ2i3f0qFNM+RD2xfiYW7Q500p96vZZehHurSppDdOYoXFAhZGurWuTeJ5SoaIsXDRpWUtKRj1/WIedWpRUz6su7qG9oibz6Tmwted0C5lSrwt19WZfYuk54k9dvbEE3vr9f6DR1S37TCqU/UjqvjRezR32RbinAuHBTv27IkrnnzyWV8pYHZoWl2iWyY+J4tW7YgNZXOFnSvrhh/W6wmBrEmdikK8yEsF875GbXpPlJ9ffhhi0eydgm8K8eS+WJf+9ErOLHTp7+vUb/R8mjikA1UuW8zttcXXN23herkm2ctp/OxVlCVT+ljhkK+ZRcAu4u/Mq78Yc7wQ8j4uU9SlvTT8m69dqq9W5aCPaye598YVT7oPnUlnzv0p9tZxSe4V/EDKe6aZj/AAACAASURBVHa9T0tLofbCX1eF4HqGJg/rKD+Ttv3TiPurvX2CRWgl+2vlskXtfsZ8ZX+17XH8ncLCBf+8Qoig/JKC91f+PmeReuPiUfRGrhyq7q9TF6ynBSu2UfYsGalS6aL0UrZM9Lr4Pkiu+6sr33vO7q9nxL2U2t/d9vZX23opkDcXtRAvlP4SL7BmLt4ov9d5z0XxDAGIJ57hil5BAASSKYHW3SJ1ubKF0xJP+mkTT95/Nx99VKsbDRRvkVOnShkrnnx75LR4czRNeBj0EL9PIefea/hsqi0eBJvV/VjeyPNbYNvb04ThJuyh8MPJs/TvjbviQf6o8M5IQWvmDZWCS8Kwnbg3/9dv3qXy9XvKN+7F38lHvUbMkZ4qy2cMkN4f2/cdjX045xtCfihmoaGQEGriFhZa3qvcnjq3rCk9VGIeSHfTjVt3Zf24D8T2+t2y63vaIDwATu2a/5xAkzBsx3ZD8oMQftL8nxl7enw+ZKb0QMmZPbOcR8kaXWSi0OG9mlPhim3k22MWDLjE5ejK9XJbfpPPD2E/n/9TXOc9+TZ72fQBIhyKH7zay7eOzepWiscpMbfyuGxYXBk4dqF4Kz5XvvHnuX8mRCQWjTg3CntTtP//Az/blj0Jfty1QApACQvzWjixN73/bow9ytfrQdU//lDe3HPpP3qB9DBYMLGX/NmVNWRvLj+dvSjXSVy72EQ5m3iSFDsWD+yJJ/bWK4uNLGId2z5HXg97b5Sp87nw5Gkn3xC7Kp44y87VdVOwXEuaIDxi+MGHCws++YSQN7xXi3gmPH/xb/l29OZtsba+3iNvuvltpbtri71p8oi1yXmXuNjWmk3wS7he2Dsoc4YXaMyANs+tLXu/eFDvQ5fqq1U53drDiXbFeyeLVPzgclqsTc45xN5/wcGBSe4V/bo0og8+7SS8/ypJETZufpS4+6cR91d7n82xM1Yo2l/tfcYqCs82X9lfbd89Z79dIr+n2OOmWJUO8nuzSIEYr0H+nm8pXjzYvgPU2l9nCc+WHeJ7+ZuVE8hsjnmJkZz3V1e/95zZXz3x3W1vf/1LvDDi70TbemGb8WdlZJ+W8rsJxTMEIJ54hit6BQEQSKYEVq6P1uXKGtV5/iGWJ2ITTz4pV0yG7qzbeoD6d2ksBQp2H7clObM98NsmX77ku1Tzk5L0qciTsGHRSOGi+5L8U9yH/nXbDshwGX6w5jwmnEODvVK+XjjCoXjCfbHnQwbhocBvmYtVaU+TRCgBh3i06TWRjp0+J99wxS0cBsN/j1v4zWzNloPkmzce21Yypk8nbyjjPhDb65dDGrjYwifijpGUeBL3hoQ5Tlv4tczxYissSnFI0/DeLSRH5sJhMgk5unK9j548owbth8vQovdECAu/yV+/7TtaMqUfpU2TktiDJK69bHNxJJ5wOAWvFRbXXn81BzXtOjr27RTfbHGYUcI3VUun9pfcE5aED8PVmg+k8iJZMXsecOG1d/7iFemV4+oasjcXDglKeKMY9+aeQ1GSYle0cB674omj9cp/t4lBts9d60ZVpfikRDyxx86VdcNzSuzmnq+7d4cGsSbsM3KuFC75gT9Xzqwy/IuFU/Y8cXdtsc3YA8P2MMcnUvCDyeo5Q+Q4CddLj2GzRThCVKzY8twCS+IXoYunOFtV1XohLRP3wOLPEwvSr76UVbylzyxznxQRiXrt7RW8Z81Ztlm+HebC+zKHTZUsVjCe54kj8cTRevXE/mrvs8negEr2V17rSX3GKnz0rs/srwnFE34Y5881i+e2kEv+bLFHJHuSqrm/cpjQgSM/0aYlo2I/P8l5f3X1e8/R/uqp7257+yufNJfwO/GDap3EflxXereheIYAxBPPcEWvIAACIKAJgbjiCb+l4i9ODr/5599bUjzh/Cf9Rs9L1IvAYrHS2xVaibel7YjFFy5xxRP2TuCHKr6558LeCBzXziIBe0awOHLym/kirCVQ/j3um1P++Ztvj1NP4eXCJ+Hw345umyPDOwaNW0R/iLwh/HDlqHBOjQ+rdxFCyUD5YJKwxBVP7PXLuVs4PpzdWRMWfribOboblRF5PLgkvIHl33E8fl+R78T2Np1/xzexeXO/QuNEfoe3K7SWIUxlP4w58SguR1eud7PwkOG5Ht48Q+Zl4cLzY/GEXajZJhyaU0N4esQt/EDG3jXsGWMrCT0teB4/n/9LhuY8fRYq851w4fwKtSqXkt49zpSED8M1WgwS1104UfHE1TVkby6J2SXuzf32vUeTZOeMeJLUemVPmiPC+8p2ihTfJL9ftSON6N1S5u+JK56wGFK2RGEpJnBJLGwnrueJPXaurBsey9HNvc1jZtboz0XOmrfl/Gq3HiIf3NkTwt21xW/C+UhuzpPExXYC0l6RwDrrixlUE0+cWZta1kmY88Q2tr29gsNzuLDQx55lc0ROJf5MnhR79ewvN8WG7Rhxf7X32VS6v9r7jHESZF/ZXxPucfwdXaBsiyTFEzX3Vw7bsSeeJLf91dXvPUf7q6e+u+3tr/ySBeKJlrt+zFgQT7RnjhFBAARAQDUCccUT7tT2VpPzdbB4wskiS4ucJ5yrYVjP5nLcg0d/FslGI+UDePu+k+TNO+doeCweCheIxKF5hfcEh8SwOJBbxFWP7N1KxNL+S71F6E3aNKmkeGILp+EHMX4w5dwXi1fviL3553GioqNlKAAnMuVwFg5r4cICTFuRj2FgtyZUV7wduS/muOmbQzJRK8cLJyz8QBoVFSUf9jkhKs/3+xO/yIf1uAKBvX45/IVzp/BDbaOa5aW4tFfkZ2HBgPsvXiSfeENfn/jBODFXWA5D4ZstTnbZq319KcRwvogJgztI4YmFJH5LyMIGCz6c98OW98GV67U9eLKwlE145rAnEQtPLJ4wZ7YJu3OPEeEBuYUHCectKVYkr7Rzh36Tac/qSVKg4vwoDdqPiHeMM3sOsXcBl/kTeskHXi6cu2aV8MyZP76ndA2/fPWGzLPCuW0SK66IJ66uIXYdT2oujsSTgyJ3BNs4KXaOTttJar3abMJiCSc+ZXvwW1rO38PrMa54wqLZEZH7ht8UX/7nhhAKFwqhKixezhNnxRNX1g3bydHN/YOHT6hE9c5SzKwhEh1/L5JE8zpt2eAT+blwd21xMkXOWTRrdHfKnjWjFEw5t8o+wYfXqlqeJ6ptmip1lJR4Ym+vyJ/nVZnYkcOk0qdLQ/O/2ipFExZP5ol/23KeGHF/tbdP2D4j7u6vjj5jvrK/uiqeqLm/OhJPktv+6ur3nqP91VPf3fb2Vw4VhHii0obuQjcQT1yAhaogAAIgYDQCfAPPD3XsIcKFT0th7xN/8QDN4gkXTmTac8RsebIOF3645pNkqgs3c35zwW/9/vz7mjyJx2KxyASpiyb3kS79fLoMix/chsNsUomcJ+vmD5P92DLQ87/Z6+Lchb9pt2jDCWNthR8mOeHd7tUT44XpsGs7e7Kw4MCFQ1KWTRsgvSsSlmviCGTON8LJL22FXVKHCjGIT/ApIpLr2U4fsNcvh93ww0psHyJh49AezYjfGA0TeS34OvnUGGbJp6/EDdvhNjtF3gt+2LTNmRM+cnsb43ZCiOK/MSsWTjipKotQXJy9Xn7TyCE1nEOBC4c9/CLEoqVT+8kwHk4iyqfLcGgEF86ZwGNwMlgOWeHEdlw4t0nLHuPjseHfc0LTB4+e0PebZ8bmfuGQniHjF0vvGlt5VYR0bFs25jlb8C/4YdiWy4Z/Tug9wTbnsB0OUXB1DZUQAlpSc+EbxYR24dwdTbp8IRPGmk1mu+wSrpXELi6p9conHHFCZC5xPz82HjYxisPM2vSaIHO+cD0WuFhM278uJuTEFXaurBuuG3Nz30HkPHlPjsU5T1hYY7HPVjgxLoucXDhMi09s4tOr+KHX3bXFHm/t+06OXbP8Web1YTuyPOE1c9gO7zO25MWJLjIv+GVS4glPPam9gvNT1G49OHYv5rA4DqviPYdDeeLun0bbXx3tE0r3V3ufMf4O84X9NeEel5jnCe/hHLbDIYNq7q9sPz4tjsNCbSU576+O1nPCLcjR/uqp7257+2ti34l8/8cn9NlOfPOCrdTrpgjxxOtMhgmDAAiAgHsE+CEuIiJKnuJiO+WF37bzKSVc+Mu/Tpsh8nQOW6gO/50f1F8SuTcCRF6MhIXfkPJNiC3ExJWZ8VGBN8Ubas5l4kx7dnXna8ic8YVEE5naxrbXL4sbnCiTc7HEzaHCv+cH3rhsErsWrsdeK1wv4VGrnPiWhR7Of8APzgmLK9fLyXW5DxZgEiuca4XFnkwZ0sX7M/Ph63LnGFi2NY/7gjhpx532SdnenTWkZC6O2LmyRuPW5fXHa+clkafC9plJqi8WJbMIrxRH9ZyZiyvrxpn+2LuKvVAS5kGytXV3bfHaY0YJcxk5M6fkWMfeXsH8eY0n9fm28TDi/mrvs6l0f7X3GcP+mvinBPurst1DyXdNYiM7+v7B/qrMXkZoDfHECFbAHEAABEBAJwKcZHbr7iPSc4ETcvKD1Y6vxslwBBQQAAEQAAEQAAEQAAEQAIEYAhBPsBJAAARAwIcJ8BtyzoHy8PETIZhkoDIi2WXa1Cl9mAguHQRAAARAAARAAARAAASeJwDxBKsCBEAABEAABEAABEAABEAABEAABEAABOwQgHiC5QECIAACIAACIAACIAACIAACIAACIAACEE+wBkAgmRMQx8SSyd41OqyQzAHh8kAABEAABEAABEAABEAABEDAfQLwPHGfHVqCAAiAAAiAAAiAAAiAAAiAAAiAAAj4AAGIJz5gZFwiCIAACIAACIAACIAACIAACIAACICA+wQgnrjPDi1BAARAAARAAARAAARAAARAAARAAAR8gADEEx8wMi4RBEAABEAABEAABEAABEAABEAABEDAfQIQT9xnh5YgAAIgAAIgAAIgAAIgAAIgAAIgAAI+QADiiQ8YGZcIAiAAAiAAAiAAAiAAAiAAAiAAAiDgPgGIJ+6zQ0sQAAEQAAEQAAEQAAEQAAEQAAEQAAEfIADxxAeMjEsEARAAARAAARAAARAAARAAARAAARBwnwDEE/fZoSUIgAAIgAAIgAAIgAAIgAAIgAAIgIAPEIB44gNGxiWCAAiAAAj4OgGrAGDydQi4fhAAARAAARAAAQUELKKtWUF7b28K8cTbLYj5gwAIgAAIgAAIgAAIgAAIgAAIgAAIeJQAxBOP4kXnIAACIAACIAACIAACIAACIAACIAAC3k4A4okKFvz3bqgKvaALrQlkThdM9x6HU1Q0u7OjeBMBfz8TpU8dRLcehHnTtDHX/xNIHeIvIkhM9PhZJJh4kMALqQIpJMjPgyNo0bV3OgiHhkfT/ScRWgDCGCoS4O+WF8R3y218t6hIVbuuUqcIILJa6XFolHaDYiTVCOC+XDWUdjvKliFEm4GS6SgQT1QwLMQTFSDq0AU2aR2gqzQkxBOVQOrUDcQTbcAnD/FEG1ZqjwLxRG2i2vQH8UQbzp4aBeKJp8hq0y/uy7XhDPFEGWeIJ8r4ydYQT1SAqEMX2KR1gK7SkBBPVAKpUzcQT7QBD/FEG86JjQLxRD/2SkaGeKKEnv5tIZ7obwMlM8B9uRJ6zreFeOI8q8RqQjxRxg/iiQr89OoCm7Re5JWPC/FEOUM9e4B4og19iCfacIZ4oh9ntUeGeKI2UW37g3iiLW+1R8N9udpEE+8P4okyzhBPlPGDeKICP726wCatF3nl40I8Uc5Qzx4gnmhDH+KJNpwhnujHWe2RIZ6oTVTb/iCeaMtb7dFwX642UYgnniAK8UQFqgjbUQGiDl1gk9YBukpDQjxRCaRO3UA80QY8xBNtOEM80Y+z2iNDPFGbqLb9QTzRlrfao+G+XG2iEE88QRTiiQpUIZ6oAFGHLrBJ6wBdpSEhnqgEUqduIJ5oAx7iiTacXRNPvPP0IP1IajsyxBNteas9GsQTtYlq2x/uy7XhjbAdZZwhnijjJ1tDPFEBog5dYJPWAbpKQ0I8UQmkTt1APNEGPMQTbTi7Jp7oNyeM7JgAxBPHjIxcA+KJka3jeG64L3fMSI0aEE+UUYR4oowfxBMV+MXtwip+MKncZ1LdYZPWCLQHhoF44gGoGnYJ8UQb2BBPtOEM8UQ/zmqPDPFEbaLa9gfxRFveao+G+3K1iSbeH8QTZZwhnijjB/FEBX56dYFNWi/yyseFeKKcoZ49QDzRhj7EE204QzzRj7PaI0M8UZuotv1BPNGWt9qj4b5cbaIQTzxBFOKJClQRtqMCRB26wCatA3SVhoR4ohJInbqBeKINeIgnrnO+cfsepUmVglKEBDtsbLFY6d+bdyh9utTP1Q8Nj6b7TyIc9oEKxiIA8cRY9nB1NhBPXCVmrPq4L9fGHvA8UcYZ4okyfrI1xBMVIOrQBTZpHaCrNCTEE5VA6tQNxBNtwEM8cZ7zH5euUdNuo+nho6ey0UfvF6JpI7pQQIB/op3sOXiS+oyaRxERkfLvlUoXpYlDOpDZHBN4CvHEefZGqgnxxEjWcH0uEE9cZ2akFrgv18YaEE+UcYZ4oowfxBMV+OnVBTZpvcgrHxfiiXKGevYA8UQb+hBPnOdcq9VgSpkihOaN70H//Hub6rYdSv06N6ZGNcs91wl7p5Sr24PqVi1NvTrUp8tXb1D9dsOpb6eG1LRuJYgnzmM3XE2IJ4YziUsTgnjiEi7DVcZ9uTYmgXiijDPEE2X8IJ6owE+vLrBJ60Ve+bgQT5Qz1LMHiCfa0Id44hznew8eU8kaXWjhxN70/rtvyUY9hs2WITmr5wx5rpOtu49Qv9Hz6dCmGTJkh0tf4YVy+Z8btGbeUIgnzmE3ZC2IJ4Y0i9OTgnjiNCpDVsR9uTZmgXiijDPEE2X8IJ6owE+vLrBJ60Ve+bgQT5Qz1LMHiCfa0Id44hzn8xf/pjpthtLetZMpa+b0stHMxRtpw86DtH/dlOc62XXghBBXZtGRLbMobZqU8u9zlm2mlRv2SkGFC8J2nGNvtFoQT4xmEdfmA/HENV5Gq437cm0sAvFEGWeIJ8r4QTxRgZ9eXWCT1ou88nEhnihnqGcPEE+0oQ/xxDnOR388R616jqfDm2fQC2ljPEkWrtxO85ZvoRM75z3Xyf2Hj6msCNt5KVtmalqnIj149ISWr99NFoslVjyJirZSaES0cxNALcMQ4JQ1IUH+9DQsyjBzwkScJxAUYCayEoVHWZxvhJqGIZAy2F8Iz1EkcnGjeJCAvAdDcZsAxBO30f3XEAljVYCoQxcQT3SArtKQEE9UAqlTNxBPtAEP8cQ5zjbPk33rJlOWTI49T7jXi5eu0uR56+hvke8ke5ZM9Nsff1O2FzPGhu1ERVuk9wmKdxHghL/BgX70DOKJdxnu/7MNDPAT/7JSRCTEE280oBRPhOjMJ5mheI6A9NBCcZsAxBO30UE8UQGdrl1APNEVv6LBIZ4owqd7Y4gn2pgA4olznG05TxZN6kPF38knG3UfOpOu37qXaM6ThL0+eRpKxap0oA5Nq1PnljXlnxG24xx7o9VC2I7RLOLafBC24xovo9XGfbk2FkHYjjLOEE+U8ZOt4XlC4rhGouhoE4WEeI9ajE1ahcWvUxcQT3QCr9KwEE9UAumgG4gnznOu0WKQzF8yZ2x3unr9jsiBMkScntOIGtcqLztp1m2M8DDJSKP7t5E/37rzQIT4pKKbd+7TqKnL6Njp3+jQxumUKmUIxJM42E13b5I1w4vOG0LnmhBPdDaAwuEhnigEqHNz3JdrYwCIJ8o4QzxRxs/nxZPISKLjJ/3o4GEiPxFq+kllC+XP5x0CCjZpFRa/Tl1APNEJvErDQjxRCSTEE9VAXvjrKjXp8gWxFwmXksUK0PSRXSkwMMa9+aNa3Shn9hdp+YwB8udhE5fSum0H5L/zvJ6Tpo/qKsUVW/F1zxPzzavkv/VL8v/xAEW/mpciG31Olhy5VLOXpzqCeOIpstr0C/FEG86eGgX35Z4iG79fiCfKOEM8UcZPtvZFzxObaHJIiCbPQkWGtTgl9+sWqv6pldKkNraIgk1ahcWvUxcQT3QCr9KwEE9UAumgG3ieuM752o07lDpVCkoj/rNXnj4Lk14nL2XNRAEBzyff81XxJK5okpBf5AcfU1SttmRNGZOU14gF4okxrCLexZE7WUsgnhjDfu7OAvfl7pJzrR3EE9d4JaytqnjCNx17Dp6kS1euPzcrdn9NERKkbLYGbe1L4gmLJsdO+NHh7/8TTV7OaaUS71vp0WOiPXtNFB5hIn5ZV76chYoXdefrTxtDY5PWhrMnRoF44gmq2vUJ8UQb1hBPtOGc2Ci+Jp6Yb18j/23LyP/4fonD6h9IUWWqkyV/MTL/cpQC9q6P+X3KVBRVrRVFlqqqn3HsjAzxxJBmcXpSEE+cRmXIirgv18YsEE+UcVZNPPl6+0EaMmGxnA27ufpzDEecsnfNZBlP7K3FarUSZ88P8OdM3vGLL4gniYkm2bNbqXwZK72W6z+B5MlTE+34xkxnf43xRsmW1Uo1q1voxczG80LBJu2tn0YS+4uJ0qcOolsPwrz3Inx45hBPtDE+xBNtOCc2iq+IJ1I02b6c/I/tixFHgkIoqnR1iqpQL56HifnOdQr4ajL5/f6TrGfJnosimvYkS8439DNSIiNDPDGUOVyeDMQTl5EZqgHuy7UxB8QTZZxVE0/K1+tBKVKE0KrZgyllimBlszJg65Ub99GU+WvpxM55PiWeREaJnCb/9zR5+ixGEMnyopXKlbXSm7mT9ir5408zbdlmogcPY9p8WCKaKpYzloCCTdqAHzQnpwTxxElQBq0G8UQbw0A80YazL4onMaLJV0I02RsjhoSkoOjSNSmqfB2ypkiVJHj/s8fJf90cMt+6KutEFa9AkbXbkTVVWv2MFWdkiCeGMIPbk4B44jY6QzTEfbk2ZoB4ooyzauJJyRpdqHLZYjSg62fKZmSw1n9evkaNOo2SSeQ47MiXxJOjx8303UET2USTTBktwtOEKG9e50JxWHjZf8BE3x+J8dZJl054oVSz0quvONfe00sBm7SnCXuuf4gnnmOrRc8QT7SgTATxRBvOviSemB4/oID1c/4LzxFCSVTF+hRV6lOyhjjvXRywb71IKLuMTOGhZA0W3irVWlBkmZhjnvUsEE/0pK98bIgnyhnq2QPuy7WhD/FEGWfVxJMBYxbQ6bMXaeeK8cpmZLDWkVHRdPP2Pdq65wgtXrXDbfGEfS7ip1U12IUmmM7FP8y0fGVM6BWLHmU/stLbhdwTPW7eMtGmrWa6di2GAPdTuaJV92ONsUkbew3amx3EE++1Hc8c4ok29jOSeMKn2SSWD63sh0USDYfVhpDnRkmuYTuBi8eQ/4n9MiSHvUyihLcJix/uFCnEbJxP/j/skc0tmbJRZPM+FJ3rLXe6U6UNxBNVMOrWCcQT3dCrMjDuy1XB6LATiCcOEdmtoJp4smLDHho9fQVVr1SCcmTL/NygrRp+QkH/P/JP2ZT1ac1HEo6ftcpt8USfWbs/6qy5fsSiR4kPoqlSeXXCbY6fMNPuvWaKEElnU4RYqWN7i64n8mCTdn996N0S4oneFlA2PsQTZfycbW0E8eTHny9Qx/5TYo8ATjj3Q5tmUPp0xj19xVnWCeslR/HEfO0vCh7VTl5q6Li1ZE3zgrt44rUzX7lAAatnkt+l8zLkJ2zoYtX6dnWCEE9cJWas+hBPjGUPV2eD+3JXiblXH+KJe9xsrVQTTz7r/IX0PEmqHN48g15I6703SPbEk/uPI5RZwWCtfxbJXpevEm+HxVHDA3qI5Jzi5By1yuPHJlqx1kp/XjJRvjxWaqFjlFfalIH0OCySLNHqiENqMUI/jgmYRcLY1MEB9PBp8vrsOb7y5FEjJIhD+UwUGi5i+1A8RoA5Bwc+n+TcYwMm0nGDDiOkx8m4ge3opeyZhZdJ/GN9c4ijfs1mb/LLdI5echRPAmcOIP9fT1Bk2VoUWbeDcyBcqBU4bxj5//Q9RRf6gMLbD3ehpXpVIZ6ox1KPniCe6EFdvTEhnqjH0l5PEE+UcVZNPFE2DeO3tieehEZEG/8CXJjhsDHRdPeeierXNtOHxV1o6GTVx0+IRoyzUJg4KKVVUzO9XcDJhipXCwowU2SkhdwLRlJ5MujOJQIcUBYg7Bcu7IfifQT8+WFZ/H8UhEuPGs9sMhHvc3qW9yq3o5qVS4l8aI31nIbmYyc38cTvz7MUNLE7WQODKWz0yngn6agFl8N4goY2I3PoM4poM4iiinykVtdO9wPxxGlUhqzornjibaH1hoSvwqQgnqgA0YkuIJ44AclOFY+IJ7fuPKCw8HDK+mLGZBPL7CthO6fPmGnjZrPwErJS926eE4VOnTbLPCipUlqpWycLBQVr7/2BTVrZ5qFna4Tt6Elf+dgI21HO0JkejBC206rneIoSucO+nNbfmSknmzrJTTwJHtORzFcuUmR1kdj140Yes5P/0T0U+OV4Ic6I8J3hyzwi0tibPMQTj5lWk47dFU80mRwGcUgA9+UOEalSAeKJMoyqiifL1u2iKQvWUwQntfh/KVeyCA3r2cJrY5qtVqvwToiiddu+o6kL1tH3m2eS2c9M/n7/uUL/ezdUmRUM1HriFD96JEJr6tS0UMECnn2rP3+RH10VSWSLFLZQjU89O1ZiiLFJG2jhuTgViCcuAjNYdYgn2hjECOLJzv3HqNeIOTRuUDviEJ2EpUCeXOQnvlOTW0lO4omfCKUJEiE11tTpKOyLFWQNCPSouYKm9ia/33+iqPfKUETLAR4dK2HnEE80xa36YBBPVEeqaYe4L9cGN8QTZZxVE09WbdpHo6Yup1dzZqWP3i9EmdKno0PHfqajp85RvjdeobXzhpJJuBB7Wzl34TLVbTss3rQ/eDc/LZjYK/Z3yUU8OSaOJt7+yQBNLwAAIABJREFUjZkyZLBIbxBPl7t3TTRtVowI1bJZNL3ysrbeJ9ikPW1hz/UP8cRzbLXoGeKJFpSNcVQx5zz55fxfSV4wEsZqsxaUjBI8tAWZb12lyEbdKLJkVSVdOdXW/OAOBQ1rKY8xDu80iqLzF3OqnRqVIJ6oQVG/PiCe6MdejZFxX64GRcd9QDxxzMheDdXEk8qN+8hxEh5VvEgc7zt53lrx+3GUM/uLymZr0NbJQTyJEnkbJwivk9BQEzWqb6E8b3pePGFzfnfYj/btN3k8TCixpYNN2qAfKCemBfHECUgGrgLxRBvjGMHz5OKlq/TgoUh0lUQpXCB3PE9Obch4fpTk4nnif+QbClw+iawZslDoqOWeB/f/EQIO76CAFVPImiothY34kqwhKTUZG+KJJpg9NgjEE4+h1aRj3JdrgpkgnijjrJp4UrhiG2pSuwL1aFcv3oyuXLtFLKzMGdudShUvpGy2Bm2dHMSTQ0LE2CNEjGxZrdS+jedynSRmwhlz/Oj2bROV+jCaypfVzvsEm7RBP1BOTAviiROQDFwF4ok2xjGCeKLNlRpvlOQgnpgiIyh4YGPiRK4RbQdTVOFSmoLmBLWcqDbq/UoU0fQ/b19PTgLiiSfper5viCeeZ+zJEXBf7km6//UN8UQZZ9XEk9qth9CtO/fpuw3T4x07OH7WKvpS5ELZu2aSSCCbQdlsDdra28WT8DATTZwmTi4JN1GrFtH08kvaCRhs0n+vm2jugpjwnS7toylTZm3GxyZt0A+UE9OCeOIEJANXgXiijXGMIp789scVmjR3Lf32x98imXwEZc+SiRrVLEd1qpZOlscUs3WTg3gSsGcdBWyYT5acuSms/2xtFm2cUUx3b1LwcBG+I0Sc8G7jKTpPYY/PAeKJxxF7dACIJx7F6/HOcV/uccRyAIgnyjirJp4cOvYLte87iQIDA+jdgm9Spgxp6fsTZ+nOvYcyB8rsMd2VzdTArb1dPNkrPE4OCs+T13JZqNln2oTrJDTn9p1mOnbCTFmzWKlDW208X7BJG/hD5WBqEE+813Y8c4gn2tjPCOLJyTO/U7NuY+QF5341B2V4IQ2dOntRJpavU/UjGt6rhTYwNB7F28UTkzguOHhAAzKFibwjvaZQ9Gv5NSYYM5z/gU0UuGYWWdJloPBhi8kalMKj84B44lG8Hu8c4onHEXt0ANyXexRvbOcQT5RxVk084WmwWDJh9mr668q/FB1tkSfs1K9Wltp+VlWKKnoXT53j7s3iybNnJpnrJFroFe1aR1P2bNp4fSRcCxERRNNF8lg+6eeTjy1UvKjnRRxs0np/It0fH+KJ++yM0BLiiTZWMIJ4Ur/dcLp89QZ9u34KpQgJlhfOp9gNHr+YNu48JLxVp1HG9Gm1AaLhKN4unvhvWkiBu9ZQVL73KKLLaA3JPT9U0LjO5Hf5d4osVZUiG3bz6FwgnngUr8c7h3jiccQeHQD35R7FC/FEJbyqiidx58Q3R954uo47XL1ZPNkhTtc5Kk7ZyZvHQg3reV6wsMf3jz/NtGyFOAban+jzztGUJo1nhRxs0u6sdmO0gXhiDDu4OwuIJ+6Sc62dEcQTzofGITq9OzSIN/nL/9ygKk360fwJvajEe/p4NbhG07Xa3iyemB7dF7lOPiNTVASFDRZhO9lede3iVa5tvn2Ngoc0l72GCS8Yiwe9YCCeqGw8jbuDeKIxcJWHw325ykCT6A6eJ8o4KxJPooS7QlhYBKVMEewzQkliuL1VPHn0SOQ6mRqTa6SrOJo4oziiWO+y9ms/OvurSZMQImzSelvb/fEhnrjPzggtIZ5oYwUjiCecMD4kOIg2LBoZ76JXbNhLo6d/RV8vHEF5Xs+pDRANR/Fm8YRPueHTbqKKlqWIFv01pJb0ULb8K9b0mSmMw3cCgjwyL4gnHsGqWacQTzRD7ZGBcF/uEazPdQrxRBlnReLJ9n1Hqc/IubRpySgaMXkZnfrlQpKzObx5hjiONrWy2Rq0tbeKJ5u2munUaTO9XchCtarrL5yweTmMaMqMmOS1dWtHU4G3POd9gk3aoB8oJ6YF8cQJSAauAvFEG+MYQTxZtGoHTZ63looXyUelRP4zDtE5LHKkfXPgOGUQob17RDL55Oil6q3iienudQoZ1FQu0LAxq0SukYzaLFYnRgn+oh2Zr/5FkWVrUWTdDk60cL0KxBPXmRmpBcQTI1nD9bngvtx1Zu60gHjiDrX/2igSTziD/prN+6lTi5p09NQ5unzlRpKzadWoinj7FKhstgZt7Y3iyf0HQqSYHuN10utzz4fIuGK6U2fMtGmzmUJCrNSji4WCgj0joGCTdsUqxqoL8cRY9nB1NhBPXCXmXn0jiCccwjtx7hpauuabeBdRIG8umjS0ozh5xzgP5+5RTryVt4ongfNHkP/pQxRZRggU9TwjULjL2Xz9bwoe0Vo2D+87g6JfyeNuV0m2g3iiOlJNO4R4oilu1QfDfbnqSBPtEOKJMs6KxJO4Q9+684ACA/wpXdpU8Wb0LDRcHmH8co4Xk+XbJb5YbxRPbOExxd6zUJXKxvA6ibtwFi31o7+vmDzqFYNNWtnmoWdriCd60lc+NsQT5Qyd6cEI4oltns9Cw4jznISKUN+XsmWmzBnTOXMJXlvHG8UT85ULFDymE1kDgyls9EqypjSet3DANyspYPMSsmTKRmEjvlR9fUA8UR2pph1CPNEUt+qD4b5cdaQQTzyAVDXxpEGHEVQw72s0oGvjeNM8d+Ey1W07jHauGE85s2f2wCXo36W3iSc3b5lo1lw/8hOOJz2F10mqlJ7x7FBimbieMS2bRdMrL6s/R2zSSiykb1uIJ/ryVzo6xBOlBJ1rr5d4gnxoRN4ongRN+Jz8/vqVIqu3oMiPGzm3yHSoFTyiFZmvX6HISg0oskYrVWcA8URVnJp3BvFEc+SqDoj7clVxJtkZPE+Ucfa4eHLl2i3ihHGr5wwhdtNNjsXbxJOvVpnpwkUzlSwRTRXKqS9KqGXjw9/70e59JpErx0qdO0WTcGxStWCTVhWnpp1BPNEUt+qDQTxRHWmiHeolniAfmveJJ37nfqSgGf3IkuYFCh/1lUjIatwwa/PVPyn4i/ZyzYeNWS3ysmRQ7QMF8UQ1lLp0BPFEF+yqDYr7ctVQ2u0I4okyzorFkwUrttHTZ2G0duu3lDnDC1T6g7djZxQREUm7vztBdx88ppM75wlPB7Oy2Rq0tTeJJ1evmmj+Yj8KCrJSz24WCvZQPhG1TDVzjh/dum2iTz62UPGi6oYXYZNWy0ra9wPxRHvmao4I8URNmkn3pZd4gnxo3ieeBI1sQ37/XqbIht0oslRVbRaoglECVkwVJwJtp4iK9SiqZhsFPcVvCvFENZS6dATxRBfsqg2K+3LVUEI88SBKxeJJsSod6MnT0ESnyGLJe4Xy0Gd1KlCZDwp78DL07dqbxJNFS0QukX9MVKGslUp+GK0vOCdGv3zZTIuXmSlNaiv16q7ufLFJO2EAg1aBeGJQwzg5LYgnToJSWE0v8STutH01H5o3he34n/yWAheNJmuGLBQ6arnCVadNcw7b4fAda1AIhY1fK/O0qFEgnqhBUb8+IJ7ox16NkXFfrgZFx33A88QxI3s1FIsnts77jZ5PuV/NQa0afqJsRl7Y2lvEExZNWDzhHCc9ukWTv8phMJ4y3QzhfXJbeJ80bmihN3Or532CTdpTFvN8vxBPPM/YkyNAPPEk3f/6NoJ44qv50LxJPAma0ov8LpyhiNYDKeqd0tosThVGCZwxgPzPnaCIBl0o6qNqKvRIBPFEFYy6dQLxRDf0qgyM+3JVMDrsBOKJQ0R2K6gmnvz06x+0ftt31KJBZXrt5Wyxg46ftYqyZE5PTetWUjZTA7f2FvFk1x4Tff+DH5UtbaXSpdT14vCkeU6dFkcXbzXTa7ks1OwziCeeZO0tfUM88RZLJT5PiCfa2M/I4klyz4fmLeKJKTyUQj6PER6ezdmjzcJUaRQWTlhAsWTISmGjlqnSK8QTVTDq1gnEE93QqzIwxBNVMDrsBOKJQ0TaiCft+06i8xev0P51U+LlNpm7bAvNWLyBTuycSylC1HGrVHbJ6rf2FvGET9jhk3batoqmHNmNmyg2MQt9MdaPwiNM1KWjhTJlVEdAwSat/mdBqx4hnmhF2jPjQDzxDNeEveopnvh6PjRvEU/8zhyhoLlDKTrvOxTedaw2C1PFUYIHNSXz3esULk58jC74vuKeIZ4oRqhrBxBPdMWveHDclytG6FQHEE+cwpRkJdU8T0rW6ELVP/6QerWvH2+w67fuUfl6PXDajjI7KW795KmJxk8SiWJFgtiBfbzH68R24bv3mujwET969x0LVasC8UTxgvDyDiCeeLcBIZ5oYz89xRNfz4fmLeJJwMppFHBoG0XW7UCRZWtpszBVHCXg4DYKWDWNonMXpPAekxT3DPFEMUJdO4B4oit+xYNDPFGM0KkOIJ44hcnz4gkLJPnefIWmj+wab7BDx34h9kr5euEIyvN6TmWzNWhrb/A8+emMmTZsNtNb+axUv473iSePHplo4lQ/eVxx757RFCxOC1JasEkrJahfe4gn+rFXY2SIJ2pQdNyHnuKJbXa+mg/NW8ST4P4NyfzgDoUNWUSWrN53j2aKCKPgPvWIw4/CBs4jS45cjj8YdmpAPFGET/fGEE90N4GiCeC+XBE+pxtDPHEaVaIVVfM86TtqHm3b+wMtmtxHnrDDJ+38e+MOdRk0nS5euiqPKg4MDFA2W4O29gbxZP0GM/181kzVP7XQO4XV8dzQ2hyr1prp/G9mqlTBSiXeVy4AYZPW2oLqjQfxRD2WevQE8UQb6kYQT3w1H5o3iCfmm/9Q8LCWZEmXkcLGrNJmUXpgFP+NCyhw91qKKlqWIlr0VzQCxBNF+HRvDPFEdxMomgDuyxXhc7oxxBOnUXlWPLl7/xF93Kg3PQsNl8LJC2lT0517D+WgA7s1oUY1yymbqYFbe4N48sV4kTMkzEQ9xSk7adMq99rQwxyX/zbR4i/9VDu2GJu0HlZUZ0yIJ+pw1KsXiCfakDeCeGK0fGjhEZHy3iTbixnIZDI5NERkVDTdunOfsmfJ6LBu3AreIJ4EfLuRAtbOpsgSlSnysx4uXZ+RKpvv36bgAY3klELHryNr6nRuTw/iidvoDNEQ4okhzOD2JHBf7jY6lxpCPHEJ13OVVfM84Z6fPA2lhSu30+mzF6WIkitnVqpfvQwVKfCGslkavLXRxZN/r5to7gI/mWiVE656c5kx20y375ipYT0L5c2j7FqwSXvvSoB44r2245mrJp7ws693asGaGNAI4olR8qFZrVYaM2MFrdiwV7JnT9i543pQscJ5E7VFVHQ09R4xhw78cEb+PSQ4kD5vXYfqVSsjf7Z528ZtnDN7Ztq5Yrz8lTeIJ7FH/bYdSlGFP9RkTXpqkMCFo8j/x+8oslIDiqzRyu1hIJ64jc4QDSGeGMIMbk8C9+Vuo3OpIcQTl3A9V1lV8UTZVLy3tdHFk4OH/WjvfhMVL2qhTz5WJjjobSXbscWvvGylls2Uhe5gk9bbmu6PD/HEfXZGaKmaeGKEizHwHIwgnhglH9rRH89Rq57jaf6EXiK0+E0aMWUZ7TpwnI5tn0tm8/MeKEtW76QpC9bRvrWTKVOGdLRs3S6aOHcNHdkyi1KlDKE+I+fS31dv0qh+/z2ohwQHUY6smbxGPEnRoULMXKduIWtQiIFXsuOp+f11noImdJXXETZ+LVkD3TvdEeKJY9ZGrgHxxMjWcTw33Jc7ZqRGDYgnyiiqKp7cvH2fOL756bPQ52b1aYUPKICzfSbDYnTxZNFSP/r7iomaNrbQ6695t3jCy0etY4uxSXvvhxHiiffajmcO8UQb+xlBPDFKPrRB4xbR2d8u0aYloyT86zfvUvn6PemrmQOpcP7czxnki2nLacvuI3Tg62nS6+SPS9eoeouBtH35WHrlpSxSPLn/8AktmNgrUWMa3fPE79yPFDSjH0W/lp/Ce03RZkF6eJTgL9qR+epfFNGwK0WV+tSt0ZSLJ3yPZXZrbDRSTgDiiXKGevaA+3Jt6EM8UcZZNfHk+OnfqEX3sUnO5tCmGZQ+XWplszVoayOLJyK8m0aNiRGtRgyJMihB16a1Sxxb/L04triISHxbQyTAdbdgk3aXnP7tIJ7obwMlM4B4ooSe822NIJ4YJR8a35+8kDYNTR7WMRbgW6Wb04TBHeiTcsWeg8piSd12wyhNqhTUuWVN2rjjEKVMERIrlrB48u2R0zIsOX26NKKP4lSyWIHYfowungSsn0sB+76myGrNKbJyY+cXlYFr+p/8lgIXjSZLhqwUNmqZWzNVLp64NSwaqUQA4olKIHXqBvfl2oCHeKKMs2riSaOOI+nG7Xs0ZkBbatl9HK2ZN1QkZMtInQdMJYuINV49Z4iymRq4tZHFk/O/m2nVGjPletVCzZu4LzQYCb/t2GKeU//e0RQS4l7SA2zSRrKqa3OBeOIaL6PVhniijUWMIJ7wlRohH1qtVoMp3xuv0Ki+/4XZFCzXkgZ2/UzkZiv7nEGehYZR654TZP62azduy/8d3b8NVa9UQtZduXGfPEkwJChQetyeOfcnjejdkmpXKSX/HhVtobAIZaGlnlwlUf2bE/37N/kPm0v06pueHErTvqO61CR69ID8uo8m09vvuzy2WSQRDgr0EzlrksfLJpcBeHmDQH8/cQVWiohKHve7Xm4Ol6efIshf7pv83IjiOQKpQpLn6beeIxa/Z9XEkw+qdaKmdSpR68ZVqFC5VrRi1iB6+63XyRZnvHfNJMoqstsnx2Jk8WTbDjMdP6ne8b5Gsd+qdeLY4vNmqlDWSiU/dO8GFeKJUazp+jwgnrjOzEgtIJ5oYw2jiCfaXK39UdjzhD1EJg11zvNk8PjFMvn9lqWjiZPNzli8gRas2CZfDOV/89XnBmvTayLde/CIvl44Qv4tMtoqk8YasZju3aboHnVFFtxUZJ6zzYhTdHtO1u0ryLpuAVGewmTu53o4kllE3PAD3JNQiCduG0HHhkGBwoDiuTs8EuKJjmZwe+hUIf70TAiXFpjPbYbONEyTInmm0XDm2tWoo5p4UqxKB2pe/2Pq0LQ68b+7tqpFjWtVkG9marQYJJO0lXgvvxpzNlwfRhZPJk7xo0ePTdSpfTS9mDn5KLmcw4VzuaRKaaU+Pd27QYV4YriPktMTgnjiNCpDVoR4oo1ZjCKeGCEfGuc8OXfhMm1YNFLC//fGHarQoFeSOU840e07Bd+kcYPayfoWi5UKlG1Bn7epQ20aV33OgEMnLqETP/1GO74aJ/9m5LCdgMM7KGDFFIp6rwxFtBygzWLUaBTT08cU0quWHC1s4Dyy5Mjl0sgI23EJl+EqI2zHcCZxaUK4L3cJl9uVEbbjNjrZUDXxpFrzgZQ9S0aaM7Y7dR08nY6dOk+DujWhbXt/oMPHf6EjW2dR2tQplc3WoK2NKp7cu2+iqTP8RJy2lfr2ck9gMChyOS3bscX160TTW/lcF4awSRvZuvbnBvHEe23HM4d4oo39jCCeGCUf2g8nf6XWvSbIFzlF385DLHbsOXgy9rSdaQu/lqfv2MQPvo85dOwXWr9gOOXKmZU2fXOYWICxJYxlz5RGNcvR669kp5/P/yVzvvELo76dGhpePAmcN5z8fzpMEc36UFTxmBN3klMJWDGVAg5vp6hi5SmieV+XLg3iiUu4DFcZ4onhTOLShHBf7hIutytDPHEbnbriydqtB+jiX//QQCGYcBb7T5r0owjOVioKv6XhtzXJtRhVPDl+wkzbdpqpUEEL1a6R/Hzgfjxtps1bzZTzJSu1buG6OIRN2ns/kRBPvNd2EE+0s50RxBOj5EPj0Bs+nnjtlm+lAfz8zDRvXE96/9235M/9Ry+g7ft+oJ/3LZY/37n3kIZP/pK+++En+XPG9GnlvUzDGuXkz+yZcv3WvVhjcrLYycM6U4qQIPk7I3uehHT7lEwRYRQ6YT1ZU6XVbkFqNJL5+hUKHhGT2yZ0/Dqypk7n9MgQT5xGZciKEE8MaRanJ4X7cqdRKaoI8UQRPvU8T2Yu3kg379ynkX1ayhlFRkXTL+JtzGsvZ6O0aZKnx4kNvVHFkxWrzfT7BTPVqWWhgvmTn3jC/MdMEIndQk3UoV20yKnjmvcJNmllm4eerSGe6Elf+djwPFHO0JkejCCeGC0fGid+vXPvAeXImpnMZpNDjHwvc1Mkw8+RNdNzdR8+eir74nxuKUKC4/3dqOKJ+c+zFDyxuwxn4bCW5FqCpvcjv/M/UuTHDSmyesx9qTMF4okzlIxbB+KJcW3jzMxwX+4MJeV1IJ4oY6ha2E6rHuPp/sPHsfHEyqblXa2NKp6MFEcURwrnH84JwrlBkmPZvc9Eh7/3o7cLWahWddcEImzS3rsiIJ54r+145hBPtLGfEcQTX82HZlTxJGDzYgr4ZhVFVKpPUTVaa7MQdRjF/9wJCpwxgKwpU1HY6FVkDYwvbiU1JYgnOhhLxSEhnqgIU4eucF+uDXSIJ8o4qyaezP9qK81cspF+3LWAAuRRYfoWTu7278079GKm9E7Nh0OMbty+L98guTp/I4onl/820eIv/ShrFit1aOt6SIu+1nN+dCXHFmOTdp6z0WpCPDGaRVybD8QT13i5W9sI4omv5kMzqngSPKYjma9cpPDuEyn6jULuLi2vaBc8qCmZ716nyIbdKLLU80l+E7sIiCdeYdokJwnxxLvth/tybewH8UQZZ9XEk9/+uEL12g2jBtXL0iflij83qwJ5cskYYy3Kzv3HqO8X8yg6OsYToXvbutS6UZUkh548by0tWrUj9u/9OjeiJnUqyp837/qeBowRx94lKCe/mU8hwYHyt0YUT/bsN9Ghw37yGF8+zjc5l9VrzXTuNzOVK2Olj0o6LxRhk/beVQHxxHttxzOHeKKN/YwgnvhqPjQjiiemJw8ppHcd6YUROm2rNotQx1ECDm6jgFXTyJIhK4WNWubUTCCeOIXJsJUgnhjWNE5NDPflTmFSXAniiTKEqoknDTqMkDlOkiqHNs2g9OlSK5utE62fhYZR8aodpVjSoVkN2i5O+xk4diFtWzaGXhUZ8xMWzq7fY9hsGt2/DVUpX5zWbP6WRk//ir5eOILyvJ5TZtgfMmGx/Dlu4Qz7JlNMvLQRxZM58/3o+g0TtWwWTa+8nLzFk8vi2OLFbhxbjE3aiQ+UQatAPDGoYZycFsQTJ0EprGYE8WT/4VN078FjqlP1I3k1vpIPzYjiif/x/RS4ZAxFF3yfwsU9W3IvnBQ3uE89MoWHUninURSdv5jDS4Z44hCRoStAPDG0eRxODvflDhGpUgHiiTKMisQTvgniwmEuFy9dpQcPnyQ5m8IFcpO/n+fDeXbsO0a9R86hU7sXUFBggJwPJ6z7TBwh2LF5jefm13fUPDpx5jfav25K7N9K1uhC9aqVoS4ta0nxhDPunxb9JVWMJp48eWqi8ZP8KEBc/uD+UcpWiJe0th1bXKdWtEiO65xYhE3aS4ybyDQhnniv7XjmEE+0sZ8RxJMmXUbT02ehPpcPzYjiCQsnLKBENOxKUaU+1WYR6jyK/8YFFLh7LUW/+TaFfz7B4WwgnjhEZOgKEE8MbR6Hk8N9uUNEqlSAeKIMoyLxpH674ZQtSwaaMrwzrdiwl7JmTk9lPyyibEYKWy9cuZ0Wr95BR7bMiu2JvWLYU2RU35ij6+KWoROX0PfHf6G9ayfH/pqPVuTM+uMHt5fiCXuufFi0AAUFBVCJd/NTrSofxcuLYjTx5OdfzLR+o5nezG2hxg1dS6KqEL9uzU/9ZKZNW8yUI7uV2rZyLnQHm7Ru5lI8MMQTxQh17QDiiTb4jSCesCcnh78e2z5Hm4s2yChGFE84ZIdDd0JFCItVhLL4QjE/uEvB/RvISw0bsogsWXPavWyIJ969KiCeeLf9cF+ujf0gnijjrEg8qd16iAyFmTikA7FAUTDvazSga2NlM1LYeuLcNbRj39F4niQtuo8Vp82koBmjuj7X+7HT56ll93FUodS79NH7hejPv/+VQlCFku9I8eTkmd9pw46D9IIIOfrn31u079ApqlS6KE0e1jG2rwdPxJE2Biqr1hH9+BNRzaomKvG+c14YBpq+21MZMopIvOCk7p2JsjtxX5gmRQA9CYski2/oS25zNWJDs0iflCo4gB49M9ZnT01W/Ml1fJCqmiNq11dwoDCgCHsMC3dO6NRuZslrJOYcHOh5j0971G6KROwVGvSk8YPa08dliiYvwHauxmjiifnKBQoe04ksmbJT2IilPmMHvtDApWPJ/9g+cWxxI3FscQuIJ8nY+hBPvNu4EE+0sR/EE2WcFYknS1bvpOmLN1DnFjVp/bYDlDP7i9SoZvlEZ/TBe/ldPsXGnUtz1fOEx+CYbG736Mkzypf7ZdouxJf2TavJsJ2EZenab2jC7NX0095FsdfzLNxYoTEDR1jpsYigGtzHTJky+o54sv0bK+3aT/S+uD9vWMfxYyc/VEREWshi9R1G7nymjNjGLB68AwPMFBaBh28j2sfRnAL+nzw88v9JvR3V98Tfk7M4ZePlJ1TGIPE50bPwy4vjp39LcgqHN8+gF9J6Ph+a1gyMJp4EfLOSAjYvoajS1SmivnjD4EPF7/wpCprelywvZBLHFq+EeJKMbQ/xxLuNC/FEG/tBPFHGWZF4cv/hY+ozch4dOXnW4Sy0Shhry3nCOUoC/5/zpFiVDtSsbqVEc54knPj3J85S294TafmMgVRE5GlJWHYdOCESzM6iEzvnUYqQIPlnI4Xt3Lhpotnz/ChNGiv1+ty3HixtuV6Cg600oI/ja8cm7fBja9gKCNsxrGmcmhjCdpzCpLiSXmE7cfOhccjOpSvXk7yWdk2qxZ5cp/iCDdTGPsbmAAAgAElEQVSB0cST4IndyfznWacTpxoIpSpTsYUshfWbRZaX30iyT4TtqIJbt04gnuiGXpWBcV+uCkaHnUA8cYjIbgVF4omt59CwCKrffjjlfjW79NhIrLz2cnYymx17Ayi7HBKJ6cKo6Cft5TzaN63+3Gk7B4+eoWGTltK88T3FfHPEiB837lCmDOmIj1vuPnQmZRT/Xj1niPzbnGWbKf+bueidgrnliQEsrPj7+9OWpV/ETtVI4snhI360e6+J3i1ioWpVfS8eZZE4dedvcfoO53rhnC/2CjZppZ82/dpDPNGPvRojQzxRg6LjPvQST4yYD80xLXVrGEk84dNmQj6PuTcLnbqFrEEh6l6sF/QWsGYWBRzYRJHlalNknfZJzhjiiRcY084UIZ54t/1wX66N/SCeKOOsinjCU+A3TXxyrxYn6ji65G17fqC+X8yLrda1VW1q1yQms/w2cXQxn7DD4kiBvLnk78rX60HXb90jP+FKzrlPRvVtHfsmbNC4RbRx56HYvrJnySiFl7jHHhtJPFmyzEyXLpupYT0L5c3je+LJ8ZNm2rbDTAXyW6huLYgnjj4r3vp3iCfearmYeUM80cZ+eoknRsyHpg3x/0Yxknjif/owBc4f7vSJM1qz0mI8v7/OU9CErmRNnY5Cx4vEcEkUiCdaWMNzY0A88RxbLXqGeKIFZSKIJ8o4qyaeKJuG+q2jRSw9J3jN9mKG2PCdpEa5c++hSDQaTi9lyyQEoOe9Y/hvN27fozSpUlDG9Gmf68Yo4kmEyJ05aoy/nN8gcUTx/6OW1Idr4B5DQ000ZoKfEMKIBvaNEl5CSU8Wm7SBDelgahBPvNd2EE+0s51e4okR86FpRz1mJCOJJwFfTaaA73dSZK22FFmhrtYoDDNecP+GZH5wh8J7TKTo3IUSnRfEE8OYy62JQDxxC5thGuG+XBtTQDxRxjnZiifKsLjW2ijiyYWLZvpqlZly5rBS65aOc364dpXeU/vLr8z0519mqlcnmvLnSzoZLDZp77FpwplCPPFe20E80c52eoknRsyHph1144knNtEgbPB8smR7VWsUhhkvYJNI9L9rNUWWrEqRjbpBPDGMZdSbCMQT9Vjq0RPuy7WhDvFEGWeIJ8r4ydZGEU+27zTTsRNmKlvGSqVL+q54cuqMmTZtNlPeNy3UsH7SoTvYpFVY/Dp1AfFEJ/AqDYuwHZVAOuhGL/HENi0j5UPThvh/oxjF88R8/QoFj2hF1lRpKXTCeq0xGGo8v2uXKWhUG7KmSEWhkzZCPDGUddSZDMQTdTjq1Qvuy7UhD/FEGWeIJ8r4GUo8mTbTj+7eM1HbVtGUI7vvHr8bHmGiL8aKuB1RBvUT4UuBiRsZm7QKiz+xLjjyzcPLD+KJh2ynUbcQT7QBrbd4wldppHxo2lCPGcUo4knAvq8pYP1ciipekSKa9dYSgSHHChrRmvyu/00RnUdT1FvvPTdHhO0Y0mxOTwriidOoDFkR9+XamAXiiTLOHhFPnoWGUYBINhEQYCfhhLJ5G6q1ETxPHj0y0cSpfhT0P/bOAz6K4ovjv9urofcuSlEUQYoiAgoKIl16L9IRkN57J5QQeq+CSO9NpApIEQEpf1BQpEnvLblc+89uSCCQkLvb3bm9uzefDx+F7My8+b25ze733rzHyvT2c6NMr6YEVMGYxUsF/HVWQPWqThQqEH/0Cd2kVRCe05AETzgJrdI0BE9UEvalYbUAT/isVHuzaAWemCf1gv7MUUS17Af7h59rTyjOFhl/WgLjunmwFy2DqKa9CZ5w1l/t6QieqK2wuuPTc7m6+saMTvBEns6KwRO7w4ExU5dgxcZfEMUyl/bt2AgNa3wJMeu+mWUu/XHaAHmWari3+vAk8a/yfz8iYP0mAe+zHB91Wa6PYG8nTumwcrUeuXI68U0jgieBth8Invi3Rwme8PEfwRM+Osc3i1bgSZK2ZSXzxGMq4nGVYG/CvVuw9G0glWuOZMeYXMa4oakUeeLfO4TgiX/7j+AJH/8RPJGns2LwZNOOg+g5bAZKFSuAIyfOQiwPLMITscyvWO5337rJSJ0yuTxrNdpbfXiS+MKXLBdw5k8BVas48WGh4CtR/LJCNjsQOsYAO/tvnx4OhIS8eo6EbtKJ7yutXkHwRKuecc8ugifu6ST3KoInchX0vr8W4Inh9GGYJveF4613Ye012fvFBFhP85iO0P97BlGtBsJe+LM4qyN44t/OJnji3/6j53I+/iN4Ik9nxeCJGGHyZrZMCB/cDjVaDEDNSqUkeHLl2i2Uq98DP0zph0L53pZnrUZ7awGeDGMlim2sVHG3Tg6kTKlywgmN+uFls5at1ON/p3WoVMGJokVeBUp0k/YTR8ZjJsET//WdaDnBEz7+I3jCR+f4ZtECPDEunwbjrjWwVWwEW5VvfCeGxmYWNRG1sRf6FFGtBxE80Zh/5JhD8ESOer7vS8/lfHxA8ESezorBkyIV2uDbJlXRon7FeOHJqjlD8W7u7PKs1WhvX8OTS5d1mDNfj/TpnOjQjqJOYraJGIkjRuQkVLqZbtIa/UC5YRbBEzdE0vAlBE/4OEdr8CSY8qFpAZ5YBjeDcOMKrD0mwpEzL59N5wez6B7dR0jP2pKlERPWsSM8SWKtpsgTP3Dga0wkeOLf/qPncj7+I3giT2fF4Emj70bg/sPHWL9gJGqxUMiYyJMh4xZg+Ybd+P2nWQixJFD2RN4afN7b1/Bkxy4dftmrxycfO1GxPMGTFzfEcFZ1J4pV34kvIodu0j7/6HhtAMETr6XTREeCJ3zcoAV4Eqz50HwNT2Jze4QkRUT4Wj4bzo9mMU/sCf2fx1gFop6sElF0XhixETzxIyfGYyrBE//2Hz2X8/EfwRN5OisGT06eOY96bYciZQr2izoyCnlyvQGHw4nTZy+gad3y6NG2njxLNdzb1/Bk5hw9/ruqQ5OGTuTORfDkxa2yaq2A4ycEfPWlC58Wj5tIl27SGv5QJWIawRP/9Z1oOcETPv7TAjwJ1nxovoYnxj0bYVwykR1NKcmOpgRuwn5vP0mG/T/BtGgc7Hk/QlSHUIIn3gqpsX4ETzTmEA/NoedyDwXz8nJZ8ER8zRS8nDhAuikGT0Q9RIAyfMIinPn7ogRO0qVJiaZ1yuMb9kcQxIoxgdl8CU8iI3UYOUYvCTt0IMuOSi2OAuf+FrDoRwGZM7rQtg3Bk0DZHgRP/NuTBE/4+E8L8CRY86H5Gp6YZwyC/vh+RDXuBnvx8nw2nB/NorM+RUjnqpLFEWGsElHS6EpEFHniR06Mx1SCJ/7tP4InfPwnC57wMVHTsygKT15cqcvlgk4XuMDkxbX6Ep6c+p8Oy1fpkTOHE00bU9RJfJ82ES6JkKlzBwfSpH6eTJdu0pq+N73WOIIn/us70XKCJ3z8pwV4Eqz50HwNT0K6V4fuyWNEjvwRztTp+Ww4P5vFPH0g9CcOwFa/E2wlKxM88TP/xWcuwRP/diI9l/PxH8ETeTorDk+cThcePX76ilXicZ5Abb6EJ1u2CjhwSECZ0i6U+jRuZEWg6u3putZtFHDkqIAvPnfhi5LPNaKbtKdKaud6gifa8YU3lhA88UY1z/toAZ4Eaz40X8KTmISoriTJEDFujecbJ0h6GI78AtOc4XDkzg9rt3CCJwHgd4In/u1Eei7n4z+CJ/J0VgyeXLh8HaOmLMb+3/8nHdl5ue1dOxlpUiWXZ61Ge/sSnixYJOD8vwIa1HXi3TwUeRLfFvn3goD5CwWkTuVCl44ETxL8GImBYn5S5ZrgiUZvhm6aRfDETaFkXqYFeBKs+dB8CU/0f/0B84QecOQpAGvnMJm7KHC762xRsHRlETr2KESMXg5XitR0bMfP3U3wxL8dSPCEj/8InsjTWTF48k2nUBw7dQ5NapdD1kzpoNdH5+GIadXKlYDJZJRnrUZ7+xKexBxJEaGACAeoxa/AmHF6PH6iQ9vWDmTOFK0T3aT9d7d4Ak/EvFaEFbXla4InfPyhBXgirjQY86H5Ep4Yd62Gcfl02L6oDluddgpvtsDKFmiaFwrD4Z2w1WwN25e1CZ4ovFt4D0fwhLfiys5Hz+XK6pnQaARP5OmsGDwp9FUr1KxYEv07N5ZnkR/29hU8efJUh9FhehgZkxrQh5LFvm7rbNoi4NBhASWKOVCuLMETP/yYxTHZE3iipbWKO0+zmaA4UiaCJ3x2pVbgyYurDZZ8aL6EJ2IVGbGajK1RV9hKVOCz2fx0Fv2pQzBP7Q9n9ncQ2WcqwRM/9WOM2QRP/NuBBE/4+I/giTydFYMnddsMYck4U2D6qC7yLPLD3r6CJ+JxHfHYTtasLrRpQflOXrd1Ll/WYfZ8PVKkcKF752it6Cbthx+2Zyb7KzzxX8WVtZzgibJ6JjSaluBJsOVD8yU8MY/+DvoLf8HacxIcOd7js9n8eJaQbuzozlOWXHfo9xAyZUXq5Gbcuh/pxysKXtMJnvi37+m5nI//CJ7I01kxePLr4VNo3SMMc8f1RKYMaV6xKnvWjAFbrthX8OTgbwI2/ySgcCEnqlWhgwmJfRTCJujx8KEOLZo58OYbLoIniQmm4Z8TPNGwc9wwjeCJGyIpcIkW4Emw5kPzJTxJ0rastHsiJqyHyxyiwE4K7CGMiyfAuG8TbFWawlWlEcETP3Y3wRM/dh59qcnNeQRP5EmtGDw59+8V1Gw5MN5ksaKJlDBWnqPi6712g4CjxwRULO/EJx8TPElM4Z+367Bvvx4fF3GicgUnwZPEBNPwzwmeaNg5bphG8MQNkRS4RAvwJFjzofkKngi3r8EyoAlcaTMhYvgiBXZR4A+hP3cc5vDucKbLDPvIRQRP/NjlBE/82HkET7g5j+CJPKkVgyfisZ2zDKB0bF4DmTOmZQljxQP0z9vnxQvBaIibRFae6drp7avIk5lz9fjvPx2aNnYiZw6CJ4ntiGvXdZg+Sw+LxYW+PR0ETxITTMM/1xw88aNKRVpwK8ETPl7QAjzxt3xo12/dRYpkSZAkxOKWk27cuofk0vXmONf7Cp7oj++HecYgOD4oBmvboW6tgS4CQnrUgu7xA9gGzEKqvO/RsR0/3RQET/zUcc/MpmM7fPxH8ESezorBkyIV2qB6hc/Qt2MjeRb5YW9fwZNhoQbYbECv7g4kTUKVdtzZOhMm63H3ng5NGjpR/EMT7j6ywu4g7dzRTkvXaA6eaEkcP7CF4AkfJ2kBnvhLPrS///0PTTqNxIOHTyTnlCpWABOHdmAJ2Q3xOmvbnt8xdtpS3Lr7gEXcOpAvTw5MH90VKZMnla73FTwxblkM4/oFsJWvD1vV5nw2WgDMYlw5E8YdK2EvXw8pv2lP8MRPfUrwxE8dR/CEq+MInsiTWzF4IobmGlhkiZjzJNiaL+DJvfs6jJ/0PIoi2DT3dr07d+mwe68eBQs48e03BE+81dHX/Qie+NoD8uYneCJPP3d7+xSePCst5S/50Gq0GMC+hAjBzDFdcfnqLdRuPQi9v2uIBtXLvCK3ze5AwS9boGGNsujToQGePI3EV/W7o1alUujapo5P4YlpznAYjvyCqJb9YP/wc3e3StBfJ1w6C0toezhTp0fyaasJnvjpjiB44qeOI3jC1XEET+TJrRg82bTjIHoOm4GxA9oiS6a0r1iV/92crxzlkWe6dnr7Ap78+ZeAH5cJ0nEd8dgONfcUuHtXhwlT9DCx8s6TRhso8sQ92TR3FcETzbnEI4MInngkl9cX+xSePLPaH/Kh3b3/CJ9V64A5YT1Q7KP3Jcu7Dp6GqzduY+n0ga/oL8KSjyt+i17t66NJ7XLSz9v0HCc940wLja446KvIE8vQFhCuXYJ1wBw4srzp9d4Jxo5irhgxZ0zI0Om4kz53MErg92smeOLfLqRjO3z8Jw+ePPtmhI+pmpxFMXhSj52tPXnmfIKLpISxyvr/l3167NipkxLFigljqbmvwNQZety4qUPbZga88RYd23FfOe1cSfBEO77wxhKCJ96o5nkfLcATf8iHdubcRdRqNQjbl4cj87NqgVPmrcHqLXuwc8X4eIXvP3ou1mzZi6rlSqBA3lwYNXUJ5oX3RKF8b/sUnsRU2nk6fZvnGybIexg3LIBx82IYvqqBh9XbBrka/rl8gif+6bcYqwme8PGfPHjCx0Ytz6IYPBG/Xbr/4HGCay2U/20Y9JQwVqnNsHylHqdO61DtaycKFyR44omuexl42sbAU+GCOtSqbqecJ56Ip5FrCZ5oxBFemkHwxEvhPOymBXjiD/nQDh45jRbdxmDfuslInTK5pPKcHzdh5qL1OLxlZryq79p/TIpOeSdHNpz661/kyfUG5rBjy2lSRfcXc2lZbQ4PPSbvctelf2Dv3wK6bDlhGDlP3mDB2PvGVdh6NIAuZWoYJq8JRgX8fs1GQ3SxCpudnov90ZkhJr1033RSKkJV3ZfUEn8uL1UnDaDBFYMnAaSJx0vxxbGdydME3LotoE0LB7JmpbuMJ0578ECHcRP1LEcPMLAPe7jVkX6e6KeFawmeaMEL3ttA8MR77TzpqQV44g/50GIiT3asCEem9GkkiV8XeRJzzGfqyM74vHhBiMlmm3UZhZxvZsH3E/vEvrw9tfKFJziwHa5ZI+D6pAyENv092Sp07TMFXINaAZf+hq5nOPBeIdLFzxSwsJdvuFyItBE88TPXSeaKkUNPIu1wEj1R1X0pk7LcBdS8VkAxeLJ68x78c/FqgoZ816wGQiwmrw3VckdfwJOBQ6OpYf8+dil/BzXPFJg5h5V5vqpDnZpO5Huffsl6pp7vryZ44nsfyLGA4Ikc9dzvqwV44g/50GJgiJjw/pMP80oCdxk0Bddu3o0358nW3YdZ1MlU/LJ6ItKlSSldP3neasxevBEndkRHfPgi54lhzWyYfl4OW7UWsJWr5/5GoStjFTBv/RH6tfNh+7wabHXbkzJ+pgAd2/Ezh71kLh3b4eM/OrYjT2fF4Emr7mE4dOz0K9Y4HNEvpr+um4JUKZPJs1ajvXnDEzFfh5i3I3UqF7p05PzNlkZ94KlZBw4J2LJVQN53nahXh+CJp/r5+nqCJ772gLz5CZ7I08/d3lqAJ/6SD61as/5ImSIppo/qgivXbrMcKANZQtgGrKLOl5LcYgRN1kzpMLJPK1y4fB2VGvdG/Wpl0LNdPTyNsKJ+u6FIljQJVswa7DN4YprSF4b/HYa1/XA48hV1d5vQdS8oYLx+AcYhreBMmRaRo5aSNn6mAMETP3MYwROfOIzgiTzZFYMnCZnRousYdvbXERvKKs9cbfbmDU9OnBSwco2Ad/M40aAuvfh7syuskTqMGKOHkQXwDOhr92YI6uNDBQie+FB8BaYmeKKAiG4MoQV44i/50M6ev4LGHUbg8ZMISdnPiubHpGEdYXoW2lmqRidkz5oRiyb3lX6+atMelhdlI/67flv6e3FWpad/5ybIljm9z+CJpW8DCPduIWL4D3ClzejGDqFLXlZA/N1i6sWidu7dRuSAWXBmyUEi+ZECBE/8yFnxmEqRJ3z8R/BEns6qw5NDx86geZfR2LY0jJUwTifPWo325g1Ptm7X4df9epT61IEypSlfh7fb4vuFRvxzwYUmDZ3InYsg1Ot11LEfa2evETzxdtdrox/BEz5+0AI84bNS5WYRYUjyZEmQgv1xp4lHe1KlSPbKsWTex3Z01giEdP4aLpMFERM3uGM6XROPAuLvliQrp8H+82rYqjaDrXwD0smPFCB44kfOInjiM2cRPJEnverw5PTZC6jdejBmje2OEkXyybNWo715w5NFPwo497eAOrUcyJdXOy+0GnVPgmYdPWLG2k0OfFzEicoVCJ74k/8InviTt161leAJH/9pAZ4Eaz403vBE/88pmMO6wJEjL6w9J/LZYAE4i/i7JeWFk4gYybR8Kw+svaYE4CoDd0kET/zbtxR5wsd/BE/k6awYPNm572hs+Kpokotlu37w6AlWbvwFDx8/xaGN02LDX+WZrEzvBw+fsFJm9thkb4mNevP2fQiCLt7recMTsVKMWDGmQzsn0qejl/7EfJfQz20RZgwb60Dy5C706EK5Y7zV0Rf9CJ74QnXl5iR4opyWrxtJC/AkWPOh8YYnxj0bYVwyEbZPK8LWsAufDRaAs4i/W1InN+Nxky+hs1kRMXYlXMmikwJT074CBE+076PXWUjwhI//CJ7I01kxeJJQUrjC+d9Bi/oVpXJ+WmjieeaW3cfi5JnzkjliAriF7AxzTHnCl20UE8O17DZGyrovthzZM2NeeC9kSJcq9lKe8CTKBgwPja60M3Qg5eqQs6fEm3TPQTY8fMRA1LcOpM9AUTxy9OTZl+AJT7WVn4vgifKaxjeiFuBJQisN9HxovOGJackkGPZsQFTd72D/vCqfDRaAs8TAkwdh/WE48gtsjbrCVqJCAK40MJdE8MS//UrwhI//CJ7I01kxeGKz2VkkR9xv700mAwx6VnNdQy1sxjKs2LAba+YOQ9IkIajXdogERKaFxv9NTYf+k3CVnYGeNLwjzCxxXL1vhyDXW1kxc0w3n8CTS5d1mDNfj4zsRb89e+Gn5r0C4k16/pIoHDos4KsyLnxagvT0Xk2+PQme8NVb6dkIniitaPzjaRmeBHo+NN7wxDyuK/R/n4S1axgcbxfgs8ECcJYYeHLv580wzQ+FI/8nsLYbFoArDcwlETzxb78SPOHjP4In8nRWDJ7IM4Nf79K1u6BC6aLo0ZZlU2dNzJg/cOw8nNo1HzqdmBTzebv/4DFKVP0OYQPbSn3EtuHn/eg9clac63lGnvx+RMD6TQI+yO9Erep0ZEfOzhFv0geP2rDgBx2yv+FCy2YET+ToybMvwROeais/F8ET5TWNb0Qtw5NAz4fGG56EdKoCXVQkIsLWwJU0GZ8NFoCzxMCT29fuIqRrdASPmIBXTMRLTfsKEDzRvo9eZyHBEz7+I3giT2dZ8GT01CU4euKsWxbMDe+JZElD3LpWzYs+KNMcg7s1RY2KJaVpjp48J5Un3LduMlKnTB5najFnS/Eq7RE+uD3KfV5E+tnx0/+gAfsWYtfKCbFHd3jCk41bBPxGkRKKbJGYm3TfQdHRUf16OmC20NEdRcRVeRCCJyoLrPLwBE9UFvjZ8FqAJ/6WD00pz/CEJ8L927D0qQ9nijSIHL1MqSUE5Tgx8OTW/UiYw7tDf+44rG2HwvFBsaDUw98WTfDE3zwW116CJ3z8R/BEns6y4MkYEZ6cOueWBXPCevgcnohJbPN90QxjB7RFxTLRkSRnzl1ErVaDsGXxGGTPmuGVtYiVgi5cvoYOzWvAaDRIkSciQHkRntx/zBKRcGrTZgPnLwAtv9Hh3XfoRV+O7CmSGPEk0obZC1w4/ZcODesChT6QMyL15aWAXgCSWox4+JTfZ4/X2oJhHouJOZBF+kVaKdpLTX9bTHpIWvuw+Us+NKUl4glPDKcPw8RytzneKwxrx9FKLyWoxnsRnhh3rIRx5UzYi5dHVOPnR7WDShA/WyzBEz9z2EvmEjzh4z+CJ/J0lgVP5E3tm95i5MmQ7s1QvcJnkgGvizwRfy5W5RHzpBxjkEiMnBFzu/z596U4x3aeWvklbu3e34WoKGBIXwGpUxE8kbOLxBcLq82JfQddWLbKhcLsmHjThnGPbskZn/qqp4B4xM5sFBAZRS/f6qms3shGkX6xZnPQ0UP1VAb0giB9TnzZ/CUfmtIa8YQnxm0rYFw9C7YytWCr1UbppQTVeC/CE92dawjp34Qdg0rBjkOtCiod/HWxBE/81XPRdhM84eM/gifydFYcnpz79wr+99cFPI2wIscbmfBRwXdhNGgnaayY86RimU/Q/VsWZsCaWEp5UNj8eHOexCft1037IUmIGUunD4z9sXrHdsQX+eeA5BGrCjN2vJ5FwAAD+vADNvK2mHZ7x9yk794HwpiuZrML/XrRy7h2PfbcMjq24w9eSthGOrbDx39aOLbDZ6UezCL+SuXAyHnCE9OC0TAc2o6ob3rA/slXHohBl76swIvwRPyZZUhzCNcvw9pzEhw53iPBNK4AwRONOygR8wie8PGf38MTTr/HE/KGYvAkitXQFSvT7PvtZJy5UqZIilljuyNfnhx8dkQis4ydvlQCJmvnDUeSJBapes6L1XYmzlmFrbt/w+YfokNfxbwn4rfcDocDS9bswNQFa/H9xD74qEAeDvAk7mLO/SNg0WIB2bOx5KbN6SVf7oZ68SY9baYe12/o0KKpA29mp4geudqq3Z/gidoKqzs+wRN19Y0Z3VfwZOO2A/iXHXd1p7VsUBkhFpM7l/rVNTzhiWXEtxCu/IPIPlPhzP6OH+kkRp75NjIqMXhiWDMbpp+Xw1auHmzVWviRtsFpKsET//Y7wRM+/vN7eMJHpgRnUQyejJz0Axav3o6GNcrii+IF2ZGS5Djw+/8w+8eN0uS/rJ6kiQiUh4+fokXXMRAz/Ystc4Y0WDS5HzJnTCv9vc/I2di04wBO7Jgn/X3bnt/ReeAU6f/TsDWN6f8tin30fhxB1Ys8ieu3Xw/osXWbDh996MTXlSjcXe5n58Wb9I6dOvyyT49Pizvw1ZcET+Rqq3Z/gidqK6zu+ARP1NU3ZnRfwZNvOoXi9+N/ubXI+JK1u9VR4xfxhCdJ2paV1Hg6fZvGVdG+eS9Hnuj/OQVzWBc4srwJ64A52l9AkFtI8MS/NwDBEz7+I3giT2fF4Mln1TrgnZxvQKyq82L7addv6DZkGlbOHoL33n5TnrUK9r57/xGibDZkSp/mtaPa7A5cvnoTGdKmSjDhLS94smqtgOMnBFSq4ETRIgRP5G6HF2/SV67oMGueHunTOdGhHWkrV1u1+xM8UVthdccneKKuvr6GJ3xWp+1ZeMET4cYVWAY3gzN9VkQOXaBtUfzAupfhiWhySPea0D15iIjhC+FKm9kPVhG8Jl0C+FkAACAASURBVBI88W/fEzzh4z+CJ/J0VgyeFK3UFlXKFkf/zo3jWCQmV63ZcqB0dKdEkXzyrNVob17wZPosPa5dp6MlSm2Dl2/So8bqWa4eHbp0dFAyXqVEVmkcgicqCctpWIInfISOiTzx8fHg2MXevH2fVViyskjPdJqIRFXTC7zgieHYHphmDYO94KeIajNIzSUFxdjxwRPTwjEwHNgGW512sH1RPSh08NdFEjzxV89F203whI//CJ7I01kxeNKqexgOHTuN5TMHI0+uN6Q8IbfvPkCv4TNx8OhpHNg4DSmSJZFnrUZ784InA4caJAX69HAgJISOlsjdDi/fpFevE/DHcQEVWWTPJxTZI1deVfsTPFFVXtUHJ3iiusTSBL46tvPy6hau2Irxs1eySnHPS4uX+awwBndrJh2HDcTGC54YNyyAcfNi2Co2gq3KN4EoJdc1xQdPDMf2MUA1BI48BWHtPJarPTSZZwoQPPFML61dTfCEj0cInsjTWTF4cum/m6jcpDdLrOqEyWSUQIkIT8TWqWVNtG5URZ6lGu7NA57cviNg0lQByZK50LMrJYtVYju8fJP+3xkBy1YIyJ3TiSaN6OiOEhqrNQbBE7WU5TMuwRM+OmsBnixZuwPDJyySErOXKlYA6dOkwt5DJ6QvVfK+8xb7wmWQ9GVLoDVe8MTEvrAy/PEroloPgL1QyUCTkft64oMnuqhIhHSKfoaNCF8HV0hgfhHIXWwVJiR4ooKoHIckeMJHbIIn8nRWDJ6IZtx78AhzftyEU3/+G1uquF610iic35+yv3suKA94Qi/2nvslsR4v36StkTqMGBNdVlssBS2WhKamTQUInmjTL+5aRfDEXaXkXacFeFKhYXQetC2Lx8RZzNwlmxE+czn799HInjWjvIVqsDcveGIZ2BTCrf8QOXg+nBmzaVAJ/zIpPngirsA0uS8Mpw8jqkVf2D/6wr8WFUTWEjzxb2cTPJHhP/E7CDcPJRA8kaEz66oYPBEjT97Ikj4gv0FKTGIe8GTnbh1279GjRDEHypV189ORmOFB/vP4btLzFwr494KA+nWceO9dij7R6hYheKJVz7hnF8ET93SSe5UW4Emhr1qhcc2y6NqmTpzliM8MIliZPqoLSn5SQO5SNdefFzyhSjvKuj4heGLcsxHGJRMlcCICFGraVIDgiTb94q5VBE/cVUredQRP5OmnGDyp13Yo/rt2C41qfoU6X3+O1CkD8xxzfHLzgCdLlgs486eAGlWdKFiAXurlbfvo3vHdpA8cFLDlZwGFCzpR7WvSWQmd1RiD4IkaqvIbk+AJH621AE/EhPE3b9/DL6snQRCeH88ZM3UJvme5ULYvG8cSyKblIwjHWXjAE+HiWVhGtYczWy5E9pvBcXWBO1VC8ES4fweWPvXgsoQgYvz6wBXAz1dG8MS/HUjwhI//CJ7I01kxePLr4VOYsXA9jp48K1n0RfFC+KZOORQp+K48C/2gNw94MmGKHnfv6vBtKweyZKbIEyW2RXw36Tt3dJg4VY8kLCFvb5aYl5o2FSB4ok2/uGsVwRN3lZJ3nRbgyd5DJ/Ftr3FSLrSPPsiD9GlTQnxeEHOiiTlQpoV2kbdIjfbmAU8MB7bCtDAM9qJfIqppL40q4V9mJQRPxFVYRraFcPlvljQ2jCWPDbxoKf/yVPzWEjzxby8SPOHjP4In8nRWDJ7EmCGWIly+fhd+XLsdDx4+Qbo0KdG0Tnk0rv0VDProfBKB1njAk5hKO0MH2gNNPp+tJ6GbdPgkPe7fJ1DFyzHelFEleMLLO+rMQ/BEHV1fHlUL8ES0SYQlY6ctxflLV6Wk8mKFnbpfl2aJ5CtLUCUQGw94Ylw5A8Ydq2Cr0Rq2srUDUUbua3odPDFuWgTjxoWsXHENVra4LXfbaMLEFSB4krhGWr6C4Akf7xA8kaez4vAkxhyn04UJs1dATAontr1rJwdsSUK14cnVazrMmK1H2rROdGpPR0nkbfnnvRO6SW/ZKuDAIQFffO7CFyUp+kQpvZUch+CJkmryH4vgCR/NtQBPXs6H5nK5giI3Gg94Yp7UC/ozRxHVYSTseYvw2VQBPsvr4Ilw6Rwsoe3gSpsJEcMXBbgS/rk8gif+6bcYqwme8PEfwRN5OisOT27cuoflG3ZBLE8oRp4kCTGjXtUy6NiiBqteYpBnrUZ7qw1Pjh0XsGadgPfec6J+bYInSm2DhG7S5/8VsGCRIB2PEo9JUdOeAgRPtOcTTywieOKJWt5fqwV4Eqz50HjAE0uvOhAe3kNk6BI4U6XzfqNQz1gFXgdPxIssvetBeHAH1gFz4MjyJimnMQUInmjMIR6aQ/DEQ8G8vFw9eCK+owpeWuU/3RSDJy/nPCmU720p50npEoWh1we2kGrDEzGBqZjIlCIhlP1gve4mPWykATZ2QkrMeyLmP6GmLQUInmjLH55aQ/DEU8W8u14L8CRY86GpDU90Tx4jpHt1uEwWREzc4N0GoV6vKJAYPBEr7oiVd2xVW8BWvh4pqDEFCJ5ozCEemkPwxEPBvLxcPXjipUF+1k0xeCJ+u/TPhf9YpZ0v0KhG2YDMnp+Qb9WGJ9//IOCf86x8Los6EaNPqCmjwOtu0ktZdaPTrLqRWHFHrLxDTVsKEDzRlj88tYbgiaeKeXe9FuBJjOXBlg9NbXiiP3sc5vHd4cidH9Zu4d5tEOrlMTwxnD4M0+S+cOR8D9Yek0hBjSlA8ERjDvHQHIInHgrm5eUET7wU7lk3xeDJ2fNXkOvNLAEfZRKf3GrDkzHhejx+rGP5Thws7wlFQcjb8s97v+4m/Qc7KrWaHZV6910nGtQheKKU5kqNQ/BEKSV9Mw7BEz66awmexKw4WPKhqQ1PDLvXwrRsKuwlqyCqfkc+GyoIZkks8kSUIKRjZehsVkSMXQlXspRBoIr/LJHgif/4Kj5LCZ7w8R/BE3k6KwZP5Jnh373VhCcRETqEjo2uUkSVdpTdJ6+7ST9luo9iuotpegb0pQpHyiovfzSCJ/I19OUIBE/4qK8leBJs+dDUhifGH8Jh/HULbPU7wVayMp8NFQSzuANPTDOHwPDHPkQ17gZ78fJBoIr/LJHgif/4iuCJ73xF8ESe9gRP5Okn9VYTnly4IGDeQkpeqoCbXhkiMcI9c64e//2nwzeNnMiVk6JP1PCBt2MSPPFWOW30I3jCxw9agCfBmg9NbXhiHtMR+n/PwNp9PBy58vHZUEEwizvwxHBgG0wLx8DxQTFY2ZF1atpRgOCJdnzhjSWJPZd7Myb1eVUBgifydgXBE3n6qQ5PDh4WsHmLgIIFnKhRlV7gFXBX7BCJ3aR/2avHjl06FC3iRKUKpL2S2ssdi+CJXAV925/gCR/9tQBPgjUfmtrwJKRTFeiiIhExYT1c5hA+GyoIZnEHnugeP0BIj1qSGk+nbwsCVfxniQRP/MdX8Vma2HO5f69OO9YTPJHnC4In8vRTHZ6s2yjgyFEB5co6UaIYvcAr4C634cm16zpMn6VH8uQu9OhCJYuV1F7uWARP5Cro2/4ET/jorwV4Eqz50NSEJ7o71xHSvzGcqdMjcuSPfDZTkMziDjwRpTCP7Qz9+f/B2m4YHPk/CRJ1tL9Mgifa99HrLCR4wsd/BE/k6UzwRJ5+qsOTWfP0uHJFhyYNncidi+CJAu5yG56IF4p5T8T8Jx3aOZE+HemvpP5yxiJ4Ikc93/cleMLHB1qAJ3xWqr1Z1IQn+hMHYJ4+EPb3iyDqu5HaW7wfW+QuPDH+vBzGNbNh+7QibA27+PGKA8t0gif+7U+CJ3z8R/BEns6y4IlYmvjBoyduWVAgb+6ArcSjZs6TYaEG2GyQIh/ECAhqyingzk36eeSPi0X+UPSJcurLG4ngiTz9fN2b4AkfDxA84aNzfLOoCU+MPy2Bcd082MrWhq1Ga98tMgBndheeCNcvwzKkOVxJUyAibFUAKuGfSyJ44p9+i7Hanedy/16hNqwneCLPD7LgiXiW+eSZ825ZsHftZKRJldyta/3tIrXgyb0HOoyfqIfF4kLfnvTirvS+cOcm/ddZAYuXCngzuwstmpIPlPaBt+MRPPFWOW30I3jCxw8ET17V2Rplw+27D5AlY1rodDrVHKEmPDHNHQnD77sQ1bQ37EXLqLaGYBzYXXgiaiMenRKPUFl7TYHjrTzBKJfm1kzwRHMu8cggd57LPRqQLo5XAYIn8jaGLHjyz8WrePDQ3ciTXBR54qGv/jrHXtyX0Iu7h7K5fbk7N2kx6keM/hFbPwawzAxkUfO9AgRPfO8DORYQPJGjnvt9CZ4818rlciF08mIsXr1d+keTyYgZo7uiaKH34hW0Tc9x2PfbyVd+1rlVLbRqWBm9hs/Exu0H4vw8e9YM2LJ4jPRvasIT87CW0F+9iMh+M+DMlsv9DUFXJqqAJ/DEuHImjDtWwlaxIWxVmiY6Nl2gvgIET9TXWM0Z3HkuV3P+YBmb4Ik8T8uCJ/KmDpzeakWe7N2nx7adVO1FrZ3i7k164WIBf/8joHZNB/K/T/BELX94Mi7BE0/U0t61BE/4+ITgyXOdDx45jRbdxmDW2O4oUiAPho5fiK27f8OhTTMgCK9GoFy/dRePHj+NHeBphBUNWHLQYT2bo0bFkug5bAYuXrmB4b1bxF4TYjEjW+b0qsOTJG3LSnNQpRflP0eewBP9X8dhntAdjjffgbX3VOWNoRE9VoDgiceSaaqDu8/lmjLaD40heCLPaYrCkxu37uGP//2NJ08jXrGqStniMBqjv8EPtKYWPFmxSo+T/9Ph60pOfPQhJStVet+4e5P+jZWL3sjKRX+Q34la1ckPSvvBm/EInnijmnb6EDzh4wtfwRMt5kPrP3ouTv35L9bOHy6Jf+3GHXxZtxt+mNIPhfK9nahDJsxeiSVrd2DfuikwGvQSPLn34DFmh3WPt69akSfC1X9hGdYazkxvIHLQvETtpgs8U8ATeCKOHFsyOnwtXCFJPZuMrlZcAYInikvKdUB3n8u5GhWAkxE8kedUxeDJb8f+RLMuoxK0hnKeeO6oyTP0uHVTh5bNHciejSIePFfw9T3cvUk/fKRD2Hg9zGYX+vWivCdK+8Gb8QieeKOadvoQPOHjC1/BEy3mQxOfT1KnTIHwwe1ixX//86YYO6AtKpYp+lqH3L3/CJ/X7ISBXb5BrcqlpGtFeLJr/zEUzv8Oy+eWgo3xCT4rmj92HLXgieHwTpjmhcJeuBSiWvXns5GCaBZP4YlY9UisfmRt3geOIqWDSCltLpXgiTb94q5V7j6XuzseXRe/AgRP5O0MxeCJGM4qhrmG9m2N5l1GY9nMQSwhWzp813cCnOys8VL2CyZQm1qRJwOHRkfq9O9jBzueTU1hBTy5SceCrGYMZL1BIEthV3g8HMETjyXTVAeCJ3zc4St4osV8aDVaDEDed97C8F7Pj9l8UKY5+nVshLpVX//SOyhsPnbv/wO7Vk6IPeLz45odOPfvFYSYTVLE7fHT/2Boj+aoWamk5FyH0wWrTXnY7lg2G46Ni6Gv2Rz6ak34bKQgmkVgSYRNRgGRUe75zrljHewLxkMoURaGb/sFkVLaXKpRL0iG2RwUJaxND73eKotJjyibU3pvpKaeAknMgXkSRD3F4o6sGDwp/nV7NKlVDi0bVkKBMi2weGp/FHw/N2LOGW9fNg6ZWXb7QGxqwJObLOJkCos8SZnShW6d3PslHojaqrkmT+DJzzt02PerHp+WcOCrMnRTV9Mv7oxN8MQdlbR7DcETPr7xFTzhszrPZhEjT8QIkXGDPIs8uXLtFsrV74GwgW1RoXTCESqtuofh7v2HWDVnqGRYlN2Jp5Eq/O6e0Ac4cRD4bhhQ+FPPRKCrE1VAYO/eSS1GPHrKssW70XR3b8HVvQ6QPBUwcY0bPegSNRWwmPUAe/GOjCJ4oqbOao2dIokRjyNtcJL71JJYGjdVMvpGXo7AisGTopXaomnd8mjbpCrE/+/YogYa1igrfTNTrVl/KUlbiSL55Niq2b5qwJMTp3RYuVqPt3M70bgB3UXUcL4n8OTSZR3mzNcjfQYXOnyrwgOxGgsM4DEJnvi3cwme8PGfVuCJFvKhiTlPTp+9gNVzGXRg7er12yhbr3uiOU869J+Ev/6+hJ+Xhr3WaWJ0yuE//sTmH0ZL16l1bCekX0Po7t5E5NDv4Uyfhc9GCqJZPD22I0pjGdQMws0riOwzDc7siefPCSI5uS+Vju1wl1zRCT15Lld04iAbjI7tyHO4YvDk66b9kDVTOkwf1QUdB0zCoaNn0L9TY6mUn1jub/+GqUiZPDCTaakBT8QqO2K1HYp0kLfBX9fb05v0iNF6WK06dO/iQIrkFH2inmcSH5ngSeIaafkKgid8vKMFeKKVfGgHfv8fWnYfK32R83HBdyHCjm17fo+ttjNxziqp+k4M/BA99CeDJjVbDsS00C4oVaxAHKcNGDMPDaqXQe63suLEmfNSzjfxC6Ne7eurBk901giEdP5aGp8q7ajzGfIGnpiWToHhl3WwVW0OW/lo/1PzjQIET3yju1KzevpcrtS8wTYOwRN5HlcMnizfsBvnzl9GPwZMxCz2FRv3RlRUdNhjq4aV0blVLXmWari3GvBk8RIBf50TpOouYpUXasor4OlNesVqASdPCahcwYmPi5BPlPeI+yMSPHFfKy1eSfCEj1e0AE+0kg/NxUL5xfLEy9fvksTXs9wIM0d3Q7GP3pf+3mfkbGzacQAndjyvYNO4w0ipXHFMhZ4XvfZlna64dvNu7D+JyWLDB3+HJCFm6d/UiDzRnz8N89hOLLrhHRblQKVx1fgUeQNPDKd+g2lqPzhz5UNk9/FqmEVjuqlAIMMTXVQkDIe2s1NJLrhSpXv+J0VqN9XR/mWePpdrf0XatJDgiTy/KAZPXjbDZnfgJPs2JtebWZAyRWBGnMSsWQ14Ej5Jj/v3dWjPjohkZEdFqCmvgKc3abFstFg+OncuJ5o0JHiivEfcH5HgiftaafFKt+CJjllOtz5Z7tMCPNFaPrSnEVbcvnsf2TJniE3+6q3IDx4+kcYS87klCbHEGUYNeGLcuxHGHyfCXqwcopqyEsn0a8hb1yXYzxt4Ir7UiiWLJWgWvo6VLE6iuF00oHsKBCI8ER7ehX7nGim6SRcZEa8QrjQZ4EyVPhqopEnPklqklf7f8VYeuNJmdk88DVzl6XO5Bkz2SxMInshzmyx4Ync4EBkZhaRJLNCxDOXB2pSGJ2LAzvDQ6EzIQwfag1VW1dft6U3aGqnDiDEsGRlrA1gFJCPlW1LdRwlNQPDEZ9IrMrFb8ESRmYJ7EC3Ak2DNh6YGPDEtY8dDdrPjIbW+ha1MzeDe3Cqt3ht4IppintQL+jNHEdV6EOyFKJGvSu5JdNhAgifCtUswbFsOw4Gtset2ZsgGV8o00X9nESi6R/fYn/vQPX2coDa2L2vBXrGxX0A9T5/LE90QdEG8ChA8kbcxZMGTTTsOouewGVJI69DwhTh68myC1uxbNxmpUyaXZ60HvZ2sTODVG7eRMX0aGA3RL7yJtUv/3WTfRqX3+NsopeHJZZacdLaYnDSdEx3a0VdLifnN2597c5Oeu0CPi5d0aFjPiTzvkG+81V5uP4InchV89uzF/uML7E3wRBn/JTaKFuBJsOZDUwOemMO7QX/uBKwdR8PxXuHE3E8/90IBb+GJccdKGFfOhK1EBdgadfViZuqihAKBAE/0f/0BvQhN/nc4VhLHu4Xg+LI27O8XSVAm4d4tQIQpjx9B9+A2dNcuwrhthXS9i1WDslVrAXvx8krIrNoY3jyXq2ZMAA9M8ESec2XBEzGZ2rJ1O9G+WXUcPHoaFy5dT9CaFg0qIcRikmetm7237DyEXiNmwvGsznuX1rXRks2fUJs8bzV+WLWNlcZyws76VCrzCYb3aiFdvm7rr+gbOvuVrr//NCt2PUrDkyNHBazbKCBfXhfq1KLKLm663ePLvLlJ79uvx8/bdfiwsBNVKxM88Vh0hToQPFFISB8NQ/CEj/BagCfBmg9NDXgS0q269A1zxOjlcAVQngM+nwb3ZvEWngjsRdUytCU7OpEOkaFL3JuMrlJcAX+GJ/rDOyXYIVz+O1YXe7GysJetC2fmN73SSnfnBoyrZsBwbJ/UX6wGFdW4O5zZcno1ntqdvHkuV9umQByf4Ik8r8qCJy9OvXPfUdy9/wi1KpeSZ5HM3k8jIvFJ5XYSLGn7TTVsYtV++o2ag40LQ5Ej+6vn/sRoGTEpXEw2/Zjs+vPG90LRQu9h7U/7MHDsPKyaMzSOZWKG/ZijSkrDk01bBBw6LKBMaRdKfUrwROaWSLC7NzfpW7cFTJ4msPPtLvTuQb5RyzeJjUvwJDGFtP1zgid8/KMFePLySoMlH5rS8ET35DFCuleHyxKCiPHr+WygIJzFW3giSmXpUx/C/duIHDjH65fdIJRc0SX7Izwx7F4Lw8/LIUWOsOZKmhz2TyvBUbo6nCmeHdGRqZL+7HEYlk6GnkE+sYlQxlajDVzJUsocWdnu3jyXK2tBcIxG8ESenxWDJyKAePI0AqvnDpNnkczem3ccQo9h03H059kwm6KTUogJ6xqxEoLtmlZ7ZfTd+/9A+74TsG7+COTOkVX6eaGvWqFH23pSGUIRngwJ/x7H2HgJNaXhycIfBPx9XkCDuk68m4eiG2RuiQS7e3uTDp/Ikvk+0KFtawcyZ9JiRsvAz7RJ8EStTwWfcQme8NHZV/CE8qEpX21HuHIelhFt4MjyFqwDEn4e4bOzAncWOfDEtGgcDPt/Yjlp2rCcNIFbYVLL3vc3eGKaNxKGw9EVwFxpM8FWtjYcxcvBZYyu2qV0E3MmGdfNlRLPiiDWXvkbTeVP8va5XGmdAn08gifyPKwYPBk56QfpiMuhTdPlWSSz95wfN2He0s3Yv/55Gb96bYdCjBSJOYrz4hRWlp21evP+uHLtFlo3rIKHrCzh1t2/STAlVcpkEjwRI1c+/Tg/zGYjSnyUDzUqlYqTR0VpeCJGNogRDh1YpZ30VGlH5o5IuLu3N+nYyKAvWGTQZxR9opqDXjMwwRNfqK7cnARPlNPydSP5Cp5oOR8aH+WVhyf6Ewdgnj4Q9nwfI6r9CF7LCLp55MATw9G9MM0eKuWjEfPSUOOvgD/BE9PCMCkZrAgxbE16cks0rHvyCMYNC1j1nugINgc7EmSv1wGOdwrwd9hLM3r7XO5zw/3MAIIn8hymGDy5ceseytbrhjH9v0X5Lz6WZ5WM3mEzlmEzS2S7c8X42FGadRmFZEmTYPLwjvGOPHb6Uqxn4EeMVLl28y5qVCyJQd2+gUGvx+/H/8LqzXuQOlVyXL56Ezv2HkW5zz9G+OB2sWPdf8LK4yjYeg9iIXVsyNAhoIouCur68lApkhjxJNKGZ6lx3J7p7DkdZs134Y1sQKfn28Dt/nShfAX0ApDUYsTDp8p+9uRbRiO4o4DFyBzIKrRFRhF8dEcvb6+xGPWwmJjWnJtW86HxlEHpYzviN8ZitR1bycqw1e/EcylBNZcceKKLeIKQrtERzhETN8Blilu+OqiE9NFi/QWeiCXHxdLj4h6J6jwGjhzvcVdMf/Vi9FGec8elue0fl0ZUsz7c7XhxQoInfOQneCJPZ8XgiQgofjv2Z4LW8Kq242nkiRhl0nXwNPy6booUabJtz+/oNmQaOjSvgVYNK7+yngXLf8LYaUvxx/a5sdEnTyOVKyf85AnQZ6iLJaMFRg/xRR0MeRvKn3pbTHpYbU5W7c3zozfd+7sglpQOHaRjpbr9adWBYauYb8jMXsDp5ds//Wk0RL/Q2+x0LFFND+oZZRQ/J75sWsmHxlsDxeHJmtkwsbwI9motEVWuLu/lBM18cuCJKJIlrAuEf05J0UFilBA1vgr4AzwxrpgO487V0tGcqE6j4MiVj69IL81mOPoLDKtmQbh7U4o+iWo/3Gfgj+AJn61A8ESezorBE/HIzr+XriVoTZvGX3OpthOT80TMUWJ6lvOkaKW2+KZ2uXhzngwKmw/x4W7v2smxttdtM4RFqoRgbnjPV9azdfdhBlum4vCWmSxpaPSZRCWP7Vy9psOM2XpkyuhCuzb0ray87f363nJu0kuWCTjzl4DqVZ0oVIBeANX0U3xj07Ed3oorOx8d21FWz4RG89WxnRft0Uo+ND6KP59FaXhimjMchiO/IKp5H9iLlOa9nKCZTy48Mf70I8spMR/2UlURVe+7oNFNKwvVOjwxrpoJ4/aVcBlMsHYeDaePwUmM34Tb12Ae0xG6R/dZFExeRHUZo1reldftFTnP5VrZg/5gB8ETeV5SDJ7IM0O53k+eRuLjit/i2yZfsz9VX6m2s+fgcQwetwAzx3TD2zmyYfn6XVJC2HGD2rHjOEVwngGgr7/pK0WddG5VC9MXrkO+PDnx4QdvS9WEWvdgZxQNBqxf8PzMsZLw5MyfApYsF6REsWLCWGrqKSDnJn3kGCsnvUFA3nedqFeH/KSel+IfmeAJb8WVnY/gibJ6ahmeaCUfGh/F1YMn5rEdoT9/BtYeE+DI+T7v5QTNfHLhiXDpLCyh7eHMkA2RQ+YHjW5aWaiW4Ylxy2IY1y+QpLJ2HgtHnoJakU2yQwQopnEscur+HZ8BFDnP5ZoSU+PGEDyR5yBF4YlYuWbx6u24eOU6urSujQqli6Jv6GxkSJdaAhG82sZtB9BrxMzY6Tq2qIk2jatIf9/IShf3Gj4TS1nitfzv5YTT6cKoKT+yZLf7EBFplfKeVCpTDP06NWL5RgzoP3ou1mzZGztW1kzpJPDyYtljJeHJwd8EbP5JQNEiTlSqQC/lau4ZOTfppxE6jBqrZ0e3gAF9lTu2peZ6A2lsgif+7U2CJ3z8p4XIE63kQ+OjuHrwxNKnnvRSEznyRzhTp+e9nKCZTy48EYUS856I+U/IV/y3jVbhnLJ92wAAIABJREFUiXHbChhXz5IEiWLVSe15i/AXx40ZdXduwBzWyWcARc5zuRvLo0ueKUDwRN5WUAyeHD15FmJ4rniURaxg06t9AzSs8SVmLFyPyfNWxznmIs9k93o7WBZQMcFrloxpY4/vJNbz0n83kC1zBghC3FwjTyOsuH7rLlIkS4J0aV6tia4kPNmyVcCBQwLKlXWiRDGCJ4n5TM7P5d6kxeNV4jGrpo2dyJmDfCXHF572JXjiqWLaup7gCR9/aAGeaCUfGh/F1YMnSdqWlQZ/On0b76UE1XxKwBPTvFBWfnYnoup3hL1k9Bd31PgooEV4EpPsWVTAyqp/Oj4oxkcML2cRAYolvCt0Yg4Uzkd45D6Xe7nkoOtG8ESeyxWDJ+36jMf9B4+xeGp/1Gw5EDVZOV8Rnvz1z2XUaDEAK2cPwXtvvynPWo32VhKeLFnBcmmcEVC3thPvv0cv5Gq6XO5NeuduHXbv0eOTj52oWJ58paavXh6b4AlPtZWfi+CJ8prGN6IW4IlW8qHxUVwdeKK7cw0h/ZvAlSYDIkYs5r2UoJpPCXhiOLQDpgUsESh7SRZflqnxU0Br8MS4bzOMi6Orf0a1HsDKEZfkJ4aMmYT7t2Ee24k7QJH7XC5jyUHVleCJPHcrBk8KfdUKnVrWRNM65SVYEgNPbt25j89rdsYPU/qhUL635Vmr0d5KwpOYaIY2LRzImtXzKjAalUiTZsm9Sf93VYeZc/RIlcqFrh0puS9PJxM84am28nMRPFFeU63CEz4r1d4sSiaMFUuJmsO7w5E7P6zdwrW32ACySAl4onv8ACE9akkVS8SSxdT4KaAleGI4tJ1BtNHR4ISVABZLAftTEwGKKawrBAZveUWgyH0u9yd9fWkrwRN56isGT6o164+0aVJg7rieceDJkrU7MHzCIuxfPxUpUySVZ61GeysJT8Q8GmI+jZ7dHKziD8ETNV2uxE06xl+d2juQNi35S01/vTg2wZMElBZPHPrBNiR4wueTooXIE3Glt+8+kPKNXbxyA1XKFkPh/O9g2bqdyJA+Nb4oXoiPGJxnURKeGA5ug+n7MdLLl/gSRk09BZSAJ6J1ltB2EC6dY4lBw1hi0ALqGUwjx1FAK/DEcGQ3THOiC0tENe4Ge/Hyfukp3cN7rAoPy4HCCaAo8Vzul0JzNprgiTzBFYMna3/ah36j5rCKNR/j8B9nUKpYQSk/yOzFG/FZ0fyYMbqbPEs13FspeGJjeUeHjWQZSFkbOpCSkKrtciVu0mvXCTh6XED5r5wo/gkd3VHbZzHjEzzhpbQ68xA8UUfXl0fVAjy59N9NVG7SG2IeMrH17dhIOtIrJmPftOMgy4c2Awa9no8gHGdREp7EVOmwlasHW7UWHFcRfFMpBU+M6+bB+NMS2MrWhq1G6+AT0kcr1gI80Z85CvOkXtHgpF4HVrb6ax+pocy0EkAZ3w3C9cvRESidQuEyJ1Fm8JdGUeK5XBXDAmxQgifyHKoYPBHNmPPjJkyauyr2IUn8t08K58XYgW2RJlVyeZZquLdS8OT2HQGTpgpIk8aFzt/RMRC1Xa7ETTqmtPRbbznRvAnBE7V9RvCEl8LqzkPwRF19Y0bXAjwZMXERduw7innhvdB18NTYI70xSeY3LAxFzuyZ+QjCcRZF4QnLmSDmTrA17Azbp5U4riL4plIKngj/nIIlrAucWXMisv/z6o/BpyjfFfsanuisETAPaQHh3i3YqjSFrWJDvgKoNJt4FM0U3g36axdVBShKPJerJEFADUvwRJ47FYUnoilipZ1/L13Dk6eReDNbxnir08gzWXu9lYInf/8jYOFiQarcIlZwoaauAkrcpG02Fi0UGh0t1K+nA2aLH5yZUFdWLqNT5AkXmVWbhOCJatLGGVgL8KRIhTZo2aAy2jSuEudI7517D1Gyekd8P7EPPiqQh48gHGdREp6YJ/WG/swRTZc45SitqlMpBU9EI0M6fw3xZToydCmcqdKqajcNHq2Ar+GJcfk0GHetgTNjNkQOnh9QbtE9ecQAShforzKA8ta7iOo8hkWghCi6RiWeyxU1KEAHI3giz7GKwZMO/SfhvdzZ0a5ptTgWnTxzHs27jsaWxWMCFqQoBU+OHhOwdoOAwgWdqPY1wRN5Wzvx3krdpL//QcA/51mFpFoOvJ+X4Eniysu/guCJfA19OQLBEz7qawGefFWvOz78IA9C+7aKA0/2HDyOtr3HY8eKcGRKn4aPIBxnURKeWIY0l0LmIwfOhTNzdo6rCL6plIQnpllDYDi2D1Hf9ID9k6+CT0wfrNiX8ER/4S+YR38nrdraYxIcOd/zgQLqTql78hjmCewIz5XzsBcuhahW/RWdUKnnckWNCsDBCJ7Ic6pi8KQeK8f2wXu52HnmuCFq127exZd1ukoljAu+n1uetRrtrRQ82bFTh1/26VH6cxc+L0nHdtR2t1I36YO/Cdj8k4ACHzhRsxpBL7X9Jo5P8ISHyurNQfBEPW1fHFkL8CRsxjIsXLEVg7s1xYLlW1GrUkm89/ab6DRwMpIlCcHPS8P4iMF5FiXhSUjHStDZohAxaSNcRjPnlQTXdErCk5gytWq8ZAaXV9xfrS/hiXloS+lYi/3zqoiqGw1RArHp7tyAZVgrKarKVqcdbF9UV2yZSj2XK2ZQgA5E8ESeY2XDk2OnzsFmc2BQ2DzkYOeWm9apEGuRzW7Hqk17sHX3b1JSuCQhFnnWarS3UvBk1VoBx08IqFHViYIF6CVcbXcrdZO+d1+H8ZP0MJtd6NeLoJfafiN4wkNhdecgeKKuvjGjawGe2OwOtO4xFr8d+zPOopMlDcGCCb0lkBKITSl4onv8kJW9rQlX0mSICFsTiFJpak1KwhMx74WlbwM4Q5IgMnydptYZqMb4Cp6Yti6DYe0cdjwrHayD56qWUFUrftOfOADz9IGSOdYeE1mUTV5FTFPquVwRYwJ4EIIn8pwrG54U+qoVoliek4SayWRkQKU8OrWsKc9SDfdWCp7MXaDHxUs6KfGomICUmroKKHmTFhP9igl/Wzd3IFs2Orqjruco8kRtfdUen+CJ2gpHj68FeBKz0iMnzuLE6X/w4NET5H4rK0p/Wihgv1AR16wUPBEu/w3LyLZwZmOJR/tR4lG1PzlKwhPRVstQljz02iVFXzDV1sCfx/cFPNGxMr4h/ZtEg4QOo+DI+6E/S+i27SIsEqGRK3kq6UihK1kKt/smdKGSz+WyjQngAQieyHOubHhy5txFRLEau31GzkLuHFnRov7zTPBmBk7eyfkGBEEnz0qN91YKnoSz6IX7LIqhS0cHUqeiF3C13a7kTXrrNh1+PaBHyU8d+LI0+U5t39GxHbUVVnd8gifq6hszuhbgyfSF6/BGlgyo/GWxOIs+9+8VjJ+1AmGsGl8gRqUqBU/0f/wK88zBcHxQDFZ2PJqaugooDU+MK2fAuGOVVHVFrL5CTV0FfAFPzGIVmnMnVMkBoq5a8kc3j+0I/fkzcLxdANau8o9gKvlcLn91gTsCwRN5vpUNT2KmF6vrCIKAEItJnkV+2FspeDJwaHTVlqED7X6ogv+ZrORN+sJFHeZ9r0fGDC60/5aO7qi9GwieqK2wuuMTPFFXXy3Bk8TyoS2bOQj58uTgIwjHWZSCJ2LlDrGCR6DnUeDomtdOpTQ80Z8+AvPk3qw6SR5Ye03RyjID1g7e8MRwcBtM34+RjmZZBy+AK0XqgNU2voXpHt6DZXhr6B7dh61cPdiqtZC1fiWfy2UZEuCdCZ7Ic7Bi8EQ048ate/jjf3+zMsURr1hVpWxxGI3RcCDQmhLw5OFDHcIm6JE8uQs9utDLN489ovRNesRoPaxWHboz/6VgfqSmngIET9TTlsfIBE94qKyNYzvxwRO7w4Fl63Zi5KTFVG0nka1gXDmTRS6shK1Ga9jK1uazcYJ4FqXhiShlkrZlJUUjwtfCFZI0iNVVf+k84YmYj8g8sDGEiKeIatwN9uLl1V+gBmfQnz8N89hOkmVidJwYJedtU/q53Fs7Ar0fwRN5HlYMnojJ4Jp1GZWgNXvXTkaaVMnlWavR3krAk0uXdZgzXy/lyxDzZlBTXwGlb9LLV+px6rQOX1dy4qMPKWeNmh4keKKmuuqPTfBEfY3FGXx5bCexfGiifYXzv4NFk/vyEYPzLEpFnphmDWPlbvcgqmV/2D8sxXkVwTedGvDExPa44fRhRDXvA3uR0sEnKscV84QnpgWjYDi0gx1Z+YAdWRnHcZXam8q4fQWMq2axRLkhiBwwG660Gb0yUunncq+MCIJOBE/kOVkxeNKg3TBcv3UXoX1bo3mX0RBDcbNkTIfv+k6A0+XC0mdZmeWZq83eSsCTE6cErFwtIN/7LtSpSfCEh6eVvkmLlZLEiknvvO1Eo/oET9T0IcETNdVVf2yCJ+pr7Gt4IpYnfhIRiR9Xb0f6tKlQttRHsYs2GY0oUSQf3s2dnY8QPphFKXhiHv0d9Bf+YglHJ7GKFu/5YCXBNaUa8CT26FXRLxHVtFdwCcp5tbzgif7ccZjDu0urixi+kMGCzJxXqr3pzNMGQH/yIJxZcjCAMssrA5V+LvfKiCDoRPBEnpMVgyfFv26PJrXKoWXDSihQpgUWT+2Pgu/nxsEjp9Gi2xhsXzYOmTOmlWetRnsrAU/27tNj204dShR3oNyXdOSDh6uVvklbI3UYMUYvmU55a9T1IMETdfVVe3SCJ2orHD2+LyNPYlb4+/G/2DHGpCx5fDY+i9bILErBE0uvOhBYXoHI0KWsDGpgPkNpxGWSGWrAE+HGZVgGN2fVSFIiYuxKLS034GzhAU90NissQ1pCd+e6lONDzPVBDdBZn8I8oi2EW1dh/7g0opr18VgWpZ/LPTYgSDoQPJHnaMXgSdFKbdG0bnm0bVIV4v93bFEDDWuUhZhRv1qz/pg1trv0TVMgNiXgyfpNAn4/IqByBSc+LkJRCzz2iRo36dnz9Lh8RSdFnogRKNTUUYDgiTq68hqV4AkfpbUAT8SVBmM+NKXgSUy+jKfTt/HZNEE+ixrwRJTU0qc+hPu3WbnpGazsdK4gV1m95fOAJ8bVs2DctgKOzG/COnCOeovxw5HFstxieW6x2Rp1ha1EBY9WocZzuUcGBMnFBE/kOVoxePJ1037Imikdpo/qgo4DJuHQ0TPo36kxNm4/gH2/ncT+DVORkn37FIhNCXiycLGAv/8R6KWb4wZR4yYdE0Ek5jwRc59QU0cBgifq6MprVIInfJTWAjwJ1nxoSsAT8Rtcy8Bv4GRHAiLZ0QBq6iugFjwxLpkI456NFKmgsgvVhif6qxdhHtZSWkVkn2lwZn9b5RX53/BiHhgxH4ykUf+ZcGbN6fYi1Hgud3vyILqQ4Ik8ZysGT5Zv2I1z5y+jHwMm127cQcXGvREVZZOsa9WwMjq3qiXPUg33VgKeTJ6ux61bOnzHytxmYOVuqamvgBo36Rs3dZg6Q48UKVzo3ply16jlRYInainLZ1yCJ3x01gI8CdZ8aErAE/1ff8A8oQdLSFmAJaQM47NpgnwWteCJ/sQBmFnuP2eufIjsPj7IVVZv+WrDE0toOwiXzsFWugZstduqtxA/H9m4eDyM+zbDlSYDi7aaCVeSZG6tSI3ncrcmDrKLCJ7Ic7hi8ORlM2x2B06eOY9cb2ZByhSBGXESs2Yl4MmwkQbY7ED/PnaYjPKcSr3dU0Ctm3T4RD3uP9ChPQNhGQmEuecMD68ieOKhYBq7nOAJH4doAZ74Wz40MfF9imRJkCTEIstJSsATw8GfYfp+LOyUaFSWLzzprBY80VkjENL5a8mUiAnrpaok1JRXQE14Yty5GsYV01nuoXSwDl0Al9Gs/AICaETLsNYQrv4Lx3uFYe042q2VqfVc7tbkQXQRwRN5zlYcnjidLjx6/PQVqwIZoMiFJxEROoSO1cNscaFfT4pWkLel3e+t1k1642YBv/0uoGxpFz77lPz53CM69r/KRFURPHF/n2vxSoInfLyiBXjiL/nQ/v73PzTpNBIPHj6RnFOqWAFMHNoBRqPhFWfF5HJ7+Qd6vYATO+ZFvyBbHbj3OEqWo42bf4Bxw/ewVWgI29dNZY1Fnd1TQC14Is5untgT+j+PIar1INgLfeqeQXSVRwqoBU+EuzdhZkl/xWSx1g6j4Mj7oUd2BePFYkJdy4hvoYt44vZxNbWey4NR/9etmeCJvB2hGDy5cPk6Rk1ZjP2//w8Ox6u5HvaunYw0qZLLs1ajveXCk2s3dJg+Uy9FKYjRCtTUUiDuy7taN+mz5wT8sETAm2+40KIZ+VMNbxI8UUNVfmMSPOGjtRbgib/kQ6vRYgCSJgnBzDFdcfnqLdRmL7i9v2uIBtXLvOIsGwsTvXDlepx/n7N4E/b+dgL7109VDJ6YFo2DYf9PXiVe5LPDAm8WNeGJccdKGFfOlJJoisk0qSmvgFrwxDRnOAxHfvG6iozyK/WPEWOOq4nWisd3nNlen/9Eredy/1CLn5UET+RprRg8+aZTKI6dOocmtctJiWP1+uiSrTGtWrkSMAXoeRS58OTMXwKWLBOQh1VnaciqtFDjo4CaN+mBQ6O/rRQjicSIImrKKkDwRFk9eY9G8ISP4lqAJ/6QD+3u/Uf4rFoHzAnrgWIfvS85p+vgabh64zaWsjwVibWHLNr206rfxYEtSkSexEQqiCHvYug7NfUVUBOeCNcuskokLeFMmRaRo5aqv5ggnEENeBLjN1HOyNAl0rEdau4rIB49FI8gOjNnR+TAua/tqOZzufsWB/6VBE/k+VgxeFLoq1aoWbEk+nduLM8iP+wtF54c/E3A5p8EFGUliiuxUsXU+Cig5k06pnpSnZoO5Huf4InSHiV4orSifMcjeMJHby3Ak5dXqsV8aGfOXUStVoOwfXk4MmdII5k8Zd4arN6yBztXJJ7cc+Skxayy4H7sWTMJhmdfHCkBTyyDmkG4eQWRg+bBmekNPpsmyGdRE56I0saULLYOmANHljeDXG3ll68GPDHNHQnD77tgK14etsbdlDc6wEcU8/2Yh7SAcO8WbGVrw1ajdYIrVvO5PMBl9mh5BE88kuuVixWDJ3XbDEGa1CmkUsXB1uTCk59+FrD/oIByZV0oUYyOefDaP2repA8xILaJAbECHzhRsxoBMaV9SvBEaUX5jkfwhI/eWoQnfFbu2SwHj5xGi25jsG/dZKROGX28eM6PmzBz0Xoc3jLztYPduHUPpWt3wcg+rVCVRdjGNAfL/2azy7v3RzYqJQ1nmfczYKLklJ551burdex0r9EgIMomz3cJzR41ZwycuzfB2KAd9BXremck9UpQAfHZQGx2hzJfWrlu/AdrtwbSmOaJy6FLm5HU90IB19+nYR0cXZ3IPGgqdG/ni3cUk1GQ7psuZdznhaXB0cViins6JDhWrdwqFYMnvx4+hdY9wjB3XE9kevbNzYtmZs+aEYIQfVMLtCYXnixdLuD0nwLq1GJRCnnpjsFrf6gJT+7d12H8JD2r2OBC7x4ExJT2KcETpRXlOx7BEz56awGePHkaidWb92D73iMsl8jNVxa+Zt5wpEzu24p8MZEnO1aEI1N6zyJPeg6bgaMnz2LbsnHQiW/ez1oUewF4HMFK6HnZdA/vQ9elBpAsBZwT13o5CnXzVAE9e05NFmLEgyfykv0mNK/u6F7opg6C691CcPUY56l5dH0iCoSY2ZFp9uYdEaXMc5duzkjoDmyHq/hXcLXoTfrLUWDFLAg/LZXKF2P4/HgrTqVKasKjCBtE+ExNPQXSJDepN3gQjKwYPBGzz9dsOTDeZLGijpQwNuHdNGOOHlev6tC6hQPZstINg9fnTk14Iq5h4lQBd+4IaNPSgaxZyK9K+pXgiZJq8h+L4AkfzbUATwaHLcCKjbuRJ9cbyJIxHYRn3wzHKDCqb2vZZYHlqhmT80T88ueTD/NKw3UZNAXXbt59bc6T85euoUqTPpg0rCPKfBY3J4ncYzvCxbOwjGoP5xu5Edl3utwlUn83FVD72A6VLHbTEV5epuSxHeHWf7AMbCpZEslKEzvTZ/XSKuoWo4B5WCvor15IMGmy2s/l5IloBejYjrydoBg8EY/tnGUApWPzGsicMS1LGCvEsezz4oVYKGRghgnJjTwZM06Px0906NnVgWTJ6CVb3pZ2v7faN+ktWwUcOCTg85IOlP6c/Oq+ZxK/kuBJ4hpp+QqCJ3y8owV4IuZD+4L9/g8f3I7Por2cpVqz/kiZIql09PjKtdssB8pA9GrfAA1rfCmNKCbFF5Phi8dzYlqr7mEMsNzBxoWhr8wqF54Yju2DadYQ2AuWQFSbwV6uirp5qoDa8ES0xzyuK/R/n4S13TA48n/iqYl0/WsUUBKemBaOgeHANtiLfIGo5n1JdwUUEG5cgWVwM2kka4dQVvL5ozijqv1crsASAmIIgify3KgYPClSoQ2qV/gMfTs2kmeRH/aWC09iKrMMHeh9iK8fyuZzk9W+SZ//V8CCRQKysKiTb1n0CTXlFCB4opyWvhiJ4Akf1bUATyo26oUCeXMjtO9z6MBn9Z7Ncvb8FTTuMIJ9kREhdfysaH4poiSmSmCpGp0gHj9eNDn6JerkmfOo13ZonAo9L84oF54Yd66GccV02L6oDlsdbYMnz5TW9tU84ImRHV0wrpsLe8kqiKrfUduC+Jl1r4Mn4le67may0d29iZB+DaXVU9SJspsg5t7mSpYSkYMXwJU0WewEaj+XK7sS/x2N4Ik83ykGT8RvZQwsskQMew22Jgee3LmjY8c79CzZrgudO9ALNs+9w+MmPWykgSW/Anp1dyBpEoo+Ucq/BE+UUtI34xA84aO7FuDJwhVbMX72Sla1Jjw2GSuf1Xs3y3/XbyN5siRIwf7IabLhyfLpMO5aDVutNrCVqSXHFOrrgQI84Ilw5R9YRnwrlbwVS99SU04BpSJPjIvGwbj/J9gLl0JUq/7KGaj2SJ4QIrVtec345vBu0J87AccHxWBlEDqm8Xgu9+GyNTM1wRN5rlAMnmzacRBi4rSxA9oiS6a0r1iV/92crxzlkWe6dnrLgSf/nBfw/Q8CcuZwomljd5m4dtbuz5bwuEkvWSbgzF8CalR1omAB8q9S+4XgiVJK+mYcgid8dNcCPBETxharEh05YTYZX1n49mXh0nGZQGty4Ylp5hAY/tiHqNYDYC9UMtDk0ex6eMATcfEhPWpB9/gBIofMhzNDNs3q4W+GKQFP4kSdDJwDZ2YqKa30PhAe3IGZHd/RRUbA2rwPHEVKS1PweC5Xei3+OB7BE3leUwyeiOGrYhhrQk1rCWMfPHzCIgLsSJcmpVsKiqG81igb0rJyzC83OfDkyDEB6zYIKMRerKuzF2xq/BTgcZM+cpT5d6MgVVESqylRU0YBgifK6OirUQie8FFeC/Dku74TsWv/MXxSOK+UD82gj5v7rGf7+ixhbOCV4ZULTyyh7SFcOgtrrylwvJWHz4ahWdj+1CF1cjNu3Y9UVQ3T9yyfxsFt0pEs8WgWNWUUUAKeGH+cCOPejZRvSBmXJDiK4fBOmOaFwmUJYWWMGURMmZbgicqaxwxP8ESe0IrBE7Hazv0HjxO0plD+t195aJJnune9RQjSsvvYWNAjJoBbyM4wx5QnfHlUMYS325BpOH32gvSjbJnTSwnjCr6fO/ZSOfBkxy4dftmrxxcsoegXLLEoNX4K8IAnDx/pEDZeD7PZhX69yL9KeZfgiVJK+mYcgid8dNcCPClaqS1Klyis+ZwnSntELjyJiUyIGL0crhSplTaPxktAAV7wxHBkN0xzRsCetwiiOowkfyikgFx4IkZEWHrXk6wRq1yJ1a6oqaeAadYwGI7tgePtArB2DSN4op7UcUYmeCJPaMXgiTwz+PUOm7EMKzbsxpq5w1gOihCW8G0IcmTPjGmhXeI1okXXMbj34JFUrlBgFYQ69p+EG7fuYdWc52f05MCT1esE/HFckKJOxOgTavwU4AFPxNVMnaHHjZs6NG/qwFvZKe+JEh4meKKEir4bg+AJH+21AE/EKjbv5MyGMQO+5bNojcwiB57obFaEdKwsreTp9G0aWVFwmMELnuginiCkazXyscLbSi48MS2dDMMv66UqSGI1JGrqKqCLeCod3xEe3kVUvQ5IU7UO7j6ywu6gZ2U1lSd4Ik/doIMnpWt3QYXSRdGjbTRZXrVpDwaOnYdTu+ZDp9O9omaFhj3xZraMmDG6m/QzMfnd5HmrcXjLzNhr5cCTed/rceGiTsp3IuY9ocZPAV7wZNtOHfbu0+PTEg58VYZ+ISjhYYInSqjouzEInvDRXgvwZPOOQ+g9cibE3CYZ0qXis3ANzCIHngg3LrNyns3hTJ+FVfr4XgOrCR4TeMETUVHzmI7Q/3uGlWwdxUq2fhg8Iqu4UjnwhKJOVHTMa4bWnzsOc3h3uAwmJAtbiHtJ0hE8UdkVBE/kCRx08OSDMs0xuFtT1KgYnYDt6MlzUnnCfesmx1sJYMPP+9mD3yxWajEXGtT4EiMn/YBWDSqjWb0KisCT8RP1LLJFJ1XaESvuUOOnAC94cvGyDnPn65Exgwvtv6WjO0p4mOCJEir6bgyCJ3y01wI8adZlFH479meCC07ody8fhdSbRQ480Z85CvOkXnDkKQhr57HqGUkjv6IAT3hi3PwDjBu+ZzlParDcJ23JGwooIAeeGJdPYxWu1rCjVB+xo1ShClhDQ7irgPFZdTEhRx5E9plK8MRd4by8juCJl8I96xZU8MTlciHfF82kikAVyxSVJDhz7iJqtRqELYvHIHvWDK+oeeHydTRoPww5s2fBiTP/sIpBeiye0g9533kr9toHT2xee6Fb3+iu4+jIq9caettR/CX7NNIOh1N9aNVvCDs/awUG9taxyhLqz+etJv7STy/okMRiwKOn3n/2/GWtgWinxRSdNDQyimCimv6xhjUNAAAgAElEQVQ1G/WwmMTalb5r67b+in8vXUvQgDaNv0aIxeQ7A1WaWQ48MbASqSZWKtVerCyimvRUyUIaNj4FeMIT/YU/YR7dAc6M2RDJEmZSk6+At/BEPDZi6VVXMsDaYyIcOfPKN4ZG8EgBy5DmEK5fhqNaM1jLNfCoL13smQIETzzT6+WrgwqeiIsXI0+GdG+G6hU+k7RILPLkyzpdUfrTwujbsRHECj0dB0zCsVPncPTn2bEJcJ+wF3Bv2v2HOgwa4USK5MCw/q8eGfJmTOrjvgIh7AUu0uYAY2qqtwWLXTh2AqhXU4diH6s+XcBPIJ6ws7AXwwh6+fZLXxtZRQt2TpJVPKOjimo60MDydJmNvoUnaq5Py2PLgSfGDQtg3LwYtoqNYKvyjZaXGXC28YQnongh3atD9+QxIoYvgittpoDTk/eCvIUnxpUzYNyxiqK9eDvshfmEK+dhGdFG+hdK1quuIwieyNM36OCJmPOkYplP0P3baMK8cuMvGBQ2P96cJw8fP0Wxyu0wqm9rVPmq+DPYcpYd8xmJZTMHIV+eHNK/eZvz5NIVHebM0yNbVhdat6BvYOVtZc978zq2I1omJgUWkwO/m8eJBnXphdFzb8XtQcd25Cro2/50bIeP/lo4tsNnpdqbRQ48MS1kZWwPbENU426wFy+vvcUFsEW84Ylp7kgYft8FW/1OsJWMThJMzXsFvIEnuscPYOnbADpbFKv4Mo5VfvnAewOopywFUu9dDeuPrMrR/9k7CzgnjvaP/3b3ktzhTpFCcSlSaLEWl6LFneJO4XB3dz3ssFKkuHuRYi1WnEILFApFijt3sc07s3lD7+AkyW6S3WTm//l/3peXnZlnnmeyzHz3EeKNZRwcDpvO/7wSZSlIoc4MnshTpKLw5OCv57By4z7cuvMvenZoICVmHTR+IUkSlxw92teXJ6lCvSfPWy0Bk81LxiBBgmA07hS92s7MRRuw5+BJ7FwxUZqRllmk5YkXTumLROT54VOW4tDxczi8adY7zxN34cmFSxzWbxTwaV4bGtVn8EQhEzs9jDfhydsIDhMmC9DpgKED3fNUcnphAfCgP8KT1284vHwJ8v/2/wRxziiQ34ZgUuba3xqDJ96xKIMn3tFzTLPIgSeGGX0g/Hkexu6TYM1dyHeLCMCZvQ1Pgk7sg37pRFgLlICx839VHANQ9Yos2R14otu4ALq960ioTh4SsjNLETnYIO5pgJ7LX5IP1jxJpGwpU4tU4OkqnYXgf8cg9xSkUC8GT+QpUjF4cuai3SMjQYgBRpMZ/b9rimYkwer8ZVvfVaehf+frRr1JaPnhy1f/lkRJlyYFlocNRrq0KaU/Dxy3EDv2H8OF/UukP9MQnclzV+P8ZZrvhEeubJnQr0tjFPks97uluAtPjv4i4Kf9HEp+SaqwVGRvBm/vDW/CE7q28EUC7t7j0PJbEdmyMu8TOfbWIjx59pzD3bsEjrzi8OqVDc9f8BIkefUK5L/HHLZHUiyhYAERxYrYkO4j/3lHMHgiZ/c735fBE+d1pfSTcuBJ8NAW4B/fR+TI7yGmyai0aGy8ODTgbXhCvR5C+taHTR+MiJnbmG1kasBVeBLN64QkZ6ZJmlnznQboufz5rX8gjGgLLjKCeAJNIZ5ABX0nkJ/OzOCJPMMqBk+6DJxOLgCvsXLOENRrNwz1qpeR4Mmff/2Dum2HYv3CkciTI7M8aRXs/fT5K5jMZnyUOoVTo1LoYjZbkDJ5kg+edxeebN/J4+RvPKpXIZejouwy7ZQhFHzI2/Dk58MCfj5Icp4UE1G1MrO3HFNqCZ5YiKPRz4dIuWoCS2NrwcE2JEpoQ8KE/0EUI0kw/O+D//6cIb1NgiifFdT+3mHwRM7ud74vgyfO60rpJ+XAkwSdK0nivJ23V2mx2HjxaMDb8ISKEzyuM/h/rpPKSuSimItdFOVsUlfhiW7zYuj2rGZeJ3KUrmBfx7kcv+6VPLLEJClgHLYYtoSJFJyFDcXgibw9oBg8KfR1e3RvVw+tGlaRYIkDnjx68hxl6/XAClKhplC+HB9IS7+laj1VqrvwZPmPPK5d59GssYhcObV/IZK3Fb3f29vwhHqdUO+TlCls6N6VhWnJsbhW4MnvV3js/onDi/97lpQoLuKjtEBicg5IkIACEyBJHNWXKDz57TSHsyRnDmG9UqOgpdBnNhQvakPyZNr0RmHwRM7ud74vgyfO60rpJ92FJ46qH7ZESRExeb3SYrHxVAhPdFuWQLd7FcyVGsBctwOzkQwNuAJPuLev7blOjMTDgZQmtpISxaz5VgNRz+WGuUMhXDwOa/7iMHYZ7VvB/Gx2Bk/kGVQxeFK79RByKUyCxVP7RYMnqzbvx5gZy/Hr1jmkRCu5KfhhcxeehM0X8Oghh+86WZE2jTYvQVo2p7fhCdUVzXtC85/0DLVq9uKrBpurHZ5QWLJlG4frN+yVTmhS6Dq1bEidyj1ISsHJ+Ys8Tv3G4f6//+FmGv5VlJz38uR2b1xf2ZLBE+9oXi3wRAv50JS2iLvwxFG+1po5J4wD5igtFhtPhfCE/+sSgqf0hJg+CyKHLmA2kqEBV+CJbiuparVrJcSM2RA5eL6MWVlXpTQQ9VxOq1AZRrUFBcqmVgNgKVZBqWkCfhwGT+RtAcXgyebdRzF4wiJULlsUp85dQZkSnyFViqRYuHI7ShXLj/kTe8uTVMW93YUnY8YHkdAhYFA/q/Q1mTXvasAX8IRW3KGVd6pVFVG8iLYuvN61TtyzqRmeHCThWQdIeBZtBr0Nlb+24YvCytn67l3ijXKWw/kLPGhIEG2JE9Ok0yIyfayN9wiDJ975NakBnmglH5rSFnEXngSdOQT9wjGwFCoJU4fhSovFxlMhPKEihfSsKeV4iJywGmJSew4+1lzXgCvwxFEmmno1UO8G1nyvgffP5cIfZ2GY2Q82Qwgihy2CLUUa3wvpBxIweCLPiIrBEyrGoh93YNZiUifd+t9FoXjhvJg8rDNSJEssT1IV93YHnkRGchg3SZAuV4MHsBAOX5jXF/Dk98sc1qwXkD2biBbNlLtQ+0J/vpxTjfDkxk0eW3dwePrUDk5oFa3qBJLRXCaeaCYTKYFNAMoJ4o1CPdhoa9RAxKd51L+vGDzxxI74cEw1wBOt5UNTyjLuwhPdvnXQbVgAc/m6MDforJQ4bBwnNeCLnCdUNP2CUQg6e4SVp3bSTrE95iw8Cfp1N/TLp0JMmQ6RY5bJnJV1V0oDMZ3LdWvmQHdwM6xZSDWkfqwakhK6ZvBEnhYVhSdUFFpp5+bt+3jzNhKZM6aVvE/8vbkDT2gug7nhAtKktqFrZwZPfLFHfAFPjASajSXQjLZRw1jJYnftriZ48vo1h527eVwiYIy2ZEltqFnDJgEybzQa0rNqDf8uRKjq1yJobhU1NwZPPGGdD+s5qgGeuJsPzRMa8uaYbsOT/18UzA07w1yurjdFZnMRDfgKnjgu85ZCpYnH0VBmCzc14Cw8CabhIPdvw9ywC/md1XFzNtZNaQ3EdC7nzCYYxnYE/+COlBOI5gZiTZ4GGDyRpz/F4En7PlOknCYtG1RG/jxZ5Umlsd7uwJM/r/JYuZpHjuwimjdV90VHY+ZwWlxfwBMq3OKlAm7d5tCsCUkUnIPZ3mmDRXlQLfDk+Eke+w5wMJns4OQrUna8fFkbdEHurEpen63befx2xp5jpTip3lWNVPFSa2PwxDuWUQM8CdR8aO7CE8P84RDO/wpTx+GwfFbSOxuFzfJOA76CJ/yzR1LyUjEkASKnbWEWcVMDzsATR44Zm86AyIlrYSM6Z00dGojtXM7fu4ng0fZkyjQvEM0PxJr7GmDwxH3d0Z6KwROa2yRsyUYpZCdThjRoQSBKrcpfIUFIsDwJNdDbHXhy/BSPnbt4kuxRRI1q6r3kaED9bovoK3hy5KiAveTCXeRzEd9UZ7Z3x4BqgCeHjgjY/7MdmmQgCWFrfyP6PPHz0V8F/LTPLlNukkS2aUN17i8GT9zZ9a73UQM8CdR8aO7Ck+CxncDf+QuRA+dAzJTTdaOzHrI04Ct4QoU2jG4H4d4tGPtMhzVbPlnrCNTOzsATmlOI5haylKkFU+OugaoqVa47rnM5LSlNS0uLaTMicsT3qpRfK0IxeCLPUorBEyqG2WzBnkOnsGLDXly8cgOCwKNGxRISSMmdPZM8SVXc2x14svsnHr8e5/F1RRtKkq/VrHlfA76CJw9Ifoo5pNJSEpLks09PZnt3LO9reHL6LE+q6di9PNQWJkPLI69ZZ5ctU0ab5OEUEuKZvCvu2I72YfDEXc251k8N8IRKHIj50NyFJyF96oJ780oqU0zLFbPmXQ34Ep7oNi6Abu86mKs0hblWa+8u3E9miw+ecC+fIaR/Q2m1kaOXQUyVzk9W7h/LiO9cbpgUCuHmFZgr1oe5Xkf/WLQPVsHgiTylKwpPoopy/8ETLF61E7RUMW1Ht4QheVL/TBrrDjyhlxt6yWlYz4p8n6rrYiNvS2mnd3wvaU+uZPJ0Aa9ecVK+G5r3hjXXNOBLeHL9Lx7LVtrhBE0IW0yFVZPukIo8VEaamDp5MhtatxCRjPynWhqDJ96xhFrgCV1toOVDcweecGYjQkJrSJvj7by93tkkbJZoGvAlPBH+PAfDjL6SxxH1PGLNdQ3EB090W5ZAt3sVLHmLwNRtnOsTsB4e1UB853L+6UMYRrYFZ4qEsdcUWHMU9Kg8/jo4gyfyLOsReEJLE67YsA97Dp6UpMuV7WOsmD3Yb0N43IEn4YsF0JKjHdpYkZF8HWbN+xqI7yXtSYm2kPwUp0l+isqVbPiqBPM+cVXXvoIn9De7iOSssRKTlSppRaXy6v3tPnvOYckPPF684Mi714aWBKCkS6sOeRk8cXXHu/e8GuBJoOZDcwee8P/+g+CRbZhbunvbXZFevoQndAEh3b+RLobM88g9c8YHTxzliSk4oQCFNXVpwJlzedCxvdAvmwQxSQoYSfgOy1njug0ZPHFdZ1F7KAZPnjx7iQ07DmHlxn14/PSFFLJTr1ppfFv/a2TLnF6elCrv7Q48mThVwJs3HPqSsI3EJHyDNe9rwJmXtKekuvInL1VI+eQTEW3IpZY11zTgC3jy+AmP8MUcaMWkAvlF1K+jfru9jSAeKCt43LvPSUlsG5McKDRJta8bgyfesYAa4Emg5kNzB54Il0/DEDYA1jyFYQyd6J1NwmaJpgFfwxPDvGEQLhyDqdUAWIpVYNZxUQNxwZOg4+TS/QO5dKchOTNGspwZLqrWK487ey7Xh49E0LmjsGb9FMa+M7wimz9NwuCJPGsqBk8adx4l5TnJkSUjWjasjOoVikOv18mTTiO93YEnw0bZy3EoU672wxKVGlGdT8V09iXtCSFpednR4+17YOhAC3SB8VNRTJXehic0xGreQh60LDEtQdyime8BhLPKtJCK2KtJmODVa/ZQo9o1RRT+zLfyM3jirPXkPacGeEJXEIj50NyBJ7qjO6FbOR2WL6vA1Ly3POOz3m5pwNfwJOjwNuhXzYKlSHmY2gx0aw2B3CkueBI8vgv429dgatQVlrK1AllNql27s+dy7s1rGMaR8sUkjIe9L503J600pduzBimHTnG+E3vyAw0oBk9oRv18ubIge5YMAadmV+HJ06ccZswWpBwEvUJZyIavNoyzL2lPybd0OY8bN3k0biAibx7fXmY9tUZPjetNeELzhiwgHifU84QmYG3VwoogH5QilqvLHaS61wlS5Ys2X+dqYfBErjWd668WeBJV2kDJh+YWPNnyPcnH8CPM37SCuVoz54zMnlJUA76GJ6xksTxzxgZPhBtXYJgcCltwiL08sd7/K4HK06RvertyLuf/uY7gcZ0lQc3NesBcsrpvhNbIrBScGGYOAM2tlWztUY1IrU4xFYMn6lyed6RyFZ7QCzO9OGfNIqJVc3Zp9o6VPpzFlZe0J2Q8doLHrj285AVAvQFYc14D3oIn1ENoyTJ7fiKa2Ld9axGGYO2G2UUtr9ymlRWfZPLNWhg8cX6vy3lSbfAkkPKhuQNP9EsnIujEPpha9IOlRCU5pmd93dSAr+EJFTt4RGvwD+7A2H82rJ/kcnMlgdktNniiXzIOQad+hrlcXZgb2i/crKlPA66ey4NOHYB+yXhpIca+M0kYT171LUoFEkngZNZAKZ+SNc/nSDlypgqk0q4IsuDJL6cuYfjkJVgyfYBUVefMhauxamLxtH5IlDBEu5qKQ3JX4cmZczw2b+XxWUERdWt579JMr0k0wMflRj9We09Ml8Vzt4OrL2l354mt35MnHGbOEaRkngP6Mg8kV/TrLXhCK9bQ6jpJk9rQsZ1I3mG+gQ2u6Ca+Z1et5XHlDx4JE9jQpaPok5xLDJ7EZyVl/l4N8CRQ86G5A08M03pDuHYBxh6kikQuVkVCmV+Ba6OoAZ7o1s6F7udNzAPJNdNJT8cET1h5YjcU6aMu7pzLdevmQXdgo1TaPXLYItgSJ/OR9OqclpZ21s/sD84YIVUnolWKWM4TebaSDU9GTl2KRVP7YTWFJ5euxSrNoil9GTz5v3YOHORw8LCAsqWtKF9W+5cxeVvQd73deUkrLe20WQJMRqBtaxtSp/JDQqW0wv4/njfgycYtPM6d5yW41aGtiBQp/OO3aiLeNOGLBDx6xCFDBgKF2nof3DF44qEfxnvDqgGeBGo+NHfgSfCQFuCf3Efk6GUQU6XzziZhs0TTgBrgSdDlU9CHDYKYPDUix/3ILOSCBmKCJ7qdK6Db9gOs+YvD2GW0C6OxR72tAXfP5YYZfSD8eZ4lkH3PYMKNy9DPIqE6FJzkLgRTlzGw6fQMnsjc2LLgicy5/aa7q54njkuZGhI3+o0R3FiIuy9pN6aKtcuevRx+OSagNCl7W1HFZW+VXLMSY3kanuzZR+zyqyAl8m3X2op0H/kHOHHo/ukzkgB3AQ+jkUORz0V8U9274I7BEyV+BfGPoQZ4Eqj50NyBJwk620N13s7bG79x2RMe0YAa4AldWEhodZKbwES+pC+GmC6TR9bqj4PGBE9C+jUA9+o5jN0mwJr3c39ctt+syd1zOffmFQxjSQLZZ49gLlUD5qbd/UYn7i4kJo8Tx1jM88Rdrdr7KQZPug2ZhTzZM6FLq9rRJKIVeNr0mohdKychVYqk8qRVaW9X4cmSZTz+/puX8p3QvCes+UYD7r6klZT2DsmlsWCxgJTEq6F7V+97ACi5Fm+O5Ul4QpOq0uSqtPnzb/SvGzx+IGWMaatfV0SBfN57FzF44p1fixrgiXdWqr5ZXIUn/PPHCB7YBGKSFCSh5Rr1LShAJFILPNEvGI2gs4dhrtUW5iqNA0T78pf5PjwRSE4MA8mJIaZMh8gxy+RPwEbwqAbknMuFu3/DMKa9JJ+pRR+SN6qyR2VV8+BxgRMqN4Mn8qynGDyhrrkF8mTDoNDoGeLvP3yKig17YeWcIfjs0+zypFVpb1fhyXQSqvHsOSddlumlmTXfaEDOS1pJiSdNE6QSuN26iCx0x0nFegqeRAUKgVAF6dBRAfsP2DMhdWpvRfp03nkfMXji5EaX+Ziv4AnLhwa4Ck+oe7VhcneSIDQ3SRQaJtPyrLu7GlALPHFc+mnCWJo4ljXnNPA+PDFMCgW9SJoad4OlTE3nBmFP+UwDcs/lQacPQr9orCR/5MC5EDPl8NlafDUxLcdN82dFzXHyviwMnsizjmx4cpbkOTGbrRg+ZQmyZEqHVg2rvpPIbLFgw47D2HPwJE7tmk9yB/hnaTBX4cmwUfY6p6OGWeRZj/WWpQG5L2lZk0fp7CghW6GcDWVKMe8TZ/TqCXhiMgGzSALfl684lCe2KBsgtli1hiSQ/dOeFLdLBxEhJMeLpxuDJ57WsH18X8ITteZDM5KkP4+fvkD6tCnBcc6lUBdFG+49eIzECRMgaZKEThnPVXjiOPRbCpWGqcNQp+ZgDymvAbXAEy7iLUJ61bJfAieshpg0pfKL9cMRo8ITRylbVp5YO4ZW4lyu2xAO3b710m/GOHh+QCWQpeBET/K/8OT94UgOG5P1GTyR95uQDU8Kfd0eJpqBMJam1+sIUKmC7u3qyZNUxb1dgSevyMVs8nQBCUnVjv692UXZl2ZV4iWthPw3SQjX9ySUK11aGzp3ZHvCGZ16Ap5s3c7jtzO85P1DvYACpVFoNH8hj8dPeGQmpYvbkhLGnm4Mnnhaw76FJ95ZnWuz2Gw2jA9biZUb90kd6dlk/sReKFYoT6wDvXj5Bn1GzcOvv12SnimcPyeWk0SetPUfE47t+45F65spQxopRJk2V+GJbu866DYugLlifZjrdXRtcexpxTSgFnhCF2SY1R/ClTMwNQmFpfQ3iq3RnweKCk8cpb/N5Ul54gasPLEW7K7UudwwpScEUp7XmiUPjP1maWHpsmV0FpzQiRg8kadu2fDkyrVbMJktGDhuAbJnyYC2Taq/k8hADic5s34Mnnfu6468pfiutyvw5M4dkuNiieCzKhe+05L6ZlbqJa3EysZPFhARwaFXqBXJknn+y78SMvtyDKXhya1/OCz+XpCW1IUArI8IyAqkRhPIzp1PKj8RDl7ySyu+rujZ9TN44p3d5SvPk6irU0s+tOOnL6Nt70lYMLkPihTMhVHTl0lesSd2zI/xjEK9TSo26gWeeKd0blkbVcoVJV5pbwjktnsA9Bs9H7fuPMCYAW3fLTck2ICM6VJLf3YVnuhXhyHo0FaYGn0HS9noueO8s1vYLFQDaoIndD/QfWHNUxjG0InMQE5owAFPXj9+hpA+daUerHqVE4pTySNKncujJpCl4JECSH9uroATqgcGT+TtBtnwxDH9zdv3cf7yDZT4PC/Spk7+Tqo9B08hTapkKJTPf+POXIEnl37nsHaDgE/ziGjUIHC+bsvbpp7prdRLWgnpNm/lceYcjypfi/iyONsX8elUSXhCPS9mzxPw/AUnherQkJ1AbFev8Vixyp5Atgl5N+Uh7yhPNQZPPKXZ6OOqAZ6oJR/akImLcemPm9j8/RhJSfcfPCFwpDdWzB4c4/lk+95j6D82XHo+R5aMHxiMwpNnL15j4ZQ+MRrTVXhimDcMwoVjMHYaCWvBL72zQdgsH2hATfDEkUSYChkxbTNsIc6FjAWyWR3wJHLTcui2LGHliTW2GZQ8l/P3bsIwoRupWmWEqWU/WIrbq5n5W+NvX4UhfCS4pw+jlSOOa50MnsjbBYrBE3ow2bH/OA6unxEtJpi6tv50+Dcp50mQYP+y62/NFXhylJQ//YmUQf2qhBWVKwXmJU0t9lfyJS13Tdeu81j+I49MGW1o18bzYRNy5fV1fyXhyXZSWeckqbATaOE6Mdnw4BEBB37moCNpmTqR/CdUJ55oDJ54QqsfjulLeKK2fGite05A8qRJMG1El3eK+rRsK0we2hnVKhT7QHn07LLr5xMoWTQ/rt+8K1ULDCXhx8UL55WepfDk51/PSqE8KZIlIWMUR6li+d+N4yo8CR5DymzevYFIEqMvZszmnQ3CZvlAA2qCJ1Q4w4TvINy6CmObgbAWKc8sFo8GHPDEHFoP/MunMHafJF0oWdOGBpQ+lwedOQT9QjswjxwwB2LmnNpQhJNSCud+gX7JOKmsuTXXZzB9NxY2nT7e3gyexKuiOB9QDJ7QijolixbAiD6tok149cYd1GkzJNavN/LEV0dvV+CJ46JWvYqIYkU9czFRh1bUL4XSL2m5Kx4zgYRNmDj062VFokQMrMWlT6XgSaCH68SkYwrxKMxLTsLHvutkJbkh5O7sD/szeKK8TmMa0ZfwRG350Oq2HYq8OT/BmP7/hdkUqNAGg0O/RaNaH15Km3YZjb9u3UObxtWk5LKbdx/F8TOXsWnJGBKOnBE/btqPazfvIMSgx7nfrxPP278wqm8b1KteWjIFDfsxW53/Nz6iQw3gzSuEhG8HEiX2zgZhs3ygAQ4cgoI4mC3O286TarRsWQHz2oXgi5aFoftIT07lF2MLPA/r8QOImDkcXPpMCJ683C/WFSiL0Afx5LdnA/0/pZppdTis236UhjMMnAo+3xdKDe3TcSzbVsK8eoEkg1CuOvTt+jktj0Hnn84MTitA5oOKwZNStbtJMcGDuzePJtIf12+jXrth+HHuUBTM659fU1yBJ9QtnrrHN20kIncudfzjLHMPaba72uDJ+o08LlziUaOaiKJfsL0R18ZSAp6QVE0II9V1Aj1c5309R00gmyO7iOZNld+LTsMTGkWk/PSafWe5Krgv4Yna8qFRzxPqITJ1uHOeJxSefJw+DSYOsSdvtRIQ8kXVjuj47Tfo1OLDkqft+0zB0+cvsWHRKOl5o1nE6wjnKurRkpL8d9XJF0MDxHm7XDUze15BDVAH6cQhejx/TeI5VdC4e3+DH9YGNkMIxDk7VCCRukVIEEw+Qo3qCtvVi7B92xNiWZZoV90Wiy5dskR6vIogXhQKO2DzYUPAnf9VmszWYQjEotr24uIXTwB37Cf7ehp1gVipvktmTpnEA1/FXJJA2w8rBk9oIrYzF6/hly2zSUliwzutUNdWGs5zbPtcJEmUQNvaikV6V+AJza3w8BFJQEeSUtLqKqz5TgNqgyeXr/BYvY5H1iwiWjVnN8a4doYS8GTnbh7HT/JIk9qGrp0V/pfad9takZlp5Z15C8gXIJJAtlJ5G0qVVFY/TsMTRVYTuIP4Ep44tK6WfGg0tPjy1b+xcfFoSbR7/z5GpcZ9Ys15Qp+nAMgBQyg8KfR1OykpfkzVA4dP+R6nzv2BnSvsiT1dCdsR7t2CYXQ7iB99jMjhSwJ3w6pg5WoL26EqCRnyLbgnD2DqOg6WT4uoQEvqFSHx87uwDmwFVp5YvTaKSzJPnsv1S8Yj6NQBaXpz3Q4wV2qgOSVxEW+gnzNEqiREgaqp4wgpobSrjYXtuKqx6M8rBpDz9ywAACAASURBVE/oIaN+++HS6F+QTPYfpU6Bg8fO4fWbCNSpWiqaq6w8kdXX2xV4MpaEZhhJaMagflYEBzN44ktrevIl7c66aKWT8ZOCJOI+sK8VISFsf8SmR7nw5DaperWIVL2ijYHMmLUcNYEshUsUMinVGDxRSpNxj6MGeKKWfGjHfvsd7fpMlqrtFP0sNyjs2EvysTmq7cxctEGqvuOAHzREp22vSZhBvmKXKV4QyzfsxbTwtVhJDq6ffZodQyctQdM6FZD9kwy4cOUGqGdLs7qV0P+7JpJRXIEnQb+fgn72IFjyfgFTt/He2Rxslhg1oEZ4QktY01LW5pLVYG7Wk1kuDg0k/HEabEd2wVyBlPyuz0p+a22zePpcrtvyPXS77SE85tI1YG7SXTMq4h/fhz5sEPiHdyAmTw0TqcBFgbs7jcETd7T2Xx/F4Akdksb9jibl/2gcMP1KkyJZYjSpXQHtiZurLsh/46uchSfGSA5jJwkknhYYNsg5d1555mW949KAp1/S7mj/x7U8/viDR51a5CtnQeZ94il4MiNMAC3PG8jVdZzZnwcPkwSyBzmpdDMt4axUY/BEKU2qH56oJR+azWaTyhOv3fqzpDRB4BE+sTdKfPGp9OeB4xYSL9ljuLD/P8+PGQvXY+FKkoPk/60fASMtG1SW/kTXdf/h03d/R5PFThvR9Z3nrSvwRHd4O3SrZrLLsXd+FnHOokZ4Itz4HYbJPWBLlBQRk9erQEvqFIGWpw0Z2Jjcik2IGLMCtpRp1SkokypWDXjjXO5431IhLJ+XgandENVbhL9+EbQiG/f2Nayf5IaJhJXaEidzW24GT9xWndRRUXgSVRSaLI3nOXnSaaS3s/DkwUMOc+YLSJ3Ghm4kESNrvtWAN17Srq7w/AUeGzbzyJVDRLMmDJ7Epj85nid79nL45Rj5HZJKMt26MB3Ht0fD5vJ49JjH1xVsKPmVMu8tBk/i07oyf68GzxO15UN7G2HE46fPkTFdGqfOKPT5fx89xcfpUkNHy1BFaS9evpHGSkcSyiYICY72dy7Bk82LoduzGuZarWGu0lQZ47NR3NKAGuEJXUhIn7qgcMDYdyasWe0Vn1iLrgHqnUO9dLhCX+JNB5ZcV4v7w1vncoHkPzHMt0dLWHMUIDBijBTqpcYW9NvP0C8eJ4lmKVQapg5DZYvJ4Ik8FSoKTx48eiZ5n7x5G/GBVN9U+vKDg4c80dXT21l48idJFLuSJIz1VBJG9WhEG5J46yXtijZoSBcN7aJtKPFOeu+s7spQfv2su/Dk7l0O4Yvt+qWeFNSjgrW4NXD/AYd54Xad9ehqRYoU8nXG4Il3dp0a4Emg5kNzBZ44YvFNrfrDUqyidzYHmyVGDagVnuhWTIPuFxKOQvI00HwNrH2ogeBBTcE/ewSh7xS8ylqQqUiDGvDmuVy4cRn6WQNAE3Zb02WGucckiElSqEprui1LSJjRKkkm8zctYa72rSLyMXgiT42KwZOTZ/+QYn5ja0c2h0lhPN5q1PPl3oPHSEtyr3g6ZMhZeHLiFI8du3gU+VzEN9XZF29v7YXY5vHmS9qVtS5byeP6Xzwa1rMi36fyL6quzK2VZ92FJyxcxz0L7z3A4chRARnS29CxnXzvEwZP3LODq73UAE8CNR+aK/AkeEpP8CQBoLHXFPIVlF36XN3nSj6vVngSdOkkSRQ5GGLq9Igc9YOSS/aLsd55EqRND2HiSlKxhYXGa9Gw3j6X83dvwDCzP7hXzyEmSwlTj8kQ07qXR0RJffOP7pJQzjAIV05Lw5raDSYhRmUVm4LBE3mqVAye0LJ+1LV1/KAOaNNzItaED0f6tKnQddAMiCTWeDWJ1fJW23XgBPqPJXW9Sd4V2np2aIB2TavHOP0P6/Zg0hw71YvaaNLbH2YOxJY9v2DQ+IUf/P1vuxcgJNhe6slZeOIIF1DS/d1bOvXHebz9knZWh6fP8NiynceneW1oVF/+RdXZebX0nDvwxAEAWLiOe5Z2gKeqX4soUVwe/GXwxD0buNpLDfCEyhyI+dBcgSchg5uBe/qQ5GlYRvI0pHPVzOx5BTWgVnhClxjSo6b0lTxy2CKI5Es5a/9pwDCjL4Q/z4H/NhRcxToMnmh0c/jiXM49+ZcAlAGgwMKWMBFM342DNUsen2iQe/kMQduXQXfEnmuL5jkyfTeW5DnJpag8DJ7IU6di8OTLmt+hRf3KaNesOgpWaPsuI/3x0yRjPSljvG/NVCku2NPtbUQkitfoIsGSzi1rY8e+Yxg8YRG2LxuPLJk+PJS8ePUGDx8/iybWdwNnIF/urCT5Wxds3n0UwyYveVeu0PEgzbDPcfacLs7Ck7XrBVy6zKEB8SjIzzwKPL0V4h3fFy/peIUiD0REcBg/mSUWjktXrsKTqKEnLFzHmV344TOOkCea8Dq0ixXJkrnvFcXgiXs2cLWXWuBJVLkDJR+aK/AkQedKkoreztvrqonZ8wprQM3wRL9gNILOHia5cdqQ3Dj2qk6sAfz92wge1RY2nR66sE0kd0UCBk80ujF8dS7nXr+EIWwg+NtXyT4ywNR+CKz5i3tNi1xkBIL2rELQvg3gLCZpXkvR8rDUaU88YlIpLgeDJ/JUqhg8KVa9M1o1qoLOLWqB/vfQtnWlsn208k7t1kOk8oBfFcknT1oneu/cfwJ9R8/DmZ8WwqDXST0o2PmWyNKlVe14R3CEH239YRyyZU4vwZOR037AWTJebM1ZeLKA5Fq4Q3IutGtjRaaM7l884l0Ee8ApDfjqJe2McIuXCrh1m0PTRiJy55L3ld+Z+bT2jKvwxJH0tGxpK8qXZb89d+296ycex47zyPSxDe1au+8VxeCJuxZwrZ+a4An9UEGTr77fPkqd/N2HCNdWp+6nnYUnNEcDzdVAXcYjx69W96ICQDo1wxPh1AEYloyHNXNOGAfMCQBrOLdE/apZCDq8DZbS38DQtjf5XG9j8MQ51anuKV+ey6lXlz58BAmVOSN5oFgLfAUrKWdMq9t4sul+3oigHSukhNC0WXMVhLleJ4gfZ/fYtAyeyFOtYvCkZqvByPBRKsyb0BOhQ2fhxJkrGNK9ObYTz4+jJy/i121zkDRxQnnSOtF70Y87sGT1Tvy69b9/WBp3HgXqKTKmf9t4R6jRYiByZMmA6SO7Ss9SeEI9V0oWzQ+DQYevvsiHutXLRMuj4iw8mTRNwOvXHPr0tCJJYnaBi9cYHn7Aly/p+JZ2/CSPnbt5fEbKFdclZYtZi64BV+AJzdVBQ3ZYlSv5u8hMwsjD5gp4/pxD9aoiihVxb28yeCLfFs6MoAZ4cvffx2gZOi5aWd+osns7H5ozelPiGafhCcl1QnOeWLPkhbHfTCWmZmPI0ICa4Qn9Oh3Ss6a0usjxqzzyRVqG6nzSleokuF8DcGYjCWdajITZsjF44hNLKDOpGs7luuVToft197sFiRmywlquNixflIHNkECZhZJRgk4eQNCWxeBJyCZtYrpMsNTtCEu+oorNEdtADJ7IU7Fi8GTttoO4duMfDCbA5P6DJ6jWfABMJrMkXftmNdCjfX15kjrZe8r8Ndi5/zgOrJv+rgdNZJsoYQKEjQmNc5Q9B0+h14g5+Gn1FAkE0fbb+T+xcedhJCfJbv+59xD7j5xB5bJFpZAeR3v51r7O+FqvgfYnpo2P70n2997QQKIQHd4aLaBu5GprL15yGDnehmBS/XKcvZoaa1E0QMugJzAE4XVE3L+9l0SPY6bYYCGPde8MZM7E1ChXAzdvEYAyH6R6GjCgJ4fkyV3//Rh09uo9RrP73ity1xEI/Q1BAvHA5H26VPox5ciJi2jTuCrmL9uK/t81QcoUSTEhbCVSp0yGNaRc5PslgH0qsEKTOwtPHN4ENBkgTQrImm81oGZ4QjVjmNVf+jJuahIqeVoEeqNf7XVr50lf6409piBxAvIPE/M80ey2UAM8ocqjQEMg3kxBpMIV9/qFpE+bIQSWIuUkkCKmz+K2jmnyZ2HTQgj3/pbGoGE5lpqtYClR2e0xXe3I4ImrGov+vGLw5H0xzBYrLl65IYW+JE3ieY8Tx/zuep7Q5LLlG/REqWIF4vRQWbp2NybPXY1z+xa/8z557URW78dPyCVusogU5KIxrL9vD7Pytoz/9E5gEBBpspKExupc07TZNty+Q0rqtuOQ03Pee+pcfDxSEXaCYL1A4Ffcl+8Va2z47SxQiBSwaNnEnqPIl436afjDr3/9FhuOHgOyfEKgVCfX9aoPIn1IziiT2T3PFV/aUEtz64J4GHS+3XFl6nZHza+/Qtc2dVD46/ZS/rDc2TORjxz2ENtj2+ciSSLlvuapxT7OwhPdntXQbV4M09cNpfh21nyrAbXDk6BDW6FfHQZrnsIwhk70rbJUMHvwsJYk0ec9mDoOh+WzkgyeqMAmckRQCzyJuoYgEi4XdGibVBHN0cRMOWEp8w2sBKbQHCkxNe7ta6mKD14/B2e1n1WDdq2E8Ac5lJImhiSA9evGsFSoJ+Xr8WZj8ESetj0GT+SJ5X5vx4GM5ijR/z/nCc3B0rJB5ThznqzZcgCjpi/DoY0zkYp8FYutObxTTu0KR4IQ+w/GmbCdGzd5LF3O45PMNrRpyb62um9h5Xqq8SUddXVHfxHw034OX5DS1jVZaetohncmbIfmF6J5hmjr18uKRIlUSsmU29JeG8lE8pnNnCPg1SsOtWqI+LywaxCEhe14x1RqCNuh//5S71OaxL1AhTYYN7A9alQsgZu374OGyS6e1g/FC+f1jkK8OIvT8GTVTOgOb4epcTdyGLeHZLDmOw2oHZ7wz58geGBjSUER0zbDFuK9j5O+s0rMMwf9fgr62YOkL/c0jIk25nmiNiu5Jo+az+X8/VsEomyFcHyvVPWKNuqNYi1c2r7IV8+kvCXc88eguaziaubydWGp2oxU00nimoIUeprBE3mK9Dt48uZtJIpW64ROLWqS/6/1QbWdw8fPY8TUpQif1JvkNskoac9IwotK1uqKOlVLYVDot9E0Om/ZFuTLlRWfF8iBp89foUPfKQgi5Sa2Lh377jln4MnZ8zw2bWE5LORtV2V7O/2Sph9uXbsbKiIozSsxbZaAkBAbBvZlwC2qUp2BJ3PDBfz7gJMSxNJEsawpqwEHEKaMOrSra3mcGDxR1haxjaYGePJ14z4oXCAnJgzqgObdxoEmjl1CgMmqzfulMJ5dKychU4Y03lGIF2dxFp7o5wwGdeM2ktxs1gIlvCghmyomDagdnlCZDRO+g3DrKoxtBpIv3+UD1pD6sEEIunwK5tptYa5sB0oMnmh7Ozh9LvfhMik4CTq5H/zBzST0hsQxx9Ikj5QkySAmTgYkSgYb+U8bDdH5qrL3StJTx+AYvhsyeCJvA/kdPKHq2L73GPqPDX+nmdC29dCxuT02lCaw7T8mHKvnDUP+PFml/23Bim2Y/f0mHNkU9kGI0ZCJi7Fp15F3Y9FcKBS8RC177Aw8+fmwgJ8PcihTyooK5dgXcHnbVpneWnhJh80X8Oghh7aksklmUuGENbsG4oMn5y/w2LCZl7xNqNcJa57RwOZtPM6c5ZE1i4hWzZ0njAyeeMYe74+qBngyknysuHz1FtaED8eJs1fQpud/oQaF8+fEcnIB8sfmLDwxjG4nHcAjhy6QFUfvjzr0xZq0AE90u1dBt2UJLIVKw9RhqC/U5PM5+Ud3ETyslSRHxOQN777gM3jic9PIEkAL5/KoCxRIKA9//ZIdjCQiUQvkP0UCTGwp08nSg6c7M3giT8N+CU+oSmgOE5rgNX3alO/Cd9xVFS2v+O+jp1JcdkwhPc7AE+p1Qr1Pan1DXNwLOX/JcFdm1i9+DWjhJX2AALeDBLyVKCaiamW2bxxWjQuemEly2KkzSD6UCA4N61mR71MGneL/Nbj3hDGSw4zZPN685aSqULQ6lDONwRNntCT/GTXAk/dXcenPmzj4yznp40Xp4gX8skyxdKEj+ZievSbxbfG0kNDqpFKICRFTN8GWIFF8j7O/97AGtABPaPhA8Kh2Up6EiFk7PKwRdQ6vWzsXup83wVKsIkyt+r8TksETddrLWam0cC53di1qfo7BE3nW8Vt4Ik8trvV2Bp7QfCfUzb3ltyKyZXXuguGaFOxpVzWghZf0A+J1Mod4nyRMaEP/3syDwhl4so+UJT5MyhNnyGBDx7ZMZ67+Llx9/uo1HitWkcSkBhu6dxVJZbP4YRWDJ65q2b3n1QhP3FuJ9no5A0+4iDcI6VU7oC/BarOsFuAJ1VnIkG/BPXkAU9dxsHxaRG1q9Kg8nCkSwX3q28sTD5oH8eP/MuozeOJR1Xt8cC2cyz2uBC9MwOCJPCUrCk8eP30hhcXcuvMA31QqAeqSSxOxpkmdHOW+LCRPUhX3dgaezJrD4/ETHt26iEidisETNZhTKy9pmveE5j/p1N6K9Oniv5iqQbeeliE2z5NnLzhMn2lPEtu5gxXpPmL68rQt6PjrN/G4cJFHnlwimjSK//3G4Ik3rAIweOIdPcc0izPwhIbr0LAdMV0mRA5b7Dth2czvNKAVeKLbuAC6vetg/qoqzN/2CigLBh3cAv2a2bB+kgvG/rOjrZ3BE21vBa2cy7WtZYDBE3kWVAye3L77kGTOHyCFy9BGE682q1sRNGfIjv3HcWrXfJKnwH6p8bfmDDwZP1lABAkjGEASfyYgCUBZ870GtPKS3rOXwy/HBJQuaUXF8mzv0J0TGzxZtY7HlSs8Cn8monbN+C/xvt+F/iEBfbdNm8XDaOTQvKmIHNnj1j2DJ96xO4Mn3tGz2/Dkz/MwzOgDa46CMPaa4jth2cyagyfCjd9hmNwDtoSJETFlY0BZMHhoC/CP75NwnQEkbKcCgyd+ZH2tnMu1rnIGT+RZUDF4Mnbmcuw/eoZk0e+PXiPmoF71MhI8OXPxqpRhf9uy8ciaSd0JdNxVpTPwZNioIGn4UcMs7k7D+imsAa28pB0ld1OmoGERLAwlNnhy6zaHxUsFkuPIhp6hIhImYKBJ4Z9MnMOdPsNjy3YeSZLY0KdH3PuUwRPvWIbBE+/o2V14EnT6IPSLxsJSuAxM7Yf4Tlg2s+bgCRU4pE9dqTSqse9MWLP6X7nvmLalcOUMDLP6E2iUiECjTR88wjxPoqjER5Ui5bxOtHIul7NGNfRl8ESeFRSDJ0WqdkS7pjWkqjZ12w59B0+ePHuJ0nVC8cPMgfiiYC550qq0d3zw5PVrDpOmCVIugH4sb4VqrKill/TEqQLevOFY2Nf/d09Mnidhc3k8esyjciUbvirBIJMvfmjzFwm4d4+TSkPTEtGxNQZPvGMdBk+8o2e34QkpdalfMweWMjVhatzNd8KymTUJT3QrpkH3yy6YKzWAuW6HgLCigVTKFC4cg7lKE5hrtWHwxM+srqVzuZZVz+CJPOspBk++btwHnxfIhfGD2keDJ4ePn0fnAdOxf900fJQ6hTxpVdo7PnjiSPqZOo0N3TqxS51azKill/SOXTxOnOKlMte03HWgt/fhyW+neWzdwSNpUht6d2f68dX+cLzr6Pw9Q61InixmgMLgiXcspBZ4YrPZcO3mXfxDwntz58iEDB+lwl9/3yWJsEP89lzgTM4T3dal0O1aCfM3LWGu9q13NgWbJU4NaCXnCV1E0KWT0M8ZTMqipkXEmBV+b1n+6UMED24mrTNi4lrYkiRn8MTPrK6lc7mWVc/giTzrKQZPpsxfg2Xr9mBE71ZYunYP6lcvjTw5MqP7sDAkShCCn1b7bzxvfPDkrxs8fljBI8snIlq3YHkY5G1Z5Xpr6SX99988lizjkS6tDZ07MjgQFZ4YTRwpTcwjkpTNbdZYRK6c7Dem3K/E9ZG2EYh1isCsrFlEtGoesy0YPHFdr+70UAM8efn6LRp3GiklkqfNkQ+taZfR+OfeQxzZHObO0lTfxyl4snIGdEd3wNykO8yla6h+TYEgoJbgCbWHo9Q1TThMEw/7c9NtCIdu33pYCpWEqcPwGJfKwna0vQO0dC5Xt6Y5Il7s3r8MnsiznmLwxGyxokPfyTh59o9oEiUiX5aWzhgggRR/bfHBkwuXOKzfKCBfXhsa1mcXX7XsA629pMdNEiRAwKrIRE8Yu2sPj2MneKkEOC0FzppvNUD36IwwHm9JEllaeYdW4Hm/MXjiHRupAZ4s+nEH5izdjL6dG2PJqh1o3bialA/t0LHz6DJwOvatmUqgcErvKMSLszgDT/ThIxB07hfpIkgvhKz5XgNagyf6BaMRdPYwCWFpS0JZGvtegR6UIKRnTXCRETD2nAJrzoIMnnhQ174aWmvncl/pSe68DJ7I06Bi8MQhxukLV3Hh8l948eoNsn+SAeVLFiLVZYLlSany3vHBk+MneezczaNYERHVq7LLnVrMqbWXNA1LoeEpbB/9B08u/2UCLQNOGysDrpZfFnDmHI/NW2MPo2LwxDu2UgM8KVO3O6qWL4YBXZtGC+l99OQ5ytbr4bf50JyBJ7RaCq2aEtl7GsTs+b2zKdgscWpAa/BEOHUAhiXjYyzb60+m1h3dCd3K6fGW9WaeJ9q2utbO5VrVNoMn8iynODyRJ442e8cHT/bu53DkF4Hlq1CZebX2kn74kMPs+QIMpJrM4AGB7cHkCNuZPNsEGhbHgJLKflxEHEfy2JhKbDN44h17qQGelKrdDXWqlkKvjg2jwZMr126hfvvh2LF8Aj75+CPvKMSLszgDT4KHtwb/8A4iR3wPMW1GL0rHpopNA1qDJ1zEG4T0qi1Vn4kcOF/Kf+KPzTC6PYR7f8PclIS4lYo9xI3BE21bX2vncq1qm8ETeZaTBU8WrtyOy1f/dkqCsQPa+a0HSnzwZPM2HmfO8qhZQ8QXhZnniVMbxgsPafEl7biQ1q4lonDBwN1LQQJw704wZi+0IDiYlCbuJiIkhJUm9sLPxukp4koey+CJ02qU9aAa4EnP4bPx62+/Y8OiUQgdMkuqxFe7Skm06TkRV2/ewW+7wiEIdu8xf2rOwBN66aWXX1pylV5+WfO9BrQGT6jG9MTzJIh4oJgrN4a5dlvfK1FhCYRr52GY1ge24BBE0kSx+ti92Rk8UVj5Xh5Oi+dyL6tIkekYPJGnRlnwpP+YcJw6b89x8vjpC1itItKmjp79+sGjZ9DrdTi4YQaSJk4oT1qV9o4Pnvy4hscff/Jo0pDE/+cO3Auv2synxZe0IxwiQ3obOrYLXO8TesCdPkvAoydADRIKV5SExHm3xZ2My7uyqHc2R5Wo95PHMnjiHZupAZ7Qs0HVZv1IDhyjtGiaBy0i0iidFyYO6YgaFUt4RxlensUZeJKgcyVJqrfz9npZOjZdbBrQIjwR/roEw5SeBMAlJiBuo98ZV79gJMnrchTmcnVgbtglzvUxeKJt82vxXK5FjTN4Is9qsuBJ1KkbdRwpud7Sw1DUNnzK9zhGvjrt/nEyeJ5eOPyvxQdPFiwRcOcOh3atrcj0Mfs6rpYdoNWX9OjxQTCbSY4PUvaalr8OxHbipIAduzmkTiVKuU5YU6cGaCWk6TPtyWMbNxCRN4/dVgyeeMdeaoAnEhyIiARNHHv+979Aq+9k+yQ9vq1XCflyZfGOInwwS3zwhH/+GMEDm0BMkoJ8TV/jAwnZlDFpQIvwhK4jeFRb8Pdvw9R2ECxflPMb43JvXpHfSWNwZhMiR5LwtjRxh7cxeKJt02v1XK41rTN4Is9iisGTIlU7kgz6ldCjff1oEl28cgONO4/C5u/HIEcW/4zpjQ+e0C/kz55z6N7VipQpAvOyK2+beqa3Vl/Sjq/5n5MQsFokFCzQ2pu3dq8Tkwlo28qKzJnYb0rNe+D8BR4bNvNIlIiGV1mh0zF44i17qQWeeGu9aponXnjyz3UEj+sMMX0WRA5doCbRA1oWrcKToENboV8dBjFbPkT2me43NtStmQPdwc2w5P0Cpm7j410XgyfxqkjVD2j1XK5qpcYgHIMn8iymGDyhbrm0ws7RzbOjeZj8sG4PJs1ZhQWT++CrIvnkSavS3vHBk7ETBNAvsIP7WWEg+RlYU4cGtPqSdiSO1QUBA/papMtoILWt20nVoTM8CpLXSb26lkBaumbXuoh4390m3nclv7Li6wo25nniJUv6Cp78cuoS7j8gMXVOtFqVvyLvMPIy87MWHzwRLp+GIWwArHkKwxg60c9Wr93laBWecKZIBPepTzw0jIgctRRi6gzaNcL/Jedev0BIX/sHWWPfWbBmzRPvmhg8iVdFqn5Aq+dyVSuVwRPFzaMYPFm5cR/GzVqBXNk+RuWyRZEpQxqcvXQN67YfQpJECbBv7TToaJZHP2zxwZNho+wHw1HD2EVPTebX8kvacRmtUY3k+/gicLxPHj4iFYfm2d8j44YGwcJFqmlLMVli0cCjxzzC5v5XUjrrx+S/cxxevSXxZ6x5TAO+gifU25R6nTrTjmwOQ4pkiZ15VFPPxAdPgk7sh37pBFiKlIepzUBNrc2fhdUqPKE2oaV8aUlfS9laMDXqqnkz6daHQ7d/Pay5CsLYY4pT62HwxCk1qfYhLZ/LVatUBk8UN41i8IRKRqvvhC3ZKCWCc7R0aVJgxuhufh3bHBc8efGCw9SZAhIntqFvz8BN8Kn4zlVgQC2/pB2hEKlT29Ctc+Dsq8VLBdy6zaHUVyKa1dPj4XMGTxT4KXhliF17eBw7wSNTRhK+8x3Jf8Xgicf17it48uZtJEw0MRNpPYbZvVGnjfgu2nq/GzgDos2G1fOGeVwPvpggPnhCL4X0cmguV5ckwezsCxHZnDFoQMvwRLh3C4bR7WDTGRA5ZX2cVWnUbnzqdRI8qKmU68TYawqsOQo6JTKDJ06pSbUPaflcrlqlMniiuGkUhSdUOovVilv//ItnL14jfdqUSP9RKsWFVtuAccGTe/c5zF8oIF1aGzp3DJxLrtpsFJM8Wn9Jj51Ivj5P0QAAIABJREFUwsGMHNqTRMQfB0Ai4t+v8Fizjiclz23o35tU9kphYPBECz+0/8sYNXlsyybAZwWZ54mnzecreBJ1XcWqd0bLBpXRpVXtaMulieTb9ZmMfWumkn8fU3paFV4fPz54ErR5EfR71sBcqy3MVRp7XT42Ycwa0DI8oSsyTOwG4e8/YG7WE+aS1TRrZsfvw5olD4z9Zjm9DgZPnFaVKh/U+rlclUpl8ERxsygOT67dvIPf//xbKkuYhVTf+eKz3H4bruOwRlzw5PpfPJat5JE9q4gW3wZOeIXiO9UDA2r9Je34kl+wgIh6tf1/b1EPLurJVaeWiCKFbUiRmMETD/wsPDrkhUs81m/kkYR44g3pyyPSwsJ2PKlwNcCTUrW7IXXKZNi4eHS0pe49/JvklTJ/Ym+UKpbfk2rwydjxwRP9sikIOrYHpua9Yfmyik9kZJN+qAGtw5OgY3uhXzYJ1nSZYRy2SJMm5t6+tnudGCNIuM5kErbzmdPrYPDEaVWp8kGtn8tVqVQGTxQ3i2LwxGQyo9uQWTh68mI0IZMmSSgli/XnkoRxwZNz53ls3MKjQH4R9ev4/wVX8R3qwQG1/pJ+8pTDzNn2/B+D+lsRbPDfZMSHjgrYf4DDR8SDqwvx4KIHXF/DE6pt/yy+7sEfHRnaka+nXCmgXDmWB8qT2lYDPBkzYzlWbd6PVg2roEq5ovg4fRqc+/06xsxYhodPnuPghpmqynny76OnUp62BCHBTpuGll9+TrxtM6ZL/S5hfnzwxDBnCIRLJ2Ak+WGsBUo4PRd70LMa0Do8odoJ6VkTXCQBD8Rjg3puaK3pti6FbtdKSXZXvE7oOhk80Zq1o8ur9XO5VrTPqu3Is5Ri8IQmi6VJY2m54nJffobkJAEcdctd+ON2ScJDG2f5rQdKXPDkl2MC9uzlUKK4iKpfM3gib7sq29sfXtJLfhDw9y1O2lt0j/lje/3aXprYTO7ZHdpakTGDTRXwxB917Y01RUse28mK1Gn8F/p5Q59xzaEGeGIkH1Y69J2C387/+YGo4wa2B622o4Z2/eZdtOg+Di9evpHEKVOiIGaO6hZnJaBdB05gNIFAjj5rwoe/+1AULzz5f3iFs1VE1KCjQJDBH+CJbt086A5shKVEJZha9NOU2aJ5nXSbAGvez12Sn8ETl9Sluof94VyuOqXGIBCDJ/KspBg8oa65ObN+jMXTor+od/98Er1HzsX6hSORJ0dmedKqtHdc8ISCEwpQaHlOWqaTNfVowB9e0pd+57B2g4AUyW3o0c0/99eGzTxogtwC+Yj3Vl07IFKD54l6drL2JDl4SMCBQxzSfURyQXXwz32rBquoAZ449HD+8l+4QP7/6fNXyEByoVE4QcN51NLqth2KhAlCED6pF/659wgNOgzHgK7N0LROhRhF3LH/OPqNno/6NcpIH41oxaAEIYZ3HivxwZOQIc3BPfkXkaOXQUyVTi1qCHg5/AGe8I/uInhYK8mWEdO2wBaSQDN21e1cAd22HyBmzIbIwfNdlpvBE5dVpqoO/nAuV5VCYxGGwRN5VlIMntCkcN9U+hJDejSPJtEf12+jXrthUujOV0XyyZNWpb3jgic0ZIeG7tSuKaLwZ/7pGaBSs8Qrlr+8pMdPFhARwaF1CxFZPvGvPXbnLocFi+2hSf16WZEokd1LgcGTeLe3qh8wBAVh3BQb+WLPoVoVEcWL+te+VYvy1QRPqE4ePn6OSKORJIhNpSpPVAp06AegRVP6osQXn0rm6zViLu49eBxjNSAbqRJUtl4P6YPRwil9YjR3fPAkQedK9svtDHK5NWjncquWve0pOfwBnlDdGGb0gfDneZjrd4K5Qj1PqUvRcTnjWwQPJLlOIt7A1HUcLJ8WcXl8Bk9cVpmqOvjLuVxVSo1BGAZP5FlIMXjSvs8UnDh7GWvDRyBXto9JFUoOj5++QP8x4Th+5jKObZ8rxRH7Y4sLniz/kce16zyaNRGRKwe7IKjJ/v7ykt6zj3g3/Srg07w2NKrvX1/x54YL+PcBh/JlyWWl9H9rY/BETb8k12VJHBKEP65xCF9ig14HhH5nRZIkLHzHdU3G3UMt8GTZuj2YvnA9aG40R6tQqjBG9G6tinwnV67dQv32w7Fv7TSkS5NCEnH2kk3YuOswDqyb/oGSH5FcLRSe5M+TFWYSTxgRaZQ+DvXs0FDyPpGgiNGKZ69NMRqIM0UipPs30t+9nbdXabOz8WRowF/gSdCZQ9AvHAMxdXpEjvpBhka811W3ezV0Wxa77XVCJWXwxHv28sRM/nIu94RulByTwRN52lQMnty++xA1WgyA1SpCT07DFJRQeEJb93b10OFb+0HBH1tc8ISWKablijuSXA0ZSK4G1tSjAX95ST8nFWimkUo0tA3oY0WCBP6xzxzJlqm3CfU6idoYPFHP78gdSSg8IYQdi36wgpagzkaqkbVk1cjcUWWcfdQAT2iyWJo0NkumdPZQnRTJcOTEBemjSt6cn5APLsOljy2+bMdPX0bb3pNwdEsYkidNLImy6McdCF++Fad2hX8gGk142+y7MShfsjDKf1VIShY7feE6sr7PEDYmVHpeJN4pFmvM72Lb438REdoQSJkWCcLW+XLpbO73NEB3YpDAw0zOslpvbzvUAF6/JJVrpoPP51ruEK+v3WRERJc6sJFKO4Y+4yEUdi8XksDb3yVW0T/OQV63g48n1JHfnoX89pj1PGsIfRDv2Qn8fHTF4AnV07MXr6QDx6U/br4rVdy4dnkUzp/Tr9UYFzyhl1p6ue0ZaiVJdNnrQE0bwV/gCdXpDyt4/HWDR6XyNpQqqX3vEzP5QD11hkDeIxwaEm+afMSrJmpj8ERNvyTXZXHAk/sPLZgRJoA6JDRpKCJPbu1fWFzXhud6qAGeVG1mz4O2a+WkaAtdvGonpoWvJf/7RGTKkNZzSnBiZIfnyf510/BR6vg9Txzw5OCGGe/ytixdu1taz/l9iyUYZDSLePU25lLc3M0/IYzrAlvmnLAOmeeEhOwRb2lAIJXcqPfC81cxew15Sw4l5uE3LQG/cyXEwiUhdh6pxJAeG0PYsxbc+nDYSIll66glbs+TIJiAeQIu3xLPL9a0p4HkifV4Sd6b1ljAs/ZWpE6JUyW1e0iy5p4GFIMn1MX1weNnGN2vjXuSaLhXXPBk2CjyIidtyECL5J7Omno04E/w5Ar5er9qHY+kSW3o3V37h4a9+zkc+UWQvLWo19b7jcET9fyO3JHEAU/o5fLEKR47dvFIlNCe9Fivd2dE1icmDagBnhT6uj2a16uEXh2Jp0WURr1VKViZN6EnShcv6FMDOnKeLJ7aD8U/zyvJ0nP4bNx/+DTGnCf0Q1HJWt3w/fQBKFoot/T8wpXbMYOEJl088L1UrjiusB1aopiWKqY5HWhuB9bUowF/CduhGuWePkTI4GaSciMnroGYxA4G1dY4s9Ge6+TNSxg7joD1M/e8Tui6WNiO2qzrmjz+dC53beXefZqF7cjTt2LwpG2vSZLnycbFo+VJpMHescETE/lwMWaCHZ6MGkbqrLKmKg3420t64lQBb95waN5URI7s2v2CHzUMqXNHK0ku+aHHFoMnqvopuSxMVHhCOzvCG4sVEVG9qnb3rsuK8HAHNcATmjD+IfmwcmjjLAkqONqkOavwA8mFsm/NVPIbT+lhTcQ/fO3WQ5A0SUIJ5ty5/5jkQBmG/t81JZV0KkqdW3YfL1UJouWVaaProoljl80aJH046kjKMX+UJiVWzB4s/X1c8CTo2B7ol02BpTgpJdtSW6Vk49ektp/wJ3hCLWGYOxTCxeMwf9MS5mrfqtI4tKwyLa8spsuEyGGLZcnI4Iks9fm8s7+dy32u0FgEYPBEnmUUgycLVmzD7O834fSeharKoi9PPc71jg2ePHvOYfosAclIuE4vErbDmro04G8v6f0HOBw6KiBXThHNGit8AaXhkQoPGdtuoB401JOmcCERtb+JeVIGT9T1W3JVmvfhycOHHGbPt+ft6dTeivTpWIijqzqN6Xk1wJMjJy6iU/+pUi60LwrkImEuSfHLqUtSTjSaA2Xu+J5KLFX2GFdv3EHzbmPx+k2ENFapYvkxa3SoJDdtZep2l8KLlocNkv588/Z9KU/Kg0fPpD9n+yQDqSrY+13YT1zwRPfTWug2LYS5UgOY63aQLTsbQDkN+Bs8Cbp0Evo5g4nXSXLifbJWOUUpOFJIn3qS14mpw1BYCpWWNTKDJ7LU58POFKzb4G/nch8qNM6pGTyRZxnF4AktSdyQuNs1rlUe1SoU/0Cq/LmzQiCJgPyxxQZPHGVW06e3oVM7Bk/UZnt/e0lH9diIWtZXbXqPS55btzksXiqQC4uN5AkSkTCW5LcMnmjJqh/K+j48oU/s2sPj2AkeqVPb0K0ze18qYWE1wBO6DgpLJs9djRu370lJ5VMkS4xGNcuTRPI13sEJJdarxBh3/32MxCThvbPVAe8/eIKgIOFd7hOHDHHCE5LbQbd/vQROKEBhTT0a8Dd4QjUbTEJ3eBLCIzckxhNWCjq4Bfo1sxWrCsTgiSes5L0x/e1c7j3NuTYTgyeu6ev9pxWDJ407j8LFKzdilebI5jBVlCSUp66Ye8cGT/68ymPlal4KoaChFKypSwP++JJ2lMYuW8qK8uW09/U+bC6PR495VK5kw1clYr9AM3iirt+Sq9LEBE9omON0kjyWhp59XdGGkl8ygOKqXt9/Xg3w5P18aDTUxdfVdeTq1Zn+ccET/dIJCDqxXwrZoaE7rKlHA/4ITxwlgK15CsMYOlE9yqZgp38j8C+fwtRuMCyfl5UtG4MnslXo0wH88VzuU4XGMjmDJ/Ksohg8uXbzjlSuL7ZWKH8OUv7N7pathvbi5RuYLRakSpFUtjixwZPTZ3ls2cajUEERdWoxeCJb0QoP4I8v6T+vEWC3ikdCknyzf29tXT5PneaxbQePFMntiUPjagyeKPxj8PJwMcETKsKVP0ji47U8+ZIP9OhqRZIk2gOAXlZlnNOpAZ4Eaj60uOCJYdYACFdOS8liadJY1tSjAX+EJxwpVxzSt56k5IixK2FLkUYVCtcd2Q7djzMV8zqhi2LwRBWmdVsIfzyXu60MD3Zk8ESechWDJ4eOnZc8S/LnyRpNIpqt/udfzqLBN2VVkQuFxjO36zP5nZcMTQC3jMQwO8oTvq9OWinAROtovtfWLRiBvDk/kf7X2ODJEZJ/Yi/JQ0G/oNIvqaypSwP++pKePF3Aq1ecpkq/RpCSxAuXcHj8hJfytdC8LXE1Bk/U9VtyVZrY4AkdZwWBf1cJBMyaRUSr5gw6u6rbqM+rAZ4Eaj60uOBJ8LjO4P+5jsiBcyBmyinHxKyvwhrwR3hCVaRfNBZBpw/CXLE+zPU6Kqw194ZzhBOZWg+EpWh59wZ5rxeDJ4qo0WeD+Ou53GcKjWViBk/kWUQxeELDdgrkyYZBofayaI52++4DUo6wPzYtGYOcWTPKk1aB3lPmr8G6bQexiVQFSpggBI07j0SWTOliTVp3gySFs1r/+wp+7vfrGDFlKX5aPUXKvE9bbPDEEcNfuZJIQhDYJUAB8yk6hL++pA8eEXDgZw7Zsopo+a029h31lqFeM7lykGS3TeKXmcETRX8KXh8sLnjy8iWHGbMFEMdANKhnRf5PGXh210BqgCeBmg8tTngysAn4548RSbwARJV4Abi7x/ytn7/CE+HaeRim9YEtOAQR07f63GxBv+6GfvlURb1O6KIYPPG5aWUJ4K/ncllK8UBnBk/kKdXj8OTMxWtSBvst349F9iwZ5EmrQO/yDXqiavli6Nu5sTTahh2HMWzyElz6+Xun4rBpecK0qZNHgy2xwZN1G3lcvMSjXm0RBQvEfyFUYHlsCBc04K8v6devOUyaZg+R69XdimRJ1X35/O0Mj63beSRKRBOFiggJiV9eBk9c2OgqfDQueELF/eUYjz17eWkv9OomwhAc/55Q4TJ9LpIa4Emg5kOLC54k6GzPc/J23l6f7xEmQHQN+Cs8oasMHtYS/KN7MLXqD0sxewluX7V3Xieu5v2xF2WJtTF44iuLKjOvv57LldGOcqMweCJPl7LhSfs+U/D0+UvQnCchwQZkTJf6nURmixV//X1Xgg0H1k2XJ6lCvQtUaIMRvVuhbjV7OTQH3Dm6JQzJkyaOc5bDx8+j84Dp2LVyolSy0NFigyc/rODx1w0ezZuJyJGNwROFTKjYMP78kv6R5I34g+SPyE72XQuy/9TanjzhMCfc7mXQuoWILJ84JyuDJ2q1qHNyxQdP6ChzSOniB6SE8ReFRdSs4dy+cG72wHjKGMnho1Q6hBh8l2uMWu0vjeVDU2p3xAZPuIi3COlVSzUeAEqt11/G8Wd4EnRwM6lsMwfWLHlh7DfTZybTbVsK3U577hWag0XJxuCJktr0/lj+fC73vjZjn5HBE3nWkA1PugycLiWKvUJKFYcE6/FJxo/eSURhypdF8qFquaJI//8QF3niyutNs/znK9cak4d2JuWUi0mDXbl2C/XbDydAZBIBIrEn0aJ9KzXqjc8L5MLEIdHjRV+9JTe/GNqkmTbc/xfo041DhvTyZGe9lddAwpAgRBgtEP3wXnbnLjB1tv3zTKumHArmV15/Sow4ZZYNd+8D5UoBNavRT0rONZ5UPQ8xBOFNRMy/PedGYU/5SgMGHTEgMbfRFPuP7/YdUn1njn0Ph3bikCWzr6TV3rxPnnJEdyJ6dxXIv2tE1z5sDx8/h14XRDzgEkWT4m2EEQ8fP0PmjGmd8vr04RLcmjo2eMI/uks8AFpBTJUOkaOXuTU26+Q5DfgzPOEi3pDqNg3BmU0wtR8GS2HyD6+Xm3DxOAxzh0qzGruMhjV/cUUlYPBEUXV6fTAGT7yjcgZP5OlZNjxxTL98/U9InzYVKpQqLE8iD/emnicj+7RGnar2fzSc9TzZvPsoBk9YhP3rpn2QXPZVxIcJZenYw8fa8JIk7hwxkENSlYdOeFjtqhw+Ibl8R5isEAkY88e2fbcN+w9yIKl9MKgvSI4fda1y604bfj5Mvo4TJ67+PV2Tjec4hOhJWVsCv1jTngb0QfRCz8FEvBPjaus3kxCe43Brj2hPK8pI/DbCDp0eP+bQrYOAgp/6Fp7Elg/t8tW/0aDDiHg/XCijFe+PEhs8EW78DsPkHrBmzQNj31neF4zNGKcG/Bme0IXrtv1AvD5WwKYPhnEASVicLpPXdgQNGTKM6wQuMgLmKk1grtVG8bkZPFFcpV4dkMET76ibwRN5elYMnmzZ8wtmLlqP+RN7R0sMS8sUJkwYjFmjQ+VJqlBvmvOkWoXi6NOpkTTi+u2HMHzK93HmPKHhR2XqhqJy2aIY3qvlB5LEFrYzbBSpt0naqGHsgqeQ+RQdJhBe0mHzBDx6xCFPblGqvqOWdus2h8VL7eEE3bqISJ3KNdlY2I5aLOmeHM6E7dCRjSaSPDaMx5s3pGrZV6RqWQX/BJ3uaTHmXkt+EPD3LQ7Jk9kwrL9AEij6LmyHShh7MvmHJJl8P6yeN+yDKn1K6sNXY8UKT879AkP4CFgLlICRJNpnTV0a8Hd4QrVtmNkPwh9nJe8n45D5sBnsX1bo29V5/0/X7MaZIgk4IVWmHtyBNXchGLtPcm0AJ59m8MRJRan0sUA4l6tB9QyeyLOCYvCEHpBsog1rwodHk2jbT79iwLgFOLxpFlImTyJPWgV6T563WgImm0n1nwQJgtG4U/RqOzMXbcCegyexc8XEd7MtXbsb08LX4uCGmVI55vdbTPCEll4dP1mAQW/D4AFxf11VYFlsCDc0EAgvaZozguaOoK1RfSs+zev7y2ekkcOsOTxoYtvqVUQUK+oaOKFrYfDEjQ2voi7OwhMqMs0bRfNH0da2lRWZM/l+D6tIldFEWU+SlF8gScqDSYLdju1EZM/ku5wnC1dux5u3kVi77WekSZkcZb/87J2sJpMZPx06hSfPX+G3XeEQBN96x3jCnrHBE93RndCtnA7zl1Vgbt7bE1OzMWVoIBDgCQ3fMYzpAP7pQ1jyFYXpu7EyNOZcV334SASdOypVlzIOWQBbSELnOrr4FIMnLipMZY8HwrlcDSpn8ESeFRSDJ1/W/A5NaldAtzZ1o0n0+OkL4rXRHcvDBqNw/hzypFWg98vXb0G9YajLMG3p0qSQZEuXNqX054HjFmLH/mO4sH+J9OeISBPo2prWqfCuQs/7YsQET2gizJlzBKRIbkOPbgyeKGA6xYcIlJf0oaMC9h8gYS6kckmPrs5Vs1Fc2VEGXLOOx+9XeFmllBk88aSFPD+2K/CESrNzN4/jJ3nixWhDKPFUcqYik+dXoa4ZDpES5ftJiXLa2re24uOPbfBltZ1i1Tvj9RsSQxRDo7CkSMHc+LZ+JZT7spC6FKmQNLHCk90/Qrfle5grN4a5dluFZmPDKKWBQIAnVFf8vZsIHt1BUpu5TnuYv26olAo/GEf38ybo1s6FTWcgoUJhENNn8dhcDJ54TLVeGThQzuVeUWYckzB4Is8CisGTGi0GStV21i0YEU2iddsPYsSUpR9UqJEntvzeT8kXL5PZ/EH+EndGjgme3PqHhCV8L+DjjDa0b8PgiTt69XSfQHpJz18o4N5934fvnDvPY+MW+5fx7gTkJEzgnhcBgyee/nV4dnxX4QmVJox4UD0inlQ5soto3tR1byXPrsi3o18mMHI1gZK01a8rokA+u358CU8cGqGepzmyZETbJtV8qyQvzx4rPCHVTnSk6om5YWeYy0X/2ORlEdl0MWggUOAJXbpw6gAMS8ZLWjD2mgprjgKK7wnhxhWS48cetm/qMByWQiUVnyPqgAyeeFS9Hh88kM7lHldmHBMweCJP+4rBk1mLNyB8+Ta0aVwNVcsXRZpUyXHw13OYtmCtJOHRzbPB856KppSnBLm9Y4InV8hhdhU5zObJRXJNNGIHfbk69kT/QHpJP31Gc0f8P3yngYhP83h/T754QcJ15gogzBLNGovIldN9GRg88cQvwntjugNPqDffbAJQrIRFf1NdRJHP3d8/3lup52f6h4D6hQTU01a6pBUVy/8HJNUAT879fl0KlW3duCqyZf6v7NykOavwEfH8bNGgsueV5IMZYoMn+kVjEXT6IExtBsJSpLwPJGNTxqWBQIInVA+6VTOhO7wdtoSJSDjNIojJ7F7YSjTu1XMEj2oH7vULmCvWh7le9EqVSszx/hgMnnhCq94bM5DO5d7T6oczMXgiT/uKwROaVLVd70n47fyf0STS63VYOLkPviiYS56kKu4dEzw5dZrHth08ChcSUfsbdshXo/kC7SV97ASPXXt4n4XvLFgs4M5dDl8UFlGzhrzfBIMnavxFOS+TO/CEjn76LI8t23iSIwPo2smKlCnd81xyXlJ1P/mMAMl54TwiI4lXGQGiTQgYjdrUAE869Z+KK9du48C66dFym8xfthVhSzbi1K75pCJYsLoV7YZ0scETw4y+EP48B2PoRFjzqLs6oRvL1nyXQIMn1GDBNJHrP9chfpwdkYPmKWZDw6RQCDevwJo9P4y9pyk2blwDMXjiFTV7bJJAO5d7TJHxDMzgiTzNKwZPHGKcPPsHzl++LiWKo1+ZSpcoiKSJPZMYSt7SlesdEzw5SOLPD5D48/e/BCo3q/sjeTKjuvtSeb9nIL6kFy4R8M8d74fvHDxMfg+kbHIyUgEktIsVQfZCVG43Bk/cVp0qOroLT6jw1KOPevalTmNDNwJQArXRpOThi3k8fcohfTobOrX/UBdqgCelandDrSol31W4c9jr/sOnqNiwV8BV2zGMbg/h3t+IHBIOMUPWQN2+ql13IMIT/vkTkkCWeIi8eQ1zyeowN+sh2z66/4eniUlTwjh0IfFs+bDYguxJYhiAwRNPaNV7Ywbiudx72v1vJgZP5GldcXhCxXkbEQkduR3pdDJvSPLW5rXeMcGTHbt4nDjFoxqpJlLcjWoiXhM+gCcKxJf0c/KlOuz/oTONvBS+c4fAmgUE2tBGL3j0oie3MXgiV4O+7S8HntDyxWFzebx8yUnvVvqODcRGf1P0t5UksQ1dOonEe+PD35Ua4AkFJHlzfYJZo+15DxztyImLoF4pGxaNQu7smfzOhLF5noT0awAazhA5cQ3EJCn8bt1aX1AgwhNqM+HaBRim2as/GUlImVVGSFnQ2aPQLxgpjRU5cC7ETN4rFsHgibZ/gYF4LveFxRg8kad1xeCJhQSi0xjmdSS2mZYhHBT6LZrVrYh67YaRcr06/Dh3qDxJVdw7JniyZr2A3y9zJHmflSTvk39ZVPHyNStaoL6kT58hoQ/bvRO+Q14FpCyxIF10K5FcDKVITgYlGoMnSmjRd2PIgSdUahr+RcPAaGv5rShVbgqk5ihJbNDb0KGdDalTxbx+NcCT/mPCsX3fMSye1k+qsEMr7dz79zG6DZmFazfvSKWKaXivv7XY4EmCzpWkpb6dt9dDS6a55diZw13lBio8ofrS7V8P3fpwO/QYusCtqji0io9hQjdwZqNsCOOODRk8cUdr6ukTqOdyb1uAwRN5GlcMnuzYfxz9Rs9HGRKmc/rCVYS2rSfBk027jmDIxMU4uiUMyZN6x21Pnkpc7x0TPFnyg4C/b3Fo1VxE1iyBdbB3XYO+6RHIL+llK3lc/4tHvrw2NKyvDNCIyYrrNgi4+DuHTKRsajtSPlWpxuCJUpr0zThy4QmV2hEaScsWdyPlixORMsaB0Gg4KF07bfGBIzXAkyfPXqJK077EI9UogRN6Dnj89IUk/+DuzdG0TgW/NFtM8IR7/RIhfetJyTkjpmzyy3VrfVGBDE+o7fThIxF07ijEFGlIAtkFsIU4H3bPRbwh4T8dwD99CEvZWjA16ur17cDgiddVruiEgXwuV1SR8QzG4Ik8bSsGT6iHSeaMH2HaiC74p/5FAAAgAElEQVSo23Yo6lUvI8GTO/cfoXKTvlgxezAK/a+984COomrD8JsNSQi9d1GxIAgi/HQLvYP0JogU6UV6bwLSi/TeBSlSpagUEVSagohSBEQRRHonffPfO3FDyobs7sxOSd45h6OQufd+83yzszfP3FJIv6F76rC4V9qZPJk5V2yrecMHXcWc/Oxibj4P8xFIzg/phw99MH22DSEhPmgmpu8U1Hj3netiS9m1G23K1rL+4u149852pE+v3eeA8sR8nyd3ItJCnsj2Fi/zxV+XfJSRJ1IkJPXj7O82rFoTtSWxXIhcLkj+tMMM8kTG9/BREBat3o7jv55TJEq+vDnRtG4FFCv8cpJNmTN5Yrt6Sew+0g727HkQPHJpkr12K19YcpcnPqHBCJALyF67jIhXiiLkw4kupzNgen/4njmOiOcLIKT/DJfLaXki5YmWNPWvKzn3y/WkTXmijrZm8qREjY7o1Kou2jWv6VSeJNV5zRK/M3kyfpKv6CT6oH+fiGTzRlTdrah/6eT+kP7lVxvk8P/UqcQirl3tyi48WhwHvvPFrr1R25JnyWxH08aRmgtEyhMtMmVcHVrJEzkdbOY8IQHFbjM1qtpRpnTSFShnzwlx8lmUOHmjTASqVUn882oWeeK405LTemjO5InvuRNiXYm+iHjpNYT0nmLcB5AtJ0ggucsTCcZ2/bIQKF3gExKEsGrNEP52Hdju3xFzzR7A5+5N+Ny/C9y/Lf57R2xDLP7fblfOtV06B2WBWLFjT2S6jIbcZZQnhmDXrNHk3i/XDGQiFVGeqCOtmTxp2e1j3L3/EFuXjUWj9sOjR558NGUZ1n2xDz9+uQCBKf3VRWvS0s7kyfBRUYvljhoebtKoGRYf0sDqdTacOWPDq2L6TlOV03fu3vXB55tsuPR3lDgpK36RrS5+ofXGQXniDar61amVPJERnxb372fiPpZH544RyJk9camg35Vq09LvQpx8+p84KfSqmGrX0LUpcGaQJ8l1PTRn8iTF8f1iIc3RCC/6FkI7DNfm5mAtmhKgPInC6fvLQQTMdf0ejUybAZFCnMideiKee0XTnLhTGeWJO7TMdy775frkhPJEHWfN5MnJ03+gWedRSJ8uNYKCQ5H/hWcQEWHHqd//ROum1dGvczN1kZq4dFx58uixDyZM9lXe5A/q51on18SXl2RD40NavEgS9+oMMX1HjpKSu5Y8kzsSucUfd4+fjtuw8ysfsVi0D9KkiUSj+pFeXeuH8sTdDJnrfC3libyyL7bbcPQnGzJlikRXIVD8ktD6o+fO27BydZQcckecyPPNIE+S63poTuXJt1vhv2Ymwt6ujbDmH5rrQ8loFAKUJ09uhBSbF8Fv/zbYM2QBMmRW5Ijyx/F38V/5d3vGrKa5eyhPTJMKjwJhv9wjbG4XojxxG1msAprJE1mrFChjPlmJ0+f/UsRJlkzp0bpJdbwv/thsUW+jk+IRV57I9R5mzfNVdkCQCxnyMCcBPqSj8nLmrA2r10b9ciYPKT/kQrIFxZ/n8j5dpEj5suULH5wWdcijgFg7pV7tSM2mACV051CemPMz5WpUWsuTcDHAb+4CscbOTZuyDohcDyQpHHJRZ7m4szzcFSeyjBnkSXJdD82ZPPH7Yjn8dnyKsJotEVbn/aRwiya5a6A8sXZKKU+snT/2y/XJH+WJOs6aypOYoURGRsLHJ+kKk5jXGlee/PmnDUtW2PCs+MWzXWuOPFF3i3qvNB/ST9jKXzp//iUSJ0/acPfek89t2rSReLWAkCliu+28eWKLFLkGw+YtPpAjreSisLVqRKJoEX1+aaU88d7nQo+atZYnMubrYoHuuQt8hbgHWja34+WX9LkXvcXrwh82LP80SpwUFlN1Grs4VSdmPGaQJ8l1PTSn8uSz6cqb/NBm3RFe7h1v3TqsVwUByhMV8ExQlPLEBElQEQL75SrguVGU8sQNWE5OVSVPLvx5BfcePHIpgiIFX1S2KUyKR1x58qvYmnWd2KK14Ct2NGti7Q58UsyX45r4kHae3b/EmiUnfvFRthiWC3E6jnRSpIjRKC+9CPx6CjgmpurI4xkhVRo1tIstSN2f7uPp/UV54ik5c5TzhjyRV3bshBR6UfdlowZ2vFbIms9fKU7kGidSBHkqTiQDM8iT5LoemjN54r9gFFIcPyDWOxkm1j152xwfRkYRiwDlibVvCMoTa+eP/XJ98kd5oo6zKnki1ziRU3VcOQ5snolMGdK6cqrlzokrTw4fsWH7lzaULGFH7RrW7LxbLgkeBMyHdOLQ5OgSKVKkEHR2VK4Yibff1H90FeVJ4rkz8xnekieKQBFSb/MX/23n+47Yzvd1az2D/7gYtcaJWnFiFnmSXNdDcyZPAqb0hu/5k2Knnclix50iZv6IJtvYKE+snXrKE2/nT36feu9FOPvl3s5fVP2UJ+o4q5InF/76B/fuuzry5IVkM/Jkzzc++PaALyqUj0SFt/X/xVLdLZF8SvMh7XquQ0OjdjU58QtwXrwVz5AhEs3FqKqcOfQbbRIzWsoT13NnxjO9KU/k9f4sRqBs/G8ESu2adpQsbg2BElOcvCJGLr6rcuSiGUaeyHwkx/XQnMmTlCPbwHbtMoKHL4Y9Z14zfjSTfUyUJ9a+BShPrJ0/9sv1yR/liTrOquRJQk0/DgqGX4oUYseDqO16k/oRd+TJ1m02/HjMBit12pN6jpxdHx/SnmX94UMfBAREGrqjCeWJZ7kzSylvyxN5nTGn8FStHIk3y5pbZF8Ua2XJESdy8Vs1U3Vi5tgs8iRmTMllPTRn8iSwb334PHqIoEkbEJkmnVk+jowjBgHKE2vfDpQn1s4f++X65I/yRB1nzeRJuBhjPHH2Z1i/7VuxXWkYBvdoiRYNKkOutB/g74fVc4api9TEpePKk9XrbDgj3tI3bWwXi21a442nifF6LTQ+pL2G1usVU554HbFXG9BDnsgLiDkCxcwjAeU6QytW+iJMiBNPdtVJKFlGyROuhwY4kyepOldRUvV47i6vfr5YuecEKE88Z2eGkpQnZsiC5zGwX+45O3dKUp64Qyv+uZrJk+17DqH/6HkoV6YIfvrld/Ro11CRJ5t2HsDQCYvx3ZaZYkHJ5LHmyaKlvrgkOsNypx254w4PcxLgQ9qceXElKsoTVyiZ9xy95IkkIBc+Xi8W8JZH6ZJ21KxuLqHtLXEir9coecL10OLLE9v920g5oCki02ZA0MT15v1wJvPIKE+sfQNQnlg7f+yX65M/yhN1nDWTJ3KEybN5cmDqyC5o0G4YGtYqp8iTy1dvoFrzfvh01hAULfSSumhNWjruyJPps224dcuG7l3syJrFXB11kyI0JCw+pA3BrkmjlCeaYDSsEj3libzImCNQSomFvGuZZCFvGdeuvT548MAHRV6zo2E9bb8vjJInXA/NiTy58gdSjumIiFzPIWTYQsM+e2z46QQoT6x9h1CeWDt/7Jfrkz/KE3WcNZMnJWp0RKdWddGueU2n8mTDolF45cWkuUBaXHny8URfZYvXgX0jkCoVR56ou0W9V5oPae+x9XbNlCfeJuzd+vWWJ/Jqfjlpw+ebonYJKFbEjnp1tRUV7hC7f98Hm7b6QG5JLA9v7cxmlDxxh4Xe54aIacU3b99DruyZ4ePjfBcxLWKKO23H9/QxBMwYgIj8ryOk5yQtmmAdXiBAeeIFqDpWSXmiI2wvNMV+uRegOqmS8kQdZ83kSctuH+Pu/YfYumwsGrUfHj3y5KMpy7Dui3348csFCEzpry5ak5aOK0+Gj4paKHfUcDGBnYcBBGSHOHFpxYe0AanRqEnKE41AGlSNEfJEXmrMKTxyPSq5LpXeh9zKftceG8Tv8MiQPhIN6tvxnJemd5pFnuz74Wes2rgbf13+F706NEaNiqUweNxCZMuSET3bN9IlBXKh2nEzVylxyMNfrMU2b0JvlCpawGn7N27dRfmGPeP9bNbYD1GhbFEMGDMf23YfjPXzvLmzYeeqicq/xZUnKY7uhf+ScQgvXgGh7Qbrcs1sxH0ClCfuMzNTCcoTM2XD/VjYL3efmSclKE88ofakjGbyRG5FKOc5p0+XGkHBocj/wjOIiLDj1O9/onXT6ujXuZm6SE1cOqY8kcOvJ03zRerUkRjQx9y7O5gYqS6h8SGtC2avNEJ54hWsulVqlDyRFyi33P5MLOotj1fyi+2Am+ojUG7d9sEGMfLl8pWo0Q5y+lDVKnaxM533sJtBnhw7+Tve6z4WqQIDIEd9DOj6rjKld96KrZi5ZCOO7pyv/Mzbx6GfTqFdn4lYMKkvShTJj1HTVuCrfUdwePs82GzxR6Bcv3kXFRr1FFORuyLfszmjw8udI4uIN6Wyxttfl69hzMB20T8LTBmAPDmzKn+PK0/8vtkIv3VzEVa+HsKadvX25bJ+DwlQnngIziTFKE9MkggPw2C/3ENwbhajPHETWJzTNZMnsl4pUMZ8shKnz/+liJMsmdKjdZPqeF/8cdY5URe6eUrHlCdXr/lg7nxfZM8Wia6dKE/Mk6X4kfAhbebsPD02yhPr5k5GbqQ8ke3/esoH6z6PWkT2hXx2lPgfUNCLO6Pt/84Xu8XaJvLIlEmMNhFrm+TNk/joOLVZNoM86TJoGu7ee4hVs4cqu+851kM7e+FvZYrv5ws/QoGXnlV7qYmWlwvX/3rmIjYvHaOce/XaLVRu2ifB9dgc8mTTkjF4OV+eePVLeXJHXNfCyX2dth1PnmxeDL+v1iCsbhuEVX830Xh5gjEEKE+M4a5Vq5QnWpE0ph72y/XhTnmijrOm8iRmKHKIrDfnE6u7bG1Lx5Qncg778k9tyPe8Ha3f0+eNprZXk3xq40PaurmmPLFu7mTkRssTGcNvp23YtDlq+ow80qWLRMniQPH/2cXIAm3EhpTpG8Vok2vXo8TJm29EoGolbep25Q4wgzwpWrU9PvygofIiJeZi8o5pMXotJt+m13ix4186ZVF7x/Fq+daYNKwzalYqFQ+nQ54ULpBP2Smw4MvPomXDKtG7Bkp58s0Px1Gs8MvIlCGdqKM03ipVOLqeePJk5RT4/fAlwlr0QtibNV1JH88xgADliQHQNWyS8kRDmAZUxX65N6DL30WjRts6DsoTdZy9Jk/UhWWt0jHlyYlfbNggOuSFX41E44YceWLmTPIhbebsPD02yhPr5k5GbgZ5IuOQ4uT4cRsOHfGBnFYjD18xIKVwITveKBOpjCD09Ph6jw+++z5qdIvcda1Rw0jkzO55fZ7EYQZ5Uq/NUGTOlA6Lp/SPJU8+27xHGan6w9bZynRfbx9S3BR8+TmMGfBkms1rldpiSI+WaFq3Yrzm7z14hI+mLEfObJkg/3/7nkNIlyYVvl4zGQFivZTVm/bg3MXLCAzwx8+/nceJUxcwql9bMbLmbaUu+QIpwv4k30GTByHip+8R2GcsfIu/6e3LZf0eE/CBr5jGFWHnyy+PERpY0PbfItB28fnjYT0Cvjbbf89N5s+b2UvhG1umeLOtpFg35YkGWY0pTw4esmHn1zaULmlHzer88tUAr9eqoDzxGlqvV0x54nXEXm3ALPIk5kWeO2/DwUPA+f92wJE/e0ZMrSldyq7I8ISO0FAo4uXuHR/cvuuDkBA7zl2w4cp/a5tULB+J8m8bI9LNIE82f/kdhoxfhGrlS+Loz6dRrszrypTehau2KSM15k3o49V7zVG5HHkiR4hMGeHayJO4Qf3+x2XUbzsUS6cNRMmir8SLuX3fySL/9yF3FpRHcKgd9x//N6xJ/D3F+G6w/XEa4QNmwP7Cq7pcMxtxn0AK4TvTpfbH7fvig83DcgRSB4pFpIQ4eRRszDPXcsBMFnDmdP649ygU4UyfVzOTLYP31xnz6gUYXDnliQYJiClPHG8bK1WMRLk3+enXAK/XqqA88Rpar1dMeeJ1xF5twIzyxHHBt2754KAYiSJHpIT9t2FamjSRKF7UjgwZfXDndiRu37Hhzl3g9m3gcZDz7W5z5YpEw3fsyKpi9IraJJhBnshrWLR6O2Ys3qCsheY4ShcriEnDOwuhkVbtZbpUXq55Ihew37h4tHL+P//eRJVmfRNc8yRupfcfPkaZ2l0we2xPlC/7erw2R0xeKuTQGez4dILys7jTdlIOawXbzasIHrUM9qy5XYqZJ+lPgNN29GeuZYuctqMlTf3rYr9cH+actqOOM+WJOn5RnbBbQdG1bN5qw7GfbahXx45iorPNw7wE+JA2b24Si4zyJDFC5v65meWJg1xIqA+OHffBoUNCmNxzLkjkuRkzRCJz5khkE5usZMokp+hATFOJVNZQMfowizyRHOROOxcvXcWjx8F4Nk92ZfSJnsfBH3/DB30nKbvtlHz9FUjZsWv/j9G77UxftEHZfcchP3buPSzEWAgqvFEU/mJLpJFTluHrb4/i243TlXVPhk1cgnfrV8KLz+XGL2KxfDmypUWDKmI3oebKZcWVJ4G93oFPcBCCpm5BZGAqPS+dbblBgPLEDVgmPJXyxIRJcSMk9svdgKXiVMoTFfBEUcoTdfyU0jHlyarPbDh7zobmYvvLAmIbTB7mJcCHtHlzk1hklCeJETL3z60gT2ISPH3Whp9/BuTvvFky+SCbGE2SMWPUWiZmPswkT4zmJNcgkdsTr9v6jRKKr5jzPV9MGSpTPGoKzaCxC8W6Jgfxy54lyt/ldKPhk5ZEj5bxF+ucTBEjZSq+WUz5eeUmvXH1uhh69N8hpyBNHdktetvluPIkVecqypmP5+4yGgXbfwoByhNr3x6UJ9bOH/vl+uSP8kQdZ03lyc3b97Bt90H8dfka6lQpo6xCv3bLXvFGLiMqlC2qLlITl44pT+Yv9lXmun/QNkKXrShNjMX0ofEhbfoUJRgg5Yl1cycjt5o8sSptypP4mZOjSW7evos8ObPBJhYGfdoRJibeX795RzklV/bM8XYQvHf/kVJXTvGzVIEpY1UVU57Ybl9HyiEtYM+QBcHjPrPq7ZQs4qY8sXaaKU+snT/2y/XJH+WJOs6ayZNLV66jdquB0W9pBosV7Fs0qAw5z1iuUn905zykkNsYJMEjpjyZNt1XGeLds1uEGMJt/LDtJIhbs0viQ1ozlLpXRHmiO3JNG6Q80RRngpVRnujD2VkrseTJpd+RclxX2J95EcGD5xoXFFtOlADlSaKITH0C5Ymp05NocOyXJ4pIkxMoT9Rh1EyefDx9JfZ8dwxLpg5A75GzxXZ95RR5cuzk73iv+1h8sWIc8uXNqS5aN0rbxRaB/1y7iexZM8FPLp/u4nH9plgFUBzZsmRwsUTsaTujx6VAmFhgf/CACKQMoDxxGaIBJ/IhbQB0jZqkPNEIpEHVUJ7oA57yRB/OicmTFKeOwn/mYIQXLI7Q7uOMC4otJ0qA8iRRRKY+gfLE1OlJNDj2yxNFpMkJlCfqMGomT0rU6IgP3q2Nju/VQYN2w6Llya079/F2/R5YPn0QihfJry5aF0vLhd4GfDw/ehRMrw6NRWy1EiwtRcuUeWuxevMehIpF7eRcaMe85y1ffY/B4xbGK/vjlwsQmNJf+feYI0+GjxLbpIlj1PD/tmlwMWaepj8BPqT1Z65Vi5QnWpE0ph7KE324U57owzlReXJoF/yXT0R4qcoIbT3AuKDYcqIEKE8SRWTqEyhPTJ2eRINjvzxRRJqcQHmiDqNm8qSq2PLvf6/lx7jB7WPJk/2HTqDzwGnYs34qcohRIN4+HgcFo7TYTlDKks7v18N2sQbLkPGLsE2MfHk+gZEvjqlF7VvURuPa5ZRdAfLkFFsniMOxaNyGRaNihS5X2PfxiZov7ZAnd+/6YOoMX6RLG4m+vbhNsbdzrbZ+PqTVEjSuPOWJcey1aJnyRAuKiddhFnmSHNdDizltx2/XevhtXICwSo0Q1qhj4onjGYYRoDwxDL0mDVOeaILRsErYL9cHPeWJOs6ayZPJYuTGivVfYWSf1li27is0qvU2Crz0LD4cPhNpUgXi6zWT1UXqYukdew6j3+i5OPb1QgSI1fHlUfadrmgpthDs0rpevFrkavly1fz+YnvB9xtXi/dzKU8+mrocx0V9CR0OeXLlHx/MX+SLnDki0bkD5YmLKTPsND6kDUOvumHKE9UIDa2A8kQf/GaQJ8l1PbRY8kSIEylQwuq3R1jVJvokn614RIDyxCNspilEeWKaVHgUCPvlHmFzuxDlidvIYhXQTJ7IVek79JuEI8fPxGogTepALPtkoCJS9DgWrd6OJWt24Iets6Oba9Z5FORIkTED2sULwTEtp2q54jh74W/4+/mhcR25XkvUtoJSnsiRK2+WLIyAAD+8UbwQGoj1XGKuo+KQJ7+LLYo/FVsVv5jPjlYtzb2FpR65MHsbfEibPUMJx0d5Yt3cycgpT/TJnxnkidnWQ9OHPBBTnvivmIgUB3ch9P1+CC9dVa8Q2I4HBChPPIBmoiKUJyZKhgehsF/uATQPilCeeAAtRhHN5Imjzp9++R2/nLqAew8eKcKi4ptF423hpy7kp5eWI2B2iN199q6fFn1im17jkSZ1Kswc0yNe4TnLt2D20k2KLCn8yvM4eeYiVm3cheG9WqFp3Yr48cRZbNyxHxkzpMXf/1zHngPHUK18SUwd2SW6rgdBUeubHPkJ+Gx9JIqLXZlbNH36FojeZMC6XSOQOmUK0cENh1jyhofFCMgdRgMDUuBRMNcWsljqlHADUtgAkcOQMEpmb+bPX6zfFeAvWBt4mGk9ND0xxJInswYjxW9HEdr1Y4QXKqlnGGzLTQKUJ24CM9nplCcmS4ib4VCeuAnMw9MpTzwE918xzeTJJws/x3PP5EDNiqXg/990GXWheVba3ZEnUp58tmk3DmyeGd1g+76TERQcgk9nDYkXxLJ1X2LSnDX4effi6NEnDx6L7XXEsXe/D77YGYnyb0aibi3KE88yqF8pRZ6ERkAuGMzDWgRswp4E+vtSnlgrbdHR+vvJX+h9EBrG6Y3eTKG/2GnOaHlilvXQvMnZWd0x5YncptgmtisOGTALEc/ps3C+3tebVNqjPLF2JilPrJ0/yhN98kd5oo6zZvKk5/BZ2LX/R0WcNKjxFlo2rJLgAq3qQn56aceaJ3KNEofEKVWrs7KeibM1Txzn/7xrEfz8onbKkSNVHj8Owdr5I+I19tW+o8pWzEd3zhcjagKUnzum7Xz5tQ0/HLKhauVIvFmWvxR4M89a1M2HtBYUjamD03aM4a5Vq5y2oxXJp9djhmk7ZlkPTR/iT1qJJU+GtIDt9nUEjfkUkZmz6x0K23ODAOWJG7BMeCrliQmT4kZI7Je7AUvFqZQnKuCJoprJExnGr2fFlJcNu7F9z0Flm+CCLz+H1k2ro2q5ErHWCFEX8tNLP3ocjJI1O6FTq3fEn7rxdtuRu/+MnLIM8yf2wUvP58Hdew/xdoMeYped8hjcoyWOnjiDdr0nonvbBkodc1dsQaH8+cROQi/h9t0HYl2XyUiRIgW2Lvs4OhCHPNmw2YYTv9hQr64dxYpwOLo386xF3XxIa0HRmDooT4zhrlWrlCeek5Rjdlz9djGDPDHLemieE/esZEx5kqpz1Bpqj+fu8qwyltKNAOWJbqi90hDliVew6lYp++X6oKY8UcdZU3niCEVu9SvXHfl0wy6cOX9JGQFyYNMMse5IoLpoXSy9bddBDPh4fvTZPdo1RMf36ih/3ya2Lh4wZj7WzB2OwgXyKf8m1zHpNVIMpxXCRx5yTZOJwzoiha8v5DbGm3YeiK4rd44siniJue2xQ56sWGXD+Qs2tGxux8svudq9dfGieJrmBPiQ1hypbhVSnuiG2isNUZ54BWu8Ss0gTxxBGb0emj7En7TikCc+IY8R2LMuIgMCEfTJVr3DYHtuEqA8cROYyU6nPDFZQtwMh/1yN4F5eDrliYfg/ivmFXkSFByqyJOVn3+Ncxcvw1csWifXFEmfNrW6aN0oLUWIXOA1V/bMLq3BEh4Rgcv/3ECWTOnjSZ7HQSH498ZtpEuTSvl53MMhT+Yu8MXVf33Q8YMI5M7FdTTcSJchp/IhbQh2TRqlPNEEo2GVUJ7og94M8sQs66HpQzy+PLHdvIqUw1qJ6To5xLSdlXqHwfbcJEB54iYwk51OeWKyhLgZDvvlbgLz8HTKEw/BeUOeyGk7crSJFCdSXshRGu83qYb6Yg2UVIEp1UVq4tIOeTJ5mi/uP/BBrw8jkDG9jvLEnXHcJuaod2h8SOtNXLv2KE+0Y2lETZQn+lA3gzwxy3po+hCPL098L55GwMQeYqHYV8SCsU8Wptc7HrbnGgHKE9c4mfUsyhOzZsa1uNgvd42T2rMoT9QR1GzkSa8Rs/D1tz8q0VSvUBKtm1SPnhajLkTzl3bIk+GjohacHTYoXCw+a/64k3uEfEhb9w6gPLFu7mTklCf65M8M8kReqRnWQ9OHuBN58stBBIhpwhGFSyOky2i9w2B7bhKgPHETmMlOpzwxWULcDIf9cjeBeXg65YmH4P4rppk8GTvjUzyTKxsa1HwbqVMl3VEmznBLeRIc4oOxE3yVH48aHq4uKyytCwE+pHXB7JVGKE+8glW3SilP9EFtFnniuFqj10PTh3pUK441T1L88CX8V05BeNnqCH2vj54hsC0PCFCeeADNREUoT0yUDA9CYb/cA2geFKE88QBajCKayRN1YVi7tJQnt2/74JNZvsp0HTlth4f5CfAhbf4cJRQh5Yl1cycjpzzRJ39mkydmWA9NH/JP5Infl2vgt2UxQqs1RXi9D/Rqnu14SIDyxENwJilGeaJnIuTGGHLdAO0O9su1Y/m0mihP1HFWJU/kTjprNu9Ft7b1ceinU7j499UEo/ng3doITOmvLlqTlpby5NJlHyxa4qssFCsXjOVhfgJ8SJs/R5Qn1s3R0yKnPNEnr2aRJ8lxPTTHyBO/9XPht3cjwkFeFvEAACAASURBVBp1Qlilhvoknq14TIDyxGN0pihIeWKKNHgcBPvlHqNzqyDliVu44p2sSp7s2HMY/UbPxealYzDmk5X48cTZBKP5bstMMSojrbpoTVpaypPTZ234bK1N2aJYblXMw/wE+JA2KEcaLHDMkScG5U6jZilPNAKZSDVmkCfJdT00hzzxXzIOKY7uRWibQQgvWVGfxLMVjwlQnniMzhQFKU9MkQaPg2C/3GN0bhWkPHELl7byRO6oExwSKnbSCYCPj4+6SCxcWsqTn47ZsGWbDcWK2FGvLuWJFdLJh7QVsuQ8RsoT6+ZORk55ok/+zCBP5HpoecV6aPWT2XpoDnkSMGMAfE8fQ0j38Ygo+D99Es9WPCZAeeIxOlMUpDwxRRo8DoL9co/RuVWQ8sQtXNrKk5i1zV2xRVkwtnblMrEaOXfxMqYtWI/Jwzsn2e2KpTz59jtf7NnrgzfKRqBaZR23KVaX/2Rdmg9p66af8sS6uaM80S93ZpAn+l2tuVpyyJOUYzrCduUPBA+ZB3ueF8wVJKOJR4DyxNo3BeWJtfPHfrk++aM8UcdZ1bSdmE036zwKrxV4AYN7tIgV0dXrt1G5SW+snT8ChfI/ry5ak5aW8mT7ThsOH7WhelU7ypbmyBOTpipWWHxIWyFLzmOkPLFu7ihP9MudUfKE66E9WTA25cBmsN27heBxa2DPkFm/5LMljwhQnniEzTSFKE9MkwqPAmG/3CNsbheiPHEbWawCXpUn4RERWLtlL8bOWIU966ciR9ZM6qI1aWkpT9Zv8MXJ33zQsJ4dRV6jPDFpqihPrJAYF2KkPHEBkolP4bQdfZJjlDzhemhP5EmqzlWUZD+eu0ufpLMVVQQoT1ThM7ww5YnhKVAVAOWJKnwuF6Y8cRmV0xNVy5OiVdsjNDTsqVEUK/wyVs4crC5SE5eW8mTZShv+uGjDey3seOkFyhMTpys6ND6krZAl5zFSnlg3dzJyyhN98meUPOF6aFHy5O612wjsWx+RgakRNHWzPklnK6oIUJ6owmd4YcoTw1OgKgD2y1Xhc7kw5YnLqLwjT1as/wqPgoKxeuNuZM2cAVXKFY9uyN/PD2+UKIRXXsyrLkqTl5byZNZcX1y/4YPOHSKQMwfXPDF5ypTw+JC2QpYoT6ybpYQjpzzRJ6tGyZOYV5dc10OT8uTehT+QcmQb2LPlQfBHS/VJOltRRYDyRBU+wwtTnhieAlUBsF+uCp/LhSlPXEblHXniqFVuU5wubWq8nC+PuogsWFrKkwlTfPHokQ/69ooQHChPrJBGPqStkCXKE+tmifLE6NyZQZ4k1/XQFHly4hhSTu6FiBcKIaTvNKNvB7bvAgHKExcgmfgUyhMTJ8eF0NgvdwGSBqdQnqiDqHraTszmr924g59/O49Hj4PiRVWnSln4+aVQF61JS0t5MnxU1LWNGh5u0igZVlwCfEhb957gtB3r5k5GzpEn+uTPrPIkOayHJuXJgwN74b/gI4S//iZCO47QJ+lsRRUByhNV+AwvTHlieApUBcB+uSp8LhemPHEZldMTNZMnR46fQZte4xOM5sDmmciUIa26aE1a+vzfwRg/2RcBAZEYMiDCpFEyLMqTpHMPUJ5YO5eUJ/rkz0h5ktzXQ5Py5OGOjfD7bDrC3qyFsBY99Uk6W1FFgPJEFT7DC1OeGJ4CVQFQnqjC53JhyhOXUXlXnrzbZTT+vXEb4wZ3QNteE5StiXNlz4Jugz+BPTISa+YOVxepiUufOBuCmXNsyJQpEj27UZ6YOFWxQuND2iqZih8n5Yl1cycjpzzRJ39GypPkvh6aIk/WLYXfF8sQVqMFwt5prU/S2YoqApQnqvAZXpjyxPAUqAqA/XJV+FwuTHniMirvypOy73RFq0bV8EGLWihSqR1WzR6K1199EYd+OoV2fSZi99opyJk9s7poTVr64PFgLF7mi7x5IvFBW8oTk6YpXlh8SFslU5Qn1s2U88gpT/TJqJHyxHGFyXU9NClPHi2eihT7tiC0aTeEl6+rT9LZiioClCeq8BlemPLE8BSoCoD9clX4XC5MeeIyKu/Kk1K1OqN10+ro3Kou5P/3aNcALRpUwbmLl1GvzVAsmNRX2XknKR67vgvB2vU2FMhvR/Om3KbYKjnmQ9oqmaI8sW6mKE+MzJ1R8uT6zbs4ceq88p1/9dot3HvwKEEMRQq+CF9fm5GYvNK2Ik+mDUeKY98i9IOhCP9fOa+0w0q1JUB5oi1PvWujPNGbuLbtsV+uLc+EaqM8UcdZszVP3mk9BLlzZMHc8b3QY9gMHD52GkM/fA/bdh/Ed0dO4ocvZiO92I0nKR6bvwzBtp02/K+YHXVrU55YJcd8SFslU5Qn1s0U5YmRuTNKnmzfcwj9R8/D5qVjMGziEpw8/UeCGJLqemhSnjwe1QO+504gpOdkROQvYuStwLZdJEB54iIok55GeWLSxLgYFvvlLoJSeRrliTqAmsmTdV/sw7k//sYQIUzkm6aa7w1EaGiYEl37FrXRs30jdZGauPSnG4Kxb78vyr0ZgUoVuU2xiVMVKzQ+pK2SKcoT62aK8sTI3BklT27cuqvsvKeMPLl+G/fuP23kyQumGnki125LlyYVUgWmVJU6RZ70bwXfq38hZNgiROR6VlV9LKwPAcoTfTh7qxXKE2+R1ade9sv14Ux5oo6zZvIkbhhh4RHK26YXns2F9OmS5ogTxzUvWBmCIz/aULOGHaVLcOSJultSv9J8SOvHWuuWuGCs1kT1rY9rnujD2yh5os/VadvK+YtX0OrDsdGip1yZIpg+qjv8/FI4bWjAmPnKyNqYR97c2bBz1UTln6Q8Ce78Dnwe3kPQpM8RmSa9tgGzNq8QoDzxClbdKqU80Q21Vxpiv9wrWONVSnmijrPX5Im6sKxVeurcEJw6Y0PjhhEo/CpHnlgle3xIWyVT8eOkPLFu7mTklCf65M8M8uTQsVPY+tX36N2xCbJkSo9NOw9g4aptSJ0qEOMGtceLz+fWB0YirTRoN0yJaf7E3vj7nxto3GEEBnZrgXfrV3JaUk5L+uvyNYwZ2C7654EpA5AnZ9ZoeRLyXtQ6J4/n7jLFNTKIxAlQniTOyMxnUJ6YOTuJx8Z+eeKMtDiD8kQdRc3kSa8Rs/DDj785jcZfvLkpV+Z1NH2nAgoXyKcuYhOWHj0lFH9d8kHr9+zI9zxHnpgwRU5D4kPaKpmiPLFuppxHTnmiT0bNIE/a9BqPazfuYMenE5QpPJWb9FYkyuOgYOTNnR0bFo3SB8ZTWrl99wHeqtcdiyb3Q5nirypn9h45B/9cu4k1c4cnKE/u3HuIhZP7Ov150I2bCOlaTxlxIkee8LAGAcoTa+QpoSgpT6ydP/bL9ckf5Yk6zprJk26Dp+Pw8VOoVr5kdESPg0Lw1b4jijAJDg5Vdt4Z3b8tGtR8W13UJis9aHQIbty0oVunCGTLxpEnJktPguHwIW2VTFGeWDdTlCdG5s4M8kRKicZ1yosd+Bpi8Wc7MHX+OuxeOwX3Hz6GHO1xdOc81euLqGV8+txfaNR+BHavm4qc2TIp1c1asgkbd+7H3vXTEpQn3/xwHMUKv4xMGdKhZqXSeKtU4ehzgy5cQMig9xGR81mEDF+kNkSW14kA5YlOoL3UDOWJl8DqVC375fqApjxRx1kzeVKjRX/RcSiCwT1axIro/Q/Hwd/PT3k703ngNPz599XoOcHqQjdP6e4DQxEU5IP+fSKQJjXliXky8/RI+JC2SqYoT6ybKcoTI3NnFnnSsmFVdHyvDjr2n4Iz5y/h243T8ehxMErW7ITl0weheJH8RmLCoZ9OoV2fifhuy0xkTJ9WiWXR6u2Yv3KrkDvznca2etMe5YVQYIC/sjjuiVMXMKpfWzSsFfVyKPzXY3god9spWBSphs8w9PrYuHsEbDYf2O3sy7lHzRxn+/hExRHJ9JkjIW5Gwc+em8A8PN1XPON4eE5AM3lStGp7ZVrOwG7vxopmxuINWLr2Sxz/eiFWbd6FCTM/wy97lngesQlLfvBh1K5Co4aHmzA6hpQQAcoT694bXPPEurmTkXPajj75M4M8kS9NTpw6j7bNamLagvVoIvoJI3q/L0aqnkbbXhPEy5QJyvQdIw/HyJM966ciR1bXRp7Ejbd938m4ffd+1DQkMXv38Xe7EDrrI0QUL4fwDsOMvDy27QaBFOKXivRp/HHrfogbpXiqWQikSemnmJOHIeyPmyUn7sSRJV0A7j4MRTjlpTvY3D43ewZ1u8m53WASK6CZPGn4wXD8cekqDm2bgwB/8fD675AjUuR84sPb52LO8i2Yt2JLkpIn9+4DfYaFiWHHkRjYLyKJ3R5J+3IoT6ybX8oT6+aO8kS/3JlBnsh+QdOOI8UaJyHiezIAX302WUxzSYuW3T7G2QuXRN9gHuTbRiMPx5oni6f0R+n/FVRCkeu4yTVaElrzJG68IyYvxdGfzyhru8jj4RfrEb5yOsLL10Vo025GXh7bdoMAp+24AcuEp3LajgmT4kZI7Je7AUvFqZy2owKeKKqZPJEr6rfrPRG+vjaUeP0VZMmYHkdPnFEWipNvmeTbJrlw3L+iM+LYyk9d6OYoffmfSIycEI6sWezo3oWLxZojK65FwYe0a5zMeBbliRmz4npMHHniOis1Z5pBnsj4Q0LDcOnKNbzwbO5oUXLk+BlkzJAGLz2fR80lala2XpuhSJ8uNeaO74XLV2+KNVCGY0DXd9GiQWWlDTkFOXeOLBgrdgiSx7CJS5SdeF58Ljd+Of2H0r9p0aCKKNNc+fmDVQsQsWUFwuq0RljN2NOZNQuaFWlOgPJEc6S6Vkh5oituzRtjv1xzpE4rpDxRx1kzeSLD+PHEWUyY/ZnyNikiwq6sqP9u/cpo36K20mE6e+FvZVTKc8/kUBe1iUqf/j0SU2aH49m8kWjXmiNPTJSaREPhQzpRRKY9gfLEtKlxKTDKE5cwqT7JLPJEXohcH+S3s38qI1CeF32A4uIli18KX9XXqFUFv/9xGe91/xgPHwUpVcrFX2eM7gH//0bSlmvwoTK9aOXMwcrP5a5BcmSK45DnTx3ZTRldI4/7cyfA/s0XCHv3Q4S9VVurMFmPlwlQnngZsJerpzzxMmAvV89+uZcB/1c95Yk6zprKE3WhWLP04Z/sWLgiAq8WsKNpY448sVIW+ZC2UrZix0p5Yt3cycgpT/TJnxnkSagYddJ96Ax8d+RkrIuWozwWTOqLQvmf1weGi61c+fcm0qZJhXTiT2LHvfuPcPP2XeTMnjnejkH3JwyC/acDCO04AuGvv5lYVfy5SQhQnpgkER6GQXniITiTFGO/XJ9EUJ6o46ypPNn3w89YtXE3/rr8L3p1aIwaFUth8LiFyJYlI3q2b6QuUpOW3v2tHWs2RqBkCTtq16A8MWmanIbFh7SVskV5Yt1sxY+c8kSfbJpBnoyd8anSL5BTWiqUfV1M1UmLgz/+hoWrtykQvt04w1QjULTKzL2hnRH5+0kE950G+wuFtKqW9XiZAOWJlwF7uXrKEy8D9nL17Jd7GfB/1VOeqOOsmTw5dvJ3MeR1rDJkVc5vdswVnrdiK2Yu2ahs9+cYzqouZG1KyzdGYeHhytQiVw/5Bu2fa7eUt0yORXE3bY/A9q/tqFA+EhXe5rQdV1ma4Tw+pM2QBc9i4MgTz7iZpRTliT6ZMIM8eated7yc7xksnto/1kV/+c0R9PloDj5f+BEKvPSsPkB0bOVej2aI/PcygkcugT37Mzq2zKbUEKA8UUPP+LKUJ8bnQE0E7Jeroed6WcoT11k5O1MzedJl0DTcvfcQq2YPhdx5p2GtcspCa3KdkwbthpmmgyTnM3/QdxJOigXe5CEXgFsh5jA7tid0BknOhe45fKYYUXNN+fGHHzREh5Z1lP9fviYCBw6KUSc17ShZnCNP1N2O+pbmQ1pf3lq2RnmiJU3966I80Ye5GeRJqVqdUadKWQzt+V6siz5z/pLSV5BTd94okfRGZtxtXV3sV/wQQVM2ITJVGn0SzlZUE6A8UY3Q0AooTwzFr7px9stVI3SpAsoTlzAleJJm8qRo1faKVGjdpLoiSxzy5MatuyjfsCc+nTUERQu9pC5aDUpPnrcW67/Yh02LRyN1qkA06/wRns+bE3PG9XJau5z/XLVZX5Qs+go+eLe2uIYXlQXvHCNWZi0Kx88nI9FMrHdSUKx7wsM6BPiQtk6u4kZKeWLd3MnIKU/0yZ8Z5En7vpNx+PgprJs/EvlfeAY+Pj5inZB7GDBmPuQufQe3zXFpfRF9iGnXyt0mUeucPJ67S7tKWZPXCVCeeB2xVxugPPEqXq9Xzn651xErDVCeJM5Z/kZvS+A0zeSJ3OYvc6Z0WDylfyx58tnmPRjzyUr8sHW2sg2g0UfFxr2UtVj6dW6mhLJh+34Mn7QEv36zVOnUxT0Gjl2Ar/YdFdOO5iGFb/ydAcZOC8cff0bttCN33OFhHQJ8SFsnV3EjpTyxbu5k5JQn+uTPDPLk0pXrqN1qoLIDn9y5Ri7EKuWJPGKO4tSHiH6tSHliT58ZwePX6NcoW1JNgPJENUJDK6A8MRS/6sbZL1eN0KUKKE9cwpTgSZrJk81ffoch4xehWvmSOPrzaZQr87oyOmPhqm3Kln/zJvRRF6lGpV+r1BYj+7RGg5pvKzUeO3lO2Z7wuy0zkTF92nityPnacn2THNky49/rt1Dg5efEei7NkSdnVuXcwaPDcP0m0L2LHVmzcOSJRmnSpRo+pHXB7JVGKE+8glW3SilP9EFtBnkir/TOvQdYtHo7fj1zMXqr4mb1KqJY4Zf1AWFAK4o8yZMPwUPmG9A6m/SUAOWJp+TMUY7yxBx58DQK9ss9JedeOcoT93jFPVszeSIrlp2jGYs3KG+YHEfpYgUxaXhnZBIr7Bt9REZGolCFNpg0rDNqViqlhHP63F9o1H4Edq6aiLy5s8UL8dXyrfHCc7nR9J0KikSZs3wzgkNCse/zT5S3aN36h4m/Ax8Ph1gQ1+grZPvuEEidMgWCQiNgt3PEkDvczHCuzeaDQH9fPAoON0M4jMFNAv5+cjCkD0LDuMi2m+jcOt0/ha/43kpo4KlbVXl08sYd+/H90V+VUZu1xbon8kVKcjmkPIkoUAwhPSYkl0tOEtdJeWLtNFKeWDt/lCf65I/yRB1nTeWJDEXutHPx0lU8ehyMZ/Nkd2s3G3WX4lppOfLko75tUL/GW0qBxEaeSHkydlB71K32hnK+XDy2ftuhWDN3OAoXyIcPPgxT/n3a+PhTflyLiGcZRUCRJyHhoDsxKgOetyvcCQIDUlCeeI7Q0JIBKcQv9CKHIWEcrefNRPj72gyTJ+NnrcbKz7+OdXmDe7RQtixODoeUJ+ElKyK0zaDkcLlJ5hopT6ydSsoTa+eP8kSf/FGeqOOsuTxRF473S8s1T2pWKo2+nZoqjX2+7VuMmLw0wTVP5PnvVH0DPds3Us53jFRZPn0QihfJr8iT1KkjMaAP36B6P3vatsCHtLY89ayN03b0pK19W5y2oz1TZzUaNW1H7mond9kpV6YIpo/ugbCwMHQeOA3Hfz2Hw9vnITClvz4ADGxFypOwig0Q1rizgVGwaXcJUJ64S8xc51OemCsf7kbDfrm7xDw7n/LEM26OUqrkidzut/+YeS5FsFastC8XijP6mDR3jSJMNi8Zg1SpUqJZp9i77UxftEEsEHsEOz6NGmo7df46rNywS9mdJ13a1Ipo+e7ISXwv1khJFZhSkSfZs0WiayfKE6Nz6277fEi7S8w851OemCcXnkRCeeIJNffLGCVPTv3+Jxp3GIlN4nv25Xx5lMAdWxNvWDQKr7yY1/2LsVgJRZ7Ua4ewalGL0/OwBgHKE2vkKaEoKU+snT/2y/XJH+WJOs6q5Mn5i1cwY8mGBCM4fOw05BsoeRzYPNMU657cf/gY7XpPhOzcySNntkxYOXMIcmbPrPx90NiF2L7nIH7Zs0T5u5yG1GnAFBw5fkb5e6rAAMwe20vZulgeUp7ke96O1u85G37+tI2O1CWOpdUT4ENaPUOjaqA8MYq8Nu1SnmjDMbFajJIncp2TDv0mi5cMs5AhfRolTDmVt2TNTuL7syfKl309sdAt/3MpT0Lf64PwstUtfy3J6QIoT6ydbcoTa+eP/XJ98kd5oo6zKnmSUNNyasvoaStw4tQFZXvi/l2aK2uGONsKWF34npe+ffeBWKwwDDmyZnKpkrv3HkKKl2dyZY11HVKeFH41Eo0bcuSJSyBNdBIf0iZKhpuhUJ64Ccxkp1Oe6JMQo+WJnB4rXzjIIzwiAmNnrFJ2uiuU/7loAHL9Mbn4elI7pDwJ6TIaEYVLJ7VLS9LXQ3li7fRSnlg7f+yX65M/yhN1nDWVJ5euXMO4maux/9AJpTPUS6wT0rx+ZfiJFf/VHeYdwSHlSemSdtSszoUP1eVY/9J8SOvPXKsWKU+0ImlMPZQn+nA3Wp64cpVmGZXqSqzunKPIk/4zEPF8AXeK8VyDCVCeGJwAlc1TnqgEaHBx9sv1SQDliTrOmsiT6zfvYsq8tdi2+yB8xer+HVrUQdvmNaPfOKkL0dylpTypXDESb7/JkSfmzlT86PiQtlrGnsRLeWLd3MnIKU/0yZ9R8kROd712445LF5knZ1bIrceT2iHlSdCYFYjMnDOpXVqSvh7KE2unl/LE2vljv1yf/FGeqOOsSp7IOczTF32OVRt3K1G0aFAZXVvXV6bqJJdDypN6dewoVpQjT6yWcz6krZYxyhPrZix25JQn+mTSKHmiz9WZuxVFnnyyFZEBgeYOlNHFIkB5Yu0bgvLE2vljv1yf/FGeqOOsSp44FoWTc5p7dWgi1g/JmGA0b5UuosH0HXUX643SUp40b2pHgfyUJ97g6806+ZD2Jl3v1s2RJ97l66g9UvyPN8YEUJ7okz/KE304O2tFypPHc3cZFwBb9ogA5YlH2ExTiPLENKnwKBD2yz3C5nYhyhO3kcUqoIk8cSWEpDqvWcqT9m0i8Mwz8tcMHlYiwIe0lbIVO1bKE+vmTkZOeaJP/ihP9OHsrJV7nRvi0ZhPjQuALXtEgPLEI2ymKUR5YppUeBQI++UeYXO7EOWJ28i0kyec1xy1VXHP7hHIlJHyRN2tqH9pPqT1Z65Vi5QnWpE0ph7KE324U57ow9mpPBnQDo/6zzIuALbsEQHKE4+wmaYQ5YlpUuFRIOyXe4TN7UKUJ24j006eqGs6aZSW8mTwgAikDKA8sVpG+ZC2WsaexEt5Yt3cycgpT/TJH+WJPpydypOxffGo42jjAmDLHhGgPPEIm2kKUZ6YJhUeBcJ+uUfY3C5EeeI2MsoTdchilz57LhJpMwVrWSXr0okAH9I6gfZCM5QnXoCqY5WUJ/rApjzRh7OzVoL+uIA76XMZFwBb9ogA5YlH2ExTiPLENKnwKBD2yz3C5nYhyhO3kVGeqEMWv/Q/t4K0rpL16UCAD2kdIHupCcoTL4HVqVrKE31AU57ow9mpPAmJwJ2HocYFwJY9IkB54hE20xSiPDFNKjwKhP1yj7C5XYjyxG1klCfqkFGeaM3PqPr4kDaKvPp2KU/UMzSyBsoTfehTnujDmfLEOM5at0x5ojVRfeujPNGXt9atsV+uNVHn9VGeqOOsarcddU0nndIceWLNXPIhbc28yagpT6ybOxk55Yk++aM80Ycz5YlxnLVumfJEa6L61kd5oi9vrVtjv1xropQn3iBKeaIBVcoTDSAaUAUf0gZA16hJyhONQBpUDeWJPuApT/ThTHliHGetW6Y80ZqovvVRnujLW+vW2C/XmijliTeIUp5oQJXyRAOIBlTBh7QB0DVqkvJEI5AGVUN5og94yhN9OFOeGMdZ65YpT7Qmqm99lCf68ta6NfbLtSZKeeINopQnGlClPNEAogFV8CFtAHSNmqQ80QikQdVQnugDnvJEH86UJ8Zx1rplyhOtiepbH+WJvry1bo39cq2JUp54gyjliQZUKU80gGhAFXxIGwBdoyYpTzQCaVA1lCf6gKc80Ycz5YlxnLVumfJEa6L61kd5oi9vrVtjv1xropQn3iBKeaIBVcoTDSAaUAUf0gZA16hJyhONQBpUDeWJPuApT/ThTHliHGetW6Y80ZqovvVRnujLW+vW2C/XmijliTeIUp5oQJXyRAOIBlTBh7QB0DVqkvJEI5AGVUN5og94yhN9OFOeGMdZ65YpT7Qmqm99lCf68ta6NfbLtSZKeeINopQnGlClPNEAogFV8CFtAHSNmqQ80QikQdVQnugDnvJEH86UJ8Zx1rplyhOtiepbH+WJvry1bo39cq2JUp54gyjliQZUKU80gGhAFXxIGwBdoyYpTzQCaVA1lCf6gKc80Ycz5YlxnLVumfJEa6L61kd5oi9vrVtjv1xropQn3iBKeaIBVcoTDSAaUAUf0gZA16hJyhONQBpUDeWJPuAzpkmJwAAffRqzSCshoWG4efsecmXPDB8f77EJConAnYehFqHCMB0EKE+sfS9Qnlg7f+yX65O/XJkD9WkoibZCeaJBYilPNIBoQBV8SBsAXaMmKU80AmlQNZQn+oDnyJMnnCMjIzFu5iqs2rhb+Ud/fz/Mm9AbpYoWSDQZO/ceRt9RczF5eGfUqFhKOX/AmPnYtvtgrLJ5c2fDzlUTlX+jPEkUqylPoDwxZVpcDoryxGVUpjyR/XJ90kJ5oo4z5Yk6fkppyhMNIBpQBR/SBkDXqEnKE41AGlQN5Yk+4ClPnnA+9NMptOszEQsm9UWJIvkxatoKfLXvCA5vnwebLeERKCdP/4EW3cYgIsIeS570Hz0Pf12+hjED20U3EpgyAHlyZqU80ef29korlCdewapbpZQnuqH2SkPsl3sFa7xKKU/UcaY8UceP8kQDfkZVwYe0UeTVt0t5op6hkTVQnuhDn/LkCeehExbjjDPYKAAAIABJREFU1zMXsXnpGOUfr167hcpN++DTWUNQtNBLThNy9fptvPP+IPTu2EQZtTJhSMfokSdSnty59xALJ/d1WtatkSc2UYVdn3uCrTydAOWJte8QyhNr54/9cn3yR3mijjPliTp+lCca8DOqCj6kjSKvvl3KE/UMjayB8kQf+pQnTzi36TUeGdOnw9SRXaL/8dXyrTFpWGfUrBQ1FSfm8TgoWIiTwShX5nUM69UKr1VqG0+efPPDcRQr/DIyZUgn6iiNt0oVjq7CLXmiz+3AVlwgQHniAiQTn0J54kpy5Ei7SFdO1P0c9sv1QU55oo4z5Yk6fpQnGvAzqgo+pI0ir75dyhP1DI2sgfJEH/qUJ084N2g3DAVffg5jBjyZZiOFyJAeLdG0bsVYCbHbIyFlizyWThuoTOuJK09Wb9qDcxcviwV5/fHzb+dx4tQFjOrXFg1rva2UE0usiD/m/AVFn7vPoq2I3yttYiFheQ/wsB4BxyLQ/OxZL3cyYvmstSsPT2vGb5WonzZV1SrXYGSclCca0OeaJxpANKAKyhMDoGvUJOWJRiANqobyRB/wlCdPOEsZIkeITBmR+MiTf/69iSrN+qJC2aJIkyZqV4Ivvv4BhfI/L0RLBTSoGSVIYh7t+07G7bv3sWHRKOWfg0MjcPdRmD6JZiuaEUghfnlLn8Yft+6HaFYnK9KPQJrAFIq5fBgcoV+jbEkzAlnSBeCu2KUsnPJSM6bOKsqRMaVX69e2cjmnVc5tNc9BeaJBLihPNIBoQBWUJwZA16hJyhONQBpUDeWJPuApT55wlmuenPr9T2xcPFr5R4cgcbbmycNHQZizbHOsJC1f/xVK/68gmtQpj2rlS8ZL4IjJS3H05zPY8ekE5WectqPPPa51K5y2ozVRfevjtB19eWvdGvvlWhN1Xh+n7ajjTHmijh9LkwAJkAAJkAAJmJzAwR9/wwd9Jym77ZR8/RVI2bFr/4/Ru+1MX7RB2X3HIT/iXk7caTvDJi7Bu/Ur4cXncuMXsSOPHNnSokEVDOja3OQkGB4JkAAJkAAJkICnBChPPCXHciTgjID5RpcxTyRAAiSQ7AnINRDk9sTrtn6jsPD1tWH+hD4oU/xV5e+Dxi7E9j0H8cueJU5ZxZUnlZv0htyNx3HIxWKnjuyGVIEByZ41AZAACZAACZBAUiVAeZJUM8vrIoGkQoBCKqlkktdBAoYTeBwUgpu37yJPzmzK4oRqjnv3Hyl15cyeWUgTK80hV3PVLEsCJEACJEACyZcA5YmK3MuOU1h4OLJkSq+iFhbVi4BcPV++fZRvHOMe8mf/XLuJ7FkzwS+Fr14hsR0XCdy59wBB4peeXDmyOC3Bz6KLIA04TX7mbt6+h/sPHyNX9iwITOkfL4p/b9xGujSp+AuoAflhk9oS4HeJtjz1qC08IkLssGNzKtP43aJHBjxrIyw8Alev3ULWzBmcfq/IWvnd4hlbPUoFBYfi3+u3kCZ1oJJD9sv1oG6RNuRuS+rebXj1QilPPMArF5OTc6dPinnO8sgtfqFbMXMwcohfvHmYk4D8Ba77kBlKcLPGfhgryJ17D2PAx/MRESGHOAC9OjTGB+/WMueFJLOoZMdHbjEqO7DyyJQhLTq/X09Za0Ae/Cya+4Y4cvwMOg6YgtDQqF1HpLjs06kp3m9cTfn7+YtX0OrDsdH5LVemCKaP6g4/P7FjAg8SsBgBfpdYLGEi3MdBwajWvB+6tK6H5vWivlf43WL+PE6auwbL1n4ZHWixwi/jk1HdkDljOn63mD996DxwGvYfOhEd6bN5smPFjMHRL6P5LLVAEpNxiJQnHiR/8ry1WP/FPmwSq/anThWIZp0/wvN5c2LOuF4e1MYi3iawYft+jJm+UvkFTm49GVOeyI5T6dpdFFkifynfvvsghoxfhG0rxik55WEsAflWaeXnX+PdBpWRNnUqzFq6EWvFmgWHts1RRinws2hsfhJr/fDx0zh19k/UrlIGGdKlwdiZq5Q1J378coHyplCKMfkMnT+xN/7+5wYadxiBgd1aRMuxxOrnz0nALAT4XWKWTLgex+BxC7Hlq++VAkN7vhdLnvC7xXWORpy5+LMdyPdsTpQqWgB/XLqK97qPVaR8z/aNlHD43WJEVlxvc8Lsz1Dl7f+h8Cv5cOnKNTTqMBLN61ZEf7HgNp+lrnPkmcYQoDzxgHvFxr1Qo2Ip9OvcTCktfzkfPmkJfv1mKXx8TDzOyINrTQpFHj0Ohpz2IXdXCAwIiCVPduw5jH6j5+LY1wsR4O+nXG7Zd7qipdg1Qb6J4mEuAhdFJ6l2q0HKjhlvlCgEfhbNlZ/Eolm0ejtmiy1gj2yfiwdiBN9b9bpj0eR+0Yt29h45R5k+t2bu8MSq4s9JwFQE+F1iqnS4FIycThgcEor6bYeid8cmseQJv1tcQmiak7oMmobLV29i67KPcfvuA363mCYziQciX2yWEi8xu7xfF+1b1AafpYkz4xnGEqA88YC/XHV/ZJ/WaFDzbaX0sZPnhPX+GN9tmYmM6dN6UCOL6EGg5/BZCBdzZGOOPJG/zC1ZswM/bJ0dHUKzzqOU7SfHDGinR1hsww0Cy9d/hYnijcW+DZ8oc2T5WXQDnoGnHjh8Emu37MXh46fQV0zbaSreMJ0+9xcatR+B3eumIme2qCmPs5Zswsad+7F3/TQDo2XTJOA+AX6XuM/MLCVK1eqsjFiIOW2H3y1myU7icYSFhSsvvaqVL6n02/jdkjgzM5wRIqTJtAXrsfe7Y8p0nbkTeiN92tTgs9QM2WEMTyNAeeLm/SHXzihUoQ0mDeuMmpVKKaUdD+qdqyYib+5sbtaYzE43cBEgZ/JEDs3dsedQrF/W2vQaLxawSoWZY3oks+SY+3J/PXsR73YZjca1y2NYr1bK4r/8LJo7Z47oPtu8Bxt3HMD5P6+gvZgiJ0d1HfrpFNr1mRhLOstO0/yVW3F053xrXBijJIH/CPC7xLq3Qlx5wu8Wa+Wy2+Dp2H/4BL5aPUnZ+YrfLdbIn5yeI9c+OXfxsljPLp2y9IH8HYrPUmvkLzlHSXniQfblG4mP+rZB/RpvKaU58sQDiAYU4cgTA6Br1OSff/+rrIfxWsEXsGBi3+gdk/hZ1AiwTtXIESidxAKyX4g1hULEcHk58mTP+qnRi21z5IlOiWAzmhPg21LNkepWYUIjT9jP0y0FHjf0sVjPbvWmPVg+fRCKF8mv1ON4ocnvFo+x6lpQysr6bYcp4mvu+F4ceaIrfTbmCQHKEw+oybmwNSuVVoafy+Pzbd8q62lwzRMPYOpYxJk8ccytPC7WPPH/b80T2ZGSC49xzRMdk/OUpk79/idadPsYbxQvhE9Gd0MK3ydbSfOzaI4cuRqF3D2pUuPeyjon+V/Mq8xLXzylP0r/r6BSRa8Rs3D1+m2ueeIqUJ5nGgL8LjFNKtwOxJk84XeL2xh1LSC3BJeL+2/fcxDLPhmEYoVfim7fseYJv1t0TYmqxvp8NAcX/vwHm5eOiV7zhP1yVUhZ2IsEKE88gCu3SJPCZPOSMUiVKiWadeJuOx5g1K2I3II4IiICcjHK8IhwzBjdAylSpIDN5gO5mGzJmp3QqdU74k9d7rajW1Zca0huBy7XoCldrCCGiN0QbP8tyJwmdaAyR5afRdc4GnWW3ClJ7rLzZqnCInc2RTLv/f4YDmyaifTpUqNem6HKf+XbJrnYX6P2wzGg67toIXZX4kECViLA7xIrZSsq1nDRL7CL/sFb9XugW5v6aPpOheiXKPxuMXc+HVvdTh3ZFflfeCY62Nw5s8IvhS+/W0ycvnsPHmHSnDV4r1FV5BO7Wh779Rza950kvveriO//5uyXmzh3DC2KAOWJB3fC/YeP0a73RMg34vKQix2unDlEGXLGw3wEFq7ahk8Wfh4rsH5dmqF1k+rKv23bdRADPn6yxkKPdg3R8b065ruQZBjR+m37MHLysnhXXlaMQlk4uS/4WTT3TSGnMsgF4RxHqsAATBjSERXfLKb80+9/XFYW234odt6Rx1tCski56RgFZu6rY3QkEJsAv0usdUe8/+E4/HjibKygN4mXYi/ny8PvFpOnUo4WcnxvxAxVjlx46fk8/G4xcf5kv61emyG4duNOdJTyu3/aR90RmNKf/XIT546hRRGgPFFxJ8ihgaFhYdHz9VVUxaIGE5CjU/7+5zpyCQHGX9wMToYHzfOz6AE0nYrIt7uykyTnNefKnkUZ8RX3uPLvTaRNkwrpxB8eJGBlAvwusXL24sfO7xZr55PfLebNn5QoN27eQY5smZFajOKPe/BZat7cJffIKE+S+x3A6ycBEiABEiABEiABEiABEiABEiABEngqAcoT3iAkQAIkQAIkQAIkQAIkQAIkQAIkQAIk8BQClCe8PUiABEiABEiABEiABEiABEiABEiABEiA8oT3AAmQAAmQAAmQAAmQAAmQAAmQAAmQAAl4RoAjTzzjxlIkQAIkQAIkQAIkQAIkQAIkQAIkQALJhADlSTJJNC+TBJI7gUgBIP4+L8mdCq+fBEiABEiABEiABEiABEjAFQKUJ65Q4jkkQAIkQAIkQAIkQAIkQAIkQAIkQALJlgDlSbJNPS+cBEiABEiABEiABEiABEi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"cell_type": "code", - "execution_count": 25, + "execution_count": 3, "metadata": { "id": "zuvGHWID04p_" }, @@ -103,7 +103,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 4, "metadata": { "id": "WPCybXIJ04qA" }, @@ -132,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -153,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 6, "metadata": { "id": "etMzRtx404qA" }, @@ -177,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 7, "metadata": { "id": "N3FtAhrT04qB" }, @@ -198,7 +198,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 8, "metadata": { "id": "-9OVt0EQ04qB" }, @@ -208,9 +208,9 @@ "output_type": "stream", "text": [ "Halt: Maximum number of iterations (5) reached.\n", - "Estimated parameters: [7.38910820e-05 2.07135001e-06]\n", - "Initial gravimetric energy density: 386.13 Wh.kg-1\n", - "Optimised gravimetric energy density: 397.79 Wh.kg-1\n" + "Estimated parameters: [6.92072167e-05 2.26386226e-06]\n", + "Initial gravimetric energy density: 386.81 Wh.kg-1\n", + "Optimised gravimetric energy density: 404.95 Wh.kg-1\n" ] } ], @@ -245,13095 +245,25 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 9, "metadata": { "id": "ZVfozY0A04qC" }, "outputs": [ { "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "fill": "toself", - "fillcolor": "rgba(255,229,204,0.8)", - "hoverinfo": "skip", - "line": { - "color": "rgba(255,255,255,0)" - }, - "showlegend": false, - "type": "scatter", - "x": [ - 0, - 5, - 10, - 15, - 20, - 25, - 30, - 35, - 40, - 45, - 50, - 55, - 60, - 65, - 70, - 75, - 80, - 85, - 90, - 95, - 100, - 105, - 110, - 115, - 120, - 125, - 130, - 135, - 140, - 145, - 150, - 155, - 160, - 165, - 170, - 175, - 180, - 185, - 190, - 195, - 200, - 205, - 210, - 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}, - "zaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - } - }, - "shapedefaults": { - "line": { - "color": "#2a3f5f" - } - }, - "ternary": { - "aaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "title": { - "x": 0.05 - }, - "xaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - }, - "title": { - "text": "Cost Landscape", - "x": 0.5, - "y": 0.9 - }, - "width": 600, - "xaxis": { - "range": [ - 0.000065, - 0.0001 - ], - "title": { - "text": "Positive electrode thickness [m]" - } - }, - "yaxis": { - "range": [ - 2e-6, - 9e-6 - ], - "title": { - "text": "Positive particle radius [m]" - } - } - } - } + "image/svg+xml": [ + "70μ80μ90μ100μ2μ3μ4μ5μ6μ7μ8μ9μ−400−380−360−340−320Cost LandscapePositive electrode thickness [m]Positive particle radius [m]" + ] }, "metadata": {}, "output_type": "display_data" @@ -14440,7 +333,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.11.7" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/examples/notebooks/spm_scipy_DifferentialEvolution.ipynb b/examples/notebooks/spm_scipy_DifferentialEvolution.ipynb index 1b399872c..d106fed38 100644 --- a/examples/notebooks/spm_scipy_DifferentialEvolution.ipynb +++ b/examples/notebooks/spm_scipy_DifferentialEvolution.ipynb @@ -17,7 +17,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -30,27 +30,27 @@ "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: pip in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (23.3.2)\n", - "Requirement already satisfied: ipywidgets in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (8.1.1)\n", - "Requirement already satisfied: comm>=0.1.3 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipywidgets) (0.2.0)\n", - "Requirement already satisfied: ipython>=6.1.0 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipywidgets) (8.18.1)\n", - "Requirement already satisfied: traitlets>=4.3.1 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipywidgets) (5.14.0)\n", - "Requirement already satisfied: 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+101,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -120,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 4, "metadata": { "id": "sBasxv8U04p3" }, @@ -141,7 +141,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -178,7 +178,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 6, "metadata": { "id": "zuvGHWID04p_" }, @@ -206,7 +206,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 7, "metadata": { "id": "WPCybXIJ04qA" }, @@ -239,7 +239,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 8, "metadata": { "id": "etMzRtx404qA" }, @@ -264,7 +264,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 9, "metadata": { "id": "-9OVt0EQ04qB" }, @@ -294,7 +294,7 @@ }, { "cell_type": "code", - "execution_count": 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" + "image/svg+xml": [ + "05100.660.680.70.720.740.760.7805100.660.6650.670.6750.68Negative electrode active material volume fractionPositive electrode active material volume fractionParameter ConvergenceFunction CallFunction CallNegative electrode active material volume fractionPositive electrode active material volume fraction" ] }, "metadata": {}, @@ -581,90 +422,8 @@ "outputs": [ { "data": { - "text/html": [ - " \n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - " \n", - " " + "image/svg+xml": [ + "0.50.550.60.650.70.750.80.40.450.50.550.60.650.70246810Cost LandscapeNegative electrode active material volume fractionPositive electrode active material volume fraction" ] }, "metadata": {}, @@ -672,32 +431,8 @@ }, { "data": { - "text/html": [ - "
" + "image/svg+xml": [ + "0.60.650.70.750.80.850.90.50.550.60.650.70.750.80.40.81.21.622.4Cost LandscapeNegative electrode active material volume fractionPositive electrode active material volume fraction" ] }, "metadata": {}, From d7a71a75b24dd3c245a13b2543be720a241d5ccc Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Thu, 22 Feb 2024 09:29:43 +0000 Subject: [PATCH 28/64] fix missed deletion during merge --- setup.py | 51 --------------------------------------------------- 1 file changed, 51 deletions(-) delete mode 100644 setup.py diff --git a/setup.py b/setup.py deleted file mode 100644 index 8d53b6e92..000000000 --- a/setup.py +++ /dev/null @@ -1,51 +0,0 @@ -from distutils.core import setup -import os -from setuptools import find_packages - -# User-friendly description from README.md -current_directory = os.path.dirname(os.path.abspath(__file__)) -try: - with open(os.path.join(current_directory, "README.md"), encoding="utf-8") as f: - long_description = f.read() -except Exception: - long_description = "" - -# Defines __version__ -root = os.path.abspath(os.path.dirname(__file__)) -with open(os.path.join(root, "pybop", "version.py")) as f: - exec(f.read()) - -setup( - name="pybop", - packages=find_packages("."), - version=__version__, # noqa F821 - license="BSD-3-Clause", - description="Python Battery Optimisation and Parameterisation", - long_description=long_description, - long_description_content_type="text/markdown", - url="https://github.com/pybop-team/PyBOP", - install_requires=[ - "pybamm>=23.5", - "numpy>=1.16", - "scipy>=1.3", - "pandas>=1.0", - "pints>=0.5", - ], - extras_require={ - "plot": ["plotly>=5.0", "kaleido>=0.2"], - "all": ["pybop[plot]"], - "docs": [ - "sphinx>=6", - "pydata-sphinx-theme", - "sphinx-autobuild", - "sphinx-autoapi", - "sphinx_copybutton", - "sphinx_favicon", - "sphinx_design", - "myst-parser", - ], - }, - # https://pypi.org/classifiers/ - classifiers=[], - python_requires=">=3.8,<=3.12", -) From 183df28aaf283c85179916bd82bded32bc1a92b2 Mon Sep 17 00:00:00 2001 From: NicolaCourtier <45851982+NicolaCourtier@users.noreply.github.com> Date: Fri, 23 Feb 2024 14:35:44 +0000 Subject: [PATCH 29/64] Update test_plots.py --- tests/unit/test_plots.py | 31 +++++++++++++++---------------- 1 file changed, 15 insertions(+), 16 deletions(-) diff --git a/tests/unit/test_plots.py b/tests/unit/test_plots.py index 11e935f06..fff8074b0 100644 --- a/tests/unit/test_plots.py +++ b/tests/unit/test_plots.py @@ -13,15 +13,9 @@ def model(self): # Define an example model return pybop.lithium_ion.SPM() - @pytest.mark.unit - def test_model_plots(self): - # Test plotting of Model objects - pass - @pytest.fixture - def problem(self, model): - # Define an example problem - parameters = [ + def parameters(self): + return [ pybop.Parameter( "Negative particle radius [m]", prior=pybop.Gaussian(6e-06, 0.1e-6), @@ -34,12 +28,14 @@ def problem(self, model): ), ] + @pytest.fixture + def dataset(self, model): # Generate data t_eval = np.arange(0, 50, 2) values = model.predict(t_eval=t_eval) # Form dataset - dataset = pybop.Dataset( + return pybop.Dataset( { "Time [s]": t_eval, "Current function [A]": values["Current [A]"].data, @@ -47,13 +43,19 @@ def problem(self, model): } ) - # Generate problem + @pytest.mark.unit + def test_dataset_plots(self, dataset): + # Test plotting of Dataset objects + pybop.plot_dataset(dataset, signal=["Voltage [V]"]) + + @pytest.fixture + def problem(self, model, parameters, dataset): return pybop.FittingProblem(model, parameters, dataset) @pytest.mark.unit - def test_problem_plots(self): + def test_problem_plots(self, problem): # Test plotting of Problem objects - pass + pybop.quick_plot(problem, title="Optimised Comparison") @pytest.fixture def cost(self, problem): @@ -63,9 +65,6 @@ def cost(self, problem): @pytest.mark.unit def test_cost_plots(self, cost): # Test plotting of Cost objects - pybop.quick_plot(cost.x0, cost, title="Optimised Comparison") - - # Plot the cost landscape pybop.plot_cost2d(cost, steps=5) @pytest.fixture @@ -84,4 +83,4 @@ def test_optim_plots(self, optim): pybop.plot_parameters(optim) # Plot the cost landscape with optimisation path - pybop.plot_cost2d(optim.cost, optim=optim, steps=5) + pybop.plot_optim2d(optim, steps=5) From 4cf9108c85a11caf38f7efc94f5adee57d292e34 Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Fri, 1 Mar 2024 12:30:47 +0000 Subject: [PATCH 30/64] Revamp model, problem, and cost object from numpy arrays to dictionary. Update tests, add base model classes to init, cleaner multi-signal interaction --- examples/scripts/spm_CMAES.py | 4 +- examples/scripts/spm_adam.py | 17 ++- examples/scripts/spm_scipymin.py | 4 +- examples/standalone/problem.py | 16 +-- pybop/_problem.py | 39 ++++-- pybop/costs/design_costs.py | 14 ++- pybop/costs/fitting_costs.py | 127 ++++++++++++++------ pybop/models/base_model.py | 31 +++-- pybop/models/empirical/__init__.py | 2 +- pybop/models/lithium_ion/__init__.py | 2 +- pybop/models/lithium_ion/echem_base.py | 11 +- pybop/plotting/plot_problem.py | 24 ++-- tests/integration/test_parameterisations.py | 8 +- tests/unit/test_cost.py | 6 +- tests/unit/test_models.py | 4 +- tests/unit/test_problem.py | 4 +- tests/unit/test_standalone.py | 11 +- 17 files changed, 212 insertions(+), 112 deletions(-) diff --git a/examples/scripts/spm_CMAES.py b/examples/scripts/spm_CMAES.py index 170d560bc..816f6798a 100644 --- a/examples/scripts/spm_CMAES.py +++ b/examples/scripts/spm_CMAES.py @@ -33,11 +33,13 @@ "Time [s]": t_eval, "Current function [A]": values["Current [A]"].data, "Voltage [V]": corrupt_values, + "Bulk open-circuit voltage [V]": values["Bulk open-circuit voltage [V]"].data, } ) +signal = ["Voltage [V]", "Bulk open-circuit voltage [V]"] # Generate problem, cost function, and optimisation class -problem = pybop.FittingProblem(model, parameters, dataset) +problem = pybop.FittingProblem(model, parameters, dataset, signal=signal) cost = pybop.SumSquaredError(problem) optim = pybop.Optimisation(cost, optimiser=pybop.CMAES) optim.set_max_iterations(100) diff --git a/examples/scripts/spm_adam.py b/examples/scripts/spm_adam.py index b8f3d2f50..56d315f47 100644 --- a/examples/scripts/spm_adam.py +++ b/examples/scripts/spm_adam.py @@ -20,7 +20,7 @@ ] # Generate data -sigma = 0.001 +sigma = 0.01 t_eval = np.arange(0, 900, 2) values = model.predict(t_eval=t_eval) corrupt_values = values["Voltage [V]"].data + np.random.normal(0, sigma, len(t_eval)) @@ -31,14 +31,23 @@ "Time [s]": t_eval, "Current function [A]": values["Current [A]"].data, "Voltage [V]": corrupt_values, + "Bulk open-circuit voltage [V]": values["Bulk open-circuit voltage [V]"].data, } ) +signal = ["Voltage [V]", "Bulk open-circuit voltage [V]"] # Generate problem, cost function, and optimisation class -problem = pybop.FittingProblem(model, parameters, dataset) -cost = pybop.SumSquaredError(problem) -optim = pybop.Optimisation(cost, optimiser=pybop.Adam, verbose=True) +problem = pybop.FittingProblem(model, parameters, dataset, signal=signal) +cost = pybop.RootMeanSquaredError(problem) +optim = pybop.Optimisation( + cost, + optimiser=pybop.Adam, + verbose=True, + allow_infeasible_solutions=True, + sigma0=sigma, +) optim.set_max_iterations(100) +optim.set_max_unchanged_iterations(20) # Run optimisation x, final_cost = optim.run() diff --git a/examples/scripts/spm_scipymin.py b/examples/scripts/spm_scipymin.py index 759f8c2e6..db1d783d0 100644 --- a/examples/scripts/spm_scipymin.py +++ b/examples/scripts/spm_scipymin.py @@ -21,12 +21,12 @@ parameters = [ pybop.Parameter( "Negative electrode active material volume fraction", - prior=pybop.Gaussian(0.6, 0.05), + prior=pybop.Gaussian(0.6, 0.02), bounds=[0.5, 0.8], ), pybop.Parameter( "Positive electrode active material volume fraction", - prior=pybop.Gaussian(0.48, 0.05), + prior=pybop.Gaussian(0.48, 0.02), bounds=[0.4, 0.7], ), ] diff --git a/examples/standalone/problem.py b/examples/standalone/problem.py index 5a29138e8..bc9cd31d5 100644 --- a/examples/standalone/problem.py +++ b/examples/standalone/problem.py @@ -14,10 +14,13 @@ def __init__( model=None, check_model=True, signal=None, + default_variables=None, init_soc=None, x0=None, ): - super().__init__(parameters, model, check_model, signal, init_soc, x0) + super().__init__( + parameters, model, check_model, signal, default_variables, init_soc, x0 + ) self._dataset = dataset.data # Check that the dataset contains time and current @@ -37,8 +40,7 @@ def __init__( raise ValueError( f"Time data and {signal} data must be the same length." ) - target = [self._dataset[signal] for signal in self.signal] - self._target = np.vstack(target).T + self._target = {signal: self._dataset[signal] for signal in self.signal} def evaluate(self, x): """ @@ -55,7 +57,7 @@ def evaluate(self, x): The model output y(t) simulated with inputs x. """ - return x[0] * self._time_data + x[1] + return {signal: x[0] * self._time_data + x[1] for signal in self.signal} def evaluateS1(self, x): """ @@ -73,8 +75,8 @@ def evaluateS1(self, x): with given inputs x. """ - y = x[0] * self._time_data + x[1] + y = {signal: x[0] * self._time_data + x[1] for signal in self.signal} - dy = np.dstack([self._time_data, np.zeros(self._time_data.shape)]) + dy = [self._time_data, np.zeros(self._time_data.shape)] - return (np.asarray(y), np.asarray(dy)) + return (y, np.asarray(dy)) diff --git a/pybop/_problem.py b/pybop/_problem.py index 2ecc59515..e2aaee3d0 100644 --- a/pybop/_problem.py +++ b/pybop/_problem.py @@ -1,4 +1,5 @@ import numpy as np +import pybop class BaseProblem: @@ -27,6 +28,7 @@ def __init__( model=None, check_model=True, signal=["Voltage [V]"], + default_variables=[], init_soc=None, x0=None, ): @@ -45,6 +47,11 @@ def __init__( self._time_data = None self._target = None + if isinstance(model, (pybop.BaseModel, pybop.lithium_ion.EChemBaseModel)): + self.default_variables = default_variables + else: + self.default_variables = [] + # Set bounds self.bounds = dict( lower=[param.bounds[0] for param in self.parameters], @@ -148,10 +155,13 @@ def __init__( dataset, check_model=True, signal=["Voltage [V]"], + default_variables=["Time [s]", "Discharge capacity [A.h]"], init_soc=None, x0=None, ): - super().__init__(parameters, model, check_model, signal, init_soc, x0) + super().__init__( + parameters, model, check_model, signal, default_variables, init_soc, x0 + ) self._dataset = dataset.data self.x = self.x0 @@ -161,12 +171,13 @@ def __init__( # Unpack time and target data self._time_data = self._dataset["Time [s]"] self.n_time_data = len(self._time_data) - target = [self._dataset[signal] for signal in self.signal] - self._target = np.vstack(target).T + self._target = {signal: self._dataset[signal] for signal in self.signal} # Add useful parameters to model if model is not None: self._model.signal = self.signal + self._model.default_variables = self.default_variables + self._model.n_parameters = self.n_parameters self._model.n_outputs = self.n_outputs self._model.n_time_data = self.n_time_data @@ -193,14 +204,14 @@ def evaluate(self, x): y : np.ndarray The model output y(t) simulated with inputs x. """ - if (x != self.x).any() and self._model.matched_parameters: + if np.any(x != self.x) and self._model.matched_parameters: for i, param in enumerate(self.parameters): param.update(value=x[i]) self._model.rebuild(parameters=self.parameters) self.x = x - y = np.asarray(self._model.simulate(inputs=x, t_eval=self._time_data)) + y = self._model.simulate(inputs=x, t_eval=self._time_data) return y @@ -229,7 +240,7 @@ def evaluateS1(self, x): t_eval=self._time_data, ) - return (np.asarray(y), np.asarray(dy)) + return (y, np.asarray(dy)) class DesignProblem(BaseProblem): @@ -255,10 +266,13 @@ def __init__( experiment, check_model=True, signal=["Voltage [V]"], + default_variables=["Time [s]", "Current [A]", "Discharge capacity [A.h]"], init_soc=None, x0=None, ): - super().__init__(parameters, model, check_model, signal, init_soc, x0) + super().__init__( + parameters, model, check_model, signal, default_variables, init_soc, x0 + ) self.experiment = experiment # Build the model if required @@ -278,8 +292,9 @@ def __init__( # Add an example dataset for plotting comparison sol = self.evaluate(self.x0) - self._time_data = sol[:, -1] - self._target = sol[:, 0:-1] + self._time_data = sol["Time [s]"] + self._capacity_data = sol["Discharge capacity [A.h]"] + self._target = {key: sol[key] for key in self.signal} self._dataset = None def evaluate(self, x): @@ -307,6 +322,8 @@ def evaluate(self, x): return sol else: - predictions = [sol[signal].data for signal in self.signal + ["Time [s]"]] + predictions = {} + for signal in self.signal + self.default_variables: + predictions[signal] = sol[signal].data - return np.vstack(predictions).T + return predictions diff --git a/pybop/costs/design_costs.py b/pybop/costs/design_costs.py index 5af2ddfe6..f2b452af4 100644 --- a/pybop/costs/design_costs.py +++ b/pybop/costs/design_costs.py @@ -57,9 +57,13 @@ def update_simulation_data(self, initial_conditions): if self.update_capacity: self.problem.model.approximate_capacity(self.problem.x0) solution = self.problem.evaluate(initial_conditions) - self.problem._time_data = solution[:, -1] - self.problem._target = solution[:, 0:-1] - self.dt = solution[1, -1] - solution[0, -1] + + if "Time [s]" not in solution: + raise ValueError("The solution does not contain time data.") + self.problem._time_data = solution["Time [s]"] + self.problem._capacity_data = solution["Discharge capacity [A.h]"] + self.problem._target = {key: solution[key] for key in self.problem.signal} + self.dt = solution["Time [s]"][1] - solution["Time [s]"][0] def _evaluate(self, x, grad=None): """ @@ -123,7 +127,7 @@ def _evaluate(self, x, grad=None): self.problem.model.approximate_capacity(x) solution = self.problem.evaluate(x) - voltage, current = solution[:, 0], solution[:, 1] + voltage, current = solution["Voltage [V]"], solution["Current [A]"] negative_energy_density = -np.trapz(voltage * current, dx=self.dt) / ( 3600 * self.problem.model.cell_mass(self.parameter_set) ) @@ -181,7 +185,7 @@ def _evaluate(self, x, grad=None): self.problem.model.approximate_capacity(x) solution = self.problem.evaluate(x) - voltage, current = solution[:, 0], solution[:, 1] + voltage, current = solution["Voltage [V]"], solution["Current [A]"] negative_energy_density = -np.trapz(voltage * current, dx=self.dt) / ( 3600 * self.problem.model.cell_volume(self.parameter_set) ) diff --git a/pybop/costs/fitting_costs.py b/pybop/costs/fitting_costs.py index 8db3111c6..d8bb212a3 100644 --- a/pybop/costs/fitting_costs.py +++ b/pybop/costs/fitting_costs.py @@ -39,10 +39,23 @@ def _evaluate(self, x, grad=None): """ prediction = self.problem.evaluate(x) - if len(prediction) < len(self._target): - return np.float64(np.inf) # simulation stopped early + for key in prediction: + if key not in ["Time [s]", "Discharge capacity [A.h]"]: + if len(prediction.get(key, [])) != len(self._target.get(key, [])): + return np.float64(np.inf) # prediction doesn't match target + + e = np.array( + [ + np.sqrt(np.mean((prediction[signal] - self._target[signal]) ** 2)) + for signal in prediction + if signal not in ["Time [s]", "Discharge capacity [A.h]"] + ] + ) + + if self.n_outputs == 1: + return e.item() else: - return np.sqrt(np.mean((prediction - self._target) ** 2)) + return np.sum(e) def _evaluateS1(self, x): """ @@ -65,24 +78,38 @@ def _evaluateS1(self, x): If an error occurs during the calculation of the cost or gradient. """ y, dy = self.problem.evaluateS1(x) - if len(y) < len(self._target): - e = np.float64(np.inf) - de = self._de * np.ones(self.n_parameters) - else: - dy = dy.reshape( - ( - self.problem.n_time_data, - self.n_outputs, - self.n_parameters, - ) - ) - r = y - self._target - e = np.sqrt(np.mean((r) ** 2)) - de = np.mean((r.T * dy.T), axis=2) / np.sqrt( - np.mean((r.T * dy.T) ** 2, axis=2) + + for key in y: + if key not in ["Time [s]", "Discharge capacity [A.h]"]: + if len(y.get(key, [])) != len(self._target.get(key, [])): + e = np.float64(np.inf) + de = self._de * np.ones(self.n_parameters) + return e, de + + r = np.array( + [ + y[signal] - self._target[signal] + for signal in y + if signal not in ["Time [s]", "Discharge capacity [A.h]"] + ] + ) + + if self.n_outputs == 1: + r = r.reshape(self.problem.n_time_data) + dy = dy.reshape(self.n_parameters, self.problem.n_time_data) + e = np.sqrt(np.mean(r**2)) + de = np.mean((r * dy), axis=1) / np.sqrt( + np.mean((r * dy) ** 2, axis=1) + np.finfo(float).eps ) + return e.item(), de.flatten() - return e, de.flatten() + else: + r = r.reshape(self.n_outputs, self.problem.n_time_data) + e = np.sqrt(np.mean(r**2, axis=1)) + de = np.mean((r[:, :, np.newaxis] * dy), axis=1) / np.sqrt( + np.mean((r[:, :, np.newaxis] * dy) ** 2, axis=1) + np.finfo(float).eps + ) + return np.sum(e), np.sum(de, axis=1) class SumSquaredError(BaseCost): @@ -128,13 +155,22 @@ def _evaluate(self, x, grad=None): """ prediction = self.problem.evaluate(x) - if len(prediction) < len(self._target): - return np.float64(np.inf) # simulation stopped early + for key in prediction: + if key not in ["Time [s]", "Discharge capacity [A.h]"]: + if len(prediction.get(key, [])) != len(self._target.get(key, [])): + return np.float64(np.inf) # prediction doesn't match target + + e = np.array( + [ + np.sum(((prediction[signal] - self._target[signal]) ** 2), axis=0) + for signal in prediction + if signal not in ["Time [s]", "Discharge capacity [A.h]"] + ] + ) + if self.n_outputs == 1: + return e.item() else: - return np.sum( - (np.sum(((prediction - self._target) ** 2), axis=0)), - axis=0, - ) + return np.sum(e) def _evaluateS1(self, x): """ @@ -157,22 +193,33 @@ def _evaluateS1(self, x): If an error occurs during the calculation of the cost or gradient. """ y, dy = self.problem.evaluateS1(x) - if len(y) < len(self._target): - e = np.float64(np.inf) - de = self._de * np.ones(self.n_parameters) - else: - dy = dy.reshape( - ( - self.problem.n_time_data, - self.n_outputs, - self.n_parameters, - ) - ) - r = y - self._target - e = np.sum(np.sum(r**2, axis=0), axis=0) - de = 2 * np.sum(np.sum((r.T * dy.T), axis=2), axis=1) + for key in y: + if key not in ["Time [s]", "Discharge capacity [A.h]"]: + if len(y.get(key, [])) != len(self._target.get(key, [])): + e = np.float64(np.inf) + de = self._de * np.ones(self.n_parameters) + return e, de + + r = np.array( + [ + y[signal] - self._target[signal] + for signal in y + if signal not in ["Time [s]", "Discharge capacity [A.h]"] + ] + ) - return e, de + if self.n_outputs == 1: + r = r.reshape(self.problem.n_time_data) + dy = dy.reshape(self.n_parameters, self.problem.n_time_data) + e = np.sum(r**2, axis=0) + de = 2 * np.sum((r * dy), axis=1) + return e.item(), de + + else: + r = r.reshape(self.n_outputs, self.problem.n_time_data) + e = np.sum(r**2, axis=0) + de = 2 * np.sum((r[:, :, np.newaxis] * dy), axis=1) + return np.sum(e), np.sum(de, axis=1) def set_fail_gradient(self, de): """ diff --git a/pybop/models/base_model.py b/pybop/models/base_model.py index 1f56f9b4e..ab739bf77 100644 --- a/pybop/models/base_model.py +++ b/pybop/models/base_model.py @@ -58,6 +58,8 @@ def __init__(self, name="Base Model"): self.parameters = None self.dataset = None self.signal = None + self.non_parameters = None + self.default_variables = [] self.matched_parameters = {} self.non_matched_parameters = {} self.fit_keys = [] @@ -353,11 +355,14 @@ def simulate(self, inputs, t_eval) -> np.ndarray[np.float64]: self.built_model, inputs=inputs, t_eval=t_eval ) else: - return [np.inf] + return {signal: [np.inf] for signal in self.signal} - predictions = [sol[signal].data for signal in self.signal] + predictions = { + signal: sol[signal].data + for signal in (self.signal + self.default_variables) + } - return np.vstack(predictions).T + return predictions def simulateS1(self, inputs, t_eval): """ @@ -399,20 +404,20 @@ def simulateS1(self, inputs, t_eval): t_eval=t_eval, calculate_sensitivities=True, ) + predictions = {signal: sol[signal].data for signal in self.signal} - predictions = [sol[signal].data for signal in self.signal] + dy = np.asarray( + [ + sol[signal].sensitivities[key].toarray() + for signal in self.signal + for key in self.fit_keys + ] + ).reshape(self.n_parameters, self.n_time_data, self.n_outputs) - sensitivities = [ - np.array( - [[sol[signal].sensitivities[key]] for signal in self.signal] - ).reshape(len(sol[self.signal[0]].data), self.n_outputs) - for key in self.fit_keys - ] - - return np.vstack(predictions).T, np.dstack(sensitivities) + return predictions, dy else: - return [np.inf], [np.inf] + return {signal: [np.inf] for signal in self.signal}, [np.inf] def predict( self, diff --git a/pybop/models/empirical/__init__.py b/pybop/models/empirical/__init__.py index 587906276..6a28b0a98 100644 --- a/pybop/models/empirical/__init__.py +++ b/pybop/models/empirical/__init__.py @@ -1,4 +1,4 @@ # # Import lithium ion based models # -from .ecm import Thevenin +from .ecm import ECircuitModel, Thevenin diff --git a/pybop/models/lithium_ion/__init__.py b/pybop/models/lithium_ion/__init__.py index d61591b4f..4dca05ea0 100644 --- a/pybop/models/lithium_ion/__init__.py +++ b/pybop/models/lithium_ion/__init__.py @@ -1,4 +1,4 @@ # # Import lithium ion based models # -from .echem import SPM, SPMe +from .echem import EChemBaseModel, SPM, SPMe diff --git a/pybop/models/lithium_ion/echem_base.py b/pybop/models/lithium_ion/echem_base.py index 7e5c869fa..6a642ff34 100644 --- a/pybop/models/lithium_ion/echem_base.py +++ b/pybop/models/lithium_ion/echem_base.py @@ -223,9 +223,14 @@ def approximate_capacity(self, x): # Calculate average voltage positive_electrode_ocp = self._parameter_set["Positive electrode OCP [V]"] negative_electrode_ocp = self._parameter_set["Negative electrode OCP [V]"] - average_voltage = positive_electrode_ocp(mean_sto_pos) - negative_electrode_ocp( - mean_sto_neg - ) + try: + average_voltage = positive_electrode_ocp( + mean_sto_pos + ) - negative_electrode_ocp(mean_sto_neg) + except TypeError: + average_voltage = positive_electrode_ocp([mean_sto_pos]).evaluate()[0][ + 0 + ] - negative_electrode_ocp(mean_sto_neg) # Super hacky, needs to be fixed # Calculate and update nominal capacity theoretical_capacity = theoretical_energy / average_voltage diff --git a/pybop/plotting/plot_problem.py b/pybop/plotting/plot_problem.py index 57aebe048..67f63d757 100644 --- a/pybop/plotting/plot_problem.py +++ b/pybop/plotting/plot_problem.py @@ -32,37 +32,37 @@ def quick_plot(problem, parameter_values=None, show=True, **layout_kwargs): parameter_values = problem.x0 # Extract the time data and evaluate the model's output and target values - reference_time_data = problem.time_data() + xaxis_data = problem.time_data() model_output = problem.evaluate(parameter_values) target_output = problem.target() # Create a plot for each output figure_list = [] - for i in range(0, problem.n_outputs): + for i in problem.signal: default_layout_options = dict( title="Scatter Plot", xaxis_title="Time / s", - yaxis_title=pybop.StandardPlot.remove_brackets(problem.signal[i]), + yaxis_title=pybop.StandardPlot.remove_brackets(i), ) # Create a plotting dictionary if isinstance(problem, pybop.DesignProblem): trace_name = "Optimised" - opt_time_data = model_output[:, -1] + opt_time_data = model_output["Time [s]"] else: trace_name = "Model" - opt_time_data = reference_time_data + opt_time_data = xaxis_data plot_dict = pybop.StandardPlot( x=opt_time_data, - y=model_output[:, i], + y=model_output[i], layout_options=default_layout_options, trace_names=trace_name, ) target_trace = plot_dict.create_trace( - x=reference_time_data, - y=target_output[:, i], + x=xaxis_data, + y=target_output[i], name="Reference", mode="markers", showlegend=True, @@ -71,12 +71,12 @@ def quick_plot(problem, parameter_values=None, show=True, **layout_kwargs): if isinstance(problem, pybop.FittingProblem): # Compute the standard deviation as proxy for uncertainty - plot_dict.sigma = np.std(model_output[:, i] - target_output[:, i]) + plot_dict.sigma = np.std(model_output[i] - target_output[i]) # Convert x and upper and lower limits into lists to create a filled trace - x = reference_time_data.tolist() - y_upper = (model_output[:, i] + plot_dict.sigma).tolist() - y_lower = (model_output[:, i] - plot_dict.sigma).tolist() + x = xaxis_data.tolist() + y_upper = (model_output[i] + plot_dict.sigma).tolist() + y_lower = (model_output[i] - plot_dict.sigma).tolist() fill_trace = plot_dict.create_trace( x=x + x[::-1], diff --git a/tests/integration/test_parameterisations.py b/tests/integration/test_parameterisations.py index e9d7cd9af..10d1278c1 100644 --- a/tests/integration/test_parameterisations.py +++ b/tests/integration/test_parameterisations.py @@ -81,6 +81,7 @@ def test_spm_optimisers(self, optimiser, spm_costs, x0): if optimiser in [pybop.CMAES]: parameterisation.set_f_guessed_tracking(True) + parameterisation.cost.problem._model.allow_infeasible_solutions = False assert parameterisation._use_f_guessed is True parameterisation.set_max_iterations(1) x, final_cost = parameterisation.run() @@ -126,12 +127,15 @@ def spm_two_signal_cost(self, parameters, model, x0): { "Time [s]": solution["Time [s]"].data, "Current function [A]": solution["Current [A]"].data, - "Terminal voltage [V]": solution["Terminal voltage [V]"].data, + "Voltage [V]": solution["Voltage [V]"].data, + "Bulk open-circuit voltage [V]": solution[ + "Bulk open-circuit voltage [V]" + ].data, } ) # Define the cost to optimise - signal = ["Terminal voltage [V]", "Time [s]"] + signal = ["Voltage [V]", "Bulk open-circuit voltage [V]"] problem = pybop.FittingProblem( model, parameters, dataset, signal=signal, init_soc=init_soc ) diff --git a/tests/unit/test_cost.py b/tests/unit/test_cost.py index 29172932a..7d3a4ea63 100644 --- a/tests/unit/test_cost.py +++ b/tests/unit/test_cost.py @@ -122,7 +122,7 @@ def test_costs(self, cost): ) # Test type of returned value - assert type(cost([0.5])) == np.float64 + assert np.isscalar(cost([0.5])) if isinstance(cost, pybop.ObserverCost): with pytest.raises(NotImplementedError): @@ -137,7 +137,7 @@ def test_costs(self, cost): if isinstance(cost, pybop.SumSquaredError): e, de = cost.evaluateS1([0.5]) - assert type(e) == np.float64 + assert np.isscalar(e) assert type(de) == np.ndarray # Test option setting @@ -187,7 +187,7 @@ def test_energy_density_costs( cost = cost_class(problem) # Test type of returned value - assert type(cost([0.5])) == np.float64 + assert np.isscalar(cost([0.5])) assert cost([0.4]) <= 0 # Should be a viable design assert cost([0.8]) == np.inf # Should exceed active material + porosity < 1 assert cost([1.4]) == np.inf # Definitely not viable diff --git a/tests/unit/test_models.py b/tests/unit/test_models.py index ce9be93e8..2e7f7ea5e 100644 --- a/tests/unit/test_models.py +++ b/tests/unit/test_models.py @@ -202,9 +202,9 @@ def test_simulate(self): model.signal = ["y_0"] inputs = {} t_eval = np.linspace(0, 10, 100) - expected = y0 * np.exp(-k * t_eval).reshape(-1, 1) + expected = y0 * np.exp(-k * t_eval) solved = model.simulate(inputs, t_eval) - np.testing.assert_array_almost_equal(solved, expected, decimal=5) + np.testing.assert_array_almost_equal(solved["y_0"], expected, decimal=5) with pytest.raises(ValueError): ExponentialDecay(n_states=-1) diff --git a/tests/unit/test_problem.py b/tests/unit/test_problem.py index 0cdfd8bce..5b436a319 100644 --- a/tests/unit/test_problem.py +++ b/tests/unit/test_problem.py @@ -167,7 +167,7 @@ def test_problem_construct_with_model_predict( assert problem._model._built_model is not None with pytest.raises(AssertionError): np.testing.assert_allclose( - out["Terminal voltage [V]"].data, - problem_output, + out["Voltage [V]"].data, + problem_output["Voltage [V]"], atol=1e-5, ) diff --git a/tests/unit/test_standalone.py b/tests/unit/test_standalone.py index 4ba611a96..f5b0a33e6 100644 --- a/tests/unit/test_standalone.py +++ b/tests/unit/test_standalone.py @@ -53,13 +53,18 @@ def test_standalone_problem(self): # Test the Problem with a Cost rmse_cost = pybop.RootMeanSquaredError(problem) - x = rmse_cost([1, 2]) + rmse_x = rmse_cost([1, 2]) + rmse_grad_x = rmse_cost.evaluateS1([1, 2]) - np.testing.assert_allclose(x, 3.138, atol=1e-2) + np.testing.assert_allclose(rmse_x, 3.05615, atol=1e-2) + np.testing.assert_allclose(rmse_grad_x[1], [-0.81758337, 0.0], atol=1e-2) # Test the sensitivities sums_cost = pybop.SumSquaredError(problem) - sums_cost.evaluateS1([1, 2]) + x = sums_cost.evaluateS1([1, 2]) + + np.testing.assert_allclose(x[0], 934.006734006734, atol=1e-2) + np.testing.assert_allclose(x[1], [-334.006734, 0.0], atol=1e-2) # Test incorrect number of initial parameter values with pytest.raises(ValueError): From 3428c97608d788c9342d6fd429f1eb4272e7888c Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Fri, 1 Mar 2024 14:13:02 +0000 Subject: [PATCH 31/64] Fix ukf examples, temporarily limits ukf to signal output model --- examples/scripts/exp_UKF.py | 5 ++- pybop/observers/observer.py | 64 +++++++++++++++++------------ pybop/observers/unscented_kalman.py | 3 +- 3 files changed, 42 insertions(+), 30 deletions(-) diff --git a/examples/scripts/exp_UKF.py b/examples/scripts/exp_UKF.py index 965775ecd..77e0c6456 100644 --- a/examples/scripts/exp_UKF.py +++ b/examples/scripts/exp_UKF.py @@ -42,7 +42,8 @@ simulator = pybop.Observer(parameters, model, signal=["2y"], x0=x0) simulator._time_data = t_eval measurements = simulator.evaluate(x0) -measurements = measurements[:, 0] + +measurements = measurements["2y"] # Verification step: Compare by plotting go = pybop.PlotlyManager().go @@ -85,7 +86,7 @@ # Verification step: Find the maximum likelihood estimate given the true parameters estimation = observer.evaluate(x0) -estimation = estimation[:, 0] +estimation = estimation["2y"] # Verification step: Add the estimate to the plot line4 = go.Scatter(x=t_eval, y=estimation, name="Estimated trajectory", mode="lines") diff --git a/pybop/observers/observer.py b/pybop/observers/observer.py index 0a93fe8a4..173d9f3c3 100644 --- a/pybop/observers/observer.py +++ b/pybop/observers/observer.py @@ -53,6 +53,7 @@ def __init__( self._state = model.reinit(inputs) self._model = model self._signal = self.signal + self._n_outputs = len(self._signal) def reset(self, inputs: Inputs) -> None: self._state = self._model.reinit(inputs) @@ -78,9 +79,7 @@ def observe(self, time: float, value: Optional[np.ndarray] = None) -> float: self._state = self._model.step(self._state, time) return 0.0 - def log_likelihood( - self, values: np.ndarray, times: np.ndarray, inputs: Inputs - ) -> float: + def log_likelihood(self, values: dict, times: np.ndarray, inputs: Inputs) -> float: """ Returns the log likelihood of the model given the values and inputs. @@ -93,16 +92,22 @@ def log_likelihood( inputs : Inputs The inputs to the model. """ - if len(values) != len(times): - raise ValueError("values and times must have the same length.") - log_likelihood = 0.0 - self.reset(inputs) - for t, v in zip(times, values): - try: - log_likelihood += self.observe(t, v) - except Exception: - return np.float64(-np.inf) - return log_likelihood + if self._n_outputs == 1: + signal = self._signal[0] + if len(values[signal]) != len(times): + raise ValueError("values and times must have the same length.") + log_likelihood = 0.0 + self.reset(inputs) + for t, v in zip(times, values[signal]): + try: + log_likelihood += self.observe(t, v) + except Exception: + return np.float64(-np.inf) + return log_likelihood + else: + raise ValueError( + "Obersever.log_likelihood is currently restricted to single output models." + ) def get_current_state(self) -> TimeSeriesState: """ @@ -156,17 +161,24 @@ def evaluate(self, x): inputs[param.name] = x[i] self.reset(inputs) - output = [] - if hasattr(self, "_dataset"): - ym = self._target - for i, t in enumerate(self._time_data): - self.observe(t, ym[i]) - ys = self.get_current_measure() - output.append(ys) + if self._n_outputs == 1: + signal = self._signal[0] + output = [] + if hasattr(self, "_dataset"): + ym = self._target[signal] + for i, t in enumerate(self._time_data): + self.observe(t, ym[i]) + ys = self.get_current_measure() + output.append(ys) + else: + for t in self._time_data: + self.observe(t) + ys = self.get_current_measure() + output.append(ys) + + out = {signal: np.vstack(output) for signal in self._signal} + return out else: - for t in self._time_data: - self.observe(t) - ys = self.get_current_measure() - output.append(ys) - - return np.vstack(output) + raise ValueError( + "Observer is currently restricted to single output models." + ) diff --git a/pybop/observers/unscented_kalman.py b/pybop/observers/unscented_kalman.py index 62b9d0a79..62e98d917 100644 --- a/pybop/observers/unscented_kalman.py +++ b/pybop/observers/unscented_kalman.py @@ -62,8 +62,7 @@ def __init__( self._time_data = self._dataset["Time [s]"] self.n_time_data = len(self._time_data) - target = [self._dataset[signal] for signal in self.signal] - self._target = np.vstack(target).T + self._target = {signal: self._dataset[signal] for signal in self.signal} # Add useful parameters to model if model is not None: From 43521dae672d82ede892d4c21afa2eb4a53146a7 Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Sat, 2 Mar 2024 19:05:46 +0000 Subject: [PATCH 32/64] default_variables to additional_variables w/ docstrings, updt. observer tests, dict output observer.evaluate() --- examples/scripts/exp_UKF.py | 6 ++--- pybop/_problem.py | 38 ++++++++++++++++++++++------- pybop/models/base_model.py | 5 ++-- pybop/observers/observer.py | 33 +++++++++++++------------ pybop/observers/unscented_kalman.py | 5 +++- tests/unit/test_observers.py | 4 +-- 6 files changed, 57 insertions(+), 34 deletions(-) diff --git a/examples/scripts/exp_UKF.py b/examples/scripts/exp_UKF.py index 77e0c6456..bbbc6f950 100644 --- a/examples/scripts/exp_UKF.py +++ b/examples/scripts/exp_UKF.py @@ -43,15 +43,15 @@ simulator._time_data = t_eval measurements = simulator.evaluate(x0) -measurements = measurements["2y"] - # Verification step: Compare by plotting go = pybop.PlotlyManager().go line1 = go.Scatter(x=t_eval, y=corrupt_values, name="Corrupt values", mode="markers") line2 = go.Scatter( x=t_eval, y=expected_values, name="Expected trajectory", mode="lines" ) -line3 = go.Scatter(x=t_eval, y=measurements, name="Observed values", mode="markers") +line3 = go.Scatter( + x=t_eval, y=measurements["2y"], name="Observed values", mode="markers" +) fig = go.Figure(data=[line1, line2, line3]) # Form dataset diff --git a/pybop/_problem.py b/pybop/_problem.py index e2aaee3d0..f1f0e8c82 100644 --- a/pybop/_problem.py +++ b/pybop/_problem.py @@ -16,6 +16,8 @@ class BaseProblem: Flag to indicate if the model should be checked (default: True). signal: List[str] The signal to observe. + additional_variables : List[str], optional + Additional variables to observe and store in the solution (default: []). init_soc : float, optional Initial state of charge (default: None). x0 : np.ndarray, optional @@ -28,7 +30,7 @@ def __init__( model=None, check_model=True, signal=["Voltage [V]"], - default_variables=[], + additional_variables=[], init_soc=None, x0=None, ): @@ -48,9 +50,9 @@ def __init__( self._target = None if isinstance(model, (pybop.BaseModel, pybop.lithium_ion.EChemBaseModel)): - self.default_variables = default_variables + self.additional_variables = additional_variables else: - self.default_variables = [] + self.additional_variables = [] # Set bounds self.bounds = dict( @@ -146,6 +148,12 @@ class FittingProblem(BaseProblem): Dataset object containing the data to fit the model to. signal : str, optional The signal to fit (default: "Voltage [V]"). + additional_variables : List[str], optional + Additional variables to observe and store in the solution (default: []). + init_soc : float, optional + Initial state of charge (default: None). + x0 : np.ndarray, optional + Initial parameter values (default: None). """ def __init__( @@ -155,12 +163,13 @@ def __init__( dataset, check_model=True, signal=["Voltage [V]"], - default_variables=["Time [s]", "Discharge capacity [A.h]"], + additional_variables=[], init_soc=None, x0=None, ): + additional_variables += ["Time [s]", "Discharge capacity [A.h]"] super().__init__( - parameters, model, check_model, signal, default_variables, init_soc, x0 + parameters, model, check_model, signal, additional_variables, init_soc, x0 ) self._dataset = dataset.data self.x = self.x0 @@ -176,7 +185,7 @@ def __init__( # Add useful parameters to model if model is not None: self._model.signal = self.signal - self._model.default_variables = self.default_variables + self._model.additional_variables = self.additional_variables self._model.n_parameters = self.n_parameters self._model.n_outputs = self.n_outputs self._model.n_time_data = self.n_time_data @@ -257,6 +266,16 @@ class DesignProblem(BaseProblem): List of parameters for the problem. experiment : object The experimental setup to apply the model to. + check_model : bool, optional + Flag to indicate if the model parameters should be checked for feasibility each iteration (default: True). + signal : str, optional + The signal to fit (default: "Voltage [V]"). + additional_variables : List[str], optional + Additional variables to observe and store in the solution (default: []). + init_soc : float, optional + Initial state of charge (default: None). + x0 : np.ndarray, optional + Initial parameter values (default: None). """ def __init__( @@ -266,12 +285,13 @@ def __init__( experiment, check_model=True, signal=["Voltage [V]"], - default_variables=["Time [s]", "Current [A]", "Discharge capacity [A.h]"], + additional_variables=[], init_soc=None, x0=None, ): + additional_variables += ["Time [s]", "Current [A]", "Discharge capacity [A.h]"] super().__init__( - parameters, model, check_model, signal, default_variables, init_soc, x0 + parameters, model, check_model, signal, additional_variables, init_soc, x0 ) self.experiment = experiment @@ -323,7 +343,7 @@ def evaluate(self, x): else: predictions = {} - for signal in self.signal + self.default_variables: + for signal in self.signal + self.additional_variables: predictions[signal] = sol[signal].data return predictions diff --git a/pybop/models/base_model.py b/pybop/models/base_model.py index ab739bf77..8e291676f 100644 --- a/pybop/models/base_model.py +++ b/pybop/models/base_model.py @@ -58,8 +58,7 @@ def __init__(self, name="Base Model"): self.parameters = None self.dataset = None self.signal = None - self.non_parameters = None - self.default_variables = [] + self.additional_variables = [] self.matched_parameters = {} self.non_matched_parameters = {} self.fit_keys = [] @@ -359,7 +358,7 @@ def simulate(self, inputs, t_eval) -> np.ndarray[np.float64]: predictions = { signal: sol[signal].data - for signal in (self.signal + self.default_variables) + for signal in (self.signal + self.additional_variables) } return predictions diff --git a/pybop/observers/observer.py b/pybop/observers/observer.py index 173d9f3c3..ce482ec8d 100644 --- a/pybop/observers/observer.py +++ b/pybop/observers/observer.py @@ -22,6 +22,8 @@ class Observer(BaseProblem): Flag to indicate if the model should be checked (default: True). signal: List[str] The signal to observe. + additional_variables : List[str], optional + Additional variables to observe and store in the solution (default: []). init_soc : float, optional Initial state of charge (default: None). x0 : np.ndarray, optional @@ -37,10 +39,13 @@ def __init__( model: BaseModel, check_model=True, signal=["Voltage [V]"], + additional_variables=[], init_soc=None, x0=None, ) -> None: - super().__init__(parameters, model, check_model, signal, init_soc, x0) + super().__init__( + parameters, model, check_model, signal, additional_variables, init_soc, x0 + ) if model._built_model is None: raise ValueError("Only built models can be used in Observers") if model.signal is None: @@ -161,24 +166,20 @@ def evaluate(self, x): inputs[param.name] = x[i] self.reset(inputs) - if self._n_outputs == 1: - signal = self._signal[0] - output = [] - if hasattr(self, "_dataset"): + output = {} + ys = [] + if hasattr(self, "_dataset"): + for signal in self._signal: ym = self._target[signal] for i, t in enumerate(self._time_data): self.observe(t, ym[i]) - ys = self.get_current_measure() - output.append(ys) - else: + ys.append(self.get_current_measure()) + output[signal] = np.vstack(ys) + else: + for signal in self._signal: for t in self._time_data: self.observe(t) - ys = self.get_current_measure() - output.append(ys) + ys.append(self.get_current_measure()) + output[signal] = np.vstack(ys) - out = {signal: np.vstack(output) for signal in self._signal} - return out - else: - raise ValueError( - "Observer is currently restricted to single output models." - ) + return output diff --git a/pybop/observers/unscented_kalman.py b/pybop/observers/unscented_kalman.py index 62e98d917..e0dd7b8f6 100644 --- a/pybop/observers/unscented_kalman.py +++ b/pybop/observers/unscented_kalman.py @@ -50,10 +50,13 @@ def __init__( dataset=None, check_model=True, signal=["Voltage [V]"], + additional_variables=[], init_soc=None, x0=None, ) -> None: - super().__init__(parameters, model, check_model, signal, init_soc, x0) + super().__init__( + parameters, model, check_model, signal, additional_variables, init_soc, x0 + ) if dataset is not None: self._dataset = dataset.data diff --git a/tests/unit/test_observers.py b/tests/unit/test_observers.py index ab77428c5..e2c44d74c 100644 --- a/tests/unit/test_observers.py +++ b/tests/unit/test_observers.py @@ -62,7 +62,7 @@ def test_observer(self, model, parameters, x0): observer.observe(-1) with pytest.raises(ValueError): observer.log_likelihood( - t_eval, np.array([1]), inputs=observer._state.inputs + {"2y": t_eval}, np.array([1]), inputs=observer._state.inputs ) # Test covariance @@ -81,7 +81,7 @@ def test_observer(self, model, parameters, x0): "Output": expected, } ) - observer._target = expected + observer._target = {"2y": expected} observer.evaluate(x0) @pytest.mark.unit From 67d28876c50e303eb359707f80023f38fbbc0a74 Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Mon, 4 Mar 2024 13:50:13 +0000 Subject: [PATCH 33/64] Fix integration test logic, add gradient landscape plots, pin pytest version due to breaking change in 8.1.0 --- examples/scripts/exp_UKF.py | 5 +- examples/scripts/spm_adam.py | 39 ++++++-- noxfile.py | 2 +- pybop/costs/fitting_costs.py | 13 +-- pybop/plotting/plot_cost2d.py | 47 ++++++++- pyproject.toml | 2 +- tests/integration/test_parameterisations.py | 100 +++++++++++--------- 7 files changed, 141 insertions(+), 67 deletions(-) diff --git a/examples/scripts/exp_UKF.py b/examples/scripts/exp_UKF.py index bbbc6f950..d7d838cc0 100644 --- a/examples/scripts/exp_UKF.py +++ b/examples/scripts/exp_UKF.py @@ -86,10 +86,11 @@ # Verification step: Find the maximum likelihood estimate given the true parameters estimation = observer.evaluate(x0) -estimation = estimation["2y"] # Verification step: Add the estimate to the plot -line4 = go.Scatter(x=t_eval, y=estimation, name="Estimated trajectory", mode="lines") +line4 = go.Scatter( + x=t_eval, y=estimation["2y"], name="Estimated trajectory", mode="lines" +) fig.add_trace(line4) fig.show() diff --git a/examples/scripts/spm_adam.py b/examples/scripts/spm_adam.py index 56d315f47..78d484b45 100644 --- a/examples/scripts/spm_adam.py +++ b/examples/scripts/spm_adam.py @@ -20,34 +20,50 @@ ] # Generate data -sigma = 0.01 -t_eval = np.arange(0, 900, 2) -values = model.predict(t_eval=t_eval) -corrupt_values = values["Voltage [V]"].data + np.random.normal(0, sigma, len(t_eval)) +init_soc = 0.5 +sigma = 0.003 +experiment = pybop.Experiment( + [ + ( + "Discharge at 0.5C for 3 minutes (1 second period)", + "Charge at 0.5C for 3 minutes (1 second period)", + ), + ] + * 2 +) +values = model.predict(init_soc=init_soc, experiment=experiment) + + +def noise(sigma): + return np.random.normal(0, sigma, len(values["Voltage [V]"].data)) + # Form dataset dataset = pybop.Dataset( { - "Time [s]": t_eval, + "Time [s]": values["Time [s]"].data, "Current function [A]": values["Current [A]"].data, - "Voltage [V]": corrupt_values, - "Bulk open-circuit voltage [V]": values["Bulk open-circuit voltage [V]"].data, + "Voltage [V]": values["Voltage [V]"].data + noise(sigma), + "Bulk open-circuit voltage [V]": values["Bulk open-circuit voltage [V]"].data + + noise(sigma), } ) signal = ["Voltage [V]", "Bulk open-circuit voltage [V]"] # Generate problem, cost function, and optimisation class -problem = pybop.FittingProblem(model, parameters, dataset, signal=signal) +problem = pybop.FittingProblem( + model, parameters, dataset, signal=signal, init_soc=init_soc +) cost = pybop.RootMeanSquaredError(problem) optim = pybop.Optimisation( cost, optimiser=pybop.Adam, verbose=True, allow_infeasible_solutions=True, - sigma0=sigma, + sigma0=0.05, ) optim.set_max_iterations(100) -optim.set_max_unchanged_iterations(20) +optim.set_max_unchanged_iterations(45) # Run optimisation x, final_cost = optim.run() @@ -64,3 +80,6 @@ # Plot the cost landscape with optimisation path pybop.plot_optim2d(optim, steps=15) + +# Plot the cost and gradient landscapes +pybop.plot_cost2d(cost, gradient=True, steps=3) diff --git a/noxfile.py b/noxfile.py index c732bc3f9..5784e90dd 100644 --- a/noxfile.py +++ b/noxfile.py @@ -33,7 +33,7 @@ def coverage(session): "--cov-report=xml", ) session.run( - "pytest", "--plots", "--cov", "--cov-append", "--cov-report=xml", "-n", "1" + "pytest", "--plots", "--cov", "--cov-append", "--cov-report=xml", "-n", "0" ) diff --git a/pybop/costs/fitting_costs.py b/pybop/costs/fitting_costs.py index d8bb212a3..eb21001a5 100644 --- a/pybop/costs/fitting_costs.py +++ b/pybop/costs/fitting_costs.py @@ -98,16 +98,17 @@ def _evaluateS1(self, x): r = r.reshape(self.problem.n_time_data) dy = dy.reshape(self.n_parameters, self.problem.n_time_data) e = np.sqrt(np.mean(r**2)) - de = np.mean((r * dy), axis=1) / np.sqrt( - np.mean((r * dy) ** 2, axis=1) + np.finfo(float).eps + de = np.mean((r * dy), axis=1) / ( + np.sqrt(np.mean((r * dy) ** 2, axis=1) + np.finfo(float).eps) ) return e.item(), de.flatten() else: r = r.reshape(self.n_outputs, self.problem.n_time_data) e = np.sqrt(np.mean(r**2, axis=1)) - de = np.mean((r[:, :, np.newaxis] * dy), axis=1) / np.sqrt( - np.mean((r[:, :, np.newaxis] * dy) ** 2, axis=1) + np.finfo(float).eps + de = np.mean((r[:, :, np.newaxis] * dy), axis=1) / ( + np.sqrt(np.mean((r[:, :, np.newaxis] * dy) ** 2, axis=1)) + + np.finfo(float).eps ) return np.sum(e), np.sum(de, axis=1) @@ -162,7 +163,7 @@ def _evaluate(self, x, grad=None): e = np.array( [ - np.sum(((prediction[signal] - self._target[signal]) ** 2), axis=0) + np.sum(((prediction[signal] - self._target[signal]) ** 2)) for signal in prediction if signal not in ["Time [s]", "Discharge capacity [A.h]"] ] @@ -217,7 +218,7 @@ def _evaluateS1(self, x): else: r = r.reshape(self.n_outputs, self.problem.n_time_data) - e = np.sum(r**2, axis=0) + e = np.sum(r**2, axis=1) de = 2 * np.sum((r[:, :, np.newaxis] * dy), axis=1) return np.sum(e), np.sum(de, axis=1) diff --git a/pybop/plotting/plot_cost2d.py b/pybop/plotting/plot_cost2d.py index 6e4291173..30c2ef661 100644 --- a/pybop/plotting/plot_cost2d.py +++ b/pybop/plotting/plot_cost2d.py @@ -3,7 +3,9 @@ import numpy as np -def plot_cost2d(cost, bounds=None, steps=10, show=True, **layout_kwargs): +def plot_cost2d( + cost, gradient=False, bounds=None, steps=10, show=True, **layout_kwargs +): """ Plot a 2D visualisation of a cost landscape using Plotly. @@ -54,6 +56,23 @@ def plot_cost2d(cost, bounds=None, steps=10, show=True, **layout_kwargs): for j, yj in enumerate(y): costs[j, i] = cost(np.array([xi, yj])) + if gradient: + grad_parameter_costs = [] + + # Determine the number of gradient outputs from cost.evaluateS1 + num_gradients = len(cost.evaluateS1(np.array([x[0], y[0]]))[1]) + + # Create an array to hold each gradient output & populate + grads = [np.zeros((len(y), len(x))) for _ in range(num_gradients)] + for i, xi in enumerate(x): + for j, yj in enumerate(y): + (*current_grads,) = cost.evaluateS1(np.array([xi, yj]))[1] + for k, grad_output in enumerate(current_grads): + grads[k][j, i] = grad_output + + # Append the arrays to the grad_parameter_costs list + grad_parameter_costs.extend(grads) + # Import plotly only when needed go = pybop.PlotlyManager().go @@ -80,6 +99,32 @@ def plot_cost2d(cost, bounds=None, steps=10, show=True, **layout_kwargs): elif show: fig.show() + if gradient: + grad_figs = [] + for i, grad_costs in enumerate(grad_parameter_costs): + # Update title for gradient plots + updated_layout_options = layout_options.copy() + updated_layout_options["title"] = f"Gradient for Parameter: {i+1}" + + # Create contour plot with updated layout options + grad_layout = go.Layout(updated_layout_options) + + # Create fig + grad_fig = go.Figure( + data=[go.Contour(x=x, y=y, z=grad_costs)], layout=grad_layout + ) + grad_fig.update_layout(**layout_kwargs) + + if "ipykernel" in sys.modules and show: + grad_fig.show("svg") + elif show: + grad_fig.show() + + # append grad_fig to list + grad_figs.append(grad_fig) + + return fig, grad_figs + return fig diff --git a/pyproject.toml b/pyproject.toml index 36eb03a43..3287989fd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -41,7 +41,7 @@ dev = [ "nox", "nbmake", "pre-commit", - "pytest>=6", + "pytest<=8", "pytest-cov", "pytest-mock", "pytest-xdist", diff --git a/tests/integration/test_parameterisations.py b/tests/integration/test_parameterisations.py index 10d1278c1..33ad4f364 100644 --- a/tests/integration/test_parameterisations.py +++ b/tests/integration/test_parameterisations.py @@ -8,6 +8,12 @@ class TestModelParameterisation: A class to test the model parameterisation methods. """ + @pytest.fixture(autouse=True) + def setup(self): + self.ground_truth = np.array([0.55, 0.55]) + np.random.normal( + loc=0.0, scale=0.05, size=2 + ) + @pytest.fixture def model(self): parameter_set = pybop.ParameterSet.pybamm("Chen2020") @@ -18,21 +24,17 @@ def parameters(self): return [ pybop.Parameter( "Negative electrode active material volume fraction", - prior=pybop.Gaussian(0.6, 0.02), - bounds=[0.375, 0.7], + prior=pybop.Gaussian(0.55, 0.05), + bounds=[0.375, 0.75], ), pybop.Parameter( "Positive electrode active material volume fraction", - prior=pybop.Gaussian(0.5, 0.02), - bounds=[0.375, 0.625], + prior=pybop.Gaussian(0.55, 0.05), + bounds=[0.375, 0.75], ), ] - @pytest.fixture - def x0(self): - return np.array([0.63, 0.51]) - - @pytest.fixture(params=[0.3, 0.7]) + @pytest.fixture(params=[0.4, 0.7]) def init_soc(self, request): return request.param @@ -40,20 +42,24 @@ def init_soc(self, request): def cost_class(self, request): return request.param + def noise(self, sigma, values): + return np.random.normal(0, sigma, values) + @pytest.fixture - def spm_costs(self, parameters, model, x0, cost_class, init_soc): + def spm_costs(self, model, parameters, cost_class, init_soc): # Form dataset - solution = self.getdata(model, x0, init_soc) + solution = self.getdata(model, self.ground_truth, init_soc) dataset = pybop.Dataset( { "Time [s]": solution["Time [s]"].data, "Current function [A]": solution["Current [A]"].data, - "Terminal voltage [V]": solution["Terminal voltage [V]"].data, + "Voltage [V]": solution["Voltage [V]"].data + + self.noise(0.002, len(solution["Time [s]"].data)), } ) # Define the cost to optimise - signal = ["Terminal voltage [V]"] + signal = ["Voltage [V]"] problem = pybop.FittingProblem( model, parameters, dataset, signal=signal, init_soc=init_soc ) @@ -74,10 +80,14 @@ def spm_costs(self, parameters, model, x0, cost_class, init_soc): ], ) @pytest.mark.integration - def test_spm_optimisers(self, optimiser, spm_costs, x0): + def test_spm_optimisers(self, optimiser, spm_costs): # Test each optimiser - parameterisation = pybop.Optimisation(cost=spm_costs, optimiser=optimiser) + initial_cost = spm_costs(spm_costs.x0) + parameterisation = pybop.Optimisation( + cost=spm_costs, optimiser=optimiser, sigma0=0.05 + ) parameterisation.set_max_unchanged_iterations(iterations=25, threshold=5e-4) + parameterisation.set_max_iterations(125) if optimiser in [pybop.CMAES]: parameterisation.set_f_guessed_tracking(True) @@ -102,35 +112,34 @@ def test_spm_optimisers(self, optimiser, spm_costs, x0): parameterisation.optimiser.set_population_size(-5) parameterisation.optimiser.set_population_size(5) - parameterisation.set_max_iterations(125) x, final_cost = parameterisation.run() elif optimiser in [pybop.SciPyMinimize]: parameterisation.cost.problem._model.allow_infeasible_solutions = False - parameterisation.set_max_iterations(125) x, final_cost = parameterisation.run() else: - parameterisation.set_max_iterations(125) x, final_cost = parameterisation.run() # Assertions - np.testing.assert_allclose(final_cost, 0, atol=1e-2) - np.testing.assert_allclose(x, x0, atol=5e-2) + assert initial_cost > final_cost + np.testing.assert_allclose(x, self.ground_truth, atol=2.5e-2) @pytest.fixture - def spm_two_signal_cost(self, parameters, model, x0): + def spm_two_signal_cost(self, parameters, model, cost_class): # Form dataset init_soc = 0.5 - solution = self.getdata(model, x0, init_soc) + solution = self.getdata(model, self.ground_truth, init_soc) dataset = pybop.Dataset( { "Time [s]": solution["Time [s]"].data, "Current function [A]": solution["Current [A]"].data, - "Voltage [V]": solution["Voltage [V]"].data, + "Voltage [V]": solution["Voltage [V]"].data + + self.noise(0.002, len(solution["Time [s]"].data)), "Bulk open-circuit voltage [V]": solution[ "Bulk open-circuit voltage [V]" - ].data, + ].data + + self.noise(0.002, len(solution["Time [s]"].data)), } ) @@ -139,54 +148,55 @@ def spm_two_signal_cost(self, parameters, model, x0): problem = pybop.FittingProblem( model, parameters, dataset, signal=signal, init_soc=init_soc ) - return pybop.SumSquaredError(problem) + return cost_class(problem) @pytest.mark.parametrize( - "optimiser", + "multi_optimiser", [ pybop.SciPyDifferentialEvolution, - pybop.IRPropMin, + pybop.Adam, pybop.CMAES, ], ) @pytest.mark.integration - def test_multiple_signals(self, optimiser, spm_two_signal_cost, x0): + def test_multiple_signals(self, multi_optimiser, spm_two_signal_cost): # Test each optimiser + initial_cost = spm_two_signal_cost(spm_two_signal_cost.x0) parameterisation = pybop.Optimisation( - cost=spm_two_signal_cost, optimiser=optimiser + cost=spm_two_signal_cost, optimiser=multi_optimiser, sigma0=0.05 ) parameterisation.set_max_unchanged_iterations(iterations=15, threshold=5e-4) - parameterisation.set_max_iterations(100) + parameterisation.set_max_iterations(125) - if optimiser in [pybop.SciPyDifferentialEvolution]: + if multi_optimiser in [pybop.SciPyDifferentialEvolution]: parameterisation.optimiser.set_population_size(5) x, final_cost = parameterisation.run() # Assertions - np.testing.assert_allclose(final_cost, 0, atol=1e-2) - np.testing.assert_allclose(x, x0, atol=5e-2) + assert initial_cost > final_cost + np.testing.assert_allclose(x, self.ground_truth, atol=2.5e-2) - @pytest.mark.parametrize("init_soc", [0.3, 0.7]) + @pytest.mark.parametrize("init_soc", [0.4, 0.7]) @pytest.mark.integration - def test_model_misparameterisation(self, parameters, model, x0, init_soc): + def test_model_misparameterisation(self, parameters, model, init_soc): # Define two different models with different parameter sets # The optimisation should fail as the models are not the same second_parameter_set = pybop.ParameterSet.pybamm("Ecker2015") second_model = pybop.lithium_ion.SPM(parameter_set=second_parameter_set) # Form dataset - solution = self.getdata(second_model, x0, init_soc) + solution = self.getdata(second_model, self.ground_truth, init_soc) dataset = pybop.Dataset( { "Time [s]": solution["Time [s]"].data, "Current function [A]": solution["Current [A]"].data, - "Terminal voltage [V]": solution["Terminal voltage [V]"].data, + "Voltage [V]": solution["Voltage [V]"].data, } ) # Define the cost to optimise - signal = ["Terminal voltage [V]"] + signal = ["Voltage [V]"] problem = pybop.FittingProblem( model, parameters, dataset, signal=signal, init_soc=init_soc ) @@ -204,22 +214,20 @@ def test_model_misparameterisation(self, parameters, model, x0, init_soc): # Assertions with np.testing.assert_raises(AssertionError): np.testing.assert_allclose(final_cost, 0, atol=1e-2) - np.testing.assert_allclose(x, x0, atol=5e-2) + np.testing.assert_allclose(x, self.ground_truth, atol=2e-2) - def getdata(self, model, x0, init_soc): + def getdata(self, model, x, init_soc): model.parameter_set.update( { - "Negative electrode active material volume fraction": x0[0], - "Positive electrode active material volume fraction": x0[1], + "Negative electrode active material volume fraction": x[0], + "Positive electrode active material volume fraction": x[1], } ) experiment = pybop.Experiment( [ ( - "Discharge at 1C for 3 minutes (1 second period)", - "Rest for 2 minutes (1 second period)", - "Charge at 1C for 3 minutes (1 second period)", - "Rest for 2 minutes (1 second period)", + "Discharge at 0.5C for 3 minutes (1 second period)", + "Charge at 0.5C for 3 minutes (1 second period)", ), ] * 2 From b6a073ba39a29c2147e380eb60b4391d8fe0d58f Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Mon, 4 Mar 2024 14:23:45 +0000 Subject: [PATCH 34/64] Add tests for gradient plots, up coverage --- tests/unit/test_plots.py | 28 +++++++++++++++++++++------- 1 file changed, 21 insertions(+), 7 deletions(-) diff --git a/tests/unit/test_plots.py b/tests/unit/test_plots.py index fff8074b0..bc721f029 100644 --- a/tests/unit/test_plots.py +++ b/tests/unit/test_plots.py @@ -13,18 +13,32 @@ def model(self): # Define an example model return pybop.lithium_ion.SPM() + # @pytest.fixture + # def parameters(self): + # return [ + # pybop.Parameter( + # "Negative particle radius [m]", + # prior=pybop.Gaussian(6e-06, 0.1e-6), + # bounds=[1e-6, 9e-6], + # ), + # pybop.Parameter( + # "Positive particle radius [m]", + # prior=pybop.Gaussian(4.5e-06, 0.1e-6), + # bounds=[1e-6, 9e-6], + # ), + # ] @pytest.fixture def parameters(self): return [ pybop.Parameter( - "Negative particle radius [m]", - prior=pybop.Gaussian(6e-06, 0.1e-6), - bounds=[1e-6, 9e-6], + "Negative electrode active material volume fraction", + prior=pybop.Gaussian(0.68, 0.05), + bounds=[0.5, 0.8], ), pybop.Parameter( - "Positive particle radius [m]", - prior=pybop.Gaussian(4.5e-06, 0.1e-6), - bounds=[1e-6, 9e-6], + "Positive electrode active material volume fraction", + prior=pybop.Gaussian(0.58, 0.05), + bounds=[0.4, 0.7], ), ] @@ -65,7 +79,7 @@ def cost(self, problem): @pytest.mark.unit def test_cost_plots(self, cost): # Test plotting of Cost objects - pybop.plot_cost2d(cost, steps=5) + pybop.plot_cost2d(cost, gradient=True, steps=5) @pytest.fixture def optim(self, cost): From ee4cdffce74bb053821d0a76d26ce570f0f748dd Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Mon, 4 Mar 2024 16:30:12 +0000 Subject: [PATCH 35/64] Set default SciPyMinimize method to Nelder-Mead, clean-up repo --- examples/scripts/spm_scipymin.py | 4 ++-- pybop/optimisers/scipy_optimisers.py | 5 +++-- pyproject.toml | 2 +- tests/unit/test_plots.py | 14 -------------- 4 files changed, 6 insertions(+), 19 deletions(-) diff --git a/examples/scripts/spm_scipymin.py b/examples/scripts/spm_scipymin.py index db1d783d0..759f8c2e6 100644 --- a/examples/scripts/spm_scipymin.py +++ b/examples/scripts/spm_scipymin.py @@ -21,12 +21,12 @@ parameters = [ pybop.Parameter( "Negative electrode active material volume fraction", - prior=pybop.Gaussian(0.6, 0.02), + prior=pybop.Gaussian(0.6, 0.05), bounds=[0.5, 0.8], ), pybop.Parameter( "Positive electrode active material volume fraction", - prior=pybop.Gaussian(0.48, 0.02), + prior=pybop.Gaussian(0.48, 0.05), bounds=[0.4, 0.7], ), ] diff --git a/pybop/optimisers/scipy_optimisers.py b/pybop/optimisers/scipy_optimisers.py index 7e717b81e..f4ea49a72 100644 --- a/pybop/optimisers/scipy_optimisers.py +++ b/pybop/optimisers/scipy_optimisers.py @@ -12,7 +12,8 @@ class SciPyMinimize(BaseOptimiser): Parameters ---------- method : str, optional - The type of solver to use. If not specified, defaults to 'COBYLA'. + The type of solver to use. If not specified, defaults to 'Nelder-Mead'. + Options: 'Nelder-Mead', 'Powell', 'CG', 'BFGS', 'Newton-CG', 'L-BFGS-B', 'TNC', 'COBYLA', 'SLSQP', 'trust-constr', 'dogleg', 'trust-ncg', 'trust-exact', 'trust-krylov'. bounds : sequence or ``Bounds``, optional Bounds for variables as supported by the selected method. maxiter : int, optional @@ -27,7 +28,7 @@ def __init__(self, method=None, bounds=None, maxiter=None): self._max_iterations = maxiter if self.method is None: - self.method = "COBYLA" # "L-BFGS-B" + self.method = "Nelder-Mead" def _runoptimise(self, cost_function, x0, bounds): """ diff --git a/pyproject.toml b/pyproject.toml index 3287989fd..36eb03a43 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -41,7 +41,7 @@ dev = [ "nox", "nbmake", "pre-commit", - "pytest<=8", + "pytest>=6", "pytest-cov", "pytest-mock", "pytest-xdist", diff --git a/tests/unit/test_plots.py b/tests/unit/test_plots.py index bc721f029..d2f8387af 100644 --- a/tests/unit/test_plots.py +++ b/tests/unit/test_plots.py @@ -13,20 +13,6 @@ def model(self): # Define an example model return pybop.lithium_ion.SPM() - # @pytest.fixture - # def parameters(self): - # return [ - # pybop.Parameter( - # "Negative particle radius [m]", - # prior=pybop.Gaussian(6e-06, 0.1e-6), - # bounds=[1e-6, 9e-6], - # ), - # pybop.Parameter( - # "Positive particle radius [m]", - # prior=pybop.Gaussian(4.5e-06, 0.1e-6), - # bounds=[1e-6, 9e-6], - # ), - # ] @pytest.fixture def parameters(self): return [ From 66efaba7832774844a5449024e845d7daeb95b11 Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Fri, 8 Mar 2024 09:55:58 +0000 Subject: [PATCH 36/64] unicode fix for win notebooks, update prediction shape checks, remove Discharge capacity as default additional_variable --- conftest.py | 5 +++ pybop/_problem.py | 11 +++---- pybop/costs/base_cost.py | 1 + pybop/costs/design_costs.py | 1 - pybop/costs/fitting_costs.py | 60 +++++++++++++----------------------- 5 files changed, 32 insertions(+), 46 deletions(-) diff --git a/conftest.py b/conftest.py index 3641cbd11..d8e79fcd8 100644 --- a/conftest.py +++ b/conftest.py @@ -1,6 +1,7 @@ import pytest import matplotlib import plotly +import sys plotly.io.renderers.default = None matplotlib.use("Template") @@ -42,6 +43,10 @@ def pytest_configure(config): config.addinivalue_line("markers", "plots: mark test as a plot test") config.addinivalue_line("markers", "notebook: mark test as a notebook test") + if sys.platform.startswith("win"): + # Set the output encoding to UTF-8 on Windows + sys.stdout = open(sys.stdout.fileno(), mode="w", encoding="utf8", buffering=1) + def pytest_collection_modifyitems(config, items): options = { diff --git a/pybop/_problem.py b/pybop/_problem.py index f1f0e8c82..d1e6d394a 100644 --- a/pybop/_problem.py +++ b/pybop/_problem.py @@ -147,9 +147,9 @@ class FittingProblem(BaseProblem): dataset : Dataset Dataset object containing the data to fit the model to. signal : str, optional - The signal to fit (default: "Voltage [V]"). + The variable used for fitting (default: "Voltage [V]"). additional_variables : List[str], optional - Additional variables to observe and store in the solution (default: []). + Additional variables to observe and store in the solution (default additions are: ["Time [s]"]). init_soc : float, optional Initial state of charge (default: None). x0 : np.ndarray, optional @@ -167,7 +167,7 @@ def __init__( init_soc=None, x0=None, ): - additional_variables += ["Time [s]", "Discharge capacity [A.h]"] + additional_variables += ["Time [s]"] super().__init__( parameters, model, check_model, signal, additional_variables, init_soc, x0 ) @@ -271,7 +271,7 @@ class DesignProblem(BaseProblem): signal : str, optional The signal to fit (default: "Voltage [V]"). additional_variables : List[str], optional - Additional variables to observe and store in the solution (default: []). + Additional variables to observe and store in the solution (default additions are: ["Time [s]", "Current [A]"]). init_soc : float, optional Initial state of charge (default: None). x0 : np.ndarray, optional @@ -289,7 +289,7 @@ def __init__( init_soc=None, x0=None, ): - additional_variables += ["Time [s]", "Current [A]", "Discharge capacity [A.h]"] + additional_variables += ["Time [s]", "Current [A]"] super().__init__( parameters, model, check_model, signal, additional_variables, init_soc, x0 ) @@ -313,7 +313,6 @@ def __init__( # Add an example dataset for plotting comparison sol = self.evaluate(self.x0) self._time_data = sol["Time [s]"] - self._capacity_data = sol["Discharge capacity [A.h]"] self._target = {key: sol[key] for key in self.signal} self._dataset = None diff --git a/pybop/costs/base_cost.py b/pybop/costs/base_cost.py index bdafec3c4..6cbd2a19a 100644 --- a/pybop/costs/base_cost.py +++ b/pybop/costs/base_cost.py @@ -33,6 +33,7 @@ def __init__(self, problem): self.bounds = problem.bounds self.n_parameters = problem.n_parameters self.n_outputs = problem.n_outputs + self.signal = problem.signal def __call__(self, x, grad=None): """ diff --git a/pybop/costs/design_costs.py b/pybop/costs/design_costs.py index f2b452af4..e1fb38cb4 100644 --- a/pybop/costs/design_costs.py +++ b/pybop/costs/design_costs.py @@ -61,7 +61,6 @@ def update_simulation_data(self, initial_conditions): if "Time [s]" not in solution: raise ValueError("The solution does not contain time data.") self.problem._time_data = solution["Time [s]"] - self.problem._capacity_data = solution["Discharge capacity [A.h]"] self.problem._target = {key: solution[key] for key in self.problem.signal} self.dt = solution["Time [s]"][1] - solution["Time [s]"][0] diff --git a/pybop/costs/fitting_costs.py b/pybop/costs/fitting_costs.py index eb21001a5..3e7349110 100644 --- a/pybop/costs/fitting_costs.py +++ b/pybop/costs/fitting_costs.py @@ -39,16 +39,14 @@ def _evaluate(self, x, grad=None): """ prediction = self.problem.evaluate(x) - for key in prediction: - if key not in ["Time [s]", "Discharge capacity [A.h]"]: - if len(prediction.get(key, [])) != len(self._target.get(key, [])): - return np.float64(np.inf) # prediction doesn't match target + for key in self.signal: + if len(prediction.get(key, [])) != len(self._target.get(key, [])): + return np.float64(np.inf) # prediction doesn't match target e = np.array( [ np.sqrt(np.mean((prediction[signal] - self._target[signal]) ** 2)) - for signal in prediction - if signal not in ["Time [s]", "Discharge capacity [A.h]"] + for signal in self.signal ] ) @@ -79,20 +77,13 @@ def _evaluateS1(self, x): """ y, dy = self.problem.evaluateS1(x) - for key in y: - if key not in ["Time [s]", "Discharge capacity [A.h]"]: - if len(y.get(key, [])) != len(self._target.get(key, [])): - e = np.float64(np.inf) - de = self._de * np.ones(self.n_parameters) - return e, de + for key in self.signal: + if len(y.get(key, [])) != len(self._target.get(key, [])): + e = np.float64(np.inf) + de = self._de * np.ones(self.n_parameters) + return e, de - r = np.array( - [ - y[signal] - self._target[signal] - for signal in y - if signal not in ["Time [s]", "Discharge capacity [A.h]"] - ] - ) + r = np.array([y[signal] - self._target[signal] for signal in self.signal]) if self.n_outputs == 1: r = r.reshape(self.problem.n_time_data) @@ -156,16 +147,14 @@ def _evaluate(self, x, grad=None): """ prediction = self.problem.evaluate(x) - for key in prediction: - if key not in ["Time [s]", "Discharge capacity [A.h]"]: - if len(prediction.get(key, [])) != len(self._target.get(key, [])): - return np.float64(np.inf) # prediction doesn't match target + for key in self.signal: + if len(prediction.get(key, [])) != len(self._target.get(key, [])): + return np.float64(np.inf) # prediction doesn't match target e = np.array( [ np.sum(((prediction[signal] - self._target[signal]) ** 2)) - for signal in prediction - if signal not in ["Time [s]", "Discharge capacity [A.h]"] + for signal in self.signal ] ) if self.n_outputs == 1: @@ -194,20 +183,13 @@ def _evaluateS1(self, x): If an error occurs during the calculation of the cost or gradient. """ y, dy = self.problem.evaluateS1(x) - for key in y: - if key not in ["Time [s]", "Discharge capacity [A.h]"]: - if len(y.get(key, [])) != len(self._target.get(key, [])): - e = np.float64(np.inf) - de = self._de * np.ones(self.n_parameters) - return e, de - - r = np.array( - [ - y[signal] - self._target[signal] - for signal in y - if signal not in ["Time [s]", "Discharge capacity [A.h]"] - ] - ) + for key in self.signal: + if len(y.get(key, [])) != len(self._target.get(key, [])): + e = np.float64(np.inf) + de = self._de * np.ones(self.n_parameters) + return e, de + + r = np.array([y[signal] - self._target[signal] for signal in self.signal]) if self.n_outputs == 1: r = r.reshape(self.problem.n_time_data) From 6c6494aa8dfb721e1a7fd0ed88f5615ece08df91 Mon Sep 17 00:00:00 2001 From: NicolaCourtier <45851982+NicolaCourtier@users.noreply.github.com> Date: Fri, 8 Mar 2024 19:47:56 +0000 Subject: [PATCH 37/64] Update spm_MLE example --- examples/scripts/spm_MLE.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/scripts/spm_MLE.py b/examples/scripts/spm_MLE.py index 7aa4254a7..6e822c4a7 100644 --- a/examples/scripts/spm_MLE.py +++ b/examples/scripts/spm_MLE.py @@ -54,7 +54,7 @@ print("Estimated parameters:", x) # Plot the timeseries output -pybop.quick_plot(x[0:2], likelihood, title="Optimised Comparison") +pybop.quick_plot(problem, parameter_values=x[0:2], title="Optimised Comparison") # Plot convergence pybop.plot_convergence(optim) @@ -65,6 +65,6 @@ # Plot the cost landscape pybop.plot_cost2d(likelihood, steps=15) -# Plot the cost landscape with optimisation path and updated bounds +# Plot the cost landscape with optimisation path bounds = np.array([[0.55, 0.77], [0.48, 0.68]]) -pybop.plot_cost2d(likelihood, optim=optim, bounds=bounds, steps=15) +pybop.plot_optim2d(optim, bounds=bounds, steps=15) From 9b0373477eacd7ca84286dee38d7dea2eac99de4 Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Wed, 13 Mar 2024 13:00:40 +0000 Subject: [PATCH 38/64] Updt. cost2d/optim2d x0 shape/colour, revert conftest win platform unicode, add infeasible unit test --- conftest.py | 5 ---- pybop/plotting/plot_convergence.py | 30 ++++++++++----------- tests/integration/test_parameterisations.py | 4 +-- tests/unit/test_optimisation.py | 11 ++++++++ 4 files changed, 28 insertions(+), 22 deletions(-) diff --git a/conftest.py b/conftest.py index d8e79fcd8..3641cbd11 100644 --- a/conftest.py +++ b/conftest.py @@ -1,7 +1,6 @@ import pytest import matplotlib import plotly -import sys plotly.io.renderers.default = None matplotlib.use("Template") @@ -43,10 +42,6 @@ def pytest_configure(config): config.addinivalue_line("markers", "plots: mark test as a plot test") config.addinivalue_line("markers", "notebook: mark test as a notebook test") - if sys.platform.startswith("win"): - # Set the output encoding to UTF-8 on Windows - sys.stdout = open(sys.stdout.fileno(), mode="w", encoding="utf8", buffering=1) - def pytest_collection_modifyitems(config, items): options = { diff --git a/pybop/plotting/plot_convergence.py b/pybop/plotting/plot_convergence.py index b59631798..53324b574 100644 --- a/pybop/plotting/plot_convergence.py +++ b/pybop/plotting/plot_convergence.py @@ -98,35 +98,35 @@ def plot_optim2d(optim, bounds=None, steps=10, show=True, **layout_kwargs): # Import plotly only when needed go = pybop.PlotlyManager().go - # Plot the initial guess + # Plot the optimisation trace + optim_trace = np.array([item for sublist in optim.log for item in sublist]) + optim_trace = optim_trace.reshape(-1, 2) fig.add_trace( go.Scatter( - x=[optim.x0[0]], - y=[optim.x0[1]], + x=optim_trace[:, 0], + y=optim_trace[:, 1], mode="markers", - marker_symbol="x", marker=dict( - color="red", - line_color="midnightblue", - line_width=1, - size=12, + color=[i / len(optim_trace) for i in range(len(optim_trace))], + colorscale="YlOrBr", showscale=False, ), showlegend=False, ) ) - # Plot the optimisation trace - optim_trace = np.array([item for sublist in optim.log for item in sublist]) - optim_trace = optim_trace.reshape(-1, 2) + # Plot the initial guess fig.add_trace( go.Scatter( - x=optim_trace[:, 0], - y=optim_trace[:, 1], + x=[optim.x0[0]], + y=[optim.x0[1]], mode="markers", + marker_symbol="circle", marker=dict( - color=[i / len(optim_trace) for i in range(len(optim_trace))], - colorscale="YlOrBr", + color="mediumspringgreen", + line_color="mediumspringgreen", + line_width=1, + size=14, showscale=False, ), showlegend=False, diff --git a/tests/integration/test_parameterisations.py b/tests/integration/test_parameterisations.py index 33ad4f364..0aecf6158 100644 --- a/tests/integration/test_parameterisations.py +++ b/tests/integration/test_parameterisations.py @@ -91,7 +91,7 @@ def test_spm_optimisers(self, optimiser, spm_costs): if optimiser in [pybop.CMAES]: parameterisation.set_f_guessed_tracking(True) - parameterisation.cost.problem._model.allow_infeasible_solutions = False + parameterisation.cost.problem.model.allow_infeasible_solutions = False assert parameterisation._use_f_guessed is True parameterisation.set_max_iterations(1) x, final_cost = parameterisation.run() @@ -115,7 +115,7 @@ def test_spm_optimisers(self, optimiser, spm_costs): x, final_cost = parameterisation.run() elif optimiser in [pybop.SciPyMinimize]: - parameterisation.cost.problem._model.allow_infeasible_solutions = False + parameterisation.cost.problem.model.allow_infeasible_solutions = False x, final_cost = parameterisation.run() else: diff --git a/tests/unit/test_optimisation.py b/tests/unit/test_optimisation.py index 6569d1ad5..7e1a4b10a 100644 --- a/tests/unit/test_optimisation.py +++ b/tests/unit/test_optimisation.py @@ -122,6 +122,17 @@ def test_halting(self, cost): with pytest.raises(ValueError): optim.set_max_unchanged_iterations(1, threshold=-1) + @pytest.mark.unit + def test_infeasible_solutions(self, cost): + # Test infeasible solutions + for optimiser in [pybop.SciPyMinimize, pybop.GradientDescent]: + optim = pybop.Optimisation( + cost=cost, optimiser=optimiser, allow_infeasible_solutions=False + ) + optim.set_max_iterations(1) + optim.run() + assert optim._iterations == 1 + @pytest.mark.unit def test_unphysical_result(self, cost): # Trigger parameters not physically viable warning From e7aef79b6b102bf0ca67096f1bee6f20f36a09fb Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Wed, 13 Mar 2024 13:43:23 +0000 Subject: [PATCH 39/64] Updt SciPy & BaseOptimiser for maximum iterations limit - fixes #237 --- pybop/_optimisation.py | 5 +++-- pybop/optimisers/base_optimiser.py | 2 +- pybop/optimisers/scipy_optimisers.py | 16 ++++------------ 3 files changed, 8 insertions(+), 15 deletions(-) diff --git a/pybop/_optimisation.py b/pybop/_optimisation.py index d5114b283..04e0b4ad2 100644 --- a/pybop/_optimisation.py +++ b/pybop/_optimisation.py @@ -175,15 +175,16 @@ def _run_pybop(self): final_cost : float The final cost associated with the best parameters. """ - x, final_cost = self.optimiser.optimise( + result = self.optimiser.optimise( cost_function=self.cost, x0=self.x0, bounds=self.bounds, maxiter=self._max_iterations, ) self.log = self.optimiser.log + self._iterations = result.nit - return x, final_cost + return result.x, self.cost(result.x) def _run_pints(self): """ diff --git a/pybop/optimisers/base_optimiser.py b/pybop/optimisers/base_optimiser.py index 29cc219a2..f8796bc99 100644 --- a/pybop/optimisers/base_optimiser.py +++ b/pybop/optimisers/base_optimiser.py @@ -38,7 +38,7 @@ def optimise(self, cost_function, x0=None, bounds=None, maxiter=None): self.cost_function = cost_function self.x0 = x0 self.bounds = bounds - self.maxiter = maxiter + self._max_iterations = maxiter # Run optimisation result = self._runoptimise(self.cost_function, x0=self.x0, bounds=self.bounds) diff --git a/pybop/optimisers/scipy_optimisers.py b/pybop/optimisers/scipy_optimisers.py index f4ea49a72..5fc12ea69 100644 --- a/pybop/optimisers/scipy_optimisers.py +++ b/pybop/optimisers/scipy_optimisers.py @@ -80,7 +80,7 @@ def cost_wrapper(x): else: self.options.pop("maxiter", None) - output = minimize( + result = minimize( cost_wrapper, x0, method=self.method, @@ -89,11 +89,7 @@ def cost_wrapper(x): callback=callback, ) - # Get performance statistics - x = output.x - final_cost = cost_function(x) - - return x, final_cost + return result def needs_sensitivities(self): """ @@ -182,7 +178,7 @@ def callback(x, convergence): (lower, upper) for lower, upper in zip(bounds["lower"], bounds["upper"]) ] - output = differential_evolution( + result = differential_evolution( cost_function, bounds, strategy=self.strategy, @@ -191,11 +187,7 @@ def callback(x, convergence): callback=callback, ) - # Get performance statistics - x = output.x - final_cost = output.fun - - return x, final_cost + return result def set_population_size(self, population_size=None): """ From afd4990f84ff4acfd0d07a944beea270df9c118e Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Wed, 13 Mar 2024 15:25:37 +0000 Subject: [PATCH 40/64] add infeasible cost tests, remove redundant scipyminimise maxiter options check --- pybop/optimisers/scipy_optimisers.py | 13 ++++--------- tests/unit/test_cost.py | 2 +- 2 files changed, 5 insertions(+), 10 deletions(-) diff --git a/pybop/optimisers/scipy_optimisers.py b/pybop/optimisers/scipy_optimisers.py index 5fc12ea69..010c653b5 100644 --- a/pybop/optimisers/scipy_optimisers.py +++ b/pybop/optimisers/scipy_optimisers.py @@ -49,9 +49,10 @@ def _runoptimise(self, cost_function, x0, bounds): A tuple (x, final_cost) containing the optimized parameters and the value of `cost_function` at the optimum. """ - # Add callback storing history of parameter values self.log = [[x0]] + self.options = {"maxiter": self._max_iterations} + # Add callback storing history of parameter values def callback(x): self.log.append([x]) @@ -74,12 +75,6 @@ def cost_wrapper(x): (lower, upper) for lower, upper in zip(bounds["lower"], bounds["upper"]) ) - # Set max iterations - if self._max_iterations is not None: - self.options = {"maxiter": self._max_iterations} - else: - self.options.pop("maxiter", None) - result = minimize( cost_wrapper, x0, @@ -158,6 +153,8 @@ def _runoptimise(self, cost_function, x0=None, bounds=None): A tuple (x, final_cost) containing the optimized parameters and the value of ``cost_function`` at the optimum. """ + self.log = [] + if bounds is None: raise ValueError("Bounds must be specified for differential_evolution.") @@ -167,8 +164,6 @@ def _runoptimise(self, cost_function, x0=None, bounds=None): ) # Add callback storing history of parameter values - self.log = [] - def callback(x, convergence): self.log.append([x]) diff --git a/tests/unit/test_cost.py b/tests/unit/test_cost.py index 7d3a4ea63..b6f4daf33 100644 --- a/tests/unit/test_cost.py +++ b/tests/unit/test_cost.py @@ -153,10 +153,10 @@ def test_costs(self, cost): for i in range(len(record)): assert "Non-physical point encountered" in str(record[i].message) - if isinstance(cost, pybop.RootMeanSquaredError): # Test infeasible locations cost.problem._model.allow_infeasible_solutions = False assert cost([1.1]) == np.inf + assert cost.evaluateS1([1.1]) == (np.inf, cost._de) # Test exception for non-numeric inputs with pytest.raises(ValueError): From db284402100e0b1bb8c3ed0d4b6dad98f849ed8f Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Fri, 15 Mar 2024 10:08:06 +0000 Subject: [PATCH 41/64] Updt grad descent hypers for likelihood tests, add tol arg to scipy optimisers, pass optimiser final cost as is --- pybop/_optimisation.py | 8 ++++---- pybop/costs/_likelihoods.py | 2 +- pybop/optimisers/scipy_optimisers.py | 10 ++++++++-- tests/integration/test_parameterisations.py | 12 ++++++++---- 4 files changed, 21 insertions(+), 11 deletions(-) diff --git a/pybop/_optimisation.py b/pybop/_optimisation.py index b0a819052..a7bd6732e 100644 --- a/pybop/_optimisation.py +++ b/pybop/_optimisation.py @@ -156,8 +156,6 @@ def run(self): x, final_cost = self._run_pints() elif not self.pints: x, final_cost = self._run_pybop() - if not self._minimising: - final_cost = -final_cost # Store the optimised parameters if self.cost.problem is not None: @@ -374,8 +372,10 @@ def _run_pints(self): # Store the optimised parameters self.store_optimised_parameters(x) - # Return best position and score - return x, f if self._minimising else -f + # Return best position and the score used internally, + # i.e the negative log-likelihood in the case of + # self._minimising = False + return x, f def f_guessed_tracking(self): """ diff --git a/pybop/costs/_likelihoods.py b/pybop/costs/_likelihoods.py index 239535739..5af2805a9 100644 --- a/pybop/costs/_likelihoods.py +++ b/pybop/costs/_likelihoods.py @@ -96,7 +96,7 @@ def _evaluateS1(self, x, grad=None): for key in self.signal: if len(y.get(key, [])) != len(self._target.get(key, [])): likelihood = np.float64(np.inf) - dl = self._de * np.ones(self.n_parameters) + dl = self._dl * np.ones(self.n_parameters) return -likelihood, -dl r = np.array([self._target[signal] - y[signal] for signal in self.signal]) diff --git a/pybop/optimisers/scipy_optimisers.py b/pybop/optimisers/scipy_optimisers.py index a3bac83d7..d3a6177c6 100644 --- a/pybop/optimisers/scipy_optimisers.py +++ b/pybop/optimisers/scipy_optimisers.py @@ -20,10 +20,11 @@ class SciPyMinimize(BaseOptimiser): Maximum number of iterations to perform. """ - def __init__(self, method=None, bounds=None, maxiter=None): + def __init__(self, method=None, bounds=None, maxiter=None, tol=1e-5): super().__init__() self.method = method self.bounds = bounds + self.tol = tol self.options = {} self._max_iterations = maxiter @@ -79,6 +80,7 @@ def cost_wrapper(x): x0, method=self.method, bounds=bounds, + tol=self.tol, options=self.options, callback=callback, ) @@ -126,8 +128,11 @@ class SciPyDifferentialEvolution(BaseOptimiser): The number of individuals in the population. Defaults to 15. """ - def __init__(self, bounds=None, strategy="best1bin", maxiter=1000, popsize=15): + def __init__( + self, bounds=None, strategy="best1bin", maxiter=1000, popsize=15, tol=1e-5 + ): super().__init__() + self.tol = tol self.strategy = strategy self._max_iterations = maxiter self._population_size = popsize @@ -178,6 +183,7 @@ def callback(x, convergence): strategy=self.strategy, maxiter=self._max_iterations, popsize=self._population_size, + tol=self.tol, callback=callback, ) diff --git a/tests/integration/test_parameterisations.py b/tests/integration/test_parameterisations.py index c9ac02b0e..73c87de45 100644 --- a/tests/integration/test_parameterisations.py +++ b/tests/integration/test_parameterisations.py @@ -70,7 +70,7 @@ def spm_costs(self, model, parameters, cost_class, init_soc): model, parameters, dataset, signal=signal, init_soc=init_soc ) if cost_class in [pybop.GaussianLogLikelihoodKnownSigma]: - return cost_class(problem, sigma=[0.05, 0.05]) + return cost_class(problem, sigma=[0.03, 0.03]) else: return cost_class(problem) @@ -123,7 +123,11 @@ def test_spm_optimisers(self, optimiser, spm_costs): assert parameterisation._max_iterations == 125 elif optimiser in [pybop.GradientDescent]: - parameterisation.optimiser.set_learning_rate(0.02) + if isinstance(spm_costs, pybop.GaussianLogLikelihoodKnownSigma): + parameterisation.optimiser.set_learning_rate(1.8e-5) + parameterisation.set_min_iterations(150) + else: + parameterisation.optimiser.set_learning_rate(0.02) parameterisation.set_max_iterations(150) x, final_cost = parameterisation.run() @@ -196,9 +200,9 @@ def test_multiple_signals(self, multi_optimiser, spm_two_signal_cost): # Test each optimiser parameterisation = pybop.Optimisation( - cost=spm_two_signal_cost, optimiser=multi_optimiser, sigma0=0.05 + cost=spm_two_signal_cost, optimiser=multi_optimiser, sigma0=0.03 ) - parameterisation.set_max_unchanged_iterations(iterations=15, threshold=5e-4) + parameterisation.set_max_unchanged_iterations(iterations=35, threshold=5e-4) parameterisation.set_max_iterations(125) initial_cost = parameterisation.cost(spm_two_signal_cost.x0) From 61d7d7ae5d6e0d1a385a6db020ad31b947fc2cec Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Fri, 15 Mar 2024 10:42:38 +0000 Subject: [PATCH 42/64] Split kaleido dependancy to avoid windows hang --- pyproject.toml | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 36eb03a43..311e71c0b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -26,7 +26,12 @@ dependencies = [ ] [project.optional-dependencies] -plot = ["plotly>=5.0", "kaleido>=0.2"] +# Split kaleido into two dependencies to avoid Windows hang +# See: https://github.com/plotly/Kaleido/issues/110 +plot = ["plotly>=5.0", + "kaleido==0.1.0.post1; sys_platform == 'win32'", + "kaleido>=0.2; sys_platform != 'win32'", +] docs = [ "pydata-sphinx-theme", "sphinx>=6", From 744d1663f32db734653c31d6970da62636ae21a0 Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Fri, 15 Mar 2024 12:08:31 +0000 Subject: [PATCH 43/64] small refactors and cleanup --- pybop/_problem.py | 12 +++++++++--- pybop/costs/_likelihoods.py | 11 ++++------- pybop/costs/design_costs.py | 6 +++--- pybop/models/lithium_ion/echem_base.py | 6 ++---- 4 files changed, 18 insertions(+), 17 deletions(-) diff --git a/pybop/_problem.py b/pybop/_problem.py index 3fa07ec63..de01c6007 100644 --- a/pybop/_problem.py +++ b/pybop/_problem.py @@ -49,7 +49,7 @@ def __init__( self._time_data = None self._target = None - if isinstance(model, (pybop.BaseModel, pybop.lithium_ion.EChemBaseModel)): + if isinstance(model, pybop.BaseModel): self.additional_variables = additional_variables else: self.additional_variables = [] @@ -186,7 +186,10 @@ def __init__( init_soc=None, x0=None, ): - additional_variables += ["Time [s]"] + # Add time and remove duplicates + additional_variables.extend(["Time [s]"]) + additional_variables = list(set(additional_variables)) + super().__init__( parameters, model, check_model, signal, additional_variables, init_soc, x0 ) @@ -308,7 +311,10 @@ def __init__( init_soc=None, x0=None, ): - additional_variables += ["Time [s]", "Current [A]"] + # Add time and current and remove duplicates + additional_variables.extend(["Time [s]", "Current [A]"]) + additional_variables = list(set(additional_variables)) + super().__init__( parameters, model, check_model, signal, additional_variables, init_soc, x0 ) diff --git a/pybop/costs/_likelihoods.py b/pybop/costs/_likelihoods.py index 5af2805a9..51254f7f0 100644 --- a/pybop/costs/_likelihoods.py +++ b/pybop/costs/_likelihoods.py @@ -91,7 +91,6 @@ def _evaluateS1(self, x, grad=None): Calls the problem.evaluateS1 method and calculates the log-likelihood """ - y, dy = self.problem.evaluateS1(x) for key in self.signal: if len(y.get(key, [])) != len(self._target.get(key, [])): @@ -100,16 +99,15 @@ def _evaluateS1(self, x, grad=None): return -likelihood, -dl r = np.array([self._target[signal] - y[signal] for signal in self.signal]) + likelihood = self._evaluate(x) if self.n_outputs == 1: r = r.reshape(self.problem.n_time_data) dy = dy.reshape(self.n_parameters, self.problem.n_time_data) - likelihood = self._evaluate(x) dl = self.sigma2 * np.sum((r * dy), axis=1) return likelihood, dl else: r = r.reshape(self.n_outputs, self.problem.n_time_data) - likelihood = self._evaluate(x) dl = self.sigma2 * np.sum((r[:, :, np.newaxis] * dy), axis=1) return likelihood, np.sum(dl, axis=1) @@ -176,28 +174,27 @@ def _evaluateS1(self, x, grad=None): """ sigma = np.asarray(x[-self.n_outputs :]) if np.any(sigma <= 0): - return -np.float64(np.inf), self._de * np.ones(self.n_parameters) + return -np.float64(np.inf), -self._dl * np.ones(self.n_parameters) y, dy = self.problem.evaluateS1(x[: -self.n_outputs]) for key in self.signal: if len(y.get(key, [])) != len(self._target.get(key, [])): likelihood = np.float64(np.inf) - dl = self._de * np.ones(self.n_parameters) + dl = self._dl * np.ones(self.n_parameters) return -likelihood, -dl r = np.array([self._target[signal] - y[signal] for signal in self.signal]) + likelihood = self._evaluate(x) if self.n_outputs == 1: r = r.reshape(self.problem.n_time_data) dy = dy.reshape(self.n_parameters, self.problem.n_time_data) - likelihood = self._evaluate(x) dl = sigma ** (-2.0) * np.sum((r * dy), axis=1) dsigma = -self._n_times / sigma + sigma**-(3.0) * np.sum(r**2, axis=0) dl = np.concatenate((dl, dsigma)) return likelihood, dl else: r = r.reshape(self.n_outputs, self.problem.n_time_data) - likelihood = self._evaluate(x) dl = sigma ** (-2.0) * np.sum((r[:, :, np.newaxis] * dy), axis=1) dsigma = -self._n_times / sigma + sigma**-(3.0) * np.sum(r**2, axis=0) dl = np.concatenate((dl, dsigma)) diff --git a/pybop/costs/design_costs.py b/pybop/costs/design_costs.py index e1fb38cb4..16dd8a0f3 100644 --- a/pybop/costs/design_costs.py +++ b/pybop/costs/design_costs.py @@ -1,7 +1,7 @@ -import pybop import numpy as np import warnings +from pybop import is_numeric from pybop.costs.base_cost import BaseCost @@ -114,7 +114,7 @@ def _evaluate(self, x, grad=None): float The negative gravimetric energy density or infinity in case of infeasible parameters. """ - if not all(pybop.is_numeric(i) for i in x): + if not all(is_numeric(i) for i in x): raise ValueError("Input must be a numeric array.") try: @@ -173,7 +173,7 @@ def _evaluate(self, x, grad=None): float The negative volumetric energy density or infinity in case of infeasible parameters. """ - if not all(pybop.is_numeric(i) for i in x): + if not all(is_numeric(i) for i in x): raise ValueError("Input must be a numeric array.") try: with warnings.catch_warnings(): diff --git a/pybop/models/lithium_ion/echem_base.py b/pybop/models/lithium_ion/echem_base.py index 6a642ff34..c4664a31a 100644 --- a/pybop/models/lithium_ion/echem_base.py +++ b/pybop/models/lithium_ion/echem_base.py @@ -227,10 +227,8 @@ def approximate_capacity(self, x): average_voltage = positive_electrode_ocp( mean_sto_pos ) - negative_electrode_ocp(mean_sto_neg) - except TypeError: - average_voltage = positive_electrode_ocp([mean_sto_pos]).evaluate()[0][ - 0 - ] - negative_electrode_ocp(mean_sto_neg) # Super hacky, needs to be fixed + except Exception as e: + raise ValueError(f"Error in average voltage calculation: {e}") # Calculate and update nominal capacity theoretical_capacity = theoretical_energy / average_voltage From c1b38546872bbe2c3200b3a0bc4f90e6711d22bc Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Fri, 15 Mar 2024 12:33:52 +0000 Subject: [PATCH 44/64] Updt changelog --- CHANGELOG.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 7bfc4528f..043f2d30d 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,6 +2,8 @@ ## Features +- [#198](https://github.com/pybop-team/PyBOP/pull/198) - Adds default subplot trace options, removes `[]` in axis plots as per SI standard, add varying signal length to quick_plot, restores design optimisation execption. +- [#224](https://github.com/pybop-team/PyBOP/pull/224) - Updated prediction objects to dictionaries, cost class calculations, added `additional_variables` argument to problem class, updated scipy.minimize defualt method to Nelder-Mead, added gradient cost landscape plots with optional argument. - [#218](https://github.com/pybop-team/PyBOP/pull/218) - Adds likelihood base class, `GaussianLogLikelihoodKnownSigma`, `GaussianLogLikelihood`, and `ProbabilityBased` cost function. As well as addition of a maximum likelihood estimation (MLE) example. - [#185](https://github.com/pybop-team/PyBOP/pull/185) - Adds a pull request template, additional nox sessions `quick` for standard tests + docs, `pre-commit` for pre-commit, `test` to run all standard tests, `doctest` for docs. - [#215](https://github.com/pybop-team/PyBOP/pull/215) - Adds `release_workflow.md` and updates `release_action.yaml` From 716c671733b8dc70244e9d4caa478df0fa12e118 Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Tue, 19 Mar 2024 12:08:27 +0000 Subject: [PATCH 45/64] updt coverage, bugfix sigma check/wrap --- examples/standalone/problem.py | 4 ++-- pybop/costs/_likelihoods.py | 10 +++++----- tests/unit/test_cost.py | 1 + tests/unit/test_likelihoods.py | 11 ++++++----- 4 files changed, 14 insertions(+), 12 deletions(-) diff --git a/examples/standalone/problem.py b/examples/standalone/problem.py index bc9cd31d5..45f5a6025 100644 --- a/examples/standalone/problem.py +++ b/examples/standalone/problem.py @@ -14,12 +14,12 @@ def __init__( model=None, check_model=True, signal=None, - default_variables=None, + additional_variables=None, init_soc=None, x0=None, ): super().__init__( - parameters, model, check_model, signal, default_variables, init_soc, x0 + parameters, model, check_model, signal, additional_variables, init_soc, x0 ) self._dataset = dataset.data diff --git a/pybop/costs/_likelihoods.py b/pybop/costs/_likelihoods.py index 51254f7f0..25408db44 100644 --- a/pybop/costs/_likelihoods.py +++ b/pybop/costs/_likelihoods.py @@ -16,11 +16,11 @@ def set_sigma(self, sigma): Setter for sigma parameter """ - if sigma is not type(np.array([])): - try: - sigma = np.array(sigma) - except Exception: - raise ValueError("Sigma must be a numpy array") + if not isinstance(sigma, np.ndarray): + sigma = np.array([sigma]) + + if not np.isreal(sigma).all(): + raise ValueError("Sigma must contain only numeric values") if np.any(sigma <= 0): raise ValueError("Sigma must not be negative") diff --git a/tests/unit/test_cost.py b/tests/unit/test_cost.py index 693e3944b..7dc81ebab 100644 --- a/tests/unit/test_cost.py +++ b/tests/unit/test_cost.py @@ -195,6 +195,7 @@ def test_energy_density_costs( assert cost([0.4]) <= 0 # Should be a viable design assert cost([0.8]) == np.inf # Should exceed active material + porosity < 1 assert cost([1.4]) == np.inf # Definitely not viable + assert cost([-0.1]) == np.inf # Should not be a viable design # Test infeasible locations cost.problem._model.allow_infeasible_solutions = False diff --git a/tests/unit/test_likelihoods.py b/tests/unit/test_likelihoods.py index 02db1e87f..2aeb8ed72 100644 --- a/tests/unit/test_likelihoods.py +++ b/tests/unit/test_likelihoods.py @@ -74,6 +74,8 @@ def test_base_likelihood_init(self, problem): assert likelihood.bounds == problem.bounds assert likelihood._n_parameters == 1 assert np.array_equal(likelihood._target, problem._target) + with pytest.raises(ValueError): + likelihood.set_sigma("Test") @pytest.mark.unit def test_base_likelihood_call_raises_not_implemented_error(self, problem): @@ -126,9 +128,8 @@ def test_gaussian_log_likelihood(self, problem): assert np.all(grad_likelihood <= 0) @pytest.mark.unit - def test_gaussian_log_likelihood_call_returns_negative_inf_for_non_positive_sigma( - self, problem - ): + def test_gaussian_log_likelihood_call_returns_negative_inf(self, problem): likelihood = pybop.GaussianLogLikelihood(problem) - result = likelihood(np.array([-0.5])) - assert result == -np.inf + assert likelihood(np.array([-0.5])) == -np.inf # negative value + with pytest.raises(ValueError): + assert likelihood(np.array([0.3])) == -np.inf # parameter value too small From a479136c2bdf8dbca25d67c7d8717f079b792c79 Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Tue, 19 Mar 2024 15:02:20 +0000 Subject: [PATCH 46/64] coverage, bugfix model.simulateS1 --- pybop/costs/_likelihoods.py | 16 ++++++++-------- pybop/models/base_model.py | 4 +++- tests/unit/test_cost.py | 2 ++ tests/unit/test_likelihoods.py | 23 +++++++++++++++++++---- 4 files changed, 32 insertions(+), 13 deletions(-) diff --git a/pybop/costs/_likelihoods.py b/pybop/costs/_likelihoods.py index 93fc46e6c..dc045f0bd 100644 --- a/pybop/costs/_likelihoods.py +++ b/pybop/costs/_likelihoods.py @@ -64,18 +64,17 @@ def _evaluate(self, x, grad=None): Calls the problem.evaluate method and calculates the log-likelihood """ - prediction = self.problem.evaluate(x) + y = self.problem.evaluate(x) for key in self.signal: - if len(prediction.get(key, [])) != len(self._target.get(key, [])): + if len(y.get(key, [])) != len(self._target.get(key, [])): return -np.float64(np.inf) # prediction doesn't match target e = np.array( [ np.sum( self._offset - + self._multip - * np.sum((self._target[signal] - prediction[signal]) ** 2) + + self._multip * np.sum((self._target[signal] - y[signal]) ** 2) ) for signal in self.signal ] @@ -92,6 +91,7 @@ def _evaluateS1(self, x, grad=None): the log-likelihood """ y, dy = self.problem.evaluateS1(x) + for key in self.signal: if len(y.get(key, [])) != len(self._target.get(key, [])): likelihood = np.float64(np.inf) @@ -144,10 +144,10 @@ def _evaluate(self, x, grad=None): if np.any(sigma <= 0): return -np.inf - prediction = self.problem.evaluate(x[: -self.n_outputs]) + y = self.problem.evaluate(x[: -self.n_outputs]) for key in self.signal: - if len(prediction.get(key, [])) != len(self._target.get(key, [])): + if len(y.get(key, [])) != len(self._target.get(key, [])): return -np.float64(np.inf) # prediction doesn't match target e = np.array( @@ -155,8 +155,7 @@ def _evaluate(self, x, grad=None): np.sum( self._logpi - self._n_times * np.log(sigma) - - np.sum((self._target[signal] - prediction[signal]) ** 2) - / (2.0 * sigma**2) + - np.sum((self._target[signal] - y[signal]) ** 2) / (2.0 * sigma**2) ) for signal in self.signal ] @@ -173,6 +172,7 @@ def _evaluateS1(self, x, grad=None): the log-likelihood """ sigma = np.asarray(x[-self.n_outputs :]) + if np.any(sigma <= 0): return -np.float64(np.inf), -self._dl * np.ones(self.n_parameters) diff --git a/pybop/models/base_model.py b/pybop/models/base_model.py index cde839b01..0816cc211 100644 --- a/pybop/models/base_model.py +++ b/pybop/models/base_model.py @@ -410,7 +410,9 @@ def simulateS1(self, inputs, t_eval): for signal in self.signal for key in self.fit_keys ] - ).reshape(self.n_parameters, self.n_time_data, self.n_outputs) + ).reshape( + self.n_parameters, sol[self.signal[0]].data.shape[0], self.n_outputs + ) return y, dy diff --git a/tests/unit/test_cost.py b/tests/unit/test_cost.py index 7dc81ebab..69abef2a7 100644 --- a/tests/unit/test_cost.py +++ b/tests/unit/test_cost.py @@ -161,6 +161,8 @@ def test_costs(self, cost): cost.problem._model.allow_infeasible_solutions = False assert cost([1.1]) == np.inf assert cost.evaluateS1([1.1]) == (np.inf, cost._de) + assert cost([0.01]) == np.inf + assert cost.evaluateS1([0.01]) == (np.inf, cost._de) # Test exception for non-numeric inputs with pytest.raises(ValueError): diff --git a/tests/unit/test_likelihoods.py b/tests/unit/test_likelihoods.py index 2aeb8ed72..ed29f196d 100644 --- a/tests/unit/test_likelihoods.py +++ b/tests/unit/test_likelihoods.py @@ -128,8 +128,23 @@ def test_gaussian_log_likelihood(self, problem): assert np.all(grad_likelihood <= 0) @pytest.mark.unit - def test_gaussian_log_likelihood_call_returns_negative_inf(self, problem): + def test_gaussian_log_likelihood_returns_negative_inf(self, problem): likelihood = pybop.GaussianLogLikelihood(problem) - assert likelihood(np.array([-0.5])) == -np.inf # negative value - with pytest.raises(ValueError): - assert likelihood(np.array([0.3])) == -np.inf # parameter value too small + assert likelihood(np.array([-0.5, -0.5])) == -np.inf # negative sigma value + assert ( + likelihood.evaluateS1(np.array([-0.5, -0.5]))[0] == -np.inf + ) # negative sigma value + assert likelihood(np.array([0.01, 0.1])) == -np.inf # parameter value too small + assert ( + likelihood.evaluateS1(np.array([0.01, 0.1]))[0] == -np.inf + ) # parameter value too small + + @pytest.mark.unit + def test_gaussian_log_likelihood_known_sigma_returns_negative_inf(self, problem): + likelihood = pybop.GaussianLogLikelihoodKnownSigma( + problem, sigma=np.array([0.2]) + ) + assert likelihood(np.array([0.01])) == -np.inf # parameter value too small + assert ( + likelihood.evaluateS1(np.array([0.01]))[0] == -np.inf + ) # parameter value too small From 18a1a4e331a36eb99d3e330ad545d0b5bff05f1d Mon Sep 17 00:00:00 2001 From: NicolaCourtier <45851982+NicolaCourtier@users.noreply.github.com> Date: Wed, 20 Mar 2024 11:55:49 +0000 Subject: [PATCH 47/64] Update to n_time_data and add two_signal test --- pybop/costs/_likelihoods.py | 12 +++--- tests/unit/test_likelihoods.py | 68 +++++++++++++++++++++------------- 2 files changed, 48 insertions(+), 32 deletions(-) diff --git a/pybop/costs/_likelihoods.py b/pybop/costs/_likelihoods.py index dc045f0bd..7a01bb108 100644 --- a/pybop/costs/_likelihoods.py +++ b/pybop/costs/_likelihoods.py @@ -9,7 +9,7 @@ class BaseLikelihood(BaseCost): def __init__(self, problem, sigma=None): super(BaseLikelihood, self).__init__(problem, sigma) - self._n_times = problem.n_time_data + self.n_time_data = problem.n_time_data def set_sigma(self, sigma): """ @@ -54,7 +54,7 @@ def __init__(self, problem, sigma): super(GaussianLogLikelihoodKnownSigma, self).__init__(problem, sigma) if sigma is not None: self.set_sigma(sigma) - self._offset = -0.5 * self._n_times * np.log(2 * np.pi / self.sigma0) + self._offset = -0.5 * self.n_time_data * np.log(2 * np.pi / self.sigma0) self._multip = -1 / (2.0 * self.sigma0**2) self.sigma2 = self.sigma0**-2 self._dl = np.ones(self._n_parameters) @@ -124,7 +124,7 @@ class GaussianLogLikelihood(BaseLikelihood): def __init__(self, problem): super(GaussianLogLikelihood, self).__init__(problem) - self._logpi = -0.5 * self._n_times * np.log(2 * np.pi) + self._logpi = -0.5 * self.n_time_data * np.log(2 * np.pi) self._dl = np.ones(self._n_parameters + self.n_outputs) def _evaluate(self, x, grad=None): @@ -154,7 +154,7 @@ def _evaluate(self, x, grad=None): [ np.sum( self._logpi - - self._n_times * np.log(sigma) + - self.n_time_data * np.log(sigma) - np.sum((self._target[signal] - y[signal]) ** 2) / (2.0 * sigma**2) ) for signal in self.signal @@ -190,12 +190,12 @@ def _evaluateS1(self, x, grad=None): r = r.reshape(self.problem.n_time_data) dy = dy.reshape(self.n_parameters, self.problem.n_time_data) dl = sigma ** (-2.0) * np.sum((r * dy), axis=1) - dsigma = -self._n_times / sigma + sigma**-(3.0) * np.sum(r**2, axis=0) + dsigma = -self.n_time_data / sigma + sigma ** -(3.0) * np.sum(r**2, axis=0) dl = np.concatenate((dl, dsigma)) return likelihood, dl else: r = r.reshape(self.n_outputs, self.problem.n_time_data) dl = sigma ** (-2.0) * np.sum((r[:, :, np.newaxis] * dy), axis=1) - dsigma = -self._n_times / sigma + sigma**-(3.0) * np.sum(r**2, axis=0) + dsigma = -self.n_time_data / sigma + sigma ** -(3.0) * np.sum(r**2, axis=0) dl = np.concatenate((dl, dsigma)) return likelihood, np.sum(dl, axis=1) diff --git a/tests/unit/test_likelihoods.py b/tests/unit/test_likelihoods.py index ed29f196d..029f34401 100644 --- a/tests/unit/test_likelihoods.py +++ b/tests/unit/test_likelihoods.py @@ -53,22 +53,30 @@ def dataset(self, model, experiment, x0): ) @pytest.fixture - def signal(self): - return "Voltage [V]" + def one_signal_problem(self, model, parameters, dataset, x0): + signal = ["Voltage [V]"] + return pybop.FittingProblem( + model, parameters, dataset, signal=signal, x0=x0, init_soc=1.0 + ) - @pytest.fixture() - def problem(self, model, parameters, dataset, signal, x0): - problem = pybop.FittingProblem( + @pytest.fixture + def two_signal_problem(self, model, parameters, dataset, x0): + signal = ["Time [s]", "Voltage [V]"] + return pybop.FittingProblem( model, parameters, dataset, signal=signal, x0=x0, init_soc=1.0 ) - return problem + @pytest.mark.parametrize( + "problem_name, n_outputs", + [("one_signal_problem", 1), ("two_signal_problem", 2)], + ) @pytest.mark.unit - def test_base_likelihood_init(self, problem): + def test_base_likelihood_init(self, problem_name, n_outputs, request): + problem = request.getfixturevalue(problem_name) likelihood = pybop.BaseLikelihood(problem, sigma=np.array([0.2])) assert likelihood.problem == problem - assert likelihood.n_outputs == 1 - assert likelihood._n_times == problem.n_time_data + assert likelihood.n_outputs == n_outputs + assert likelihood.n_time_data == problem.n_time_data assert np.array_equal(likelihood.get_sigma(), np.array([0.2])) assert likelihood.x0 == problem.x0 assert likelihood.bounds == problem.bounds @@ -78,37 +86,43 @@ def test_base_likelihood_init(self, problem): likelihood.set_sigma("Test") @pytest.mark.unit - def test_base_likelihood_call_raises_not_implemented_error(self, problem): - likelihood = pybop.BaseLikelihood(problem) + def test_base_likelihood_call_raises_not_implemented_error( + self, one_signal_problem + ): + likelihood = pybop.BaseLikelihood(one_signal_problem) with pytest.raises(NotImplementedError): likelihood(np.array([0.5, 0.5])) @pytest.mark.unit - def test_base_likelihood_set_get_sigma(self, problem): - likelihood = pybop.BaseLikelihood(problem) + def test_base_likelihood_set_get_sigma(self, one_signal_problem): + likelihood = pybop.BaseLikelihood(one_signal_problem) likelihood.set_sigma(np.array([0.3])) assert np.array_equal(likelihood.get_sigma(), np.array([0.3])) @pytest.mark.unit def test_base_likelihood_set_sigma_raises_value_error_for_negative_sigma( - self, problem + self, one_signal_problem ): - likelihood = pybop.BaseLikelihood(problem) + likelihood = pybop.BaseLikelihood(one_signal_problem) with pytest.raises(ValueError): likelihood.set_sigma(np.array([-0.2])) @pytest.mark.unit - def test_base_likelihood_get_n_parameters(self, problem): - likelihood = pybop.BaseLikelihood(problem) + def test_base_likelihood_get_n_parameters(self, one_signal_problem): + likelihood = pybop.BaseLikelihood(one_signal_problem) assert likelihood.get_n_parameters() == 1 @pytest.mark.unit - def test_base_likelihood_n_parameters_property(self, problem): - likelihood = pybop.BaseLikelihood(problem) + def test_base_likelihood_n_parameters_property(self, one_signal_problem): + likelihood = pybop.BaseLikelihood(one_signal_problem) assert likelihood.n_parameters == 1 + @pytest.mark.parametrize( + "problem_name", ["one_signal_problem", "two_signal_problem"] + ) @pytest.mark.unit - def test_gaussian_log_likelihood_known_sigma(self, problem): + def test_gaussian_log_likelihood_known_sigma(self, problem_name, request): + problem = request.getfixturevalue(problem_name) likelihood = pybop.GaussianLogLikelihoodKnownSigma( problem, sigma=np.array([1.0]) ) @@ -119,8 +133,8 @@ def test_gaussian_log_likelihood_known_sigma(self, problem): assert np.all(grad_likelihood <= 0) @pytest.mark.unit - def test_gaussian_log_likelihood(self, problem): - likelihood = pybop.GaussianLogLikelihood(problem) + def test_gaussian_log_likelihood(self, one_signal_problem): + likelihood = pybop.GaussianLogLikelihood(one_signal_problem) result = likelihood(np.array([0.5, 0.5])) grad_result, grad_likelihood = likelihood.evaluateS1(np.array([0.5, 0.5])) assert isinstance(result, float) @@ -128,8 +142,8 @@ def test_gaussian_log_likelihood(self, problem): assert np.all(grad_likelihood <= 0) @pytest.mark.unit - def test_gaussian_log_likelihood_returns_negative_inf(self, problem): - likelihood = pybop.GaussianLogLikelihood(problem) + def test_gaussian_log_likelihood_returns_negative_inf(self, one_signal_problem): + likelihood = pybop.GaussianLogLikelihood(one_signal_problem) assert likelihood(np.array([-0.5, -0.5])) == -np.inf # negative sigma value assert ( likelihood.evaluateS1(np.array([-0.5, -0.5]))[0] == -np.inf @@ -140,9 +154,11 @@ def test_gaussian_log_likelihood_returns_negative_inf(self, problem): ) # parameter value too small @pytest.mark.unit - def test_gaussian_log_likelihood_known_sigma_returns_negative_inf(self, problem): + def test_gaussian_log_likelihood_known_sigma_returns_negative_inf( + self, one_signal_problem + ): likelihood = pybop.GaussianLogLikelihoodKnownSigma( - problem, sigma=np.array([0.2]) + one_signal_problem, sigma=np.array([0.2]) ) assert likelihood(np.array([0.01])) == -np.inf # parameter value too small assert ( From 19b247c394146e97e2dec2f26d4ea7a909d30258 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 20 Mar 2024 11:56:09 +0000 Subject: [PATCH 48/64] style: pre-commit fixes --- pybop/costs/_likelihoods.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pybop/costs/_likelihoods.py b/pybop/costs/_likelihoods.py index 7a01bb108..b681100a6 100644 --- a/pybop/costs/_likelihoods.py +++ b/pybop/costs/_likelihoods.py @@ -190,12 +190,12 @@ def _evaluateS1(self, x, grad=None): r = r.reshape(self.problem.n_time_data) dy = dy.reshape(self.n_parameters, self.problem.n_time_data) dl = sigma ** (-2.0) * np.sum((r * dy), axis=1) - dsigma = -self.n_time_data / sigma + sigma ** -(3.0) * np.sum(r**2, axis=0) + dsigma = -self.n_time_data / sigma + sigma**-(3.0) * np.sum(r**2, axis=0) dl = np.concatenate((dl, dsigma)) return likelihood, dl else: r = r.reshape(self.n_outputs, self.problem.n_time_data) dl = sigma ** (-2.0) * np.sum((r[:, :, np.newaxis] * dy), axis=1) - dsigma = -self.n_time_data / sigma + sigma ** -(3.0) * np.sum(r**2, axis=0) + dsigma = -self.n_time_data / sigma + sigma**-(3.0) * np.sum(r**2, axis=0) dl = np.concatenate((dl, dsigma)) return likelihood, np.sum(dl, axis=1) From 695c2c980031142ff57d184fe3be346e28642099 Mon Sep 17 00:00:00 2001 From: NicolaCourtier <45851982+NicolaCourtier@users.noreply.github.com> Date: Wed, 20 Mar 2024 12:39:10 +0000 Subject: [PATCH 49/64] Test standard plot and plot_trajectories --- tests/unit/test_plots.py | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/tests/unit/test_plots.py b/tests/unit/test_plots.py index 8e793e495..bc53f6638 100644 --- a/tests/unit/test_plots.py +++ b/tests/unit/test_plots.py @@ -8,6 +8,16 @@ class TestPlots: A class to test the plotting classes. """ + @pytest.mark.unit + def test_standard_plot(self): + # Test standard plot and removal of brackets + plot_dict = pybop.StandardPlot( + x=np.ones(10), + y=np.ones((2, 10)), + trace_names=["Trace [1]", "Trace [2]"], + ) + plot_dict() + @pytest.fixture def model(self): # Define an example model @@ -46,6 +56,11 @@ def dataset(self, model): @pytest.mark.unit def test_dataset_plots(self, dataset): # Test plotting of Dataset objects + pybop.plot_trajectories( + dataset["Time [s]"], + dataset["Voltage [V]"], + trace_names=["Time [s]", "Voltage [V]"], + ) pybop.plot_dataset(dataset, signal=["Voltage [V]"]) @pytest.fixture From 47c00589c18bac078e02a15d9b97ae1d67aa8272 Mon Sep 17 00:00:00 2001 From: NicolaCourtier <45851982+NicolaCourtier@users.noreply.github.com> Date: Wed, 20 Mar 2024 12:47:21 +0000 Subject: [PATCH 50/64] Add test_with_ipykernel --- tests/unit/test_plots.py | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/tests/unit/test_plots.py b/tests/unit/test_plots.py index bc53f6638..3704867ea 100644 --- a/tests/unit/test_plots.py +++ b/tests/unit/test_plots.py @@ -106,3 +106,12 @@ def test_optim_plots(self, optim): # Plot the cost landscape with optimisation path pybop.plot_optim2d(optim, steps=5) + + @pytest.mark.unit + def test_with_ipykernel(self, dataset, cost, optim): + import ipykernel + pybop.plot_dataset(dataset, signal=["Voltage [V]"]) + pybop.plot_cost2d(cost, gradient=True, steps=5) + pybop.plot_convergence(optim) + pybop.plot_parameters(optim) + pybop.plot_optim2d(optim, steps=5) From 1a80b6fa4cab9e28efe8b51bdc47e4a1e7ee5c62 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 20 Mar 2024 12:47:56 +0000 Subject: [PATCH 51/64] style: pre-commit fixes --- tests/unit/test_plots.py | 1 - 1 file changed, 1 deletion(-) diff --git a/tests/unit/test_plots.py b/tests/unit/test_plots.py index 3704867ea..77f052ece 100644 --- a/tests/unit/test_plots.py +++ b/tests/unit/test_plots.py @@ -109,7 +109,6 @@ def test_optim_plots(self, optim): @pytest.mark.unit def test_with_ipykernel(self, dataset, cost, optim): - import ipykernel pybop.plot_dataset(dataset, signal=["Voltage [V]"]) pybop.plot_cost2d(cost, gradient=True, steps=5) pybop.plot_convergence(optim) From fa551a0403c9f29f67cd6c4d87dc03ac01420600 Mon Sep 17 00:00:00 2001 From: NicolaCourtier <45851982+NicolaCourtier@users.noreply.github.com> Date: Wed, 20 Mar 2024 13:12:39 +0000 Subject: [PATCH 52/64] Re-add test_with_ipykernel --- tests/unit/test_plots.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/tests/unit/test_plots.py b/tests/unit/test_plots.py index 77f052ece..8a415108a 100644 --- a/tests/unit/test_plots.py +++ b/tests/unit/test_plots.py @@ -109,6 +109,9 @@ def test_optim_plots(self, optim): @pytest.mark.unit def test_with_ipykernel(self, dataset, cost, optim): + import ipykernel + + print(ipykernel.__version__) pybop.plot_dataset(dataset, signal=["Voltage [V]"]) pybop.plot_cost2d(cost, gradient=True, steps=5) pybop.plot_convergence(optim) From b6ce663c8616b7589edcfce731a888fb47cf8694 Mon Sep 17 00:00:00 2001 From: NicolaCourtier <45851982+NicolaCourtier@users.noreply.github.com> Date: Wed, 20 Mar 2024 13:39:27 +0000 Subject: [PATCH 53/64] Update test_plots.py --- tests/unit/test_plots.py | 30 ++++++++++++++++++++++-------- 1 file changed, 22 insertions(+), 8 deletions(-) diff --git a/tests/unit/test_plots.py b/tests/unit/test_plots.py index 8a415108a..12f749580 100644 --- a/tests/unit/test_plots.py +++ b/tests/unit/test_plots.py @@ -10,11 +10,12 @@ class TestPlots: @pytest.mark.unit def test_standard_plot(self): - # Test standard plot and removal of brackets + # Test standard plot + trace_names = pybop.StandardPlot.remove_brackets(["Trace [1]", "Trace [2]"]) plot_dict = pybop.StandardPlot( - x=np.ones(10), + x=np.ones((2, 10)), y=np.ones((2, 10)), - trace_names=["Trace [1]", "Trace [2]"], + trace_names=trace_names, ) plot_dict() @@ -64,18 +65,31 @@ def test_dataset_plots(self, dataset): pybop.plot_dataset(dataset, signal=["Voltage [V]"]) @pytest.fixture - def problem(self, model, parameters, dataset): + def fitting_problem(self, model, parameters, dataset): return pybop.FittingProblem(model, parameters, dataset) + @pytest.fixture + def experiment(self): + return pybop.Experiment( + [ + ("Discharge at 1C for 10 minutes (20 second period)"), + ] + ) + + @pytest.fixture + def design_problem(self, model, parameters, experiment): + return pybop.DesignProblem(model, parameters, experiment) + @pytest.mark.unit - def test_problem_plots(self, problem): + def test_problem_plots(self, fitting_problem, design_problem): # Test plotting of Problem objects - pybop.quick_plot(problem, title="Optimised Comparison") + pybop.quick_plot(fitting_problem, title="Optimised Comparison") + pybop.quick_plot(design_problem) @pytest.fixture - def cost(self, problem): + def cost(self, fitting_problem): # Define an example cost - return pybop.SumSquaredError(problem) + return pybop.SumSquaredError(fitting_problem) @pytest.mark.unit def test_cost_plots(self, cost): From f198cbe0773ef81d632e11a608525c4c54c28b30 Mon Sep 17 00:00:00 2001 From: NicolaCourtier <45851982+NicolaCourtier@users.noreply.github.com> Date: Wed, 20 Mar 2024 13:46:55 +0000 Subject: [PATCH 54/64] Rename plot_cost2d as plot2d --- pybop/__init__.py | 2 +- pybop/plotting/plot_convergence.py | 2 +- pybop/plotting/plot_cost2d.py | 6 ++---- tests/unit/test_plots.py | 8 ++++---- 4 files changed, 8 insertions(+), 10 deletions(-) diff --git a/pybop/__init__.py b/pybop/__init__.py index b85f2b847..05fd61dcf 100644 --- a/pybop/__init__.py +++ b/pybop/__init__.py @@ -112,7 +112,7 @@ # from .plotting.plotly_manager import PlotlyManager from .plotting.quick_plot import StandardPlot, StandardSubplot, plot_trajectories -from .plotting.plot_cost2d import plot_cost2d +from .plotting.plot_cost2d import plot2d from .plotting.plot_dataset import plot_dataset from .plotting.plot_convergence import plot_convergence, plot_optim2d from .plotting.plot_parameters import plot_parameters diff --git a/pybop/plotting/plot_convergence.py b/pybop/plotting/plot_convergence.py index 29ad582a7..52e238f0a 100644 --- a/pybop/plotting/plot_convergence.py +++ b/pybop/plotting/plot_convergence.py @@ -94,7 +94,7 @@ def plot_optim2d(optim, bounds=None, steps=10, show=True, **layout_kwargs): cost = optim.cost # Create the cost landscape - fig = pybop.plot_cost2d(cost, bounds=bounds, steps=steps, show=False) + fig = pybop.plot2d(cost, bounds=bounds, steps=steps, show=False) # Import plotly only when needed go = pybop.PlotlyManager().go diff --git a/pybop/plotting/plot_cost2d.py b/pybop/plotting/plot_cost2d.py index 29c3bbd08..767ff9759 100644 --- a/pybop/plotting/plot_cost2d.py +++ b/pybop/plotting/plot_cost2d.py @@ -3,9 +3,7 @@ import numpy as np -def plot_cost2d( - cost, gradient=False, bounds=None, steps=10, show=True, **layout_kwargs -): +def plot2d(cost, gradient=False, bounds=None, steps=10, show=True, **layout_kwargs): """ Plot a 2D visualisation of a cost landscape using Plotly. @@ -147,6 +145,6 @@ def get_param_bounds(cost): if param.bounds is not None: bounds[i] = param.bounds else: - raise ValueError("plot_cost2d could not find bounds required for plotting") + raise ValueError("plot2d could not find bounds required for plotting") return bounds diff --git a/tests/unit/test_plots.py b/tests/unit/test_plots.py index 12f749580..dc0d54d11 100644 --- a/tests/unit/test_plots.py +++ b/tests/unit/test_plots.py @@ -94,14 +94,14 @@ def cost(self, fitting_problem): @pytest.mark.unit def test_cost_plots(self, cost): # Test plotting of Cost objects - pybop.plot_cost2d(cost, gradient=True, steps=5) + pybop.plot2d(cost, gradient=True, steps=5) # Test without bounds for param in cost.problem.parameters: param.bounds = None with pytest.raises(ValueError): - pybop.plot_cost2d(cost, steps=5) - pybop.plot_cost2d(cost, bounds=np.array([[0.5, 0.8], [0.4, 0.7]]), steps=5) + pybop.plot2d(cost, steps=5) + pybop.plot2d(cost, bounds=np.array([[0.5, 0.8], [0.4, 0.7]]), steps=5) @pytest.fixture def optim(self, cost): @@ -127,7 +127,7 @@ def test_with_ipykernel(self, dataset, cost, optim): print(ipykernel.__version__) pybop.plot_dataset(dataset, signal=["Voltage [V]"]) - pybop.plot_cost2d(cost, gradient=True, steps=5) + pybop.plot2d(cost, gradient=True, steps=5) pybop.plot_convergence(optim) pybop.plot_parameters(optim) pybop.plot_optim2d(optim, steps=5) From 9ef38974a99913b02b17654f2c6ae4dfeceac410 Mon Sep 17 00:00:00 2001 From: NicolaCourtier <45851982+NicolaCourtier@users.noreply.github.com> Date: Wed, 20 Mar 2024 14:15:39 +0000 Subject: [PATCH 55/64] Merge plot_optim2d with plot2d --- pybop/__init__.py | 2 +- pybop/plotting/plot_convergence.py | 86 ------------------------------ pybop/plotting/plot_cost2d.py | 59 ++++++++++++++++++-- tests/unit/test_plots.py | 4 +- 4 files changed, 59 insertions(+), 92 deletions(-) diff --git a/pybop/__init__.py b/pybop/__init__.py index 05fd61dcf..dd49e2e5e 100644 --- a/pybop/__init__.py +++ b/pybop/__init__.py @@ -114,7 +114,7 @@ from .plotting.quick_plot import StandardPlot, StandardSubplot, plot_trajectories from .plotting.plot_cost2d import plot2d from .plotting.plot_dataset import plot_dataset -from .plotting.plot_convergence import plot_convergence, plot_optim2d +from .plotting.plot_convergence import plot_convergence from .plotting.plot_parameters import plot_parameters from .plotting.plot_problem import quick_plot diff --git a/pybop/plotting/plot_convergence.py b/pybop/plotting/plot_convergence.py index 52e238f0a..e1814b4ad 100644 --- a/pybop/plotting/plot_convergence.py +++ b/pybop/plotting/plot_convergence.py @@ -56,89 +56,3 @@ def plot_convergence(optim, show=True, **layout_kwargs): fig.show() return fig - - -def plot_optim2d(optim, bounds=None, steps=10, show=True, **layout_kwargs): - """ - Plot a 2D visualization of a cost landscape using Plotly with the optimisation trace. - - This function generates a contour plot representing the cost landscape for a provided - callable cost function over a grid of parameter values within the specified bounds. - - Parameters - ---------- - optim : object - Optimisation object which provides a specific optimisation trace overlaid on the cost landscape. - bounds : numpy.ndarray, optional - A 2x2 array specifying the [min, max] bounds for each parameter. If None, uses `get_param_bounds`. - steps : int, optional - The number of intervals to divide the parameter space into along each dimension (default is 10). - show : bool, optional - If True, the figure is shown upon creation (default: True). - **layout_kwargs : optional - Valid Plotly layout keys and their values, - e.g. `xaxis_title="Time [s]"` or - `xaxis={"title": "Time [s]", "titlefont_size": 18}`. - - Returns - ------- - plotly.graph_objs.Figure - The Plotly figure object containing the cost landscape plot. - - Raises - ------ - ValueError - If the cost function does not return a valid cost when called with a parameter list. - """ - # Extract the cost function from the optimisation object - cost = optim.cost - - # Create the cost landscape - fig = pybop.plot2d(cost, bounds=bounds, steps=steps, show=False) - - # Import plotly only when needed - go = pybop.PlotlyManager().go - - # Plot the optimisation trace - optim_trace = np.array([item for sublist in optim.log for item in sublist]) - optim_trace = optim_trace.reshape(-1, 2) - fig.add_trace( - go.Scatter( - x=optim_trace[:, 0], - y=optim_trace[:, 1], - mode="markers", - marker=dict( - color=[i / len(optim_trace) for i in range(len(optim_trace))], - colorscale="YlOrBr", - showscale=False, - ), - showlegend=False, - ) - ) - - # Plot the initial guess - fig.add_trace( - go.Scatter( - x=[optim.x0[0]], - y=[optim.x0[1]], - mode="markers", - marker_symbol="circle", - marker=dict( - color="mediumspringgreen", - line_color="mediumspringgreen", - line_width=1, - size=14, - showscale=False, - ), - showlegend=False, - ) - ) - - # Update the layout and display the figure - fig.update_layout(**layout_kwargs) - if "ipykernel" in sys.modules and show: - fig.show("svg") - elif show: - fig.show() - - return fig diff --git a/pybop/plotting/plot_cost2d.py b/pybop/plotting/plot_cost2d.py index 767ff9759..d03fcb59e 100644 --- a/pybop/plotting/plot_cost2d.py +++ b/pybop/plotting/plot_cost2d.py @@ -3,7 +3,9 @@ import numpy as np -def plot2d(cost, gradient=False, bounds=None, steps=10, show=True, **layout_kwargs): +def plot2d( + cost_or_optim, gradient=False, bounds=None, steps=10, show=True, **layout_kwargs +): """ Plot a 2D visualisation of a cost landscape using Plotly. @@ -12,8 +14,12 @@ def plot2d(cost, gradient=False, bounds=None, steps=10, show=True, **layout_kwar Parameters ---------- - cost : callable - The cost function to be evaluated. Must accept a list of parameter values and return a cost value. + cost_or_optim : a callable cost function, pybop Cost or Optimisation object + Either: + - the cost function to be evaluated. Must accept a list of parameter values and return a cost value. + - an Optimisation object which provides a specific optimisation trace overlaid on the cost landscape. + gradient : bool, optional + If True, the gradient is shown (default: False). bounds : numpy.ndarray, optional A 2x2 array specifying the [min, max] bounds for each parameter. If None, uses `get_param_bounds`. steps : int, optional @@ -36,6 +42,15 @@ def plot2d(cost, gradient=False, bounds=None, steps=10, show=True, **layout_kwar If the cost function does not return a valid cost when called with a parameter list. """ + # Assign input as a cost or optimisation object + if isinstance(cost_or_optim, pybop.Optimisation): + optim = cost_or_optim + plot_optim = True + cost = optim.cost + else: + cost = cost_or_optim + plot_optim = False + # Set up parameter bounds if bounds is None: bounds = get_param_bounds(cost) @@ -91,6 +106,44 @@ def plot2d(cost, gradient=False, bounds=None, steps=10, show=True, **layout_kwar # Create contour plot and update the layout fig = go.Figure(data=[go.Contour(x=x, y=y, z=costs)], layout=layout) + + if plot_optim: + # Plot the optimisation trace + optim_trace = np.array([item for sublist in optim.log for item in sublist]) + optim_trace = optim_trace.reshape(-1, 2) + fig.add_trace( + go.Scatter( + x=optim_trace[:, 0], + y=optim_trace[:, 1], + mode="markers", + marker=dict( + color=[i / len(optim_trace) for i in range(len(optim_trace))], + colorscale="YlOrBr", + showscale=False, + ), + showlegend=False, + ) + ) + + # Plot the initial guess + fig.add_trace( + go.Scatter( + x=[optim.x0[0]], + y=[optim.x0[1]], + mode="markers", + marker_symbol="circle", + marker=dict( + color="mediumspringgreen", + line_color="mediumspringgreen", + line_width=1, + size=14, + showscale=False, + ), + showlegend=False, + ) + ) + + # Update the layout and display the figure fig.update_layout(**layout_kwargs) if "ipykernel" in sys.modules and show: fig.show("svg") diff --git a/tests/unit/test_plots.py b/tests/unit/test_plots.py index dc0d54d11..1384272ea 100644 --- a/tests/unit/test_plots.py +++ b/tests/unit/test_plots.py @@ -119,7 +119,7 @@ def test_optim_plots(self, optim): pybop.plot_parameters(optim) # Plot the cost landscape with optimisation path - pybop.plot_optim2d(optim, steps=5) + pybop.plot2d(optim, steps=5) @pytest.mark.unit def test_with_ipykernel(self, dataset, cost, optim): @@ -130,4 +130,4 @@ def test_with_ipykernel(self, dataset, cost, optim): pybop.plot2d(cost, gradient=True, steps=5) pybop.plot_convergence(optim) pybop.plot_parameters(optim) - pybop.plot_optim2d(optim, steps=5) + pybop.plot2d(optim, steps=5) From f42298f60199d4b2a566a7ad9f55623ff4ca2c3a Mon Sep 17 00:00:00 2001 From: NicolaCourtier <45851982+NicolaCourtier@users.noreply.github.com> Date: Wed, 20 Mar 2024 14:16:58 +0000 Subject: [PATCH 56/64] Update scripts to plot2d --- examples/scripts/ecm_CMAES.py | 4 ++-- examples/scripts/exp_UKF.py | 2 +- examples/scripts/spm_CMAES.py | 4 ++-- examples/scripts/spm_IRPropMin.py | 2 +- examples/scripts/spm_MLE.py | 2 +- examples/scripts/spm_SNES.py | 2 +- examples/scripts/spm_UKF.py | 2 +- examples/scripts/spm_XNES.py | 2 +- examples/scripts/spm_adam.py | 2 +- examples/scripts/spm_descent.py | 2 +- examples/scripts/spm_pso.py | 2 +- examples/scripts/spm_scipymin.py | 2 +- examples/scripts/spme_max_energy.py | 2 +- 13 files changed, 15 insertions(+), 15 deletions(-) diff --git a/examples/scripts/ecm_CMAES.py b/examples/scripts/ecm_CMAES.py index da5835ade..0e0f5476f 100644 --- a/examples/scripts/ecm_CMAES.py +++ b/examples/scripts/ecm_CMAES.py @@ -97,8 +97,8 @@ pybop.plot_parameters(optim) # Plot the cost landscape -pybop.plot_cost2d(cost, steps=15) +pybop.plot2d(cost, steps=15) # Plot the cost landscape with optimisation path and updated bounds bounds = np.array([[1e-4, 1e-2], [1e-5, 1e-2]]) -pybop.plot_optim2d(optim, bounds=bounds, steps=15) +pybop.plot2d(optim, bounds=bounds, steps=15) diff --git a/examples/scripts/exp_UKF.py b/examples/scripts/exp_UKF.py index d7d838cc0..340524fde 100644 --- a/examples/scripts/exp_UKF.py +++ b/examples/scripts/exp_UKF.py @@ -112,4 +112,4 @@ pybop.plot_parameters(optim) # Plot the cost landscape with optimisation path -pybop.plot_optim2d(optim, steps=15) +pybop.plot2d(optim, steps=15) diff --git a/examples/scripts/spm_CMAES.py b/examples/scripts/spm_CMAES.py index 816f6798a..89131604f 100644 --- a/examples/scripts/spm_CMAES.py +++ b/examples/scripts/spm_CMAES.py @@ -68,7 +68,7 @@ pybop.plot_parameters(optim) # Plot the cost landscape -pybop.plot_cost2d(cost, steps=15) +pybop.plot2d(cost, steps=15) # Plot the cost landscape with optimisation path -pybop.plot_optim2d(optim, steps=15) +pybop.plot2d(optim, steps=15) diff --git a/examples/scripts/spm_IRPropMin.py b/examples/scripts/spm_IRPropMin.py index 277b9f089..973e2f8a9 100644 --- a/examples/scripts/spm_IRPropMin.py +++ b/examples/scripts/spm_IRPropMin.py @@ -52,4 +52,4 @@ # Plot the cost landscape with optimisation path bounds = np.array([[0.5, 0.8], [0.4, 0.7]]) -pybop.plot_optim2d(optim, bounds=bounds, steps=15) +pybop.plot2d(optim, bounds=bounds, steps=15) diff --git a/examples/scripts/spm_MLE.py b/examples/scripts/spm_MLE.py index 6e822c4a7..0fb860883 100644 --- a/examples/scripts/spm_MLE.py +++ b/examples/scripts/spm_MLE.py @@ -67,4 +67,4 @@ # Plot the cost landscape with optimisation path bounds = np.array([[0.55, 0.77], [0.48, 0.68]]) -pybop.plot_optim2d(optim, bounds=bounds, steps=15) +pybop.plot2d(optim, bounds=bounds, steps=15) diff --git a/examples/scripts/spm_SNES.py b/examples/scripts/spm_SNES.py index e36639760..421d386a1 100644 --- a/examples/scripts/spm_SNES.py +++ b/examples/scripts/spm_SNES.py @@ -51,4 +51,4 @@ pybop.plot_parameters(optim) # Plot the cost landscape with optimisation path -pybop.plot_optim2d(optim, steps=15) +pybop.plot2d(optim, steps=15) diff --git a/examples/scripts/spm_UKF.py b/examples/scripts/spm_UKF.py index 067a554d7..e629d3f39 100644 --- a/examples/scripts/spm_UKF.py +++ b/examples/scripts/spm_UKF.py @@ -76,4 +76,4 @@ # pybop.plot_parameters(optim) # # Plot the cost landscape with optimisation path -# pybop.plot_optim2d(optim, steps=15) +# pybop.plot2d(optim, steps=15) diff --git a/examples/scripts/spm_XNES.py b/examples/scripts/spm_XNES.py index 3321eb9ba..ef9a9e03c 100644 --- a/examples/scripts/spm_XNES.py +++ b/examples/scripts/spm_XNES.py @@ -52,4 +52,4 @@ pybop.plot_parameters(optim) # Plot the cost landscape with optimisation path -pybop.plot_optim2d(optim, steps=15) +pybop.plot2d(optim, steps=15) diff --git a/examples/scripts/spm_adam.py b/examples/scripts/spm_adam.py index 6be9849c7..687ff3b36 100644 --- a/examples/scripts/spm_adam.py +++ b/examples/scripts/spm_adam.py @@ -78,4 +78,4 @@ def noise(sigma): # Plot the cost landscape with optimisation path bounds = np.array([[0.5, 0.8], [0.4, 0.7]]) -pybop.plot_optim2d(optim, bounds=bounds, steps=15) +pybop.plot2d(optim, bounds=bounds, steps=15) diff --git a/examples/scripts/spm_descent.py b/examples/scripts/spm_descent.py index 624c95aab..a474234cf 100644 --- a/examples/scripts/spm_descent.py +++ b/examples/scripts/spm_descent.py @@ -55,4 +55,4 @@ # Plot the cost landscape with optimisation path bounds = np.array([[0.5, 0.8], [0.4, 0.7]]) -pybop.plot_optim2d(optim, bounds=bounds, steps=15) +pybop.plot2d(optim, bounds=bounds, steps=15) diff --git a/examples/scripts/spm_pso.py b/examples/scripts/spm_pso.py index 819a7454c..74d0ea4cc 100644 --- a/examples/scripts/spm_pso.py +++ b/examples/scripts/spm_pso.py @@ -52,4 +52,4 @@ pybop.plot_parameters(optim) # Plot the cost landscape with optimisation path -pybop.plot_optim2d(optim, steps=15) +pybop.plot2d(optim, steps=15) diff --git a/examples/scripts/spm_scipymin.py b/examples/scripts/spm_scipymin.py index 759f8c2e6..1573701ba 100644 --- a/examples/scripts/spm_scipymin.py +++ b/examples/scripts/spm_scipymin.py @@ -53,4 +53,4 @@ pybop.plot_parameters(optim) # Plot the cost landscape with optimisation path -pybop.plot_optim2d(optim, steps=15) +pybop.plot2d(optim, steps=15) diff --git a/examples/scripts/spme_max_energy.py b/examples/scripts/spme_max_energy.py index c42495904..4d4e3c735 100644 --- a/examples/scripts/spme_max_energy.py +++ b/examples/scripts/spme_max_energy.py @@ -66,4 +66,4 @@ # Plot the cost landscape with optimisation path if len(x) == 2: - pybop.plot_optim2d(optim, steps=3) + pybop.plot2d(optim, steps=3) From 3ca404d9b19600859a79401b30c3d2d5e66150a9 Mon Sep 17 00:00:00 2001 From: NicolaCourtier <45851982+NicolaCourtier@users.noreply.github.com> Date: Wed, 20 Mar 2024 14:18:18 +0000 Subject: [PATCH 57/64] Update notebooks to plot2d --- examples/notebooks/spm_Adam.ipynb | 4 ++-- examples/notebooks/spm_CMAES.ipynb | 4 ++-- examples/notebooks/spm_electrode_design.ipynb | 2 +- examples/notebooks/spm_scipy_DifferentialEvolution.ipynb | 4 ++-- 4 files changed, 7 insertions(+), 7 deletions(-) diff --git a/examples/notebooks/spm_Adam.ipynb b/examples/notebooks/spm_Adam.ipynb index d3860b53d..9d0c755c1 100644 --- a/examples/notebooks/spm_Adam.ipynb +++ b/examples/notebooks/spm_Adam.ipynb @@ -443,10 +443,10 @@ ], "source": [ "# Plot the cost landscape\n", - "pybop.plot_cost2d(cost, steps=15)\n", + "pybop.plot2d(cost, steps=15)\n", "# Plot the cost landscape with optimisation path and updated bounds\n", "bounds = np.array([[0.6, 0.9], [0.5, 0.8]])\n", - "pybop.plot_optim2d(optim, bounds=bounds, steps=15);" + "pybop.plot2d(optim, bounds=bounds, steps=15);" ] }, { diff --git a/examples/notebooks/spm_CMAES.ipynb b/examples/notebooks/spm_CMAES.ipynb index 73b783ce6..03093236a 100644 --- a/examples/notebooks/spm_CMAES.ipynb +++ b/examples/notebooks/spm_CMAES.ipynb @@ -433,10 +433,10 @@ ], "source": [ "# Plot the cost landscape\n", - "pybop.plot_cost2d(cost, steps=15)\n", + "pybop.plot2d(cost, steps=15)\n", "# Plot the cost landscape with optimisation path and updated bounds\n", "bounds = np.array([[0.6, 0.9], [0.5, 0.8]])\n", - "pybop.plot_optim2d(optim, bounds=bounds, steps=15);" + "pybop.plot2d(optim, bounds=bounds, steps=15);" ] }, { diff --git a/examples/notebooks/spm_electrode_design.ipynb b/examples/notebooks/spm_electrode_design.ipynb index 8b44b8f56..538c9f976 100644 --- a/examples/notebooks/spm_electrode_design.ipynb +++ b/examples/notebooks/spm_electrode_design.ipynb @@ -310,7 +310,7 @@ ], "source": [ "if len(x) == 2:\n", - " pybop.plot_optim2d(optim, steps=3)" + " pybop.plot2d(optim, steps=3)" ] } ], diff --git a/examples/notebooks/spm_scipy_DifferentialEvolution.ipynb b/examples/notebooks/spm_scipy_DifferentialEvolution.ipynb index 21e3c1ac4..2d9f77cd4 100644 --- a/examples/notebooks/spm_scipy_DifferentialEvolution.ipynb +++ b/examples/notebooks/spm_scipy_DifferentialEvolution.ipynb @@ -441,10 +441,10 @@ ], "source": [ "# Plot the cost landscape\n", - "pybop.plot_cost2d(cost, steps=15)\n", + "pybop.plot2d(cost, steps=15)\n", "# Plot the cost landscape with optimisation path and updated bounds\n", "bounds = np.array([[0.6, 0.9], [0.5, 0.8]])\n", - "pybop.plot_optim2d(optim, bounds=bounds, steps=15);" + "pybop.plot2d(optim, bounds=bounds, steps=15);" ] }, { From 302add72f1fc237fbadc961c2d1f911d807b5e34 Mon Sep 17 00:00:00 2001 From: NicolaCourtier <45851982+NicolaCourtier@users.noreply.github.com> Date: Wed, 20 Mar 2024 14:39:58 +0000 Subject: [PATCH 58/64] Update spm_MLE.py --- examples/scripts/spm_MLE.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/scripts/spm_MLE.py b/examples/scripts/spm_MLE.py index 0fb860883..3b319ff12 100644 --- a/examples/scripts/spm_MLE.py +++ b/examples/scripts/spm_MLE.py @@ -63,7 +63,7 @@ pybop.plot_parameters(optim) # Plot the cost landscape -pybop.plot_cost2d(likelihood, steps=15) +pybop.plot2d(likelihood, steps=15) # Plot the cost landscape with optimisation path bounds = np.array([[0.55, 0.77], [0.48, 0.68]]) From fe090f1e1f757f253b198ecda3347b53342e2792 Mon Sep 17 00:00:00 2001 From: NicolaCourtier <45851982+NicolaCourtier@users.noreply.github.com> Date: Wed, 20 Mar 2024 14:41:47 +0000 Subject: [PATCH 59/64] Update filename to plot2d --- pybop/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pybop/__init__.py b/pybop/__init__.py index dd49e2e5e..82b7ea6ed 100644 --- a/pybop/__init__.py +++ b/pybop/__init__.py @@ -112,7 +112,7 @@ # from .plotting.plotly_manager import PlotlyManager from .plotting.quick_plot import StandardPlot, StandardSubplot, plot_trajectories -from .plotting.plot_cost2d import plot2d +from .plotting.plot2d import plot2d from .plotting.plot_dataset import plot_dataset from .plotting.plot_convergence import plot_convergence from .plotting.plot_parameters import plot_parameters From 7b8c7f0688affa0fbedf6dea4765cb92e0437485 Mon Sep 17 00:00:00 2001 From: NicolaCourtier <45851982+NicolaCourtier@users.noreply.github.com> Date: Wed, 20 Mar 2024 14:42:42 +0000 Subject: [PATCH 60/64] Rename plot_cost2d.py to plot2d.py --- pybop/plotting/{plot_cost2d.py => plot2d.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename pybop/plotting/{plot_cost2d.py => plot2d.py} (100%) diff --git a/pybop/plotting/plot_cost2d.py b/pybop/plotting/plot2d.py similarity index 100% rename from pybop/plotting/plot_cost2d.py rename to pybop/plotting/plot2d.py From 8bfaeba8b5d7138d2bb5fc80b6bef56265609ccb Mon Sep 17 00:00:00 2001 From: NicolaCourtier <45851982+NicolaCourtier@users.noreply.github.com> Date: Wed, 20 Mar 2024 14:55:35 +0000 Subject: [PATCH 61/64] Fix typo in error message --- pybop/observers/observer.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pybop/observers/observer.py b/pybop/observers/observer.py index ce482ec8d..0bb8b08ed 100644 --- a/pybop/observers/observer.py +++ b/pybop/observers/observer.py @@ -111,7 +111,7 @@ def log_likelihood(self, values: dict, times: np.ndarray, inputs: Inputs) -> flo return log_likelihood else: raise ValueError( - "Obersever.log_likelihood is currently restricted to single output models." + "Observer.log_likelihood is currently restricted to single output models." ) def get_current_state(self) -> TimeSeriesState: From cb705052b3bd93f72578777d2bfb2b55304319e5 Mon Sep 17 00:00:00 2001 From: NicolaCourtier <45851982+NicolaCourtier@users.noreply.github.com> Date: Wed, 20 Mar 2024 14:57:33 +0000 Subject: [PATCH 62/64] Reduce init_soc in test_model_misparameterisation --- tests/integration/test_parameterisations.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/integration/test_parameterisations.py b/tests/integration/test_parameterisations.py index 73c87de45..d4fb2b5ae 100644 --- a/tests/integration/test_parameterisations.py +++ b/tests/integration/test_parameterisations.py @@ -215,7 +215,7 @@ def test_multiple_signals(self, multi_optimiser, spm_two_signal_cost): assert initial_cost > final_cost np.testing.assert_allclose(x, self.ground_truth, atol=2.5e-2) - @pytest.mark.parametrize("init_soc", [0.4, 0.7]) + @pytest.mark.parametrize("init_soc", [0.4, 0.6]) @pytest.mark.integration def test_model_misparameterisation(self, parameters, model, init_soc): # Define two different models with different parameter sets From 76b5c19598a13ae9b24aab61fb382530535ded49 Mon Sep 17 00:00:00 2001 From: NicolaCourtier <45851982+NicolaCourtier@users.noreply.github.com> Date: Thu, 21 Mar 2024 09:59:05 +0000 Subject: [PATCH 63/64] Update ipykernel print to assert Co-authored-by: Brady Planden <55357039+BradyPlanden@users.noreply.github.com> --- tests/unit/test_plots.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/unit/test_plots.py b/tests/unit/test_plots.py index 1384272ea..4b070d165 100644 --- a/tests/unit/test_plots.py +++ b/tests/unit/test_plots.py @@ -125,7 +125,7 @@ def test_optim_plots(self, optim): def test_with_ipykernel(self, dataset, cost, optim): import ipykernel - print(ipykernel.__version__) + assert ipykernel.__version__ >= 0.6 pybop.plot_dataset(dataset, signal=["Voltage [V]"]) pybop.plot2d(cost, gradient=True, steps=5) pybop.plot_convergence(optim) From e513c33232d2b3d4adcfe52df42abd6419eac301 Mon Sep 17 00:00:00 2001 From: NicolaCourtier <45851982+NicolaCourtier@users.noreply.github.com> Date: Thu, 21 Mar 2024 10:21:09 +0000 Subject: [PATCH 64/64] Fix version check --- tests/unit/test_plots.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tests/unit/test_plots.py b/tests/unit/test_plots.py index 4b070d165..b60ca2a85 100644 --- a/tests/unit/test_plots.py +++ b/tests/unit/test_plots.py @@ -1,6 +1,7 @@ import pybop import numpy as np import pytest +from packaging import version class TestPlots: @@ -125,7 +126,7 @@ def test_optim_plots(self, optim): def test_with_ipykernel(self, dataset, cost, optim): import ipykernel - assert ipykernel.__version__ >= 0.6 + assert version.parse(ipykernel.__version__) >= version.parse("0.6") pybop.plot_dataset(dataset, signal=["Voltage [V]"]) pybop.plot2d(cost, gradient=True, steps=5) pybop.plot_convergence(optim)